From 5496a404d8e0fdc391c2185468f7c78f59182477 Mon Sep 17 00:00:00 2001 From: mcditooss Date: Tue, 7 Nov 2023 15:52:55 +0900 Subject: [PATCH 01/44] fixed typo added new classes --- .../heom/heom-1a-spin-bath-model-basic.md | 4 +- ...1b-spin-bath-model-very-strong-coupling.md | 2 +- ...eom-1d-spin-bath-model-ohmic-fitting.ipynb | 1744 +++++++++++++++++ .../heom-1d-spin-bath-model-ohmic-fitting.md | 913 +++------ 4 files changed, 1983 insertions(+), 680 deletions(-) create mode 100644 tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb diff --git a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md index 4aa6a779..87f98ecc 100644 --- a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md +++ b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md @@ -64,9 +64,9 @@ density is given by: \begin{equation*} c_k = \begin{cases} - \lambda \gamma (\cot(\beta \gamma / 2) - i) \ + \lambda \gamma (\cot(\beta \gamma / 2) -i) \ & k = 0\\ - 4 \lambda \gamma \nu_k / \{(nu_k^2 - \gamma^2)\beta \} \ + 4 \lambda \gamma \nu_k \{(\nu_k^2 - \gamma^2)\beta \} \ & k \geq 1\\ \end{cases} \end{equation*} diff --git a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md index e4851516..a99285e5 100644 --- a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md +++ b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md @@ -71,7 +71,7 @@ As an example, the Matsubara decomposition of the Drude-Lorentz spectral density \begin{equation*} c_k = \begin{cases} \lambda \gamma (\cot(\beta \gamma / 2) - i) & k = 0\\ - 4 \lambda \gamma \nu_k / \{(nu_k^2 - \gamma^2)\beta \} & k \geq 1\\ + 4 \lambda \gamma \nu_k / {(\nu_k^2 - \gamma^2)\beta \} & k \geq 1\\ \end{cases} \end{equation*} diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb new file mode 100644 index 00000000..20d12b8b --- /dev/null +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb @@ -0,0 +1,1744 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "259274b8", + "metadata": {}, + "source": [ + "# HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions" + ] + }, + { + "cell_type": "markdown", + "id": "3cfc6dd5", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded \n", + "in a set of auxiliary density matrices.\n", + "\n", + "In this example we show the evolution of a single two-level system in contact with a single bosonic environment.\n", + "\n", + "The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", + "\n", + "The bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", + "\n", + "In the example below we show how to model an Ohmic environment with exponential cut-off in three ways:\n", + "\n", + "* First we fit the spectral density with a set of underdamped brownian oscillator functions.\n", + "* Second, we evaluate the correlation functions, and fit those with a certain choice of exponential functions.\n", + "* Third, we use the available OhmicBath class \n", + "\n", + "In each case we will use the fit parameters to determine the correlation function expansion co-efficients needed to construct a description of the bath (i.e. a `BosonicBath` object) to supply to the `HEOMSolver` so that we can solve for the system dynamics." + ] + }, + { + "cell_type": "markdown", + "id": "376044c7", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c9091371", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "import qutip\n", + "from qutip import (\n", + " basis,\n", + " expect,\n", + " liouvillian,\n", + " sigmax,\n", + " sigmaz,\n", + " spost,\n", + " spre,\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver,\n", + " BosonicBath,\n", + " FitSpectral,\n", + " FitCorr,\n", + " OhmicBath,\n", + ")\n", + "\n", + "# Import mpmath functions for evaluation of gamma and zeta\n", + "# functions in the expression for the correlation:\n", + "\n", + "from mpmath import mp\n", + "\n", + "mp.dps = 15\n", + "mp.pretty = True\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "278b856d", + "metadata": {}, + "source": [ + "## Helper functions\n", + "\n", + "Let's define some helper functions for plotting the resutls" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3b43e49b", + "metadata": {}, + "outputs": [], + "source": [ + "# Solver options:\n", + "\n", + "options = {\n", + " \"nsteps\": 15000,\n", + " \"store_states\": True,\n", + " \"rtol\": 1e-14,\n", + " \"atol\": 1e-14,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "962cbe21", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian, bath and system measurement operators:" + ] + }, + { + "cell_type": "markdown", + "id": "04c0d02a", + "metadata": {}, + "source": [ + "### System Hamiltonian" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d4f6ca24", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the system Hamiltonian\n", + "eps = 0 # Energy of the 2-level system.\n", + "Del = 0.2 # Tunnelling term\n", + "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "11929825", + "metadata": {}, + "outputs": [], + "source": [ + "# Initial state of the system.\n", + "rho0 = basis(2, 0) * basis(2, 0).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "a50797e2", + "metadata": {}, + "source": [ + "### System measurement operators" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "56677852", + "metadata": {}, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresonding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresonding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "1b17b0e6", + "metadata": {}, + "source": [ + "### Analytical expressions for the Ohmic bath correlation function and spectral density" + ] + }, + { + "cell_type": "markdown", + "id": "02b425b6", + "metadata": {}, + "source": [ + "Before we begin fitting, let us examine the analytic expressions for the correlation and spectral density functions and write Python equivalents. \n", + "\n", + "The correlation function is given by (see, e.g., http://www1.itp.tu-berlin.de/brandes/public_html/publications/notes.pdf for a derivation, equation 7.59, but with a factor of $\\pi$ moved into the definition of the correlation function):\n", + "\n", + "\\begin{align}\n", + "C(t) =& \\: \\frac{1}{\\pi}\\alpha \\omega_{c}^{1 - s} \\beta^{- (s + 1)} \\: \\times \\\\\n", + " & \\: \\Gamma(s + 1) \\left[ \\zeta \\left(s + 1, \\frac{1 + \\beta \\omega_c - i \\omega_c t}{\\beta \\omega_c}\\right) + \\zeta \\left(s + 1, \\frac{1 + i \\omega_c t}{\\beta \\omega_c}\\right) \\right]\n", + "\\end{align}\n", + "\n", + "where $\\Gamma$ is the Gamma function and\n", + "\n", + "\\begin{equation}\n", + "\\zeta(z, u) \\equiv \\sum_{n=0}^{\\infty} \\frac{1}{(n + u)^z}, \\; u \\neq 0, -1, -2, \\ldots\n", + "\\end{equation}\n", + "\n", + "is the generalized Zeta function. The Ohmic case is given by $s = 1$.\n", + "\n", + "The corresponding spectral density for the Ohmic case is:\n", + "\n", + "\\begin{equation}\n", + "J(\\omega) = \\omega \\alpha e^{- \\frac{\\omega}{\\omega_c}}\n", + "\\end{equation}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e4905d21", + "metadata": {}, + "outputs": [], + "source": [ + "def ohmic_correlation(t, alpha, wc, beta, s=1):\n", + " \"\"\" The Ohmic bath correlation function as a function of t\n", + " (and the bath parameters).\n", + " \"\"\"\n", + " corr = (\n", + " (1 / np.pi) * alpha * wc**(1 - s) * beta**(-(s + 1)) * mp.gamma(s + 1)\n", + " )\n", + " z1_u = (1 + beta * wc - 1.0j * wc * t) / (beta * wc)\n", + " z2_u = (1 + 1.0j * wc * t) / (beta * wc)\n", + " # Note: the arguments to zeta should be in as high precision as possible.\n", + " # See http://mpmath.org/doc/current/basics.html#providing-correct-input\n", + " return np.array([\n", + " complex(corr * (mp.zeta(s + 1, u1) + mp.zeta(s + 1, u2)))\n", + " for u1, u2 in zip(z1_u, z2_u)\n", + " ], dtype=np.complex128)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "edd66606", + "metadata": {}, + "outputs": [], + "source": [ + "def ohmic_spectral_density(w, alpha, wc):\n", + " \"\"\" The Ohmic bath spectral density as a function of w\n", + " (and the bath parameters).\n", + " \"\"\"\n", + " return w * alpha * np.e**(-w / wc)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7c32be4c", + "metadata": {}, + "outputs": [], + "source": [ + "def ohmic_power_spectrum(w, alpha, wc, beta):\n", + " \"\"\" The Ohmic bath power spectrum as a function of w\n", + " (and the bath parameters).\n", + " \"\"\"\n", + " return (\n", + " w * alpha * np.e**(-abs(w) / wc) *\n", + " ((1 / (np.e**(w * beta) - 1)) + 1) * 2\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "bac74291", + "metadata": {}, + "source": [ + "### Bath and HEOM parameters" + ] + }, + { + "cell_type": "markdown", + "id": "38f903f4", + "metadata": {}, + "source": [ + "Finally, let's set the bath parameters we will work with and write down some measurement operators:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c1ea19c7", + "metadata": {}, + "outputs": [], + "source": [ + "Q = sigmaz()\n", + "alpha = 3.25\n", + "T = 0.5\n", + "wc= 1.0\n", + "s= 1" + ] + }, + { + "cell_type": "markdown", + "id": "f71d98fb", + "metadata": {}, + "source": [ + "And set the cut-off for the HEOM hierarchy:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "15a566eb", + "metadata": {}, + "outputs": [], + "source": [ + "# HEOM parameters:\n", + "\n", + "# The max_depth defaults to 5 so that the notebook executes more\n", + "# quickly. Change it to 11 to wait longer for more accurate results.\n", + "max_depth = 5" + ] + }, + { + "cell_type": "markdown", + "id": "65099a5a", + "metadata": {}, + "source": [ + "## Building the HEOM bath by fitting the spectral density" + ] + }, + { + "cell_type": "markdown", + "id": "c32b82b4", + "metadata": {}, + "source": [ + "We begin by fitting the spectral density, using a series of $k$ underdamped harmonic oscillators case with the Meier-Tannor form (J. Chem. Phys. 111, 3365 (1999); https://doi.org/10.1063/1.479669):\n", + "\n", + "\\begin{equation}\n", + "J_{\\mathrm approx}(\\omega; a, b, c) = \\sum_{i=0}^{k-1} \\frac{2 a_i b_i w}{((w + c_i)^2 + b_i^2) ((w - c_i)^2 + b_i^2)}\n", + "\\end{equation}\n", + "\n", + "where $a, b$ and $c$ are the fit parameters and each is a vector of length $k$." + ] + }, + { + "cell_type": "markdown", + "id": "66a3d71b", + "metadata": {}, + "source": [ + "With the spectral density approximation $J_{\\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. This can be done quickly using the FitSpectral bath, which takes the target spectral density as an array and fits it to the series of **k** underdamped harmonic oscillators with the Meier-Tannor form" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "08dc9a17", + "metadata": {}, + "outputs": [], + "source": [ + "w = np.linspace(0, 15, 20000)\n", + "J = ohmic_spectral_density(w, alpha, wc)" + ] + }, + { + "cell_type": "markdown", + "id": "a649c903", + "metadata": {}, + "source": [ + "We first initialize our FitSpectral class" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "fb5a13de", + "metadata": {}, + "outputs": [], + "source": [ + "fs=FitSpectral(T,Q,Nk=4)" + ] + }, + { + "cell_type": "markdown", + "id": "070ea1da-e3a0-4a45-b894-edc793e8d6d7", + "metadata": {}, + "source": [ + "To obtain a fit we simply pass our desired spectral density and range, into the get_fit method" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "10fbe33a-682c-44e7-8c69-fb85d192b7d6", + "metadata": {}, + "outputs": [], + "source": [ + "fs.get_fit(J,w)" + ] + }, + { + "cell_type": "markdown", + "id": "237c5e0f-16df-4e07-ab25-50d575bf5bd0", + "metadata": {}, + "source": [ + "To obtain an overview of the results of the fit we may call the summary method" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "155ced50-5009-4589-bd31-b3e0e38f3ae0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results of the fitting the Spectral density with 4 terms: \n", + " \n", + " Parameters| lam | gamma | w0 \n", + " 1 | 6.07e-01 | 1.01e+00 |1.00e-01 \n", + " 2 |-4.44e+00 | 4.31e+00 |3.96e+00 \n", + " 3 | 7.93e+00 | 2.30e+00 |1.00e-01 \n", + " 4 | 1.07e-02 | 3.09e-01 |1.00e-01 \n", + " \n", + "A normalized RMSE of 2.64e-06 was obtained for the Spectral density \n", + " The current fit took 13.199548 seconds\n" + ] + } + ], + "source": [ + "fs.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "b637d86d-1e1f-4d14-ad37-b0a5db8b69e8", + "metadata": {}, + "source": [ + "By default the get_fit method, has a threshold normalized root mean squared error (NRMSE) of $5\\times 10^{-6}$ and selects the number of oscillators automatically to obtain that value, one may on the other hand specify the Number of oscillators that can be done using the optional argument N, or may want a more accurate NRMSE, which can be specified with the final_rmse optional argument" + ] + }, + { + "cell_type": "markdown", + "id": "b601eb73-7b1f-4a0b-a988-d66212351c70", + "metadata": {}, + "source": [ + "or by requiring a lower NRMSE" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e00be6c9-e1a4-4968-bfad-bc566207cbe5", + "metadata": {}, + "outputs": [], + "source": [ + "fs.get_fit(J,w,final_rmse=2e-6)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "367f5c67-dfb8-4e05-8f7f-886257d3e10a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results of the fitting the Spectral density with 5 terms: \n", + " \n", + " Parameters| lam | gamma | w0 \n", + " 1 | 9.15e-02 | 6.03e-01 |1.00e-01 \n", + " 2 | 2.39e+00 | 1.50e+00 |1.00e-01 \n", + " 3 | 5.37e+00 | 2.28e+00 |1.15e+00 \n", + " 4 | 1.18e-03 | 1.54e-01 |1.00e-01 \n", + " 5 |-3.75e+00 | 4.31e+00 |4.17e+00 \n", + " \n", + "A normalized RMSE of 1.28e-06 was obtained for the Spectral density \n", + " The current fit took 24.697593 seconds\n" + ] + } + ], + "source": [ + "fs.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "cec2e350-406b-4e84-9772-a1041fd8c0bd", + "metadata": {}, + "source": [ + "Alternatively one may choose the number of oscillators in the fit instead of a desired NRMSE" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d9476fb9-d87f-494e-abe1-e253d8977251", + "metadata": {}, + "outputs": [], + "source": [ + "fs.get_fit(J,w,N=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "bafef06d-2656-4fd9-8306-72b61cd4160c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results of the fitting the Spectral density with 3 terms: \n", + " \n", + " Parameters| lam | gamma | w0 \n", + " 1 | 6.07e-01 | 1.01e+00 |1.00e-01 \n", + " 2 |-4.44e+00 | 4.31e+00 |3.96e+00 \n", + " 3 | 7.93e+00 | 2.30e+00 |1.00e-01 \n", + " 4 | 1.07e-02 | 3.09e-01 |1.00e-01 \n", + " \n", + "A normalized RMSE of 2.64e-06 was obtained for the Spectral density \n", + " The current fit took 4.815641 seconds\n" + ] + } + ], + "source": [ + "fs.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "194bfe00", + "metadata": {}, + "source": [ + "Let's take a closer look at our last fit by plotting the contribution of each term of the fit:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "1b7296fb", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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", 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m4r1Qa20l8F7w5QHdEVe3+eILt25lxHrP9OoFvw3m8rff7m0sIiKSsPr3788f//hHZs6cyeLFi6murqampoYff/yRBx98sHHWGum6tE8age3Dtr9uo1xo33ZxjKV71dU1teLtsIO3sURy7rlu/dBDsHGjt7GIiIikOSWNMDBsu61ZzUP7iowxPeIYT/eZP98ljltvDXEaP6tLttsO9t8fqqthxgyvoxEREUlrShrdsDoh1W2UC98XMcMyxkwzxswyxsxam4g9klsKzYf5s595G0dbzj3XTW+YmUyjQ4mIiKQe/SaOIWvtvcC94Ab39jic6JIhaTz4YFi6FHJyvI5EREQkrSlphIqw7XygvJVy+a18Jnn94Q9w4IEwfLjXkbTO59Pg3iIiIglAt6dhRdj2oDbKhfaVB3tTJ7/SUvfM4DbbeB1JdGVlrhf1smVeRyIiIpKWlDQ27zG9faulmvZp0EAvnH02nHce3Hef15GIiIikpbRPGq213wFLgi9/GamMMaYA2DP48vXuiCvuPv4YjjvODWeTDE4/3a3vuw/q672NRUREJA2lfdIYFMqcjjPGDI+w/xygB+AHHumuoOLqk0/giSfgww+9jqR99t4bxoyBlSvh+ee9jkZERCTtJFXSaIzpZYwpDS00xZ8f/n7LcRSNMdONMTa4DI9Q9Y3AKlxnl5eMMeOCn8s2xpwF/DlY7l5r7YK4fLnuNm+eW2+7rbdxtJcxcOaZbvsf//A2FhERkTSUVEkj8DmwNmwZEnz/0hbv39GRSq21ZcChwHrcjC+zjDHluPmm7wKycbelL+z6V0gQyZY0Apx8MuTlwZtvwoLUyN1FRESSRbIljXFjrZ0NjAVuBr4HsoAq4H3gDOAga22tdxHGWDImjcXFcPzxbvvuuz0NRUREtnTVVVdhjOGAAw7wOhSJg6Qap9FaO7yTn5sOTG9HudXARcElda1fD2vXQo8eMHiw19F0zFlnwfffw6RJXkciIiItzJkzB4Bx48Z5HEl069ev5/nnn+fNN99kzpw5LF68mIaGBvr06cP48eM5+eSTOfLII70OM6EkVdIoMRJqZRwzxj0rmEzGj4d33/U6ChERieDzzz8HkiNp7N+/Pw0NDY2vc3NzycrKYvny5SxfvpznnnuOgw46iKeffpr8/Pw2akofuj2djnJy4IgjYL/9vI5ERERSxPLly1m9ejUAu+yyi8fRRNfQ0MCuu+7KXXfdxY8//khNTQ2VlZUsXLiQ0047DYBXXnmF3/3udx5HmjjU0piOJkyAf//b6yi65vPP4a674JxzYKedvI5GRCTthW5N9+rVi5EjR3ocTXRvvfUWkydP3uL94cOHc//995OZmck999zDww8/zHXXXceQIUMi1JJe1NIoyenBB+H++9UhRkQkQYSSxtZaGZ9++mmKioowxnDqqaeyefPm7gxvC5ESxnCh1kaAWbNmxTucpKCkMR3NmgVr1oC1XkfSeaHbBY88AhUV3sYiIiKtJo319fVccMEFTJkyhbq6Ou6++24eeOABcnNzvQiz3cLj8/v9HkaSOJQ0ppu6OthtNxgwILmn49tuO9eDurLSJY4iIuKpSD2nly5dyqRJk7j11lsZOnQo7733XtI8I/j22283bu+www7eBZJAlDSmm0WLIBCAoUMhO9vraLomNEPM3Xcnd6upiEiSW7duHcuWLQOaksbXXnuNnXfemY8//pj99tuP2bNnM2HChKh1PfjggxhjOr2EJ3udtWnTJv7yl78AsOeeezJ69Ogu15kK1BEm3fzwg1tvtZW3ccTCUUdBaSl8+aWbS3viRK8jEpEU8z8vfMO3K8q9DiMmthtYxNWHjY1L3aFWxqKiIkaMGMHVV1/NNddcg7WWK664gmuuuYaMjPa1U+Xl5dGvX79Ox5LdxQaRQCDASSedxMqVK8nJyeH222/vUn2pREljuvnxR7feemtv44iFnByYOhWuv961NippFBHxRChpHDp0KL/85S/573//S8+ePXnooYf41a9+1aG6jj32WI499th4hNku559/Pi+++CIAd911Fz/72c88iyXRKGlMN6GWxlRIGgGmTYOamqaOMSIiMRSvlrlUE0oav/76a77++mv69OnDhx9+yNZJ9rvmkksu4Y477gDg5ptvZurUqR5HlFj0TGO6SaXb0+C+x223wVj9YBcR8UooaTzhhBPIyMhg7dq1vPPOOx5H1TGXXXYZf//73wG44YYbuOCCC7wNKAGppTHdpNLt6UisTb6pEUVEklhZWRk//fQTAFdeeSXjx4/nwgsv5Mwzz2TYsGHs18HZx5544gnOP//8Tsfz7LPPsvvuu3foM5deeik33ngjANdffz2XXHJJp4+fypQ0ppu33nKtjaNGeR1JbD39tHu28YYbYK+9vI5GRCRtfP7551hrycvLY/To0Wy33XZ8//333HXXXfz617/mgw8+YGwH7gbV1NQ0TkfYGXV1dR0qf8kllzS2MF5//fVceumlnT52qtPt6XQzcKAb3zAnx+tIYmvuXPjsM80QIyLSzUK3pnfYYQd8Ph8At912GwceeCBlZWUccsghHUoCTznlFKy1nV723nvvdh8rPGG88cYblTBGoaRRUsPpp0NGBjzzjJvtRkREukUoadxpp50a3/P5fDz55JNsv/32LF68mMMOO4zq6mqPIozs8ssvb0wYb7rpJi6++GKPI0p8ShrTybPPwjHHuMQq1QwZAoce6ma5eeABr6MREUkboaRx5513bvZ+UVERL774Iv369eOzzz7jxBNPJBAIeBHiFpYsWcL1118PQEZGBn/729/o379/q0voecd0p6QxnXz0ETz1FMyf73Uk8RGaIeaee9ysNyIiElfV1dUsWLAAaN7SGDJs2DCef/558vLy+Pe//50wt3/Dk9dAIMDq1avbXCorKz2MNnGoI0w6WbjQrUeO9DaOeDngABg+3H3PN96AAw/0OiIRkZSWn59PQ0NDm2V23XXXhLs1PXz4cKymn+0wtTSmk8WL3XrYMG/jiBefzw32DfDgg56GIiIikmrU0phOUj1pBDetYN++cNxxXkciIiKSUpQ0povqali7FrKyYMAAr6OJn3794LTTvI5CREQk5ej2dLpYssSthwxxQ9Okg+pqiPKsjYiIiLRPmmQPgjFw9NHp0znkhhvcQOYvvuh1JCIiIilBt6fTxejRbqq9dJGZCWVlboaYI47wOhoREZGkp5ZGSU0nn+ymSnztNfjpJ6+jERERSXpKGtPF99/D8uXpM+h1SQkce6zbvvdeb2MRERFJAUoa08Wpp8LgwfDOO15H0n1CM8TMmAF1dd7GIiIikuSUNKaLdBijsaWJE2HHHd1QQ//+t9fRiEgC0qwgkmrieU0raUwHdXWwYoXrQT14sNfRdB9jmlob337b01BEJPH4fD78fr/XYYjElN/vx+fzxaVu9Z5OB8uWuWcZBw2C7Gyvo+leJ5wAEybA+PFeRyIiCSY/P5/KykqKi4u9DkUkZiorK8nPz49L3WppTAehW9PDh3sahieKipQwikhERUVFbNiwQa2NkjL8fj8bNmygqKgoLvUraUwHodlghg71Ng6vzZvnZokREQEKCwspKChg8eLFbNq0iYaGBj3jKEnHWktDQwObNm1i8eLFFBQUUFhYGJdj6fZ0Oli+3K3T6XnGln7/e7jzTnjgATjlFK+jEZEEYIyhb9++VFRUUF5ezpo1a9TqKEnJ5/ORn59PaWkphYWFGGPichwljelg2jTYe2/o08frSLyzyy5ufffdShpFpJExhqKiorjdzhNJJbo9nQ5KS2H33WGbbbyOxDvHHgs9e8Inn8Dnn3sdjYiISNJR0ijpoaAAfvtbt33PPd7GIiIikoSUNKaDadPgwguhqsrrSLz1u9+59SOPQEWFt7GIiIgkGSWNqa6+Hu6/H269Nf3GaGxp7FjYc0+orHSJo4iIiLSbksZUt2oVWAv9+kFWltfReO/MMyEvD9av9zoSERGRpKLe06lOw+009+tfw8EHg2aAEBER6RAljalu2TK3HjTI2zgSRXa2btOLiIh0gm5Pp7pQS6OSxubq6uCJJ2DjRq8jERERSQpKGlOdbk9HduKJcNxx8M9/eh2JiIhIUlDSmOoGDoTddoPRo72OJLEcf7xb33knBALexiIiIpIEjCZnj4/x48fbWbNmeR2GtMbvh622gsWL4YUX4NBDvY5IRETEM8aY2dba8W2VUUujpCefD37/e7d9663exiIiIpIElDSmMmtd7+mGBq8jSUynnQb5+fDf/8I333gdjYiISEJT0pjKNmyAIUOgb1+vI0lMvXrBySe77dtu8zYWERGRBKekMZWtWOHW/fp5G0ciO/dcGDPGdRYSERGRVmlw71S2apVbDxjgbRyJbNtt4dtvwRivIxEREUloamlMZaGksX9/b+NIdEoYRUREoupSS6MxZhTwc2Ag0AfIBdYDa4F5wAfW2uquBimdpKSx/ZYuhTvugD331PA7IiIiEXQ4aTTG/ByYBvwSiNbDosEYMwd4BPiXtbas4yFKp61e7dZKGqN77jm4/nr48EMljSIiIhG0+/a0MeZEY8xXwPvAyUA/wABVwBLgC+Aj4DtcS6MFsoDdgFuB5caY+4wxQ2L5BaQNeqax/U4+GYqK4P33Yc4cr6MRERFJOFGTRmPM3saYWcA/ge2BjcB9wEnAKGttkbV2hLV2nLV2D2vtdtba/kAxsA9wBfAxkA+cBnxnjPmLMaYwPl9JGl12GTz5JEya5HUkia+w0I3bCBrsW0REJIKo0wgaY0IT874G3A28bK2t7/CBjBkO/BY4FygBpltr/9zRepKFphFMQj/9BNts42aLWbTIzdstIiKSBmI1jeBrwM+ttQdZa5/rTMIIYK1dZK39X2AYrvVxQ2fqEYmbkSPhqKOgvh5uv93raERERBJK1JZG6RzPWxrr6uCKK2DQILjoIu/iSDaffAITJ0LPnrB8ORQUeB2RiIhI3MWqpVGS0erVcNNNcOONXkeSXHbbDa66Cl56SQmjiIhIGM0Ik6o0RmPnXXON1xGIiIgknE4njcaYYlxv6q2AQsAPLAVmWWtXxSQ66TyN0Rgb1dWQn+91FCIiIp7rVNJojPkClzBGnH/NGLMAeAy4x1q7utPRSeeppbFrli+H0093yffs2ZpqUERE0l5nn2ncMfhZ08oyGrga+NEYc1kM4pSOUtLYNb17w+efu+XNN72ORkRExHOdTRofAM7CDd49CjeMzjbAvsAlwKtAA25A778YY+7teqjSIUoauyY3F849122rM5GIiEjnkkZr7WnW2nustW9ba3+w1i611v5orZ1prb3JWnswLpG8G9fyeJox5shYBi5RFBTAkCEweLDXkSSvs85yzzO+9hp89ZXX0YiIiHgqbkPuWGtXWWvPBq7CJY5nx+tYEsHf/gZLlrjBqqVzSkqapha8/npvYxEREfFYd4zTeCNQC4zrhmOJxNZFF7lpBR9/3E0zKCIikqa6I2nM6cZjSYjf73UEqWH4cDjxRMjMhE8/9ToaERERz8RscO/gM4uTge+AlUAdMAQ4FZc4fhyrY0kUVVVQVARDh8LChV5Hk/yuvRb++ld1KhIRkbQWyxlhRgK/B1pOZm2AhcB5MTyWtGXtWggE1NoYK4MGeR2BiIiI52J5y/hl4DrgfdzsMAZYAZwDjLLWzo3hsaQta9e6dZ8+3saRahoa4NFHYc0aryMRERHpdjFLGq2186y1f7TW7gX0Bc7FjdV4B3BTrI4j7aCkMT7OPhtOOAFuucXrSERERLpdp5JGY0xpW/uttZustXcC2wHPAb83xpzRmWNJJ4RawpQ0xtbUqW59xx2wcaO3sYiIiHSzzrY0LjDGnG2MafPz1toaXEeYBtwMMtIdQi2Nfft6G0eqmTgR9t0XKipc4igiIpJGOps0FgO3A98ZY042xmS3UbYSlzSO6uSxpKN0ezp+rrrKrW+5xSWPIiIiaaKzSePvgQpgK2AGsMIYc7cx5mhjzIhQC6Qxph8uuczDJY/SHY46yiU1++3ndSSpZ++9YffdYcMGtTaKiEhaMda2HCGnnR80ZgBwLXAS4KP5UDsWqAfCWyAfsdb+tpNxJp3x48fbWbNmeR2GxMObb7qEvFcvNw5mz55eRyQiItIlxpjZ1trxbZXpdO9pa+1Ka+1UXGeXu4Ay3DA7JlhvTtjr74DLOnuscMaYQmPMdGPMXGNMpTGmzBjzmTHm4ii3yduqc7oxxrZj2ToW30GS3D77wPHHwzXXQF6e19GIiIh0iy4P7m2t/R7XO/o8YFdgJ9xA3z2AcmA28Ly1trarxzLGDAPeBoYH36rGJafjg8sJxph9rbWd7dpaD2xoY39DJ+vtXjNmQE6Ou02tpCb2jHHjNYqIiKSRmM0IY60N4KYKjMt0gcYYH/ACLmFcCfzWWvvf4POTU4D7gJ2BR4CDO3mYD621e3c9Wo+dfz5UVsKmTUoau0N9PWRleR2FiIhIXMVyRph4OwXYIbh9tLX2v+CSVWvtE8DvgvsOMsbs60F8iaGmxiWMWVlu/mmJn0AA/vQnGDEC1q3zOhoREZG4Sqak8eTgeqa19qMI+x/HzXENkDYdbrYQPtyOMd7GkuoyMuCzz2D5crjhBq+jERERiauoSaMx5hJjTEzvcRpjJhhjDupA+XzgF8GXr0QqY1038FeDLw/oWoRJTGM0dq//+R+3vuMOWL3a21hERETiqD0tjdcDPxljLjTGFHflYMaYPYwxL+Kee5zQgY9uS1OsX7dRLrSvvzGmpBMhjjXGfG2MqQn2zP7OGHOfMWbnTtTlDc0G07123RUOOwyqq+Haa72ORkREJG7akzReBxQBNwIrjTFPBwfxjpqVGGOygq2KfzbG/Ai8g+uk8hnwnw7EOTBse3kb5cL3DWy1VOtKcQlqqFf2KOB0YLYx5ppO1Nf91NLY/a65xj0KcPfd8NNPXkcjIiISF1GTRmvtH3HJ08O43tZHAU/iEshFxpjnjDH3G2OuDyaHdxhjHjXGfIwbcudj4EpgBPAT8Btr7URr7VcdiLMwbLu6jXLh+wpbLbWl73HjSI4Gcq21vYEC4EDckEEGuMoYc3FblRhjphljZhljZq0NJW/drbLSPWunpLH77LgjnHSS60X9xz96HY2IiEhcdGhGGGPMQGAaMBUYHLYrUiWhXhgNwEvAPcBrthNT0BhjfoMbSgdgG2vtD62U2x94Pfhy91Y6zHT02LnAu7jb6ZXAYGttWbTPeTojTCDgEpicHG+On46WLIFRo+DnP4fXXoPsTo0zLyIi4on2zAjToXEarbUrgOnAdGPM9sAkYDfcreA+QC6wHlgLfItLtj6w1lZ0OPrmwj+f30a58H1dPSYA1trNxpgrgTdwA5bvCzwbi7rjJiNDCWN3GzoUvvzSJY7qtS4iIimo04N7W2u/xnU8uSt24bRqRdj2IKC1W9uDWvlMV4W3WI6MYb2SSkaP9joCERGRuEmWcRrnAYHg9vZtlAvtW2WtbWs6wNR15JGwyy7wzTdeR5K+vv0WLrjAPSYgIiKSItqVNBpjbjTG/MYY40lTirW2Gvgg+PKXkcoYYwyu4wo0PdcYKxPDthe2WioRzJ0Ln38OmTGbIVI6wu+HQw6BW2+FRx6JXl5ERCRJtLel8SLgX8C3xphyY8y7xphbjDEnGWPGBud/jrd/BteTjTG7Rdg/haZbxw+1t9JgstnW/hwgNABfFfBme+v2xPr1bl1a6m0c6crng6uvdttXXglVVd7GIyIiEiPtTfY24npDG1xnkD2Ac4EHcc8XlhtjPjTG3G6MOdUYs6MxxhfjWP8JzA3G8ExofmljTIYxZgpwX7DcK9baZomdMWa6McYGl+Et6p1kjPmvMeZEY8zgsM9kBY/xHq6zD8D/Wms3xfh7xU5DA2za5DpiFBd7HU36+u1vYdw4WLYMrr/e62hERERiol33MK21vY0xQ4FdWiz9g0Xycbdww1sAa40xc4E5uLEOP7DWzutsoNbaBmPMr4CZwHDgv8aYalzimxss9jlwQgerNrge0aEktAbXotgTyAqWCQB/tdYmdgawaZNbFxe7Fi/xRkYG3HIL7LmnSxqnToVhw7yOSkREpEva/eCbtXYJsISwmVyMMf2AcTRPJIcGd+fixjYcH1Z+KTADuLkzw/BYaxcZY3YELsENMj4CqAe+AR4DbrfW1nWw2rnB+n4O7ICbFaYYN1D4t7iWxnuttXM7Gm+3C92a7t3b2zgE9tgDjjsOHn8cLr/crUVERJJYhwb3bleFxvRiyxbJrWka7NsCK4HDrbWzY3rwBOLJ4N4ffQS77w677QYff9y9x5YtLVkCY8aAtbBgAQwZ4nVEIiIiEcV8cO/2sNZuxHUWaXyu0BjTA3fr+hDgZNxg4C8ZY3a01q6JdQxpq29fuOQSGDQoelmJv6FD4aGH3PONShhFRCTJxbylMeoBjekNvAbsDFxvrb2iWwPoJp5OIygiIiLSAe1paez2wb2ttetx81cbXMujSOoLBODBB5ueOxUREUkyXs0I8zmwGdeRRWLlm2/g7bdh9WqvI5GWLr0UTj0V/vAHryMRERHpFE+SRuvuib9H05A2Egt33AGTJ8PTT3sdibR0xhmQlQX33w8ffBC9vIiISILxbO5pa+2BQJFXx09JoVufJSXexiFbGjPGDb0DcOaZUF/vbTwiIiId5FnSCNCJMRWlLRqnMbFdeSWMHAlff+3mphYREUkiniaNEmMbNri1ksbElJcHd97ptq++2o3jKCIikiSUNKYStTQmvl/+EqZMgepqtTaKiEhSifng3uIhPdOYHG6+GXbZBS6+2OtIRERE2k1JY6rYvNm1XmVmQmGh19FIWwYN0tA7IiKSdHR7OlXk5MCKFfDVV2BM9PKSGFavhltu8ToKERGRqNTSmCqMgQED3CLJob4eJk6ERYvc3NRHH+11RCIiIq1SS2Oas9by9fIyXpm7ki+XbiIQ6N65yNNaVpabKQbg7LNh3Tpv4xEREWmDWhpTxbvvwo03wv77w7nntusjP6yp5OInv+DLZWWN740sLeBPh23H3qP7xitSCXfmmfDkk/DOO+7f7bHHvI5IREQkIrU0pooFC+CFF+CLL9pVfP6qco666wOWbqzh2iO356Xz9uCmY34GBk554DNuf/N73GyPElcZGW5qwfx8ePxxePRRryMSERGJSEljqujAcDvlm+s57cFZ5Gdn8tw5v+CE3YYxdmBPjtplMK+cvydH7jyIv7+xgFvf/D7OQQsAW2/d1Bnm7LNh8WJPwxEREYlESWOq6MDA3te9NI+VZTXcdeIuDCnJb7YvJ9PHTcf8jCnjBnPLf7/nic80a0m3OP10OPxwKC+H//7X62hERES2oKQxVYSmEIzS0jh/VTlPzFrKaXuMYJehvSKWMcbwl6N2YM9tSvl/z33D18vLIpaTGDIG7rsP3n4bTjvN62hERES2oKQxVbSzpfHvry+gR3Ym50zeus1ymb4Mbjl2J0rysznn0TlU1zXEKlJpTZ8+MGmS11GIiIhEpKQxVbTjmcYf11byxrermbrHCIrzs6NW2btHDrcetxOL11dzw2vfxSpSaY833oDJk93tahERkQSgpDFVTJzohtsZNKjVIo9+soTMDMMJE4e2u9rdRvbm5J8P48EPFzFr0YZYRCrR+P1u/Ma334Zp00C92EVEJAEoaUwV118Pr78Oo0ZF3L253s/Ts5dx4Pb96VuY26GqL/vlGAb2zOOyZ76iriEQi2ilLT6fG7uxRw944gn4xz+8jkhERERJY7p4c94aymrq+c2u7W9lDCnIyeTPR4zlp7VV/PPDRbEPTrY0apQbvxHgwgth9mxv4xERkbSnpDEVBALw449NPagjeHnuSkp7ZDNxZPQheSLZZ0w/Jo/uw61vfs+a8s2djVQ64thj3biNdXUwZQps2uR1RCIiksaUNKaC9evdANHbbBNxd3VdA2/OX81B2w/Al2E6fZg/HTaW2gY/f3tVnWK6zU03wbhxsHAhTJ3qdTQiIpLGlDSmgo0b3bpX5HEXZ85fy+b6AAfvMKBLhxlRWsBpe4zkmTnL+HzJxi7VJe2Uk+Oebxw1Cs47z+toREQkjSlpTAWh25bFxRF3vzlvNb3ys9h1RPQpBqP5/T5bU9ojm7+8Ml9zU3eXkSPh229h7729jkRERNKYksZU0EZLYyBgeff7deyxTZ8u3ZoO6ZGTyfn7jeLThRt4c96aLtcn7eTzNW2/8gp88YVnoYiISHpS0pgK2kga560qZ11lLZO2KY3Z4Y6bMISRpQX87dX5NPg1BE+3euUVOOQQN0/12rVeRyMiImlESWMqaOP29LsL1gEwaVSfmB0uy5fBZb8czfdrKnl69rKY1SvtMHky7LorLFkCRxwBNTVeRyQiImlCSWMqaKOl8b3v1zKmfyH9ijo2oHc0B47tzy5Di7n5vws0L3V3ys2Ff/8bhgyBDz+Ek05yQy6JiIjEmZLGVPCb37jblief3Ozt2gY/sxZvZI+tY3drOsQYw5UHb8vq8lpmvL8w5vVLGwYMcP/ePXvCM8/AJZd4HZGIiKQBJY2pYNgw+OUvYbvtmr09d1kZdQ0BJsSg13Qk44eXcODYftz9zk+sq6yNyzGkFWPHwrPPQlYW3Hwz3H671xGJiEiKU9KYwj5b5G5bjx8WefzGWLjsl2Ooqfdz+5vfx+0Y0op99oEZM6BfP5g40etoREQkxSlpTAV33AF//CMsWtTs7VmLNjCyTwG9e+TE7dBb9enB8bsO4ZFPlrBwXVXcjiOtOPFEWLAAJkzwOhIREUlxShpTwT//CddeC2uaxk0MBCyzFm9kwrD43JoOd/6+o8jJzOD6V+fH/VgSQVFR0/bDD8M773gXi4iIpCwljakgQu/pH9ZWUlZTz/jh8bs1HdKnMIff7bUVr3y9itmLNb2gZ2bOdL2pDzsMPvvM62hERCTFKGlMBRHGafxs0QYAJgyPf0sjwOl7jqBvYQ7XvTxP0wt6ZdIkOO44qKhwHaPmzvU6IhERSSFKGpOdtRGTxq+WllFSkM2w3vndEkZ+diYX7T+K2Ys38to3q7vlmNKCzwcPPeRaGjdscAOBa7pBERGJESWNya6iAvx+KChww68EfbW8jO0H9cSYrs833V6/HjeYbfr24G+vzqde0wt6IysLnnwSDj4Y1q93PaxnzfI6KhERSQFKGpNdqJUx7HnGzfV+FqyuYMdBPbs1lExfBn84aAwL11Xx+KdLuvXYEiY3143hePjh7nnX44+HBs3aIyIiXaOkMdnV1cG228I22zS+9e3KcvwByw6DuzdpBNhnTF8mjizhlv9+T8Xm+m4/vgTl5MBTT8EZZ7hZYzIzvY5IRESSnJLGZLf11vDtt/DWW41vzV1WBsCOHiSNxhiuOGhb1lfVce+7P3X78SVMVhbcey/suGPTe99rEHYREekcJY0paO7yMkp75NC/KNeT4/9sSDGH/Wwg9733EyvLajyJQSJ48EHXKn333V5HIiIiSUhJY7KLMLzN3GVl7DCoqFs7wbR02YGjsRaue1kDfieMFStcp6mzzoKrr4547YiIiLRGSWOyu+UW13P6T38CoLquge/XVLDD4GJPwxpSks9Ze2/FC1+u4KMf13saiwRdeSXcdx9kZMD//i+ceirU1nodlYiIJAkljclu40aornaJADBvZTkBCzt0c8/pSM7caysG98pj+vPf0KAheBLD6afDv/8NeXlu+sl99mk2/aSIiEhrlDQmuxZD7ny7sgKA7QYWtfKB7pOb5eP/Hbod362u4F8fL/Y6HAn51a/g/fdh8GD48EM3JI+IiEgUShqTXWje6eBsMN+tKqcwN5OBPb3pBNPSAdv1Y9KoPtz0+gLWVuhWaMLYZRc3P/WBB8Idd3gdjYiIJAEljcmuxRSC81dWMKZ/oaedYMIZY7j6sO3Y3ODnL6/M8zocCde/P7z6qutRDa5jzGOPubE/RUREWlDSmOzK3JiMFBdjreW7VRWM6e/9relwW/XpwbRJI3l2znLe+36t1+FIax54AH7zG9hrL1iiGX1ERKQ5JY3JLpQ09uzJ8k01VNQ2MLp/obcxRXDuPtswsrSAK56dS3WdprRLSNttB0OGwMcfw847w8svex2RiIgkECWNye6KK+D662HIEL5b5TrBbDsg8ZLG3CwffzlqB5ZtrOGm1xd4HY5EMnEifP45HHQQbNgAhxwCv/+9650vIiJpT0ljsjvuOLj0Uujdm/nBpHFUv8RLGgF2G9mbE3YbyowPFvLl0k1ehyOR9O4NL74If/2rm4bwzjtdq+OPP3odmYiIeExJYwqZv6qCwb3yKMzN8jqUVl1+0Bj6FuZy2dNfsbne73U4EklGBlx+OXz6KYwdCz4fDBzodVQiIuIxJY3JrLYW7r0Xnn0WgPkryxmTgM8zhivKzeIvR+3Ad6sr+Pvr33kdjrRlp51g1iz3bGNenntv/Xr3zKOIiKQdJY3JbP16+N3v4JxzqG3w89O6qoTsBNPS5DF9OWniMO57byEf/LDO63CkLbm5MHx40+tLL4Xdd4dzzmnqhCUiImlBSWMyC+s5/cOaSvwBm3DD7bTmyoO3ZWSfAi5+8kvKquu9Dkfaw1p3m9rng7vucr2tn3zSvS8iIilPSWMyCyWNRUWNPacT/fZ0SF62j1uP3Zl1lbVc+Z+5WCUeic8YuOYamDMHdtsNVqyAY4914zp+/rnX0YmISJwpaUxm5eVu3bMn36+pJDPDMLy0wNuYOmCHwT256IBRvPTVSh7+RINJJ40ddoAPPoB77oHSUnjvPZgwARZrfnERkVSmpDGZhbU0/rimkuGlBWT5kuuf9MxJWzF5dB/+/MK3fKFheJKHzwfTpsH338NFF8HUqTBsmNtnLVRUeBufiIjEXHJlGNJcWEvjD2sr2apP8rQyhmRkGG4+dif6FOZwziNz2FCleY+TSnEx/P3vrtUx5I03XAL5l79AVZVnoYmISGwpaUxmlZUA1PUsZvH6arbu28PjgDqnOD+bu08cx9qKWi544gv8AT3fmHSMadp+8UXYuBGuvBJGjoSbbmq8VkVEJHkpaUxm558PtbUsufBK/AGbtEkjuOcb//fwsby7YC3XvjTP63CkK2691bU27rorrFkDF18MQ4fCn/4Ea9d6HZ2IiHSSksZkl53ND+VuyJqt+iRv0ghw3K5DmfqLEcz4YCH/+miR1+FIZxkD++3nBgF/4QU3ruPGjfDnP7vB6EVEJCkpaUwBP6xxt/6SPWkEuOqQbdl3TF+mv/At7yxQq1RSMwYOPdT1tH7vPTj6aDj77Kb9jz8OL70EgYB3MYqISLspaUxmF1wAEyfy4zcLGdgzl4KcTK8j6jJfhuG243dmVL9CznlkDl8v16wjKWGPPeDpp6FXL/e6vt71uj70UNh6a7jhBlin2YFERBKZksZk9vXX8Mkn/FDewFZJ/DxjSwU5mcw4ZTw987L47YxPG1tSJYU0NLg/eoYPh4UL4bLL3GwzRx4Jzz0HdepFLyKSaJQ0JrPycgIYfqy2KXFrOtyAnnk8fPpuZBjDifd/wtIN1V6HJLGUl+cSxR9+gOefh4MOAr8f/vMfOOII+PBDryMUEZEWlDQms7IyVhX2ptpPUvecbs2I0gL+ddquVNc1cNL/fcKqss1ehySx5vPBYYfByy/D0qXuNvVBB8GkSU1lpk6FSy+FTz7RPNciIh5S0pjMysr4ofcQIDWTRoBtBxTx4NRdWVdZxzH3fMSyjWpxTFkDB8Ill7gEMiP4o2ndOnjoIbjxRpg40Q0afuGFMHOmbmGLiHQzJY3JLCxpTLXb0+F2GdqLh0/fjU3VdRxz90csWqdZRtJGSQm8/bYbk3TQINcaecstsM8+0Lu3Sx5FRKRbKGlMVnV1sHkzP5YOoWdeFqU9sr2OKK52GlLMY9MmsrkhwDH3fMT8VeVehyTdISPD9by+5RZYsgQ++sjdqt5+ezdF4dixTWX/8hfXCvncc25cSBERiSlj9YxQXIwfP97OmjUrfgeoqYFLL+XY3Ak0bLUNz5y1e/yOlUC+X13Bif/3CdW1fv5x4jj22KbU65DEK6tWQf/+bttad+t66VL32hj42c9g773d8otfQKmuFRGR1hhjZltrx7dVRi2NySovD+64gx+LB7JVnwKvo+k22/Qr5N9n/4JBvfI45YFPefKzpV6HJF4JJYzgksYHH3RTFU6aBFlZ8MUXroXyiCPgzjubyi5f7mar2ayOVSIiHZH8o0GnsU3VdayrrEvZTjCtGVicx1Nn/pyzH5nDZc98xU/rqrjkgFFk+vQ3UNrKyHDPOe6zj3tdU+MSw7ffhnfecbe4Q554ws2HnZUFO+7oWiRDy447Ng1ALiIizSRd0miMKQQuBo4GRgB+YAHwOHC7tbbTXSqNMf2Ay4BDgaFADfAN8E/g/2wi3cvfsIEfP/oGSN2e020pzM1ixikTuPr5b7j7nR+Zu3wTtx23M7175HgdmiSCvDyYPNktLRUUuGchv/0WZs92S8igQbBsWdPrp55yt71Hj4aePeMft4hIAkuqZxqNMcOAt4HhwbeqAR8QyhQ+B/a11nb4KXhjzDjgNaB38K1KIJemxPp14FfW2tr21Bf3Zxqfe44n/nQnlx90Pu9cujfDeqfPLeqWnvxsKX987mtKC7K568Rx7DSk2OuQJBmUl8OXXzZfhg1ziSJARQUUFTWV79fPJY9jxrj1kUfCiBHexC4iEmPteaYxaVoajTE+4AVcwrgS+K219r/GmAxgCnAfsDPwCHBwB+vuCbyISxjnAydZa2cZY7KBM4CbgQOC67Nj8oW6qqyMH0sGk239DO6V73U0njpmwhC2G1jE7/41myl3f8iF+4/id5O2wpdhvA5NEllREey5p1siqaqCY46B+fNhwQJYvdot777r9m+7bVPSeNNN8OijblrE0DJsmGu5HDTIJZwiIkkuaZJG4BRgh+D20dbajwCstQHgiWDy+ChwkDFmX2vtmx2o+xKgP+529MHW2oXBuuuAO40xRcB1wDRjzC3W2gUx+UZdUV7OD72HMDJQpeQI2H5QT146bw+u/Pdcrn/1O96ev5a/H/MzhpSkd0ItXdC/v3v+ESAQcD2zv/vOLfPnw3bbNZX9+ustb3WHbLcdfOMeJcFaOPZY15N74EAYMAD69nWv+/Rx7+XrmhWRxJQ0t6eNMe8CewIzrbX7RNhvgB9xzzk+ZK09uQN1L8Y9w/iAtXZqhP09cK2bPYD/tdZeHa3OuN+evvZaJi3pxw69Mrnzr6fE7zhJxlrLs3OWc/Xz32CAqw7ZlmPGDyFDibXE07p1bh7tRYualsWLYcUKGDWq6ZZ3eXnbz0Y+8ACccorbfuIJuOeepoSypMR9trjYbR91VNPn1q93z2rm5sbl64lI6kuZ29PGmHzgF8GXr0QqY621xphXgbNwt5LbW/doXMLYVt2Vxpj3gIOCdUdNGuNtc3kFS4t35MictV6HklCMMRw9bjC7jijhkqe+5A/PzuXZOcu57qjt2bpvodfhSaoqLXXLxIltl8vKgqefdslkaFm3DtaudesBA5rKzp/f+ow3vXs3TxrHj3eJana2Syp79oTCQpdInnqqW0J1PvCAez+09OjRtD1pUlPiuWoVNDS41zk5bp2Z6cbAFJG0lBRJI7AtTWNKft1GudC+/saYEmvthnbUvX2Ez7dW90HAdm2U6TYLK/zYogy2LvR5HUpCGlKSz2NnTOSp2Uu57uX5HHTre5y111actffW5GXrnIlH8vLg6KPbV3bqVDcoeSip3LgRyspg0yaXxIXLynJLXR2sWeOWkP33b9qePx+uv771Y65Y0ZS4Tp0Kr7T4Ozojwx378MPhscfce2vWwIEHuvdDS1aWSzCzsuCqq2DnnV3ZF16A115rvj+09OoF55zTdKxHHnFJa2Ym+Hxuychw6+22cy24oePPndt8f/j2z37m6gA3q1BNTeSy+fku4Qb3OEJ5uUuQIy05Oe5zAH6/Kx+pnEiKSZakcWDY9vI2yoXvGwi0J2nsaN1Fxpge1trKdtQdNz/Uuh9YWxWn9vSBXZGRYTh2wlD23bYf17z4Lbe99QNPzV7GpQeO5oidBumWtSS2IUPc0h4LFrjnJTdvdknlpk1QWek68wwd2lRu223hr39t2hdaQq8Lw1rjS0rcM5abN0NtrVv7/S7pamhoKldZ6QZSb820aU3bH37YfKD1cMOGNU8azzsPNrTyI/zaa+HKK932u+/ClCmtH3/dOtcyC3D66fDGG5HLHXUUPPOM2168GEaObL3O116DA4I3tK66Cv72t8jlRoyAn35qej1ggPtOkRLM6dPhkktcueefh5NPbj1pnTfP/fsAHH98U+eslg45BO69120vXQq7t5g5LDyxffhh19IM7vv84x+R6xw8GN5/v+n1Lru46y1SnZdcAmed5bZffRXOPTdynQCffdaUtE+dCh98ELnOAw+EW29128uWNf07RHLffe4PL3AD/d9//5ZljHGd1V59tem9PfZwf6BFcv757joCdy1dfHHrx3/33abv9LvfuWlQI9l/f/j739328uVwcIu+vMcc466zBJAsSWP4fcXqNsqF72vvvcjO1r1F0miMmQZMAxga/oM6DjZNOZ6895Yz8tD94nqcVFDaI4dbjtuZ43cdyjUvzeOiJ7/kgQ8WcdUh2zJxZO/oFYgkA2NcS2ZeXvPb3OFGj4bLL29ffQ8/vOV7DQ0ugQx/Fn7QIJgzpymxrK2F+npXtr7etfSF/OpXLhEN3x/aDh/eCFwyVFHh9oda80LrUCsjuOc9J09u2t+ybFZW81i32WbLMn5/80HdjXHxWBt58YXdrQi1WIbvD2nZZ6C21rUGR1Jf37xceCLWUni969e7FuJIwpPuhobmY5C2FD5D0qZNLnGOpGUL6qJFrc/1Hv4dKivdc7+tCQSatpctc38IRbJ92M3B+nqXQLemqqppe/Xqpg5pLVW2+HU+b17rf7CsDXskrKzMtXK3xu9v2l64sPWy4ddzXR189VXz/aHENwEkRUcYY8xvcEPpAGxjrY145Rlj9seNpwiwe6iHdZS6rwSuDb7MstY2tFLuDCD4JxsDrbUr26o37h1hgEDAqrWsgwIBy3NfLuf6V79jZdlm9ti6lPP324YJw0u8Dk1EUom1LhEKTzBDCXekJTu76bGDujqXyLRWtm9fl6yCa0mtbWX44NzcplbWhgZYGfZrq+Xv/j593B8c0PQoRCQ+X/MW8EWLmhK+lnWWlDQl4xUV7jnZ1owc2XSuli6F6urIdfbo4Vo7wZ2nthLRoUNdeXDHXreu+X5rsdZS78vCv80oGgIBGvyWhvnf4W9owG/BHwC/tW7bWvwlvQkU96IhYPGXlxNYuZoGawlYguWbygYGD6aBDALW4l+1hoaamqZyjXWCPzsHf3ExgYCloa6BwNq1TfusxZ+Ty9F7jWHswPhOMJAyHWGAirDttsajCN9X0Wqptusuj2HdcaWEseMyMgxH7jyYX44dwMMfL+aed39kyt0f8Yute3PBfqOUPIpIbBjTPGGELZ9FbU12dtPt52hKS9tXLjOz/Y879OrV6nSa1lrqGwI0BALUN1jqeg+g3h9oXOoabNP2Bj/1a9fSEHrtL6C+IRBW3uIPWOoDAfxLfqIhYF3iFrA0hPb5A8G1xR8I0BCopsG/urFseJnQ59w6gD+wxtXtt9QHXH0N/mD9we1AY076U8Tvu6U2Et8trO9A2dXNXvkyjFuMwZexmV13qI570tgeyZI0hre9DwK+aqXcoFY+05G6W0saQ3WXe/08o3RdXraPMyaN5MSJw3jkk8Xc/c5PTLn7I3YeWszpe4zkwLH9NJe1iHjKWpcs1Tb4qW0IUNcQoLYhQG2Dv2m7PkCd309tfSCsjD9YrrXygcbkrS4sgWvwB6jzNyV99Q0tXgfLxZMvw5AZWnwZwbUhMyPD7fOF9mc0387IIDer5ecywuoywbozItRt8GVkkNVYxr3OzDBkZBh8GeDLyAgmcMHtDMgwrr6MDIL7mpYMEzymcXVkBt8L1Z8RSgh9pvGzrj6T0A1CyZI0zgMCuB7U29PK0Dg09YRe1c6e09C8x/T2wWO1Vfe37axXkkBeto/T9xzJCbsN48lZS3ngg4Wc8+gcBhXnccruwzlmwhB65mVFr0hEUp61ljp/gM11AWrq/dTU+9kcWtf52dzgpya4b3NoX4v3a4Plaxr3hSV6wYSutj6YJPoDW9yd7YzszAxyMjPIyfSRk5lBdmYG2T6XdGX53HZuVgZZuZmNr7OC+7Iym15n+jKC+4P7GveHvfZlkJ3pkqnQdrN9vgyyGvc3JW+hZMqo13lCS4pnGqHZ4N5vWWv3jbDfAD8AI+n84N4zrLWnRdhfgGuTTpzBvSUu/AHLm/NW83/vL+SThRvIyczg4B0GcOyEIew2okQ/0ESSQIM/QHW9n+paP1V1DU3rugaq68Ler/NTVdtiHSxfXd/gkrr6QGNiWFPv71QS58sw5GX5yM3ykZedQW6mj7xs9zonM4PcLN8WiV1oyQ69l9XidcTtDHKyfGT7MprK+zL0c0vaJZWeaQT4Jy5pnGyM2c1a+0mL/VNwCSPAQx2s+yHgj8Bxxpg/W2sXtdh/Di5h9NPUIUdSkC/DcMDY/hwwtj/frCjj8U+X8p8vlvPvz5czorSAKeMHc/hOgxhUnOd1qCIppcEfoKrWT/nmeiprG6isbaBicz0Vmxuo2OxeV24OvhfcrmolCaxtCEQ/YJAvw1CQ7aMgJ5P8sHXfwtzGRC83K4O8rKZELzfLF3ztEsDcbF9TUhhccrMzGl9n6VEXSRHJ1NKYCczBzT+9HDjZWvtmcM7po4H7gSLgFWvtwS0+O52mWVxGtEwKjTE9gfm4+ae/BX5rrZ1tjMkGTgNuAbKBf1hrz25PvGppTB01dX5e+Xolj3+2lE8Xuqcexg3rxaE7DuCQHQbQt0hTt0l6s9ZSWdtAWU1941LeuG6gIpgAVoYlgC3fq6n3Rz1OhoEeOZkU5mbRIyeTHrkuwcvP9lGQnUl+TnCdnUlBjq/5OttHfo5b54WVV0uciNOelsakSRoBjDHDgZnA8OBb1bjnHEO/tT8H9rXWbmzxuem0kTQGy4wDXgNCA/dVBOsNPdD2OvAra20rYxs0p6QxNS1eX8WLX63khS9XMH9VBcbArsNLOGBsf/Yd05fhpQVehyjSKYGApaK2oTHZa5n8tVzC3y/f3IA/0PrvEmOgR3Ymhbku0WtM/HIzKcwJvp8TfB18z21nBcu6JS/LpwRPJE5SLmkEMMYUApcARwEjcB1kFgCPAbdba7cYObU9SWOwXD/gcuBQYAiwGddR5p+45x3bfc9DSWPq+2FNBS98uZKX567k+zWuQ/3IPgXsO6Yv+4zpx/jhvXRbSjyzud7Phqq6yEt1HRsqw7ar6thUXUcbeR+ZGYaeeVn0zMuiKLhuuRTlZUbcX5CdmdA9QkUkRZPGZKGkMb0sWV/NW/NX89Z3a/n4x/XU+QPkZ/sYP7yEn4/sze5b9WbswCIN4yOdEghYKjY3sL6qlo3VdayvbJH8VW+ZGFbXRb7dm2GgpCCbXvnZlBQ0Lb3ysynObz0hzM9WK59IKlPS6CEljemrqraB939Yx4c/rOPDH9c3tkIW5mSy64gSdhnWi52HFLPjkGJ65CRTXzSJlbqGQGPyt7G6jvVVdWyscusNVbVsrKp3CWJVvdtXXdfq7d+8LF+z5K/NJT+bnnlZavUTkS0oafSQkkYJWVtRy8c/refDH9fzycL1/LTWzYdqDGzTtwc7D+nFz4YUs93AIkb3KyQv2xelRkkkoU4gG6qaJ38bgy1+zd4LtgxW1EacrRRjoDgvi14F2fQu2LIlsHeP4Logh5IeLgnU9SIisaCk0UNKGqU1ZdX1fLFsE58v2cgXSzfxxdJNbKquB1zSMKJ3AWMGFDKmfxFj+heyVd8eDOmVT3ambm13h7qGAJuq69hYXR92u7eWDVX1bl3t1qFWwo1V9dT5Iz/unJ2ZQe8WCV9JMCHs1WJdUuBaAfUIg4h4QUmjh5Q0SntZa1myoZp5KyuYv6qc+cH14g3VjQMJ+zIMg3vlMbx3ASNK3TKsdz4Di/MY0DOXwlzNWhNJgz/Appp6NlY1JYEbg8//bQw+E+jWwTJVrbcCAhTlZoa1/uVQUpDVbN0yGSzQc4AikiRSbXBvkZRkjGFY7wKG9S7gl9v3b3y/qraBBasr+GltFYvWV/HTuioWrati1qINVLXo5FCYk8mA4lz698xjYM9c+hXl0rtH063N3gU5wVucydWSZa2lpt5PZW0D5TUNweFd3HAv5cGhXsoat5vGBQwfGqY1+dm+xpa/XgXZDO+d3+x1Sb47XyU9mm4Pqze8iKQzJY0iCaogJ5Odh/Zi56G9mr1vrWVtZS2L11ezsmwzKzfVsLJsMys21bCqfDPfrihnXWXrw4n2zGsa+64gxy2FOW4Q5B45WeRlZ5Dt85GVacj2uenJmuaUdfPEujiC8WBbvHa9feuCc+fW+93cuqF1nd82blfX+amua6Cqzk91bXDdOMtHA9XtmLYtJzOj2TAvpT2yGdmngJ55WVsmgQVZjQlgbpaeBRQR6QgljSJJxhhD38Jc+ha2PhNNgz/QeDt2fVVts6FYQrdgQ9OwldXUs3xjNVW1rkWvuq6hzfH6uqopETXkZwdn9AjO1FGcnx2cwWPLWTyKgolhUW7TmIBFuVlK/kREuomSRpEUlOnLoE9hDn0Kc4DCDn/eHwi2BvoD1IdaDBssdf4ADYEABtfaGHpcL/TUXui1L8MlheEtlaFWSj3jJyKSnJQ0isgWfBkGX4ZPrXgiItJIT3WLiIiISFRKGkVEREQkKiWNIiIiIhKVkkYRERERiUpJo4iIiIhEpaRRRERERKJS0igiIiIiUSlpFBEREZGolDSKiIiISFRKGkVEREQkKmOt9TqGlGSMWQssjvNhSoF1cT5GutE5jS2dz9jTOY0tnc/Y0zmNve44p8OstX3aKqCkMYkZY2ZZa8d7HUcq0TmNLZ3P2NM5jS2dz9jTOY29RDmnuj0tIiIiIlEpaRQRERGRqJQ0Jrd7vQ4gBemcxpbOZ+zpnMaWzmfs6ZzGXkKcUz3TKCIiIiJRqaVRRERERKJS0igiIiIiUSlp9JgxptAYM90YM9cYU2mMKTPGfGaMudgYk93FuvsZY/5ujPnOGFNjjNlgjHnPGHO6McbE6jskCmNMb2PMqcaYh40x3xpjqowxtcaYZcaY/xhjjuxC3dONMbYdy9ax/E5eMsac0s7vvF8XjpE212g7z2VomdmJ+lPuGjXG5BtjDjLG/NEY86wxZnHY95jezjrieo0ZY7YyxtxjjFlojNlsjFljjHnNGHN0V+uOh66cU2PMIGPM2caYp4wxPwTPZ03wuz9mjNmni7E92M5rOLMrx4m1Lp7Tbvl/G6vrNKFOfLoxxgwD3gaGB9+qBnKA8cHlBGPMvtbajZ2oexzwGtA7+FYlUAjsEVymGGN+Za2t7cp3SDCraH5NbwbqgUHB5XBjzCvAr6211Z08Rj2woY39DZ2sN5EFgLVt7O/UNZSG1+jqKPuzgJLg9mddOE4qXaO7Ai939sPxvsaMMQcDTwH5wbfKg8c6ADjAGPMAcJpNrM4DnTqnxpghuAkrwhPt6uDr4cHlOGPMDGCatdbfhRg3A2Vt7E+k8wldvE6D4vb/NpbXqVoaPWKM8QEv4P6jrQT2t9YW4P5RjwMqgJ2BRzpRd0/gRdxFMR+YYK0tBAqA3+MuzgOAm7v8RRJLJvApcDawlbU2z1rbAxgB/F+wzEHAPV04xofW2v5tLIu69A0S09Io3/m9jlaYjtdolHPYH7gurPj/tVZPO6TaNboReBO4ATge98dhVPG+xowxI4AncT+zPwBGW2t7Aj2B/w0WOxW4tDP1x1lnzqkPlyC+CZwMDAr+zuoBjAWeC5abCkzvYnxPRLmGu5KQxkunrtMwcfl/G/Pr1FqrxYMFOA3315IFfh5h//Fh+/ftYN1/Dn6uGhgRYf8Vwf0NwCivz0UMz+nkKPvvDjunQzpY9/Tg5972+nt24/k8JfidF8Wh7rS8RqOck2+D3/m9Tn4+5a5RwBfhvUXB7zk9ymfjeo0B/wp+fiVQHGH/PcH9ZUAvr89lV88pLsnYpY39BnglWE8FkNuJ2B4Mfv5Br89Td5zTYLm4/r+N9XWqlkbvnBxcz7TWfhRh/+PAwuD2bztYd6j849bahRH23467TeMDTuhg3QnLWhvtObDw1hvPp2NKc2l5jbbGGLM7sG3w5f1expJIbNdalOJ2jRljCoDQs2D/sNZuilDsL8F1EXBER+qPp86eU2ttmbV2Thv7LTAj+LIHTddzyuvidRo38bhOlTR6wBiTD/wi+PKVSGWC/wFfDb48oAN1jwaGRqm7EgjdUmx33Slgc9i2z7Mo0pyu0YhOC67Lcc8eSRd0wzW2B5AXpf5FwLxO1p+s9DM2scT8OlXS6I1taTr3X7dRLrSvvzGmpI1y4baP8Pm26t6unfWmgr3Dtud2so6xxpivgz0GK4M9Mu8zxuwcg/gSVR9jzOzg960xxvxkXA/1vTtZn67RMMaYHsAxwZeP2s530gpJx2u0pXhfY+H1f9OO+sd2sP5ktXdwXQcs6EI9+xpjFgR7+ZYbN7rILcaYbboeYsKKx//bmF+nShq9MTBse3kb5cL3DWy1VNfqLgr+0kppxphi3DNM4J4Z+66TVZXikv5QT/dRwOnAbGPMNV2NM0HlA7vgfhFk4DoWnQDMNMbM6MTwF7pGmzsOdzsPYnNrOh2v0ZbifY2F6t8YJckP1d/en99JK9jh4szgyyesteVdqG4wMBJ3Defjkp/zga+NMWd1KdDEFY//tzG/TpU0eqMwbLutf8jwfYWtluq+upOSMSYD9zDwANzwMOd2oprvgcuA0bgHvHvjemEeCMzGPQR+lTHm4pgEnRhWAP8D/Az3nUtwP8B/Afw3WOZUOt77VNdoc6cH119aa2d3oZ50vEZbE+9rLFQ2WqtwaH8qX78YY/JoGtJlPU1/oHfUHFzP9uFATvBnThHuubwfgWzgLmPMr7sacwKJ5//bmF+nSholHdwKHBrcPtta+2VHK7DWPmKtvcFau8BaWx98r85a+zruuZHQuHrTg0N9JD1r7evW2unW2q9scBw7a63fWvsh7gdaaIiNs1P8tlHcGGPGArsFX3aplTEdr1HxXvBOw6PAONwwRr+x1rbVutsqa+1t1to7rbWLQ51LrLXV1tpncf9PFgWL3mhMagz+n2z/b5U0eqMibDu/1VLN91W0Wqr76k46xpgbcX+5AlxorZ3RVvnOsNZuBq4MvuwB7BvrYyQaa20AuCT4MgM4rAMf1zXaJNTKuJlOjMnaXml4jcb7GguVbavu8P0pef0Gxxt+GNfrtgGXML4ej2NZa9cD1wZfDsONY5zSYvD/NubXqZJGb6wI2x7URrnwfStaLdW1usuDvQhTjjHmeiDUpH+ptfaWOB4ufNikkXE8TsKw1v4ArAu+7Mh31jUKGDdN6InBl8/YTsz81EHpdI3G+xoL1d8rOBpGtPrb+/M7aYQljMcCfuBEa+3TcT5sOl3DIV35zjG/TpU0emMebmo2aN67qaXQvlXW2ramFwoX3lOwPXV/2856k4ox5gaaRri/zFp7o5fxSDO6Rp3DcQ+/g8ZmjLV4X2Ph9bfV4zRUf1s9V5NOMGF8BNeJK5QwPuFtVBJBzK9TJY0eCPZi+iD48peRygSf1zgw+LLdzf3BXsFLotRdAOzZ0bqTRfCWdOjW6WXW2hu64bATw7YjDSSccowxW9GU9LT7O+sabRS6Nf0D8E43HC9trtFuuMbeB2qi1D+MpgGuU+YaDksYw1sYH++mw6fNNRymK9855tepkkbv/DO4nmyM2S3C/ik0NUU/1MG6Q+WPM8YMj7D/HNzzEX7i+ByVF4IJY+iW9CWxSBijPXBtjMmh6VmbKtz8o0mtHd/Z4OZYBddq/mIHD5G21yiAMWYosF/w5YzgYP5dqS/trtF2iNs1Zq2tAp4JvjyrlQ4KlwfXFcB/OlJ/ogomjI/iEsYG4IRYJYztuIZLaHq+bxnweSyO66V4/7+Ny3Ua63kOtbR7PshM4CvcnI/LCM4vjUvkp+DmgbTAyxE+O52mOZSHR9jfEzfPpMU1N48Lvp8NnIUbdsYCd3l9HmJ8Tv8Wdl4u7OBnWz2nwF64IWZOBAaHvZ+FezD507DPXub1eYjRuRwe/F6/w/3xYsKuz4m42YpC33mL60jXaLuvt3pggK7RNr93L1yLdmhZEvwe17d4v0csrzGa5kG2rewfgZuG0ALvAtsE3y8A/oT7Yyohz3dnziluhpdHw67bKZ04bqvnFDgJeBY3vE7fsPfzcB1tFoRdw8d6fQ5jdE67/P+2u69Tz090Oi+4X8wLwy6KKlxTcuj1HCJMIE6UX8jBMuNwnRRC5cpxgzOHXr+GGwfL8/MQo3M5NOy7+YFVUZZL2ntOcbMc2LClGljb4nz6gWu9Pg8xvjbDv/Pm4Hfe3OL9GUCmrtEOndsM3NAhFniuA59Ly2s07FxFWx6M5TVGlF/GwTIH435uh+rbhGuBC71+gOAfXIm0dOacApPC3q8j+s/YLRK7ts4pcEqLY1cG/+3Cz+dm3LBpnp/DGJ3TLv+/7e7rtKMzOUgMWWsXGWN2xD1/dxTuL4J63F/FjwG3W2vrOln37OAYcJfjxigcgrtovsbdGp9h3bApqSKjxXa/KOU7MvvDXNy/0c+BHXB/LRbj/oN/i5u/9l5rbWenJkxEq3GDoP8c2Anog/tLejPuD50PcdfQB61VEE0aXqMh++GGDIHYdYBJx2s0qnhfY9bal4M/wy8H9sfNqLEJ9wf/PdbaZ9r4eLIJ/xmbRfSfsXlR9rc0E7gKdw1vC/TGtRaX4577fQt3Thd2sN5E1i3/b2N5nYZuOYmIiIiItEodYUREREQkKiWNIiIiIhKVkkYRERERiUpJo4iIiIhEpaRRRERERKJS0igiIiIiUSlpFBEREZGolDSKiIiISFRKGkVEREQkKiWNIiIiIhKVkkYRERERiUpJo4iIiIhEpaRRRERERKJS0igikoCMMWcbY6wxZpMxpm87ys8Ilv+qO+ITkfSjpFFEJMEYYwYCfwm+/H/W2jXt+NinwfX2xpiS+EQmIulMSaOISOK5BigCfgL+0c7PfBdcG2D7eAQlIulNSaOISAIxxowCfht8ea21tqGdH10Wtr1VbKMSEVHSKCKSaM4DfMBG4NEOfK42bLswphGJiACZXgcgIiKOMcYHnBB8+bS1dnOL/dsBBwM/WWufbfHx7LBtf/yiFJF0pZZGEZHEsStQHNx+I8L+E4AbgH0i7Bsatr00tmGJiChpFBFJJDuFbc+JsH/P4Pq7CPt2DtueFauARERClDSKiCSOkcG1BRaH7zDG9AJ+Hny5MsJn9wuuF1hrV8QnPBFJZ0oaRUQSR4/guj5Cr+nf0PQcel34DmPMAGDf4MvH4xeeiKQzJY0iIomjPLjODiaCABhj8oFLgVAL4pAWn/t/QBZQA9wd7yBFJD0paRQRSRyzw7avNsZkGmN6Ag8Dw4DrgvtONcYUGmOyjTFXAGcF3/+ztTbSrWsRkS4z1lqvYxAREcAYkwt8DowJvlWDuyWdBdwJXAAsAEbQNC5jTnA9AzjDWhvornhFJL2opVFEJEEEx2XcF3gEWBt8ex6uJfG84HOORwLv4p5r3AzMBKZYa09Twigi8aSWRhERERGJSi2NIiIiIhKVkkYRERERiUpJo4iIiIhEpaRRRERERKJS0igiIiIiUSlpFBEREZGolDSKiIiISFRKGkVEREQkKiWNIiIiIhKVkkYRERERiUpJo4iIiIhE9f8BGknpQQQxkvoAAAAASUVORK5CYII=", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the components of the fit separately:\n", + "plt.rcParams['font.size'] = 25\n", + "plt.rcParams['figure.figsize'] = (10,5)\n", + "def plot_fit(func,J, w, lam, gamma, w0):\n", + " \"\"\" Plot the individual components of a fit to the spectral density. and how they contribute to the full as an animation \"\"\"\n", + " total=0\n", + " plt.plot(w, J, 'r--', linewidth=2, label=\"original\")\n", + " for i in range(len(lam)):\n", + " component=func(w,[lam[i]],[gamma[i]],[w0[i]])\n", + " total+=component\n", + " plt.plot(w, J, 'r--', linewidth=2, label=\"original\")\n", + " plt.plot(w,total,label=rf\"$k={i+1}$\")\n", + " plt.xlabel(r\"$\\omega$\")\n", + " plt.ylabel(r\"$J(\\omega)$\")\n", + " plt.legend()\n", + " plt.pause(1)\n", + " plt.show()\n", + "def plot_fit_components(func,J, w, lam, gamma, w0):\n", + " \"\"\" Plot the individual components of a fit to the spectral density. and how they contribute to the full as an animation \"\"\"\n", + " total=0\n", + " plt.plot(w, J, 'r--', linewidth=2, label=\"original\")\n", + " for i in range(len(lam)):\n", + " component=func(w,[lam[i]],[gamma[i]],[w0[i]])\n", + " plt.plot(w,component,label=rf\"$k={i+1}$\")\n", + " plt.xlabel(r\"$\\omega$\")\n", + " plt.ylabel(r\"$J(\\omega)$\")\n", + " plt.legend(bbox_to_anchor=(1.04, 1))\n", + " plt.show()\n", + "lam, gamma, w0 = fs.params_spec\n", + "plot_fit(fs.spectral_density_approx,J, w, lam, gamma, w0)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "9da8c597-557d-4bb6-b20b-c81b0ac09eb9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_fit_components(fs.spectral_density_approx,J, w, lam, gamma, w0)" + ] + }, + { + "cell_type": "markdown", + "id": "c868d316", + "metadata": {}, + "source": [ + "And let's also compare the power spectrum of the fit and the analytical spectral density:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "9f95a7ee", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.rcParams['figure.figsize'] = (10,5)\n", + "\n", + "def plot_power_spectrum(alpha, wc, beta, lam, gamma, w0, save=True):\n", + " \"\"\" Plot the power spectrum of a fit against the actual power spectrum. \"\"\"\n", + " w = np.linspace(-10, 10, 50000)\n", + " s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta)\n", + " s_fit = fs.spec_spectrum_approx(w)\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " axes.plot(w, s_orig, 'r', linewidth=2, label=\"original\")\n", + " axes.plot(w, s_fit, 'b', linewidth=2, label=\"fit\")\n", + "\n", + " axes.set_xlabel(r'$\\omega$', fontsize=28)\n", + " axes.set_ylabel(r'$S(\\omega)$', fontsize=28)\n", + " axes.legend()\n", + "\n", + " if save:\n", + " fig.savefig('powerspectrum.eps')\n", + "\n", + "\n", + "plot_power_spectrum(alpha, wc, 1/T, lam, gamma, w0, save=False)" + ] + }, + { + "cell_type": "markdown", + "id": "1a2c9e67", + "metadata": {}, + "source": [ + "Now that we have a good fit to the spectral density, Let us obtain its dynamics, by passing our FitSpectral bath specifications into the HEOMSolver" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "0e86f101-2a45-47ba-bd4a-be014f5025c4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10.0%. Run time: 1.44s. Est. time left: 00:00:00:12\n", + "20.0%. Run time: 2.61s. Est. time left: 00:00:00:10\n", + "30.1%. Run time: 3.73s. Est. time left: 00:00:00:08\n", + "40.1%. Run time: 4.82s. Est. time left: 00:00:00:07\n", + "50.1%. Run time: 5.87s. Est. time left: 00:00:00:05\n", + "60.1%. Run time: 7.00s. Est. time left: 00:00:00:04\n", + "70.1%. Run time: 8.39s. Est. time left: 00:00:00:03\n", + "80.1%. Run time: 10.14s. Est. time left: 00:00:00:02\n", + "90.2%. Run time: 11.82s. Est. time left: 00:00:00:01\n", + "100.0%. Run time: 13.55s. Est. time left: 00:00:00:00\n", + "Total run time: 13.55s\n" + ] + } + ], + "source": [ + "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + "options = {'nsteps':15000, 'store_states':True, 'rtol':1e-12, 'atol':1e-12, 'method':\"bdf\"}\n", + "Ltot = liouvillian(Hsys) + fs.terminator\n", + "HEOM_spectral_fit = HEOMSolver(Ltot, fs.Bath_spec, max_depth=4, options=options,)\n", + "result_spectral=HEOM_spectral_fit.run(rho0,tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "ac8e7a45-9325-4f0c-9620-ae6330c1f2d5", + "metadata": {}, + "source": [ + "Now if we want to see the systems's behaviour as we change the Number of terms in the fit, we may use this auxiliary function" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "0a755790-1e0d-4bb8-9ea9-5dddf26764ce", + "metadata": {}, + "outputs": [], + "source": [ + "def generate_spectrum_results(Q,beta, N, Nk, max_depth):\n", + " \"\"\" Run the HEOM with the given bath parameters and\n", + " and return the results of the evolution.\n", + " \"\"\"\n", + " fs=FitSpectral(T,Q,Nk)\n", + " fs.get_fit(J,w,N)\n", + " Ltot = liouvillian(Hsys) + fs.terminator\n", + " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + "\n", + " # This problem is a little stiff, so we use the BDF method to solve\n", + " # the ODE ^^^\n", + " print(f'Starting calculations for N={N} and max_depth={max_depth} ... \\n ')\n", + " HEOM_spectral_fit = HEOMSolver(\n", + " Ltot, fs.Bath_spec, max_depth=max_depth, options=options,\n", + " )\n", + " results_spectral_fit = (HEOM_spectral_fit.run(rho0, tlist))\n", + " print('\\n')\n", + " return results_spectral_fit" + ] + }, + { + "cell_type": "markdown", + "id": "6d3c564b", + "metadata": {}, + "source": [ + "Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "fb82f454-778b-4399-982c-8799b44d243d", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_result_expectations(plots, axes=None):\n", + " \"\"\" Plot the expectation values of operators as functions of time.\n", + "\n", + " Each plot in plots consists of (solver_result,\n", + " measurement_operation, color, label).\n", + " \"\"\"\n", + " if axes is None:\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " fig_created = True\n", + " else:\n", + " fig = None\n", + " fig_created = False\n", + "\n", + " # add kw arguments to each plot if missing\n", + " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", + " for result, m_op, color, label, kw in plots:\n", + " exp = np.real(expect(result.states, m_op))\n", + " kw.setdefault(\"linewidth\", 2)\n", + " if color == 'rand':\n", + " axes.plot(\n", + " result.times, exp,\n", + " c=np.random.rand(3,), label=label, **kw,\n", + " )\n", + " else:\n", + " axes.plot(result.times, exp, color, label=label, **kw)\n", + "\n", + " if fig_created:\n", + " axes.legend(loc=0, fontsize=12)\n", + " axes.set_xlabel(\"t\", fontsize=28)\n", + "\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "6197bb61", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting calculations for N=1 and max_depth=5 ... \n", + " \n", + "10.0%. Run time: 0.06s. Est. time left: 00:00:00:00\n", + "20.0%. Run time: 0.09s. Est. time left: 00:00:00:00\n", + "30.1%. Run time: 0.11s. Est. time left: 00:00:00:00\n", + "40.1%. Run time: 0.13s. Est. time left: 00:00:00:00\n", + "50.1%. Run time: 0.15s. Est. time left: 00:00:00:00\n", + "60.1%. Run time: 0.16s. Est. time left: 00:00:00:00\n", + "70.1%. Run time: 0.18s. Est. time left: 00:00:00:00\n", + "80.1%. Run time: 0.19s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 0.21s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 0.23s. Est. time left: 00:00:00:00\n", + "Total run time: 0.23s\n", + "\n", + "\n", + "Starting calculations for N=2 and max_depth=5 ... \n", + " \n", + "10.0%. Run time: 0.22s. Est. time left: 00:00:00:02\n", + "20.0%. Run time: 0.32s. Est. time left: 00:00:00:01\n", + "30.1%. Run time: 0.42s. Est. time left: 00:00:00:00\n", + "40.1%. Run time: 0.51s. Est. time left: 00:00:00:00\n", + "50.1%. Run time: 0.60s. Est. time left: 00:00:00:00\n", + "60.1%. Run time: 0.67s. Est. time left: 00:00:00:00\n", + "70.1%. Run time: 0.74s. Est. time left: 00:00:00:00\n", + "80.1%. Run time: 0.81s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 0.87s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 0.93s. Est. time left: 00:00:00:00\n", + "Total run time: 0.93s\n", + "\n", + "\n", + "Starting calculations for N=3 and max_depth=5 ... \n", + " \n", + "10.0%. Run time: 0.48s. Est. time left: 00:00:00:04\n", + "20.0%. Run time: 0.67s. Est. time left: 00:00:00:02\n", + "30.1%. Run time: 0.86s. Est. time left: 00:00:00:02\n", + "40.1%. Run time: 1.05s. Est. time left: 00:00:00:01\n", + "50.1%. Run time: 1.23s. Est. time left: 00:00:00:01\n", + "60.1%. Run time: 1.47s. Est. time left: 00:00:00:00\n", + "70.1%. Run time: 1.66s. Est. time left: 00:00:00:00\n", + "80.1%. Run time: 1.84s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 2.01s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 2.19s. Est. time left: 00:00:00:00\n", + "Total run time: 2.19s\n", + "\n", + "\n", + "Starting calculations for N=4 and max_depth=5 ... \n", + " \n", + "10.0%. Run time: 1.54s. Est. time left: 00:00:00:13\n", + "20.0%. Run time: 2.41s. Est. time left: 00:00:00:09\n", + "30.1%. Run time: 3.23s. Est. time left: 00:00:00:07\n", + "40.1%. Run time: 4.25s. Est. time left: 00:00:00:06\n", + "50.1%. Run time: 5.08s. Est. time left: 00:00:00:05\n", + "60.1%. Run time: 5.82s. Est. time left: 00:00:00:03\n", + "70.1%. Run time: 6.72s. Est. time left: 00:00:00:02\n", + "80.1%. Run time: 7.61s. Est. time left: 00:00:00:01\n", + "90.2%. Run time: 8.37s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 9.13s. Est. time left: 00:00:00:00\n", + "Total run time: 9.13s\n", + "\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Generate results for different number of lorentzians in fit:\n", + "\n", + "results_spectral_fit_pk = [\n", + " generate_spectrum_results(Q,1/T, n, Nk=1, max_depth=max_depth)\n", + " for n in range(1,5)\n", + "]\n", + "\n", + "plot_result_expectations([\n", + " (\n", + " result, P11p, 'rand',\n", + " f\"P11 (spectral fit) $k_J$={pk + 1}\",\n", + " )\n", + " for pk, result in enumerate(results_spectral_fit_pk)\n", + "]);" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c322425b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting calculations for N=4 and max_depth=5 ... \n", + " \n", + "10.0%. Run time: 2.63s. Est. time left: 00:00:00:23\n", + "20.0%. Run time: 3.99s. Est. time left: 00:00:00:15\n", + "30.1%. Run time: 5.54s. Est. time left: 00:00:00:12\n", + "40.1%. Run time: 6.76s. Est. time left: 00:00:00:10\n", + "50.1%. Run time: 7.95s. Est. time left: 00:00:00:07\n", + "60.1%. Run time: 9.13s. Est. time left: 00:00:00:06\n", + "70.1%. Run time: 10.53s. Est. time left: 00:00:00:04\n", + "80.1%. Run time: 11.74s. Est. time left: 00:00:00:02\n", + "90.2%. Run time: 13.28s. Est. time left: 00:00:00:01\n", + "100.0%. Run time: 14.78s. Est. time left: 00:00:00:00\n", + "Total run time: 14.78s\n", + "\n", + "\n", + "Starting calculations for N=4 and max_depth=5 ... \n", + " \n", + "10.0%. Run time: 4.37s. Est. time left: 00:00:00:39\n", + "20.0%. Run time: 7.30s. Est. time left: 00:00:00:29\n", + "30.1%. Run time: 10.56s. Est. time left: 00:00:00:24\n", + "40.1%. Run time: 13.65s. Est. time left: 00:00:00:20\n", + "50.1%. Run time: 16.66s. Est. time left: 00:00:00:16\n", + "60.1%. Run time: 19.72s. Est. time left: 00:00:00:13\n", + "70.1%. Run time: 22.78s. Est. time left: 00:00:00:09\n", + "80.1%. Run time: 26.43s. Est. time left: 00:00:00:06\n", + "90.2%. Run time: 30.58s. Est. time left: 00:00:00:03\n", + "100.0%. Run time: 35.11s. Est. time left: 00:00:00:00\n", + "Total run time: 35.11s\n", + "\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# generate results for different number of Matsubara terms per Lorentzian\n", + "# for max number of Lorentzians:\n", + "\n", + "Nk_list = range(2, 4)\n", + "results_spectral_fit_nk = [\n", + " generate_spectrum_results(Q,1/T, 4, Nk=Nk, max_depth=max_depth)\n", + " for Nk in Nk_list\n", + "]\n", + "\n", + "plot_result_expectations([\n", + " (\n", + " result, P11p, 'rand',\n", + " f\"P11 (spectral fit) K={nk}\",\n", + " )\n", + " for nk, result in zip(Nk_list, results_spectral_fit_nk)\n", + "]);" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "a5ce8797", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting calculations for N=4 and max_depth=2 ... \n", + " \n", + "10.0%. Run time: 0.07s. Est. time left: 00:00:00:00\n", + "20.0%. Run time: 0.09s. Est. time left: 00:00:00:00\n", + "30.1%. Run time: 0.11s. Est. time left: 00:00:00:00\n", + "40.1%. Run time: 0.14s. Est. time left: 00:00:00:00\n", + "50.1%. Run time: 0.16s. Est. time left: 00:00:00:00\n", + "60.1%. Run time: 0.23s. Est. time left: 00:00:00:00\n", + "70.1%. Run time: 0.24s. Est. time left: 00:00:00:00\n", + "80.1%. Run time: 0.26s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 0.28s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 0.29s. Est. time left: 00:00:00:00\n", + "Total run time: 0.29s\n", + "\n", + "\n", + "Starting calculations for N=4 and max_depth=3 ... \n", + " \n", + "10.0%. Run time: 0.17s. Est. time left: 00:00:00:01\n", + "20.0%. Run time: 0.23s. Est. time left: 00:00:00:00\n", + "30.1%. Run time: 0.28s. Est. time left: 00:00:00:00\n", + "40.1%. Run time: 0.33s. Est. time left: 00:00:00:00\n", + "50.1%. Run time: 0.38s. Est. time left: 00:00:00:00\n", + "60.1%. Run time: 0.43s. Est. time left: 00:00:00:00\n", + "70.1%. Run time: 0.48s. Est. time left: 00:00:00:00\n", + "80.1%. Run time: 0.53s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 0.57s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 0.62s. Est. time left: 00:00:00:00\n", + "Total run time: 0.62s\n", + "\n", + "\n", + "Starting calculations for N=4 and max_depth=4 ... \n", + " \n", + "10.0%. Run time: 0.51s. Est. time left: 00:00:00:04\n", + "20.0%. Run time: 0.78s. Est. time left: 00:00:00:03\n", + "30.1%. Run time: 1.04s. Est. time left: 00:00:00:02\n", + "40.1%. Run time: 1.27s. Est. time left: 00:00:00:01\n", + "50.1%. Run time: 1.48s. Est. time left: 00:00:00:01\n", + "60.1%. Run time: 1.70s. Est. time left: 00:00:00:01\n", + "70.1%. Run time: 1.93s. Est. time left: 00:00:00:00\n", + "80.1%. Run time: 2.16s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 2.38s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 2.58s. Est. time left: 00:00:00:00\n", + "Total run time: 2.58s\n", + "\n", + "\n" + ] + }, + { + "data": { + "image/png": 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b9XsHmDp1KuXl5UlfU6dOTRrzrW99i+LiYo455hiOOOIIpkyZ0u7ziUhmCGqNEYDcbj+jtNvcHZ/plvPcPuC9DvWfNm0aubm59O3bl/POO49bb72103OqqamhrKwsvh0KhWhqamLNmjVUVFTsV4gAzJ49m6FDhwJw22238Z3vfIfzzz+f7du3x+dZjBw5kiuvvJKnn36ac845hzfeeIPq6mruvfdecnO9/yucdtppafO67rrr4ucBuOiii+LvZ8yYwd13380bb7zBBRdccEifP5WysrK0l2kuuOACzjjjDK655hpqa2tZsWIFxx9/PHl5eQDs3LmT6667jqqqKvbu3cuMGTP2K3YA/vznP3c4rwcffJCf/exnvP7667z88svxkRIRyR5B3dYLGhmRg/DMM89QU1NDVVUVDz74YJfM6+jXrx+1tbXx7aOOOooFCxYwZ84cDj/8cC655BKqq6vj7YkFwvDhw6murqaqqorq6ur9/mV/1113sXXrVsAb2Rk+fHi8EGmPxPMAPP7444wbNy5+/FWrVrFjx46D/dhtqq2tpby8/ID9TU1NrF27lnHjxtGvXz8mTJjAokWL4qMlMTNnzuSSSy5h6dKlLFu2jM9+9rOdllsoFOK0005j06ZNPPTQQ512XBHpHvuWgtfIiCTo6IhFT3LCCSewfv16Pve5z8X3zZw5k5kzZ7Jnzx6uvvpqbrrpJp544gnAKyxiNm7cyODBgxk6dCgjRozgvfeS/x6HDh3Kxo0bCYfDBxQkZpY0JnF/VVUVV155JUuWLGHixImEQiHGjRuHc+6gP3db51+7di2XXnrpAftXrVpFUVERI0eOBIhfqtm6dStf+cpXAHjllVfo379//FKLmfHFL37xgGOde+65LF26NOn5J02axKJFi9LmHg6HNWdEJAtpZER6hHA4TGNjI5FIhEgkQmNjY/yuk3RtyUyZMoVXXnklvr1u3TpefPFFmpqaKCwspKioiFAoFG//+c9/zqZNm9i5cyd33XUXM2bMYMKECfTp04d58+bR0NBAJBJh1apVvPnmm4C3bscRRxzBzTffTH19PY2Njbz22msADBw4kA8//DDt562vr8fMqKjw/hXx6KOPxifzHqpk529qamL58uWcddZZB/R/++23GTt2bLyIueCCC1i4cOF+IyNLlixh+vTpbZ570aJF1NXVJX21LkS2bdvG008/TV1dHZFIhMWLF/Pb3/6WM8444yA/uYgERcWI9Ahz586lqKiIe+65hyeffJKioqL4bb/p2pK57LLLWLhwIQ0NDYD3h/jmm29mwIABDBo0iG3btnHXXXfF+8+cOZOzzz6bkSNHMnLkSG6//XZCoRDPPfccK1asYMSIEQwYMIArrriC3bt3A8Tb33//fYYNG8aRRx7J7373O8C7XXXu3LmUl5dz3333Jc1xzJgxXH/99UycOJGBAweycuVKTj311E75XSY7/7PPPsvkyZMZPHjwAf1b3747fPhwKisrqampYezYsQBs3bq13XcztZeZ8dBDD3HkkUfSr18/brjhBhYsWNBlc2ZEpOsEWYxYZwwpS8eNHz/eLVu27ID9a9eu7dCdDz3ZrbfeyuGHH853v/vdtP0qKyt55JFHkl5y6ElOPvlkfv3rX3P88ccfVPwvfvELqqqquPvuuwH4+9//zimnnNKZKbaLvuMimce5KK9vOgFHmIlHvtMli56Z2XLn3PhkbZozIhkrceRD4B//+MchxV9++eVce+21TJo0iUgkwrhx4wIpRkQk87REd+EIB7L6KqgYEek18vLytFS7iCQV5OqroGJEeoANGzYEnYKISFYLcsEz0ARWERGRXq8l/rTeYEZGVIyIiIj0crGRkSCeSwMqRkRERHq9IG/rBRUjIiIivV7WzBkxs/5mNsvMnjSzNWZWb2ZNZrbJzJ4xs6+04xgDzezHZrbOzBrMbKeZLTWzKyzV+tf7x48ys4fN7CMzazSzbWa22MwubOdn+Kyf/yY/9y1m9icza9dykWb2Bb//loTP/qSZdd4DPkRERLpZU8R7ZldBaFAg5+/I3TSftOrfCLQAQ/zXBWa2CPiqc25v62AzOwlYDMSWgKwDyoDT/NdFZvZl51xTspOb2RTgD0Cxv2uPf6yzgbPN7FHgcpdiFTczuwJ4KOEz7AYGAtOAaWb2A+fcnFQf3szmAHf6m84//xDga8AMM7vWOaf7JkVEJOvsGxkZGMj5O3KZJhd4A/gWMMo5V+ScKwVGAL/2+5wLPNw60Mz6An/GKx7eBT7nnCsDSoDZeEXN2cBPkp3YzEYAv8crRF4DRjvn+gJ9gR/63WYB308RPxH4hf8ZngGGOufKgYqEfO80s4tTxF/MvkLkYaDCjx/qHy8X+IV/HhERkawRdc20RHcAOeSFBgSSQ0eKkTOccyc75x5yzsWf4OWc2+Ccu4J9f9QvNbOhrWJvAAYBDcAU59wyP7bZOfdz9v2hv8rMjk5y7h/iFS6fAFOdc+v9+Drn3J3AL/1+t5lZvyTx84EQsBK42Dm3yY//1Dl3Dd6IDcB8MwslBvrb8/3Nxc65a5xzn/rxm4AZ/nET+4mIiGSF5sh2wJEfGkCO5QWSQ7uLEefcS210+XXC+9Zrz1/m/3zaOfdRktif4V22CeFd9ogzsxIgNifkIedcTZL4u/2fffAuuyTGj8S7DARwn3OuJU38cODzrdpO9/cDHLA+uXOuGfixv3mafz4REZGs0OzPFwnqEg107t00jQnv46MLZjYaGOZv7v/8cZ9zrg5Y6m+e3ar5NKCojfgNwNoU8YnPW/9rsnjgVaC2jfhavEtEySTmdeDz3eWg3HLLLSxYsCDoNA5aZWUlL7zwQtK2devWceKJJ1JWVsZPf/pTAI477jhefvnllMebMGECq1ev7opURaQX21eMBDN5FTq3GJmc8H5lwvvER4yuShMfaxvTan9ifLr/Esfij0sRv805ty1ZoHMugjeXJV38Wr9fsvhtwPYU8T1KZWUlRUVFlJaWMnDgQGbNmkVdXR0ADzzwAOPHj6egoIBvfOMb+8Wla0tm+/btPP7441x99dVd8Cnalq6Q6Azz589n8uTJ1NbWct111wGwevVqJk+enPL8N9xwA3fccUfKY+7atQszY+LE/acuXX311Xzve9/r3A+QxHvvvUdhYSGXXnppl59LRDrPvjtpsnxkxMzKgVv8zaXOuXUJzYMT3m9Oc5hYWx8zK00SvyvZXTpJ4ge32j+4VXt3x/c4zz33HHV1dbz11lu8+eabzJ07F4DBgwdz++23881vfvOAmHRtyTz22GNMmTKFoqKitjsHIBwOH1J8VVUVxx3Xsbr1y1/+Mi+99BJbtmxJ2r5ixQoGDRrEmjVr9uuzYsUKxo0bdyjptsu3v/1tPve5z3X5eUSkczWHPwGy/DKNmeUATwBHAE3Ad1p1KUt4n66YSGwrS/I+XWxie1mr/UHHx5nZVWa2zMyWbd++PVW3rDFkyBDOPfdcVq3yBqWmT5/OtGnT6N+//wF907Uls2jRIk4//fT99s2bN48hQ4ZQVlbG6NGjWbJkCeCNItx9992MGTOGfv36MWvWLBobvauG1dXVXHjhhVRUVDBixIj4JZGYjz/+mOnTp1NRUUH//v2ZPXs2X//619m4cSPnn38+paWlzJ8/P36eefPmccIJJ1BSUkI4HOaee+5h1KhRlJWVMWbMGP70pz+1+dnOOOMMXnrpJWbPnk1paSnr16+PH/+FF15Ief7CwkJOOukknn/++aTHXbFiBePHj+ess87i2WefBSASibBy5UpOPPHEdv3eD9bTTz9NeXk5Z555ZpeeR0Q6X1MPuUzzf4Cp/vtvOefe6YRj9kjOuV8658Y758ZXVASzyl1n+vjjj1m4cGGX/KFbuXIlo0ePjm+vW7eOBx54gDfffJPa2loWL15MZWVlvP2pp55i8eLFfPDBB6xfv565c+cSjUY5//zzGTt2LJs3b2bJkiUsWLCAxYu9m6cikQhTp05l+PDhbNiwgc2bN3PJJZfwxBNPMGzYsPgI0I033hg/z29/+1v+8pe/UFNTQ25uLqNGjWLp0qXs3r2bO++8k0svvTTlyEXMiy++yKRJk3jggQeoq6vj6KP3v4Es3fmPPfZY3nkn+f/F3n77bcaNG8e0adN45plnAHj33XeJRCIce+yx7fq9A0ydOpXy8vKkr6lTpx7Qf8+ePdxxxx38+Mc/TnI0Ecl0zfHLNMEsBQ8dW/TsAGZ2H946IQDfc879R5JutQnvi/EWC0umOOF9bZL3ie3p4mtb7Q86/qBVzHq/sw6V1vZHj+pQ/2nTppGbm0vfvn0577zzuPXWWzs9p5qaGsrK9g0yhUIhmpqaWLNmDRUVFfsVIgCzZ89m6FDvjvLbbruN73znO5x//vls3749Ps9i5MiRXHnllTz99NOcc845vPHGG1RXV3PvvfeSm+v9X+G0004jneuuuy5+HoCLLroo/n7GjBncfffdvPHGG1xwwQWH9PlTKSsrS3uZ5oILLuCMM87gmmuuoba2lhUrVnD88ceTl+fdrrdz506uu+46qqqq2Lt3LzNmzNiv2AH485//3KGc/u3f/o3LL798v9+LiGSP5kjsMk1wIyMHXYyY2Xzgen/z+865BSm6Vie8H0LqYmSI/3OPf3dN6/h+ZlacZt7IkFb9W8cPIb108Z89hPge55lnnuGLX/xil56jX79+1Nbuq+uOOuooFixYwJw5c1i9ejXnnHMO999/P4MHe1N0Ev8QDh8+nOrqaqqqqqiurqa8vDzeFolEmDRpEuCN7AwfPjxeiLRH6z+4jz/+OPfffz8bNmwAoK6ujh07dnT047ZbbW3tfp8npqmpibVr1zJu3Dj69evHhAkTWLRoUXy0JGbmzJnMnj2bqVOn4pyLX+o6WCtWrOCFF17g7bffPqTjiEgwnIv664wEO2fkoIoRM7sXbyEzgBudc/el6Z54B83x7LsFt7XYXStr0sQfB7zZRnzrO25i8YebWYVz7oDJGv7CZsekiZ8KHGtmoWR31JjZ4XiruSaLP2gdHbHoSU444QTWr1+/34TImTNnMnPmTPbs2cPVV1/NTTfdxBNPPAF4hUXMxo0bGTx4MEOHDmXEiBG89957Sc8xdOhQNm7cSDgcPqAgSfWopMT9VVVVXHnllSxZsoSJEycSCoUYN24cKZ5I0CGpzr927dqkd6usWrWKoqIiRo70lrmJXarZunUrX/mK99ioV155hf79+8cvtZhZ0qLy3HPPZenSpQfsB5g0aRKLFu27k/3ll19mw4YNDBvm3b1fV1dHJBJhzZo1vPXWWx34xCIShJboThwt5Ob0JZRTGFgeHZ4z4l+aSSxE7k3X37+zZqO/+aUUxywBJvmbrWfnvYq3cmu6+OFA7KJ46/i/JbxPGg+cyr6Jp6niy4B/ShGfeNy/pejT44XDYRobG4lEIkQiERobG+N3naRrS2bKlCm88sor8e1169bx4osv0tTURGFhIUVFRYRC+xbL/fnPf86mTZvYuXMnd911FzNmzGDChAn06dOHefPm0dDQQCQSYdWqVbz5plfPTpgwgSOOOIKbb76Z+vp6Ghsbee01bymZgQMH8uGHH5JOfX09ZkZs/s+jjz4an8x7qJKdv6mpieXLl3PWWQcuZfP2228zduzYeBFzwQUXsHDhwv1GRpYsWcL06dPbPPeiRYuoq6tL+kosRACuuuoqPvjgA1asWMGKFSu45pprOO+88+LzckQks2XCJRroYDHiFyKxSzM3tFWIJHjc/3mJmVUmaf82UApEgKcSG5xz9cAf/c1r/efctHaT/7MW71kxifEf4hU0ANebJV3r9mb/ZxXw363aXvH3J/aL848X+528mrhUfm8zd+5cioqKuOeee3jyyScpKiqK3/abri2Zyy67jIULF9LQ4NWhTU1N3HzzzQwYMIBBgwaxbds27rpr34K4M2fO5Oyzz2bkyJGMHDmS22+/nVAoxHPPPceKFSsYMWIEAwYM4IorrmD37t0A8fb333+fYcOGceSRR/K73/0O8BZcmzt3LuXl5dx3X/KBvzFjxnD99dczceJEBg4cyMqVKzn11FM75XeZ7PzPPvsskydPjl+aStT69t3hw4dTWVlJTU0NY8eOBWDr1q3tvpupvYqLixk0aFD8VVpaSmFhIT1hgrZIb9DkPyAvqKf1xjnn2vUC5uE9rdbhTVbtSGxfYIsfuxo4yd+fD1yLd0uwAx5MET8Cb7l4h1csfMbfXwLcAUT9thtTxE8Ewn6fPwJD/P2HAQ8mfK6LU8RfnNDnQeAwf/8Q/3jOP/7E9v5OTjrpJJfMmjVrku7vjW655Rb3k5/8pM1+w4cPd3/729+6PqGATZgwwa1cufKg4x966CF38803x7dff/31zkirw/QdF8kc1Xuecq9uHO3e+/T2Lj8XsMyl+JvYrjkjZjYMiE25jwI3mdlNaULucwnzSJxzu81sKt4D6cYAy8ysFigEYiMVzwNJl4l0zn3kPzn3D3iXc9ab2W680ZTYWP1jQNKRGufc62Z2DfAQMB2YbmY1eEVS7OL8D5xzv08R/3szG4P3QL9rgWv885f7XcLAtc6511P+RqTDEkc+BP7xj38cUvzll1/Otddey6RJk4hEIowbN45TTjmlk7ITkWyUCUvBQ/snsOa0et/WlNvS1jucc8vN7Di8SypTgaFAPd4E0f8E/sM5F011QOfcQjM7wY8/C2+l0xrgLeBh59wfU8X68Y+Y2Vt4l1ROx5twug14HfiZc+7FNuLnmNl/4y3qNhHoh7fq6ivA/c655eniRYKWl5fHI488EnQaIpJBmvw5I0EuBQ/tLEac9yC65NP7O8A5txX4V/91MPEfAFcdwvnfotVTgTsY/yKQtmiR7he7rVZERDomE57YC537oDwRERHJIvHVV3Oz6G4aERER6RmccwnPpdHIiIiIiHSziKsj6vaSY8WELOUzXruFipEM5DphBU+RTKTvtkjmaE6YvJpq1efuomIkw4RCIVpaWoJOQ6RLtLS0dOhZQCLSdZrCsdVXg71EAypGMk55eTlbt24lGk15l7NIVopGo2zdupW+fZMtoiwi3a3ZX301E4oR/RMlwwwYMIBNmzaxbt26oFMR6XQlJSUMGDAg6DREBGiKbAGgIPeIgDNRMZJxcnJy4k9AFRER6SrxYiTo59KgyzQiIiK9UlM4Vowc+PDN7qZiREREpBdqilQDmXGZRsWIiIhIL+Oci9/amx9SMSIiIiLdLBytIeoaCVkZuTkHPNu226kYERER6WUy6RINqBgRERHpdZriq6+qGBEREZEANIX9kREVIyIiIhKETFrwDFSMiIiI9DrN4cy5kwZUjIiIiPQ68QmsKkZEREQkCLpMIyIiIoGJuhb/ib2WEU/sBRUjIiIivYpXiDjyQxXkWF7Q6QAqRkRERHqVfU/rzYxLNKBiREREpFeJPa03PwOe1hujYkRERKQXaY5PXh0UcCb7qBgRERHpRWIjI7pMIyIiIoHYt8aILtOIiIhIAJp0mUZERESC1BSOPbFXIyMiIiLSzcLROiJuDzlWQG5Ov6DTiVMxIiIi0ks0hb35IvmhQZhZwNnso2JERESkl4hNXi3MPTLgTPanYkRERKSXaApvBqAgNCTgTPanYkRERKSXaIz4xUhu5kxeBRUjIiIivUZsZKRQIyMiIiIShPhlmlwVIyIiIhKAfZdpVIyIiIhIN4tE6wlHd2HkkZ9TEXQ6+1ExIiIi0gvEn0mTOxizzPrzn1nZiIiISJdo9Bc8y7TJq6BiREREpFfI1MmroGJERESkV2jK0MmroGJERESkV2jM0DVGQMWIiIhIr6CREREREQlUpj6XBlSMiIiI9HiR6F5aoju9NUZCmbXGCKgYERER6fEyeY0RUDEiIiLS4zX5a4xk4iUaUDEiIiLS48WeSVOYgZNXQcWIiIhIj5fJC56BihEREZEerzGD76QBFSMiIiI9XpMu04iIiEiQGsMfAxoZERERkQCEo3WEo7vIsYKMXGMEVIyIiIj0aE3hTQAUhI7MyDVGQMWIiIhIj9YY3ghAYe6RAWeSmooRERGRHiw2X6Qwd1jAmaSmYkRERKQHa/Qv0xTmDg04k9RUjIiIiPRgjZHYyIgu04iIiEgAdJlGREREAuNcZN9S8Bm6xgioGBEREemxmiKf4AiTl1NBKKco6HRSUjEiIiLSQ8Uu0RRl8CUaUDEiIiLSY8WXgc/gyaugYkRERKTHaopPXs3c23pBxYiIiEiP1ZAFd9KAihEREZEea9/IiC7TiIiISAD2LXimyzQiIiLSzcLRPYSju8mxIvJyBgSdTloqRkRERHqgxoRLNGYWcDbptbsYMbNiMzvXzG43s/9rZlVm5vzXnDZi5yT0Tfc6qo3jjDKzh83sIzNrNLNtZrbYzC5s52f4rJk9aWabzKzJzLaY2Z/M7Ix2xn/B77/Fj9/kH++z7YkXERHpLvFiJJTZl2gAcjvQdwKw8BDP1wLsTNMeTtVgZlOAPwDF/q49QH/gbOBsM3sUuNw551LEXwE8xL7PvBsYCEwDppnZD5xzc9Kcfw5wp7/p/PMPAb4GzDCza51zj6T5bCIiIt0mG57WG9PRyzS7gCXAvcA/A590MP7/OecGpXltSBZkZiOA3+MVIq8Bo51zfYG+wA/9brOA76eInwj8Aq8QeQYY6pwrByqAh/1ud5rZxSniL2ZfIfIwUOHHD/WPlwv8wj+PiIhI4BrDVUDPK0aWOucOc8590Tl3o3PuaaCpqxJr5YdACV7xM9U5tx7AOVfnnLsT+KXf7zYz65ckfj4QAlYCFzvnNvnxnzrnrgEWx/qZWSgx0N+e728uds5d45z71I/fBMzwj5vYT0REJFAN8WJkeMCZtK3dxYhzLtKViaRiZiVAbE7IQ865miTd7vZ/9sG77JIYPxI4zd+8zznXkiZ+OPD5Vm2n+/sB7mod6JxrBn7sb57mn09ERCRQsZGRorzKYBNph2y4m+Y0IPaowUXJOviXd9b6m2e3aj4r4f1fU5zjVaC2jfhavEtEySTmdVaKPiIiIt0iEm2gObIVI4+C0BFBp9Om7i5GjjOzVWbWYGZ1ZrbOzH5lZiemiTk+4f3qNP1Wxc6RIn6bc25bskB/1OfdNuLXphod8o+7PUW8iIhIt2oMbwSgIHcIZh25VyUY3V2MDACOBfYCBcDRwBXAcjObmyJmsP9zl3Nub5pjb27Vv3X8ZtLrqngREZFuFb9EkwXzRaD7ipH3gBuB0UChc64/3oTUc4DlgOFNPr0+SWyZ/zNdIZLYXtZqf9DxcWZ2lZktM7Nl27dvT9VNRETkkOybvFoZbCLt1C3FiHPuKefcvc659bEJpM65Zufc83hzQt70u84xs77dkVMQnHO/dM6Nd86Nr6ioCDodERHpoRo0MtIxzrlG4FZ/sxQ4s1WX2MTSYtKLtde22h90vIiISLdqDG8AoChPxUhHvJ7wvvWtsdX+z35mlq4gGNKqf+v4IaTXVfEiIiLdqqEle9YYgcwpRtJZlfA+3Z0qsbteWt9xE4s/3MySXhvxFzY7po34Y1sviJYQfzjeaq7J4kVERLpNJLqXluj2rLmtFzKnGDkl4f1HrdpeBRr8919KFmxmw/Hu0gF4vlXz3xLeJ40HTmXfxNNU8WXAP6WITzzu31L0ERER6XKx23oLc4eS4t/QGafLixFr47nFZlYA/Lu/WY/37Js451w98Ed/89oUE1xv8n/W4j0rJjH+Q7yCBuB6M8tLEn+z/7MK+O9Wba/4+xP7JeafB8TuAnrVP5+IiEggGvz5ItlyiQY6WIyYWT8zGxB7JcQXJ+43s9KEsM+b2QtmdqmZHZlwrDwzOxNYCpzs7/5hiuXe78ArVI4AnjOzz/jHKDGzO4Br/H5znXO7ksTfCESAscDTZjbEjz/MzB4Ezo31a72wmb99o785xcweNLPD/PghwNPACf7xb0RERCRA2bbGCHhPm+2It9n3nJZE32f/J+b+J/AN/73h3SFzJoCZNeAVFn2B2ChFFLjHOZf0QXPOuY/8J+f+AZgErDez3Xh338TGoB7De5pwsvjXzewa4CFgOjDdzGr8HGIjNz9wzv0+RfzvzWwM3pN7rwWu8c9f7ncJA9c6515PFi8iItJd4muMZMEzaWK6Y87ISuAGvEst6/Hmf5T7P98BHgDGOeduS3cQ59xCvBGIXwEb8J5XU4M3R+OrzrlZzjmXJv4RvBGY3+CtlloMbMO7rHOmc25OG+efg1dQPePHFfvH+Q1win98ERGRQMXmjPTYkRHnXGVHT+Cc+5R9T7U9JM65D4CrDiH+LeBrhxD/IvDiwcaLiIh0tdhtvdlUjGTK3TQiIiJyiMLROv+23nzyQ4OCTqfdVIyIiIj0EPtu6x2GWfb8ic+eTEVERCSthvgy8JWB5tFRKkZERER6iIYWb6mrotwRAWfSMSpGREREeoiGsLeIeVGeihEREREJQEOLV4wU57Z+5mxmUzEiIiLSAzgX1ciIiIiIBKc5spWoayAvpz+5Ocke45a5VIyIiIj0AA1hf/Jqlo2KgIoRERGRHiE2XyTb7qQBFSMiIiI9wt74yEh2TV4FFSMiIiI9QkPLBkAjIyIiIhKQBo2MiIiISFAi0XqaI59g5FEYGhJ0Oh2mYkRERCTLxZ9Jk1uJWSjYZA6CihEREZEsF38mTRbe1gsqRkRERLJefOXVLJy8CipGREREst7e2BojWTh5FVSMiIiIZL1GjYyIiIhIULL5AXkxKkZERESyWGNkE1HXRH5oILk5ZUGnc1BUjIiIiGSxvS3vA1CcOyrgTA6eihEREZEs1uAXI0V5nwk4k4OnYkRERCSLxUdG8jQyIiIiIgHYV4wcFXAmB0/FiIiISJby7qTxVl9VMSIiIiLdzruTppH80OHk5vQJOp2DpmJEREQkSzXE76TJ3lERUDEiIiKStfbG76RRMSIiIiIB2NvyAZDd80VAxYiIiEjW2tvyHqBiRERERAKw/5002bvGCKgYERERyUpNkc1EXSN5ORXk5vQNOp1DomJEREQkC+27RJO9y8DHqBgRERHJQvsmr2b3JRpQMSIiIpKVesIy8DEqRkRERLLQ3pb1gC7TiIiISACcCyeMjBwdcDaHTsWIiIhIlmkIb8DRQkFoCLk5pUGnc8hUjIiIiGSZ+pZ1AJTkjQ44k86hYkRERCTL7G32ipHifBUjIiIiEoB6f/JqSQ+YLwIqRkRERLLOvjtpNDIiIiIi3Swc3UNTpJocK6Aod1jQ6XQKFSMiIiJZJLYMfFHuUZjlBpxN51AxIiIikkXqm2N30vSM+SKgYkRERCSrxG7r7Sl30oCKERERkayyt4etMQIqRkRERLKGc9H4nJGesAx8jIoRERGRLNEUqSbi6snLGUB+qH/Q6XQaFSMiIiJZYt8y8D1nVARUjIiIiGSN+ua1QM+avAoqRkRERLJGfYtXjJTkjQk4k86lYkRERCRLxEZGSvNVjIiIiEg3a4ns8peBL6IotzLodDqVihEREZEssO8SzdGYhQLOpnOpGBEREckCdc09c74IqBgRERHJCvUtawAoyT824Ew6n4oRERGRLNBTJ6+CihEREZGMF4nupSH8EUZuj1oGPkbFiIiISIbzVl51FOWNIsfyg06n06kYERERyXDxSzR5PW++CKgYERERyXjx23p74HwRUDEiIiKS8eqaVwNQopERERER6W5R18LelveAnnlbL6gYERERyWh7W97D0UJh7nByc0qDTqdLqBgRERHJYHXNKwEozT8+4Ey6jooRERGRDFbXvAqA0vz/FXAmXUfFiIiISAaLFSNlKkbAzIrN7Fwzu93M/q+ZVZmZ819z2nmMgWb2YzNbZ2YNZrbTzJaa2RVmZu2IH2VmD5vZR2bWaGbbzGyxmV3YzvN/1syeNLNNZtZkZlvM7E9mdkY747/g99/ix2/yj/fZ9sSLiIh0RCTaSH3LeiCnx95JA5Dbgb4TgIUHeyIzOwlYDPT3d9UBZcBp/usiM/uyc64pRfwU4A9Asb9rj3+ss4GzzexR4HLnnEsRfwXwEPs+825gIDANmGZmP3DOzUmT/xzgTn/T+ecfAnwNmGFm1zrnHknzKxAREekQb32RCMV5nyGUU9xm/2zV0cs0u4AlwL3APwOftCfIzPoCf8YrHt4FPuecKwNKgNlAC15R8ZMU8SOA3+MVIq8Bo51zfYG+wA/9brOA76eInwj8Aq8QeQYY6pwrByqAh/1ud5rZxSniL2ZfIfIwUOHHD/WPlwv8wj+PiIhIp9g3ebXnXqKBjhUjS51zhznnvuicu9E59zSQdBQjiRuAQUADMMU5twzAOdfsnPs5+/7QX2VmyZ4A9EO8wuUTYKpzbr0fX+ecuxP4pd/vNjPrlyR+PhACVgIXO+c2+fGfOueuwRuxAZhvZqHEQH97vr+52Dl3jXPuUz9+EzDDP25iPxERkUMWW+xMxYjPORc5hPNc5v982jn3UZL2n+FdtgnhXfaIM7MSIDYn5CHnXE2S+Lv9n33wLrskxo/EuwwEcJ9zriVN/HDg863aTvf3A9zVOtA51wz82N88zT+fiIjIIYuPjOT13Nt6oRvupjGz0cAwf3NRsj7OuTpgqb95dqvm04CiNuI3AGtTxJ+V8P6vKdJ8FahtI74W7xJRMol5nZWij4iISLuFo3U0hD/CyKMkf3TQ6XSp7ri1N7GcW5WmX6yt9VOAEuNXtyP+uBTx25xz25IF+qM+77YRvzbV6JB/3O0p4kVERDrMu6XXUZI3mhzLDzqdLtUdxcjghPeb0/SLtfUxs8T1bmPxu5xze9sRP7jV/sGt2rs7XkREpMN6w2JnMd1RjJQlvE9XTCS2lSV5ny42sb2s1f6g4+PM7CozW2Zmy7Zv356qm4iISEIx0rPni4BWYO1WzrlfOufGO+fGV1RUBJ2OiIhksLrm/wFUjHSW2oT36VZsSWyrTfK+rdVeYu21rfYHHS8iItIhTZGtNEWqCVkJxXlHBZ1Ol+uOYqQ64f2QNP1ibXv8u2tax/czs3QFQSy+utX+6lbt3R0vIiLSIXVN7wBQmj+WVstf9UjdUYwk3kGTbqwp1rYmTXy6O1Vi8a3vuInFH25mSa+N+AubHdNG/LGtF0RLiD8cbzXXZPEiIiIdsqd5BQBlBeMCzaO7dHkx4pxbB2z0N7+UrI+/sNkkf/P5Vs2v4q3cmi5+OBB7glDr+L8lvE8aD5zKvomnqeLLgH9KEZ943L+l6CMiItIutU0rACjLHxtsIt2kuyawPu7/vMTMKpO0fxsoBSLAU4kNzrl64I/+5rX+c25au8n/WYv3rJjE+A/xChqA680sL0n8zf7PKuC/W7W94u9P7BfnH+96f/NV/3wiIiIHJeqa43fSqBhJwsz6mdmA2Cshvjhxf6t1QgDuw3uuTDHwF/8JvphZvpldC/zI7/fL2HNnWrkDqAeOAJ4zs8/48SVmdgdwjd9vrnNuV5L4G/EKnbHA02Y2xI8/zMweBM6N9Wu9sJm/faO/OcXMHjSzw/z4IcDTwAn+8W9ERETkENQ3r8PRTFHuCPJC5UGn0y06OjLyNt5Ko7HXUH//91vtfyAxyDm3G5gKfIq3wuoyM9uD9zyaB4F8vMsj30t2Uv95NhfjreUxCVhvZjXAbuAHgAGP4T1NOFn863gFSxiYDmwys13ADuBav9sPnHO/TxH/e/88+P13+PGb/OOFgWv884iIiBy02ua3ASjLHxdsIt2o29YZcc4tx5uA+hPgPSAPb7TjVeBK4FznXMqnADvnFuKNQPwK2ID3vJoavDkaX3XOzXLOuTTxjwAnA7/BWy21GNiGd1nnTOfcnDbynwOc6fff5sdv9o93in98ERGRQ1LbyyavAliav9/ShcaPH++WLVsWdBoiIpJhllWfQVOkmnED/6tHPSDPzJY758Yna9MKrCIiIhmiObKtVy12FqNiREREJEPUxhc7O6FXLHYWo2JEREQkQ+yJTV7tRfNFQMWIiIhIxtjTtByAPvmfDTiT7qViREREJANEonupb14N5FBWcGLQ6XQrFSMiIiIZoLb5f3CEKck7htyc1muH9mwqRkRERDLAniZvuYc+BUnvfu3RVIyIiIhkgPh8kYKTAs6k+6kYERERCVjUtcRXXlUxIiIiIt2uvnktUddAYW4l+aEBQafT7VSMiIiIBKw3zxcBFSMiIiKB29PszRfp2wsv0YCKERERkUA5F02YvKqREREREelmDeEPCUdryA8dTkHoyKDTCYSKERERkQDtbvwH4I2KmFnA2QRDxYiIiEiAdjd5xUjfglMCziQ4KkZEREQC4lx0XzFSqGJEREREutnelvWEo7vJDx1BYWho0OkERsWIiIhIQGKjIuUFJ/fa+SKgYkRERCQwNY1/B6Bv4ckBZxIsFSMiIiIBcC7MnqY3AehboGJEREREulld81oiro7C3GEU5A4OOp1AqRgREREJwL5benv3qAioGBEREQnE7iZ/voiKERUjIiIi3S3qmuPPo+lbOCHgbIKnYkRERKSb7Wl6i6hroDjvaPJDhwedTuBUjIiIiHSzmsbXACgvPDXgTDKDihEREZFuFitG+hWeFnAmmUHFiIiISDdqjuygvmUNOVZAWf5JQaeTEVSMiIiIdKOaxtcB6FPwOUI5hQFnkxlUjIiIiHSjmsZXAc0XSaRiREREpJs45/ZNXi1QMRKjYkRERKSb7G1ZR0t0B/mhwynO+0zQ6WQMFSMiIiLdZFfCqIiZBZxN5lAxIiIi0k1qGv8bgPIi3dKbSMWIiIhINwhHa/0l4EOaL9KKihEREZFuUNP4Go4wfQpOJC9UHnQ6GUXFiIiISDfY2fASAP0KvxBwJplHxYiIiEgXcy7CLn++yGFFpwecTeZRMSIiItLF6ppXEo7uoiB0JEW5o4JOJ+OoGBEREeliOxtfBuCwosm6pTcJFSMiIiJdbFfDywD0K5ocaB6ZSsWIiIhIF2oKf0J9y7vkWDF9CyYEnU5GUjEiIiLShXY2enfRlBf+EzmWH3A2mUnFiIiISBfaufdvAPQvOjPgTDKXihEREZEuEo7uZnfTG0BI64ukoWJERESki+xseBlHmL4FE7TqahoqRkRERLrIpw0vANC/6IsBZ5LZVIyIiIh0gUi0gZrGpQAcpmIkLRUjIiIiXaCm8VWirpHS/LEU5A4MOp2MpmJERESkC+gSTfupGBEREelkUdccf0qvipG2qRgRERHpZDWNrxNxeyjO+wxFeSOCTifjqRgRERHpZDv2LgRgQNGUgDPJDipGREREOlHUNbHTny8yoPjcgLPJDipGREREOtGuhqVEXD0leWMoyqsMOp2soGJERESkE8Uv0RTrEk17qRgRERHpJJHo3vhTegcUfyngbLKHihEREZFOsqvxFaKugbL8sRTmHhl0OllDxYiIiEgn2R6/RHNewJlkFxUjIiIinaAlUsOuhpeBHPoXnxN0OllFxYiIiEgn2NGwCEcL5YX/REFIz6LpCBUjIiIinWB7/TMAHF58QbCJZCEVIyIiIoeooeUjapvfIceKOUzPoukwFSMiIiKHaNveZwEYUHQOoZyigLPJPipGREREDoFzUbbX/xcAFSW6RHMwVIyIiIgcgj1Ny2iKVJMfOoK+BROCTicrqRgRERE5BFvr/wjA4cVfxkx/Vg+GfmsiIiIHKRzdw6cNiwE4vPTCgLPJXipGREREDtL2+ueIukb6FpxCUe6woNPJWt1WjJjZN8zMteOV8p4oMxtoZj82s3Vm1mBmO81sqZldYWbWjhxGmdnDZvaRmTWa2TYzW2xm7SpnzeyzZvakmW0ysyYz22JmfzKzMzryuxARkeznnGNr/R8AGFh6UcDZZLfcAM4ZBbanaW9KttPMTgIWA/39XXVAGXCa/7rIzL7snEsVPwX4A1Ds79rjH+ts4GwzexS43DnnUsRfATzEvt/ZbmAgMA2YZmY/cM7NSfO5RESkB6lrXkV9y7vk5pTTv+isoNPJakFcpvnYOTcozWtp6wAz6wv8Ga94eBf4nHOuDCgBZgMteEXFT5Kd0MxGAL/HK0ReA0Y75/oCfYEf+t1mAd9PET8R+AVeIfIMMNQ5Vw5UAA/73e40s4s79qsQEZFstbX+94C34mqO5QecTXbLljkjNwCDgAZginNuGYBzrtk593PgTr/fVWZ2dJL4H+IVLp8AU51z6/34OufcncAv/X63mVm/JPHzgRCwErjYObfJj//UOXcN3ogNwHwzCx3iZxURkQwXidazfe9fABhYqn+HHqpsKUYu838+7Zz7KEn7z/Au24SAryU2mFkJEJsT8pBzriZJ/N3+zz54l10S40fiXQYCuM8515Imfjjw+ZSfQkREeoRte58j6vZSlv9ZivNGBZ1O1sv4YsTMRgOxKcqLkvVxztUBscs7Z7dqPg2Irc2bKn4DsDZFfOKFwL+mSPNVoDZFvIiI9CDOOT6pfQqAI8q+1kZvaY8gipEKM1tuZnX+HTEf+neoTE7R//iE96vSHDfWNiZN/Op2xB+XIn6bc25bskDnXARvLkuyeBER6UF2N/2DveH3yMupoH+R/v3ZGYIoRoqBzwLN/vlH4F1aecnM/sPMWt/hMzjh/eY0x4219TGz0iTxu5xze9sRP7jV/sGt2jsaLyIiPciWOm9UZFDpJeRYXsDZ9AzdWYxUAz8AxgKFzrnD8AqTU4EX/D6zOPCOmLKE9+mKicS2siTv08Umtpe12n+o8XFmdpWZLTOzZdu3p7u7WUREMlFTuJqdDUsw8hikiaudptuKEefc8865Oc65/4mtBeKcizjn/h9wDvBfftdvmdlnuiuv7uSc+6VzbrxzbnxFRUXQ6YiISAdtqfstEKV/8Tnkh/Tf8c6SERNYnXNRvNt3wcvp/ITm2oT3xaSW2Fab5H262MT22lb7DzVeRER6gEi0Ib7i6hGlmrjamTKiGAFwzr0P7PA3RyY0VSe8H5LmELG2Pf7dNa3j+5lZuoIiFl/dan91q/aOxouISA+wrf7/Eo7WUJo/lrL8cUGn06NkTDGSRuIdNMen7LWvbU2a+HR3usTiW99xE4s/3MySjsn5C50dkyJeRESynHMRqmsfA2BI2Sza8Tg06YCMKUbMbBQwwN+ML2zmnFsHbPQ3v5QitgSY5G8+36r5VbyVW9PFDweOTRH/t4T3SePxJuHGJq62jhcRkSz3acPfaIx8TGHuMD2Hpgt0SzHS1hN1/fZ7/c0o3nNoEj3u/7zEzCqTHOLbQCkQAZ5KbHDO1QN/9Dev9Z9z09pN/s9avGfPJMZ/iFfQAFxvlvQ+rpv9n1XAfydpFxGRLOWcY3PtfwAwuPQb6Kkfna+7RkaGm9kbZna1mY2MFSdmlmNmp+CtjPoVv+/D/mhIovvwnitTDPzFf4IvZpZvZtcCP/L7/TL23JlW7gDqgSOA52J365hZiZndAVzj95vrnNuVJP5GvEJnLPC0mQ3x4w8zsweBc2P9/AXQRESkh9jTvJy65v8hN6ecw0u+0naAdFjrBca60uf8F0CTmdXiXdooSOjzKHBd60Dn3G4zm4r3QLoxwDI/vhCIjVQ8D3wv2Ymdcx/5T9T9A97lnPVmthtvNCVW4j7GvtGZ1vGvm9k1wEPAdGC6mdXgPfU3NurzA+fc79P9AkREJPts3vMrAAaVziSUU9RGbzkY3TUyshX4DvAbvAmme4ByoAVvGfX/AE5zzn3TORdOdgDn3HK8Cag/Ad7DK0Lq8S6hXAmcG1u/JEX8QuAE4FfABrzn1dTgzQn5qnNulnPOpYl/BDjZ/wyb8UZptuFd1jnTOTenrV+CiIhkl9rmlexqfIUcK2Zw6deDTqfHsjR/f6ULjR8/3i1btizoNEREJI21O77NzoYlDCm7nMry7wedTlYzs+XOufHJ2jLmbhoREZFMUt/8LjsblpBjhQwumxV0Oj2aihEREZEkPt7zEAADS2aQHxrQRm85FCpGREREWtnb8h6fNizGyGdIn28GnU6Pp2JERESklard/weAgaVfpSA0MOBsej4VIyIiIglqm1aws+EFcqyIoX2uDTqdXkHFiIiIiM85x4bdPwZgcOll5IeSPpJMOpmKEREREV9N46vsaXqT3Jy+DOlzedDp9BoqRkRERADnolTt/gkAQ8quIjenT8AZ9R4qRkRERIAdDX+lvmUN+aHDOaL0a0Gn06uoGBERkV4v6pqoqrkfgKF9vk0opzDgjHoXFSMiItLrba59lKbIJorzPsPAkguDTqfXUTEiIiK9WlP4EzbteRiAEeW3YdadD7QXUDEiIiK9XNXuHxN1DfQvOovywlOCTqdXUjEiIiK91p6mt9i+9zmMfCrLbwo6nV5LxYiIiPRKzkX4cNe/AzCkzzcpzD0y4Ix6LxUjIiLSK22p+w31LavJDw3kyLKrgk6nV1MxIiIivU5TeEt8gbOR5bcTyikOOKPeTcWIiIj0Ks45Ptj1A6JuL4cVnUX/4rOCTqnXUzEiIiK9yqcNi9nV+DIhK2Vkv9uDTkdQMSIiIr1IOLqbD3fNBWB4+Q0UhAYGnJGAihEREelFPtx1Fy3RHfTJP4lBJRcHnY74VIyIiEivsGPvX9m+97/IsUJGHfYjzPQnMFPofwkREenxmiPb+GDXnQBU9v0+xXkjA85IEqkYERGRHs05x3s7byMc3U154WkMKp0ZdErSiooRERHp0T6pf5qaxqXk5vTlqMP+HTMLOiVpRcWIiIj0WPXN77Kh5h4ARvX7ge6eyVAqRkREpEcKR2t599P/TdQ1cXjJVxlQ/KWgU5IUVIyIiEiP45zj/Z230RiuoiTvGEaWa3GzTKZiREREepwtdU/wacPzhKyE0f0XEMopDDolSUPFiIiI9Ch7mpazoWY+AEcddhdFeZXBJiRtUjEiIiI9RmN4E2t3zMYRZnDpvzCg+JygU5J2UDEiIiI9Qjhax9od1xKO7qK88DQqy78fdErSTipGREQk6zkXYf2nN7C35T2Kckcyuv9PMMsNOi1pJxUjIiKS1ZxzfFQzj12NL5Ob05djBzxEbk5Z0GlJB6gYERGRrLa59ldsqXscI49j+v+UorzhQackHaRiREREstbWuj9Stft+wDi6/3z6Fp4cdEpyEFSMiIhIVvq04UXe3/VvAIwsv50BxecGnJEcLBUjIiKSdXY1LGXdju8CUYb2+RZHlH0t6JTkEKgYERGRrLKrYSlrd3wbRzODSmcytM93gk5JDpGKERERyRq7Gl/dV4iU/DMjy/8NMws6LTlEKkZERCQr7Gx4hbXbv7WvEOl3hwqRHkLFiIiIZLxt9f/F2h2xQuQSRvbTiEhPouXpREQko22ufYwNNfcAMKTsSob3/VcVIj2MihEREclIzkWp2n0/m2sfAaCy/CaGlM0KOCvpCipGREQk40Si9azfeRM7G14AQnzmsH/n8JJpQaclXUTFiIiIZJSmcDVrd3yL+pZ3CVkfjhmwgPLCfwo6LelCKkZERCRj7G58g3Wf/ist0R0U5g7n2AEPUZw3Mui0pIupGBERkcA5F2Vz7a+p2v0TIErfglMY3X8BeaHyoFOTbqBiREREAtUSqeG9nbewq/ElAI4su5phfb+Dmf5E9Rb6X1pERAKzq/FV3t95K82RbeTm9OUzh83jsKLJQacl3UzFiIiIdLtItIENu+/lk7rfAFCWfyJH97+XwtwjA85MgqBiREREulVt0wrW77yZxvAGjFyG9f0OQ8quwCwUdGoSEBUjIiLSLcLRPVTV3M8n9b8DHEW5R3F0//mU5o8JOjUJmIoRERHpUs45duz9Cx/V3ENLdAdGLkPKvsnQvt8mxwqCTk8ygIoRERHpMnXNq9lQM5/dTf8AoE/+SYw6bA7FeZ8JNjHJKCpGRESk0zWFt1C1ewHb9z4LOHJz+lLZ9/scXjIdMz0wXvanYkRERDpNS2QXm2v/gy11jxN1TRh5HFF2KUP7XENuTt+g05MMpWJEREQO2b4i5Cmibi8A/YvOpbL8XynMHRpwdpLpVIyIiMhBawpvobruCT6pezpehPQr/DxD+8ymrOCEgLOTbKFiREREOqy2aSXVdY+xY+9fgQgQK0K+TVnB2GCTk6yjYkRERNol6pr5tOFvfFL7G/Y0L/f3hhhQPIXBpbMoK/hfgeYn2UvFiIiIpLW35X0+qfsD2/c+Qzi6G4CQlTGo9GKOKP0aBbmDA85Qsp2KEREROUBzZAef7l3M9r3PUdu8Ir6/JO8YBpZcREXJBeTmlAaXoPQoKkZERATwlmv/dO/f2LH3L9Q0/R2IAhCyEiqKz2dg6VcpyTsOMws2UelxVIyIiPRiDeGN7Gp4iZ0NL7GnaRmOMABGHuWFn6ei+DwOKzqTUE5xwJlKT6ZiRESkF4lEG6ltfpuaxv/HzoaXaAi/n9Aaom/BKQwoPo/+RWeRFyoPKk3pZVSMiIj0YFHXQl3zSnY3/oPdTX9nT9PbOJrj7SEro1/RJA4r/AL9ij6vVVIlECpGRER6kJbILmqb3/FeTSuobX4nvhiZxyjJG0PfwpPpV3g6fQpOIsfyAstXBFSMiIhkrXB0D/XN71Lf8i51zaupbV5BY7jqgH5FuSPoWziRvgUn07dgAnmhfgFkK5KaipEOMLMy4HrgQmAE3rKD64GngZ8555rThIuIHJSoa6YxvJGGlg+pb3k3XoA0RaoP6JtjhZTmH09Z/jjK8sdSVjCW/NDhAWQt0n4qRtrJzIYDLwOV/q69QAEw3n99zczOdM7tCiRBEclqzkVpie6gwS86GsIf+T830BjeRGzJ9UQ5VkBx3mhK8kZTkncsZQVjKc47WpddJOuoGGkHMwsBz+EVIluAy5xzL5hZDnAR8CvgROApYEpQeYpI5oq6FloiO2iKbKEpvJnGyGaawpsT3m/Zb2Lp/nIoDA2lKG+EV3zkH0NJ3jEU5Q7HTP8Zl+ynb3H7fAOIPXThQufc6wDOuSjwO78o+Q1wrj86siSYNEWkOzkXJRzdQzi6i5boLpoj22mJbKc5so3myHaao9u9n5FthKNtD5rm5vSjMHcoRbkjKMob4f3MHUFR3nByrKAbPpFIMFSMtM+/+D9fihUirTwN/DvePJLLABUjIlnCuQgRV08kWkc4WkfE1RGJ1hN2tf6+GlqiNYQju7yf0RpaorsIR2sIR/cQW6W0bTnk5fSnIDSQgtwjKcwdQkFoCAW5Q/z3g7WwmPRaKkbaYGbFwKn+5qJkfZxzzsz+ClwLnN1duYn0ZM45HC0410LUteBoIeqaibomoq4x/oq4RqKugWg0cTv2aiDqmoi4Bq8tWu8XG7HCo77Vba8dF7Iy8kL9yM0pJz9UQX5OBXmhCvJDh3vb/isvpz/eFV8RaU3FSNuOBXL896vS9Iu1DTKzw5xzO7s2re7hnGu9p0Pbrs3+B5yxi8+f/nxtHq/NfMAR9fdHvfM7wN/n5eO9vKt8rtX+qH+OJPtxsF9bNH7G2LES97WOcf6++G/FRXFEcETAeT9d7CfRffv8/ezXvu+91y+aZF9iv3BCUdG8771rIZq4TXN8v6Ml/f90ncYIWSmhnFJyrZRQTgmhnFJ/Xwl5OeXk+q+8nH7khsr9ff3Iy+mrORsinUD/L2pb4rOxN6fpl9g2GOiWYuTNTW8wdU5Zd5xKAmOA/kXdWTrnEW8twDb/JdIzjf1fH7H4f3fPPRkqRtqW+Jc+3XhuYlvS6sDMrgKuAhg2bNihZ+aLRnQbn4iIdK5IJKftTp1ExUg3cs79EvglwPjx49u6ftAunz3iJDY83LDfPjvg334d29bjwaWnOuAqm4ikZNZ5/2hui4qRttUmvE831T2xrTZlr04WCoUoCZV21+lEREQ6XfeNwWSvxPWWh6Tpl9h24BrNIiIikpSKkbatZd9CAsen6Rdr+6Sn3EkjIiLSHVSMtME5txd4zd/8UrI+5k2yOMfffL478hIREekpVIy0z3/6P79gZicnab8IGOm/f7x7UhIREekZVIy0z38CK/FuO/mjmZ0JYGY5ZhZ7UB7AIj2XRkREpGN0N007OOfCZvZl4CW8J/e+YGZ78Yq5Qr/b28DXgslQREQke2lkpJ2ccxuAE4Af4i397vCWYVwO3ACc4pxr+7GcIiIish+NjHSAc64WuNN/iYiISCfQyIiIiIgESsWIiIiIBErFiIiIiARKxYiIiIgESsWIiIiIBErFiIiIiARKxYiIiIgESsWIiIiIBErFiIiIiATKnHNB59Armdl2oKoTDzkA2NGJxxPpSvq+SjbR97VzDHfOVSRrUDHSQ5jZMufc+KDzEGkPfV8lm+j72vV0mUZEREQCpWJEREREAqVipOf4ZdAJiHSAvq+STfR97WKaMyIiIiKB0siIiIiIBErFiIiIiARKxUgWM7MyM5tjZivNrM7MdpvZm2Z2vZnlB52f9Axm1t/MZpnZk2a2xszqzazJzDaZ2TNm9pV2HGOgmf3YzNaZWYOZ7TSzpWZ2hZlZO+JHmdnDZvaRmTWa2TYzW2xmF3bOp5SezsxuNjMXe7XRV9/XbqY5I1nKzIYDLwOV/q69QAgo8LffBs50zu3q9uSkRzGzFiA3YVcjEAFKEvYtAr7qnNubJP4kYDHQ399VBxQmHPN54MvOuaYU558C/AEo9nftAUrZ94+pR4HLnf5jJimY2WhgBd73DgDnXNKiQt/XYGhkJAuZWQh4Dq8Q2QKc5ZwrwfvyXwLUAicCTwWVo/QoucAbwLeAUc65IudcKTAC+LXf51zg4daBZtYX+DPef9jfBT7nnCvDK2RmAy3A2cBPkp3YzEYAv8f7br8GjHbO9QX6Aj/0u80Cvn/oH1N6IjPLwfueFgKvt9FX39egOOf0yrIXcDng/NfEJO3/nNB+ZtD56pXdL+ALbbT/IuH7NrRV24/8/XuBEUlib/Hbw8DRSdqf8Nu3AOVJ2h/223cD/YL+XemVeS/gf/vfkSeBObHvaoq++r4G9NLISHb6F//nS865ZJX+08BH/vvLuicl6amccy+10eXXCe9bL5kd+/497Zz7iAP9DG8YPAR8LbHBzEqA2DX2h5xzNUni7/Z/9gGmtZGn9DL+SMW/A58C32tHiL6vAVExkmXMrBg41d9clKyP80rwv/qbZ3dHXtKrNSa8D8Xe+Nfph/mbqb6rdcBSf7P1d/U0oKiN+A3A2hTxIr/Cu8Tyr8657ek66vsaLBUj2edY9v3vtipNv1jbIDM7rGtTkl5ucsL7lQnvj094357v6phW+xPjV7cj/rg0faSXMbMrgTOBF5xzj7cjRN/XAKkYyT6DE95vTtMvsW1wyl4ih8DMyvGuowMsdc6tS2ju6He1j5mVJonf5ZLcpZMkXt9zAcDMhgD3Ag3A1e0M0/c1QCpGsk9Zwvt0X/jEtrKUvUQOkn+XwhPAEUAT8J1WXQ71u1qWpD1dvL7nEvMw3h0sc5xzH7YzRt/XAKkYEZGD9X+Aqf77bznn3gkyGREAM7sUOA9vXZH7g81G2kvFSPapTXhfnLLX/m21KXuJHAQzuw9v3QWA7znn/iNJt0P9rtYmaU8Xr+95L2dmhwML8Bblu9I5F+5AuL6vAVIxkn2qE94PSdMvsa06ZS+RDjKz+cD1/ub3nXMLUnTt6Hd1j3+3Quv4fv5dZG3F63su8/AWLPsl8K6ZlSa+gPhjMhL2x/bp+xogFSPZZy0Q9d8fn6ZfrO0T59zOrk1Jegszu5d9q0fe6Jy7L033xDsS2vNdXZMmPt2dB7H4dHcwSO8wwv95Ld7IQ+vXLQl9Y/vm+9v6vgZIxUiW8Wdpv+ZvfilZH/9BTuf4m893R17S8/mXZm7wN290zt2brr9/Z81GfzPVd7UEmORvtv6uvop3N0S6+OF4t7snixdpN31fg6ViJDv9p//zC2Z2cpL2i4CR/vv23F8vkpZfiMQuzdzQViGSIPb9u8TMKpO0fxvvIWIRWj1LyTlXD/zR37zWf25Iazf5P2uBZ9qZk/RQzrnJzjlL9QJ+kNA3tv+7CYfQ9zUoQa9Hr1fHX3gPLvsfvGccbMJ//gxecXkR3nMPHLAw6Fz1yv4X3nX42LNnvtfB2L54z+lweMPSJ/n78/GG0pv8tgdTxI/AW37bAf8NfMbfXwLcgXfJ0uGN1AT+u9Irs1+0/WwafV8Depn/i5Is41ftL+E9uRe8e9dz2PeI7LfxipRd3Z6c9BhmNgyo8jejQNoltYH7XKt5JEkeyV6L9z3N87c7+kj23Xj/Oo0tPf8Y8E2n/5hJG8xsDnAneCMjKfro+xoAXabJUs57xsEJeI+lXoVXbbcAy/Gu65+iQkQ6QU6r9wPbeJW2PoBzbjnehL6fAO/h/Ue9Hu8a+5XAuan+w+7HL8T7rv8K2ID3/I8a4G/AV51zs/Qfduks+r4GQyMjIiIiEiiNjIiIiEigVIyIiIhIoFSMiIiISKBUjIiIiEigVIyIiIhIoFSMiIiISKBUjIiIiEigVIyIiIhIoFSMiIiISKBUjIiIiEigVIyISI9nZt8wM+e/KoPOR0T2p2JEREREAqViRERERAKlYkREREQCZc65oHMQEekSZjYZeKkdXb/gnHu5S5MRkZQ0MiIiIiKB0siIiPRYZlYCjAAuAOb6u88Bqlt1/cg5V9+duYnIPrlBJyAi0lX8AmOVmY1P2L3eObchoJREJAldphEREZFAqRgRERGRQKkYERERkUCpGBEREZFAqRgRERGRQKkYERERkUCpGBGR3kALKolkMBUjItIbNCa8LwgsCxFJSsWIiPQGWxLejwosCxFJSiuwikhv8Dbe6Egh8CMzawGqgKjfvtk51xBUciK9nZ5NIyK9gpnNA25M0ayn9ooESJdpRKS3uBm4ElgK7AQiwaYjIjEaGREREZFAaWREREREAqViRERERAKlYkREREQCpWJEREREAqViRERERAKlYkREREQCpWJEREREAqViRERERAKlYkREREQCpWJEREREAqViRERERAKlYkREREQC9f8BcW6QAgOmcPUAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Generate results for different depths:\n", + "\n", + "Nc_list = range(2, max_depth)\n", + "results_spectral_fit_nc = [\n", + " generate_spectrum_results(Q,1/T, 4, Nk=1, max_depth=Nc)\n", + " for Nc in Nc_list\n", + "]\n", + "\n", + "plot_result_expectations([\n", + " (\n", + " result, P11p, 'rand',\n", + " f\"P11 (spectral fit) $N_C={nc}$\",\n", + " )\n", + " for nc, result in zip(Nc_list, results_spectral_fit_nc)\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "16dc2201", + "metadata": {}, + "source": [ + "We now combine the fitting and correlation function data into one large plot." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "83e44b5f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "t = np.linspace(0, 15, 100)\n", + "C =ohmic_correlation(t, alpha=alpha, wc=wc, beta=1/T)\n", + "w2 = np.concatenate(\n", + " [np.linspace(-10, -0.1, 5000),\n", + " np.linspace(0.1, 10, 5000)],\n", + ")\n", + "S=ohmic_power_spectrum(w2,alpha=alpha,beta=1/T,wc=wc)\n", + "\n", + "\n", + "fs.fit_plots(w,J,t, C,w2,S);" + ] + }, + { + "cell_type": "markdown", + "id": "66ab9ccd", + "metadata": {}, + "source": [ + "## Building the HEOM bath by fitting the correlation function" + ] + }, + { + "cell_type": "markdown", + "id": "d6ac709f", + "metadata": {}, + "source": [ + "Having successfully fitted the spectral density and used the result to calculate the Matsubara expansion and terminator for the HEOM bosonic bath, we now proceed to the second case of fitting the correlation function itself instead.\n", + "\n", + "Here we fit the real and imaginary parts separately, using the following ansatz\n", + "\n", + "$$C_R^F(t) = \\sum_{i=1}^{k_R} c_R^ie^{-\\gamma_R^i t}\\cos(\\omega_R^i t)$$\n", + "\n", + "$$C_I^F(t) = \\sum_{i=1}^{k_I} c_I^ie^{-\\gamma_I^i t}\\sin(\\omega_I^i t)$$\n", + "\n", + "Analogously to the spectral density case, one may use the FitCorr class" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "04c40eac-5dbb-4f08-aa3d-c2b729be6719", + "metadata": {}, + "outputs": [], + "source": [ + "fc=FitCorr(Q)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "d866a44f", + "metadata": {}, + "outputs": [], + "source": [ + "t = np.linspace(0, 25, 1500)\n", + "C = ohmic_correlation(t, alpha=alpha, wc=wc, beta=1/T)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "4140f6ec-e30f-44b1-88d0-f43208ada2c0", + "metadata": {}, + "outputs": [], + "source": [ + "fc.fit_correlation(t,C,Ni=3,Nr=3)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "7a06d9db-f5a0-48f5-b60c-1a7e33143e71", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit correlation class instance: \n", + " \n", + "\n", + "Results of the fitting the Real Part with 3 terms: |\t Results of the fitting the Imaginary Part with 3 terms: \n", + " | \n", + " Parameters| lam | gamma | w0 | \t Parameters| lam | gamma | w0 \n", + " 1 | 2.23e+00 |-2.20e+00 |2.45e-05 |\t 1 |-3.32e+00 |-9.96e-01 |1.66e-01 \n", + " 2 |-9.25e-01 |-4.96e+00 |3.91e+00 | \t 2 |-8.59e-01 |-1.09e+00 |1.37e+00 \n", + " 3 | 2.18e-01 |-3.36e-01 |1.50e-18 | \t 3 |-3.07e-01 |-1.19e+00 |2.70e+00 \n", + " | \n", + " A normalized RMSE of 4.48e-05 was obtained for the real part | \t A normalized RMSE of 1.21e-04 was obtained for the imaginary part \n", + "\t \t \t \t \t \t The current fit took 24.255618 seconds\n" + ] + } + ], + "source": [ + "fc.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "88ff5472-93c0-4f15-9f9a-dcd4b70b14dd", + "metadata": {}, + "outputs": [], + "source": [ + "fc.fit_correlation(t,C,final_rmse=1e-4)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "509dc9e0-89c4-43af-b245-b4e1d25f639b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit correlation class instance: \n", + " \n", + "\n", + "Results of the fitting the Real Part with 3 terms: |\t Results of the fitting the Imaginary Part with 5 terms: \n", + " | \n", + " Parameters| lam | gamma | w0 | \t Parameters| lam | gamma | w0 \n", + " 1 | 2.23e+00 |-2.20e+00 |2.45e-05 |\t 1 |-1.32e+00 |-1.13e+00 |4.34e-36 \n", + " 2 |-9.25e-01 |-4.96e+00 |3.91e+00 | \t 2 |-3.70e-01 |-9.44e-01 |2.11e+00 \n", + " 3 | 2.18e-01 |-3.36e-01 |1.50e-18 | \t 3 |-1.51e-01 |-1.03e+00 |3.32e+00 \n", + " | \t 4 |-2.48e+00 |-8.63e-01 |1.37e-01 \n", + " | \t 5 |-7.36e-01 |-9.14e-01 |1.13e+00 \n", + " | \n", + " A normalized RMSE of 4.48e-05 was obtained for the real part | \t A normalized RMSE of 8.94e-05 was obtained for the imaginary part \n", + "\t \t \t \t \t \t The current fit took 106.264649 seconds\n" + ] + } + ], + "source": [ + "fc.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "acaf610e", + "metadata": {}, + "source": [ + "Now we construct the `BosonicBath` co-efficients and frequencies from the fit to the correlation function:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "c136b8c8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mcditoos/qutip_gsoc_app/qutip/solver/heom/bofin_baths.py:1545: RuntimeWarning: divide by zero encountered in divide\n", + " (((1 / (np.e**(w * beta) - 1)) + 1) * 2)\n", + "/home/mcditoos/anaconda3/envs/qutip-dev/lib/python3.12/site-packages/matplotlib/cbook.py:1699: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return math.isfinite(val)\n", + "/home/mcditoos/anaconda3/envs/qutip-dev/lib/python3.12/site-packages/matplotlib/cbook.py:1345: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return np.asarray(x, float)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "t = np.linspace(0, 15, 100)\n", + "C = ohmic_correlation(t, alpha=alpha, wc=wc, beta=1/T)\n", + "fc.fit_plots(w, J, t, C, w2, S,beta=1/T)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "7c296e7d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n", + "10.0%. Run time: 0.02s. Est. time left: 00:00:00:00\n", + "20.0%. Run time: 0.04s. Est. time left: 00:00:00:00\n", + "30.1%. Run time: 0.05s. Est. time left: 00:00:00:00\n", + "40.1%. Run time: 0.06s. Est. time left: 00:00:00:00\n", + "50.1%. Run time: 0.08s. Est. time left: 00:00:00:00\n", + "60.1%. Run time: 0.09s. Est. time left: 00:00:00:00\n", + "70.1%. Run time: 0.10s. Est. time left: 00:00:00:00\n", + "80.1%. Run time: 0.12s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 0.13s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 0.14s. Est. time left: 00:00:00:00\n", + "Total run time: 0.15s\n", + "3\n", + "10.0%. Run time: 0.48s. Est. time left: 00:00:00:04\n", + "20.0%. Run time: 0.72s. Est. time left: 00:00:00:02\n", + "30.1%. Run time: 0.95s. Est. time left: 00:00:00:02\n", + "40.1%. Run time: 1.17s. Est. time left: 00:00:00:01\n", + "50.1%. Run time: 1.38s. Est. time left: 00:00:00:01\n", + "60.1%. Run time: 1.62s. Est. time left: 00:00:00:01\n", + "70.1%. Run time: 1.88s. Est. time left: 00:00:00:00\n", + "80.1%. Run time: 2.13s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 2.35s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 2.56s. Est. time left: 00:00:00:00\n", + "Total run time: 2.56s\n", + "4\n", + "10.0%. Run time: 3.28s. Est. time left: 00:00:00:29\n", + "20.0%. Run time: 5.09s. Est. time left: 00:00:00:20\n", + "30.1%. Run time: 6.64s. Est. time left: 00:00:00:15\n", + "40.1%. Run time: 8.21s. Est. time left: 00:00:00:12\n", + "50.1%. Run time: 10.13s. Est. time left: 00:00:00:10\n", + "60.1%. Run time: 11.67s. Est. time left: 00:00:00:07\n", + "70.1%. Run time: 13.36s. Est. time left: 00:00:00:05\n", + "80.1%. Run time: 15.16s. Est. time left: 00:00:00:03\n", + "90.2%. Run time: 16.67s. Est. time left: 00:00:00:01\n", + "100.0%. Run time: 18.21s. Est. time left: 00:00:00:00\n", + "Total run time: 18.21s\n" + ] + } + ], + "source": [ + "def generate_corr_results(N, max_depth):\n", + " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + " t = np.linspace(0, 15, 100)\n", + " C = ohmic_correlation(t, alpha=alpha, wc=wc, beta=1/T)\n", + " fc.fit_correlation(t,C,Ni=N,Nr=N)\n", + " HEOM_corr_fit = HEOMSolver(\n", + " Hsys, fc.Bath_corr, max_depth=max_depth, options=options,\n", + " )\n", + "\n", + " results_corr_fit = (HEOM_corr_fit.run(rho0, tlist))\n", + "\n", + " return results_corr_fit\n", + "\n", + "\n", + "# Generate results for different number of lorentzians in fit:\n", + "results_corr_fit_pk = [\n", + " print(f\"{i + 1}\") or generate_corr_results(i, max_depth=max_depth,\n", + " )\n", + " for i in range(1,4)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "9d68342a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_result_expectations([\n", + " (\n", + " result, P11p, 'rand',\n", + " f\"P11 (correlation fit) k_R=k_I={pk + 1}\",\n", + " )\n", + " for pk, result in enumerate(results_corr_fit_pk)\n", + "]);" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "e835851c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", + "\n", + "plot_result_expectations([\n", + " (\n", + " results_corr_fit_pk[0], P11p,\n", + " 'y', \"Correlation Function Fit $k_R=k_I=1$\",\n", + " ),\n", + " (\n", + " results_corr_fit_pk[2], P11p,\n", + " 'y-.', \"Correlation Function Fit $k_R=k_I=3$\",\n", + " ),\n", + " (results_spectral_fit_pk[0], P11p, 'b', \"Spectral Density Fit $k_J=1$\"),\n", + " (results_spectral_fit_pk[2], P11p, 'g--', \"Spectral Density Fit $k_J=3$\"),\n", + " (results_spectral_fit_pk[3], P11p, 'r-.', \"Spectral Density Fit $k_J=4$\"),\n", + "], axes=axes)\n", + "\n", + "axes.set_yticks([0.6, 0.8, 1])\n", + "axes.set_ylabel(r'$\\rho_{11}$', fontsize=30)\n", + "axes.set_xlabel(r'$t\\;\\omega_c$', fontsize=30)\n", + "axes.legend(loc=0, fontsize=20);" + ] + }, + { + "cell_type": "markdown", + "id": "7f846c98-15f1-46a6-a6e2-22ddc298981c", + "metadata": {}, + "source": [ + "# Using the Ohmic Bath class\n", + "\n", + "While the two classes above are designed for general fits of either correlation functions or spectral densities, as the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced shortly by using the OhmicBath class. By default the method fits using the spectral density, however it can use the correlation function if method is specified" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "a7bce5bb-da4d-4c28-8830-4679eaf83a14", + "metadata": {}, + "outputs": [], + "source": [ + "obs=OhmicBath(T,Q,alpha,wc,s,rmse=1e-5,method='spectral')" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "0fec43b0-d490-4f81-8bd2-2c52d94540e4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results of the fitting the Spectral density with 4 terms: \n", + " \n", + " Parameters| lam | gamma | w0 \n", + " 1 | 6.07e-01 | 1.01e+00 |1.00e-01 \n", + " 2 |-4.44e+00 | 4.31e+00 |3.96e+00 \n", + " 3 | 7.93e+00 | 2.30e+00 |1.00e-01 \n", + " 4 | 1.07e-02 | 3.09e-01 |1.00e-01 \n", + " \n", + "A normalized RMSE of 2.64e-06 was obtained for the Spectral density \n", + " The current fit took 28.250522 seconds\n" + ] + } + ], + "source": [ + "obs.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "9f4b887e-398b-44de-a1fa-1ce47881932d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "obs.fit.fit_plots(w,J,t, C,w2,S);" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "1bad7407-9533-4718-9d70-18ac45f2ce8a", + "metadata": {}, + "outputs": [], + "source": [ + "obc=OhmicBath(T,Q,alpha,wc,s,rmse=1e-4,method='correlation')" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "a4c18a34-25a8-4db3-a90a-0179730ddb4c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit correlation class instance: \n", + " \n", + "\n", + "Results of the fitting the Real Part with 5 terms: |\t Results of the fitting the Imaginary Part with 4 terms: \n", + " | \n", + " Parameters| lam | gamma | w0 | \t Parameters| lam | gamma | w0 \n", + " 1 | 2.12e+00 |-2.51e+00 |9.30e-01 |\t 1 |-3.36e+00 |-2.20e+00 |9.56e-01 \n", + " 2 | 3.60e-02 |-1.40e-01 |5.02e-08 | \t 2 |-3.36e+00 |-4.32e-01 |4.61e-03 \n", + " 3 | 6.41e+00 |-7.66e-01 |7.27e-05 | \t 3 | 4.27e-01 |-4.29e+00 |4.30e+00 \n", + " 4 |-5.80e+00 |-7.52e-01 |9.31e-02 | \t 4 |-3.36e+00 |-1.23e+00 |2.01e-01 \n", + " 5 |-1.25e+00 |-4.63e+00 |2.98e+00 | \n", + " | \n", + " A normalized RMSE of 2.31e-06 was obtained for the real part | \t A normalized RMSE of 7.29e-06 was obtained for the imaginary part \n", + "\t \t \t \t \t \t The current fit took 17.603134 seconds\n" + ] + } + ], + "source": [ + "obc.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "01e0dda9-23f9-4dfe-a78f-05ab02ae6a34", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "obc.fit.fit_plots(w, J, t, C, w2, S,beta=1/T)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "8ed890a6-2918-4d6f-9ec4-027dd60b3239", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10.0%. Run time: 9.32s. Est. time left: 00:00:01:23\n", + "20.0%. Run time: 14.89s. Est. time left: 00:00:00:59\n", + "30.1%. Run time: 20.69s. Est. time left: 00:00:00:48\n", + "40.1%. Run time: 28.31s. Est. time left: 00:00:00:42\n", + "50.1%. Run time: 37.21s. Est. time left: 00:00:00:37\n", + "60.1%. Run time: 44.90s. Est. time left: 00:00:00:29\n", + "70.1%. Run time: 52.61s. Est. time left: 00:00:00:22\n", + "80.1%. Run time: 60.75s. Est. time left: 00:00:00:15\n", + "90.2%. Run time: 68.44s. Est. time left: 00:00:00:07\n", + "100.0%. Run time: 76.71s. Est. time left: 00:00:00:00\n", + "Total run time: 76.71s\n" + ] + } + ], + "source": [ + "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + "\n", + "HEOM_ohmic_corr_fit = HEOMSolver(Hsys, obc.bath, max_depth=5, options=options,)\n", + "Ltot = liouvillian(Hsys) + fs.terminator\n", + "HEOM_ohmic_spectral_fit = HEOMSolver(Hsys, obs.bath, max_depth=5, options=options,)\n", + "\n", + "#results__ohmic_corr_fit = (HEOM_ohmic_corr_fit.run(rho0, tlist))\n", + "results_ohmic_spectral_fit = (HEOM_ohmic_spectral_fit.run(rho0, tlist))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8417c361-7971-413b-8b12-702fb4d73b27", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10.0%. Run time: 30.90s. Est. time left: 00:00:04:37\n", + "20.0%. Run time: 58.34s. Est. time left: 00:00:03:52\n", + "30.1%. Run time: 84.19s. Est. time left: 00:00:03:15\n", + "40.1%. Run time: 112.52s. Est. time left: 00:00:02:48\n", + "50.1%. Run time: 139.78s. Est. time left: 00:00:02:19\n", + "60.1%. Run time: 167.44s. Est. time left: 00:00:01:51\n", + "70.1%. Run time: 194.66s. Est. time left: 00:00:01:22\n", + "80.1%. Run time: 229.82s. Est. time left: 00:00:00:56\n", + "90.2%. Run time: 260.57s. Est. time left: 00:00:00:28\n" + ] + } + ], + "source": [ + "results_ohmic_corr_fit = (HEOM_ohmic_corr_fit.run(rho0, tlist))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0cee16c9-e11d-4ee6-8b13-0ea24f6e9c25", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", + "\n", + "plot_result_expectations([\n", + " # (\n", + " # results_corr_fit_pk[0], P11p,\n", + " # 'y', \"Correlation Function Fit $k_R=k_I=1$\",\n", + " # ),\n", + " (\n", + " results_corr_fit_pk[2], P11p,\n", + " 'y-.', \"Correlation Function Fit $k_R=k_I=3$\",\n", + " ),\n", + " (results_spectral_fit_pk[0], P11p, 'b', \"Spectral Density Fit $k_J=1$\"),\n", + " (results_spectral_fit_pk[2], P11p, 'g--', \"Spectral Density Fit $k_J=3$\"),\n", + " (results_spectral_fit_pk[3], P11p, 'r-.', \"Spectral Density Fit $k_J=4$\"),\n", + " (results_ohmic_spectral_fit, P11p, 'g-.', \"Spectral Density Fit Ohmic Bath\"),\n", + " (results_ohmic_corr_fit, P11p, 'k-.', \"Correlation Fit Ohmic Bath\")\n", + "\n", + "], axes=axes)\n", + "\n", + "axes.set_yticks([0.6, 0.8, 1])\n", + "axes.set_ylabel(r'$\\rho_{11}$', fontsize=30)\n", + "axes.set_xlabel(r'$t\\;\\omega_c$', fontsize=30)\n", + "axes.legend(loc=0, fontsize=20);" + ] + }, + { + "cell_type": "markdown", + "id": "e10f9641", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "362da2bd", + "metadata": {}, + "outputs": [], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "dbdf051c", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8598f078", + "metadata": {}, + "outputs": [], + "source": [ + "assert np.allclose(\n", + " expect(P11p, results_spectral_fit_pk[2].states),\n", + " expect(P11p, results_spectral_fit_pk[3].states),\n", + " rtol=1e-2,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4cfe9b59-006e-460a-a48d-e901122eb111", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md index 591e2d51..fcef10c8 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.5 + jupytext_version: 1.15.2 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -27,11 +27,11 @@ The properties of the system are encoded in Hamiltonian, and a coupling operator The bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions. -In the example below we show how to model an Ohmic environment with exponential cut-off in two ways: +In the example below we show how to model an Ohmic environment with exponential cut-off in three ways: * First we fit the spectral density with a set of underdamped brownian oscillator functions. - * Second, we evaluate the correlation functions, and fit those with a certain choice of exponential functions. +* Third, we use the available OhmicBath class In each case we will use the fit parameters to determine the correlation function expansion co-efficients needed to construct a description of the bath (i.e. a `BosonicBath` object) to supply to the `HEOMSolver` so that we can solve for the system dynamics. @@ -40,14 +40,8 @@ In each case we will use the fit parameters to determine the correlation functio ## Setup ```{code-cell} ipython3 -import contextlib -import dataclasses -import time - import numpy as np from matplotlib import pyplot as plt -from scipy.optimize import curve_fit - import qutip from qutip import ( basis, @@ -61,6 +55,9 @@ from qutip import ( from qutip.solver.heom import ( HEOMSolver, BosonicBath, + FitSpectral, + FitCorr, + OhmicBath, ) # Import mpmath functions for evaluation of gamma and zeta @@ -76,61 +73,7 @@ mp.pretty = True ## Helper functions -Let's define some helper functions for plotting results and timing how long operations take: - -```{code-cell} ipython3 -def coth(x): - """ Vectorized hyperbolic cotangent of x. """ - return 1. / np.tanh(x) -``` - -```{code-cell} ipython3 -def plot_result_expectations(plots, axes=None): - """ Plot the expectation values of operators as functions of time. - - Each plot in plots consists of (solver_result, - measurement_operation, color, label). - """ - if axes is None: - fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - fig_created = True - else: - fig = None - fig_created = False - - # add kw arguments to each plot if missing - plots = [p if len(p) == 5 else p + ({},) for p in plots] - for result, m_op, color, label, kw in plots: - exp = np.real(expect(result.states, m_op)) - kw.setdefault("linewidth", 2) - if color == 'rand': - axes.plot( - result.times, exp, - c=np.random.rand(3,), label=label, **kw, - ) - else: - axes.plot(result.times, exp, color, label=label, **kw) - - if fig_created: - axes.legend(loc=0, fontsize=12) - axes.set_xlabel("t", fontsize=28) - - return fig -``` - -```{code-cell} ipython3 -@contextlib.contextmanager -def timer(label): - """ Simple utility for timing functions: - - with timer("name"): - ... code to time ... - """ - start = time.time() - yield - end = time.time() - print(f"{label}: {end - start}") -``` +Let's define some helper functions for plotting the resutls ```{code-cell} ipython3 # Solver options: @@ -155,7 +98,7 @@ And let us set up the system Hamiltonian, bath and system measurement operators: ```{code-cell} ipython3 # Defining the system Hamiltonian -eps = 0.0 # Energy of the 2-level system. +eps = 0 # Energy of the 2-level system. Del = 0.2 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` @@ -247,25 +190,11 @@ def ohmic_power_spectrum(w, alpha, wc, beta): Finally, let's set the bath parameters we will work with and write down some measurement operators: ```{code-cell} ipython3 -# Bath parameters: - -@dataclasses.dataclass -class OhmicBathParameters: - """ Ohmic bath parameters. """ - Q: object = dataclasses.field(default_factory=sigmaz, repr=False) - alpha: float = 3.25 - T: float = 0.5 - wc: float = 1.0 - s: float = 1 - - def __post_init__(self): - self.beta = 1 / self.T - - def replace(self, **kw): - return dataclasses.replace(self, **kw) - - -obp = OhmicBathParameters() +Q = sigmaz() +alpha = 3.25 +T = 0.5 +wc= 1.0 +s= 1 ``` And set the cut-off for the HEOM hierarchy: @@ -290,141 +219,106 @@ J_{\mathrm approx}(\omega; a, b, c) = \sum_{i=0}^{k-1} \frac{2 a_i b_i w}{((w + where $a, b$ and $c$ are the fit parameters and each is a vector of length $k$. -```{code-cell} ipython3 -# Helper functions for packing the paramters a, b and c into a single numpy -# array as required by SciPy's curve_fit: - -def pack(a, b, c): - """ Pack parameter lists for fitting. """ - return np.concatenate((a, b, c)) ++++ +With the spectral density approximation $J_{\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. This can be done quickly using the FitSpectral bath, which takes the target spectral density as an array and fits it to the series of **k** underdamped harmonic oscillators with the Meier-Tannor form -def unpack(params): - """ Unpack parameter lists for fitting. """ - N = len(params) // 3 - a = np.array(params[:N]) - b = np.array(params[N:2 * N]) - c = np.array(params[2 * N:]) - return a, b, c +```{code-cell} ipython3 +w = np.linspace(0, 15, 20000) +J = ohmic_spectral_density(w, alpha, wc) ``` -```{code-cell} ipython3 -# The approximate spectral density and a helper for fitting the approximate -# spectral density to values calculated from the analytical formula: +We first initialize our FitSpectral class -def spectral_density_approx(w, a, b, c): - """ Calculate the fitted value of the function for the given - parameters. - """ - return np.sum( - 2 * a[:, None] * np.multiply.outer(b, w) / ( - ((w + c[:, None])**2 + b[:, None]**2) * - ((w - c[:, None])**2 + b[:, None]**2) - ), - axis=0, - ) +```{code-cell} ipython3 +fs=FitSpectral(T,Q,Nk=4) +``` +To obtain a fit we simply pass our desired spectral density and range, into the get_fit method -def fit_spectral_density(J, w, alpha, wc, N): - """ Fit the spectral density with N underdamped oscillators. """ - sigma = [0.0001] * len(w) +```{code-cell} ipython3 +fs.get_fit(J,w) +``` - J_max = abs(max(J, key=abs)) +To obtain an overview of the results of the fit we may call the summary method - guesses = pack([J_max] * N, [wc] * N, [wc] * N) - lower_bounds = pack([-100 * J_max] * N, [0.1 * wc] * N, [0.1 * wc] * N) - upper_bounds = pack([100 * J_max] * N, [100 * wc] * N, [100 * wc] * N) +```{code-cell} ipython3 +fs.summary() +``` - params, _ = curve_fit( - lambda x, *params: spectral_density_approx(w, *unpack(params)), - w, J, - p0=guesses, - bounds=(lower_bounds, upper_bounds), - sigma=sigma, - maxfev=1000000000, - ) +By default the get_fit method, has a threshold normalized root mean squared error (NRMSE) of $5\times 10^{-6}$ and selects the number of oscillators automatically to obtain that value, one may on the other hand specify the Number of oscillators that can be done using the optional argument N, or may want a more accurate NRMSE, which can be specified with the final_rmse optional argument - return unpack(params) -``` ++++ -With the spectral density approximation $J_{\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. +or by requiring a lower NRMSE ```{code-cell} ipython3 -w = np.linspace(0, 25, 20000) -J = ohmic_spectral_density(w, alpha=obp.alpha, wc=obp.wc) - -params_k = [ - fit_spectral_density(J, w, alpha=obp.alpha, wc=obp.wc, N=i+1) - for i in range(4) -] +fs.get_fit(J,w,final_rmse=2e-6) ``` -Let's plot the fit for each $k$ and examine how it improves with an increasing number of terms: - ```{code-cell} ipython3 -for k, params in enumerate(params_k): - lam, gamma, w0 = params - y = spectral_density_approx(w, lam, gamma, w0) - print(f"Parameters [k={k}]: lam={lam}; gamma={gamma}; w0={w0}") - plt.plot(w, J, w, y) - plt.show() +fs.summary() ``` -The fit with four terms looks good. Let's take a closer look at it by plotting the contribution of each term of the fit: +Alternatively one may choose the number of oscillators in the fit instead of a desired NRMSE ```{code-cell} ipython3 -# The parameters for the fit with four terms: - -lam, gamma, w0 = params_k[-1] - -print(f"Parameters [k={len(params_k) - 1}]: lam={lam}; gamma={gamma}; w0={w0}") +fs.get_fit(J,w,N=4) ``` ```{code-cell} ipython3 -# Plot the components of the fit separately: - -def spectral_density_ith_component(w, i, lam, gamma, w0): - """ Return the i'th term of the approximation for the spectral density. """ - return ( - 2 * lam[i] * gamma[i] * w / - (((w + w0[i])**2 + gamma[i]**2) * ((w - w0[i])**2 + gamma[i]**2)) - ) +fs.summary() +``` +Let's take a closer look at our last fit by plotting the contribution of each term of the fit: -def plot_spectral_density_fit_components(J, w, lam, gamma, w0): - """ Plot the individual components of a fit to the spectral density. """ - fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - axes.plot(w, J, 'r--', linewidth=2, label="original") +```{code-cell} ipython3 +# Plot the components of the fit separately: +plt.rcParams['font.size'] = 25 +plt.rcParams['figure.figsize'] = (10,5) +def plot_fit(func,J, w, lam, gamma, w0): + """ Plot the individual components of a fit to the spectral density. and how they contribute to the full as an animation """ + total=0 + plt.plot(w, J, 'r--', linewidth=2, label="original") for i in range(len(lam)): - axes.plot( - w, spectral_density_ith_component(w, i, lam, gamma, w0), - linewidth=2, - label=f"fit component {i}", - ) - - axes.set_xlabel(r'$w$', fontsize=28) - axes.set_ylabel(r'J', fontsize=28) - axes.legend() - - return fig - + component=func(w,[lam[i]],[gamma[i]],[w0[i]]) + total+=component + plt.plot(w, J, 'r--', linewidth=2, label="original") + plt.plot(w,total,label=rf"$k={i+1}$") + plt.xlabel(r"$\omega$") + plt.ylabel(r"$J(\omega)$") + plt.legend() + plt.pause(1) + plt.show() +def plot_fit_components(func,J, w, lam, gamma, w0): + """ Plot the individual components of a fit to the spectral density. and how they contribute to the full as an animation """ + total=0 + plt.plot(w, J, 'r--', linewidth=2, label="original") + for i in range(len(lam)): + component=func(w,[lam[i]],[gamma[i]],[w0[i]]) + plt.plot(w,component,label=rf"$k={i+1}$") + plt.xlabel(r"$\omega$") + plt.ylabel(r"$J(\omega)$") + plt.legend(bbox_to_anchor=(1.04, 1)) + plt.show() +lam, gamma, w0 = fs.params_spec +plot_fit(fs.spectral_density_approx,J, w, lam, gamma, w0) +``` -plot_spectral_density_fit_components(J, w, lam, gamma, w0); +```{code-cell} ipython3 +plot_fit_components(fs.spectral_density_approx,J, w, lam, gamma, w0) ``` And let's also compare the power spectrum of the fit and the analytical spectral density: ```{code-cell} ipython3 +plt.rcParams['figure.figsize'] = (10,5) + def plot_power_spectrum(alpha, wc, beta, lam, gamma, w0, save=True): """ Plot the power spectrum of a fit against the actual power spectrum. """ w = np.linspace(-10, 10, 50000) - s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta) - s_fit = ( - spectral_density_approx(w, lam, gamma, w0) * - ((1 / (np.e**(w * beta) - 1)) + 1) * 2 - ) - + s_fit = fs.spec_spectrum_approx(w) fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) axes.plot(w, s_orig, 'r', linewidth=2, label="original") axes.plot(w, s_fit, 'b', linewidth=2, label="fit") @@ -437,114 +331,84 @@ def plot_power_spectrum(alpha, wc, beta, lam, gamma, w0, save=True): fig.savefig('powerspectrum.eps') -plot_power_spectrum(obp.alpha, obp.wc, obp.beta, lam, gamma, w0, save=False) +plot_power_spectrum(alpha, wc, 1/T, lam, gamma, w0, save=False) ``` -Now that we have a good fit to the spectral density, we can calculate the Matsubara expansion terms for the `BosonicBath` from them. At the same time we will calculate the Matsubara terminator for this expansion. +Now that we have a good fit to the spectral density, Let us obtain its dynamics, by passing our FitSpectral bath specifications into the HEOMSolver ```{code-cell} ipython3 -def matsubara_coefficients_from_spectral_fit(lam, gamma, w0, beta, Q, Nk): - """ Calculate the Matsubara co-efficients for a fit to the spectral - density. - """ - # initial 0 value with the correct dimensions: - terminator = 0. * spre(Q) - # the number of matsubara expansion terms to include in the terminator: - terminator_max_k = 1000 - - ckAR = [] - vkAR = [] - ckAI = [] - vkAI = [] - - for lamt, Gamma, Om in zip(lam, gamma, w0): - - ckAR.extend([ - (lamt / (4 * Om)) * coth(beta * (Om + 1.0j * Gamma) / 2), - (lamt / (4 * Om)) * coth(beta * (Om - 1.0j * Gamma) / 2), - ]) - for k in range(1, Nk + 1): - ek = 2 * np.pi * k / beta - ckAR.append( - (-2 * lamt * 2 * Gamma / beta) * ek / - ( - ((Om + 1.0j * Gamma)**2 + ek**2) * - ((Om - 1.0j * Gamma)**2 + ek**2) - ) - ) - - terminator_factor = 0 - for k in range(Nk + 1, terminator_max_k): - ek = 2 * np.pi * k / beta - ck = ( - (-2 * lamt * 2 * Gamma / beta) * ek / - ( - ((Om + 1.0j * Gamma)**2 + ek**2) * - ((Om - 1.0j * Gamma)**2 + ek**2) - ) - ) - terminator_factor += ck / ek - terminator += terminator_factor * ( - 2 * spre(Q) * spost(Q.dag()) - - spre(Q.dag() * Q) - - spost(Q.dag() * Q) - ) - - vkAR.extend([ - -1.0j * Om + Gamma, - 1.0j * Om + Gamma, - ]) - vkAR.extend([ - 2 * np.pi * k * obp.T + 0.j - for k in range(1, Nk + 1) - ]) - - ckAI.extend([ - -0.25 * lamt * 1.0j / Om, - 0.25 * lamt * 1.0j / Om, - ]) - vkAI.extend([ - -(-1.0j * Om - Gamma), - -(1.0j * Om - Gamma), - ]) - - return ckAR, vkAR, ckAI, vkAI, terminator +tlist = np.linspace(0, 30 * np.pi / Del, 600) +options = {'nsteps':15000, 'store_states':True, 'rtol':1e-12, 'atol':1e-12, 'method':"bdf"} +Ltot = liouvillian(Hsys) + fs.terminator +HEOM_spectral_fit = HEOMSolver(Ltot, fs.Bath_spec, max_depth=4, options=options,) +result_spectral=HEOM_spectral_fit.run(rho0,tlist) ``` +Now if we want to see the systems's behaviour as we change the Number of terms in the fit, we may use this auxiliary function + ```{code-cell} ipython3 -def generate_spectrum_results(obp, params, Nk, max_depth): +def generate_spectrum_results(Q,beta, N, Nk, max_depth): """ Run the HEOM with the given bath parameters and and return the results of the evolution. """ - lam, gamma, w0 = params - ckAR, vkAR, ckAI, vkAI, terminator = ( - matsubara_coefficients_from_spectral_fit( - lam, gamma, w0, beta=obp.beta, Q=obp.Q, Nk=Nk, - ) - ) - Ltot = liouvillian(Hsys) + terminator + fs=FitSpectral(T,Q,Nk) + fs.get_fit(J,w,N) + Ltot = liouvillian(Hsys) + fs.terminator tlist = np.linspace(0, 30 * np.pi / Del, 600) - with timer("RHS construction time"): - bath = BosonicBath(obp.Q, ckAR, vkAR, ckAI, vkAI) - HEOM_spectral_fit = HEOMSolver( - Ltot, bath, max_depth=max_depth, options=options, - ) - - with timer("ODE solver time"): - results_spectral_fit = (HEOM_spectral_fit.run(rho0, tlist)) - + # This problem is a little stiff, so we use the BDF method to solve + # the ODE ^^^ + print(f'Starting calculations for N={N} and max_depth={max_depth} ... \n ') + HEOM_spectral_fit = HEOMSolver( + Ltot, fs.Bath_spec, max_depth=max_depth, options=options, + ) + results_spectral_fit = (HEOM_spectral_fit.run(rho0, tlist)) + print('\n') return results_spectral_fit ``` Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow. +```{code-cell} ipython3 +def plot_result_expectations(plots, axes=None): + """ Plot the expectation values of operators as functions of time. + + Each plot in plots consists of (solver_result, + measurement_operation, color, label). + """ + if axes is None: + fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) + fig_created = True + else: + fig = None + fig_created = False + + # add kw arguments to each plot if missing + plots = [p if len(p) == 5 else p + ({},) for p in plots] + for result, m_op, color, label, kw in plots: + exp = np.real(expect(result.states, m_op)) + kw.setdefault("linewidth", 2) + if color == 'rand': + axes.plot( + result.times, exp, + c=np.random.rand(3,), label=label, **kw, + ) + else: + axes.plot(result.times, exp, color, label=label, **kw) + + if fig_created: + axes.legend(loc=0, fontsize=12) + axes.set_xlabel("t", fontsize=28) + + return fig +``` + ```{code-cell} ipython3 # Generate results for different number of lorentzians in fit: results_spectral_fit_pk = [ - generate_spectrum_results(obp, params, Nk=1, max_depth=max_depth) - for params in params_k + generate_spectrum_results(Q,1/T, n, Nk=1, max_depth=max_depth) + for n in range(1,5) ] plot_result_expectations([ @@ -562,7 +426,7 @@ plot_result_expectations([ Nk_list = range(2, 4) results_spectral_fit_nk = [ - generate_spectrum_results(obp, params_k[-1], Nk=Nk, max_depth=max_depth) + generate_spectrum_results(Q,1/T, 4, Nk=Nk, max_depth=max_depth) for Nk in Nk_list ] @@ -580,7 +444,7 @@ plot_result_expectations([ Nc_list = range(2, max_depth) results_spectral_fit_nc = [ - generate_spectrum_results(obp, params_k[-1], Nk=1, max_depth=Nc) + generate_spectrum_results(Q,1/T, 4, Nk=1, max_depth=Nc) for Nc in Nc_list ] @@ -595,160 +459,17 @@ plot_result_expectations([ We now combine the fitting and correlation function data into one large plot. -```{code-cell} ipython3 -def correlation_approx_matsubara(t, ck, vk): - """ Calculate the approximate real or imaginary part of the - correlation function from the matsubara expansion co-efficients. - """ - ck = np.array(ck) - vk = np.array(vk) - return np.sum(ck[:, None] * np.exp(-vk[:, None] * t), axis=0) -``` - -```{code-cell} ipython3 -def plot_cr_fit_vs_actual(t, ckAR, vkAR, C, axes): - """ Plot the C_R(t) fit. """ - yR = correlation_approx_matsubara(t, ckAR, vkAR) - - axes.plot( - t, np.real(C), - "r", linewidth=3, label="Original", - ) - axes.plot( - t, np.real(yR), - "g", dashes=[3, 3], linewidth=2, label="Reconstructed", - ) - - axes.legend(loc=0) - axes.set_ylabel(r'$C_R(t)$', fontsize=28) - axes.set_xlabel(r'$t\;\omega_c$', fontsize=28) - axes.locator_params(axis='y', nbins=4) - axes.locator_params(axis='x', nbins=4) - axes.text(0.15, 0.85, "(a)", fontsize=28, transform=axes.transAxes) - - -def plot_ci_fit_vs_actual(t, ckAI, vkAI, C, axes): - """ Plot the C_I(t) fit. """ - yI = correlation_approx_matsubara(t, ckAI, vkAI) - - axes.plot( - t, np.imag(C), - "r", linewidth=3, label="Original", - ) - axes.plot( - t, np.real(yI), - "g", dashes=[3, 3], linewidth=2, label="Reconstructed", - ) - - axes.legend(loc=0) - axes.set_ylabel(r'$C_I(t)$', fontsize=28) - axes.set_xlabel(r'$t\;\omega_c$', fontsize=28) - axes.locator_params(axis='y', nbins=4) - axes.locator_params(axis='x', nbins=4) - axes.text(0.80, 0.80, "(b)", fontsize=28, transform=axes.transAxes) - - -def plot_jw_fit_vs_actual(bath_fit, obp, axes): - """ Plot the J(w) fit. """ - [lam, gamma, w0] = bath_fit - [alpha, wc] = [obp.alpha, obp.wc] - - w = np.linspace(0, 25, 20000) - - J_orig = ohmic_spectral_density(w, alpha=alpha, wc=wc) - J_fit = spectral_density_approx(w, lam, gamma, w0) - - axes.plot( - w, J_orig, - "r", linewidth=3, label=r"$J(\omega)$ original", - ) - axes.plot( - w, J_fit, - "g", dashes=[3, 3], linewidth=2, label=r"$J(\omega)$ Fit $k_J = 4$", - ) - - axes.legend(loc=0) - axes.set_ylabel(r'$J(\omega)$', fontsize=28) - axes.set_xlabel(r'$\omega/\omega_c$', fontsize=28) - axes.locator_params(axis='y', nbins=4) - axes.locator_params(axis='x', nbins=4) - axes.text(0.15, 0.85, "(c)", fontsize=28, transform=axes.transAxes) - - -def plot_sw_fit_vs_actual(bath_fit, obp, axes): - """ Plot the S(w) fit. """ - [lam, gamma, w0] = bath_fit - [alpha, wc, beta] = [obp.alpha, obp.wc, obp.beta] - - # avoid the pole in the fit around zero: - w = np.concatenate( - [np.linspace(-10, -0.1, 5000), - np.linspace(0.1, 10, 5000)], - ) - - s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta) - s_fit = ( - spectral_density_approx(w, lam, gamma, w0) * - ((1 / (np.e**(w * beta) - 1)) + 1) * 2 - ) - - axes.plot(w, s_orig, "r", linewidth=3, label="Original") - axes.plot(w, s_fit, "g", dashes=[3, 3], linewidth=2, label="Reconstructed") - - axes.legend() - axes.set_ylabel(r'$S(\omega)$', fontsize=28) - axes.set_xlabel(r'$\omega/\omega_c$', fontsize=28) - axes.locator_params(axis='y', nbins=4) - axes.locator_params(axis='x', nbins=4) - axes.text(0.15, 0.85, "(d)", fontsize=28, transform=axes.transAxes) - - -def plot_matsubara_spectrum_fit_vs_actual( - t, C, matsubara_fit, bath_fit, obp, -): - """ Plot the Matsubara fit of the spectrum . """ - fig = plt.figure(figsize=(12, 10)) - grid = plt.GridSpec(2, 2, wspace=0.4, hspace=0.3) - - [ckAR, vkAR, ckAI, vkAI] = matsubara_fit - - plot_cr_fit_vs_actual( - t, ckAR, vkAR, C, - axes=fig.add_subplot(grid[0, 0]), - ) - plot_ci_fit_vs_actual( - t, ckAI, vkAI, C, - axes=fig.add_subplot(grid[0, 1]), - ) - plot_jw_fit_vs_actual( - bath_fit, obp, - axes=fig.add_subplot(grid[1, 0]), - ) - plot_sw_fit_vs_actual( - bath_fit, obp, - axes=fig.add_subplot(grid[1, 1]), - ) - - return fig -``` - ```{code-cell} ipython3 t = np.linspace(0, 15, 100) -C = ohmic_correlation(t, alpha=obp.alpha, wc=obp.wc, beta=obp.beta) - -ckAR, vkAR, ckAI, vkAI, terminator = ( - matsubara_coefficients_from_spectral_fit( - lam, gamma, w0, beta=obp.beta, Q=obp.Q, Nk=1, - ) +C =ohmic_correlation(t, alpha=alpha, wc=wc, beta=1/T) +w2 = np.concatenate( + [np.linspace(-10, -0.1, 5000), + np.linspace(0.1, 10, 5000)], ) +S=ohmic_power_spectrum(w2,alpha=alpha,beta=1/T,wc=wc) -matsubara_fit = [ckAR, vkAR, ckAI, vkAI] -bath_fit = [lam, gamma, w0] -plot_matsubara_spectrum_fit_vs_actual( - t, C, matsubara_fit, - bath_fit, obp, -); +fs.fit_plots(w,J,t, C,w2,S); ``` ## Building the HEOM bath by fitting the correlation function @@ -757,302 +478,67 @@ plot_matsubara_spectrum_fit_vs_actual( Having successfully fitted the spectral density and used the result to calculate the Matsubara expansion and terminator for the HEOM bosonic bath, we now proceed to the second case of fitting the correlation function itself instead. -Here we fit the real and imaginary parts seperately, using the following ansatz +Here we fit the real and imaginary parts separately, using the following ansatz $$C_R^F(t) = \sum_{i=1}^{k_R} c_R^ie^{-\gamma_R^i t}\cos(\omega_R^i t)$$ $$C_I^F(t) = \sum_{i=1}^{k_I} c_I^ie^{-\gamma_I^i t}\sin(\omega_I^i t)$$ -```{code-cell} ipython3 -# The approximate correlation functions and a helper for fitting -# the approximate correlation function to values calculated from -# the analytical formula: - -def correlation_approx_real(t, a, b, c): - """ Calculate the fitted value of the function for the given parameters. - """ - a = np.array(a) - b = np.array(b) - c = np.array(c) - return np.sum( - a[:, None] * np.exp(b[:, None] * t) * np.cos(c[:, None] * t), - axis=0, - ) - - -def correlation_approx_imag(t, a, b, c): - """ Calculate the fitted value of the function for the given parameters. - """ - a = np.array(a) - b = np.array(b) - c = np.array(c) - return np.sum( - a[:, None] * np.exp(b[:, None] * t) * np.sin(c[:, None] * t), - axis=0, - ) - - -def fit_correlation_real(C, t, wc, N): - """ Fit the spectral density with N underdamped oscillators. """ - sigma = [0.1] * len(t) - - C_max = abs(max(C, key=abs)) - - guesses = pack([C_max] * N, [-wc] * N, [wc] * N) - lower_bounds = pack([-20 * C_max] * N, [-np.inf] * N, [0.] * N) - upper_bounds = pack([20 * C_max] * N, [0.1] * N, [np.inf] * N) - - params, _ = curve_fit( - lambda x, *params: correlation_approx_real(t, *unpack(params)), - t, C, - p0=guesses, - bounds=(lower_bounds, upper_bounds), - sigma=sigma, - maxfev=1000000000, - ) - - return unpack(params) - - -def fit_correlation_imag(C, t, wc, N): - """ Fit the spectral density with N underdamped oscillators. """ - sigma = [0.0001] * len(t) - - C_max = abs(max(C, key=abs)) - - guesses = pack([-C_max] * N, [-2] * N, [1] * N) - lower_bounds = pack([-5 * C_max] * N, [-100] * N, [0.] * N) - upper_bounds = pack([5 * C_max] * N, [0.01] * N, [100] * N) - - params, _ = curve_fit( - lambda x, *params: correlation_approx_imag(t, *unpack(params)), - t, C, - p0=guesses, - bounds=(lower_bounds, upper_bounds), - sigma=sigma, - maxfev=1000000000, - ) - - return unpack(params) -``` +Analogously to the spectral density case, one may use the FitCorr class ```{code-cell} ipython3 -t = np.linspace(0, 15, 15000) -C = ohmic_correlation(t, alpha=obp.alpha, wc=obp.wc, beta=obp.beta) - -params_k_real = [ - fit_correlation_real(np.real(C), t, wc=obp.wc, N=i+1) - for i in range(3) -] - -params_k_imag = [ - fit_correlation_imag(np.imag(C), t, wc=obp.wc, N=i+1) - for i in range(3) -] +fc=FitCorr(Q) ``` ```{code-cell} ipython3 -for k, params in enumerate(params_k_real): - lam, gamma, w0 = params - y = correlation_approx_real(t, lam, gamma, w0) - print(f"Parameters [k={k}]: lam={lam}; gamma={gamma}; w0={w0}") - plt.plot(t, np.real(C), label="C_R(t) analytic") - plt.plot(t, y, label=f"C_R(t) k={k + 1}") - plt.legend() - plt.show() +t = np.linspace(0, 25, 1500) +C = ohmic_correlation(t, alpha=alpha, wc=wc, beta=1/T) ``` ```{code-cell} ipython3 -for k, params in enumerate(params_k_imag): - lam, gamma, w0 = params - y = correlation_approx_imag(t, lam, gamma, w0) - print(f"Parameters [k={k}]: lam={lam}; gamma={gamma}; w0={w0}") - plt.plot(t, np.imag(C), label="C_I(t) analytic") - plt.plot(t, y, label=f"C_I(t) k={k + 1}") - plt.legend() - plt.show() +fc.fit_correlation(t,C,Ni=3,Nr=3) ``` -Now we construct the `BosonicBath` co-efficients and frequencies from the fit to the correlation function: - ```{code-cell} ipython3 -def matsubara_coefficients_from_corr_fit_real(lam, gamma, w0): - """ Return the matsubara coefficients for the imaginary part - of the correlation function. - """ - ckAR = [0.5 * x + 0j for x in lam] # the 0.5 is from the cosine - # extend the list with the complex conjugates: - ckAR.extend(np.conjugate(ckAR)) - - vkAR = [-x - 1.0j * y for x, y in zip(gamma, w0)] - vkAR.extend([-x + 1.0j * y for x, y in zip(gamma, w0)]) - - return ckAR, vkAR - - -def matsubara_coefficients_from_corr_fit_imag(lam, gamma, w0): - """ Return the matsubara coefficients for the imaginary part - of the correlation function. - """ - ckAI = [-0.5j * x for x in lam] # the 0.5 is from the sine - # extend the list with the complex conjugates: - ckAI.extend(np.conjugate(ckAI)) - - vkAI = [-x - 1.0j * y for x, y in zip(gamma, w0)] - vkAI.extend([-x + 1.0j * y for x, y in zip(gamma, w0)]) - - return ckAI, vkAI +fc.summary() ``` ```{code-cell} ipython3 -ckAR, vkAR = matsubara_coefficients_from_corr_fit_real(*params_k_real[-1]) -ckAI, vkAI = matsubara_coefficients_from_corr_fit_imag(*params_k_imag[-1]) +fc.fit_correlation(t,C,final_rmse=1e-4) ``` ```{code-cell} ipython3 -def corr_spectrum_approx(w, ckAR, vkAR, ckAI, vkAI): - """ Calculates the approximate power spectrum from ck and vk. """ - S = np.zeros(len(w), dtype=np.complex128) - for ck, vk in zip(ckAR, vkAR): - S += ( - 2 * ck * np.real(vk) / - ((w - np.imag(vk))**2 + (np.real(vk)**2)) - ) - for ck, vk in zip(ckAI, vkAI): - S += ( - 2 * 1.0j * ck * np.real(vk) / - ((w - np.imag(vk))**2 + (np.real(vk)**2)) - ) - return S +fc.summary() ``` -```{code-cell} ipython3 -def plot_jw_correlation_fit_vs_actual(matsubara_fit, obp, axes): - """ Plot J(w) from the correlation fit. """ - [ckAR, vkAR, ckAI, vkAI] = matsubara_fit - [alpha, wc] = [obp.alpha, obp.wc] - - w = np.linspace(0.001, 25, 20000) - - J_orig = ohmic_spectral_density(w, alpha=alpha, wc=wc) - J_fit = np.real( - corr_spectrum_approx(w, ckAR, vkAR, ckAI, vkAI) / - (((1 / (np.e**(w * obp.beta) - 1)) + 1) * 2) - ) - - axes.plot( - w, J_orig, - "r", linewidth=3, label=r"$J(\omega)$ original", - ) - axes.plot( - w, J_fit, - "g", dashes=[3, 3], linewidth=2, label=r"$J(\omega)$ fit", - ) - - axes.legend(loc=0) - axes.set_ylabel(r'$J(\omega)$', fontsize=28) - axes.set_xlabel(r'$\omega/\omega_c$', fontsize=28) - axes.locator_params(axis='y', nbins=4) - axes.locator_params(axis='x', nbins=4) - axes.text(3, 1.1, "(c)", fontsize=28) - - -def plot_sw_correlation_fit_vs_actual(matsubara_fit, obp, axes): - """ Plot S(W) from the correlation fit. """ - [ckAR, vkAR, ckAI, vkAI] = matsubara_fit - [alpha, wc, beta] = [obp.alpha, obp.wc, obp.beta] - - # avoid the pole in the fit around zero: - w = np.concatenate([ - np.linspace(-10, -0.1, 5000), - np.linspace(0.1, 10, 5000), - ]) - - s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta) - s_fit = corr_spectrum_approx(w, ckAR, vkAR, ckAI, vkAI) - - axes.plot( - w, s_orig, - "r", linewidth=3, label="Original", - ) - axes.plot( - w, s_fit, - "g", dashes=[3, 3], linewidth=2, label="Reconstructed", - ) - - axes.legend() - axes.set_ylabel(r'$S(\omega)$', fontsize=28) - axes.set_xlabel(r'$\omega/\omega_c$', fontsize=28) - axes.locator_params(axis='y', nbins=4) - axes.locator_params(axis='x', nbins=4) - axes.text(0.15, 0.85, "(d)", fontsize=28, transform=axes.transAxes) - - -def plot_matsubara_correlation_fit_vs_actual(t, C, matsubara_fit, obp): - fig = plt.figure(figsize=(12, 10)) - grid = plt.GridSpec(2, 2, wspace=0.4, hspace=0.3) - - ckAR, vkAR, ckAI, vkAI = matsubara_fit - - plot_cr_fit_vs_actual( - t, ckAR, vkAR, C, - axes=fig.add_subplot(grid[0, 0]), - ) - plot_ci_fit_vs_actual( - t, ckAI, vkAI, C, - axes=fig.add_subplot(grid[0, 1]), - ) - plot_jw_correlation_fit_vs_actual( - matsubara_fit, obp, - axes=fig.add_subplot(grid[1, 0]), - ) - plot_sw_correlation_fit_vs_actual( - matsubara_fit, obp, - axes=fig.add_subplot(grid[1, 1]), - ) -``` +Now we construct the `BosonicBath` co-efficients and frequencies from the fit to the correlation function: ```{code-cell} ipython3 t = np.linspace(0, 15, 100) -C = ohmic_correlation(t, alpha=obp.alpha, wc=obp.wc, beta=obp.beta) - -matsubara_fit = [ckAR, vkAR, ckAI, vkAI] - -plot_matsubara_correlation_fit_vs_actual( - t, C, matsubara_fit, obp, -) +C = ohmic_correlation(t, alpha=alpha, wc=wc, beta=1/T) +fc.fit_plots(w, J, t, C, w2, S,beta=1/T) ``` ```{code-cell} ipython3 -def generate_corr_results(params_real, params_imag, max_depth): - ckAR, vkAR = matsubara_coefficients_from_corr_fit_real( - *params_real - ) - ckAI, vkAI = matsubara_coefficients_from_corr_fit_imag( - *params_imag - ) - +def generate_corr_results(N, max_depth): tlist = np.linspace(0, 30 * np.pi / Del, 600) + t = np.linspace(0, 15, 100) + C = ohmic_correlation(t, alpha=alpha, wc=wc, beta=1/T) + fc.fit_correlation(t,C,Ni=N,Nr=N) + HEOM_corr_fit = HEOMSolver( + Hsys, fc.Bath_corr, max_depth=max_depth, options=options, + ) - with timer("RHS construction time"): - bath = BosonicBath(obp.Q, ckAR, vkAR, ckAI, vkAI) - HEOM_corr_fit = HEOMSolver( - Hsys, bath, max_depth=max_depth, options=options, - ) - - with timer("ODE solver time"): - results_corr_fit = (HEOM_corr_fit.run(rho0, tlist)) + results_corr_fit = (HEOM_corr_fit.run(rho0, tlist)) return results_corr_fit # Generate results for different number of lorentzians in fit: results_corr_fit_pk = [ - print(f"{pk + 1}") or generate_corr_results( - params_real, params_imag, max_depth=max_depth, + print(f"{i + 1}") or generate_corr_results(i, max_depth=max_depth, ) - for pk, (params_real, params_imag) - in enumerate(zip(params_k_real, params_k_imag)) + for i in range(1,4) ] ``` @@ -1089,6 +575,75 @@ axes.set_xlabel(r'$t\;\omega_c$', fontsize=30) axes.legend(loc=0, fontsize=20); ``` +# Using the Ohmic Bath class + +While the two classes above are designed for general fits of either correlation functions or spectral densities, as the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced shortly by using the OhmicBath class. By default the method fits using the spectral density, however it can use the correlation function if method is specified + +```{code-cell} ipython3 +obs=OhmicBath(T,Q,alpha,wc,s,rmse=1e-5,method='spectral') +``` + +```{code-cell} ipython3 +obs.summary() +``` + +```{code-cell} ipython3 +obs.fit.fit_plots(w,J,t, C,w2,S); +``` + +```{code-cell} ipython3 +obc=OhmicBath(T,Q,alpha,wc,s,rmse=1e-4,method='correlation') +``` + +```{code-cell} ipython3 +obc.summary() +``` + +```{code-cell} ipython3 +obc.fit.fit_plots(w, J, t, C, w2, S,beta=1/T) +``` + +```{code-cell} ipython3 +tlist = np.linspace(0, 30 * np.pi / Del, 600) + +HEOM_ohmic_corr_fit = HEOMSolver(Hsys, obc.bath, max_depth=5, options=options,) +Ltot = liouvillian(Hsys) + fs.terminator +HEOM_ohmic_spectral_fit = HEOMSolver(Hsys, obs.bath, max_depth=5, options=options,) + +#results__ohmic_corr_fit = (HEOM_ohmic_corr_fit.run(rho0, tlist)) +results_ohmic_spectral_fit = (HEOM_ohmic_spectral_fit.run(rho0, tlist)) +``` + +```{code-cell} ipython3 +results_ohmic_corr_fit = (HEOM_ohmic_corr_fit.run(rho0, tlist)) +``` + +```{code-cell} ipython3 +fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) + +plot_result_expectations([ + # ( + # results_corr_fit_pk[0], P11p, + # 'y', "Correlation Function Fit $k_R=k_I=1$", + # ), + ( + results_corr_fit_pk[2], P11p, + 'y-.', "Correlation Function Fit $k_R=k_I=3$", + ), + (results_spectral_fit_pk[0], P11p, 'b', "Spectral Density Fit $k_J=1$"), + (results_spectral_fit_pk[2], P11p, 'g--', "Spectral Density Fit $k_J=3$"), + (results_spectral_fit_pk[3], P11p, 'r-.', "Spectral Density Fit $k_J=4$"), + (results_ohmic_spectral_fit, P11p, 'g-.', "Spectral Density Fit Ohmic Bath"), + (results_ohmic_corr_fit, P11p, 'k-.', "Correlation Fit Ohmic Bath") + +], axes=axes) + +axes.set_yticks([0.6, 0.8, 1]) +axes.set_ylabel(r'$\rho_{11}$', fontsize=30) +axes.set_xlabel(r'$t\;\omega_c$', fontsize=30) +axes.legend(loc=0, fontsize=20); +``` + ## About ```{code-cell} ipython3 @@ -1106,3 +661,7 @@ assert np.allclose( rtol=1e-2, ) ``` + +```{code-cell} ipython3 + +``` From 47e7793d6f98c10e401d4d39af6e2a2f932fcfca Mon Sep 17 00:00:00 2001 From: mcditooss Date: Wed, 8 Nov 2023 11:25:14 +0900 Subject: [PATCH 02/44] modified tutorial to match fitting classes --- ...eom-1d-spin-bath-model-ohmic-fitting.ipynb | 1744 ----------------- .../heom-1d-spin-bath-model-ohmic-fitting.md | 6 +- 2 files changed, 1 insertion(+), 1749 deletions(-) delete mode 100644 tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb deleted file mode 100644 index 20d12b8b..00000000 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb +++ /dev/null @@ -1,1744 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "259274b8", - "metadata": {}, - "source": [ - "# HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions" - ] - }, - { - "cell_type": "markdown", - "id": "3cfc6dd5", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded \n", - "in a set of auxiliary density matrices.\n", - "\n", - "In this example we show the evolution of a single two-level system in contact with a single bosonic environment.\n", - "\n", - "The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", - "\n", - "The bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", - "\n", - "In the example below we show how to model an Ohmic environment with exponential cut-off in three ways:\n", - "\n", - "* First we fit the spectral density with a set of underdamped brownian oscillator functions.\n", - "* Second, we evaluate the correlation functions, and fit those with a certain choice of exponential functions.\n", - "* Third, we use the available OhmicBath class \n", - "\n", - "In each case we will use the fit parameters to determine the correlation function expansion co-efficients needed to construct a description of the bath (i.e. a `BosonicBath` object) to supply to the `HEOMSolver` so that we can solve for the system dynamics." - ] - }, - { - "cell_type": "markdown", - "id": "376044c7", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c9091371", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "import qutip\n", - "from qutip import (\n", - " basis,\n", - " expect,\n", - " liouvillian,\n", - " sigmax,\n", - " sigmaz,\n", - " spost,\n", - " spre,\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver,\n", - " BosonicBath,\n", - " FitSpectral,\n", - " FitCorr,\n", - " OhmicBath,\n", - ")\n", - "\n", - "# Import mpmath functions for evaluation of gamma and zeta\n", - "# functions in the expression for the correlation:\n", - "\n", - "from mpmath import mp\n", - "\n", - "mp.dps = 15\n", - "mp.pretty = True\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "278b856d", - "metadata": {}, - "source": [ - "## Helper functions\n", - "\n", - "Let's define some helper functions for plotting the resutls" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "3b43e49b", - "metadata": {}, - "outputs": [], - "source": [ - "# Solver options:\n", - "\n", - "options = {\n", - " \"nsteps\": 15000,\n", - " \"store_states\": True,\n", - " \"rtol\": 1e-14,\n", - " \"atol\": 1e-14,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "962cbe21", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "And let us set up the system Hamiltonian, bath and system measurement operators:" - ] - }, - { - "cell_type": "markdown", - "id": "04c0d02a", - "metadata": {}, - "source": [ - "### System Hamiltonian" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d4f6ca24", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the system Hamiltonian\n", - "eps = 0 # Energy of the 2-level system.\n", - "Del = 0.2 # Tunnelling term\n", - "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "11929825", - "metadata": {}, - "outputs": [], - "source": [ - "# Initial state of the system.\n", - "rho0 = basis(2, 0) * basis(2, 0).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "a50797e2", - "metadata": {}, - "source": [ - "### System measurement operators" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "56677852", - "metadata": {}, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresonding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresonding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "1b17b0e6", - "metadata": {}, - "source": [ - "### Analytical expressions for the Ohmic bath correlation function and spectral density" - ] - }, - { - "cell_type": "markdown", - "id": "02b425b6", - "metadata": {}, - "source": [ - "Before we begin fitting, let us examine the analytic expressions for the correlation and spectral density functions and write Python equivalents. \n", - "\n", - "The correlation function is given by (see, e.g., http://www1.itp.tu-berlin.de/brandes/public_html/publications/notes.pdf for a derivation, equation 7.59, but with a factor of $\\pi$ moved into the definition of the correlation function):\n", - "\n", - "\\begin{align}\n", - "C(t) =& \\: \\frac{1}{\\pi}\\alpha \\omega_{c}^{1 - s} \\beta^{- (s + 1)} \\: \\times \\\\\n", - " & \\: \\Gamma(s + 1) \\left[ \\zeta \\left(s + 1, \\frac{1 + \\beta \\omega_c - i \\omega_c t}{\\beta \\omega_c}\\right) + \\zeta \\left(s + 1, \\frac{1 + i \\omega_c t}{\\beta \\omega_c}\\right) \\right]\n", - "\\end{align}\n", - "\n", - "where $\\Gamma$ is the Gamma function and\n", - "\n", - "\\begin{equation}\n", - "\\zeta(z, u) \\equiv \\sum_{n=0}^{\\infty} \\frac{1}{(n + u)^z}, \\; u \\neq 0, -1, -2, \\ldots\n", - "\\end{equation}\n", - "\n", - "is the generalized Zeta function. The Ohmic case is given by $s = 1$.\n", - "\n", - "The corresponding spectral density for the Ohmic case is:\n", - "\n", - "\\begin{equation}\n", - "J(\\omega) = \\omega \\alpha e^{- \\frac{\\omega}{\\omega_c}}\n", - "\\end{equation}" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e4905d21", - "metadata": {}, - "outputs": [], - "source": [ - "def ohmic_correlation(t, alpha, wc, beta, s=1):\n", - " \"\"\" The Ohmic bath correlation function as a function of t\n", - " (and the bath parameters).\n", - " \"\"\"\n", - " corr = (\n", - " (1 / np.pi) * alpha * wc**(1 - s) * beta**(-(s + 1)) * mp.gamma(s + 1)\n", - " )\n", - " z1_u = (1 + beta * wc - 1.0j * wc * t) / (beta * wc)\n", - " z2_u = (1 + 1.0j * wc * t) / (beta * wc)\n", - " # Note: the arguments to zeta should be in as high precision as possible.\n", - " # See http://mpmath.org/doc/current/basics.html#providing-correct-input\n", - " return np.array([\n", - " complex(corr * (mp.zeta(s + 1, u1) + mp.zeta(s + 1, u2)))\n", - " for u1, u2 in zip(z1_u, z2_u)\n", - " ], dtype=np.complex128)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "edd66606", - "metadata": {}, - "outputs": [], - "source": [ - "def ohmic_spectral_density(w, alpha, wc):\n", - " \"\"\" The Ohmic bath spectral density as a function of w\n", - " (and the bath parameters).\n", - " \"\"\"\n", - " return w * alpha * np.e**(-w / wc)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7c32be4c", - "metadata": {}, - "outputs": [], - "source": [ - "def ohmic_power_spectrum(w, alpha, wc, beta):\n", - " \"\"\" The Ohmic bath power spectrum as a function of w\n", - " (and the bath parameters).\n", - " \"\"\"\n", - " return (\n", - " w * alpha * np.e**(-abs(w) / wc) *\n", - " ((1 / (np.e**(w * beta) - 1)) + 1) * 2\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "bac74291", - "metadata": {}, - "source": [ - "### Bath and HEOM parameters" - ] - }, - { - "cell_type": "markdown", - "id": "38f903f4", - "metadata": {}, - "source": [ - "Finally, let's set the bath parameters we will work with and write down some measurement operators:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c1ea19c7", - "metadata": {}, - "outputs": [], - "source": [ - "Q = sigmaz()\n", - "alpha = 3.25\n", - "T = 0.5\n", - "wc= 1.0\n", - "s= 1" - ] - }, - { - "cell_type": "markdown", - "id": "f71d98fb", - "metadata": {}, - "source": [ - "And set the cut-off for the HEOM hierarchy:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "15a566eb", - "metadata": {}, - "outputs": [], - "source": [ - "# HEOM parameters:\n", - "\n", - "# The max_depth defaults to 5 so that the notebook executes more\n", - "# quickly. Change it to 11 to wait longer for more accurate results.\n", - "max_depth = 5" - ] - }, - { - "cell_type": "markdown", - "id": "65099a5a", - "metadata": {}, - "source": [ - "## Building the HEOM bath by fitting the spectral density" - ] - }, - { - "cell_type": "markdown", - "id": "c32b82b4", - "metadata": {}, - "source": [ - "We begin by fitting the spectral density, using a series of $k$ underdamped harmonic oscillators case with the Meier-Tannor form (J. Chem. Phys. 111, 3365 (1999); https://doi.org/10.1063/1.479669):\n", - "\n", - "\\begin{equation}\n", - "J_{\\mathrm approx}(\\omega; a, b, c) = \\sum_{i=0}^{k-1} \\frac{2 a_i b_i w}{((w + c_i)^2 + b_i^2) ((w - c_i)^2 + b_i^2)}\n", - "\\end{equation}\n", - "\n", - "where $a, b$ and $c$ are the fit parameters and each is a vector of length $k$." - ] - }, - { - "cell_type": "markdown", - "id": "66a3d71b", - "metadata": {}, - "source": [ - "With the spectral density approximation $J_{\\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. This can be done quickly using the FitSpectral bath, which takes the target spectral density as an array and fits it to the series of **k** underdamped harmonic oscillators with the Meier-Tannor form" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "08dc9a17", - "metadata": {}, - "outputs": [], - "source": [ - "w = np.linspace(0, 15, 20000)\n", - "J = ohmic_spectral_density(w, alpha, wc)" - ] - }, - { - "cell_type": "markdown", - "id": "a649c903", - "metadata": {}, - "source": [ - "We first initialize our FitSpectral class" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "fb5a13de", - "metadata": {}, - "outputs": [], - "source": [ - "fs=FitSpectral(T,Q,Nk=4)" - ] - }, - { - "cell_type": "markdown", - "id": "070ea1da-e3a0-4a45-b894-edc793e8d6d7", - "metadata": {}, - "source": [ - "To obtain a fit we simply pass our desired spectral density and range, into the get_fit method" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "10fbe33a-682c-44e7-8c69-fb85d192b7d6", - "metadata": {}, - "outputs": [], - "source": [ - "fs.get_fit(J,w)" - ] - }, - { - "cell_type": "markdown", - "id": "237c5e0f-16df-4e07-ab25-50d575bf5bd0", - "metadata": {}, - "source": [ - "To obtain an overview of the results of the fit we may call the summary method" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "155ced50-5009-4589-bd31-b3e0e38f3ae0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Results of the fitting the Spectral density with 4 terms: \n", - " \n", - " Parameters| lam | gamma | w0 \n", - " 1 | 6.07e-01 | 1.01e+00 |1.00e-01 \n", - " 2 |-4.44e+00 | 4.31e+00 |3.96e+00 \n", - " 3 | 7.93e+00 | 2.30e+00 |1.00e-01 \n", - " 4 | 1.07e-02 | 3.09e-01 |1.00e-01 \n", - " \n", - "A normalized RMSE of 2.64e-06 was obtained for the Spectral density \n", - " The current fit took 13.199548 seconds\n" - ] - } - ], - "source": [ - "fs.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "b637d86d-1e1f-4d14-ad37-b0a5db8b69e8", - "metadata": {}, - "source": [ - "By default the get_fit method, has a threshold normalized root mean squared error (NRMSE) of $5\\times 10^{-6}$ and selects the number of oscillators automatically to obtain that value, one may on the other hand specify the Number of oscillators that can be done using the optional argument N, or may want a more accurate NRMSE, which can be specified with the final_rmse optional argument" - ] - }, - { - "cell_type": "markdown", - "id": "b601eb73-7b1f-4a0b-a988-d66212351c70", - "metadata": {}, - "source": [ - "or by requiring a lower NRMSE" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "e00be6c9-e1a4-4968-bfad-bc566207cbe5", - "metadata": {}, - "outputs": [], - "source": [ - "fs.get_fit(J,w,final_rmse=2e-6)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "367f5c67-dfb8-4e05-8f7f-886257d3e10a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Results of the fitting the Spectral density with 5 terms: \n", - " \n", - " Parameters| lam | gamma | w0 \n", - " 1 | 9.15e-02 | 6.03e-01 |1.00e-01 \n", - " 2 | 2.39e+00 | 1.50e+00 |1.00e-01 \n", - " 3 | 5.37e+00 | 2.28e+00 |1.15e+00 \n", - " 4 | 1.18e-03 | 1.54e-01 |1.00e-01 \n", - " 5 |-3.75e+00 | 4.31e+00 |4.17e+00 \n", - " \n", - "A normalized RMSE of 1.28e-06 was obtained for the Spectral density \n", - " The current fit took 24.697593 seconds\n" - ] - } - ], - "source": [ - "fs.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "cec2e350-406b-4e84-9772-a1041fd8c0bd", - "metadata": {}, - "source": [ - "Alternatively one may choose the number of oscillators in the fit instead of a desired NRMSE" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "d9476fb9-d87f-494e-abe1-e253d8977251", - "metadata": {}, - "outputs": [], - "source": [ - "fs.get_fit(J,w,N=4)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "bafef06d-2656-4fd9-8306-72b61cd4160c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Results of the fitting the Spectral density with 3 terms: \n", - " \n", - " Parameters| lam | gamma | w0 \n", - " 1 | 6.07e-01 | 1.01e+00 |1.00e-01 \n", - " 2 |-4.44e+00 | 4.31e+00 |3.96e+00 \n", - " 3 | 7.93e+00 | 2.30e+00 |1.00e-01 \n", - " 4 | 1.07e-02 | 3.09e-01 |1.00e-01 \n", - " \n", - "A normalized RMSE of 2.64e-06 was obtained for the Spectral density \n", - " The current fit took 4.815641 seconds\n" - ] - } - ], - "source": [ - "fs.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "194bfe00", - "metadata": {}, - "source": [ - "Let's take a closer look at our last fit by plotting the contribution of each term of the fit:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "1b7296fb", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the components of the fit separately:\n", - "plt.rcParams['font.size'] = 25\n", - "plt.rcParams['figure.figsize'] = (10,5)\n", - "def plot_fit(func,J, w, lam, gamma, w0):\n", - " \"\"\" Plot the individual components of a fit to the spectral density. and how they contribute to the full as an animation \"\"\"\n", - " total=0\n", - " plt.plot(w, J, 'r--', linewidth=2, label=\"original\")\n", - " for i in range(len(lam)):\n", - " component=func(w,[lam[i]],[gamma[i]],[w0[i]])\n", - " total+=component\n", - " plt.plot(w, J, 'r--', linewidth=2, label=\"original\")\n", - " plt.plot(w,total,label=rf\"$k={i+1}$\")\n", - " plt.xlabel(r\"$\\omega$\")\n", - " plt.ylabel(r\"$J(\\omega)$\")\n", - " plt.legend()\n", - " plt.pause(1)\n", - " plt.show()\n", - "def plot_fit_components(func,J, w, lam, gamma, w0):\n", - " \"\"\" Plot the individual components of a fit to the spectral density. and how they contribute to the full as an animation \"\"\"\n", - " total=0\n", - " plt.plot(w, J, 'r--', linewidth=2, label=\"original\")\n", - " for i in range(len(lam)):\n", - " component=func(w,[lam[i]],[gamma[i]],[w0[i]])\n", - " plt.plot(w,component,label=rf\"$k={i+1}$\")\n", - " plt.xlabel(r\"$\\omega$\")\n", - " plt.ylabel(r\"$J(\\omega)$\")\n", - " plt.legend(bbox_to_anchor=(1.04, 1))\n", - " plt.show()\n", - "lam, gamma, w0 = fs.params_spec\n", - "plot_fit(fs.spectral_density_approx,J, w, lam, gamma, w0)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "9da8c597-557d-4bb6-b20b-c81b0ac09eb9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plot_fit_components(fs.spectral_density_approx,J, w, lam, gamma, w0)" - ] - }, - { - "cell_type": "markdown", - "id": "c868d316", - "metadata": {}, - "source": [ - "And let's also compare the power spectrum of the fit and the analytical spectral density:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "9f95a7ee", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams['figure.figsize'] = (10,5)\n", - "\n", - "def plot_power_spectrum(alpha, wc, beta, lam, gamma, w0, save=True):\n", - " \"\"\" Plot the power spectrum of a fit against the actual power spectrum. \"\"\"\n", - " w = np.linspace(-10, 10, 50000)\n", - " s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta)\n", - " s_fit = fs.spec_spectrum_approx(w)\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " axes.plot(w, s_orig, 'r', linewidth=2, label=\"original\")\n", - " axes.plot(w, s_fit, 'b', linewidth=2, label=\"fit\")\n", - "\n", - " axes.set_xlabel(r'$\\omega$', fontsize=28)\n", - " axes.set_ylabel(r'$S(\\omega)$', fontsize=28)\n", - " axes.legend()\n", - "\n", - " if save:\n", - " fig.savefig('powerspectrum.eps')\n", - "\n", - "\n", - "plot_power_spectrum(alpha, wc, 1/T, lam, gamma, w0, save=False)" - ] - }, - { - "cell_type": "markdown", - "id": "1a2c9e67", - "metadata": {}, - "source": [ - "Now that we have a good fit to the spectral density, Let us obtain its dynamics, by passing our FitSpectral bath specifications into the HEOMSolver" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "0e86f101-2a45-47ba-bd4a-be014f5025c4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10.0%. Run time: 1.44s. Est. time left: 00:00:00:12\n", - "20.0%. Run time: 2.61s. Est. time left: 00:00:00:10\n", - "30.1%. Run time: 3.73s. Est. time left: 00:00:00:08\n", - "40.1%. Run time: 4.82s. Est. time left: 00:00:00:07\n", - "50.1%. Run time: 5.87s. Est. time left: 00:00:00:05\n", - "60.1%. Run time: 7.00s. Est. time left: 00:00:00:04\n", - "70.1%. Run time: 8.39s. Est. time left: 00:00:00:03\n", - "80.1%. Run time: 10.14s. Est. time left: 00:00:00:02\n", - "90.2%. Run time: 11.82s. Est. time left: 00:00:00:01\n", - "100.0%. Run time: 13.55s. Est. time left: 00:00:00:00\n", - "Total run time: 13.55s\n" - ] - } - ], - "source": [ - "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - "options = {'nsteps':15000, 'store_states':True, 'rtol':1e-12, 'atol':1e-12, 'method':\"bdf\"}\n", - "Ltot = liouvillian(Hsys) + fs.terminator\n", - "HEOM_spectral_fit = HEOMSolver(Ltot, fs.Bath_spec, max_depth=4, options=options,)\n", - "result_spectral=HEOM_spectral_fit.run(rho0,tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "ac8e7a45-9325-4f0c-9620-ae6330c1f2d5", - "metadata": {}, - "source": [ - "Now if we want to see the systems's behaviour as we change the Number of terms in the fit, we may use this auxiliary function" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "0a755790-1e0d-4bb8-9ea9-5dddf26764ce", - "metadata": {}, - "outputs": [], - "source": [ - "def generate_spectrum_results(Q,beta, N, Nk, max_depth):\n", - " \"\"\" Run the HEOM with the given bath parameters and\n", - " and return the results of the evolution.\n", - " \"\"\"\n", - " fs=FitSpectral(T,Q,Nk)\n", - " fs.get_fit(J,w,N)\n", - " Ltot = liouvillian(Hsys) + fs.terminator\n", - " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - "\n", - " # This problem is a little stiff, so we use the BDF method to solve\n", - " # the ODE ^^^\n", - " print(f'Starting calculations for N={N} and max_depth={max_depth} ... \\n ')\n", - " HEOM_spectral_fit = HEOMSolver(\n", - " Ltot, fs.Bath_spec, max_depth=max_depth, options=options,\n", - " )\n", - " results_spectral_fit = (HEOM_spectral_fit.run(rho0, tlist))\n", - " print('\\n')\n", - " return results_spectral_fit" - ] - }, - { - "cell_type": "markdown", - "id": "6d3c564b", - "metadata": {}, - "source": [ - "Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "fb82f454-778b-4399-982c-8799b44d243d", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_result_expectations(plots, axes=None):\n", - " \"\"\" Plot the expectation values of operators as functions of time.\n", - "\n", - " Each plot in plots consists of (solver_result,\n", - " measurement_operation, color, label).\n", - " \"\"\"\n", - " if axes is None:\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " fig_created = True\n", - " else:\n", - " fig = None\n", - " fig_created = False\n", - "\n", - " # add kw arguments to each plot if missing\n", - " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", - " for result, m_op, color, label, kw in plots:\n", - " exp = np.real(expect(result.states, m_op))\n", - " kw.setdefault(\"linewidth\", 2)\n", - " if color == 'rand':\n", - " axes.plot(\n", - " result.times, exp,\n", - " c=np.random.rand(3,), label=label, **kw,\n", - " )\n", - " else:\n", - " axes.plot(result.times, exp, color, label=label, **kw)\n", - "\n", - " if fig_created:\n", - " axes.legend(loc=0, fontsize=12)\n", - " axes.set_xlabel(\"t\", fontsize=28)\n", - "\n", - " return fig" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "6197bb61", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting calculations for N=1 and max_depth=5 ... \n", - " \n", - "10.0%. Run time: 0.06s. Est. time left: 00:00:00:00\n", - "20.0%. Run time: 0.09s. Est. time left: 00:00:00:00\n", - "30.1%. Run time: 0.11s. Est. time left: 00:00:00:00\n", - "40.1%. Run time: 0.13s. Est. time left: 00:00:00:00\n", - "50.1%. Run time: 0.15s. Est. time left: 00:00:00:00\n", - "60.1%. Run time: 0.16s. Est. time left: 00:00:00:00\n", - "70.1%. Run time: 0.18s. Est. time left: 00:00:00:00\n", - "80.1%. Run time: 0.19s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 0.21s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 0.23s. Est. time left: 00:00:00:00\n", - "Total run time: 0.23s\n", - "\n", - "\n", - "Starting calculations for N=2 and max_depth=5 ... \n", - " \n", - "10.0%. Run time: 0.22s. Est. time left: 00:00:00:02\n", - "20.0%. Run time: 0.32s. Est. time left: 00:00:00:01\n", - "30.1%. Run time: 0.42s. Est. time left: 00:00:00:00\n", - "40.1%. Run time: 0.51s. Est. time left: 00:00:00:00\n", - "50.1%. Run time: 0.60s. Est. time left: 00:00:00:00\n", - "60.1%. Run time: 0.67s. Est. time left: 00:00:00:00\n", - "70.1%. Run time: 0.74s. Est. time left: 00:00:00:00\n", - "80.1%. Run time: 0.81s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 0.87s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 0.93s. Est. time left: 00:00:00:00\n", - "Total run time: 0.93s\n", - "\n", - "\n", - "Starting calculations for N=3 and max_depth=5 ... \n", - " \n", - "10.0%. Run time: 0.48s. Est. time left: 00:00:00:04\n", - "20.0%. Run time: 0.67s. Est. time left: 00:00:00:02\n", - "30.1%. Run time: 0.86s. Est. time left: 00:00:00:02\n", - "40.1%. Run time: 1.05s. Est. time left: 00:00:00:01\n", - "50.1%. Run time: 1.23s. Est. time left: 00:00:00:01\n", - "60.1%. Run time: 1.47s. Est. time left: 00:00:00:00\n", - "70.1%. Run time: 1.66s. Est. time left: 00:00:00:00\n", - "80.1%. Run time: 1.84s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 2.01s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 2.19s. Est. time left: 00:00:00:00\n", - "Total run time: 2.19s\n", - "\n", - "\n", - "Starting calculations for N=4 and max_depth=5 ... \n", - " \n", - "10.0%. Run time: 1.54s. Est. time left: 00:00:00:13\n", - "20.0%. Run time: 2.41s. Est. time left: 00:00:00:09\n", - "30.1%. Run time: 3.23s. Est. time left: 00:00:00:07\n", - "40.1%. Run time: 4.25s. Est. time left: 00:00:00:06\n", - "50.1%. Run time: 5.08s. Est. time left: 00:00:00:05\n", - "60.1%. Run time: 5.82s. Est. time left: 00:00:00:03\n", - "70.1%. Run time: 6.72s. Est. time left: 00:00:00:02\n", - "80.1%. Run time: 7.61s. Est. time left: 00:00:00:01\n", - "90.2%. Run time: 8.37s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 9.13s. Est. time left: 00:00:00:00\n", - "Total run time: 9.13s\n", - "\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Generate results for different number of lorentzians in fit:\n", - "\n", - "results_spectral_fit_pk = [\n", - " generate_spectrum_results(Q,1/T, n, Nk=1, max_depth=max_depth)\n", - " for n in range(1,5)\n", - "]\n", - "\n", - "plot_result_expectations([\n", - " (\n", - " result, P11p, 'rand',\n", - " f\"P11 (spectral fit) $k_J$={pk + 1}\",\n", - " )\n", - " for pk, result in enumerate(results_spectral_fit_pk)\n", - "]);" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "c322425b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting calculations for N=4 and max_depth=5 ... \n", - " \n", - "10.0%. Run time: 2.63s. Est. time left: 00:00:00:23\n", - "20.0%. Run time: 3.99s. Est. time left: 00:00:00:15\n", - "30.1%. Run time: 5.54s. Est. time left: 00:00:00:12\n", - "40.1%. Run time: 6.76s. Est. time left: 00:00:00:10\n", - "50.1%. Run time: 7.95s. Est. time left: 00:00:00:07\n", - "60.1%. Run time: 9.13s. Est. time left: 00:00:00:06\n", - "70.1%. Run time: 10.53s. Est. time left: 00:00:00:04\n", - "80.1%. Run time: 11.74s. Est. time left: 00:00:00:02\n", - "90.2%. Run time: 13.28s. Est. time left: 00:00:00:01\n", - "100.0%. Run time: 14.78s. Est. time left: 00:00:00:00\n", - "Total run time: 14.78s\n", - "\n", - "\n", - "Starting calculations for N=4 and max_depth=5 ... \n", - " \n", - "10.0%. Run time: 4.37s. Est. time left: 00:00:00:39\n", - "20.0%. Run time: 7.30s. Est. time left: 00:00:00:29\n", - "30.1%. Run time: 10.56s. Est. time left: 00:00:00:24\n", - "40.1%. Run time: 13.65s. Est. time left: 00:00:00:20\n", - "50.1%. Run time: 16.66s. Est. time left: 00:00:00:16\n", - "60.1%. Run time: 19.72s. Est. time left: 00:00:00:13\n", - "70.1%. Run time: 22.78s. Est. time left: 00:00:00:09\n", - "80.1%. Run time: 26.43s. Est. time left: 00:00:00:06\n", - "90.2%. Run time: 30.58s. Est. time left: 00:00:00:03\n", - "100.0%. Run time: 35.11s. Est. time left: 00:00:00:00\n", - "Total run time: 35.11s\n", - "\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# generate results for different number of Matsubara terms per Lorentzian\n", - "# for max number of Lorentzians:\n", - "\n", - "Nk_list = range(2, 4)\n", - "results_spectral_fit_nk = [\n", - " generate_spectrum_results(Q,1/T, 4, Nk=Nk, max_depth=max_depth)\n", - " for Nk in Nk_list\n", - "]\n", - "\n", - "plot_result_expectations([\n", - " (\n", - " result, P11p, 'rand',\n", - " f\"P11 (spectral fit) K={nk}\",\n", - " )\n", - " for nk, result in zip(Nk_list, results_spectral_fit_nk)\n", - "]);" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "a5ce8797", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting calculations for N=4 and max_depth=2 ... \n", - " \n", - "10.0%. Run time: 0.07s. Est. time left: 00:00:00:00\n", - "20.0%. Run time: 0.09s. Est. time left: 00:00:00:00\n", - "30.1%. Run time: 0.11s. Est. time left: 00:00:00:00\n", - "40.1%. Run time: 0.14s. Est. time left: 00:00:00:00\n", - "50.1%. Run time: 0.16s. Est. time left: 00:00:00:00\n", - "60.1%. Run time: 0.23s. Est. time left: 00:00:00:00\n", - "70.1%. Run time: 0.24s. Est. time left: 00:00:00:00\n", - "80.1%. Run time: 0.26s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 0.28s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 0.29s. Est. time left: 00:00:00:00\n", - "Total run time: 0.29s\n", - "\n", - "\n", - "Starting calculations for N=4 and max_depth=3 ... \n", - " \n", - "10.0%. Run time: 0.17s. Est. time left: 00:00:00:01\n", - "20.0%. Run time: 0.23s. Est. time left: 00:00:00:00\n", - "30.1%. Run time: 0.28s. Est. time left: 00:00:00:00\n", - "40.1%. Run time: 0.33s. Est. time left: 00:00:00:00\n", - "50.1%. Run time: 0.38s. Est. time left: 00:00:00:00\n", - "60.1%. Run time: 0.43s. Est. time left: 00:00:00:00\n", - "70.1%. Run time: 0.48s. Est. time left: 00:00:00:00\n", - "80.1%. Run time: 0.53s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 0.57s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 0.62s. Est. time left: 00:00:00:00\n", - "Total run time: 0.62s\n", - "\n", - "\n", - "Starting calculations for N=4 and max_depth=4 ... \n", - " \n", - "10.0%. Run time: 0.51s. Est. time left: 00:00:00:04\n", - "20.0%. Run time: 0.78s. Est. time left: 00:00:00:03\n", - "30.1%. Run time: 1.04s. Est. time left: 00:00:00:02\n", - "40.1%. Run time: 1.27s. Est. time left: 00:00:00:01\n", - "50.1%. Run time: 1.48s. Est. time left: 00:00:00:01\n", - "60.1%. Run time: 1.70s. Est. time left: 00:00:00:01\n", - "70.1%. Run time: 1.93s. Est. time left: 00:00:00:00\n", - "80.1%. Run time: 2.16s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 2.38s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 2.58s. Est. time left: 00:00:00:00\n", - "Total run time: 2.58s\n", - "\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Generate results for different depths:\n", - "\n", - "Nc_list = range(2, max_depth)\n", - "results_spectral_fit_nc = [\n", - " generate_spectrum_results(Q,1/T, 4, Nk=1, max_depth=Nc)\n", - " for Nc in Nc_list\n", - "]\n", - "\n", - "plot_result_expectations([\n", - " (\n", - " result, P11p, 'rand',\n", - " f\"P11 (spectral fit) $N_C={nc}$\",\n", - " )\n", - " for nc, result in zip(Nc_list, results_spectral_fit_nc)\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "16dc2201", - "metadata": {}, - "source": [ - "We now combine the fitting and correlation function data into one large plot." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "83e44b5f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "t = np.linspace(0, 15, 100)\n", - "C =ohmic_correlation(t, alpha=alpha, wc=wc, beta=1/T)\n", - "w2 = np.concatenate(\n", - " [np.linspace(-10, -0.1, 5000),\n", - " np.linspace(0.1, 10, 5000)],\n", - ")\n", - "S=ohmic_power_spectrum(w2,alpha=alpha,beta=1/T,wc=wc)\n", - "\n", - "\n", - "fs.fit_plots(w,J,t, C,w2,S);" - ] - }, - { - "cell_type": "markdown", - "id": "66ab9ccd", - "metadata": {}, - "source": [ - "## Building the HEOM bath by fitting the correlation function" - ] - }, - { - "cell_type": "markdown", - "id": "d6ac709f", - "metadata": {}, - "source": [ - "Having successfully fitted the spectral density and used the result to calculate the Matsubara expansion and terminator for the HEOM bosonic bath, we now proceed to the second case of fitting the correlation function itself instead.\n", - "\n", - "Here we fit the real and imaginary parts separately, using the following ansatz\n", - "\n", - "$$C_R^F(t) = \\sum_{i=1}^{k_R} c_R^ie^{-\\gamma_R^i t}\\cos(\\omega_R^i t)$$\n", - "\n", - "$$C_I^F(t) = \\sum_{i=1}^{k_I} c_I^ie^{-\\gamma_I^i t}\\sin(\\omega_I^i t)$$\n", - "\n", - "Analogously to the spectral density case, one may use the FitCorr class" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "04c40eac-5dbb-4f08-aa3d-c2b729be6719", - "metadata": {}, - "outputs": [], - "source": [ - "fc=FitCorr(Q)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "d866a44f", - "metadata": {}, - "outputs": [], - "source": [ - "t = np.linspace(0, 25, 1500)\n", - "C = ohmic_correlation(t, alpha=alpha, wc=wc, beta=1/T)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "4140f6ec-e30f-44b1-88d0-f43208ada2c0", - "metadata": {}, - "outputs": [], - "source": [ - "fc.fit_correlation(t,C,Ni=3,Nr=3)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "7a06d9db-f5a0-48f5-b60c-1a7e33143e71", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fit correlation class instance: \n", - " \n", - "\n", - "Results of the fitting the Real Part with 3 terms: |\t Results of the fitting the Imaginary Part with 3 terms: \n", - " | \n", - " Parameters| lam | gamma | w0 | \t Parameters| lam | gamma | w0 \n", - " 1 | 2.23e+00 |-2.20e+00 |2.45e-05 |\t 1 |-3.32e+00 |-9.96e-01 |1.66e-01 \n", - " 2 |-9.25e-01 |-4.96e+00 |3.91e+00 | \t 2 |-8.59e-01 |-1.09e+00 |1.37e+00 \n", - " 3 | 2.18e-01 |-3.36e-01 |1.50e-18 | \t 3 |-3.07e-01 |-1.19e+00 |2.70e+00 \n", - " | \n", - " A normalized RMSE of 4.48e-05 was obtained for the real part | \t A normalized RMSE of 1.21e-04 was obtained for the imaginary part \n", - "\t \t \t \t \t \t The current fit took 24.255618 seconds\n" - ] - } - ], - "source": [ - "fc.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "88ff5472-93c0-4f15-9f9a-dcd4b70b14dd", - "metadata": {}, - "outputs": [], - "source": [ - "fc.fit_correlation(t,C,final_rmse=1e-4)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "509dc9e0-89c4-43af-b245-b4e1d25f639b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fit correlation class instance: \n", - " \n", - "\n", - "Results of the fitting the Real Part with 3 terms: |\t Results of the fitting the Imaginary Part with 5 terms: \n", - " | \n", - " Parameters| lam | gamma | w0 | \t Parameters| lam | gamma | w0 \n", - " 1 | 2.23e+00 |-2.20e+00 |2.45e-05 |\t 1 |-1.32e+00 |-1.13e+00 |4.34e-36 \n", - " 2 |-9.25e-01 |-4.96e+00 |3.91e+00 | \t 2 |-3.70e-01 |-9.44e-01 |2.11e+00 \n", - " 3 | 2.18e-01 |-3.36e-01 |1.50e-18 | \t 3 |-1.51e-01 |-1.03e+00 |3.32e+00 \n", - " | \t 4 |-2.48e+00 |-8.63e-01 |1.37e-01 \n", - " | \t 5 |-7.36e-01 |-9.14e-01 |1.13e+00 \n", - " | \n", - " A normalized RMSE of 4.48e-05 was obtained for the real part | \t A normalized RMSE of 8.94e-05 was obtained for the imaginary part \n", - "\t \t \t \t \t \t The current fit took 106.264649 seconds\n" - ] - } - ], - "source": [ - "fc.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "acaf610e", - "metadata": {}, - "source": [ - "Now we construct the `BosonicBath` co-efficients and frequencies from the fit to the correlation function:" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "c136b8c8", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/mcditoos/qutip_gsoc_app/qutip/solver/heom/bofin_baths.py:1545: RuntimeWarning: divide by zero encountered in divide\n", - " (((1 / (np.e**(w * beta) - 1)) + 1) * 2)\n", - "/home/mcditoos/anaconda3/envs/qutip-dev/lib/python3.12/site-packages/matplotlib/cbook.py:1699: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return math.isfinite(val)\n", - "/home/mcditoos/anaconda3/envs/qutip-dev/lib/python3.12/site-packages/matplotlib/cbook.py:1345: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "t = np.linspace(0, 15, 100)\n", - "C = ohmic_correlation(t, alpha=alpha, wc=wc, beta=1/T)\n", - "fc.fit_plots(w, J, t, C, w2, S,beta=1/T)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "7c296e7d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2\n", - "10.0%. Run time: 0.02s. Est. time left: 00:00:00:00\n", - "20.0%. Run time: 0.04s. Est. time left: 00:00:00:00\n", - "30.1%. Run time: 0.05s. Est. time left: 00:00:00:00\n", - "40.1%. Run time: 0.06s. Est. time left: 00:00:00:00\n", - "50.1%. Run time: 0.08s. Est. time left: 00:00:00:00\n", - "60.1%. Run time: 0.09s. Est. time left: 00:00:00:00\n", - "70.1%. Run time: 0.10s. Est. time left: 00:00:00:00\n", - "80.1%. Run time: 0.12s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 0.13s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 0.14s. Est. time left: 00:00:00:00\n", - "Total run time: 0.15s\n", - "3\n", - "10.0%. Run time: 0.48s. Est. time left: 00:00:00:04\n", - "20.0%. Run time: 0.72s. Est. time left: 00:00:00:02\n", - "30.1%. Run time: 0.95s. Est. time left: 00:00:00:02\n", - "40.1%. Run time: 1.17s. Est. time left: 00:00:00:01\n", - "50.1%. Run time: 1.38s. Est. time left: 00:00:00:01\n", - "60.1%. Run time: 1.62s. Est. time left: 00:00:00:01\n", - "70.1%. Run time: 1.88s. Est. time left: 00:00:00:00\n", - "80.1%. Run time: 2.13s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 2.35s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 2.56s. Est. time left: 00:00:00:00\n", - "Total run time: 2.56s\n", - "4\n", - "10.0%. Run time: 3.28s. Est. time left: 00:00:00:29\n", - "20.0%. Run time: 5.09s. Est. time left: 00:00:00:20\n", - "30.1%. Run time: 6.64s. Est. time left: 00:00:00:15\n", - "40.1%. Run time: 8.21s. Est. time left: 00:00:00:12\n", - "50.1%. Run time: 10.13s. Est. time left: 00:00:00:10\n", - "60.1%. Run time: 11.67s. Est. time left: 00:00:00:07\n", - "70.1%. Run time: 13.36s. Est. time left: 00:00:00:05\n", - "80.1%. Run time: 15.16s. Est. time left: 00:00:00:03\n", - "90.2%. Run time: 16.67s. Est. time left: 00:00:00:01\n", - "100.0%. Run time: 18.21s. Est. time left: 00:00:00:00\n", - "Total run time: 18.21s\n" - ] - } - ], - "source": [ - "def generate_corr_results(N, max_depth):\n", - " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - " t = np.linspace(0, 15, 100)\n", - " C = ohmic_correlation(t, alpha=alpha, wc=wc, beta=1/T)\n", - " fc.fit_correlation(t,C,Ni=N,Nr=N)\n", - " HEOM_corr_fit = HEOMSolver(\n", - " Hsys, fc.Bath_corr, max_depth=max_depth, options=options,\n", - " )\n", - "\n", - " results_corr_fit = (HEOM_corr_fit.run(rho0, tlist))\n", - "\n", - " return results_corr_fit\n", - "\n", - "\n", - "# Generate results for different number of lorentzians in fit:\n", - "results_corr_fit_pk = [\n", - " print(f\"{i + 1}\") or generate_corr_results(i, max_depth=max_depth,\n", - " )\n", - " for i in range(1,4)\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "9d68342a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", - "\n", - "plot_result_expectations([\n", - " (\n", - " results_corr_fit_pk[0], P11p,\n", - " 'y', \"Correlation Function Fit $k_R=k_I=1$\",\n", - " ),\n", - " (\n", - " results_corr_fit_pk[2], P11p,\n", - " 'y-.', \"Correlation Function Fit $k_R=k_I=3$\",\n", - " ),\n", - " (results_spectral_fit_pk[0], P11p, 'b', \"Spectral Density Fit $k_J=1$\"),\n", - " (results_spectral_fit_pk[2], P11p, 'g--', \"Spectral Density Fit $k_J=3$\"),\n", - " (results_spectral_fit_pk[3], P11p, 'r-.', \"Spectral Density Fit $k_J=4$\"),\n", - "], axes=axes)\n", - "\n", - "axes.set_yticks([0.6, 0.8, 1])\n", - "axes.set_ylabel(r'$\\rho_{11}$', fontsize=30)\n", - "axes.set_xlabel(r'$t\\;\\omega_c$', fontsize=30)\n", - "axes.legend(loc=0, fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "id": "7f846c98-15f1-46a6-a6e2-22ddc298981c", - "metadata": {}, - "source": [ - "# Using the Ohmic Bath class\n", - "\n", - "While the two classes above are designed for general fits of either correlation functions or spectral densities, as the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced shortly by using the OhmicBath class. By default the method fits using the spectral density, however it can use the correlation function if method is specified" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "a7bce5bb-da4d-4c28-8830-4679eaf83a14", - "metadata": {}, - "outputs": [], - "source": [ - "obs=OhmicBath(T,Q,alpha,wc,s,rmse=1e-5,method='spectral')" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "0fec43b0-d490-4f81-8bd2-2c52d94540e4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Results of the fitting the Spectral density with 4 terms: \n", - " \n", - " Parameters| lam | gamma | w0 \n", - " 1 | 6.07e-01 | 1.01e+00 |1.00e-01 \n", - " 2 |-4.44e+00 | 4.31e+00 |3.96e+00 \n", - " 3 | 7.93e+00 | 2.30e+00 |1.00e-01 \n", - " 4 | 1.07e-02 | 3.09e-01 |1.00e-01 \n", - " \n", - "A normalized RMSE of 2.64e-06 was obtained for the Spectral density \n", - " The current fit took 28.250522 seconds\n" - ] - } - ], - "source": [ - "obs.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "9f4b887e-398b-44de-a1fa-1ce47881932d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "obs.fit.fit_plots(w,J,t, C,w2,S);" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "1bad7407-9533-4718-9d70-18ac45f2ce8a", - "metadata": {}, - "outputs": [], - "source": [ - "obc=OhmicBath(T,Q,alpha,wc,s,rmse=1e-4,method='correlation')" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "a4c18a34-25a8-4db3-a90a-0179730ddb4c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fit correlation class instance: \n", - " \n", - "\n", - "Results of the fitting the Real Part with 5 terms: |\t Results of the fitting the Imaginary Part with 4 terms: \n", - " | \n", - " Parameters| lam | gamma | w0 | \t Parameters| lam | gamma | w0 \n", - " 1 | 2.12e+00 |-2.51e+00 |9.30e-01 |\t 1 |-3.36e+00 |-2.20e+00 |9.56e-01 \n", - " 2 | 3.60e-02 |-1.40e-01 |5.02e-08 | \t 2 |-3.36e+00 |-4.32e-01 |4.61e-03 \n", - " 3 | 6.41e+00 |-7.66e-01 |7.27e-05 | \t 3 | 4.27e-01 |-4.29e+00 |4.30e+00 \n", - " 4 |-5.80e+00 |-7.52e-01 |9.31e-02 | \t 4 |-3.36e+00 |-1.23e+00 |2.01e-01 \n", - " 5 |-1.25e+00 |-4.63e+00 |2.98e+00 | \n", - " | \n", - " A normalized RMSE of 2.31e-06 was obtained for the real part | \t A normalized RMSE of 7.29e-06 was obtained for the imaginary part \n", - "\t \t \t \t \t \t The current fit took 17.603134 seconds\n" - ] - } - ], - "source": [ - "obc.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "01e0dda9-23f9-4dfe-a78f-05ab02ae6a34", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "obc.fit.fit_plots(w, J, t, C, w2, S,beta=1/T)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "8ed890a6-2918-4d6f-9ec4-027dd60b3239", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10.0%. Run time: 9.32s. Est. time left: 00:00:01:23\n", - "20.0%. Run time: 14.89s. Est. time left: 00:00:00:59\n", - "30.1%. Run time: 20.69s. Est. time left: 00:00:00:48\n", - "40.1%. Run time: 28.31s. Est. time left: 00:00:00:42\n", - "50.1%. Run time: 37.21s. Est. time left: 00:00:00:37\n", - "60.1%. Run time: 44.90s. Est. time left: 00:00:00:29\n", - "70.1%. Run time: 52.61s. Est. time left: 00:00:00:22\n", - "80.1%. Run time: 60.75s. Est. time left: 00:00:00:15\n", - "90.2%. Run time: 68.44s. Est. time left: 00:00:00:07\n", - "100.0%. Run time: 76.71s. Est. time left: 00:00:00:00\n", - "Total run time: 76.71s\n" - ] - } - ], - "source": [ - "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - "\n", - "HEOM_ohmic_corr_fit = HEOMSolver(Hsys, obc.bath, max_depth=5, options=options,)\n", - "Ltot = liouvillian(Hsys) + fs.terminator\n", - "HEOM_ohmic_spectral_fit = HEOMSolver(Hsys, obs.bath, max_depth=5, options=options,)\n", - "\n", - "#results__ohmic_corr_fit = (HEOM_ohmic_corr_fit.run(rho0, tlist))\n", - "results_ohmic_spectral_fit = (HEOM_ohmic_spectral_fit.run(rho0, tlist))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8417c361-7971-413b-8b12-702fb4d73b27", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10.0%. Run time: 30.90s. Est. time left: 00:00:04:37\n", - "20.0%. Run time: 58.34s. Est. time left: 00:00:03:52\n", - "30.1%. Run time: 84.19s. Est. time left: 00:00:03:15\n", - "40.1%. Run time: 112.52s. Est. time left: 00:00:02:48\n", - "50.1%. Run time: 139.78s. Est. time left: 00:00:02:19\n", - "60.1%. Run time: 167.44s. Est. time left: 00:00:01:51\n", - "70.1%. Run time: 194.66s. Est. time left: 00:00:01:22\n", - "80.1%. Run time: 229.82s. Est. time left: 00:00:00:56\n", - "90.2%. Run time: 260.57s. Est. time left: 00:00:00:28\n" - ] - } - ], - "source": [ - "results_ohmic_corr_fit = (HEOM_ohmic_corr_fit.run(rho0, tlist))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0cee16c9-e11d-4ee6-8b13-0ea24f6e9c25", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", - "\n", - "plot_result_expectations([\n", - " # (\n", - " # results_corr_fit_pk[0], P11p,\n", - " # 'y', \"Correlation Function Fit $k_R=k_I=1$\",\n", - " # ),\n", - " (\n", - " results_corr_fit_pk[2], P11p,\n", - " 'y-.', \"Correlation Function Fit $k_R=k_I=3$\",\n", - " ),\n", - " (results_spectral_fit_pk[0], P11p, 'b', \"Spectral Density Fit $k_J=1$\"),\n", - " (results_spectral_fit_pk[2], P11p, 'g--', \"Spectral Density Fit $k_J=3$\"),\n", - " (results_spectral_fit_pk[3], P11p, 'r-.', \"Spectral Density Fit $k_J=4$\"),\n", - " (results_ohmic_spectral_fit, P11p, 'g-.', \"Spectral Density Fit Ohmic Bath\"),\n", - " (results_ohmic_corr_fit, P11p, 'k-.', \"Correlation Fit Ohmic Bath\")\n", - "\n", - "], axes=axes)\n", - "\n", - "axes.set_yticks([0.6, 0.8, 1])\n", - "axes.set_ylabel(r'$\\rho_{11}$', fontsize=30)\n", - "axes.set_xlabel(r'$t\\;\\omega_c$', fontsize=30)\n", - "axes.legend(loc=0, fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "id": "e10f9641", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "362da2bd", - "metadata": {}, - "outputs": [], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "dbdf051c", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8598f078", - "metadata": {}, - "outputs": [], - "source": [ - "assert np.allclose(\n", - " expect(P11p, results_spectral_fit_pk[2].states),\n", - " expect(P11p, results_spectral_fit_pk[3].states),\n", - " rtol=1e-2,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4cfe9b59-006e-460a-a48d-e901122eb111", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md index fcef10c8..d6b42553 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md @@ -580,7 +580,7 @@ axes.legend(loc=0, fontsize=20); While the two classes above are designed for general fits of either correlation functions or spectral densities, as the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced shortly by using the OhmicBath class. By default the method fits using the spectral density, however it can use the correlation function if method is specified ```{code-cell} ipython3 -obs=OhmicBath(T,Q,alpha,wc,s,rmse=1e-5,method='spectral') +obs=OhmicBath(T,Q,alpha,wc,s,rmse=9e-5,method='spectral') ``` ```{code-cell} ipython3 @@ -661,7 +661,3 @@ assert np.allclose( rtol=1e-2, ) ``` - -```{code-cell} ipython3 - -``` From bb3044d16134767abb14ef67eefcca080694d3db Mon Sep 17 00:00:00 2001 From: mcditooss Date: Thu, 9 Nov 2023 05:26:09 +0900 Subject: [PATCH 03/44] added quick plots for spectrum, correlation and spectral density to first tutorial --- .../heom/heom-1a-spin-bath-model-basic.md | 21 ++++++++++++++++++- 1 file changed, 20 insertions(+), 1 deletion(-) diff --git a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md index 87f98ecc..baab2a7b 100644 --- a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md +++ b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md @@ -5,7 +5,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.14.5 + jupytext_version: 1.15.2 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -507,6 +507,25 @@ plot_result_expectations( ); ``` +The built-in class also allows us to quickly obtain the bath spectrum, correlation function, and spectral density + +```python +w=np.linspace(-10,10,1000) +w2=np.linspace(0,10,1000) +fig, axs = plt.subplots(2, 2) +axs[0, 0].plot(w,bath.power_spectrum(w,T)) +axs[0,0].set(xlabel=r'$\omega$',ylabel=r'$S(\omega)$') +axs[0, 1].plot(w2,bath._spectral_density(w2)) +axs[0,1].set(xlabel=r'$\omega$',ylabel=r'$J(\omega)$') +axs[1, 0].plot(w2,bath.CI(w2)) +axs[1,0].set(xlabel=r'$t$',ylabel=r'$C_{I}(t)$') +axs[1, 1].plot(w2,bath.CR(w2)) +axs[1,1].set(xlabel=r'$t$',ylabel=r'$C_{R}(t)$') +fig.tight_layout() +plt.show() + +``` + We can compare the solution obtained from the QuTiP Bloch-Redfield solver: ```python From 9f32c2a298f6b91e298039ec33b00e6d870ff0e5 Mon Sep 17 00:00:00 2001 From: mcditooss Date: Fri, 10 Nov 2023 13:28:41 +0900 Subject: [PATCH 04/44] fixed refactors from classes --- tutorials-v5/heom/heom-1a-spin-bath-model-basic.md | 2 +- tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md index baab2a7b..db4ef00e 100644 --- a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md +++ b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md @@ -515,7 +515,7 @@ w2=np.linspace(0,10,1000) fig, axs = plt.subplots(2, 2) axs[0, 0].plot(w,bath.power_spectrum(w,T)) axs[0,0].set(xlabel=r'$\omega$',ylabel=r'$S(\omega)$') -axs[0, 1].plot(w2,bath._spectral_density(w2)) +axs[0, 1].plot(w2,bath.spectral_density(w2)) axs[0,1].set(xlabel=r'$\omega$',ylabel=r'$J(\omega)$') axs[1, 0].plot(w2,bath.CI(w2)) axs[1,0].set(xlabel=r'$t$',ylabel=r'$C_{I}(t)$') diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md index d6b42553..03deab07 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md @@ -318,7 +318,7 @@ def plot_power_spectrum(alpha, wc, beta, lam, gamma, w0, save=True): """ Plot the power spectrum of a fit against the actual power spectrum. """ w = np.linspace(-10, 10, 50000) s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta) - s_fit = fs.spec_spectrum_approx(w) + s_fit = fs.power_spectrum(w,T) fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) axes.plot(w, s_orig, 'r', linewidth=2, label="original") axes.plot(w, s_fit, 'b', linewidth=2, label="fit") @@ -504,7 +504,7 @@ fc.summary() ``` ```{code-cell} ipython3 -fc.fit_correlation(t,C,final_rmse=1e-4) +fc.fit_correlation(t,C,final_rmse=5e-5) ``` ```{code-cell} ipython3 From 2d6f5ecedb1fd470b15c22acf7cfc320eb626947 Mon Sep 17 00:00:00 2001 From: Gerardo Jose Suarez <31544007+mcditoos@users.noreply.github.com> Date: Mon, 20 Nov 2023 15:35:58 +0900 Subject: [PATCH 05/44] Update tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md Co-authored-by: Paul --- tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md index 03deab07..9c34cfa2 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md @@ -221,7 +221,7 @@ where $a, b$ and $c$ are the fit parameters and each is a vector of length $k$. +++ -With the spectral density approximation $J_{\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. This can be done quickly using the FitSpectral bath, which takes the target spectral density as an array and fits it to the series of **k** underdamped harmonic oscillators with the Meier-Tannor form +This type of fit can be performed quickly using the FitSpectral bath, which takes the target spectral density as an array and fits it with a series of **k** underdamped harmonic oscillators with the Meier-Tannor form. ```{code-cell} ipython3 w = np.linspace(0, 15, 20000) From ffd3b16659fb3a98e83f8a4a4cb66d5cf545c6c2 Mon Sep 17 00:00:00 2001 From: Gerardo Date: Thu, 25 Jan 2024 02:01:35 +0100 Subject: [PATCH 06/44] updated the tutorial --- .../heom-1d-spin-bath-model-ohmic-fitting.md | 634 +++++++++++------- 1 file changed, 396 insertions(+), 238 deletions(-) diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md index 9c34cfa2..c903ec06 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.15.2 + jupytext_version: 1.16.1 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -46,17 +46,13 @@ import qutip from qutip import ( basis, expect, - liouvillian, sigmax, sigmaz, - spost, - spre, ) from qutip.solver.heom import ( HEOMSolver, - BosonicBath, - FitSpectral, - FitCorr, + SpectralFitter, + CorrelationFitter, OhmicBath, ) @@ -71,10 +67,6 @@ mp.pretty = True %matplotlib inline ``` -## Helper functions - -Let's define some helper functions for plotting the resutls - ```{code-cell} ipython3 # Solver options: @@ -98,13 +90,9 @@ And let us set up the system Hamiltonian, bath and system measurement operators: ```{code-cell} ipython3 # Defining the system Hamiltonian -eps = 0 # Energy of the 2-level system. -Del = 0.2 # Tunnelling term +eps = 0 # Energy of the 2-level system. +Del = 0.2 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() -``` - -```{code-cell} ipython3 -# Initial state of the system. rho0 = basis(2, 0) * basis(2, 0).dag() ``` @@ -148,39 +136,39 @@ J(\omega) = \omega \alpha e^{- \frac{\omega}{\omega_c}} ```{code-cell} ipython3 def ohmic_correlation(t, alpha, wc, beta, s=1): - """ The Ohmic bath correlation function as a function of t - (and the bath parameters). + """The Ohmic bath correlation function as a function of t + (and the bath parameters). """ - corr = ( - (1 / np.pi) * alpha * wc**(1 - s) * beta**(-(s + 1)) * mp.gamma(s + 1) - ) + corr = (1 / np.pi) * alpha * wc ** (1 - s) + corr *= beta ** (-(s + 1)) * mp.gamma(s + 1) z1_u = (1 + beta * wc - 1.0j * wc * t) / (beta * wc) z2_u = (1 + 1.0j * wc * t) / (beta * wc) # Note: the arguments to zeta should be in as high precision as possible. # See http://mpmath.org/doc/current/basics.html#providing-correct-input - return np.array([ - complex(corr * (mp.zeta(s + 1, u1) + mp.zeta(s + 1, u2))) - for u1, u2 in zip(z1_u, z2_u) - ], dtype=np.complex128) + return np.array( + [ + complex(corr * (mp.zeta(s + 1, u1) + mp.zeta(s + 1, u2))) + for u1, u2 in zip(z1_u, z2_u) + ], + dtype=np.complex128, + ) ``` ```{code-cell} ipython3 def ohmic_spectral_density(w, alpha, wc): - """ The Ohmic bath spectral density as a function of w - (and the bath parameters). + """The Ohmic bath spectral density as a function of w + (and the bath parameters). """ - return w * alpha * np.e**(-w / wc) + return w * alpha * np.e ** (-w / wc) ``` ```{code-cell} ipython3 def ohmic_power_spectrum(w, alpha, wc, beta): - """ The Ohmic bath power spectrum as a function of w - (and the bath parameters). + """The Ohmic bath power spectrum as a function of w + (and the bath parameters). """ - return ( - w * alpha * np.e**(-abs(w) / wc) * - ((1 / (np.e**(w * beta) - 1)) + 1) * 2 - ) + bose = (1 / (np.e ** (w * beta) - 1)) + 1 + return w * alpha * np.e ** (-abs(w) / wc) * bose * 2 ``` ### Bath and HEOM parameters @@ -193,8 +181,8 @@ Finally, let's set the bath parameters we will work with and write down some mea Q = sigmaz() alpha = 3.25 T = 0.5 -wc= 1.0 -s= 1 +wc = 1.0 +s = 1 ``` And set the cut-off for the HEOM hierarchy: @@ -221,7 +209,7 @@ where $a, b$ and $c$ are the fit parameters and each is a vector of length $k$. +++ -This type of fit can be performed quickly using the FitSpectral bath, which takes the target spectral density as an array and fits it with a series of **k** underdamped harmonic oscillators with the Meier-Tannor form. +With the spectral density approximation $J_{\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. This can be done quickly using the FitSpectral bath, which takes the target spectral density as an array and fits it to the series of **k** underdamped harmonic oscillators with the Meier-Tannor form ```{code-cell} ipython3 w = np.linspace(0, 15, 20000) @@ -231,139 +219,177 @@ J = ohmic_spectral_density(w, alpha, wc) We first initialize our FitSpectral class ```{code-cell} ipython3 -fs=FitSpectral(T,Q,Nk=4) +fs = SpectralFitter(T, Q, w, J) ``` -To obtain a fit we simply pass our desired spectral density and range, into the get_fit method +To obtain a fit we simply pass our desired spectral density and range, into the ``get_fit`` method. The number of exponents we'll use in our bath is given by Nk ```{code-cell} ipython3 -fs.get_fit(J,w) +bath, fitinfo = fs.get_fit(Nk=1) ``` -To obtain an overview of the results of the fit we may call the summary method +To obtain an overview of the results of the fit we may take a look at the summary from the ``fitinfo`` ```{code-cell} ipython3 -fs.summary() +print(fitinfo["summary"]) ``` -By default the get_fit method, has a threshold normalized root mean squared error (NRMSE) of $5\times 10^{-6}$ and selects the number of oscillators automatically to obtain that value, one may on the other hand specify the Number of oscillators that can be done using the optional argument N, or may want a more accurate NRMSE, which can be specified with the final_rmse optional argument - -+++ - -or by requiring a lower NRMSE +We may see how the number of exponents chosen affects the fit since the approximated functions are available: ```{code-cell} ipython3 -fs.get_fit(J,w,final_rmse=2e-6) +plt.plot(w, J, label="Original") +plt.plot(w, bath.spectral_density(w), "-.", label="Fitted") +plt.plot(w, bath.spectral_density_approx(w), label="Effective Fitted") +plt.show() ``` +Here we see that out approximated or effective spectral density is worse than the one we obtained for the fit, this happens because we are not using enough exponentials from each of the underdamped modes to have an appropiate fit. All modes have the same number of exponents, and we set it to 1 which is not enough to model a bath with the temperature considered, let us repeat this with a higher number of exponents. + ```{code-cell} ipython3 -fs.summary() +bath, fitinfo = fs.get_fit(Nk=5) +plt.plot(w, J, label="Original") +plt.plot(w, bath.spectral_density(w), "-.", label="Fitted") +plt.plot( + w, + bath.spectral_density_approx(w), + label="Effective Fitted", + marker="o", + color="r", + markevery=200, + linestyle="None", +) +plt.legend() +plt.show() ``` -Alternatively one may choose the number of oscillators in the fit instead of a desired NRMSE +Since the number of exponents increases simulation time one should go with the least amount of exponents that correctly describe the bath properties (Power spectrum, Spectral density and the correlation function). When the number of exponents is not specified it defaults to 5 + ++++ + +By default the ``get_fit`` method, has a threshold normalized root mean squared error (NRMSE) of $5\times 10^{-6}$ and selects the number of oscillators automatically to obtain that value, one may on the other hand specify the Number of oscillators that can be done using the optional argument N, or may want a more accurate NRMSE, which can be specified with the final_rmse optional argument ```{code-cell} ipython3 -fs.get_fit(J,w,N=4) +bath, fitinfo = fs.get_fit(final_rmse=1e-6) +print(fitinfo["summary"]) ``` +Alternatively one may choose the number of oscillators in the fit instead of a desired NRMSE + ```{code-cell} ipython3 -fs.summary() +fittedbath, fitinfo = fs.get_fit(N=4) +print(fitinfo["summary"]) ``` Let's take a closer look at our last fit by plotting the contribution of each term of the fit: ```{code-cell} ipython3 # Plot the components of the fit separately: -plt.rcParams['font.size'] = 25 -plt.rcParams['figure.figsize'] = (10,5) -def plot_fit(func,J, w, lam, gamma, w0): - """ Plot the individual components of a fit to the spectral density. and how they contribute to the full as an animation """ - total=0 - plt.plot(w, J, 'r--', linewidth=2, label="original") +plt.rcParams["font.size"] = 25 +plt.rcParams["figure.figsize"] = (10, 5) + + +def plot_fit(func, J, w, lam, gamma, w0): + """Plot the individual components of a fit to the spectral density. + and how they contribute to the full fit one by one""" + total = 0 + plt.plot(w, J, "r--", linewidth=2, label="original") for i in range(len(lam)): - component=func(w,[lam[i]],[gamma[i]],[w0[i]]) - total+=component - plt.plot(w, J, 'r--', linewidth=2, label="original") - plt.plot(w,total,label=rf"$k={i+1}$") + component = func(w, [lam[i]], [gamma[i]], [w0[i]]) + total += component + plt.plot(w, J, "r--", linewidth=2, label="original") + plt.plot(w, total, label=rf"$k={i+1}$") plt.xlabel(r"$\omega$") plt.ylabel(r"$J(\omega)$") plt.legend() plt.pause(1) plt.show() -def plot_fit_components(func,J, w, lam, gamma, w0): - """ Plot the individual components of a fit to the spectral density. and how they contribute to the full as an animation """ - total=0 - plt.plot(w, J, 'r--', linewidth=2, label="original") + + +def plot_fit_components(func, J, w, lam, gamma, w0): + """Plot the individual components of a fit to the spectral density. + and how they contribute to the full fit""" + plt.plot(w, J, "r--", linewidth=2, label="original") for i in range(len(lam)): - component=func(w,[lam[i]],[gamma[i]],[w0[i]]) - plt.plot(w,component,label=rf"$k={i+1}$") + component = func(w, [lam[i]], [gamma[i]], [w0[i]]) + plt.plot(w, component, label=rf"$k={i+1}$") plt.xlabel(r"$\omega$") plt.ylabel(r"$J(\omega)$") plt.legend(bbox_to_anchor=(1.04, 1)) plt.show() -lam, gamma, w0 = fs.params_spec -plot_fit(fs.spectral_density_approx,J, w, lam, gamma, w0) + + +lam, gamma, w0 = fitinfo["params"] +plot_fit(fs._spectral_density_approx, J, w, lam, gamma, w0) ``` ```{code-cell} ipython3 -plot_fit_components(fs.spectral_density_approx,J, w, lam, gamma, w0) +plot_fit_components(fs._spectral_density_approx, J, w, lam, gamma, w0) ``` And let's also compare the power spectrum of the fit and the analytical spectral density: ```{code-cell} ipython3 -plt.rcParams['figure.figsize'] = (10,5) +plt.rcParams["figure.figsize"] = (10, 5) -def plot_power_spectrum(alpha, wc, beta, lam, gamma, w0, save=True): - """ Plot the power spectrum of a fit against the actual power spectrum. """ + +def plot_power_spectrum(alpha, wc, beta, save=True): + """Plot the power spectrum of a fit against the actual power spectrum.""" w = np.linspace(-10, 10, 50000) s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta) - s_fit = fs.power_spectrum(w,T) + s_fit = fittedbath.power_spectrum_approx(w) fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - axes.plot(w, s_orig, 'r', linewidth=2, label="original") - axes.plot(w, s_fit, 'b', linewidth=2, label="fit") + axes.plot(w, s_orig, "r", linewidth=2, label="original") + axes.plot(w, np.real(s_fit), "b", linewidth=2, label="fit") - axes.set_xlabel(r'$\omega$', fontsize=28) - axes.set_ylabel(r'$S(\omega)$', fontsize=28) + axes.set_xlabel(r"$\omega$", fontsize=28) + axes.set_ylabel(r"$S(\omega)$", fontsize=28) axes.legend() if save: - fig.savefig('powerspectrum.eps') + fig.savefig("powerspectrum.eps") -plot_power_spectrum(alpha, wc, 1/T, lam, gamma, w0, save=False) +plot_power_spectrum(alpha, wc, 1 / T, save=False) ``` -Now that we have a good fit to the spectral density, Let us obtain its dynamics, by passing our FitSpectral bath specifications into the HEOMSolver +Now that we have a good fit to the spectral density, Let us obtain its dynamics, by passing our ``FitSpectral`` bath specifications into the ``HEOMSolver`` ```{code-cell} ipython3 tlist = np.linspace(0, 30 * np.pi / Del, 600) -options = {'nsteps':15000, 'store_states':True, 'rtol':1e-12, 'atol':1e-12, 'method':"bdf"} -Ltot = liouvillian(Hsys) + fs.terminator -HEOM_spectral_fit = HEOMSolver(Ltot, fs.Bath_spec, max_depth=4, options=options,) -result_spectral=HEOM_spectral_fit.run(rho0,tlist) +HEOM_spectral_fit = HEOMSolver( + Hsys, + fittedbath, + max_depth=4, + options=options, +) +result_spectral = HEOM_spectral_fit.run(rho0, tlist) ``` Now if we want to see the systems's behaviour as we change the Number of terms in the fit, we may use this auxiliary function ```{code-cell} ipython3 -def generate_spectrum_results(Q,beta, N, Nk, max_depth): - """ Run the HEOM with the given bath parameters and - and return the results of the evolution. +def generate_spectrum_results(Q, N, Nk, max_depth): + """Run the HEOM with the given bath parameters and + and return the results of the evolution. """ - fs=FitSpectral(T,Q,Nk) - fs.get_fit(J,w,N) - Ltot = liouvillian(Hsys) + fs.terminator + fs = SpectralFitter(T, Q, w, J) + bath, _ = fs.get_fit(N, Nk=Nk) tlist = np.linspace(0, 30 * np.pi / Del, 600) # This problem is a little stiff, so we use the BDF method to solve # the ODE ^^^ - print(f'Starting calculations for N={N} and max_depth={max_depth} ... \n ') + print(f"Starting calculations for N={N}, Nk={Nk} + and max_depth={max_depth} ... + ") HEOM_spectral_fit = HEOMSolver( - Ltot, fs.Bath_spec, max_depth=max_depth, options=options, + Hsys, + bath, + max_depth=max_depth, + options=options, ) - results_spectral_fit = (HEOM_spectral_fit.run(rho0, tlist)) - print('\n') + results_spectral_fit = HEOM_spectral_fit.run(rho0, tlist) + print(" +") return results_spectral_fit ``` @@ -371,10 +397,10 @@ Below we generate results for different convergence parameters (number of terms ```{code-cell} ipython3 def plot_result_expectations(plots, axes=None): - """ Plot the expectation values of operators as functions of time. + """Plot the expectation values of operators as functions of time. - Each plot in plots consists of (solver_result, - measurement_operation, color, label). + Each plot in plots consists of (solver_result, + measurement_operation, color, label). """ if axes is None: fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -388,10 +414,15 @@ def plot_result_expectations(plots, axes=None): for result, m_op, color, label, kw in plots: exp = np.real(expect(result.states, m_op)) kw.setdefault("linewidth", 2) - if color == 'rand': + if color == "rand": axes.plot( - result.times, exp, - c=np.random.rand(3,), label=label, **kw, + result.times, + exp, + c=np.random.rand( + 3, + ), + label=label, + **kw, ) else: axes.plot(result.times, exp, color, label=label, **kw) @@ -407,17 +438,20 @@ def plot_result_expectations(plots, axes=None): # Generate results for different number of lorentzians in fit: results_spectral_fit_pk = [ - generate_spectrum_results(Q,1/T, n, Nk=1, max_depth=max_depth) - for n in range(1,5) + generate_spectrum_results(Q, n, Nk=1, max_depth=max_depth) for n in range(1, 5) ] -plot_result_expectations([ - ( - result, P11p, 'rand', - f"P11 (spectral fit) $k_J$={pk + 1}", - ) - for pk, result in enumerate(results_spectral_fit_pk) -]); +plot_result_expectations( + [ + ( + result, + P11p, + "rand", + f"P11 (spectral fit) $k_J$={pk + 1}", + ) + for pk, result in enumerate(results_spectral_fit_pk) + ] +); ``` ```{code-cell} ipython3 @@ -426,17 +460,20 @@ plot_result_expectations([ Nk_list = range(2, 4) results_spectral_fit_nk = [ - generate_spectrum_results(Q,1/T, 4, Nk=Nk, max_depth=max_depth) - for Nk in Nk_list + generate_spectrum_results(Q, 4, Nk=Nk, max_depth=max_depth) for Nk in Nk_list ] -plot_result_expectations([ - ( - result, P11p, 'rand', - f"P11 (spectral fit) K={nk}", - ) - for nk, result in zip(Nk_list, results_spectral_fit_nk) -]); +plot_result_expectations( + [ + ( + result, + P11p, + "rand", + f"P11 (spectral fit) K={nk}", + ) + for nk, result in zip(Nk_list, results_spectral_fit_nk) + ] +); ``` ```{code-cell} ipython3 @@ -444,32 +481,154 @@ plot_result_expectations([ Nc_list = range(2, max_depth) results_spectral_fit_nc = [ - generate_spectrum_results(Q,1/T, 4, Nk=1, max_depth=Nc) - for Nc in Nc_list + generate_spectrum_results(Q, 4, Nk=1, max_depth=Nc) for Nc in Nc_list ] -plot_result_expectations([ - ( - result, P11p, 'rand', - f"P11 (spectral fit) $N_C={nc}$", - ) - for nc, result in zip(Nc_list, results_spectral_fit_nc) -]); -``` - -We now combine the fitting and correlation function data into one large plot. - -```{code-cell} ipython3 -t = np.linspace(0, 15, 100) -C =ohmic_correlation(t, alpha=alpha, wc=wc, beta=1/T) -w2 = np.concatenate( - [np.linspace(-10, -0.1, 5000), - np.linspace(0.1, 10, 5000)], -) -S=ohmic_power_spectrum(w2,alpha=alpha,beta=1/T,wc=wc) - - -fs.fit_plots(w,J,t, C,w2,S); +plot_result_expectations( + [ + ( + result, + P11p, + "rand", + f"P11 (spectral fit) $N_C={nc}$", + ) + for nc, result in zip(Nc_list, results_spectral_fit_nc) + ] +); +``` + +#### We now combine the fitting and correlation function data into one large plot. Here we define a function to plot everything together + + +```{code-cell} ipython3 +def gen_plots(fs, w, J, t, C, w2, S): + def plot_cr_fit_vs_actual(t, C, func, axes): + """Plot the C_R(t) fit.""" + yR = func(t) + + axes.plot( + t, + np.real(C), + "r", + linewidth=3, + label="Original", + ) + axes.plot( + t, + np.real(yR), + "g", + dashes=[3, 3], + linewidth=2, + label="Reconstructed", + ) + + axes.set_ylabel(r"$C_R(t)$", fontsize=28) + axes.set_xlabel(r"$t\;\omega_c$", fontsize=28) + axes.locator_params(axis="y", nbins=4) + axes.locator_params(axis="x", nbins=4) + axes.text(0.15, 0.85, "(a)", fontsize=28, transform=axes.transAxes) + + def plot_ci_fit_vs_actual(t, C, func, axes): + """Plot the C_I(t) fit.""" + yI = func(t) + + axes.plot( + t, + np.imag(C), + "r", + linewidth=3, + ) + axes.plot( + t, + np.real(yI), + "g", + dashes=[3, 3], + linewidth=2, + ) + + axes.set_ylabel(r"$C_I(t)$", fontsize=28) + axes.set_xlabel(r"$t\;\omega_c$", fontsize=28) + axes.locator_params(axis="y", nbins=4) + axes.locator_params(axis="x", nbins=4) + axes.text(0.80, 0.80, "(b)", fontsize=28, transform=axes.transAxes) + + def plot_jw_fit_vs_actual(w, J, axes): + """Plot the J(w) fit.""" + J_fit = fs.spectral_density_approx(w) + + axes.plot( + w, + J, + "r", + linewidth=3, + ) + axes.plot( + w, + J_fit, + "g", + dashes=[3, 3], + linewidth=2, + ) + + axes.set_ylabel(r"$J(\omega)$", fontsize=28) + axes.set_xlabel(r"$\omega/\omega_c$", fontsize=28) + axes.locator_params(axis="y", nbins=4) + axes.locator_params(axis="x", nbins=4) + axes.text(0.15, 0.85, "(c)", fontsize=28, transform=axes.transAxes) + + def plot_sw_fit_vs_actual(axes): + """Plot the S(w) fit.""" + + # avoid the pole in the fit around zero: + s_fit = fs.power_spectrum_approx(w2) + + axes.plot(w2, S, "r", linewidth=3) + axes.plot(w2, s_fit, "g", dashes=[3, 3], linewidth=2) + + axes.set_ylabel(r"$S(\omega)$", fontsize=28) + axes.set_xlabel(r"$\omega/\omega_c$", fontsize=28) + axes.locator_params(axis="y", nbins=4) + axes.locator_params(axis="x", nbins=4) + axes.text(0.15, 0.85, "(d)", fontsize=28, transform=axes.transAxes) + + def plot_matsubara_spectrum_fit_vs_actual(t, C): + """Plot the Matsubara fit of the spectrum .""" + fig = plt.figure(figsize=(12, 10)) + grid = plt.GridSpec(2, 2, wspace=0.4, hspace=0.3) + + plot_cr_fit_vs_actual( + t, + C, + lambda t: fs.correlation_function_approx(t), + axes=fig.add_subplot(grid[0, 0]), + ) + plot_ci_fit_vs_actual( + t, + C, + lambda t: np.imag(fs.correlation_function_approx(t)), + axes=fig.add_subplot(grid[0, 1]), + ) + plot_jw_fit_vs_actual( + w, + J, + axes=fig.add_subplot(grid[1, 0]), + ) + plot_sw_fit_vs_actual( + axes=fig.add_subplot(grid[1, 1]), + ) + fig.legend(loc="upper center", ncol=2, fancybox=True, shadow=True) + + return plot_matsubara_spectrum_fit_vs_actual(t, C) +``` + +#### And finally plot everything together + +```{code-cell} ipython3 +t = np.linspace(0, 15, 1000) +C = ohmic_correlation(t, alpha, wc, 1 / T) +w2 = np.concatenate((-np.linspace(10, 1e-2, 100), np.linspace(1e-2, 10, 100))) +S = ohmic_power_spectrum(w2, alpha, wc, 1 / T) +gen_plots(fittedbath, w, J, t, C, w2, S) ``` ## Building the HEOM bath by fitting the correlation function @@ -487,160 +646,159 @@ $$C_I^F(t) = \sum_{i=1}^{k_I} c_I^ie^{-\gamma_I^i t}\sin(\omega_I^i t)$$ Analogously to the spectral density case, one may use the FitCorr class ```{code-cell} ipython3 -fc=FitCorr(Q) -``` - -```{code-cell} ipython3 -t = np.linspace(0, 25, 1500) -C = ohmic_correlation(t, alpha=alpha, wc=wc, beta=1/T) -``` - -```{code-cell} ipython3 -fc.fit_correlation(t,C,Ni=3,Nr=3) -``` - -```{code-cell} ipython3 -fc.summary() +t = np.linspace(0, 15, 1500) +C = ohmic_correlation(t, alpha=alpha, wc=wc, beta=1 / T) ``` ```{code-cell} ipython3 -fc.fit_correlation(t,C,final_rmse=5e-5) +fc = CorrelationFitter(Q, T, t, C) ``` ```{code-cell} ipython3 -fc.summary() +bath, fitinfo = fc.get_fit(Ni=4, Nr=4) +print(fitinfo["summary"]) ``` -Now we construct the `BosonicBath` co-efficients and frequencies from the fit to the correlation function: - ```{code-cell} ipython3 -t = np.linspace(0, 15, 100) -C = ohmic_correlation(t, alpha=alpha, wc=wc, beta=1/T) -fc.fit_plots(w, J, t, C, w2, S,beta=1/T) +gen_plots(bath, w, J, t, C, w2, S) ``` ```{code-cell} ipython3 def generate_corr_results(N, max_depth): tlist = np.linspace(0, 30 * np.pi / Del, 600) - t = np.linspace(0, 15, 100) - C = ohmic_correlation(t, alpha=alpha, wc=wc, beta=1/T) - fc.fit_correlation(t,C,Ni=N,Nr=N) + bath, _ = fc.get_fit(Ni=N, Nr=N) HEOM_corr_fit = HEOMSolver( - Hsys, fc.Bath_corr, max_depth=max_depth, options=options, + Hsys, + bath, + max_depth=max_depth, + options=options, ) - results_corr_fit = (HEOM_corr_fit.run(rho0, tlist)) + results_corr_fit = HEOM_corr_fit.run(rho0, tlist) return results_corr_fit # Generate results for different number of lorentzians in fit: results_corr_fit_pk = [ - print(f"{i + 1}") or generate_corr_results(i, max_depth=max_depth, + print(f"{i + 1}") + or generate_corr_results( + i, + max_depth=max_depth, ) - for i in range(1,4) + for i in range(1, 4) ] ``` ```{code-cell} ipython3 -plot_result_expectations([ - ( - result, P11p, 'rand', - f"P11 (correlation fit) k_R=k_I={pk + 1}", - ) - for pk, result in enumerate(results_corr_fit_pk) -]); +plot_result_expectations( + [ + ( + result, + P11p, + "rand", + f"P11 (correlation fit) k_R=k_I={pk + 1}", + ) + for pk, result in enumerate(results_corr_fit_pk) + ] +); ``` ```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) -plot_result_expectations([ - ( - results_corr_fit_pk[0], P11p, - 'y', "Correlation Function Fit $k_R=k_I=1$", - ), - ( - results_corr_fit_pk[2], P11p, - 'y-.', "Correlation Function Fit $k_R=k_I=3$", - ), - (results_spectral_fit_pk[0], P11p, 'b', "Spectral Density Fit $k_J=1$"), - (results_spectral_fit_pk[2], P11p, 'g--', "Spectral Density Fit $k_J=3$"), - (results_spectral_fit_pk[3], P11p, 'r-.', "Spectral Density Fit $k_J=4$"), -], axes=axes) +plot_result_expectations( + [ + ( + results_corr_fit_pk[0], + P11p, + "y", + "Correlation Function Fit $k_R=k_I=1$", + ), + ( + results_corr_fit_pk[2], + P11p, + "k", + "Correlation Function Fit $k_R=k_I=3$", + ), + (results_spectral_fit_pk[0], P11p, "b", "Spectral Density Fit $k_J=1$"), + (results_spectral_fit_pk[3], P11p, "r-.", "Spectral Density Fit $k_J=4$"), + ], + axes=axes, +) axes.set_yticks([0.6, 0.8, 1]) -axes.set_ylabel(r'$\rho_{11}$', fontsize=30) -axes.set_xlabel(r'$t\;\omega_c$', fontsize=30) +axes.set_ylabel(r"$\rho_{11}$", fontsize=30) +axes.set_xlabel(r"$t\;\omega_c$", fontsize=30) axes.legend(loc=0, fontsize=20); ``` # Using the Ohmic Bath class -While the two classes above are designed for general fits of either correlation functions or spectral densities, as the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced shortly by using the OhmicBath class. By default the method fits using the spectral density, however it can use the correlation function if method is specified + As the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced shortly by using the OhmicBath class. This allows for rapid implementation of fitted ohmic baths via the correlation function or spectral density ```{code-cell} ipython3 -obs=OhmicBath(T,Q,alpha,wc,s,rmse=9e-5,method='spectral') +obs = OhmicBath(T, Q, alpha, wc, s) ``` ```{code-cell} ipython3 -obs.summary() +Obath, fitinfo = obs.make_correlation_fit(t, rmse=2e-4) +print(fitinfo["summary"]) ``` ```{code-cell} ipython3 -obs.fit.fit_plots(w,J,t, C,w2,S); -``` - -```{code-cell} ipython3 -obc=OhmicBath(T,Q,alpha,wc,s,rmse=1e-4,method='correlation') -``` - -```{code-cell} ipython3 -obc.summary() -``` - -```{code-cell} ipython3 -obc.fit.fit_plots(w, J, t, C, w2, S,beta=1/T) +tlist = np.linspace(0, 30 * np.pi / Del, 600) +HEOM_ohmic_corr_fit = HEOMSolver( + Hsys, + Obath, + max_depth=5, + options=options, +) +results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist) ``` ```{code-cell} ipython3 -tlist = np.linspace(0, 30 * np.pi / Del, 600) - -HEOM_ohmic_corr_fit = HEOMSolver(Hsys, obc.bath, max_depth=5, options=options,) -Ltot = liouvillian(Hsys) + fs.terminator -HEOM_ohmic_spectral_fit = HEOMSolver(Hsys, obs.bath, max_depth=5, options=options,) - -#results__ohmic_corr_fit = (HEOM_ohmic_corr_fit.run(rho0, tlist)) -results_ohmic_spectral_fit = (HEOM_ohmic_spectral_fit.run(rho0, tlist)) +Obath, fitinfo = obs.make_spectral_fit(w, rmse=2e-4) +print(fitinfo["summary"]) ``` ```{code-cell} ipython3 -results_ohmic_corr_fit = (HEOM_ohmic_corr_fit.run(rho0, tlist)) +HEOM_ohmic_spectral_fit = HEOMSolver( + Hsys, + Obath, + max_depth=5, + options=options, +) +results_ohmic_spectral_fit = HEOM_ohmic_spectral_fit.run(rho0, tlist) ``` ```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) -plot_result_expectations([ - # ( - # results_corr_fit_pk[0], P11p, - # 'y', "Correlation Function Fit $k_R=k_I=1$", - # ), - ( - results_corr_fit_pk[2], P11p, - 'y-.', "Correlation Function Fit $k_R=k_I=3$", - ), - (results_spectral_fit_pk[0], P11p, 'b', "Spectral Density Fit $k_J=1$"), - (results_spectral_fit_pk[2], P11p, 'g--', "Spectral Density Fit $k_J=3$"), - (results_spectral_fit_pk[3], P11p, 'r-.', "Spectral Density Fit $k_J=4$"), - (results_ohmic_spectral_fit, P11p, 'g-.', "Spectral Density Fit Ohmic Bath"), - (results_ohmic_corr_fit, P11p, 'k-.', "Correlation Fit Ohmic Bath") - -], axes=axes) +plot_result_expectations( + [ + # ( + # results_corr_fit_pk[0], P11p, + # 'y', "Correlation Function Fit $k_R=k_I=1$", + # ), + ( + results_corr_fit_pk[2], + P11p, + "y-.", + "Correlation Function Fit $k_R=k_I=3$", + ), + (results_spectral_fit_pk[0], P11p, "b", "Spectral Density Fit $k_J=1$"), + (results_spectral_fit_pk[2], P11p, "g--", "Spectral Density Fit $k_J=3$"), + (results_spectral_fit_pk[3], P11p, "r-.", "Spectral Density Fit $k_J=4$"), + (results_ohmic_spectral_fit, P11p, "g-.", "Spectral Density Fit Ohmic Bath"), + (results_ohmic_corr_fit, P11p, "k-.", "Correlation Fit Ohmic Bath"), + ], + axes=axes, +) axes.set_yticks([0.6, 0.8, 1]) -axes.set_ylabel(r'$\rho_{11}$', fontsize=30) -axes.set_xlabel(r'$t\;\omega_c$', fontsize=30) +axes.set_ylabel(r"$\rho_{11}$", fontsize=30) +axes.set_xlabel(r"$t\;\omega_c$", fontsize=30) axes.legend(loc=0, fontsize=20); ``` From 7b84b9e260d1f134075d8be98e02bd45ec46f4c2 Mon Sep 17 00:00:00 2001 From: Paul Menczel Date: Thu, 25 Jan 2024 17:26:31 +0900 Subject: [PATCH 07/44] Updated notebook 1a --- .../heom/heom-1a-spin-bath-model-basic.md | 105 ++++++++---------- 1 file changed, 45 insertions(+), 60 deletions(-) diff --git a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md index db4ef00e..b9fc065b 100644 --- a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md +++ b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md @@ -5,7 +5,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.15.2 + jupytext_version: 1.16.1 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -64,9 +64,9 @@ density is given by: \begin{equation*} c_k = \begin{cases} - \lambda \gamma (\cot(\beta \gamma / 2) -i) \ + \lambda \gamma (\cot(\beta \gamma / 2) - i) \ & k = 0\\ - 4 \lambda \gamma \nu_k \{(\nu_k^2 - \gamma^2)\beta \} \ + 4 \lambda \gamma \nu_k / \{(\nu_k^2 - \gamma^2)\beta \} \ & k \geq 1\\ \end{cases} \end{equation*} @@ -128,15 +128,8 @@ def dl_matsubara_params(lam, gamma, T, nk): """ ckAR = [lam * gamma * cot(gamma / (2 * T))] ckAR.extend( - 4 - * lam - * gamma - * T - * 2 - * np.pi - * k - * T - / ((2 * np.pi * k * T) ** 2 - gamma**2) + (8 * lam * gamma * T * np.pi * k * T / + ((2 * np.pi * k * T)**2 - gamma**2)) for k in range(1, nk + 1) ) vkAR = [gamma] @@ -148,21 +141,6 @@ def dl_matsubara_params(lam, gamma, T, nk): return ckAR, vkAR, ckAI, vkAI ``` -```python -def dl_corr_approx(t, nk): - """Drude-Lorenz correlation function approximation. - - Approximates the correlation function at each time t to nk exponents. - """ - c = lam * gamma * (-1.0j + cot(gamma / (2 * T))) * np.exp(-gamma * t) - for k in range(1, nk): - vk = 2 * np.pi * k * T - c += (4 * lam * gamma * T * vk / (vk**2 - gamma**2)) * np.exp( - -vk * t - ) - return c -``` - ```python def plot_result_expectations(plots, axes=None): """Plot the expectation values of operators as functions of time. @@ -333,8 +311,8 @@ Below we show how to use this built-in functionality: # Compare to built-in Drude-Lorentz bath: with timer("RHS construction time"): - bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) - HEOM_dlbath = HEOMSolver(Hsys, bath, NC, options=options) + dlbath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) + HEOM_dlbath = HEOMSolver(Hsys, dlbath, NC, options=options) with timer("ODE solver time"): result_dlbath = HEOM_dlbath.run(rho0, tlist) # normal 115 @@ -349,6 +327,30 @@ plot_result_expectations( ); ``` +The `DrudeLorentzBath` class also allows us to easily evaluate analytical expressions for the power spectrum, correlation function, and spectral density. In the following plots, the solid lines are the exact expressions, and the dashed lines are based on our approximation of the correlation function with a finite number of exponents. + +```python +w = np.linspace(-10, 10, 1000) +w2 = np.linspace(0, 10, 1000) +fig, axs = plt.subplots(2, 2) + +axs[0, 0].plot(w, dlbath.power_spectrum(w)) +axs[0, 0].plot(w, dlbath.power_spectrum_approx(w), '--') +axs[0, 0].set(xlabel=r'$\omega$', ylabel=r'$S(\omega)$') +axs[0, 1].plot(w2, dlbath.spectral_density(w2)) +axs[0, 1].plot(w2, dlbath.spectral_density_approx(w2), '--') +axs[0, 1].set(xlabel=r'$\omega$', ylabel=r'$J(\omega)$') +axs[1, 0].plot(w2, np.real(dlbath.correlation_function(w2))) +axs[1, 0].plot(w2, np.real(dlbath.correlation_function_approx(w2)), '--') +axs[1, 0].set(xlabel=r'$t$', ylabel=r'$C_{R}(t)$') +axs[1, 1].plot(w2, np.imag(dlbath.correlation_function(w2))) +axs[1, 1].plot(w2, np.imag(dlbath.correlation_function_approx(w2)), '--') +axs[1, 1].set(xlabel=r'$t$', ylabel=r'$C_{I}(t)$') + +fig.tight_layout() +plt.show() +``` + We also provide a legacy class, `HSolverDL`, which calculates the Drude-Lorentz correlation functions automatically, to be backwards compatible with the previous HEOM solver in QuTiP: @@ -400,7 +402,8 @@ $ C(t)=\sum_{k=0}^{\infty} \frac{c_k}{\nu_k} = 2 \lambda / (\beta \gamma) that are kept in the hierarchy, and treat the residual as a Lindblad. This is clearer if we plot the correlation function with a large number of -Matsubara terms: +Matsubara terms. To create the plot, we use the utility function of the +`DrudeLorentzBath` class mentioned above. ```python def plot_correlation_expansion_divergence(): @@ -410,8 +413,8 @@ def plot_correlation_expansion_divergence(): t = np.linspace(0, 2, 100) # correlation coefficients with 15k and 2 terms - corr_15k = dl_corr_approx(t, 15_000) - corr_2 = dl_corr_approx(t, 2) + corr_15k = dlbath.correlation_function(t, Nk=15_000) + corr_2 = dlbath.correlation_function(t, Nk=2) fig, ax1 = plt.subplots(figsize=(12, 7)) @@ -432,8 +435,7 @@ def plot_correlation_expansion_divergence(): ax1.set_ylabel(r"$C$") ax1.legend() - -plot_correlation_expansion_divergence(); +plot_correlation_expansion_divergence() ``` Let us evaluate the result including this Ishizaki-Tanimura terminator: @@ -507,25 +509,6 @@ plot_result_expectations( ); ``` -The built-in class also allows us to quickly obtain the bath spectrum, correlation function, and spectral density - -```python -w=np.linspace(-10,10,1000) -w2=np.linspace(0,10,1000) -fig, axs = plt.subplots(2, 2) -axs[0, 0].plot(w,bath.power_spectrum(w,T)) -axs[0,0].set(xlabel=r'$\omega$',ylabel=r'$S(\omega)$') -axs[0, 1].plot(w2,bath.spectral_density(w2)) -axs[0,1].set(xlabel=r'$\omega$',ylabel=r'$J(\omega)$') -axs[1, 0].plot(w2,bath.CI(w2)) -axs[1,0].set(xlabel=r'$t$',ylabel=r'$C_{I}(t)$') -axs[1, 1].plot(w2,bath.CR(w2)) -axs[1,1].set(xlabel=r'$t$',ylabel=r'$C_{R}(t)$') -fig.tight_layout() -plt.show() - -``` - We can compare the solution obtained from the QuTiP Bloch-Redfield solver: ```python @@ -660,8 +643,8 @@ def pade_corr(tlist, lmax): tlist_corr = np.linspace(0, 2, 100) cppLP, etapLP, gampLP = pade_corr(tlist_corr, 2) -corr_15k = dl_corr_approx(tlist_corr, 15_000) -corr_2k = dl_corr_approx(tlist_corr, 2) +corr_15k = dlbath.correlation_function(tlist_corr, Nk=15_000) +corr_2k = dlbath.correlation_function(tlist_corr, Nk=2) fig, ax1 = plt.subplots(figsize=(12, 7)) ax1.plot( @@ -774,16 +757,18 @@ plot_result_expectations( ### Next we compare the Matsubara and Pade correlation function fits -This is not efficient for this example, but can be extremely useful in -situations where large number of exponents are needed (e.g., near zero -temperature). +Fitting the correlation function is not efficient for this example, but +can be extremely useful in situations where large number of exponents +are needed (e.g., near zero temperature). We will perform the fitting +manually below, and show in notebook 1d how the `CorrelationFitter` +class can be used to perform such fits with less effort. First we collect a large sum of Matsubara terms for many time steps: ```python tlist2 = np.linspace(0, 2, 10000) -corr_15k_t10k = dl_corr_approx(tlist2, 15_000) +corr_15k_t10k = dlbath.correlation_function(tlist2, Nk=15_000) corrRana = np.real(corr_15k_t10k) corrIana = np.imag(corr_15k_t10k) @@ -850,7 +835,7 @@ with timer("Correlation (real) fitting time"): for i in range(kR): poptR.append(fitter(corrRana, tlist2, i + 1)) -corrRMats = np.real(dl_corr_approx(tlist2, Nk)) +corrRMats = np.real(dlbath.correlation_function_approx(tlist2)) kI = 1 # number of exponents for imaginary part poptI = [] From 318e0fd7913f78d2a8161f314efe212351f57932 Mon Sep 17 00:00:00 2001 From: Paul Menczel Date: Fri, 26 Jan 2024 11:55:07 +0900 Subject: [PATCH 08/44] Updated HEOM notebooks 1b and 1c with new functionality --- ...1b-spin-bath-model-very-strong-coupling.md | 54 ++++++------------- .../heom-1c-spin-bath-model-underdamped-sd.md | 29 +++++++++- 2 files changed, 43 insertions(+), 40 deletions(-) diff --git a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md index a99285e5..f06f3e0a 100644 --- a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md +++ b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.5 + jupytext_version: 1.16.1 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -71,7 +71,7 @@ As an example, the Matsubara decomposition of the Drude-Lorentz spectral density \begin{equation*} c_k = \begin{cases} \lambda \gamma (\cot(\beta \gamma / 2) - i) & k = 0\\ - 4 \lambda \gamma \nu_k / {(\nu_k^2 - \gamma^2)\beta \} & k \geq 1\\ + 4 \lambda \gamma \nu_k / \{(nu_k^2 - \gamma^2)\beta \} & k \geq 1\\ \end{cases} \end{equation*} @@ -199,8 +199,9 @@ P12p = basis(2, 0) * basis(2, 1).dag() Let us briefly inspect the spectral density. ```{code-cell} ipython3 +bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) w = np.linspace(0, 5, 1000) -J = w * 2 * lam * gamma / ((gamma**2 + w**2)) +J = bath.spectral_density(w) # Plot the results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -261,48 +262,19 @@ axes.legend(loc=0, fontsize=12); matsBath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) padeBath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) -# We will compare against a summation of {lmaxmats} Matsubara terms -lmaxmats = 15000 -exactBath = DrudeLorentzBath( - Q, lam=lam, gamma=gamma, T=T, Nk=lmaxmats, combine=False, -) - - -def CR(bath, t): - """ C_R, the real part of the correlation function. """ - result = 0 - for exp in bath.exponents: - if ( - exp.type == BathExponent.types['R'] or - exp.type == BathExponent.types['RI'] - ): - result += exp.ck * np.exp(-exp.vk * t) - return result - - -def CI(bath, t): - """ C_I, the imaginary part of the correlation function. """ - result = 0 - for exp in bath.exponents: - if exp.type == BathExponent.types['I']: - result += exp.ck * np.exp(exp.vk * t) - if exp.type == BathExponent.types['RI']: - result += exp.ck2 * np.exp(exp.vk * t) - return result - fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(16, 8)) ax1.plot( - tlist, CR(exactBath, tlist), - "r", linewidth=2, label=f"Mats (Nk={lmaxmats})", + tlist, np.real(matsBath.correlation_function(tlist)), + "r", linewidth=2, label=f"Exact", ) ax1.plot( - tlist, CR(matsBath, tlist), + tlist, np.real(matsBath.correlation_function_approx(tlist)), "g--", linewidth=2, label=f"Mats (Nk={Nk})", ) ax1.plot( - tlist, CR(padeBath, tlist), + tlist, np.real(padeBath.correlation_function_approx(tlist)), "b--", linewidth=2, label=f"Pade (Nk={Nk})", ) @@ -312,11 +284,13 @@ ax1.legend(loc=0, fontsize=12) tlist2 = tlist[0:50] ax2.plot( - tlist2, np.abs(CR(matsBath, tlist2) - CR(exactBath, tlist2)), + tlist2, np.abs(matsBath.correlation_function_approx(tlist2) + - matsBath.correlation_function(tlist2)), "g", linewidth=2, label="Mats Error", ) ax2.plot( - tlist2, np.abs(CR(padeBath, tlist2) - CR(exactBath, tlist2)), + tlist2, np.abs(padeBath.correlation_function_approx(tlist2) + - padeBath.correlation_function(tlist2)), "b--", linewidth=2, label="Pade Error", ) @@ -358,6 +332,8 @@ axes.legend(loc=0, fontsize=12); ## Simulation 4: Fitting approach +We will perform the fitting manually here, and show in notebook 1d how the `CorrelationFitter` class can be used to perform such fits with less effort. + ```{code-cell} ipython3 def wrapper_fit_func(x, N, args): """ Fit function wrapper that unpacks its arguments. """ @@ -415,7 +391,7 @@ def fitter(ans, tlist, k): # Correlation function values to fit: tlist_fit = np.linspace(0, 6, 10000) -corrRana = CR(exactBath, tlist_fit) +corrRana = np.real(matsBath.correlation_function(tlist_fit)) # Perform the fit: kR = 3 # number of exponents to use for real part diff --git a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md index a20dd73b..0542e6a0 100644 --- a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md +++ b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.5 + jupytext_version: 1.16.1 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -426,6 +426,33 @@ plot_result_expectations([ ]); ``` +The `UnderDampedBath` class also allows us to easily evaluate analytical expressions for the power spectrum, correlation function, and spectral density. In the following plots, the solid lines are the exact expressions, and the dashed lines are based on our approximation of the correlation function with a finite number of exponents. In this case, there is an excellent agreement. + +```{code-cell} ipython3 +w = np.linspace(-3, 3, 1000) +w2 = np.linspace(0, 3, 1000) +t = np.linspace(0, 10, 1000) +bath_cf = bath.correlation_function(t) # uses numerical integration + +fig, axs = plt.subplots(2, 2) + +axs[0, 0].plot(w, bath.power_spectrum(w)) +axs[0, 0].plot(w, bath.power_spectrum_approx(w), '--') +axs[0, 0].set(xlabel=r'$\omega$', ylabel=r'$S(\omega)$') +axs[0, 1].plot(w2, bath.spectral_density(w2)) +axs[0, 1].plot(w2, bath.spectral_density_approx(w2), '--') +axs[0, 1].set(xlabel=r'$\omega$', ylabel=r'$J(\omega)$') +axs[1, 0].plot(t, np.real(bath_cf)) +axs[1, 0].plot(t, np.real(bath.correlation_function_approx(t)), '--') +axs[1, 0].set(xlabel=r'$t$', ylabel=r'$C_{R}(t)$') +axs[1, 1].plot(t, np.imag(bath_cf)) +axs[1, 1].plot(t, np.imag(bath.correlation_function_approx(t)), '--') +axs[1, 1].set(xlabel=r'$t$', ylabel=r'$C_{I}(t)$') + +fig.tight_layout() +plt.show() +``` + ## Compare the results +++ From 1f21342757ca8214e0ac1de81f9eb68954a15929 Mon Sep 17 00:00:00 2001 From: Paul Menczel Date: Sat, 27 Jan 2024 00:05:00 +0900 Subject: [PATCH 09/44] Revised tutorial notebook 1d --- .../heom-1d-spin-bath-model-ohmic-fitting.md | 74 ++++++++++--------- 1 file changed, 39 insertions(+), 35 deletions(-) diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md index c903ec06..78ee6bad 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md @@ -209,14 +209,14 @@ where $a, b$ and $c$ are the fit parameters and each is a vector of length $k$. +++ -With the spectral density approximation $J_{\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. This can be done quickly using the FitSpectral bath, which takes the target spectral density as an array and fits it to the series of **k** underdamped harmonic oscillators with the Meier-Tannor form +With the spectral density approximation $J_{\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. This can be done quickly using the `SpectralFitter` class, which takes the target spectral density as an array and fits it to the series of **k** underdamped harmonic oscillators with the Meier-Tannor form ```{code-cell} ipython3 w = np.linspace(0, 15, 20000) J = ohmic_spectral_density(w, alpha, wc) ``` -We first initialize our FitSpectral class +We first initialize our SpectralFitter ```{code-cell} ipython3 fs = SpectralFitter(T, Q, w, J) @@ -237,36 +237,49 @@ print(fitinfo["summary"]) We may see how the number of exponents chosen affects the fit since the approximated functions are available: ```{code-cell} ipython3 -plt.plot(w, J, label="Original") -plt.plot(w, bath.spectral_density(w), "-.", label="Fitted") -plt.plot(w, bath.spectral_density_approx(w), label="Effective Fitted") +fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5)) + +ax1.plot(w, J, label="Original spectral density") +ax1.plot(w, bath.spectral_density_approx(w), label="Effective fitted SD") +ax1.set_xlabel(r'$\omega$') +ax1.set_ylabel(r'$J$') +ax1.legend() + +ax2.plot(w, np.abs(J - bath.spectral_density_approx(w)), label="Error") +ax2.set_xlabel(r'$\omega$') +ax2.set_ylabel(r'$J$') +ax2.legend() + plt.show() ``` -Here we see that out approximated or effective spectral density is worse than the one we obtained for the fit, this happens because we are not using enough exponentials from each of the underdamped modes to have an appropiate fit. All modes have the same number of exponents, and we set it to 1 which is not enough to model a bath with the temperature considered, let us repeat this with a higher number of exponents. +Here we see a surprisingly large discrepancy in our approximated or effective spectral density. This happens because we are not using enough exponentials from each of the underdamped modes to have an appropiate fit. All modes have the same number of exponents, and we set it to 1 which is not enough to model a bath with the temperature considered, let us repeat this with a higher number of exponents. ```{code-cell} ipython3 bath, fitinfo = fs.get_fit(Nk=5) -plt.plot(w, J, label="Original") -plt.plot(w, bath.spectral_density(w), "-.", label="Fitted") -plt.plot( - w, - bath.spectral_density_approx(w), - label="Effective Fitted", - marker="o", - color="r", - markevery=200, - linestyle="None", -) -plt.legend() +print(fitinfo["summary"]) + +fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5)) + +ax1.plot(w, J, label="Original spectral density") +ax1.plot(w, bath.spectral_density_approx(w), label="Effective fitted SD") +ax1.set_xlabel(r'$\omega$') +ax1.set_ylabel(r'$J$') +ax1.legend() + +ax2.plot(w, np.abs(J - bath.spectral_density_approx(w)), label="Error") +ax2.set_xlabel(r'$\omega$') +ax2.set_ylabel(r'$J$') +ax2.legend() + plt.show() ``` -Since the number of exponents increases simulation time one should go with the least amount of exponents that correctly describe the bath properties (Power spectrum, Spectral density and the correlation function). When the number of exponents is not specified it defaults to 5 +Since the number of exponents increases simulation time one should go with the least amount of exponents that correctly describe the bath properties (Power spectrum, Spectral density and the correlation function). When the number of exponents is not specified it defaults to 5. +++ -By default the ``get_fit`` method, has a threshold normalized root mean squared error (NRMSE) of $5\times 10^{-6}$ and selects the number of oscillators automatically to obtain that value, one may on the other hand specify the Number of oscillators that can be done using the optional argument N, or may want a more accurate NRMSE, which can be specified with the final_rmse optional argument +By default the ``get_fit`` method, has a threshold normalized root mean squared error (NRMSE) of $5\times 10^{-6}$ and selects the number of oscillators automatically to obtain that value. One may also specify the number of oscillators that is used with the optional argument N, or may want a more accurate NRMSE, which can be specified with the final_rmse optional argument ```{code-cell} ipython3 bath, fitinfo = fs.get_fit(final_rmse=1e-6) @@ -292,7 +305,6 @@ def plot_fit(func, J, w, lam, gamma, w0): """Plot the individual components of a fit to the spectral density. and how they contribute to the full fit one by one""" total = 0 - plt.plot(w, J, "r--", linewidth=2, label="original") for i in range(len(lam)): component = func(w, [lam[i]], [gamma[i]], [w0[i]]) total += component @@ -319,19 +331,16 @@ def plot_fit_components(func, J, w, lam, gamma, w0): lam, gamma, w0 = fitinfo["params"] -plot_fit(fs._spectral_density_approx, J, w, lam, gamma, w0) +plot_fit(SpectralFitter._meier_tannor_SD, J, w, lam, gamma, w0) ``` ```{code-cell} ipython3 -plot_fit_components(fs._spectral_density_approx, J, w, lam, gamma, w0) +plot_fit_components(SpectralFitter._meier_tannor_SD, J, w, lam, gamma, w0) ``` And let's also compare the power spectrum of the fit and the analytical spectral density: ```{code-cell} ipython3 -plt.rcParams["figure.figsize"] = (10, 5) - - def plot_power_spectrum(alpha, wc, beta, save=True): """Plot the power spectrum of a fit against the actual power spectrum.""" w = np.linspace(-10, 10, 50000) @@ -378,9 +387,7 @@ def generate_spectrum_results(Q, N, Nk, max_depth): # This problem is a little stiff, so we use the BDF method to solve # the ODE ^^^ - print(f"Starting calculations for N={N}, Nk={Nk} - and max_depth={max_depth} ... - ") + print(f"Starting calculations for N={N}, Nk={Nk} and max_depth={max_depth} ... ") HEOM_spectral_fit = HEOMSolver( Hsys, bath, @@ -388,8 +395,6 @@ def generate_spectrum_results(Q, N, Nk, max_depth): options=options, ) results_spectral_fit = HEOM_spectral_fit.run(rho0, tlist) - print(" -") return results_spectral_fit ``` @@ -499,7 +504,6 @@ plot_result_expectations( #### We now combine the fitting and correlation function data into one large plot. Here we define a function to plot everything together - ```{code-cell} ipython3 def gen_plots(fs, w, J, t, C, w2, S): def plot_cr_fit_vs_actual(t, C, func, axes): @@ -643,7 +647,7 @@ $$C_R^F(t) = \sum_{i=1}^{k_R} c_R^ie^{-\gamma_R^i t}\cos(\omega_R^i t)$$ $$C_I^F(t) = \sum_{i=1}^{k_I} c_I^ie^{-\gamma_I^i t}\sin(\omega_I^i t)$$ -Analogously to the spectral density case, one may use the FitCorr class +Analogously to the spectral density case, one may use the `CorrelationFitter` class ```{code-cell} ipython3 t = np.linspace(0, 15, 1500) @@ -679,7 +683,7 @@ def generate_corr_results(N, max_depth): return results_corr_fit -# Generate results for different number of lorentzians in fit: +# Generate results for different number of exponentials in fit: results_corr_fit_pk = [ print(f"{i + 1}") or generate_corr_results( @@ -735,7 +739,7 @@ axes.legend(loc=0, fontsize=20); # Using the Ohmic Bath class - As the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced shortly by using the OhmicBath class. This allows for rapid implementation of fitted ohmic baths via the correlation function or spectral density + As the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced quickly by using the OhmicBath class. This allows for rapid implementation of fitted ohmic baths via the correlation function or spectral density ```{code-cell} ipython3 obs = OhmicBath(T, Q, alpha, wc, s) From 7d59db3b74d22605d219a7ef9c90ffc26da12d32 Mon Sep 17 00:00:00 2001 From: mcditooss Date: Sun, 4 Feb 2024 20:15:41 +0100 Subject: [PATCH 10/44] updated according to new ansatz --- ...eom-1d-spin-bath-model-ohmic-fitting.ipynb | 1735 +++++++++++++++++ ...om-1e-spin-bath-model-pure-dephasing.ipynb | 1053 ++++++++++ 2 files changed, 2788 insertions(+) create mode 100644 tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb create mode 100644 tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb new file mode 100644 index 00000000..0d63b9e4 --- /dev/null +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb @@ -0,0 +1,1735 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b7b4c8ea", + "metadata": {}, + "source": [ + "# HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions" + ] + }, + { + "cell_type": "markdown", + "id": "e15a6ba7", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded \n", + "in a set of auxiliary density matrices.\n", + "\n", + "In this example we show the evolution of a single two-level system in contact with a single bosonic environment.\n", + "\n", + "The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", + "\n", + "The bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", + "\n", + "In the example below we show how to model an Ohmic environment with exponential cut-off in three ways:\n", + "\n", + "* First we fit the spectral density with a set of underdamped brownian oscillator functions.\n", + "* Second, we evaluate the correlation functions, and fit those with a certain choice of exponential functions.\n", + "* Third, we use the available OhmicBath class \n", + "\n", + "In each case we will use the fit parameters to determine the correlation function expansion co-efficients needed to construct a description of the bath (i.e. a `BosonicBath` object) to supply to the `HEOMSolver` so that we can solve for the system dynamics." + ] + }, + { + "cell_type": "markdown", + "id": "7e81fb53", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "07a1946f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "import qutip\n", + "from qutip import (\n", + " basis,\n", + " expect,\n", + " sigmax,\n", + " sigmaz,\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver,\n", + " SpectralFitter,\n", + " CorrelationFitter,\n", + " OhmicBath,\n", + ")\n", + "\n", + "# Import mpmath functions for evaluation of gamma and zeta\n", + "# functions in the expression for the correlation:\n", + "\n", + "from mpmath import mp\n", + "\n", + "mp.dps = 15\n", + "mp.pretty = True\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7939c2d0", + "metadata": {}, + "outputs": [], + "source": [ + "# Solver options:\n", + "\n", + "options = {\n", + " \"nsteps\": 15000,\n", + " \"store_states\": True,\n", + " \"rtol\": 1e-14,\n", + " \"atol\": 1e-14,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "0f4446b2", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian, bath and system measurement operators:" + ] + }, + { + "cell_type": "markdown", + "id": "db34c1d9", + "metadata": {}, + "source": [ + "### System Hamiltonian" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b13df20f", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the system Hamiltonian\n", + "eps = 0 # Energy of the 2-level system.\n", + "Del = 0.2 # Tunnelling term\n", + "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()\n", + "rho0 = basis(2, 0) * basis(2, 0).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "c97942a7", + "metadata": {}, + "source": [ + "### System measurement operators" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "35c11edc", + "metadata": {}, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresonding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresonding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "3ce72545", + "metadata": {}, + "source": [ + "### Analytical expressions for the Ohmic bath correlation function and spectral density" + ] + }, + { + "cell_type": "markdown", + "id": "5ab9f93c", + "metadata": {}, + "source": [ + "Before we begin fitting, let us examine the analytic expressions for the correlation and spectral density functions and write Python equivalents. \n", + "\n", + "The correlation function is given by (see, e.g., http://www1.itp.tu-berlin.de/brandes/public_html/publications/notes.pdf for a derivation, equation 7.59, but with a factor of $\\pi$ moved into the definition of the correlation function):\n", + "\n", + "\\begin{align}\n", + "C(t) =& \\: \\frac{1}{\\pi}\\alpha \\omega_{c}^{1 - s} \\beta^{- (s + 1)} \\: \\times \\\\\n", + " & \\: \\Gamma(s + 1) \\left[ \\zeta \\left(s + 1, \\frac{1 + \\beta \\omega_c - i \\omega_c t}{\\beta \\omega_c}\\right) + \\zeta \\left(s + 1, \\frac{1 + i \\omega_c t}{\\beta \\omega_c}\\right) \\right]\n", + "\\end{align}\n", + "\n", + "where $\\Gamma$ is the Gamma function and\n", + "\n", + "\\begin{equation}\n", + "\\zeta(z, u) \\equiv \\sum_{n=0}^{\\infty} \\frac{1}{(n + u)^z}, \\; u \\neq 0, -1, -2, \\ldots\n", + "\\end{equation}\n", + "\n", + "is the generalized Zeta function. The Ohmic case is given by $s = 1$.\n", + "\n", + "The corresponding spectral density for the Ohmic case is:\n", + "\n", + "\\begin{equation}\n", + "J(\\omega) = \\omega \\alpha e^{- \\frac{\\omega}{\\omega_c}}\n", + "\\end{equation}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "51dcd19d", + "metadata": {}, + "outputs": [], + "source": [ + "def ohmic_correlation(t, alpha, wc, beta, s=1):\n", + " \"\"\"The Ohmic bath correlation function as a function of t\n", + " (and the bath parameters).\n", + " \"\"\"\n", + " corr = (1 / np.pi) * alpha * wc ** (1 - s)\n", + " corr *= beta ** (-(s + 1)) * mp.gamma(s + 1)\n", + " z1_u = (1 + beta * wc - 1.0j * wc * t) / (beta * wc)\n", + " z2_u = (1 + 1.0j * wc * t) / (beta * wc)\n", + " # Note: the arguments to zeta should be in as high precision as possible.\n", + " # See http://mpmath.org/doc/current/basics.html#providing-correct-input\n", + " return np.array(\n", + " [\n", + " complex(corr * (mp.zeta(s + 1, u1) + mp.zeta(s + 1, u2)))\n", + " for u1, u2 in zip(z1_u, z2_u)\n", + " ],\n", + " dtype=np.complex128,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "165a7345", + "metadata": {}, + "outputs": [], + "source": [ + "def ohmic_spectral_density(w, alpha, wc):\n", + " \"\"\"The Ohmic bath spectral density as a function of w\n", + " (and the bath parameters).\n", + " \"\"\"\n", + " return w * alpha * np.e ** (-w / wc)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "48b0422d", + "metadata": {}, + "outputs": [], + "source": [ + "def ohmic_power_spectrum(w, alpha, wc, beta):\n", + " \"\"\"The Ohmic bath power spectrum as a function of w\n", + " (and the bath parameters).\n", + " \"\"\"\n", + " bose = (1 / (np.e ** (w * beta) - 1)) + 1\n", + " return w * alpha * np.e ** (-abs(w) / wc) * bose * 2" + ] + }, + { + "cell_type": "markdown", + "id": "d32921ba", + "metadata": {}, + "source": [ + "### Bath and HEOM parameters" + ] + }, + { + "cell_type": "markdown", + "id": "e124f6e7", + "metadata": {}, + "source": [ + "Finally, let's set the bath parameters we will work with and write down some measurement operators:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "29f1c71a", + "metadata": {}, + "outputs": [], + "source": [ + "Q = sigmaz()\n", + "alpha = 3.25\n", + "T = 0.5\n", + "wc = 1.0\n", + "s = 1" + ] + }, + { + "cell_type": "markdown", + "id": "f506bdd3", + "metadata": {}, + "source": [ + "And set the cut-off for the HEOM hierarchy:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5faa0dbb", + "metadata": {}, + "outputs": [], + "source": [ + "# HEOM parameters:\n", + "\n", + "# The max_depth defaults to 5 so that the notebook executes more\n", + "# quickly. Change it to 11 to wait longer for more accurate results.\n", + "max_depth = 5" + ] + }, + { + "cell_type": "markdown", + "id": "baa5d001", + "metadata": {}, + "source": [ + "## Building the HEOM bath by fitting the spectral density" + ] + }, + { + "cell_type": "markdown", + "id": "a1acb05a", + "metadata": {}, + "source": [ + "We begin by fitting the spectral density, using a series of $k$ underdamped harmonic oscillators case with the Meier-Tannor form (J. Chem. Phys. 111, 3365 (1999); https://doi.org/10.1063/1.479669):\n", + "\n", + "\\begin{equation}\n", + "J_{\\mathrm approx}(\\omega; a, b, c) = \\sum_{i=0}^{k-1} \\frac{2 a_i b_i w}{((w + c_i)^2 + b_i^2) ((w - c_i)^2 + b_i^2)}\n", + "\\end{equation}\n", + "\n", + "where $a, b$ and $c$ are the fit parameters and each is a vector of length $k$." + ] + }, + { + "cell_type": "markdown", + "id": "67d25b0d", + "metadata": {}, + "source": [ + "With the spectral density approximation $J_{\\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. This can be done quickly using the `SpectralFitter` class, which takes the target spectral density as an array and fits it to the series of **k** underdamped harmonic oscillators with the Meier-Tannor form" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d7506510", + "metadata": {}, + "outputs": [], + "source": [ + "w = np.linspace(0, 15, 20000)\n", + "J = ohmic_spectral_density(w, alpha, wc)" + ] + }, + { + "cell_type": "markdown", + "id": "2a844471", + "metadata": {}, + "source": [ + "We first initialize our SpectralFitter" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f1b662cf", + "metadata": {}, + "outputs": [], + "source": [ + "fs = SpectralFitter(T, Q, w, J)" + ] + }, + { + "cell_type": "markdown", + "id": "9df4a73b", + "metadata": {}, + "source": [ + "To obtain a fit we simply pass our desired spectral density and range, into the ``get_fit`` method. The number of exponents we'll use in our bath is given by Nk" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "636e8699", + "metadata": {}, + "outputs": [], + "source": [ + "bath, fitinfo = fs.get_fit(Nk=1)" + ] + }, + { + "cell_type": "markdown", + "id": "db8dcad0", + "metadata": {}, + "source": [ + "To obtain an overview of the results of the fit we may take a look at the summary from the ``fitinfo``" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "874aa5a1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result of fitting The Spectral Density with None terms: \n", + " \n", + " Parameters| lam | gamma | w0 \n", + " 1 | 2.39e+00 | 1.50e+00 |1.00e-01\n", + " 2 |-3.75e+00 | 4.31e+00 |4.17e+00\n", + " 3 | 5.37e+00 | 2.28e+00 |1.15e+00\n", + " 4 | 9.15e-02 | 6.03e-01 |1.00e-01\n", + " 5 | 1.18e-03 | 1.54e-01 |1.00e-01\n", + " \n", + "A normalized RMSE of 1.28e-06 was obtained for the The Spectral Density\n", + " The current fit took 5.916326 seconds\n" + ] + } + ], + "source": [ + "print(fitinfo[\"summary\"])" + ] + }, + { + "cell_type": "markdown", + "id": "5feb8869", + "metadata": {}, + "source": [ + "We may see how the number of exponents chosen affects the fit since the approximated functions are available:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7d1aab7a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5))\n", + "\n", + "ax1.plot(w, J, label=\"Original spectral density\")\n", + "ax1.plot(w, bath.spectral_density_approx(w), label=\"Effective fitted SD\")\n", + "ax1.set_xlabel(r'$\\omega$')\n", + "ax1.set_ylabel(r'$J$')\n", + "ax1.legend()\n", + "\n", + "ax2.plot(w, np.abs(J - bath.spectral_density_approx(w)), label=\"Error\")\n", + "ax2.set_xlabel(r'$\\omega$')\n", + "ax2.set_ylabel(r'$J$')\n", + "ax2.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d2770aea", + "metadata": {}, + "source": [ + "Here we see a surprisingly large discrepancy in our approximated or effective spectral density. This happens because we are not using enough exponentials from each of the underdamped modes to have an appropiate fit. All modes have the same number of exponents, and we set it to 1 which is not enough to model a bath with the temperature considered, let us repeat this with a higher number of exponents." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "29567a2c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result of fitting The Spectral Density with None terms: \n", + " \n", + " Parameters| lam | gamma | w0 \n", + " 1 | 2.39e+00 | 1.50e+00 |1.00e-01\n", + " 2 |-3.75e+00 | 4.31e+00 |4.17e+00\n", + " 3 | 5.37e+00 | 2.28e+00 |1.15e+00\n", + " 4 | 9.15e-02 | 6.03e-01 |1.00e-01\n", + " 5 | 1.18e-03 | 1.54e-01 |1.00e-01\n", + " \n", + "A normalized RMSE of 1.28e-06 was obtained for the The Spectral Density\n", + " The current fit took 6.213607 seconds\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bath, fitinfo = fs.get_fit(Nk=5)\n", + "print(fitinfo[\"summary\"])\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5))\n", + "\n", + "ax1.plot(w, J, label=\"Original spectral density\")\n", + "ax1.plot(w, bath.spectral_density_approx(w), label=\"Effective fitted SD\")\n", + "ax1.set_xlabel(r'$\\omega$')\n", + "ax1.set_ylabel(r'$J$')\n", + "ax1.legend()\n", + "\n", + "ax2.plot(w, np.abs(J - bath.spectral_density_approx(w)), label=\"Error\")\n", + "ax2.set_xlabel(r'$\\omega$')\n", + "ax2.set_ylabel(r'$J$')\n", + "ax2.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "59078b9a", + "metadata": {}, + "source": [ + "Since the number of exponents increases simulation time one should go with the least amount of exponents that correctly describe the bath properties (Power spectrum, Spectral density and the correlation function). When the number of exponents is not specified it defaults to 5." + ] + }, + { + "cell_type": "markdown", + "id": "0d6950f4", + "metadata": {}, + "source": [ + "By default the ``get_fit`` method, has a threshold normalized root mean squared error (NRMSE) of $5\\times 10^{-6}$ and selects the number of oscillators automatically to obtain that value. One may also specify the number of oscillators that is used with the optional argument N, or may want a more accurate NRMSE, which can be specified with the final_rmse optional argument" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "6d5c1dae", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result of fitting The Spectral Density with None terms: \n", + " \n", + " Parameters| lam | gamma | w0 \n", + " 1 | 8.94e-01 | 1.15e+00 |1.10e-01\n", + " 2 | 1.87e-02 | 3.46e-01 |3.62e-01\n", + " 3 | 8.27e+00 | 5.41e+00 |1.00e-01\n", + " 4 | 7.69e+00 | 2.38e+00 |1.00e-01\n", + " 5 |-1.27e+01 | 4.92e+00 |2.77e+00\n", + " 6 | 1.52e-03 | 1.38e-01 |2.62e-01\n", + " 7 | 2.69e-03 | 1.74e-01 |1.00e-01\n", + " \n", + "A normalized RMSE of 8.05e-07 was obtained for the The Spectral Density\n", + " The current fit took 52.747275 seconds\n" + ] + } + ], + "source": [ + "bath, fitinfo = fs.get_fit(final_rmse=1e-6)\n", + "print(fitinfo[\"summary\"])" + ] + }, + { + "cell_type": "markdown", + "id": "0f8a973b", + "metadata": {}, + "source": [ + "Alternatively one may choose the number of oscillators in the fit instead of a desired NRMSE" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a8e16132", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result of fitting The Spectral Density with 4 terms: \n", + " \n", + " Parameters| lam | gamma | w0 \n", + " 1 | 1.56e+00 | 9.46e-01 |2.11e+00\n", + " 2 | 1.67e+00 | 9.17e-01 |1.13e+00\n", + " 3 | 1.00e+00 | 1.03e+00 |3.32e+00\n", + " 4 | 6.80e-01 | 8.68e-01 |1.19e-01\n", + " \n", + "A normalized RMSE of 4.39e-05 was obtained for the The Spectral Density\n", + " The current fit took 1.023160 seconds\n" + ] + } + ], + "source": [ + "fittedbath, fitinfo = fs.get_fit(N=4)\n", + "print(fitinfo[\"summary\"])" + ] + }, + { + "cell_type": "markdown", + "id": "51abdb8c", + "metadata": {}, + "source": [ + "Let's take a closer look at our last fit by plotting the contribution of each term of the fit:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "b73396a9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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pc8Y28fHx3u309PQynXfjxo38+uuvbNiwgdTUVLKzs3G5XN7X8/LyvNvJycllD1yNqViUGuunTQcBuKRfW5OTlBARweiLO7Fo4iqmrNzLg0PbYLepg19ERCpRZiaU5Rfbk93HduhQ2Y49fgIRwyjbcQDHr12Xl1e2Y09WKB05UrZj09LKlq0SOJ3OUvcclpyM5XQ6derk3Xa73ezatavUcyUdvbfOV0rmbdu2bL9XlbVdRdSvX/+MbY7eswicsCzJ8ZYtW8b999/PihUrypzh6AypNZ2KRamR8otcLNmRCsCghDiT05R2Qdt6xIYGkpJVwIKthxjS3jf3E4iIiJxURAQ0anTmdie7B61u3bIdGxFRet9iKdtxAIGBpfcdjrIdGxZ24nPR0WU7Nsa8uQyO7+WqU6dOmY47vt2RI0dO2fZUPY4VVbIwioqKKtMxZW1XEYHH/505g6MT85zMpEmTGD16dKkexLIoPP5DjhpKxaLUSCt2pZFX5KJeRBDtGvj2H8yzFWi3MrxbI/67aBeT5qxmyI4iuOIKs2OJiEhNdfwQ0fKo6CQe4eGwd2/Fjr388oof++qrnkcVdvzi72UtfIKCgk57npKsVt+OWjpdsVWdJSYmMmbMGG+hWLduXe68804uuOACWrZsSd26dXE4HN4/z8TERJo3b25mZL9TsSg1z8aNLHjpG2jUk0F17aWmi64qRnWsy38X7eKXA4WkfPgkcZdeCj68t0BERESqpsjIyFL7WVlZZTru+LUCK7Pn7nglr1XW+//K2s5M48ePJz8/H/AM3V2yZMlpZ5At68+qJtHNUlLzzJ/PguCGAJx/+E+Tw5xc66Z16ZqzH5fVxrTwVp6b+UVERKTGCwsLw+FwePd37dpVpuN27NhRav90S1f4WpMmTbzbZZ1Z1FczkFamn376ybv95JNPnnGpkX379lV2pCpHxaLUOLtXrGNnbDx2l5O+g7qYHeeUru3iWUR2UqehGBMmmJxGRERE/KVr167e7ZIzo55OyXUOo6Ojadasma9jnVKPHj282wsXLizT/X0LFiyoxES+UXLinpLf46ksWbKkMuNUSSoWpcZZlOyZ8ar7/q2Ed+9ibpjTuPyGoTicBeyMbczKbQdg3TqzI4mIiIgf9O/f37s9ZcqUMk2W8uWXX3q3+/Xr59fbbC699FLv9sGDB8+4IH1hYSGffvppJac6e0VFRd7tM/15ut1uJk6cWNmRqhwVi1KzJCezNMwzC1pf4wgEBJgc6NTCHIFcFuH5z2FSpwtBvYsiIiK1wq233urdPnjwIOPHjz9t+ylTppTqgbz99tsrK9pJdezYkd69e3v3H3zwQQ4fPnzK9k8//TSJiYl+SHZ2GjRo4N1evHjxadu+8cYb7Ny5s7IjVTkqFqVGMZYsZXkTz3pFvZtEnqG1+a69xvPJ4qy2/cj+ZopnPSsRERGp0RISErjmmmu8+//617+YOnXqSdsuW7aM2267zbvfuXNnLr/88krPeLzXX3/d2/u2a9cuzj//fJYuXVqqzZEjR7j//vt56aWXyrwkiJkGDhzo3X722WdJPsUanZ999hmPPvqov2JVKSoWpUbZsWwth0OjCSoqoHOfjmbHOaNz2zWkhZFDbqCDmS16wX//a3YkERER8YN///vf3glVnE4nI0aMYPjw4Xz11VcsXLiQqVOncscdd9C/f3/vTKjBwcF8/vnn2EyYQf28887j6aef9u5v3LiR8847jxYtWjBo0CDOPfdc6tevz1tvvQXAxx9/XOr445f+qAruvfdebwG8d+9eunbtyrPPPstPP/3EggUL+OijjxgyZIh3eY077rjD5MT+p6UzpEZZuisN2sC5yZsJOm+s2XHOyGKxMKpXc15akcI3nYZy3QfvwCOPaBkNERGRGi4uLo4FCxYwdOhQ9havKzlt2jSmTZt20vbh4eF89913dOrUyZ8xS3n66aex2+0888wz3vv9du3aVWpGV4fDwXvvvcfQoUNLHXv8kiFVQffu3Xnuued44oknADh06FCpgrikUaNG8fjjj/Phhx/6M6Lp1LMoNUd+PssMzz9EvXP3QTUY/gAwfOg52Aw3qxq1489sN/z6q9mRRERExA/atm3L2rVrue+++wgNDT1pm4CAAK6//no2bNjA+eef79+AJ/Gvf/2L1atX89e//pXWrVsTEhJCZGQk55xzDg899BDr16/n5ptvJiUlxXuMw+EgJCTExNSn9q9//YuPPvqIuLi4k75ev359xo8fz6RJk6rk2t2VzWIYhmF2CPG9zMxMIiMjycjIICIiwuw4fmEsWcK5X+0iNTSKyam/0OO/r5sdqczueHUW81LhzpbB/OuOC6AW/mMkIiKnl5+fz65du2jevDnBwcFmxxEfy8/PZ+HChezcuZO0tDQiIiJo0qQJ559/frX8XW7KlCne+zJ79+59wv2NVU1+fj6LFi1i48aN5OXlERcXR6tWrejXr58pw35L5vLF+76itYGGoUqNsatOY1JDjxBkuOh06QCz45TLtZedy7zP/2DqATcPuQwC7SoWRUREapPg4GAuvPBCs2P4TMl7Fvv06WNikrIJDg5m6NChJwyfre00DFVqjNVZngLrnGZ1CLp6mMlpymdQQl3qhgeRmlPIL1sOmh1HRERE5ARlHZD4+eef88MPP3j3x4wZU0mJpLKpWJQaY3XSEQC6NokyN0gF2G1WRnSLB2DS70meJzVCXERERKqQZ599ljvvvJMFCxbgdDpPeH3v3r088MADpYrDYcOGmTopj5wdDUOVGmP1nnQAujaJNjdIBY06N573ft3Br1tTONDvAurffzeMHGl2LBEREREA8vLy+PDDD/nwww8JDg4mISGB2NhYnE4nycnJ7Nixo1T7pk2b8v7775uUVnyhVvUsHjp0iNmzZ/Pss89y5ZVX0qBBAywWi/fx6aef+iXHzp07eeqpp+jevTt169bF4XDQsmVLrr76ar799tuTflIjp5e7I5Et+zIA6Nq46k3NXBYt6obRM8qCGwvfuuvCu++aHUlERETEy2o9Vjrk5+ezdu1afvnlFxYuXHhCoTho0CCWLVvmXUtSqqda0bN44MABevfuze7du82OwoQJE3jkkUcoKCgo9fzOnTvZuXMn06dPp3fv3nz55Ze0aNHCpJTVz7rvfsFFPepnHabBpC9gbNVfY/FkRg45hxXfruObc4Zyzwd/wbplC7Rta3YsEREREZ555hkGDBjA3Llz+eOPP9ixYwdpaWk4nU6ioqJo2LAhffv25ZprruGCCy4wO674QK0oFvPz86tEofjcc8/x1FNPefetVivt27cnJiaGP//8k/379wOwbNkyBg4cyIoVK2jQoIFZcauV1TsPQWg9uu7bCjdeYXacCrusUwOembKaPdENWN64I30++ghefdXsWCIiIiIEBARw8cUXc/HFF5sdRfykVg1DBahbty4XX3wxTzzxBNOnT/fbdX/88Ueefvpp736fPn3YvHkz69ev59dff2Xv3r18/fXXhIWFAZ4bhEfqfrUyW53t+avcdf9W6NzZ5DQVFxJo54pz6gPwTaeh8NlnUFhocioRERERqY1qRbEYExPD5MmTSUxMJCUlhdmzZ/Pcc88xbJh/llcwDINHHnnEO91wQkIC8+bNo02bNt42VquVa6+9lmnTpnmfW7x4cal9OYWCAtaHxAHQObAQQkJMDnR2RvVrDcAPCX3JyMyFmTNNTiQiIiIitVGtKBYjIiK45ppraNq0qSnXnz17NmvXrvXuT5gwgZBTFDRDhgzh2muv9e6/9NJLlZ6vuktbu4n94XUAaN801uQ0Z69L4yjahFooCAjiu3YD4MMPzY4kIiIiIrVQrSgWzTZ16lTvdvPmzbnwwgtP235siclZVqxYwd69eystW02wcdU2AJql7SO8YzuT05w9i8XCqIEJAHzT6UL48UdISjI5lYiIiIjUNioW/WDWrFne7YsuugiLxXLa9v379yc0NPSkx8uJNiYeAqBDyg7o2NHkNL5xdbd4AnCzvkFrNsU2BT8t6yIiIiIicpSKxUqWkpLCgQMHvPt9+vQ54zF2u50ePXp499etW1cp2WqKjemedSnbH9wJ55xjchrfiA0LYkiLKAC+6XwhlPg7JCIiIiLiDyoWK9nmzZtL7bds2bJMx5Vsd/w5pLSN1nAA2mcfgMaNTU7jO0eHok7vexUFE94yOY2IiIiI1DYqFitZYmJiqf0mTZqU6biS7Y4/hxyTm53HrvB6AHSIDYYzDPGtTga0rkuDyGDS813M3XjQ7DgiIiIiUsuoWKxkWVlZpfYjIyPLdFxERMQpz3EyBQUFZGZmlnrUBptT8zEsFuqGBxE35Suz4/iUzWrhmu7xAHzzhya4ERERERH/UrFYybKzs0vtBwcHl+k4h8NxynOczIsvvkhkZKT30bgGDcc8nU37MgDo0DACYqv/shnHG9nd83P8bfth9h7KhF27TE4kIiIiIrWFisVK5nQ6S+3b7fYyHVeyXVFR0RnbP/bYY2RkZHgfSbVkqYXNBzy9ru0bRJyhZfXUJDaE81rEYBgw+cZ/wrBhYBhmxxIRERGRWkDFYiULCQkptZ+fn1+m40q2K7mMxqkEBQURERFR6lEbbD/o6XVtUy/c5CSV59qenvtXv23RB9eGjbBypcmJRERERKQ2ULFYycLCwkrt5+Xllem43NzcU55DPAzDYNu2vQC0nj3F5DSV56IO9YmwukmOjGNRsy7w2WdmRxIRERGRWkDFYiWrU6dOqf39+/eX6biSazPG1sB78Xzh0J79pAeGYHW7aDl/ltlxKk1wgI3h3RoB8L8ul8D//gcFBSanEhEREZGaTsViJUtISCi1v2fPnjIdV/Kew7Zt2/o0U02xfc02AJqkHyS4TSuT01Su0f1bA/Bzq57sL7LArJpbHIuIiIhI1aBisZK1bt261GQ1a9asKdNxq1ev9m63a9fO17FqhG3bPb20rQ/vhhpeULeuF07PKAsuq41JnS6ETz81O5KIiIiI1HAqFitZYGAgvXr18u7/9ttvZzzmwIEDbN++3bs/YMCASslW3W0rntymdWoSHNeDWxONvvAcAL7ufBHO2XPg4EGTE4mIiIhITaZi0Q+GDRvm3Z43bx4Hz/BL/pdffundjoqKUrF4CtvzLAC0ObynxvcsAlzcqSGxFHIgvA6/NOvmuXdRREREqr3BgwdjsViwWCx8/PHHZscR8VKx6AfXX389QUFBgGfNxFdeeeWUbbOzs3nrrbe8+6NHjyYgIKDSM1Y3hmGwze5ZHqRV9kGIjzc5UeULstsY2TEOgIldL9WsqCIiIjXE2rVrvdtdu3Y1MYn5EhMTCQ0N9RbPFouFcePGmR2r1lKxWEGJiYll/kscHx/P2LFjvfsTJkxgypQTl3ooKiri1ltv9U6C43A4ePzxx32evSY4lJZNelAoVreLVnVCwVo7/irfcInnP5CFLbqz54JLobDQ5EQiIiJyNvbs2UNqairguX2pQ4cOJicy11133VVqCTkxV+34DRu48847CQ4OPuFR3jYVNW7cOFq39sxo6XK5GDVqFDfddBNTpkxh/vz5vPfee5x77rl8++233mNeffVVGjZs6JPr1zTb1/4JQOOMgwS3bmlyGv9pEhvCgMaedTf/N/RmCAw0OZGIiIicjZKTGnbo0IHAWvx/+8SJE/nxxx/NjiEl2M/cpGYoKiqi4Axr0zmdTpxOZ6VcPzo6mpkzZzJkyBCSkpJwu91MnDiRiRMnnrT9ww8/zF//+tdKyVIT7PpzL2CjRVoydKj59yuWNPr8BBZ+sZLJfyTxj6GtCbLbzI4kIiIiFVRypvxu3bqZF8Rkhw8f5h//+AfgWQkgIyODffv2mZxKak3PYlXQpk0b1q1bx+23347D4Thpm3bt2jFjxgxefvllP6erXhIj6gHQrHl9GDTI5DT+NbhtHPUigkjNKeTHjZoRVUREpDor2bNYm+9X/Mc//sHhw4cBeO+99zRnRxVRa3oWP/30Uz714dp0zZo1wzCMch8XFRXFhx9+yJtvvskvv/xCUlISOTk5NGjQgHPOOadW/yNRHrsKPb1pza++GPo0MzeMn9ltVq7r0YQJP//Jlwv/5MpdK+Cqq8yOJSIiIhVQslisrT2Lc+fO9Y62u/XWW7USQBVSa4rFqiY8PLzUkhpSPompOQA0rxNqchJzXNezMW/P28by5Gy2v/AArfr1gzp1zI4lIiIi5ZCWluad2NBqtdK5c+fTtn/11Vd57LHHcLlcgGe+jXfeeada3+eYm5vLXXfdBUCdOnV49dVXTU4kJWkYqlQ7LrfBnlTPLFnNYmtnsdgg0sFgPDOnTew4FL76yuREIiIiUl4l71dMSEggJCTkpO2ys7MZOXIkDz/8MC6Xi8DAQN59910++OCDal0oAjz55JPs2rULgNdee43Y2FiTE0lJKhal2tl34AiFLjeBVgsNI4LMjmOa0YPbAzC14wXkff6lyWlERESkvMoyBHXr1q307NnTO2N+/fr1mT9/vrc3rjpbuXIlEyZMAGDgwIHccsstJieS46lYlGpn15qtADRJ2Y3tnrtNTmOeAUPOpUluGpnBYXxfEAHr15sdSURERMrhTJPbTJ8+nZ49e7J582YAevXqxcqVKznvvPP8lrGyOJ1O7rjjDm9P6XvvvWd2JDkJFYtS7STuOgBAsyP7oHlzk9OYx2q1cEMDz/bErpfAZ5+ZG0hERETK5VQ9i263m8cff5zhw4eTmZkJwG233cavv/5a7jW4P/30UywWi88fZztx5Ouvv+4dhvvII4/Qtm3tWgqtutAEN1Lt7DqYCdTxrLHY8mKz45hq5E0X8sb4paxr0IZ1339Np5ecYNfbWkREwDAM8opcZseo0hwBNiwWiynXzsvLY+vWrd79oz2LqampXH/99fz0008ABAQE8Oabb9ao9bd37NjBM888A0CrVq14/PHHTU4kp6LfKqXaScx2QmBxz2LLlmbHMVVsfD0uyd3DjIiWTGzam1fmzoVLLzU7loiIVAF5RS7aP/Wj2TGqtE3PXkRIoDm/Dq9fv947q2nz5s2Jiopi5cqVjBgxgt27dwNQr149Jk+eTP/+/St8nUaNGnHRRRf5JPPx562osWPHkpeXB8C7775LcHCwr2KJj6lYlGon0e2Z9UvFoseN5zVjxgb4rt0A/vXFV0SqWBQREanyjh+C+sknn3DPPfeQn58PQI8ePZg6dSrx8fFndZ2hQ4cydOjQszqHL33yySf8/PPPAIwePZohQ4aYnEhOR8WiVCtOl5ukwEgAmhu5EBVlbqAq4NxRF5Ow8GO2xjRmyq5cbjtyBKKjzY4lIiImcwTY2PSs73uUahJHgM20a5csFhctWsSUKVO8+7fccgvvvfdejetxS0lJ4Z///CcA0dHRvPHGGyYnkjNRsSjVyv5DmTitNgKdhdSrH2N2nCrBEhjIjeHZPAl8OfQmbg0IwJy7L0REpCqxWCymDbGUMyu5xmJKSop3e+zYsTV2ZtD77ruPtLQ0AF566SXi4uJMTiRnotlQpVpJ2uJZtDU+IwWrhqB6XfXM3wgJtLGjKIBlBwvMjiMiIiKn4XK5WLdunXf/sssu825/8803bNu2zYxYlWrp0qVMmjQJgD59+nDnnXeanEjKQh83SbWyd8c+ABplpkBrFYtHhYcFc1XXRvxv+R4mLt9Nn5axZkcSERGRU9i6dat3gpf69eszZcoUzj//fJYtW8aRI0e44oorWLZsGdE+uK3kp59+4vXXXz/r8xzvwQcfLNe9kAcPHvRuL126FKu17H1WzzzzjHf2VIBdu3bRrFmzMh8vFadiUaqVvUdygRAapx+Elt3NjlOl3NirKf9bvocfNxwgJSufuPCadZ+DiIhITVHyfsXOnTsTFBTEtGnT6NmzJ0lJSWzbto2RI0cyZ84c7Ge5JFZycjI//uj7WXGvu+46n59Tqh4NQ5VqZW/j1gDE33ULXH21yWmqlvYNI+jWJAqn2+Cb5z4Et9vsSCIiInISJe9X7Ny5M+DpYZwxYwYhISEA/Pzzz9x7771mxKsUAQEBREZGlvlRcv3LoKCgUq+Vp1dSzo7+pKVaSTqSC0DjFo00E+pJ3LjBs4DvV1mhuH791eQ0IiIicjLH9ywe1bVrV7744gtvofTee+/x1ltvndW1xowZg2EYPn+MGTOmXDkuu+wy0tPTy/xo0qSJ99hHH330lK9J5VKxKNVKUppnfH98tMPkJFXTpf3aEp2bQXJkHPO//snsOCIiInISJ+tZPGr48OGl7s974IEHKmUYqUhZqFiUaqPA6eJglmeh2sYxISanqZqCrx7GyG2LAJiYGQI5OSYnEhERkZL27NlDamoq4BlemZCQcEKbJ5980ntPoMvl4tprr2Xz5s1+zSkCKhalGtmXuB/DAAcuYhfMMztO1eRwcH0zz8Q2vzbpTNLX083NIyIiIqWU7FXs0KHDKSew+fjjj+nRowcAGRkZXHHFFd4iU8RfVCxKtbF3y24A4g/vxTLze5PTVF3NbxxB/12rMCxWvlz4p9lxREREpIRT3a94PIfDwfTp02nYsCEAO3bsYMSIERQVFVV6RpGjVCxKtZGUlAJAfEYKNG1qcpoqrG9fRu/3/Ec0OaI1BYm7TQ4kIiIiR5W1WARo2LAhM2bMwOHwzNXw66+/cvfdd1dqPpGStM6iVBt7UzKBOp41Fpv2NztO1WWxMGRwF+qnHOZAeB3mfPYDw57WfywiIiJVwfTp08vV/txzzyU3N7dywlRhiYmJZkcQ1LMo1UhSlmfYReOMg+pZPAP7LTdz3VrPzGlfJhWBYZicSERERESqGxWLUm3sLfT8dY1XsXhmzZpxnSMTm9vFijot2frzMrMTiYiIiEg1o2JRqo19Vs8snw1z0qBBA5PTVH31bxvN0ML9AHyZHWZyGhERERGpblQsSrVQ5HKTEhQOQIOIILDZTE5UDVx/PTf+dQQAU1fvI6fAaXIgEREREalOVCxKtZCSfAjDYiXAVUSd+jFmx6k2zmsZS/M6oWQXOJmxZp/ZcURERESkGlGxKNXCgT/3AFAvKxWr7lcsM6vVwuheTQCYuGw3hia6EREREZEyUrEo1cI+l2eVl4YBbujVy+Q01cuINlEEWgw27c9k9dpdZscRERERkWpCxaJUC/uDIwCof965cNddJqepXqInvMbl634BYOL05SanEREREZHqQsWiVAv7M/IBaBAVbHKSauimm7hxzQ8AzMxxkJ5baHIgEREREakOVCxKtbA/vbhYjFCxWG7t2tG1UQTtD+6g0BbAtzN/NzuRiIiIiFQDKhalWth/JAeABlEOk5NUT5ZbbuGGNXMA+GblXk10IyIiIiJnpGJRqoX925MAaPj4gyYnqaauu44rti8hqKiAbZYw1u1OMzuRiIiIiFRxKhalyivKzeOQo3iCmyMHTU5TTcXGEnnhYC7ZtgSAb2YsMzmQiIhUlEaHiNQeZr/fVSxKlXdwRxKGxUqgs4jYetFmx6m+brmFUet+AuC75ELyCl0mBxIRkfKwWj2/trndbpOTiIi/HH2/H33/+5uKRanyDuzaB0C97FSsjRqZnKYau/hieufuJz79AFnWQH78fYfZiUREpBzsdjsWi4WCggKzo4iIn+Tn52OxWLDb7aZcX8WiVHn7kg8D0CDzEMTHm5ymGgsMxHrD9YxcPw+AbxZuMzmQiIiUh9VqxeFwkJOTY3YUEfGTzMxMwsLC1LMocioHDmUC0CArVcXi2brzTkZcdi4WYEmGhaS0XLMTiYhIOYSFhZGTk0NhodbMFanpcnJyyM/PJyIiwrQMKhalytuX6Rlu0yBLPYtnrWNH4h9/kH6t6wAweeVekwOJiEh5REZGYrfb2bt3Ly6X7j0XqalycnJISkoiNDSUsLAw03KYM/hVpBwO5rshTD2LvjTy3MYs+vMw3/6RxP2DW2OzWsyOJCIiZWC322ncuDGJiYls376dyMhIwsLCsNlsWCz6t1ykujIMA7fbTX5+PpmZmeTn5xMaGkp8fLxpQ1BBxaJUAwddNgDistOgYUOT09QMF7avR0SwnX0Z+Sz58xD9E+LMjiQiImUUFBRE8+bNSU9PJyMjgyNHjpgdSUR8xGKxEBYWRmxsrKn3Kh6lYlGqvBSbA4C4QCAw0NwwNUTwnkSuytvN55ZGfDN7Ff0TLjY7koiIlENgYCBxcXHUrVsXp9OpIakiNYDVasVut5teIJakYlGqNMMwSImoA26Ie+xBs+PUHL//zqjPXuHzMRP4cX8hGblFRIYEmJ1KRETKyWKxEBAQQECA/g0XEd+rOmWryElk5jkpLF57uO7IYeaGqUmGDaND3iESDiVSaLExa2Wi2YlEREREpIpRsShVWkpWPgCRjgCCA2wmp6lBHA4so0Zx9Yb5AEybv9HkQCIiIiJS1ahYlCrtYPGyGXHhQSYnqYFuuYWrNi3AYrj5PTdAay6KiIiISCkqFqVKS1m/BYA4Vx5kZJicpobp25f69aLou3stANMWbDI5kIiIiIhUJSoWpUpLWbYagLhff4JVq0xOU8NYLHDzzd6hqFP/2INhGCaHEhEREZGqQsWiVGkp2cXDUHOOQKNGJqepgW66iYu3LcFRmE+iO4jVe7RWl4iIiIh4qFiUKi2l0PO1bnaaisXK0Lw5oX16cvG2JQBM+3G1yYFEREREpKpQsShVWorhWTeqnlEAoaEmp6mhbrmFqzd6hqJ+vzuPQqfb5EAiIiIiUhXYzQ4gckqGQYrdUyDGhWqx4UpzzTX0jYwibksgKVmFzN+awkUd6pudSkRERERMpp5FqbqOHCElNAqAuMhgc7PUZBER2EYM56qu8QBMW5VsciARERERqQpULEqVlb0nmdxABwBxseEmp6n5ru7quSf0ly0ppOcWmpxGRERERMymYlGqrIO79wMQWpBLaP04k9PUfO0aRNCuQQSFLjczV+wyO46IiIiImEzFolRZKftTgeJlMxo0MDlNLbBhA8PX/gTAtJ/XmxxGRERERMymYlGqrBSn569n3dx0FYv+YLMx7Jt3sLpdrCxysCc11+xEIiIiImIiFYtSZR3q3AOAepcOgeHDTU5TC7RrR1z7VvTdvRaAaT+uMjmQiIiIiJhJxaJUWSlZBQDUDQ8Gm83kNLVEiTUXp6/dj2EYJgcSEREREbOoWJQq63D20WIxyOQktch113HRrj9wFOazyxLC2t2pZicSEREREZOoWJQq63C2Z/mG2LBAk5PUIrGxhF40hIv+XArA9O+XmxxIRERERMyiYlGqrNQ1GwGoM+c7k5PUMrfcwlXFQ1G/35NPkcttciARERERMYOKRamacnJIdXn+etZZutDkMLXMJZfQL3svdbKPkGoLZtHqRLMTiYiIiIgJVCxKlWTs20dqSCQAsdFhJqepZQIDsV93LVdu/hWAabP/MDmQiIiIiJhBxaJUSZlJ+ymyBQAQGxdtcppa6NZbvbOizs0NISu/yORAIiIiIuJvKhalSjq89yAA4QU5BDeoZ3KaWqhrVzr+8h0t64ZSYFiYs+GA2YlERERExM9ULEqVdPhAGgCxORnQoIHJaWonS/v2DO8WD8D0NckmpxERERERf1OxKFVSaloWALG56SoWTXRl54YALNmRyv6MPJPTiIiIiIg/qViUKik1IxeAOjnpUL++uWFqscYxIfRsFo1hwHczlpodR0RERET8yF5ZJ05OTmbTpk3s3r2bQ4cOkZOTA0BoaCh169aladOmdOjQgYYNG1ZWBKnGDucWQSTE5moYqqkOHuSq7z5iRafhTPt9N2NvNjuQiIiIiPiLz4rFI0eOMGPGDH788UcWLFhASkpKmY6Li4tj4MCBXHTRRVx55ZXExsb6KpJUY4cLPV/rFGRDtGZDNU1cHJclrWJc+yvYEhrH5uUbaNero9mpRERERMQPznoY6uzZs7nqqqto0KABt99+O9988w0HDx7EMIwyPQ4ePMjkyZO54447aNiwIcOGDWPWrFm++N6kGktN8BQkdTq1BYvF5DS1mMVC5C2juWDHCgCmT1tsciARERER8ZcKFYtut5tPPvmEhIQELr/8cr7//nsKCwu9BeBRQUFBNG/enG7dutG3b1/OO+88unbtSrNmzQgKCvK2O3pcUVERM2fO5Morr6R169Z89NFHuFyus/8updpJrd8YgNg7bjE5iXDzzVy1ZSEAM3JCcBUUmhxIRERERPyh3MNQJ0+ezOOPP87OnTsBvMVhcHAwffv2ZeDAgfTo0YNzzjnnjPcjJicns379ev744w9+/fVXFi9eTH5+PgA7d+7kL3/5C//3f//Hiy++yKhRo8obVaqxw9megiQ2LNDkJEJcHIM6NCQyL4sDoTEsnzSH826+0uxUIiIiIlLJLEbJrsAzGDhwIL/99hvgKRLtdjuXXXYZo0eP5pJLLiE0NPSswuTm5jJ79mz+97//MXPmTIqKijwhLRb69u3LwoULz+r8tUlmZiaRkZFkZGQQERFhdpxyO2fcj2TlO5n3wEBaxYWZHUfmzOHxN2fyv66XcM2RLbz2/oNmJxIRERGRMqpobVCuYaiLFi3CMAzq1KnDM888Q3JyMtOmTeOaa64560IRICQkhBEjRjBlyhSSk5N59tlniYuLwzAMFi/WvVK1RUFWDln5TgDqqGexahg6lKsPbwRgTkgT8nbtMTmQiIiIiFS2chWLcXFxjB8/nj179vDkk09St27dyspFnTp1eOKJJ9i9ezdvvvlmpV5LqpbUr6cAYHc5iZj0P5PTCAA2G90vH0h8+gGyg0KY9+l3ZicSERERkUpWrmJxx44d3HfffaUmp6lsQUFB3H///d57JKXmSz2UDnjWWLSGnX2PtfiG9bZbuXrTAgCm78wGt9vcQCIiIiJSqcpVLPpiqGlFhYSEmHZt8a/DR7IBiM1Nh3r1zA0jxzRtyrD6nn8yfo0/h9QczYoqIiIiUpOd9TqLIr52ONMzI25sbgbExZmcRkpqNelTOsVH4jRg5voDZscRERERkUqkYlGqnNRczyy4dXPSVSxWNRYLV3VpBMC01ckmhxERERGRyqRiUaqc1OLRjTH5WRAdbW4YOcEVnRtis1pYk5TOzkPZZscRERERkUpi99WJDh8+zG+//cbixYvZvHkz27dvZ//+/RQUFGAYBvXq1aNJkyb06NGDvn37MnjwYKJVCMhJpLk8n2HEWp1gsZicRo5XNzyI/i1iWLA9len/Gs8D7z4GNpvZsURERETEx3xWLMbFxWEp8Yu9YRilXt+7dy/JycksXbqUt956C7vdzpAhQ/jLX/7CsGHDfBVDqju3myOWAACiA9XxXVVdvXgaC+oNYLqlPv/48Ucsl15qdiQRERER8TGf/zZuGMYJhWLJ145+LSoqYs6cOQwfPpyuXbuyYsUKX0eR6ujIEdIc4QBEhwaaHEZO5cIr+xJakMue6Aas+mya2XFEREREpBL4rGcRPEVgkyZNaN68OY0aNaJu3bpYLBYMw2Dfvn3s2bOH9evXk5ub620PsHbtWvr168frr7/Ovffe68tIUt2kpHDEEQFATHiwyWHkVBxXXMpFX41jaos+TEsPpPuePdCkidmxRERERMSHfFYszpkzh3PPPZeYmJjTtnO5XKxevZqZM2fy1Vdf8eeff2KxWHA6nfz9738nPDycMWPG+CqWVDctWpAWtwuKDKJvvM7sNHIqdjtXt4tlagHMbNuPp/77EYHPPWN2KhERERHxIZ8NQ73wwgvPWCgC2Gw2zj33XMaNG8fWrVuZNm0azZs3Bzw9jffffz8HDmj9ttqqyB5AVpGnxzmmRxdzw8hpnXfnKOKy00h3RPDrjyugqMjsSCIiIiLiQ6bPIDJs2DBWrlxJ7969AcjOzua9994zOZWY5UiuZ90MiwUiHQEmp5HTscU3YliRZ63FaQ27wHffmRtIRERERHzK9GIRIDIykq+++gq73TMqdubMmSYnErOk53p6p6IcAdisWjajqrvqoq4AzGvVk4z/fmJyGhERERHxpSpRLAI0bdqUbt26YRgGu3btMjuOmCRt4VIAoimC4omQpOpqP2wIbTL2U2gPZM7+QvjzT7MjiYiIiIiPVJliESA/Px/AO1uq1D5Hvp8DQMy2jZCWZnIaOROLzcZVDWwATOswCBYuNDmRiIiIiPiKX4rFoqIili1bRnZ29klfdzqdvPTSS6xbtw6LxUITTcFfa6UV37MYnZsJdeuanEbK4qqbLgJgWZNOJI+4weQ0IiIiIuIrPl1n8VQyMzM577zzsFgs1K9fn/j4eGJiYggICCA1NZUNGzaQnZ2NxeK5R+3aa6/1Ryypgo7kuwGIcRVAUJDJaaQsGjZvSO8Wu1m2M40Za5K55/xWZkcSERERER/wS7F4lGEYHDhw4ISlMQzD8G4PHz6cJ554wp+xpApJc3k+MIi2uUxOIuVxdddGLNuZxrRVydw9sKX3gx8RERERqb78MgzV4XAwYsQImjZtimEY3geAxWLhggsu4IMPPmD9+vV8++23BAYG+iOWVDX5+RyxBQMQE6Biozq55JwGBNqt/JmSzcY9qXD4sNmRREREROQs+aVYDAkJYfLkyezatYu9e/fyySefcPXVVxMYGIjb7Wb+/Pm8/vrrpKSk+COOVFUpKaSFRAAQHaI1FquTiOAAhraOBeCb+1+Ef/zD5EQiIiIicrb8Phtqw4YNueWWW5gyZQr79u1j3LhxhIaGsnXrVoYOHcrHH3/s70hSVaSkcMRRXCyGB5scRsrruu6NAJjWrCd5U6bBccPNRURERKR68Vmx6HQ6y31MdHQ0Tz31FOvXr6d9+/a43W7uvvtu1q1b56tYUp0cOkTa0WIxMtTkMFJefds3pDF5ZAWH8UOLXvDBB2ZHEhEREZGz4LNisWPHjsyZM6dCxzZt2pSZM2cSFBSE0+nktdde81UsqU4OH/b2LMbEhJscRsrLarVwbc+mAHzd+SJ47z0oLDQ5lYiIiIhUlM+KxW3btnHZZZdx+eWXs3r16nIf36xZM7p3745hGPzyyy++iiXVSAFWcoJCAIipG2VuGKmQawafg9Vw83vjDmwvsMHUqWZHEhEREZEK8vk9i7Nnz+bcc8/liiuuYOHCheU6Ni0tDYBDhw75OpZUA+nDRgBgs1oIv36UyWmkIupHBnNBnGdyokmdL4S33zY5kYiIiIhUlM+KxaeeeorAwEDvshg//PADgwYNolWrVjz77LMsX74ct9t9yuPff/99tmzZAkBMTIyvYkk1kpbjGbIYHRKA1eb3uZfER667pAsAUzoOpmD5Cli50txAIiIiIlIhFuPogoc+8Oeff/LXv/6VefPmlb5I8QLdoaGhdOzYkYSEBOrVq0dQUBBpaWn89ttvrFu3DsMwsFgsXHjhhcyePdtXsWqlzMxMIiMjycjIICIiwuw4ZbJk+2Fu+HA5rePC+OmBgWbHkQpyutz0fWomB1023pnxEpf3agmffmp2LBEREZFaq6K1gd2XIVq3bs3cuXP56aefePLJJ1mxYoX3NcMwyM7OZvny5SxfvrzUccfXq2PHjvVlLKkm0nKLexZDA01OImfDbrMyqk8L3v5tN193vojLv34eXn0V6tY1O5qIiIiIlEOljPUbOnQoy5YtY968eYwYMQK7/VhNerKOzKM9jwD/+Mc/uOqqqyojllRxR979CICYHVtMTiJna9R5LbAYBr8168ruv/3T7DgiIiIiUgGVemPYBRdcwOTJk9m/fz+TJk3irrvuol+/fsTFxXnvbQSIi4vjmmuuYe7cuVo2oxZL27EbgOhd20xOImercUwIA1pEA/DlBaPVqygiIiJSDfl0GOqpxMTEMHLkSEaOHOl9zjAM8vPzsdlsBAZq2GGt53ZzxPD8dYyxnnoiJKk+bhnYil93/cGk35P4x5A2OAJtZkcSERERkXIwbcpJi8WCw+FQoSge6emkOcIBiA60nKGxVAcD28TRJCaEjLwiZqxJNjuOiIiIiJST1ieQquHwYY44PDMzRYUEmBxGfMFmtXBT76YAfLZoB8azz8HSpSanEhEREZGyUrEoVcPhw2QEhwEQHRZschjxlZHnxhNsg82Hcvnjg6/h5ZfNjiQiIiIiZaRiUaqGw4dJD/YMQ42KDDE5jPhKVEggV3VpBMBn3S+H776DP/80OZWIiIiIlEW5isVnn32WnJycyspySjk5OTz77LN+v6740eHDpBffsxgZXfaFQqXqu6lvcwDmtDmPg6HRMH68uYFEREREpEzKVSyOGzeOli1b8tJLL5Genl5JkY5JT0/nxRdfpEWLFjzzzDOVfj0xj+vQYTKLh6FG1Y0yN4z4VIeGkfSID8dps/Nll4vhk08gNdXsWCIiIiJyBuUehnro0CH+9a9/0aRJE+655x7++OMPn4dasWIFY8eOpUmTJjzxxBMcOnTI59eQqiUzNd27HVkv1rwgUilu7t8KgC+7XEp+oRPef9/kRCIiIiJyJuUqFhcsWECnTp0wDIPs7Gzef/99evXqRUJCAo8++ijz588nPz+/3CFyc3P56aef+Oc//0mrVq3o06cPH374IdnZ2RiGQefOnZk/f365zyvVR/q5fQAIcxcR0KypyWnE1y7uWJ9GYXZSQ6OY1vECePttKCgwO5aIiIiInIbFMAyjPAcYhsEXX3zB888/z/bt2z0nsRxbFy8gIIB27drRsWNHWrRoQaNGjYiKisLhcGAYBvn5+Rw5coTk5GR27NjBhg0b2LJlC06ns9Q1AFq1asWTTz7JjTfeWOoacmaZmZlERkaSkZFBRETVvwdw9Z4jXP2fJTSKcrD40QvMjiOV4KPfdvHczE20SE1i3of3YP30E7jlFrNjiYiIiNR4Fa0Nyl0sHuV2u/nmm2946623WLZsWemTlrOwOz5C7969uf/++xk5ciRWqyZsrYjqViwu2JrCmE9+p32DCH64v7/ZcaQSZBc46fPcXLKcBv+d8ixDQ/JhzRrQB0EiIiIilaqitUGFKzGr1cp1113HkiVLWLduHY888gitW7cGPMXf8Y+jTvVaq1atePjhh1m7di1Llizh2muvVaFYi2TkFQEQFRJgchKpLGFBdkYXz4z6357DISkJdu0yOZWIiIiInIrdFyfp2LEjL774Ii+++CKJiYn8+uuvrFy5kk2bNrF7924OHz7sXXIjNDSUOnXq0LRpU9q3b0/37t0ZMGAAzZs390WUMlmyZAmfffYZixYtIjk5GcMwiI+Pp1+/ftxyyy307dvX59esyDDad999l7vuusvnWaqi9NRMQMViTXdr3+Z8tHAnKxp3ZPXSDXRt0dDsSCIiIiJyCj4pFktq1qwZzZo145YqeC9STk4O9913Hx9//PEJr23evJnNmzfz3//+l1tvvZW3336b0NBQE1LWQk4n6U89C/1vJHLBzzC6u9mJpJLUiwhmWLd4vl25l//+sZ//JKhYFBEREamqfF4sVlUul4vhw4czd+5c73MOh4MOHTpgt9vZtGkTmZme3q1PPvmE5ORkfvjhB2w2m8+zDBgwAIfDccZ2TZo08fm1q6S0NNId4QBEuTVDZk13Z/8WfLtyL3M2HCDxcA7N6uhDGREREZGqqNYUi08++WSpQvHOO+/kpZdeIiYmBvD0Or788ss899xzAMydO5ennnqKF154wedZPvvsM5o1a+bz81Zbhw6REVxcLAbXmr+StVZC/XAuaBvHL1tS+M+C7bzSrx64XNCokdnRRERERKSEcs8gs6saTkixb98+3nzzTe/+TTfdxAcffOAtFMFzL+Wzzz7LE0884X3ujTfeYN++fX7NWisdPkx6cBgAUaGBJocRf/jbBa0AmPr7HpI69YAS7zsRERERqRrKXSy2bNmS6OhoBg0axAMPPMAXX3zB+vXrcblclZHPJ8aPH09+fj4AISEhjB8//pRtn3zySRo3bgxAfn4+EyZM8EfE2u3wYe8w1MjwMw/PleqvW5No+jWPwmmx8l7XK2HiRM/sqCIiIiJSZVRobYrMzEwWLlzIhAkTGDNmDF26dCEsLIwePXrwl7/8hXfffZelS5eSm5vr67wVMm3aNO/2qFGjSvUoHi8wMJBbb73Vuz916tRKzSaU7lmMCjM5jPjLfRe2A2DyOUPZ74iEN94wOZGIiIiIlFShYvH4NRINw6CgoIBVq1bx0Ucf8be//Y1+/foRERFBu3btuOGGG3j11Vf56aefOHz4sE+/gTPZunUr27dv9+5ffPHFZzzmkksu8W5v376drVu3Vko2KZaWduyexdiyLxIq1VvP5jH0ig+n0B7A+z1HwAcfgJ//fRARERGRUyv3bCLfffcda9as8T5K3sN4tHi0WCzeYnLbtm1s27aNSZMmeds1bNiQrl270rVrV7p06ULXrl0rbcKXtWvXltrv06fPGY/p1q0bgYGBFBYWArBu3ToSEhIqJZ+AkZZGuqMDAFF1oswNI35138XtGf3hcr7qfBH3LPuGuHfegXHjzI4lIiIiIlSgWLz88su5/PLLvftZWVnewnH16tWsWbOGTZs2eQut4wtIgOTkZPbt28esWbO854mMjKRLly707NmT/v37M2DAAMLDw8/qmwPP+olHBQYGeu9HPJ2j7Xbs2HHCOXzhoYceYtOmTSQlJVFUVERsbCytW7dm4MCB3HLLLTRv3tyn16vqso9k4qrjWaIkql6syWnEn85rGUu3eg5WHYT3el3DU2+/DQ8+CD5474uIiIjI2anQMNSSwsPD6d+/P/feey8ff/wxq1atIjs7mzVr1vDJJ59w//33M3DgQCIjI0sdd/ww1vT0dH799VdeffVVrrzySurVq8d1113H4sWLzypfYmKidzs+Ph6LxVKm40qucVjyHL7w7bffsmnTJrKyssjPzyc5OZkFCxbwzDPP0KZNG+666y7y8vJ8es2qLD3Dc29rUFEBwXF1TE4j/mSxWPj7ZecAMLHrpewrssK775qcSkRERESgktZZtNvtdOrUiU6dOnHLLbd4n09MTCzVA7lmzRqSSsyAWLIXMj8/n8mTJzN58mSGDx/Ohx9+eELBWRZZWVne7fIcHxFx7N65kufwhTp16tCyZUvCwsLIyMhgy5YtZGdnA+B0Onn//fdZsWIF8+fPL3PmgoICCgqOLWifmZnp08yVKePBR2D6biJDAqGOisXapn/rOvSqF8zyg/BW3+t56bXX4K9/hdBQs6OJiIiI1Gpn3bNYHs2aNeOqq67imWeeYcaMGezevZvU1FTmzZvHa6+9xo033ki7du287Y/2Pk6dOpVevXpVaHKco0UYQHBwcJmPcziOLeFQ8hwV1b59e8aPH8+OHTs4dOgQy5YtY968efz+++8cOXKEmTNn0qlTJ2/71atXc91115X5/C+++CKRkZHeR1mG21YV6bH1AYiKCYeAAJPTiL9ZLBYeHt4NgMnnDGGnMwA+/tjkVCIiIiLi12LxZKKjo7ngggt44IEH+Pzzz9m4cSOpqal8+eWXDB482Ntu27ZtjB49utzndzqd3m27vewdqSXbFhUVlfu6x9u4cSP3338/LVq0OOm1LrvsMpYvX85ll13mfX7OnDl8//33ZTr/Y489RkZGhveRVI3WrEvP89zfGuUINDmJmKV702iGxDtwWW28/vc34c47zY4kIiIiUuuZXiyeTFRUFNdffz0//fQTM2fOJLR4ONq8efOYP39+uc4VEhLi3c7Pzy/zcSXbhvppOFxwcDBfffUV9erV8z739ttvl+nYoKAgIiIiSj2qi/RcTzEeGaJexdrswRHnYrHArNxQNqQWnPkAEREREalUVbJYLOnSSy/l3RITXvzvf/8r1/FhYccWeS/PpDG5ubknPUdlCw8P5+677/buL1q0qFxFbrVTWEjGkhUARBXkmBxGzNSuQQRXdm4IwKs/am1TEREREbNV+WIR4IYbbiA21rOkwpIlS8p1bJ0SE6bs37+/zMcdOHDAu3302v4yaNAg73Z+fn61GlJabikpZHw/G4CoP5aaHEbM9o8hbbBbLfy67RCLtx+GmvxBiYiIiEgVVy2KRYvFQvv27TEMg3379pXr2ISEBO92ampqqR7D0ylZoLVt27Zc1zxb9evXL7VfkYl9qo20NNIdnjX1ooJsJocRszWrE8roXp5la55790dc7dtDgYakioiIiJihWhSLcOzew/IuY1FydlWANWvWnPGY5ORkDh06dMpzVLbjC9qS913WOGlppAd7isVIh+5ZFPj7kDZEuArYEhDFt+GtNDOqiIiIiEmqTbE4YcIEPvjgA26//fZyHdezZ0+CgoK8+7/99tsZj1m0aJF3Ozg4mJ49e5brmmdr48aNpfbj4uL8en2/SksjPdhzT2hUWNAZGkttEB0ayH3dPMPHX+1/M9mvvqHeRRERERETVJtisU2bNtxxxx28//775TouLCys1BIcX3755RmPKdlm8ODBfpsN9aivv/7au92sWTMaNGjg1+v7VVoaGUeHoUbU4B5UKZebR/ajWUE6h8OieS++N3zwgdmRRERERGqdalMsno0xY8Z4t9etW3fatQtXrVrF7NmzT3qsP3z33XfMnDnTu3/VVVf59fp+l5rqHYYaFR1uchipKgLtVh69oDkA/+1xNcnj34UczZYrIiIi4k+1oli85ppr6Ny5s3d/7NixbNmy5YR2+/fv58Ybb8TlcgHQpUsXRowYcdJzJiYmYrFYvI9x48adtF1GRgYjRoxg5cqVZ8z51VdfccMNN3j3Q0JCeOSRR854XLVWYhhqZJ1Ik8NIVXLR5X3olXeAgoAgXu5wGbzzjtmRRERERGoVu9kB/MFisfDf//6XgQMHkpeXx/79++nVqxd33303AwYMwG63s2LFCt555x0OHjwIgMPh4IMPPsBisZzVtQ3DYOrUqUydOpW2bdty0UUX0aVLFxo0aEBoaChZWVmsX7+eb7/9lt9//71U5k8++eSEmVFrmvy0dApiPfcqRsb5d4kSqdosFgtPjuzOFd8n8V378xn92fP0GjsWoqLMjiYiIiJSK9SKYhGgR48eTJw4kRtvvJG8vDwyMzN5+eWXefnll09o63A4mDhxIj169PBphi1btpy0R/N44eHhvP/++4waNcqn16+K0jNyIRasbhdh9VQsSmkd+3Xh+kkL+V9oS57qcyMzX3+TgOeeMTuWiIiISK1QK4ahHjV8+HBWrlzJkCFDTtpjaLFYGDx4MH/88QfDhw/3yTUdDgd/+ctf6NChwxl7KSMjI7nvvvvYsGED119/vU+uX9VlBnsmD4rMz8Yaq2JRTvTQPZcQnZfJ1rrN+PyXzVBiWRsRERERqTwWwzAMs0OYISkpicWLF5OcnAxAo0aN6Nu3L40bN660ax45coQ1a9aQkpLC4cOHSU9PJyQkhJiYGDp16kSnTp2w2XyzMH1mZiaRkZFkZGQQERHhk3NWhj8S07jmvaU0jXHw60OD4CyH/UrN9NX9L/KYoxNhVoNfHhpEXLR/ZygWERERqc4qWhvUmmGox2vcuDHXXXedX68ZHR3NoEGD/HrNqi4jrwiACEegCkU5pWufvYevv1jH2r2ZvDj3T968tovZkURERERqvFo1DFWqnsx8T7EY6QgwOYlUZdbISJ676hwsFpi2OpnlO1PNjiQiIiJS46lYFFNl5jkBiHDU2k5uKaNO8VFc37MJAE/N2EhRXr7JiURERERqNhWLYp7Dh8n45HMAIpISzc0i1cJDFyYQHWRl68EsPv7bi2bHEREREanRVCyKeQ4eJHPHHgAi9+0xOYxUB9FBVh779VMA3ozqRNJPC80NJCIiIlKDqVgU86SmepfOiAjWMFQpA7udkTcMoffudeQHBPPE5DUYbrfZqURERERqJBWLYp60NDKCwgCICAkyOYxUF5bbbuWFHT8S6Czi15iWfP/hDLMjiYiIiNRIKhbFPGlpx3oWw4NNDiPVht1Oy6cf4q9LJwHw7KYC0tNzTA4lIiIiUvOoWBTzpKWRGVRcLEZqkXUph0sv5a6gQ7Q6vIfDweG89Pb3ZicSERERqXFULIp50tLIDC4ehhodYXIYqVYsFoJeeYkX57wDwNc54Sxfr0mSRERERHxJxaKYJy2NjOJiMbJOpMlhpNo591x6DOjM9WvmAPDYxOUUOF0mhxIRERGpOVQsimncR46QFRQCQESdaJPTSLX0f//Ho0v/R53sI+y0hPDud2vMTiQiIiJSY6hYFNNkZ+RgWDx/BSPqxZqcRqqlZs2I/NtdPL3Jc8/if1amsD0ly+RQIiIiIjWDikUxTcbgCwEIdDsJjokyN4xUX088weWzP2dQQl0KXW4e/nYdLrdhdioRERGRak/Fopgmc/QtAERGhoJVfxWlghwOLEFBPH/1OYQG2li1J50vliaanUpERESk2tNv6GKazDwnABHBdpOTSE3QKMrBo5e0BeCV2ZvZeyTX5EQiIiIi1ZuKRTFNRl4RABGOAJOTSE0xOiyLHll7yXUaPD5pFYah4agiIiIiFaViUcxhGGTmFQIQqWJRfMQ6fjwvff0cgc5CFiZmMHVVstmRRERERKotFYtijn37yPzb3wGI2LDW3CxSc7zwAi0LM7h/8VcAPDdjHYeyCkwOJSIiIlI9qVgUcxw5QmagA4AIZ77JYaTGqF8fnnqKv6yYSvuDO0gvNBj33QazU4mIiIhUSyoWxRzp6WQGhQEQGai/huJDf/87Ae3a8srst7C5Xcxaf4C5Gw+YnUpERESk2tFv6WKO9HQygz3Foia4EZ8KCIB336XjwR3cuWIqAE9OXeedUElEREREykbFopjjyBEyg0IBiAgJMjmM1Dj9+8OYMfx98Vc0T0vmYE4RL83ebHYqERERkWpFxaKYIz2djOKexcjwYJPDSI30yisEh4fy4py3AfhqRRJLdhw2OZSIiIhI9aFiUcyRnk5mcHHPYmSYyWGkRqpbF15+md5JGxi9+gcAHpu6nrxCl8nBRERERKoHFYtijpLDUKNVLEoluf12uOUWHv3nCBpEBrM7NZc3520zO5WIiIhItaBiUcyRnu4tFiNjI00OIzWW1Qqffkr40At4/qqOAHy4aCfr9qabm0tERESkGlCxKKYoOpJOTlAIABF1ok1OI7XB4Hb1uLJzQ9wGPPztOgqdbrMjiYiIiFRpKhbFFFmPPO7dDm8YZ2ISqU2evqwt0VYXWw5k8f6vO8yOIyIiIlKlqVgUU2S2agdAWJAde4jD5DRSKxQVEXv15Tw9400A3v75T7anZJkcSkRERKTqUrEopji6QHpEsN3kJFJrBARAmzYM27SAQTt+p9Bt8PC363C5DbOTiYiIiFRJKhbFFJn5xcWiI8DkJFKrvPwylsaNeeHHfxNakMuqPel8sTTR7FQiIiIiVZKKRfE/t5vMxSsAiLBozTvxo4gIeP99GmYd5tFfPwXglTlbSErLNTeXiIiISBWkYlH8LzOTjPFvAxCxeYPJYaTWueQSuOkmRq+eTc+kDeQWuXlkyjoMQ8NRRUREREpSsSj+l55OZlAYABE2/YIuJnjzTaz16/HKDxMILspnyY5U/rdij9mpRERERKoUFYvif0eOkBkcCkCkblkUM8TGwscf0yx9Pw8t/ByA/5u5kb1HNBxVRERE5CgVi+J/6elkBnmKRc2GKqa55BIYO5YxK2fSfe8mcooMHpu6XsNRRURERIqpWBT/S08nI7h4GGqwuhbFRK+9hq15M14J3kOQ3cqiPw8z6fcks1OJiIiIVAkqFsX/0tOPDUMNCzI5jNRqYWGwciUtP3ybf16YAMALszazLz3P5GAiIiIi5lOxKP535MixCW7CQ0wOI7VeVBQAt/VrTtcmUWQVODUcVURERAQVi2KG9HQyinsWIyLDTA4j4mGzWnj1mk4E2iz8uu0Qk1fuNTuSiIiIiKlULIr/lVw6Iybc5DAix7RaPI8HFv8PgOemreVARr7JiURERETMo2JR/C8vj6wgz/DT8JhIk8OIlJCWxh0Lv6Lzvm1kuSw8/uVyDUcVERGRWkvFovhd4bvvUxDgmdgmIqGlyWlESrjtNuyjRvLq7PEEOov4ZU82U3/fY3YqEREREVOoWBS/y8ov8m6HhQabmETkOBYLvP8+bcJt3F88HPWZqWs4mKnhqCIiIlL7qFgUv8vKdwIQGmjDZrWYnEbkOJGR8PXXjF05g3P2/0kmdh567xcNRxUREZFaR8Wi+N3RYjE8OMDkJCKn0LMn9hee541ZbxDoLGRhmsHE2WvMTiUiIiLiVyoWxe+yXn0DgPC8LJOTiJzGgw/SuncnHl3wKQAvzN/NjuQ0czOJiIiI+JGKRfEvl4vM2T8BEH74gMlhRE7DaoUvvmDMkY30TVxDvi2ABz5ZQpHLbXYyEREREb9QsSj+lZVFZnAoAOEWl8lhRM4gOhrrlG95beX/iLDD2mwL7/yy3exUIiIiIn6hYlH8KyPj2BqLVk0YItVAly40WL+S50d2BeCd+dtZveeIyaFEREREKp+KRfGvzEyygjw9ixE2k7OIlFVwMFd2bsiVnRvichv8Y9IacvMLzU4lIiIiUqlULIp/ZWR4i8XwQP31k+rluWEdqR8RTGJqLs/d8zoUqmAUERGRmku/rYt/lRiGGhFsNzmMSPlEhgTwetYfAHwV14kf/vECaP1FERERqaFULIp/lexZdASaHEak/PpePYi7/pgGwCMB7Ul6/d8mJxIRERGpHCoWxb8yM49NcBOiYlGqoZ49efC2wXRL3kxWcBj3riukaNYPZqcSERER8TkVi+JfGRlkBRb3LIaHmBxGpGICbriet5rkEpGfzZqGCbz21newebPZsURERER8SsWi+Ffz5mTFxgEQXr+OyWFEKi7+uSd4JW0ZAO93vYL5t/0TDhwwOZWIiIiI76hYFP8aNYrM2HoAhHftZHIYkbNgtXLxf57jpj2egvHBHqM5OPw6yMoyOZiIiIiIb6hYFL/Lyi8CIFyzoUp1FxbGv14aS7sjSaSFRPLXFpdSOPZus1OJiIiI+ISKRfGrQqebAqcbgIjgAJPTiJy94Cbx/Oe28wgvyOWP+A7830V3mR1JRERExCdULIpfHe1VBAhTz6LUEM17deKNgfUB+HRzBtNW7zU5kYiIiMjZU7EofpV13WgAQp0F2KwWk9OI+M7Qq/pz7wWtAHhs6no27csEwzA5lYiIiEjFqVgUv8rafwiA8IIck5OI+N7fh7RhYJu65Be5GfvFH6RfdyN8+qnZsUREREQqRMWi+FVWoQuAcGeByUlEfM9mtTDhui40jnaQdCSP+4wEnHfcCZMnmx1NREREpNxULIr/GAaZTs9muFF0+rYi1VRUSCDv3dgdh+FiYYvuPDfodhg9Gn74wexoIiIiIuWiYlH8Jy+PLHswAOG4TA4jUnk6NIrkzRvPBeCz7lfw2TkXwYgRsGCBucFEREREykHFovhPRgZZQaEAhFvdJocRqVwXn9OQRy5qA8Azg+9kQYP2cPnlsHChyclEREREykbFovhPZiZZQSEAhNtMziLiB3ed34qRXRvittr427BH2eqoA5dcoh5GERERqRZULIr/lOhZjAjQshlS81ksFl4Y0ZmeTaPIDgrhtmue5oDVAZdeCj//bHY8ERERkdNSsSj+k5FxrGcx2G5yGBH/CLRbef/mHrSIDSE5Mo6bRz1LumGDV17ROowiIiJSpalYFP8pec9icIDJYUT8Jzo0kM9u70W98CC21W3K7be+Rt5Xk8CiHnYRERGpulQsiv90705Wtx4AhHdIMDmMiH81jgnh89t7ERFsZ2VEPH/97k+KXJroSURERKouFYviP82bkxlbD4Dwdm1MDiPifwn1w/loTA+C7FZ+2ZLCw9+uw+U2IDkZPv7Y7HgiIiIipahYFL/Kyi8CdM+i1F49msXw7xu6YbNamLY6mUe/+h33RRfD7bfD00/rPkYRERGpMlQsil9l5TsBFYtSuw1pX4/x13bBaoHJ6w/xeONBuLHAs8/C3/4GLpfZEUVERERULIof7dhBVm4hABEB+qsntdsVnRvyZnHB+HXni3jywrsxAP7zHxgxAnJyzI4oIiIitZx+Yxe/KXr4EfJdniF24emHTU4jYr5hXRrx+qjOWCzwZddLefKiezw9jDNmwMCBsH+/2RFFRESkFlOxKH6TlVPg3Q6rG2NiEpGq4+qu8bx6jadgnNjlUh686mGKrDZYuRJ69YL1682OKCIiIrWUikXxm6w8zxDUkMI87BHhJqcRqTqu6R7P+Gu7YLdamJbQn7uvf458WwAkJUHfvvDjj2ZHFBERkVpIxaL4TVZe8UyohXlg1V89kZKGdWnEBzd3J8huZV58J8bc/gbZgQ7IyoJVq8yOJyIiIrWQfmMXv8ks9MzwGO4qOENLkdrpgrb1+Oy2noQF2VkW3Zxr736XA9feDI8+anY0ERERqYVULIrfZHlWzSDcXWhuEJEqrHeLWL66szexoYFsDK7DVefcxKb9WaUb5eaaE05ERERqFRWL4h9FRWRZPGsrhhtOk8OIVG3nxEcy/a99aVk3lANZBYx8bwnzt6R4Xpw/H5o3h7lzzQ0pIiIiNZ6KRfGPjAyygkIBCLe6TQ4jUvU1jglh6j19Oa9lLDmFLm7/7Hc+/n4VxqhRkJICF18MTzwBTn34IiIiIpVDxaL4R2amt1iMsJmcRaSaiHQE8OmtPRl1bjxuA55dvJ+/j/gXefYgMAx44QUYNAj27jU7qoiIiNRAKhbFPzIyyAoKASA8wGJyGJHqI9Bu5eURnXjisnbYrBZmRLXm6ocnsjumkafBb79Bly4wc6apOUVERKTmUbEo/tGpE1m3jwUgfFB/k8OIVC8Wi4U7+rfgyzt6UScskC0uB1fc8wHzel/maZCaCldcAX//O+TlmZpVREREag4Vi+IfNhtZFE9wUyfa5DAi1VPvFrHMvLc/XZtEkVlkcMfAu3n6Ly+TbwvwNJgwAbp3hw0bzA0qIiIiNYKKRfGbzPwiAMKDA0xOIlJ91Y8M5uu/9ObWvs0A+Cy6A8MencTWhq08DZKTISLCvIAiIiJSY6hYFL/JyvfM2hgebDc5iUj1FmS38fQVHfjk1h7UCQtkqzOQK8dM4LNhd+MePwGaNDE7ooiIiNQAKhbFP377jazkAwCEp6eaHEakZhiUEMfs+wdwfkJdClwGT7e9jOsKE9h1OOdYo7Q0eOklKCgwL6iIiIhUSyoWxT/mzSMrw/MLbPi+PSaHEak56oYH8cmYHjw7rAMhgTZW7Erj4vEL+WDhDlxuAx5+GB57zDNj6qJFZscVERGRakTFovhHyXUWI0NNDiNSs1gsFm7u04wf/z6A/q3rUOB0838/bGH4+Pmsn/2bp9GWLTBgAIwdC+nppuYVERGR6kHFovhFUVY2eYHBAIRHh5ucRqRmahwTwue39eSVEZ0ID7azNiWPK296ncdvHMeR4OL33QcfQEICfPIJuN3mBhYREZEqTcWi+EV2Tr53Oyw60sQkIjWbxWJhVI/G/PzAQK7q0hAD+F+jcxn094lM7HUVLosVUlLgttugd29YvtzsyCIiIlJFqVgUv8jKLQTAUZhPgIpFkUoXFxHM+Ou6MukvvWlbP5x0w8YT59/Bpfd/xi8tzsUA+P13T8H40ENmxxUREZEqSMWi+EXm0WUzCnIgXMNQRfylV4tYZt7bj3FXtCci2M7WoGhuGzmOa+94i1UNEzyNOnY0N6SIiIhUSSoWxS+yCoqLxcI8CAw0OY1I7WK3WRnTtzkLHx7E2AEtCLRbWRHbguE3vc7YsRPYPGRY6QMOHYLCQnPCioiISJWhYlH8IqvIACDclX+GliJSWaJCAnns0nYs+Of5jDo3HqsFfoxqySVvL+bOz/9g3d50T8O77oK2beHLLzUJjoiISC2mYlH8Isvl+RruLjI3iIjQMMrBK9d0Zs7fB3B5pwZYLPDTpoNc+c5ibnljLr8v34KxaxfceCN06gRffw0ul9mxRURExM9ULIpfZDVtCUBEsN3kJCJyVJt64bxzQzd++sdAhndrhM1q4deUIkbe+ApX3fQ6M9oNoGjzFrj+eujQASZOBKfT7NgiIiLiJyoWxS+y7rwbgPChF5icRESO1youjDdGdeGXBwdyfc/GBNqtrG2YwP1XPky/uz7m371HcmT3PrjpJs/w1I8/hoICs2OLiIhIJVOxKH7hneBGPYsiVVbT2FBeHN6JJY9ewAND21AnLJCD4bG8OvAWet/zKf+47AGWFwRj3H47jB5tdlwRERGpZPrNXfwiK99zr2J4cIDJSUTkTOqEBXHf4NaMHdiCWev289Fvu9i4L5NpHS9gWscLaJG6l2u7N2J4VgF1w4PMjisiIiKVRD2L4hfedRbVsyhSbQTZbQzvFs/Me/sx/a99ua5HY0JssDM2nhcTLfR58Wfu+OwPvl+7j7wf5sCVV8Ls2ZoMR0REpIawGIZhmB1CfC8zM5PIyEgyMjKIiIgwN8zq1dz87kIWxrTitfqZXPP3683NIyIVll3gZObafXz9exJrktK9z4e4Crlwy2KGbfqVfkYaAXfeAbfdBvXqmRdWREREgIrXBurmkcp38CBZecU9i4cPmBxGRM5GWJCd63o24bqeTdh2MIsZa5KZsWovezNgeodBTO8wiOjcDIYsWsGFn11N/y7NCL7jNhg0CGw2s+OLiIhIOahnsYaqUj2L33zDkLmpbK/ThP9F7Oa8x+8xN4+I+JRhGKxOTOW7GUuZmZTP4YAQ72uOwnwG7lrJhanbuOC8dkTdfw80aGBiWhERkdpHPYtSdWVlkRUUCkBEuMPkMCLiaxaLhW7N69Dt71fwhMvN8iUb+WnWMuZmB7EvLJY5CX2ZQ19sbhddv97MgE7ZDGhTl3MaRWKzWsyOLyIiIqegnsUaqkr1LI4fT/s98eQGOvi1bRZNx1xnbh4R8QujoICNk2Yyd8EG5hLLlrjmpV6PCgmgnzWTAY58+l89iAYtGpmUVEREpGaraG2gYrGGqkrFovPZ52iV2w2AVb0MYq6+3NQ8ImKClBT27khmoTWWhdsOsXjHYbKKZ0k+qkl+Oj3rBNCrb0d6dWpG4xgHFot6HkVERM6WhqFKlZWdlQvF81qER4ebG0ZEzBEXR3xcHDcAN/RqgtPlZs285Sx8/h0WNu/Guvqt2RMcxZ5s+PbHXfDjLhpYiujZNIoeXZrRtUk0CfXCsdu04pOIiIi/qFiUSpeVkw8REFyUT0BkQ7PjiEgVYLdZOffC3pxbJ5AHJk8ma9rHrCwKZnnjc1jRuAPrGrRmvy2AGYk5zEjcCEBwgJVzGkXSpXEUnRtH0aVxFI2i1PsoIiJSWVQsSqXLzC2ECAgvyIVw9SyKSDGLBbp3h+7dCX/xRc5fs4bzJ0+GqR+RtyOR1Q0TWN64IyubncPall3JKnDye+IRfk884j1FnbBAOsVH0a5BOO0bRNKuQTjNYkOxauIcERGRs6ZiUSrd0fuSwgtywOzJdkSkarJYoGtXz+P//g/H9u2cN2sW582cCdZNuJ9+hJ2Hc1iTlM6apCOs/fl3NgfHcjgbftmSwi9bUrynCgm00bZ+OO0aRNC+YQTtGkTQKi6MiOAAE79BERGR6kcT3NRQVWmCm5/en8ydu0Lo7M5gxnMjICjI1DwiUs0YhqeYPCo/H6KjyXe62VivBRvjWrI5rjmb4lqwJa4ZBfbAk56mXkQQreLCaFU3jFb1wj1f48KoExaooawiIlKjaYIbqbKyevSBXWuJSGipQlFEyu/4Qs7lgldfJfjnn+k+fz7dk7d4X3JarCTGNGRjXAtPATn0arYQSkpWAQczPY/F21NLnS7SEUCruDCaxobQLDaUprEhNInxbEeFBKiQFBGRWks9izVUVepZ/GxJIk9/t5FLz6nPf0Z3NzWLiNQwTiesWgU//wwLFsDSpZCVdez1PXugcWMy8orYcSib7d//zI65v7G9SQJ/htcjyR3E6f4TDA+20zQ2hKaxoTSNCaFpbAjx0SE0jHLQIDKY4ABbZX+HIiIiZ009i1JlZeUXARAepPuFRMTH7Hbo2dPzeOwxT6/j+vXw22+waRM0bgx4eg+7NYmm286l8N0E7+H59kB2RjdiR4dz2dP6HHbHNSHREcsedwAHcpxk5TvZkJzJhuTMk16+TlggDaMcNIx00DDKQaNoB42igj3PRTmICQnUZDsiIlJt1cpiccmSJXz22WcsWrSI5ORkDMMgPj6efv36ccstt9C3b99Kvf7OnTv59NNPmTVrFnv27CE7O5uGDRvSqVMnRo8ezVVXXYXdXnN+NFmpGYDnE3oRkUpls0GXLp7HyRQVQWgo5OQAEOwspP2hXbRfsAsWTD7W7pJLyJ/xPXvSctmdmsvu1Bx2b9nNbqedfXlu9qXnkVvo4nB2IYezC1m3N+OklwuwWagbFkTdiGDqhQcRFxFEvfBg4iKCiIsIJi48iLjwYGJDVVSKiEjVU6uGoebk5HDffffx8ccfn7bdrbfeyttvv01oaKjPM0yYMIFHHnmEgoKCU7bp3bs3X375JS1atKjwdarMMFSXi8cuu5+vulzCP3Yv5P6vXjYvi4gIeHoft26FP/6A33/3fF2zxjNxzlEPPwwvl/j3yu2GyEjIzobwcIw2bchodw7JLduzr2Fz9kXVZ19QBHtznOxLz2Nfeh4pWQWU9X9Yu9VCnbAg6oYHERMaSGxoILFhgcSEBhEbGkhMaCAxYYHUCQ0iJiyQ0ECb7qUUEZEy0zDUM3C5XAwfPpy5c+d6n3M4HHTo0AG73c6mTZvIzPQMM/rkk09ITk7mhx9+wGbz3f0ozz33HE899ZR332q10r59e2JiYvjzzz/Zv38/AMuWLWPgwIGsWLGCBg0a+Oz6psjOJivIU3SH22rN5xIiUpXZbNC+vedx882e55xO+PNPzxDW9eth8ODSx+ze7SkUAbKysKxcSdTKlUQBHUq2i4+HGTOgW18KnW4OZxeQknyIg4czSQkIJSW7kJTMAg5m5ZOSWUBKVgGpOQU43QYHMvM5kJlPWQTard4iMjYsiOiQACIdAUQ5AohwBBAVEujZP+553WMpIiLlUWuKxSeffLJUoXjnnXfy0ksvERMTA3h6HV9++WWee+45AObOnctTTz3FCy+84JPr//jjjzz99NPe/T59+vDpp5/Spk0bANxuN5MnT+aOO+4gOzubvXv3MnLkSH777TefXN80mZlkBYUAEB5gNTmMiMgp2O3Qrp3nMWrUia9brXD//Z4eya1bITGRk3Yb7t0Lxf+vBNqtnnsXv5kDY8dCQAA0aQLNm0OzZp7CslEjiho2IjW2PgfDYzlsCSI1p5C04kdqdiGpOQXe7bScQvKKXBQ63ezPyGd/RtmKy6OC7NYSBWQgEQ7PdniwnYhgO2HBdsKCAggLthMebCc86OhzdsKLn7dpuKyISK1RK4ah7tu3j5YtW5JfPMTopptu4vPPPz9p2yeffJLnn38egODgYHbs2EHDhg3P6vqGYdC1a1fWrl0LQEJCAqtWrSIkJOSEtvPmzWPo0KHe/alTp3L11VeX+5pVZhjqpk0Mf/EHVjVqx3vpS7j4Pd8U3yIipsrPh+3bYdu2YwXk1q2wa5enYCx53/ljj8FLL535nJ06QfH/E14ff+yZ3TUuDurVg7g48mLqkBoYRmqe01NE5hSSnltIRl4RGXlFpOcWf80rIjOvyPua20f/24cE2ggrLiLDgwM8BWXxfkigjZDAo19P3HYE2ggNshEScGw72G7T/ZoiIpVMw1BPY/z48d5CMSQkhPHjx5+y7ZNPPslnn31GUlIS+fn5TJgwgZdfPrv77GbPnu0tFMFz3+LJCkWAIUOGcO211zJp0iQAXnrppQoVi1VGVpa3ZzHCcfKFskVEqp3gYOjY0fM4k3bt4MorPYXkrl3HhrMer1GjE58bP94zLLYEBxBvtRJft+6xIvKee6Dk/xX5+Z5lRGJiICYGd3Q02bZAMvKc3qKyZGGZXVBEVr6T7HwnWQVHvxaRne8ku8AzK2yB0w1AbqGL3EIXKVmnvve+vBwBnsLREegpJEOCPAWmI8BGUICnoAwOsBIcUPzVbiM4wEZQ8XaQ9zUbwfYS20efL24TZLfqXk8RkXKoFcXitGnTvNujRo3yDj09mcDAQG699VaeffZZwNOzd7bF4tSpU73bzZs358ILLzxt+7Fjx3qLxRUrVrB3717i4+PPKoNpMjOP3bMYomJRRGqhm28+dm+kYUBqqmf9x+RkTy9kcrLn0anTicfu3Xvyc7rdcPCg57F+PVxzTenX9+yBCy7w7lqBiMBAImJiaFxcQBITA7Gxnl7PuLhjx+7Y4XlEREBErGdin4gICoMcZBe5T1pIHi0w8wqd5BQXk7mFTnILXeQVusgpdJJ33PO5hS7vJfOKXOQVufCHIPuxojPIbiPQbiXQZiXAbiXIZvXsFz8XaLcSUPw16BTPBx53nPf54447+nyA1YLdZsVusxBg9Xy1Wy0qYkWkSqrxxeLWrVvZvn27d//iiy8+4zGXXHKJt1jcvn07W7duJSEhocIZZs2a5d2+6KKLzvgfQv/+/QkNDSWneGr3WbNmMXbs2Apf31RZWWQFFt+zGOYwOYyIiMksFqhTx/Po1u3M7b/55lhRmJJy4nZKChQWli72AI4cOfFchYVw4IDnUdLxQ2QnT/YMnT1OoMVCTHg4MRERnkKySxf48svSjf79b0/hGxoKISHHvsaW3I+E+HjcdeqS7ywuIAtc5BY5ySlwFReVxYVmkYv8Ihf5RW7yi1wUOI9+Pfac9/Xi5wpKtMsvcpHvdOMqMQa3wOmmwOkmI+/Mf/z+ZLdaSheQNk/BebSYDPBuWwko/mq3FT9f4vUA28lft9uOFaoBNgs267GH3WrBavFc32a1YrMce75km9LHWLFawW61nqaNBWvJ54vPq8JYpPqo8cXi2uPu/+jTp88Zj+nWrRuBgYEUFhYCsG7dugoXiykpKRwo8R9zWa5vt9vp0aMHCxYs8F6/unJlZpETVAeA8HAViyIi5TJkyOlfNwzIyPAMiy2pbl3P8h9paZ7CMS2t9KP4w0gAoqNLH1s8M/hJr5WZeez1yMgT23zxBSxffvrMAE89hfWZZ4rvabSDKwMaxnm+j6Cg03994w1IKDEH7dq18NVXx9oEBkJIgGdCoeJHkT2A/OBQ8i+8uFSxWZC4h8L0DAotNgotNoosVgotVgqxUoCVQiwUGlbP81gpdLkpLC42C51uior3jz5f6HRT4HJTdNxzhSXaOV3uk94/6nQbON0G+bjP/OdXzdlKFI92qwWbzVKqQLWW+Gq1eF6zWDzHWS1Hnweb5eg+nnbFhajNQul2R4/ztil57Iltju5bLBZsxec+eqzVQjlylT6X9WguC8CxfUuJryXbWTi2X/KrpcR5LJQ+3vu89xjPeY6d/9j3U3L/aBurxQIWTp7tuMwq+muHGl8sbt682bsdGBhI48aNz3jM0XY7duw44Rxnc32Ali1blum4li1beovFs7m+2bIzsoHiYjHC9+tWiojUahYLREWd+HyLFqXXiTxeQYGniDxyxFNQlXTBBZ7lRY4Whqd6nKxYLFmEns7x9+3n53t6Pos/pD2t4lnLvdavP/33CgQAAXXqEH7oUOkXHnn5xN7Rk7nhhhPbNW3qKbxtttM/Xn/dc8/qUVu24B4zhiJ7IE57AM6AQIrsATjtARTZAnDa7RTZirefehqnw0GRy8DpMihauhTnkmU4rZ7C1mm14SwucousNpwWT2HrjIymqM95OF1unG6DIpcb5/oNODOzPa9jwWWx4sKCy2LBxdHnLLgio3BGROJyG56Hy40z5RBu8LTBgssAFxacWDzPGxZcgMtmx2ngPfZUzvS6VB9WigvO4q+eovNoQWnBYrOWLmRdzlLHWSh+FBejHD2f3Y7FbvMWzBbDgMLCEm0pve19zoLFEYzFavU+h8uFpcSxJa9NiWtbrBYsoaHH9i1gyc/H4nSe5LqeHe9+YCDWEMex78MClsxMLMHBXNmzOUPb16v8H0YlqfHFYmJionc7Pj6+zJ+CNGnSxFssljzH2Vz/6HnLev1TneNkCgoKKCg4NtlA5qk+GfazzKxcAIKKCgiMMnFWVhEROSYoCOrX9zyON2TImXs04eRLh3z+uaeAys31FI45Oce2S349fgiuYUDXrp6iMT/fU8we/ZqX57lH86jje1ELyjjRzvFFMUBRUcWPzcw89WRFJR1fQGdmYl2+nCAg6EzHvv0ixJaYZ+HrNfD+/535mueeC0+NKf3cK3dBWZbj+te/4KHnj+3n5EBY2JmPA1i0CPr1AzwzwbunTsN57bW4rDZPcWq14bTacBcXukefd4aG4vpjlbeIdLrduF56Bff33+OyWDEsFlxWK27L0Yen2HVbrbh79cb14D9xG4bn4QbXP/+JkZbmaVP88BxvwW2xeY83rr4a17k9io8zcB8+jOv9/3rOe/QaJa9ptWJYrJ7nrxmJOzQUl7v4utv+xL12bXHektcrvn7xOQgJwd39XNyGgQG4DTC2bsXIyPS2NywWDIsFt8WCQfFXixUjKgp3nbqeYw3Pn7GRlOTNZ1DiuJLnwoLbEYLbZi0+zrNkm+Fyec9dUW4AA1zFX48xPI+iivaUn8U9zOllfF+fVPmWIzomD8g4yfM5tG9WV8ViVZaVleXdjjzZp6CnUHJK2ZLnOJvrlydDea//4osv8swzz5QvnB9kXT0KvthAeGgQnNfL7DgiIuIrJ/vwtWvXip2rfn1YterUrzudxwrI4/8fvewymD//WGFZVHTyx/FFJsAll3hmkz3VMUcfJ5v1tl07zxBgl+v0j+Ov6y7HL882W+n9sq52Zj3JL/9lve7xx5YnbwkWiwUbBjaXE4p7k06pMATqh5d+zpkKezee+UId6kPn45Y427nUM8nTmdw6BAa3Pra/xYCFn535OID//N2zXupRExbB9BfPfFxCAny4pfRzl74As2ef+dj77oOHJ5R+zmYr289o1iy49JJj+/PmQYml2gzwFJfFX93FY1TdFgvu9AwMq9VT2BoGxtNP4/73f4qLUTBKFKvewhZw9+2L8dnnxcWtp8B1Xz0c9/bt3vZYLJ6yskSxawDuv90Lo2/wFLeAcTgV4+qrS7TDe53jjzX+8x9o1hyD4mvO+xnjrbe87bBQuv3RwjoqCv7zn+JrFhfk//4Pxorlxe2Lv9+Sxx7d7t0bbr7Zk93t+SDAePQxjEsvoUer2DP/fKqwGl8sZpf41C/4ZP9RnILDcez+uuyyfHJYhuuXJ0N5r//YY4/xwAMPePczMzPLNOS2sjVt1YhJfwnzDDepU8fsOCIiUh3Z7Z5H6EluZzhVD2lZjBnjeVTEkiUVO653b88v9263p5h0Ok9daB6/Fto998DIkceO93QRnbh9sj+nt9+G9PRj7Y7/enS7devSxwUHe+4JPdNxbje0alX62C5d4D//wdudBSfftp/k19GRI6Ft2xPbH3+ONm1OPPaf//QU8qc65ujX7t1LHxcb6+lZPd0xR78e/6FF165Q4vewU6pb98Tnrr7a872WdLIPBvr3P/G5++4r24cIJQtbgMaN4d57vbsW4LiPJo4JDij9IULfXpBfht+N27SBOsf9XbzsfNh3kp/Z8XokQNMSverRNrhiwIntTva9d2wE8SUm/UpvDoO7nPmaERHQ5bgljHo1A9vhMx/bszH0aVb6uW71oFdjiI868/FVmMUwyvoxVfU0ZMgQfv75Z8Azy+jChQvLdNxNN93ExIkTARg8eDDz5s2r0PWff/55nnzySe++y+XCerJP/I7z0UcfcccddwBgs9lwOs/wqdxxKrrwpoiIiIiI1CwVrQ0qPki5mggpcRN9fn7ZxyGXbBt6sk/oKnD98mTw1fVFREREREQqosYXi2ElbsrOyyv7okq5ubknPcfZXL88GXx1fRERERERkYqo8cVinRL3ye3fv7/Mx5VcGzE2tuI3ptY57j69smbw1fVFREREREQqosYXiwkJCd7t1NTUUj12p5OUlOTdbnv8TccVvD7AnrLMzuXD64uIiIiIiFREjS8W27VrV2p/zZo1ZzwmOTmZQyUW7j3+HOXRunVr7CVm+SrL9QFWr17tk+uLiIiIiIhURI0vFnv27ElQ0LFlb38rw4K0ixYt8m4HBwfTs2fPCl8/MDCQXr2OrS9YlusfOHCA7du3e/cHDDjJVMEiIiIiIiKVqMYXi2FhYQwePNi7/+WXX57xmJJtBg8efNazkQ4bNsy7PW/ePA4ePFjm60dFRalYFBERERERv6vxxSLAmBIL7q5bt47vv//+lG1XrVrF7NmzT3psRV1//fXe3s2ioiJeeeWVU7bNzs7mrbfe8u6PHj2agICAs84gIiIiIiJSHrWiWLzmmmvo3Lmzd3/s2LFs2bLlhHb79+/nxhtvxOVyAdClSxdGjBhx0nMmJiZisVi8j3Hjxp3y+vHx8YwdO9a7P2HCBKZMmXJCu6KiIm699VbvJDgOh4PHH3+8TN+jiIiIiIiIL9nP3KT6s1gs/Pe//2XgwIHk5eWxf/9+evXqxd13382AAQOw2+2sWLGCd955xztE1OFw8MEHH2CxWHySYdy4ccyePZs///wTl8vFqFGjuOGGG7jqqquIiYlh69atvPvuu6xbt857zKuvvkrDhg19cn0REREREZHysBiGYZgdwl+mTp3KjTfeSF5e3mnbORwOJk6cyPDhw0/ZJjExkebNm3v3n3766dP2LgJs27aNIUOGlFoW41QefvhhXn755TO2O5XMzEwiIyPJyMggIiKiwucREREREZHqraK1Qa0YhnrU8OHDWblyJUOGDDlpj6HFYmHw4MH88ccfpy0UK6pNmzasW7eO22+/HYfDcdI27dq1Y8aMGWdVKIqIiIiIiJytWtWzWFJSUhKLFy8mOTkZgEaNGtG3b18aN27sl+tnZWXxyy+/kJSURE5ODg0aNOCcc86ha9euPjm/ehZFRERERAQqXhvU2mKxplOxKCIiIiIiUPHaoFZMcFMbHf0MIDMz0+QkIiIiIiJipqM1QXn7CVUs1lBZWVkAfhtWKyIiIiIiVVtWVhaRkZFlbq9hqDWU2+1m3759hIeH+2z5j4rKzMykcePGJCUlaUhsDaGfac2kn2vNo59pzaSfa82jn2nNU9V+poZhkJWVRcOGDbFayz7HqXoWayir1Up8fLzZMUqJiIioEm8W8R39TGsm/VxrHv1Mayb9XGse/Uxrnqr0My1Pj+JRtWrpDBERERERESkbFYsiIiIiIiJyAhWLUumCgoJ4+umnCQoKMjuK+Ih+pjWTfq41j36mNZN+rjWPfqY1T035mWqCGxERERERETmBehZFRERERETkBCoWRURERERE5AQqFkVEREREROQEKhZFRERERETkBCoW5QRLlixh7NixtG/fnsjISCIiImjfvj1/+ctfWLx4caVff+fOnTz11FN0796dunXr4nA4aNmyJVdffTXffvstTqez0jPUFOnp6UybNo377ruPAQMGUL9+fYKCgggLC6NJkyZcccUVjB8/niNHjlTK9S0WS7kf7733XqVkqSkWLFhQoT/XLVu2VEoevV/PTmJiYoV+niUfiYmJZ51D79XyOXToELNnz+bZZ5/lyiuvpEGDBqX+bD799NMKn3v9+vU88MADdOrUiZiYGMLCwkhISGD06NHMmTPHd9/EaRw4cICXX36ZPn360KBBA4KDg2nWrBkXX3wxn376KXl5eX7J4U++/pnm5uYye/ZsHnroIYYMGUJ8fDwOh4OQkBAaNWrEhRdeyAsvvMC+ffsq5xsCmjVrVu739aOPPlppeczgy59rRf+9rsz3rV/eq4ZIsezsbOO2224zgNM+br31ViM7O7tSMowfP94ICgo67fV79+5t7Nixo1KuX1Ns3rzZuPzyy43AwMAz/jwBIyQkxHjzzTcNt9vt0xxlufbxj3fffdenGWqa+fPnV+jPdfPmzT7Povfr2du1a1eFfp5HH3a73UhLSzvrHHqvls3+/fuNpk2bnvHP5pNPPin3uYuKiozHHnvMsFqtpz33ZZddZqSkpPj+myv21VdfGZGRkafNkJCQYKxatarSMviTr3+mBw4cMK699lojJCSkTO+jgIAA47HHHjMKCgp8/r2V5fs6/vHII4/4PIcZKuO9WtF/r2fPnl0p36O/3qt2RACXy8Xw4cOZO3eu9zmHw0GHDh2w2+1s2rSJzMxMAD755BOSk5P54YcfsNlsPsvw3HPP8dRTT3n3rVYr7du3JyYmhj///JP9+/cDsGzZMgYOHMiKFSto0KCBz65fk2zYsIGZM2eWes5ms9GqVSvq1auHy+Vi8+bNpKWlAZ5PQP/xj3+wceNGPvjgAywWi88zDRgwAIfDccZ2TZo08fm1a6rg4GAGDhxYprZhYWE+vbber77hcDi46KKLytze7Xbz008/efcvuugioqOjfZpJ79VTy8/PZ/fu3ZVy7rFjx/Lxxx979wMCAmjfvj1hYWFs2bKF1NRUAGbNmsWQIUNYvHixz9/XX3zxBTfffHOp59q0aUODBg1ITEz0fu9bt27l/PPPZ8mSJXTo0MGnGfzN1z/TpKQkJk2aVOo5i8VCixYtqF+/PjabrdS/kUVFRbz44ousWbOG6dOnExgY6LMsJfXo0YOYmJgztktISKiU6/tbZb5Xjyrrv91169b1+bX9+l71UXEr1dxjjz1W6pOIO++800hNTfW+np2dbTz55JOl2jz++OM+u/6cOXMMi8XiPXefPn2MrVu3el93uVzG119/bYSFhXnb9O3b12fXr2kmT57s7XW46qqrjOnTpxsZGRml2rjdbmP69OlGo0aNSv1c//Of//gsR8nz7tq1y2fnrc1K9iw2bdrUlAx6v5rnxx9/LPW++uabb3xyXr1Xy6Zkz0LdunWNiy++2HjiiSeM6dOnn1XP4vvvv1/q+CuvvNLYu3ev9/XCwkLj7bffNux2u7fNDTfc4NPvbd26daVGCrRp08b4448/SrWZO3euUa9ePW+bFi1aGHl5eT7N4W++/pn+/vvvBmBYLBZj8ODBxpdffmkcOnTohHYLFiww2rdvX+oaDz/8sE+/t5I9a/Pnz/fpuau6ynivHt+zaBZ/v1dVLIqRnJxsBAcHe/9C3XTTTads+8QTT3jbBQcHG8nJyWd9fbfbbXTu3LlUl3lOTs5J2/7000+l3qhTp0496+vXRNOnTzfuuOMOY/fu3Wdsu2fPHqN+/freP9M6deoYhYWFPsmhX0B9z+xiUe9Xc91www3eP8/o6GgjPz/fJ+fVe7VsMjIyjMmTJxuJiYknvFbRX0BzcnJK/Rt8/vnnG06n86RtP/zwQ287i8VirFy5sqLfygmuuOKKUv8PHDhw4KTtNmzYUOoX1TfeeMNnGczg65/pypUrjREjRhgbN248Y9v09PRSBWNgYOAp/9wrojYXi5XxXq0qxaK/36sqFsV46KGHvH+RQkJCSvUoHq+goMBo3LixTz8FmzVrVqk335w5c07b/tprr/W27dmz51lfX078VHvevHk+Oa9+AfU9s4tFvV/Nk5GRYTgcDu+f59133+2zc+u9evYq+gvov//971IF4KZNm07bvlevXt72o0aNOsvUHhs3biyV/7333jtt+0ceecTbtn79+obL5fJJjqrmbHqLy+r40QIffvihz85dm4vF06nOxaIZ71XNhipMmzbNuz1q1KjTjmkPDAzk1ltv9e5PnTr1rK9f8hzNmzfnwgsvPG37sWPHerdXrFjB3r17zzpDbXfFFVeU2q+smTOl+tP71TyTJ08uNbPdLbfcYmIa8ZWS76mBAwfSrl2707Yv+Z764f/bu/egqK47DuDfhZWHPFQUBZWXjogPUPE1anwVDEZaayWjMZmSTK2O1tZaHJvONFbbOploHmB0TEiiTjViFCSBmAcB1EG04SEviUi1EUR8IAiUBRGQ2z+op3tZHrvshWXZ72eGmXvWc+/5cdcfd3977z3366/x5MkTRWNwdHTEK6+80mX/jRs3iuX79+/jn//8p9ExWKqgoCDZPcI8/lJXTJGrLBYtXHFxMW7evCnay5cv73adF154QSzfvHkTxcXFRsXw1VdfieWQkJBuJ1dZuHAhHBwcOlyfeqb9FwTPJjMiao/5ajr/+Mc/xLKfnx/mzp1rwmhICRqNBmlpaaJt6DFYo9HgwoULRsehnZfPPfdctxPnjBs3TjYRSvsJ1Uh/1tbWGDJkiGjz+EtdMUWusli0cPn5+bL2vHnzul0nMDBQNltXQUFBj8evqKjA/fv3DRpfrVZj9uzZioxPbdrPGDZy5EgTRUL9GfPVdH788Uekp6eLNs8qDgzXrl1Dc3OzaOuTU25ubvD29hZtY3NKkiRcvXrVoBja92Ne99zjx49RUVEh2jz+UmdMlassFi1cUVGRWLaxsYGHh0e367Tvp70NY8YHgPHjx+u1nnY/Y8anNu0vJ9b3D5AhduzYgSlTpsDZ2Rn29vYYO3Ysli5dit27d+PWrVuKj2cJampqsGbNGnh7e8Pe3h5OTk7w8fHBqlWrcPDgQcW/oWa+ms6xY8cgSRKAtseU/PKXv+y1sZirfac/5NTt27dRX19v0hgsWUJCAlpbW0W7N46/APDOO+9gxowZGDp0KGxtbeHu7o758+fjT3/6k6wAIf2Fh4djwoQJcHBwgIODAzw9PbF8+XLs27dP9gWAUkyVqywWLVxJSYlYHjt2rN7P19N+vpb2NowZv/12+2J8Ampra7F//37RDggIwOTJkxUfJy4uDteuXUNdXR0aGxtRXl6OCxcu4K9//St8fX2xadMm2f1Y1L3a2lrExsaitLQUjY2N0Gg0KCkpQUJCAn73u9/B09MTBw4cUGw85qtpSJKEY8eOiXZwcDDGjBnTa+MxV/uOdj6o1Wq9n0WqZE4pkdelpaXiywzSX0tLC958803RHjlyJIKCgnplrK+++gp5eXmora1FU1OTuH9t7969mDZtGl588UXx7GXSz/Hjx3Hz5k00NDSgoaEBZWVlSEpKwuuvvw4vLy/s3LkTT58+VWw8U+Wq2qDeNODU1dWJZe1r5rvj7Ozc4TaMGd+QGJQan4Dt27fLLi3cs2dPr4wzYsQIjB8/Ho6OjqitrcX169eh0WgAtB0wo6OjkZmZifPnzxv0f9HSeXt7Y8yYMbC1tUVlZSWuXbuGlpYWAG3F5NatW5GXl4fDhw8bPRbz1TQuXrwoO6PX25egMlf7jnY+ODk5wcpKv+/wlcwpJfK6tbUVDQ0NsvuTqXtvvfWW7KzeG2+8AVtb214Za8iQIfD19YWzszM0Gg1u3LghikNJknDmzBlkZ2fj4sWLel1lRoC7u7u4sqe6uhpFRUVobGwEADQ2NmLPnj3IysrCl19+iUGDBhk9nqlylWcWLdyzDwAAYGdnp/d62jN3aW/DmPENiUGp8S3dJ598Iisi1q5dqzMzqjEmT56MqKgo/Pvf/8bDhw/x/fffIyUlBVlZWaiursbZs2cREBAg+ufm5uKll15SbPyByMrKCsHBwThx4gSqqqpw69YtpKenIzU1Ffn5+aiursYHH3yAESNGiHWOHDmCvXv3Gj0289U0tCe2cXZ2xi9+8QvFx2Cumoapj8Edrd+TvFYiDkuTlJSEXbt2ifb8+fPxm9/8RtExvL29sWfPHhQWFqKmpgaZmZlISUnB999/j8rKSqSlpWHRokWif2lpKX72s5+hqalJ0TgGCpVKhTlz5uDjjz/G3bt3cffuXVy+fBmpqanIyclBTU0NYmJiZPcUJyUlYevWrYqMb6pcZbFo4Z6dgQDaLoHRl3Zf7ZvzjRnfkBiUGt+SpaWlYcuWLaLt4+OD6OhoRcf44Ycf8Pvf/x7jxo3T+Te1Wo3Q0FBkZGQgNDRUvP7tt9/iyy+/VDSOgWTRokVITk7Gyy+/3OFjbhwdHbFp0ybk5OTIDlh/+9vf8ODBA6PGZr72vYaGBsTGxor2mjVrdA78SmCumoapj8HtYzAkjvb9mNv6Kyoqwrp168S9isOGDUNMTAysra0VHefChQv485//jClTpuj8m0qlwsKFC3H+/Hls2LBBvJ6fn6/4Z4GBwsvLCxkZGfj1r3/d4SXjtra2WLduHXJycjBz5kzxenR0tCKTQJkqV1ksWrjBgweL5WenzvWh3deYy060xzckBqXGt1R5eXlYuXKl+PZw5MiR+Pbbb01ySZmdnR1OnjyJUaNGideUvM/OUnl4eODUqVOi3dDQYPSlqMzXvvf555/LLj0y5SyozFXlmfoY3D4GQ+Jo34+5rZ+ysjKEhISguroaQNv+P3v2LLy8vEwSj5WVFQ4dOgR/f3/xGvPaOMOGDUN8fLw48ydJEg4ePGj0dk2VqywWLZz281kMmbCgoaGhw20YM74hMSg1viUqLi5GSEgIamtrAbT9Ufvuu+/g6+trspicnJywefNm0b548aJBH5yoY3PmzMGSJUtEOzk52ajtMV/7nvYlqOPHj8dzzz1nwmiYq0oz9TG4o/V7ktdKxGEJHjx4gODgYJSVlQFoOxP1xRdfYP78+SaNS61WY/v27aJ948YNnUdqkWE8PT1ll+obe/wFTJerLBYtnPZ9Tffu3dN7Pe0JUYYPH67I+IbEoNT4lubWrVsIDg4WUzo7OTnhm2++wbRp00wcGbB06VKx3NjYKA6mZBzt/fqvf/3LqG0xX/tWeXk5UlNTRbu/PFuRuaoc7ZzSaDR630ukZE4pkddOTk6KTOAxkD169AjLli0Tf4fVajVOnTqFZcuWmTiyNtp5DRh/vCD5Pi0pKTH6XlBT5SqLRQs3ceJEsVxVVaXz7UNntD8c+Pn5KTI+0PYMmb4c35LcuXMHQUFBuHPnDoD/X/oyd+5cE0fWxs3NTdaurKw0USQDi/Z+NXafMl/71vHjx8U9TSqVCuHh4SaOqA1zVTn9Iad8fX1lj81iXivvP//5D0JCQsTMp1ZWVvj000/x85//3MSR/R/zWnnt92lVVZVR2zNVrrJYtHCTJk2StfPy8rpdp7y8HA8fPux0G4aYMGGC7MZbfcYH2mbiU2J8S/Hs0pdn0+8/u/RFexY0U2v/RUX7a/OpZ7T3q7H7lPnat7QvQV2yZInJ7mlqj7mqnJ4cg5ubm/HDDz90ug1DOTo6YuzYsQbFADCv9VVfX48VK1YgOzsbQNsXP0eOHMHatWtNHJkc81p5Su9TU+Uqi0ULN2fOHNkzfdLT07td5+LFi2LZzs4Oc+bM6fH4NjY2sjNb+ox///593Lx5U7T7U8HTH1VVVSE4OBjFxcUAgEGDBiEuLq7fXPryjPaHH6Bt0h0ynvZ+NXafMl/7TmZmJq5fvy7a/eUSVIC5qqRx48bJPvzpk1NXrlyRfQhVIqe0t6FPDM3NzcjIyFA0hoGosbERK1euxKVLl8Rrhw4d6lf5/AzzWnna+9TW1laRSQRNkassFi2co6MjgoKCRPvEiRPdrqPdJygoyOgZ0LQvw0hJSel2en/t8YcOHcqDVBdqa2sREhKCwsJCAIC1tTViYmLw05/+1MSR6frss8/Esre3d4fTUpNhGhoakJiYKNpKTKLAfO0b2mcVHRwcEBYWZsJo5Jirylq5cqVYjo2N7fa+Ju2cmjJlCsaPH290DNp5XVRUJDsT0ZHExEQxS6+VlZWiz+cdKJqamhAWFoZz586J1yIjI7Fp0yYTRtU57by2s7PDjBkzTBiN+ZMkCadPnxbtefPmKbJdk+SqRBbv9OnTEgDxk5iY2GnfK1euSNbW1qJvbGys0eOXlZVJtra2YpsRERGd9q2rq5M8PT1F3y1bthg9/kCl0WikBQsWiH1lZWUlffrpp6YOq0MJCQmy/4Pbtm0zdUgDQkREhGy/fvHFF0Zvk/na+548eSK5uLiI/fbqq6+aOiSBudo57f1y9OhRvdfLzMyUrfv+++932resrExycnISfd9++20FIm87Xri6uortrl69utO+LS0t0uzZs0Xf0NBQRWLoj3r6nra0tEhhYWGy9d98883eC9RIWVlZko2NjYh11apVpg6pV/X0fTXE+++/LxsnKipKke2aIldZLJLU2toqTZs2Tfxncnd3l4qKinT63b17V5o0aZLoN336dKm1tbXDbd66dUuWJLt27eoyhq1bt4q+1tbWUlxcnE6fpqYm6cUXXxT97O3tpfLy8h79zgNdY2OjFBwcLPaVSqWSDh8+bPR29X1fa2pqpNWrV0vZ2dndbjMmJkZycHAQ2xw8eLB07949o2MdiJKSkqSIiAiprKysy35NTU3S66+/LnuvAgMDma9mIi4uTvZ+nDt3zuBtMFf7njEfQFeuXCnWdXR0lNLT03X61NbWSgsXLhT93NzcpIaGBr1j6u5Lh/fee0/WPzIyUqdPa2urtG3bNtmx5cqVK4b8qmalJ+9pa2urFB4eLlv3L3/5S6/E1NV7GhYWJp07d67Tv/vPpKSkyIoPlUol5ebmKhZvf9ST97WwsFD61a9+JV2/fr3Lfq2trVJUVJTsxMro0aPNOlf/P1MBWSyVSoWPP/4YixcvxuPHj3Hv3j3MnTsXmzdvxqJFi6BWq5GZmYmDBw+KS87s7e3x0UcfyWZlMsbu3bvxzTff4MaNG3j69CnWrFmDl19+GatWrYKLiwuKi4vxwQcfoKCgQKzz9ttvY/To0YqMP9Ds378fKSkpoj106FCcPn1adklEV5YtWyZ75pKhJElCfHw84uPj4efnh5CQEEyfPh3u7u5wcHBAXV0drl69iri4OGRlZYn1VCoVjh49qjODGLVpaGjAe++9h6ioKCxYsACLFy/G1KlTMWLECNjY2KCyshKZmZk4ceKEbPYzFxcXxMTEMF/NhPYlqF5eXrJnZSqNuWq4DRs24Pjx49326ehyw86eSbl//35cvnwZlZWV0Gg0CAoKwvr16/H888/D0dERBQUFOHDggJikzMrKCh999BHs7e2N/4X+Z8uWLYiLi8Ply5cBAH/4wx+QmpqKV155BW5ubigpKcHhw4dl90lt374dgYGBisVgKkq+p7GxsTh27Jho29nZISMjA8uXL9crloCAAOzbt0+vvl1JSUnBmTNn4OnpiRUrVmDGjBnw8PCAk5MT6uvrUVxcjISEBNllsgCwd+9eTJ8+3ejx+wMl39fm5mYcOXIER44cwcyZM/GTn/wE06ZNw8iRI2Fvb4/q6mrk5ubi5MmTsvvNbW1t8dlnn5l3rvaoxKQB6cyZM5K9vb3s24qOfuzt7aUzZ850uS1Dz1RIkiQVFxdLHh4e3Y4PQPrjH/+o0G89MO3atUuv/djZT2ffaun7vlZXVxs8ppOTkxQTE9N7O2UA+Pzzzw3erxMmTJBycnK63C7ztf948OCBpFarxb7buXNnj7bDXO09r776ao//tnbl0qVLssuPO/uxtraWDhw4oFes+vxd11ZRUSH5+/vr9busW7dOevr0qV5x9HdKvqdHjx416vi7ePHiLmPV9z0dMmSIQePa2NhI7777rpF7sn9R8n3Nzc01eBtubm5ScnKyXrH251zlBDckrF69GleuXEFwcHCHZyBUKhWCgoKQnZ2N1atXKz6+r68vCgoKsH79+k6/gZk0aRISEhKwd+9exccn5djb22Pjxo2YMmVKt2ezhgwZgq1bt6KwsBDr1q3rowjNk5+fH9auXSubPbEz3t7e2LdvH3Jzc3tlogLma++IiYlBS0uLaPf2sxWZq/3H/PnzUVBQgLCwMNkjarTNnj0baWlp+O1vf9srMbi6uiIzMxM7duzodOZGLy8vfPLJJ4iJiYGVFT9G9lcbN25EYGAgrK2tu+xnb2+P1157Dbm5uYiIiOij6MyPu7s7wsPD9ZpQatSoUXjjjTdw9epVBAcH90o8fZmrqv9Vs0QyZWVluHTpEsrLywEAY8aMwYIFC+Dh4dEn49fV1eHcuXMoKytDfX093N3d4e/vz9m5zFB1dTXy8vJQUVGByspK1NTUYPDgwXBxcUFAQAACAgK6PZiRrtu3b+PatWuorKxEZWUl6uvr4ezsjJEjR2LWrFmKzJCoL+brwMBc7T8ePnyItLQ03LlzB01NTRg9ejRmzZqFiRMn9lkMjY2NuHDhAkpKSlBdXY1Ro0bBz88P8+bNU+ySdup9Go0Gubm5uH//PiorK1FdXQ1bW1sMGzYMkydPRmBgIGxsbEwdpll58OABCgoK8PDhQ1RWVqKurg6Ojo4YMWIEZsyYgUmTJvVpjvR2rrJYJCIiIiIiIh28foCIiIiIiIh0sFgkIiIiIiIiHSwWiYiIiIiISAeLRSIiIiIiItLBYpGIiIiIiIh0sFgkIiIiIiIiHSwWiYiIiIiISAeLRSIiIiIiItLBYpGIiIiIiIh0sFgkIiIiIiIiHSwWiYiIiIiISAeLRSIiIiIiItLBYpGIiIiIiIh0sFgkIiIiIiIiHSwWiYiIiIiISAeLRSIiIiIiItLBYpGIiIiIiIh0sFgkIiIyY7t374ZKpYJKpYKvry+ampoMWj8pKUmsr1KpUFFR0UuREhGRuWGxSEREZKZu3LiBt956S7QjIyNhY2Nj0DZmzZola6enpysSGxERmT8Wi0RERGZqy5YtePLkCQBg+fLlCA0NNXgbw4cPh6enp2hfunRJsfiIiMi8sVgkIiIyQ8nJyUhOThbtv//97z3elo+Pj1guKioyKi4iIho4WCwSERGZoZ07d4rlF154QedyUkOMGTNGLN+8edOouIiIaOBgsUhERGRmUlNTkZGRIdo7duwwanuurq5i+d69e0Zti4iIBg4Wi0RERGbmww8/FMs+Pj5YsmSJUdtTqVRi+dk9kERERGpTB0BERET6q6qqQkJCgmiHh4fLij1t9fX1ePz4MQDA2dm505lSJUnqcJmIiCwbzywSERGZkdTUVDQ3N4t2SEhIp31fe+01uLq6wtXVFdnZ2Z32u3v3rlgeNWqUMoESEZHZY7FIRERkRs6fPy+WHRwcMHv27E77ZmVlieWpU6d22u/27dtiWfsxGkREZNlYLBIREZmRwsJCsTx16lSo1R3fUVJeXo7S0lIAgJubG5ydnTvs19LSgqtXr4p2V8UnERFZFhaLREREZuTGjRtieeLEiZ32034G49ixYzvtl5ubi4aGBtFesGCBkRESEdFAwWKRiIjITLS2tuLBgwei3dX9hYmJiWLZxcWl035nz54Vy2q1GkFBQUZGSUREAwWLRSIiIjPR2Ngoa9va2nbY79GjR/j6669Fe9CgQR32kyQJJ0+eFO3g4GAMHz5cgUiJiGggYLFIRERkJqytrWWPyXj06FGH/Q4ePIgnT56IvlVVVR32S0xMlF3WumHDBgWjJSIic6eS+EAlIiIis+Hm5iYuRQ0ICEB+fr7s30tLSzF16lRoNBosXboU58+fh6OjI6qqqmTPWaypqcHMmTPx448/AgD8/f2Rn5/f6TMbiYjI8vDMIhERkRlZuHChWC4oKMCHH34o2iUlJQgNDYVGo4Gvry9eeuklAIBGo8E777wj+pWWlmLFihWiULS2tkZ0dDQLRSIikuGZRSIiIjOSnJyM559/Xvaan58fXFxccOXKFXH56XfffQc3Nzf4+/uLfgEBAbCzs0NOTg5aWlrE65GRkdi2bVtf/QpERGQmWCwSERGZmYiICERGRnb4b2q1GocOHRL3H4aFhSE+Pr7Dvo6OjoiKisL69et7LVYiIjJfLBaJiIjMUHx8PKKjo5GXl4dHjx7B1dUVS5cuxY4dOzB9+nTRr7GxEXv27MGpU6dw+/ZtDB48GD4+PggNDcXmzZsxevRo0/0SRETUr7FYJCIiIiIiIh2c4IaIiIiIiIh0sFgkIiIiIiIiHSwWiYiIiIiISAeLRSIiIiIiItLBYpGIiIiIiIh0sFgkIiIiIiIiHSwWiYiIiIiISAeLRSIiIiIiItLBYpGIiIiIiIh0sFgkIiIiIiIiHSwWiYiIiIiISAeLRSIiIiIiItLBYpGIiIiIiIh0sFgkIiIiIiIiHSwWiYiIiIiISAeLRSIiIiIiItLxX9lDZstDKnHzAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the components of the fit separately:\n", + "plt.rcParams[\"font.size\"] = 25\n", + "plt.rcParams[\"figure.figsize\"] = (10, 5)\n", + "\n", + "\n", + "def plot_fit(func, J, w, lam, gamma, w0):\n", + " \"\"\"Plot the individual components of a fit to the spectral density.\n", + " and how they contribute to the full fit one by one\"\"\"\n", + " total = 0\n", + " for i in range(len(lam)):\n", + " component = func(w, [lam[i]], [gamma[i]], [w0[i]])\n", + " total += component\n", + " plt.plot(w, J, \"r--\", linewidth=2, label=\"original\")\n", + " plt.plot(w, total, label=rf\"$k={i+1}$\")\n", + " plt.xlabel(r\"$\\omega$\")\n", + " plt.ylabel(r\"$J(\\omega)$\")\n", + " plt.legend()\n", + " plt.pause(1)\n", + " plt.show()\n", + "\n", + "\n", + "def plot_fit_components(func, J, w, lam, gamma, w0):\n", + " \"\"\"Plot the individual components of a fit to the spectral density.\n", + " and how they contribute to the full fit\"\"\"\n", + " plt.plot(w, J, \"r--\", linewidth=2, label=\"original\")\n", + " for i in range(len(lam)):\n", + " component = func(w, [lam[i]], [gamma[i]], [w0[i]])\n", + " plt.plot(w, component, label=rf\"$k={i+1}$\")\n", + " plt.xlabel(r\"$\\omega$\")\n", + " plt.ylabel(r\"$J(\\omega)$\")\n", + " plt.legend(bbox_to_anchor=(1.04, 1))\n", + " plt.show()\n", + "\n", + "\n", + "lam, gamma, w0 = fitinfo[\"params\"]\n", + "plot_fit(SpectralFitter._meier_tannor_SD, J, w, lam, gamma, w0)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "e50c6ab7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_fit_components(SpectralFitter._meier_tannor_SD, J, w, lam, gamma, w0)" + ] + }, + { + "cell_type": "markdown", + "id": "949b87bc", + "metadata": {}, + "source": [ + "And let's also compare the power spectrum of the fit and the analytical spectral density:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "4d4a94c1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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/yf6PPPIIu3fv9kBlpyYlJcVoN7drKth3gd25c6e7SzKdQruIiHi/EzZXqqyEvDz7YbNTY44ZPK0/4dgfalu9osYNBYq/SEtLY9q0acbxQw89xOzZsxvtu2LFCm688UbjOCMjgwsvvNDtNZ7o73//uzEavWvXLs4880yWL1/eoE9hYSF33303Tz/9tNNLWZppwoQJRvuxxx5rcsOkt99+mz//+c+eKstUCu0iIuL9Tlin/cABx2G9zRWbFHbJeWSwHoCthxLxgcUzxEQvv/yy8eBjTU0Nl19+OZdddhkffPABixYtYvbs2dx8882MGzfOWDkmIiKC//znPy7d7dRZp59+Oo888ohxvHHjRk4//XR69erFxIkTGTFiBJ06deKf//wnAP/+978bnH/ikpXe4Pe//73xg8i+ffsYNmwYjz32GD/88AMLFizgjTfeYNKkScaykDd7zVqu7qMlH0VExPudMD3m+Cg7OLkHz+DBZMb8h1XWUQCsWXyUsy+KauEkCVQdOnRgwYIFTJ48mX379gHw2Wef8dlnnzXaPzY2li+//LLJXVA94ZFHHiEkJIRHH33UmA++a9euBivgREZG8sorrzB58uQG55641KU3GD58OI8//jh/+ctfADh8+HCDH0zqu/LKK3nwwQd5/fXXPVmix2mkXUREvF8zob2FleDsLBYyRzoeZvvpY/+f/yqnpn///qxfv5677rqL6OjoRvuEhoZyzTXXsGHDBs4880zPFtiIhx56iLVr13LnnXfSt29foqKiiI+PZ8iQIdx7771kZ2fzm9/8hkOHDhnnREZGEhXlnT/APvTQQ7zxxht06NCh0T/v1KkTL7zwAh999JFHH/41i8Xmr1tpCSUlJcTHx1NcXExcXJzZ5YiItN0f/wjHHwhctozXNozh1lvthzNnYrSbs/Ffixj8P+MBuLzXz8zacZrLyquoqGDXrl307NmTiIgIl11XvENFRQWLFi1i586dFBQUEBcXR7du3TjzzDN98vv1008/Nebtjx49+qT5796moqKCxYsXs3HjRsrLy+nQoQN9+vThjDPOMGU6Uv26XPHfvbN5LWCmx1RXV/Pzzz+zbNkysrKy2Lx5M3v27KGoqIjq6mri4uLo2rUrI0aM4IorrmDy5Mku32q4vp07d/LWW2/x9ddfs3fvXqxWK507dyY9PZ3rrruOqVOnEhISMP/ziIg071RH2oH+155G9P9YKSOG1XudPEkE+3z1c845x+wyXKb+nPYxY8aYWIlzIiIimDx58knTegJNwKTCBx98kOeee67JPy8oKKCgoID169fzxhtvMHToUP7973832GTBVWbMmMH9999PZf0vIexBfufOnXz++eeMHj2a9957j169ern8/iIiPscFoT04Pobh8etYVDyUvTVdOPTTXjqM6NbyiSI+wGazOTVF5D//+Q/ffPONcXz99de7sSpxpYCZ037iLKDo6GjS09OZMGECZ555Jv37928wsr5u3TrGjx/PkiVLXFrH448/zh/+8AcjsAcFBTF48GDGjx/fYE3SFStWMGHCBA7UXyJBRCRQnRDaDx50HDob2gEyB5Ub7Z/e2eSCwkS8w2OPPcYtt9zCggULqKk5eVnTffv28ac//alBSL/kkktMfXhWWidgRtojIyO58MILufjiixk/fjxpaWkn9Tl8+DAzZszg6aefpra2FqvVyrXXXssvv/xCTEzMKdfw3XffNXjyecyYMbz11lv069cPsO/o9cknn3DzzTdjtVrZt28fV1xxhct/cBAR8TkuGGkHGDGlHRzb7Xz1j6Wc33x3EZ9RXl7O66+/zuuvv05ERARpaWm0a9eOmpoacnNz2bFjR4P+3bt3Z+bMmSZVK20RMKH98ccfb7FP+/bteeKJJ+jRowe33HILADk5OXzyyScNdkhrC5vNxv3332+M+KelpTFv3rwGT2wHBQVx1VVX0a5dO2Pe1tKlS/nss8+49NJLT+n+IiI+rf7mSmFhRmiPiYHWLHyReVUvODZ2snprHNTVgRufXxLxlPqzBSoqKli/fn2TfSdOnMj7779vrEUvvkGfVI24+eab6d27t3G8YMGCU77m3LlzG/wHNGPGjCaXWJo0aRJXXXWVcfz000+f8v1FRHxaEyPtrc0cvfqFkBRq3wznp6p0bFnZLipQxFyPPvooc+fO5Y9//CPjxo2jc+fOREREEBISQnJyMunp6dxxxx3897//5ccff6STUxsciDcJmJH21jrttNOMXyUdrD95so3qb4Hcs2fPFp9Cv+222/joo48AWLVqFfv27SPVmW3/RET8Ub3QXmELp7jY3m5taLdYYETvQr7fHEcendg36wu6Ds1wYaEi5ggNDeXcc8/l3HPPNbsUcRONtDeh/kMcsbGxp3y9r7/+2mhPmTKlxSe8x40b12Azh/rni4gEnHqh/VCxY8v1tgwWjhjv+GxdPTf/lMoSEfEUhfZGVFdXN9ho4FTXMD106FCD0XpnrhcSEkJmZqZxnJWVdUo1iIj4tOOhPSSEvMOOr662TMnNPLed0f5pYwTU1p5qdSIibqfQ3oiHHnrICNlJSUmnvIbppk0NlxWrP1++OfX7nXgNEZGAcjy0h4eTX29wvH371l8qc6TjN52rK9Nhw4ZTLE5ExP0U2rFPhTlw4ACff/4555xzDs8++yxg34Hrgw8+oF27di1coXm7d+9ucNytm3ObedTvd+I1REQCSr3QfuSI4+22fDx37gyd4soA+IkR2BYuckGBIiLuFbAPoiYnJ3Ok/id/PRaLhcmTJ/P3v/+dwYMHn/K9SktLGxzHx8c7dV5cXFyT12hMZWVlg11WS0pKnKxQRMTL1QvtBQWOt9sS2i0WyBxaw1eLoIhEtn+zlb53uaZMERF30Uh7I8aOHcvtt9/OwIEDXXI9q9Xa4DgiIsKp8yIjI5u8RmOeeuop4uPjjVfXrl1bV6iIiLc6HtrDwhqMtCclte1ymWc5Fhj4aXk1nLBrtoiItwnY0H722WczZcoUpkyZwplnnkn//v2NjQmWLFnCZZddxujRo9m1a9cp3+vE7YRDQpz7BUf9ftXV1S32f+CBByguLjZeOTk5rStURMRbHd9cyQXTYwAyRzm+/laX9INt206hOBER9wvY6THH10Cvr6CggNdff53HHnuMsrIyVq9ezYQJE/jpp5/o0KFDm+914iZKFRUVTW6sdGK/4+ov/9iU8PBwwsPDW+wnIuJzXDinHWDECEd7NZmwaBH069f2+kRE3CxgR9obk5SUxH333cfixYuNtdlzcnK45557Tum6MTExDY7Ly8udOu/o0aNNXkNEJKC4OLQnJ0OPFPvAyM+cRu3CJadYoIiIeym0N2LYsGE89NBDxvGHH35IQf0nn1opOTm5wfGBAwecOq/+2u6nuoKNiIjPqqmBujp7u96DqMHB4ORz/Y3KPD0UgKNEs+lH5z6XRUTMotDehGnTphntmpoaVq9e3eZrpaWlNTjeu3evU+fVn5Pev3//Nt9fRMSn1VsVq/5Ie2KifSWYthoxMthor97fGZwcUBERMYNCexNOXHmlqeUhndG3b98GD5WuW7fOqfPWrl1rtAcMGNDm+4uI+LQmQvup/gKy3qbT9nntK1ac2gVFRNxIob0JxcXFDY4TEhLafK2wsDBGjRplHC9Z0vLcyYMHD7J9+3bjePz48W2+v4iIT6sX2qtCozm+bcWphvbhw8FisS/1+BMjYPnyU7ugiIgbKbQ3YdGihjvk9e7d+5Sud8kllxjtefPmkZeX12z/9957z2gnJCQotItI4KoX2gtJNNqnGtrj4iCtTy0A68mgamnbp0GKiLibQnsjqqqqeOKJJ4zj3r17nzQvvbWuueYaYznG6upqnnnmmSb7Wq1W/vnPfxrH1113HaGhoad0fxERn1UvtB+xOXZTcsXz+SNG2acuVhFO1k9V4MSeGCIiZgiI0P7DDz9w7733sn///hb7HjhwgIsuuqjBvPM///nPjfbdvXs3FovFeE2fPr3J66ampnLbbbcZxzNmzODTTz89qV91dTU33HCD8bBqZGQkDz74YIt1i4j4reMbK9EwtLd1N9T66s9r/6lqCKxff+oXFRFxg4DYXKmsrIznnnuOf/zjH5x++umMGzeOIUOGkJycTFRUFFarlZ07d7J48WK++OKLBuujX3zxxdx0000uqWP69OnMnTuXbdu2UVtby5VXXsm1117L1KlTSUpKYsuWLfzrX/8iKyvLOOfZZ5+lc+fOLrm/iIhPqj/SXptgtF0x0n7iw6i3r1jRcOclEREvERCh/bi6ujqWLFni1IOgADfccAOvvPIKllNZU6yexMRE5syZw6RJk8jJyaGuro53332Xd999t9H+9913H3feeadL7i0i4rPqhfaCWsfC7K4I7RkZEBxso7bWYl9BZvnf4He/O/ULi8+rqqrizTffZNasWWRnZ1NQUED1selTGRkZrFu3juuvv563334bgEceeaTZ37iLnKqAmB4zYsQI/vSnPzFw4MAWA3hYWBiXX345Cxcu5N///jdhYWEuraVfv35kZWVx0003ERkZ2WifAQMG8MUXX/C3v/3NpfcWEfFJ9Ufaq+OMtitCe1QUDB5kX0FmI4MoW7ru1C8qPq+0tJQzzjiD22+/3Vg8olrPO4jJAmKkPTU1lb///e/8/e9/p6ioiPXr17Nz507y8/OprKwkOjqaxMREBgwYQEZGBhEREU5dt0ePHthstlbXk5CQwOuvv87zzz/Pjz/+SE5ODmVlZaSkpDBkyBCGDRvW6muKiPit+iPt1bFG2xVz2gEyRwaxPgvqCGbdngTG5uVBx46uubj4pAcffLDBpordu3enT58+xp4rrVlRbvfu3fTs2dM4bktuEIEACe31JSQkMGHCBCZMmGB2KcTGxjZYClJERBpRL7QXVUcZ7cTExjq3XmYmvP66vb2aTMauWAH6bA5YtbW1xpQXgKeffpr777/fxIpE7AJieoyIiPiw+qG9yhHa4+Mb69x69R9GXcbp2hk1wG3dupXSYzt4hYSEcM899zTZ96233sJms2Gz2TSfXdxOoV1ERLxbvdBeXOl4FugUNqpuID0dYmPqAFjMOGyrf3LNhcUnFRQUGO1OnToZU2JEzKbQLiIi3q3+SHul45mjuLjGOrdecDCcPta+SMFBUtix6gho3nHAqv/AaXBwsImViDSk0C4iIt6t3uZKReX2naVjY8GVA6DjxjlWFltcmgE7d7ru4uL1FixYYGyUOHHiROP9PXv2NNhE8fjruOuvv77JDRbfeustLBZLg4dQgUav19IGjSKg0C4iIt6u/kj7sdDuqvnsx40f72gvZhz8pCkyIuJdNFFLRES8W/057UdDAdfNZz8uMxPCQmqpqglmEePhp5lw1VWuvYl4raSkJKZMmQLY57QfX+4xIiKizavNdenShSlTplBeXs6iRYuM94/f50R9+vRp030kcCi0i4iIdzsW2qsI5Wil/WvL1aE9IgJGnlbLklXB7KAPB5bsIMW1txAvlp6ezrfffgvYp8ocnyLTsWNH4/3Wmjx5MpMnTz5pnfa2Xk9E02NERMS7HQvtxTjmxLh6egzAuLMdO2AvXhsDdXWuv4mISBsptIuIiHdrJLS7eqQdYNw4R3txZSZs3er6m4iItJGmx4iIiHc7FtqLSDDeckdoP/10sFhs2GwWx8Oo/fu7/kbHjBgBBw+67fI+r1MnPQ8sUp9Cu4iIeLdGQrs7psfEx8PQ3qWs3R5HFukULf2IhF+5/j7HHTwIubnuu76I+BeFdhER8W4eGmkHGHdWKGu3g40gli6s4QL33AawjyRL0/TvR6QhhXYREfFuxzZXcvecdoBxkyP556v29uJtHbmgpsa1uzjVo6kfItIaehBVRES8W0UF4P7pMXDCw6g1Y2DTJvfcSESklRTaRUTEu3lwekzHjtA3uQCA1WRSvmyte24kItJKCu0iIuLdjo20e2J6DMAZw+33qyaMn34odN+NRERaQaFdRES8mwenxwCccX6c0V6yJsJ9N5KAEBTUMGrZbDaTKhFfp9AuIiLe7XhoD0oy3nLrSPu5MUZ7SU4P7YwqpyQ6OrrBcXl5uUmViK9TaBcREe92fEdUS4LxljtH2vv2hfZhRQAsrR1F3Y5d7ruZ+L2EhAQiIhy/sdmxY4eJ1YgvU2gXERHvdnyk3ZIIQHg4RLhx1orFAmf02g9AMQls/FIhS9ouODiY9PR043jGjBnU6bc30gYK7SIi4t2Oh3abfXjdnVNjjjtjdK3RXjKvwv03FL927bXXGu033niDLl26MHnyZKZOnWq8PvzwQxMrFF+gzZVERMS7mRHaL2kHb9nbS7JiucP9txQ/9j//8z988cUXzJ8/H4CDBw9y8ODBBn2GDh1qQmXiSzTSLiIi3q2ykjoslNbZH+hz53z244adn0IkRwFYktfX/TcUvxYaGsoPP/zAW2+9xYUXXkjXrl2JjIw0uyzxMRab1h7yWyUlJcTHx1NcXExcXFzLJ4iIeBubDYKDKbbFkkAxAJMmwQ8/uP/WZyWuZX7RMAD2/HSYbsPbN9m3oqKCXbt20bNnzwYPHYqI/3LVf/fO5jWNtIuIiPeqrgabjVJijbc8NQZxRtpho730k/2euamISBMU2kVExHsdm89egiOpeyy0j3d8RS5ZWNtMTxER91NoFxER73Vsjfb6I+2xsU11dq3Rl3UmCHtYX7KpnWduKiLSBIV2ERHxXiaOtMcN70u6JRuA7OKuFBd75r4iIo1RaBcREe91LLSbMaed0FBOT94GgI0gflqs7edFxDwK7SIi4r0aGWn31PQYgJEDrUZ71ZxDnruxiMgJFNpFRMR7NTKn3ZMr2I4c71jGbdVyPYwqIuZRaBcREe9l8kh72pQexFICwKrtiZ67sYjICRTaRUTEe5k5px0ISh9MJqsB2H80kdxcz91bRKQ+hXYREfFeJo+0ExvLyIStxuHqlXUevLmIiINCu4iIeC+T57QDZPYrMdqrftC6jyJiDoV2ERHxXmaPtAMjTw8x2quWVXv25iIixyi0i4iI9zJ5TjtAl7E9SGE/AKu3xFHXzAwZm83moapExGye/u9doV1ERLzXCSPtQUEQGenZEizpQxjJKnsdlRFs3Xpyn6Ag+9dpXXOJXkT8yvH/3o//9+9uCu0iIuK9Thhpj4sDi8XDNfTuzYiQ9cbh2rUndwkJCcFisVB5bA6+iPi/iooKLBYLISEhLXd2AYV2ERHxXsdC8PGRdk/PZwcgOJihPR0PoK5bXXVSl6CgICIjIykrK/NkZSJiopKSEmJiYjTSLiIi0thIuxmGDXd8Xa5bVt5on5iYGMrKyqiqOjnUi4h/KSsro6KigjgPfigptIuIiPeqqKCWIMqIAUwaaQc6j+pKMocBWPtLGI09fxYfH09ISAj79u2jtrbWwxWKiKeUlZWRk5NDdHQ0MTExHruvZybhiIiItEVFBVYcX4pmjbRbMtIZxlp+4BwOl0Zy4AB07tywT0hICF27dmX37t1s376d+Ph4YmJiCA4OxuLxifgi4io2m426ujoqKiooKSmhoqKC6OhoUlNTPTY1BhTaRUTEm1VWmrpGu2HIEIbyb37gHADWrTs5tAOEh4fTs2dPioqKKC4uprCw0LN1iojbWCwWYmJiaNeunUfnsh+n0C4iIt6rosLUNdoNyckMTdgDRfbDtT/bOP/8xkfPw8LC6NChA+3bt6empkZTZUT8QFBQECEhIR4P6vUptIuIiPeqqPCOkXZg2KAqWGpvr1tRATS/YLzFYiE0NJTQ0FD3Fycifk8PooqIiPfylpF2oN/oJCI5CsC6tdpESUQ8S6FdRES8l7fMaQeChw5hCNkAbN8fTWmpebWISOBRaBcREe/lRSPtpNtXkDkuK8vEWkQk4Ci0i4iI9/KiOe3078+QoF+Mww0bTKxFRAKOQruIiHgvbxppDwtjUDfHnJiNWVoVRkQ8R6FdRES8lxfNaQcYdFq40d6wutzESkQk0Ci0i4iI9/KmkXagfWYPOpAHwMYtweYWIyIBRaFdRES8lzfNaQcYPJjB2CezHyqJ5PBhk+sRkYCh0C4iIt7Ly0baGTSIQWw0DjdubKaviIgLKbSLiIj38rI57XTvzuCwbcahVpAREU9RaBcREe9kszUYaQ8OhshIk2sKCmJwr6PG4cb11SYWIyKBRKFdRES8U3U12GzGSHtsLFgsJtcEDDwtwmhv+KnCxEpEJJAEVGgvKiris88+46677mL8+PF06tSJ8PBwYmJi6NatGxdddBEvvPAChYWFbrm/xWJp9euVV15xSy0iIl6vwh6IrcQAEBNjZjEOCaf1IpUcADZuC8NmM7kgEQkIIWYX4AmbN2/m3nvv5fvvv6eqquqkP6+qqqKsrIycnBzmzJnDQw89xJNPPsndd9+NxRuGdUREAlFlJeAI7abPZz9u8GAGsZF9dKWwLJwDB6BzZ7OLEhF/FxChfcOGDcyZM6fBe8HBwfTp04eOHTtSW1vLpk2bKCgoAODo0aP88Y9/ZOPGjbz66qtuCe7jx48n0onJmd26dXP5vUVEfEJFBTa8b6SdQYMYzId8x7mAfQUZhXYRcbeACO3HhYSEcOGFF3L99dczceJE4uqtHWaz2fjyyy+58847yc3NBeD111/ntNNO44477nB5LW+//TY9evRw+XVFRPxGRQXlRGI7NpPTa0J7ly4MitwFxzZE3bgRJk82tyQR8X8BMac9NDSUm2++mR07dvDZZ59xySWXNAjsYJ9vfskll7B8+XI6depkvP/www9TXa3VAUREPK6iwhhlBy8K7RYL/fs4vhe2ZJ887VJExNUCIrRfcsklvPbaa05NNenatSuPPvqocZyfn8+iRYvcWZ6IiDSmstI7QzuQNizaaG9eV25iJSISKAIitLfWRRdd1OB48+bNJlUiIhLAvHWkHUga3pMO5AGweUeoydWISCBQaG9EUlJSg+OSkhKTKhERCWAVFZThGNH2ptDO4MGksQWAg8VRFBebXI+I+D2F9kbs2bOnwXGHDh1MqkREJIB58Ug7gwbRH8dvYbdsMbEWEQkICu2NmD17doPjMWPGuPwe9957L4MGDSIuLo7IyEhSU1OZOHEi06dPZ9euXS6/n4iIzzlhTnt0dDN9Pa1DB/pH5xiHmkUpIu6m0H6C4uJiZsyYYRynp6czcOBAl99n1qxZ/PLLL5SWllJRUUFubi4LFizg0UcfpV+/ftx+++2Ul+vhJhEJYN480m6x0L+nY9WYzWv1eS0i7hVQ67Q745577uHgwYPG8RNPPOGW+yQnJ9O7d29iYmIoLi5m8+bNWK1WAGpqapg5cyarVq1i/vz5xMfHO3XNyspKKo/tIAiaiy8iPs6bQzvQf2gEbLC3t/xcBrS8YZ6ISFtppL2e119/nTfeeMM4vuqqq05aSeZUDBw4kBdeeIEdO3Zw+PBhVqxYwbx581i9ejWFhYXMmTOH9PR0o//atWu5+uqrnb7+U089RXx8vPHq2rWry2oXEfE4Lw/t3UenEE4FAJu36etURNxLnzLHLFq0iDvvvNM47tmzJzNnznTpPTZu3Mjdd99Nr169TvqzkJAQLrjgAlauXMkFF1xgvP/tt9/y1VdfOXX9Bx54gOLiYuOVk5PT8kkiIt7Ki9dpBwhOH0RftgGwLS+OmhqTCxIRv6bQDqxbt46LL76Yqir7/MQOHTrw7bffOj0txZUiIiL44IMP6Nixo/Heiy++6NS54eHhxMXFNXiJiPis8nKvDu31V5CprgtBawiIiDsFfGjfsmULU6ZMofjYIruJiYl8//339OvXz7SaYmNjueOOO4zjxYsXU1FRYVo9IiKm8PbQnpRE/5h9xqFWkBERdwro0L5r1y4mTZrEoUOHAHtYnjt3LhkZGSZXBhMnTjTaFRUVmuoiIoHHy+e0A/Tv7hhQ2bKm1MRKRMTfBWxo37dvH2effTb79tlHSaKiopgzZw6jRo0yuTK7Tp06NTjOz883qRIREZOUl3vvjqjHpKWHG+3NqxTaRcR9AjK05+XlMWnSJGMTo/DwcD7//HPGjx9vcmUOR48ebXAcFRVlUiUiIibx9ukxQNrp7Yz2Zu2KKiJuFHCh/ciRI0yaNIktx/acDg0NZdasWUyePNnkyhrauHFjg+MOHTqYVImIiElOCO2RXrgMeuyINLpg/43t5lw9/C8i7hNQob24uJgpU6awYYN9N4zg4GDef/99LrzwQpMrO9mHH35otHv06EFKSoqJ1YiImKDenPboqDqCvPEba+BA0rAPAh2pjKGgwOR6RMRveeNHoFuUlZVxwQUXsGbNGgCCgoJ4++23mTZtmsmVnezLL79kzpw5xvHUqVPNK0ZExCz1Rtq9cWoMAHFx9I0+YBxu22ozsRgR8WcBEdorKyuZOnUqS5cuBcBisfDaa69x3XXXndJ1d+/ejcViMV7Tp09vtF9xcTGXX3658QNDcz744AOuvfZa4zgqKor777//lOoUEfFJvhDagX6pjmeQtq4uNrESEfFnIWYX4AkzZsxg3rx5xnFCQgIff/wxH3/8sVPnT548mXvuuafN97fZbMyePZvZs2fTv39/pkyZwtChQ0lJSSE6OprS0lKys7OZNWsWq1evNs6zWCy8+eabJ60kIyISEOqH9liLycU0rV//II7NkGHrykL4fYKp9YiIfwqI0H7iSiyFhYV89913Tp/vytC8efNmNjuxA0dsbCwzZ87kyiuvdNm9RUR8SXV5DZVEABAT472hve+IePjC3t62qdrcYkTEbwXE9BizRUZGcuuttzJo0CAslua/eOLj47nrrrvYsGED11xzjYcqFBHxPmVljrY3T4/pOS6VYGoA2Lo3wuRqRMRfWWw2m56a8aDCwkLWrVvHoUOHyM/Pp6ioiKioKJKSkkhPTyc9PZ3g4GCX3KukpIT4+HiKi4uJi9NSZCLiW3JTRpB68CcALr8cZs0yuaCmFBbSJ+kIO+hDTPBRSqqjaGF8RkTE4GxeC4jpMd4kMTGRiRMnml2GiIjXs5Y7BjC8eaSdxET6hf/Mjso+WGujOHgQtEqviLiapseIiIhXslY6xpW8OrQD/ToUGe2tq4qa7Cci0lYK7SIi4n1sNqwVocZhdLSJtTihb686o71t6SETKxERf6XQLiIi3qeqCiuOpO71I+0ZkUZ767qjzfQUEWkbhXYREfE+FRXGGu3gA6F9bHujvXW7vlpFxPX0ySIiIt6n3sZK4P2hveuZvQmnAoBth7Ral4i4nkK7iIh4Hx8L7UEdkukTvAuA7WUp1NaaXJCI+B2FdhER8T4+FtoB+iUeBqCKcHI2FJtcjYj4G4V2ERHxPj42px2gb2qF0d764z4TKxERf6TQLiIi3qe8nDIfWj0GoN9Ax2ZQW1drpF1EXEuhXUREvI8vTo/JTDDa2zbVmFeIiPglhXYREfE+Phja+57V1Whv3RfZTE8RkdZTaBcREe9zwpx2b98RFaDj4PbEUgrA1sIOJlcjIv5GoV1ERLyPD460W4Is9I3eD8Du2lSq8ktMrkhE/IlCu4iIeJ96oT00uJawMJPrcVK/TvYHUOsIZuePu80tRkT8ikK7iIh4n3qhPSbCdx7q7Ne7zmhvXXrYxEpExN8otIuIiPepN6c9JtJ3thftmxFltLdllZtYiYj4G4V2ERHxPvVH2iPrWujsPfqN62i0t+4MbqaniEjrKLSLiIjXsR2tF9qjfSe09x3rWDVm26F4EysREX+j0C4iIl6nwlqD7dhXVIwPLPd4XGKSheSQQgC2VnSDsjKTKxIRf6HQLiIiXsdaajPavrDcY339kvIByCWVsnXbTK5GRPyFQruIiHgdq9XRjom1mFdIG/TrVmG0ty/YZ2IlIuJPFNpFRMTr1A/t0T4W2vsODDXaW3/SBksi4hoK7SIi4nWsRx1fTzFxvrUKS7+RiUZ762bfeYhWRLybQruIiHidBqE9IcTESlqv7+ntjfa23KhmeoqIOE+hXUREvI613DG6HpPgWyPtffo5vlq3lnaCiopmeouIOEehXUREvE6D0B7rW19V0dGQGnUEgK30g61bTa5IRPyBb30SiohIQLBWOh7m9LUlHwH6dioF4AjJFKzabnI1IuIPFNpFRMTrWKvCjLYvhva0fo515rcsO2JiJSLiLxTaRUTE65RV+fZIe9ppjqK3ZFWaWImI+AuFdhER8TrW6nCj7ZOhfUyS0d6yO7yZniIizlFoFxERr2OtiTDaPhnaBzoepN1SkAzV1SZWIyL+QKFdRES8S00NVptjffPoaBNraaPu3SE8qAqALbZ+sF0Po4rIqVFoFxER71JejhXH8LovjrQHB0Of5CIAttGXmuxN5hYkIj5PoV1ERLyLH4R2gP697CPt1YSxe9l+k6sREV+n0C4iIt6loqJBaI+KaqavF0tLdzyAumXtURMrERF/oNAuIiLepd5Ie3RIBUE++k2VNirRaG/ZajGxEhHxBz76USgiIn6rXmiPCfXdNc7TBoUY7S2HEqGmxsRqRMTXKbSLiIh3KS+nDPuSMTFhVSYX03ZpaY72lro+sGuXecWIiM9TaBcREe9Sb057TLjvrm+ekAAdoq0AbCENNm40tyAR8WkK7SIi4lVqSsupIBKAmAjfnlKS1tX+AOpBUij+eYfJ1YiIL1NoFxERr1JW6JjHHhNZa2Ilpy5tYL157auKTaxERHydQruIiHgVa6FjdD060mZiJacuLTPOaG/Z7Nt/FxExl0K7iIh4FWuRI7THRNWZWMmpa7CCzL5oqPXt3xyIiHkU2kVExKtYi+uFdh/dDfW4/v0d7S21vWHPHvOKERGfptAuIiJexVrimEYSHePbmxL17AmhQfYfQraQBr/8YnJFIuKrFNpFRMSrlJU6psTExvl2aA8Jgd6dygDYRl9qN2wyuSIR8VUK7SIi4lWsVkc7Os73v6bS+tl/c1BBJHtX55lcjYj4Kt//NBQREb9iLXOMrsfEhzTT0zekDYs22ls2+O4OryJiLoV2ERHxKmVHHe2YBD8I7YNDjfaW3RFg09KPItJ6Cu0iIuJVrEeDjXZ0QmgzPX1DWpqjvbmqJ+TkmFeMiPgshXYREfEq1grH6HpMUpiJlbjGgAGO9i8M1AoyItImCu0iIuJVyiodI+0x7cJNrMQ1kpKgU7x9zo9Cu4i0lUK7iIh4FWulY0qMP0yPARjUrxqAfNpzaI2mx4hI6ym0i4iIV7FWOabExMT69jrtxw08LdJo/7JOK8iISOsptIuIiFcpq3ZMiYmJMbEQFxo0zPGDyC87tYKMiLSeQruIiHgVa02E0Y6ObqajDxk40NHeWNELDhwwrxgR8UkK7SIi4lWstfbQHkQtEREtdPYR9UO7HkYVkbZQaBcREe9hs1FWZ5//HRNUjsU/prTTrh10jNMKMiLSdgEV2ouKivjss8+46667GD9+PJ06dSI8PJyYmBi6devGRRddxAsvvEBhYaHba9m5cycPP/www4cPp3379kRGRtK7d28uvfRSZs2aRU1NjdtrEBHxOtXVWLFPZI8OqTC5GNca2Ne+gswhOpK/Zo/J1YiIr7HYbP7/NMzmzZu59957+f7776mqavmp/aioKJ588knuvvtuLG4Y5pkxYwb3338/lZWVTfYZPXo07733Hr169WrzfUpKSoiPj6e4uJi4uLg2X0dExGOKikhKrKOQJPpG7WNrWarZFbnM726t5OXX7A/ZLhzyO8ZnvWRyRSLiDZzNawEx0r5hwwbmzJnTILAHBweTlpbG+PHjGTt2LElJScafHT16lD/+8Y/ceuutuPpnmscff5w//OEPRmAPCgpi8ODBjB8/npSUFKPfihUrmDBhAgf0sJKIBJKjRynD/vRpTKh/LY04aJhjVZxfdoRpBRkRaZWACO3HhYSEMHXqVD7//HMKCgrYvHkzCxcuZMmSJeTn5/P555/TpUsXo//rr7/OK6+84rL7f/fddzzyyCPG8ZgxY9i0aRPZ2dksXLiQffv28eGHHxJzbI2zffv2ccUVV7js/iIi3q6q6ChV2MNtdFi1ydW4VoMVZI72hEOHzCtGRHxOQIT20NBQbr75Znbs2MFnn33GJZdcctKvHywWC5dccgnLly+nU6dOxvsPP/ww1dWn/sVhs9m4//77jZH7tLQ05s2bR79+/Yw+QUFBXHXVVXz22WfGe0uXLm1wLCLiz8oKHNMGY8L9bKR9kKOth1FFpLUCIrRfcsklvPbaa3Tr1q3Fvl27duXRRx81jvPz81m0aNEp1zB37lzWr19vHM+YMYOoqKhG+06aNImrrrrKOH766adP+f4iIr6gQWiP8K8H8pOToX1sOaDQLiKtFxChvbUuuuiiBsebN28+5WvOnj3baPfs2ZNzzjmn2f633Xab0V61ahX79u075RpERLydtdDxm83oiDoTK3GPgb3tvz04SAoFP+82txgR8SkK7Y2o/1Aq2J/qPVVff/210Z4yZUqLq9KMGzeO6HpbAdY/X0TEX9UP7TFR/hfaB51W72HUn/1rSUsRcS+F9kbs2dNw/dwOHTqc0vUOHTrEwYMHjeMxY8a0eE5ISAiZmZnGcVZW1inVICLiC8qK64X2aP9bXWXgaY4tXjfuCG+mp4hIQwrtjag/lQWcC9nN2bRpU4Pj3r17O3Ve/X4nXkNExB9Zi2qNdr1fNvqN+g+jbijtDvn55hUjIj5Fof0ExcXFzJgxwzhOT09nYP11utpg9+7dDY6deSD2xH4nXkNExB9ZSxxTYo6tfutXhgxxtLMZAhqQEREnKbSf4J577mkwleWJJ5445WuWlpY2OI6Pj3fqvPrLUp54jcZUVlZSUlLS4CUi4kvKrI4pMTFx/vcV1a4ddI63ApBFOraNWkFGRJzjf5+Ip+D111/njTfeMI6vuuqqk1aSaQur1drgOCIioomeDUVGRjZ5jcY89dRTxMfHG6+uXbu2rlAREZPV/6iLjvXPr6j0NPuyloUkkbsq1+RqRMRX+OcnYhssWrSIO++80zju2bMnM2fOdMm1a2oarjUcEhLi1Hn1+zmzwdMDDzxAcXGx8crJyWldoSIiJrOWOdoxCc59Vvqa9JGOgZustbXN9BQRcfDPT8RWWrduHRdffDFVVfb1czt06MC3337r9DSWlpy4iVJFRUWTGyud2O+4aCeeyAoPDyc8XKsRiIjvKjvqWA7XX0P7kJGOz//sHVGcb2ItIuI7An6kfcuWLUyZMoXi4mIAEhMT+f777+nXr5/L7hFzwtNU5eXlTp139OjRJq8hIuKPrEeDjXZ0QqiJlbhPeobjB5Os0h5QVGRaLSLiOwI6tO/atYtJkyZx6NAhAGJjY5k7dy4ZGRkuvU9ycnKD4wMHDjh1Xv0HYtu1a+fSmkREvJG1whHaY9r5528O+/eHkCD7tJgs0rWCjIg4JWBD+759+zj77LPZt28fYJ/CMmfOHEaNGuXye6WlpTU43rt3r1Pn1Z+T3r9/f5fWJCLijcoqHFNiYpLCTKzEfcLCYECnQgA205+qrM0mVyQiviAgQ3teXh6TJk1i165dgH0u+Oeff8748ePdcr++ffs2eKh03bp1Tp23du1aoz1gwABXlyUi4nWsVY4pMdHtnFtpyxcN6W9foKCGUDYvPWJyNSLiCwIutB85coRJkyaxZcsWAEJDQ5k1axaTJ0922z3DwsIajOAvWbKkxXMOHjzI9u3bjWN3/UAhIuJNrFWO0fWY9pHN9PRt6aMdD6Nmra9rpqeIiF1Ahfbi4mKmTJnChg0bAAgODub999/nwgsvdPu9L7nkEqM9b9488vLymu3/3nvvGe2EhASFdhEJCGXV9nnswdQQHuuf02MA0sfGGu2sXXHN9BQRsQuY0F5WVsYFF1zAmjVrAAgKCuLtt99m2rRpHrn/NddcYyzHWF1dzTPPPNNkX6vVyj//+U/j+LrrriM01D9XURARqc9aY/+cjKYMS5Clhd6+66QVZJzY9VpEAltAhPbKykqmTp3K0qVLAbBYLLz22mtcd911p3Td3bt3Y7FYjNf06dOb7Juamsptt91mHM+YMYNPP/30pH7V1dXccMMNxsOqkZGRPPjgg6dUp4iIr7DW2qfExAQdbaGnb+vcGRLD7TtJZTMENuthVBFpnn/uXHGCGTNmMG/ePOM4ISGBjz/+mI8//tip8ydPnsw999xzynVMnz6duXPnsm3bNmpra7nyyiu59tprmTp1KklJSWzZsoV//etfZGVlGec8++yzdO7c+ZTvLSLiC8rqAiO0WyyQnlrAwh3R7KcL+SsWkZyZaXZZIuLFAiK019+kCKCwsJDvvvvO6fM7derkkjoSExOZM2cOkyZNIicnh7q6Ot59913efffdRvvfd9993HnnnS65t4iIL7Da7Ls/RwdXmlyJ+6UPqmPhDns7e2kJE39vbj0i4t0CYnqMN+nXrx9ZWVncdNNNREY2vjLCgAED+OKLL/jb3/7m4epERMxTVWmjGvvDpzGhFSZX434NHkbN9t/5+yLiGhabzWYzu4hAVVpayo8//khOTg5lZWWkpKQwZMgQhg0b5pLrl5SUEB8fT3FxMXFxWp1ARLxb4cFKklLsD6Kel7iCbwpGm1yRe61aaWPUaHtYvzHmY94ovdLkikTEDM7mtYCYHuOtYmNjGywFKSISyKyHy4Fjq8eEVZtbjAcMGmzBQh02glhv7QVlZRAdbXZZIuKlND1GRES8gj2028VE+H9oj46GPnGHANjIIGo2bjG5IhHxZgrtIiLiFcoKHA+fxkTUmliJ52R0Lwaggki2zd9ncjUi4s0U2kVExCtYjzhCe3RUnYmVeE5GhqO9frl/L3MpIqdGoV1ERLyCtaDKaMdEB8YaCRkT4o32+l/0mJmINE2hXUREvEJZcY3Rjo4OjCUQMyZ1MNrrc9ubWImIeDuFdhER8QrWeqE9JraZjn6ka/cgEoJLAMg62hsq/H99ehFpG4V2ERHxCtZixzz2mNjA+HqyWCC93X4AcknlyKodJlckIt4qMD4VRUTE65VZHfPYo+OCTazEszJ6W432+u/zTKxERLyZW556OXjwIKtXryYrK4vdu3eTm5uL1WqlvLycyMhIoqOj6dKlCz169CA9PZ3MzExSUlLcUYqIiPgIa73QHpMQOA9lZgwPgeX29vpVlZxlbjki4qVc9qm4aNEiPvvsM7755hu2b9/e6vN79+7Neeedx9SpU5k4caKryhIRER9hLXM8fBqTGGpiJZ6VcVY7eMneXr81wtxiRMRrnVJoz8vL41//+hdvv/02e/fuBcBmc4yUWCwtP/1/vP/27dt56aWXeOmll+jSpQvXX389d9xxh0bgRUQCRNlRx3dGdGKYiZV41qDJnQmiljqCyTrY0exyRMRLtWlO+65du7jxxhvp0aMHjz/+OHv27GkQ1o+z2WzYbDZiYmJo3749qamptG/fnujoaOPPGjtn3759PPnkk/Ts2ZPrr7+eHTv0YI6IiL+zljvmsce0CzexEs+KjAmmX/geADZW9qa6rKqFM0QkELVqpP3w4cP89a9/5c0336Smpuak0J2YmMiECRPIzMwkPT2dfv360aVLFyIjI0+6Vnl5Obm5uWzZsoXs7GxWr17NwoULKSgoAOzhvaqqinfeeYf333+fG264gccff5wOHTqcdC0REfF91nLHV1JMcmBNE8nomMfmvb2oIpwt329j8KV9zS5JRLyM06H9hRde4NFHH6WkpKRBWO/Tpw9XXHEFl112GcOHD3f6xpGRkfTp04c+ffpwwQUXGO+vWbOG2bNnM2vWLLZt24bNZqOmpobXX3+djz76iOnTp/OHP/zB6fuIiIhvKKtyfCVFJ5882OPPMvpX8pF9linr/5uv0C4iJ7HYGpuj0oigoCAsFgs2m42QkBCuuOIKbrvtNsaPH++24hYvXszMmTP55JNPqK6uthdssVBbW+u2e/qTkpIS4uPjKS4uJi4uzuxyRESaNSJ+K2tK+hFMDdUFViyJCWaX5DHfPLKSCx4bBcC9oxfzzPJxJlckIp7ibF5r1Zz2sLAwfv/737N9+3bee+89twZ2gHHjxvHuu++yY8cO7rrrLiIiAuvXpSIigcRabZ/HHoMVS0y0ydV4Vsa5jkUX1u+IMbESEfFWTof23/72t2zdupUZM2bQrVs3d9Z0ktTUVF544QW2bNnCb3/7W4/eW0REPKOsxh7aoymD0MBZ8hGg88hUkjgCQFZBF5OrERFv5PT0GPE9mh4jIr4kMbiYorp4+gVtY0tt4M3pPivuJ+aXjgAgb2cZHXoG1m8bRAKVW6bHiIiIuIu1LgqAmOBykysxR0a3AqO9/ptcEysREW+k0C4iIqarqoIa7FNiokMqTa7GHBlDHL/4Xr+o2MRKRMQbKbSLiIjprKWOwBoTGqChfXy80V6fra9nEWlInwoiImI6a4FjF9CYsMDcEXTAud0JpgaArJxEk6sREW/Tqh1RW6u6uprdu3dTUlJCVVUVISEhdOnShZSUFCwWiztvLSIiPqQsvxw4tnpMeI25xZgkokcn+gdvZmPtADZZU6mqgrAws6sSEW/h0tD+008/sXjxYhYtWsS6devYt28fdXV1J/ULCwtj+PDhjBs3jkmTJnHWWWcpxIuIBDDrEceUmJiIwAztWCxkJO1j4+EBVBPGppUlZIzTyl8iYufS0D5y5EgjfDe3kmRlZSXLly9n+fLlPPPMM3To0IHrrruOP/3pT3Tu3NmVJYmIiA9oENojTx7sCRQZva28f9jeXv/dQYV2ETG4bU67xWJpdPT8xPdtNht5eXk8//zz9OnTh3vvvZfKysB8CElEJFCVFTrmsUdHBXBoH+4YS1u/IjCXvhSRxrl8TvvxEfbg4GA6depEamoqkZGRWCwWampqyMnJITc3l+rqauOc4yG+oqKCf/zjH/zwww988cUXdO/e3dXliYiIF7IWOr4TYgJ4T6GMs5PhZXs7a0u4ucWIiFdxaWj//e9/z4gRI8jMzKRv374EBwc32q+uro7s7GyWLFnC119/zY8//khVVRUWiwWbzUZWVhaTJk1i8eLFdOrUyZUlioiIF7IWOeaxx8SYWIjJOo7rR3sOcZgOrM/riM0GeuRLRMDF02NmzJjBr3/9a/r3799kYAcICgoiIyODO++8k2+++Yb9+/fz5JNPEh9vX6PWYrGwc+dObrnlFleWJyIiXqqspNZoR8cG7mrEluR2ZIRtBuBwdSIHDzT9fJiIBBav+GRMSkrigQceYOvWrZx99tnYbDZsNhvffPMNCxYsMLs8ERFxs9ISRziNjfeKrybTZKTkGe31CwpNrEREvIlXfTImJyfzzTffkJmZabz3wQcfmFiRiIh4grXU8fBpTHzTv6kNBBn9HQ/lrv/xiImViIg38arQDhAaGspTTz1lHC9atMjEakRExBNKrY6J27GJbt33z+tljIky2uvX1jbTU0QCideFdoCxY8cC9pVo9u/fb3I1IiLibqVljq+jmKTA3ga0/6RUQrGPtmftjDW5GhHxFl4Z2g8fPmy06y8NKSIi/sl61DElJrZdYIf2sIwBDGATAJuLOlJRYXJBIuIVPBrai4qK2LlzZ7N9CgsLufXWWwH7KjJaq11ExP+VljumxMS2jzCxEi8QE0NGzA4Aagnhlw2Bu9mUiDh4dOLgypUrOf/884mKiqJv37507dqVDh06EBERwdGjR9m1axfLly831mwHmDp1qidLFBERE1grQ412dHKkiZV4h4xuRbzzi729fn4Bp41INrcgETGdx5/2sdlsHD16lPXr17N+/fqT/qy+jIwMHnzwQU+WJyIiJiitsk+JicZKUGwAb4l6TEa6DY6H9iUlcK9Cu0ig82hoP77hUv1wbqm31VvHjh1JSUmhS5cuTJkyhVtuuYXwcG3jLCLi70qr7J/1MVghWqE9Y0ICfGhvZ20I7CUwRcTOo6F90qRJ5ObmsnLlSpYuXcp3333Hhg0bAHt4P3z4MMOGDePhhx9usFa7iIj4N2uNfR57LKUQ3c7kaszX/vS+dOIAB0lh/b4kbDaoN8YlIgHI46vHpKSkMHXqVJ599lmysrLYvXs3Tz75JN26daOuro7vvvuOMWPG8MADD3i6NBERMUlprX1t8liLFUJDW+gdANLSyCALgIKqWHJzTa5HRExn+pKP3bp144EHHmD79u383//9H7GxsdTV1fHMM8/wpz/9yezyRETEzaqqoNpmD+oxwVrfEIDwcDKScozD9T9rkyWRQOfS0L537942nxscHMztt9/OqlWrSElJwWazMWPGDJYsWeLCCkVExNuUljrasaHl5hXiZTL6lBnt9fMLTKxERLyBS0P7wIEDeeKJJ6iqqmrzNfr168eLL75oHL/88suuKE1ERLxU/dAeE9r27w9/kzHCMU1o/Ur9BkIk0Lk0tB89epRHHnmEAQMG8MEHH5y0hKOzzj//fKOtkXYREf9mLXZM/YiNUGg/rt+EFMKoBCBrW4BvOCUi7pnTvmvXLn71q18xcOBA3nvvPWpqalp1fklJCWBfGvLw4cPuKFFERLxE6WHHKHJMROu+L/xZ6GlDGMRGALbmJ1GumUMiAc2lof3SSy/FZrNhsViw2Wxs2bKF3/zmN3Tu3Jn/9//+H+vWrXPqOs8++6zRjouLc2WJIiLiZayHHWk0NlIPXBp69SIj2B7a6wjm2ArJIhKgXLpO+6effspXX33F7373O3Jycozwnp+fz/PPP8/zzz9PcnIyZ555JsOGDWPAgAF06tSJ+Ph4ysvL2bJlC++//z5ff/21selSz549XVmiiIh4mdL8SqMdG11nYiVeJiiIjC75cGyNh/UrK8jM1DQZkUDl8s2VLrroIs466yyefvppZsyYgdVqNQL48ekus2bNYtasWU1e4/hceIvFwrRp01xdooiIeJHSI4557DExJhbihTKG1DlC+8JC+F2KuQWJiGncMqc9Ojqaxx9/nB07dnD33XcTExPTIIiDPZg39qrfp0+fPtxxxx3uKFFERLyEtbDaaMfGmliIF0ofF2+0s9a3bXEHEfEPbt1cqX379jz//PPk5uby8ssvM2rUKGPKTH0Wi+WkMJ+Zmck333xDjIZdRET8WmmRYx57THywiZV4n3Zj+tGFfQCs35NAGxdlExE/4PLpMY2JiYnhjjvu4I477qCoqIgFCxaQnZ3Ntm3b2Lt3L2VlZVRXV9OxY0f69u3L1KlTOeusswgKMn3DVhERcTNrSb0lH+P1ud/AkCFksIxcUimuimLvXuje3eyiRMQMHgnt9SUkJDB16lSmTp3q6VuLiIgXKi1xDB/HJoU20zMAJSaSEbeLb+wrIbN+nY3u3S3m1iQiptCQhoiImKq01BFCYxIV2k+U0afMaK9fWGReISJiKoV2ERExlbXMEdpjk8NNrMQ7ZYwIM9pZK7XDkkigUmgXERFTlR51PHwak6x1yE/UZ0IXIrCH9fVb9EONSKBSaBcREVNZKxyhPbZDpImVeKeQYUMYjH071O1HEikra+EEEfFLTof2zMxM5s+f785aWvTjjz8ycuRIU2sQERHXKq2wT/8Ip4LQRC3ze5K+fckIsod2G0FkZ5tcj4iYwunQvmbNGiZNmsSkSZOYN2+eO2s6yQ8//MDZZ5/N5MmTWbNmjUfvLSIi7lVaZQ/tMVi1u1JjQkLI6HzYOFy/uqqZziLir1o9PWb+/PlMmTKFoUOH8sorr1BSUuKOuigtLeVf//oXQ4cO5dxzz2XBggUnbcokIiK+z1ptn6cdS6lCexMyBjvWstcKMiKByenQ/v3335OWlmbsWJqdnc2dd95JSkoKl156Ke+88w4HDx48pWIOHDjAO++8w6WXXkqnTp343e9+R3Z2tnHPAQMG8P3335/SPQ4fPszcuXN57LHHuPjii0lJSTF2ZLVYLLz11lundP3m1L+Ps69XXnnFbfWIiHiD0too4NhIe6TmtDdmyBnxRjtrfZ2JlYiIWZzeXGnSpElkZWXx8ssv89RTT3Ho0CEAysvL+fLLL/nyyy8B6Nu3L5mZmQwZMoS+ffuSmppKhw4diIyMJCwsjKqqKsrLy8nLyyM3N5etW7eSnZ3N6tWr2b59u3G/+qPqHTt25MEHH+SOO+4gJKRt+0EdPHiQ0aNHs2fPnjadLyIirldbC+V19hVjYoOPgkUbBzUmcUx/urGHvXQna08CdXWgTcNFAkurEnBISAh33303t9xyCy+99BIvvvgiubm52Gw2LBYLNpuNrVu3sm3btlYXcjykH78OQGpqKnfffTf/8z//Q+Qpjr5UVFR4VWAfP368U3+nbt26eaAaERFzWK2OdmyI1iBv0pAhZLCcvXSntDqC3buhVy+zixIRT2rTsHVUVBT33Xcff/rTn/j444958803mT9//klzzp2Zg245YVTFYrFw9tlnc9NNNzFt2jSCg4ObOLPt2rdvz/DhwxkxYgQjRoxg6tSpLr9HS95++2169Ojh8fuKiHiT0lJHOyZUD1g2qX17MmJ28NWxH3LWr7PRq5d+KyESSNo21+T4ySEhXHvttVx77bXs37+fL774gm+//ZYlS5ZQWFjo1DVsNhuJiYmMHz+ec88915hn7mpJSUl88sknZGZm0r17d5dfX0REWs9aagPs4TM2vNLcYrxcRu8yWG9vr19SyqWXxZlbkIh41CmF9vo6d+7MHXfcwR133AHAzp07yc7OZvfu3ezfvx+r1UplZSXh4eHExMTQuXNnevbsyeDBg+nlgd/xxcXFMW3aNLffR0REnFeaXwnY57THhNeYW4yXy8gMM0J71sqjgEK7SCBxWWg/Ua9evTwSxkVExHdZD5dzPLTHRiq0N6fXuC5EvV7GUaJZvznc7HJExMP07LmIiJjGPtJuFxutpQybEzx0CEOwb4e6syARN22TIiJeSqFdRERMUz+0x0RrA71m9e9PRlC2cZid3UxfEfE7rQrtxcXFzJo1i127drmrnoBx7733MmjQIOLi4oiMjCQ1NZWJEycyffp0/fsVkYBhLaw22toMtQVhYWR0OmQcrl9T3UxnEfE3rQrtX375JVdddRV9+vShXbt2vPfee+6qy+/NmjWLX375hdLSUioqKsjNzWXBggU8+uij9OvXj9tvv53y8tatWVxZWUlJSUmDl4iINystdMxjj4nTEoYtyRhca7SzFhWZV4iIeFyrHkT96quvjLXXq6qqOO+881p1s/Lycr799lvWrVtHcXEx7dq1o2vXrkyZMsUtyzx6s+TkZHr37k1MTAzFxcVs3rwZ67FdRmpqapg5cyarVq1i/vz5xMfHt3A1u6eeeopHH33UnWWLiLiUtcQxjz023vX7cvibIeMT4Xt7e/1aTScSCSStCu1LliwxNkP61a9+RVJSktPnvvvuu/zxj3+koKDgpD87vqHS888/z8CBA1tTkk8ZOHAgt956KxdddNFJK+vU1NTw3Xff8eCDD5KVlQXA2rVrufrqq5k7d65T13/ggQf405/+ZByXlJTQtWtX1/0FRERcrLS4XmhPdNuCZn4jbswgerCL3fRkQ04cdXUQpKfTRAKC0/+p5+TkcPDgQWOk/brrrnP6Jm+//Ta//e1vOXLkCDab7aRXXV0dP/zwA8OGDeOll15q/d/CR2zcuJG777670aUwQ0JCuOCCC1i5ciUXXHCB8f63337LV1995dT1w8PDiYuLa/ASEfFmpVZHOyYx1LxCfMXQocYKMtbqCPbsMbkeEfEYp0P71q1bjXZCQgJnnHGGU+fl5eVx1113YbPZsFgsxqu+4+9VV1dz9913889//tPZsvxOREQEH3zwAR07djTee/HFF02sSETEfUqtju+D2GStPd6ipCSGxO01DrPXa5lMkUDhdGjfvXs3YA/Yo0aNcvoGf//73yktLTWC+vHwPnLkSK688krOOeccoqOjjfdtNhv33HMPq1atat3fxI/ExsYaO8sCLF68mIqKChMrEhFxj5Iyxzz2+PZhJlbiO4b0dXwfZC86ecqpiPgnp0N7cXGx0e7du7dT59TU1PDvf/+7QWDv0aMHa9asYcWKFXz44Yd8++235OXl8dBDDxkj7rW1tdx4443GVJxANHHiRKNdUVFBTk6OidWIiLhHyVHHPPbYTtEmVuI70kdFGu2sFUdNrEREPMnp0F5/+cF27do5dc6CBQuMB0+Pj6S/+uqrZGRkNOgXGRnJ448/zssvv2wE9U2bNvHll186W57f6dSpU4Pj/Px8kyoREXGfknL76Ho4FYQl6zkcZ/Q9qyth2Delyt4aYXI1IuIpTof22Hq7XlRVVTl1zpw5cxocDxgwgEmTJjXZ/7bbbuPiiy82jl955RVny/M7R482HD2JiooyqRIREfcprbKH9jhKQA/POyU0cygD2ATA1oJ2VFa2cIKI+AWnQ3v90fXDhw87dc78+fONeeoWi4Vp06a1eM5f//pXwD4yv2jRImpra1s4wz9t3LixwXGHDh1MqkRExH1KquxTPRTaW6FrV4aE2ReHqLUFs2mTyfWIiEc4Hdr79u1rtNeuXdti/yNHjrBhw4YG751//vktnjd8+HBjScSKigqn7uWPPvzwQ6Pdo0ePgNt8SkT8n80GJbX23yLGUQoRmurhFIuFId2KjMPsxUVNdhUR/+F0aD/ttNOIjIzEZrOxbt069u7d22z/uXPnNniQND4+nszMTKfuNXLkSKNdf6nJQPHll182mFo0depU84oREXGTykqottnXZo8LKYMTlgOWpqWnO/5dZS/UCjIigcDp0B4SEsJZZ50F2KeuPPPMM832/+STT4y2xWJh4sSJJ63P3pTU1FSjXVhY6GyJHrd79+4Ga89Pnz690X7FxcVcfvnlrFmzpsVrfvDBB1x77bXGcVRUFPfff7+rShYR8RolJY52XJiWtW2NIWc6pqxmZQXuSmsigaRVe0b/7ne/4+uvv8ZmszFz5kzOPfdcLrzwwpP67d27l7lz5zaYz95Yv6ZERzuW/Sqp/6l+im655RbeeeedFvvcfvvtJ71/Kuuk22w2Zs+ezezZs+nfvz9Tpkxh6NChpKSkEB0dTWlpKdnZ2cyaNYvVq1cb51ksFt58882TVpIREfEHDUO7nqZsjc5n9SeRAgpJIjsn0exyRMQDWhXap0yZwujRo1m5ciW1tbVcccUVPProo9x5551G0D5y5Ag33ngjNTU1xsh6aGhog1VhWlJ/TfjQUNdta11dXU1lC4/Z19TUUFNT47J7nmjz5s1s3ry5xX6xsbHMnDmTK6+80m21iIiYqSS/Cji2ekykc6uSiZ0lrR9DLMtZZBvH/ookCgogKcnsqkTEnZyeHnPcf/7zH6KiorBYLFRWVvLAAw/QoUMHTjvtNEaMGEH37t1PWjXm4osvdnptd4C8vDyjHRMT09oSvU5kZCS33norgwYNanGKUHx8PHfddRcbNmzgmmuu8VCFIiKeV5Ln2P8jLsp9gyV+KSSEIe0PGofZK8pMLEZEPKFVI+0Affr04ZNPPuHyyy+noqICm81GeXk569ata9DveDi1WCw8+OCDrbrHqlWrjLYrV0156623eOutt1x2vR49eji1a2t4eDgzZ84E7HP0161bx6FDh8jPz6eoqIioqCiSkpJIT08nPT2d4ODgFq4oIuL7Sg4eBeIBiIuuM7cYH5SeVgmH7O3sHw4y4XzndisXEd/U6tAOcO655/Ldd9/xm9/8xngYsyn33nsvQ4cOdfra+/fvZ8eOHcZxnz592lKi10pMTGTixIlmlyEiYrqSw47pinFxepiytYaMjobF9nbWqvLmO4uIz2v19JjjzjjjDH755Rf+93//l8GDB2Oz2Rq8EhISePbZZ3nqqadadd333nvPaIeFhTVYH15ERPxHyZFqox0Xp+UeW2vwuY6V1rK3aY17EX/XppH24yIiIvjzn//Mn//8Z/Ly8sjJyaGwsJB27dqRkZHR6mkex1elOT5yn5mZSVhY2KmUKCIiXqqkwDGPPS6hzWNIASt29CB6sIvd9GTDkRTq6iBI/xpF/NYphfb6OnbsSMeOHU/pGu+88w47d+40QrumkYiI+K+SwlqjHZfksq+jwBEVxZCYXey29sRaF82ebVX0TNNAl4i/8pqfycvLy40HVo8/3HnZZZeZWZKIiLhRSbFjHntcO9ct7xtI0rs7FrvP/jbXxEpExN28ZmgjMjKSRYsW8fPPP7N27Vr2799PRkaG2WWJiIiblJQ65rHHtQ83sRLfNWRoMGy0t7MWFnLx3T3NLUhE3MZrQjtAr1696NWrF9OmTTO7FBERcbMSq+OXvXEdI02sxHcNOas9HFu/ITtbK/CI+DOvmR4jIiKBpeSoY7GCuE5RJlbiu/pe1J8w7EtnZudqS1QRf6bQLiIipigptz80GUwNke19f/drM4S2T2BA2E4AtpZ3pbJMO8uK+CuFdhERMUVJpT20x1KKJT7O5Gp8V3rKYQBqCWHT1ztNrkZE3EWhXURETFFSZZ/HHkcJxCm0t9WQwY657NnfHzCxEhFxJ4V2ERExRUmNfR57HKUQqQdR22rIuASjnbWmuumOIuLTFNpFRMTjqquh3BYBQFxIGVgsLZwhTRlysWOZx+xd0SZWIiLupNAuIiIeV1rqaMeFlptXiB/o3D+OxKAiALKLu9l/IhIRv6PQLiIiHtcgtIdVmleIH7BYID15PwD76ULBss0mVyQi7qDQLiIiHleSX2W04yKqmukpzhjS1/GDT/bXe02sRETcRaFdREQ8ruTgUaMdF6W1xU/VkNGOuexZy8tMrERE3EWhXUREPK4kzzGPPS661sRK/MOQc7sY7ewtoSZWIiLuotAuIiIeV3LYMZ0jLtbWTE9xxuBRjpH27PwUqNKUIxF/o9AuIiIeV5JfL7THKbSfqthY6Bl9CIANtkHUZW80uSIRcTWFdhER8biSI4557HGJwSZW4j+G9LAvyWMllj3fbzG5GhFxNYV2ERHxuJICR2iPTQozsRL/kT48xGivW1BkXiEi4hYK7SIi4nFFhY4pMQkd9OCkKwyd3MFor80OaaaniPgihXYREfG44hJHO6FjhHmF+JFhp0ca7bUH9TCqiL9RaBcREY8rKnXMY49PiTKxEv/RsyfEh9rXaF9ry4ANG0yuSERcSaFdREQ8rqjMMSUmITXGxEr8h8UCQ7sVApBLKod/zDa5IhFxJYV2ERHxuOJy+8OnYVQS0THe5Gr8x7Dhjq/1tfOOmFiJiLiaQruIiHhcUaV9/nU8xVgSE8wtxo8Mm5xstNeus5hYiYi4mkK7iIh4XFG1fQfPBIogSnPaXWXYKMfymT/ndYbSUhOrERFXUmgXERGPqquDktpjoT3Eap+MLS4xYABEBNtXjVnLMFizxuSKRMRVFNpFRMSjrFaow756THxoucnV+JeQEBjSrRiAbfSjdNFakysSEVdRaBcREY8qLqwz2gkRCu2uNmyEY2Ol9fMOm1iJiLiSQruIiHhU0f6jRjshUhsAudqwMx2r8azNCm6mp4j4EoV2ERHxqKLcMqMdH11jYiX+qcGyj8U9ITfXxGpExFUU2kVExKOKD9QbaY+ra6antMWQIRBksf97XcswWLnS5IpExBUU2kVExKOK8iqNdkK8zcRK/FNUFPRPtQKwgcFULtMKMiL+QKFdREQ8quiwYx57fKK+htxhWGYoADWEsnGBHkYV8Qf6tBQREY8qPlJrtBOSQ5rpKW112thIo/3ThgiorW2mt4j4AoV2ERHxqKL6Sz62DzWxEv81cqSjvbpyCGzaZF4xIuISCu0iIuJRRUWOHVDjO0aYWIn/GjbM8TDqKkbqYVQRP6DQLiIiHlVc6vjqSUiJbKantFV0NAzuZV+lZyODKFu6ztyCROSUKbSLiIhHFZU55rEnpMaYWIl/GzkuHIBaQli7qNTkakTkVCm0i4iIRxWV28OkhTpiOseZXI3/yhzjeF5g9c4kKCtrpreIeDuFdhER8ajiCvs89niKCUpKMLcYP1b/YdRVtkxYo/XaRXyZQruIiHhUUXUUAAkU2XcCErcYNAgiQmsAWE0mLF9uckUicioU2kVExKOKa+3z2OODy8BiaaG3tFVoKJw2uBqAHfThyIJskysSkVOh0C4iIh5TUQGVNvuc9oRQzbF2t5HjHUtq/rSsCmw2E6sRkVOh0C4iIh5TVODYWCk+osLESgJD5kjHbzJWlaTBtm0mViMip0KhXUREPKZ4n2PpwYSoKhMrCQwNdkYlE5YtM68YETklCu0iIuIxRTn1QntMrYmVBIbevSEx1j6vfSWjsC1VaBfxVQrtIiLiMUW5jnns8XGaX+1uFguMGm3/qj9ER3YuzDG5IhFpK4V2ERHxmIL9jnnsSUkmFhJAxo4PNtrLtiVDUZF5xYhImym0i4iIxxQcdMxjT0rWV5AnnH66o72UsbBihXnFiEib6RNTREQ8pjDfMY89qWOIiZUEjlGjIDjIvmrPUsbqYVQRH6XQLiIiHlNQ4GgndopouqO4THQ0DB1k3xl1I4MoWrje5IpEpC0U2kVExGMKihxfO0ldo02sJLCMnRgGgI0glq8MgpoakysSkdZSaBcREY8pLHVMiUnqFmNiJYGl/rz2ZZWnQXa2ecWISJsotIuIiMcUlIUb7cQe8SZWEljGjnW0Na9dxDcptIuIiMcUVEQCEI2VsI6JJlcTOFJToVunSsC+yVL1kpUmVyQirRVwof3w4cPMnTuXxx57jIsvvpiUlBQsFovxeuuttzxSx86dO3n44YcZPnw47du3JzIykt69e3PppZcya9YsajTfUET8UGGVfUpMkqUQQrR6jCeNnRAKwFGiWb+g0ORqRKS1AuYT8+DBg4wePZo9e/aYXQozZszg/vvvp7KyssH7O3fuZOfOnXz++eeMHj2a9957j169eplUpYiIa9lsUFAbB0BSSKnJ1QSe088I4oOP7O2lB3sxIicHunY1tygRcVrAjLRXVFR4RWB//PHH+cMf/mAE9qCgIAYPHsz48eNJSUkx+q1YsYIJEyZw4MABs0oVEXGp8qM2KrEv85gYftTkagLP+PGO9gLOhMWLTatFRFovYEJ7fe3bt+fcc8/lL3/5C59//rnH7vvdd9/xyCOPGMdjxoxh06ZNZGdns3DhQvbt28eHH35ITIz918f79u3jiiuu8Fh9IiLuVLDHMbqeFFVuYiWBafBgSIq170i7kAnULVRoF/ElATM9JikpiU8++YTMzEy6d+/u8fvbbDbuv/9+bDYbAGlpacybN4+oqCijT1BQEFdddRXt2rVj8uTJACxdupTPPvuMSy+91OM1i4i4UuGeEuDY9JiYKnOLCUBBQTDhzCA++woKSSLrhzyGml2UiDgtYEba4+LimDZtmimBHWDu3LmsX+/YhW7GjBkNAnt9kyZN4qqrrjKOn376abfXJyLibgU5ZUY7Ma7OxEoC18TJjrG6+bu6w6FDJlYjIq0RMKHdbLNnzzbaPXv25Jxzzmm2/2233Wa0V61axb59+9xWm4iIJxTkOqbEJCXaTKwkcJ15pqO9gDNhyRKzShGRVlJo95Cvv/7aaE+ZMgWLxdJs/3HjxhEd7djiu/75IiK+qDDPMSUmqV3zn4HiHoMGQXKcfSGEhUygdoHmtYv4CoV2Dzh06BAHDx40jseMGdPiOSEhIWRmZhrHWVlZbqlNRMRTCg459p9I7BAwj1R5Ffu8dvsPTMUksP77PJMrEhFnKbR7wKZNmxoc9+7d26nz6vc78RoiIr6m4IhjSkxSp3ATKwlsE88JM9rzt3SG4mITqxERZym0e8Du3bsbHHfr1s2p8+r3O/EajamsrKSkpKTBS0TEWxQUOabEJKU2/iC+uF/Dee0TYOlS02oREecptHtAaWnDnf/i4+OdOi8uLq7JazTmqaeeIj4+3nh11U53IuJFCkuCjXZi1xgTKwlsAwdC+7gKABYxnpoFehhVxBcotHuA1WptcBwREeHUeZGRkU1eozEPPPAAxcXFxisnJ6d1hYqIuFGB1TElJql7rImVBDaLBc6aaG+XEM/Kb46YW5CIOEWh3QNqamoaHIeEOPcAVv1+1dXVLfYPDw8nLi6uwUtExFsUlNsHLIKpIbZ7ksnVBLYplzgGj77/JRWOHjWxGhFxhkK7B5y4iVJFRYVT59XvV3/5RxERX1RYaf8cS7QUYQkLNbmawHZs020AvrNNhhUrzCtGRJyi0O4BMTEN526Wl5c30bOho/VGPk68hoiIrzlSY3+eJylED8mbLTUVBqUWAbCaTAq+XWVuQSLSIoV2D0hOTm5wfODAAafOq7+2e7t27Vxak4iIJ1UfraYE+5S95IiWn9ER9zvnXPuDwXUE8985zg0miYh5FNo9IC0trcHx3r17nTqv/oOk/fv3d2lNIiKelL+1wGgnRysgeoMp0xwPA3+3ubvmtYt4OYV2D+jbt2+Dh0rXrVvn1Hlr16412gMGDHB1WSIiHpO/w7GBT3JslYmVyHHjx0N4sH2Rg+9tk7At0XrtIt5Mod0DwsLCGDVqlHG8ZEnLa+IePHiQ7du3G8fjx493S20iIp6Qv9sxJaZ9Uq2JlchxkZEwfqB9ucccurH5k2yTKxKR5ii0e8gll1xitOfNm0deXl6z/d977z2jnZCQoNAuIj4tf59jNawTHvMRE0253LEy2XffmViIiLRIod1DrrnmGsLD7RuLVFdX88wzzzTZ12q18s9//tM4vu666wgN1fJoIuK78g84psQkd3Jurwpxv3Muc8xrn5szGIqKzCtGRJql0H4Kdu/ejcViMV7Tp09vsm9qaiq33XabcTxjxgw+/fTTk/pVV1dzww03GA+rRkZG8uCDD7q8dhERT8o/ZDPayV3Cm+kpnjR4MHSNKQRgARMo/Vbz2kW8VUCF9ltuuYWIiIiTXq3t01bTp0+nb9++ANTW1nLllVfy61//mk8//ZT58+fzyiuvMGLECGbNmmWc8+yzz9K5c2eX3F9ExCz5Rxzt5G5RTXcUj7JY4MJxRQBUEc68dw82f4KImCagfkdZXV1NZWVls31qamqoqalxy/0TExOZM2cOkyZNIicnh7q6Ot59913efffdRvvfd9993HnnnW6pRUTEk/KLHFP8knvGNtNTPO3C69vzr7n29ldLk7jU3HJEpAkBNdLuDfr160dWVhY33XQTkZGRjfYZMGAAX3zxBX/72988XJ2IiHvkWx1TYpL7JppYiZzorItjiAqyr53/ddHp1B1ofqEEETGHxWaz2VruJu5QWlrKjz/+SE5ODmVlZaSkpDBkyBCGDRvmkuuXlJQQHx9PcXExcXFxLrmmiEhbDI/8hZ8rBhJCNVW1IViCLGaXJPVc0vcXvtw+EIAVj//AqL9MNrkikcDhbF4LqOkx3iY2NrbBUpAiIv7qcFU8AMlBBViCOppcjZzooovgy+ft7Tmzqxj1F3PrEZGTaXqMiIi4l81Gfp19SkxyWInJxUhjLrizh9H+amMv8woRkSYptIuIiFsdPVhCOfYVY5Ijy0yuRhqT0juKETGbAVhfNYCc5ftMrkhETqTQLiIibpW/rdBoJ8c0v4KXmOeiTMdyj3Ne2m1eISLSKIV2ERFxq/xdjikxyfHVJlYizbnw+mSj/eV/o02sREQao9AuIiJulb/nqNFObqcFy7zVsGsHkBqUC8CPeQMpPuKePUtEpG0U2kVExK3y91UY7eT2WurRW1lCgrm0VxZg3x31m5d2mlyRiNSn0C4iIm6Vf8AxJaZ959BmeorZLr3U8ZuQzz6uMrESETmRQruIiLhV/uE6o53cXXOlvdm4/0mnHfkAfLO5FxUVLZwgIh6j0C4iIm516Eiw0U7upd2ZvVlIj1QuTlgMQFldFD/MLjW5IhE5TqFdRETcKq8owmh37J9oYiXijMvOPGK0P3st38RKRKQ+hXYREXGrvDLHlJgOvWJMrEScMemmHsRgH2H/cnkyNVpERsQrKLSLiIhb5VUmAJAUVEhomFaP8XYRZ4/lvKDvAThSGcviRVqmU8QbKLSLiIj7VFeTV9cegI5hRebWIs6JjOSyQVuMw8/eKDCxGBE5TqFdRETcpmz3YcqwT4npGG01uRpx1vlXxxFGJQCffR2GTYPtIqZTaBcREbfJ2+QYpe0Yr/UDfUXc1LOYxDwA9hXH8tNPJhckIgrtIiLiPnnbHUsGdkzWE40+Y8AALm232Dj87MNKE4sREVBoFxERN8rbXW60O3bUQ6g+w2Lh4kssBFELwKcfVmmKjIjJFNpFRMRt8nKqjHbHLiEmViKt1eHKMxmHfbR96/5YNm40uSCRAKfQLiIibpN30DE826FHlImVSKtNmMC0sK+Mw1mfaKhdxEwK7SIi4jZ5R4KNdsfe2ljJp0REcNmZjgeJZ71b3kxnEXE3hXYREXGbvKIIo92xf6KJlUhbdL5iLGNZAsDGnVFs2mRyQSIBTKFdRETcJq8s2mhrpN0HnX8+V/CJcThrlom1iAQ4hXYREXGbvIoEAOIsJUREavUYn9O5M5cN2W4czvqg2sRiRAKbQruIiLhHdTV5dckAdAwvMrcWabOul2UymuUAZG0KZetWkwsSCVAK7SIi4hYVOYcpIR6AjlGlLfQWr3XhhUzDMS/m009NrEUkgCm0i4iIW+RtzDfaHeO1o6bPOu00Lk9eZBzO+rjOxGJEApdCu4iIuMWBzcVGu1P7WhMrkVMSFESPS4cxgtUA/LwuiJ07Ta5JJAAptIuIiFvs31ZmtLukmliInLpLL9UUGRGTKbSLiIhb7N9bY7Q79wg3sRI5ZWedxbSY74zDTzRFRsTjFNpFRMQt9h9wLPHYuZ/WaPdp4eH0vmggw/gZgNU/BbFnj8k1iQQYhXYREXGL3Pwwo915cJKJlYhLXHaZpsiImEihXURE3GJ/iWN0vcvAeBMrEZc491ymhX5pHM6aZTOxGJHAo9AuIiJusb88EYBISznxCdoN1efFxNDvvN4MIQuA5cst7Ntnck0iAUShXUREXK+igv21HQHoHH4EizK7fzhhFZnZs02sRSTAKLSLiIjLHd15kCLsI+2dY0pMrkZc5qKLmBb0mXGoKTIinqPQLiIiLrc/+4jR7pxUYWIl4lLt2jFwYkcG8AsAS5bAgQMm1yQSIBTaRUTE5fZvcuyG2qWjdkP1K5deyhV8AoDNZtEUGREPUWgXERGX27/TMbreuWuwiZWIy11+OdMsjqSuKTIinqHQLiIiLrc/xzG63rlXhImViMt16sTgM5PpxxYAFi2CvDyTaxIJAArtIiLicvsPOr5eOqfFmliJuIPl6quMVWTq6ix8/rm59YgEAoV2ERFxudwCx+i6dkP1Q5ddxrTgz41DTZERcT+FdhERcbnckjij3blvtImViFskJzN0cnt6sQOA+fMhP9/kmkT8nEK7iIi4Vl0dORXtAUgKLiJamd0v1Z8iU1urKTIi7qbQLiIiLlV78DD76AJAt+gjLfQWnzV1KtNCvjAOZ32iKTIi7qTQLiIiLnVw7QFqCAWgW5LV5GrEbeLjGXF+B7qzG4D//tfGEf2MJuI2Cu0iIuJSe9cXGu1uKTUmViLuZrnmamOjpZraIG20JOJGCu0iIuJSezcfNdpdu+trxq9ddBFXR3xuHH74fp15tYj4OX2aioiIS+3d5dhYqVtapImViNtFR3PatN70ZjsACxZZOHjQ5JpE/JRCu4iIuNTe/SFGu1t6gnmFiEdYfvsbruZDwL7R0qxZJhck4qcU2kVExKX2FjjWeOw2rJ2JlYhHTJzIVR0WGIcfvVttXi0ifkyhXUREXCrHmghAMDWkdAs1uRpxu+BgBl8/goFsBGDJylByckyuScQPKbSLiIjrlJezt6YzAKnhhwkONrke8QjLb37NVXxkHH/yiYnFiPgphXYREXGZsq25HCEZgG5xReYWI54zaBBXDdpoHH74ZrmJxYj4J4V2ERFxmZyfDxvtbu0V3AJJ2q0TGMpaAFZviGTnTpMLEvEzCu0iIuIyezeUGO1uqdrWPqBccw1XBTnmxXz0QW0znUWktRTaRUTEZXZvrTLa3XqFNNNT/E779lx1Vr5x+NEbVhOLEfE/Cu0iIuIyO3dZjHavoXEmViJm6Hn3xYxkJQDrd8WzebPJBYn4EYV2ERFxmZ15UUa71+gOJlYipjj3XK6On2scfvRaSTOdRaQ1Aja0L1u2jNtuu42BAwcSHx9PXFwcAwcO5NZbb2Xp0qVuuafFYmn165VXXnFLLSIi7rCz2L6ZUhC1dBsQ3UJv8TshIVxxQ6xx+OF/qrDp0QYRlwi40F5WVsZNN93E2LFjefXVV9m0aRMlJSWUlpayadMmXnvtNc444wxuvPFGysrKzC5XRMR3lJezo7obAN3C8wgLM7keMUXqH6YxjkUAbM5PZs3qOpMrEvEPAfWUUG1tLZdddhnff/+98V5kZCSDBg0iJCSEX375hZIS+6/y3nzzTXJzc/nmm28IdsPuIOPHjycyMrLFft26dXP5vUVE3KFw/V6KSAOgV0IB0NncgsQc3bvz60Gfs3jjeADe+d8cRnze3eSiRHxfQIX2v/71rw0C+y233MLTTz9NUlISYB+F/9vf/sbjjz8OwPfff8/DDz/Mk08+6fJa3n77bXr06OHy64qImGXnykNwPLSnVJhbjJjqij/35ve/rqCSCD74NoHnqiE01OyqRHxbwEyP2b9/P88//7xx/Otf/5pXX33VCOwA0dHRPPbYY/zlL38x3vvHP/7B/v37PVqriIgv2rHOscRf794mFiKmS7hqCheH2wfJDlfG892HhSZXJOL7Aia0v/DCC1RU2Ed+oqKieOGFF5rs+9e//pWuXbsCUFFRwYwZMzxRooiIT9u5zbGZTq/BUc30FL8XGspvLnIE9f/8TYNfIqcqYEL7Z599ZrSvvPLKBiPsJwoLC+OGG24wjmfPnu3W2kRE/MHOfY75D71GNP0ZK4FhylNn0p5DAHy5sTdFh6paOENEmhMQoX3Lli1s377dOD733HNbPOe8884z2tu3b2fLli1uqU1ExF/szI832r1GtTexEvEGoX26c03vVQBUEsEnD60ztyARHxcQoX39+vUNjseMGdPiOaeddhph9dYry8rKcnldIiJ+w2Zjx9FOACQEFZPU3vWrbonv+fW9KUb7nY+0BqjIqQiI0L5p0yajHRYWZsxXb86J/epfwxXuvfdeBg0aRFxcHJGRkaSmpjJx4kSmT5/Orl27XHovERF3q9p3iL02+2dmz5jDJlcj3mL4LacxIHwHAItLh7LrCw2AibRVQIT23bt3G+3U1FQsFotT59VfI73+NVxh1qxZ/PLLL5SWllJRUUFubi4LFizg0UcfpV+/ftx+++2Ul5e79J4iIu6yY1EuddhH1/t1KDK3GPEaliALvz7/iHH8n0d2mFiNiG8LiNBeWlpqtOPj45vp2VBcXFyj13CF5ORkRo0axdlnn82IESOIiYkx/qympoaZM2cyduxYiouLnb5mZWUlJSUlDV4iIp6wZXmB0U7rXdtMTwk0v/7bEIKw/3/ijfXDqT1wyOSKRHxTQIR2q9WxdnBERITT59XfsbT+Ndpq4MCBvPDCC+zYsYPDhw+zYsUK5s2bx+rVqyksLGTOnDmkp6cb/deuXcvVV1/t9PWfeuop4uPjjZcz04BERFxhy4Zqo52W4fznrPi/1L6RnNtzKwA5dOOH+34wuSIR3xQQob2mpsZoh4Q4vwls/b7V1dXN9HTOxo0bufvuu+nVq1ej97rgggtYuXIlF1xwgfH+t99+y1dffeXU9R944AGKi4uNV05OzinXLCLijC27HA8Zpo1NNrES8Ua3/Lmd0X7t43jQ9E+RVguI0B4V5djk4/gGS86o3zc6OtqlNTUlIiKCDz74gI4dOxrvvfjii06dGx4eTlxcXIOXiIgnbDnsWJe93/hOJlYi3uiCGzrQKcK+2dKXVVPI++dHJlck4nsCIrTXny/emoc7jx492ug13C02NpY77rjDOF68eHGrftgQEfGo2lq2lNun43UJOUhsgpZ7lIZCQ+GGayoBqCGUt57Jg1o9+yDSGgER2pOTHb+qPXDggNPnHTx40Gi3a9eumZ6uN3HiRKNdUVGhqS4i4rWOrN3LEeyfs2mJeshQGnfTQ47fwLxecBm2WZ+aWI2I7wmI0J6Wlma0jxw50mAEvTn1g3L//v1dXldzOnVq+Ovl/Px8j95fRMRZWxY6BjjSUp37fJXA07s3nDXUvsrQdvqy4OH/gs1mclUiviMgQvuAAQMaHK9bt67Fc3Jzczl82LFByInXcLcTf7CoPy9fRMSbbPnJsSRuWn/n9sGQwHTLfYlG+5WtZ8H8+SZWI+JbAiK0jxw5kvDwcON4yZIlLZ6zePFiox0REcHIkSPdUltTNm7c2OC4Q4cOHr2/iIiztmx2jJamZeoBeGnapZdZaB9nf0brUy4n95FXTa5IxHcERGiPiYnh7LPPNo7fe++9Fs+p3+fss8/22Ooxx3344YdGu0ePHqSkpHj0/iIiztq8z/GgftqZ+qySpoWHw22/sy8PWksIrywZBMuWmVyViG8IiNAOcP311xvtrKysZtc+//nnn5k7d26j53rCl19+yZw5c4zjqVOnevT+IiKtkV3YBYBorHTPSDC3GPF6t/9PECHBdQDM5DYqHv5fkysS8Q0BE9qnTZtGRkaGcXzbbbexefPmk/odOHCAX/3qV9QeW4pq6NChXH755Y1ec/fu3VgsFuM1ffr0RvsVFxdz+eWXs2bNmhbr/OCDD7j22muN46ioKO6///4WzxMRMUNZTgE7a3sAMCh2L0EB860ibdWlC0w79rV6mA589N92sHy5uUWJ+ADntwf1cRaLhddee40JEyZQXl7OgQMHGDVqFHfccQfjx48nJCSEVatW8dJLL5GXlwdAZGQkr776KhbLqT1YZbPZmD17NrNnz6Z///5MmTKFoUOHkpKSQnR0NKWlpWRnZzNr1ixWr17doOY333zzpJVkRES8xS9z9wD2jZUGpxaZWov4jt/fHcSHH9vbL/J7fjP9L1i++9bcokS8XMCEdoDMzEzeffddfvWrX1FeXk5JSQl/+9vf+Nvf/nZS38jISN59910yMzNdWsPmzZsbHeE/UWxsLDNnzuTKK6906f1FRFwpe3GR0R4ySMv3iXPGjIHhp9Wx5ucg1jCC5d+XcPqKFTB6tNmliXitgPtF5mWXXcaaNWuYNGlSoyPoFouFs88+m59++onLLrvMJfeMjIzk1ltvZdCgQS2O2sfHx3PXXXexYcMGrrnmGpfcX0TEXTZk1xntwadr5RhxjsUCd93tiCB/5x5oYoqpiNhZbLbA3dkgJyeHpUuXkpubC0CXLl0YO3YsXbt2dds9CwsLWbduHYcOHSI/P5+ioiKioqJISkoiPT2d9PR0goNdswV4SUkJ8fHxFBcXExenL1MRcb3JiT8xr2gEAAc3FdKxf2ILZ4jYVVZCz542DhywYKGOTQwgbf5MOPNMs0sT8Shn81pAh3Z/p9AuIm5ls5ESnMdBWyeSg45wqKYdp/gIkASYZ5+F++6zt2/idV4f+RqsWIH+jySBxNm8FnDTY0RExDXyN9oDO8Dg+H3KWdJqt90G8fH2scP/8BtyV+2D2bNNrkrEOym0i4hIm2z4dp/RHtK9xMRKxFfFxcGdd9p/2qsmjOf5Izz4IFRXm1yZiPdRaBcRkTZZt8RqtIdk6OtE2uauuyAiwj7aPpPbKNh6GN54w+SqRLyPPmVFRKRNfs4ONdqnnZVgXiHi0zp2hBtvtI+2W4m1j7ZPnw5Wa/MnigQYhXYREWmTNbkdAQilisGX9Da5GvFl990Hocd+BnyBP5CfVwNPPWVuUSJeRqFdRERarezwUTZX9gRgcOQOwuMjTK5IfFn37nDzzfa2lVie4T547jnYscPcwkS8iEK7iIi02vrPd1GHfU+J4V0PmVyN+IOHHoLwcHv7JX7HwapE+NOfzC1KxIsotIuISKut+aHAaA8fVtdMTxHndOkCd9xhb5cTxVM8AF9+Cd99Z25hIl5CoV1ERFrt53WOr4/TJrUzsRLxJ3/+M0RF2duvcDu76AF33w1VVabWJeINFNpFRKTV1uR0ACCEatIv1UOo4hodO9ozOkAV4dzP32DLFnj+eXMLE/ECCu0iItIq5cVV/FJhfwh1UPgOItpFm1yR+JM//xk62H8m5BOuZCmnw6OP6qFUCXgK7SIi0io/zdpFLSEAjEg9YHI14m/i4uDxxx3Hf+R56sor4PbbwWYzrzARkym0i4hIqyz76ojRPn1krYmViL+68UYYPNjeXs1I3udamDcP3n3X3MJETKTQLiIirbJ8TZjRHnNJBxMrEX8VEgL/+Ifj+F6epYh4+OMf4fBh8woTMZFCu4iIOM1mg2X77fPZEykg7ZL+Jlck/mryZLj4Ynv7ICk8xJNw5IjjSVWRAKPQLiIiTtu5+giH6+xLPI5O3EJQRFgLZ4i03YsvQvSx55z/xR2sZCR88AF8/LG5hYmYQKFdRESctuz93UZ7zMAS8wqRgNCtm33hGAAbQdzGTGoItu/CtH+/ucWJeJhCu4iIOG35IscmN6dPijKxEgkUd98NGRn29nqG8iz3QkEB3HyzVpORgKLQLiIiTluy1f7gaRC1jLyur8nVSCAICYFXX4WgY4nlER5lPekwdy7MnGlucSIepNAuIiJOOZxTQXaZfffTYWEbie3byeSKJFCMHAn33WdvVxPGr3mHSsLgT3+CjRvNLU7EQxTaRUTEKQv/7diRcmK/XBMrkUD06KOOaTLZpPMwj0F5OVx5JZSVmVuciAcotIuIiFN+nOMIRmdN0aox4llhYfCf/9j/CfAs9zKPs+GXX+B3vzO3OBEPUGgXERGnzN9on88eTA1n3Kz12cXz0tPhiSfsbRtBXMsH5NIZ3nrL/hLxYwrtIiLSogM7jrK5vAcAmeHZxPbvYm5BErDuuQfOO8/ePkx7ruZDqgmB//kfWL/e3OJE3EihXUREWrTgjXrz2QccMLESCXRBQfDOO9C1q/14CeN4kP+1z2+/5BI4fNjcAkXcRKFdRERa9P2cSqM98bxIEysRgXbt7Juihobaj5/jXv7Dr2HPHrjiCqiuNrdAETdQaBcRkWbV1cHcTT0AiKKMcbcPMrcgEWD0aHj+ecfxzbzOYs6AhQvtOzKJ+BmFdhERadbab/PIq0kG4OyENUR062ByRSJ2//M/9hfY12+/lM/YQS/417/gpZfMLU7ExRTaRUSkWd/M3Gu0zx9bYmIlIg1ZLDBjBpxzjv34CMmcy7ccpCPcdRfMnm1ugSIupNAuIiLN+mZxrNE+79auJlYicrKQEPjoIxgwwH68nb5M4TsKbfFw7bWweLG5BYq4iEK7iIg0KT+3kpWF/QAYFLyZ7hcOMbkikZMlJMB330G3bvbjLDK4gK+xVobAxRfbN2AS8XEK7SIi0qSvntuC7dhXxfn9d9rX2xPxQl27wrx50OHYIxfLOZ0L+JrSohr7/JkdO5q/gIiX06eviIg0adYsm9G+7DcxJlYi0rK+feH77yE+3n68iAlM5gcKc8vgrLNg925T6xM5FQrtIiLSqKJDVfywzz5RONWyj5G/G2lyRSIty8iwj7gnJdmPVzKas/iRw3uP2oN7To65BYq0kUK7iIg06stnNlFNGADT+mUTFBVhckUizhkxAhYscEyVWccwTmcZ23YFw8SJCu7ikxTaRUSkUbM+rjPa034bbWIlIq03ZAgsWgRdutiPt9OX0axg8Y4UGDsWtm41t0CRVlJoFxGRkxzZV853OQMBSLEcYMxdmSZXJNJ6aWmwbJk9wAMU0I5JzOM/OWfCGWfA2rWm1ifSGgrtIiJykg//soEqwgG4ZuB6gqIjTa5IpG26dYMlSxwbMFURzm/5D3ccfpTKCefYh+NFfIBCu4iInOTN2fFG+/p7kk2sROTUxcXBnDlw++2O917hDs4o/YY9k26Cd981rzgRJym0i4hIA9nf7WdNqX1DpeHh2Qy5frjJFYmcutBQ+Ne/4I03ICLCvpTpT2QytHoVH/z6a/jrX6GuroWriJhHoV1ERBp4c/oeo33D5H1gsZhYjYhr3XgjLFtmoVcve3AvIpFr+YCrnxhEwWU3w9GjJlco0jiFdhERMZQVVPLmSvva7OFUcPWT6SZXJOJ6w4bBmjUWrrnGsXnYR1zN4C+e4NP+D2HbopVlxPsotIuIiOGdP/5MkS0BgGt7LKddehdzCxJxk4QEeP99Cx9+CIkxVQAcoDPTcp7nwkG72PnyXHMLFDmBQruIiABgq7Pxz487Gse/fyTJxGpEPOOqq2DDljDOH2813vumdgqDfncmj4/5mqMFFSZWJ+Kg0C4iIgB8/7e1bKroBcC4mJ8Z9ltNjZHA0LkzzFkQw6x3y+kSWQBABZE8vOIC+nYs5o1H9lBTY3KREvAU2kVEBFudjceeCjWO7765TA+gSkCxWODy6yLZdDCRP561jmDsKX1/TUdufqw7GV0O8/mntVpgRkyj0C4iIvz3ubUsK7VvGzkwbDuXPnO6yRWJmCM2zsI//juUDV/uYmr8j8b7vxxqz6XTgslIK+eDD6C21sQiJSAptIuIBDhbnY1Hnwg2jv962yGCQoObOUPE//W/qC+fHTqDxb9+ldEsN97fsD2Sa6+FAWl1vPaaVogUz1FoFxEJcF/ct5QlpRkA9A/bwRXPjTK5IhEvERbGGf+5lWVLbHzd9fYG4X3bjiBuvRVSU23cey/s3m1emRIYFNpFRAJYZeFR/t+MVOP4yf9XRHCYRtlF6rOMPZ3zt/+TZU8u4L+h5zIRx7SZwkILzz0HvXrBxRfD559DVZV5tYr/UmgXEQlgL1y+mB01PQA4M2k9lz4x3NyCRLxVWBiWBx/grE0v8+OUZ1jNCH7D24RRCYDNBl99BZdeCl26wB/+AOvWmVqx+BmLzWaztdxNfFFJSQnx8fEUFxcTFxdndjki4mW2fraRjMt6UUEkFupY+9keMqb2NLssEe9ns8HcufD//h+HNuXzGrfwL+4gl9STug4cCNOm2V+DB2tRJjmZs3lNod2PKbSLSFNqy6uYkLyBpUdPA+DuUSt4YcVok6sS8TE1NfD66/Dww9QcLmAek3iL6/mcqVQScVL3fv3g8sth6lQYMQKCNN9BUGgXFNpFpGlPnj6Hvyy/EIDeYXvJyutEVEKYyVWJ+KjSUnjpJfj73+HIEQpJ4COu4h1+zTLGNnpKcjJMmQLnnQfnnAPt23u4ZvEaCu2i0C4ijfrhwflMeWoCNoKwUMeC13cw/qa+Zpcl4vtKS+H//g+eew7y8wHIpTOfcSmzIn/D4ooR1NlOHl63WGD4cJg4ESZMgLFjISHBw7WLaRTaRaFdRE6ybXY2Yy5P4QjJADx+8Sr+8sVIk6sS8TNWK/z73zBjBuzcabydRwe+DLmcuSk3MO/IMEqPhjR6usUCQ4faA/y4cTB6NHTu7KHaxeMU2kWhXUQaOLBkB2MnBLOrrgcA53dZz1d70gkK1pNxIm5RWwtz5sDzz8PChQ3+qIpQlneextwedzA3P5OsrSfPga+vSxfIzISRI+2vESMgPt6dxYunKLSLQruIGA4s3cnkidVsrE4DYEj0DhZt60xCSqTJlYkEiKws+0Or774LhYUn/fHhtDNYNPT3LAqbxMKsJLKy7IvUNKdPH0hPt7+GDLH/s1cvPeDqaxTaW7Bs2TLefvttFi9eTG5uLjabjdTUVM444wx++9vfMnZs4w+OuMrOnTt56623+Prrr9m7dy9Wq5XOnTuTnp7Oddddx9SpUwkJafzXZs5SaBcRgJ3fbGbyxRHsrO0BQPfQXJatj6HzAA3TiXhcRYV9B6Y33oD//rfxZN63L4VnT2NxypUsKx3M6p9DWL3aPmW+JVFR9qUlBw+GtDT7q18/e5gPD3f530ZcQKG9CWVlZdx11138+9//brbfDTfcwIsvvkh0dLTLa5gxYwb3338/lZWVTfYZPXo07733Hr169WrzfRTaReSHR5ZwzeMDOGJrB9gD+7yFYfQZo6UqREyXmwuffgoffwxLlzbeJyYGJk2i7qxJbO1xDqsK+rD6JwurVkF2NpSXO3eroCDo2dMe4I+/evaE7t3tLzfEHXGSQnsjamtrOf/88/n++++N9yIjIxk0aBAhISH88ssvlJSUGH92zjnn8M033xAc7LotvR9//HEefvhh4zgoKIiBAweSlJTEtm3bOHDggPFnqamprFq1ipSUlDbdS6FdJHBVlVby5LmLeWLZROqwf4YNiNjFD6sT6DI40eTqROQkOTnwySfwxRf2AF9b23i/9u3tT6ieeSa1Y8ezM2IgWRuDycqyh/isLNixo/W3b98eevSwv7p3d/wzNdX+EGxysqbd/P/27jyqqTP/H/j7soVABEREQDZFUKwda61YasWNqfb0N9bt59baaUcd23GGPzozeuq4dLHTYs/vnFo941hrta06ba3Ujl+3Yq2j2KlYUdEqWxWNgGxfBELAALm/P1JuE0hCQgK5kPfrnHtyn+TZQm4ePlye+9zuwqDdjDVr1uCtt96S0suXL8fbb7+N4OBgAIaz8Onp6XjjjTdMyrz55ptOaf/48eN48skn0fYjT05Oxu7du5GQkAAA0Ov12L9/P5YtWwaNRgMAmDBhArKysrrUHoN2Ivd0budVLF/pjSv3h0vP/Z+IC/j43Aj0j+TpNCLZq6kBvv4aOHLEcOfVykrLef39DVeltl2hmpQETf8o5OULKCiAyZafb1jYpiu8vYHwcEMA334bPBgYNMgQ+IeEAA7O7nU7DNrbKS0tRVxcHJqamgAAS5Yswccff2w277p167Bx40YAgK+vL3766SdEOLjWkiiKGDNmDC5fvgwAGD58OHJycuDn59ch74kTJ/DrX/9aSmdkZGD27Nl2t8mgnci9FJy4jbW/K8V+9S93NvVECzbMyMbf/ieZq8QQ9UZ6PZCTY1h95tQp4MwZoLbWepmQEMOVqW3bqFHAAw9AVPXD3buGAL6wELh1Cygu/mUrKen84ldb9O9vCOBDQw2PbZtxOjjYsBZ9//6GVXDc+Sw+g/Z2Vq1ahXfeeQcA4OfnB7VaLZ1hb0+n02HYsGFQq9VS2fT0dIfaP3LkCJ566ikpfezYMUyfPt1i/oULF+Kzzz4DACQlJeHcuXN2t8mgnajvE0XgzI48bN5Yh4PqsdJUGAB4SJmPD3d7YMx83jiJqM9obQUuXzYE8N9/D2RnG6JvW7RNao+PN2zDhhkeY2MBb2/odMCdO78E8bduAaWlhmC+tNSwWTvp31WCYAjc+/e3vAUFGbZ+/YCAAMOj8ebra6inN2LQ3k58fDyKiooAAM8//zx27dplNf+GDRvw+uuvAwCGDRuGwsJCh9pftmwZdu7cCQAYMmQIfvrpJwhWjq5vv/0WU6dOldJqtRqRkZF2tcmgnajvKsq6i0/f/An/OhWOa02mF6yHChVYP/cafv/x4/BW8v/URH1eeTlw/rwhgM/OBi5dMjxnK0/PXyaxR0cDUVEdt5/jCJ0OKCv7JYhvC+orKgwBvfFmy2o3zuLpaTmgb3tepTKsruPvb9iM99unVaqeWwefQbuR/Px8jBgxQkp/+umnWLBggdUy33//PZKTk6V0Xl4ehg8fbqWEdeHh4bh79y4A4MUXX8S2bdus5m9paUFQUBAaGhoAAP/85z+xYsUKu9pk0E7Ud9y7o0HW7iJkHmzAiR/DcK0prkOeMKEcf5p8BWl7kqCK4HeeyK1VVhquTL161fB45Qrw449dn9QeEACEhRkmrw8aZJjrYvzYth8cbIh2PT3R1NQxkG/bamrMb/fuWb4GtydFRBj+GOkJtsZrbnEKpm0eeRvjYNyShx9+GD4+PtDpdACA3NzcLgftFRUVUsBua/teXl4YN24cTp06JbVPRH2ftrIBN86UoOh8DfIvNyHnRwUulIXjp+YYAA+ZLTPB/yJeWliD//v/kuETmNqj/SUimRo4EJg61bC1EUXDGfiiIsOk9qIi031rp8br6gxbQYFt7QcGwjcoCFH9+yPK3HyXiJ9PfbfbRH8V6kUV7rWoUHPfDzW1HlJAX1tr6GJdneHReGv/nFbr2I9PjktgukXQfv36dWnfx8cHUVFRnZZpy/fTz+smGdfhSPsAEBfX8QyZOXFxcVLQ7kj7RNSz9K0idPX30VDRgPqKRmgqtNBU34fmf3XQ/K8O9TUtqK5oQUW5iPJqL1TUKlCuUeFO0wCU6cMAJFit3wOtSPK7ijmPlWPBmjhETxnTM2+MiHo3QTCcLQ8LAx5/3PQ1UTRExWq16Xb7tuHxzh1DwG/rnJfaWsNm63z7ti4CCPh5iwYMc1ZUKkCpNExcN7eF+QKxps+1+PhBI/RDvahCfasf6lv9oGlVQtuqQEOrAtoWBRpafNDQ7ANtsw8adN5o0HlDq/NCw30vhIWJAILs6nt3c4ugvbi4WNqPjIy0OpfcWHR0tBS0G9fhSPtt9dravqU65Gb3siz8z7GOh5O1yVeWXhJF85+PM+tydfvW5qTZW6Zr79HV7VtqQ7BYyvCaubostW2lX134XFpFDzTrPX/evNAseqJZ9DLd4I1meP98Majvz5vjlNBitH8RxsVWYep0b0x+cQSC4kc7pW4iIgCGgL7tas8HH7Scr7HRMIG9vNz8o7k5Ly0tXe+XVtul0+ZeMITcQV1tNyYGQHFXS3cLtwja643+Kgy046oC43lF9Q5cTdG+rK19sLf9+/fvm9xl1fhGUd0t92ILDpQ83nlGIjJLgB6hHlUY6l+OYQNrMSy6GXEjvPGrqSFIfGoovPx+5eouEhEZzni33UbVFqIINDR0DOQ1ms63hgbDY3090NT0y2bljvJO4+3d/W3YyS2Cdo3RRRe+vraf+VIqlWbrcKR9e/pgb/tvvfUWXnvtNfs6R0Q28UDrz+fRm+EttMAbLYZHoQXeHi3wFloNjx6t8PbQw9uzFSqfZqh8W9BP2QKVnx4qfxH9+gGqfgKCQn0wKMYXg4b1Q2hCEELi+8PTJxRAqKvfKhGR8wjCL3PWbZiebBO93rCMjXEg3z6ob9tvbASam61vOl3H5wYOdE5fncgtgvYWo3/LeNlxmy7jvM3NzU5p354+2Nv+K6+8gpdffllK19XV2TR/3xnWZ4zBnytKzb4meFiepmDpNXufByyvz9pj7dtbl5VZWl2qy0KFPfJenP1zcdJ76Updlj8Yz58350x5ISKiLvLw+GX+uhtxi6Dd+K6jbXdEtYVxXn8HLiNuf9fTpqYms3dCdbR9hUIBhUJhfwedICgmEEExPbSgKREREZGbcYubxqpUKmm/sbHR5nJaowsfjOtwpH17+uCs9omIiIiod3OLoD0kJETaLysrs7mc8drqAwYMcEr79vTBWe0TERERUe/mFkG78U2RqqurTc5gW6NWq6V94zuqOtI+ANy+fbtH2yciIiKi3s0tgvbExEST9KVLlzotU1JSgsrKSot12CM+Pt7kolJb2geAixcvOqV9IiIiIurd3CJoT0pKMrlAMysrq9MyZ86ckfZ9fX2RlJTU5fZ9fHwwfvx4u9q/e/cuioqKpHRKSkqX2yciIiKi3s0tgnaVSoVp06ZJ6b1793ZaxjjPtGnTHFo9BgCefvppaf/EiRMoLy+3uf2goCAG7URERERuzC2CdgB4/vnnpf3c3FwcOnTIYt6cnBwcPXrUbNmuWrRokXS2v7m5GZs2bbKYV6PR4L333pPSzzzzDLxleGcuIiIiIuoZbhO0z5s3D6NHj5bSK1asQF5eXod8ZWVlePbZZ9Ha2goAeOihhzB37lyzdRYXF0MQBGl79dVXLbYfGRmJFStWSOnNmzfjwIEDHfI1NzfjhRdekC5WVSqVWLNmjU3vkYiIiIj6Jre4uRIACIKAHTt2YNKkSWhsbERZWRnGjx+Pl156CSkpKfDy8kJ2dja2bt0qTV1RKpV4//33IVi7daUdXn31VRw9ehSFhYVobW3F/PnzsXjxYsyaNQvBwcHIz8/Htm3bkJubK5V55513EBER4ZT2iYiIiKh3EkRRFF3diZ6UkZGBZ599ttMbHCmVSuzZswdz5syxmKe4uBhDhgyR0hs2bLB6th0ACgoKkJqaarKcoyWrVq1Cenp6p/ksqaurQ2BgIGpraxEQENDleoiIiIioe9gar7nN9Jg2c+bMwYULF5Cammr2DLogCJg2bRp++OEHqwF7VyUkJCA3NxdLly6FUqk0mycxMRFfffWVQwE7EREREfUdbnem3ZharcbZs2dRUlICABg8eDAmTJiAqKioHmm/vr4eJ0+ehFqtRkNDA8LDw/Hggw9izJgxTqmfZ9qJiIiI5M3WeM2tg/a+jkE7ERERkbxxegwRERERUR/BoJ2IiIiISOYYtBMRERERyRyDdiIiIiIimWPQTkREREQkcwzaiYiIiIhkjkE7EREREZHMMWgnIiIiIpI5Bu1ERERERDLHoJ2IiIiISOYYtBMRERERyZyXqztA3UcURQBAXV2di3tCREREROa0xWltcZslDNr7sPr6egBAVFSUi3tCRERERNbU19cjMDDQ4uuC2FlYT72WXq9HaWkp+vXrB0EQur29uro6REVFQa1WIyAgoNvbo96PxwzZi8cM2YvHDNnDFceLKIqor69HREQEPDwsz1znmfY+zMPDA5GRkT3ebkBAAAdGsguPGbIXjxmyF48ZskdPHy/WzrC34YWoREREREQyx6CdiIiIiEjmGLST0ygUCmzYsAEKhcLVXaFegscM2YvHDNmLxwzZQ87HCy9EJSIiIiKSOZ5pJyIiIiKSOQbtREREREQyx6CdiIiIiEjmGLQTEREREckcg3Zy2L179/DNN98gPT0d8+bNQ2xsLARBkLZXX33Vofpv3LiB9evXY+zYsRg4cCCUSiXi4uIwe/ZsfPHFF2hpaXHOGyFZKS4uNjmObN2OHTvm6q6Tk3333XdYsWIFRo4cicDAQAQEBGDkyJH4/e9/j7Nnz7q6eyQDp06d6tJ4kZeX5+quUzeorKzE0aNH8frrr2PmzJkIDw83+dx3797d5bqvXLmCl19+Gb/61a8QHBwMlUqF4cOH45lnnun23z+8Iyo5JCEhAUVFReiuRYg2b96M1atX4/79+ybP37hxAzdu3MDBgwfx6KOPYu/evRg6dGi39IGIXKOhoQFpaWn48MMPO7x2/fp1XL9+HTt27MALL7yALVu2wN/f3wW9JCK5uHv3Lh599FHcunXL6XW3tLRg/fr1SE9Ph16vN3mtoKAABQUF2LdvH5566ins2rULAwcOdHofGLSTQwoLC7ut7jfeeAPr16+X0h4eHhg5ciSCg4NRWFiIsrIyAMD333+PSZMmITs7G+Hh4d3WH3Kt6dOn25SvOwZK6nmtra2YM2cOvv76a+k5pVKJBx54AF5eXrh27Rrq6uoAALt27UJJSQmOHDkCT09PV3WZZMLX1xeTJk2yKa9Kperm3lBPampq6paAHQBWrFhhcgLB29sbI0eOhEqlQl5eHqqrqwEAhw8fRmpqKs6ePev840skcgAAEYAYGBgoTpkyRVy1apX4+eefi+Hh4dJrGzZssLveY8eOiYIgSHUkJyeL+fn50uutra3ip59+KqpUKinPhAkTnPjOyNVu3rwpfbYcqtzPK6+8YvL5L1++XKyurpZe12g04rp160zyrFmzxoU9Jlf69ttvpeMgJibG1d0hFzH+vTFw4EBxxowZ4tq1a8WDBw+ajBW7du2yq97t27eblJ85c6Z4584d6XWdTidu2bJF9PLykvIsXrzYye9OFPmbkByyd+9eMT8/X9Tr9SbPx8TEdDlo1+v14ujRo6Xyw4cPFxsaGszmzczMNPkiZWRkdPWtkMwwaHdfJSUloq+vr/TZL1myxGLetWvXSvl8fX3FkpKSHuwpyQWDdhJFUaytrRX3798vFhcXd3itq0F7Q0ODGBYWJpWdPHmy2NLSYjbvBx98IOUTBEG8cOFCV9+KWbwQlRyyePFiJCQkQBAEp9V59OhRXL58WUpv3rwZfn5+ZvOmpqZiwYIFUvrtt992Wj+IyDXeffddNDU1AQD8/Pzw7rvvWsy7bt06REVFATD8a3zz5s090UUikqGAgADMmzcPMTExTqtz9+7duHv3LgBAEAT84x//sDgNb+nSpRg/fjwAQBRFpKenO60fAFePIRnKyMiQ9ocMGYInnnjCav4VK1ZI+9nZ2bhz50639Y2Iut+XX34p7c+fPx/BwcEW8/r4+OCFF16Q0sbjBxGRo4zHlEmTJiExMdFqfuOY5MiRIx0W0nAEg3aSncOHD0v706dP7/Qs/sSJE01WjTAuT0S9S35+PoqKiqT0jBkzOi3z5JNPSvtFRUXIz8/vlr4RkXvRaDQ4ffq0lLZ3PNJoNDh16pTT+sOgnWSloqJC+jcUACQnJ3daxsvLC+PGjZPSubm53dI3Iup+xlPjANvGgIcffhg+Pj5SmmMAETnDtWvX0NzcLKVtGY/CwsIQGxsrpZ05HjFoJ1m5fv26STouLs6mcsb52tdBfcNzzz2H+Ph4+Pv7w9/fH9HR0ZgxYwY2bdqEiooKV3ePnMT4++vj4yPNV7emfT6OAe7t3r17mD9/PmJjY6FUKtGvXz8MGTIEs2bNwtatW6WlQok6I7eYhEE7yUpxcbFJOjo62qZyxvna10F9wyeffIKioiJotVpotVqo1WocP34cq1evRkxMDNatW4fW1lZXd5McZPz9jYyMtPkid44B1Ka2thb79+/HrVu30NTUBI1Gg+LiYnz11Vf405/+hOjoaGzZssXV3aRewHgs8fLysvleMN01HvHmSiQr9fX1JunAwECbygUEBFisg/qG8PBw6cxZTU0Nrl+/Lq0w0tTUhI0bN+L8+fM4dOgQvL29Xdxb6irj76+t33+AYwCZio2NxeDBg6FQKFBVVYVr166hpaUFgCGoT0tLw6VLl7Bz504X95TkzHgs6devHzw8bDvX3V3jEc+0k6xoNBqTtK+vr03llEqlxTqodxIEAUlJSdixYwdKS0tRWlqK7777Dt988w1ycnJw79497Nu3z2Tu4PHjx5GWlua6TpPDjL+/tn7/AY4B7s7DwwOpqanYu3cvqqurcfPmTWRlZeGbb77B5cuXUVNTg23btiEkJEQq8+GHHzp9ST7qW+Q2HjFoJ1lpOxPSxsvLtn8GGeczvmiEeq+YmBicO3cOy5YtM/svSYVCgUWLFiEnJwdjx46Vnt++fTsvROzFjMcAW7//7fNyDHA/KSkpyMzMxOLFi80uEapSqfDiiy8iJyfH5A/9119/HeXl5T3YU+pN5DYeMWjvQ/bs2QNBEJy+7d69u8feQ/ubKLVNf+iMcT7j5R/JeeR6fPXv3x8ZGRnSWRBRFLF161YnvGNyBeMxwNbvf/u8HAPIkqioKHz22WdSWqvVcooMWSS38YhBO8mKSqUySTc2NtpUTqvVWqyD+r7o6GgsXLhQSmdmZrqwN+QI4++vrd9/gGMA2S4pKQmTJ0+W0hwvyBK5jUe8ELUP8ff3x+DBg7ul3p5iPN8QAMrKyjBgwIBOyxmv7W5LfrKf3I+vKVOmSGfti4uLodPpTNbupt7BeAwoKyuzuRzHALLHlClTpJveFBQUuLYzJFvG45FGo4FGo7EpCO+u8YhBex8ye/ZszJ4929XdcMjw4cNN0rdv38aoUaM6LadWq6X9ESNGOL1fJP/jKywszCRdXV1t8/JcJB/GY0B1dTW0Wm2HaXPmcAwgexiPF1VVVS7sCcmZuZhk5MiRnZbrrvGI02NIVuLj400u4Lh06ZJN5S5evCjtJyYmOrtb1AsY/zsS6Hh9BPUO7b+/towBJSUlqKystFgHUXvG4wXHCrKkK+NRc3MzfvzxR4t1OIJBO8mKj48Pxo8fL6WzsrI6LXP37l0UFRVJ6ZSUlG7pG8mb8SCpUCjsWuOb5CMpKQkKhUJK2zIGnDlzRtr39fVFUlJSt/SN+g7j8SI0NNSFPSE5Gzp0KCIjI6W0LePRhQsXTP4odGZMwqCdZOfpp5+W9k+cONHpclx79+6V9oOCghi0uyFRFPH5559L6eTkZBf2hhyhUqkwbdo0KW38/bbEOM+0adO4egxZpdVq8e9//1tKP/bYYy7sDcndzJkzpf39+/dDp9NZzW88Hj3wwAOIi4tzWl8YtJPsLFq0SDrT1tzcjE2bNlnMq9Fo8N5770npZ555hnfDdENbt241WZt91qxZrusMOez555+X9nNzc3Ho0CGLeXNycnD06FGzZYnMWbduHSoqKqQ0xwuyxnhMqaqqwvbt2y3mvXPnDj766COzZZ1CJOoGMTExIgARgLhhwwa7y6elpUnlPT09xS+++KJDHp1OJ86bN0/Kp1QqxZKSEif0nlzt6tWr4u9+9zsxLy/Paj69Xi++++67oqenp3QcREREiFqttod6St1Br9eLo0ePlj7T8PBw8fr16x3ylZaWiomJiVK+hx56SNTr9S7oMbnS8ePHxZdffllUq9VW8+l0OnH16tXS8QJAfPjhh3nMuAnjz33Xrl12lZ05c6ZUVqVSiVlZWR3y1NbWihMnTpTyhYWFOf13kSCKoujcPwPInWzcuBEbN27s8Pz9+/elfU9PT7N3EsvPz0dMTIzZemtqajB+/HgUFhYCMNyievHixZg1axaCg4ORn5+Pbdu2mZxd3bp1K1auXOnoWyIZuHTpEsaMGQMAGDt2LKZOnYrRo0cjNDQUSqUSNTU1uHjxIv71r38hLy9PKqdQKJCZmYmJEye6quvkJOfPn8ekSZOktZEDAgLw0ksvISUlBV5eXsjOzsbWrVul6XNKpRL/+c9/MG7cOFd2m1zg4MGDmD17Njw8PDBhwgRMmjQJo0aNQkhICHx8fFBVVYXs7Gzs3bvXZFWP4OBgfPfddx1WCKHebfny5fjkk086PG8cl3h5ecHT07NDHks3UCouLsa4ceOklYYUCgWWLl2KJ554AiqVCrm5udiyZQtu3rwJwBCzHDx4EL/5zW+c8ZZ+4dQ/AcjtbNiwweSvV3u2mzdvWq07Pz9fjIqKsqmuVatW9cwbph5x8eJFu4+nsLAwMTMz09VdJyc6cOCAqFQqO/3slUqleODAAVd3l1zkyy+/tHu8iI+PF3NyclzddeoGv/3tb7scl1hz9uxZMTg4uNM6PD09xS1btnTLe+OcdpKthIQE5ObmYunSpVAqlWbzJCYm4quvvkJ6enoP9466U3h4OJ577jmbLuAZNGgQ1q5diytXriA1NbUHekc9Zc6cObhw4QJSU1MhCEKH1wVBwLRp0/DDDz9gzpw5LughycGIESOwYMECk1U+LImNjcWmTZtw8eJF6b95RLZ47LHHkJubi7lz55qdPQAA48aNw+nTp/HHP/6xW/rA6THUK9TX1+PkyZNQq9VoaGhAeHg4HnzwQQ66bqC8vBy5ubmorKxEVVUV6uvroVKpEBISgjFjxiAxMdFsQEd9i1qtxtmzZ1FSUgIAGDx4MCZMmICoqCgX94zk5Pbt27h27RqqqqpQVVWFhoYGBAQEIDQ0FI888ohTV/Ig91VZWYnTp0/jzp070Ol0iIiIwCOPPNLtU60YtBMRERERyRynxxARERERyRyDdiIiIiIimWPQTkREREQkcwzaiYiIiIhkjkE7EREREZHMMWgnIiIiIpI5Bu1ERERERDLHoJ2IiIiISOYYtBMRERERyRyDdiIiIiIimWPQTkREREQkcwzaiYiIiIhkjkE7EREREZHMMWgnIiIiIpI5Bu1ERERERDLHoJ2IiIiISOYYtBMRERERyRyDdiIiIiIimWPQTkREREQkcwzaiYhINubOnQtBECAIAvz8/FBcXNyletLS0qR6BEFAdna2cztKRNTDGLQTEZEsHDp0CBkZGVJ69erViI2N7VJdjzzyiEn6zJkzjnSNiMjlGLQTEZHLaTQarFy5UkrHxsZi9erVXa5v3LhxJunTp093uS4iIjlg0E5ERC6Xnp4OtVotpd944w34+vp2ub74+Hh4enpK6UuXLjnSPSIilxNEURRd3QkiInJfFRUViIuLg0ajAQAkJCTg2rVrJkF3V0RGRqKkpAQA4OHhAa1WC4VC4XB/iYhcgWfaiYjIpd566y0pYAeAv/3tbw4H7IAhaG+j1+u7fFErEZEcMGgnIiKXqa+vx86dO6X0gAEDsHDhQqfUrVQqTdJ1dXVOqZeIyBUYtBMRkcvs2bMH9fX1UnrJkiXw8fFxSt2CIJikdTqdU+olInIFL1d3gIiI3NdHH31kkl6yZInV/JmZmWhtbQUAJCUlITg42GLelpYWk7SXF3/lEVHvxQtRiYjIJWpqahASEgK9Xg8ACAkJQUVFRYcz5G1KS0sxePBgKV1YWIhhw4ZZrH/IkCEm89jVarXJPHciot6E02OIiMglTp06JQXsADB58mSLATsAnDt3Ttr38/PD0KFDLeZtbW2VVo4BAB8fH4SHhzvYYyIi12HQTkRELnHlyhWT9JgxY6zmP3v2rLQfHx8PDw/Lv8KuXLmC5uZmKT127FinrEhDROQqDNqJiMglCgsLTdKJiYlW8x8/flzaj4qKspo3KyvLJD1x4kQ7e0dEJC8M2omIyCVu375tkg4LC7OY99atW7h69aqUDg0NtVr34cOHTdKpqald6CERkXwwaCciIpdoaGgwSQcGBlrMu2/fPpO0r6+vxbzV1dU4efKklA4NDcXUqVO72EsiInlg0E5ERC5hPOccABobG83ma2lpwfbt202e02q1Fut9//33TdZkX7x4MeezE1Gvx6CdiIhcYtCgQSbp/Px8s/k++OAD3Lp1C4IgSNNibt68aTZvVVUVNm3aJKUVCgX+/Oc/O6nHRESuw6CdiIhcIj4+3iTdfgoMABQUFGD16tUAgCeeeAIREREAgP/+97+orq42yavT6bBo0SLcu3dPeu4Pf/gD12Ynoj6BQTsREbnErFmzTNKHDx/GX/7yF5SXl6OxsREZGRmYPHky6urqIAgCXnvtNenmSjqdDs8++yzUajWamppw8uRJTJw4ESdOnJDqGzVqFN58882efEtERN2Gd0QlIiKXaG1tRXJyMs6fP99p3r/+9a/YtGkTtmzZgrS0tE7zDxkyBCdOnLB6AyYiot6EZ9qJiMglPD09sW/fPgwbNsxqvrS0NKSnpwMAli9fjtGjR1vN/+STTyIrK4sBOxH1KTzTTkRELlVXV4dt27bhiy++wM2bN1FXV4eBAwfi8ccfx8qVK5GSkmKSv7a2Fn//+99x8OBB3Lp1C97e3oiIiEBKSgoWLVrE5R2JqE9i0E5EREREJHOcHkNEREREJHMM2omIiIiIZI5BOxERERGRzDFoJyIiIiKSOQbtREREREQyx6CdiIiIiEjmGLQTEREREckcg3YiIiIiIplj0E5EREREJHMM2omIiIiIZI5BOxERERGRzDFoJyIiIiKSOQbtREREREQyx6CdiIiIiEjmGLQTEREREckcg3YiIiIiIpn7/3qrpyqdnJKiAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_power_spectrum(alpha, wc, beta, save=True):\n", + " \"\"\"Plot the power spectrum of a fit against the actual power spectrum.\"\"\"\n", + " w = np.linspace(-10, 10, 50000)\n", + " s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta)\n", + " s_fit = fittedbath.power_spectrum_approx(w)\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " axes.plot(w, s_orig, \"r\", linewidth=2, label=\"original\")\n", + " axes.plot(w, np.real(s_fit), \"b\", linewidth=2, label=\"fit\")\n", + "\n", + " axes.set_xlabel(r\"$\\omega$\", fontsize=28)\n", + " axes.set_ylabel(r\"$S(\\omega)$\", fontsize=28)\n", + " axes.legend()\n", + "\n", + " if save:\n", + " fig.savefig(\"powerspectrum.eps\")\n", + "\n", + "\n", + "plot_power_spectrum(alpha, wc, 1 / T, save=False)" + ] + }, + { + "cell_type": "markdown", + "id": "598d9240", + "metadata": {}, + "source": [ + "Now that we have a good fit to the spectral density, Let us obtain its dynamics, by passing our ``FitSpectral`` bath specifications into the ``HEOMSolver``" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "d928c1b1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [***** 23% ] Elapsed 239.49s / Remaining 00:00:13:21" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[21], line 8\u001b[0m\n\u001b[1;32m 1\u001b[0m tlist \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m30\u001b[39m \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39mpi \u001b[38;5;241m/\u001b[39m Del, \u001b[38;5;241m600\u001b[39m)\n\u001b[1;32m 2\u001b[0m HEOM_spectral_fit \u001b[38;5;241m=\u001b[39m HEOMSolver(\n\u001b[1;32m 3\u001b[0m Hsys,\n\u001b[1;32m 4\u001b[0m fittedbath,\n\u001b[1;32m 5\u001b[0m max_depth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m,\n\u001b[1;32m 6\u001b[0m options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[1;32m 7\u001b[0m )\n\u001b[0;32m----> 8\u001b[0m result_spectral \u001b[38;5;241m=\u001b[39m HEOM_spectral_fit\u001b[38;5;241m.\u001b[39mrun(rho0, tlist)\n", + "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/heom/bofin_solvers.py:1037\u001b[0m, in \u001b[0;36mHEOMSolver.run\u001b[0;34m(self, state0, tlist, args, e_ops)\u001b[0m\n\u001b[1;32m 970\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun\u001b[39m(\u001b[38;5;28mself\u001b[39m, state0, tlist, \u001b[38;5;241m*\u001b[39m, args\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, e_ops\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 971\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 972\u001b[0m \u001b[38;5;124;03m Solve for the time evolution of the system.\u001b[39;00m\n\u001b[1;32m 973\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1035\u001b[0m \u001b[38;5;124;03m list of attributes.\u001b[39;00m\n\u001b[1;32m 1036\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1037\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrun(state0, tlist, args\u001b[38;5;241m=\u001b[39margs, e_ops\u001b[38;5;241m=\u001b[39me_ops)\n", + "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/solver_base.py:158\u001b[0m, in \u001b[0;36mSolver.run\u001b[0;34m(self, state0, tlist, args, e_ops)\u001b[0m\n\u001b[1;32m 153\u001b[0m stats[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpreparation time\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m time() \u001b[38;5;241m-\u001b[39m _time_start\n\u001b[1;32m 155\u001b[0m progress_bar \u001b[38;5;241m=\u001b[39m progress_bars[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprogress_bar\u001b[39m\u001b[38;5;124m'\u001b[39m]](\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28mlen\u001b[39m(tlist)\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprogress_kwargs\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 157\u001b[0m )\n\u001b[0;32m--> 158\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m t, state \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_integrator\u001b[38;5;241m.\u001b[39mrun(tlist):\n\u001b[1;32m 159\u001b[0m progress_bar\u001b[38;5;241m.\u001b[39mupdate()\n\u001b[1;32m 160\u001b[0m results\u001b[38;5;241m.\u001b[39madd(t, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restore_state(state, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m))\n", + "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/integrator/integrator.py:201\u001b[0m, in \u001b[0;36mIntegrator.run\u001b[0;34m(self, tlist)\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 188\u001b[0m \u001b[38;5;124;03mIntegrate the system yielding the state for each times in tlist.\u001b[39;00m\n\u001b[1;32m 189\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;124;03m The state of the solver at each ``t`` of tlist.\u001b[39;00m\n\u001b[1;32m 199\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m tlist[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[0;32m--> 201\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mintegrate(t, \u001b[38;5;28;01mFalse\u001b[39;00m)\n", + "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/integrator/qutip_integrator.py:55\u001b[0m, in \u001b[0;36mIntegratorVern7.integrate\u001b[0;34m(self, t, copy)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mintegrate\u001b[39m(\u001b[38;5;28mself\u001b[39m, t, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 55\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ode_solver\u001b[38;5;241m.\u001b[39mintegrate(t, step\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_failed_integration()\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_state(copy)\n", + "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/integrator/explicit_rk.pyx:269\u001b[0m, in \u001b[0;36mqutip.solver.integrator.explicit_rk.Explicit_RungeKutta.integrate\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/integrator/explicit_rk.pyx:304\u001b[0m, in \u001b[0;36mqutip.solver.integrator.explicit_rk.Explicit_RungeKutta.integrate\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + "HEOM_spectral_fit = HEOMSolver(\n", + " Hsys,\n", + " fittedbath,\n", + " max_depth=4,\n", + " options=options,\n", + ")\n", + "result_spectral = HEOM_spectral_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "bcde5056", + "metadata": {}, + "source": [ + "Now if we want to see the systems's behaviour as we change the Number of terms in the fit, we may use this auxiliary function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd426cb2", + "metadata": {}, + "outputs": [], + "source": [ + "def generate_spectrum_results(Q, N, Nk, max_depth):\n", + " \"\"\"Run the HEOM with the given bath parameters and\n", + " and return the results of the evolution.\n", + " \"\"\"\n", + " fs = SpectralFitter(T, Q, w, J)\n", + " bath, _ = fs.get_fit(N, Nk=Nk)\n", + " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + "\n", + " # This problem is a little stiff, so we use the BDF method to solve\n", + " # the ODE ^^^\n", + " print(f\"Starting calculations for N={N}, Nk={Nk} and max_depth={max_depth} ... \")\n", + " HEOM_spectral_fit = HEOMSolver(\n", + " Hsys,\n", + " bath,\n", + " max_depth=max_depth,\n", + " options=options,\n", + " )\n", + " results_spectral_fit = HEOM_spectral_fit.run(rho0, tlist)\n", + " return results_spectral_fit" + ] + }, + { + "cell_type": "markdown", + "id": "5ed40998", + "metadata": {}, + "source": [ + "Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "739772ec", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_result_expectations(plots, axes=None):\n", + " \"\"\"Plot the expectation values of operators as functions of time.\n", + "\n", + " Each plot in plots consists of (solver_result,\n", + " measurement_operation, color, label).\n", + " \"\"\"\n", + " if axes is None:\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " fig_created = True\n", + " else:\n", + " fig = None\n", + " fig_created = False\n", + "\n", + " # add kw arguments to each plot if missing\n", + " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", + " for result, m_op, color, label, kw in plots:\n", + " exp = np.real(expect(result.states, m_op))\n", + " kw.setdefault(\"linewidth\", 2)\n", + " if color == \"rand\":\n", + " axes.plot(\n", + " result.times,\n", + " exp,\n", + " c=np.random.rand(\n", + " 3,\n", + " ),\n", + " label=label,\n", + " **kw,\n", + " )\n", + " else:\n", + " axes.plot(result.times, exp, color, label=label, **kw)\n", + "\n", + " if fig_created:\n", + " axes.legend(loc=0, fontsize=12)\n", + " axes.set_xlabel(\"t\", fontsize=28)\n", + "\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63e1b711", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting calculations for N=1, Nk=1 and max_depth=5 ... \n", + " [*** 14% ] Elapsed 0.19s / Remaining 00:00:00:01" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 1.29s*] Elapsed 1.29s / Remaining 00:00:00:00\n", + "Starting calculations for N=2, Nk=1 and max_depth=5 ... \n", + " Total run time: 1.99s*] Elapsed 1.99s / Remaining 00:00:00:00\n", + "Starting calculations for N=3, Nk=1 and max_depth=5 ... \n", + " Total run time: 4.79s*] Elapsed 4.79s / Remaining 00:00:00:00\n", + "Starting calculations for N=4, Nk=1 and max_depth=5 ... \n", + " Total run time: 14.24s*] Elapsed 14.24s / Remaining 00:00:00:00\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generate results for different number of lorentzians in fit:\n", + "\n", + "results_spectral_fit_pk = [\n", + " generate_spectrum_results(Q, n, Nk=1, max_depth=max_depth) for n in range(1, 5)\n", + "]\n", + "\n", + "plot_result_expectations(\n", + " [\n", + " (\n", + " result,\n", + " P11p,\n", + " \"rand\",\n", + " f\"P11 (spectral fit) $k_J$={pk + 1}\",\n", + " )\n", + " for pk, result in enumerate(results_spectral_fit_pk)\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26604acf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting calculations for N=4, Nk=2 and max_depth=5 ... \n", + " Total run time: 28.27s*] Elapsed 28.27s / Remaining 00:00:00:00\n", + "Starting calculations for N=4, Nk=3 and max_depth=5 ... \n", + " Total run time: 49.26s*] Elapsed 49.26s / Remaining 00:00:00:00\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#generate results for different number of Matsubara terms per Lorentzian\n", + "#for max number of Lorentzians:\n", + "\n", + "Nk_list = range(2, 4)\n", + "results_spectral_fit_nk = [\n", + " generate_spectrum_results(Q, 4, Nk=Nk, max_depth=max_depth) for Nk in Nk_list\n", + "]\n", + "\n", + "plot_result_expectations(\n", + " [\n", + " (\n", + " result,\n", + " P11p,\n", + " \"rand\",\n", + " f\"P11 (spectral fit) K={nk}\",\n", + " )\n", + " for nk, result in zip(Nk_list, results_spectral_fit_nk)\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d189e62", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting calculations for N=4, Nk=1 and max_depth=2 ... \n", + " Total run time: 1.08s*] Elapsed 1.08s / Remaining 00:00:00:00\n", + "Starting calculations for N=4, Nk=1 and max_depth=3 ... \n", + " Total run time: 1.68s*] Elapsed 1.68s / Remaining 00:00:00:00\n", + "Starting calculations for N=4, Nk=1 and max_depth=4 ... \n", + " Total run time: 4.80s*] Elapsed 4.80s / Remaining 00:00:00:00\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generate results for different depths:\n", + "\n", + "Nc_list = range(2, max_depth)\n", + "results_spectral_fit_nc = [\n", + " generate_spectrum_results(Q, 4, Nk=1, max_depth=Nc) for Nc in Nc_list\n", + "]\n", + "\n", + "plot_result_expectations(\n", + " [\n", + " (\n", + " result,\n", + " P11p,\n", + " \"rand\",\n", + " f\"P11 (spectral fit) $N_C={nc}$\",\n", + " )\n", + " for nc, result in zip(Nc_list, results_spectral_fit_nc)\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "0c48fbd1", + "metadata": {}, + "source": [ + "#### We now combine the fitting and correlation function data into one large plot. Here we define a function to plot everything together" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab417c68", + "metadata": {}, + "outputs": [], + "source": [ + "def gen_plots(fs, w, J, t, C, w2, S):\n", + " def plot_cr_fit_vs_actual(t, C, func, axes):\n", + " \"\"\"Plot the C_R(t) fit.\"\"\"\n", + " yR = func(t)\n", + "\n", + " axes.plot(\n", + " t,\n", + " np.real(C),\n", + " \"r\",\n", + " linewidth=3,\n", + " label=\"Original\",\n", + " )\n", + " axes.plot(\n", + " t,\n", + " np.real(yR),\n", + " \"g\",\n", + " dashes=[3, 3],\n", + " linewidth=2,\n", + " label=\"Reconstructed\",\n", + " )\n", + "\n", + " axes.set_ylabel(r\"$C_R(t)$\", fontsize=28)\n", + " axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=28)\n", + " axes.locator_params(axis=\"y\", nbins=4)\n", + " axes.locator_params(axis=\"x\", nbins=4)\n", + " axes.text(0.15, 0.85, \"(a)\", fontsize=28, transform=axes.transAxes)\n", + "\n", + " def plot_ci_fit_vs_actual(t, C, func, axes):\n", + " \"\"\"Plot the C_I(t) fit.\"\"\"\n", + " yI = func(t)\n", + "\n", + " axes.plot(\n", + " t,\n", + " np.imag(C),\n", + " \"r\",\n", + " linewidth=3,\n", + " )\n", + " axes.plot(\n", + " t,\n", + " np.real(yI),\n", + " \"g\",\n", + " dashes=[3, 3],\n", + " linewidth=2,\n", + " )\n", + "\n", + " axes.set_ylabel(r\"$C_I(t)$\", fontsize=28)\n", + " axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=28)\n", + " axes.locator_params(axis=\"y\", nbins=4)\n", + " axes.locator_params(axis=\"x\", nbins=4)\n", + " axes.text(0.80, 0.80, \"(b)\", fontsize=28, transform=axes.transAxes)\n", + "\n", + " def plot_jw_fit_vs_actual(w, J, axes):\n", + " \"\"\"Plot the J(w) fit.\"\"\"\n", + " J_fit = fs.spectral_density_approx(w)\n", + "\n", + " axes.plot(\n", + " w,\n", + " J,\n", + " \"r\",\n", + " linewidth=3,\n", + " )\n", + " axes.plot(\n", + " w,\n", + " J_fit,\n", + " \"g\",\n", + " dashes=[3, 3],\n", + " linewidth=2,\n", + " )\n", + "\n", + " axes.set_ylabel(r\"$J(\\omega)$\", fontsize=28)\n", + " axes.set_xlabel(r\"$\\omega/\\omega_c$\", fontsize=28)\n", + " axes.locator_params(axis=\"y\", nbins=4)\n", + " axes.locator_params(axis=\"x\", nbins=4)\n", + " axes.text(0.15, 0.85, \"(c)\", fontsize=28, transform=axes.transAxes)\n", + "\n", + " def plot_sw_fit_vs_actual(axes):\n", + " \"\"\"Plot the S(w) fit.\"\"\"\n", + "\n", + " # avoid the pole in the fit around zero:\n", + " s_fit = fs.power_spectrum_approx(w2)\n", + "\n", + " axes.plot(w2, S, \"r\", linewidth=3)\n", + " axes.plot(w2, s_fit, \"g\", dashes=[3, 3], linewidth=2)\n", + "\n", + " axes.set_ylabel(r\"$S(\\omega)$\", fontsize=28)\n", + " axes.set_xlabel(r\"$\\omega/\\omega_c$\", fontsize=28)\n", + " axes.locator_params(axis=\"y\", nbins=4)\n", + " axes.locator_params(axis=\"x\", nbins=4)\n", + " axes.text(0.15, 0.85, \"(d)\", fontsize=28, transform=axes.transAxes)\n", + "\n", + " def plot_matsubara_spectrum_fit_vs_actual(t, C):\n", + " \"\"\"Plot the Matsubara fit of the spectrum .\"\"\"\n", + " fig = plt.figure(figsize=(12, 10))\n", + " grid = plt.GridSpec(2, 2, wspace=0.4, hspace=0.3)\n", + "\n", + " plot_cr_fit_vs_actual(\n", + " t,\n", + " C,\n", + " lambda t: fs.correlation_function_approx(t),\n", + " axes=fig.add_subplot(grid[0, 0]),\n", + " )\n", + " plot_ci_fit_vs_actual(\n", + " t,\n", + " C,\n", + " lambda t: np.imag(fs.correlation_function_approx(t)),\n", + " axes=fig.add_subplot(grid[0, 1]),\n", + " )\n", + " plot_jw_fit_vs_actual(\n", + " w,\n", + " J,\n", + " axes=fig.add_subplot(grid[1, 0]),\n", + " )\n", + " plot_sw_fit_vs_actual(\n", + " axes=fig.add_subplot(grid[1, 1]),\n", + " )\n", + " fig.legend(loc=\"upper center\", ncol=2, fancybox=True, shadow=True)\n", + "\n", + " return plot_matsubara_spectrum_fit_vs_actual(t, C)" + ] + }, + { + "cell_type": "markdown", + "id": "7e97bb39", + "metadata": {}, + "source": [ + "#### And finally plot everything together" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89a13ac5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t = np.linspace(0, 15, 1000)\n", + "C = ohmic_correlation(t, alpha, wc, 1 / T)\n", + "w2 = np.concatenate((-np.linspace(10, 1e-2, 100), np.linspace(1e-2, 10, 100)))\n", + "S = ohmic_power_spectrum(w2, alpha, wc, 1 / T)\n", + "gen_plots(fittedbath, w, J, t, C, w2, S)" + ] + }, + { + "cell_type": "markdown", + "id": "bc59037f", + "metadata": {}, + "source": [ + "## Building the HEOM bath by fitting the correlation function" + ] + }, + { + "cell_type": "markdown", + "id": "5c3a7b08", + "metadata": {}, + "source": [ + "Having successfully fitted the spectral density and used the result to calculate the Matsubara expansion and terminator for the HEOM bosonic bath, we now proceed to the second case of fitting the correlation function itself instead.\n", + "\n", + "Here we fit the real and imaginary parts separately, using the following ansatz\n", + "\n", + "$$C(t) = \\sum_{k=1}^{n} (a_{k}+ i d_{k}) e^{-b_{k} t}e^{i c_k t}$$\n", + "\n", + "Analogously to the spectral density case, one may use the `CorrelationFitter` class" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48917666", + "metadata": {}, + "outputs": [], + "source": [ + "t = np.linspace(0, 15, 500)\n", + "C = ohmic_correlation(t, alpha=alpha, wc=wc, beta=1 / T)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7bbcd148", + "metadata": {}, + "outputs": [], + "source": [ + "fc = CorrelationFitter(Q, T, t, C)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18c67d04", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit correlation class instance: \n", + " \n", + "Result of fitting The Real Part Of |Result of fitting The Imaginary Part \n", + " the Correlation Function with 3 terms: | Of the Correlation Function with 3 terms: \n", + " | \n", + " Parameters| a | b | c | d | Parameters| a | b | c | d \n", + " 1 | 1.08e-01 |-2.36e-01 || 1.13e-06 |2.76e+00 | 1 | 1.51e+00 |-3.82e+00 || 2.00e+00 |1.94e+00 \n", + " 2 |-6.76e+00 |-1.73e+00 ||-2.87e-01 |1.48e+01 | 2 |-1.17e+01 |-4.49e-01 || 1.74e-08 |-1.34e-01 \n", + " 3 | 8.15e+00 |-2.35e+00 ||-1.00e+00 |3.83e+00 | 3 | 2.16e+01 |-1.41e+00 ||-1.55e-08 |-1.81e+00 \n", + " | \n", + "A normalized RMSE of 5.90e-05 was obtained for the The Real Part Of |A normalized RMSE of 4.67e-05 was obtained for the The Imaginary Part \n", + " the Correlation Function | Of the Correlation Function \n", + " The current fit took 4.625643 seconds | The current fit took 0.544415 seconds \n", + "\n" + ] + } + ], + "source": [ + "bath, fitinfo = fc.get_fit(Ni=3, Nr=3)\n", + "print(fitinfo[\"summary\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b5f867b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gen_plots(bath, w, J, t, C, w2, S)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85ec990b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n", + " Total run time: 1.57s*] Elapsed 1.57s / Remaining 00:00:00:00\n", + "3\n", + " Total run time: 5.95s*] Elapsed 5.95s / Remaining 00:00:00:00\n", + "4\n", + " Total run time: 17.48s*] Elapsed 17.47s / Remaining 00:00:00:00\n" + ] + } + ], + "source": [ + "def generate_corr_results(N, max_depth):\n", + " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + " bath, _ = fc.get_fit(Ni=N, Nr=N)\n", + " HEOM_corr_fit = HEOMSolver(\n", + " Hsys,\n", + " bath,\n", + " max_depth=max_depth,\n", + " options=options,\n", + " )\n", + "\n", + " results_corr_fit = HEOM_corr_fit.run(rho0, tlist)\n", + "\n", + " return results_corr_fit\n", + "\n", + "\n", + "# Generate results for different number of exponentials in fit:\n", + "results_corr_fit_pk = [\n", + " print(f\"{i + 1}\")\n", + " or generate_corr_results(\n", + " i,\n", + " max_depth=max_depth,\n", + " )\n", + " for i in range(1, 4)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "145acb4d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (\n", + " result,\n", + " P11p,\n", + " \"rand\",\n", + " f\"P11 (correlation fit) k_R=k_I={pk + 1}\",\n", + " )\n", + " for pk, result in enumerate(results_corr_fit_pk)\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "199f302e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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1Sp8+fdQyvr6+qcroPo01MzNT9u3bl+7+slJWURRl8+bNavnFixenWSY6Olpp1aqVAijly5dX4uPjU6zP7In048ePlbCwsHRjiI2NVVq3bq0+yU9ISEhVRp+n3vqUM/T1sLS0TPMp8tOnT9WnoLVq1Uo33szoPsn28fFRgoKC0n09fPgw1faGbiGRk+P94Ycf1Hq6du2qxMTEpFkuMTExVUtdfa+/PmXz03cgp9c/s3ORlfOqry5duiiA4uXlpX4WFRWVomXM4MGDldjYWIPsLyPZvQfTfWWlJbuiGP/4TXGMr5IWEiJTlStX5vz58wwePBhbW9s0y3h6erJ9+3a+++47I0dnWs/f/He0Yusd/iaMRAghRF7w5MkTdTmjgaD1ERsby5IlSwCoVq0aU6ZMSVVGo9Hg6+tLsWLFAJg3b16GdQ4YMIDWrVvrtX99yn777bcAdO3alSFDhqRZxsbGRo3r5s2bWe5X7urqStGiRdNdb2VlxaxZSSNL37p1S33CaGi5cT1Gjx5NixYtUn3u4uKi9s8/f/484eHhOQsemD9/PjVr1kz35evrm+N9ZCa7x6vVatVrXLp0aVauXKmOc/YqMzOzXGupm5+/A3nh+usj+e83efyE69ev07hxY9atW4elpSW+vr4sWbIEKysr0wWZiwr78ecHhW6WjeXLl6c7eFR2lC9fXp0PPSuKFi3KkiVLmDt3Ln/88Qd37twhKiqKUqVKUbNmTby8vAwWY37S6ZfPiaq4GHte0urFOWKjorC2tzd1WEIIkS0BAfWJiyv43c+srEpSv35ArtT94sULddk+h/8fBAYG8vz5cyApOWBubp5mOScnJ3r06MH8+fMJDg7m/v37lCpVKs2yffr00Xv/mZUNDQ0lMDAQgB49emRY1tPTE1dXV548ecKff/7JO++8o3ccr4qNjeXhw4dERkaqAwfq/rY5d+4c9erVy3b96TH29dA9hpCQkAIx4n52j/fs2bOEhoYCSV2VHRwcci3GjMh3IHdFRESog2fWqVOHnTt30rdvX54/f06JEiXYvHkzb775ptHiCQoKynEd7u7uepc1xfEb+xgLgkKXkMhrHB0d6dy5s6nDyDNeq+DO71Z16RR3HFfCWDfmW7wXT898QyGEyIPi4h4QFxdq6jDyNUdHR3U5eaaN7Lpw4YK63KhRowzLNmrUSB3H4cKFC+ne/NSqVUvv/WdWNiDg36SOt7c33t7eetX74EHWk15RUVH89NNPrF+/nosXL2bYL1+3lYoh5cb1eLWPuC4XFxd1WTfRlV2TJ09O84m+MWX3eM+cOaMum3Ja+fz8HcgL1z8zuq2b9uzZw549e1AUhYYNG7J161ZKly6daR21atVKcZOd3GKmYcOGTJo0idq1a+sdT40aNTIvZECGPP5bt25RtmzZTMsb+xgLAklIiDwntHFNOHocgJfrT0PeGndICCH0ZmVV0tQhGEVuHqerq6u6/PDhwxzV9ezZM3U5s+4fJUv+e0y6273K2dlZ7/1nVvbRo0d616Xr5cuXWSp/8+ZNWrVqRUhIiF7lo6OjsxNWpnLjetjZ2aW7zszs357KmQ2MmF9k93h1k0zp3dgbg3wHcpfuDfnu3bsBaNWqFbt27Uq3i46umJgYLl26hIuLC6NHj1Y/O378OFu3bmX37t34+/tTvXr1XIk/pwx1/K6urnolI0T2SEJC5DkdF43leNXTLGIkayN7UNzvIB07vm3qsIQQIstyqxtDYaL79O306dMGq1ej0WS4Xt/umOk1Mc9OWd0bpDVr1ujd+iIrSRGAfv36ERISgkajYeDAgfTq1QtPT0/c3NzUH+larVaNNztdU7PKUNdDZF1m595Y5DtgeMk35B4eHri4uBAYGMjJkye5ePEidevWzXT7c+fOkZCQQKNGjVK1BunWrRvbtm1j6dKlfP/993rFo9siJrvc3d0zHANHl6GOX5+yyYx9jAWBJCREnuNepTw93xjF//7XD4BRo8Lo2NHEQQkhhDCJatWqqWMlHDt2jIiIiGxP/anbXPvBgwdUrlw53bK6rTF0t8tNyYP2QdLNWW40/b18+TLHjye1Qhw3bhwzZsxIs1xYWJjB9/2qvH49ckPyE/rksTrSk9PuSZnRbXl07969NKepNYbC+B0wpuQb8gYNGjBnzhwaNGjA/fv36dSpE/7+/pm2jklOAqd1Q96mTRu2bdumTsusj5o1a+offDqWLVvGgAED9CprqOPPyhg6xj7GgkBm2RB50po1bYCk5nh37nRj+fLfTBqPEEII09BoNOoPs6ioKHVE/uzQvcH/66+/Mix76tSpNLfLTboDWu/bty9X9nHx4kV1uVevXumW0x3PIi2GeKqe169HbkgeEyV5IMf0XLlyJVfj0L3BPHr0aJa3N1SrisL4HciIIVurJCQkEBwcDCS1NCtdujTbt2/HxsaG0NBQOnfunGl3rORBdtNKSNy4cQPAZMmszOT28QvDkYSEyJPKly9BixZ+/7wzY+moxyTEx5k0JiGEEKYxZswYtV/4pEmTuHz5sl7babVaVq9erb6vV6+e2gx2xYoV6fYhf/HiBRs3bgSSWmgYq499xYoVqVatGgDr16/n9u3bBt9HQkKCupzR2BMLFizIsB4bGxt1OTY2Nlux5PXrkRs8PDyApGNKL+kQFxfHli1bcjWO2rVrU6ZMGQCWLFlCZGRklrY3xPWHwvkdyIihzivApUuX1DqSu741aNCApUuXAuDv769Og5qe5BYCr87+d+bMGXx9fXFycmLo0KF6x6QoSo5f+rYcMOTxZ6WFhDGPsaCQhITIs7Zu7Y6H2VE20IOjL4exrMOHpg5JCCGECZQuXZp58+YBSa0kmjdvzpEjRzLcJjg4mLZt2zJ79mz1M2tra4YMGQIktRSYOnVqqu0UReHDDz9UB/378EPj/t/z9ddfA0mDqXXr1i3D5tCxsbH4+voSExOjd/2VKlVSl1esWJFmmfnz5/Pbb79lWI/uDeH169f13r+u/HA9DK158+bqclr97hVF4eOPP+bevXu5GoeZmRlffPEFAHfv3uWDDz4gLi7tBz9arTZVPIa4/lA4vwMZMdR5hZQDOuqOxePt7c2ECRMA2LBhQ5rnHJISYxcuXMDMzIwVK1YwZcoUxo8fT7du3WjYsCHFixfnwIEDFC9ePEdx5hZDHb+zs7OaSBS5Q8aQEHmWs7M949/dSY8dmwBos28nj29dx61cBRNHJoQQwtgGDhzI3bt3mTRpEo8ePaJFixa0adOGzp074+npSdGiRXn27Bl///03O3fuZM+ePSQmJqaakm7SpEls3bqVGzduMG3aNC5cuMCgQYN47bXXCAkJYd68eRw+fBiAJk2aMGzYMKMep7e3N3v37mXFihUEBgZSrVo1hg8fTvPmzXFzcyMqKorr169z7Ngxtm7dyrNnz/jggw/0rt/Ly4saNWpw4cIF5s+fz/Pnz+nTpw+lSpXizp07rF69ms2bN9O0aVNOnDiRYT02NjbExMQwceJELCwsKF++vDpGQunSpbG1tc00nrx+PQzNy8uLxo0bc/LkSRYvXkxcXBz9+/enSJEiXL16lQULFnD48GGaNGnCn3/+mauxjBo1Cj8/P/bv38+2bduoWbMmI0eOpH79+tjZ2fHgwQNOnjzJunXr6N27d4pBDQ11/aHwfQcyYsjzmnxD7uLigru7e4p106ZN49KlS2zdupWpU6fi6elJjx49UpQ5f/488fHxAKlu2j08PDhy5IjayiYvMtTx54fuGsePH+fatWvqe91ZdK5du8by5ctTlM9zLTAUUaCFh4crgBIeHm7qULIlMSFROWheX3mImzKIJUqzN34ydUhCiEIuOjpaCQ4OVqKjo00dSqG0ZcsWpXz58gqQ6at69erK3r17U9UREhKiVK1aNcNtmzZtqjx9+jTNGCZPnqyWy0xWyiZLSEhQxo4dq5ibm2d6jPb29srLly9TbL9s2TJ1fUhISKr6z5w5ozg7O6dbZ82aNZV79+6p7ydPnpxmnGPHjk23jkOHDukVi6IY73ocOnQoVXxZpVtHeuclM5cuXVKKFy+e7rF++umnGZ43Qx5vVFSU0r1790y/Z2kdqz7XX1EK1ncgp9dfn3Oh73nNTKtWrRRAadmyZZrrIyMjlTp16iiAYmtrq/j7+6dYv3DhQgVQxo0bpyiKomi1WuXu3bvKoEGDFEBp06aN3rGYgqGOf+zYscYIN0f69++v1/+JWf2/KFl2f/foex8qXTZEnmZmbkbkj19TmUssZTDH/tdfzZILIYQofLp168aVK1dYs2YNffv2pUqVKjg7O2NhYYGLiwt169Zl5MiRHDx4kKCgINq0aZOqjvLly3Pu3DnmzZtH8+bNKVasGJaWlpQoUYJ27dqxatUqjh49arKR/M3Nzfnuu+8IDg7ms88+w8vLC2dnZ8zNzXF0dKR69er06dOHFStWcP/+fb2fmCarU6cOZ8+eZcSIEZQrVw5LS0tcXFxo2LAhs2fP5tSpU3r10Z85cyaLFy+mWbNmuLi4ZGkKVF15/XoYWtWqVTl9+jQ+Pj6UK1cOKysr3NzcaNeuHTt37tR7CkVDsLOzY9OmTfzxxx/069cPDw8PbG1tcXR0pGrVqnTr1o21a9eq3Tt0Ger6Q+H7DmTEUOf13LlzAKlaiSWzt7dn+/btlChRgujoaDp37kxoaKi6/tUBHTUaDaVLl2bhwoW4u7uzb9++FE/l8xpDH7/IPRpFkUl9C7KIiAiKFClCeHh4tqdJyws8Pbdx+XJXAIoXX8WDB33zzLzZQojCJSYmhpCQEDw8PFIMQCaEEEIUFA0aNCAgIIDr16/z+uuvp1g3fvx4vv32W7755hvGjRtnoghzV/LxX716lYoVK5o6HJPK7u8efe9DpYWEyBf8/N5EowkH4NGjPnz7adrzpgshhBBCCCGyLz4+nqCgIIoWLZoqGQHQqVMnALZt22bs0Iwi+fidnJyoUEHGrsttkpAQ+ULFim707HmUEjxgDX0Z/MMcnl7L3Tm6hRBCCCGEKGwuXrxIbGxsquk+kzVq1IiSJUsSEBDA3bt3jRxd7ks+/rp160qLbCOQhITIN5Yv78Bs85H0Zh0lCONEs16mDkkIIYQQQogCJbPxEzQaDR06dEBRFLZv327M0IxCxo8wLhlDooArKGNIJFsxYyfdvu6FI5EA/DVnNo0++czEUQkhChMZQ0IIIYQQhYWMISGEjg/Gv8ccl3/nn3b84r/Ev3xpwoiEEEIIIYQQQmSHJCREvqLRQPsdnxFAPQCqJT5iX7uOJo5KCCGEEEIIIURWSUJC5DsNm7zGyjd8SPzn69vy2HFuHT5s2qCEEEIIIYQQQmSJJCREvjRha19+MUvqumFHHPc69ULRak0clRBCCCGEEEIIfUlCQuRLJUpY8/zTPtymDABNXjzk6OBBJo5KCCGEEEIIIYS+JCEh8q0vpzdlXNEp6vuay9fx8Px50wUkhBBCCCGEEEJvkpAQ+Za1tYYey1uxht4AuBDH323fNXFUQgghhBBCCCH0IQkJka917lyeDY0H84RiADR7EMr/vvzSxFEJIYQQQgghhMiMJCREvjdreX0+NZulvveYNZewW7dMGJEQQgghhBBCiMxIQkLke1WqOFFsdG120R6AUko8Z9u0MXFUQgghhBBCCCEyIgkJUSBMn1GHCS5TeYEDAC3//puA7783cVRCCCGEEEIIIdIjCQlRINjbm/HJXBfG8a36mfW4CURFRpowKiGEEEIIIYQQ6ZGEhCgw+vWrQGDDlpzgDXbwHu/GD+PriRNNHZYQQgghhBBCiDRYmDoAIQxFo4H5C1+jRV0/whVnIJoffvCkZ8+eNG7c2NThCSGEEEIIIYTQIS0kRIFSp44z3QfdBTSAHTCLwYMHExsba+LIhBBCCCGEEELokoSEKHBmzaqOs/Ozf9714E5wMTYMHmzSmIQQQgghhBBCpCQJCVHgODub85//JCUkOuBHMFf4vzVrCN6+3cSRCSGEEEIIIYRIJgkJUSCNHFmRmjWv04o/cOcRtsCTfv2Ij483dWhCCCFErlq+fDkajQaNRsPNmzclFmFS8h34l6nORXx8PFZWVmg0GmbMmGG0/eYVhf348zpJSIgCycwM5s8vytdMJ4Ty7OYd+r2w5ptvvjF1aEIIIXIoPj6e9evX079/fzw9PSlWrBiWlpa4urpSr149fHx8OHDgAFqt1tShigLm8OHD6g2lPq/ly5ebOmRhQPn1+l+8eFF9KFe7dm0TR2N8Bfn4IyIiWL9+PZ999hnNmzenYsWKFClSBCsrK4oXL06LFi3473//y9OnT00darokISEKrKZNi9Gx503e4H+8yz5uM4fp06dz9uxZU4cmhBAim7Zv307VqlXx9vZm5cqVXL58mWfPnpGQkMDTp085ffo0CxYsoHXr1nh6erJz505Th1wgyVPvwk2uf+7IrfOq+9u3Tp06Bqs3vyjIx3/q1Cm8vb2ZM2cOR48e5fr160RERBAfH8/jx485cuQIX375JVWrVmXv3r2mDjdNMu2nKNB++qkKlXZHQoQG6EdCwq8MGDAAf39/LC0tTR2eEEKILPj222+ZMGECiqIA8M4779C5c2eqVatG0aJFefbsGVeuXMHPz4/9+/fz999/M2HCBN577z0TR154DRgwgAEDBpg6jFzh4+PDyJEjMyzj7u5upGjyroL6HcjO9TfVuUi+IS9WrFih/E4W9OMvU6YMLVu2pF69epQpU4ZSpUqh1Wq5e/cumzdvZuvWrTx58oROnTrh7+9PrVq1TB1yCpKQEAVa8eIWTJ36iE8+KfrPJ77cOFeb1cOGMXDZMlOGJoQQIgtWrVrF+PHjAXBzc2PDhg20bNkyVbl33nmHUaNGERQUxJgxY/J0M1WRvxUvXpwaNWqYOgxhIvnp+iffkBe07gr6KsjH37JlS27fvp3u+h49evDbb7/RtWtX4uLimDp1Klu2bDFihJmTLhuiwPvoo8rUrHkdgDbc4QK2vL98ORd37zZxZEIIIfRx7949fHx8ALCzs+Pw4cNpJiN01axZk/379/P5558bI0QhhMizzp8/DxS87gr6KsjHb25unmmZLl26ULVqVQCOHj2a2yFlmSQkRIFnZgaLFztjZpbI/7GJsrzACQjv0YO42FhThyeEECITc+fOJSoqCoCpU6dSrVo1vbYzMzOjb9++aa6Li4vD19eXli1b4ubmhpWVFSVLluTdd99l9erVGQ6IOWXKFLWfN0B4eDjTpk3Dy8uLokWLphjMLitlX3Xq1CmGDh1K5cqVcXBwwN7enqpVqzJq1CiuXr2q1zlIz4ULF5g+fTpt27bF3d0da2trHBwcqFSpEv379+fkyZNpbpc8qN/AgQPVzzw8PFIN5nf48GFA/z7xhrweMTExzJo1i7p16+Lo6IijoyMNGzZk3rx5JCQkZP1kGdCAAQPQaDSUL18+w3IZnbfcON4TJ04wZMgQqlSpgpOTEw4ODlStWpUuXbqwcuVKIiIigKxf/8yORVdh+A6kdy6yc171devWLcLCwoD0b8hDQ0Np0qQJGo0Ga2trFi1alOX95FWF/fiT2dvbA0l/G3mOIgq08PBwBVDCw8NNHYrJDRwYpDjxXLmNu6KAooCyvVMnU4clhMhnoqOjleDgYCU6OtrUoRQKWq1WcXNzUwDF3t7eIP+f3bx5U/H09FSAdF9vvvmm8vTp0zS3nzx5slru77//VsqXL59q+2XLlmW5bLL4+HjFx8cnw/gsLS2VRYsWpRnfsmXL1HIhISGp1h86dCjDupNfX331Vba3PXTokF6xGPp6PHjwQKldu3a69XTs2FFJTExMsx596B7/5MmTs7x9//79FUApV65chuUyOm+GPN6XL18q3t7emV7P5GPN6vXP7FiS5ZfvQE6vf3rnIjvnVV+//fabuv358+dTrT9y5IhSokQJBVBKlSqlnDhxIsv7yMsK+/EriqIEBwcr5ubmCqDUr18/y9tn93ePvveh0kJCFBrff18Ny2IJDOPfrGfz33/n4p49JoxKCCFERoKDg3n8+DEAzZo1w8nJKUf1RUZG0qpVKy5dugQkNWX9/fffCQgIYNOmTTRv3hyA48eP06FDBxITEzOsr3v37oSGhjJ69Gj2799PQEAA69ato0qVKtkuO3jwYObPnw9A+/btWb16NadOncLf35/FixdTvXp14uPjGTZsGH5+flk+BwkJCdjb29OjRw8WLFjA4cOHOX36NHv27OH777+nXLlyAMycOZNlr4y31KBBA4KCgpg+fbr62d69ewkKCkrxatCggV6xGPp6dOvWjUuXLvHRRx+xf/9+AgMDWbt2LZ6engD4+fmxePFi/U5UPpCT49VqtXTu3Jl169YBUKlSJebOncuxY8cIDAxkx44djB8/nooVK6rbGPr6g3wHIHfOa7Lk8ROsrKzUZvvJfvjhB95++20ePnxI48aNCQgI4I033sjRseQ1hfX4X758ydWrV5kzZw4tW7ZU/24+/vhjE0eWhiynSES+Ii0kUlq0KEQBRfmVgWoriWOOjkpsTIypQxNC5BPSQsK41qxZoz7dGj9+fI7r+/zzz9X6vv7661TrtVqt0qdPH7WMr69vqjK6T2PNzMyUffv2pbu/rJRVFEXZvHmzWn7x4sVplomOjlZatWqlAEr58uWV+Pj4FOszeyL9+PFjJSwsLN0YYmNjldatW6tP8hMSElKV0eeptz7lDH09LC0t03yK/PTpU/UpaK1atdKNNzO6T7J9fHyUoKCgdF8PHz5Mtb2hW0jk5Hh/+OEHtZ6uXbsqMen8FkpMTFRCQ0P1ji8rx6Io+es7kNPrn9m5yMp51VeXLl0UQPHy8lI/i4qKStEyZvDgwUpsbKxB9peR5P3l5PVqi7LMGPv4TXGMyXS/P2m9Pv/8c0Wr1Wa5XmkhIYQBDRlSnoYNr/EpcwjlNQDefPGC3T17mjgyIYQQaXny5Im6XKJEiRzVFRsby5IlSwCoVq0aU6ZMSVVGo9Hg6+tLsWLFAJg3b16GdQ4YMIDWrVvrtX99yn777bcAdO3alSFDhqRZxsbGRo3r5s2bWe5X7urqStGiRdNdb2VlxaxZs4Ck/tfJTxgNLTeux+jRo2nRokWqz11cXNT++efPnyc8PDxnwQPz58+nZs2a6b58fX1zvI/MZPd4tVqteo1Lly7NypUrsba2TnMfZmZmvPbaa4YN/B/5+TuQF66/PpL/fpPHT7h+/TqNGzdm3bp1WFpa4uvry5IlS7CysjJdkLmosB8/JB37yZMnmTVrljrOSl4i036KQkWjgcWLS1K3rjXDEhexkw4ANN++nQt79lCjXTsTRyiEEELXixcv1OXkQbmyKzAwkOfPnwNJyYH0Rid3cnKiR48ezJ8/n+DgYO7fv0+pUqXSLNunTx+9959Z2dDQUAIDA4Gkqdoy4unpiaurK0+ePOHPP//knXfe0TuOV8XGxvLw4UMiIyPVgQMVRVHXnzt3jnr16mW7/vQY+3roHkNISEiBGHE/u8d79uxZQkNDARg6dCgODg65FmNG5DuQuyIiItTBM+vUqcPOnTvp27cvz58/p0SJEmzevJk333zTaPEEBQXluA53d3e9y5ri+I19jLq6dOlC/fr1AYiOjub69ets3LiRbdu20adPH3744Qc6dOiQ4/gMTRISotCpVcsBH5/LzJv3HsvpzwBWUBQ4/3//R+zjx1jb2Jg6RCFEAVG/fn0ePHhg6jByXcmSJQkICMiVuh0dHdXl5Jk2suvChQvqcqNGjTIs26hRI3UchwsXLqR781OrVi29959ZWd1z6O3tjbe3t171Zuc7FhUVxU8//cT69eu5ePFihv3ydVupGFJuXI9X+4jrcnFxUZd1E13ZNXny5DSf6BtTdo/3zJkz6vJbb71l+MD0lJ+/A3nh+mdGt3XTnj172LNnD4qi0LBhQ7Zu3Urp0qUzraNWrVopbrKTW8w0bNiQSZMmUbt2bb3jqVGjRpbizylDHv+tW7coW7ZspuWNfYy6ihYtmqL1W4MGDejVqxerVq2if//+dO7cmV9//ZUBAwaYLMa0SEJCFErffFOFTZse88nDubRhH69xn7ciI9nYrRs9du0ydXhCiALiwYMH6lNIkT2urq7q8sOHD3NU17Nnz9TlzLp/lCxZMs3tXuXs7Kz3/jMr++jRI73r0vXy5csslb958yatWrUiJCREr/LR0dHZCStTuXE97Ozs0l1nZvZvT+XMBkbML7J7vLpJpvRu7I1BvgO5S/eGfPfu3QC0atWKXbt2pdtFR1dMTAyXLl3CxcWF0aNHq58dP36crVu3snv3bvz9/alevXquxJ9Thjp+V1dXvZIReVW/fv3YsWMHGzdu5MMPP6Rz585Z+r8rt0lCQhRKjo4a5s5NoHdvN4azED86AdB2925ObdlCw/ffN3GEQoiCQPcHdEGWm8ep+/Tt9OnTBqs3s360ul0WMpJeE/PslNW9QVqzZo3erS+y+sOyX79+hISEoNFoGDhwIL169cLT0xM3Nzf1R7pWq1Xj1fdc5IShrofIurzSp1y+A4aXfEPu4eGBi4sLgYGBnDx5kosXL1K3bt1Mtz937hwJCQk0atQoVWuQbt26sW3bNpYuXcr333+vVzy6LWKyy93dPcMxcHQZ6vj1KZvM2Meor86dO7Nx40aioqLYvXs3vXv3Nmj9OSEJCVFo9epVioULr7LjSEdW8AH9WUkRIK5fPyJbt8Yhh1PLCSFEbnVjKEyqVaumjpVw7NgxIiIisj31p25z7QcPHlC5cuV0y+q2xtDdLjclD9oHSTdnudH09/Llyxw/fhyAcePGMWPGjDTLhYWFGXzfr8rr1yM3JD+hTx6rIz057Z6UGd2WR/fu3UtzmlpjKIzfAWNKviFv0KABc+bMoUGDBty/f59OnTrh7++faeuY5CRwWjfkbdq0Ydu2beq0zPqoWbOm/sGnY9myZXp3OTDU8WdlDB1jH6O+3Nzc1OVbt24ZtO6cklk2RKGl0cCiRWWxto7hY37kDkkDyLwZHY3fe++ZODohhBCQdGOe/MMsKipKHZE/O3Rv8P/6668My546dSrN7XKTl5eXurxv375c2cfFixfV5V69eqVbLrNkmiGequf165EbksdESR7IMT1XrlzJ1Th0bzCPHj2a5e0N1aqiMH4HMmLI1ioJCQkEBwcDSS3NSpcuzfbt27GxsSE0NJTOnTtn2h0reZDdtBISN27cADBZMiszuX38+Y1u91FTDWKbHklIiEKtcmVrPv/8DuEUZRBL1c87HT/O0aVLM9hSCCGEsYwZM0btFz5p0iQuX76s13ZarZbVq1er7+vVq6c2g12xYkW6fchfvHjBxo0bgaQWGsbqY1+xYkWqVasGwPr167l9+7bB95GQkKAuZzT2xIIFCzKsx0ZnAOjY2NhsxZLXr0du8PDwAJKOKb2kQ1xcHFu2bMnVOGrXrk2ZMmUAWLJkCZGRkVna3hDXHwrndyAjhjqvAJcuXVLrSO761qBBA5b+8/vW399fnQY1PcktBHSTpZA0KKqvry9OTk4MHTpU75gURcnxS9+WA4Y8/qy0kDDmMWbFpk2b1GVDtOIwJElIiEJv0qRKVKhwlwO05hdGAmAP2I4YwbMsNEMTQgiRO0qXLs28efOApFYSzZs358iRIxluExwcTNu2bZk9e7b6mbW1NUOGDAGSWgpMnTo11XaKovDhhx+qg/59+OGHhjoMvXz99ddA0mBq3bp1y7A5dGxsLL6+vsTExOhdf6VKldTlFStWpFlm/vz5/PbbbxnWo3tDeP36db33rys/XA9Da968ubqcVr97RVH4+OOPuXfvXq7GYWZmxhdffAHA3bt3+eCDD4iLi0uzrFarTRWPIa4/FM7vQEYMdV4h5YCOumPxeHt7M2HCBAA2bNiQ5jmHpMTYhQsXMDMzY8WKFUyZMoXx48fTrVs3GjZsSPHixTlw4ADFixfPUZy5xVDH7+zsrCYS86Lly5dn+n/A3Llz2fXPoP3ly5c36lSv+pAxJEShZ2UFixfb0aoVjOW/tGU3FQkhKj6er0aMYFEuP6UQQgiRuYEDB3L37l0mTZrEo0ePaNGiBW3atKFz5854enpStGhRnj17xt9//83OnTvZs2cPiYmJqaakmzRpElu3buXGjRtMmzaNCxcuMGjQIF577TVCQkKYN28ehw8fBqBJkyYMGzbMqMfp7e3N3r17WbFiBYGBgVSrVo3hw4fTvHlz3NzciIqK4vr16xw7doytW7fy7NkzPvjgA73r9/LyokaNGly4cIH58+fz/Plz+vTpQ6lSpbhz5w6rV69m8+bNNG3alBMnTmRYj42NDTExMUycOBELCwvKly+vjpFQunRpbG1tM40nr18PQ/Py8qJx48acPHmSxYsXExcXR//+/SlSpAhXr15lwYIFHD58mCZNmvDnn3/maiyjRo3Cz8+P/fv3s23bNmrWrMnIkSOpX78+dnZ2PHjwgJMnT7Ju3Tp69+6dYlBDQ11/KHzfgYwY8rwm35C7uLjg7u6eYt20adO4dOkSW7duZerUqXh6etKjR48UZc6fP098fDxAqpt2Dw8Pjhw5orayyYsMdfx5vbvGlClT+Oyzz3j//fd58803qVChAg4ODrx48YKgoCDWrFmj/ltuZWXF4sWLsbDIYykARRRo4eHhCqCEh4ebOpQ8r2/fSwooyhscVz4zG6hoQAGUjRs3mjo0IUQeEh0drQQHByvR0dGmDqVQ2rJli1K+fHmFf/6NzuhVvXp1Ze/evanqCAkJUapWrZrhtk2bNlWePn2aZgyTJ09Wy2UmK2WTJSQkKGPHjlXMzc0zPUZ7e3vl5cuXKbZftmyZuj4kJCRV/WfOnFGcnZ3TrbNmzZrKvXv31PeTJ09OM86xY8emW8ehQ4f0ikVRjHc9Dh06lCq+rNKtI73zkplLly4pxYsXT/dYP/300wzPmyGPNyoqSunevXum37O0jlWf668oBes7kNPrr8+50Pe8ZqZVq1YKoLRs2TLN9ZGRkUqdOnUUQLG1tVX8/f1TrF+4cKECKOPGjVMURVG0Wq1y9+5dZdCgQQqgtGnTRu9YTMFQxz927FhjhJtt5cqV0+v/Q3d3d2Xfvn3Z2kd2f/foex8qXTaE+MePP1bBxeUZ/6Mp32uXovwzFaiPjw/37983cXRCCCEgaaq5K1eusGbNGvr27UuVKlVwdnbGwsICFxcX6taty8iRIzl48CBBQUG0adMmVR3ly5fn3LlzzJs3j+bNm1OsWDEsLS0pUaIE7dq1Y9WqVRw9etRkI/mbm5vz3XffERwczGeffYaXlxfOzs6Ym5vj6OhI9erV6dOnDytWrOD+/ft6PzFNVqdOHc6ePcuIESMoV64clpaWuLi40LBhQ2bPns2pU6f06qM/c+ZMFi9eTLNmzXBxccnSFKi68vr1MLSqVaty+vRpfHx8KFeuHFZWVri5udGuXTt27typ9xSKhmBnZ8emTZv4448/6NevHx4eHtja2uLo6EjVqlXp1q0ba9euVbt36DLU9YfC9x3IiKHO67lz5wBStRJLZm9vz/bt2ylRogTR0dF07tw5xcCHrw7oqNFoKF26NAsXLsTd3Z19+/Zx7dq1bMVmDIY+/rzq4MGDLFiwgJ49e1KrVi1KlCiBhYUFDg4OVKhQgffff59ly5Zx5coVWrdubepw06RRFJnUtyCLiIigSJEihIeHZ3uatMJk+fJ7DBz4GgDW1neIja0GRNKxfXu279yZZ+bqFkKYTkxMDCEhIXh4eKQYgEwIIYQoKBo0aEBAQADXr1/n9ddfT7Fu/PjxfPvtt3zzzTeMGzfORBHmruTjv3r1KhUrVjR1OCaV3d89+t6HSgsJIXT07/8ab72VlO2NjS2Djc10mgGzd+/m938GwBFCCCGEEKKgio+PJygoiKJFi6ZKRgB06pTUinjbtm3GDs0oko/fycmJChUqmDqcAk8SEkLo0Gjg11/LYm2dNFrtmzFVOQxUBjxnzuSGzvztQgghhBBCFDQXL14kNjY21XSfyRo1akTJkiUJCAjg7t27Ro4u9yUff926daV1tBFIQkKIV1SsaMWXXz4A4BBvc8asOgAPFYWPPvggxRzuQgghhBBCFCSZjZ+g0Wjo0KEDiqKwfft2Y4ZmFPll/IiCQsaQKOBkDInsiY+H6tVDuXq1NFW4zP/ZjOCbmCNogcmTJ6eY+koIUbjIGBJCCCGEKCxkDAkhTMDSEn79NekP5wpV+a92B5iVBZLmLs7tucGFEEIIIYQQoqCThIQQ6WjWzJH+/a8DEBfngLv7fAC0Wi2De/fmRUSEKcMTQgghhBBCiHxNEhJCZGDu3NdxdX0GwO3b71K+/DCaAH43b7KuY0fTBieEEEIIIYQQ+ZgkJITIgLOzhh9/TFTfuz3uyzGgAtDz6FF2L1hgstiEEEIIIYQQIj+ThIQQmfD2dqNdu2sA+Ec1Y1fRZgAUAZxGjyb09m0TRieEEEIIIYQQ+ZMkJITIhEYDS5Z44ODwAoC+z/24Z+UAQNOEBHa//TZardaUIQohhBBCCCFEviMJCSH0ULq0OTNnJiUkIijCCNtfSU5BfHDtGmu/+MJ0wQkhhBBCCCFEPiQJCSH0NHLkazRpchMAv/AerCv7LgBWQL05cwg6dcp0wQkhhBBCCCFEPiMJCSH0pNHAihWlsbaOBmDQ7S1cdyoGgCdwvn17YmJiTBihEEIIIYQQQuQfkpAQIgsqVbJk4sSnAMRhwzD7ZcRoNAD0efaM5d7epgxPCCGEEEIIIfINSUgIkUVffulOzZp3AfjjfkdW1+qhruv8228c2rTJVKEJIYQQQgghRL4hCQkhssjCAlascMXcPB4AnwsruFimHAClgJh+/Xj08KEJIxRCCCGEEEKIvE8SEkJkg5eXDWPG3AcgIdEaH+v5hFtaAtA+NpYNrVrJVKBCCCGEEEIIkQFJSAiRTdOnl+X11x8AcOxae9a93V9dNyQ4mFVffWWq0IQQQgghhBAiz5OEhBDZZGMDS5c6odEktYT45PAPnHuzIQC2QN1Zszj9v/+ZMEIhhBBCCCGEyLskISFEDjRvbsegQUkDXMbE2DM8Ygb3i7kAUBO48O67vHjxwoQRCiGEEEIIIUTeJAkJIXJo7tyyvPbaEwD+Ov8O6zr7qFOBeoSH8/GIEaYMTwghhBBCCCHyJElICJFDjo6wdKm1+n7C2q8IHurNTCsrWgLL1q5l1apVpgtQCCGEEEIIIfIgSUgIYQBt2zoyYMBNAGJiHBjiP5wSC74n8Z/1I0eO5OrVqyaLTwghhMhty5cvR6PRoNFouHnzpqnDKXAK0/k11bHGx8djZWWFRqNhxowZRtuvEIWZJCSEMJCffipPqVJJXTfOnHmLq1dhwIABAERGRtKnVy/i4uJMGKEQQuRvUVFRLFq0iPfeew93d3dsbGxwcHDg9ddfp0mTJowYMYL169dz//59U4cqsunw4cPqjajuy8LCAhcXFzw8PHjrrbf45JNP2LJli/y/mkeldx3Tey1fvtzUIQNw8eJF4uPjAahdu7aJozG8R48esWPHDiZNmkT79u1xdXVVr0Hyb1YhjE0SEkIYyKtdN+bOHcAHH7SkSpUq1AdWnD7N/IEDTRegEELkY6dOnaJGjRoMHz6cXbt2ERoaSmxsLFFRUYSEhHDy5EkWLlyIt7c3Xl5epg431xSmp+S6EhMTCQsL4+bNmxw7dowffviB7t274+7uzvTp00lISDB1iCZV2L4XuXW8Z8+eVZfr1KljsHrzihIlStCxY0emTZvGnj17ePr0qalDEgILUwcgREHSrp0j/fvfZsWKssTEOPDZZ+XY/PEgqoz8EkvAau1a9nfrRuv33zd1qEIIkW9cu3aN1q1bExERAUCnTp3o3r07lStXxsrKiidPnnDu3Dn279/PoUOHTBytMBQfHx9Gjhypvo+MjCQsLIzz589z8OBBDhw4wOPHj5k4cSJ+fn7s2LEDNzc3E0acuwYMGJAvn2K/eh3T4u7unuK9qY41OSFRrFixVDEVNGXKlMHT05N9+/aZOhRRyElCQggD++mnsuzb95T794tx5kxz1t8MZFS5cpS6dYsnwOfDhrHnjTcoVaqUqUMVQoh8YcKECWoyYunSpQxMo7VZ69at+fzzz3n8+DEbN240dogiFxQvXpwaNWqk+rx9+/Z8+eWXXLx4kX79+nHmzBlOnTpFt27dOHjwIFZWViaIVqQnveuYFyUnJApidw2ASZMm0aBBAxo0aECJEiW4efMmHh4epg5LFHLSZUMIA3NygqVLbdT33/80jPvff8S6SpV4Ezj/7BkffPABWq3WdEEKIUQ+kZiYyI4dOwCoX79+mskIXW5ubowaNcoYoQkTq169OidOnFC76Bw/fhxfX18TRyXys/PnzwMFs7sGwNSpU+nQoQMlSpQwdShCqCQhIUQuaNfOnv797wBJs24M/aYub+xfR/HXXgPgwIEDfPfdd6YMUQgh8oXHjx/z8uVLACpWrJjteqZMmaL2OQd4/vw5kydPpnr16jg4OODi4kKLFi1Ys2aN3nWeOnWKoUOHUrlyZRwcHLC3t6dq1aqMGjVK75mVTpw4wZAhQ6hSpQpOTk44ODhQtWpVunTpwsqVK9WWIcmDBOomZDw8PFINDnj48OF0jzk8PJxp06bh5eVF0aJFUw0meOHCBaZPn07btm1xd3fH2toaBwcHKlWqRP/+/Tl58qTe58ZYbG1tWbVqlXqMs2fPVgclTEt2r9mr5zImJoZZs2ZRt25dHB0dcXR0pGHDhsybNy/T8Szu3bvHV199Rd26dSlSpAhWVlaULFmSmjVr4u3tzfLly9XrriutcROy8r2Ij4+nZMmSaDQa2rdvn2GMkPR9SN7+m2++ybS8IaU3RkR2/g70devWLcLCwoD0ExKhoaE0adIEjUaDtbU1ixYtyvJ+hBCvUESBFh4ergBKeHi4qUMpdMLDFaVUqacKKAooyvjxc5SDBw8oGo1GAZQiZmbKsQMHTB2mECKLoqOjleDgYCU6OtrUoRQKT58+VQAFUGrXrp3teiZPnqzWc+PGDaVChQrq+1df3bt3V+Lj49OtKz4+XvHx8Ul3e0CxtLRUFi1alG4dL1++VLy9vTOsA1AmT56sKIqiHDp0KNOygHLo0KE0j/nvv/9Wypcvn6r8smXLslT/V199le4xLVu2TC0XEhKSlcuj0o0j+dj10aZNG3W7EydOpFqf02umey4fPHig1K5dO916OnbsqCQmJqZZz9GjRxUnJ6dMz7Ofn1+qbdM6v1n9XnzxxRcKoJiZmSl3797N8Jx+8sknCqCYm5tnWvZV2b2OGR3rq/Xq+3egr99++03d/vz586nWHzlyRClRooQCKKVKlUrze5bfhISEqMfcv39/U4cj8qjs/u7R9z5UWkgIkUucnODXX//tujF37hDs7EKZNGkSdQB/rZYrnTvz6NEjk8UohBB5nYuLC+XKlQPg3LlzfPfddznu8tazZ09CQkIYMWIEBw4cwN/fn19//ZXKlSsDsHnzZj799NN0tx88eDDz588HksYzWL16NadOncLf35/FixdTvXp14uPjGTZsGH5+fqm212q1dO7cmXXr1gFQqVIl5s6dy7FjxwgMDGTHjh2MHz8+RYuQBg0aEBQUxPTp09XP9u7dS1BQUIpXgwYN0oy5e/fuhIaGMnr0aPbv309AQADr1q2jSpUqACQkJGBvb0+PHj1YsGABhw8f5vTp0+zZs4fvv/9evQYzZ85k2bJlWTndRvHOO++oy8eOHUu1PqfXTFe3bt24dOkSH330Efv37ycwMJC1a9fi6ekJgJ+fH4sXL061XWxsLL169SIiIgJHR0fGjh3L7t27CQwM5OTJk2zYsIExY8ZQpkwZvY87q9+LIUOGAEnfwZUrV6Zbb3x8PKtXrwagTZs2lC5dWu+YclNO/w4ykjx+hJWVFVWrVk2x7ocffuDtt9/m4cOHNG7cmICAAN54440cHYsQ4h85yZaIvE9aSJhev3531VYSXl6HlfA7gUqEhYWS/OHU2rXTfZIihMh7pIWE8c2ePTvF089y5copH374obJmzRrl2rVretWh+4QbUNauXZuqTEREhPrk28zMLM2npJs3b1brWLx4cZr7io6OVlq1aqUASvny5VO1tvjhhx/UOrp27arExMSkWU9iYqISGhqa4rOstELQPWYzMzNl37596ZZ9/PixEhYWlu762NhYpXXr1ur5T0hISFXGlC0kDhw4oG43aNCgFOsMcc10z6WlpWWaT+CfPn2qPkGvVatWqvUHDx7MsAVEsvj4+DR/t2V0frNy7t966y0FUCpVqpRuma1bt6r1bd68OcP60qJ7HX18fJSgoKB0Xw8fPszSseqzPju6dOmiAIqXl5f6WVRUVIqWTIMHD1ZiY2MNsj/df4+y+0pu4ZRd0kJC6ENaSAiRz82bV5pSpZ4BcOZMc6b/dAztjH8z+x+dO8f8L74wVXhCCJHnffLJJwwaNEh9f+vWLebNm0efPn2oWLEiJUuWpFevXvj5+aEoSqb1dejQAW9v71SfOzo6qn3CtVotCxYsSFXm22+/BaBr167q0+ZX2djYMG/ePABu3ryZoj+7Vqtl1qxZAJQuXZqVK1dibW2dZj1mZma89s/YQzk1YMAAWrdune56V1dXihYtmu56KysrNe5bt26pT5PzimLFiqnLyeMAJMvpNXvV6NGjadGiRarPXVxc1LENzp8/T3h4eIr1Dx48UJffeuutdOu3sLDAyckp3fU5lXwOrl69yokTJ9Isk9wKxtXVlY4dO+Zof/Pnz6dmzZrpvvLKQKTJ3+nk8SOuX79O48aNWbduHZaWlvj6+rJkyRKZxUUIA5NpP4XIZU5OsGyZLe3aJb3/8cdhtN+9jmotW1Li0CGKAg3mzOFIu3Y0z+DHohAi/6lfH3TuQQqskiUhICD36jczM+PXX3/l//7v/5gzZw4HDx5M0W3j4cOHbNiwgQ0bNlC/fn3Wr19PhQoV0q0vo5k6GjZsSPXq1bl48SIHDhxIsS40NJTAwEAAevTokWHMnp6euLq68uTJE/7880+1S8HZs2cJDQ0FYOjQoTg4OGR88AbSp0+fLJWPjY3l4cOHREZGqudaN9lz7tw56tWrZ9AYc0L3PL548UJdNsQ1e1VG51L3nISEhKQYHFF3uu9ly5bx8ccfZxhPbunevTsfffQRz58/Z9myZTRt2jTF+ocPH7J7924A+vbtWyhuwCMiItTBM+vUqcPOnTvp27cvz58/p0SJEmzevJk333zToPsMCgrKcR3u7u4GiEQI05KEhBBG0LatLcOGhbJoUWni4mwZOaomf+79hqde71Hs2TMaAou6dKHqjRsyFZMQBciDB/DPvacwgHbt2tGuXTvCwsI4ceIEAQEBBAYGcuzYMfVpdEBAAM2aNSMwMDDFDaCuzPqXN2zYkIsXL3L16lXi4uLUG7IAnayLt7d3mq0s0qL7ZPzMmTPqckZPyQ2tVq1amZaJiorip59+Yv369Vy8eJHExMR0yz558sSQ4eWYbhJCt3WBIa7Zq14dX0CXi4tLmjEBvPnmm7z++uvcuHGDMWPGsGbNGrp27Urz5s2pX7++0W78bW1t6d27N76+vmzcuJEff/wRe3t7df2qVavUmUJ0WyZl1+TJk5kyZUqO68lNui1+9uzZw549e1AUhYYNG7J161a9xtCoUqUKf//9N0+fPk3xPUhPjRo1chKyEAWGJCSEMJK5c0uzf/8jQkKKc/lyA7765hd+3u1HXOM3sVIUhr18yeQ2bZh0+jTm5uamDlcIYQAlS5o6AuMw9nE6OzvToUMHOnToACQ9zV+7di2fffYZYWFh3L9/n4kTJ7JkyZI0ty9evHiG9ScnhhVFISwsTH2f3UGIk6cthZQ38uklTHKDs7Nzhutv3rxJq1atCAkJ0au+6OhoQ4RlMLrnVfdm0BDX7FV2dnbprjMz+7c39KsJHUtLS/z8/OjevTuXLl3C398ff39/IClJ0Lx5c/r160fPnj1z/XfA0KFD8fX15cWLF2zZsoUPPvhAXZfcXaNBgwbUrFkzV+PIK3QTEsmtQ1q1asWuXbvS7VKlKzIykmvXrlG2bFm9khFCiH9JQkIII7Gzg9Wri9CsWSJarTlLlgzj3XcX0PLbb7H66isAxpw/z7zPP+fjuXNNHK0QwhBysxuD+Je1tTUDBw7ktddeo90//eO2bt3KokWLUtwgJtNoNBnWl944FLo3mGvWrNGr1QGknwzILA5DyuwGt1+/foSEhKDRaBg4cCC9evXC09MTNzc39YZMq9Wq9egzVocx6bY8SZ45BAx/zXKqWrVqBAUF4efnh5+fH0eOHOH69etER0erT+bnzJnDrl27Mk2c5USdOnWoV68egYGBLFu2TE1I/PXXXwQHBwOGaR2RXyQnJDw8PHBxcVFnPrl48SJ169bVa3utVqtX2WQXLlzIbrgqd3f3DMd+ESI/kISEEEb0xhvWfP75Q/773xIkJlry0Uet+Ovkc6L3tKD44cM4A41++IFD7drRsm1bU4crhBD5Stu2bSlTpgx37twhLCyMp0+f4ubmlqrcw4cPM5xaMfmpukajSXFjqjtwokajyVaTa1dXV3X53r17KW6eTeXy5cscP34cgHHjxjFjxow0y706WGResn//fnVZt6+/Ia6ZoZmbm9OlSxe6dOkCwP3799m9eze+vr4EBgYSGBjI8OHD2bZtW67GMWTIEAIDAzly5Ag3btzg9ddfV1tH2Nra6t29pSBITkg0aNCAOXPm0KBBA+7fv0+nTp3w9/fPtDXT6dOnAbKUkDBE65Nly5YxYMCAHNcjhCnJLBtCGNm0aSWoXj2pb+qtW9X55NNLuGxeRdg/TfwaA1e6dcuw/6oQQoi06c5KkVbrCEBtJp+e5PWVKlVK0a/fy8tLXd63b1+24tO9YTl69GiWt8+NVhUXL15Ul3v16pVuuYA82uTnwoULHDx4EIAyZcpQv359dZ0hrlluK1WqFIMGDeLPP/9Uvx87duzIUreY7HwvevfujZ2dHYqisGLFCqKjo1m/fj0A3bp1o0iRIlmu01gM+XeQkJCgtgqpXbs2pUuXZvv27djY2BAaGkrnzp0zvRbZSUgIIZJIQkIII7OygnXr3LC0jANg/fpBrN+9jiJ79hD3z3+wI16+5Oc2bTIcUEwIIURKL1++VG8snJyc0u3LvWLFinTrCAgIUJtSvzrLQsWKFalWrRoA69ev5/bt21mOsXbt2mrrjCVLlhAZGZml7W1sbNTl2NjYLO8/LckDGELGYyekNQ2qqUVHR/PBBx+oXUg+//xzLCz+bQBsiGtmLJaWljRv3hxIuibPnz/Xe9vsfC+cnJzUmUdWrFjB5s2b1cFhBw8erPe+TcGQfweXLl1S66hduzaQ1FJi6dKlQFKCMqOZeeDfLkNZSUgoipLjl7SOEAWBJCSEMIGaNc2ZOjUCAEUx44svunPb7QmxOs1kPwsK4sfPPjNViEIIkSdERkbSqFEjduzYkWKqz1dptVpGjx6tzmzQqVOndJ+i/v7772zcuDHNfQ0bNgxIal0xfPjwVGW+/vprAGJiYujWrRuPHz9ON6bY2Fh8fX2JiYlRPzMzM+OLL74A4O7du3zwwQfExcWle0z37t1L8Zlu0/Hr16+nu++sqFSpkrqcXrJm/vz5/PbbbwbZn6EEBwfz5ptvqjeDzZs3x8fHJ1W5nF4zQzl27BjXrl1Ld31cXBxHjhwBkqYxTau7UXqy+70YMmQIALdu3WLs2LFA0jgKLVq00LsOUzDk34HugJbJCQlImpVlwoQJAGzYsIGpU6emuX1sbCzBwcGULFnSqAPVClFQyBgSQpjI2LGubN9+n7/+KsWDBx589NFxNm8azJO9e3E9cgQXoPGPP7K/TRtav/uuqcMVQgiTOXXqFB07dqR06dJ06dKFJk2aUK5cORwdHXn+/Dlnzpxh6dKlBAUFAVCkSBGmTZuWbn3169end+/eHDlyhO7du+Pk5MT58+f57rvvuHLlCgCjRo1KcwBEb29v9u7dy4oVKwgMDKRatWoMHz6c5s2b4+bmRlRUFNevX+fYsWNs3bqVZ8+epZjBILluPz8/9u/fz7Zt26hZsyYjR46kfv362NnZ8eDBA06ePMm6devo3bt3iikTvby8sLGxISYmhokTJ2JhYUH58uXV7imlS5fG1tY2S+fXy8uLGjVqcOHCBebPn8/z58/p06cPpUqV4s6dO6xevZrNmzfTtGlTTpw4kaW6c+LRo0cpBv6LiooiLCyM8+fPc/DgQfbv36+2jGjcuDGbN2/G0tIyVT2GuGaGcPDgQaZNm0azZs147733qFWrFm5ubkRHR/P333+zYMECten/kCFDUrT0yEx2vxdNmzbF09OTS5cuqV1FBw4caNQBV7PDkH8HyQkJFxcX3N3dU6ybNm0aly5dYuvWrUydOhVPT0+1VUmy8+fPk5CQkC+6axw/fjxFUkx3dppr166xfPnyFOWlBYYwCkUUaOHh4QqghIeHmzoUkYZr17SKre1LBRQFFOWXX6Yr2mfPlDAXFyX5w19sbJSbN2+aOlQhxD+io6OV4OBgJTo62tShFArR0dFKyZIlFUCvV6VKlZSAgIBU9UyePFktc+PGDcXDwyPdOt5//30lPj4+3ZgSEhKUsWPHKubm5pnGY29vr7x8+TJVHVFRUUr37t0z3X7y5Mmpth07dmy65Q8dOpTmMWfmzJkzirOzc7r11qxZU7l3716GcS1btkxdHxISkuk+03Lo0CG9rzWguLm5KTNmzMjweilKzq+ZvudSN37da/FqHRm9unXrlua/L5mdX32/F6+aPXu2Ws7MzEy5fft2hseoD93zkNZ3JTP6fJeye7yvatWqlQIoLVu2THN9ZGSkUqdOHQVQbG1tFX9//xTrFy5cqADK119/rfc+TaV///5Z+vsSQlGy/7tH3/tQ6bIhhAlVqKBh9ux/m+pOnDiI4Ht7cdq1i/h/nk6MjInB9+23DdZXWAgh8pPkgeVOnDjB1KlTad++Pa+//jr29vaYm5vj5ORE1apV6dmzJ2vXruXChQvUq1cvwzo9PDwIDAxk/PjxeHp6YmdnR5EiRXjrrbfU1gAZPZ02Nzfnu+++Izg4mM8++wwvLy+cnZ0xNzfH0dGR6tWr06dPH1asWMH9+/fTfFJrZ2fHpk2b+OOPP+jXrx8eHh7Y2tri6OhI1apV6datG2vXrlW7d+iaOXMmixcvplmzZri4uGQ6pac+6tSpw9mzZxkxYgTlypXD0tISFxcXGjZsyOzZszl16pRJm6ObmZlRpEgRypYtS7NmzRgzZgxbtmzh7t27jB8/PtPWBIa4Zjk1duxYdu3axSeffELjxo0pW7YsNjY22NjYUL58eXr27MnOnTvZsmVLijES9JXd70W/fv3U5datW2c4A01eYqi/g3PnzgEpu2vosre3Z/v27ZQoUYLo6Gg6d+5MaGioul4GtBQiZzSKkscmkhYGFRERQZEiRQgPD8fJycnU4Yg0KAq0bv2AgwdLAtCsmR9799ZCmbcRu7FjCQC6A+2GD8+TA4oJUdjExMQQEhKCh4dHtm4ahGlMmTJF7QMuP32E+NfBgwfVAVw3bNiQqkuCyFijRo04deoUN2/epFy5cqYORwiDy+7vHn3vQ6WFhBAmptHAqlUlcXZOGuTy2LGOfPvtemw/+4TbkyfzjrU1t4CFCxdmODK8EEIIIURWJc8mUaxYMTp37mziaPKXxMREgoKCKFasmCQjhMgmSUgIkQeUKgVLlvzb1HDWrA85cnQRZadM4ceFC9XPR4wYkWI0aCGEEEKI7Lp58yabNm0CkgaztLa2NnFE+UtwcDDR0dF4eXmZOhQh8i1JSAiRR3TrZk///vcBiImxx8enAU+f+tO/f3916jnzmBjmtG9PWFiYKUMVQgghRD4VGhrK1atX2bdvH926dSM+Ph4bGxvGjBlj6tDynYCAACBplhchRPZIQkKIPOSXX0pRvnzSFEyXLzfg88//IiHhBT/++CP/V6MGp4DFDx4wrWNHtFqtaYMVQgghRL7Tp08fKleuTNu2bTlz5gwA//nPfyhdurSJI8t/9uzZA0C7du1MHIkQ+ZckJITIQ+ztYf36opibJwCwcqUPGzbMw9ramiVvvkk1wBoYeOIE386YYdJYhRBCCJF/2dnZUadOHZYvX57mbC4iY2fOnGHbtm3UqlWLpk2bmjocIfItmWWjgJNZNvKnKVOeMnVqMQBKlgzh2LGzVCzXgTAvL+5cvEh34JpGw969e2ndurVpgxWikJFZNoQQovCaOXMmly5dUsfeOHToEI0aNTJxVELkHpllQ4hCaOLEYjRs+BCABw88+PDDWGIS7+P8xx/s/PprrpI0bZ23tze3b982bbBCCCGEEIXA48ePGT9+PLt27eLdd9/l5MmTkowQIockISFEHmRuDuvXF8fB4SUAe/f24ocflqG4FePLqVN57733AHj69Cndu3cnNjbWlOEKIYQQQhR4bm5uaLVaHj9+zObNm6lVq5apQxIi35OEhBB5lIeHhp9//rdH1YwZH3Hy5HzMzMxYtWpVUrMpoI+/P/8ZPNh0gQohhBBCCCFENkhCQog8rH9/e7p1ewRAZKQzI0fWICzMH2dnZ36fN4+TGg0fA53XrGHFokWmDVYIIYQQQgghskASEkLkYRoN/PprcUqVeg7A2bMt+PrrwyQkRFLjjTfwcHUFoCGQ4OPDX3/9ZbpghRBCCCGEECILJCEhRB5XtCisWeOARqMFYOHCMWzd+gMULYrTvn3EmZsDMFirZUvbtjx48MB0wQohhBBCCCGEniQhIUQ+0LKlBZ9//hyAxERLPvnEm2vXNkOdOpgtWaKWmx4ezuQ2bYiLizNRpEIIIYQQQgihH0lICJFPzJjhQr16SeNJ3LtXAR8fhZcvb2AxYABRw4YBYAVMDgpi4pAhJoxUCCGEEEIIITInCQkh8glLS9i0yU2dCvTAgf/jv/9dhVYbh/28eUTUqwfAa0CnVav4df58E0YrhBBCCCGEEBmThIQQ+YiHh4ZFizTq+++++5wDB+aCpSVOu3YR6eICQFMg4cMP+fPPP00UqRBCCCGEEEJkTBISQuQz3t629O//BICYGHtGjWrDvXt7oHhxHPbuJf6fQS6Ha7Vsad+ee/fumTJcIYQQQgghhEiTJCSEyId8fV2pVOkpANeueTFmzC1iY+9B/fpoFi1Sy80ID2dimzbExsaaKlQhhBBCCCGESJMkJITIh+zsYNMmF6yskmbT2LRpOAsXzkNRErEYNIiX/wxqaQ1MvXiR8YMGoSiKCSMWQgghhBBCiJQkISFEPlW7toZZs/6d3nPixE/566+fAbDz9eWFlxcA7kCXtWtZ/MsvpghTCCGEEEIIIdIkCQkh8rHRox14992k8SQiIlzx8anN06fHwNISx927iXJ2BqAZEP/xxxw/ftyE0QohhBBCCCHEvyQhIUQ+ptHAypWulCoVDsDZsy0ZP/5P4uOfQokS2O/ZQ8I/g1yO0mrZ9N573Llzx5QhCyGEEEIIIQQgCQkh8r1ixWDtWgfMzBIBWLLkU9asmZ00ZkTDhmjmzwcgCgiNiKBz585ERUWZMGIhhBBCCCGEkISEEAVCixbmjBuXlGTQai344otRnDuXNNuG+dChvBw3ju7u7mwBzpw5Q//+/dFqtSaMWAghhBBCCFHYSUJCiAJi6lQnmjZNmgr0yRN3hg0rT1jYSQDsvvmG2Xv24OjoCMCWLVv4z3/+Y7JYhRBCCCGEEEISEkIUEObmsGlTMYoVewGAv39bxo8/kjSeBFC9enXWrVuHRqMB4MTUqWzcsMFk8QohhChYli9fjkajQaPRcPPmTVOHU+AUpvNrqmONj4/HysoKjUbDjBkzjLZfIQozSUgIUYCUKgXr1tmi0SR1x1i06HNWr/4ORUl6/9577/H9zJn8AuwHgvr2JSAgwHQBCyFEFkRFRbFo0SLee+893N3dsbGxwcHBgddff50mTZowYsQI1q9fz/37900dqsimw4cPqzeiui8LCwtcXFzw8PDgrbfe4pNPPmHLli3ExcVlXqkwuvSuY3qv5cuXmzpkAC5evEh8fDwAtWvXNnE0xjN27NgU1+Pw4cOmDkkUIpKQEKKAad3agvHjIwHQas0ZO/Zjzpz5RV0/pmFDRv6zPDUhgS/efZd79+6ZIFIhhNDfqVOnqFGjBsOHD2fXrl2EhoYSGxtLVFQUISEhnDx5koULF+Lt7Y2Xl5epw801hekpua7ExETCwsK4efMmx44d44cffqB79+64u7szffp0EhISTB2iSRW270VuHe/Zs2fV5Tp16his3rzs3LlzzJ0719RhiELMwtQBCCEMb+pUJ44cecbx4y48eVKa4cOrsHfvUVxc3kLTogUJU6agnTqVoYrC4ceP6dKlC0eOHMHW1tbUoQshRCrXrl2jdevWREREANCpUye6d+9O5cqVsbKy4smTJ5w7d479+/dz6NAhE0crDMXHx4eRI0eq7yMjIwkLC+P8+fMcPHiQAwcO8PjxYyZOnIifnx87duzAzc3NhBHnrgEDBjBgwABTh5Flr17HtLi7u6d4b6pjTU5IFCtWLFVMBZFWq2Xo0KEkJCRQvHhxHj16ZOqQRCEkCQkhCqCk8SRcqFnzBU+eOBIQ0IZx42by889VsbIqjsWkSTx95x0O9+4Nt2/j7+/PkCFDWL16tTrGhBBC5BUTJkxQkxFLly5l4MCBqcq0bt2azz//nMePH7Nx40ZjhyhyQfHixalRo0aqz9u3b8+XX37JxYsX6devH2fOnOHUqVN069aNgwcPYmVlZYJoRXrSu455UXJCorB01/jpp5/w9/enatWqdO3alW+//dbUIYlCSLpsCFFAlSwJ69bZqeNJLFnyBatWfYeiJIJGQ7GmTfn999+xt7cHYO3atfIfkRAiz0lMTGTHjh0A1K9fP81khC43NzdGjRpljNCEiVWvXp0TJ06oXXSOHz+Or6+viaMS+dn58+eBwtFd486dO0ycOBGA+fPnSyJPmIwkJIQowN55x5wJE6KApPEkvvzyMwID56jra9euzerVqwHoBdhMmMC2bdtMEaoQQqTp8ePHvHz5EoCKFStmu54pU6aofc4Bnj9/zuTJk6levToODg64uLjQokUL1qxZo3edp06dYujQoVSuXBkHBwfs7e2pWrUqo0aN4urVq3rVceLECYYMGUKVKlVwcnLCwcGBqlWr0qVLF1auXKm2DEkeJFA3IePh4ZFqcEDdwehePebw8HCmTZuGl5cXRYsWTTWY4IULF5g+fTpt27bF3d0da2trHBwcqFSpEv379+fkyZN6nxtjsbW1ZdWqVeoxzp49Wx2UMC3ZvWavnsuYmBhmzZpF3bp1cXR0xNHRkYYNGzJv3rxMx7O4d+8eX331FXXr1qVIkSJYWVlRsmRJatasibe3N8uXL1evu660xk3IyvciPj6ekiVLotFoaN++fYYxQtL3IXn7b775JtPyhpTeGBHZ+TvQ161btwgLCwPST0iEhobSpEkTNBoN1tbWLFq0KMv7yStGjhxJZGQk/fv3p0WLFqYORxRmiijQwsPDFUAJDw83dSjCRBISFOWtt54poCigKPXr71UePdqfosyRVq2U5AIfW1kpZ8+eNVG0QuR90dHRSnBwsBIdHW3qUAqFp0+fKoACKLVr1852PZMnT1bruXHjhlKhQgX1/auv7t27K/Hx8enWFR8fr/j4+KS7PaBYWloqixYtSreOly9fKt7e3hnWASiTJ09WFEVRDh06lGlZQDl06FCax/z3338r5cuXT1V+2bJlWar/q6++SveYli1bppYLCQnJyuVR6caRfOz6aNOmjbrdiRMnUq3P6TXTPZcPHjxQateunW49HTt2VBITE9Os5+jRo4qTk1Om59nPzy/Vtmmd36x+L7744gsFUMzMzJS7d+9meE4/+eQTBVDMzc0zLfuq7F7HjI711Xr1/TvQ12+//aZuf/78+VTrjxw5opQoUUIBlFKlSqX5PcsvNmzYoACKi4uL8ujRI0VRUn7Hs3P+RMGV3d89+t6HSgsJIQo4c3PYsMEZV9ekmTeSxpMIJDb235k1mn3wgbo8Oy6Ob9u04cGDB0aPVQghXuXi4kK5cuWApNHgv/vuO7RabY7q7NmzJyEhIYwYMYIDBw7g7+/Pr7/+SuXKlQHYvHkzn376abrbDx48mPnz5wNJ4xmsXr2aU6dO4e/vz+LFi6levTrx8fEMGzYMPz+/VNtrtVo6d+7MunXrAKhUqRJz587l2LFjBAYGsmPHDsaPH5+iRUiDBg0ICgpi+vTp6md79+4lKCgoxatBgwZpxty9e3dCQ0MZPXo0+/fvJyAggHXr1lGlShUAEhISsLe3p0ePHixYsIDDhw9z+vRp9uzZw/fff69eg5kzZ7Js2bKsnG6jeOedd9TlY8eOpVqf02umq1u3bly6dImPPvqI/fv3ExgYyNq1a/H09ATAz8+PxYsXp9ouNjaWXr16ERERgaOjI2PHjmX37t0EBgZy8uRJNmzYwJgxYyhTpozex53V78WQIUOApO/gypUr0603Pj5ebUHZpk0bSpcurXdMuSmnfwcZSR4/wsrKiqpVq6ZY98MPP/D222/z8OFDGjduTEBAAG+88UaOjsVUnj9/zscffwzAd999V6AHghX5RE6yJSLvkxYSItnBg4mKmVmCAoqi0SQqv/zyqZKYGKeujxszRm0l8QSUbjVrKlFRUSaMWIi8SVpIGN/s2bNTPP0sV66c8uGHHypr1qxRrl27plcduk//AGXt2rWpykRERKhPvs3MzNJ8Srp582a1jsWLF6e5r+joaKVVq1YKoJQvXz5Va4sffvhBraNr165KTExMmvUkJiYqoaGhKT7LSisE3WM2MzNT9u3bl27Zx48fK2FhYemuj42NVVq3bq2e/4SEhFRlTNlC4sCBA+p2gwYNSrHOENdM91xaWlqm+QT56dOn6hP0WrVqpVp/8ODBDFtAJIuPj0/zd1tG5zcr5/6tt95SAKVSpUrpltm6data3+bNmzOsLy2619HHx0cJCgpK9/Xw4cMsHas+67OjS5cuCqB4eXmpn0VFRaVoyTR48GAlNjbWIPvT/fcou6/kFk5ZMXToUAVQ3njjDUWr1aqfSwsJkR5pISGEMIhWrcyYPDkWAEUxY9y4cRw79l91veXs2cS8/TYAxYBvg4Lw6dkzx08ihRAipz755BMGDRqkvr916xbz5s2jT58+VKxYkZIlS9KrVy/8/PxQFCXT+jp06IC3t3eqzx0dHdU+4VqtlgULFqQqkzz4b9euXdWnza+ysbFh3rx5ANy8eTNFf3atVsusWbMAKF26NCtXrsTa2jrNeszMzHjttdcyPR59DBgwgNatW6e73tXVlaJFi6a73srKSo371q1b6tPkvKJYsWLqcvI4AMlyes1eNXr06DT73Lu4uKhjG5w/f57w8PAU63VbHr711lvp1m9hYYGTk1O663Mq+RxcvXqVEydOpFkmuRWMq6srHTt2zNH+5s+fT82aNdN95ZWBSJO/08njR1y/fp3GjRuzbt06LC0t8fX1ZcmSJfl68Mfjx4+zZMkSLCwsWLBggcysJvIESUgIUYh8/bUdbdsm/VCLiHBlxIjW3LmzJWmluTk2W7cS808T4crAwB07mPDFFyaKVgghkpiZmfHrr7+ye/duWrdujZlZyp8vDx8+ZMOGDXTq1ImGDRty/fr1DOvLaKaOhg0bUr16dQAOHDiQYl1oaCiBgYEA9OjRI8N9eHp64urqCsCff/6pfn727FlCQ0MBGDp0KA4ODhnWYyh9+vTJUvnY2Fhu375NcHAwFy5c4MKFCymSPefOnTN0iDmiex5fvHihLhvimr0qo3NZr149dTkkJCTFulKlSqnLpuz20r17dzX5lFYcDx8+ZPfu3QD07ds3X9+A6ysiIkIdPLNOnTrs3LmT+vXrExQURIkSJfjjjz/w8fEx6D5f7WaSnVeXLl303l9cXBzDhg1DURQ++eQTatasadDjESK7LEwdgBDCeMzMYN06Z+rUCef27SJcvtyQ0aPPsWbNJeztPcHJCZsDB4itUwfr589pAdyYM4fFVaowdNgwU4cvRP40Z07SK6dWrwbdp7KHD0PfvknLn36a9Er24gX80589R5o3h1dnnWjVCv7+Gxwc4PLlnO8jC9q1a0e7du0ICwvjxIkTBAQEEBgYyLFjx9Sn0QEBATRr1ozAwMAUN4C6Mutf3rBhQy5evMjVq1eJi4tTb8gCAgLUMt7e3mm2skiL7pPxM2fOqMsZPSU3tFq1amVaJioqip9++on169dz8eJFEhMT0y375MkTQ4aXY7pJCN3WBYa4Zq96dXwBXS4uLmnGBPDmm2/y+uuvc+PGDcaMGcOaNWvo2rUrzZs3p379+ka78be1taV37974+vqyceNGfvzxR3UKcIBVq1apM4XotkzKrsmTJzNlypQc15ObdFv87Nmzhz179qAoCg0bNmTr1q16jaFRpUoV/v77b54+fZrie5CeGjVq5CTkLPvmm2+4dOkSZcuWZfLkyUbdtxAZkYSEEIWMszNs2+ZEkyZxxMVZsX37UGbNGsvXX3+NhYUTlCuH9e7dJLz1Fhbx8QwCxo8YwX4Pjwyb+woh0hERAf88Ec+R2NjU75PrfXWKQEUxzD7Tuul8+DCpbkfHnNefTc7OznTo0IEOHToASU/z165dy2effUZYWBj3799n4sSJLFmyJM3tixcvnmH9JUqUAEBRFMLCwtT3jx49yla8ydOWQsob+fQSJrnB2dk5w/U3b96kVatWqZ7qpyc6OtoQYRmM7nnVvRk0xDV7lZ2dXbrrdFvvvJrQsbS0xM/Pj+7du3Pp0iX8/f3x9/cHkpIEzZs3p1+/fvTs2RNzc/Nsxa2voUOH4uvry4sXL9iyZQsf6AxundxqokGDBoXmKbpuQiK5dUirVq3YtWtXul2qdEVGRnLt2jXKli2rVzLC2C5fvqx2Xfr5559TJKCEMDVJSAhRCNWtq+GXXxSGDk16P3PmFGrV+pquXb9P6k/YuDEWq1dDz54AfKMofNC5M6/5+6tNmYUQenJyAkOMUP/qj2Jr63/rfbW/uUZjmH3+03w9hRIlIDw8qYVEHmFtbc3AgQN57bXXaNeuHQBbt25l0aJFqbp3AJn2m05vHArdG8w1a9bo1eoA0k8GGLP/dmY3uP369SMkJASNRsPAgQPp1asXnp6euLm5qTdkWq1WrUefsTqMSbflSfLMIWD4a5ZT1apVIygoCD8/P/z8/Dhy5AjXr18nOjpafTI/Z84cdu3alWniLCfq1KlDvXr1CAwMZNmyZWpC4q+//iI4OBgwTOuI/CI5IeHh4YGLi4s688nFixepW7euXttrtVq9yia7cOFCdsNVubu7Zzj2S7K5c+cSFxfH66+/zsuXL1m/fn2G8fzxxx9qK6GOHTtKAkPkKklICFFIDRlizYkTESxf7kRsrB0ffeRD5co/U6PGR0kFevRA+/ffmE2cCMDC6Gh6vfMOi86eVZ8WCiH08Gp3CkNp0QLu3k17naNj+uty6o8/cqdeA2jbti1lypThzp07hIWF8fTp0zSntHv48GGGUysmP1XXaDQpbkx1B07UaDTZanLtqpPkuXfvXoqbZ1O5fPkyx48fB2DcuHHMmDEjzXKvDhaZl+zfv19dfvPNN9VlQ1wzQzM3N6dLly5q///79++ze/dufH19CQwMJDAwkOHDh7Nt27ZcjWPIkCEEBgZy5MgRbty4weuvv662jrC1tdW7e0tBkJyQaNCgAXPmzKFBgwbcv3+fTp064e/vn2lrptOnTwNkKSFhiNYny5YtY8CAAZmWi/2nhd2NGzf0uq7Tpk1Tl0NCQiQhIXKVDGopRCE2f74TtWo9ByA0tBI+PmV59uyQut5swgTi/xm8yxZY+OABQ9u2zbApqxBCmJLurBRptY4A1Gby6UleX6lSpRT9+r28vNTlffv2ZSs+3RuWo0ePZnn73GhVcfHiRXW5V69e6ZbTHY8hL7lw4QIHDx4EoEyZMtSvX19dZ4hrlttKlSrFoEGD+PPPP9Xvx44dO7LULSY734vevXtjZ2eHoiisWLGC6Oho9cl5t27dKFKkSJbrNBZD/h0kJCSorUJq165N6dKl2b59OzY2NoSGhtK5c+dMr0V2EhJCiCSSkBCiELOxgd9+K0qRIkkJhuPHuzBx4lFiYv55sqrRYLl0KbGNGwNQEph+7hzDvb1lOlAhRJ7z8uVL9cbCyckp3b7cK1asSLeOgIAAtenyO++8k2JdxYoVqVatGgDr16/n9u3bWY6xdu3aauuMJUuWEBkZmaXtbWxs1OXYV8cVyabkAQwh47ET0poG1dSio6P54IMP1C4kn3/+ORYW/zYANsQ1MxZLS0uaN28OJF2T58+f671tdr4XTk5O6swjK1asYPPmzergsIMHD9Z736ZgyL+DS5cuqXXUrl0bSGopsXTpUiApQZnRzDzwb5ehrCQkFEXJ8Uuf1hEAy5cvz7Qu3YEuDx06pH5evnx5vY9JiOyQhIQQhZyHB6xZ82/f9AULvmblym/QauOSPrCywnrHDmLLlgWgFuD9++98/dVXJohWCFHYREZG0qhRI3bs2JFhIlSr1TJ69Gh1ZoNOnTql+xT1999/Z+PGjWnua9g/MwqZmZkxfPjwVGW+/vprAGJiYujWrRuPHz9ON6bY2Fh8fX2JiYlRPzMzM+OLf6ZTvnv3Lh988AFxcXHpHtO9e/dSfKbbdDyz6U31ValSJXU5vWTN/Pnz+e233wyyP0MJDg7mzTffVG8GmzdvnubUjDm9ZoZy7Ngxrl27lu76uLg4jhw5AiRNY5pWd6P0ZPd7MWTIEABu3brF2LFjgaRxFFrozuiTBxny70B3QMvkhAQkzcoyYcIEADZs2MDUqVPT3D42Npbg4GBKlixp1IFqhSgoZAwJIQTvvWfO+PEv+eYbO7Rac776ajJVq/6Ht96anlSgWDGs9+8nrl49rCIjeRfYN2sWSypXVn/MCCFEbjl16hQdO3akdOnSdOnShSZNmlCuXDkcHR15/vw5Z86cYenSpQQFBQFQpEiRFH2gX1W/fn169+7NkSNH6N69O05OTpw/f57vvvuOK1euADBq1Kg0B0D09vZm7969rFixgsDAQKpVq8bw4cNp3rw5bm5uREVFcf36dY4dO8bWrVt59uxZihkMkuv28/Nj//79bNu2jZo1azJy5Ejq16+PnZ0dDx484OTJk6xbt47evXunmDLRy8sLGxsbYmJimDhxIhYWFpQvX17tnlK6dGlsbW2zdH69vLyoUaMGFy5cYP78+Tx//pw+ffpQqlQp7ty5w+rVq9m8eTNNmzblxIkTWao7Jx49epRioL2oqCjCwsI4f/48Bw8eZP/+/WrLiMaNG7N582YsLS1T1WOIa2YIBw8eZNq0aTRr1oz33nuPWrVq4ebmRnR0NH///TcLFixQm/4PGTIkRUuPzGT3e9G0aVM8PT25dOmSOojhwIEDjTrganYY8u8gOSHh4uKCu7t7inXTpk3j0qVLbN26lalTp+Lp6am2Kkl2/vx5EhISpLuGENmliAItPDxcAZTw8HBThyLyuIQERWnV6rmSNF+golStelK5eXNVykKHDysJ5ubKClCsQDE3N1d2795tmoCFMJHo6GglODhYiY6ONnUohUJ0dLRSsmRJBdDrValSJSUgICBVPZMnT1bL3LhxQ/Hw8Ei3jvfff1+Jj49PN6aEhARl7Nixirm5eabx2NvbKy9fvkxVR1RUlNK9e/dMt588eXKqbceOHZtu+UOHDqV5zJk5c+aM4uzsnG69NWvWVO7du5dhXMuWLVPXh4SEZLrPtBw6dEjvaw0obm5uyowZMzK8XoqS82um77nUjV/3WrxaR0avbt26pfnvS2bnV9/vxatmz56tljMzM1Nu376d4THqQ/c8pPVdyYw+36XsHu+rWrVqpQBKy5Yt01wfGRmp1KlTRwEUW1tbxd/fP8X6hQsXKoDy9ddf673PvEj3+5mV8ycKvuz+7tH3PlS6bAghADA3h40bi1CmTFJz58uXGzFyZDzh4TqDmDVvjnlgIGc+/pg4kqZT6969u/pERwghDC15YLkTJ04wdepU2rdvz+uvv469vT3m5uY4OTlRtWpVevbsydq1a7lw4QL16tXLsE4PDw8CAwMZP348np6e2NnZUaRIEd566y21NUBGT6fNzc357rvvCA4O5rPPPsPLywtnZ2fMzc1xdHSkevXq9OnThxUrVnD//v00n9Ta2dmxadMm/vjjD/r164eHhwe2trY4OjpStWpVunXrxtq1a9XuHbpmzpzJ4sWLadasGS4uLplO6amPOnXqcPbsWUaMGEG5cuWwtLTExcWFhg0bMnv2bE6dOmXS5uhmZmYUKVKEsmXL0qxZM8aMGcOWLVu4e/cu48ePz7Q1gSGuWU6NHTuWXbt28cknn9C4cWPKli2LjY0NNjY2lC9fnp49e7Jz5062bNmSYowEfWX3e9GvXz91uXXr1hnOQJOXGOrv4Ny5c0DK7hq67O3t2b59OyVKlCA6OprOnTsTGhqqrpcBLYXIGY2i5LGJpIVBRUREUKRIEcLDw3F6dZ56IdJw9iw0aRJHTEzSyPJffDGO6dPHYGX171SfiYmJ9OzZky1btgBQonhx/jx5Eg8PD1OELIRRxcTEEBISgoeHR7ZuGoRpTJkyRe0DLj99hPjXwYMH1QFcN2zYkKpLgshYo0aNOHXqFDdv3qRcuXKmDkcIg8vu7x5970OlhYQQIoU6dWDx4n//aZg7dypr1kz9d5BLkp40rVq1iqZNm1IV+P3RI4a9/TZPnz41fsBCCCGEyLbk2SSKFStG586dTRxN/pKYmEhQUBDFihWTZIQQ2SQJCSFEKn37WvDxx1EAJCRY8dlnkzhxYkqKMra2tuyYOpU/zcxoCPwSEkK/9u2zNG+6EEIIIUzn5s2bbNq0CUgazNLa2jqTLYSu4OBgoqOj8fLyMnUoQuRbkpAQQqRp9mx73noraT7ysLCSjBjRkZCQpSnKFK1XD7uKFQGIBM74+9O3b18SExONHa4QQggh9BAaGsrVq1fZt28f3bp1Iz4+HhsbG8aMGWPq0PKdgICkcbYaN25s4kiEyL8kISGESJOFBWzZUgR390gAgoOb8NFHWsLD//dvoaJFsdq/n6fvvce7dnY8ALZu3cqnn34qfbSFEEKIPKhPnz5UrlyZtm3bcubMGQD+85//ULp0aRNHlv/s2bMHgHbt2pk4EiHyL0lICCHS5eoKv//ugLV10vgRO3YMYcaM34iNvfdvobJlKbZjB8u3bFFHuP7pp5+YM2eOKUIWQgghhB7s7OyoU6cOy5cvT3M2F5GxM2fOsG3bNmrVqkXTpk1NHY4Q+ZbMslHAySwbwhBWrkygf/+kKdUsLOJYuHAUAwbMw8wsZV/TpUuXMnjwYByB1sD/rVtHr169jB+wELlIZtkQQojCa+bMmVy6dEkde+PQoUM0atTIxFEJkXtklg0hhMl98IEFo0e/BJIGufzii/9w/Pj4VN0yBg0axPeffsoxYAuwvV8/jhw5YvyAhRBCCCEM7PHjx4wfP55du3bx7rvvcvLkSUlGCJFDFqYOQAiRP8yZY8fZsy84dsyRZ89KMXx4D3bsWEiFCiNSlPukVCk0/ywvS0iga4cOuJ48SfXq1Y0ftBBCCCGEgbi5uaHVak0dhhAFirSQEELoJWmQS0dKl06aDvTy5Ub4+Djw7NkfKcppPvsMbf/+ANgAayIjGfX229y+fdvYIQshhBBCCCHyMElICCH05uYGO3bYY2ubNMjl/v19mTDhCC9fXvu3kEaD2eLFJLz9NgAuwPKHD+nbqhVPnjwxQdRCCCGEEEKIvEgSEkKILKlTB1atMlffL1w4mQULvichIfzfQpaWWGzdSnyNGgCUB+Zdv06vdu2IjIw0arxCCCGEEEKIvEkSEkKILHv/fXMmT44BQFHMmDjxO37/fSxabcK/hZycsNy3j3h3dwBqAZMCA+ndtStxcXEmiFoIIYQQQgiRl0hCQgiRLZMm2dC1a1Jrh5cvnRg9eiyBgVNSFipVCss//iDB2RmAt4CBBw4w6IMPZFAoIYQQQgghCjlJSAghssXMDFatcqBmzRcA3LtXgeHDW3L79tKUBStVwmL/fhJsbQHoCry1YQOfjBmTatpQIYQQQgghROEhCQkhRLbZ28OOHY64ur4E4MyZt/n44xiePz+asmC9elhs347WImmm4WGA688/8+233xo5YiGEEEIIIUReIQkJIUSOlC0L27fbYWmZNH7Eb7+NZMaM7URHh6Qs2Lo1ZqtWoWg0AEwE7k2YwOLFi40csRBCCCGEECIvkISEECLH3ngD5s/XqO/nzp3J8uX/ISEhImXBXr3Q/Pij+vYn4ODw4WzdutVIkQphONLlSAghhBAFXW7/3pGEhBDCIAYPNufjj5Nm3khMtOSrr2axd+/nKEpiyoKjR6OMGwck/QO0UlFY3KsXhw8fNm7AQmSTuXnStLcJCQmZlBRCCCGEyN+Sf+8k//4xNElICCEMZvZsG955JwqAiAhXfHw+5fTpKanKaWbMQBk0CAArYFV8PL07duTMmTNGjFaI7LGwsMDa2prw8HBThyKEEEIIkavCw8OxtrbG4p+x4AxNEhJCCIOxsIBNm+ypUiVpOtA7d6oydGgLbt16ZZwIjQbNwoVoO3Qg2twcb+B+ZCRt27blypUrxg9ciCzQaDQULVqUFy9eEBYWZupwhBBCCCFyRVhYGC9evKBo0aJoNJrMN8gGjSKdYAu0iIgIihQpQnh4OE5OTqYORxQSISHQoEE0T58mTfX53ntLWLGiDMWKtU1ZMDqal+fP0/rTT/nf//4HgLu7O8ePH6dcuXLGDlsIvSmKwsOHDwkLC8POzg4HBwdsbGwwMzPLtf+whRBCCCFyk6IoaLVaYmJiiIyM5OXLlzg7O1OiRIks/77R9z5UEhIFnCQkhKn873/QsmU8cXGWAPj4TOC///XGwaFGqrLPnz+nRYsWnDt3DoBqFSpw8PhxSpYsadSYhciq8PBwIiIiePnyJVqt1tThCCGEEELkmJmZGXZ2djg5OVGkSJFs1SEJCQFIQkKY1tq1Wvr0SeoZptFo+eab4XzyyTSsrVMnGh49ekSzN9+kx9WrdAY+9PRk1/HjuLi4GDlqIbJOq9WSkJAgSQkhhBBC5GtmZmZYWFhgZpaz0R0kISEASUgI05s8OY7//McKABubKJYsGUGvXgsxN7dLVTb8iy8oMns2AEeAcQ0asPfgQRwdHY0ZshBCCCGEECIH9L0PlUEthRC5asoUK3r2jAYgJsaeMWNmcvjwGBQl9ZPkIgMHkli0KAC/A3/6+9OpUyeio6ONGLEQQgghhBDCGCQhIYTIVRoNLF9uS6NGSdOBPnlSGh+fEVy4MDl14WrVMN+/n7tTp7LM2RmAw4cP06NHD+Lj440ZthBCCCGEECKXSUJCCJHrbGzAz8+ecuVeAnD1al18fOpx586vqQvXr4/7pEns3r0bBwcHAHbs2MEHH3xAYmKiMcMWQgghhBBC5CJJSAghjMLNDXbtssPRMRaAEye68NlnL3j27ECa5Rs1aoSfnx/W1ta8C1Rfv54Rw4cjw94IIYQQQghRMEhCQghhNNWqwZYt1pibJ7V02LRpDFOn7iMyMijN8i1atOB/o0fzG/A14Prrr3zxxReSlBBCCCGEEKIAkISEEMKoWreG+fM16vuff57JvHk/EhNzN83ydStVwvKf5W+Bl99/z9SpU3M/UCGEEEIIIUSukoSEEMLohg41Y/z4OAAUxYzJk39m7dqvSEgIT1142DD473/Vt77A9alTmTlzppGiFUIIIYQQQuSGQp2Q+N///sfw4cOpVq0aRYoUwcnJiWrVqjFs2DBOnDiRq/uOiYlh1apV/N///R8VK1bEyckJKysrXF1dqV+/PqNGjeLPP//M1RiEMKXp063o3TsGgLg4Wz79dC67d3+IVhuXuvAXX8D48erb5cDpceOYO3eucYIVQgghhBBCGJxGKYSdsaOiovjoo49YunRphuUGDhzIzz//jL29vUH3f+DAAQYNGsSdO3cyLfvee+/x66+/UqJEiWztKyIigiJFihAeHo6Tk1O26hAit8TFQevWLzl61A4Ad/e/2bBhLk2a+KLRaFIWVhT46COYNw+ABOB9oM28eYwaNcq4gQshhBBCCCHSpe99aKFLSCQmJvLuu++yb98+9TNbW1uqV6+OhYUFwcHBREREqOvatGnDrl27MDc3N8j+d+zYQdeuXUlISFA/S26ZYWdnx4MHD7h8+TJarVZdX6VKFY4fP46rq2uW9ycJCZHXPX8OTZq85PLlpKRE9eon2LhxH9WqpTFOhFYLw4fDkiUAxAGdga4LFzJs2DCjxSyEEEIIIYRIn773oYWuy8bEiRNTJCOGDh3K3bt38ff3588//+TevXtMnDhRXb9v3z4mTZpkkH0/f/6cQYMGqckIR0dHli5dypMnT/jzzz85ePAgFy9e5NatW/Tu3Vvd7sqVK3z++ecGiUGIvKZoUdi7144SJaIBuHixKT4+Nbl7d2HqwmZmsGABSr9+AFgBW4GNw4ezYsUKo8UshBBCCCGEyLlC1ULi3r17VKhQgZiYpH7r/fr1Y+XKlWmWnThxItOnTwfAxsaG69ev89prr+Vo/wsWLMDHx0d9v2PHDt577710y3ft2pXffvsNAEtLSx49ekTRokWztE9pISHyizNn4M0343j50gqAHj2+55dfquDq2iF14YQElN690WzaBEAU8K5Gw4g1a/D29jZi1EIIIYQQQohXSQuJNPzwww9qMsLOzo4ffvgh3bITJ06kTJkyQNIAlD/++GOO93/s2DF1uUaNGhkmIwAmTJigLsfHx+Pv75/jGITIq7y8YPNmK8zNEwHYuPEzpk07RETEqdSFLSzQrFmD0rkzAPbADkVhXt++bNmyxYhRCyGEEEIIIbKrUCUktm3bpi736NEDFxeXdMtaWVkxcOBA9f3WrVtzvP/Hjx+ryzVq1Mi0/KtldLcXoiBq3x58ff8dzPLnn2fx448LefnyWurClpZoNmxAadcOAEdgp1bLdz178vvvvxspYiGEEEIIIUR2FZqExJUrV7h27d+bmnb/3MRkpH379urytWvXuHLlSo5icHBwUJfj4tKY2vAVsbGxKd47OzvnaP9C5AfDhpkxfnzSOCuKYsa0ab4sWzaZ2NgHqQtbW6PZuhWlVSsAigK7ExOZ8v777Nmzx3hBCyGEEEIIIbKs0CQkzp07l+J9kyZNMt2mbt26WFlZqe/Pnz+foxgaNmyoLv/5558pZtpIy5EjR9RlS0vLFNsLUZBNn27BgAFJCbn4eGvGjp3Ppk0fk5AQnrqwrS2a339HadoUgGLAnoQEvujUif379xsxaiGEEEIIIURWFJqExKVLl9RlKysrdXyIjLxaTreO7Ojfvz92dklTG96/f58ZM2akW/b58+eMGzdOfT9gwACKFSuWo/0LkV9oNLB4sTXvvps088bLl0589NGP7N49gsTEmNQb2Nuj2bUL5Z+k3U3gbnw8nTp1SjGrjhBCCCGEECLvKDQJiZs3b6rL7u7uaDSa9AvrKFu2bJp1ZEepUqVYunQplpaWAEyZMoVevXpx9OhRXrx4QUJCAnfv3mX58uXUq1eP4OBgAFq0aMHs2bNztG8h8hsLC9i0yZZGjV4CEBZWEh+f/3D8uA+Kkph6AycnNHv2kDh0KD926MBzkgak7dSpE3v37jVq7EIIIYQQQojMFZqExIsXL9TlIkWK6L2d7hQlunVkV8+ePdm3bx+enp4AbNiwgebNm+Pk5ISlpSVlypRh4MCB3Lhxg2LFivHVV1+xd+9evafsjI2NJSIiIsVLiPzKzg527bKjSpWklhKhoZUYPnwkp09/QpozFjs7Y75oEcu3buX9998Hkv4mOnfuLGNKCCGEEEIIkccUmoREZGSkumxjY6P3dra2tmnWkRMtWrRg165ddOjQId0ylpaWDBo0CB8fnxTjWGTm22+/pUiRIupLn64pQuRlLi6wf78tr72W1FXjypUGjBjRgb//npruNpaWlqxbt47u3bvjDGyKjeWzTp3YvXu3kaIWQgghhBBCZKbQJCR0B5C0sLDQezvdsvHx8TmOIzo6mg8//JBKlSqxY8cOAOzs7GjYsCGtWrWiZs2amJubEx8fz6xZs6hYsSLffPON3vWPGzeO8PBw9XXnzp0cxyyEqZUpA/v22VCkSNLsNAEBbRg1qhK3b/+c7jaWlpasnTePgKJF6QjsjY/n886d2bVrl5GiFkIIIYQQQmSk0CQkkgeThKR+5frSLWtvb5+jGOLi4njvvff45ZdfSEhIoEiRIvz666+EhYXx119/cfDgQc6fP8/Dhw/54osv0Gg0xMfHM2HCBCZMmKDXPqytrXFyckrxEqIgqF4ddu60wto6Kbl48GAfPvssgYcPN6S7jaW5OR7u7gCYA4nx8XTt2pWdO3caI2QhhBBCCCFEBgpNQsLBwUFdjo6O1nu7ly9fpllHdkyfPp1Dhw4BSV1BDh06xKBBg1J1yShWrBj//e9/+emnn9TPvv32W06dOpWj/QuR3zVtChs3WmBmpgVg8+ZPmDLlDM+epTO9p6srmkOH0LZuzX/bteMKSYnBbt26qS2UhBBCCCGEEKZRaBISrq6u6vL9+/f13u7Bgwfqck6m3YyJieHHH39U3w8bNgwvL68Mt/nwww+pXbs2AIqi8PPP6TdPF6Kw6NQJFiz4d5acBQtmMnv2JiIiAtLewNUVs337mOXnR69evYB/kxJ+fn7GCFkIIYQQQgiRhkKTkKhSpYq6/PTp0xQtHzKiOwZD1apVs73/U6dOpZjxolOnTnpt17FjR3X56NGj2d6/EAXJ0KEapk37d+rP777z5ZdffiIq6nK621hYWLBq1Sq8vb0xB76Lj+fLbt34/fffjRCxEEIIIYQQ4lWFJiGRPM1msrNnz2a6TWhoKI8fP063jqwIDQ1N8V7f2S90y+m21hCisJswwZxRo5LGk9BqLZg8eTG//jqZ6Oib6W5jYWHByqVLOVKuHJ8A+xIS+Or999m6datxghZCCCGEEEKoCk1ComHDhlhbW6vvjx8/nuk2x44dU5dtbGxo2LBhtvevu2/QfxwL3ZYculOQClHYaTTw008W9O6dNPNGfLw1X375K2vXfkZsbPrdsiyio3nD0REAd5KSEhP+7/9Yt26dMcIWQgghhBBC/KPQJCQcHBx4++231fdr1qzJdBvdMm+//XaOZtkoVapUiveBgYF6badbrnTp0tnevxAFkZkZLF9uRYcOsQDExDjwySeL2bJlJPHxT9PeyNkZzR9/oNSsCSQlJf7QapneuzdLly41UuRCCCGEEEKIQpOQABgwYIC6fP78+QwHtDt9+jS7d+9Oc9vsqFevXoqEhq+vL1qtNsNt7ty5w5YtW9T3zZs3z1EMQhRElpawaZM1zZsnJSVevHBh9Ghfdu4cQkJCRNobubmhOXgQpVYtAEoBh4EfBw/G19fXKHELIYQQQghR2OWLhMSWLVt4/fXXqVChQo7q6d69uzprBcDw4cO5fDn1IHj379+nb9++JCYmDZpXp04d3n///TTrvHnzJhqNRn1NmTIlzXJWVlb06dNHfR8QEMCIESOIj49Ps/y9e/fo3Llziq4dgwYNyvQYhSiMbGzAz8+a+vVjAHj2rBQjRszl4MGBJCamM4CtmxuaQ4dQ6tVLegscApaOGsWcOXOME7gQQgghhBCFWL5ISERGRnLz5k1u3ryZo3o0Gg2LFy9Wx2K4f/8+jRo14quvvmLXrl3s27eP6dOn4+XlxaVL/8/efYdHUe1hHP/upncIJSGQhBJ67yX0KiCKgigIUkRRuaKAomChKEVFRbFiRVCRLgLSBCnSe+8JndATEggpu/ePCRsCAUJ6eT/PMw+7szNnztw7YPbN75yzDzDmbZg0aRImk+leTafIiBEj8PHxsb3/7rvvqFixImPGjGHRokWsWbOGOXPmMHDgQCpUqMC2bdtsx/bt25datWqluQ8iuZWHByxe7EyFCkYoERZWnH793mPNmj5YLDHJn+TtbVRK1K9vvAX+AWYOHsz777+fOR0XEREREcmjTFar1ZrVnbifyZMn07t3b0wmk61qIS1mz55N9+7d7zuxpIuLC1OnTuXxxx+/6zGhoaGUKFHC9n748OF3rZIAY3WPDh06cPLkyRT3t0uXLkydOhUHB4cUn3NTREQEXl5ehIeH4+np+cDni+Q0Z85AgwbRhIY6A1CmzGZ++WUider8iMlkl/xJV69i7dAB08qVAEQC7YGGw4bx/vvvp0sgKSIiIiKSV6T0e2iOqJBIb48//jhbtmyhZcuWyX7RMJlMtGjRgs2bN98zjEiNatWqsWvXLl577TW8vb3veWzNmjWZNm0af/zxR6rCCJG8qEgRWL7cmSJFjDklDh6sxfPP92HHjv5YrXeZt8XDA9PChdCqFQDuwN/AxjFjGDx4MDkgtxURERERyXEytELi+PHj6dLOjBkzeP3119OtQuJWJ06c4L///uPUqVOAsZJFcHAw/v7+6Xqd5MTFxbFjxw527tzJxYsXuXHjBp6enhQtWpTatWunSx9UISF51d690KhRDJcuOQJQt+4CfvhhORUqjL97xUN0NHTuDAsWGG+BzoD/Cy/w5ZdfYjbnyQxXREREROSBpPR7aIYGEmazOd1Kna1Wa4YEErmdAgnJyzZtgmbNYomKMiqMmjb9g0mT9lG69Ii7nxQTA127wuzZxlvgKcCzZ0++//577O3tM7zfIiIiIiI5WbYZsmG1WtNlExF5ULVrw/z5Djg5xQHw779P0r9/SY4eHXf3kxwd4Y8/jFACcASmAzcmT+bJJ5/kxo0bGd9xEREREZE8IEMrJOzsjAnkfH19KVOmTKrbOXv2LAcOHFCFRCqoQkIEFi6Ejh3jiY01/k1q3/47vvwygsDAwXc/KT4e+vaFn3823gKVAP9WrZg9ezbu7u4Z3m8RERERkZwoWwzZKFu2LIcPH6Zp06b8888/qW4nvVfZyEsUSIgY5s6Fzp0txMcbhWGPPvolEyfG4+8/4O4nWSzw0kvw7bcMt7dnVJxRaVGvXj0WLlxI/vz5M6HnIiIiIiI5S7YYslGzZk2sVivbtm3LyMuIiNxXx47w229mzGZjpY0//+zPoEFxnDz59d1PMpvh669h/nxar1iBl5cXAOvXr6dJkyacPXs2E3ouIiIiIpI7ZWggUatWLQDCw8M5cuRIRl5KROS+unSByZPNmExGKDFz5iDeeOMyp0//cPeTTCZo357ghg1ZuXIlhQsXBsBu1y4aBQcTEhKSGV0XEREREcl1MiWQANi8eXNGXkpEJEW6d4dJkxJX//ntt2G89dZxzp795b7nVq1alTVr1tC9UCE2AO8cPUqT4GD27t2bgT0WEREREcmdMnT9uho1alC1alUAzp8/n+p2GjZsyE8//ZRe3RKRPK5vXxM3blj53/+MYOLnn0fi6Pgmo0b9jo9P13ueW7pAASZHR2MGngE2nDlD48aN+fvvv6ldu3bGd15EREREJJfI0EktJetpUkuRu/vkEyuDBydWS/TvP4h3321A4cKd733i3LlYn3ySFa6utL5yhXjA3d2defPm0axZs4zttIiIiIhINpctJrUUEcnOBg0yMXp0Yib75ZefMG7cv1y48Oe9T+zYEdPq1dQ6eJCGTZoAEBkZSdu2bZk3b15GdllEREREJNdQICEiedqwYSbefTcxlPj00y/44IOFXLy44N4n1qmDZ6FC/P333zz88MMAlLhxg5cee0xDzEREREREUkCBhIjkeSNGmBgyxGJ7P378t4wbN58LF+bf91wXFxdmz57NwEce4R/gX4uF9/r0YcyYMWhEnIiIiIjI3SmQEJE8z2SCcePMDBx4ayjxNePGLeDChfsPwXBwcODjq1fxA4KAtcCMt97ilVdewWKx3OdsEREREZG8SYGEiAhGKPHxx2YGDUoMED7++GvGjl3E+fNz73/+L79grVABAF9gJbBr4kS6du3KjRs3MqbTIiIiIiI5WI4IJGbNmkXJkiUpVapUVndFRHIxkwnGjzfz2muJocQnn3zF2LHLOH9+1r1PLlYM0+rV0KABAJ7AIiB++nTatWtHRERExnVcRERERCQHyhGBRGRkJKGhoYSGhmZ1V0QklzOZ4MMPzbz+emIo8emnXzB69ArOnZt+75O9vWHpUkiY5NIJmA6UWb6cpk2bcvbs2YzruIiIiIhIDpMjAgkRkcxkMsEHH5h5443EUOKzz75g9OjVhIVNu/fJrq4wZw707g0Y/8h+DTy6bRsN6tfn8OHDGddxEREREZEcRIGEiEgyTCYYO9bMm28mrpTx+ecTGTPmP8LCfr33yfb28MMP8Oabtl3DgTdCQ2lYvz5btmzJoF6LiIiIiOQc9hnZ+PHjx9OlnQsXLqRLOyIiD8JkgjFjTJjNVsaMMQFGKGGxvMJbb1nw9e1x75PHjgUfHxg4EIB+QKELF2jTpAm/z5lDq1atMuEuRERERESyJ5PVarXe/7DUMZvNmEymdGnLarViMpmIj49Pl/byioiICLy8vAgPD8fT0zOruyOSI1mt8M47VkaPTvz3rH//V3nrrWoUKdLr/g389hvWnj0xxcUBxgocnezs+PiHH+jZs2fGdFpEREREJIuk9Htohg/ZsFqt6bKJiGQVkwnee8/E228n/lv05ZcTeO+93Zw+/f39G+jWDdOCBVjd3ABoAiyPj+etXr0YNWqU/o0TERERkTwpQysk7OzsAPD19aVMmTKpbufs2bMcOHBAFRKpoAoJkfQ1fLiVUaMSKyV6936HkSML4e8/4P4nb9qEtV07TAnD0E4C7YBavXvz7bff4uDgkDGdFhERERHJRCn9HpqhgUTZsmU5fPgwTZs25Z9//kl1O5MnT6Z3794KJFJBgYRI+nvvPSvvvpsYSnTrNpbRo6F48aH3P/ngQawPPYQpJASAHUB1oGWrVsycOVN/T0VEREQkx8sWQzZq1qyJ1Wpl27ZtGXkZEZFM9c47JsaPT8xyf/ttKIMHu3L48Nv3H35Rpgymdeugdm1i3Nx4xsEBK7B06VIaNWrEqVOnMrbzIiIiIiLZRIYGErVq1QIgPDycI0eOZOSlREQy1eDBJr76KvH97Nmv8OqrgRw4MPD+oYSPD6xYgeOaNXy5fDne3t4A7Ny5k3r16rFr164M7LmIiIiISPaQKYEEwObNmzPyUiIime7FF+Hnn8FstgCwYMFzvPxyLfbs6YfVep/hZW5uUK0aDRs2ZO3atZQoUQIHoNPJkzQJDk7TMDcRERERkZwgQwOJGjVqULVqVapUqcL58+dT3U7Dhg356aef+PHHH9OxdyIiadezJ/z2mxl7eyOUWLasO/37t2bHjp5YLLEpaqNs2bKsW7uWuQUKMAGYevUqndq04Zdffsm4jouIiIiIZLEMndRSsp4mtRTJHH/+CV26xBMTY6wuVLfuAr744ieqV5+KnZ3z/RvYsQNrnTqYYmK4DjQCtgAjRozg3XffxWQy3acBEREREZHsIVtMaikiklc8+ijMm2eHs7MxVGPDhvb06/cCmzZ1IT4+6v4NVK2KackSrN7eTH3oIbYk7B4xYgQ9evQgOjo64zovIiIiIpIFFEiIiKSTNm1g0SI73NziANi6tSXPPTeE//7rRFxcxP0baNIE09Gj9F24kPHjx9uqIn799VdatGiRpqFvIiIiIiLZTYYM2YiKimLXrl1ERUURFxeHj48PJUuW1JCBLKAhGyKZb/16aNMmjogIewCCgrbxxRdDaNbsVxwdC6e4nblz5/L000/z7rVrHAb+KVGC+fPnU6FChQzquYiIiIhI2qX0e2i6BRJxcXFMnTqVL774gh07dmCxWO44xs/Pj+bNm9O6dWsee+wxXF1d0+PScg8KJESyxtat0Lp1LBcvOgBQrNhBJk7sx0MP/Yyzc2CK2zk+bBgBY8cC8CEwxsOD6TNn0rp164zotoiIiIhImmXqHBKhoaHUrVuXZ599lm3bthEfH4/Var1jO3XqFFOnTuWZZ57Bz8+PV155hVOnTqVHF0REspUaNWDNGgeKFjVW2jh5sgzPPTeFWbN6ERW1N8XtBMQmrtQxBPjp6lU6t23L119/nd5dFhERERHJVGkOJC5cuEBwcDDbt2/nZrGFyWS6Y0b4W/dZrVYiIiL44osvKF++POPHj0+2okJEJCcrVw7WrnWgdGkjVLhwoRgvvTSTX399hfDw9Slr5KOP4MsvsdoZq3c8BqywWHjvpZd49dVXiY+Pz6Dei4iIiIhkrDQP2XjqqaeYPn16krABoGLFipQrVw4nJyeuX7/OkSNHOHToENevXzcufMvxJpOJ1q1bM336dDw8PNLSHbmNhmyIZL1z5+Chh2LZts0YvuHsHMmYMU/Ss+cAvL3bpKyRRYuwdumC6epVAE4CDwPF2rfn999/17+dIiIiIpJtZMocEufPn6do0aK2IRoAnTp1YuzYsQQFBd1xfGxsLOvWreOvv/5iypQpnDt3DpPJZAslKleuzKpVq/TFOR0pkBDJHiIioEOHOFatMia6dHC4wbvvduf55ztTuPCTKWtk9254+GE4dgyASKArcKxyZebPn09AQEDGdF5ERERE5AFkyhwSK1euJC7OWN7OZDLRt29fZsyYkWwYAeDg4EDjxo356KOPOHHiBJ988gn58uWzhRK7du3iiSee0PANEcl1PD1h0SJ7OnQwhljExjoxfPg0xo9fwqlTKZwPolIl2LAB6tYFwB34E2ixaxe1a9Xiv//+y5jOi4iIiIhkgDQFEjcnpLRarTg7O/Pxxx+n+FwHBwdeffVVtm3bRvXq1W3tLFu2jE8//TQt3RIRyZZcXGD2bDueecYIXS0WOz766Afef/8IoaHvkaKCNR8fWLECunQBjH/EPwVGnT9P66ZN+fHHHzPuBkRERERE0lGaAonIyEjAqI6oX79+qsYwBwQEsHz5cipXrmyrlHj//fe5dOlSWromIpIt2dvDTz+ZeeWVxPDhm2/G89Zb9hw69CpWawoqxFxc4Pff4e23bbv6AQvi4hjy7LO8+uqrtuo1EREREZHsKk2BhJOTk+21r69vqtvx9PRk+vTp2NvbYzKZiIiI4I8//khL10REsi2zGT791MR77yXu++23obzySlV27nwGi+VGyhp57z345Resjo4ANAU2Af989hnt2rXj8uXLGdF9EREREZF0kaZAokCBArbXaa1oKFu2LF27drWVLP/5559pak9EJDszmYwChy+/BJPJ+Hdv0aI+9OvXjQ0bHiMuLjxlDfXogWnlSkgIhUsAawGXpUupU6cO+/bty5gbEBERERFJozQFEiVKlACMuR+2bduW5s48/vjjttcHDhxIc3siItndSy/BH3+YcHQ0JrvcsKEdffqMYPnyR7hx41TKGqlXDzZtgpo1AfAA5gDuhw9Tr149Fi5cmCF9FxERERFJizQFErVr18bFxQWAc+fOpfmH3lKlSgFGwBEWFpamtkREcoonnoDFi+3w9DTmfdi/vw59+nzPvHlPERW1J2WNFCsGq1bBU08BMKNAAbZjLLn08MMP89FHH6Vs0kwRERERkUySpkDCzc2N5557zvZ+8ODBXL9+Pc2dAu65VqmISG7TtCmsWWOPn18sAKdOlaZfv5lMm9afK1dWpawRV1f47Tf45RcePnyYTp06AUbIO2TIEJ555hmio6Mz6A5ERERERB5MmgIJgBEjRlCwYEEADh48SJcuXYiJiUlVWzeHaZhMJvz9/dPaNRGRHKVyZVi/3oHy5Y1KicuXffjf/+bz3Xcfcu7cjJQ1YjJBjx645cvH9OnTGTFiBADtgYNTp9KoUSOOHz+eMTcgIiIiIvIA0hxI5MuXj6+//tpWCrxw4UIaNWrE0aNHH7itH374wfa6efPmae2aiEiO4+9vVEoEBxtzSkRHuzN06BwmTPiLkyc/e6C2zGYzw4cPZ+n48fwBrATKb95MzZo1WbFiRfp3XkRERETkAaQ5kADo1KkT/fv3t4USmzZtokKFCgwcOJBDhw6lqI0xY8bw999/YzKZMJvN9OvXLz26JiKS43h7w9KldnTsaAEgPt6BsWN/4b33TnP48OtYrZYHaq/ljh24Ac5Ac+DChQu0atWKTz/9VPNKiIiIiEiWMVnT6adRi8XC008/zR9//IHJZMJqtWIymQBj8svmzZtTv359ypYtS5EiRbCzs+PMmTNs2rSJb775htWrV9t+MB41ahRvv/12enQrz4uIiMDLy4vw8HDNyyGSw8THw8svW/n6a5Nt32OPTWTkyHVUrPgTZrNTyhqKiYFXXiFu3ToeL1SIv5Yts33UrVs3vvvuO1xdXdO7+yIiIiKSR6X0e2i6BRJgTJw2cuRIRo8ejcVise27GUzc71xvb2/Gjh2bZKJMSRsFEiI5m9UKY8fCW28l7mvQ4E8++ugratf+DQeHAilvLCqKeGdn3n33XcaMGQOAKxBUpQpz5syhZMmS6dt5EREREcmTUvo9NF2GbNxkMpkYMWIEa9asITg4+I5SYKvVetfNZDJRtWpVzpw5w/z58zl9+nR6dk1EJEcymWDYMPjpJ7C3N4LetWsfpXfv0SxZ8gjXrh1OeWNubtjZ2TF69GhmzZpFNVdXjgI1d+6kVq1aLFmyJGNuQkREREQkGelaIXG7VatW8eOPPzJnzhyuXr2aeNEUVEwAFC5cmBo1alCzZk1q1KhBjRo1CAgIyKju5kqqkBDJPZYtg06d4oiIsAfAx+cYH374NI88Mo58+Ro+WGNXr3KjWjWcEiYg/goYCIwcO5Y33ngjxf9Oi4iIiIjcLkuGbNxNfHw8mzZtYtWqVWzatImtW7cSEhKStCO3/PB7a5du/6HY29vbFlLcLDmWu1MgIZK77NkDbdvGceKEEUq4uYUzcmRXunfvjo9Pt5Q3FBMDgwbBl1/adq0BOgMNO3Xip59+wsPDI307LyIiIiJ5QrYKJJJz5coVtm7dypYtW9iyZQtbt27lyJEjdw0jbu+myWQiPj4+0/qbUymQEMl9zp6Fhx+OY8sWI5Sws4tl4MAX6N+/OIGBbz9YdcPPP2N94QVMN24AcAroBFwpW5ZZs2ZRsWLF9L8BEREREcnVsn0gkZyIiAi2bt1qCyq2bt3KwYMH7wgpbs45oUDi/hRIiOROUVHQrZuFefMSpwLq1m0Mw4YdpHz5b1O+AgfA5s3w2GNw8iQAMcAAYIqLC99OmkT37t3Tt/MiIiIikqvlyEAiOZGRkWzbti1JNcWBAwewWq0KJFJAgYRI7hUfD4MHW/nss8SKiGbNpjFmzI/UrDkNBwfvlDd27hx06QIrV9p2TQFeAJ554QU+/fRTnJ2d06/zIiIiIpJr5ZpAIjnXrl1j+/btNGjQIKu7ku0pkBDJ/b74Al55xYrFYgQTlSqtYfz412jUaCqurkEpbyg2Ft54Az791LZrF8YQDs+aNZk5cybFixdP176LiIiISO6TJct+ZhZXV1eFESIiCf73P5g714Srq1E1tnt3Q/r0mcrs2T24cmVVyhtycIBPPoHp08HdHYDKwGYgcMsWatSowYIFC9L/BkREREQkT8qRgYSIiCTVoQOsXm2Hr28cAKdPB/HCC4uYNOlDzpz54cEae+IJY16JChUA8ARmAW9dvkzHhx/m7bff1pA5EREREUkzBRIiIrlEjRqwcaM9VasaYUFUlBdDh/7J6NE7OXjwVSyWuJQ3VrYsbNgA3RKXEh0MLAd+HD2a1q1bExYWlr43ICIiIiJ5igIJEZFcxN8f/vvPjo4dLQBYLHZ8+eVnDBpUga1bHyU29krKG3N3h6lT4csvsTo4ANAI2AqcX76cGjVqsGbNmnS/BxERERHJGxRIiIjkMm5uMGuWmbfeSty3YMHz9O37OitWPMS1awdT3pjJBC+9hGn1aiPtAMLt7TkKnD59mqZNmzJ+/HgsFkv63oSIiIiI5HoKJEREciGzGd5/3yhwcHIywoIdO5rSu/evzJr1DJcuLXuwBuvWha1b4fHH8V6+nDrNmgEQHx/P66+/TocOHbhw4UI634WIiIiI5GYKJEREcrGnn4Z//zVTuPDNyS5L8eKLi/nuuwmcOvUlD7Tyc8GCMGsWhRo1YsmSJQwbNgyAUkD4woVUrVqVlStXZsBdiIiIiEhupEBCRCSXq1cPNm9OOtnlsGF/MnbsIQ4efAmLJfaB27S3t2f06NEs/esvZtvb8y/wzOnTtGjWjFGjRmkVDhERERG5LwUSIiJ5wM3JLh97zKiIMCa7nMCgQTXZvLk9MTHnU9VuyyNHqBIXhz3QC3C2Whk+fDitWrXizJkz6dZ/EREREcl9FEiIiOQRbm4wc6YpyWSXCxf2pXfvkSxa1I6rV7c+eKP9+8O772J1dWXlSy9x3Wz8Z2XFihVUrVqVxYsXp1PvRURERCS3USAhIpKH3Jzs8tdfwdnZmOxy79769OnzJ1OmDCIs7NcHa9DeHkaOxHT0KM9/+SUrVqzAz88PgPjz52n30EMMHTqU2NgHHxYiIiIiIrmbAgkRkTyoWzf47z8zxYoZk11evOjHK68sYfz4FRw+PBCLJe7BGvTxAaBx48bs2LGDR9u2ZSGwBPhp3DiaNGnCsWPH0vcmRERERCRHUyAhIpJH1agBW7bY06SJUSkRF+fI+PHf89prQWzZ0jbV80oULFiQOeXLUxdoAWwHXNeto1q1asydOzedei8iIiIiOZ0CCRGRPKxwYVi61MyAAYn7/vyzP88++y5Llz6UunklANMjj0DC0A1fjEqJ165c4YnHHuOFF17g2rVrae+8iIiIiORoCiRERPI4Bwf47DP46SdwcjKqJXbtakSvXn/y228DOHt26oM32qQJbN8ObdoAxn9s3gLWAEu//ZYaNWqwbdu29LoFEREREcmBFEiIiAgAvXrBqlVmihaNB+DChWK8/PIyPvtsSermlShUCBYuhHHjsNrbA1AXYwhH7QMHqFunDuPHj8disaTnbYiIiIhIDqFAQkREbOrUgc2b7QgONkKC2Fhnxo37hSFDSiTMKxH2YA2azfDGG5jWroWgIAA8gCnAT3FxvPf667Rp04bTp0+n742IiIiISLanQEJERJLw9YXly8288ELivjlzBtC790gWLmxHePh/D95o7dqwdatRhpHgaYxqiahly6hcubImvBQRERHJYxRIiIjIHRwd4euv4bvvwNHRqJbYs6cBvXv/zY8/juDEiU+xWq0P1qiHhzFRxe+/g5cXACWAVUD/S5fo/Nhj9OvXj6ioqPS9GRERERHJlhRIiIjIXfXtC//9ZyYw0JhX4sqVwrz22iLef/8se/Z0IS4u4sEbfeopY8LL4GAA7IFRwArg70mTqFmzJlu3pm51DxERERHJORRIiIjIPdWqBVu22NGmjVEpYbHYMWnSB7zwwtOsXNmcqKg9D95o8eLw778wYgRWs/GfokbATqDKgQPUq1ePDz/8kPj4+PS6DRERERHJZhRIiIjIfRUoAAsWmBk+HEwmY6jGf/91pGfP35k+vQ9hYb8+eKP29jB8OKZVqyAwEIB8wPuAKTaWN954g+bNmxMSEpJu9yEiIiIi2YcCCRERSRE7OxgxAhYsMJE/v1G5cOpUaV58cQWffbaIgwf7Y7HcePCGg4ONIRxPPYXVwYHFPXsSazIBsGrVKqpUqcIPP/zw4HNWiIiIiEi2pkBCREQeSNu2sHWrHTVrGkM4btxwZezYKbz+eiU2bGhBdPTxB280Xz747TdM27fz8s8/s2LFCgITqiY8IiPp37cvjzzyCGfPnk3HOxERERGRrKRAQkREHljx4rBmjZnnnkvcN2/ei/TqNYF58zpy4cL8B2/UZIIKFQBo0qQJO3fupG+vXswBNgHH58+nUqVKzJo1Kz1uQURERESymAIJERFJFWdnmDQJfvwRnJyMaomDB2vRp88KvvzyJw4ffg2LJSbV7Xt6evJdyZLUBSoDU4FLFy/SuXNnnnnmGa5cuZIetyEiIiIiWUSBhIiIpEnv3rB+vZmgIGNeiagoL0aMmMWQIYFs2NCC69dDU994x45QpQpWOzumNmnCzVkkpkyZQuXKlVm2bFlauy8iIiIiWUSBhIiIpFm1asbSoE89lTjx5Jw5L9Or16fMm/cY58/PTV3DlSvDxo2Y/v6bcStW8Msvv+Dp6QlAxMmTtGrVigEDBnDt2rW034SIiIiIZCoFEiIiki48PeG330x8+23SIRzPPvsvX301lUOHXkndKhxOTtCqFSaTiR49erBr1y5aNm3K38CfwB8TJ1K9enXWr1+fnrcjIiIiIhlMgYSIiKQbkwmef94YwlG69K1DOGbyxhtBrF/flOvXj6TpGgEBASxu2ZIGwCPAbqDqwYMEBwfz+uuvc/369TTfh4iIiIhkPAUSIiKS7m4O4ejaNekQjt69J/Lnn504d25Gmto3V60KhQsDUAiYDkyzWJg8fjzVqlVj7dq1aWpfRERERDKeAgkREckQHh7w668mJk1KOoSjb9+VfPHFTA4ceIH4+FTO/fDww7B7Nzz+uG3XE8AeoMrBgzRs2JDBgwdrbgkRERGRbEyBhIiIZBiTCZ57DjZsMFOmjBFKREV58d57f/Dqq3VYvboxkZE7Utd4oUIwcyZMmwYFChi7gBnANKuVXz75hGrVqrFmzZr0uRkRERERSVcKJEREJMNVrQqbN5t5+unEIRyLFvWhR4/f+P335zl58jOsVus9WrgLkwmefBL27ElSLdEF2AtUPXSIxo0b8+qrrxIVFZX2GxERERGRdKNAQkREMoWHB0ydauKXX8Dd3Zjw8uTJMrz00mrGjDnOjh0PExNzLnWN+/gY1RK//55stcSvn31G1apVWbVqVfrcjIiIiIikmQIJERHJVD16wLZtdtSubQzhiItz5OuvP+a55wawaFELLl5clLqGTSZ46imjWuKxx2y7b1ZLVD9yhCZNmjBgwABVS4iIiIhkAwokREQk0wUFwZo1ZoYMSdy3eXMbevVaxvfff8bhw4OwWG6krnEfH5g1K9lqiT+AaRMnUqlSJZYsWZLW2xARERGRNFAgISIiWcLRET74AJYuBV9fYwjH5cs+vPnm3wwd6s+6dQ2JitqXusbvUS2xDygQGkqbNm145plnuHDhQtpvRkREREQemAIJERHJUi1bws6ddrRvnzip5cyZA+nVaxKzZj3NqVPfpG7CS0i2WiLa2Zm9CR9PmTKF8uXL8+uvv6b+GiIiIiKSKgokREQkyxUqBH/9ZeLzz8HJyZhb4vDh6jz33H+MG7eXHTs6cOPGmdQ1fmu1xJNP4vfXX0z8/nvy5csHwIULF+jevTvt2rUjNDQ0fW5IRERERO5LgYSIiGQLJhO8/DJs3GimQgUjlIiJceGLLz7nuecGsHBhK86fn5X6C/j4wLRpmFq25Nlnn2Xfvn088cQTlAKWAycXLaJixYp8+umnxMfHp8s9iYiIiMjdmayqUc3VIiIi8PLyIjw8HE9Pz6zujohIily/DkOHwmefJe7z8LjEq6++SNeuzpQu/Tn29l5pv5DVyvkqVSi0ezexQAdgMVC7dm2+//57qlSpkvZriIiIiOQxKf0eqgoJERHJdlxcYMIEWLIE/PyMaoWrV715770/GDCgFcuXN+TKlZVpv1BYGIXi4gAI9/BgdcLuTZs2UbNmTYYNG8b169fTfh0RERERuYMCCRERybZatYJdu+zo0iWxmG/Zsu706LGAn34ayZEjrxMfH536C/j6wrZt8M47FJw5k8WrV1OuXDkA4uLi+GDsWKpWrcqyZcvSeisiIiIichsFEiIikq15e8O0aSamTgUvL2NuiXPnAhg8eBnDhvmwbl0wkZE7Un8BZ2cYNQpat6Zhw4Zs376d4cOHU87engNA5UOHaNWqFU8//TRnz55Nn5sSEREREc0hkdtpDgkRyU2OH4eePa38+6/Jtq9EiV0MG/YsrVo9hr//65jN9mm/kNVKZMOGuK9dC8ACoD9wxcuLMWPG0K9fP+zs7NJ+HREREZFcSHNIiIhIrhMQAP/8Y2L8eHB0NKolQkIq88IL/zFiRDSbNjUiKmpv2i8UFYV7wrKgAO2BvUC/8HBe6d+f+vXrs3Xr1rRfR0RERCQPUyAhIiI5itkMgwfD5s1mqlQxQon4eAd+/nkkPXp8yfTp3Tl+/AMslrjUX8TdHebPhxkzwM8PAFfgA2Ab4LhpE7Vr1+aVV14hIiIizfckIiIikhcpkBARkRypcmXYtMnMO++AnZ0x+vDQoRo8//x6Ro0KZ9OmxkRF7Uv9BUwm6NwZ9u2DAQOMJASoBKwBvrVYmPr555QrV44ZM2agEZAiIiIiD0aBhIiI5FiOjsZ8lOvXm6hQwaiWiItz5IcfxtCr1wRmzXqK48c/xGqNT/1FPD3hs89g40aoWdO2uy+wH2h15gxdunShXbt2HDlyJG03JCIiIpKHKJAQEZEcr1Yt2LLFzBtvgNlsVCrs31+Hvn03MGbM+YS5Jfan7SI1a8KGDTBxInh4AFAImAysAEIXLaJSpUqMHDmS69evp+1aIiIiInmAVtnI5bTKhojkNevXQ69eFg4cSMzcK1Zcy5tvPk/jxj3x9x+EyZTGFTJOn4ZXXzXmmEgQA3wKvAcUKl6cCRMm8Mgjj2Ayme7SiIiIiEjupFU2REQkT6pXD7ZtMzNoEJhMRua+Z08Dnn12E2PGnGPTpiZpr5bw84Pp02HhQihRAgBH4A2MYRy1Q0Pp2LEj7du359ChQ2m7loiIiEgupUBCRERyHRcX+PhjWLXKRKlSxtwSMTEufPvtR/ToMYFp03pw7NgYLJbYtF2obVvYvRveftuY0AIoBkzHmPzy77//plKlSgwbNoyoqKi0XUtEREQkl1EgISIiuVbDhrBjh5lXXkmsljh4sBb9+q3lnXfiWbeuPhERm9J2EVdXeO892LMH2rUDIKRZM8L9/QGIiYlh7NixlCtXjunTp2s1DhEREZEECiRERCRXc3ODCRPgv/8SV+KIj3dgypR3ePrpX/jll4EcPjyY+Pg0VjAEBcGCBTBvHiWmTWPfvn0MGzYMR0dHzED1kyd58sknadGiBXv27EnzfYmIiIjkdAokREQkT6hfH7ZuNfPuu2Bvb1QpHDtWgQEDVvHGGwGsXFmXS5eWpv1CHTpA4cK4ubkxevRodu/ezYSKFZkH/AucXbGCqlWrMmjQIMLDw9N+PREREZEcSqts5HJaZUNE5E47d8Kzz1rYvDkxl/fxCWXw4Od5+GE/goI+xsGhQPpcLCICa4kSmC5dAqAxsDrho8KFC/P+++/Tp08f7OzSuPKHiIiISDahVTZERETuokoVWLfOzPjx4OJiDOMICyvOkCFLGDSoCcuWNeDcuT/SZ74HT09MU6ZAqVLEP/UUrUaNwtnZGYBz587x/PPPU7NmTf7999+0X0tEREQkB1GFRC6nCgkRkXs7fBiee87Kv/+abPvy5z/L//73Cp07X6dMma9wdi6W9gtFR8P165A/P6GhobzxxhvMmD6dn4DJwArg8ccf56OPPqJkyZJpv56IiIhIFlGFhIiISAoEBcE//5iYNAk8PY1qicuXfXnvvT/o2/cl/vyzLSdPfobFEpe2Czk7Q/78ABQvXpw//viDfUOH0hNYDswCts2eTfny5XnzzTeJiIhI2/VEREREsjkFEiIikueZzfDcc7B3r5lHH03cv2nTQzzzzEZGjjzLhg0NiIjYmK7XLbt7t+3148A+YERMDF9+8AFlypThhx9+ID4+Pl2vKSIiIpJdKJAQERFJULQozJ0Lc+ZAsWJGtURMjAvffz+Wbt1+ZPLkwRw8+BKxsVfS54Jz58JPP4GvLwBOwFDgINA2LIzn+valdu3arFq1Kn2uJyIiIpKNKJAQERG5TceORrXEwIFgNhtTLYWGVmLAgNUMGlSdZcvqERb2W9onvTSboVcvOHgQhg4FR0cAigA/ARsBl23baNKkCZ06deLw4cNpu56IiIhINqJAQkREJBkeHvDJJ7Bpk4latRKDhwULnqNbt1V8/vlCtm9vxbVrB9PnYmPGwL598Pjjtt21gP+AaSTOL/HKK69w4cKFtF9TREREJIspkBAREbmHGjVg/XoTEyeCh4cxjOPKlcKMGTOVvn3fZPbsxwkJGUF8fHTaL1ayJMyaBcuXG2uTJngS2A+Mi4tjyuefExQUxIcffkh0dDpcU0RERCSLKJAQERG5Dzs7+N//YN8+M507J+7furUlffpsZsQIE2vW1OLSpaXpc8FmzWDrVvjmGyhcGABHYDBwGOgTHs47b7xB2bJlmTp1KhaLJX2uKyIiIpKJTNY0D4CV7Cyl67+KiEjKLVgA/ftbOHYsMdf39Q2hf/9X6djRiaCg8Tg7B6TPxa5ehQ8/hI8/huvXbbuPAq8Bc4AaNWrw0Ucf0bx58/S5poiIiEgapPR7qCokREREHlD79rBnj5khQ8De3sj1z54twTvv/Enfvr2YPbs9x46NTp9hHB4e8N57xsSXPXuCyQRASaBmwiFbt26lRYsWPPzww+zduzft1xQRERHJBAokREREUsHNDT74AHbuNNGiRWKx4YYN7ejVazPvvGNh9epaXLy4IH0uWKwY/PyzMZSjRQsoVIgGc+dSrVo12yELFiygcuXKPP/885w+fTp9risiIiKSQTRkI5fTkA0RkYxntcLMmTBwoIVTpxKzfh+fUP73v1fp0CGO0qUn4OoalH4XPH0aihbFYrEwdepU3nrrLXqdPEkh4H0g0sWFAQMG8MYbb5A/f/70ua6IiIhICqT0e6gCiVxOgYSISOaJjIT334dPPrEQG5sYTNSp8zcDBrxG/fqPERg4FDs7t3S/9vXDh7GvUAGH2FguAIHANSBfvny88cYbDBgwAFdX13S/roiIiMjtNIeEiIhIJnN3h3HjYNcuM61aJeb9Gze2pXfvrbz7rgsrV9bg3LkZpPfvA1z27MHBwQGAPTVqEOfoCMCVK1cYOnQoQUFBfP3118TGxqbrdUVERERSS4GEiIhIOitbFhYvNjFzJvj7G0tyxsY68euvb/HUU8v4/PM5bN/egsjI3el30UcfhSNH4JVXaLJoEYcOHaJPnz6YzWZcgWZnzvC/l16ifPny/P7771oqVERERLKchmzkchqyISKStaKiYMwYGD/eQkxM4u8BKlZcy//+N5DmzWtSvPhIHB0LZcj19+3bx5YnnqD7nj3sBoYBfwFVq1Zl7NixPPTQQ5gSVu4QERERSQ8asiEiIpINuLnB6NHGMI727RN/B7BnTwNefHEDL79clwULGnHixMdYLDHpfv3yxYrR/eRJACoB84A1gMeOHbRr146mTZuydu3adL+uiIiIyP0okBAREckEZcrA/PkmFi2C8uUTh0ssWdKTbt02M3LkJVavrsH583PTd34JDw+YOxfq1bPtCgZWA/OBy6tWERwcTIcOHdi2bVv6XVdERETkPhRIiIiIZKI2bWDHDjOffw758xvBRHS0Oz/8MJonn5zPl1/+mjC/xI70u2jTprB2LcyZA+XL23a3B7YDvwEH58+nRo0adOrUiV27dqXftUVERETuQoGEiIhIJnNwgJdfhkOHzPzvf2BnZ1REhIUVZ+TIGfTqNZzff+/NgQPPERMTlj4XNZmgY0fYtQt+/BH8/QHjB4GuwF7gZ2D77NlUrVqVp556in379qXPtUVERESSoUBCREQkixQoABMnwo4dJlq3ThymsXNnE/r128ygQXVZsKAhx46NIz4+On0uamcHvXvDwYPw8cdQsKCxG+gJHAC+tVpZ98cfVKpUiR49enDo0KH0ubaIiIjILRRIiIiIZLGKFWHRIhN//QWlSxvDOKxWMwsX9qVbt+0MHx7NypU1CQv7Das1nZbrdHaGQYMgJMRYBiR/fgDsgeeAQ8BEi4XlU6dSvnx5+vTpw9GjR9Pn2iIiIiJo2c9cT8t+iojkLDEx8MUXMGqUhfDwxN8beHufoVev4TzxxHbKlh1H/vzN0/fC4eEwYQJ88glERNh2RwMjgXGAvb09vXv35u233yYgICB9ry8iIiK5Rkq/hyqQyOUUSIiI5EwXLsB778FXX1mJizPZ9gcG7qVfvyG0a2ehVKkPcHevnL4XvnzZGMrx2WcQGQnAQBcXJly/bjvEwcGB5557jqFDh1KsWLH0vb6IiIjkeAokBFAgISKS0x0+DEOHwsyZSfdXq7aCF154nSZNqlC8+CicndM5GLhwAT78EBYu5Mry5Xz65Zd8+umnXL16lSLADSDS0ZHevXvz5ptvUrx48fS9voiIiORYCiQEUCAhIpJbrF0Lr71mZd06U5L9LVtOpW/f96hd+3ECAt7E3t4rfS8cH29MhAlcunSJjz/+mMoffED7+Hi+BMYC1+zteeaZZxg6dChBQUHpe30RERHJcRRICKBAQkQkN7FaYfZsePNNC4cPJ84v4eBwg8cf/4yePb+lcuVX8PN7AbPZMWM6cegQ1nLlMFksXAQCgaiEj8xmM926deOtt96iXLlyGXN9ERERyfZS+j1Uq2yIiIjkECYTdOoEe/aY+fxzKFDAWHEjNtaJP/4YQpcumxk16jSrVlUnLOxXrNb49O+EuzumF14AR0dc3n6bwe++i5eXUZVhsViYM3UqFSpU4KmnnmL37t3pf30RERHJNVQhkcupQkJEJPcKD4exY2HCBCs3biQO5fD2PsMzz4yiS5f1lC49kgIFOmAyme7RUiqcOgWenuDhQXh4OBMnTuT3jz9m7ZUr/Ap8ABwHHnvsMd555x2qV6+evtcXERGRbEtDNgRQICEikhccPw4jR8LPP1uxWBKDBz+/I/Tu/Q4dO4ZSqtRo8udvlqH9iOnfH8evvgIgFvgZY46JEKB9+/a888471K1bN0P7ICIiIllPQzZERETyiIAA+OEH2L3bRKdOiftPny7F6NG/8eSTX/PNN+PZvr0VEREbM6wfjr6+4O4OgAPwHHAQ+Ak4uGAB9erVo0WLFixbtgz9PkRERERUIZHLqUJCRCTv2bQJhg2zsmxZ0mEalSuv5rnnhtKsWWFKlHgPN7eK6X/xS5dgwgT4/HNjTEkCCzALGAdsBWrWrMmbb77JY489hl3CKh4iIiKSO2jIhgAKJERE8rJ//oGhQ61s2pQ0mKhXbz7PPvsOwcGVKV58JC4uJdL/4leuwMSJ8OmncPlyko+WYAQTK4DSpUszZMgQevTogZOTU/r3Q0RERDKdAgkBFEiIiOR1VivMmQNvvWVl//6kwUTjxjPp1Ws0DRrUJTBwGM7OAenfgYgI+PprI5gIC0vy0QaMYOJPwLdIEQYNGkS/fv3w8PBI/36IiIhIplEgIYACCRERMcTFwZQpMHy4hRMnEqeQMpksNG06nV69xlCvXiMCAobi7Fws/TsQHQ2TJ8OHH8LRo0k+2oexKsdvgFu+fPTv358BAwZQuHDh9O+HiIiIZDgFEgIokBARkaSio+G772DMGAtnzyYNJlq0+I2ePcdRt24zAgKG4uTkl/4diIuDmTNh3DjYsSPJRyeABsBJwNnZmWeffZbXXnuN4sWLp38/REREJMMokBBAgYSIiCTv+nVjJMW4cRbOn08MJszmeFq3/oVnnvmQWrVaExDwJk5ORdK/A1YrLFoEY8fC6tUAHPP2pnREBLFxcbf0x0znzp0ZPHgwderUSf9+iIiISLrTsp8iIiJyVy4uMGgQhISY+eADKFDAAoDFYseiRb3p0WMnAwdW4s8/m3L48EBu3Dibvh0wmaBtW1i1CtasgQ4dCPzmG44cPcrAgQNxc3MDYLDFwpbp06lbty6NGzfmzz//xGKxpG9fREREJEuoQiKXU4WEiIikxNWrxkqd48dbuHIl8fcV9vYxtG37I08/PYGaNdsTEDAER0efDO/PxYsXmffGG/T+4QcswEfAmwmflS5dmoEDB9KzZ09cXV0zvC8iIiLyYFQhISIiIinm4QFvvQWhoWZGjABPT6MKIS7Okb/+eoGnn97FgAEVmDmzBYcOvcqNG6cytD8FChSgd2QkYPywcqVI4rCRQ4cO8dJLLxEQEMC7775L2G2rd4iIiEjOoAqJXE4VEiIikhqXLsEnn8Bnn1mIjEw6x0SLFr/RvftH1K3bgICAN3BxKZExnbh8Gb79FmbNwrJ6NYuWL+fjjz9m+fLlVAUaAT8BcU5OdO/enUGDBlGhQoWM6YuIiIikmCa1FECBhIiIpM3Fi/DZZ/D55xbCw5OuytGkyQx69BhL/frVCAwciqtr2YzphNVqzDmRYNu2bUQ/9hj1jx3jEvA18AVwFmjbti2DBg2iRYsWmG45R0RERDKPAgkBFEiIiEj6uHIFvvgCPv3UwqVLSUd8Nmw4h+7dR9OwYRCBgcNwd6+SsZ0JC4OiRSE+3rbrBvAbMAHYCVSoUIEBAwbQvXt32wSZIiIikjkUSAigQEJERNLX1avGcqEff2zh3LmkwUTdugt45pn3aNTIl8DAt/D0rJ1xHdmxwxhT8vvvEBub5KN/gc+AeYBX/vz07duX/v37ExgYmHH9ERERERsFEgIokBARkYxx7RpMmgQffmjhzJmkwUS1aivo2nUcrVtDYOAb5MvXLOOGT5w6BRMnwjffQHh4ko9CMIZy/ABcNZvp2LEjr7zyCo0aNdJwDhERkQykQEIABRIiIpKxoqPhxx9h3DgLJ04kDSaCgrbRtes42rc/TokSgylU6DFMJruM6UhkJEyebKxdevBgko+igMnA58ABoFq1agwYMICuXbvi7OycMf0RERHJwxRICKBAQkREMkdMDEyZAh98YOHQoaTBhJ/fYZ58cjwdO64hKGgAPj7PYGeXQUGAxQKLFxszcS5efMfHizCGcywGChQsSL9+/XjxxRcpWrRoxvRHREQkD1IgIYACCRERyVzx8TB3LowbZ2Xz5qTDIvLnP0vnzhPo1GkW5cr1wc/vRRwc8mVcZ/bvN4ZzTJ4MUVFJPtoL1MCYDNPe3p7HHnuMl156iSZNmmg4h4iISBopkBBAgYSIiGQNqxWWL4cPPrCydGnSL/hubuE88sjXPPHED1Sp0pFixV7FySkDKxSuXDHGlUycCKGhAGwqVowGZ88SFxeX5NAKFSrw0ksv0aNHD/13U0REJJUUSAigQEJERLLeli3wwQcwc6YVqzUxnHBwuEGbNj/z1FOfUbNmPfz9X8fNrXzGdSQ+Hv76yxjOMXw4p8uU4euvv2bSpElcPneO34BpwJ+Ak5sbPXr04KWXXqJy5coZ1ycREZFcSIGEAAokREQk+zh0CMaPh59/thITkxhMmEwWgoP/5IknPqFpU2/8/QeSL1/mDZ2IiYlh0+uvE/z554CxKkffWz5v2LAhL730Ep06dcLR0TFT+iQiIpKTpfR7qPmun4iIiIiko9Kl4dtvITTUxJAh4OFhAcBqNbNmzWO88spqunR5m08//YYNG+pw9uwULJaYDO+Xo6MjwZcu2d5f7dABNzc32/u1a9bQrVs3/P39eeuttzh+/HiG90lERCQvUIVELqcKCRERya6uXDECis8/t3D6dNLfkfj4HOPxxz+nY8f5lC3bEz+/fjg4FMi4zlgsxqQXc+fCxIlEXL3KlClT+Oqrr6i6dy9vA18BU4BIs5mHH36Y559/noceegg7uwxaylRERCSH0pANARRIiIhI9hcTA9Onw8cfW9m+PekwDVfXCNq1+57OnSdRvXozihV7FVfXspnWN6vVSkSVKnjt3g1AJPAbMAnYAvj7+/Pss8/Sp08f/P39M61fIiIi2ZkCCQEUSIiISM5htcK//8Inn1iZPz9pMGE2x9Oo0Sy6dPmYhg0L4e8/iHz5mmX8PBNRUdC2LaxefcdHWzGCid+AKLOZtm3b8vzzz9OuXTvs7e0ztl8iIiLZmAIJARRIiIhIzrR/P0yYAJMnW4iOTjqco2LF/+jceQJt2hylRIkBFC78FGazU8Z2aNcu+PprmDIFIiOTfBSFsTrHJGAj4OfnR58+fXj22WcpXrx4xvZLREQkG1IgIYACCRERydnOn4dvvoEvvrBw7lzSYKJQoRM88sjXdOw4hwoVOuHn9wLOzsUytkORkTBtGkyaBJs23fHxDuA7YCoQYTLRunVrnn/+eTp06ICDg0PG9k1ERCSbUCAhgAIJERHJHaKj4bffjOEce/YkHabh4BBNixa/89hjXxEcXJyiRV/Gy6tRxg/n2L4dvvsOpk6FiIgkH10DpmOEE2sBHx8fevXqRd++fQkKCsrYfomIiGQxBRICKJAQEZHcxWqFf/6Bzz+H+fOtWK1JQ4dKldbw2GMTadv2MIGBL+Dj0w07O7e7tJZOoqKMWTknTYL16+/4eA/QBdib8L5x48b07t2bzp074+7unrF9ExERyQIKJARQICEiIrnX0aPw1Vfw/fcWwsOTDucoWPBUwnCOPyhf/lGKFn0JF5eSGd+pXbuMqokpU4x1TYFr9vb4Wa2Ex8cnOdTd3Z0nnniCPn36EBwcnPEVHSIiIplEgYQACiRERCT3i4oyRk1MnGhhz56kwYSDww2aNv2DTp0m0qCBL0WL/o/8+VthMpnv0lo6uX4dZs40qiYqVSJsxAgmT57Mjz/+yIEDB/gYuAH8BBwCgoKC6NWrFz179qRYsQyeB0NERCSDKZAQQIGEiIjkHVYrrFgBEyfCvHlWLJakFQflym2kQ4dvaN9+CyVL9sTXtycODgUyvmNxcZCwDKjVamXT0qVUa9cOx/h4LgBFgLiEQ00mE61ataJ379507NgRZ2fnjO+fiIhIOlMgIYACCRERyZtCQxOHc1y+nLQaws3tCm3aTObRR3+kdu0q+Pm9gKdng8wbMvH339ChA8THs79VK/rHx7N8+XLbxxWAfYBXvnx07dqV3r17U6tWLQ3pEBGRHEOBhAAKJEREJG+7ds1YneOrr6xs23bnF/qqVf/lkUe+5qGHDhIQ8Cy+vj2wt/fK+I6FhRnjTNq0gUqVCA0NZfLkycz74QfWnzjBceBnYDJwAqhUqRI9evSgW7duGtIhIiLZngKJFFi7di2TJ09m9erVnDp1CqvVSrFixWjYsCE9e/YkODg4w/sQGxvLkiVLmDFjBps3b+bMmTNcu3YNHx8fihQpQq1atWjWrBnNmjUjf/78D9y+AgkRERFjOMemTfD11zBtmoXo6KRVE/nzh9G27Q888sgUqlYNTqiaqJXp/bR88gnmwYMT3wPLgSnAbCDKZKJ58+Z0796dTp064eHhkel9FBERuR8FEvcQFRXFgAED+PHHH+95XO/evZk4cSJubhmzXNjatWt5/vnn2bNnz32P7d+/P1988cUDX0OBhIiISFKXL8Mvv8A331jYvz9pMGEyWahT528eeeQbWrQ4i7//8xQu3BV7+0xannPxYvj4Y1i2zEhRbnENmANMBZYCji4udOzYke7du9O6dWvsE+apEBERyWoKJO4iPj6edu3asWTJEts+FxcXKlasiL29PXv37iUiIsL2WevWrVm4cCF2dnbp2o9ffvmF3r17Y7FYbPvy5ctHyZIlyZcvH+Hh4ezfv5+oqChAgYSIiEh6s1ph5UqjamL2bCtxcUmHdBQqdIK2bX+kffsZVKvWCF/fZ/HwqJk5czkcP26kJj//DEeO3PHxWeB3jMqJbUDhwoXp2rUr3bt3p2bNTOqjiIjIXSiQuIthw4YxduxY2/vnnnuOcePG4e3tDRjVEx988AHvvfdeknNGjx6dbn2YOXMmTz75pC2MqF69OuPGjaN58+ZJfrthsVjYvHkzM2bMwNXVlZEjRz7wtRRIiIiI3N/Zs/DjjzBpkoVjx+6smqhZcynt2v1A69ZHCQx8Bh+f7jg4eGd8x6xWWL/emG9i2jS4dOmOQ/ZiBBO/Ysw3Ua5cOXr06MHTTz9NYGBgxvdRRETkNgokknH69GlKlSpFdHQ0AD169OCXX35J9th33nmH999/HwBnZ2eOHDmCn59fmvsQFhZG+fLluXz5MgCdOnVi2rRpGVZmqUBCREQk5eLjjVET33wDCxbcuXSop+cFWrf+hfbtf6FOnfL4+j5L/vzNMZnMd2kxHcXEGCt0TJ0K8+YZ72/zL/ADxrAOgMaNG9OtWzc6d+5MgQKZsMSpiIgICiSSNWTIED766CMAXF1dOXHihK0y4nYxMTEEBQVx4sQJ27kffPBBmvvQvXt3fv31VwDKlCnDzp07cXJySnO7d6NAQkREJHVOnYLJk+GHHywcPXpn4FChwjratfuetm3XU7Lkk/j69sLZOSBzOnflCsyYAVOmwOrVST6aAXS57XB7e3tatWpF165defTRR/UzgYiIZCgFEskoXbo0hw8fBqBXr1789NNP9zx++PDhjBo1CoCgoCAOHTqUpuufPXsWf39/4uLiAJg7dy6PPvpomtq8HwUSIiIiaWOxwL//wvffw+zZFm7cSBpOuLhcpXnzabRr9yMNGnjh5/csBQs+gtmccb9wSCIkxFjbdMoUOHCAsG+/5fvz55kyZQoHDhzABfgcmAn8A9g7O9O+fXueeuop2rdvj4uLS+b0U0RE8gwFErc5cOAA5cqVs72fNm0aTz755D3PWb9+PfXr17e9379/P2XLlk11H8aNG8fQoUMBKFKkCCdOnEj3yTJvp0BCREQk/Vy6BL/+Ct99Z2HXrjurJgID99Kmzc+0a7eQChVa4OvbE3f36pkzyaTVCps3Q9Wq4OiI1Wpl27Zt7Bk5kh7z5gEwERhwyynu7u507NiRrl270qpVKxwcHDK+nyIikuul9HtoJgx4zB527NiR5P2tQcPd1KhRA0dHR9v7nTt3pqkPt67s8dBDD2V4GCEiIiLpy9sbXn4Zduwws3EjPP88eHgkrph17FgFJk36kMcf30H37g/x0Ucfsnp1LY4f/4gbN05nbOdMJqhdGxJ+djGZTNSoUYMetyxfbn3kEQoXLpx4SmQk+6dOpX379vj6+tKvXz9WrFhBfHx8xvZVRESEPBRI7Nu3z/ba0dERf3//+55z+3G3tvGgrFYrW7Zssb2vV68eACEhIQwdOpTKlSvj5eWFu7s7JUuWpGvXrsyYMSPJsqAiIiKSPdz87v/tt3DmjJmffoKGDROLTi0WOzZubMt7702jXbt/6N/fk+++68L27Q8RFvY78fHXM6+zP/4Ic+bAs88yYPZsTp06xZIlS+jTpw/PuLiwCTgEDLx0iTWTJtG8eXP8/f15+eWXWbVqlcIJERHJMHlmyEbv3r35+eefAShZsiRHklnTOznNmzdnxYoVtjZ+/PHHVF3/6NGjlCpVyvZ++fLl7Nu3j9dee43r1+/+Q0nVqlWZPn06ZcqUSdV1NWRDREQk8xw5Ar/8ApMn37l8KECxYgdp02YyDz00l8qVG+Dj8wxeXg0zZ0hHMiwtW2L+558k+3ZjTIw5A9gH+Pr68vjjj/PEE0/QqFEjVXiKiMh9acjGba5evWp77eXlleLzbv0f79Y2HtTFixeTvJ87dy79+/e3hRGBgYE0bdqUWrVqJRkmsmPHDurXr8/evXtTdJ0bN24QERGRZBMREZHMUaoUjBwJR4+aWbECevYEN7fEaseTJ8vwww+j6dJlF926PcmHH05ixYoqhIaO5Pr1o5neX/PTT0OLFmBO/JGwEjAS2AvsAV44e5ZVX31Fs2bN8PPz48UXX+Sff/6xTdItIiKSWnkmkIiMjLS9dnZ2TvF5t848fWsbD+rKlStJ3n/++ecAlC1bllWrVhEaGsqKFSvYtGkT586dY+DAgbZjL126ROfOnYlJZr3x240dOxYvLy/blpKhKSIiIpK+zGZo2hR+/hnOnjUzeTI0b55YlGq1mtm6tSVjx06hffv1PPtsaSZMeJkNGxpx6tSXxMScy5yO9u4Ny5YZa5x+/jncNsdWBWA4sAujWuLlc+dY+803tGzZEj8/P/r168fSpUsVToiISKrkmUDi1v9Q2tvbp/i8W4+NjY1N9fVv3Lhxxz4/Pz9WrVpFo0aNkuz38vLik08+4e2337bt27dvH5MnT77vdYYOHUp4eLhtO3HiRKr7LCIiImnn7g7PPAP//GMiNBTeew9KlUqsmoiOdmP58m4MG7aANm1m078/TJrUiR072nL27BTi4lJfoZlivr7GbJ1r18Lx4zBhAgQHJzmkHPA2sAM4ALx6/jwbJ02idevW+Pr60rdvXxYvXpymn5dERCRvyTOBhKurq+11dHR0is+79Vi3W2apflDJnTt69OgkM13fbvjw4UkqHFIyf4WTkxOenp5JNhEREckeAgPh7bfh0CEza9ZAv37g7Z04aWR4eCH+/LM/L7+8mrZtv+T114/x22+N2bPnKS5cmIfFcv9qyTTz94dXXoE1a+DkSaNyonFjYybPBGWAYcA24BuMoak//PADDz30EIUKFaJ79+7MmjUrTdWlIiKS++WZQMLd3d32+l6TSN7u2rVrybaRlusDODg40KVLl3ueY29vz1NPPWV7v3nzZqKiolLdBxEREckeTCajAOGbb+DMGTvmzYMnnwRn58TKiTNnSjJ16tv07LmNjh3f4J13VvHnn9U5cKAfV66sxGrNhJW4ihY1KidWrjSGdXzxhTEW5ZY5J2Lq1k3yi58b4eGc/vVXnuzcmUKFCvHII4/w448/cv78+Yzvr4iI5Ch5JpAoWLCg7fWZM2dSfN7Zs2dtrwsUKJDq6xcqVCjJ+3LlyiX5j/fd1KhRw/Y6Li5OQzBERERyGUdH6NABpk2Dc+eM+SZat7ZiNifOOXH4cHW++WY8nTrtomvXJxk9+heWLKnCkSOvc/XqNjJl0bQiRaB/f1ixAk6fhq+/htateXnJEs6fP8+sWbPo1q0bj7q4sBwIA7pGR/PXX3/x7LPP4uvrS5MmTZgwYQKhoaEZ318REcn28kwgUbZsWdvrixcvJql8uJdbA4By5cql+voBAQFJAghvb+8UnXd7CHL58uVU90FERESyNw8PY76JxYtNnDplYsIEqF07sRLCajWzbVtzxo//gYcf3kr37k0YM+ZTli+vRUjIO0RG7syccMLHB154ARYvBk9PXF1defzxx/n111/59YknACgAxOXPbzvFYrGwadUqRg0cSIkSJahevTqjRo1i585M6rOIiGQ7eSaQKF++fJL327dvv+85p06dSlJeeHsbD8JsNicJNJKb5DI5t8938SArhIiIiEjO5etrTOWwcaOZAwdg+HAICkoMJ+LiHFm//mHGjfuFtm3X0r17LcaO/YAVK2oREvIukZG7s+SLvl2nTsb4kyJF+PnkSdatW8eQIUMoXbo0nYBzwDIgePt2vhs+nKpVqxIUFMRrr73GmjVriI+Pv88VREQktzBZ80gkHRkZScGCBW1BwAcffMCQIUPuec60adPo2rUrYAQBFy5cSNPEloMGDeLTTz8FwMfHJ8lwkLv5/PPPeeWVV2zvT506hZ+fX4qvGRERgZeXF+Hh4ZrgUkREJIezWmHzZvj9d5g+PZ5Tp+zuOMbBIZp69RbStOkftGhxmICADhQu/ARubhUzt7MWS5K5JqxWK1dbt8Zz2bIkh20C5gJ/AnswqkPbtWvHww8/TJs2bfDy8srETouISHpI6ffQPBNIALRv356FCxcCUKVKFXbs2HHP4zt06MD8+fNt5958nVpr1qxJssTnzp07qVy58j3Pefjhh1mwYAEA/v7+HD9+/IGuqUBCREQkd7JYYN06mD4dZsyI58yZO8MJJ6dr1Ks3n2bNptOsWQgBAY9QqFAX3NxSX/WZJsOHw6+/wpEjyX4cAsxP2P4FLPb2NG7cmA4dOtChQwdKlSqVeX0VEZFUUyCRjBkzZiRZ2WLevHl06NAh2WO3bt1KnTp1bGWDM2bMoHPnzmm6vsVioWLFiuzfvx+Arl278ttvv931+O3bt1OzZk0sFqM883//+x8TJ058oGsqkBAREcn9LBZjlc7p02HmzHjCwu4MJ5ydI2nQ4C8aN55Js2YnCAh4mEKFOuPqWh7TLUt6ZjirFfbsgTlzYO5c2Lo12cMigSXAX8BCjKEe5cqVs4UT9evXx97ePtO6LSIiKadAIhlWq5Xq1avbKiOKFCnC8uXL75is8syZM7Ro0YJ9+/YBUK1aNbZu3Zrsf6xDQ0MpUaKE7f3w4cMZMWLEXfswZ84cHn/8cdv7Tz75hIEDB95x3PHjx2nRogWHDx8GwNHRkYMHDxIYGJjyG0aBhIiISF4THw+rViWGExcuJF85UafOIho1mk2zZvspWbIVBQs+jodHrcwNJwCOHYM//4T58+HffyE29o5DLMBGjMqJP4HdGBOEt23blg4dOtCmTRvy5cuXmb0WEZF7UCBxF5s2baJJkyZcv34dAE9PT1588UUaN26Mvb09Gzdu5IsvviAsLAwAFxcXVq5cSe3atZNt70EDCYAePXowdepU2/vmzZvTo0cPSpQoQVRUFKtWreLrr78mIiLCdsxXX33Fiy+++MD3q0BCREQk74qLM77jT58Os2bFc+nSneGEnV0sNWr8Q6NGs2nefBNlyjSiYMHH8fJqiNmcyRUIERGwdCn89RcsWAAXLtxxyJ9Ax9v22dvb06hRI9q1a0fbtm2pUKFC5gcrIiJio0DiHmbPnk337t1tocTduLi4MHXq1CQVDbdLTSARExPDE088wbx58+7bV5PJxOjRoxk6dOh9j02OAgkREREBo/BgxQqYPRvmzk1+WIfJZKFy5TU0ajSbpk3/pVKlmhQs+Bj587fEzi6TV/qKj4eNG41wYv582LULgHV9+vDp1assWrSIq1ev4ghMAZZjDO04gbHc+kMPPUTbtm1p0aIFHh4emdt3EZE8ToHEfezbt48BAwbwzz//3LEklslkonnz5nz++edUqFDhnu2kJpC46bvvvmPs2LGEhIQk+3mjRo14//33ady4cYraS44CCREREbldfLwxIebs2TB7djzHjt0ZTgCUKbM5YVjHIqpVK0OhQo/h7d0Oe/ss+IIfGmpUTXTqBL6+xMTEsHr1ag5+8QUvzp0LwE9An9tOc3BwoGHDhraAolKlSqqeEBHJYAokUujEiRP8999/nDp1CoCiRYsSHByMv79/pvVh06ZN7Nmzh7Nnz+Lk5ESRIkVo1KgRRYsWTXPbCiRERETkXqxWY17J2bONYR0HDiQfTgQE7KNBg3kEBy8iONiNwoUfpkCBh3F2LpbJPb7NG2/Ahx8CsOCZZ/jszBlWrlxJTEwMXsAvwKKELQQoVqyYLZxo2bKlfj4SEckACiQEUCAhIiIiD2bfvpuVExa2bjUne4yX13nq159PcPCfNGlyHn//1hQo0AF39+qZX31gsRiJyqJF0L8/5M9PVFQU//77L2cnTuTZxYtthx4A/k7YVgFx9vYEBwfbAooqVaqoekJEJB0okBBAgYSIiIikXmiosTrn7NkW1q41YbHc+WXdwSGamjWX0aDBPJo02Uq5cnUoUKAD+fI1y/x5J243YADcZcn0a8C/GOHEYuAQ4OPjQ8uWLWnVqhWtWrXCz88v07oqIpKbKJAQQIGEiIiIpI/z52HhQpg3z8rixRaiopIf2lGu3EYaNJhHw4ZLqVWrGAULdqBAgfY4OhbK5B5jVE/s2AF//21UUKxda0ygkYxjwNKE7R/gIlCxYkVbONGkSRPc3Nwyr+8iIjmYAgkBFEiIiIhI+ouONlbsmDcP/vwzjjNnkl8e1McnlAYN/qJu3b9p1Og6RYu2pkCBdri5ZdHQiCtXYNkyI5z4+284fTrZwyzANhIDipWA2cGB4OBgW0BRo0YN7OySD2VERPI6BRICKJAQERGRjHVzUkwjnIhnx47kv6Q7OV2jevXl1K27kIYNt1KpUiUKFGhH/vwtsbfPgp9RrFZjKdFFi2DpUli9Gm7cuOOwaCB/wp+38vb2pkWLFraAonjx4pnQaRGRnEGBhAAKJERERCRzHT8Of/1lDO1YscJKbGzyE2MGBu6lbt2F1Ku3mIYNwcenNd7ebXFzq5g11RPXr8OaNbBkiRFQ7NgBwLnKlXm3QQOWLl3K0aNHAZgI5MOonvgdiAVKlSpF8+bNad68Oc2aNcPHxyfz70FEJJtQICGAAgkRERHJOhER8M8/xtwTCxbcfWiHi8tVatVaSt26C2nUaAdly9akQIG25MvXAnt790zudYKwMKPz+fJBu3YAHDlyhGWLF9Nt4EA8YmK4DBTEGOJxkz0QB1SoUMEWTjRt2hRvb+9MvwURkayiQEIABRIiIiKSPVitsHPnzXAinnXrzMmu2gFQqtT2hOqJpdSv72SrnnB1LZf1y3IePgzVq0NkJBebNuWr5s1ZunQp69evJzY2liVAYWA5sAJjedEIk4lq1arZAopGjRrp5zIRydUUSAigQEJERESyp0uXjJERCxfCwoVxXLiQfPWEm1s41asvp1atJTRosJvKlcvg7d2afPla4OhYMJN7nSAmBtavB2dnqFMHgGvXrrF2xQqadOyIQ1yc7dB4YCuJAcUaINrOjtq1a9sCigYNGuDq6poFNyIikjEUSAigQEJERESyP4sFtmxJrJ7YvNmM1Zp8JYSf3xFq1VpCrVpLadToIgEBweTP3xovr/qYzU6Z3PPbhITAE08Ys3ze5UfsWGAjRkCxHFgHWB0dqV+/Pk2aNKFJkybUq1dPAYWI5GgKJARQICEiIiI5z7lzxuIXixdbWbw4nosXk6+eMJvjKF9+A7VrL6F27VXUq+dO4cItyZ+/Fa6u5bNueMfly7BqFSxfbqyPumvXXQ+NxgglVmIM71gPxNrbU7t2bRo3bkzjxo0JDg7Gy8src/ouIpIOFEgIoEBCREREcjaLBbZvNxa/WLw4nv/+M9115Q43tyvUqPFPwvCOnVSqVJ78+VuRP39LHB0LZW7Hb3XuHKxcaQQUy5fDwYN3PTQGeAsYf8s+s9lMtWrVbAFFo0aNKFgwi4ariIikgAIJARRIiIiISO4SFWV8tzcCijj270++egLAz+8w1asvp0aN5TRseI6SJauTP39zvLwaYW+fhT8XnTplVE6sWGEEFKGhST5+tUgRPjtzxva+MDAUo4JiFXARYxWPJk2a2EIKPz+/zOu/iMh9KJAQQIGEiIiI5G4nThiTYy5ZYmXJknguX757QFGixC6qV19O9er/0qhRJP7+tcmXrzleXg2ws8vCORuOHYPVq41hHqtWwYoVhJnNrF69mlWrVuE0bx4fHTsGwBiMCorblSpVisaNG9OwYUOCg4MpU6ZM1q9IIiJ5lgIJARRIiIiISN4RHw/btt2snrCwfj3ExCQ/vMNsjqdMmS0JFRSradAgDj+/YPLla46nZx3MZsdM7v09vPwyfPEFAN936sR3J06wZcsW4uPjCQIWA6uB/xK2fUCBggVp0KABwcHBNGjQgFq1auHs7JxltyAieYsCCQEUSIiIiEjede0a/PefMSpi2bI4tm61w2JJvmrA3j6GChXWUaPGcmrUWEv9+k4ULtyYfPma4+FRHZPJLpN7f4vLl40bWb0a3noLPD2JjIxk7dq1RH3+OY8tWJD0cIyJMv8D1mKs6hHn6EjNmjUJDg62hRSFCxfO/HsRkTxBgYQACiREREREbrpyJXHxi2XLYtmzx+Guxzo7R1Gx4lqqVFlF9epbqFfPCR+f+nh5NcbDoyZm893PzVRjx8KoURAdfddD4oBtGOHEzSqK00BQUJAtoAgODqZcuXKYzclXlIiIPAgFEgIokBARERG5m7Aw+PdfI6D4559Yjhy5e8jg4BBN+fIbqFp1JdWqbaRBAxO+vnXJl68xHh51sLPLwuEQN27Ali2wdq1RSfHff3D+/D1POUZiOLEW2Al45c9PgwYNqF+/PnXr1qV27dpablREUkWBhAAKJERERERS6tixmwtfWPnnnzhOn757QGFnF0u5cpuoUmUV1aqtpX79WIoVq4WXV+OESTLdMrHnt7Fa4cgRI5i4GVLs2XPPUyoCe295bwasJhPly5enXr161KtXj7p161KxYkXs7LJw+IqI5AgKJARQICEiIiKSGlYrhIQYQzxWrrTy77+xhIbefaJLszmeoKBtVK26kqpV/6N+/UgCA6uRL18TPD0b4OCQPxN7n4zLl2H9+sQKig0b4Pp1AKJdXHiqZUv+W7eOCxcuAPAe8DSwHngHOJLQjLu7O7Vq1bIFFPXq1cPX1zcLbkhEsjMFEgIokBARERFJLydOJAYUK1fGcvDgvVfiCAzcS6VK/1Gp0n/UqnWOSpX8yZcvGE/PBri4lMraZTljY2HHDiOcuHYNhg7FarVy6NAhNmzYQO1hwyh38iQA/sDJW06tAZTDCCuOAoGBgbZwom7dutSoUUMreojkcQokBFAgISIiIpJRzp41Fr5YuRJWroxh9+57BxT584dRseJaKldeQ9Wq+6hTx52CBevi6RmMh0eN7LXUaIcO8M8/WL28WDtjBus3bGDDhg2sX7+egSdOMDDhsPMYq3isBzYkvL7m4EDVqlWpW7cutWrVonbt2pQrV05DPUTyEAUSAiiQEBEREcksFy/CmjVGQLF6dSzbt9sRF3f3VSscHa9TrtymhCqKjdSvfwN//yp4eQXj5dUAB4cCmdj7ZMTGwsmTUKJEkt0xtWrhuGXLXU/bD2xK2DYD2wGTqys1atSgdu3a1KpVi1q1ahEUFKRVPURyKQUSAiiQEBEREckq167Bxo3GqIg1a+JYu9ZKRMS9lwu9dZhHzZpnqFSpWMIwj/q4upbFZMoGX+CXLzduav16Yy6KixfveXg8sAcjnNiMEVTsBFy8vKhZs2aSkCIwMDBrh7KISLpQICGAAgkRERGR7MJigb17b84raWX16ntPlAng6XmR8uU3UL78eipV2k2dOlCsWEU8Pevh4VEXR8eCmdT7u7Ba4ejRxHBiwwbYts2orriHy0AB4PYvIgULFrSFEzeDCj8/v4zqvYhkEAUSAiiQEBEREcnOzpxJXPhizZoYtm+3v+cwD4CAgH2UL7+BChXWU63aKapVy4e3d208Pevh7l4Vs9kpk3p/FzduwO7dsGkTbN5sbLt3Q3y87ZC93t60dHLizJkztn3TMSbQ3AwMAa4n7C9SpAg1atSgevXqVK9enRo1aqiSQiSbUyAhgAIJERERkZzk1mEe69bFs2FDPBcu3LuKwtk5ijJlNlOhwnoqVNhC7drXKFUqCE/Punh61sPZuXjWf3m/ft1Y1WPzZiOoqFgRhgzh9OnTbN68mU0bNzJw/Hi8b9zgMuB92+mPAwWBrcAuwDV/fqpVq2YLKmrUqEGZMmU0caZINqFAQgAFEiIiIiI5mdUKISHGiIj1662sWxfDjh0OxMbeu4qicOHjCUM9NlChwmFq1XKkSJHKeHjUwsOjFo6OhTLpDlIoPBzq1IGDB7keHMyCV181gopNm9iyZQuzw8NpnnBoHLAP2HbLth2IdXWlSpUqSaopKlWqhJNTFleMiORBCiQEUCAhIiIikttER8P27TdDCgvr18dy7Ni9v3SbTBb8/Q9QtuxmypTZTJUqJ6he3ZnChaskhBQ1cXDInzk3cC/h4XDpUpKVPawWC5b8+bGLiLjnqUdJGlJsA87b2VGxUiVbFUXVqlWpUqUK+fLly7h7EBEFEmJQICEiIiKS+4WFGfNJGiFFLBs3moiKsr/nOWZzPMWL76FMmc2UK7eJKlXCqF7djQIFquHhUQt39xrY23tk0h3cg8VijGHZti1x27MH4uLue2oYieHEJmBOwv6AgACqVKliCyiqVKlC6dKlNeRDJJ0okBBAgYSIiIhIXhQfb6zoYUzZYGXjxhvs2uVATMy9v3Db28dQsuROypbdTNmyW6ha9QJVquTD27t6QkhRFTs7t0y6i3u4OXHmrSHFjh3GJBx3sR6of9u+jsBVjGVIrzo7U6lSpTuCCm/v22e0EJH7USAhgAIJERERETHExCQufrFpk4WNG2PYu9eR+Ph7z0fh4BBNiRK7KV16G0FB26lc+SLVqjlRqFBF3N2r4+5ePeuXHwUjhTl0KDGg2LrV+PPSJQD2NWzIF1WqsGPHDnbu3MnVq1c5BgQAV4DbB6z4YCxPWrhYMVs4cTOoKFOmDPb2965AEcnLFEgIoEBCRERERO7u5uIXt4YUBw86YbXee1UOszkef/8DBAVto3TpbVSocJLq1c0ULVoad/fqeHhUx8kpIOtX97Ba4cQJ2LkTfH2hVq2E3VZO7NhBQPXqAOwtWJCO+fNz+PBhbn49Wgw0A/ZjVFDsBHYk/HnJ0ZEKFStSqVIlKlWqRMWE1wEB2eCeRbIBBRICKJAQERERkQdz9apRWGCEFPFs3RrL4cP3DykAfHxCEyoptlGu3CGqVbNQsqQ/Hh7V8PCojotLWczmbFJZcPUqzJxphBUlS8LLLxMVFcXu3bvZuXMnT776Kp53GQJyCdgN7Lll2w1Eu7tTsWJFW0Bx888iRYooqJA8RYGEAAokRERERCTtIiON7+1bt8LWrRa2br3B3r2OxMbefxJIL6/ztpAiKGg/lSrdoEKFfHh7V8TNrQpubpVxcMiX8TfxIG7cgGefhZ07se7bhykFE2gCnCNpSLEdY+6KfPnyJQkobv5ZqFA2W35VJJ0okBBAgYSIiIiIZIyYGGPiTGO6Bitbt0azY4c9UVEO9z3X3j6GgIB9lCq1M2ESzbNUrWoiMNAfDw8jpHBxKZM9qiliYmD/fiORubnt3g2nTt331E1Andv2tQEiMAILp0KFbAFFxYoVKV++POXKlaNw4cKqqJAcTYGEAAokRERERCTzWCxw+HDivJJbttxg+3a4cMEpRed7eZ2nZMmdlCq1k1Kl9lGxYhSVKrlSsGB53Nyq4O5eBUfHwhl7Eyl15YqRyOzebSxDenM7e9Z2yKaKFXmnWDH27NnDyZMnATgKlMAIJbxua7IEEAnE5ctH+QoVKFeuHOXLl7dtgYGBWppUcgQFEgIokBARERGRrGW1wunTxuSZO3fCjh0x7NgRy8GDLvdd4QOMCTSLFTtoCypKlz5OpUpQurQPnp6VcXWtiJtb+eyxHCnAxYuJ4USZMtCiBQBXrlxh/+bN1GvVCoC9np40d3EhLCzMduoijAqKS8A+jAk1b/3zrJMTQWXL2iopbgYVpUuXxsXFJVNvU+ReFEgIoEBCRERERLKnGzdg376bIYWVHTui2bnTxPnzzik639k5ioCAfRQvvofixfdQpswFKlSAoKDCeHhUwM2tIq6u5bGzc83gO3kAV6/C5MlGVUXJkjBkCBcuXGD37t3s27ePJ4cMwTsy8q6nRwOHuDOsOAj4lix5R1BRrlw5vL29M+PORJJQICGAAgkRERERyVnCwmDXrptBRRw7dsSwb58TMTEpG6rg7BxJ8eJ7E4KKvZQpc5GKFaFkycK4u1dMCCrKZa+gAozxLgMHGsNA9u+HhCEeKRWKEU5sAd6+ZX/BggUpU6bMHVtQUJCqKiTDKJAQQIGEiIiIiOR8sbFw6NDNOSWt7N59g927LYSGuqRoOVIAV9cIAgNvDSouJQQVvri53RpUZJMv6VevwoEDRhnJ/v3Gtm8f1kOH7rnqxxag1m37PgN8MCop3gdiEvYHBAQkG1YEBgZib58NJhSVHEuBhAAKJEREREQk97p2zfiebswtaWX37uvs2QPHjqU8qHBzC8fffz8BAfsJCDhAqVIXKVfOSpkynuTLVxpX13K4upbDwSGbrHwRGwtHj9oCipthhXXfPkwREZxp1owZHTuyb98+9u/fz6FDh/j31CmCMCbM9Lituf8BNTGGghxM2I7Z21MkKCjZsMLX1zd7/O8g2ZoCCQEUSIiIiIhI3hMVZXxP37MnaVBx/HjKh2mYzXEULXrEFlYUL36csmVjKF/eEV9ff1tQ4eJSCrP5/kudZjir1VjhIyYGAgMT98fFYXV3x3TjBpeLF+fLZ5/l4MGDHDx4kAMHDjD1yhXaJ9PcSYxw4tAtfx4Czru5USwoiKCgIEqVKkWpUqVsr4sVK6ZVQARQICEJFEiIiIiIiBgiI42igptBxa5d19m/H06cSHlFBUD+/GG2oCIw8BClSoVTrpyJoKACuLuXTQgryuLgkD8D7+YBxMRASIiR1NSoYdtttVqJL10a+yNHUtyUBTgFHAaO3LLNBuwcHSlRokSSkOLmn8WLF8fJKWXLv0rOp0BCAAUSIiIiIiL3c+2aMUeFMQrCyt690ezfH8ehQ85ER6e8+sHR8TrFih2iaNFDFCt2iMDAMwQFxVK6tAMBAYVxdS2Ni4ux2du7Z+AdPYBr1+DwYTh40NgOHYKDB7EePIjpwoWUNQG4A7d+sewGlMcILuYCESYTAQEBSUKKW1+7u2eT/z0kXSiQEECBhIiIiIhIalkscOLErXNKxrJ3bzQHDthz7tyDTX7p6hphCyqMsOI8pUrFUbasA0WL+iUEFWVwcSmVfSbWvHzZFlBw8KARXBw5gvXIEUwXL9oOO1ugAC80bMiRI0c4cuQI169fZw7QMeHzAODELc22Ah4iaYVFdOHC+JcqRYkSJZJsxYsXx9/fHweHbDAsRlJMgYQACiRERERERDLClSvGIhiJVRXX2LfPyrFjLsTGPtg8Ch4el5KEFcWLXyIoKI6yZZ3w8fG3VVW4uJTEbM4mwx7Cw+HIEWMzmaBzZ8AYBnLmzBm8GjbELSSEODs7nunUicMhIRw+fJjLly8zDnjjtuZuDgUJwVjCNOSW18fNZkzFihFQsmSygUWRIkUwm82ZdeeSAgokBFAgISIiIiKSmeLi4PjxW0c/xHLgwHUOHTJz/LgrFsuDfXHOl+8cRYsews/vKH5+RwkIiKBkyVhKlXLC378Arq6lcHEpibNzyewzZwUk/g9w4QL07GnbffnyZaydO+O9fPkDNReLUWVxa2DxHXAOcHJyIjAw8I6w4mZgUaBAAa0MkskUSAigQEJEREREJLu4ObfkoUPGduDADQ4cuMGhQ3acPu36QBNrAjg5XaNIkaP4+R3Bz+8oRYuepUSJaEqWNBMU5IaXV3FbWOHs7I/JlE1WwDh92igvuVlhceQIhIZiDQlJ8bwVACUxgombHgeewwgsJgHbEvZ7eHgQEBBAYGBgsn/6+flpdZB0pkBCAAUSIiIiIiI5wfXrcPRoYmHBgQPRHDwYw+HD9pw9m/LlSm8ymSwULHgKP78jFClylKJFQwkMvEqJEvEEBTlRpIhPkuoKe3uPDLirVIiMhNBQYwsJSfwzJMQILMLDAbCYTAx68UWOnjhBSEgIISEhDI2K4q2EZtoCi25pNhj4GTgOHEv48+Z2ymwmvmhRfEuUSDawCAgIwM3NLVNuP7dQICGAAgkRERERkZzu2jXjO/mRI0ZocehQNIcPR3P0qDEMJCbG/oHbdHWNwNc3FF/fUHx8QvHzO0dAQDTFi0PJks74+BTGxaU4zs7FcXYOxN7eK/1vLDWuXDFCijNnoG1b226r1cqNp57Cefp0ACYNHMiWqChCQ0MJCQmhYUgIP8bF3bPpcyQNKm4NLiLz58e1eHECAgOTrbQoVKiQhoXcQoGEAAokRERERERyM4sFTp0yggpj9EM8Bw9e4+jReEJCnLh8OXUrdtwtsAgMtFKypDO+voVxdg5MCCyK4+CQL31vLDWsVmPOipAQqFYNHB0TP/rmG6xvvIE5IiLVzUcDzwNTbtnnAdQDwhwcsBQtindAAMWKFUt28/HxyTOTbyqQEECBhIiIiIhIXhYeboQVR4/C4cNWDh+O5vDhG4SEGPNWPOiKIDe5uFy1BRa+vqEUKRKGv380xYtbKVHCiSJFCiVUWATg5OSPg0M2qSAIDzdmHb19O3YMy7FjmE6fxmSx3PX0DsD8W943AP5LeP0Z8Optx48ATgMngTA7O+KLFMEtIIBi/v7Jhha+vr7Y2z94xUt2o0BCAAUSIiIiIiKSvPh4Y+TDzSkbQkPjOXr0GiEhcRw7ZsfJk26pDiwcHa9TuPAJChU6QeHCJ/DxOY2fXxT+/nH4+9tRvLgLBQr44uTkj7OzP05OAdljHou4OGPSzYSQ4tbQwnrsGGETJnDY2Znjx49z7NgxCi5fznPLlgEw3MWFUdev25rKD1xK5hLRGAHFze3ULa/PmM3E+vjgHBCAXzKhRdGiRfHz88PxluqP7EiBhAAKJEREREREJHUslsTA4tgxCAmJ5+jR64SExCYEFqmbv+Imd/fLttDCx+c4Pj7nKVbsOsWKWQgIcCAw0B0Pj6IJoUUATk5FMZud0u8G08OOHTB9Opw8CT17ElW3LqdOneLkyZNErl3LI++8k6pm40isrPgT+PCWz0qXLs3BgwfT3vcMpEBCAAUSIiIiIiKSMSwWOHvWCCuMxTAsHDlyjWPHYjhxwszp0y5ERqY+QDCZLOTPH0bhwicoXPh4QqVFBEWKxFK0qAl/fwf8/V3x9PTFyamobbOzyyYrYkREwH//GZN8nDxp2ywnTmA9cQK7q1dT1Mw3wIu3vG/SpAn//vtvRvQ43SiQEECBhIiIiIiIZJ2bUzacOGFsoaHRHDt2nWPHLJw6Zc+ZM25pqrIA8PS8SMGCp2xb4cIX8fW9jp9fHMWKmSlWzJkiRfLj7GwEFo6ORXF0LIzJlMUTTF69aoQVtwUWttDi5EnsLl5k1xNPsKh2bVvlRaVKlRgxYkTW9v0+FEgIoEBCRERERESyL4sFzp0zwgpjqgYLx45dJzT0BidPwokTjpw/74rVmrbwwMHhRpLQolChM/j4XMXPLwY/PyhWzAF/f3c8PX1xdLwZXBTB3t49ne40laKjjXkt3LO4Hw9IgYQACiRERERERCRni4kx5pk8ceJmMUEcx45d4+TJGE6dgjNnHAkLcyU2Nu2rU3h6XiR//rMUKHCGAgXOULDgRQoXvoaPTyy+vuDn50CxYi54e3vj5FQER8ciODr64uBQIOsrLrIRBRICKJAQEREREZHcz2KBCxduHQFh5cSJaE6cuM6pU/GcPm3HmTMuhIe7pMv1nJ2j8PY+i7f3mYTwIoxChaLw8bmBr68FPz87ihRxxs/PE2dn34TgogiOjj6Yzdl7hYz0oEBCAAUSIiIiIiIiN127ZlRbJAYXcRw/HsXJk7GcOgVhYfacO+fGjRsO6XI9szkOb+8wvL3P4O19NqHqIpxChWIoXDgeX18TPj4OFCniQsGC+XFy8sHR0QcHBx8cHQthMqVu2dWspkBCAAUSIiIiIiIiD8JqNSbjPHPGWEXkzBk4deoGp05d5/TpGM6cMYKLsDAXIiLSp+ICwMEhmvz5z5E/f1jCdo6CBSMoVOgGhQvHUbgw+Po6EBBQmIoVe6XbdTNCSr+Hpn2QjYiIiIiIiEguYTJBvnzGVr78zb1OCVtS0dGJoYWxxXPy5DVOn77B6dPxhIWZCQtz5sIFNyyWe88xERvrzLlzAZw7F3DP44oXDyEkJDV3lv0okBARERERERFJBWdnKF7c2Ax2gEfClig+Hs6fJ6G6As6etXLmzHXOnLnO2bNxhIVZOXfOnvPnnbl82fWe4UXBglcz5maygAIJERERERERkQxkZwe+vsZmMAGuCVtS8fHGBJ1hYbeGF9EJ4UUsZcp4Z2LPM5YCCREREREREZFsws4OfHyMzWACXBK23EULpYqIiIiIiIhIplMgISIiIiIiIiKZToGEiIiIiIiIiGQ6BRIiIiIiIiIikukUSIiIiIiIiIhIplMgISIiIiIiIiKZToGEiIiIiIiIiGQ6BRIiIiIiIiIikukUSIiIiIiIiIhIplMgISIiIiIiIiKZToGEiIiIiIiIiGQ6BRIiIiIiIiIikukUSIiIiIiIiIhIplMgISIiIiIiIiKZToGEiIiIiIiIiGQ6BRIiIiIiIiIikukUSIiIiIiIiIhIplMgISIiIiIiIiKZToGEiIiIiIiIiGQ6BRIiIiIiIiIikukUSIiIiIiIiIhIprPP6g5IxrJarQBERERkcU9EREREREQkL7j5/fPm99G7USCRy129ehUAf3//LO6JiIiIiIiI5CVXr17Fy8vrrp+brPeLLCRHs1gsnD59Gg8PD0wmU1Z3564iIiLw9/fnxIkTeHp6ZnV3RFJNz7LkJnqeJbfQsyy5iZ5nyQmsVitXr17Fz88Ps/nuM0WoQiKXM5vNFCtWLKu7kWKenp76h1VyBT3LkpvoeZbcQs+y5CZ6niW7u1dlxE2a1FJEREREREREMp0CCRERERERERHJdAokJFtwcnJi+PDhODk5ZXVXRNJEz7LkJnqeJbfQsyy5iZ5nyU00qaWIiIiIiIiIZDpVSIiIiIiIiIhIplMgISIiIiIiIiKZToGEiIiIiIiIiGQ6BRIiIiIiIiIikukUSEiWWbt2Lf369aNChQp4eXnh6elJhQoVeP755/nvv/+yunuSy5w/f56///6bUaNG8cgjj1CkSBFMJpNt+/nnn1Pd9q5duxg0aBBVqlTB29sbd3d3ypYty9NPP82iRYtS3e7Ro0d59913qVmzJoUKFcLFxYVSpUrx2GOPMXPmTOLi4lLdtuRMV65cYc6cOQwYMIDGjRvj6+uLk5MT7u7uBAQE0KFDByZMmMDly5dT1b6eZckssbGxbNiwgU8//ZTevXtTv359/Pz8cHV1xcHBgQIFClCtWjX69u3L4sWLsVgsD3wNPc+SHYSGhuLm5pbkZ44RI0Y8UBt6liVXs4pkssjISGufPn2swD233r17WyMjI7O6u5LDnTlzxhoYGHjf5+2nn3564LZjY2OtQ4cOtZrN5nu23b59e+u5c+ceqO0JEyZYnZyc7tluvXr1rEeOHHngfkvOs2/fPuvDDz9sdXR0vO+zDFhdXV2tn376qdVisaSofT3Lktlee+21FD3LN7dq1apZt27dmqK29TxLdtKmTZs7npHhw4en6Fw9y5IXKJCQTBUXF2dt3bp1kn/sXFxcrLVq1bLWq1fP6unpmeSz1q1bW+Pi4rK625KDhYSEpOiH3dQEErcHaw4ODtaqVatag4ODrQUKFEjyWZUqVaxXr15NUbujRo1Kcq7ZbLZWqlTJ2rhxY2uRIkWSfFasWDHr6dOnH7jvkrPMmDHjjmfWzs7OWrZsWWvjxo2twcHBVm9v7zuO6du3b4pCCT3LktkGDx6c5P9/Nzc3a5UqVaxNmjSxNm3a1FquXLk7voS5u7tbV69efd+29TxLdjFlypRkf+ZIaSChZ1nyAgUSkqmGDh2a5B+55557znrx4kXb55GRkdZ33nknyTHDhg3Lwh5LTndrIFGoUCHrQw89ZH377betc+fOTVMg8e233yY5/5FHHrGePHnS9nlMTIx14sSJVnt7e9sx3bp1u2+7ixYtsppMJts59evXtx44cMD2eXx8vHXatGlWd3d32zHBwcEP1HfJeW4GEvb29taOHTta586daw0PD09yjMVisc6dO9datGjRJM/mV199dc+29SxLVnj77betDz/8sHXSpEnW/fv3J3vMuXPnrG+99ZbVzs7O9oz4+/vf80uXnmfJLs6fP28tWLCgFbCWL1/e6ufn90CBhJ5lySsUSEimOXXqlNXZ2dn2j1uPHj3ueuzbb79tO87Z2dl66tSpTOyp5Cbh4eHWGTNmWENDQ+/4LLWBRFRUlNXX19d2btOmTe9ayfP999/bjjOZTNYtW7bctV2LxWKtWrWq7fiyZctao6Kikj126dKlSfo/e/bsFPdfcp65c+da+/btaz127Nh9jz1+/HiS57NgwYLWmJiYZI/Vsyw5wXfffZfkGfnxxx+TPU7Ps2Qn3bt3tz0HK1euTDJ89H6BhJ5lyUsUSEimef31123/qLm6uiapjLjdjRs3rP7+/rbjhwwZkok9lbwitYHEl19+meQ//nv37r3n8XXr1rUd36VLl7set2DBgiR9WrRo0T3bffLJJ23H1qlTJ8X9l9zv9t+sLVu2LNnj9CxLTlGqVCnbM/LMM88ke4yeZ8kuFi9ebHsGevfubbVarQ8USOhZlrxEq2xIppkzZ47tdZcuXfD29r7rsY6OjvTu3dv2fvbs2RnaN5EHcevz2KRJE8qXL3/P4/v162d7vXDhQm7cuHHfdkuUKEHr1q1T3O7GjRs5efLkPY+XvKNDhw5J3u/fvz/Z4/QsS05Ro0YN2+uzZ88me4yeZ8kOrl27xgsvvABAwYIF+eijjx64DT3LkpcokJBMceDAAQ4fPmx7/9BDD933nLZt29peHz58mAMHDmRI30QeRGRkJKtWrbK9f9BnOTIykn///TfZ4xYsWGB73aZNG0wm0z3bbdSoEW5ubsmeL3nb7YFvRETEHcfoWZac5NblBz08PO74XM+zZBfvvPMOISEhAIwfP54CBQo80Pl6liWvUSAhmWLHjh1J3tevX/++59SoUQNHR0fb+507d6Z7v0Qe1N69e4mNjbW9T8mz7OvrS/HixW3vk3uWz507l+S3filp197entq1a9+zXcmbjh07luR94cKF7zhGz7LkFLGxsaxbt872PrlnSs+zZAdbtmzhs88+A4zKhp49ez5wG3qWJa9RICGZYt++fbbXjo6OgwVjSgAAE2tJREFU+Pv73/ec24+7tQ2RrHL7c1iqVKkUnXfrcck9yxnVruRNtw9zS+4HTz3LklO89dZbti9S3t7e9OrV645j9DxLVouLi6Nv377Ex8fj6OjIN998k6p29CxLXmOf1R2QvCE0NNT2ulixYvctEbspICCAI0eO3NGGSFa59Tm0t7enSJEiKTovICAg2Tbutu/W49PSruQ94eHhtt/QAVSpUoUKFSrccZyeZcmu4uLiOH/+PBs2bOCrr75i6dKlADg7O/P7778nWwKv51my2scff8z27dsBeOONNyhXrlyq2tGzLHmNAgnJFFevXrW99vLySvF5np6eybYhklVufQ49PDwwm1NWaHa/Z/n2fSn9e6K/I3K7wYMHJynLff/995M9Ts+yZCcFCxbk4sWLyX5mMplo1aoVH3/8MZUqVUr2GD3PkpWOHDnCyJEjAQgKCmLYsGGpbkvPsuQ1GrIhmSIyMtL22tnZOcXnubi4JNuGSFbJqGf59n0pbVt/R+RW33//PT/88IPt/ZNPPnnHihs36VmWnCI4OJgXXngh2Uqfm/Q8S1bq168f169fB+Drr79+oGfwdnqWJa9RhYRkiltnx7a3T/ljd+uxt07wI5JVMupZvrXdB2lbf0fkplWrVtG/f3/b+xIlSvDtt9/e9Xg9y5KdtGjRgvDwcABu3LjB2bNnOXjwIBaLhTVr1rBmzRpq167NH3/8QYkSJe44X8+zZJWffvqJf/75B4Cnn36ali1bpqk9PcuS1yiQkEzh6upqex0dHZ3i82499tZlh0SySkY9y7e2e/P42/elpl3JG7Zv384jjzxCTEwMYKyqsWjRonuW5OpZluzkjz/+uGPfpUuX+P777xk1ahRRUVFs2rSJJk2asHnz5jtWjtHzLFnh3LlzvPbaawDkz5+fTz75JM1t6lmWvEZDNiRTuLu7217fLGlLiWvXriXbhkhWyahn+fZ9KW1bf0fkwIEDtGnTxvbb5fz587NkyRLKlClzz/P0LEt25+3tzZAhQ1i9ejUeHh4AnDhxgsGDB99xrJ5nyQoDBgzg0qVLAIwbNy7ZJZYflJ5lyWsUSEimKFiwoO31mTNnUnzerROzJTertkhmu/VZjoyMTPF4yvs9y7e2Cyn/e6K/I3lbSEgILVu25Ny5c4AxAdrff/9N1apV73uunmXJKapXr85bb71lez9t2jTbl8Cb9DxLZlu3bp2tsqd+/fo899xz6dKunmXJaxRISKYoW7as7fXFixeTpK33cuLECdvr1C6fJJKebn2WAY4fP56i8+73LGdUu5J7nTx5khYtWnDy5EnAKMedP38+devWTdH5epYlJ+ncubPtdVxcHJs2bUryuZ5nyWxhYWG21+vWrcNsNmMyme66HTt2zHb8yJEjk3x263KaepYlr1EgIZmifPnySd7fXKf5Xk6dOsX58+fv2oZIVkjNsxwbG8uePXvu2gZA6dKlk0wclZJ2AbZt23bPdiV3CgsLo2XLloSEhADg5OTE3Llzady4cYrb0LMsOYm/v3+S97cvEarnWXILPcuS1yiQkExRp04dnJycbO/XrFlz33NWr15te+3s7EydOnUypG8iD6JkyZIUK1bM9j4lz/KWLVuSVAUl96XR0dExyW+2U9Lu2bNnOXz48D3bldzn4sWLtGzZkgMHDgDg4ODAzJkzadWq1QO1o2dZcpKbc6TclC9fviTv9TxLZnNwcMDLyyvFm8lksp3r5OSU5DOzOfErmZ5lyWsUSEimcHd3p0WLFrb3v/76633PufWYFi1aaGZfyTYeeeQR2+sZM2bYVja4m1uf5YoVK1KqVKlkj3v00Udtr5ctW5akHPR+7ebLl08/KOQB4eHhtGnTht27d/P/9u4/pqr6j+P4614QBdGUojRUBDTBIksll84fS7HNH6TTVkZLjeyXZls6s6x0zmppc0upVsaCol+a6a1czqIm6pY/R5AZkoqhpkL+RETAe75/uM6XC9zL73Mv1+dju9v9nPP+fM5H/Myxl+d8jiQFBATo888/14QJE5o0HmsZbUV2drZLu661x3qGlcaPH69z5841+NOrVy+z78KFC92ek1jLuL4QSMAyM2bMML/n5ubqu+++c1u7b98+/fDDD3X2Bbyt+nosKSnRBx984Lb22LFjysjIqLNvTdOmTTPvJKqsrNTy5cvd1paWlmrVqlVmOzk5We3atWvA7NFWXbp0SePHj9fevXslSXa7XRkZGS7P1jcWaxltQUVFhZYtW2a2Y2Jiaj0PL7Ge4T9Yy7iuGIBFnE6nMWDAAEOSIcno3r27ceDAgVp1J06cMOLi4sy6u+66y3A6nV6YMfzdf2tMkvHxxx83qm9SUpLZNzQ01Ni+fXutmvPnzxvDhw8367p162aUlZV5HHfu3LlmfUBAgPH111/XqqmoqDCmTp1q1gUHBxvHjx9v1PzRtpSXlxtjxowx/85tNpuRlpbWImOzlmG1LVu2GPPnz2/Q3/WJEyeMsWPHuvx7vWbNGrf1rGf4qsjISHNtLF68uN561jKuFzbDMIzWCDqAuuzevVsjR440333cuXNnPfPMMxoxYoQCAwO1a9cupaammreQBQcHa+vWrUpISPDmtNHGzZo1S59++mmt41euXDG/BwYGKiAgoFZNeXl5nWMWFhYqISFBJSUlkq49D5qSkqKxY8cqNDRUubm5Wr16tbnpoN1u18aNGzVx4kSPcz179qyGDBmigoICs98jjzyiSZMmKSwsTPn5+Xr//feVm5tr9klNTdXs2bPr+SmgLVu+fLlefPFFs921a9dG7auTmJioefPm1XmOtQyrbdy4UZMnT5bdbtfQoUM1fPhwxcfH66abblJISIhKS0t1+PBhbdu2TQ6Hw+XZ+KSkJG3cuNHlefzqWM/wVb179zbftLF48WItWbLEYz1rGdcNbyciuP6sX7/eCA4Odvnfjro+wcHBxvr16709XfiB6dOn17ve3H082bFjhxEWFlbvGAEBAcbq1asbPN/8/HyjZ8+eDZrfggULmvvjQRuwePHiJq9hScb06dM9js9ahpU2bNjQpHU8c+ZM48qVK/WOz3qGL2rsHRKGwVrG9YFAAl7xxx9/GGPGjDFsNlutf/hsNpsxevRoY//+/d6eJvxEawUShmEYx44dM6ZMmWIEBgbW2T8hIcHYsWNHo+d89uxZIyUlxW14FxcXZzgcjqb8ONAGtXYgYRisZVinqKjIeOGFF4z+/fvX+XtA9U9QUJAxZcoUY+vWrY26BusZvqYpgYRhsJbh/3hkA15VVFSkHTt26Pjx45KkiIgIDRs2rNb7xgFfV1xcrOzsbB07dkwVFRW69dZbNXjw4Do3XmuMixcv6ueff1ZRUZEuXbqk7t27Kz4+XnfffXcLzRxwxVqGlc6dO6fffvtNhw8fVklJia5cuaKOHTuqa9euiouL04ABA9ShQ4cmj896hr9gLcNfEUgAAAAAAADL8dpPAAAAAABgOQIJAAAAAABgOQIJAAAAAABgOQIJAAAAAABgOQIJAAAAAABgOQIJAAAAAABgOQIJAAAAAABgOQIJAAAAAABgOQIJAAAAAABgOQIJAAAAAABgOQIJAAAAAABgOQIJAAAAAABgOQIJAAAAAABgOQIJAADgs9566y3ZbDbzs3nzZm9PCQAAtBACCQAA4LNyc3Nd2vHx8V6aCQAAaGkEEgAAwGdVDyTCwsIUERHhxdkAAICWRCABAAB8UmVlpfLz8832nXfe6cXZAACAlkYgAQAAfNKBAwdUWVlptnlcAwAA/0IgAQAAfFLN/SO4QwIAAP9CIAEAAHxSXl6eS5tAAgAA/0IgAQAAfEZsbKz5is/ly5e7nBsyZIjLK0Crf1566aVmX/v3339XUFCQOWZMTIzKy8ubPN6IESNc5rhp06ZmzxEAAH9CIAEAAHxCWVmZCgoKmtR3wIABzbq20+nUE0884bJnxerVq9WhQ4cmj3nPPfe4tLdu3drksQAA8EcEEgAAwCfk5eXJ6XQ2qW9zA4lVq1Zp586dZnvy5MkaN25cs8YkkAAAwDObYRiGtycBAABQVlam06dPS5J27typhx9+2Dw3b948zZkzx23fyMhI2Wy2Jl339OnTiomJUWlpqSSpXbt2ys/PV1RUVJPG+8+BAwfUv39/s92uXTtdvnxZAQEBzRoXAAB/EejtCQAAAEhSSEiIevfuLUnasGGDy7n77rvPPNfSli5daoYRkvT44483O4yQpJ49e7q0KysrdfToUUVHRzd7bAAA/AGPbAAAAJ+zZ88el3ZCQkKrXOfw4cP68MMPzXb79u21aNGiFhk7NDRUXbp0cTnW1D0yAADwRwQSAADA5+zevdv8HhkZqfDw8Fa5zooVK1w2snzooYdq3dnQHDUDieLi4hYbGwCAto5AAgAA+JRz587pr7/+Mts1N4dsKRcuXFBmZqbLsSeffLJFr1FzX4uKiooWHR8AgLaMQAIAAPiUPXv2qPqe2631uEZmZqbL3hFxcXEaNmxYq1zrP1evXm3V8QEAaEsIJAAAgE+p/riG1HqBhMPhcGknJSV5rDcMQ7GxserRo4d69Oiht99+u95rnDp1yqV98803N36iAAD4Kd6yAQAAfEr1DS3tdrsGDRrU4tcoLy9Xdna2y7HExESPff7880/l5+eb7fr2miguLlZZWZnLsV69ejVypgAA+C/ukAAAAD6l+h0S/fr1U6dOnVr8Gvv27VN5ebnZttvtuvfeez32+fXXX13a8fHxHuurhxeSFBgYqNtuu62RMwUAwH8RSAAAAJ9x6tQpFRUVme3W2tCyZlgQFRWlkJAQj322bNlSq48n27dvd2kPHDhQHTt2rHduVVVV+vLLL5WcnKy+ffuqc+fO6tixo/r166eJEydqzZo1vK0DAOAXeGQDAAD4DKv2j6gZSPTp08djvdPp1I8//mi2b7zxRgUHB3vs88svv7i0R4wYUe+8srKylJKSoqNHj9Y6d/DgQR08eFDff/+9jhw5ojfeeKPe8QAA8GUEEgAAwGdU3z9CkgYPHtwq16l5h0F4eLjH+qysLP37779m+4YbbvBYf/78+VqBxKRJkzz2+eijj/TUU0/J6XQqKChI06ZN07hx4xQVFSWn06mCggJt2rRJ33zzTavdOQIAgJUIJAAAgM/Izc01v9tsNt1xxx2tcp2am00GBQV5rM/IyGhU/WeffabKykqzHRUV5fGVog6HQ08//bScTqdiY2P17bffqm/fvi41Q4YM0aOPPqpDhw6pa9euHq8PAEBbQCABAAB8RvX9I0JCQhq050JT2Gw2l/b58+fd1hYWFuqrr75yOVb9bomaqqqq9M4777gcmzVrltv6kpISzZw5U1evXlVERIR++uknRUREuK2PiYlxew4AgLaETS0BAIDPsNv//6vJpUuXVFBQ0CrXqfnKzr1797qtXbBggaqqqmS323X77bdLuhYiuNtYcuXKlTp48KDZDg8P13PPPed2/Ndee01nz56VJKWlpXkMIwAA8CcEEgAAwGfExsa6tJOSkpSZmamcnBwVFhaan7o2fWyMmq/sLCwslMPhqFW3atUqrVu3TpL0wAMPKDIyUpJkGIYyMzNr1TscDi1atMjl2LJlyxQaGlrnPC5fvqxPPvlE0rVNL++///7G/2EAAGijbIZhGN6eBAAAgCRt27atQW+j6N27t44cOdLk61y4cEG33HKLysvLzWOdOnXS66+/rlGjRqm0tFRpaWlKS0uTdG3PiJycHL333ntKTU2VdO2RkhUrVmjUqFEqLi5WZmam0tLSVP1XqwcffFBr1651Ow+Hw2Fudvnuu+/q2WefbfKfCQCAtoY9JAAAgM8YPny4VqxYoYULF+rq1atu6wYNGtSs63Tu3FkLFy7UkiVLzGMXL17U3Llz66xfuXKl4uLiNGnSJDOQKCsr0+zZs91eY9y4cUpPT/c4j5ycHPP70KFDGzx/AAD8AY9sAAAAnzJ//nzl5OTo+eef18CBA9WlSxcFBAS41DQ3kJCkV155RdOnT/dY0759e6WmpprBw+jRo/XYY4957BMcHKxXX31VDodDISEhHmv/+ecf83u3bt0aOHMAAPwDj2wAAIDr2ubNm5Wenq5du3bp5MmTkq49EjJ27FjNmTNHffr0cal3Op1KS0tTenq69u/fr7KyMoWHhys6Olrjx49XcnJyrU0z3ZkxY4b5StG///67wf0AAPAHBBIAAABe8vLLL+vNN9+UJK1bt05Tp0718owAALAOj2wAAAB4SWJiovl96dKlunjxotva48eP68yZM1ZMCwAAS3CHBAAAgBeNHDlS2dnZkqTo6GjNmTNHCQkJCg0N1ZkzZ5SXl6fNmzcrKytLJ0+eVFhYmJdnDABAyyCQAAAA8KLi4mJNmDBBu3bt8lgXHR2tQ4cOWTQrAABaH4EEAACAl1VVVemLL77Q2rVrtW/fPpWUlCgoKEjdu3dXVFSUEhMTNXHiRPXr18/bUwUAoMUQSAAAAAAAAMuxqSUAAAAAALAcgQQAAAAAALAcgQQAAAAAALAcgQQAAAAAALAcgQQAAAAAALAcgQQAAAAAALAcgQQAAAAAALAcgQQAAAAAALAcgQQAAAAAALAcgQQAAAAAALAcgQQAAAAAALDc/wCaTcCjLF1rLAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", + "\n", + "plot_result_expectations(\n", + " [\n", + " (\n", + " results_corr_fit_pk[0],\n", + " P11p,\n", + " \"y\",\n", + " \"Correlation Function Fit $k_R=k_I=1$\",\n", + " ),\n", + " (\n", + " results_corr_fit_pk[2],\n", + " P11p,\n", + " \"k\",\n", + " \"Correlation Function Fit $k_R=k_I=3$\",\n", + " ),\n", + " (results_spectral_fit_pk[0], P11p, \"b\", \"Spectral Density Fit $k_J=1$\"),\n", + " (results_spectral_fit_pk[3], P11p, \"r-.\", \"Spectral Density Fit $k_J=4$\"),\n", + " ],\n", + " axes=axes,\n", + ")\n", + "\n", + "axes.set_yticks([0.6, 0.8, 1])\n", + "axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", + "axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", + "axes.legend(loc=0, fontsize=20);" + ] + }, + { + "cell_type": "markdown", + "id": "9f3aaff7", + "metadata": {}, + "source": [ + "# Using the Ohmic Bath class\n", + "\n", + " As the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced quickly by using the OhmicBath class. This allows for rapid implementation of fitted ohmic baths via the correlation function or spectral density" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41cc501a", + "metadata": {}, + "outputs": [], + "source": [ + "obs = OhmicBath(T, Q, alpha, wc, s)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88e222ba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit correlation class instance: \n", + " \n", + "Result of fitting The Real Part Of |Result of fitting The Imaginary Part \n", + " the Correlation Function with None terms: | Of the Correlation Function with None terms: \n", + " | \n", + " Parameters| a | b | c | d | Parameters| a | b | c | d \n", + " 1 | 1.08e-01 |-2.36e-01 || 1.13e-06 |2.76e+00 | 1 | 1.51e+00 |-3.82e+00 || 2.00e+00 |1.94e+00 \n", + " 2 |-6.76e+00 |-1.73e+00 ||-2.87e-01 |1.48e+01 | 2 |-1.17e+01 |-4.49e-01 || 1.74e-08 |-1.34e-01 \n", + " 3 | 8.15e+00 |-2.35e+00 ||-1.00e+00 |3.83e+00 | 3 | 2.16e+01 |-1.41e+00 ||-1.55e-08 |-1.81e+00 \n", + " | \n", + "A normalized RMSE of 5.90e-05 was obtained for the The Real Part Of |A normalized RMSE of 4.67e-05 was obtained for the The Imaginary Part \n", + " the Correlation Function | Of the Correlation Function \n", + " The current fit took 5.148684 seconds | The current fit took 1.124591 seconds \n", + "\n" + ] + } + ], + "source": [ + "Obath, fitinfo = obs.make_correlation_fit(t, rmse=1e-4)\n", + "print(fitinfo[\"summary\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68f7ff0d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 19.60s*] Elapsed 19.60s / Remaining 00:00:00:00\n" + ] + } + ], + "source": [ + "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + "HEOM_ohmic_corr_fit = HEOMSolver(\n", + " Hsys,\n", + " Obath,\n", + " max_depth=5,\n", + " options=options,\n", + ")\n", + "results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d981536c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result of fitting The Spectral Density with None terms: \n", + " \n", + " Parameters| lam | gamma | w0 \n", + " 1 | 2.21e+00 | 1.25e+00 |1.00e-01\n", + " 2 | 3.27e+00 | 1.44e+00 |1.80e+00\n", + " \n", + "A normalized RMSE of 9.11e-05 was obtained for the The Spectral Density\n", + " The current fit took 0.177001 seconds\n" + ] + } + ], + "source": [ + "Obath, fitinfo = obs.make_spectral_fit(w, rmse=1e-4)\n", + "print(fitinfo[\"summary\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13300786", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ 1% ] Elapsed 0.05s / Remaining 00:00:00:04" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 2.23s*] Elapsed 2.23s / Remaining 00:00:00:00\n" + ] + } + ], + "source": [ + "HEOM_ohmic_spectral_fit = HEOMSolver(\n", + " Hsys,\n", + " Obath,\n", + " max_depth=5,\n", + " options=options,\n", + ")\n", + "results_ohmic_spectral_fit = HEOM_ohmic_spectral_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "765a0633", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", + "\n", + "plot_result_expectations(\n", + " [\n", + " # (\n", + " # results_corr_fit_pk[0], P11p,\n", + " # 'y', \"Correlation Function Fit $k_R=k_I=1$\",\n", + " # ),\n", + " (\n", + " results_corr_fit_pk[2],\n", + " P11p,\n", + " \"y-.\",\n", + " \"Correlation Function Fit $k_R=k_I=3$\",\n", + " ),\n", + " (results_spectral_fit_pk[0], P11p, \"b\", \"Spectral Density Fit $k_J=1$\"),\n", + " (results_spectral_fit_pk[2], P11p, \"g--\", \"Spectral Density Fit $k_J=3$\"),\n", + " (results_spectral_fit_pk[3], P11p, \"r-.\", \"Spectral Density Fit $k_J=4$\"),\n", + " (results_ohmic_spectral_fit, P11p, \"g-.\", \"Spectral Density Fit Ohmic Bath\"),\n", + " (results_ohmic_corr_fit, P11p, \"k-.\", \"Correlation Fit Ohmic Bath\"),\n", + " ],\n", + " axes=axes,\n", + ")\n", + "\n", + "axes.set_yticks([0.6, 0.8, 1])\n", + "axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", + "axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", + "axes.legend(loc=0, fontsize=20);" + ] + }, + { + "cell_type": "markdown", + "id": "fb593683", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ec29f78", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "QuTiP: Quantum Toolbox in Python\n", + "================================\n", + "Copyright (c) QuTiP team 2011 and later.\n", + "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross and Asier Galicia.\n", + "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", + "Original developers: R. J. Johansson & P. D. Nation.\n", + "Previous lead developers: Chris Granade & A. Grimsmo.\n", + "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", + "\n", + "QuTiP Version: 5.0.0.dev0+9557c82\n", + "Numpy Version: 1.26.0\n", + "Scipy Version: 1.11.3\n", + "Cython Version: 3.0.3\n", + "Matplotlib Version: 3.8.0\n", + "Python Version: 3.12.0\n", + "Number of CPUs: 16\n", + "BLAS Info: Generic\n", + "INTEL MKL Ext: False\n", + "Platform Info: Linux (x86_64)\n", + "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", + "================================================================================\n", + "Please cite QuTiP in your publication.\n", + "================================================================================\n", + "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" + ] + } + ], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "e9ff4ad3", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69332bfe", + "metadata": {}, + "outputs": [], + "source": [ + "assert np.allclose(\n", + " expect(P11p, results_spectral_fit_pk[2].states),\n", + " expect(P11p, results_spectral_fit_pk[3].states),\n", + " rtol=1e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(P11p, results_corr_fit_pk[2].states),\n", + " expect(P11p, results_spectral_fit_pk[3].states),\n", + " rtol=1e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(P11p, results_ohmic_spectral_fit.states),\n", + " expect(P11p, results_spectral_fit_pk[3].states),\n", + " rtol=1e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(P11p, results_corr_fit_pk[2].states),\n", + " expect(P11p, results_ohmic_corr_fit.states),\n", + " rtol=1e-2,\n", + ")" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb new file mode 100644 index 00000000..e8337970 --- /dev/null +++ b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb @@ -0,0 +1,1053 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f4febf25", + "metadata": {}, + "source": [ + "# HEOM 1e: Spin-Bath model (pure dephasing)" + ] + }, + { + "cell_type": "markdown", + "id": "3464d460", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices.\n", + "\n", + "In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", + "\n", + "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian (see paper), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", + "\n", + "In the example below we show how to model the overdamped Drude-Lorentz Spectral Density, commonly used with the HEOM. We show how to do the Matsubara and Pade analytical decompositions, as well as how to fit the latter with a finite set of approximate exponentials. This differs from examble 1a in that we assume that the system and coupling parts of the Hamiltonian commute, hence giving an analytically solvable ''pure dephasing'' model. This is a useful example to look at when introducing other approximations (e.g., fitting of correlation functions) to check for validity/convergence against the analytical results. (Note that, generally, for the fitting examples, the pure dephasing model is the 'worst possible case'. \n", + "\n", + "### Drude-Lorentz spectral density\n", + "\n", + "The Drude-Lorentz spectral density is:\n", + "\n", + "$$J(\\omega)=\\omega \\frac{2\\lambda\\gamma}{{\\gamma}^2 + \\omega^2}$$\n", + "\n", + "where $\\lambda$ scales the coupling strength, and $\\gamma$ is the cut-off frequency.\n", + "We use the convention,\n", + "\\begin{equation*}\n", + "C(t) = \\int_0^{\\infty} d\\omega \\frac{J_D(\\omega)}{\\pi}[\\coth(\\beta\\omega) \\cos(\\omega \\tau) - i \\sin(\\omega \\tau)]\n", + "\\end{equation*}\n", + "\n", + "With the HEOM we must use an exponential decomposition:\n", + "\n", + "\\begin{equation*}\n", + "C(t)=\\sum_{k=0}^{k=\\infty} c_k e^{-\\nu_k t}\n", + "\\end{equation*}\n", + "\n", + "The Matsubara decomposition of the Drude-Lorentz spectral density is given by:\n", + "\n", + "\\begin{equation*}\n", + " \\nu_k = \\begin{cases}\n", + " \\gamma & k = 0\\\\\n", + " {2 \\pi k} / {\\beta \\hbar} & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "\\begin{equation*}\n", + " c_k = \\begin{cases}\n", + " \\lambda \\gamma (\\cot(\\beta \\gamma / 2) - i) / \\hbar & k = 0\\\\\n", + " 4 \\lambda \\gamma \\nu_k / \\{(nu_k^2 - \\gamma^2)\\beta \\hbar^2 \\} & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." + ] + }, + { + "cell_type": "markdown", + "id": "8f3fa39c", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f647d330", + "metadata": {}, + "outputs": [], + "source": [ + "import contextlib\n", + "import time\n", + "\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "import scipy\n", + "from scipy.optimize import curve_fit\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " basis,\n", + " expect,\n", + " liouvillian,\n", + " sigmax,\n", + " sigmaz,\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver,\n", + " BosonicBath,\n", + " DrudeLorentzBath,\n", + " DrudeLorentzPadeBath,\n", + " CorrelationFitter\n", + ")\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "1ca0dac8", + "metadata": {}, + "source": [ + "## Helper functions\n", + "\n", + "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e8ba478d", + "metadata": {}, + "outputs": [], + "source": [ + "def cot(x):\n", + " \"\"\" Vectorized cotangent of x. \"\"\"\n", + " return 1. / np.tan(x)\n", + "\n", + "\n", + "def coth(x):\n", + " \"\"\" Vectorized hyperbolic cotangent of x. \"\"\"\n", + " return 1. / np.tanh(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0baeb70e", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_result_expectations(plots, axes=None):\n", + " \"\"\" Plot the expectation values of operators as functions of time.\n", + "\n", + " Each plot in plots consists of (solver_result, measurement_operation,\n", + " color, label).\n", + " \"\"\"\n", + " if axes is None:\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " fig_created = True\n", + " else:\n", + " fig = None\n", + " fig_created = False\n", + "\n", + " # add kw arguments to each plot if missing\n", + " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", + " for result, m_op, color, label, kw in plots:\n", + " if m_op is None:\n", + " t, exp = result\n", + " else:\n", + " t = result.times\n", + " exp = np.real(expect(result.states, m_op))\n", + " kw.setdefault(\"linewidth\", 2)\n", + " axes.plot(t, exp, color, label=label, **kw)\n", + "\n", + " if fig_created:\n", + " axes.legend(loc=0, fontsize=12)\n", + " axes.set_xlabel(\"t\", fontsize=28)\n", + "\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c1bfde92", + "metadata": {}, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\" Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "364b8c32", + "metadata": {}, + "outputs": [], + "source": [ + "# Solver options:\n", + "\n", + "options = {\n", + " \"nsteps\": 15000,\n", + " \"store_states\": True,\n", + " \"rtol\": 1e-14,\n", + " \"atol\": 1e-14,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "46438b6a", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian, bath and system measurement operators:" + ] + }, + { + "cell_type": "markdown", + "id": "51fd743a", + "metadata": {}, + "source": [ + "Here we set $H_{sys}=0$, which means the interaction Hamiltonian and the system Hamiltonian commute, and we can compare the numerical results to a known analytical one. We could in principle keep $\\epsilon \\neq 0$, but it just introduces fast system oscillations, so it is more convenient to set it to zero." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b0c8e116", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the system Hamiltonian\n", + "eps = 0.0 # Energy of the 2-level system.\n", + "Del = 0.0 # Tunnelling term\n", + "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4c0b88e8", + "metadata": {}, + "outputs": [], + "source": [ + "# System-bath coupling (Drude-Lorentz spectral density)\n", + "Q = sigmaz() # coupling operator\n", + "\n", + "# Bath properties:\n", + "gamma = 0.5 # cut off frequency\n", + "lam = 0.1 # coupling strength\n", + "T = 0.5\n", + "beta = 1. / T\n", + "\n", + "# HEOM parameters:\n", + "# cut off parameter for the bath:\n", + "NC = 6\n", + "# number of exponents to retain in the Matsubara expansion\n", + "# of the correlation function:\n", + "Nk = 3\n", + "\n", + "# Times to solve for\n", + "tlist = np.linspace(0, 50, 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "94b66256", + "metadata": {}, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresonding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresonding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "eb8c9bb2", + "metadata": {}, + "source": [ + "To get a non-trivial result we prepare the initial state in a superposition, and see how the bath destroys the coherence." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "be5d671d", + "metadata": {}, + "outputs": [], + "source": [ + "# Initial state of the system.\n", + "psi = (basis(2, 0) + basis(2, 1)).unit()\n", + "rho0 = psi * psi.dag()" + ] + }, + { + "cell_type": "markdown", + "id": "ae75e9c8", + "metadata": {}, + "source": [ + "## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9dcd6c27", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.010007858276367188\n", + " [***** 20% ] Elapsed 0.65s / Remaining 00:00:00:02" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 3.26s*] Elapsed 3.26s / Remaining 00:00:00:00\n", + "ODE solver time: 3.265465021133423\n" + ] + } + ], + "source": [ + "with timer(\"RHS construction time\"):\n", + " bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", + " HEOMMats = HEOMSolver(Hsys, bath, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMats = HEOMMats.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5b2960ff", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results so far\n", + "plot_result_expectations([\n", + " (resultMats, P11p, 'b', \"P11 Matsubara\"),\n", + " (resultMats, P12p, 'r', \"P12 Matsubara\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "7fac31d9", + "metadata": {}, + "source": [ + "## Simulation 2: Matsubara decomposition (including terminator)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "bc0d12f5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.020837783813476562\n", + " [****** 24% ] Elapsed 0.80s / Remaining 00:00:00:02" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 3.59s*] Elapsed 3.59s / Remaining 00:00:00:00\n", + "ODE solver time: 3.596360683441162\n" + ] + } + ], + "source": [ + "with timer(\"RHS construction time\"):\n", + " bathMats = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", + " _, terminator = bathMats.terminator()\n", + " Ltot = liouvillian(Hsys) + terminator\n", + " HEOMMatsT = HEOMSolver(Ltot, bathMats, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMatsT = HEOMMatsT.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "1616f9a2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "plot_result_expectations([\n", + " (resultMats, P11p, 'b', \"P11 Matsubara\"),\n", + " (resultMats, P12p, 'r', \"P12 Matsubara\"),\n", + " (resultMatsT, P11p, 'b--', \"P11 Matsubara and terminator\"),\n", + " (resultMatsT, P12p, 'r--', \"P12 Matsubara and terminator\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "78f2f403", + "metadata": {}, + "source": [ + "## Simulation 3: Pade decomposition\n", + "\n", + "As in example 1a, we can compare to Pade and Fitting approaches." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "3552840e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.02041316032409668\n", + " [ 2% ] Elapsed 0.07s / Remaining 00:00:00:03" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 3.64s*] Elapsed 3.63s / Remaining 00:00:00:00\n", + "ODE solver time: 3.6367034912109375\n" + ] + } + ], + "source": [ + "with timer(\"RHS construction time\"):\n", + " bathPade = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", + " HEOMPade = HEOMSolver(Hsys, bathPade, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultPade = HEOMPade.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "a334df89", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "plot_result_expectations([\n", + " (resultMatsT, P11p, 'b', \"P11 Matsubara (+term)\"),\n", + " (resultMatsT, P12p, 'r', \"P12 Matsubara (+term)\"),\n", + " (resultPade, P11p, 'b--', \"P11 Pade\"),\n", + " (resultPade, P12p, 'r--', \"P12 Pade\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "f46548c3", + "metadata": {}, + "source": [ + "## Simulation 4: Fitting approach" + ] + }, + { + "cell_type": "markdown", + "id": "6c58c376", + "metadata": {}, + "source": [ + "To do this we calculate the correlation function using 15000 Matsubara terms." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "2ebacd9c", + "metadata": {}, + "outputs": [], + "source": [ + "tfit=np.linspace(0,5,5000)\n", + "lmaxmats = 15000\n", + "cc=bath.correlation_function(tfit,Nk=lmaxmats)\n" + ] + }, + { + "cell_type": "markdown", + "id": "41c3aa42", + "metadata": {}, + "source": [ + "In order to obtain the fit quickly we provide tight bounds on the parameters of\n", + "the fit, through two lists called lower and upper,similarly we guess close\n", + "parameters to the one expected " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ff64254a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit correlation class instance: \n", + " \n", + "Result of fitting The Real Part Of |Result of fitting The Imaginary Part \n", + " the Correlation Function with 4 terms: | Of the Correlation Function with 1 terms: \n", + " | \n", + " Parameters| a | b | c | d | Parameters| a | b | c | d \n", + " 1 | 6.91e-02 |-5.43e+00 | 6.67e-07 |-9.63e-02 | 1 | 9.86e-02 |-5.00e-01 |-9.97e-07 |-5.00e-02 \n", + " 2 | 9.83e-02 |-6.75e+01 |-6.57e-07 |-8.97e-02 | \n", + " 3 | 9.48e-02 |-5.13e-01 | 1.44e-07 |-9.63e-02 |A normalized RMSE of 1.96e-14 was obtained for the The Imaginary Part \n", + " 4 | 1.00e-01 |-1.65e+03 |-9.81e-07 |-9.57e-02 | Of the Correlation Function \n", + " | \n", + "A normalized RMSE of 3.34e-05 was obtained for the The Real Part Of | \n", + " the Correlation Function | \n", + " The current fit took 1.287672 seconds | The current fit took 0.043288 seconds \n", + "\n" + ] + } + ], + "source": [ + "lower=[-0.1,-np.inf,-1e-6,-0.1]\n", + "upper=[0.1,0,1e-6,0.1]\n", + "guesses=[0.09,-10,0,np.imag(cc[0])]\n", + "fc= CorrelationFitter(Q,T,tfit,cc)\n", + "fbath,fitinfo=fc.get_fit(Nr=4,Ni=1,lower=lower,upper=upper,guesses=guesses,sigma=1e-2)\n", + "print(fitinfo['summary'])" + ] + }, + { + "cell_type": "markdown", + "id": "507c9394", + "metadata": {}, + "source": [ + "We can now visualize the approximation of the correlation function through the\n", + "different techniques" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "eba87b42", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tplot=np.linspace(0,10,2000)\n", + "plt.plot(tplot,np.real(bath.correlation_function(tplot)),linewidth=2\n", + " ,label=f'Original with {lmaxmats} exponents')\n", + "plt.plot(tplot,np.real(bathPade.correlation_function_approx(tplot)),linewidth=2\n", + " ,label=f'Pade',marker='D',markevery=100,color='k')\n", + "plt.plot(tplot,np.real(bath.correlation_function_approx(tplot)),linewidth=2\n", + " ,label=f'Matsubara',marker='x',markevery=110,markersize=10)\n", + "plt.plot(tplot,np.real(bathMats.correlation_function_approx(tplot)),linewidth=2\n", + " ,label=f'Matsubara with Terminator',marker='o',markevery=130)\n", + "plt.plot(tplot,np.real(fbath.correlation_function_approx(tplot)),'-.'\n", + " ,label='Fit',linewidth=2,color='r')\n", + "plt.xlabel('t',fontsize=15)\n", + "plt.ylabel(r'$C_{R}(t)$',fontsize=15)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "bd3a1d31", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tplot=np.linspace(0,10,2000)\n", + "plt.plot(tplot,np.imag(bath.correlation_function(tplot)),linewidth=2\n", + " ,label=f'Original with {lmaxmats} exponents')\n", + "plt.plot(tplot,np.imag(bathPade.correlation_function_approx(tplot)),linewidth=2\n", + " ,label=f'Pade',marker='D',markevery=100,color='k')\n", + "plt.plot(tplot,np.imag(bath.correlation_function_approx(tplot)),linewidth=2\n", + " ,label=f'Matsubara',marker='x',markevery=110,markersize=10)\n", + "plt.plot(tplot,np.imag(bathMats.correlation_function_approx(tplot)),linewidth=2\n", + " ,label=f'Matsubara with Terminator',marker='o',markevery=130)\n", + "plt.plot(tplot,np.imag(fbath.correlation_function_approx(tplot)),'-.'\n", + " ,label='Fit',linewidth=2,color='r')\n", + "plt.xlabel('t',fontsize=15)\n", + "plt.ylabel(r'$C_{R}(t)$',fontsize=15)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "f9ffc5f8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.02409219741821289\n", + " [ 0% ] Elapsed 0.07s / Remaining 00:00:00:00" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 65.48s*] Elapsed 65.48s / Remaining 00:00:00:00\n", + "ODE solver time: 65.47719120979309\n" + ] + } + ], + "source": [ + "\n", + "with timer(\"RHS construction time\"):\n", + " HEOMFit = HEOMSolver(Hsys, fbath, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultFit = HEOMFit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "a184570f", + "metadata": {}, + "source": [ + "## Analytic calculations" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c7b8e0e2", + "metadata": {}, + "outputs": [], + "source": [ + "def pure_dephasing_evolution_analytical(tlist, wq, ck, vk):\n", + " \"\"\"\n", + " Computes the propagating function appearing in the pure dephasing model.\n", + "\n", + " Parameters\n", + " ----------\n", + " t: float\n", + " A float specifying the time at which to calculate the integral.\n", + "\n", + " wq: float\n", + " The qubit frequency in the Hamiltonian.\n", + "\n", + " ck: ndarray\n", + " The list of coefficients in the correlation function.\n", + "\n", + " vk: ndarray\n", + " The list of frequencies in the correlation function.\n", + "\n", + " Returns\n", + " -------\n", + " integral: float\n", + " The value of the integral function at time t.\n", + " \"\"\"\n", + " evolution = np.array([\n", + " np.exp(-1j * wq * t - correlation_integral(t, ck, vk))\n", + " for t in tlist\n", + " ])\n", + " return evolution\n", + "\n", + "\n", + "def correlation_integral(t, ck, vk):\n", + " r\"\"\"\n", + " Computes the integral sum function appearing in the pure dephasing model.\n", + "\n", + " If the correlation function is a sum of exponentials then this sum\n", + " is given by:\n", + "\n", + " .. math:\n", + "\n", + " \\int_0^{t}d\\tau D(\\tau) = \\sum_k\\frac{c_k}{\\mu_k^2}e^{\\mu_k t}\n", + " + \\frac{\\bar c_k}{\\bar \\mu_k^2}e^{\\bar \\mu_k t}\n", + " - \\frac{\\bar \\mu_k c_k + \\mu_k \\bar c_k}{\\mu_k \\bar \\mu_k} t\n", + " + \\frac{\\bar \\mu_k^2 c_k + \\mu_k^2 \\bar c_k}{\\mu_k^2 \\bar \\mu_k^2}\n", + "\n", + " Parameters\n", + " ----------\n", + " t: float\n", + " A float specifying the time at which to calculate the integral.\n", + "\n", + " ck: ndarray\n", + " The list of coefficients in the correlation function.\n", + "\n", + " vk: ndarray\n", + " The list of frequencies in the correlation function.\n", + "\n", + " Returns\n", + " -------\n", + " integral: float\n", + " The value of the integral function at time t.\n", + " \"\"\"\n", + " t1 = np.sum(\n", + " (ck / vk**2) *\n", + " (np.exp(vk * t) - 1)\n", + " )\n", + " t2 = np.sum(\n", + " (ck.conj() / vk.conj()**2) *\n", + " (np.exp(vk.conj() * t) - 1)\n", + " )\n", + " t3 = np.sum(\n", + " (ck / vk + ck.conj() / vk.conj()) * t\n", + " )\n", + " return 2 * (t1 + t2 - t3)" + ] + }, + { + "cell_type": "markdown", + "id": "da570ee1", + "metadata": {}, + "source": [ + "For the pure dephasing analytics, we just sum up as many matsubara terms as we can:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "81f82fd3", + "metadata": {}, + "outputs": [], + "source": [ + "lmaxmats2 = 15000\n", + "\n", + "vk = [complex(-gamma)]\n", + "vk.extend([\n", + " complex(-2. * np.pi * k * T)\n", + " for k in range(1, lmaxmats2)\n", + "])\n", + "\n", + "ck = [complex(lam * gamma * (-1.0j + cot(gamma * beta / 2.)))]\n", + "ck.extend([\n", + " complex(4 * lam * gamma * T * (-v) / (v**2 - gamma**2))\n", + " for v in vk[1:]\n", + "])\n", + "\n", + "P12_ana = 0.5 * pure_dephasing_evolution_analytical(\n", + " tlist, 0, np.asarray(ck), np.asarray(vk)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "96b1ad9d", + "metadata": {}, + "source": [ + "Alternatively, we can just do the integral of the propagator directly, without using the correlation functions at all" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "923533f8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_106800/917460483.py:15: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n", + " If increasing the limit yields no improvement it is advised to analyze \n", + " the integrand in order to determine the difficulties. If the position of a \n", + " local difficulty can be determined (singularity, discontinuity) one will \n", + " probably gain from splitting up the interval and calling the integrator \n", + " on the subranges. Perhaps a special-purpose integrator should be used.\n", + " scipy.integrate.quad(integrand, 0, np.inf, args=(lam, gamma, T, t))[0]\n" + ] + } + ], + "source": [ + "def JDL(omega, lamc, omega_c):\n", + " return 2. * lamc * omega * omega_c / (omega_c**2 + omega**2)\n", + "\n", + "\n", + "def integrand(omega, lamc, omega_c, Temp, t):\n", + " return (\n", + " (-4. * JDL(omega, lamc, omega_c) / omega**2) *\n", + " (1. - np.cos(omega*t)) * (coth(omega/(2.*Temp)))\n", + " / np.pi\n", + " )\n", + "\n", + "\n", + "P12_ana2 = [\n", + " 0.5 * np.exp(\n", + " scipy.integrate.quad(integrand, 0, np.inf, args=(lam, gamma, T, t))[0]\n", + " )\n", + " for t in tlist\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "6d8cafce", + "metadata": {}, + "source": [ + "## Compare results" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "e0600fe9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_result_expectations([\n", + " (resultMats, P12p, 'r', \"P12 Mats\"),\n", + " (resultMatsT, P12p, 'r--', \"P12 Mats + Term\"),\n", + " (resultPade, P12p, 'b--', \"P12 Pade\"),\n", + " (resultFit, P12p, 'g', \"P12 Fit\"),\n", + " ((tlist, np.real(P12_ana)), None, 'b', \"Analytic 1\"),\n", + " ((tlist, np.real(P12_ana2)), None, 'y--', \"Analytic 2\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "1106097d", + "metadata": {}, + "source": [ + "We can't see much difference in the plot above, so let's do a log plot instead:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "8caa163c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + "\n", + "plot_result_expectations([\n", + " (resultMats, P12p, 'r', \"P12 Mats\"),\n", + " (resultMatsT, P12p, 'r--', \"P12 Mats + Term\"),\n", + " (resultPade, P12p, 'b-.', \"P12 Pade\"),\n", + " (resultFit, P12p, 'g', \"P12 Fit\"),\n", + " ((tlist, np.real(P12_ana)), None, 'b', \"Analytic 1\"),\n", + " ((tlist, np.real(P12_ana2)), None, 'y--', \"Analytic 2\"),\n", + "], axes)\n", + "\n", + "axes.set_yscale('log')\n", + "axes.legend(loc=0, fontsize=12);\n", + "#axes.set_xlim(0,1)\n", + "#axes.set_ylim(0.4,0.5)" + ] + }, + { + "cell_type": "markdown", + "id": "587f2206", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "51cafb9e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "QuTiP: Quantum Toolbox in Python\n", + "================================\n", + "Copyright (c) QuTiP team 2011 and later.\n", + "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross and Asier Galicia.\n", + "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", + "Original developers: R. J. Johansson & P. D. Nation.\n", + "Previous lead developers: Chris Granade & A. Grimsmo.\n", + "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", + "\n", + "QuTiP Version: 5.0.0.dev0+9557c82\n", + "Numpy Version: 1.26.0\n", + "Scipy Version: 1.11.3\n", + "Cython Version: 3.0.3\n", + "Matplotlib Version: 3.8.0\n", + "Python Version: 3.12.0\n", + "Number of CPUs: 16\n", + "BLAS Info: Generic\n", + "INTEL MKL Ext: False\n", + "Platform Info: Linux (x86_64)\n", + "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", + "================================================================================\n", + "Please cite QuTiP in your publication.\n", + "================================================================================\n", + "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" + ] + } + ], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "40a80b9c", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "88cf1b93", + "metadata": {}, + "outputs": [], + "source": [ + "assert np.allclose(\n", + " expect(P12p, resultMats.states[:15]), np.real(P12_ana)[:15],\n", + " rtol=1e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(P12p, resultMatsT.states[:100]), np.real(P12_ana)[:100],\n", + " rtol=1e-3,\n", + ")\n", + "assert np.allclose(\n", + " expect(P12p, resultPade.states[:100]), np.real(P12_ana)[:100],\n", + " rtol=1e-3,\n", + ")\n", + "assert np.allclose(\n", + " expect(P12p, resultFit.states[:50]), np.real(P12_ana)[:50],\n", + " rtol=1e-3,\n", + ")\n", + "assert np.allclose(P12_ana, P12_ana2, rtol=1e-3)" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From e979c6cd59e80fb94e6467bb352fcaf07895cf0f Mon Sep 17 00:00:00 2001 From: mcditooss Date: Tue, 5 Mar 2024 17:39:55 +0100 Subject: [PATCH 11/44] minor changes --- ...eom-1d-spin-bath-model-ohmic-fitting.ipynb | 175 +++++++++--------- ...om-1e-spin-bath-model-pure-dephasing.ipynb | 68 ++++--- 2 files changed, 119 insertions(+), 124 deletions(-) diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb index 0d63b9e4..5b7838e5 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb @@ -398,7 +398,7 @@ " 5 | 1.18e-03 | 1.54e-01 |1.00e-01\n", " \n", "A normalized RMSE of 1.28e-06 was obtained for the The Spectral Density\n", - " The current fit took 5.916326 seconds\n" + " The current fit took 13.957773 seconds\n" ] } ], @@ -476,7 +476,7 @@ " 5 | 1.18e-03 | 1.54e-01 |1.00e-01\n", " \n", "A normalized RMSE of 1.28e-06 was obtained for the The Spectral Density\n", - " The current fit took 6.213607 seconds\n" + " The current fit took 13.026487 seconds\n" ] }, { @@ -548,7 +548,7 @@ " 7 | 2.69e-03 | 1.74e-01 |1.00e-01\n", " \n", "A normalized RMSE of 8.05e-07 was obtained for the The Spectral Density\n", - " The current fit took 52.747275 seconds\n" + " The current fit took 61.909602 seconds\n" ] } ], @@ -584,7 +584,7 @@ " 4 | 6.80e-01 | 8.68e-01 |1.19e-01\n", " \n", "A normalized RMSE of 4.39e-05 was obtained for the The Spectral Density\n", - " The current fit took 1.023160 seconds\n" + " The current fit took 0.980358 seconds\n" ] } ], @@ -724,7 +724,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -772,24 +772,14 @@ "name": "stdout", "output_type": "stream", "text": [ - " [***** 23% ] Elapsed 239.49s / Remaining 00:00:13:21" + " [* 3% ] Elapsed 0.18s / Remaining 00:00:00:05" ] }, { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[21], line 8\u001b[0m\n\u001b[1;32m 1\u001b[0m tlist \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m30\u001b[39m \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39mpi \u001b[38;5;241m/\u001b[39m Del, \u001b[38;5;241m600\u001b[39m)\n\u001b[1;32m 2\u001b[0m HEOM_spectral_fit \u001b[38;5;241m=\u001b[39m HEOMSolver(\n\u001b[1;32m 3\u001b[0m Hsys,\n\u001b[1;32m 4\u001b[0m fittedbath,\n\u001b[1;32m 5\u001b[0m max_depth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m,\n\u001b[1;32m 6\u001b[0m options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[1;32m 7\u001b[0m )\n\u001b[0;32m----> 8\u001b[0m result_spectral \u001b[38;5;241m=\u001b[39m HEOM_spectral_fit\u001b[38;5;241m.\u001b[39mrun(rho0, tlist)\n", - "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/heom/bofin_solvers.py:1037\u001b[0m, in \u001b[0;36mHEOMSolver.run\u001b[0;34m(self, state0, tlist, args, e_ops)\u001b[0m\n\u001b[1;32m 970\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun\u001b[39m(\u001b[38;5;28mself\u001b[39m, state0, tlist, \u001b[38;5;241m*\u001b[39m, args\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, e_ops\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 971\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 972\u001b[0m \u001b[38;5;124;03m Solve for the time evolution of the system.\u001b[39;00m\n\u001b[1;32m 973\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1035\u001b[0m \u001b[38;5;124;03m list of attributes.\u001b[39;00m\n\u001b[1;32m 1036\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1037\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrun(state0, tlist, args\u001b[38;5;241m=\u001b[39margs, e_ops\u001b[38;5;241m=\u001b[39me_ops)\n", - "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/solver_base.py:158\u001b[0m, in \u001b[0;36mSolver.run\u001b[0;34m(self, state0, tlist, args, e_ops)\u001b[0m\n\u001b[1;32m 153\u001b[0m stats[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpreparation time\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m time() \u001b[38;5;241m-\u001b[39m _time_start\n\u001b[1;32m 155\u001b[0m progress_bar \u001b[38;5;241m=\u001b[39m progress_bars[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprogress_bar\u001b[39m\u001b[38;5;124m'\u001b[39m]](\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28mlen\u001b[39m(tlist)\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprogress_kwargs\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 157\u001b[0m )\n\u001b[0;32m--> 158\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m t, state \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_integrator\u001b[38;5;241m.\u001b[39mrun(tlist):\n\u001b[1;32m 159\u001b[0m progress_bar\u001b[38;5;241m.\u001b[39mupdate()\n\u001b[1;32m 160\u001b[0m results\u001b[38;5;241m.\u001b[39madd(t, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restore_state(state, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m))\n", - "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/integrator/integrator.py:201\u001b[0m, in \u001b[0;36mIntegrator.run\u001b[0;34m(self, tlist)\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 188\u001b[0m \u001b[38;5;124;03mIntegrate the system yielding the state for each times in tlist.\u001b[39;00m\n\u001b[1;32m 189\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;124;03m The state of the solver at each ``t`` of tlist.\u001b[39;00m\n\u001b[1;32m 199\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m tlist[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[0;32m--> 201\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mintegrate(t, \u001b[38;5;28;01mFalse\u001b[39;00m)\n", - "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/integrator/qutip_integrator.py:55\u001b[0m, in \u001b[0;36mIntegratorVern7.integrate\u001b[0;34m(self, t, copy)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mintegrate\u001b[39m(\u001b[38;5;28mself\u001b[39m, t, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 55\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ode_solver\u001b[38;5;241m.\u001b[39mintegrate(t, step\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_failed_integration()\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_state(copy)\n", - "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/integrator/explicit_rk.pyx:269\u001b[0m, in \u001b[0;36mqutip.solver.integrator.explicit_rk.Explicit_RungeKutta.integrate\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/integrator/explicit_rk.pyx:304\u001b[0m, in \u001b[0;36mqutip.solver.integrator.explicit_rk.Explicit_RungeKutta.integrate\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 4.05s*] Elapsed 4.04s / Remaining 00:00:00:00\n" ] } ], @@ -814,7 +804,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "bd426cb2", "metadata": {}, "outputs": [], @@ -850,7 +840,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "739772ec", "metadata": {}, "outputs": [], @@ -895,7 +885,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "63e1b711", "metadata": {}, "outputs": [ @@ -904,25 +894,25 @@ "output_type": "stream", "text": [ "Starting calculations for N=1, Nk=1 and max_depth=5 ... \n", - " [*** 14% ] Elapsed 0.19s / Remaining 00:00:00:01" + " [*********52% ] Elapsed 0.90s / Remaining 00:00:00:00" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " Total run time: 1.29s*] Elapsed 1.29s / Remaining 00:00:00:00\n", + " Total run time: 1.99s*] Elapsed 1.99s / Remaining 00:00:00:00[*********57%* ] Elapsed 1.02s / Remaining 00:00:00:00\n", "Starting calculations for N=2, Nk=1 and max_depth=5 ... \n", - " Total run time: 1.99s*] Elapsed 1.99s / Remaining 00:00:00:00\n", + " Total run time: 2.08s*] Elapsed 2.08s / Remaining 00:00:00:00\n", "Starting calculations for N=3, Nk=1 and max_depth=5 ... \n", - " Total run time: 4.79s*] Elapsed 4.79s / Remaining 00:00:00:00\n", + " Total run time: 4.64s*] Elapsed 4.64s / Remaining 00:00:00:00\n", "Starting calculations for N=4, Nk=1 and max_depth=5 ... \n", - " Total run time: 14.24s*] Elapsed 14.24s / Remaining 00:00:00:00\n" + " Total run time: 12.89s*] Elapsed 12.89s / Remaining 00:00:00:00\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -953,7 +943,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "26604acf", "metadata": {}, "outputs": [ @@ -962,14 +952,14 @@ "output_type": "stream", "text": [ "Starting calculations for N=4, Nk=2 and max_depth=5 ... \n", - " Total run time: 28.27s*] Elapsed 28.27s / Remaining 00:00:00:00\n", + " Total run time: 25.44s*] Elapsed 25.44s / Remaining 00:00:00:00\n", "Starting calculations for N=4, Nk=3 and max_depth=5 ... \n", - " Total run time: 49.26s*] Elapsed 49.26s / Remaining 00:00:00:00\n" + " Total run time: 55.59s*] Elapsed 55.59s / Remaining 00:00:00:00\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -1002,7 +992,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "1d189e62", "metadata": {}, "outputs": [ @@ -1011,16 +1001,16 @@ "output_type": "stream", "text": [ "Starting calculations for N=4, Nk=1 and max_depth=2 ... \n", - " Total run time: 1.08s*] Elapsed 1.08s / Remaining 00:00:00:00\n", + " Total run time: 0.83s*] Elapsed 0.83s / Remaining 00:00:00:00\n", "Starting calculations for N=4, Nk=1 and max_depth=3 ... \n", - " Total run time: 1.68s*] Elapsed 1.68s / Remaining 00:00:00:00\n", + " Total run time: 1.38s*] Elapsed 1.37s / Remaining 00:00:00:00\n", "Starting calculations for N=4, Nk=1 and max_depth=4 ... \n", - " Total run time: 4.80s*] Elapsed 4.80s / Remaining 00:00:00:00\n" + " Total run time: 3.94s*] Elapsed 3.94s / Remaining 00:00:00:00\n" ] }, { "data": { - "image/png": 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DePY8xZEjRxAYGCj+GRujqkp05s2bh9mzZ+OLL77QmfAgIqKKUT/Ow5Php2HlbQt5Vx84jDS+rLsmPHeC7+TkhEGDBlXlWKrNvn378Pvvv4vtZcuWGVzXukuXLhgyZAi2bt0KAFi0aJHBBD82NlbnK/X58+cbnD1v0aIFPv74YyxevBgAsGTJEnz00Ud6NzBKT0/HqlWrxPYnn3xiMMnw8vLCvHnz8MEHHwAAVq1ahdmzZ8PFxUVvf6q8ffv2IScnB1lZWQCK/h7s2LEDQFHtevHfLWP76VOchP/66686CX5GRgYiIyMxbNgwBAcHw9HREefPn8f+/ftLJZjR0dGQyWTo2rWruIrOiy++iMGDB4t9li1bhvDwcHTs2BEffPABGjVqhKysLMTFxWHPnj3iBkhff/01wsPD0b59e3z22Wdo2rQpkpKSsHv3bqxatQqOjo5o2bKlGHPkyJGwtrZGUFAQHB0dy/x9vvHGG1i7di2Cg4PRqlUrXLhwAV999VWVlASVpbzxajQanDt3DqNHjy4zTkxMDARBKFXH3rdvX1hbW2PNmjVwcnISP6wkJydX+L05OjpW+pulv//975g5cyZ69OiB3r1749dff9V53dADyUREVJr6QQ4KjyYBAKT1bAAzS/AhVAMA4s93331n9HV3797VubaqjB49WozZuHFjQaPRlNn/yJEjOuN48OCB3n7z5s0T+ygUCiErK6vMuLdv39aJe/LkSb391q9fL/aRSCTCnTt3yoyblZUlODg4iNds2LChzP4lZWRkCACEjIwMg33y8vKE2NhYIS8vr0KxLcV3330nABDOnz9fbl9/f3+dP0ftn7t371a4nyEdO3YUevXqpXMuPz9fGD9+vNCqVSvByclJsLOzE4KCgoRZs2YJOTk5giAIwqxZswQAwoULF4Q+ffoICoVCcHR0FIYOHSokJSWVus/du3eF9957T2jQoIFgbW0teHh4CK+88oowf/58nX6xsbHCW2+9Jbi5uQk2NjaCn5+fMGrUKCE/P1/sM23aNKF+/fqCVCoVAAhHjx7VGVNKSkqp+6enpwujR48WPD09BXt7eyE8PFz45ZdfhIiICCEiIkLsV/xnVN7v7ujRowIAYfv27Trn9V1vaLyCIAiHDx8Wf49l+dvf/iYAEG7evFnqtW7dugkAhE6dOonn1q1bJwQGBpYZszpEREQY/PtozL+3tf3fACKiisjd9UBIkG4UEqQbhYz5V2rknsbka8XqRILv7e0txhw/fny5/ZVKpU7C/O9//1tvvw4dOoh9evToYdRYgoKCxGs+++wzvX3efvttsU9wcLBRcbt37y5e8/bbbxt1TTEm+OZpx44dgpWVlZCQkFCh68pKpsl477zzjvDKK69Uedy0tDTB09NTmD9/vpCTkyPk5uYKp06dEsaMGVPl96pK/DeAiOiZ7JU3xQQ/e01cjdyzIgl+rd/JNjk5WWdd/rCwsHKvkclkaNu2rdi+fPlyqT6CIOjstGlM3JL99MUFoFNOVJVxybIMHDgQbdu2xcKFC009lDrn9u3b2Lp1q1hSV5VcXV1x6NAh/PLLL/Dz84OPjw8++ugjtG/fvsrvRURE1UP9+NnzW1Y+5rWiIGDmO9mOGDECZ86cEdeAd3NzQ/PmzfHaa69h1KhRerdeL6nkOtoBAQFG3TsgIEDctl7fWtz3798Xd+KsaFxDYwOKVjop3j31eePeunWr3HW7yfxJJBJ8++232L17NzQajd7nNah63L9/HytWrEB4eHi1xG/ZsmWVrhJGREQ1SzvBl5phgm/WGcP69esRFxeH3Nxc5Obm4sGDBzhw4ACmTp0Kf39/zJgxo9zt2+Pj43XaxbtXlke7X8kYVRX33r174so+xR4+fKiz7v3zxFUqlaU2xtJWUFCAzMxMnR8yTy+88AKmT59eoeR+9uzZEATB4Go3VL7IyEiMHTvW1MMgIiIzpTODX58JfoX4+PggLCwMr732Glq3bq2zbnx+fj7mz5+P3r176yTEJRWvYFLM2dnZqHs7OTkZjFFVcTUaDXJzc6s8rr442hYuXAhnZ2fxp2HDhkbdg4iIiIgATXGCL5NA6i4vu7MJmFWCL5FI0K5dO3z77bd49OgRHj16hNOnT+Pw4cOIiYnB06dPsWnTJp1NXA4cOICJEycajJmdna3TLmtzKW12ds8+jZWMUVVx9cWprrjapk2bhoyMDPHnwYMHRt2DiIiIiJ7N4Eu9bCGRlt7E0dTMKsH39/fH2bNnMWbMGL1b2svlcgwdOhQxMTE6a06vWrXK4IOlKpVKp21sXbp2P33fEFRFXH2xqyuuNrlcDicnJ50fIiIiIiqfoNJAk1y0yak5lucAZpbgG8vFxQXR0dHi7LYgCFixYoXeviU3E8rPzzfqHtr9HBwcqiWuvtjVFZeIiIiIKk+TnA9oip6htPJmgl+l/Pz88Pbbb4vtgwcP6u2nUCh02mVtS69Nuza+ZIyqiqsvTnXFJSIiIqLKUz9+NqlqjivoABac4ANFK10Ui4+PR2FhYak+JVcSefz4sVGxtdfOd3Nzq5a4jo6OsLa2rvK4gP4xExEREVHlmPsKOoCFJ/je3t467bS0tFJ9goKCdNr37983Krb2g6fBwcGlXg8MDIRE8uyhiqqK6+rqqpPkP09cDw8PuLq6GnUdERERERlP80grwWeJTtUrWZZSsn4dAJo1a6bzAOqlS5eMin3x4kXxOCQkpNTrCoUCvr6+VR635PmqjEtERERElaNONO9NrgALT/CvXr0qHsvlcr1rxtvY2OhsAX/y5Mly4yYmJursJtupUye9/bTPGxNXqVTi7NmzFYp79uzZUivr6PPLL7+UG5eIiIiIKoclOtVIEARs27ZNbIeFhRns269fP/H40KFDSEpKKjP2xo0bxeN69eoZTJi14167dk1nFl2f3bt3ixtQSaVS9OnTp9y4mZmZ2LNnT5lxL1y4gBs3bui9noiIiIiqjk6JDmfwq9aKFSt01r7v37+/wb5Dhw6FXF60y5hSqcSSJUsM9s3OzsY///lPsR0VFVXqQdhivXr1goeHh9ieP3++wbhqtRqLFy8W2z179oSnp6fevm3atEGLFi3E9sKFC6FWqw3GXrBggXj8wgsv6OwRQERERERVRyzRkUog9TS/XWwBM0rwr169itGjR+vMROsjCAKWLVuGTz75RDxXv359jB071uA1vr6+GDdunNhetmwZdu7cWaqfUqnEu+++Kz7Yamdnh+nTpxuM6+DggGnTpont6OhoLF26VO+YJ02ahPPnzwMo2rF37ty5BuNKJBLMmzdPbJ8/fx6TJ0+GIAil+v7jH//A999/L7bnzZun8/AvGWft2rWQSCTij0wmg6+vL9599108fPhQ7JeVlYUpU6agW7du8PDwgEQiwezZs0vFM7ZfeebOnYvmzZtDo9FU4t3VjNOnT2P27Nl4+vRptcQv/jOKj48vt+/WrVvRokUL2NnZQSKR4NKlS6WuL2u8a9asQYMGDZCTk2P0+H766Sfx74++b/MGDhxo8EN9Tbh06RJ69+4NPz8/2NnZwdXVFWFhYdiwYYPJxkREZIl0drG1MptUWpdQCWPGjBHkcnmpHwDij0wm09unpIsXL4rXhIaGCpMnTxY2bNgg/Pzzz8Ivv/wi7N69W5gzZ44QHBysE18ulwsnTpwod6xPnjwRmjVrJl4nlUqFd955R9ixY4dw5MgRYeXKlUKrVq10Yq9YsaLcuAUFBcIrr7yic90bb7whbN68WTh69Kjw3XffCeHh4TqvT5o0yajf71tvvaVzXXh4uLB27Vrh6NGjwubNm4U33nhD5/W33nrLqLglZWRkCACEjIwMg33y8vKE2NhYIS8v77nuYe6+++47AYDw3XffCWfOnBGOHDkizJ49W5DL5ULjxo2F7OxsQRAE4e7du4Kzs7PQqVMnYcyYMQIAYdasWaXiGduvLA8fPhQcHByE7du3V8E7rH5fffWVAEC4e/dutcQv/jMqL35ycrJgbW0t9OnTRzh27Jhw5swZIScnR0hOThbOnDkj5OfnlztepVIpNGvWTJg5c6bR45s3b54AQLC1tRU+//zzUq/7+/sL3bt3NzpeVTt69Kgwbtw4Yf369cKRI0eEPXv2CG+//bYAQJg3b16519f2fwOIiIyhUamFBOtNQoJ0o5DU5qcavbcx+VqxSiX4I0eO1EkwK/JTknaCb+yPt7e3cPDgQaPHe+PGDaFhw4ZGxZ4yZYrRcZOTk4WWLVsaFXfo0KGCWq02Km5ubq4QGRlpVNzOnTsLubm5Ro9ZGxP8Z8nj+fPndc7PmDFDACBs2LBBEARB0Gg0gkajEQRBEFJSUgwm7sb2K8uUKVOEBg0aGP33xdQqmuDn5ORUKL6xCf7JkycFAMLWrVvL7FfeeP/2t78Jzs7ORo9zwIABgpubmzBixAghODhY57XU1FQBgDB9+nSjYtWk9u3bCw0bNiy3X23/N4CIyBiqxFwhQbpRSJBuFFLeOFqj965Igm823yv4+PhgxIgRCAgIKLevl5cXvvjiC1y5cgVdunQx+h6BgYG4fPkyRo8eDTs7/Q9FhISE4IcfftCply+Ph4cHzp07h8mTJ+tdyQcA/P39sXr1amzatAlSqXG/djs7Oxw6dAiLFi0y+NW+p6cnFi5ciMOHDxt8T/T8OnToAAC4d+8eAIglGOUxtp8hhYWFWLNmDYYNG1bq70tKSgrGjh2Lhg0bQi6Xw8PDA6+++ioOHToEAJg9e7ZYJjJw4EA4OTnB2dkZ77zzDlJSUkrd69atWxg2bBg8PT0hl8sREhKCb775plS/69evY+jQofDy8oJcLoefnx9GjBiBgoICzJ49G5MnTwYANG7cWHz/x44d0xlTTEwMBg0aBBcXF/G/9bi4OLz77rto1qwZ7O3t0aBBA/Tp0wdXrlyp8O9t1KhRCA8PBwAMGTIEEokEnTt3BqBb4lPeeIGi528yMzOxZcsWo+594cIFhIaGYuDAgbh+/brOKl8XLlwAALz88ssVfk/Vzd3dXWcpYSIiMkx7F1tzXUEHACr1r/ratWuxdu3aKhmIl5cX1q1bBwBISkrC5cuXkZKSgtTUVGRlZUGhUMDd3R2tW7dGSEjIcydP9erVw+rVq/GPf/wDR44cwYMHD5CTkwMfHx+0bNkSrVu3fq64tra2WLJkCebOnYtjx44hPj4e6enp8PLyQnBwMMLCwp5rzFKpFFOnTsVf//pXnDhxArdv30Zqairc3d0REBCATp068f+cq1HxcqnaD1PXhLNnzyItLU1nt+Ziw4cPR0xMDBYsWIDAwEA8ffoUMTExpTZ6GzBgAAYPHozx48fj6tWrmDFjBmJjY3H27FnxwfHY2Fi88sor8PPzw9///nd4e3vjwIEDmDhxIlJTUzFr1iwAwO+//47w8HC4u7tj7ty5aNasGR4/fozdu3ejsLAQY8aMwZMnT7B8+XJER0fDx8cHANC8eXOdMQ0cOBBvv/02xo8fL9a3P3r0CG5ubli0aBE8PDzw5MkTrFu3Du3bt8fFixdLbVZXlhkzZqBdu3aYMGECvvzyS0RGRsLJyalUP2PG6+3tjeDgYOzduxfvvfdemfdNS0vD/fv3MWzYMHTv3h0KhQI7d+4UH5avbIIvCEKZD9prK+/fA41GA41Gg/T0dGzfvh0HDhzAihUrnmtcRER1jebRsz2YzHUFHaCSCX518fLyQteuXav1Ho6OjtWynKStrS169OhR5XFlMhlee+01vPbaa1UeuzKuTL4EZbrS1MMQWbtYo+VXLz339Wq1GiqVCvn5+Th+/Djmz58PR0dH9O3bt+oGaYQzZ84A0J8Qnjp1CmPGjMH7778vntP3d3ngwIHiilHdunWDl5cXoqKisG3bNkRFRQEAPv30Uzg6OuLkyZNiIty1a1cUFBRg0aJFmDhxIlxcXPDpp59CJpPh3LlzOh92iuM4OjrCz88PANC6dWs0atRI7/saOXIk5syZo3OuU6dOOkvRqtVq9O7dGy1atMCqVavw9ddfl/3L0hIQECAm6c2aNRO/gSnJ19fXqPG+/PLL4jcjZYmJiQEAhIaGwtbWFr169cLOnTsxc+ZMAEUJvouLCxo3bixek5aWhtmzZ+OHH35Aeno66tevj1GjRmHKlCmwsrLSiX/8+HG9H/b0uXv3rsH3AwB/+ctfsGrVKgBF+4T885//1FmEgIiIDNOZwTfTXWwBM03wyXIo05UofFJo6mFUmZIJYcuWLbFy5Up4eXnV6DgePXoEiUQCd3f3Uq+1a9cOa9euhZubG7p06YLQ0FC9S7kWJ9/FBg8ejJEjR+Lo0aOIiopCfn4+Dh8+jA8++AD29vY6G6r16tULK1aswK+//oqIiAgcP34co0ePrvQ3GW+++WapcyqVCkuWLMGGDRsQFxcHpfLZB8Zr165V6n6V5enpieTkZKhUqjJnxotn6IuXqC3+piIuLg5NmzbFhQsXdD6spaSk4NVXX0VERATOnDmDBg0a4MqVK5g+fbreb/pCQ0PFVbjKU79+/TJfnz59OsaMGYPk5GTs2bMHH374IXJycjBp0iSj4hMR1WU6u9jW1hIdImsX/XsEmEplx/O///0PISEhkMlk8PLyEks3alpeXh6sra1LzeQCRUtAzp8/H6tXr8aMGTOgUCgwYMAALFmyBN7e3mI/7WOg6FsgNzc3sZQnLS0NKpUKy5cvx/Lly/WOIzU1Fenp6VCr1fD19a30+9L3+/z000/xzTffYOrUqYiIiICLiwukUinGjBmDvLw8PVFqjq2tLQRBQH5+PhQKhcF+MTExOjP0vXv3hq2tLXbs2IGxY8ciPj4eb731lth/woQJeOmll/Dtt9+K51q2bGlwUzuFQoGXXnrJqDGXV6Lj5+cnfnvRq1cvAMC0adMwcuTIGi9FIyKyNJawyRXABJ8qqTLlMOYoJCQEbdq0MfUw4O7ujsLCQuTk5MDBwaHUa0uXLsXSpUtx//597N69G5999hmSk5Oxf/9+sV9iYiIaNGggtlUqFdLS0uDm5gYAcHFxgZWVFYYPH44JEyboHUfjxo1hb28PKysrJCQkVPp96Zud3rBhA0aMGIEvv/xS53xqairq1atX6XtWxpMnTyCXy8tM7oGiBF97hl6hUKBbt27YuXOnOKtf/L9xcXHYsWMHYmNjjR5HVZbolNSuXTv8+9//xp07d5jgExGVo3gNfIAJPhFVUHBwMADg9u3baNWqlcF+fn5++PDDD3H48GGcOnVK57WNGzfq7Gq8bds2qFQqcVUZe3t7REZG4uLFi2jVqhVsbGwM3iciIgLbt2/HggUL9JYNARB3i67orLtEIhGvLbZ37148fPgQTZs2rVCsijBmvHfu3Cn1oHBJGRkZuHPnDgYOHKhzfuDAgRg1ahSio6MBPHue4siRIwgMDBT/jI1RlSU6JR09ehRSqRRNmjSp0HVERHWRWKIjKdroylwxwSeqoH379iEnJwdZWVkAilai2bFjB4Cikgd7e/sK9dOnOAn/9ddfdRL8jIwMREZGYtiwYQgODoajoyPOnz+P/fv3l0owo6OjIZPJ0LVrV3EVnRdffBGDBw8W+yxbtgzh4eHo2LEjPvjgAzRq1AhZWVmIi4vDnj17cOTIEQDA119/jfDwcLRv3x6fffYZmjZtiqSkJOzevRurVq2Co6MjWrZsKcYcOXIkrK2tERQUBEdHxzJ/n2+88QbWrl2L4OBgtGrVChcuXMBXX31VJSVBZSlvvBqNBufOncPo0aPLjBMTEwNBEHQ+TAFA3759YW1tjTVr1sDJyUn8sJKcnFzh9+bo6Fjpb5bGjh0LJycntGvXDl5eXkhNTcX27duxdetWTJ48mbP3RERGKC7RkXrYQmJtNqvNl1bdi/KT+eNGV4Y3utLH39/f4IZj2psmGdvPkI4dOwq9evXSOZefny+MHz9eaNWqleDk5CTY2dkJQUFBwqxZs8QNmWbNmiUAEC5cuCD06dNHUCgUgqOjozB06FAhKSmp1H3u3r0rvPfee0KDBg0Ea2trwcPDQ3jllVeE+fPn6/SLjY0V3nrrLcHNzU2wsbER/Pz8hFGjRok7wwqCIEybNk2oX7++IJVKBQDC0aNHdcaUkpJS6v7p6enC6NGjBU9PT8He3l4IDw8XfvnlFyEiIkKIiIgQ+xm70dXRo0cFAKV2ANZ3vaHxCoIgHD58WPw9luVvf/ubAEC4efNmqde6desmABA6deoknlu3bp0QGBhYZszq8N///lfo2LGj4O7uLshkMqFevXpCRESEsH79eqOur+3/BhARlUej0QgJ8s1Fu9i23lvj96/IRlcSQRCEGvs0QWYpMzMTzs7OyMjI0LtmOADk5+fj7t27aNy4MWxtzfcrqdpk586dGDJkCO7du6dTS1+e2bNnY86cOUhJSTFYTkPlGz58OO7cuVOq9Kmynjx5gpCQEEycOBGffPKJuCnZd999p/PQrbnhvwFEVNepU/KR6F1Udinv4QP3vcY9G1VVjMnXipnxdwtEddvAgQPRtm1bLFy40NRDqXNu376NrVu3VmhHa2O5urri0KFD+OWXX+Dn5wcfHx989NFHaN++fZXfi4iIqo76odYDtg0Ml9maA9bgE5kpiUSCb7/9Frt374ZGo4FUys/jNeX+/ftYsWIFwsPDqyV+y5YtdVY8IiIi86d5qLWLrRmvgQ8ALNEhlugQUbn4bwAR1XU5/4nD0w/OAQDqrWwHh7HVt9KbPizRISIiIiKqQupHWjP4Dcx7Bp8JPhERERFRObRr8KVmXoPPBJ+IiIiIqBzqh5zBJyIiIiKqNdR/bnIFGymk7vKyO5sYE3wiIiIionIU72JrVd8OEonExKMpGxN8IiIiIqIyCPlqaNIKAJj/GvgAE3wiIiIiojKJ5Tkw/zXwASb4RERERERl0n7AVmrmD9gCTPCJiIiIiMqks4JOfZboEBERERFZNJboEBERERHVIrpr4HMGn4iIiIjIomm0drE1902uACb4RACAtWvXQiKRiD8ymQy+vr5499138fDhQ7FfVlYWpkyZgm7dusHDwwMSiQSzZ88uFc/YfuWZO3cumjdvDo1GU4l3VzNOnz6N2bNn4+nTp9USv/jPKD4+vty+W7duRYsWLWBnV7RW8aVLl0pdX9Z416xZgwYNGiAnJ8fo8f3000/i35+LFy+Wen3gwIHw9PQ0Ol51W716NSQSCRQKhamHQkRk9liiQ2TBvvvuO5w5cwYHDx7E+++/j82bN6Njx45iopeWlob//Oc/KCgoQP/+/Q3GMbZfWR49eoQlS5Zg7ty5kErN/z/V06dPY86cOdWW4BsrJSUFw4cPR0BAAPbv348zZ84gMDAQvXv3xpkzZ+Dj41PueEeOHAkHBwcsWbLE6PvGxMQAAGxtbbFz5069r7/88svP96aq2MOHDzFp0iTUr1/f1EMhIrIIxSU6ElcbSOxkJh5N+cw/ayCqQS+88AI6dOiAyMhIzJo1C1OmTMHdu3exa9cuAIC/vz/S09Nx/PhxLFy40GAcY/uVZdmyZahXrx4GDhz4XNebu9zc3PI7PYebN29CqVTinXfeQUREBDp06AB7e3t4eHigQ4cOkMvL315cJpNh3LhxWLZsmdHjjImJgZubGwYPHlwqwU9LS8O9e/cQGhr6XO+pqo0fPx6dOnVC165dTT0UIiKzJwiCOINvCfX3ABN8ojJ16NABAHDv3j0AEEswymNsP0MKCwuxZs0aDBs2rNTsfUpKCsaOHYuGDRtCLpfDw8MDr776Kg4dOgQAmD17tlgmMnDgQDg5OcHZ2RnvvPMOUlJSSt3r1q1bGDZsGDw9PSGXyxESEoJvvvmmVL/r169j6NCh8PLyglwuh5+fH0aMGIGCggLMnj0bkydPBgA0btxYfP/Hjh3TGVNMTAwGDRoEFxcXBAQEAADi4uLw7rvvolmzZrC3t0eDBg3Qp08fXLlypcK/t1GjRiE8PBwAMGTIEEgkEnTu3BmAbolPeeMFgKioKGRmZmLLli1G3fvChQsIDQ3FwIEDcf36dVy9elXnNQBmMYO/YcMGHD9+HP/6179MPRQiIougSSsACotKZS2hPAcAzP87BiITiouLAwB4eHjU6H3Pnj2LtLQ0REZGlnpt+PDhiImJwYIFCxAYGIinT58iJiYGaWlpOv0GDBiAwYMHY/z48bh69SpmzJiB2NhYnD17FtbW1gCA2NhYvPLKK/Dz88Pf//53eHt748CBA5g4cSJSU1Mxa9YsAMDvv/+O8PBwuLu7Y+7cuWjWrBkeP36M3bt3o7CwEGPGjMGTJ0+wfPlyREdHi2UwzZs31xnTwIED8fbbb2P8+PFi2dOjR4/g5uaGRYsWwcPDA0+ePMG6devQvn17XLx4EUFBQUb/3mbMmIF27dphwoQJ+PLLLxEZGQknJ6dS/YwZr7e3N4KDg7F371689957Zd43LS0N9+/fx7Bhw9C9e3coFArs3LkTLVq0AFD5BF8QBKjVaqP6ymSG/1lPTk7Gxx9/jEWLFsHX1/e5xkJEVNfoPmBrGTP4TPCpUqLfyUdemmDqYYjs3CQYuMH2ua9Xq9VQqVTIz8/H8ePHMX/+fDg6OqJv375VOMrynTlzBoD+hPDUqVMYM2YM3n//ffFcv379SvUbOHCgWEPerVs3eHl5ISoqCtu2bUNUVBQA4NNPP4WjoyNOnjwpJsJdu3ZFQUEBFi1ahIkTJ8LFxQWffvopZDIZzp07p/NhpziOo6Mj/Pz8AACtW7dGo0aN9L6vkSNHYs6cOTrnOnXqhE6dOolttVqN3r17o0WLFli1ahW+/vrrsn9ZWgICAsQkvVmzZuI3MCX5+voaNd6XX35Z/GakLMX196GhobC1tUWvXr2wc+dOzJw5E0BRgu/i4oLGjRuL16SlpWH27Nn44YcfkJ6ejvr162PUqFGYMmUKrKysdOIfP35c74c9fe7evWvw/fzlL39BUFAQPvjgA6NiERFRyU2uOINPdUBemoCcZFOPQlvlPmyUTAhbtmyJlStXwsvLq1JxK+rRo0eQSCRwd3cv9Vq7du2wdu1auLm5oUuXLggNDRVn5LUVJ9/FBg8ejJEjR+Lo0aOIiopCfn4+Dh8+jA8++AD29vZQqVRi3169emHFihX49ddfERERgePHj2P06NGV/ibjzTffLHVOpVJhyZIl2LBhA+Li4qBUKsXXrl27Vqn7VZanpyeSk5OhUqnKnBkvnqEvrrEv/qYiLi4OTZs2xYULF3Q+rKWkpODVV19FREQEzpw5gwYNGuDKlSuYPn263tKu0NBQnD9/3qgxG3pwdufOndizZw8uXrxYqfIxIqK6Rv3QslbQAZjgUyXZuUlQ2aS6KhWN5/n973//Q0hICGQyGby8vMTSjZqWl5cHa2vrUjO5QNESkPPnz8fq1asxY8YMKBQKDBgwAEuWLIG3t7fYT/sYKCrdcHNzE0t50tLSoFKpsHz5cixfvlzvOFJTU5Geng61Wl0lJR36fp+ffvopvvnmG0ydOhURERFwcXGBVCrFmDFjkJeXpydKzbG1tYUgCMjPzy9zOcmYmBidGfrevXvD1tYWO3bswNixYxEfH4+33npL7D9hwgS89NJL+Pbbb8VzLVu2xJ49e/TGVygUeOmll4was74PItnZ2ZgwYQL+7//+D/Xr1xdXDiosLAQAPH36FNbW1nBwcDDqHkREdYn60bMZfClLdKguqEw5jDkKCQlBmzZtTD0MuLu7o7CwEDk5OaWSLnd3dyxduhRLly7F/fv3sXv3bnz22WdITk7G/v37xX6JiYlo0KCB2FapVEhLS4ObmxsAwMXFBVZWVhg+fDgmTJigdxyNGzeGvb09rKyskJCQUOn3pW/meMOGDRgxYgS+/PJLnfOpqamoV69epe9ZGU+ePIFcLi93rfiSS2AqFAp069YNO3fuFGf1i/83Li4OO3bsQGxsrNHjqGyJTmpqKpKSkvD3v/8df//730td4+Lign79+omrRRER0TNqC9vkCmCCT2SWgoODAQC3b99Gq1atDPbz8/PDhx9+iMOHD+PUqVM6r23cuFFnWcZt27ZBpVKJq8rY29sjMjISFy9eRKtWrWBjY2PwPhEREdi+fTsWLFigt2wIgLj8ZEVn3SUSSamlK/fu3YuHDx+iadOmFYpVEcaM986dO6UeFC4pIyMDd+7cKbWc6cCBAzFq1ChER0cDePY8xZEjRxAYGCj+GRujsiU63t7eOHr0aKnzixYtwvHjx7Fv3z6Df65ERHWdTg0+Z/CJaqd9+/YhJycHWVlZAIpWotmxYweAotp1e3v7CvXTpzgJ//XXX3US/IyMDERGRmLYsGEIDg6Go6Mjzp8/j/3795dKMKOjoyGTydC1a1dxFZ0XX3wRgwcPFvssW7YM4eHh6NixIz744AM0atQIWVlZiIuLw549e3DkyBEAwNdff43w8HC0b98en332GZo2bYqkpCTs3r0bq1atgqOjI1q2bCnGHDlyJKytrREUFARHR8cyf59vvPEG1q5di+DgYLRq1QoXLlzAV199Ve2rvJQ3Xo1Gg3PnzmH06NFlxomJiYEgCKXWuO/bty+sra2xZs0aODk5iR9WkpOTK/zeHB0dK/XNkq2trfh3StvatWthZWWl9zUiIioi7mJrLYXUvfy9VMwBE3yiCvrggw/EdfEBYPv27di+fTsA3fIIY/vp07BhQ3Ts2BE//PADxo4dK563tbVF+/btsX79esTHx0OpVMLPzw9Tp07FlClTdGJER0dj9uzZWLlyJSQSCfr06YOlS5fqzNQ3b94cMTExmDdvHr744gskJyejXr16aNasGXr16iX2e/HFF3Hu3DnMmjUL06ZNQ1ZWFry9vfHaa6+J8Tp37oxp06Zh3bp1+Pbbb6HRaHD06NFyk8dly5bB2toaCxcuRHZ2Nl5++WVER0fjiy++KPO6yipvvMeOHUNGRkaph5VLKl5Bp+SKRy4uLoiMjMTPP/+MsLAwsTzJz88PDx48qPo3RERE1aJ4mUyr+naQSC1jkQKJIAjm84QkmURmZiacnZ2RkZGhd81wAMjPz8fdu3fRuHFj2NrWrrp7c7Vz504MGTIE9+7d06mlL8/s2bMxZ84cpKSksOyiEoYPH447d+6UKn2qrCdPniAkJAQTJ07EJ598Im5K9t133+k8dGtu+G8AEdVFQr4ajxy2AgBsXnGHxy/dTDYWY/K1YtzJlshMDRw4EG3btsXChQtNPZQ65/bt29i6dSsWL15c5bFdXV1x6NAh/PLLL/Dz84OPjw8++ugjtG/fvsrvRURElaN+rL1EpmXU3wMs0SEyWxKJBN9++y12794NjUYDqZSfx2vK/fv3sWLFCoSHh1dL/JYtW+qseEREROZJ+wFbqYWsgQ+wRIfAEh0iKh//DSCiuih36z2kDysq1XRa/BIcJ5W9slp1YokOEREREVElaW9yZUklOkzwiYiIiIj00FjgJlcAE3wiIiIiIr0scZMrgAk+EREREZFe4iZX4Aw+1WJ8JpuobuJ/+0RUF6kTimbwJS42kNhZzuKTTPDJKNbW1gCA3NzccnoSUW1U/N9+8b8FRES1naARoC7exbah5ZTnAFwHn4xkZWWFevXqITk5GQBgb28PicQytmsmoucnCAJyc3ORnJyMevXqwcrKytRDIiKqEZqkfECpAQBY+TLBp1rK29sbAMQkn4jqjnr16on/BhAR1QXqBznisYwz+FRbSSQS+Pj4wNPTE0ql0tTDIaIaYm1tzZl7Iqpz1AlaD9g2dDDhSCqOCT5VmJWVFf/PnoiIiGo1dcKzGXxLK9HhQ7ZERERERCWoH2itgW9hJTpM8ImIiIiIStBJ8DmDT0RERERk2YrXwAeY4BMRERERWbziGXyphxwSW8t69pAJPhERERGRFkGlgfqRZW5yBTDBJyIiIiLSoX6cB2gEAICVr2UtkQkwwSciIiIi0mHJK+gATPCJiIiIiHToPGDLBJ+IiIiIyLLpJPgNmOATEREREVk0lugQEREREdUiTPCJiIiIiGoRdUJO0YGEJTpERERERBZP3OTK2w4Sa8tLly1vxERERERE1UQoVEOTlA/AMstzACb4REREREQi9cM8oGiPK1j5MsEnIiIiIrJoOktk1tUEPyUlBfv27cPcuXPRt29f+Pj4QCKRiD9r166tULzc3Fzs27cPkydPRpcuXeDr6ws7OzvY29ujQYMG6NatGxYsWIBHjx5VKK72mIz9+fe//12heyQmJmLx4sUICwuDj48PbG1t0ahRI/To0QNr165FXl5eheIVe/r0Kb755ht07twZvr6+kMvl8PX1RefOnfHNN9/g6dOnzxWXiIiIiHRZ+go6ACB73gsTExPRoUMH3Lt3r0oGkpSUhI8++gh79uxBbm6u3j55eXl49OgRDh48iDlz5mDSpEmYPXs2bGxsqmQMlbFlyxaMHz8eGRkZOufv3buHe/fu4cCBA1i0aBE2b96M1q1bGx338OHDGDFiRKkPNA8fPsTDhw9x/PhxfPnll1i3bh26dOlSJe+FiIiIqK6qDTP4z53g5+fnV1lyDwAPHjzA1q1bdc5JJBI0adIE3t7esLKywq1bt/D48WMAgFKpxMKFC3Hp0iXs2rWrQkl+p06dYGdnV24/Pz8/o+KtX78eI0aM0DkXGBgIHx8fxMfHi7+nGzduoHPnzjh9+jRatGhRbtwjR46gZ8+eUCqV4rlGjRrB398fjx8/xs2bNwEAjx49Qs+ePfHzzz8jMjLSqDETERERUWnqBznicZ2bwdfm4eGB0NBQtGnTBm3atEH//v2fO5ZEIsFrr72G9957D926dYO7u7vO68ePH8df/vIXxMbGAgD27duHGTNmYPHixUbfY926dWjUqNFzj1HblStX8P7774vtwMBAbNq0CaGhoeK5gwcPYvjw4UhKSkJmZib69u2Lq1evwtbW1mDcpKQkDBo0SEzuvb29sX79ep1Z+t9++w1RUVG4efMmVCoVBg0ahNjYWHh5eVXJeyMiIiKqa7RLdGQNHUw4kuf33DX4rq6u2L59O+Lj45GcnIx9+/Zh3rx56Nev3/MNRCrFm2++iT/++AOHDh3CsGHDSiX3ABAREYHTp0+jefPm4rmlS5ciKSnped9KpXz++ecoKCgAALi7u+PEiRM6yT0AdO3aFYcPH4ZcLgcA3LlzBytXriwz7oIFC5Ceng4AkMvlOHz4cKkSnDZt2uDEiRPi7+nJkyf48ssvq+R9EREREdVFYomOlQRSH8OTsebsuRN8JycnDBo0CP7+/lUykJdffhk7duzQSdwNcXZ2xj/+8Q+xXVhYiB9//LFKxlERsbGx2LNnj9ieP3++wdnzFi1a4OOPPxbbS5YsgUaj0ds3PT0dq1atEtuffPKJwd+Ll5cX5s2bJ7ZXrVolfjAgIiIiooopnsG3qm8HiZVlLjhpmaMG8Prrr+vU0V+/fr3GxxAdHS0eKxQKREVFldl/7Nix4nFiYiLOnDmjt9/evXtRWFgIoKhkSfs6fd555x04OBR9hVRQUICffvrJqPETERER0TNCngqa1KLKDEt9wBaw4ATfysoKzs7OYjszM7PGx7B3717xODw8HAqFosz+TZo0QVBQkNg29K2DdtygoCA0bty4zLgKhQLh4eHlxiUiIiIiw2rDCjqABSf4eXl5SE5OFtuenp41en9BEHDlyhWxHRYWZtR12v0uX76st8/vv/9eLXGJiIiIyDB1wrM9iyx1BR3AghP8H374QaeG3dhEGAAmT56MFi1awMnJCXZ2dvD19UVkZCRmz56Nu3fvGhXj/v37yMl5toxSQECAUddp97t27Vqp11UqFeLi4ioV99atW1CpVEZdR0RERERFasMSmYCFJvgqlUpntRhPT0+8/vrrRl+/Y8cOxMbGIisrC/n5+Xj48CGOHTuGOXPmIDAwEOPHjy9319n4+HidtrFr5mv3u3fvHgRB0Hn94cOHOuveP09cpVJZ4Z1+iYiIiOo63RIdy1wiE6iidfBr2qJFi3TKY7744gtxCUpjuLu7IyAgAAqFAhkZGbh+/Tqys7MBFH14WLVqFc6dO4ejR4/q1Plry8rK0mkb6leSk5OTeKzRaJCbmys+IFtVcfXF0VZQUCAu7QmY5vkFIiIiInOj0loDnzP4NejAgQOYNWuW2H7llVfwl7/8pdzrmjdvjqVLl+L27dtISUnBr7/+ikOHDuH8+fNIT0/Hjz/+iFatWon9L168iLfffttgvOIPBMXK2rRKW8kddEvGqa642hYuXAhnZ2fxp2HDhkbdg4iIiKg2U9/TKtHxY4JfI65du4ahQ4eKtfcuLi7YtGkTrKysyr326tWr+Oijj9CkSZNSr8lkMvTu3Rtnz55F7969xfP79+/XWedeW8kad5nMuC9DSvbTLsepzrjapk2bhoyMDPHnwYMHRt2DiIiIqDZT3/8zwbe1gtTTMje5AiwowX/w4AG6d+8ubuJkb2+PH3/8sco22gKKZss3b96ss1nV8uXL9fa1t9f9VJefn2/UPUr20y7Pqc642uRyOZycnHR+iIiIiOoyQRDEGXyZnz0kEomJR/T8LCLBT0pKQpcuXcSZZrlcjl27duGVV16p8ns5Ojrigw8+ENu//PKL3iS75Jr35T2UWyw3N1enXTJOdcUlIiIiIsM0aQUQctUAACs/y33AFrCABP/Jkyfo2rUrbt68CaCoFGXr1q3o2rVrtd0zMjJSPM7Pz9dbwuLu7q7Tfvz4sVGxExMTxWNHR0dYW1tXeVwAcHNzM+o6IiIiIgLU97QesPVngl9tMjMz0b17d3HFHKlUig0bNqBfv37Vel9vb2+ddmpqaqk+gYGBOl/d3L9/36jY2h8WgoODS73u6uqqk+Q/T1wPDw+4uroadR0RERERlXjAlgl+9cjJyUGvXr3w22+/AQAkEgn++9//YsiQIdV+75LlLiXr4oGiEhhfX1+xfenSJaNiX7x4UTwOCQnR20f7fFXGJSIiIiL9xAdsAchYolP18vPz0bdvX5w6dUo8969//QsjR46skftfvXpVp+3p6am3X6dOncTjkydPlhtXqVTi7Nmzeq83FPfs2bNG7Ur7yy+/lBuXiIiIiPRTcQa/+hQWFuLNN9/EkSNHxHP/+Mc/MH78+Bobw5YtW8TjRo0awcfHR28/7VKha9eu6cyi67N7925xAyqpVIo+ffqUGzczM9PgUp3FLly4gBs3bui9noiIiIjKxxKdaqJWqzFs2DD89NNP4rkvv/wSH3/8cY2NYffu3fjxxx/Fdv/+/Q327dWrFzw8PMT2/PnzDfZVq9VYvHix2O7Zs6fBbwbatGmDFi1aiO2FCxdCrVYbjL1gwQLx+IUXXkBoaKjBvkRERERUmliiYyWBVQO7sjubObNJ8AVBwHvvvYedO3eK52bOnIlp06ZVKm5GRgbefPNNXLhwody+mzdvxrBhw8S2vb09pk6darC/g4ODzviio6OxdOnSUv0EQcCkSZNw/vx5AEXPE8ydO9dgXIlEgnnz5ont8+fPY/LkyRAEoVTff/zjH/j+++/F9rx58yx63VYiIiIiUygu0bFqYAeJzGxS5OciEfRljUZ6//33sX79+lLnCwoKxGOZTKZ3p9mSa8tv27ZN5wFaW1tbREREGD2WVq1aYcmSJaXOP336FC4uLgCKVq3p3r07XnrpJfj4+MDBwQFZWVm4cuUKduzYISbgQFGSvWXLFgwePLjM+xYWFiIyMhKnT58Wz73xxhuIioqCt7c34uPjsWbNGp0a/UmTJuGrr74q9z0NHjwY27dvF9vh4eEYM2YM/P39kZiYiI0bN+p82/DWW29h27Zt5cYtKTMzE87OzsjIyOCmV0RERFTnaLKVeOxclHPZdPSAx7HqW479eVUkX5NV5kZKpVInmddHpVIZ9ZBoyZVr8vPzceDAAaPHYsyOr9evX8f169fL7efo6IhVq1aVm9wDgI2NDXbt2oXXX39dXM7zxx9/1Em8tQ0dOlSnVKcs69atQ2pqKo4ePQqg6EFeQw/zdu7cGevWrTMqLhERERE9o1N/b+Er6ABmVKJTXezs7DB27Fi0aNGi3NIVZ2dnTJw4EX/88QeGDh1q9D08PDxw7tw5TJ48Gc7Oznr7+Pv7Y/Xq1di0aROkUuN+7XZ2djh06BAWLVpksF7f09MTCxcuxOHDh2FnZ9n1YkRERESmoJ3gyyz8AVugkiU6liY9PR2XLl1CcnIyUlNT8fTpU9jb28PV1RWtWrVCq1at9JYTVUR+fj6OHTuG+Ph4pKenw8vLC8HBwQgLC6tUbbxKpcKJEydw+/ZtpKamwt3dHQEBAejUqRNkskp9EcMSHSIiIqrTslfeQsaHRaXa9f7dDg7vNzXxiEqrSL5WpxJ80o8JPhEREdVlGdMvIXtxLADAbV8kbLvpXyLdlCqSr9X6Eh0iIiIiorLUpjXwASb4RERERFTHiWvgA5D52ZtwJFWDCT4RERER1WnFa+BLPeSQ2FXu2UZzwASfiIiIiOosoVANzaM8ALWjPAdggk9EREREdZg6IQ/4c8mZ2rAGPsAEn4iIiIjqsNq2Bj7ABJ+IiIiI6jBVLVtBB2CCT0RERER1mPYKOizRISIiIiKycLVtDXyACT4RERER1WE6a+AzwSciIiIismzFNfgSRxkk9axNPJqqwQSfiIiIiOokQSNA/SAXQFF5jkQiMfGIqgYTfCIiIiKqkzSJeUChBkDtecAWYIJPRERERHWUqhaugQ8wwSciIiKiOqo2rqADMMEnIiIiojpKHc8En4iIiIio1lDdzRaPZU0UJhxJ1WKCT0RERER1kvqu1gx+Yyb4REREREQWTRVfNIMvcZRB6mpj4tFUHSb4RERERFTnCGqN+JCtVWNFrVkDH2CCT0RERER1kDohD1AJAABZo9pTngMwwSciIiKiOkit9YCtVZPas4IOwASfiIiIiOognRV0atEDtgATfCIiIiKqg3TWwGeJDhERERGRZdOdwWeJDhERERGRRdOpwecMPhERERGRZVP9ucmV1NMWUgeZiUdTtZjgExEREVGdIuSpoHmcB6D2raADMMEnIiIiojpGde/ZA7a1bQ18gAk+EREREdUx6rtaK+jUsiUyASb4RERERFTHqOK1VtBpxBIdIiIiIiKLpr6jtYIOZ/CJiIiIiCybSqtEp7btYgswwSciIiKiOkZdXKJjJYFVQ3vTDqYaMMEnIiIiojqleBdbq4b2kFjXvnS49r0jIiIiIiIDNE8LITxVAqid9fcAE3wiIiIiqkOKZ++B2rmCDsAEn4iIiIjqkNq+gg7ABJ+IiIiI6hBVfO1eQQdggk9EREREdYj6rvYMPkt0iIiIiIgsmk4NPmfwiYiIiIgsm/rPEh2JnRWkXrYmHk31YIJPRERERHWCoBHEGnyrRg6QSCQmHlH1YIJPRERERHWCJjEPyFcDAKwa1c7yHIAJPhERERHVEarbWvX3AUzwiYiIiIgsmiouSzyWNXU04UiqFxN8IiIiIqoTtDe54gw+EREREZGF057BtwrgDD4RERERkUVTFc/gSyWQNaqdm1wBTPCJiIiIqA4QBAGqW0Uz+FYN7SGRW5l4RNWHCT4RERER1XqaJ4UQMpQAAFnT2lt/DzDBJyIiIqI6QH1bq/6+Se2tvweY4BMRERFRHaCK01pBhzP4RERERESWTXVHaw38WryCDsAEn4iIiIjqAJ0Z/Fq8Bj7ABJ+IiIiI6gDtTa5q8xr4ABN8IiIiIqoDije5knrbQuogM/FoqhcTfCIiIiKq1TTZSmiS8gEAsqa1e/YeYIJPRERERLWc+rZ2/T0TfCIiIiIii1ZcngMAVrX8AVuACT4RERER1XKqO3VnBR2ACT4RERER1XLaM/gs0SEiIiIisnDaS2TyIVsiIiIiIgtXPIMvcbGB1MXGxKOpfkzwiYiIiKjWEgrUUD/IBQDImtb++nugChL8lJQU7Nu3D3PnzkXfvn3h4+MDiUQi/qxdu/a5Y1+5cgWffvopWrVqBVdXVygUCgQFBSEqKgr79+9/7rh37tzBzJkzERoaCg8PD9jZ2SEgIAADBgzAjh07oFKpnituYmIiFi9ejLCwMPj4+MDW1haNGjVCjx49sHbtWuTl5T1X3KdPn+Kbb75B586d4evrC7lcDl9fX3Tu3BnffPMNnj59+lxxiYiIiGo71d1sQCg6rgv19wAA4Tk9fvxY8Pf3F1D0KzP4891331U4tlKpFKZNmyZIpdIyY/fu3VtITk6uUOylS5cKcrm8zLgdOnQQbt++XaG4mzdvFpydncuMGxQUJMTExFQo7qFDh4T69euXGbd+/frCwYMHKxRXW0ZGhgBAyMjIeO4YREREROYod0+CkCDdKCRINwoZMy6ZejjPrSL52nPP4Ofn5+PevXvPe3mZxo0bh4ULF0Kj0QAArK2t8eKLL+LVV1+Fm5ub2G/v3r3o0qULsrOzDYXSMW/ePHz88ccoKCgAAEilUrzwwgvo1KkTfHx8xH6//vorIiIi8PjxY6Pirl+/HkOHDkVGRoZ4LjAwEBEREfD39xfP3bhxA507d8bVq1eNinvkyBH07NkTjx49Es81atQIERERCAwMFM89evQIPXv2xNGjR42KS0RERFRXqO9or4FfN2bwq6QG38PDAz169MAXX3yBXbt2VSrWf/7zH/z3v/8V23379sXdu3dx6dIlnDx5Eo8fP8by5cshk8kAAJcvX8a4cePKjXvgwAHMmjVLbIeFheHatWu4cuUKjh8/joSEBGzZsgUKRVFtVkJCAt56661y4165cgXvv/++2A4MDMRvv/2GGzdu4NixY4iPj8fPP/8MLy8vAEBmZib69u2L/Pz8MuMmJSVh0KBBUCqVAABvb28cPHgQd+/exbFjx3Djxg2cP39eTPRVKhUGDRqEpKSkcsdMREREVFeo4urWGvgAnr9EJyMjQ9i+fbsQHx9f6jU8Z4lOTk6O4O3tLV7buXNnQaVS6e27evVqsZ9EIhEuXLhgMK5GoxFefPFFnVKZnJwcvX0PHjyoM/7o6Ogyx9ynTx+xr7u7u5CYmKi33x9//KFTGvT111+XGff//u//xL5yuVy4evWq3n6JiYmCu7u72HfixIllxtWHJTpERERUW6X0OiKW6Kge55p6OM+tRkp0nJycMGjQIJ0SlMpau3YtEhMTAQASiQT/+te/YGVlpbfv6NGj0b59ewCAIAhYvHixwbj79u3D77//LraXLVsGe3t7vX27dOmCIUOGiO1FixYZjBsbG4s9e/aI7fnz54sz9SW1aNECH3/8sdhesmSJWIJUUnp6OlatWiW2P/nkEzRv3lxvXy8vL8ybN09sr1q1Cunp6QbHTERERFSXqG4XzeBL7K0g9bI18WhqhlktkxkdHS0eR0REICQkpMz+2qU5P/30k1hbX1bcxo0bo1u3bkbHPXfuHBISEsqNq1AoEBUVVWbcsWPHiseJiYk4c+aM3n579+5FYWEhgKIPOtrX6fPOO+/AwcEBAFBQUICffvqpzP5EREREdYGg1IibXMkCnSCRSEw8opphNgl+dnY2Tpw4IbZ79OhR7jU9e/bUuf7YsWN6++3du1c87t69e7l/uB07dhQT5pLXG4obHh4u1u8b0qRJEwQFBYntH3/8sdy4QUFBaNy4cZlxFQoFwsPDy41LREREVJeo7mYD6qI1MmXN6sYDtoAZJfixsbHiA6VA0UOw5fH29kajRo3E9uXLl0v1SU5OFst+jI0rk8nQtm3bMuMKgoArV65UKG7JfvriAtApJ6rKuERERER1iermsxV0ZIFM8GvctWvXdNoBAQFGXafdr2SM6ox7//595OTkVHlclUqFuLi4SsW9devWc2/WRURERFRbqG5liseyZk4mHEnNkpl6AMXi4+PFY5lMprMufVn8/Pz0xjB0Tru/qePeu3cPgiDolAw9fPhQ55uM54mrVCrx6NEjg9cWFBToPK+QmZmptx8RERGRJeMMvollZT37A3B0dIRUatzQnJyefRrTjmHonLOzs9nE1Wg0yM3NrfK4+uJoW7hwIZydncWfhg0bGnUPIiIiIkuiuqk9g88Ev8Zp70Zra2v8EkZ2dnZ6Yxg6Z2zsmoirL051xdU2bdo0ZGRkiD8PHjww6h5ERERElqR4Bl/qLofUVW7i0dQcsynR0a4ZL96l1hjafbVLW/TFrUjsmoirL3Z1xdUml8shl9edv+RERERU92iyldA8ygMAyILqTv09YEYz+NobT+Xn5xt9nXZf7aUt9cWtSOyaiKsvdnXFJSIiIqpLVLe06u/rUHkOYEYJvvYa8nl5eUZfp13Drm8d+pLnjI1dE3H1xamuuERERER1SV19wBYwowTf3d1dPM7Ozi6zhlyb9hr3bm5uZcYFgMePH5tNXEdHR1hbW1d5XED/mImIiIjqirq6RCZgRgm+9g6vQNE688bQfkA0ODi4xuIGBgbqLG9ZVXFdXV11kvznievh4QFXV1ejriMiIiKqjTiDbwZCQkJ02pcuXSr3GqVSiatXrxqMAQDNmjXTeQDVmLgAcPHixTLjKhQK+Pr6VnnckuerMi4RERFRXSEukSkBZAF1q3TZbBL8Jk2a6CTMJ0+eLPeaCxcu6NSed+rUqVQfGxsbtG/fvkJxExMTdXaT1Re35Hlj4iqVSpw9e7ZCcc+ePWvUrrS//PJLuXGJiIiI6gJBEMQZfCs/B0jszGbhyBphNgk+APTt21c83r59OwoLC8vsv3HjRvG4RYsWCAgI0NuvX79+4vGhQ4eQlJRkdNx69eoZTJi14167dk1nFl2f3bt3ixtQSaVS9OnTp9y4mZmZ2LNnT5lxL1y4gBs3bui9noiIiKiu0aQUQMgoWjK8rpXnAGaW4I8aNUo8Tk1NxapVqwz2TUhIwLp16/ReW9LQoUPFdd+VSiWWLFlisG92djb++c9/iu2oqKhSD8IW69WrFzw8PMT2/PnzDcZVq9VYvHix2O7Zsyc8PT319m3Tpg1atGghthcuXAi1Wm0w9oIFC8TjF154AaGhoQb7EhEREdV2OjvYBtatB2wBM0vw27ZtqzOLP336dJw6dapUv8zMTAwbNkycDff29saECRMMxvX19cW4cePE9rJly7Bz585S/ZRKJd59913xwVY7OztMnz7dYFwHBwdMmzZNbEdHR2Pp0qWl+gmCgEmTJuH8+fMAAIlEgrlz5xqMK5FIMG/ePLF9/vx5TJ48GYIglOr7j3/8A99//73Ynjdvns7Dv0RERER1TV1+wBYAJIK+rNFI77//PtavX1/qfEFBgXgsk8lgZWVVqo+hDZzi4+PRtm1bpKamAijadXX06NHo1q0bFAoFLl++jOXLl+Pu3bsAikpddu3aZbDcpVh6ejrat2+PW7duidcNGzYM/fv3h6urK27cuIGVK1fi8uXL4jUrVqwo84MDABQWFiIyMhKnT58Wz73xxhuIioqCt7c34uPjsWbNGp0a/UmTJuGrr74qMy4ADB48GNu3bxfb4eHhGDNmDPz9/ZGYmIiNGzfixx9/FF9/6623sG3btnLjlpSZmQlnZ2dkZGTAyanufcolIiKi2iVj2iVkL4kFALj91Bm23eubeESVV5F8rVIJ/qhRo3TKZCqirNuePn0affr0wZMnT8qMYWVlhaVLl+LDDz806p43b95Ely5ddJaUNGTKlCk6JTVlSUlJweuvv44rV66U23fo0KHYsGEDpNLyvzzJy8tD7969cfTo0XL7du7cGT/99BPs7OyMGrM2JvhERERUm6S9eQL5uxIAAF5xfSFrbPmr6FQkXzOrEp1ir7zyCi5fvow333xTZ4lLbW3btsWJEyeMTu6BorXrL1++jNGjRxtMhENCQvDDDz8YndwDRevOnzt3DpMnT4azs7PePv7+/li9ejU2bdpkVHIPFJUIHTp0CIsWLTJYr+/p6YmFCxfi8OHDz5XcExEREdU2YomOjRRWfvamHYwJVGoGvyakpKTgxIkTSEhIQGFhIerXr482bdqU2sCqorKysnDkyBE8ePAAOTk58PHxQcuWLdG6detKxc3Pz8exY8cQHx+P9PR0eHl5ITg4GGFhYZWqjVepVDhx4gRu376N1NRUuLu7IyAgAJ06dTL4IchYnMEnIiKi2kJQa/BIsQ0o1EDW3BleV3qbekhVosZKdKh2YIJPREREtYXqbjaSmu4GANj294XbztqxP5DFl+gQERERET2Pur5EJsAEn4iIiIhqEdUtrSUym9W9JTIBJvhEREREVIvU9TXwASb4RERERFSLqK5niMcs0SEiIiIisnDKa0U1+BJXG0g95CYejWkwwSciIiKiWkGTUQjNozwAgHWIc6WWKLdkTPCJiIiIqFZQXddaQSe4bpbnAEzwiYiIiKiWUGon+CFM8ImIiIiILJr2DL51sLMJR2JaTPCJiIiIqFZQXdNaQYcz+ERERERElq24REdibwUrPwcTj8Z0mOATERERkcUTCtRQ384GAMiCnCCR1s0VdAAm+ERERERUC6huZQEaAUDdXkEHYIJPRERERLWA6hqXyCzGBJ+IiIiILJ7y+rMHbOvyCjoAE3wiIiIiqgV0ZvDr8Ao6ABN8IiIiIqoFVDf+TPCtJJA1czTtYEyMCT4RERERWTRBI4gJvixAAYmNlYlHZFpM8ImIiIjIoqnv5UDIUwMAZHW8/h5ggk9EREREFo472Opigk9EREREFq14B1sAsK7jS2QCTPCJiIiIyMLprqDDEh0m+ERERERk0VRaa+DLgjiDzwSfiIiIiCyWIAhQ/jmDL21gB6mTtYlHZHpM8ImIiIjIYmmS8yGkFwLgDrbFmOATERERkcXiDralMcEnIiIiIoul1E7wOYMPgAk+EREREVkwVexT8dj6BSb4ABN8IiIiIrJgyqvPVtCxbsEEH2CCT0REREQWShAEqP4oSvClPnaQuspNPCLzwASfiIiIiCySJikfmrQCAJy918YEn4iIiIgskvIPrQ2uWH8vYoJPRERERBZJdfWpeGzdop7JxmFumOATERERkUXSnsHnCjrPMMEnIiIiIouk1FoiU9acCX4xJvhEREREZHG0V9CxauwAqcLaxCMyH0zwiYiIiMjiqO/nQshWAQCsOXuvgwk+EREREVkc1R9PxWPZC/VMNg5zxASfiIiIiCwOd7A1jAk+EREREVkcpdYMvjVn8HUwwSciIiIii6MqnsG3kkAW5GTawZgZJvhEREREZFEEtQbKa0UJvqyZIyS2ViYekXlhgk9EREREFkV1Oxso0AAAZKy/L4UJPhERERFZFJXODrb1TDcQM8UEn4iIiIgsivLqU/GYK+iUxgSfiIiIiCyK9gw+S3RKY4JPRERERBZFnMG3kULW1NGkYzFHTPCJiIiIyGIIBWqobmYBAKxDnCCRMZ0tib8RIiIiIrIYquuZgFoAAMiaszxHHyb4RERERGQxlL+ni8fWL7qYcCTmiwk+EREREVkM5eWn4rF1q3omG4c5Y4JPRERERBZDeZkz+OVhgk9EREREFkEQBCh/fwoAkHrawsrbzrQDMlNM8ImIiIjIImge50GTWgCA5TllYYJPRERERBahePYeYHlOWZjgExEREZFF0Km/5wy+QUzwiYiIiMgicAbfOEzwiYiIiMgiiDP4NlLIgp1MOxgzxgSfiIiIiMyekKeC6kYWAMC6uTMk1kxjDeFvhoiIiIjMnvJqBqARALD+vjxM8ImIiIjI7LH+3nhM8ImIiIjI7HEFHeMxwSciIiIis6e8/FQ8lnEGv0xM8ImIiIjIrAmCAOXvRTP40gZ2sHKTm3hE5s2sEvz4+HhIJJJK/cTHx1dZ3P3791do/Hfu3MHMmTMRGhoKDw8P2NnZISAgAAMGDMCOHTugUqme6/eSmJiIxYsXIywsDD4+PrC1tUWjRo3Qo0cPrF27Fnl5ec8Vl4iIiMgSqO/nQshQAgCsW3H2vjwyUw+gKslkMjg7O5vk3suWLcPUqVNRUFCgc/7OnTu4c+cOdu3ahQ4dOmDjxo1o0qSJ0XG3bNmC8ePHIyMjQ+f8vXv3cO/ePRw4cACLFi3C5s2b0bp16yp5L0RERETmpHj2HgCsX6xnuoFYCLNK8O3s7NC9e3ej+2s0Ghw8eFBsd+/eHS4u5X+qM/YeHh4eRvWbN28eZs6cKbalUimaN28OV1dX3Lp1C48fPwYA/Prrr4iIiMC5c+fg4+NTbtz169djxIgROucCAwPh4+OD+Ph43Lt3DwBw48YNdO7cGadPn0aLFi2MGjMRERGRpdB9wJYz+OUxqwTfy8urQmUxP//8s06CP3LkSKOuq2jpTVkOHDiAWbNmie2wsDCsXbsWgYGBAIo+hGzfvh1jxoxBdnY2EhIS8NZbb+HkyZNlxr1y5Qref/99sR0YGIhNmzYhNDRUPHfw4EEMHz4cSUlJyMzMRN++fXH16lXY2tpW2fsjIiIiMjXdJTLrmWwclsKsavArat26deKxi4sL+vbtW6P3FwQBU6dOhSAUbboQFBSEQ4cOick9UDSbP2TIEHz//ffiuVOnTum09fn888/Fch93d3ecOHFCJ7kHgK5du+Lw4cOQy4seNLlz5w5WrlxZJe+NiIiIyFwUz+BL7Kwga+Zo4tGYP4tN8DMzM3WS5LfffltMdGvKvn378Pvvv4vtZcuWwd7eXm/fLl26YMiQIWJ70aJFBuPGxsZiz549Ynv+/Pnw8vLS27dFixb4+OOPxfaSJUug0WiMfQtEREREZk2TqYQ6LhsAIHvBGRIri01fa4zF/oa2b9+us3qMseU5VSk6Olo8bty4Mbp161Zm/3HjxonH586dQ0JCQrlxFQoFoqKiyow7duxY8TgxMRFnzpwpsz8RERGRpVBefCIe27zsasKRWA6LTfC1y3OCg4PRvn37Gh/D3r17xePu3btDIpGU2b9jx45wcHDQe72huOHh4VAoFGXGbdKkCYKCgsT2jz/+WGZ/IiIiIkuhvKj1gC0TfKNYZIJ/584dnYdUTTF7n5ycjMTERLEdFhZW7jUymQxt27YV25cvXy7VRxAEXLlypUJxS/bTF5eIiIjIEhVeeDaDzwTfOBaZ4P/vf/8TH2yVSqUYPnx4ha4fMWIEmjVrBgcHBzg4OMDPzw89evTAkiVLkJycbFSMa9eu6bQDAgKMuk67X8kYAHD//n3k5ORUeVwiIiIiSySW6FhLYd3CNPsdWRqLS/AFQcD//vc/sd2lSxc0aNCgQjHWr1+PuLg45ObmIjc3Fw8ePMCBAwcwdepU+Pv7Y8aMGVCr1WXGKLljrp+fn1H31u5naNfdysa9d++e+AGIiIiIyFJpclRQXc8EAFi3dIZEbmXiEVkGs1oH3xi//PIL7t69K7afpzzHx8cHjRo1gp2dHdLT03Ht2jXk5+cDAPLz8zF//nycP38ee/bsgbW1td4YWVlZOm1jd9B1cnIyGKOq4mo0GuTm5urU+2srKCjQ2XE3MzPTqHsQERER1STlpXTgzzlL69YszzGWxc3gaz9c6+TkhAEDBpR7jUQiQbt27fDtt9/i0aNHePToEU6fPo3Dhw8jJiYGT58+xaZNm9CoUSPxmgMHDmDixIkGY2ZnZ+u0jd1cys7OzmCMqoprKHaxhQsXwtnZWfxp2LChUfcgIiIiqklcQef5WFSCn5ubi+3bt4vtwYMHl0ps9fH398fZs2cxZswY+Pj4lHpdLpdj6NChiImJ0dlMatWqVQYfWFWpVDptmcy4L0O0+ymVymqJayh2sWnTpiEjI0P8efDggVH3ICIiIqpJSp0HbF1MOBLLYlEJ/vfff69TwlLVq+e4uLggOjpanDUXBAErVqzQ27fkhlbFJT7l0e6nr4SmKuIail1MLpfDyclJ54eIiIjI3BQWL5FpJYF1y3omHYslsagEX7s8JyAgAOHh4VV+Dz8/P7z99tti++DBg3r7lVybXnvTrbLk5uYajFFVcQ3FJiIiIrIUQp4KqtgMAICshTMkdhb36KjJWEyC//DhQxw+fFhsV+fa95GRkeJxfHw8CgsLS/Vxd3fXaT9+/Nio2Npr57u5uVVLXEdHR4MPBxMRERFZAuXlp4C66AlbGz5gWyEWk+CvX78eGo0GQNFDsyNGjKi2e3l7e+u009LSSvXR3jkWKFq/3hja9e7BwcGlXg8MDNTZEbeq4hIRERFZksIYrR1sQ1l/XxEWk+Brl+d07twZ/v7+1XavkuUuJeviAaBZs2Y6D7ZeunTJqNgXL14Uj0NCQkq9rlAo4OvrW+VxiYiIiCyJ9go6XCKzYiwiwT937hyuX78utquzPAcArl69Kh7L5XK9a9Hb2Nigffv2YvvkyZPlxk1MTERcXJzY7tSpk95+2ueNiatUKnH27Nly4xIRERFZCmXMnwm+VALrFzmDXxEWkeBrz947ODjgzTffrLZ7CYKAbdu2ie2wsDCDffv16yceHzp0CElJSWXG3rhxo3hcr149g4m4dtxr167pzM7rs3v3bnF1IalUij59+pTZn4iIiMicCQVqKP/48wHbYCdIHfiAbUWYfYJfWFiILVu2iO1BgwZV6woxK1as0Fn7vn///gb7Dh06FHK5HEDRLPqSJUsM9s3OzsY///lPsR0VFWXwQdhevXrBw8NDbM+fP99gXLVajcWLF4vtnj17wtPT02B/IiIiInOn/CMDUBY9e2ndmrP3FWX2Cf6ePXvw5MmzGqyKludcvXoVo0ePxo0bN8rsJwgCli1bhk8++UQ8V79+fYwdO9bgNb6+vhg3bpzYXrZsGXbu3Fmqn1KpxLvvvis+MGtnZ4fp06cbjOvg4IBp06aJ7ejoaCxdulTvmCdNmoTz588DKHr4eO7cuYbfJBEREZEFEMtzANiEsv6+oiSCIAimHkRZ+vbtiz179gAo2pH27t27OqvMlOfSpUto3bo1ACA0NBSvvfYaXnzxRXh6esLOzg7p6em4ePEiNm/erFPnL5fLcfDgQXTs2LHM+Onp6Wjfvj1u3boFoKhEZtiwYejfvz9cXV1x48YNrFy5UudbgRUrVmDChAllxi0sLERkZCROnz4tnnvjjTcQFRUFb29vxMfHY82aNTo1+pMmTcJXX31l9O+mWGZmJpydnZGRkcFNr4iIiMjknv7lHHJWFT236H60C+SdWJ1QkXzNrBP85ORkNGjQACqVCgAwY8aMCs9Qayf4xvL29sb69evRpUsXo/rfvHkTXbp00Vmq0pApU6bolNSUJSUlBa+//jquXLlSbt+hQ4diw4YNkEor/qUME3wiIiIyJ8nt9kN54QkgAXyevAWpE/f3qUi+ZtYlOps2bRKTewDPtfa9j48PRowYgYCAgHL7enl54YsvvsCVK1eMTu6BorXrL1++jNGjR8POzk5vn5CQEPzwww9GJ/cA4OHhgXPnzmHy5Ml6V/IBir7VWL16NTZt2vRcyT0RERGRORHy1VD+XrQGvizEmcn9czDrGfyqlpSUhMuXLyMlJQWpqanIysqCQqGAu7s7WrdujZCQkAqV/+iTlZWFI0eO4MGDB8jJyYGPjw9atmxZ4W8RSsrPz8exY8cQHx+P9PR0eHl5ITg4GGFhYZUeM2fwiYiIyFwU/pqKlFd/BgDYj2wCl/92MPGIzENF8rU6teaQl5cXunbtWq33cHR01FnmsqrY2tqiR48eVR6XiIiIyJwUnk8Tj63buplwJJaLNR1EREREZDYKzz1L8G3aMcF/HkzwiYiIiMhsiDP4cimsW+p/BpHKxgSfiIiIiMyC5kkB1LeyABRtcCWxsTLxiCwTE3wiIiIiMgva9fc27dxNOBLLxgSfiIiIiMyCToLPB2yfGxN8IiIiIjILSj5gWyWY4BMRERGRyQmCgMLzTwAAEhcbWAUoTDwiy8UEn4iIiIhMTn0/F5rkfABF5TmV3cizLmOCT0REREQmx/KcqsMEn4iIiIhMjjvYVh0m+ERERERkcror6LiacCSWjwk+EREREZmUoNJA+VtRgm/l7wArLzsTj8iyMcEnIiIiIpNSXcuEkKsGwPXvqwITfCIiIiIyqcKzqeKxNR+wrTQm+ERERERkUoW/PkvwuYJO5THBJyIiIiKTKjzzZ4Ivk8CmDR+wrSwm+ERERERkMuq0AqiuZwIArF92hcROZuIRWT4m+ERERERkMkrt8pwwdxOOpPZggk9EREREJlNw5lmCLw/zMOFIag8m+ERERERkMoVnUsRjzuBXDSb4RERERGQSgkoD5bk/N7jys4eVr72JR1Q7MMEnIiIiIpNQXn76bIMrludUGSb4RERERGQSOuU5r7A8p6owwSciIiIikyg8rb2CDmfwqwoTfCIiIiIyieINriT2VrBuVc+0g6lFmOATERERUY1TP8yF+l4OAMC6nRsk1kxLqwp/k0RERERU4wrPsDynujDBJyIiIqIaV6D1gK2c699XKSb4RERERFTjtGfwrTswwa9KTPCJiIiIqEYJeSooY9IBALJgJ1i5yU08otqFCT4RERER1ajC354ASg0AwIaz91WOCT4RERER1ajCX5LFY5uOniYcSe3EBJ+IiIiIalTBiWcJvrwTE/yqxgSfiIiIiGqMoNKIO9hKG9jBqrGDiUdU+zDBJyIiIqIao4x5AiFHBQCQd/SERCIx8YhqHyb4RERERFRjCn7RWv+e9ffVggk+EREREdWYQq36exvW31cLJvhEREREVCMEjYCCk0UJvtRdDlmIk4lHVDsxwSciIiKiGqH64ymEp0oARctjsv6+ejDBJyIiIqIaUfCL9vKYHiYcSe3GBJ+IiIiIakThiWcP2HKDq+rDBJ+IiIiIqp0gCOIMvsTJGtat6pl2QLUYE3wiIiIiqnaqm1nQJOUDAGxe9YDEimlodeFvloiIiIiqnfbymPIIludUJyb4RERERFTtdB6wZf19tWKCT0RERETVShAEFBbX39tbwfplFxOPqHZjgk9ERERE1Up9Jxvq+7kAAJswd0hsrEw8otqNCT4RERERVauCI0nisfw1bxOOpG5ggk9ERERE1argSKJ4LI/0MuFI6gYm+ERERERUbQSNgIKjRTP4EidrWIe6mnhEtR8TfCIiIiKqNqqrGdCkFAAoWh5TImP6Wd34GyYiIiKiaqNTnvMay3NqAhN8IiIiIqo2BYe1E3w+YFsTmOATERERUbUQVBoU/LmDrdTTFrIWziYeUd3ABJ+IiIiIqoXytycQslQAilbPkUgkJh5R3cAEn4iIiIiqBevvTYMJPhERERFVi+LlMQHW39ckJvhEREREVOWEPBUKTqUAAKwaOUDWRGHiEdUdTPCJiIiIqMoVnkkFCjQAOHtf05jgExEREVGVyz+iVZ4Tyfr7msQEn4iIiIiqnO7690zwaxITfCIiIiKqUurUfCjPpwEAZC3rwcrbzsQjqluY4BMRERFRlSo4lAgIRce23X1MO5g6iAk+EREREVWpggOPxWN5Nyb4Nc3sEvxjx45BIpFU+Of69etG3+PKlSv49NNP0apVK7i6ukKhUCAoKAhRUVHYv3//c4/9zp07mDlzJkJDQ+Hh4QE7OzsEBARgwIAB2LFjB1Qq1XPFTUxMxOLFixEWFgYfHx/Y2tqiUaNG6NGjB9auXYu8vLznHjMRERFRVRIEAfk/FyX4EnsryMM9TDyiukciCIJg6kFoO3bsGCIjIyt83bVr1xAcHFxmH5VKhZkzZ2Lx4sXQaDQG+/Xu3RvfffcdPDyM/wu5bNkyTJ06FQUFBQb7dOjQARs3bkSTJk2MjrtlyxaMHz8eGRkZBvsEBQVh8+bNaN26tdFxtWVmZsLZ2RkZGRlwcnJ6rhhEREREAKD8PR3JL+8DAMh71Yf7ns6mHVAtUZF8TVZDY3outra2iIiIMKqvQlH+5gnjxo3Df//7X7FtbW2N5s2bQ6FQ4Pr160hLK3oYZO/evejSpQtOnTplVNx58+Zh5syZYlsqlaJ58+ZwdXXFrVu38Phx0afYX3/9FRERETh37hx8fMr/umr9+vUYMWKEzrnAwED4+PggPj4e9+7dAwDcuHEDnTt3xunTp9GiRYty4xIRERFVl+LZewCw7cHyHJMQzMzRo0cFFD2WIfj7+1dZ3FWrVolxAQh9+/YVEhISxNcLCwuF5cuXCzKZTOwzbNiwcuPu379fkEgk4jVhYWHCjRs3xNfVarWwZcsWQaFQiH1effXVcuNevnxZkMvl4jWBgYHCb7/9ptPn559/Fry8vMQ+TZo0EfLy8irwWymSkZEhABAyMjIqfC0RERGRtpQuh4QE6UYhQbpRUN5kblFVKpKvmV0NfnXIzc3FrFmzxHbnzp0RHR2NBg0aiOesra3x4Ycf4t///rd4bvPmzYiJiTEYVxAETJ06FcKfVU5BQUE4dOgQAgMDxT5SqRRDhgzB999/L547deqUTlufzz//XCz3cXd3x4kTJxAaGqrTp2vXrjh8+DDkcjmAomcAVq5cWWZcIiIiouqiyVai4JcUAIBVEwWsmjqaeER1U51I8NeuXYvExKLNFiQSCf71r3/ByspKb9/Ro0ejffv2AIoS+MWLFxuMu2/fPvz+++9ie9myZbC3t9fbt0uXLhgyZIjYXrRokcG4sbGx2LNnj9ieP38+vLz0bxDRokULfPzxx2J7yZIlZT5fQERERFRdCo4lA8qiPMS2mw8kEomJR1Q31YkEPzo6WjyOiIhASEhImf3HjRsnHv/0008GH5zVjtu4cWN069bN6Ljnzp1DQkJCuXEVCgWioqLKjDt27FjxODExEWfOnCmzPxEREVF1KDjwSDzm8pimU+sT/OzsbJw4cUJs9+jRo9xrevbsqXP9sWPH9Pbbu3eveNy9e/dyP6V27NgRDg4Oeq83FDc8PLzcB32bNGmCoKAgsf3jjz+W2Z+IiIioOogP2MokkL+mv/qAql+tT/BjY2OhVCrFdlhYWLnXeHt7o1GjRmL78uXLpfokJyeLZT/GxpXJZGjbtm2ZcQVBwJUrVyoUt2Q/fXGJiIiIqpPqdhbUcdkAAJtXPSB1tDbxiOous07wnz59isGDB6NRo0aws7ODo6MjGjdujP79+2PFihXIzMwsN8a1a9d02gEBAUbdW7tfyRjVGff+/fvIycmp8rhERERE1Slfa/da2+4szzEls07wMzIysH37dty7dw/5+fnIzs5GfHw8fvjhB/zf//0f/Pz8sHz58jJjxMfHi8cymcyo9ecBwM/PT28MQ+e0+5s67r1798SVfYiIiIhqQv7eh+KxvHt9E46EzHqjKwBo1KgRGjRoALlcjtTUVMTGxkKlUgEo+gAwceJEXLp0CWvWrNF7fVZWlnjs6OgIqdS4zzTaO4RpxzB0ztnZ2WziajQa5Obm6tT7aysoKNB5cNiYb0KIiIiIDNHkqFBwNAkAYOVrD+sX65l2QHWc2c3gS6VSdOnSBRs3bkRaWhru3r2LkydP4vDhw/j999+Rnp6OlStXwt3dXbzmv//9r8HlLLOzs8VjW1tbo8dhZ2enN4ahc8bGrom4hmIXW7hwIZydncWfhg0bGnUPIiIiIn0KDj4GCv5cHrN3fS6PaWJml+B36tQJBw8exLBhw+Dq6lrqdYVCgfHjxyMmJkbnQdi5c+ciKSmpVP/i2X6gqETHWNp9tR/S1Re3IrFrIq6h2MWmTZuGjIwM8efBgwdG3YOIiIhIn/y9z5bHtO3doIyeVBPMLsE3VsOGDbF161axnZubq7dMR3vjqfz8fKPja/fVV+pSckMrY2PXRFxDsYvJ5XI4OTnp/BARERE9D0EjiPX3EjsrLo9pBiw2wQeAdu3aoXPnzmL74MGDpfporyGfl5dndOzc3Fy9MQydMzZ2TcQ1FJuIiIioqil/S4MmqWiiUf66NyR2Zv+IZ61n0Qk+AERGRorHN2/eLPW6dq1+dnZ2mbXp2rTXuHdzcyszLgA8fvy4VB9TxXV0dIS1NdeeJSIiouqnU57zBstzzIHFJ/je3t7icWpqaqnXtXd4BYrWmTeGdl16cHBwjcUNDAzUeTClquISERERVYf8H58tj2nbm8tjmgOLT/C1S1NK1q8DQEhIiE770qVL5cZUKpW4evWqwRgA0KxZM50HW42JCwAXL14sM65CoYCvr2+VxyUiIiKqauqEXCgvpQMArENdYVW/dC5GNc/iE3ztRNzT07PU602aNNFJmE+ePFluzAsXLuh8cOjUqVOpPjY2Nmjfvn2F4iYmJiIuLq7MuCXPGxNXqVTi7Nmz5cYlIiIiqko6s/cszzEbFp3g5+bmYvfu3WL7lVde0duvb9++4vH27dtRWFhYZtyNGzeKxy1atEBAQIDefv369ROPDx06pHeZTkNx69WrZzAR14577do1ndl5fXbv3i1ukCWVStGnT58y+xMRERFVhTyd8hwm+ObCohP8GTNmIDk5WWz3799fb79Ro0aJx6mpqVi1apXBmAkJCVi3bp3ea0saOnQo5HI5gKJZ9CVLlhjsm52djX/+859iOyoqyuCDsL169YKHh4fYnj9/vsG4arVaZ5Ovnj176v0mg4iIiKgqaXJUKDhStMiHtL4drF92MfGIqJhZJfg///wz/vrXvyIhIaHMfkqlEp999hm+/vpr8dzLL7+sM1OvrW3btjqvTZ8+HadOnSrVLzMzE8OGDRNnw729vTFhwgSD4/D19cW4cePE9rJly7Bz506943333XfFB2bt7Owwffp0g3EdHBwwbdo0sR0dHY2lS5eW6icIAiZNmoTz588DACQSCebOnWswLhEREVFV0dm9thd3rzUnEkEQBFMPotiuXbswYMAASKVSvPrqq4iIiMALL7wAd3d32NjYIDU1FefOncPGjRt1Vo1xdXXF6dOnS61soy0+Ph5t27YVV9qRy+UYPXo0unXrBoVCgcuXL2P58uW4e/cugKJSl127dpVb7pKeno727dvj1q1b4nXDhg1D//794erqihs3bmDlypW4fPmyeM2KFSvK/OAAAIWFhYiMjMTp06fFc2+88QaioqLg7e2N+Ph4rFmzRqdGf9KkSfjqq6/KjKtPZmYmnJ2dkZGRwU2viIiIyChPRp5G3oZ4AIDb7giW6FSziuRrZpngV0SzZs2wdetWtG7duty+p0+fRp8+ffDkyZMy+1lZWWHp0qX48MMPjRrDzZs30aVLF50PHYZMmTJFp6SmLCkpKXj99ddx5cqVcvsOHToUGzZsgFRa8S9lmOATERFRRQiFajz2iYbwVAmJoww+SW9CIrcy9bBqtYrka2ZVohMcHIwhQ4borHpjSKNGjbBkyRJcvHjRqOQeKHoI9/Lly3jzzTd1lrjU1rZtW5w4ccLo5B4oWrv+8uXLGD16NOzs7PT2CQkJwQ8//GB0cg8AHh4eOHfuHCZPngxnZ2e9ffz9/bF69Wps2rTpuZJ7IiIioooqOJYM4akSQNHDtUzuzYtZzeBru3//PmJjY5GamorU1FTk5OTAyckJnp6eaNOmjcGVbYyVkpKCEydOICEhAYWFhahfvz7atGlTZpmPMbKysnDkyBE8ePAAOTk58PHxQcuWLY3+EGJIfn4+jh07hvj4eKSnp8PLywvBwcEICwurdM0bZ/CJiIioItI/OIfc/xQt/e26NRx2g/xMPKLaz2JLdMg0mOATERGRsQS1BokNd0GTlA/YWsEn+U1IHfRXRlDVsdgSHSIiIiIyb4VnUouSewC23X2Y3JshJvhEREREZLS8758tKmI3oKEJR0KGMMEnIiIiIqMIgoD87//cr0gmge0bXBrTHDHBJyIiIiKjKGPSob6XAwCQR3pB6mJj4hGRPkzwiYiIiMgoOuU5A7lyjrligk9ERERERskvTvAlgG0/lueYKyb4RERERFQu5ZWnUF3PBADYvOoBKy/9m3uS6THBJyIiIqJy5W69Jx7bDWZ5jjljgk9EREREZRIEAXnFCb5Uwp1rzRwTfCIiIiIqk/K3J1DfyQZQtHoOy3PMGxN8IiIiIipTnnZ5zhB/E46EjMEEn4iIiIgMEjQCcrf9meBbS2E3wNe0A6JyMcEnIiIiIoMKT6VA8zAPAGDbzRtSV7mJR0TlYYJPRERERAaxPMfyMMEnIiIiIr0ElQZ5O+4XNWytYNuX5TmWgAk+EREREelVcDQJmpQCAIBt7/qQOlqbeERkDCb4RERERKRX3pZn5Tn2LM+xGEzwiYiIiKgUIU+FvJ1F5TkShQy2veqbeERkLCb4RERERFRK3q4ECFkqAIDdID9I7GQmHhEZiwk+EREREZWSu/6ueGw/vLEJR0IVxQSfiIiIiHSoH+eh4GAiAMDK3wE2nTxNPCKqCCb4RERERKQjd1M8oBEAAPbvNIJEKjHtgKhCmOATERERkUgQBOT+747YZnmO5WGCT0REREQi5aV0qP7IAADYhLlD1szJxCOiimKCT0REREQiPlxr+ZjgExEREREAQFBqkLcpvqghl8JuMDe3skRM8ImIiIgIAJB/4DE0KQUAALs+vpC62Jh4RPQ8mOATEREREQAg97vb4rEdy3MsFhN8IiIiIoL6cR7yf3wIAJD62MG2h4+JR0TPiwk+ERERESF33R1A9efa96OaQCJjmmip+CdHREREVMcJGgE5a56V5ziMDjDhaKiymOATERER1XEFR5OgvpMNAJB38YasscLEI6LKYIJPREREVMflro4Tjx3GNDXhSKgqMMEnIiIiqsPUKfnI+z4BACD1kMO2XwMTj4gqiwk+ERERUR2Wu/4uoNQAAOxHNIHExsrEI6LKYoJPREREVEcJgoDc1c8errUfw4drawMm+ERERER1VOEvKVDdyAQA2ER4wjrQycQjoqrABJ+IiIiojsr55qZ4zIdraw8m+ERERER1kDohF3nfPwAASL1sYTeooYlHRFWFCT4RERFRHZTzn1uAumjnWof3m/Lh2lqECT4RERFRHSMUqJHz7Z8P18okcBjH8pzahAk+ERERUR2Tt/0+NMn5AAC7AQ1hVd/exCOiqsQEn4iIiKiOydZ+uPbDQBOOhKoDE3wiIiKiOqTwXCqU59IAANYv1oPNqx4mHhFVNSb4RERERHWIzuz9hCBIJBITjoaqAxN8IiIiojpCnZiHvG33AQASVxvYD/M38YioOjDBJyIiIqojcr65CRRqAAAOowMgsZOZeERUHZjgExEREdUBmmwlslfeKmpYS6H4vyDTDoiqDRN8IiIiojogd+0dCOmFAAD7of6wasClMWsrJvhEREREtZyg0iD7H9fFtuLTEBOOhqobE3wiIiKiWi4v+gHU8TkAAHl3H1i3rGfaAVG1YoJPREREVIsJgoDsv18T24q/cva+tmOCT0RERFSLFZ5IhvK3JwAA69YukL/mZeIRUXVjgk9ERERUi2V9pTV7/2kIN7aqA5jgExEREdVShRefoGDfIwCAlZ897N7yM/GIqCYwwSciIiKqpbIWXBWPFVOaQ2LN1K8u4J8yERERUS2k/OMp8r9/AACQ+tjB4d0AE4+IagoTfCIiIqJaKOvLZ7P3jpNCILG1MuFoqCYxwSciIiKqZZQ3MpG37R4AQOohh/37TU08IqpJTPCJiIiIapnsRVcBoehY8UkwpA4y0w6IahQTfCIiIqJaRHUnG7kb4wEAEhcbOPwl0LQDohrHBJ+IiIioFsn68g9AXTR9r/goCFJHaxOPiGoaE3wiIiKiWkJ5IxO5/7sLAJA4W0PxYZCJR0SmwASfiIiIqJbImn1ZnL13nBQCqYuNiUdEpsAEn4iIiKgWKLyUjrxt9wEUrZzjMJGz93WVWSb4T58+xffff4+JEyeiU6dO8Pb2hlwuh0KhgJ+fH/r06YOlS5ciPT3dqHjx8fGQSCQV/tm/f3+Fxn3nzh3MnDkToaGh8PDwgJ2dHQICAjBgwADs2LEDKpXqeX4dSExMxOLFixEWFgYfHx/Y2tqiUaNG6NGjB9auXYu8vLzniktERES1R+aM38Vjx+ktIFWw9r6ukgiCIJh6EMWuX7+OyZMn4+eff0ZhYWG5/e3t7bFgwQJ89NFHkEgkBvvFx8ejcePGFR7Pvn370KNHD6P6Llu2DFOnTkVBQYHBPh06dMDGjRvRpEkTo8ewZcsWjB8/HhkZGQb7BAUFYfPmzWjdurXRcbVlZmbC2dkZGRkZcHJyeq4YREREZDoFp1OQ2vEgAMCqoT28rvfhxla1TEXyNbNaFPWPP/7Ajz/+qHPOysoKTZs2hZeXF9RqNa5du4YnT54AAHJzc/HJJ5/g6tWr+M9//lNmkq+te/fuRvXz8PAwqt+8efMwc+ZMsS2VStG8eXO4urri1q1bePz4MQDg119/RUREBM6dOwcfH59y465fvx4jRozQORcYGAgfHx/Ex8fj3r2iDSxu3LiBzp074/Tp02jRooVRYyYiIqLaQRAEZH6hNXs/oyWT+7pOMCPbt28XAAgymUzo37+/sGvXLiEjI0Onj0ajEXbt2iU0aNBAQNEWDgIA4V//+pfBuHfv3tXpW5X2798vSCQSMXZYWJhw48YN8XW1Wi1s2bJFUCgUYp9XX3213LiXL18W5HK5eE1gYKDw22+/6fT5+eefBS8vL7FPkyZNhLy8vAq/h4yMDAFAqd81ERERmb+8/Q+FBOlGIUG6UXgctFvQKNWmHhJVg4rka2ZVg29tbY0xY8bg9u3b+P7779GvX79SX0FIJBL069cPZ86cgbe3t3h+5syZUCqVNTpeQRAwdepUCH9WOQUFBeHQoUMIDHy2oYRUKsWQIUPw/fffi+dOnTql09bn888/F8t93N3dceLECYSGhur06dq1Kw4fPgy5XA6g6BmAlStXVsl7IyIiIvMnqDXImHJRbDvNbgmJzKzSOzIBs/ob0K9fP3z77bfw8/Mrt2/Dhg0xZ84csZ2amooTJ05U5/BK2bdvH37//dlXYsuWLYO9vb3evl26dMGQIUPE9qJFiwzGjY2NxZ49e8T2/Pnz4eXlpbdvixYt8PHHH4vtJUuWQKPRGPsWiIiIyILl/vcOVH8UPadn3c4NdoP9TTwiMgdmleBXVJ8+fXTa169fr9H7R0dHi8eNGzdGt27dyuw/btw48fjcuXNISEgoN65CoUBUVFSZcceOHSseJyYm4syZM2X2JyIiIsunyVIic+Zlse38t5chkRr3PCLVbhad4Lu6uuq0MzMza/T+e/fuFY+7d+9e7kO+HTt2hIODg97rDcUNDw+HQqEoM26TJk0QFPRsrduSDyoTERFR7ZO9JBaa5HwAgO2bDSF/1bjFQaj2s+gEv3gVmWKenp41du/k5GQkJiaK7bCwsHKvkclkaNu2rdi+fPlyqT6CIODKlSsViluyn764REREVHuoHuQg6+s/KxespXBe+JJJx0PmxaITfO1SFsD4ZHjEiBFo1qwZHBwc4ODgAD8/P/To0QNLlixBcnKyUTGuXbum0w4ICDDqOu1+JWMAwP3795GTk1PlcYmIiKj2yPz8dyBfDQBQfBgIWYCjiUdE5sRiE/yMjAwsW7ZMbLdq1QrNmzc36tr169cjLi4Oubm5yM3NxYMHD3DgwAFMnToV/v7+mDFjBtRqdZkx4uPjddrGPBhcsl/JGFUV9969e+LKPvoUFBQgMzNT54eIiIgsQ8GZFORtjAcASFxt4Pj5C6YdEJkdi03w//rXv+qUyMyfP9/oa318fBAWFobXXnsNrVu3hq2trfhafn4+5s+fj969e5e57GZWVpZO29nZ2ah7ay/7WTJGVcXVaDTIzc012HfhwoVwdnYWfxo2bGjUPYiIiMi0BLUGGR/+JradZrWE1MXGhCMic2SRCf7q1auxZs0asT1kyJBSK+pok0gkaNeuHb799ls8evQIjx49wunTp3H48GHExMTg6dOn2LRpExo1aiRec+DAAUycONFgzOzsbJ229oeEstjZ2RmMUVVxDcUuNm3aNGRkZIg/Dx48MOoeREREZFo5q+KgvJQOALB+sR4cxjcz8YjIHFlcgn/ixAlMmDBBbDdu3BirVq0q8xp/f3+cPXsWY8aMgY+PT6nX5XI5hg4dipiYGJ3NpFatWmXwgVWVSqXTlslkRo1fu5++bwiqIq6h2MXkcjmcnJx0foiIiMi8qZPzkfnFs/13nJe35aZWpJdF/a24dOkS+vbti8LCQgBFq+bs37/f6DKW8ri4uCA6OlqcNRcEAStWrNDbt+SGVvn5+UbdQ7uf9pKZVRnXUGwiIiKyXJnTLkHIKJrAsx/ZhMtikkEWk+DfuHED3bt3R0ZG0W5tLi4u+PnnnxEYGFil9/Hz88Pbb78ttg8ePKi3X8m16fPy8oyKr10br299+6qIayg2ERERWaaC0ynIXXsHACBxtobTopdMOyAyaxaR4N+9exddunQRl7B0dHTEvn378OKLL1bL/SIjI8Xj+Ph48RsDbe7u7jrtx48fGxVb+8FgNze3aonr6OgIa2tro64jIiIi8yYoNXg64bzYdpr/Iqw8jXtGj+oms0/wExIS8PrrryMhIQFAUQnLjz/+iPbt21fbPb29vXXaaWlppfpo7xwLFK1fbwztB1qDg4NLvR4YGKizI25VxSUiIiLLlP23a1BdfgoAsG7tAodxTU07IDJ7Zp3gJyUloUuXLrh79y6AoodDd+3ahU6dOlXrfUuWu5SsiweAZs2a6TzYeunSJaNiX7x4UTwOCQkp9bpCoYCvr2+VxyUiIiLLo7yRicx5f+5wL5Wg3qr2kFiZdfpGZsBs/4akpaWhS5cuuHHjBgDA2toaO3bsQNeuXav93levXhWP5XK53od4bWxsdL5FOHnyZLlxExMTERcXJ7YNfVDRPm9MXKVSibNnz5Ybl4iIiCyHoBHwdOxZoEADAFB8GgybUFcTj4osgVkm+BkZGejevTv++OMPAICVlRU2bdqEN954o9rvLQgCtm3bJrbDwsIM9u3Xr594fOjQISQlJZUZe+PGjeJxvXr1DCbi2nGvXbumMzuvz+7du8UNsqRSaZl7AhAREZFlyFkVh8KTKQAAqwAFHGe1NPGIyFKYXYKfk5OD3r1748KFCwCKEtZ169Zh0KBBNXL/FStW6Kx9379/f4N9hw4dCrlcDqBoFn3JkiUG+2ZnZ+Of//yn2I6KijL4IGyvXr3g4fFs6auydulVq9VYvHix2O7Zsyc8PT0N9iciIiLzp07IRea0ZxN8Lv9uB6m9cXvjEJlVgl9QUID+/fvj1KlTAIp2oP32228RFRX13DGvXr2K0aNHi6U+hgiCgGXLluGTTz4Rz9WvXx9jx441eI2vry/GjRsntpctW4adO3eW6qdUKvHuu++KD8za2dlh+vTpBuM6ODhg2rRpYjs6OhpLly7VO+ZJkybh/PmiJ+slEgnmzp1r+E0SERGR2RMEAeljz0LIKtr80v69AMhf8y7nKqJnJIIgCKYeRLElS5Zg6tSpYtvFxQXt2rUz+vquXbvir3/9q865S5cuoXXr1gCA0NBQvPbaa3jxxRfh6ekJOzs7pKen4+LFi9i8eTOuX78uXieXy3Hw4EF07NixzHump6ejffv2uHXrFoCibxyGDRuG/v37w9XVFTdu3MDKlSt1vhVYsWKFzm68+hQWFiIyMhKnT58Wz73xxhuIioqCt7c34uPjsWbNGp0a/UmTJuGrr74q57dUWmZmJpydnZGRkcFdbYmIiEwse+UtZHxYNHkn9bGD15XekLrYmHhUZGoVydfMKsGfPXs25syZ89zXjxw5EmvXrtU5p53gG8vb2xvr169Hly5djOp/8+ZNdOnSRWepSkOmTJmiU1JTlpSUFLz++uu4cuVKuX2HDh2KDRs2QCqt+JcyTPCJiIjMg+pWJpJf3gchVw0AcPuxM2x71jfxqMgcVCRfM6sSnerg4+ODESNGICAgoNy+Xl5e+OKLL3DlyhWjk3ugaO36y5cvY/To0bCzs9PbJyQkBD/88IPRyT0AeHh44Ny5c5g8ebLelXwAwN/fH6tXr8amTZueK7knIiIi8yCoNHgy8oyY3DuMa8rknp6LWc3gV7ekpCRcvnwZKSkpSE1NRVZWFhQKBdzd3dG6dWuEhITobDL1PLKysnDkyBE8ePAAOTk58PHxQcuWLSv8LUJJ+fn5OHbsGOLj45Geng4vLy8EBwcjLCys0mPmDD4REZHpZS74A1kzi0p6rZoq4BnTC1IHPlhLRSy2RIdMgwk+ERGRaRX+loaUV38GVAIglcDjl66w6eBu6mGRGWGJDhEREZGF0GQq8WTYqaLkHoDjZ82Z3FOlMMEnIiIiMhFBEPB03Fmob2cDAKzbucFxxgsmHhVZOib4RERERCaSu/o28rYV7ZMjcbaG66ZX/7+9Ow+vqrr/Pf45c05mQkSmMIiCoIIDgxQFaxC0KLVI0TpcxaHUX3tpr7ZavUXU0qdObW2hTnWobe3VqyKtVSnaqgzaHxqL0YJpQcAkgEAg83Cm9fsjsD0nZDgJSXay8349z3my9t5rr/MNZCWf7OxBLr/H5qrQ2xHwAQAAbBD+qFzl3yuwlvs9NkXekek2VgSnIOADAAB0s1h1WAe+sV6qP3RLzBtPUHD+MJurglMQ8AEAALqRMUYHr/2HIlsqJUm+CdnKeuB0m6uCkxDwAQAAulH1A1tU/2KxJMmV6VO/Z8+SK4Xz7tF5CPgAAADdpP713aq8/UNrud/vvyTfaJ5Bg85FwAcAAOgGke3Vjfe7jx263/0dJyt44RCbq4ITEfABAAC6WKwqrLJ5a2UOhCRJKXMGK2PJKTZXBaci4AMAAHQhE43pwOUbFCkslyR5TshQv99/SS63y97C4FgEfAAAgC5U8f1/quHVXZIkV7ZP/VdNlzvLb3NVcDICPgAAQBepfujfqvlVUeOC16X+z58t34lZ9hYFxyPgAwAAdIH6V0tV8d0vnlSb/chkBc4daGNF6CsI+AAAAJ0s9I/9OnDpeuuOOem3jlPawlE2V4W+goAPAADQicKbK7T/ordkaqOSpOD8YcpcNsHmqtCXEPABAAA6SeSzGpVd8KZ1O8xA/rHq97up3DEH3YqADwAA0Ami++tVdsGbipbUSpJ8Z+Qo58XpcgU8NleGvoaADwAAcJRiBxpUNvtNRT6plNR4r/v+fzlH7gyfzZWhLyLgAwAAHIVYeUj7z39T4U0HJUnuwUHlvvZleQak2FwZ+ioCPgAAQAfFKsPaf8GbChcckCS5B6Yo9418eUem21wZ+jICPgAAQAfEKkIqm/OmwhvLJEnuYwLKfT1fvjGZNleGvs5rdwEAAAC9TbSsQWXxR+77B5T7Rr5843hKLexHwAcAAGiH6J467Z/9d0U+rpDUGO77rzlXvpOz7S0MOISADwAAkKTIZzXaf97fFN1aLUlyDwoqd825HLlHj0LABwAASEL443KVzXnLus+9Z3iacl8/V95RGTZXBiQi4AMAALSh4c3PVXbJWpmKsKTG+9znvn6uvHlpNlcGHImADwAA0IraP+7QwWv/IYVjkiTfxBz1//MMeY4N2lwZ0DwCPgAAQDOMMaq+b7Mqb//QWhf4ymDlPHuW3GlEKPRcfHUCAAA0YRqiKv/f76v2iW3WutQbjlf2iolyeXmMEHo2Aj4AAECc6O46Hfj6OoXe3W+ty1w2Qek/HCeXy2VjZUByCPgAAACHhP57v8rmr1NsV13jihSP+v1milIvH2FrXUB7EPABAAAk1fz2U5XfuFEKNV5M68lLVc6L0+U/I8fmyoD2IeADAIA+LVYbUcV3C1T75Bfn2/unD1DOc2fJMyDFxsqAjiHgAwCAPiv8r3IduGyDIpsrrHVp/3WCsn5+hlw+LqZF70TABwAAfY4xRrWPb1PF/ymQqYtKklxpXmWvmKjU/3WczdUBR4eADwAA+pTYgQaVf/s91f3/z6x13vHZynn2LPnGZNpYGdA5CPgAAKDPqPtLqcoX/bdie+qtdWk3nqCsB06XK8VjY2VA5yHgAwAAx4tVhFRx8weqfepTa52rn1/9Hpms4PxhNlYGdD4CPgAAcLT6v+5S+aKNihbXWusCFwxWv8cmyzM41cbKgK5BwAcAAI4U3V2nipsKEs61d2V4lfWzM5R67XE8lRaORcAHAACOYqIx1Ty2VZW3fyhTGbbWB849VtmPnynv8DQbqwO6HgEfAAA4RmjjfpV/t0DhjWXWOnf/gDLvO02pV4/kqD36BAI+AADo9aIltar4v5tU94cdCetTrzlOmfeeKk8uT6RF30HABwAAvVasNqLqB7ao+r7N1gOrJMk7NlPZD01WYPoAG6sD7EHABwAAvY4Jx1T75DZVLvtYsV111npXP78y7zxFaYtOkMvntrFCwD4EfAAA0GuYaEx1/2+nKu/6SNFPq7/Y4HUp7b9GK3PJyXLnBOwrEOgBCPgAAKDHM9GY6l8qUeXdHynyr4qEbSkXDVHmPafKd2KWTdUBPQsBHwAA9FgmFFXtH3ao+v7Nivy7KmFbIH+gMn88Xv4puTZVB/RMBHwAANDjxGoiqn1iq6p/9omiJbUJ2/xTc5X54wkKfPlYm6oDejYCPgAA6DGiJbWqfuQ/qn1sq2JlDQnb/OcMUMatJylw3kDuZw+0goAPAABsZYxRaP0+1az4t+peKpaiJmF7yoVDlP7DcQpMPcamCoHehYAPAABsEasKq+65nap56N8Kf1ieuNHrUvDrw5Rx60nynZJtR3lAr0XABwAA3cYYo9CGfap98lPVPb9TpjaasN19bIrSvnm80r55vDyDU22qEujdCPgAAKDLRUtrVfvMDtU+te2Iu+FIkm9yf6V/e7SCXx8mV8BjQ4WAcxDwAQBAl4jur1f9C8WqfW6nQuv2Somn1suV6VPqN4Yr9dpR8k/sb0+RgAMR8AEAQKeJHWhQ3culqntupxre2HPEBbOS5P/ysUq75jilzMuTO5UoAnQ2ZhUAADgqke3Vqv9zier+XKLQun3NhnrvmEwFLxuu1CtGyDsqw4Yqgb6DgA8AANrFRGMKFxxQ/Su7VPfnEkUKy5vt5xmWquClwxW8bIR8E7K5dz3QTQj4AACgTZHPatSwZrcaXt+j+r/tkTkYarafZ1S6gl8dqpSL8+SfmiuXm1APdDcCPgAAOEJ0b71C6/aqYe1eNbyxR5FPKlvs65vcX8G5Q5Uyd4i847I4Ug/YjIAPAAAU2Vmj0Nq9ali/V6F1+xQpajnQu7J9Cpw7UCmzBillzmDuVw/0MAR8AAD6mFhNROF/HlDovTKF3zug0Dv7FC2ubXkHj0v+M3MVOG+gUs4bJN/EHLm87u4rGEC7EPABAHAw0xBVeHNFY5B/v0yh98oU+bhCih15pxuL1yXfxBwFzhog/9kDFDj7GLmz/N1XNICjQsDvJd555x09/fTTWrdunUpLS2WM0dChQ3XWWWfp6quv1rRp0+wuEQBgI2OMYrvrFC4sP/Q6qPBH5Y3nzkdaCfOSXEGP/FNzG8P8WcfINyVX7jQiAtBbuYwxrc962KqmpkaLFy/Wk08+2Wq/hQsXavny5UpLS2v3e1RWViorK0sVFRXKzMzsaKkAgG5gYkbRklpFiioV+XelIkVVCm+uUKSwXLGyhrYH8LjkOzlLvon95Z/UX75J/eU7KUsuH6fcAD1Ze/Iav573YNFoVPPmzdOaNWusdcFgUCeddJK8Xq82b96sysrGi6CeeuoplZaW6tVXX5XH47GrZABAJzDGyBwMKfJptSL/rjoU5CuttqmNJjeQ1yXv2Cz5TsmW/4wc+SbmyHdaDkfnAYdjhvdgS5YsSQj3N9xwg+655x7l5ORIajy6f++99+rHP/6xJGnNmjW644479JOf/MSWegEAyYtVhxXdUaPI9uovPm6vUWRH47KpDLdrPPfAFPlOyZZvfD/5xmfLNz5b3hMz5fJz0AfoazhFp4fatWuXRo0apfr6eknSVVddpd/97nfN9l2yZImWLVsmSUpJSdG2bds0ePDgpN+LU3QAoPMYYxTb36BoaZ2iJbWKldYquqtO0dLaxnW7ahUtqZUpb1+AlyR5XPIcly7f6Ax5x2TKOzqz8eOJmfIMSOn8TwZAj9GevEbA76FuueUW3X///ZKk1NRUFRcXW0fumwqFQjr++ONVXFxs7Xvvvfcm/V4EfABonYnEFCtrUGxvg6J76xXbW6/Y/gbF9tYfWm5QbF+9orvrFN1VJ4ViHX8zr0ue4WnyjkiXZ0SavCdkHAryGfIel84ReaCP4hx8B3jppZes9oIFC1oM95Lk9/u1cOFC3X333ZKklStXtivgA0BfYSIxxQ6GFDsYkjn0MXYgpFj54XaDzMFwY7s8pNi+Q+H9QIPUWYfD/G55hgTlGZraGORHpsszIl3ekWnyjEyXZ0hQLg8XvALoOAJ+D1RUVKStW7day+eff36b+1xwwQVWwN+6dauKioo0ZsyYLqsRALqDMUYKx2TqojI1EcWqIjJVYZmqiGKVYZmqcNy6sGJVYZnKSOPHqrBMdVy/gyGZqkiX1uvK8cszJFWewcHGj0OC8gxOtQK9e0hQ7v4BuVyuLq0DQN9GwO+BPvzww4TlqVOntrnP6aefLr/fr1AoJEkqLCwk4APoNCYak2mISaGYTEO0sX3oozm0Tg1x2+L6mYaYTH1EpjYqUxtpDOuH2wnr4j7GbVfUvjNJXUGP3MemyD0gRZ5jAnIPSJH7mBS5BwTkOaZxvXtAijwDAnLnBjh9BkCPQMDvgbZs2WK1/X6/8vLy2tzncL9t27YdMQaA5hljGk+7OPwxaqSYkYkaKaa49qHXofXGasdvj9s/brti+qKPtV1SNNbMezSOkTB+xEiRmBQxMvEfw3HL4SbbDy0rEovbduhIeAtjKRKTiRiZUGJwP9y2M2QfLVeqR64Mn9zZfrlz/HJl++XuF/dqui7ni7YryI9JAL0P37l6oB07dljtoUOHJv2n3GHDhlkBP36Mnubl/GLVN3TDUa7elEeSqrWbP6Fk3q4jJbW5j2m22b7xmlnZa74eXIde3cwtyX/old79b29xub74J3A3tl3x693NbHe5Gutv0sda35yKQ68dzW2MHHrVduqnBsCZ8qa5NenWlq+VtAMBvweqqqqy2llZWUnvF39FdfwYTTU0NKih4YunHR5+WFZ3OVCVroYot3MD0Ixe84sYADRKK6qxu4QjcJl+D1RdXW21U1KSD8LBYLDZMZr66U9/qqysLOuVzClAAAAA6B04gt8DRSJf3OXB603+vyi+bzjc8gNUbrvtNt10003WcmVlZbeG/K8+6pVpWl9zpyE1XdXsWQuutvs0HbvZPs2N3YF6knivIz/VDn7uHf48OvBvZjeO6qIj+LpBO/Elg47wZyZ/tkV3IeD3QKmpqVb78JNskxHfNy0trcV+gUBAgUCgY8V1guzTMmx7bwAAAKfjFJ0eKD39iyvc6urqkt6vtvaLC8LixwAAAEDfQcDvgXJzc6327t27k95vz549Vrt///6dWhMAAAB6BwJ+DxT/gKqysrKEI/OtKS4uttonnnhip9cFAACAno+A3wONHTs2YXnTpk1t7lNaWqp9+/a1OAYAAAD6BgJ+DzR58uSEi2DXr1/f5j7r1q2z2ikpKZo8eXKX1AYAAICejYDfA6Wnpys/P99afuaZZ9rcJ75Pfn5+q3fRAQAAgHMR8Huoa665xmoXFhbq5ZdfbrHvBx98oNdee63ZfQEAANC3EPB7qPnz52vChAnW8qJFi/TJJ58c0W/37t268sorFY1GJUmnnnqqLrnkkm6rEwAAAD0LD7rqoVwul37zm99oxowZqqur0+7duzVlyhTdeOONmj59urxerzZu3KgVK1bo888/lyQFg0E99thjcvW0p5ACAACg27iMMTyZuQdbuXKlrrzyyjYfeBUMBvWHP/xB8+bNa/d7VFZWKisrSxUVFcrMzOxoqQAAAOgi7clrnKLTw82bN08FBQWaOXNms0fmXS6X8vPz9f7773co3AMAAMBZOEWnFxg7dqxef/11FRcXa8OGDSotLZUkDRkyRNOmTVNeXp7NFQIAAKCnIOD3Inl5ebrsssvsLgMAAAA9GKfoAAAAAA5CwAcAAAAchIAPAAAAOAgBHwAAAHAQAj4AAADgIAR8AAAAwEEI+AAAAICDEPABAAAAByHgAwAAAA5CwAcAAAAchIAPAAAAOIjX7gJgP2OMJKmystLmSgAAANCcwzntcG5rDQEfqqqqkiTl5eXZXAkAAABaU1VVpaysrFb7uEwyvwbA0WKxmHbt2qWMjAy5XK5uec/Kykrl5eWpuLhYmZmZ3fKeAI4ecxfonZi7vZ8xRlVVVRo8eLDc7tbPsucIPuR2uzV06FBb3jszM5NvNEAvxNwFeifmbu/W1pH7w7jIFgAAAHAQAj4AAADgIAR82CIQCGjp0qUKBAJ2lwKgHZi7QO/E3O1buMgWAAAAcBCO4AMAAAAOQsAHAAAAHISADwAAADgIAR8AAABwEAI+us0777yjRYsWady4ccrKylJmZqbGjRunb37zm9qwYYPd5QG9Unl5uV566SUtXrxY06dP18CBAxUIBJSenq5hw4bpoosu0oMPPqiDBw92aPyPPvpIN910k8aPH6+cnBylp6drzJgxuuKKK7R69eoO1/3pp5/qjjvu0BlnnKFjjjlGwWBQo0aN0te+9jW98MILikQiHR4b6O127NihtLQ0uVwu63XnnXe2awzmbh9ngC5WXV1trr32WiOp1dfChQtNdXW13eUCvcKWLVvMhRdeaPx+f5tzS5JJTU01v/jFL0wsFktq/HA4bG677TbjdrtbHXfOnDlm79697ar9wQcfNIFAoNVxzzzzTLNt27aO/NMAvd7s2bOPmBNLly5Nal/mLowxhoCPLhWJRMysWbMSJn8wGDQTJ040Z555psnMzEzYNmvWLBOJROwuG+jxnn/++SN+sHo8HjNmzBgzffp0M23aNJOTk3NEn+uvvz6pkN/0l3Kfz2cmTJhgpk2bZvr375+wbfz48aaqqiqpuu++++6Efd1utzn55JPN9OnTzaBBgxK2DR061Ozateto/6mAXuX3v/99s8E52YDP3IUxBHx0sdtuuy1h0t9www2mrKzM2l5dXW2WLFmS0Of222+3sWKgdzgc8L1er7n44ovNqlWrTEVFRUKfWCxmVq1aZYYMGZIwxx566KFWx3700UcT+s+dO9eUlJRY20OhkFm+fLnxer1Wn8svv7zNmlevXm1cLpe1z9SpU01RUZG1PRqNmmeffdakp6dbfaZNm9bOfxmg99q3b5/Jzc01kszYsWPN4MGD2xXwmbs4jICPLlNaWmpSUlKsyX7VVVe12PdHP/qR1S8lJcWUlpZ2Y6VA77Nq1Spz/fXXm507d7bZ97PPPjMDBw605lhubq4JhULN9q2pqUnoe84557T4V7XHH3/c6udyuUxBQUGLNcRiMTNhwgSr/5gxY0xNTU2zfV9//fWEkLJy5co2P0fACa688krr6/7tt982w4cPTzrgM3cRj4CPLvODH/zAmuSpqakJR+6bamhoMHl5eVb/W265pRsrBZyv6ZG9N954o9l+v/71rxN+8G/evLnVcadMmWL1X7BgQYv9XnnllYT3X716davjXnrppVbfyZMnt/0JAr3cX//6V+trfuHChcYY066Az9xFPO6igy7z0ksvWe0FCxYoJyenxb5+v18LFy60lleuXNmltQF9zUUXXZSw/MknnzTbL37uzZgxQ2PHjm113EWLFlntV199VQ0NDW2OO3LkSM2aNSvpcTdu3KiSkpJW+wO9WW1trb71rW9JknJzc3X//fe3ewzmLuIR8NElioqKtHXrVmv5/PPPb3OfCy64wGpv3bpVRUVFXVIb0Bc1/QW7srLyiD7V1dVau3attdzeeVtdXa233nqr2X6vvPKK1Z49e7ZcLler45599tlKS0trdn/AaZYsWaLt27dLkh544AH179+/Xfszd9EUAR9d4sMPP0xYnjp1apv7nH766fL7/dZyYWFhp9cF9FU7d+5MWB4wYMARfTZv3qxwOGwtJzNvBw4cqBEjRljLzc3bvXv3as+ePe0a1+v1atKkSa2OCzhBQUGBfvnLX0pqPPJ+9dVXt3sM5i6aIuCjS2zZssVq+/1+5eXltblP037xYwA4Ok1Pe2vuB3XTOTdq1Kikxo7v19y87apxgd4uEono+uuvVzQald/v1yOPPNKhcZi7aIqAjy6xY8cOqz106NA2/6R32LBhw5odA0DHVVRUWEcIJWn8+PEaN27cEf3i55zX69WgQYOSGr+tedt0XXz/oxkX6O1+9rOfadOmTZKkW2+9VSeeeGKHxmHuoikCPrpEVVWV1c7Kykp6v8zMzGbHANBxN998c8Kf2ZctW9Zsv/g5l5GRIbc7uR8Rbc3bpuuS/Z7A9wM42bZt23TXXXdJko4//njdfvvtHR6LuYumCPjoEtXV1VY7JSUl6f2CwWCzYwDomMcff1xPPPGEtXzppZcecUedw7pq3jZdl+zYfD+Aky1atEh1dXWSpIcffrhdc64p5i6aIuCjS0QiEavt9XqT3i++b/wFQwDab+3atfr2t79tLY8cOVKPPvpoi/27at7Gj9uesfl+AKd66qmn9Le//U2SdMUVV2jmzJlHNR5zF00R8NElUlNTrXZ9fX3S+8X3jb/NFoD22bRpk+bOnatQKCSp8a45q1evbvVP7F01b+PHbc/YfD+AE+3du1ff//73JUn9+vXTz3/+86Mek7mLpgj46BLp6elW+/CfIJNRW1vb7BgAkldUVKTZs2eroqJCUmOIWLNmjUaPHt3qfl01b5uuS3Zsvh/AiRYvXqwDBw5Iku65555mb1nbXsxdNEXAR5fIzc212rt37056v/gLAdv7oA8A0vbt2zVz5kzt3btXUuMFd6+99pomTJjQ5r7x87a6ujrpc2fbmrfx40rJf0/g+wGc5t1339Vzzz0nqfFWtTfccEOnjMvcRVMEfHSJMWPGWO2ysrKE3+ZbU1xcbLU7erswoK8qKSlRfn6+9Wj41NRU/eUvf9GUKVOS2j9+3krSZ599ltR+bc3brhoX6G0+//xzq/3uu+/K7XbL5XK1+Ip/QN1dd92VsC3+9pPMXTRFwEeXGDt2bMLy4fv8tqa0tFT79u1rcQwALfv88881c+ZM63H3gUBAq1at0vTp05MeoyPzNhwO61//+leLY0jSCSeckHDRXTLjStI///nPVscF0Ii5i6YI+OgSkydPViAQsJbXr1/f5j7r1q2z2ikpKZo8eXKX1AY4TVlZmWbOnKmioiJJks/n0wsvvKDzzjuvXeMcd9xxGjp0qLWczLwtKChI+Atdc79Q+P3+hL8iJDPunj17tHXr1lbHBXobn8+nrKyspF/xD4kMBAIJ2+Lvdc/cRVMEfHSJ9PR05efnW8vPPPNMm/vE98nPz+fKeyAJFRUVmj17tj7++GNJksfj0R//+EddeOGFHRpv7ty5Vvv555+37sLTkvh5e9JJJ7X4KPuvfvWrVvuNN95IOFWhrXGzs7MJCXCEOXPmqLy8POlX/BNhf/jDH7a4TWLuIhEBH13mmmuusdqFhYV6+eWXW+z7wQcf6LXXXmt2XwDNq6mp0Zw5c1RQUCBJcrvdevrppzV//vwOjxk/9/bv39/qffNLSkr09NNPN7tvU9/4xjesv+qFw2Hdd999Lfatrq7Wr371K2v5iiuukM/nS6J6oO9i7iKBAbpILBYzEyZMMJKMJDNo0CCzZcuWI/rt2rXLjB071up36qmnmlgsZkPFQO9RX19vZs6cac0bl8tlnnjiiU4Ze+7cuda46enpZv369Uf0qaioMGeffbbVb+DAgaa2trbVcRcvXmz193g85oUXXjiiTygUMvPnz7f6BYNBU1pa2imfF9DbDB8+3JoLS5cubbM/cxeHuYwxpvt+nUBf895772nGjBnWvXMzMzN14403avr06fJ6vdq4caNWrFhh/ckvGAzq7bff1qRJk+wsG+jx7rvvPt16663Wcr9+/dp13cp5552nm2++udltO3bs0KRJk7R//35Jjef+XnfddZo1a5bS09NVWFio5cuXWxf0ut1urVq1ShdddFGr73nw4EFNmTJF//nPf6z9Lr/8cl188cXKyclRUVGRHn74YRUWFlr7rFixIuFpvEBfMmLECOtOOkuXLtWdd97Zan/mLix2/4YB53vxxRdNMBi0fqtv6RUMBs2LL75od7lAr7B06dI251Rrr6uvvrrV8Tds2GBycnLaHMfj8Zjly5cnXXdRUZHJy8tLqsZbbrnlKP+VgN6tvUfwjWHuohHn4KPLzZs3TwUFBZo5c2bCHQEOc7lcys/P1/vvv6958+bZUCGApr70pS+psLBQl1xyScJt8uJNmjRJa9eu1Xe+852kxx09erQKCwt13XXXKRgMNttn7Nix+tOf/qR77723Q7UDfRlzF5LEKTroVsXFxdqwYYNKS0slSUOGDNG0adOUl5dnc2UAWrJv3z6tXbtWJSUlCoVCGjx4sCZOnHjEQ3Daq6qqSn//+99VXFysmpoaDRo0SKeccopOO+20Tqoc6NuYu30XAR8AAABwEE7RAQAAAByEgA8AAAA4CAEfAAAAcBACPgAAAOAgBHwAAADAQQj4AAAAgIMQ8AEAAAAHIeADAAAADkLABwAAAByEgA8AAAA4CAEfAAAAcBACPgAAAOAgXrsLAACgM2zatEmrVq2ylr/3ve8pOzvbtnoAwC4uY4yxuwgAAI7Wb3/7Wy1cuNBa3r59u0aMGGFfQQBgE07RAQAAAByEgA8AAAA4CAEfAAAAcBACPgAAAOAgBHwAAADAQbiLDgCgV3O5XO3e580339Q555zT+cUAQA/AEXwAAADAQXjQFQCgV/N4PJIkY4xisdgR65vTkaP+ANBbcAQfANCrRSIRRSIRPfHEEwnrt27dam1r+poxY4ZN1QJA1yPgAwAAAA5CwAcAAAAchIAPAAAAOAgBHwAAAHAQAj4AAADgIAR8AAAAwEEI+AAAAICDEPABAAAAByHgAwAAAA5CwAcAAAAchIAPAHAEn8+XsByNRm2qBADsRcAHADhCRkZGwvLBgwdtqgQA7EXABwA4wogRIxKW33vvPXsKAQCbuYwxxu4iAAA4WpFIRLm5uaqoqJAkDR48WI8//rjOOeccBYNBm6sDgO7DEXwAgCN4vV4tXLjQWt61a5e+8pWvKDU1VampqUpPT7de69ats7FSAOhaBHwAgGMsW7ZMZ5111hHr6+rqVFNTY724ABeAkxHwAQCOkZaWprfeekvPPvusFixYoNGjRysjI0NuNz/uAPQdnIMPAAAAOAiHNAAAAAAHIeADAAAADkLABwAAAByEgA8AAAA4CAEfAAAAcBACPgAAAOAgBHwAAADAQQj4AAAAgIMQ8AEAAAAHIeADAAAADkLABwAAAByEgA8AAAA4CAEfAAAAcBACPgAAAOAgBHwAAADAQQj4AAAAgIMQ8AEAAAAH+R9QBxYKzScEgwAAAABJRU5ErkJggg==", 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", 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" ] @@ -1060,7 +1050,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "ab417c68", "metadata": {}, "outputs": [], @@ -1195,13 +1185,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "89a13ac5", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1242,7 +1232,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "48917666", "metadata": {}, "outputs": [], @@ -1253,7 +1243,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "7bbcd148", "metadata": {}, "outputs": [], @@ -1263,7 +1253,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "18c67d04", "metadata": {}, "outputs": [ @@ -1274,34 +1264,36 @@ "Fit correlation class instance: \n", " \n", "Result of fitting The Real Part Of |Result of fitting The Imaginary Part \n", - " the Correlation Function with 3 terms: | Of the Correlation Function with 3 terms: \n", + " the Correlation Function with 3 terms: | Of the Correlation Function with 5 terms: \n", " | \n", - " Parameters| a | b | c | d | Parameters| a | b | c | d \n", - " 1 | 1.08e-01 |-2.36e-01 || 1.13e-06 |2.76e+00 | 1 | 1.51e+00 |-3.82e+00 || 2.00e+00 |1.94e+00 \n", - " 2 |-6.76e+00 |-1.73e+00 ||-2.87e-01 |1.48e+01 | 2 |-1.17e+01 |-4.49e-01 || 1.74e-08 |-1.34e-01 \n", - " 3 | 8.15e+00 |-2.35e+00 ||-1.00e+00 |3.83e+00 | 3 | 2.16e+01 |-1.41e+00 ||-1.55e-08 |-1.81e+00 \n", - " | \n", - "A normalized RMSE of 5.90e-05 was obtained for the The Real Part Of |A normalized RMSE of 4.67e-05 was obtained for the The Imaginary Part \n", - " the Correlation Function | Of the Correlation Function \n", - " The current fit took 4.625643 seconds | The current fit took 0.544415 seconds \n", + " Parameters| a | b | c | Parameters| a | b | c \n", + " 1 | 2.24e-01 |-3.43e-01 |6.57e-18 | 1 |-5.20e+00 |-4.65e+00 |1.20e+00 \n", + " 2 |-9.60e-01 |-4.96e+00 |3.80e+00 | 2 | 2.77e+00 |-4.68e+00 |2.77e+00 \n", + " 3 | 2.26e+00 |-2.23e+00 |4.28e-12 | 3 |-1.68e+00 |-3.68e-01 |4.72e-03 \n", + " | 4 |-6.72e+00 |-2.13e+00 |4.69e-01 \n", + "A normalized RMSE of 8.22e-05 was obtained for the The Real Part Of | 5 |-4.63e+00 |-1.04e+00 |7.08e-02 \n", + " the Correlation Function | \n", + " |A normalized RMSE of 5.01e-06 was obtained for the The Imaginary Part \n", + " | Of the Correlation Function \n", + " The current fit took 0.092525 seconds | The current fit took 1.349405 seconds \n", "\n" ] } ], "source": [ - "bath, fitinfo = fc.get_fit(Ni=3, Nr=3)\n", + "bath, fitinfo = fc.get_fit(Ni=5, Nr=3)\n", "print(fitinfo[\"summary\"])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "2b5f867b", "metadata": {}, "outputs": [ { "data": { - "image/png": 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KIiPgzAoifahUQFKS1FGYP3d3wIRb6T148EA8rlevXqX6qlu3bql9l2bevHlo0qRJpcYtkJSUpFWwcu7cuaXOVACAqVOnYtOmTTh79qxBxi/KxcUFa9asEWd0FOXg4ICJEyeKu6ecPn261L4019mXxcbGBgsXLhRnyOzevRtvvvlmBSMnqn4EQShWe2DUqFHFzouOjsa7774rtr/66iutdgErKyu89tpraNasGfr27QulUolVq1bhnXfeQYMGDYqdP378eHFGhZeXF44dO4aAgIBi58lkMrRr1w7t2rUrsVhuTk4Opk+fLrbbt2+Po0ePFvsdUa9ePezatQsjRozA7t27AQALFy7Eq6++Wux3dVFJSUkICAjA6dOn4eLiovWYra0tli5dijt37oi1cTZu3IiJEycW62fPnj3i8ezZs7Xi1mRtbY3+/fujf//+yM/PLzO2ykhLS8PgwYOxa9cucRZfAVdXV6ONq2nlypU4efKk2P7666/F7b+L8vT0xJQpUzB58mSt2TiVNWXKFCQnJwMAOnfujMOHD5f4N6Z9+/Y4duwYevTogStXriAqKgrLli3T2gGswPz588U+bWxssH//fnTo0EHrHBcXF3zzzTews7PD559/brCvh4i0MVlBpI+kJMDLS+oozF9CAuDpabLhCl5cACj2orSiil6v2XdJ5HJ5sR0/KuPw4cNiITArKytMmDCh3GsmT55stGTF888/DycnpzLP6dGjh3hsqGnInTt3Fo8vXLhgkD6JqrKIiAjMnj0bf//9t3jfCy+8gLZt2xY7d8WKFeIbw/79+5eYqNDUo0cPTJo0CWvWrIFKpcLatWuxaNEirXMOHjyIK1euiO1169aVmKgoqqSp8tu2bUNCQgIAdWLjp59+KjWZaWFhgXXr1uHo0aNIS0uDUqnE2rVrsXDhwnLHXrt2bZl/E2bMmCEmK86fP1/isr/o6GjxuKzlA5qKJhEMSaFQ4IcffjDqGGVRKpX49ttvxfbw4cNLTVRosrCwgJ2dnUFiuH79Ov755x8A6iTRr7/+WmYy3M7ODt9//734d+X777/H3LlztZLwmZmZ2Lx5s9h+5513iiUqNM2bNw9//PEHbt68Wdkvh4hKwGQFEVUbmpXXra2tK9VX0evL+ySoefPmcHNzq9SYmjTfmLdt21anT8r69u1rsPGL6tq1a7nn+Pr6isdlrU/XFBERgcOHDyM4OBgPHjwQ34SU5NGjR8jMzDTYC92a7Nsz3+LbM9+W+ngT9yY4Mq7srTCf2PgEbiaV/gJ9ZteZmNl1ZqmPhyWGod//yq4rc/iVw2jq0bTUxw35dZQXrykFBwfjqaee0rovNzcXMTExuHXrlljHBgCefPJJ/PDDDyX2o/mmq7SZAEW9+OKLWLNmDQCIdWg0/fbbb+Jxq1atMGzYMJ36LcmuXbvE4169epWYcNFUq1YtvPDCC1i3bp14fXnJimbNmqFnz55lntO1a1dYWFhApVIhJycHERERxWayadbvuHr1arH/H1MbPHgwvL29JRv/zJkzuHfvntj+5JNPTB7DL7/8Iv4sDBkyBA0bNiz3mk6dOqFRo0YIDw9HXFwcbty4oZVsO3r0qFj7QiaTYerUqWX2J5fLMXnyZJ0SNURUcUxWEFG14eLiIi7XqOyWoUWvLy9ZoMuLpIrQfBHYrFkzna4p2IowKyvLoLEAQO3atcs9RzOJkJmZWea5N27cwPTp03Hw4EGtN17lSUlJYbLCAFJzUhGTFlPq4842zuX2EZ8RX2YfqTll/wwqVcoyry84pyyG/DrKi9eUHj58iP3795d5TuPGjTF37ly8+OKLJS7PunPnjtY2kX369NFp7JYtW4rHV65cgSAIWv1rTvsvaelJRZw7d048HjRokE7XPPPMM2KyIjQ0FGlpaWUWF9Ul0Wprawt3d3fx70dJydaOHTuKS1A+/fRTeHt7Y+zYsUYralwezZlsUtD8Pqhfvz7at28vaQwVSda3bNkS4eHhAIDLly9rJSvOnz8vHjdv3lwrCV+aQYMGMVlBZCRMVhBRteHq6iq+2EyqZE2Ross+yps1YehdKlJSUsTjiixpcXZ2NkqyorIzVTSdOHECgwYNKjehURLN2TOkPyeFE3wcfUp9vJZ9rXL7qGVfCynZKaU+7qQoe9mQ3EJeZgwF55TFkF9HefGam4iICPz333+l1pH577//xGO5XI7Ro0dXeIy8vDykpqbC2Vmd9FGpVOKbPABlTo8vj1Kp1ErKlrb7Q1GtW7cWj1UqFSIiIrTuK0qXRCtQfrJ14sSJ+Oqrr5CWloasrCyMGzcOs2bNwuDBg9GnTx9069bNYDWLdGHoBHlFhYWFiceV+T6oDM3v8Z9++klcylOekJAQ8TgxMVHrsYIdSQDtxF1ZmjRpAisrK3EXGCIyHCYriPTh7q6ux0Blc3c36XANGzYU141eu3at2CeCFVHSNmxlsTBwIdGKzDYwxHWmkpqaijFjxohvBhwdHfHqq69iwIABaNKkCWrXrg1bW1utddj6/h9S6Qyx5KG85RXlaerRFNEzo8s/sQzm8HUYQ+/evXHs2DGxrVQqERMTg6CgIHz99dc4deoUlEolFi1ahLy8PHz99dfF+tBM2CqVynJnapQmJSVFTFY8fPhQ63eMZyVqEhWdveDh4aHTdUXP09y+tST6JFpL+j1ap04dbN++HWPGjBFjT0xMxMaNG7Fx40YA6qVww4YNw6RJk9CmTZsKj1sRUm/jrJnQr8z3gb5UKpXW95BmHZWK0PxgAND+fnLX8TWMpaUlnJ2diyU+iKjymKwg0oeFhUkLR5Juunfvjn379gFQvykuuha1IjRrRjRu3BheJi6oqjmbQtf6D0Dll78Y2/r168WCeq6urjh37lyZu5ykpaWZKjQisyWXy1GvXj3Uq1cPQ4cOxeTJk8U6Fd988w369etXbBlFRkaGQcZWqVTicdGZTQqFQu9+i/ala1Kh6JimnG3Vv39/hIWF4ZtvvsGmTZtw//59rcejo6OxatUqrF69GuPGjcOqVauMtmzN0AnyitJ83ivzfaCvrKwsre9NfRXto6CwNVCxRJcUzwFRTSDtbzoiIgPq1auXVluzEFxFREREaK1bLdqvKWhux6frzhp37941yhIQQzp48KB4/Pbbb5eZqACgteaeiNQzjVatWqW19GHq1Klab7IA7YRn/fr1IQiCXrf69euX2CdQ/FPpiiiYrVFA18Rk0YRsZXd+qigvLy8sXrwYMTExuHr1KlauXInRo0drLRUUBAE///wzXnjhBZPGZkjlJQI0n/fKfB/oy97eHlZWVmL72LFjen1/F90CWHPXq4oky5lYJzIOJiuIqNro2bOn1prh9evX6/XmffXq1VrTgF977TWDxFcRgYGB4vHVq1fLneoMQGvauLnSXKOu+TWW5t9//zVmOERVkpWVFVasWCG2IyMjxR08CmjOBouKiip3RyNd2NnZaS0/uHXrlt59OTg4wNbWVmxHRETodJ1mTQFAmiUIgDpp1Lp1a0ybNg1bt25FfHw8/v77b60k0u7du7WKQEpFc4aArnUVyvubo1kLpDLfB5Wh+X9vqBg0f24iIyN1uiY5OdnsZzUSVVVMVhBRtSGTybS254uOjsZnn31WoT6uX7+OZcuWie0uXbqgS5cuBotRV/369RNfYObm5uLnn38u95qCCvnmTPOFsi61KArWghORtl69emHAgAFi+8svv9RKzgYGBopLBfLz83H8+HGDjKv5+/DEiROV6qtdu3bisebOIGU5e/aseOzq6qo180NKcrkcgwcPxqFDh7Tqahw4cKDYuZpLOExRZ0gzwaRL4vvu3btIT08v8xzN74PLly9XetmRPs+JZgyHDx+u1PgFNL8nr1y5gvz8/HKv0Vw2SkSGxWQFEVUrr732mtaLjSVLlui8HCQ+Ph4jRowQ31DL5XIsX77cKHGWx93dHSNHjhTbCxYsKPaJoqa1a9dWiVkI3t7e4vHp06fLPHfbtm0Ge4NFVB198skn4nF8fLxWwtLFxQWdOnUS299//71Bxuzfv794/OeffxbbOakievbsqdVX0aUsJfnll1/E4x49ephdAV5PT090795dbMfHxxc7x97eXjw2xdI9zWWFmjthlGbXrl3lntO3b1+xEHJWVhY2b96sf4DQ7znRTNbt3LkTcXFxlYoB0P6eTE5O1lq6WBp9l5wSUfmYrCCiasXa2hpbtmwRi5qpVCq8/PLLWLBgQZnTX0+fPo2ePXuKu4kAwGeffabTUgVjWbhwofh1PHz4EH379sXevXu1PnXKyMjAwoULMW3aNNjY2MDBwUGqcHXSu3dv8XjlypW4du1aiecdOHAA48ePN1FURFVTjx49tH6mlixZolX4cMaMGeLxrl27sHPnzkqPOXHiRPH3UmZmptZstoqaMGGCeBwfH4+lS5eWef6ff/6pNQNj4sSJeo9dURWZAaE5K6Gkba81l1CUlYQ2lPbt24vHZ86cQUxMTKnnpqSk4Kuvviq3T29vb4waNUpsf/zxx5VKFujznLz00kviLJbs7GxMnTq10jNVAgICtLZinTt3bpmzK0JDQyudqCGi0jFZQUTVTtOmTbFv3z6xAJhSqcTcuXPh7++P9957D3/88QeOHz+Ov//+G8uXL8cTTzyBnj17aq15nT17Nj788EOJvgK1hg0bYtWqVeInh1FRUXj66afh6+uLPn36oEuXLvDy8sLHH3+M/Px8fPXVV1pbrZljdfLXX39dXKeempqKrl27Yvbs2di7dy9OnDiBzZs3Y9SoURg4cCAyMjIkqRdCVJV8/PHH4nFsbCzWr18vtseMGYOuXbsCUL/ZHjt2LDZt2lRun9evX8fkyZNLXH7m7u6Od999V2xv3rwZb775Zpk1MRITE0tMRDRt2hSjR48W2x999BG2b99eYh9nz57Fq6++KrbbtGmDZ555ptyvxVD69euHNWvWlFubYN++fTh69KjYLqlAs2by4MqVK0avN9StWzfUqlULgHpJ0LRp00osoJmSkoIRI0YgOlq3LYXnzZsn/j5PTEzEE088UWbtCJVKhd9++w3Xr18v9pg+z4m9vb3WUs8dO3bgxRdfLLfYZUpKClauXInnn3++xMc/+OAD8fjChQuYOnUqlEplsfOio6MxbNiwEh8jIsPg1qVEVC317NkTJ0+exMsvv4ygoCAA6jf7X3/9dZnXOTk5YdGiRZgyZYoJoizf+PHjoVKp8Pbbb4trgmNjY7V2ybC0tMTChQvx5ptv4vPPPxfvL1pt3xzUqVMHa9euxbhx4yAIAtLT0/HVV1+V+Elez549sWLFCvz4448SREpUNfTv3x9dunQRazksWrQIr732GqysrGBhYYGtW7ciMDAQ9+/fR1ZWFl555RV89913GD16NNq0aQNnZ2dkZmYiLi4OV65cwaFDh8QZT5pL6jTNnTsXx48fF5dprVq1Cn/99RdeeukldO7cGW5ubkhLS0NYWBiOHTuGvXv3wtvbW2umR4FVq1bh5MmTiI+Ph1KpxKhRozBixAiMGTMGPj4+SExMxJ49e7Bx40bxTaGNjQ3+97//icsQTOHOnTuYOnUqZs6ciQEDBqBr164ICAiAm5sb8vPzce/ePezZswfbtm0TEwEdOnTAwIEDi/UVEBCAtm3bIigoCIIgoG/fvmjdujX8/Pwglxe+NF+3bp1Bts22tLTEO++8I74J37VrF7p27Yo33ngD/v7+SE9Px5kzZ7Bu3TokJCSgT58+uHXrVpkzMACgefPmWL58OSZNmgRAPcugRYsWeO655zBw4ED4+vpCpVIhJiYG586dw44dOxAbG4ujR4+iefPmBnlOpkyZgrNnz+J///sfAGDLli3Yt28fxo4dix49eogzNpKTk3H9+nWcOXMGhw4dQm5uLjp37lzi1zV69GgMHToUu3fvFsc8f/48Jk2ahICAAGRlZeHUqVNYs2YNHj16hG7duuHevXs6J3mIqAIEIhIyMjKEixcvChkZGVKHQgaWn58vbNiwQejcubNgYWEhACjx5uPjI8yYMUNISEjQue958+aJ148bN65CcWmOHRERUe75kZGRwuzZs4UWLVoIDg4OgqOjoxAQECBMmTJFuHr1qiAIgpCXlydYWVmJ/cbHx5fY14YNG8RzevfuXeqY9erVE887evRouTFGRERofV1l2b17t9CgQYMS/y9cXV2Fjz76SMjLyxMEQbfnqjL/F0TmYNy4cTr9XJbk77//1vo5+fHHH7Uev3v3rtC2bdtSf/+VdluzZk2pY2ZkZAhDhgzRua969eqV2ldoaKjg6+urUz+Ojo7l/j7SfC7nzZun03NY3u87zcd1uTVq1EiIjIwsdbwLFy4ILi4uZfZR9PddRX8na8rNzRV69+5dbtwBAQFCQkJChcb66aefBLlcrvNzU1p/+jwngqD+O//uu+9W+Pu7c+fOpX5NaWlpQteuXcvtw9fXV4iMjKzU/01JCl6Xrl+/XlizZo2Qn59f6T6JqiImK4gEJitqivj4eGH37t3CunXrhC+++EJYtmyZsGXLFuHKlStSh2YQFy9eFF8s1a5dW+pwypWXlyecOHFCWLFihbBw4UJh7dq1wv79+4WcnBypQyMyucokKwRBENq3by9e7+/vLyb7CuTm5grff/+90Lhx4zLffDk4OAhDhgwRtmzZImRlZZU5pkqlErZs2SI0b9681P5kMpnQoUMHYcOGDWX2lZSUJLz99tuCvb19if1YWVkJL7zwgnD37t1ynwtjJCt+/fVXYfjw4YKzs3OZz5+Hh4fwwQcfCGlpaeWOGR0dLXz00UdCly5dBDc3t2Jv+A2ZrBAE9WudqVOnCpaWlsXiVigUwsSJE8W4KzpWaGioMGrUKK2EedGbl5eXMH36dCExMdFgz4mms2fPCk8//XSZiROZTCa0bdtWWLBggXDv3r0yv6asrCzh/fffF2xtbYv1Y2lpKQwfPlz8UIDJCiLjkAmCCfZMIjJzmZmZCA0NRUBAgFg4jKiqmTZtGlavXg0AGDFiRKlrv4moZrtz5w7OnTuHhIQEpKWlwd7eHrVq1UKzZs3QqlUrWFlZVbjP27dv49y5c4iPj0dmZiYcHR3RsGFDdOzYUat4Ynmys7Nx4sQJ3LlzB8nJyXByckLdunXRp08fODk5VTguQ1OpVLh+/TrCwsIQHR2NtLQ0WFtbw93dHa1atUK7du30ev5MKTExEYcOHUJUVBQsLS1Rt25d9O3bV6vmkb7S0tJw4sQJ3Lt3D8nJyVAoFPD29kbLli3RunVrk+zekpaWhlOnTokxWFpawsXFBY0aNULr1q21tpbVtb9Dhw4hIiICgiDA19cXPXr0gI+Pj5G+gsLXpcHBwcjJycHrr7+utb0rUU3BZAURmKwg8yUIgk4v7o4cOYIBAwaIVct37dqFoUOHGjs8IiIiMjAmK4jU+F1PRGTGfvrpJzz33HPYs2dPidX2k5KS8Pnnn2PQoEFioqJDhw4YPHiwqUMlIiIiIjIY7gZCRGTGlEol/vjjD/zxxx+wsrJC48aNxUrocXFxCAsL09pX3s3NzeRV8omIiIiIDI3JCiIiM6Y57TMvLw/Xr18vcY96AGjbti1+++03NG3a1FThEREREREZBZMVRERm7LXXXkOzZs2wb98+nDt3DuHh4UhMTEROTg6cnJxQq1YtdO3aFcOGDcPQoUNNUryMiIiIiMjYmKwgIjJjFhYW6NWrF3r16iV1KEREREREJsMCm0RERERERERkVpisICIiIiIiIiKzwmQFEREREREREZkVJiuIiIiIiIiIyKwwWUFEREREREREZoXJCiIiIiIiIiIyK0xWEGkQBEHqEIiIiIioBuPrUSI1JiuIAFhaWgIA8vPzJY6EiIiIiGqygtejfF1KNR2TFUQArKysIJPJkJmZKXUoRERERFSDZWZmQhAE5ObmAgBkMpnEERFJg8kKIgAWFhZwdnbGw4cPpQ6FiIiIiGqwpKQkpKenQ6lUQqFQMFlBNRaTFUSPubq6IjMzE2lpaVKHQkREREQ1UFpaGrKzs8V/PTw8pA6JSDJMVhA95uLiAkdHR9y6dYsJCyIiIiIyqbS0NNy6dQuZmZlISUmBSqWCv7+/1GERSUYudQBE5sLCwgKNGjVCcHAwbt68CRsbG7i7u8POzg6WlpacgkdEREREBiMIAvLz85GZmYmkpCRkZ2cjMzMT0dHRSExMhJOTE/z8/KQOk0gyTFYQabCwsEBAQAD+/fdfxMfHIysri0kKIiIiIjIaQRCQnp6OtLQ0pKam4sGDBxAEAd27d4ejo6PU4RFJRiZwI1+iYvLy8nDkyBGEhoZCEATY29vD2toaFhZcOUVERERElVcwsyIvLw9KpRKZmZlQKpVwdHREz5490bp1a35oRjUakxVEpcjPz0d8fDzu3buHmzdvIiMjAyqVCvyRISIiIiJDkclksLCwgKenJxo3bgw/Pz+4uroyUUE1HpMVRDrQzHwTERERERmKTCaDlZUVLC0tpQ6FyKwwWUFEREREREREZoUL8ImIiIiIiIjIrDBZQURERERERERmhckKIiIiIiIiIjIrTFYQERERERERkVlhsoKIiIiIiIiIzAqTFURERERERERkVpisICIiIiIiIiKzwmQFEREREREREZkVJiuIiIiIiIiIyKwwWUFEREREREREZoXJCiIiIiIiIiIyK0xWEBEREREREZFZYbKCiIiIiIiIiMwKkxVEREREREREZFaYrCAiIiIiIiIis8JkBRERERERERGZFSYriIiIiIiIiMisMFlBRERERERERGaFyQoiIiIiIiIiMitMVhARERERERGRWWGygoiIiIiIiIjMCpMVRERERERERGRWmKwgIiIiIiIiIrPCZAURERERERERmRUmK4iIiIiIiIjIrDBZQURERERERERmhckKIiIiIiIiIjIrcqkDINNQqVSIjY2Fo6MjZDKZ1OEQEZEEBEFAWloa6tSpAwsLfl5BxsPXHUREBFTutQeTFTVEbGws/Pz8pA6DiIjMQFRUFHx9faUOg6oxvu4gIiJN+rz2YLKihnB0dASg/iZxcnKSOBoiIpJCamoq/Pz8xL8JRMbC1x1ERARU7rUHkxU1RMEUTCcnJ75oICKq4Tgtn4yNrzuIiEiTPq89uGCViIiIiIiIiMwKkxVEREREJvTvv/9i8uTJaN68OZydneHk5ITmzZvj9ddfx+nTp40+/p07dzB37lx06NABnp6esLW1hb+/P0aMGIFt27ZBqVQaPQYiIqLyyARBEKQOgowvNTUVzs7OSElJ4XRMIqIain8LpJWRkYG3334b69evL/O8CRMmYMWKFbC3tzd4DMuWLcP777+PnJycUs/p0qULfvnlFzRs2FDvcfi9RkREQOX+HnBmBREREZGR5efnY+TIkVqJCltbW3Ts2BFdunTRegG3YcMGjBw5Evn5+QaNYcGCBZgxY4aYqLCwsEDLli3Rq1cveHt7i+edPXsWvXv3xv379w06PhERUUXU6GTFgwcPsHfvXnz22WcYOnQovL29IZPJxNvPP/9stLE1x9H19v333xstHiIiIjKeTz75BAcOHBDbkyZNQnR0NC5cuIAzZ84gNjYWn3zyifj4gQMHMHfuXIONv3//fsybN09sd+3aFaGhoQgJCcHx48cRHR2N3377DQ4ODgCA6OhojBkzxmDjExERVVSN3A0kLi4OXbp0wd27d6UOhYiIiKq52NhYfPfdd2L75Zdfxrp167TOsbe3x2effQZBEPD5558DAL799ltMmzYNderUqdT4giDg/fffR8HK36ZNm+LQoUOws7MTz7GwsMBzzz0Hd3d3PPnkkwCA06dPY8eOHRgxYkSlxiciItJHjUxWZGdnm1WiolevXrC1tS33vLp165ogGiIiIjKkpUuXIjs7GwBgZ2eHpUuXlnruJ598go0bNyIqKgrZ2dlYtmwZFi9eXKnx9+7di6tXr4rtZcuWaSUqNPXv3x/PPfccfv/9dwDAokWLmKwgIiJJ1MhkhSZPT0906NABHTt2RMeOHTF8+HCTx7Bx40bUr1/f5OMSERGR8e3YsUM8fvbZZ+Hm5lbqudbW1pgwYQI+++wzAMD27dsrnazYvn27eNygQQMMGDCgzPMnT54sJivOnz+P6Oho+Pr6VioGIiKiiqqRNSvc3NywdetWREZGIiEhAXv37sWCBQswbNgwqUMjIiKiaiQsLAzh4eFi+6mnnir3mkGDBonH4eHhCAsLq1QM//zzj3g8cOBAyGSyMs/v2bOn1k4kmtcTERGZSo1MVjg5OWH06NGoV6+e1KFULaGhwMWLUkdBRERUZWguvwDUhS3L0759e1hbW4vt4OBgvcdPSEhAXFxchcaXy+UIDAw0yPhERET6qvHLQEhHycnA0KFAdDSwfj3wwgtSR0RERGT2QkNDxWNra2v4+fmVe03Bebdv3y7WR2XGBwB/f3+drvP398exY8cqPb7ejh4FliwBcnOBnBygQQNg0ybTx0FEpicI6ptKVXirSNtY52q2NeMs7VbeOZV93JRjTJsGODsb//++CCYrqHxKJfDcc0B4OAQAwotjYZGcrP6mJSIiolJFRkaKx76+vuUuwShQt25dMVmh2Udlxi/oV9fxS+ujJDk5OcjJyRHbqampOo1Tqvh4YN++wvajR5Xrj6gqUanUSbrs7MJ/c3OBvDz9bkWuzc/LQW5eNnLzcpCrzEFefi6UKiVslTJ45lkB+fnqm1JZeJyfD1W+EkcdEpEv5EMp5CNfpf5XKeSL9/WKt0HddEut6zRv15xzsL1hDlRCPlSCABU0bjIBggB8dbDsp2dlJ+BiHUAlK/nWLQqYeab06/NlQL9x2tcIAITHv54FAF8eBp6IKL2PfY2A2U8Wni/ItP+VAbi+quyvY8pgYG/j4mMX9DE0DFhTxiq8fBngObvk8fH4ePN2YPiN0vvY2hx4eWTh2AXXFUj5ErBVAnjxRSYryEx9/z1w6BBiHYG+44BwN+DVg29j3ZgxkHl5SR0dERGR2UpLSxOPnSvwQs/JyanEPiozfkViqOj4X375JT799NOKBVcWhUK7rZEIITI5QVAnDDIyit8yM0u+v+CWnQ0hJxs5OZnIystCVl4msvOykKXMRlZ+NrLzc5ClykHrWBVqPcpTj5OXVyyEcz7AT+2BXEv1Lc+i8DjXUv2G++jGsr+M0c8C2wMAQQFAUfzx50OALX+Wfr3KAug/t+wxtv0O1C3jzfE1L2BeGavRZDokK47WB7Y3L/1xS1XZ18sAHK9f9jnJ5WzU+MgGCKlV9jnlSbAH7rroH4MMwMNyzlGWU/RBJQNyysgICLrl142GyQoz8N577+H69euIiopCXl4e3N3d0bhxY/Tu3Rvjxo1DgwYNpA1w8mTg+nWsDluDmx7qu35sp8Kb679Amw+WShoaERGROUtPTxePbWxsdL5Oc0tzzT4qM35FYqjo+B9++CFmzpwptlNTU3Va8lIqjZodAJisoMrJz1cvaU5MVN8ePQJSUnS+XbNLx10nAWkKIN26+K1NHDDpchnDWwC2OrzJHxVV+uMRrsAPHcruo+AT/dLIhLLffJb3xra8JACg/lor04fmzITSWAhlPAj1G/CyyMq5XhdW+YBtXmFfssf9av5bHtdsoFa69nWafblllX29TAAaJZU8dkFfTuX86nTJBtre1+hTo2/Nf8XlICbGZIUZ2LZtm1Y7JiYGMTExOHbsGBYuXIiJEyfiu+++03rhYFJWVsDq1fhn9i8ACqd1bvnvd7TBUmliIiIiqgKUSqV4LJfr/rJL89y8Ej5l1Wf8isRQ0fEVCgUURWdDVIZCgZN11Z/C5loCL93PhrvheqeqThCAtDQgNlZ9u39ffXvwoDAh8fiW9fABolUP8cAO6pu9+t9kW/Wn46kK4LdtZb+5nDeo7E/yR14vO1khV6nfpJf1Rj67nB9NWx1+DeRZAtb5pT/eOBkIjFGfY52vfsNtnQ9YqdQxdokuu38ZgI9OqL8WS6Hw65I/vlkKQLv7ZffR8x6wZ7M64VDarTzf7Qc+P1L69bbKsq+XAcj6HLCEDBYyC8hkFpBZqP+FhcbNRVZ4LJNp/TsqVYZRP0PdLukGAA1Leezx4z/ekgHhpVxbcNyu9MdkMhluXSjjWpkM8JUBfqU8JpNhoEyGgZElPwaZDBj0+F+J3ocyWWEGPDw84O/vDwcHB6SkpODGjRvipxhKpRJr167F+fPncfToUZ2nbxp67Wi+Kh9PNH4Sd2//KU432u8Qh0VpaYCjY6X6JiIiqq7s7OzE4+zsbJ2v0zxXcxvRyoxf0G/R+4w5vt6srbGpTeEnyX03M1lRo2RmAnfvAhERQGSk+nbvnpiYyEyIQaw8CzGOQJMkwLuMyT//NAfGPFv2cBt2AXZlJANcyvnRTbcu+3EAeFJdggY2SvWbadu8wmMbJdDiQdnX94kEgtYACsEC1jI5rC2sxJuVpRWsLawh97cCrKzVHzSWcPvCygpfJGvcZ61xbGkJ+FkCPSzVx3K5+t8it88ty34c5Txe29ISgzTPsbAofitIDJTS9tVsl3MuSjpXJoONjvWDSFpMVkikefPmeP311zFkyBA0bNhQ6zGlUon9+/djzpw54nZhV65cwfPPP4+9e/fq1L+h145aWljimxf/h29cdsPvzTxEOwPXPYDck8dg/fQQg41DRERUnTg4OIjHWVnlzOnVkJmZWWIflRm/IAZdkhWGGl9vCgUUGp+O5qhyTR8DGVdmJhAWBty4ob6FhamTEhERQEICgMeFDnsCd1yBu85ATGsgtjuQorGa6addwKtXSh/GM6P8UFIUZScrhoYBdVPUU+odch/fBDkcLG3hYGkLTwsHoIMrYG+vfbOzU38ibWODvQoFYGOjrsei+W9J9xV9TKGAs0KBNgVv7olqCCYrJPLff/+V+phcLsfgwYPRr18/jB49Gv/8oy4Du2/fPvz1118YMqT85IDB144C6l+4nTuj571T2NIKyJUDN879g9ZMVhAREZXIw8NDPL5/v5z50Rri4uLEY3d3/ecUaI5fEIMu/RlqfL0pFFBoTGfPUem/FIYkplSqExFXrqhv//0H3LiBBw/uItkWaJpU+qUyAMs7A/Fl5Mtiy5ngWy8FGBsMeGQCnpmAZ74CnpZOcJc7wlXhAhdbV3gN8wKcXdW7HZRwG+bggGFFkxAVWNZFRPrhT5kZs7GxwZYtW9C4cWPEx8cDAFasWKFTssLga0cLdOqEcbtOocc9oG0c0KRjrOHHMLJDhw7hySfVew21b98eFy9e1HkrOUMZP348Nm5Ul2z+5ptvtBJLRERUfTRt2lQ8TkpKQmZmpk4zG6KiCivtNWvWzCDjA8C9e/fQsmVLk42vN2tr7ZkVMpW6SKKlpeljId0Jgjox8e+/wNmzQFAQMm4EI9g5B5e9gaDaQKgPcKMNkGSnLux3ZW3ZXTZ4WJissMkDfNIAn1SgTpr6uOsjB6B5XaBOHcDLC/D0BDw8xFt9Dw/84u6ubru7Fy/eSkRmi8kKM+fo6IgpU6Zg/vz5AICTJ08iOzu7QhXFDap1awz8FsDjdXewKn2GiDnKy8vDW2+9JbYXL15s8kQFAHz22Wf47bffkJOTg08//RQvvvgiatWq5P5HRERkdgICArTaQUFB6NatW5nXxMTE4MGDwgXsRfuoiMaNG0Mul4uFNoOCgvD000+Xe92VK4Xz6iszvt4UCq1CgTlyALm5khV5o1Lk5QHnzwOnTgGnT6uTFEmFUyWWdQZmvqPe8rIkYR7qnRtKLaro6Ylldz0gz/NF/VpN4VrHH7I2PurEhLe3+sbvCaJqi8mKKqBv375isiI7OxtRUVFo3LixNMG0bq3dvnNHXY25ihTZXL16NW7cUG/+3KdPH/Tv31+SOOrWrYvXX38dK1asQGpqKj755BOsW7dOkliIiMh4OnXqBIVCIRa9PnXqVLnJipMnT4rHNjY26NSpk97jW1tbo3Pnzjh9+rQ4fnni4uIQHh4utnv16qX3+HqzttZeBmIJ9falfGMqvTt3gP371bcjR9SvA0tRL6XkREWdVKBZIhCQCGT514V9o+ZA06ZAo0ZA/fpAgwZAvXqAgwP0/+4noqqOFVqqgNq1a2u1ExMTJYoEQEBA8SmYoaHSxFJBGRkZ+OKLL8T2Bx98IGE0wKxZs8St4TZs2IDbt2+XcwUREVU1Dg4O6Nevn9j+5Zdfyr1G85x+/fpVejeOYcOGiceHDh0Sl5bqMr6Li4s0yYoiBTZzLaGeWUGmJwjAhQvA++9D2bQx/u3jj7lbp6KT1y78Ur/0RAUAtL8PtI8FJl4GVp12wb83uuNR/nuICdyCw+9cwcqtGbC/dRfYuxdYuhR4803gmWeAFi0AKQq7EpFZYbKiCtCsyA0U34bMpGxs1BlvTRqfvpizVatWIeFxdelWrVph4MCBksZTr149jBkzBoB6B5gFCxZIGg8RERnH+PHjxePg4GD89ddfpZ57+fJlrZ2/NK/V1wsvvCDWscrLy8OSJUtKPTc9PR3Lly8X2y+++CKsrKwqHUOFKRRwzAVcs4DaaYBcBfXMCjKdK1eAmTOR3qgufp/QCc/dWQKvEeHoPhFY0Bu44AOcqFfKtd7ewOjRqPvRElwacxA//vQAUw88RNctp+D82RLg+eeBtm3VhSqJiErBZEUVUHTnEC8vL4kieazoEpRbt6SJowLy8vK0XnxNnjxZwmgKacaxZcuWClWKJyKiqmH06NFo06aN2J48ebK4JFHT/fv38dJLLyE/X73+oW3bthg1alSJfUZGRkImk4m3guWiJfH19dX6e7Ns2TL8+eefxc7Ly8vDhAkTcO/ePQCAra0t5syZo9PXaHDW1njtMpC8GLj/DTAqFExWmEJSErBiBdCuHdC+Pd679h28XojG82OAP1oCD4uswnlQMOmnVStgyhRg82b11qMxMcDWrcB77wH9+6uLWxIRVRBrVlQBv/32m3hcv359eHt7SxgN1OsJNVWBZMXWrVsRExMDQL3+98UXX5Q4IrXevXujUaNGCA8PR25uLtasWYPPPvtM6rCIiMiAZDIZfvjhB/Tu3RtZWVm4f/8+OnfujClTpqBXr16Qy+U4f/48Vq5cKS7RsLW1xbp16wxWBHr+/PnYu3cvbt26hfz8fDz77LMYO3Yshg8fDjc3N4SFhWHNmjUIDg4Wr/nqq69Qp04dg4xfYSXtaMZlIMZz8SLw3XfAtm1az7NdHpClMbHGJQt48g4wKM4RAxv0R52nhwPfPameSUFEZGBMVpi53bt34++//xbbw4cPly6YAkVnVlSBZSDr168XjwcMGAAXFxfpgilizJgx+PLLLwEAGzduxKeffirJDiVERGQ8gYGB2Lx5M1566SVkZWUhNTUVixcvxuLFi4uda2tri82bNyMwMNBg47u6uuLvv/9G//79ERUVBZVKhc2bN2Pz5s0lnj979mxMmzbNYONXmIUFIJcDSs39SzmzwqBUKuCvv4BvvwVOnCjxlDHXge87AiNDgTGP6qBXz5cgf2800KGD+v+IiMiI+FvGQHSdjpmSkoJRo0bh0qVL5fa5ZcsWjB07Vmzb2dnh/fffN1TI+mvUCPcdgF9bAV91A05nhkkdUZliYmJw9OhRsT1y5MgK95GSkoJTp05h/fr1+Prrr/HFF19g9erV+PPPPxEdHV2p+DTjuXfvHo4fP16p/oiIyDyNHDkSly5dQv/+/UtMSstkMvTr1w8XL17U629VeZo0aYLg4GBMnDgRtqXsqhEQEIBdu3aVmEQxOWtr7TZnVhiGIAC7diG5UytEjh9eaqICAFrY+CFW8SHWfHEVT5yMhvzLxUBgIBMVRGQSNXZmxaRJk7Bp06Zyz3njjTeK3Z+dna33uIIgYPv27di+fTuaNWuGgQMHom3btvD29oa9vT3S0tIQEhKCbdu24cKFC+J1MpkMGzZsKLYziCTq1kVQbeDFx8toPz7+CN2zs9XFN83Qrl27oFKpxPaTTz6p03WhoaH47bff8M8//+DKlStafRTVsmVLvPvuu3j55ZdhUcE/4B06dICbmxuSk5MBADt27ECfPn0q1AcREVUNAQEBOHjwIKKionD69GlxiaKPjw+6d+8OPz8/nfqpX78+BEGo8PguLi748ccf8d133+HIkSOIiopCRkYGvL290apVK7Rr167CfRqNQgFoFhnnzIrKO3AAD+e/j8X2QVgxEOh/B9j1W5FzFApg5Ejg1Vche+IJWDIxQUQSqbHJiry8PHHP89IolUooNacfGtiNGzdKLLBVlKOjI9auXYtnn33WaLFUiJ8ffDR2qop1hLqQkr+/ZCGVZd++feJx48aNdV5/27VrV6SkpOh07rVr1zB+/Hhs3boVv/76K5ycnHSOTyaToXfv3tixYwcAYM+ePVi2bJnO1xMRUdXj5+eH559/XrLxHR0dtbY0NUucWWE4d+4gc+ZbWJG4B4t6AY8eT6zZ3Qy44QE0SwRQpw7w1lvA668Dbm6ShktEBNTgZIVUbG1t8frrr+P06dO4fv16mZ+KODs7Y9y4cZg1axbq1q1rwijL4eyMOoI9gAwAQIwTgKgos01WnDp1SjzWd/1vkyZN0Lx5c9SvXx+Ojo4QBAEPHjxAUFAQzp8/L/4//vPPP3jllVewc+fOCvUfGBgoJivCw8MRGxsrXVEzIiIic1C0yCZnVlRcVhaExYuwdecXmNlPiRiNiTPWSmDSZcCxXhPgu0+AZ58tniAiIpJQjU1W/Pzzz/j5558N1p+u0zEVCgXWrl0LAHj48CGCgoKQkJCAxMREPHr0CHZ2dnBzc0Pr1q3RunVrWFpaGixGQ3L3qAtrZShy5Y9nVkRFSR1SiW7fvo2HDx+K7VatWul8bZcuXTB69GgMHjy4zB1YIiIiMH36dPz1118A1MtOfv/9dzz33HM6j9W6dWut9oULF8z/Ey8iIiJjYrKics6fR9aElzCswy0cHFF4t4UKGHcVmB9ZH3Xf/0KdpDDT15tEVLPV2GSFOXB1dUXfvn2lDkMvMr+6qJMWikhXIMaMkxUhISFa7cZFdzIpg+bykbI0aNAAO3fuxLBhw8SdW5YuXVqhZEWTJk202sHBwUxWEBFRjXbXBXj1FSDXEngqHPiIy0B0k5sLfPYZ8OWXsFWp4NSy8KGnbwJfXXRF89lfAePGqXdcISIyU6yYQ/rx80Odx3Urku2A7KgIaeMpRWRkpFbb19fXKONYWFhg3rx5Yvvs2bNISkrS+XofHx+tdtG4iYiIapocGyscaQicqqeuq8CZFTqIigJ69wYWLlRvTQrgu31A8wRg5+8W+NvzbTQ/dweYOJGJCiIye0xWkH6KFtlMuC1dLGWIjY3Vant5eRltrKJLTM6dO6fztXZ2dnB0dBTbBdXhiYiIaiqFZeEykBw5WGCzPIcOAe3bA2fPat3tlwpcO98Bw367AtnSZYCLizTxERFVEFOqpB8/P/j8C7hlAj5pQGaCeb65Tk9P12qXtq98eX3s2LEDR48eRXBwMO7fv4/U1FRkZ2eXWackOjq6QuPY2toiLS2txLiJiIhqGoVV4ZbouZbgzIqyLFsGvPMOUPR1iVwOzJsH2QcfcCYFEVU5nFlB+vHzwzcHgKQlQPAaoOV/D6SOqERFt6e1rkCVa6VSia+//hp16tTBK6+8gg0bNuDSpUuIjY1Feno6lEol8vPztW6aNAt76kKhUUgsKyurQtcSERFVNwp5YbIih8mKkuXnI+edt5D/zoziiYqGDYFz54CPP2aigsiEDh06BJlMBplMhg4dOpT44ebPP/8sniOTyQy+BFypVKJJkyaQyWSwtLTExYsXDdq/qTBZQfrx9YWF5s/dw4dARoZk4ZRGUaSSeK6OU0iVSiXGjh2L9957T5ztUFHZ2dkVOl8zsaLPDBAiIqLqRHNmBZeBlCA7GynPj8BTD1dixlOA1tuhIUOAS5fUy0KIyGTy8vLw1ltvie3FixdDJpOZPA65XI7PP/8cAKBSqfDWW2/ptHOluWGygvTj51f8PjPcEcTBwUGrreuMhW+//RZbt24V2wqFAq+88gp++eUXBAUF4cGDB8jMzIRKpYIgCOJNU0V/IWRmZorH9vb2FbqWiIioulFYFSbuObOiiOxs3H/uafT0/AvHGgArOwNfd3v82KefAjt3sjYFkQRWr16NGzduAAD69OmD/v37SxbLmDFj0Lp1awDq4v9btmyRLBZ9MVlB+rG3B1xdte+rYI0GU6hTp45WOz4+vtxrcnNz8cUXX4jt2rVr4/Lly9i4cSPGjh2LNm3awMPDA7a2tlqZ0srUmcjMzNS6vujuIERERDWNpbUCFuoNLTizQlN2NuKeG4y+dY8ipJb6Lo8MoFeMJbBpEzB3LmDBl/hEppaRkaH1HuKDDz6QMBpAJpNh9uzZYnv+/PlQKpUSRlRx/E1G+iv6hjouTpo4ytCgQQOtti67bJw8eRIpKSlie9GiRWjevHm51+mSCClN0bjq16+vd19ERETVgkKBKReBN88BL4SAMysAIDcXD54djH6+RxDmob6r/kPg9O/26Lz+APDSS9LGR1SDrVq1CgkJCQDUuwQOHDhQ4oiA559/Hn6PZ8TfunULmzdvljiiimGygvRXq5Z22wyTFS1bttRq37x5s9xrwsLCtNqDBg3SaazKFK4pOmbBlC0iIqIaS6HAyj3Air3ArDNgskIQkPz6y+jvcwTXH+/EXvcRcPRPBzT54zDwxBOShkdUk+Xl5WH58uVie/LkyRJGU8jS0hITJ04U2999952E0VQckxWkv9q1tduVmFlgLP7+/nDVWK4SEhJS7jWPHj3SarsWXe5Sij/++KNCsWkqGldgYKDefREREVULRXfwquHLQIR5czFG+APBj19++aQCR/50QP1th4DOnaUNjqiG27p1qzhT2sbGBi+++KLEERV69dVXxaXrwcHBOHLkiMQR6Y7JCtJfFZhZAQC9evUSjy9cuFDu+Y6OjlptXbYSCgkJwa5duyocWwHNuPz9/VmzgoiIqMiOXjV6ZsVPP0G24HNMuwDY5gFe6cDR323gv5WJCiJzsH79evF4wIABcDGjArd+fn7o0qWL2N6wYYOE0VQMN10m/dWqhTn9gL2NgAf2wLXr0XCROqYSPPXUU2IiITw8HDExMWUmA1q0aKHV/uGHH7BkyZJSz3/48CFefPFF5Ofn6xWfIAg4fvy42NZ12QkREVG1xpkVaqdPA2+8AQAYGQoc3wCoLGVovHYbExVEZiAmJgZHjx4V2yNHjqx0nzdu3EBQUBBiYmJgaWkJX19f9OnTBx4eHnr1N3LkSJw5cwYAsGPHDqSnpxfbNdEccWYF6a92bdxzBoK8gRgnICH1vtQRlWjo0KGw0KiKfejQoTLP7969u9Yvgm+++QarV68ucSvSixcvolevXggJCdF7u9FLly4hOTlZbA8fPlyvfoiIiKoVzqxQz1p99llAo4J/YCzQ+ePvgcGDJQyMiArs2rULKpVKbD/55JN693Xs2DF06dIFAQEBeOGFF/Duu+/inXfewZgxY+Dt7Y0RI0bg7t27Fe5XM6aMjAwcPHhQ7xhNickK0l+tWvDMKGw+SDe/mhWAevvSJzSKTm3fvr3M8xUKBT7++GOxrVKpMG3aNDRr1gzTpk3DvHnz8Pbbb6NTp04IDAzEtWvXAADLli3TKz7NeHx8fNC3b1+9+iEiIqpWanqyQqkEnn8eiI3Vvv/994HXX5cmJiIqZt++feJx48aNUadOHb36+fbbb9G/f3+cO3euxMeVSiV27tyJFi1alPvha1GtW7eGu7u72N6zZ49eMZoal4GQ/mrXhpdGsiIhLwXIzwcsLaWLqRQTJ04Uf6gPHDiAlJQUODs7l3r+9OnTcfnyZfzvf/8T77t582aJu4nIZDIsXLgQEydOxGuvvVbh2LZt2yYejxs3TmsWCBERUY1V05eBLFoEaCwTBQA8+SSwcKE08RBRiU6dOiUe61sk/59//sG7774LQRBgZWWFfv36oWXLlrC0tMTNmzexb98+ZGVlAVDPjBg6dCiOHDmiVYuiLDKZDB06dMCBAwcAQGsJujnjuyLSX61a2skKOwF48EC6eMowevRo+Pr6AgCys7N12mN448aNWLVqFWoX3fXkMQsLC/Tt2xeHDx/Ghx9+qFdcJ06cwK1btwAAVlZWmDp1ql79EBERVTsKBdKsgXh74J4zoMrJljoi07lwAfj0U+37/PyAX381yw+FiGqq27dv4+HDh2K7VatWevUza9YsCIKAHj164ObNm9i7dy+++uorLFq0CNu3b8fdu3cxbNgw8fysrCyMGzcO2dm6/15s3bq1eBweHl5sB0RzxJkVpD8PD3hlFjYf2EO9fWkpb+6lJJfLMX36dLz33nsAgLVr12LatGnlXjd16lRMmjQJ586dQ3BwMB49egQXFxd4e3ujc+fOxaZ5lVTXoizr1q0Tj5977jnuAkJERFTA2hpjngX2N1I3Hx7MMstC3gaXmYmbU56Fl1wJl4JSFZaWwO+/A3oW1yMi4wgJCdFqN27cWK9+cnJy0KFDB+zbt6/EOnienp7Ytm0bhgwZIi47uXnzJlavXo2ZM2fqNEaTJk3EY0EQEBISgp49e+oVr6lwZgXpTy6Hl5Wr2Eywh9luXwqoEw+1Hm+3GhISgv379+t0nZWVFXr06IGpU6dizpw5mDp1KkaMGKH3erQCUVFR+OOPPwAAlpaWmDt3bqX6IyIiqlYUCigK60oiR1kzZlZkz/sIw7tEouVUYL//4zs/+gjo2lXSuIiouMjISK12wUzuipLJZPjhhx/KLNgvl8uxbt062Nraivd9//33On9YWvRD0aKxmyMmK6hSPB28xOOEgpkVZsrOzg5z5swR24sWLZIwGvUuI3l5eQCA8ePH652JJSIiqpYUCig0dgXPUdaAAptBQfj8yjKEeqp3Wvv4CUDVKRDQKPxNROYjtkgBXC8vr1LOLFvPnj3Rrl27cs/z8/PT2hr11q1bYrH/8hRd2h4TE1OxICXAZAVVSm1nH8w4A3xxCHjlKsw6WQEAU6ZMQUBAAAD11kCHDx+WJI6oqCisXbsWAODo6IjPP/9ckjiIiIjMlrU1rDWTFfnVPFmRn4+wGS9jSTf1p6RW+cDP/8hhsfF/gJWVxMERUUnS09O12pqzHipiyJAhOp87dOhQrXZpu4cUVTS2orGbIyYrqFLsPOvgu/3Ah6eAp2/BrJeBAOolHcuXLxfb77//foXrTBjC3LlzxYI48+bNK7WIJxERUY1VdBlIfvXeDUT4/ntMq3cNeY/rZ84+DbR4/WOgWTNpAyOiUuUU2VLZuuguRjpq06aNzue2bdtWq339+nWdrlMU2Q66YHcRc8YCm1Q5j2tAiMx8ZgUA9O/fX5IEhaYNGzZgw4YNksZARERk1qyttZaB5FbnZMXDh/hj8wc4/JS6Wf8hMCe+CfDBB9LGRURlKpoAyNVzi+VaRd9TVeBczd1IylI0saLvLBBTYrKCKqcKJiuIiIioCig6s0JVfZMVOQs/xftdCqdkr9gL2K1dCxR5I0RE5sXBwUGrre9shbIKa5Z3rq7LOTIzM7XaFRlTKlwGQpXj6andTkyUJg4iIiKqXooW2KyuyYrbt7Hx5ErcdVE3B4QDz7QcCfTpI2VURKSDorsDxuv5wW1GRobe5xZNmJSmaGxFdwcxR5xZQZVTdL9vJiuIiIjIEKytMekSMOgWoMgHmicqy7+mKvr4Y7x6MR/IBz7tDSw5KgeOLZY6KiLSQYMGDbTa+u6wkZCQoPO5RZMOrq6uOl1XNLb69evrPKZUOLOCKqekmRUS14MgIiKiakChgP9DoPddoEs04JQtAMpqlrC4dg34/XfIVcDrl4CIZUCb56cDjRpJHRkR6aBly5Za7Zs3b+rVT1BQkM7nXr16VavdvHlzna4LCwvTardq1UrnMaXCZAVVTtGZFdnZQAWmMRERERGVqKSq+noWrzNbn36q9SGPtb0T8NFHEgZERBXh7++vNbMhJCREr37+/vtvnc/dvXu3Vrtz5846XacZW6NGjXSekSElJiuocjw8kG4NXPMCjtcD7jqDS0GIiIio8koqLlmkmn2VFhwMbNumfd877wBV4A0EERXq1auXeHzhwgW9+jhx4kSxGRMliY6Oxvbt28V248aNi83uKIkgCLh06ZLY7t27t15xmhqTFVQ5Tk7Y08wSraYCfSYA25qDyQoiIiKqvOqerFiwQLvt4gLMmCFFJERUCU899ZR4HB4erlfdCkEQMGnSpDJ3E8nPz8cbb7yhtavHG2+8AZlMVm7/wcHBSEpKEtuDBg2qcIxSYLKCKkcmg7u1s9hMtAPw4IF08RAREVH1UJ2Xgdy+Dfz5p/Z9M2eqExZEVKUMHToUFhaFb6sPHTpU4T4UCgUuXLiAQYMG4e7du8UeT0xMxJgxY/DPP/+I9zVp0gRTp07Vqf+DBw+Kx7a2thgwYECFY5QCkxVUaR42buJxkh04s4KIiIgqrzrPrPjuO+2C5I6OwNtvSxcPEemtTp06eOKJJ8S25jINXX399deQyWQ4fvw4mjRpgsGDB2P27Nn48MMPMWrUKNSrVw87duwQz7e1tcXGjRthY2OjU/+aMQ0fPhyOjo4VjlEK3LqUKs3dwQtAOIDHMyuYrCAiIqLKsrIqfl91mFmRlISDR36EvD7QJxKQAcDrrwPOzmVfR0Rma+LEieKMigMHDiAlJQXOFfiZfuaZZ5CTk4PZs2cjNzcXe/bswZ49e0o8197eHjt27ECXLl106js6Ohpnz54V2xMmTNA5LqlxZgVVmrtzbfE4yRZcBkJERESVZ2GBKDc5VgUC33YFTtVFtZhZIaxZg5l9cvDEeKDtG8Aje0tg+nSpwyKiShg9ejR8fX0BANnZ2di8eXOF+5g1axYOHDiADh06lPi4paUlhg0bhmvXruHJJ5/Uud/169dDeDyTq3nz5hW6VmqcWUGVZuteG3a5QKY1l4EQERGR4YTVluPNwUoAwJwTQI+qnqzIy8ORnd/i2hB10yEXcBn2HODnJ21cRFQpcrkc06dPx3vvvQcAWLt2LaZNm1bq+ePHj8f48eOL3d+vXz9cvHgRoaGhCAoKQkxMDCwsLODr64u+ffvC09OzQnHl5+dj/fr1YnvmzJkVul5qTFZQ5Xl4wD1NnaxItANwm8kKIiIiqjyFhRWAbABAriWq/jKQ3buxrMlDsTn9HID/zZIuHiIymKlTp+Lrr79GfHw8QkJCsH//fgwcOFCvvgICAhAQEFDpmP744w+xYKe/vz/GjRtX6T5NictAqPI8PeH+eAedJFtAeJAgbTxERERULSgsCncEyZGjyi8DubdhKf5uoj72TQFGOHcG2reXNigiMgg7OzvMmTNHbC9atEjCaNSWLFkiHs+fPx9yedWaq8BkBVWehwe2bgXufQukLAJkiUnlX0NERERUDoVl4Y4gOVV9ZsXt2/g5/RQEmbo56TJgNVm3bQeJqGqYMmWKOCPi2LFjOHz4sGSxbN26FUFBQQCATp064cUXX5QsFn0xWUGV5+GBRsmAXypglwfWrCAiIiKDUFhWn5kVqh/WYUNb9bFMAMZHOANjxkgaExEZlpWVFZYvXy6233//fbG4pSkplUp89NFHAACZTIaVK1dCJpOZPI7KqlrzQMg8FS30kpQE5OcDlpbSxENERETVgrW8yMyKqpqsyMvDkYPrEDlU3RxwG6g7YjxgaytpWERkeP3795ckQaFJLpfj5s2bksZgCJxZQZXn4aHdFgTg4cOSzyUiIiLSUbGZFVV1GciBA2h05xHeOw14pQMTLwOYPFnqqIiIzBqTFVR5RZMVAJeCEBERUaXZyG3gngnUSQVcs1B1Z1Zs3oz6j4AlB4Hobx8X1jRApX8iouqMy0Co8hQKwNERSEsrvI/JCiIiIqokdwsHJC7RuKN/FZxZkZoK7NwpNq1UAF56RbJwiIiqCs6sIMMoOrviwQNp4iAiIqLqw9pau10VZ1bs2AFkZxe25XLg2Weli4eIqIpgsoIMo2iygjMriIiIqLIUCu12VaxZ8csv2u2nnip5CS0REWnhMhAyiLRarljYH0iyBZokAe8xWUFERESVVdVnVsTFAYcPa9/30kvSxEJEVMUwWUGG4e6OxQ3Uh30jgPe4DISIiIgqq+jMiqqWrNi5E1CpCtuOjsCQIZKFQ0RUlXAZCBmEg3ttWCvVx0m24DIQIiIiqryqvgxk+3bt9jPPAHZ20sRCRFTFMFlBBiHz9IJ7lvo40Q5MVhAREVHlVeVlIMnJ2Jh0GC+MAn5rCaRZAxg5UuqoiIiqDCYryDA8POCRqT5MsgOEBwnSxkNERERVX1WeWfH33/i1uQq/tQJeGA3cqW2tLq5JREQ6Yc0KMgwPD7g/TlbkyIHMRw9gL21EREREVNVZW2PCMCDMA5CrgBNVaGZFys7fcbSl+rjeI6B1u6cABwdJYyIiqko4s4IMw91dnFkBAImZSdLFQkRERNWDQoGg2sAZP+CcD6rOMpCMDOyPOIg8S3VzaBggGzlK2piIiKoYJivIMNzdxZoVAJAkZFStqZpERERkfhQKWOerD3PlgJBbRZIVBw5gV8M8sTk03EJdXJOIiHTGZSBkGB4eaJEA9I4E3DMBGyWApCTA21vqyIiIiKiqsraGIr+wmZubBUXpZ5uNvL3/YE9j9bFzNtC7bi/AzU3aoIiIqhgmK8gw3Nzw1nngrfMa9zFZQURERJWhUEChLGzmKnPMP1khCDgVtAuPBqubg24BVoM4q4KIqKK4DIQMQy4HXFy07+P2pURERFQZRWZW5ORlSxeLrq5fx37nwtdAz9wEMGiQdPEQEVVRnFlBhuPuDjx6VNhOYpFNIiIiqoQiMyuqRLJi716MD1Iviz3gDzyZXQcICJA6KiKiKofJCjIcd3fg9u3CNpMVREREVBkKhfbMivwqUGBz7140SwSaJQLv/Qvg9WcAmUzqqIiIqhwmK8hwPDy021wGQkRERJVhbY2B4YBnBqDIBxwzleVfI6W0NODkSe37uASEiEgvTFaQ4bi7a7c5s4KIiIgqQ6HAuKvAuKuP2w1VkoZTrqNHgbzCLUshlwNPPCFdPEREVRiTFWQ4TFYQERGVKyQkBBs2bMChQ4cQHR2N3Nxc+Pj4oGPHjnj55Zfx1FNPGWVclUqF8+fP4/Dhwzh//jyuXbuGhIQE5OTkwNXVFQ0aNEC3bt3wyiuvoG3btkaJocKsrbXbOWa+DOTwYe129+6Ak5M0sRARVXFMVpDhPF4GIgDIsgLsuAyEiIhIpFQqMXfuXCxevBgqlfYMgZs3b+LmzZv49ddfMXjwYGzYsAGenp4GG3vmzJnYsmUL4uLiSnw8ISEBCQkJOHfuHL777jsMGzYMa9euRa1atQwWg14URTYqNfdkxdGj2u3+/aWJg4ioGmCyggzH3R1PjAP+9QPkKiD9CGdWEBERFZg8eTLWr18vtq2srNC8eXM4ODjgxo0bSHo8I/Gff/5B//79cfr0aTg4OBhk7HXr1iEjI0Prvtq1a6Nu3bqwt7dHTEwMbt68KT62a9cuBAUF4eTJk/Dz8zNIDHopmqzIzZUmDl0kJgIhIdr39e0rTSxERNWAhdQBUDXi4QGlBZAjBzKsgezkBKkjIiIiMgvr1q3TSlQMHToUERERCAoKwqlTp3D//n2sWLECcrn6c6Tg4GBMnjzZ4HG0aNEC3333HW7duoX79+/j3LlzOHLkCMLCwnDr1i0MGzZMPPfu3bsYM2YMBEEweBw6q0rLQI4f127b2QGBgdLEQkRUDTBZQYbj7g6PzMJmUiZnVhAREWVmZmLevHliu0+fPti+fTt8fHzE+6ysrPDmm2/i+++/F+/bsmULLl++bJAYAgMDsWfPHly7dg0zZsxAo0aNip3TqFEj7Ny5Ey+99JJ437lz57Bz506DxKCXkmZWSJk8KYNw7CheGgl80xW4UhvqehVFky1ERKQzJivIcNzd4a6RrEhUpgBKM99ijIiIyMh+/vlnsVaETCbD6tWrYWlpWeK5EydOROfOnQEAgiBg8eLFBonh6NGjGKTjFprLly+Hvb292N6+fbtBYtBL0Tf7gmC2ry1uXtiPX1oD7w4EPnkCXAJCRFRJTFaQ4Xh4aM+ssAXw8KFk4RAREZkDzTf7vXv3RkBAQJnnay7/2LNnD3JMvPTB1dUV3bt3F9s3btww6fhaFApkyYFYRyDCBUhRwDyXgiQk4EReuNjseRdMVhARVRKTFWQ47u5wzypsJtqB25cSEVGNlp6ejhMnTohtXbYl1ZwBkZ6ejmPHjhkjtDK5ubmJx6mpqSYfX6RQ4LeWgM8soOEM4LeWMM8im8eP40S9wmavBBugQwfp4iEiqgaYrCDDUSjgkV+4tpTJCiIiqumuX7+OvLw8sd21a9dyr6lduzbq168vtoODg40RWpnu3r0rHnt5eZl8fJG1NRT5hc0cOcxzZsWJEzj5OFlhmwd0aNQLsLKSNiYioiqOyQoyKA+5k3icZAv1Nl5EREQ1VGhoqFbb399fp+s0zyvah7HFxsbi/PnzYluXBIvRKBRQaJSoyLGEWc6suHvlKO66qI+7RgHWPXpLGg8RUXUglzoAql7aCrXww+4H8MgEWsWDMyuIiKhGi4yMFI/lcjm8vb11uq5u3bol9mEKn332GfLzC6czvPDCCyYdX0tVmFmRkYGTGdfFZs97ACZ0ky4eIqJqgskKMihfhzp47d9rhXdwZgUREdVgaWlp4rGjoyMsLHSb1OrkVDhTUbMPYztx4gR++OEHsT1y5Ei0a9eu3OtycnK0CoEarM6FlVXxmRXmlqy4cAGnfQq3U+0ZZQEEBkoYEBFR9cBlIGRY7u7abc6sICKiGiw9PV08trGx0fk6W1vbEvswppiYGDz77LNQqVQA1EU2ly9frtO1X375JZydncWbn5+fYYKSyaCwKKz9kCOH+S0DOXMGfSKBF4OBJolAJ7dWgMbWr0REpB8mK8iwmKwgIiISKZWF0wLkct0ntGqeq1mg01gyMjIwbNgwxMfHAwBkMhnWr18PHx8fna7/8MMPkZKSIt6ioqIMFptCppGsMMeZFf/+i+f+AzZvB8JWAo6dekgdERFRtVCjkxUPHjzA3r178dlnn2Ho0KHw9vaGTCYTbz///LNJ4rhz5w7mzp2LDh06wNPTE7a2tvD398eIESOwbds2rRc6Zs/DQ7vNZSBERGRmNm/erPX33lC3kl432NnZicfZ2dk6x6h5rr2RP6XPzc3FiBEjcOnSJfG+7777DsOGDdO5D4VCAScnJ62boSgsrcVjs5tZIQjA2bPa93VjvQoiIkOokTUr4uLi0KVLF61tuaSybNkyvP/++1rrPAF1AuPOnTvYuXMnunTpgl9++QUNGzaUKMoK4MwKIiIikYODg3iclZWl83WZmZkl9mFo+fn5eOGFF3Dw4EHxvk8//RTTp0832pgV1TjTBifWA9b5gHc6gDfNaGZFeHjxD2ak3D2FiKgaqZHJiuzsbLNIVCxYsABz584V2xYWFmjevDnc3Nxw69Yt3L9/HwBw9uxZ9O7dG+fPn9e5irhkOLOCiIjMnL29vc7LGyrab1EeGn8X09PTkZ6erlPyIS4uTjx2L/pBgIGoVCpMmDAB27dvF+977733tF6bmAN7S1v0jNC4w5yWgfz7r3a7dm2gfn1JQiEiqm5qZLJCk6enJzp06ICOHTuiY8eOGD58uEnG3b9/P+bNmye2u3btip9//hlNmjQBoH4BsXXrVrz22mtIT09HdHQ0xowZg1OnTpkkPr1xZgUREZm5ESNGYMSIESYZq2nTplrte/fuoXnz5uVep1nzoVmzZgaPCwCmTJmCTZs2ie1p06ZhyZIlRhmrUhQK7bY5LQM5c0a73bUrIJNJEwsRUTVTI5MVbm5u2Lp1KwIDA1GvXj2Tjy8IAt5//30Ignqbq6ZNm+LQoUNa61otLCzw3HPPwd3dHU8++SQA4PTp09ixY4fJXmDpxd0dNzyAa15Aki0w/FYSaqlUgI5btREREVUnAQEBWu2goKBykxV5eXn477//Su3DEGbMmIF169aJ7YkTJ2LFihUGH8cgrK212+Y0s+LiRe12ly7SxEFEVA3VyHeQTk5OGD16tCSJCgDYu3cvrl69KraXLVumlajQ1L9/fzz33HNie9GiRUaPr1I8PPBzW2DMs8AbQ4DrbiogJUXqqIiIiCTRsGFD+Pr6im1dZkheunRJq2ZFr169DBrTnDlzsGzZMrH94osvYt26dZCZ64wAc51ZkZsLhIRo39exozSxEBFVQzUyWSE1zbWhDRo0wIABA8o8f/LkyeLx+fPnER0dbbTYKs3dHe6Fr6+QaAcuBSEiohpt6NCh4vHWrVuRW86b7V9++UU8btGiBfz9/Q0Wy+eff44vv/xSbI8aNQobN26EhTnPgCyarDCXmRXXruFYnVwcagg8snl8X/v2koZERFSdmPFfpurrn3/+EY8HDhxY7icZPXv21CrapXm92bGzg0du4eqiJCYriIiohhs/frx4nJiYiLVr15Z6bnR0NDZu3FjitZW1bNkyfPLJJ2L7mWeewZYtW2BpaWmwMYzCXJeBXLqEL3sAT74CuH4A3G9ZD3BxkToqIqJqg8kKE0tISNCq8N1Vh+2t5HI5AgMDxXZwcLBRYjMImQzuVoV7qyfagTuCEBFRjRYYGKg1u2LOnDk4ffp0sfNSU1MxduxYpKWlAQBq166NadOmldm3TCYTb2UlNn788Ue88847YnvAgAHYtm0brKysKvjVSMBMl4EIly7iyuNN2jwygNotOksbEBFRNVMjC2xKKTQ0VKut69ROf39/HDt2rMQ+zI2HwhVAMgB1kU3OrCAioppu2bJl+Pfff5GYmIj09HT069cPEydOxIABA+Dg4IDg4GCsWLECERHqPTotLCywbt062NraVnrs+/fvY/LkyWJhb0C9jfuwYcN07mPfvn2VjkNv1tb4oT2QYgM45AJvmMnMitj/zuJBf/VxuzhA1oH1KoiIDInJChOLjIzUatetW1en6zTPK9pHSXJycpCj8cc8NTVVp3EMwd3eA8BtAJxZQUREBAD169fHrl27MGTIECQnJyMnJwerV6/G6tWri51raWmJpUuXYsiQIQYZOycnByqVSuu+EydOGKRvk1Ao8FE/4IE9UP8h8IY5zKzIzUVQ0nWx2e4+gPEdpIuHiKga4jIQEyuY2lnA2dlZp+ucnAqXVhTtoyRffvklnJ2dxZufn1/FAq0ED8da4jFrVhAREal169YNwcHBGDVqFOTykj8vCgwMxIkTJ/Dmm2+aODozplBAoVQf5shhHjUr/vsPVzyVYrNtHFhck4jIwDizwsTS09O12jY2NqWcqU1zGmjRPkry4YcfYubMmWI7NTXVZAkLF5fakAmAXR4gV4HJCiIiosd8fHywbds2PHjwACdOnEB0dDRyc3NRp04ddOzYEU2bNq1Qf5pLO0pTv359nc4zW9bWUOSrD3MsYR7JikuXcKV2YbOdlR+LaxIRGRiTFSamVCq12qV9slKU5nl5eXnlnq9QKKAoWpDKRCw9vJC5ELAp+FJHcRkIERGRJk9PT4waNUrqMKqGojMr0sxgGcilS2JxTbtcoHFjFtckIjI0LgMxMTs7O612dna2Ttdpnqe5jalZcncvTFQAnFlBRERE+jPDmRU5Vy/BOh+QCUDreMCyQ2D5FxERUYVwZoWJOTg4aLWzsrKKJTBKkpmZWWofZsfdXbvNZAURERHpS6GA4nG5LqUloMrNkfbTNpUKiqv/4cYZIN0aeGAH4Pe2UkZERFQtcWaFiXl4eGi179+/r9N1cXFx4rF70WSAuSnyNXI3ECIiItKbQiHOrACAnNws6WIBgDt3gMcfIjnkAg0eAWjdWtKQiIiqI6POrIiLi8OFCxcQHByMyMhIxMTEID09HVlZWbC1tYW9vT18fHxQv359tG7dGoGBgfD29jZmSJIrWjjr3r17aNmyZbnXRUVFicfNmjUzeFwGVdLMCkEAZDJp4iEiIqKqS6FA7XTANwVQ5APKXN2W0BpNSIh229MTqFWr5HOJiEhvBk9WnDhxAjt27MCePXsQHh5e4ev9/f0xaNAgDB8+HH379jV0eJJr3Lgx5HK5WGgzKCgITz/9dLnXXblyRTwOCAgwWnwGUXRmRW4ukJ4OODpKEw8RERFVXQoFftum0R6YX+qpJhEcrN1u1YofyBARGYFBloHEx8dj/vz5aNCgAfr27Yvly5fj1q1bEARB562yCs4NDw/HypUr0b9/f9StWxdz587VealEVWBtbY3OnQsrRp86darca+Li4rQSP7169TJKbAZT0jIV1q0gIiIifRTd3UzH4uRGU3RmRatW0sRBRFTNVSpZERERgVdffRX169fHggULcPfu3RKTEwWJCAcHB3h6esLX1xeenp6wt7cvNaEhCAKio6OxcOFCNGjQAOPHj8ft27crE67ZGDZsmHh86NAhxMfHl3n+L7/8Ih67uLiYf7LCyQkouiUrkxVERESkDxsb7bbUu4EUnVnBehVEREahV7LiwYMHeOONN9CsWTNs3LgROTk5WgkHV1dXjBgxAl988QX+/vtv3Lx5ExkZGUhJSUFcXBzu3r2LuLg4pKamIiMjAzdv3sRff/2FL774AiNGjICrq6vYlyAIyM3NxaZNmxAQEIDJkycjISGh8l+5hF544QUoHn9KkJeXhyVLlpR6bnp6OpYvXy62X3zxRVhZWRk9xkqRyRDUxAnDngd6vAqs6wAW2SQiIiL9mNPMisxMoOgyZ86sICIyigrXrFi6dCk+/fRTpKamaiUoGjVqhDFjxmDkyJHo0KGDzv3Z2tqiUaNGaNSoEQYPHizef+nSJWzfvh3btm0Tl5QolUr8+OOP+P333zF//nzMmDGjouEbTWRkJBo0aCC2582bh/nz55d4rq+vLyZPniwmIZYtW4Zu3bph1KhRWufl5eVhwoQJuHfvHgD1czVnzhzjfAEGlu7hhN3NkgEAnWLAmRVERDUMi2yTwRRNVkg5s+K//yAIAsQKFTIZ0KKFdPEQEVVjFU5WzJw5EzKZDIIgQC6XY8yYMZg8ebLBlyZ06NABHTp0wMKFC3Hy5EmsXbsWW7duRV5eHlJTUzFr1qxKJSsmTZqETZs2lXvOG2+8Uez+bANk9OfPn4+9e/fi1q1byM/Px7PPPouxY8di+PDhcHNzQ1hYGNasWYNgjamGX331FerUqVPpsU3Bw9YdQCQAINEOnFlBRFQDsMg2GYU5LQMJCcFLI4FzvkDLBODHaw3gYWcnXTxERNWYXruBWFtb4/XXX8esWbNQt25dQ8dUTM+ePdGzZ08sWrQIX3/9NdatW4ecSv6hysvLK7cPpVIp7tphaK6urvj777/Rv39/REVFQaVSYfPmzdi8eXOJ58+ePRvTpk0zSizG4OFYuIVXki04s4KIqJqKj4/HmjVrsHHjRnEmoObMS5kOuyQUnF9QZHvlypXw8fHB+PHjMWXKFM64qOnMaRlISAiCawG33YB7zoBzXhvpYiEiquYqXLNi3LhxuHnzJpYtW2aSRIUmX19fLF26FGFhYRg3bpxJxzaGJk2aIDg4GBMnToStrW2J5wQEBGDXrl1YvHixiaOrHFeX2pA9fq2aaAcmK4iIqhkW2SaTMaNlIHkhQQh7vEN700TAqhWTFURExiITdN1blIwqLS0NR44cQVRUFDIyMuDt7Y1WrVqhXbt2Buk/NTUVzs7OSElJgZOTk0H6LNOHH8JdtQjJdoB/MhAe/xzw22/GH5eIiEpliL8FDx48wCeffIINGzZAqVQWSza4ubmhd+/eCAwMROvWrdGkSRP4+PiUmJTPyspCTEwMwsLCEBISggsXLuD48eNITk7WOk8mk8HS0hITJkzAggUL4OXlpVfsZDoGfd1x9ix+eb0rVnQGciyBb07Y4InrWYYJtIKuN3NHixfU35/PXQN+e+FPYORISWIhIqoKKvP3QK9lIOX5448/0KpVKzRt2hQWFpXaHbXGcHR01NrStMpzd4f7PSDZjjMriIiqCxbZJkkoFIh3UNeJAIBEea40cSQn4z/LwkRaywSwuCYRkREZJZPw/PPPo2XLlnBxcTFG91QVuLvDI1N9mGID5CWzwCYRUVU3c+ZMMVEhl8vxwgsv4NixY7h58yYWLlxYoURFWQoKbIeFheH48eMYO3YsrKysIAiCWGSbahAbGyg0SojlyFRAfr7p4wgNxTWNST0tki2Bhg1NHwcRUQ1htGkPgiAYZNcMqqI8PDDsBjD5IvDRCUCZ/EDqiIiIyACsra3x1ltvITw8HL/88ovBdwMrqmfPnti8eTNu376Nt99+GzZFd4ag6k+hgEIjN5EjhzR1K27cwH+ayQrbeoCVlenjICKqIYyyDIQI7u54/7RG2+6hZKEQEZFhjBs3Dp999hn8/PxMPnZBke1Zs2Zh3rx5Jh+fJKRQaM+ssIQ6WWHqLUNDQ/Gf5+OQlIC/H4trEhEZk1knK9zc3NCqVSt06NAB3377rdThUEV4eGi3MzOBrCyglF1PiIjI/G3YsEHqEODn54f169dLHQaZko2N2cysWHkduO4JpCkAy17NTR8DEVENYtbVL9PS0nDy5EksW7ZM6lCootzdi9/HIptERNXeH3/8gdDQUKhUKqlDoeqipJkVUiw1Dg1FvwjgrfPAnJMAmjUzfQxERDWI3jMrDhw4gJs3b6J169Zo1aoVXF1dDRkXVXUuLoBMBmhuaZeUBPj6ShYSEREZ3/PPPw+ZTAZ7e3ukpqZKHQ5VB+ZQsyIrC4iI0L4vIMC0MRAR1TB6JyvOnDmDzz77TGzXqVMHrVq1QuvWrQ0SGIBie7dTFWJpCbi5ac+mSOSOIERENQGLbJNByeVokCLDjDMCFPlAj3sw/cyKW7e0P4ABgKZNTRsDEVENU6maFYIgQCaTQRAExMTEIDY2Fvv37xfvy8/PR6tWrdCxY0fx1rZtWygUinL7TkxMFKeQ6nI+mSF3d+1kBZeBEBERUUXJZGiaboPv9mcV3mfqmRWhodptPz/AwcG0MRAR1TB6JyvsHldg1pz9oJm8KGhfv34d169fx//+9z/1gHI5mjdvjg4dOogJjDZt2sCqyNZPO3bsEI89ihZrpKqhaN0KzqwgIiIdscg2aVEo1EsxCkidrOASECIio9M7WfHee+9h8uTJuHr1KoKDg3H16lVcvXoV165dE6d+CoIgJi4Kkhh5eXkIDg5GcHCwWFXcysoKLVu2RNu2bdGwYUNER0djw4YNkMlkAIA2bbg1VFUkeLgjwxp4YAdky4EAzqwgIiIdFRTZPnXqFJMVBNjYaLdNvQzkxg3tNotrEhEZXaWWgTg5OaFnz57o2bOneJ9KpYJcLodMJoOFhQWeffZZXLx4Ebdv3xbPKZrAyM3NxZUrV3DlypUSzxk9enRlwiSpuLvD8z0g2wpokQBcY7KCiKhaYJFtMrmiS4I5s4KIqNqrVLKiJBYWFlrHv/76KwAgNTUVly5dwsWLF8VbhEZVZc3kRMG/giCga9euePnllw0dJpmAzMMTXhnAPRf17Arc5zIQIqLqgEW2yeSkTFaoVLgTfwPbuwFNE4EO94E6TFYQERmdwZMVBYq+yHByckLfvn3Rt29f8b5Hjx5pJS+uXr2KyMhIqFQq+Pr64rnnnsPcuXO1EiBUhbi7w+uOOlmRaAfkJyXCUuqYiIjIIFhkm0xKymUg0dE4XSsX7w1QN7/eD8ziTiBEREZnlGRFamoqgoKCEBISUuZ5Li4u6N+/P/r37691v0qlYoKiOvDwgNfjbwGVBZCcch+e0kZEREQGwCLbZHJSzqy4dQs3NWqGN82wAWrVMt34REQ1lFGSFQ4ODujRowd69Oih1/VMVFQTXl7wzCxsPsh4wGQFEVE1wCLbZGqCjQIxTkCOJSBXAfVMObMiPBxhGjmzJo71gcffn0REZDxGWwZCBC8veGUUNhOyE9FcEPgHnoioGmCRbTIlQaGA30z1cZco4IyJZ1aEPZ5ZIc8HGtRpYbqxiYhqMCYryHg8PbWTFVZ5QHo64OgoXUxERGQ0LLJNxmKhsIE8H1BaAjlymHQZiCr8Fm61VB83fAhYNWpisrGJiGoyJivIeIrMrHhgByAhgckKIqJqjkW2yeBsbKAoSFZYwqQFNmNjbiCrnfq4SRKAdo1MNjYRUU3GZAUZj4MDBkQrcHJ9DjwzAN9UAA8eAP7+UkdGRERGwiLbZBQKBRRKIMPaxDMrVCrcTokUm/4PATRubJqxiYhquAq/GggMDMTRo0eNEYvOjhw5gk6dOkkaA+lAJkNt+1rocQ9omgTY50E9s4KIiKqtgiLbU6ZM0et6JiqoRAoFFPnqw1xTzqyIiUFufi4CHgAKJeCfDKARZ1YQEZlChV8RXLp0Sfwk5NChQ8aIqVQHDx5Ev3798OSTT+LSpUsmHZv05OWl3WaygoiIiCrKxgYKpfowxxKmm1lx6xaevANcXwVkLgQmh9oBtWubZmwiohpO748vjh49ioEDB6Jt27b4/vvvkZqaasi4RGlpaVizZg3atm2Lp556CseOHSu2FpbMGJMVREREVFkaMytMugwkPFw8tBAA64aNuasZEZGJVDhZceDAATRt2lTcPz0kJATTpk2Dt7c3RowYgU2bNiEuLq5SQd2/fx+bNm3CiBEjULt2bbz55psICQkRxwwICMCBAwcqNQaZCJMVREREVFmPa1YAJi6wqZGsAMB6FUREJlThApv9+/dHcHAwVq1ahS+//BIJj998ZmVlYffu3di9ezcAoHHjxggMDESrVq3QuHFj+Pr6wsvLC7a2trC2tkZubi6ysrIQHx+PmJgY3Lx5EyEhIbhw4QLCNf4waM6iqFWrFubMmYMpU6ZALmdt0CrB01O7/eCBNHEQERFR1WVjg//9DORZqmtHoKfploFoYb0KIiKT0esdv1wux/Tp0zFp0iSsXLkSK1asQExMDARBEPdGv3nzJm4V/QWvA8291guOfX19MX36dEydOhW2trb6hExS4cwKIqJqIzAwEEuWLNHagtTUjhw5gg8++ADnz5+XLAaSgEKBNvEabQmWgQBgsoKIyIQqVXLbzs4Os2fPRkREBDZv3ox+/fpBVsI6voLlG2XdipLJZOjfvz+2bNmCiIgIzJo1i4mKqojJCiKiaoNFtkkyCoV22xTLQFQqLgMhIpKQQdZSyOVyjB07FmPHjkVsbCx27dqFffv24dSpU3j48KFOfQiCAFdXV/Tq1QtPPfUUhg4dCm9vb0OER1Ly8sKJesCJekCCPTAzPBb1pY6JiIgq5ejRozh69ChatWqFN954A2PHjoWTk5PBx0lLS8PmzZuxdu1ahISEAIA4i5NqGBsb7bYpZlbExhZPinBmBRGRyRi88EOdOnUwZcoUcX/1O3fuICQkBJGRkYiNjUV6ejpycnKgUCjg4OCAOnXqoEGDBmjZsiUaNmxo6HBIal5e2O8PfNFL3RxyKxn1VSrAolKTeoiISAIHDhzA22+/jRs3bgCAWGR71qxZGDBgAEaOHIknn3wStSuxteP9+/dx6NAhbN++HQcOHED24zeLBbMwAwICsHz58sp/MVS1SDGzIiICAgAxNWZrC/CDNCIikzF6lcqGDRsyCVGTeXnBK6OwmWCrAh4+BNzdpYuJiIj0wiLbJBkpZlZERGDyEOCfxoD/Q2BTsA/qcVYPEZHJ8K89GZenp1ayIt4B6h1BmKwgIqqSWGSbJFF0ZoWJkhXhbkCsk/rmlNjA+GMSEZGIc/HJuBQKeAl2YjPeHiyySURUDbDINpmURMtAbruqD12zANe6TYw/JhERiTizgozO28YDwD0AQJwDmKwgIqpGWGSbTMLGBsfrAWd9gRw5MCE6E35GHjIn8jai6quP/ZMBNOXMCiIiU2KygozO2742CpIV9x3BZAURUTXFIttkNAoF/m4CfN1d3eyzNcvoyYq7ieEQHk8W8n8IoAGTFUREpsRkBRmdi5s3FEr1JyGcWUFEVHOwyDYZjEIBRX5hM0eVa9zxcnMRmR0nNhswWUFEZHKSJitu3bqF8PBwyOVytGnTBl5eXhW6PiUlBc7OzkaKjgxF5lUL7e4DSgsgIBGA7IHUIREREVFVYmMDhbKwmaPKAwQBMNbuHPfu4a7GS8x6KWCygojIxCRJVoSFheHll1/GpUuXxPtkMhmGDBmC5cuXw8+v9Il9UVFR2L17N3bt2oUTJ06I+6+TGfPywpnPNdqjObOCiIiIKqDozAoLAVAqASsr44wXEYFIl8Jm/Tx7wMWltLOJiMgITJ6sSEpKQp8+fZCQkKBVAVwQBOzevRvnz5/HiRMn4O/vLz4WFhaGP/74Azt37kRQUJB4fklVx8kMFZ0xw2UgREREVBEKhfbMCjnU25caMVnxwjWg4UPgrgvQ0q6+ccYhIqJSmTxZsWzZMsTHx0Mmk8Hd3R1PP/00fHx8EBsbi7179+L+/ft49dVXcfz4cZw4cQIfffQR/v33X/F6zT3YO3XqZOrwSR+entrt+Hhp4iAiIkkFBQWhZcuWkMtZMosqyMZGe2aFJdTblzo4GGe8iAi0TABaFny+MrKpccYhIqJSmfzVwp49ewAAbdu2xaFDh+Dq6io+lpWVhbfeegsbNmzAsmXLMHv2bCiVSjFBYWFhgZ49e2LkyJEYOXIkfH19TR0+6aNWLe02kxVERDVS+/btYW1tjRYtWqBdu3Zo37492rdvjzZt2sDW1lbq8Mic2djApqSZFcYSGandZr0KIiKTM3my4tatW5DJZFi0aJFWogIAbG1t8eOPPyIiIgKzZ89GXl4eAKBBgwaYMWMGnn/+eXgW/ZSezJ+3t3b70SMgKwvgC1MiohonNzcXQUFBCAoKwoYNGwCoP4xo0qSJVgKjXbt2LKJNhWxs4JoF1H0E2CgBh1yoX0sYS0SEdpvJCiIikzN5siI9PR2AemZFad577z0cPXoUMpkMffv2xd9//w0bGxsTRUgGVzRZAQBxcfzDT0RUw8ydOxdXrlzB5cuXERMTI96fn5+P0NBQ3LhxA1u2bBHvr1+/frEERq2is/WoZrCxwbAwYFiYxn3GLLJeNFlRv77xxiIiohKZPFlRUBjT3t6+1HPat28vHn/++edMVFR1Tk7qWRSan4Dcv89kBRFRDTN//nzxODExEZcvX8aVK1fEBMbt27e1im9HREQgMjISO3bsEO+rXbs22rVrhw4dOuDTTz81ZfgkJZkMUCi0l34Ya2ZFRkbxYuB8zUJEZHJmWeFKM5HRsmVLCSMhg5DJ1LMr7twBAOTLAMvYWImDIiIiKXl4eGDAgAEYMGCAeF96erqYvChIYISGhkKpLCxWcP/+fdy/fx979+5lsqKmsbXVTlYYa2ZF0XoVAGdWEBFJQLJkha7bjjoYq8ozmZTKuzZaP3UHMY5A0yTg7P37UodERERmxsHBAT179kTPnj3F+3JzcxEcHKyVwAgJCUG2MZcAkHkqOtPWWN8Dd+9qt2vVAuzsjDMWERGVSrJkxRNPPIFWrVqhZcuW4r8snll9WXjXQYI98MgWuO8A9TIQIiKiclhbW6Njx47o2LGjeJ9KpcKNGzckjIokUTRZYaxlIPfu4e8mQLo1UP8R0MHHD1bGGYmIiMogWbLi/PnzOH/+vNZ9Hh4eaNmyJZo0aSJRVGQ03t6onQ48sAfiHAAhOha6za0hIiJzEx4ejm7duqFZs2Zo27Yt2rZti7Fjx5qsxpSFhQWaN29ukrHIjBTdRcxYMyvu3cOS7sDJeupm2nVfJiuIiCRgYeoBP/nkEzzzzDOoU6cOBEHQuj148ADHjh3DunXrxGUizs7OeOKJJzB79mxs3boVEUWrM1PV4O0N7zT1Ya4cePjgnrTxEBGR3t58800kJibi9OnTWLVqFS5fvsxi2GR8JpxZcffxrrnumYCDb0PjjENERGUy+cwKzWJYulQCT0tLw/Hjx3H8+HHxPldXV3Ts2BGBgYFYsGCBSeMnPXl7o7bGRJq4RzFwky4aIiLS04ULF3DgwAHxQ4VBgwZhxYoVEkdFNYKJZlbkRUUi2l99XP8RgOZ1jTIOERGVTdLdQPStBJ6cnIwDBw7g4MGDTFZUFd7e8E4vbN7PjAcn8BIRVT1r164FoN6K3M7ODt9//73ORbPLc+PGDTRq1AhyuVluVkYSy7SzQt/XgCwroEMssMFIMyuikyKgejz3uN4jAHWZrCAikoLZvRpgJfBq6nHNigJxyhQgLw+w4ipQIqKqZOfOnWJyYtasWfD19TVY33/99Rc++eQTtGzZEu3bt8fo0aO1PtCoLkJCQrBhwwYcOnQI0dHRyM3NhY+PDzp27IiXX34ZTz31lCRxDR48GHv27BHb9erVQ2RJ23hKxMrGDucff7s5Z8M4Myvy83E3K05s1ksBkxVERBIxu2RFSVgJvBoomqxwABAfDxjwRS4RERlXWFgYkpOTAai3IJ8wYYJB+581axa2bt2Kixcv4sqVKzh8+DBu375t0DGkpFQqMXfuXCxevBgqlUrrsZs3b+LmzZv49ddfMXjwYGzYsMGku6Rt2bJFK1FhjuQKW1ioAJUFkC2HcZIVsbGIdij8v6nLZAURkWT0LrAZHh4OLy8v9OrVC2+//TbWr19v0pkOrARexbi7IzDeEt/uA7ZsA4aFgduXEhFVMVevXgWgTlS0a9cO9evXN2j/FhYW+OabbwCol5lERkbi2LFjBh1DSpMnT8aXX34pJiqsrKzQpk0bdO/eHe7u7uJ5//zzD/r374/09PTSujKo5ORkzJgxwyRjVYbM1g42j1cFZ8thnAKb9+4hxrGw6ZNlBXh4GH4cIiIql97JClYCpwqxsIC/whvvnAWevwY0SgaTFUREVUxiYqJ4HBAQYJQxevbsic6dO4vtXbt2GWUcU1u3bh3Wr18vtocOHYqIiAgEBQXh1KlTuH//PlasWCHW6wgODsbkyZNNEtvMmTORkJAAmUyGJ554wiRj6sXGBraPkxVZVjDOzIp796C0eLzMBICPfW3AQDVZiIioYvRKVrASOOmlTh3tNpMVRERVyqNHj8RjHx8fo43z5ptviscHDx402jimkpmZiXnz5ontPn36YPv27VrPoZWVFd588018//334n1btmzB5cuXjRrb4cOHsXHjRgDAhAkTtGqGmR0bG5PMrPjoJPBoEZC+EAi0b2z4MYiISCd6JSs0K4Hb2toavBK45s4fVI14e2u3mawgIqpSrK2txWOFQmG0cQYOHAiZTAZBEBAaGoqUlBSjjWUKP//8M+Li1EUbZTIZVq9eDUtLyxLPnThxojizRBAELF682GhxZWVlibM3PDw8sGTJEqONZRC2ttrJCiPNrChgnwdY+dU3/BhERKQTvZIVBZXAZTKZUSqBOzg4oGPHjnj99ddx4MABg/VNEmOygoioSnN2dhaPNZeEGJqHhwdat24ttkNDQ402lils375dPO7du3e5S2g0l3/s2bMHOTk5Rolr3rx5YgHTb775RqtuhlmysYFtnvowy4gzK7SwuCYRkWQqnKwoqAQuCAIAGKUSeOvWrXH58mX89NNPmDJlikH7JwkVTVbExkoTBxER6aVBgwbicXBwsFHH0nxDHx4ebtSxjCk9PR0nTpwQ27psSzpo0CCt641RZPTKlSv47rvvAKiXpbzyyisGH8PgbG3x6hVgzglgzkkYfWYFACYriIgkVOFkBSuBk944s4KIqEpr0aIFAPXf54sXLxp1eYaXl5d4/PDhQ6ONY2zXr19HXl6e2O7atWu519SuXVvr9ZWhE0P5+fmYNGkSlEolrK2ttepkmDUbG7xzFlh4BPiIyQoiomqvwskKVgInvTFZQURUpXl7e6NZs2YAgNzcXGzatMloY7m6uorHptrC0xiKLmHx9/fX6TrN8wy9DOa7777DpUuXAAAffPABmjZtatD+jcbWVrtt6GUgqamARhFZAExWEBFJqMLJClYCJ715e+OhDbDfH/ipHXDOMg7Iz5c6KiIiqoBRo0YBUM+u+Pzzz5GWlmaUcVJTU8Xjqrw1emRkpHgsl8vhXTRxX4q6Gm+SNfuorIiICHFnksaNG2POnDkG69voin4fGHpmRVRU8fsMWJeNiIgqpsLJClYCJ735+OCKN/DUy8Brw4A/m6mA+HipoyIiogqYNGkSrKysIJPJ8ODBA7z66qtGGSdK442j2Rd+LINmMsfR0REWFrq99HJyciqxj8p64403kJmZCQBYvXq1wV7L5eTkIDU1VetmcMaeWVF0CYiXV/ExiYjIZCqcrGAlcNKblxd8M+ViM9oJJX+KQUREZqtu3bqYNGmSWGh7+/btWrMhDUWzKKUhdx0zNc0lLBWZIWKr8SbZUMtg/ve//4m7rL300kvo37+/QfoFgC+//BLOzs7izc/Pz2B9i0wws2JJdyBwEjD8eeBGgKdh+yciogqpcLKClcBJbxYW8HUsXDrEZAURUdW0cOFC8c2oIAhYs2YNRo0aZbBP03ft2oWEhAQA6qUTXbp0MUi/UlAqleKxXC4v40xtmudqFujUV2JiImbOnAlAXQ+koJi5oXz44YdISUkRb1HG+PteNFlh6JkVsbH4zxO46APsagbk1/Yq/xoiIjKaCicrWAmcKsPOuy7c1LNPEeUEIDpa0niIiKjinJ2d8dtvv8HGxkZcsrlz5060atUK27dvr1TfaWlp+OCDDwCodx7r3Lkz7OzsDBG2aPPmzZDJZAa//fzzz8XG0ow9uwIzATTPtbe3r9TXCwAzZsxAUlISAGDx4sVar7EMQaFQwMnJSetmcEWXZBh6ZkVMDGI0wvZxb1D6uUREZHQVTlawEjhVip8ffB9/8BbjBKii7pV9PhERmaWuXbvi999/F+tXAOo6E2PGjEGnTp3w559/iktFdJWUlIThw4cjLCxMvO/tt982aNym5uDgIB5nVWAmQEFdiaJ96GPfvn345ZdfAADdunXDa6+9Vqn+JGNjg0c2QLgbcM0LSFMZfmZFjKP60C4XcPZmsoKISEq6z0fUMGrUKCxcuFCsBD5u3Dg4OjoaOrZqUwmcNPj5wS8KCK4N5FkCCffDUVvqmIiISC/PPPMM9u3bh1GjRuHRo0fiLIuLFy/i2WefRa1atTB8+HAMHToUgYGBpRbKjI+Px6ZNm/Dtt98iPj5eTH60bNkSo0ePNnjc9vb2RtnRrKQZEB4eHuJxeno60tPTdUo+xMXFiceVLTA6ffp0AOqlJWvXrhWf3yrH1hbfdQE+66Nu7t+UjQGCABjq64mJQUwb9aFPGiBrZLxd74iIqHx6JSsmTZqEJUuWQKlUipXAt27daujYqk0lcNLg6wvf/wqb0cmRTFYQEVVhffr0weXLlzF27FicOXNGfCMsCALi4uKwdu1arF27FgBQp04d+Pn5wcXFBTY2NkhJScHdu3cREREhXlOQ8HB0dMQff/xhlJhHjBiBESNGGKXvopo2barVvnfvHpo3b17udZqvgQpmtOor/vHOW0qlEq1atdL5urt372olNubNm4f58+dXKpZKsbGBbWEJEGTJAeTmAgba0ST1QTTSHnflmwrACAktIiLSXYWXgQCsBE6V8HgZiIUK8E0B0pPvSx0RERFVUr169XDq1CmsWrUKbm5uYtJBM3EhCAJiYmJw7tw57N+/H7t27cKxY8dw584d8fGCRIWzszO2bdtW7I1+VaRZLBwAgoKCyr0mLy8P//1XmNkv2keNZWsLG41kRbYchiuymZODmNwksemTCqBOHcP0TUREetErWQGwEjjpyc8Ps/4Fcj4Hor4D+lxKBjQqpRMRUdUkk8kwZcoUREZG4ssvv0TdunW1khCayYui12kmNTp16oTz58/jySefNPWXYBQNGzbU+sDl1KlT5V5z6dIlrZoVvXr1qlQMmluKlndTaMxSkMlkWo9JviTXxqZ4ssJQRTbj4rSLa6aBMyuIiCSmd7KiqlcCJ4n4+cFWCchVj9sqFaCxLpeIiKo2e3t7vP/++7hz5w4OHz6Mt99+Gy1atBBfK5R0c3FxwfDhw7F3716cPXsWjRs3lvrLMKihQ4eKx1u3bkVubm6Z5xcUwwTUu7D5+/tXavy7d+/i0aNHOt0KXn8B6pm0pT0miSIzK7KsYLiZFTExqP8IWHAEeOMC0OO+FeDiYpi+iYhIL3rVrChQUAl89OjR4h7gBZXAO3TogPfffx8jR46sUCGnpKQkPPvss9WqEjhp8PBQry3NySm8LyoK4DIfIqJqRSaToW/fvujbty8A9e4Wt2/fRnR0NNLT02FpaQl3d3fUqlULTZs2rbpFH3Uwfvx4rF69GgCQmJiItWvX4q233irx3OjoaGzcuFHrWnpMoTDezIrYWDRKBj4uWIHsX9dwhTuJiEgves+sKFBQCdzlcfa5aCVwHx8fTJ06Ffv27RP39y5JfHw8vv76a7Rq1QrHjh0Tp4UaqxI4SUQmK56Y0CgiRkRE1ZOdnR1atWqFQYMGYcyYMRg5ciR69+6NZs2aVetEBQAEBgZqza6YM2cOTp8+Xey81NRUjB07FmlpaQCA2rVrY9q0aWX2rbnEptonNuRy2KgKX7oaNFkRE6PdZr0KIiLJVWpmRYGqWAlc07///ouNGzfi5MmTiImJgSAI8PX1RY8ePTBu3Dh0797d4GPq88JszZo1eOONNwwei8n5+QG3bxe2796VLhYiIiITWLZsGf79918kJiYiPT0d/fr1w8SJEzFgwAA4ODggODgYK1asEF8PWVhYYN26dbC1tZU4cvNia6EAoF76YdACm7Gx2m3WqyAikpxBkhVAYSXw77//HnPnzkVSUpLWG/KCnUNiYmIQW+QPQsFjALQqgf/xxx9GrQSekZGBt99+G+vXry/2WGhoKEJDQ/HDDz9gwoQJWLFiRYn7p5Me6tfXbkdGShEFERGRydSvXx+7du3CkCFDkJycjJycHKxevVpcHqLJ0tISS5cuxZAhQySI1Lx1eWiHS2uzYKMEaqWDMyuIiKoxgyUrgMJK4K+88gpWrlyJ77//Hncff2pe1kyColubderUCZs2bTJqga38/HyMHDkSBw4cEO+ztbVFixYtIJfLcf36dXFnkw0bNiAmJgZ79uyBpaWlwWPp1auXTp+c1K1b1+BjS6JBA+02kxVERFQDdOvWDcHBwZg+fTp27doFZQm7YQUGBmLp0qXo1q2bBBGaP2dLe7SP0VhWzJkVRETVlkzQnNZgYIIg4NixY9i1axeOHDmC69evQ6VSlXiuq6srevfujcmTJ2PgwIHGCkk0Z84cfPnll2J70qRJWLRoEdzc3ACoZ10sXrwYCxYs0Lpm4cKFBhlfM3nzf/buOzyqMu3j+HfSeyDUNHpHOoQmHVFkRcResLvs6sruq+6y6urqWnF3FdS1F1yxrChiQxFEqvSuNOkptISQ3jPvHxNOZtLL9Pw+1zWX5555zjl3nIQ8uecpR44coUPF0QZ2lpmZSWRkJBkZGURERNR+giP9979wyy3lcc+esGeP6/IREWki3Op3QRN35swZVq9eTVJSEoWFhcTExDB48GCHjih1Jod9r3XvDgcOlMeffgpXXmn/6370EVx3XeOvKyLSxDXm94FdR1ZU5K4rgaekpPDCCy8Y8YwZM3jjjTds2oSGhvKPf/wDs9nMk08+CcDzzz/PPffcQ4yGBjZOx46sbg9PjoajzeCvGw9yu9msVbdFRKTJaNWqFVfa44/spqbiSFQ77gZiQyMrRERczqHFiorOrwTep08fZ962krlz55Jf9sstJCSEuXPnVtv2kUce4b333iMxMZH8/HzmzZvHnDlznJSpl+rQgTw/WFa2bfyv4UVw+jS0aePavERERMS9BQXZxvaYBpKZSWppNr+0h9gsiMuEIH0wJSLico3eutQTff7558bxNddcY0z9qEpAQAC33XabES9atMihuTUJMTF0zC6vkx1pjtatEBERkdo5YmRFSgqr2sPY26DrLHhhGFpgU0TEDTS5YsX+/fs5ePCgEV9yySW1njN58mTj+ODBg+zfv98huTUZvr60i4g3wqPNULFCREREaueIkRXJySRbTaOOLQmpXBQRERGna3LFip07d9rEw4cPr/WcgQMHEhAQYMS7du2ye15NTVC7TsRYNlvhSDOgbF95ERERkWo5oliRkkJyeHkYG9y68dcUEZFGc+qaFe5g7969xnFAQADx8fE1tLZtd+jQoUrXsIc///nP7Nmzh8TERIqKimjRogVdu3ZlzJgx3HLLLXSsuNWnN+jYkY7nICUCTodB7tFfCXF1TiIiIuLeQkL45wjICYAWuXCvI0ZWhGtxTRERd9DkRlYctZpuEBcXV+cdSNq1a1flNezh008/Zc+ePWRlZZGfn09ycjIrV67k8ccfp1u3bvzud78jz177iLuLDh3omF4eHj5l3wKQiIiIeKGQEP4xBh4fC68PBnJzG3/NiiMrWnjhh0QiIh6oyY2syMrKMo4jIyPrfJ71nrDW17CHli1b0rlzZ8LCwsjIyGDfvn1kZ2cDUFxczOuvv86mTZv48ccf65xzQUEBBQUFRpyZmWnXnButY0e6LCsPD507wgWuy0ZEREQ8QWgoIUWQHQi5/kCOHYoVyckklw2mCC+A8Oj2jb+miIg0WpMbWXG+CAAQVHHeYw2CrRZasr5GQ/Xq1Yu5c+dy6NAhzpw5w4YNG1i+fDmbN28mPT2dr7/+mr59+xrtt2/fznXXXVfn6z/zzDNERkYaj7pMd3Gqzp0Zcwz+tB5e/gb67j4NRUWuzkpERETcWUgIIWXdhVx/7DKywpySbIysiM0EYjUNRETEHTS5YkVxcbFx7OdX94El1m2L7PBH9S+//MIf//hHOnXqVOW9pkyZwsaNG5kyZYrx/HfffcdXX31Vp+s/+OCDZGRkGI/ExMRG52xXXbsy9ii8sBTu2Qwd00q1I4iIiIjUrGKxIien0ZfMPJ1Ebtk66rFZaNtSERE30eSKFSEh5cs45tdjb27rtqGhoXbNqTpBQUF89NFHtGnTxnjupZdeqtO5gYGBRERE2DzcSlSU5WHt119dk4uIiIh4hgrFCnNuI4sVpaVEHj9F7pPw64vwn2/QyAoRETfR5IoVYWFhxnF9Fq3MtRpmaH0NRwsPD+f3v/+9Ea9Zs6ZeRRa31rWrbXzwoGvyEBEREc9gVawo8YHCvEYWK86cgeJigouhy1nonoZGVoiIuIkmV6xo2bKlcXzixIk6n3fy5EnjuEWLFnbNqTbjxo0zjvPz891vSkdDVSxWaGSFiIiI1MSqWAGQW9jIdcRSUmxjHx+wGtEqIiKu0+SKFd27dzeO09LSbEZM1MS6QNCjRw+751WTtm3b2sSpqalOvb/DqFghIiIi9REaSodz0OMMDEyB4vxGLrCZnGwbt20Lvr6Nu6aIiNhFk9u6tGfPnjbxjh07GDFiRI3nJCcnc+bMmWqv4WgVCyrW6254NBUrREREpD5CQnj1G6s4vrBx16s4skLrVYiIuI0mN7IiISGBwMBAI167dm2t56xZs8Y4DgoKIiEhwSG5VeeXX36xiVu3bu3U+ztMly628dGjUNjIToeIiIh4r4of2DR269KKIyu0XoWIiNtocsWKsLAwJkyYYMQffPBBredYt5kwYYLTdgM57+OPPzaOO3ToQHR0tFPv7zBdu1Jigv0t4PMesKRzKRw54uqsRERExF1VLFY0dutSjawQEXFbTa5YAXDrrbcax7t27eKrr76qtu22bdv49ttvqzzXGb788ku+/vprI542bZpT7+9QzZqRE92CHvfC9Ovg6VHA/v2uzkpERETcVcViRX4+lJY2/HoaWSEi4raaZLHiqquuol+/fkY8c+ZM9u3bV6ndiRMnuOmmmygpKQGgf//+XHnllVVe8+jRo5hMJuPx2GOPVdkuIyODK6+8kq1bt9aa50cffcQNN9xgxCEhIcyePbvW8zxJRJfexGZajve2BHOFKS8iIiIihqrW7arHVvQVFZ5IYur18LvfwPz+aGSFiIgbaXILbAKYTCbefPNNxowZQ15eHidOnGDo0KH8/ve/Z/To0fj5+bFp0yZefvllTp06BUBwcDBvvPEGJpOpUfc2m80sWrSIRYsW0aNHDy6++GL69+9PdHQ0oaGhZGVlsXv3bj799FM2b95sk/O7775baWcQj9erFz3PrCY5As6GwJkD2/CSFTlERETE3qqaipubW/XzdZCSkcRXZRvFpYbArRpZISLiNppksQJgyJAhLFiwgJtuuom8vDwyMzOZM2cOc+bMqdQ2ODiYBQsWMGTIELvmsG/fvipHdFQUHh7O66+/zjXXXGPX+7uF3r3puQSWd7aEe1N2qVghIiIiVatqZEVDF9ksKCC5ON0IYzPRyAoRETfSJKeBnDd9+nS2bt3KxIkTqxwxYTKZmDBhAlu2bGH69Ol2uWdwcDC//e1v6d27d62jNCIjI5k1axY///wz119/vV3u73Z69aJn+a6w7Mk+0ri5pyIiIuK9goMrP9fQYsWJEyRHlIexWWjNChERN9JkR1ac17NnT5YtW0ZiYiLr1q0juWyhpdjYWEaOHEl8fHydrtOhQwfMZnOt7QIDA3n99dcBSE9PZ8eOHZw+fZrU1FTOnTtHSEgIUVFR9O3bl759++Lr69vwL84T9OpFL6tixd7IIjh+HDp0cFlKIiIi4qZ8fVnZ1Z9ZE4vI9Yf71sPdDd0RJCWF5PDyMDbfH5o1s0uaIiLSeE2+WHFefHw81113nVPv2bx5c8aNG+fUe7qdNm3oWRQJZACWRTb55RcVK0RERKRK+aGB7G5TBMDpUBo+siI52XZkRWAraOTaZCIiYj9NehqIuAGTiVYdexOVC8FF4F8K7Nnj6qxERETETYX6lk8FyfWn4cWKiiMrwrVehYiIO9HICnE5U6/e/PzKT7TJAR8z4KftS0VERKRqIX52KlYkJ5NkPbIiqn3jEhMREbtSsUJc74ILiM62infudFkqIiIi4t5C/Mt3BGnsyIqJSdAiDzICISRGxQoREXeiYoW4Xv/+tvEvv0BhIQQEuCQdERERcV8hAXYqViQn8/dVVvG/tROIiIg70ZoV4nr9+tnGRUVat0JERESqFBIQZhzn+gON2A3ERqzWrBARcScqVojrRUZCp062z23f7ppcRERExK2FBFUoVjRkZIXZDGXb1RtiNLJCRMSdaBqIuIf+/eHw4fJ4xw5XZSIiIiJuLDg4ggfXQGghdE4H4hpQrMjKqjwiQyMrRETciooV4h4GDIBFi8pjFStERESkCj4hoTz9g9UTExtQrKg4qgIgOrrBOYmIiP1pGoi4B6tFNktNkPPzdigtdV0+IiIi4p5CQmzjhkwDqbheRVQUBAdX3VZERFxCxQpxD/37czAKLpoBUbPh4YQsOHjQ1VmJiIiIu7FHsULrVYiIuD0VK8Q9xMbSPKINyztDRhBsjgE2bHB1ViIiIuJu7DSyIiUccvzLYq1XISLidlSsEPdgMtFiwAg6n7WE26KhaMM61+YkIiIi7qdisaIhW5cmJzP5Rgh7GNo8AOYYrVchIuJuVKwQ9zFsGEPKRmXm+8Mve1e7Nh8RERFxP6GhtnEDR1YkR1gOQ4rAFBvX+LxERMSuVKwQ9zF0KAlWU0g3Ze9vWAdEREREvJcdpoHkn0gkrewysVlozQoRETekYoW4j8GDGXLCZISbo82wbZsLExIRERG3ExJCSjjsagMb4qAgP7vel0jOSDKOYzPRmhUiIm5IxQpxH6GhDGzZB9+yHUvXxwM//eTSlERERMTNhITwwCTo93sYfiekkFW/80tLSc4/bYQaWSEi4p5UrBC3EpIwkgEnLMe/tIa0dctcm5CIiIi4l9BQQorKw5zCei6weeYMySElRqiRFSIi7snP1QmI2Bg7lgf+8Sp5/jDqGEQVb4DiYvDTt6qIiIgAYWG2xYqiehYrkpONxTUBYnNM0Lq1fXITERG70V+A4l7GjOHaX6yfyLasW5GQ4KqMRERExJ2EhxNWWB5mm4qhsBACAup2fkoKyeHlYax/C/D1tW+OIiLSaJoGIu6lTRvo2dP2uZUrXZKKiIiIuKHwcMILysOsQCCrHutWJCczex2seQc++hQuCIi3e4oiItJ4KlaI+xk71jb+8UeXpCEiIiJuKDyccKuRFVkB1K9YkZJC22y48Dhc9zM0b9Pe7imKiEjjqVgh7qdisWL1asjPd0kqIiIi4mZCQ4mwGlmR2YCRFTa0E4iIiFtSsULcz/jxYDKVx7m5loKFiIiIiI8P4aYgI6z3NJCUFNtYO4GIiLglLbAp7qdlS8uCmhs3lj/37bcwaZLrchIRERG3MT49kp//k094IbTMRSMrRES8kEZWiHu69FLbeMkS1+QhIiIibicyMJLeZ6BdBpZtTDWyQkTE66hYIe7p0ks51BwemgCDfgvvBR+AgwddnZWIiIi4g/Bw27iuxYqCAkhNtX1OIytERNySihXingYO5GR8M54ZBdti4OtuwOefuzorERERcQcNLVacOFH5OY2sEBFxSypWiHvy8WFownRa5FrCb7tC3mf/c21OIiIi4h4aWqxITuaL7vD4GHhrIJyJCoLISPvnJyIijaZihbgtv6uvZep+y3FOACxP3wrHjrk2KREREXG9hhYrUlJY3AMeGwd3TYUTnVrZ7kAmIiJuQ8UKcV/jxnFFUpgRLu4BfPqp6/IRERER99CIkRXJEeVhbESc/XISERG7UrFC3Je/Pxf1m05ooSX8ogcUffC+a3MSERER12vEyIrkslMDiyGqTXv75iUiInajYoW4taDrbmLKActxWgh8n70TduxwaU4iIiLiYuHhvDIE/nwR/N/FNGhkRWwmmGK0uKaIiLtSsULc2/jx3JTS0ggX9wDefdd1+YiIiIjrhYfz9gD410h4aSiYszLrdFrOyUQygizHsVloJxARETemYoW4N19fLhl/F3duhW8XwKvfAB98APn5rs5MREREXCU8nPCyaaIlPpCfU7diRfK548ZxXCYQE+OA5ERExB5UrBC353/rHbz5FVxyEPxKgbQ0+PhjV6clIiIirhIeTnhBeZiVn1H7OWYzybmnjDA2E42sEBFxYypWiPvr3BkmTrR9bt48MJtdk4+IiIi4Vng4EdbFioI6rFmRmYlPXj4JSZZCRbsMNLJCRMSN+bk6AZE6mTULli8vj3fsgNWrYcwYl6UkIiIiLmI1DQQgqyi79nOSkxlzDDa+ZfWcihUiIm5LIyvEM0yZYhlhYe3pp12Ti4iIiLhWhWkgmcU5tY+4TE62jVu2hKAg++cmIiJ2oWKFeAYfH/jjH22f+/572LDBNfmIiIiI61QcWeFvhry8ms9JSrKN4+Lsn5eIiNiNihXiOe68E9q0sX3u7393TS4iIiLiOuHhtMuA/idg9FEshYusWtatULFCRMSjqFghniM4GP7yFwDy/OA/Q+Dopu9hyRIXJyYiIlJ3u3fv5r777qNv375ERUURFhZG9+7dufHGG/nuu++clofZbObHH3/k7rvvpn///rRu3ZqgoCDi4+NJSEjgrrvu4sMPP+TkyZNOy6nOwsO5dQdsfx1WzYfRx1CxQkTEy5jMZm2p0BRkZmYSGRlJRkYGERERrk6n4XJzWTeyHVdNSONkONy4Cxb80h127YKAAFdnJyLi1rzmd4GHKi4u5tFHH2XOnDmUlpZW227KlCm8++67tGrVymG57Nmzh9/+9resW7eu1rZTpkzh66+/rtf1Hf69ZjaDvz+UlJQ/t20bDBhQ/TlTpth+wPHkk/Dww/bPTUREDI35faCRFeJZQkLofe8/KPK1hB/0hS1Z++E//3FtXiIiIrWYOXMmzzzzjFGo8Pf3p1+/fowcOZIWLVoY7b755hsmTpxIdnYddrhogGXLljFo0CCbQkVoaCj9+vVj/PjxJCQk0KxZM4fc225MJggPt31OIytERLyKihXicZrd+jsePdLOiP9wKZT8/RE4etR1SYmIiNTgjTfe4J133jHiqVOncuTIEXbs2MHatWs5ceIEL730En5+ll3ld+3axcyZM+2ex7p167j88svJz88HoFOnTixcuJDU1FR27NjBDz/8wMaNG0lPT2f37t08/vjjxLnrH/X1LFaUJiViM5zYXb8uEREBNA2kyfC2ob+F61bT76Mx7CsbIfviErg3eAysWGHZOURERCrxtt8FniI3N5fOnTsbaz+MHTuW5cuX4+vrW6nt22+/zZ133gmAyWRiy5YtDBw40C555OXl0adPHw4dOgTAyJEj+e677wgLC7PL9a055XutTx/4+efy+IMP4IYbqm6bm8uC4aH8fgrEZ8ITK+DKz/dB9+6OyU1ERABNA5EmKGDkaN4onWLED06E/T+vgnnzXJiViIhIZfPnzzcKFSaTiVdeeaXKQgXAHXfcwdChQwHLAphz5syxWx5PPfWUUaho0aIFixcvdkihwmkqTlVJT6++bXIyiRGQHQh7W4HZBMTGOjI7ERFpJBUrxGONevZDfrfP0snKCYBrroa8v82GDRtcnJmIiEi5RYsWGcdjxoyhZ8+eNba3nv6xZMkSCgoKGp1DQUEBr732mhE/8sgjtGzZstHXdanmzW3jc+eqb5uURJLVB3rxJaHgyYUaEZEmQMUK8VwREfzrzk/oddoStsiDHIpg+nRISXFtbiIiIkB2djarV6824ksuuaTWcyZPnmxz/sqVKxudx+eff05aWhoAgYGB3HzzzY2+pstVLFbUNLIiKYnEyPIwPkyjKkRE3J2KFeLRQidM5pOw23j8R1j2X2iZC5w4AVdcATk5rk5PRESauD179lBUVGTEw4cPr/Wctm3b0qFDByPetWtXo/P4/vvvjeMRI0bQvOIf+p6oWTNG3AHR98MFd1N7saJsZIVfCbRp2cEZGYqISCOoWCEer/dTb/Co/0R8rZeK3bTJMsLCDkNnRUREGmrv3r02cefOnet0nnW7itdoiE2bNhnHw4YNA+DUqVM89dRTDBo0iKioKEJCQmjfvj3Tpk3jnXfeobCwsNH3dajmzTkVCifD4UQYdZ4GEpMFvnHxzshQREQaQcUK8Xx+fvDxx9Cxo+3z338PN94IVp9oiYiIONNRq221/fz8iI6OrtN57dqVb9F9tJFbcxcVFdkUPLp27cpnn31Gr169+Nvf/sa2bdtIT08nLy+P48eP88UXX3DHHXfQvXt3Nm7cWKd7FBQUkJmZafNwuGbNaG7ZgZVzQVCafrbapnnJx0gNtRzHZaJtS0VEPICKFeIdWrSAr7+GqCjb5z/7zDIlJDfXNXmJiEiTlpWVZRyHh4fjU8ftta23d7O+RkOcO3eO0tJSI966dSvXXnstZ89a/rhv27Yto0ePZtiwYYSGhhrtjh49ytixY+u0ZsYzzzxDZGSk8YiPd8LIhebNaZ5nOSz1gaystGqbJp89YhzHq1ghIuIRVKwQ79GrF3z3XaXVvQu/+wbzJRdDWvWdGBEREUfIzs42joOCgup8XnBwcJXXaIhzFaZH/Oc//6GkpIS2bdvy5ZdfkpKSwqpVq1i/fj2pqak8++yzxtaq+fn5XHfddaSmptZ4jwcffJCMjAzjkZiY2Kic66R5c2NkBUB6fvUjK1ofPs2n/4MXvoMbd6FihYiIB1CxQrzLkCHw5ZcQEgKAGbh1GlzXZi3ZwweBHRYpExERqavi4mLj2M/Pr87nWbctauR0xqq2Pg0NDWXlypVcdtllmEwm4/mgoCBmz57N66+/bjx36tQpXnjhhRrvERgYSEREhM3D4Zo1M0ZWAKTnZ1TdrqCAiORUrtwLf9oAlx1AxQoREQ+gYoV4n3HjYNkyaNaMf46Ej/rAJxfAgEuOsWF6AnzyiaszFBERF1qwYAEmk8nuj/nz51e6V0hZ8RwsoxTqyrqt9dSMhqjq/AceeIDu3btXe84dd9xhs3PJO++806gcHKLiyIqSbCgpqdyuqu3MVawQEXF7KlaIdxoxAlatomdJFOFlHygdbAEX3ljAY69eS8FtN4MzFv8SEZEmLcxqamJeXl4NLW3lWq21FFZhemNjcjjvpptuqvU86zYnT57kwIEDjcrD7qKiiLL6X3o2mKp3BKk4JSU0FCIjHZmZiIjYQd3HI4p4mr59ueyz3WybMYWbOu1gYxyU+MDjY+Hj1Pf5zyXLmPD0RzB2rIsTFRERZwoNDSU2NtYh162oZcuWxnF2djbZ2dl1Kj6cPHnSOG7RokWj8mrWrBl+fn7GlJTw8HC6dOlS63kDBw60iQ8fPky3bt0alYtdtWjBuCPw3PfQKheGJAOpqZZFt60dO2Ybx8eD1dQXERFxTypWiHeLiaHLkg2svft3PPXjfJ4YYylY7G8JEy8+yfw/jeOWvjPgn/+ENm1cna2IiDjBFVdcwRVXXOGUe1WcanH8+HF69epV63nWC1T26NGjUTn4+/vTuXNn9u/fD0BUxZ2zqlGxSJKent6oPOwuKIghmWEM+clqAdLUVKg4vaXi1q8VtzoXERG3pGkg4v0CA/F76x3+fuu7bFkQwvCy/l/bLJi2D3j/fUvH5uWXoZGLmImIiFjr2bOnTbxjx45azykqKuKXX36p9hoN0bt3b+O4qgU3q1JxjY367GbiNFYjVwBLsaKiisWKDh0clY2IiNiRihXSNJhMcOut9F/+M2v3jeDtL+A/SyDyfH8tIwPuvdey/enHH4PVfvQiIiIN1alTJ+KsFnNcu3Ztreds3brVZs2K0aNHNzqPMWPGGMdnzpwhJyen1nOOHDliE7dxxxGIdSlWVPg6NLJCRMQzqFghTUvHjvisWs3tt7/E9OQqtlU7eBCuvx4GDYIvvlDRQkREGm3q1KnG8cKFCyksLKyx/QcffGAc9+7dm86dOzc6h+nTpxtblJaUlLBixYpaz/n++++N48DAQAYMGNDoPOyuDsWKw6m/8soQ+LYLnAhDIytERDyEihXS9Pj6wh/+APv3w403Vt1mxw5+/NM0/nJDK068+TzUccisiIhIRbfeeqtxnJqayuuvv15t26SkJN57770qz22MuLg4LrroIiOeM2cOZrO52vbJycn897//NeKLLrqI4OBgu+RiV7UVK4qLWeuXwj1T4NKb4JPeqFghIuIhVKyQpqttW1iwAFauhISESi8/MQb+2fMsHY/fz523RrHzqVlw6pTz8xQREY82ZMgQm9EVDz30EOvWravULjMzkxtuuIGsrCwA2rZtyz333FPjtU0mk/GorbDxzDPPGKMr1q1bx3333UdpFSMI09PTufLKK408zufslmorViQncziy/GvseA5NAxER8RAqVoiMGQMbNsCiRVC2iNmxSPgp3vJygR+83SOX/sUvMfqhaD65awRFK5ZDDZ9IiYiIWJs3b56xjWl2djYTJkzgnnvu4YsvvuCHH37ghRdeoH///qxZswYAHx8f3njjDbuOZhg4cKBN0WHu3LkkJCTw2muvsXLlSpYuXcoTTzxBz5492bhxo9HuL3/5C8OHD7dbHnZVsVhx5oxtfPQoR5qVhx3zgytvbSoiIm5JW5eKgGUBziuugKlT4eOPaT9nDkfm7mbuMHh1CGQFWpqtaWdmDeuJ+eYiFv4tjhEX32mZSlKH/epFRKTp6tChA1988QWXXXYZZ8+epaCggFdeeYVXXnmlUltfX1/mzp3LZZddZvc8nnzySdLS0njttdcAy2KeW7durbb93XffzdNPP233POymVSuORcLBKEiOgKvOpBBi/frRoxxpXh52jGxv+Z0vIiJuTyMrRKz5+lqKDzt3Ev3JEuYUjSH53/Cfb6Cn1Yc1Z4Oh+44keOwx6NoVhg2zbH2qaSIiIlKNESNGsGvXLq688kr8/Kr+vGjIkCGsXr2aP/zhDw7L49VXX+Xzzz+32c60on79+rF48WL+85//4Ovr67BcGi06mkfGw8Rb4JYr4FhOsu3rR44YIyta5kBYfOMXKxUREefQyAqRqphMMHkyTJ5M+KZN3P3KK/z+7Y9ZEV3AS0OhVQ60yLNqv3Gj5TFrFgwfDpdfbnl07+6yL0FERNxPbGwsn376KWfOnGH16tUkJSVRWFhITEwMgwcPpns9f2/UtEhmTaZNm8a0adP4+eef2bFjBydOnMDHx4c2bdowbNgwunjKiMHoaGLKl9YgpSCVnqWl4GP5PC732EGS21te65SO1qsQEfEgKlaI1CYhARISMD3/PBP++18mvPYapQf2V93WbIaffsL800/8Y8lsLilqR8KF12K6ZDKMGAGBgc7NXURE3FKrVq248sorXZ0GF1xwARdccIGr02i46GhiM8vD5NBSyyKbrVsDsD9lN+YOltd6pgKjVKwQEfEUmgYiUldRUfCnP8Hevfj8tB7uuafywl5lNsfCY+Ng2KTjdCj8J/c/O57VfSIomjwJ/vUv2LkTqliBXUREROqhTRtissvDlHDgxAlLYDaTlXyY7qngWwq9zmAspC0iIu5PIytE6stksqxRMWwYvPACLFtm2QL1q68g29JjWtirvPnxZvD8CHh+RCGR+cuYuHkZl34INye1wG/4SBg50jLqYvBgCApyzdckIiLiifz8iPFtDqQDVsWKfv3g9GlG/5LNvl+g0BeKfIAePVyZrYiI1IOKFSKN4e8Pl15qeRQUwIoV8MUXPLh0MT1TT7GwFyzvBMVla5NlBMFnvWBzDNw2Nw2+/NLyAAgIgEGDygsXAwdadhnx0QAoERGR6sSERWNTrEhJsbywb5/RJqAEAvyDoF075ycoIiINomKFiL0EBhqLckaVvsLtmzdz+5dfcnbZN3ydt5Nvu8D3neFsCEw+CJU2TisshPXrLQ8s27B1KgzFZ8BAS+Fi4EAYMMCyaGdAgNO/PBEREXcUHdUOk3kPZhMciwQSEy0v7N1r27B7d8uuXyIi4hFUrBBxBB8fGDoUhg4liqe4OS2Nm3/8kZJlS9m0cgmRR1JqPP1UKHSdBRH5OQw6sYYhv6xh8DLodwo6Z/ri27kr9Opl++jeXdNIRESkyQno0Jn4DEiMhCJf4MgRywtWIysATQEREfEwKlYAP/30E++99x5r1qwhOTkZs9lMXFwcF154IbfccgsjR4506P0PHz7M/Pnz+eabbzh+/DjZ2dnExMTQt29fbrzxRqZNm1btfuziIVq0gKuuwveqqxgOcPQorFtX/ti927KTSJktMZb/ZgbBjx0tj/MCi0vokbqPbz7YR+yiReUv+PhAhw7QuXPlR6dOEBbmhC9URETEyTp2ZO0/oHUOBJYAo1WsEBHxBiZzQzfo9gI5OTnMmjWLd955p8Z2t912Gy+99BKhoaF2z2HevHnMnj2bgoKCatsMGzaMDz74gE6dOjX4PpmZmURGRpKRkUFERESDryMOkpEBGzdaChdbtrAmZT3Pd09ncywkV/F2+ZVAztOWObjVWd4Jcv2hYzrEZUKzyNaYOnexzNeNjy9/xMVZ/tuqldbHEPFy+l0gzuLU77VFi8B6G9j4eDh+HNq3t/z3vI8+guuuc2wuIiJiozG/D5pssaKkpIRLL72U77//3nguODiY3r174+fnx549e8jMLN+4e9KkSSxZsgRfO851fOKJJ3j00UeN2MfHh169ehEVFcWvv/7KifNbbwFxcXFs2rSJ6OjoBt1LHVQPdOIEbNvGia2r2HJoNVvP7eWXwEx+bm0pUux8rebTJ94MP1jVt0ILLUWL+AzLf6f8ClftsTohIKC8cBEdDW3bQps2lsf547ZtLUUNf3+HfMki4lj6XSDO4tTvte3bLes6nWcyQWqqZVSjtR07LLuEiIiI06hY0QAPPfQQzzzzjBHfddddPPvss0RFRQGWURdz5szhiSeesDnnqaeessv9ly5dyuTJkzn/v3/48OHMnz+fbt26AVBaWsrChQu58847yS7bDnPkyJGsXbu2QfdTB9VLZGTA3r2U/PIzvnv3wZ49lsexY5WadvwjHG1e/aVmr4Vnl1f/+skwuHeyZVhtmxzLf6PyoHkeRAU2o3l4K+LDY/GPamXpEEZFWf5b1XHz5qCpTCIup98F4ixO/V47d87ye8baG29wbtZvaZZfFvv6QlYWBAc7NhcREbGhYkU9paSk0LlzZ/LzLb/BZsyYwX//+98q2z7yyCM8+eSTAAQFBXHo0CFiYmIadX+z2cyAAQPYuXMnAN27d2fbtm2EhIRUart8+XIuuugiI160aBFXXHFFve+pDqqXy862zM399Vc4dAgOHeLD7PUcKEjmuE82iRGQFGFZfCynbCORl7+BezZXf8ktMTDktzXfdv9L0C2t+teXd4Jt0RBRABG+IUQERhARFElEYAThIc1oHtKCqPDWEBEBkZGW/1ofW/83PFzTVEQaSb8LxFmc/r0WHQ0nTxph6dAEosZtIqIAphyAV5P6WUZWiIiIUzXm90GT/Khz7ty5RqEiJCSEuXPnVtv2kUce4b333iMxMZH8/HzmzZvHnDlzGnX/b7/91ihUgGXdiqoKFQATJ07k2muv5X//+x8Azz77bIOKFeLlwsJg8GDLo8wN5w/y8uDwYTh8GPOhQ2QkHSTxzEFah6ZB/CnLfvQllRe/OF2HJVqi8mp+/Yvu8PLQ81Fu2aO8Mzk8EX56seZrPDAJinwgtAhCzf6EmgII9Qki1DeIUL8Q+he3pL1fCwgNrdsjJMSya0pQkOUTtvPH5x8qiIiIeJ5+/WyKFbuObSIjCDKCLCMFSUhwXW4iItIgTbJY8fnnnxvH11xzjTH1oyoBAQHcdttt/OMf/wAsIxsaW6xYZLWDQ8eOHZk0aVKN7WfOnGkUKzZt2kRSUhJxcXGNykGakOBg6N0bevfGBDQrexhKSiwdvMREyyMpCU6eZOKpFI5uPc6prBOczkvldNE50gPNnA2G9GA4G0z58NpqZAbW/HpE9evKGt4caNkVxaKo7JFjvP7aV78yc2v1569tBzdcaVmzI7QIgosguBiCii3HQcXw6jcQUlR2QkCAbfEiOJhtbUpJiTAR5BdEsF8QQf4hBPsHExQQQnBACGH+YYQHhFnOrfjw96/6+dpeq/i6HdfLERHxOv36wdKlRvhtl/KXJh4G7hri/JxERKRRmlyxYv/+/Rw8eNCIL7nkklrPmTx5slGsOHjwIPv376d79+4NzuGbb74xji+++GJMJlON7UeNGkVoaCg5OTnG+TNnzmzw/UVs+PpCbKzlMWyY8XQA0L7sAUBpKaSlWQobp05Z/jvijOW5tDQ4e7bS8f9tyGHqfkvRIivQ8l/rR7+TVeRjxUz5tJXqhBbV/Hp6kGX6S01e+9oqKCy0PKwW2H2xJ7zXv/rzL/kVvv2g5nsMvwNKTZbFUQNKLNvrGcfFMHMrXHi8+vOPNzOxrKsvgfgSgA+BZj/LscmPAHzx9/FjyLlQfPz8LeuDVPEo8PfB7OeLv28AvjW0q/Xh42P5vvH1bfixs65hMlkeIuLd+vc3Dot84KWh5S9NPgiMH+/0lEREpHGaXLHCevoFWBa2rM3AgQMJCAigsLAQgF27djW4WHH69GlOWg1TrMv9/fz8GDJkCCtXrjTuL+J0Pj6WnUBatYI+fep0Sv+CAvpbFzHS0iyLhGZkWIoB7TOgZ6bl+Pxz1v/NymL7a5YtWHMCIKeK/w48UXMOJiA6q/yckipmeQTWsAUsQF4t/1IGF9f8OsDm2Krvfd4lB+HCGs7f1tbMnVOKgepvVvBEzdvZ3j4dPuxrOTaZwb8E/ErBv9RyPOVXmL+4pq8CLrnJ8l//kvLzrK9x1zZISK7+/H0t4bOe4GsG31LLf/1Ky499S+GO7eBTw2pKu9pYpin5lpada7Y9bp5n+day4eNjKVr4+ICPD2eDwcfkY3lgqnTsiwmTj2+l81xyXPG5sWPhnntqfqNEmpoxY8jzg1mT4a1B5U/3PwGdWnaFzp1dl5uIiDRIkytW7N271zgOCAggPj6+1nPOtzt06FClazTm/gCd6/jLs3PnzkaxojH3F3GqwEDLomcN3HLXVFpKn6ys8uJFdjbk5NTr8ZucHFK+L4+LCvPIL8ojz1xEvh/k+9X8hzHA9T9Dn9OWtnll5+T5lx8PTar5/BKT5Q/pmooVtRVMCuswC8S/lmsUWV3DbIJCPyi0ej27llEsZmBpl5rbTDxcc7Hi59bwtwk1X+OO7TW//tQo+OSC6l+fvgc++6TCk6Wllv+Wrc/S4sGa7/G/hXDNL9W//kV3uHWa5XunuseReTV/b/11IvzQsfrzxx6Fv6+q4sSQEBUrRCqKiSGo/yA2x9rOC3zgJ+A3v3FNTiIi0ihNrlhx9OhR4zguLq7WKRjntWvXzihWWF+jMfc/f9263r+6a1SloKCAgoLyBQEyrYa0i3gMHx/LTiCRkVCHwmJd+Jc9wktLoaAA8vMtj7y88uMKz00re1TZLi8Pwguhc2H5FJLCQigqMo59CwvJ/6oQc2EBxUWFFJYWUlhcSEFpIYUlhRSUFtE2veb5LINS4I0vocDPUrgo8C37r5/luNRkGUVSk15nYMxRyxDpIl8o9rE9bptd8/mldfjn0reWwk9JHa5RW/GouJY1UP1Ka369Lltg1ZZDgR+cq2UHRFMt1zjUHLbEVv96q9xqXtAisCJVMl1/Ax8+u5WLZkBKBMzcAtfv8YEv7nV1aiIi0gBNrliRlZVlHEdG1jKR3Yr1NivW12jM/euTQ33v/8wzz/D444/XLzmRpsTHx7L4aHAtf3HakYnyYkmlzVbMZigutilyWD86FxbSuajI0qa6x101vFZczGM1nVtcDJ2K4XdWcYX7+RQXkb2xgCJzCUXmYuO/xaUlFGE5bhfmB/1NlhEMpaWW/1odjyou5Mul+ZSYSymhlBJzKcWUH5dSCgE+5edUsbv21XugZ6ql8FHsYxmxYn08KKXm98FsgksPWIov1T1aVlcoKBNWCN1Tqz/fTO3FIxOW6SulJktOlV6vrtihYoVI1e6+m14ffsivL20lKwDa5AD/eAw6dnRxYiIi0hBNrliRnV3+0WFQUFANLW0FW/1BY32Nxty/PjnU9/4PPvgg9913nxFnZmbWacqLiLiIyWTZ/cPf3zLM3w2ZqKLIUk8xZY86M5srFT6uO39cRTGk0rHZbHmUlhrFD5/SUr45Xwixet7meEY1z5f999LSUi6t7Rr/reEapaV8YnVsLi3FXFpCaWkJpeZSSktLMA00w0DfyucOGNDId0HESwUFwdKlhDz8MCEnT8K118J117k6KxERaaAmV6woLi5fnM7Pr+5fvnXboqJath+o4/3rk0N97x8YGEhgYC37RoqIuDuTybIDiZczlT00ZkKkkVq0gNdec3UWIiJiB02uXxRi9Yllfn5+nc+zbhsa2vDPFkMqfGJa1xzsdX8RERERERERd9fkihVhYWHGcV5eXp3Py80tn8BsfY3G3L8+Odjr/iIiIiIiIiLurskVK1q2bGkcnzhxos7nnTx50jhu0aKFXe5fnxzsdX8RERERERERd9fkihXdu3c3jtPS0mxGLNQkMTHROO7Ro4dd7g9w/Phxp95fRERERERExN01uWJFz549beIdO3bUek5ycjJnzpyp9hr10bVrV5vFMutyf4Dt27fb5f4iIiIiIiIi7q7JFSsSEhJsdslYu3ZtreesWbPGOA4KCiIhIaHB9w8ICGDo0KH1uv/Jkyc5ePCgEY8ePbrB9xcRERERERFxd02uWBEWFsaECROM+IMPPqj1HOs2EyZMaPRuHJdffrlxvHz5ck6dOlXn+zdr1kzFChEREREREfFqTa5YAXDrrbcax7t27eKrr76qtu22bdv49ttvqzy3oa6//npjdEdRURHPPfdctW2zs7N58cUXjfjGG2/E39+/0TmIiIiIiIiIuKsmWay46qqr6NevnxHPnDmTffv2VWp34sQJbrrpJkpKSgDo378/V155ZZXXPHr0KCaTyXg89thj1d4/Li6OmTNnGvG8efP47LPPKrUrKiritttuMxbhDA4O5qGHHqrT1ygiIiIiIiLiqfxqb+J9TCYTb775JmPGjCEvL48TJ04wdOhQfv/73zN69Gj8/PzYtGkTL7/8sjFFIzg4mDfeeAOTyWSXHB577DG+/fZbfv31V0pKSrjmmmu44YYbmDZtGlFRUezfv59XX32VXbt2Gef885//JCYmxi73FxEREREREXFXTbJYATBkyBAWLFjATTfdRF5eHpmZmcyZM4c5c+ZUahscHMyCBQsYMmSI3e7fvHlzvv76ayZOnEhiYiKlpaUsWLCABQsWVNn+L3/5C/fcc4/d7i8iIiIiIiLirppssQJg+vTpbN26lVmzZvHDDz9gNpttXjeZTIwfP54XX3yRXr162f3+3bp1Y9euXTzwwAN8+OGH5OXlVWrTs2dPnn32WaZOndqoe53/2jIzMxt1HRER8VznfwdU/H0nYm/qd4iICDSu72Eyq8cCQGJiIuvWrSM5ORmA2NhYRo4cSXx8vFPun5WVxYoVK0hMTCQnJ4fo6Gj69OnDgAED7HL9pKQkp30tIiLi3hITE4mLi3N1GuLF1O8QERFrDel7qFjRRJSWlpKSkkJ4eHiD193IzMwkPj6exMREIiIi7JyhuIreV++j99Q72eN9NZvNZGVlERMTg49Pk1xjW5zEHv0O0L9n3kjvqXfS++p97PWeNqbv0aSngTQlPj4+dvsULSIiQv8IeSG9r95H76l3auz7GhkZacdsRKpmz34H6N8zb6T31DvpffU+9nhPG9r30McqIiIiIiIiIuJWVKwQEREREREREbeiYoXUWWBgIH//+98JDAx0dSpiR3pfvY/eU++k91WaIn3fex+9p95J76v3cYf3VAtsioiIiIiIiIhb0cgKEREREREREXErKlaIiIiIiIiIiFtRsUJERERERERE3IqKFSIiIiIiIiLiVlSsEBERERERERG3omKF1Oinn35i5syZ9OrVi8jISCIiIujVqxe//e1vWbdunavTkzpauXIlJpOp3o99+/a5OvUm68yZM3z77bf84x//YOrUqURHR9u8N/Pnz2/wtXfv3s19991H3759iYqKIiwsjO7du3PjjTfy3Xff2e+LEBv2fE+PHj3aoJ9pvb/iCdT38A7qe3gW9Tu8k6f3PfwafKZ4tZycHGbNmsU777xT6bW9e/eyd+9e3nzzTW677TZeeuklQkNDXZCliPc5efIkw4YN49ixY3a/dnFxMY8++ihz5syhtLTU5rUDBw5w4MABPvzwQ6ZMmcK7775Lq1at7J5DU+TI91TEm6jvIeJ86nd4J2/pe6hYIZWUlJQwffp0vv/+e+O54OBgevfujZ+fH3v27CEzMxOAd999l+TkZJYsWYKvr6+rUpZ6CAoKYsyYMXVqGxYW5uBspKL8/HyH/WKZOXOmzR8B/v7+9OrVi7CwMPbt20daWhoA33zzDRMnTmTdunX6HrADR76n51188cV1aqeOoLgr9T28m/oe7kv9Du/kNX0Ps0gFDz74oBkwHnfddZc5LS3NeD07O9v8yCOP2LR56KGHXJix1ObHH3803qv27du7Oh2pwZEjR4z3qlWrVuZLLrnE/Le//c28ePFim5+5d999t17Xff31123Onzp1qjkpKcl4vbCw0PzSSy+Z/fz8jDY33HCDnb+6pskR76n1NfWrXLyB+h7eR30Pz6B+h3fylr6HejhiIzk52RwUFGR8E86YMaPatn/729+MdkFBQebk5GQnZir1oQ6D58jIyDAvXLjQfPTo0UqvNfSXS05Ojrlt27bGuWPHjjUXFxdX2fatt94y2plMJvPWrVsb+qVIGUe8pypWiDdR38M7qe/hGdTv8E7e0vfQAptiY+7cueTn5wMQEhLC3Llzq237yCOPEB8fD1iGGs2bN88ZKYp4tYiICK666irat29vt2vOnz+fkydPAmAymXjllVeqHTp9xx13MHToUADMZjNz5syxWx5NlSPeUxFvor6HiOuo3+GdvKXvoWKF2Pj888+N42uuuYaoqKhq2wYEBHDbbbcZ8aJFixyam4g0jPXP5pgxY+jZs2eN7WfOnGkcL1myhIKCAoflJiKivoeId1G/Q+xFxQox7N+/n4MHDxrxJZdcUus5kydPNo4PHjzI/v37HZKbiDRMdnY2q1evNuL6/lxnZ2ezcuVKR6QmIqK+h4iXUb9D7EnFCjHs3LnTJh4+fHit5wwcOJCAgAAj3rVrl93zEpGG27NnD0VFRUZcl5/rtm3b0qFDByPWz7WIOIr6HiLeRf0OsScVK8Swd+9e4zggIMCYE1qTiu2sryHu6dy5c1xzzTV06NCB4OBgwsPD6dixI9OmTePll182toYT71DxZ7Jz5851Os+6nX6u3d/NN99M165dCQ0NJTQ0lHbt2nHJJZfw3HPPcfr0aVenJ1It9T2aBvU9mg71O5oOZ/Q9VKwQw9GjR43juLg4TCZTnc5r165dldcQ95SRkcHChQs5duwY+fn5ZGdnc/ToUb744gvuvfde2rVrx0svveTqNMVOrH8m/fz8iI6OrtN5+rn2LO+//z4HDx4kNzeX3NxcEhMTWbp0KbNnz6Z9+/Y88sgjlJSUuDpNkUrU92ga1PdoOtTvaDqc0ffws1Ou4gWysrKM48jIyDqfFxERUeU1xH116NCB2NhYAgMDSU1NZc+ePRQXFwOWDsWsWbPYsWMHb7/9toszlcay/pkMDw/Hx6duNWr9XHuW6Oho4xPL9PR09u7da+yukJ+fz5NPPsnmzZv56quv8Pf3d3G2IuXU92g61PdoGtTvaDqc0ffQyAoxZGdnG8dBQUF1Pi84OLjKa4j78PHxYeLEiXzwwQekpaVx5MgR1q5dyw8//MDOnTtJT0/n1VdfpWXLlsY577zzjraP8gL6ufZOJpOJhIQE3nzzTVJSUkhJSeGnn37ihx9+YNu2bZw7d44PP/zQZg7w0qVLmTVrluuSFqmC/o3yXup7NE36mfZeruh7qFghhvPVbbAM26or67bWC+qI+xg9ejTLli3jhhtuqHJLuLCwMH73u9+xbds2m39g/vGPf3Dq1CknZir2pp9r79S+fXs2btzInXfeWeUQ28DAQK6//nq2bdvGoEGDjOdff/11LVwmbkX/Rnkv9T2aJv1Mey9X9D1UrBBDSEiIcXx+CE9dWLcNDQ21a07iXPHx8fzvf/8z4tzcXA3H9HD6uW7amjdvzqJFi4xPt8xmMy+//LKLsxIpp3+jRH0P76KfabFn30PFCjGEhYUZx3l5eXU+Lzc3t8priGdKSEhg7NixRrxs2TLXJSONpp9radeuHdddd50R62da3In+jRJQ38Ob6GdawH59DxUrxGA9Z/DEiRN1Pu/kyZPGcYsWLeyak7jGuHHjjOMDBw64MBNpLOuf6+zs7DrPA9XPtXex/pk+evQohYWFLsxGpJz6HnKe+h7eQf0OOc8efQ8VK8TQvXt34zgtLc2mwlmTxMRE47hHjx52z0ucr23btsZxamqqCzORxrL+uQY4fvx4nc7Tz7V3sf6ZBsu/8SLuQH0POU99D++gfoecZ4++h4oVYujZs6dNvGPHjlrPSU5O5syZM9VeQzyTdWfReu6heJ6G/FwXFRXxyy+/VHsN8TwV/wDUz7W4C/U95Dz1PbyD+h1ynj36HipWiCEhIYHAwEAjXrt2ba3nrFmzxjgOCgoiISHBIbmJc1n/wmjdurULM5HG6tSpE3FxcUZcl5/rrVu32vyCGT16tENyE+ex/pkODAwkMjLShdmIlFPfQ85T38M7qN8h59mj76FihRjCwsKYMGGCEX/wwQe1nmPdZsKECVq91wvk5uby5ZdfGvGIESNcmI3Yw9SpU43jhQsX1jpn0Prnunfv3nTu3NlhuYnjmc1mPvnkEyMePny4C7MRsaW+h4D6Ht5G/Q6xV99DxQqxceuttxrHu3bt4quvvqq27bZt2/j222+rPFc81yOPPMLp06eNeNq0aa5LRuzC+mczNTWV119/vdq2SUlJvPfee1WeK57p5ZdfttnfXD/T4m7U9xD1PbyL+h1it76HWcRKaWmpuV+/fmbADJijo6PNe/furdQuJSXF3LNnT6Nd//79zaWlpS7IWGqzdOlS83333WdOTEyssV1hYaF59uzZxnsKmAcOHKj31Y1Yvzfvvvtuvc6dOnWqcW5YWJh57dq1ldpkZGSYR40aZbRr27atOTc3107ZS1Ua8p7+/PPP5ttvv928b9++GtuVlpaa586da/b19TXuERMTo/dU3I76Ht5HfQ/voH6Hd/KkvoepLGERw+bNmxkzZoyxN3JERAS///3vGT16NH5+fmzatImXX36ZU6dOARAcHMyqVasYMmSIK9OWaixevJgrrrgCHx8fRo4cyZgxY7jgggto2bIlAQEBpKamsmnTJj744AOblZijoqL46aefKq3qLI5311138f7771d6vqCgwDj28/PD19e3Upv8/Pwqr3n06FGGDBlirLAeGBjIHXfcwaRJkwgLC2PXrl289NJLHDlyBAAfHx8WL17MZZddZo8vqcmz53u6Y8cOBgwYAMCgQYMYP348/fr1o3Xr1gQHB5Oens727dv56KOP2Ldvn3FeYGAgy5YtY9SoUfb6skTsRn0P76K+h2dRv8M7eUXfo0ElDvF6n332mTk4ONim8lbVIzg42PzZZ5+5Ol2pweeff17r+1jx0bVrV/O2bdtcnXqTdcstt9T7PTv/qMm6devMUVFRtV7D19fX/NJLLznpq20a7Pmebt++vd7XaNu2rXnZsmUu+MpF6k59D++hvodnUb/DO3lD30NrVkiVpk+fztatW5k4cSImk6nS6yaTiQkTJrBlyxamT5/uggylrnr06MG1115rszJzdTp06MBzzz3H9u3bjeqpeI8RI0awa9currzySvz8/KpsM2TIEFavXs0f/vAHJ2cndRUdHc3NN99cpwXI2rRpw9/+9jd2797NxIkTnZCdSMOp7+E91PcQUL/Dm7iq76FpIFKrxMRE1q1bR3JyMgCxsbGMHDmS+Ph4F2cm9XX8+HH27NlDamoqqamp5OTkEBERQevWrRk8eLBWX25Czpw5w+rVq0lKSqKwsJCYmBgGDx6sobce5tSpU+zatYszZ86QmppKVlYWYWFhtGzZkgEDBtCzZ88q/+gTcXfqe3gP9T0E1O/wJs7se6hYISIiIiIiIiJuRdNARERERERERMStqFghIiIiIiIiIm5FxQoRERERERERcSsqVoiIiIiIiIiIW1GxQkRERERERETciooVIiIiIiIiIuJWVKwQEREREREREbeiYoWIiIiIiIiIuBUVK0RERERERETErahYISIiIiIiIiJuRcUKEREREREREXErKlaIiIiIiIiIiFvxc3UCItI0Pffcc+Tm5gIwbNgwLrnkEhdnJCIiIt5K/Q4Rz2Mym81mVychIk1LRkYGzZo1M+J58+Yxa9Ys1yUkIiIiXkv9DhHPpGkgIuJ0O3futIn79u3rokxERETE26nfIeKZVKwQEafbtWuXTdynTx8XZSIiIiLeTv0OEc+kYoWIOJ31JxwxMTG0aNHChdmIiIiIN1O/Q8QzqVghIk5n3WnQpxsiIiLiSOp3iHgmFStExKlKS0v5+eefjVjzRkVERMRR1O8Q8VwqVoiIw2VlZeHj44PJZMLX15e8vDzjtX/+85+YTKYqHx9//HGj7nvllVca1woJCeHo0aMNus6sWbNs8tq0aVOj8hIRERHHUb9DxDuoWCEiDrdjxw4asktyY4ZqfvXVVyxatMiIZ8+eTYcOHRp0rcGDB9vEa9asaXBeIiIi4ljqd4h4BxUrRMThdu/eja+vL76+vphMJpvXzj9f8RESEkL37t0bdL/s7GzuueceI+7QoQOzZ89ucP5DhgyxiVevXt3ga4mIiIhjqd8h4h1UrBARh7v77rspLi6muLiYa6+91ni+V69exvMVHzk5Ofj5+TXofnPmzCExMdGIn3jiCYKCghqcf9euXfH19TXiHTt2NPhaIiIi4ljqd4h4BxUrRMSptmzZYhxXHOZoD6dPn2bu3LlG3K1bN66//vpGXdPPz4+2bdsacVJSEgUFBY26poiIiDie+h0inkvFChFxmoyMDA4dOmTEjug0PPPMM2RnZxvxww8/bPPpREPFxcUZx6WlpQ1eNEtEREScQ/0OEc+mYoWIOM3WrVttFryyd6chKyuLt99+24hbtGjBddddZ5drBwcH28SZmZl2ua6IiIg4hvodIp5NxQoRcRrroZh+fn7079/frtdfsGABWVlZRjxjxgwCAgLscu2KC3QVFhba5boiIiLiGOp3iHi2hq0iIyLSANadhl69elX61KCx3nvvPZt4xowZNbZftmwZJSUlACQkJBAVFVVt2+LiYpu4oYtwiYiIiHOo3yHi2fRdLyJOY91pGDRokF2vnZ6ezubNm424ZcuWDBgwoNr2KSkpTJo0yYh//fXXGjsN1qt8A8TGxjYiWxEREXE09TtEPJumgYiIU6Snp3PkyBEjtve80ZUrV1JaWmrEY8eOrTSE0trGjRuN45CQEDp16lRt25KSEpKTk404ICCA6OjoRmYsIiIijqJ+h4jnU7FCRJzC+tMNsH+nYffu3TZxTZ9uAKxbt8447tq1Kz4+1f9zuHv3boqKiox40KBBdlnpW0RERBxD/Q4Rz6dihYg4hXWnwd/fn379+tn1+r/++qtN3LNnzxrbL1261DiOj4+vse3atWtt4lGjRtUpp19++YX777+fQYMG0aJFCwIDA+nQoQMTJkzghRdeICkpqU7XERERkfpRv0P9DvF8WrNCRJzCutNwwQUXEBgYaNfrHz9+3CZu27ZttW2PHTvGzz//bMStW7eu8drffPONTTxx4sQa2+fk5PCHP/yB9957z2bLtPP3PnbsGCtWrKCwsJDZs2fXeC0RERGpP/U7yu+tfod4KhUrRMQpdu7caRzbe+swsPyithYZGVlt2w8//NAmDgoKqrZtWloaK1asMOLWrVszfvz4GvMYP348mzZtwmQyce2113LzzTfTv39/goKCOHbsGN9//z2vvPIKCQkJtX1ZIiIi0gDqd6jfIZ5PxQoRcYqjR48axzUtKtVQ1nM7AfLy8qpsV1xczOuvv27zXG5ubrXXfeONN2z2Nr/hhhuqnTdqNpu58sor2bRpEwEBAXz22Wf85je/sWkTFRXFgAEDmDVrVo3zVUVERKTh1O+wUL9DPJm+Y0XE4UpKSmxWzHbEnMk2bdrYxPv376+y3VtvvcWxY8cwmUzGMEzr1cKtpaam8txzzxlxYGAg999/f7U5zJ8/35iT+sYbb1TqMFgLDg62+5BUERERUb+jKup3iCdSsUJEHM7X15e4uDgjfvfdd3njjTc4c+ZMpbmVDdW1a1ebuOKQS4ADBw4YczUnTZpETEwMAOvXryctLc2mbWFhIddffz3nzp0znrv77rttvg5rxcXFPPzwwwCMGzeOW265pcFfi4iIiDSc+h0i3kHFChFximuvvdY4LiwsZObMmbRu3Ro/Pz/j0axZM5tPQupj2rRpNvE333zDAw88wKlTp8jLy2PRokWMHTuWzMxMTCYTjz/+OLGxsUY+N910E4mJieTn57NixQpGjRrF8uXLjetdcMEFPPXUU9Xef9WqVZw4cQKABx54oEFfg4iIiNiH+h0ins9ktld5UUSkBllZWVx88cWsX7++2jYXXngha9asadD1S0pKGD58OJs3b6617Z///Geee+45XnrpJWbNmlVr+44dO7J8+fIa57zOnj2b5557juDgYNLT0zXUUkRExIXU7xDxfBpZISJOER4ezurVq3nnnXe49NJLiY2NrfSLdeDAgQ2+vq+vLx9++CFdunSpsd2sWbOYM2cOAHfddVet+65PnjyZtWvX1ro41/ktzOLj49VhEBERcTH1O0Q8n0ZWiIhXyczM5NVXX+XTTz/lyJEjZGZm0qpVKy688ELuueceRo8ebdM+IyODp59+msWLF3Ps2DH8/f2JiYlh9OjRXH/99TVuF2Zt0qRJLFu2jN69e9vspS4iIiLeS/0OEcdRsUJExA6uvvpqPv30UwIDA8nOzsbPTztDi4iIiGOo3yFNgaaBiIjYwbBhwwAoKChg3rx5NbataX91ERERkdqo3yFNgUZWiIjYQVpaGl26dOHcuXP4+/tz//33c+2119K+fXsKCws5ePAgK1as4MMPP2T+/PkMHTrU1SmLiIiIh1K/Q5oCFStEROxkxYoVXHnllTZ7pFfk5+dHZmYmwcHBzktMREREvI76HeLtVKwQEbGj5ORkXn75ZZYuXcqhQ4fIy8ujRYsWREdHM3r0aKZOnVrnxbNEREREaqJ+h3gzFStERERERERExK1ogU0RERERERERcSsqVoiIiIiIiIiIW1GxQkRERERERETciooVIiIiIiIiIuJWVKwQEREREREREbeiYoWIiIiIiIiIuBUVK0RERERERETErahYISIiIiIiIiJuRcUKEREREREREXErKlaIiIiIiIiIiFtRsUJERERERERE3IqKFSIiIiIiIiLiVvxcnYA4R2lpKSkpKYSHh2MymVydjoiIuIDZbCYrK4uYmBh8fPR5hTiO+h0iIgKN63uoWNFEpKSkEB8f7+o0RETEDSQmJhIXF+fqNMSLqd8hIiLWGtL3ULGiiQgPDwcs3yQREREuzkZERFwhMzOT+Ph443eCiKOo3yEiItC4voeKFU3E+SGYERER6jSIiDRxGpYvjqZ+h4iIWGtI30MTVkVERERERETErahYISIiIiIiIiJuRcUKEREREREREXErKlaIiIiIiIiIiFtRsUJERERERERE3IqKFSIiIiIiIiLiVlSsEBERERERERG3omKFiIiIiIiIiLgVFStERERERERExK2oWCEiIiIiIiIibsXP1QmIUFICS5fC1q3QvDlcdhm0b+/qrERERERERMRFVKwQ10pKIvOay3ndtI3dbaDLWfjt3/5E2wceg4cfBpPJ1RmKiIiIiIiIk6lYIa6TkcHhqaMYN+Yox5uVPz2/fwn7//4I/hkZ8M9/uiw9EREREZEGSU+Hp5+GzEwYNw4uu4yCQD8C/QJdnZmIx1CxQlwm7757uXSkbaHCpxRe/wr8S4F//QuGDoWrrnJViiIiIiIi9XfDDfDddwAc/98bPPlpAD8OjOLnvxxVwUKkjrTAprjGjh08c+R99re0hL1Ow4//C2bxJyYuOmzVbuZMOHvWJSmKiIiIiNRbURF8/70R3jsZ3uxTyMGik7w9d4YLExPxLCpWiEtkzHmcucMsx/4l8MmSEMZ+u5fLHnjTtuHZs/Dkk85PUERERESkITIyoLTUCB9ZXf7SUycWUvDOGy5ISsTzqFghznfqFG8mfUlW2Qi4W3dA7zsetOwAcvvtMH26bfuXX4akJKenKSIiIiJSbxkZNuHgFLh8n+U4JQI+eHMWHD3q/LxEPIyKFeJ88+dz+9ZS3voCLjoE928LhHvusbxmMlnWqggIKG9fVAQvveSaXEVERERE6qNCsQLgwTXlx/8cVEDpbbfajL4QkcpUrBDn++wzovLgju3w/fvQ/aLroXnz8tc7drSMsLD22muW1ZTtYPny5ZhMJkwmE4MGDcJsNtvlurU5ePAg/v7+mEwmYmNjyc7Odsp9RURExLUc2fdYuXKlcW2TycTKlSurbFdcXEy3bt0wmUz4+vqyZcsWu+UgFVQsVrRowdDJdzL6qCXc1wq+OrEKFixwemoinkTFCnGulBTYvNn2uRtvrNzuvvssoyzOy8yEDz9s9O2Lioq49957jXjOnDmYrO/jQF26dOGuu+4CICUlhSeeeMIp9xURERHXcWXfw5qfnx9Plq0DVlpayr333uu0D2yanLJixZsD4dsukNI2FP79b/5yoJXR5KnRYH7iH1Bc7KosRdyeihXiXF9+aRtHRsKYMZXbde0Kl19Orj+8NRCmXQel777T6Nu/8sor7NtnmTQ4duxYJk6c2Ohr1scjjzxCYKBlsY65c+dyVPMVRUREvJqr+x7Wrr76avr27QvAhg0b+Oijj1yWi1fLzKTAF+6eApfeBJdMOg0REVz617fpe9LSpHUOZCYessuHcSLeSsUKcS6rbZwAmDwZ/P2rbnvXXdw0He6aCl/0gNWnN8PPPzf41jk5OTz99NNG/Ne//rXB12qo6OhoZsywbFlVWFjI448/7vQcRERExDncoe9hzWQy8Ze//MWIH3vsMYr1yb795eezpxUU+1rCvjlhAJh+8xv+c6g7a96Brz+EyAIsu97pPRCpkooV4jxmM6xebfvcJZdU337SJK5JbmaE7/cF5s9v8O3/85//cPr0aQD69OnDxRdf3OBrNcYDDzxgHL///vscOnTIJXmIiIiIY7lL38PaddddR3x8PAC//vorC7Rugv3l53MoqjzsnR9hOTCZuHDWv7jwuFXbX3+FJUucmp6Ip1CxQpxn715IS7N9rqopIOf5+XH5yNuJyLeEC3tD7mcfN2jl5KKiIl588UUjnjlzZr2vYS/du3dn7NixAJSUlDBv3jyX5SIiIiKO4U59D2u+vr7ccccdRvzCCy+4MBsvlZdHelB52NIUUh5MmQIDB9q2f+MN5+Ql4mFUrBDnqTiqIj4e2rev8ZTgm27jqj2W46xA+CIsufICnXWwcOFCkpOTAQgKCuLGqhb1dCLrTsK7775Lpp12OhERERH34G59D2u33367scjnrl27WLFihYsz8jL5+aQHl4fNfULLA5MJ7r7btv2330JionNyE/EgKlaI0+St/ZFJM+DBCbCiI5ZRFbWtht27NzPS443wk97Ap5/W+97vvFO+OOekSZNo1qxZva9hT5dffrmx0GZ2djYLFy50aT4iIiJiX+7W97AWHx/PsGHDjPjdd991YTZeKD+fc1YjK2yKFQDXXgvh4eVxaSm80/iF5EW8jZ+rE5CmY8eRDSybBMs6w+lQGD9yZO0nmUyMGn0TbbKf4VQYfNcFcv73CaHPPVd7oaNMcnIyP/74oxFPnz69oV8C2dnZrFu3jqSkJFJTUzGbzURFRdGtWzcGDhxIREREna4THh7OxIkT+eabbwDL2hXWoy1ERETsraioiG3btvHTTz+xa9cu9u3bx7Fjxzh37hxFRUVEREQQHx/P4MGDufrqq7nooovw8dHnWg1hr75HUlISa9euJTk5GV9fX+Li4hg8eDAdOnRodI7Tp09n/fr1AHz++edkZ2cTFhbW6OsKlaaBNPMPt309LAxuuAFef738ubffhkceAf3MiRhUrBDnyMpiU2n5akJDUoDBg+t0qu9V1zDtkWd4fTDk+8NS/+NM37698ny/anzxxReUWq1zcdFFF9UrdYAffviBZ555hlWrVlW7arafnx8jRozg1ltv5ZZbbqm1g3fRRRcZxYo1a9aQmppKy5Yt652biIhIXTz00EP861//qvb1s2fPcvbsWXbu3Mnbb79N//79eeeddxgwYIATs/QOje177N27lz/+8Y8sX74cs9ls85rJZGLcuHH8+9//pn///g3O0TqnnJwcli1bxhVXXNHg64mV/HzyrDa7a16xWAFw1122xYrERFi/HuryYZ5IE6HSnTjHzp1sjikPh5zwgQsuqNu5/fpxVXpbJh2E176CUceAsj/y6+K7774zjrt27UpMTEwNrW1lZWUxbdo0Jk6cyA8//FDj9l7FxcWsXr2a22+/vU5rUIwbN844Li0tZenSpXXOS0REpL4q/tEbGhpK3759GTNmDGPHjqVHjx42hfYdO3YwevRo1q5d6+xUPV5j+h4LFy6kf//+LFu2rNJ7Bpb3ccWKFQwfPpwPP/ywwTn27duXFi1aGPES7UhhP/n5zF8MpY9B7pPQIaB15TaDBkGvXhxtBk+Pgok3Q8nC/zk5URH3ppEV4hzbtrEt2nIYUAx9WvaCoKCazznPZGLi4GuZaL1rxpIllqFydWDdyRoyZEhdMyY9PZ1Ro0bxyy+/2DwfFxfH2LFjiYmJISAggNTUVHbt2sXWrVspKCio8/UvuOACgoODycvLA2DVqlVutfiWiIh4l+DgYH7zm98wdepURo8eTffu3Su1OXPmDPPmzePZZ5+lpKSE7OxsbrjhBvbs2aMpAvXQ0L7H0qVLueGGG2w+HImIiGDy5Ml07tyZvLw8tm3bxpo1a8jPz+f222/n6aefblCOJpOJQYMG8f333wOWfojYSVnfzgQEFwPBIVW3u/pq/rj/cb7sYQlXffMR40vnaiqISBkVK8QpCrdv4UA7y3HPVAgYULcpIIZLLwXrYsXGjZCaCrVMmzh06BDp6elG3KdPnzrdrrS0lBtvvNGmUNGuXTteeOGFauedZmZmsnjxYp5//vk63cPHx4fevXuzZcsWADY3YJcTERGRunriiSdqbdOqVSuefPJJOnTowF133QVAYmIiCxcu5LbbbnN0il6hoX2PjIwMbr/9dptCxa233sqLL75IeLjtNIKdO3dy/fXXs3fvXh566KEG59q3b1+jWHHw4EHOnTvnVguBeqz8fNu4ug/orr6aG68tL1Z8EJ3K+A0bYMQIx+Yn4iFUthOnOHBwIyVl3229T1Pn9SYMo0dDiFVV2myGOkyb2L17t03ctWvXOt3ugw8+4NtvvzXibt26sX79+hoXyIqIiODmm29mx44dREZG1uk+3bp1M45/+eUXSkpK6nSeiIiII91555107tzZiFeuXOm6ZDxMQ/sezz33HCkpKUY8Y8YM3n333UqFCoB+/fqxYsUK4uPj6zWqsyLrfojZbK6UuzRQXYsVvXtzmU8Pwsvewk97Qf7Cjxybm4gHUbFCHK+khF+yDxthrzNA3771u0ZQEEyYYPtcHeZWHj161CaOi4ur9Ryz2cycOXOM2M/Pj48//rhe801NddypJDY21jguKiqy6aSIiIi40kCrDxZOnjzpwkw8S0P6HkVFRbz99ttG3KJFC1588cUaz2nbti0vvPBCg3I8z7ofApVzlwYqmwZiCA6utmnw9Gu5Yq/lODMIVm7+xPKhnIioWCFOcOwYHc8Uc/cmGHMUBqcAPXvW/zqXXmobf/cd1DISoeIf/61bV7HAUQW7du2ymf4xbdo0h62E3rZtW5s4OTnZIfcRERGpL+vpCFV9ui9Va0jfY/369Zw6dcqIZ8yYUafpGNOnT6ddu3b1zvE89UMcpK4jKwCmT2fq/vJwScRp2LvXMXmJeBgVK8Tx9u4lIRn+swRWzoeL06OgVav6X6diseLsWdi+vcZTsrOzbeLgGirb51Uc6nr99dfXKb2GqJhPxXxFRERcoaioiPXr1xvx8OHDXZiNZ2lI32PDhg028W9+85s63ctkMjFlypS6J1eB+iEOUp9iRZ8+XJQfg3/Z52/fdAVzPXa9E/FmKlaI4+3bZxv36AF1nCZho107sJpbCcAPP9R4SsV5nAEBAbXe5ueff7aJhw0bVrf8GiAwMNAmzqs4bFBERMQFHn74YWPqR1RUFLfeeqtrE/IgDel77K3wSXq/fv3qfL/+/fvXuW1F6oc4SD2mgWAyETHpMkYds4SHo2D/yoWOy03Eg2g3EHG8ikPZevRo+LUmTGB75gG+7A4rO8DSH5cTMHt2tc0r/hIuLCys9RZpaWnGsclkqjRE0p4qdmjq8umLiIiIvRUXF3PmzBk2btzIK6+8wrJlywAICgrio48+okWLFi7O0HM0pO9hvXuIj48PLWvZ7cxamzZt6p5cBeqHOEZhYR5X3AAhRTDwBDxY08gKgEsv5YbHXqddBkz5FeKPbIOMDKjjgu0i3krFCnG8iiMrGrJexXkTJvDCyVd5v+wDh5/Wr2ZsQQFU6BicV3FP+Lp8YpCVlWUch4SE4OPAva5zc3Nt4tDQUIfdS0RExFrLli1tCvTWTCYTF110Ef/+97+54IILar1WQUGBzR++mZmZdsvT0zSk72E9/SLEevezOmhM30H9EMfILsljSdlg4OyAOhQrJkzgjqsDuGP7+cJWCSxbBldd5dA8RdydpoGIY5nN9h1ZMXYsF5VvLMKPMYWwcWO1zSvu4GG9eFV1IiIijOPc3FxKS0vrn2cdVcyn4qrcIiIirjBy5Eh+97vf0atXrzq1f+aZZ4iMjDQe8fHxDs7QfTWk72Fd4KhYQKhNTk5OvdpbUz/EMXKLywtUoUXUPA0EIDQUxo61fe677+yel4inUbFCHOvsWcvDWmOKFS1aMC6ktxGu6EiN61Z07NjRJq7LKtfWQ13NZjMnTpyof551ZJ2Pn5+fOgkiIuI0EyZM4OKLL+biiy9m7Nix9OjRwxhNuHbtWqZPn86wYcM4cuRIrdd68MEHycjIMB6JiYmOTt9tNaTv0bx5c+O4tLSU1NTUOt+vLsWQ6lTMrUOHDg2+lpTLMZePMgopouYFNs+bPNk2rmVdNpGmQMUKcayKHRxfX2jkL8K4kZPpVvY7fEMc5KxcVm3bikNXDxw4UOv1+/TpYxNvrGHkRmPt31++V1Xv3r3x9fV12L1ERESs/e9//+O7777ju+++48cff2Tv3r2cOXOGOXPmGNMBNm/ezJgxYzh9+nSN1woMDCQiIsLm0VQ1pO/Rs8IU2Z07d9b5fvVpW5F1PwQq94GkAYqLyfUpH5UbWkjdihUTJtjGR4/C4cNVNhVpKlSsEMc6fJidbeBQcyj2AeLjwa+RS6WMH8+4o5bDYl9Ye3IjVLPVVufOnW0+rdi9e3etlx9bYRjehx9+2NBMa1RaWsqePXuMeMiQIQ65j4iISF1FRUXxl7/8hTVr1hAeHg5AYmIi999/v4sz8xwN6XtU3HnsmzpuXWk2m/n666/rl6AV69y6dOlik7c0UF4eOVYbwITUZRoIQO/e0KqV7XMrVtg1NRFPo2KFONaRI8yYDl3+CJF/hdJOHWs/pzajRjH+WPm37o/tSuGnn6ptPnr0aON48+bNtV6+T58+Np8sLF68mO3btzcw2er9/PPPNotujRkzxu73EBERaYgBAwbw8MMPG/HHH3/M2YrTOqVa9e17DB8+3GZXj/fff5+MjIxaz/v88885fvx4g3I0m81s3brViNUPsZP8fHL8y8PQuk4D8fGB8eNtn9NUEGniVKwQhzIfOczhsiJ9XCb4dOzU+IuGhTE2aqARrugIrFlTbfNLLrnEOD548GCd5o7+9a9/NY5LSkq47rrr6rV2hdlsrrXNjz/+aBybTCYuvvjiOl9fRETE0a6y2omguLi4Tn90i0V9+x7+/v7cfvvtRpyamsqf/vSnGs85ffo0//d//9fgHHft2mWzG8zkimsmSMPk55NrVayo85oVUHkqyIoVlsXqRZooFSvEoU4l7jOGwnVOBzraYWQF0DphHFP3wW+3wJ/XUWOxYurUqTbbjy5fvrzW619//fVMmTLFiA8cOMCwYcNYvHhxtedkZ2ezYMECBgwYUKdPQ87vYQ+WVddbVRz6JyIi4kIVd/SobptTqawhfY/Zs2fb7CQyf/587rzzTpst1c/bvXs348eP5/jx4wRWs317baz7IcHBwUyaNKlB15EK8vKIyYKbdsL0PdDrDHWbBgIwYQJ5frC4B8yaDPM6noaff3ZouiLurJGLB4jU7MjZQ8Zxp3Sgkx1GVgCMGsUXU/9ZHgdthIICqOIXdkxMDOPHjzc6CosWLeKWW26p8fImk4n//ve/jB49ml9++QWA48ePc8UVVxAXF8e4ceOIjY3F39+ftLQ0du/ezZYtW+q0lzpAVlaWTcdlxowZdTpPRETEWSoW3ps1a+aaRDxQQ/oekZGRvP3221x22WUUFxcD8Pbbb/Ppp59y6aWX0rFjR/Lz89m+fTurVq2itLSUgIAAnn766QatKbJo0SLjeNq0acYaJdJI+fkMTYahn5fFJhP4+9d4iqFTJ3I6x3PFdZbddIYmwR9//BG08Kk0USpWiOOUlHA896QRtj+H3UZWMHKkbZyfD9u2wfDhVTa/4447jA7D999/T0ZGBpGRkTXeIioqip9++onrr7+eJUuWGM8nJSXx/vvvNyr9r776ioICy7ZWISEhXHPNNY26noiIiL2tXr3aJu7cubOLMvFMDel7XHLJJXzwwQfMmDGDwsJCwFI0+uijjyq1DQwM5O23327QtudJSUls2LDBiG+77bZ6X0OqkZ9vGwcFWQoWddRy+AR6nJnPvlawNRpy1/5IyKxZdk5SxDNoGog4TkoKiWElRhifif2KFVFRUGFrsJqmglx11VXExcUBkJ+fz4IFC+p0m4iICL755hu+/vprRo4caTOksyJ/f3/Gjx/PggULat2y7a233jKOb731Vn1aJSIibqWwsJAnn3zSiDt37kz37t1dmJHnaWjf45prrmHHjh1MnDgRUxV/5JpMJkaPHs26deu48cYbG5TbO++8Y6yv1atXLy666KIGXUeqUHGUbV2ngJw3ahQjLQMrKPaFzb+u0roV0mRpZIU4zuHDJFn9zR5XEAitW9vv+qNG2c7jW7MG/vKXKpv6+fnxxz/+kT//+c8AvP7669xzzz11vtWUKVOYMmUKZ8+eZe3atZw4cYK0tDT8/PyIioqiW7duDBw4kLCwsFqv9euvv7Jy5UoAfHx8+OMf/1jnPERERBpi2bJlfP/99/zf//2fzboIVTlx4gS33norO3bsMJ6zXnha6qYxfY+ePXuybNkykpKSWL16NSkpKfj6+hIbG8uQIUPoaPXhz9ixY+u0sPd5JSUlvPPOO0Z833331flcqYOqRlbUx6hRjHwJ3i5bS35deDpjDh6Erl3tk5+IB1GxQhzn+HESrYoV8ZHx9RoGV6tRo+DVV8vjdeugtNSy9VMV7r77bv71r39x6tQpdu/ezdKlS+u9A0dUVBRTp05tTNb861//MjoVN910E926dWvU9URERGqTk5PDv/71L55//nlGjBjBqFGj6NOnDy1btiQkJITs7GwOHz7MmjVr+OKLL8jNzTXOnTp1KnfccYcLs/dcje17xMXFccMNN9g1p08++YRjx44BlhEzta2lIfXU2GJFly6MzGsJpAKwLh7LB3IqVkgTpGKFOE5SEgsWQdJySIyE2G52mgJy3oUX2sbp6fDLL9UuQhQSEsJDDz1kjGR49tlnnb5d6MmTJ3nvvfcAy7SRv//97069v4iING2lpaWsXbuWtWvX1qn9bbfdxmuvvVbldASpnTv0PSp67rnnjOPHHnsMPz/9OWBXFYsV9Z0GYjLRte9YWuV8yplQ+CkeStesxsdqa1uRpqJJr1lx5swZvv32W/7xj38wdepUoqOjMZlMxmP+/PlOyePw4cM8+uijDBo0iFatWhEcHEznzp254oor+PTTT40VoT1OUhLBxdD1LIw/An6x8bWfUx/x8dC+ve1zNaxbAfD73/+enj17ArBy5Up++OEH++ZUiyeeeMJYWPNPf/oTney1O4qIiEgNBg8ezH333UevXr1qLTwEBARw5ZVXsmrVKt555x0CAgKclKV3cnXfw9rChQuN6T0JCQkNXvNCalBxzYr6jqwATKNGM/K45fhcMOzdtcIOiYl4niZZSj158iTDhg0zhsC50rx585g9e7bxB+x5hw8f5vDhwyxevJhhw4bxwQcfeN4ftsnJtnHZIlN2NWoUZ84cY207WNcOnlmzCv+77662ub+/Py+++KKxkNTs2bPZvHmzUz4xOnjwIG+++SYA0dHRPPLIIw6/p4iICFimE/z73//m3//+N+fOnWPnzp0cPnyY1NRUCgoKCA0NpXnz5vTs2ZN+/foR1IA/sKRqrux7WCsuLubhhx8GLIt0vvzyyxox4wj5+eT4g18pBJSAqSE/S6NGMX4BnAmFkcch9HAinDgB0dH2z1fEjTXJYkV+fr5bFCqeeOIJHn30USP28fGhV69eREVF8euvv3LixAkANmzYwJgxY9i0aRPRnvSPVFKSbeygYsWfchfwYV9LePVXqxhayykTJ06s10JU9tKlSxdjGzIRERFXadasGWPGjGHMmDGuTqXJcFXfw5qfnx8HDhxwaQ5NQn4+V1wHyzqDbymkb/QnvL7X6NOHe/dFcO+mzPLnNmyAK66wZ6Yibq9JTwMBaNWqFZdccgl/+9vfWLx4sdPuu3TpUpv1CoYPH87evXvZvXs3q1atIikpiY8//tjYXSIpKYmrr77aafnZhTOKFSNGMOp4ebgm8BSkpNj/PiIiIiIitcnLI9ffcljiA8H+IfW/hq8vJCTYPrdxY+NzE/EwTXJkRVRUFAsXLmTIkCG0r7jmgROYzWZmz55tVNi7d+/O8uXLCQkp/8fMx8eHa6+9lhYtWhjDBtetW8fnn3/OFZ5QVS0shFOnbJ+LjbX/fXr1YlRaKJADwNp28MD69XDllfa/l4iIiIhITcqmgQAEFINfUAOKFQBDh8Ly5eXxhg2Nz03EwzTJkRURERFcddVVLilUAHz77bfs3LnTiOfNm2dTqLA2ceJErr32WiN+9tlnHZ6fXZRNYbHhiJEVPj707DKc5mVrGa1tB6Xrf7L/fUREREREapOfT07ZmrShRdR/N5Dzhg2zjTdvBk9ddF+kgZpkscLVFi1aZBx37NiRSZMm1dh+5syZxvGmTZtIqji9wh1VzDE4GJo3d8itfIaP4MKyqSBpIbB/948OuY+IiIiISI2spoGEFNGg3UAAy8gKa7m58MsvjUpNxNOoWOEC33zzjXF88cUX17oS86hRowgNDa3yfLeVlMTzw+GeS+GJ0ZDdPhocteL0sGGMslovde253ZZpKCIiIiIizmQ1DSS0kIYXK1q1goo7AWoqiDQxKlY42enTpzl58qQRDx8+vNZz/Pz8GDJkiBHv2rXLIbnZVVISX3SHVxLg0fHgG+OA9SrOGzaMEYnl4Ya2xVC2h7iIiIiIiNPk59uOrGjoNBCoPLpCi2xKE6NihZPt3bvXJu7cuXOdzrNuV/Eabik5mRNl+zRF5kNwjAPXB2nenIER3QgrgOGJ0DMVWL/ecfcTEREREalCcV4OhWVbGIQ2ZhoIwLBhlJpgfwt4vy+kbVtrlxxFPEWT3A3ElY4ePWoTt2vXrk7nWbereI2qFBQUUFBQYMSZmZk1tHaA5GROltVX2mbjmJ1ArAQnjOTsnAP4l5Y9Eb8e/vhHh95TRERERMSaKb+Ape9Djj9EFAB3NaJYMXQofx8LT46xhF989CtTz52DZs0an6iIB9DICifLysqyiSMjI+t0XkRERLXXqMozzzxDZGSk8YiPj69foo2UcyaFrEDLcdtsoG1bx95w+PDyQgVoTp+IiIiIOJ1vfgGTDsEV+2DCERo3DaR/fwakln+2vCkWy64gIk2EihVOlp2dbRMH1XFoWLDVP3QVr1GVBx98kIyMDOORmJhY6zn2dCoj2Thumw20aePYG1bc3unYsaq3TxURERERcZS8PNu4MdNAAgNJiLrACDfGonUrpElRscLJiivsj+znV7eZONbtioqKam0fGBhIRESEzcOZTuSdMY6dMrKiVy8ID7d9TutWiIiIiIgz5efbxo0pVgCx/UcTXTaoenMslG5Q/1aaDhUrnCwkJMQmzq/4D1o1rNtZb2PqlgoKOEn56A+njKzw9a28YrKKFSIiIiLiTBX79o2ZBgKYhg4joWzAckYQ/Lr/JzCbG3VNEU+hYoWThYWF2cR5FYeKVSM3N7faa7id06eJyoPL9sOQZOiahuOLFQAVt4FVsUJEREREnMme00AAhpUXKwA2hZyDw4cbd00RD6HdQJysZcuWNvGJEydo0aJFreedPHnSOK5Le5c6dYpxR2Hc0bLYzw+aN3f8fSsWK7ZsgcJCCAhw/L1FREREROw8DYQOHUjIaQacAyyLbM7YuBE6d27cdUU8gEZWOFn37t1t4uPHj9fpPOsFMnv06GHXnOzu1CnbuHVr8HHCt1rZNBAzcKg5bGxZADt3Ov6+IiIiIiJg92kgmEwMbmdZSD4iH0ygXe+kydDICifr2rUrfn5+xkKbO3bs4NJLL631vO3btxvHPXv2dFh+dmE1CgRw/OKa50VFUdy9Kx2m/EpyBPQ+DT9v3gxDhjjn/iIiIiLSpP0SmsOG9hBaBMMToX1jR1YAzQZfyK/Pf0endPAxA8O0fak0DRpZ4WQBAQEMtVoIcu3atbWec/LkSQ4ePGjEo0ePdkhudlNxZIUz1qso4zdkqGVBT2BPK8jYXPv/XxERERGRRjObWRFTwJ2Xw/VXwdp2NH4aCMCQIXQ5W1aoAMvI4Qo7DIp4IxUrXODyyy83jpcvX86pin/cV/DBBx8Yx82aNVOxoiZDhzK8bMaM2QQbj6pYISIiIiJOUFRErtW49ZAiGj8NBGDQINs4Lw/27m38dUXcnIoVLnD99dcTGBgIQFFREc8991y1bbOzs3nxxReN+MYbb8Tf39/hOTaKK4sVCQkMTyoP15sTISPDefcXERERkaYpL488q256SBH2GVnRogW0b2/73Natjb+uiJtTscJOjh49islkMh6PPfZYtW3j4uKYOXOmEc+bN4/PPvusUruioiJuu+02YxHO4OBgHnroIbvnbneuLFb068ewk+Ul7fXxWHYFERERERFxpPx8cq2KFcHF2KdYAZVHV6hYIU1Aky1W3HXXXQQFBVV61LdNQz322GN07doVgJKSEq655hpmzJjBZ599xo8//shrr73G4MGD+fTTT41z/vnPfxITE2OX+ztSyakTFFl/ZzlrgU2AwEA6duhP67J1KzbGQulGrZgsIiIiIg6Wn0+e1TSQYHtNAwEVK6RJarK7gRQVFVFQUFBjm+LiYmPXDntr3rw5X3/9NRMnTiQxMZHS0lIWLFjAggULqmz/l7/8hXvuucchudjb9tIUhjwKkfkwayP8w5kjKwDT0GEMS9rClz3gXDAc3LWSbjzs1BxEREREpInJzXXMNBCoXKzYscOyyKZfk/1zTpqAJjuywh1069aNXbt2cccddxBcTdW1Z8+efPHFF8yZM8fJ2TVQURFnSrIAyAgCkxnnTgMBSEggIdlyGFoIRw5vA7O55nNERERERBojN9d2GojJ337FBKtiRakJskryYN8++1xbxE012VLc/PnzmT9/vt2u16FDB8wN+IO4WbNmvPXWW7zwwgusWLGCxMREcnJyiI6Opk+fPgwYMMBuOTpFWhqpIeVhy1ygVSvn5pCQwC1/gKn7odcZ8DWfheRkiItzbh4iIiIi0nTk5uJXCiGFkOcPwX52mgIC0LIlh3pHc2vCCXa0hZt3wn+2boULLrDfPUTcTJMtVrib8PBwmy1NPVZqauViRVSUc3Po2pU4UyRxp612Adm0ScUKEREREXGc3Fz++zn893MwA8SE2vXyLXsMYm37rwHYGoNl3YpbbrHrPUTciaaBiH2lpXHG6t/lVqZQ58+l8/GBhATb5zZtcm4OIiIiItK05OYahybAFGLfYkXkgGF0SbMc72wDxdu04514NxUrxL4qjqwIcvKoivMqFis2bnRNHiIiIiLSNFgVKwAICam6XUMNGsTAE5bDfH/Ym7gdSkrsew8RN6JihdhXxWJFSAvX5FGxWLFli/4xFxERERHHcUKxYtCJ8nBb83wtsileTcUKsa+0NM5YFyvC27omj4rFiuxs/WMuIiIiIo7j6GJFq1YMKipfuH5rDJYP5ES8lIoVYl+pqfxzGXy8EF75GoJaOHnb0vPatoV27Wyf07oVIiIiIuIoFYsVofZdswJgQPwQ43hbNJZFNkW8lIoVYl9paQxLgmt/gd9vAVq4aBoI2IyuKDVByaYNrstFRERERLybo0dWAFH9h9Mx3XK8vS2UbNXICvFe2rpU7Cs11TZu2dI1eQAkJPD1rk+ZNxQ2x8LCbT9wkeuyERERERFv5oRiBYMG8fhfIaAEBp4An/wdlnXZfH3tfy8RF1OxQuyrYrHCxSMrzi2A5Z0t4aaCw1yUlwfBwa7LSURERES8UnFuNpNugeAiGHASnnRQsWLGLusn8izrsvXubf97ibiYpoGIfaWl2cauHFkxaBAJKSYj3BRthu3bXZePiIiIiHitvLwsfuwIS7rBhjgcM7KidWuIi7N9Tv1b8VIqVoh9udM0kLAwukT3plmeJdwYB+aNG12Xj4iIiIh4rbz8LOM4uAjHFCsABgywjVWsEC+lYoXYT1ERZGTYPufKaSCAT8JQhqRYjk+FQeL2lS7NR0RERES8U15+tnEc4sxixbZtjrmPiIupWCH2c/Zs5edcObICICGBhOTycFOidgQREREREfvLK8wxjoOLcVyxYuBA23jHDjCbHXMvERdSsULsJzWVrdEwvz980R3OhABRUa7NaehQhiaVh5v8T1eeqiIiIiIi0kg2xQpnjqw4dw6OHnXMvURcSMUKsZ+0NBb1hNumwbTrYWfnUPD3d21OvXsz5GyQEW6KBTZvdl0+IiIiIuKVcovyjGOHTgOJj6/8gaDWrRAvpK1LxX5SU0m32hW0eVBz1+Vynp8fbXsM5sE1a+mRCsOSgA6bYPJkV2cmIiIiIl4kr7i8WOHQaSAmE+YB/VlwZgVbYyxPzd2+HaZPd8z9RFxExQqxn7Q0zloVK6JCXLu4piEhgaefX1sea0cQEREREbGz6LNF/HYL5PrDgBM4rlgBmAYM5MHSFSRHQEQ+PL99m4bMi9dRsULsJy2N9PIZFzQPb+W6XKwNHWobb9pkWYTIZHJNPiIiIiLidXonF/L6PqsnHFisYMAAPezG0QAAhG5JREFUBn0FyRGQGQSHD22hi+PuJuISKsCJ/Zw7Z0wDMZkhIsJNihUJCbZxWhocPuyaXERERETE+5jNkJtr+5yDixUDT5SHW31Pw6lTjrufiAuoWCH2k55uTANpngc+zV28E8h57dtD69a2z2kqiIiIiIjYS1ERlJTYPufIYkW3bgw8G2CE26PRIpvidVSsEPtJTzemgTTPB5o1c2U25UymyqMrNm1yTS4iIiIi4n0qjqoAxxYrfH0Z2OICI9ymYoV4IRUrxG7M59IJLAG/EsvICpq7wW4g51Vct0IjK0RERETEXpxdrABieg2lTbbleFs0mLdvc+j9RJxNxQqxG1P6OVL+DYVPwJp3ca9iRcWRFdu3Q2Gha3IREREREe9SVbEiNNShtzQNGGisW5EWAon7Nzv0fiLOpt1AxH7OnQPABAQV4z7TQAASEij0hS+7w8ZYCCss4O+7dsHgwa7OTEREREQ8XcVihY8PBARU3dZeBgxgzHwo8IWBJ8Dn6DHIzISICMfeV8RJVKwQ+0lPt43daWRFs2b4dO3GLdMOkBsA7c/B3zduVLFCRERERBovN5eMQDCbILgIAoKDMZlMjr3nBRcwe6Mfs9cVlz+3cyeMGuXY+4o4iaaBiH2YzcbICoM7jawA/IYMZVDZULljzeDUlpWuTEdEREREvEVuLrMmQ/O/QtAjcCAm0PH3DAyEXr1sn9umdSvEe6hYIfaRnV15uyZ3GlkBMHQoQ5PKw03H1rsuFxERERHxHjk55PqXh8H+jl1c0zBggG2sHUHEi6hYIfZRcQoIuGexIrk83GhKrjpvEREREZH6yM0lz6pYEeKsYsXAgbaxihXiRVSsEPuoOAXEZILwcJekUq2+fRl6unyho42xwJYtrstHRERERLxDbq7tyIpAx+4EYqg4smLPHigocM69RRxMxQqxj4ojFJo1s6yC7E4CAojrMoC2WZZwcyyUbtBUEBERERFppNxc8qy2LggODHPOffv1s42Li+Hnn51zbxEHc7O/JsVjpafz1CiYcDNcdQ0kxrrZqIoypqHDjKkgGUFwYNePrk1IRERERDyf1TSQwGLwCXHSyIqICOjSxfY5TQURL6FihdjHuXPsaAsrOsFnvcAc6ab7OyckMPYojDwO/7ceArfvtuxkIiIiIiLSUFbTQIKLgBAnrVkBMGAAJSbY0wo+64mKFeI1/GpvIlIH6emcDS4Pm4e2cF0uNRk6lD/dCH/acP6JNDh2DDp0cGFSIiIiIuLRrKaBBBfj9GLFRSEL+bGjJTy7fBNutsy9SINoZIXYx7lzpJcVK3xLISzcTYsVnTpBiwq5bdzomlxERERExDvk5vLJQvjufZi/GKcXK3qfLg+3p+6GkhLn3V/EQVSsEPtIT+dckOWwWT6Ymke5Np/qmEyQkGD73KZNrslFRERERLxDbi7Dk+DiQzDpEE4vVgw8UR5ub14ABw447/4iDqJihdhHejqZgZbDyHyguRsPPhs61DbWyAoRERERaYzcXNvYmcWKNm0YWNzKCLdFo3UrxCuoWCF2YT6XTsb5YkUBlq1L3VXFYsXWrVBU5JpcRERERMTzubJYAfTqMJiAYsuxihXiLVSsELvIy0ij2Ndy7PYjK4YMsY3z87UftYiIiIg0nIuLFf79B9GnbN2K/S0he+dmp95fxBFUrBC7MGdk8Kf1cPs2uOQg7l2saNGi8n7UmgoiIiIiIg1VsVgRGurc+1utW2E2wa7kbWA2OzcHETtTsULsIjQ1gxeWwttfwux1uPc0EICEBMzAwSj4oA+kb17t6oxERERExFO5eGSFdbEiMh9OmrPg+HHn5iBiZ36uTkC8RGambezuxYqhQ/nbyQ95erQl/HrNWqa4NiMRERER8VSuLlZ06MDVSRFMmpdJx3QwgWXdivbtnZuHiB1pZIU0XkkJZGfbPhcR4Zpc6mroUPqdKg83liZWLriIiIh4mXPnzvH5558za9YsRo8eTdu2bQkMDCQsLIx27dpx2WWXMXfuXNLT012dqohHOVGaycsJ8PYA2BqN84sVJhMteg6i0/lCBWiRTfF4GlkhjZeVVfk5dy9W9OvH0JN+gGXZ5I2xwObNMGGCS9MSERFxhH379vHnP/+Z77//nsLCwkqvFxYWkpOTQ2JiIl9//TUPP/wwTz31FH/84x8xmUxVXFFErO0LyuLeSy3Hf10Dg4KDnZ/EgAHw44/lsYoV4uE0skIar6oRCe5erAgKol2n/rQpGxCyKRZKN25wbU4iIiIO8vPPP/P111/bFCp8fX3p3r07o0ePZuTIkURFRRmv5ebm8n//93/89re/xaxF+kRqZjaTXZJnhGGFQHi48/MYMMA2VrFCPJyKFdJ4VRUrwsKcn0c9mYYOY2iS5fhcMBzc+WPNJ4iIiHg4Pz8/pk2bxuLFizl79iz79u1j1apVrF27ltTUVBYvXkxsbKzR/q233uK1115zYcYiHqCwkBzfUiMMLcI9ihVJSZCa6vw8ROxExQppvIwM2zg8HHw84FsrIYGE5PJw44kt2uJJRES8kr+/P3feeSeHDh3i888/5/LLLyeiwihIk8nE5Zdfzvr162nbtq3x/KOPPkpRUZGzUxbxHNnZZAeUh2GFuOaDu+7dISjI9jmNrhAPpjUrpPEyMzkdCiUmiCyA4MgIPGJ269ChDH2sPNwYnsGMpCSIj3dZSiIi4h1OnjzJ5s2b2bVrF0ePHiU5OZns7Gzy8vIIDg4mNDSU2NhYOnToQN++fRkyZAjR0dEOy+fyyy/n8ssvr1Pb+Ph4Hn/8cWbOnAlAamoqq1evZoLWdRKpWoViRairihV+ftC3L2zaVP7c9u1w0UXOz0XEDlSskMbLzGT2RJhfNvLs5+8C6e3ajOqma1eGZEdiMmcQWghmgI0bVawQEZEGWb16NZ9//jlLlizh4MGD9T6/c+fOTJ48mWnTpjFu3DgHZFh3l112mVGsAMsCnSpWiFQjK4sc//IwrBDn7wZy3oABRrGixASmbVs1lF48looV0niZmWQGloeRgZGuy6U+TCYi+w9l38vf0/ks+JqBXhvhqqtcnZmIiHiIU6dO8eqrr/Lee+9x/PhxAJsFKeuyk8b59gcPHuTll1/m5ZdfJjY2lltvvZXf//73Dh1xUR3rxTYBMrW9t0j1Ko6s8AkEX1/X5DJwIO/1gzcHwY62sH7ZRvq4JhORRlOhTRovM5MMq+lxkcHNXZdLfQ0dSre0skIF2A6bExERqcaRI0e4/fbb6dChA0888QTHjh2rctcMs9mM2WwmLCyMVq1aERcXR6tWrQgNDTVeq+qcpKQknnrqKTp27Mitt97KoUOHnPFlGY4dO2YTt27d2qn3F/Eo2dkElkCrHAgugjD/UNflMmAAp8JgXTvICYBtRccgO9t1+Yg0gsNGVrjbXE1xoIwMMspGVviUQliYZxUrbGzZAsXFljl/IiIiFZw5c4ZHHnmEd999l+Li4krFhubNmzNmzBiGDBlC37596datG7GxsQQHB1e6Vl5eHsnJyezfv5/du3ezefNmVq1axdmzZwFL0aKwsJD333+fDz/8kNtuu40nnnjCKYWDRYsW2cTDhw93+D1FPFZ2No+thMdWlsWdXdgX7tOHgad8AMvuJNvawi07d8LIka7LSaSB7PoXmTfN1ZR6sBpZEVEApggPmQYClYsVubmwaxcMHOiafERExG3NnTuXxx9/nMzMTJsiRZcuXbj66quZPn06gwYNqvP1goOD6dKlC126dGHKlCnG81u3bmXRokV8+umn/Prrr5jNZoqLi3nrrbf43//+x2OPPcaf/vQne35pNjIyMpg3b54R9+3bl169ejnsfiIer+LIBVcsrnleUBADIroB+wDYHo1lkU0VK8QDNXoayKlTp3jsscfo2LEj48aN48UXXzR+sVY1tLEq59uen6s5ceJE2rVrx6OPPsqJEycam6I4WmamMbIiogCI9KBiRcuW0LWr7XM//eSaXERExK3dd999RqHCz8+P66+/npUrV3LgwAGeeuqpehUqajJo0CCeeuop9u/fz6pVq7jhhhvw9/fHbDaTmZnJ/fffb5f7VOf+++/n5MmTRvzkk0/Wek5BQQGZmZk2D5EmIyvLNnZlsQJo0XsI7c5Zjre3hdLt21yaj0hDNbhY4e1zNaUerBbYjCwAKuzb7vZGjLCNVawQEZFqBAQEcO+993Lw4EE++OADRo8e7dD7jRo1igULFnDo0CFmzZpFUFBQ7Sc1wltvvcXbb79txNdeey2XXXZZrec988wzREZGGo947awlTYk7jawAGDCAgWWf92YHwsEDG1ybj0gD1btYcebMGX73u9/Ro0cP3nvvPQoKCmwKDs2bN+eKK67g6aef5uuvv+bAgQPk5OSQkZHByZMnOXbsGCdPniQzM5OcnBwOHDjAV199xdNPP80VV1xB8+blc7ys52r27NmTmTNncvr0aft85WI3hVnnyC/brikyH88rVlSch7t+vWvyEBERt3bLLbdw4MAB5s2bR7t27Zx677i4OObOncv+/fu55ZZbHHKP1atXc8899xhxx44def311+t07oMPPkhGRobxSExMdEiOIm6pYrEiPNw1eZxnVawA2JZ1AAoLXZePSAPVa82KpjJXU+rH91wmW1+HjEAIKgZGeFixwmpkRYkJzp06SosTJ0ALvoqIiJV3333X1SkQHx/PO++8Y/fr7tixg6lTp1JY9gdN69at+e6774is49TOwMBAAgMDa28o4o3cbWRF//42xYrtrUq47pdfYMAA1+Uk0gD1GlnRVOZqSv34ZmYx8ASMOwrDk/C8kRW9epHZMpwJN0Pzv8JN09HoChERaTL279/PxRdfTEZGBmAZJfv999/TrVs3F2cm4iHcrVjRrBlD/Nvz1zXwySfwh01YFtkU8TD1ngbi7XM1pQEqLqLlacUKX1/CBwxldxvICoSNcVD60zpXZyUiIuJwR44cYeLEicY02/DwcL799lv69evn4sxEPIibLbAJ0LrnYJ75Aa7eA/GZqFghHqlexQpvn6spDVSxWOFJu4GUMY0YyfCy6bXpwXBg5wrXJiQiIuJgSUlJTJgwgaSkJABCQkL4+uuvGVpxW28RqVl2NiPugHG3wJ8uwS2KFZWmfKhYIR6oXmtWePNcTWmgkpLKQ988bWQFwIgRjFgOX/awhD+d202PggLQ/FsREfFCp06dYuLEiRw5cgSwrDmxePFih4+YFfFGhTmZrC/bAKfEB9cvsAmVixU7d0JpKfg0eDNIEaerV7FCpJKKw97AM4sVQ4da1tso81NMCbdv21Z5pxAREZF6KCoq4ujRo2RmZlJYWIifnx+xsbFER0djMplcklNaWhoTJ05k//79APj7+/Ppp59y0UUXuSQfEU+Xk1c+yji0EPccWZGdDQcPgtaiEQ+iYoU0TsUpIOCZxYrISAY364VfyR6KfWF9HPDTTypWiIhIvWzZsoU1a9awevVqduzYQVJSEqWlpZXaBQQEMGjQIEaNGsXEiRMZP368U4oXGRkZXHzxxfz8888A+Pr68uGHH/Kb3/zG4fcW8VbZBeX94TB3KVZER0ObNnDqVPlz27apWCEeReOApHGqKla4wz/QDRAy9EIGnLQc72kN6RtXuTYhERHxOAkJCTzwwAN8+eWXHDt2jJKSEsxmc6VHQUEB69ev57nnnmPSpEnExMTwwAMPkJKS4rDccnJymDJlClu3bgXAx8eH9957j6uuusph9xRpCnIKyqdEu02xAmDgQNtY61aIh3F4saKoqIhff/2VrVu3sn79ejZv3kxKSgpms9nRtxZnyMxkeSd4biS8OhhOtg4BX19XZ9Uww4czIrE83HRkLej7VEREGshkMlU5WqLi82azmVOnTvHCCy/QpUsX/vznP1NQUGDXXAoKCpg2bRrr1q0zcnjzzTe58cYb7XofkaYouyjHOA4twn2KFVpkUzyc3aeBuPvwR7GzjAy+7A4vlS0cPvDzENq6NqOGGzGCmx6GQSkwIhE6pafDsWPQoYOrMxMREQ9y/gMZX19f2rZtS1xcHMHBwZhMJoqLi0lMTCQ5OZmioiLjnPN9oPz8fJ5//nmWLVvGF198Qfv27e2S07x581i+fLkRN2vWjE8++YRPPvmkTudfdNFF3H///XbJRcSrmM3kFOcZoVuNrBgwgORwWN4JtkXDdUmbGG42g/7mEg9h92JFQkKC8Qu3ptET54c/nh8C2bp1a2688Ubuu+8+YmJi7J2WOEpmJlkB5WF4kAeuV3Fe164MLmjB4F1p5c+tX69ihYiI1Nm9997L4MGDGTJkCF27dsW3mtGGpaWl7N69m7Vr1/LNN9+wYsUKCgsLMZlMmM1mdu3axcSJE1mzZg1t2zb+Y4Dc3FybOD09naVLl9b5fHvkIOKVCgvJ9i0xwtBC3GM3EIABA1jdHm69whK2WpHB8ORkiItzbV4ideTQaSDuNPxRHCQzkyyr3T09ulhhMlVeUPOnn1yTi4iIeKR58+YxY8YMevToUW2hAizrRfTr14977rmHJUuWkJKSwlNPPUVkZCRg6SsdPnyYu+66y1mpi0hDZGXRPgPu+wl+uwUGp+A+Iys6dmRgVqgRbotGU0HEozikWHF+8SgfHx9iYmIYOnQoY8eOZdy4cYwaNYr27dvj5+dntIPKwx+HDh3KsWPHHJGe2FNWFtlWIyvCgiJdl4s9jBhhG6tYISIiThAVFcWDDz7IgQMHmDBhgtFHWrJkCStXrmz09R977LEqF/qs62P+/PmNzkHEK2Vnc8Fp+Pf38PrXMPkg7lOs8PGha/uBhJV9Bry9LSpWiEex+zQQdx3+KA6SnW07DSSkmctSsYuKIyt27oScHAgNrbq9iIiIHbVs2ZIlS5Zw4YUXsnnzZgA++ugjxo4d69rERKRq2dmVnwsJcX4e1fAZMJB+p9awrh0cbQ5nd24gytVJidSR3UdWaPhjE5OVZUwDCSoCvzAPngYCMGSI7W4mJSWwaZPr8hERkSbH39+fZ555xohXr17twmxEpEYVixUhbrYz3sCBDDxRHm5P3uq6XETqyeFbl9aVo4c/ioNYjawId6fVjxsqNBT697d9rmybNxEREWcZOXIkYJlam5KS4uJsRKRaFYsV7rK45nkDBtgUK7b5noa0tOrbi7gRtylWnHd++OOQIUOM5z766CMXZiQ1ys4mJgtiMyEmC88vVgBceKFtvGaNa/IQEZEm68yZM8ax9RanIuJmsrJsY3frC/fowcBUfyPcHg3s2OGydETqw+2KFaDhjx4lO5t170DS87DjNdzvH+iGGDWK3a3hgUkw4g746uRqKC52dVYiIuIFzp07x+HDh2tsk56ezm9/+1vAMi22ffv2zkhNRBqi4sgKd+sL+/vTM7oPsZlw0SEYkowW2RSPYfcFNu1Fwx89hLv/A90QF17Ikebw77KNQVYfz+eyHTtg8GCXpiUiIp5v48aNXHrppYSEhNC1a1fi4+Np3bo1QUFB5ObmcuTIEdavX28sOg4wbdo01yYtItXzgL6wf/9BJD2/rfyJTipWiGdw22KFhj96CHefp9cQbdowPKATYPnk66d4LFNBVKwQERE7MJvN5ObmsnPnTnbu3FnpNWv9+vXjoYcecmZ6IlIf2dkkh4PZBGGFEBkWisnVOVU0YIBtrJEV4iGcPg1Ewx+9jAdUkxui1dDxdClbe2hLDBSsXenSfERExDuc3ynt/ELiFYsTbdq0oX///kyZMoUXX3yRDRs2EO4NHwSIeKvsbGZMh/j7oPlfIS/CfbYtNVQsVuzbx/+3d9/hUZVpH8e/k94gdAi9V6WHonSQYkEQxN5FRPfFXd1FcW2LrgruriJYwLWDuIpYEJAiIE0FaUHpPYQeIL3nvH9MMplJnUk7k5nf57rOxXnOnHIP057c5ykkJZkTi4gLKr1lhZo/ehh3H1SotPr356qv/8uh2pDuB9sO/MRVhgEWt8uVi4hIFTJs2DBiYmL49ddf2bRpEytWrOD3338HrDdozp8/T7du3XjuueccBhsXETeVkEBizsx4FgOCQ8PNjacwnTuDjw9kZ1vLhgFRUdC3r7lxiZTAlAE27Zs/fv/993zwwQe8/fbbfPTRR6xbt460tDTb3YbOnTur+aM789CWFfTvz9XRecWN4XHWLLSIiEgZRUREMGbMGF577TWioqI4duwY//znP2natCnZ2dmsWLGCvn37Mm3aNLNDFZGSJCbakhVh6WAJc8OWUCEh0L694zZ1BZEqoNKTFWr+6GE8NVnRvDn9U+vZihuaoilMRUSkQjRt2pRp06Zx6NAh3n77bapVq0Z2djYzZ87k8ccfNzs8ESlOYiJJOTODhqbjvnVhjVshVVClJytymz8uXryYJ554giuuuMIhaXH+/HkaNGjAc889x5/+9CcCAwMrO0RxVkYGpKU5bnPXL2hXWSy07zyEOjnd+TY1hewNmkJXRESKd+LEiVIf6+vry8MPP8yWLVuIiIjAMAxmzZrFxo0byzFCESlXdt1AwpSsEClXpnQDUfNHD5GUxNftofNkuPp+WNIWz5gNJIel/wAe2AGPb4YPvoXsjWpZISIixevYsSMvvfQS6enppT5H27ZtmT17tq381ltvlUdoIlIR4uJIyklWhGYA4W44ZgXYkhWZPvB7PTgQE2W98SjixkxJVuSn5o9VVGIip6rB7vqwuSlcDsJ9s8ml0b8/r66Gf6+EMfvA79gJiI4u+TgREfFaycnJPP/883To0IGFCxcW6O7qrGuvvda2rpYVIu4rPf4SaTlTFlRLw32TFV27srMBVJsGVz4C/+6ZAXv3mh2VSLHKPVmh5o9eJDGRBLteOmHpQGioaeGUu44doWZNx20at0JERJxw9OhR7rzzTjp27MiCBQvIzMx06fj4+HjAOsbX+fPnKyJEESkHcalxtvVwd05W1KpFq9AmtsTKtobA9u2mhiRSknJPVqj5oxdJSCAhIK9YzfAHf3/z4ilvPj7Qv7/jtvUat0JERIo2duxYDMPAYrFgGAb79+/n7rvvpmHDhvz1r39l586dTp3ntddes61Xr169gqIVkbKqcSGBXe/A+g9g+lrAjT+v1Tr3pN0F63pUfUjbtsXcgERKUO7JCjV/9CJ2UzUBVPMNNi+WipI/WaGWFSIiUoyvvvqKb7/9lsaNGwPYkhYXLlzg9ddfp0ePHtSvX59bbrmFV199lW+//ZZff/2Vffv2sWPHDj7//HNGjx7Nf/7zHywWCxaLhRYtWpj8rESkUIaB/+UEOp+F/ieg+2nct2UFQM+eRJ6yrmb4QtT+n8yNR6QEfhV14tzmj9OnT+eZZ57hlltuwc/P+cup+WMVkK8bSDU/D+oCkit/smLPHoiNhdq1zYlHRETc3g033MCQIUN49dVXmTVrFomJiVgsFiCvXrNo0SIWLVpU5Dlyb/ZYLBbGjx9fKXGLiIuSkiAry3GbOycrevUi8lv4tIu1uDVhP5Hp6RAQUPxxIiYp95YVav7oRRITHbqBhAV40OCaubp3h5AQx23qCiIiIiUIDQ3lxRdf5PDhwzz22GOEhYU5JCAA29Tt+Rf7fVq3bs3kyZPNeRIiUry4uILb3DlZYdeyAmBr/SyIijIvHpESlHuyQs0fvUj+lhVBbvzlXFr+/nDVVY7b1q41JxYREaly6taty+uvv05MTAxvvfUWvXv3ttWN7OXWeSAviREZGcmyZcsI86SZtkQ8SWHJCne+yVqjBl3DWuOX0xhkayNg61ZTQxIpToV0A1HzRy+RmMgdUdDtNCQGQLUgN/5yLovBgzm5ZTVrm8OGZvCPn1cSYXZMIiJSpYSFhTF58mQmT57M5cuXWbduHbt37+bgwYOcOHGCpKQkMjIyqF+/Pm3atGHMmDEMGTIEHx+3mGVeRAqT023dJiTE7QebD+rRmyvPHWJHBJwPgbQtPxOo1lvipipszIrc5o9Tpkzh5Zdf5oMPPiAhIQFwbP5YmNy7C4ZhqPmjO0tM5C77lmNjPDRZMWQIb6+BV3KGrxj25X4mnDsH9eqZG5eIiFRJNWrUYMyYMYwZM8bsUESkLPK3rHDnLiC5evXivVcXUCsFml8GS8dtZkckUqQKT9er+aMHy0k+2Xjq69SjBwPOBtmK65sB69aZFo6IiIiIuIEqmqzocRpaXAYLwN69Bev0Im6iwlpW5Kfmjx4oMdGx7KnJCn9/rmreD5/s1WT7wIamWMetmDDB7MhERERExCxxcXzVAQ7UhvA0uN0/hBpmx1SSrl3Bzw8yM61lw4Bt22DQIDOjEilUpSUr7Kn5o4fwlmQFUH3AcLoeWs32hrC7Plz8ahW1zA5KRERERMwTF8fCK+Grjtbi9VurQLIiKAg6d4bt2/O2bd2qZIW4JTVbkNLLn6yoVs2cOCrD4MEMPG5dNSzwU+ZhOHWq+GNERERExHPFxRFnNzNeeGht82JxRWSkY3nLFnPiECmBkhVSel7UsoJu3RhyNsRWXNsCTWEqIuJlIiMjWWvyd/+aNWvo1auXqTGISI74eOJyhjWzGFCtWhVJVuT/DtH0peKmlKyQ0vOmZIWvL/2bD8Qn21pco2SFiIjX2bZtG8OGDWPYsGGsXr26Uq+9atUqhg4dyjXXXMO2bRq9X8Qt2LWsqJ4GPuE1TA3HaflbVhw/DufOmROLSDGUrJBSS02OJ6o+HK4Jl4Pw7GQFED5wOP1PwLDDcGcUGGt+NDskERExwdq1axkxYgRdu3bl3XffJT4+vkKuk5CQwDvvvEPXrl0ZOXIk69atK3LadxExQVycrWVFeCpVYzYQgI4dITTUVsy2oNYV4paUrJBSO+BziS6TofVj8Ldr8PhkBYMHs/YjWPUpPLURLEePWTPRIiLiFVauXEm7du1sU6zv3r2bRx99lIiICMaOHcunn37KmTNnynSN06dP8+mnnzJ27FgaNGjAn/70J3bv3m27ZocOHVi5cmU5PSMRKRO7lhXhaVSdZIWvL3Tvzt+HQJ8HofUUMLb8anZUIgW4NBtIZGQkM2fOZPDgwRUVT4nWrFnDU089xRYNBGO6pIwk23poBp6frLjySiy1a0NsbN62tWvh3ntNC0lERCrPsGHDiIqK4q233uKVV17hXE6z6ZSUFL777ju+++47ANq0aUNkZCRXXnklbdq0oXHjxtSrV4/g4GACAgJIT08nJSWFs2fPEhMTw4EDB9i9ezdbt27l0KFDtuvZt6KoX78+Tz/9NJMnT8bPz5TJ3EQkn7SES6T6W9erVMsKgF69+PX8Bn5tbC2e2raBRuZGJFKAS792uX01Bw8ezFNPPcWwYcMqKq4CVq1axauvvsq6desq7ZpSvKSMZNt6aDqen6zw8YGBA2Hx4rxtSlaIiHgVPz8/HnvsMSZOnMicOXOYPXs2MTExGIaBxWLBMAwOHDjAwYMHXT53bnIi9zwAjRs35rHHHuORRx4hODi4XJ+LiJRNSuJlOp+BuCBoHA9Ur252SM6LjKTXe/BjS2txy5nfGGsYYLGYG5eInVJ1A/G0vpqbN29m0qRJdOzYkfDwcKpXr07Hjh156KGH2LRpU7lfD6wVEVeXd999t0JiKZWsLJLIsBVDM3Do++axhgxxLP/4I6j/sIiI1wkJCWHq1KkcPXqU+fPnM3ToUCyFVPJzu28Ut+RnsVgYNmwYCxcu5OjRozzxxBNKVIi4oRqxSex6F469AQu/osq1rOgVk1f8NTwRDh82Lx6RQriUrPC0vppJSUk88MADXH311cybN4+9e/cSHx9PQkICe/fu5b333qNfv37cf//9JCUllXxCb5KSQlJAXjE0He9MVsTEwN695sQiIiKm8/Pz4/bbb2fVqlWcOHGCt956ixtuuIEaNWo4fYPFMAxq1KjBjTfeyDvvvEN0dDQrV67klltuwdfXt4KfgYiUWlycY7kqJSuaN6dPSt5Uqz83BjZvNi8ekUK41A3Ek/pqZmVlcdNNNzkkPoKDg+nUqRN+fn7s2bPH1mLkww8/JCYmhmXLllVIpWHAgAFO3TFp2rRpuV+71JKSSPLPK4ZmACEhpoVTadq3h8aN4eTJvG2rVllHVRYREa/WsGFDJk+ezOTJkwE4cuQIu3fv5tixY5w6dYrExETS0tIIDAwkLCyMhg0b0qJFC6644gpatmxpcvQi4pLUVEhPd9xWlZIVFgsNuvWn+aVvOFYTtjaCzJ834Xf33WZHJmLj8l/9ntJX89lnn3VIVEycOJFXX32VWrVqAdZWFzNmzODFF18ErK1KnnvuOf75z3+WWwy5Pv74Y5o3b17u561Qycne2bLCYoHhw+GDD/K2rVwJjz1mXkwiIuKWWrZsqSSEiKfK36oCqlayAqBvX/pssSYrUvxh9561dDM7JhE7pZ66tCr31Tx16hSvv/66rXzXXXcxb948W6ICIDQ0lOnTp/PMM8/Ytv3nP//h1KlT5RZHlVZYywpv6U87fDjZFoiqD7N6w8nf1kBamtlRiYiIiEhl8ZBkRV+7xsI/px6ChATz4hHJp9TJilxVsa/mG2+8QWpqKmBNurzxxhtF7vvss8/SpEkTAFJTU5k1a1a5x1MlJSfzyFb44y3YMg/6nw2yzpbhDYYO5d9XQZfJ8OdR8EPjVPXxExEREfEm+ZMVgYHWpSrp2ZNhx32ZuhEWfw4TfjdgyxazoxKxKdeJuqtKX82vv/7atj5hwgSHFhX5BQQEcN999zF9+nQAFi9ezIwZMyo8RreXlETNVKiZmlOu7QVdQHLVqUO/4HbAfgBWt4QHV62CwYPNjUtERCpcXFwcq1atokePHrRo0cLscETELPlnQ6xqrSoAgoPp2KQ7M1Zvzdv2888wdKh5MYnYKddkRX7u2Fdz//79DoN4jhw5ssRjRo0aZUtWHDp0iP3799OuXbsKi7FKSE52LHvDeBV2IiPHEJ46g7ggWNUSslauwPfll80OS0REKth3333HvffeC0CNGjV48803ueOOO8wNSkQqX/6WFdWrmxNHWfXtC1vtkhVqLSxupEKTFe5o165dDuW+ffuWeEz37t1ts5gAREVFKVmRP1nhDTOB2PEbPpKh78xgcUe4GALbz2wn8vx5qFvX7NBERKQCLVmyxNbNNT09nVGjRrl0fEpKCj/88AM7d+4kLi6O2rVr06RJE0aMGEFERERFhCwiFSEujr9dA0vbQngqLNwfRHOzYyqNq66CN9/MK//yC2Rne0/3bnFrXpes2Lt3r209ICDANh5FcXL3O3z4cIFzlIe//e1v7Nmzh+joaDIyMqhduzZt2rRh4MCB3HPPPe7ZzDQpybHsZS0r6NuX4X8PYHFHawJrZUuI/PFHuPVWkwMTEZGKtHHjRtuA4nfeeWexXUnzmz9/Pn/5y1+4ePFigccsFgtDhw7l9ddfp6OmwxZxf3FxHK0Je3PuU/meqsItK+xdugQHDkD79ubEI2LH5ZTZhg0bSKjCo8QeO3bMtt64ceNCZzApTNOmTQs9R3lYtGgRe/bsISEhgdTUVGJiYli3bh3/+Mc/aNu2LQ8//DApKSkunTMtLY34+HiHpVx5ecsKAgMZXv9qW3FFa6xTmIqIiMeKjo7mzJkztpYVrnT/+Pjjj7nnnnuIjY0tdGa07OxsVq1aRbdu3ZgzZ05FPQURKS+XLxNnN55meGht82IpiyZNoGFDx23qCiJuwuVkxcCBA6lRowZt2rTh5ptv5uWXX2b58uWcOXOmIuIrd/aJlnAXBsKpbtcPrbyTNXXq1KF3794MHTqUnj17EhYWZnssMzOTuXPncvXVVxNX2BRJRXjllVcIDw+3Lc60IHFJ/pYV3pasAFoMGkObWOv6z40hft0KcHIGHBERqXoOHDhgW69Rowb9+vVz6rizZ88yZcoUDMPAYrHYFnu52zIyMnjsscd4075Ztoi4n4sXuRRsXfXJhrAa9cyNp7QsFmtXEHs//2xOLCL5lKobiGEYHD58mCNHjrB48WLb9rp169KtWzfb0rVrV9q0aVNuwZaHxMRE23pQUJDTxwUHBxd6jtLq2LEjDz30EDfccEOBQUgzMzNZsWIFTz/9NFFRUQDs2LGDW2+9leXLlzt1/mnTpvH444/byvHx8eWbsPDyATYBuOYaRvwAl4PgmsMQF3uK6nv3gprvioh4pNyWlRaLhd69ezt93L///W8SEhJsCQrDMPDx8SEyMpLmzZtz+fJlNm/eTGJiIhaLBcMweOKJJ+jTpw+9evWqiKciImUVG0tsTheQWingU7uOufGURd++sGhRXlnJCnETpUpW5L8bkNsc8ty5c6xcuZKVds3hQ0ND6dKliy150a1bN6644gr8/f3LEHbpZWZm2tb9/Jx/+vb7ZmRklDmOP/74o9hrXXfddQwdOpTx48ezdOlSAH744QeWLFnCDTfcUOL5AwMDCazIuZ6Tkni1H6T5Qr0kmOyFLSto355X9jZk1g+n8MltULF8uZIVIiIeyr6FY6tWrZw6JjMzkw8++MAhUdG8eXO+/vprunTpYtsvJSWFl19+mVdeeQWArKws7r//fnbv3u10l1URqUQXLxLbzLpaOwVo4vz4NW7nqqs4H2IdLPTnxjDy0B+MvXwZatQwOzLxci53A5k4cSKRkZEEBwfb+lnmsv8xzX0sMTGRzZs389ZbbzFx4kRbN4du3bpx33338eabb1bqOBghdn9Up6amOn2c/b6hldSKICgoiIULF1K/fn3bttmzZ1fKtUuUnMwbfeCFwTCjH97ZssJiIeya6/ISFQA5iSUREfE89uNH1a7tXP/0devW2QbUzO0GMm/ePIdEBVhbcL744ou89dZbtrrV3r17+e6778opehEpT+mXLpCQc1+wdjLgwmC7bqdbNw408OO+MTCvJ3zfFvj1V7OjEnE9WTF37lx++eUXEhIS2LNnD5999hlTp05l+PDh1K1b16kERkZGBrt27eKTTz7hL3/5C4MGDaJGjRq0bt3aNg7GsmXLiI2NLZ9nacd+PAhXBq1Mtuv2YH+OilatWjUmT55sK2/YsMGlJEuFSUoiKadxTGg6XjlmBQDXXedY3rCh4LzbIiLiEapVq2Zbz53OvCTff/+9Q7lDhw4MGzasyP0nTZrE6NGjbeV3333XxShFpDJcTLpgW6+dQtVOVgQG0j2iO35Z1uLPTdAgm+IWSj11qcVioX379rRv355b7aZrPH36NDt37nRYDh8+THZ2tsOxuewTG0eOHOHo0aMO42C0b9+eIUOGcNttt3FV/sFfSqFOnbz+ZKdPn3b6OPsBRJ29m1JeBg8ezAsvvABYW3hER0ebPhaIkZxEUoB1PTQDqOWFLSsAhg6FgADIrbRmZsKqVTB+vLlxiYhIubP//T9//rxTx6xdu9Y2DoXFYmG8E78Pzz77LN999x2GYbB+/XqysrLw9fUtddwiUv6CL1zmPz/AhRBoG0vVTlYAwX360/30FrY0tk7HGrt5LbX5h9lhiZdzuWVFSSIiIhg1ahTTpk3jf//7H/v37ycuLo6NGzcyZ84cHnzwQXr06EFgYKBDogIKb4Wxd+9e3n77bfr370+HDh1YZD/4Sym0a9fOth4bG+vQYqI40dHRtvX2lTzvcIMGDRzKFy5cKGLPypOakoiR83J5dcuKsDAYNMhxm7qCiIh4JPsbBTt27Chx/9jYWH7//XeHbddee22Jx/Xo0cM2+HZqaqpT1xKRSpSdTfjZOP7yC/xzDdyziyqfrKB/f/qdyCtuPP1r3s04EZOUe7KiMKGhoVx11VU88sgjzJs3jy1btpCYmMjvv//O/PnzeeKJJxg6dCi1a9cushuJYRjs37+fW265hWuvvdalaTztdejQwaG8c+fOEo+JiYlxuIOS/xwVLX9CJcQNEgNJqfG29dAMvHPMilz5u4IsWwZ2LYlERMQzdO/e3TZm186dOzlx4kSx+y9fvtyhThMeHk5kZKRT17KfBcR+ylQRcQOXLxecrt4DkhX97b7SNkSkw2+/mRePCJWUrCj0wj4+dOzYkdtvv53XXnuNVatWce7cOaKjo1myZAkvvvgiY8aMoV69erYf+txmlCtWrGDAgAFOt4qw16tXL4dZMjZu3FjiMRs2bLCtBwUFVfo0YvlnDqlXz/x5nJPSk2zrXt2yAgomK86dg23bzIlFREQqjJ+fH0OGDAGsN1FmzpxZ7P5ffvmlbd1isTB48GCnZ/Zo3Lixbf3SpUuliFZEKkzOoLkOqnqyolYt+oXm3ZDd0BRYv968eEQwMVlRlEaNGnHdddfx97//ncWLF9vGwJg6dSrh4eGAtYLw+++/8+ijj7p8/rCwMIYOHWorL1iwoMRj7PcZOnRopc0Gkuvzzz+3rTdv3pyIiIhKvX5hktITbete37KiVSto1440X1jVEr5uj7qCiIh4qD/96U+AtS4yd+7cAgNo5jpx4gTLly+33WgBuP76652+jn1dIz4+vpg9RaTS5U9WBAV5xI27On2G0CGnMfn2CEjauMbcgMTruV2yojCdO3fm1Vdf5ciRI7YRsg3DYP78+aVqGnnvvffa1qOioliyZEmR+27fvp3ly5cXemxl+O677xwqQmPGjKnU6xfFJyWVbqeh7QVoHI9HfEGXRep1I2jwVxh+N/xtOBhLC6+8iohI1TZixAj69OmDxWIhKyuLm2++mZkzZ5KUlNfiMDY2lvvvv5/MzEzbNn9/f4dZPkpi393V39+/fIIXkfKRP1lR1VtV5BowgFEH4ZrD8NxPkPHrz5CVZXZU4sWqRLIiV40aNVi0aJGtv2d2djaffvqpy+cZP368w/zmkyZNYt++fQX2O336NHfeeSdZOR/Srl27Mm7cuELPeezYMSwWi23Jnb0jv7i4OMaNG8c2J7oJLFy4kNtvv91WDgkJ4cknnyzxuMrQ/nQG2+fC/jkwfS1en6wIuu5GuuZMGHO4Fhw4ug3sZpARERHP8cknnxASEoLFYiEtLY1p06ZRr149unfvTs+ePWnWrFmBWUBGjx7t0mxiZ8+eta1X5pTpIuIED05W/HslrPwUnl0PNS4kghPj+4lUlFJPXWoWPz8/nnrqKVvS4KeffnL5HBaLhffee4+BAweSkpLC6dOn6d27N5MnT2bAgAH4+fmxZcsW5syZY6ssBAcHM2/ePKf7mhbFMAwWL17M4sWLad++PSNGjKBr165EREQQGhpKQkICu3fvZtGiRWzdutUh5g8//LDAzCCmyT9eiDd3AwHo14/rXwxkXYs0AL5vC+2WL4f77jM5MBERKW+tW7fmyy+/ZNy4caSmpmIYBikpKQUG7c6tM1gsFp5++mmXrrFlyxbbujt0/xQRO56arGjQANq2BfuW6+vXQ48e5sUkXq3KJSsABgwYYFs/fPhwqc4RGRnJ/PnzufPOO0lJSSE+Pp4ZM2YwY8aMAvsGBwczf/58p0fwdta+ffsKbdGRX7Vq1Zg7dy4TJkwo1+uXmmGAXXNXwOtbVhAQwPURA/krKwFrsuKJJUuUrBAR8VAjR45kxYoV3H333bbWlUX529/+RteuXZ0+96lTpxzqN61bty5LqCJS3mJj+bUR+BhQJxlaeEqyAmDAgILJir/8xbx4xKtVqW4guWrXro2PjzX0i4WNxuukm266iW3btjFs2LBCKxkWi4WhQ4fy22+/cdNNN5X6OvaCg4N56KGH6NSpU4mtNMLDw5kyZQq///47t912W7lcv1ykpxecmtPbW1YAbYffRutY6/qGZnB57fKCLVBERMRj9OvXjz179vDyyy9zxRVX2KZfz11q1KjBa6+9xiuvvOLSee0H9g4ICKBNmzblHbqIlMXFi9w3Bno9BFc+gue0rABrssLehg0F6/0ilaRKtqwAaNOmDQcOHCA9Pb1M5+nQoQOrVq0iOjqaTZs2ERMTA1hnJbn66qtp0qSJU+dp3ry5w1zqRQkMDGTu3LmAdSqynTt3cu7cOS5cuMDly5cJCQmhVq1adO7cmc6dO+Pr61v6J1dRCvsD3NtbVgCWG27gui8szKptkOUDKxulMmHVKrjxRrNDExGRChIUFMRTTz3FU089xdmzZ4mOjubSpUvUrl2bLl26uPw7njvLSO4NjcjISAICAioidBEprYsXic3pmV07Gc9KVgwc6FiOjYW9e6FTJ3PiEa9WZZMVe/fuJS4uzmFch7Jo0qQJt956a7mcy1k1a9Zk8ODBlXrNcpG/CwioZQVA7dpcH9SZWewCrF1BJnz9tZIVIiJeon79+tSvX79M5/j00085cuSILVlRJesJIh7OuBhLbEvreu0UoJkHJSuaNoVmzeD48bxtP/2kZIWYokp2A8kVHh7OsGHDzA7D+6hlRZEGDLybamnQ6RzWeaqXLAG7qetERESKkpKSYhuIM7e1Znl1QxWR8hMXf56snL+iaicDLsz0UyXk7wqyfr05cYjXq7ItK8RE+VtW+PmBmqgCEDB2PEc6PEEdWz7norWvn+6MiYhICYKDg1m/fj3bt29nx44dnDp1ymGqdRFxD7HJF2zrtVPwrG4gAAMGkDX/U3bXhw1NYfT2NTQzDCjjrIgirlKyQlyXnMxzg2HhFRCcCQt/CEQNw3I0bUqd9t1h+/a8bV9/rWSFiIg4pWXLlrRs2ZLx48ebHYqIFCE2NW+Af48bswJg4EBm9YEnRliL/t+f5+EDB6BdO3PjEq9TpbuBiEmSkjhVDQ7Vht31ITsk2OyI3MvYsY7lb76xTvcqIiIiIlVbdjaxmQm2oke2rGjdmn7JdWzFtc2BH380LRzxXkpWiOuSk0mxa5MT4qfxKhzkT1ZER8O2bebEIiIiIiLlJz6e2KC8m1Ae2bLCYqF7l5FUT7UW17SA7NWrzI1JvJKSFeK6pCSS/fOKwQFKVjjo2BFat3bc9s03poQiIiIiIuXo4kVu2w3nZsLeOXD7bjwvWQH4DRvOoGPW9QuhsDtqtQaNl0qnZIW4LjmZFPtkRWCYebG4I4ulYOuKr75SVxAREXEL58+fZ/ny5UyfPp3Ro0cTERGBxWKxLR999JHZIYq4r4sX8TWgbjK0vwB1M/whNNTsqMrf0KEMPZpX/LFeoloKS6XTAJviuvzdQJSsKGjsWHjttbzyvn2wezd07mxeTCIi4tXOnDlDnz59OH78uNmhiFRd5887lmvV8sxZMho2ZKilJXAEgB9bwOOrV0Pv3ubGJV5FLSvEdXbdQCwGBIQoWVFA797QpAkXQuCDbvBFJ+B//zM7KhER8WKpqalKVIiU1blzjuX69c2JoxJ07H09DXLGEl3fDDJWrzA3IPE6SlaI6+y6gQRngCXEA5u+lZWPD+duuZ4Gf4UHboQZV2NNVqgriIiIuIG6desycuRInnnmGb7RuEoizvOiZIVl2DUMPQr1EuH6A3B5x8+QlGR2WOJF1A1EXJeUxF9+hlPVwALQSgNsFqbehPvo+t932NYQtjeEIxcP03L7dujRw+zQRETEC9WqVYsvv/ySyMhImjVrZnY4IlVT/mRFvXrmxFEZBg7knQk+hKVkW+v8ZMKGDTBypMmBibdQywpxXUoKD+yAZ9fDM+uBECUrCtWzJ+PP5I0O/T91BRERERNVr16d8ePHK1EhUhZnzzqWPTlZUa0a1br3xWFEjtWrzYpGvJCSFeK6lBTHcnCwOXG4O4uFWzpNsBU/vwL44gt1BRERERGpqrypZQXANdc4lletMicO8UpKVojrlKxwWosJk+gTbV2PagB7ko/Dli3mBiUiIiIipXIqPobbxsGfR8K37fDoMSsAGDbMsRwVVbB1iUgFUbJCXKdkhfO6dOHWc3Vtxc+vQF1BRERERKqo46ln+PxKmNUHfmyJ57es6NULqlVz3LZmjTmxiNdRskJcp2SF8ywWJnS9A59sa3HhFWB88T/IyjI3LhERERFxjWFwNv2SrdggEc9PVvj7w6BBjttWrjQlFPE+SlaI65SscEnELQ8y9CgMPQJPboKs06dg3TqzwxIRERERV1y+zNmgvBtO9b0hWQEFu4IsXw7Z2ebEIl5FyQpxnZIVrunUiWW/d2H1J/DgdvDLBj75xOyoREREyk1aWhrx8fEOi4jHOXeOM2F5xfpJeEey4tprAUj1g5WtYKvvWdi2zeSgxBsoWSEuS0pP4tdGsLsenA1FyQon+N11j+OGr76CxERzghERESlnr7zyCuHh4balSZMmZockUv7OneOsfbIiO8Q76sGtW/NHZHNqT4URd8F/+gJLl5odlXgBJSvEZfsD4+kzETo/Av8YhHd8SZfVbbeBr29eOSkJvv7avHhERETK0bRp04iLi7Mt0dHRZockUv7OnbPeqMvRILhu0ft6mLb9b8Q/p+fHitaQuXSJuQGJV1CyQlyWkplqWw/OQMkKZzRoACNGOG5TVxAREfEQgYGBVK9e3WER8Thnzzq0rKhXrYF5sVQy/+tvZMQh6/qlYPjl3HY4dcrcoMTjKVkhLkvJSrOthyhZ4by773Ys//gjnDxpTiwiIiIi4ppz5+h3AkYehP7HIbBehNkRVZ5+/bj2ZJCtuKwNsGyZefGIV1CyQlyWnJ2XrAjORMkKZ40eDfZ3mgwDFiwwLx4RERERcd65c7y6GpYvgPUf4h2Da+by92dk82tsxaVt0LgVUuGUrBDXZGaS4pM3VZFaVrggOBgmTLAVDYCPP7YmLURERETEvZ0751j2pmQFUH/UeCJjrOtRDeDkzysgNbX4g0TKQMkKcU1KCsn+eUWNWeGiu+/mRDi8NADa/R8cPb0XfvnF7KhEREREpCRnzzqWvSxZwahRXHswr7iscQr89JN58YjHU7JCXJOSQop9skLdQFzTrx+fDazFs0PgYG34uCvw3ntmRyUiIiIiJcnfsqJ+fXPiMEvdulwXcAUAV5zNuWmpriBSgZSsENekpJDil1dUNxAXWSzc1echcnvSfNwFsj9fCHFx5sYlIiIiIsXz8m4gAD36T+DY67D7HbgrCvj+e3VplgqjZIW4JiWFh7bBkTfgj7fgmsMoWeGiRvc9xjVHLQAcqwlrIlI10KaIiIiIO0tLg8uXHbd5YbLC5/obaGZ/j+3oUfjjD9PiEc+mZIW4JiWFaunQ4jJ0PA/hWX7g51fiYWKnQQMe9OtlK77bE5g3T1lpEREREXd16lTBbQ0bVn4cZuvSBRo3dty2eLE5sYjHU7JCXJOS4lhWq4pSuXHCczRIsK5/0x5OHdkFv/1mblAiIuLxJk6cSFBQUIHF1X1EvM7Jk1wMhozcv55CQiA83NSQTGGxwNixjtu+/NKcWMTjKVkhrlGyolz4Dx/Jg0esP3BZPvB+N6ytK0RERCpQRkYGaWlpBRZ7mZmZJe4j4nViYhg/AQKfhYgnILlZQ+sf7t7o5psdy7//Dvv2mROLeDQlK8Q1SlaUDx8fJnafaBtoc0VrYOFCiI83NSwRERERKcTJk5ysDoYFkgIgpEETsyMyz1VXQYMGjtu++sqcWMSjKVkhrlGyotw0vf8vvLDewpLP4KcPgaQk+PBDs8MSEREP9tFHH2EYRqkWEW9mxFiTFQCN44FGjUyNx1S+vjBunOM2dQWRCqBkhbhGyYry07Ahz9a+iesPgG9uHXD2bMjONjUsEREREXF06fRRUvyt643jKTjIpLcZPx6Ac6EwrwfsjdkFBw+aHJR4GiUrxDVKVpSvKVMcy4cPw7Jl5sQiIiIiIoWKuXjMtu71LSsA+vfn217hRDwBk26AT7oAixaZHZV4GCUrxDUpKfynLzw5DKYPRMmKsurf3zoFlL033zQnFhEREREp1Mmk07Z1JSsAX1/69hxjKy7qCMYidQWR8qVkhbgmJYWPu8DMfvBKP5SsKCuLBR57zHHbqlWwZ4858YiIiIiIo+xsTmbE2oqN1A0EgHrj7mbgcev6odqwO2YHHDliblDiUZSsENekpNj664VkoGRFebjtNqhTx3Hb7NnmxCIiIiIijs6d42Ro3phialmRY8AAxp0ItRUXdURdQaRcKVkhrklJITknWRGciZIV5SEoCB56yHHbJ5/AxYvmxCMiIiIieU6eZNI2WP0xfPQ19DjrA/Xrmx2V+fz8GNtuDJacgeK/6gB8/rmpIYlnUbJCXJOSQoqfdTVYLSvKzyOPgK8v6b4wvzOsapAMb71ldlQiIiIiEhNDwwQYehTu2QUNqje0Tt8pNBx3L1efsK7vqZfTFWT3bnODEo+hZIW4JiWF1NxkhVpWlJ9GjTh5xw20eAzuugn+PhSMWW9AUpLZkYmIiIh4t5gYx7K6gOQZPJhbT4bbih93BT7+2LRwxLMoWSGuSUkhLSdZEaRkRblq9PgL1E22rm9tBGurX4QPPjA1JhERERGvd/KkY1mDa+bx9eXW7vcQnAEjD8LAY8D8+ZCZaXZk4gGUrBCXZKYkkZXzrlGyonxZunThycS8aUz/2R/4978hI8O8oERERES8nVpWFKv2PQ9z+l+wfAHccAA4exZWrjQ7LPEASlaISzLSkukbDd1OQ9tYlKwoZxMefJ3WOTNjrWkJP2cdh//9z9ygRERERLxZ/pYVSlY46tCB8M6RjtvUFUTKgZIV4pLgpHQ2vw/b58J/v0PJinLmO2AQ0063spX/OQB49VXIzi76IBERERGpOCdOOJbVDaSge+5xLH/7LVy6ZE4s4jGUrBDXpKQ4lpWsKF8WC3feMYOml63FpW1hR+wf8M03ZkYlIiIi4p2ysuDYMcdtLVuaEopbu/VW8PfPK6elwRdfmBePeAQlK8Q1SlZUuIDRY5l6pIGtPLs38Pzzal0hIiIiUtlOnuSLdpm80g8+vwLiA1GyojC1a8MNNzhuU1cQKSMlK8Q1SlZUPB8f7r/tNSJjYNZymLMM+P13+PJLsyMTERER8S5HjrDgSnh6GNw2HuJqBkPdumZH5Z7ydwX5+WfYv9+cWMQjKFkhrlGyolIET7idX3/uxJRfISR3MpAXXrA2RRQRERGRynHkCEdrWlf9s6Bh3VZgsZgbk7saNcohkWMAzJ1rWjhS9SlZIa5RsqJy+Phg+cd0x2379sHnn5sTj4iIiIgXMo4e4WgN63rzy+DbQl1AiuTvD3ffTXR1+PsQ6PowpH/8ASQlmR2ZVFFKVohrlKyoPGPGQNeujtv+8Q/IzDQjGhERERGvc+H4XhIDrestLqHxKkry8MM8MQJeHgBRDeCrhnHw2WdmRyVVlJIV4jzDgNRUx21KVlQcHx9rcsLewYPw3/+aE4+IiIiIlzl67oBtvcVllKwoSevWPGrpZSvO6QW89Zb17wgRFylZIc5LTWVJW2j8OLSeAh91RcmKinbDDRAZ6bjt+echPt6ceERERES8yNGEE7Z1taxwzoC7n+OKs9b1zU1h+7ldsGmTuUFJlaRkhTgvJYW4IIipDodrQZI/SlZUNIsFZs503HbuXMFtIiIiIlK+EhI46ptgK7ZUssIpllGj+NOROrbyW5FYW1eIuEjJCnFeSgqpfnnFoEyUrKgMgwbB6NEA/NgChtwDl+f8C06eNDcuEREREU929Cg1U6D7KaiRktMNpHlzk4OqAnx8uHPYXwjP6T3+2ZUQu/RLOH3a3LikylGyQpynZIV5Zszg9assDLsH1raAF3unwTPPmB2ViIiIiOc6epRJ22DbPLg0A3oQobqvk0Lvf5j7d/sCkOoP73fOgvfeMzkqqWqUrBDnpaYWTFYEBpoWjldp355x3e8kOMNafLM37P/+Y/j1V3PjEhEREfFUR444FC2attR5tWrxSOObsOSMq7mmBfD22wUH6xcphpIV4rz8yQoff+uYClIpmj77L/72WwAAmb7w55FgPDIZsrJMjkxERETEAx065Fhu0cKcOKqo1g8/zfS1sOoTWD4fOHsWPvjA7LCkClGyQpxXIFkRYF4s3qhePaYOeY7GcdbiD23gi7Qd8O675sYlIiIi4on27nUst2tnThxVVdeuPBN4DcOOgO325syZkJFhZlRShShZIc7Ln6zwVReQyhb6l6nM3t3YVp4yCi5Nf9qaqRYRERGR8rNnj2O5Y0dz4qjKnn7asXz8OHz2mTmxSJWjZIU4LzWV0fvh3yvgnz9C8/QQsyPyPv7+jPn7p4zJSfSfC4Mne8fD1KnmxiUiIiLiSWJjC94MUrLCdQMHwlVXOW575RV1YxanKFkhzktNZcBxePxneHoDNMoKNTsi7zRoELODxlItDULTodM54JNPYNUqsyMTERER8Qz5u4D4+0OrVubEUpVZLAVbV+zfD19/bU48UqUoWSHOS0tzLAcFmROH0PjVt1m4LJg9b8FjuROCPPggJCSYGpeIiIiIR9izh0tBYOSW27a1JizEdddeC126OG57+WUwjML3F8mhZIU4L/9UQ0pWmKdBA6576F80jbPbduKEuoOIiIiIlIPsPX/Q+HGo/STcNg51ASmLwlpX7NgBS5eaE49UGUpWiPOUrHAvDz8MgwY5bnv3XVizxpRwRERERDzFyUPbSQ6AS8GQFICSFWU1bhy0aUOaL8ztAXfeBMa0pzR2hRRLyQpxnpIV7sXHB95/H0LyDXT6wAMQH29OTCIiIiIeYN/5vDEr2l9AyYqy8vWFZ57h2jvg4RtgQWf4JvMP67hrIkVQskKclz9ZEaipS03XsiW8+qrjtmPHYPJk9QMUERERKY24OPZZYm3FDudRsqI83HEH/3e2ma049RpIf/4ZSE42MShxZ0pWiPPUssI9PfooDBjgsCn9f5/Bp5+aFJCIiIhIFbZ3L3vr5BXbX/SBNm3Mi8dT+Ppy45/fZeAxa/FQbXi70SmYNcvUsMR9KVkhzktL47eG8Gsj2F0PJSvchY+PtQldeDgA65pD6ynwy0sPw8GD5sYmIiIiUtXs3k1U/bxih2ot1KK4nFhGjODflyJt5ekD4eIbr8CFCyZGJe5KyQpxXmoqt46HPhNh8L0oWeFOmjWD995jbXMYejdEh8OE61OIvfvmgi1iRERERKRIWVt/ZWcD63rLi1Djip7mBuRJLBZ6PPcud+2yFi8Fw0vdEuDFF82NS9ySkhXivNRU0nytq0GZKFnhbm6+mf5D7+PqaGsxOhxubbOLzD89ovErRERERJx0YN9mkgOs693OAD2VrChX3bvzz+pjCM6wFuf0gv2fz4Fdu8yNS9yOkhXivNRUUv2sq0pWuCe/WbNZuKMldZOs5dWt4C+nPoR588wNTERERKQqSE2lzS8H+P0t+GQxPPwbEBlZ4mHimiYvvM4Tv1r/FG1+GRJ9s2HSJE1lKg6UrBDn5U9WqO+e+wkNpdGn37L42yD8c77r5/SGtz96BH7+2dzYRERERNxdVBR+GVl0Og93RcGwoxbo1s3sqDxP8+Y8ddVUXl4Nu96BHqeBX3+Fd981OzJxI0pWiPPS0mzJikC1rHBfV1xBvxc/Zt6SvE1Thmezasp1cOKEeXGJiIiIuLvffnMst2sH1aubE4uHC532PNPOtSU4027jtGlw6pRpMYl7UbJCnJaZmkymxqyoGiZM4N7hU/nbJmsxywfWhl+Ca6+Fy5dNDU1ERETEbeVPVmi8iooTFFSwJUVCAkyZYk484naUrBCnpaWn2NaVrKgCXn6ZVxjGjfvgnz9aF/74A8aNg/R0s6MTERERcT9btzqWNV5FxRo8GO65x3HbV1/B11+bE4+4FSUrxGmpGcm2dSUrqgBfX3z/9wVfRbXn6Q1gyd2+Zg088ABkZ5sZnYiIiIh7SUqCPXsct6llRcX717+gdm3HbQ8+CCdPmhOPuA0lK8RpNRMyOfMaHHsd63gIGmDT/dWsie/S5VC/vuP2+fPhscc0pamIiIhIrs2bHW/m+PpC166mheM16tSB//zHcdvFi3DXXZodxMspWSFO80lLp34SNIuDRgmoZUVV0bw5LF0KISGO2+fMgSefVMJCREREBGDtWsdyZGTB+pNUjLvusnZVBrIt8M/+8NeAdTBjhrlxiamUrBDnpaY6lpWsqDp69IAvvgA/P8ftr70GL7xgSkgiIiIi7iRp/Wr63Q9PDoP1zbCOpyCVw2KB997DaNKYMbfCM0Ph31fBNwuehV9+MTs6MYmSFeI8JSuqtuuugwULwCfvY5/qB7f/MZ0/nn5QLSxERETEeyUk8MuZbWxqCjP7wYIrUbKistWsiWXBZ4w8bBtpjXtHZ3N44jg4d87EwMQsSlaI85SsqPomTICPPgKLhWwL3D0WFl4J/bLf56fHxmjQTREREfFOGzawvklePWjASV+4+moTA/JS/fszedSz3PyHtRgXBKMGnyL2lhsgLc3c2KTSKVkhzsufrNAAm1XTXXfB3LnEB8LRGtZNl4NhePh3fDF5AGRkmBqeiIiISKVbu5YNzfKKA2p313gVJrE88yzvnetDx5zGFAdrw40tt5D6sFoCexslK8Q5hlEwm6mWFVXXxInUeOt91n5iYdRB66Z0P7il4SZemNSW7Iux5sYnIiIiUonS167m58bW9eaXoMlVI80NyJv5+RH++dcsWxtBgwTrpk1N4e6k+WS/NtPc2KRSKVkhzklPL7hNyYqq7f77CVvwJd8u8uP+7Xmb/9HsGDc+1Yy4P7YXfayIiIiIp4iO5ucLO0n1txb7n0DjVZitQQOaLVzO918HEZrzZ8jSNrD330/Bl1+aG5tUGiUrxDmpqfzaCP5vFPztGvilMUpWeIJx4/D/bin/XR3MzJXgk9NV8/tGSUz6Zx/48Udz4xMRERGpaN98w3ft8oojToVAv37mxSNWXbrQ498L+eJLaBQPP30Enc4Bt98O331ndnRSCZSsEOekpvJHPZjTG/51NUTVR8kKTzF8OJZ1P/G3Iw34YT7USoa6STBzWQZccw1Mnw5ZWWZHKSIiIlIhjK8X821767pvNoxqfz34+5sblFiNGcO1D7zKwTeh56mcbZmZcPPN8MMPpoYmFU/JCnFOaiqpfnnFoEw0wKYniYyELVu4pnpXfpsH3y6EpnFYxyp5/nm49lo4f97sKEVERETK14UL8NNPzFsCj/0Cd0RBrTG3mR2V2Js6leBJjzpuS0+HsWPVCtjDKVkhziksWaGWFZ6lSRPYsIEWg8bQ92S+x1auhM6d4fvvTQlNREREpEJ89x2WbIMhR+GNH+DjlSEwfLjZUYk9iwXefBMeeMBxe2oqXHcdLF5sTlxS4ZSsEOekpTkmK7Is4OdX9P5SNYWFwVdfwauvgq+v42NnzsANN8D990NcnDnxiYiIiJSnzz5zLI8apSlL3ZGPD8ydC3fe6bg9LQ3Gjydr9pvmxCUVSskKcU7+lhU+AdYsp3geHx948klYswYiIgo+/uGHTJzciFfm3U1qRkrlxyciIiJSHg4dKtiNYPx4c2KRkvn6wocfWsersLM1wqDzvsfY89QDkJ1tUnBSEZSsEOcUlqwQzzZgAOzYYb3DYGdpG/hvuySePv0pHZ6vzZdrZmMYhklBioiIiJTSvHmO5dq1YcwYU0IRJ/n5WVvDTJwIwIlwGH0b7KkHvXw/4JP7e8DFiyYHKeVFyQpxTv5kha8G1/QK9evD0qXw3/9CtWoA7IjIm+L0WGAKEzZMof8/mrLuj6UmBioiIiLigrQ06116e/fcozHZqgI/P2uXkOnT8c2GiETr5qQAuKfFTu78c1PiN681N0YpF0pWiHNSU2l+Ga4+AT1OQThKVngNi8U6oNHu3TBkCM+sh13vwjWH83bZZDnJ4EXXM/il1qw/uNq8WEVERESc8cUX1plA7OXcrZcqwGKBZ5+l0awP2PCxD/dvz3toQaskun0xhA3/fkzdQqo4JSvEOWlpPP4zbPwAfpsH7dKrmx2RVLZmzWD1apg/nyss9VnxKXy/ANrbzWi6LuswX/19LLz7rnWEZhERERF3k5mJ8dKLHK5pt23gQGjf3rSQpJTuu4/Q71fy/s91WbgIqudUP4/UhAGJb/LXB5vCgQPmxiilpmSFOCf/H55qIuedLBa44w7Yvx/Ln//MdUd8+f1tmP8VtL0AQRnw1PJEmDwZmjeHGTM0c4iIiIi4l4ULWZNxkDZT4PZxcKgW8PjjZkclpTV0KOzYwa01+rHzXegbnfdQ26gY6NzZWifNyDAvRikVJSvEOUpWiL3wcHj9ddi1C98bx3DHbvjjbdjwYV6/Qc6ehaeegqZN4c9/hr17zYxYREREBNLTMab/g78PBcMCC6+ELf1aWKdnl6qrUSNYs4YWD01lwwcwZymMOggPbsc6PslTT0GnTvDNN6CB4asMJSvEOUpWSGE6dYKvv4bNm/HrN4CepwrZJz4eZs2Cjh1h0CAemXUNb2+eRWxybKWHKyIiIl7utdf41u8wvza2Fjudg1sefN3aelSqNn9/mDED39U/8uiFFixdAD72eYmDB2HsWBg8GH791bQwxXlKVohz8icrAjXAptjp2xfWrYNVq2DYsCJ3O7T7J965vJpHV/2Z+jPrMmJWJO//9p4SFyIiIlLx9u/n4mvTeeS6vE0vRrfG9/rR5sUk5W/IENi9G8uf/1x4Euqnn6BPHzKuGcK3X75IVlZmpYcozlGyAti8eTOTJk2iY8eOhIeHU716dTp27MhDDz3Epk2bKvz6R44c4bnnnqNHjx7UrVuX4OBgWrVqxdixY1m0aBGZmW7wAUpLcyyrZYXkZ7FYExWrVsHWrTB+fIEfiGVt8tazLAYrL//Gg0sfosHMeoycFcmczW+QkpFSyYGLiHgns+s/IpUqLQ3uvZc/DUvntHU2dkYcgjHPLVSrCk8UGmrtsrx5M/TqVeguX55dy5g9z9F2WhgzZt/K2djjlRyklMRiGN7baScpKYkpU6bwwQcfFLvffffdx+zZswkNDS33GGbNmsWTTz5JWv5kgJ0+ffqwYMECWrZsWerrxMfHEx4eTlxcHNWrl2Imj2eegX/+M698662wcGGp4xEvceQIvPcevP8+nLdOG7KrPizoDF90guM1HHcPT4Xzh8fhf8ON1sRHRETlxyziwcr8WyAeoTLqP3qviVsxDHjoId7c/V8eG2XdVCMFfs96iEavzTU3Nql42dnWqWqfegqOWxMSBtDtYdjVIG83vywYnd2aewY+xojBEwn0U0vy8lCW3wOvTVZkZWVx7bXXsnLlStu24OBgOnXqhJ+fH3v27CE+Pt722PDhw1m2bBm+vr7lFsOLL77Ic889Zyv7+PjQsWNHatWqxcGDBzl9+rTtscaNG7NlyxYiSvnHW5krDX/9K/z733nle++FDz8sVSzihdLSrGNbzJ1r7S6C9UdiayP4sqM1cXGiBtz8B3zxpd1x7dtb+xUOHgyDBkHduqRkpBDsH1z5z0HEA+gPSKms+o/ea+JWXn2VtGem0e1h2FvXuumz9fW47bsj1jvw4h1SU+Gtt+Bf/8I4c4ZVreBfV8GqVgV3DU/34bmgETx+25vQunXlx+pByvR7YHipadOmGVj/XjIAY+LEiUZsbKzt8cTEROPZZ5912Ofpp58ut+v/8MMPhsVisZ27b9++xv79+22PZ2VlGZ9//rkRFhZm2+fqq68u9fXi4uIMwIiLiyvdCR591Oj8MEbEExi9H8QwHn641LGIlztxwjBmzjSMrl0Nw3qvw8gGY1d965K7rbAltW0rI+w5P+PKFyOM+9673nh78yxja8xWIy0zzexnJVIllPm3QKq8yqr/6L0mbiE72zCmT7fVI86HWOuxzw73M4wdO8yOTsySkmIY775rGC1aGAYYh2piTBuKUf+vGLyQt/y3W04dtEMHw/jrXw1jzRrDSFOd01Vl+T3wypYVp06dolWrVqTmDBp511138cknnxS677PPPstLL70EQFBQEIcPH6Zhw4Zlur5hGHTr1o1du3YB0K5dO7Zv305ISEiBfVevXs0111xjKy9evJixY8e6fM0y3+F48EHq13yfc2HQ/BIc5TF44w3XzyNib88eWLwYliyBLVtK3H1TE+j3QMHtAYYPXXwa0rl2R6YPfYmG7Xqq/6lIIXS327tVZv1H7zUxXWIiTJwIn3/usDnVDwIX/A/LhAkmBSZuIzPTOpXp3LmwejUZPrCyFfzvCus4awdmQ638Q6kFB0OfPtC/P0d6tiKtc0faNe2Oj0VDQRZF3UBcNHXqVF577TUAQkJCiI6OplatWoXum56eTuvWrYmOjrYdO2PGjDJdf9myZVx3Xd4wxD/88AMjRowocv9bb72V//3vfwD06tWLX0sx1U6ZKw133UV44/nEB0H787A37El49VXXzyNSlNOnYelS+P57WLvWOuVpPitbwdRr4Pd6kFXEb0LsDKjlXx3atYNWraxN9+z+PRtmISywGqEBavYp3kd/QHq3yqz/6L0mplqyBB57DI4eLfjY66/Dn/9c6SGJmzt0CObNgwUL4NQpMn3AL7v4Q6aMgtm9oVa6L30zI+hZsxNdWvejS8/raN6sixIYOZSscFGbNm04dOgQAPfeey8fljD2wvPPP8/06dMBaN26NQcPHizT9R988EHef/99AFq0aMHhw4exFHMXeO3atQwZMsRWjo6OpnHjxi5ds8yVhptvJrD9ItL9oOtp2NHgeXjhBdfPI+KMzEzYscOatFizBjZuhKQk28PJ/taBOrc2gq0Nrf/urwMRCXDq38WcFxh3mw+L22XTIC2AVtnhtAyoR5OwRjSu1YImEe3o0LwnrVr2tGbORTyM/oD0bpVZ/9F7TSpdRgbHv/6QmI9mc9Xy3wvf56234JFHKjcuqVqys631zs8/h0WLbAPEF6bnQ7CtiAZn1dItTD7XlBn+11pvmLVpY12aNoVCWtN7srL8HvhVUExua//+/bYfaoCRI0eWeMyoUaNsP9aHDh1i//79tGvXrtQxLF261LY+YsSIYhMVAP379yc0NJSknD/Wli5dyqRJk0p9/dLITk0hPefdEpSJpi6ViuXnB5GR1mXqVMjIgN27rV1Ftmwh5Ndf6bt3L31P5uVaEwPgpBPff4fDrWnyM4HpnOE8mzgPKX9ADBADj7wNby3DOuBWnTpQt651yVlPrlWNH4JjqFetAfXCG1KvVhPCazfCUqMGVK8O4eHW+EVE3Ig71H9Eylv2ubPsXrOQb39bwDepO9lRN5M2rWGfBXzsb8eGh8PHH8ONN5oWq1QRPj4wYIB1mT0btm6FZcusrX+3b3fY9fbd0CgeNjeBC/ka7CYEGPgfOQ5r3il4jZo1oVEjzjeryztt42hcrRGN6rSgcd3WNKrfmvD6zbDUrWvdz8vrlF737HPHicjVt2/fEo/p3r07AQEBpKenAxAVFVXqH+tz585x5swZl67v5+dHZGQk63JmUYiKiirVtcsiLT3Ztq5khVQ6f3/o3t26PPywdVt8vLX1xe7d8PvvhO3eTfvffwcKdh+xd/UJCEuHwzXhTLWCjzfOPTwpybocd5xz+1hdGPcokACcygkvC2qmQM1UqJEKn38fRHMj3Jo5L2SJrmZwMdSHsKBqhAVVJywonODgavgEBkFAQMHF37/w7faP+/oWv/ioKaKINzO7/iPiMsOwzt5w+TJcvAjR0XDiBBeO7+X98yvYmHWUzXVSuRgCVMtZgIO1YW1zGJrbA6R/f/joI2jZ0oxnIVWZr691fIo+fWD6dLhwATZtsra82LCBx7du4/GfMzGAw7VgZwNry99dDSCqPnQ9U8R5L12CS5c4EAfPRwJsh4tYl/0Qmg4NE6BuEny7rDp1QutCjRpQrZp1qV7dtp4QFoBPtWqEVKuNJTTU+jdaYKD139zFvmy/XgUSIe4fYTnbu3evbT0gIIAmTZqUeEzufocPHy5wjrJcH6BVq0LmyilEq1atbMmKsly/tFLT80aXUbJC3EL16jBwoHXJZRjWyswff8Dhw9bl0CHrv0eOQFqatdVEjsQAOFYDYqpZW2VEh8OgY8Vf9lwhQ11k+MK5MOsC4JucCvGpRZ7jjeHwn6vsNmQBiRB60ZpI6RsNX/+v+Dhe7g9pvtbPY2FL1zPQ4nK+g+ySF2kBPlwM9cHP4os/vvhbfPG3+OFv8cXi62fdz2KxJjkslsKX8n6sqO35FdUarSL2dcdrDRwIkycXvq9IEcyu/7gsKgo++MBWNDAwDAMDg2yMnKlKrNsC8cWC3eckXw/nJDJIJdO6f+5xQDbZGFgHaa5jBBd6bO62I77xZOYckXeOvPWIrBDqZBdSN8o5X4IlnT/8LmFYsHseOJzjqrR6BOBb4Nhcf/hf4phvgm3/7HzPp2Z2AINTI4p9Hgf94jEs4G9Y8DN88Dcs+Bs+BOQs/vg4tkgoIpYyb8vKsk5rnp6et+SUM1OS8LsUB3Fx1u35JNSAp/5cSIxAj1MwZh90PA80aQIvvwx33KFBt6V81KljbZ2T20InKQm2bsWyYwetd+2i9a5djN/4h7VFMNbplIoTU0SL4KQAa9LtYG0IiY2HM0XfiPu/MfBxV/C5DNXTHJdq6dD/OPx9QxEHWyzg7883HSwE4EswfgQbfgRb/Amy+BNo8adJejC+fv7WgUibNy/hGZU/r0tWHDt2zLbeuHHjErtg5GratKntx9r+HGW5fu55nb1+UecoTFpaGmlpabZyfCGDFboiLSNfsiIwsEznE6kQFou1L2Bhn6vsbIiJsbaUiImBkycJO3mSK2JiuOLkSeu23aes42UUo9lleHk1nA+1Ji5yl8tBcCkI4oOsLSyKkxhQ+PakAOty2Ylc4Ot9CjY5tDdnKTy6Nd/GrCzrAmyuD0PuLfxYn2zwz4bo/0Dd5ML3AfjHQPiyk7WprW82+Bp56z4GRJ6CN34o/nncO8Y6Bon9cT4GWACLAffsKj6BtLeOdXArS06NIPc4+39f/hFCMoo+x+IOsKWR43H252p90RpHcWZcbR1hvijXH4Aep4t+/EhNWHBl8df462YIzsQ6noqSFeIis+s/Ljt0CGbNYkNTGHB/8bue+hdEJBb9+FOjYE7voh+/+gRs/KDoxwGu+iucDSv68VnLYUox45/vLIfn8W4Jz6NvNGz+qPhrDHocThXTZfJfK+CJn4t+fHc9uP9GCMgqevnPCqidfwYFOwuvgJ+aQ7ovxFWHi8F5S2wIjN8Dn3xd9PHN4qBaGiQEQu1k6+s3/DCM3g9N4oHeveHtR+HWW60tD0UqSmgoDBpkXXJlZMC+fbBnD5ZDh+Dgwbwl3/gXA47DtwvzbprFVM/5txqcqma9GVZc/QUgPudPsmwfuBxsXexVSyt4jI1hYKSnU9xEk5dfgfA0bPXHyuZ1yYqEhATbenh4uNPH2Q8GYn+OslzflRhcvf4rr7zCP/7xD9eCK0ZYUgbvfG+tjLe4BNyolhVSxfj4WO+yFHc3MTvbeifn/HlrU7/z5wust7h0iWlxcXA6565PXJy1S0q2dSyMrPz9ZAsx4DgYFmvSIsnf+m9iTqIiMcDa9K8kxf1hDDlJxWJkFtMrJNsH0pwYBTumOvxRr+jHg0uIAeCb9hBXzNdJ35PFJyuiw+GdyOKv8fy64n/sl7eG//Yo+vFhh0tOVrzSv/jnEZFYfLLicE14bkjRjwP83xbn/k9FClPR9Z/yvkmSy5mUilHCTiWdw5mR5i0l7FTSOSrjeZT0nQ3Ff/eDNdlQnEvB8Fuj4vd5dXXxj69vBnN7Fv340RrFH+9jwPzF0DYW2l0AS3CwNUHx91EwZgy0bVv8CUQqkr8/XHmldckvIcF6cyznplmDmBhG55ZjYuDIBYiNte4HZDjRg/eKc9abZQmB1sRFfCAkBEByzo2x6sUlKyi5PhmY+51gUuLP65IViYl5KesgF7oyBNvNDGB/jrJc35UYXL3+tGnTePzxx23l+Ph4p5p8FiUsKYOH7QcBVzcQ8UQ+PtbBjGrWdK2yYxjWpoBxcfjmJjASEyElBZKTCyx3pKRwh/22y8nWfTMy8prDdk53bB6bnu7w+NLPMkjxs/7IpPpBmt16qp+1VUNx6iTDmL3WimuGr/UH0f7fTB+7H6giBGRZ+1VmW6xJmmyLdUrZ3Aq3rxMV5+ySKuflMF9VWRv/qvGweIKKrv+U902SXNXToPfJgi2m7P8t6Q/sTufg2gOOx/kYeevtL5Qcx/g91oSk/XXtW4F1KnrCAMCahH7sl+KfR2jBHg8Orjtg7cOeP/7cf5vGlfw8bvnD2novw6fg93+6b07LhGJk+li/24uaPhxKfj0K+20JyLS2xqidDI2KyonVq2dtOdmkCaNbtIBu3axjWbVtWyX63otQrRq0b29dipOeDrGx+MfGWm+YxcZab4wlJBRYpickwMmccmqqbclMSyEhOxXS04Civ1x8DJi5ElL8IcUv799UP+t3gn/u59Wkz5jXfbIz7Zp4+7nwn26/b0ZGCe1xnLy+KzG4ev3AwEACy7OrxuOPw7lzeR+CNm3K79wiVZ3FAmFh1qVRCbecyskAw3BMbuR28XBy6ZaVxdcl7XeNYU3EGIa15YhhOCxzcpb8jxvZ2WQb2RgNs2GAT7Hn2Jd1mSwjiywjm+zsLLKMLOuxhoFhZNNgQjW4xW6Kr3x9n/sYqWzLtv6l4dCP3DBsdzrDnm0IFt8i+1L/1TjPHUZeP3Bs57H+W6tNMEwvZG4yu/N9aRwmM6cHemH7dbq+HlwfXuixAN1IZmnWycJf7Byhf20B+For6CIuquj6T3nfJKF5c5g4kc7AL5CXNSys+8qdRWzP2TYpZyny+NoWyD9uaL79Zhd3vMUCzYs/viXwRklj1VxRzDWAERYLI4o7HmBEMTECbxZ3LMBo4Maijx8CZKZDFtlkWAzSyXJcLNnU+EtNwKfAsbmeIJ57s1IJCAimun8YtYJqEhwQiiUwMG/wv4drWAcUrFHDOpNHeLi6dIj3CAiAiAjrUkp+QM3cQna2ta6YmmodGyb377m0NAIzM/lbRoa1G3Rh/96cs16zZjFXqzhel6wIsZvXNjW1hI7lduz3DQ0tpqO4C9fPPW/+bRV5/VLLnYFBRNyDxZI3G4ibsYD9EHHFKmJ6cqdVB7qX8RztKPh3iquuKePxdYBry3gOkeJUdP2n3G+SdO8O8+aV3/mkXPnmLKVpZ9skZxGRSuLjkzcDSBXjdXPZhYXljY6UklLM6D/5JCfnjTJnf46yXN+VGMrr+iIiIuJ9zK7/iIiIuMrrkhV16tSxrZ8+XcxoZ/mcOZM3UW7t2rXL5fquxFBe1xcRERHvY3b9R0RExFVel6xo1y6vsW9sbKzDHYPiREdH29bblzQoipPXBzhx4kSlXl9ERES8j9n1HxEREVd5XbKiQ4cODuWdO3eWeExMTAzn7ebFzX8OV7Rp08ZhsCpnrg+wY8eOcrm+iIiIeB+z6z8iIiKu8rpkRa9evRwGgNq4cWOJx2zYsMG2HhQURK9evUp9/YCAAHr37u3S9c+cOcOhQ4ds5QEDBpT6+iIiIuJ9zK7/iIiIuMrrkhVhYWEMHTrUVl6wYEGJx9jvM3To0DLPxnHjjTfa1levXs3Zs2edvn6NGjWUrBARERGXuEP9R0RExBVel6wAuPfee23rUVFRLFmypMh9t2/fzvLlyws9trRuu+02292NjIwMZs6cWeS+iYmJvPlm3qzYd9xxB/6aZ1pERERcZHb9R0RExBVemawYP348Xbp0sZUnTZrEvn37Cux3+vRp7rzzTrKysgDo2rUr48aNK/Scx44dw2Kx2JYXXnihyOs3btyYSZMm2cqzZs3iq6++KrBfRkYG9913n20QzuDgYJ5++mmnnqOIiIiIvYqo/4iIiFQUv5J38TwWi4X33nuPgQMHkpKSwunTp+nduzeTJ09mwIAB+Pn5sWXLFubMmWProhEcHMy8efOwWCzlEsMLL7zA8uXLOXjwIFlZWUyYMIHbb7+dMWPGUKtWLfbv388777xDVFSU7ZjXXnuNhg0blsv1RURExLu4Q/1HRETEWRbDMAyzgzDL4sWLufPOO0lJSSl2v+DgYObPn89NN91U5D7Hjh2jRYsWtvLzzz9fbOsKgAMHDjBs2DCHacGKMnXqVGbMmFHifkWJj48nPDycuLg4qlevXurziIhI1aXfAoHyrf8URe81ERGBsv0eeGU3kFw33XQT27ZtY9iwYYXeMbBYLAwdOpTffvutVD/UJWnbti1RUVE88MADBAcHF7pPhw4d+Pbbb8uUqBARERHJZXb9R0RExBle3bLCXnR0NJs2bSImJgaARo0acfXVV9OkSZNKuX5CQgJr1qwhOjqapKQkIiIiuPLKK+nWrVu5nD8uLo4aNWoQHR2tOxwiIl4qPj6eJk2acPnyZcLDw80OR9xARdV/VO8QEREoW91DyQovcfLkyUpLvIiIiHuLjo6mcePGZochHkz1DhERsVeauoeSFV4iOzubU6dOUa1atVIPkpWbFdNdEs+i19Xz6DX1TOXxuhqGQUJCAg0bNsTHx6t7gkoFK496B+j7TKo2vX+lKiuv929Z6h5eORuIN/Lx8Sm3u2jVq1fXF64H0uvqefSaeqayvq7q/iGVoTzrHaDvM6na9P6Vqqw83r+lrXvotoqIiIiIiIiIuBUlK0RERERERETErShZIU4LDAzk+eefJzAw0OxQpBzpdfU8ek09k15X8UZ630tVpvevVGXu8P7VAJsiIiIiIiIi4lbUskJERERERERE3IqSFSIiIiIiIiLiVpSsEBERERERERG3omSFiIiIiIiIiLgVJStERERERERExK0oWSHF2rx5M5MmTaJjx46Eh4dTvXp1OnbsyEMPPcSmTZvMDk+ctG7dOiwWi8vLvn37zA7da50/f57ly5czffp0Ro8eTUREhMNr89FHH5X63Lt37+bxxx+nc+fO1KpVi7CwMNq1a8cdd9zBDz/8UH5PQhyU52t67NixUn2m9fpKVXH58mV+/PFHZsyYwfjx42nevLnDe/mFF14o0/mPHDnCc889R48ePahbty7BwcG0atWKsWPHsmjRIjIzM8vniYjXUd1Z3EmVr08aIoVITEw07r//fgModrnvvvuMxMREs8OVEqxdu7bE17KwZe/evWaH7nVOnz5tNGvWrMTX5sMPP3T53BkZGca0adMMHx+fYs993XXXGefOnSv/J+elKuI1PXr0aKk+08uXL6+4JypSTtq0aWNYLJZi38vPP/98qc//xhtvGIGBgcWev0+fPsbhw4fL70mJx1PdWdyJp9Qn/ZxJaIh3ycrK4qabbmLlypW2bcHBwXTq1Ak/Pz/27NlDfHw8AB9++CExMTEsW7YMX19fs0IWFwQFBTFw4ECn9g0LC6vgaCS/1NRUjh8/XiHnnjRpEh988IGt7O/vT8eOHQkLC2Pfvn3ExsYCsHTpUoYNG8amTZv0HigHFfma5hoxYoRT+9WtW7dC4xApDwcPHqywc7/44os899xztrKPjw8dO3akVq1aHDx4kNOnTwPwyy+/MHDgQLZs2UJERESFxSOeQXVncTceU58sU6pDPNK0adMcsmITJ040YmNjbY8nJiYazz77rMM+Tz/9tIkRS0nsW1Y0a9bM7HCkGPZ3zOvWrWuMHDnSeOaZZ4xvvvmmTJnwuXPnOhw/evRo4+TJk7bH09PTjdmzZxt+fn62fW6//fZyfnbeqSJe0/wtK0Q8Se77Ojw83Bg8eLAxdepU44svvjAiIiLK1LLihx9+cGix0bdvX2P//v22x7OysozPP//cCAsLs+1z9dVXl+MzE0+lurO4G0+pT6qGIw5iYmKMoKAg25vrrrvuKnLfZ555xrZfUFCQERMTU4mRiiuUrKg64uLijC+//NI4duxYgcdK++OSlJRkNGjQwHbsoEGDjMzMzEL3/e9//2vbz2KxGNu2bSvtU5EcFfGaKlkhnmzBggXG/v37jezsbIft9k2aXU1WZGdnG126dLEd365dOyMpKanQfVetWuXw+Vq8eHFpn4p4AdWdxR15Sn1SA2yKgzfeeIPU1FQAQkJCeOONN4rc99lnn6VJkyaAtanRrFmzKiNEEY9WvXp1xo8fT7NmzcrtnB999BFnzpwBwGKx8PbbbxfZ9PSBBx6gd+/eABiGwYwZM8otDm9VEa+piCe7/fbbadu2LRaLpdzOuXz5cnbt2mUrz5o1i5CQkEL3HTZsGLfccout/Oqrr5ZbHOJ5VHcWd+Qp9UklK8TB119/bVufMGECtWrVKnLfgIAA7rvvPlt58eLFFRqbiJSO/Wdz4MCBdOjQodj9J02aZFtftmwZaWlpFRabiEhlsP8ebNGiBcOHDy92f/vvwS1btnDy5MkKi02qNtWdxVuYUZ9UskJs9u/fz6FDh2zlkSNHlnjMqFGjbOuHDh1i//79FRKbiJROYmIi69evt5Vd/VwnJiaybt26ighNRKTSLF261LY+YsSIEltt9O/fn9DQ0EKPF8mlurN4C7Pqk0pWiI1980iAvn37lnhM9+7dCQgIsJWjoqLKPS4RKb09e/aQkZFhKzvzuW7QoAHNmze3lfW5FpGq7Ny5c7amy+Dc96Cfnx+RkZG2sr4HpTCqO4u3MKs+qWSF2Ozdu9e2HhAQYOtTV5z8+9mfQ9zT5cuXmTBhAs2bNyc4OJhq1arRokULxowZw5w5c2xTa4lnyP+ZbNWqlVPH2e+nz7X7u/vuu2nTpg2hoaGEhobStGlTRo4cycyZMzl37pzZ4YmYSt+DUlFUdxZvYdb3qJIVYnPs2DHbeuPGjZ0e2Kpp06aFnkPcU1xcHF9++SXHjx8nNTWVxMREjh07xrfffsv//d//0bRpU2bPnm12mFJO7D+Tfn5+REREOHWcPtdVy6effsqhQ4dITk4mOTmZ6OhoVqxYwZNPPkmzZs149tlnycrKMjtMEVPk/w6z/34rjr4HpSSqO4u3MKs+6efyEeKxEhISbOvh4eFOH1e9evVCzyHuq3nz5jRq1IjAwEAuXLjAnj17yMzMBKzJjClTprBz507ef/99kyOVsrL/TFarVg0fH+dy1PpcVy0RERG21lKXLl1i7969ttHpU1NTeemll9i6dStLlizB39/f5GhFKlf+7zBn6zj6HpSSqO4s3sKs+qRaVohNYmKibT0oKMjp44KDgws9h7gPHx8fhg0bxoIFC4iNjeXo0aNs3LiRH3/8kV27dnHp0iXeeecd6tSpYzvmgw8+0LSVHkCfa89ksVjo1asX7733HqdOneLUqVNs3ryZH3/8ke3bt3P58mU+++wzh76iK1asYMqUKeYFLWKS/N9hzn4X6ntQSqLfWPEWZr3XlawQm9w762Bt3uMs+33tB14R9zFgwABWrVrF7bffXuiUWmFhYTz88MNs377d4Y+b6dOnc/bs2UqMVMqbPteeqVmzZvz66688+OCDhTbFDAwM5LbbbmP79u306NHDtn3u3LkazE28jv33IDj/XajvQSmJfmPFW5j1XleyQmxCQkJs67nNh51hv6/9NF9S9TRp0oT//e9/tnJycrK6glRx+lx7t5o1a7J48WLbXRDDMJgzZ47JUUlVMn/+fCwWS7kvH330UaU9B/vvQXD+u1Dfg1IS/caKtzDrva5khdiEhYXZ1lNSUpw+Ljk5udBzSNXUq1cvBg0aZCuvWrXKvGCkzPS5lqZNm3LrrbfayvpMi7fJ/x3m7HehvgelJPqNFW9h1ntdA2yKjf14BadPn3b6OPu5y2vXrl2uMYk5Bg8ezLp16wA4cOCAucFImdh/rhMTE0lMTHTqx0Kfa88yePBg253sY8eOkZ6eTkBAgLlBSZUQGhpKo0aNKuS8lcX+exCsdRxnvtf0PSglUd1ZvIVZ9UklK8SmXbt2tvXY2FiSk5MLNJ0sTHR0tG29ffv2FRKbVK4GDRrY1i9cuGBiJFJW9p9rgBMnTtCxY8cSj9Pn2rPYf6bB+h3v7LRj4t3Gjh3L2LFjzQ6jTAr7HrziiitKPE7fg1IS1Z3FW5hVn1Q3ELHp0KGDQ3nnzp0lHhMTE8P58+eLPIdUTfZNtpz50RX3VZrPdUZGBn/88UeR55Cqx/4zDfpci3dp06aNwyBvznwPAuzYscO2ru9BKYzqzuItzKpPKlkhNr169SIwMNBW3rhxY4nHbNiwwbYeFBREr169KiQ2qVz2Xyz16tUzMRIpq5YtW9K4cWNb2ZnP9bZt2xz+uB0wYECFxCaVx/4zHRgYSHh4uInRiFSugIAAevfubSs78z145swZDh06ZCvre1AKo7qzeAuz6pNKVohNWFgYQ4cOtZUXLFhQ4jH2+wwdOlQjGnuA5ORkvvvuO1v5qquuMjEaKQ+jR4+2rX/55Zekp6cXu7/957pTp060atWqwmKTimcYBl988YWt3LdvXxOjETHHjTfeaFtfvXp1idNy238P1qhRQ8kKKZTqzuJNzKhPKlkhDu69917belRUFEuWLCly3+3bt7N8+fJCj5Wq69lnn+XcuXO28pgxY8wLRsqF/WfzwoULzJ07t8h9T548yccff1zosVI1zZkzh6ioKFtZn2nxRrfddpvtDnhGRgYzZ84sct/ExETefPNNW/mOO+7A39+/wmOUqkl1Z/EWptQnDRE72dnZRpcuXQzAAIyIiAhj7969BfY7deqU0aFDB9t+Xbt2NbKzs02IWEqyYsUK4/HHHzeio6OL3S89Pd148sknba8pYHTv3l2vqxuxf20+/PBDl44dPXq07diwsDBj48aNBfaJi4sz+vfvb9uvQYMGRnJycjlFL4UpzWv6+++/G/fff7+xb9++YvfLzs423njjDcPX19d2jYYNG+o1lSqrWbNmtvfy888/7/LxU6ZMsR3v6+trLFq0qMA+6enpxvjx4237BQcHGzExMeUQvXgq1Z2lqqlK9UlLTsAiNlu3bmXgwIG2OXSrV6/O5MmTGTBgAH5+fmzZsoU5c+bYmlAGBwfz008/ERkZaWbYUoRvvvmGsWPH4uPjw9VXX83AgQO54oorqFOnDgEBAVy4cIEtW7awYMEChxF7a9WqxebNmwuM/isVb+LEiXz66acFtqelpdnW/fz88PX1LbBPampqoec8duwYkZGRttldAgMDeeCBBxg+fDhhYWFERUUxe/Zsjh49CoCPjw/ffPMNN9xwQ3k8Ja9Xnq/pzp076datGwA9evRgyJAhdOnShXr16hEcHMylS5fYsWMHCxcuZN++fbbjAgMDWbVqFf379y+vpyVSIV566SVeeumlAtvtPy++vr4Og2bm2r9/P82aNSv0vJcuXaJ3794cPHgQsH7P3X777YwZM4ZatWqxf/9+3nnnHYeWSHPmzOHRRx8t61MSD6e6s7gjj6hPlirFIR7vq6++MoKDgx0yb4UtwcHBxldffWV2uFKMr7/+usTXMf/Spk0bY/v27WaH7rXuuecel1+z3KU4mzZtMmrVqlXiOXx9fY3Zs2dX0rP1DuX5mu7YscPlczRo0MBYtWqVCc9cxHXPP/98qT8vR48eLfbc+/fvN5o0aeLUuaZOnVo5T1g8gurO4m48oT6pMSukUDfddBPbtm1j2LBhWCyWAo9bLBaGDh3Kb7/9xk033WRChOKs9u3bc8sttziM4FuU5s2bM3PmTHbs2GG7cyue46qrriIqKopx48YVekcSIDIykvXr1/OnP/2pkqMTZ0VERHD33Xc7NVBV/fr1eeaZZ9i9ezfDhg2rhOhE3Fvbtm2JiorigQceIDg4uNB9OnTowLfffsuMGTMqOTqpylR3Fm9RmfVJdQOREkVHR7Np0yZiYmIAaNSoEVdffTVNmjQxOTJx1YkTJ9izZw8XLlzgwoULJCUlUb16derVq0fPnj0164MXOX/+POvXr+fkyZOkp6fTsGFDevbsqW4/VczZs2eJiori/PnzXLhwgYSEBMLCwqhTpw7dunWjQ4cOhVaaRQQSEhJYs2YN0dHRJCUlERERwZVXXqlkvZSZ6s7iLSq6PqlkhYiIiIiIiIi4FXUDERERERERERG3omSFiIiIiIiIiLgVJStERERERERExK0oWSEiIiIiIiIibkXJChERERERERFxK0pWiIiIiIiIiIhbUbJCRERERERERNyKkhUiIiIiIiIi4laUrBARERERERERt6JkhYiIiIiIiIi4FSUrRERERERERMStKFkhIiIiIiIiIm5FyQoR8UgrVqzAYrFgsVioUaMGmZmZZockIiIiHkr1DpHyp2SFiHik7777zrY+atQo/Pz8TIxGREREPJnqHSLlT8kKEfFI33//vW199OjRJkYiIiIink71DpHyZzEMwzA7CBGR8rRjxw66d+8OgJ+fH+fPn6dGjRrmBiUiIiIeSfUOkYqhlhUi4nGWLFliWx8wYIAqDCIiIlJhVO8QqRhKVoiIx7HvN3rDDTeYGImIiIh4OtU7RCqGuoGIiEc5deoUjRs3Jver7fDhw7Rs2dLkqERERMQTqd4hUnHUskJEPMp3331nqzB06tRJFQYRERGpMKp3iFQcJStEpFyNGzfONs94SEgIx44dK9V5pkyZYjuPxWJhy5YtTh1n3xTT2dG4zY5ZRERESsfs33DVO0QqjpIVIlJulixZwuLFi23lJ598kubNm5fqXD179nQob9iwocRjkpKSWLt2ra3sTKXB7JhFRESkdMz+DVe9Q6RiKVkhIuUiMTGRRx991FZu3rw5Tz75ZKnPFxkZ6VBev359icesXLmS1NRUAOrVq0evXr2K3d8dYhYRERHXucNvuOodIhVLyQoRKRczZswgOjraVn7xxRcJCgoq9fnatGmDr6+vrbxz584Sj7Fvinn99dfj41P8V5w7xCwiIiKuc4ffcNU7RCqWZgMRkTI7d+4crVq1IjExEYC2bduyZ88ehx/Q0mjcuDExMTEA+Pj4kJycTGBgYKH7Zmdn06BBA86fPw/AN998w4033ujWMYuIiIjr3OE3XPUOkYqnlhUiUmavvPKK7ccX4O9//3uZf3zB+gOcKzs7u9gBqH755RdbhSEoKIhrrrmm2HO7Q8wiIiLiOnf4DVe9Q6TiKVkhImWSkJDA+++/byvXrl2bW2+9tVzOHRwc7FCOj48vcl/7pphDhw4lJCSkyH3dJWYRERFxjbv8hqveIVLxlKwQkTKZP38+CQkJtvJdd91FQEBAuZzbYrE4lNPT04vc15Wpw9wlZhEREXGNu/yGq94hUvH8zA5ARKq2jz/+2KF81113Fbv/qlWryMrKAqBXr17UqlWryH0zMzMdyn5+hX9lHT58mL179wLWH+0bbrjB7WMWERER17nDb7jqHSKVQ+9mESm1S5cusXXrVlu5Tp06dOvWrcj9T506xfDhw23lgwcPFvsDbD9iNkCjRo0K3e/bb7+1rffs2ZOIiAi3j1lERERc4y6/4ap3iFQOdQMRkVJbt24d2dnZtvKgQYMKNEe09+uvv9rWQ0JCaNmyZZH7ZmVl2Ua3BggICCiyMrBkyRLbeklNMd0lZhEREXGNu/yGq94hUjmUrBCRUtu9e7dDubg7BQCbNm2yrbdp06bY+ch3795NRkaGrdyjR49CR82+dOkSGzdutJVLaorpDjGLiIiI69zhN1z1DpHKo2SFiJTawYMHHcodOnQodv8VK1bY1ps0aVLsvvYVAYD+/fsXut+yZcts/TWbNWtGly5dij2vO8Sc3x9//METTzxBjx49qF27NoGBgTRv3pyhQ4fy+uuvc/LkSafOIyIi4snc4Tdc9Q6RyqMxK0Sk1E6cOOFQbtCgQZH7Hj9+nN9//91WrlevXrHnXrp0qUN52LBhhe5nPxp3SXc3wD1izpWUlMSf/vQnPv74YwzDKHDt48ePs2bNGtLT03nyySeLPZeIiIinc4ffcNU7RCqPkhUiUmpJSUkO5fDw8CL3/eyzzxzKQUFBRe4bGxvLmjVrbOV69eoxZMiQAvtlZGQ43IEoqd+oO8RsH8eQIUPYsmULFouFW265hbvvvpuuXbsSFBTE8ePHWblyJW+//Ta9evUq6WmJiIh4PLN/w1XvEKlcSlaISKnZ95MESElJKXS/zMxM5s6d67AtOTm5yPPOmzfPYZ7w22+/vdA+mD/99BNxcXEAVK9enUGDBrl9zACGYTBu3Di2bNlCQEAAX331Fddff73DPrVq1aJbt25MmTKl2P6qIiIi3sLs33DVO0Qql96JIlJq9evXdyjv37+/0P3++9//cvz4cSwWi61J49GjRwvd98KFC8ycOdNWDgwM5Iknnih0X/ummCNGjMDf39/tYwb46KOPbHdm5s2bV6DCYC84OJjAwMAiHxcREfEWZv+Gq94hUrmUrBCRUmvTpo1DOX/zRYADBw7Y+j0OHz6chg0bAvDzzz8TGxvrsG96ejq33XYbly9ftm175JFHaNy4caHXd2XqMHeJOTMzk7///e8ADB48mHvuucepuEVERLyd2b/hqneIVDJDRKSUVq5caQAOyxNPPGGcOXPGSE5ONr766isjIiLCAAyLxWL88ssvxnXXXWfbd+TIkcaJEyeMlJQU48cffzR69erlcK4rrrjCSE5OLvTau3btsu3n6+trxMbGun3MhmEYq1evtu27dOnSUv2/i4iIeCPVO1TvEO+iZIWIlFpmZqYRGRlZ4Ee4sOVvf/ubYRiG8eabbzq1f4sWLYzDhw8Xee2XXnrJtu/AgQOrRMyGYRhTp041ACM4ONhITU11Om4RERFvp3qH6h3iXdQNRERKzdfXl88++4zWrVsXu9+UKVOYMWMGABMnTixxTvJRo0axceNGWrZsWeQ+rk4d5g4xQ94UZk2aNFGfUBEREReo3uFazKB6h1RtFsPIN8muiIiL4uPjeeedd1i0aBFHjx4lPj6eunXr0q9fPx599FEGDBjgsH9cXBwvv/wy33zzDcePH8ff35+GDRsyYMAAbrvttmKn3gI4c+YMDRs2tM0RfuDAgQJ9Qt0t5lzDhw9n1apVdOrUyWEudREREXGO6h2qd4h3ULJCRKqc9957j4ceegiA9u3bs3fvXpMjct7NN9/MokWLCAwMJDExET8/zSAtIiLizlTvEDGHuoGISJVj3xTT2dG43UWfPn0ASEtLY9asWcXuW9z86iIiIlI5VO8QMYdaVohIlTNz5kzbD+ptt91Gu3btTI7IebGxsbRu3ZrLly/j7+/PE088wS233EKzZs1IT0/n0KFDrFmzhs8++4yPPvqI3r17mx2yiIiIV1O9Q8QcSlaIiFSyNWvWMG7cOIc50vPz8/MjPj6e4ODgygtMREREPI7qHVJVKVkhImKCmJgY5syZw4oVKzh8+DApKSnUrl2biIgIBgwYwOjRo50ePEtERESkOKp3SFWkZIWIiIiIiIiIuBUNsCkiIiIiIiIibkXJChERERERERFxK0pWiIiIiIiIiIhbUbJCRERERERERNyKkhUiIiIiIiIi4laUrBARERERERERt6JkhYiIiIiIiIi4FSUrRERERERERMStKFkhIiIiIiIiIm5FyQoRERERERERcSv/D5xAjQP9AKY9AAAAAElFTkSuQmCC", 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", 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" ] @@ -1316,7 +1308,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "85ec990b", "metadata": {}, "outputs": [ @@ -1325,11 +1317,18 @@ "output_type": "stream", "text": [ "2\n", - " Total run time: 1.57s*] Elapsed 1.57s / Remaining 00:00:00:00\n", + " [***** 23% ] Elapsed 0.15s / Remaining 00:00:00:00" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 0.68s*] Elapsed 0.68s / Remaining 00:00:00:00\n", "3\n", - " Total run time: 5.95s*] Elapsed 5.95s / Remaining 00:00:00:00\n", + " Total run time: 3.65s*] Elapsed 3.65s / Remaining 00:00:00:00\n", "4\n", - " Total run time: 17.48s*] Elapsed 17.47s / Remaining 00:00:00:00\n" + " Total run time: 24.37s*] Elapsed 24.37s / Remaining 00:00:00:00\n" ] } ], @@ -1362,13 +1361,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "145acb4d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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bCiwtLY0TJ06wZMkSwHpXLKOv7vXr1/nmm28A6wgIGcdeunQJT0/PXD/WDgsLY/LkyVnG5n3nnXfo0qUL7du359lnn6VBgwacP3+elStX8umnn+Lt7c1LL73E6tWr6d69O5MmTcLf35+5c+eyZs0a3nrrLbuH4vLrvvvuY+7cudx555088cQTtGvXDhcXF06dOkVERAQDBw7M8TmYvn378s477/DAAw8wduxYLl++zNtvv53tyA/NmzdnwYIFLFy4kHr16uHm5kbz5s2zPe/UqVPp1asX3bt356mnnsLV1ZVp06axf/9+5s+fn+unt8WhefPmbNmyhVWrVlGtWjW8vb1p1KiRbX/GUKr5qa1x48Zs3bqVnj170rVrVzZt2lTo7gZ+fn4899xzTJgwgXnz5tmGgatTp06hZ1Z74okn8PLyYuzYsVy7do0PPvigUD+LzN+//Dh48CAHDx4ErPNOXL9+3fY726RJkywjsBQboxgAtq+ZM2cW+flnzZpld40///wzT8e98sortmPq1q2br2vGxMQYgBETE1OQkvMl8uDPxvD/N96466W7jSe/eK3YryciZVNCQoJx8OBBIyEhwdGlFLmZM2cagLFr165c23br1s3u/4TMXxEREbZ2x44dy7Fd7dq1c73OkSNHDJPJZCxatCjLvoMHDxr33nuvERAQYLi6uhq1atUyRo0aZSQmJtra7Nu3z+jfv7/h6+truLq6GqGhoVn+j4yIiDAAY/HixVmuMXLkSMPT0zPb2lJSUoy3337bCA0NNdzc3AwvLy8jJCTEGDdunPH777/bfa+6detmd+wXX3xhNGrUyLBYLEa9evWMqVOnGjNmzDAA49ixY7Z2x48fN3r37m14e3vbfc8yvq83vpatW7caPXr0MDw9PQ13d3ejQ4cOxqpVq+za5PRzzvg+ZP75ZSen40eOHJnlZ7pnzx6jc+fOhoeHhwHYfR+OHDliAMbSpUtver2Mc9/4czh16pQREhJi1KlTxzh69Giu57hZ7YZh/d2uVauW0bBhQyM1NTVP58sJYIwfP95u2/z58w1nZ2dj9OjRRlpaWp7O061bN6Np06aFqiXDSy+9lOPv4ksvvXTTY3P7dy8/ec1kGHno0ZxPmf+qmDlzZo7zuBfU//73P7spK2NiYrI8KJCd9957jyeffBKw3pq/2SDiSUlJdoOcx8bGUrNmzTxfqzBW7v6Oj2eNAcC7ekcWPZ/zDEgiUnElJiZy7Ngx6tatm+WBLSkeGSMw3PjQkpRtEydOZPbs2Rw9ejTbmfmk9Mjt373Y2Fh8fX3zlNdK5+N6ubh27Zrdel7/8c88WPiN57jR1KlT8fX1tX2V5DSKYU1uBZO1f1ZC3M1nzBERkZIzdepUNm3axK5duxxdihSR6OhoPvroI15//XUF4AqmTIbgzOMgAnl+02Zul5KSctO2zz33HDExMbavkydP5r/QAvLx8MTZ0zq0TGr8GRKSEkvs2iIikrNmzZoxc+ZMu4ezpWw7duwYzz33HA888ECRnC89PZ3U1NSbfpUmGZN15PSVlpbm6BKLTZkMwTdOipGYmLeQmLldbrPBWCwWfHx87L5KksXrr6eH01PY+uueEr22iIjkbNiwYfTv39/RZUgRadWqFU8//XSRPaw3efJkXFxcbvp1/PjxIrlWUahfv/5Naw0PD3d0icWmTN73zzwDDViHc8lptrjMrl+/nuM5Shu/SsHEn9sJwI9H99M7tODD54iIiEjJGDt2LP369btpm8zD5DnaqlWr7J6ButHNxpku68pkCL5xvLmzZ89mmW0mO5k/vspLe0eqVaUep361Lh8/d/TmjUVERKRUqF69eqkKubnJaZi7iqBMdoe4cVy6P//8M0/HZe7Xm3nO8tIotO7fY+RFx+jhOBEREZGiVCZDcMOGDe0ectuzZ0+ejvv5559ty40bNy7qsopUq7p/h/SkhCs3aSkiIiIi+VUmQ7Crq6vd1JDbtm3L9Zhz585x5MgR23rXrl2LpbaiUjMgCJOz9eG91ESFYBEREZGiVCZDMMDAgQNty5s2beL8+fM3bT937lzbcqVKlUp9CAZwcrf2W05LuExyLkO6iYiIiEjeldkQfP/999vmNk9JSeGtt97KsW3G/NgZHnzwQVxcXIq9xsJycfvr4T0jlUNnjju0FhEREZHypFSF4OPHj2MymWxfL7/8co5tg4ODGTdunG39/fffZ+nSpVnapaSkMHr0aNvDc+7u7jz//PNFXntxcPfwty0fOKkRIkRERESKSqFC8COPPIKbm1uWr/y2KaiXX36Zhg0bAtYZT4YMGcLw4cNZunQpERERfPLJJ7Rp04YlS5bYjvnvf/9bZoYu8fH+eyi4YxfyNgKGiIiIiOSuUCE4JSWFpKSkLF+Zpaam5tqmoPz8/Fi9ejU1a9YErFMVfvXVVwwePJgePXrw6KOPsnfvXlv7CRMmMH78+CK5dkkIrFTFtnz+ylkHViIiUrJmzZpl98mgs7MzwcHBjB49mtOnT9u1ffHFF+nXrx81atTAZDIxatSobM954MAB/vnPf9KxY0c8PT0xmUxs2bIlX3WlpKQQEhLCG2+8UcBX5lhhYWGEhYUV6Nhp06Yxa9asLNszPsXNbl9J+PDDD2nQoAGurq6YTCaio6MZNWoUderUsWv3+uuvs2LFiizHf/vtt3h5eWV5X+Vk1KhRRTLhVnbv8WrVqnHffffx+++/F/r8GUwmE4899lihzxMWFkazZs0KfZ6zZ8/y4osv0rFjRwIDA/Hx8eHWW2/ls88+K/EpmktVd4iCuOWWW9i7dy8PPfQQ7u7u2bZp3LgxX3/9NW+++WYJV1c4tSvXtC1fidY89SJS8cycOZOoqCg2btzII488wvz587ntttuIj4+3tXn33Xe5fPkyAwYMwNXVNcdz7d69mxUrVuDv71/gqWCnTZvG1atX+de//lWg48uynEJwtWrViIqKom/fviVe0549e3j88cfp3r07mzdvJioqCm9vbyZOnMjy5cvt2uYUgsPDw2nXrp3DukpmvMc3bdrEY489xsqVK+nSpQtXr151SD3F7ccff2T27NmEh4cze/Zsli5dSrdu3Xj00Ud55JFHSrSWQs0YN2vWrCL9y69OnToYhpHv4ypVqsTnn3/Ou+++y+bNmzl58iTx8fFUq1aN5s2b06pVqyKrsSR1a3IrS//6Hb4Wc/LmjUVEyqFmzZrRpk0bALp3705aWhqvvvoqK1as4MEHHwQgLi4Os9l6T2fOnDk5nmv48OGMHDkSgCVLlrBq1ap81ZKamsp///tfxowZg6enZ0FeToGkpKTY7hTe6Pr163h4eJRYLdmxWCx06NDBIdc+cOAAYO162a5dO9v2+vXr5+s848ePZ+jQoUyZMsX26XJJyfweDwsLIy0tjZdeeokVK1YwevToEq2lJHTu3JmjR4/aDVDQq1cvkpOT+eijj3jllVdK7GdQ5u8EZ+bt7c3AgQN57LHHeOaZZxgxYkSZDcAADavVwuxm7RecHHdCw6SJSIWXEbZOnDhh25YRgHOT13Y5WblyJadPn2b48OFZ9v3666/cf//9VKlSBYvFQq1atRgxYoRd97/9+/czcOBA/Pz8cHNzo2XLlnz55Zd259myZQsmk4k5c+bwn//8hxo1amCxWDhy5IjtY/h9+/bRu3dvvL29bXe0k5OTmTJlCiEhIVgsFipXrszo0aO5ePFirq/rlVdeoX379vj7++Pj40Pr1q2ZMWOG3U2pOnXqcODAASIjI20f32d0N8ipO8S2bdsIDw/H29sbDw8POnXqxJo1a+zaZHQJiIiI4NFHHyUwMJCAgAAGDRrEmTM3ny01LCyMYcOGAdC+fXu7rjA3docwmUzEx8fz5Zdf2urP3C2kf//+eHl5MX369Fy/X9nZvn07gYGB9OvXz+5TioLICMS5Df1aUIZh8Pzzz+Pi4lLg11sYfn5+2Y7QlfFHzKlTp0qslkLdCZbi5+ZTm+uJlzBSE/j+t72ENb3V0SWJSCk3eddZYpNLtm/dzfi4OjGpbbUiOVfGpEeVK1cukvPlx5o1awgKCqJJkyZ223/55Re6dOlCYGAgkydPpmHDhpw9e5aVK1eSnJyMxWLh8OHDdOrUiaCgID744AMCAgL46quvGDVqFOfPn2fChAl253zuuefo2LEjn3zyCWazmaCgIMAadgcMGMC4ceN49tlnSU1NJT09nYEDB7J161YmTJhAp06dOHHiBC+99BJhYWHs3r07x+6CYA2x48aNo1atWgDs2LGDf/3rX5w+fZpJkyYBsHz5cgYPHoyvry/Tpk0DsA1Tmp3IyEh69epFixYtmDFjBhaLhWnTptG/f3/mz5/P0KFD7do//PDD9O3bl3nz5nHy5Emefvpphg0bxubNm3O8xrRp05g/fz5Tpkxh5syZhISE5Pi+iIqKokePHnTv3p2JEycC4OPjY9vv6upqC+mTJ0/O8ZrZWbRoESNGjGDMmDF8+OGHODk55ev4Gx07dgywdvfMLD09nfT09FyPN5lMOdaQlJTEqFGjWLNmDatWraJPnz6FqjUtLS1Pn+CbzeZc/wjdvHkzzs7OWV53cVIILuX8/Wtz/cKPAGz/9UeFYBHJVWxyGleTSk8ILoy0tDRSU1NJTEwkMjKSKVOm4O3tzYABA0q8lqioKFq3bp1l+7///W+cnZ3ZuXOnXQjL6K4B1tGMkpOTiYiIsH3Ue+eddxIdHc0rr7zCuHHj8PX1tbWvX78+ixcvznKtlJQUJk2aZPcx+YIFC1i3bh1Lly5l0KBBtu2hoaG0bduWWbNm8eijj+b4umbOnGlbTk9PJywsDMMweP/995k4cSImk4lWrVrh7u6Oj49Pnro+PPvss/j5+bFlyxbbQ2T9+vWjZcuWPPXUUwwZMgSTyWRr36dPH7vx/K9cucKECRM4d+4cVatWzfYaTZo0sXV7yNylIDsdOnTAbDZTuXLlHOtv3bo1U6dOJT4+Ps/dXd58801eeOEFXn/99Sx/yORV5vf49u3bmTJlCl27ds3yHh8zZkyWTw6y061bt2wf+Lxy5QoDBw7k2LFjbN26ldDQ0ALVm1l4eDiRkZG5ths5cuRNu89u2LCBOXPm8MQTTxAQEFDouvJKIbiUa1CjEad+tS4f/nOfY4sRkTLBx7Vwd6KKWmHquTGwNG/enI8//pgqVarkcETxOXPmDG3btrXbdv36dSIjI3nooYduend68+bNhIeHZ+nrOGrUKNauXUtUVJTdXbl77rknx3PduG/16tVUqlSJ/v37k5qaatvesmVLqlatypYtW24agjdv3szrr7/Orl27iI2Ntdt34cKFfH+v4+Pj+eGHH3j00UftRlFwcnJi+PDhPPPMMxw+fJiQkBDbvhsDX4sWLQBrt5ecQnBRCwoKIj09nXPnzuXap9gwDMaNG8eXX37JvHnzGDJkSIGve+N7PONh/hv7gL/88st5GuXB29s7y7Zjx47RsWNH3Nzc2LFjB8HBwQWuN7NPP/2UuLi4XNsFBgbmuO+nn35iyJAhdOjQgalTpxZJXXmlEFzKDe54O1u+fQMwuHp+P4Zh2P31LCJyo6LqelAazJ49m8aNG+Ps7EyVKlWoVs1xry0hISHLOPdXr14lLS0t11Bx+fLlbGvPGLf+8uXLdttzep0eHh52H+ODte9odHR0jiNjXLp0Kce6du7cSe/evQkLC2P69OkEBwfj6urKihUreO2110hISLjp68rO1atXMQwjX6/3xrt/GV0tCnL9gsr42eblmsnJySxcuJCmTZtyxx13FOq6Ge/xuLg4Fi5cyKeffsr999/P2rVr7drVqlUrT+E1u4ywc+dOLl26xGuvvVZkARigQYMGee4OkZ2ff/6ZXr160bBhQ7755pubdrEpDgrBpVz9qsG4VmpAcvTvpF47yeb9uwlv3jb3A0VEyoHGjRvf9GPukhQYGMiVK1fstvn7++Pk5JTrwzwBAQGcPZt1vPeMh79uvFOW082O7LZnPEy2bt26bI/J7s5ghgULFuDi4sLq1avtAn52Q4nllZ+fH2azOV+vtzTI+NnmpTaLxUJERAS33347PXv2ZN26dfj5+RXoupnf4xkjoHz++ecsWbKEwYMH29oVpjvE0KFDqVq1Ki+88ALp6em8+OKLBar1RoXpDvHzzz/Ts2dPateuzYYNG+y6A5UUheAyoEZwa45FWwfOXvHDOoVgEREHCAkJ4ehR+yns3d3d6datG4sXL+a1117LMUCFh4ezfPlyzpw5Yzdr6ezZs/Hw8CjUEGP9+vVjwYIFpKWl0b59+3wdmzH0WuYHqRISErIdas5iseTpLqmnpyft27dn2bJlvP3227aH8jImtAoODi7Rh58y5Fb/H3/8QUBAQJ67f7Rq1YrIyEh69uxJWFgYGzdutD3AWBhvvfUWS5cuZdKkSQwaNMh2F7Uw3SHAOqmMt7c3Tz75JPHx8UXS9aCg3SH27NlDz549CQ4OZuPGjQX+A6KwFILLgP4d+vLB/oUA/Hlip4OrEREpXSIjI21DgaWlpXHixAmWLFkCWO+KZfTVvX79Ot988w1gHQEh49hLly7h6emZ68faYWFhTJ48OcvYvO+88w5dunShffv2PPvsszRo0IDz58+zcuVKPv30U7y9vXnppZdYvXo13bt3Z9KkSfj7+zN37lzWrFnDW2+9Vai7YPfddx9z587lzjvv5IknnqBdu3a4uLhw6tQpIiIiGDhwIHfffXe2x/bt25d33nmHBx54gLFjx3L58mXefvvtbD+Wbt68OQsWLGDhwoXUq1cPNzc3mjdvnu15p06dSq9evejevTtPPfUUrq6uTJs2jf379zN//nyHdOtr3rw5W7ZsYdWqVVSrVg1vb28aNWpk279jxw66deuWr9oaN27M1q1b6dmzJ127dmXTpk2F7m7g5+fHc889x4QJE5g3b55tGLg6depkmQUvv5544gm8vLwYO3Ys165d44MPPijUzyLz9y+vDh8+TM+ePQF47bXX+P333+1myKtfv37Jjf5iSJ7ExMQYgBETE1Pi105LSzP6TbjN6DO+vtFnfAMj6rd9JV6DiJQ+CQkJxsGDB42EhARHl1LkZs6caQDGrl27cm3brVs3A8j2KyIiwtbu2LFjObarXbt2rtc5cuSIYTKZjEWLFmXZd/DgQePee+81AgICDFdXV6NWrVrGqFGjjMTERFubffv2Gf379zd8fX0NV1dXIzQ01Jg5c6bdeSIiIgzAWLx4cZZrjBw50vD09My2tpSUFOPtt982QkNDDTc3N8PLy8sICQkxxo0bZ/z+++9236tu3brZHfvFF18YjRo1MiwWi1GvXj1j6tSpxowZMwzAOHbsmK3d8ePHjd69exve3t5237OM7+uNr2Xr1q1Gjx49DE9PT8Pd3d3o0KGDsWrVKrs2Of2cM74PmX9+2cnp+JEjR2b5me7Zs8fo3Lmz4eHhYQB234cjR44YgLF06dKbXi/j3Df+HE6dOmWEhIQYderUMY4ePZrrOW5Wu2FYf7dr1aplNGzY0EhNTc3T+XICGOPHj7fbNn/+fMPZ2dkYPXq0kZaWlqfzdOvWzWjatGmhajGMv193Tl83vo9ulNu/e/nJaybDKMAUbRVQbGwsvr6+xMTEZHkooSSMm/YMfx5cCkD90OH875GXSrwGESldEhMTOXbsGHXr1s3ywJYUj4wRGG58aEnKtokTJzJ79myOHj2a7cx8Unrk9u9efvJauZoxrjwb3OXvsR//PB7lwEpERCquqVOnsmnTJnbt2uXoUqSIREdH89FHH/H6668rAFcwCsFlRK8W7XHxqQNASswRthz40bEFiYhUQM2aNWPmzJmcO3fO0aVIETl27BjPPfccDzzwQJGcLz09ndTU1Jt+lSYZk3Xk9JWWVj4m3smOQnAZEly7o2154dZlDqxERKTiGjZsGP3793d0GVJEWrVqxdNPP11kD+tNnjwZFxeXm34dP368SK5VFOrXr3/TWsPDwx1dYrHRff8yZFjYvby6bz4Ap07scHA1IiIicqOxY8fSr1+/m7bJPEyeo61atYqkpKQc999snOmyTiG4DOnUqAWulRpaJ86IO8Gan7bRt3UXR5clIiIif6levXqpCrm5yWmYu4pA3SHKmLp1O9mWl25TlwgRERGRglAILmPG9LwPTNaZfc7/uY3k1BQHVyQiIiJS9igElzEtajfEPbAFAOmJV5jz3WoHVyQiIiJS9igEl0FNG3WzLX+7a6UDKxEREREpmxSCy6BxvYZgcvECIPrMTq7ExTi4IhEREZGyRSG4DAoOCMKrShsAjLQkpm9c6OCKRERERMoWheAyqn3TMNvyzr3fOK4QEZFiMGvWLEwmk+3L2dmZ4OBgRo8ezenTp+3avvjii/Tr148aNWpgMpkYNWpUtuc8cOAA//znP+nYsSOenp6YTCa2bNmSr7pSUlIICQnhjTfeKOArc6ywsDDCwsIKdOy0adOYNWtWlu3Hjx/HZDJlu68kfPjhhzRo0ABXV1dMJhPR0dGMGjWKOnXq2LV7/fXXWbFiRZbjv/32W7y8vLK8r3IyatQovLy8Cl13du/xatWqcd999/H7778X+vwZTCYTjz32WKHPExYWRrNmzYqgInj44Ydp1qwZlSpVwt3dnVtuuYWnn36aS5cuFcn580ohuIwa1a0fTh7VALh+aT+Hzxx3bEEiIsVg5syZREVFsXHjRh555BHmz5/PbbfdRnx8vK3Nu+++y+XLlxkwYACurq45nmv37t2sWLECf3//As+CNW3aNK5evcq//vWvAh1fluUUgqtVq0ZUVBR9+/Yt8Zr27NnD448/Tvfu3dm8eTNRUVF4e3szceJEli9fbtc2pxAcHh5Ou3bteP7550uoansZ7/FNmzbx2GOPsXLlSrp06cLVq1cdUk9JiI+PZ+zYscybN481a9bw8MMP89lnn9GtWzeSk5NLrA5NllFGBfhUwrfqrVz5wzo6xBcb5vHmKMf8AouIFJdmzZrRpo21+1f37t1JS0vj1VdfZcWKFTz44IMAxMXFYTZb7+nMmTMnx3MNHz6ckSNHArBkyRJWrVqVr1pSU1P573//y5gxY/D09CzIyymQlJQU253CG12/fh0PD48SqyU7FouFDh06OOTaBw4cAOCRRx6hXbt2tu3169fP13nGjx/P0KFDmTJlCjVr1izSGnOT+T0eFhZGWloaL730EitWrGD06NElWktJmT9/vt16jx498Pb25p///Cfbtm2jR48eJVKH7gSXYT1b/X0n49ChdaSnpzuwGhGR4pcRtk6cOGHblhGAc5PXdjlZuXIlp0+fZvjw4Vn2/frrr9x///1UqVIFi8VCrVq1GDFihN10tPv372fgwIH4+fnh5uZGy5Yt+fLLL+3Os2XLFkwmE3PmzOE///kPNWrUwGKxcOTIEdvH8Pv27aN37954e3vb7mgnJyczZcoUQkJCsFgsVK5cmdGjR3Px4sVcX9crr7xC+/bt8ff3x8fHh9atWzNjxgwMw7C1qVOnDgcOHCAyMtL28X1Gd4OcukNs27aN8PBwvL298fDwoFOnTqxZs8auTUaXgIiICB599FECAwMJCAhg0KBBnDlz5qZ1h4WFMWzYMADat29v1xXmxu4QJpOJ+Ph4vvzyS1v9mbuF9O/fHy8vL6ZPn57r9ys727dvJzAwkH79+tl9SlEQGYH4/PnzhTpPTgzD4Pnnn8fFxaXAr7c4VK5cGSDbP/aKi+4El2H3tO/Osm9DSI3+lZT4M2w5uJsezdrlfqCISBl15MgR4O//MEvSmjVrCAoKokmTJnbbf/nlF7p06UJgYCCTJ0+mYcOGnD17lpUrV5KcnIzFYuHw4cN06tSJoKAgPvjgAwICAvjqq68YNWoU58+fZ8KECXbnfO655+jYsSOffPIJZrOZoKAgwBp2BwwYwLhx43j22WdJTU0lPT2dgQMHsnXrViZMmECnTp04ceIEL730EmFhYezevRt3d/ccX9fx48cZN24ctWrVAmDHjh3861//4vTp00yaNAmA5cuXM3jwYHx9fZk2bRpgvQOck8jISHr16kWLFi2YMWMGFouFadOm0b9/f+bPn8/QoUPt2j/88MP07duXefPmcfLkSZ5++mmGDRvG5s2bc7zGtGnTmD9/PlOmTGHmzJmEhITk+L6IioqiR48edO/enYkTJwLg4+Nj2+/q6moL6ZMnT87xmtlZtGgRI0aMYMyYMXz44Yc4OTnl6/gbHTt2DIBbbrnFbnt6enqebnaZTKYca0hKSmLUqFGsWbOGVatW0adPn0LVmpaWZvfHUk7MZnO2f4SmpqaSlJTEnj17mDhxIl26dKFz586Fqik/FILLMB8PLwKrteRc9K8ALNoyXyFYRHj8zbu4Epf7HcCS4u9dmQ+eWVGgY9PS0khNTSUxMZHIyEimTJmCt7c3AwYMKNoi8yAqKorWrVtn2f7vf/8bZ2dndu7caRfCMrprALz88sskJycTERFh+7j9zjvvJDo6mldeeYVx48bh6+tra1+/fn0WL16c5VopKSlMmjTJ7mPyBQsWsG7dOpYuXcqgQYNs20NDQ2nbti2zZs3i0UcfzfF1zZw507acnp5OWFgYhmHw/vvvM3HiREwmE61atcLd3R0fH588dX149tln8fPzY8uWLbaHyPr160fLli156qmnGDJkCCaTyda+T58+fPDBB7b1K1euMGHCBM6dO0fVqlWzvUaTJk1s3R4ydynITocOHTCbzVSuXDnH+lu3bs3UqVOJj4/Pc3eXN998kxdeeIHXX389yx8yeZX5Pb59+3amTJlC165ds7zHx4wZk+WTg+x069Yt2wc+r1y5wsCBAzl27Bhbt24lNDS0QPVmFh4eTmRkZK7tRo4cmeWTgh07dtCxY0fb+p133smCBQsK/UdEfigEl3H9bu3BjN+WY6Ql8efRCK4nJeJhcXN0WSLiQFfiLnI5ung+Si1pNwaW5s2b8/HHH1OlSpUSr+XMmTO0bdvWbtv169eJjIzkoYceuund6c2bNxMeHp6lv+moUaNYu3YtUVFRdnfl7rnnnhzPdeO+1atXU6lSJfr3709qaqpte8uWLalatSpbtmy5aQjevHkzr7/+Ort27SI2NtZu34ULF/L9vY6Pj+eHH37g0UcftRtFwcnJieHDh/PMM89w+PBhQkJCbPtuDHwtWlhnRj1x4kSOIbioBQUFkZ6ezrlz53LtU2wYBuPGjePLL79k3rx5DBkypMDXvfE93rhxY77++uss3QJefvnlPI3y4O3tnWXbsWPH6NixI25ubuzYsYPg4OAC15vZp59+SlxcXK7tAgMDs2xr3rw5u3bt4vr16+zZs4c33niDXr16sXnz5hLr564QXMb1Dm3P7A2hJF/YiZFyjSXfr2FE95z/8RSR8s/fu+S7CtxMYeqZPXs2jRs3xtnZmSpVqlCtWrUirCx/EhIScHOzv8lw9epV0tLScg0Vly9fzrb26tWr2/ZnltPr9PDwsPsYH6x9R6Ojo3McGeNmw07t3LmT3r17ExYWxvTp0wkODsbV1ZUVK1bw2muvkZCQcNPXlZ2rV69iGEa+Xm9AQIDdekZXi4Jcv6AyfrZ5uWZycjILFy6kadOm3HHHHYW6bsZ7PC4ujoULF/Lpp59y//33s3btWrt2tWrVylN4zXyHPcPOnTu5dOkSr732WpEFYIAGDRrkuTvEjTw9PW1377t27Ur79u3p0KEDn376KU8++WSR1XgzCsFlnLe7J9Wrh3L8wk4A1n2/SCFYpIIraNeD0qhx48Y3/Zi7JAUGBnLlyhW7bf7+/jg5OXHq1KmbHhsQEMDZs2ezbM94+OvGO2XZBZmctmc8TLZu3bpsj8nuzmCGBQsW4OLiwurVq+0CfnZDieWVn58fZrM5X6+3NMj42ealNovFQkREBLfffjs9e/Zk3bp1+Pn5Fei6md/jGSOgfP755yxZsoTBgwfb2hWmO8TQoUOpWrUqL7zwAunp6bz44osFqvVGhekOcaM2bdpgNpv57bffiqS2vFAILgfuurUrH/62krTr57l69idOXDhF7aCi+0tPREQgJCSEo0eP2m1zd3enW7duLF68mNdeey3HABUeHs7y5cs5c+aM7W4oWO8Cenh4FGqIsX79+rFgwQLS0tJo3759vo7NGHotcz/MhISEbIeas1gsebpL6unpSfv27Vm2bBlvv/227aG89PR0vvrqK4KDg7M89FUScqv/jz/+ICAgIM/dP1q1akVkZCQ9e/YkLCyMjRs32h5gLIy33nqLpUuXMmnSJAYNGmS7i1qY7hBgnVTG29ubJ598kvj4eKZOnVroWgvTHeJGkZGRpKen06BBg0LXlVcKweVAl8Yt+dg/lLTrGwCDryIW8sLQ/zi6LBGREhEZGWkbCiwtLY0TJ06wZMkSwHpXLKOv7vXr1/nmG+sMmzt27LAde+nSJTw9PXP9WDssLIzJkydnGZv3nXfeoUuXLrRv355nn32WBg0acP78eVauXMmnn36Kt7c3L730EqtXr6Z79+5MmjQJf39/5s6dy5o1a3jrrbfsHorLr/vuu4+5c+dy55138sQTT9CuXTtcXFw4deoUERERDBw4kLvvvjvbY/v27cs777zDAw88wNixY7l8+TJvv/12tiM/NG/enAULFrBw4ULq1auHm5sbzZs3z/a8U6dOpVevXnTv3p2nnnoKV1dXpk2bxv79+5k/f36Od7qLU/PmzdmyZQurVq2iWrVqeHt706hRI9v+HTt20K1bt3zV1rhxY7Zu3UrPnj3p2rUrmzZtKnR3Az8/P5577jkmTJjAvHnzbMPA1alTJ8ssePn1xBNP4OXlxdixY7l27RoffPBBoX4Wmb9/ebV69WqmT5/OgAEDqF27NikpKezevZv33nuPBg0a8PDDDxe4nnwzJE9iYmIMwIiJiXF0Kdn6z5xpRp/xDYw+4+sbAyZ0NtLS0hxdkogUs4SEBOPgwYNGQkKCo0spcjNnzjQAY9euXbm27datmwFk+xUREWFrd+zYsRzb1a5dO9frHDlyxDCZTMaiRYuy7Dt48KBx7733GgEBAYarq6tRq1YtY9SoUUZiYqKtzb59+4z+/fsbvr6+hqurqxEaGmrMnDnT7jwREREGYCxevDjLNUaOHGl4enpmW1tKSorx9ttvG6GhoYabm5vh5eVlhISEGOPGjTN+//13u+9Vt27d7I794osvjEaNGhkWi8WoV6+eMXXqVGPGjBkGYBw7dszW7vjx40bv3r0Nb29vu+9Zxvf1xteydetWo0ePHoanp6fh7u5udOjQwVi1apVdm5x+zhnfh8w/v+zkdPzIkSOz/Ez37NljdO7c2fDw8DAAu+/DkSNHDMBYunTpTa+Xce4bfw6nTp0yQkJCjDp16hhHjx7N9Rw3q90wrL/btWrVMho2bGikpqbm6Xw5AYzx48fbbZs/f77h7OxsjB49Os95oVu3bkbTpk0LVYthGMahQ4eMwYMHG7Vr1zbc3NwMNzc3IyQkxHj66aeNy5cv53p8bv/u5SevmQwjDz2ahdjYWHx9fYmJicnyUEJpELn/J/7f4imkXN4LwDNjZxHWoouDqxKR4pSYmMixY8eoW7dulge2pHhkjMBw40NLUrZNnDiR2bNnc/To0RKdrEHyL7d/9/KT1zRjXDnRoVEznH3+7mO19LuFDqxGRKR8mjp1Kps2bWLXrl2OLkWKSHR0NB999BGvv/66AnAFo592OWFxceXWOk3YfsoLI+UaR3/fTNz1WLw9St9daxGRsqpZs2bMnDmTc+fOOboUKSLHjh3jueee44EHHiiS8+VlZrfSFLZzm/XtZjPQlXW6E1yO3B7aBtfK1mFWjLQkFm1b4diCRETKoWHDhtG/f39HlyFFpFWrVjz99NNF9rDe5MmTcXFxuenX8ePHi+RaRaF+/fo3rTU8PNzRJRab0vOniBRaq7qNcPWsSdJf65t2LOGh3iMcWpOIiEhFMnbsWPr163fTNpmHyXO0VatWkZSUlOP+m40zXdYpBJcjTk5OdGkQwsbzdUmNPUb0hYP8+udhQmrlfwgTERERyb/q1auXqpCbm5yGuasI1B2inOnZoi1Ofn+/oedtWeDAakRERERKJ4XgcqZxcF283APB7ALAz3tXk5qW4uCqRKQ4aaRLEakoivLfO4XgcsZkMhHeuCmulVsBkJp4lfU/bnJwVSJSHDKeME9NTXVwJSIiJSMlxXpjryhGrFAILoe6N28L7nVs6yu3LXZcMSJSbJycnHByciI2NtbRpYiIFDvDMIiJicFiseDi4lLo8+nBuHKoTlB1qnj7ctotkPTES/x5bBuXoi8QWCnI0aWJSBEymUwEBQVx9uxZLBYLnp6eRTbMk4hIaWEYBikpKcTExHDt2jVq1KhRJOdVCC6nwpu2ZH70nySdXA9GOvO2LOLxux5zdFkiUsR8fX1JSEjg0qVLXLx40dHliIgUG4vFQo0aNXKdDjmvFILLqbBmbfhqxzbb+ne7lvGvgeN1l0iknDGZTFSrVo2goCBbXzkRkfLGycmpSLpAZKYQXE5VqRRAfb9K/BHXnJTL+4iP+ZOdh3bQvklHR5cmIsUgo3+wiIjkjR6MK8d6NG+D2beJbX3BlnkOrEZERESk9FAILse6Nm1NSiqYXK1THh7+9Vvi4qMdW5SIiIhIKaAQXI5V8vSmZfVqWKp0AsBIT2bJ1qUOrkpERETE8RSCy7nuzduS7vb3HOYbohZpdikRERGp8BSCy7nOIaEYCTG4+Fv7BkdfPsq+oz85uCoRERERx1IILufcLW50atgYZ/9Q27b5EXpATkRERCo2heAKoGdoe5JSnTA5ewKw98B64hPiHFyViIiIiOMoBFcAreqF4E0ilmqdAUhPTWTl9yscW5SIiIiIAykEVwBOZjM9mrcF95q2bWu2L3RgRSIiIiKOpRBcQYSHtich7hIulW4B4PKFX/n1xH4HVyUiIiLiGArBFUT9qsHU9vPDOfBW27b5m+c6sCIRERERx1EIrkB6hrYnxbBgcnID4Me9a0hMuu7gqkRERERKnkJwBdKjeVtSYs/aHpBLS7nO2h9WObgqERERkZKnEFyBBPhUomXdRuBVz7bt6616QE5EREQqHoXgCqZnaHsSYy/j7FMXgPNn9/LH6V8dXJWIiIhIyVIIrmA6h4TibCThUqWjbdvcb+c4sCIRERGRkqcQXMG4W9zo0rglqXjaHpDb+fMqEpLiHVyZiIiISMlRCK6AeoZ2IDH6T9xqdAUgNeU663Z87eCqREREREqOQnAF1LJuIwK8vMGroW3b8si5GIbhwKpERERESo5CcAXkZDbTvXlbEuMu4eIXAsDFC4f59djPDq5MREREpGQoBFdQPVu0JzXhCq5VO9u2LYjQDHIiIiJSMSgEV1D1qgZTr0oNUgw3TC5eAOzeu464+GjHFiYiIiJSAhSCK7CeoR1IvPIH7sHhAKSnJbH6+yUOrkpERESk+CkEV2DhLdphJt1uBrmV383TA3IiIiJS7ikEV2B+Xj60a9iMhOgzuFZuCUD01T/Zc/h7xxYmIiIiUswUgiu43i07kppwBZegDrZtC/WAnIiIiJRzCsEVXLuGzfD18CI5zQmzxQ+AvYe+5UrMBQdXJiIiIlJ8FIIrOBdnZ3q0aEfi5aO41+wFgJGextdbFzi4MhEREZHioxAs9G7ZEYw0DI9agAmAb7YvJC0t1bGFiYiIiBQThWChftVgGlStyfUrJ7BUbQ/Atbjz/HBgi2MLExERESkmCsECQK+WHUhLjMalcjvbtkWb5ziwIhEREZHioxAsAPRo3g5nsxNJyak4eVQB4PCR7zl76U8HVyYiIiJS9BSCBQBfTy86NGpO4pWjuNfs/ddWg+WR8xxal4iIiEhxUAgWm94tO4GRTrpbNTA5A7Bxx2KSkhMdXJmIiIhI0VIIFpu2DZrg5+nD9UtHcavRFYDEhBi2/LjKwZWJiIiIFC2FYLFxcnIiPLQdaUmxuFRua9u+6NvZGIbhwMpEREREipZCsNjp3bIjAAnXYnCpdAsAZ84d4tCxnx1ZloiIiEiRUggWO3WCqtOoem2Srv6BW63bbdsXb/7SgVWJiIiIFC2FYMmiV8uOgEGqyRuzqy8AP+xdx5WYC44tTERERKSIKARLFt2bt8HFyZnrFw7gXucOAIz0NFZvXeDgykRERESKhkKwZOHt7kmXJq1IT7mO2SeEjLfJqq3zSElNdmxxIiIiIkVAIViydUfrzgBcv3wMt+rW5Wvxl9i+Z70jyxIREREpEgrBkq0WtRtSzS+Q5LgzuFbratuuB+RERESkPFAIlmyZzWb6tOoEQFJiAs4+dQH44889HDm535GliYiIiBRakYTg77//nnHjxtGkSRN8fX3x8fGhSZMmjB07lu3btxfFJXIUFxfH+++/T58+fQgODsbNzY2AgABatGjBk08+yc8/a3zbgurdsiNmk4mEi4fwqNPXtn1ZxGwHViUiIiJSeCajEFOBxcfH8/jjj/PFF1/ctN3o0aP58MMP8fT0LOilsrVgwQLGjx/PlStXcmxjMpkYP348b731Fu7u7gW+VmxsLL6+vsTExODj41Pg85Q1k+ZNY8dv+/Ct3Zn4vf/FSInHycmVua9tw9fL39HliYiIiNjkJ68V+E5wWloagwYNsgvA7u7utGnThg4dOthdeObMmQwaNIi0tLSCXi6Ld999l/vvv98uAFevXp1u3brRoUMHPDw8ADAMg//973/ce++9RXr9iiLjAbn48wfxqN0HgLS0ZNZ9v8iRZYmIiIgUSoFD8MSJE9mwYYNt/ZFHHuHUqVPs2rWLqKgozpw5w8SJE237N2zYwKRJkwpX7V8iIyN56qmnbOs1a9Zk3bp1nD59mi1bthAVFcXly5eZOnUqTk5OAKxZs6bIrl+RtGvYDH8vX1ITr+Ic0BowAbAici5paamOLU5ERESkgArUHeLMmTPUr1+fxMREAIYPH87s2dn3E504cSJTpkwBwM3NjaNHj1K9evVClAwdOnTghx9+AMDX15eff/6ZunXrZtt2+vTpjB07FgCLxcLvv/9OzZo1833NitodAuCLTStYsG09bv4NMMf8RNJ56/d+4iMf0ym0l4OrExEREbEq9u4Q7733ni0Ae3h48N577+XYduLEibbQmZiYyPvvv1+QS9rs37/fFoABnn/++RwDMFjvULdr1w6ApKQk3n777UJdvyK6/a9RIhKvHMWj9u227UsjNFyaiIiIlE0FCsHLly+3LQ8ZMgR//5wfkHJ1dWX06NG29WXLlhXkkjYRERF26/fdd1+ux9x///121y/Es4AVUo2AIELr3AIYJCan4+RVA4CDR3Zw/Mxvji1OREREpADyHYIPHz7MkSNHbOt9+vTJ9Zg77rjDtnzkyBEOHz6c38vanDhxwrbs4+NDrVq1cj2mRYsWtuVTp07x008/Ffj6FZVtBrnze/GqN9C2fVnETEeVJCIiIlJg+Q7Bv/zyi916x44dcz2mdevWuLq62tb37t2b38vaxMTE2Ja9vb3zdMyNfUL27NlT4OtXVJ0bt8TLzZ30lARMHnUwOVuHu9u882ui4y47uDoRERGR/Ml3CD506JBt2dXVNU8Pmd3YLvM58itz8I2Li8vTMbGxsXbrBw8eLPD1KyqLiyvhLdoDEHduHx51/h4u7Ztt8x1ZmoiIiEi+5TsEHz9+3LYcHByMyWTK03GZuy1kPkd+BQcH25ZjY2P5888/cz1m3759dut5uX5SUhKxsbF2XxVdn7+6RCRfO4dXjW5kvH1WRH5FSmqyAysTERERyZ98h+DMd199fX3zfFzmLgl5vYObnS5dutitL1y4MNdjFixYYLeel+tPnToVX19f21dBhlUrb+pXDeaW6rUBuHrhCG7VraE47tolvvtpjSNLExEREcmXfIfga9eu2Zbd3NzyfFzmKYsznyO/2rRpwy233GJbnzp1qt3DcjeaOXMmO3bssNuWl+s/99xzxMTE2L5OnjxZ4JrLk4y7wQmXDuNTf4Bt++Jvv9CoGyIiIlJm5DsEp6b+PUuYs7Nzno/L3DYlJSW/l7Uxm8288sortvWrV6/StWtXNm7caNcuMTGR//73v7aJMjLL/BpyYrFY8PHxsfsS6NG8Le6uFjDSSbx+HZdKjQA4cfoQ+4/ucnB1IiIiInmT7xDs4eFhW86YMCMvMrf19PTM72Xt3HffffzjH/+wrf/555/07t2b4OBgunfvTqdOnQgMDGTChAmkpqZSr149u6Hc8tONQ+x5WNzo0dw6+UjMmR/xbjDItm/pt184qiwRERGRfMl3CPby8rItJyQk5Pm469evZ3uOgpo2bRqvvvqq3dBrp0+fZsuWLURFRREfHw9Yu09s2LCBtLQ0W7tKlSoV+voVWd82twGQnpKAm3cwZrcAAH7Y9y1nL+X+oKKIiIiIo+U7BAcGBtqWz549m+fjzp07Z1sOCAjI72WzMJlMvPjiixw5coQXX3yRDh06EBgYiIuLC9WqVaNnz57MnDmT77//nvr169uNCJGXCTYkZw2q1aRRjToAnPvjezxtk2cYfL1FUymLiIhI6ZfvENyoUSPb8uXLl+3u8N5M5gfLQkJC8nvZHNWsWZNXX32VqKgoLl68SHJyMmfOnGHjxo2MGjUKFxcXYmJi7Ga5a9OmTZFdv6Lq99fd4JT4CwTW6orJyQLAuu8XE59Q8NE/REREREpCvkNw48aN7dbzMvva6dOnuXjxYo7nKG7fffed3cgF7du3L9Hrl0fdmrbB02Id8ePCnz/jXqsXAEnJ19kQtdiRpYmIiIjkKt8huF27dlgsFtv6tm3bcj1m69attmU3NzfatWuX38sWyrx582zL7dq1o169eiV6/fLIzdWVnqHWPyZiLxwioMHfw6Uti/iStPS0nA4VERERcbgCPRgXHh5uW587d26ux2RuEx4eXujRIfLj8OHDLF261Lb+yCOPlNi1y7uMB+TAIP36VSxV2gJw6eppduzd5LjCRERERHKR7xAMMGrUKNvy3r17WbVqVY5tf/rpJ9auXZvtscUtKSmJRx55xDYucUhICCNGjCix65d3dYKq06xWfQBOHd2GT+bh0jZruDQREREpvQoUggcPHkxoaKhtfdy4cfz6669Z2p09e5Zhw4bZhidr2bIl99xzT7bnPH78OCaTyfb18ssv37SGOXPmkJycnOP+8+fPc9ddd9m6YphMJj799FO7IdWk8Pq26QpAemoi/r7Vcfa2Tqt86I8f+e3EPkeWJiIiIpKjvE/5lonJZGL69Ol069aNhIQEzp49S/v27Xn00Ufp2rUrzs7O7Ny5k//973+cP38esE6b/Nlnn2EymYqk8EceeYQnnniCAQMG0LlzZ+rWrYuLiwtnz54lIiKCRYsWER0dbWs/bdo0unbtWiTXlr/d1rgVH7svIjYhnmOHNuHZ4B5ifn4HgGWbZ/Ds6PccW6CIiIhINkxG5mET8mnZsmUMGzYs10kz3N3d+eqrrxg0aFCObY4fP07dunVt6y+99NJN7wa7ubmRlJSUa42enp68//77PPTQQ7m2vZnY2Fh8fX2JiYnRFMo3+Gz9UpZEWfsAt+gylkPfPEx6cgxmkxMzX4kgyL+6gysUERGRiiA/ea1A3SEyDBo0iB9//JGePXtme4fXZDIRHh7O7t27bxqAC+Luu+/G398/x/2enp48+OCDHDhwoNABWG7uzlu72JYv/7kbj78mz0g30lgRMdNRZYmIiIjkqFB3gjM7efIk27dv5/Tp0wDUqFGDzp07U7NmzaI4fbbS09P55ZdfOHLkCOfPnycuLo7KlStTs2ZNunbtiru7e5FdS3eCb27Cl++x59hhAJp0HsvhVQ9CejIWVw++mrINLw99z0RERKR45SevFahPcHZq1qzJfffdV1SnyxOz2UyrVq1o1apViV5Xsup76222EOx6/SwetW/n+rFVJCVf55tt8xjS+x8OrlBERETkb4XqDiGSoVNIKH6e1r+49u1bT+WQewFrF5nlEV+SnJJ7/20RERGRkqIQLEXCxdmZPq07AZCWmkx1d2fcqlv7CkfHXSRi90pHliciIiJiRyFYikzfNrdhNlnfUgf3foPvLUNs+5Zs+pz09HRHlSYiIiJiRyFYikyQrz+dQloAcCX6PI2q18U1oBkAp84fZffBSEeWJyIiImKjECxFqn/bbrblc0e34tkw893g6Y4oSURERCQLhWApUi3rNqJWYFUADh39iSYNO+HsZR0mb9+RnRw+sdeR5YmIiIgACsFSxEwmEwPa/X03mMsH8Wx4r211qe4Gi4iISCmgECxFLrxFe9xdLQD88PN66ja6HbOlEgDb9qzn7KU/HVidiIiIiEKwFANPN3d6hrYHICklmapcwbP+3QAYRjrLN2sqZREREXEshWApFpkfkPtlz1qq3DIQk5MbAOujlhB77aqjShMRERFRCJbiUSeoOqF1bgHg1KWzNPWz4FHnDgCSUxJYvXWuI8sTERGRCk4hWIpN5gfkTv0eSaVbBpPxlvs6cjZJyYkOqkxEREQqOoVgKTadGoUS6F0JgN2Hf6J9rZq4BXcFIPbaFTb+sNSB1YmIiEhFphAsxcbJyYk729wGQLphkHx+D94Nh9r2L9k0nbS0VEeVJyIiIhWYQrAUqztbd8bZ7ATAlp8i6HhLKJagtgCcv3yKyJ/WOLI8ERERqaAUgqVY+Xv7cluTVgDEXL+GX9KfeDW637Z/0YZPSE9Pd1R5IiIiUkEpBEuxG9AuzLb83Y8bCG3YDhf/JgCcOPs7O/dvdlBlIiIiUlEpBEuxa1KzHg2r1QLgyNmTNHGPxzvT3eCFGz7BMAxHlSciIiIVkEKwFDuTycTdHXrY1nfv/ZaGDbri7FMXgF+P72Hf7z84qjwRERGpgBSCpUR0bdoafy8fAKJ+3UOnyk52fYMXbvjEUaWJiIhIBaQQLCXC1dmFfm2sYwSnGwbHj3xPzfo9cPKsBsBPv27j9z/3O7JEERERqUAUgqXE9G1zGy5OzgCs+2kbPYJ98Mo0bvDCDR87qjQRERGpYBSCpcT4efkQ1qwNANcSE0i8cIDKDe7AbPEH4PtfNnDy3FFHligiIiIVhEKwlKjMD8it3rmZ8Fr+eDUcDIBhGCzeNN1RpYmIiEgFohAsJapBtZo0q9UAgD8vnSMg5SyV6g/A5OIFwOadK7h49YwjSxQREZEKQCFYStzdHbrbltft2kzX2kF41rsLgLT0VJZ++4WDKhMREZGKQiFYSlynRqEE+Vr7Ae86coDmXkl4Nbgbk5MFgLXbFxAdd9mRJYqIiEg5pxAsJc7JyYkB7brZ1r/7OZKOtarjUacvAMkpiayMnO2o8kRERKQCUAgWh7ijdWcsLq4AbNizg7AqFrwa3gsm6xBqKyNnc+16rCNLFBERkXJMIVgcwtvdk16h7QFITEli7287ubVWbTxq9wYgPiFOd4NFRESk2CgEi8Pc1f7vB+S+3rmFO2p5W6dSNlnflssjZnI98ZqjyhMREZFyTCFYHKZW5Wq0qd8EgPPRlzlz+hAtguvhXrMnANeux7D6u68cWaKIiIiUUwrB4lCDOobblpdGbaJvHV+8Gz1Axltz6eYZJCTFO6g6ERERKa8UgsWhbq3fmLpBNQA4dOoYqbGnaVSjAe41rV0lYq9dZc3W+Y4sUURERMohhWBxKJPJxD2Z7gYv27GJvnV88Gr0AGCybvv2cxKTExxUoYiIiJRHCsHicGHN2+Dv5QPA9kO/EEAc9ao3xK2GdSzhq3GXWLd9oSNLFBERkXJGIVgcztXZhYHtwgAwMFjxQwR9a/viHfKgrc3ijZ+RnJLkoApFRESkvFEIllKhb5uutskz1u+JoqGXQc1qt+BWvQsAV2IvsCFqsSNLFBERkXJEIVhKBR8PT/q06gRAUkoya3/cyh21ffEOGWZrs3DDJ7obLCIiIkVCIVhKjbs79MD018NwX+/cwq0BrlSpEoKlagcALkWfY9MPyx1ZooiIiJQTCsFSalT3r0znxqEAXLkWy9YDP/51N3i4rc2ijZ+QmpbiqBJFRESknFAIllLlno49bctLo76lc1UPAqs2wVKlLQDnL59i886vHVWeiIiIlBMKwVKqNK1Vn8bBdQE4duE0+44f5vaaPnZ3gxesn0ZaWqqjShQREZFyQCFYSh37u8Gb6FbDG/+qzXGt3BqAs5f+ZPOulY4qT0RERMoBhWApdTo3bknVSgEA/Hj0EOcun6V3TR+8G/99N3jeug/VN1hEREQKTCFYSh0ns5m7O/SwrS/+fiM9gr3xr9oS1yDr3eBzl06y6YdljipRREREyjiFYCmV+rTqhLebBwAR+3YRdy2a22v54NN4lK3N/LUfkZKa7KAKRUREpCxTCJZSyd3iRv923QBIS09n6Y5v6RHsTaUqzbFUaQfAhatn2BC1xJFlioiISBmlECyl1l3tu2NxdgFg7Y/bSUlO4PZaPng3HmlrM3/9R5pFTkRERPJNIVhKrUqe3tz+11TKiSlJrNwVSXiwN5WCmuBWzbr9cvR51m5f6MgyRUREpAxSCJZS7Z5OPTGbrG/Tr3+IwJSeSu+a3ng3HmFrs3DDxyQmJziqRBERESmDFIKlVKvmF0i3ptYRIWKuX2P9z98TXtMH38BGuFW/DYCrsRf5Zus8R5YpIiIiZYxCsJR6Q7rcblteErUJi8mgV03vv/oGmwBYtPFTEpOuO6hCERERKWsUgqXUq181mDYNmgBwPvoykQd+pGewDz4B9XALto4gEXPtCiu/m+PIMkVERKQMUQiWMmFo57/vBi/avgF3ZxO9avrgHTKCjLfxko3TiU+Ic1CFIiIiUpYoBEuZ0KJOQ0Jq1AHgj/On2XXkAL2CffDxr4N7TevscnHXo1kZOduBVYqIiEhZoRAsZYLJZLLrG7xo2wY8XMz0DPbBu/Fw+GsEiWXfzuDa9VhHlSkiIiJlhEKwlBmdGrUgOKAKAHtP/M6hU8esD8j51sS9Vm8AriXEsjziC0eWKSIiImWAQrCUGWazmXs797KtL9q2AU8Xp7/6Bj8IJicAlkfMJObaFUeVKSIiImWAQrCUKeEt2hHg7QvA97/+wp8Xz9Krlg8+vsF41LkDgITEeBZt+MSRZYqIiEgppxAsZYqrswv3dOwJgIHBgq3r8XA2c0ctH7xDhoHZFYBV333FxatnHFmqiIiIlGIKwVLm9L21C97ungBs3reLs1cuEl7TGz+fKng1uBuAlNRk5n7zoSPLFBERkVJMIVjKHHeLG3d3sA6Llm6ks3D7BixOZu6s44vXLfdhcrEG5I07lnLq/B+OLFVERERKKYVgKZPuaheGh8UNgI17dnAx5iph1b0J8PbDq+FQwBqQZ69+15FlioiISCmlECxlkpe7BwPaWqdMTklLZcn3G3FxMtGvji+eDQZhtlQCYOvPa/n9z/0OrFRERERKI4VgKbMGdQzH4uwCwDc/buPqtVi6VPOiipc33iHDbe1mrXzbUSWKiIhIKaUQLGVWJU9v7mxzGwBJqSks27EZZ7OJAXV98ajbFyePqgD89Os29v62w5GlioiISCmjECxl2r2deuHi5AzAqp2RxCXE06GqJ9W93PFuPNLWbubKtzEMw1FlioiISCmjECxlWqBPJXq37AjA9eREvv5hC2aTiYF1K+FeKxxnnzoA/Hp8Dzv2bXJgpSIiIlKaKARLmTekS2/MJutbefkPEVxPSuTWIA9qebvh3WSMrd2Xq94hLT3NUWWKiIhIKaIQLGVeNb9AerRoC0BcQjyrd3+H2WTirnqVcKvWCRf/xgCcOPs7W3avdGSpIiIiUkooBEu5cF+XPpgwAbD0+29JSkkmNMCder4WfJo+ZGs3Z837pKQmO6pMERERKSUUgqVcqFW5Krc1aQXA1fhY1v30PSaTibvrVcJSuRWWoDYAnL98im+2zXdkqSIiIlIKKARLuXH/bX1sywu3byA5NYUmfm6E+Lnhnelu8Ly1/yM+Ic4RJYqIiEgpoRAs5Ub9ajXpcEtzAC7FXmX9z9a7wYPrV8LV7xbca4YDEBt/lcUbP3VkqSIiIuJgCsFSrgwL62tbnr91PcmpKdT1sdAmyMM6UoTZOsPc8oiZXLx6xlFlioiIiIMpBEu5ckv12lnuBgPcXa8Srl5V8ax/FwDJKUnMWf2+o8oUERERB1MIlnInu7vBVT1cuK2aF96NHsDk4gXApp3LOHb6V0eVKSIiIg6kECzlTk53g/vX9cXNzRfvRg8CYBgGM1a86bA6RURExHEUgqVcyu5usJ/FmV41vfGsfxdOHlUA+PHQVn46tM1RZYqIiIiDKARLuZTT3eA+tX3xsthPpzzj6zdJT093SJ0iIiLiGArBUm5ldzfYw9lM3zq+uNfsgUulhgD8ceoQEbu/dlSZIiIi4gAKwVJu3VK9Nu2zuRvco4Y3AW4u+DQbZ2v75ap3SE5JckidIiIiUvIUgqVcG94t691gFycTA+tVwhLUCkuVdgBcvHqWr7d86agyRUREpIQpBEu5dkuN7O8Gd6rqSXVPF3yaPULGr8HCDR8Te+2qo0oVERGREqQQLOVedneDzSYT99SrhItvPTxq9wYgPiGO+es/clSZIiIiUoIUgqXcu/Fu8LqftgMQGuhOA18L3o1HYXKyALD6u7mcvnDcUaWKiIhICVEIlgpheKaRIuZ9t5bE5GRMJhNDGvjh5FEZzwb3ApCalsKMFW84qkwREREpIQrBUiHcUr02nUNaAnDlWiyrdkUCUN/XQtsgD7wa3YfZLQCAqL2b2HM4ylGlioiISAkokhD8/fffM27cOJo0aYKvry8+Pj40adKEsWPHsn379qK4RI4SExOZM2cO9957Lw0aNMDHxwdXV1cCAwNp06YN48ePJypKgUZgRPd+mDABsHDbeq4nJQJwT/1KuLq449P07wk0Plv2GmnpaQ6pU0RERIqfyTAMo6AHx8fH8/jjj/PFF1/ctN3o0aP58MMP8fT0LOilsrVp0ybGjBnDyZMnc23bt29fZsyYQZUqVQp0rdjYWHx9fYmJicHHx6dA5xDHm7r0CyL27QJgRPf+DOt2JwALf7/K+j+juRTxT1KifwfgiQdeo0+noQ6rVURERPInP3mtwHeC09LSGDRokF0Adnd3p02bNnTo0MHuwjNnzmTQoEGkpRXdnbXVq1dzxx132AVgHx8fOnToQI8ePWjSpAlm898vb82aNXTr1o1Lly4VWQ1S9owI64fZZH1fLPl+I7HX4wHoV8cHLxdnfFr809b2y1XvEJ8Q55A6RUREpHgVOARPnDiRDRs22NYfeeQRTp06xa5du4iKiuLMmTNMnDjRtn/Dhg1MmjSpcNX+JTo6mjFjxpCamgqAt7c3X3zxBZcuXSIqKopvv/2WAwcOcOLECR544AHbcYcPH+app54qkhqkbKoREETvlh0AuJ6UyJLvNwLg6eJE/7q+WAJb4FajKwDRcZdZuOFjh9UqIiIixadAIfjMmTO8++67tvXhw4fz2Wef4e/vb9vm6enJ5MmTefHFF23b3nnnHc6cOVOIcq0WLFjAxYsXbevz589n9OjRuLi42LULDg5m7ty53HXXXbZt8+bNIzo6utA1SNk1rFtfXJycAVj+QwRXr8UC0L2GN0Huzvg0Gwtm63tpecRMzl3KvbuNiIiIlC0FCsHvvfceiYnWh4o8PDx47733cmw7ceJEatasCVgfYnv//fcLckk7W7dutS03a9aMvn373qQ1vPDCC7bllJQUdu3aVegapOwKquTPnbd2ASApJZkF29YD4Gw2Mbi+H86e1fBqcA8AqakpzPj6TYfVKiIiIsWjQCF4+fLltuUhQ4bY3QG+kaurK6NHj7atL1u2rCCXtJP5LnCzZs1ybX9jm8zHS8V03219sDhb7/au3vUdF2Os0yW3ruxOQ18LXo0ewGypBMC2n9ex/4j+cBIRESlP8h2CDx8+zJEjR2zrffr0yfWYO+64w7Z85MgRDh8+nN/L2vHy8rItJycn59o+KSnJbt3Pz69Q15eyL8DblwHtwgBISUtl3ndrAawTaDT0w+ziiXeTTEOmLX2N9PR0R5QqIiIixSDfIfiXX36xW+/YsWOux7Ru3RpXV1fb+t69e/N7WTvt2rWzLUdFRdkekMtJZGSkbdnFxcXueKm4hnbpjYerGwDrft7O2SvWTwjq+VhoX8UDjzp9cPatB8DvJ/fz7c4VjipVREREili+Q/ChQ4dsy66urrb+vjdzY7vM5yiIkSNH4uHhAcDZs2d57bXXcmwbHR3Nc889Z1sfNWoUAQEBhbq+lA8+Hl4M6tgDgLT0dOZErrHtG1TPDxcnJ3wzDZk2a+XbJCTFl3idIiIiUvTyHYKPHz9uWw4ODsZkMuXpuFq1amV7joKoVq0aX3zxhW00iJdffpn77ruP7777jri4OFJTUzl16hSzZs3i1ltv5eDBgwCEhYXx9ttvF+raUr7c07En3m7WP6i+/WUnx86fBiDQ3ZmewT5YKrfCrVonAK7EXmDh+k8cVquIiIgUnXyH4Li4vycP8PX1zfNxmSfPyHyOgho6dCgbNmygcePGACxcuJBu3brh4+ODi4sLNWvWZPTo0fzxxx8EBATw7LPPsn79+jzP9paUlERsbKzdl5Q/nm7uDO1yOwAGBjO//dq2r29tX7xczPg0/4dtyLSlmz/nzMXjjihVREREilC+Q/C1a9dsy25ubnk+zt3dPdtzFEZYWBjffPMN/fr1y7GNi4sLY8aM4dFHH7Xrl5ybqVOn4uvra/vKS7cPKZsGtg8j0LsSADt+28f+E9YHPz1czAyqVwlnrxp4NRgMWIdM+3Rpzt1vREREpGzIdwjO/BCas7Nzno/L3DYlJSW/l80iISGBxx57jIYNG7J69WrAOmZxu3bt6NGjB82bN8fJyYmUlBT++9//0qBBA15//fU8n/+5554jJibG9pV5emYpXywurgzv/vcfUp9vWo5hGADcVt2Lml4ueIU8iNktEICd+yPYeWCLI0oVERGRIpLvEJzxQBpgmzAjLzK39fT0zO9l7SQnJ9O3b18++ugjUlNT8fX1ZcaMGVy9epUffviBb7/9lr1793L+/HmefvppTCYTKSkpvPDCC3YTZ9yMxWLBx8fH7kvKr96hHagZWBWAgyf/YMdh6wgmZpOJ+xv6Y3Z2x6f5OFv7T5e8SnJKUrbnEhERkdIv3yE48xi9CQkJeT7u+vXr2Z6jIKZMmUJERARg7WYRERHBmDFjsnR3CAgI4K233uKDDz6wbZs6dSo7d+4s1PWl/HFycmJM+EDb+hfffk3aX+MCN/Jzo02QB+7B3XENbAHAmYsnWBEx0yG1ioiISOHlOwQHBgbals+ePZvn486dO2dbLswQZTdOvTx27FhatWp102Mee+wxQkNDATAMgw8//LDA15fyq1NIKE2CreMCn7h4lk2/7LDtu7e+H65OZnxbPEbGr838ddO4FH0uu1OJiIhIKZfvENyoUSPb8uXLl+3u8N5M5j61ISEh+b2szc6dO+1GahgwYECejuvfv79t+bvvvivw9aX8MplMjOl5l219dsRqkv/qvx7o7kyfWj64VKqPRz3reykx+Tozlr/piFJFRESkkPIdgjOGJMuwZ8+eXI85ffo0Fy9ezPEc+XH69Gm79byO2pC5Xea70iKZtajTkPYNmwFwMfYqK3f9PdvgHbV98LM44dNkFGZXax/xLT+uYt8Rda8REREpa/Idgtu1a4fFYrGtb9u2Lddjtm7dalt2c3Mr1LTFma8Nee+XnPmOdebh2kRuNKbnXZiwTgIz/7u1XEuwvncsTmYG1/fD7OqDd9OHbO0/XjyZtLSbT90tIiIipUuBHowLDw+3rc+dOzfXYzK3CQ8PL9ToENWqVbNb//HHH/N0XOZ2NWrUKPD1pfyrW6UG4S2sf6jFJV5n0fYNtn3tq3jQwNeCR507cKnUEIBjp3/lm23zHVKriIiIFEy+QzDAqFGjbMt79+5l1apVObb96aefWLt2bbbHFsStt95qF6KnTZtG+l9P8efk5MmTLF261LberVu3QtUg5d+I7v1xcbKObb18x2Yux0YD1n7D9zf0w2Rywjf0X7b2s9e8R8y1K44oVURERAqgQCF48ODBttEWAMaNG8evv/6apd3Zs2cZNmwYaWlpALRs2ZJ77rkn23MeP34ck8lk+3r55Zezbefq6sqDDz5oW9+9ezf/+Mc/cpyA48yZMwwcONCu28SYMWNyfY1SsVX1C6Bf264AJKWmMHvLatu+Oj4WOlfzxDWgKe61egFw7XoMs1b+P4fUKiIiIvlXoBBsMpmYPn26rW/t2bNnad++Pc8++yzffPMNGzZsYMqUKbRq1YpDhw4B1n64n332GSaTqdBFv/zyy1SpUsW2Pn36dJo2bcrrr7/OunXr2LZtG8uXL+fJJ5+kSZMm/Pzzz7a2Dz/8MG3atCl0DVL+PXDbHXhYrFODr//5e46d//uhzHvq+WFxMuHTbCwmZ+sEMuujFvHr8T2OKFVERETyyWRkzA9bAMuWLWPYsGG5Ppzm7u7OV199xaBBg3Jsc/z4cerWrWtbf+mll3K8GwzWUSn69+/PqVOn8lzvkCFD+Oqrr3BxccnzMRliY2Px9fUlJiZGs8dVIAu3rWfGphUAtGnQhNeH/d0FYt2JGBYfjeba70uI3fcxAPWDm/D+08twcsr7lOIiIiJSNPKT1wp0JzjDoEGD+PHHH+nZs2e2d3hNJhPh4eHs3r37pgG4IFq2bMm+fft46qmn8Pf3v2nbW2+9lQULFrBw4cICBWCpuO5q350gX+v7a/eRg+w+ctC2r2dNH6p5OONZ/26cfa2TbBw9dZDVW+c5pFYRERHJu0LdCc7s5MmTbN++3TaOb40aNejcuXOex/EtjNTUVH755Rf27t3L5cuXSUpKwsfHhxo1atC2bdsiqUF3giuub/fu5M1l1imS61WpwUfjnsfJbP378eCVBP7fngskXz7ApcjHAfBw82L6xA34+wY5rGYREZGKKD95rchCcHmnEFxxpaen8/jnb/HbmRMA/GfgcG5v1cm2/5P9F9l14TrRP/0/rh//BoBut/bj2dHvOaJcERGRCqvEukOIVARms5mxvf/uzjNr80oSkpNs60Ma/PWQXNOHbTPJRf64mp9/3V7itYqIiEjeKASL5EGLOrfQsZF1WMDLcTEsjfrWts/fzZn+dXwxW3zxaTbWtv2jRS+TnJKU5VwiIiLieArBInn0cK+7MJusvzKLtm3gSlyMbV+vvx6Sc699O64BzQA4feEYSzZNd0itIiIicnMKwSJ5VDOwKv3a3AZAYkoSX0b8PYGGs9nEA7f4YzKZ8W35BPwVlhesn8aZiyccUq+IiIjkTCFYJB+GhfXFwzVjAo3tdhNoNPF3p22QBy6+9fBsYJ0ZMSU1mY8XT0bPn4qIiJQuCsEi+VDJ05v7busDQLphMH3jMrv9GQ/JeTceiZN7ZQB2H4xk+571JV6riIiI5EwhWCSf7u5gP4HGrt8P2PbZHpJzdsenxXjb9k+Wvsr1xGslXquIiIhkTyFYJJ8sLq6MCR9oW/9k/RJS09Js6xkPyblV74KlSjsALkefZ86a90q6VBEREcmBQrBIAXRv3pYmwdapkk9eOseqXZG2fX8/JGfCt+W/MDlZAFi5ZTaHT+x1SL0iIiJiTyFYpABMJhOP3nGvbX3OltXExP/d3aGJvzvtq3jg7Fkdr8YjAEg30vlg3gukpqWUeL0iIiJiTyFYpIAa1ahDr9AOAFxLTGD2llV2+4c28MfD2YxXg8E4+9YH4I/Th1i+eWaJ1yoiIiL2FIJFCmFM+EDcXKzdHdbs3mo3ZJqvxYl76lfCZHamUqt/28YO/uqb9zV2sIiIiIMpBIsUQoBPJe6/7XbAOmTaJ+sW240J3LW6F/V9LLj6h+BZ/24AklOS+N+CSRo7WERExIEUgkUK6Z6OPalSKQCAn48dJurw3w+/mU0mRoT442QC7yajcfIIsrY7vJ1vd65wRLkiIiKCQrBIobm6uDC29yDb+mfrl5Kc+vfDb8FervSu6YPZ2R3flv9n2z592etEx10uyVJFRETkLwrBIkWgS+NWtKjdEIAzVy+y4ocIu/396/oS6OaEW9X2uAWHARAbf5Xpy14v6VJFREQEhWCRImEymXi0z72YTSYA5kWu5eq1WNt+i5OZYY2sXSZ8W4zH7OIFwOZdX/Pjoa0lX7CIiEgFpxAsUkTqV6tJn9adAbienMgXm1bY7W8e4E6bIA+c3Pzxbj7Otv1/CyaRmHS9JEsVERGp8BSCRYrQqB4D8LS4A7B+TxSHTv5ht/++hn64O5nwqH0HroGhAJy7fJKvvvmgxGsVERGpyBSCRYpQJU9vRnTvZ1v/3zcLSUtPt637WZwZVN8Pk8lEpVZPYjK7ArB88xeaUllERKQEKQSLFLEBbbtRN6gGAL+f/ZO1P2232x9Ww4u6Pq44e9fEK2QYYJ1S+d2vniE5JanE6xUREamIFIJFipiTkxPj7xxqW5/57dfEXr9mWzebTIwMCcDJBF63DMWlUgMATpz9nQXrPy7xekVERCoihWCRYtCiTkO6N28LQFxCPDO/XWm3v6aXK3fU9rVOqdx6AiaTEwCLNnzC0VMHS7xeERGRikYhWKSYjO01CHdXCwDf/LiN386csNvfr44v1TyccalUH89GDwCQlp7KO189Q2paSpbziYiISNFRCBYpJgE+lRjWrS8ABgb/W7OA9EwPybmYrd0iTIB3yIO4+NQF4I9Th1i88TNHlCwiIlJhKASLFKO7O/SgVmBVAH49fZwNe3bY7W9YyY3uwd6YzC743vq0rVvEvLX/48TZ30q8XhERkYpCIVikGDnf8JDcjE3LiUuIt2szqF4l/C1OuPo1wrPhEABS01J456tnSUtLLdF6RUREKgqFYJFi1qpeCF2btAYg5vo1Zkesttvv7mxmRIh1SmXvxiNw8a4FwG8n9rI8YmbJFisiIlJBKASLlICxt9+DxcU6McaqXZEcOXvSbn/zAHc6VPHE5OSKb+unABMAs1e/y6nzf9x4OhERESkkhWCREhDk688DXe8AIN0weH/1PLuZ5MA6pbK3ixnXgKZ4NhgEQEpqMu/OfZa09LQSr1lERKQ8UwgWKSGDO/a0PSR3+PRx1v64zW6/t6sT99/ib11uMgYXr+oAHPzjJ1Zu+bJkixURESnnFIJFSoiLszOP97vftj5j0wquXou1a9MuyIPQQHfMzm74tHrKtn3myrf589yREqtVRESkvFMIFilBLercQq/QDgDEJyXw6fqldvtNJhPDbvHH3cmEpXIonvX/7hbx/+ZM0GgRIiIiRUQhWKSEPdJ7EN5uHgBs3reTn//41W6/v5szQxtau0X4NHsYV++agHW0iIUbPinZYkVERMophWCRElbJ05uHe91tW/9g9XySU+ynSe5SzZPmAW6YnCz43Pqs3SQaR07uL9F6RUREyiOFYBEHuL1VJ5rUrAfA6SsXWLR9g91+k8k6pbK7swlX/xA8b7H2JU5LT+Xt2U+TnJJU4jWLiIiUJwrBIg5gNpt5ot8DmE3WX8H5W9dx+vIFuzZ+Fmce+KtbhHfjYVgqNQTgxNnfmbPmvRKtV0REpLxRCBZxkLpVanBPx3AAUtJS+XDNAgzDsGvTsaonoYHumMwu+Nw6AbPZBYCl337O/qO7S7xmERGR8kIhWMSBhoX1JcjXerf3pz8OsWW/fbA1mUyMaOSPh7MZF996eDYeCYBhGPy/OU+TkBRf4jWLiIiUBwrBIg7k7mph/B1DbOsfr1tM7HX7YFvJ4syDf02i4XXLENwCmgJw7tJJPl/+ZskVKyIiUo4oBIs4WMeQUDqFhAIQHR/H9A1Ls7RpX8WD1pXdMZmc8Ln1GZyc3QD4Zts8fjz4XYnWKyIiUh4oBIuUAo/deR8eFmuwXb8nip+OHrLbbzKZGNYoAC8XM85eNfBqOta27925zxEXH12S5YqIiJR5CsEipUCgTyUe7jXItv7+6nkkJifbtfF1dWLYX90iPOoNwKNKGwAux5zn/fkvZHmoTkRERHKmECxSStzZujPNajUA4OzVS8zZsjpLm7ZVPGkT5IHJZMK79VO4WHwA2L5nPRt3ZO1GISIiItlTCBYpJcxmM08OeBAXJ2cAlkZt4vczf2ZpN+wWf3xczTi5V8ar5ZO27Z8seZUzF0+UWL0iIiJlmUKwSClSM7AqD3a7E4B0w+CdlXNITUuza+Pt6sTokEAA3Gt0xavOHQAkJMXz1pf/JjXNfgpmERERyUohWKSUubdTL+oG1QDg6LlTLI3alKVNi0B3wmp4AeDVYjxu3sEAHD7+C/PXfVRyxYqIiJRRCsEipYyLszNPDhiG2WQCYM6WNVmmVAYY0sCPKu7OmJ3d8br1WUwmJwAWrJvGwT9+KtGaRUREyhqFYJFSKCS4Dne17wFAcmoK762am2X0B4uTmYebBmI2gat/Y7wbDwcg3Ujnv1/+h/iEuBKvW0REpKxQCBYppUb26E+VSgEA/HL8N9b9/H2WNvV8LPSr4wuA5y0P4Fm5OQDnLp/kkyWvllyxIiIiZYxCsEgp5e5q4Yl+D9jWP1u/lMtxMVna9a3tS10fV0xmJzxbP4OziwcAm35Yxnc/rSmxekVERMoShWCRUqxNgyb0bNEegPikBD5YPS9Ltwhns4mHmwTiajbh7FkNr9DHbfs+nD+Ri1fPlGjNIiIiZYFCsEgp948+g6nk6Q1A1OG9fLt3Z5Y2VT1cGNrQDwD3mj3xqWXtT3wtIZa3Zv2HtLTUkitYRESkDFAIFinlfDy8eDxTt4iP1y7KtltEt+peNA9wx2Qy4dHiCdy8qgCw/+gu5q37X4nVKyIiUhYoBIuUAV0at6R7szYAxCVe5/1sRoswmUyMDgnAy8WM2dULr1tfsA2bNn/dR/zyW1SJ1y0iIlJaKQSLlBHj7xxq6xax47d92XaL8LU4MbqxdUQJ14Cm+DYdDYBhGPz3y6eIjrtccgWLiIiUYgrBImVEXrtFtAz0oEewNSy7NxyKT1XrHeTLMed556tnSE9PL5mCRURESjGFYJEyJC/dIgCG1Pcj2NMFk8mMe+tncHP3B2DXgS2siJhZojWLiIiURgrBImXM+DuH4ufpA+TcLcLFycS4ZoG4mE04ufnj0XqCbd/MlW9z+MTeEqtXRESkNFIIFiljrN0i7retT1u7kMux0VnaVfd05b6/hk1zq9IWv0b3AZCalsIbM5/QtMoiIlKhKQSLlEGdG7eke/O2AFxLTOC9bCbRAOuwaa0rW2eQc2s8Gp/KTQA4d+kkH8x/MdtjREREKgKFYJEyavwdQ2zdIn7IoVuEyWRiZIg/fhYnTGZn3Fo/j6urJwDf/bSGDVGLS7RmERGR0kIhWKSM8vHw4on+f48WMW3tQi7EXMnSzsvFiUeaBGIC67TKrf5j2/fx4skcO324JMoVEREpVRSCRcqwTiGhhLdoB1i7Rfy/FbOzHQKtkZ8b/er4AuBWoxv+DQYAkJSSyGszHlP/YBERqXAUgkXKuPF3DCXQx/oA3M/HDvP1zi3Ztutfx5cGvhYALE3/gU9AQwBOXzjGB/NfUP9gERGpUBSCRco4L3cPnr5rhG39843LOXHhbJZ2TmYTjzQJxMPZjMnJgtutE3G1eAHw3U/fsOq7OSVWs4iIiKMpBIuUA63qhTCoQzgAKWmpvLlsJimpqVnaBbo789Bf0yo7e9XAp9VTtn3Tl03l8PFfSqZgERERB1MIFiknxoQPpHblagAcOXeSryLXZNuuZWUPete0TqvsWv02AhvdC1jHD379i8eJi48ukXpFREQcSSFYpJxwdXHhmUGjcTY7AbBw23oO/Hk027b31Pejno8rAC6NH8avSjMALlw5zduzn8724ToREZHyRCFYpBxpUK0mI7r3ByDdMHhr+SyuJyVmaedsNjGuaWVr/2CzM66tXsDdvRIAOw9EsGTTZyVZtoiISIlTCBYpZ+7t3IumNesDcPbqJT5dvyTbdoHuzjzUxNo/2MkjCK9bn8NkMgHw5ap32PvbjpIpWERExAEUgkXKGSezmQl3j8Td1Toc2tqfthP1a/YPvLUM9KB3Teuscy5Bbajc1DrKRLqRzhsz/48rsRdLpmgREZESphAsUg5V86/Mo32G2NbfWfkVV6/FZtv2nvqVbP2DnRo+SGCNtgBcjbvE1C8eJzUtpfgLFhERKWEKwSLl1O2tOtKxUSgAMdev8XYOs8k5m038o9lf/YNNTjiFPoOXVxAA+4/s4vPlb5Zo3SIiIiVBIViknDKZTPxf/wfw87R2d9h15AArfojItm2AW6b+wW5+eLSZiJOTMwBfb5nF5p0rSqRmERGRkqIQLFKO+Xn58PTdI23rMzat4MjZk9m2bRnoQZ9a1sDs7N+Eyq2esO37YP6LHD11sHiLFRERKUEKwSLlXJsGTRjcsSdgnU3u9SUzSEhOyrbtoHqVCKlkfaDOVPMOqjbsB0BSSiJTpo/XRBoiIlJuKASLVACjwwfSsFotAE5dPs/Haxdl287JbGJcs8r4WZwwmUyYmownsEoTAM5dPsmbs54kLT2txOoWEREpLgrBIhWAi7Mzzw9+CDcX613edT9/T+T+H7Nt6+PqxD+aVcbJBCYnV5xaTcTTww+AHw9tZc7q90qqbBERkWKjECxSQdQICOKxO4fa1t9bNZdzVy9n27aBr4X7GvoD1ok0vNu+iDljOuYNH7P9lw3FX7CIiEgxUggWqUB6texAWLM2AMQnJfDGsi9IS8u+e0P3Gl50qOIJgDmgJdVbjrPt+39znubkuaPFX7CIiEgxUQgWqUBMJhNP9HuAKpWsw6EdPPkHc7/7Jse2I0L8CfZyASCt9iCq1+8FQEJiPJOnP0p8QlzJFC4iIlLEFIJFKhhPN3eeu2cMZpP113/ed2vZe/z3bNtanMz8s1ll3J1NmEwm0ps+QWDlBgCcOv8Hb836tx6UExGRMkkhWKQCalKzHiO6W4c/SzcM3lg2k5j4a9m2reLhwsNNAgEwO7vj3PolPNx9Adh5IIIvV/2/kilaRESkCCkEi1RQQ7vcTmidWwC4FHuVt5bPynZaZbBOpNG3tnUiDbNndfzbT7Q9KLd442eaUU5ERMochWCRCsrJbObZQaPx9fACrNMqL9q+Mcf2d9WrRPMANwDS/VtRs81jtn3vzXuew8d/Kd6CRUREipBCsEgFFuBTiWfvGYMJEwCzNq9k34ns+webTSbGNqlMFQ9nAJJr9Kd244EApKQmM/mzR7kcfb5kChcRESkkhWCRCu7W+o15oOsdAKQb6by+ZAbR8dmP+uDhYuZfzYNwd7I+KJfc6FGqB7cC4ErsBSZPf5Sk5MQSq11ERKSgFIJFhGFhfW39gy/HxfDWspz7B1fzdOGRpoGYAJPZhfTQF6nkWx2A307s5YP5L2AYRkmVLiIiUiAKwSKCk9nMc/eMwc/T+vDb7qMHWbBtfY7tQwM9uKteJQDMlkp4tnsFVxd3ADbv+pql335e7DWLiIgURpGE4O+//55x48bRpEkTfH198fHxoUmTJowdO5bt27cXxSXs1KlTB5PJVOCvWbNmFXlNImWdv7cvz94zGrPJ2j94dsQqfjn2W47t+9b24dbKHgAY3vWo0fE5274vvn6LnfsjirdgERGRQihUCI6Pj+ehhx6ic+fOfPbZZxw6dIjY2Fji4uI4dOgQ06dPp0uXLowZM4b4+PiiqrnQAgICHF2CSKnUql4Iw7r1BazjB09dOoOr12KzbWsymRjTOMA2o1xSYGfqt34IAMMweGPm/3Hs9K8lU7iIiEg+mYwCdt5LS0vjzjvvZMOGDbZt7u7uNG3aFGdnZw4ePEhs7N//efbu3ZtvvvkGJyenQhc9cuRIzp/P+1PoO3bsICYmBgB/f3/Onj2Lq6trvq4ZGxuLr68vMTEx+Pj45OtYkbIkLT2d57/6kJ//sAbYVnUb8frwx3EyZ/8388WEFKbsPse1lHQMIx23/W9y7PdNAFT2q8Z7Ty3F3zeoxOoXEZGKKz95rcAh+Pnnn2fq1Km29UceeYQ33ngDf39/wHqX+M033+TVV1+1O+a1114ryOUKLCYmhqpVq5KYaH1i/Z///CcfffRRvs+jECwVydVrsTz6yWtc+esu8INd72Rkj/45tj90JYF3frlAugFGWhKmXRM4c2Y/AA1rNeet/5uHm6t7idQuIiIVV37yWoG6Q5w5c4Z3333Xtj58+HA+++wzWwAG8PT0ZPLkybz44ou2be+88w5nzpwpyCULbNGiRbYADDBq1KgSvb5IWeTn5cPzgx+y9Q+e+9037Di8L8f2jf3dua+hHwAmJwu0egk/32oA/P7nPt7+8qkcR5sQERFxhAKF4Pfee88WLD08PHjvvfdybDtx4kRq1qwJQGJiIu+//35BLllgX375pW25cePGtG3btkSvL1JWtahzC2PC77Ktv7lsJqcvX8ixfXiwD91rWGefw+KPT4cpuFk8Adj+y3pmrXq7OMsVERHJlwKF4OXLl9uWhwwZYncH+Eaurq6MHj3atr5s2bKCXLJAjhw5Yjc6xciRI0vs2iLlwb2de9GlsXUyjPikBCYv/IyE5KQc29/f0J8mftaplVM86xDc+WXMJus/M4s3fsb67xcVf9EiIiJ5kO8QfPjwYY4cOWJb79OnT67H3HHHHbblI0eOcPjw4fxetkBmz55tWzabzQwfPrxEritSXphMJp66awQ1A6sCcOzCad5bNTfHyTCczCYebVaZan9NrXy9Umtu6fikbf+HCyax53BU8RcuIiKSi3yH4F9++cVuvWPHjrke07p1a7vRGPbu3Zvfy+abYRjMmTPHtt6rVy+qV69e7NcVKW88LG68NHQs7q4WACL27eLrnVtybu9i5vEWQXi5WP95ia16JyEt7wMgLT2V1z4fz8lzR4u9bhERkZvJdwg+dOiQbdnV1dXW3/dmbmyX+RzFJTIykuPHj9vW9UCcSMHVqlyN/wwcYVv/dP0S9p84kmP7IA8X/tmsMk7W5+qIqTuGevVvA+BaQiyTPnmY6LjLxVqziIjIzeQ7BGcOlsHBwZj+eno8N7Vq1cr2HMUl8wNxvr6+3HXXXcV+TZHyrGvT1tzbqRdgHUt4yuLpXI6LybF9Iz83RjSyTkxjMjmR2HQC1as2AuDcpZO8/MlYEpMTir9wERGRbOQ7BMfFxdmWfX1983xc5rHaMp+jOMTHx7NkyRLb+pAhQ3Bzc8vXOZKSkoiNjbX7EqnoxoQPJLTOLQBcuRbLa4unk5qWlmP7LtW96FPrr999Zw9c2kzGz6cKAIdP/MIbXzxBWlpqsdctIiJyo3yH4GvXrtmW8xMs3d3/Hig/8zmKw7Jly+yuUZCuEFOnTsXX19f2lZduHyLlnZOTE88PfohAH+uYwPv/PMpnG5be9Jh76leiZaD19z/FNZDALlNxd7MOpfbD/s18vGRyjg/aiYiIFJd8h+DU1L/v2jg7O+f5uMxtU1JS8nvZfMncFaJBgwZ06tQp3+d47rnniImJsX2dPHmyKEsUKbP8vHyYOOQRXJysv9Mrfohgw56cR3wwm0w80iSQ2t7Wh2Pj3WpTt+sUnJ1cAFizdR6LN35a/IWLiIhkku8Q7OHhYVvOPBNbbjK39fT0zO9l8+zkyZNERETY1gs6NrDFYsHHx8fuS0SsGgfX5Z93DLGtv79qHodO/pFjezdnM4+3qIy/xQmAaK/mNOn6vG3/zJVvs3nnimKrV0RE5Eb5DsFeXl625YSEvD/Ucv369WzPUdTmzJljm57VZDIxYsSIXI4QkYLo2+Y2+rXpCkBKWiqvLPyUS7HRObavZHHm/0KDcHe2Pkx7ya8roR3/adv/7tzn+Pnw98Vas4iISIZ8h+DAwEDb8tmzZ/N83Llz52zLAQEB+b1snmWeIKN79+52o1KISNH65x1DaFG7IWB9UO7lBZ+QlJKcY/saXq481jzINnTahaqDaN5yMACpaSlMmf5Pjp3+tdjrFhERyXcIbtSokW358uXLdnd4byZzn9qQkJD8XjZPduzYYTcbnaZJFilezk5OvDjkEar4WqdO/+3MCd5dmfOMcgAhfm6MCskYOs3EpbpjCbklDIDrideY+PFDXLx6pthrFxGRii3fIbhx48Z263v27Mn1mNOnT3Px4sUcz1FUMj8Q5+XlxT333FMs1xGRv1Xy9OaV+x/FzcU6o9zmfTtZ/P3Gmx7TqZoXd9W1DrFoMjlxrfHT1A5uAcDl6PO88NFoYq9dLd7CRUSkQst3CG7Xrh0Wi8W2vm3btlyP2bp1q23Zzc2Ndu3a5feyuUpKSmLhwoW29cGDBxfrA3gi8rd6VYOZcPffn7zM2LiCnb/vv+kx/er4cls16/MB6U5umFu/QlCAtfvSyXNHmfTJwyQm5e2TJhERkfwq0INx4eHhtvW5c+fmekzmNuHh4cUSTleuXMnVq3/fOVJXCJGS1aVJK4Z16wuAgcHUJV9w8tK5HNubTCaGNfKnqb91vPEkZx/8Or9JJe/KABw+/gtTPh9PSmrOfYxFREQKKt8hGOwnn9i7dy+rVq3Kse1PP/3E2rVrsz22KGXuClGnTh26detWLNcRkZwN63YnnUNaAhCflMBL8z/mWkLOd3OdzSYebVaZYC/rmMFxzkHUDHsLDzdvAH48tJV35jxjG/FFRESkqBQoBA8ePJjQ0FDb+rhx4/j116xPdJ89e5Zhw4aR9te0qi1btsyxn+7x48cxmUy2r5dffjnP9Zw/f57169fb1keMGIHJZMrz8SJSNMxmMxPuHkndoBoAnLp8gSmLP7/p1Mruzmb+LzSIADfrGMKXXWrRqMcbuP7Vx3jLj6v4dOkUzSonIiJFqkAh2GQyMX36dNtUyGfPnqV9+/Y8++yzfPPNN2zYsIEpU6bQqlUrDh06BFinTf7ss8+KJZzOnTvXbiY7jQ0s4jjuFjdevv8f+Lhbuz399MchPvpm4U1DrJ/FmX+HVsHLxfpP0nm3EEJ7vIrZbA3GKyNns2D9tOIvXkREKowChWCAtm3b8tVXX9mCcGxsLG+++SZ9+/bl9ttvZ+LEiZw/fx6wBuCvvvqKtm3bFk3VN8jcFeK2226jfv36xXIdEcmban6BvHzfP2xTK6/5cSvLdmy+6TFVPV14IjQIV7P1D+XTnrfSrscLtv2zV7/Lmq3ziq9oERGpUAocggEGDRrEjz/+SM+ePbO9w2symQgPD2f37t0MGjSoMJfK0Z49e9i7d69tXQ/EiZQOzWo34N8Dh9vWP1u/lKhff7npMfV8LPyzeWXbZBp/+nSjY7f/s+3/aNFLbP15bfYHi4iI5IPJKKKOdidPnmT79u2cPn0agBo1atC5c2dq1qxZFKd3uNjYWHx9fYmJicHHx8fR5YiUGbMjVvFV5DcAuLlYeGfMf2hQ7eb/LkSdu8bnBy/b1muemcMPO2YB4OzkwsvjPuXWJl2LrWYRESmb8pPXiiwEl3cKwSIFYxgGbyz9goj9uwEI9K7EB488Q6BPpZset/7PWBYduZpxEqod+4gf9ywHwOLixpTxM2nWoHi6WImISNmUn7xWqO4QIiK5MZlM/GfgCJoE1wPgUlw0k+ZNIyE56abH3V7Lhz61fDJOwoV6j9KiSS8AklISmfTJwxw+sfcmZxAREcmZQrCIFDtXFxdeum8cVSoFAHDk3EneWDqTtFzG/72nfiU6VbWOMpGGE7GNn6JJwy4AJCTGM/GjMRw/81vxFi8iIuWSQrCIlAg/Lx+mPDAeD4t1hriow78wY+Pymx5jNpkYGRJAaIB1FJpkw5nE5i9wS902AMRdj+b5D0dw+sLxYq1dRETKH4VgESkxtYOqMXHIWMwm6z89S6I28fUPW256jLPZxD+aBRJSyTp5RiKu0PoV6tZsDsDVuEs89+Fwzl85Xay1i4hI+aIQLCIl6tb6jflX3/ts69PWLmLboT03PcbVycxjLYKo6+0KwDXDDbd2r1Gz2i0AXLx6luc/HMGVmAvFVreIiJQvCsEiUuL6trmN+2/rA4CBdfSIA38evekx7s5m/q9lEDU8XQCIMTzx6/Qm1SrXAeDMxRM8/7+RxF67Wqy1i4hI+aAQLCIOMarHAHq2aA9AcmoKk+Z/zMlL5256jJeLE/9uGUSQu3UmukuGD1W7vk1lvxoAnDj7O8//byRx12OKt3gRESnzFIJFxCFMJhNPDhhGq3ohAMQlxPP8V//jStzNA2wlizP/aVkFP4sTAOcNP+r0+H/4+QQBcPTUQV74cCTXrscW7wsQEZEyTSFYRBzGxdmZSUPGUq9KMADnoy/z4ryPSEhKvOlxge7WIOzlYv0n7LRRmZBe7+LrZR2C7feT+3nxo9HEJ8QV7wsQEZEySyFYRBzK082d1x4cT2UfPwCOnD3Jq4unk5qWdtPjqnm68O+WQbg7mQA4YVSl2e3v4+vlD8DhE78oCIuISI4UgkXE4QJ8KvHasMfwcrOOB7z7yEHeXz2P3GZ1r+1t4YnQICx/BeFjRjWa93kfH09roP71+B4mffwQ1xOvFe8LEBGRMkchWERKhTpB1Xn5vn/g4mR96O3/t3ff4VFUCxvA3y3pjfROGqQQCARC7xA6ggKiIIqIin4qei0oXhH16tVrV+zX3q6KFFFp0qt0CIQQUgjpve4m2TrfHwtDloQkm+yShH1/z5PH3TNnzpzxcXZfZ8+cs+XEAXyz8/cW9+vZzR6PxvnAVmoIwpn6APSb8h5cHLsBAM5mHsfzH9+LOpXSYn0nIqKuhyGYiDqNuNBILLvlbvH9j3s2Yd3fO1rcL8rdHo/EeUN+6RMtXR+IhGnvwdnRDQCQnHEUz398L+pVtZboNhERdUEMwUTUqYzuPQAPTr5VfP/x5tXYdupQi/v18nDAw318IDfcEEaqLgiDp70HJwdXAMCZ9CN44dP7Ua+us0i/iYioa2EIJqJO55Yh43DHqKni+zfXf4tD50+3uF8fTwc82Mcbl4YII0UXjGHT34eTgwsA4NT5v/HCJwzCRETEEExEndRdY6djesIoAIBe0ONfv/wXpy+mtbhfPy9HLIn1xqUhwkjWBWP49PfgaO8MADh1/iCe/2gxxwgTEVk5hmAi6pQkEgkemnobRscOAHBpVbkfP0ZGYW6L+w7wccR9vbxwKQfjjC4EI296VwzCp9MPc/o0IiIrxxBMRJ2WTCrFslvuxoCIGACAUlWHf36/CvnlJS3uO8jXCYt7eYpB+LQuDCNu+kB8WO5s5nEsX3UXapSVFuo9ERF1ZgzBRNSp2cjleP62JYgJCgMAlCuqsfy791HWwvLKADDUz9koCJ/RBWPY9A/g6myYRzgt+zSeWXUnqhTlluo+ERF1UgzBRNTpOdja4V/zH0KItz8AoKCiFM9+twrVtS2P6x3q52w8NEIbgCHTPkA3Fy8AQGZuCp5+7w5UVJdaqvtERNQJMQQTUZfg6uiEV+9cCl83w7LIF4rz8Oz3q6Csb3mmh8F+TlgS6yU+LHdG44eB0z6Ep5svAOBiQRqWvTcfpZWFFus/ERF1LgzBRNRleLl2w2t3PQp3J8Pcv+fzL+K5Hz5EnVrV4r4DfQ1B+PL0aclqL/Sf9gG83QMAALlFmVj27nwUl+dbrP9ERNR5MAQTUZcS6OmD1+5aClcHJwBAck4GXvjfx1BrNC3um+DjhAd7X5lHOLneA32nfgA/z2AAQEFpNp56dx7ySy5arP9ERNQ5MAQTUZcT5huIV+9cCic7BwDAiQup+Ncvn0Gj1ba4b7y3Ix7q4y2uLHe2zg2xUz5AoI/hwbvi8jw89c48ZOWft1j/iYio4zEEE1GX1DOgO15Z8DDsbewAAIfSzuDVNV9Cp9O1uG9fL0c81McH8kufgCm1zoia+D66+/UEAJRXF+Opd+fhXNZJS3WfiIg6GEMwEXVZvYLD8a/5/wdbuQ0AYF/KCbz527fQ6/Ut7hvn5YBH+vjA5tLTcudqHRE6/j306N4HAKCorcLy9+/CidQDljsBIiLqMAzBRNSl9Q2LxAu3PwAbmRwAsD3pMN77438QBKHFfXt7OuDROG/YXRoknF5nB6+Rb6J3j8EAgHp1LZ7/eDH2n9pquRMgIqIOwRBMRF1eQo9e+Oet90IqMXykbTq+Dx9t+qVVQTjGwwGP9/OF46WxEdl1cjgMfhkJvccDALRaDf79+cP46+81ljsBIiK67hiCieiGMCy6L56edTckl5bF+O3wrlYH4R5udlgW7wtXG8NHYl6dFNq4ZzFywEwAgF7Q4+3vn8b6nV9brP9ERHR9MQQT0Q1jbJ+BePLmu9oUhINdbPF0fz+428kAAMX1QHnPR5E4fIFY59M1L+O7P99tVXtERNS5MQQT0Q1lQr8heOLmO9sUhP2cbPBMfz/4OBjGF1eoBeQGLcZNiQ+JdX7c9AE++fVfrXr4joiIOi+GYCK64UzsN7RREP548+pWBWEvBzme6e+HQCfDjBPVGj3OeczBnGnPiHU27P4W//n6Mag1La9UR0REnRNDMBHdkCb2G4onZl4JwusP7Wx1EHazk2FZf1+EudoCAJRaPY45TsTtt7wMqdQwXGLP8Y1Y8dE9UNbVWO4kiIjIYhiCieiGNTG+7UHY2UaGJ/r5IqqbYTEOlU7AfulQzJv7LuxsDSvVJaUdwpPv3I7SykLLnQQREVkEQzAR3dDaE4Qd5FI81tcH/bwMoVcnALvUvTDntk/h6uwOAMjKT8Xjb92K7MJ0y50EERGZHUMwEd3wJsYPxeMzFxgF4VV//q9VD7fZyqT4v97eGOnvDAAQAOxUBmLynM/h5xkEACipKMATb9+G5IxjFjsHIiIyL4ZgIrIKk+KHGQXhP47uxVu/fQddK4KwTCrBwmgPTAtxFcv2VXfDoBmfIiIoFoBhmeVnP7gLB079ZZkTICIis2IIJiKrMSl+GJbNultcWe6vU3/jtTVfQqvTtbivRCLBrAh3zI90vxSjgaNV9ghPfBf9okcAANQaFV75/CH8sfcHS50CERGZCUMwEVmV8XGD8M9bF0N+aZaH3cnH8K9fPoNaq2nd/kGuWBLrBfmlJJxcLYXzoH9hVMKV1eU+/HklvvrtDc4lTETUiTEEE5HVGdmrP1bevgQ2MsOiGAdTk7Dyfx+jXq1u1f4DfZ3wWF8f2MsMSThToYcy+nHcNPY+sc4vf32K1756FCp1vflPgIiI2o0hmIis0uDIPnj5jodgZ2OYC/hYRgpW/PghalWtC60xHg5Y1t8PrraGj9GCWi0yfO/A7TOeE4db7D2xCc+8vwCVNWWWOQkiImozhmAislrx4dF4dcEjcLS1BwCcyjqPZ79bBUVdbav2D3GxxbMDriyzXKnW4ZDdOCycvwr2to4AgHNZJ/GPN+dwCjUiok6GIZiIrFrvkB74z8JH4WJvCK1nczPx5NfvoEJR3ar9vR1s8OwAP4RfWl1OpRPwV3005i34Ep5uvgCAwrIcPP7WrTiZetAyJ0FERCZjCCYiqxcVGIrX7/4H3BwNcwFnFuXiH1++icKK1g1jcLGV4al4XwzwNgRpvQBsqfTGhFu/RHhQDABAWVeD5z5chK0Hf7XMSRARkUkYgomIAET4BeHte56At6thJbj88hL848s3cKEor1X728qkeKC3FyZ1vzKX8J5ye8RM+gAJvcYAAHR6Ld754Rl8veEtzhxBRNTBGIKJiC4J9vLDO4ufRLCXHwCgrKYKT371NlJyMlu1v1Qiwdwe7rgj0kOcS/hEBeAw8EVMHnGnWO/nrR/jP18/hnp1nblPgYiIWokhmIioAR83D7y96AlEBoQAAGrqa7Hs2/dwNP1sq9sYF+SCR+K8YSs1ROGMGi2KQu7FHTOeFWeO2HN8I5a9Mw8lFQXmPwkiImoRQzAR0VXcnJzx+sLH0C8sCgCg0qjx/I8fYdeZo61uo6+XI57u7ws3W8OiHEV1WhxxmIB75r8PBzsnAEBazhk8+sYspFw4Yf6TICKiZjEEExE1wdHOHi/PfwgjYvoBALR6HV799Uv8cWRPq9sIdbXDPxP8EOBkAwBQaPTYUh+DO+78Cr6eQQCAiuoSPP3eHdh+eJ3Zz4GIiK6NIZiI6BpsbWzwz1vvw+T4YQAAAQLe//N/+Hbn7xAEoVVteNrLsXyAH2I9DHMR6wRgY7knxs7+En16DAIAaLRqvPntU/hi/X+g0+ssczJERGSEIZiIqBkyqRT/mLEAc4dPFMu+370Rb//2HbS61gVWR7kUj8b5YHyQi1i2p1QGv1GvY+Kw28WyX7f9Fy9+ugTKuhrznQARETWJIZiIqAUSiQT3TrgFSybNgeTSvA9bTh7E8//7CHWtXGZZJpVgfqQH7orygOzS1BHJlTqUhf8f7rp5BaRSw9jhI8m78I+35iC/JMsSp0JERJcwBBMRtdLsoePxz1sXw0ZmWCb5aPpZk1aXA4DRgS54vJ8vnOSGj9+CWg0O2o7DAws/g4tjNwBATmEGHn1jNo6n7DP7ORARkQFDMBGRCUbFDsCrdy6Fs70DACCtIBuPfv46ckuLWt1GtLs9/pngB39HQ5hWavX4vToU8+/6Ft39egAAFLVVWPHRPfhl6yetHn9MREStxxBMRGSiuNCeePueJ8XV5Qory/DYF2/gbCsX1QAAX0cbPJvgj94NHpj7vdgJQ2Z+hkG9xwEA9IIeX214E698/jBq6xXmPxEiIivGEExE1AahPgF4795lCPcNBABU1ymx7Jt3sT/lZKvbcJRLsTTOBxOCrzwwt69EgMPAF3DrpEcgkRgGD+8/tQWPvTEbOYUZZj0HIiJrxhBMRNRGXq7d8NaiJxB/aVENtVaDl37+DL8e2NbqIQwyqQS39/TA3dEekF96YC6tSoOzHrPxwJ0fwsnBEJBzijLw6JuzcODUXxY5FyIiayMRONisVaqrq+Hm5oaqqiq4urp2dHeIqBPRaLV4e8N32J50WCyb0n8EHpl2O+QyWavbyahS4aPTJahUG6Zek0uAqd5KbP7tSWQVnBfr3TbxQdw5/THIpK1vm4jIGpiS1xiCW4khmIiaIwgCfti9Ed/u+kMsiw+LwnNz74OLg1Or26lS6fDRmRKkV6nEshE+MhQefgN7jv8plg2IGYmn734HLk7dzNJ/IqIbgSl5jcMhiIjMQCKRYMGYaXhm1iJxCrUTF1Lx2BdvIr+8pNXtuNnJ8FS8L8YFNhgnXKyDEPcM7pyxXJxP+FjKXjzy+s1IvZhk3hMhIrISDMFERGY0Lm4QXl/4GNwcnQEAOaWFePTz15Gc3fqH2uRSCe6I8sCiaE9cmk4YGdVqHHWchIcW/Rduzh4AgKKyXDz5zm34ffd3nEaNiMhEHA7RShwOQUSmKCgvwYofP0J2aSEAwEYmx+Mz78T4uEEmtXOhWoUPT5egQmUYJyyTAFN8Vdiz8Vmcyzop1hvVfyqWzntFfJCOiMgacUywBTAEE5GpFHW1eHn15ziemSKWLRg9FQtGT4NU2vof4qrVOnx8pgTnK6+MEx7gaQOkfYXfd30llgV6h+LZxasQHhRjnhMgIupiGIItgCGYiNpCq9Phw40/489je8WyETH98NTNC+FgZ9/6dvQC1mRUYGtOjVjm5yjHYJzCt2v+CWWdodzWxg7/d+tKTBx6qzjPMBGRtWAItgCGYCJqK0EQsObgdvx361oIMHzkhvkE4oXbl8Dfw9ukto4V1+KrlFLU6Qzt2EolmOajxJbfn0F6TrJYL3HwLDw09wXY2zma70SIiDo5hmALYAgmovY6fP4MXl3zJZSqOgCAi4MTnrv1XsSHR5vUTlGtBh+fKUGOQiOWDfexgeL0J9i0/0exLMS/J5bf8x5C/CPNcwJERJ0cQ7AFMAQTkTnklBZi5f8+QW5ZEQBAKpFiyaTZuHnwWJOGL6h1evx4vgJ7CxRiWXdnW/TTHMLXa55HvboWAGBnY4/7Zz+HKcNv4/AIIrrhMQRbAEMwEZmLsr4Or675EofTzohlk/oNxSPT58FWbmNSW/sKFPghtRxqveGj3EEuwXSvGvy27imjVeaG95uER+f/Gy6ObuY5CSKiTogh2AIYgonInHR6Pb7ZsQE/7dsilsUEheH525bA08W0oJqrUOOj0yUoqtOKZSN85ag5/Sk27bsyPMLb3R/L7n4HvSMS2n8CRESdEEOwBTAEE5El7DpzFG+t/xYqrWF8r4ezG1bedj9igsNNaqdOq8fX58pwtLhWLAtyssFAnMQ3a1ZAUVsFwDD8Yv7UR3D7pP+D7NLqc0RENwqGYAtgCCYiS0kvyMELP32C4qpyAIBcKsMDk+fgpoGjTRrHKwgC9uQr8L+0Cmj0V2aPmO6nxs5NzyE546hYt3ePgVi28G14u/ub92SIiDoQQ7AFMAQTkSVVKKrx8urPcfpimlg2Pm4Qlk6fDwdbO5PaylWo8WlyKfKVV2aPGORtB/uLv+CXLR9AL+gBAC6O3fDoHf/G8L4TzXMSREQdjCHYAhiCicjStDodvti2DmsObhfLQn0C8Pzc+xHk5WtSWyqdHj+nVWB3/pXZI7zt5RjvfBHfrX4aJRUFYvnEoXOwZPZzcLR3bv9JEBF1IIZgC2AIJqLrZU/yMbz123eoUxuWSXa0s8eTNy/EiJh+Jrd1pFiJb1LKxMU1ZBJgaoAUZ/a+hv0nrzyU5+cZjCfueoMPzRFRl8YQbAEMwUR0PWWXFOKlnz9FdmmhWDZ3+EQsGjcDMplpD7SV1GnwWXIpMqvVYllUNzv0VOzGd7/9G3UqJQDDQ3O3Trgfd0xdChu5rXlOhIjoOmIItgCGYCK63upU9Xh7w/fYnXxMLOsbGoln5yyGu7Npn0NavYD1mZXYlF0tljnKpZjuU4utG5/H2cwrxwgPisGyhW9xpTki6nIYgi2AIZiIOoIgCFh/aCc+27oGOr3hgTYPZzcsn30P+oaZHlJTyuvwRUoZKlQ6sWywtz0c89bi503vQ6szPExnI7fFPTOXYcbouyCVSs1zMkREFsYQbAEMwUTUkZKzM/Dy6v+irObyfL8S3DF6KuaPmgqZiSFVqdHhu9RyHGkwp7CHnQyTu5Xgl3XLkV2YLpb3ixqGxxf8h1OpEVGXwBBsAQzBRNTRKhTVeG3NlzhxIVUs6xsaiWdm32PyKnOCIOBQkRLfp5aLD81JACQG2KLizBfYsOtrsa6zgyuWzFmB8YNuNmneYiKi640h2AIYgomoM9Dp9fh53xZ8u/N36C99fLs5OuPpWYuQ0KOXye2V1mnxRUopzleqxLLuzrYYbnseX//6LMoqi8TyQb3HYuntL8Ozm2nTtRERXS8MwRbAEExEnUlSVhpeW/MlSmsqxbLbR0zCwrE3mTx7hF4QsCW7GusyK3HppjDkEmBSgBRpB9/BrqMbxLrODq64f/ZzSBx8C+8KE1GnwxBsAQzBRNTZVCkVeGP9NzicdkYsiw2OwPI598DHzcPk9i7WqPH5WeOV5sJdbRGvP4Xv17+IiuoSsXxQ7Fg8Mu9f8Orm176TICIyI4ZgC2AIJqLOSK/XY+3fO/DFtnXi7BEuDk54fMYCDG/D4hoanYD1FyqxJbsal78c5FJgqr8UqX+/i51HfhPrOjm4YMns55A4eBbvChNRp8AQbAEMwUTUmaXkXsC/V3+OoqpysWxK/+F4YNIcONjZm9xeelU9vjxbhqI6rVjWw80O8fpT+HbdC0Z3hQfGjsHSeS/zrjARdTiGYAtgCCaizq6mTol3NnyPfSknxbJADx88M3sRogJDTW5PpdNjXWYltuXUiHeFbaUSTAmQIPXge9hxZL1Y18nBBffe8gwmDZ3Lu8JE1GEYgi2AIZiIugJBELD5xAF8tOkXqDSGZZJlUinuGjMdc0dMMnlOYQA4X1mPr1LKUNzgrnBkNzv00yfhm7Urje4K9+kxCEvnvYIg37D2nwwRkYkYgi2AIZiIupK8smK8tuZLpOZfFMt6d++Bp2fdDd9unia3p9Lp8WtGJXbk1ohltlIJJvtLcP7Q+9hxeJ1YbiO3xbzJD2NO4r2wkdu270SIiEzAEGwBDMFE1NVodTp8t+sP/LxvizinsJOdA5ZOn4exfQa2qc1zFfX4KqUUpfVXll0OdbHFQOk5/LzhJRSW5Vwp94/E0vmvICYsvn0nQkTUSgzBFsAQTERd1emLaXh97ddGD82N7TMQD0+9DS4OTia3V6fVY21mJXbmXhkrLJUA4/1tUHX2G2zY+RX0gmGmColEgukjF+DuGU/A0d7ZHKdDRHRNDMEWwBBMRF2Zsr4Oq/78CTtOHxbLPF3c8I8ZCzCoZ+82tZlepcI358qM5hX2dZBjnGsRfvvjRaTnJIvlXt388NDcFzAkLrHtJ0FE1ILrHoIPHDiAb775Bnv37kVeXh4EQUBQUBBGjBiBhQsXYvjw4e09RIs0Gg22bt2K1atX4+jRoygoKEBtbS18fX3h7++PhIQEjB07FmPHjoW7u7vJ7TMEE9GNYEfSYaz68ycoVXVi2ZT+w3H/xNlwsncwuT2NXsDGi1X4M6tKXG0OAEb42cMhdwN+2vw+VOorxxrebxKWzH4O3u7+7ToPIqKmXLcQrFQqsXTpUnz55ZfN1lu0aBFWrVoFJyfTf3ZrjQMHDuD+++9HcnJyi3UfeughfPDBByYfgyGYiG4UJVUVeHvDdziWkSKW+bh54ImZdyI+PLpNbeYr1fg6pRwZ1SqxzM1Whqk+tdi59d84fm6fWG5v64g7pi7FzWMXQi6zafuJEBFd5bqEYJ1Oh6lTp2Lr1q1imYODA2JjYyGXy3H27FlUV1eL2yZOnIiNGzeavKZ9S7799lssWrQI+ksrJQFAt27dEB4ejm7duqGqqgrnzp2DUqkEwBBMRAQYplLbeGwfPtu6BnXqK8F15qAxuCfxZjjY2pncpl4QsCtPgV8zKqBqcFs4ztMeoYoD+N/vr6JKcWVccoh/Tzx024vo02NQ+06GiOgSU/Ka6RNGXrJixQqjAHzfffchNzcXR44cwcGDB5Gfn48VK1aI27du3Yrnn3++rYdr0q+//moUgOPj47FlyxaUlJTg2LFj2L59O44ePYrq6mocOnQITz75JDw9TZ8aiIjoRiORSDAtYSQ+efA5xIX0FMt/O7wLD37yCpKzM0xuUyqRYFyQC14eHIA4zytDK5LK6rFZMwC33r0GU0bMFxfTuFiQhmXvzseb3z6JiurS9p8UEZEJ2nQnOD8/HxEREaivrwcA3Hnnnfj222+brLtixQq8/PLLAAB7e3tkZGQgICCgHV02KCoqQkxMDCoqKgAAs2fPxk8//QS5XN7utpvCO8FEdKPS6/X47fAufLltPVRaw0NuEkhwy5BxWDjupjbdFRYEAUeKa/FTWgWq1FemU/N3tMEopzz8senfSMs+LZY7Obhg4fTHMXXkfMik5v3FkIish8WHQyxbtgxvvPEGAMDR0RE5OTnw8PBosq5arUaPHj2Qk5Mj7vuf//zH1EM2smDBAvzwww8AgMjISCQlJcHOzvQP6tZiCCaiG11uaRHeWP8NUnIviGV+3Tzx2E13oH9ETJvarNPqsT6zEtsbTKcGAIO97eFeuhW/bHwHirorQ+d6BMfi4dteQlRo37aeBhFZMYuH4J49eyI9PR0AcPfdd+Orr75qtv7KlSvx0ksvAQB69OiBtLQ0Uw9ppLCwEMHBwdBqDUt4rl+/HjNnzmxXmy1hCCYia6DT67HmwDZ8u+sPqLVXpj6bFD8M90+c1aZ5hQHgYo0a36eWIbNaLZY5yqWY5KtH6uEPse3QWrFcIpFg4pA5WHjTE3B39Wr7yRCR1bHomODU1FQxAAPA5MmTW9xnypQp4uv09HSkpqaaelgjX3/9tRiA/f39MX369Ha1R0REBjKpFHNHTMQnD/wTfRqMFd5y4gDu+/Al7Dt7ok3thrjYYvkAP9wV5QFHueGrp1arx7o8oC76H3ji/m8RGhAFwDCUYsvB1bj3pUSs2f45NFp1c00TEbWJySH41KlTRu+HDh3a4j79+/eHre2V9eOTkpJMPayRhg/kTZ482ewzThARWbsgL1+8sfAxLJ0+H4629gCAckU1XvrlM7z082cor6kyuU2pRILRgS54ZUgAhvlduaN8oUaNn0oDMXDGl7hr5nJxZbnaegU+X/caHnxlKg6f2WmeEyMiusTkEJyScmVeSVtbWwQHB7e4z9X1GrZhKkEQcOzYMfH9kCFDAAAXLlzA8uXL0adPH7i5ucHZ2Rnh4eGYN28eVq9ebTSFGhERtUwqlWJ6wkj896HnMbjBqnL7Uk7gvg9fwpYTB9CWWTZdbWVY3MsLy+J9EeBkmCdYALC/uB77bSfgzsXrMXHoXHEWibySLKz85D6s+GgxcgpNn7WCiKgpJo8JXrRoEb7++msAQHh4ODIyWveBNG7cOOzcuVNso6UFNq4lMzMTERER4vsdO3YgJSUFTz75JOrq6q65X9++ffHLL78gMjKyVcdRqVRQqa7MnVldXY3g4GCOCSYiqyQIAnadOYqPNv2CqlqFWN4npCeWTpuHEJ+2rQCn1QvYmVeD3zIrUddgbmF/RzlGOBVi27Y3kJxxVCyXSeWYOeYuzJv8MJwd+VlMRMYsOia4pqZGfO3m5tbq/Rp2pGEbpiorKzN6v379ejz00ENiAA4JCcGYMWOQkJBgNATj1KlTGDp0KM6ePduq47z66qtwc3MT/1pzx5uI6EYlkUgwts9AfP7QSoztM1AsP30xDQ9+8gq+2v4b6tWmj92VSyWYEOyKfw8NxEh/Z0gulRfUarG6xAvdx72HB+e/Ca9ufgAAnV6LtTu+xL0vJWLjvp+g02nNcXpEZIVMDsEKxZU7APb29q3ez8HhysTpDdswVWVlpdH7999/HwAQFRWFPXv2ICsrCzt37sSRI0dQXFyMf/zjH2Ld8vJyzJkzB+pWfFAvX74cVVVV4t/lKd6IiKyZm5Mzls++B/9e8Aj83Q0zN2j1Ovxv72bc/9FLOJLW8vL1TXG1leHuGE88l+CHCNcr012eKqvHn6p4TF/wC+ZOegi2NoZtVYpyrPrpOfzfq9NxOHlXm4ZlEJF1MzkEX56VAYBJC1M0rKvRaJqp2byGQxQuCwgIwJ49ezBy5Eijcjc3N7z99tt47rnnxLKUlBR88803LR7Hzs4Orq6uRn9ERGSQ0KMXPvu/FZg/agrklxa3KKwswz9/+AAv//JflFVXtqndUFc7LB/gi3t7ecLN1tCuVgC25mtwxv1W3H//OoyIvzLjUHZhOlZ+fC+e/WAhMnJb90sfERHQhhDs6Ogovr68YlxrNKzr5NS2eSavte8rr7wCHx+fa+6zcuVKo+EMbR2PTEREV9jZ2OLucTPwyYPPoW/olect9pw9jsUfvIj1h3ZCp9M100LTJBIJhvo545UhAZga4gr5pTESlWod1hbYQdNnOR5e/K3RghonUw/gkf/MxJvfPoWSivx2nxsR3fhMDsHOzs7i6+YeRLtabW1tk2205/gAYGNjg7lz5za7j1wux+233y6+P3r0KJRKZZv7QEREV3T39sPrCx/DUzcvhJvjpenN1PX4aNMveOiz13D6YtsWSHKQSzE7wh0vDQ5AP68rQ+pyFBqsrQhESOJHeGD+W/DzNNzkEAQB2w+vw70vTcDXG96Csq7tz58Q0Y3P5BDs5XVl9Z6CgoJW71dYWCi+9vT0NPWwIm9vb6P30dHRRnenr6V///7ia61WyzG+RERmJJFIMKHfEHzx8AuY0n+EWJ5ZlIsnvnobr/76BUrbOETC19EGj8T54Kl4X4S4XHng+Ux5PX6v74dRc3/EnTOXw9nR8LC2WqPCz1s/xuIXx+OPPd9Dq2v7EDwiunGZHIKjoqLE12VlZUZ3eJvTMHRGR0ebelhR9+7djUKvh4dHq/a7OnhXVFS0uQ9ERNQ0V0cn/GPGHXh38VPo4X9lGNrOM0dxz6oX8NPeLUbLMZsi2t0ezyX4YXGMJ9ztDOOFBQD7itTYZzsBty9ah5ljFkEuM8w9XKUox4e/vID7/zUJO49s4HzxRGTE5BAcExNj9P7kyZMt7pOXl4eSkpJrtmEKqVRqFKKbelCuKVePXzZlZgsiIjJNr+BwrLrvGSydPh+uDoZnOeo1Kny5fT2WfPQyDp8/06Z2pRIJhvk7499DAjArvBvsZIYBwyqdgI0FAtL9F+L+Jeswqv80cZ+C0my8/s3jeOQ/M3H4zE7OJEFEANoQggcNGgQ7uyvT1+zbt6/Fffbu3Su+tre3x6BBg0w9rJHRo0eLry9cuNCqfa6u5+vr264+EBFR82SXVpz78pEXcdPAUZBeXgGuvBjP/fghVvz4EfLLS1popWm2Mimmhbrh1aGBGB1wZX7hCpUO6wsdUBv7NB649wf0jRwq7pOZl4KVn9yHJ9+5HWfSj7T39Iioi2vTg3Hjx48X3//www8t7tOwzvjx49s1OwQAzJo1S3xdVFSE06dPt7jP1q1bxdfBwcEICAhoVx+IiKh1XB2d8Mi0efhwybPo3b2HWH7o/Gnc9+FL+GLbeijrW/+gdUNutjLcFe2Jlwb7o4/nlYfn8pUabCj3g8uw1/Hg3f9Fz+59xG1nM4/hqXfnYcVHizmtGpEVM3nZZABYvXq10YwMGzZswE033dRk3ePHj2PQoEHiNDmrV6/GnDlz2thdA71ej9jYWJw7dw4AMG/ePPz444/XrH/y5EkMGDBAHA/28MMPY9WqVSYd05Rl+IiIqGmCIGDn6SP4719rUVZTJZZ3c3LBXWNvwpT4YZDJZG1uP7WiHmsyKpFRbTxULtbdDuH1x7Bx+/vIKcww2jaq/zTcNf0fCPQJbfNxiahzMCWvtSkEC4KA+Ph4nDp1CgDg7++PHTt2NHrgraCgAOPHj0dKSgoAoF+/fjh+/DgkEkmjNrOyshAWFia+X7lyJV544YVr9mHdunVGd4Tffvtto9XhLsvOzsb48eORnp4OALC1tcX58+cREhLS+hMGQzARkTnVqerx455NWPv3DmgaLH0c4u2PJZPmIKFHrza3LQgCTpXWYU1mJfKVxg/hJXjZwrtyL37/6wMUN5hPWCqVIXHwLZg36SH4eQVf3SQRdREWD8EAcOTIEYwePVqcK9jV1RUPPvggRo0aBblcjsOHD+ODDz5AUVERAMOyybt378bAgQObbM/UEAwAd955J77//nvx/bhx43DnnXciLCwMSqUSe/bswccff4zq6mqxzkcffYQHH3zQ5PNlCCYiMr+C8hJ8sW099pw9blSeENEL902chTDfwDa3rRcEHCxUYn1mJcpVVxbtkEmAoT62sMvfhA3bP0GVovzKNqkcE4bMwu2T/g++nkFtPjYRdYzrEoIBYO3atViwYEGLi2Y4ODjg+++/N7pze7W2hGC1Wo1bb70VGzZsaLGvEokEr7zyCpYvX95i3aYwBBMRWU5ydgY+3fIrzuVliWVSiQRT+o/AXWOnw9257Z+7Gr2AXXk1+COrCgrNlWnS5BJgqJcM+ovrsHH3l0aLaxjC8GzcPulBhmGiLsSUvGbyg3ENzZo1C8eOHUNiYmKTQxwkEgnGjx+Po0ePNhuA28rW1ha//fYbPvvsM6MAfbWRI0di165dbQ7ARERkWbHdI/DevcuwfPY98HEzzP+uFwT8eWwvFr2/Ev/bswn1anWb2raRSjAh2BWvDQ3EjDA3cVo1rQDsLdHhkPMMTL9rPWZPfBhODi4AAJ1ei80HfsbiFxPx3o//RFF5nnlOlIg6jXbdCW4oJycH+/fvR16e4YMiMDAQw4cPR3Dw9RtbdeTIESQnJ6OwsBB2dnbw9/fHyJEjERjY9p/TLuOdYCKi60OlUWPd3zvx097NqFVfmePdw9kNC8ZMw+T4YZC34+G5GrUOW3OqsT23Birdla9AG6kEw7wEqDPXYdOer1FbrxC3yWU2mDBkNm6b9CB8Pdr/nUJElnHdhkNYE4ZgIqLrq0JRje92/YGNx/ZB3+CrKtDDB3ePm4GRveIhlbb9B80atQ6bs6uxI7cGav2V9m2lEgz3EqDKXIuNe75pMgzfOuF++Ht1b/OxicgyGIItgCGYiKhjXCwuwNc7NmD/uZNG5T39u+OexJsxIKLtq5ACQJVah80Xq7AzTwFNgzBsJ5NguJcedelrsXHvN6irV4rbpBIpRidMx9wJDyA0ILJdxyci82EItgCGYCKijpWSk4kvtq1H0sU0o/L4sCjck3gzogJD29V+pUqLjRersTu/Btorz8/BVirBYA8dVJlrsWXft0ZhGACGxiXitokPIiq0b7uOT0TtxxBsAQzBREQdTxAEHE0/iy+3r0dGYa7RtpG9+mPh2Ono7u3frmNUqLTYmFWNPfk10Db4hpRJgEHueiDnd/y17ztUKyuM9ouPGo7bJj2AuJ5DmnxYnIgsjyHYAhiCiYg6D71ej93Jx/D1jg0oqCgVyyWQYGyfgVgweiqCvHzbdYzyei22ZFdjT77CaMywVAL0d5fCsXAL/tr3Fcoqi4z2iw7th9smPYjBvccxDBNdZwzBFsAQTETU+Wi0Wmw6vh8/7N6ICuWVhZGkEgnGxw3GHaOnIsDDu13HqFbr8FeO4QG6+gazSUgAxHnI4Vm+C9v3fIGC0myj/UIDojAn8V6M6j8NNnLbdvWBiFqHIdgCGIKJiDqvOrUKvx/ejV/2b0V1nfEDbBP6DcH8UVPg7+7VrmMoNTrsyK3BXzk1UDYcNAygVzc5/GsOYc++z3Gx4LzRNs9uvrh5zN2YMvx2cR5iIrIMhmALYAgmIur8alX1+O3wLvy6/y/U1NeK5TKpFJPih2H+yCnw6ebRrmPUa/XYna/AluxqVKl1RttCneUIVZ3E8UNf4VzWSaNtDvZOmDLsNswcczd8PALa1QciahpDsAUwBBMRdR3K+jqsO7QTaw5sg1JVJ5bLpTJM7j8Mc4dPgp+7Z7uOodEJ2FegwKbsKpTVG4dhLzsZYqWZyEj6AUfO7EDDr1qpVIbRA6Zh1rjF6BEc264+EJExhmALYAgmIup6FHW1WPv3dqw9uMNo9TmZVIrxcYNx+4hJ7X6ATqsXcLRYic3Z1chRaIy2OdtIEe9QhrLU1dh9ZB00WuOln/tFDcPs8YsxIGYUH6IjMgOGYAtgCCYi6rqqa5VYc3Ab1h/aiTq1SiyXQIJRsf0xb+RkhPsFtesYgiDgbEU9Nl+sxtmKeqNtNlIJBripoLv4B3Ye/LHR9GrBfhGYOXohxg+6GfZ2ju3qB5E1Ywi2AIZgIqKur7pWifWHdmL9oR1Q1NcZbRsaFYd5I6cgOii03cfJrlFjS3Y1Dhcr0WB2NcOMEt0kcC7Zif1/f4v8kotG+zk7uGLSsLm4adQC+Hq2L5QTWSOGYAtgCCYiunEo6+vwx9E9WHNwOyqVNUbb+ofHYP6oyegT0rPdQxTK6rX4K8cw17BKZ/x1G+QoRXDdCZw7+T8kZxwx2iaVSDEkLhEzR9+FPj0Hc6gEUSsxBFsAQzAR0Y2nXq3GpuP7sPrANpRWGw9RiAkKw63DJmBodF/IpNJ2HUep0WF3ngLbcmsazSjhYiNFb5s8lKauxYETfzQaNxwWGI2Zo+/CmIQZsLO1b1c/iG50DMEWwBBMRHTjUms12HbqEH7et8VoBToACPDwxpyhiZjQbwjsbNq36IXhIbpabMutxoVq47ArkwBxLmpI8zbhwOGfUVZlvBKdq5M7Jg+bi6kj5nGoBNE1MARbAEMwEdGNT6fTYVfyMazevxWZRXlG29wcnTFz0BjMGDQaro7O7T5WRpUK23KqcbSk1mjcMACEOcvgX/M3Tp/4CalXzTcskUiQ0Gs0po2Yj4TY0ZBJZe3uC9GNgiHYAhiCiYishyAIOJaRgtX7t+LEhVSjbXZyG0yKH4bZQ8fDv51LMgNAeb0WO/NqsCdfAYXGeCU6N1sZoiRZKEldi0OnNkGrM56Czcc9AJOH345Jw26Fh2v7+0LU1TEEWwBDMBGRdUrLz8avB/7C7uTj0AtXQqpUIsGImHjMHpaImKCwdh9HrdPj7yIltuXUIE9pHHYlAKId62BTsBXHjv+Kkop8o+0yqRzD+k7AtJHzEddzCB+kI6vFEGwBDMFERNatsKIM6/7ejk3HD6BeozLaFh0YiluGjMPIXv0hl7VveIIgCEitVGFHbg1OlDYeKuFtJ0F31SnkpqzHyXN7cPXXeJBvOKaOmIfEwbPg4ujWrr4QdTUMwRbAEExERIBhruE/ju7Bb4d2oUJZbbTN08UN0xNGYVrCSHRzcmn3sSpUWuzNV2BPvgIVKuNZJeRSIMauCvrcTThyfC0qa8qMttva2GFEv8mYNPRWTrNGVoMh2AIYgomIqCG1RoOdZ45g3d87kVmUa7TNRibHuD4DcfOQcYho50p0AKDTCzhVVoeduTWNVqMDgEAHwLfmMC4kr0NyxuFG2/29umPS0FuROHgWPLu1b5loos6MIdgCGIKJiKgpgiDg9MU0rPt7Jw6mnoL+qq/VuJCeuHnwWLPMNwwARbUa7MpTYF+BArVa4wfpbKQS9JQVQXXxd5xI+hOK2iqj7VKJFAmxozFp6FwM6j0GcplNu/tD1JkwBFsAQzAREbWksKIMG47swubj+xsty+zj5oGpA0ZgcvwweLi0f6yuWqfHkeJa7MqrQeZVcw4DgJetDv41R5B3/g+cSTvYaLu7ixfGD74Fk4beiiDf8Hb3h6gzYAi2AIZgIiJqrTpVPbYlHcL6Q7uQU1potE0mlWJETDymJ4xCXGj7l2YGgByFGnvzFThYqGx0d1gqASJsKiDL/wtJSb+htLKw0f69wgdg4pDZGBE/BU4O7R/LTNRRGIItgCGYiIhMpdfrcTzzHNYf2okjackQYPyV293LD9MSRmJC3yFwdnBs9/E0OgHHS2uxN1+BlCbGDrvKgRDNGZSlb8TJszsazTtsa2OHIX0SMX7wLRgQPQIymbzdfSK6nhiCLYAhmIiI2qOgohQbj+3D5uP7UVWrMNpmJ7fBmD4DcVPCKEQGhpjleCV1GuwrUGJ/QeOZJQAg2FYJx+LdSD+7ATmFaY22u7t4YUzCTRg/+BaEB8ZwdgnqEhiCLYAhmIiIzEGt1WB/ykn8cXQvTl9sHD57+nfHlAEjMLZ3ApzsHdp9PL0g4ExZPfYWKHCqtBa6q771ZRAQimxoc7chOXkTqpUVjdoIDYjC+EE3Y2zCDM4uQZ0aQ7AFMAQTEZG5ZRXn48+je/HXqb9RqzIevmAnt8Go2AGY3H8YenfvYZY7sVVqHf4uVOJAgQK5V61KBwBOUh0C65NQdWELTqfuglZrXEcqkaJf1DCMGzQTQ/okcvwwdToMwRbAEExERJZSp1Zh1+kj+P3oHqQX5DTaHuTpg0nxwzCh7xCzzCwBADk1ahwoVODvIiWq1fpG271l9fCo3I/ctE1IyzrRaLutjR0Gxo7BmAE3YWDsGNjZ2pulX0TtwRBsAQzBRER0PaTlZ2Pzif3YkXQESpXxNGtSiRSDI3tjcv/hGNQjFrJ2LtEMGBbiOFtRjwMFCpworYPm6nWaAQSiGDZFO5GeshHF5bmNtjvYO2FY3ASMHnAT4qOHcf5h6jAMwRbAEExERNeTSqPGvpST2Hx8P05lnW+03cPZDYl9B2N83CCE+Qaa5Zi1Wj2OFitxoECJtCpVo+0SCAhQp0Eo2IXzqX+hWlHeqI6rkztG9JuM0QOmoXePQZCaYYEQotZiCLYAhmAiIuoo+eUl2HLiALaePIiymqpG23v4BSOx72CM7TMQ7s7m+Y4qqdPgYKESh4qUKKzVNtouhx4BqrNQ5+1AauoOKOtqGtXxdPPFqP5TMWrANESF9OUME2RxDMEWwBBMREQdTafT4WjGWWw+fgB/n0+CTn/1whhSJPTohcS+gzE0Kg52NrbtPqYgCMhRaHCoSInDRUqUNzHdmh008K9LgjJnB1JSd0GtaTxHsbe7P4b3m4QR/aYgJiyed4jJIhiCLYAhmIiIOpNKZQ12nTmK7acOITX/YqPtjnb2GNWrPxL7DkHv7hFmCZ16QUBGlQqHi2pxpFiJGk3jB+rsBRW8FUdRfXE70jIONFqQAwA8XH0wvN9EDO83Cb0jBnJRDjIbhmALYAgmIqLOKrukENuTDmHbqUMoqW48z6+vmwfGxQ3CmN4JZhs/rNMLSKmox+EiJY6X1KLu6gmIAdjqFPCoOgxF7m5kXjjUZCB2c/bA0LgJGBE/GX0jh/ChOmoXhmALYAgmIqLOTq/X4/TFdGw79Tf2nD2OOnXjh9tCfQIwpncCRscOQKCnj1mOq9EJOF1eh6PFSpwsrYOqiUAs1ynhVXMUtbl7kJF5ABqtulEdZ0c3DO2TiOH9JqFf1DBOu0YmYwi2AIZgIiLqSurVahxMPYW/Tv2N4xkp0DfxdR8ZEIIxvRMwKrY/fNw8zHJcjU5AcnkdjpXU4kRpLeq0TQXiOngpjqM+fy/S0/c2OYbYztYBA2JGYkifRAzqPQZuzubpH93YGIItgCGYiIi6qvKaKuw9ewK7zhxFck5Gk3V6d4/AmN4JGNmrv9lmmNBeGjJxtFiJEyV1UGobjyGW6OrgqTgJTcE+XMjYg3pVbaM6UokUvcIHYEjceAzpk4hAn1Cz9I9uPAzBFsAQTEREN4LiynLsTj6GXWeOIq0gu9F2qUSCvmFRGNmrP4ZH9zVrIE6trMex4locL6lt8qE6QaeGa/VJSEoOIu/CPtQoG89DDADBfhEY0mc8hvZJRFRoP840QSKGYAtgCCYiohtNXlkxdp45il2njyC7tLDRdgkk6B3SAyNi+mF4TD+zDZnQ6QWkValwrKQWJ0tqm5x2TRB0cFSch33Z3yjJ3o/i0gtNttXNxRODe4/DoN7j0C9qKBztnc3SR+qaGIItgCGYiIhuVIIgIKs4H7vOHMWuM0dRUFHaZL2owFCMjInHiF7xCPDwNtuxsxUanCytxcmSOmQrGj8wBwDy2ly4Vh5Bdd4+5OSeQlPxRS6zQe8eAzGw12gMjB2DIN9wLtBhZRiCLYAhmIiIrIEgCEgvyMG+lBPYe/YEcsuKmqwX7huEEb3iMTImHt29/cwWNsvqtWIgTq2sRxMTTUBQVcC16hg0hQeQe/FQkw/WAYCvZ5AYiOMih8De1sEsfaTOiyHYAhiCiYjI2giCgIslBdh39gT2pZxAZlFek/UCPXwwNCoOQ6LiEBscDplMZpbj12r0OF1WhxOltThTVtfkXMR6bT1sK0/BruIYSnMOoqIyv8m2bOS26Bs5BAm9xmBg7GgEeIeYpY/UuTAEWwBDMBERWbu8smLsSzmBfWdPNLlKHQC4ODhhcM/eGBLVBwMiesHJ3jx3Xy8/WHe6rA5JZXUoqtU2qiMIAqDIhkv1CdQXHkJe7gnomligAwACfcIwIGYk4qOHI67nYI4lvkEwBFsAQzAREdEVxZXl2JdyAvvPnUJydnqT8xDLpTL0DYs03CWOjINPN/PN9VtUq8HpsjqcLqvDucp6NDH7GvSaWthXnYK87AiKsw+iuqa4ybZkUjliwuIRHz0c/WNGoGf3PpBJzXM3m64vhmALYAgmIiJqWnWtAofTkvF3ahKOpp9FrbrpMboRfkEYGhWHQT37IDKgu9mmNlPp9DhXUY+ksjokldZdY7YJAbqaTDhWHIOq6AiK8k9Br29cDwCcHVzRN3Io+seMQHz0cPh7dTdLP8nyGIItgCGYiIioZRqtFkkX0/B3ahIOpiahuKrpuX7dHJ0xIKIXBvaMRUJEL7g5mWc4giAIyFdqxGET6VWqJh+u02sUQHkS7CpOoLrgKCoqGs+ZfJmfVzD6R49E/+jhiIscAhdHN7P0lcyPIdgCGIKJiIhMIwgCMovycDA1CYdSk645jlgCCSIDQzCwRywG9oxFZEAIZGa6S1yn1eN8ZT2Sy+txtrwOBU2MJQYArbIQdpUnICk7gZLcI6irr266rxIJIoJ6Ia7nEPSNHILYiAQ4ObiYpa/UfgzBFsAQTERE1D5l1ZU4lHYGR9KScSLz3DWHTbg6OGFAj14Y2CMWCT16oZuT+UJmWb0WZ8vrLoXi+iaXchYEHbQVabCvOgl18TGUFiRBp286PEulMvQM7o24yMHo29MQiu3tHM3WXzINQ7AFMAQTERGZj0arxdmcTBxJT8aRtGRcKG56+jUJJOgZ0B39w6MRHx6N2OAI2NrYmKUPekFAdo0ayeX1SC5vZuiEtg6a0iTYVZ5AfclJlJekXbNNmVSOqNA4xPUcgrjIIegV1h92tvZm6S+1jCHYAhiCiYiILKekqgJH05NxJD0ZxzOufZfYTm6D3iE9EB8ejf7hMQj3DTTbA3b1Wj1SK+txrqIeKRX1yFVo0FRI0qmqoC07BbvK06grOYmKssxrtimX2yA6pB9iIxLQu0cCYsL6c/iEBTEEWwBDMBER0fWh1elwNicDR9IMofhai3QAhgfs+oVFoX94NPpHxMC3m6fZ+qHQ6JBaocK5S8E4X9n0nMO6+nJoy07BtvI0aotPoKqZh+ykEinCAqPRu8dAxEYkIDYiAR6u5lmCmhiCLYIhmIiIqGNUKKpxIvMcTmSew7HMcyitrrhm3QAPb8SHRaNvWCTiQnrCw8V8MzlUqXVIrTAE4nMV9Siqa3qcsK6uFOqSk5BXJqGu+CQU1dcO8QAQ4B2C2IiB6H0pFAd4h5htGWprwxBsAQzBREREHU8QBOSWFRkCccY5nMpKRa2q6aETABDs5Ye+oT0RF2r+UFyh0oqBOLVChZL6ZkJx2WnIKpOhLTuDyrJ0oMmBFgburt6IjRiA2PAExITFIzwoBjZyW7P1+0bGEGwBDMFERESdj06nw/n8izh26U5xSk4mtNdYBAMAgjx90Tc0EnGXgrGnmUNxWqUKaZUqnK+qR941xhTr1Qqoy5MhqTgDoeIMqkpSrrm8MwDY2tihR3BvxITFIyYsHtGh/eDZzdds/b6RMARbAEMwERFR51enqkdyTiaSss4jKSsNqflZ0OmbWFP5koahuHf3HvB2czdbX5QaHdKrLoXiShWyapqefULQqaGuOAdd2RlIKs9AUXIGGrWy2bZ93AMQfSkU827xFQzBFsAQTERE1PWYGop93DwQ2z0CscERiO0egVCfALMt3KHW6ZFZrUZaZT3OV6qQUa2CqolULAg6aKsyoS47C1SlQFt+FsoWxhXbyG3Rs3tvRIcaQnFkSBy83f2tbmwxQ7AFMAQTERF1fXVqFc5eCsWnss4jNa/5UOxoZ49eQeGGYNw9AlGBoXCwtTNLX3R6AblKNTKq1MioUiGjqplxxfUV0JSnQFtxFpKqc1CWpkCrvfZYaADo5uKJyO5x6Nm9NyJD4hAZEoduLuabPaMzYgi2AIZgIiKiG0+dWoWUnEwkXUxDcnYGzuVlQaVRX7O+TCpFD79g9Ooegd7dI9ArKByert3M1p8qtQ6ZlwJxZrUKF6rVUOubuFus10FbbbhbrK80hOO6Fu4WA4ZhFD1D+hhC8aWAfCPNW8wQbAEMwURERDc+rU6HjMIcJGdn4Ex2Bs7mZKBcUd3sPl6u7ogODEVMUBiig0LR0z8E9rbmGZ+r1QvIa+3dYlUlNOVnoS5PBapSoapIhUbVfN8BINAnDJEhfRDZ3XC3ODwwussu/cwQbAEMwURERNZHEAQUVJQaheKLJQXN7iOVSBHuG4jooFBEB4YhOigMQZ4+ZlvZrkqtw4UqFbJq1LhQbfinQtN4SIcgCNDVFkBTkQp1RSokVedRV34eOm1ds+1LJBIE+oShR1AsIoJ7ISLI8OfqbL6HBi2FIdgCGIKJiIgIAKprFTibcwHJORk4l3sB5/Mvok6tanYfJzsHRDW4WxwZEAJ3Z/PkCUEQUFqvRVa1Ghdq1Mi6FIyv+dBdTQ40FeegrkgFqs6jviIDev21p2i7zNvdHz2CY8VQHBHcC17dOtfDdwzBFsAQTERERE3R6fXILinAudwLOJeXhZTcC7hYXAChmQUxAMMwisiA7ujp3x09L/3TXMFYLwgorNUiq1olBuNshRraJp4BFPQaaKoyoalIhaYyDfqqdKiqLkBoRTB2dXa/FIoN4Tg8KBqB3qGQyeRmOQ9TMQRbAEMwERERtVatqh7n8y8iJfcCzuVmITXvQotjiwHLBuPL44uzqtXIVqiRXaNGrkJzjQfvNNBWZ0NTlQZNZTp0VemGf7YwlAIwLO4R4tcTYUHRCAuIRnhgNCKCY6/LA3gMwRbAEExERERtJQgCiqvKcS43C+fyLiAtPxvpBTmoVTc/zRkAeLu6i4G4h38wIvyC4eniZpZhCDq9gMJajRiKs2vUyFZoUNvELWNB0EOnzIem0hCINVWGcKytr2jxOA/MWYGZYxa2u78tYQi2AIZgIiIiMie9Xo/88hKcz7+ItIJsk4Kxq4MTIvyCEO4XhAi/YIT7BqK7tz/kMlm7+yUIAsrqdVcFYzUqVI2XoxYEAfr6Mmgq06CpyoCmKhO66gxoavKABsNB/vXw90iIHtLuvrWEIdgCGIKJiIjI0vR6PfLKi5GWn21yMJZLZQjx8Ue4b5AYkMN9g+Dq6GSWvlWrdYYhFErDMIpchRoFSg20TSRJvbYO2uosw1jjqgwsuulxTO8ZaJZ+NNtHhmDzYwgmIiKijtAwGGcW5SGzMBcZhbmoULY8xhgwDKcwBOJAhPoEINQnAIGePrCV27S7b1q9gKJajSEUK9XIuRSOr75r/FS8L6Ld7dt9vJYwBFsAQzARERF1JuU1VQ1CcQ4yi/KQU1oEvXDtZaAvk0qkCPL0QahPAEJ8/MVwHODuDZkZhlQoNToxGOcqNJgd0Q3ONu1vtyUMwRbAEExERESdnUqjxsXiAmQU5Yp3jDOLclGrank4BQDYyOQI9vJrFI593TzMttiHJTEEWwBDMBEREXVFgiCgsLIMWcX5uFhcgKzifGQV5yOntBAaXdNLMF/N3sYOwV6+6O7th2AvP8NrL38EeHjDRt4xcwI3hSHYAhiCiYiI6Eai0+mQX1HSKBznlhW3akgFYBhW4e/udSkg+yPYy1cMyS4O5nkgzxQMwRbAEExERETWQK3VIK+sWAzFWcUFuFicj8LKUuhNiI3uTq5iKE7sOxix3SMs2GsDU/Ja57l/TUREREQdzlZugzDfQIT5Gk9pptZokFdejJzSImSXFiJH/CuCSqNu1E6FshoVymokXUxD75Ae1yUEm4IhmIiIiIhaZGvTdDjW6/Uora5sFIxzSgvFpaK7e/l1RJebxRBMRERERG0mlUrh080DPt08kNCjl9E2RV0tckoLEeLt30G9uzaGYCIiIiKyCGcHR8QEh3d0N5rU+Sd8IyIiIiIyM4ZgIiIiIrI6DMFEREREZHUYgomIiIjI6jAEExEREZHVYQgmIiIiIqvDEExEREREVochmIiIiIisDkMwEREREVkdhmAiIiIisjoMwURERERkdRiCiYiIiMjqMAQTERERkdVhCCYiIiIiq8MQTERERERWhyGYiIiIiKwOQzARERERWR2GYCIiIiKyOgzBRERERGR1GIKJiIiIyOowBBMRERGR1WEIJiIiIiKrwxBMRERERFaHIZiIiIiIrI68ozvQVQiCAACorq7u4J4QERERUVMu57TLua05DMGtVFNTAwAIDg7u4J4QERERUXNqamrg5ubWbB2J0JqoTNDr9cjPz4eLiwskEonFj1ddXY3g4GDk5OTA1dXV4scjIvPgtUvUdfH67foEQUBNTQ0CAgIglTY/6pd3gltJKpUiKCjouh/X1dWVFyJRF8Rrl6jr4vXbtbV0B/gyPhhHRERERFaHIZiIiIiIrA5DcCdlZ2eHlStXws7OrqO7QkQm4LVL1HXx+rUufDCOiIiIiKwO7wQTERERkdVhCCYiIiIiq8MQTERERERWhyGYiIiIiKwOQ3AncuDAASxZsgS9evWCm5sbXF1d0atXL9x///3Yv39/R3ePqMuqrKzEunXrsHTpUowaNQp+fn6ws7ODs7MzunfvjptuugnvvvsuKioq2tT+6dOn8fjjjyMuLg4eHh5wdnZGVFQU7rjjDmzevLnN/c7MzMTzzz+PAQMGwNvbGw4ODoiIiMAtt9yCX3/9FVqtts1tE3V1WVlZcHJygkQiEf9eeOEFk9rgtWvlBOpwCoVCuOeeewQAzf4tWrRIUCgUHd1doi4jJSVFmD59umBra9vi9QVAcHR0FN555x1Br9e3qn2NRiMsX75ckEqlzbY7bdo0obi42KS+v/vuu4KdnV2z7Q4ZMkTIyMhoy78aoi5v0qRJja6JlStXtmpfXrskCILAENzBtFqtMHHiRKOLw8HBQUhISBCGDBkiuLq6Gm2bOHGioNVqO7rbRF3C6tWrG335yGQyISoqShg1apQwfPhwwcPDo1Gde++9t1VB+Or/ebWxsRH69u0rDB8+XPD09DTaFhcXJ9TU1LSq3y+99JLRvlKpVOjdu7cwatQowd/f32hbUFCQkJ+f395/VURdynfffddkuGxtCOa1S4LAENzhli9fbnRR3HfffUJZWZm4XaFQCCtWrDCq8+yzz3Zgj4m6jsshWC6XCzfffLOwfv16oaqqyqiOXq8X1q9fLwQGBhpdZx999FGzbX/66adG9WfMmCHk5uaK29VqtbBq1SpBLpeLdebPn99inzdv3ixIJBJxn6FDhwqpqanidp1OJ/z000+Cs7OzWGf48OEm/psh6rpKSkoELy8vAYAQExMjBAQEmBSCee3SZQzBHSgvL0+wt7cXL4Y777zzmnWfe+45sZ69vb2Ql5d3HXtK1DWtX79euPfee4WLFy+2WDc7O1vw8/MTrzMvLy9BrVY3WVepVBrVHTNmzDV/ofn888/FehKJRDh27Ng1+6DX64W+ffuK9aOiogSlUtlk3b/++svoi3zt2rUtniPRjWDBggXif/e7d+8WQkJCWh2Cee1SQwzBHeipp54SLwJHR0ejO8BXU6lUQnBwsFh/2bJl17GnRNbh6jtE27Zta7Lehx9+aPTlePbs2WbbHTx4sFh/7ty516z3559/Gh1/8+bNzbZ72223iXUHDRrU8gkSdXFbtmwR/5tftGiRIAiCSSGY1y41xNkhOtC6devE13PnzoWHh8c169ra2mLRokXi+7Vr11q0b0TW6KabbjJ6f+7cuSbrNbz+Ro8ejZiYmGbbXbJkifh648aNUKlULbYbFhaGiRMntrrdw4cPIzc3t9n6RF1ZbW0tHnjgAQCAl5cX3njjDZPb4LVLDTEEd5DU1FSkp6eL7ydPntziPlOmTBFfp6enIzU11SJ9I7JWV/+PaHV1daM6CoUCe/bsEd+beu0qFArs2rWryXp//vmn+HrSpEmQSCTNtjty5Eg4OTk1uT/RjWbFihW4cOECAODNN9+Ep6enSfvz2qWrMQR3kFOnThm9Hzp0aIv79O/fH7a2tuL7pKQks/eLyJpdvHjR6L2Pj0+jOmfPnoVGoxHft+ba9fPzQ2hoqPi+qWu3uLgYhYWFJrUrl8sxcODAZtsluhEcO3YM7733HgDDHdyFCxea3AavXboaQ3AHSUlJEV/b2toiODi4xX2urtewDSJqv6uHGTX1ZXb1dRcREdGqthvWa+ratVS7RF2dVqvFvffeC51OB1tbW3zyySdtaofXLl2NIbiDZGVlia+DgoJa/Onksu7duzfZBhG1T1VVlXinCQDi4uLQq1evRvUaXndyuRz+/v6tar+la/fqsob129MuUVf31ltv4eTJkwCAp59+GtHR0W1qh9cuXY0huIPU1NSIr93c3Fq9n6ura5NtEFH7PPHEE0Y/ab788stN1mt43bm4uEAqbd3HaEvX7tVlrf1c4GcC3cgyMjLw4osvAgB69OiBZ599ts1t8dqlqzEEdxCFQiG+tre3b/V+Dg4OTbZBRG33+eef44svvhDf33bbbY1mirjMUtfu1WWtbZufCXQjW7JkCerq6gAAH3/8sUnX3NV47dLVGII7iFarFV/L5fJW79ewbsMB/kTUNnv27MFDDz0kvg8LC8Onn356zfqWunYbtmtK2/xMoBvVV199he3btwMA7rjjDiQmJrarPV67dDWG4A7i6Ogovq6vr2/1fg3rNpxehYhMd/LkScyYMQNqtRqAYTaIzZs3N/tzpqWu3YbtmtI2PxPoRlRcXIwnn3wSAODu7o6333673W3y2qWrMQR3EGdnZ/H15Z96WqO2trbJNojINKmpqZg0aRKqqqoAGL5ot27disjIyGb3s9S1e3VZa9vmZwLdiJYuXYry8nIAwGuvvdbkdIWm4rVLV2MI7iBeXl7i64KCglbv1/DBHVMnCicigwsXLiAxMRHFxcUADA/JbNq0CX379m1x34bXrkKhaPVYvpau3YbtAq3/XOBnAt1oDh48iJ9//hmAYZrC++67zyzt8tqlqzEEd5CoqCjxdVlZmdH/ETYnJydHfN3WaWKIrFlubi7Gjx8vLlPq6OiIP/74A4MHD27V/g2vXQDIzs5u1X4tXbuWapeoqykqKhJfHzx4EFKpFBKJ5Jp/DRe5efHFF422NZx6jNcuXY0huINcvV755TkQm5OXl4eSkpJrtkFEzSsqKkJiYqK49KqdnR3Wr1+PUaNGtbqNtly7Go0GycnJ12wDAHr27Gn0oExr2gWAEydONNsuERnw2qWrMQR3kEGDBsHOzk58v2/fvhb32bt3r/ja3t4egwYNskjfiG5EZWVlSExMRGpqKgDAxsYGv/76KyZMmGBSO+Hh4QgKChLft+baPXbsmNGvPU2FbltbW6O70a1pt7CwEOnp6c22S9TV2NjYwM3NrdV/DRebsrOzM9rWcC5gXrt0NYbgDuLs7Izx48eL73/44YcW92lYZ/z48XyalKiVqqqqMGnSJJw5cwYAIJPJ8OOPP2L69Oltam/GjBni69WrV4uzS1xLw2s3Njb2msuqzpw5U3y9bds2o5+FW2q3W7du/CKlG8K0adNQWVnZ6r+GK68988wz19wG8NolYwzBHejuu+8WXyclJeH333+/Zt3jx49j06ZNTe5LRNemVCoxbdo0HDt2DAAglUrxzTffYM6cOW1us+H1V1pa2uy8wrm5ufjmm2+a3Pdq8+bNE38h0mg0eP31169ZV6FQ4P333xff33HHHbCxsWlF74msF69dMiJQh9Hr9ULfvn0FAAIAwd/fX0hJSWlULz8/X4iJiRHr9evXT9Dr9R3QY6Kupb6+XkhMTBSvHYlEInzxxRdmaXvGjBliu87OzsK+ffsa1amqqhJGjhwp1vPz8xNqa2ubbXfp0qVifZlMJvz666+N6qjVamHOnDliPQcHByEvL88s50XU1YSEhIjXwsqVK1usz2uXLpMIgiBcv8hNVzty5AhGjx4tzivo6uqKBx98EKNGjYJcLsfhw4fxwQcfiD+tODg4YPfu3Rg4cGBHdpuoS3j99dfx9NNPi+/d3d1NGks/YcIEPPHEE01uy8rKwsCBA1FaWgrAMBZx8eLFmDhxIpydnZGUlIRVq1aJD+FJpVKsX7/+mssxX1ZRUYHBgwcjLS1N3G/+/Pm4+eab4eHhgdTUVHz88cdISkoS9/nggw+MVr0jsiahoaHiDBErV67ECy+80Gx9Xrsk6ugUToKwZs0awcHBQfw/w2v9OTg4CGvWrOno7hJ1GStXrmzxumrub+HChc22v3//fsHDw6PFdmQymbBq1apW9zs1NVUIDg5uVR+XLVvWzn9LRF2bqXeCBYHXLhlwTHAnMGvWLBw7dgyJiYlGT7leJpFIMH78eBw9ehSzZs3qgB4SUVOGDRuGpKQkzJ4922iKpIYGDhyIPXv24OGHH251u5GRkUhKSsLixYvh4ODQZJ2YmBj89ttv+M9//tOmvhNZM167BAAcDtHJ5OTkYP/+/cjLywMABAYGYvjw4QgODu7gnhFRc0pKSrBnzx7k5uZCrVYjICAACQkJjSbSN1VNTQ127NiBnJwcKJVK+Pv7o0+fPoiPjzdTz4msG69d68UQTERERERWh8MhiIiIiMjqMAQTERERkdVhCCYiIiIiq8MQTERERERWhyGYiIiIiKwOQzARERERWR2GYCIiIiKyOgzBRERERGR1GIKJiIiIyOowBBMRERGR1WEIJiIiIiKrwxBMRERERFZH3tEdICKi6+fkyZNYv369+P6xxx5Dt27dOqw/REQdRSIIgtDRnSAiouvj66+/xqJFi8T3Fy5cQGhoaMd1iIiog3A4BBERERFZHYZgIiIiIrI6DMFEREREZHUYgomIiIjI6jAEExEREZHV4ewQRERWQCKRmLzPzp07MWbMGPN3hoioE+CdYCIiIiKyOlwsg4jICshkMgCAIAjQ6/WNypvSlrvHRERdBe8EExFZAa1WC61Wiy+++MKoPD09Xdx29d/o0aM7qLdERJbHEExEREREVochmIiIiIisDkMwEREREVkdhmAiIiIisjoMwURERERkdRiCiYiIiMjqMAQTERERkdVhCCYiIiIiq8MQTERERERWhyGYiIiIiKwOQzARkRWxsbExeq/T6TqoJ0REHYshmIjIiri4uBi9r6io6KCeEBF1LIZgIiIrEhoaavT+yJEjHdMRIqIOJhEEQejoThAR0fWh1Wrh5eWFqqoqAEBAQAA+//xzjBkzBg4ODh3cOyKi64d3gomIrIhcLseiRYvE9/n5+Zg6dSocHR3h6OgIZ2dn8W/v3r0d2FMiIstiCCYisjIvv/wyRowY0ai8rq4OSqVS/ONDc0R0I2MIJiKyMk5OTti1axd++uknzJ07F5GRkXBxcYFUyq8EIrIeHBNMRERERFaH/9tPRERERFaHIZiIiIiIrA5DMBERERFZHYZgIiIiIrI6DMFEREREZHUYgomIiIjI6jAEExEREZHVYQgmIiIiIqvDEExEREREVochmIiIiIisDkMwEREREVkdhmAiIiIisjoMwURERERkdRiCiYiIiMjqMAQTERERkdVhCCYiIiIiq8MQTERERERW5/8B/+WQrgwrBLgAAAAASUVORK5CYII=", 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", 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" ] @@ -1393,13 +1392,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "199f302e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -1449,7 +1448,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "id": "41cc501a", "metadata": {}, "outputs": [], @@ -1459,7 +1458,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "id": "88e222ba", "metadata": {}, "outputs": [ @@ -1470,16 +1469,18 @@ "Fit correlation class instance: \n", " \n", "Result of fitting The Real Part Of |Result of fitting The Imaginary Part \n", - " the Correlation Function with None terms: | Of the Correlation Function with None terms: \n", - " | \n", - " Parameters| a | b | c | d | Parameters| a | b | c | d \n", - " 1 | 1.08e-01 |-2.36e-01 || 1.13e-06 |2.76e+00 | 1 | 1.51e+00 |-3.82e+00 || 2.00e+00 |1.94e+00 \n", - " 2 |-6.76e+00 |-1.73e+00 ||-2.87e-01 |1.48e+01 | 2 |-1.17e+01 |-4.49e-01 || 1.74e-08 |-1.34e-01 \n", - " 3 | 8.15e+00 |-2.35e+00 ||-1.00e+00 |3.83e+00 | 3 | 2.16e+01 |-1.41e+00 ||-1.55e-08 |-1.81e+00 \n", + " the Correlation Function with 3 terms: | Of the Correlation Function with 5 terms: \n", " | \n", - "A normalized RMSE of 5.90e-05 was obtained for the The Real Part Of |A normalized RMSE of 4.67e-05 was obtained for the The Imaginary Part \n", - " the Correlation Function | Of the Correlation Function \n", - " The current fit took 5.148684 seconds | The current fit took 1.124591 seconds \n", + " Parameters| a | b | c | Parameters| a | b | c \n", + " 1 | 2.24e-01 |-3.43e-01 |6.57e-18 | 1 |-5.20e+00 |-4.65e+00 |1.20e+00 \n", + " 2 |-9.60e-01 |-4.96e+00 |3.80e+00 | 2 | 2.77e+00 |-4.68e+00 |2.77e+00 \n", + " 3 | 2.26e+00 |-2.23e+00 |4.28e-12 | 3 |-1.68e+00 |-3.68e-01 |4.72e-03 \n", + " | 4 |-6.72e+00 |-2.13e+00 |4.69e-01 \n", + "A normalized RMSE of 8.22e-05 was obtained for the The Real Part Of | 5 |-4.63e+00 |-1.04e+00 |7.08e-02 \n", + " the Correlation Function | \n", + " |A normalized RMSE of 5.01e-06 was obtained for the The Imaginary Part \n", + " | Of the Correlation Function \n", + " The current fit took 0.141887 seconds | The current fit took 3.001019 seconds \n", "\n" ] } @@ -1491,7 +1492,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "68f7ff0d", "metadata": {}, "outputs": [ @@ -1499,7 +1500,7 @@ "name": "stdout", "output_type": "stream", "text": [ - " Total run time: 19.60s*] Elapsed 19.60s / Remaining 00:00:00:00\n" + " Total run time: 105.94s*] Elapsed 105.94s / Remaining 00:00:00:00\n" ] } ], @@ -1516,7 +1517,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "d981536c", "metadata": {}, "outputs": [ @@ -1531,7 +1532,7 @@ " 2 | 3.27e+00 | 1.44e+00 |1.80e+00\n", " \n", "A normalized RMSE of 9.11e-05 was obtained for the The Spectral Density\n", - " The current fit took 0.177001 seconds\n" + " The current fit took 0.296087 seconds\n" ] } ], @@ -1542,7 +1543,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "13300786", "metadata": {}, "outputs": [ @@ -1550,14 +1551,14 @@ "name": "stdout", "output_type": "stream", "text": [ - " [ 1% ] Elapsed 0.05s / Remaining 00:00:00:04" + " [ 2% ] Elapsed 0.12s / Remaining 00:00:00:05" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " Total run time: 2.23s*] Elapsed 2.23s / Remaining 00:00:00:00\n" + " Total run time: 2.40s*] Elapsed 2.40s / Remaining 00:00:00:00\n" ] } ], @@ -1573,13 +1574,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "765a0633", "metadata": {}, "outputs": [ { "data": { - "image/png": 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UFMQLL7yQr2Mpakrr8T99+pQrV66wcOFCunXrpn5v/vOf/xg5sizkuoikmEsrmc7LlJXc1pBQFEW5dOmSUr16db32OXXq1Hwdb3GqIaEoilKttq167GMH9zR2OEIIkYnUkChcGzZsUP9fmDZtWr7zmzJliprf9OnTM61PTU1VhgwZoqZZsmRJpjTp38aamJgoBw4c0Lm/3KRVFEXx8/NT069YsSLLNHFxcUr37t0VQKlVq5aSlJSUYX1Ob6QfPHigREZG6owhISFB8fDwUN/kJycnZ0qjz1tvfdIZ+nqYm5tn+Rb50aNH6lvQpk2b6ow3J+nfZI8fP14JCQnROUVERGTa3tA1JPJzvIsWLVLz6devnxIfH59lupSUlEw1dfW9/vqkLU73QH6vf07nIjfnVV99+/ZVAMXd3V39LDY2NkPNmNGjRysJCQkG2V928voMln7KTU12RSn84zfGMaZJf/9kNU2ZMkVJTU3Ndb5SQ6IEql+/PufOnWP06NFYW1tnmcbNzY1du3Yxb968Qo7OuDo0bKXO7wsoep2uCCGEKFwPHz5U5ytVqpSvvBISEli5ciUADRs2ZObMmZnSaDQalixZQvny5QFYvHhxtnmOGDECDw8PvfavT9qvvvoKgH79+jFmzJgs01hZWalx3bhxI9ftyitUqEDZsmV1rrewsGD+/PmAtv112htGQyuI6zFp0iS6du2a6XNHR0e1ff65c+eIiorKX/DA0qVLadKkic5pyZIl+d5HTvJ6vKmpqeo1rlatGuvWrVP7OXueiYlJgdXULc73QFG4/vpI+/6m9Z9w7do12rVrx6ZNmzA3N2fJkiWsXLkSCwsL4wVZgEr78YP22E+ePMn8+fPVflaKklI37OeaNWt0dh6VF7Vq1VLHQ8+NsmXLsnLlSr799lt+/fVXbt26RWxsLFWqVKFJkya4u7sbLMbi5MtvVrB1X31Ihtt3YomO/gcHB8ecNxRCiCLq1q2F3Lq1MN/5uLmtp1y5rupyZORRLl7UdthYvfr7VK/+vrouOfkJAQFu+d5n2bJdaNhwQ4bPzpzpztOnlzE1taNt27/yvY+cPHnyRJ1P65Qrr4KDg3n8+DGgLRzQ1Tu5g4MDAwYMYOnSpYSGhnL37l2qVKmSZdohQ4bovf+c0oaHhxMcHAxoh2rLjpubGxUqVODhw4f88ccfvPTSS3rH8byEhAQiIiKIiYlROw5M/9vm7NmztGzZMs/561LY1yP9MYSFhZWIHvfzerxnzpwhPDwc0DZVtrOzK7AYsyP3QMGKjo5WO89s3rw5v/zyC2+++SaPHz+mUqVK+Pn50bFjx0KLJyQkJN95ODs7653WGMdf2MeYXt++fWnVSvtyNy4ujmvXrrF161Z27NjBkCFDWLRoEb169cp3fIZW6gokihp7e/sMw4GWdnXq16N8NRMe3UxFeQoT3unH+nXHjB2WEELkWXJyNImJ4fnOR1ESMi2n5ZucHP18aoPsMynpYabPEhMjSEwMx9TUPt/568Pe/tl+0kbayKvz58+r823bts0mpXZ9Wj8O58+f1/nw07RpU733n1PaoKAgdd7b2xtvb2+98r13757eMaSJjY3l+++/Z/PmzVy4cCHbdvnpa6kYUkFcj+fbiKfn6PjsBUf6gq688vHxyfKNfmHK6/GePn1anTfmsPLF+R4oCtc/J+lrN+3bt499+/ahKApt2rRh+/btVKtWLcc8mjZtmuEhO63GTJs2bfjss89o1qyZ3vE0btw4V/HnlyGP/+bNm9SoUSPH9IV9jOmVLVs2Q+231q1bM2jQIH788UeGDx9Onz59WLVqFSNGjDBajFmRAglR5DSu48Kxm9cA+C3wDyNHI4QQ+WNm5oCFRc4/enKi0VhmWk7L18zs+Y4eNQbZp7l5hUyfWVhUIjk5ClPTwnmjWqHCsxgiIiLyldc//zwbUjqn5h+VK1fOcrvnlStXTu/955T2/v37eueV3tOnT3OV/saNG3Tv3p2wsDC90sfFxeUlrBwVxPWwsbHRuc7E5FlL5Zw6Riwu8nq86QuZdD3YFwa5BwpW+gfyvXv3AtC9e3f27Nmjs4lOevHx8Vy8eBFHR0cmTZqkfvb777+zfft29u7dS2BgII0aNSqQ+PPLUMdfoUIFvQojiqqhQ4eye/dutm7dyjvvvEOfPn1y9X9XQZMCCVHkLFqwiQ4921C1PlzrlMShU1d4qUU9Y4clhBB58nxzCkMpV64rL7xwO8t1Zmb2OtflV/PmvxZIvrqkf/t26tQpg+WbUztafZtj6qpinpe06R+QNmzYoHfti9z+sBw6dChhYWFoNBpGjhzJoEGDcHNzw8nJSf2Rnpqaqsabl6apuWWo6yFyr6i0KZd7wPDSHshdXFxwdHQkODiYkydPcuHCBVq0aJHj9mfPniU5OZm2bdtmqg3i6enJjh07WL16Nd98841e8aSvEZNXzs7O2faBk56hjl+ftGkK+xj11adPH7Zu3UpsbCx79+5l8ODBBs0/P6RAQhQ5zd1b037CFxxWPgXg7Y1fcbXFaiNHJYQQwhgaNmyo9pVw/PhxoqOj8zz0Z/rq2vfu3aN+/fo606avjZF+u4KU1mkfaB/OCqLq719//cXvv/8OwCeffMIXX3yRZbrIyEiD7/t5Rf16FIS0N/RpfXXokt/mSTlJX/Pozp07WQ5TWxhK4z1QmNIeyFu3bs3ChQtp3bo1d+/epXfv3gQGBuZYOyatEDirB/KXX36ZHTt2qMMy66NJkyb6B6+Dr6+v3k0ODHX8uelDp7CPUV9OTk7q/M2bNw2ad37JKBuiSFoz/h008WUBuGa7lq3Hzhk3ICGEEEah0WjUH2axsbFqj/x5kf4B/88//8w2bUBAQJbbFaT0HVofOHCgQPZx4cIFdX7QoEE606XvzyIrhnirXtSvR0FI6xMlrSNHXS5dulSgcaR/wPztt9yPamaoWhWl8R7IjiFrqyQnJxMaGgpoa5pVq1aNXbt2YWVlRXh4OH369MmxOVZaJ7tZFUhcv34dwGiFWTkp6OMvbtI6sQWM1omtLlIgIYokZycHethP1S6YpPLZgt4kJSYZNyghhBBG8d5776ntwj/77DP++ku/0T1SU1NZv369utyyZUu1GuzatWt1tiF/8uQJW7duBbQ1NAqrjX3dunVp2LAhAJs3b+bvv/82+D6Sk5PV+ez6nli2bFm2+VhZWanzCQkJ2aTUrahfj4Lg4uICaI9JV6FDYmIi27ZtK9A4mjVrRvXq1QFYuXIlMTExudreENcfSuc9kB1DnVeAixcvqnmkNX1r3bo1q1drax0HBgaqw6DqklZD4PnR/06fPs2SJUtwcHBg7NixesekKEq+J31rDhjy+HNTQ6IwjzE3fvrpJ3XeELU4DEkKJESRtek/k6l6xQnHNXBp90169dW/J3MhhBAlR7Vq1Vi8eDGgrSXRpUsXjh3LfgSm0NBQXnnlFRYsWKB+ZmlpyZgxYwBtTYFZs2Zl2k5RFN555x2107933nnHUIehl+nTpwPaztQ8PT2zrQ6dkJDAkiVLiI+P1zv/evWe9cm0du3aLNMsXbqUnTt3ZptP+gfCa9eu6b3/9IrD9TC0Ll26qPNZtbtXFIX//Oc/3Llzp0DjMDEx4cMPPwTg9u3bDBs2jMTExCzTpqamZorHENcfSuc9kB1DnVfI2KFj+r54vL29+fRTbbPoLVu2ZHnOQVswdv78eUxMTFi7di0zZ85k2rRpeHp60qZNGypWrMihQ4eoWLFivuIsKIY6/nLlyqkFiUXRmjVrcvw/4Ntvv2XPnj0A1KpVq1CHetWH9CEhiqwytlY0e1yJvTe0P8YOn/iLK1dCqFevaJXqCSGEKHgjR47k9u3bfPbZZ9y/f5+uXbvy8ssv06dPH9zc3Chbtiz//PMPly9f5pdffmHfvn2kpKRkGpLus88+Y/v27Vy/fp3Zs2dz/vx5Ro0aRdWqVQkLC2Px4sUcPXoUgPbt2/PWW28V6nF6e3uzf/9+1q5dS3BwMA0bNmTcuHF06dIFJycnYmNjuXbtGsePH2f79u38888/DBs2TO/83d3dady4MefPn2fp0qU8fvyYIUOGUKVKFW7dusX69evx8/OjQ4cOnDhxItt8rKysiI+PZ8aMGZiZmVGrVi21j4Rq1aphbW2dYzxF/XoYmru7O+3atePkyZOsWLGCxMREhg8fTpkyZbhy5QrLli3j6NGjtG/fnj/+KNiRxiZOnIi/vz8HDx5kx44dNGnShAkTJtCqVStsbGy4d+8eJ0+eZNOmTQwePDhDp4aGuv5Q+u6B7BjyvKY9kDs6OuLs7Jxh3ezZs7l48SLbt29n1qxZuLm5MWDAgAxpzp07R1KStnby8w/tLi4uHDt2TK1lUxQZ6viLenONmTNn8sEHH9C/f386duxInTp1sLOz48mTJ4SEhLBhwwb1b7mFhQUrVqzAzKyIFQEookSLiopSACUqKsrYoeRJYkKi4lAVRWODUtetjjL78yfGDkkIUcrFxcUpoaGhSlxcnLFDKZW2bdum1KpVSwFynBo1aqTs378/Ux5hYWGKq6trttt26NBBefToUZYx+Pj4qOlykpu0aZKTk5WpU6cqpqamOR6jra2t8vTp0wzb+/r6quvDwsIy5X/69GmlXLlyOvNs0qSJcufOHXXZx8cnyzinTp2qM48jR47oFYuiFN71OHLkSKb4cit9HrrOS04uXryoVKxYUeexvv/++9meN0Meb2xsrOLl5ZXjfZbVsepz/RWlZN0D+b3++pwLfc9rTrp3764ASrdu3bJcHxMTozRv3lwBFGtrayUwMDDD+h9++EEBlE8++URRFEVJTU1Vbt++rYwaNUoBlJdfflnvWIzBUMc/derUwgg3z2rWrKnX/4fOzs7KgQMH8rSPvP7u0fc5VJpsiCLN3MKcLz75Bjuz37l68Spfz7cjn8PQCyGEKMY8PT25dOkSGzZs4M0336RBgwaUK1cOMzMzHB0dadGiBRMmTODw4cOEhITw8ssvZ8qjVq1anD17lsWLF9OlSxfKly+Pubk5lSpVokePHvz444/89ttvRuvJ39TUlHnz5hEaGsoHH3yAu7s75cqVw9TUFHt7exo1asSQIUNYu3Ytd+/e1fuNaZrmzZtz5swZ3n77bWrWrIm5uTmOjo60adOGBQsWEBAQoFcb/blz57JixQo6deqEo6NjroZATa+oXw9Dc3V15dSpU4wfP56aNWtiYWGBk5MTPXr04JdfftF7CEVDsLGx4aeffuLXX39l6NChuLi4YG1tjb29Pa6urnh6erJx40a1eUd6hrr+UPrugewY6ryePXsWIFMtsTS2trbs2rWLSpUqERcXR58+fTJ0fPh8h44ajYZq1arxww8/4OzszIEDB7h69WqeYisMhj7+ourw4cMsW7aMgQMH0rRpUypVqoSZmRl2dnbUqVOH/v374+vry6VLl/Dw8DB2uFnSKIoM6luSRUdHU6ZMGaKiovI8TFpR8Pbb8MMP2vkxY2DFCuPGI4QoveLj4wkLC8PFxSVDB2RCCCFESdG6dWuCgoK4du0atWvXzrBu2rRpfPXVV3z55Zd88sknRoqwYKUd/5UrV6hbt66xwzGqvP7u0fc5VGpIiGLh888h7T5etQrW+G42bkBCCCGEEEKUQElJSYSEhFC2bNlMhREAvXv3BmDHjh2FHVqhSDt+BwcH6tSpY+xwSjwpkBDFQsWK8Nln4FhuO+VrmjHqHW8CAncbOywhhBBCCCFKlAsXLpCQkJBpuM80bdu2pXLlygQFBXH79u1Cjq7gpR1/ixYt0Gg0xg6nxJMCCVFsTJoEZuUG8vBGCspT8Bza19ghCSGEEEIIUaLk1H+CRqOhV69eKIrCrl27CjO0QlFc+o8oKaQPiRKupPQhkebzWYvx+XIS/DtU9gcferDg6wPGDUoIUapIHxJCCCGEKC2kDwkh0pnx2Tu4ulVUl/+7+iCR/9w1YkRCCCGEEEIIIfJCCiREsaLRwJrll7Fy0i4nPoKXetUzblBCCCGEEEIIIXJNCiREsdO2TRm6tBoG//Yxcyowlo0bfIwblBBCCCGEEEKIXJECCVEs/bh2LVXr/duGKRkmTP2c5KQk4wYlhBBCCCGEEEJvUiAhiiUnJxjudRgTe+1y1B3o36+WUWMSQgghhBBCCKE/KZAQxdZMnxdwrddeXfY/dIfzf/gbMSIhhBBCCCGEEPqSAglRbFlYwMIvT1CujikASgL0Huxp5KiEEEIIIYQQQuhDCiREsfbKKxra1vUHG+1y2I1kPn+7q1FjEkIIIYQQQgiRMymQEMXe/y3pSa0a7dTl2euOcffyaSNGJIQQQgghhBAiJ1IgIYq92rVhkOevWNfSNt1IjgOPV9sYOSohhBBCCCGEENmRAglRIsz41Jpqtv8FS+3yhWvJzJnSw7hBCSGEEEIIIYTQSQokRIlgYwNfzRxPlWaO6mdfr95PQmK8EaMSQgghhBBCCKGLFEiIEqN/f6hd5hxm1aFMdbDygoV/fmvssIQQQgghhBBCZEEKJESJodHAD99Ww8p8DVHD4UE1mHVsFlceXTF2aEIIIYQQQgghniMFEqJEadQIRr8+HALeByAhJYG3dr+FoihGjkwIIYQQQgghRHpSICFKnJkzoULI5xBZC4DAM0d5a1Qzo8YkhBBCCCGEECIjKZAQJU7ZsjBvji3s/oG6/4O4lbByXQjbV003dmhCCCGEEEIIIf4lBRKiRBoxAlqX9+DhP2akxgCpMOGTL4wdlhBCCFFirVmzBo1Gg0aj4caNG8YOp8QpTefXWMealJSEhYUFGo2GL76Q341CFAYpkBAlkokJLP6vhrg7/mgqgkM1DV989ZWxwxJCCJEPsbGxLF++nNdeew1nZ2esrKyws7Ojdu3atG/fnrfffpvNmzdz9+5dY4cq8ujo0aPqg2j6yczMDEdHR1xcXOjcuTOTJ09m27ZtJCYmGjtkkQVd11HXtGbNGmOHDMCFCxdISkoCoFmzktfc9/79++zevZvPPvuMnj17UqFCBfUajBgxwtjhiVJKCiREidWmDQzp0YOylj8QHZ7I0aMfGzskIYQQeRQQEEDjxo0ZN24ce/bsITw8nISEBGJjYwkLC+PkyZP88MMPeHt74+7ubuxwC0xpekueXkpKCpGRkdy4cYPjx4+zaNEivLy8cHZ2Zs6cOSQnJxs7RKMqbfdFQR3vmTNn1PnmzZsbLN+iolKlSrz++uvMnj2bffv28ejRI2OHJARmxg5AiII0bx7s3PkWAOvXw+jR0LWrcWMSQgiRO1evXsXDw4Po6GgAevfujZeXF/Xr18fCwoKHDx9y9uxZDh48yJEjR4wcrTCU8ePHM2HCBHU5JiaGyMhIzp07x+HDhzl06BAPHjxgxowZ+Pv7s3v3bpycnIwYccEaMWJEsXyL/fx1zIqzs3OGZWMda1qBRPny5TPFVNJUr14dNzc3Dhw4YOxQRCknBRKiRKtQAb76CsaN0y5PmAAbNxxhl+8X+Hx/yLjBCSGE0Munn36qFkasXr2akSNHZkrj4eHBlClTePDgAVu3bi3sEEUBqFixIo0bN870ec+ePfnoo4+4cOECQ4cO5fTp0wQEBODp6cnhw4exsLAwQrRCF13XsShKK5Aoic01AD777DNat25N69atqVSpEjdu3MDFxcXYYYlSTppsiBJvzBht8w2AxNQXadm5O7MWH2bfxnnGDUwIIUSOUlJS2L17NwCtWrXKsjAiPScnJyZOnFgYoQkja9SoESdOnFCb6Pz+++8sWbLEyFGJ4uzcuXNAyWyuATBr1ix69epFpUqVjB2KECopkBAlnokJLF0KGvN4wh2PkBoDigIj3v6YhPhYY4cnhBAiGw8ePODp06cA1K1bN8/5zJw5U21zDvD48WN8fHxo1KgRdnZ2ODo60rVrVzZs2KB3ngEBAYwdO5b69etjZ2eHra0trq6uTJw4kStXruiVx4kTJxgzZgwNGjTAwcEBOzs7XF1d6du3L+vWrVNrhqR1Epi+QMbFxSVT54BHjx7VecxRUVHMnj0bd3d3ypYtm6kzwfPnzzNnzhxeeeUVnJ2dsbS0xM7Ojnr16jF8+HBOnjyp97kpLNbW1vz444/qMS5YsEDtlDAreb1mz5/L+Ph45s+fT4sWLbC3t8fe3p42bdqwePHiHPuzuHPnDh9//DEtWrSgTJkyWFhYULlyZZo0aYK3tzdr1qxRr3t6WfWbkJv7IikpicqVK6PRaOjZs2e2MYL2fkjb/ssvv8wxvSHp6iMiL98Dfd28eZPIyEhAd4FEeHg47du3R6PRYGlpyfLly3O9HyFERtJkQ5QKLVrAxHFWrDz6HVR8F+5DxBMY/lJdNv8uvbELIURRlb76/cWLFw2SZ1hYGB4eHly7dk39LDY2lmPHjnHs2DF27tzJpk2bMDPL+mdScnIy7777LkuXLs207tKlS1y6dIkVK1bwf//3f4wdOzbLPOLi4hg9ejSbNm3SmceuXbvw8fFh5syZeTvQdK5cucLLL7+sswPAo0eP0q1bt0yfJyYmcvXqVa5evcq6dev4+OOP+aqIjVrVqFEjPDw8OHDgAOHh4QQGBvLCCy9kSGOIa5YmIiKCV155hbNnz2b4PDAwkMDAQA4cOMDOnTsxMcn83u/48eP06tUrU4FDREQEERERnD9/ns2bN1OhQgV69eql7ynQi7m5OcOGDWP+/PnquapWrZrO9KtXrwbA1NSU4cOHGzSWoiinDi1/++03BgwYQEREBFWqVMHPzy/TfSaEyD2pISFKjdmzocyDSVRoXQu0LzjY+r97HNgy36hxCSGE0M3R0ZGaNWsCcPbsWebNm0dqamq+8hw4cCBhYWG8/fbbHDp0iMDAQFatWkX9+vUB8PPz4/3339e5/ejRo9UH2549e7J+/XoCAgIIDAxkxYoVNGrUiKSkJN566y38/f0zbZ+amkqfPn3Uwoh69erx7bffcvz4cYKDg9m9ezfTpk3LUCOkdevWhISEMGfOHPWz/fv3ExISkmFq3bp1ljF7eXkRHh7OpEmTOHjwIEFBQWzatIkGDRoA2gd2W1tbBgwYwLJlyzh69CinTp1i3759fPPNN+o1mDt3Lr6+vrk53YXipZdeUuePHz+eaX1+r1l6np6eXLx4kXfffZeDBw8SHBzMxo0bcXNzA8Df358VK1Zk2i4hIYFBgwYRHR2Nvb09U6dOZe/evQQHB3Py5Em2bNnCe++9R/Xq1fU+7tzeF2PGjAG09+C6det05puUlMT69esBePnll7MtuChM+f0eZCetQMLCwgJXV9cM6xYtWsSLL75IREQE7dq1IygoSAojhDAURZRoUVFRCqBERUUZO5Qi4ccfFQXrB4rFCyignarYo8Q9jTZ2aEKIYiIuLk4JDQ1V4uLijB1KqbFgwQL1bzag1KxZU3nnnXeUDRs2KFevXtUrDx8fnwx5bNy4MVOa6OhopVmzZgqgmJiYKOfOncuUxs/PT81jxYoVWe4rLi5O6d69uwIotWrVUpKSkjKsX7RokZpHv379lPj4+CzzSUlJUcLDwzN85uvrq24bFham9zGbmJgoBw4c0Jn2wYMHSmRkpM71CQkJioeHh3r+k5OTM6XJTWy6HDlyRM3Dx8dH7+0OHTqkbjdq1KgM6wxxzdKfS3Nzc+XIkSOZ8nj06JFSqVIlBVCaNm2aaf3hw4fVPPz9/XUeS1JSUpa/27I7v7k59507d1YApV69ejrTbN++Xc3Pz88v2/yykv46jh8/XgkJCdE5RURE5OpY9VmfF3379lUAxd3dXf0sNjZW8fb2Vvc1evRoJSEhwSD7S//3KK+Tr69vvmIICwtT8xo+fLhBjkuUPHn93aPvc6jUkBClypAh0KVNBUzj5kFF7Wd3n8DoV1yz31AIIYTRTJ48mVGjRqnLN2/eZPHixQwZMoS6detSuXJlBg0ahL+/P4qi5Jhfr1698Pb2zvS5vb292iY8NTWVZcuWZUqT1lyhX79+6tvm51lZWbF48WIAbty4kaE9e2pqKvPna2vmVatWjXXr1mFpaZllPiYmJlStWjXH49HHiBEj8PDw0Lm+QoUKlC1bVud6CwsLNe6bN29mqN5eFJQvX16dT+sHIE1+r9nzJk2aRNcsxhB3dHRU+zY4d+4cUVFRGdbfu3dPne/cubPO/M3MzHBwcNC5Pr/SzsGVK1c4ceJElmnSasFUqFCB119/PV/7W7p0KU2aNNE5FZWOSNPu6bTmGteuXaNdu3Zs2rQJc3NzlixZwsqVK2UUFyEMTPqQEKWKRgNLlkCzZlMp3/O/PNp9GxTY9PsdRm79hpcGfGDsEIUQJUirVpDuGaTEqlwZgoIKLn8TExNWrVrFG2+8wcKFCzl8+HCGZhsRERFs2bKFLVu20KpVKzZv3kydOnV05pfdSB1t2rShUaNGXLhwgUOHMg4PHR4eTnBwMAADBgzINmY3NzcqVKjAw4cP+eOPP9QmBWfOnCE8PByAsWPHYmdnl/3BG8iQIUNylT4hIYGIiAhiYmLUc52+sOfs2bO0bNnSoDHmR/rz+OTJE3XeENfsedmdy/TnJCwsLENfBFWqVFHnfX19+c9//pNtPAXFy8uLd999l8ePH+Pr60uHDh0yrI+IiGDv3r0AvPnmm6XiATw6OlrtX6V58+b88ssvvPnmmzx+/JhKlSrh5+dHx44dDbrPkJCQfOfh7OxsgEiEMC4pkBClTsOG8P778PWSAMzbVyXpf9pRN4aOnsL1XmOwtilj7BCFECXEvXvw77OnMIAePXrQo0cPIiMjOXHiBEFBQQQHB3P8+HH1bXRQUBCdOnUiODg4wwNgejm1L2/Tpg0XLlzgypUrJCYmqg9kQelKXby9vbOsZZGV9G/GT58+rc5n95bc0Jo2bZpjmtjYWL7//ns2b97MhQsXSElJ0Zn24cOHhgwv39IXQqSvXWCIa/a85/sXSM/R0THLmAA6duxI7dq1uX79Ou+99x4bNmygX79+dOnShVatWhXag7+1tTWDBw9myZIlbN26le+++w5bW1t1/Y8//qiOFJK+ZlJeGapj1oKUvsbPvn372LdvH4qi0KZNG7Zv365XHxoNGjTg8uXLPHr0KMN9oEvjxo3zE7IQJYYUSIhSacYM2LixCo+SfEiqNAsi4F4MvPtqM1YcvWHs8IQQJUTlysaOoHAU9nGWK1eOXr16qaMQJCQksHHjRj744AMiIyO5e/cuM2bMYOXKlVluX7FixWzzr1SpEqCtERAZGaku379/P0/xpg1bChkf5HUVmBSEcuXKZbv+xo0bdO/enbCwML3yi4uLM0RYBpP+vKZ/GDTENXuejY2NznXpR9Z4vkDH3Nwcf39/vLy8uHjxojoqB2gLCbp06cLQoUMZOHAgpqameYpbX2PHjmXJkiU8efKEbdu2MWzYMHVdWnON1q1b06RJkwKNo6hIXyCRVjuke/fu7NmzR2eTqvRiYmK4evUqNWrU0KswQgjxjBRIiFLJzg6++w769/fBsc9q/tl9C1Jg5bGb9F83hx7Dphs7RCFECVCQzRjEM5aWlowcOZKqVavSo0cPALZv387y5cuzHHpRo9Fkm5+ufijSP2Bu2LBBr1oHoLswIKc4DCmnB9yhQ4cSFhaGRqNh5MiRDBo0CDc3N5ycnNQHstTUVDUfffrqKEzpa56kjRwChr9m+dWwYUNCQkLw9/fH39+fY8eOce3aNeLi4tQ38wsXLmTPnj05FpzlR/PmzWnZsiXBwcH4+vqqBRJ//vknoaGhgGFqRxQXaQUSLi4uODo6qiOfXLhwgRYtWui1fWpqql5p05w/fz6v4aqcnZ2z7ftFiOJACiREqdWvH/TooWHfr39g1qkGyUe1bWRHjJ/BX71GUdbRMB2JCSGEKByvvPIK1atX59atW0RGRvLo0SOcnJwypYuIiMh2aMW0t+oajSbDg2n6jhM1Gk2eqlxXqFBBnb9z506Gh2dj+euvv/j9998B+OSTT/jiiy+yTPd8Z5FFycGDB9X59G39DXHNDM3U1JS+ffvSt29fAO7evcvevXtZsmQJwcHBBAcHM27cOHbs2FGgcYwZM4bg4GCOHTvG9evXqV27tlo7wtraWu/mLSVBWoFE69atWbhwIa1bt+bu3bv07t2bwMDAHGsznTp1CiBXBRKGqH3i6+vLiBEj8p2PEMYko2yIUkujgcWLwSqpGmaPF8K/v00jnsI4j+ZGjU0IIUTepB+VIqvaEYBaTV6XtPX16tXL0K7f3d1dnT9w4ECe4kv/wPLbb7/levuCqFVx4cIFdX7QoEE60wUV0So/58+f5/DhwwBUr16dVq1aqesMcc0KWpUqVRg1ahR//PGHen/s3r07V81i8nJfDB48GBsbGxRFYe3atcTFxbF582YAPD09KVOm6PapZcjvQXJyslorpFmzZlSrVo1du3ZhZWVFeHg4ffr0yfFa5KVAQgihJQUSolSrU0fbn0T8mXcp06IhmGs/33rqAesWTjBucEIIIXLl6dOn6oOFg4ODzrbca9eu1ZlHUFCQWpX6+VEW6tatS8OGDQHYvHkzf//9d65jbNasmVo7Y+XKlcTExORqeysrK3U+ISEh1/vPSloHhpB93wlZDYNqbHFxcQwbNkxtQjJlyhTMzJ5VADbENSss5ubmdOnSBdBek8ePH+u9bV7uCwcHB3XkkbVr1+Ln56d2Djt69Gi9920MhvweXLx4Uc2jWbNmgLamxOrVqwFtAWV2I/PAsyZDuSmQUBQl35PUjhAlgRRIiFJvyhRo2FBD1NE9mL34rMT9P58s5f7dq0aMTAghRExMDG3btmX37t0Zhvp8XmpqKpMmTVJHNujdu7fOt6g///wzW7duzXJfb731FqCtXTFu3LhMaaZP1/YxFB8fj6enJw8ePNAZU0JCAkuWLCE+Pl79zMTEhA8//BCA27dvM2zYMBITE3Ue0507dzJ8lr7q+LVr13TuOzfq1aunzusqrFm6dCk7d+40yP4MJTQ0lI4dO6oPg126dGH8+PGZ0uX3mhnK8ePHuXpV9++KxMREjh07BmiHMc2quZEueb0vxowZA8DNmzeZOnUqoO1HoWvXrnrnYQyG/B6k79AyrUACtKOyfPrppwBs2bKFWbNmZbl9QkICoaGhVK5cuVA7qhWipJA+JESpZ2EBP/wAnTrVhCczoM7ncA00ZcH/f+sZ3X+msUMUQohSLSAggNdff51q1arRt29f2rdvT82aNbG3t+fx48ecPn2a1atXExISAkCZMmWYPXu2zvxatWrF4MGDOXbsGF5eXjg4OHDu3DnmzZvHpUuXAJg4cWKWHSB6e3uzf/9+1q5dS3BwMA0bNmTcuHF06dIFJycnYmNjuXbtGsePH2f79u38888/GUYwSMvb39+fgwcPsmPHDpo0acKECRNo1aoVNjY23Lt3j5MnT7Jp0yYGDx6cYchEd3d3rKysiI+PZ8aMGZiZmVGrVi21eUq1atWwtrbO1fl1d3encePGnD9/nqVLl/L48WOGDBlClSpVuHXrFuvXr8fPz48OHTpw4sSJXOWdH/fv38/Q8V9sbCyRkZGcO3eOw4cPc/DgQbVmRLt27fDz88Pc3DxTPoa4ZoZw+PBhZs+eTadOnXjttddo2rQpTk5OxMXFcfnyZZYtW6ZW/R8zZkyGmh45yet90aFDB9zc3Lh48aI61OnIkSMLtcPVvDDk9yCtQMLR0RFnZ+cM62bPns3FixfZvn07s2bNws3NTa1VkubcuXMkJycXi+Yav//+e4ZCsfSj01y9epU1a9ZkSC81MEShUESJFhUVpQBKVFSUsUMp8t56S1HQpChlvGspddugMAPF6Wsn5X7MfWOHJoQoQuLi4pTQ0FAlLi7O2KGUCnFxcUrlypUVQK+pXr16SlBQUKZ8fHx81DTXr19XXFxcdObRv39/JSkpSWdMycnJytSpUxVTU9Mc47G1tVWePn2aKY/Y2FjFy8srx+19fHwybTt16lSd6Y8cOZLlMefk9OnTSrly5XTm26RJE+XOnTvZxuXr66uuDwsLy3GfWTly5Ije1xpQnJyclC+++CLb66Uo+b9m+p7L9PGnvxbP55Hd5OnpmeXfl5zOr773xfMWLFigpjMxMVH+/vvvbI9RH+nPQ1b3Sk70uZfyerzP6969uwIo3bp1y3J9TEyM0rx5cwVQrK2tlcDAwAzrf/jhBwVQpk+frvc+jWX48OG5+n4JoSh5/92j73OoNNkQ4l9z50JFJxOiNl3jau0+YAoPnj5gwp4JRW54MyGEKC3SOpY7ceIEs2bNomfPntSuXRtbW1tMTU1xcHDA1dWVgQMHsnHjRs6fP0/Lli2zzdPFxYXg4GCmTZuGm5sbNjY2lClThs6dO6u1AbJ7O21qasq8efMIDQ3lgw8+wN3dnXLlymFqaoq9vT2NGjViyJAhrF27lrt372b5ptbGxoaffvqJX3/9laFDh+Li4oK1tTX29va4urri6enJxo0b1eYd6c2dO5cVK1bQqVMnHB0dcxzSUx/NmzfnzJkzvP3229SsWRNzc3McHR1p06YNCxYsICAgwKjV0U1MTChTpgw1atSgU6dOvPfee2zbto3bt28zbdq0HGsTGOKa5dfUqVPZs2cPkydPpl27dtSoUQMrKyusrKyoVasWAwcO5JdffmHbtm0Z+kjQV17vi6FDh6rzHh4e2Y5AU5QY6ntw9uxZIGNzjfRsbW3ZtWsXlSpVIi4ujj59+hAeHq6ulw4thcgfjSJPWiVadHQ0ZcqUISoqCgcHB2OHU+Rt2gSDBwO2EZhMakSq1SMAfF9awrD243T22C6EKD3i4+MJCwvDxcUlTw8NwjhmzpyptgGXnz5CPHP48GG1A9ctW7ZkapIgste2bVsCAgK4ceMGNWvWNHY4QhhcXn/36PscKk9XQqQzaBC88goQW4lU/yUA1D4F7748ga8mehg3OCGEEEIIA0sbTaJ8+fL06dPHyNEULykpKYSEhFC+fHkpjBAij6RAQoh0NBpYsgSsrIALA6h6pgrXf4YnT2H28l85H7TX2CEKIYQQQhjEjRs3+OmnnwBtZ5aWlpZGjqh4CQ0NJS4uDnd3d2OHIkSxJQUSQjyndm3w8dHO3w3/GP79P8baCZKfRBsvMCGEEEKIfAoPD+fKlSscOHAAT09PkpKSsLKy4r333jN2aMVOUFAQoB3lRQiRNzLspxBZ+OAD2LABzgdOwm7wauqa/c3/jt7A2kb64RBCCCFE8TVkyBCOHTuW4bPPP/+catWqGSmi4mvfvn0A9OjRw8iRCFF8SQ0JIbJgbg7LlwNoiNl4miuh//DwkRRGCCGEEKJksLGxoXnz5qxZsybL0VxE9k6fPs2OHTto2rQpHTp0MHY4QhRbMspGCSejbOTP+PGwbJl2/vXXYdcubT8TD29fwdTGhnKO8jZBiNJGRtkQQojSa+7cuVy8eFHte+PIkSO0bdvWyFEJUXBklA0hjOirr6ByZe28vz9s3QofTXuZ2g3qM7R7U+MGJ4QQQgghCs2DBw+YNm0ae/bs4dVXX+XkyZNSGCFEPkkfEkJko2xZ+O9/4Y03tMuTN7zG3YMHIR5+OfsPP3w1knGf+Bo1RiGEEEIIUfCcnJxITU01dhhClChSQ0KIHPTvD/36aefvXh8O6fot+nDGGq5fDTBOYEIIIYQQQghRjEmBhBA50Ghg8WIoUwa4MAAzy5ehoXbdkxQY2KUTKSnJRo1RCCGEEEIIIYobKZAQQg9Vq8KCBdr55F2bsXzJBuy1y0F3EpnxVlejxSaEEEIIIYQQxZEUSAihp9GjoVs3IL4cCbt3Qr9n6xb4nuDk0Q3GCk0IIYQQQgghih0pkBBCTxoNrFgB1tbAdQ9MIkdAe+26JAWG9B5KbOxjI0YohBBCCCGEEMWHFEgIkQt16sDnn2vnU/cuwbqtE1TSLl9/ovB27+ZGi00IIYQQQgghihMpkBAil957D1q1ApKtidu8D5N+gKl23fpfb7LNd7oRoxNCCCGEEEKI4kEKJITIJTMzWLVK+y/3WsDl6eDxbP34t7/g9q3zRotPCCGEEEIIIYoDKZAQIg+aNoWPPtLOpx6ZSZm6LaGudvlBIgzt2k6GAhVCCCGEEEKIbEiBhBB5NH06uLoCiilRG7Zi2ccSbLXrjl6P5fMJLxk1PiGEEEIIIYQoyqRAQog8srKClSu1o28QWRvl9/8Dz2frv1x+jKMHVhktPiGEEEIIIYQoyqRAQoh86NABJkzQzif+OYoKDq9BB+1yMjC031giI+8YLT4hhBBCCCGEKKqkQEKIfPrqK6hZE0DDwzWrse5qDdW060zNFP66HmTM8IQQQgghhBCiSJICCSHyyd5eO+oGALEVSfHfhKY/uDSFvyfB9AvfkaqkGjVGIYQQQgghhChqpEBCCAN48UV4+23tfOK5PrS4doakEc4o5vBr2K8s+N8C4wYohBBCCCGEEEWMFEgIYSBffw01amjng39pRn/Nj2jQAPDpr5/y6/lfiIuLNmKEQgghhBBCCFF0SIGEEAaSoekGsHJ6V8Y1/giAaiHJDG7Zi4lvtDZSdEIIIYQQQghRtEiBhBAG9NJLMG6cdj42FkKXfEaTx2W5uQsiEsH3l8ts8Z1m3CCFEEIIIYQQogiQAgkhDCx9043fjlgSW8EVumuXK5aD+tUaGi84IYQQooCsWbMGjUaDRqPhxo0bxg6nxClN59dYx5qUlISFhQUajYYvvvii0PYrRGkmBRJCGJiDA6xc+e+CYsLd77dh/4IdL3Sz5fK1e7i//KZR4xNCiOIqNjaW5cuX89prr+Hs7IyVlRV2dnbUrl2b9u3b8/bbb7N582bu3r1r7FBFHh09elR9EE0/mZmZ4ejoiIuLC507d2by5Mls27aNxMREY4cssqDrOuqa1qxZY+yQAbhw4QJJSUkANGvWzMjRGFZ0dDSbN2/mgw8+oEuXLtStW5cyZcpgYWFBxYoV6dq1K19//TWPHj0ydqiilJECCSEKgIcHvPWWdj7uflXcj9/h+KEYypSrZNzAhBCimAoICKBx48aMGzeOPXv2EB4eTkJCArGxsYSFhXHy5El++OEHvL29cXd3N3a4BaY0vSVPLyUlhcjISG7cuMHx48dZtGgRXl5eODs7M2fOHJKTk40dolGVtvuioI73zJkz6nzz5s0Nlm9REBAQgLe3NwsXLuS3337j2rVrREdHk5SUxIMHDzh27BgfffQRrq6u7N+/39jhilLEzNgBCFFSzZ8Pe/fCrVvw20F7li9/NjQowIMbFwm7HUKbjgOMF6QQQhQDV69excPDg+ho7UhFvXv3xsvLi/r162NhYcHDhw85e/YsBw8e5MiRI0aOVhjK+PHjmTBhgrocExNDZGQk586d4/Dhwxw6dIgHDx4wY8YM/P392b17N05OTkaMuGCNGDGCESNGGDuMXHv+OmbF2dk5w7KxjjWtQKJ8+fKZYioJqlevTrdu3WjZsiXVq1enSpUqpKamcvv2bfz8/Ni+fTsPHz6kd+/eBAYG0rRpU2OHLEoBKZAQooCkNd145RXt8ocfQo8eUKsWzJk3kG8/34ppCpy62ghn50ZGjVUIIYqyTz/9VC2MWL16NSNHjsyUxsPDgylTpvDgwQO2bt1a2CGKAlCxYkUaN26c6fOePXvy0UcfceHCBYYOHcrp06cJCAjA09OTw4cPY2FhYYRohS66rmNRlFYgUdKaawB069aNv//+W+f6AQMGsHPnTvr160diYiKzZs1i27ZthRihKK2kyYYQBejll2HMGO18TAyMGgWTd05kxqqt/PMUHiTAgO5tSUkp3VVNhRBCl5SUFHbv3g1Aq1atsiyMSM/JyYmJEycWRmjCyBo1asSJEyfUJjq///47S5YsMXJUojg7d+4cUPKaawCYmprmmKZv3764uroC8NtvvxV0SEIAUiAhRIH75huoXl07f+QI3LhUB/oAdtrP/rgSy0dvdTZafEIIUZQ9ePCAp0+fAlC3bt085zNz5ky1zTnA48eP8fHxoVGjRtjZ2eHo6EjXrl3ZsGGD3nkGBAQwduxY6tevj52dHba2tri6ujJx4kSuXLmiVx4nTpxgzJgxNGjQAAcHB+zs7HB1daVv376sW7dOrRmS1klg+gIZFxeXTJ0DHj16VOcxR0VFMXv2bNzd3SlbtmymzgTPnz/PnDlzeOWVV3B2dsbS0hI7Ozvq1avH8OHDOXnypN7nprBYW1vz448/qse4YMECtVPCrOT1mj1/LuPj45k/fz4tWrTA3t4ee3t72rRpw+LFi3Psz+LOnTt8/PHHtGjRQu1UsHLlyjRp0gRvb2/WrFmjXvf0suo3ITf3RVJSEpUrV0aj0dCzZ89sYwTt/ZC2/ZdffpljekPS1UdEXr4H+rp58yaRkZGA7gKJ8PBw2rdvj0ajwdLSkuXLl+d6P0Wdra0toL3HhSgUiijRoqKiFECJiooydiil2sGDigLaydIqRWn7f90UhqOgQQEUE1B+3jbf2GEKIfQQFxenhIaGKnFxccYOpVR49OiRAtq/lc2aNctzPj4+Pmo+169fV+rUqaMuPz95eXkpSUlJOvNKSkpSxo8fr3N7QDE3N1eWL1+uM4+nT58q3t7e2eYBKD4+PoqiKMqRI0dyTAsoR44cyfKYL1++rNSqVStTel9f31zl//HHH+s8Jl9fXzVdWFhYbi6PKn0caceuj5dfflnd7sSJE5nW5/eapT+X9+7dU5o1a6Yzn9dff11JSUnJMp/ffvtNcXBwyPE8+/v7Z9o2q/Ob2/viww8/1P7uMDFRbt++ne05nTx5sgIopqamOaZ9Xl6vY3bH+ny++n4P9LVz5051+3PnzmVaf+zYMaVSpUoKoFSpUiXL+6y4Cw0NVUxNTRVAadWqlbHDEUVEXn/36PscKjUkhCgEL70EkyZp5xPiTUjYtJFyrmWhq/azVGDU4A+5deu8kSIUQoiiydHRkZo1awJw9uxZ5s2bR2pqar7yHDhwIGFhYbz99tscOnSIwMBAVq1aRf369QHw8/Pj/fff17n96NGjWbp0KaDtz2D9+vUEBAQQGBjIihUraNSoEUlJSbz11lv4+/tn2j41NZU+ffqwadMmAOrVq8e3337L8ePHCQ4OZvfu3UybNi1DjZDWrVsTEhLCnDlz1M/2799PSEhIhql169ZZxuzl5UV4eDiTJk3i4MGDBAUFsWnTJho0aABAcnIytra2DBgwgGXLlnH06FFOnTrFvn37+Oabb9RrMHfuXHx9fXNzugvFSy+9pM4fP3480/r8XrP0PD09uXjxIu+++y4HDx4kODiYjRs34ubmBoC/vz8rVqzItF1CQgKDBg0iOjoae3t7pk6dyt69ewkODubkyZNs2bKF9957j+pp1Sr1kNv7Ysy/7UhTU1NZt26dznyTkpJYv349AC+//DLVqlXTO6aClN/vQXbS+o+wsLBQmy2kWbRoES+++CIRERG0a9eOoKAgXnjhhXwdS1Hx9OlTrly5wsKFC+nWrRspKSkA/Oc//zFyZKLUyE9piSj6pIZE0REbqyj16z+rKTFs1gGFz1Co/axEv21dGyUpKcHYoQohsiE1JArfggULMrz9rFmzpvLOO+8oGzZsUK5evapXHunfcAPKxo0bM6WJjo5W33ybmJhk+ZbUz89PzWPFihVZ7isuLk7p3r27Aii1atXKVNti0aJFah79+vVT4uPjs8wnJSVFCQ8Pz/BZbmohpD9mExMT5cCBAzrTPnjwQImMjNS5PiEhQfHw8FDPf3JycqY0xqwhcejQIXW7UaNGZVhniGuW/lyam5tn+Qb+0aNH6hv0pk2bZlp/+PDhbGtApElKSsryd1t25zc3575z584KoNSrV09nmu3bt6v5+fn5ZZtfVtJfx/HjxyshISE6p4iIiFwdqz7r86Jv374KoLi7u6ufxcbGZqjJNHr0aCUhwTC/09L/PcrrlFbDKbfSn7+spilTpiipqakGOU5R/EkNCSFKCBsbWLcOTP791m2c7cHwelPBE7U/iT+vPuWjcV2MFqMQQhRFkydPZtSoUeryzZs3Wbx4MUOGDKFu3bpUrlyZQYMG4e/vj6IoOebXq1cvvL29M31ub2+vtglPTU1l2bJlmdJ89dVXAPTr10992/w8KysrFi9eDMCNGzcytGdPTU1l/vz5AFSrVo1169ZhaWmZZT4mJiZUrVo1x+PRx4gRI/Dw8NC5vkKFCpQtW1bnegsLCzXumzdvqm+Ti4ry5cur82n9AKTJ7zV73qRJk+jatWumzx0dHdW+Dc6dO0dUVFSG9ffu3VPnO3fW3XeUmZkZDg4OOtfnV9o5uHLlCidOnMgyTVotmAoVKvD666/na39Lly6lSZMmOqei0hFp2j2d1n/EtWvXaNeuHZs2bcLc3JwlS5awcuXKEj2KS/PmzTl58iTz589X+0sRoqDJsJ9CFKK2bWHaNJgzB5KTIWDuHFq8c5hTXsGwFlDg29Un6fzqPPr0/8jY4QohDGDhHwtZ+MfCHNO1qNKCn71/zvBZ7029OXX3VI7bvt/+fd5v/6yJwZOEJ7j9n5te8e0atIuWVVuqy7sv7+bt3W/nuJ2dhR1/vfOXXvvILxMTE1atWsUbb7zBwoULOXz4cIZmGxEREWzZsoUtW7bQqlUrNm/eTJ06dXTml91IHW3atKFRo0ZcuHCBQ4cOZVgXHh5OcHAwoB0iLztubm5UqFCBhw8f8scff6hNCs6cOUN4eDgAY8eOxc7OLvuDN5AhQ4bkKn1CQgIRERHExMSo5zp9Yc/Zs2dp2bKlrs0LXfrz+OTJE3XeENfsedmdy/TnJCwsLEPniFWqVFHnfX19jVYl3svLi3fffZfHjx/j6+tLhw4dMqyPiIhg7969ALz55psl+gE8TXR0tNp5ZvPmzfnll1948803efz4MZUqVcLPz4+OHTsadJ8hISH5zsPZ2TlP2/Xt25dWrVoBEBcXx7Vr19i6dSs7duxgyJAhLFq0iF69euU7PiH0IQUSQhSyGTPgl1/g9Gm4eMGcMdf8uFyvMTHdYuFXbV25sW9+TPNWPalZs6mxwxVC5FN0QjThT8JzTFe9TOZ24w+ePtBr2+iEjD3yKyh6bQeQmJKYYTkuKU6vbe0t7PXK35B69OhBjx49iIyM5MSJEwQFBREcHMzx48fVt9FBQUF06tSJ4ODgDA+A6eXUvrxNmzZcuHCBK1eukJiYqD6QBQUFqWm8vb2zrGWRlfRvxk+fPq3OZ/eW3NCaNs35/5PY2Fi+//57Nm/ezIULF9S25Fl5+PChIcPLt/SFEOlrFxjimj3v+f4F0nN0dMwyJoCOHTtSu3Ztrl+/znvvvceGDRvo168fXbp0oVWrVoX24G9tbc3gwYNZsmQJW7du5bvvvlNHVgD48ccf1ZFC0tdMyisfHx9mzpyZ73wKUvoaP/v27WPfvn0oikKbNm3Yvn27Xn1oNGjQgMuXL/Po0aMM94EujRs3zk/I+VK2bNkMNaJat27NoEGD+PHHHxk+fDh9+vRh1apVjBgxwmgxitJDCiSEKGQWFtqmGy1bQmIirPqmFrO2ruWzBC+4CVyDB/EwyOMFjof+g5lZyX8zIURJ5mDpQDX7nH/MOtk4ZfmZPts6WGas3q1Bo9d2ABamGf/GWJtb67WtnUXhvNnPSrly5ejVq5f6Bi8hIYGNGzfywQcfEBkZyd27d5kxYwYrV67McvuKFStmm3+lSpUAbY2AyMhIdfn+/ft5ijdt2FLI+CCvq8CkIJQrVy7b9Tdu3KB79+6EhYXplV9cXJwhwjKY9Oc1/cOgIa7Z82xsbHSuMzF51hr6+QIdc3Nz/P398fLy4uLFiwQGBhIYGAhoCwm6dOnC0KFDGThwIKampnmKW19jx45lyZIlPHnyhG3btjFs2DB1XVpzjdatW9OkSZMCjaOoSF8gkVY7pHv37uzZs0dnk6r0YmJiuHr1KjVq1NCrMKKoGjp0KLt372br1q2888479OnTJ8e/HULklxRICGEEjRvDF1/Ahx9qu7hc/WF/Rn87nlVxS2EZ8AROXollythOLPL909jhCiHy4fnmFLnxfBMOfdlb2nP7/dt52rZX/V553tZYLC0tGTlyJFWrVqVHjx4AbN++neXLl2d4QEyTU9toXf1QpH/A3LBhg161DkB3YUBhttHO6QF36NChhIWFodFoGDlyJIMGDcLNzQ0nJyf1gSw1NVXNR5++OgpT+ponaSOHgOGvWX41bNiQkJAQ/P398ff359ixY1y7do24uDj1zfzChQvZs2dPjgVn+dG8eXNatmxJcHAwvr6+aoHEn3/+SWhoKGCY2hHFRVqBhIuLC46OjurIJxcuXKBFixZ6bZ+amqpX2jTnz+d/ZDVnZ+ds+37Jiz59+rB161ZiY2PZu3cvgwcPNmj+QjxPCiSEMJLJk+Hnn+H4cbhxA1L3LKRpixOc8zoHawAFvl8TQKcec+g/cLqRoxVCiKLvlVdeoXr16ty6dYvIyEgePXqEk1PmmicRERHZDq2Y9lZdo9FkeDBN33GiRqPJU5XrChUqqPN37tzJ8PBsLH/99Re///47AJ988glffPFFlume7yyyKDl48KA6n76tvyGumaGZmprSt29f+vbtC8Ddu3fZu3cvS5YsITg4mODgYMaNG8eOHTsKNI4xY8YQHBzMsWPHuH79OrVr11ZrR1hbW+vdvKUkSCuQaN26NQsXLqR169bcvXuX3r17ExgYmGNtplOntH395KZAwhC1T3x9fQ3erCL938ybN28aNG8hsiKjbAhhJKamsGYNpDXb9F1hxfgKm7GpYwMvaj9TgDHDZnDpUta9YAshhMgo/agUWdWOANRq8rqkra9Xr16Gdv3u7u7q/IEDB/IUX/oHlt9++y3X2xdErYoLFy6o84MGDdKZLn1/DEXJ+fPnOXz4MADVq1dXO+sDw1yzglalShVGjRrFH3/8od4fu3fvzlWzmLzcF4MHD8bGxgZFUVi7di1xcXFs3rwZAE9PT8qUKZPrPAuLIb8HycnJaq2QZs2aUa1aNXbt2oWVlRXh4eH06dMnx2uRlwKJoiqt012g0DrdFaWbFEgIYUS1a8O33z5bnjXJja86L4YXgPrazx4nwsherxglPiGEKE6ePn2qPlg4ODjobMu9du1anXkEBQWpVamfH2Whbt26NGzYEIDNmzfz999/5zrGZs2aqbUzVq5cSUxMTK62t7KyUucTEhJyvf+spHVgCNn3nZDVMKjGFhcXx7Bhw9QmJFOmTMHM7FkFYENcs8Jibm5Oly7aob+Tk5N5/Pix3tvm5b5wcHBQRx5Zu3Ytfn5+auewo0eP1nvfxmDI78HFixfVPJo1awZoa0qsXr0a0BZQZjcyDzxrMpSbAglFUfI9FUSnkz/99JM6X1r6EBHGJQUSQhjZmDHQs6d2/t49+HXhCLybekM/oByULQtOXrVJSdXd27kQQpRUMTExtG3blt27d2cY6vN5qampTJo0SR3ZoHfv3jrfov78889s3bo1y3299dZbgLZ2xbhx4zKlmT5d24QuPj4eT09PHjx4oDOmhIQElixZQnx8vPqZiYkJH374IQC3b99m2LBhJCYmZrl9amoqd+7cyfBZ+qrj165d07nv3KhXr546r6uwZunSpezcudMg+zOU0NBQOnbsqD4MdunShfHjx2dKl99rZijHjx/n6tWrOtcnJiZy7NgxQPtmOqvmRrrk9b4YM2YMoK2aP3XqVEDbj0LXrl31zsMYDPk9SN+hZVqBBGhHZfn0008B2LJlC7Nmzcpy+4SEBEJDQ6lcuXKhdlSbW2vWrMnxvv7222/Zs2cPALVq1TL4UKdCZEX6kBDCyDQaWLUKmjaFhw9h104N/+35AwFVA7B57SZ/VU3mZ6sQ5vw2B5+uPsYOVwghCl1AQACvv/461apVo2/fvrRv356aNWtib2/P48ePOX36NKtXryYkJASAMmXKMHv2bJ35tWrVisGDB3Ps2DG8vLxwcHDg3LlzzJs3j0uXLgEwceLELDtA9Pb2Zv/+/axdu5bg4GAaNmzIuHHj6NKlC05OTsTGxnLt2jWOHz/O9u3b+eeffzKMYJCWt7+/PwcPHmTHjh00adKECRMm0KpVK2xsbLh37x4nT55k06ZNDB48OMOQie7u7lhZWREfH8+MGTMwMzOjVq1aavOUatWqYW1tnavz6+7uTuPGjTl//jxLly7l8ePHDBkyhCpVqnDr1i3Wr1+Pn58fHTp04MSJwmtCeP/+/Qwd/8XGxhIZGcm5c+c4fPgwBw8eVGtGtGvXDj8/P8zNzTPlY4hrZgiHDx9m9uzZdOrUiddee42mTZvi5OREXFwcly9fZtmyZWrV/zFjxmSo6ZGTvN4XHTp0wM3NjYsXL6pDnY4cObJQO1zNC0N+D9IKJBwdHXF2ds6wbvbs2Vy8eJHt27cza9Ys3Nzc1Folac6dO0dycnKRb64xc+ZMPvjgA/r370/Hjh2pU6cOdnZ2PHnyhJCQEDZs2KB+vy0sLFixYkWu7kEh8kwRJVpUVJQCKFFRUcYOReRg505F0Y65oSg2NopyNjRWORJ2RDGZZaIwE0UzU6McvHbQ2GEKUerFxcUpoaGhSlxcnLFDKRXi4uKUypUrK2i71clxqlevnhIUFJQpHx8fHzXN9evXFRcXF5159O/fX0lKStIZU3JysjJ16lTF1NQ0x3hsbW2Vp0+fZsojNjZW8fLyynF7Hx+fTNtOnTpVZ/ojR45kecw5OX36tFKuXDmd+TZp0kS5c+dOtnH5+vqq68PCwnLcZ1aOHDmi97UGFCcnJ+WLL77I9nopSv6vmb7nMn386a/F83lkN3l6emb59yWn86vvffG8BQsWqOlMTEyUv//+O9tj1Ef685DVvZITfe6lvB7v87p3764ASrdu3bJcHxMTozRv3lwBFGtrayUwMDDD+h9++EEBlOnTp+u9T2OoWbOmXvefs7OzcuDAAWOHK4qQvP7u0fc5VJpsCFFE9OkDY8dq558+hTHDbehQrSuzu2nf8ikojF4zkBEetbl/P8yIkQohROFJ61juxIkTzJo1i549e1K7dm1sbW0xNTXFwcEBV1dXBg4cyMaNGzl//jwtW7bMNk8XFxeCg4OZNm0abm5u2NjYUKZMGTp37qzWBsjuzaCpqSnz5s0jNDSUDz74AHd3d8qVK4epqSn29vY0atSIIUOGsHbtWu7evZvlm1obGxt++uknfv31V4YOHYqLiwvW1tbY29vj6uqKp6cnGzduVJt3pDd37lxWrFhBp06dcHR0zHFIT300b96cM2fO8Pbbb1OzZk3Mzc1xdHSkTZs2LFiwgICAAKNWRzcxMaFMmTLUqFGDTp068d5777Ft2zZu377NtGnTcnyTa4hrll9Tp05lz549TJ48mXbt2lGjRg2srKywsrKiVq1aDBw4kF9++YVt27Zl6CNBX3m9L4YOHarOe3h4ZDsCTVFiqO/B2bNngYzNNdKztbVl165dVKpUibi4OPr06ZOh48fi0qHl4cOHWbZsGQMHDqRp06ZUqlQJMzMz7OzsqFOnDv3798fX15dLly7h4eFh7HBFKaJRlCI2kLQwqOjoaMqUKUNUVBQODg7GDkfkIDYW3N3hyhXt8vTpMOvzVHpt7MXZg3uJ2gWxj6Fr83IcCrqPqalUpROisMXHxxMWFoaLi0ueHhqEccycOVNtAy4/fYR45vDhw2oHrlu2bMnUJEFkr23btgQEBHDjxg1q1qxp7HCEMLi8/u7R9zlUakgIUYTY2sKGDZD2oufLL+F/J0zo59qPe2Uh9t+OpAPPRnL61C9Gi1MIIYQQJUPaaBLly5enT58+Ro6meElJSSEkJITy5ctLYYQQeSQFEkIUMa1bQ1r/ZampMHQoNHfshKWTNfQHK0dYt+YzWrWWHw1CCCGEyLsbN26owzyOHDkSS0tLI0dUvISGhhIXF4e7u7uxQxGi2JICCSGKoI8/hrSRlm7cgP/6uLKq9yoGerzOnb/+xnNY1kNPCSGEEEJkJzw8nCtXrnDgwAE8PT1JSkrCysqK9957z9ihFTtBQUGAdpQXIUTeSAN0IYogU1P48Udo1gyio7Xzr73mzeZh3lmmT05OxMzMopCjFEIIIURxM2TIEI4dO5bhs88//5xq1aoZKaLia9++fQD06NHDyJEIUXxJDQkhiqhateD//u/Z8ttvw61bGdOE3Qyhb00bhns2KtTYhBBCCFG82djY0Lx5c9asWZPlaC4ie6dPn2bHjh00bdqUDh06GDscIYotGWWjhJNRNoo3RYHBg2HzZu1y165w6JC2BoX/0RW88eZbJPw78tTCr4cx+cO1RotViNJCRtkQQojSa+7cuVy8eFHte+PIkSO0bdvWyFEJUXBklA0hSjGNBpYuhbQhwY8ehQUL4FbULbyOTySh1bO0H320jl9/XWWUOIUQQgghSroHDx4wbdo09uzZw6uvvsrJkyelMEKIfJICCSGKuLJltX1IaDTa5enT4e6l6kxs8w64A621nycpMKj3WG7ePGesUIUQQgghSiwnJydSU1N58OABfn5+NG3a1NghCVHsSYGEEMVAly7wySfa+eRk8PaGT9vMo51zO3gFqKFd9yBWod9L7YmPjzFarEIIIYQQQgihDymQEKKYmDkT0kaVun4dJk00Z0v/rTjaO8IAwF677vTVp4we1NJYYQohhBBCCCGEXqRAQohiwtwcNm2CtD5hNm2CX3dWZ6vXVkzsTWAQYKpdt3HXZeZ/NcRosQohhBBCCCFETqRAQohipFYtWLHi2fLEieCc9CJfdv8SqgGvP1v36acbOXhweWGHKIQQQgghhBB6kQIJIYqZAQNgzBjt/NOnMGgQ/KfVVDxdPaE58G9nz0kKvOn5NmFhp4wVqhBCCCGEEELoJAUSQhRD330Hbm7a+TNn4OOPNfj29aVB+QbwMlBTu+5+jEI/j47ExUUbK1QhhBBCCCGEyJIUSAhRDNnYwObNYGmpXf7uO/jtoAM7Bu7AztpO28llGe26s9fiGDGgBampqUaLVwghhBBCCCGeJwUSQhRTTZvCN988Wx4xAhwS3VjdezXYAgMBM+26rbuv8fWXg40QpRBCCCGEEEJkTQokhCjGJkyAPn20848ewZtvgqfrG0xpPwWqkqGTy88+28LevUuMEqcQQgghhBBCPE8KJIQoxjQaWLUKnJ21y0ePwty58NVLX9GtVjdoBrTTrktSYOgb7xAZecdY4QohhBBCCCGESgokhCjmypeH9evB5N9vs48P/HHCjM1em6lqXxU8ABcwNYGu7cvjUKaSUeMVQgghhBBCCJACCSFKhC5dYMYM7XxKCnh7g+ZpRbYN2Ia5uTmVX4VqnrCt40M+O/KZcYMVQgghhBBCCKRAQogSY8YM6NpVOx8eDkOHQpuq7Tgw9ACr/7OH8CamAHz5+5dsOb/FeIEKIYQQQgghBFIgIUSJYWoKGzdCxYra5f37Yd486FqrKz3r9eSbl58NyTFy10hmjezIyhWTjRStEEIIIYQQorSTAgkhSpAqVbSFEhqNdnn6dPjtN+38u23fZUTzEZgkQ5X1ccxcc4JJ4xfx22/rjRewEEIIIYQQotSSAgkhSpgXX4TP/u0mIjVV25/E/fug0WiY0n4KDphz3Uq7Pj4Ftv/wrfGCFUIIIYQQQpRaUiAhRAk0YwZ066adv3NH259EaipEJUQRawH0ArMaMOrlmizaEGzUWIUQQgghhBClkxRICFECpfUnUenfET4PHICvvoIXqr/A/736f9SqUIsdS79j1f4bRo1TCCFEybFmzRo0Gg0ajYYbN24YO5wSpzSdX2Mda1JSEhYWFmg0Gr744otC268QpZkUSAhRQlWuDBs2POtP4rPP4NgxGNtyLBcnXqTXq+9m2ubOnUuFHKUQQugvNjaW5cuX89prr+Hs7IyVlRV2dnbUrl2b9u3b8/bbb7N582bu3r1r7FBFHh09elR9EE0/mZmZ4ejoiIuLC507d2by5Mls27aNxMREY4cssqDrOuqa1qxZY+yQAbhw4QJJSUkANGvWzMjRFJ6pU6dmuB5Hjx41dkiiFJECCSFKsBdfBB8f7Xz6/iSszKwypFMUhc8GuVO/pitr1nxkhEiFECJ7AQEBNG7cmHHjxrFnzx7Cw8NJSEggNjaWsLAwTp48yQ8//IC3tzfu7u7GDrfAlKa35OmlpKQQGRnJjRs3OH78OIsWLcLLywtnZ2fmzJlDcnKysUM0qtJ2XxTU8Z45c0adb968ucHyLcrOnj3Lt99Kf2LCeMyMHYAQomCljbTx669w9y68+Sbs3att1gHwJOEJXYZX4fSWWAAmvPU1DRq0pH37AUaMWgghnrl69SoeHh5ER0cD0Lt3b7y8vKhfvz4WFhY8fPiQs2fPcvDgQY4cOWLkaIWhjB8/ngkTJqjLMTExREZGcu7cOQ4fPsyhQ4d48OABM2bMwN/fn927d+Pk5GTEiAvWiBEjGDFihLHDyLXnr2NWnJ2dMywb61jTCiTKly+fKaaSKDU1lbFjx5KcnEzFihW5f/++sUMSpZAUSAhRwpmaaptuNG8OERFw8CB88YW2CYeiKPRY34PTrrHQBAiBuCTo/5o3f5x2pWbNpsYOXwgh+PTTT9XCiNWrVzNy5MhMaTw8PJgyZQoPHjxg69athR2iKAAVK1akcePGmT7v2bMnH330ERcuXGDo0KGcPn2agIAAPD09OXz4MBYWFkaIVuii6zoWRWkFEqWlucb3339PYGAgrq6u9OvXj6+++srYIYlSSJpsCFEKVK4MmzaByb/f+JkztR1dajQa3m37LmiA3kBV7fq7kan0eqkdT548NFLEQgihlZKSwu7duwFo1apVloUR6Tk5OTFx4sTCCE0YWaNGjThx4oTaROf3339nyZIlRo5KFGfnzp0DSkdzjVu3bjFjxgwAli5dKgV5wmikQEKIUqJbN/j8c+28osDgwXDzJgxsPJCPO3wM5oA3mNhr05y/Gkf/15uQklK62+UKIYzrwYMHPH36FIC6devmOZ+ZM2eqbc4BHj9+jI+PD40aNcLOzg5HR0e6du3Khg0b9M4zICCAsWPHUr9+fezs7LC1tcXV1ZWJEydy5coVvfI4ceIEY8aMoUGDBjg4OGBnZ4erqyt9+/Zl3bp1as2QtE4C0xfIuLi4ZOocMH1ndM8fc1RUFLNnz8bd3Z2yZctm6kzw/PnzzJkzh1deeQVnZ2csLS2xs7OjXr16DB8+nJMnT+p9bgqLtbU1P/74o3qMCxYsUDslzEper9nz5zI+Pp758+fTokUL7O3tsbe3p02bNixevDjH/izu3LnDxx9/TIsWLShTpgwWFhZUrlyZJk2a4O3tzZo1a9Trnl5W/Sbk5r5ISkqicuXKaDQaevbsmW2MoL0f0rb/8ssvc0xvSLr6iMjL90BfN2/eJDIyEtBdIBEeHk779u3RaDRYWlqyfPnyXO+nqJgwYQIxMTEMHz6crl27GjscUZopokSLiopSACUqKsrYoYgiICVFUXr1UhRtkYSitG6tKPHxipKckqy8uuFVhZkojEcxMUcB7TR2VCtjhy1EkRIXF6eEhoYqcXFxxg6lVHj06JH696hZs2Z5zsfHx0fN5/r160qdOnXU5ecnLy8vJSkpSWdeSUlJyvjx43VuDyjm5ubK8uXLdebx9OlTxdvbO9s8AMXHx0dRFEU5cuRIjmkB5ciRI1ke8+XLl5VatWplSu/r65ur/D/++GOdx+Tr66umCwsLy83lUaWPI+3Y9fHyyy+r2504cSLT+vxes/Tn8t69e0qzZs105vP6668rKSkpWebz22+/KQ4ODjmeZ39//0zbZnV+c3tffPjhhwqgmJiYKLdv3872nE6ePFkBFFNT0xzTPi+v1zG7Y30+X32/B/rauXOnuv25c+cyrT927JhSqVIlBVCqVKmS5X1WXGzZskUBFEdHR+X+/fuKomS8x/Ny/kTJldffPfo+h0oNCSFKERMTWLcOXFy0y4GBMHkymJqYstFzIw3KN4BKkDrg2XChK1YHMf/rocYLWghRqjk6OlKzZk1A2xv8vHnzSE1NzVeeAwcOJCwsjLfffptDhw4RGBjIqlWrqF+/PgB+fn68//77OrcfPXo0S5cuBbT9Gaxfv56AgAACAwNZsWIFjRo1Iikpibfeegt/f/9M26emptKnTx82bdoEQL169fj22285fvw4wcHB7N69m2nTpmWoEdK6dWtCQkKYM2eO+tn+/fsJCQnJMLVu3TrLmL28vAgPD2fSpEkcPHiQoKAgNm3aRIMGDQBITk7G1taWAQMGsGzZMo4ePcqpU6fYt28f33zzjXoN5s6di6+vb25Od6F46aWX1Pnjx49nWp/fa5aep6cnFy9e5N133+XgwYMEBwezceNG3NzcAPD392fFihWZtktISGDQoEFER0djb2/P1KlT2bt3L8HBwZw8eZItW7bw3nvvUb16db2PO7f3xZgxYwDtPbhu3Tqd+SYlJbF+/XoAXn75ZapVq6Z3TAUpv9+D7KT1H2FhYYGrq2uGdYsWLeLFF18kIiKCdu3aERQUxAsvvJCvYzGWx48f85///AeAefPmleiOYEUxkZ/SElH0SQ0JkZVTpxTF0vJZTYl167Sf//XgL6Xs3LLamhKvPXvTYKpB8fP7yrhBC1FESA2JwrdgwYIMbz9r1qypvPPOO8qGDRuUq1ev6pVH+rd/gLJx48ZMaaKjo9U33yYmJlm+JfXz81PzWLFiRZb7iouLU7p3764ASq1atTLVtli0aJGaR79+/ZT4+Pgs80lJSVHCw8MzfJabWgjpj9nExEQ5cOCAzrQPHjxQIiMjda5PSEhQPDw81POfnJycKY0xa0gcOnRI3W7UqFEZ1hnimqU/l+bm5lm+QX706JH6Br1p06aZ1h8+fDjbGhBpkpKSsvzdlt35zc2579y5swIo9erV05lm+/btan5+fn7Z5peV9Ndx/PjxSkhIiM4pIiIiV8eqz/q86Nu3rwIo7u7u6mexsbEZajKNHj1aSUhIMMj+0v89yuuUVsMpN8aOHasAygsvvKCkpqaqn0sNCaGL1JAQQhicuzuk7/dr3DgICYEGFRrw0xs/YaoxhdZAO+36FAVGDPmEwMBdRolXCFG6TZ48mVGjRqnLN2/eZPHixQwZMoS6detSuXJlBg0ahL+/P4qi5Jhfr1698Pb2zvS5vb292iY8NTWVZcuWZUqT1gt9v3791LfNz7OysmLx4sUA3LhxI0N79tTUVObPnw9AtWrVWLduHZaWllnmY2JiQtWqVXM8Hn2MGDECDw8PnesrVKhA2bJlda63sLBQ475586b6NrmoKF++vDqf1g9Amvxes+dNmjQpyzb3jo6Oat8G586dIyoqKsP6e/fuqfOdO3fWmb+ZmRkODg461+dX2jm4cuUKJ06cyDJNWi2YChUq8Prrr+drf0uXLqVJkyY6p6LSEWnaPZ3Wf8S1a9do164dmzZtwtzcnCVLlrBy5cpi3fnj77//zsqVKzEzM2PZsmVqnyhCGJMM+ylEKTVqFPzxB6xcCXFx4OkJQUHwUu2X+G/P/zJhzwR4GTT/gHIZYhKgb09PTp4+S/XqxWP4LiGKhIULtVN+rV8P6R+Cjh6FN9/Uzr//vnZK8+QJ/Ft9PF+6dNGOG5xe9+5w+TLY2cFff+V/H3owMTFh1apVvPHGGyxcuJDDhw9naLYRERHBli1b2LJlC61atWLz5s3UqVNHZ37ZjdTRpk0bGjVqxIULFzh06FCGdeHh4QQHBwMwYMCAbGN2c3OjQoUKPHz4kD/++ENtUnDmzBnCw8MBGDt2LHZ2dtkfvIEMGTIkV+kTEhKIiIggJiZGPdfpC3vOnj1Ly5YtDRpjfqQ/j0+ePFHnDXHNnpfduUx/TsLCwjJ0jlilShV13tfXV602X9i8vLx49913efz4Mb6+vnTo0CHD+oiICPbu3QvAm2++WawfwPUVHR2tdp7ZvHlzfvnlF958800eP35MpUqV8PPzo2PHjgbdZ0hISL7zcHZ21jttYmIib731FoqiMHnyZJo0aZLv/QthCFIgIUQp9t//wqlT2unqVRgxArZvh/Gtx3Px4UX+G/BfFC8wXQUpEXDnUSqvvtSW/wXdxN6+grHDF6J4iI6Gfx9A8yUhIfNyWr7P98ivKIbZ58Mshv6NiNDmbW+f//xzqUePHvTo0YPIyEhOnDhBUFAQwcHBHD9+XH0bHRQURKdOnQgODs7wAJheTu3L27Rpw4ULF7hy5QqJiYnqA1lQUJCaxtvbO8taFllJ/2b89OnT6nx2b8kNrWnTpjmmiY2N5fvvv2fz5s1cuHCBlJQUnWkfZnVvGFH6Qoj0tQsMcc2e93z/Auk5OjpmGRNAx44dqV27NtevX+e9995jw4YN9OvXjy5dutCqVatCe/C3trZm8ODBLFmyhK1bt/Ldd99ha2urrv/xxx/VkULS10zKKx8fH2bOnJnvfApS+ho/+/btY9++fSiKQps2bdi+fbtefWg0aNCAy5cv8+jRowz3gS6NGxfuy50vv/ySixcvUqNGDXx8fAp130JkRwokhCjFrKzAzw9atoTISNi5ExYsgA8/hIWvLOTyo8vsv7aflCFgthySY+D85af079OUvQf/xtRU/oQIkSMHBzBEh3DPV+u3tHyW7/PVuzUaw+yzQhYFj5UqQVSUtoaEkZQrV45evXrRq1cvQPs2f+PGjXzwwQdERkZy9+5dZsyYwcqVK7PcvmLFitnmX6lSJUBbIyAyMlJdvn//fp7iTRu2FDI+yOsqMCkI5cqVy3b9jRs36N69O2FhYXrlFxcXZ4iwDCb9eU3/MGiIa/Y8GxsbnetMTJ61hn6+QMfc3Bx/f3+8vLy4ePEigYGBBAYGAtpCgi5dujB06FAGDhyIqalpnuLW19ixY1myZAlPnjxh27ZtDBs2TF2X1lyjdevWpeYtevoCibTaId27d2fPnj06m1SlFxMTw9WrV6lRo4ZehRGF7a+//lKbLv33v//NUAAlhLHJ04QQpZyLi7Ym+GuvaZc//hhat4auXc3Y4rWF9qvac5GLJL8JpishJRkOHrnLhPEd+GH5n8YNXoji4PnmFIbStSvcvp31Ont73evy69dfCybffLC0tGTkyJFUrVqVHj16ALB9+3aWL1+e4QExTU7tpnX1Q5H+AXPDhg161ToA3YUBhdl+O6cH3KFDhxIWFoZGo2HkyJEMGjQINzc3nJyc1Aey1NRUNR99+uooTOlrnqSNHAKGv2b51bBhQ0JCQvD398ff359jx45x7do14uLi1DfzCxcuZM+ePTkWnOVH8+bNadmyJcHBwfj6+qoFEn/++SehoaGAYWpHFBdpBRIuLi44OjqqI59cuHCBFi1a6LV9amqqXmnTnD9/Pq/hqpydnbPt+yXNt99+S2JiIrVr1+bp06ds3rw523h+/fVXtZbQ66+/LgUYokBJgYQQgldfhRkzYPZsSE2FgQMhOBicncvg7+1P25VteVT5ESkDQLNR2wXz/3adJvbbx9jaljV2+EIIAcArr7xC9erVuXXrFpGRkTx69CjLIe0iIiKyHVox7a26RqPJ8GCavuNEjUaTpyrXFdLVOrlz506Gh2dj+euvv/j9998B+OSTT/jiiy+yTPd8Z5FFycGDB9X59G39DXHNDM3U1JS+ffvSt29fAO7evcvevXtZsmQJwcHBBAcHM27cOHbs2FGgcYwZM4bg4GCOHTvG9evXqV27tlo7wtraWu/mLSVBWoFE69atWbhwIa1bt+bu3bv07t2bwMDAHGsznTp1CiBXBRKGqH3i6+vLiBEjckyX8G+Tv+vXr+t1XWfPnq3Oh4WFSYGEKFAyyoYQAgAfH3j5Ze38/fvQv7+2iXodxzpsH7gdcxNzqA/Kq+BaHmw+aYimFHR0JYQoXtKPSpFV7QhArSavS9r6evXqZWjX7+7urs4fOHAgT/Glf2D57bffcr19QdSquHDhgjo/aNAgnenS98dQlJw/f57Dhw8DUL16dVq1aqWuM8Q1K2hVqlRh1KhR/PHHH+r9sXv37lw1i8nLfTF48GBsbGxQFIW1a9cSFxenvjn39PSkTJkyuc6zsBjye5CcnKzWCmnWrBnVqlVj165dWFlZER4eTp8+fXK8FnkpkBBCaEmBhBACAFNT2LgRatXSLgcEwMSJ2r7xOtfszLJey7Ays6JpEwf+mggBUWcZsn0IKam6Oz0TQojC9PTpU/XBwsHBQWdb7rVr1+rMIygoSK26/PwoC3Xr1qVhw4YAbN68mb///jvXMTZr1kytnbFy5UpiYmJytb2VlZU6n/B8R6d5lNaBIWTfd0JWw6AaW1xcHMOGDVObkEyZMgUzs2cVgA1xzQqLubk5Xbp0AbTX5PHjx3pvm5f7wsHBQR15ZO3atfj5+amdw44ePVrvfRuDIb8HFy9eVPNo1qwZoK0psXr1akBbQJndyDzwrMlQbgokFEXJ96RP7QiANWvW5JhX+o4ujxw5on5eK+2HoRAFRAokhBCq8uVhxw6wttYur1oFP/ygnR/lPoork66wfvLv2Ftpe9ff+ddOphyYQsS9q1y+/IeRohZClGQxMTG0bduW3bt3Zxjq83mpqalMmjRJHdmgd+/eOt+i/vzzz2zdujXLfb311luAtnbFuHHjMqWZPn06APHx8Xh6evLgwQOdMSUkJLBkyRLi4+PVz0xMTPjwww8BuH37NsOGDSMxMVHnMd25cyfDZ+mrjl+7dk3nvnOjXr166ryuwpqlS5eyc+dOg+zPUEJDQ+nYsaP6MNilSxfGjx+fKV1+r5mhHD9+nKtXr+pcn5iYyLFjxwDtMKZZNTfSJa/3xZgxYwC4efMmU6dOBbT9KHRNP8RwEWTI70H6Di3TCiRAOyrLp59+CsCWLVuYNWtWltsnJCQQGhpK5cqVC7WjWiFKCulDQgiRQfPmsHIlpA2z/u670KQJdOgAzg7OODs4s23ANl7d+CrJqcn8vHERu3svItnJnJOnLlKpUh2jxi+EKHkCAgJ4/fXXqVatGn379qV9+/bUrFkTe3t7Hj9+zOnTp1m9ejUhISEAlClTJkMb6Oe1atWKwYMHc+zYMby8vHBwcODcuXPMmzePS5cuATBx4sQsO0D09vZm//79rF27luDgYBo2bMi4cePo0qULTk5OxMbGcu3aNY4fP8727dv5559/MoxgkJa3v78/Bw8eZMeOHTRp0oQJEybQqlUrbGxsuHfvHidPnmTTpk0MHjw4w5CJ7u7uWFlZER8fz4wZMzAzM6NWrVpq85Rq1aphnVaqrCd3d3caN27M+fPnWbp0KY8fP2bIkCFUqVKFW7dusX79evz8/OjQoQMnTpzIVd75cf/+/Qwd7cXGxhIZGcm5c+c4fPgwBw8eVGtGtGvXDj8/P8zNzTPlY4hrZgiHDx9m9uzZdOrUiddee42mTZvi5OREXFwcly9fZtmyZWrV/zFjxmSo6ZGTvN4XHTp0wM3NjYsXL6qdGI4cObJQO1zNC0N+D9IKJBwdHXF2ds6wbvbs2Vy8eJHt27cza9Ys3Nzc1Folac6dO0dycrI01xAirxRRokVFRSmAEhUVZexQRDHz/vuKom2woSiVKinK7dsZ168MXqnUeA+lTBUUtP1cKh6dKxsnWCEKUVxcnBIaGqrExcUZO5RSIS4uTqlcubL6dyanqV69ekpQUFCmfHx8fNQ0169fV1xcXHTm0b9/fyUpKUlnTMnJycrUqVMVU1PTHOOxtbVVnj59mimP2NhYxcvLK8ftfXx8Mm07depUnemPHDmS5THn5PTp00q5cuV05tukSRPlzp072cbl6+urrg8LC8txn1k5cuSI3tcaUJycnJQvvvgi2+ulKPm/Zvqey/Txp78Wz+eR3eTp6Znl35eczq++98XzFixYoKYzMTFR/v7772yPUR/pz0NW90pO9LmX8nq8z+vevbsCKN26dctyfUxMjNK8eXMFUKytrZXAwMAM63/44QcFUKZPn673Poui9Pdnbs6fKPny+rtH3+dQabIhhMjSvHnQvbt2PiICvLy0nVymefj0IX+XhagBYGUFNc3g25n/NUqsQoiSK61juRMnTjBr1ix69uxJ7dq1sbW1xdTUFAcHB1xdXRk4cCAbN27k/PnztGzZMts8XVxcCA4OZtq0abi5uWFjY0OZMmXo3LmzWhsgu7fTpqamzJs3j9DQUD744APc3d0pV64cpqam2Nvb06hRI4YMGcLatWu5e/dulm9qbWxs+Omnn/j1118ZOnQoLi4uWFtbY29vj6urK56enmzcuFFt3pHe3LlzWbFiBZ06dcLR0THHIT310bx5c86cOcPbb79NzZo1MTc3x9HRkTZt2rBgwQICAgKMWh3dxMSEMmXKUKNGDTp16sR7773Htm3buH37NtOmTcuxNoEhrll+TZ06lT179jB58mTatWtHjRo1sLKywsrKilq1ajFw4EB++eUXtm3blqGPBH3l9b4YOnSoOu/h4ZHtCDRFiaG+B2fPngUyNtdIz9bWll27dlGpUiXi4uLo06cP4eHh6nrp0FKI/NEoShEbSFoYVHR0NGXKlCEqKgoHBwdjhyOKmYcPoVUruHlTuzx2LCxfrp0/dfcUnX07E5sUy8tnrPm/2Qeo27ij7syEKCHi4+MJCwvDxcUlTw8NwjhmzpyptgGXnz5CPHP48GG1A9ctW7ZkapIgste2bVsCAgK4ceMGNWvWNHY4QhhcXn/36PscKjUkhBA6Vaig7eQy7W/PihXPOrlsUaUFm702812P79i7PSZTYUR2nc8JIYQQomhIG02ifPny9OnTx8jRFC8pKSmEhIRQvnx5KYwQIo+kQEIIkS13d21BRJpJk+B//9PO96rfi3fbvouJJuOfktvXztLL3Ykffni3ECMVQgghRG7cuHGDn376CdB2ZmlpaWnkiIqX0NBQ4uLicHd3N3YoQhRbUiAhhMjRm2/Ce+9p55OSoH9/eG4kOtWmvd/wUpPm7D33D5Mm/pdffvmu0OIUQgghRPbCw8O5cuUKBw4cwNPTk6SkJKysrHgv7T96obegoCBAO8qLECJvpEBCCKGX+fMhbVjye/egXz94fpj2ree3MuyPKdz6d+TPpBTwfuM9AgN3FWqsQgghhMjakCFDqF+/Pq+88gqnT58G4PPPP6datWpGjqz42bdvHwA9evQwciRCFF9SICGE0IuZGWzdCjVqaJcDArSdXKb1DZeqpLLoz0Ukm8LTfmD/byfdT+Lg9R6eXL78h3ECF0IIIUQmNjY2NG/enDVr1mQ5movI3unTp9mxYwdNmzalQ4cOxg5HiGJLRtko4WSUDWFoZ85Ahw7w9Kl2ed48mDpVO38/9j7tVrYj7HEYJIDDDxD9j3ZdLWdzTvwZQtWqDYwStxCGIqNsCCFE6TV37lwuXryo9r1x5MgR2rZta+SohCg4MsqGEKJIad4cfvzx2fLHH8Pu3dr5irYV2TNkD+WsyoElRI8CBxvtuhu3k+jxUkseP75X6DELIYQQQuTXgwcPmDZtGnv27OHVV1/l5MmTUhghRD5JgYQQItc8PWHWLO28osDgwXDhgnbZtYIrOwbuwNzEHOwgeizYm2vXhVyM5bWejYmPjzFO4EIIIYQQeeTk5ERqaioPHjzAz8+Ppk2bGjskIYo9KZAQQuTJ9Ong5aWdf/IEeveGR4+0y11qdcG3j692oRw8GQ02//61+d/JR/T3bERKSnLhBy2EEEIIIYQoMqRAQgiRJyYmsGaNtgkHwPXrMGCAdlhQgCFNhzCn2xztQmVIGAoWGu3inr1/M2pkK1JTUws7bCGEEEIIIUQRIQUSQog8s7WFXbugYkXt8q+/wuTJz9ZP6zSNt1u+DUCKC5j0B9N/16378SwffyzDZAkhhBBCCFFaSYGEECJfatSAHTvA/N9+Iv7v/+CHH7TzGo2Gxa8upq9rXwDiG4PtK8+2nT//IPPnDyvcgIUQQgghhBBFghRICCHy7YUXnhVCALzzDhw9qp03NTFlo+dGOlTXjtEd3R7Kv/As7ccf/ciaNR8VXrBCCCGEEEKIIkEKJIQQBjFy5LPmGsnJ2g4vr17VLlubW/Oz98+4VXAD4JEHVGqiXZeqwLixX+Pvv6jwgxZCCCGEEEIYjRRICCEM5uuv4ZV/m2Q8egS9ekFkpHbZ0dqRfW/uo5p9NdBARD+oVku7LikZ/jpz0igxCyGEEEIIIYxDCiSEEAZjZgabN0PDhtrlS5e0NSXSRt6oUaYGe4fspYxlGTCB8KFQryI07gS1PPsbL3AhhBBCCCFEoZMCCSGEQZUtC7t3g5OTdvnXX2HCBFAU7XKTSk3YNWgXFqYWVLGvhEl/CHkRhmwfwsFrB40WtxBCCCGEEKJwSYGEEMLgXFxg506wtNQur1wJ33zzbH2XWl048OYBLr97lRdeGQlAUmoS/bb048/bf7Le92Nu3jxX+IELIYQQQgghCo0USAghCsQLL4Cv77PlqVO1hRRputTqgp2lHctfX64OCxqbGMvclzswbNQ8XurehoiIa4UasxBCCCGEEKLwSIGEEKLAeHvDzJnaeUWBIUPg1KmMacxMzNjUfxOdanSi+kM4eSUFBbh6PYHvFr5T2CELIYQQQgghCokUSAghCtRnn8Hgwdr5p0/h9dchPDxjmpjEGB7FPeKWE0SNBkcTGNjUkTlf/VL4AQshhBBCCCEKhRRICCEKlEYDq1Zpm3AA3LmjLZSIjX2WpoxlGeqUqwOAUtmU2e+/zMbTDzAxkT9RQgghhBBClFTya18IUeCsrLT9R7i4aJdPn9Y230hJ0S6bm5qzxWsLQ5sO5cw7F5gwf3+mwojQ0KMkJycWbuBCCCGEEEKIAiMFEkKIQuHkpB0O1MFBu7xrF3z44bP11ubWrOu3jgYVGmTadsWUvrR378bgwU1JTU0tpIiFEEIIIYQQBUkKJIQQhaZhQ/DzA1NT7fK338L33+tOn5KawuCPG/PhN7uIToSffrrEW2+1kUIJIYQogtasWYNGo0Gj0XDjxg1jh1Oi3LhxQz23a9asMXY4BaakHGfaMcxM69m7iCsp590Y5O9e/kmBhBCiUHl4wLJlz5bfey/jcKBpEpIT6L2pN5usL1C1C2j+/XzVqmAmT+5eCJEKIYqS2NhYli9fzmuvvYazszNWVlbY2dlRu3Zt2rdvz9tvv83mzZu5e/eusUMV+TBixAj1x336ycrKikqVKtGgQQP69evHF198wannh20SRUpW11HXNGLECGOHC8C1a9f4/PPP6dChA9WqVcPS0pIKFSrQtGlT3n33XY4fP27sEEuk9AUiWU22trbUrl2bAQMG8PPPPxs7XGFgUiAhhCh0Y8bA9OnaeUXRDg968mTGNLejb/Nn+J8AXOwGTVs/W/f998f46KMehRStEMLYAgICaNy4MePGjWPPnj2Eh4eTkJBAbGwsYWFhnDx5kh9++AFvb2/c3d2NHW6B6tq1KxqNhq5duxo7lEKVkJDA/fv3uXz5Mjt37mT69Om0bNmSNm3acOTIEWOHZ3Sl5S1tQR1nQkICkydPxs3NDR8fH/73v/9x584dEhMTefToESEhIfz3v/+lc+fOvPbaa9y+fdtg+xY5e/r0KWFhYfz000/06dOHV199ladPnxbY/qTGSOEyM3YAxvS///2PtWvXcvz4ccLDw1EUBWdnZzp27Mjw4cPp0KFDge07Pj6en376iZ9//pnTp09z//594uPjcXBwoFatWrRt25Y333yT9u3bF1gMQhjT55/DjRuwfj3Ex2tH3jh5EupoB9ugjmMd9gzZQ/e13YlNiuXsa9A6DgLPa9d//fV+rKz6MmvWTmMdghCiEFy9ehUPDw+io6MB6N27N15eXtSvXx8LCwsePnzI2bNnOXjwoDyYljD79++natWqAKSmpvL48WPu3bvHn3/+yc6dO7l+/TqBgYG89NJLTJ8+nVmzZhk54oJTq1YtFEUxdhh50qpVK3x9fbNNU65cOcA4x/nkyRP69Omj/v1wdHRkxIgRvPjii1SqVImoqCiCgoLw9fXlr7/+Ys+ePbRv354DBw7g5uZWqLEWlKJ0f/Xp04c5c+Zk+CwqKorg4GAWLVpEWFgYe/fuZfz48axdu9ZIUQqDUkqhmJgYZdSoUQqQ7TRy5EglJibG4Ps/ePCgUr169Rz3Dyivvfaacu/evTzvKyoqSgGUqKgoAx6BEIaRkKAo3bopiraehKLUq6coDx5kTHPo2iHFYraFwkwUZqJ0rJfxOzJnzhvGCV6UWnFxcUpoaKgSFxdn7FBKhQEDBqjf99WrV2eb9v79+8rixYsLKTLj6NKliwIoXbp0MXYomfj6+qrXKiwsLE95DB8+XK88UlJSlNWrVys2NjZq+v/7v//LW+AlgCHOvaGlxWPIe7UgjvONN95Q83zxxReV+/fvZ5kuMTFRmTJlipq2Tp06SnR0dJZp09L4+PgYJMaSLiwsTD1nw4cP15nu3r17SsWKFRVA0Wg0yp07dwo8Hl9f32zTFsXvnqHl9XePvs+hpa7JRkpKCp6enqxevVr9zNramlatWtGuXTsc0oYAAHx9ffH09CQlbWxCA9i9ezc9e/bk1q1b6mcODg60a9eO7t2707BhwwzDHf7yyy906dKFhw8fGiwGIYoKCwvYvl3b2SXAlSvQpw/ExT1L82LtF9ncfzMmGu334vch0K3ms/XTp//E11+/WYhRCyEKS0pKCrt37wa0b1lHjhyZbXonJycmTpxYGKEJIzMxMWHkyJHs378fMzNthd8PPviAe/fuGTkyUZxs3bqVn376CQB3d3f8/f1xcnLKMq25uTnz589n3LhxgLa/iY8//rjQYhVQqVIlhg0bBoCiKAQGBho5ImEIpa5AYsaMGRw4cEBdHjt2LLdv3yYwMJA//viDO3fuMGPGDHX9gQMH+Oyzzwyy78ePHzNq1CiSk5MBsLe3Z/Xq1Tx8+JA//viDw4cPc+HCBW7evMngwYPV7S5dusSUKVMMEoMQRU3ZsrB3L1Spol3+3/9g6FBIP5BGP7d+rHh9hbp8ZAS8WO3Z+o8/3sD3348tlHiFEIXnwYMHajvhunXr5iuvWrVqZeg8LzAwEG9vb6pXr46VlRXVq1dnxIgRXLx4Ua/8bt++zSeffEKLFi0oV64cVlZW1KhRg4EDB+rddOTBgwdqB3oVK1bE0tKS6tWr06FDBz7//HMuXbqkpk3r7PHYsWMAHDt2LFPHb7Vq1cqQ//M9/f/666+88cYbVK9eHXNz80zpz58/z5w5c3jllVdwdnbG0tISOzs76tWrx/Dhwzn5fGc/RUDHjh2ZPHkyoG0O++2332abPiAggLFjx1K/fn3s7OywtbXF1dWViRMncuXKFZ3bPd93QWpqKsuXL+eFF16gXLly2Nra0rRpU7744osc27YHBwczevRo6tevj62trXr/tWzZkokTJ/Lzzz9nqj6vq0370aNH0Wg0GQrrXFxcMt0bR48e5eHDh1haWqLRaBg/fny2MQL4+/ur22/cuDHH9IZgiOPMjblz56rzy5cvx9raOsdt5s+fT+XKlQFYvXo19+/fz3GbtL83ad+ratWqMXTo0Gz/3jx/zyUmJrJw4UJatWpFmTJlcHR0pGvXrvzyyy8Ztnvy5Alff/017u7uODg4ULZsWTw8PDh8+LDOfeWmz4QTJ04wZswYGjRogIODA3Z2dri6utK3b1/WrVunNq8rKOn/bsXHx2eZ5u7duyxZsgQvLy/q1auHra2tet779OnDli1bdI7YptFocHFxUZdHjhyZ6T7LbvSU/PxtKLXyXnmj+AkPD1esrKzUajVDhw7VmXb69OlqOisrKyU8PDzf+1+6dGmGqua7d+/ONn3fvn3VtObm5kpkZGSu9ylNNkRxceqUotjaPmu+8cEHmdMs/N9CtekGPigvVXr2fTLRoCxd+k7hBy5KHWmyUXgePXqkfsebNWuWr7xq1qypVgdetWqVYmZmlmVTSUtLS2Xz5s3Z5rVy5UrF2to62yaXo0ePVpKSknTmsX79esXW1jbbPGrWrKmmT9+UQZ/0ipKx2vi0adOyTX/kyBG9mpJ+/PHHOo+pMJtspHfr1i3FxMREAZR69eplmSYpKUkZP358tsdmbm6uLF++PMdjO3/+vNK9e3ed+bRp00Znk9+FCxeqsWY3PXnyJMN2uqqQ63vdjhw5oijKs+YJZcqUyfFvWL9+/RRAKVu2bJ7+3qXtOzdNNgx1nPo4e/asut0LL7yg/4EpivLJJ5+o23733XeZ1qf/7v33v//V+ffGxsZGOXbsWJb7SH/PnT17Vmnbtq3O4/7mm28URVGUmzdvKo0aNcoyjUajUX788ccs96VPE4WnT58q3t7eOV6DvDRT0bfJhqIoGZrN/PHHH5nWJycn6/Ud8/DwyPQ9UxQlx+2eP0ZD/W0oygq6yUapKpD48MMPM/wBePTokc60CQkJGfp5mDp1ar73P3jwYDW/xo0b55g+MDAww0184MCBXO9TCiREcbJnj6KYmj4rlPj++8xpPj/6eYZCCY8Kz74jpiYoq1ZNKfzARakiBRKFK60gAVDmzp2rpKSk5CufZs2aKebm5krV/2fvrqOjuNoADv9240YguLtD0eDuLS5FigQvWty1LdqP4u4UKE6LuzsECe6BAsEJSYgnO98fQza7xJMNkrzPOXvIzJW5s8bOO1cyZVLmzJmjnDt3Tjl27JgybNgwxcrKSgEUc3Nz5dy5c5HWs2zZMqP/y+fMmaOcPHlSuXTpkrJlyxblhx9+0KcPHDgw0jpWrVqlz2Ntba307dtX2b17t3Lp0iXl+PHjyty5c5W6desquXLl0pd5+vSpcu3aNaV06dIKoJQuXVq5du2a0ePOnTtGxwk7xnfffacAStGiRZXly5cr58+fV44dO2Z0IXXgwAHFzs5OadmypbJw4ULl6NGjyqVLl5S9e/cqf/75p9HrENVcHl8qIKEoilKoUCF9uefPn0dI79Chgz79+++/V9asWaOcP39euXDhgrJkyRKji7jt27dHe24VKlRQtFqt4uLiouzatUu5ePGi8s8//yjly5fX54kscOPm5qa/UMqZM6fy559/KocOHVIuX76snDhxQlm+fLnSvn17xd7ePtYBiQ8fPijXrl1TJkyYoE/ft29fhPdG2EXQvn379Pn+/vvvKJ/P169fKxYWFgqg9OzZM7YvgxFTBiTiep6xMWfOHH1dkydPjsOZKcqZM2f0ZVu0aBEhPSytXLlyikajUYoVK6YsX75cuXDhgnL8+HFlwIAB+vdCtmzZlMDAwAh1GL7nypYtq5ibmyu9evVSDhw4oLi6uipLly5VMmbMqACKVqtVrl27ppQqVUqxsbFRhg8frhw9elS5cOGCMnPmTMXR0VEBFAcHB+Xly5cRjhVTQCI0NFSpXbu2Pk/evHmVGTNmKCdOnFAuXryo7Ny5Uxk5cqSSJ0+eRA1IvHz5UkmfPr0CKFmyZIn0eQsODla0Wq1So0YN5X//+5+yd+9e5eLFi8rRo0eV5cuXG31OO3ToEKH8tWvXjD4nEyZMiPA+M3wOTfHd8LWTgIQJ5cmTR/9m6NixY4z5x44dq8+fJ0+eBB/f8IPcunXrGPP7+/sbBSTWrl0b52NKQEJ8axYvDg9IaDSKsnGjcbpOp1OGHRimD0poxqLUSmkclFi9euSXabxIFiQg8XlNmzbN6P/C7NmzK3369FHWrl2r3L9/P9b1GF5QZ8+ePdKL1sOHD+vvZJYuXTpC+n///aefRNHFxSXKHhBhvRG0Wm2EIMGzZ8/0daRLl065du1alG1+8uRJhH1xmdTS8HmrWbOmEhAQEGXe169fR9sTMzAwUP87Jnv27EpISEiEPF8yINGuXTt9uePHjxulbd68WZ+2ZMmSSMv7+/vr72zmyJEjwmtreG5ApHeaAwIClCJFiiiAkjp16gh1jBkzRgEUOzu7aCcsf//+fYTAW0wXjLF97nU6nZIjRw4F1DvEUZk+fbq+PldX1yjzRSesfGTBM8PHw4cPTX6esdG1a1ej4EZc+Pn5Rdsrx/C98sMPP0R64WwYXNm6dWuEdMNz1Wg0yj///BMhz9WrVxUzMzMFUNKmTatYWVkpZ8+ejZBv165d+rqmT58eIT2m533mzJn69KZNm0b5XRIaGhqvXuWGx2/cuHGE98ipU6eUOXPm6K/lLCwslC1btkRal06nU+7duxft8cKu8TQajXL37t1o2xOXSS3j+93wtZOAhIncvn3b6M0SU3dMRTGOfgLK7du3E9SGsK5vgNKsWbMY879//97o+Lt3747zMSUgIb5Fo0aFByUsLRXl8GHjdJ1Op/Td3VcflDAbg1IzRfhnxcIcZcOG379M40WSJwGJzys0NDTalbHSp0+vtGrVStm+fbui0+mirMcwILF58+Yo8xl26z9//rxR2qBBgxRAyZQpU7Svf3BwsJI5c2YFUEaNGmWUNnz4cH39kV1gxCQ+AQmtVmuS2d+vXLkS7UXqlwxI9OvXT19u27ZtRmmlSpXSX0hF5+bNm/o6Dhw4YJRmeG7R/YZbuHChPp+bm5tRWrdu3RRAKVGiRKzPK4wpL9R/++03/fviv//+izRP0aJFFVB718RXVJ/ZTx+G7+XPGZAwHBr96WsVGylTplQAxcnJKUJaWL3W1taR9khQFEXx9vZWLC0tFUAZMGBAhHTDc23VqlWU7Qj7ToDo776HfQdG9jmI7nkPDQ3Vf59lzpw50mEOCWV4/JgeLVu2jPDdHFchISFKmjRpFECZNm1atO2JS0Aivt8NX7vEDkio0xInA25ubkbb5cuXj7FMyZIlsbS0JCgoCICrV6+SP3/+eLehTJky/PPPPwCcOXOGkJAQ/czQkQmbuArUmX3LlCkT72ML8S35/Xd4/hyWL4egIHXljWPHoEQJNV2j0TCz3kx8g3xZfmU5oWZwvC/UnAWHPkBwCAwb8BtNmgzG0tL6y56MSPamn5nO9DPTE1zPmmZrqJajmn776KOjtNuqrjAzsPxABpYfqE/zCfSh4LyCCT5m1RxVWdtsrdG+GqtqcPftXewt7bnd53aCjxETrVbLsmXL+PHHH5k+fTqHDh0ymozs5cuXbNiwgQ0bNlC6dGnWr19P7ty5o6wvVapUNG7cOMr0zp07s2DBAgAOHjyIs7OzPm3btm0ANGzYEGvrqL9bzM3NKV++PJs3b+bMmTNGaWET0OXMmTPadphSxYoVI0xgGZPAwEBevnzJhw8f9M+3YjDRopubG6VKlTJlMxPE3t5e/7ePj4/+72fPnnHx4kUAWrZsGW0dBQsWJE2aNPrJxmvVqhVpvrZt20ZZh+Fz8vDhQ7777jv9dsaPszffvHmT8+fPf7HfdZ07d+bXX38lNDSUVatWMXr0aKP0ixcvcu3aNX3epMrwfWJnZxfn8nZ2drx//z7aSRxr165NunTpIk1zcHAgb9683Lhxg4cPH0Z7rNatW0eZ9t133+mvGVq1ahVtvsePH8d4rE9duXKFZ8+eAepiAIaftS9h586d2Nvbky9fPhwdHWPMr9PpePHiBT4+PgQHB+v3Z8mShTdv3kS4RkyI+H43JHfJJiBhOIutpaUlWbNmjbFMWL4HDx5EqCM+XFxc+P333/Hz8+P58+dMnDiRcePGRZr3/fv3jBgxQr/dsWNHUqdOnaDjC/Gt0Ghg0SJ49Qp27gQfH/j+e3UFjly51DxajZbFDRfjH+LPuuvrCLaAk32h+ky4GwRTF02WYIT4KngHevPM51mC6wkMCYywHVavd6DxD2IFxSTHfOMXccnpl74veebzDAdLhwTXHxf16tWjXr16eHp6curUKVxdXbl48SInTpzAy8sLAFdXVypXrszFixf1F3+fKlGiRLQ3A4oXL66/GXH9+nX9fi8vL+7fvw/AokWLWLRoUazabbgMZXBwsL7OypUro9FoYlVHQsX2h6+vry+zZ89m/fr13LhxI9plz7+25cgNLy4Nl3B3dXXV/92mTRvatGkTq/qiWz60QIECUaY5OTlF2qaw40+ePJnAwEAqVqxIvXr1qF+/PpUrV6ZQoUKf7f2QOXNm6taty+7du1m5ciWjRo0yOvaKFSsA9Xdwu3YJX1a7atWqcV794nNwcAj/Dvvw4UOcy4eVMXy/fSq69wqEv18+fa98Kl++fFGmpUyZMk75YjrWpy5fvqz/u0qVKnEqGx8uLi4RVvoIDAzk8ePHbNmyhYkTJ7J8+XLOnz/PoUOHIg34KIrC2rVrWbZsGefOncPfcD35T5jyuyy+3w3JXbIJSDx69Ej/d5YsWWL9pZ8tWzZ9QMKwjvjImDEjy5cvp3379gQHBzN+/Hhu3bpFr169KFGiBDY2Nrx48YKDBw/y+++/6yOY1apVY9q0aQk6thDfGnNz2LABatWCM2fg5UuoWxdOnYKw/3vMtGasarIKv2A/tt3ZRqAVONSH9xmhy7XxZC1WgfJZY+4NJURiSmGVgswOmWPOGAMrc6sI22H1prAy/kGsQWOSY6axTRNhX3q79HgFeGFv+WXukqVKlYoGDRrQoEEDQP2h+vfffzNo0CA8PT15/vw5Y8aMYenSpZGWj+puZRhzc3OcnJx48eIF79690++PzdJ+kTFc5u3du3f6XgZRBUwSQ6pUqWLM8+jRI2rUqIG7u3us6ozuB/6XYHhRYfjD3xSv26dsbW2jTNNqtfq/Pw3oFChQgHXr1tGtWzc8PT3ZuXMnO3fuBCBNmjTUq1eP7t27U7ly5Xi1OS66devG7t27efDgASdOnNBfaIZ9ngAaN26cpG+GGZ7bixcvKFasWKzL+vv76y8qo3uOonuvQPj7JbrgX0z1GL7nYpMvpmN9yvCz9Tm/twxZWVmRL18+RowYQenSpalTpw7Xr19n8ODB/PXXX0Z5AwICaNasGXv27IlV3ab8Lovvd0Nyl2wCEoaRqNh07wljGPU0RTSrVatWpE+fnl69enHr1i19N9PIpE6dmm7duvHrr79iaWkZq/oDAwMJDAy/i5bYawELkZhsbdUeEpUqwa1bcP8+1K8Phw9D2I0NCzMLNrTYQJMNTaiavSpHch/B98F+CPpA3TV1OdD+AEVS5efcuS3UqNHly56QSJY+HU5hKtVyVOPpwKeRpjlYOUSZllCHXQ4nSr3xZWVlRadOnciUKRP16tUDYOvWrSxevNjoB2CY2NyQMByaEMbwB2T//v3p0iV23ydR/f/9ue6GA5iZmcWYp3379ri7u6PRaOjUqROtW7emYMGCpE2bFisrNRim0+n0dUX2HH1JhndxDe8SG75ua9eujXVvkdgEceKjefPm1KpViw0bNrBv3z5OnDjB69evefPmDWvWrGHNmjW4uLiwfPnySN+/ptKgQQMyZMjAixcvWLFihT4g8e+//+Lp6Qkk7eEagFEA4vLly9StWzfWZa9cuaIfyhSXQMa37nN+b0Wldu3aFCtWDDc3N9avX8+CBQuMhtxMnDhRH4yoWrUqvXv3pmTJkmTIkAEbGxv956pKlSqcOHHiq/suS46STUDCsCtWdOM+P2VjYxNpHQlRrVo1du/eTd++ffWR8U9ZWFjQuXNnevbsGetgBMDkyZP59ddfTdJOIb4GTk6wbx9UqABPn4KrKzRvrgYqwj4aVuZW7GyzEzOtGf3K9qPhuoYccj+ET5APLefXIveCAE75hrB6zXNathwd/QGFEN+kunXrkjVrVp48eYKnpydv374lbdq0EfK9fPky2npCQkL0F2SGd9oN74L6+flRpEiROLfRyckJrVaLTqfDw8MjzuUTy+3btzl58iQAI0aMYOLEiZHmC3tevjZPnjzhzp07AOTPn9/odTd83TQaTbxeN1NzdHSke/fudO/eHVDnlNi+fTtz5szBw8ODVatWUaJECfr165dobTA3N8fFxYWpU6eyadMm5syZg729vX64RpYsWahTp06iHf9rYDj8YPv27QwfPjzWZbdv367/+3P0aPmS0qQJ7ynn4eGRoPn0TKVAgQK4ubkRHBzMnTt3KFmyJKAGSsN6x1WqVInDhw9HGdj7Wr/PkqPEC71+ZUJCQvR/Rzd29FOGeQ0nQokvf39/+vTpQ968efXBCFtbW8qUKUONGjUoWrQoZmZmBAcH87///Y88efIwadKkWNc/YsQIvLy89I8nT54kuM1CfGlZs8LevRA2TPLAAejUCQzmtcNMq961s7GwYXub7VTPUR37AKi29ANH3ocQFAzdu4zF0/PruQgQQphWpkyZ9H9H9SP0ypUrRr8JPuXm5qafzNrw4jVt2rRkzqwOgzl48GC87qpZWFjo64zvnbnEuEN548YN/d/RTZ5nOB/D12T27Nn6u9VNmzY1SisRNhsysH///s/artgqVKgQw4cP5+zZs/o7vRs3boxTHfF5X3Tt2hWNRoOvry+bNm3i6dOnHDhwAFDH8SdmD434MuX7v1ixYvreDWfOnIn1+9vHx0c/x4GVlVW0n5mkIOxiH+D48eNfsCXhDL/DDa/P3r17p5//pWXLllG+hz98+KAPYkbma+gJkpx8fd80icRwTE9AQECsyxnmjc8MvIaCgoKoX78+8+bNIyQkBEdHR5YtW4anpyfnzp3j0KFDXL16lZcvXzJkyBA0Gg3BwcGMGjWKUaNGxeoYVlZWpEiRwughRFJQuLDaKyKsg9Pff8PgwerioJ+ytbClj3Mf/Kw17G4H31uCLfD3/4aTKlWmiAWEEN88Pz8/bt68CajDLQ17Nxh69+4dO3bsiLKe5cuX6//+dJWFRo0aAeoM6Zs3b45XOxs2bAiAu7u7ftWOuAjr5Wk4PDOhDH/cRzd3wsKFC012TFM5efIkM2fOBNTnpn///kbpefLkoVChQgCsX7+e//777zO3MPayZs2qH24S14n2DHv/xva9kSdPHqpWrQqoE1muWrUKnU6nH7bzNYrPeUZn2LBh+r+7d+8eq/kEhgwZor/o7dSpU4zz0nzrihUrpl8MYOnSpSbrMR5fiqLoV84BtTdPmNh+ly1btizaG82mfp+J6CWbgIThEjVxmbzE8M2c0GVuJkyYwJEjRwB1KMiRI0fo3LlzhCEZqVOn5o8//mD27Nn6fZMnT+b8+fMJOr4Q37qKFdWJLsMC3jNmQGTzveoUHVNOTUGHwhtHsGpmxpYVv/FDj9j3NhJCfHkfPnygbNmy7Ny502ipz0/pdDr69u2rn+upUaNG0d7hGjhwYKRDN44dO8bixYsBdYk2wyU/Qb0QCZtPoUePHjHeUd29ezdXr1412tenTx/9DY6ff/7ZaCWPTz19GnEekLBJ5R4+fGiysc958+bV/71q1apI8yxYsIB///3XJMczBZ1Ox8qVK6lbt67+ImTWrFmkT58+Qt6wZS3DJrt7/fp1lPUGBgYyf/78ON28iq1///2X9+/fR5n+5MkTbt9Wl9LNmTNnnOo2nGwwbDL22OjatSug9tiZM2cOoI67j27p3C8pvucZlTZt2tCsWTNAnUeiUaNGUb4/goODGTp0qH6FnZw5czJ16tQEt+Frp9VqGTJkCKB+J3Xo0EHfi+xTn2M42vz58/ULDRQrVkzfcw3Unmxhq4msX78+0nZeuHAhwlK3n0qdOrX++swU7zMRvWQzh4Th+Kfnz5/Hupzhsk8JmWk4ICCAWbNm6be7d+9u1I0wMn369GHp0qW4ubmhKApz5sxh9erV8W6DEElBo0bqkqDduqnbQ4eqq264uITn0Wq07PppFzX+qkG+1PmYNWAWWVJkMapHp9Nx+fJuSpVq8BlbL4SIq/Pnz9OwYUMyZ85MkyZNKF++PNmzZ8fBwYH3799z+fJlli9fzrVr1wB1fP7vv/8eZX3FihXj5s2blCpVihEjRlCmTBkCAwPZvXs3M2bMICQkBHNzc+bNmxehbM6cOVm4cCGdOnXi3bt3VKxYkfbt29OgQQOyZctGSEgIT58+5fz582zevJkHDx6wY8cOo4kUM2TIwIIFC+jQoQOvXr2iTJkydOvWje+//54MGTLw4cMHrl+/zvbt27lz506EH8MVKlRgxYoVvHr1ioEDB9KuXTv9ZN0WFhZkz549zs9xiRIlKFKkCNevX2fBggW8f/+etm3bkjFjRp48ecKaNWvYvHkzFStW5NSpU3GuP77u3r2rvxur0+nw8vLixYsXnDt3jn/++Ue/GplWq2XcuHH6ORk+1aZNG/bt28eqVau4ePEihQoV4ueff6Zq1aqkTZsWX19f/WoTW7du5d27d3To0MHk5zNz5kzatm1L/fr1qVGjBgULFsTR0RFPT09cXV2ZM2eO/qZZz54941R3iRIlsLa2JiAggDFjxmBubk6OHDn0XdYzZ85sNC9amObNm9O3b188PT31QbqveTLL+J5ndJYvX86bN284fvw4Bw8epECBAnTu3JkaNWqQPn16vL29cXV1ZdmyZfqAUaZMmdi+fXuy6Yncu3dvduzYwYEDB/jnn38oWrQovXr1onTp0tja2vLixQvOnj3LunXr+Omnnxg/fny8j/X+/fsIgdqgoCAePXrE5s2bWbduHaB+7j8NCGm1Wtq2bcu8efO4cuUKlStXZsCAAeTJkwcvLy92797N/Pnzsbe3J1OmTNy9ezfSNpibm+Ps7MypU6dYvnw5JUqUoHjx4lhYWADqfEBR9cIT8aAkE3PnzlUA/cPX1zdW5dKmTasvM2/evHgf/9ixY0bHP3ToUKzKjR49Wl8mW7ZscT6ul5eXAiheXl5xLivE12zCBEVRB2woipmZovzzT8Q8b/3eKsGhwRH2f/jwXuldOK1iboayevXIxG+sSFL8/f2VmzdvKv7+/l+6KUmev7+/kiFDBqP/P6N75M2bV3F1dY20ruzZsyuA4uLioixZskQxNzePtA5LS0tl3bp10bZr/fr1SooUKWJsj1arVQ4fPhxpHStXrlRsbGyiLZ89e/YI5Xx8fJRcuXLFKn/Y/nHjxsX4XF++fFlJlSpVlG0pWrSo4uHhEW2dK1as0Ke7u7vHeMzIuLi4xPr1BpQyZcooR48ejbHekJAQZejQoYqZmVmMddrZ2Sl+fn7xOjd3d3d9vhUrVhilVa1aNcZjm5mZKZMmTYpTvWGGDh0aZb1HjhyJss19+vTR53N0dIxw7vEVVmfVqlVjXSYxzzM6/v7+St++faP8XjB81KlTR3n8+HG09cX2sxf2nojsOYrte27cuHH6fNEJ+2xF9r0Sm+fd19dXadGiRYzPT2y+b6I7fmwe9vb2yurVqyOt6/3790rx4sWjLOvk5KQcO3Ys2udeURRl586dikajifEcTfHd8LWL7++e2F6HJpseEgULFjTavnLlChUqVIi2zLNnz4y6bX1aR1w8e/bMaDtsLFZMDPMZ9tYQIrkbORJevIC5cyE0FFq1gt27oWbN8DxONhGj11efXWZJ7QrMu6V2x+3UcRLBwcF06vTH52q6ECKWrK2tefbsGWfPnuXgwYOcPXuWO3fu8PLlSwICArCzsyNTpkwUK1aMxo0b07x581itTNW1a1eKFCnCjBkzOHnyJG/evCFt2rTUrFmTYcOG6ecciEqrVq2oU6cOixcvZu/evdy8eRNPT08sLCzIkCEDhQsXpnr16rRo0SLK/+9dXFyoU6cO8+bNY+/evTx48AA/Pz8yZMhAtmzZ+P7772nTpk2Ecvb29pw+fZrJkyezf/9+Hj9+HO1Y6dgqXrw4V65cYfLkyezZswcPDw8cHBzIkycPLVu2pHfv3nFapczULC0tcXR0JGXKlBQqVAhnZ2fq169P8eLFY1XezMyMqVOn0qVLFxYvXszhw4d59OgR3t7e2Nraki1bNooXL06dOnVo2rRpnO+yx8bGjRs5ePAgBw4c4MqVK7x48YI3b95gbW1Njhw5qFKlCj169KBo0aLxqn/KlCnkzZuXv/76ixs3buDl5WW07GlU2rdvz9y5cwF1UtPEOHdTiu95Rsfa2prZs2fzyy+/sGbNGvbt28ejR494+/YtDg4OZMqUiapVq/Ljjz/q591Ibmxtbdm0aRNHjhxhxYoVnDx5khcvXmBubk7mzJkpVKgQLVq00M+1Y0oWFhY4OjpSsGBBateuTZcuXYwmMTbk6OjIqVOnmD59Ohs3buTevXuYm5uTNWtW6tevT79+/YzmnYhK/fr1OXToELNmzeLChQu8fv3aJAsciIg0ipI8Fl/98OEDadKk0U9MMnXqVIYOHRptmfXr1+t/DFhbW/PmzZt4T2y5detWmjdvrt92c3OL1VrYM2fOZMCAAYD6AYtu7GFkvL29cXR0xMvLK9l0KxPJh06nDtVYs0bdtrODQ4egbNnI8995c4fSi0qS4rUfZebBvx/nPjLTwtx5fejRY87nabj4pgUEBODu7k7OnDm/6AWaiJscOXLw+PFjXFxc9DPkCyHUCf7C5pI4d+4cZcqU+cItEkJ8TeL7uye216HJalLLmga3TteuXRtjGcM8NWvWTNAqG4aT8ABGs8NGxzCf4aQtQgh1csvly9V5JQB8feH77+HjUPIIeuzqwYcQPzxSgWtvaKEOBSRUB716zWX69K9zVnEhhBAisYStLFOkSBEJRgghPrtkE5AA6Nixo/7vq1evRrvs16VLl9izZ0+kZeOjVKlSRgGN+fPnRztjOKizLW/ZskW/nVy7iAkRHQsLdeWN6tXVbU9PqFMH7t+PmHdN0zXkccoDwNNUcK4XtPsYlFAUGDRoJZMmJe31xIUQQogwp0+f5vTp04C6cowQQnxu30RAYsuWLeTKlSvBSxC1aNGCYsWK6bd//vln/Wy5hp4/f067du3049GKFy9uNNzC0KNHj9BoNPpHVLPKWlpa0rZtW/22q6srPXr0iHIskoeHB40bNzZaovRrnvVYiC/J2hq2bYOwGzsvXkCtWvDJ1C1kTpGZIy5HyJ1K/S55kgqO9YIuFuF5Ro3awNixjT9Ty4UQQojP6/Hjx9y9e5d///2Xdu3aAZAuXTo6dZJegkKIz++bmNTyw4cP+gv/hNBoNCxZsoSqVavi7+/P8+fPKVu2LD179qRKlSqYm5tz/vx55s6dq1/6yMbGhsWLFyf42ADjx49n27Zt+rqXLFnC0aNH6dixIyVLlsTe3p7Xr19z/PhxVqxYgZeXl75s165dKV26dILbIERS5eAAe/ZA1apw/To8fgy1a8Px42Cw6i9ZUmThiMsRqq6sivt7d56kgr29ofd8mPdxuerff9+Or28t/vzz4Jc5GSGEECKRVK1alcePHxvtmzNnDra2tl+oRUKI5OybCEiYkrOzM2vWrKFdu3b4+/vj7e3N1KlTI6xjC2owYs2aNTg7O5vk2BkzZmTv3r00bNiQp0+fAnDv3j1GjRoVbbmWLVsyf/58k7RBiKTMyQn274dKleDhQ7h1C+rVg8OHwXAunayOWTnicoTqq6rj/t6dZylhSx8YOA+mq/PeMn36Ifz9KzJ37gn9+uZCCCFEUuHg4ECRIkUYNWoU9evX/9LNEUIkU8nyV3azZs24ePEitWrVirTng0ajoWbNmri6utKsWTOTHrt48eJcu3aNwYMH4+QUcUlCQ6VKlWL9+vVs2LABCwuLaPMKIVQZM8LBgxC2GtTFi9CwIRiMfgIge8rsHOt4TD9840UKWNsXhhusdrZgwWm6dnWOcb4XIcTX79GjRyiKIitsiGQv7LPg7e3N6dOnJRghhPiiEnXZz//++88k9WzatIkhQ4ag0WgSvM7wp548ecKpU6d49nGweebMmalYsWKU64abUkhICG5ubly9epW3b98SGBhIihQpyJw5M87OziZpgyz7KZKrmzehShV4+1bd/uEH+OcfsLQ0zvfM+xk1/6rJnbd3AMgSaE3nWQH85heep3XrQqxZ44aZWbLrVCYiIct+CiGEECK5SOxlPxM1IKHVak0y9wKAoiiJEpBI6iQgIZKzCxegRg348EHdbtkS1q4F80/iCi8+vKDmXzW5+fomABmDrek1K4CxHyDsC7JJk9xs2HAdS0u5AE3uJCAhhBBCiOQisQMSiT5kQ1EUkzyEECKunJ1hxw6wslK3N26Ezp3h07hmBvsMHHE5QtF0RQF4bhHAnP5WTHEAs495/v33AfXr5yYw0A8hhBBCCCFEwiVq/+Ow3hEZMmQgX7588a7nxYsX3Llzx1TNEkIkI9Wqwdat0KQJBAfD6tVqgGLRIjCcqzKdXTqOuByh9uraXH5xmVfmgUzsq2HqPIURXhAMZLCyxcJC7ogLIYQQQghhCokakMiTJw/379+nQIECHDp0KN71rFq1StZGFkLE2w8/qL0jWrRQe0csXarOJTF3LhiOKkttm5pDHQ5RZ00dXD1c8bZUGNcbpi+EpVZQdHg3WXFDCCGEEEIIE0nUX9alSpVCURQuX76cmIcRQogYNWkCf/8d3iti/nwYNAg+HRGWyiYVB9sfpFyWcgDobG3Y2AjcusOwQ8MYf3S8fhiZrL4hhBBCCCFE/CVqQKJ06dIAeHl58eDBg8Q8lBBCxKhlS1i1KrxXxIwZMGpUxKCEo7Uj+9rto1H+RpzqfIo6nX6Hj2V+PfYrg/cP5uTSCRQuZM/t2yc/70kIIYQQQgiRRCTqkI2wgASAq6sruXPnTszDCSFEjNq1g8BA6NpV3Z48GaytYexY43wprFKwrfU2AEpkLIG9pT0D9g0A4Ny86azeAa+BypWrcvToAQoXrvEZz0IIIYQQQohvX6IGJEqWLEmxYsUAeP36dbzrqVSpEitWrDBVs4QQyVyXLmpQondvdXvcOHWiy2HDoi7Tv1x/bC1s+eXfn2l5DxagBiRyWluSLdt3n6PZQgghhBBCJCmJGpCwt7c3yfwRuXPnlt4VQgiT6tVLDUoMHKhuDx+uBiX69488v6IoXH5+mUBz6N8KZmyBgy/MWXbuGg4OaT5bu4UQQgghhEgqZLp4IUSyNWAATJpkvL1gQeR5dYoO32BfADRaLVfaV2T7Ex/SZcpjlC8w0C+xmiuEEEIIIUSSIgEJIUSyNmKEOmQjTK9e6rKgnzLTmrGyyUr6OPdhddPVrBh6Eo21tVGeZ0d2UDh/KmbM6JzIrRZCCCGEEOLbl6hDNoQQ4lswbhwEBMDUqep2t27qyhvduhnn02q0zPlhTqR1HNuzhF8adOeBDgYOXMHz50+ZMmUvWq3EfYUQQgghhIiM/FIWQiR7Go262kbYfBIA3bvD4sWxKz/8wHDmruhONV34vv/97wAdO5YgNDTEtI0VQogoPHr0CI1Gg0ajYeXKlV+6OYwfP17fnuRq5cqV+ufg0aNHX7o58XL06FH9ORw9evRLNydWksLz/qV07NgRjUZDjhw5vnRTRDIhAQkhhEANSkybBoMGhe/7+WdYtCj6cssuLWPq6alsLgxPfgSDKSlYvfoq9evnwt/fO1HaLERyFBwczPr163FxcaFgwYKkTp0aCwsL0qRJQ6lSpejZsycHDx5Ep9PFXJkQsWR4UR6bx9cQEAI4efIk/fr1o1ixYqRNmxYrKysyZcpEhQoV+PXXX7l3796XbmKSZBgQ+fSh1WpJkSIFRYoUoUePHly6dOlLN1eIL+qbCEhs2bKFXLlyyUobQohEpdHA//4HQ4aE7+vRI+qJLkEdxqHVqF+l/xSGgx1giQbMPqbv2/eEatVy8O7d08RruBDJxLZt2yhQoABt2rThr7/+4vbt27x7946QkBDevn3LpUuXWLhwIbVr16ZgwYLs2rXrSzc5yZE7z6aVWHejnz59SoMGDahcuTKzZ8/m6tWrvHnzhqCgIJ4/f86ZM2cYP348hQsXpn///gQGBpr0+CJqiqLg4+PDjRs3WLRoEc7OzowdOzZRjymfW/E1+ybmkPjw4YO+G6IQQiQmjUadS0KjgT/+UPf16qXOKdGrV8T8nUp0wt7Snp+2/kSILoTDucCzO6xfBh1CwB84f96TChXysW/fWbJn/+6zno8QScXkyZMZNWoUiqIAUKtWLRo3bkyhQoVImTIl7969486dO+zYsYMDBw5w9+5dRo0aRf369b9wy5Ov8ePHM378+C/dDJPr2bMnvSL7D8FAlixZADXg0LFjx8/QqnC3bt2iTp06PH2qBsLz589Pp06dKF26NKlSpeLly5ccPnyYFStW8PbtW2bNmoWbmxvbt2/HwcHhs7Y1sXyJ5z0qEyZMoHHjxvptnU7H69evOXr0KDNnzuTDhw/8/vvv5MyZk06dOn3BlgrxZXwTAQkhhPicNBqYMgW0WvVfgN691aBE794R8/9Y+EfsLO1osbEF/iH+XM4IQ3vB1kXQNhDeAXfu+FOhQin27t1L0aI1P+v5CPGtW716NSNHjgQgbdq0bNiwgerVq0fIV6tWLXr37s21a9fo378/b9++/dxNFclAunTpKFKkyJduRqR8fHxo0KCBPhgxaNAgJk+ejIWFhVG+77//nqFDh9KmTRsOHTrE0aNH6dq1Kxs2bPgSzU7SMmfOHOn7pWbNmlSrVo3atWujKAp//PGHBCREsvRNDNkQQojPTaOBSZPUZUHD9OkDc+dGnv+HvD9woP0BUlqnBMDdCTr8AhvtIdvHPB4eIVSpUpvjx9ckatuFSEo8PDzo2bMnALa2thw9ejTSYIShokWLcuDAAQYPHvw5mijEV2PYsGE8fPgQgJ9//plp06ZFCEaESZs2LTt27KBEiRIAbNy4kU2bNn22tgo1KFGqVCkAbt++jbe3zDklkp9EDUj8999/Jnm8efMmMZsphBCR0mhg4kQYNSp8X9++MHt25PkrZqvI8Y7HyWifEYDXdtCkL6xKBWH3Rt6/V6hXrz1btkxJ3MYLkUTMmDEDX19fAH799VcKFSoUq3JarZZ27dpFmX7y5Enat29Pjhw5sLa2JmXKlJQoUYLRo0fz+vXrKMt9uuKATqdj+fLlVK9enfTp06PVavVdxeOS19CdO3f45ZdfKFy4MI6OjtjY2JArVy46deqU4Anwrl+/zoQJE6hbty5ZsmTBysoKe3t78ubNi4uLC2fPno32vA3v4ObMmTPChH2GqzDEdpWNR48eMWDAAAoXLoyDgwO2trbkzZuXn3/+mWvXrkVbNqz+sKEhFy5coE2bNvpzy5w5M+3bt+fWrVuxe4JMLKqx+2HPzapVqwB4/PhxpBMgxsWrV69Yvnw5ABkyZGDatGkxlrGxsWGRwezNU6bE7v+mjRs3UrNmTdKmTYuNjQ358+dn6NChvHv3Lsoyn86X8eLFCwYPHky+fPmwtbUlc+bMtGzZkhs3bhiVe/ToEb/88gv58uXDxsaG9OnT07ZtWx48eBDlsWI7Z4JOp2PdunU0b96cbNmyYWNjQ+rUqSlWrBidO3dm7969hIQk7mpZhvOHBAQERJrnc35uP/X+/XvGjh1L4cKFsbOzI2XKlFSpUoW1a9fG63yFiEBJRBqNRtFqtSZ5hNUl4sbLy0sBFC8vry/dFCG+WTqdoowerSjqoA31MXNm1PndPd2VvLPzKoxHYTyKw3hLZV8WrVIZFD4+zMxQZs3q+vlOQpiMv7+/cvPmTcXf3/9LNyXJ0+l0Stq0aRVAsbOzM8n/ZaGhoUrv3r31n8XIHo6Ojsr+/fsjLX/kyBF9vj179ii1atWKUN7FxSXOecP89ttvirm5eZRt02g0ytixYyNtm7u7uz7fihUrom17dI/hw4fHu+yRI0f0ZcaNG6ffH5VVq1YpVlZWUdZnZmamTJo0KcryYfnGjRunzJkzJ8rnztbWVjl27FiU9cTE8PzHjRsX63IrVqzQl3N3d9fvN3xuonvExaxZs/TlRo4cGaey5cuX15d1c3MzSjM894MHDyo//fRTlO3NkyeP8vz580iP4eLiogBK9uzZlStXrigZMmSI8rU6ceKEoiiKcujQIcXR0THSfKlSpVKuX78e6bGiet4Nubu7K8WLF4/Tezq2DI8f2WfRUOnSpRVAsba2VnQ6XYT0z/25NXydbt26peTIkSPKcr17947zcyO+PfH93RPb69BEn0NC+Tj5lBBCfKs0GvjtN/Xf339X9/XvDyEhxsuEhsmRMgcnO5/k+7Xfc+n5JXwIoml3K/5ep5D6VhD/AqGh0K/fUh49esC0aQfRamUEnRCfunnzpr63QuXKlUmRIkWC6xw+fDjz5s0D1DuFw4YNo2TJkvj6+rJ9+3bmzp2Ll5cXDRo04Pz58xQrVizKuoYNG8bVq1dp1KgRHTt2JHv27Lx8+TLSbtexyTt27Fh+//glU6FCBTp37kzhwoWxsLDgzp07zJ07lzNnzvDbb7+RJk0a+vbtG6dzDwkJwc7Ojvr161OjRg0KFChAihQpePXqFTdu3GD27Nk8fvyYKVOmkC9fPqO7qs7Ozly7do1t27YxevRoAPbt20emTJmMjpEzZ85Yt2fXrl107NgRRVGwt7dn0KBB1KpVC3Nzc06fPs3kyZN58+YNI0eOJGXKlPqhO5HZt28f586d47vvvqNfv34ULVoUf39//vnnH2bNmoWfnx/t27fn3r17WFpaxul5Swy9evWiRYsWjB49mm3btpEpUyb27duXoDqPHz+u/7thw4ZxKtuoUSPOnDkDwIkTJ/juu8gnYB47diynT5+mSZMmdOjQQf8+njdvHrt27eL+/fsMGDCAdevWRXksPz8/mjZtSlBQEJMmTaJq1aqYmZmxd+9eJk2apH+tDhw4QNOmTUmRIgW//fYbZcuWJSQkhC1btjBz5kw8PT3p0qVLlL0DovPy5UsqVqyIh4cHADVq1MDFxYUCBQqg0Whwd3fn8OHDiT6E5ciRI/peTw0bNoy0V8yX+tz6+fnRqFEj3r59y+jRo6lVqxb29vZcvnyZX3/9ladPnzJv3jwaNmxI3bp1Tfm0iOQm/rGSmIX1bsiUKZNSrVq1eD8KFCggPSTiSXpICGFa48YZ95T47Te1B0VkvAK8lOorq+t7Slj8aq6sLmev9P7kDkOXLqU+6zmIhJEeEp/P2rVr433HNzJXr15VtFqtAihFihRRPD09I+TZs2ePPk+ZMmUipH96x3HMmDFRHi8uec+fP68/7ujRoyPNExoaqrRr104BFAcHhwjtj6mHxOvXryM95zCBgYFK7dq19XdHQ0JCIuSJzZ3nMNH1kAgKClIyZ86sAIq9vb1y+fLlCHkePXqkZMyYUX/X/PXr1xHyGD6/P/zwgxIYGBghz4QJE/R5tm7dGm2bo2L4Wvbs2VO5du1alI+XL1/qy8X0fBnejU6oPHnyKICi1Wrj/P20b98+fTu7detmlPbp+3jChAkRyut0OqVOnToKoJibmyuvXr2KkCfsXAElTZo0yv379yPkmTdvnj5P2rRplbx580Za15AhQ/T5Ll26FCE9pue9SZMm+vSpU6dG+bx8+PBBeffuXZTpUTE8/oQJE4zeH25ubsqhQ4eUsWPHKilSpFAAJWPGjMqdO3ciretzf24NX6eUKVNG2gvl3r17irW1tQIojRo1irY+8e1L7B4SiRqQyJcvn6LVapUaNWokqJ6VK1dKQCKeJCAhhOn9/rtxUGL48KiDEv7B/kqzDc30QQnNeI0ytgrKtI//2VtrUA7sW/R5T0AkiAQkPh/DLuizZs1KcH09e/bU13fmzJko83Xt2lWf7/z580Zphhdn+fLli/THf3zyNm/eXAGUUqVKRdptO4ynp6d+iMOSJUuM0mIKSMTGlStX9HW4urpGSDdVQGLDhg36tMmTJ0dZx5o1a/T5/vjjjwjpYWnW1tZGgQBD3t7eiqWlpQIoAwYMiLbNUYlt13cwHtLxOQMSKVOmVEAdyhBXhq97s2bNjNIMzz269+fevXv1+bZt2xYh3fBCd8GCBZHW4efnp7/QBZS9e/dGmu/hw4fRfjdE97zfunVL0Wg0CqA0btw40voTyvD40T2srKyUoUOHKk+ePEnQ8Uz5uTV8nWbPnh1lvtatW8f7/Sa+LYkdkEjUPsKlSpVCURQuX76cmIcRQojPavRo+PPP8O0pU9QhHDpdxLzW5tZsbLGR7iW7A6Cg8FsNuNkQllpAlgaWWOTL/3kaLr6I6dOnkyVLlgQ/Pp107OjRo/q06dOnG6X5+PiY5Jht27aNcD41atQgS5YsFChQIDGfNv15hLGzs0twfQcPHgSgUKFClCtXLsp83bp1i1AmMq1atcLMzCxWx44ub3BwMHv27AGgRYsW0U5mmDJlSooWLQqg72IfX4GBgfz333/cvHmT69evc/36daOhtm5ubgmqPzphz6tGo6Fz585R5vvxxx9xdHQ0KhOZ2rVrky5dukjTHBwcyJs3L4B+BYqkKOzzEp/PimGZ6FZ6+Omnn6J8f4atFgHRP88ajYaWLVtGmmZjY6N/rVKlSkWdOnUizZczZ04cHBxiPFZkdu/erX+fDxgwIE5lTS0wMJDVq1ezevXqWA9z/1yfW41Gw08//RRletjr7enpyfv3701yTJE8JeocEqVLl2b9+vV4eXnx4MEDcufOnZiHE0KIz2bgQLCxgV691O3Zs8HPDxYuhE+vOcy0ZixssJB0dumYcGICADvKp8I1kyf3MwZRZ00dVjddTcvCLQkNDeHu3dMULFjlM5+RSCze3t48e/YswfUEBgZG2A6r99MLCEVRTHLMyFa5evnyJc+ePdNfDCQmw2OErbQRX4GBgdy7dw+AsmXLRpu3RIkSWFhYEBwczPXr16PMF9U4+7jmvXnzJn5+fgCMGDGCEYbrDUfjxYsXsT5+GF9fX2bPns369eu5ceMGoaGhUeZNzFXOwp7XHDlyRBlIALC0tKREiRIcPXo02tcipgCZk5MTYBzkiq9x48bpV/X4mjg4OPD+/Xs+fPgQ57KGZaKbqyW65znsOYbon+c0adIY5f1UypQpAciTJ0+MwTkfH584v6ZhN0otLCyiDUyayooVKyKspvPhwwdu3brF4sWLWbp0KSNHjsTNzY1169ZFes5f4nObJk0aUqdOHWX6p6932OsmRFwlekAijKurqwQkhBBJSs+eYGsLnTurvSOWLgV/f1i5Esw/+XbVaDT8XuN30tqlZfud7WxosYH2/7Tn6v09BIUG0Xpza16+e8KdobNYee0Ja9b8QZMmQ77IeQnTSpEiBZkzZ05wPVZWVhG2w+r99AJCo9GY5Jhp0qSJsC99+vR4eXlhb2+f4PrjcvyXL18mqC5PT0/93+nTp482r4WFBalTp+bFixfRLmOYKlWqWB8/uryvXr2KdT2GwoIYsfXo0SNq1KiBu7t7rPL7+/vHp1mxEva8xvRagLqEpWGZyNja2kZbR9jEwdFdyH3rUqdOzfv37/H29sbf3x8bG5tYlzX8fEV3ERrd82w4OXN0z3NsX6vEek3DLtidnJwifK9+Lvb29jg7O+Ps7Ey6dOmYNGkSGzZsoE6dOhF6DH2pz21sn39I2p8rkfgSNSBRsmRJ/ezU0a3pHZNKlSqxYsUKUzVLCCFMxsUFrK2hXTt11Y21ayEgAP7+GyKbyP2Xsr/Q27k3ZloztrXeRo+dPVh+ZTmKonCr3WAW/Kfma9VqKHfu1CZHjuKf9XyE6Q0cOJCBAweavN5q1arx9OnTSNMcHByiTEuow4cPJ0q9kTFc4SJsJnpTiO6ua5jYdJ+O7XCNmPIa/pj/3//+R7169WJVZ1y75rdv3x53d3c0Gg2dOnWidevWFCxYkLRp0+ovzHQ6nb6tse1CnhCmei2E+nl58OABOp0ONze3ON39N/x8RbeyTFISm/fe5zBo0CCmTJmCTqdj2bJlEQISX+PnVghTStSARNjSMAmVO3du6V0hhPhqtWqlBiVatoSgINiyBZo2Vf+1to6Y30yr/miwMLNgaaOlWJtbs+L0fFz84RmwHZj0cz0JRohkr1ChQqRJk4Y3b95w4sQJvL294730p2EPhZiGOoSEhOjvxkfXtdxUDO9IBwcHU6RIEZMf4/bt25w8eRJQh4VMnDgx0nyGPUkSU9jzGpthJ2F37z/Ha/Etq1KlClu3bgVg+/btcQpIbN++Xf935cqVTd62r0lYz6u3b98SFBT0xZeBdXJyIm3atLx8+ZJr164ZpX1tn1shEoMsfC+EECbQuDFs3x4egNi9Gxo0gJiGvb/0fcme+3vwt4RKPeCXbDC7W00Gzd6T+I0W4iun0Wj0Y699fX1ZunRpvOuysrLST5Z37ty5aPNevnyZ4OBggEQJDnyqcOHC+oui/fv3J8oxbty4of+7devWUeZzdXWNth5T3VUOe14fPXoU7ZCV4OBg/c2tz/FafG6mvEvfunVr/R3zFStWxHouiQsXLnD27FlAnT8lLnOjfItKliwJqO+thE4MayohISEA+u+dMF/b51aIxCABCSGEMJG6dWHvXgjrRX3okLrPyyvqMjpFh4OVOnFfltQ58N64kr6LI84kP3duT549u5UYzRbiq9a/f3/9WOaxY8dy+/btWJXT6XSsWbPGaF+tWrUAdRLJsAuwyBgGPsLKJCZbW1tq1qwJqKunnD9/3uTHCLvggejnnli4cGG09VgbdPv6dKLVuAh7XhVFYfny5VHm27x5M14fv0Q/x2vxuYU9nwl5LsOkT58eFxcXQO15MmRIzPMQ+fv78/PPP+u3hw0bluB2fO3q16+vv0CfMWPGF24NuLu78/btWwCyZs1qlPa1fW6FSAyJEpDw9fXl7NmzHDp0iH379nHlypVolxASQoikompVOHgQPq5Sx6lTUK0aRDUfXyaHTJzodIIOxTpwstNJmpZ1MUoP0YWwoEZh+vZdSJky3+Hquj3yioRIojJnzszcuXMB9fdF1apVOXbsWLRlbt68Sd26dZk2bZrR/p49e+onYuvevbv+QtfQ/v37WbZsGQBlypTB2dnZFKcRo1GjRukvklq3bs2DBw+izBsaGsrff/8dp3lCwnqHAKxatSrSPAsWLODff/+Ntp6MGTPq/46ujTFp2rQpmTJlAmDSpEmRLlX45MkTBg8eDKhBm06dOsX7eF+rsOfz1atXJlkB5I8//iBnzpyAepE6ZMiQCHfdw7x584ZGjRrpe6A0b96cVq1aJbgNX7t8+fLRtGlTALZt28b//ve/KPP6+vom6nAInU7H8OHD9ds//PCDUfrX9rkVIjGYbA6JkJAQ1qxZw9y5c3Fzc0On00XIkylTJmrUqEGdOnVo2rRpjLO3CiHEt6hcOTh8WO0d8eYNXLkClSvDgQOQPXvE/CmsUrCqScQfGp7+nmwf0IDpR24C4OERQrVqjVm1ajLNmw+PkF+IpKpTp048ffqUsWPH8urVK6pVq0adOnVo3LgxBQsWJGXKlLx79467d++ya9cu9u7dS2hoaITJ+YoWLcqgQYP43//+x7Vr1yhZsiTDhg2jRIkS+Pn5sWPHDmbPnk1oaCiWlpYsWrTos51jxYoVGTt2LL/++ivu7u4UL16cLl26UKdOHTJmzEhgYCCPHj3izJkzbN68GQ8PD65du0aWLFliVX+JEiUoUqQI169fZ8GCBbx//562bduSMWNGnjx5wpo1a9i8eTMVK1bk1KlT0dZjbW1NQEAAY8aMwdzcnBw5cugDPZkzZ47V6g4WFhYsXryYhg0b4uPjQ6VKlRgyZAg1a9bE3Nyc06dPM2XKFP1wjmnTpkW66su3rkKFCoB6YdqjRw/69u1L6tSp9cGpPHnyxKk+R0dHtm/fTt26dfHw8GDatGns2LGDzp074+zsjKOjI69eveLw4cMsX75cf2e+SpUq+kBccjB//nzOnj2Lh4cHQ4cOZe/evXTs2JECBQqg0Wh49OgRR48eZf369WzevJlq1arF+1jPnj2LsGStr68vN2/eZMmSJfphI6lTpzYKTsDX97kVIlEoJuDu7q6ULFlS0Wq1ilarVTQaTZSPsDyOjo7KL7/8ojx9+tQUTRBR8PLyUgDFy8vrSzdFiGTn1i1FyZJFUUB9ZM6sKDduxK6sd4C3knNmTqX4zygPtCglQeHjw0yLMmlS68RtvIiSv7+/cvPmTcXf3/9LNyXZ2bJli5IjRw79ZyG6R+HChZV9+/ZFqCM0NFTp1atXtGUdHR0jLasoinLkyBF9viNHjkTb3rjkDTNjxgzFysoqxvOztLRU7t27Z1TW3d1dn75ixYoIdV++fFlJlSpVlHUWLVpU8fDw0G+PGzcu0jYOHTo0yjoMz3PcuHH6/VFZuXJltOdrZmamTJo0KcryMbU1TNWqVRVAqVq1arT5omL4WsZ0LEMrVqzQl3N3d4+QHhoaqpQrVy7K84+vx48fK/Xq1YvxfWRubq706dMn2u+zuLyPo3uOXFxcFEDJnj17tHXE9rXKnj27AiguLi4R0mJ63hVFUR48eKAUKVIkxucotp/dqI4fm0fOnDmVixcvRlrX5/7cxvZ1is1zLJKG+P7uie11aIKHbLx584aKFSty5coV/TIzGo0mwuQphvsURcHb25u5c+dSsGBBpk2bFmmPCiGE+JYVKKAO2ciXT91+9kztKRHT8HBFUWi9pTXu7925khEqDoBNNtDkY3qoDkaOXE/HjiUICQlKzFMQ4qvSrFkz7ty5w9q1a2nXrh358+cnVapUmJub4+TkRMmSJenVqxeHDh3i2rVr1KlTJ0IdWq2WefPmcfz4cdq2bUu2bNmwsrIiRYoUFC9enJEjR3Lv3r1Iy34O/fv358GDB4wZM4Zy5cqRJk0azM3NsbOzI1++fDRv3pyFCxfy7NmzON89L168OFeuXKFHjx5kz54dCwsLnJycKFOmDNOmTeP8+fNGXbujMmXKFJYsWULlypVxcnKK0/Knn3JxceH27dv069ePggULYmdnh42NDblz56Zbt25cvnyZESNGxLv+r51Wq2X//v2MHj2aYsWKYW9vb5IJCLNly8aePXs4fvw4ffr0oUiRIjg5OWFhYUH69OkpW7YsY8eO5caNG8yZM8dojoHkIleuXFy5coWVK1dSv359MmbMiKWlJWnSpKFYsWJ069aNgwcPUqVKFZMf29ramixZslC/fn0WLVrE9evX9ZNtfupr/NwKYUoaRUnYYrWtW7dm48aNRsEGUGeMLlCgAFZWVvj7+/PgwQPu3buHv7+/emCD/BqNhjp16rBx40YcHBwS0hzxCW9vbxwdHfHy8or3UmlCiIR59Qq+/x7Clnm3s4Nt2+DjHHaR2nZ7G222tME/RP3OtA2CY8tg00v4wyBf5crp2LbtMqlSZUq8ExBGAgICcHd3J2fOnMnyR7wQQgghko/4/u6J7XVoggISr1+/JnPmzISGhuoDEc2bN2fy5MmRRu3DltfZsWMHq1ev5tWrV2g0Gn1QomjRohw/flwunE1IAhJCfB28vaFRIwibi8/SEtavh4/zakXK1cOVhusa8uLDCwDMdLB2M3y4CT2AsLm38+SxZteuw+TLVz5Rz0GoJCAhhBBCiOQisQMSCRqycezYMf1yNBqNhq5du7Jp06YouxBaWFhQpUoV/ve///HkyROmT59OypQp9UGJa9eu8eOPP8rwDSFEkpMiBezZowYlAIKCoEULiGa1O0pnKs25rucomq4oAKFaaPMj/FcV9gOpPua7fz+AcuUqsm/fgkQ9ByGEEEIIIUwpQQGJZ8+eAeqwC2tra/78889Yl7WwsKB///5cvnyZEiVK6Os5ePDgV7EmsBBCmJqNDWzZAh06qNs6HXTpAp+sTGgkm2M2TnY+Sd3cdQFQNPBbdZj3I5wBwsK/np4KDRr0Yvr0pLcsnhBCCCGESJoSFJD48OEDoPaOKF++fLzmf8iWLRuHDx+maNGi+p4SEyZM4N27dwlpmhBCfJXMzWHFCujfP3zfkCEwYgRENYAuhVUKdv60kx6leuj3bSkMbXubc1wLYVNRhITAoEEr6dixBEFBAYl2DkIIIYQQQphCggISVlZW+r8zZMgQ73pSpEjBxo0bMTc3R6PR4O3tzYYNGxLSNCGE+GpptTB9Ovz+e/i+KVPU3hLBwZGXMdeaM7/+fP6s8yca1EmBL6YNoeWkfKxOYU5fg7yrVl2hSpXMvHz5IPFOQgghhBBCiARKUEAiderU+r8T2qMhf/78tGnTRj855rZt2xJUnxBCfM00Ghg9GubNU/8GtedE48bg6xtVGQ0Dyw9ka6ut2JjbAHAy4C6VR6Wnd/40LAYsPuY9d+4dpUsX5OnTG4l+LkIIIYQQQsRHggISOXPmBNS5Hy5fvpzgxjRr1kz/9507dxJcnxBCfO169YING9RVN0Cd+LJ6dXj9OuoyTQo04Xin42SwV3umPfB/RjmXIMzK23AISPsxX/HsqcmUqWCitl8IIYQQQoj4SlBAwtnZGRsb9S7dq1ev2L17d4Iakzt3bkANcLx8+TJBdQkhxLfixx9h3z5wdFS3L1yAChXgQTQjLj5dgeN9kDdd6vqzrDGcB8qlhrpThqPVJuhrXgghhBBCiESToF+qdnZ2dOvWTb89aNAg/P39E9woINq1SoUQIqmpVg1OnIDMmdXt+/fVoMTFi1GXyeaYjVOdT9EwX0P9vjUlzRj0I7j2gr6H+jNw30BCdaEAnD27GV/f94l3EkIIIYQQQsRBgm+djR8/njRp0gBw9+5dWrZsSVBQULzqChumodFoyJo1a0KbJoQQ35SiReHMGShUSN1+9QqqVlV7T0TFwcqBf1r9w/CKw9FqtPzT6h+ydu5HiJmaPuPsDOr/XR/XWaOpV6slzs6ZuXv3TOKfjBBCCCGEEDFIcEAiZcqULFiwQD8Z5e7du6lcuTIPHz6Mc13Lli3T/12jRo2ENk0IIb45WbOqPSUqVVK3fX2hQQNYvTrqMmZaMybXmsyNXjdomL8hM+vNZHGDxZhrzQHwOLmPngMm4uWrcOuWH8OHd/gMZ5L0KVGt0yqEEEIIkUQk9u8dkwwubt68Ob1799Y39sKFCxQqVIgBAwZw7969WNUxadIk9uzZg0ajQavV8vPPP5uiaUII8c1xcoL9+6FpU3U7JAQ6dICpUyG6/xMKpCmg/7tbqW4caHcAa3Nraj+EZQrkAnLZaViy5FDinkASFzYvh06n+8ItEUIIIYRIXKGh6tDfxJqXzNxUFc2aNYs3b96wYcMGNBoNQUFBzJ49m9mzZ+Ps7EyNGjUoX748+fPnJ2PGjJiZmfH8+XMuXLjAwoULOXHiBKBGYMaNG0euXLlM1TQhhPjm2NjApk3wyy8wf766b/hwePoUZs4EM7OY67j84jIBIQFMrwhPHGGza0osNv1N6tTZErXtSZ2FhQVmZmb4+vpiZ2f3pZsjhBBCCJFo/Pz8MDMzw8LCIubM8WCygIRWq+Xvv/8mf/78TJw4UX/nSFEULly4wIULF6ItrygKTk5OTJ482WiiTCGESK7MzGDuXHWiy1Gj1H1z58J//8Hff0N018KKonDmafhcEaEtmlFw6WKsHVMb5bt/7Tgdfm7G0qWbKVSoWiKcRdKj0WhwcHDA29ubtGnTotFovnSThBBCCCFMTlEUvL29cXBwSLTfOybtd6HRaBg/fjwnT56kYsWKEcabKIoS5UOj0VCsWDGeP3/Ozp078fDwMGXThBDim6TRwMiRsGIFmH8MIW/frk52+fx5dOU0rG+xnrFVxjKmyhi2tNoSIRhxZPd82pSoypkzbylXrgZbtkxOxDNJWhwdHQkODsbDw0PmkhBCCCFEkqMoCh4eHgQHB+MYtjZ9ItAoifhL6vjx4yxfvpx//vkHHx+f8IPGMrqSLl06SpYsSalSpShZsiQlS5YkWzbpahwX3t7eODo64uXlJUupCvGNO3gQmjcHb291O1s22LULihSJe12Hr++Aqo3p9U7hzsd9Gg0MGVKXyZN3J9o4waTEx8eHp0+fYmFhQYoUKbC1tcXMzEx6TAghhBDim6QoCqGhofj5+eHt7U1wcDBZsmTBwcEhznXF9jo0UQMSYUJDQ7lw4QLHjx/nwoULXLp0CXd3d+OGGPyAM2zSpz/snJyc9EGKSZMmJW7DkwAJSAiRtNy4AT/8oA7bAEiRAjZvhtq1Y1/HskvL6L6zO63ddMzfCh2A7QbptWtnZsOG86RKlcmUTU+S/Pz88PLywsfHRz/pkxBCCCHEt8zMzAwHBwccHR2xtbWNVx1fVUAiMu/fv+fSpUtcvHiRixcvcunSJR48eBBlMOLTZmo0GvnxFwsSkBAi6XnxAho2BFdXddvcHBYuhC5dYi7r9sKNMkvLEBQaBED+13B2EcwOgXEG+bJnt2TTpo04Ozc2/QkkQYqiEBwcLCtvCCGEEOKbptVqsbCwSHCPz68+IBEZb29vLl26pA9UXLp0ibt370YIUoTNOSEBiZhJQEKIpMnXF9q2hW3bwveNGAETJkB0oy2CQ4MZtH8Qc87P0e+zD4SjK+D5C2gHeH3cb20N//tfD/r0WZAo5yCEEEIIIZKmbzIgEZkPHz5w+fJlo94Ud+7c0Y9vEdGTgIQQSVdoKAwerC4DGqZVK1i5Ug0mRGfllZX02NmDwNBAAMwUDXN2KtS+CC0AN4O8rVsXYunSU9jZpTTtCQghhBBCiCQpyQQkIuPn58eVK1eoUKHCl27KV08CEkIkfXPnQr9+EDZaoGJF+PdfSJMm+nIXnl2g6YamPPN5BoAGDS2vKazYAr2BFQZ5Cxa0ZevWfRQoUCkxTkEIIYQQQiQhsb0O/SanUbe1tZVghBBCfNSnjxqACJtz6NQpKFcObt2KvpxzZmcudr9IpWxqkEFBYUNRKNoXplnDMiCso8WtW36UKVOFdevGJ9JZCCGEEEKI5OabDEgIIYQw1rAhnDgBGTOq2w8eqEGJvXujL5fePj2HOhyiV+le+n0PUkOZ8ZmoVzIvZ4DcH/f7+Cj89NOv9O5diZCQoEQ5DyGEEEIIkXxIQEIIIZKIkiXh3DkoXlzd9vaG+vVh1iyIbnCepZkl8+rPY2nDpViaWQLwIMCDkq298OpSE1egiUH++fNPUbFiBp49i6ELhhBCCCGEENGQgIQQQiQhWbPCyZPQtKm6rdNB//7w888QFEOnhi4lu3Ci0wmypsgKwEu/V9TMdpSl/2vKRjMN/wPMPuZ9+tAbC4sYZs4UQgghhBAiGhKQEEKIJMbODjZvhlGjwvctWQJ16sDbt9GXLZO5DJd+vkTtXLUBCFVCGeL7DxkGK3yfBo4AWcxgytwxpEuXM/FOQgghhBBCJHkSkBBCiCRIq4UJE2DNGrCyUvcdOwZlysDNm9GXTWObhj1t9zCmyhj9vnc2ULy3hkdFwbYb/Hx/KiuvrNSnu7tfwsPjTiKciRBCCCGESKokICGEEElY27Zw9CikT69uP3wI5cvDnj3RlzPTmvFb9d/Y2WYnKa1TAtDyuzZM6/UddzOAf4g/nbZ1otO2TnhdvcCPdStTvHghdu2ak6jnI4QQQgghkg6NokQ31Zn41sV2/VchRNL25Ak0agRXrqjbWi38+Sf06wcaTfRl3T3dmXRiEnN/mItO0dFvbz+WXFoCgFUwdJ+jZY63DoBMmcx5+NALKyvbRDwbIYQQQgjxNYvtdaj0kBBCiGQgsskuBwyAbt0gMDD6sjlT5WRJoyVYmVthY2HD4oaLWdN0DVZmVvx0DYZ766iOOuHlivnTJBghhBBCCCFiRQISQgiRTEQ22eWyZVCtGnh4xK2uYhmKodVo+auUGUOawTYL2Dp7EHUa9zPKFxQUkPCGCyGEEEKIJEkCEkIIkYyETXa5di1Yf1y18+xZKF0azpyJfT2D9w/GP8SfUCWUyzUK8vjiQRr1nWaUJ+TlcypXzkTPnuUJDPQz4VkIIYQQQoikQAISQgiRDP30E5w6pQ7lAHj+HKpWhaVLY1d+XfN1NCnQhO/Sf8fF7hcpUrSmUfqT1w8YWSQ35897snDhWcqUSc+dO6dMfBZCCCGEEOJbJgEJIYRIpkqWBFdXNRABEByszinRuzcEBUVfNpVNKra23MrhDoexsbAxSnvm/YwT7SqT8Y0/5h/3Xb36gdKlK7Nq1QjTn4gQQgghhPgmSUBCCCGSsXTp4MAB+OWX8H3z50PNmvDyZfRlNRoNqW1TG+278+YOeefkZXjh5zR1hJNAjo9pHz4odOw4hdatC+Hj88aUpyGEEEIIIb5BEpAQQohkzsICZs2CFSvAykrdd/IklCoFFy7Evp6AkABabm6Jf4g/Txwhfz8Nd4vCZaCFQb4NG25RtGhmTp5cZ8rTEEIIIYQQ3xgJSAghhACgY0c4fhwyZ1a3nz2DypXhr79iV97KzIpOxTthrlUHagRpFTq1MKPrj7BWA4uBsAVBHz8Oonr1nxg1qgGhoSGmPhUhhBBCCPENkICEEEIIvTJl1HklKlZUtwMDwcVFHdIR07wSGo2G/uX6c7LTSbI7ZgcgVAllS2HINhAqpIVLQMmP+UNCYNKkXVSsmB5390uJdk5CCCGEEOLrJAEJIYQQRjJkgMOHoUeP8H1z5kC1avD0aczly2Ypy6WfL9EofyP9vpcOULK3GVsrwhlgKKD5mHbu3DuKFy8tE14KIYQQQiQzEpAQQggRgaUlLFgAS5aofwOcOaOuzHH4cMzlnWyc+LfVv8ysOxNLM7WCIEIZU8eMyl1gnJ0FB4FMH/N7e6sTXrZqVRBv71eJck5CCCGEEOLrIgEJIYQQUeraFU6dguzqCAxev4batWHqVFCU6MtqNBr6levHua7nyJ86P6AO4TifFTrOrU2pqmW5CjQ1KLNx422++y4rJ06sTZTzEUIIIYQQXw8JSAghhIhW6dJw8SLUq6du63QwfDg0bQpeXjGXL56hOBe7X6RLiS76fZse76Z4k5fcG9+FzRoNSzCe8PKnnzoRFBRg8nMRQgghhBBfDwlICCGEiFHq1LBzJ4wbB5qPkz9s26YGK65ejbm8naUdSxstZUOLDThaOQLwyOsRlTQrGbqoOTVSqcuDlkL9j2nR3D+wtLROrNMRQgghhBBfAQlICCGEiBUzMxg/HnbtglSp1H3370O5crB6dezqaFm4JW493KiQtQKgDuH402MzBftqOVAajmog9w9wNsU7gkOD9eU8PT3Q6XQmPiMhhBBCCPElaRQlplHA4lvm7e2No6MjXl5epEiR4ks3RwiRRDx6BC1aqEM5wvToATNngpVVzOVDdCH8fux3JpyYgE5RAw0Z7DKQyv0Ft9KpeZwzObOm2Rpy+1lTvul32Npbs3r1QbJmLWLy8xFCCCGEEKYT2+tQCUgkcRKQEEIkloAA+OUXdSWOMKVKwcaNkCtX7Oo49ugY7f5ph72lPWe7nGXO+TmMPzqeUCUUAHuNNUPmaRn3yg+AggVtuX7dB61WOvgJIYQQQnytYnsdKr/ohBBCxIu1NSxeDMuXh/eKuHgRSpSALVtiV0fVHFVx6+HGttbbcLR2ZHSV0ZzpcoZ8qfMB0P9oACVf+fGx0wRTJv0mwQghhBBCiCRCftUJIYRIkE6d4OxZyJtX3fb2Vodz9O0LgYExl3eycdIHIACcMzuzqcUmLM0sWVwKMmaAKxr4s+/3NGoyyKhsYKCfKU9FCCGEEEJ8RhKQEEIIkWDFi4OrK7RuHb5v7lyoWBEePIhbXSG6EH7e9TNBoUG8sodafRx489efDJy92yhf6Lu3VKuWlQ4diuHj8ybhJyGEEEIIIT4rCUgIIYQwiRQp4O+/YdEi4yEcJUvC5s1xq6te7nqYaczI45SHewMeUrTdQKP0R2/uM61ILs6efcfq1VcpUiQTBw8uNdGZCCGEEEKIz0EmtUziZFJLIcSXcOUKtGwJ9+6F7+vTB6ZNi90qHADnnp5Dq9HinNnZaH9QaBB/tSpAyBZ3BgABH/drtdC5c2lmzNiHvb2TKU5DCCGEEELEg0xqKYQQ4ospXlztHdGmTfi+uA7hKJulbIRgxEPPh2SdkZWFTu40tYPLQFgOnQ6WLnWlcOEM7N+/yBSnIYQQQgghEpEEJIQQQiQKBwdYu1ZdiePTIRwbNsS9vlBdKB3+6cAr31dczAy5BmhxLQqngcmA5cd8//0XTL16PejSpbTMLSGEEEII8RWTgIQQQohEo9FAt25w7hzk+7iQhre3Ovll587w4UPs6/IL9iOtXdrwbXMdXVpaUtsFutqovSXKfExTFFi+/CKFC2diz575JjsfIYQQQghhOhKQEEIIkeiKFVNX4WjbNnzfihVqb4lLl2JXh4OVA1tbbmVZo2XYW9oD6nwSR3NCjoEaLheBU8AfQNg0FU+eBFO/fm86diyBt/crU56SEEIIIYRIIAlICCGE+CwcHGDNGvjrL7BX4wncuwflysGff6pzQMREo9HQuURn3Hq4USV7Ff1+XwuFzq0sqdseOtnAFaDcxzRFgVWrrlCoUGZ27Zpj6tMSQgghhBDxJAEJIYQQn1X79nD5Mjh/nI0yOBgGD4bvv4cXL2JXR65UuTjicoRZ9WZhY24DqL0ljuTWkGOghrcVsnES+BOw/ljm2bMQGjT4hQ4diklvCSGEEEKIr4AEJIQQQnx2efLAyZMwdGj4vv371aEde/bErg6tRssvZX/BrYcbFbNWBEBBwddCwaWVBeeW/8rA1KlxAyoalFu9+ioFCmTm3LktJjsfIYQQQggRdxKQEEII8UVYWsLUqXDgAGTIoO579Qp++AEGDIDAwNjVkzd1Xo51PMafdf7E2lztD/HA8wGV/hvPkKUtydqsEceAGYDNxzKhATry5i1r4jMSQgghhBBxIQEJIYQQX1StWnD1KtSvH75v5kx1bombN2NXh5nWjIHlB3L558uUzawGGhQUprkt4LtqN2nS0Yp2NnAVKG8OP49ohZNTFpOfixBCCCGEiD0JSAghhPji0qaFHTtg9myw+rhExpUrUKqUui82E14CFEhTgFOdTzG11lQszSwBuP/uPrtyBJFzoIYLhcG6Dfzut44+u/vgE+gDwN27Z6hTJwt37pxKhLMTQgghhBCR0SiKonzpRojE4+3tjaOjI15eXqRIkeJLN0cIIWJ09Sq0aWPcO6J2bXWZ0MyZY1/Pzdc3cfnXBVcPVwB+LPQj/3n9x7ln5/R5sqbIyqrCY5jSsjf7vYKxsYG///6DJk2GmOp0hBBCCCGSndheh0oPCSGEEF+V774DV1fo1y9834EDULQobNgQ+3oKpS3EmS5nmFhjIjlT5mRJwyWc6nyKGXVnYGthC8ATrye87dKdG17BANhbaKhcuZUpT0cIIYQQQkRBekgkcdJDQgjxLTtwADp2BA+P8H1t28LcuZAyZezrCQwJxMrcSr/90PMhjdY14tnjG5xeDpnfQH9rLVVm9qPjz9ONyup0OrRaid8LIYQQQsSW9JAQQgjxzatdG65dg5Ytw/etXav2ojhyJPb1GAYjACzNLPnP6z/e20KJPhYsrmXLrI0bIwQjHt4/T9GiDmzd+kdCTkMIIYQQQkRCAhJCCCG+ak5OsH49rFkDjo7qvidPoGZNGDwYAgLiXueyS8vwCVIntGxWpAUVlh/AoWFzozw3T2+n/3cVuHnTj+bNh9GgQQ4eP76a0NMRQgghhBAfSUBCCCHEV0+jUYdqXL0K1aqp+xQF/vwTnJ3h8uW41Te26lhWNl5J4bSFmV9/PhWyVjBK9/B+xoMOTXnrH6rft2vXY4oUKcakSa0JCQlK4BkJIYQQQggJSAghhPhmZMsGhw7BtGlgqa7qyfXrUKYM/PorBAfHrh6NRoNLcReu9rxKSuuURmmr3VbTdHV90Oo4ASwFnD6mffgAo0ZtoFgxJ06cWGuisxJCCCGESJ5kUsskTia1FEIkVdeuQbt2aq+JMCVKwKpV6ooc8fHM+xmF5hfCO9AbG3MbKt72Z+U2sPSBocBKg7xaLbRrV4wZM3bi5JQlAWcihBBCCJG0yKSWQgghkrSiReHCBRgzBszM1H2XL0OpUjBpEoSExL3OHXd34B3oDYB/iD+H82rJO8CMleVhqQaOAYU+5tXp4K+/3MiXLztLlw5Ep9OZ5LyEEEIIIZILCUgIIYT4Zllawm+/wdmzUOhjpCA4GEaNggoV4NatuNXXo3QPDnc4TL7U+QDQKTr8taGMrW9N/j5gmxEuA5MBm49l3r7V0a3bDKpUSc+NG4dNdWpCCCGEEEmeBCSEEEJ880qXhosXYdgwdSgFqL0nSpSA//0PQkOjL2+oes7qXO1xlfFVx2Nppk5UERASwIPUULmXFT83gl6WcAOob1Du1Kk3lCxZkwEDqvPhwzuTnZsQQgghRFIlAQkhhBBJgrU1TJkCp05B/vzqvsBAGDoUKleGu3djX5eVuRXjqo3jao+rVM9RXb8/IDSQtaUtyDoANHXLsQPYCoTNIBEUBDNnHiVfvvSsXj1ShnEIIYQQQkRDAhJCCCGSlHLl1LkkBg5UlwsFOHMGihVTe0vEZW6J/Gnyc6jDIf5q8hdpbNMAEKwLxtsGWjXX4bZpDk1z5uQmMBD4OJUFz5+H0KHDZE6dWmfKUxNCCCGESFJklY0kTlbZEEIkZydPQseO8OBB+L5SpWDZMjVAERdv/d4y7OAwll1ept9npjGjf6nejD9rjdWUadwN0fELcBj4oUJ6dp16YYrTEEIIIYT4psgqG0IIIZK9SpXAzQ369QvvLXHxojrnxJgx6pCO2Eptm5qljZZyrOMxCqYpCECoEsqfrrPJ47iKrP10uOeFg0CP7JCqcyU8/T315XU6HZs3T5RhHEIIIYQQH0lAQgghRJJmZwczZ6pzS4StxBESAhMmqJNenj4dt/qqZK/ClR5XmFB9AlZmVgC89H3JSwdo19mRRm1gy4+w9ukW8s/Nz8orK9GFBLOkZ31+/HE05cql5uLFnaY9SSGEEEKIb5AEJIQQQiQL5cvDpUswdiyYm6v7bt1Se1H06wcfPsS+LkszS0ZVGcX1Xtf5Ie8P+v3LGy+ncu+p+KWyA+C132s6bevEpA75GL94LwAXLrzH3d3NZOclhBBCCPGtkjkkkjiZQ0IIISK6ehW6dAFX1/B92bPD4sVQp07c6lIUhR13d7D3/l7m158PwBOvJwzaP4hNNzdhHwDuM+FMAPQD8pR0Yv/FtyY7FyGEEEKIr43MISGEEEJE4bvv1JU3pk0DGxt13+PHULcudOoE797Fvi6NRkOj/I30wQiArI5Z2dBiA6UylkLnYEuzVlAwFez7oRIrtp80Kq/T6WjduhA7dsw0wZkJIYQQQnw7JCAhhBAiWTI3h0GD1N4S1aqF71+5Up1rYsMGSEgfwn9u/8PF5xfxC/bjRiEnRs9uQp7V28icuaA+T0CQH+sqFWHDhls0ajSA2rUzc/Pm0fgfVAghhBDiGyIBCSGEEMlanjxw6JA6XCOsR+HLl9C6NXz/PTx8GL967S3tye6YHYAlDZewru1WNE5O+nRFUZjTpwxbztzS7zt40IPixavz88/lePfuabzPSQghhBDiWyABCSGEEMmeVgvdusHNm9C4cfj+ffugcGGYPBmCguJWZ53cdbjZ+yYrGq+gaYGmaMLWHQUevHvAzLMzyX/sBpuAZUD6j2nBwbB48Tny5s3On392JCQkjgcWQgghhPhGyKSWSZxMaimEEHH377/Qty88NeikULgwLFyorsqRUA3XNWTX3V1Ym1nR8EoAM/eB/QeYBEwHDEMQ+fPb8OefU6lfv2/CDyyEEEII8RnIpJZCCCFEPDVpovaWGDBA7T0BcOMGVK6s9qSIy6SXnzr08BA77+5EQcE/NIB/i1uSe4CWGZVhvBncApob5L9zx58GDX6hdu3MXLt2KAFnJYQQQgjxdZGAhBBCCBEJBweYPh0uXIDSpcP3L10KBQrAmjXxm/SycvbKTKs9jRRW6t2CoNAg/M10/FHPjuz94XJB2AwcAYoZlDt40IOSJWvh4lIcD487CTgzIYQQQoivgwQkhBBCiGiULAlnz8KcOWqQAuD1a2jfHmrXhrt341afpZklgyoM4l7fe3Qr2Q0N6twSvsG+vHQAl3Z2lOkKqdLDRWAJ4fNLhITAX3+5kT9/AUaO/AFf3/cmOkshhBBCiM9PAhJCCCFEDMzMoE8fuHULWrQI33/oEHz3HYwbB/7+casznV06FjdczMXuF6mcrbJ+v2+wLxezainXx4qZ3YrQNV067gFjANuPeT58gMmT95A7dxoWLfoloacnhBBCCPFFSEBCCCGEiKXMmWHTJti5E7KrK3oSGAi//QaFCsG2bXEfxlEiYwmOdTzGhhYbyJoiKwA6RUdAaCATcz9j7qYhWI8ewW82NtwDuhD+n/fLl6GcOXPCVKcnhBBCCPFZSUBCCCGEiKP69dVJLocOBXNzdd+jR+pkmPXrw/37catPo9HQsnBLbve5za/VfsXG3AYAzwBP+h4ZQpH0W9h2YA4BjSqzGLgC1APsLWDKlI0mOy8hhBBCiM9JAhJCCCFEPNjZwdSpcPUq1KwZvn/PHnWJ0DFjwM8vbnXaWtgytupY7vS5Q9uibfX77769S5ODXSlX6TYF+sDLnLDKFjTdYcz5P3jx4YU+78iR9WnZsiDu7pcSeopCCCGEEIlKoyjxmSNcfCtiu/6rEEKI+FMU2LxZXSb02bPw/dmzw8yZ0LgxaDRxr/fCswsM2j+IE/+FD8vQoCGTfUYUDw88Pn6t21nYMaj8IDptf0HRuYv5EAK2tvDo0SPSps2esJMTQgghhIij2F6HSg8JIYQQIoE0GvjxR7h9G4YNAwsLdf/jx9C0KfzwA9y7F/d6nTM7c6zjMf5p9Q95nfIC0O67dtzv94B+zabqlw71DfZl6c7fuDZnMZoQtWzDevkkGCGEEEKIr5oEJIQQQggTsbeHKVPg2jV1SdAwe/dCkSIwahT4+satTo1GQ5MCTbjR6waz681mYo2JWJtbM7TiUO73vU/P0j3RarSU9IAaWngIdMnvxKT/rTWqx9/fmxkzOhMYGMdxJEIIIYQQiUQCEkIIIYSJ5c8P+/apwziyqgtnEBQEkyZBgQLw999xX43DwsyCvmX7ktUxq35fWru0FM9QHJ2i49h3DmQdAK7187P0xG1y5SodXtjXl5ntazJw4Apy53Zk4cK+hIaGmOBMhRBCCCHiTwISQgghRCLQaKB5c7h1C0aOBEtLdf/Tp9C2LVSsCBcuJOwYgSGBjDs6DgCfIB+mt15BwTX7IG1afZ4QXQjrulVm5hZXAJ49C6Fnz7kULOjAhg2/odPpEtYIIYQQQoh4koCEEEIIkYjs7GDiRHUYR/364fvPnIEyZcDFBTw84le3lbkV/7b6l0rZKtGkQBM6luhI9pTh80YEhASw9tRCvt96me1AdYOy9+4F0Lr1OJydndi/f1H8GiCEEEIIkQCyykYSJ6tsCCHE12XfPnU1jlu3wvfZ2cGIETBwINjYxL1ORVHwDfbF3tJev0+n6HBe4swTryfkvPOamfug3FM4CIwALn5SR5Uq6Zg4cSaVKrWJz2kJIYQQQujJKhtCCCHEV6huXXBzg9mzIVUqdZ+vL4weDQULwqZNcZ9fQqPRGAUjANZfX8+l55d47feai9nMqPmzNT/8BJnTwAVgE5DPIP/x46+oXPknatfOzLlzWxJyikIIIYQQsSIBCSGEEOIzs7CAvn3VpUD79AEzM3X/48fQsiVUqwaXLyfsGCG6EJxsnAAIVULxDw3gcEFLSva1oH1TKJMCbgBLgMwG5Q4e9KB8+RZ8/302Ll7cmbBGCCGEEEJEQwISQgghxBeSOjXMmaP2mKhTJ3z/8eNQqhR07QrPn8ev7g7FOvDwl4eMqjwKWwtbAIJCgwhUgvmnlC0FBpgzsC40toX7wGwgw8eyigJ79z6hTJmGNGqUi6tXDyTkNIUQQgghIiVzSCRxMoeEEEJ8GxQFdu1S55G4dy98v60tDBkCgweDvX3U5aPz3Oc5vx//ncUXFxOqhOr321nYoQT4s+JRCVrufIi/pycLgCnAa4PyWi106eLM4sXn49cAIYQQQiQrMoeEEEII8Q3RaKBBA7h+Hf78Exwd1f1+fvDrr5A3LyxeDCEhca87o0NG5tefz63et2hVuJV+v2+wL35mOoaUfM3KnROwGD+GgSlS4I4alHD6mE+ng4wZMyX0FIUQQgghjEgPiSROekgIIcS36c0b+P13mD/fOAhRsCD88Ye6hKhGE7+6r7y4wtgjY9lxd4fR/nyp8/Gr8xCyL9xAkb8PogSpQzkWp9Li5u5BKsf0+rxPnlwnMNCfPHmc49cIIYQQQiRZ0kNCCCGE+IalSQOzZqnLg7ZoEb7/1i1o2BBq1ICLn67dGUvFMxRne5vtnO1yljq5wyevuPv2Lm32dqN+gYtkGggLKkCTtPC8l45SK8uz7NIygkODwcODYf1aUKhQGdq0KcyzZ7eiOZoQQgghROSkh0QSJz0khBAiaTh9Wp1H4swZ4/1t28KECZAjR/zrPvH4BKOPjOb44+P6fVZmVlTIWoFjD4+gM7h9kSNlDpZuteH747cIBmxtwP3RQ9Klyxn/BgghhBAiSZEeEkIIIUQSUqECnDoFmzdDnjzh+9euhfz5YehQ8PSMX92Vs1fmqMtRDrQ/QNnMZQHoV7Yfh10Oc6LrKaNeFOYPHlH0+C0GAfZAx7ZlIwQjXr1yj19DhBBCCJGsSA+JJE56SAghRNITFASLFqmTXb59G74/ZUoYNgx++UVdnSM+FEVh171dlMtSjjS2afT799/fT9t/2hL65g2/HYFul8BjQA/shw4nbdrs+nwe9y6Rr0QpKlfOyq+/zqJMmabxPEshhBBCfKukh4QQQgiRRFlaQt++8OCBGoCwslL3v38PI0ZA7tywYAEEB8e9bo1GQ4N8DYyCEQBnn53ljd8bPG1hTvt8TFrZhZxj/zAKRly7vJ+5hZ3x9YW9e59QtmwzatXKxMmT6xJwtkIIIYRIqiQgIYQQQnyjHB1hyhS4exc6dwbtx//VX7yAXr2gQAH4+2912c6EUBSFvff3AmCmMWNPuz2M/WkRODjo8/gE+nDql8ZkCdaRzqDsoUPPqVz5J6pUSc/hw8sS1hAhhBBCJCkSkBBCCCG+cdmywbJlcP06NG8evv/hQ3XSyxIlYNcuiO8gTY1Gw4lOJ1jffD1jq44lV6pcmGnN9Ombb26m9+7ePLUIoL0luAMzgIwGdZw48YqaNbtStqwTW7f+gS6hURIhhBBCfPNkDokkTuaQEEKI5OfCBRg5Eg4eNN5fqRJMnqz+ayoBIQHknp0bDx8PUlilINTbm8GnoP85sA6E5cAU4Mkn5QoWtGXQoB64uEzG3NzSdA0SQgghxBcnc0gIIYQQyZSzMxw4oAYknJ3D9588CZUrQ4MGcOWKaY619dZWPHw8APAO9MbXCmZ8n4LMA2FCdWhtDfeBJUBeg3K3bvnRtet0cuWyZ9o0F/z9vU3TICGEEEJ8MyQgIYQQQiRRNWvCuXOwZYs6n0SYXbvUYRwtWqjDPBKiTZE27Gizg9KZSuv3eQd642cFc+s4knWQhpG1oYEd3AI2AiUNyj95EsyQIX+RNWsqRo1qwLt3TxPWICGEEEJ8M2TIRhInQzaEEEIAhITA6tUwbhw8MRg/odFAy5bq/oIF41+/oijse7CPX4/9ytmnZ43SHCwdCA7wo+NVLXPdMqN1f8RB1KEchz+px95ew9Gj2ylVqkH8GyOEEEKIL0qGbAghhBBCz9wcOnVSV+SYPRsyZFD3Kwps2ACFC0O7dmp6fGg0GurlqcfpzqfZ324/lbKFT1ThE+RDgDaUdWVtmbqiC15rllK7WDEOAeeAZoDmY940juYUL14vAWcqhBBCiG+FBCSEEEKIZMTaGvr2VVfg+PNPSJtW3a8osHat2kuiUyc1PT40Gg21c9fmRKcTnOh0gh/y/qBP8wr0YtTRMWR/MpDh/6vLg02LCc5hxhbgJtDMTkPdztXRaI1/ngwaVIsjR1bEr0FCCCGE+GrJkI0kToZsCCGEiI6vL8ybB3/8AW/fhu83N4eOHWH0aMiePWHHcHvhxpRTU9h4YyM6JXy5T0szSyy0FuR/5Mu4Y7C6GGwuDIXSFmJw+cH8VKQNbv26UHbB3wA0apSLbdseJKwxQgghhEh0sb0OlYBEEicBCSGEELHh46MO5Zg2Dd6/D99vYQGdO8Pw4ZAjR8KOcf/dff536n+sdFtJUGiQfn+rwq24/OIyd98ajxdp9Do1mea9ZeHH7QkTfmTUqI0Ja4QQQgghEp3MISGEEEKIWHNwgFGj4NEjGD8ewn47BAfDokWQNy906QL378f/GHmc8rCo4SLc+7kzuPxg7C3tSW2TmmWNlnGr9y3+bfUvFbNW1Odvc/QtM1CXDC2V1Zq+feca1efqup2+fSvz5EkClwoRQgghxBchPSSSOOkhIYQQIj7evYPp02HWLPjwIXy/Vgs//aQGLwyXEo3XMfzfcf3Vdapkr2K0v9G6Rpz47wTB3u/pdQ4GPMtMRreHYGkZnunKFX7qWId1bq+xsIBGjfIxfPj/KF26UcIaJYQQQogEkx4SQgghhIg3JyeYMEHtMTFmDDg6qvt1OlizBgoVglat4Nq1BBzDxilCMOLFhxfse7CP9wHv0djb49WvO9YXrxoFIx6/f8zubnXY7vYaUHtxbNlyF2fnxlSunI6tW/9Ap9MhhBBCiK+bBCSEEEIIEaXUqeG339TAxO+/q4EKUFfl2LgRvvsOmjaFixdNc7yXH15SJF0RAHqW7smihotIZeukT/cN8mXZ3knUufiaW8AQwPC+y8mTr2nefBh58tgydWpbfHzemKZhQgghhDA5GbKRxMmQDSGEEKbk4wMLFqhLhr56ZZz2ww9qb4py5RJ2DEVROProKPnT5CeTQyb9/jd+b8gzOw9ajZY0Tz0ZdQLaXIMAHSwDZgL/fVKXg4OG1q1LM2jQDPLnr4gQQgghEp8M2RBCCCGEyTk4wNCh4O4OM2ZAxozhabt3Q/nyUL067Nun9qKID41GQ/Wc1Y2CEQALLizAK9ALzwBP7qWB/m1SkmkQzKsInazgAbARMAw7+PgoLFlygUKFKlG7dmZ27ZojwzmEEEKIr4QEJIQQQggRZ7a20L8/PHwI8+ZB1qzhaUePQr16ULIkbNgAISGmOaaNhQ2prFPpt98HvOetHfzRMCWZhmjoXR++Sw0nAVegAxA284ROBwcPetCgwS8ULGjPjBmd8fV9b5qGCSGEECJeZMhGEidDNoQQQnwOQUGwejVMnQr37hmn5c4NgwdDx45gbZ2w43wI+sCKyyuYcXYG7u/djdJszG1AUSh7P4CxZ62ofieQl8AiYAHw4pO6UqbUcOfOA9Kly5mwRgkhhBDCSGyvQyUgkcRJQEIIIcTnFBoK//4LU6aAq6txWvr0aq+Knj3DV+2IrxBdCP/c+ocZZ2dw5ukZozStRkupjKWYVXAA5dadQLPqL4J8fdkEzAIufMxXrkwqzpx7l7CGCCGEECICmUNCCCGEEJ+dmRk0bw7nz8PBg1C7dnjay5cwYgRkywbDh8Pz5/E/jrnWnB8L/8jpLqc53/U8bYu2xUJrAYBO0XHB4wIVDv1E2eKurD04nXe/j6BCSjgHnAFqpNRSoXUdQnTh40lCQoKoUiUdEye25N27p/FvnBBCCCFiRXpIJHHSQ0IIIcSXdvGiOpRj82bjiS4tLdVhHIMHQ968CT/Oc5/nLHBdwELXhbz2e22Ulso6Fb6BPtS4E8LQ0/BrVTiWE7KmyEqP0j3oWqQDZ+rVocn5WwBUr56Bw4cTEDERQgghkjEZsiEACUgIIYT4ety7B9OmwcqV6pwTYTQaaNwYBg6ESpXU7YQICAlg/fX1zDo3iysvrhildS3ZlWOPjnHvnfFEFz/dNCPLxlD++Li9Zs1o2rb9XZ8eEhJESEgQ1tb2CWucEEIIkQzIkA0hhBBCfFXy5oVFi+DRI3XpUAcHdb+iqPNOVKkCZcrA+vUJW5nD2tyajsU7cqn7JY51PEbTAk3RarTkdcrLogaLuN3nNnvb7qVhvob6MvVuhzIVcAN61S9Aq1ZjwisMDWXD+t/IlCkFvXtX4u7dMxGOKYQQQoi4kx4SSZz0kBBCCPG1ev9eDVDMng0eHsZp2bLBL79A164JnwAT4NH7Rzz1fkqlbJWM9ldYVoErL66gKDrK3QtkQWAtCqzbr++moSgKr5fOon2/wez3DwVAq4UqVTLQvfvP/PjjSMzNLSMcTwghhEjOZMiGACQgIYQQ4usXFAQbN8Kff8KVK8ZpDg5qUOKXXyBHDtMe99brWxSaXwiAPE55GFV5FK2LtMbaPHxt0mOPjqFUrsbCp7AF+LTjRvr0ZrRqVYk+faaSN29Z0zZQCCGE+EbJkA0hhBBCfBMsLaFdO7h0CQ4fhgYNwtN8fGDGDMidG1q2hHPnTHdcc605bYu2xdLMkl6le9GxeEejYMTf1/5m4fHpaLWwHngM/ApkNqjj5ctQZs8+RoEC5ahSJT2rV48kKCjAdI0UQgghkjDpIZHESQ8JIYQQ36Lbt2HmTFi1CgI+ub6vUAH694emTcHcPOHHeuX7ChtzGxysHPT7bry6QZEFRTDXmmNtbk265x8YeAY6XAWbINgDLAZ2A7pP6kuTRkvLluXp23cKBQpUQgghhEhuZMiGACQgIYQQ4tv2+jUsXAhz58KrV8ZpWbJAz57QrRukTWva4/be1Zv5rvON9jlaORLo582PVxX6XIAyHvAUWA4sBZ58UodGA+XLp6ZLl060bfsrVla2pm2kEEII8ZWSgIQAJCAhhBAiaQgIgL//hunT4cYN4zQrK2jTBvr2hZIlTXO8e2/vscB1ASuurOB9wHujNAutBVbmVmTw+EC/c9D+KtgHwn7UXhM7gNBP6nNy0vL333OpW7enhFxRagAAU85JREFUaRoohBBCfMUkICEACUgIIYRIWhQFDh1SV+bYuVPdNlSxohqYaNYMLCwSfjy/YD/WX1/P4ouLOfcs4gQWKaxS4KS155LTCFItWwtnz/IcWAEsAR59zGdpDv89vU/69LkT3ighhBDiKycBCQFIQEIIIUTS9fAhzJ8PS5eCl5dxWqZM6nCO7t0hXTrTHM/thRtLLi1h9dXVeAd6G6XZmNvQsnBLuqesSfmt5/FZtgB7v1AOAfO18KxaOo7udcfWInzYRteupfH09KZLl1+oV68XWq3MNS6EECJpkICEACQgIYQQIunz9YU1a2DOnIjDOSwtoVUrddnQ0qVNczy/YD823tjI4ouLOfP0TIT0fE75ePT6Ho1vKfQ9D9fTQa/6kMI6BW2KtKFzic4UmrScTEsW4RMMNjbg4fGclCkzmKaBQgghxBcmAQkBSEBCCCFE8qEocOSIGpjYvh10nyx/4ewMPXpA69Zga6L5Ja+9vMaSS0v4y+0vvAKNu2lUzFqR136vefDqLqFm4fsd/WH7NGgQCj5A08a52frvfaOyBw8upXz5FtjZpTRNQ4UQQojPKLbXodI3UAghhBBJgkYDNWrAP//AgwcwZAikShWefuECdOmiDufo1w9u3Ur4MYumL8rs72fjMciDVU1WUTFrRX3avB/mcbv3bY51PUnn4p2xNVejIOWfQgUdvADmVyrAoCETjep8d+owDRp0I0MGJ376qQhHjqxC92l0RQghhEgCpIdEEic9JIQQQiRnfn7q6hzz58PlyxHTq1ZV55po2lQd3mEKN17dYNe9XQytONRo/+QTkxl5eCQprFJg5unNn88K02n4eihSRJ/nwNm/ca/Yjp91xj/Pcue2omXLGnTu/Ct58jibpqFCCCFEIpEhG7Fw+vRpVq1axYkTJ3j27BmKopAlSxYqVaqEi4sLFStWjLmSBAoODmb//v1s2rQJV1dXnj9/jp+fH+nTpydjxoyULl2a6tWrU716dVIZ3uaJJQlICCGEEOpwjgsXYMECWL9eXUbUULp0au+J7t0hR47EOL5C0QVFufFaneRi90+7yeqYlSLpwoMRQaFB/NbEiaY7fZkNbAL8P6lHowFn51S0adMMF5ffSJUqk+kbK4QQQiSQBCSi4evryy+//MLy5cujzdepUyfmzJmDnZ1dorTj9OnTdO/enRufzsAVid69ezN37tw4H0MCEkIIIYQxT0/46y9YuBBu3zZO02jg++/VuSZ++AHMzCKvI65CdCHMODOD5VeW42TjxKnOp4zSl11axpFHR3iyfS2jTkCth/AB2Ii6hOjpSOq0soIaNbLRoUNnmjUbhqWltWkaK4QQQiSQBCSiEBoayg8//MD+/fv1+2xsbChcuDDm5ubcvHkTb+/wpbzq1KnD7t27MTPVL5KP/vrrLzp16mQ0JjRlypTkypWLlClT4uXlxe3bt/H19QUkICGEEEKYmqLAsWNqr4mtWyEkxDg9Sxbo3Bk6dTJdrwlFUfAM8MTJxsloX765+bj/7j4aNNhb2mPh6UPXS9DtEuTxhPvAWmA18CCSelOl0tCw4Xd06jSQKlXayRKiQgghviiZ1DIKY8aMMQpGdOvWjadPn3LhwgXOnDmDh4cHY8aM0afv37+fsWPHmrQNmzdvNgpGlChRgn379vH69WsuXrzIoUOHcHV1xdvbm3PnzjF48GBSp05t0jYIIYQQyZ1GA9WqwYYN8OQJTJwI2bOHpz99Cr/9BrlyQd26sHEjBAYm9Jgao2AEwNWXV7n/Tl1lQ0HBJ8iHd3Ywo6oF3w2yoVAv2OMMfWzgHmpviV6AYS2engp//eVG9eou5M5tw4gR38tEmEIIIb56yaqHhIeHB7lz5ybg48DR9u3b89dff0Wad8yYMUyYMAEAa2trHjx4QKZMCR+n+fLlSwoWLIinpycAzZs3Z/369Zibmye47shIDwkhhBAi9kJDYd8+dTjHrl0Rlw5NnRo6dFDnmyhc2HTHfej5kJVXVrLKbRX/ef0XId3WwpaQ4EAqPAxllnt+vjvnTlBQEHtRe03sAAxjJRUqpObUqTema6AQQggRB9JDIhIzZ87UByNsbW2ZOXNmlHnHjBlD1qxZAQgICGDWrFkmacOgQYP0wYh8+fKxdu3aRAtGCCGEECJuzMzUuSO2b4f//lN7TeTKFZ7+9i3MmKEujFG+PCxbBh8+JPy4uVLl4rfqv+Hez52jLkfpUqILKazCf8D5BfsRRChHc0Gb1mbM2DOOdwv+R6PKldmEuoToYqDKx/wtWjU1qj8w0I969bIxe3Z33r17mvAGCyGEECaQrHpI5M2bl/v31S6RHTt2ZMWKFdHmHzduHL/99hsAefLk4d69ewk6/osXL8iaNSshHwep/vvvvzRu3DhBdcZEekgIIYQQCaPTwdGjsHSpOtfEp8M27O2hdWu110TZsupQEFPwD/Znx90drL66mr339xKiM57kQqvRUid3HTL5W5B6ww46X4YCb6F2Azhb2p5mxZrRpkgbaqYvz57GNWh85BIA33+fjd27H5umkUIIIUQkZFLLT9y5c4cCBQrot9evX0+rVq2iLXP27FnKly+v3759+zb58+ePdxumTJnCiBEjAMiYMSNPnjwx+WSZn5KAhBBCCGE6797B2rWwZAlcuxYxvWBB6NgR2rUDE4z01Hvt+5r119fz19W/cPVwjZBuobUghaUDOR+8wy09BBt0vux83wH7NT7M/ri9YsVQOnacqk/38XnDzp1zadp0MNbW9qZrtBBCiGRLhmx8ws3NzWjbMNAQlZIlS2Jpaanfvnr1aoLaYDiZZr169RI9GCGEEEII03Jygr59wc0Nzp+H7t3BwSE8/dYtGDYMsmZVlw9dvx78/RN+3LR2aelbti8Xul3gVu9bjKw0kmyO2fTpmRwy8XzwCyYPP0D70p1xtHLUp9W84sMM4CTQpXZufvxxRHjF3t5smdqPn376lfTpU9CyZUG2b59OSEhQwhsthBBCxCDZBCRu3bql/9vS0lI/P0R0Ps1nWEdcKYrCxYsX9dvlypUDwN3dnREjRlC0aFEcHR2xt7cnV65ctGnThk2bNskM2UIIIcRXSKMBZ2dYtAieP4cVK6BSpfB0nQ727oU2bSBjRvj5Zzh9Wl1qNKEKpCnAxJoTce/nzrGOx+haoivdS3XHwsyCWrlqsazxMl4MfkH+1PmxMbehU3MzmrSCNI0rsXTPHezsUgIQqgtl75Su7Jj4NwDe3gqbNt2mceNBZMhgQ/v237Fr1xwJTgghhEg0yWbIRqdOnVi5ciUAuXLl4sGDyFbxjqhGjRocOXJEX8fy5cvjdfyHDx+SO3du/fbhw4e5desWgwcPxj+aWyfFihVj48aN5MuXL17HlSEbQgghxOfz4AH89ResWgWPI5mmIW9ecHGB9u0hW7aI6aby+P1jcszKAUDBNAUZUWkEDfI1IJVNKn2eI+5HCK5Vg5CH6kod2wG/SOpKnVpL3bqFaN26C99/3wtzc8tIcgkhhBDhZMjGJ3x8fPR/Ozo6RpPTmOGTZ1hHXL19+9Zo+99//6V37976YET27NmpVq0apUuXNhom4ubmRvny5bl582asjhMYGIi3t7fRQwghhBCfR+7c8Ouv8PAhHDmiBh/s7MLT792D0aMhRw6oVQtWrzbNKh2f+hD0gfp562OuNadt0ba0L9beKBjRZksbhh0YxuJSYJkT1mjgFbAOaAwYhhzevtXx99/XadRoAOnSWdOmTWG2bfuToKAA0zdcCCFEspJsAhIfDP63t7a2jnU5GxubSOuIq/fv3xttz56tTi2VP39+jh8/zqNHjzhy5AgXLlzg1atXDBgwQJ/33bt3tGjRgqCgmLtMTp48GUdHR/0jNkNThBBCCGFaWi1UqwYrV8KLF2qPiRo1wtMVBQ4dgg4dIH16+Okn2LULgoNNc/zC6Qqz86edvBz8kp7OPY3Sbr6+yfrr67nw/AJbCkOTrnakHAa9GkO2LPAvanBiDWpwwsqgrKenwvr1N2nSZDDp09vSqlVBtm79Q4ITQggh4iXZBCTCltoEMDc3jyanMcO8wQn4lRD46RphQKZMmTh+/DiVK1c22u/o6Mj06dMZPXq0ft+tW7dYtWpVjMcZMWIEXl5e+seTJ0/i3WYhhBBCJJy9vRp4OHQIHj2C339Xe1KE8fODdeugQQN1ZY7evU0334STjRNONk5G+449Oma07Rvsywdr+KsE1O5pS8ph0K8xZM+qBideA38DTQHDWzrv3yts3Hib5s2H8f33uRLeWCGEEMlOsglI2Nra6v8OCIh9FN8wr51hn8s4iqzsxIkTSZcuXZRlxo0bZ9TDITbzV1hZWZEiRQqjhxBCCCG+Dtmzq0M27t2DkyfVyS6dDOIFb97A/PlQsaIatBg9Wl25w5R6OvfkwS8PmFhjIkXTFTVK8wv2w8sGVpWAuj1sSTEczo9zoU2VKmzVaHgNrAdaADYG5WrXrmZUT1BQACtXDsPb+5VpGy+EECJJSTYBCXv78HW1o5tE8lN+fuHTOxnWkZDjA1hYWNCyZctoy5ibm9O6dWv9tqurK76+vvFugxBCCCG+DhqNGnRYuFBdpWP7dmjVCgxHlbq7w8SJUKgQlCgB06bBs2emOX6uVLkYWXkkV3te5Xbv20yoPoFi6YsZ5fEL9sPHGmppVlGq7Qcm7xzOi1njqFQqH+s1as+JTUDebGY8y2WHq4criqKAvz8H/xhIp05/kDZtekaO/ME0jRZCCJHkJJuARJo0afR/P3/+PNblXrx4of87derU8T5+2rRpjbYLFChg1GsjKiVLltT/HRISIkMwhBBCiCTG0hIaNoT16+HVK3W+iTp11Hkowly5AkOGQNas6lwUy5aBp6dpjp8/TX5GVRnFlR5XuNvnLpNqTKJEhhJGeS49v8TIC5PJ6/krxVu+w3E49GsADrnhxU+hzL21FOclzuSYlYMlU1qyf8wCAIKCIFu23EZ1+fq+5/btk6ZpvBBCiG9asglI5M+fX//327dvjXo+RMcwAFCgQIF4Hz9btmxGAQgnJ6docof7NAjiaapfH0IIIYT46jg4qPNN7Nun9oaYOROcncPTlf+3d9/hVVSJ/8ff96aHhBKKhJCEJJSE3hFCW0NRpOiKuq669rYqW3TtLq4/d3V17ajrKl91F3sBbDRRaUrvEEJJT0ghkJBC6p3fH2NuckmH5KZ9Xs8zT2bunDlzJh4w+XDmHMNcvePWW83JMGfPNpcZzclpnPv369qPhyc9zM47dnL03qM8E/UMo/xHOZQ5ceYE3l26E/7wczz16ETyPC32c4k5ibh++TVXAHcBAV2sXHnlXyouLijgi//+jYiISYSHe/PnP1/Eli2fY7PZGucBRESkVWk3gURERITD8e7du+u8JiUlhczMzBrraAir1eoQaFQ3yWV1zp7voiErhIiIiEjr1bMn/OEPsHUrxMTAwoXQt2/F+ZISc2WOG26AHj1g3jx4/304j1XKHYT5hfHgxAfZfvt2YhfE8uy0ZxnTy0xHrhp0FfdPuJ8NN20g9b5U3pz9Jj19emLFyn9GQvIgeLZbRxKTc+naNQgAm2Fjy8sPsvbulwCIiTnDiy/+wIUXzicoyINbbx3NihWvU1pa96piIiLSNrSbQGLs2LF4eFQsXLVxY91DBTds2GDf9/T0ZOzYsefVhilTptj34+Li6nXN2eUuuOCC82qDiIiItD79+8MTT8Dhw2ZA8ac/QUBAxfniYnMeiuuug+7d4de/ho8/hsaaeiqkSwh/ifwLW2/bSvwf4vnLhIpRDz19enJZ+GVk5Gdgw0bCQH++WDifk4f3YK00OnRz8maS/7eI0QaMPqv+lJRSFi/ewaxZd9Otmyfz5oXx1lt/IisrsXEeQEREWqR2E0j4+PgQFRVlP37//ffrvKZymaioqPNaZQPg17/+tX0/PT2dffv21XnN6tWr7fuBgYH06tXrvNogIiIirZfFYr7C8cILkJhortSxYAH4+1eUKSqCpUvhN78xw4mrroLPPjOXF20MwZ2DCe4c7PDZzuM7cbWaS6XfMOwGPr3yU4K69LGf/+PKP/LkuifZ2gsu7gLbgETgVeAiwKVSXTk5Bl9+Gcvtt7/EBRcEM26cHwsXXs6BA983zgOIiEiLYTGMxljlunX49NNPHVa2+PLLL5kzZ061ZXfu3MnYsWMpKyuzXzt//vzzur/NZmPQoEEcOnQIgGuuuYYPPvigxvK7d+9m1KhR9vcq77nnHl599dUG3fP06dN06tSJnJwcLQEqIiLSRtlsZjjxySdm+JCeXrVMhw7m5Jnz58PFF5vHjSm3KJcVR1cw7IJhDOhWMXdX3Kk4Ql8JBcCCBTcXN/xOFTP/INy4B0Ydhyzg61+2VUBNb5306ePO9OnDuPzyG7nkkt837gOIiEijqe/voe0qkDAMgxEjRrBnzx4A/P39+f7776tMVnn8+HGioqKI/mXh7+HDh7Nz504sFkuVOuPj4wkJCbEfL1y4kCeeeKLGNixdutRhpMQLL7zAn/70pyrlEhMTiYqK4ujRowC4u7tz+PBhgoODq5StjQIJERGR9qWsDNavrwgnTpyoWsbLywwlfv1rc2LMzp2brj2Lti7i3hX3VnvO09UT97xCZhyFm3dBVBxgg3WY4cRXQHUvuY4e3Zlt2zTRt4hIS6VAogbbtm1jypQpnDlzBoCOHTty1113MXnyZFxdXdm6dSuLFi0i/Zd/WvDy8mLdunWMqTzFdSUNDSQArr/+epYsWWI/vuiii7j++usJCQkhPz+f9evX88Ybb3D69Gl7mddff5277rqrwc+rQEJERKT9Ki2FH380w4nPP4eTJ6uWcXWFqCgznJg3z1y9ozHZDBubkzez7NAylh5aytGTR6st52Z1w1paxhMd53LXfi86fb0G48QJojGDia+BnwAb8Nhjc/h//+9L+7VlZaXMnBnEuHEjueKK3zNy5KzGfQgREWkQBRK1+OKLL7juuuvsoURNvLy8WLJkicOIhrOdSyBRXFzMlVdeyZdffllrOQCLxcLf//53Hn744TrLVkeBhIiIiIC5KscPP8AXX8CyZdW/1mGxwMSJZjhx+eXQwIGZdTIMg4OZB1l2aBlfH/maLclbMKj6o6ir1ZUpQZOZ7TGE/j/so/ey7xmaASeA+SPg8OwLmDf2cmb1m8VFvSLZPXsmE9dvB2DatF6sWZPSuA0XEZEGUSBRh+joaBYsWMDatWs5+1tgsVi46KKLeOWVVxg4cGCt9ZxLIFHurbfe4umnn65xxY1Jkybx1FNPMXny5HrVVx0FEiIiInK2sjL4+WcznPjiC0hIqL7cqFFmOHHFFTBgQPVlzkdGfgYrjqzgq8NfserYKvKK86ot5+HiQbesIuYdhs8jIN234tysOFemvFfKg78c/+tfN3Dffe/az+fnZzN9el+mTh3LvHm3MGbM5Vit7WZedxGRZqFAop6SkpLYtGkTKSlmkh4QEEBkZCSBgYFOa8O2bds4cOAAaWlpeHh44O/vz6RJkwiovJ7XOVIgISIiIrUxDNi50wwmPv8cYmKqLxceDnPnmtuFF4KLS/XlzlVRaRHrE9bz9eGv+erwV8RlV/yDTVevrmy+ZTPfHv2Wrw5/xbr4dZTYSgB4Zg08sAligM+uGM11/3qLPn2GA1B2MosVMyKZs6PioXr0cGHKlL5ccslc5s1bgJ9f78Z9EBERUSAhJgUSIiIi0hDR0RUjJ3burL5Mt27mZJjz5sH06Y2/YodhGESfiLaHE339+vLOvHfs5/OK84hcHMnejL24W92JSC7m5bJpTHnuE+jSBTDnrrj35p50fzeTv9VwHxcXGD68E1FRF3LZZTczbtx8jZ4QEWkECiQEUCAhIiIi5y4+HpYuNcOJn34ylxc9m4cHTJtmjpyYPRt69Wr8dtgMG1ZLRVCQW5RL12e7UmIrIaRzCF9d8xV+Xn74+/oDsOv4LjILMjn025ncuxWOACuAlcCPQGEN9/HzszJhQhDTp09jzpy7CAkZ2fgPIyLSDiiQEECBhIiIiDSOzEz49lv48ktYtQry86svN2ZMxasdQ4aYE2U2tqyCLJ7/+XlWHF1BZGAki2Ytcjg/+j+j2XF8B25WN7qcLmH6Mbh5J0xOhGLDXFZ0xS9b9Wt+mEJDPZg8eSAzZ85j3rw/4eWln6VEROpDgYQACiRERESk8RUWmit2fPmluaWmVl8uOBjmzIFZs2DqVPDyavy2lNnKcLFWTGiRnpdOz+d7VinnYnHBpbSMwWlw1QG4bh8E5JqBxApgNeboieqm1bRY4Pjxo1xwQVjjP4CISBukQEIABRIiIiLStMonxSwPJ3bvrr6clxdcdJEZTlxyCVRapKxRpeels2jrIlYcXcGO4ztqLOdmdcM3t4QbTvdhYXwfOv34M8VFRWwB1mAGFNsAGxAR7s3BaMchIbfdNoakpDSion7FnXe+gK9vt6Z5IBGRVkiBhAAKJERERMS5EhPhq6/McOKHH6CkpPpyERFmODFrFkycCO7ujd+WjPwMVh1dxYqjK1h9bDVZZ7KqLedicWGs/2imW/sx9VAhvh98xuhUOAW81AOeirIyccZEpodOZ3rodEb9411C3nmT5EIDdzfIOpmFj4+fvb6iogI8PLwb/4FERFoJBRICKJAQERGR5nP6NKxda8498e23Nb/a4eNjrtZRPnqiEVY+r6LMVsb21O2sOraKNbFr2Jy8mVJbabVlXSwu+BTamBVjkO0JK/pXnLPaYP+zMKEQsoFx4/zYvLlS0FFSwvRZfTh2LIsJE/oTFXUxs2bdodc9RKRdUSAhgAIJERERaRkMA/burQgnalq1A2DYsIrRE+PGgZtb47cntyiXH+N/ZE3sGtbEruHQiUMO55dfvRwDg9XHVrM6djVHT5rTXwafhH3/Bu9iWD9hCKVP3MP06bcDEHsqlu6XXUfwxp85VenZLBbo39+LyMiBTJs2m5kzb8XPr3fjP5SISAuhQEIABRIiIiLSMp08CWvWmOHEihXmKh7V6djRnHtixgxzC2uigQbJp5NZc8wMJ35K+olD9xzC09XTfv6p9U/x+A+PA9DJpQPzsrrz3jUfw9ix9jILlt3Bn+f/h9+UwXagrIZ7Wa0waJAPEycOZdq0eUyffrPmoBCRNkWBhAAKJERERKTls9lgx46K0RPbtpkjKqoTGloRTlx0EXTq1PjtMQwDy1nrlV7xyRV8Ef0FAFtu3UJQpyB6+pireexL38e7u9/lu5/f5+V30pmcaK7WsQH4/pdtD1DTD91ubjB0aCcmTBjGRRfNJirqJgUUItKqKZAQQIGEiIiItD4ZGbByJaxaBatXw4kT1ZdzcTFf6SgPKMaMAVfXpmnThoQNfHPkG7ambGXN9Wsclhp9ZuMzPLz2YQAsWHC1QfApg2mxcMMuuDAVsoB1VAQU0bXcy8UF7r13Ki+++EPTPIyISBNTICGAAgkRERFp3Ww2cynR1avNbePGmlfu6NQJoqIqAoqmWlr0bDOXzGT1sdXVnrNgwa3MIPQkzDwKN+2CYRlwHPgRM5z4ATh21nXPPfc77r//PfvxyZPJ3HffXKZMmc6MGTfTq9eApnkYEZFGoEBCAAUSIiIi0rbk58O6dRUBRXQtQw3CwszXOsq3Hj2apk1ZBVmsS1jHD3E/8EP8DxzIPFBjWQsWJl4whqnFvZh86Azj10TT4WgiiZiveKwHPukIE//fNC6fdA2TgycTVuzDlzdfzmUrNgNw7bVDWLJkb9M8jIhII1AgIYACCREREWnbkpLMyTFXrza/njxZc9nBgyvCiSlToHPnpmlTel46P8b/yA/xZkBxOOtwjWVdra50tfrgl5rNgs0wNQF+dSOk+VaUuT22C13/e4qnfzn+z3/+yG23vWg/n5Cwl8jIUYwbF0JkZCQXXXQVQ4fOxGq1Ns0DiojUQYGEAAokREREpP0oK4NduypGT/z8MxQXV1/WaoVRoyoCiokTwdu7adqVcjrFDCd+GUERlx1XpUyAbwC3jLiF9Ynr2Zy8mcLSQvu5V76F67bCRuDbmf146M3PCA4eCsCRzd+yY841XHPitEN9nTtbGD78AsaPH8GkSbOZPPm3dOjQuWkeUETkLAokBFAgISIiIu1XQQFs2gTffw9r15oredhs1Zd1c4Px4ysCinHjwN29adqVlJPEhsQNrE9Yz4bEDRzMPMjNw29m8bzFABSVFrEtdRvzP5lPen46roaF6YcNnnS5iNGvLTXXQgUKSwv5w5U+hC8r4yGghuzF/nwRET6MHRvOxInTiIq6jt69BzXNA4pIu6dAQgAFEiIiIiLlsrNh/fqKgGL//prLenvDhAkwebL5esfYseDp2TTtyszP5EzpGYI6Bdk/O3nmJN2e7YaBwYieI/j37H8T2DEQf19/ABZtXcSxk8fI/vdLvPItuJXAVuAnYNMvX2t5ewWA3r3dGD06iAkTJvCrX81nxIhZuLg00TIlItKuKJAQQIGEiIiISE3S0+HHH82A4vvv4ejRmst6eJijJqZMMUOK8eOhQ4ema1tmfiYvbX6J9YnriQyM5JlpzzicD3k5hPjseMCcKNO30CAiE6bFwvV7oN9JiMEMJ8q3I3Xcc/XqN5k+/Xb7sc1m0zwUInJOFEgIoEBCREREpL4SEuCHHyoCipSUmsu6usKYMRUjKCIj7W9SNLnU3FQCXgiotYx7KQTlwIQk+G20KzNiSsnEHDlRPopiOxWvebi5Qs7pHLy8fnmIsjIe/es8PvroO4YPD+Lxx19m+PBLmuyZRKRtUSAhgAIJERERkXNhGBAXZ77isW6ducVVnYvSzmqFESMqRlBMmgR+fk3TtlJbKXvT9/JT0k/2LSEnodZrunr6McGzHxOyvInce4pBq3fjfQZ2AN+5whPjIPCKQMb1HseFARdy1Yd7ufft91leWAbAtm3LGT16rr2+nTu/Zd++DUydeo19gk0RkXIKJARQICEiIiLSWJKSzICiPKSIiam9fESEOXKifOvbFyyWpmlb8ulkfk762Qwokn9i5/GdlNpKa72mZy5cdQC8S+CZSY7nVv8XXo6FVYCHF+TkllTML7FjB/c/ch3Prz4EwAUXuDB8eC/GjRtJZOQlREZerRU9RNo5BRICKJAQERERaSppabBhgxlOrF8P+/bVXr5HD3OizIkTzYBi5MimW8mjoKSA7anb+SnpJzYlbeKnpJ84ecZxmsuFUxYy0n8km5M3szl5M1uSt1BQWgDA02tgwRYo6tKFI1+9zdhxvwbgYOZBDv/uUt5YGc/qGu7t6gr9+3szYkQYo0ePJTJyDiNGXIKraxM9rIi0OAokBFAgISIiIuIsWVmwcaMZUGzcCLt2QWktgxQ8Pc15KMpHUEyY0HSveRiGweGsww4BxVtz3iIyKNJeZmPiRia9Yw6V6OfXDy9c2TD9QzqGDwNgd9puNiVuYsQV95CTDOuAzcA2oKCO+3t6Qnh4R0aM6MuYMRcyYcJcBg+O0qoeIm2UAgkBFEiIiIiINJeCAti6FTZtMreffoKcnNqvceZrHmd7fdvr3P3t3QC8cekb3Dn6Tvu5nMIcOv+zM1aLlQ5nbISfgMkJcOUBGJkKBzHDic3AFiC6Hvfr1MlCWtppPD19muBpRKQ5KZAQQIGEiIiISEths8HBgxUBxcaNtU+UCeaIiXHj4MILza9jx0KXLk3TPsMwSMhJYEvyFsYHjieoU5D93Hex3zH9f9Orvc5qgx75MDgdouJg/gHomg27MFfy2PbL1/izrgsL8+Do0cLKDeD63w0jLu44I0aE87e/fYifX+9GfUYRcQ4FEgIokBARERFpyY4frwgoNm2q+zUPgPDwipDiwgth8GBz3oamtCN1B69sfYUtyVuIyapjNk/Ax+rJRNdQRme6Mzo6m5GbE/HMtrEDM5xY5QWbR1kZf3Mko3uNZnSv0cx57H+MWLmKY8UG7m5wOjcfDw9ve51ffvkCWVlpjB8/l/79J2C1WpvugUXkvCiQEECBhIiIiEhrUvk1j82bYcsWyMys/Rpvbxg9umIUxYUXQq9eTdfGU2dOsS11G1uSt7AlZQvbUreRkZ9R53VuZfCrWPjtfjjQHZ6bWOmkAcf+BRfmQyYweHAH9u3Lqzj/+edc/MjNrDp8GgBfXwvh4Z0YOjSMkSPHMnbsxQwffrEmzhRpIRRICKBAQkRERKQ1MwzztY7ycGLzZnMURUlJ7dcFBprhxLhxZlgxciQ01Y+ChmGQkpvC9tTt7Ejdwfbj29meup0TBSeqlP3ntH8CsC11G9tStpGQkwCARwl88DlcfggOjR1G1vMPMnHiNQC8v/d9+l91F/NjckmspR2entCvnw+DBwczcuQoxo6dzpgxc/Hy0s/AIs6mQEIABRIiIiIibU1hIeze7RhSxMfXfo3FAgMGmOHE6NHm6h7Dh5ujK5qCYRgk5iSy4/gOtqeaAcWO4zs4fM9hunp3tZd7/qfnuX/N/QAM6j6IriVurJv7BYSEkFecx9eHv+brmK948boP2FgIOzHnptgJHK9HO1xcIDTUk8GDezNixDBGj76ISy75fVM8sohUokBCAAUSIiIiIu1BenpFOLFli/naR15e7ddYrTBoUEVAMXo0DB0KHh5N00bDMLCctWTIvd/ey6JtiwBYf+N6JgVPsp+rvAxp+cSZEZkwMRFmHQH/NIgucwwpYutoQ0iIB7GxhQ6frVnzH7p27c3QodP0yodII1EgIYACCREREZH2qKzMXNFj+3Zz27YN9uyB4uLar3NzM0OJ8pEUo0eboYWbW9O00zAMYk/Fsj11O7P7z6aDewf7uVe3vMqClQtquRi6FEK/LBiXDLNjXRiQUEZ8UUVIsQtzSVLbL5cMnuzH4g9WMLjHYLzdvGHpUvr+/mqOpZXg4wOnThU5hBJFRQUOE2uKSP0okBBAgYSIiIiImIqLYf9+M5woDyn27zfDi9p4eJgreYwYYW4jR5qhRVO97lFud9puvor5il1pu9idtpu47DrWSP1FsGs3hhd2ZkDSGQbsSWFaDKSdgVcC4f0pQF+wYKFf136sXZhIn5OFlAH9+3sSE3OmoqK0NKZfO5K9+zMYMMCPQYPCGD58DKNGTWPo0Bm4u3s2yXOLtAUKJARQICEiIiIiNTtzxhw5UTmkOHTInEyzNlarOSdFeUhRvvn5NV1bswuz2ZO2h11pu+whxcHMg5Taal8ntV9ZZ/ofzSbNB3YEVHzeqQASn4WXge2d3Oh1zSjeeONnALYkbyFg/k38aks0R6up080NQkO9GDDAn0GDBjBkyFhGjpxOv37jtRypCAok5BcKJERERESkIXJzzZU8ykOKXbvg8OG6QwqA4OCqIUVAgDmpZlMoLC3kQMYBdqfttocUu9N2k1+Sby+T/KdkPFw92Je+j73pe/n04KdsStoEwLgkuOGQB3de9SyWBQsotZVSaivlkbWPcN+vX+TaAtgN5NSzPV5eEBbWgf79ezF48ECGDh3HyJEzCQkZ2ejPLtKSKZAQQIGEiIiIiJy/vDzYuxd27jQDil27zNc96lp+FKBbt4pwYuhQcxswANybaP5Im2Ej7lQc+zL2EZ0ZzUMTH3KYTPOPK//Iy1teBuDtOW/Tp3MfokKjAPg56WcmvjMRLxdPBsUXMCYFfhULPdLgZA7sB/YCe4AjVMxNUZs+fdyJiyty+Gz79i/x9+9HQEBEozyzSEujQEIABRIiIiIi0jSKi82JM8sDil27zOVI61rdA8xXHiIiKgKK8q1nz6YbTVFu1dFVrDy6kr0Ze/nwig/p0aGH/dyb29/kzm/urPY6Fxv0zIMBJ2BsCoxKAI90OJUL0ZhhxQHg7Jkuhk/y45OlmwntEoqL1QVWrqT/rXM5klJC9+5Wjh8vwsXF1V4+Pz8bL6+OevVDWjUFEgIokBARERER57HZ4OhRx5Bi1y7IzKzf9d26VQ0pBg40X4Vwhv/u+S8vbX6JA5kHKC6rY0mSX3jhRkRpZwZmuRARl0vfo/l0SYKUIvioO6yaBAwFT1dPIrpF8NXjMYRkFlAChIZ6cOxYpWVIY2OZcdMENu/KIDTUh759/RkwoB+DB49m2LCp9O8/QUuTSqugQEIABRIiIiIi0rwMA1JTzckz9+6t2A4dqnuFDzAn0Ozf3zGkGDQI+vQxzzWFUlspR7KOsDd9L/sy9nEg8wAHMg5w7NQxbEbdL2oMybASmWDjcFf4PrTic68iSH0aFgK7O7gQMDecDz7YD8Da2LUEXnUbl+2II7qGet3dISjIk759uxMeHkZExFCGDp3EkCHT6NCh83k/t0hjUSAhgAIJEREREWmZioogOtoxpNi7F9LT63e9t7f52segQY5bUFDTBRWFpYXEnIjhQOYBDmYetH89evKoQ1ARc08MJWUlHMg8wP6M/Xx75Ft2HN8BQGQC3LTflZuveArLgw9ypuQMMVkxvLvrXf509cvcnWvOUZHcgHZZreDv70poqB8DBgQxZMhwFix4q3EfXqQBFEgIoEBCRERERFqX9HTYt88xpDhwwJyzoj46dDBf8zg7qAgMbNrVPmJOxHAw8yDRJ6J5YuoTWC0VqcgDax7guZ+eA+D1Wa8T2iWUmX1nAuZEmhP+bwIAXQog7BQMTYchSeCZBiUnIbUIYjDnqjgK1L7QKQQGupGY6PgN+8c/fkN29inCw4fy298uxNPTp5GeXqQqBRICKJAQERERkdavpASOHDHDiX37zIBi/36Ija3fcqQAvr7VBxVNuSxpuXXx61gTu4YDmQd4bdZr9PLtZT/3zq53uPnLm2u93qMUAnNgYCaMSoYuKWBkQU4uHDbgEOZWPp9o16Ee/O7Z39O/a3/6d+3PmH8uYcL777E/z4aLC+Tn5+Ph4W2v//33H+fw4f1ERAxnyJBJ9Os3AXd3z8b/Rki7oUBCAAUSIiIiItJ2FRSYc1EcPGiGFOVbXFz9g4qOHSE8vOoWFtZ0S5NW9kPcD7y35z1ismKIzowmpyin3te6YCWk2Juex/N4cD0MOwav9YF/XgQEVZSLeQlGZEMBEBTkRkKCOXoitygX66LXuO6F/8ey1AJ7eVdX6N3bnT59/Ojbtzf9+0cwcOAoBg2aQlDQUK0AInVSICGAAgkRERERaX/y882gonJIceAAxMfXvw4XFzOUqC6s6NKladptGAYZ+RkcOnGoYssyvyZkJ2BQ869uEwMj8TyRTVLmMWJ8Kq3cYcD7n8LAg7A3wI8zj1/LHXe8AsC7u9+l+1U38fgR2FXPNnp5Qe/engQH+xEa2puwsH707z+M8PDx9O8/QWGFAAok5BcKJERERERETHl55kSalUOK6GhISKj/iAqAHj2qDyqCg5tuQs0zJWc4cvKIY1hx4hAxWTEUlBQQfXc04d3CMQyD1NxUXtv2Gk9vfBqAQX7h9DlRyvI5H+Ayegw5hTlMfW8qhaWFTFlxiB6HoSwL8nIhpQQOA0eAwtoadBarFfLzcx3mpvjuu7fJzEwmImIcAwf+Sq+BtCP1/T3U1YltEhERERERaTY+PjBmjLlVVlBgzlFx6JDjFhMDZ85UrScjw9zWr3f83NMT+vWr2Pr3r9i/4ILzm6vCy82LoRcMZegFQx0+txk2Uk6n4O/rD4DFYiGgYwA9fXri7uJOcVkxf5/+DPPC59mvOXLyCLvTdgNwaAxQ6fvhVQJB2XBxJgQmgUcGlJ6C3F/CiiNAAlUn1vT2s7Dk4EeEdQkjzC+M3l+t442/3c8XR81XULZtW87o0XPt5bdv/5K9ezcwYMBoBg6cRJcuvZD2RyMk2jiNkBAREREROTc2GyQlVQ0qDh2CtLSG1eXr6xhWVA4tunZtmvaX2cpIzEmkm3c3fD187Z9/GfMlV3xyBaW2utbrcORpcyEk340+GaX0SChl4GHwTYMtXvDeWOBXFWWXf2TlH4dsbMEMYnJzT9GhQ2dKbaXYVq3kicfu4umdFYub+nWx0jvQm8DArgQH9yY0tC9hYYPo338MYWFjHSbhlJZPr2wIoEBCRERERKQpZGebIygqhxTR0ebKHyUlDaurS5fqg4p+/aBTpyZpPiVlJcRnx3Pk5BGOZB0xv/6yn5CTgM2w1VlHeFFHhh7Lo8yw8fkgx3N//w7CNsIeK8RfNZAPPjwAwNaUrfx49TgObIL/1rOtLlbo3sOF3r19CQrqTnBwICEh/ejbdygTJ16Fr2+3Bj69NDUFEgIokBARERERcabSUkhMhMOHzddAKm9xceaoi4bo3t0MJkJDzUk2K3/t2bNpliwtKi0i9lQssadiOXbqmMPX2FOxFJaas0t8PP9jLg+/nMScRI6ePMq6hHX2eSsCDF/GxhbxypiF9L73EQCm/2862Wey6fnjdvrshOwTkJsPmUUQb0DqObR1y5YvGDv2cvvxDz+8x+rVHxEaGs60adcTEjLyvL8f0nCaQ0JERERERMTJXF3NsCA0FC6+2PFccbEZSlQOKcqDi6Sk6ifWzMw0t59+qnrOy8sxoKi8HxICHh7n9gwerh5EdI8gontElXM2w0ZaXhrHTh4jvFs4bi5uhPmZ80YUlxXby9150QM8uvBR+/GZkjN8F/udeTDgl62SToUw5AT0TAbfdHA5CaWnIa8AThRDggEnq2nrm/HL+bHkCCGdQxjxUxw//P0Znkk4Baxk8WJXh0Biz55VPPPMnwkKCqRPnzBCQwcTGjqSkJARuLo6YY1XqUIjJNo4jZAQEREREWn5zpwxX/eobmRF6jkMHbBYICCg6qiK8q9duzbN6IrcolzisuPo4tmFwE6B9s+PZB1h0OuDKLE18H2WX3QtciEgrYxx+2HWNjgG/GU8GDMryjy1FuI3wNu/HK9fv4RJk64FYM17C8m473muy8qvUreLFbp3d8G/Vwd69+5K797+9OkTQp8+4YSFDadfv7F07NjjnNrdXumVDQEUSIiIiIiItHYFBebIimPHzNCi8te4OHPkRUP5+kKfPjVvXbo0fmBRZisj+XQysadiSchJID473mFLOp1U59wVwV7+jDrlRVlqMstDHR98cDp88gbsBXbfEslfnv2IbEsxpWWlPHn7AEYuhfvOse2dfC307OVJQEBn7r33T1x22V8qnqvMnBzUxUUvIJRTICGAAgkRERERkbbMZoOUlKpBRfnXrKxzq/fswCI42PHYz6/xA4uSshKSTyc7BhU5FfvJp5N5dNKjPPmrJzEMg4z8DI5kHWHyu5MxMOju3Z1fdRjMvT1mM3HuPeDuTtR/o/g+7nusNgjIgK5J4JsJbifBOA1n8iG7CDJKq38lpDpPvXQD99/9Jh6uHnD6ND+9/TRTH3yG7t1dueHG6fzjH986lF+/fglBQYMJDBzcbkILBRICKJAQEREREWnPcnLMcKK6wCIpqeErgpTz8ak6qqI8tAgOhm7dmiawKC4rpoN7B/tnmfmZXPnplcRnxzM+cDwfXvGhwzX9Xu3H0ZNH61V/h1zomQKd08H7BFiyoTQPcgvgdBEkA2UAtwCB0N27O5dmdObX/zrC3F/q+OMfp/Liiz+Y7S0tpuixR/H9578AcHGBHl1d6OHvjb9/ZwICehAQEEhQUCjBwRH06TOMPn2GtYn5LDSppYiIiIiISDvXqROMGGFuZysrg+PHIT7e3BISKvbLj2sKLPLyYP9+c6uOpycEBlZsQUGOXwMDzVEYDeHm4oabi5vDZ907dOfHG38EoLp/a5/bfy5HTh4hMSeRxJxEThWeqrH+fF84Fg6EO37eoRg+/hR6ZcNvLobDPc3PMwsy2Z6fyUw3GFICiR4QGNjHft28Nybz8j+32I/LyuB4RhnHM3LZsycXSAJ2ONzLxQpd/axc0NMb/16d6NWrB717BzB2bBRz5vyx9m9QK6QREm2cRkiIiIiIiMi5sNkqAouzw4ryz85l/opynTvXHlj07g3ujTxYIK84j6ScJJJOJ9lDCof9nCSKyoocrunftT9PRz1NQnYCyaeTSc5NZvWx1WQXZgPw8HqYWRbMlL/+H7apU3l9++ss2rqIzIw4pmwtJn4/lORBfiGcKoHsBi79CjB3bijLlx9rhO+Ac+iVDQEUSIiIiIiISNOw2SAtzTGsiIszXwVJTDS/5uaee/0WC1xwgWNQERBQdfP0bKwnMkdZZBZkkpRTEVL4evhy84ibHcoNeWMI+zP24+HiQeyCWMqMMvuqIn9c+Ude3vJyzTcpAWsO+KVBp0zwPGkel+WZq61kF8Gps0OLSdDrsl48MeUJbht1W+M9cBPRKxsiIiIiIiLSZKxW6NXL3MaPr75MTk5FOFE5qCj/mpxc8ygLwzADj7Q02Lat5nb4+VUfVFTe6junhcVioUeHHvTo0INRvUbVWG7N9WtIykni5JmT9OrYy+GczbDRwa0D+SVVlxgFwA1s3eBENzhR0w1K4d33IDQJXhgIywZCam4qBzIP1P0QrYhGSLRxGiEhIiIiIiItlc0GGRmOIcXZwUVamhlOnA9397pDi169wMOjcZ7LMAxOF50mJTeFlNMppOSmkHw62b5f/nl6fnqNddw//n4K87LJOJ3KseJ0jmQd4fph17No1qLGaWQT0isbAiiQEBERERGR1q24GFJTzYAiJcXckpMr9lNSzPPnumJIZX5+0LMn+Ps7bmd/5uvbOKuIFJcVczz3eJXg4njecd677D1crRUvNTz83cOM6z2Oy8IvO/8bNzEFEgIokBARERERkbbPZoMTJxxDiuq2UzUvstEg3t5VQ4rqjrt3N19taQypuan4uPvQ0aPl/16nQEIABRIiIiIiIiLlCgrM0RQ1BRZpaebKIoWFjXM/FxdzYs7KgUXPnuZnZ2+dOjXOqIuWQJNaioiIiIiIiFTi7Q19+5pbTQzDnIzz+PGKgKJ8O/s4O7v2+5WVmQFIamrdbfPwgB49qg8rKm89e5qvlrQFCiREREREREREfmGxQOfO5hYRUXvZwkLHkKKmACM93XytpDZFRRWTetYmPByioxvyRC2XAgkRERERERGRc+DpCX36mFttysogM7MinKhtO3Gi9vDiggsa8wmalwIJERERERERkSbk4mK+atGzZ91ly8rMUKKmwCI8vOnb6ywKJERERERERERaiPKJMNvSSIiaNNICJCIiIiIiIiIi9adAQkREREREREScToGEiIiIiIiIiDidAgkRERERERERcToFEiIiIiIiIiLidAokRERERERERMTpFEiIiIiIiIiIiNMpkBARERERERERp1MgISIiIiIiIiJOp0BCRERERERERJxOgYSIiIiIiIiIOJ0CCRERERERERFxOgUSIiIiIiIiIuJ0CiRERERERERExOkUSIiIiIiIiIiI0ymQEBERERERERGnUyAhIiIiIiIiIk6nQEJEREREREREnE6BhIiIiIiIiIg4nQIJEREREREREXE6BRIiIiIiIiIi4nSuzd0AaVqGYQBw+vTpZm6JiIiIiIiItAflv3+W/z5aEwUSbVxubi4AgYGBzdwSERERERERaU9yc3Pp1KlTjectRl2RhbRqNpuN1NRUfH19sVgszd2cGp0+fZrAwECSkpLo2LFjczdH5JypL0tbov4sbYX6srQl6s/SGhiGQW5uLr169cJqrXmmCI2QaOOsViu9e/du7mbUW8eOHfUXq7QJ6svSlqg/S1uhvixtifqztHS1jYwop0ktRURERERERMTpFEiIiIiIiIiIiNMpkJAWwcPDg4ULF+Lh4dHcTRE5L+rL0paoP0tbob4sbYn6s7QlmtRSRERERERERJxOIyRERERERERExOkUSIiIiIiIiIiI0ymQEBERERERERGnUyAhIiIiIiIiIk6nQEKazU8//cQdd9zBwIED6dSpEx07dmTgwIHcfvvtbNq0qbmbJ21MZmYmK1as4Mknn2Tu3Ln4+/tjsVjs27vvvnvOde/bt48///nPDB06FD8/P3x8fBgwYADXXnstK1euPOd6Y2Nj+etf/8qoUaPo3r07Xl5ehIWFcfnll/PZZ59RWlp6znVL65Sdnc3SpUtZsGABkydPpmfPnnh4eODj40NQUBBz5szhpZde4tSpU+dUv/qyOEtJSQlbtmzhxRdf5KabbmL8+PH06tULb29v3Nzc6Nq1K8OHD+fWW29l1apV2Gy2Bt9D/Vlagvj4eDp06ODwM8cTTzzRoDrUl6VNM0ScLC8vz7j55psNoNbtpptuMvLy8pq7udLKHT9+3AgODq6zv73zzjsNrrukpMR4+OGHDavVWmvdl156qZGRkdGgul966SXDw8Oj1novvPBC49ixYw1ut7Q+0dHRxuzZsw13d/c6+zJgeHt7Gy+++KJhs9nqVb/6sjjb/fffX6++XL4NHz7c2LlzZ73qVn+WlmTmzJlV+sjChQvrda36srQHCiTEqUpLS40ZM2Y4/GXn5eVljB492rjwwguNjh07OpybMWOGUVpa2tzNllYsLi6uXj/snksgcXaw5ubmZgwbNsyIjIw0unbt6nBu6NChRm5ubr3qffLJJx2utVqtxuDBg43Jkycb/v7+Dud69+5tpKamNrjt0rp8+umnVfqsi4uLMWDAAGPy5MlGZGSk4efnV6XMrbfeWq9QQn1ZnO2+++5z+O/foUMHY+jQocaUKVOMqVOnGuHh4VV+CfPx8TE2bNhQZ93qz9JS/O9//6v2Z476BhLqy9IeKJAQp3r44Ycd/pK77bbbjKysLPv5vLw84/HHH3co88gjjzRji6W1qxxIdO/e3bj44ouNxx57zFi2bNl5BRJvvvmmw/Vz5841kpOT7eeLi4uNV1991XB1dbWX+e1vf1tnvStXrjQsFov9mvHjxxsxMTH282VlZcZHH31k+Pj42MtERkY2qO3S+pQHEq6ursZll11mLFu2zMjJyXEoY7PZjGXLlhkBAQEOffP111+vtW71ZWkOjz32mDF79mzjP//5j3Ho0KFqy2RkZBiPPvqo4eLiYu8jgYGBtf7Spf4sLUVmZqbRrVs3AzAiIiKMXr16NSiQUF+W9kKBhDhNSkqK4enpaf/L7frrr6+x7GOPPWYv5+npaaSkpDixpdKW5OTkGJ9++qkRHx9f5dy5BhL5+flGz5497ddOnTq1xpE8b7/9tr2cxWIxduzYUWO9NpvNGDZsmL38gAEDjPz8/GrLrlmzxqH9X3zxRb3bL63PsmXLjFtvvdVISEios2xiYqJD/+zWrZtRXFxcbVn1ZWkN3nrrLYc+8n//93/VllN/lpbkuuuus/eDdevWObw+Wlcgob4s7YkCCXGav/zlL/a/1Ly9vR1GRpytqKjICAwMtJd/4IEHnNhSaS/ONZB47bXXHP7nf/DgwVrLjxs3zl7+qquuqrHcN99849CmlStX1lrv1VdfbS87duzYerdf2r6z/2Xtu+++q7ac+rK0FmFhYfY+8rvf/a7aMurP0lKsWrXK3gduuukmwzCMBgUS6svSnmiVDXGapUuX2vevuuoq/Pz8aizr7u7OTTfdZD/+4osvmrRtIg1RuT9OmTKFiIiIWsvfcccd9v1vv/2WoqKiOusNCQlhxowZ9a5369atJCcn11pe2o85c+Y4HB86dKjacurL0lqMHDnSvp+WllZtGfVnaQkKCgq48847AejWrRvPPfdcg+tQX5b2RIGEOEVMTAxHjx61H1988cV1XnPJJZfY948ePUpMTEyTtE2kIfLy8li/fr39uKF9OS8vjx9//LHact988419f+bMmVgsllrrnTRpEh06dKj2emnfzg58T58+XaWM+rK0JpWXH/T19a1yXv1ZWorHH3+cuLg4AP71r3/RtWvXBl2vviztjQIJcYo9e/Y4HI8fP77Oa0aOHIm7u7v9eO/evY3eLpGGOnjwICUlJfbj+vTlnj170qdPH/txdX05IyPD4V/96lOvq6srY8aMqbVeaZ8SEhIcjnv06FGljPqytBYlJSX8/PPP9uPq+pT6s7QEO3bs4OWXXwbMkQ033HBDg+tQX5b2RoGEOEV0dLR9393dncDAwDqvObtc5TpEmsvZ/TAsLKxe11UuV11fbqp6pX06+zW36n7wVF+W1uLRRx+1/yLl5+fHjTfeWKWM+rM0t9LSUm699VbKyspwd3fn3//+9znVo74s7Y1rczdA2of4+Hj7fu/evescIlYuKCiIY8eOValDpLlU7oeurq74+/vX67qgoKBq66jps8rlz6deaX9ycnLs/0IHMHToUAYOHFilnPqytFSlpaVkZmayZcsWXn/9ddasWQOAp6cnH374YbVD4NWfpbk9//zz7N69G4AHH3yQ8PDwc6pHfVnaGwUS4hS5ubn2/U6dOtX7uo4dO1Zbh0hzqdwPfX19sVrrN9Csrr589mf1/XOiPyNytvvuu89hWO5TTz1VbTn1ZWlJunXrRlZWVrXnLBYL06dP5/nnn2fw4MHVllF/luZ07Ngx/va3vwHQt29fHnnkkXOuS31Z2hu9siFOkZeXZ9/39PSs93VeXl7V1iHSXJqqL5/9WX3r1p8Rqeztt99m8eLF9uOrr766yoob5dSXpbWIjIzkzjvvrHakTzn1Z2lOd9xxB2fOnAHgjTfeaFAfPJv6srQ3GiEhTlF5dmxX1/p3u8plK0/wI9JcmqovV663IXXrz4iUW79+PXfffbf9OCQkhDfffLPG8urL0pJERUWRk5MDQFFREWlpaRw+fBibzcbGjRvZuHEjY8aM4eOPPyYkJKTK9erP0lzeeecd1q5dC8C1117LtGnTzqs+9WVpbxRIiFN4e3vb9wsLC+t9XeWylZcdEmkuTdWXK9dbXv7sz86lXmkfdu/ezdy5cykuLgbMVTVWrlxZ65Bc9WVpST7++OMqn508eZK3336bJ598kvz8fLZt28aUKVPYvn17lZVj1J+lOWRkZHD//fcD0KVLF1544YXzrlN9WdobvbIhTuHj42PfLx/SVh8FBQXV1iHSXJqqL5/9WX3r1p8RiYmJYebMmfZ/Xe7SpQurV6+mf//+tV6nviwtnZ+fHw888AAbNmzA19cXgKSkJO67774qZdWfpTksWLCAkydPAvDMM89Uu8RyQ6kvS3ujQEKcolu3bvb948eP1/u6yhOzVTertoizVe7LeXl59X6fsq6+XLleqP+fE/0Zad/i4uKYNm0aGRkZgDkB2ooVKxg2bFid16ovS2sxYsQIHn30UfvxRx99ZP8lsJz6szjbzz//bB/ZM378eG677bZGqVd9WdobBRLiFAMGDLDvZ2VlOaSttUlKSrLvn+vySSKNqXJfBkhMTKzXdXX15aaqV9qu5ORkoqKiSE5OBszhuF9//TXjxo2r1/Xqy9KazJ8/375fWlrKtm3bHM6rP4uzpaen2/d//vlnrFYrFoulxi0hIcFe/m9/+5vDucrLaaovS3ujQEKcIiIiwuG4fJ3m2qSkpJCZmVljHSLN4Vz6cklJCQcOHKixDoB+/fo5TBxVn3oBdu3aVWu90jalp6czbdo04uLiAPDw8GDZsmVMnjy53nWoL0trEhgY6HB89hKh6s/SVqgvS3ujQEKcYuzYsXh4eNiPN27cWOc1GzZssO97enoyduzYJmmbSEOEhobSu3dv+3F9+vKOHTscRgVV90uju7u7w79s16fetLQ0jh49Wmu90vZkZWUxbdo0YmJiAHBzc+Ozzz5j+vTpDapHfVlak/I5Usp17tzZ4Vj9WZzNzc2NTp061XuzWCz2az08PBzOWa0Vv5KpL0t7o0BCnMLHx4eoqCj78fvvv1/nNZXLREVFaWZfaTHmzp1r3//000/tKxvUpHJfHjRoEGFhYdWWmzdvnn3/u+++cxgOWle9nTt31g8K7UBOTg4zZ85k//79ALi4uPDBBx8we/bsc6pPfVlai/Xr1zscV9f31J/FmS699FKys7PrvQUFBdmvfeihh2o8B+rL0r4okBCnufHGG+37e/fu5auvvqqx7M6dO1mxYkW114o0t8r98cSJE7z55ps1lk1OTua9996r9tqzXXPNNfaRRCUlJTz77LM1ls3Ly+OVV16xH1977bW4ubnVo/XSWuXn53PppZeyY8cOAKxWK++9957Du/UNpb4srUFxcTFPPfWU/TgsLKzK+/Cg/ixth/qytCuGiJPYbDZj2LBhBmAAhr+/vxEdHV2lXGpqqhEREWEvN3z4cMNmszVDi6WtK+9jgPHOO+806Nq5c+far/Xx8TE2btxYpUxOTo4xadIke7mePXsaBQUFtda7YMECe3kXFxfjs88+q1KmuLjYmD9/vr2cl5eXkZKS0qD2S+tSWFhoTJs2zf7f3GKxGIsXL26UutWXxdlWr15t3H///fX6b52ammrMmDHD4e/rt956q8by6s/SUgUHB9v7xsKFC+ssr74s7YXFMAyjKYIOkeps27aNKVOm2Nc+7tixI3fddReTJ0/G1dWVrVu3smjRIvsQMi8vL9atW8eYMWOas9nSyt12223873//q/J5UVGRfd/V1RUXF5cqZQoLC6utMz4+njFjxnDixAnAfB/0lltuYcaMGfj4+LB3715effVV+6SDVquVZcuWMWfOnFrbeurUKcaNG8eRI0fs1/32t7/lsssuw8/Pj5iYGN544w327t1rv2bRokXcfffddXwXpDV79tlnefDBB+3HXbp0adC8OtOnT+e+++6r9pz6sjjbsmXLuPzyy7FarUyYMIFJkyYxZMgQunXrhre3N3l5ecTGxrJhwwaWL1/u8G783LlzWbZsmcP7+JWpP0tL1adPH/tKGwsXLuSJJ56otbz6srQbzZ2ISPvz+eefG15eXg7/2lHd5uXlZXz++efN3VxpA2644YY6+1tNW202bdpk+Pn51VmHi4uL8eqrr9a7vTExMUZgYGC92vfAAw+c77dHWoGFCxeecx8GjBtuuKHW+tWXxZmWLl16Tv34pptuMoqKiuqsX/1ZWqKGjpAwDPVlaR8USEizOHjwoDFt2jTDYrFU+YvPYrEYUVFRxoEDB5q7mdJGNFUgYRiGkZycbFxxxRWGq6trtdePGTPG2LRpU4PbfOrUKeOWW26pMbyLiIgwli9ffi7fDmmFmjqQMAz1ZXGepKQk489//rMxcODAan8OqLy5u7sbV1xxhbFu3boG3UP9WVqacwkkDEN9Wdo+vbIhzSopKYlNmzaRkpICQEBAAJGRkVXWGxdp6TIzM1m/fj3JyckUFxfTq1cvRo8eXe3Eaw2Rm5vL999/T1JSEvn5+fj7+zNkyBBGjBjRSC0XcaS+LM6UnZ3Nnj17iI2N5cSJExQVFdGhQwe6dOlCREQEw4YNw9PT85zrV3+WtkJ9WdoqBRIiIiIiIiIi4nRa9lNEREREREREnE6BhIiIiIiIiIg4nQIJEREREREREXE6BRIiIiIiIiIi4nQKJERERERERETE6RRIiIiIiIiIiIjTKZAQEREREREREadTICEiIiIiIiIiTqdAQkREREREREScToGEiIiIiIiIiDidAgkRERERERERcToFEiIiIiIiIiLidAokRERERERERMTpFEiIiIhIi/XPf/4Ti8Vi31auXNncTRIREZFGokBCREREWqy9e/c6HA8ZMqSZWiIiIiKNTYGEiIiItFiVAwk/Pz8CAgKasTUiIiLSmBRIiIiISItUUlJCTEyM/Xjo0KHN2BoRERFpbAokREREpEWKjo6mpKTEfqzXNURERNoWBRIiIiLSIp09f4RGSIiIiLQtCiRERESkRdq3b5/DsQIJERGRtkWBhIiIiLQY4eHh9iU+n332WYdz48aNc1gCtPL28MMPn/e99+/fj7u7u73OsLAwCgsLz7m+yZMnO7Txm2++Oe82ioiItCUKJERERKRFKCgo4MiRI+d07bBhw87r3jabjVtvvdVhzopXX30VT0/Pc65z7NixDsfr1q0757pERETaIgUSIiIi0iLs27cPm812TteebyDxyiuvsGXLFvvx5ZdfzqxZs86rTgUSIiIitbMYhmE0dyNERERECgoKyMjIAGDLli385je/sZ+77777uOeee2q8Njg4GIvFck73zcjIICwsjLy8PADc3NyIiYkhJCTknOorFx0dzcCBA+3Hbm5unDlzBhcXl/OqV0REpK1wbe4GiIiIiAB4e3vTp08fAJYuXepw7qKLLrKfa2xPPvmkPYwAuPnmm887jAAIDAx0OC4pKSEhIYHQ0NDzrltERKQt0CsbIiIi0uJs377d4XjMmDFNcp/Y2Fj+85//2I89PDx49NFHG6VuHx8fOnfu7PDZuc6RISIi0hYpkBAREZEWZ9u2bfb94OBgunfv3iT3ee655xwmsrz66qurjGw4H2cHEpmZmY1Wt4iISGunQEJERERalOzsbI4ePWo/PntyyMZy+vRplixZ4vDZ7bff3qj3OHtei+Li4katX0REpDVTICEiIiItyvbt26k853ZTva6xZMkSh7kjIiIiiIyMbJJ7lSsrK2vS+kVERFoTBRIiIiLSolR+XQOaLpBYvny5w/HcuXNrLW8YBuHh4fTu3ZvevXvzr3/9q857pKenOxz36NGj4Q0VERFpo7TKhoiIiLQolSe0tFqtjBo1qtHvUVhYyPr16x0+mz59eq3XHDp0iJiYGPtxXXNNZGZmUlBQ4PBZUFBQA1sqIiLSdmmEhIiIiLQolUdIDBgwAF9f30a/x86dOyksLLQfW61Wxo8fX+s1mzdvdjgeMmRIreUrhxcArq6u9O/fv4EtFRERabsUSIiIiEiLkZ6eTlJSkv24qSa0PDssCAkJwdvbu9ZrVq9eXeWa2mzcuNHheOTIkXTo0KHOtpWWlvLRRx9x7bXX0q9fPzp27EiHDh0YMGAAc+bM4a233tJqHSIi0ibolQ0RERFpMZw1f8TZgUTfvn1rLW+z2VizZo39uGvXrnh5edV6zQ8//OBwPHny5DrbtXbtWm655RYSEhKqnDt8+DCHDx/m66+/Ji4ujn/84x911iciItKSKZAQERGRFqPy/BEAo0ePbpL7nD3CoHv37rWWX7t2LVlZWfbjTp061Vo+JyenSiBx2WWX1XrN22+/zR133IHNZsPd3Z1rrrmGWbNmERISgs1m48iRI3zzzTd88cUXTTZyRERExJkUSIiIiEiLsXfvXvu+xWJh8ODBTXKfsyebdHd3r7X8e++916Dy77//PiUlJfbjkJCQWpcUXb58OXfeeSc2m43w8HC+/PJL+vXr51Bm3LhxXHfddRw7dowuXbrUen8REZHWQIGEiIiItBiV54/w9vau15wL58JisTgc5+Tk1Fg2Pj6ejz/+2OGzyqMlzlZaWsrLL7/s8Nltt91WY/kTJ05w0003UVZWRkBAAN999x0BAQE1lg8LC6vxnIiISGuiSS1FRESkxbBaK340yc/P58iRI01yn7OX7NyxY0eNZR944AFKS0uxWq0MGjQIMEOEmiaWfOGFFzh8+LD9uHv37tx777011v/Xv/6VU6dOAbB48eJawwgREZG2RIGEiIiItBjh4eEOx3PnzmXJkiXs3r2b+Ph4+1bdpI8NcfaSnfHx8SxfvrxKuVdeeYVPP/0UgHnz5hEcHAyAYRgsWbKkSvnly5fz6KOPOnz21FNP4ePjU207zpw5w3//+1/AnPRy5syZDX8YERGRVspiGIbR3I0QERERAdiwYUO9VqPo06cPcXFx53yf06dPc8EFF1BYWGj/zNfXl7///e9MnTqVvLw8Fi9ezOLFiwFzzojdu3fz+uuvs2jRIsB8peS5555j6tSpZGZmsmTJEhYvXkzlH62uvPJKPvnkkxrbsXz5cvtkl6+99hq///3vz/mZREREWhvNISEiIiItxqRJk3juued46KGHKCsrq7HcqFGjzus+HTt25KGHHuKJJ56wf5abm8uCBQuqLf/CCy8QERHBZZddZg8kCgoKuPvuu2u8x6xZs3j33Xdrbcfu3bvt+xMmTKh3+0VERNoCvbIhIiIiLcr999/P7t27+cMf/sDIkSPp3LkzLi4uDmXON5AAeOyxx7jhhhtqLePh4cGiRYvswUNUVBS/+93var3Gy8uLxx9/nOXLl+Pt7V1r2ePHj9v3e/bsWc+Wi4iItA16ZUNERETatZUrV/Luu++ydetW0tLSAPOVkBkzZnDPPffQt29fh/I2m43Fixfz7rvvcuDAAQoKCujevTuhoaFceumlXHvttVUmzazJjTfeaF9SNDExsd7XiYiItAUKJERERESaySOPPMLTTz8NwKeffsr8+fObuUUiIiLOo1c2RERERJrJ9OnT7ftPPvkkubm5NZZNSUnh5MmTzmiWiIiIU2iEhIiIiEgzmjJlCuvXrwcgNDSUe+65hzFjxuDj48PJkyfZt28fK1euZO3ataSlpeHn59fMLRYREWkcCiREREREmlFmZiazZ89m69attZYLDQ3l2LFjTmqViIhI01MgISIiItLMSktL+fDDD/nkk0/YuXMnJ06cwN3dHX9/f0JCQpg+fTpz5sxhwIABzd1UERGRRqNAQkREREREREScTpNaioiIiIiIiIjTKZAQEREREREREadTICEiIiIiIiIiTqdAQkREREREREScToGEiIiIiIiIiDidAgkRERERERERcToFEiIiIiIiIiLidAokRERERERERMTpFEiIiIiIiIiIiNMpkBARERERERERp1MgISIiIiIiIiJO9/8Bc9Gjx/fGPicAAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] @@ -1628,7 +1629,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "2ec29f78", "metadata": {}, "outputs": [ @@ -1646,7 +1647,7 @@ "Previous lead developers: Chris Granade & A. Grimsmo.\n", "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", "\n", - "QuTiP Version: 5.0.0.dev0+9557c82\n", + "QuTiP Version: 5.0.0.dev0+12d694b\n", "Numpy Version: 1.26.0\n", "Scipy Version: 1.11.3\n", "Cython Version: 3.0.3\n", @@ -1680,7 +1681,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "id": "69332bfe", "metadata": {}, "outputs": [], diff --git a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb index e8337970..18871983 100644 --- a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb +++ b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb @@ -317,16 +317,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "RHS construction time: 0.010007858276367188\n", - " [***** 20% ] Elapsed 0.65s / Remaining 00:00:00:02" + "RHS construction time: 0.006853342056274414\n", + " [****** 24% ] Elapsed 0.57s / Remaining 00:00:00:01" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " Total run time: 3.26s*] Elapsed 3.26s / Remaining 00:00:00:00\n", - "ODE solver time: 3.265465021133423\n" + " Total run time: 2.89s*] Elapsed 2.89s / Remaining 00:00:00:00\n", + "ODE solver time: 2.894904851913452\n" ] } ], @@ -382,16 +382,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "RHS construction time: 0.020837783813476562\n", - " [****** 24% ] Elapsed 0.80s / Remaining 00:00:00:02" + "RHS construction time: 0.016600847244262695\n", + " [ 0% ] Elapsed 0.00s / Remaining 00:00:00:00" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " Total run time: 3.59s*] Elapsed 3.59s / Remaining 00:00:00:00\n", - "ODE solver time: 3.596360683441162\n" + " Total run time: 3.04s*] Elapsed 3.04s / Remaining 00:00:00:00[*** 12% ] Elapsed 0.43s / Remaining 00:00:00:03\n", + "ODE solver time: 3.038472890853882\n" ] } ], @@ -453,7 +453,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "RHS construction time: 0.02041316032409668\n", + "RHS construction time: 0.013526439666748047\n", " [ 2% ] Elapsed 0.07s / Remaining 00:00:00:03" ] }, @@ -461,8 +461,8 @@ "name": "stdout", "output_type": "stream", "text": [ - " Total run time: 3.64s*] Elapsed 3.63s / Remaining 00:00:00:00\n", - "ODE solver time: 3.6367034912109375\n" + " Total run time: 3.15s*] Elapsed 3.15s / Remaining 00:00:00:00[*********73%***** ] Elapsed 2.27s / Remaining 00:00:00:00[*********79%****** ] Elapsed 2.47s / Remaining 00:00:00:00\n", + "ODE solver time: 3.149986505508423\n" ] } ], @@ -525,7 +525,7 @@ "metadata": {}, "outputs": [], "source": [ - "tfit=np.linspace(0,5,5000)\n", + "tfit=np.linspace(0,2,5000)\n", "lmaxmats = 15000\n", "cc=bath.correlation_function(tfit,Nk=lmaxmats)\n" ] @@ -553,17 +553,16 @@ "Fit correlation class instance: \n", " \n", "Result of fitting The Real Part Of |Result of fitting The Imaginary Part \n", - " the Correlation Function with 4 terms: | Of the Correlation Function with 1 terms: \n", + " the Correlation Function with 3 terms: | Of the Correlation Function with 1 terms: \n", " | \n", " Parameters| a | b | c | d | Parameters| a | b | c | d \n", - " 1 | 6.91e-02 |-5.43e+00 | 6.67e-07 |-9.63e-02 | 1 | 9.86e-02 |-5.00e-01 |-9.97e-07 |-5.00e-02 \n", - " 2 | 9.83e-02 |-6.75e+01 |-6.57e-07 |-8.97e-02 | \n", - " 3 | 9.48e-02 |-5.13e-01 | 1.44e-07 |-9.63e-02 |A normalized RMSE of 1.96e-14 was obtained for the The Imaginary Part \n", - " 4 | 1.00e-01 |-1.65e+03 |-9.81e-07 |-9.57e-02 | Of the Correlation Function \n", - " | \n", - "A normalized RMSE of 3.34e-05 was obtained for the The Real Part Of | \n", + " 1 | 1.00e-01 |-5.52e-01 | 9.23e-07 |-9.34e-02 | 1 | 9.86e-02 |-5.00e-01 |-9.97e-07 |-5.00e-02 \n", + " 2 | 7.98e-02 |-7.48e+00 | 5.85e-07 |-9.97e-02 | \n", + " 3 | 1.00e-01 |-1.19e+02 |-8.30e-07 |9.93e-02 |A normalized RMSE of 1.02e-14 was obtained for the The Imaginary Part \n", + " | Of the Correlation Function \n", + "A normalized RMSE of 8.26e-05 was obtained for the The Real Part Of | \n", " the Correlation Function | \n", - " The current fit took 1.287672 seconds | The current fit took 0.043288 seconds \n", + " The current fit took 0.553227 seconds | The current fit took 0.026110 seconds \n", "\n" ] } @@ -572,8 +571,9 @@ "lower=[-0.1,-np.inf,-1e-6,-0.1]\n", "upper=[0.1,0,1e-6,0.1]\n", "guesses=[0.09,-10,0,np.imag(cc[0])]\n", + "sigma=1e-2\n", "fc= CorrelationFitter(Q,T,tfit,cc)\n", - "fbath,fitinfo=fc.get_fit(Nr=4,Ni=1,lower=lower,upper=upper,guesses=guesses,sigma=1e-2)\n", + "fbath,fitinfo=fc.get_fit(Nr=3,Ni=1,lower=lower,upper=upper,guesses=guesses,sigma=sigma)\n", "print(fitinfo['summary'])" ] }, @@ -594,7 +594,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -629,7 +629,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -666,16 +666,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "RHS construction time: 0.02409219741821289\n", - " [ 0% ] Elapsed 0.07s / Remaining 00:00:00:00" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 65.48s*] Elapsed 65.48s / Remaining 00:00:00:00\n", - "ODE solver time: 65.47719120979309\n" + "RHS construction time: 0.0073528289794921875\n", + " Total run time: 3.75s*] Elapsed 3.75s / Remaining 00:00:00:00\n", + "ODE solver time: 3.749444007873535\n" ] } ], @@ -829,7 +822,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_106800/917460483.py:15: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n", + "/tmp/ipykernel_55362/917460483.py:15: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n", " If increasing the limit yields no improvement it is advised to analyze \n", " the integrand in order to determine the difficulties. If the position of a \n", " local difficulty can be determined (singularity, discontinuity) one will \n", @@ -876,7 +869,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -912,7 +905,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -967,7 +960,7 @@ "Previous lead developers: Chris Granade & A. Grimsmo.\n", "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", "\n", - "QuTiP Version: 5.0.0.dev0+9557c82\n", + "QuTiP Version: 5.0.0.dev0+12d694b\n", "Numpy Version: 1.26.0\n", "Scipy Version: 1.11.3\n", "Cython Version: 3.0.3\n", @@ -1022,6 +1015,7 @@ " expect(P12p, resultFit.states[:50]), np.real(P12_ana)[:50],\n", " rtol=1e-3,\n", ")\n", + "\n", "assert np.allclose(P12_ana, P12_ana2, rtol=1e-3)" ] } From 841122b496d1cc2fe8b0bae88170d24676c521ab Mon Sep 17 00:00:00 2001 From: mcditooss Date: Wed, 6 Nov 2024 21:52:56 +0100 Subject: [PATCH 12/44] The new environment class, refactoring of first 3 notebooks --- .../heom/heom-1a-spin-bath-model-basic.ipynb | 2061 +++++++++++++++++ ...spin-bath-model-very-strong-coupling.ipynb | 900 +++++++ ...om-1c-spin-bath-model-underdamped-sd.ipynb | 1018 ++++++++ ...eom-1d-spin-bath-model-ohmic-fitting.ipynb | 826 ++----- ...om-1e-spin-bath-model-pure-dephasing.ipynb | 382 +-- tutorials-v5/heom/heom-2-fmo-example.ipynb | 805 +++++++ .../heom/heom-3-quantum-heat-transport.ipynb | 803 +++++++ .../heom/heom-4-dynamical-decoupling.ipynb | 904 ++++++++ ...om-5a-fermions-single-impurity-model.ipynb | 828 +++++++ ...eom-5b-fermions-discrete-boson-model.ipynb | 528 +++++ tutorials-v5/heom/heom-index.ipynb | 56 + 11 files changed, 8212 insertions(+), 899 deletions(-) create mode 100644 tutorials-v5/heom/heom-1a-spin-bath-model-basic.ipynb create mode 100644 tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb create mode 100644 tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb create mode 100644 tutorials-v5/heom/heom-2-fmo-example.ipynb create mode 100644 tutorials-v5/heom/heom-3-quantum-heat-transport.ipynb create mode 100644 tutorials-v5/heom/heom-4-dynamical-decoupling.ipynb create mode 100644 tutorials-v5/heom/heom-5a-fermions-single-impurity-model.ipynb create mode 100644 tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.ipynb create mode 100644 tutorials-v5/heom/heom-index.ipynb diff --git a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.ipynb b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.ipynb new file mode 100644 index 00000000..6a76d09c --- /dev/null +++ b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.ipynb @@ -0,0 +1,2061 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "92158a9d", + "metadata": {}, + "source": [ + "# HEOM 1a: Spin-Bath model (introduction)" + ] + }, + { + "cell_type": "markdown", + "id": "fe8ddb3e", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The HEOM method solves the dynamics and steady state of a system and its\n", + "environment, the latter of which is encoded in a set of auxiliary density\n", + "matrices.\n", + "\n", + "In this example we show the evolution of a single two-level system in contact\n", + "with a single Bosonic environment. The properties of the system are encoded\n", + "in a Hamiltonian, and a coupling operator which describes how it is coupled\n", + "to the environment.\n", + "\n", + "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", + "\n", + "In the example below we show how to model the overdamped Drude-Lorentz\n", + "Spectral Density, commonly used with the HEOM. We show how to do this using\n", + "the Matsubara, Pade and fitting decompositions, and compare their\n", + "convergence.\n", + "\n", + "### Drude-Lorentz (overdamped) spectral density\n", + "\n", + "The Drude-Lorentz spectral density is:\n", + "\n", + "$$J_D(\\omega)= \\frac{2\\omega\\lambda\\gamma}{{\\gamma}^2 + \\omega^2}$$\n", + "\n", + "where $\\lambda$ scales the coupling strength, and $\\gamma$ is the cut-off\n", + "frequency. We use the convention,\n", + "\\begin{equation*}\n", + "C(t) = \\int_0^{\\infty} d\\omega \\frac{J_D(\\omega)}{\\pi}[\\coth(\\beta\\omega) \\\n", + " \\cos(\\omega \\tau) - i \\sin(\\omega \\tau)]\n", + "\\end{equation*}\n", + "\n", + "With the HEOM we must use an exponential decomposition:\n", + "\n", + "\\begin{equation*}\n", + "C(t)=\\sum_{k=0}^{k=\\infty} c_k e^{-\\nu_k t}\n", + "\\end{equation*}\n", + "\n", + "As an example, the Matsubara decomposition of the Drude-Lorentz spectral\n", + "density is given by:\n", + "\n", + "\\begin{equation*}\n", + " \\nu_k = \\begin{cases}\n", + " \\gamma & k = 0\\\\\n", + " {2 \\pi k} / {\\beta } & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "\\begin{equation*}\n", + " c_k = \\begin{cases}\n", + " \\lambda \\gamma (\\cot(\\beta \\gamma / 2) - i) \\\n", + " & k = 0\\\\\n", + " 4 \\lambda \\gamma \\nu_k / \\{(\\nu_k^2 - \\gamma^2)\\beta \\} \\\n", + " & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." + ] + }, + { + "cell_type": "markdown", + "id": "e9e16cf9", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "46b6f2dd", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "import contextlib\n", + "import time\n", + "\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "from scipy.optimize import curve_fit\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " basis,\n", + " brmesolve,\n", + " destroy,\n", + " expect,\n", + " liouvillian,\n", + " qeye,\n", + " sigmax,\n", + " sigmaz,\n", + " spost,\n", + " spre,\n", + " tensor,\n", + ")\n", + "from qutip.core.environment import (\n", + " DrudeLorentzEnvironment,\n", + " ExponentialBosonicEnvironment,\n", + " system_terminator\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver,\n", + " HSolverDL,\n", + ")\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "c00385b8", + "metadata": {}, + "source": [ + "## Helper functions\n", + "\n", + "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "87a25d2e", + "metadata": {}, + "outputs": [], + "source": [ + "def cot(x):\n", + " \"\"\"Vectorized cotangent of x.\"\"\"\n", + " return 1.0 / np.tan(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "aad8604f", + "metadata": {}, + "outputs": [], + "source": [ + "def dl_matsubara_params(lam, gamma, T, nk):\n", + " \"\"\"Calculation of the real and imaginary expansions of the Drude-Lorenz\n", + " correlation functions.\n", + " \"\"\"\n", + " ckAR = [lam * gamma * cot(gamma / (2 * T))]\n", + " ckAR.extend(\n", + " (8 * lam * gamma * T * np.pi * k * T /\n", + " ((2 * np.pi * k * T)**2 - gamma**2))\n", + " for k in range(1, nk + 1)\n", + " )\n", + " vkAR = [gamma]\n", + " vkAR.extend(2 * np.pi * k * T for k in range(1, nk + 1))\n", + "\n", + " ckAI = [lam * gamma * (-1.0)]\n", + " vkAI = [gamma]\n", + "\n", + " return ckAR, vkAR, ckAI, vkAI" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6512fe07", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_result_expectations(plots, axes=None):\n", + " \"\"\"Plot the expectation values of operators as functions of time.\n", + "\n", + " Each plot in plots consists of (solver_result, measurement_operation,\n", + " color, label).\n", + " \"\"\"\n", + " if axes is None:\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " fig_created = True\n", + " else:\n", + " fig = None\n", + " fig_created = False\n", + "\n", + " # add kw arguments to each plot if missing\n", + " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", + " for result, m_op, color, label, kw in plots:\n", + " exp = np.real(expect(result.states, m_op))\n", + " kw.setdefault(\"linewidth\", 2)\n", + " axes.plot(result.times, exp, color, label=label, **kw)\n", + "\n", + " if fig_created:\n", + " axes.legend(loc=0, fontsize=12)\n", + " axes.set_xlabel(\"t\", fontsize=28)\n", + "\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "78c51da3", + "metadata": {}, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\"Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a4dba89e", + "metadata": {}, + "outputs": [], + "source": [ + "# Default solver options:\n", + "\n", + "default_options = {\n", + " \"nsteps\": 1500,\n", + " \"store_states\": True,\n", + " \"rtol\": 1e-12,\n", + " \"atol\": 1e-12,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "996f8f86", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian, bath and system measurement operators:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5e5ddd12", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the system Hamiltonian\n", + "eps = 0.5 # Energy of the 2-level system.\n", + "Del = 1.0 # Tunnelling term\n", + "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2562dd97", + "metadata": {}, + "outputs": [], + "source": [ + "# Initial state of the system.\n", + "rho0 = basis(2, 0) * basis(2, 0).dag()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "14508538", + "metadata": {}, + "outputs": [], + "source": [ + "# System-bath coupling (Drude-Lorentz spectral density)\n", + "Q = sigmaz() # coupling operator\n", + "\n", + "# Bath properties:\n", + "gamma = 0.5 # cut off frequency\n", + "lam = 0.1 # coupling strength\n", + "T = 0.5\n", + "beta = 1.0 / T\n", + "\n", + "# HEOM parameters\n", + "NC = 5 # cut off parameter for the bath\n", + "Nk = 2 # terms in the Matsubara expansion of the correlation function\n", + "\n", + "# Times to solve for\n", + "tlist = np.linspace(0, 50, 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "47573ae2", + "metadata": {}, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresonding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresonding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "081c3008", + "metadata": {}, + "source": [ + "### First of all, it is useful to look at the spectral density\n", + "\n", + "Now we are ready to begin. Let's look at the shape of the spectral density given the bath parameters we defined above:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7ea119e6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_spectral_density():\n", + " \"\"\"Plot the Drude-Lorentz spectral density\"\"\"\n", + " w = np.linspace(0, 5, 1000)\n", + " J = w * 2 * lam * gamma / (gamma**2 + w**2)\n", + "\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " axes.plot(w, J, \"r\", linewidth=2)\n", + " axes.set_xlabel(r\"$\\omega$\", fontsize=28)\n", + " axes.set_ylabel(r\"J\", fontsize=28)\n", + "\n", + "\n", + "plot_spectral_density()" + ] + }, + { + "cell_type": "markdown", + "id": "660e3fc7", + "metadata": {}, + "source": [ + "Next we calculate the exponents using the Matsubara decompositions. Here we\n", + "split them into real and imaginary parts.\n", + "\n", + "The HEOM code will optimize these, and reduce the number of exponents when\n", + "real and imaginary parts have the same exponent. This is clearly the case\n", + "for the first term in the vkAI and vkAR lists." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "640975c2", + "metadata": {}, + "outputs": [], + "source": [ + "ckAR, vkAR, ckAI, vkAI = dl_matsubara_params(nk=Nk, lam=lam, gamma=gamma, T=T)" + ] + }, + { + "cell_type": "markdown", + "id": "9305b68b", + "metadata": {}, + "source": [ + "Having created the lists which specify the bath correlation functions, we\n", + "create an `ExponentialBosonicEnvironment` from them and pass the environment to the `HEOMSolver` class.\n", + "\n", + "The solver constructs the \"right hand side\" (RHS) determinining how the\n", + "system and auxiliary density operators evolve in time. This can then be used\n", + "to solve for dynamics or steady-state.\n", + "\n", + "Below we create the bath and solver and then solve for the dynamics by\n", + "calling `.run(rho0, tlist)`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "1132fb8e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.0045168399810791016\n", + " [ 0% ] Elapsed 0.00s / Remaining 00:00:00:00" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 2.07s*] Elapsed 2.07s / Remaining 00:00:00:00[*********99%***********] Elapsed 2.04s / Remaining 00:00:00:00\n", + "ODE solver time: 2.067253828048706\n" + ] + } + ], + "source": [ + "options = {**default_options}\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " env = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI)\n", + " HEOMMats = HEOMSolver(Hsys, (env,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMats = HEOMMats.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7f220aa4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (resultMats, P11p, \"b\", \"P11 Mats\"),\n", + " (resultMats, P12p, \"r\", \"P12 Mats\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "a5e26830", + "metadata": {}, + "source": [ + "In practice, one would not perform this laborious expansion for the\n", + "Drude-Lorentz correlation function, because QuTiP already has a class,\n", + "`DrudeLorentzBath`, that can construct this bath for you. Nevertheless,\n", + "knowing how to perform this expansion will allow you to construct your own\n", + "baths for other spectral densities.\n", + "\n", + "Below we show how to use this built-in functionality:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4b5a6c06", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.009504079818725586\n", + " [ 0% ] Elapsed 0.01s / Remaining 00:00:00:00" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 2.08s*] Elapsed 2.08s / Remaining 00:00:00:00[*********59%** ] Elapsed 1.22s / Remaining 00:00:00:00\n", + "ODE solver time: 2.0851950645446777\n" + ] + } + ], + "source": [ + "# Compare to built-in Drude-Lorentz bath:\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " dlenv = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T,Nk=100)\n", + " dlenv_approx=dlenv.approx_by_matsubara(Nk=Nk)\n", + " HEOM_dlbath = HEOMSolver(Hsys, (dlenv_approx,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " result_dlbath = HEOM_dlbath.run(rho0, tlist) # normal 115" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "fb58dfcb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (result_dlbath, P11p, \"b\", \"P11 (DrudeLorentzBath)\"),\n", + " (result_dlbath, P12p, \"r\", \"P12 (DrudeLorentzBath)\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "dbacfc84", + "metadata": {}, + "source": [ + "The `DrudeLorentzEnvironment` class also allows us to easily obtain the power spectrum, correlation function, and spectral density. The approximated Environment is a `BosonicEnvironment` where the effective power spectrum, correlation function, and spectral density from the approximation is also accessible. In the following plots, the solid lines are the exact expressions, and the dashed lines are based on our approximation of the correlation function with a finite number of exponents." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "9e2e1c45", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "w = np.linspace(-10, 20, 1000)\n", + "w2 = np.linspace(0, 20, 1000)\n", + "fig, axs = plt.subplots(2, 2)\n", + "\n", + "axs[0, 0].plot(w, dlenv.power_spectrum(w))\n", + "axs[0, 0].plot(w, dlenv_approx.power_spectrum(w), '--')\n", + "axs[0, 0].set(xlabel=r'$\\omega$', ylabel=r'$S(\\omega)$')\n", + "axs[0, 1].plot(w2, dlenv.spectral_density(w2))\n", + "axs[0, 1].plot(w2, dlenv_approx.spectral_density(w2), '--')\n", + "axs[0, 1].set(xlabel=r'$\\omega$', ylabel=r'$J(\\omega)$')\n", + "axs[1, 0].plot(w2, np.real(dlenv.correlation_function(w2)))\n", + "axs[1, 0].plot(w2, np.real(dlenv_approx.correlation_function(w2)), '--')\n", + "axs[1, 0].set(xlabel=r'$t$', ylabel=r'$C_{R}(t)$')\n", + "axs[1, 1].plot(w2, np.imag(dlenv.correlation_function(w2)))\n", + "axs[1, 1].plot(w2, np.imag(dlenv_approx.correlation_function(w2)), '--')\n", + "axs[1, 1].set(xlabel=r'$t$', ylabel=r'$C_{I}(t)$')\n", + "\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b5967613", + "metadata": {}, + "source": [ + "We also provide a legacy class, `HSolverDL`, which calculates the\n", + "Drude-Lorentz correlation functions automatically, to be backwards\n", + "compatible with the previous HEOM solver in QuTiP:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "2677dce6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.002721548080444336\n", + " Total run time: 2.08s*] Elapsed 2.08s / Remaining 00:00:00:00\n", + "ODE solver time: 2.083860158920288\n" + ] + } + ], + "source": [ + "# Compare to legacy class:\n", + "\n", + "# The legacy class performs the above collation of coefficients automatically,\n", + "# based upon the parameters for the Drude-Lorentz spectral density.\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " HEOMlegacy = HSolverDL(Hsys, Q, lam, T, NC, Nk, gamma, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultLegacy = HEOMlegacy.run(rho0, tlist) # normal 115" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "e15f2f54", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (resultLegacy, P11p, \"b\", \"P11 Legacy\"),\n", + " (resultLegacy, P12p, \"r\", \"P12 Legacy\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "fa3f616d", + "metadata": {}, + "source": [ + "## Ishizaki-Tanimura Terminator\n", + "\n", + "To speed up convergence (in terms of the number of exponents kept in the\n", + "Matsubara decomposition), We can treat the $Re[C(t=0)]$ component as a\n", + "delta-function distribution, and include it as Lindblad correction. This is\n", + "sometimes known as the Ishizaki-Tanimura Terminator.\n", + "\n", + "In more detail, given\n", + "\n", + "\\begin{equation*}\n", + "C(t)=\\sum_{k=0}^{\\infty} c_k e^{-\\nu_k t}\n", + "\\end{equation*}\n", + "\n", + "since $\\nu_k=\\frac{2 \\pi k}{\\beta }$, if $1/\\nu_k$ is much much smaller than\n", + "other important time-scales, we can approximate,\n", + "$ e^{-\\nu_k t} \\approx \\delta(t)/\\nu_k$, and $C(t)=\\sum_{k=N_k}^{\\infty}\n", + "\\frac{c_k}{\\nu_k} \\delta(t)$\n", + "\n", + "It is convenient to calculate the whole sum\n", + "$ C(t)=\\sum_{k=0}^{\\infty} \\frac{c_k}{\\nu_k} = 2 \\lambda / (\\beta \\gamma)- i\\lambda $\n", + ", and subtract off the contribution from the finite number of Matsubara terms\n", + "that are kept in the hierarchy, and treat the residual as a contribution in \n", + "Lindblad form.\n", + "\n", + "This is clearer if we plot the correlation function with a large number of\n", + "Matsubara terms. To create the plot, we use the utility function of the\n", + "`DrudeLorentzBath` class mentioned above." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "62f328f9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_correlation_expansion_divergence():\n", + " \"\"\"We plot the correlation function with a large number of Matsubara terms\n", + " to show that the real part is slowly diverging at t = 0.\n", + " \"\"\"\n", + " t = np.linspace(0, 2, 100)\n", + "\n", + " # correlation coefficients with 15k and 2 terms\n", + " corr_15k = dlenv.correlation_function(t)\n", + " corr_2 = dlenv_approx.correlation_function(t)\n", + "\n", + " fig, ax1 = plt.subplots(figsize=(12, 7))\n", + "\n", + " ax1.plot(\n", + " t, np.real(corr_2), color=\"b\", linewidth=3, label=rf\"Mats = {Nk} real\"\n", + " )\n", + " ax1.plot(\n", + " t, np.imag(corr_2), color=\"r\", linewidth=3, label=rf\"Mats = {Nk} imag\"\n", + " )\n", + " ax1.plot(\n", + " t, np.real(corr_15k), \"b--\", linewidth=3, label=r\"Mats = 15000 real\"\n", + " )\n", + " ax1.plot(\n", + " t, np.imag(corr_15k), \"r--\", linewidth=3, label=r\"Mats = 15000 imag\"\n", + " )\n", + "\n", + " ax1.set_xlabel(\"t\")\n", + " ax1.set_ylabel(r\"$C$\")\n", + " ax1.legend()\n", + "\n", + "plot_correlation_expansion_divergence()" + ] + }, + { + "cell_type": "markdown", + "id": "b66a8145", + "metadata": {}, + "source": [ + "Let us evaluate the result including this Ishizaki-Tanimura terminator:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "019fcbd4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.0070781707763671875\n", + " [ 1% ] Elapsed 0.01s / Remaining 00:00:00:01" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 2.26s*] Elapsed 2.26s / Remaining 00:00:00:00\n", + "ODE solver time: 2.259308338165283\n" + ] + } + ], + "source": [ + "# Run HEOM solver and include the Ishizaki-Tanimura terminator\n", + "\n", + "# Notes:\n", + "#\n", + "# * when using the built-in DrudeLorentzEnvironment the terminator (L_bnd) is\n", + "# available from bath.terminator().\n", + "# \n", + "# * in the legacy HSolverDL function the terminator is included automatically\n", + "# if the parameter bnd_cut_approx=True is used.\n", + "\n", + "op = -2 * spre(Q) * spost(Q.dag()) + spre(Q.dag() * Q) + spost(Q.dag() * Q)\n", + "\n", + "approx_factr = (2 * lam / (beta * gamma)) - 1j * lam\n", + "\n", + "approx_factr -= lam * gamma * (-1.0j + cot(gamma / (2 * T))) / gamma\n", + "for k in range(1, Nk + 1):\n", + " vk = 2 * np.pi * k * T\n", + "\n", + " approx_factr -= (4 * lam * gamma * T * vk / (vk**2 - gamma**2)) / vk\n", + "\n", + "L_bnd = -approx_factr * op\n", + "\n", + "Ltot = -1.0j * (spre(Hsys) - spost(Hsys)) + L_bnd\n", + "Ltot = liouvillian(Hsys) + L_bnd\n", + "\n", + "options = {**default_options, \"rtol\": 1e-14, \"atol\": 1e-14}\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI)\n", + " HEOMMatsT = HEOMSolver(Ltot, (bath,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMatsT = HEOMMatsT.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "f6721af4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (resultMatsT, P11p, \"b\", \"P11 Mats + Term\"),\n", + " (resultMatsT, P12p, \"r\", \"P12 Mats + Term\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "123ca99d", + "metadata": {}, + "source": [ + "Or using the built-in Drude-Lorentz environment we can write simply:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "82e8f41a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.007383823394775391\n", + " Total run time: 2.10s*] Elapsed 2.10s / Remaining 00:00:00:00\n", + "ODE solver time: 2.0973634719848633\n" + ] + } + ], + "source": [ + "options = {**default_options, \"rtol\": 1e-14, \"atol\": 1e-14}\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath,delta = dlenv.approx_by_matsubara(Nk=Nk,compute_delta=True)\n", + " Ltot = liouvillian(Hsys) + system_terminator(Q,delta)\n", + " HEOM_dlbath_T = HEOMSolver(Ltot, (bath,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " result_dlbath_T = HEOM_dlbath_T.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "c4a87dc6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (result_dlbath_T, P11p, \"b\", \"P11 Mats (DrudeLorentzBath + Term)\"),\n", + " (result_dlbath_T, P12p, \"r\", \"P12 Mats (DrudeLorentzBath + Term)\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "6a7ef2f8", + "metadata": {}, + "source": [ + "We can compare the solution obtained from the QuTiP Bloch-Redfield solver:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "8d002cce", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 1.75s*] Elapsed 1.75s / Remaining 00:00:00:00\n", + "ODE solver time: 1.7543323040008545\n" + ] + } + ], + "source": [ + "\n", + "options = {**default_options}\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultBR = brmesolve(\n", + " Hsys, rho0, tlist, a_ops=[[sigmaz(), dlenv]], options=options\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "96fe6f5f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (resultMats, P11p, \"b\", \"P11 Mats\"),\n", + " (resultMats, P12p, \"r\", \"P12 Mats\"),\n", + " (resultMatsT, P11p, \"b--\", \"P11 Mats + Term\"),\n", + " (resultMatsT, P12p, \"r--\", \"P12 Mats + Term\"),\n", + " (resultBR, P11p, \"g--\", \"P11 Bloch Redfield\"),\n", + " (resultBR, P12p, \"g--\", \"P12 Bloch Redfield\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "aaa12be7", + "metadata": {}, + "source": [ + "## Padé decomposition" + ] + }, + { + "cell_type": "markdown", + "id": "911d3acc", + "metadata": {}, + "source": [ + "The Matsubara decomposition is not the only option. We can also use the\n", + "faster-converging Pade decomposition." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "470eed60", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def deltafun(j, k):\n", + " if j == k:\n", + " return 1.0\n", + " else:\n", + " return 0.0\n", + "\n", + "\n", + "def pade_eps(lmax):\n", + " Alpha = np.zeros((2 * lmax, 2 * lmax))\n", + " for j in range(2 * lmax):\n", + " for k in range(2 * lmax):\n", + " # Fermionic (see other example notebooks):\n", + " # Alpha[j][k] = (deltafun(j, k+1) + deltafun(j, k-1))\n", + " # / sqrt((2 * (j + 1) - 1) * (2 * (k + 1) - 1))\n", + " # Bosonic:\n", + " Alpha[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1)) / np.sqrt(\n", + " (2 * (j + 1) + 1) * (2 * (k + 1) + 1)\n", + " )\n", + "\n", + " eigvalsA = np.linalg.eigvalsh(Alpha)\n", + " eps = [-2 / val for val in eigvalsA[0:lmax]]\n", + " return eps\n", + "\n", + "\n", + "def pade_chi(lmax):\n", + " AlphaP = np.zeros((2 * lmax - 1, 2 * lmax - 1))\n", + " for j in range(2 * lmax - 1):\n", + " for k in range(2 * lmax - 1):\n", + " # Fermionic:\n", + " # AlphaP[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1))\n", + " # / sqrt((2 * (j + 1) + 1) * (2 * (k + 1) + 1))\n", + " # Bosonic [this is +3 because +1 (bose) + 2*(+1) (from bm+1)]:\n", + " AlphaP[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1)) / np.sqrt(\n", + " (2 * (j + 1) + 3) * (2 * (k + 1) + 3)\n", + " )\n", + "\n", + " eigvalsAP = np.linalg.eigvalsh(AlphaP)\n", + " chi = [-2 / val for val in eigvalsAP[0:lmax - 1]]\n", + " return chi\n", + "\n", + "\n", + "def pade_kappa_epsilon(lmax):\n", + " eps = pade_eps(lmax)\n", + " chi = pade_chi(lmax)\n", + "\n", + " kappa = [0]\n", + " prefactor = 0.5 * lmax * (2 * (lmax + 1) + 1)\n", + "\n", + " for j in range(lmax):\n", + " term = prefactor\n", + " for k in range(lmax - 1):\n", + " term *= (chi[k] ** 2 - eps[j] ** 2) / (\n", + " eps[k] ** 2 - eps[j] ** 2 + deltafun(j, k)\n", + " )\n", + "\n", + " for k in range(lmax - 1, lmax):\n", + " term /= eps[k] ** 2 - eps[j] ** 2 + deltafun(j, k)\n", + "\n", + " kappa.append(term)\n", + "\n", + " epsilon = [0] + eps\n", + "\n", + " return kappa, epsilon\n", + "\n", + "\n", + "def pade_corr(tlist, lmax):\n", + " kappa, epsilon = pade_kappa_epsilon(lmax)\n", + "\n", + " eta_list = [lam * gamma * (cot(gamma * beta / 2.0) - 1.0j)]\n", + " gamma_list = [gamma]\n", + "\n", + " if lmax > 0:\n", + " for ll in range(1, lmax + 1):\n", + " eta_list.append(\n", + " (kappa[ll] / beta)\n", + " * 4\n", + " * lam\n", + " * gamma\n", + " * (epsilon[ll] / beta)\n", + " / ((epsilon[ll] ** 2 / beta**2) - gamma**2)\n", + " )\n", + " gamma_list.append(epsilon[ll] / beta)\n", + "\n", + " c_tot = []\n", + " for t in tlist:\n", + " c_tot.append(\n", + " sum(\n", + " [\n", + " eta_list[ll] * np.exp(-gamma_list[ll] * t)\n", + " for ll in range(lmax + 1)\n", + " ]\n", + " )\n", + " )\n", + " return c_tot, eta_list, gamma_list\n", + "\n", + "\n", + "tlist_corr = np.linspace(0, 2, 100)\n", + "cppLP, etapLP, gampLP = pade_corr(tlist_corr, 2)\n", + "corr_15k = dlenv.correlation_function(tlist_corr, Nk=15_000)\n", + "corr_2k = dlenv.correlation_function(tlist_corr, Nk=2)\n", + "\n", + "fig, ax1 = plt.subplots(figsize=(12, 7))\n", + "ax1.plot(\n", + " tlist_corr,\n", + " np.real(cppLP),\n", + " color=\"b\",\n", + " linewidth=3,\n", + " label=r\"real pade 2 terms\",\n", + ")\n", + "ax1.plot(\n", + " tlist_corr,\n", + " np.real(corr_15k),\n", + " \"r--\",\n", + " linewidth=3,\n", + " label=r\"real mats 15000 terms\",\n", + ")\n", + "ax1.plot(\n", + " tlist_corr,\n", + " np.real(corr_2k),\n", + " \"g--\",\n", + " linewidth=3,\n", + " label=r\"real mats 2 terms\",\n", + ")\n", + "\n", + "ax1.set_xlabel(\"t\")\n", + "ax1.set_ylabel(r\"$C_{R}(t)$\")\n", + "ax1.legend()\n", + "\n", + "fig, ax1 = plt.subplots(figsize=(12, 7))\n", + "\n", + "ax1.plot(\n", + " tlist_corr,\n", + " np.real(cppLP) - np.real(corr_15k),\n", + " color=\"b\",\n", + " linewidth=3,\n", + " label=r\"pade error\",\n", + ")\n", + "ax1.plot(\n", + " tlist_corr,\n", + " np.real(corr_2k) - np.real(corr_15k),\n", + " \"r--\",\n", + " linewidth=3,\n", + " label=r\"mats error\",\n", + ")\n", + "\n", + "ax1.set_xlabel(\"t\")\n", + "ax1.set_ylabel(r\"Error\")\n", + "ax1.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "e44fe78d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.006168365478515625\n", + " Total run time: 2.10s*] Elapsed 2.10s / Remaining 00:00:00:00\n", + "ODE solver time: 2.1024391651153564\n" + ] + } + ], + "source": [ + "# put pade parameters in lists for heom solver\n", + "ckAR = [np.real(eta) + 0j for eta in etapLP]\n", + "ckAI = [np.imag(etapLP[0]) + 0j]\n", + "vkAR = [gam + 0j for gam in gampLP]\n", + "vkAI = [gampLP[0] + 0j]\n", + "\n", + "options = {**default_options, \"rtol\": 1e-14, \"atol\": 1e-14}\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI)\n", + " HEOMPade = HEOMSolver(Hsys, (bath,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultPade = HEOMPade.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "2b539e1f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (resultMats, P11p, \"b\", \"P11 Mats\"),\n", + " (resultMats, P12p, \"r\", \"P12 Mats\"),\n", + " (resultMatsT, P11p, \"y\", \"P11 Mats + Term\"),\n", + " (resultMatsT, P12p, \"g\", \"P12 Mats + Term\"),\n", + " (resultPade, P11p, \"b--\", \"P11 Pade\"),\n", + " (resultPade, P12p, \"r--\", \"P12 Pade\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "59fdf064", + "metadata": {}, + "source": [ + "The Padé decomposition of the Drude-Lorentz bath is also available via a\n", + "built-in class, `DrudeLorentzEnvironment` bath. Similarly to the terminator\n", + "section when approximating by Padé one can calculate the terminator easily by\n", + "requesting the approximation function to compute delta\n", + "\n", + "Below we show how to use the built-in Drude-Lorentz Environment to obtain a\n", + "Padé decomposition approximation and its terminator (although the terminator \n", + "does not provide much improvement here,because the Padé expansion already fits \n", + "the correlation function well):" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "d475f81a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.0066945552825927734\n", + " Total run time: 2.12s*] Elapsed 2.12s / Remaining 00:00:00:00\n", + "ODE solver time: 2.12541127204895\n" + ] + } + ], + "source": [ + "options = {**default_options, \"rtol\": 1e-14, \"atol\": 1e-14}\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " env_approx,delta = dlenv.approx_by_pade(Nk=2,compute_delta=True)\n", + " Ltot = liouvillian(Hsys) + system_terminator(Q,delta)\n", + " HEOM_dlpbath_T = HEOMSolver(Ltot, (env_approx,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " result_dlpbath_T = HEOM_dlpbath_T.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "b0b7b87f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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KTEx0fVQaKOecc44++OAD1zRBycnJHgNvxo0bp5deekkPPPCAunfvrkqVKikkJEQjR47UiRMnXMcdOnTI4yNap6Qzkv2+ffskmZHOBTl8+LBiYmLyfc55zaioqKK/yf9x/sdbs2ZNj/2luQeHDh2SlPd9OvflHoG+b98+HTlypMAZHoozNZPzuvnVvWbNmnlCRn7H7du3T1u2bClwEN2Z9alSpUqeYyIjIz1+DgqTkZGh/v37KzMzUwsXLnT1Rc0tISHBNQhRkjp37qzmzZurU6dOevbZZzV16lRJ5g+hb7/9Vo8++qjOP/98xcfHu8JYcepUmvf16quvevTLXr16tUaNGqXPPvvM43ueu0tGcR06dEhZWVmaPn26pk+fnu8xZ96rgn6mo6Oj8/zbiYiIUOXKlfMcGxERoZMnT+bZ73x9cb7HpeHTsHrs2DG1adNGI0aMKDDh57Zt2zb1799ft9xyi9599139+OOPuuOOO1StWrUivR4AgkGzZs08/iMOtKioqLPWx9m/dMqUKR77Dx48qIoVK7oeV6lSJd+WljP3Of/Tnj59eoEjnc/Wyux8/eHDhws8piCfffaZHA6HLrzwQo/9+a1j7vwP+dSpUx77CwpwRX3vVapU0Zdffplv/XK3WhbGed3U1FTVrl3b47k9e/bkCUf5vceqVauqQoUKmjlzZr7XKE3Ayk9mZqauuuoqbd26VcuWLctT77Np3bq1JOmXX36RJKWlpWn+/PmaMGGCHnzwQddxp06dKtHPRkmdOcjv6NGjkqRWrVp5bV7USpUqKTQ0VDfccIPuvPPOfI858w/F/O63tzi/v97++SiIT8Nqv3791K9fvyIfP2PGDNWtW1fTpk2TZH6hr1q1Sv/+979tG1ZzcszmhdkgAMCWHA6HIiMjPfZ98cUX+uuvv9SwYUPXvp49e+rpp5/WL7/84tEV4Mzppbp06aKKFStq48aN+Q6+KkyzZs0kmUFgxfHmm29q4cKFGjp0qOrWrVvo8c6g8euvv6pPnz6u/Z999pnHcU2aNFGNGjU0e/ZsjRs3zhUSduzYoeXLl3u04g4cOFAffPCBsrOzdcEFFxSr/mdyfqT/7rvv6vzzz3ftT0lJ0aZNm/Twww8Xeo6BAwdqypQpqlKlSr6t4iXh/FnJr9Xt5ptv1pIlS7Rw4UJX+CyqdevWSXJ3/3A4HLIsK8/P5uuvv67s7Owi1ykYREdHq2fPnlq7dq1at2591rmX/eHPP/9UlSpV/NZ1yVYRa8WKFerdu7fHvj59+uiNN95QZmamT+f6K6nPP5fGjJHuu08aNUrywSwyAMqhpUuXukajZ2dna8eOHfroo48kSd27d3fNHHD8+HEtWLBAklx91pYuXaqDBw8qJiamWA0GBRk4cKBmzZqlpk2bqnXr1lq9erWeeeaZPK1iY8aM0cyZMzVgwABNnjzZNRvA5s2bPY6LjY3V9OnTNXz4cB0+fFiDBw92jb7/5ZdfdODAAb3yyisF1qd27dpq0KCBVq5cqbvvvjvP8ydOnHB9L06cOKE///xT8+bN0/z589W9e3fNmDGjSO87KSlJF198saZOnapKlSqpXr16+vbbb/XJJ594HBcSEqInnnhCI0eO1BVXXKFbbrlFR44c0cSJE/N0Dbj22mv13nvvqX///vrnP/+pDh06KDw8XLt379bixYt12WWX6YorrnAdn56e7rrvuVWrVk3du3fXrbfequnTpyskJET9+vVzzQZQp04djR07ttD3OGbMGH388ce68MILNXbsWLVu3Vo5OTnauXOnvv76a91zzz3FDtWtWrWSJD3//PMaPny4wsPD1aRJE82YMUPvvPOO7rrrLsXExHj0sYyPj1fz5s1dj48cOeJ6PjMzU5s2bdKUKVMUGRnpalmMj4/XhRdeqGeeeUZVq1ZV/fr1tXTpUr3xxhseLf6SXCtUvfbaa4qLi3N1d8nv43+7ev7559W1a1d169ZNt99+u+rXr6+MjAxt2bJFn3/+ub777ju/1WXlypXq3r27T1tvPRQ6BMtLJFlz58496zGNGjWy/vWvf3ns+/HHHy1J1p49e/J9zcmTJ620tDTXtmvXLr/NBpCTY1kdO7pHB48e7fNLAmVeYSNIg5lzNoAzR17nxzlCOb8t94hm58j0/Lbco9DPdp2zjTi3LMv6+++/rZtvvtmqXr26FR0dbXXt2tVatmyZ1b17d6t79+4ex27cuNG65JJLrKioKKty5crWzTffbH366af5jsReunSpNWDAAKty5cpWeHi4VatWLWvAgAHWhx9+WGi9H330UatSpUrWyZMn87yf3N+DmJgYq0GDBtbgwYOtDz/8MN8R2Tpj1HVuqamp1uDBg63KlStbCQkJ1vXXX2+tWrXKYzYAp9dff91q1KiRFRERYTVu3NiaOXOmNXz48Dz3ITMz0/r3v/9ttWnTxoqKirJiY2Otpk2bWrfddpv1xx9/FPhecm/O73t2drb11FNPWY0bN7bCw8OtqlWrWtdff721a9euPN+Xgu7z0aNHrUceecRq0qSJFRERYSUkJFitWrWyxo4d6zFDRUHfp3r16uWZrWL8+PFWzZo1rZCQENe9d45EP9v7cZ4v93OhoaFW3bp1rcGDB1tr1671uM7u3butq666yqpUqZIVFxdn9e3b1/rvf/+bb52mTZtmJScnW6GhoR73r6DvTX73riico/ELmw2hMPl9v7dt22bddNNNVq1atazw8HCrWrVqVufOna3JkyfnuX5+/46GDx9uxcTE5Nlf0PegXr161oABAzz2bdmyJd8ZLc7kzdkAHJZVwNBIL3M4HIUOsGrcuLFGjBjhscTfjz/+qK5duyo1NTXfzusTJ07U448/nmd/Wlqa4uPjvVL3gmRkSNdcI+XuevTLL1IxP9kAkMvJkye1bds2JScnl2gADcqHPXv2KDk5WW+//baGDBkS6OoA5cajjz6qt99+2zUXc0EK+12enp6uhISEIuU1W31onZSUlKcz+v79+xUWFlZgU/348eOVlpbm2nbt2uWPqkqS4uKkhQulf/3Lve+MhSQAAD5Qs2ZNjRkzRv/6179st8oQUFYdOXJEL730kqZMmeKVpVuLylZhtVOnTlq0aJHHvq+//lrt27cvsL9qZGSk4uPjPTZ/u+suyTnDyiefSGf06wYA+MAjjzyiq666Sn/99VegqwKUC9u2bdP48ePzzJ3saz4Nq0ePHtW6detcI/i2bdumdevWaefOnZJMq2juZcxGjRqlHTt2aNy4cdq0aZNmzpypN954Q/fee68vq1lqcXFS376mfPCgdMa8vAAAH4iLi9OECRNUp06dQFcFKBfOPfdc3Xffff4bWPU/Pg2rq1at0rnnnutaEWHcuHE699xz9dhjj0kyc8M5g6tk5ghbsGCBlixZorZt2+qJJ57QCy+8YNtpq3LLtRiEirEACQAAAM7CbwOs/KU4HXa9aetWyTnd4CWXSF9/7bdLA2UKA6wAIPiV2QFWwaxBA8m5stnPP5vJrAAAAFA6hFUvcTiktm1NOS1NytW7AQAAACVEWPWiXKsL6n9LFwMAAKAUCKteRFgFAADwLsKqFxFWAQAAvIuw6kWNGknOAW+EVQAouq+++krh4eGaN29eoKsCwGYIq14UFia1aGHKW7dKJ04Etj4A7GXWrFlyOByuLSwsTLVr19aIESPyrML0yCOPaODAgapVq5YcDoduvPHGfM/5+uuv6/LLL1f9+vVVoUIFNWzYULfffrtSU1OLVKcePXp41KlChQpq06aNpk2b5rVlTJcsWSKHw6ElBUxCvXv3bl1//fV6/vnndfnllxf5vMuWLVNkZKR27Njh2pf7/YSEhCguLk4NGzbU1VdfrY8++sjvS7PeeOONql+/fole26NHD7Vs2dK7FfKD5cuXa+LEiTpy5EiJXl+/fn2Pn8moqCg1bNhQ48aN08GDB0t0zo0bN2rixInavn17nufs8H0+83dDQVtJf5a85dtvv1VsbKzfV40jrHpZkybmq2VJ27YFti4A7OnNN9/UihUrtGjRIt1yyy2aPXu2unXrpmPHjrmO+b//+z8dOnRIl156qSIiIgo814QJExQbG6spU6boyy+/1P3336/58+erXbt22rdvX5Hq06BBA61YsUIrVqzQnDlzVKtWLY0dO1bjx48v9XstTFZWloYMGaJbb71Vd9xxR5FfZ1mWxowZo1tuuUX16tXzeM75fpYvX6558+bpwQcf1IkTJ3T11VerR48eSktL8/bbQC7Lly/X448/XuKwKkldunRx/UwuXLhQt912m1599VX1dS4XWUwbN27U448/nm9YtYMBAwa43q9zk6TBgwd77Js7d25A69mrVy916NBBDz30kF+vG+bXq5UDzoUBJNO62rx54OoCwJ5atmyp9u3bS5J69uyp7OxsPfHEE5o3b56uu+46SVJGRoZCQkx7wjvvvFPgudauXavq1au7Hnfv3l3nnXeezj//fP3nP//RI488Umh9KlSooI4dO7oe9+vXT02bNtWLL76oyZMnKzw8vETvsyjCwsL0448/Fvt1X375pdasWaP3338/z3Nnvh9JGjlypN58803ddNNNuvXWWzVnzpwCz21Zlk6ePKkKFSoUu17B5Pjx44qOjg50NfJVsWJFj3vYs2dPZWRk6IknntDvv/+uxo0bB7B2Z7dkyRL17NlT27ZtK3JLaLVq1VStWrU8+xMTE/P8LJdEZmam69Oc0rrzzjs1ZMgQTZ482W9LHdOy6mW5w+qWLYGrB4Dg4fzPKPfH2c6gWpjcQdWpXbt2Cg0N1a5du0pUn/DwcLVr107Hjx/XgQMHtGXLFo0YMUKNGjVSdHS0atWqpUGDBmn9+vV5Xrt582b17dtX0dHRqlq1qkaNGqWMjIx8r/PNN9+oV69eio+PV3R0tLp06aJvv/22SHV85ZVXdP7556uJ8+OsIhgxYoT69++vDz/80ON77XA4NHr0aM2YMUPNmjVTZGSk3nrrrQK7L2zfvl0Oh0OzZs3y2D9r1iw1adJEkZGRatasmd5+++1863H69GlNnjxZTZs2VWRkpKpVq6YRI0bowIEDRX4vTjk5OXr66add56pevbqGDRum3bt3exzn/Kj7+++/V+fOnRUdHa2bbrpJkllJ6N5771VycrIiIiJUq1YtjRkzxqOlP/f36Z133lGzZs0UHR2tNm3aaP78+a5jJk6cqPvuu0+SWULd+fH1kiVLzvpRd48ePQp9rwkJCZLk8cfTqlWrdO2117q6wdSvX1//+Mc/PO7vrFmzdPXVV0syodd5zTPvX0pKirp166bo6Gg1aNBATz75pN+7jRTmjz/+0NChQ1W9enXXz9lLL73kcYzz5/add97RPffco1q1aikyMlJbtmzRjTfeqNjYWG3evFl9+vRRTEyMatSooSeffFKStHLlSnXt2lUxMTFq3Lix3nrrrTx1GDRokGJjY/Wf//zHL+9ZomXV6wirgI+0by/t3RvoWrglJUmrVnnlVFv+98siv5aVkli6dKmys7PVwtmJvgS2bt2qsLAwVapUSVu2bFGVKlX05JNPqlq1ajp8+LDeeustXXDBBVq7dq0rMO7bt0/du3dXeHi4Xn75ZSUmJuq9997T6NGj85z/3Xff1bBhw3TZZZfprbfeUnh4uF599VX16dNHX331lXr16lVg3U6fPq1vvvlGd911V7Hf16WXXqoFCxZo2bJlHt0H5s2bp2XLlumxxx5TUlKSqlevXqzwOGvWLI0YMUKXXXaZnn32WaWlpWnixIk6deqUxx8eOTk5uuyyy7Rs2TLdf//96ty5s3bs2KEJEyaoR48eWrVqVbFadG+//Xa99tprGj16tAYOHKjt27fr0Ucf1ZIlS7RmzRpVrVrVdWxqaqquv/563X///ZoyZYpCQkJ0/Phxde/eXbt379ZDDz2k1q1ba8OGDXrssce0fv16ffPNN3I4HK5zfPHFF0pJSdGkSZMUGxurp59+WldccYV+++03NWjQQCNHjtThw4c1ffp0ffLJJ6rxv6UdmzdvrhYtWrg+3nZasWKFxo0bl+dn1bIsZWVlSTLLdqakpGjatGnq0qWLkpOTXcdt375dTZo00bXXXqvKlSsrNTXV9YfMxo0bVbVqVQ0YMEBTpkzRQw89pJdeeknnnXeeJOmcc85xnWfv3r267rrrdM8992jChAmaO3euxo8fr5o1a2rYsGFFvh++tHHjRnXu3Fl169bVs88+q6SkJH311Ve6++67dfDgQU2YMMHj+PHjx6tTp06aMWOGQkJCXH/YZmZm6sorr9SoUaN033336f3339f48eOVnp6ujz/+WA888IBq166t6dOn68Ybb1TLli3Vrl0713kjIiLUuXNnffHFF5o0aZJ/3rxVxqSlpVmSrLS0tIBcf/9+yzI9Vi2rd++AVAEIaidOnLA2btxonThxwvOJWrXc/7jssNWqVez39uabb1qSrJUrV1qZmZlWRkaGNX/+fKtatWpWXFyctXfv3nxfFxMTYw0fPrxI10hPT7eaNWtm1alTx8rIyCj0+O7du1stWrSwMjMzrczMTGvPnj3Wgw8+aEmyrr766nxfk5WVZZ0+fdpq1KiRNXbsWNf+Bx54wHI4HNa6des8jr/kkkssSdbixYsty7KsY8eOWZUrV7YGDRrkcVx2drbVpk0bq0OHDmet808//WRJsj744IMC309BFi5caEmynnrqKdc+SVZCQoJ1+PBhj2MXL17sUW+nbdu2WZKsN99801XvmjVrWuedd56Vk5PjOm779u1WeHi4Va9ePde+2bNnW5Ksjz/+2OOcKSkpliTr5ZdfLvJ72bRpkyXJuuOOOzz2O78/Dz30kMe5JFnffvutx7FTp061QkJCrJSUFI/9H330kSXJWrBggWufJCsxMdFKT0937du7d68VEhJiTZ061bXvmWeesSRZ27ZtK7DulmVZmzdvtqpUqWL17NnTOnXqlGt/vXr1LEl5tg4dOlipqalnPWdWVpZ19OhRKyYmxnr++edd+z/88MN872Xu781PP/3ksb958+ZWnz59zno95zWd/34yMzOtb775xpJkbdmyxWN/dnZ2oefKTZJ15513uh736dPHql27dp58M3r0aCsqKsr18+v8ub3wwgvznHP48OF5fv4yMzOtatWqWZKsNWvWuPYfOnTICg0NtcaNG5fnPA8//LAVEhJiHT16tMD6F/i7/H+Kk9foBuBlVatK8fGmTMsq4EVJSVKtWvbZkpJK/FY6duyo8PBwxcXFaeDAgUpKStLChQuVmJhYqm/RyZMndeWVV2rHjh368MMPFRsbW6TXbdiwQeHh4QoPD1fNmjX17LPP6rrrrnN9zJeVlaUpU6aoefPmioiIUFhYmCIiIvTHH39o06ZNrvMsXrxYLVq0UJvck05LGjp0qMfj5cuX6/Dhwxo+fLiysrJcW05Ojvr27auUlJQ8H0HntmfPHkn5d4EojGVZ+e6/6KKLVKlSpWKfT5J+++037dmzR0OHDvVohaxXr546d+7scez8+fNVsWJFDRo0yOO9t23bVklJSQXOmJCfxYsXS1KemSI6dOigZs2a5elSUalSJV100UV56tOyZUu1bdvWoz59+vTJtwtEz549FRcX53qcmJio6tWre3zsXhR79+5V3759VaNGDc2dOzfPIMKuXbsqJSVFKSkp+vHHH/XGG2/owIEDuuiiizxmBDh69KgeeOABNWzYUGFhYQoLC1NsbKyOHTvm8bNZmKSkJHXo0MFjX+vWrYv0vnr16uX69xMeHq6LL75YktSwYUOP/c5uFyVx8uRJffvtt7riiisUHR3tca/69++vkydPauXKlR6vueqqq/I9l8PhUP/+/V2Pw8LC1LBhQ9WoUUPnnnuua3/lypULvLfVq1dXTk6O9vrp0y66AXiZw2G6AqxZI23fLp0+LZ1lIC+AovLSR+528Pbbb6tZs2YKCwtTYmKi66PS0jh16pSuuOIK/fDDD5o/f74uuOCCIr/2nHPO0QcffOCaJig5Odlj4M24ceP00ksv6YEHHlD37t1VqVIlhYSEaOTIkTqRa46+Q4cOeXxE65R0RrB3zlIwePDgAut0+PBhxcTE5Puc85pRzomti8H5H2/NmjU99pfmHhw6dEhS3vfp3Jd7BPq+fft05MiRAmd4KM7UTM7r5lf3mjVr5gkZ+R23b98+bdmypcBBdGfWp0qVKnmOiYyM9Pg5KExGRob69++vzMxMLVy40NUXNbeEhATXIERJ6ty5s5o3b65OnTrp2Wef1dSpUyWZP4S+/fZbPfroozr//PMVHx/vCmPFqVNp3terr77q0S979erVGjVqlD777DOP73nuLhnFdejQIWVlZWn69OmaPn16vsecea8K+pmOjo7O828nIiJClStXznNsRESETp48mWe/8/XF+R6XBmHVB5xhNSdH2rnTsx8rADRr1szjP+LSOnXqlC6//HItXrxYn3766Vn7e+YnKirqrPVx9i+dMmWKx/6DBw+qYsWKrsdVqlTJt6XlzH3O/7SnT59e4Ejns7UyO19/+PDhAo8pyGeffSaHw6ELL7zQY3/uFlEn53/Ip06d8thfUIAr6nuvUqWKvvzyy3zrl7vVsjDO66ampqp27doez+3ZsydPOMrvPVatWlUVKlTQzJkz871GaQJWfjIzM3XVVVdp69atWrZsWZ56n03r1q0lSb/8b9WdtLQ0zZ8/XxMmTNCDDz7oOu7UqVMl+tkoqTMH+R09elSS1KpVK6/Ni1qpUiWFhobqhhtu0J133pnvMWf+oZjf/fYW5/fX2z8fBSGs+kDduu7yrl2EVQC+42xR/e677/TJJ5+oT58+Xr+Gw+FQZGSkx74vvvhCf/31lxrm+gXXs2dPPf300/rll188ugKcOb1Uly5dVLFiRW3cuDHfwVeFadasmSQzCKw43nzzTS1cuFBDhw5V3dy/qAvgDBq//vqrx/f1s88+8ziuSZMmqlGjhmbPnq1x48a5QsKOHTu0fPlyj1bcgQMH6oMPPlB2dnaxWr/z4/xI/91339X555/v2p+SkqJNmzbp4YcfLvQcAwcO1JQpU1SlSpV8W8VLwvmzkl+r280336wlS5Zo4cKFrvBZVOvWrZPk7v7hcDhkWVaen83XX39d2dnZRa5TMIiOjlbPnj21du1atW7d+qxzL/vDn3/+qSpVqpS661JREVZ9IPfvwJ07A1cPAMFr6dKlrtHo2dnZ2rFjhz766CNJZi5V58wBgwcP1sKFC/Xwww+rSpUqHv3W4uPj1dwLkz0PHDhQs2bNUtOmTdW6dWutXr1azzzzTJ5WsTFjxmjmzJkaMGCAJk+e7JoNYPPmzR7HxcbGavr06Ro+fLgOHz6swYMHu0bf//LLLzpw4IBeeeWVAutTu3ZtNWjQQCtXrtTdd9+d5/kTJ064vg8nTpzQn3/+qXnz5mn+/Pnq3r27ZsyYUaT3nZSUpIsvvlhTp05VpUqVVK9ePX377bf65JNPPI4LCQnRE088oZEjR+qKK67QLbfcoiNHjmjixIl5ugZce+21eu+999S/f3/985//VIcOHRQeHq7du3dr8eLFuuyyy3TFFVe4jk9PT3fd99yqVaum7t2769Zbb9X06dMVEhKifv36uWYDqFOnjsaOHVvoexwzZow+/vhjXXjhhRo7dqxat26tnJwc7dy5U19//bXuueeeYofqVq1aSZKef/55DR8+XOHh4WrSpIlmzJihd955R3fddZdiYmLO+rN65MgR1/OZmZnatGmTpkyZosjISFfLYnx8vC688EI988wzqlq1qurXr6+lS5fqjTfe8Gjxl+Raoeq1115TXFycq7tLfh//29Xzzz+vrl27qlu3brr99ttVv359ZWRkaMuWLfr888/13Xff+a0uK1euVPfu3X3aeuuh0CFYQSbQswFYlmXNneseMPzEEwGrBhCUChtBGsycswGcOfI6P84RyvltuUc0F3SMJKt79+5Fus7ZRpxblmX9/fff1s0332xVr17dio6Otrp27WotW7bM6t69e55rbNy40brkkkusqKgoq3LlytbNN99sffrpp/mOxF66dKk1YMAAq3LlylZ4eLhVq1Yta8CAAdaHH35YaL0fffRRq1KlStbJkyfzvJ/c34OYmBirQYMG1uDBg60PP/ww3xHZOmPUdW6pqanW4MGDrcqVK1sJCQnW9ddfb61atcpjNgCn119/3WrUqJEVERFhNW7c2Jo5c6Y1fPhwj9kALMuMvv73v/9ttWnTxoqKirJiY2Otpk2bWrfddpv1xx9/FPhe8ru32dnZ1lNPPWU1btzYCg8Pt6pWrWpdf/311q5du/J8Xwq6z0ePHrUeeeQRq0mTJlZERISVkJBgtWrVyho7dqzHDBUFfZ/q1auXZ7aK8ePHWzVr1rRCQkJc9945Er2wn9UzZwMIDQ216tataw0ePNhau3atx3V2795tXXXVVValSpWsuLg4q2/fvtZ///vffOs0bdo0Kzk52QoNDfW4fwV9b/K7d0XhHI1f2GwIhcnv+71t2zbrpptusmrVqmWFh4db1apVszp37mxNnjw5z/Xz+3c0fPhwKyYmJs/+gr4H9erVswYMGOCxb8uWLfnOaHEmb84G4LCsAoZGBqn09HQlJCQoLS1N8c5h+X62Zo3knJLslluk114LSDWAoHTy5Elt27ZNycnJJRpAg/Jhz549Sk5O1ttvv60hQ4YEujpAufHoo4/q7bffds3FXJDCfpcXJ68xdZUP5F59jG4AAOB9NWvW1JgxY/Svf/3LdqsMAWXVkSNH9NJLL2nKlCleWbq1qOiz6gNVq0pRUdLJk2aAFQDA+x555BFFR0frr7/+8tsa5UB5tm3bNo0fPz7P3Mm+Rlj1AYfDDLL6/XfTsmpZZh8AwHvi4uLyLDEJwHfOPfdcj4UD/IVuAD7i/CP/6FEpLS2wdQEAAAhWhFUfYfoqoHTK2NhPAChXvPk7nLDqI7mnH9y9O3D1AIKNs9N+VlZWgGsCACgp5+9wbwzEIqz6SO5lp1NTA1cPINiEhoYqNDRU6enpga4KAKCE0tPTXb/PS4sBVj5So4a7TFgFis7hcKh69epKTU1VZGSkYmJi/LdKCgCgVCzL0rFjx5Senq4aNWp45fc3YdVHcres7tkTuHoAwSghIUEnTpzQwYMHXUuOAgCCg8PhUMWKFZWQkOCV8xFWfYSWVaDkHA6HatSooerVqyszMzPQ1QEAFEN4eLhXPv53Iqz6SGKimVvVsmhZBUrKW/2dAADBiwFWPhIeLlWrZsq0rAIAAJQMYdWHnF0B9u6VWLoaAACg+AirPuQcZJWZKR06FNi6AAAABCPCqg8xyAoAAKB0CKs+RFgFAAAoHcKqDzHXKgAAQOkQVn0oMdFdZl5zAACA4iOs+lD16u7yvn2BqwcAAECwIqz6UO6wun9/4OoBAAAQrAirPkRYBQAAKB3Cqg9VrCiF/W9BW8IqAABA8RFWfcjhcLeuElYBAACKj7DqY7nDqmUFti4AAADBhrDqY86wmpUlHTkS0KoAAAAEHcKqjzHICgAAoOQIqz5GWAUAACg5wqqPEVYBAABKjrDqY7mXXCWsAgAAFA9h1cdYchUAAKDkCKs+RjcAAACAkiOs+hhhFQAAoOQIqz5WrZq7TFgFAAAoHsKqj1WoIMXFmTJhFQAAoHgIq36Qe8lVAAAAFB1h1Q+cYfXvv6XTpwNbFwAAgGBCWPWD3P1WDx4MXD0AAACCDWHVD6pUcZcPHQpcPQAAAIINYdUPqlZ1lwmrAAAARUdY9QNaVgEAAEqGsOoHhFUAAICSIaz6Qe5uAAywAgAAKDrCqh/QsgoAAFAyhFU/IKwCAACUDGHVD3KHVboBAAAAFB1h1Q8qV3aXaVkFAAAoOsKqH4SHSwkJpkxYBQAAKDrCqp84uwIQVgEAAIqOsOonzumr/v5bys4ObF0AAACCBWHVT5wtq5ZlAisAAAAKR1j1E6avAgAAKD7Cqp8wfRUAAEDxEVb9JPeSq7SsAgAAFA1h1U/oBgAAAFB8hFU/oRsAAABA8fk8rL788stKTk5WVFSU2rVrp2XLlp31+Pfee09t2rRRdHS0atSooREjRuhQGWiKpGUVAACg+HwaVufMmaMxY8bo4Ycf1tq1a9WtWzf169dPO3fuzPf4H374QcOGDdPNN9+sDRs26MMPP1RKSopGjhzpy2r6BX1WAQAAis+nYfW5557TzTffrJEjR6pZs2aaNm2a6tSpo1deeSXf41euXKn69evr7rvvVnJysrp27arbbrtNq1at8mU1/YKWVQAAgOLzWVg9ffq0Vq9erd69e3vs7927t5YvX57vazp37qzdu3drwYIFsixL+/bt00cffaQBAwYUeJ1Tp04pPT3dY7Mj+qwCAAAUn8/C6sGDB5Wdna3ExESP/YmJidq7d2++r+ncubPee+89DRkyRBEREUpKSlLFihU1ffr0Aq8zdepUJSQkuLY6dep49X14S4UKUnS0KdOyCgAAUDQ+H2DlcDg8HluWlWef08aNG3X33Xfrscce0+rVq/Xll19q27ZtGjVqVIHnHz9+vNLS0lzbrl27vFp/b3K2rhJWAQAAiibMVyeuWrWqQkND87Si7t+/P09rq9PUqVPVpUsX3XfffZKk1q1bKyYmRt26ddPkyZNVo0aNPK+JjIxUZGSk99+AD1SpIu3aZcKqZUkFZHYAAAD8j89aViMiItSuXTstWrTIY/+iRYvUuXPnfF9z/PhxhYR4Vik0NFSSaZENds6W1awsyaZdawEAAGzFp90Axo0bp9dff10zZ87Upk2bNHbsWO3cudP1sf748eM1bNgw1/GDBg3SJ598oldeeUV//vmnfvzxR919993q0KGDatas6cuq+gXTVwEAABSPz7oBSNKQIUN06NAhTZo0SampqWrZsqUWLFigevXqSZJSU1M95ly98cYblZGRoRdffFH33HOPKlasqIsuukhPPfWUL6vpN2dOX9WgQeDqAgAAEAwcVln4fD2X9PR0JSQkKC0tTfHx8YGujofHHpOeeMKUFyyQ+vULbH0AAAACoTh5zeezAcCNhQEAAACKh7DqR5Uru8t//x24egAAAAQLwqofVarkLhNWAQAACkdY9aPcLauHDweuHgAAAMGCsOpHtKwCAAAUD2HVj2hZBQAAKB7Cqh/RsgoAAFA8hFU/ioiQYmJMmZZVAACAwhFW/czZukrLKgAAQOEIq37m7Ld6+LBUttYOAwAA8D7Cqp85W1ZPn5ZOnAhsXQAAAOyOsOpnzAgAAABQdIRVP2PJVQAAgKIjrPpZ7umraFkFAAA4O8Kqn9GyCgAAUHSEVT+jZRUAAKDoCKt+RssqAABA0RFW/YyWVQAAgKIjrPoZLasAAABFR1j1M1pWAQAAio6w6me0rAIAABQdYdXP4uMlh8OUaVkFAAA4O8Kqn4WEuLsC0LIKAABwdoTVAHCGVVpWAQAAzo6wGgDOfqtHjkg5OQGtCgAAgK0RVgPAGVYtS0pLC2xdAAAA7IywGgC5p6+i3yoAAEDBCKsBkHv6KvqtAgAAFIywGgC0rAIAABQNYTUAaFkFAAAoGsJqANCyCgAAUDSE1QCgZRUAAKBoCKsBkLtllbAKAABQMMJqAORuWaUbAAAAQMEIqwFAyyoAAEDREFYDgJZVAACAoiGsBkCFClJkpCnTsgoAAFAwwmqAOFtXaVkFAAAoGGE1QJz9VmlZBQAAKBhhNUCcLavHj0unTgW2LgAAAHZFWA0QBlkBAAAUjrAaICy5CgAAUDjCaoCw5CoAAEDhCKsBQssqAABA4QirAULLKgAAQOEIqwFCyyoAAEDhCKsBQssqAABA4QirAULLKgAAQOEIqwFCyyoAAEDhCKsBkrtllbAKAACQP8JqgFSs6C7TDQAAACB/hNUACQuTEhJMmbAKAACQP8JqADm7AtANAAAAIH+E1QByDrI6fFiyrMDWBQAAwI4IqwHkbFnNzpaOHg1sXQAAAOyIsBpATF8FAABwdoTVAModVhlkBQAAkBdhNYCYaxUAAODsCKsBRDcAAACAsyOsBlDullW6AQAAAORFWA0gWlYBAADOjrAaQAywAgAAODvCagAxwAoAAODsCKsBRDcAAACAsyOsBhADrAAAAM6OsBpAMTFSeLgp07IKAACQF2E1gBwOd1cAWlYBAADyIqwGmLMrAC2rAAAAeRFWA8zZspqRIWVmBrYuAAAAdkNYDbDcg6yOHAlYNQAAAGyJsBpgTF8FAABQMMJqgLGKFQAAQMEIqwHGKlYAAAAF83lYffnll5WcnKyoqCi1a9dOy5YtO+vxp06d0sMPP6x69eopMjJS55xzjmbOnOnragYM3QAAAAAKFubLk8+ZM0djxozRyy+/rC5duujVV19Vv379tHHjRtWtWzff11xzzTXat2+f3njjDTVs2FD79+9XVlaWL6sZUKxiBQAAUDCfhtXnnntON998s0aOHClJmjZtmr766iu98sormjp1ap7jv/zySy1dulR//vmnKv+vybF+/fq+rGLA0bIKAABQMJ91Azh9+rRWr16t3r17e+zv3bu3li9fnu9rPvvsM7Vv315PP/20atWqpcaNG+vee+/ViRMnCrzOqVOnlJ6e7rEFEwZYAQAAFMxnLasHDx5Udna2EhMTPfYnJiZq7969+b7mzz//1A8//KCoqCjNnTtXBw8e1B133KHDhw8X2G916tSpevzxx71ef39hgBUAAEDBfD7AyuFweDy2LCvPPqecnBw5HA6999576tChg/r376/nnntOs2bNKrB1dfz48UpLS3Ntu3bt8vp78CW6AQAAABTMZ2G1atWqCg0NzdOKun///jytrU41atRQrVq1lJCQ4NrXrFkzWZal3bt35/uayMhIxcfHe2zBpGJFd9nu3QCOHZPGjpXq1JEaNZKeekoqw2PfAACADfgsrEZERKhdu3ZatGiRx/5Fixapc+fO+b6mS5cu2rNnj44ePera9/vvvyskJES1a9f2VVUDKjxcioszZTu3rJ48KfXuLU2bJu3eLW3ZIj34oDR8uGRZga4dAAAoq3zaDWDcuHF6/fXXNXPmTG3atEljx47Vzp07NWrUKEnmI/xhw4a5jh86dKiqVKmiESNGaOPGjfr+++9133336aabblKFChV8WdWAcnYFsHPL6sMPS85xcaGh7v3vvy/NmBGYOgEAgLLPp2F1yJAhmjZtmiZNmqS2bdvq+++/14IFC1SvXj1JUmpqqnbu3Ok6PjY2VosWLdKRI0fUvn17XXfddRo0aJBeeOEFX1Yz4JyDrA4ftmcr5dat0vPPm3JUlPTzz9Lcue7nH3tMCrJJGAAAQJBwWJYd41HJpaenKyEhQWlpaUHTf7VXL+m770w5I0OKjQ1sfc40erT00kumPHGiNGGCKV93nWlZlUxgDeJJGQAAgB8VJ6/5fDYAFM7Oq1gdPiy9+aYpx8RId9/tfm7SJCnsf5OfvfyydOqU/+sHAADKNsKqDdh5+qo5c6Tjx035pps8g/U550iDB5vywYPSvHl+rx4AACjjCKs2YOeW1f/3/9zlm2/O+/wtt7jL//mP7+sDAADKF8KqDdi1ZXXvXmnpUlNu3Fhq3TrvMT16mBZWyfS73bfPb9UDAADlAGHVBuwaVufNc89OcPXVUn4Lj4WEmOckc+xnn/mtegAAoBwgrNqAXbsBfPWVu3z55QUfd+WV7nLuKa0AAABKi7BqA3ZsWc3Kck+nVaWKdN55BR/bvr3kXGDsm2/M9FsAAADeQFi1ATu2rKakuCf679XLfNxfEIdDGjTIlDMz3f1cAQAASouwagN2bFn95ht3+ZJLCj++d293edEi79cHAACUT4RVG7BjWP3xR3f5oosKP75nTyk01JS//to3dQIAAOUPYdUGYmPdQc8O3QBycqSffjLlxEQpObnw1yQkSB06mPLmzdLu3b6rHwAAKD8IqzbgcLhbV+3Qsvr779KRI6bcsWP+U1blp1cvd3nZMq9XCwAAlEOEVZtwDrKyQ8vqypXucseORX9dt27u8g8/eK8+AACg/CKs2oSzZTUtzUwbFUgrVrjLxQmrHTu6Zw3I3ecVAACgpAirNpF7kJXzI/hAcbashoSYOVSLKj7evSTrr7+a4A0AAFAahFWbsMtcqxkZ0n//a8qtWpnBX8XRtav5almeLbQAAAAlQVi1CbtMX7V6tZkNQCpeFwAnZ1iV6LcKAABKj7BqE3ZpWV23zl1u1674r+/SxV0mrAIAgNIirNqEXVpWf/3VXW7Tpvivr11bqlfPlFNSAj9YDAAABDfCqk3YJaz+8ov5GhIitWxZsnOcf775evy4WSAAAACgpAirNmGHbgBZWdKGDabcqJEUHV2y8zjDqmRaVwEAAEqKsGoTdmhZ/e036dQpUy5JFwCn3NNdrVpVujoBAIDyjbBqE7lbVgMVVkvbX9Up98AsWlYBAEBpEFZtokoVdzlQYdXZX1VyT+5fEgkJUpMm7nOePl26egEAgPKLsGoTubsBHDwYmDrkDqulaVmV3F0BTp+W1q8v3bkAAED5RVi1ibAwqWJFUz50KDB1cHYDqFTJTEFVGgyyAgAA3kBYtRFnV4BAtKweOSLt2WPKLVtKDkfpzpc7rDLICgAAlBRh1UaqVjVfjxzx/2T6uedDbdas9Odr3dodeHN3LwAAACgOwqqNOFtWLcv/c616O6zGxkoNG5ryf//LSlYAAKBkCKs24mxZlfzfbzV3WG3a1DvnbNvWfD15Uvr9d++cEwAAlC+EVRvJHVb93W910yZ32Rstq5I7rErSunXeOScAAChfCKs2knuuVX+HVWfLaoUKUp063jln7umv6LcKAABKgrBqI4HqBnD6tLR1qyk3aSKFeOmngpZVAABQWoRVGwlUy+qWLVJ2til7qwuAJNWs6Q7ghFUAAFAShFUbCVTLqi8GV0lm6ipnV4D9+6W9e713bgAAUD4QVm0kUC2ruQdXeTOsSnQFAAAApUNYtRE7tKx6sxuARFgFAAClQ1i1kcqV3eVAtKw6HFKjRt49NzMCAACA0iCs2khEhBQfb8r+alm1LHfLanKyFBXl3fM3bWrel0TLKgAAKD7Cqs04+636q2X1r7+kY8dM2dtdACQpPFxq2dKUf/vNfS0AAICiIKzajLPf6uHD7umkfOm339zlJk18cw1nVwDLkv77X99cAwAAlE2EVZtxtqxalnTkiO+vt2WLu+zt/qpOuQdZ0W8VAAAUB2HVZnLPCOCPrgC5w2rDhr65BoOsAABASRFWbSb3XKv+GGTlj7DaurW7/OuvvrkGAAAomwirNuPvltWtW83X8HCpTh3fXKNSJfe5f/3VdHEAAAAoCsKqzfhzYQDLcresNmgghYb67lrO1tX0dGnHDt9dBwAAlC2EVZvx55KrqanSiROm7KsuAE70WwUAACVBWLUZf7as+qO/qlPusEq/VQAAUFSEVZvxZ8uqP8Nq7kFWtKwCAICiIqzajD8HWPkzrDZq5F7KlbAKAACKirBqM/6cusqfYTU01L3s6tat0tGjvr0eAAAoGwirNhMZKcXGmrK/WlZDQ6V69Xx7LYllVwEAQPERVm3I2brqy7Cae9qq+vXNPKu+xuIAAACguAirNlS9uvl66JCUne2baxw4IGVkmLKvuwA4MX0VAAAoLsKqDTnDqmX5rt+qP/urOtGyCgAAiouwakPOsCpJ+/f75hqBCKssuwoAAIqLsGpDucPqvn2+uUYgwqrk7grAsqsAAKAoCKs2lJjoLpelllWJxQEAAEDxEFZtyJ/dABwOKTnZN9fID8uuAgCA4iCs2pA/w2rdumZuV39hRgAAAFAchFUb8nVYPXxY+vtvU/ZnFwDn9SpUMGXCKgAAKExYoCuAvHwdVgPVX1VyL7uakuJedtW5Ypfd/Pe/0ptvSuvXSxER0rnnSiNGSA0aBLpmAACUH7Ss2lDVqu5yWQurknuQlV2XXT1xQho1SmrVSnruOWnRIumLL6TJk6XGjaWHH5ZOnw50LQEAKB8IqzYUHi5VrmzKZTGs2nmQVUaGdPHF0quv5v98drY0ZYo0aJB07Jh/6wYAQHlEWLUpZ1cAX4fVc87x/vkLY9fpq7KypMsvl5YvN48rVJD+/W9p714zJ+yjj5o/JCTp66+lK6+UMjMDVl0AAMoFwqpNOedaPXpUOn7cu+feutVdDkT/S7suu/rII9J335lyxYrSDz9I99xj7kXdutKkSeb5hARzzNdfm+cBAIDvEFZtypeDrJwtqzVrSjEx3j13UVSqZMKfZJ9lV3/8UXrqKVMOC5Pmz5fOOy/vcV27Sp9/bgZcSdL06dKCBf6rJwAA5Q1h1aZ8FVbT093nC0R/VSdn66odll3NypLuuMP9+MknpS5dCj6+Wzdp2jT345tukg4e9Fn1AAAo1wirNuWrsJq7C0Ag+qs62WlxgBdfdHdHOO88acyYwl8zapTUv78p79sn3Xuvz6oHAEC5Rli1KV+F1dyDqxo18t55i8su/VbT0qTHHzdlh0N6+WUzF2xhHA7pjTdM31ZJeust6aeffFZNAADKLcKqTfkqrP7xh7scyLBql5bVF16Qjhwx5WHDpAsuKPprk5LcQVeS7r5bysnxavUAACj3CKs25Y+W1UD2WbXDsqtpaWbSf8m0pj76aPHPcfvtUosWpvzzz9JHH3mvfgAAgLBqW86pqyQpNdV75w30HKtOzmVXJdOPNiPD/3V46SXPVtWSfD/Cw6Vnn3U/njjRLBwAAAC8w+dh9eWXX1ZycrKioqLUrl07LVu2rEiv+/HHHxUWFqa2bdv6toI2VaOGu+yLsJqYKMXFee+8JdGunflqWdKaNf69dmamCauSFBJillAtqd693bMHbNokzZ5d+voBAADDp2F1zpw5GjNmjB5++GGtXbtW3bp1U79+/bRz586zvi4tLU3Dhg1Tr169fFk9W4uNdYdJb4XVY8fc5wpkFwCn9u3d5VWr/HvtTz6R9uwx5csuK10rs8MhPfGE+/Hjj5vpsAAAQOn5NKw+99xzuvnmmzVy5Eg1a9ZM06ZNU506dfTKK6+c9XW33Xabhg4dqk6dOvmyerZXs6b56gxVpZV72io7hNXzz3eXU1L8e+0XXnCX77679Ofr2dNskmm9fvfd0p8TAAD4MKyePn1aq1evVu/evT329+7dW8udi6/n480339TWrVs1YcKEIl3n1KlTSk9P99jKCmdYPXrUO3067TK4yql5c/cgK3+2rK5aJTl/BFu1krp39855J01yl596ipkBAADwBp+F1YMHDyo7O1uJuUcKSUpMTNTevXvzfc0ff/yhBx98UO+9957CwsKKdJ2pU6cqISHBtdWpU6fUdbcLb/dbtVtYDQuTzj3XlLdulQ4f9s91p093l+++23yM7w1du5rVrSRp82azLCsAACgdnw+wcpyRBCzLyrNPkrKzszV06FA9/vjjaty4cZHPP378eKWlpbm2Xbt2lbrOduFsWZW80xXAbmFV8uy3unq176+Xlib9v/9nypUqSUOHevf899/vLj/9tHfPDQBAeeSzsFq1alWFhobmaUXdv39/ntZWScrIyNCqVas0evRohYWFKSwsTJMmTdIvv/yisLAwfffdd/leJzIyUvHx8R5bWeHLsBrIaaty83e/1TlzpJMnTfn666XoaO+ev39/97yry5dLP/zg3fMDAFDe+CysRkREqF27dlq0aJHH/kWLFqlz5855jo+Pj9f69eu1bt061zZq1Cg1adJE69at0wXFWVqojPBVN4AqVUyroh3kDqv+6Lf65pvu8ogR3j9/SAitqwAAeFPROoaW0Lhx43TDDTeoffv26tSpk1577TXt3LlTo0aNkmQ+wv/rr7/09ttvKyQkRC2ds8T/T/Xq1RUVFZVnf3nhzZbVEyckZw8Ju3QBkMySr/HxUnq671tWN2+WVq405datJV9N4XvttWbe1t27Tb/VDRvcra0AAKB4fNpndciQIZo2bZomTZqktm3b6vvvv9eCBQtUr149SVJqamqhc66WZ95sWd22zV22U1gNCXEvDrB7t1TA2DuvmDXLXR4xwnsDq84UESGNG+d+/MwzvrkOAADlgcOyLCvQlfCm9PR0JSQkKC0tLej7rx496l4YoHt3acmSkp/rs8/M5PeSWRK0iDOD+cX997sD3aefSpde6v1rZGVJdeua0B8WZlqqq1Xz/nWcjh411/v7b7Mk659/SrVr++56AAAEk+LkNZ/PBoCSi401H5FLpe8GYMeZAJxyr/3w44++ucaiRe7W6YEDfRtUJXPv7rzTlDMzpWnTfHs9AADKKsKqzTm7ApS2G4Cdw2qXLu6yr0bP+3pgVX7uukuKijLlV181rawAAKB4CKs2561VrP74w122W1itXl1yTq27apV7ailvOXzYdC9wXqtfP++evyDVq7uD8dGj0owZ/rkuAABlCWHV5rw1I4CzZbViRaly5VJVySe6djVfT5/2/hRW779vziuZuVXDw717/rO55x4ziEySnn/e+0EcAICyjrBqc7lnBChpWD11SnJOunDOOb4bBV8avuwKcOYsAP50zjnS4MGmvG+f9Pbb/r0+AADBzqfzrKL0atVyl//6q2Tn2LJFyskx5aZNS18nX3C2rEreDavr17uXcW3fXgrElL333+9e4vWZZ6Sbb5ZCQ/1fj7M5elRatsx0F9m71/S1rVbNTCvWtq2ZjgsAgEAgrNpc3bruckmnpN282V1u0qR09fGVRo1MODpwwMwIkJ3tnUA3c6a7fOONpT9fSbRrJ/XqJX37rfnDYd486aqrAlOX3CzL1On556WvvjKzFuQnPl665hpp1Cj3nLgAAPgL3QBszhth9bff3GW7tqw6HNKFF5rykSPSmjWlP+fp09I775hyZKQ0dGjpz1lSuZdgnTLFBMVAWrtW6tZNuuQSaf78goOqZFYXe/110zJ96aXSpk3+qycAAIRVm/N2WLVry6ok9e7tLn/9denP9/nn0qFDpnzllVKlSqU/Z0ldcol03nmmvGaNqVsgZGVJjz1mgmfuOW1r1ZJuv9307/36axNgX3xR+sc/zJyxTp9/broFPPGEe9AaAAC+xApWNmdZUoUKZpBUixbSf/9b/HNccIH088+m9fLYMXM+O9q2TWrQwJRLu2KXJA0YIC1YYMqLFkkXX1y685XW55+7V+dq08aE1hA//rmYmipde630/ffufY0bS5MnS1dcYVb2ys+xY2Zg2L/+5dlvum1b6cMP7TcVGgDA/ljBqgxxONytqzt2FP/jY8tyt6zWq2ffoCpJycnu4LN8uRn0U1J//SV9+aUp160rXXRR6etXWgMHmhZNSfrlF2nuXP9de+tWqXNnd1ANDZUmTTID0K6+uuCgKkkxMabV9Y8/pAcecPclXrfO9GH95BOfVx8AUI4RVoOAM6wePWr6cxbHvn1SWpop27kLgNMll5ivmZnS0qUlP8/bb7tnQBgxwr8tmAVxOExAdJowwQwk87VNm0x/4O3bzePatU2r9aOPFm+Uf4UK0pNPSikp7p+l9HQzWGzSpMD3wwUAlE02+C8chSlNv9VgGFyVW+5+q199VbJzWJY9ZgHIT9++UseOprxhg+cysL6wdq0Jqs45eps3l376yXOqsOI691wTWK+91r1vwgRp2DDTXQUAAG8irAYBb4XVYGhZ7dnT/ZH0Z5+VrLVu8WL3il29ekn163uteqXmcEhPP+1+/PDDpnXSF1asMN/PgwfN4/POM63VuVdFK6m4OLMy2DPPuBeZePdd0y/YeT0AALyBsBoEcofVHTuK99rcc6wGQ8tqQoK7f+mOHaZlsLimT3eXb7nFO/Xypm7dTD9RSdq/34ys97bvvjNdKpxdQDp3NvuqVvXeNRwO6d57pY8/dveF/uEH03Kc++cOAIDSIKwGgeRkd/nPP4v32txzYgZDy6pkpplyKu7gnR07TIusZFoQc5/LTp56ysz9Kkn/93/uVba84YsvpP79zSh+ybQuf/21+UPAF664wqx+5VwaeOtWqVMnE44BACgtwmoQOOccd3nr1uK9dv1687VSJXeYsLvLLnN/tDxnTvG6Arz8sntg1ahRUni49+vnDcnJpguAZAZZ3XSTd+Yt/fBD6fLL3X1HBw0yc6bGxJT+3GfTrp3pC9umjXl85IjUp49ZTAAAgNIgrAaBWrXco7aLE1b//ts9L2arVu4AaHdJSaavpWT6ni5fXrTXHTkivfqqKUdESLfe6pPqec2DD0qtW5vyr7+6w2tJzZxpBj1lZZnHQ4aYj+ijokp33qKqU8e0sA4caB5nZZluGPff7/4Dwg4sy/zb2LTJLIzw/femL++6ddKuXWdfzQsA4H+E1SAQGuqeLP/PP4v+H7+zVVUyYTWY5B7BP2tW0V7zwgvuPprXXy8lJnq7Vt4VHm4CpnNA2b//bcJlcVmWNHWqdPPN7p+Nm2+W3nvP/y3LcXHSvHnSmDHufc88YxZoOHDAv3Vx2r9fmj1buvtus9hEpUpS5cpmZoSuXc2+Hj3MLAd165pW6BYtpMGDTXeNlSsJsAAQSKxgFSQGDjR9ESVp927T2lqYl16SRo825RkzpNtu8139vO3YMdPCevSoCQ+7dp19udT0dLPowZEjJtz/9ptn9wk7mz7dBCnJvNevvpK6dCnaa0+flsaONd0fnMaMkZ59NvBzy77yinTXXe65ZGvWNDMIdO/u+2tv3ix98IH06aemxbS0oqPNgLXLLzf/Fr05UA0AyqPi5LWzrFsDOzmz32pRwmowt6zGxEg33GACz7FjJmyPH1/w8c89514w4frrgyeoSuYPip9+Mi2hx46ZuVgXLDCzBpzNzp3SP/7h2U3iySfNx+526PJx++1mOdehQ03r5p49ZqaHf/7TLCIQG+vd6+3YYfo4z5599oBau7aZGaNGDalKFXfr85EjZtqtLVvMHzu5+xAfP26C76efmj8CunQxiyFceaXp/gAA8B1aVoPECy+Y/+Ql89HxiBGFv6ZLF3eQSUuTgu3bsWWLCTuWZT7S37Il/4CzfbvUrJl08qRpVd20SWrUyO/VLZWTJ02rnXMhhPBwacoUc8/P/Cj/+HET4idMcI/4j4iQXntNGj7cr9Uukr17peuu85wdoHZt03Xh2mvPvtRrYVJTpY8+Mq2oBfVtPu880yp60UVmIFiVKoWfNyvLLC/7ww+mT+uiRWY1uPx07Gi6DAwebFr3AQCFK05eI6wGiS++cA9ceegh6V//OvvxliVVrOj+eNy51GawueYaM8JdMgOSpk71fD4nx7RELlpkHo8da1pZg9GJEyawfv21e1/9+qb1tGVL09K3apUJZocOuY+pU8f0dT3/fH/XuOiys03f1ccfN8HcqVEjadw4E1orViz8PJZl+m1//bX0//6fGRiV32+wDh3MOa+5pmifQhQmJ8e0fn/6qTR3rvT77/kfd/75JrRedVVwte4DgL8RVstgWN2yxd1aeOWVhQ/E2bHDvXLTwIHS55/7tHo+s2WLGexy+rRpYVy61Mzh6TRpkmlhlMzHups3B18Lcm6Zmaa7w3PPFW3KrttvNwHeV3OoetvWrdKdd+ZdSjcy0gx2uvBCM/ApKcn0Ez161Izc//13061l2bKC//Bq0cIE+2uv9X1Q3LjRtOh+9JFnd5vczj3XBNc+fcyUXqVpQS7MyZOmL/uuXWbbudN8PXDAfA+PHTNfT582nz6EhJivUVHmjwTnVq2aafWuXdv8EVS7ttlvh24lAMoWwmoZDKvZ2eYj8JMnzeT+ha0QNH++mWNTMuFnyhTf19FXHn1UmjzZlKtXd7ciPvGEu4XZ4TCtq716Ba6e3pSSYt7311/nDa3h4WZaqnvvdc9rGmyWLDF/aCxeXLrzNG5svhdDhpiwGgi//WZ+Jj/6qOAV12JjzR9ZF1xg6tm8uZlrNza28CB47Jjp75uaaqaic4ZSZyB1hlJfiYkxf/g6t3r1PB9XrUqYBVB8hNUyGFYl01Kzbp1pETl2zL0CUn4ef1yaONGUZ882rU3BKjNT6t3bBBynqCjPj5OfesoMLCpr9uwxc4GmpprWsHPOMS2QcXGBrpl3rF0rvfWW+Wh9587Cj4+IMO+/Vy+pXz+pbVt7BaWtW93BNSWl8OOjo92tyBERZsvKcreEpqWZrjzeEBVlzp+TY/74zclxLx5RGtHReQOs83GdOmYWj6io0t+nzEzTVaYo28mT5r2FhOTdQkPN8sDR0Z5bTIz7a2ysb1vCARBWy2xYvf56M2Jckn75xT2hfH5yT3W1ZUvw9587fNgs6/n99577Q0PNx+D33ReYesE7LMsEvXXrzEf+hw6ZgWSxsaaLQ/36pkWyWTP/LXJQWtu3m3+Dy5aZbc8e718jJMRMCVa3rgmGZ241apg/bKKj8w9fWVkmDB85Yrpb7NtnWm6dm7MFd8eO0gXbsDBzH+PjTX3Cwsy/XecWEmK6KJw6lXdzhk/nFGj+Ehlpfv5iY02dneXCNuexMTHmfeYOybm/hoSY91TczbIK3iTPxw6HqUNYmPlEpihfC9oXGmqvPwwR/AirZTSsTp1qBldJZ28ttSzTWrN/v5n8/ODBsvFLJjvbjIKfPdv8x9q2rfTAA8H7UTjKD8sy4fXXX01/140bzUf6e/eagHjqlAlrmZkmxDjDTlycCZw1a7q3WrU8w6g/WgBzcszvk+3b8247dpivuT/pQNlUWKjN/Vzu/3POTBmFPT6bs/1fVtBzDofZQkLyLxf22B/H5q5/oL+2bCkNG1bw99lbmGe1jGre3F3esKHg43btMv+xSFL79mUjqErmL/vRo90LHQDBwuEwfVSTk6XLLiv4OOd/2nb7NxsSYv4ATkoyU3WdybLyhtkdO0wgd3ZlcH7NyPBsJcwtIsK0aObeKlTw3KKi8u7L7/nISHP+nBzPLSvLtNYeP+7ejh1zf3V2wThzO37cD99om8vKci/njLLriiv8E1aLg7AaRHIPICloBLLk2VfOztMZAfBkt5BaVA6HmQs5MdEMIisqy3L3nw0Pt/f7z842gTW/IHv0qAnhuR8fO+YO5bn7CTu/5uS4uwWc2S3ibFvuVrr8Nslddn5/MzNNyCzqV2/sO9OZ97awx/k5WwtsQc+d2VUiJ6d4LbnlkR3/HRJWg0iDBqbfV1qa9PPP7j5JZ1q50l1u395/9QOA4nD2qQwGoaGmW0ZZGdxY3uXX59cZZPN7fLbnSnussz6B/urc7LicdJD8moBk/go//3zpm2/M6PDdu/Nf6jH3qPmirjEPAEB5kbslGvYXEugKoHg6dHCXf/457/Pp6dKaNabcsqWZ5BsAACBYEVaDTO7+YD/9lPf5H35wf6zQo4dfqgQAAOAzhNUgkzusrliR9/ncKwIRVgEAQLAjrAaZxESpUSNTXrnSTObtZFnSp5+ackiI1L2736sHAADgVYTVIDRggPmalWXWjnfasEH64w9T7tbNniP6AAAAioOwGoQGDnSX5893lz/+2F2+6ir/1QcAAMBXCKtBqFs391x/n39uVmPJzpbeecd9zBVXBKZuAAAA3kRYDUIREdLll5vykSMmpH7yibR1q9l38cVS7dqBqh0AAID3sChAkBo92t2SOmaMWV3Fady4gFQJAADA62hZDVIdOrg/6j9xwqxFLUmXXCL17Ru4egEAAHgTYTWI/ec/UqtW7sfNm5vWVpaQAwAAZQXdAIJYlSpSSor01VdmjtU+faSoqEDXCgAAwHsIq0EuMlK69NJA1wIAAMA36AYAAAAA2yKsAgAAwLYIqwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALAtwioAAABsy+dh9eWXX1ZycrKioqLUrl07LVu2rMBjP/nkE11yySWqVq2a4uPj1alTJ3311Ve+riIAAABsyqdhdc6cORozZowefvhhrV27Vt26dVO/fv20c+fOfI///vvvdckll2jBggVavXq1evbsqUGDBmnt2rW+rCYAAABsymFZluWrk19wwQU677zz9Morr7j2NWvWTJdffrmmTp1apHO0aNFCQ4YM0WOPPVak49PT05WQkKC0tDTFx8eXqN4AAADwneLkNZ+1rJ4+fVqrV69W7969Pfb37t1by5cvL9I5cnJylJGRocqVKxd4zKlTp5Senu6xAQAAoGzwWVg9ePCgsrOzlZiY6LE/MTFRe/fuLdI5nn32WR07dkzXXHNNgcdMnTpVCQkJrq1OnTqlqjcAAADsw+cDrBwOh8djy7Ly7MvP7NmzNXHiRM2ZM0fVq1cv8Ljx48crLS3Nte3atavUdQYAAIA9hPnqxFWrVlVoaGieVtT9+/fnaW0905w5c3TzzTfrww8/1MUXX3zWYyMjIxUZGVnq+gIAAMB+fNayGhERoXbt2mnRokUe+xctWqTOnTsX+LrZs2frxhtv1Pvvv68BAwb4qnoAAAAIAj5rWZWkcePG6YYbblD79u3VqVMnvfbaa9q5c6dGjRolyXyE/9dff+ntt9+WZILqsGHD9Pzzz6tjx46uVtkKFSooISHBl1UFAACADfk0rA4ZMkSHDh3SpEmTlJqaqpYtW2rBggWqV6+eJCk1NdVjztVXX31VWVlZuvPOO3XnnXe69g8fPlyzZs3yZVUBAABgQz6dZzUQmGcVAADA3mwxzyoAAABQWoRVAAAA2BZh1a4OH5buvFOqV09q2lR66ikpKyvQtQIAAPArnw6wQgkdOCB16yb99pt734MPSitWSB99JIVx2wAAQPlAy6rdWJY0bJg7qIaHS84Vvz79VHrmmcDVDQAAwM8Iq3bz6afSl1+aclKS9Pvv0ldfSSH/u1WTJklbtwaufgAAAH5EWLWT7Gzpvvvcj6dPl+rXly65RPrnP82+kyeliRMDUTsAAAC/I6zayRdfSFu2mPKFF0pXXeV+btIkqXJlU/7gA2n3bv/XDwAAwM8Iq3bywgvu8gMPuPuqSlJsrHT77aaclSW9+KJ/6wYAABAAhFW72LpV+vZbU27YUOrbN+8xo0ebAVeS9M47ptsAAABAGUZYtYs5c9zlkSPdA6pyS0qS+vUz5T17pKVL/VM3AACAACGs2sUHH7jLQ4YUfNx117nL773nu/oAAADYAGHVDjZtktavN+WOHc0MAAUZNMj0X5XMNFd0BQAAAGUYYdUOFixwlwcPPvuxFSq4+7MeOiT99JPv6gUAABBghFU7+Oord7l//8KPHzjQXZ4/3/v1AQAAsAnCaqAdO+YeKFWnjtS0aeGv6dfPPa3V55/7rm4AAAABRlgNtKVLpdOnTblvX8+5VQtSvbrUoYMp//e/0r59vqsfAABAABFWAy13F4D85lYtSM+e7jJTWAEAgDKKsBpozrAaGir16lX01+UOq4sXe7dOAAAANkFYDaS9e6XffjPlCy6QEhKK/trOnaWwMFNessTrVQMAALADwmog/fCDu3zhhcV7bWysdP75prx5swm+AAAAZQxhNZCWLXOXu3Yt/utzdwWgdRUAAJRBhNVAcoZVh0Pq0qX4r+/Rw10mrAIAgDKIsBoo6enSL7+YcqtWUsWKxT9Hp05SyP9u4cqVXqsaAACAXRBWA2XFCiknx5S7dSvZOWJjTdCVpPXrzQIDAAAAZQhhNVBy91ctaViVzCwCkgm+q1aVrk4AAAA2Q1gNlNwf25ekv6pTx47u8k8/lfw8AAAANkRYDYTcraA1aki1a5f8XM6WVYl+qwAAoMwhrAbCli1SWpopO+dKLammTaX4eFNeuVKyrNKdDwAAwEYIq4GQkuIulzashoS4W1dTU6Xdu0t3PgAAABshrAaCN8Oq5NkVgH6rAACgDCGsBkLusNq+fenPl/sca9eW/nwAAAA2QVj1t6wsd6Bs0ECqUqX05zzvPHd5zZrSnw8AAMAmCKv+tmGDdOKEKXujC4BkZhNwht41axhkBQAAygzCqr95u7+qJDkc7tbV/fvNQCsAAIAygLDqb74Iq5J07rnuMv1WAQBAGUFY9TdnWA0J8exrWlr0WwUAAGUQYdWfTp6U1q835WbNpNhY7507d8sqYRUAAJQRhFV/WrfOzAYgebcLgCQ1bOgOv3QDAAAAZQRh1Z981V9VMt0K2rY15R07pEOHvHt+AACAACCs+pMvw6rk2W913Trvnx8AAMDPCKv+5Ayr4eFS69bePz/9VgEAQBlDWPWX9HTpt99MuXVrKTLS+9fIHVZpWQUAAGUAYdVfVq92ryzliy4AkplhICzMlJ2zDgAAAAQxwqq/+Lq/qiRFREhNm5rypk3S6dO+uQ4AAICfEFb9xR9hVXL3hc3Kcnc7AAAACFKEVX9Ztcp8jY42H9f7SqtW7vKvv/ruOgAAAH5AWPWHAwek7dtN+bzz3P1KfSH3LAOEVQAAEOQIq/7gry4AkmdYZZAVAAAIcoRVf/jpJ3f5ggt8e61ataSKFU2ZllUAABDkCKv+8PPP7rKvw6rD4W5d/esv6fBh314PAADAhwirvmZZ7rBarZpUr57vr5l7kBVdAQAAQBAjrPra1q3u1s0OHUzLp6/RbxUAAJQRhFVf82cXACemrwIAAGUEYdXXcg+u6tDBP9ds2dJdJqwCAIAgRlj1tdwtq76etsopLk5q0MCU//tfKSfHP9cFAADwMsKqL50+La1da8qNGkmVK/vv2s6uAMeOSdu2+e+6AAAAXkRY9aVff5VOnTJlf3UBcGKQFQAAKAMIq760YoW77O+wyiArAABQBhBWfWnZMne5a1f/Xjt3yyphFQAABCnCqq9YlvT996YcFye1aePf6zdsKEVFmTLdAAAAQJAirPrKH39I+/aZcteuUmiof68fGiq1aOGuy/Hj/r0+AACAFxBWfcXZqipJF14YmDo4uwJYlrRhQ2DqAAAAUAqEVV+xU1iV6LcKAACCEmHVV5xhNSpKat8+MHVg+ioAABDkCKu+sG2btGOHKXfsKEVEBKYeTF8FAACCHGHVFxYudJcvvjhw9ahWTapRw5R//dX0XQUAAAgihFVfyB1W+/ULXD0kd1eAQ4ek1NTA1gUAAKCYCKvedvKk9N13ppyUJLVtG9DqMMgKAAAEM8Kqt337rXtO0759pZAAf4vptwoAAIIYYdXbZs92l6+4InD1cKJlFQAABDHCqjcdPy7Nm2fKFStKffoEsjZG06ZSWJgpE1YBAECQIax600cfSceOmfJVV0mRkYGtj2Tq0LSpKW/eLJ0+Hdj6AAAAFIPPw+rLL7+s5ORkRUVFqV27dlq2bNlZj1+6dKnatWunqKgoNWjQQDNmzPB1Fb3DsqRnnnE/vvHGgFUlD2dXgMxM6bffAlsXAACAYvBpWJ0zZ47GjBmjhx9+WGvXrlW3bt3Ur18/7dy5M9/jt23bpv79+6tbt25au3atHnroId199936+OOPfVlN7/jsM+m//zXlTp2kLl0CW5/c6LcKAACClE/D6nPPPaebb75ZI0eOVLNmzTRt2jTVqVNHr7zySr7Hz5gxQ3Xr1tW0adPUrFkzjRw5UjfddJP+/e9/+7KapWNZ0tq10p13uvc98IDkcASuTmcirAIAgCDls7B6+vRprV69Wr179/bY37t3by1fvjzf16xYsSLP8X369NGqVauUmZmZ72tOnTql9PR0j81vLEtq0kQ67zzpr7/Mvosvli691H91KArCKgAACFI+C6sHDx5Udna2EhMTPfYnJiZq7969+b5m7969+R6flZWlgwcP5vuaqVOnKiEhwbXVqVPHO2+gKBwO6Zxz3I8bN5befdderaqSVLOmVLmyKRNWAQBAEPH5ACvHGcHNsqw8+wo7Pr/9TuPHj1daWppr27VrVylrXEx9+0r9+5vBVatWSWeEbVtwONytq3v2mKVXAQAAgkCYr05ctWpVhYaG5mlF3b9/f57WU6ekpKR8jw8LC1OVKlXyfU1kZKQiAzlF1D//aTa7a9VKWrLElNevl3r0CGRtAAAAisRnLasRERFq166dFi1a5LF/0aJF6ty5c76v6dSpU57jv/76a7Vv317h4eG+qmr5QL9VAAAQhHzaDWDcuHF6/fXXNXPmTG3atEljx47Vzp07NWrUKEnmI/xhw4a5jh81apR27NihcePGadOmTZo5c6beeOMN3Xvvvb6sZvlAWAUAAEHIZ90AJGnIkCE6dOiQJk2apNTUVLVs2VILFixQvXr1JEmpqakec64mJydrwYIFGjt2rF566SXVrFlTL7zwgq666ipfVrN8aNHC9F21LMIqAAAIGg7LOYKpjEhPT1dCQoLS0tIUHx8f6OrYS+PG0h9/SBUqSBkZUmhooGsEAADKoeLkNZ/PBgAbcXYFOHFC2ro1sHUBAAAoAsJqeUK/VQAAEGQIq+VJ7rC6bl3AqgEAAFBUhNXy5Nxz3eU1awJXDwAAgCIirJYndetKzsUVVq82MwMAAADYGGG1PHE4pPPOM+X9+83SqwAAADZGWC1v2rVzl1evDlw9AAAAioCwWt44W1YlwioAALA9wmp5k7tllUFWAADA5gir5U1yslSxoinTsgoAAGyOsFre5B5klZpqNgAAAJsirJZHdAUAAABBgrBaHjEjAAAACBKE1fIo94wAq1YFrh4AAACFIKyWR+ec4x5k9fPPrGQFAABsi7BaHoWESBdcYMr79kk7dgS2PgAAAAUgrJZXHTu6yytXBq4eAAAAZ0FYLa8IqwAAIAgQVssrZzcASVqxInD1AAAAOAvCanlVqZLUtKkpr10rnTwZ2PoAAADkg7Banjm7AmRmmsAKAABgM4TV8ix3v9XlywNXDwAAgAIQVsuzrl3d5aVLA1cPAACAAhBWy7PmzaWqVU152TIpOzuw9QEAADgDYbU8czik7t1N+cgR6ddfA1odAACAMxFWyztnWJXoCgAAAGyHsFre9ejhLi9ZEqhaAAAA5IuwWt61aCFVrmzKy5ZJOTmBrQ8AAEAuhNXyLiTE3RXg8GHmWwUAALZCWIXUu7e7vHBh4OoBAABwBsIqpH793OUFC/x77cxMac0a6euvpVWrpBMn/Ht9AABga4RVSPXqmTlXJemnn6RDh3x/zR07pNtvN/1l27WT+vSRzj/fzPs6cqS0a5fv6wAAAGyPsAqjf3/zNSfHtHL6imVJL75owvGMGdLRo57PHz8uvfGG1KyZ9PbbvqsHAAAICoRVGP7oCnDqlGk1vesuE0olKTZW+sc/pEcekW64QYqPN/uPHZOGD5cef9w3dQEAAEEhLNAVgE107SrFxUkZGdL8+SZYRkZ67/ynTklXXukZhO+8U5o8WapY0b3vyBHpnnukmTPN44kTpQoVpPvv915dAABA0KBlFUZEhHTppaZ85Ih3uwJkZUnXXusOqlFR0uzZpjtA7qAqmcdvvCE995x734MP+n/gFwAAsAXCKtz+8Q93efZs75133Dhp3jxTrlBB+vJLE17PZuxY6YknTNmypOuvl7Zt816dAABAUCCswu2SS9yrWX36qek3Wlr/+Y80fboph4dLn33mXoSgMA8/LF1+uSn//bc0YgQrbAEAUM4QVuEWESENHmzKx4+bYFkay5aZfqlOM2ZIF19c9Nc7HNKsWVJysnm8dKnpIgAAAMoNwio85e4K8NprJT/Pjh3SVVeZSf8l6Z//lG66qfjnSUjwrMe990qpqSWvFwAACCqEVXi68EKpSRNTXrJE2rCh+Oc4dsx8fH/ggHl88cXSv/9d8jpdfLF0442mnJ4uPfZYyc8FAACCCmEVnkJCpDvucD+eNq14r8/JMcFy3TrzuGFDac4cKayUs6Q984x7DtaZM6X160t3PgAAEBQIq8hr+HB3MJw1q3ij8CdMkD76yJTj4ky/V+egrdKoWtUMuJJMIL7vvtKfEwAA2B5hFXklJEhjxphyVpYJoEXx3ntmkn/JtNDOnm2WTfWWu++W6tUz5a++kn780XvnBgAAtkRYRf7GjnVP2P/OO9J33539+AULPAdQPfusNGCAd+sUFWVWtHJiKVYAAMo8wiryV7GiNHWq+/GNN0r79uV/7IIFZinV06fN41tvNaP/feG666QGDUx50SJpxQrfXAcAANgCYRUFu/VWqVs3U961S+rfX9qzx/18VpYJtIMGSadOmX3XXCO99JKZI9UXwsOlhx5yP540yTfXAQAAtuCwLMsKdCW8KT09XQkJCUpLS1O8c5AQSi41VTr/fOmvv8zjihXNXKwVKphVrrZudR979dWm32p4uG/rlJkpNW4sbd9uHv/8s6kjAAAICsXJa7Ss4uxq1DCDmerWNY+PHJFeeUV67jl3UHU4zEj9Dz7wfVCVzDXGj3c/fvZZ318TAAAEBGEVhWvRQkpJkUaMyDtf6sUXSytXmlkAQvz44zRsmFStmil/9JFZMQsAAJQ5hFUUTfXqZjL+vXulxYulb74xXQMWLZI6dPB/faKipNGjTTk7W3r+ef/XAQAA+Bx9VhG8Dhww3RNOnpRiY80gMOd0WwAAwLbos4ryoVo1M6WWJB09Kv3nPwGtDgAA8D7CKoLb2LHuabKef97MFAAAAMoMwiqCW+PGZp5XyfSh/fDDwNYHAAB4FWEVwW/cOHf5//5PKlvdsAEAKNcIqwh+F14onXuuKa9aJf34Y2DrAwAAvIawiuDncHi2rj73XODqAgAAvIqwirLhmmvMaluSNG+e9OefAa0OAADwDsIqyoaICPciAZYlvfBCYOsDAAC8grCKsuO226QKFUz5jTektLTA1gcAAJQaYRVlR5Uq0rBhpnz0qPT664GtDwAAKDXCKsqWMWPc5RdekLKyAlYVAABQeoRVlC1Nm0r9+5vyzp3S3LmBrQ8AACgVwirKnrFj3WWmsQIAIKgRVlH29OoltWplyitXmg0AAAQlwirKHofDs3X1//4vcHUBAAClQlhF2fSPf0jVq5vyxx9LO3YEtj4AAKBECKsom6KipDvuMOXsbGn69MDWBwAAlAhhFWXX7bdLkZGm/J//SBkZga0PAAAoNsIqyq7q1aXrrjPl9HTp1VcDWx8AAFBshFWUbffcYwZcSdIzz0jHjgW2PgAAoFgIqyjbmjeXrr7alPfvl2bMCGx9AABAsfg0rP7999+64YYblJCQoISEBN1www06cuRIgcdnZmbqgQceUKtWrRQTE6OaNWtq2LBh2rNnjy+ribLuscfcratPP03rKgAAQcSnYXXo0KFat26dvvzyS3355Zdat26dbrjhhgKPP378uNasWaNHH31Ua9as0SeffKLff/9dl156qS+ribKuRQvpmmtMef9+6eWXA1sfAABQZA7LsixfnHjTpk1q3ry5Vq5cqQsuuECStHLlSnXq1EmbN29WkyZNinSelJQUdejQQTt27FDdunULPT49PV0JCQlKS0tTfHx8qd4DypCNG6WWLSXLkhISpC1bpKpVA10rAADKpeLkNZ+1rK5YsUIJCQmuoCpJHTt2VEJCgpYvX17k86SlpcnhcKhixYr5Pn/q1Cmlp6d7bEAezZtLN95oymlp0oQJAa0OAAAoGp+F1b1796q6cwWhXKpXr669e/cW6RwnT57Ugw8+qKFDhxaYuqdOnerqE5uQkKA6deqUqt4ow/71LykmxpRffdW0tgIAAFsrdlidOHGiHA7HWbdVq1ZJkhzOQS25WJaV7/4zZWZm6tprr1VOTo5ePksfw/HjxystLc217dq1q7hvCeVFjRrS+PGmnJ0tjRljugUAAADbCivuC0aPHq1rr732rMfUr19fv/76q/bt25fnuQMHDigxMfGsr8/MzNQ111yjbdu26bvvvjtrX4bIyEhFOlcpAgozbpz02mvSzp3SokXS229Lw4cHulYAAKAAxQ6rVatWVdUiDEzp1KmT0tLS9PPPP6tDhw6SpJ9++klpaWnq3Llzga9zBtU//vhDixcvVpUqVYpbRaBgFSpIL70kDRpkHo8ZI/XubVpdAQCA7fisz2qzZs3Ut29f3XLLLVq5cqVWrlypW265RQMHDvSYCaBp06aaO3euJCkrK0uDBw/WqlWr9N577yk7O1t79+7V3r17dfr0aV9VFeXNwIHuZViPHJGGDTPdAgAAgO34dJ7V9957T61atVLv3r3Vu3dvtW7dWu+8847HMb/99pvS0tIkSbt379Znn32m3bt3q23btqpRo4ZrK84MAkChnn9eSkoy5W++kSZNCmx9AABAvnw2z2qgMM8qimzpUumii6ScHLPC1ezZ0pAhga4VAABlXnHyWrH7rAJlRvfu0pQp0oMPmlkBrr9eio+X+vXzzvktS9qxQ/r5Z2nNGjOo68ABs79CBaluXTP/64UXmgULijBLBgAA5Q1hFeXb/feb1axef13KypIuu8zMFuBcQKC4LMsE048/NtvvvxftdQ0amLB8++3u7gkAAIBuAICys6V//EP68EP3vltukZ56SqpUqWiv/+knE04/+UTavr3kdYmKkm69VZo4sWjXBgAgCBUnrxFWAcm0qo4ZY6a1cqpaVbrtNmnoUKlpUynkf+MRLUvavdt8vL9wofT559L+/XnP6XBIXbtKPXpI558vNWkiJSZKYWFmydft26WVK805vvvO9J11qlZNeuYZM1MB3QMAAGUMYZWwipJ67TXpnnuko0c998fHm6AZEiLt22emvMpPWJgZtHXlldLll5vXFMXu3WaGgpdflo4fd++/7DJp5kypcuWSvBsAAGyJsEpYRWns2mUGXf2//2daXAtToYLUp48Jp4MGlS5Y7t5tVtnK3SWhTh1Tl44dS35eAABshLBKWIU37Nkjvf++tGyZtH69dPiw+ai+ShWpcWPz0X7HjqYlNTrau9f+/HNpxAjp0CHzODJSeustptYCAJQJhFXCKsqC3btNf9lly9z7/vUvafx4+rECAIJacfKaT1ewAlAKtWub1bVuusm97+GHzfRWuQdjAQBQhhFWATuLiDBzwE6d6t736qtmloDMzMDVCwAAPyGsAnbncJgBX++9J4WGmn3vvSddfbV08mRg6wYAgI8RVoFgMXSoWXQgMtI8/vRTM/vAsWOBrRcAAD5EWAWCyaWXSl98IcXEmMfffCP17l3wvK8AAAQ5wioQbHr1khYtkhISzOPly830WQcOBLZeAAD4AGEVCEadOklLlphlWSVp7Vqpe3fpr78CWi0AALyNsAoEq7Ztpe+/l2rVMo83bZK6dZO2bQtotQAA8CbCKhDMmjY1iwY0aGAeb9smde1qgisAAGUAYRUIdsnJJrA2b24e79kjXXih9OOPga0XAABeQFgFyoKaNaWlS6XzzjOPDx40g67eey+w9QIAoJQIq0BZUbWq9N130sUXm8enT0vXX2+WaM3ODmzdAAAoIcIqUJYkJEgLFki33ureN2WKCbCpqYGrFwAAJURYBcqa8HBpxgzpuefcy7MuWSK1bi3Nni1ZVkCrBwBAcRBWgbLI4ZDGjpUWL3ZPbXXwoFmydcAAafPmwNYPAIAiIqwCZVm3btK6ddKVV7r3LVwotWgh3XQToRUAYHuEVaCsq1pV+vhjae5cM2uAJOXkSG++KTVrJvXsKc2cSZ9WAIAtOSyrbHVgS09PV0JCgtLS0hQfHx/o6gD2cuyYNH269NRT0pEjeZ9v0UI691zztWZNqXp1KTJSCgszfV0zMtxbero5R+4tLc18PX1aCgkxW3i4CczVq0s1akhNmpjFDFq2lCpV8ue7BwDYRHHyGmEVKI+OHJFmzTIDsX77LXD1aNFC6tLFzAnbt6+ZzQAAUOYRVgmrQNFYlvTTT9IXX5i+rL/8ImVlle6cFSqY1ticHDO/66lTRTtnWJjUo4d02WXSNdeYllgAQJlEWCWsAiVz6pS0caO0ZYu0d6904ICUmWnCpmVJcXFSfLz7a8WKnltCggmquVmW6TJw4IC0Y4cZ1LVpk/Tzz9KaNfkvWBAWJvXvLw0fbmYvOPOcAICgRlglrALB4dgxafly07L76afS9u15j6lcWbrhBmnkSNPPFQAQ9AirhFUg+FiWtH699P770jvvSHv25D2mY0cTWocMkWJj/V9HAIBXEFYJq0Bwy86WvvlGeust6ZNPTPeE3GJjpWuvNcG1QwezCAIAIGgQVgmrQNlx+LD03nvSf/5jWl7P1KqVCa3XX2+6DAAAbK84eY1FAQDYW+XK0l13mZkKfv5ZuvVWzy4A69dL//ynmRd26FDpu+/MTAQAgDKBllUAwefoUenDD6XXXzcDtM6UlGRmERg0SOrVi/6tAGAzdAMgrALlx8aNJrS+/bZ06FDe58PCzKpcXbtKnTtLrVtLDRqY/QCAgCCsElaB8ufUKTP91bvvmsFZJ04UfGxEhFn2NTnZdB9wLi0bF2daYePipKgo95KxDoe77NxCQ/PfYmPNFkIvKwAoCGGVsAqUbydOmL6r8+dL339vWl/9yeEwiyYkJJgtMdEE4lq1zFanjtS4sWnhjYjwb90AwAYIq4RVALkdOmT6tqakmNWzNm6Ufv+99EvLllZIiFS/vgmujRub1t7mzaVmzUxLL1NyASijCKuEVQCFycqS9u+X/vrLLEBw8KAZuOXcTp40CxXk5JjNWc7Odn89c8vKkjIyzPKyaWlmO3Ik7zyxRVGpkju4NmvmLtepQxcDAEGPsEpYBWAXlmVC619/uYPxtm3SH3+Y1t3ffzcBt6iio/MG2GbNpHPOYdAYgKBBWCWsAggWliXt2yf99pu0ebPporBpk9l27y76ecLD3V0JateWatRwb0lJUsWKpv9sXBwtswACjrBKWAVQFqSnmwDr7Gfr/LptW8kXPnA4TGBNSDCzFkREmC0yMm85PNy95X58tnKFCmZwWVyc+Zq7XKEC/XABSCpeXuMzIwCwq/h4qUMHs+V28qTpPpC7FdY5aCwz8+zntCwTgtPTfVfvgoSGmpBcsaLZKlVyl4vymLALlEuEVQAINlFRZnGD1q0992dlSTt3SqmpntvevZ6DvtLSzONjx6TTp0s2AKwksrOlw4fNVhIREcULt5UqSTExni3E+W2hoV55ewB8g7AKAGVFWJiZu7VBg+K9zrJMkDx1yoRXZ4DNzDTb6dPu8pmPzywfP26CsHNWBOdX5+acIeHIEXPN4jh92szgsH9/8V5XGIfDHVzDwvJ+zW9fUZ5zdqeIjPR+OSKCVmaUG4RVACjvHA538IqJ8c81Lcu07P79tzu8Orcz9+V3TFqaOYe36uIM6cEkdx/jwsKtswX5zFXYCnpcnGMD9Vp/XYc/CgKOsAoA8D+Hw700bZ06xX99drZptT1boP37bxOIc7cKF7SdPm26UTi3zMz8vzo3O3AG7KNHA12Tss8XIdnhKNkm+fa1F1wg3Xtv4L7X+SCsAgCCT2iou2+qvzm7TeQXZJ1lZ5B0dqlwdrHwV/nUKe+1PMO98Ed5YMP3SVgFAKA4cnebsLOsLHeIzcx0r8aWeyW2M8vFfeyrY8vDa50r4525BZoNuz3Y/F8aAAAoEX/3Q4b35Bdic4fZsz1f2tdVqBC4910AwioAAICd5O5jCrHmHgAAAGyLsAoAAADbIqwCAADAtgirAAAAsC3CKgAAAGyLsAoAAADbIqwCAADAtgirAAAAsC3CKgAAAGyLsAoAAADbIqwCAADAtgirAAAAsC3CKgAAAGyLsAoAAADbIqwCAADAtgirAAAAsC3CKgAAAGyLsAoAAADbIqwCAADAtgirAAAAsC3CKgAAAGyLsAoAAADbIqwCAADAtgirAAAAsK2wQFfA2yzLkiSlp6cHuCYAAADIjzOnOXPb2ZS5sJqRkSFJqlOnToBrAgAAgLPJyMhQQkLCWY9xWEWJtEEkJydHe/bsUVxcnBwOh1+umZ6erjp16mjXrl2Kj4/3yzXhPdy/4Mc9DH7cw+DHPQxu/r5/lmUpIyNDNWvWVEjI2XullrmW1ZCQENWuXTsg146Pj+cfaBDj/gU/7mHw4x4GP+5hcPPn/SusRdWJAVYAAACwLcIqAAAAbIuw6gWRkZGaMGGCIiMjA10VlAD3L/hxD4Mf9zD4cQ+Dm53vX5kbYAUAAICyg5ZVAAAA2BZhFQAAALZFWAUAAIBtEVYBAABgW4TVUnr55ZeVnJysqKgotWvXTsuWLQt0lVCA77//XoMGDVLNmjXlcDg0b948j+cty9LEiRNVs2ZNVahQQT169NCGDRsCU1nkMXXqVJ1//vmKi4tT9erVdfnll+u3337zOIZ7aG+vvPKKWrdu7Zp0vFOnTlq4cKHree5fcJk6daocDofGjBnj2sc9tLeJEyfK4XB4bElJSa7n7Xr/CKulMGfOHI0ZM0YPP/yw1q5dq27duqlfv37auXNnoKuGfBw7dkxt2rTRiy++mO/zTz/9tJ577jm9+OKLSklJUVJSki655BJlZGT4uabIz9KlS3XnnXdq5cqVWrRokbKystS7d28dO3bMdQz30N5q166tJ598UqtWrdKqVat00UUX6bLLLnP9Z8j9Cx4pKSl67bXX1Lp1a4/93EP7a9GihVJTU13b+vXrXc/Z9v5ZKLEOHTpYo0aN8tjXtGlT68EHHwxQjVBUkqy5c+e6Hufk5FhJSUnWk08+6dp38uRJKyEhwZoxY0YAaojC7N+/35JkLV261LIs7mGwqlSpkvX6669z/4JIRkaG1ahRI2vRokVW9+7drX/+85+WZfFvMBhMmDDBatOmTb7P2fn+0bJaQqdPn9bq1avVu3dvj/29e/fW8uXLA1QrlNS2bdu0d+9ej/sZGRmp7t27cz9tKi0tTZJUuXJlSdzDYJOdna0PPvhAx44dU6dOnbh/QeTOO+/UgAEDdPHFF3vs5x4Ghz/++EM1a9ZUcnKyrr32Wv3555+S7H3/wgJ69SB28OBBZWdnKzEx0WN/YmKi9u7dG6BaoaSc9yy/+7ljx45AVAlnYVmWxo0bp65du6ply5aSuIfBYv369erUqZNOnjyp2NhYzZ07V82bN3f9Z8j9s7cPPvhAa9asUUpKSp7n+DdofxdccIHefvttNW7cWPv27dPkyZPVuXNnbdiwwdb3j7BaSg6Hw+OxZVl59iF4cD+Dw+jRo/Xrr7/qhx9+yPMc99DemjRponXr1unIkSP6+OOPNXz4cC1dutT1PPfPvnbt2qV//vOf+vrrrxUVFVXgcdxD++rXr5+r3KpVK3Xq1EnnnHOO3nrrLXXs2FGSPe8f3QBKqGrVqgoNDc3Tirp///48f5XA/pyjIbmf9nfXXXfps88+0+LFi1W7dm3Xfu5hcIiIiFDDhg3Vvn17TZ06VW3atNHzzz/P/QsCq1ev1v79+9WuXTuFhYUpLCxMS5cu1QsvvKCwsDDXfeIeBo+YmBi1atVKf/zxh63/DRJWSygiIkLt2rXTokWLPPYvWrRInTt3DlCtUFLJyclKSkryuJ+nT5/W0qVLuZ82YVmWRo8erU8++UTfffedkpOTPZ7nHgYny7J06tQp7l8Q6NWrl9avX69169a5tvbt2+u6667TunXr1KBBA+5hkDl16pQ2bdqkGjVq2PvfYMCGdpUBH3zwgRUeHm698cYb1saNG60xY8ZYMTEx1vbt2wNdNeQjIyPDWrt2rbV27VpLkvXcc89Za9eutXbs2GFZlmU9+eSTVkJCgvXJJ59Y69evt/7xj39YNWrUsNLT0wNcc1iWZd1+++1WQkKCtWTJEis1NdW1HT9+3HUM99Dexo8fb33//ffWtm3brF9//dV66KGHrJCQEOvrr7+2LIv7F4xyzwZgWdxDu7vnnnusJUuWWH/++ae1cuVKa+DAgVZcXJwrt9j1/hFWS+mll16y6tWrZ0VERFjnnXeeaxod2M/ixYstSXm24cOHW5Zlpu2YMGGClZSUZEVGRloXXnihtX79+sBWGi753TtJ1ptvvuk6hntobzfddJPr92W1atWsXr16uYKqZXH/gtGZYZV7aG9DhgyxatSoYYWHh1s1a9a0rrzySmvDhg2u5+16/xyWZVmBadMFAAAAzo4+qwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALCtsEBXAACQ17p16zRv3jzX4zFjxqhixYoBqw8ABArLrQKADc2aNUsjRoxwPd62bZvq168fuAoBQIDQDQAAAAC2RVgFAACAbRFWAQAAYFuEVQAAANgWYRUAAAC2xWwAAGAjDoej2K9ZvHixevTo4f3KAIAN0LIKAAAA22JRAACwkdDQUEmSZVnKycnJsz8/JWmNBYBgQcsqANhIVlaWsrKy9MYbb3js37Jli+u5M7fu3bsHqLYA4HuEVQAAANgWYRUAAAC2RVgFAACAbRFWAQAAYFuEVQAAANgWYRUAAAC2RVgFAACAbRFWAQAAYFuEVQAAANgWYRUAAAC2RVgFABsKDw/3eJydnR2gmgBAYBFWAcCG4uLiPB7//fffAaoJAAQWYRUAbKh+/foej1NSUgJTEQAIMIdlWVagKwEA8JSVlaWqVasqLS1NklSzZk29/vrr6tGjhypUqBDg2gGA/9CyCgA2FBYWphEjRrge79mzR/3791d0dLSio6MVGxvr2pYtWxbAmgKAbxFWAcCmJk+erK5du+bZf+LECR07dsy1MfgKQFlGWAUAm4qJidGSJUv0wQcf6JprrlHjxo0VFxenkBB+dQMoP+izCgAAANviz3MAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADY1v8HgRTOUW9oh04AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (result_dlpbath_T, P11p, \"b\", \"P11 Padé (DrudeLorentzBath + Term)\"),\n", + " (result_dlpbath_T, P12p, \"r\", \"P12 Padé (DrudeLorentzBath + Term)\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "a5a10572", + "metadata": {}, + "source": [ + "### Next we compare the Matsubara and Pade correlation function fits\n", + "\n", + "Fitting the correlation function is not efficient for this example, but\n", + "can be extremely useful in situations where large number of exponents\n", + "are needed (e.g., near zero temperature). We will perform the fitting\n", + "manually below, and then show how to do it with the built-in tools\n", + "\n", + "For the manual fit we First we collect a large sum of Matsubara terms for \n", + "many time steps:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "e73fb7aa", + "metadata": {}, + "outputs": [], + "source": [ + "tlist2 = np.linspace(0, 2, 10000)\n", + "\n", + "corr_15k_t10k = dlenv.correlation_function(tlist2, Nk=15_000)\n", + "\n", + "corrRana = np.real(corr_15k_t10k)\n", + "corrIana = np.imag(corr_15k_t10k)" + ] + }, + { + "cell_type": "markdown", + "id": "80afb2db", + "metadata": {}, + "source": [ + "We then fit this sum with standard least-squares approach:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "05fc75f6", + "metadata": {}, + "outputs": [], + "source": [ + "def wrapper_fit_func(x, N, args):\n", + " \"\"\" Fit function wrapper that unpacks its arguments. \"\"\"\n", + " x = np.array(x)\n", + " a = np.array(args[:N])\n", + " b = np.array(args[N:2 * N])\n", + " return fit_func(x, a, b)\n", + "\n", + "\n", + "def fit_func(x, a, b):\n", + " \"\"\" Fit function. Calculates the value of the\n", + " correlation function at each x, given the\n", + " fit parameters in a and b.\n", + " \"\"\"\n", + " return np.sum(\n", + " a[:, None] * np.exp(np.multiply.outer(b, x)),\n", + " axis=0,\n", + " )\n", + "\n", + "\n", + "def fitter(ans, tlist, k):\n", + " \"\"\" Compute fit with k exponents. \"\"\"\n", + " upper_a = abs(max(ans, key=abs)) * 10\n", + " # sets initial guesses:\n", + " guess = (\n", + " [upper_a / k] * k + # guesses for a\n", + " [0] * k # guesses for b\n", + " )\n", + " # sets lower bounds:\n", + " b_lower = (\n", + " [-upper_a] * k + # lower bounds for a\n", + " [-np.inf] * k # lower bounds for b\n", + " )\n", + " # sets higher bounds:\n", + " b_higher = (\n", + " [upper_a] * k + # upper bounds for a\n", + " [0] * k # upper bounds for b\n", + " )\n", + " param_bounds = (b_lower, b_higher)\n", + " p1, p2 = curve_fit(\n", + " lambda x, *params_0: wrapper_fit_func(x, k, params_0),\n", + " tlist,\n", + " ans,\n", + " p0=guess,\n", + " sigma=[0.01 for t in tlist],\n", + " bounds=param_bounds,\n", + " maxfev=1e8,\n", + " )\n", + " a, b = p1[:k], p1[k:]\n", + " return (a, b)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "1db2c659", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation (real) fitting time: 0.8875892162322998\n", + "Correlation (imaginary) fitting time: 0.016649723052978516\n" + ] + } + ], + "source": [ + "kR = 4 # number of exponents to use for real part\n", + "poptR = []\n", + "with timer(\"Correlation (real) fitting time\"):\n", + " for i in range(kR):\n", + " poptR.append(fitter(corrRana, tlist2, i + 1))\n", + "\n", + "corrRMats = np.real(dlenv_approx.correlation_function(tlist2))\n", + "\n", + "kI = 1 # number of exponents for imaginary part\n", + "poptI = []\n", + "with timer(\"Correlation (imaginary) fitting time\"):\n", + " for i in range(kI):\n", + " poptI.append(fitter(corrIana, tlist2, i + 1))" + ] + }, + { + "cell_type": "markdown", + "id": "8e6ebb4b", + "metadata": {}, + "source": [ + "And plot the results of the fits:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "ebcb68f7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define line styles and colors\n", + "linestyles = [\"-\", \"--\", \"-.\", \":\", (0, (3, 1, 1, 1)), (0, (5, 1))]\n", + "colors = [\"blue\", \"green\", \"purple\", \"orange\", \"red\", \"brown\", \"cyan\", \"magenta\"]\n", + "\n", + "# Define a larger linewidth\n", + "linewidth = 2.5\n", + "\n", + "# Create a single figure with two subplots\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", + "\n", + "# Plot the real part on the first subplot (ax1)\n", + "ax1.plot(tlist2, corrRana, label=\"Analytic\", color=colors[0], linestyle=linestyles[0], linewidth=linewidth)\n", + "ax1.plot(tlist2, corrRMats, label=\"Matsubara\", color=colors[1], linestyle=linestyles[1], linewidth=linewidth)\n", + "\n", + "for i in range(kR):\n", + " y = fit_func(tlist2, *poptR[i])\n", + " ax1.plot(tlist2, y, label=f\"Fit with {i} terms\", color=colors[(i + 2) % len(colors)], linestyle=linestyles[(i + 2) % len(linestyles)], linewidth=linewidth)\n", + "ax1.set_ylabel(r\"$C_{R}(t)$\")\n", + "ax1.set_xlabel(r\"$t$\")\n", + "ax1.legend()\n", + "\n", + "# Plot the imaginary part on the second subplot (ax2)\n", + "ax2.plot(tlist2, corrIana, label=\"Analytic\", color=colors[0], linestyle=linestyles[0], linewidth=linewidth)\n", + "\n", + "for i in range(kI):\n", + " y = fit_func(tlist2, *poptI[i])\n", + " ax2.plot(tlist2, y, label=f\"Fit with {i} terms\", color=colors[(i + 3) % len(colors)], linestyle=linestyles[(i + 1) % len(linestyles)], linewidth=linewidth)\n", + "ax2.set_ylabel(r\"$C_{I}(t)$\")\n", + "ax2.set_xlabel(r\"$t$\")\n", + "\n", + "ax2.legend()\n", + "\n", + "# Add overall plot title and show the figure\n", + "fig.suptitle(\"Comparison of Analytic and Fit to Correlations (Real and Imaginary Parts)\", fontsize=16)\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "469a8a2e", + "metadata": {}, + "outputs": [], + "source": [ + "# Set the exponential coefficients from the fit parameters\n", + "\n", + "ckAR1 = poptR[-1][0]\n", + "ckAR = [x + 0j for x in ckAR1]\n", + "\n", + "vkAR1 = poptR[-1][1]\n", + "vkAR = [-x + 0j for x in vkAR1]\n", + "\n", + "ckAI1 = poptI[-1][0]\n", + "ckAI = [x + 0j for x in ckAI1]\n", + "\n", + "vkAI1 = poptI[-1][1]\n", + "vkAI = [-x + 0j for x in vkAI1]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "59fa79c9", + "metadata": {}, + "outputs": [], + "source": [ + "# overwrite imaginary fit with analytical value (not much reason to use the\n", + "# fit for this)\n", + "\n", + "ckAI = [lam * gamma * (-1.0) + 0.0j]\n", + "vkAI = [gamma + 0.0j]" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "e07b01f5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.0052492618560791016\n", + " Total run time: 3.21s*] Elapsed 3.21s / Remaining 00:00:00:00[*********59%** ] Elapsed 1.90s / Remaining 00:00:00:01\n", + "ODE solver time: 3.20740008354187\n" + ] + } + ], + "source": [ + "options = {**default_options}\n", + "\n", + "NC = 4\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI)\n", + " HEOMFit = HEOMSolver(Hsys, (bath,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultFit = HEOMFit.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "ed997ede", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (resultFit, P11p, \"b\", \"P11 Fit\"),\n", + " (resultFit, P12p, \"r\", \"P12 Fit\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "27d6df70", + "metadata": {}, + "source": [ + "Now we use the built-in functions. The `BosonicEnvironment` class, includes a \n", + "method that performs this fit automatically. More information on how the\n", + "built-in functios work can be found in `HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions`" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "d69384a6", + "metadata": {}, + "outputs": [], + "source": [ + "tlist3 = np.linspace(0, 2, 200)\n", + "envfit, fitinfo =dlenv.approx_by_cf_fit(tlist=tlist3,full_ansatz=True,maxfev=1e6,Ni_max=1,Nr_max=3)" + ] + }, + { + "cell_type": "markdown", + "id": "c115cb93", + "metadata": {}, + "source": [ + "The approx_by_cf_fit method outputs a `ExponentialBosonicEnvironment` object,\n", + "which contains a decaying exponential representation of the original \n", + "environment , and a dictionary containing all information related to the fit.\n", + "The dictionary contains a summary of the fir information and the normalized \n", + "root mean squared error, which assesses how good the fit is. " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "a2ebc66f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation function fit:\n", + "\n", + "Result of fitting the real part of |Result of fitting the imaginary part \n", + "the correlation function with 3 terms: |of the correlation function with 1 terms: \n", + " | \n", + " Parameters| a | b | c | d | Parameters| a | b | c | d \n", + " 1 | 1.02e-01 |-5.71e-01 |-9.61e-08 |2.16e+00 | 1 | 1.12e-02 |-5.01e-01 |-6.77e-03 |-5.00e-02 \n", + " 2 | 7.96e-02 |-8.08e+00 | 2.70e-06 |1.76e+00 | \n", + " 3 | 2.34e-01 |-2.06e+02 | 0.00e+00 |0.00e+00 |A normalized RMSE of 7.91e-03 was obtained for the the imaginary part\n", + " |of the correlation function. \n", + "A normalized RMSE of 2.04e-04 was obtained for the the real part of | \n", + "the correlation function. | \n", + "The current fit took 35.864703 seconds. |The current fit took 0.005103 seconds. \n", + "\n" + ] + } + ], + "source": [ + "print(fitinfo['summary'])" + ] + }, + { + "cell_type": "markdown", + "id": "e4ad68e3", + "metadata": {}, + "source": [ + "We can then compare the result of the built-in fit with the manual fit" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "a7742b6c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a single figure with two subplots\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", + "\n", + "# Plot the real part on the first subplot (ax1)\n", + "ax1.plot(tlist2, corrRana, label=\"Original\", marker=\"o\", markevery=500)\n", + "ax1.plot(tlist2, fit_func(tlist2, *poptR[-1]), color=\"r\", label=\"Manual Fit\")\n", + "ax1.plot(tlist2, np.real(envfit.correlation_function(tlist2)), \"k--\", label=\"Built-in fit\")\n", + "ax1.set_ylabel(r\"$C_{R}(t)$\")\n", + "ax1.set_xlabel(r\"$t$\")\n", + "ax1.legend()\n", + "\n", + "# Plot the imaginary part on the second subplot (ax2)\n", + "ax2.plot(tlist2, corrIana, label=\"Original\", marker=\"o\", markevery=500)\n", + "ax2.plot(tlist2, fit_func(tlist2, *poptI[-1]), color=\"r\", label=\"Manual Fit\")\n", + "ax2.plot(tlist2, np.imag(envfit.correlation_function(tlist2)), \"k--\", label=\"Built-in fit\")\n", + "ax2.set_ylabel(r\"$C_{I}(t)$\")\n", + "ax2.set_xlabel(r\"$t$\")\n", + "ax2.legend()\n", + "# Add an overall title and adjust layout\n", + "plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "305001b1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.005854129791259766\n", + " [ 0% ] Elapsed 0.01s / Remaining 00:00:00:00" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 3.73s*] Elapsed 3.73s / Remaining 00:00:00:00\n", + "ODE solver time: 3.734066963195801\n" + ] + } + ], + "source": [ + "options = {**default_options}\n", + "\n", + "NC = 4\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " HEOMFit_2 = HEOMSolver(Hsys, (envfit,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultFit_2 = HEOMFit_2.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "14c9c43c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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qzNKlSyv8DE59jRkzBgCTycTkyZNtfbZs2cLkyZPZt2+fQ2s5Fx+XXq0BiepiD6vB+QqrIiLSsM2cOZP27dtTVFTEzz//zLRp00hJSWHjxo0EBwcD8PLLL9OlSxdGjRrF+++/X+VYa9as4YsvvqB79+4MGzaMr7/+usb1tGzZktmzZwNQWlrKpk2bmDJlCosXL2bbtm0EBQXVeMz58+cTFhZ21nOWL1/OlClTGD9+PI0aNarWuE888QT3339/jeupjueee46hQ4dWOBYREQHAihUriD+5cTzWsDplyhSGDBni8PB8NgqrTuIf5k+OqQlNjBwaFSmsiohIw9apUyd69eoFwNChQzGbzTz99NN88cUX3HTTTQAUFBTg5WX90Pejjz6qcqxbbrmFW2+9FYDPP//8vMJqYGAg/fr1s72/8MILCQgI4I477mDZsmUMHz68xmN27969xn2qo1WrVk4ZF6BNmzYVfg6nquq4q2kZgBNl+1lnV6PLD2FYDDdXIyIi4jlOBqFTP3Y/GVTPpbrn1VR4eDgAvr6+tmNVfQQ/efJkTCZThWOnLwOorM9DDz0EQFJSku1j93MtYaisBpPJxD333MNHH31EcnIyQUFBdO3alQULFpx1rJo4dRnArFmzuO666wDrPzZO1n7qEgdn0cyqE33W9nG2bSzlEHF8lmvQOMJ07k4iIiINwK5duwCIiopyWw3l5eWAfRnA1KlTadmyJQMGDHDK9SZMmEBOTg6vvfYa8+bNIzbWOqnVoUOH8xrvm2++YfXq1UydOpWQkBBeeOEFrr76arZv307Lli2rNYbFYrH9HE7y8TkzHl5xxRU899xzPProo7z++uv06NEDcO6sr60ep1+hAdve/QY+2mhtpx+GxhHurUdEROqgl16yvs6lRw/46quKx0aNgt9/P3ffSZOsr5MKCiA5ueqvnwez2Ux5eTnFxcWkpKTwzDPPEBoayqhRo2o17vnavHlzhRlUgLZt2/LNN9/g7+/vlGvGx8fTvHlzwLpkoLbrPouKivjhhx8IDQ0FoEePHsTFxfHpp5/y8MMPV2uMsWPHnnFs586dtG7dusKxqKgo2rRpA1jDtSuXCCisOlGs/R4r0tPhPP/hJCIiDVl+Ppx213ylEhLOPHbkSPX65udXfG8YFfud/vXzcHq46dy5M2+88QYxMTG1Hvt8tGrVijlz5gDWGda9e/fy/PPPM2zYMJYuXWoLZq5isViwWCy29yaTCW9v77P2GTp0qC2oAsTExBAdHV1hacXps6be3t4Vli88//zzXHTRRRXOSajsd8mNFFad6NSwekhbrYqIyPkIC4Nmzc59XmUfp0dFVa/v6Xewm0wV+53jDvfq+PDDD0lOTsbHx4eYmBjbR+DuEhAQYLvhC6xhesiQITRr1ownn3ySTz75xKX1TJ06lSlTptjet2jR4pxbRJ28a/9U/v7+FBUVAdats5KSkip8fcmSJQwZMsT2vmXLlhV+Dp5IYdWJmjUupBO7iSaT8k3NgPbuLklEROqa2nwEf/qygOoKDYW0tPPrW4Xk5GSPD0WxsbFERkbyxx9/2I4FBARQUlJyxrlZWVkOvfaf//xnRo4caXvviKUIcXFxrF69usKxdu3a1XpcV1NYdaJW2avYiHXvsqVL/wE8796CREREpEppaWlkZWVVuOEpMTGRzMxMDh8+bFuyUFpaynfffXde1zgZQk/Ofp4UFxdHXFzceVZeOT8/P4f+A6Gq2p1NYdWJQltF29reOZlurERERMTzpaSkcOTIEcB6Q1Zqaiqff/45AIMHD7btHFBYWMjChQsBWLlypa1vVlYWwcHBjBgx4pzXKioqsvU1m83s3buXF154AYAHHnjAdt7YsWN58sknueGGG3jooYcoLi7m1VdfxWw2n9f32LlzZwBeeeUVbr31Vnx9fWnXrl2FtaeeqlOnTgC8/fbbhIaGEhAQQFJSUqXLERxJYdWJGrW1h9WAPIVVERGRs3nqqadISUmxvV+6dKltD9JT11pmZmba9vw86eR+oNVZ6wmwZ88e+vfvD1j3bW3atCldu3bltddeY/DgwbbzkpKS+PLLL3n00UcZM2YMsbGxTJo0iSNHjlRYY1pdQ4YM4ZFHHuGDDz7gnXfewWKxnLGO1FMlJSUxffp0XnnlFYYMGYLZbGbmzJln3VvWEUyGYdSr3erz8/MJDw8nLy/vnI88czZzmQX8fPHGwpagXnQ4vvrcnUREpEEpLi5m7969JCUlERAQ4O5yRBziXL/XNclreoKVE3n7epHtZf3IIqxEM6siIiIiNaWw6mRHfa1LASLMmXrkqoiIiEgNKaw6WUGQNawGUsyxjGNurkZERESkblFYdbLiUPtNVjnbtBRAREREpCYUVp2srIk9rObvUlgVERERqQmFVWeLtN5gVYIfxw7mubkYERERkbpFYdXJ9o26jzDyCKCYTfGXubscERERkTpFDwVwssbNQyk40c7UKgARERGRGtHMqpNF25esKqyKiIiI1JDCqpMprIqIiIicP4VVJ4uOMPMYz/AK9zH8t6fdXY6IiIhLzZo1C5PJZHv5+PgQHx/PbbfdxsGDByuc+/jjjzNy5EiaNWuGyWSq8pnz7777LqNHjyYxMZHAwEBat27NX//6V9LT06tV05AhQyrU5OvrS2JiInfccQepqann/b0mJiZWqHnfvn2YTCZmzZplO7Z8+XImT57M0aNHqz3u+PHjSUxMPO+6KrN06dIKP4NTX2PGjAHAZDIxefJkW58tW7YwefJk9u3b59BazkVrVp0sJMyLx3iWQIrZeagT8IS7SxIREXG5mTNn0r59e4qKivj555+ZNm0aKSkpbNy4keDgYABefvllunTpwqhRo3j//ferHOupp55i6NChPPfcczRr1ozt27fz9NNP8+WXX7Ju3TpiYmLOWU/Lli2ZPXs2AKWlpWzatIkpU6awePFitm3bRlBQUI2/x/nz55/zOffLly9nypQpjB8/nkaNGlVr3CeeeIL777+/xvVUx3PPPcfQoUMrHIuIiABgxYoVxMfH245v2bKFKVOmMGTIEIeH57NRWHUyk5eJbO9o4s37aVSqdQAiItIwderUiV69egEwdOhQzGYzTz/9NF988QU33XQTAAUFBXh5WT/0/eijj6oca926dUSfss5u8ODB9OjRg969e/POO+/w+OOPn7OewMBA+vXrZ3t/4YUXEhAQwB133MGyZcsYPnx4jb/H7t2717hPdbRq1cop4wK0adOmws/hVFUddzUtA3CBfH/r/6CaGFmYS81urkZERMT9TgahUz92PxlUz+XUoHpSz5498fb25sCBA+ddU3h4OAC+vr62Y1V9BD958mRMJlOFY6cvA6isz0MPPQRAUlKS7WP3pUuXnrWuymowmUzcc889fPTRRyQnJxMUFETXrl1ZsGDBWceqiVOXAcyaNYvrrrsOsP5j42Ttpy5xcBbNrLrAseBoKARvLGTtziEyOcrdJYmIiLjVrl27AIiKcszfiSkpKZjNZjp27FjtPuXl5YB9GcDUqVNp2bIlAwYMcEhNp5swYQI5OTm89tprzJs3j9jYWAA6dOhwXuN98803rF69mqlTpxISEsILL7zA1Vdfzfbt22nZsmW1xrBYLLafw0k+PmfGwyuuuILnnnuORx99lNdff50ePXoAzp31tdXj9CsIpaERcMTazt+brbAqIiLV9tJL1te59OgBX31V8dioUfD77+fuO2mS9XVSQQEkJ1f99fNhNpspLy+nuLiYlJQUnnnmGUJDQxk1alTtBsa6fOCuu+4iISGB22+/vVp9Nm/eXGEGFaBt27Z88803+Pv717qmysTHx9O8eXPAumSgtus+i4qK+OGHHwgNDQWgR48exMXF8emnn/Lwww9Xa4yxY8eecWznzp20bt26wrGoqCjatGkDWMO1K5cIKKy6gDk8wtY+lprtxkpERKSuyc+H026ar1RCwpnHjhypXt/8/IrvDaNiv9O/fj5ODzedO3fmjTfeqNbNUGdTXFzMNddcQ2pqKj/99BMhISHV6teqVSvmzJkDWGdY9+7dy/PPP8+wYcNYunSpLZi5isViwWKx2N6bTCa8vb3P2mfo0KG2oAoQExNDdHR0haUVp8+aent7V1i+8Pzzz3PRRRdVOCehsl8mN1JYdQFLk0hbuzgty42ViIhIXRMWBs2anfu8yj5Nj4qqXt/Tb2A3mSr2O8cN7tXy4YcfkpycjI+PDzExMbaPwGujpKSEq6++mmXLlrFgwQL69u1b7b4BAQG2G77AGqaHDBlCs2bNePLJJ/nkk09qXV9NTJ06lSlTptjet2jR4pxbRJ28a/9U/v7+FBUVAdats5KSkip8fcmSJQwZMsT2vmXLlhV+Dp5IYdUFvKLsv0yl6ZpZFRGR6qvNR/CnLwuortBQSEs7v75VSU5OdmgoKikpYfTo0SxZsoQvv/ySYcOG1XrM2NhYIiMj+eOPP2zHAgICKCkpOePcrCzHTj79+c9/ZuTIkbb3jliKEBcXx+rVqysca9euXa3HdTWFVRfwbWoPq+WHFVZFRERq4+SM6k8//cS8efO49NJLHTJuWloaWVlZFW54SkxMJDMzk8OHD9uWLJSWlvLdd9+d1zVOhtCTs58nxcXFERcXd56VV87Pz8+h/0CoqnZnU1h1Ad82ifzIRWQTQYl/63N3EBERaYBSUlI4csR6R7LZbCY1NZXPP/8csO6lenLngDFjxrBo0SIee+wxIiIiWLlypW2MsLCwat1dX1RUZOtnNpvZu3cvL7zwAgAPPPCA7byxY8fy5JNPcsMNN/DQQw9RXFzMq6++itl8fltRdu7cGYBXXnmFW2+9FV9fX9q1a1dh7amn6tSpEwBvv/02oaGhBAQEkJSUVOlyBEdSWHUBvwt6czE/AjAhAm5xcz0iIiKe6KmnniIlJcX2funSpbY9SE9da3lyL9Fnn32WZ599tsIYgwcPPue+pQB79uyhf//+gHV/16ZNm9K1a1dee+01Bg8ebDsvKSmJL7/8kkcffZQxY8YQGxvLpEmTOHLkSIU1ptU1ZMgQHnnkET744APeeecdLBbLGetIPVVSUhLTp0/nlVdeYciQIZjNZmbOnHnWvWUdwWQYhuHUK7hYfn4+4eHh5OXlnfORZ66Slma/S/Pqq2HePPfWIyIinqO4uJi9e/eSlJREQECAu8sRcYhz/V7XJK/pCVYucOrseLaWrIqIiIhUm8KqCwQGWl8ADr55UERERKRe05pVF/nauII2bMSy3RfY7e5yREREROoEhVUXiTOl05wDlJl9MCwGJi/TuTuJiIiINHBaBuAihYHWhau+lFNw0AHPrRMRERFpABRWXaQk2H6XVd4e3WUlIiIV1bPNeaSBc+Tvs8Kqi5SF2cNqwT6FVRERsfL19cVkMnH8+HF3lyLiMIWFhYD197u2tGbVRYwm9rBaeEBhVURErLy9vQkPD+fIkSOUlJQQFhaGj48PJpPubZC6xzAMCgsLyczMpFGjRnh7e9d6TIVVV4mMtDVLDimsioiIXdOmTQkMDCQzM5P8fN3XIHVfo0aNaNq0qUPGUlh1EZ8Y+8xqeYY2WxURETuTyUSjRo0IDw/HbDZTXl7u7pJEzpuvr69DZlRPcnpYnTFjBv/6179IT0+nY8eOTJ8+nUGDBlV5/uzZs3nhhRfYuXMn4eHhXHbZZbz44otEnPoYqDrIP85ev5GlmVURETmTyWTCx8cHHx/NJYmc5NQbrObOncsDDzzAY489xrp16xg0aBAjRoxg//79lZ6/bNkyxo0bxx133MHmzZv57LPPWL16NRMmTHBmmS4RlGAPq6ZchVURERGR6nBqWH3ppZe44447mDBhAsnJyUyfPp2EhATeeOONSs9fuXIliYmJ3HfffSQlJTFw4ED+8pe/sGbNGmeW6RLBXVpxL69yI7P5Ju7P7i5HREREpE5wWlgtLS1l7dq1DB8+vMLx4cOHs3z58kr7DBgwgLS0NBYuXIhhGBw+fJjPP/+cK664osrrlJSUkJ+fX+HliRq3asJ/uJdPuJF1lq7uLkdERESkTnBaWM3KysJsNhMTE1PheExMDBkZGZX2GTBgALNnz2bs2LH4+fnRtGlTGjVqxGuvvVbldaZNm0Z4eLjtlZCQ4NDvw1HCwuDkEqRsrQIQERERqRanPxTg9H3iDMOocu+4LVu2cN999/Hkk0+ydu1avv32W/bu3cvEiROrHP+RRx4hLy/P9jpw4IBD63cUkwmaNLG2s7QZgIiIiEi1OO12w8jISLy9vc+YRc3MzDxjtvWkadOmccEFF/DQQw8B0KVLF4KDgxk0aBDPPPMMsbGxZ/Tx9/fH39/f8d+AE7QPO0SzzAxiM3OBYe4uR0RERMTjOW1m1c/Pj549e7J48eIKxxcvXsyAAQMq7VNYWIiXV8WSTu7TVR+emfxq5g38Tk++KbmY4twid5cjIiIi4vGcugxg0qRJvPvuu7z//vts3bqVBx98kP3799s+1n/kkUcYN26c7fwrr7ySefPm8cYbb7Bnzx5+/fVX7rvvPvr06UNcXJwzS3WJomD7U6xyd2nhqoiIiMi5OHXX4bFjx5Kdnc3UqVNJT0+nU6dOLFy4kBYtWgCQnp5eYc/V8ePHU1BQwH/+8x/+9re/0ahRIy666CKef/55Z5bpMqWhEZBubRfsyya2d7x7CxIRERHxcCajPny+for8/HzCw8PJy8sjLCzM3eVUsKTfwwz9zRq81734I93/dpGbKxIRERFxvZrkNafvBiB2plMeGVucpi0BRERERM5FYdWFvCKb2NrlR3LdWImIiIhI3aCw6kJ+MY1tbXO2wqqIiIjIuSisupB/rH1mlZwc9xUiIiIiUkcorLpQYJx9ZtU7TzOrIiIiIueisOpCoS3sM6vex/LcWImIiIhI3eDUfValorD2ccRzgFwa069tED+6uyARERERD6ew6kIh4d4c9omnvBxytApARERE5Jy0DMCFTCZofGLZaq7CqoiIiMg5Kay6WJMTy1a1GYCIiIjIuWkZgIuNsXyKH5toXJBLedFL+AT6urskEREREY+lsOpio45+SB++ASA79Uki2ke5uSIRERERz6VlAC5WEmLfvip/n9YCiIiIiJyNwqqLmcPsDwYoPKi7rERERETORmHVxYxG9plVhVURERGRs1NYdTFTE/vMatlhLQMQERERORuFVRfzjrLPrJZlamZVRERE5GwUVl3ML8Y+s2pka2ZVRERE5GwUVl0sMM4eVk1HNbMqIiIicjYKqy4WFG9fBuCdr5lVERERkbPRQwFcLDQpkjX0JJfGHPTryEB3FyQiIiLiwRRWXaxR60iasgaAgREw3r3liIiIiHg0LQNwMX9/CAqytnO1ZFVERETkrBRW3aDxiXuscrRkVUREROSsFFbdoMmJe6w0syoiIiJydgqrbvBEzgP8QRd2FCdQlHXc3eWIiIiIeCzdYOUGCZb9dGEjABn7cgmMDHZzRSIiIiKeSTOrblAWYn8wwLEDWgsgIiIiUhWFVTcwh9sfDHA8TWFVREREpCoKq25gNLLPrJaka0sAERERkaoorLqBV6R9ZrX0sGZWRURERKqisOoGPtH2mVVzlmZWRURERKqisOoG/jH2sGpka2ZVREREpCoKq24Q2My+DMArTzOrIiIiIlVRWHWD4Hj7zKpPvmZWRURERKqihwK4QVjbpjzO0+TSmJCYDgxwd0EiIiIiHkph1Q3C44J5lscB6Ku5bREREZEqKSq5gbc3NGpkbedoyaqIiIhIlRRW3aTxiWWruVqyKiIiIlIlLQNwk/jwAgyyiMrJwTB3x+StfzeIiIiInE4JyU1e2j+GvbRklaUXBQfz3V2OiIiIiEdSWHWT0mD79lX5qVoLICIiIlIZhVU3KQu1Pxjg2AGFVREREZHKKKy6iSXcPrNamKYtAUREREQqo7DqJqYm9rBaclgzqyIiIiKVUVh1E69I+zKA8sOaWRURERGpjMKqm/jF2GdWzdmaWRURERGpjMKqm/jH2mdWTXqMlYiIiEilFFbdJKiZfWbVK08zqyIiIiKVUVh1k5Dm9plV32OaWRURERGpjB636ibh7ZrSmQ3k0IQurZuwyN0FiYiIiHgghVU3CQ73YZtPZ8rLIUZPWxURERGplJYBuInJBE1OrATQ/VUiIiIilVNYdaPGJ+6xytX9VSIiIiKV0jIAN7rctIgRbKNJfg7lRU/iE+jr7pJEREREPIrCqhuNzZlBXxYAkL3vXiKSo91ckYiIiIhn0TIANyoNsW9flb9PC1dFRERETqew6kbmUPuDAQoPauGqiIiIyOkUVt3IaGyfWS06qJlVERERkdMprLqRqYl9ZrX0sGZWRURERE6nsOpGPtH2mdXyTM2sioiIiJxOYdWN/Jraw6qRo5lVERERkdMprLpRQKx9GYApVzOrIiIiIqdTWHWj4AT7zKp3vmZWRURERE6nhwK4UWhiBLtpSQ5NSPNu4e5yRERERDyOwqobNWoVQQy7ARgYA2PdXI+IiIiIp9EyADfy84PgYGs7R0tWRURERM6gsOpmTU4sW1VYFRERETmTwqqbNT6xIUBuLhiGe2sRERER8TRas+pmf8t7khak0Lgkl6IjywmKDnF3SSIiIiIeQ2HVzdqUbaE/PwOQsS9HYVVERETkFFoG4GZlIfYHAxTs116rIiIiIqdSWHUzc7j9wQCFabrLSkRERORUTg+rM2bMICkpiYCAAHr27Mkvv/xy1vNLSkp47LHHaNGiBf7+/rRq1Yr333/f2WW6TxP7zGpxumZWRURERE7l1DWrc+fO5YEHHmDGjBlccMEFvPXWW4wYMYItW7bQvHnzSvtcf/31HD58mPfee4/WrVuTmZlJeXm5M8t0K68I+8xqWaZmVkVERERO5dSw+tJLL3HHHXcwYcIEAKZPn853333HG2+8wbRp0844/9tvvyUlJYU9e/bQ5MQGpImJic4s0e18o+0zq+YjmlkVEREROZXTlgGUlpaydu1ahg8fXuH48OHDWb58eaV9vvrqK3r16sULL7xAs2bNaNu2LX//+98pKipyVplu5x9rn1klVzOrIiIiIqdy2sxqVlYWZrOZmJiYCsdjYmLIyMiotM+ePXtYtmwZAQEBzJ8/n6ysLO666y5ycnKqXLdaUlJCSUmJ7X1+fr7jvgkXCGxmD6teRzWzKiIiInIqp99gZTKZKrw3DOOMYydZLBZMJhOzZ8+mT58+XH755bz00kvMmjWrytnVadOmER4ebnslJCQ4/HtwppAE+zIA3wLNrIqIiIicymlhNTIyEm9v7zNmUTMzM8+YbT0pNjaWZs2aER4ebjuWnJyMYRikpaVV2ueRRx4hLy/P9jpw4IDjvgkXCGsVxb+ZxGM8w6KIm91djoiIiIhHcVpY9fPzo2fPnixevLjC8cWLFzNgwIBK+1xwwQUcOnSIY8eO2Y7t2LEDLy8v4uPjK+3j7+9PWFhYhVddEhYbzEOmf/Mcj7HId5S7y6mW48ehtNTdVYiIiEhD4NRlAJMmTeLdd9/l/fffZ+vWrTz44IPs37+fiRMnAtZZ0XHjxtnOv/HGG4mIiOC2225jy5Yt/Pzzzzz00EPcfvvtBAYGOrNUt/HygsYnVgLkePgqgD174KKLICQEGjWCBx+E4mJ3VyUiIiL1mVO3rho7dizZ2dlMnTqV9PR0OnXqxMKFC2nRogUA6enp7N+/33Z+SEgIixcv5t5776VXr15ERERw/fXX88wzzzizTLdr0sQaVHM9+P6qtL1lLOryGCuOTwECKSqC6dNh716YN88aukVEREQczWQYhuHuIhwpPz+f8PBw8vLy6sySgL69LexYk0cTctlZloiXj+clv7ntnmTsjqf5hBuYFPNfco+aKCmBFuzjhb9lcv2LfdxdooiIiNQRNclrnpeKGqDX9l1JLk3YTSsK9nve9OqKTw8wase/ALiW/7H56z18+SX8if/yB13pNP0O8o9a3FyliIiI1EcKqx6gNLiRrZ231/MWrubcP5lArItTdwy/lya9W3HpcIOnI6YTTj4dzJv4YcIcN1cpIiIi9ZHCqgcoD7M/GOB4mmfNrO5anctFGf8FIN8rnOTZj1u/YDIR8JL9kbnJX0yjrLRerSgRERERD6Cw6gGMcPuDAYoOetbM6qZHZ9tmVXcNGId3pL3WZuOGsb1JfwCSzZtYPn2VW2oUERGR+kth1RNE2GdWSzI8Z2bVYoGWKTNt71tMnXDGOaXj/2xrF//nXZfUJSIiIg2HwqoH8DlltrI803NmVjd9k0qXst8B2BXWg4ihXc44p8NT11FgCgWg/4G55GaUuLRGERERqd8UVj2Ab4x9ZtWS7Tkzq/tnLLC1c4eMrvQc77BgtrW3fi2MAta9+KMLKhMREZGGQmHVAwTE2cOqKddzZlab/PqVrd3i3quqPC/45mtsbfP/5ju1JhEREWlYFFY9QHC8fRmAV75nzKxmZsK/CibyETezNagH0cM6V3luu3uHU4j1cbjd9n1J8XGzq8oUERGRek5h1QOENLfPrPoVeMbMakoKfMHVjOMjPrhnDZhMVZ7rHRrE1uaXspPWfMYYVv543IWVioiISH3m4+4CBBq1jmQQP5NDE1onRfOluwsCli61t4cMrTqonrRryn+54Tbr7Oo/l8OQUU4qTERERBoUhVUPEBjqw2r/QZSUgI+HTEqeDKve3nDBBec+f+jlgbb2Dz84pyYRERFpeLQMwEM0ObESIMcDVgFk7iukxZaFhJJPr14QGnruPtHR0LWrtf3775Cd7dwaRUREpGFQWPUQjU/cY5XrAfdX7fpkNQu5glwaM9nnmWr3GzbM+mcjI4dVC7OcVJ2IiIg0JAqrHmKwz6/cyiz+fPwlSgvcu7F+/g+/AeCNhcjOsdXud2XMKjbSiRwi8HnzP84qT0RERBoQrVn1ELdkvkh/vgDgyJ4biOoa57ZagjetsrWbXdO32v06XRRNJJsBCN/8q8PrEhERkYZHM6seoizEvn1Vfqr71gJYLJB0xDqzeswUQtOhydXuG9mzBRk+zQDokLeCooJyp9QoIiIiDYfCqocwh9kfDFCY5r67rPYsO0S8kWZtR/TG5ONd/c4mE6kJAwEI4Tjb5v7hjBJFRESkAVFY9RBGY/vManG6+2ZWD365xtY+ltynxv0t/Qfa2tlfLnNITSIiItJwKax6CK9I+8xq6WH3zayWrLLPhgb0717j/rHX2cNqwBqFVREREakdhVUP4RNln1k1H3HfzGrQjvW2dtyIrjXu32JkZwpM1o1ZWx1ejmE4qjIRERFpiBRWPYR/U/vMqpHtvpnVZtnWmdVCAokZ2KbG/U0+3uxu0huAWOMQB3475ND6REREpGFRWPUQgXH2sOp11D1hNfNgGcVmXyyYSA3tXLObq05R0K63rb3/f6sdVZ6IiIg0QAqrHiKkuX0ZgE++e8LqH1t86cBWQingszFzz3ucwAutYdWMFzlr9zqqPBEREWmAFFY9RFhSBEcJZxetyDSi3FLDHyfurSokmOYXJp73OAm3XsQgfiacPF42HnBIbSIiItIw6QlWHiK8RSP8vI5isUDvGLjKDTWsX29vd635vVU2Me0bsy9+EMfTYO1a64MGvPTPIhERETkPihAewtsbGp9YtpqV5Z4aTs6s+vhAhw61G6tXL+ufBQWwY0ftxhIREZGGS2HVg0RGWv/Mznb9tctKLLy3qS8fcgsPx8zE37924/W232PFmjVVnyciIiJyNloG4EEiIqx/5udDWRn4+rru2qnLDtCHVfRhFWuMPOC2Wo3Xr10u9/IRvVlNyNvd4eZJjilUREREGhSFVQ9yS/7rPMRiIsgmd9vnRHeOcdm1M1O20vpEuzgxudbjdW1fwkXcD8CajRmAwqqIiIjUnJYBeJDk0vWM5ksGsYz8nYddeu3CtVttbd8utQ+rER2bkullDduJeesxLHqUlYiIiNScwqoHMTeKsLWPp7r2Liuvndts7SYX1D6sAhyI6AZApJHFwVUHHTKmiIiINCwKqx7EdPIOK6D4oGvvsmqcbp9Zjb+4vUPGPNa6m62dtmC9Q8YUERGRhkVh1YN4RdtnVkvTXTezahiQcNwaVg97xxLYNNwh4/r36WZrFy5f75AxRUREpGFRWPUg/nH2mVVLputmVjM2ZRFpWMNxeiPHLAEAaHpZN1s7YPt6h40rIiIiDYfCqgcJaGafWTVlu25mNe1H+3rV4/GOWQIA0HxYGwoJBCAuc73DxhUREZGGQ2HVg4Qm2WdWffJcN7Na8Jt9vSrJjptZ9fL1Zk9IFwASy3dzdH++w8YWERGRhkFh1YOEJdlnVv0KXDezupwB/J1/8S53EDysn0PHzm3ezdbe99UGh44tIiIi9Z8eCuBBGic1wowX3lgIKnTdzOpPhzuyhI4AHBnt2LGLB17Mx1uOsZ5udMhqTjfHDi8iIiL1nMKqB/Hx9+Zdv4kcLQ0kP6g1U1103W0nlqxGRlpfjhR2+xiGvz0GgNsPwO2OHV5ERETqOYVVD/N/Ca+zezc0LsclYTUvD9LTrW0HLle16dwZvLzAYoH16x0/voiIiNRvWrPqYU7ObObmQnm586+387ccerCWUPJp77iNAGyCgqBtW2t70yYoK3P8NURERKT+Ulj1MBH2e6zIzXX+9YoW/MhaepFPODem/9sp1+jaFQIppFvpb+xc4drHyIqIiEjdprDqYSIjwQszTcgm+1CJ069XunmnrR3UPsEp1xhf8hYFhPIb/cj57yKnXENERETqJ4VVD3PT9icpw5dsIin9eaXTr+eTusvWbtKnjVOuEdG9Od5YAChf84dTriEiIiL1k8Kqh/FpFIwXBgBFac7fvios0x5W4wa1cso1EkZ2tbVD9yqsioiISPUprHoY7yj7otWydOev74w9bg2rR7yiCWoa5pRrxHSLJctkvXOsee4fGBbDKdcRERGR+kdh1cP4xdk3OrVkOjesHj14nKYW675VGSGtnXYdk5eJ1MbdAIgyjpC5IcNp1xIREZH6RWHVwwQ0O2U7gGznLgM49MtuWzs/xjnrVU8qSLIvBUj7RksBREREpHoUVj1MSKJ9ZtX7qHNnVnNW29ermhOdN7MK4NPTHlaPLVvv1GuJiIhI/aGw6mHCkuwzq34Fzp1ZLd1k37bKr6Nzw2rUxfaw6rtVM6siIiJSPQqrHqZxqya2dtBx586slqXZ14427uXcsJo0oj2l+AIQk6GwKiIiItWjsOph/IJ8yKURAMElzp1ZfabJy4RQQFfW0+zSTk69ll+IH3sCOgJglJRSfMwFz5IVERGROk9h1QPl+VjXrTYuP+LU6+zaBccJIbNpV0IiA5x6LYD3L51LI3Jpwy627PBx+vVERESk7lNY9UDPt36HgfxCH2MV5U6agDx2DDJOrAJo7dwVADYxg9qSd2LW+A+tBBAREZFq0PSWB0prPYRft1nb2dkQE+P4a+y271rlsrDapYu9rbAqIiIi1aGw6oGio+3tzEznhNXcBb/yDjPZRWuSwkYBHRx/kdN0tW8IoLAqIiIi1aKw6oFOD6vOYCxfwQTeA2ClJQFXhNXoaLgn/CO65qXQftkuDMsSTF4mp19XRERE6i6FVQ+UFJDOSNYQTSYla/rAsM4Ov4bXHvsDAcJ7OffpVae62W8uffkGyuHQylTiBiS67NoiIiJS9+gGKw/UIetnvmYU7zGBkBXfO+UaIRn2sBp3oYsWrQLHWneztQ8t0loAEREROTuFVQ8U2OKUdQCHnbMOILrAGlZzTY0JT2pyjrMdx6+3feFq4QqFVRERETk7hVUPFNLKfkeVd7bjw2rR0RKamfcDkB7kullVgKbD7WE1YNt6l15bRERE6h6tWfVAjdraZ1b98xwfVtN+2UsbDACORrk2rCZd3IrjBBFMIU0zPXtmdcsPh9jzytf8FjQUo01bLroIhg4Fk+4JExERcRnNrHqgiDZNMJ/4TxN03PFhNXvlTlu7rLlrw6qPvzd7gqw3jDUv20NhRr5Lr18dmbsLWNTqbtpdksDIBRNJ/XQlzz4Lw4ZBnz6wcaO7KxQREWk4FFY9kI+fF1mmKADCS5ywDGCT/eYq3w6u2wngpKyEbrb2vq89K/ntXZ5OTvIARuyZgTcWAKKx/zdYswb69jH44fXt7ipRRESkQVFY9VBH/axLAZqUZ4JhOHRs065Ttq3q6dqZVQCjs33das5Sz1kKcHhbLubBQ2lftgmA4wSx+tLHuPWzK/n4Y+jYEcBgSvE/GHhPV1Y+84Nb6xUREWkItGbVQx0LjIYSCKSY44ePEdw01GFj/2oaSCrHaM0ukge5Pqw2GtwVPre2TevXu/z6lSkrsbC3/5/oV26dMU3zTcTvp+/oPbAtAJ2Ba6+F9y76L3eveBGA9k+MYUfX32h7ZTt3lS0iIlLvaWbVQxWF2m+yyt3u2KUA7xz7E+P5gCsb/0qTdlEOHbs6kkZ1Zi7X8wjP8bHPeJdfvzJLxr5Bv6PfAZDtFYn/r0uIPhFUTwoIgIk/Xc/quFEANCIP05hrKT5a7PJ6RUREGgqFVQ9V2iSGbJqwlfbkHipy2LglJbDfumsVrV0/qQpA4+ahPJQwl//jEf67b4CjVznU2L6f99P/y4dt7w+//AlRvRMrPdc7wJeO62azI8B6k1ib0s2suPQpV5QpIiLSICmseqhlV79EJNl0YCupoZ0cNu7evfYlsK1aOWzYGut6Ytlqfj7s2+e+OgDSx9xLKMcAWNHpTjrcd/FZzw+KDsH7k9mU4AfAhateZOPbK5xep4iISEOksOqhomPsm3lmOnAVwN5Nx/HH+rF1G9dvBGDTpYu9/Ycb77FasgSeOzKBDXTmsHcsnRe9UK1+rUZ3ZsVlUwDwxoLvA3djKTM7s1QREZEGSWHVQ0Wf8sRVR4bVwNnvUkgQqTTnwsJvHTdwDXXtCn6U0J3fyfz2d7fUYBjw2GOwgCvpxnqWv/ArIfGNqt1/4Py/sy3AOkXcvmgdK/4yyzmFioiINGAKqx7KWWHVtHsXXhg05wAxrR23w0BN9Wh6iGOE8Ds96fnFE26pYeFCWHHi0/vkDl6Muj+pRv19AnwofHa67X3bDx7l2CHPe8iBiIhIXaaw6qGaBubxNnfyNSMZ/O0jDhs3KN2+x2rTgW66wwpIGhDLMUIAaJblnnUAzz9vbz/9NHh713yMHpOGsDxuDABZlibM/dd+B1UnIiIi4IKwOmPGDJKSkggICKBnz5788ssv1er366+/4uPjQ7du3ZxboIeKae7PnbzLSL4h6dAyh40blWcNqwWEENkh+hxnO4+3j4k9od0AaGo+SP4uxz+p62y2zF7Hfb+MoT/LSW5vMHr0+Y8VM/P/GGf6iE5s4p8fdaKgwGFlioiINHhODatz587lgQce4LHHHmPdunUMGjSIESNGsH//2Wef8vLyGDduHMOGDXNmeR4tNCqAHBoD0KjwkEPGLCsso1nZPgAOBrbG5GU6ewcny23Vy9be++lql14754mXGcP/WM4FvDroM7xq8b+EVsNbYdx0Mxa8yc6G115zXJ0iIiINnVPD6ksvvcQdd9zBhAkTSE5OZvr06SQkJPDGG2+ctd9f/vIXbrzxRvr37+/M8jxell8cAFFlhxzyyNVDK1LxpRyA3Aj3LQE4yad/b1v72E+rXHbdjN8P0XfvJwDkmJow4LmRtR7ziSewBd4XX7RuySUiIiK157SwWlpaytq1axk+fHiF48OHD2f58uVV9ps5cya7d+/mqaeqt9F6SUkJ+fn5FV71RX5QLGB95Oqxg3m1Hu/ICvt61ZIE94fVuNF9bO3ATa6bWd127+u20L6h/0SCIoNqPWbbtnDzzdZ2s9yN/DDx81qPKSIiIk4Mq1lZWZjNZmJiYiocj4mJISMjo9I+O3fu5OGHH2b27Nn4+PhU6zrTpk0jPDzc9kpISKh17Z6isFGcrZ29sfZLAY7/YQ+rPu3duMnqCa0uasERk/Vxr4lHVjlk9vhcyo6X0mnFOwCU4ku7V+922NhPPGZhHtewkS5cNOdOig7Xn384iYiIuIvTb7AymSquizQM44xjAGazmRtvvJEpU6bQtm3bM75elUceeYS8vDzb68CBA7Wu2VOUR8Xa2ke3ptd6PGOXPayGdnf/zKq3j4ldja1LAZpYsjmyaq/Tr7numW+INI4AsCb+amJ7xp2jR/W1butFo8RGADQyjrL+rrcdNraIiEhD5bSwGhkZibe39xmzqJmZmWfMtgIUFBSwZs0a7rnnHnx8fPDx8WHq1Kn88ccf+Pj48NNPP1V6HX9/f8LCwiq86o04e5Aq2l37mdWgg56xbdWpCtrblwLs/9wF61Znvm9r+vzlDocPH/XiP7Bg/cdYyy9fwlxY4vBriIiINCROC6t+fn707NmTxYsXVzi+ePFiBgwYcMb5YWFhbNy4kfXr19teEydOpF27dqxfv56+ffs6q1SP5dfCPrNallr7sHpP2If0ZSW3+31MdNfYc3dwgcALrTOr+0kgdUexU6915I9D9Dy8EICD3gn0eMjxu010urY9v0ZdDUCMOZ11kz5y+DVEREQakuotDD1PkyZN4pZbbqFXr17079+ft99+m/379zNx4kTA+hH+wYMH+fDDD/Hy8qJTp04V+kdHRxMQEHDG8YYiqPUpH1Gn124ZQHk5rN/fhDL6Uty+LyYPeRxE81uH0vT/0jlMUy4rhWuceK2tj37EhVgA2NZ3PM38z+MpANXg98Q/4b55AETNegHjP7dh8nHOtUREROo7p4bVsWPHkp2dzdSpU0lPT6dTp04sXLiQFi1aAJCenn7OPVcbsvBuScziVtKJxTvsQgbVYqwDB6CszNpu7RkrAABo3i4QS1QgHIFVJ+6xqmRJc60ZBlh+tj9coeXU8Y6/yAl97unDqkcvos+xn2hRspONT8+n85QxTrueiIhIfWYyDBfcgu1C+fn5hIeHk5eXV+fXrxYUwMlvYehQqGLZbrUsXgwndxH7xz8qPmrU3a64AhZaP51n925o2dLx11i1Cvr2NejPCm5q8St373vI8Rc5xZJHFzN0mvUHviOsJ22PrnZOChcREamDapLXPOTDYKlMaCiEhFjbh2q5ZPX4op+5n+lcwQI6xubUvjgH6mO/x4oVy53zb6eZMwFMrGAAAU84N6gCDJpyMRv9egDQNn8tu978wenXFBERqY8UVj1c7In7oGq5ZJXGKV8wnQdZwJV0NjbUvjAHurBbHi/zAGvoSauptzp8/KIi+MT6wCqCguD66x1+iTP4+Jo4dMvDtvf7/zXX+RcVERGphxRWPVxcHPhQRmh+GsePlp33OIEeuG3VSb0vDOJO3qEnv9Ni7xKHPxzgq0+Ok3fiAWDXXWedsXaFQS9fw3z/sYziS4bve5tTtrkVERGRalJY9XCPHrybEvxJI4GsFTvPe5yIXGtSKiKAmO6O2wjfEUIa+7I5rD8AseVp5K5Pdej4HScNZwX9+DNvcds4s0PHPpugUG+2PTWHrxmF2fDihRdcdmkREZF6Q2HVw/k0DsUL60xj3tbzW7hqLjUTX7obgDT/Vnj5eN5/9pyOF9raez/42WHjHvxxG53yltOP33jQ73UuHOLa7/2uu+w3yc2aBQcPuvTyIiIidZ7npRapwGgWb2sf3552XmMcWpGKP6UAZEW0c0hdjhZ8mX1jrrKffnHYuPuemmlr7x92OyYv196RHx5uDaxg3Tps1lPOf6RsTZiP5LDtwTfZ1O5a5iQ+TMeO0KYN9O4NN98Mi2//hCOf/gQWi7tLFRGRBkph1cP5t21ua5fvPXBeYxxO2WZrF7fwzLDa/ta+lOILQNOdjplZtZSU0XbFBwCU4kvH525yyLg19cADMMRvOd9yKQ+/15rc33a4pY5TFe85xIYBEymNiaf99L/Sacc8wlP/YMsW2LUL1qyB2bMhduazRI0dRmZwErsefB2KnfuUMRERkdMprHq48I4JtrZX2vk9QOH47/aw6tOpfa1rcoaoFkFsDuwFQIviHRRsOb9gfqpN/1pElOUwAL9Fj6JZt6haj3k+YmLg/h4/cynf442FfROnuaUOAMxmtt72AubWbemy4i0CjSLbl8LJIzAQGje2bgkbRh4d2AJAdPF+Wk+/h8ON25P+3jfuql5ERBoghVUPF9XTPrMamHV+YdW0Y7ut3bivZ86sAqR3Gm5r75rxfa3HK3/zXVvbMv72Wo9XG93fvotcGgHQZf2H5P227ewdnKBo+352xg8ledY/CTaOA5BPKF+0uI9vn15N0oFfOH4ccnKsE6g//BLA59fOYWnAZbYxYopTiZ0wku1dr8ecme3y70FERBoehVUPF92+CccJAqBR/vmF1fBD9mAUP8xzw2rY9fZQZFn0ba3GOropja4HrTOAaV4J9H3y0lqNV1stOofxU0/rwwi8sbD/1idcev3M+b9S1KkXbTKs64HNePFFs7s4sHQPo/e9wmWP9yI23tv2kC0/P+g90J/rP7+egQWL+HrK7/zif7FtvHYbPiO7eTdyv1nu0u9DREQaHoVVD+flbSLd1zq7GlOy/7z2IF1T1oW19GCvdysaJTZycIWO0+3O3uTQGIAW+37GMJ//TT3b/jkTb6z9N/W+nYBgb4fUWBt9Pr6fDGIA6Lz9c7K+Xe2S665ZZeHw9ffSpPwIAKmmFix8aClXHXidjoMjz9nfxweufLI73TK/56PhH5FFBADRJWmEjBzMnr/PcGr9IiLSsCms1gE5IdawGkwhBak1e1RqXh5MKHyNXqzl1v7nv0+rK4SEe/Ne+xcZydckWnazc/f5/Xoa5WYSFr8HgAUTSU+7dwnASQntg/llyJO291l3PuLwByCcbulSGHKRF6PLPyObJvwaMIyiZb9z5QuDbLOo1RUaZuKW725mx6d/sMLXutWYFxbuebnVicfZioiIOJ7Cah1QGGFft5q5pmZLAbbbl6vSPtm12zadD+O22/mGkRwnhO++O78x1i05yqqy7pTjzcpGl9Hukubn7uQigz+awB5TSwDap/1I2oyvnHat776DESPg+HHYQyvu77GMtrsX0X5Ak1qNO+C6ZiTu/pGPEh7lfl5hkeVSbr8dHnlEO1yJiIjjKazWARsueoAB/Eo8B9gR0KVGfU8Nq+08d7mqzaWnLC39+uvzG+PNzyK4hvk0Zz+H/vaSYwpzkOh4P9Zc+3+29z5/vx/jeKFjL2IYrLtvJtdeWWrbaeqKK+DdX5OJivN1yCViE3z4055n8br3Htux//s/uP46g6Lt57e2WkREpDIKq3VAQM+OrGAAB4knNa1may+3bbV/zNzeM3etqqBLF2jRwtr+6SfIruEN59nZ8PHH1nZBSByXPeB53/TIWWP4JcB6s1LT4lRWPeXAraAsFnZedg/dX7udWWU34k05114L8+ZBQIDjLgPWtayvvgqvvQZeJ/6fJHHev7F06EjuRwscezEREWmwFFbrgMREe3tvDR+AdMWH17OT1nzNSJJjjzqyLKcwmeBPVxUyljnMNV/L7rv+XaP+77wDRSe2Dr3jDggJcUKRtRQUbKLo+df4ne4MZilX//e6GofySpWXs2vQeNp8b73h6Rrm8fSwFObMsd7d7yz33GOdBb884Cde4B8EW44RPm4Uh+6d5vQ1uSIiUv+ZDKN+/W2Sn59PeHg4eXl5hJ18KHsdt3s3tG5tbV97LXz+efX7pvq1pkXZbo4RTGBpPt6+nv/vk3Vf7qf7aOv0ampwMi0KNlOdu4HKsvL4e9sveTN3LGUmf3buhFatnF3t+TEMGHmFwcJF1u/r+uthzpxqfZuVKylhd78babV+HgDlePPhkJmM//EW26yns238rZB9Q2/jyqJPbccO9B1Dwg8zPetfDWVlcPiwdUPZsjLrQtuwMGjUCKKicNkPTESkAatJXtP/K9cBzZvDJV4/chevM3h59Z9+VJh5jISyPQDsC+pYJ4IqQLdRzVnlPxCAFse3cvjLldXqt/nuGbySeyv7SGRK34UeG1TBGkrfeddEkxP3On36Kbz3Huc3E1lQwN6uV9mCagl+fHDFZ9z2k+uCKkDnvkH02jWHt+KnYsGauhN++5zDSf0wb9l+jt7OU14Oa9fCrFnwxD25WPz8ISEBunaFXr2gTx/rGpmmTSn1C+ZwTGfS+l1L2gc/amJYRMQD1I300sD5+sJ/vO/jde7hzvQp1d5/NHXRZryw/m2b3ayzM0t0KJMJDl0+wfY+feo75+xTnnWUpM9fACCaTC69y4OT6glxcfDGG9a2CQtH/vwY+296pEZjlO/cy6GkASRtt26dUEggs8d+ze1fX33+s7S1EBtn4tadT/DKRV+Rh/VfyjFZmynr3J2jz81wzbKA48fJense23rdxEvdPyIiwppJb7sNnnm9EaVUvSbCz1xMTOYm4n+bxwPjc2nWDP70J+s/JA7vK7J+zCEiIi7l4+4CpHqyw1tB1hYCKCF740EiuiWcs0/O0o22trlD3QmrAL2mjSFv/n2Ek0/79Z9QljYN3/iYKs/fdMdLdLMcBWBxzC1cdksd2PoA68f/y3816PfqjdxgzIVP4JDJTNxHz5/z4+hjny7EfPM44sqsC16PEs6XExZw+zsDXVF6lQIC4IEfRvLh46vp/dxoOrCVAEsRAY/dTcbcL4n55XNMYaGOvejRo2TNWkDue/NI2PItkZYiIoFUssjnllNONLGIEQAc84/E7O2HBRMBpfmEl2eTxF5asws/yljOANLTrcsz5syB0XzHfK4mO7o9pquuoskdV0Pv3lo2ICLibEY9k5eXZwBGXl6eu0txqB86P2AY1nkpY/PrS6rVJ6X7fbY+q5//0bkFOsG8lpNs9W8Y8Y8qzyvalWYUmEIMA4wSfI1Vc/e4sMraKyszjP90eN32vRpgHOx7tWEcPlxln0WLDGNm8F2287fR1vj0me0urLp6ln5zzHg/6G5bncvpZwy/xGJsd0Sphw8beS++bezrcJlRavKt8PM7+TpMlBEfVWyMHWsYL71kGN9/bxhpaYZRXn7mcIWFhrFtm2HM+7TMePXeHcZllxlGSIh9uLeZcMb4eWHNjOwb7jIs3y82jNJSB3xTIiINQ03ymsJqHfHD6Ndsf0GumPButfqsazzU1idjY6aTK3S8VV8cNIrxMwwwjpmCjZJd+ys9b3O70bbv86uEu1xcpWPk5xvGv9u/bZTjZfteCn1CjMNj7zEs/5tnGEuXGmUrVhvffWcYF19sPSWYAmM3ScZ3vpcbv3yd6+5voUpZWYYx5YLvjP3EG31ZYYBheHsbxk03GcaGdeWGsXq1YVgs1R4vM9Mwfrp3XoWf1amvDKKNT5v82fh43HfGhjUlNRn6DGVlhrFihWE8+qhhPBX7pvEzA6u87vGAxkb2FbcYlsU/nP8FnaG01DBycuyv3FzDyMurPLGLiLhITfKadgOoI5Y/sYgBz1wOwLKBDzPwl3PcaGUYZHtHE2FkcdjUlGhzulvWMNbW/Ph7ufrgfwDY0f5K2m75ssIt8/sefYvEaRMBOEw0Ocu2knxB7Z7Q5C4lJTD94q+5Y9ltRHLmXla/mC7kQiOlwrGxF6bzr4+aktDcs//jGgZ89VkJ9z3kz/5TnhkwmvnM5xoKQuMo7nUBIQO6Eti6mXUtgdmM+UgOxzbtY2OLkSwqHsrixbBmDcQZaaRhXwqTSnNSmlyDZfQ19P/bANp1qNl+xNW1axd89/ER8j76ii575nMJi/GntMI5b4b/k913/h+jRlnv3fL3tVh3HPBx4KorsxkyMiAtDQ4cwNKlG5lhrTlwAA4cgIL1u7nmpQsIKM7D11xc5TAlvsHM+ctSijv1Ijoa4uOhRckOIlctxCs+znp3Z0ICNG0K3s75mYpIw1STvKawWkfs+HYPbUdYbxpaHT+a3gfmn/X87M0ZRHSKtZ7f+BJ653zv9BqdYUNKLtFDkmnKYQB2PvwebabdDkDWW/8jbOKf8KMMgE+u/pQ/zbvObbU6gmHAf1/KoPixp7mp5D0CKLF9LY8wGnEUMNGyJUyeDDffXIvtrtzg+HF4+WWYPt36AIclDGEIKefs9xr3cB+vVTj2KdeRGdYG4+pruPDBnnTuYnLpzyItDRZ8UkDGzEW02zqfK/iGMAroy0pW0RewZu5xHdfy2oYLKUjqinfH9oT2bod3m1YQHQ2RkRAaag2C/v4QEVHxIt9/j2XzFop2pFG6Jw3LgTT8Mg4QlHcIb0u57bRJ3q/wsvk+2/tYDnGIZtX6PjqyiS10tL0fxwd8wPgK55i9fDjeOJ7y2AS8E5sT1L45vu1awoQJiIicj5rkNd1gVUckDkmkkECCKCI6c/M5zz+wcCMn/9oraFG3bq46VZfBjZk56k1u++pq5nE1f51xI08lQFCgQZ97phB5IqjOjX2Aa+fU7aAK1uB509+acuwvrzP77efI+GgxgXu34Ft6nNKQJtw6rJRR1/lz5ZXWXSLqmuBgePxxePBBeP89g1/+fTPH9wczlCUEUVRlv778Zmt37gyXXw5xV37Gtf3dd39TfDxMfCgUHrqew4evZ+5nJex+P4Xf/+gNJzbsKC4G/7W/4kchETtWwI4VUMW/Mw81SubhK7dw7Jg11Ofmwut/TKF36XKCgeCz1BJrPlDhfR7h7KMF+YSRRzjHCcaCF6YTu4P4UE4oBYRSwFEaVRyL9DPG97aUE5a9D7L3wSZgAaR6JTHmrQm0aIHtdfnSfxCduQn/1gn4tW6OKTLCusfuqS9fX2jWzDpbe1JJCaxebf2BlZRY/ywutj7h48SfRmERlsJijv/5QQqDo2xf9l28kMiPXj5xbhGUl1s3nTCZrL8cJhOYTJgDQ/nt/5YQFITtFbNwJiFbV+MdGohvWBBeocHWX9LgYGutJ9vNmkGbNmf5LyAizqSZ1Tpkc0BPOpb8jhkvOHYc7+Cqn5/5yr9K+fgff9Cb1Vz2j66Mev4CF1bqWKWl8OSAH/jX2qFYsH8UeSOzmc3NzAsdR//N7xGboH971UW7d8PSH8o58P1WzNt34Zd1CFN5GeWGN96NQimJTSS0a0vaDGtO377W3ODJsrNhwQJYsgRSUmDkvtf4G/8mkdSz9ttIJ7qwscKxhYxgBN9WOJZFBAdIsL2yAhPYGzeQrPYDSUiwhuiEBOvEbWioPW/5+lpXI1gs1lUEhYVw9Kj1lZtrX1VgbNtOxN41+B9Jo/Fx61Was5/m7CeCHFsdKVx4xqz4rwxgACvO+TP6oN1zzGv3CN7e1n1ww/LT+Djl3DucAHRmA5uw/wN8PDOZye3n7HeYaNsnNCd9zE3cxH/P2Xdp3I28deFsW94ODoZ/vtYMv9JjmP2DMQcGYwQFYwSHYAoOhsBA62y5txd5t0+itGd/vLysh/xSdxLyyjPWhc5mA8Ny4mWAYVR8v/2fMyn3CcBstv53i1kyh5ifPwOs59q2grOtnLa2CxKS2XH78/j6Wlef+PhAm//cT9CB7dbs7u0Fvj6YTr58fDD5+eDl4435ytFw1VW2vqbSEnjqKftAJ1/e3mceGzUKYk7ZtSUtzfqPELB/BFTZn76+cNllFX/oGzZY+5+rb9Om1ud0n2rZMutfHOfq27ZtxX80HT8O69efvd/JdvfuFR8NmJEBBw+e+5oBAdDutN1q9u61Xvts/UwmaNLE+uCSkwyj4nZ6Z+sfE2P9vTypqAiysiqOf/Iajn42dxU0s1pPZcV0hP2/442FfT9uJ3FU1yrP/W2dH2vozRp6c+cNLizSCfz84PGlF3PgL/DfU/5emctYuieXcOvS24iKrkOfhUsFrVpBq1Y+8JfOQN39FOCkiAi49VbrC2D//ntZs+ZePll7jLzVOzC2bScoez/BhUeI4ghBFOKNmQNUDGteXvBJ2F382uhmypvG49U8nuA2ccS2DCQhAdomwEXxzng4WLsTL+vfZ/v3Q2oqrEmFgzuOc3zbAcx795OW6YdXtjVEnRRzWhisyrbt8NUpz4loQmDVJ58m8LQZ+KJT+pbhQ/mJv9ZMGJgw8MKCCYNjnPmDCqKwWtfccSiYOXMqHnuYPPw5DsX5kFd139u/G8v/Tnk/gEx+5cNqXbflD29zDHtwmMpmnmDeOfvtXp3NyNNO+40V9GH1Ofs+9UkiU7nK9r6xqYgc4/lq1TvuX53ZGGwPq8NzlvH8/j+ds1++VyMGdsytcGxq2suMzp11zr7fho/lny0q/sdZvOV6osvP/ITgdM+0fJ+FMbfZJuBbFe5h1u/V2/rvlosOkhsYZ5vAv3rXR4zf8o9z9jsQ3olHr9xoy4YA/1h8Jx3Tfzxn3287/53/9f0XcCJbWiy89V71ZvxfvXIxO5pfbOvbYd8S/rrgijPOe/uqb/C96nJuu61aw7qMwmodUtSuG5v3r2UzHYnc60XiWc49+Y/ZgADo1MkV1TlXSAjMng133w3ffmv9C3L4cB8GDbq9Tq3ZlIaneXPri2tCgB4nXtaJn+xs66feZWXQrhR2+FX8BNrb+0p3lk5goHUSyD4RFAy0P/Gy1p2WZg2zqakwe98usrdnUbZ7P0FZ+/E+loep8BjehccItBwjhGP4UsbvJ34GJx0nmBf5G8UEUGoKoNzH+jL8/LH4B2L2D8TwD4TAQJpFtSc2xFpbYCCE+F3DI775+IQG4h/sQ2DgmbPIJ/+cUm4N4IWF1tdPma/wS94TGMdPHCg8jlfhcbyLj+NTfAzf0uMEcZxV9DntJ2OwlWRCKSCY47bX6TfbWc+s+H9QJ5dinI/Tx3JG3/LTYoGXUV7FmWfastOH9ae8r+5u12YLbKz4oQJHq/lzOppnnYStMF41++7eAyv22N/n1eC/zY8/UWHBTNtq9juaBx9/XPHYLXDKqvGqbdwI757yc/IC3qrmdb/6Gk6NwyOAv1Zy3hdfQoAXCqty/vLumMSIxZMAeOYYXFTFebm51ruWAbp1q5trG6syYID1JVLX+flBbKy7q6gdX19ISrK+rExA1IlXT9t5hmEN5aWl1tA41mz9+N9stn567O8fgJ/fi/j7n8+mA34nXuej+YlX5QzDGm6PHYN/nVhLfOwYHDtm4uDx1Sfa9uOFeWWUHi3Eu6QQS7kFw2LQ2LcJN3nZQ7N3aU/uL96Gt7cJb2/w8jbZX14V3/+1cTBeJz5x9/IC75K/8e+yP1f4eNfkZbL9aTJZj1m8fZnqZ/0Zl5VZ//yi8Af+V2qmrNyEpdyCpbQco8z6OtmmvJwcr0iGmqx9ysuB0jAmHF2CyVyOUW7GZC7HZLZ+0WS2/kc8eeyQkYj/KWvIt1q68pjxvC2gn/GnyfpnKf4Enfb31MLyq9lvtDqzj1Hx/WavTgSe9jvzRtl9BBvHMGFdTnH6n5zou/G0T3KyiORF/lbhmlXVfvy0VeTr6M5/uLvq7/XEn4eIO+P3bCGXs+/E9FNl/U62157yvynrd2HiI24+6/VO/nmYig/VyaApnzHmjGtk0JQkD5wA0prVOmTTJuvNJQA33ACffFL5eStfX8v8e35gNb3p8Zc+vPimwz8nFBERqdNO3SjZYjlz8+TqHjvf/idrqOzPs33NWX1Pvpo0OXNZrTNozWo91batdXebkhLrXpNVKf9sPs/zLAA/e88BxrqmQBERkTri1HWj2kbYs+mh1nWInx/0OLHU69Cu4+QcrHyrn/A/7HfoJtxQd3cBEBEREVFYrWNuj/iS9XQlnzBSZyw44+tleYW0O2rdk3Kvd2sSB8a7ukQRERERh1FYrWPatPOiKxvwxkLR0lVnfH3HhytsT3TalzhEd8qLiIhInaawWse0uL6vrR2+9czNt4/OW2J/M3SoK0oSERERcRqF1TqmRe9odntbNwFul7uSksNHK3w9ZtVXtnbi+CEurExERETE8RRW6xiTCXa1tT51wgcz2175zva1zF930rrQumPwH4F9SbrgzP3cREREROoShdU6KGDMSFu7ZP43tvaef9kf6pc+4FqX1iQiIiLiDAqrdVC3eweRTygAbXcswHK8CAyD6MX2Z7g1f0BhVUREROo+hdU6KDzKj1WxVwHQyJLL5sf/y9rfTVxfOIsPGMfKoItIvqKlm6sUERERqT2F1TrK9767bO2Y1x7nvltyWUsvxvMB615YrC2rREREpF5QWK2jBj7UnyVhowB43vx3lm9tBEDLlnD7BP1nFRERkfpBqaaO8vaGqPnvcEPAF7zE3wATgYEwezb4+7u7OhERERHH8HF3AXL+Ol0UzZT1V9H0DbBYYOJE6NDB3VWJiIiIOI7Cah3Xrh1Mn+7uKkREREScQ8sARERERMRjKayKiIiIiMdSWBURERERj6WwKiIiIiIeS2FVRERERDyWwqqIiIiIeCyFVRERERHxWAqrIiIiIuKxFFZFRERExGMprIqIiIiIx1JYFRERERGPpbAqIiIiIh5LYVVEREREPJbCqoiIiIh4LIVVEREREfFYCqsiIiIi4rEUVkVERETEYymsioiIiIjHUlgVEREREY+lsCoiIiIiHkthVUREREQ8lsKqiIiIiHgshVURERER8VgKqyIiIiLisRRWRURERMRjKayKiIiIiMdSWBURERERj6WwKiIiIiIey+lhdcaMGSQlJREQEEDPnj355Zdfqjx33rx5XHLJJURFRREWFkb//v357rvvnF2iiIiIiHgop4bVuXPn8sADD/DYY4+xbt06Bg0axIgRI9i/f3+l5//8889ccsklLFy4kLVr1zJ06FCuvPJK1q1b58wyRURERMRDmQzDMJw1eN++fenRowdvvPGG7VhycjKjR49m2rRp1RqjY8eOjB07lieffLJa5+fn5xMeHk5eXh5hYWHnVbeIiIiIOE9N8prTZlZLS0tZu3Ytw4cPr3B8+PDhLF++vFpjWCwWCgoKaNKkiTNKFBEREREP5+OsgbOysjCbzcTExFQ4HhMTQ0ZGRrXG+Pe//83x48e5/vrrqzynpKSEkpIS2/v8/PzzK1hEREREPI7Tb7AymUwV3huGccaxynzyySdMnjyZuXPnEh0dXeV506ZNIzw83PZKSEiodc0iIiIi4hmcFlYjIyPx9vY+YxY1MzPzjNnW082dO5c77riDTz/9lIsvvvis5z7yyCPk5eXZXgcOHKh17SIiIiLiGZwWVv38/OjZsyeLFy+ucHzx4sUMGDCgyn6ffPIJ48eP57///S9XXHHFOa/j7+9PWFhYhZeIiIiI1A9OW7MKMGnSJG655RZ69epF//79efvtt9m/fz8TJ04ErLOiBw8e5MMPPwSsQXXcuHG88sor9OvXzzYrGxgYSHh4uDNLFREREREP5NSwOnbsWLKzs5k6dSrp6el06tSJhQsX0qJFCwDS09Mr7Ln61ltvUV5ezt13383dd99tO37rrbcya9YsZ5YqIiIiIh7IqfusuoP2WRURERHxbB6xz6qIiIiISG0prIqIiIiIx3LqmlWpneLtqax5fwPBYd50u2cgpnAtaxAREZGGRTOrHuqbiV/Ton0Ag164kh6PX07PiH3s/Gilu8sSERERcSmFVQ/0wz8Xc9VbI8jE/vCEdeYuDLm1OYd+3OrGykRERERcS2HVwxzbe4Rb/9UR84kVGsOabaN9sHV7r0NGHPdenwEWiztLFBEREXEZhVUP89w1qzlkxAFwadwGvt/fnpQt0UR5ZwMwL2covzyy0J0lioiIiLiMwqonSUsj4I9VRJCFHyW8Pi8OLy+Ibh7A9IcPM5r5/MwgBs7/m2ZXRUREpEFQWPUkb7zBk8YUDpDA0ts+pFXfSNuXbnymA/OHvMoglmHauQMWLHBjoSIiIiKuobDqKUpK4O23AQj0Kaf/M1ecec7f/mZvz5jhosJERERE3Edh1VN8/z1kZVnb114LcXFnnnP55dCihbW9eDEcPuy6+kRERETcQGHVQ6S8sp4sIqxvbrml8pO8vMi75jbe43aGWb5n9kPrXVafiIiIiDsorHqA0qOFXP3j3TQlg+t8v8C4+JIqz93Y7RYm8B4/MYyPv9YTrURERKR+U1j1ACmv/kEuTTDjg29CDCZ/vyrPHXBzSxJ80gH46WgPju094qoyRURERFxOYdUDfDuv0Na+6hrvs57r5QUjO+8DoBR/fpi+yZmliYiIiLiVwqoH+HZLcwC8MHPJPe3Pef7IP9k//l/wtfZbFRERkfpLYdXNDvyyjy1lbQDoE7qNJi1Cz9ln6MR2BGKdjf1mX0eMcrNTaxQRERFxF4VVN/vuzT229mV9cqrVJzDUhyFNtwGQYTRl+5dbnVKbiIiIiLsprLrZt0v8be3LxkVXu9+QfsW29tL/pju0JhERERFPobDqRpbScn5M7wBAE1MOvf7Uptp9h4xtamsvXel/ljNFRERE6i6FVTfaPH87R2kMwOC4XXj7Vv8/R49rkwg1FQCwNL2d1q2KiIhIvaSw6kZ5y7fQnd/xwsygPiU16uvja+LRTl/xBhNZagyG9eudU6SIiIiIG/m4u4CGbODBufzO/8gnFOPBZTXu//Bf8+Gut6xvUpZCr56OLVBERETEzTSz6i6GAcusATUszET4gI41H2PwYHv7l18cVJiIiIiI51BYdZddu+DwYWt7wADwPvuTqyrVvj00amRt//abNQCLiIiI1CMKq25SunS5/c3Agec3iJcXed2HsJiLeSbjDrLWpzmkNhERERFPobDqJvf+O5Ek9jCOD0jvMOy8x3mm+O8MZzFP8Awr5uxzXIEiIiIiHkBh1U1WpUazjyRmcxOhF3Q573H6Dgm0tX9LKT7LmSIiIiJ1j8KqGxRlHWdTcWsAOgTsISQ66LzH6ndDoq29clujWlYmIiIi4lkUVt3gj//tohxfAHonHK7VWPFdmhDnnQHA6ry2WErKal2fiIiIiKdQWHWD1d/l2Nq9etb+Dv5+cfsByCecbV/tqPV4IiIiIp5CYdUNVq+zP4uh9+VRtR6vb/dSW3vVgsxajyciIiLiKRRW3WD1wTgAfCmly+iWtR6vx9BwW3vd75ZajyciIiLiKRRWXSx//1G2lyUB0DV4F/6hfrUes/voRFt73b4mtR5PRERExFMorLrY75/txjjxY++dlOWQMSMSQ2nucxCAdcda6yYrERERqTcUVl1s9Y95tnbvvufxiNUq9I3dTzfWcR2fcXydbrISERGR+kFh1cXu9n2HXxnAdO5n6PW1v7nqpLl3/8I6evA+dxC6Y63DxhURERFxJ4VVFwtav5wBrOD+4PdIHNbKYeOaenS3v1m3zmHjioiIiLiTwqorZWbCfuueqPTsCd6OWwZA91PC6u+/O25cERERETdSWHWl1avt7d69HTt2ZCQkJACQt24PhllbWImIiEjdp7DqQvM/LOAlHuQXBlLSra/Dx38u+Flas5NGBQdIW77f4eOLiIiIuJrPuU8RR5m5NImvuQGAnbGptHbw+AURiew+Meq6BWkkDEp08BVEREREXEszqy5iWAxWZyUC0Mh0lFZDmzv8Gj0GBNja65YXO3x8EREREVdTWHWRg6sPkWGJAaBX4z2YvEwOv0a3K5rZ2n/sDHL4+CIiIiKuprDqIqv/Z19D2rt9gVOu0fKCWII4DsDG7FinXENERETElRRWXWT1MvvH8r0HBZzlzPPn7WOiU0gqALvLW3As7ahTriMiIiLiKgqrLrJ6e5it3ftax69XPalzfC4ABl5s/maf064jIiIi4goKqy5gmC2sybE+raqp12Ga9XLeR/RdOhu29safc512HRERERFXUFh1gd0/pXKURgD0ikrF5Ph7q2w6D2pka2/YYFR9ooiIiEgdoLDqAr/NO2hr9+lU5NRrdb7CvsRgw/5GTr2WiIiIiLPpoQAukJC5lpvZyyr60HdYiFOvFdkyjJlN/ka7nOV0tOwHIw2nTuWKiIiIOJHCqgtcePATLuQ365uJOU6/3vgBO2DBSjgGpKZCYqLTrykiIiLiDFoG4GylpbBunbXdti00buz8a3bubG9v3Oj864mIiIg4icKqs/3xhzWwAvTp45prnhpWN2xwzTVFREREnEBh1ckyftjEMYKtb/r2dck1y5K78APDeJkHeOeLKJdcU0RERMQZtGbVyR5+rzUfkUdHNvNlvIUkF1zT0rotl/EtZnzounEbd7rgmiIiIiLOoJlVJ1t1IBYL3myjPbFDk11yTf8QX9r5Wx+7uqWkJWUFxefoISIiIuKZFFadKG9/HttKWwLQNWgXAeH+Lrt2l6aZAJThx/Zv97rsuiIiIiKOpLDqRGvm7sY48SPu2+qIS6/duX2Zrb3xJ9deW0RERMRRFFadaPm3+bZ2n37eLr12537BtvaG38tdem0RERERR1FYdaKf14fa2oNubuHSa3cZ0czW3rgn+CxnioiIiHguhVUnKSssY0VOOwCaeaeTODDepddv3juGMJN1ZndDjmuvLSIiIuIoCqtOsm7uDo4TAsCF8XsweZlcen2Tl4nOodYdAQ5YmnF0d7ZLry8iIiLiCAqrTvLz/+w3NV04wOyWGrq0OGprb1yQ6pYaRERERGpDDwVwkrQt+XhhxoI3F94Q55YaenY36LxxA13YQPABL6CHW+oQEREROV+aWXUGi4XpR8eTQxMWhY0leWQrt5Rxx13+bKArH3MLPXJ/dEsNIiIiIrWhmVVnWLcOcnMJBy67qBRcvF7VpmNHMJnAMGDjRvfUICIiIlILmll1hkWL7O1LL3VfHSEh0NL6BC02bQKze9bOioiIiJwvhVUnMBZ9a38zYoT7CgHo0gWAoiKDkm167KqIiIjULQqrDpa79ygJy+dwK7P4NuFOaOHahwGcbo7pT7RnKyEc49sPM91ai4iIiEhNKaw62JfPbuIg8XzIrXwfdZO7y8ErMYHttMeCNxtWFbu7HBEREZEaUVh1sDlfBNja102McGMlVl0uaWprb9zp78ZKRERERGpOYdWBjmzN4ofsbgAk+hyg3x0d3VsQ0HpoAv5YZ1Q3ZDY9x9kiIiIinkVh1YHen7QJ84ndwG7otdvlj1itjI+/Nx2DrDdW7SxLpOjIMTdXJCIiIlJ9Tg+rM2bMICkpiYCAAHr27Mkvv/xy1vNTUlLo2bMnAQEBtGzZkjfffNPZJTpE8dFipn+fDIAJC7dPTXRvQafo0iwHAAvebFmoHQFERESk7nBqWJ07dy4PPPAAjz32GOvWrWPQoEGMGDGC/fv3V3r+3r17ufzyyxk0aBDr1q3j0Ucf5b777uN///ufM8t0iLdu/40MSwwA1zT7jTaXJLq3oFN07mDfX3XD0hw3ViIiIiJSM04Nqy+99BJ33HEHEyZMIDk5menTp5OQkMAbb7xR6flvvvkmzZs3Z/r06SQnJzNhwgRuv/12XnzxRWeWef4MA1JTWXD3Ih6Z39t2+J/PhruxqDN1GRhma29Yb3FjJSIiIiI147SwWlpaytq1axk+fHiF48OHD2f58uWV9lmxYsUZ51966aWsWbOGsrKySvuUlJSQn59f4eVS/fuzccbPFBEEwF87/kzvWzu4toZz6HJFgq29cV/YWc4UERER8SxOC6tZWVmYzWZiYmIqHI+JiSEjI6PSPhkZGZWeX15eTlZWVqV9pk2bRnh4uO2VkJBQ6XlOYTLBwIFcwK8AXNX0N15e3td116+m6OQIor2OALAhr7l1RlhERESkDnD6DVYmU8U74g3DOOPYuc6v7PhJjzzyCHl5ebbXgQMHallxDd14I30eG87SV/5g/sE++Id55l6mb3WZwc8MYrvRFg4dcnc5IiIiItXi46yBIyMj8fb2PmMWNTMz84zZ05OaNm1a6fk+Pj5ERFS+wb6/vz/+/m4MiKNHEzB6NIPdV0G1jL74GKxfZn2zcSM0a+begkRERESqwWkzq35+fvTs2ZPFixdXOL548WIGDBhQaZ/+/fufcf73339Pr1698PX1dVapDUOXLvb2hg3uq0NERESkBpy6DGDSpEm8++67vP/++2zdupUHH3yQ/fv3M3HiRMD6Ef64ceNs50+cOJHU1FQmTZrE1q1bef/993nvvff4+9//7swyG4ZTw+rGje6rQ0RERKQGnLYMAGDs2LFkZ2czdepU0tPT6dSpEwsXLqRFixYApKenV9hzNSkpiYULF/Lggw/y+uuvExcXx6uvvsq1117rzDIbhvbt+cVrMOstnclZnMxT7q5HREREpBpMhlG/bg3Pz88nPDycvLw8wsK0TdOpOgbsYktJa/wo4dhxL3yDtLRCREREXK8mec3puwGI5+gSkwlAKf7s+H6fe4sRERERqQaF1Qakc/tSW3vjj5lurERERESkehRWG5Au/YJt7fWrSs9ypoiIiIhnUFhtQHpcZX+61++7Qt1YiYiIiEj1KKw2ILHdmxLjZf34f21uSwxLvbq3TkREROohhdUGxGSCHhGpAOQYTdi/PM3NFYmIiIicncJqA9Oz3XFbe+2XCqsiIiLi2RRWG5geFwTa2r8vL3ZjJSIiIiLnprDawPQYFU8bdjCWOXQrXO7uckRERETOyqmPWxXP06J/HDsioyErCw5GgfGodTGriIiIiAfSzGpDYzJBjx7W9pEjcPCge+sREREROQuF1YaoZ097e+1a99UhIiIicg4Kqw3RiZnVYvxJT9nh5mJEREREqqaw2gBlJ/WiK+sJ4Rh3zh7s7nJEREREqqQbrBqgJt1bcNCUg9nw4bcj1idZmbx0k5WIiIh4Hs2sNkAmLxN9I/cAkGVEsiflgJsrEhEREamcwmoD1a+z/UlWKz/Xk6xERETEMymsNlD9Lg6xtVcuK3NjJSIiIiJVU1htoPrc2BoTFgB+2x3p5mpEREREKqew2kCFt2hEsp913eq6420pyi12c0UiIiIiZ1JYbcD6tUgHoBxf1n22083ViIiIiJxJYbUB69fXsLV//SrHjZWIiIiIVE5htQEbeF2srZ26+ZgbKxERERGpnB4K0IC1H9mar0Jv4oKCRTQ5aoA5C7y93V2WiIiIiI1mVhswk5eJKy8ppgm5cPQobNzo7pJEREREKlBYbegGD7a3U1LcV4eIiIhIJRRWG7pTw+rPP7uvDhEREZFKaM1qQ9e5M/OCb2Hh8QvZ8FUPVpoNvLxN7q5KREREBNDMqnh58XHoX3mPCawu78G6T7XfqoiIiHgOhVXhkgtLbO1FMzPcWImIiIhIRQqrwoi7kmztRb81dvn1czans+OrrWT9cRAM49wdREREpMFQWBUSB7egve9uAFbmdyBnX77Tr1lw4CgvjviBjn47iOgUS7urkonq1oydMQPh0UchR0/UEhEREYVVOWFEx/0AWPBm8atbnXqtRU+tJDmxkIe+vZgtZW1tx/0ppuWRlTBtGrRrBwsXOrUOERER8XwKqwLA5dcF29oLvzY75RqGxeD/hv/EFVP7cNASZzveN3QLN7Vaya3xP+Htc+JXMisLRo6k8OW3nFKLiIiI1A0KqwLAoLs6E8wxABbsbk9pYblDxzcsBvd1S+GRxRdhnPi1uzRqLVu/28/K/A58vKsfbx24HPbsgVGjANhltKTDpEuZeZseViAiItJQKawKAP6NArkyfj0AOUYTfnjZgY9eNQymXbiI/2wcYjv09CU/syijB+2HN694bkICzJ9P5r1PM5gUUknkL7P6s+KtDY6rR0REROoMhVWxueFP9ocBfP9JluMGfuUVbv51Iq3YhQkLH/7lVx7//kJMXlU8fMDLi6jpj3FNV+tNX2X4MeauKA5vOuK4mkRERKROMBlG/dorKD8/n/DwcPLy8ggLC3N3OXVKSW4hj8e8zZiyT+gTvgNT5mHw86vdoD/8AJdeChYLh4hl2Z8/4vq3hlWra3lxORfHbiLlaDcARsas4qtDvasOuSIiIlIn1CSvaWZVbPwbB/Gv61bTl1WY8o7Cd9/VbsDdu+H668FiASDu0duqHVQBfAJ8mPtLM5p6HQZgweE+fPq3lbWrSUREROoUhVWp6IYb7O333jvvYfLSCnii32JKco9bD4wcCU8/XeNxYjpF8Z9Je2zv732lDTl7jp53XSIiIlK3KKxKRZddBnHWbaWMr76meMf+Gg9hLrNwU6/tPJM1kaEsIaP1QPj4Y/A6v1+3a1/ox9Wx1hnVI0YkU6/TzVYiIiINhcKqVOTrS+H4u3iLP9PVWMc/bjxQ4yEeG7qcbw73AmC7qT3H3/gQwsPPvyaTiVe/aE4ghQC8/nt/dv5Y8xAtIiIidY/Cqpyh6MY7mMRLbKQLb6/tyaFN1X/06Qd3r+L5XwcC4E05nz67i1YXJ9W6pvg+cTw00Dq7Wo4v3/x9Sa3HFBEREc+nsCpniOjYlLu6LgeghACeHbe9Wv2WvbeNO2d0s72fftUShj3Sx2F1PTS3F6P9FrKCfjywfjxs0HIAERGR+k5hVSr10MwOBGG9OerNdX1Y8/m+s56/adEBRt8ZTRnWra7uavcj98y/2KE1hcSFMf+FnfTjN+uB87hhS0REROoWhVWpVHT3Zjw19GcALHhz6zgL+UdKKj138/cHuWhkINlGEwAuabSKV9YOBJMT9kP9858hJsba/vxz2LTJ8dcQERERj6GwKlV68IshdPHbCsCWopZc12U7x3LLKpyzc+7vXHBZCEcskQD0CdzAZ2ta4hPs75yiAgPhH/8AwIKJ5Q986pzriIiIiEdQWJUq+YYF8vkcM42x3mD1fUYXusdmsPCfKbBgAdx+O61u6M0lxvcA9ArYyHfrmxLeKtK5hU2cyLfhY+nOOi74cSp/fLHXudcTERERt1FYlbNqc3Unvnx2M+EcBWBXSQIbXlgEV14JM2fihYVZjOeh+E9YsjWWRm2jnV9UUBA7h0xgA10B+PdDGc6/poiIiLiFwqqc06BHB/HbJ3sZHLgKgI5stn8xNJTgFybzwt7rCEl08ozqKW6f0ZsmJ2Z8P9nVi7T1WS67toiIiLiOyTAMw91FOFJ+fj7h4eHk5eURFhbm7nLqF7OZ3/+znJbpv9LI5xh07GidYQ0JcUs5T/ZfzNMrLwHgoQG/8sKvF7ilDhEREamZmuQ1hVWpsw6vTaNFr0hKCCDMlM+BdF/CYgLdXZaIiIicQ03ympYBSJ0V0zOeca2sDy/IN8J455717i1IREREHE5hVeq0Sc/bb+ia/kUiZSUWN1YjIiIijqawKnVa+2s7cWXErwCklcfy+RN/uLkiERERcSSFVanzJj1ob7/0ZhD1axW2iIhIw6awKnXe4If709v/D27gE14vuAXTpo3uLklEREQcRGFV6jyTtxe/Pv8rn3AjfVgN06e7uyQRERFxEIVVqRd87xgH4eHWN7NnQ2amewsSERERh1BYlfohJATuvNPaLimBN95wbz0iIiLiEAqrUn/cey8lXoF8wDgueHYERzOK3V2RiIiI1JLCqtQfzZvzeJs5jOcDlpf14d37daOViIhIXaewKvXKnVOb29qvzo+nvEz7WImIiNRlCqtSr7S9vhtXNv4FgANlsfxv6mY3VyQiIiK1obAq9c6D95Tb2v9+zU8PCRAREanDFFal3hnyxCC6+W4CYHVeW5bP2e/mikREROR8KaxKvWPy9WHSNam29y8/nu3GakRERKQ2FFalXhr76gXEmtIBmL+nC3t+P+regkREROS8KKxKveQX3Yh7+q4BwII3r9y3y80ViYiIyPlQWJV66y+vdCCQQnwow1j3BxTrIQEiIiJ1jcKq1FsRfVoxs/cb7KAtrxZOgPffd3dJIiIiUkMKq1KvjX1zKEnss76ZNg1KStxaj4iIiNSMwqrUbz16wMiR1nZaGnzwgXvrERERkRpxaljNzc3llltuITw8nPDwcG655RaOHj1a5fllZWX885//pHPnzgQHBxMXF8e4ceM4dOiQM8uU+u6JJwAoxp//PJzG4f2aXRUREakrnBpWb7zxRtavX8+3337Lt99+y/r167nllluqPL+wsJDff/+dJ554gt9//5158+axY8cORo0a5cwypb7r04eUPg/Rkj3cmzuVyX/a7u6KREREpJpMhuGch1Fu3bqVDh06sHLlSvr27QvAypUr6d+/P9u2baNdu3bVGmf16tX06dOH1NRUmjdvfs7z8/PzCQ8PJy8vj7CwsFp9D1J/ZP6wgdaXJFJAGF6Y2bj8GB36h7u7LBERkQapJnnNaTOrK1asIDw83BZUAfr160d4eDjLly+v9jh5eXmYTCYaNWpU6ddLSkrIz8+v8BI5XfTFXXik27eAdd/Vh27S0hIREZG6wGlhNSMjg+jo6DOOR0dHk5GRUa0xiouLefjhh7nxxhurTN3Tpk2zrYkNDw8nISGhVnVL/fXA3P4kcACAhXuTWfR+upsrEhERkXOpcVidPHkyJpPprK81a6xPDjKZTGf0Nwyj0uOnKysr44YbbsBisTBjxowqz3vkkUfIy8uzvQ4cOFDTb0kaiMC2CUy70j6rP/FuL44VOGUVjIiIiDiIT0073HPPPdxwww1nPScxMZENGzZw+PDhM7525MgRYmJiztq/rKyM66+/nr179/LTTz+ddS2Dv78//v7+1SteGrwbP76c96OX8VPJQPYXx/DYmK288l2yu8sSERGRKtQ4rEZGRhIZGXnO8/r3709eXh6rVq2iT58+APz222/k5eUxYMCAKvudDKo7d+5kyZIlRERE1LREkSqZwkJ5+9USOv2liGICefX7ZC7/+AiX3hzl7tJERESkEk5bs5qcnMxll13GnXfeycqVK1m5ciV33nknI0eOrLATQPv27Zk/fz4A5eXljBkzhjVr1jB79mzMZjMZGRlkZGRQWlrqrFKlgWn152E81/VT2/tbbvchK6PcjRWJiIhIVZy6z+rs2bPp3Lkzw4cPZ/jw4XTp0oWPPvqowjnbt28nLy8PgLS0NL766ivS0tLo1q0bsbGxtldNdhAQOZf7f7iSywN+xI8SJpc9RsT0J9xdkoiIiFTCafusuov2WZXqyvrmNw6MupvulrXWA2++CX/5i0OvUVYGa9bAtvVF5OzM4W9X7oDycmjUCJo1I9svlsAgE0FBDr2siIiIR6tJXlNYlYbtP/+Be++1tr284L33KL1xPH5+5z9kYSF893Up89/KZMGvjcgtDQEglkMcolmFc/8eNIPXi+9gRM9Mbv17FCOv9cfb+/yvLSIiUhd4xEMBROqEe+6Bhx6yti0WXrptA31bpLPpD3ONhsnJgQ/fKuLqPmlEhpVwzQ1+fLQk3hZUAUo4c9eK1YUdKLb4MX91PKPH+tM+KptP3jlG/fonpIiIyPnTzKqIxQIPPMCnr2UwFuuNV36mUu668SgPPBtNixZn6ZuVxbKXVjHk/y7FbJw5JRpGHpd7f0//Fodo38bM8O5HwNvbmm537uT+n69lTunVZFJxO7cLWqXzn7nRdOupaVYREal/tAxAYVVqyjDY/Lf3uf7lfmyhY4UvdY49QrdOZiKivSk6buGC+H3cEvol/PgjrF5NoRFAJFkUYV14GkMGV/l/y9UX5jD0r+3xH3ERBARUft3ycsxLfubb537n3yk9WWIMtX3J11TGMw/l87fnIrQ0QERE6hWFVYVVOU/FPyxj6nUbefnoeIoJrPScK/mKr7iqwrH7mY5PsD/XXFpIv4nd8B56IfjUcBvj/ftZOG4Ok1JGsZ32tsNDkzP48remhIbW+NsRERHxSAqrCqtSG0VFHHn1E958Po+vcgeylp4YpyzvbsE+9pFkfdO5M1x2GVx9NfTta71Jq5ZKf1rGU9du4vmjf8bAi7HM4ZMHV2P61wtoilVEROoDhVWFVXEEw4AtW8hf9CsHtxWQc7iMoBAvouL9ib+gBfTuDc2anXuc81FQwNLrXuff33VkLmMJogiuuAL++1/Q77WIiNRxCqsKq1JfvPWWdceC8hNP2OrShbzPvie8bczZ+4mIiHgwbV0lUl/85S/w3XfQuDEAezYUkNwB/vVIjpsLExERcQ2FVRFPd9FFsHIl+fEduITFpJtj+Mf/NeGfd2RpP1YREan3FFZF6oK2bQld/h23R35tO/TC+5H8+eojmGv2/AIREZE6RWFVpI4wJcTz2JabeCPhOUxYAHj3yyhuGHaEkhI3FyciIuIkCqsidUlUFBM33s2c5Kn4UgrA5ylRjBpwhOPH3VybiIiIEyisitQ14eFcv+YffNXraQIpBOD736O4pEcWublurk1ERMTBFFZF6qKgIC779QkWD51GOEcBWLEjksFdcigsdG9pIiIijqSwKlJX+flxweLJpFz1MtEcBuCatFcJeucVNxcmIiLiODV8eLmIeBRvb7rOe4pltz3H3A+LeYxn4QEgMxOeftohj38VERFxJ/1NJlLXeXnRZtZjPP6kN6aTx557Dm64ge1/FLuzMhERkVpTWBWpD0wmmDIFXn3VNpv6w2c5dOzmw4Q/HddOASIiUmcprIrUJ/feC199RWZQItfxGWZ8eG9OMD3bH2P9encXJyIiUnMKqyL1zRVXEL3iS16OeJZgjgGwPS2E3j3N/POBEo4dc3N9IiIiNaCwKlIfdenC+B2P8vtFD9GDtQCUW7x54RV/2rco5I0Zhp56JSIidYLCqkh91aQJbX+YwYp/r+Apn2fxx3qz1cGcIO6620TLuGJee8Xi5iJFRETOTltXidRnJhN+k+5h8tV7uXncX5m07Gq+ZhQAh3ICOPDkm5B/BK66Cjp3prDIxIED0LIl+PpWPqRhQHExBAZWPL5sGezZAwUFkJ9v/bO0FMLDra/oaOu4rVpBRISTv28REak3TIZhGO4uwpHy8/MJDw8nLy+PsLAwd5cj4jkMAxYtYu2DH/PMjuv4kqvYQBc6sdn69bg4libdxtBfnwEgJLCcRiFm/HwtlJZ7UVoKxSVeFBT70CriKDv/8m/IyYHcXMjJYdSqx/j66IXnLOPKTnv46t+7oFcvaNLEVprJdI6OIiJSb9Qkr2lmVaShMJng8svpedllzF+wgIxXbqHpki1w8p+rhw6x9VC27fRjRT4cK6r8/yLys0rh2WcrHAvjlmqV0XLTV3Dpg9Y3yckwcCA9f3iB5h1CuHyUD9dcA5GRNf7uRESkntLMqkhDlpoKX38N33wDy5ax6NhAZjGeAySQRzi5NKYcH/wotb3CyCeKI3zDyApDfcel7ArqQlhQOaE+hYSZjuFrlJJ/3JvcAh/SacoeWnIZ33IVX9n67aIVbdhle+/jbeHSi83cOM6XUaMgJMRlPw0REXGRmuQ1hVURsbJYrItON2+G9HTIyICiIjCbrV8LCrIvQA0Pt36E37ix9c8mTSAsrOrHu5rNkJUFO3fC9u2wcSMsXw7r1rG4fAg3MZsjRJ/RLci/nDHXeTH+di8GD9bTY0VE6guFVYVVkbqhsBBSUrB8tYDf5+3js8wL+YQ/cYDmZ5zapkUpm3b44efnhjpFRMShapLXNE8hIu4TFAQjRuD1xuv0yljA86suYt89/+bn8Cv5C2/SiFzbqR1SF+J30UB4/31OPtnAbHZX4SIi4iqaWRURz1NWBt9+S/F7s/nyay9mWW7hr7zBKL62fj04mNIxN9Lm21cZcqk/48ebtExARKQO0TIAhVWR+uPIEfj4Y3jvPet62hPmM5prmG973yLezK23ezNunHUvVxER8VxaBiAi9UdUFDz4oPWmrN9+gz//GUJDySS6wjKB1DRvpv5/e3cfHXV153H8Mw+ZIYSQAAFCDA8RRAICW0AtHpQULErVxXK6YmVdiqd7VheUVN09WtuF3UXh2KO2FB9WoT5sTxfbAopVW1KEgLq4kYeaJoBgIkR5iEAIISSTzMzdP37klwwJDKIwd+D9Ouee5PdwMzfzJfDJ5f5+v/+QBg2SRv1NRA8/LK1d6zyYAACQvJhZBZB86uul5cvVuOTXWrUhUy/pB/qTblBUvnan3jC2Vn9cnyb5W+8ZW1vr3NAAAJAYLAMgrAIXj127pBdf1N6lb+vXB67XMt2uLRrlHn5Yj+mxzo9KV10ljRql8GX5Sp19l9LTpUtyPcrK8igry3kQQVqaFAhIwaDz8Yc/dCZ2W2zbJr33nuTzOetjfb6OW1qaNGFCAt4LAEgShFXCKnDxCYelP/9ZWrVK1as2qujzfK3WJN2p/9b1WuOe9oku1SB9ckZfsvxvH1J+5j5nwxg99/G3dM8HM+P269ftqHY/sdy5D21mptSnjwoXXap3P0hR//7SgAGtbfBgZ42tn+cJAriI8LhVABcfv1+68UbpxhvV62mj6eXlmr5mjfS/PaX3+0l79kiSQgpqgtZolwbpgHorpE6n/JLBVb+VVOluR9VFUvyw6qs5KN11V8y+XXpDm3SzNm1qf34gYHTZZVJ+vkff/a50xx1n9B0DwEWBsArgwuPxSMOGOe2++5x9+/ZJ5eUaun271ux4XaqslNl/QMf31epgdVQHm7uqUZ0UUlBNCiikoLK1P+bLfktr9YJ+qIh8isqriHwdtgzVthuSTxF5FelwXW1Tk0dlZc7NDvLK/qA7Kv8iDR0q5ecrmjdQU6elaOBAKT9fGjLE+dijxzl55wDAOiwDAADJWUbQ0OA8Vev4cec2Ah5P7Dltt41x+jQ3x7bjx6UjR5xWUyMdPizt3StVVam5ar8+/8zo0/Al2q3++kQDtV1DVK6h+liD1ayAfqWZmqmX3JfZ7R+oAeFd7Ybbs6dRfr5HgwZJ/fo5bepULhwDkBxYBgAAX5bfL6WnO+0cSZE0IBrVgAMHpJ07pR07pO0fSDteUXjbTlVUepRlqmP6bA93fNPYL77w6IsvpPXrW/fd0PwHZYzsKeXmStnZevZ5n154wbl4rEcPZ/lsamr7lpcnTZ4c+/VLSqRQqPVis1O1Tp2ci8oA4FxhZhUAbBEKOSG2vNy59UB5uUxZufZ+fEzbmgdqm/K1XUO0TfnapnztVx+3a4qa1KhO8urEX+k+nx4MLNITDf8c92WvH7xHRT96qzWBpqRo+L/eqL/uiT9N+7NHjujB2Y1SSooUCOjA4RR9Y2xQgYBHwaATdjMznWvNTm7Tpkm9erV+raYmZ8I6GPyybxyAZMPMKgAko2BQuuIKp53gkXRJOKxLKip0/YkAq/IXpW3bVFv+uXY39NQe9dMh9WgNqpIUiSjUEJFPYUXi/FUf+LhUuueemH1N2i4pflgNPPpv0qO/dLcb1U/7tPuMvt2CQ8vVa7jc9Po/a3L1g3/pqdRUo8xMj3szhZZw2/J5To50992xX6uiwlnF0blz64xx585OhgaQ3AirAGA7v9+5x9XgwdKUKe7ujGhUI6qqNKKyUvrsM6lqyImPVdLnn+uXh5/UokM/UW2dR4fUQ0fVVQ1KVYNSdVyd3c9PvpBMkv5RL+iAersXmzUp4La22/20J6afkUd9tcc93qBUNSq1w2+r27/PkfS5u12jOZJ+roYGjxoanGviOnJp9xrd7fltzDTtfQ8O0ZvFXdqd6/E4b5/f7yxXuPde6bHH2ozXOG+rz9d63smt5dijj0pjxrT2LS2Vnngitu+JCea2k9QKBJyHsHm9sX0rKk69vKKlX5cuzjIO4GJGWAWAZOX1Sv37O+0UPJIyw2Fl1tY6F3zV1jrLDUIhqbHRaaGQ1HC78//wJy4Ue7C5WWoKSc3HWi8ea3O89fMUqXmKuz2guVl7jn/PeZ0TLdQYVY26xbTD6q6e+iJmrL1UrWu1XjXqpiPKVI26qV7tA2i3w5+0m1o9rjWS2j+JwZjWIUtS8y+fk36z0H2CQ9QX0K5dfz2jt/vBY/OkS8rdJFm17xt6+U/3nVHf+7N/I3UKuk+d+NULQ/Xz3+fG7XfDDdIf/xi7b/hw51kYp1tLHAhIhYXOUosW1dXS7NntH2px8gMuvF7pJz+JXaJRUiK98Ub8fpmZ0p13xo63uNj5Haqjfi2fe71S377OnS7a+uAD56PX23Fr6ZuT4wT7FqGQdOiQ88tKvJaZGXvtZMsf7Y7O9Xrb78O5R1gFgAud3+9cYZWg+10FQyFl19Yqu02AVU2NVHPdiY9Ou+PIEd1R82jMvuaaYzoSTXfD6xFlKqCmdq9xk97UAH3abuY4pKCcxRB+heVX9rGd0rHWZQoRpaiHDioin5qV0ubc9usH/O8XS1rnbod1SFL8sBpQSJ6/nx6zr0mLJc2K3/dPb0idp8ldABwIqGH/BjU291dj4+n7fvHEK9Lb77iJru5Yb/3ud/PjvqYk3et7Rr161bp9P9z4Df3n8olx++X1OqY7O70dk0afeuxKvb4xO27fu793UM/+dG9Mii0YP0iNIW/cviuXhXTrrXL7btzoVUFB/O9TcpaPdGpzu+W5c6WFC+P3u+46J4i3dfXV0ubN8QPy3LnSgw+29jt82JnhP5Nw/eab0ogRrX1ff126//7T9/F6pe7dYy/IlKSf/lR6663W8556Sho37szet/OJsAoAOLeCQWearu1U3RlKMUY96+rUs02AddqdrbcHq6nRA8cPS41FrbPFHbVI5ETr7X4eCId1MNK/zbGIZIyMpKi8bshtVorSVB8ztgl6R9t1uXtOy3nNSolZNhHp4N66U/S6+qoq5ryO2ihtdtJUQ4PbN18fKU1H2p3b9nWbFVBKyXtSyctuv6gGSTqzsOr9+ROSKtztiGZJih9WvdX7pdtui9kX0SpJt8Tv+/tXpd/PjtkXVaOk+FfceW//O0lvuNtG49X2F4vT8fToLnlbp1JNaJ6kH8Xvt/F9Kfe2mFTYtO8thcPD4vZt/tlT0ktL3anaSKS7Dh1ae0bjDf/DXVL6Tvc1j1ZPUkXFI3H7ZQVqpZunx4y3cvOPtPmzAvec2ocWSLemxCZpCxBWAQD28nikrl2ddprlDl8rY+SJROQLh+WLRBSMRDpcBtGluVmXt91/8jKJpibn/6ObmqRQQevyi6YmTQqFNCnUKDUdbd3vnnvy51fEbL8RmtF6LBzu+FuQs364rQH6VLvVr90DLdput3x+SZu1xJITri/Xjrj9Tg70kvRP+i9N0upT9onKq6i8ulIl7frep0VqVop7TtvWtm9fVcX066FD+q5WyMgTt3mP10lqfR8HqUwTtOaU50fllZFHw5q2SJ/Hvk9DtVVeNcZ9zazqcqm6zO3nVXcN1K4zGm/gL/8nqbVvUL2VpS/i9ktrqnGmZdvwaar8am59L95bL/XqeI15InHrKgAAklUkEhuMWwJsJCJFo7EfO9p3umNf9nzbjhkTv53peYk+93yaOlVavvycvwy3rgIA4GLg87XeqwsXtnMZmNueb+GfJcIqAACA7Tyei/ZxcfEvsQMAAAAShLAKAAAAaxFWAQAAYC3CKgAAAKxFWAUAAIC1CKsAAACwFmEVAAAA1iKsAgAAwFqEVQAAAFiLsAoAAABrEVYBAABgLcIqAAAArEVYBQAAgLUIqwAAALAWYRUAAADWIqwCAADAWoRVAAAAWIuwCgAAAGsRVgEAAGAtwioAAACsRVgFAACAtQirAAAAsBZhFQAAANYirAIAAMBa/kQP4OtmjJEkHT16NMEjAQAAQEdaclpLbjudCy6s1tXVSZL69u2b4JEAAADgdOrq6pSRkXHaczzmTCJtEolGo9q7d6/S09Pl8XjOy2sePXpUffv2VVVVlbp27XpeXhNfH+qX/Khh8qOGyY8aJrfzXT9jjOrq6pSTkyOv9/SrUi+4mVWv16vc3NyEvHbXrl35AU1i1C/5UcPkRw2THzVMbuezfvFmVFtwgRUAAACsRVgFAACAtQirX4NgMKi5c+cqGAwmeig4C9Qv+VHD5EcNkx81TG421++Cu8AKAAAAFw5mVgEAAGAtwioAAACsRVgFAACAtQirAAAAsBZh9St65plnlJeXp06dOmn06NHasGFDooeEU1i/fr1uueUW5eTkyOPx6LXXXos5bozRvHnzlJOTo9TUVBUUFKisrCwxg0U7CxYs0JVXXqn09HT16tVLt956q3bs2BFzDjW027PPPqsRI0a4Nx0fO3as3n77bfc49UsuCxYskMfjUWFhobuPGtpt3rx58ng8MS07O9s9bmv9CKtfwauvvqrCwkI98sgj2rJli6699lpNnjxZe/bsSfTQ0IH6+nqNHDlSixcv7vD4448/rieffFKLFy9WSUmJsrOz9e1vf1t1dXXneaToSHFxsWbNmqWNGzeqqKhI4XBYkyZNUn19vXsONbRbbm6uFi5cqA8//FAffvihJkyYoClTprj/GFK/5FFSUqLnn39eI0aMiNlPDe03bNgw7du3z22lpaXuMWvrZ3DWrrrqKnP33XfH7BsyZIh56KGHEjQinClJZuXKle52NBo12dnZZuHChe6+xsZGk5GRYZ577rkEjBDxVFdXG0mmuLjYGEMNk1W3bt3MkiVLqF8SqaurM5dddpkpKioy48ePN3PmzDHG8DOYDObOnWtGjhzZ4TGb68fM6llqamrSpk2bNGnSpJj9kyZN0vvvv5+gUeFsVVZWav/+/TH1DAaDGj9+PPW0VG1trSSpe/fukqhhsolEIlq2bJnq6+s1duxY6pdEZs2apZtuuknXX399zH5qmBx27typnJwc5eXl6fbbb1dFRYUku+vnT+irJ7GDBw8qEomod+/eMft79+6t/fv3J2hUOFstNeuonrt3707EkHAaxhjdf//9GjdunK644gpJ1DBZlJaWauzYsWpsbFSXLl20cuVKDR061P3HkPrZbdmyZdq8ebNKSkraHeNn0H5XX321XnnlFQ0ePFgHDhzQ/Pnzdc0116isrMzq+hFWvyKPxxOzbYxptw/Jg3omh9mzZ+ujjz7Su+++2+4YNbTb5Zdfrq1bt+rIkSNavny5ZsyYoeLiYvc49bNXVVWV5syZo9WrV6tTp06nPI8a2mvy5Mnu58OHD9fYsWM1cOBAvfzyy/rmN78pyc76sQzgLGVlZcnn87WbRa2urm73Wwns13I1JPW037333qtVq1Zp7dq1ys3NdfdTw+QQCAQ0aNAgjRkzRgsWLNDIkSP1i1/8gvolgU2bNqm6ulqjR4+W3++X3+9XcXGxFi1aJL/f79aJGiaPtLQ0DR8+XDt37rT6Z5CwepYCgYBGjx6toqKimP1FRUW65pprEjQqnK28vDxlZ2fH1LOpqUnFxcXU0xLGGM2ePVsrVqzQO++8o7y8vJjj1DA5GWMUCoWoXxKYOHGiSktLtXXrVreNGTNG06dP19atW3XppZdSwyQTCoW0bds29enTx+6fwYRd2nUBWLZsmUlJSTFLly415eXlprCw0KSlpZlPP/000UNDB+rq6syWLVvMli1bjCTz5JNPmi1btpjdu3cbY4xZuHChycjIMCtWrDClpaXm+9//vunTp485evRogkcOY4y55557TEZGhlm3bp3Zt2+f244fP+6eQw3t9vDDD5v169ebyspK89FHH5kf//jHxuv1mtWrVxtjqF8yans3AGOooe0eeOABs27dOlNRUWE2btxobr75ZpOenu7mFlvrR1j9ip5++mnTv39/EwgEzKhRo9zb6MA+a9euNZLatRkzZhhjnNt2zJ0712RnZ5tgMGiuu+46U1pamthBw9VR7SSZF1980T2HGtrtrrvucv++7Nmzp5k4caIbVI2hfsno5LBKDe02bdo006dPH5OSkmJycnLM1KlTTVlZmXvc1vp5jDEmMXO6AAAAwOmxZhUAAADWIqwCAADAWoRVAAAAWIuwCgAAAGsRVgEAAGAtwioAAACsRVgFAACAtQirAAAAsBZhFQAAANbyJ3oAAID2tm7dqtdee83dLiwsVGZmZsLGAwCJwuNWAcBCL730kmbOnOluV1ZWasCAAYkbEAAkCMsAAAAAYC3CKgAAAKxFWAUAAIC1CKsAAACwFmEVAAAA1uJuAABgEY/H86X7rF27VgUFBV//YADAAsysAgAAwFo8FAAALOLz+SRJxhhFo9F2+ztyNrOxAJAsmFkFAIuEw2GFw2EtXbo0Zv+uXbvcYye38ePHJ2i0AHDuEVYBAABgLcIqAAAArEVYBQAAgLUIqwAAALAWYRUAAADWIqwCAADAWoRVAAAAWIuwCgAAAGsRVgEAAGAtwioAAACsRVgFAAulpKTEbEcikQSNBAASi7AKABZKT0+P2a6pqUnQSAAgsQirAGChAQMGxGyXlJQkZiAAkGAeY4xJ9CAAALHC4bCysrJUW1srScrJydGSJUtUUFCg1NTUBI8OAM4fZlYBwEJ+v18zZ850t/fu3avvfOc76ty5szp37qwuXbq4bcOGDQkcKQCcW4RVALDU/PnzNW7cuHb7GxoaVF9f7zYuvgJwISOsAoCl0tLStG7dOi1btky33XabBg8erPT0dHm9/NUN4OLBmlUAAABYi1/PAQAAYC3CKgAAAKxFWAUAAIC1CKsAAACwFmEVAAAA1iKsAgAAwFqEVQAAAFiLsAoAAABrEVYBAABgLcIqAAAArEVYBQAAgLUIqwAAALAWYRUAAADWIqwCAADAWoRVAAAAWIuwCgAAAGsRVgEAAGCt/wcqXWyHmdnUngAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (resultFit, P11p, \"b\", \"P11 Fit\"),\n", + " (resultFit, P12p, \"r\", \"P12 Fit\"),\n", + " (resultFit_2, P11p, \"r--\", \"P11 Built-in-Fit\"),\n", + " (resultFit_2, P12p, \"b--\", \"P12 Built-in-Fit\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "fa7e9764", + "metadata": {}, + "source": [ + "## A reaction coordinate approach" + ] + }, + { + "cell_type": "markdown", + "id": "da4b539f", + "metadata": {}, + "source": [ + "Here we construct a reaction coordinate inspired model to capture the\n", + "steady-state behavior, and compare to the HEOM prediction. This result is\n", + "more accurate for narrow spectral densities. We will use the population and\n", + "coherence from this cell in our final plot below." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "f8f73a68", + "metadata": {}, + "outputs": [], + "source": [ + "dot_energy, dot_state = Hsys.eigenstates()\n", + "deltaE = dot_energy[1] - dot_energy[0]\n", + "\n", + "gamma2 = deltaE / (2 * np.pi * gamma)\n", + "wa = 2 * np.pi * gamma2 * gamma # reaction coordinate frequency\n", + "g = np.sqrt(np.pi * wa * lam / 2.0) # reaction coordinate coupling\n", + "# reaction coordinate coupling factor over 2 because of diff in J(w)\n", + "# (it is 2 lam now):\n", + "g = np.sqrt(\n", + " np.pi * wa * lam / 4.0\n", + ") #\n", + "\n", + "NRC = 10\n", + "\n", + "Hsys_exp = tensor(qeye(NRC), Hsys)\n", + "Q_exp = tensor(qeye(NRC), Q)\n", + "a = tensor(destroy(NRC), qeye(2))\n", + "\n", + "H0 = wa * a.dag() * a + Hsys_exp\n", + "# interaction\n", + "H1 = g * (a.dag() + a) * Q_exp\n", + "\n", + "H = H0 + H1\n", + "\n", + "energies, states = H.eigenstates()\n", + "rhoss = 0 * states[0] * states[0].dag()\n", + "for kk, energ in enumerate(energies):\n", + " rhoss += states[kk] * states[kk].dag() * np.exp(-beta * energies[kk])\n", + "\n", + "rhoss = rhoss / rhoss.norm()\n", + "\n", + "\n", + "class ReactionCoordinateResult:\n", + " def __init__(self, states, times):\n", + " self.states = states\n", + " self.times = times\n", + "\n", + "\n", + "resultRC = ReactionCoordinateResult([rhoss] * len(tlist), tlist)\n", + "\n", + "P12RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 1).dag())\n", + "P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag())" + ] + }, + { + "cell_type": "markdown", + "id": "81955bbf", + "metadata": {}, + "source": [ + "## Let's plot all our results\n", + "\n", + "Finally, let's plot all of our different results to see how they shape up against each other." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "c45ce240", + "metadata": {}, + "outputs": [], + "source": [ + "rcParams = {\n", + " \"axes.titlesize\": 25,\n", + " \"axes.labelsize\": 30,\n", + " \"xtick.labelsize\": 28,\n", + " \"ytick.labelsize\": 28,\n", + " \"legend.fontsize\": 28,\n", + " \"axes.grid\": False,\n", + " \"savefig.bbox\": \"tight\",\n", + " \"lines.markersize\": 5,\n", + " \"font.family\": \"STIXgeneral\",\n", + " \"mathtext.fontset\": \"stix\",\n", + " \"font.serif\": \"STIX\",\n", + " \"text.usetex\": False,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "4189a448", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 15))\n", + "\n", + "with plt.rc_context(rcParams):\n", + "\n", + " plt.sca(axes[0])\n", + " plt.yticks([expect(P11RC, resultRC.states[0]), 0.6, 1.0], [0.32, 0.6, 1])\n", + " plot_result_expectations(\n", + " [\n", + " (resultBR, P11p, \"y-.\", \"Bloch-Redfield\"),\n", + " (resultMats, P11p, \"b\", \"Matsubara $N_k=2$\"),\n", + " (\n", + " resultMatsT,\n", + " P11p,\n", + " \"g--\",\n", + " \"Matsubara $N_k=2$ & Terminator\",\n", + " {\"linewidth\": 3},\n", + " ),\n", + " (\n", + " resultFit,\n", + " P11p,\n", + " \"r\",\n", + " r\"Fit $N_f = 4$, $N_k=15\\times 10^3$\",\n", + " {\"dashes\": [3, 2]},\n", + " ),\n", + " (\n", + " resultRC,\n", + " P11RC,\n", + " \"--\", \"Thermal\",\n", + " {\"linewidth\": 2, \"color\": \"black\"},\n", + " ),\n", + " ],\n", + " axes=axes[0],\n", + " )\n", + " axes[0].set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", + " axes[0].legend(loc=0)\n", + " axes[0].text(5, 0.9, \"(a)\", fontsize=30)\n", + " axes[0].set_xlim(0, 50)\n", + "\n", + " plt.sca(axes[1])\n", + " plt.yticks(\n", + " [np.real(expect(P12RC, resultRC.states[0])), -0.2, 0.0, 0.2],\n", + " [-0.33, -0.2, 0, 0.2],\n", + " )\n", + " plot_result_expectations(\n", + " [\n", + " (resultBR, P12p, \"y-.\", \"Bloch-Redfield\"),\n", + " (resultMats, P12p, \"b\", \"Matsubara $N_k=2$\"),\n", + " (\n", + " resultMatsT,\n", + " P12p,\n", + " \"g--\",\n", + " \"Matsubara $N_k=2$ & Terminator\",\n", + " {\"linewidth\": 3},\n", + " ),\n", + " (\n", + " resultFit,\n", + " P12p,\n", + " \"r\",\n", + " r\"Fit $N_f = 4$, $N_k=15\\times 10^3$\",\n", + " {\"dashes\": [3, 2]},\n", + " ),\n", + " (\n", + " resultRC,\n", + " P12RC,\n", + " \"--\",\n", + " \"Thermal\",\n", + " {\"linewidth\": 2, \"color\": \"black\"},\n", + " ),\n", + " ],\n", + " axes=axes[1],\n", + " )\n", + " axes[1].text(5, 0.1, \"(b)\", fontsize=30)\n", + " axes[1].set_xlabel(r\"$t \\Delta$\", fontsize=30)\n", + " axes[1].set_ylabel(r\"$\\rho_{01}$\", fontsize=30)\n", + " axes[1].set_xlim(0, 50)" + ] + }, + { + "cell_type": "markdown", + "id": "d087bf9f", + "metadata": {}, + "source": [ + "And that's the end of a detailed first dive into modeling bosonic environments with the HEOM." + ] + }, + { + "cell_type": "markdown", + "id": "cb5966e6", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "f415b943", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "QuTiP: Quantum Toolbox in Python\n", + "================================\n", + "Copyright (c) QuTiP team 2011 and later.\n", + "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", + "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", + "Original developers: R. J. Johansson & P. D. Nation.\n", + "Previous lead developers: Chris Granade & A. Grimsmo.\n", + "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", + "\n", + "QuTiP Version: 5.1.0.dev0+7941773\n", + "Numpy Version: 2.1.3\n", + "Scipy Version: 1.14.1\n", + "Cython Version: 3.0.9\n", + "Matplotlib Version: 3.9.2\n", + "Python Version: 3.12.7\n", + "Number of CPUs: 16\n", + "BLAS Info: Generic\n", + "INTEL MKL Ext: None\n", + "Platform Info: Linux (x86_64)\n", + "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", + "================================================================================\n", + "Please cite QuTiP in your publication.\n", + "================================================================================\n", + "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" + ] + } + ], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "2555e881", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "7d97aa9c", + "metadata": {}, + "outputs": [], + "source": [ + "# Check P11p\n", + "assert np.allclose(\n", + " expect(P11p, resultMatsT.states),\n", + " expect(P11p, resultPade.states),\n", + " rtol=1e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(P11p, resultMatsT.states),\n", + " expect(P11p, resultFit.states),\n", + " rtol=1e-2,\n", + ")\n", + "\n", + "# Check P12p\n", + "assert np.allclose(\n", + " expect(P12p, resultMatsT.states),\n", + " expect(P12p, resultPade.states),\n", + " rtol=1e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(P12p, resultMatsT.states),\n", + " expect(P12p, resultFit.states),\n", + " rtol=1e-1,\n", + ")" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter" + }, + "kernelspec": { + "display_name": "qutip-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb new file mode 100644 index 00000000..4138ea3d --- /dev/null +++ b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb @@ -0,0 +1,900 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "37e47748", + "metadata": {}, + "source": [ + "# HEOM 1b: Spin-Bath model (very strong coupling)" + ] + }, + { + "cell_type": "markdown", + "id": "01ab22bd", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices.\n", + "\n", + "In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", + "\n", + "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian, the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", + "\n", + "In the example below we show how to model the overdamped Drude-Lorentz Spectral Density, commonly used with the HEOM. We show how to do this using the Matsubara, Pade and fitting decompositions, and compare their convergence.\n", + "\n", + "This notebook shows a similar example to notebook 1a, but with much stronger coupling as discussed in [Shi *et al.*, J. Chem. Phys **130**, 084105 (2009)](https://doi.org/10.1063/1.3077918). Please refer to notebook HEOM 1a for a more detailed explanation.\n", + "\n", + "As in notebook 1a, we present a variety of simulations using different techniques to showcase the effect of different approximations of the correlation function on the results:\n", + "\n", + "- Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator\n", + "- Simulation 2: Matsubara decomposition (including terminator)\n", + "- Simulation 3: Pade decomposition\n", + "- Simulation 4: Fitting approach\n", + "\n", + "Lastly we compare the results to using the Bloch-Redfield approach:\n", + "\n", + "- Simulation 5: Bloch-Redfield\n", + "\n", + "which does not give the correct evolution in this case.\n", + "\n", + "\n", + "### Drude-Lorentz (overdamped) spectral density\n", + "\n", + "The Drude-Lorentz spectral density is:\n", + "\n", + "$$J_D(\\omega)= \\frac{2\\omega\\lambda\\gamma}{{\\gamma}^2 + \\omega^2}$$\n", + "\n", + "where $\\lambda$ scales the coupling strength, and $\\gamma$ is the cut-off frequency. We use the convention,\n", + "\\begin{equation*}\n", + "C(t) = \\int_0^{\\infty} d\\omega \\frac{J_D(\\omega)}{\\pi}[\\coth(\\beta\\omega) \\cos(\\omega \\tau) - i \\sin(\\omega \\tau)]\n", + "\\end{equation*}\n", + "\n", + "With the HEOM we must use an exponential decomposition:\n", + "\n", + "\\begin{equation*}\n", + "C(t)=\\sum_{k=0}^{k=\\infty} c_k e^{-\\nu_k t}\n", + "\\end{equation*}\n", + "\n", + "As an example, the Matsubara decomposition of the Drude-Lorentz spectral density is given by:\n", + "\n", + "\\begin{equation*}\n", + " \\nu_k = \\begin{cases}\n", + " \\gamma & k = 0\\\\\n", + " {2 \\pi k} / {\\beta } & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "\\begin{equation*}\n", + " c_k = \\begin{cases}\n", + " \\lambda \\gamma (\\cot(\\beta \\gamma / 2) - i) & k = 0\\\\\n", + " 4 \\lambda \\gamma \\nu_k / \\{(nu_k^2 - \\gamma^2)\\beta \\} & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." + ] + }, + { + "cell_type": "markdown", + "id": "21530878", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "adf8edee", + "metadata": {}, + "outputs": [], + "source": [ + "import contextlib\n", + "import time\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " basis,\n", + " brmesolve,\n", + " expect,\n", + " liouvillian,\n", + " sigmax,\n", + " sigmaz,\n", + ")\n", + "from qutip.core.environment import (\n", + " DrudeLorentzEnvironment,\n", + " system_terminator\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver,\n", + ")\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "cefeed3a", + "metadata": {}, + "source": [ + "## Helper functions\n", + "\n", + "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d883bc7f", + "metadata": {}, + "outputs": [], + "source": [ + "def cot(x):\n", + " \"\"\" Vectorized cotangent of x. \"\"\"\n", + " return 1. / np.tan(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "52048f66", + "metadata": {}, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\" Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "48891158", + "metadata": {}, + "outputs": [], + "source": [ + "# Solver options:\n", + "\n", + "options = {\n", + " \"nsteps\": 15000,\n", + " \"store_states\": True,\n", + " \"rtol\": 1e-14,\n", + " \"atol\": 1e-14,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "4c19dbbf", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian, bath and system measurement operators:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5b8ef999", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the system Hamiltonian\n", + "eps = .0 # Energy of the 2-level system.\n", + "Del = .2 # Tunnelling term\n", + "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "51a11f44", + "metadata": {}, + "outputs": [], + "source": [ + "# Initial state of the system.\n", + "rho0 = basis(2, 0) * basis(2, 0).dag()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a04bea10", + "metadata": {}, + "outputs": [], + "source": [ + "# System-bath coupling (Drude-Lorentz spectral density)\n", + "Q = sigmaz() # coupling operator\n", + "\n", + "# Bath properties (see Shi et al., J. Chem. Phys. 130, 084105 (2009)):\n", + "gamma = 1. # cut off frequency\n", + "lam = 2.5 # coupling strength\n", + "T = 1. # in units where Boltzmann factor is 1\n", + "beta = 1. / T\n", + "\n", + "# HEOM parameters:\n", + "\n", + "# number of exponents to retain in the Matsubara expansion of the\n", + "# bath correlation function:\n", + "Nk = 1\n", + "\n", + "# Number of levels of the hierarchy to retain:\n", + "NC = 13\n", + "\n", + "# Times to solve for:\n", + "tlist = np.linspace(0, np.pi / Del, 600)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "48e6e8e0", + "metadata": {}, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresonding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresonding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "6a25f082", + "metadata": {}, + "source": [ + "### Plot the spectral density\n", + "\n", + "Let us briefly inspect the spectral density." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cc3f7e50", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bath = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=500)\n", + "w = np.linspace(0, 5, 1000)\n", + "J = bath.spectral_density(w)\n", + "\n", + "# Plot the results\n", + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + "axes.plot(w, J, 'r', linewidth=2)\n", + "axes.set_xlabel(r'$\\omega$', fontsize=28)\n", + "axes.set_ylabel(r'J', fontsize=28);" + ] + }, + { + "cell_type": "markdown", + "id": "d029267b", + "metadata": {}, + "source": [ + "## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ade152c2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.007765769958496094\n", + " Total run time: 1.52s*] Elapsed 1.51s / Remaining 00:00:00:00\n", + "ODE solver time: 1.5161352157592773\n" + ] + } + ], + "source": [ + "with timer(\"RHS construction time\"):\n", + " matsBath=bath.approx_by_matsubara(Nk=Nk)\n", + " HEOMMats = HEOMSolver(Hsys, (matsBath,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMats = HEOMMats.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "9f1b4246", + "metadata": {}, + "source": [ + "## Simulation 2: Matsubara decomposition (including terminator)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3ead8e19", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.011697769165039062\n", + " Total run time: 1.71s*] Elapsed 1.70s / Remaining 00:00:00:00\n", + "ODE solver time: 1.7070674896240234\n" + ] + } + ], + "source": [ + "with timer(\"RHS construction time\"):\n", + " matsBath,delta=bath.approx_by_matsubara(Nk=Nk,compute_delta=True)\n", + " terminator = system_terminator(Q,delta)\n", + " Ltot = liouvillian(Hsys) + terminator\n", + " HEOMMatsT = HEOMSolver(Ltot, (matsBath,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMatsT = HEOMMatsT.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f00137ef", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + "\n", + "P11_mats = np.real(expect(resultMats.states, P11p))\n", + "axes.plot(\n", + " tlist, np.real(P11_mats),\n", + " 'b', linewidth=2, label=\"P11 (Matsubara)\",\n", + ")\n", + "\n", + "P11_matsT = np.real(expect(resultMatsT.states, P11p))\n", + "axes.plot(\n", + " tlist, np.real(P11_matsT),\n", + " 'b--', linewidth=2,\n", + " label=\"P11 (Matsubara + Terminator)\",\n", + ")\n", + "\n", + "axes.set_xlabel(r't', fontsize=28)\n", + "axes.legend(loc=0, fontsize=12);" + ] + }, + { + "cell_type": "markdown", + "id": "cf65d1c7", + "metadata": {}, + "source": [ + "## Simulation 3: Pade decomposition" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a2f13484", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# First, compare Matsubara and Pade decompositions\n", + "padeBath = bath.approx_by_pade(Nk=Nk)\n", + "\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(16, 8))\n", + "\n", + "ax1.plot(\n", + " tlist, np.real(bath.correlation_function(tlist)),\n", + " \"r\", linewidth=2, label=f\"Exact\",\n", + ")\n", + "ax1.plot(\n", + " tlist, np.real(matsBath.correlation_function(tlist)),\n", + " \"g--\", linewidth=2, label=f\"Mats (Nk={Nk})\",\n", + ")\n", + "ax1.plot(\n", + " tlist, np.real(padeBath.correlation_function(tlist)),\n", + " \"b--\", linewidth=2, label=f\"Pade (Nk={Nk})\",\n", + ")\n", + "\n", + "ax1.set_xlabel(r't', fontsize=28)\n", + "ax1.set_ylabel(r\"$C_R(t)$\", fontsize=28)\n", + "ax1.legend(loc=0, fontsize=12)\n", + "\n", + "tlist2 = tlist[0:50]\n", + "ax2.plot(\n", + " tlist2, np.abs(matsBath.correlation_function(tlist2)\n", + " - bath.correlation_function(tlist2)),\n", + " \"g\", linewidth=2, label=\"Mats Error\",\n", + ")\n", + "ax2.plot(\n", + " tlist2, np.abs(padeBath.correlation_function(tlist2)\n", + " - bath.correlation_function(tlist2)),\n", + " \"b--\", linewidth=2, label=\"Pade Error\",\n", + ")\n", + "\n", + "ax2.set_xlabel(r't', fontsize=28)\n", + "ax2.legend(loc=0, fontsize=12);" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "facc16ac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.0061817169189453125\n", + " Total run time: 1.02s*] Elapsed 1.02s / Remaining 00:00:00:00\n", + "ODE solver time: 1.0189778804779053\n" + ] + } + ], + "source": [ + "with timer(\"RHS construction time\"):\n", + " HEOMPade = HEOMSolver(Hsys, (padeBath,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultPade = HEOMPade.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "fa05670c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "fig, axes = plt.subplots(figsize=(8, 8))\n", + "\n", + "axes.plot(\n", + " tlist, np.real(P11_mats),\n", + " 'b', linewidth=2, label=\"P11 (Matsubara)\",\n", + ")\n", + "axes.plot(\n", + " tlist, np.real(P11_matsT),\n", + " 'b--', linewidth=2, label=\"P11 (Matsubara + Terminator)\",\n", + ")\n", + "\n", + "P11_pade = np.real(expect(resultPade.states, P11p))\n", + "axes.plot(\n", + " tlist, np.real(P11_pade),\n", + " 'r', linewidth=2, label=\"P11 (Pade)\",\n", + ")\n", + "\n", + "axes.set_xlabel(r't', fontsize=28)\n", + "axes.legend(loc=0, fontsize=12);" + ] + }, + { + "cell_type": "markdown", + "id": "e5bb519f", + "metadata": {}, + "source": [ + "## Simulation 4: Fitting approach\n", + "\n", + "In `HEOM 1a: Spin-Bath model (introduction)` a fit is performed manually, here\n", + "we will use the built-in tools. More details about them can be seen in \n", + "`HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions`" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "2968c281", + "metadata": {}, + "outputs": [], + "source": [ + "lower = [0, -np.inf, -1e-6, -3]\n", + "guess = [np.real(bath.correlation_function(0))/10, -10, 0, 0]\n", + "upper = [3.5, 0, 1e-6, 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d25032d3", + "metadata": {}, + "outputs": [], + "source": [ + "tfit=np.linspace(0,100,10000)\n", + "envfit,fitinfo = bath.approx_by_cf_fit(tlist=tfit,Nr_max=3,Ni_max=1,full_ansatz=True,\n", + " sigma=0.1,maxfev=1e6,target_rsme=None,\n", + " lower=lower,upper=upper,guess=guess)" + ] + }, + { + "cell_type": "markdown", + "id": "0f3851dc", + "metadata": {}, + "source": [ + "We can quickly compare the result of the Fit with the Pade expansion" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "de33d3f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8))\n", + "\n", + "ax1.plot(\n", + " tlist, np.real(bath.correlation_function(tlist)),\n", + " \"r\", linewidth=2, label=f\"Exact\",\n", + ")\n", + "ax1.plot(\n", + " tlist, np.real(envfit.correlation_function(tlist)),\n", + " \"g--\", linewidth=2, label=f\"Fit\",marker=\"o\",markevery=50\n", + ")\n", + "ax1.plot(\n", + " tlist, np.real(padeBath.correlation_function(tlist)),\n", + " \"b--\", linewidth=2, label=f\"Pade (Nk={Nk})\",\n", + ")\n", + "\n", + "ax1.set_xlabel(r't', fontsize=28)\n", + "ax1.set_ylabel(r\"$C_R(t)$\", fontsize=28)\n", + "ax1.legend(loc=0, fontsize=12)\n", + "\n", + "ax2.plot(\n", + " tlist, np.imag(bath.correlation_function(tlist)),\n", + " \"r\", linewidth=2, label=f\"Exact\",\n", + ")\n", + "ax2.plot(\n", + " tlist, np.imag(envfit.correlation_function(tlist)),\n", + " \"g--\", linewidth=2, label=f\"Fit\",marker=\"o\",markevery=50\n", + ")\n", + "ax2.plot(\n", + " tlist, np.imag(padeBath.correlation_function(tlist)),\n", + " \"b--\", linewidth=2, label=f\"Pade (Nk={Nk})\",\n", + ")\n", + "\n", + "ax2.set_xlabel(r't', fontsize=28)\n", + "ax2.set_ylabel(r\"$C_I(t)$\", fontsize=28)\n", + "ax2.legend(loc=0, fontsize=12)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "945d3613", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.10657048225402832\n", + " Total run time: 9.50s*] Elapsed 9.50s / Remaining 00:00:00:00\n", + "ODE solver time: 9.499839067459106\n" + ] + } + ], + "source": [ + "with timer(\"RHS construction time\"):\n", + " # We reduce NC slightly here for speed of execution because we retain\n", + " # 3 exponents in ckAR instead of 1. Please restore full NC for\n", + " # convergence though:\n", + " HEOMFit = HEOMSolver(Hsys, (envfit,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultFit = HEOMFit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "86bbc4d9", + "metadata": {}, + "source": [ + "## Simulation 5: Bloch-Redfield" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "0830344b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 0.78s*] Elapsed 0.78s / Remaining 00:00:00:00\n", + "ODE solver time: 0.7874143123626709\n" + ] + } + ], + "source": [ + "with timer(\"ODE solver time\"):\n", + " resultBR = brmesolve(\n", + " Hsys, rho0, tlist,\n", + " a_ops=[[sigmaz(), lambda w: bath.power_spectrum(w)]], sec_cutoff=0, options=options,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "f01ff43a", + "metadata": {}, + "source": [ + "## Let's plot all our results\n", + "\n", + "Finally, let's plot all of our different results to see how they shape up against each other." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "7d86d8e6", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate expectation values in the bases:\n", + "P11_mats = np.real(expect(resultMats.states, P11p))\n", + "P11_matsT = np.real(expect(resultMatsT.states, P11p))\n", + "P11_pade = np.real(expect(resultPade.states, P11p))\n", + "P11_fit = np.real(expect(resultFit.states, P11p))\n", + "P11_br = np.real(expect(resultBR.states, P11p))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "573f5fa0", + "metadata": {}, + "outputs": [], + "source": [ + "rcParams = {\n", + " \"axes.titlesize\": 25,\n", + " \"axes.labelsize\": 30,\n", + " \"xtick.labelsize\": 28,\n", + " \"ytick.labelsize\": 28,\n", + " \"legend.fontsize\": 28,\n", + " \"axes.grid\": False,\n", + " \"savefig.bbox\": \"tight\",\n", + " \"lines.markersize\": 5,\n", + " \"font.family\": \"STIXgeneral\",\n", + " \"mathtext.fontset\": \"stix\",\n", + " \"font.serif\": \"STIX\",\n", + " \"text.usetex\": False,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "bf2aceb5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", + "\n", + "with plt.rc_context(rcParams):\n", + " # Plot the results\n", + " plt.yticks([0.99, 1.0], [0.99, 1])\n", + " axes.plot(\n", + " tlist, np.real(P11_mats),\n", + " 'b', linewidth=2, label=f\"Matsubara $N_k={Nk}$\",\n", + " )\n", + " axes.plot(\n", + " tlist, np.real(P11_matsT),\n", + " 'g--', linewidth=3,\n", + " label=f\"Matsubara $N_k={Nk}$ & terminator\",\n", + " )\n", + " axes.plot(\n", + " tlist, np.real(P11_pade),\n", + " 'y-.', linewidth=2, label=f\"Padé $N_k={Nk}$\",\n", + " )\n", + " axes.plot(\n", + " tlist, np.real(P11_fit),\n", + " 'r', dashes=[3, 2], linewidth=2,\n", + " label=r\"Fit $N_f = 3$, $N_k=15 \\times 10^3$\",\n", + " )\n", + " axes.plot(\n", + " tlist, np.real(P11_br),\n", + " 'b-.', linewidth=1, label=\"Bloch Redfield\",\n", + " )\n", + "\n", + " axes.locator_params(axis='y', nbins=6)\n", + " axes.locator_params(axis='x', nbins=6)\n", + " axes.set_ylabel(r'$\\rho_{11}$', fontsize=30)\n", + " axes.set_xlabel(r'$t\\;\\gamma$', fontsize=30)\n", + " axes.set_xlim(tlist[0], tlist[-1])\n", + " axes.set_ylim(0.98405, 1.0005)\n", + " axes.legend(loc=0)" + ] + }, + { + "cell_type": "markdown", + "id": "ab946655", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "8619c5a9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "QuTiP: Quantum Toolbox in Python\n", + "================================\n", + "Copyright (c) QuTiP team 2011 and later.\n", + "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", + "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", + "Original developers: R. J. Johansson & P. D. Nation.\n", + "Previous lead developers: Chris Granade & A. Grimsmo.\n", + "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", + "\n", + "QuTiP Version: 5.1.0.dev0+7941773\n", + "Numpy Version: 2.1.3\n", + "Scipy Version: 1.14.1\n", + "Cython Version: 3.0.9\n", + "Matplotlib Version: 3.9.2\n", + "Python Version: 3.12.7\n", + "Number of CPUs: 16\n", + "BLAS Info: Generic\n", + "INTEL MKL Ext: None\n", + "Platform Info: Linux (x86_64)\n", + "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", + "================================================================================\n", + "Please cite QuTiP in your publication.\n", + "================================================================================\n", + "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" + ] + } + ], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "a87d6c51", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "e2deba88", + "metadata": {}, + "outputs": [], + "source": [ + "assert np.allclose(P11_matsT, P11_pade, rtol=1e-3)\n", + "assert np.allclose(P11_matsT, P11_fit, rtol=1e-3)" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "qutip-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb new file mode 100644 index 00000000..58ed5e6d --- /dev/null +++ b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb @@ -0,0 +1,1018 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "95cc68d0", + "metadata": {}, + "source": [ + "# HEOM 1c: Spin-Bath model (Underdamped Case)" + ] + }, + { + "cell_type": "markdown", + "id": "472e03c8", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices.\n", + "\n", + "In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", + "\n", + "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", + "\n", + "In the example below we show how to model the underdamped Brownian motion Spectral Density.\n", + "\n", + "Note that in the following, we set $\\hbar = k_\\mathrm{B} = 1$.\n", + "\n", + "### Brownian motion (underdamped) spectral density\n", + "The underdamped spectral density is:\n", + "\n", + "$$J_U = \\frac{\\alpha^2 \\Gamma \\omega}{(\\omega_c^2 - \\omega^2)^2 + \\Gamma^2 \\omega^2)}.$$\n", + "\n", + "Here $\\alpha$ scales the coupling strength, $\\Gamma$ is the cut-off frequency, and $\\omega_c$ defines a resonance frequency. With the HEOM we must use an exponential decomposition:\n", + "\n", + "The Matsubara decomposition of this spectral density is, in real and imaginary parts:\n", + "\n", + "\n", + "\n", + "\\begin{equation*}\n", + " c_k^R = \\begin{cases}\n", + " \\alpha^2 \\coth(\\beta( \\Omega + i\\Gamma/2)/2)/4\\Omega & k = 0\\\\\n", + " \\alpha^2 \\coth(\\beta( \\Omega - i\\Gamma/2)/2)/4\\Omega & k = 0\\\\\n", + " -2\\alpha^2\\Gamma/\\beta \\frac{\\epsilon_k }{((\\Omega + i\\Gamma/2)^2 + \\epsilon_k^2)(\\Omega - i\\Gamma/2)^2 + \\epsilon_k^2)} & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "\\begin{equation*}\n", + " \\nu_k^R = \\begin{cases}\n", + " -i\\Omega + \\Gamma/2, i\\Omega +\\Gamma/2, & k = 0\\\\\n", + " {2 \\pi k} / {\\beta } & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "\n", + "\n", + "\n", + "\\begin{equation*}\n", + " c_k^I = \\begin{cases}\n", + " i\\alpha^2 /4\\Omega & k = 0\\\\\n", + " -i\\alpha^2 /4\\Omega & k = 0\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "\\begin{equation*}\n", + " \\nu_k^I = \\begin{cases}\n", + " i\\Omega + \\Gamma/2, -i\\Omega + \\Gamma/2, & k = 0\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." + ] + }, + { + "cell_type": "markdown", + "id": "5ecff67f", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "54f4f7ff", + "metadata": {}, + "outputs": [], + "source": [ + "import contextlib\n", + "import time\n", + "\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " basis,\n", + " brmesolve,\n", + " destroy,\n", + " expect,\n", + " qeye,\n", + " sigmax,\n", + " sigmaz,\n", + " tensor,\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver,\n", + ")\n", + "from qutip.core.environment import (\n", + " UnderDampedEnvironment,\n", + " ExponentialBosonicEnvironment\n", + ")\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "713e16b7", + "metadata": {}, + "source": [ + "## Helper functions\n", + "\n", + "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e375ecb7", + "metadata": {}, + "outputs": [], + "source": [ + "def cot(x):\n", + " \"\"\" Vectorized cotangent of x. \"\"\"\n", + " return 1. / np.tan(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f158bbeb", + "metadata": {}, + "outputs": [], + "source": [ + "def coth(x):\n", + " \"\"\" Vectorized hyperbolic cotangent of x. \"\"\"\n", + " return 1. / np.tanh(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "62db5351", + "metadata": {}, + "outputs": [], + "source": [ + "def underdamped_matsubara_params(lam, gamma, T, nk):\n", + " \"\"\" Calculation of the real and imaginary expansions of the\n", + " underdamped correlation functions.\n", + " \"\"\"\n", + " Om = np.sqrt(w0**2 - (gamma / 2)**2)\n", + " Gamma = gamma / 2.\n", + " beta = 1. / T\n", + "\n", + " ckAR = [\n", + " (lam**2 / (4*Om)) * coth(beta * (Om + 1.0j * Gamma) / 2),\n", + " (lam**2 / (4*Om)) * coth(beta * (Om - 1.0j * Gamma) / 2),\n", + " ]\n", + " ckAR.extend(\n", + " (-2 * lam**2 * gamma / beta) * (2 * np.pi * k / beta) /\n", + " (((Om + 1.0j * Gamma)**2 + (2 * np.pi * k / beta)**2) *\n", + " ((Om - 1.0j * Gamma)**2 + (2 * np.pi * k / beta)**2)) + 0.j\n", + " for k in range(1, nk + 1)\n", + " )\n", + " vkAR = [\n", + " -1.0j * Om + Gamma,\n", + " 1.0j * Om + Gamma,\n", + " ]\n", + " vkAR.extend(\n", + " 2 * np.pi * k * T + 0.j\n", + " for k in range(1, nk + 1)\n", + " )\n", + "\n", + " factor = 1. / 4\n", + "\n", + " ckAI = [\n", + " -factor * lam**2 * 1.0j / Om,\n", + " factor * lam**2 * 1.0j / Om,\n", + " ]\n", + " vkAI = [\n", + " -(-1.0j * Om - Gamma),\n", + " -(1.0j * Om - Gamma),\n", + " ]\n", + "\n", + " return ckAR, vkAR, ckAI, vkAI" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3c3083d2", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_result_expectations(plots, axes=None):\n", + " \"\"\" Plot the expectation values of operators as functions of time.\n", + "\n", + " Each plot in plots consists of: (solver_result, measurement_operation,\n", + " color, label).\n", + " \"\"\"\n", + " if axes is None:\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " fig_created = True\n", + " else:\n", + " fig = None\n", + " fig_created = False\n", + "\n", + " # add kw arguments to each plot if missing\n", + " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", + " for result, m_op, color, label, kw in plots:\n", + " exp = np.real(expect(result.states, m_op))\n", + " kw.setdefault(\"linewidth\", 2)\n", + " axes.plot(result.times, exp, color, label=label, **kw)\n", + "\n", + " if fig_created:\n", + " axes.legend(loc=0, fontsize=12)\n", + " axes.set_xlabel(\"t\", fontsize=28)\n", + "\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1e702554", + "metadata": {}, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\" Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "20425aac", + "metadata": {}, + "outputs": [], + "source": [ + "# Solver options:\n", + "\n", + "options = {\n", + " \"nsteps\": 15000,\n", + " \"store_states\": True,\n", + " \"rtol\": 1e-14,\n", + " \"atol\": 1e-14,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "7f76b8d1", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian, bath and system measurement operators:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "df4a5c49", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the system Hamiltonian\n", + "eps = .5 # Energy of the 2-level system.\n", + "Del = 1.0 # Tunnelling term\n", + "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8258fd6b", + "metadata": {}, + "outputs": [], + "source": [ + "# Initial state of the system.\n", + "rho0 = basis(2, 0) * basis(2, 0).dag()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5176eb82", + "metadata": {}, + "outputs": [], + "source": [ + "# System-bath coupling (underdamed spectral density)\n", + "Q = sigmaz() # coupling operator\n", + "\n", + "# Bath properties:\n", + "gamma = .1 # cut off frequency\n", + "lam = .5 # coupling strength\n", + "w0 = 1. # resonance frequency\n", + "T = 1.\n", + "beta = 1. / T\n", + "\n", + "# HEOM parameters:\n", + "\n", + "# number of exponents to retain in the Matsubara expansion of the\n", + "# bath correlation function:\n", + "Nk = 2\n", + "\n", + "# Number of levels of the hierarchy to retain:\n", + "NC = 10\n", + "\n", + "# Times to solve for:\n", + "tlist = np.linspace(0, 50, 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f2723068", + "metadata": {}, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresonding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresonding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "4ea690dd", + "metadata": {}, + "source": [ + "### First let us look at what the underdamped spectral density looks like:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f3569f92", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_spectral_density():\n", + " \"\"\" Plot the underdamped spectral density \"\"\"\n", + " w = np.linspace(0, 5, 1000)\n", + " J = lam**2 * gamma * w / ((w0**2 - w**2)**2 + (gamma**2) * (w**2))\n", + "\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " axes.plot(w, J, 'r', linewidth=2)\n", + " axes.set_xlabel(r'$\\omega$', fontsize=28)\n", + " axes.set_ylabel(r'J', fontsize=28)\n", + "\n", + "\n", + "plot_spectral_density()" + ] + }, + { + "cell_type": "markdown", + "id": "5dfb0800", + "metadata": {}, + "source": [ + "The correlation functions are now very oscillatory, because of the Lorentzian peak in the spectral density." + ] + }, + { + "cell_type": "markdown", + "id": "42d073aa", + "metadata": {}, + "source": [ + "### So next, let us plot the correlation functions themselves:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "864e7b29", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def Mk(t, k, gamma, w0, beta):\n", + " \"\"\" Calculate the Matsubara terms for a given t and k. \"\"\"\n", + " Om = np.sqrt(w0**2 - (gamma / 2)**2)\n", + " Gamma = gamma / 2.\n", + " ek = 2 * np.pi * k / beta\n", + "\n", + " return (\n", + " (-2 * lam**2 * gamma / beta) * ek * np.exp(-ek * np.abs(t))\n", + " / (((Om + 1.0j * Gamma)**2 + ek**2) * ((Om - 1.0j * Gamma)**2 + ek**2))\n", + " )\n", + "\n", + "\n", + "def c(t, Nk, lam, gamma, w0, beta):\n", + " \"\"\" Calculate the correlation function for a vector of times, t. \"\"\"\n", + " Om = np.sqrt(w0**2 - (gamma / 2)**2)\n", + " Gamma = gamma / 2.\n", + "\n", + " Cr = (\n", + " coth(beta * (Om + 1.0j * Gamma) / 2) * np.exp(1.0j * Om * t)\n", + " + coth(beta * (Om - 1.0j * Gamma) / 2) * np.exp(-1.0j * Om * t)\n", + " )\n", + "\n", + " Ci = np.exp(-1.0j * Om * t) - np.exp(1.0j * Om * t)\n", + "\n", + " return (\n", + " (lam**2 / (4 * Om)) * np.exp(-Gamma * np.abs(t)) * (Cr + Ci) +\n", + " np.sum([\n", + " Mk(t, k, gamma=gamma, w0=w0, beta=beta)\n", + " for k in range(1, Nk + 1)\n", + " ], 0)\n", + " )\n", + "\n", + "\n", + "def plot_correlation_function():\n", + " \"\"\" Plot the underdamped correlation function. \"\"\"\n", + " t = np.linspace(0, 20, 1000)\n", + " corr = c(t, Nk=3, lam=lam, gamma=gamma, w0=w0, beta=beta)\n", + "\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " axes.plot(t, np.real(corr), '-', color=\"black\", label=\"Re[C(t)]\")\n", + " axes.plot(t, np.imag(corr), '-', color=\"red\", label=\"Im[C(t)]\")\n", + " axes.set_xlabel(r't', fontsize=28)\n", + " axes.set_ylabel(r'C', fontsize=28)\n", + " axes.legend(loc=0, fontsize=12)\n", + "\n", + "\n", + "plot_correlation_function()" + ] + }, + { + "cell_type": "markdown", + "id": "ed47f7f9", + "metadata": {}, + "source": [ + "It is useful to look at what the Matsubara contributions do to this spectral density. We see that they modify the real part around $t=0$:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "233372e0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_matsubara_correlation_function_contributions():\n", + " \"\"\" Plot the underdamped correlation function. \"\"\"\n", + " t = np.linspace(0, 20, 1000)\n", + "\n", + " M_Nk2 = np.sum([\n", + " Mk(t, k, gamma=gamma, w0=w0, beta=beta)\n", + " for k in range(1, 2 + 1)\n", + " ], 0)\n", + "\n", + " M_Nk100 = np.sum([\n", + " Mk(t, k, gamma=gamma, w0=w0, beta=beta)\n", + " for k in range(1, 100 + 1)\n", + " ], 0)\n", + "\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " axes.plot(t, np.real(M_Nk2), '-', color=\"black\", label=\"Re[M(t)] Nk=2\")\n", + " axes.plot(t, np.real(M_Nk100), '--', color=\"red\", label=\"Re[M(t)] Nk=100\")\n", + " axes.set_xlabel(r't', fontsize=28)\n", + " axes.set_ylabel(r'M', fontsize=28)\n", + " axes.legend(loc=0, fontsize=12)\n", + "\n", + "\n", + "plot_matsubara_correlation_function_contributions()" + ] + }, + { + "cell_type": "markdown", + "id": "94059ab4", + "metadata": {}, + "source": [ + "## Solving for the dynamics as a function of time" + ] + }, + { + "cell_type": "markdown", + "id": "74a740c6", + "metadata": {}, + "source": [ + "Next we calculate the exponents using the Matsubara decompositions. Here we split them into real and imaginary parts.\n", + "\n", + "The HEOM code will optimize these, and reduce the number of exponents when real and imaginary parts have the same exponent. This is clearly the case for the first term in the vkAI and vkAR lists." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b1528829", + "metadata": {}, + "outputs": [], + "source": [ + "ckAR, vkAR, ckAI, vkAI = underdamped_matsubara_params(\n", + " lam=lam, gamma=gamma, T=T, nk=Nk,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ff4c0d2b", + "metadata": {}, + "source": [ + "Having created the lists which specify the bath correlation functions, we create a `BosonicBath` from them and pass the bath to the `HEOMSolver` class.\n", + "\n", + "The solver constructs the \"right hand side\" (RHS) determinining how the system and auxiliary density operators evolve in time. This can then be used to solve for dynamics or steady-state.\n", + "\n", + "Below we create the bath and solver and then solve for the dynamics by calling `.run(rho0, tlist)`." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "73c7e130", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.028772354125976562\n", + " Total run time: 17.14s*] Elapsed 17.14s / Remaining 00:00:00:00******** 36% ] Elapsed 4.79s / Remaining 00:00:00:08[*********62%** ] Elapsed 10.76s / Remaining 00:00:00:06[*********63%** ] Elapsed 10.98s / Remaining 00:00:00:06\n", + "ODE solver time: 17.142221450805664\n" + ] + } + ], + "source": [ + "with timer(\"RHS construction time\"):\n", + " bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI)\n", + " HEOMMats = HEOMSolver(Hsys, (bath,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMats = HEOMMats.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "88fcd2c0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_result_expectations([\n", + " (resultMats, P11p, 'b', \"P11 Mats\"),\n", + " (resultMats, P12p, 'r', \"P12 Mats\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "af7419ef", + "metadata": {}, + "source": [ + "In practice, one would not perform this laborious expansion for the underdamped correlation function, because\n", + "QuTiP already has a class, `UnderDampedEnvironment`, that can construct this bath for you. Nevertheless, knowing how\n", + "to perform this expansion is an useful skill.\n", + "\n", + "Below we show how to use this built-in functionality:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "f447f515", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.11582255363464355\n", + " Total run time: 14.42s*] Elapsed 14.42s / Remaining 00:00:00:00*********60%** ] Elapsed 9.39s / Remaining 00:00:00:06\n", + "ODE solver time: 14.422335863113403\n" + ] + } + ], + "source": [ + "# Compare to built-in under-damped bath:\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = UnderDampedEnvironment(lam=lam, gamma=gamma, w0=w0, T=T)\n", + " bath_approx=bath.approx_by_matsubara(Nk=Nk)\n", + " HEOM_udbath = HEOMSolver(Hsys, (bath_approx,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " result_udbath = HEOM_udbath.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "d06dc1ca", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_result_expectations([\n", + " (result_udbath, P11p, 'b', \"P11 (UnderDampedEnvironment)\"),\n", + " (result_udbath, P12p, 'r', \"P12 (UnderDampedEnvironment)\"),\n", + " (resultMats, P11p, 'r--', \"P11 Mats\"),\n", + " (resultMats, P12p, 'b--', \"P12 Mats\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "d7cb76af", + "metadata": {}, + "source": [ + "The `UnderDampedEnvironment` class also allows us to easily evaluate analytical expressions for the power spectrum, correlation function, and spectral density. In the following plots, the solid lines are the exact expressions, and the dashed lines are based on our approximation of the correlation function with a finite number of exponents. In this case, there is an excellent agreement." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "437de6ef", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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HtY4m/NwZhZ1MdyKEXzGW5RFDPkFK+Xu7orBzWs/xKiHqXkAVdt26dWPu3LnMmjWL9u3b8+KLLzJp0iRuv/12raMJf3faShM66bETwq/cl/MS6ywP0TT3ZwB2NL2X5mWf8EX83zVOJgJNQN08AXDNNddwzTXXaB1DBJryHrtS1USQYkOnypJiQvgTg8vdM6c3u6+tM5otODBQapMPccK7AqrHTgjNON3DM2WK+25YucZOCP9iLC/sdEZ3YWcxlC8p5pD3uvCugOuxE0ILW2KH8t99cVwWfJCHHJ+hl8JOCL9iUG3AqR67+KLt/Nf4Ds5jKUB3DZOJQCOFnRBecNzQkFWutoSaYvmk7Cj2kBRaax1KCFFnTOWFncEcDEC4/Rj99CvYXZanZSwRgGQoVggvqFhSLMvSjOcd9/C1eZi2gYQQdcqkuodijSZ3Yac3h7i/dtk0yyQCkxR2QnhBUv5a7tAvpB37AHA4ZUkxIfyJCXcBZ7RU3DxR/qcq050I75KhWCG8oN3xRdxj/I6v7Q5+oTehDrkrVgh/8o2rH8FqCb1DGwBgsLh77kxS2Akvk8JOCC+omJC4qXMf6y2fcbikITBM00xCiLrhcqk8b3OvN74+PA4AY/lQrBkp7IR3yVCsEF6glE9Q7NS7Z6OXu2KF8B9Wh6vy7xVLipmC3D12ZlWusRPeJYWdEF5Q0WPnqijskMJOCH9RZrURRQFBlGEun7/OZHH32FkUO6rLda6XC1GnZChWCC/QqeWFncF9QbUBWStWCH9hL8hmo2UkdlWPQX8TAOaIODqWvU8ZJjY7XFhM0o8ivEP+pQnhBRU9dmp5YSc9dkL4D1tpsftPjJXbLEYD+YRixUSpXXrshPdIYSeEF1T02Knlyw0Z5Bo7IfyGzVYCgFUxVW4z6HUY9Qogy4oJ75LCTggvmB12F/fY/kF2TC8ADNJjJ4TfsJeVAmDDXGX784ZPed04GfuJI1rEEgFKrrETwgv26FuwxtWAgRGNmePsix0jt2odSghRJ5xWd4+dXTFW2T5EWUGM/iT7CnOBZhokE4FIeuyE8AJ7+V1xuuAo/m5/iKftI1BVWX1C1NyyZcu49tprSUxMRFEUvvnmm3O2X7JkCYqinPHYuXOndwIHEIetvMdOV7XHzqa4v7Zbi72eSQQu6bETwgu6lvxGM/0xopyJldscLrXyGhwhzqe4uJiOHTtyzz33cNNNN9X4dbt27SI8PLzy69jYWE/EC2iO8h47h3J6YWcC9dTzQniDFHZCeMFtJTNJNR5kbcmlWFAw4sThcGHUS6e5qJkhQ4YwZMiQWr8uLi6OyMjIGrW1Wq1YradWSigoKKj18QJRkSmWuc7euEKb0OpP2+3lhZ5LCjvhRfJbRQgv0JXPW2fWw07LPWy1jMBRJr80hed17tyZhIQEBgwYwOLFi8/ZdsKECURERFQ+kpKSvJTSt2WHtmWM/WG+b3B3le12nXtCcumxE94khZ0QXqAvn+7EGBRSuc1pl6WGhOckJCTw/vvvM2fOHL7++mtatWrFgAEDWLZs2VlfM27cOPLz8ysfGRkZXkzsu6x2913uZoO+ynaH3t1j5yy/Bk8Ib5ChWCG8oGLeOr0xCJeqoFNUHA67xqmEP2vVqhWtWp0aGOzZsycZGRlMnDiRfv36Vfsas9mM2Wyu9jlxdjZrGWZsWAxVr5l1lt9Modqkx054j0/02NntdjIyMti1axfHjx/XOo4QtaYvH4rVG4w4yt92Tof02Anv6tGjB3v27NE6ht9pe3AGuyx3c3vOxCrbv0h8ii5lU9kef61GyUQgqreFXVFREe+99x79+/cnIiKCJk2a0LZtW2JjY0lJSeH+++9n7dq1WscUokYqJiTWG004yjvKnXbpsfNn9fED6caNG0lISNA6hv+xlwGn1oKu4DJHcZxwip366l4lhEfUy6HYN998k//85z80adKE6667jqeffppGjRoRFBTE8ePH+eOPP1i+fDkDBw6kR48evP3227Ro0ULr2EKc1Z977JwVPXZOKez8TVFREZ9//jmzZs1izZo1Ve4wbdy4MYMGDeKBBx6gW7duF7TvvXv3Vn594MABNm3aRHR0NMnJyYwbN47MzEw++eQTACZNmkSTJk1o164dNpuNzz77jDlz5jBnzpyL/0ZFFYrDXdip+qrD2EEmd0FXcQ2eEN5QLwu7FStWsHjxYjp06FDt8927d+fee+9l6tSpfPTRRyxdulQKO1GvPe18GJ2zjKfDEnAo7redS26e8Cue/kC6bt06Lr/88sqvx44dC8Bdd93FjBkzyMrKIj09vfJ5m83GE088QWZmJkFBQbRr144ffviBoUOH1t03LQBQHO6bI1RTSJXt7YpXMd6wgLCj/aDKRChCeE69LOy++uqrGrUzm82MGjXKw2mEuHgLnV1wuFSeDQpjqdIdnaOENvpgrWOJOuTpD6T9+/c/52olM2bMqPL1k08+yZNPPlnj/YsLp3O4b45QjFWHYpNLdtDTsIjV+WFaxBIBql4Wdn82YcIENm3axNGjRwkJCaFNmzbccMMN9O7dW+toQtSIqqo4XO5fyAadjldMD5NVWsZ3wYnneaXwJfKBNHDpy3vslNN67DC657GrGKoVwhvq7c0TFd5//30KCwtp3LgxBoOBOXPm0LdvXwYNGsTJkye1jifEeTkcDq7T/c4Q3WpMihND+TJijvL1Y4UQvs3gdBd2OnPVwq6iB0/vlHnshPfU+x67AwcOnLFtzZo1jBw5kocffpjPP/9cg1RC1JzdWsJbpncBKOUJDIqCHicOp1xQ7a9kpCGw7DC0IdupEhaeXGX7qcLOWt3LhPCIet9jV53u3bszbdo05s2bp3UUIc7Lbjt1UjeaLUwvfZR9ljsIOrJSw1TCk2SkIbB8ah7OA/a/U5bYo8p2ncl9Ha3eKUOxwnvqfY/dn02fPp3Q0FBMJhPffPMNsbGxF7W/CRMm8Mwzz/D4448zadKkugkpxGlstvI5rlQFvd6AC/cUCKpMd+K3ZKQhsJTY3L3vFdObVNCb3T12Bpf02Anv8anCbvXq1Xz11VecPHmSoUOHXlSP3dq1a3n//fe55JJL6jChEGeqWBPWjgGzToezYroTh0PLWMLLKkYa+vbtq3UUUcdsVvd7PPj0wq68x04KO+FNPjUUO3XqVPLy8vj+++/Zv3//Ba88UVRUxO23384HH3xAVFTUWdtZrVYKCgqqPISoLUf5UKy9/HOUS5Eeu0Ayffp0vvrqK7799lv++9//XvRIg6h/vrfdw27zHUQUH6yyvTSxB32tb/J00D+1CSYCUr0v7Pr168eqVasqv1YUhSFDhvD555/z7LPPXtA+H374Ya6++mquvPLKc7abMGECERERlY+kpKQLOp4IbA57eWFX0VNX8acUdgFh9erVjBw5khtvvJG8vDy5NtgPBalWTIoTc1DVu2JNwWFkqPEcdYZqlEwEonpf2F1yySX06dOHXr168frrr7Nw4UJWrFjBRx99RGlp7W8h/+KLL9iwYQMTJkw4b9tx48aRn59f+cjIyLiQb0EEuIrCzkHVwk567AJDXY00iPrJ6XBgVtzvZXNQ1QIuyOjunS+zy9RGwnvq/TV277zzDqNGjeK1115j/PjxFBYWAu6eu5deeqlW+8rIyODxxx9n4cKFWCyW87Y3m82YzebzthPiXEqDEhhtG0V4SBDj+fNQrFxj56/69evHq6++So8e7rskK0YaEhISGDp0KPfcc4/GCUVdKSkuoGJdiaCQqitMBDsLeNowC7Md4NwjRELUlXpf2AG0bduW6dOn8+GHH7Jv3z5OnjxJSkoK8fHxtdrP+vXrycnJIS0trXKb0+lk2bJlvPPOO1itVvR6/Tn2IETtlRnC+cbVh6blk5fut7QluwSMJrnWyl9VjDR0796dm266iQ4dOhAaGsqsWbMuaKRB1F/WkiLCcN/1brZUXSbQgpWRhu+wq/J7RXiPTxR2FfR6PS1btrzg1w8YMICtW7dW2XbPPffQunVrnnrqKSnqhEfYHe5hGKPefeXDd9H38PPRo0yIrn5NUeH76nKkQdRv1lL3/9tSzIToql7dZLa4h2aNihOH3YbBaPJ6PhF46mVhl56eTnJy8vkblsvMzKRRo0bnbRcWFkb79u2rbAsJCaFBgwZnbBeizhQdZaBuHRFqAtAPY8WSYk657saf1dVIg6jfrCVFAJQqFk5bKRZL8KktZaXFhEphJ7ygXt480a1bN+6//37WrFlz1jb5+fl88MEHtG/fnq+//tqL6YSoneDcTXxgeoP7Sz4CwFDec+dwqVrGEnUsPT292u0VIw3du3evUtRlZmZ6K5rwoBKXkZ+dndmgP3NO1D8PzVpLi7wZSwSwetljt2PHDl566SWuuuoqjEYjXbt2JTExEYvFwokTJ9i+fTvbtm2ja9euvPbaawwZMuSCj7VkyZK6Cy5ENVwO9+SlTp0RgNuOvsZE809sOPA49H5ey2iiDnXr1o3rrruO+++/n+7du1fbJj8/ny+//JL//ve/PPjggzz66KNeTinq2smgZEbY/0Gr6DAGnfacotNRphqxKHaspSWa5BOBp14WdtHR0UycOJF///vfzJ8/n+XLl3Pw4EFKS0uJiYnh9ttvZ/DgwTJ8KnyC6nBPd1Kx4oQeFbPiAFkY3K948wOpqD9KbO67209fTqyCVTFhwY69rNibsUQAq5eFHcC2bdswm83ceOON3HjjjVrHEeKCVfTYucp77NDpK57QKJHwBPlAGphKKwo7Y/WFXSlBRFCMtaTQm7FEAKu3hd3YsWNp164db7zxRuW2b7/9lpkzZxIXF8eYMWNo2rSphgmFqBm1orBT3IWdWlHgSWHnlywWi3wgDSAN985ml3kCGwr6A/874/lnQ19g7zErL4c093o2EZjqbWG3efNmnn/+1PVHO3bs4OabbyYuLg6r1coXX3zB5s2bSUxM1DClEOenOqv22Km68rdd+XbhP0aOHEnnzp3p0qULl1xyiUxwHgBUawFmxY6h/G730x0PTuVQ3kkKHDKdlvCOelvY5efnV1mb9ZNPPqFp06Zs27YNh8PBNddcw8svv8xbb72lYUohzq+ix66ip07Vu1c9UaSw8zsbN27k008/pbS0FIPBQOvWrenSpQtdunShc+fOdO7cmdBQWTfUr5S5h1hdxur/v4aa3b9mi8qkh154R72c7gSgcePGZGVlVX79888/c8stt6DX6zGbzYwbN46FCxdqmFCImjkY0Z1n7PexIbr8YnmDey4rKez8z+rVqyksLOSPP/5g+vTpDBo0iIyMDF544QUuu+wyIiMjad26NY888gi7du2q1b6XLVvGtddeS2JiIoqi8M0335z3NUuXLiUtLQ2LxULTpk2ZOnXqBX5n4qxs5YWdKazap/s4VvEPwxeEZK/2ZioRwOptYTdw4MDK6+sOHTrExo0bGThwYOXzzZo1IyMjQ6t4QtRYdlBzZjoHcCjCPQVGUUgyvzvbcdQglxH4I51OR9u2bbn99tuZOHEiv/zyC8eOHePAgQP873//4+abb2b16tV07tyZ3377rcb7LS4upmPHjrzzzjs1an/gwAGGDh1K37592bhxI8888wyPPfYYc+bMudBvTVRDZyufn85cfWHXuXQVDxvmEZG33oupRCCrt0Oxzz77LJ07d6Zp06aUlZWRlJREnz59Kp8/evSoDGkIn2B3Vl1S7FCja/n3puYMi0zkOi2DCa9KSUkhJSWFYcOG8eKLL/Liiy/y1FNP8fvvv9fo9UOGDKnVFClTp04lOTmZSZMmAdCmTRvWrVvHxIkTuemmmy7kWxDVMNjdhZ3OEl7t85U9eVa5K1Z4R73tsWvUqBFr167lhhtuYMiQIXz99dcoyqmLU3/99deLWjdWCG8JLzpAL90fxDqzATAb3G87mywpFtDuvPNONm/e7LH9r1y5kkGDqk6ZO3jwYNatW4fdbq/2NVarlYKCgioPcW5Gh7uw0wdV32OnlvfkVfbsCeFh9bbHDtyfcF9//fVqn9u+fTt/+ctfvJxIiNrrmD2HB01fsTLvHmAApvLCzmqXwi6QpaSksHLlSo/tPzs7+4x1aePj43E4HOTl5ZGQkHDGayZMmMALL7zgsUz+aK8ulWJXKYbwxtU+r5jdI0t6m/TYCe+o14XduXzyySdaRxCiRhRXee+I3n1XbJOcn9lofo4D2e2Bn7QLJjTXoUMHj+7/z6McAKqqVru9wrhx4xg7dmzl1wUFBVVmJxBnetd4F3ttRcxK6lHt84rZPUSrd8jKE8I7fLawE8JX6Bxl7r8Y3NOcGHUKUUoRWU4ZmhGe07BhQ7Kzs6tsy8nJwWAw0KBBg2pfYzabZe69WqqYxiTMUv2vU32wu7AzSWEnvKTeXmMn/IfqCuwhR53TXdgpRndhpyv/06DKdCfCc3r27MmiRYuqbFu4cCFdu3bFaDRqlMr/FFndhV3FfHWnMwZFAGBySmEnvEMKO+FR21f+SOkLDVn97n1aR9GM3mUFQDEFu782uXtEDC4p7ETNFRUVsWnTJjZt2gS4pzPZtGkT6enpgHsY9c4776xsP3LkSA4dOsTYsWPZsWMH06ZN46OPPuKJJ57QIr5fcjmdrOEO1ppHEqZWf6OJs3F3rrb+h+fMT3o5nQhUMhQrPOroypkUqyn8dCSIS7UOoxF9eY+dzhjk/rr8TwPV35koRHXWrVvH5ZdfXvl1xbVwd911FzNmzCArK6uyyANITU1l/vz5jBkzhnfffZfExETeeustmeqkDhUX5ROmWAnGSllo9dOdBIVFs01NJcYmQ9zCO6SwEx5ldhTSTbebhc6u2ByuyjtCA4mxvMfOYC4v6Ezl19pJj52ohf79+1fe/FCdGTNmnLHtsssuY8OGDR5MFdhKik4SBthUPeby9/fpKpcUs8oHOeEdUtgJjwp2ua8rOUkohWV2GoQG3qfWb8zX4izpxIAGbQHQm9y/AIzSYyeETysrOAFAiRJEpK76D63hRnhQ/x2RahE26+WYzBZvRhQBSAo74VEmVykAw3S/U3wihwahgTd1ws+63ux1FnFFVFMA9EHhbHI1o0AXTj+NswkhLlxJQR4ARUoYkWdpExZs5inDF+gUlbwTOcQ0TPZaPhGYAm9cTHiVyVkCQG/9Nqx5BzROo41SmxMAi9H9dtNHN2GY7UUecD6lZSwhxEUqy88FoFhf/fV1ADq9ngIlxN3uRI5XconAJoWd8ChzeY8dgLXohIZJtNPKvo1Oyl6CFPc1dZVLijlc57xmSghRv9mLjwNQZjh7YQdQqLifL87P83gmIWQoVniURT1V2NmKA7Owe8vxH0LNpWRYrwDiMRv0ALhUcLhUjPrqVwEQQtRvJ1whrHa1pjDk3OuWl+jDwAHWAinshOdJj53wqLvVf5KruifodBSf1DaMRsy4e+pMFvc8diZs/G5+lLXmkdhKZJF1IXzV5pDeDLc9z28pD5+znbW8R89RdMwbsUSAk8JOeIyqqmy3xfG7qx0ArrJ8jRN5n8Nuw6i4r7EzW9zX2ZhMZhopx4hVCrBZy7SMJ4S4CCdK3He2RwafeyUPmykSAGdJYI5aCO+Swk54TJndhapCseqe3kO1Bt7aqGWlp5YRsgSHAqA3GLCr7uFYW5ksMySEr8ovdffGRwWbztnOYY4EQC057ulIQkhhJzynpPAEYwxf8Vf9rwAo1kKNE3mf9U+Fnbl8KBbAivsXga2sxOuZhBB148H0f7DW/BCtCn4/Z7vtKbdxjfXf/Br1Fy8lE4FMCjvhMWX5OTxumItOUXnQNoYVEVdrHcnrKnrkylQjyp8mMC1V3JOU2koCr9gVwl+EOo4Tq+QTbDn3pMP66Kb8oTbliC3US8lEIJO7YoXHlBW7i5YcNZKfXN0wKYkaJ/K+ih45q2Liz6d+q2IBFWylgTc8LYS/CHW6b36yhMecs13FNXgnS2UZQeF5UtgJj7GXuG+WKFbdy4gVWx1axtFEiT6Cl+23Emwx8diftlt1QeAEe6n02Anhq8LVQlAgJPLchV2ccpL79d8Tm2cBengnnAhYUtgJj7GVFy2hShnX6VbQKL8B0E3bUF5WoItgqvM6mgaFVCnsjhiTKbG7KFXlLSiELyoqOEFo+aTjkbGNztk2TlfIs8aZHCuLAN70QjoRyALqGrsJEybQrVs3wsLCiIuLY9iwYezatUvrWH7LUeYu7GKVfN4yvcNfCj7VOJH3FZX3UoZaqhZw78c+w7W2lzgcnqZFLCHERTqRcxiAEtVMSFjkOdtGlBd+UWoBDrsMxwrPCqjCbunSpTz88MOsWrWKRYsW4XA4GDRoEMXFMuWEJzjKrx8rLJ/uxOwKvDtA7fnZXKLsI1lfdZqDEJO70CuxO7WIJYS4SIV5mQAc10Wft21UTAJOVUGnqJzMzfJ0NBHgAmocaMGCBVW+nj59OnFxcaxfv55+/fqd0d5qtWK1Wiu/LiiQVQJqw1U+b91xfQPCXIcJ+tPyYoEiKmMR88wvsrGwN3Bd5fZgk3seu1Jb4F13KIQ/OF7mYqWzLfaQeBqfp63eYCBPiSCGk5zMPUxMYopXMorAFFA9dqfLz3df3B8dXf0nrgkTJhAREVH5SEpK8mY8n7c9egDXW8czK3IkACFq4PXYqWXuDwMOY9VpDoac+IylptG02v+xFrGEEBdpj7ENf7X/H7OTnq9R+5P6BgAUH8v0ZCwhArewU1WVsWPH0qdPH9q3b19tm3HjxpGfn1/5yMjI8HJK33ZcDWez2pz8Bh0AsCj2gLu+pGK1DedphV0YJaTocjCXyaLgQviinEL3aE5smLlG7YuN7g4E20kZihWeFVBDsX/2yCOPsGXLFn777beztjGbzZjNNXvTijNV3DgQHh5Vua24MJ+I6FitInmdYnPfQKKaqhZ2qtG9bqxil+s7hfBFuQXudZ5rWthZLbFQBs6Co56MJURg9tg9+uijzJs3j8WLF9O48fmujhAXqkneYu7Xf09TNR2r6p6gs7TopLahvExnK5+A2BxWZbtidi8vpnME3vC0EP7gb/ufZK35IToWn3s5sQpbmtzN1db/sDjsGg8nE4EuoHrsVFXl0UcfZe7cuSxZsoTU1FStI/m1DscXcbdxCauKG/Mv3cOcsMJYJYyGWgfzIoPdXdjpLOFVtismd4+d3hF4N5QI4Q8ibNnEKvlkhdZsmTBzw9ZsUx0kFps8nEwEuoDqsXv44Yf57LPPmDlzJmFhYWRnZ5OdnU1pqfxy9QSD090bpbOE8VvQZSxwdafQFVhD20ZH9YWdzhxa/rwMxYqamzx5MqmpqVgsFtLS0li+fPlZ2y5ZsgRFUc547Ny504uJ/ZPqchHvzAYgqlHzGr2mcZR72qfDJ+T3jfCsgOqxmzJlCgD9+/evsn369Oncfffd3g/k54xO9wlMbw4j1GwESgNuWbFfzFewpDiZtNi2VbYbQ9zXHZqdslasqJnZs2czevRoJk+eTO/evXnvvfcYMmQI27dvJzk5+ayv27VrF+Hhpz5YxMYGzjWunnLy2FGilPKbJxrXrLBLCnFxn/4Hkk7kA309mE4EuoAq7FRV1TpCQDGV99gZgkLpouymuW4/zuNxQOD8Ypmn9mW/owtfNKx657UpIp6Drniy1WhaapRN+JY33niD++67jxEjRgAwadIkfvrpJ6ZMmcKECRPO+rq4uDgiIyO9lDIw5B3eQxSQQzRxQSE1ek1iZBDPGT8HIP9EHhFR515fVogLFVBDscK7zC53j50xKIxbSmbxtukdQrJWapzKu44Xu6d3aRBS9boaY3I3+tve5DH171rEEj7GZrOxfv16Bg0aVGX7oEGDWLFixTlf27lzZxISEhgwYACLFy8+Z1ur1UpBQUGVhzhTYfY+AI4ba37FcHBoBMdx95zmHt7jkVxCgBR2woOCyickNgeHV07Q6ypfPzYQOOx2Ust2kKwcJSq4aud4RJD7LuGCUjsul/Qki3PLy8vD6XQSHx9fZXt8fDzZ2dnVviYhIYH333+fOXPm8PXXX9OqVSsGDBjAsmXLznocmZS9Zmx5BwAoCkqs1evyDO5CsDBrX51nEqJCQA3FCu8KVYtBgaDwBhwzuIcrVGvgFHYFx48y1+Seld5h+VuV58LLCzuXCoVWR2WhJ8S5KIpS5WtVVc/YVqFVq1a0atWq8uuePXuSkZHBxIkTq11CEdyTso8dO7by64KCAinuqnHQHoXL2RZbZOtava7QkghFu7Hm7PVQMiGksBMeYrU7GG57jjCllHejG+GqmKA3gAq7whNHiQZOEkqksepQrMWo53/mF4hTj1N89AcimrSqfidCADExMej1+jN653Jycs7oxTuXHj168Nlnn531eZmUvWa+cfRglb0lb3TqWKvX2aKaQ9ESdMd2eyiZEDIUKzyk0Opks9qc31wdCA0NqVx5oXLC3gBQfCIHgEIlvNrnk5VcknW5lOTnejOW8EEmk4m0tDQWLVpUZfuiRYvo1atXjfezceNGEhIS6jpewNmb4z6PNY+r2Rx2FYwN3T184UUH6jyTEBWkx054RGGZe1qTULMBvU5BKZ/HTW8LnB47a4G7sCs2RFb7fIkuFFzHKc0/5sVUwleNHTuWO+64g65du9KzZ0/ef/990tPTGTlyJOAeRs3MzOSTTz4B3HfNNmnShHbt2mGz2fjss8+YM2cOc+bM0fLb8HknCoopLcoHgmgWW7vCLiq5A6yFBPshVJcLRSd9K6LuSWEnPKI0L5379d9jNTYEBqMPdU9xYrad0DaYF9kK3D1xpcbIap8v0YeDC2yF0mMnzm/48OEcO3aM8ePHk5WVRfv27Zk/fz4pKSkAZGVlkZ6eXtneZrPxxBNPkJmZSVBQEO3ateOHH35g6NChWn0LfiF7x29ss9zHFqU1Ieara/XaRi06Msw2nr2uRH4tthEXZvFQShHIpLATHuHM2c2zxpkccKUA/8TZuDv/WPMASmhTXtU6nJe48jMBsAXFVft8mbkB2MGeX/1djUKcbtSoUYwaNara52bMmFHl6yeffJInn3zSC6kCS8GhLQCop63/XBMWSxD50R0pyitmR1ahFHbCI6QfWHiEvfg4AGV691BFSHwLvnL2Z6mthZaxvMpQeAQAV3ijap+3BZdf9F4ohZ0QvkI5sgGAkgbtz9Oyeh0aRQCwJeNkXUUSogrpsRMe4SjJB8BmcBd2DULdd4UeL7adc4oGf7JU35M1DhPtG/Wo9nk1tCHkgL7kqJeTCSEuVGzBNgAsKV0v6PWXRZ+gs+FjErZEwIAP6jKaEIAUdsJDXKXuws5udA9XRAcb6avbQrRaQGFRH8LDqr9T1J/8YOvEAUcLvmhafWGni0rmkCuOY86aLUkkhNBWceFJkp3poEDj9n0uaB/tol20NvzEsfwIuYFCeIT8ixIeoZa47/R0mtzDDhaTganGSfzXNJn8o4e0jOYVqqpy5KR7SbXEiKBq29hb38Bltkm8rrvHm9GEEBfowKal6BWVHKKJTWxyQfto0q4HdlVPA/LJSpelxUTdk8JOeIS+xH2npyv01I0DJ3XuIq/4WJYmmbwpJzePLq6tNNSdpGFE9RdIx4e7J4LNLijzZjQhxAUq3PELAOnhaRe8D0twKPuNzQHI3LCgTnIJ8WdS2AmPMFvzANCHnVok+6TRXeQV5R7UIpJX5excwSzTf/jS/B9MhurfZo2jggH3nH8nim3ejCeEuAALilsw29EfW8trLmo/xxPcS7rp9/9SF7GEqEIKO+ERk833cqftKeypl1duKwly3x1qP3ZQo1TeU3JkOwDHglLO2ibIpGda0FusNT9Ezo7l3oomhLgAeUVWPs1pylOOB0jtO/yi9hXVcQgAzYvW4bDLhzpRt6SwEx6xqSSWZa6OhMc1qdzmCG8MgC4/Q6NUXpS7E4CyiObnbBZvLCFWyafwiKwdKUR9tuCPbFwqdGwcQcJZrputqRadLuMkoYRTzM41P9VRQiHcpLATdc7lUjlW5P4UGhNmqtyuj3b3XgWVHNEklzdF5bt77IyJ557rqjgkGQBHjlxELUR9VrbyQ9opBxjavuH5G5+H3mBgd1R/DqsxrNt58KL3J8SfyXQnos6dOJHH/cq35OgiaRAypHJ7SFwzAKKth7WK5hXWshKa2PeDAgntzj0lgiu2DZz4HsuxbV5KJ4Sorcz9O7j35NuMMKscaTKwTvapH/ISfadtIfiQkVtsDoJN8utY1A3psRN1Lnf/Zp4yfsGTpq+q3DiQ0Korz9nvZrT1QYqtDg0TetaBrSswKQ5OEEZikzbnbBvZ/FIAGpfuRHW5vBFPCFFLGT/9F52issWSRmKTVnWyzy4tkkluEEqxzcn/1vv3h13hXVLYiTpXmLkLgDxT4yrbo2Pi+Sn4Wta5WrEzu1CLaF5xcvN8AA6Edj7v5KMp7XrgUHXEcJKjmfu9EU8IUQv5x47SLvsbANTuI+tsv4qicF+fVAw42Pfrx9ht1jrbtwhsUtiJOufI2wtAUeiZd4S2SXCvOLH9SL5XM3nT5OJ+jLPfR0H7u87bNigkjEMG988pc+tST0cTQtTSji+fJ0wp5YCuCR0uu6lO931LWmO+sYznBfsbrP/fa3W6bxG4pLATdc500t3zpEY3PeO5nglwk24Zpo0feTuWVxzIK2ZZlpEvXANo1/vaGr3maGxvfnO2Y1226uF0Qoja2LdlBV2yvwKgsN/z6PT6Ot2/xWSguN1tALTf9Q45mQfqdP8iMElhJ+pcw+IdAIQkdTzjucuiT/C6aSqDcmbgcjq9Hc3jZq12L5fWv2UssWHmGr1GufIF/mZ/lqmHk3G6pLgToj4oLi1D+eYhTIqTjcG96dDvBo8cp9uNo9llaE2oUkrex3fIkKy4aFLYiTp17OhhGqlHcakKKR0vO+P55p37U0AIURSw7ffvvR/Qg44dPcxf1gznet1v/O3SpBq/rltqNFHBRk6U2Fm8M8eDCYUQNWF3unhs9lbeKhtKNjEk3/X+ea+XvVA6vZ6gW96jSA2irW0rGyff7ZcfeoX3SGEn6tT+rSsBSNc3JjyywRnPG01mdsQMBsC2drpXs3navln/oKWSzqNBP3F5q7jzv6CcQa/jlq5JxHKSfQunejChEOJ8Sqx2HvhkHb/szOFHXT+y7vyNBvGNz//Ci5DcshN7ek/EqSp0Pzmf9W/dhrWsxKPHFP5LCjtRpz7La04f6ySWt3j6rG0a9B0BQKeCJez/Y7W3onnUmrlv0f3kfFyqgm3gS7W+FufOLpEsMY/hwZNvsPnXLzyUUghxLns3/8ahV3uxZ/c2LEYdU/+WRuemCV45dudBf2Njt1dxqDoan1zDve8vZm+O/84eIDxHCjtRZ04U21i47SiH1Tja9hp61nbNO/ZmQ+hl6BUV19yHKC323ZOX6nKxevbLpG16HoDVSffQtsdVtd5Po4YJbE34CwCJy56Wi6iF8KKjh/ex+q07aPL1tbRx7uYFyxd8PuJSLm9d8573utD1mgfY1v99xunG8PsRuGrScv4zdx3ZGXu9mkP4NinsRJ35csFiSu1O2iWG0yU56pxtE4e/wQnCae7cx7J3HiSnsMxLKevO3u2b2PrqQC7dMQG9orIm+lp63Pv6Be+vw+0vcUCXQiwnKP3oGjL376jDtEKIPysrKWLTL1+w8dWhNPigK5cen4dBcbEh9DK6jJpBWkq0Jrk6Xn4z/3n8fq5sE4/DpWJf9zENPuzOhteuYcOP0336g7DwDkVVVbkNr4YKCgqIiIggPz+f8PBwrePUKxsXfkaH3x/lB1cPQm95nwEdzn/zwM7VC1F//Ad3lj1JmTmGe3o34ZYOkTROiPdC4tpTXS52HT7Kb4dKmb81i6DDy/ncNAGbqmdDy8e59K/PXfQF1kcO7ET/8VDiOUaJamZzswdpf/0YwiK0+SVTH8j7Tjv+9LMvLDjBH7kutmaeZOW+Y4w6+CjdlJ2Vz28zdUA34DnaXDpYw5RVLd+Ti+vrh7isdFHlNqtqZK+5DQUNexDcpBtxnYfSMDIERVE0TCrq0sW+7wKysJs8eTKvvfYaWVlZtGvXjkmTJtG3b9/zvs6fTnJ1Je/IIfZ++xLdsme7e62irqb74zNr/Po92fn8/X9b2XLYPWHxz6YnsOhVssIvwdGgFZaGrQiJaUx4TCJRcY0wW0I88n24XCrFNgfFViclmVsozj1M6fHDOPOPoM/PIKJwL43sh5jn7MEzjvsBMOpUPoj7H82veYLGzdvXWZaczAMcm3E7bezu9WP/5bqf3Fa30b9VLJ3iDaQ2bIDBaKqz49V38r47pbbnrqVLlzJ27Fi2bdtGYmIiTz75JCNH1nz1BF/62ZfYHBw/eoSi7N2UHM/Clrcf5cRBgooO0cB6mARXDl2sUzlJGACjDf/jNsMS9sddSXz/B0ht203j7+Ds9m1dRc6KT2mS9RMJ5FZuz1eD6Wj9gHCLkaaxodzn+h8xJhu6yGTM0UlYIuMIiYonPLohYRHRdT4Pn/CMi33fBdyqw7Nnz2b06NFMnjyZ3r1789577zFkyBC2b99OcnKy1vHqFdXlwmYrw1pWSqHTwLFSlbwiK/ZD64jY+zWRJ7bR0r6THooKCqyJHEqXUTNqdYwWDSP4ZlRvfvwjm3krNtMkKxuD6qJxfhbk/wR/WmVrqfMS7lefIdikJ9io5zP7GJyKEZdiwKXoUBUdKu4/95nb8HXkvbhUFadLZVzuUxhdVvQ40KlO9KoDverAopayQW3Jg9bHK4+zzXwPIUo1c0kp0E2/h8uaxtK3RQzXdUokLuyaC/zpnl1co1Rinl7O2u+mYN76ObPKemPdmsUPW7N42jCTu/U/kalvSL6pIaXBCbiCY1CCItEFR3Gy6XWEhoYSZNQTUpaDhVKM5iCM5mBMlmBMZgsGgxG93uCx6RuEZ9T23HXgwAGGDh3K/fffz2effcbvv//OqFGjiI2N5aab6nYFhZpSVRWrw4W1tAh7SQEOWxk2aylOWyl2qxWnvRSnrYy8qEsow0Kp3UlQ9joicteDtQCdrQidrQiDowiDoxizs5hxhifYURJBic3JPwxf8LBhXvUHV6Bv2FEcKS3olBRJ/2avEJsYQ5wPvA+adehBsw49UF0uMvZt5cimn9FnrOBYqYrerqOgzMGmjJO0N/1Aqu4oZJ25jww1lhuMUwizGAk26Rlb+g4x6jGchmCchhBcxmBcxhAwmLGbItmd8ldMBh1GvY6kvN+xqEXoDWZ0RhM6QxAGownFYAS9GXvcJeh0oNcpmIsy0bus6HQGdHoDOr0end6Aotej1xvRh8RUttW5HO4/dXoURXE/fOD/R30XcD12l156KV26dGHKlCmV29q0acOwYcOYMGFClbZWqxWr9dQv+IKCApKSks5bRX+66hDFq2bQt+QXQC1/gAJQ/uOeGvE4R/TuW+j7lf7CoJIfytu42yuc+t8yOfQR9huaoarQ17qEG0u//lNb3O3L9/tO8ENs07cFoJf9d+4sm4mC6j52ZQ4VVHjHfD9rDZ1RVZUejrU8bn0fI3ZM2DGpNiyKvTLDg7bR/OTqDsB1ut95y/Ru5XM7jW2x9hpLx8tvPuvPpKYKTh5j/7qFlGZswnh8D+GlGYQ7jhOtnmSeqxdP2N29DQYc7LXcedb9/OLszH32f1R+vcN8N0GKrdq2a1ytuMX2T/d+dQoLTE+i1+kpNMVQZonDFZqAMaEtDZp2olGzDhhNNZt4uC64XCpbMvNZtD2bNQeO80jWM1ymbDxr+/ZlH1JEMACvGt7jFsPZlynrZp3CSV0kOkXhGf0n3MRinIoOF3qc6HBVPBQdj5gncEzfAAX4q+1rhjp+BkBVKv51nfrX+K/g58g2uO8kvMb6A0NsP5U/X/GvUAFFYa+hBb80e5qJN585kfWf+VKvkSfV5twF8NRTTzFv3jx27Dh1rebIkSPZvHkzK1eurPYYF3rOe2/pPoLXT6VbyXIMqq384T6PGHFgVO0MdbxGutN9ScGzhs+43zD/rPsbYH2NfWojAMYavuQxwzdnbXuj9V9sUFsCcLfpZx7Uf0ehPopCSyK28BQMMc0IbtiChOYdiY5rdNb9+Cqrw8nenCLSj5UQsXU6uhP7MRdnEmrLJcRZQLirgFCllD9cTbjG9lLl634x/Z1mumoqQCDdFUs/238rv55nepZLdNXf0JWrhtPNemqaptmm8Vyq21lt22LVTDvrqWmuZhhfob9+c7Vt7aqe1vbP3GcXBSbp3+JKZR2u084lFb8tL1c+wK6YURR4xvUBA1lVbVuAv5kmUaxz99w+4Picgc7llb953U69ZkzQvzmuc0/d9Vfb/xjs+LXyuapt4bng5ziqbwjAddbvGWpbWNlCBTYZL2Fd6yd56YYO1X7PFaTHrhZsNhvr16/n6aerTsUxaNAgVqxYcUb7CRMm8MILL9T6OAfzionI2087Q/X/YAH2Zx7lD9U9rNhNf5jWxrNfKJ95NI8tagwAafqjNDfuO2vbY8ePs8vlvri2o/4Yqcb0s7YtKDjJAVcxAB10+SSYTnXxc9rlGg31BTQMtdAg1ITZcimrnMfRJ3QgqcsgWie3OOsxais8sgGdrvwr8Ncq21WXi4FFJfzm1FFqc1JSZmPb4Zk4rIW4HA5QnbicLlCdqC4nJkssE6M7olPc88Rtz3oDnU5BZzCg05tQ9AZ0BhPm4DASw2NYF9OEULMBs0GHopz9jl5v0+kUOiVF0ikpEgCn8xcyDmzn5JH9lOUdxHEiA13pMfS2Aoz2AlrFNqTA6qLM4URXZiHfFYJJtWPGjk6p+hnOruqwO92nMyNlhBpKqw+hQvbJYo4Q5M5kyCPJcKTyudNlHMtnnxoKwFBDNs0M1f9CyLMa2XNULgSvidqeuwBWrlzJoEGDqmwbPHgwH330EXa7HaPReMZrLvSctz+3mNbHD9HacJbzmAI616mC0Vb+q8eqGrEpBmyYsGPErphwKEZaxIcTZY7CYtRjdKSxtqwMlzEUlykMxRyGYglDbwnHEBTOc0mXEhXTkJgwMyGmoSjKm3hngpL6wWzQ0y4xgnaJEdDhmWrbWMtKiMs/yY+uUIqsDoqtDo4f+ifHivNQrUW4rEVgK0Kxl6A4bRQroQyLScTmdGFzqOTmtmObLRy9akev2jG47BhUGwou8pUwEiIsOF0qLlXF7giigBB0qvujob78I6JRceI87X5NHa6zfl86XFVW4jHo7Jh19rO2LyxzYC3fv9FYTJS+4KxtjxaUkV/+b9BoOE6i4ehZ22YeLyYLCwCKIY9kQ+bZ2x4rYH/5uc9lyKGp4WCV5/eUxJBx3PPzEwZUj92RI0do1KgRv//+O7169arc/tJLL/Hxxx+za9euKu0v9NPrH5n55O7bSHhh+S3qikLFDchK+cePY7E9cJojAIXgwgOEFZ4q1hRFd+p1ikJBTCcc5igUFMwlmYTm76t8vvKC2fJjFEe1wRkUjQIYS44SVLgfpbxHBcX9t4r9WiOb47C42xqsxwkqTMdoCcFotmAwWSqH78yWIEwmi3SR+zDV5cJut1FWVoLLbsfpcuA0R+FEcZ88i/NQywpwOR2oLgcup7P87+4/i6Pbourd1/UZCzIwlmSjqiqo6qk/cYGqUhjTCafe3WtoLjyApSAdtbyX2P2nCriwGSNRG3ene+q5bwyRHrvan7sAWrZsyd13380zz5z6Zb9ixQp69+7NkSNHSEg4s/y50HPexvQT5B/cSETZEXRGM3qj+xyiN1kwmIIwmizoopIwmy2YDDpMOhWTQS4HCDQup7PynON0qTitRah2Ky6XC1V1oVacT1DBpeIMjcelqqgqKCV5YC9xnz5U97nG5XKVn1Nc2CJSUdEBKrrCLBRrgfuc5Ko4R7kq910a1QaXokcFjAXp6EtzK0fTTv+zKKYDqt6MqoK54BDGkuzyp6u2U1EpiumIS+/+AGwuPIS56LD7s2/5f+yWaIyJHenQOOKcPyfpsbsAp989pKpqtXcUmc1mzObaD7u1bxQBjfoD/Wv4ioZAzxq2jQM617BtDNCuhm2jgeY1bCt8jaLTYTJbMJkt1TeIqs31pdHAuYdPT4kB6u9F6b6mpueuc7WvbnuFCz3ndU6OguQrav06EVh0ej06wFhxD4c5suYvjqz5Mo3EhdUiVS0yUJvZCWKAtFq0rzsB9XEpJiYGvV5PdnZ2le05OTnEx9fPKTaEEOJCzl0NGzastr3BYKBBgzOX+xNC+IeAKuxMJhNpaWksWrSoyvZFixZVGd4QQoj65ELOXT179jyj/cKFC+natWu119cJIfxDQBV2AGPHjuXDDz9k2rRp7NixgzFjxpCenl6ruZ2EEMLbznfuGjduHHfeeepO8ZEjR3Lo0CHGjh3Ljh07mDZtGh999BFPPPGEVt+CEMILAu4au+HDh3Ps2DHGjx9PVlYW7du3Z/78+aSkpGgdTQghzup8566srCzS00/dBZ+amsr8+fMZM2YM7777LomJibz11luazWEnhPCOgLor9mLl5+cTGRlJRkZGwN6dJ4S3VdyZefLkSSIizn03mahbcs4Twvsu9pwXcD12F6Ow0D3nVlJSLe7OEULUicLCQinsvEzOeUJo50LPedJjVwsul4sjR44QFhbmEwsuV1T98mlbfhZ/5ms/C1VVKSwsJDExEZ3Me+ZVNT3n+dq/KU+Tn8cp8rM4paY/i4s950mPXS3odDoaN26sdYxaCw8PD/g3VAX5WZziSz8L6anTRm3Peb70b8ob5OdxivwsTqnJz+Jiznny8VcIIYQQwk9IYSeEEEII4SeksPNjZrOZf/7znxe0RJC/kZ/FKfKzEHVN/k1VJT+PU+RncYq3fhZy84QQQgghhJ+QHjshhBBCCD8hhZ0QQgghhJ+Qwk4IIYQQwk9IYSeEEEII4SeksBNCCCGE8BNS2AWAgwcPct9995GamkpQUBDNmjXjn//8JzabTetoXjN58mRSU1OxWCykpaWxfPlyrSN53YQJE+jWrRthYWHExcUxbNgwdu3apXUs4Qfk/eW2bNkyrr32WhITE1EUhW+++UbrSJqQc80pU6ZM4ZJLLqlcbaJnz578+OOPHj2mFHYBYOfOnbhcLt577z22bdvGm2++ydSpU3nmmWe0juYVs2fPZvTo0Tz77LNs3LiRvn37MmTIENLT07WO5lVLly7l4YcfZtWqVSxatAiHw8GgQYMoLi7WOprwYfL+OqW4uJiOHTvyzjvvaB1FU3KuOaVx48a8/PLLrFu3jnXr1nHFFVdw/fXXs23bNo8dU+axC1CvvfYaU6ZMYf/+/VpH8bhLL72ULl26MGXKlMptbdq0YdiwYUyYMEHDZNrKzc0lLi6OpUuX0q9fP63jCB8l76/qKYrC3LlzGTZsmNZRNCfnmqqio6N57bXXuO+++zyyf+mxC1D5+flER0drHcPjbDYb69evZ9CgQVW2Dxo0iBUrVmiUqn7Iz88HCIh/B8Iz5P0lakLONW5Op5MvvviC4uJievbs6bHjGDy2Z1Fv7du3j7fffpvXX39d6ygel5eXh9PpJD4+vsr2+Ph4srOzNUqlPVVVGTt2LH369KF9+/ZaxxE+St5f4nzkXANbt26lZ8+elJWVERoayty5c2nbtq3Hjic9dj7sX//6F4qinPOxbt26Kq85cuQIV111FTfffDMjRozQKLn3KYpS5WtVVc/YFkgeeeQRtmzZwqxZs7SOIvyAvL/E2ci5Blq1asWmTZtYtWoVDz30EHfddRfbt2/32PGkx86HPfLII9x6663nbNOkSZPKvx85coTLL7+cnj178v7773s4Xf0QExODXq8/o/cgJyfnjF6GQPHoo48yb948li1bRuPGjbWOI3yYvL/Euci5xs1kMtG8eXMAunbtytq1a/nvf//Le++955HjSWHnw2JiYoiJialR28zMTC6//HLS0tKYPn06Ol1gdNaaTCbS0tJYtGgRN9xwQ+X2RYsWcf3112uYzPtUVeXRRx9l7ty5LFmyhNTUVK0jCR8n7y9RHTnXnJuqqlitVo/tXwq7AHDkyBH69+9PcnIyEydOJDc3t/K5hg0bapjMO8aOHcsdd9xB165dK3sr09PTGTlypNbRvOrhhx9m5syZfPvtt4SFhVX2skRERBAUFKRxOuGr5P11SlFREXv37q38+sCBA2zatIno6GiSk5M1TOZdcq455ZlnnmHIkCEkJSVRWFjIF198wZIlS1iwYIHnDqoKvzd9+nQVqPYRKN599101JSVFNZlMapcuXdSlS5dqHcnrzvZvYPr06VpHEz5O3l9uixcvrvY9dtddd2kdzavkXHPKvffeW/neiI2NVQcMGKAuXLjQo8eUeeyEEEIIIfxEYFxoJYQQQggRAKSwE0IIIYTwE1LYCSGEEEL4CSnshBBCCCH8hBR2QgghhBB+Qgo7IYQQQgg/IYWdEEIIIYSfkMJOCCGEEMJPSGEnhBBCCOEnpLATQgghhPATUtgJv/bII4/Qp0+fap9r0qQJ//nPf7ycSAghPEfOecKgdQAhPGX79u1MmTKFZcuWVft8mzZt2LRpk3dDCSGEh8g5T4D02Ak/9tprr9GtWzd69+5d7fPR0dEcPXrUy6mEEMIz5JwnQAo74accDgdz5szhpptuqtz24IMP8tFHH1V+XVhYSEhIiBbxhBCiTsk5T1SQwk74pX379lFYWEiHDh0AcLlcfPXVV4SGhla22bJlC23atNEqohBC1Bk554kKUtgJv3Ty5EmAypPaTz/9xIkTJzCZTACsWbOGQ4cOMWzYMI0SCiFE3ZFznqggN08Iv5SSkoKiKMyaNYuQkBD+/ve/M3ToUL799luaNGnCgw8+yBVXXEG/fv20jiqEEBdNznmigqKqqqp1CCE8YcKECbz88ssEBQXx73//m+7du3P99deTk5PDtddey+TJk4mOjtY6phBC1Ak55wmQwk4IIYQQwm/INXZCCCGEEH5CCjshhBBCCD8hhZ0QQgghhJ+Qwk4IIYQQwk9IYSeEEEII4SeksBNCCCGE8BNS2AkhhBBC+Akp7IQQQggh/IQUdkIIIYQQfkIKOyGEEEIIPyGFnRBCCCGEn5DCTgghhBDCT0hhJ4QQQgjhJ6SwE0IIIYTwE1LYCSGEEEL4CSnshBBCCCH8hEHrAL7E5XJx5MgRwsLCUBRF6zhCBARVVSksLCQxMRGdTj6LepOc84Twvos950lhVwtHjhwhKSlJ6xhCBKSMjAwaN26sdYyAIuc8IbRzoec8KexqISwsDHD/sMPDwzVOI0RgKCgoICkpqfL9J7xHznlCeN/FnvOksKuFiqGI8PBwOckJ4WUyFOh9cs4TQjsXes6TC1aEEKKemDx5MqmpqVgsFtLS0li+fPlZ22ZlZXHbbbfRqlUrdDodo0ePrrbdnDlzaNu2LWazmbZt2zJ37lwPpRdC1AdS2AkhRD0we/ZsRo8ezbPPPsvGjRvp27cvQ4YMIT09vdr2VquV2NhYnn32WTp27Fhtm5UrVzJ8+HDuuOMONm/ezB133MEtt9zC6tWrPfmtCCE0pKiqqmodwlcUFBQQERFBfn6+DEsI4SWB8r679NJL6dKlC1OmTKnc1qZNG4YNG8aECRPO+dr+/fvTqVMnJk2aVGX78OHDKSgo4Mcff6zcdtVVVxEVFcWsWbPOmylQfvZC1CcX+76Ta+w8YNnuXLI3L6StdQvxXa4mtk1fkOuDhBBnYbPZWL9+PU8//XSV7YMGDWLFihUXvN+VK1cyZsyYKtsGDx58RgFYwWq1YrVaK78uKCi44GMLz7GXFbH7t6+xpa9Db81HCY0ntsOVNLxkAOj0WscTGpPCzgOW7s4levMC2hvmwd6p7Le0J+KWd2jQtLPW0YQQ9VBeXh5Op5P4+Pgq2+Pj48nOzr7g/WZnZ9dqnxMmTOCFF1644OMJz3K6VGavzaDlglvpqm479cRRYN97ZH3fGPsV/yK5181aRRT1gFxj5wHdmkQT3rwXyy39KVVNNC37g+BPBrNv6Uytowkh6rHT74JTVfWi7wauzT7HjRtHfn5+5SMjI+Oiji3qTn6pnfs+Xsszc7cyyXodmcSxLHIYvzS8l2VBA8hXQ0hwHKbhTw8y86flyFVWgUt67DzgqvYNof1DwEPs3L2L4i8fIM2xiZRfH2YfLppd9jetIwoh6pGYmBj0ev0ZPWk5OTln9LjVRsOGDWu1T7PZjNlsvuDjCc8oOJbFf2Z8w5LcZCxGHZcPHk5cjydoZDj1Kzwj6yirZv8fq3PNTFtcwG7rdp6/pi06nVwGFGikx87DWrdsRZsnFrIseBAGxUXjxaPJ3rlS61hCiHrEZDKRlpbGokWLqmxftGgRvXr1uuD99uzZ84x9Lly48KL2KbyrrLiAvClXM77gOYYG7+SrB3txX59UjIaq/TJJCfEMevx9Gg/5OwAzVhxk4vfrweXSIrbQkPTYeUGwxUy3xz5n9RvXUlRq5f0FJ/m4mROLUS5yFUK4jR07ljvuuIOuXbvSs2dP3n//fdLT0xk5ciTgHibNzMzkk08+qXzNpk2bACgqKiI3N5dNmzZhMplo27YtAI8//jj9+vXjlVde4frrr+fbb7/l559/5rfffvP69ycugKqy64N76OjYxzElgrF/uYLmjSPO2lxRFO7tk0qYxcDbcxZxw7p/sDn/Wjre8YoXQwutSWHnJUEWE8n3z+TqyWs5nu3kzZ93M25IG61jCSHqieHDh3Ps2DHGjx9PVlYW7du3Z/78+aSkpADuCYlPn9Ouc+dTN2StX7+emTNnkpKSwsGDBwHo1asXX3zxBf/3f//Hc889R7NmzZg9ezaXXnqp174vceE2f/tfOp78GbuqJ2PgVDq1rdkNeDd3TSJyTz4tdmXi2vseB9b2J7XbEA+nFfWFzGNXC3Uxp9PP248y4pN16HUK39/XjjbNUuo4pRD+ReZS04787LVz4sh+jO/3IpRSlqY8zmX3jK/V61VVZfkbt9Ov8AeOKjGEjVlDcHgDD6UVdeli33dyjZ2XXdk2npvbhfKG/m0iPx+My249/4uEEEIEDlUl6/OHCKWU7frW9Prbc7XehaIodLj3HTJoSLyax65PHvdAUFEfSWGngSeHtqe3bhsJriy2ffdfreMIIYSoR3b9/g1ti1dhVQ0ow97BaDRe0H6ioqLJvXISAJ3zvmP/pqV1mFLUV1LYaSC2QQO2tBgFQNKWt7EWn9A4kRBCiPpAVVX+b3MUL9pvZ2nDu2nTodtF7a9LnyGsDBsMgOv7v6O6nHURU9RjUthppMdfRnOQRCIpYPvc17SOI4QQoh74adtR1mYUM1N3HZ1u/0+d7LPZXydSpAZRYFP5df22879A+DQp7DQSbLGQccljADTZ+yn2siKNEwkhhNCS0+Fg4oLtAIzom0pcuKVO9huXmMzsrjO50fYC/1l2HIdT5rbzZ1LYaajb1fdymHiiKGD79+9oHUcIIYSG/pg/lXcKHuN6yybu79e0Tvd9y8C+RAWb2J9bzJwNh+t036J+kcJOQxazmb0t7gMgfPvnuORTlBBCBCTV6SBm07u01mVwY5Mywi0XdsPE2YRZjDx8eXPCKOHYT69it5XV6f5F/SGFnca6XD+KF9QHuKbknyzdk6d1HCGEEBrYtfhzGrmOcFINpcP1YzxyjL9dmsw3ln8xyvEpm36c5pFjCO1JYaex8NAwdN3uoZggPlt1SOs4QgghNKCseQ+ATQm3EB0V7ZFjWEwGclOvB6DB5qkySuSnpLCrB26/NBmAX3cdJSPnuMZphBBCeFP6tlW0sm3DruppcfVjHj1Wm+vGUKKaaeo6xOZlcz16LKENKezqgaaxoTyUdIgfjM+QM+dJreMIIYTworxf3TfPbQjtR6OkVI8eKyIqlm0JwwAwrHzbo8cS2pDCrp4Y0DqOtrpDtDg6H1tZidZxhBBCeEFJfi5tjy0AwNjzQa8cM3noEzhUHR1sGzm0Y71Xjim8x6cLu8mTJ5OamorFYiEtLY3ly5efte3XX3/NwIEDiY2NJTw8nJ49e/LTTz95Me25dbpsGNnEEE4x2xd/oXUcIYQQXjBvZzEjbaOZYxhKp56DvXLM+OSWbA3tBcCRX6d45ZjCe3y2sJs9ezajR4/m2WefZePGjfTt25chQ4aQnp5ebftly5YxcOBA5s+fz/r167n88su59tpr2bhxo5eTV89gMLAv8WoAdFuksBNCiEDw+ZrDLHF15li/f6PTe+9XsunSEdhVPYdzjlNkdXjtuMLzFFVVVa1DXIhLL72ULl26MGXKqU8bbdq0YdiwYUyYMKFG+2jXrh3Dhw/n+eefr1H7goICIiIiyM/PJzw8/IJyn8uBnZtI/eIyHKqOglGbiY5PrvNjCOFrPP2+E2cnP3vP2pVdyOBJyzDoFFY/M4AGoWavHVt1OfnL69+x/piRF69vxx09m3jt2OLcLvZ955M9djabjfXr1zNo0KAq2wcNGsSKFStqtA+Xy0VhYSHR0We/rdxqtVJQUFDl4UmprTux09Aag+Ji78/TPXosUXdUl4t9m5ax6uNneO5/6/i/b7by4fL97M7O1zqaEKIey/r+PzxhmM1NzVSvFnUAik7PNb06AjBrTYZXjy08yycLu7y8PJxOJ/Hx8VW2x8fHk52dXaN9vP766xQXF3PLLbectc2ECROIiIiofCQlJV1U7po40fwmAGL3f+3xY4mLt+u3uRx8KY1m31xLjwPvsmr9ej5blc6/f9jBx2//i93/uZT96xZqHVMIUc847VY6Hv6cRwzfcnOyNmuFD+vUCJNeR0n2Lnbv3q5JBlH3fLKwq6AoSpWvVVU9Y1t1Zs2axb/+9S9mz55NXFzcWduNGzeO/Pz8ykdGhuc/1bS+8i6+c/bk5dJh7Msp9PjxxIVxWEvYOPkeWv18N6mO/ZSoZtaH9OX2Hqk8dkVzLm8Vyy36pbS076Tp9zez/v2HcNltWscWQtQTO5fNIYoCcomkw2XDNMkQFWJiUvz3LDH/neM/v6lJBlH3DFoHuBAxMTHo9fozeudycnLO6MU73ezZs7nvvvv46quvuPLKK8/Z1mw2YzZ7t3s8KiaeOU3Hs2RXLu23ZvPogDCvHl+cn7X4BIfevp7OZZsBWB79F1rf+m/S4hJI+1O77Mxv+P3Lp+md/wNpR2ay842dpD76LeZguVZJiEDn2DgLgF1xQ+hj8u7vmT9r1P4yWPYZLXMWYLNaMXn5d56oez7ZY2cymUhLS2PRokVVti9atIhevXqd9XWzZs3i7rvvZubMmVx99dWejnnBhnZIAOCHrVkaJxGnczicHHj7elqWbaZQDWJVrw/p+9hHxMYlnNG2YaNkeo+ZyYqu/6VItdC6dAOH3hqKvVR6YoUIZMUFJ2hVuBKA2F53aJqlXd9hHCOSaArYunSOpllE3fDJwg5g7NixfPjhh0ybNo0dO3YwZswY0tPTGTlyJOAeRr3zzjsr28+aNYs777yT119/nR49epCdnU12djb5+fXvAvfBbRvSRp/JVXnTObR7i9ZxRDlVVXnh+x38M/86Dqnx7Bv6BT0G3Xze1/W65m72XfUZhWoQLcu2snPKbagupxcSCyHqo53L/odFsZOhJNLykp6aZjEYTextOAQAddNMTbOIuuGzhd3w4cOZNGkS48ePp1OnTixbtoz58+eTkpICQFZWVpU57d577z0cDgcPP/wwCQkJlY/HH39cq2/hrCKCjbwc9iWjDV9z5PfPtY4jys1em8Gnqw6xhjbsuPEXOl3av8av7dhzILsGfIRVNbD+uIXPVh7wXFAhRL2m3+FeozUjcTCKTvtfwwn97gGgQ/FKjuXW7AZEUX/55DV2FUaNGsWoUaOqfW7GjBlVvl6yZInnA9Uha6vrYfNaGmb8CLyidZyAl75tJR99txOI44lBrbiqY+3vkO7a72pmF3/Jv5aWYfhhN+2SGtAlOaruwwoh6q1iq4OthaGkKKHE9RiudRwAktteyn59Kk2dB9i0+BMa3CJrlvsy7T8qiGq1uuxW7KqeVNchDu3erHWcgOYoLcQ4527m6p5mRNIRHrqs2QXv65arruDqSxJwuFSemL2R0iLPzo0ohKhfftmZw3O2u/hLyAyatbtU6ziVclJvACB47w8aJxEXSwq7eioiOpZdQe7JIzNXz9U4TWD744vnSHBlU6iE8OAt16PTnX9KnbNRFIWXhnWgfVgR4wv+jz0f3VeHSYUQ9d38Le6b4gZfklQvhmErNLn8bh6yPc7NBY+TlV+qdRxxEerPvypxhtJU98oaEemLztNSeMrR/Ztpe/ATAPZ2/SexsWef97CmIoKNvHB5A3rptnPJiYXsXiWfkIUIBMWFJ8nZtQpQK2c/qC8aNkrhWPIQyjDzwxaZkcGXSWFXj6X0cq9C0dq2jbwceaN5napy4qvRmBQn68yX0nvoned/TQ2l9bqSlQ2GARC88B84bWV1tm/huyZPnkxqaioWi4W0tDSWL19+zvZLly4lLS0Ni8VC06ZNmTp1apXnZ8yYgaIoZzzKyuTfmxZ2LJ/L14Zn+Cb4Jdol1r/5LK/t6C42520+onEScTGksKvH4pJasl+fSjEWtmxYpXWcgLNn1Txal27AqhqJvunNixqCrU6r214jl0gauzLZPOflOt238D2zZ89m9OjRPPvss2zcuJG+ffsyZMiQKnf3/9mBAwcYOnQoffv2ZePGjTzzzDM89thjzJlTdS6y8PBwsrKyqjwsFos3viVxGtfO+QBY4zrWaJUkbxvSviGPGeYyMecBDu/foXUccYGksKvnlnaeRJr1PWbleH6dWnGK6nKh+/VFAFbH3EDTlu3q/BgxMbHsbDsagGa73qfwZG6dH0P4jjfeeIP77ruPESNG0KZNGyZNmkRSUhJTpkyptv3UqVNJTk5m0qRJtGnThhEjRnDvvfcyceLEKu0URaFhw4ZVHsL77HYbLQtWABDZ+TqN01QvJszC4JA9tNRlcmjZZ1rHERdICrt6rnuXztgxsHxPLmV2mdTWW5btzGRRaSuOqeG0vOl5jx2n5w2PsF+XQgTF7PzqBY8dR9RvNpuN9evXM2jQoCrbBw0axIoVK6p9zcqVK89oP3jwYNatW4fdbq/cVlRUREpKCo0bN+aaa65h48aNZ81htVopKCio8hB1Y9fan4mkiJOE0jzt3MtZaqm05fUAxGf8qHEScaGksKvn2iaEkxhhoczuZOXOw1rHCQiqqjLxl0O87LiND7t+S8NEz/WWGoxGjvUYB0DU4cUcKyj22LFE/ZWXl4fT6Txjrev4+Pgz1sSukJ2dXW17h8NBXl4eAK1bt2bGjBnMmzePWbNmYbFY6N27N3v27Kl2nxMmTCAiIqLykZQkIwV1pXDzPAD2RvRGbzBqnObsmvf7Kw5VR3PnPjL3bdU6jrgAUtjVc4qi8FjCdpaaxhC0xHM9R+KU5Xvy2JqZj8WoY8TldT8Ee7quVw7nldCnGWJ9iXeXHvL48UT9dfp1V6qqnvNarOra/3l7jx49+Nvf/kbHjh3p27cvX375JS1btuTtt9+udn/jxo0jPz+/8pGRkXEx344op7pcJOUsAUDfZoi2Yc4jMjaB7ZbOABz5XZYY80VS2PmAdsmxpOhySD62AtXl0jqO3zs5bxzdlR3c2jWJBqFmjx9P0enocd0I7Bj4bPUhcgrljsVAExMTg16vP6N3Licn54xeuQoNGzastr3BYKBBgwbVvkan09GtW7ez9tiZzWbCw8OrPMTFO7R7M43VLGyqgZa9hmkd57yKm18DQFT6Qo2TiAshhZ0PaHHpUGyqgURyOLh7i9Zx/NrutT9zXdFXfGaawIPdvPdLrV+LGDonR+Jy2PjhJ7m2JdCYTCbS0tJYtKjqnJWLFi2iV69e1b6mZ8+eZ7RfuHAhXbt2xWisfqhPVVU2bdpEQkL9mkPN3/10JIhbbf/HzOhRhITX/2UEm/W9Gaeq0Nyxl9zDe7WOI2pJCjsfYAkJZ3fQJQBkrf9e4zT+rXjJGwBsjBpMQmKy146rKApjuofyq+nvDN/6IAXHj3rt2KJ+GDt2LB9++CHTpk1jx44djBkzhvT0dEaOHAm4h0nvvPPUXIojR47k0KFDjB07lh07djBt2jQ++ugjnnjiico2L7zwAj/99BP79+9n06ZN3HfffWzatKlyn8I7Fu06zipXW/TdfWOlmbiGSay39GCOsw+/75Q57XyNFHY+ojipPwAh6Yu1DeLHsg5so2OR+w7EuKv+4fXj9+ncAZshlGDFyq5vX/P68YW2hg8fzqRJkxg/fjydOnVi2bJlzJ8/n5SUFACysrKqzGmXmprK/PnzWbJkCZ06deLFF1/krbfe4qabbqpsc/LkSR544AHatGnDoEGDyMzMZNmyZXTv3t3r31+gOlZkZX36CQCuaFP9sHp9tLn3ZP5uH8WXB0xaR/ELxVYHz8zdyh+Z+R4/lqJWXG0rzqugoICIiAjy8/O9fu1J+s71JH9xBWWqEdeTBwgOCfPq8QPB6vdGcWnW52w0d6fzOG2WcVvz/Qd0X/cExwkn+KmdWIJCNMlRn2j5vgt08rO/eL8tmM3u375me9TlTPy77/SUph8rod9ri9HrFNY+eyXRIVLgXYyVX73Bls3rWBw+jFn/uPmcN0Vd7PtOeux8RFLLzhylARbFzu41C7SO43fKSotpmeWejsCVdq9mOboMvotsYoimgM0/fqRZDiFE3TDs+IZ7DQu4JXSz1lFqJblBMG0bhtFaPcC6VTJSdDFUl5Pk7e/xoOEHHks55PFVR6Sw8xGKTse2+Gv51HElK47W3zmQfNWWhZ8QRSHZxNDx8r9olsNgNHGo2W0AxPwxTe6CFsKHuZwumua7l4MMbTtY4zS1Ny7qF34wP0Pcuje1juLTti/7mkZqNvlqCB2G3O/x40lh50Ps/cbxnONevsyI0DqK31m8N599rgQONrkZw1nuKPSWNkMfpVQ10cx1gB2r5A5ZIXzVvu1rieM4paqJ5t0Gnf8F9UzjrlcD0KZkHcWFJ7UN48Ncq98H4I/46wgLj/T48aSw8yG9mjXAoFM4eKyEg3myQkFd2ZFVwJTcDlzlfJ3mw57ROg7hDeLYEjMUgIw18zROI4S4ULkbfwBgT3AnTJZgjdPUXpM2XTmsNMSs2Nn9+zdax/FJRw7uol3JWgAaD3zYK8eUws6HhFmMdEsOp6uyk21rf9U6jt/4ap17qbYr2zQkJrJ+XCAeM/gprreO56Gj15F5slTrOEKICxB2eCkAZSmXa5zkwig6HelxVwCg7pCpti7EwUVT0Ckqf5g7k9Kig1eOKYWdj3kseAH/M48ncctkraP4BVtZKSUbZmPBys1dG2sdp1Kzlm0JaXopLhU+XyXLjAnha4oLT9LK+gcAiV2v1TjNhQvrNAyAFvm/47TbtA3jY+wOJ8mZ891/73yX144rhZ2Pie3gvk6jWckmHPImu2h/LP0fL6uT+NHyf/RrHqN1nCru7Omev+yHtbuwy/9rIXzKlq1byCWCI0o8jZu11zrOBWvd9QqOEU4YJexbJ0uM1cYvO3MZWvYiL+vup/0Vt3ntuD5d2E2ePJnU1FQsFgtpaWksX778rG2zsrK47bbbaNWqFTqdjtGjR3svaB1K7dCLAkIIV0rYs/k3reP4PGWze5Hr7ITLMRj0GqepakCbeF4I+pIfHfezdclXWscRQtTCj7nR9La+xcdtPwQPT2/hSUajkT3h7mXtTmyZr3Ea3/LF2nQKCIVuIzCaPL/ueAWfLexmz57N6NGjefbZZ9m4cSN9+/ZlyJAhVWZm/zOr1UpsbCzPPvssHTt29HLauqM3GNgX0hmAE1u1mUTXX+QezaR98WoAGl2m3dx1Z2PU62jdMIRgxYpuw8daxxFC1MKy3bmAQpe2LbWOctHKuj7IbbZn+FfRjVpH8RmHjxWwdHcuALd2S/LqsX22sHvjjTe47777GDFiBG3atGHSpEkkJSUxZcqUats3adKE//73v9x5551ERNRsuhCr1UpBQUGVR31gT+kHQHjW7xon8W17fpmBUXGy19Cc5NZpWsepVuIV7pnqO5Ss4Wj6Ho3TCCFqIj3nJIeOFWHQKfRq1kDrOBetc7e+rKEDO3KtMiNDDWXOfY65xud4IPEgTWK8u4KQTxZ2NpuN9evXM2hQ1XmBBg0axIoVK+rsOBMmTCAiIqLykZTk3ar7bBI7XwVAS+s2SorrR7Hpi2L3zwXgRPP6+yk0qcUl/GHqiF5RObBoqtZxhBA1kLn0IzaYRzIh6nvCLL4/oXxEkJHuqdEA/LzjqMZp6j+n00nK4e/ppNvHwGYWrx/fJwu7vLw8nE4n8fFVF1SOj48nOzu7zo4zbtw48vPzKx8ZGRl1tu+L0ahZB47SAJPiYO+6n7WO45My92yihWMPdlVP0yvu1jrOOZVecicATTK+wel0apxGCHE++oPLiVKKSGnge3PXnc31qSrPGz6h1YqxWkep9zb//iMNyaOQIDpccavXj++ThV2F09dbU1W1TtdgM5vNhIeHV3nUB4pOxw/JTzDMOp75RS20juOTdq92r+iwLSiNBnGNNE5zbh0G/JUCQmhIHlt/+07rOEKIc3A6XTQr3gBAVPuBGqepO32aRXOvYQG9SpZQcEx67c6ldP0sAPY0GIAlyLvDsOCjhV1MTAx6vf6M3rmcnJwzevH8VYMu17NJbc7yfSe1juKTJuT2oq/1TY52f1rrKOdlCQphV4z7sgPb+s80TiOEOJd929bRgHxKMZHasZ/WcepMo9RW7Nc1Qa+o7Pn9a63j1FtFxcW0P7kYgIju3pvi5M98srAzmUykpaWxaFHVu0IXLVpEr169NErlXT3LL8jdnlXA8WKZ46w2dmUXsvtoEUd1CfTo6Rsn3og+I3jNfgtPHr+egjK71nGEEGeRu9U919u+oEswmIM0TlO3shPcK2gou2UN67P5Y8lXRCjF5CoNaNp1sCYZfLKwAxg7diwffvgh06ZNY8eOHYwZM4b09HRGjnTfRThu3DjuvPPOKq/ZtGkTmzZtoqioiNzcXDZt2sT27du1iH/R4sIs/DV6D//Rf8CeFd9oHcenfLfJPSXOZa1iiQjyjQubW3TszU8N/sZBRzQ/bs3SOo4Q4iwsh92zFZQ26q1xkroX3fk6AFoWrsFmLdM4Tf2k++N/ABxKHIKiN2iSQZuj1oHhw4dz7Ngxxo8fT1ZWFu3bt2f+/PmkpLhn68/KyjpjTrvOnTtX/n39+vXMnDmTlJQUDh486M3odWZY6B9cWrKY1TsjYOAtWsfxCarLxY2rb6WjMQalxatax6kxRVG4sUsjXl2wi683ZDK8W7LWkYQQp7HZbLQo2QQKxF5ypdZx6lzLzpeR930kMcpJ/ljzE+37Xq91pHolt9DK9II08nXFtO3rvSXETuezhR3AqFGjGDVqVLXPzZgx44xtqqp6OJF3mVsOgJyvaHR8tdZRfMaeLStoqaaTqMvC1b651nFqZVinRvyx6BP+cngpRw68R2Jqa60jCSH+ZOuho/zuHER3w366t+2pdZw6p9PrORDZm5iTP1C05TuQwq6K7zYf4Udnd44kDuLb1t01y+GzQ7ECmnUbjEPV0VjNIuvQLq3j+IQTa78EYHtYT4LDojROUzuJkUE8FLqMK/SbyFj8kdZxhBCn+e1QGW84buHTlv9FZ/CNyzxqy9B2KFlqNH+c0PtdZ8nF+nZTJgDDOiVqmkMKOx8WFhHNXmMrADLWL9A4jQ9QVZKz3D8neyvf/KRZ2nY4AI0z5qG6XBqnEUL82Yp9eQB+sdrE2bTqdzP9ne/y76Lr2H20SOs49Ub6wT30yfqYFF0u11wihZ24CCcaurv7dQeXaZyk/svetYoE11FKVDOt+t6kdZwL0u6K2yhSg2ikZrNbJqcWot4oLS0lKuNnwimmV7MYreN4TLDFTO/msYCsQvFnhxZ/zD+MX/Je2IfEhpk1zSKFnY8Lb+O+QLdJwTrpwTmPrDXuuZe2BnUlMtK3hmErhIRFsD3yMgBOrv5c4zRCiAp7NixhqmEiv1iepEm0f01zcror28Sjw8XeLXW3hKcvU1WVhAz35PGlrbVfovKCCzu73U5GRga7du3i+PHjdZlJ1ELztMspVU3ku4I4cOiQ1nHqtaiMXwAoazroPC3rt+C0vwLQ8tgvWGXKASHqhYId7vNLRnhnFJ1/95lc2TSIteaHeP34Y+QdzdQ6juZ2bVlNc9dBbKqBlpffef4XeFit/vUVFRXx3nvv0b9/fyIiImjSpAlt27YlNjaWlJQU7r//ftauXeuprKIaZkswoxvNZIDtdZYd0TpN/ZVfYuPz0l6sdLalaS/tP1FdjDa9ruYYkURRyPbl32gdRwgBRB11916pTXxj0vOLERcbS74hFp2ism/FXK3jaC5vxacA7AjrQUik9sPwNS7s3nzzTZo0acIHH3zAFVdcwddff82mTZvYtWsXK1eu5J///CcOh4OBAwdy1VVXsWfPHk/mFn/SsWUqACv2HdM4Sf21ZHcuHziG8HzUyyQ19u054PQGI3vjBrHe1YLfDxZqHccvyAiEuBgFBSdpYdsJQKMuV2mcxjtyEwcAYNgT2DfuORwOmh91/wx0HYdrnMatxvPYrVixgsWLF9OhQ4dqn+/evTv33nsvU6ZMYdq0aSxdupQWLWSBem9wX6i7i3X7j+J0OtHr9VpHqncWbXdf5DuwrX+sJRx0zctcN3kVlkM67rE6CDH79JSUmigqKuLzzz9n1qxZrFmzBqvVWvlc48aNGTRoEA888ADdunXTMKXwBfvW/0JnxUm2EkvDlMCYXzKm63WQ8QGti9dQVlqsyWL39cEfKxfQiTwKCaZ1v79oHQeoRY/dV199VVnU9ezZk4KCgmrbWSwWRo0axYgRI+omoTiv9onhTDa/w3L1HvZv+U3rOPWOrfgkUbu+pAH5XOknhV2HpGiaNAimzO6qLFpFzdXXEYjJkyeTmpqKxWIhLS2N5cuXn7P90qVLSUtLw2Kx0LRpU6ZOnXpGmzlz5tC2bVvMZjNt27Zl7lwZOqtrJbvci74fjuwGiqJxGu9o2qE3uUQToljZuWq+1nE0s3P7ZopVM7uir8BoDtY6DnCBN0+sXr2asrIzL9ouKCjgH//4x0WHErVj0OtICIEQxUreVpkC43T7Vs7jRWUK/7P8m06NI7WOUycUReG6To2IoIgDK+UXdW1VjECsW7eO559/nquuuooOHTrQvHnzytGH6dOnk52dzXXXXcfSpUs9nmn27NmMHj2aZ599lo0bN9K3b1+GDBlyxtKIFQ4cOMDQoUPp27cvGzdu5JlnnuGxxx5jzpw5lW1WrlzJ8OHDueOOO9i8eTN33HEHt9xyC6tXy2o1dSkmdxUAuqaXaZzEexSdnoMN+gJQ9sf3GqfRRqnNyYuZaXS1TsE48Hmt41SqVWF344038vLLL6MoCjk5OWc8X1xczBtvvFFn4UTNWRv3ASA0U3rsTmfb5j7ppMf0Rafzn0/TNzTXsdb8EI8cfZ6TuVlax/Ep9XEE4o033uC+++5jxIgRtGnThkmTJpGUlMSUKVOqbT916lSSk5OZNGkSbdq0YcSIEdx7771MnDixss2kSZMYOHAg48aNo3Xr1owbN44BAwYwadKkavdptVopKCio8qiJL9dmMOLjdazYm1fr79vXHS+28UDJQzxjv4/kbldrHcerzO3c32/qseUBOd3Woh1HKbY5iYmO4pLWLbWOU6lWhV1KSgrff/89qqrSsWNH4uLiGDhwIE888QSffPIJr7/+OgkJCZ7KKs4hvtNgAJqX/YGtrFTjNPWH6rSTeuJ3AEI6XKtxmrqVmtqcdEMTjIqT3Ys/1TqOz6oPIxA2m43169czaFDVqXgGDRrEihXVzxW2cuXKM9oPHjyYdevWYbfbz9nmbPucMGECERERlY+kpKQa5c/b9iuD97xAyW+Ta9Ten6zaf4xDakPWxwwjpmHNfl7+olXPq5mq3siD1sf5I7NmHwL8yfK1GwCV6zs2QqlHQ/C1KuzefPNNfvvtN4xGI6tWreKDDz6gd+/e7N27lxdeeIE5c+bw6quveiqrOIcmrbuQRyRBio29GxZrHafeOLhpMeEUcUINo92lA7WOU+dyU68DIGzPN9oG8UH1aQQiLy8Pp9NJfHzVa0Dj4+PJzs6u9jXZ2dnVtnc4HOTl5Z2zzdn2OW7cOPLz8ysfGRkZNcrfLTSPmw3LaHgk8C4FqVhGrKcfLyN2NuagUDY1f4RNanMW7TzzPeTPThzL4T8Zd7HA9DQ3tqlfE1Jf0K10xcXFGAzul15/vW+uuelvFJ2Og2FpxBT+QsH2n6HXUK0j1QvHN3xLKu75hXpZtF3mxROaXn4nrj2TaGPfxtH03cQn15/hgPru9BGIBg0a0LFjRzp27Mgll1zCli1bvD4CcfqnflVVz9kTUF3707fXZp9msxmzufbvk/h2/eAPSLXuxG63YTSaar0PX9V52yugj6Ff8sNaR9HElW3jWbAtm192HGXswMA5/+z89TN6Kg4sBh1NkuvXFFoXVNhVFHWifnE26QdbfyHy6Eqto9QPqkpC1q/uv7b0z0I3vlFT/jBfQnvbZg4u/ZT4O17UOpLPePPNNwF3MfPbb79x5MgRNm7cyKZNm5g7dy4ul8trIxAxMTHo9fozetJycnLO6HGr0LBhw2rbGwwGGjRocM42Z9vnhWrcsguFBBGmlLJr21padepdp/uvr3KOpHOT/TtuMkJB8jit42ji8laxXKHfyMCcdWQfbkTDxk21juQVYbvdN63lNLmeJtpGOUONh2LPdmfW2WRmyjIj3ta4yxB+c7ZjbmlnSmwOreNoLufwHho6s7CqBlr38d+e5aKWwwCIOzhP2yA+qri4mG7dunH99dfzr3/9i2+++YZ9+/Zx4MABbrvtNq9kMJlMpKWlsWjRoirbFy1aRK9evap9Tc+ePc9ov3DhQrp27YrRaDxnm7Pt80Lp9HoOWdoCcGzHuado8ScH17snpt2vb0p4tH9MpVRbDULNPBX8HX81LObgiq+1juMVRw7tob19CwCpl9+lcZoz1biw69atG/fffz9r1qw5a5v8/Hw++OAD2rdvz9dfB8b/4PoksUlLngr5N+85rmbNAZk9/6dMM92sk3k16p80iPbf619aX/43bKqeJEc6B/fu0DqOz6kvIxBjx47lww8/ZNq0aezYsYMxY8aQnp7OyJEjAff1b3feeWodypEjR3Lo0CHGjh3Ljh07mDZtGh999BFPPPFEZZvHH3+chQsX8sorr7Bz505eeeUVfv75Z0aPHl3n+Uvi0wAwHFlX5/uur1z7lwGQG3upxkm0daLxlQBY9i/UOIl3HFjyMQDbTZcQ27i5xmnOVOMz2o4dO3jppZe46qqrMBqNdO3alcTERCwWCydOnGD79u1s27aNrl278tprrzFkyBBP5hbVUBSF3s0b8OW6w6zcd4z+reK0jqSpn7cf5RgRxHTy75NuZIM4/hv/Ap+mN+C2A3rG1r/zTL2Tnp5Oci2ui8nMzKRRo0YeTATDhw/n2LFjjB8/nqysLNq3b8/8+fNJSUkBICsrq8rISWpqKvPnz2fMmDG8++67JCYm8tZbb3HTTTdVtunVqxdffPEF//d//8dzzz1Hs2bNmD17NpdeWvfvibDmveDQ+yQUbj3vtYH+QFVVGp90r40e3GqAxmm0ldh9GBx4hzalGyguzCckLELrSB6jqioJh9yjIyWt6ue64zXusYuOjmbixIkcOXKEKVOm0LJlS/Ly8ipnZL/99ttZv349v//+uxR1GurVLIYY8rHu+FHrKJoqsjpYWb52rr8sI3YuTXrdSB4RzNuUWXkBvTi7+joCMWrUKA4ePIjVamX9+vX063dqQfkZM2awZMmSKu0vu+wyNmzYgNVq5cCBA5W9e3/2l7/8hZ07d2Kz2dixYwc33uiZX0ZNOl6GU1UocRnIzPH/dauPHNxNYzUbh6qjWdcrtY6jqaRWaRxR4jErdnat/E7rOB619481NHUdwqYaaDngb1rHqVatxyAsFgs33nijx04O4uL0ToQ15lFQAPnHbiOiQUOtI2li788f8Yl+OotCh9Is1j9vnPizK9vEE2TUc/BYCVsPn+CSpGitI9VrMgJR94LCo7k15itWZdr4b5aVxn7+eerwhgU0AvaZWtEqLErrOJpSdDoyYi8jMedL7Nt/gEH1s+CpC/87GMQu25NcnVjEzZGxWsep1gUtKVZfeGJdRV8XG9+IQ/okdIrK/rULtI6jGWXn9/TQ7eCyBif9fkgIIMRsYGSTI3xhehHrvL9rHafekxEIz2jbxD1cve7gCY2TeN6xI/txqDpOxPfUOkq9EHrJNQA0P/k7TqdT4zSe4XSpfLM1hyWuToRf/rjWcc7qogq7pUuX0r9/fwYPHsyyZe6LSHNycvj000+544476iTg2XhiXUV/cbRBdwDse5doG0QjDmsJLQrda2FGdxmmbRgv6psaSQ/dDlrmLsRpt2odxydUjEC8+eabzJ07lwULFvDZZ5/x97//nfbt22sdz+d0beLuuVp/0L+HYlVV5fn8a+lo/QB9r8Ccv+50LbtfRQHBnHQF88fOnVrH8YjV+49xtMBKuMVA/1b1s7cOLrKwGzlyJA8++CDjx49n5syZjBgxgmbNmvH999+fsYxNXfPEuor+wtTicgAaHj/79UP+bO+aHwnGylGiaR0g82kBtO9zDXlEEEERu3//Vus4Pue5557jyy+/ZPv27X7b4+BpXeNUZpvG8+mJ2yksLtE6jsfsyy0ir8iKwxBChxapWsepF4wmM680+4wBttf5MV2vdRyPKPnpBZ4yzOL21mA21N/v8aLu87dYLPz1r38FIC0tjdjYWLZv317j9QUvVMW6ik8//XSV7ReyruJHH32E3W6vnPfpz6xWK1brqZ6Pmi6IrbVmXQfj/F0h2ZVJbuYBYhsF1omnaIv74t29Uf2Ir8dvvrpmMpnYFTOImLyvKNs4G/rfonUknxIVFcWCBQuYOHEie/fuJSkpiQ4dOtC+fXvat2/PNddco3XEei8utiFBugzCKGbj1tV07nG51pE8YsXeXMDdQ2kxBs455nwuvaQNn2/byM87jvL0kNZax6lTZaXFdM/5H1caStie/Fet45zTRfXY5ebm8uWXX7JhwwbKyspITU31eFEHnltX8XQXuiC21iKiY9lnaAHAoXXzNU7jXarLRUreUgDM7a7WOI33RXR3T6jb6uRyyorzNU5Tv+3Zs4dx48Zx8uRJwD2P3LRp01izZg3Hjx9n7ty53HLLLbhcLr744gttw/oKnY7DIe0AOLH7d43DeE7b38fwjek5/hK1X+so9cplLWMx6BTSc05wKCtX6zh1atvS/xGulHCUBrS+9Cqt45zTRRV2Y8eOZcGCBYwcOZKEhAS2bdvGsGHDeOGFF5g3z/Oz4HtiXcU/u9AFseuDvLge7r8cWKZtEC/L2L6SWPU4RaqFNj0Dr7Brm9afDCWBYMXKriVSjJzLyy+/zK5du4iMjDzjOavVSllZGddddx3PPPMMn332mfcD+ihrQlcALNn+OVGxy+mkefE6Oun20SY5sOcKPV1EkJFXGvzARvMDZP76ntZx6pSy5UsADiQMRaev3720tSrszvUJt7CwkB07dnDvvfdiNBr58ssvPZEX8Ny6iqczm82Eh4dXefgKU+fhjLE9xIulNwfUvGar9h/jZ2dntoT2JiQkROs4XqfT6ziU6C5odX/8T+M09dvSpUt57LHHqn3ObDYzcuRIXnrpJS+n8n1RLfsAkFz8B06X/517DmxbTSRFFKsWmnXsq3WceicpIZ4QxUr4wZ+0jlJn8o/n0L54FQDxfTx7Y2hdqFVhV18+4XpqXUV/0rZTD75X+rE5P4j04/57EfPpZh1uwAj7PzjQ9w2to2imUb87Weq8hOmF3TlZYtM6Tr2VmZlJs2bNzvr8gw8+6JWRB3/TuEMfXKpCYyWXvfv3ah2nzuVtcS+btTf4Eowms8Zp6p/UPsMBaGPbSk72YY3T1I0dv36KSXFwQJdCarv6v5JRrQq7+vQJ1xPrKvqTYJOBzknuqQdW7PPvqQcq5BSWsSnjJABXtg3MiZkBUlt15OWYl/ja0YsftmZpHafeio6OJivr7D+f7t27s3ev/xUmnqYPiuCwqQkAWX/436Uglkz3tYMljfponKR+ik1uxT5Dc/SKyr5ls7WOUyfCd7lXnslJvV7jJDVTq8KuPn3CHT58OJMmTWL8+PF06tSJZcuW1WhdxSVLltCpUydefPHFM9ZV9DdXNnbygP47QlZP0jqKV6xf/RuNyKVj4wjiwy1ax9HUDZ0TAfh24xGNk9Rf/fr1Y8aMGWd9XqfTVbkrXtTc0fi+/OzszB9+9pnSYbPSvGQLALGXDNQ4Tf11PNl9c0Hwvh80TnLx0nMLWVuaQJ4aTtMr7tE6To3UarqTik+4Z7s71NufcEeNGsWoUaOqfa66E3bFuoqBok9sKW2Nszh5LAzV9RqKrn5f8HmxktZP4DfzOn6J+j8gsD9NX9exEdN+XEHHjO85cjCaxCattI5U7zzxxBP06NGDTp068cADD5zx/MqVK2natKkGyXyf9bJ/MuKj1TTKDeIRrcPUof2bl9NSKeMEYT4xJKeVxr1vhf3v0LZsE8fzjhId47vry327JZvXHffwc8pYPm3kG+eDWvXYySdc39K8Uz+KVQuRFHJwu39PVlxSeJyWJRsBaNIlsBfkBmgYYeGD8I941jiT9CUztI5TL6WlpTFlyhRGjRrFwIED+eabb0hPT+f48eN8++23PPXUU9x+++1ax/RJnZIj0SmQebKU7PwyrePUmY1ZpXzv7MGWsMvQ1/M7I7WU0KwDB/UpGBUnu5d57kZKT1NVlbkbMwEY1iVZ4zQ1V6vC7oknnuCDDz7g/fffr/Z5+YRbv5jMZvYEXQJA7uaFGqfxrD0r5mFSnKQriTRt3UnrOPVCaesbAUhI/w7V5dI4Tf00YsQIlixZQn5+PjfeeCOpqanExsZyww030KFDB8aMGaN1RJ8UajbQumE4CRxjy27/mevt+9w4HrE/xoEe/9Y6Sr23v9mdvGofzpd5KVpHuWA7t2+mwbH1BBlhcHvfuW67VoWdfML1PSWN3EtqWQ7/pnESz3Jsd0/EnBHb/5xzGQaS1pffjlU1kuLKYN8fq7SOU2/16dOHNWvWsH37dj799FPee+89VqxYwYIFC/zyjnlveYl3WGl5FNdm/5hP0epwsvbgcQB6NY/ROE39lzJwJJOd1zPvoIH8ErvWcS5IwZK3+co8nqkNviLUfFELdXlVrScolk+4viWm/ALfZiVbcNj8c5jc6bDTLN99p1p4p+s0TlN/hEc2YHtYTwByf/9U4zT1X+vWrbntttsYMWIEPXr00DqOzzM1dF/XGZ7jHxMVb9++jUaODGJCjLSIC9U6Tr3XLDaUlvGhOFwqP+84qnWcWrPbrLTMdY90RXb0rcnuL2jlCfmE6zuate/BCcIIUcrYv9n/ph4A2Lv+ZyIp4gRhtO42QOs49YpyiXu92KZHF8jC9sKrYtr1B6CldSulVoe2YeqAY80H/GL+B6+HzZRRgRq6rk0E1+l+x/H721pHqbUdy78migLyiKRdH9+Y5qTCRS0pJp9w6z+9Xs/+kM6UqUbS927TOo5HnNzonmJnd3gvjEaTxmnql7b9/kIBIcRznO2/f6d1HBFAYlv1xIqRGKWAnds3ah3nokUdXQmAMbmbxkl8x9WNinjL9C7XHptOYcFJrePUimOT+xKCPXGDMfjY75WLKuyEb9jb9Z9cYv2Q6cX+WXz/u+haHrI9jq3zvVpHqXdMliB2NBhImWpk5zbf/+UqfIdiDCIjqA0AJ3Ys1TjNxSnNP0ZTm3sqr6QugzVO4zuatOvFEaUhwYqV7Ut85+7Y/ON5tCtwX94T0+sujdPUnhR2AaBL21bYMLLu4AnK7P41HLc/t4iteSqL6EHHnjIMW52QQc/S3TqZ/8vsQX6pb17ELHxTcby7d8uU6ds37+xf9xM6ReWQ0ojGKWefpF9Upeh0HG48FADjjjkap6m57T/PwKzYOahLpvklPbWOU2tS2AWA5nGhxIaZsTpcbDjkX1PBV1yU26NpA8Itcn1nddq1bEFCfEOsDhfzNstKFMJ7IlpfBkBK0WacLlXjNBeudNcvAGRGdpPr62qpUV/30p4dStaSl+MbSxw697j/f+c2+wuKzvfKJN9LLGpNURQejdnAT6Yn0S/+j9Zx6lTqymd5TP811zbznVvRvU1RFG7u2hiA31av1jiNCCRJHfszk6t42X4r2zPztY5zweLy3O8bQ4vLNU7iexq17Mx+fVP3ZMVLPtc6znn9kZnPHYWj+JvjOVoMOnNFGl8ghV2AaBUXTCvdYaKOrtA6Sp05kXuEK0oWMNb4P/o1C9c6Tr12Q8d45pr+yXsn7mff9vVaxxEBQh8Uwc9NnuAHVw9Wl88B52uOZaWT7MrApSo063aV1nF80rHUawEI3/ONtkFq4Kt1GajoiGh7BZGxCVrHuSBS2AWIpC7uE1Iz+x6K8v1jOHbvb3PQKyp79c1ISG6hdZx6rUF4CLqwWACyl36kcRoRSC5NjQZg1X7fLOx+z3Rwt+0fTAu+hwaxvrP6QH2S2v9OXKpCqdVKes5JreOcVVlZGfM3HgTglq5J2oa5CFLYBYjElOakK4noFZX9axdoHadOGPe4V5vISZSbJmpC1/lvALQ8+gNWP52sWtQ/PVLC6K7soMmBL3D54HV2S/cXssTVmdwOvjksVx/ENG7OY4kzudn2L77blqd1nLP645dPWag+yLiQ7+njw6uLSGEXQDKjugNQtnuJtkHqQGlxIa2K3TPax3W7QeM0vqHtZTdznHBiOcmWxb5zh5rwbe2iXHxpfpFn1A/Zk56hdZxaUVWV3/bmAtC3RazGaXxbv7QOAMzdmImq1s8C37zlc6KUIjomBKHX+e5NMlLYBRB9M/cdanF5vj31AMCuFd8RpNjIIpZm7f1zfr66pjea2J/gXhpHv+kTjdOIPztx4gR33HEHERERREREcMcdd3Dy5MlzvkZVVf71r3+RmJhIUFAQ/fv3Z9u2qpOQ9+/vXjv5z49bb73Vg9/JmQwRDckyNEanqGRsWuzVY1+sQ7u3cE/JdPoYdtK1SZTWcXzaVe0bYjHqOJ5zhK27dmsd5wwH9+6gg3UjLlUh+coHtY5zUaSwCyBNuw3BpSo0caVz/KhvfXI+nW2bexWF9NjLfPJ2dK0kXDESgI4lqzhyqP6dXAPVbbfdxqZNm1iwYAELFixg06ZN3HHHHed8zauvvsobb7zBO//f3r3HRVXnjx9/DTAMF2EUERAFwUsgggp4AS2tLNA0c90ys7Vsy7K0cqtv2fbbdNvddNu221Zrmbmt2l1pzcy7aKaoKIQK4g0QRAQFBhAYbuf3BystASo4M2dmeD8fj3k8ZPicz+d9OJ7PvOec8/l83n2XAwcO4Ofnx+233055eXmzcrNnz+bcuXNNrw8++MCcu9KqEu9oABqyf7R429fjfPJ/mOO0nue7bMBF66h2ODbN00XLEv/dJOnmUrF5idrhtJC39T0AjrlG4h8UonI010c+ETsRb5+e7NMO56u6MSSftI35hFpT36BwyqBQprjSZfBktcOxKb0GDCVdNwRHjcKJLR+pHY4AMjIy2LhxIx999BGxsbHExsaybNky1q9fT2ZmZqvbKIrCW2+9xUsvvcTUqVMJDw/nk08+obKykk8//bRZWTc3N/z8/Jpeer2+zViMRiNlZWXNXqbg2v8mAPxLD1rtbbjWuOU2rphR0XuMypHYh5CI4Thr6hl0cROVl8qvvoGFVFZWMKjgGwAahs9WNxgTkMSuk0mMfof/q5vD93m2tfbd/zqYU8KLVTO5RbOcG2LuUDscm3Mp9v94rGY+z5292e5WIrFFe/fuRa/XM3LkyKb3YmJi0Ov17NnT+vREWVlZFBQUEBcX1/SeTqdj7NixLbZZvXo13t7eDBo0iOeee67FFb3/tXjx4qbbwXq9noAA04wM7BXZGOdA5RSnc21jkuyaqksMqPoJAJ+hE1SOxj6Exk6iQNMDveYSaVs/vfoGFpK2cQXdKKdA04OwsfeoHc51k8Suk7klxAeAnceLbHKEGsDmowUAjBnYC61WVptor6gxkzjiOZaiKoXv0mz3yq29KCgowMfHp8X7Pj4+FBQUtLkNgK+vb7P3fX19m21z//3389lnn5GYmMgf/vAH1qxZw9SpU9uM5cUXX8RgMDS9cnNN88iGc/c+FDg2jso/k7rVJHWa26mDW3DR1HIeL/oOHKZ2OHZB4+hEbuAUANyOWkdipygK3Y42PnN8pu90HJxs/zNFErtOJrpPNzx1DgRUppNx/Jja4bSboihkHj0IKMSF+V61vGjJ0UHDjJGBAKzcm6VyNPZr0aJFLQYu/PKVnNw4sru1ZaoURbnq8lW//P0vt5k9eza33XYb4eHhTJ8+na+//pqtW7dy6NChVuvT6XR4eno2e5lKUY/GQU4NWbtNVqc5VRzdDECWPgYHR/moNJWgcY3TxgyuSSXnVLrK0cChMyU8WjmHFQ13EDLhCbXDMQlZh6mT0To68JHnR4wo38ruPQUQuljtkNol+1gqK6vmcVLXi579W/9wEld377DeGLe/xvSiLWQe/pKQiOFqh2R35s2bd9URqEFBQaSlpXH+/PkWvysqKmpxRe4yP7/GiXILCgro2fPn2fELCwvb3AYgKioKrVbLiRMniIqKupbdMJn6EXOY/FUkReUh3HoNSavaehT+NwHtd6u6gdiZHoE3cMQlmvDqg5zd8h59+r2najzLdmWRo/hxbPCL6L39VY3FVGzya0hHpgZYu3Yt8fHxeHt7o9FoSE1NtUis1sghaBQAXvmJ6gbSAfn7vgag2tUPd1cXlaOxXd4eLtyuz8NfU0zx9nfUDscueXt7ExoaesWXi4sLsbGxGAwG9u/f37Ttvn37MBgMjBo1qtW6g4OD8fPzY8uWLU3v1dTUsHPnzja3ATh69Ci1tbXNkkFLCY0YxjHHAZyrqOP0hUsWb789ii5cwL2ulAZFQ/+YSWqHY3cahj8CQL+CDVyqUm+y9KyiCjalNz668MhNwarFYWo2mdh1ZGqAS5cuMXr0aJYssb5h1pYWNPIuAEJqM7hQaFvPWHnlNT6fU91P1my8XroxTwEQWfw9RQV5KkfTeQ0cOJDx48cze/ZskpKSSEpKYvbs2UyaNImQkJ+nXQgNDSUhIQFovAU7f/58Xn31VRISEjhy5AizZs3Czc2NGTNmAHDq1CleeeUVkpOTyc7OZsOGDdxzzz1ERkYyevRoi++ni9aR6MDGueD2nLTe1QcAdmRXMcL4HvO6LcXbxzbXC7Vm4WOn8ZbzbOKrF5PwU+vPkVpC7lf/xz+d3uSBvhUM8PVQLQ5Ts7lbsZenBkhKSmoaRbZs2TJiY2PJzMxs1hH+r8uJX3Z29jW3ZTQaMRp//jZhqqH/avPu3Z8sxyCC67M5lbQO78m2MRljQV4WIbWZoIHg0bY/ckltNwyP5+TmAfSvO0Hy+rfo8cjraofUaa1evZqnnnqqaZTr5MmTeffdd5uVyczMxGAwNP38/PPPU1VVxRNPPEFJSQkjR45k8+bNeHg0fkA5Ozuzbds23n77bSoqKggICGDixIksXLgQR0d15mSb4lfE5DPL6bI/AGL/oUoM1yIxsxAFBwYMsuzt6s7CwckJzzFzKV2fzr/2ZHP/yECL35ovvlBI1PkEujhWERRqG5+B18rmErurTQ3QVmLXEYsXL+aPf/yjyeqzJkV+Ywg+m43mxBbANv5TZ+3+HD+NwjHtQEL97eeyuWo0Gi4NewKSfsfAvC+oqFhIly72863Vlnh5ebFq1aorlvnl/G8ajYZFixaxaNGiVssHBASwc+dOU4VoEsO719DXaQc5JX7UNyhWuWxTbV0du48XAnBLaMvRysI07h7Wm79vzuRkYQV7j51l1MDeFm0//ds3uVFTRbZjH0Ju/LVF2zY3m7sV25GpATrKXEP/rYF+cOPSUv3Lk6irrVU5mmvjcXoDAIYgmbvOVCJum0mBxofulJGyfqna4Qg71yfqNupwoA8FZBw7evUNVJB5YCvbeJS/uK5mcK+2J3MW18fTRctToeV87bwI93UPWbTtsopyQnNWA1Ay9HG7W73IavbGElMDtJc5h/6rrX/UrZTjhhflHE/ZpXY4V1V07gwDjYcBCB5j2bUu7ZmDk5b80FkABGSuoLZOJiwW5uPoqueMLhSAgtQtVymtjvK09fTQlBHqYcTBCq8o2pOJIwYSqTnBkKr9nExrfTJuc0hZ8ybeGDiv6cHg8Q9brF1LsZpbseacGkC05Kh1JqHn7/g225HoCz6EqR3QVWw4Wc3W2heY0O0sMwJuUDscuxI2cS7fHtvPP6onMDs1n3uGmWa1ASFac6nXKDidjkvuTuB3aofTgt/5/96+vkEGaJlb7/7hHNTfSnTZNko3LYHB68zepsFQSnhW43KKBUOfxFdru6swtcVqrtiZc2oA0Tqv2N9wQAllU/pFq1+/8bujF/mhYTCVMc+oHYrdcenSlbM3v8FxJYB/bD9JbX2D2iEJO+Y9tPExkEGVyVRWqzfVRWvys44R3HCGOsWBAbF3qR1Op+A9/kUAoip2kZWRYvb2UhLeojsGzjr4EXHH42ZvTw1Wk9hdq45MDQBQXFxMamoq6emNM11nZmaSmppq8ufybMnNIT44OzqQdeESJwsr1A6nTYVl1RzIKQbgjgiZesAcHojtg3cXZ84UV5JwQFajEObjN+gmynGjm6acjIPWNbgjb1/jZ8Zx5zA8u8vACUvoEzacn9xH46BRuPideQcrFpUbeTYrmldqZ3J+xO9xsMOrdWCDiR00Tg0QERFBXFwccXFxDB48mJUrVzYr88upAdatW0dkZCQTJzZ+W5w+fTqRkZEsXdp5HxjvonPi/oAL/MnpY85s+0DtcNqUvnUlCxxX86uexfh3dVU7HLvk5uzE/Nhu/NXpQ4Ztuouamhq1QxJ2SuOoJctzBCcb/DmeY13zJ7pkN86TaQi8TeVIOhfPCS/ToGgYVrGDYwcTzdbO3zdnctHoQHLP6QyNe8Bs7ajNap6xa4+OTA0wa9YsZs2aZcaobNMkrzyiC7Zy9PQ54Dm1w2lV18zPeMzpIAe8ZIoTc7p7ZH+qfzhIV6WcpG/eIWaadf5/ELYv9+a3mPtlOiHnPbhP7WD+y1BaQmhVKmggIOZXaofTqQSHx5C8NY5hpZvI2bKUkKixJh8MmXHyFGuSswAnXp4UZtcDY2zyip0wnaDR9wIwsDadgrNnVI6mpaKCPAZVpwIQOFpGw5qTS5euHA9tfOZkQPo7GEqLVY5I2KvRob1w0EDm+XLOllapHQ4AuzPPsrx+AklOw+jdf4ja4XQ6fe5+lecb5jGn9H7Wp5l2RSSloQHjl4/wvXYBc28oY1iQl0nrtzaS2HVy3Xv15aTTABw0Cqd//ErtcFo4lbgSraaeU0798e0boXY4di9y6rPkOfjTHQNHPn9Z7XCEnerq5kx0n244U8uenzLUDgeA9SeM/LXuPnYPfx8svAqCgB69++M/5kEUHPjjt+mUVprucZCkhPcYWnOIQE0hM2+1/6RdEjtBSWA8AG6nvlc5kpb0J/8DwMW+MkLNErTOLpTe2JjQDT/3GTknDqsckbBXj+v3kap7lF77/6x2KFTX1pOYWQRA/CA/laPpvOaM7Ue/Hu5UVZTyzWemef79XN5pBqW9CkBq/8fxC7L2yb2unyR2Av/YxnVXB1anUFx8UeVofpZzKp2BdRk0KBr63/qg2uF0GuG3TOeISzTOmjoqvnoCpUGmPxGm1z9kEG4aI6EV+6mqVnewTsqBH4itP0AfT0fCe9nPRPS2xkXryN/vDGKz7nkeOPMyB3ckXH2jK6ivq6No5SN4aio54XQD0fctNFGk1k0SO0GvAUPJdeiNTlNHRuLnaofTJHfnJwBkuA7Fy6+PytF0IhoN3aa9S6Wiw8eYzbqde83SzOf7z5j0douwLQGDb6aMLnhpyknfv1nVWBz2L+Nj59d5Tf+VxRejF80NvSGIQp8bcdAo9Nn5NLlnOj790v5/Pc9g40GqFGd0dy/F0UlrwkitlyR2AoDzgXeQ29CDlBzreGBeURR+OldFkeJJzUD7WqDZFvTqG8auqDe53fg3XkqsIK+k0qT1J2/4mEP/eYc7393NJWOdSesWtkHj5MypbjcCUJVm/hUH2lJbW8OA0sb59LoMnqxaHOJng377PjlOQXhjwPDJfRgMZe2u48CWL4jNWw5A+rBXCAyNNnWYVksSOwFAz0m/56aat/h7YRQFhmq1w+HQmRL+Vh7POGUpoXGPqB1Op3T7nTPo3yeQCmMdcz9NwWiidWRzM1MI27eA17TLeLbXMdx1NjnrkjABp0F3AtDvwnbVbvkf2bMRL8oppQuhMRNUiUE05+zaBbf7V1GOG+H1GRx77x4qqq79c2n3iQs8lujEvoZQ9vW4m+g77XOFibZIYicA6OXdjWF9vFAUWJ+Wr3Y4JKScBeC28F64usqkxGpwdNDw5r1D0btq6Xv2W/Z/+OR112kovkDDF7/BTWPkiPMQJk2TpL0zGzDqLqoUZ3pSxOkj5rnlfzWXUtcCcNrrZhztdCUCW9QjOILiyZ9gRMvImiQy3riDgqKiq263LjWP3/7rAMX1Lnw64E2GPWa9k++biyR2osmdQ/zRUsfJ5C2qxlFdXcWFnzaioYFfRfZSNZbOLsDLjWXxLrzp/E9uKlxN0qcdH8FYXVPHmaW/pk9DHkV44fvQKpzkg7RTc3Hz4Jj7cAAK96+xePvVxhpCincA4BE11eLtiyvrExVHftwHVOOMlzGfe9/fxdcH86hvaLm2eUnROQ6+PZ2Sr+dTU99A/CBfXrtvJI5One+OQOfbY9GmiSEeTNLNpbuhnLzsW+gddIMqcaRt+4ylyp857BpCWL99qsQgfjYiZgx7jz1BbPb7xBz/G0lr9cRMbd/Vu8qaOmb/OxmHivH8TXuGyns+p2/PQDNFLGxJ9ZAH+OuuAA6XDCdGUSw6eCHth3WM0JRioAv9Rk6yWLvi2gWP+jXnu/rx54355BS68NxXP5GwJZFZ3sfQ9QhGYyxDe3Yfg0u3E60xMsTRgcqoOTw6JRpHO15d4koksRNNvLt355hLEN2Nh8lKXEnvWX9SJQ5dWuNycZW9RnXaE9PaxDzwF/YvLWJE4VfEpP0/dhkKuenBP6JxuPpF//zich7/LI2fcktxc44k+/4fibnB3wJRC1sQPvbXPPiDnpqLDRw/X0GIn4fF2r54dDsAp3ziiNLqLNauaB/fsNF8GNLA8t1Z/DPxFIPLf+D26s/hf5ca1sApx35U3baYx2M791q/citWNFMV0ng7oldOAg31ln+YOTcrk4jqQwAEjnvU4u2L1mkcHBg+5wP2+TWu7Dkm5x2SX5tEXm5Om9s01Dew57t/0/BOFJV5R/B0cWLVIyMlqRPNeLhoGTOgBwAbDpt2KakrKa+uZX7RndxufA33W35nsXZFx2gdHZgzth9JL47j1tExpOtv4qRuEOluw0jueR+Z8Z/R96VkwmPj1Q5VdXLFTjQTettDVP20mL7k8tP+bQyJvd2i7WdvXUaARiFdN5SwYPufIdyWaBwcGTlnKYe+DCT86OsMr/6R+5Z+jW/ErUyI6EmYXxfcdFouFJwhP2Uz3sdWMao+HYCXu6wjcM6X9OnurvJeCGs0KcwL18wEbtj/L7jtS4ss6fXtT+cw1jWg+IRyQ6gsV2grXJ0dGX7HQ3DHQ2qHYrUksRPNuHp245DXOKJKvqcy6WOwYGJXW2NkwNnGEWq1Q+63WLuifaKm/Z6zGbfy03cfsvdCKKTm801qPl84v8JQzUm6a+oI+W9Zo6LlaOD9jJjxF3SuktSJ1t0a0o047TLcao3kpP1AnyFjzN7mfw6cAGDasN4yKbGwK3IrVrTgOfphAIaUbsNQYrkJi3/asgo/LnIRPWHjZlqsXdF+vQbGcMdzH/PN3NHMGhVEvx7u9Nfko9PUUY+GPKc+pPR9DOMTyUQ9/DY61y5qhyysmKdnNw57NE5WXLTn32Zv7/SpTFYU3cfb2vf41WBfs7cnhCXJFTvRQr+oceRu6E1AQx47Etdyy68sM9dYyeFNAJwIuIcYncxdZwuGBnRlaEBXAJSKg9TXGnF096K3szu91Q1N2BinoffC7m0En99MXW2NWafCObPtQ/pqjIS6V9Cjq3zpEPZFrtiJFjQODhyNfJl44xL+kjUARWk5Z5CpHTpTwuzSB5he90f6T5xv9vaE6Wm6+ODYLQCc5ZaraL+Im6ZQjCfdMZC+y3xz2tXU1BKS/w0AtUMfMFs7QqhFEjvRqtG3T+Wsc19OFlaw68QFs7e34sdsQEPvIbfg7Rdg9vaEENbFWacjw7dxiTGHQyvM1k7yti/pyQVK8SD0lhlma0cItUhiJ1rl4aLlnmGNN9M+23XYrG2dy89l9+HGB5kfGh1k1raEENarx9jHAAir2E/x2RMmr19RFLSHPgYgq9dknHRuJm9DCLVJYifa9FBMIK9pP+Dt3GlkZ/5ktnbOrH2ZXdqnWOB3kEH+erO1I4S1KikpYebMmej1evR6PTNnzqS0tPSK26xdu5b4+Hi8vb3RaDSkpqa2KGM0GnnyySfx9vbG3d2dyZMnk5eX17IyKzFg4GBStJEcUgawJfmYyetPTzvA8NpkGhQNQeOvf+1jIayRTSZ27e0Ea2treeGFF4iIiMDd3R1/f38eeOAB8vPVX+zemgX28CDEowadppaCjX83SxuFZ3MYWvQtHpoqbhoebZY2hLB2M2bMIDU1lY0bN7Jx40ZSU1OZOfPKI8MvXbrE6NGjWbJkSZtl5s+fT0JCAp9//jm7d++moqKCSZMmUV9fb+pdMAmNRkNu3DLurlnEG0ddqTXxJOkl298B4KjnjXQLGGjSuoWwFjY5KnbGjBnk5eWxceNGAB599FFmzpzJt99+22r5yspKDh06xB/+8AeGDBlCSUkJ8+fPZ/LkySQnJ1sydJvjMnY+fL+XqOL1nM3KpFdwyFW3aY9T//kLsZpajmnDGDTqDpPWLYQtyMjIYOPGjSQlJTFy5EgAli1bRmxsLJmZmYSEtH7OXU78srOzW/29wWBg+fLlrFy5kttua1xiadWqVQQEBLB161bi41vO0G80GjEajU0/l5WVXc+udcj4yH702JzD+TIj3x8pYPIQ06xUkn3hEk8VTWa6xpW7x8k8mcJ+2dwVu8ud4EcffURsbCyxsbEsW7aM9evXk5mZ2eo2er2eLVu2MG3aNEJCQoiJieEf//gHBw8e5MyZMxbeA9sSMnI8R3SROGvqyfvPH01a97nsDKLPfw1A/U3PW2S2eSGszd69e9Hr9U1JHUBMTAx6vZ49e/Z0uN6DBw9SW1tLXFxc03v+/v6Eh4e3We/ixYub7oTo9XoCAiw/kMnZyYH7Rwaip4KCja/TUFdrknrf23GS4gZ3MvrPpu/Qm01SpxDWyOYSO1N1ggaDAY1GQ9euXdssYzQaKSsra/bqjLS3/z8Aoku+J/fkEZPVe27Nizhr6knTRRF20xST1SuELSkoKMDHx6fF+z4+PhQUFFxXvc7OznTr1q3Z+76+vm3W++KLL2IwGJpeubm5HW7/ejwYE8h3upd4tGo5aVuuf8LivMKLrE1pfLbwyXEDrrs+IayZzSV2pugEq6urWbBgATNmzMDT07PNctbw7dUahAy7jTTXEThpGiha+4JJ6jyWvI2o8h00KBrcJr4qS/oIu7No0SI0Gs0VX5cfBWnt/7+iKGY5L65Ur06nw9PTs9lLDd26uJAT8CsAvA/8nYbamuuqL/uzZ1nj9AdmBRYRFdjt6hsIYcOsJrGzVCdYW1vL9OnTaWho4P33379iWWv59moNPO/8C3WKA8GXUvkx9fqu2tXWN7B5+3ZqFEcOdhtP/8GxJopSCOsxb948MjIyrvgKDw/Hz8+P8+fPt9i+qKgIX9+OL3fl5+dHTU0NJSUlzd4vLCy8rnotJWzqAi4qnvRuOEvaurc7XE/64YOMKF7HUIdTzBxm/fstxPWymsET8+bNY/r06VcsExQURFpaWoc7wdraWqZNm0ZWVhbbt2+/6rdRnU6HTqe7evCdQFDYCNb0/xN/OuqNfksRG8PqcXV27FBdSxNP8UbxKHa6BvPBfRNMHKkQ1sHb2xtvb++rlouNjcVgMLB//35GjBgBwL59+zAYDIwaNarD7UdHR6PVapueLwY4d+4cR44c4bXXXutwvZbSzas7Owc8ztiTfyXw8DuUj/stHl27t6uOhvoGatY9g7OmnowuIxk4QvobYf+s5oqdt7c3oaGhV3y5uLg06wQvu5ZO8HJSd+LECbZu3Ur37u3rIATET5uDztObnIuV/GVDeofqOHLWwDvbGycenTl5At6+vUwZohA2Z+DAgYwfP57Zs2eTlJREUlISs2fPZtKkSc1GxIaGhpKQkND0c3FxMampqaSnN56LmZmZpKamNj2Sotfrefjhh3n22WfZtm0bKSkp/OY3vyEiIqJplKy1i7nnGc5oeuFFGcdXPt3u7XcnvM/Q2lSMipYe094xQ4RCWB+rSeyuVUc6wbq6Ou6++26Sk5NZvXo19fX1FBQUUFBQQE3N9T270Zl00Tnx+j1DACjf/ynJm1a3a/uSi0VcWj6ZGxqyiAvz5a6hppnGQAhbt3r1aiIiIoiLiyMuLo7BgwezcuXKZmUyMzMxGAxNP69bt47IyEgmTpwIwPTp04mMjGTp0qVNZd58802mTJnCtGnTGD16NG5ubnz77bc4Onbsarul6XQulIz7GwDRF78l5YfWp7RqTfbJdCIP/xmAjAGz8Q4MNUuMQlgbjWKJFd5NrLi4mKeeeop169YBMHnyZN59991mI1w1Gg0rVqxg1qxZZGdnExwc3GpdO3bs4Oabb76mdsvKytDr9RgMBtUeKrYG//n3W9x1eiGXFBdyJn9NWPRNV92m4lIFOW/FM6j2CDma3nR97iB6dxcLRCtsnZx36rGWv/2+9x7mXEE+r2nn8tncW+jT3f2K5Q2GUs69PY7QhpMcdw5jwAu70DhqLRStENfnes87m0zs1GItnZza6mqqOfHGeAZWp3ARPXkTVzFk+Jg2y5eUlJC19B6ijAcox5WLd39DUHiMBSMWtkzOO/VYy9++urqKuz88wJH8cgK93PhqTiy+nq1/Maww1jH340QezX+ZcMccah9JxLu3THEibMf1nnc2dytWqM/J2YU+T6wly6kf3THQb/09bP3yXWrrWi5TdOjAjxS9cytRxgNUK1rO3/EvSeqEEO3i4uLKxw+NIMDLldziCja9NYejh35sUe5kYQXTlu5lZ46RJx1+T+Hd30hSJzoduWLXDtby7dVaVJcXk/3+VEKrUgA4ohlAYtgrdPEfSEllLQGHXuOuyrVoNfWU4kHplJUEDb1F5aiFrZHzTj3W9rfPLa4kcel8ZtZ8QY3iyF7PeJR+49A46qjP2s2lC2d4smYu3dyc+eS3Ixjcu6vaIQvRbtd73lnNdCfC9rh4eBHy7CaOfPUnBmQuJVw5wVOHznL6YON3hVecStE61XNMfxO9fvM+QT0CVY5YCGHLArzcmPLYIo7+6yyDyncztnwDpG74uYADHAuYxMzfPIyfXp7hFZ2TXLFrB2v79mpNjKX5nN66nC+c7qKosg43rSNjPPK5sb833foPVzs8YcPkvFOP1f7tFYXc1K2U7F2JZ2kGGqWeco++uAy9m/43TgMHecpI2C4ZPGFBVtvJCWHH5LxTj/zthbA8GTwhhBBCCCEASeyEEEIIIeyGJHZCCCGEEHZCEjshhBBCCDshiZ0QQgghhJ2Qeeza4fIA4rKyMpUjEaLzuHy+yQB+y5M+TwjLu94+TxK7digvLwcgICBA5UiE6HzKy8vR6/Vqh9GpSJ8nhHo62ufJPHbt0NDQQH5+Ph4eHmg0mjbLlZWVERAQQG5urs3P/WQv+yL7YX2udV8URaG8vBx/f38cZOJZi5I+z3b3RfbD+liqz5Mrdu3g4OBA7969r7m8p6enzf9HvMxe9kX2w/pcy77IlTp1SJ9n+/si+2F9zN3nyddfIYQQQgg7IYmdEEIIIYSdkMTODHQ6HQsXLkSn06kdynWzl32R/bA+9rQvnZ09HUt72RfZD+tjqX2RwRNCCCGEEHZCrtgJIYQQQtgJSeyEEEIIIeyEJHZCCCGEEHZCEjshhBBCCDshiV0Hvf/++wQHB+Pi4kJ0dDQ//PDDFcvv3LmT6OhoXFxc6Nu3L0uXLrVQpG1bvHgxw4cPx8PDAx8fH6ZMmUJmZuYVt0lMTESj0bR4HTt2zEJRt7Ro0aIW8fj5+V1xG2s8HkFBQa3+befOndtqeWs6Frt27eLOO+/E398fjUbDN9980+z3iqKwaNEi/P39cXV15eabb+bo0aNXrXfNmjWEhYWh0+kICwsjISHBTHsgrkb6PPXPs8ukz1P/WFh1n6eIdvv8888VrVarLFu2TElPT1eefvppxd3dXcnJyWm1/OnTpxU3Nzfl6aefVtLT05Vly5YpWq1W+frrry0ceXPx8fHKihUrlCNHjiipqanKxIkTlcDAQKWioqLNbXbs2KEASmZmpnLu3LmmV11dnQUjb27hwoXKoEGDmsVTWFjYZnlrPR6FhYXN9mHLli0KoOzYsaPV8tZ0LDZs2KC89NJLypo1axRASUhIaPb7JUuWKB4eHsqaNWuUw4cPK/fee6/Ss2dPpaysrM069+zZozg6OiqvvvqqkpGRobz66quKk5OTkpSUZOa9Eb8kfZ51nGeXSZ+n/rGw5j5PErsOGDFihDJnzpxm74WGhioLFixotfzzzz+vhIaGNnvvscceU2JiYswWY0cUFhYqgLJz5842y1w+sUpKSiwX2FUsXLhQGTJkyDWXt5Xj8fTTTyv9+vVTGhoaWv29NR4LRVFadHINDQ2Kn5+fsmTJkqb3qqurFb1eryxdurTNeqZNm6aMHz++2Xvx8fHK9OnTTR6zuDLp86zrPJM+r8SygV2FtfV5ciu2nWpqajh48CBxcXHN3o+Li2PPnj2tbrN3794W5ePj40lOTqa2ttZssbaXwWAAwMvL66plIyMj6dmzJ+PGjWPHjh3mDu2qTpw4gb+/P8HBwUyfPp3Tp0+3WdYWjkdNTQ2rVq3it7/97RUXXwfrOxa/lJWVRUFBQbO/uU6nY+zYsW2eM9D2cbrSNsL0pM9rZG3nmfR51nMsfkntPk8Su3a6cOEC9fX1+Pr6Nnvf19eXgoKCVrcpKChotXxdXR0XLlwwW6ztoSgKzzzzDDfeeCPh4eFtluvZsycffvgha9asYe3atYSEhDBu3Dh27dplwWibGzlyJP/+97/ZtGkTy5Yto6CggFGjRnHx4sVWy9vC8fjmm28oLS1l1qxZbZaxxmPRmsvnRXvOmcvbtXcbYXrS51nfeSZ9nvUci9ao3ec5tau0aPLLbxSKolzxW0Zr5Vt7Xy3z5s0jLS2N3bt3X7FcSEgIISEhTT/HxsaSm5vL66+/zpgxY8wdZqsmTJjQ9O+IiAhiY2Pp168fn3zyCc8880yr21j78Vi+fDkTJkzA39+/zTLWeCyupL3nTEe3EeYhfV4jazjPpM9rZA3H4krU6vPkil07eXt74+jo2CKDLiwsbJFpX+bn59dqeScnJ7p37262WK/Vk08+ybp169ixYwe9e/du9/YxMTGcOHHCDJF1jLu7OxEREW3GZO3HIycnh61bt/LII4+0e1trOxZA02i99pwzl7dr7zbC9KTPa8nazjPp86znWID6fZ4kdu3k7OxMdHQ0W7Zsafb+li1bGDVqVKvbxMbGtii/efNmhg0bhlarNVusV6MoCvPmzWPt2rVs376d4ODgDtWTkpJCz549TRxdxxmNRjIyMtqMyVqPx2UrVqzAx8eHiRMntntbazsWAMHBwfj5+TX7m9fU1LBz5842zxlo+zhdaRthetLntWRt55n0edZzLMAK+rx2DbUQiqL8PPR/+fLlSnp6ujJ//nzF3d1dyc7OVhRFURYsWKDMnDmzqfzloea/+93vlPT0dGX58uVWMdT88ccfV/R6vZKYmNhs6HhlZWVTmV/uy5tvvqkkJCQox48fV44cOaIsWLBAAZQ1a9aosQuKoijKs88+qyQmJiqnT59WkpKSlEmTJikeHh42dzwURVHq6+uVwMBA5YUXXmjxO2s+FuXl5UpKSoqSkpKiAMobb7yhpKSkNE2HsWTJEkWv1ytr165VDh8+rNx3330thv7PnDmz2SjLH3/8UXF0dFSWLFmiZGRkKEuWLJHpTlQifZ51nGeXSZ+n/rGw5j5PErsOeu+995Q+ffoozs7OSlRUVLPh8g8++KAyduzYZuUTExOVyMhIxdnZWQkKClL++c9/WjjiloBWXytWrGgq88t9+etf/6r069dPcXFxUbp166bceOONynfffWf54P/H5fmBtFqt4u/vr0ydOlU5evRo0+9t5XgoiqJs2rSpaZ6mX7LmY3F5GoJfvh588EFFURqH/y9cuFDx8/NTdDqdMmbMGOXw4cPN6hg7dmxT+cu++uorJSQkRNFqtUpoaKiqH6adnfR56p9nl0mfp/6xsOY+T6Mo/32CUgghhBBC2DR5xk4IIYQQwk5IYieEEEIIYScksRNCCCGEsBOS2AkhhBBC2AlJ7IQQQggh7IQkdkIIIYQQdkISOyGEEEIIOyGJnRBCCCGEnZDETgghhBDCTkhiJzqF+fPnM2XKFLXDEEIIi5A+r/OSxE50CgcOHGDEiBFqhyGEEBYhfV7nJWvFCrtWW1uLu7s7tbW1Te+NGDGCffv2qRiVEEKYh/R5wkntAIQwJ0dHR3bv3s3IkSNJTU3F19cXFxcXtcMSQgizkD5PSGIn7JqDgwP5+fl0796dIUOGqB2OEEKYlfR5Qp6xE3YvJSVFOjghRKchfV7nJomdsHupqanSyQkhOg3p8zo3SeyE3Tt8+DCDBw9WOwwhhLAI6fM6N0nshN1raGggLS2N/Px8DAaD2uEIIYRZSZ/XuUliJ+zen//8Z7744gt69erFK6+8onY4QghhVtLndW4yj50QQgghhJ2QK3ZCCCGEEHZCEjshhBBCCDshiZ0QQgghhJ2QxE4IIYQQwk5IYieEEEIIYScksRNCCCGEsBOS2AkhhBBC2AlJ7IQQQggh7IQkdkIIIYQQdkISOyGEEEIIOyGJnRBCCCGEnfj/G6feZ2RPBHQAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "w = np.linspace(-3, 3, 1000)\n", + "w2 = np.linspace(0, 3, 1000)\n", + "t = np.linspace(0, 10, 1000)\n", + "bath_cf = bath.correlation_function(t) # uses numerical integration\n", + "\n", + "fig, axs = plt.subplots(2, 2)\n", + "\n", + "axs[0, 0].plot(w, bath.power_spectrum(w))\n", + "axs[0, 0].plot(w, bath_approx.power_spectrum(w), '--')\n", + "axs[0, 0].set(xlabel=r'$\\omega$', ylabel=r'$S(\\omega)$')\n", + "axs[0, 1].plot(w2, bath.spectral_density(w2))\n", + "axs[0, 1].plot(w2, bath_approx.spectral_density(w2), '--')\n", + "axs[0, 1].set(xlabel=r'$\\omega$', ylabel=r'$J(\\omega)$')\n", + "axs[1, 0].plot(t, np.real(bath_cf))\n", + "axs[1, 0].plot(t, np.real(bath_approx.correlation_function(t)), '--')\n", + "axs[1, 0].set(xlabel=r'$t$', ylabel=r'$C_{R}(t)$')\n", + "axs[1, 1].plot(t, np.imag(bath_cf))\n", + "axs[1, 1].plot(t, np.imag(bath_approx.correlation_function(t)), '--')\n", + "axs[1, 1].set(xlabel=r'$t$', ylabel=r'$C_{I}(t)$')\n", + "\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5bf0e1c3", + "metadata": {}, + "source": [ + "## Compare the results" + ] + }, + { + "cell_type": "markdown", + "id": "477ebdb7", + "metadata": {}, + "source": [ + "### We can compare these results to those of the Bloch-Redfield solver in QuTiP:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b70b75bf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 3.66s*] Elapsed 3.65s / Remaining 00:00:00:00\n", + "ODE solver time: 3.680868148803711\n" + ] + } + ], + "source": [ + "with timer(\"ODE solver time\"):\n", + " resultBR = brmesolve(\n", + " Hsys, rho0, tlist,\n", + " a_ops=[[sigmaz(), bath]], options=options,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "1e07101c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_result_expectations([\n", + " (resultMats, P11p, 'b', \"P11 Mats\"),\n", + " (resultMats, P12p, 'r', \"P12 Mats\"),\n", + " (resultBR, P11p, 'g--', \"P11 Bloch Redfield\"),\n", + " (resultBR, P12p, 'g--', \"P12 Bloch Redfield\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "b3cd2b9c", + "metadata": {}, + "source": [ + "### Lastly, let us calculate the analytical steady-state result and compare all of the results:" + ] + }, + { + "cell_type": "markdown", + "id": "9318a4d1", + "metadata": {}, + "source": [ + "The thermal state of a reaction coordinate (treating the environment as a single damped mode) should, at high temperatures and small gamma, tell us the steady-state:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "96459d81", + "metadata": {}, + "outputs": [], + "source": [ + "dot_energy, dot_state = Hsys.eigenstates()\n", + "deltaE = dot_energy[1] - dot_energy[0]\n", + "\n", + "gamma2 = gamma\n", + "wa = w0 # reaction coordinate frequency\n", + "g = lam / np.sqrt(2 * wa) # coupling\n", + "\n", + "NRC = 10\n", + "\n", + "Hsys_exp = tensor(qeye(NRC), Hsys)\n", + "Q_exp = tensor(qeye(NRC), Q)\n", + "a = tensor(destroy(NRC), qeye(2))\n", + "\n", + "H0 = wa * a.dag() * a + Hsys_exp\n", + "# interaction\n", + "H1 = (g * (a.dag() + a) * Q_exp)\n", + "\n", + "H = H0 + H1\n", + "\n", + "energies, states = H.eigenstates()\n", + "rhoss = 0 * states[0] * states[0].dag()\n", + "for kk, energ in enumerate(energies):\n", + " rhoss += (states[kk] * states[kk].dag() * np.exp(-beta * energies[kk]))\n", + "rhoss = rhoss / rhoss.norm()\n", + "\n", + "P12RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 1).dag())\n", + "P12RC = expect(rhoss, P12RC)\n", + "\n", + "P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag())\n", + "P11RC = expect(rhoss, P11RC)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "6ea44c47", + "metadata": {}, + "outputs": [], + "source": [ + "rcParams = {\n", + " \"axes.titlesize\": 25,\n", + " \"axes.labelsize\": 30,\n", + " \"xtick.labelsize\": 28,\n", + " \"ytick.labelsize\": 28,\n", + " \"legend.fontsize\": 28,\n", + " \"axes.grid\": False,\n", + " \"savefig.bbox\": \"tight\",\n", + " \"lines.markersize\": 5,\n", + " \"font.family\": \"STIXgeneral\",\n", + " \"mathtext.fontset\": \"stix\",\n", + " \"font.serif\": \"STIX\",\n", + " \"text.usetex\": False,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "96dcaaa8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", + "\n", + "with plt.rc_context(rcParams):\n", + " plt.yticks([P11RC, 0.6, 1.0], [0.38, 0.6, 1])\n", + "\n", + " plot_result_expectations([\n", + " (resultBR, P11p, 'y-.', \"Bloch-Redfield\"),\n", + " (resultMats, P11p, 'b', \"Matsubara $N_k=3$\"),\n", + " ], axes=axes)\n", + " axes.plot(\n", + " tlist, [P11RC for t in tlist],\n", + " color='black', linestyle=\"-.\", linewidth=2,\n", + " label=\"Thermal state\",\n", + " )\n", + "\n", + " axes.set_xlabel(r'$t \\Delta$', fontsize=30)\n", + " axes.set_ylabel(r'$\\rho_{11}$', fontsize=30)\n", + "\n", + " axes.locator_params(axis='y', nbins=4)\n", + " axes.locator_params(axis='x', nbins=4)\n", + "\n", + " axes.legend(loc=0)\n", + "\n", + " fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "5cf951e9", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "1f8aebfb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "QuTiP: Quantum Toolbox in Python\n", + "================================\n", + "Copyright (c) QuTiP team 2011 and later.\n", + "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", + "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", + "Original developers: R. J. Johansson & P. D. Nation.\n", + "Previous lead developers: Chris Granade & A. Grimsmo.\n", + "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", + "\n", + "QuTiP Version: 5.1.0.dev0+7941773\n", + "Numpy Version: 2.1.3\n", + "Scipy Version: 1.14.1\n", + "Cython Version: 3.0.9\n", + "Matplotlib Version: 3.9.2\n", + "Python Version: 3.12.7\n", + "Number of CPUs: 16\n", + "BLAS Info: Generic\n", + "INTEL MKL Ext: None\n", + "Platform Info: Linux (x86_64)\n", + "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", + "================================================================================\n", + "Please cite QuTiP in your publication.\n", + "================================================================================\n", + "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" + ] + } + ], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "dc20d05c", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "c0ea1cc8", + "metadata": {}, + "outputs": [], + "source": [ + "assert np.allclose(\n", + " expect(P11p, resultMats.states[-100:]), P11RC, rtol=1e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(P11p, resultBR.states[-100:]), P11RC, rtol=1e-2,\n", + ")" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "qutip-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb index 5b7838e5..7513103f 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "b7b4c8ea", + "id": "cabf955e", "metadata": {}, "source": [ "# HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "e15a6ba7", + "id": "97086a26", "metadata": {}, "source": [ "## Introduction\n", @@ -35,7 +35,7 @@ }, { "cell_type": "markdown", - "id": "7e81fb53", + "id": "6ff3b68e", "metadata": {}, "source": [ "## Setup" @@ -43,8 +43,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "07a1946f", + "execution_count": null, + "id": "507d7e77", "metadata": {}, "outputs": [], "source": [ @@ -77,8 +77,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "7939c2d0", + "execution_count": null, + "id": "e35aa06f", "metadata": {}, "outputs": [], "source": [ @@ -96,7 +96,7 @@ }, { "cell_type": "markdown", - "id": "0f4446b2", + "id": "9682b098", "metadata": {}, "source": [ "## System and bath definition\n", @@ -106,7 +106,7 @@ }, { "cell_type": "markdown", - "id": "db34c1d9", + "id": "67e767f2", "metadata": {}, "source": [ "### System Hamiltonian" @@ -114,8 +114,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "b13df20f", + "execution_count": null, + "id": "175e4f9c", "metadata": {}, "outputs": [], "source": [ @@ -128,7 +128,7 @@ }, { "cell_type": "markdown", - "id": "c97942a7", + "id": "3e170b8e", "metadata": {}, "source": [ "### System measurement operators" @@ -136,8 +136,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "35c11edc", + "execution_count": null, + "id": "f66f7b0f", "metadata": {}, "outputs": [], "source": [ @@ -151,7 +151,7 @@ }, { "cell_type": "markdown", - "id": "3ce72545", + "id": "b10c8d81", "metadata": {}, "source": [ "### Analytical expressions for the Ohmic bath correlation function and spectral density" @@ -159,7 +159,7 @@ }, { "cell_type": "markdown", - "id": "5ab9f93c", + "id": "ac2c1bfe", "metadata": {}, "source": [ "Before we begin fitting, let us examine the analytic expressions for the correlation and spectral density functions and write Python equivalents. \n", @@ -188,8 +188,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "51dcd19d", + "execution_count": null, + "id": "e445252c", "metadata": {}, "outputs": [], "source": [ @@ -214,8 +214,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "165a7345", + "execution_count": null, + "id": "7f596c55", "metadata": {}, "outputs": [], "source": [ @@ -228,8 +228,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "48b0422d", + "execution_count": null, + "id": "6361cbb5", "metadata": {}, "outputs": [], "source": [ @@ -243,7 +243,7 @@ }, { "cell_type": "markdown", - "id": "d32921ba", + "id": "828807f3", "metadata": {}, "source": [ "### Bath and HEOM parameters" @@ -251,7 +251,7 @@ }, { "cell_type": "markdown", - "id": "e124f6e7", + "id": "e8d2c4eb", "metadata": {}, "source": [ "Finally, let's set the bath parameters we will work with and write down some measurement operators:" @@ -259,8 +259,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "29f1c71a", + "execution_count": null, + "id": "84692a7a", "metadata": {}, "outputs": [], "source": [ @@ -273,7 +273,7 @@ }, { "cell_type": "markdown", - "id": "f506bdd3", + "id": "6311fc80", "metadata": {}, "source": [ "And set the cut-off for the HEOM hierarchy:" @@ -281,8 +281,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "5faa0dbb", + "execution_count": null, + "id": "4a2d75e0", "metadata": {}, "outputs": [], "source": [ @@ -295,7 +295,7 @@ }, { "cell_type": "markdown", - "id": "baa5d001", + "id": "96a8795a", "metadata": {}, "source": [ "## Building the HEOM bath by fitting the spectral density" @@ -303,7 +303,7 @@ }, { "cell_type": "markdown", - "id": "a1acb05a", + "id": "0087eff9", "metadata": {}, "source": [ "We begin by fitting the spectral density, using a series of $k$ underdamped harmonic oscillators case with the Meier-Tannor form (J. Chem. Phys. 111, 3365 (1999); https://doi.org/10.1063/1.479669):\n", @@ -317,7 +317,7 @@ }, { "cell_type": "markdown", - "id": "67d25b0d", + "id": "ee9df492", "metadata": {}, "source": [ "With the spectral density approximation $J_{\\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. This can be done quickly using the `SpectralFitter` class, which takes the target spectral density as an array and fits it to the series of **k** underdamped harmonic oscillators with the Meier-Tannor form" @@ -325,8 +325,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "d7506510", + "execution_count": null, + "id": "61cb326f", "metadata": {}, "outputs": [], "source": [ @@ -336,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "2a844471", + "id": "70b4e4c2", "metadata": {}, "source": [ "We first initialize our SpectralFitter" @@ -344,8 +344,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "f1b662cf", + "execution_count": null, + "id": "1a415eb6", "metadata": {}, "outputs": [], "source": [ @@ -354,7 +354,7 @@ }, { "cell_type": "markdown", - "id": "9df4a73b", + "id": "c7d4d10c", "metadata": {}, "source": [ "To obtain a fit we simply pass our desired spectral density and range, into the ``get_fit`` method. The number of exponents we'll use in our bath is given by Nk" @@ -362,8 +362,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "636e8699", + "execution_count": null, + "id": "e649ebea", "metadata": {}, "outputs": [], "source": [ @@ -372,7 +372,7 @@ }, { "cell_type": "markdown", - "id": "db8dcad0", + "id": "47c73e52", "metadata": {}, "source": [ "To obtain an overview of the results of the fit we may take a look at the summary from the ``fitinfo``" @@ -380,35 +380,17 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "874aa5a1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result of fitting The Spectral Density with None terms: \n", - " \n", - " Parameters| lam | gamma | w0 \n", - " 1 | 2.39e+00 | 1.50e+00 |1.00e-01\n", - " 2 |-3.75e+00 | 4.31e+00 |4.17e+00\n", - " 3 | 5.37e+00 | 2.28e+00 |1.15e+00\n", - " 4 | 9.15e-02 | 6.03e-01 |1.00e-01\n", - " 5 | 1.18e-03 | 1.54e-01 |1.00e-01\n", - " \n", - "A normalized RMSE of 1.28e-06 was obtained for the The Spectral Density\n", - " The current fit took 13.957773 seconds\n" - ] - } - ], + "execution_count": null, + "id": "6d03e729", + "metadata": {}, + "outputs": [], "source": [ "print(fitinfo[\"summary\"])" ] }, { "cell_type": "markdown", - "id": "5feb8869", + "id": "518e5698", "metadata": {}, "source": [ "We may see how the number of exponents chosen affects the fit since the approximated functions are available:" @@ -416,21 +398,10 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "7d1aab7a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "id": "ee47e95c", + "metadata": {}, + "outputs": [], "source": [ "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5))\n", "\n", @@ -450,7 +421,7 @@ }, { "cell_type": "markdown", - "id": "d2770aea", + "id": "c5c6b894", "metadata": {}, "source": [ "Here we see a surprisingly large discrepancy in our approximated or effective spectral density. This happens because we are not using enough exponentials from each of the underdamped modes to have an appropiate fit. All modes have the same number of exponents, and we set it to 1 which is not enough to model a bath with the temperature considered, let us repeat this with a higher number of exponents." @@ -458,38 +429,10 @@ }, { "cell_type": "code", - "execution_count": 15, - "id": "29567a2c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result of fitting The Spectral Density with None terms: \n", - " \n", - " Parameters| lam | gamma | w0 \n", - " 1 | 2.39e+00 | 1.50e+00 |1.00e-01\n", - " 2 |-3.75e+00 | 4.31e+00 |4.17e+00\n", - " 3 | 5.37e+00 | 2.28e+00 |1.15e+00\n", - " 4 | 9.15e-02 | 6.03e-01 |1.00e-01\n", - " 5 | 1.18e-03 | 1.54e-01 |1.00e-01\n", - " \n", - "A normalized RMSE of 1.28e-06 was obtained for the The Spectral Density\n", - " The current fit took 13.026487 seconds\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "id": "75e720a6", + "metadata": {}, + "outputs": [], "source": [ "bath, fitinfo = fs.get_fit(Nk=5)\n", "print(fitinfo[\"summary\"])\n", @@ -512,7 +455,7 @@ }, { "cell_type": "markdown", - "id": "59078b9a", + "id": "5575f325", "metadata": {}, "source": [ "Since the number of exponents increases simulation time one should go with the least amount of exponents that correctly describe the bath properties (Power spectrum, Spectral density and the correlation function). When the number of exponents is not specified it defaults to 5." @@ -520,7 +463,7 @@ }, { "cell_type": "markdown", - "id": "0d6950f4", + "id": "114bf341", "metadata": {}, "source": [ "By default the ``get_fit`` method, has a threshold normalized root mean squared error (NRMSE) of $5\\times 10^{-6}$ and selects the number of oscillators automatically to obtain that value. One may also specify the number of oscillators that is used with the optional argument N, or may want a more accurate NRMSE, which can be specified with the final_rmse optional argument" @@ -528,30 +471,10 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "6d5c1dae", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result of fitting The Spectral Density with None terms: \n", - " \n", - " Parameters| lam | gamma | w0 \n", - " 1 | 8.94e-01 | 1.15e+00 |1.10e-01\n", - " 2 | 1.87e-02 | 3.46e-01 |3.62e-01\n", - " 3 | 8.27e+00 | 5.41e+00 |1.00e-01\n", - " 4 | 7.69e+00 | 2.38e+00 |1.00e-01\n", - " 5 |-1.27e+01 | 4.92e+00 |2.77e+00\n", - " 6 | 1.52e-03 | 1.38e-01 |2.62e-01\n", - " 7 | 2.69e-03 | 1.74e-01 |1.00e-01\n", - " \n", - "A normalized RMSE of 8.05e-07 was obtained for the The Spectral Density\n", - " The current fit took 61.909602 seconds\n" - ] - } - ], + "execution_count": null, + "id": "d2b612fe", + "metadata": {}, + "outputs": [], "source": [ "bath, fitinfo = fs.get_fit(final_rmse=1e-6)\n", "print(fitinfo[\"summary\"])" @@ -559,7 +482,7 @@ }, { "cell_type": "markdown", - "id": "0f8a973b", + "id": "0962962b", "metadata": {}, "source": [ "Alternatively one may choose the number of oscillators in the fit instead of a desired NRMSE" @@ -567,27 +490,10 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "a8e16132", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result of fitting The Spectral Density with 4 terms: \n", - " \n", - " Parameters| lam | gamma | w0 \n", - " 1 | 1.56e+00 | 9.46e-01 |2.11e+00\n", - " 2 | 1.67e+00 | 9.17e-01 |1.13e+00\n", - " 3 | 1.00e+00 | 1.03e+00 |3.32e+00\n", - " 4 | 6.80e-01 | 8.68e-01 |1.19e-01\n", - " \n", - "A normalized RMSE of 4.39e-05 was obtained for the The Spectral Density\n", - " The current fit took 0.980358 seconds\n" - ] - } - ], + "execution_count": null, + "id": "192d9a35", + "metadata": {}, + "outputs": [], "source": [ "fittedbath, fitinfo = fs.get_fit(N=4)\n", "print(fitinfo[\"summary\"])" @@ -595,7 +501,7 @@ }, { "cell_type": "markdown", - "id": "51abdb8c", + "id": "65ad093e", "metadata": {}, "source": [ "Let's take a closer look at our last fit by plotting the contribution of each term of the fit:" @@ -603,51 +509,10 @@ }, { "cell_type": "code", - "execution_count": 18, - "id": "b73396a9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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6bNoErVpZnUpERESqWGKx+QvS0tLKdFzxtQKrcuSuOPe+yvr8X1n3s9Ibb7xBdnY2YN66u2jRolPOIFvW71Uw0WyoEnzmzIG5c2HkyBMffPcXDgf8859m2zDgrbeszSMiIiI+ERcXR3R0tGt727ZtZTpuy5YtHtunWrrC2xo3buxql3VmUW/NQFqVfv75Z1d7xIgRpS41smfPnqqO5HdULErwWbSoqF2G2bIsc/vt5nMZAB9+CCHyoLSIiEio69Kli6vtPjPqqbivc1i9enWaNm3q7Vgn1a1bN1d73rx5ZXq+b+7cuVWYyDvcJ+5x/xxPZpH7z5ghQsWiBJesrKIpvtu2tfQB9lLVqGFObgOQnm4WjCIiIhL0zjvvPFd78uTJZZos5bPPPnO1zz33XGw2W5VkK8lll13mau/fv7/UBelzc3P56KOPqjhV5eXl5bnapX09nU4nn376aVVH8jsqFiW4LF0K+flm+5xzrM1SFvffX9R+6y0o50xcIiIiEnhuvfVWV3v//v288cYbp9x/8uTJHiOQt912W1VFK9Fpp53G2Wef7dp+6KGHOHTo0En3HzlyJNu3b/dBssqpX7++q71w4cJT7vvaa6+xdevWqo7kd1QsSnBxvz0gEIrF9u3hoovM9tat5mQ3IiIiEtTatGnDoEGDXNtPPvkkU6ZMKXHfxYsX849//MO13blzZ6644ooqz1jcq6++6hp927ZtG+effz6///67xz5Hjx7lgQce4MUXXyzzkiBW6t27t6s9evRokk+yRufEiRN57LHHfBXLr6hYlODi/o+WPz+v6O6BB4ra775rXQ4RERHxmXfeecc1oUp+fj4DBw5kwIABfPHFF8ybN48pU6Zw++23c95557lmQo2KiuLjjz8mLCzM53nPOeccRo4c6dpeu3Yt55xzDs2bN+eCCy7gzDPPpF69erz55psATJgwweP44kt/+IP77rvPVQDv3r2bLl26MHr0aH7++Wfmzp3LBx98QN++fV3La9x+++0WJ/Y9LZ0hwcMwikYWq1Uzn1kMBJdcAk2awI4dsGULZGZCTIzVqURERKQK1alTh7lz53LRRRex+/i6kt9++y3ffvttifvHx8fz/fff06lTJ1/G9DBy5EgcDgfPPPOM63m/bdu2eczoGh0dzXvvvcdFhXdOHVd8yRB/0LVrV5599lmeeuopAA4ePOhRELsbPHgwTzzxBO+//74vI1pOI4sSPLZtgwMHzPbZZ4OXF6StMmFh5rqQM2eaS32oUBQREQkJbdu2ZeXKldx///3ExsaWuE94eDhDhgxhzZo1nH/++b4NWIInn3yS5cuXc++999KqVStiYmJITEykY8eOPPzww6xevZpbbrmFA4U/k2EWkDF++vPNk08+yQcffECdOnVK/Hi9evV44403+Oqrr3w6qZC/sBmGYVgdQrwvNTWVxMREUlJSSEhIsDqOb3z1FVx/vdkeORJGjbI0joiIiDdlZ2ezbds2mjVrRlRUlNVxxMuys7OZN28eW7du5ciRIyQkJNC4cWPOP//8gPxZbvLkya7nMs8+++wTnm/0N9nZ2cyfP5+1a9eSlZVFnTp1aNmyJeeee64lt/265/LGdV/R2kC3oUrwOO88mDjRnBH14outTiMiIiJSZlFRUVwcRD+/uD+z2CMA5pGIiorioosuOuH22VCnYlGCR4MG5rqFhWsXBrIdO8znGEVERET8hGEYZboV8+OPP+bHH390bQ8bNqwKU0lVCpCHukRCxMyZcMEF0LQprFtndRoRERERl9GjR3PHHXcwd+5c8gvXtXaze/duHnzwQY/i8KqrrrJ0Uh6pHI0siviT9eth7lyzPW4cjB1raRwRERGRQllZWbz//vu8//77REVF0aZNG2rWrEl+fj7Jycls2bLFY/8mTZowbtw4i9KKN4TUyOLBgweZMWMGo0ePpn///tSvXx+bzeZ6ffTRRz7JsXXrVp5++mm6du1K7dq1iY6OpkWLFlxzzTV88803Jf6mRkqxdi388AMkJ5tLaASqoUMhOtpsT5wIGRnW5hERERE5zu4203x2djYrV67k119/Zd68eScUihdccAGLFy92rSUpgSkkRhb37dvH2WefzY4dO6yOwtixY3n00UfJycnxeH/r1q1s3bqVqVOncvbZZ/PZZ5/RvHlzi1IGoM8+gxdeMNs//ABXXGFtnoqqXt2c0fXDDyElBb7+Gm691epUIiIiIjzzzDP06tWLWbNm8ddff7FlyxaOHDlCfn4+1apVo0GDBvTs2ZNBgwZx4YUXWh1XvCAkisXs7Gy/KBSfffZZnn76ade23W6nffv21KhRg02bNrF3714AFi9eTO/evVmyZAn169e3Km5gWbq0qN25s3U5vGH4cLNYBPjgAxWLIiIi4hfCw8O55JJLuOSSS6yOIj4SUrehAtSuXZtLLrmEp556iqlTp/qs35kzZzJy5EjXdo8ePVi/fj2rV6/mt99+Y/fu3Xz55ZfExcUB5gPC1157rc/yBTTDgGXLzHbt2pCUZG2eyureHU47zWwvXAgbNlibR0RERERCUkiMLNaoUYNJkybRrVs3mliwHIFhGDz66KMYx5+la9OmDbNnzyYmJsa1j91u57rrrqNmzZqu9V0WLlzIt99+yzXXXOPzzAFlzx44dMhsn3EGlGFKZ79ms8Ftt8G//21uf/ABvPyytZlEREREJOSExMhiQkICgwYNsqRQBJgxYwYrV650bY8dO9ajUHTXt29frrvuOtf2iy++WOX5At6aNUXtjh2ty+FNN90E4eFm++OPIS/P2jwiIiIiEnJColi02pQpU1ztZs2acfHFF59y/+HDh7vaS5YsYffu3VWWLSisXl3UDpZisVYtuPpqs33gAEybZmkcEREREQk9KhZ9YPr06a52v379sJVym+R5551HbGxsicdLCdxHFguf9QsGt91m/tm3L9SsaW0WEREREQk5IfHMopUOHDjAvn37XNs9evQo9RiHw0G3bt2Ye3xx9lWrVlVVvOBQOLJot0O7dtZm8aa+fWHbNmja1OokIiIiIhKCNLJYxdavX++x3aJFizId575f8XOIm4ICWLfObLdsWbSgfTAIC1OhKCIiIiKW0chiFdu+fbvHduPGjct0nPt+xc9RkpycHHJyclzbqampZeon4B0+DB06wNq1wfO8ooiIiIiIH9DIYhVLS0vz2E5MTCzTcQkJCSc9R0leeOEFEhMTXa9GjRqVL2igqlMH/voL0tPh/fetTlN1Cgpgxgxwm1VXRERERKQqqVisYunp6R7bUVFRZTou2u12yuLnKMnjjz9OSkqK67Vr167yBQ10YWFQrZrVKarG2rXm7aiXXab1FkVERETEZ1QsVrH8/HyPbYejbHf+uu+XV4Y19iIjI0lISPB4SZBo2RIyM832N9/A0aPW5hERERGRkKBisYrFxMR4bGdnZ5fpOPf93JfRkBAUGQk33WS2c3Lgq6+szSMiIiIiIUHFYhWLi4vz2M7KyirTcZmFI0klnEOOy82F9u2hf394802r01StYcOK2hMnWhZDREREREKHisUqVqtWLY/tvXv3luk497UZa2pB9pJt2QLr18MPP8Dvv1udpmqdfjp06mS2Fy+GjRstjSMiIiIiwU/FYhVr06aNx/bOnTvLdJz7BDVt27b1aqagsWFDUTvYv0Y2GwwdWrSt0UURERERqWIqFqtYq1atPCarWbFiRZmOW758uavdrl07b8cKDu6ja8WK8qB0443mrK8An3xiLqchIiIiIlJFVCxWsYiICM466yzX9oIFC0o9Zt++fWzevNm13atXryrJFvDcRxZDoVisWxcuvdRs794Nv/5qbR4RERERCWoqFn3gqquucrVnz57N/v37T7n/Z5995mpXq1ZNxeLJuI8stm5tXQ5f0kQ3IiIiQadPnz7YbDZsNhsTJkywOo6Ii4pFHxgyZAiRkZGAuWbiSy+9dNJ909PTedNtZs8bb7yR8PDwKs8YcAyjqFhs1AhCZXmRK66AVq3g3nvh3/+2Oo2IiIh4wcqVK13tLl26WJjEdw4ePMiMGTMYPXo0/fv3p379+q6C2Waz8dFHH1kdUYCyrRAvJ9i+fTvNmjVzbY8cOZJRo0aVuG9SUhLDhw93FYFjx47lnHPOYeDAgR775eXlceutt7omwYmOjuaJJ56omk8g0B08WLQ4fbBPbuMuMtIskm02q5OIiIiIF+zcuZPDhw8D5uNLHTp0sDhR1dq3bx9nn302O3bssDqKlEHIjCzecccdREVFnfAq7z4VNWrUKFq1agVAQUEBgwcP5uabb2by5MnMmTOH9957jzPPPJNvvvnGdczLL79MgwYNvNJ/0Am1yW3cqVAUEREJGu6TGnbo0IGIiAgL01S97OxsFYoBJGRGFvPy8sjJyTnlPvn5+eTn51dJ/9WrV2fatGn07duXXbt24XQ6+fTTT/n0009L3P+RRx7h3nvvrZIsQSHUJrcRERGRoOQ+U/4ZZ5xhXRAL1K5dm65du3LmmWdy5plncvXVV1sdSYoJmWLRH7Ru3ZpVq1bxf//3f3z++edkZWWdsE+7du148cUX6d+/vwUJA8iFF8L48WbR2LOn1WmscegQfP65+bzmbbdZnUZEREQqwH1kMRSeV6xRowaTJk2iW7duNGnSxOo4UgqbYRiG1SFCUVpaGr/++iu7du0iIyOD+vXr07FjR6/9I5GamkpiYiIpKSkkJCR45ZziR44dg/r1ITsbmjaFLVvAHjJ3lYuIhKTs7Gy2bdtGs2bNvPaYjFivSZMmrvkqFi1aRI8ePSxOZB2b26M2H374IcPcZ4EPUd667itaG2hk0SLx8fEeS2qIlEu1anD++fDTT7B9O8ybZ26LiIhIwDhy5IirULTb7XTu3PmU+7/88ss8/vjjFBQUAOZ8G2+//XbQP+co1tFQhEigGjq0qK01F0VERAKO+/OKbdq0ISYmpsT90tPTufbaa3nkkUcoKCggIiKCd999l/Hjx6tQlCqlYlECT0oKzJ0Lu3aB02l1GutcdRUkJprtSZMgPd3aPCIiIlIu7s8rnmxym40bN9K9e3fXjPn16tVjzpw53HXXXT7JKKFNxaIEnj//hAsugMaN4fHHrU5jnehouO46s52RAVOmWJtHREREyqW0yW2mTp1K9+7dWb9+PQBnnXUWS5cu5ZxzzvFZRgltKhYl8GzZUtRu3ty6HP5At6KKiIgErJONLDqdTp544gkGDBhAamoqAP/4xz/47bffyr0G90cffYTNZvP666OPPvLK10D8mya4kcDjXiy2aGFdDn/Qowe0agWbNsGcObBzpzniKiIiIc8wDLLyCqyO4deiw8M8ZuD0paysLDZu3OjaLhxZPHz4MEOGDOHnn38GIDw8nNdff13rb4slVCxK4FGxWMRmg1tugREjwDDgk0/gySetTiUiIn4gK6+A9k/PtDqGX1s3uh8xEdb8OLx69WrXrKbNmjWjWrVqLF26lIEDB7Jjxw4A6taty6RJkzjvvPMq3E/Dhg3p16+fVzIXP68EPxWLEngKi0WHAxo1sjaLP7jpJrNYBPjiCxWLIiIiAaD4Lagffvgh99xzD9nZ2QB069aNKVOmkJSUVKl+LrroIi666KJKnUNCl4pFCSyGUVQsNm1qFoyhrmlTuOceaNsWhgyxOo2IiPiJ6PAw1o32/ohSMIkOD7Osb/dicf78+UyePNm1PXToUN57771KLcIu4g36SVsCy8GDRUtEhPotqO7eecfqBCIi4mdsNptlt1hK6dzXWDxw4ICrPXz4cN577z0LEomcSLOhSmDR84oiIiIS4AoKCli1apVr+/LLL3e1v/76a/7++28rYomcQL9uksCiYlFEREQC3MaNG8nKygKgXr16TJ48mfPPP5/Fixdz9OhRrrzyShYvXkz16tUr3dfPP//Mq6++WunzFPfQQw/pWcgQoGJRAsuRIxAeDnl5KhaLMwz4809zvcWMDND6RyIiIn7J/XnFzp07ExkZybfffkv37t3ZtWsXf//9N9deey0//fQTjkrOz5CcnMzMmd6fFff666/3+jnF/+g2VAks998PWVmwbRv06WN1Gv/idMKAAfDf/8Jnn4Hb8w8iIiLiP9yfV+zcuTNgjjB+9913xMTEAPDLL79w3333WRFPxEXFogSesDBzBtC4OKuT+JewMHMZDYD8fHMZDREREfE7xUcWC3Xp0oVPPvkEm80GwHvvvcebb75Zqb6GDRuGYRhefw0bNqxSuSQwqFgUCSZDhxa1J060LoeIiIicVEkji4UGDBjAM88849p+8MEHq+Q2UpGyULEoEkzatYNu3cz28uWwerW1eURERMTDzp07OXz4MACRkZG0adPmhH1GjBjheiawoKCA6667jvXr1/s0pwioWJRAsnOnuej8Y4/Bzz9bncZ/3XJLUfvjj63LISIiIidwH1Xs0KHDSSewmTBhAt2O/wI4JSWFK6+80lVkBro77riDqKioE17l3UeqnopFCRx//w1ffgljxsDs2Van8V/XX2/OGAvw6afm84siIiLiF072vGJx0dHRTJ06lQYNGgCwZcsWBg4cSF5eXpVnrGp5eXnk5OSc8HKXn59f6j5S9VQsSuDYsaOo3aSJdTn8Xa1aULi47759KqxFRET8SFmLRYAGDRrw3XffER0dDcBvv/3G3XffXaX5RNxpnUUJHCoWy27oUJg61WxPnAiXXGJpHBERETFNLfz/uYzOPPNMMjMzqyaMRT766CM+0nrQAUEjixI4tm8vaqtYPLXLLoOaNc32X3/pVlQRERERKTeNLErg0Mhi2UVEwGuvQb160KePuQajiIiIiEg5qFiUwFFYLNaoAfHx1mYJBO6zooqIiIiIlJNuQ5XAkJ8Pu3ebbY0qioiIiIhUORWLEhj27IGCArOtYrFisrKsTiAiIiIiAUTFogQGPa9YcZ9/Dn37QseO4HRanUZEREREAoSeWZTAULMm3HOPWTSeeabVaQLLxInwyy9me8EC6NXL2jwiIiIiEhBULEpgaN8e3nnH6hSB6ZZbYNYss/3xxyoWRURERKRMdBuqSLC75pqi2WO//hqCbGFfEREREakaKhZFgl1MDFx7rdlOS4OpUy2NIyIiIiKBQcWiBIaMDKsTBDb3NRc//ti6HCIiIiISMFQsiv8zDKhTBxIS4MorrU4TmM47D5o2Nds//2wuRSIiIiIicgoqFsX/HTtmPmeXlgbZ2VanCUx2O9x8s9l2OuGzz6zNIyIiFWYYhtURRMRHrL7eVSyK/9u9u6idlGRdjkDnfivqxInmiK2IiAQMu938sc2pNXNFQkbh9V54/fuaikXxf8nJRW0VixXXsiX07Gm2a9WC1FRr84iISLk4HA5sNhs5OTlWRxERH8nOzsZms+FwWLPiodZZFP+nkUXvGTsWatYsen5RREQCht1uJzo6moyMDGrUqGF1HBHxgdTUVOLi4iwbWVSxKP7PvVhs2NC6HMGga1erE4iISCXExcVx8OBBcnNziYiIsDqOiFShjIwMsrOzqVmzpmUZdBuq+D+NLIqIiACQmJiIw+Fg9+7dFBQUWB1HRKpIRkYGu3btIjY2lri4OMtyaGRR/J+KxarhdMK6dXDaaVYnERGRMnI4HDRq1Ijt27ezefNmEhMTiYuLIywsDJvNZnU8EakgwzBwOp1kZ2eTmppKdnY2sbGxJCUlWXYLKqhYlEBQOMFNZKT5vJ1U3muvmc8v7tsHe/eCnn0REQkYkZGRNGvWjGPHjpGSksLRo0etjiQiXmKz2YiLi6NmzZqWPqtYSMWi+L/CkcWkJNBvTb0jORl27jTbX34J99xjbR4RESmXiIgI6tSpQ+3atcnPz9ctqSJBwG6343A4LC8Q3alYFP/3229mcZOfb3WS4DF0qDm6CPDxxyoWRUQClM1mIzw8nPDwcKujiEgQshmGVuYORqmpqSQmJpKSkkJCQoLVccQfnX46rFxptjdsgDZtLI0jIiIiIlWjorWB/4xxiohvDR1a1J440bocIiIiIuKXVCyKhKobboCwMLP9ySeg511ERERExI2KRfFvv/8On38O8+ZBerrVaYJL3bpw6aVme/dumDvX0jgiIiIi4l9ULIp/++QTuPFG6N0b1q+3Ok3wueWWorZuRRURERERNyoWxb8VLpsB0LChdTmC1ZVXQrVqZnvyZEhLszSOiIiIiPgPFYvi3wqLxbAw87ZJ8a6oKLj+ekhMhJtugowMqxOJiIiIiJ/QOovi35KTzT8bNCiajEW869lnzTUXo6OtTiIiIiIifkTFoviv3Fw4cMBs6xbUqlOrltUJRERERMQP6TZU8V/79xe169e3LoeIiIiISAhSsSj+a+/eoraKRd84etScgdYwrE4iIiIiIhZTsSj+S8Wibz33HNSrZy6n8ccfVqcREREREYupWBT/5V4s1qtnXY5QkZRkPicKWnNRRERERFQsih+z26FRIwgP18iiLwwcCDExZvvLLyEnx9o8IiIiImIpFYviv+68E3buhOxsuOQSq9MEv/h4GDDAbB87Bj/8YGkcEREREbGWikXxf3a71lj0laFDi9q6FVVEREQkpKlYFJEiF1xQtKbljBlF61yKiIiISMhRsSgiRcLC4OabzXZBAXz+ubV5RERERMQyKhbFPzmdcOGFcP318NprVqcJLbfcUtTWragiIiIiIUvFovinQ4dgzhz46iuYPdvqNKGlXTvo1s1sr1gBq1ZZGkdERERErOGwOoBIidzXWNSyGb43dChkZsIdd5jLl4iIiIhIyFGxKP5JxaK1hg+He+4Bm83qJCIiIiJiERWL4p/27Stqq1j0PYf+aRAREREJdXpmUfyTRhZFRERERCylYlH8k4pF/7FyJdx3H2zbZnUSEREREfEh3Wsm/sm9WKxXz7ocoe79981JbgCqVYNnn7U0joiIiIj4TpUVi8nJyaxbt44dO3Zw8OBBMjIyAIiNjaV27do0adKEDh060KBBg6qKIIFMI4v+4bLLICwMCgrgww9h1ChzW0RERESCnteKxaNHj/Ldd98xc+ZM5s6dy4EDB8p0XJ06dejduzf9+vWjf//+1KxZ01uRJJAVFovVqkFUlKVRQlqDBnD55fD995CcDDNnmgWkiIiIiAQ9m2EYRmVOMGPGDMaNG8dPP/1EXl4eAOU9pe349PwOh4NLLrmEO++8k8svv7wysUJeamoqiYmJpKSkkJCQYHWc8nvlFdi+Hex2ePNNq9OEth9+gP79zfY118CUKdbmEREREZFyqWhtUKFi0el0MnHiRF588UU2b94MlFwgRkZG0qBBA6pXr050dDSGYZCVlcXRo0fZu3cvOTk5JwY6Xjg2b96cxx57jGHDhhGm297KLeCLRfEf+fnQuLE52utwwK5deo5UREREJID4rFicNGkSTzzxBFu3bgWKisSoqCh69uxJ79696datGx07diz1ecTk5GRWr17NX3/9xW+//cbChQvJzs42gx0vGps2bcoLL7zA4MGDyxMz5KlYFK964gl44QWzPWYMPPKItXlEREREpMx8Uiz27t2bBQsWAGaR6HA4uPzyy7nxxhu59NJLiY2NLX9yN5mZmcyYMYPPP/+cadOmuW5rtdls9OzZk3nz5lXq/KFExaJ41ebN0KqV2W7VCjZuhOO/0BERERER/+aTYtFuN5dlrF27Nv/85z+56667qF27dvnTlsGhQ4d47733ePvttzlw4AA2m42CgoIq6SsYBXSxmJlpzr4ZF6eCxJ9ceCHMmWO2f/sNevWyNo+IiIiIlElFawN7eTqpU6cOb7zxBjt37mTEiBFVVigC1KpVi6eeeoodO3bw+uuvV2lf4mc+/hgSEiAmBr780uo0Uuj224vakyZZl0NEREREfKJcS2ds2bKl0realldkZCQPPPAAdxQuDC7Br3DZlexsc3RR/MOAAXDDDXDjjdCvn9VpRERERKSKlatY9HWh6C4mJsayvsXH3NforFPHuhziKSoKPvvM6hQiIiIi4iPlug1VxCf27y9q161rXQ4RERERkRCmYlH8j/vIop5V9W+adEpEREQkaKlYFP9TWCzGxZmT3Ih/yc+HKVPgoos8J70RERERkaBSrmcWT+XQoUMsWLCAhQsXsn79ejZv3szevXvJycnBMAzq1q1L48aN6datGz179qRPnz5Ur17dW91LMCm8DVXPK/qn3Fy47TY4dsx8jvHVV6FGDatTiYiIiIiXlWudxVOx2+3Y3NbEK+m07h93OBz07duXO++8k6uuusobEcRNwK6zmJsLkZFmu0cPWLTI2jxSsn/9C8aONduvv25ui4iIiIhf8sk6i2VhGEaJhWLhxwr/zMvL46effmLAgAF06dKFJUuWeDuKBKKDB4vaGln0X8OHF7Xfew+88zsnEREREfEjXrsNFcwisHHjxjRr1oyGDRtSu3ZtbDYbhmGwZ88edu7cyerVq8nMzHTtD7By5UrOPfdcXn31Ve677z5vRpJAo2UzAkO7dtC7N/z2G2zcaP55/vlWpxIRERERL/JasfjTTz9x5plnUqOUZ5cKCgpYvnw506ZN44svvmDTpk3YbDby8/P517/+RXx8PMOGDfNWLAk07drBypXmc4sqFv3bXXeZRSKYo4sqFkVERESCiteeWayo7777joceeoitW7cCEB8fz8aNG6lXr56VsQJewD6zKIEjJwcaNTJvHQ4Ph127tC6miIiIiB/ym2cWy+uqq65i6dKlnH322QCkp6fz3nvvWZxKREoVGQn/+IfZzsuDDz+0No+IiIiIeJXlxSJAYmIiX3zxBQ6HeVfstGnTLE4kImVy551F7XHjwOm0LouIiIiIeJVfFIsATZo04YwzzsAwDLZt22Z1HLHKTz/Bp5/CrFmQnW11GilN8+bQr19R+9Aha/OIiIiIiNd4dTbUyso+XhwUzpYqIejNN2HGDLN98KC56Lv4t5deMtdcbNPG6iQiIiIi4kU+GVnMy8tj8eLFpKenl/jx/Px8XnzxRVatWoXNZqNx48a+iCX+qHDpjLAwKGVmXfETnTqpUBQREREJQj4ZWUxNTeWcc87BZrNRr149kpKSqFGjBuHh4Rw+fJg1a9aQnp6OzWYD4LrrrvNFLPFH+/ebf9auDXa/uUtaRERERCTk+PQ2VMMw2LdvH/v27Tvh/UIDBgzgqaee8mUs8ReGUTSyqDUWA5PTCTNnwoUXmrOlioiIiEjA8snQTXR0NAMHDqRJkyYYhuF6AdhsNi688ELGjx/P6tWr+eabb4iIiPBFLPE3qamQm2u2VSwGnh9/hNat4bLLYNIkq9OIiIiISCX5pFiMiYlh0qRJbNu2jd27d/Phhx9yzTXXEBERgdPpZM6cObz66qscKBxVktBUeAsqqFgMRPHxsGWL2X7rLWuziIiIiEil+fyhsAYNGjB06FAmT57Mnj17GDVqFLGxsWzcuJGLLrqICRMm+DqS+Av3XxaoWAw8554LnTub7SVLzJeIiIiIBCyvFYv5+fnlPqZ69eo8/fTTrF69mvbt2+N0Orn77rtZtWqVt2JJIHFfo692betySMXYbHDffUXbGl0UERERCWheKxZPO+00fvrppwod26RJE6ZNm0ZkZCT5+fm88sor3oolgUTFYuC74YaiJU++/trz1mIRERERCSheKxb//vtvLr/8cq644gqWL19e7uObNm1K165dMQyDX3/91VuxJJA4HNCkCcTGQq1aVqeRioiOhttvN9u5ufC//1mbR0REREQqzGa4r1tRCXa73bVOIsBll13Gww8/TK9evcp8jvbt27NhwwbCw8PJycnxRqyQlZqaSmJiIikpKSQkJFgdp/wMw7ytUQLPjh3QvLm5jEaDBrB9O4SHW51KREREJGRVtDbw2sji008/TUREhGtZjB9//JELLriAli1bMnr0aP744w+cTudJjx83bhwbNmwAoEbhbWwSulQoBq4mTaB/f7O9Zw98+621eURERESkQrw2sgiwadMm7r33XmbPnu3ZyfEf/GNjYznttNNo06YNdevWJTIykiNHjrBgwQJWrVqFYRjYbDYuvvhiZsyY4a1YISngRxYlsP3yC/Tta7bPPRfmz7c2j4iIiEgIq2ht4NVisdDPP//MiBEjWHJ86nybzUZhN7YSRozcI9hsNiZPnszVV1/t7VghRcWiWMowoHt36NIF/vlP6NTJ6kQiIiIiIcuvisVCv/76K++++y7ff/89eXl5RZ2eomB88MEHNRuqFwRksXjjjeakKE2bwssvW51GKsvpBLvPl3IVERERkWL8slgsdOTIEX755RfmzJnDmjVr2LRpE/uPT6lvs9moU6cO5513HnfeeSd9C29dk0oJyGKxWjVISYHWrWHjRqvTiIiIiIgEBb8uFktiGAbZ2dmEhYURERFhRYSgFnDFYl4eFP49OOccWLjQ2jwiIiIiIkHC8tlQy8tmsxEdHa1CUUyHDxe1tcZicElPh7ffhp49ITvb6jQiIiIiUkZ6oEj8w6FDRW0Vi8HlvvvM16JF8PnnVqcRERERkTJSsSj+QcVi8Lr77qL2a6+ZM6WKiIiIiN9TsSj+4eDBoraKxeDSvbt5CyrA2rUwa5a1eURERESkTMpVLI4ePZqMjIyqynJSGRkZjB492uf9ig9pZDG4PfhgUfu116zLISIiIiJlVq5icdSoUbRo0YIXX3yRY8eOVVGkIseOHeOFF16gefPmPPPMM1Xen1hIxWJwu+oqaNbMbM+aBWvWWJtHREREREpV7ttQDx48yJNPPknjxo255557+Ouvv7weasmSJQwfPpzGjRvz1FNPcdD9FkUJTioWg1tYGPzrX0Xbr79uWRQRERERKZtyrbM4b948HnjgAVauXGkebLMB0LJlS6655hr69etHjx49iIqKKleIzMxMFi5cyMyZM5k6dSrbtm0DzLUYATp37szYsWPp1atXuc4bygJuncWpU2HuXLNofPFFSEqyOpF4W1oaNGoEKSnmmpo7d0LdulanEhEREQl6Fa0NylUsglnAffLJJzz33HNs3rzZPMnxohEgPDycdu3acdppp9G8eXMaNmxItWrViI6OxjAMsrOzOXr0KMnJyWzZsoU1a9awYcMG8vPzPfoAswgdMWIEN910k0cfUrqAKxYlNDzyCLz8stl++mnQ7eUiIiIiVc5nxWIhp9PJ119/zZtvvsnixYs9T1rOwq54hLPPPpsHHniAa6+9FrtdE7ZWhIpF8Uu7d5vPLoaFwQMPwJgxVicSERERCXo+LxbdrVmzhs8++4wpU6awadOmkjs6XkCerLuWLVsyYMAAbrzxRjp27FjZSCFPxaL4rcmT4bzzoE4dq5OIiIiIhARLi0V327dv57fffmPp0qWsW7eOHTt2cOjQIdeSG7GxsdSqVYsmTZrQvn17unbtSq9evWhWOFOiDyxatIiJEycyf/58kpOTMQyDpKQkzj33XIYOHUrPwjXhvKgit9G+++673HXXXRXqL+CKxZQUSEgA3W4sIiIiIuJVflMs+rOMjAzuv/9+JkyYcMr9br31Vt566y1iY2O91reKxVPIyoKYGHA44PLLzcluRERERETEKypaGziqMJNfKSgoYMCAAcyaNcv1XnR0NB06dMDhcLBu3TpSU1MB+PDDD0lOTubHH38kLCzM61l69epFdHR0qfs1btzY6337pcOHzT/z881n2SR0pKXB11/DP/6hUWURERERPxMyxeKIESM8CsU77riDF198kRo1agDmqOOYMWN49tlnAZg1axZPP/00zz//vNezTJw4kaZNm3r9vAFLayyGpgkT4P/+D44ehfr14bLLrE4kIiIiIm7KPdVo4RqIgWTPnj287rYI+M0338z48eNdhSKYz1KOHj2ap556yvXea6+9xp49e3yaNSSpWAxNNWqYhSKYa2uKiIiIiF8pd7HYokULqlevzgUXXMCDDz7IJ598wurVqykoKKiKfF7xxhtvkJ2dDUBMTAxvvPHGSfcdMWIEjRo1AiA7O5uxY8f6ImJoU7EYmvr3h7Ztzfb8+bBwobV5RERERMRDhRYxTE1NZd68eYwdO5Zhw4Zx+umnExcXR7du3bjzzjt59913+f3338nMzPR23gr59ttvXe3Bgwd7jCgWFxERwa233uranjJlSpVmEzyLxZo1rcshvmW3w6OPFm1rzUURERERv1KhZxbdJ1C12WwYhkFOTg7Lli1j2bJlHh9r1aoVXbp0oUuXLpx++ul06dKFWj4cPdq4cSObN292bV9yySWlHnPppZcyevRoADZv3szGjRtp06ZNlWUMeUeOFLU1shhabrgBRoyA3bvhhx9gzRo47TSrU4mIiIgIFSgWv//+e1asWOF6uT/DWFhEFhaQhmHw999/8/fff/PVV1+59mvQoMEJBWRVTfiycuVKj+0ePXqUeswZZ5xBREQEubm5AKxatUrFYlVyLxZPMeorQSgiAh56CP79b3P7pZfg44+tzSQiIiIiQAWKxSuuuIIrrrjCtZ2WluYqHJcvX86KFStYt26dq9AqXkACJCcns2fPHqZPn+46T2JiIqeffjrdu3fnvPPOo1evXsTHx1fqkwNYv369qx0REeF6HvFUCvfbsmXLCefwhocffph169axa9cu8vLyqFmzJq1ataJ3794MHTqUZs2aebU/v6diMbTdfjs8+6z59+Dzz+GZZyDUrgERERERP2Qz3O8p9ZL8/HzWrVvnKh5XrFjBypUrOXbs2IkB3IpI94XrIyMj6d+/P/fddx89e/ascJZbb72Vjz76CIDmzZu7CsDSXHjhhcyZM8d1jgkTJlQ4A3h+bqficDi47bbbeP3118u0FuPJVHThTUtccQUU/uLg4EHdihqKnnkGRo0y23feCePGWRpHREREJJhUtDaokmLxZLZv3+4xArlixQp27dp1YqjjBWRhgTVgwADef/99EhMTy93noEGDmDx5MgBdunTxeKbyVK6++mq+++471zkmTZpU7r7duReLtWrVokWLFsTFxZGSksKGDRtIT0/32L9Lly7MmTOnzJ9zTk4OOTk5ru3U1FQaNWoUGMXipk3mM2tHjsDVV0NYmNWJxNeOHoWmTSE11ZzkaMcOiI21OpWIiIhIUKhosVihCW4qqmnTpjRt2pSrr77a9d7Ro0c9iselS5eyYcMGoOgW1ilTprB69WoWLFhQ7slx3IuwqKioMh/nPqpXvJCriPbt23PnnXdy5ZVX0rx5c4+P5efnM3PmTJ544glWrVoFwPLly7n++uuZMWNGmc7/wgsv8Mwzz1Q6pyVatTJfErqqV4cnnoD0dPP5RRWKIiIiIpbz6chiWR07dowZM2YwYcIEfvnlF9dI40UXXcTMmTPLda6+ffvyyy+/AHDeeecxb968Mh1388038+mnnwLQp08fZs+eXb5PogKys7MZNGiQx7Oc33//PVdeeWWpxwb0yKKIiIiIiFSZio4sVmidxapWrVo1hgwZws8//8y0adOIPT7KMHv2bNdzhGUVExPjamdnZ5f5OPd9Y300yhEVFcUXX3xB3bp1Xe+99dZbZTo2MjKShIQEj5eIiIiIiEhF+WWx6O6yyy7j3XffdW1//vnn5To+Li7O1c7KyirzcZmZmSWeo6rFx8dz9913u7bnz59friI34GRlwSefmBPcrFtndRrxJ0eOeM6UKyIiIiI+5ffFIsANN9xAzZo1AVi0aFG5jnV/xnHv3r1lPm7fvn2udmHfvnLBBRe42tnZ2SVOAhQ09u2DW24xZ0QdPdrqNOIPjh6FJ580J7x5/nmr04iIiIiErIAoFm02G+3bt8cwDPbs2VOuY9u0aeNqHz582GPE8FTcC7S2bduWq8/Kqlevnsf2oUOHfNq/T2mNRSkuJwdeew3S0uDdd2H/fqsTiYiIiISkgCgWoejZw7S0tHId165dO4/tFStWlHpMcnIyBw8ePOk5qlrxgtb9ucugo2JRiqtXD4YPN9tZWfDqq9bmEREREQlRAVMsjh07lvHjx3PbbbeV67ju3bsTGRnp2l6wYEGpx8yfP9/VjoqKonv37uXqs7LWrl3rsV2nTh2f9u9TKhalJI88AoXX7TvvaHRRRERExAIBUyy2bt2a22+/nXHjxpXruLi4OPr06ePa/uyzz0o9xn2fPn36+Gw21EJffvmlq920aVPq16/v0/59SsWilKRBA7jzTrOdmQkvvmhtHhEREZEQFDDFYmUMGzbM1V61ahU//PDDSfddtmwZM2bMKPFYX/j++++ZNm2aa/vqq6/2af8+p2JRTuaJJyA62my/+y7s3m1tHhEREZEQExLF4qBBg+jcubNre/jw4WzYsOGE/fbu3ctNN91EQUEBAKeffjoDBw4s8Zzbt2/HZrO5XqNGjSpxv5SUFAYOHMjSpUtLzfnFF19www03uLZjYmJ49NFHSz0uoKlYlJOpVw/uu89s5+TAs89am0dEREQkxDisDuALNpuN//3vf/Tu3ZusrCz27t3LWWedxd13302vXr1wOBwsWbKEt99+m/3Hn42Kjo5m/Pjx2Gy2SvVtGAZTpkxhypQptG3bln79+nH66adTv359YmNjSUtLY/Xq1XzzzTf8+eefHpk//PDDE2ZGDTruxaKPlyiRAPDII/Dee5CaChMmmNstWlidSkRERCQkhESxCNCtWzc+/fRTbrrpJrKyskhNTWXMmDGMGTPmhH2jo6P59NNP6datm1czbNiwocQRzeLi4+MZN24cgwcP9mr/fkkji3IqNWvCgw/CqFGQnw+vvGLekioiIiIiVS4kbkMtNGDAAJYuXUrfvn1LHDG02Wz06dOHv/76iwEDBnilz+joaO688046dOhQ6ihlYmIi999/P2vWrGHIkCFe6d/vRURAQoLZrl7d2izin/79b2jZEv7zH3j5ZavTiIiIiIQMm2EYhtUhrLBr1y4WLlxIcnIyAA0bNqRnz540atSoyvo8evQoK1as4MCBAxw6dIhjx44RExNDjRo16NSpE506dSIsLMwrfaWmppKYmEhKSgoJhcWYP8vLg/Bwq1OIvyooAC9dGyIiIiKhpqK1QcgWi8Eu4IpFERERERGpEhWtDULqNlQRCRJ791qdQERERCToqVgUkcBx+LD5DGOTJjB7ttVpRERERIKaikWxzv79MHAg3HEHfPqp1WkkEPz0E7zxhvmM68MPg9NpdSIRERGRoKViUayzdy9MmQLvvw/z51udRgLBkCHQpYvZXrECPv/c0jgiIiIiwUzFolhHayxKedntnstnPPkkZGdbl0dEREQkiKlYFOuoWJSK6NMHLrnEbO/cCW+9ZW0eERERkSClYlGso2JRKmrMGLDZzPbzz5sT34iIiIiIV6lYFOuoWJSK6tQJhg0z2ykp8J//WBpHREREJBipWBTrqFiUyhg9GqKizPbbb8O2bdbmEREREQkyKhbFOioWpTKSksw1FwFyc+HNN63NIyIiIhJkVCyKdY4eLWpXq2ZZDAlgjz4KbdqYheJLL1mdRkRERCSoOKwOICHMvVisXt26HBK4EhNh7VoIC7M6iYiIiEjQUbEo1rnsMmjUyJygJDbW6jQSqFQoioiIiFQJFYtinf/7P6sTSDBasQKaNNFotYiIiEgl6ZlFEQkOhw/D8OFwxhnwzDNWpxEREREJeCoWRSQ4ZGTAJ5+AYZhLaaxbZ3UiERERkYCmYlGsYRjmS8RbGjc2Z0cFKCgwl9XQ3zERERGRClOxKNbYuRMiIqB2bbjvPqvTSLB4+GGzaASYNQumTrU0joiIiEggU7Eo1jh2DPLz4dAhyMmxOo0Ei5gYePXVou3774f0dOvyiIiIiAQwFYtiDfc1FqtVsyyGBKGBA6FfP7O9e7cmuxERERGpIC2dIdY4dqyorSUOqtTRjFymrd7Lwk2H2HYog5SsPGIjw2hZJ46zmtXkys4NqB0faXVM77HZzAluTjvNHLV+/XUYOtTcFhEREZEyU7Eo1nAvFjWyWCWOZeby+s9/89Vfu8jOc57w8S0HM5i5dj//+XE9g7om8a++ramXGGVB0irQsiU88QSMHGlOdnP33fDbb2DXzRQiIiIiZaViUayhYrFKzVi9lyenruFIRi4AHRokcFnH+nRsmEj1mAhSs/NYk5zCjDX7WLHrGF/+uYsfVu5hZP8OXNs1CZvNZvFn4AWPPGIupbF5M+TmwpEjUKuW1alEREREAoaKRbGG+zOLug3VawqcBmN+2sD4eVsBaF03jpFXduCcFjVPKAB7tqzF8N4tWLrjCM9NX8/yncd45JtV/L7lMC8O7EikI8yKT8F7oqJg3Dj4+2+44w4IC/DPR0RERMTHVCyKNTSy6HV5BU7+/dUKpq3aC8DwXs35v35tCA879a2XXZvU4Ju7zmH8vK28Omsj3y5PJvlYFv+7+UwSY8J9Eb3qXHih+RIRERGRctMDPGINFYtelZvv5O5PlzFt1V7Cw2y8NaQLj1/WrtRCsVCY3cbd57fgw1u7ER/pYMm2I9zy4RLSsvOqOLmIiIiI+CsVi2INLZ3hNYZh8NiUVcxev59Ih53xt5zJlZ0bVOhc57Wqzdd39aB6TDgrdx1j2Id/kpmb7+XEFlqwAC67DDIyrE4iIiIi4vdULIo1Ro2CyZPhgw+gZk2r0wS0137+mynLkgmz23jv5q5c0KZOpc7Xrn4Cn9x2FglRDpbuOMoDX67A6TS8lNZC77wDvXrBjBnw9NNWpxERERHxezbDMILgp0ApLjU1lcTERFJSUkhISLA6jlSR71Yk88CXKwAYM7Aj13Vr7LVzL91xhCH/+4PcfCfDezfn8Uvbee3clvj7b+jcGbKzzSU0Fi2Cs86yOpWIiIhIlatobaCRRZEAtWl/Go9NXg3APee38GqhCObENy8P6gTAuN+28u3y3V49v8+1bg3PPGO2nU647TbIybE2k4iIiIgfU7EoEoAycvK569OlZOUV0LNlTR66uE2V9HPV6Q2578KWADwxZQ2bD6RVST8+8+CDcMYZZnvtWnjhBWvziIiIiPgxFYviewUFMH06LFwIW7danSYgjfx+LVsOZlA3IZKx13chzG4r/aAK+lff1vRsWZOsvALu+WwZWbkFVdZXlXM4YMIE80+A55+HVauszSQiIiLip1Qsiu8dOwZXXAHnngv//KfVaQLOrLX7+Gbpbmw2eGvIGdSKi6zS/sLsNt64rgu14yP5e386I79fU6X9VbnOneGxx8x2fj7cfLNuRxUREREpgYpF8T2tsVhhRzJyeeJb8znFO89rTvdmNXzSb+34SMZefzo2G3z9125+XrffJ/1Wmaeego4dzfaqVebsvCIiIiLiQcWi+J7WWKwQwzB4aupqDqXn0rpuHP++qLVP+z+nRS3uPK85AI9PWcXh9AAejYuMhE8+gfBwc/utt+DgQWsziYiIiPgZFYvie+4ji9WrWxYj0Mxcu48fV+/DYbfx2uDTiQoP83mGf1/UmtZ14ziUnstTU9cQ0CvvdO4Mo0fDmWfCX39B7dpWJxIRERHxKyoWxfd0G2q5ZeUW8Oy09YC5TMZpDRMtyREVHsZrg0/HYbcxY80+vl+5x5IcXvPww+Z6i23bWp1ERERExO+oWBTfU7FYbu/9toXkY1k0rBbN3ee3tDTLaQ0Tue/CVgCM+n5tYN+OGhZWdCuqiIiIiHhQsSi+5/7Mom5DLdWuI5m899sWAJ66vB3REb6//bS4ey5oQbv6CRzNzOO56eutjuM9WVnw6KNw4IDVSUREREQsp2JRfE8ji+Xy/PT15OQ7OadFTS45rZ7VcQAID7Pz4oCO2Gzw7fJkfvs7CCaH2bQJuneHl16CoUPB6bQ6kYiIiIilVCyK76lYLLMFmw7x09p9hNltjOrfAZvNZnUkl86NqjHsnKYAPPntajJz860NVFkJCUUzov70E7z+urV5RERERCymYlF8Lzu7qK1i8aTyCpyM+mEtALf0aELruvEWJzrR/13chobVotl9NIuxszdZHady6tY1l9Mo9Nhj8Oef1uURERERsZiKRfG9Dz6AvDw4fBiaNbM6jd/6+PcdbD6QTs3YCP7V17drKpZVbKSDZ6/uAMD7C7axJjnF4kSVdNFF5jOLAPn5cP31kJpqbSYRERERi6hYFGs4HFCjhjkbpZzgYFoOb/z8NwCPXNKGxGj/nbHzwrZ1uaJTfQqcBo9PWU2BM4DXXgR49lk46yyzvXUr3H03BPJ6kiIiIiIVpGJRxA+9PHMDaTn5dEpK5NqujayOU6qnr2xPfJSD1ckpfPz7dqvjVE54OHzxhfkMI8Dnn8OECdZmEhEREbGAikURP7Ni1zG+/ms3AKP6d8Bu959JbU6mTnwUj11qLmz/ysyN7E3JsjhRJTVrBv/7X9H2vffC0qXW5RERERGxgIpF8b1//hMefhjGj7c6id9xOg1Gfm9OajPwjCTOaBw461AO6daYrk2qk5FbwKjjn0NAGzwY7rnHbOfkwLhx1uYRERER8TEVi+Jb+fnwzjvwyivw0UdWp/E73yzbzcpdx4iLdPDopW2sjlMudruN/1zTEYfdxsy1+5m1dp/VkSrvtdegRw8YORLefdfqNCIiIiI+pWJRfMt9ZsnEROty+KHU7Dxe+mkDAA/0aUWd+CiLE5Vfm3rx3NmrOQAjv19Lek6Ar70YGQm//QajRmkyJhEREQk5KhbFt1LcllZQsejhzdmbOJSeS/PasQw9vth9ILrvwlY0rhHD3pRsXpv1t9VxKi+8hJlonU7f5xARERHxMRWL4lvuI4uFs00Kmw+k8dGi7QCMvLIDEY7AvTSjI8J49urTAPho0TZW7w7wtReLW7QIOneGTZusTiIiIiJSpQL3J1IJTBpZPIFhGIz6fh35ToOL2teld+vaVkeqtN6ta9O/cwOcBjz+7SryC4JkJO7HH6F3b1izBq68Eo4dszqRiIiISJVRsSi+pWLxBDPX7mfB5kNEOOyMuLy91XG85qkr2pEQ5WBNciof/77D6jjece650Lq12d64Ea6/3py0SURERCQIqVgU31Kx6CE7r4Dnpq8DYHiv5jSuGWNxIu8x115sB8Crszay51iAr70I5q3TP/wANWua2zNnwiOPWJtJREREpIqoWBTf0jOLHsb9tpXdR7OonxjF3ee3sDqO113frRFnBtPaiwDNm8M334DDYW6//jp88IG1mURERESqgIpF8S2NLLrsPprJf+duBuCJy9oRE+GwOJH32e02/jPAXHtx1rr9zAyGtRcBzj/fXC+00N13w7x5lsURERERqQoqFsW3mjeHK66AXr2gYUOr01jqhR83kJPv5KxmNbiiU32r41SZ1nXjGd7bXHtxVDCsvVjozjvh/vvNdl4eXH01rF9vaSQRERERb1KxKL513XXmM1+//QbdulmdxjKLNh9i+uq92G0wqn8HbDab1ZGqVNCtvVjo1VehXz+zffQoXH45ZGdbm0lERETES1QsivhYfoGTUT+Yz+/dfHYT2tUP/mc3o8LDeC4Y1150OGDSJDj9dIiIgDFjICrK6lQiIiIiXqFiUcTHPlm8g7/3p1M9Jpx/X9Ta6jg+06t1ba46PQjXXoyPN9df/OUXuPZaq9OIiIiIeI2KRREfOpiW47oN8//6taFaTITFiXzrqcvbu9ZenBgsay8C1K9vrsEoIiIiEkRULIpvXXghtGoVsj9Yj/lpA2k5+XRsmMj13RpbHcfnasdH8vhlQbb24sm89RY8/DAYhtVJRERERCpExaL41tatsHkzbNpkdRKfW7rjKN8s3Q3AM1d1IMwe3JPanMx1Z5prL2bmFjAyWNZeLO6FF8yZUl95BZ57zuo0IiIiIhWiYlF8q3CdxYTgn9TFXYHT4Onv1gAw+Mwkzmhc3eJE1rHbbbwwoCPhYTZ+Xrefn9bstTqS99WuXdR++ml44w3LooiIiIhUlIpF8R3DgNRUs52YaG0WH/t8yU7W7kklIcrBo5e0tTqO5VrVjWd4rxYAPPntGg6l51icyMtuv91cVqPQv/8NEyZYl0dERESkAlQsiu9kZIDz+AyYIVQsHsnI5ZWZGwF46OI21IyLtDiRf7ivT0va1ovncEYuT0xZjRFsz/Y9+CCMHFm0fccd8PXX1uURERERKScVi+I7KW5r64VQsfjyzA2kZOXRrn4CN54VepPanEykI4zXBp9OeJiNWev2M3lZstWRvG/kSHNUEcxflNxwgwpGERERCRgqFsV3Cm9BhZB5ZnHZzqN8+ecuAJ69qgOOMF1y7to3SHCtNfnM92vZfTTT4kReZrOZt6Pefru5XVBgFoxffmltLhEREZEy0E+u4jshNrKYV+Dk8cmrMQwYeEYSZzatYXUkvzS8Vwu6NqlOWk4+D09ahdMZZLej2mwwbhzcdpu5XVBg3qKakWFtLhEREZFSqFgU3wmxYvF/87eycX8aNWIjePLydlbH8VthdhuvXtuZmIgwft96mA8WbLM6kvfZ7TB+vPncYq1aMGsWxMZanUpERETklFQsiu+4F4tBfhvqjsMZjJ1triX51OXtqBEbYXEi/9a0VixPXd4egJdmbmDFrmPWBqoKdju89x4sXQqnnWZ1GhEREZFSqVgU3+nWDd5/33yG64ILrE5TZQzD4Mlv15CT7+TclrW4pktDqyMFhCHdG3FZx3rkFRjc98UyUrLyrI7kfXY7NC42yVF+PkyZYk0eERERkVNQsSi+06yZ+dzWgw9C165Wp6ky3y5PZsHmQ0Q67Dx/zWnYbDarIwUEm83GCwM6kVQ9ml1Hsnh8yqrgW06jOKfTnPxm4EB47DFzLVIRERERP6FiUcSLDqfn8Oy0dQD8q29rmtTUc2nlkRgdzts3nIHDbuPH1fv47I+dVkeqWr/+ChMnmu0xY+DOO80JcERERET8gIpFES96+ru1HM3Mo229eG4/r5nVcQLS6Y2q8eglbQEYPW0dq3enlHJEAOvbF95915wxFczbtK+7DrKzrc0lIiIigopF8aUtW2DdOkhODsrRkx9W7mH66r047DZeubYz4VpTscJuO7cZfdrWITffyfBP/uJQeo7VkarOXXfBF19AeLi5PXmyWUQeOmRtLhEREQl5+mlWfOfhh6FDB0hKgr17rU7jVQfSshnx3RoA/nlhS05rGPxLg1Qlu93Ga9edTrNasexJyebez5aRV+C0OlbVue46+OEHiIkxtxcuhLPPhr//tjaXiIiIhDQVi+I7qalF7SBaZ9EwDJ6YsoZjmXl0aJDAvRe0tDpSUEiMDmf8zV2JjQjjj21HeH76eqsjVa1+/WDePKhXz9zesgV69ID5863NJSIiIiFLxaL4TuE6izYbxMVZm8WLpixLZvb6/YSH2Xh1sG4/9aZWdeN57brTAfho0XYm/bXL2kBVrWtX+OMP6NjR3D5yBJ57TrOkioiIiCX0U634TmGxmJBQNKFHgNt9NJNRP6wFzNlP29ZLsDhR8OnXoR7392kFwBPfrmbRliB/lq9xY1iwwBxpbNPGfJ4xSK4XERERCSwqFsV3CovFILkFNb/Ayb++XEFadj6nN6rG8F7NrY4UtP7VpxWXd6xPXoHB8E+Wsml/mtWRqlZCgvkM45w5UKOG58c0yigiIiI+omJRfKfwmcUgKRbf+nUzf+04Slykgzev74JDt59WGbvdvMX3zCbVScvOZ9iHf3IgNciXlwgPh/r1Pd/btw969YKVK63JJCIiIiFFP92Kb+TmFq0dlxD4t2ou2XaEt37dBMDz15xG45oxFicKflHhYfzvljNpViuW5GNZ/GPin6Tn5Fsdy3dyc2HQIPMW1R494PPPrU4kIiIiQU7FovhGitvC6gE+spiSmce/vlyO04CBZyRx1ekNrY4UMqrHRvDRrd2oGRvBmuRUbp/4J9l5wbdmZ4lSUsyCESArC268EYYPN9siIiIiVUDFovhGkBSLhmHwyOSV7EnJpmnNGJ65qoPVkUJOk5qxfHhrN+IiHSzeeoS7Pl1Kbn4Qr8FYqHZtc2mNW28tem/8eOjeHdYH+bIiIiIiYgkVi+Ib7mssBvBtqOPmbWXm2v1EhNl5c0gX4iIdVkcKSZ2SqjFhWDeiwu3M3XiQB75cTn5BCBSMUVEwYQJ8+CHEHL/1ec0aOPNMmDjR2mwiIiISdFQsim907gwHD5oLjY8caXWaClm0+RAv/bQBgJH929MpqZq1gUJc92Y1GH/zmUSE2ZmxZh8PTVoZGgUjwLBh8OefcNpp5nZmpvneTTfBsWMWBhMREZFgomJRfCMsDGrVgubNT5zhMQDsTcnivi/M5xQHdU3ihu6NrY4kQK/WtXn7hi447Da+W7GH+75YHhq3pAK0bw9//AG331703mefme+JiIiIeIGKRZFS5OY7ueezZRzOyKV9/QSeu/o0bFok3W9c3KEe797U1TXCOPyTv0Jn0puYGPjf/8yZURMTzQlv+vWzOpWIiIgECRWLIqdgGAZPTV3N8p3HSIhy8N5NXYkKD7M6lhRzUfu6vD/0TKLC7czZeJB/fBRiy2oMGQKrV8Mrr3i+bxiwbp01mURERCTgqVgU35g/H154Ad5+G3bssDpNmf1v/la+/ms3dhu8OaSL1lP0Y71a12bird2JjQhj0ZbDDH7vd/anZlsdy3caNYK4OM/3Jkwwn2u8/35IS7Mml4iIiAQsFYviG7NnwxNPwH33wYYNVqcpk5/X7eeFGWbWEVe05/w2dSxOJKU5q3lNPr/jbGrFRbBubyrXvLOQjftCtEjauRP+9S9zdPGtt8xnHH/4wepUIiIiEkBULIpvuC+dER9vXY4yWrsnhQe+XI5hwI1nNWbYOU2tjiRl1LlRNb69pyfNa8eyJyWbQe8uYsGmQ1bH8r0GDWDUKIiONrd374b+/WHwYNi719JoIiIiEhhULIpvuN8C5+fF4t6ULG6f+BeZuQWc27IWo/p30IQ2AaZRjRim3H0O3ZvWIC0nn6EfLuH9+VsxDMPqaL7jcMBDD8HatXDxxUXvT5oEbdvCa69Bbq51+URERMTvqVgU3wiQYvFYZi63fLCEvSnZtKgdyzs3nEF4mC6TQFQtJoKPb+vOgDMaUuA0eG76eu7/cgWZuSE08Q1As2bw00/w6afm8jVgjvQ/9JC5/umsWdbmExEREb+ln4LFN9yLxYQE63KcQlZuAbdN/ItNB9KplxDFxH90JzEm3OpYUglR4WG8em1nnunfAYfdxg8r93DNO4vYejDd6mi+ZbPBjTfC+vXmuoyFI+UbNsD06dZmExEREb+lYlF8w8+fWcwrcHLv58tYuuMoCVEOJv6jO0nVNfNpMLDZbAw9p+nxiW8i2bg/jcvfXMDXf+4KrdtSwRxZ/N//4M8/oUcPc3vUKKtTiYiIiJ9SsSi+UTiyGBkJ4f41Wud0Gjw6eRW/bjhApMPOB8O60aae/xW0Ujndm9Vg+v3n0qN5TbLyCnhk8iru/XwZKZl5Vkfzva5dYeFCWLwYqlf3/Ngrr8Bzz0FGhjXZRERExG+oWBTfKCwW/WxU0ek0eOLb1UxZlkyY3cY7N5xBt6Y1rI4lVaRuQhSf3n4Wj17SFofdxo+r93HJ2HnM2XjA6mi+Z7NBixae7x04AM88AyNGmB97913IC8FiWkRERAAVi+IrhcWiHz2vaBgGT323hi//3IXdBq8N7kzf9nWtjiVVLMxu4+7zWzD57nNoViuWvSnZ3Prhnzz41QqOZoT47KBz5kBWltnevx/uuQdat4bx4zVzqoiISAhSsSi+cdpp5qtNG6uTAGahOPL7tXz+x05sNnh1cGeuOr2h1bHEhzo3qsb0+8/ltnObYbfBlOXJ9H3tN75bkRx6zzIWuu46c6mNgQOL3tu+HYYPh5Yt4b//hexsy+KJiIiIb9mMkP2pKLilpqaSmJhISkoKCX40mucPnE6D0dPW8dGi7dhs8PKgzgzqmmR1LLHQ8p1HeWzyajbuN0fAz25eg5FXdqBd/RC+dv74w5z85qefPN9v0ABefBFuvtmSWCIiIlJ+Fa0NNLIoISW/wMmjk1fx0aLtALw4oKMKRaFL4+r8cN+5PHhRayIddhZvPcLlb87nyW9XcyRUb0096yyYMcMsGq+8suj9PXvg8GHrcomIiIjPaGQxSGlk8UQ5+QX868sVzFizjzC7jZcGdmKgCkUpZvfRTF6YsYHpq/YCkBDlYHjvFgw7pymxkQ6L01lo2TJzltQ5c2DnTs/JqnbsgGPHoHNny+KJiIjIyVW0NlCxGKRULHrKzM1n+CdLmb/pEBFhdt4c0oVLTqtndSzxY4u3HuaZH9axfq+5RmituAjuPr8lN57VmKjwMIvTWejQIXN9Rnd33QXjxsH558Pdd8PVV0NEhBXpREREpAQqFsWDXxWLy5ebP0DGx8ONN8KwYT7t/khGLrdP/JNlO48RHR7G/245k3Nb1Sr9QAl5BU6DH1bu4fXZf7PjcCYA9ROjuLNXc67r1oiYiBAeaSx06BA0auQ58U3dunDbbXDnndCkiXXZREREBNAzi+LP9u83n3uaPducWdGHth3KYMB/F7Js5zESohx8evtZKhSlzMLsNq7u0pDZD/bmhQEdqZ8Yxd6UbJ75YR09X/yV13/+O3SfaSwUEwOvvmousVFo/374z3+gWTO44gr47jut1ygiIhKAVCxK1UtNLWq7P+dUxZbuOMKA/y5k++FMGlaLZvLd59C1SXWf9S/BIzzMzpDujZnzf+fz3NWn0aRmDEcz8xj7yybOefEXnpq6mo370qyOaY2YGHM9xg0bzF8IDRoEjuMjroYB06ebt6U2aGA+2ygiIiIBQ8WiVL00tx+ifVQsTl+1lyH/+4OjmXl0Skrk23vPoVVd3xWqEpyiwsO46ewm/PrQ+bx9QxdOa5hAdp6TTxfvpN8b8xj83u98v3IPuflOq6P6ns0GffrApEnmBDjPPQeNGxd9vFo1z22AnByfRhQREZHy0QM3UvV8WCw6nQZvz9nMaz//DcBF7esy9vrT9WyZeFWY3cYVnRpwecf6/L7lMB//voOf1+9nyfYjLNl+hFpxEQw4I4lrujQMzbUa69eHJ5+Exx6DWbNg4kQ44wyzoHR37rkQGQnXXmuOSDZsaE1eERERKZEmuAlSfjXBzejRMHKk2Z42DS6/vEq6Sc/J58GvVjBr3X4Abu3ZlKcub0+Y3VbKkSKVty8lmy//3MkXS3ayP7VoxKxtvXiu6dKQq05vSL3EKAsT+pmtW6FFC8/3zjmnqHBM0rI2IiIi3qLZUMWDXxWLDz8Mr7xitn/7DXr18noX2w5lcOfHf7HpQDoRYXaeu+Y0Bp/ZyOv9iJQmr8DJnA0HmLIsmV83HCC3wLwl1WaDs5rV4JIO9bi4Qz0aVIu2OKnFFi40Z0tdt67kj595pjk5zhVXQJcuYNdTEyIiIhWlYlE8+FWxWLgGG5gLe3fp4tXTz9l4gPu/WE5adj51EyJ576audGmsiWzEeimZeUxbvYdvlyXz146jHh/rnJRIv9PqcXH7erSoHYut+C2aoWLdOvM5x0mTYO3aEz8eFQWHD5sT6YiIiEiFqFgUD35VLN5wA3zxhdnetAlatvTKafMLnLwxexPvzN2MYUDXJtV596YzqBOvW/3E/+w6ksnMtfuYuXYff+04ivu/vEnVo+nVuja9WtWmZ8uaxEeFWxfUSuvXm0Xj1Knm+qwAl11mzqjqbsQI81nHvn3NEUiHnkkWERE5FRWL4sGvisUrrzSfVQRz/bU6dSp9yn0p2dz/xXKWbD8CwI1nNWbklR2IcOhWNfF/B9Kymb3uADPW7OWPrUdct6qCOXlO18bVObdVLbo3q8HpjaoRFR5mYVqL7N4NP/4IjRrBpZcWvZ+fDzVqFE2clZAA559vzsTapw+0b3/iRDoiIiIhTsWiePCrYvGLL8xbzdLSYMwYc0SgEuZuPMCDX6/kSEYucZEO/jOgI/07N/BSWBHfyszNZ/HWw8z7+xDz/j7I1kMZHh+PCLPTKSmR7s1q0L1ZDbo2qR66I48Af/4J3buf/OM1a5oT5Zx7Lgwb5pVfTomIiAQ6FYviwa+KRS/JK3Dy6qy/ee+3LQB0aJDAOzecQdNasRYnE/GeXUcy+e3vgyzeepgl245wIM1zLUKbDVrViaNTUjU6JyXSuVE12tZLCK1R9W3b4Jdfil4HD558v6ZNi7aTk80vYAP9cklEREKLikXxEGzF4paD6Tz41QpW7k4BYGiPJjx+WbvQvD1PQoZhGOw4nMmSbeb6jUu2HWHnkcwT9osIs9OufjwdkxJpVz+BtvXiaV03PjRGIJ1OWL0aZs+GefNgwQI4csQsCHfv9rwl9V//grFjzY+deab56tYNunaF2rUt+xRERESqmopF8RAsxaJhGHz6x06en76O7DwnCVEOxgzsxKUd61sdTcQSB1KzWbk7hVW7j7n+PJaZV+K+SdWjaVsvnrb1EmhTL55WdeNoWjM2uH/J4nTCxo3mKGLfvp4fO/dcc8mOkjRpYs7U3LEjXHKJeSuriIhIkFCxWA6LFi1i4sSJzJ8/n+TkZAzDICkpiXPPPZehQ4fSs2fPKu1/69atfPTRR0yfPp2dO3eSnp5OgwYN6NSpEzfeeCNXX301jkrO7udXxeL+/RAfD9HR5Zp44kBqNo9MXsXcjeYtZj1b1uSVaztTPzHE16cTcWMYBjuPZLJydwprk1PYsC+NjfvS2JeaXeL+Nhs0SIymee1YmteKpVmtWJrXjqNZrVgaVIsmzB7Ek8M88wzMnw9//QUpKSffb8QIGD26aDs/H9580ywk27SBpCSt+ygiIgFFxWIZZGRkcP/99zNhwoRT7nfrrbfy1ltvERvr/Wfhxo4dy6OPPkpOTs5J9zn77LP57LPPaN68eYX78ZtisaCgaFr7c845+W/1i5mxei9PfLuao5l5RDrsPHZpW4b2aIo9mH+QFfGiY5m5rsJxw75UNuxLY8uBdFKz8096TESYnYbVo0lyvWI82rXjIoPjGnQ6YcsWs2j880/zz2XLIOP45EKTJsGgQUX7r19vzrJaKDoaWrc2C8fir/h4334uIiIiZaBisRQFBQVcdtllzJo1y/VedHQ0HTp0wOFwsG7dOlJTU10fu/jii/nxxx8JC/Pe7VrPPvssTz/9tGvbbrfTvn17atSowaZNm9i7d6/rY0lJSSxZsoT69St2u6XfFIspKVCtmtm++GKYOfOUux/LzGX0tHVMWZYMmJPYvHHd6bSqqx/ARCrLMAyOZOSy9VAG2w5msOVQOtsOZrD1UAY7DmeQV3Dq/w4iHHYaVoumXkIU9RKjqJMQabYToqhz/L3acZGBOdmO0wlbt5rPP/bs6TmL6ldfwfXXl+08hw+bS3sUWrUKDh2C5s3NEUmtCSkiIhZQsViKJ554ghdeeMG1fccdd/Diiy9S4/h/6hkZGYwZM4Znn33W45jnn3/eK/3PnDmTSy+9lMIvd48ePfjoo49o3bo1AE6nk0mTJnH77beTnp4OQM+ePVmwYEGF+vObYnHXLmjc2GwPHAjffHPSXX9as48R363hYFoOdhvcfX4LHujTOjB/8BQJMPkFTvamZLP7aBa7j2Ye/7OovTclC2cZ/7eoFRdB3YQo6sRHUiM2klpxEdSMi6BGbCQ14yKoGRtBzbhIasZGBMbzk7t2wc8/m0sAbdwIf/9tjkwWFHjuV6OGWSy6Gz4cxo8322Fh5r+HzZqZz0gmJZmvhg3NUcmWLX3z+YiISMhRsXgKe/bsoUWLFmRnm8/w3HzzzXz88ccl7jtixAiee+45AKKiotiyZQsNKjnNumEYdOnShZUrVwLQpk0bli1bRkxMzAn7zp49m4suusi1PWXKFK655ppy9+k3xeK6ddChg9keNgw+/PCEXQ6l5zDyu7VMX22OrLaoHctLgzrTtUl1HwYVkVPJK3Cy73gxuT81m32p2ex3vXLYl5LNgbTsUkcni4uNCDMLx7gIqsdEkBgd7vGqFuPZTjjejnRYXGTm5ZkjkRs3Fr3Cw+Hddz33u/his9AszdCh8NFHnu/ddZd5W2v9+lC3rjnaWbeu+apVyyw+RUREyqCitUFI3A/zxhtvuArFmJgY3njjjZPuO2LECCZOnMiuXbvIzs5m7NixjBkzplL9z5gxw1UogvncYkmFIkDfvn257rrr+OqrrwB48cUXK1Qs+o20tKJ2sb+YhmHw3Yo9PPPDWo5m5hFmt3FX7+bcd2GrwBhtEAkh4WF2GtWIoVGNkv/tAnA6DY5m5roKyUNpuRzKyOFIei6HM3I5lJ7DkYxcDqfnciQjl9wCJxm5BWQcySxxSZBTiQ4PcxWR8VEOYiMdxEU5iI883i58RXm2YyMcxB9/LzbSUfE7F8LDi55TPJVhw6BTJ3PNx8LXsWMn7peU5Lmdmwvjxp38vDabWTDWrWuOXPboUfSx3bvh99/Nkc6aNc0/a9SA2NhyTTImIiISEsXit99+62oPHjzYdetpSSIiIrj11lsZfXwmvClTplS6WJwyZYqr3axZMy6++OJT7j98+HBXsbhkyRJ2795NUvEfJAKF23Og7hM/7EvJ5slvV/PLhgMAtKufwMuDOnFaw0RfJxQRL7HbbcdHCSPp0ODU17JhGKTl5B8vJHM4lJ5LSmYex7JyScnK41hmHilZJb8MA7LyCsjKKzjprK9lFRFmJzoijJiIMKLDw4gu4c+YiDCiwt33cRAdXuz94x+LcNiJdNiJdISZfw4aTOT1QzxnmT161Ly1NTnZLOx274YLLvAMtmfPqYMbBhw8aL6KjzAuWgTXXXfiMeHhRYVjjRrmiOWkSZ77LFgAe/eav9wr/oqL02imiEiICfpicePGjWzevNm1fckll5R6zKWXXuoqFjdv3szGjRtpU9pvj09h+vTprna/fv2wlfKb3fPOO4/Y2Fgyjs/MN336dIYPH17h/i1VbGTRMAy+/HMX/5m+nrScfCLC7NzfpyXDe7cgPEzPJoqECpvNRkJUOAlR4TStVfaZp51Os8hMcSsm03PySM8pID07j/ScfLOdk0d6tls7J5+MnALSsvNJz8kjO88JQG6Bk9wsJylZJa9V6S0Ou82zkAy3E+mIIdLRjsh6HYjYZCdy25Kij9ltRH44n8isDCKyMgjPzCA8I43w9DTCU1MITz1GeMoxwo8eITw1kvDVewkPsxPusBO+O53whu0Jd+bjKMgnwplPeEE+4QV5hGfkEZ6aTPiW7YTXqU240/AsZN9+25zQ52Ti4szC8cYb4aWXPD92//3mn7Gx5ismxvPPwnb79uaIZyGn0yx+VYiKiPidoC8W3W//BHNimdKcccYZREREkJubC8CqVasqXCweOHCAffv2lat/h8NBt27dmDt3rqv/gOVWLO6KqsZjH/zBws3mBBCnN6rGy4M6aaZTESkzu93muv20MvILnGTkFJCem09Wbj5ZuU6y8grIzM0nO6+AzFxz5DIr13xlurWzjn88O69YO7eA3AInOXkF5OQ7yXebESjfaZCfa+4L5S1MI4+/akAs5st9ouzZu4Hdbm/UhZuKFXIn88SP2G3gCLMTEWbHkXQtjnuvwGEUEOYswOEs/NNJmLOAMMP802HUJmzc7zjsNsLsNvPPvdVx5Ocd3z+FMOcR13nCnE7zXIYTR/8rCWvT+vixdhw7thE2fjwOm0FYmB1HmB17mJ2wsDDsjjDsDgdhx/+0P/4YYRHh2G1gt9kI+3MJ9lWrsEc4sIeHExYebh4T7iDM4cDmCCPM4SCsbh1sZ59FmN2G3Wa+wtasxp6Xiz083Dz2+DnsEeFmH4Xt+Hjs0VHHj4Mwu63UX/qKiASLoC8W169f72pHRETQqFGjUo8p3G/Lli0nnKMy/QO0aNGiTMe1aNHCVSxWpn/LpaZSYLMz8YwreHlvA7KMw0SF2/m/i9twa89mwb0AuIj4LUeYncQYO4kxlSs6TyW/wGmOXOY7ycl3kpPnJCffLCRz8gvM7YLi75vFZq7rffNj+QUGecfPV9g2X0Xt3AKD/BLeL94uKDatrdOA3HwzJ44oiIsq2ye47YjndovuZTtuL7B3i+d7F9xatmMnrS72RhhEdDGbeZy8Dt+RB0sqNrt4SWyGE7thEGY4sR3/024Y2DCwGQZ2DHO7WjVsDgc2G9htYMvOxp6aau4H2F1/cvw9m/mew4E9qSE2m838uB1sBw5iz8gAm3mceYx5Xo6fw24DW2Ii9vr1zPeOF7i2v//Gbjhd+9lsNtf+5vmOv9e4EfbERPM9mw1bVib2rduK9nPlMf+02Wzmc7A2sLVrh80eVriJ7eABbAcPml+v48W162PH37MBxMRga9HcbB9/z7ZtK7asLNcjtjZsrmOxubXr1MFWp87x84HN6cS2YUNRn4XnsxX1V9imaVOz78L3UlOx7dnjuY97psJzhIVha92K4+nNjPv2mccX+xxxfa7H2wkJ2Bo2LPp6ALbNm7EV5Lt9rm5/z9w+f+rXx1a9WtF2TjZs3epxruIncGVo1QrCw4s+dOQwtgMHis7l1pfHKSIjoUULz2w7d0FGxgl5T8hdowa2evXc3jVg/YaSjyv2OdgaN4a4uKJ909PNW/eL5XTP7/pYu3aeX5P9++Ho0aJzl/B1BiAu1uwXt+/9li3Yjg8cnZjbrd96dc1b+wv3ycuDLVvNdt26xNSrRUxE4JZcgZu8jLZv3+5qJyUllfm3gY0bN3YVi+7nqEz/hecta/8nO0dJcnJyyMnJcW27rxlppa3Hcnj4hhdZmtQeDDi7eQ1eHNCpXLediYgEIsfxUbKYCKuTeHI6DfKcx4vIfKdnu8BJgWGQf7yozHcW/uks2i4o4f0CJwW7k8nPzsGZk0t+Ti4FObnk5+ZSkJtPfm4eBXl55OflU9D5dPITEovOt3cf+Uv+pMAwR2ALDMg3wGkYOI9vO202nPYwCnqfj2FAgWGYH9+2nYKDh8yP2+w4bXYK7PbjbRsFNjuGzU5BdAzOmjVxGmZupwHO1FTz/K79zWMNW+mPRBg2OwU2KKCUW2ezCwD3JVZsEFXGZ/P3pRV7IxIiIks/Lh3YdKjYm8VmFzeK/Vloa/rxE7grY97fd5bw5sknxCrihB2bS3i/DBfOriNAsV9acLLvn+HZ/ntrGbKdxPKVpe9TokxgX6l7laz434dyWLC04seyv4LHHQFK+r6WRfG/v+Xw44GKH8uW0ncp0ameMd/No5e05e7zyzZY5I+CvlhMc7sNMjGx7JOnuE8p636OyvRfngzl7f+FF17gmWeeKV84H0i/eiDLP1tHXBg8dkFTbriwPXaNJoqIWMZutxFpDyPSgXl3q7d0K9svQ0/UEe656NS7FBRATo75zKO7nY3MyYCys82PZ2eby5p4vHIgqTZc0dfz2JdfNkcdiu1v5OXhzMvHmZdHQX4Bxj9uo+CyyyhwGhiGQcGevThvusksNJ1mQWu+nBgFBTgNA6PAiWEYOD/7HKNRkvlYJgbG5CkYL7xgFqs2G0bhC5urwDWwYdSvj/HZZ2ZBbJgTQhnPPYex6HecNjuGjeN/Fh5rnsdps2Nc2AfjttvMHMeLbuOuuzHycs2Pg+tPo7BP2/Fz3PoPjNM6mscAzq3bMMaPLzo3bv0e/+W7AWbm//s/jIgIDMP8XJm/AH7//Xg/xz8vm81jG4D69TEGDTLfP36s8c0UjOOjkoY59HTCsYYNOL0LRufTzf4MMHJzMb7+2nVuw4ZHv7jl4IILMKrXMPszwNi9G2PpUldfrs/NrT8DG0Z4OFx4ofnxwrxr12Ps2+vZl0d/x4+tVQvj+Pra5rHA8uUYeXlFuY5z1fOFWRo3hho1Xet1k51trvnqvo/baJdho+i9Vq0x3JYbMo4cgQMHwPV1cu+36GtHRARGo8bH3z/e7959GFlZxY4rod/4BKhWjaIF+gyM5D0lfm4e2W1AteoYERFF++bmQkrKiV8Tj36Pv1etGkUHAtlZGLl5pfZrOMIgMtKV1wDz3xTDcPs6FctaeC673ePvjPkXx1mmXzwFgqAvFgsXuAdz3cSyio6OLvEclem/PBnK2//jjz/Ogw8+6NpOTU0t0y23Va1Tx2a8fG04Z7eoScNq0aUfICIiUlxY2ImFIkDjxuarIh5+uMS3bUDY8VeJNym3bAKL51esz/tuNV+GYU7sU1Dg+crPN/8EqFPL89gXHzXnASg89mR/1qoFTRt6Hvv6o+Z5T3WcYUDXruYsuYXaJ0DsoJL3L96+tJ05426hmmnQKN9sGwZFP4UXa9evD1ed5pk3czXsiy/92HNbQs/ORcdlZcGu2ac+prA9oJ3nkjWrIiBrKRjOE/d3346KgtvO8sz71Q74s/gIZwk614Obe3q+N+pnz5njT+bys6G7263ee/bASz+WfhzAXTeA+2DFr7/Cd7+Vflz9+vDIUM/33n7bXFe2NGdfCNf0Kdp2OosmwXJXfLl3A7jhn9CuXdF7a9fCO++U3ifAqP96bn/9tfn5lqZ9+xPzPf20+QulU+UFGDwY+rr9MurwYXjsMbM9ZAgE8KgigM0wSvqsg0ffvn355ZdfAHOW0Xnz5pXpuJtvvplPP/0UgD59+jB79uwK9f/cc88xYsQI13ZBQQF2e+m/afjggw+4/fbbAQgLCyM/P79c/VZ04U0REREREQkuFa0NgmN89BRi3H4TmZ1d9vW43PeNja3483UxxX4TWtYM3upfRERERESkIoK+WIyLi3O1s47fY10WmZmZJZ6jMv2XJ4O3+hcREREREamIoC8Wa9Uquud/7969ZT7OfW3Emu6LB1ei//Jk8Fb/IiIiIiIiFRH0xWKbNm1c7cOHD3uM2J3KruPruQC0bdvWK/0D7NxZ0tTSVde/iIiIiIhIRQR9sdjOfTYlYMWKFaUek5yczMHjUzaXdI7yaNWqFQ5H0aSzZekfYPny5V7pX0REREREpCKCvljs3r07kZFFC0ktWLCg1GPmzy+aEjsqKoru7lMVl1NERARnnVU0xXJZ+t+3bx+bNxctZNqrV68K9y8iIiIiIlIRQV8sxsXF0adP0Tovn332WanHuO/Tp0+fSs9GetVVV7nas2fPZn/xNVtO0X+1atVULIqIiIiIiM8FfbEIMGzYMFd71apV/PDDDyfdd9myZcyYMaPEYytqyJAhrtHNvLw8XnrppZPum56ezptvvunavvHGGwkPL3FZYBERERERkSoTEsXioEGD6Ny5s2t7+PDhbNiw4YT99u7dy0033URBQQEAp59+OgMHDizxnNu3b8dms7leo0aNOmn/SUlJDB8+3LU9duxYJk+efMJ+eXl53Hrrra5JcKKjo3niiSfK9DmKiIiIiIh4k6P0XQKfzWbjf//7H7179yYrK4u9e/dy1llncffdd9OrVy8cDgdLlizh7bffdt0iGh0dzfjx47HZbF7JMGrUKGbMmMGmTZsoKChg8ODB3HDDDVx99dXUqFGDjRs38u6777Jq1SrXMS+//DINGjTwSv8iIiIiIiLlYTMMw7A6hK9MmTKFm266iaysrFPuFx0dzaeffsqAAQNOus/27dtp1qyZa3vkyJGnHF0E+Pvvv+nbt6/Hshgn88gjjzBmzJhS9zuZ1NRUEhMTSUlJISEhocLnERERERGRwFbR2iAkbkMtNGDAAJYuXUrfvn1LHDG02Wz06dOHv/7665SFYkW1bt2aVatWcdtttxEdHV3iPu3ateO7776rVKEoIiIiIiJSWSE1suhu165dLFy4kOTkZAAaNmxIz549adSokU/6T0tL49dff2XXrl1kZGRQv359OnbsSJcuXbxyfo0sioiIiIgIVLw2CNliMdipWBQREREREah4bRASE9yEosLfAaSmplqcRERERERErFRYE5R3nFDFYpBKS0sD8NlttSIiIiIi4t/S0tJITEws8/66DTVIOZ1O9uzZQ3x8vNeW/6io1NRUGjVqxK5du3RLbJDQ9zQ46fsafPQ9DU76vgYffU+Dj799Tw3DIC0tjQYNGmC3l32OU40sBim73U5SUpLVMTwkJCT4xcUi3qPvaXDS9zX46HsanPR9DT76ngYff/qelmdEsVBILZ0hIiIiIiIiZaNiUURERERERE6gYlGqXGRkJCNHjiQyMtLqKOIl+p4GJ31fg4++p8FJ39fgo+9p8AmW76kmuBEREREREZETaGRRRERERERETqBiUURERERERE6gYlFEREREREROoGJRRERERERETqBiUU6waNEihg8fTvv27UlMTCQhIYH27dtz5513snDhwirvf+vWrTz99NN07dqV2rVrEx0dTYsWLbjmmmv45ptvyM/Pr/IMweLYsWN8++233H///fTq1Yt69eoRGRlJXFwcjRs35sorr+SNN97g6NGjVdK/zWYr9+u9996rkizBYu7cuRX6um7YsKFK8uh6rZzt27dX6Pvp/tq+fXulc+haLZ+DBw8yY8YMRo8eTf/+/alfv77H1+ajjz6q8LlXr17Ngw8+SKdOnahRowZxcXG0adOGG2+8kZ9++sl7n8Qp7Nu3jzFjxtCjRw/q169PVFQUTZs25ZJLLuGjjz4iKyvLJzl8ydvf08zMTGbMmMHDDz9M3759SUpKIjo6mpiYGBo2bMjFF1/M888/z549e6rmEwKaNm1a7uv6scceq7I8VvDm97Wi/15X5XXrk2vVEDkuPT3d+Mc//mEAp3zdeuutRnp6epVkeOONN4zIyMhT9n/22WcbW7ZsqZL+g8X69euNK664woiIiCj1+wkYMTExxuuvv244nU6v5ihL38Vf7777rlczBJs5c+ZU6Ou6fv16r2fR9Vp527Ztq9D3s/DlcDiMI0eOVDqHrtWy2bt3r9GkSZNSvzYffvhhuc+dl5dnPP7444bdbj/luS+//HLjwIED3v/kjvviiy+MxMTEU2Zo06aNsWzZsirL4Eve/p7u27fPuO6664yYmJgyXUfh4eHG448/buTk5Hj9cyvL51X89eijj3o9hxWq4lqt6L/XM2bMqJLP0VfXqgMRoKCggAEDBjBr1izXe9HR0XTo0AGHw8G6detITU0F4MMPPyQ5OZkff/yRsLAwr2V49tlnefrpp13bdrud9u3bU6NGDTZt2sTevXsBWLx4Mb1792bJkiXUr1/fa/0HkzVr1jBt2jSP98LCwmjZsiV169aloKCA9evXc+TIEcD8Dei///1v1q5dy/jx47HZbF7P1KtXL6Kjo0vdr3Hjxl7vO1hFRUXRu3fvMu0bFxfn1b51vXpHdHQ0/fr1K/P+TqeTn3/+2bXdr18/qlev7tVMulZPLjs7mx07dlTJuYcPH86ECRNc2+Hh4bRv3564uDg2bNjA4cOHAZg+fTp9+/Zl4cKFXr+uP/nkE2655RaP91q3bk39+vXZvn2763PfuHEj559/PosWLaJDhw5ezeBr3v6e7tq1i6+++srjPZvNRvPmzalXrx5hYWEe/0bm5eXxwgsvsGLFCqZOnUpERITXsrjr1q0bNWrUKHW/Nm3aVEn/vlaV12qhsv7bXbt2ba/37dNr1UvFrQS4xx9/3OM3EXfccYdx+PBh18fT09ONESNGeOzzxBNPeK3/n376ybDZbK5z9+jRw9i4caPr4wUFBcaXX35pxMXFufbp2bOn1/oPNpMmTXKNOlx99dXG1KlTjZSUFI99nE6nMXXqVKNhw4Ye39f//ve/Xsvhft5t27Z57byhzH1ksUmTJpZk0PVqnZkzZ3pcV19//bVXzqtrtWzcRxZq165tXHLJJcZTTz1lTJ06tVIji+PGjfM4vn///sbu3btdH8/NzTXeeustw+FwuPa54YYbvPq5rVq1yuNOgdatWxt//fWXxz6zZs0y6tat69qnefPmRlZWlldz+Jq3v6d//vmnARg2m83o06eP8dlnnxkHDx48Yb+5c+ca7du39+jjkUce8ern5j6yNmfOHK+e299VxbVafGTRKr6+VlUsipGcnGxERUW5/kLdfPPNJ933qaeecu0XFRVlJCcnV7p/p9NpdO7c2WPIPCMjo8R9f/75Z48LdcqUKZXuPxhNnTrVuP32240dO3aUuu/OnTuNevXqub6mtWrVMnJzc72SQz+Aep/VxaKuV2vdcMMNrq9n9erVjezsbK+cV9dq2aSkpBiTJk0ytm/ffsLHKvoDaEZGhse/weeff76Rn59f4r7vv/++az+bzWYsXbq0op/KCa688kqP/wf27dtX4n5r1qzx+EH1tdde81oGK3j7e7p06VJj4MCBxtq1a0vd99ixYx4FY0RExEm/7hURysViVVyr/lIs+vpaVbEoxsMPP+z6ixQTE+MxolhcTk6O0ahRI6/+Fmz69OkeF99PP/10yv2vu+46177du3evdP9y4m+1Z8+e7ZXz6gdQ77O6WNT1ap2UlBQjOjra9fW8++67vXZuXauVV9EfQN955x2PAnDdunWn3P+ss85y7T948OBKpjatXbvWI/977713yv0fffRR17716tUzCgoKvJLD31RmtLisit8t8P7773vt3KFcLJ5KIBeLVlyrmg1V+Pbbb13twYMHn/Ke9oiICG699VbX9pQpUyrdv/s5mjVrxsUXX3zK/YcPH+5qL1myhN27d1c6Q6i78sorPbarauZMCXy6Xq0zadIkj5nthg4damEa8Rb3a6p37960a9fulPu7X1M//vgjOTk5Xs0QFxfHjTfeeMr977zzTld73759/P7775XOEKr69Onj8Yyw/v+VU7HiWlWxGOI2btzI5s2bXduXXHJJqcdceumlrvbmzZvZuHFjpTJMnz7d1e7Xr1+pk6ucd955xMbGlni8VEzxXxAUTmYkUpyuV+tMnDjR1W7bti1nnXWWhWnEG9LT05k3b55ru7z/B6enpzN37txK53C/Ls8999xSJ85p3ry5x0QoxSdUk7ILCwsjMTHRta3/f+VUrLhWVSyGuJUrV3ps9+jRo9RjzjjjDI/ZulatWlXh/g8cOMC+ffvK1b/D4aBbt25e6V9MxWcMq1OnjkVJxJ/perXO1q1bWbBggWtbo4rBYd26deTl5bm2y3JN1atXj6ZNm7q2K3tNGYbB6tWry5Wh+H66risuKyuLAwcOuLb1/6+cjFXXqorFELd+/XpXOyIigkaNGpV6TPH93M9Rmf4BWrRoUabj3PerTP9iKn47cVn/ASqPhx9+mA4dOpCQkEB0dDRJSUlccMEFjBo1im3btnm9v1Bw7NgxBg8eTNOmTYmOjiY+Pp5mzZpx9dVX8/bbb3v9N9S6Xq3z8ccfYxgGYC5TcvPNN1dZX7pWfccfrqmdO3eSkZFhaYZQ9t133+F0Ol3bVfH/L8Arr7xCly5dqFatGpGRkdSvX59zzjmHxx57zKMAkbK75ZZbaNWqFbGxscTGxtK4cWMuueQSXnrpJY9fAHiLVdeqisUQt337dlc7KSmpzOvrua+v5X6OyvRf/Ly+6F8gJSWFsWPHurY7depE+/btvd7PN998w7p160hLSyM7O5vk5GTmzp3LM888Q+vWrbnrrrs8nseS0qWkpDBp0iR27NhBdnY26enpbN++ne+++4777ruPxo0b89Zbb3mtP12v1jAMg48//ti13bdvXxo2bFhl/ela9R3368HhcJR5LVJvXlPeuK537Njh+mWGlF1+fj7/+c9/XNt16tShT58+VdLX9OnTWbFiBSkpKeTm5rqeXxszZgydO3dm0KBBrrWXpWw++eQTNm/eTGZmJpmZmezatYuZM2fy6KOP0qRJE0aMGEFBQYHX+rPqWnWUa28JOmlpaa62+z3zpUlISCjxHJXpvzwZvNW/wEMPPeRxa+Fzzz1XJf3UqlWLFi1aEBcXR0pKChs2bCA9PR0w/8McN24cS5YsYc6cOeX6uxjqmjZtSsOGDYmMjOTQoUOsW7eO/Px8wCwm77//flasWMEHH3xQ6b50vVpj/vz5HiN6VX0Lqq5V33G/HuLj47Hby/Y7fG9eU964rp1OJ5mZmR7PJ0vpXnzxRY9RvaeeeorIyMgq6SsxMZHWrVuTkJBAeno6mzZtchWHhmEwefJk/vrrL+bPn1+mu8wE6tev77qz5+jRo6xfv57s7GwAsrOzee655/jzzz/54YcfCA8Pr3R/Vl2rGlkMcYU/AABERUWV+Tj3mbvcz1GZ/suTwVv9h7r333/fo4i47rrrTpgZtTLat2/PG2+8wZYtWzh48CCLFy9m9uzZ/Pnnnxw9epRp06bRqVMn1/7Lly/n+uuv91r/wchut9O3b18+++wzDh8+zLZt21iwYAG//PILK1eu5OjRo7z77rvUqlXLdcyECRMYM2ZMpfvW9WoN94ltEhISuOaaa7zeh65Va1j9f3BJx1fkuvZGjlAzc+ZMRo4c6do+55xzuOeee7zaR9OmTXnuuedYs2YNx44dY8mSJcyePZvFixdz6NAh5s2bR69evVz779ixgyuvvJLc3Fyv5ggWNpuN7t2787///Y89e/awZ88eFi1axC+//MKyZcs4duwYn3/+ucczxTNnzuT+++/3Sv9WXasqFkNc4QgEmLfAlJX7vu4P51em//Jk8Fb/oWzevHnce++9ru1mzZoxbtw4r/axdu1aHnjgAZo3b37CxxwOB5dffjl//PEHl19+uev9n376iR9++MGrOYJJr169+Pnnn7nhhhtKXOYmLi6Ou+66i2XLlnn8hzV69Gj2799fqb51vfpeZmYmkyZNcm0PHjz4hP/4vUHXqjWs/j+4eIby5Ci+n67tslu/fj1DhgxxPatYvXp1Pv/8c8LCwrzaz9y5c3nyySfp0KHDCR+z2Wycd955zJkzhzvuuMP1/sqVK73+s0CwaNKkCX/88Qe33357ibeMR0ZGMmTIEJYtW0bXrl1d748bN84rk0BZda2qWAxxMTExrnbh0HlZuO9bmdtO3PsvTwZv9R+qVqxYQf/+/V2/PaxTpw4//fSTJbeURUVF8cUXX1C3bl3Xe958zi5UNWrUiK+++sq1nZmZWelbUXW9+t63337rceuRlbOg6lr1Pqv/Dy6eoTw5iu+na7tsdu3aRb9+/Th69Chgfv2nTZtGkyZNLMljt9v573//S8eOHV3v6bqunOrVqzNlyhTXyJ9hGLz99tuVPq9V16qKxRDnvj5LeSYsyMzMLPEclem/PBm81X8o2rhxI/369SMlJQUw/1GbNWsWrVu3tixTfHw8d999t2t7/vz55frBSUrWvXt3zj//fNf2zz//XKnz6Xr1PfdbUFu0aMG5555rYRpdq95m9f/BJR1fkevaGzlCwf79++nbty+7du0CzJGoqVOncs4551iay+Fw8NBDD7m2N23adMKSWlI+jRs39rhVv7L//4J116qKxRDn/lzT3r17y3yc+4QoNWvW9Er/5cngrf5DzbZt2+jbt69rSuf4+HhmzJhB586dLU4GF1xwgaudnZ3t+s9UKsf96/r3339X6ly6Xn0rOTmZX375xbXtL2sr6lr1HvdrKj09vczPEnnzmvLGdR0fH++VCTyC2ZEjR7joootc/w47HA6++uorLrroIouTmdyva6j8/xfi+TXdvn17pZ8FtepaVbEY4tq0aeNqHz58+ITfPpyM+w8Hbdu29Ur/YK4h48v+Q8nu3bvp06cPu3fvBopufTnrrLMsTmaqV6+ex/ahQ4csShJc3L+ulf2a6nr1rU8++cT1TJPNZuOWW26xOJFJ16r3+MM11bp1a49ls3Rde19qair9+vVzzXxqt9v59NNPueqqqyxOVkTXtfcV/5oePny4Uuez6lpVsRji2rVr57G9YsWKUo9JTk7m4MGDJz1HebRq1crjwduy9A/mTHze6D9UFN76Ujj9fuGtL+6zoFmt+C8qit+bLxXj/nWt7NdU16tvud+Cev7551v2TFNxula9pyL/B+fl5bF27dqTnqO84uLiSEpKKlcG0HVdVhkZGVx22WX89ddfgPmLnwkTJnDddddZnMyTrmvv8/bX1KprVcViiOvevbvHmj4LFiwo9Zj58+e72lFRUXTv3r3C/UdERHiMbJWl/3379rF582bXtj8VPP7o8OHD9O3bl40bNwIQHh7ON9984ze3vhRy/+EHzEl3pPLcv66V/ZrqevWdJUuWsGHDBte2v9yCCrpWval58+YeP/yV5ZpaunSpxw+h3rim3M9Rlgx5eXn88ccfXs0QjLKzs+nfvz8LFy50vfff//7Xr67nQrquvc/9axoZGemVSQStuFZVLIa4uLg4+vTp49r+7LPPSj3GfZ8+ffpUegY099swZs+eXer0/u79V6tWTf9JnUJKSgr9+vVjzZo1AISFhfH5559zxRVXWJzsRF9++aWr3bRp0xKnpZbyyczM5Pvvv3dte2MSBV2vvuE+qhgbG8vAgQMtTONJ16p39e/f39WeNGlSqc81uV9THTp0oEWLFpXO4H5dr1+/3mMkoiTff/+9a5Zeu93u1fV5g0Vubi4DBw7k119/db33+uuvc9ddd1mY6uTcr+uoqCi6dOliYZrAZxgGX3/9tWu7R48eXjmvJdfq/7d3/zFVlv8fx19HCDwKWigKGBh/hFiAiLJWzMwgNdmag1ZZm7qcbs7miGb9k+lWa6kl+GOllrL1A0uBgrWagdqcuoniUXSZoxU/QlMPYOOHgMj9/ePz+dxfz24OgucgHHw+trNdF7zv636f++Y6h/e573PfBu57+/btMySZj5KSErexFRUVhp+fnxm7f/9+j9dfV1dnBAYGmmNmZ2e7jW1ubjaioqLM2FWrVnm8/uGqpaXFSElJMbfViBEjjK+//nqw0+pRcXGxy99gVlbWYKc0LGRnZ7ts1x9++MHjMZmvA6+jo8MICQkxt9uSJUsGOyUTc9W927dLXl5en5crLy93WXbr1q1uY+vq6ozg4GAzdtOmTV7I/D/vF6Ghoea4GRkZbmO7urqM5ORkMzY9Pd0rOQxFd7tPu7q6jMzMTJflP/zww4FL1EMnT540AgICzFwXLlw42CkNqLvdr/2xdetWl/Xk5uZ6ZdzBmKsUizC6u7uNadOmmX9M4eHhxoULFyxxly5dMqZOnWrGJSYmGt3d3T2O+ddff7lMknXr1vWaw+rVq81YPz8/o6CgwBLT2dlpvPjii2ac3W436uvr7+o5D3ft7e1GWlqaua1sNpuxe/duj8ft6369fv26kZGRYZw6deqOY+bn5xujR482xxw1apRx+fJlj3Mdjg4cOGBkZ2cbdXV1vcZ1dnYa77zzjsu+SkpKYr76iIKCApf9cejQoX6PwVy99zz5B/SFF14wlw0KCjKOHj1qifn333+NWbNmmXFhYWFGW1tbn3O604cOmzdvdonPycmxxHR3dxtZWVku7y0VFRX9eao+5W72aXd3t7F48WKXZd97770Byam3fZqZmWkcOnTI7ev+/5SVlbkUHzabzXA4HF7Ldyi6m/16/vx54/XXXzd+//33XuO6u7uN3NxclwMrERERPj1X//9KBbhv2Ww2ff7555o9e7Zu3Lihy5cv64knntDKlSv19NNPy9/fX+Xl5dq+fbt5ypndbteuXbtcrsrkifXr1+vnn39WVVWVbt26pZdeekmvvvqqFi5cqJCQEF28eFGfffaZKisrzWU2bdqkiIgIr6x/uNmyZYvKysrM/oMPPqh9+/a5nBLRm+eee87lnkv9ZRiGioqKVFRUpNjYWM2bN0+JiYkKDw/X6NGj1dzcrHPnzqmgoEAnT540l7PZbMrLy7NcQQz/0dbWps2bNys3N1cpKSmaPXu24uLiNH78eAUEBMjpdKq8vFzffPONy9XPQkJClJ+fz3z1Ebefgjp58mSXe2V6G3O1/5YvX66vvvrqjjE9nW7o7p6UW7Zs0fHjx+V0OtXS0qLU1FQtW7ZMc+fOVVBQkCorK7Vt2zbzImUjRozQrl27ZLfbPX9C/7Vq1SoVFBTo+PHjkqQ333xTBw8e1GuvvaawsDBVV1dr9+7dLt+Teuutt5SUlOS1HAaLN/fp/v379eWXX5r9kSNH6sSJE5o/f36fcklISNDGjRv7FNubsrIyFRYWKioqSgsWLND06dMVGRmp4OBgtba26uLFiyouLnY5TVaSNmzYoMTERI/XPxR4c7/evHlTe/bs0Z49ezRjxgw9++yzmjZtmiZMmCC73a6mpiY5HA7t3bvX5fvmgYGB+vbbb317rt5ViYlhqbCw0LDb7S6fVvT0sNvtRmFhYa9j9fdIhWEYxsWLF43IyMg7rl+S8fbbb3vpWQ9P69at69N2dPdw96lWX/drU1NTv9cZHBxs5OfnD9xGGQa+//77fm/XRx991Dh9+nSv4zJfh44rV64Y/v7+5rZbu3btXY3DXB04S5YsuevX1t4cO3bM5fRjdw8/Pz9j27Ztfcq1L6/rt7t69aoRHx/fp+eyaNEi49atW33KY6jz5j7Ny8vz6P139uzZveba1306duzYfq03ICDA+OSTTzzckkOLN/erw+Ho9xhhYWFGaWlpn3IdynOVC9zAlJGRoYqKCqWlpfV4BMJmsyk1NVWnTp1SRkaG19cfExOjyspKLVu2zO0nMFOnTlVxcbE2bNjg9fXDe+x2u1asWKHHH3/8jkezxo4dq9WrV+v8+fNatGjRPcrQN8XGxurll192uXqiO4888og2btwoh8MxIBcqYL4OjPz8fHV1dZn9gb63InN16HjqqadUWVmpzMxMl1vU3C45OVlHjhzRG2+8MSA5hIaGqry8XGvWrHF75cbJkyfriy++UH5+vkaM4N/IoWrFihVKSkqSn59fr3F2u11Lly6Vw+FQdnb2PcrO94SHh2vx4sV9uqDUxIkT9e677+rcuXNKS0sbkHzu5Vy1/beaBVzU1dXp2LFjqq+vlyRNmjRJKSkpioyMvCfrb25u1qFDh1RXV6fW1laFh4crPj6eq3P5oKamJp05c0ZXr16V0+nU9evXNWrUKIWEhCghIUEJCQl3fDODVW1trX777Tc5nU45nU61trZqzJgxmjBhgmbOnOmVKyT2FfN1eGCuDh3Xrl3TkSNH9Pfff6uzs1MRERGaOXOmpkyZcs9yaG9v16+//qrq6mo1NTVp4sSJio2N1ZNPPum1U9ox8FpaWuRwOPTPP//I6XSqqalJgYGBeuihh/TYY48pKSlJAQEBg52mT7ly5YoqKyt17do1OZ1ONTc3KygoSOPHj9f06dM1derUezpHBnquUiwCAAAAACw4fwAAAAAAYEGxCAAAAACwoFgEAAAAAFhQLAIAAAAALCgWAQAAAAAWFIsAAAAAAAuKRQAAAACABcUiAAAAAMCCYhEAAAAAYEGxCAAAAACwoFgEAAAAAFhQLAIAAAAALCgWAQAAAAAWFIsAAAAAAAuKRQAAAACABcUiAAAAAMCCYhEAAAAAYEGxCACAD1u/fr1sNptsNptiYmLU2dnZr+UPHDhgLm+z2XT16tUByhQA4GsoFgEA8FFVVVX66KOPzH5OTo4CAgL6NcbMmTNd+kePHvVKbgAA30exCACAj1q1apU6OjokSfPnz1d6enq/xxg3bpyioqLM/rFjx7yWHwDAt1EsAgDgg0pLS1VaWmr233///bseKzo62mxfuHDBo7wAAMMHxSIAAD5o7dq1Zvv555+3nE7aH5MmTTLbf/zxh0d5AQCGD4pFAAB8zMGDB3XixAmzv2bNGo/GCw0NNduXL1/2aCwAwPBBsQgAgI/ZsWOH2Y6OjtYzzzzj0Xg2m81s/+87kAAA+A92AgAAoO8aGhpUXFxs9hcvXuxS7N2utbVVN27ckCSNGTPG7ZVSDcPosQ0AuL9xZBEAAB9y8OBB3bx50+zPmzfPbezSpUsVGhqq0NBQnTp1ym3cpUuXzPbEiRO9kygAwOdRLAIA4EMOHz5stkePHq3k5GS3sSdPnjTbcXFxbuNqa2vN9u230QAA3N8oFgEA8CHnz58323FxcfL37/kbJfX19aqpqZEkhYWFacyYMT3GdXV16dy5c2a/t+ITAHB/oVgEAMCHVFVVme0pU6a4jbv9HowPP/yw2ziHw6G2tjazn5KS4mGGAIDhgmIRAAAf0d3drStXrpj93r5fWFJSYrZDQkLcxv34449m29/fX6mpqR5mCQAYLigWAQDwEe3t7S79wMDAHuMaGxv1008/mf0HHnigxzjDMLR3716zn5aWpnHjxnkhUwDAcECxCACAj/Dz83O5TUZjY2OPcdu3b1dHR4cZ29DQ0GNcSUmJy2mty5cv92K2AABfZzO4oRIAAD4jLCzMPBU1ISFBZ8+edfl9TU2N4uLi1NLSojlz5ujw4cMKCgpSQ0ODy30Wr1+/rhkzZujPP/+UJMXHx+vs2bNu79kIALj/cGQRAAAfMmvWLLNdWVmpHTt2mP3q6mqlp6erpaVFMTExeuWVVyRJLS0t+vjjj824mpoaLViwwCwU/fz8tHPnTgpFAIALjiwCAOBDSktLNXfuXJefxcbGKiQkRBUVFebpp7/88ovCwsIUHx9vxiUkJGjkyJE6ffq0urq6zJ/n5OQoKyvrXj0FAICPoFgEAMDHZGdnKycnp8ff+fv769NPPzW/f5iZmamioqIeY4OCgpSbm6tly5YNWK4AAN9FsQgAgA8qKirSzp07debMGTU2Nio0NFRz5szRmjVrlJiYaMa1t7frgw8+0Hfffafa2lqNGjVK0dHRSk9P18qVKxURETF4TwIAMKRRLAIAAAAALLjADQAAAADAgmIRAAAAAGBBsQgAAAAAsKBYBAAAAABYUCwCAAAAACwoFgEAAAAAFhSLAAAAAAALikUAAAAAgAXFIgAAAADAgmIRAAAAAGBBsQgAAAAAsKBYBAAAAABYUCwCAAAAACwoFgEAAAAAFhSLAAAAAAALikUAAAAAgMX/AU+br6wL0qkgAAAAAElFTkSuQmCC", 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7dmjTxtdRiYiIiIdFHVe/ICMjo1zPHX9WoCdn7o7nOlZ59/+V9z5feu2118jNzQWMpbvLli07ZQXZ8n6tahJVQ5WaZ8ECWLgQJk48ceO7v7Ba4d//NtoOB7zxhm/jEREREa8IDw8nJCTEeb179+5yPbdz585S16c6usLdmjZt6myXt7KouyqQetKvv/7qbD/xxBOnPWokKSnJ0yH5HSWLUvMsW1bSLke1LJ8ZN87YlwHw4YdQSzZKi4iI1HY9evRwtl0ro56K6zmHderUoXnz5u4O66R69erlbC9atKhc+/sWLlzowYjcw7Vwj+uf8WSWuf6MWUsoWZSaJSenpMR3+/Y+3cB+WnXrGsVtADIzjYRRREREarz+/fs72zNnzixXsZTPPvvM2T7nnHMwmUweia0sF198sbN98ODB0x5In5+fz0cffeThqKquoKDA2T7d36fdbufTTz/1dEh+R8mi1CwrV4LNZrTPPtu3sZTHXXeVtN94AypYiUtERESqn5tuusnZPnjwIK+99top7585c2apGchbbrnFU6GVqXPnzvTp08d5ff/993PkyJGT3j9x4kT27NnjhciqJjY21tleunTpKe995ZVX2LVrl6dD8jtKFqVmcV0eUB2SxY4dYehQo71rl1HsRkRERGq0du3accUVVzivH3vsMWbNmlXmvX/99Rc333yz87pbt24MGzbM4zEe7+WXX3bOvu3evZtzzz2XP//8s9Q9x44d4+677+b5558v95EgvjRw4EBne/LkySSe5IzO6dOn8/DDD3srLL+iZFFqFtf/aPnzfkVXd99d0n77bd/FISIiIl7z1ltvOQuq2Gw2Ro0axciRI/niiy9YtGgRs2bNYty4cfTv399ZCTU4OJiPP/4Yi8Xi9XjPPvtsJk6c6LzeuHEjZ599Ni1btmTQoEGceeaZNGrUiNdffx2ADz74oNTzxx/94Q/uvPNOZwK8f/9+evToweTJk/n1119ZuHAh77//PkOGDHEerzFu3DgfR+x9OjpDag6Ho2RmMTra2LNYHVx4ITRrBnv3ws6dkJ0NoaG+jkpEREQ8KCYmhoULFzJ06FD2F50r+e233/Ltt9+WeX9ERAQ//PADXbt29WaYpUycOBGr1cpTTz3l3O+3e/fuUhVdQ0JCeOeddxhavHKqyPFHhviDnj178vTTT/P4448DcPjw4VIJsaurrrqKRx99lPfee8+bIfqcZhal5ti9Gw4dMtp9+oCbD6T1GIvFOBfyl1+Moz6UKIqIiNQK7du3Z+3atdx1112EhYWVeU9AQADXXnstGzZs4Nxzz/VugGV47LHHWL16NXfccQdt2rQhNDSUqKgounTpwgMPPMD69eu58cYbOVT8MxlGAhnqpz/fPPbYY7z//vvExMSU+flGjRrx2muv8dVXX3m1qJC/MDkcDoevgxD3S09PJyoqirS0NCIjI30djnd89RVcc43RnjgRJk3yaTgiIiLulJuby+7du2nRogXBwcG+DkfcLDc3l0WLFrFr1y6OHj1KZGQkTZs25dxzz62WP8vNnDnTuS+zT58+J+xv9De5ubksXryYjRs3kpOTQ0xMDK1bt+acc87xybJf17jc8X1f2dxAy1Cl5ujfH6ZPNyqinn++r6MRERERKbfg4GDOr0E/v7juWexbDepIBAcHM3To0BOWz9Z2Shal5oiLM84tLD67sDrbu9fYxygiIiLiJxwOR7mWYn788cf8/PPPzuuxY8d6MCrxpGqyqUuklvjlFxg0CJo3h02bfB2NiIiIiNPkyZO59dZbWbhwIbbic61d7N+/n/vuu69UcnjppZf6tCiPVI1mFkX8yebNsHCh0Z46FaZM8Wk4IiIiIsVycnJ47733eO+99wgODqZdu3bUq1cPm81GYmIiO3fuLHV/s2bNmDp1qo+iFXeoVTOLhw8fZs6cOUyePJkRI0YQGxuLyWRyvj766COvxLFr1y6efPJJevbsSYMGDQgJCaFVq1ZcfvnlfPPNN2X+pkZOY+NG+PFHSEw0jtCorsaMgZAQoz19OmRl+TYeERERkSJml0rzubm5rF27lt9//51FixadkCgOGjSIv/76y3mWpFRPtWJm8cCBA/Tp04e9e/f6OhSmTJnCQw89RF5eXqn3d+3axa5du/juu+/o06cPn332GS1btvRRlNXQZ5/Bc88Z7R9/hGHDfBtPZdWpY1R0/fBDSEuDr7+Gm27ydVQiIiIiPPXUUwwYMIB58+bxzz//sHPnTo4ePYrNZiM6Opq4uDj69evHFVdcwXnnnefrcMUNakWymJub6xeJ4tNPP82TTz7pvDabzXTs2JG6deuyfft2kpOTAfjrr78YOHAgK1asIDY21lfhVi8rV5a0u3XzXRzuMGGCkSwCvP++kkURERHxCwEBAVx44YVceOGFvg5FvKRWLUMFaNCgARdeeCGPP/443333ndfG/eWXX5g4caLzum/fvmzevJn169fzxx9/sH//fr788kvCw8MBY4PwlVde6bX4qjWHA1atMtoNGkB8vG/jqarevaFzZ6O9dCls2eLbeERERESkVqoVM4t169ZlxowZ9OrVi2Y+OI7A4XDw0EMP4SjaS9euXTvmz59PaGio8x6z2czVV19NvXr1nOe7LF26lG+//ZbLL7/c6zFXK0lJcOSI0T7jDChHSWe/ZjLBLbfAvfca1++/Dy++6NuYRERERKTWqRUzi5GRkVxxxRU+SRQB5syZw9q1a53XU6ZMKZUouhoyZAhXX3218/r555/3eHzV3oYNJe0uXXwXhztdfz0EBBjtjz+GggLfxiMiIiIitU6tSBZ9bdasWc52ixYtOP/88095/4QJE5ztFStWsH//fo/FViOsX1/SrinJYv36cNllRvvQIZg926fhiIiIiEjto2TRC3766Sdn+4ILLsB0mmWS/fv3JywsrMznpQyuM4vFe/1qgltuMT4OGQL16vk2FhERERGpdWrFnkVfOnToEAcOHHBe9+3b97TPWK1WevXqxcKiw9nXrVvnqfBqhuKZRbMZOnTwbSzuNGQI7N4NzZv7OhIRERERqYU0s+hhmzdvLnXdqlWrcj3net/xfYiLwkLYtMlot25dcqB9TWCxKFEUEREREZ/RzKKH7dmzp9R106ZNy/Wc633H91GWvLw88vLynNfp6enlGqfaS0mBTp1g48aas19RRERERMQPaGbRwzIyMkpdR0VFleu5yMjIk/ZRlueee46oqCjnq0mTJhULtLqKiYF//oHMTHjvPV9H4zmFhTBnDrhU1RURERER8SQlix6WmZlZ6jo4OLhcz4W4LKc8vo+yPPLII6SlpTlfCQkJFQu0urNYIDra11F4xsaNxnLUiy/WeYsiIiIi4jVKFj3MZrOVurZay7fy1/W+gnKcsRcUFERkZGSpl9QQrVtDdrbR/uYbOHbMt/GIiIiISK2gZNHDQkNDS13n5uaW6znX+1yP0ZBaKCgIrr/eaOflwVdf+TYeEREREakVlCx6WHh4eKnrnJyccj2XXTyTVEYfUiQ/Hzp2hBEj4PXXfR2NZ40dW9KePt1nYYiIiIhI7aFk0cPq169f6jo5Oblcz7mezVhPB7KXbedO2LwZfvwR/vzT19F4Vvfu0LWr0f7rL9i61afhiIiIiEjNp2TRw9q1a1fqet++feV6zrVATfv27d0aU42xZUtJu6b/HZlMMGZMybVmF0VERETEw5QselibNm1KFatZs2ZNuZ5bvXq1s92hQwd3h1UzuM6uHZeU10ijRxtVXwE++cQ4TkNERERExEOULHpYYGAgZ511lvN6yZIlp33mwIED7Nixw3k9YMAAj8RW7bnOLNaGZLFhQ7joIqO9fz/8/rtv4xERERGRGk3Johdceumlzvb8+fM5ePDgKe//7LPPnO3o6GgliyfjOrPYtq3v4vAmFboRERGpcQYPHozJZMJkMvHBBx/4OhwRJyWLXnDttdcSFBQEGGcm/ve//z3pvZmZmbzuUtlz9OjRBAQEeDzGasfhKEkWmzSB2nK8yLBh0KYN3HEH3Huvr6MRERERN1i7dq2z3aNHDx9G4h2pqal8++233HXXXQwYMIBGjRoRFBREeHg4TZs2Zfjw4bz22msc09nSPqdksZL27Nnj/A2QyWRi0qRJJ703Pj6eCRMmOK+nTJnCzJkzT7ivoKCAm266yVkEJyQkhEcffdTtsdcIhw+XHE5f04vbuAoKMpLkN9+Enj19HY2IiIhU0b59+0hJSQGM7UudOnXycUSes2XLFoYPH07Dhg0ZOXIkb7zxBosXL+bgwYPk5+eTlZVFQkICs2fP5t577yU+Pp7XXnsNh8Ph69BrrVqTLN56660EBwef8KroPZU1adIk2rRpA0BhYSFXXXUVN9xwAzNnzmTBggW88847nHnmmXzzzTfOZ1588UXi4uLcMn6NU9uK27gymXwdgYiIiLiJa1HDTp06ERgY6MNoPGvDhg3Mnj2b/Px853sWi4V27doxYMAA+vXrR926dZ2fy87O5t5772X8+PFKGH3EevpbaoaCggLy8vJOeY/NZsNms3lk/Dp16jB79myGDBlCQkICdrudTz/9lE8//bTM+x988EHuuOMOj8RSI9S24jYiIiJSI7lWyj/jjDN8F4gXWa1Whg0bxtixYxk0aBCRkZHOzzkcDn744QfuuOMOEhMTAXjvvfc444wzuP32230Vcq1Va2YW/UHbtm1Zt24dt9xyCyEhIWXe06FDB77//nteeOEFL0dXzZx3HkybBvfdB/36+Toa3zhyBF5/Hd5/39eRiIiISCW5zizW9P2KAQEBjBs3jp07d/Ltt99y6aWXlkoUAUwmE5deeil//vknjRo1cr7/5JNPUlBQ4O2Qaz2TQ3O6PpGRkcHvv/9OQkICWVlZxMbG0qVLF7f9RyI9PZ2oqCjS0tJO+CaUGiA1FWJjITcXmjeHnTvBrN/9iIjUZLm5uezevZsWLVq4bZuM+F6zZs2c9SqWLVtG3759fRyR/5g2bVqpuh/z589n8ODBPozI+9z1fV/Z3EA/XfpIREQEl156Kf/+97956KGHuPHGG2v8b5PEjaKj4dxzjfaePbBokQ+DERERkco4evSoM1E0m81069btlPe/+OKLWK1WZ4HF8ePHl9r/V9MMHz681PUW121I4hVKFkWqqzFjSto6c1FERKTacd2v2K5dO0JDQ8u8LzMzkyuvvJIHH3yQwsJCAgMDefvtt5k2bVqNLojjWuwGjNkx8S4li1L9pKXBwoWQkAB2u6+j8Z1LL4WoKKM9YwZkZvo2HhEREakQ1/2KJytus3XrVnr37u2smN+oUSMWLFjAbbfd5pUYfWnv3r2lrmNiYnwUSe2lZFGqn7//hkGDoGlTeOQRX0fjOyEhcPXVRjsrC2bN8m08IiIiUiGnK27z3Xff0bt3bzZv3gzAWWedxcqVKzn77LO9FqMvzTruZxvt5/Q+JYtS/ezcWdJu2dJ3cfgDLUUVERGptk42s2i323n00UcZOXKkc+nlzTffzB9//FHhM7g/+ugj5x5Hd74++ugjt/wdnExaWhpTpkxxXnft2pWOHTt6dEw5Ua05Z1FqENdksVUr38XhD/r2hTZtYPt2WLAA9u0zZlxFRKTWczgc5BQU+joMvxYSYMFkMvlk7JycHLZu3eq8Lp5ZTElJ4dprr+XXX38FjOMmXn311Vp3/vb999/PgQMHnNfPPPOMD6OpvZQsSvWjZLGEyQQ33ghPPAEOB3zyCTz2mK+jEhERP5BTUEjHJ3/xdRh+bdPkCwgN9M2Pw+vXr6ew0EjmW7RoQXR0NCtXrmTUqFHOvXoNGzZkxowZ9O/fv9LjNG7cmAsuuMAtMR/fr6e89957vO9yjvTVV199QmVU8Q4li1L9FCeLVis0aeLbWPzB9dcbySLAF18oWRQREakGjl+C+uGHH/Kvf/2L3NxcAHr16sWsWbOIj4+v0jhDhw5l6NChVerDmxYtWlRqFrVFixZMnTrVhxHVbkoWpXpxOEqSxebNjYSxtmveHP71L2jfHq691tfRiIiInwgJsLBpsvtnlGqSkACLz8Z2TRYXL17MzJkznddjxozhnXfeqdIh7NXRmjVrGDFihPPsyJiYGObOnUtUcfV38Tr9pC3Vy+HDJUdE1PYlqK7eesvXEYiIiJ8xmUw+W2Ipp+d6xuKhQ4ec7QkTJvDOO+/4ICLf2rp1KxdccAFpaWkA1KlTh3nz5tG2bVsfR1a7qRqqVC/arygiIiLVXGFhIevWrXNeX3LJJc72119/zbZt23wRls/s3r2bIUOGOJPmiIgI5syZQ7du3XwcmejXTVK9KFkUERGRam7r1q3k5OQA0KhRI2bOnMm5557LX3/9xbFjxxg+fDh//fUXderUqfJYv/76Ky+//HKV+zne/fff75a9kPv372fw4MHs378fgNDQUGbPns1ZZ51V5b6l6pQsSvVy9CgEBEBBgZLF4zkc8PffxnmLWVng4fOPREREpHJc9yt269aNoKAgvv32W3r37k1CQgLbtm3jyiuvZO7cuVirWJ8hMTGRX35xf1Xca665psp9HDx4kCFDhrB7924AgoKC+O677xgwYECV+xb30DJUqV7uugtycmD3bhg82NfR+Be7HUaOhP/9Dz77DFz2P4iIiIj/cN2vWLzUslGjRnz//feEhoYC8Ntvv3HnnXf6IjyvSElJYciQIc6zJgMCAvjmm2+qVeXW2kDJolQ/FotRATQ83NeR+BeLxThGA8BmM47REBEREb9z/MxisR49evDJJ59gMpkAeOedd3j99derNNbYsWNxOBxuf40dO7bSMaWlpXHBBRewYcMGACwWC59//jnDhg2r0p9V3E/JokhNMmZMSXv6dN/FISIiIidV1sxisZEjR/LUU085r++77z6PLCP1laysLC655BJWrlwJgNlsZvr06VxxxRU+jkzKomRRpCbp0AF69TLaq1fD+vW+jUdERERK2bdvHykpKYCxR69du3Yn3PPEE0849wQWFhZy9dVXs3nzZq/G6Ql5eXlcdtllLF26FDCOd3n33XcZPXq0jyOTk1GBG6k+9u2Dhx6CZs2M/Ypa0162G280Ct0AfPwxvPiib+MRERERJ9dZxU6dOp20gM0HH3zAzp07+fvvv0lLS2P48OEsX76cevXqeSlS95syZQrz5893XkdHR/P111/z9ddfl+v5oUOHcv/993sqPCmDkkWpPrZtgy+/NNoOh5LFk7nmGrjvPqNi7KefwnPPQRUrqYmIiIh7nGy/4vFCQkL47rvv6NWrF0lJSezcuZNRo0bx66+/EhAQ4I1Q3S47O7vU9bFjxyq0xLZRo0buDklOQ8tQpfrYu7ek3ayZ7+I4BbvdwbKdR/jv3C3c99UanvhuAzP+SSA1O997QdSvD8WH+x44AC6/wRMRERHfKm+yCBAXF8f3339PSEgIAH/88Qe33367R+MTcaXpBqk+/DxZXL3vGI9+u4HNyeknfC44wMz1ZzXjnqFtCQ/ywrfdmDHw3XdGe/p0uPBCz48pIiIip/Vd8f+fy+nMM888YUauupo0aRKTJk3ydRhSAUoWpfrYs6ek7WfJ4jcr9/PwzHXY7A4igqxc2LkRLRuEk5qdzx/bDrPlQAbvLdnNz+uTeWv0GfRoWsezAV18MdSrBykp8M8/xlEaWooqIiIiIhWgnx6l+vDTmcVZq/bzfzPWAnBJl1ievqwzdcMCnZ9/+KL2LNx2mInfb2Tf0WyunvoXL17ZlUu7N/ZcUIGB8Mor0KiRUQzIYvHcWCIiIiJSI2nPolQfxcli3boQEeHbWIr8vecoD36zDoCb+jXnjWt7lEoUwSgLPahdDD/f3Z/zOzYkv9DOPV+t4et/Ejwb3I03wvnnK1EUERERkUpRsijVg80G+/cbbT+ZVczILeCeL9dgszu4pGssT1zSEbPZdNL7w4OsvHN9T27o0wyHAx78Zh3fr0n0YsQiIiIiIuWnZFGqh6QkKCw02n6SLD7702YSU3NoUjeEF0Z1PWWiWMxsNjH50k7c2Nf4MzwwYx3Ld6V4OlRDTo53xhERERGRGkHJolQPfrZfcUNiGl8VLSN9+cruFapwajKZmDS8Exd1bkR+oZ3xn6xk95EsT4UKn38OQ4ZAly5gt3tuHBERERGpUZQsSvVQrx7861/G+YFnnunTUBwOB5Nnb8LhgEu7x9G7Rd0K92E2m3j16u50bxJNWk4Bt3+6kpz8Qg9Ei3F0xm+/wc6dsGSJZ8YQERERkRpHyaJUDx07wltvwezZcP31Pg1l0fYjrNh9lCCrmQcvbF/pfoIDLEy9oSf1wwPZciCDJ7/f4MYoXdx4Y0n74489M4aIiIiI1DhKFkUq6H8LdgAw+qxmNI4OqVJfDSODef2aHphNMGPlfs9USL388pLqsV9/DTXkYF8RERER8SwliyIVsHLvMZbvPkqAxcStA1q4pc+zW9fnvqFtAZj0w0b2prh5/2JoKFx5pdHOyIDvvnNv/yIiIiJSIylZlOohy4MFYCrg3UW7ALi8R2Nio6o2q+jq9nNbc1aLumTnF3Lf12sptDvc1jegpagiIiIiUmFKFsX/ORwQEwORkTB8uM/COJiey6+bDwIwrn9Lt/ZtMZt4+apuhAdZWbn3GO/8sdOt/dO/PzRvbrR//dU4ikRERERE5BSULIr/S0019tllZEBurs/CmPFPAoV2B2c2q0PbhhFu7z++TiiTRnQC4NVft7EhMc19nZvNcMMNRttuh88+c1/fIiLiVQ6Hm1efiIjf8vX3u5JF8X/795e04+N9EkKh3cEXK4ziM9ed1dRj44w6ozEXdmqEze7ggW/WUVDoxnMRXZeiTp9uzNiKiEi1YTYbP7bZdWauSK1R/P1e/P3vbUoWxf8lJpa0fZQsLtlxhMTUHKJCAri4S6zHxjGZTDxzeWeiQwPYnJzOtKI9km7RujX062e069eH9HT39S0iIh5ntVoxmUzk5eX5OhQR8ZLc3FxMJhNWq9Un4ytZFP/nBzOL368xEtYR3eIIDrB4dKz64UE8OawjAFN+287Ow5nu63zKFNi9GxYuhKgo9/UrIiIeZzabCQkJIctPir6JiOelp6cTHh6umUWRk3JNFhs39vrwuQWFzNtoFLa5tHucV8a8vEdjBrRtQL7NzsMz12F3V3XUnj1LCt2IiEi1Ex4eTlZWFvn5+b4ORUQ8LCsri9zcXCIjI30Wg5JF8X8+nllcuPUQmXk24qKCOaNpHa+MaTKZ+M/lnQkNtPD3nmN8tmKfV8YVERH/FhUVhdVqZf/+/RQWFvo6HBHxkKysLBISEggLCyM8PNxncfhm8atIRfg4WfxxbTIAw7vFYTabvDZufJ1QHrygHZN+3MQLc7YwpEOMW892xG6HTZugc2f39SkiIh5ltVpp0qQJe/bsYceOHURFRREeHo7FYsFk8t7/o0TEvRwOB3a7ndzcXNLT08nNzSUsLIz4+HifLUEFJYtSHRQXuAkKgnr1vDp0Vp6N+UVnKw7v5p0lqK5u6NucH9YmsWpfKk/P3sT/Rvd0T8evvGLsXzxwAJKToW5d9/QrIiIeFxQURIsWLUhNTSUtLY1jx475OiQRcROTyUR4eDj16tXz6V7FYkoWxf8VzyzGx4OXf2u6aNth8mx2mtcLpVOc99eLW8wmnr28C8PeWMLP6w+wcOshzm0XU/WOExNhX9HS1i+/hH/9q+p9ioiI1wQGBhITE0ODBg2w2WxakipSA5jNZqxWq88TRFdKFsX//fGHkdzYbF4fev7mQwAM6dDQZ8t7OsRGMvbs5ry/ZDcTf9jIL/fUq3pF1jFjjNlFgI8/VrIoIlJNmUwmAgICCAgI8HUoIlID+U/aKnIyXbvCRRfB8OFeHbbQ7mDBViNZHNyhoVfHPt49Q9rQMDKIvSnZTP3DDWcvdu0K3boZ7eXLYevWqvcpIiIiIjWKkkWRk1i97xhHs/KJDLZyZnPvVEE9mYjgAJ4oOnvxrYU72JvihjO2xowpaU+fXvX+RERERKRGUbIochLFS1AHtY8hwOL7b5VLusTSv0198m12nvx+Iw5HFc9evO46sBQtZ/3kE9B+FxERERFx4fufgEVO5c8/4fPPYdEiyMz06tDFVVB9vQS1mMlk4qkRnQi0mPlj22F+2Xigah02bGgs7wWjiNDChVWOUURERERqDiWL4t8++QRGj4aBA2HzZq8Nm3A0mx2HMrGYTQxs28Br455Oywbh3DawJQCTf9xETn4VZwNvvLGkraWoIiIiIuJCyaL4t+JjMwAaN/basIu3HwHgjKbRRIX4V4W5fw1qTePoEJLScpm2qIrFboYPh+hooz1zJmRkVDk+EREREakZlCyKfytOFi0WY9mklyzdYSSL57T2n1nFYsEBFh6+qD0A7/yxk+S0nCp0FgzXXANRUXD99ZDlhsI5IiIiIlIjKFkU/5aYaHyMiyspxuJhhXYHS3cWJYtt6nllzIoa1jWWXs3rkFNQyAtztlSts6efhuRkmDoVGjVyT4AiIiIiUu0pWRT/lZ8Ph4yKpN5cgropKZ3U7ALCg6x0i4/22rgVYTKZeHJYJ0wm+G5NEqv2Hat8Z/XrQ0iI+4ITERERkRpByaL4r4MHS9qxsV4bdvGOwwD0aVkPqx8cmXEyXeKjuOKMeACe+nETdnsVj9IQEREREXHhvz8JiyQnl7S9mCwW71fs36a+18asrAcuaEdYoIW1Cal8tyax6h0eO2ZUoK3qGY4iIiIiUu0pWRT/5YNkMbegkL/3GEs6+7X2/2QxJjKYfw1qDcALc7dU7SiNZ54x9izeeCMsX+6mCEVERESkulKyKP7LNVn0UuGVf/YcI99mp1FkMK0ahHllzKq65ZwWNI4O4WB6Hh8t21P5juLjjX2ioDMXRURERETJovgxsxmaNIGAAK/NLC7fnQJA31b1MJlMXhmzqoIDLNw3tC0Aby/cQVp2QeU6GjUKQkON9pdfQl6emyIUERERkepIyaL4r/HjYd8+yM2FCy/0ypArdh8FoHeLul4Zz10u69GYdg0jSM+18b8/dlSuk4gIGDnSaKemwo8/ui0+EREREal+lCyK/zObvXLGYp6tkNUJqUD1SxYtZhMPXtgOgI+W7iE5LadyHY0ZU9LWUlQRERGRWk3JokiR9fvTyLfZqR8eSMv61WO/oqvz2sfQq3kd8mx2pszfXrlOBg0qOdNyzpyScy5FREREpNZRsihSZHnREtRezetWm/2KrkwmEw9f1B6Ar/9JYMehzIp3YrHADTcY7cJC+PxzN0YoIiIiItWJkkXxT3Y7nHceXHMNvPKKV4b8e09Jslhd9WxWlyEdGmJ3wJTfKjm7eOONJW0tRRURERGptZQsin86cgQWLICvvoL58z0+XKHdwcqi8xWr237F4xVXRp29LokdhzIq3kGHDtCrl9FeswbWrXNfcCIiIiJSbShZFP/kesaiF47N2JycTkaejYggKx1iIz0+nid1jIvk/I4NcTjg9d8qWRl1zBjo1Alee804vkREREREah0li+KfvJwsFh+Z0bN5HSzm6rdf8Xh3DW4DwI/rkiq3d3HCBFi/Hu6+G+rUcXN0IiIiIlIdKFkU/3TgQEnbC8liTdiv6Kpz4yiGdDBmF9/8vRJ7F61WqIZFfkRERETEfZQsin/y4syiw+Hgn73GfsWakiwC3DPEmF38YW0SOw9XYnZRRERERGo1JYvin7yYLCal5XI4Iw+r2USXxlEeHcubjNnFGOwOeGtBJfcuAqxdC3feCbt3uy84EREREfF7ShbFP7kmi40aeXSo1fuMWcUOsZGEBFo8Opa3/fu8otnFNUkkp+VUvIP33oPu3eHNN+GDD9wbnIiIiIj4NaunOk5MTGTTpk3s3buXw4cPk5WVBUBYWBgNGjSgWbNmdOrUibi4OE+FINWZF2cWV+9LBaBH02iPjuML3ZtEc1aLuizffZQPluzmsUs6VqyDiy8GiwUKC+HDD2HSJONaRERERGo8tyWLx44d4/vvv+eXX35h4cKFHDp0qFzPxcTEMHDgQC644AJGjBhBvXr13BWSVGfFyWJ0NAQHe3So4pnFmpgsAkwY2JLlu4/yxYoE7hzchsjggPI/HBcHl1wCP/wAiYnwyy9GAikiIiIiNV6Vk8U5c+YwdepU5s6dS0FBAWAUDCmvgwcPMmPGDGbMmIHVauXCCy9k/PjxXHLJJVUNTaqz22+HPXvA7NmV0nm2QjYkpQPQo0nNPCLi3LYxtG0YzraDmXy+fB+3DWxVsQ7GjTOSRTCWpSpZFBEREakVTI6KZHZF7HY706dP5/nnn2fHDqNwRlndBAUFERcXR506dQgJCcHhcJCTk8OxY8dITk4mLy/vxICKyvW3bNmShx9+mLFjx2LRsrcKS09PJyoqirS0NCIjq/ch8560JiGVy95aSp3QAFY9MdT576+m+Wblfv5vxlpiIoJY/NAggqwV+J6y2aBpU2O212qFhASP7yMVEREREfepbG5Q4WmbGTNm0K5dO8aNG8eOHTtwOBw4HA6Cg4MZPHgwkydPZs6cOezfv5+cnBx27tzJP//8w+LFi1myZAkrV65k165d5OTkkJCQwM8//8zkyZMZPHgwwcHBzv527drF+PHjadu2LV9//XVFwxQpl5IlqHVqbKIIMKJbHI0igzmUkcf3a5Iq9rDVCmPHGm2bDT7+2O3xiYiIiIj/qdDM4sCBA1myZAlgzCRarVYuueQSRo8ezUUXXURYWFiVgsnOzmbOnDl8/vnnzJ4927ms1WQy0a9fPxYtWlSl/msTzSyWz11frOaHtUncP7Qtdw5u4+twPGraop385+cttG8UwZy7+1csOd6xA9oU/f20aQNbt0INTq5FREREahKvzCwuXrwYh8NB/fr1eeqpp0hMTOTbb7/liiuuqHKiCBAaGsqoUaOYOXMmiYmJTJ48mZiYGBwOB0uXLq1y/1JNZGdDRgZUfIV0ha1OKJlZrOmu7tWUkAALWw5ksHz30Yo93Lo1DBpktLdvh8WL3R+giIiIiPiVCiWLMTExvPbaa+zbt48nnniCBg0aeCou6tevz+OPP87evXt59dVXPTqW+JmPP4bISAgNhS+/9NgwRzLzSDiag8kEXZtEeWwcfxEVEsDlZzQGYPqyPRXvYNy4kvaMGe4JSkRERET8VoWSxZ07d3LXXXcRFBTkqXhOEBQUxN13382uXbu8Nqb4WPGxK7m5EB7usWHWFJ2v2CYmvGLHSVRjY/o2B2DepoMkpeZU7OGRI+G66+Cnn+C119wem4iIiIj4lwoli+5YalpZoaGhPhtbvMz1jM6YGI8N41yCWkOPzChLu0YR9GlZl0K7g0//2luxh4OD4bPPjKMzVKFYREREpMbz7CF2IpVx8GBJu2FDjw2zNiENgG5Noj02hj8ae3ZzAL78O4HcgkLfBiMiIiIifkvJovgf15lFD+1VdTgcrNufCkDX+Jq/X9HVkA4NiYsK5mhWPrPXJVets0IlmyIiIiI1lZJF8T/FyWJ4uFHkxgP2Hc0mPddGoNVM24YRHhnDX1ktZkb3aQbAx3/uqXgHNhvMmgVDh5YueiMiIiIiNYrVXR0dOXKEJUuWsHTpUjZv3syOHTtITk4mLy8Ph8NBw4YNadq0Kb169aJfv34MHjyYOnVqz14xqYDiZage3K+4br+xBLVDbCSB1tr3O5NrejXhtfnbWLc/jY1JaXSKq8Dsan4+3HILpKYa+xhffhnq1vVYrCIiIiLiG25LFmNiYkod8u047oy8/fv3k5iYyJ9//snrr7+O1WplyJAhjB8/nksvvdRdYUh1l58Px4zCM57cr7g+0UgWuzauXUtQi9ULD+L8jo34aX0yX65I4OnLKvD3EBoKY8bAlClGxdqPP4Z77vFYrCIiIiLiG26fUnE4HCckiq6fK/5YUFDA3LlzGTlyJD169GDFihXuDkWqo8OHS9oenFlcXzSz2KWWJosA1/RuAsB3axLJya/g3sMJE0ra77wDJ/meFxEREZHqy20zi2AkgU2bNqVFixY0btyYBg0aYDKZcDgcJCUlsW/fPtavX092drbzfoC1a9dyzjnn8PLLL3PnnXe6MySpbrxwbIbd7mBD0cxil1pW3MZVv1b1aVI3hISjOfy0PpkresaX/+EOHWDgQPjjD9i61fh47rkei1VEREREvM9tyeLcuXM588wzqXuavUuFhYWsXr2a2bNn88UXX7B9+3ZMJhM2m4177rmHiIgIxo4d666wpLrp0AHWrjX2LXooWdyTkkVGno0gq5k2MeEeGaM6MJtNXNOrKS/+spUvV+yrWLIIcNttRpIIxuyikkURERGRGsXkONmaUS/5/vvvuf/++9m1axcAERERbN26lUaNGvkyrGovPT2dqKgo0tLSiIyM9HU4fuX7NYnc/eUazmgazax/9fN1OD51KD2Xvs//TqHdwa/3DqBNRSrD5uVBkybG0uGAAEhI8Og+UxERERGpnMrmBj4vA3nppZeycuVK+vTpA0BmZibvvPOOj6OSmqy4EmrX+GjfBuIHYiKDOa+9MYP75d8JFXs4KAhuvtloFxTAhx+6OToRERER8SWfJ4sAUVFRfPHFF1itxqrY2bNn+zgiqcmKK6F2rsXFbVxdW1ToZtaq/eTZKljoZvz4kvbUqWC3uzEyEREREfElv0gWAZo1a8YZZ5yBw+Fg9+7dvg5HfGXuXPj0U5g3zziWwc0K7Q42Fh+bUYuL27ga2DaGRpHBHMsu4PfNh07/gKuWLeGCC0raR464P0ARERER8Qm/SRYBcouSg+JqqVILvf463HCDkYBkZrq9+91HMsnKLyQkwEKrBrW3uI0ri9nEZT0aAzBzVWLFO/jvf2HLFvjtN48edyIiIiIi3uWVZLGgoIC//vqLzJP88G+z2Xj++edZt24dJpOJpk2beiMs8UfFR2dYLHCayrqVUbxfsXPjSCxmk9v7r66u6Gkkiwu3HiIlM69iD3ftCu3aeSAqEREREfElt56zeDLp6emcffbZmEwmGjVqRHx8PHXr1iUgIICUlBQ2bNhAZmYmJpPxw/vVV1/tjbDEHx08aHxs0ADM7v9dRnGy2KVxtNv7rs5ax0TQLT6KtfvT+H5NEjef08LXIYmIiIiIj3klWSzmcDg4cOAABw4cOOH9YiNHjuTxxx/3ZljiLxyOkplFDy1n3KD9iic18ox41u5PY+aq/ZVPFu12+OUXOO88o1qqiIiIiFRbXlmGGhISwqhRo2jWrBkOh8P5AjCZTJx33nlMmzaN9evX88033xAYGOiNsMTfpKdDfr7R9kCyaCu0szEpHVAl1LKM6BZHgMXExqR0thxIr3gHP/8MbdvCxRfDjBnuD1BEREREvMoryWJoaCgzZsxg9+7d7N+/nw8//JDLL7+cwMBA7HY7CxYs4OWXX+bQoQpWYpSapXgJKngkWdx5OIucgkLCAi20rB/m9v6ruzphgc4zF2dVptBNRATs3Gm033jDjZGJiIiIiC94vRpqXFwcY8aMYebMmSQlJTFp0iTCwsLYunUrQ4cO5YMPPvB2SOIvXH9Z4IFkcWOSsQS1U1wUZhW3KdPIM+IB+HZ1IrbCCp6ZeM450K2b0V6xwniJiIiISLXltmTRZrNV+Jk6derw5JNPsn79ejp27Ijdbuf2229n3bp17gpLqhPXM/oaNHB795uKlqB2jIt0e981xaB2MdQJDeBwRh5LdlTwzESTCe68s+Ras4siIiIi1ZrbksXOnTszd+7cSj3brFkzZs+eTVBQEDabjZdeesldYUl14uFkcaOSxdMKtJq5tHsVzly87rqSI0++/rr00mIRERERqVbclixu27aNSy65hGHDhrF69eoKP9+8eXN69uyJw+Hg999/d1dYUp1YrdCsGYSFQf36bu3a4XCwKbkoWYxVsngql/cwksVfNx0gK6+CKwZCQmDcOKOdnw/vvuvm6ERERETEW9y+Z3HOnDmceeaZDB8+nEWLFlXo2aNHjwJw+PBhd4cl1cHYsbBnD2RmwmWXubXrpLRc0nIKCLCYaNswwq191zRd46NoXi+U3AI7v26qxMzgv/5Vckbm229DQYF7AxQRERERr3Bbsvjkk08SGBjoPBbj559/ZtCgQbRu3ZrJkyezfPly7PaTF8yYOnUqW7ZsAaBu8TI2qb1M7i1AU7xfsXVMBIFWr9d1qlZMJhMjipaifr+mEktRmzWDESOMdlISfPutG6MTEREREW8xOYoPPHSD7du3c8cddzB//vzSgxT94B8WFkbnzp1p164dDRs2JCgoiKNHj7JkyRLWrVuHw+HAZDJx/vnnM2fOHHeFVSulp6cTFRVFWloakZFadjll/nZenb+NUWfE8/JV3Xwdjt/bcSiTIa/8gdVsYsVjQ6gbVsGzT3/7DYYMMdrnnAOLF7s/SBEREREpl8rmBlZ3BtGmTRvmzZvHr7/+yhNPPMEKl9L5DoeDzMxMli9fzvLly0s9d3y+OmHCBHeGJcKmZOPYDBW3KZ/WMeF0bhzJhsR0fl6fzPV9mlWsg/POgzPPhB494N//9kyQIiIiIuJRHlmPN3ToUP766y/mz5/PqFGjsFpLctKyJjJNLksO7733Xi5z8341qSZGj4Yrr4QHHnB7185KqCpuU26XdjOWov6wJqniD5tMsHw5TJsGXbu6OTIRERER8Qa3ziwe77zzzuO8887j6NGj/PbbbyxYsIANGzawfft2DhaV1DeZTMTExNC/f3/Gjx/PkOKla1L7/PQTpKVB27bw4otu6zYtp4D9x3IAJYsVMaxbLP+Zs5kVe46SmJpD4+iQinVg1t5QERERkerMo8lisbp163LllVdy5ZVXOt9zOBzk5uZisVgIDKzgfiipeQoKjEQR3H5sxuaiIzMaR4cQFRrg1r5rstioEHo3r8vy3UeZvTaJCQNb+TokEREREfEin/3q32QyERISokRRDCkpJW03J4vFlVA7ab9ihV3qrIpaiaWoxTIz4c03oV8/yM11U2QiIiIi4mlaJyb+4ciRkra7k8WimUUVt6m4izo3IsBiYlNyOtsPZlSukzvvNF7LlsHnn7s3QBERERHxGCWL4h88mSyquE2l1QkLZGDbBgD8sLaSs4u3317SfuUVcN9pPSIiIiLiQUoWxT8cPlzSdmOymG+zs/2QMSOmmcXKGeGyFLVSx7L27m0sQQXYuBHmzXNjdCIiIiLiKRVKFidPnkxWVpanYjmprKwsJk+e7PVxxYs8NLO4/VAGBYUOIoOtFa/mKQAM6RBDaKCFfUezWZOQWrlO7ruvpP3KK26JS0REREQ8q0LJ4qRJk2jVqhXPP/88qampHgqpRGpqKs899xwtW7bkqaee8vh44kMeShadS1DjIkud5ynlFxpoZWjHhkAVlqJeeim0aGG0582DDRvcFJ2IiIiIeEqFl6EePnyYxx57jKZNm/Kvf/2Lf/75x+1BrVixggkTJtC0aVMef/xxDrsuUZSayVPJYnFxm9got/VZG43oFgfA7HXJFNorsRTVYoF77im5fvVV9wQmIiIiIh5ToWRx4cKFdO3aFYfDQWZmJlOnTuWss86iXbt2PPzwwyxYsIDcSpTGz87O5tdff+X//u//aN26NX379uW9994jMzMTh8NBt27dWLBgQYX7lWpk0CC4+24YPRqaNHFbtzo2wz36t2lAVEgAhzPyWL4r5fQPlOWmmyCqKGn/9FM4eNB9AYqIiIiI25kcFaxY4XA4+OSTT3jmmWfYsWOH0YnL8r6AgAA6dOhA586dadmyJY0bNyY6OpqQkBAcDge5ubkcO3aMxMREdu7cyYYNG9iyZQs2m63UGACtW7fmiSee4Prrr9cSwgpKT08nKiqKtLQ0IiNrZ6LkcDjo+tQ8MnJtzLm7Px1UDbVKHpm1ji9WJHBNryY8P6pr5Tp58EF48UWj/eSToOXlIiIiIh5X2dygwsliMbvdztdff83rr7/OX3/9VbrTCiZ2x4fQp08f7r77bq688krMZhVsrQwli5BwNJv+/11AoMXMhqcuINCqf0tVsWznEa57dzlRIQH8/diQyv197t9v7F20WIyZ5BdecH+gIiIiIlJKZXMDa2UHNJvNXHPNNVxzzTVs2LCBzz77jFmzZrF9+/Yyy+sXJ5Any01bt27NyJEjGT16NF26dKlsWCJOG4uWoLZpGK5E0Q3OalGPmIggDmXksWjbYYYUFb2pkPh4+PJL6N8fYmLcH6SIiIiIuE2lk0VXnTt35rnnnuO5555jz549/PHHH6xcuZJNmzaxd+9ejhw54jxyIywsjPr169OsWTM6duxIz549GTBgAC2KKyV6wbJly5g+fTqLFy8mMTERh8NBfHw855xzDmPGjKFf8ZlwblSZZbRvv/02t912m9tj8UtpaRAZCW5cblxS3KZ2zqy6m8VsYljXOD5Yupsf1iZVLlkEGDXKvYGJiIiIiEe4JVl01bx5c5o3b86YMWPc3XWVZWVlcdddd/HBBx+c8LnNmzezefNm3n33XW666SbeeOMNwsLCfBBlLZSTA9HRYLXCJZfAd9+5pVvXYzPEPUZ0N5LFXzcdJDvfRmig2/8TIiIiIiJ+otb8pFdYWMjIkSOZN2+e872QkBA6deqE1Wpl06ZNpKcbycWHH35IYmIiP//8MxaLxe2xDBgwgJCQ0x8Q37RpU7eP7ZdSiqpr2mzGXjY32ZxcXAlVx2a4S7f4KJrVC2VvSjbzNx9yHqlRaRkZ8PXXcPPNbp1VFhEREZGqqzXJ4hNPPFEqUbz11lt5/vnnqVu3LmDMOr7wwgs8/fTTAMybN48nn3ySZ5991u2xTJ8+nebNm7u932rLA2cspmbnk5iaA0D72Ai39CnGcurhXeN4c8EOfliTVLVk8YMP4P/+D44dg9hYuPhi9wUqIiIiIlVW4aofu3fv9kQcHpWUlMSrLoeA33DDDUybNs2ZKIKxl3Ly5Mk8/vjjzvdeeeUVkpKSvBprreSBZLF4v2LTuqFEBge4pU8xjOhuJIh/bDtEWnZB5TuqW9dIFAGef94NkYmIiIiIO1U4WWzVqhV16tRh0KBB3HfffXzyySesX7+ewsJCT8TnFq+99hq5ubkAhIaG8tprr5303ieeeIImRYfC5+bmMmXKFG+EWLt5IllMUnEbT2nbMIL2jSIoKHQwd2Ny5TsaMQLatzfaixfD0qXuCVBERERE3KJS5wmkp6ezaNEipkyZwtixY+nevTvh4eH06tWL8ePH8/bbb/Pnn3+SnZ3t7ngr5dtvv3W2r7rqqlIziscLDAzkpptucl7PmjXLo7EJpZPFevXc0qWK23jW8KLlpz+srcLMu9kMDz1Ucq0zF0VERET8SqWSRYfD4XwVX+fl5bFq1Sref/99/v3vf3POOecQGRlJhw4duO6663jxxRf59ddfOeKaGHjB1q1b2bFjh/P6wgsvPO0zF110kbO9Y8cOtm7d6pHYpMjRoyVtNy9D1cyiZxTvVfxzZwqHMnIr39F11xlnLwL8+CNs2OCG6ERERETEHSpc4OaHH35gzZo1zpfrHsbi5NFkMjmTyW3btrFt2za++uor531xcXH06NGDHj160L17d3r06OGxgi9r164tdd23b9/TPnPGGWcQGBhIfn4+AOvWraNdu3YeiU8onSyeYta3vHILCtlxKBPQzKKnNKkbSo+m0azel8pP65K5qV8lz0kNDIT774d77zWu//tf+Phj9wUqIiIiIpVW4WRx2LBhDBs2zHmdkZHhTBxXr17NmjVr2LRpkzPROj6BBEhMTCQpKYmffvrJ2U9UVBTdu3end+/e9O/fnwEDBhARUfUqlps3b3a2AwMDnfsRT6X4vp07d57Qhzs88MADbNq0iYSEBAoKCqhXrx5t2rRh4MCBjBkzhhYtKvmDd3Xl5mRxx6FMbHYHdUIDiI0KrnJ/UrYR3eJYvS+VH9YmVT5ZBBg3Dp5+2vh38Pnn8NRTUNu+B0RERET8UKWWobqKiIigf//+3HnnnXzwwQesWrWKzMxM1qxZw4cffsjdd9/NwIEDiYoqfdbd8ctYU1NT+eOPP3jxxRcZMWIEDRs25JprrmFpFYte7Nmzx9mOj4/HVM6z3FzPOHTtwx2++eYbNm3aREZGBrm5uSQmJrJw4UKeeuop2rZty2233UZOTo5bx/Rrbk4WXfcrlvfrLRV3SddYzCZYvS+VhKNV2J8cHg533WW0CwtVGVVERETET1Q5WSyL1Wqla9eujBkzhldffZUFCxZw9OhRdu3axaxZs3jiiScYPnw4TZo0cSaMUDqBzM3NZcaMGQwYMIArr7yStLS0SsWSkZHhbB+fsJ5KZGTJ8kXXPtyhfv36nHXWWQwePJgzzzyT8PBw5+dsNhtTp06lX79+Ffoz5+XlkZ6eXupVbbz6Kvz+O3zzDdSpU+XutF/RO2IigunbyihIVKVCN2Aki8XfczNnQlZWFaMTERERkaqq8DLUqmjevDnNmzfnsssuc7537Ngx5/LVNWvWsHLlSrZs2QKULGGdNWsW69evZ8mSJdSvYAGUzMxMZzs4uPxLEkNCQsrso7I6duzI+PHjGT58OC1btiz1OZvNxi+//MKjjz7KunXrAFi9ejXXXHMNc+bMKVf/zz33HE899VSV4/SJNm2Ml5tsTDKSbO1X9LwR3eJYuiOFH9cmcceg1pXvqE4dePRRyMw09i+GhbkvSBERERGpFI/MLFZEnTp1OO+887jvvvv4+OOP2bhxIykpKXz22WcMHjzYed+2bdsYPXp0hfu32WzOttVa/tzY9d6CgiocPF5k48aN3H333SckisVjXXLJJSxfvpxLLrnE+f7cuXP58ccfy9X/I488QlpamvOVkJBQ5ZirI7vdweZkYya4Y2z5Z5Klci7sFEuAxcSWAxlsO1jFGfiHHjL2LrphKbKIiIiIVJ3Pk8WyREdHc+211/Lrr78ye/ZswopmGebPn8+CBQsq1FdoaKiznZtb/hL/rveGeWmWIzg4mC+++IKGDRs633vjjTfK9WxQUBCRkZGlXrVRwrFsMvNsBFrNtGyg2SlPiwoNYGDbGAB+WFPFpagiIiIi4lf8Mll0dfHFF/P22287rz///PMKPe+6H7AiRWOys0sKdrj24WkRERHcfvvtzuvFixdXKMmtdnJy4JNP4KefYNOmKndXXNymXcMIAix+/8+7RhjR3Thz8Ye1SaX2IFfZ0aOlix+JiIiIiFdVi5+mr7vuOurVMwppLFu2rELPuu5xTE5OLvdzBw4ccLaLx/aWQYMGOdu5ubk1e0npgQNw440wbBhMnlzl7oqL23TSfkWvGdIhhpAAC/uOZrN2f+UKUZVy7Bg89hg0bw7PPlv1/kRERESkUqpFsmgymejYsSMOh4OkpIotdWvXrp2znZKSUmrG8FRcE7T27dtXaMyqatSoUanrI0eOeHV8r/LgsRniHaGBVoZ2NJZOu2Upal4evPIKZGTA22/DwYNV71NEREREKqxaJItQsvewosdYdOjQodT1mjVrTvtMYmIihw8fPmkfnnZ8Quu677LGcXOyuDFJx2b4wohuxlLU2euSKLRXcSlqo0YwYYLRzsmBl1+uYnQiIiIiUhnVJlmcMmUK06ZN45ZbbqnQc7179yYoKMh5vWTJktM+s3jxYmc7ODiY3r17V2jMqtq4cWOp65iYGK+O71VuTBZTMvM4kG7s72yvZNGrBrRtQFRIAIcy8li+O6XqHT74IBR/3771lmYXRURERHyg2iSLbdu2Zdy4cUydOrVCz4WHh5c6guOzzz477TOu9wwePNhr1VCLffnll8528+bNiY2N9er4XuXGZLH4yIzm9UIJD/LqEaK1XqDVzEWdjeXTP651w1LUuDgYP95oZ2fD889XvU8RERERqZBqkyxWxdixY53tdevWnfLswlWrVjFnzpwyn/WGH374gdmzZzuvL7vsMq+O73VuTBY3JRvFVbRf0TeKl6L+vP4A+TZ71Tt89FEICTHab78N+/dXvU8RERERKbdakSxeccUVdOvWzXk9YcIEtmzZcsJ9ycnJXH/99RQWFgLQvXt3Ro0aVWafe/bswWQyOV+TJk0q8760tDRGjRrFypUrTxvnF198wXXXXee8Dg0N5aGHHjrtc9WaO5PFpOJKqFFV6kcq56yW9WgQEURaTgGLtx8+/QOn06gR3Hmn0c7Lg6efrnqfIiIiIlJutWKtnslk4t1332XgwIHk5OSQnJzMWWedxe23386AAQOwWq2sWLGCN998k4NFe6NCQkKYNm0aJpOpSmM7HA5mzZrFrFmzaN++PRdccAHdu3cnNjaWsLAwMjIyWL9+Pd988w1///13qZg//PDDEyqj1jiuyWIVjygpPjZDxW18w2I2cUmXWD5atocf1iYxuEPDqnf64IPwzjuQng4ffGBct2pV9X5FRERE5LRqRbII0KtXLz799FOuv/56cnJySE9P54UXXuCFF1444d6QkBA+/fRTevXq5dYYtmzZUuaM5vEiIiKYOnUqV111lVvH90tumlnMLShk5+EsQMtQfWlE9zg+WraHXzcdJCe/kJBAS9U6rFcP7rsPJk0Cmw1eeslYkioiIiIiHlcrlqEWGzlyJCtXrmTIkCFlzhiaTCYGDx7MP//8w8iRI90yZkhICOPHj6dTp06nnaWMiorirrvuYsOGDVx77bVuGd/vBQZCZFFyV6dOpbvZeiCDQruDemGBxEQEnf4B8YgeTaJpUjeE7PxC5m92UwXTe++F1q3hP/+BF190T58iIiIiclq1ZmaxWIcOHfj1119JSEhg6dKlJCYmAtC4cWP69etHkyZNytVP8+bNcThOf55cUFCQs4LrsWPHWLNmDYcOHeLIkSOkpqYSGhpK3bp16dq1K127dsViqeJMTHUzY4bxsaAAAgIq3Y1zCWpcZJWXDkvlmUwmhneN438Ld/LD2iSGFxW9qZLISNiyBWrb94aIiIiIj9W6ZLFYkyZNuOaaa7w6Zp06dRg0aJBXx6w2qpAoQklxG+1X9L0R3Y1k8Y+th0nLLiAqtGpfW0CJooiIiIgP1KplqFJzuc4sim+1bxRJ24bh5Bfambsx2TODJHuoXxERERFxUrIo1Z7d7mBzcvGxGUoW/UHxmYs/rE1yb8cpKcYexmbNYP589/YtIiIiIqUoWRTfOXgQRo2CW2+FTz+tdDd7j2aTnV9IcICZFvXD3RigVNaIbo0BWLYzheS0HPd1PHcuvPaascf1gQfAbndf3yIiIiJSipJF8Z3kZJg1C957DxYvrnQ3G5PSAGjXKBKLWcVt/EHTeqH0blEXhwNmrUp0X8fXXgs9ehjtNWvg88/d17eIiIiIlKJkUXzHTWcsqriNf7qiZzwAM1fuL1fl4HIxm0sfn/HYY5Cb656+RURERKQUJYviO+5KFlXcxi9d3CWWkAALu45ksWpfqvs6HjwYLrzQaO/bB2+84b6+RURERMRJyaL4jmYWa7TwICsXdWkEwDcr97u38xdegOLzNJ991ih8IyIiIiJupWRRfMcNyeLhjDwOZeRhMkH7RhFuCkzcpXgp6uy1SeQWFLqv465dYexYo52WBv/5j/v6FhERERFAyaL4khuSxeIlqC3qhxEWZHVHVOJGfVrUo3F0CBl5Nn7ZeMC9nU+eDMHBRvvNN2H3bvf2LyIiIlLLKVkU33FHsqglqH7NbDYxqmh20e1LUePjjTMXAfLz4fXX3du/iIiISC2nZFF859ixknZ0dKW6UHEb/zfqDOPMxSU7jrj3zEWAhx6Cdu2MRPG//3Vv3yIiIiK1nJJF8R3XZLFOnUp1sanojMVOcVHuiEg8oFm9MM+cuQgQFQUbN8Kdd0JAgHv7FhEREanllCyK71x8Mdx4I1x6KYSFVfjx7Hwbu45kAVqG6u+ucFmK6rYzF4tZLO7tT0REREQAJYviS//3fzB9Onz3XckxCBWw5UAGDgc0iAiiQUSQ++MTt7m4SyyhgRZ2H8lixe6jp3+gKtasKT1rLSIiIiKVomRRqi0Vt6k+woOsjOgWB8AXK/Z5ZpCUFJgwAc44A556yjNjiIiIiNQiShal2ioubtNJxW2qhWt7NwXg5w0HOJaV7/4BsrLgk0/A4TCO0ti0yf1jiIiIiNQiShbFNxwO41UFG5NUCbU66RofRae4SPJtdmaucvMxGgBNmxrVUQEKC41jNdy9P1JERESkFlGyKL6xbx8EBkKDBkYlywqyFdrZkqxlqNWJyWTiurOM2cUvVuxzf6EbgAceMJJGgHnzjP2wIiIiIlIpShbFN1JTwWaDI0cgL6/Cj+9JySLPZic00ELzehWvpCq+MaJbHKGBFnYe9lChm9BQePnlkuu77oLMTPePIyIiIlILKFkU33CtVhkdXeHHi5egdoiNxGyueCVV8Y2I4AAu7e7hQjejRsEFFxjt/ftV7EZERESkkpQsim+kppa069Sp8OOqhFp9ebzQjclkFLgJKjpO5dVXYcMG948jIiIiUsMpWRTfcE0WKzGzWFwJVcVtqp+u8dF0buzBQjcArVvDo48a7cJCuP12sNs9M5aIiIhIDaVkUXyjCsmiw+HQzGI1Vzy7+NnyfdjtHqpY+uCDRtIIkJ8PRz2wR1JERESkBlOyKL7humexgstQD2XkkZKVj8Vsol2jCDcHJt5wWffGRARb2X0kiz+2H/bMIMHBMHUqvP02LFsG9et7ZhwRERGRGkrJovhGFWYWi2cVWzUIIzjA4r6YxGvCgqxcfWYTAD5ausdzA513Htx2G1j070RERESkopQsim9UIVncmJQGaAlqdXdj3+aYTPDHtsPsPKzjLURERET8jZJF8Y0qHJ2h4jY1Q9N6oQxu3xCAj5ft8c6gS5bAxRdDVpZ3xhMRERGpxpQsim9MmgQzZ8L770O9ehV6tHgZaqe4KA8EJt409uzmAHyzcj/puQWeHeytt2DAAJgzB5580rNjiYiIiNQAShbFN844A0aOhJtvhoCAcj+WmWdjT0o2AB20DLXa69e6Hq1jwsnKL+Sbfzx0jEaxoUNLzl587TVYvtyz44mIiIhUc0oWpVrZXLQENTYqmLphgT6ORqrKZDI5Zxen/7nHc8doALRtC089ZbTtdrjlFsjL89x4IiIiItWckkWpVkqWoGpWsaYYeUZjIoOt7E3JZv7mg54d7L77jFltgI0b4bnnPDueiIiISDWmZFG8r7AQfvoJli6FXbsq9GhxsqhKqDVHaKCV685qBsDURRX791BhVit88IHxEeDZZ2HdOs+OKSIiIlJNKVkU70tNhWHD4Jxz4N//rtCjqoRaM93crzmBFjMr9x7jnz1HPTtYt27w8MNG22aDG27QclQRERGRMihZFO+r5BmLBYV2th7IAKBjrCqh1iQxkcGMPKMxAO/84eHZRYDHH4cuXYz2unVGdV4RERERKUXJonhfJc9Y3Hk4k/xCOxFBVprUDXF/XOJTtw5oickE8zcfZMehDM8OFhQEn3xSUon3jTfg8GHPjikiIiJSzShZFO9znVmsU6fcjxXvV+wQF4nJZHJzUOJrrRqEM7RDQwCmeXrvIhjLUSdPhjPPhH/+gQYNPD+miIiISDWiZFG8r5LLUFXcpua77dxWAHy7OpEDabmeH/CBB2DZMmjf3vNjiYiIiFQzShbF+yqZLG5MUnGbmu6MpnXo3bwuBYUO3lvshdlFi6VkKaqIiIiIlKJkUbzPdc9iOZehOhwOZyVUnbFYs90+yJhd/HT5Xo5kerlKaU4OPPQQHDrk3XFFRERE/JCSRfG+SswsJqXlkpZTQIDFRJuYCI+EJf7h3LYN6NYkmtwCu3f2Lhbbvh1694b//hfGjAG73Xtji4iIiPghJYvifZVIFov3K7aOiSDQqn+2NZnJZOKewW0A+ORPL84uRkaWVESdOxdefdU744qIiIj4Kf3ULd6X61K4pJzJ4sakNEDFbWqLc9s1oFt8FDkFhbzrrdnFhg2N4zSKPfww/P23d8YWERER8UNKFsX73n8fCgogJQVatCjXI8Uzi9qvWDuYTCbuGdIWgI+9Obs4dKixZxHAZoNrroH0dO+MLSIiIuJnlCyKb1itULeuUY2yHFQJtfbxyewiwNNPw1lnGe1du+D228Hh8N74IiIiIn5CyaL4vdTsfBJTcwAli7WJ6+zi9D/3eOfcRTCO0vjiC2MPI8Dnn8MHH3hnbBERERE/omRR/F7xrGLzeqFEButMvNrk3HYNOLNZHXIL7Ez5bZv3Bm7RAt59t+T6jjtg5UrvjS8iIiLiB5Qsivf9+9/wwAMwbVq5bt+QaBS36dQ4ypNRiR8ymUw8cnF7AL76O4EdhzK9N/hVV8G//mW08/Jg6lTvjS0iIiLiB5QsinfZbPDWW/DSS/DRR+V6ZEPRzGLnOCWLtVHPZnU5v2ND7A548Zct3h38lVegb1+YOBHeftu7Y4uIiIj4mNXXAUgt41pZMqp8yd/G4plF7VestR68sB3zNx/kl40HWbn3KD2b1fXOwEFB8Mcfxj5GERERkVpGM4viXWlpJe1yJIsZuQXsOpIFKFmszVrHRHDVmU0AeH7OFhzerE5aVqJot3tvfBEREREfUbIo3uU6sxh5+uRvc3IGAHFRwdQLD/JUVFIN3DOkLcEBZv7ec4yf1x/wXSDLlkG3brB9u+9iEBEREfECJYviXRWcWVRxGynWKCqYCQNaAfCfnzeTk1/o/SB+/hkGDoQNG2D4cEhN9X4MIiIiIl6iZFG8q6LJYpJxv4rbCMBtA1vRODqExNQc3vljp/cDOOccaGuc/cjWrXDNNUbRJhEREZEaSMmieFcFk8WNiUWVUBtrv6JASKCFxy7pAMA7f+wk4Wi2dwOIjIQff4R69YzrX36BBx/0bgwiIiIiXqJkUbyrAnsWc/IL2X7I2LPYWctQpchFnRvRp2Vd8mx2/vPzZu8H0LIlfPMNWIuKSb/6Krz/vvfjEBEREfEwJYviXRWYWdxyIB27A+qHBxEToeI2YjCZTEwa0QmzCeZsOMDi7Ye9H8S55xrnhRa7/XZYtMj7cYiIiIh4kJJF8a6WLWHYMBgwABo3PuWtG5JKlqCaTCZvRCfVRPtGkdzYtzkAj327wTfFbsaPh7vuMtoFBXDZZbDZBzOdIiIiIh6iZFG86+qrjT1ff/wBvXqd8taNiSpuIyf3fxe0IzYqmH1Hs3ntt22+CeLll+GCC4z2sWNwySWQm+ubWERERETcTMmi+C1nJVQVt5EyhAdZmXxpZwDeW7ybjUlpp3nCA6xWmDEDuneHwEB44QUIDvZ+HCIiIiIeoGRR/FK+zc7WA0Zxm06aWZSTGNqxIRd3aUSh3cEjs9ZTaHd4P4iICOP8xd9+gyuv9P74IiIiIh6iZFH80raDGRQUOogMthJfJ8TX4YgfmzS8ExHBVtbtT+ODJbt9E0RsrHEGo4iIiEgNomRRvOu886BNm9P+YL3RuQQ1SsVt5JRiIoN59GLj7MUX521l28EMH0dU5I034IEHwOGD2U4RERERN1CyKN61axfs2AHbt5/ytg2JxZVQtQRVTu+aXk0Y1K4B+TY79361hnyb3bcBPfecUSn1pZfgmWd8G4uIiIhIJSlZFO8qPmcx8tRFa4qL23SKU3EbOT2TycQLo7pSJzSAjUnpvP7bqX8Z4XENGpS0n3wSXnvNZ6GIiIiIVJaSRfEehwPSjRlDok4+Y2grtLM5WTOLUjExkcE8e3kXAP63cAcr9x7zXTDjxhnHahS791744APfxSMiIiJSCUoWxXuyssBetDzwFMniriNZ5BbYCQu00KJemJeCk5rg4i6xXN6jMXYH3PPVatJyCnwXzH33wcSJJde33gpff+27eEREREQqSMmieE+ayzl4p0gWNyQa93WMi8RsVnEbqZhJIzoRXyeEhKM5PPTNOhy+LDAzcaIxqwjGL0quu04Jo4iIiFQbShbFe4qXoMIp9ywWF7fR+YpSGVEhAbx13RkEWEzM3XiAj5bt8V0wJpOxHHXcOOO6sNBIGL/80ncxiYiIiJSTkkXxnvLOLLocmyFSGd2aRDuP0/jPz5tZm5Dqu2BMJpg6FW65xbguLDSWqGZl+S4mERERkXJQsijeU45k0W53sDmpeGZRlVCl8sae3ZwLOzWioNDBHZ+v4mhWvu+CMZth2jRj32L9+jBvHoRpP66IiIj4NyWL4j2uyeJJlqHuO5pNRp6NQKuZ1jHhXgpMaiKTycQLV3Slad1Q9h/L4Y7PVlFQ6MPzF81meOcdWLkSOnf2XRwiIiIi5aRkUbynVy947z1jD9egQWXeUrwEtUOjCAIs+ucpVRMVEsC7N55JWKCFP3el8PTsTb4NyGyGpk1Lv2ezwaxZvolHRERE5BT007h4T4sWxr6t++6Dnj3LvMVZ3Eb7FcVN2jWK4NWruwPw8Z97+Xz5Pt8G5MpuN4rfjBoFDz9snEUqIiIi4ieULIpf2Vhc3EaVUMWNzu/UiP87vy0AT36/gWU7j/g4oiK//w7TpxvtF16A8eONAjgiIiIifkDJovgNh8PhPGOxc2MVtxH3umNQa4Z1jcVmdzDh45VsTk4//UOeNmQIvP22UTEVjGXaV18Nubm+jUtEREQEJYviTTt3wqZNkJhY5uxJUloux7ILsJpNtG0Y4YMApSYzmUy8dGU3ejevS0aejbEfriAxNcfXYcFtt8EXX0BAgHE9c6aRRB7xk9lPERERqbWULIr3PPAAdOoE8fGQnHzCp9fvN2YVW8eEExxg8XZ0UgsEB1h498YzadswnIPpeYz5YAWp2T48UqPY1VfDjz9CaKhxvXQp9OkD27b5Ni4RERGp1ZQsivekuyz7K+OcxfWJqQB0jdd+RfGcqNAApt/cm9ioYHYcyuSmj/4mM8/m67Dgggtg0SJo1Mi43rkT+vaFxYt9G5eIiIjUWkoWxXuKz1k0mSD8xDMU1xdVQu0SH+3FoKQ2io0KYfrNvYkKCWD1vlRu+nAFWf6QMPbsCcuXQ5cuxvXRo/DMM6qSKiIiIj6hZFG8pzhZjIwsKehRxOFwsH5/KgBddWyGeEHbhhF8estZRARb+XvPMW6Z/jc5+X5QibRpU1iyxJhpbNfO2M943PeLiIiIiDcoWRTvKU4Wy1iCuv9YDseyCwiwmGgfq+I24h1d4qP4+ObehAdZ+WvXUcZ97CcJY2SksYdxwQKoW7f05zTLKCIiIl6iZFG8p3jPYpn7FY1Esl2jCIKsKm4j3tOjaR0+uqkXoYEWlu5IYcwHK0jPLfB1WEZ11NjY0u8dOAADBsDatb6JSURERGoVJYviHfn5JWfHRZ54huK6okqoXbQEVXzgzOZ1mX5zbyKCrKzYc5Tr3v2LlMw8X4dVWn4+XHGFsUS1b1/4/HNfRyQiIiI1nJJF8Y7iJahQ5szihsTiZDHaSwGJlNareV2+GN+HemGBbEhM58qpf5LkD+cwFktLMxJGgJwcGD0aJkww2iIiIiIeoGRRvOMUyaLD4WBdcXEbHZshPtS5cRQzbutLXFQwuw5nMfJ/y9iYlHb6B72hQQPjaI2bbip5b9o06N0bNm/2XVwiIiJSYylZFO9wPWPxuGWo+45mk55rI9Bipm1DFbcR32rZIJxvbj+b1jHhHEjP5cp3/uT3LQd9HZYhOBg++AA+/BBCQ433NmyAM8+E6dN9G5uIiIjUOEoWxTu6dYPDh42DxidOLPWp4v2KHWIjCLTqn6T4Xlx0CDNvP5t+reuRnV/IuOn/8OHS3Tj8pRLp2LHw99/QubNxnZ1tvHf99ZCa6sPAREREpCbRT+biHRYL1K8PLVueUOGxuBJqZxW3ET8SFRLARzf15ppeTbA74KkfN/HIrPXkFvjB0RoAHTvC8uUwblzJe599ZrwnIiIi4gZKFsXn1hfNLGq/ovibAIuZ50Z24dGL22MywZd/J3DFO8tIOJrt69AMoaHw7rtGZdSoKKPgzQUX+DoqERERqSGULIpP2e0OVUIVv2YymRg/oBUf39ybOqEBbEhMZ9gbS1iw5ZCvQytx7bWwfj289FLp9x0O2LTJNzGJiIhItadkUbxj8WJ47jl4803Yu9f59p6ULDLybARZzbRpGO7DAEVOrX+bBsy+qz/dmkSTllPATR/9zX9+3kyezU+WpTZpAuHHfQ998IGxr/GuuyAjwzdxiYiISLWlZFG8Y/58ePRRuPNO2LLF+XbxfsWOcZEEWPTPUfxb4+gQvp7Qhxv7NgNg2qJdXPbWMrYd9MNEbN8+uOceY3bxjTeMPY4//ujrqERERKQa0U/n4h2uR2dElByPUVwJtYuK20g1EWS1MPnSzky7oSd1wwLZnGwsS/1w6W7sdj+plgoQFweTJkFIiHG9fz+MGAFXXQXJyT4NTURERKoHJYviHa5L4FySxfWJShalejq/UyPm3tOfc9s1IN9m56kfN3HNtL/YcSjT16EZrFa4/37YuBHOP7/k/RkzoH17eOUVyM/3XXwiIiLi95QsineUkSwW2h1sTCyuhBrtg6BEqiYmIpgPx/bi6Us7ERpoYcWeo1w8ZTGv/7adfJvd1+EZWrSAuXPh00+N42vAmOm//37j/NN583wbn4iIiPgtJYviHa7JYmQkALuPZJKVX0hIgIVWDcJ8FJhI1ZhMJm7o25x59w4wZhkL7bzy6zYueX0xy3el+Do8g8kEo0fD5s3GuYwmk/H+li3w00++jU1ERET8lpJF8Y4y9iwW71fsFBeJVcVtpJqLrxPKh2N7MeWa7tQLC2T7oUyunvYXd3y2iv3H/ORcxvr1jXMZ//4b+vY1ridN8nVUIiIi4qf0E7p4R/HMYlAQBAQALsVt4rVfUWoGk8nEpd0bM/++gYw+qylmE/y0PpnBL//BK/O2kp1v83WIhp49YelS+OsvqFOn9OdeegmeeQaysnwTm4iIiPgNJYviHcXJoktxmw0qbiM1VJ2wQJ69vAs/3dWfvi3rkWez8/rvOxj44kKmL9vjH2czmkzQqlXp9w4dgqeegieeMD739ttQUOCb+ERERMTnlCyKdxQni0X7FW2FdjYmGUtTu2pmUWqoDrGRfH7rWbxz/Rk0rRvK4Yw8Jv6wkfNe+oOv/07AVugnRXCKLVgAOTlG++BB+Ne/oG1bmDZNlVNFRERqISWL4h2dOxuvdu0A2Hk4i5yCQsICLbSoH+7j4EQ8x2QycWHnWObfN5BnLutMw8ggElNzeHDmOs5/dREz/knwn8qpV19tHLUxalTJe3v2wIQJ0Lo1/O9/kJvrs/BERETEu0wOh8OPTpEWd0lPTycqKoq0tDQii2bz/MmMfxJ44Jt19G5Rl68n9PV1OCJek1tQyCd/7uXtP3ZyNMuYrYuNCmZc/5Zc06sJYUFWH0dYZPlyo/jN3Lml34+Lg+efhxtu8ElYIiIiUnGVzQ00syg+sb74fEXtV5RaJjjAwq0DWrLowUE8clF7GkQEkZyWy9OzN9Hvhd95Zd5WDqX7wezdWWfBnDlG0jh8eMn7SUmQ4idHgoiIiIhHKVkUnyhOFlUJVWqr8CArEwa2YvGDg3huZBda1A8jNbuA13/fwdnP/84dn69i+a4UfL74o3dv+OEHWLkSLr8coqPhlltK37N3L6xd65PwRERExHO0DLWG8udlqAWFdjpP/IU8m53f7x9IywbasyhSaHfwy8YDfLh0N3/vOeZ8v32jCK7v04wR3eOIDA7wYYRFjhwxzmd0ddttMHUqnHsu3H47XHYZBAb6IjoREREpQ2VzAyWLNZRfJYurVxs/QEZEwOjRbDp/JBe/vpiIICtrJ56P2WzybXwifmZTUjqf/LWHb1cnkltgFL8Jspq5sHMjrugZz9mt6mPxl++bI0egSZPShW8aNjRmH8ePh2bNfBebiIiIANqzKP7s4EFj39P8+bBnD+sTUwHo3DhKiaJIGTrGRfLcyK4sf2QIj1/SgdYx4eTZ7Hy/Jokb3l/BOS/8zn/nbmHbwQxfhwqhofDyy8YRG8UOHoT//AdatIBhw+D773Veo4iISDWkZFE8Lz29pB0Rwbr9RcVttF9R5JSiQgMY178lv947gO/v6McNfZoRFRJAclou/1u4k/NfXcSQV/7glV+3+S5xDA01zmPcssX4hdAVV4C1qKKrwwE//WQsS42LM/Y2ioiISLXhJzXapUbLcPkhNiKCDUXFbTqrEqpIuZhMJro1iaZbk2geH9aB3zYfYtaq/SzadoQdhzJ5/bftvP7bdlrHhHNxl1iGdmhIp7hI787cm0wweLDxSk6GDz6AadNg3z7j89HR0LRp6Wfy8iAoyHsxioiISIUoWRTPc0kW88Mi2LzXuNbMokjFBVktXNwllou7xJKeW8D8TQf5eX3yCYljg4ggzmsXw6D2MZzTpj7h3jy/MTYWHnsMHn4Y5s2D6dPhjDOMhNLVOecYyeKVVxozko0bey9GEREROS0VuKmh/KrAzeTJMHEiABu+nM2w1RAZbBS3MR3/w6OIVEp6bgG/bT7InPUHWLLjCNn5hc7PBVhMnNWiHgPa1ufsVvXpGOvlWcey7NoFrVqVfu/ss0sSx/h438QlIiJSA1U2N9DMoniey8ziuoIgII+u8dFKFEXcKDI4gMt7xHN5j3jybIWs2H2U37cc4vcth9ibks2SHUdYsuMIANGhAfRpUY9+revRt1V9WjUI8/73Y3IydOwImzaVvLdsmfG6914480yjOM6wYdCjB5i1xV5ERMTblCyK57kki+uzLQB00RJUEY8Jslro36YB/ds04MlhHdl1JIsFWw7x584Ulu8+Smp2AXM3HmDuxgMAxEQE0at5Xc5oVoeezerQMTaSQKuHk7N+/WDjRiNZnDHDeG3cWPL5f/4xXs8/DykpRiEdERER8Soli+J5LtVQ16cZS+O6qLiNiFeYTCZaNQinVYNwxvVvSUGhnfWJaSzbcYRlO1P4Z+8xDmXk8dP6ZH5anwwYZzp2i492Jo/d4qOIiQz2TIAdOxrL1CdOhM2bjaTxu++M81kBzjvvxETxiSeMvY5DhhgzkFb9r0xERMQTtGexhvKrPYvDh8Ps2eRaAujy4HcU2B0sfnAQTepqpkDE13ILClmTkMqqfcdYtfcYK/ce41j2iWciNogIokvjKDrHRdK5cRSdG0cRGxXsueWr+/fDzz9DkyZw0UUl79tsULduyYqFyEg499ySSqwdO55YSEdERKSWq2xuoGSxhvKrZPGLL2DTJtZmmrg06CzqhAaw6omh2rMo4occDge7j2Sxcu8xVu0zkscdhzKxl/F/inphgXSMi6R9owjaNoygXaMIWseEExrowZm+v/+G3r1P/vl69YxCOeecA2PHQkyM52IRERGpJpQsSil+lSwW+eSvvTzx3QYGtG3Axzef4oc9EfErOfmFbEpOZ0NiGhsS01ifmMb2Q5kUlpFBmkzQpE5oUfIYTtuGEbRqEE6L+mGEuev4jt274bffSl6HD5/8vubNS64TE40A4+LcE4eIiEg1oWqo4vfW708FoKv2K4pUKyGBFnoW7V8slltQyJYDGWxMSmP7wUy2Hshg+6EMjmTms+9oNvuOZjN/88FS/cREBNGifhgt6ofRvOhji/phNK0bSnCApfwBtWgB48YZL7sd1q+H+fNh0SJYsgSOHjUSwmbNSj/34oswZYrxuTPPNF69ekHPntCgQVX+ikRERGokzSzWUP44s3jRlMVsTk5n6g09uaBTI1+HIyIecCQzj20HM4wE8mAG2w5ksOtIFkez8k/6jMkEcVEhxNcJIb5OaNHHEBrXCaFJnVAaRQUTYClndVa7HbZuNWYRhwwp/blzzoGlS8t+rlkz44iOLl3gwguNpawiIiI1hGYWK2DZsmVMnz6dxYsXk5iYiMPhID4+nnPOOYcxY8bQr18/j46/a9cuPvroI3766Sf27dtHZmYmcXFxdO3aldGjR3PZZZdhrUnV/Q4eJDc4lG0HjYIUqoQqUnPVDw+ifngQZ7eqX+r9tOwCdqdksedIFruOGB93F33MyLORmJpDYmoOy3cfPaFPswlio4zkMT46hNjoYBpFBhMTaXxsFBVM/fAgLGaTcR5jhw7G63hDh0JwsHEkR1pa6c/t3Wu8vvvOuHZNFm02eP11I5Fs1w7i43Xuo4iI1Aq1amYxKyuLu+66iw8++OCU991000288cYbhIWFuT2GKVOm8NBDD5GXl3fSe/r06cNnn31Gy5YtKz2O38wsFhaC1cqquHaMvOFl6ocH8vdjQ1TcRkQAo6DOkcx89qZkkZiaw/5jOew/ls3+YzkkHsthf2oO+Tb7afuxmE00CA+iYWQQDYsSyIaRxqt+eCD1w4OoFx5I3bBAgswm2LnTSBr//tv4uGoVZGUZnc2YAVdcUdL55s1GldViISHQtq2ROB7/iohw89+QiIhI1Wlm8TQKCwsZOXIk8+bNc74XEhJCp06dsFqtbNq0ifSi8wA//PBDEhMT+fnnn7FYKrCP5jSefvppnnzySee12WymY8eO1K1bl+3bt5OcbJxx9tdffzFw4EBWrFhBbGys28b3icxMANY3agMYs4pKFEWkmMlkokFEEA0igjizjM/b7Q6OZOaRcCynKJnM5kBaLgfScjmYkcfBtFwOZeRSaHdwID2XA+m5QFoZPZWIDLYWJY/NqNejLfUH3ES90EDq56ZT71Ai9Vp2IfpABtGhAUSHBhC0bl3pDnJyYO1a43W8lBTjaI9i69bBkSPQsqUxI1mTVo2IiEiNV2v+r/XEE0+UShRvvfVWnn/+eeoW/U89KyuLF154gaeffhqAefPm8eSTT/Lss8+6ZfxffvmFiRMnOq/79u3LRx99RNu2bQGw2+3MmDGDcePGkZmZyf79+7nyyitZsmSJW8b3maIEfH2jVgB0iY/2YTAiUt2YzSZiipacuhbYcVVYlFAeSMvlYLrxOpCey4G0PA5l5JKSmc+RzDyOZuVjsztIz7WRnmtj15GsMnoLgB1bgC3Od0KsEUQ/9j1RBTnUyUojOu0I0UcOEJWTTp2cDKJzM4jKySQ6AKLyrIQfzSYyOIDwYCuWt96CadOMjiwWaNrUKNDTrJmRPMbHQ+PGxqxk69bu/wsUERGpglqxDDUpKYlWrVqRm5sLwA033MDHH39c5r1PPPEEzzzzDADBwcHs3LmTuCqWWXc4HPTo0YO1Rb+FbteuHatWrSI09MRD6efPn8/QoUOd17NmzeLyyy+v8Jh+swx10ybo1IkLbn6TrQ2a896NZzKkY0PfxSMitZbd7iA9t4AjRcljSmY+KVl5LtfF7+WTmp1PWk5BmedLVkRYYT4R2emE52UTkZdNRF4WEXlZROZlE57v8t6ZPYi483YigqyEBVkJC7IQMvFJwsKDCW3UgMBGDY0zIxs2NF716xvJp4iISDnonMVTePDBB3nxxRcBCA0NJSEhwTmjeLz8/Hxat25NQkKC89kXXnihSuP//PPPXHLJJc7ruXPncsEFF5z0/muuuYavvvoKgN69e7N8+fIKj+k3yeLy5WT3H0jne77Gbraw/NHBNIwM9l08IiLlZLc7yMizkZZdwLHsfFJzCpxJ5LGsAlJz8knLLiA1p+jz2QVk5BaQnmsr1z7LiggoLCCkII+w/BxCC3IJy88lhELCzA5Cz+hGaGwMoYFGkhmak0Vo4j7CIkIJjoogODqS4OhIQiLDCA6wEhxgJjjAQlDRx5AAS/mrzYqISLWkPYun8O233zrbV1111UkTRYDAwEBuuukmJk+eDBgze1VNFmfNmuVst2jRgvPPP/+U90+YMMGZLK5YsYL9+/cTHx9fpRh8Jj2dTTEtsZstxJCvRFFEqg2z2URUSABRIQE0rXfiSpBTybMVkpFrIzPXRkauzZlEZubZyDiaTsahFDKOpZORlkVGVi4ZdWPICI0gI9dGTn4hWTn5ZGflkm8NAKDAEkCBJYD04PATB0vMh8T9x70ZCNiAY0WvU7OYTQRbzQRjJ5hCgqxFiWSgleCgAIKDAwgODiIo0EJwgIVgq4VAq5lAq5kgq5lAi9l5XaptNRPkch1gKbknyFr6GasSVhERv1Pjk8WtW7eyY8cO5/WFF1542mcuuugiZ7K4Y8cOtm7dSrt27Sodw08//eRsX3DBBact8NK/f3/CwsLIKqrM99NPPzFhwoRKj+9TGRmsKypu0zUg18fBiIh4R5DVQlC4hfrhQZXrwGaDXbsoSNhPdvJBsg+lkHX4GNnH0sg+lk52ehZZmTnkZOeRNWky2eFRZOXZyM4vJHvtBrL+WUV2YAg51iByAwLJtQaRZw0k1xpITkAQudZAcgNKfnlXaHeQlV+I8X8dM+QDFBa9Tl69253MJlySTYszmQywmLCaiz5azFjNJgIsZqxF7wdajY9Wi4mA4o9F91ktJc9bLaaipLTo/TI+f+K9Rj8Ws8n50eK8NmM2g9VsdnnPhNlU9NGsYm4iUv3V+GRx7XHV6vr27XvaZ8444wwCAwPJzzcOkV63bl2lk8VDhw5x4MCBCo1vtVrp1asXCxcudI5fbWVksKGRUbShS6h7l2WJiNRYViu0bUtA27ZEASc9nbZ4J4nrLyGbFkBIAhw9CkcPGBVajx4t/crMxNGpE3mr15JXYCfXVkhuQSE5t04g96+/jWTS6pJkBgQaiWdx8tmrD/nnDSG/sJB8m518m52Cr2eQZ7KQbwkwXtaAkrYlgHyrlXxLAHmR0eSbLOQX2nHdCGN3QG6BndwCO8asaPVmMoHF5JJEOhNOM5bjkkyL2VRyr6Uk4SydnJa+tpjNzuTUbDJmh00mExYzRe8Zr+Lrk33OVDSu2eT6Odf+Sn/OeB9nu6zPWYriKv47MB8Xp3Mcc+kxi9smE0WvkvdNGNcml2uzyQQmSu4p+gilr02U9CciFVPjk8XNmzc724GBgTRp0uS0zxTft3PnzhP6qMr4AK1atSrXc61atXImi1UZ3+fS01lXlCx2jVYxBhERtyrrh9/u3Y3XqeTnY8rKMpaUBliIwljuytiroF8Po5L1Ca8jJe2h7WC4y9mTNhtce0b5Yp47Fy64AIfDQaHdQf4v88gfdaWRSJaRZBZYrBRYrNjMFgq+/Q6bAwoK7RQUOrB99TW23xZQYLFgMxfdY7FSYLZis1gpMBvvFzRthu3Ci7DZHUXP2rH9voCCrBzjOYvFeKbo+ZKPVgqi62APC8NmN+ItLLRTmJNDocmCzXLyH6McDrA5HNjsDi/NzcrpmIpe5qLE0WQuSTpNODDb7RTln8Y9Lh9NzgQVzEFBzsTVZAJzgQ2TvZDihdRmk+tzRf1gwhRgxRwSXDrxzcrC7HA47zOZwExJuzhuQkMxBQWV9FlYiCkjo9R9uMTpOrapTjQmswWKrsnNxZSdXfqe48YzAVitmOrUKbqn6M+blgYFBaWeo6x+QkMxRUSUPAeYDh0s6b9osFIxFI9Ttw4U/VkxmTDl5WE6duyEMVz/6+f8XOPGpftKT3OeoVt6rNJ/TwQHY6pf36UvExw8gMlmO+HP6Pp3DWCKjsZUdMZu8deG5GSjHV2Hjm3jaNOw+p7BW+OTxT179jjb8fHx5f6tUtOmTZ3JomsfVRm/uN/yjn+yPsqSl5dHXl7J/46Kz4z0tcz0LHbW6wRA5wYV2/MjIiIeEhhovI43YkTl+jObjTMls7ONH8yKP7q2iz8W/dLUZDJm0axREYT26AJ5eZCba7zy8iA3AzKL3rPZICAAuhxXnfyj3bD6pzICOs5FF8GVD5d+75nRsG3b6Z999VW4556S6717oXlz56UdEzazBbvZjM1sodBkptBswWY2U7hwEYWtWhlJpt1B4VdfY5v8tPOeQrPZSDpdnrebzNhiGlL47rsU2sFmtxvPvv4GhavXuPRf9IzJgt1kwmEyUWi2YO/ZE/uw4dgdUOhwYHc4sL/2OnabDbvJhN1kxm4yUWiyFD1jLnrfgn3AAOxNmlLocBjJ/OEU7IsWYTdbip4x4yjqo7CoH7vZbFz36o3DbKbQ7sDuAPuBA9gPHy4az+zyvNkYk6JnA4OwR0VRaHfgcBgV5O05OTjsduxF4zkwOf+MDlPl97Y6il52B0Y2X6Fyxy735lZ2W01VlnVX4ee6pEOVfDAPKOuIofLIAVIq+WxmJZ8D1m05/T0ndeD0t5Tp6Ck+l8JDhVYli/4sIyPD2Y6KOulCnhO4Vgly7aMq41ckhoqO/9xzz/HUU09VLDgv2HjxlTi+2kJssIkG557t63BERMQTzGbo0qVyz/brB8uWnfqewkIjgTzegw/C9de7JJi5UFBw4qusInHjxsHBg2Xf7/pq2bL0c1Yr9OhhxFRYiLmwkMCiNjab830KC6FOqPEqFmiDlH1gP822DHtz6Bxb+r307bBuXpm3l9I5Es4/buvMFR8byfrp3NwPRvYouV68GO4t53nTb2dAuEsBpieegPefOf1zZ58NS5eWfq93b/j775M+4gAjAZ04Ecdjj2MvWs9sP3oMR5MmzqSYovuMBNdIOouTXcePP2Dv3gOHoyhJ/fFH7Pfc65KUFiepZud4DhM4IiKxL1xYlGs6jAT0mWdx/PyzMykuHsd1XLvJhGPgQBwPPYTdXpy0OnDcfAuO1FTnuMZ4Rrv4z+owmXCMuxVH//5GvAD79+OYOPHEe4vHBiiO5dlncURGOtNdx++/4/jhBxyYSu4xFT1b1AZwNGqE4+57jHbx39NHH+HYtcv59wMU3V8yrgMTjj59cJx3nvEMRYn5Sy+V/Hlc7y/VlwkuvRRHkybOZx179+H4ec4J41CqL3BgxjHmRnAU/V04HDhWroQtW0v+fMf/3RTH0LAR9DvbuTTeATh++w0yM51jFr/P8TF36ADNmzvHJDcXFi0ynmnXjsZ1Qk76b7k6qPHJYmZmyW8ngoPLX4kzJKTkC+vaR1XGr0gMFR3/kUce4b777nNep6enl2vJrad179Kc7+vX42h2vnEumIiISEVZLFDG2cQ0bWq8KuOBByr3XOPGsGpV5Z4dP954ORxGwuiaWLomm2V54w34z39Knj3Zx7L+Xzt3rtHvqZ5zOKBnz9LPtW8PX31V9v3Ht4OOK+Y0fDgUn1NtTBmW3Y49LikGYya3uN5DGc+aAIvDgeWcc8DqMtMYGQaTnjj1eMXt1k0h2uWH+B4d4N+3lH2/63VwMMRHl453cC8oz0kE3bpB++POmr6ol7G0+3TObgU9XX7pEWeBQR1Pfn+pZ5uD62RFXgs40vz0z8XGwqDWpd/bHAvR5Yi3byxc2L7k2m6HOWV8D5d1gt/g5tChQ8n1RjNsLWfdi6u6l742b4fyTPp0bAQ3nFn6vR0/wMHjZmXLindAPxjSu+Q6JQXWfGq0B54N3ap2Xruv1fhzFocMGcJvv/0GGFVGFy1aVK7nbrjhBj791PhCDx48mPnz51dq/GeeeYYnnnjCeV1YWIjZfPolFO+//z7jxo0DwGKxYLNVbLO/35yzKCIiIiIiPlXZ3KDGH2oU6vKbyNwKrDF3vTcsLMwt41ckBneNLyIiIiIiUhk1PlkMd1k/n5OTU+7nsl3W9rv2UZXxKxKDu8YXERERERGpjBqfLNZ3WbufnJxc7udcz0asV6+eW8avSAzuGl9ERERERKQyanyy2K5dSUWwlJSUUjN2p5KQkOBst2/f/hR3ln98gH379nl1fBERERERkcqo8cliB9dqSsCaNWtO+0xiYiKHDx8+aR8V0aZNG6zWkqKz5RkfYPXq1W4ZX0REREREpDJqfLLYu3dvglzKOS9ZsuS0zyxevNjZDg4Opnfv3qe4+9QCAwM566yzKjT+gQMH2LFjh/N6wIABlR5fRERERESkMmp8shgeHs7gwYOd15999tlpn3G9Z/DgwVWuRnrppZc62/Pnz+fgwYPlHj86OlrJooiIiIiIeF2NTxYBxo4d62yvW7eOH3/88aT3rlq1ijlz5pT5bGVde+21ztnNgoIC/vvf/5703szMTF5//XXn9ejRowkICKhyDCIiIiIiIhVRK5LFK664gm7dujmvJ0yYwJYtW064Lzk5meuvv57CwkIAunfvzqhRo8rsc8+ePZhMJudr0qRJJx0/Pj6eCRMmOK+nTJnCzJkzT7ivoKCAm266yVkEJyQkhEcffbRcf0YRERERERF3sp7+lurPZDLx7rvvMnDgQHJyckhOTuass87i9ttvZ8CAAVitVlasWMGbb77pXCIaEhLCtGnTMJlMbolh0qRJzJkzh+3bt1NYWMhVV13Fddddx2WXXUbdunXZunUrb7/9NuvWrXM+8+KLLxIXF+eW8UVERERERCrC5HA4HL4OwltmzZrF9ddfT05OzinvCwkJ4dNPP2XkyJEnvWfPnj20aNHCeT1x4sRTzi4CbNu2jSFDhpQ6FuNkHnzwQV544YXT3ncy6enpREVFkZaWRmRkZKX7ERERERGR6q2yuUGtWIZabOTIkaxcuZIhQ4aUOWNoMpkYPHgw//zzzykTxcpq27Yt69at45ZbbiEkJKTMezp06MD3339fpURRRERERESkqmrVzKKrhIQEli5dSmJiIgCNGzemX79+NGnSxCvjZ2Rk8Pvvv5OQkEBWVhaxsbF06dKFHj16uKV/zSyKiIiIiAhUPjeotcliTadkUUREREREoPK5Qa0ocFMbFf8OID093ceRiIiIiIiILxXnBBWdJ1SyWENlZGQAeG1ZrYiIiIiI+LeMjAyioqLKfb+WodZQdrudpKQkIiIi3Hb8R2Wlp6fTpEkTEhIStCS2htDXtGbS17Xm0de0ZtLXtebR17Tm8bevqcPhICMjg7i4OMzm8tc41cxiDWU2m4mPj/d1GKVERkb6xTeLuI++pjWTvq41j76mNZO+rjWPvqY1jz99TSsyo1isVh2dISIiIiIiIuWjZFFEREREREROoGRRPC4oKIiJEycSFBTk61DETfQ1rZn0da159DWtmfR1rXn0Na15asrXVAVuRERERERE5ASaWRQREREREZETKFkUERERERGREyhZFBERERERkRMoWRQREREREZETKFmUEyxbtowJEybQsWNHoqKiiIyMpGPHjowfP56lS5d6fPxdu3bx5JNP0rNnTxo0aEBISAitWrXi8ssv55tvvsFms3k8hpoiNTWVb7/9lrvuuosBAwbQqFEjgoKCCA8Pp2nTpgwfPpzXXnuNY8eOeWR8k8lU4dc777zjkVhqioULF1bq73XLli0eiUffr1WzZ8+eSn09XV979uypchz6Xq2Yw4cPM2fOHCZPnsyIESOIjY0t9Xfz0UcfVbrv9evXc99999G1a1fq1q1LeHg47dq1Y/To0cydO9d9f4hTOHDgAC+88AJ9+/YlNjaW4OBgmjdvzoUXXshHH31ETk6OV+LwJnd/TbOzs5kzZw4PPPAAQ4YMIT4+npCQEEJDQ2ncuDHnn38+zz77LElJSZ75AwHNmzev8Pf1ww8/7LF4fMGdX9fK/vfak9+3XvledYgUyczMdNx8880O4JSvm266yZGZmemRGF577TVHUFDQKcfv06ePY+fOnR4Zv6bYvHmzY9iwYY7AwMDTfj0BR2hoqOPVV1912O12t8ZRnrGPf7399ttujaGmWbBgQaX+Xjdv3uz2WPT9WnW7d++u1Nez+GW1Wh1Hjx6tchz6Xi2f5ORkR7NmzU77d/Phhx9WuO+CggLHI4884jCbzafs+5JLLnEcOnTI/X+4Il988YUjKirqlDG0a9fOsWrVKo/F4E3u/poeOHDAcfXVVztCQ0PL9X0UEBDgeOSRRxx5eXlu/7OV5891/Ouhhx5yexy+4Inv1cr+93rOnDke+TN663vVyv+3d+dRUZ3nH8C/w4zAyICKIuACqBVxw12PGreCYqRaKzkazYmmNRqtrTV6jF002saTRpO6RGvUuJxqxAVFNGpUcDluVRBBRJFqI4i4gkAccETg/v7g59u5DMvAXBiG+X7O8Zz7Du9934c7Psw8c++8lwhAcXExJkyYgJMnT4rHtFotunbtCo1Gg1u3buGnn34CAGzfvh2ZmZk4duwY1Gq1YjF89tln+PTTT0XbwcEBXbp0gbu7O+7cuYNHjx4BAC5fvoxhw4YhNjYW3t7eis3fkCQnJ+PIkSOyx9RqNX72s5/B09MTxcXFSElJwfPnzwGUfgL68ccf4+bNm9i8eTNUKpXiMQ0dOhRarbbKfj4+PorP3VA5Oztj2LBhZvXV6XSKzs18VYZWq0VISIjZ/UtKShAdHS3aISEhaNasmaIxMVcrZjAYkJ6eXitjf/TRR9i2bZtoN2rUCF26dIFOp8Pt27eRnZ0NADh69CiCg4Nx8eJFxfN6586dmDp1quwxf39/eHt7Iy0tTfzuqampGD58OC5duoSuXbsqGkNdU/o5zcjIwN69e2WPqVQqtG/fHl5eXlCr1bK/ka9fv8bf//53JCYmIioqCo6OjorFYqxfv35wd3evsl+nTp1qZf66Vpu5+oa5f7s9PDwUn7tOc1Wh4pZs3J/+9CfZJxEzZsyQsrOzxc/1er20ZMkSWZ8///nPis1//PhxSaVSibEHDhwopaamip8XFxdLe/bskXQ6negzePBgxeZvaCIiIsRZh/Hjx0tRUVFSXl6erE9JSYkUFRUltW7dWva8btiwQbE4jMe9d++eYuPaM+Mzi76+vlaJgflqPSdOnJDl1b59+xQZl7lqHuMzCx4eHtLo0aOlxYsXS1FRURadWdy0aZNs/3HjxkkPHjwQPy8sLJTWrVsnaTQa0WfKlCmK/m5JSUmyKwX8/f2lq1evyvqcPHlS8vT0FH3at28vvXz5UtE46prSz2lcXJwEQFKpVFJQUJC0a9cu6dmzZyb9zp49K3Xp0kU2xyeffKLo72Z8Zu3MmTOKjl3f1Uaulj2zaC11nassFknKzMyUnJ2dxX+o999/v8K+ixcvFv2cnZ2lzMxMi+cvKSmRevToITtlnp+fX27f6OhoWaJGRkZaPH9DFBUVJX344YdSenp6lX3v378veXl5iWPaokULqbCwUJE4+AZUedYuFpmv1jVlyhRxPJs1ayYZDAZFxmWumicvL0+KiIiQ0tLSTH5W0zeg+fn5sr/Bw4cPl4qKisrtu2XLFtFPpVJJ8fHxNf1VTIwdO1b2OvD48eNy+yUnJ8veqK5atUqxGKxB6ec0Pj5eCgsLk27evFll39zcXFnB6OjoWOFxrwl7LhZrI1frS7FY17nKYpGkhQsXiv9IjRs3lp1RLOvVq1dS27ZtFf0U7OjRo7LkO378eKX9J02aJPr279/f4vnJ9FPtmJgYRcblG1DlWbtYZL5aT15enqTVasXxnD17tmJjM1ctV9M3oP/85z9lBeCtW7cq7T9gwADRf+LEiRZGXermzZuy+Ddu3Fhp/0WLFom+Xl5eUnFxsSJx1DeWnC02V9mrBbZs2aLY2PZcLFbGlotFa+QqV0MlHDx4UGxPnDix0mvaHR0d8etf/1q0IyMjLZ7feIx27dph1KhRlfb/6KOPxHZsbCwePHhgcQz2buzYsbJ2ba2cSbaP+Wo9ERERspXtpk2bZsVoSCnGOTVs2DB07ty50v7GOXXs2DG8evVK0Rh0Oh3ee++9SvvPnDlTbD9+/Bj//ve/LY7BXgUFBcm+I8zXX6qMNXKVxaKdS01Nxd27d0V79OjRVe7z9ttvi+27d+8iNTXVohiOHj0qtkNCQqpcXGXIkCFwcXEpd3+qmbIfELxZzIioLOar9fzrX/8S2wEBARgwYIAVoyEl6PV6nDt3TrSr+xqs1+tx9uxZi+Mwzsu33nqryoVz2rdvL1sIpeyCamQ+tVqNJk2aiDZff6ky1shVFot27vr167L2wIEDq9ynd+/estW6kpKSajz/06dP8fjx42rNr9Fo0K9fP0Xmp1JlVwxr2bKllSKh+oz5aj0//vgjLly4INo8q9gw3Lp1C69fvxZtc3LKy8sLfn5+om1pTkmShBs3blQrhrL9mNc19/LlSzx9+lS0+fpLFbFWrrJYtHMpKSli29HREW3btq1yn7L9jMewZH4A6NChg1n7GfezZH4qVfZyYnP/AFXHwoUL0bVrV7i5uUGr1aJNmzYYMWIEli1bhnv37ik+nz3Izc3FxIkT4efnB61WC1dXV7Rr1w7jx4/H+vXrFf+EmvlqPTt27IAkSQBKb1Py/vvv19pczNW6Ux9y6v79+8jPz7dqDPbs0KFDKCkpEe3aeP0FgK+++gq9evVC06ZN4eTkBG9vbwwaNAh//OMfZQUImW/q1Kno2LEjXFxc4OLiAh8fH4wePRorV66UfQCgFGvlKotFO5eWlia227RpY/b99Yzvr2U8hiXzlx23LuYnIC8vD2vXrhXtwMBAdOnSRfF59u/fj1u3buHFixcwGAzIzMzE2bNn8de//hX+/v6YNWuW7PtYVLW8vDxEREQgPT0dBoMBer0eaWlpOHToEH7/+9/Dx8cH69atU2w+5qt1SJKEHTt2iHZwcDBat25da/MxV+uOcT5oNBqz70WqZE4pkdfp6eniwwwyX1FRET7//HPRbtmyJYKCgmplrqNHjyIxMRF5eXkoLCwU319bsWIFevTogXfeeUfce5nMs3PnTty9excFBQUoKChARkYGTpw4gUWLFsHX1xdLlixBcXGxYvNZK1c11epNDc6LFy/EtvE181Vxc3MrdwxL5q9ODErNT8CCBQtklxYuX768VuZp0aIFOnToAJ1Oh7y8PNy+fRt6vR5A6Qvmpk2bEBsbizNnzlTr/6K98/PzQ+vWreHk5ISsrCzcunULRUVFAEqLyblz5yIxMRFbt261eC7mq3WcP39edkavti9BZa7WHeN8cHV1hYODeZ/hK5lTSuR1SUkJCgoKZN9Ppqp98cUXsrN6ixcvhpOTU63M1aRJE/j7+8PNzQ16vR537twRxaEkSThw4ACuXr2K8+fPm3WVGQHe3t7iyp6cnBykpKTAYDAAAAwGA5YvX464uDh8//33aNSokcXzWStXeWbRzr15AwAAzs7OZu9nvHKX8RiWzF+dGJSa395t2bJFVkRMmjTJZGVUS3Tp0gVr1qzBf//7Xzx79gyXL19GTEwM4uLikJOTgyNHjiAwMFD0T0hIwLvvvqvY/A2Rg4MDgoODsWvXLmRnZ+PevXu4cOECTp06hevXryMnJwfffPMNWrRoIfbZtm0bVqxYYfHczFfrMF7Yxs3NDb/61a8Un4O5ah3Wfg0ub/+a5LUScdibEydOYOnSpaI9aNAg/Pa3v1V0Dj8/PyxfvhzJycnIzc1FbGwsYmJicPnyZWRlZeHcuXMYOnSo6J+eno6xY8eisLBQ0TgaCpVKhf79++Pbb7/Fw4cP8fDhQ1y6dAmnTp3CtWvXkJubi/DwcNl3ik+cOIG5c+cqMr+1cpXFop17cwYCKL0ExlzGfY2/nG/J/NWJQan57dm5c+cwZ84c0W7Xrh02bdqk6Bw3b97EH/7wB7Rv397kZxqNBqGhobhy5QpCQ0PF48ePH8f333+vaBwNydChQxEdHY0pU6aUe5sbnU6HWbNm4dq1a7IXrL/97W948uSJRXMzX+teQUEBIiIiRHvixIkmL/xKYK5ah7Vfg8vGUJ04yvZjbpsvJSUFkydPFt9VbNasGcLDw6FWqxWd5+zZs/jLX/6Crl27mvxMpVJhyJAhOHPmDGbMmCEev379uuLvBRoKX19fXLlyBR9++GG5l4w7OTlh8uTJuHbtGvr06SMe37RpkyKLQFkrV1ks2rnGjRuL7Tenzs1h3NeSy06M569ODErNb68SExMxbtw48elhy5Ytcfz4catcUubs7Izdu3fD09NTPKbk9+zsVdu2bbF3717RLigosPhSVOZr3Tt48KDs0iNrroLKXFWetV+Dy8ZQnTjK9mNumycjIwMhISHIyckBUHr8jxw5Al9fX6vE4+DggA0bNqB79+7iMea1ZZo1a4bIyEhx5k+SJKxfv97ica2VqywW7Zzx/Vmqs2BBQUFBuWNYMn91YlBqfnuUmpqKkJAQ5OXlASj9o3by5En4+/tbLSZXV1fMnj1btM+fP1+tN05Uvv79+2P48OGiHR0dbdF4zNe6Z3wJaocOHfDWW29ZMRrmqtKs/Rpc3v41yWsl4rAHT548QXBwMDIyMgCUnomKiorCoEGDrBqXRqPBggULRPvOnTsmt9Si6vHx8ZFdqm/p6y9gvVxlsWjnjL/X9OjRI7P3M14QpXnz5orMX50YlJrf3ty7dw/BwcFiSWdXV1f88MMP6NGjh5UjA0aMGCG2DQaDeDElyxgf1//85z8WjcV8rVuZmZk4deqUaNeXeysyV5VjnFN6vd7s7xIpmVNK5LWrq6siC3g0ZM+fP8fIkSPF32GNRoO9e/di5MiRVo6slHFeA5a/XpD8mKalpVn8XVBr5SqLRTvXqVMnsZ2dnW3y6UNFjN8cBAQEKDI/UHoPmbqc3548ePAAQUFBePDgAYD/XfoyYMAAK0dWysvLS9bOysqyUiQNi/FxtfSYMl/r1s6dO8V3mlQqFaZOnWrliEoxV5VTH3LK399fdtss5rXyfvrpJ4SEhIiVTx0cHPDdd9/hl7/8pZUj+x/mtfLKHtPs7GyLxrNWrrJYtHOdO3eWtRMTE6vcJzMzE8+ePatwjOro2LGj7Iu35swPlK7Ep8T89uLNpS9vlt9/c+mL8Spo1lb2g4qy1+ZTzRgfV0uPKfO1bhlfgjp8+HCrfaepLOaqcmryGvz69WvcvHmzwjGqS6fToU2bNtWKAWBemys/Px9jxozB1atXAZR+8LNt2zZMmjTJypHJMa+Vp/QxtVausli0c/3795fd0+fChQtV7nP+/Hmx7ezsjP79+9d4fkdHR9mZLXPmf/z4Me7evSva9angqY+ys7MRHByM1NRUAECjRo2wf//+enPpyxvGb36A0kV3yHLGx9XSY8p8rTuxsbG4ffu2aNeXS1AB5qqS2rdvL3vzZ05OxcfHy96EKpFTxmOYE8Pr169x5coVRWNoiAwGA8aNG4eLFy+KxzZs2FCv8vkN5rXyjI+pk5OTIosIWiNXWSzaOZ1Oh6CgINHetWtXlfsY9wkKCrJ4BTTjyzBiYmKqXN7feP6mTZvyRaoSeXl5CAkJQXJyMgBArVYjPDwcv/jFL6wcmak9e/aIbT8/v3KXpabqKSgowOHDh0VbiUUUmK91w/isoouLC8LCwqwYjRxzVVnjxo0T2xEREVV+r8k4p7p27YoOHTpYHINxXqekpMjORJTn8OHDYpVeBwcHRe/P21AUFhYiLCwMp0+fFo+tXr0as2bNsmJUFTPOa2dnZ/Tq1cuK0dg+SZKwb98+0R44cKAi41olVyWye/v27ZMAiH+HDx+usG98fLykVqtF34iICIvnz8jIkJycnMSY8+fPr7DvixcvJB8fH9F3zpw5Fs/fUOn1emnw4MHiWDk4OEjfffedtcMq16FDh2T/B+fNm2ftkBqE+fPny45rVFSUxWMyX2vfq1evJHd3d3Hcpk2bZu2QBOZqxYyPy/bt283eLzY2Vrbv119/XWHfjIwMydXVVfT98ssvFYi89PXCw8NDjDthwoQK+xYVFUn9+vUTfUNDQxWJoT6q6XNaVFQkhYWFyfb//PPPay9QC8XFxUmOjo4i1vHjx1s7pFpV0+e1Or7++mvZPGvWrFFkXGvkKotFkkpKSqQePXqI/0ze3t5SSkqKSb+HDx9KnTt3Fv169uwplZSUlDvmvXv3ZEmydOnSSmOYO3eu6KtWq6X9+/eb9CksLJTeeecd0U+r1UqZmZk1+p0bOoPBIAUHB4tjpVKppK1bt1o8rrnPa25urjRhwgTp6tWrVY4ZHh4uubi4iDEbN24sPXr0yOJYG6ITJ05I8+fPlzIyMirtV1hYKC1atEj2XPXu3Zv5aiP2798vez5Onz5d7TGYq3XPkjeg48aNE/vqdDrpwoULJn3y8vKkIUOGiH5eXl5SQUGB2TFV9aHDqlWrZP1Xr15t0qekpESaN2+e7LUlPj6+Or+qTanJc1pSUiJNnTpVtu+nn35aKzFV9pyGhYVJp0+frvDv/hsxMTGy4kOlUkkJCQmKxVsf1eR5TU5Oln7zm99It2/frrRfSUmJtGbNGtmJlVatWtl0rv5vpQKyWyqVCt9++y2GDRuGly9f4tGjRxgwYABmz56NoUOHQqPRIDY2FuvXrxeXnGm1WmzevFm2KpMlli1bhh9++AF37txBcXExJk6ciClTpmD8+PFwd3dHamoqvvnmGyQlJYl9vvzyS7Rq1UqR+RuatWvXIiYmRrSbNm2Kffv2yS6JqMzIkSNl91yqLkmSEBkZicjISAQEBCAkJAQ9e/aEt7c3XFxc8OLFC9y4cQP79+9HXFyc2E+lUmH79u0mK4hRqYKCAqxatQpr1qzB4MGDMWzYMHTr1g0tWrSAo6MjsrKyEBsbi127dslWP3N3d0d4eDjz1UYYX4Lq6+sru1em0pir1Tdjxgzs3Lmzyj7lXW5Y0T0p165di0uXLiErKwt6vR5BQUGYPn06Ro0aBZ1Oh6SkJKxbt04sUubg4IDNmzdDq9Va/gv9vzlz5mD//v24dOkSAODjjz/GqVOn8N5778HLywtpaWnYunWr7HtSCxYsQO/evRWLwVqUfE4jIiKwY8cO0XZ2dsaVK1cwevRos2IJDAzEypUrzepbmZiYGBw4cAA+Pj4YM2YMevXqhbZt28LV1RX5+flITU3FoUOHZJfJAsCKFSvQs2dPi+evD5R8Xl+/fo1t27Zh27Zt6NOnD37+85+jR48eaNmyJbRaLXJycpCQkIDdu3fLvm/u5OSEPXv22Hau1qjEpAbpwIEDklarlX1aUd4/rVYrHThwoNKxqnumQpIkKTU1VWrbtm2V8wOQPvnkE4V+64Zp6dKlZh3Hiv5V9KmWuc9rTk5Oted0dXWVwsPDa++gNAAHDx6s9nHt2LGjdO3atUrHZb7WH0+ePJE0Go04dkuWLKnROMzV2jNt2rQa/22tzMWLF2WXH1f0T61WS+vWrTMrVnP+rht7+vSp1L17d7N+l8mTJ0vFxcVmxVHfKfmcbt++3aLX32HDhlUaq7nPaZMmTao1r6Ojo/SPf/zDwiNZvyj5vCYkJFR7DC8vLyk6OtqsWOtzrnKBGxImTJiA+Ph4BAcHl3sGQqVSISgoCFevXsWECRMUn9/f3x9JSUmYPn16hZ/AdO7cGYcOHcKKFSsUn5+Uo9VqMXPmTHTt2rXKs1lNmjTB3LlzkZycjMmTJ9dRhLYpICAAkyZNkq2eWBE/Pz+sXLkSCQkJtbJQAfO1doSHh6OoqEi0a/veiszV+mPQoEFISkpCWFiY7BY1xvr164dz587hd7/7Xa3E4OHhgdjYWCxcuLDClRt9fX2xZcsWhIeHw8GBbyPrq5kzZ6J3795Qq9WV9tNqtfjggw+QkJCA+fPn11F0tsfb2xtTp041a0EpT09PLF68GDdu3EBwcHCtxFOXuar6/2qWSCYjIwMXL15EZmYmAKB169YYPHgw2rZtWyfzv3jxAqdPn0ZGRgby8/Ph7e2N7t27c3UuG5STk4PExEQ8ffoUWVlZyM3NRePGjeHu7o7AwEAEBgZW+WJGpu7fv49bt24hKysLWVlZyM/Ph5ubG1q2bIm+ffsqskKiuZivDQNztf549uwZzp07hwcPHqCwsBCtWrVC37590alTpzqLwWAw4OzZs0hLS0NOTg48PT0REBCAgQMHKnZJO9U+vV6PhIQEPH78GFlZWcjJyYGTkxOaNWuGLl26oHfv3nB0dLR2mDblyZMnSEpKwrNnz5CVlYUXL15Ap9OhRYsW6NWrFzp37lynOVLbucpikYiIiIiIiEzw+gEiIiIiIiIywWKRiIiIiIiITLBYJCIiIiIiIhMsFomIiIiIiMgEi0UiIiIiIiIywWKRiIiIiIiITLBYJCIiIiIiIhMsFomIiIiIiMgEi0UiIiIiIiIywWKRiIiIiIiITLBYJCIiIiIiIhMsFomIiIiIiMgEi0UiIiIiIiIywWKRiIiIiIiITLBYJCIiIiIiIhMsFomIiIiIiMgEi0UiIiIiIiIywWKRiIjIhi1btgwqlQoqlQr+/v4oLCys1v4nTpwQ+6tUKjx9+rSWIiUiIlvDYpGIiMhG3blzB1988YVor169Go6OjtUao2/fvrL2hQsXFImNiIhsH4tFIiIiGzVnzhy8evUKADB69GiEhoZWe4zmzZvDx8dHtC9evKhYfEREZNtYLBIREdmg6OhoREdHi/Znn31W47HatWsntlNSUiyKi4iIGg4Wi0RERDZoyZIlYvvtt982uZy0Olq3bi227969a1FcRETUcLBYJCIisjGnTp3ClStXRHvhwoUWjefh4SG2Hz16ZNFYRETUcLBYJCIisjEbN24U2+3atcPw4cMtGk+lUontN9+BJCIi0lg7ACIiIjJfdnY2Dh06JNpTp06VFXvG8vPz8fLlSwCAm5tbhSulSpJU7jYREdk3nlkkIiKyIadOncLr169FOyQkpMK+H3zwATw8PODh4YGrV69W2O/hw4di29PTU5lAiYjI5rFYJCIisiFnzpwR2y4uLujXr1+FfePi4sR2t27dKux3//59sW18Gw0iIrJvLBaJiIhsSHJystju1q0bNJryv1GSmZmJ9PR0AICXlxfc3NzK7VdUVIQbN26IdmXFJxER2RcWi0RERDbkzp07YrtTp04V9jO+B2ObNm0q7JeQkICCggLRHjx4sIUREhFRQ8FikYiIyEaUlJTgyZMnol3Z9wsPHz4stt3d3Svsd+TIEbGt0WgQFBRkYZRERNRQsFgkIiKyEQaDQdZ2cnIqt9/z589x7Ngx0W7UqFG5/SRJwu7du0U7ODgYzZs3VyBSIiJqCFgsEhER2Qi1Wi27Tcbz58/L7bd+/Xq8evVK9M3Ozi633+HDh2WXtc6YMUPBaImIyNapJN5QiYiIyGZ4eXmJS1EDAwNx/fp12c/T09PRrVs36PV6jBgxAmfOnIFOp0N2drbsPou5ubno06cPfvzxRwBA9+7dcf369Qrv2UhERPaHZxaJiIhsyJAhQ8R2UlISNm7cKNppaWkIDQ2FXq+Hv78/3n33XQCAXq/HV199Jfqlp6djzJgxolBUq9XYtGkTC0UiIpLhmUUiIiIbEh0djVGjRskeCwgIgLu7O+Lj48XlpydPnoSXlxe6d+8u+gUGBsLZ2RnXrl1DUVGReHz16tWYN29eXf0KRERkI1gsEhER2Zj58+dj9erV5f5Mo9Fgw4YN4vuHYWFhiIyMLLevTqfDmjVrMH369FqLlYiIbBeLRSIiIhsUGRmJTZs2ITExEc+fP4eHhwdGjBiBhQsXomfPnqKfwWDA8uXLsXfvXty/fx+NGzdGu3btEBoaitmzZ6NVq1bW+yWIiKheY7FIREREREREJrjADREREREREZlgsUhEREREREQmWCwSERERERGRCRaLREREREREZILFIhEREREREZlgsUhEREREREQmWCwSERERERGRCRaLREREREREZILFIhEREREREZlgsUhEREREREQmWCwSERERERGRCRaLREREREREZILFIhEREREREZlgsUhEREREREQmWCwSERERERGRCRaLREREREREZOL/ANGhNKuBqKY5AAAAAElFTkSuQmCC", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "id": "53e63db1", + "metadata": {}, + "outputs": [], "source": [ "# Plot the components of the fit separately:\n", "plt.rcParams[\"font.size\"] = 25\n", @@ -689,28 +554,17 @@ }, { "cell_type": "code", - "execution_count": 19, - "id": "e50c6ab7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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QVqxYkYceeojrr7+e+vXrU7FiRYKDg+1/nwcOHKBu3bpmhnxRSiql9Pn5Z/joIyNRu/tuqF/f7IjO16WLsdUnJgbmzYN//4WGDc2OSkREREqxyHNqOyQlJRXqfef2WnTlSuC5HJ9V2POJhb3PTB9//DHp6emAsWV49erVF62YW9j/rcyi6q9S+ixZAkuXwogR5x+i9xR+fvD448bYZoNPPzU3HhERESn1wsLCCA4Otl/v37+/UO/bu3fvWdcXa+nhbLVq1bKPC1tJ1VkVV11p8eLF9vFrr712yRYsR44ccXVIJaKkUkqf1avzx4Wo0GWaBx80zm4ATJoEHnjoWkREREqXVq1a2ceOlWAvxrFPZHR0NHXq1HF2WBfUtm1b+3j58uWFOn+4dOlSF0bkHI4FiBz/jBey2vHnWw+kpFJKl7S0/PLjTZqYehj+ksqVM4r0ACQnG4mliIiIiAtdc8019vHMmTMLVfTlu+++s4+vvvpqLBaLS2IryM0332wfHz9+nJ8vcdY2MzOTyZMnuziqksvKyrKPL/X3mZOTw7fffuvqkEpESaWULuvXg9VqjDt2NDeWwnjiifzxp59CEat/iYiIiBTFkCFD7OPjx4/z8ccfX/T+mTNnnrWi+cADD7gqtAJddtlltG/f3n797LPPcurUqQveP2LECA4cOOCGyEqmatWq9vGqVasueu+HH37Ivn37XB1SiSiplNLFcWuANySVzZpB9+7GeN8+o2iPiIiIiIs0btyY/v37269feeUVZs2aVeC9a9as4f7777dft2zZkl69erk8xnONGTPGvpq3f/9+rr32Wv7888+z7omPj+fJJ5/kvffeK3SrFDN16dLFPn7jjTeIvUCP0ylTpvDiiy+6K6xiU1IppYvjPzCefJ7S0ZNP5o8//9y8OERERKRM+N///mcvDGO1WunXrx99+/blhx9+YPny5cyaNYsHH3yQa665xl75NSgoiK+//hpfX1+3x9uxY0dGjBhhv962bRsdO3akXr16XHfddbRp04YqVarwySefADBx4sSz3n9uSxRPMHz4cHuifPjwYVq1asUbb7zB4sWLWbp0KV999RXdunWztx158MEHTY744tRSREoPmy1/pTIqyjhT6Q1uvBFq14aDB2HvXkhNhZAQs6MSERGRUqpSpUosXbqU7t27czi3L+fs2bOZPXt2gfeHh4fz888/06JFC3eGeZYRI0bg5+fHqFGj7OcR9+/ff1YF2+DgYMaNG0f3vF1guc5tpeIJrrzySt58801effVVAE6ePHlW4uzo9ttv5+WXX+bLL790Z4hFopVKKT3274cTJ4xx+/bg5Oa7LuPra/TVXLjQaIGihFJERERcrEmTJmzatIknnniC0NDQAu/x9/fnzjvvZOvWrVx77bXuDbAAr7zyChs2bOCxxx6jYcOGhISEEBkZyeWXX85zzz3Hli1buO+++ziR9/MgRqIZ4qE/W73yyit89dVXVKpUqcCvV6lShY8//pgff/zRrcWRisNis9lsZgchzpeYmEhkZCQJCQlERESYHY57/PgjDBxojEeMgJEjTQ1HRESktEhPT2f//v3UrVuXoKAgs8MRJ0tPT2f58uXs27eP06dPExERQa1atbj22mu98ufImTNn2s+Ntm/f/rzzl54mPT2dFStWsG3bNtLS0qhUqRINGjTg6quvdtp24+zsbDZs2ECrVq0KPWdRvu+1/VVKj2uugSlTjAqwN9xgdjQiIiIiXiEoKIgbStHPTo5nKjt4QY2NoKAgunfvft62XW+ipFJKj2rVjL6Peb0fvdnBg8Y5SxERERHBZrMVagvo119/za+//mq/Hjx4sAujkjxecuhMpIxYuBCuuw7q1IHt282ORkRERMQjvPHGGzz00EMsXboUa15PcgeHDx/mmWeeOSuJvOWWW0wtLlSWaKVSxJPs2AFLlxrj8eNh7FhTwxERERHxBGlpaXz55Zd8+eWXBAUF0bhxY8qXL4/VaiU2Npa9e/eedX/t2rUZP368SdGWPWVqpfLkyZPMnz+fN954gz59+lC1alUsFov9Y/LkyW6JY9++fbz++utceeWVVKxYkeDgYOrXr89tt93GjBkzCvzti1zCtm3wyy8QG2u0FvFWgwZBcLAxnjIFUlLMjUdERETEA/g4VPVPT09n06ZN/PHHHyxfvvy8hPK6665jzZo19l6c4nplYqXy2LFjtG/fnoMHD5odCmPHjuWFF14gIyPjrM/v27ePffv2MWfOHNq3b893331HvXr1TIrSC333Hbz7rjH+5Rfo1cvceIorOtqoYDtpEiQkwLRpMGSI2VGJiIiImGrUqFF07tyZRYsWsW7dOvbu3cvp06exWq1ERUVRrVo1OnXqRP/+/bn++uvNDrfMKRNJZXp6ukcklG+++Savv/66/drHx4dmzZpRrlw5/v33X44ePQrAmjVr6NKlC3/99RdVq1Y1K1zvsn59/rhlS/PicIZhw4ykEuCrr5RUioiISJnn7+/PjTfeyI033mh2KFKAMrX9FaBixYrceOONvPrqq8yZM8dtz124cCEjRoywX3fo0IEdO3awZcsWli1bxuHDh5k6dSphYWGAcdh4wIABbovPq9ls8M8/xrhiRahRw9x4SqpdO7jsMmO8ahXs3GluPCIiIiIiF1EmVirLlSvH9OnTadu2LbVNaNNgs9l44YUXsOWe9WvcuDG//fYbISEh9nt8fHy44447KF++vL1HzapVq5g9eza33Xab22P2KkeOwKlTxrh1ayhEuWmPZrHAAw/A008b1199BaNHmxuTiIiIiMgFlImVyoiICPr3729KQgkwf/58Nm3aZL8eO3bsWQmlo27dunHHHXfYr9977z2Xx+f1tm7NH19+uXlxONM994C/vzH++mvIyjI3HhERERGRCygTSaXZZs2aZR/XrVuXG2644aL3Dxs2zD7+66+/OHz4sMtiKxW2bMkfl5akskIFuPVWY3ziBMyda2o4IiIiIiIXoqTSDebNm2cf9+jRA8sltmdec801hIaGFvh+KYDjSmXeWcTS4IEHjNdu3aB8eXNjERERERG5gDJxptJMJ06c4NixY/brDh06XPI9fn5+tG3blqVLlwKwefNmV4VXOuStVPr4QNOm5sbiTN26wf79UKeO2ZGIiIiIiFyQVipdbMeOHWdd169fv1Dvc7zv3DnEQXY2bN9ujBs0gOBgc+NxJl9fJZQiIiIi4vG0UuliBw4cOOu6Vq1ahXqf433nzlGQjIwMMjIy7NeJiYmFeo7Xi4uD5s1h27bSc55SRERERMSLaKXSxZKSks66joyMLNT7IiIiLjhHQd59910iIyPtHzVr1ixaoN6qUiVYtw6Sk+HLL82OxnWys2H+fHCoIiwiIiIi4gmUVLpYcnLyWddBQUGFel+wwzbOc+coyEsvvURCQoL9IyYmpmiBejtfX4iKMjsK19i2zdgGe/PN6lcpIiIiIh5HSaWLWa3Ws679/Aq349jxvqxC9CgMDAwkIiLirA8pJRo0gNRUYzxjBsTHmxuPiIiIiIgDJZUuFhISctZ1enp6od7neJ9jexEpgwID4Z57jHFGBvz4o7nxiIiIiIg4UFLpYmFhYWddp6WlFep9qXkrUwXMIbkyM6FZM+jTBz75xOxoXGvw4PzxlCmmhSEiIiIici4llS5WoUKFs66PHj1aqPc59rYsr8b3Bdu7F3bsgF9+gT//NDsa17riCmjRwhivWQO7dpkajoiIiIhIHiWVLta4ceOzrg8dOlSo9zkW2mnSpIlTYyo1du7MH5f2vyOLBQYNyr/WaqWIiIiIeAgllS7WsGHDs4rubNy4sVDv27Bhg33ctGlTZ4dVOjiu1p2TvJdKd99tVLkF+OYbo82IiIiIiIjJlFS6WEBAAFdddZX9euXKlZd8z7Fjx9izZ4/9unPnzi6Jzes5rlSWhaSycmW46SZjfPgw/PGHufGIiIiIiKCk0i1uueUW+/i3337j+PHjF73/u+++s4+joqKUVF6I40plo0bmxeFOKtgjIiIiTtS1a1csFgsWi4WJEyeaHY54KSWVbnDnnXcSGBgIGD0n//Of/1zw3uTkZD5xqGR699134+/v7/IYvY7Nlp9U1qwJZaXtSq9e0LAhPPYYPP202dGIiIiIl9u0aZN93KpVKxMjcZ+TJ08yf/583njjDfr06UPVqlXtibXFYmHy5Mlmh+h1/C59ixTkwIED1K1b1349YsQIRo4cWeC9NWrUYNiwYfZkcezYsXTs2JF+/fqddV9WVhZDhgyxF/MJDg7m5Zdfds0fwNudPAnx8ca4tBfpcRQYaCTTFovZkYiIiIiXO3ToEHFxcYBxZKt58+YmR+Rax44do3379hw8eNDsUEqdMrNS+dBDDxEUFHTeR1HvKa6RI0fSsGFDALKzs7n99tu59957mTlzJkuWLGHcuHG0adOGGTNm2N8zevRoqlWr5pTnlzplrUiPIyWUIiIi4gSOhSGbN29OQECAidG4Xnp6uhJKFykzK5VZWVlkZGRc9B6r1YrVanXJ86Ojo5k7dy7dunUjJiaGnJwcvv32W7799tsC73/++ed57LHHXBJLqVDWivSIiIiIOJljV4LWrVubF4gJKlasyJVXXkmbNm1o06YNt956q9khebUyk1R6gkaNGrF582b+7//+j++//560tLTz7mnatCnvvfceffr0MSFCL3L99TBhgpFcdupkdjTmOHUKvv/eOE/6wANmRyMiIiJexnGlsiycpyxXrhzTp0+nbdu21K5d2+xwShWLzWazmR1EWZSUlMQff/xBTEwMKSkpVK1alcsvv9xp39CJiYlERkaSkJBARESEU+YUD3LmDFStCunpUKcO7N0LPmVmN7uIiLhZeno6+/fvp27duk47GiTmq127tr2Wx+rVq+nQoYPJEZnH4nC8aNKkSQx2rLhfCmRnZ7NhwwZatWqFb17f80soyve9VipNEh4eflarEZEiiYqCa6+FBQvgwAFYvty4FhERESmE06dP2xNKHx8fWrZsedH7R48ezUsvvUR2djZg1CL573//W+rPYUrhaGlDxFsNGpQ/Vs9KERERKQLH85SNGzcmJCSkwPuSk5MZMGAAzz//PNnZ2QQEBPD5558zYcIEJZRip6RSvE9CAixdCjExkJNjdjTmueUWiIw0xtOnQ3KyufGIiIiI13A8T3mhIj27du2iXbt29u4EVapUYcmSJTz88MNuiVG8h5JK8T5//w3XXQe1asFLL5kdjXmCg+GOO4xxSgrMmmVuPCIiIuI1LlWkZ86cObRr144dO3YAcNVVV7F+/Xo6duzothjFeyipFO+zd2/+uF498+LwBNoCKyIiIsVwoZXKnJwcXn75Zfr27UtiYiIA999/P8uWLSty//TJkydjsVic/jF58mSn/B2I86hQj3gfx6Syfn3z4vAEHTpAw4bw77+wZAkcOmSs4IqIiJjIZrORlpVtdhgeLdjf96yKo+6UlpbGrl277Nd5K5VxcXHceeedLF68GAB/f38++ugj9U6XS1JSKd5HSWU+iwXuuw9eew1sNvjmG3jlFbOjEhGRMi4tK5tmry80OwyPtv2NHoQEmPOj+JYtW+xVXOvWrUtUVBTr16+nX79+HDx4EIDKlSszffp0rrnmmmI/p3r16vTo0cMpMZ87r3gWJZXiffKSSj8/qFnT3Fg8wT33GEklwA8/KKkUERGRizp36+ukSZN49NFHSU9PB6Bt27bMmjWLGjVqlOg53bt3p3v37iWaQ7yDkkrxLjZbflJZp46RWJZ1derAo49CkyZw551mRyMiIkKwvy/b33D+ClVpEuxfuAb0ruCYVK5YsYKZM2farwcNGsS4ceMu2exexJF+IhfvcvJkfuuMsr711dH//md2BCIiInYWi8W0rZ1yaY49Kk+cOGEfDxs2jHHjxpkQkXg7VX8V76LzlCIiIiLFlp2dzebNm+3XPXv2tI+nTZvG7t27zQhLvJx+hSTeRUmliIiISLHt2rWLtLQ0AKpUqcLMmTO59tprWbNmDfHx8fTu3Zs1a9YQHR1d4mctXryYMWPGlHiecz377LM6q+lhlFSKdzl9Gvz9IStLSeW5bDb4+2+jX2VKCqiHk4iIiJzD8Txly5YtCQwMZPbs2bRr146YmBh2797NgAEDWLBgAX4lrF0RGxvLwoXOrwI8cOBAp88pJaPtr+JdnngC0tJg/37o2tXsaDxLTg707QuffQbffQcOZyRERERE4OzzlC1btgSMFcuffvqJkJAQAH7//XeGDx9uRnjipZRUivfx9TUqnoaFmR2JZ/H1NdqLAFitRnsREREREQfnrlTmadWqFd988w0WiwWAcePG8cknn5ToWYMHD8Zmszn9Y/DgwSWKS5xPSaVIaTJoUP54yhTz4hARERGPVNBKZZ6+ffsyatQo+/Uzzzzjku2rUvooqRQpTZo2hbZtjfGGDbBli7nxiIiIiMc4dOgQcXFxAAQGBtK4cePz7nnttdfsZxazs7O544472LFjh1vjFO+jpFK8x6FDcOed8OKLsHix2dF4rvvuyx9//bV5cYiIiIhHcVylbN68+QUL8UycOJG2ub+kTkhIoHfv3vZk1Ns99NBDBAUFnfdR1HvkbEoqxXvs3g1Tp8L778Nvv5kdjecaONCokAvw7bfG+UoREREp8y50nvJcwcHBzJkzh2rVqgGwd+9e+vXrR1ZWlstjdLWsrCwyMjLO+3BktVoveY+cTUmleI+DB/PHtWubF4enq1AB8hoZHzumBFxERESAwieVANWqVeOnn34iODgYgGXLlvHII4+4ND7xXupTKd5DSWXhDRoEc+YY4ylT4MYbTQ1HREREzDcn72eDQmrTpg2pqamuCcYkkydPZrJ6eTudVirFexw4kD9WUnlxN98M5csb43XrtAVWRERERFxGK5XiPbRSWXgBAfDhh1ClCnTtavSwFBERERFxASWV4j3ykspy5SA83NxYvIFjFVgRERERERfR9lfxDlYrHD5sjLVKKSIiIiLiMZRUinc4cgSys42xksriSUszOwIRERERKYWUVIp30HnK4vv+e+jWDS6/HHJyzI5GREREREoZnakU71C+PDz6qJFctmljdjTeZcoU+P13Y7xyJXTubG48IiIiIlKqKKkU79CsGfzvf2ZH4Z3uuw8WLTLGX3+tpFJEREREnErbX0VKu9tuy6+WO20alLImxiIiIiJiLiWVIqVdSAgMGGCMk5JgzhxTwxERERGR0kVJpXiHlBSzI/Bujj0rv/7avDhEREREpNRRUimez2aDSpUgIgJ69zY7Gu90zTVQp44xXrzYaNEiIiIiIuIESirF8505Y5wDTEqC9HSzo/FOPj5w773GOCcHvvvO3HhEREREpNRQUime7/Dh/HGNGubF4e0ct8BOmWKsAIuIiIiIlJCSSvF8sbH5YyWVxdegAXTqZIwrVIDERHPjEREREZFSQX0qxfNppdJ5xo6F8uXzz1eKiIiIiJSQkkrxfI5JZfXq5sVRGlx5pdkRiIiIiEgpo+2v4vm0UikiIiIi4rGUVIrnU1LpGjk5sHWr2VGIiIiIiJdTUimeL69QT2CgcR5QSu7DD6FuXWM77OnTZkcjIiIiIl5MSaV4vryVyho1wGIxN5bSIjYWDh2CzEyYOtXsaERERETEiympFM+3bBn8+it89JHZkZQegwblj7/+2rw4RERERMTrqfqreL4WLYwPcZ4WLaBlS9i0CdauhV27oHFjs6MSERERES+klUqRsspxtXLKFPPiEBEREdN07doVi8WCxWJh4sSJZocjXkpJpUhZdddd4OtrjL/5BrKzzY1HRERE3G7Tpk32catWrUyMxD3OnDnD7NmzeeKJJ+jcuTNVqlQhMDCQsLAwatWqRe/evfn444+Jj483O1Svou2v4tn+/BP27zeK9LRuDWFhZkdUelSuDDfdBHPnGsWQli6Frl3NjkpERETc5NChQ8TFxQEQEBBA8+bNTY7IdXbu3Mlzzz3HokWLyMzMPO/rmZmZpKSkEBMTw9y5c3nllVd4++23efLJJ7GoUOQlaaVSPNs338Ddd0OXLrBjh9nRlD733Zc/1hZYERGRMmXDhg32cfPmzQkICDAxGtfaunUrc+fOPSuh9PX1pXHjxnTu3JlOnTpRrlw5+9dSU1N5+umnGTp0KDabzYyQvYqSSvFsee1EAKpXNy+O0qp3b4iKMsYzZ0JSkqnhiIiIiPts3LjRPm7durV5gbiRn58ft956K3PmzOH06dPs3LmTZcuWsXLlSk6dOsWcOXOo7vAz55dffsm4ceNMjNg7KKkUz5aXVPr6Gts1xbmCgmDgQIiMhHvugZQUsyMSERERN3FcqSzt5yn9/f158MEH2bt3L7Nnz+aWW24hIiLirHssFgu33HILf/75J1WqVLF//vXXXycrK8vdIXsVJZXi2WJjjddq1fKLyohzvfkmHD0K48eDwz+gIiIiUro5JpWlfaXylltu4YsvvqBWrVqXvLdmzZqMGjXKfn3q1CmWL1/uyvC8npJK8VyZmXDihDHW1lfXqVABgoPNjkJERETc6PTp0xw6dAgAHx8fWrZsedH7R48ejZ+fn739yNChQwsseFNa9O7d+6zrnTt3mhSJd1D1V/Fcx4/nj6tWNS8OERERkVLG8Txl48aNCQkJKfC+5ORkhgwZwowZMwCjSuzYsWN5+OGH3RGmaRyL9gAkJiaaFIl30EqleK6jR/PHSirdIz7eqLirKmciIiKlWmG2vu7atYt27drZE8oqVaqwZMmSUp9QAhw8ePCs60qVKpkUiXdQUimeS0mle731lnGm8r77YO1as6MRERERF7pUkZ45c+bQrl07duS2dLvqqqtYv349HTt2dFuMZpo1a9ZZ1x06dDApEu+gpFI8l2NSqQIyrlejhnGOFdSzUkREpJS70EplTk4OL7/8Mn379rVv+bz//vtZtmwZ1apVK9IzJk+ebD+D6cyPyZMnO+Xv4EISEhIYO3as/bpFixY0a9bMpc/0dkoqxXP5+EDNmuDvr5VKd+jXD/LOU0ydChkZ5sYjIiIiLpGWlsauXbvs13krlXFxcdx44428++672Gw2/P39+e9//8tXX31FYGCgWeG63bPPPsuxY8fs12+99ZaJ0XgHFeoRzzV0qPGRk6Mzfu4QHg59+8K338KZM/DLL9C/v9lRiYiIN7LZICvV7Cg8m38IWCymPHrLli1kZ2cDULduXaKioli/fj39+vWznyWsXLky06dP55prrin2c6pXr06PHj2cEvO587rKl19+yVdffWW/vuOOO86rBCvnU1Ipns9HC+puM2iQkVSCsQVWSaWIiBRHViq8U7StkmXOy0cgINSUR5+79XXSpEk8+uijpKenA9C2bVtmzZpFjRo1SvSc7t2707179xLN4U7Lly/nscces1/XrVuX8ePHmxiR99BP6yKS77rr8nuCzp+f3ydURERESg3HpHLFihXcf//99oRy0KBBLF++vMQJpbfZuHEjffr0sfferFSpEgsWLCAyMtLkyLyDVipFJJ+vL9x7L7z3HmRnw/ffw1NPmR2ViIh4G/8QYyVOLsy/4L6Q7uDYo/KEwy+Qhw0bxrhx40yIyFy7du2iR48eJCQkABAdHc2iRYto1KiRyZF5DyWV4plycqBbN6hUCdq1g2eeMTuisuO++4ykEowtsEoqRUSkqCwW07Z2ysVlZ2ezefNm+3XPnj2ZN28eANOmTeOZZ54pU8nU/v376datmz25Dg8PZ/78+bRs2dLkyLyLkkrxTKdOwZIlxjgxUUmlOzVtCm3bwt9/w8aNsHkztGhhdlQiIiLiBLt27SItLQ2AKlWqMHPmTK699lrWrFlDfHw8vXv3Zs2aNURHR5f4WYsXL2bMmDElnudczz77rFPOah4+fJiuXbty+PBhAEJCQpg7dy5XXXVViecua5RUimdy7FGpdiLuN2gQpKbCQw8ZbV1ERESkVHA8T9myZUsCAwOZPXs27dq1IyYmht27dzNgwAAWLFiAn1/JUoXY2FgWLlxY0pDPM3DgwBLPcfz4cbp168b+/fsBCAwMZM6cOXTu3LnEc5dFKtQjnklJpbmGDYMtW+DJJ8EJv6kUERERz+B4njJvi2eVKlX46aefCMntV/37778zfPhwM8Jzi7i4OLp162bv1env78+MGTO8qlKtp1FSKZ7JoeGskkoT+PmZ1jtLREREXOfclco8rVq14ptvvsGS+///48aN45NPPinRswYPHozNZnP6x+DBg4sdU0JCAj169GDr1q0A+Pr68v3339OrV68S/VnLOiWV4pm0UikiIiLidAWtVObp27cvo0aNsl8/88wzLtm+apaUlBR69uzJ+vXrAfDx8WHKlCn0V1/uElNSKZ5JSaXn2LQJhg+H3DMHIiIi4p0OHTpEXFwcYJwhbNy48Xn3vPbaa/Yzi9nZ2dxxxx3s2LHDrXG6QkZGBrfeeiurVq0CwGKx8MUXX3D33XebHFnpoEI94pkck8oqVcyLo6z78kujWA9AVBS8+aap4YiIiEjxOa5SNm/e/IKFeCZOnMjevXv5+++/SUhIoHfv3qxdu5by5cu7KVLnGzt2LL/99pv9OioqimnTpjFt2rRCvb979+48++yzrgrP67ksqYyNjWX79u0cPHiQkydPkpKSAkBoaCgVK1akdu3aNG/enGrVqrkqBPFmWqn0DDffDL6+kJ0NkybByJHGtYiIiHidC52nPFdwcDBz5syhbdu2HDlyhL1799KvXz8WL16Mv7+/O0J1utTU1LOu4+Pji7S1t4oWOS7KaUllfHw8P/30EwsXLmTp0qX2BqKXUqlSJbp06UKPHj3o06ePV/8GRJwoL6mMioKgIFNDKdOqVYOePeHnnyE2FhYuNBJNERER8TqFTSoBqlWrxk8//UTnzp1JS0tj2bJlPPLII3z55ZeuDlO8kMVms9lKMsH8+fMZP348CxYsICsrC4CiTplXZcrPz48bb7yRoUOH0rNnz5KEVeYlJiYSGRlJQkICERERZodTdB98AAcOgI8PlLDymJTQL79Anz7G+LbbYNYsc+MRERG3S09PZ//+/dStW5cg/bJXxOtkZ2ezYcMGWrVqhW8hd50V5fu+WCuVOTk5TJkyhffee489e/YABSeSgYGBVKtWjejoaIKDg7HZbKSlpREfH8/Ro0fJyMg4671ZWVnMnTuXuXPnUq9ePV588UUGDx5c6D+4lCL/939mR1A4aWcgJxtCypXeFhw33WRsQT561Egwjx3TOVcRERERsStyUjl9+nRefvll9u3bB+QnhEFBQXTq1IkuXbrQtm1bLr/88kuel4yNjWXLli2sW7eOZcuWsWrVKtLT0wHYt28fQ4cO5Z133uHdd9/l9ttvL2qoIq6RkwMbvoY/P4NTRtNcIqpD60HQ8XEICDU3Pmfz84PBg+Hdd8Fqha+/huefNzsqEREREfEQRdr+2qVLF1auXAkYyaSfnx89e/bk7rvv5qabbiI0tGQ/TKempjJ//ny+//575s6da99Oa7FY6NSpE8uXLy/R/GWJ129/9VQZSTBtEOz9veCvV2gEA3+ACg3cG5er7dkDDRsa44YNYdeu0rsyKyIi59H2VxHv5urtr0XqU7lixQpsNhsVKlRg1KhRxMbGMnv2bPr371/ihBIgJCSEfv36MXPmTGJjY3njjTeoVKkSNpvN3lNGyoDUVEhKgpId93W+zFT4tr+RUPoFww1vwQsH4JVj0PdLCK8Gp3bDpJsgbq/Z0TpXgwZw3XXG+N9/YcUKc+MREREREY9RpKSyUqVKfPzxxxw6dIjXXnuNihUruiouKlSowKuvvsrBgwf56KOPXPos8TBffw0RERASAlOnmh2NwWaDec9AzBoIioQh86DjcAiOBv9gaDEAhi6FypdDygn4tq9x3rI0efDB/PH06ebFISIiIiIepUhJ5d69e3niiScIDAx0VTznCQwM5Mknn7Sf4ZQyIK8dTXo6hIWZG0ueTVNh0w9g8YE7voPqV55/T3hluHcWRNWG+APw02Oet9paEn37wl13wbx58PHHZkcjIiIiIh6iSEmlM7a4FldISIhpzxY3c+xxWqmSeXHkST0NC182xte9DHWvufC9YZVgwGTw8Yedc2FLKVrRCwqC774z+lSqIrOIiIiI5CpSUiniFseP548rVzYvjjy/j4K001CpGXR66tL3V28N175gjBe+DGnxLg1PRERERMRMSirF8ziuVJp9ljZuL/zzjTHuOQZ8/Qv3vo5PQoXGkHISlr7nuvjMlp1tdgQiIiIiYjIlleJ58pLKsDCjWI+Zlo8GWzY0vAFqdyz8+/wC4Kb3jfG6iXDmkGviM4PVCrNmQffuZxfvEREREZEyyc9ZE506dYqVK1eyatUqduzYwZ49ezh69CgZGRnYbDYqV65MrVq1aNu2LZ06daJr165ER0c76/FSmuRtfzX7PGXcXtj8ozG+9sWiv7/+dVC3C+xfBkvfh1v/59z4zJKZCQ88AGfOGOcsx4yBcuXMjkpERERETOK0pLJSpUpYHJqh286penn48GFiY2P5888/+eSTT/Dz86Nbt24MHTqUW265xVlhiLfLzIT43DOIZp+nXDsebDnGKmVB1V4Lo+vr8GVX2PQ9XPMMlK/v3BjNEBICgwbB2LFGhd6vv4annjI7KhERERExidO3v9pstvMSSsev5b1mZWWxYMEC+vbtS6tWrfjrr7+cHYp4o5Mn88dmrlRmJMHG741x+0eKP0+NNtCgu5GcrvnMObF5gmHD8sfjxpWu1ikiIiIiUiROW6kEI1msVasWdevWpXr16lSsWBGLxYLNZuPIkSMcOnSILVu2kJqaar8fYNOmTVx99dWMGTOG4cOHOzMk8Tae0k5k4w+QmQQVGkG960o2V6cnYM9i2PAdXPsyhJZ3ToxmatoUunSBZctg1y7j9dprzY5KREREREzgtKRywYIFtGnThnKXOFuVnZ3Nhg0bmDt3Lj/88AP//vsvFosFq9XKU089RXh4OIMHD3ZWWOJtmjaFTZuMc5VmJZU2G/z9hTFuNxQctnUXS51roGpLOLoJ1n0FXZ4veYye4OGHjWQSjNVKJZUiIiIiZZLTtr/ecMMNl0woAXx9fWnTpg0jR45k165dzJ49m7p16wLGyuWTTz7JsWPHnBWWeJugIGjRwqgs2rKlOTHErodTu8E/BFoOLPl8Fgt0fMIY//UFWDNLPqcnuO22/JYvs2ad3V9URERERMoM01uK3HLLLaxfv5727dsDkJyczLhx40yOSsq0TVON1ya9IDDcOXM2uwXCKkPKCdj1q3PmNFtgINx/vzHOyoJJk8yNR0RERERMYXpSCRAZGckPP/yAn5+xG3fu3LkmRyRlljUTts4wxs5Ypczj6w+t7jXG60tR8jV0aP54/HjIyTEvFhERERExhUcklQC1a9emdevW2Gw29u/fb3Y4YpYFC+Dbb2HRIqNdhbv9uwjS4iGsCtS71rlzXzkIsMC+pUYPzNKgXj3o0SN/fOqUufGIiIiIiNt5TFIJkJ6bRORVh5Uy6JNP4N57jUQlOdn9z9/8o/HaYgD4+Dp37qha0KCbMV4/2blzm+k//4GdO+H3382t2CsiIiIipnBLUpmVlcWaNWtIvkCSYLVaee+999i8eTMWi4VatWq5IyzxRHktRXx9oRCFn5wqMxX+XWyML+vvmme0GWK8bvoBsq2ueYa7tWgBjRubHYWIiIiImMSpfSovJDExkY4dO2KxWKhSpQo1atSgXLly+Pv7ExcXx9atW0lOTsaS27rhjjvucEdY4onyKohWrAg+bl5I3/s7WNOMFcWqLqo82/AGCKkAKSdh7x/Q6AbXPEdERERExE3cklTmsdlsHDt27LyWITabzT7u27cvr776qjvDEk9hs+WvVJqxjXL7z8Zr0z4l7015Ib7+cHl/WDsONk8tfUllTg4sXAjXX29UhxURERGRUs8tS0HBwcH069eP2rVrY7PZ7B8AFouF66+/ngkTJrBlyxZmzJhBQECAO8IST5OYCJm5PRzdnVRaM2H3AmPctI9rn9UidyV+5zxIT3Tts9zp11+hUSO4+WaYPt3saERERETETdySVIaEhDB9+nT279/P4cOHmTRpErfddhsBAQHk5OSwZMkSxowZw4m8VSopm/K2voL7k8r9yyEj0eglWaOta59VrRVUaATWdNjxs2uf5U7h4bA3t6rtp5+aG4uIiIgUSteuXbFYLFgsFiZOnGh2OOKl3F79tVq1agwaNIiZM2dy5MgRRo4cSWhoKLt27aJ79+76j7ksc/ylgruTyrzkrkkv15/ltFjyVys3TXXts9zp6quhZe5Z1L/+Mj5ERETEo23atMk+btWqlYmRuF5WVhZr167lo48+YsiQIXTo0IFq1aoREhKCv78/5cuX54orruDBBx9k4cKF5Kj/dqE57adnq7XolSyjo6N5/fXX2bJlC82aNSMnJ4dHHnmEzZs3Oyss8SaOPQ4rVnTfc2022L3QGDft5Z5ntrjdeD2wEhIOu+eZrmaxwPDh+ddarRQREfFohw4dIi4uDoCAgACaN29uckSu9fLLL9O+fXueeeYZJk+ezJo1azh69ChpaWlYrVZOnz7Npk2b+Oqrr7jxxhu58sor2bBhg9lhewWnJZWXXXYZCxYsKNZ7a9euzdy5cwkMDMRqtfLBBx84KyzxJmYllce2QPIx8A+F2p3c88yoWlCrI2CD7T+555nucNdd+a1gpk07e0uziIiIeBTHhKl58+alvq6JY3FQgNDQUFq0aEGXLl249tpradKkCT4OO9Y2btxI586dWblypbtD9TpOSyp3795Nz5496dWrV7Ey+jp16nDllVdis9n4448/nBWWeBM/P6hdG0JDoUIF9z13T25vynpdwM+NFUub32a8bpvjvme6WnAwPPigMc7MhC++MDceERERuaCNGzfax61btzYvEDcJDg6mV69eTJgwgZ07d5KcnMymTZtYunQpS5YsYceOHRw7doxXXnkFX19fAJKTk7nrrrtITk42OXrP5vTDY/Pnz6dNmzb07t2b5cuXF+m9p0+fBuDkyZPODku8weDBcOAAJCfDrbe677n/5iaVDbq575kAzfoAFjj8F5yJce+zXenRR/PPpX7+OWRlmRuPiIiIFMhxIai0n6cEePPNN/nll1946KGHaNy4cYH3VKxYkbfeeotx48bZPxcTE8N0Vba/KKclla+//joBAQH2diG//vor1113HQ0aNOCNN95g7dq1Fz3sOn78eHbu3AlAubztc1J2uapP5LnSzkBMbkGZht3d88w84VWgdkdjXJq2wNauDX1y27IcOQKzZ5sbj4iIiBTIMaksCyuVRfHggw9Sv359+/XSpUvNC8YLOC2pHDlyJJs3b6ZbN2O1Jy+53LdvH6NGjaJjx45ERUXRsWNHhgwZwosvvsiIESMYPnw4rVq14tFHHwWMvpVXXHGFs8ISubh9S8CWDRUaG+cc3a3Zrcbr9jnuf7YrPf54/lgFe0RERDzO6dOnOXToEAA+Pj60zKvgfgGjR4/Gz8/P3n5k6NChZOb1Fy+lHBPtY8eOmRiJ5/Nz5mQNGzZk0aJFLF68mNdee42/HFoK2Gw2kpOTWbt2LWvXrj3rfecemh02bJgzwxK5sH9/M17dvUqZp1kfmP88HP7b2AIbVdOcOJzt+uuhTRto1ersBFNEREQ8guN5ysaNGxMSElLgfcnJyQwZMoQZM2YARpXYsWPH8vDDD7sjTFM5drcIDw83MRLP55KGfN27d2fNmjX89ttv9OvXDz+//Nz13AQSjNXJPE8//TS3uvM8nXiOu++GAQPguefc8zybDfbkJpXuPk+Zp7RugbVYYO1amDABWrQwOxoRERE5R2G2vu7atYt27drZE8oqVaqwZMmSMpFQZmVl8eeff9qvO3ToYGI0ns+lXd6vv/56pk+fztGjR/nxxx95+OGHufrqq6lUqZJ9eyxApUqV6N+/P4sWLVI7kbJs3jyYMQN+/rlIb3P8b6lITmzPbSUSkp/YmcFeBbaUnT30cek/LyIiIlIClyrSM2fOHNq1a8eOHTsAuOqqq1i/fj0dO5r4M5MbvfLKK/Ytr+XKlWPw4MHmBuThnLr99ULKlSvHgAEDGDBggP1zNpuN9PR0fH19S31PHCmErCxISDDGhWwnknnwIMffe5+UVauwBAcT2fNmKj75JL6RkYV75v7c6sS1O7q3lci5mvaBX5+D2HWlawusiIiIeKwLrVTm5OTw6quv8t5779l/aX///ffz2WefERhYtJ+XJk+ezJAhQ5wTsINJkyY5PcmzWq2cPHmStWvX8tlnn7F4sdEdICgoiB9++IHy5cs79XmljVuSyoJYLBaCg4PNerx4mri4/HEhksr03bs5eO995OQmorbMTOK//4HklauoPXkS/tWqXfqZeUll3c7Fidh5witDzasgZg3s+hWuKmVnipOTYfJk+OEH+P13CAoyOyIREXExm81GmjXN7DA8WrBf8FlHwNwpLS2NXbt22a/zVirj4uK488477QmVv78/H330EY899pgpcbpahQoViHP8GdSBxWKhe/fujBkzhssuu8zNkXkf05JKkbOcOpU/vkRSmZOSwuHhw8lJSCDo8sup+sYosuPjOfra62QdOkTMsGHU/mEqvmGhF54k2woHVhpjs5NKgKa9jKRy59zSl1QOH24klQDffw/3329qOCIi4npp1jSu+v4qs8PwaGvvWkuIf8HFcVxty5YtZGdnA1C3bl2ioqJYv349/fr14+DBgwBUrlyZ6dOnc8011xT7OdWrV6dHjx5OifnceV2tU6dOPPzwwzRr1szlzyoNlFSKZyhCUhn31VdkHTyEX7Wq1JwwHr/oaABqf/sNB+4YSMa/ezjx/ntUffPNC09ydBNkJEJQFFTxgEIyTXrColfhwCpIPQ0hpahX6yOP5CeVH34IQ4a4rw+piIiInOfcra+TJk3i0UcfJT09HYC2bdsya9YsatSoUaLndO/ene7dTaqwXwhdu3YlIXfXW0ZGBseOHWP37t3k5OSwcuVKVq5cSdu2bfnxxx+pW7euydF6NiWV4hlOnswfXySptMbFETdxEgCVX3zRnlAC+FetSrUPRnNo0GDOTJ9BxE03EXqhw+T7lxmvda4GH98Sh19i5epBpeZwYhvsXghX3Gl2RM7Trh106gSrVsG2bbBoEbjgt5YiIuI5gv2CWXvX2kvfWIYF+5l3DMwxqVyxYgUzZ860Xw8aNIhx48YRVAaOq/z444/nfe706dN8+eWXvPHGG6SkpPD333/TpUsX1q1bR6VKlUyI0jsUqTxj3l+uu6WkpPDGG2+4/bniRoVcqTwzbRq29HSCLr+c8AJ+8xXarh3Rd90FwPH3/4Mtd2vHefKSyrpdih2y0zXtZbzunGtuHK7wzDP54w8/NC8OERFxC4vFQoh/iD4u8mHWeUo4u0fliRMn7ONhw4YxefLkMpFQXki5cuV4/vnnWbFihb03ZUxMDM8++6zJkXm2IiWVI0eOpH79+rz33nucOXPGRSHlO3PmDO+++y716tVj1KhRLn+emKgQSaUtK4v4H6YCUO6+ey/4j3GFxx/DJyKCjF27SJhTQO9HawYcWmOMPeE8ZZ4mPY3XPb9DZqq5sTjbLbdA3raRRYtg61Zz4xERESmjsrOz2bx5s/26Z8+e9vG0adPYvXu3GWF5nFatWvHKK6/Yr6dOncrp06dNjMizFXn768mTJ3nllVd45513uOeee7j//vtp06aNU4P666+/+Oqrr/jhhx9ISUnBZrOZ+tsccYNCJJUpa9ZgPXEC33LliLjI9km/6GgqDBvKidEfcGrCeCJvvQWLr8MW18N/gzUdwipDxcbO+hOUXJUWEFkLEg7B3j/yVy5LA19feOopePJJ4/qjj+Crr0wNSUREpCzatWsXaWlGZd4qVaowc+ZMrr32WtasWUN8fDy9e/dmzZo1RDscMSquxYsXM2bMmBLPc65nn33WLWc1+/fvz4svvggYLUf+/vtvlxQeKg2KlFQuXbqUJ598kk2bNpGcnMz48eMZP348DRo04LbbbqNHjx506NChyEvmqamprFq1ioULFzJnzhz2798PYO+N07JlS8aOHVukOcXLXHedUbzl1CmoWXCfxsQFCwCIuLEHlkv0No0eOJC4CV+QdfAQSYsXE3HjjflfdGwl4km/rLBYjNXKtZ8bW2BLU1IJRoGe1183+pF++y288w5Urmx2VCIiImWK43nKli1bEhgYyOzZs2nXrh0xMTHs3r2bAQMGsGDBAvz8SlZ+JTY2loULF5Y05PMMHDjQ6XMWpOY5P5NeqP2IFDGp7Ny5M//88w/ffPMNb731Fnv27AFgz549jB49mtGjR+Pv70/Tpk257LLLqFevHtWrVycqKorg4GBsNhvp6enEx8cTGxvL3r172bp1Kzt37sRqtdqfk5dMNmjQgNdee4177rlHK5Wl3a23Gh8XYMvKIum33wEI73HjBe/L4xMaSvQ993Dqf/8jbsIXhPfokf/fkKf0pyxI015GUrlrvtH2xLcU1dIKD4ehQ2H0aMjMhM8+A21rFxERcSvH85QtW7YEjBXLn376iauvvprU1FR+//13hg8fzueff25SlJ4hrzJsnqioKHMC8QJF/onVYrFw3333cc899zBt2jQ++eQT1qxZY/96ZmYmmzdvPmuv9qXkJZF52rdvz5NPPsmAAQPw8SnSsU8ppVLXrSMnIQHfChUIaXNlod4Tfc/dxH31Fenbt5O2YSMhrVtBVhocXmfcUOdqF0ZcTDXbQ0h5SI2Dg6ugngcVEnKGJ54wtr76+kJu2XIRERFxn3NXKvO0atWKb775hv79+2Oz2Rg3bhxNmzbliSeeKPazBg8ezODBg0sSrqmWL19+1nX9+vVNisTzFTtj8/HxYeDAgaxevZrNmzfzwgsv0LBhQ8BIEs/9yHOhrzVo0IDnn3+eTZs2sXr1au644w4llGKXsno1AGFXX332+ciL8IuOJuLmmwGIn/qD8cnY9ZCTBeFVIdoD+w35+kGjm4zxznnmxuIKNWrA1Klw6BC8/77Z0YiIiJQ5Ba1U5unbt+9ZxTGfeeYZl2xf9QaZmZm89dZb9uv69evTuLEH1eLwME7J2i677DLeffdddu3axb59+5g0aRKPP/44119/PfXr1ycyMhJfX198fX2JjIykfv36XH/99Tz++ONMmjSJvXv3snv3bt577z0uv/xyZ4R0UatXr2bYsGE0a9aMyMhIIiIiaNasGUOHDmXVqlUueabFYinyx7hx41wSi0dKSIBzVqwdpawyksrQjh2KNG30ncae+6QFC7HGx8PBP40v1GrvWecpHdlbi8y76N+J1+rXD9TnSURExO0OHTpkPxcYGBhYYJL02muv2c8sZmdnc8cdd7Bjxw63xukKixcv5rnnnuPIkSOXvPfo0aP07t37rAQ8r2CPFMzpB7bq1KlDnTp1GDRokLOnLrGUlBSeeOIJJk6ceN7XduzYwY4dO/jiiy8YMmQIn376KaGhoSZEWQalpUFUFPj5Qc+eMGfOWV+2xseTnvuPWWiHoiWVwZdfTlDz5qRv20bCrNmUDzCSU2p1dELgLlLvWvAPhcTDcGQDVG9tdkQiIiJSCjgmSc2bN79gIZ6JEyeyd+9e/v77bxISEujduzdr166lfPnyborU+VJSUvjggw/48MMP6dixI9dccw2XX345FSpUICQkhOTkZPbt28eKFSv46aefSE3Nb+/Wp08fHnjgAROj93ylqArIxWVnZ9O3b18WLVpk/1xwcLD9G2r79u0kJiYCMGnSJGJjY/n111/xLeRWy6Lo3LkzwcHBl7yvVq1aTn+2R8qrpGW1GmftzpG6di3YbAQ2bIhfxYpFnj5q4B0ce+11zsyYTrmrN2MBqF205NSt/IOhQVfY8TPs+rV0J5VJSTBtGtx/v+euHIuIiJQSFzpPea7g4GDmzJlD27ZtOXLkCHv37qVfv34sXrwYf39/d4TqMjk5OaxcuZKVK1cW6v4hQ4Ywbtw4FQ29hDKTVL722mtnJZQPPfQQ7733HuXKlQOM3168//77vPnmmwAsWrSI119/nbffftvpsUyZMoU6deo4fV6vdYkelan//ANAyFVXFWv6iJtu5vjb75C5/wDpDTIIrhYJlZoVay63adLLSCp3zoPrXzU7GteYOBH+7/8gPh6qVoXc868iIiLiGoVNKgGqVavGTz/9ROfOnUlLS2PZsmU88sgjfPnll64O0yXatGnDM888w4IFC9ixY8d5hUIdBQQE0Lt3b5544gk6d/bAbgEeqMhJ5f79+6lb1wMLnFzEkSNH+Oijj+zX9957LxMmTDjrntDQUN544w1sNpv9UO6HH37IY489RrVq1dwab5lziaQybdMmAIKvuKJY0/uGhRLetSuJ8+aRcCCY4PZXgY/zV6CdqtENYPGFE9shbi+UL4XVxsqVMxJKgPfeU1IpIiLiYnPOOWJ0KW3atDlrG6g3q1GjBmPGjGHMmDGcOXOGTZs2sW/fPk6dOkVGRgahoaFER0fTtGlTWrZsSVBQkNkhe5UiJ5V5hXeuuOIKWrVqRatWrbjiiito1qyZS7aKOsPHH39Mem77gpCQED7++OML3vvaa68xZcoUYmJiSE9PZ+zYsbyvKpWudZGkMiczk4ztxnnK4Csu/hu1i4m8pQ+J8+aReCiYytWuwuM3MARHGy1P9i8zVis7Fb+ct8fq0weaNIGdO2HFCli1Cjp1MjsqERERKeWioqLo0qULXbqUstZtJipW9dfExESWL1/O2LFjGTx4MFdccQVhYWG0bduWoUOH8vnnn/Pnn396zG82Zs+ebR/ffvvt9i2vBQkICGDIkCH261mzZrk0NuHspPKcA+AZ27djy8rCt3x5/KtXL/YjQjt0wDfYRnaGL8knwoo9j1s17W28lsbWIgA+PvDCC/nX+uWNiIiIiFcqVlJ5bo9Jm81GRkYG//zzD1999RWPP/44V199NRERETRt2pS77rqL0aNHs3jxYk45JhBusGvXLvbs2WO/vvHGGy/5nptuusk+3rNnD7t27XJJbJLr9On88Tkrlfatry1alOiAtCXhAJG1UgBIWLWt2PO4VePc/w5j1kLyCXNjcZW77jJ6VwL88gts3WpuPCIiIiJSZEXe/vrzzz+zceNG+8f+/fvtX8tLMi0Wiz3p3L17N7t37+bHH3+031etWrWzts62atXKZYVrNuUmJXk6FKIlRevWrQkICCAzMxOAzZs3q9mpKzkmleesItuTykscJr+kg6uJqJ3G6V1hJC9bQU5KCj6e3jImsgZUa2W0Fdk1H670vDY9JRYQAM8+C08/bVz/5z/w9dfmxiQiIiIiRVLkpLJXr1706tXLfp2UlGRPMDds2MDGjRvZvn27PSE7N9EEiI2N5ciRI8ybl7+tL++cZrt27bjmmmvo3Lkz4eHhJfrDAWc1aw0ICKBmzZqXfE/efXv37j1vDmd47rnn2L59OzExMWRlZVG+fHkaNmxIly5dGDRokNcVQiqxiySV6bnnKYMuu6xkzzj0J0HRWfhXDCPrZDJJS5cS2bNnyeZ0hyY9jaRy57zSmVQCPPggvPmm8d/B99/DqFFQ1r4HRERERLxYsba/OgoPD+eaa65h+PDhTJw4kX/++Yfk5GQ2btzIpEmTePLJJ+nSpQuRkZFnve/c7bNnzpxh2bJljB49mj59+lC5cmUGDhzIqlWrShTfgQMH7OMaNWoUegulY49IxzmcYcaMGWzfvp2kpCTS09OJjY1l6dKljBo1ikaNGvHwww+Tlpbm1Gd6tAsklTmpqWQePAhAUJMSrhQfXI3FAhHXGivVSQsWlGw+d2mS+wucfUshI8nUUFwmLAyeyC1ElJ1tVIIVEREREa9R4qSyIH5+frRo0YJBgwbx0UcfsWTJEk6fPs2+ffuYNWsWr732Gr1796ZmzZpn9YhxTDTT09OZPn06nTt3ZsCAASQkJBQrlqSk/B/Ez01sLyYiIqLAOZyhQoUKXHXVVXTt2pU2bdoQFpZfOMZqtTJ+/Hg6depUpD9zRkYGiYmJZ314jY8+gj/+gBkzIDra/umMf/8Fmw3fChXwK6DVSKElxMKZg2DxIaLffQAkLze2wHq8ik2gXD3IzoA9v5kdjes88QTkfc/NnAne8L+NiIiIiADF2P5aEnXq1KFOnTrceuut9s/Fx8fbt81u3LiR9evXs3PnTiB/6+ysWbPYsmULK1eupEIRk4vk5GT7uCj9ZoKDgwuco7iaNWvG0KFD6d27N/Xq1Tvra1arlYULF/Lyyy+zefNmwGhOO3DgQObPn1+o+d99911GjRpV4jhN0bCh8XGO9NwCSUGNGpVs/pg1xmuVywlseSX+tWuRdfCQd2yBtViM1crVnxhbYJvfZnZErhEdDS+/DMnJxvlKTz/vKiIiIiJ2LlmpLIro6Giuv/56nnnmGb7++mu2bdtGXFwc3333HV27drXft3v3bu6+++4iz2+1Wu1jP7/C59CO92ZlZRX5uefatm0bTz755HkJZd6zevbsydq1a+npkOQsWLCAX375pVDzv/TSSyQkJNg/YmJiShyz2TJ2GkllYJMmJZso5m/jteZVWCwWIm40qqp63RbY3YvAmmluLK70wgvG2cqLtPwREREREc9jelJZkKioKO68804WL17M3LlzCc1dtfjtt99YsmRJkeYKCQmxj9PT0wv9Psd7Q920ahIUFMQPP/xA5cqV7Z/79NNPC/XewMBAIiIizvrwdum7c1cqS3qeMmat8VqjHQARN/YAvGgLbI02EFoJMhLg4EqzoxEREREROYtHJpWObr75Zj7//HP79ffff1+k9zueVyxK8ZvU1NQC53C18PBwHnnkEfv1ihUripQMe520NPjmG5g3D7Zvt3/aZrORsWs3AIElaeeSlQbHjC3F1GxrzNekCQG1a2PLyCBp6dLiz+0uPr75PSt3zrv4vaXJ6dNnF3ESEREREY/k8UklwF133UX58uUBWL16dZHe63gG8+jRo4V+37Fjx+zjvGe7y3XXXWcfp6enl4qtrBd07Bjcdx/06gVvvGH/tPXoUXKSksDPj8CStJc4shFyrMZKX1RtwGhvE37jjQAkLVhYkujdJ28L7M5fISfH3FhcLT4eXnkF6tSBt982OxoRERERuQSvSCotFgvNmjXDZrNx5MiRIr23scMqV1xc3FkrkBfjmMg1KemZviKqUqXKWdenTp1y6/Pd6gLtRDL27wcgoFYtLAEBxZ//8F/Ga812RtGbXOE3dAcgeeVKcrxhJbhuZwgIg6QjcHSD2dG4VkYGfPghJCXB55/D8eNmRyQiIiIiF+EVSSXkn40sanuPpk2bnnW9cePGS74nNjaWkydPXnAOVzs38XU8F1rqXCCpzNyXm1TWK8EqJUCMQ1LpIKhZM/yrVcOWlkZKCXuhuoV/EDQ0EmF2zDU3FlerUgWGDTPGaWkwZoy58YiIiIjIRXlNUjl27FgmTJjAAw88UKT3tWvXjsDAQPv1ypWXLnSyYsUK+zgoKIh27dpd5G7n27Zt21nXlSpVcuvz3epCSWXuSmWJtr7abPlJZY2z/ze0WCyEdTOqCyctWlz8Z7iTfQtsGThX+fzzkPd9+7//abVSRERExIN5TVLZqFEjHnzwQcaPH1+k94WFhZ3VmuS777675Hsc7+natavbqr/mmTp1qn1cp04dqlat6tbnu9UFt7/uAyCgTgmSyjMHIeUE+PhBtSvO+3J4t24AJC1dis0JbWNcrmF38PGHU7vg1L9mR+Na1arB0KHGODUV3nvP3HhERERE5IK8JqksicGDB9vHmzdvvmjvx3/++Yf58+cX+F53+Pnnn5k7N39746233urW57vdBVcqDwAl3P6a15+ySgvwDz7vyyFXXolvdDQ5CQmkrltX/Oe4S1Ak1L3GGJeF1cqXX4bg3P/dPv8cDh82Nx4RERERKVCZSCr79+9Py5Yt7dfDhg1j586d59139OhR7rnnHrKzswG44oor6NevX4FzHjhwAIvFYv8YOXJkgfclJCTQr18/1q9ff8k4f/jhB+666y77dUhICC+88MIl3+fVCkgqc1JSsOZW3y3R9ld7kZ6rCvyyxdeXsOuNSrtJi38r/nPcqUlP47UsJJVVqsDw4cY4IwPefNPceERERESkQGUiqbRYLHzxxRcE5656HD16lKuuuooXX3yRX3/9lUWLFvHWW2/RqlUrduzYAUBwcDATJkzA4lAxtDhsNhuzZs2iTZs2NG3alKeeeorJkyezcOFCVq5cyfz58/nPf/5Du3btuOuuu0hJSbHHPGnSpPMqwZY6jkllbuuWjAMHAPCNjsY3Kqr4c9uL9LS94C32LbC//47NG1p1NL7ZeD38NyQdu/i9pcHzz0NEhDGeOBH27jU3HhERERE5j5/ZAbhL27Zt+fbbb7nnnntIS0sjMTGR999/n/fff/+8e4ODg/n2229p2/bCyUhx7Ny5s8AV0nOFh4czfvx4br/9dqc+3yMVsFKZv/W1XvHnzUyBY1uMcY0LF1oK7dgRn5AQrMePk751K8EtWhT/me4QUQ2qt4HYdbDrV2hzv9kRuVb58vDMMzByJFit8MEHxlZYEREREfEYZWKlMk/fvn1Zv3493bp1K3AF0mKx0LVrV9atW0ffvn2d8szg4GCGDh1K8+bNL7nqGRkZyRNPPMHWrVu58847nfJ8jxcQkL8SFR0N5Fd+Dahbp/jzHtkAtmwIrwqRNS54m09gIKGdOwPaAuuxnn4aGjSAd96B0aPNjkZEREREzlFmVirzNG3alMWLFxMTE8OqVauIjY0FoHr16nTq1ImaNWsWap46depgs9kueV9gYKC9Ym18fDwbN27kxIkTnDp1ijNnzhASEkK5cuVo0aIFLVq0wNfXt/h/OG80fbrxmpUF/v4AZOZufw2sU6f489pbibSFSyTz4d26kbRgAUm//UalZ58p/jPdpUkv+H0U7FsG6YkQFGF2RK4VEQE7d0JZ+94QERER8RJlLqnMU7NmTQYOHOjWZ0ZHR3Pddde59ZleIzehBMg8HGN8qmat4s93OLfy6wWK9DgK69IZ/P3J3L+fjL17Caxfv/jPdYeKjaB8Q4j7F/YshssKLiZVqiihFBEREfFYZWr7q3iHrMPG6nFAzQtvW70om82hSM+Fz1Pm8Q0PJ7R9e0BbYL3G0aNmRyAiIlIqdO3a1d7NYOLEiWaHI15KSaV4lJyUFLLj4gDwL+RW5POc3gepp8A3AKq2vPT9OFSB/c1LksqmvY3X3YsgK93cWNwpLs44Y1m7NnjL/1YiIiIebNOmTfZxq1atTIzEfAcOHCA0NLRQbQPlbEoqxTzHj0O/fvDQQ/DttwBk5q5S+kRG4hseXrx587a+Vm0JfoGFekt41+vBYiF961ayvGEVrFprCK8GmUmw9w+zo3GfBQvg44+NM7jPPQfe0AZGRETEQx06dIi43F/mBwQE0Lx5c5MjMtfDDz9Mamqq2WF4JSWVYp6jR2HWLPjyS1ixAoCs2MMABNQo5tZXcCjSc+mtr3n8KlQgOPe3c0m//V78Z7uLjw80v9UYb5ttaihudeedkPdb1I0b4fvvTQ1HRETEm23YsME+bt68OQEBASZGY65vv/2WhQsXmh2G11JSKeYpoEdlVkxekZ5ibn0FOFz485SOvG4LbPPctje7foWsNHNjcRcfn7PbirzyCqSXoe2/IiIiTrRx40b7uHXr1uYFYrJTp07x9NNPA0aniGrVqpkckfdRUinmKSCpzIzJW6msXrw5M5Lh+DZjXNSksruRVKauW4c1Pr54z3enGm0gsiZkJsMeL1hddZauXeHGG43xoUPw6afmxiMiIuKlHFcqy/J5yqeffppTp04BMG7cOPwduhJI4SipFPNcbKWyRjFXKmPXgy0HImpARNF+yxRQsyaBTZpAdjbJf3jBOUWLBZrdYozL0hZYgPffz+8/+vbbRgEfERERKRLHpLKsrlQuWrSIb3NrewwZMoTOnTubHJF3UlIp5ilopTL3TKV/cduJ2Le+ti3W28Nv6A5A0qLFxXu+u9m3wM4vO1tgAVq0gMGDjXFCArzzjqnhiIiIeJvTp09z6NAhAHx8fGjZ8uIV80ePHo2fn5+9KurQoUPJzMx0R6guk5qaysMPPwxAhQoVGO14xEaKREmlmOecpNJms5EVU8JCPTG5lV+LUKTHUcQNNwCQsno12cnJxYvBnaq3hshakJUC/3pJIuwsb7wBQUHG+L//hf37zY1HRETEiziep2zcuDEhISEF3pecnMyAAQN4/vnnyc7OJiAggM8//5wJEyZ4fWGf1157jf25Pz988MEHlC9f3uSIvJeSSjHPOUml9eRJbBkZ4OODf9WqRZ/PZstvJ1LE85R5Ahs0IKBePWxZWSQvWVqsOdzKYimbVWABatQwelYCZGbCJ5+YG4+IiIgXKczW1127dtGuXTtmzJgBQJUqVViyZIl9dc+brV+/nrFjxwLQpUsXBg0aZHJE3k1JpZjHsRhOVBRZuT0q/apUxlKc33yd3gdpp8E3EKq0KHZY+VtgFxV7DrdqfpvxunsBZJax3kovvACNGxsJ5X/+Y3Y0IiIiXuNSRXrmzJlDu3bt2LFjBwBXXXUV69evp2PHjm6L0VWsVisPPvigfeV13LhxZofk9ZRUinkck8roaLKOHgHAv7hlnA+vM16rtgS/4m/HyNsCm7xiBTne0AC3WiuIqg1ZqfCvlyTCzhIZCdu2wfDhoEptIiIihXahlcqcnBxefvll+vbtS2JiIgD3338/y5YtK3KrjcmTJ9vPYDrzY/LkySX6s48ZM8a+/feFF16gSZMmJZpPwM/sAKQMu/lmqFnTKLQSGor16FEA/KsWN6nMO09ZvCI9eQKbNsW/Rg2yDh8mecVKInrcUKL5XM5iMVYrV31sbIHN2w5bVvj6mh2BiIicw2azYUsrQwXkisESHIwlr5K5m6WlpbFr1y77dd5KZVxcHHfeeSeLFxt1Gvz9/fnoo4947LHHTInTFfbu3cuoUaMAaNCgAS+//LLJEZUOSirFPP/3f2ddZh09BoB/lSrFm8+eVLYpSVRYLBbCb7iB0xMnkrRokecnlZCfVO5eAOmJEBRhdkTm2bgRateG6GizIxERKbNsaWnsan2l2WF4tMb/rMdygeI4rrZlyxays7MBqFu3LlFRUaxfv55+/fpx8OBBACpXrsz06dO55ppriv2c6tWr06NHD6fEfO68xTVs2DDScn/h8fnnnxOUV/RPSkRJpXiMrGNGUulXtRhJZWYqHN9qjEu4UgkQcUN3Tk+cSPLSpeRkZuLj6dXNqraECo3g1G7Y8Qu0utvsiNwvLg5efhm++AKeeAI+/tjsiERERDzSuVtfJ02axKOPPkp6ejoAbdu2ZdasWdQobjX+XN27d6d79+4lmsOZJk2axO+//w7A3XffTbdu3UyOqPRQUikew779tUoxKr8e3QQ5VgirApEl+wcQIKhFC/wqV8Z6/Dgpq1YRft11JZ7TpSwWaHE7/PEWbP6xbCaVKSnwzTdGFeD//heGDoVmzcyOSkSkTLIEB9P4n/Vmh+HRLMHBpj3bMalcsWIFM2fOtF8PGjSIcePGlboVvBMnTvB/ubvkoqOj+fDDD02OqHRRUinmsNmMV4ezBFl5SWW1YiSVjltfnXA+weLjQ3j37sR/+y1JixZ7flIJcPkAI6ncvxwSj0BEMc+meqtatYxqsCNHQna20W5kwQKn/PcgIiJFY7FYTNvaKZfm2KPyxIkT9vGwYcNKbSXUJ554gtO57ezee+89KlWqZHJEpYuqv4o5Dh2CgACoWBGGDycnPZ3s3GqwxTpT6aQiPY7Cc7drJP3xB7asLKfN6zLRdaBWB8AGW2aYHY05nnvOSC4BFi2COXNMDUdERMTTZGdns3nzZvt1z5497eNp06axe/duM8JyqT///JMff/wRgA4dOvDQQw+ZHFHpo5VKMceZM2C1wqlTkJGBNfc8pSU4GJ/IyKLPl9dOxIlJZUibK/EtV47s06dJ+esvwjp1ctrcLtPidjj0J2yeBp2eMDsa9wsJgTFjYMAA4/qJJ6B7dwgLMzcuERERD7Fr1y57oZoqVaowc+ZMrr32WtasWUN8fDy9e/dmzZo1RDuh4N3ixYsZM2ZMiec517PPPluks5rHjx+3j//88098fAq/rjZq1Ch7tViA/fv3U6dOnUK/v6xQUinmcOxRGRVlL9LjX7Vq0ctrJ8RC0hGw+EK1K5wWosXXl/CuXTkzfTpJixZ7R1LZ7Fb49Xk4vgWOb4PKzc2OyP369YMePWDhQjh8GEaNgtGjzY5KRETEIziep2zZsiWBgYHMnj2bdu3aERMTw+7duxkwYAALFizAz69kqUJsbCwLFy4sacjnGThwoNPnlJLR9lcxx5kz+ePoaLKO5BXpKcHW18rNISC05LE5CL/BaCeS9Ntv2HJLb3u0kHLQKLd09+ZpAMSnZPLNmoM8/M16eny0nPbv/E7XMUsZ9s06Jq7cz8mkDBMDdgGLxSjUExhoXH/0EWzdam5MIiIiHsLxPGXLli0BY8Xyp59+IiT3HOzvv//O8OHDzQjPJfz9/YmMjCz0h+MCR2Bg4FlfK8oqZ1milUoxh2NSGRVF1jEjqSxWOxEXnKfME9r+KnwjI8mOiyP1778Jbd/e6c9wuha3w8655Gyexhsp/Zi6Ppb0rJzzbtt7MoWF247zzq876H9lDZ7q1ogqkaWk0luDBkZ7kREjjKI9jzwCy5aB/o9ARETKuHNXKvO0atWKb775hv79+2Oz2Rg3bhxNmzbliSeKf5xm8ODBDB48uCThOkXPnj054/iz5yXUqVPH3q/zxRdfZOTIka4JrBTRT1hijnOSSuvRvO2vxahY6oLzlHks/v721crEefOcPr9LNOxBln84PklH2PXXAtKzcmheLYLnejTm6/vb8cvjV/Pdg1fx0k1NuKJmFNYcG1P/jqHrmKVMWxeDLa8yr7d7/nkjuQTIzITcim8iIiJlWUErlXn69u171vnBZ555xiXbV6X0UVIp5nA8Uxkd7XCmsogrldZMOLrRGLsgqQSIyK2KlrhoMbbMTJc8w1myc2y8s3g/09PaAHB/6Gq+e/Aq5g6/mseua0DnRhW5vEYknRpUYFiX+sx5rBMzH+lAq1pRpGRm8/yMzTwzbRMZVi/Y6nspQUEwfjx8/jmsXg0VKpgdkYiIiKkOHTpEXFwcYGzrbNy48Xn3vPbaa/Yzi9nZ2dxxxx3s2LHDrXGK91FSKeY4d/vr0SMA+BX1TOXxrWBNh6AoKF/faeE5CmnbBr+KFclJSCB51SqXPMMZsrJzeHLqBiYs38e0bKOvZjfbGjpV97to8aMra5djxsMdeeHGJvj5WJi9IZZ7v/qLhFQvaKNyKddfDw8/DL6+ZkciIiJiOsdVyubNm1+wEM/EiRNp29b4ZX1CQgK9e/e2J6MiBVFSKeZw1vZXx62vLmpyb/H1JfymGwFInPerS55RUpnWHB759h/mbj6Kv6+FB+7oDxWbYrGmwdZL96z09bHwyLX1mTSkLeGBfvy1/zT3TfqLpPRSkFiKiIgIcOHzlOcKDg5mzpw5VKtm/Fy2d+9e+vXrR5Y39O0WUyipFHM4bH/N9vcnJyUFAP8qlYs2jwuL9DiKzN0Cm/THH+Tk9nbyFDabjRdnbea3HccJ9PNhwn1t6H1FdWh9r3HDP98Ueq5rGlZk2sMdiA7xZ1PMGQZP+pvUTKuLIjfBypVw882Q+9+biIhIWVLYpBKgWrVq/PTTTwQHBwOwbNkyHnnkEZfGJ95L1V/FHCNHwuDBcOYM1tzzez7h4fjklrIuNHtS2cap4Z0rqEUL/GvUIOvwYZKXLCHi5ptd+ryi+HDxbmb9E4uvj4Vx917JdY0rGV9oMRAWjzDOnB7bAlUuL9R8TatG8M0DV3HXF2tYfzCeJ6duZPw9V+Lj45qVYLf53/9g+HCw2eD118EFzZhFREQ82Zw5c4p0f5s2bUhNTXVNMB7swIEDZofgdbRSKeZo3Rr69oX778cab1Tl9KtUqWhzpJyC+P3GuPqVTg7wbBaLxZ5IJvzqOVtgf9oYy6d/7AHgndsuy08oAULLQ5Pc5LcIq5UAl1WPZNKQtgT4+bB4+3HeX7jTWSGbp3v3/N6VH38Ma9eaGo6IiIhIaaGkUkxnPXECAL9KFYv2xrzzlBUaQ3CUc4MqQF4V2JRly8lOTHT58y7l3+NJvDhzCwCPXlufO9rWOv+mVvcZr5t/hKz0Is1/Ze1yjO7fAoDxy/Yxe8PhEsVrukaNIK9Mek4OPPAAZGSYG5OIiIhIKaCkUkxnPXkSAP+irlS6aetrnsBGDQloUB9bVhZJi39zyzMvJCXDysPfrictK5tODcrz7A3nlwQHoP51EFED0s/AzrlFfs4tV1Rn+PVGr8eXZ21lz4mkEkTtAZ55xlglB9i2Dd5919x4REREREoBJZXiftnZMG8erFoF+/blr1RWLOpKpXuTSovFYi/YkzD3F7c880JG/LyNvSdTqBwRyNiBrfC90HlHH1+44i5jvH5ysZ71VLdGdGpQnrSsbB797h/SMr24h6WfH0ycaLwCvP02bN5sbkwiIiIiXk5JpbjfmTPQqxdcfTU8/jhZ9u2vRVipzMmG2H+MsYsrvzqK6N0HgNQ1a8k6csRtz3W0aNsxZqw/jMUCn97ZmgphgRd/Q+v7wOIDB1bAiaKfjfT1sfDxHa2oGB7I7uPJjPh5azEj9xAtW8KLLxpjqxXuvVfbYEVERERKQEmluN+5PSpPGNtfi5RUntwFmUngHwoVmzo3vosIqFGdkHbtwGYj4eef3fbcPKdTMnl5tnGOcug19WhXt9yl3xRVExrnFuz5+8tiPbdieCBjB16BxQLT1h1m8fbjxZrHY7z6KlyeWw1382ajGrGIiIiIFIuSSnE/hx6VRlJZjJXKvK2v1VuDr3s740TeeisACbPnYLPZ3PZcm83Gq3O2cCo5k0aVw3i6e6PCv7ntg8brpqmQUbxzkR3rV2DoNfUAeGnWZuKSvXh1LzAQvvkG/P2N608/hdyzvSIiIiJSNEoqxf0cViptUVH2Qj3FSirddJ7SUUSPG7CEhJB58CBpGza67bkLtx3j1y3H8POx8OHtVxDk71v4N9e7Fso3NFZ3N00tdgxPd29Eo8phnErO5NU5W92aVDtdy5bwxhvQpg2sWwdFPdMrIiIiIoCSSjGDQ1KZExKCLd1odVGkQj157UTceJ4yj09oKBHduwOQUMQmwsWVlpnNm3N3AEb7kMuqRxZtAoslf7Xy7y+hmMlgkL8vH95+BX4+FuZvPcbPm8w5V+o0zz0Hq1dDkyZmRyIiIiLitZRUivs5JJXW3CqcPpGR+AReouBMnvQEOJlbcKa6+1cqASJvuw2AxPnzyUkvWv/H4hi3bC+xZ9KoHhXMI9c2KN4kV9xpnEE9uRMOrCx2LJdVj2T49Q0BGPnzNu/eBuvrm78FVkRERESKRUmluJ/DmUorRisM/0pFWKWM/QewQVQtCK/s5OAKJ6RdW/yrVSMnKYmk33936bNiTqcybtleAF7t2ZTggCJse3UUFAktbjfGf00oUUyPXlefplUjiE/N4q15O0o0l0dJS4MXXoDcc74iIiIicmlKKsX9HFYqs7KtAPhVLMJ5yljztr7msfj4EHnrLYBRsMeV3p63gwxrDh3rl+fGy6qUbLJ2Q43XnXMh/kCxp/H39eG9vpdjscDsDbEs210Kitz8+y+0awf/+Q8MGgQ5OWZHJCIiIuIVlFSK+zluf83MAopapMf8pBLyq8CmrFpF5uFYlzxj5b+nWLDtGL4+Fkb2aY7FYinZhJWbQf3rwZYDaz4v0VQta0YxuGMdAF6ZvYXUTGvJYjNbRER+BdgFC+Cjj8yNR0TEA3l1gTYRKZKifL8rqRT3cziDaE1NBYqQVNpsDpVfzU0qA2rVIqRDe7DZODNjutPnz8rOYeQv2wC4r0NtGlUOd87EHYcbr/98A2nxF7/3Ev7vhsZUjwrmcHwaY3/71wnBmahyZaPNSJ4XX4S//zYvHhERD+LjY/zImKNdHCJlRt73e973/8UoqRT3++oryMqCuDisGUaRl0JXfo3fD6lx4BsAVS53YZCFE33HQADOzJyJLSvLqXN//edB9pxIpnxoAE91K0JPykupdx1UvgyyUmDdpBJNFRrox5u3Ngfgy5X72Rqb4IwIzdO9u3GmEsBqhYEDITHR3JhERDyAn58fFouFjAwvLs4mIkWSnp6OxWLBz+/SPeGVVIo5/PygXLmi96jM2/patSX4FbJarAuFd70e3woVyD55iqQ/ljht3pNJGXy8eDcAz9/YmMhgJ1YotVjyVyvXjgdryX5AuL5JZXq1qEp2jo2XZm0hO8fLt0a9+SZcdZUx3rcPHnmk2C1YRERKCx8fH4KDg0lJSTE7FBFxk8TERMLCwrRSKZ7Pmltl06+w1V89ZOtrHou/P1F9+wJw5scfnTbv6IU7Scqw0qJGJAOurOm0ee2a94XwqpB8DLbMKPF0r/duRniQH1tiE/j6zwMlj89M/v7www/GGUuA77+HiRPNjUlExAOEhYWRkpJCZmam2aGIiIulpKSQnp5ORN7PQ5egpFJMY7PZ7CuV/oVeqcxLKs3pT1mQqNsHgMVCyurVZB46VOL5NsacYdq6wwCM7NMcH58SFucpiF8AXPWwMV79aYkrnVYKD+LFm5oA8MHCXRxNSCtphOaqWxe++CL/+rHHYP168+IREfEAkZGR+Pn5cfjwYbKzs80OR0RcJCUlhZiYGEJDQwkLCyvUey69QVbE2R5/HIKDyalRE1vubzt9C3OmMisNjm0xxh6yUgkQUKMGoVdfTcqKFZyZNo1K//d/xZ4rJ8fGiJ+N4jz9Wtegda1oZ4V5visHw4oxcHIH7JoHTXuXaLo729Zi1j+xrD8Yz8iftzH+Xs9J/Ivl9tth2TL47DPIyIDx42FCyfp7ioh4Mz8/P2rWrMmBAwfYs2cPkZGRhIWF4evrW/Lq5CLiUnm/CEpPT8fX9+ye5zabjZycHNLT00lMTCQ9PZ3Q0FBq1KhRqK2vABabakOXSomJiURGRpKQkFDoZWu3sFqN7YVARrt27EtIxCcyksZr11z6vYfWwMQeEFYZnt1lnA30EEm//cbhx4fjGx1Ng6VL8Aks3nnPaetieH7GZsIC/fjj/7pQKTzIyZGe44+3YPloo+jRsBUl/jvddSyJnp+swJpjY8K9V3JD8xL21TRbRgZcdx3ccAO89hqc84+wiEhZlJmZyZkzZ0hISMBq9fJ2UiJlRE5ODjExMdSsWfOCiaLFYiEsLIyIiIhCn6XMo5VKcS+HSprW4GBISMSvfPnCvdfxPKUHJZQAYddei1/VqliPHiVx7lyi+vUr8hyJ6Vn8Z8FOAJ7s2tD1CSVA+0eNfpXHtsDuBdD4phJN17hKOEM71+OzpXsZ8fM2OjaoQFigF/8zExhorFb6O7FQkoiIlwsICKBSpUpUrFgRq9WqrbAiXiA5OZmePXuybt26Are0+vj44OfnV6RE0pEX/7QnXikhv+WENXc1r+hJpedtq7T4+VHunrs5MfoDTk/5msi+fYu8FeiT3/7lVHIm9SqGMqhjHdcEeq6QctD2QVj1MSx7HxrdWOKEffj1DZm7+SiHTqfy4aLdvN67mXNiNUtBCWVODhTzH10RkdLCYrHg7++Pv37xJuLxMjMzOXjwIAEBAQQFOX/hQj8ViXs5rFRm5/a88atYoXDvzWsn4kHnKR1F9e+PJTiYjN27SV27tkjv3XMiicmrDwAwondzAvzc+K3Z4XHwD4EjG2DPbyWeLjjAlzdvvQyAyav3s+Wwl/euPNfq1dCyJfz7r9mRiIiIiHgEJZXiXo4rlRbjPz/f8oVIKhNiITEWLD5QrZWroisR38hIom67DYDTU74u9PtsNhsjf96ONcdG92aV6dKokO1VnCWsIrS53xgvfc8pPRm7NKpIn5bVyLHBS7M3Y80uWXVZj/Hrr9ClC2zdCr17w5kzZkckIiIiYjolleJejkllbvJSqO2veVtfKzWHgFBXROYU0ffeA0Dy0qVkHjhQqPcs3HaclXtOEeDnw2s9Tdoq2vEJ8AuG2HXG2UoneLVXUyKC/Ngam8jXfx50ypymu/pqaNTIGO/aBQMHGsWnRERERMowJZXiXg5JZXbuD+N+FYqQVNZs54qonCawbl3CunQBm43T33x7yfvTs7J5a952AIZ1rket8iGuDrFg4ZXhqmHG+LdRkFPyogtG78qmAIxZtIsjZ7y8dyVARAT88gvk/SJk4UJ4/nlzYxIRERExmZJKcS/H6q8ZGQD4FmalMib3jKKHJ5UA5QbdB8CZWbOwxsdf9N7xy/ZxOD6NqpFBPHJtfXeEd2FXPwVBkUbfys0/OmXKgW1r0qZ2NCmZ2YzM7b/p9erVgxkzIPdMMB99BF99ZW5MIiIiIiZSUinu5bj9Nc1YufKrcIkzlVnpcHSTMfaCpDKkQweCmjXDlpZG/DffXPC+w/GpfLZ0DwAv39yUkACTizEHR8PVzxjjJe8Yf+8l5ONj4Z2+l+PnY2HR9uMs3HasxHN6hGuvhf/9L//6kUdg+XLTwhERERExk5JKca969aBXL2zXXEN2SgpQiDOVRzdBdiaEVIDoum4IsmQsFgvlhxlbSU9/+x3ZyckF3vfurzvJsOZwVd1y9GpR1Z0hXthVwyC8GiTEwLqJTpmyUeVwhnWpB8DIn7eRnFFKziAOHQpPPGGMs7Lg1lthxw5TQxIRERExg5JKca877oBffiHnl1+w5Z6pvOT218N/Ga81rypxD0V3Ce/ejYB69chJTOTM1KnnfX31nlPM23IUHwuM7NO8yD0tXcY/GK590RgvHw1pF9++W1jDr29IrXIhHE1I58NFu50yp0cYMwZ69DDG8fHQsyekl3yFV0RERMSbKKkUU1jj4gDwCQ/HJzDw4jd70XnKPBYfH8o/9BAAcZOnkOOQaFizcxj5i3G+8N72tWlaNcKUGC/oiruhYhNIOw1L33fKlEH+vrxVGntX+vnB9OlwxRUQEADvvw8uaCgsIiIi4smUVIoprKdOAYXY+mqzQUzeSqX3JJUAkb164letKtmnTnFm5kz7579Zc5Ddx5OJDvHn6e6NTIzwAnz94MZ3jfFfE+DETqdM27lRRW65ohT2rgwPN/pX/v47DBhgdjQiIiIibqekUkyRnbtSeckiPWcOQfJx8PGDaq3cEJnzWPz9Kf/AAwDEffElORkZnEzKsG///L8ejYkKCTAzxAurfz007gm2bFjwopHcO8GrPZvZe1dOKS29KwGqVjV6WIqIiIiUQUoqxb2uvx4aNsT66qsA+F4qqcxbpazSwjjv52Wi+vfHr3JlrMeOcebHH3l/wU6SMqxcXj2SgW1rmR3exfV4C3wDYN8S2PWrU6asGB7ISzeXst6VF/Lpp/Dcc05LyEVEREQ8lZJKca99+2DPHqynclcqi1Kkxwv5BAZS4ZFHADj22Th+WbsXgFG3NMfXx0OK81xIuXrQ4XFjvPBlyHJOAnhHG6N3ZWpmNiNKS+/Kc737rlEZ9oMP4K23zI5GRERExKWUVIp75faptOYW5/GrcImk0l6kp60ro3KpqH598a9ZE8uZeG7Zt5Lb29Sgda1os8MqnGueNVqMxB8wqsE6gY+PhXf7Xo6/r4XF24+zYOtRp8zrUSpWzB+//jp8/LFpoYiIiIi4mpJKcR+bDRITAcj29wcu0U4kMwWObTXGXrpSCcbZyl03DQRgwL9Lea5TNZMjKoLAMLj5P8Z41Vg47pyVxYaVwxnWuT4Ar8zeyqnkDKfM6zEefNBoN5Ln6adhonP6foqIiIh4GiWV4j4pKZBjVPy0+hj/6V20UE/sP0ahmIjqEFnDHRG6xOmUTF5JqMaB8MqEZaVhm/qd2SEVTdPe0KQX5Fjh5ycgJ9sp0w7v2oAmVcKJS8nk5VlbsJW2s4fPPAMjRuRfP/QQTJtmXjwiIiIiLqKkUtwnIb83oRUjgbjomcq8ra81vHfrK8DohTs5k5HNH536AnB68mSyjhwxOaoiunk0BEZA7Dr4+yunTBno58uHt1+Bv6+FRduPM/OfWKfM61FGjDBWKcH4hcpddymxFBERkVJHSaW4T+7WVxuQbTVWu3zLX2Sl8vDfxqsXb33951A8U/+OAeCOJ+8mpG1bbBkZnBjzocmRFVFENeiWu+r2+yiId047kGbVIuy9Okf9vI3D8alOmddjWCzGNtgHHzSus7ONxHLqVHPjEhEREXEiJZXiPrkrlTk+Pvatjhcs1GOz5bcTqdnOHdE5XVZ2Di/N3ILNBv1a16BN3fJUevEFsFhInDeP1H82mB1i0Vx5P9TqAJnJMOdR+1bmkhrWuT5X1o4mKcPKc9M3k5NTyrbBWiwwfjzk9iwlO9vYGpuSYm5cIiIiIk6ipFLcJ6/yq68vAD5hYfgEBRV8b9xeSDsNvoFGj0ov9MWKfew6nkS50ABe6Wn0Zgxu3pzIvrcBcPy997A5KTFzCx8fuPUz8A+Fgythzf+cMq2vj4UxA1oSEuDLn/vi+GrlfqfM61F8fGDCBONcZYUKsGgRhIaaHZWIiIiIUyipFPfJTSqz/fyAQp6nrNYK/AJcHZnTHYxLYexv/wLwas+mlAvN/zNUfPJJfEJCSN+8mcRffjErxOIpVw9ufMcY//6G06rB1qkQyqs9mwHwn4U72RhzxinzehQfHxg3Dtavh8suMzsaEREREadRUinu07YtfPkl1vvvB8D3YpVfD3vv1lebzcYrs7eSYc3h6gYVuK1V9bO+7l+pEuWHDQPg+H9Gk+1QwMgrtB4EjW6E7EyYNQyszmkHcme7mtx8eRWysm0M/+EfEtKynDKvR/HxgVq1zv6c1QqzZpkTj4iIiIgTKKkU96lbFx54AGv7DsClVirzkkrvK9Ize0MsK/ecItDPh7dvuwyLxXLePeWGDCagXj2y4+K8r2iPxQJ9PoWQ8nB8Cyx6zUnTWni3bwtqRAcTczqNl2ZtLn1tRs6Vk2MU8enXD1580ThLLCIiIuJllFSK21njTgEXKdKTngAndhhjL1upjEvO4M252wF4qlsjapcv+NycT0AAVUeNBODMtGneV7QnrBLc+rkx/ms8bJvtlGkjg/35712t8fOx8OuWY3y39pBT5vVYf/wBU6YY4/ffh6FDjUI+IiIiIl5ESaW4XXbcaQB8y10gqTy8DrBBdB0jefEir/+0jfjULJpUCefBa+pe9N6Qtm2J7Gf0rjw2YgS2LC/b7tmoB1yd24Pxp+Fwao9Tpr2iZhQv3NgEgDfmbmfLYS/bHlwU3brB558bq78AX34Jd9wB6enmxiUiIiJSBEoqxX327oXt28k+cgQA33LRBd+Xt/W1hnetUv6y6QjzthzFz8fCBwNa4u976W+vSv/3f/hGR5Px77+c+uILN0TpZNe9CrU7QWYSTB8EWWlOmfaBq+vStUklMq05DPtmHaeSnXNu0yM9/DD88AP4+xvXM2cayeapU+bGJSIiIlJISirFfZ57Dpo3x7poEQB+5coVfJ8XFuk5kZTOaz9tBeDx6xtwWfXIQr3PLzqayi+/BMCpzz4nfft2l8XoEr5+0O8rCK0Ix7fCz8Odci7Qx8fCh3dcQd0KoRxJSOex7/4hK9uL2q8U1R13wC+/QEiIcb1qFbRvD7t3mxuXiIiISCEoqRT3SUwEIDu3T6VvdAFJZU5O7vZXvKZIj81m4+VZWzmTmkXzahE8dl2DIr0/olcvwrt3B6uVIy+8QE6Gl63KRVSF/pPAxw+2TIcVY5wybWSwPxPuvZLQAF/W7j/N2/N2OGVej9WjByxfDlWqGNd790KHDrBihblxiYiIiFyCkkpxn3P7VBa0/fXkTshIBP9QqNTMndEV26x/Yvltx3H8fS2Mub1w214dWSwWqowaiW/58mT8u4eTn3ziokhdqO41cPNoY/zHm7DDOf03G1YO58M7rgBg8uoDTF8X45R5PdaVV8LatXD55cb16dPw1luqCisiIiIeTUmluE9CAjYcVioL2v4as9Z4rXGlsbXSwx2OT2XkL9sAo9prkyoRxZrHr1w5qr75BgCnJ04i5a+/nBaj27S5H9oNNcazhsHRzU6ZtkfzKjzRtSEAL8/ewuq9pfysYa1asHKlsXLZuLFx3rKAtjQiIiIinkJJpbhPQoI9ocRiwTcq6vx7Dv9tvHpBkR5rdg5PTd1IUrqVK2pGMaxzvRLNF3799UY1WJuNI//3HNa4OCdF6kY93oV610JWCnzXH+IPOGXap7o2pOflVcnKtjHsm/X8ezzJKfN6rIgI44zlkiVw7i9ftGopIiIiHkZJpbhPYiLWvFXKyEgseQmmo7yVSi84T/npH3tYdzCesEA/PhnYCr8ibnstSJVXXiGgfn2sJ05w5PkXsOV4WXEaXz8YMMXYupx8HL7pC8knSzytj4+xtbhN7WiS0q0MnvQ3JxJLedsNf3+oWvXszx07Bp07w6ZN5sQkIiIiUgAlleIemZmQnk527pbWAre+psRBXG6vwxpt3Bhc0f21/zSf/vEvAG/fdhm1yoc4ZV6fkBBqfPwRlqAgUlatIm7CBKfM61bBUXDPLIisBaf3GiuWGSVfWQzy9+WL+9pQt0IosWfSuH/K3yRnWEser7fIzIT+/Y2tsR06wPffmx2RiIiICKCkUtzFXqQn7zxlAUV68lYpKzSGkAu0G/EACalZPDV1Azk26Ne6BrdcUd2p8wc2bEiV118H4OQnn5KyerVT53eLiKpw72wIKQ9HN8LUuyAztcTTRocGMHlIW8qHBrA1NpEHp/xNelZ2yeP1BgkJRmIJkJYGd98Nw4YZYxERERETKakU98hNKvO2v/oV1E7k0J/Ga6327oqqyGw2G8/P3MSRhHTqlA9h1C3NXfKcqL63Edm3L+TkcPjpZ8g8cMAlz3GpCg3g7ukQEAb7l8PUO52SWNYuH8qkIW0JC/Rjzb7TPPztejKtXrZNuDgqVjRajgwZkv+5CROgXTvYUcrbrYiIiIhHU1Ip7mHvUXmR7a95K5UenFSOX76PhduOE+Drwyd3tiIs0HUVaquMeJ3gli3JSUgg5pFHyc79O/Qq1a+Eu2cYieW+pU5LLFvUiGLi4LYE+fuwdNdJnpy6AWt2GUgsg4Jg4kSYNAlCcrdcb90KbdrAlCnmxiYiIiJllpJKcY+WLeHkSbIHDQIK2P6alQax/xhjD00qV+85xX8W7ARgRJ9mtKgR5dLn+QQGUuO/n+JXpQqZ+/cT+8yz2KxeeIawdoezE8sfBkJmSomnbVe3HBPubUOArw/ztx7j2embykZiCTB4MPz9N1x2mXGdmmp87p574MwZEwMTERGRskhJpbiHry9UqIA12zj/dt721yMbICcLwipDdF0TAry4owlpDP/BOEfZ/8oa3NWullue61exIjU/+x+W4GBSVq7k2Kg3sHljSwnHxHL/MpjSB1JPl3jaNnVDef6WEAKj/mH+oR/o9d2LjFz9Bm+teYv3/nqPcZvGMfvf2aw5uob49Hgn/EE8SLNmsHYtPPhg/ue++874nIiIiIgbeX53eSlVsk8bicR5218PrTFea17lcY3eM605PPrdP8SlZNKsagRv3XoZFjfGGNSsGdX+8z6xTz7FmenT8S1fjkpPPeW25ztN7Q5w7xz4fgDEroOJN8K9syCyRqGnSM1K5c+jf/LX0b/469hf7DljVAsOyO28EWuDmf9e+P2VQyrTqlIrOlbrSKfqnagUUqkEfyAPEBICX3wB118PjzwCAwdCjx5mRyUiIiJljJJKcavseCOp9Dt3+2teUlmrg5sjujibzcarc7aw4dAZIoL8GHfPlQT5F9Bf08Uiuncne8QIjo0YQdy48fiVK0+5++51exwlVrMtDFkA3/aFU7vgqxvgnplQqekF35KVncXyw8uZf2A+yw8vJ816drXTckHlaBDVAEt2FKt3p2G1BlCzXDA9LqtIijWBE6knOJR4iENJhzieepwFBxaw4MACAFpVakWver24ofYNRAVFufJP7lp33glXXw3R53xf2WxGEZ9mzcyJS0RERMoEJZXiHitWwMqVWA/HAuesVObkQExeUulZ5ym/WLGPaesO42OBT+5s5bR+lMURfcftZMef5uTHYzn+zjv4hIcTddutpsVTbJWawAOL4BuHxLLfV9DohrNuO5F6ghm7ZzB993ROpZ2yf75GWA2urn417aq2o3Wl1pQPLm//2tomcdw/+W/2nspmaVIEk4a0pXJEEADJmcnsOL2DNUfXsDp2NdvitrHhxAY2nNjAu3+9S486Pbin6T1cVuEy9/w9OFvNmud/buJEeOghePxxePttCA93f1wiIiJS6llsXnlASy4lMTGRyMhIEhISiIiIMDscGDEC2xtvsLNRY7BYaLBsGf6Vc7centgBn7UH/xB48RD4+psba67F248z9Jt12GwwonczhnQy/6ynzWbj+LvvEv/1N2CxUPWtt4jq19fssIon9TT8eA8cXAVYoPsb0HE4+xMP8MXmL5i/fz5Wm1GYqEJwBXrV68WNdW+kWblmF91+vCnmDA9M+ZtTyZlUiwxi0pB2NK5yfjJ1PMVYtZy3bx47Tue35GhZsSWDmw/m+lrX42Px4mPnhw5B8+aQnGxc16gBn30GvXubG5eIiIi4natzAyWVpZTHJZVPP431k0/4t2EjAJps3oQlIMD42rqJMPdpqNsZBv1iYpD5th1JYMC4P0nNzObuq2q5/RzlxdhsNo6/+Sbx3/9gJJZvvkFU//5mh1U81kz49f/gnyns8/djXJ2WLLCewobxz1LrSq25s8mddK3VFf8i/LIh5nQqgyb9xb6TKYQH+vH5PVdydcMKF7x/W9w2vt/xPfP3zycrJwuARtGNGNZiGN1qd/PO5NJqhbFj4bXXIM1hy/CAAcbnq1Y1LzYRERFxK1fnBl74k5J4paQksv2M3dY+ISH5CSV43HnKowlpPDhlHamZ2VzdoAIj+zT3mIQSwGKxUPm114i+5x6w2Tj66mvE//CD2WEVj18AR657gRdbduPW6lWZbz2JDRvXVm7L1J5TmXLTFG6se2OREkqAmuVCmPVIR9rVKUdShpVBk/7iyxX7Llg5t3n55rx99dss6r+IoS2GEuYfxu743Ty77Fn6/dyP3w/97n1Vd/384NlnYds2uMFha/H06dCkCXz4IWRmmhefiIiIlBpKKsU9kpLI9jUK3PhGRZ39NcfKryY7k5rJfV/9xdGEdOpXDOV/d7XG39fzvk0sFguVX3mZcoPuA+DYqDc4MXasVyU+SZlJfLT+I3rP6cO8xN3YLBa6pmczLfYon65fQPMj20o0f1RIAF8/0I6+rauTnWPjrXk7eGLqRlIzL9zrs0JwBYa3Gs6Cfgt4uOXDhPmHsefMHp5a8hSDFwxm88nNJYrJFHXrwoIF8O23UCF3tTYx0Ug4W7aERYvMjU9ERES8nuf9tCylU1IS1tyk0s+xSE/iEThzECw+UKOtScEZ0jKzeWDKOv49kUyViCCm3N+OyBDPON9ZEIvFQqUXX6TC8McBiPt8HEdfeRVbVpbJkV1cVk4WP+z8gZ6zejJx60QyczJpV6UdP/5/e/cdH0WdP378Nds3vRdIQkLvVVCKgAYFRVFBReVsZ0G/np6nP/W8s93p3VnOs57t7AULRVARkSJKUXovoQZCSAippCe7O78/JpnsQhKSzYa099PHuFM+8/l8diYfdt/7mfnMZV/y8g3L6dPpPKgshnl3wPx7oOyk12XZzEZevGYQf5vSD5NB4dutx7jqv2s4eKKo3v2CrcHcM/gefpj2A7cPuB2r0cqmrE3M+H4GD654kCMnj3hdpxahKDBjhjYS7O231zy2Z88eWLiwZesmhBBCiDZP7qlsp1rdPZVjxpC3YyeZMTEEjB9P/Ftvaut3zIM5t0LMQLhrZYtVr9LpYuYnG1m+J4sgm4nZd42qdXCX1ipv9mwyn3wKXC78x4yh839exNgazrsbVVX5+ejPvLjhRVJPpgKQFJzEg8MeZGzc2JpLjF1O+Pl5+Pk5QIXgeLjiv9B1XJPKX3col//7bBPZReXYzUb+NqUf15wT16BLmzOLM/nvlv+yYP8CVFRMBhPTe01n5sCZhNpCz7h/q7NxI9x7L+zbB3v3nv4oEiGEEEK0K3JPpWgf3C9/jah5BARpa7XXFnyUiMul8sjcbSzfk4XVZOC9W4a3qYASIPSaa4j77+soNhvFq1aRes21lB840NLV0h3IP8BdS+/i3uX3knoylTBbGI+d+xjzpsxjXPw4z8DOYIQLHoVbFkJIFyhIg4+nwPcPQUWx13UYkRTGwvvGMLJrOKWVTh6eu417Zm2ioOTMPbsx/jE8Pfpp5kyZw5jOY3C4HHy2+zMmz5vMBzs+oNxZ7nW9WsSwYbB6Nfz22+kB5b//Dc88A8XeH2shhBBCdCzSU9lOtbqeyq5dySwuJi80jPA77iDqwQe09W+PhYytcPX70H/aWa+Wy6Xyl6+388X6NIwGhbd/N4wJfaPPej18pXTnTo7eey+OYxkY/P3p9PxzBCYnt1h9CsoLeGPLG3yZ8iVO1YnZYOamvjdx+4DbCbAEnDmD8iJY8rg2QjBASAJc8gL0muR1nZwulXd+OciLP6bgcKnEBtv459QBXNArqsF5/JbxGy9ueJE9uXsA6BzQmT8O/SOTEie1qkGdGi0rC7p10x5DEh0NTz6pXS5rbr2XgQshhBDizOSRIsIrrS6ojIwk3WzmZFAwUY88Qvitt0B5ITybAKoL/rQLgjuf1Sqpqspf5+9g1tojGBR4afpgrhh8duvQHBy5uaT/8X5K1q8HIOz3vyfq/j96jrjb3HVwOZizdw6vb3mdgvICAJITknnwnAeJD4xvfIYHlsM392m9lgC9JsMlz2pBppe2puVz/5dbOJSt9chNHdKZxy/rS6h/w46T0+Xku4Pf8ermV8kqyQJgQMQA/t85/4+h0UO9rleL+vJL7d5Lp7NmXWIiPPoo3HILnMW/ISGEEEL4jgSVwiutLqi84AKOZB6nWFXp9NyzBF9xBRz4CT65EoIT4E/bz2p1VFXlyW928vGvh1EU+M+1g7hqSNxZrUNzUisrOf78C+R98gkAtn796PTvF7AmJTV72Wsz1vLsumfZn78fgO4h3XlkxCOcF9vES5wriuGXF2DNa+BygMkO5z8II+8Bi59XWZZUOHjxx718sPoQLhXC/S08cXlfpgzq1OAex1JHKZ/s+oT3tr9HiaMEgAkJE7h/2P10CeriVb1aVEoK/PWvMHeu5/r4ePjzn+H3vwebrWXqJoQQQgivSFApvNLqgkrg4JVXUb5nD/H/e4eA88+Hn/4FPz8LA66Faf87a/VwuVT+/t0uPlyTiqLAC1cP4uph7SegdFe4dCkZf30MZ0EBip8f0Y/+mZCrr26WSzTTCtN4ccOLLDuyDNBGUP3D4D9wdc+rMRlMvisoaw8sfBAOr9KWA2Phgr/A4Bna/Zhe2Hwkjz/P3U7K8UIAzusaxpOX96NPbMPbTnZpNm9seYO5++biUl2YFBPTe7fhwXzWroWnntIeR+KuUyd49lm48cYWqZYQQgghGk8G6hHthjM3FwBjaNUjRY78qr2exUF6HE4Xj8zdxodrUgF4duqAdhtQAgROmEDSgvn4jRiBWlJC5uNPkHbbbVQcPeqzMgrKC3hxw4tcMf8Klh1ZhlExckPvG1h41UKu632dbwNKgKjecMt3MO09bSCfwgz45l54czSkLAIvficbkhDKt/eO4YGLemI1GfjtYC6TX13JX7/eTm5xRYPyiLBH8MTIJ5h7+VzO73w+DrWND+Zz7rmwaJEWXF5+ec36Y8cgJ6fl6iWEEEKIVkd6Ktup1tZTqaoqewYOgspKui9fhjk6SrufsrIY7l4D0f2avQ7lDif3f7GFRTsyMRoUnp82kGntOKB0pzqd5H74ESdefRW1vBzFbifqT/cTOmMGitG73r1yZzmf7/6cd7a/Q2FFVQ9f7Hk8MvwRuod292X16+Yoh/Xvao8gKcvX1sUOgrEPQ69LwdD4382O5pXwr0V7WLgtA4Agm4mZ47pxy6hE/K0ND5B/PfYrL254kZS8FKAdDOazaZM2KuxPP8GRIxDoNkLy4cOQnw+DBrVY9YQQQghRN7n8VXiltQWVzsJC9g4fAUCvLZsx5O6Gd8aDNRgeSfXqy39jlFQ4mPnJRlbuy8ZiNPDq9UOY1D+mWctsjSoOHybj8ScoWbcOAGvv3kT/5VH8R4xocB4u1cXCgwt5bfNrZBRrgVf3kO48MOwBxnQe0zIBU2kerHoZ1v1P+6ECILo/jH0I+lzu1WWxvx3M4W/f7mJ3xkkAIgIs3D2+OzPOTcBmblh+tQ3m0yesD/83+P8YFzeubQaX2dkQEeG57q674O23Yfx4uPtuuPJKGdRHCCGEaEUkqBReaVVB5ebNVNx5JwcKi1DMZnpv3wa/vQk//Bl6XAwzZjdr8bnFFdz+0Xo2HcnHbjbyv5vOYUyPiDPv2E6pLhf5s+eQ9eKLuE5qAVPgpElEP/T/MHeue/RbVVVZkbaCN7a+oT9KI8ovinuH3MvlXS/H6OX9jD5VnAO//RfWvgNVvaeEJsG5d8GQGWBt3PNHnS6Vb7ce46Wlezmcow3CExts486xXZk+PB4/S8N6LksdpXy882Pe3/G+PphPn7A+3D3obsbHj2+bwWW17GxtEJ+yspp10dFw221w553QpQ0OViSEEEK0MxJUCq+0qqDyhx8oueoqDndJxOzvT/eNG+Crm2HXfLjwcRj7/5qt6EPZxdz6wTpSc0oIspn44NYRDOvSBgdNaQaOvDxOvPoq+V9+BS4XitVK6PXXE37H7ZjCw/V0qqry89GfeWPLG+zO3Q1AgDmA2wbcxu/6/A6bqRWOBFqSC2vfgrVv11wWaw2CITfCuXdCaGKjsqt0upiz8SivLttHRoEWPIX6mblpZCI3j0okrIGPIckty+WjnR/x+Z7PKXWUAlpwOXPQTC6IvwCD0gZvcy8pgQ8/hFdegb17PbcpClx6Kdxxh/Yqz7sUQgghWoQElcIrrSqo/OorCn9/G0fj4rBFRpL0y8/wYm8oyoRbvofE0c1S7MbDudz+0QbySirpHGLnw1uH0yO6cT1VHUFZSgrH//kvStauBUDx8yPsxhsJu/UWVhdu5Y2tb7ArZxcAdpOdGX1mcHPfmwmxhbRgrRuoohi2fqH1jOfsq1qpQLcLYOhN2n2XJmuDsyurdDJn41H+t/Kg3nNpMxu4elgcN56XSK+Yhv195ZXl6cFldc9lUnASN/W9icu7XY7V2PA6tRqqCsuXw1tvwfz54HB4bo+IgA0bpOdSCCGEaAESVAqvtKqg8r33yH/wQTJiYvFPSiJh1hvwyiAwmOHRNDDbfV7kwm0Z/OmrLVQ4XAyMC+bdm88hKrAV9qi1EqqqUrxqFSdefoWynTsBKLUZ+GGIyqJzDJSH+HF97+u5pd8tbfPxGC4XHFiuXRp7YHnNer9wGHgdDL0Rovo0ODunS2XRjgze+vkAO9JP6utHJIbxu5FdmNQvBovpzL2OeWV5fLzrY77Y8wVFlUUAhNnCuK73dUzvNZ0wW1jD32NrkpEB778P77yjDeoD0L271pPpfqlveTlY22AALYQQQrQxElQKr7SqoPLll8l++mlOREYRPGgQnf5yJXw9E+KGw+1LfVqUy6Xy+k/7+c8S7TK8i/pG88p1gxt871tHVlhRyJyU2Wyb9y6TluaRcEJb7zIZ8LvsUjrdPhNr97M0qmtzyj0Emz+FLZ9pjyOpFt0f+k+F/tMafHmsqqr8eiCHj389zJLdx3G6tH9OIwIsTB0ax1VDOjfoWZdFFUXM3TeXz3Z/pg9+ZDVauSTpEq7peQ0DIga0zfsunU748Uf46CMYOhQefthz+/DhWlB5zTVw9dVQzz29QgghhPCeBJXCK60qqPz73zn++n/JDQsjbPw4oidbYeOHMOpeuPgZnxVTVO7ggS+38OOu4wDcOjqRxyb3xWhog1/Gz6KD+QeZvXc2X+//muKqkVMjreH8oeQ8Bv14iIot2/S0/qNGETJ9OoEXXoDS1u+Pczpg/1LY9DHsWwwut8s1O5+jBZd9LoOQhAZll1lQxhfrj/D5uiMcP1nzTMreMYFcNaQzVwzuTExw/b3lla5KlqQu4aNdH+mXHAP0Cu3FNT2vYXLXyQRYAhr3PlurgwehWzfPdaNG1QSYcR3jcT9CCCHE2SBBpfBKqwoqH3qIY59+RkFwMFFTpxLeeRmc2A3XzYLek31SxKHsYu78eAP7soqwGA08c1V/rj0n3id5t0eVzkqWpS3jq5SvWJ+5Xl/fNbgrt/S7hcldJ2MxaoPPlGzeTO7771O4dJl23xxgjIwgZOo0Qq65BktcO+hdKsmF3d/CjrmQuhJUV8226AHQaxL0ugRih5zx8TeVThc/7cli3qZ0lu/JosKp5aUocG5SGJP6xXBxvxg6hdR92beqqmw5sYXZKbNZnLqYClcFoN3TOjFxIpd1vYxzos9pHSPuemv1am102F27at9+zjlw2WXaNOTMx10IIYQQdZOgUnilVQWVd93Fke8WUhwQQOxtNxFS/Ky2/qED4N/0R3v8lJLFfZ9vprDMQXSQlbd+N4whCW3wvr+z4ED+Ab458A0L9i8gpywHAINiYGzcWKb3ms6oTqPqHIG04uhR8r+aTf68eTizs/X19nOGEXzZ5QROvBhTaDs47oXHYdcC2Pk1pP3mGWAGxEDPidDtQkgaC3713/NYUFLJd9uP8fWmdDYczvPYNigumIn9Y7i4bwzdIv3rvLw1vyyfbw58w+y9s0k9maqvj7JHcWnXS7ms62X0DO3ZNi+PBS2onD1bm6ru5/Vgs0FODvj5nf26CSGEEO2EBJXCK60qqLzhBg6tWUOZzU7c/00lMPd1CO8B925oUrYOp4uXl+7jvyv2o6owrEsob/5uqAzIc4rs0mwWHVrEtwe+1R8JAhBhj2Baj2lM6zGN2IDYBuenVlRQuHw5eV9+Scmvv9VsMJsJGDOGoEsvJWDcWIwt/XfnC8U5sH8JpHwP+5dBRZHbRgViB0HX8dqUcF69g06l5ZaweGcmi3dmsuFwHu7/8saF2hnbM5KxPSIZ3T2cQNvplxarqsqmrE18d/A7FqcuprD6OZxoPczJCckkJyTTN7xv2w0wd+/Wgsv582HzZm3dpZfCwoWe6R5/XLsXc8IErUfTJPdMCyGEEPWRoFJ4pVUFlZdfzr5du3GYzSTeMwZ7zlfa8wKveN3rLDMLyrjv882sS80FYMa5CTx5eb8GjbjZEeSV5bEibQU/Hv6RX4/9ilN1AmBSTIyJG8OUblMYHz8es6Fp90VWZmRw8vvvKfhuIeW7awJWTCb8Rwwn4MJkAi+8AHOnTk0qp1VwlGuXxu5bCgdXaJdwuzNaodMQSDgXEkZC/Ll19mRmFZaxdFcWi3ZksPZgrn6JLIDRoDAsIZQxPSIYkRTG4PgQbGbPy1wrnBWsTF/JwoML+TntZ/3yWIBov2guTLiQ5IRkhkYNxWxso/e+Hj0K338P8fFwySU16x0OCAuDwqqgOigIxo+H5GRt6tvXc4RZIYQQQkhQKbzTqoLKzz9nzzP/QHU66XZ7LJaijXDFf2HI77zKbkVKFg98tZXc4goCrCb+OXUAUwa1g6CliY4XH2d52nKWHV7GhuMb9EASYEDEAC7rehmTkiY122Mqyvfvp+C77yhcspSKAwc8tln79CFgzGj8R47EPnQoBls76E0uzISDP2sB5sEVUHjs9DQRPbXgMv5cLeCM7A1Gz161kgoHvx3M4Ze92fyy9wQHs4s9tluMBgbGBTMiKYwRSWEM6xLq0ZNZWFHIL0d/YdmRZaxKX0Wpo1Tf5mfyY0TMCEZ2GsmoTqPoEtSl7fZiVlu/HkaMqHt7eLg24M+YMXDLLRAVddaqJoQQQrRWElQKr7SmoNJVUkLK0GEA9Lw2B6OhHO7dBOHdzrCnp0qnixd/3MtbP2sBS79OQfz3hqEkRvj7vM5tgdPlZHv2dlYfW83q9NVsz97usb13WG+SE5KZmDiRpOCks1q3itRUCpctp/Cn5ZRu2qw9J7KKYrXiN2wofiNH4n/eedh69277I8mqKuQc0O7BPPIbpK2F7L2npzPZtEeXdBoMsYNrDTTTckv4ee8JfjuYw7pDuWQVlntkoSjQIyqAgXEhDIoLZlB8CL1jgrCYDJQ5ylibsZZlR5bx89GfyS3L9di3k38nRnYaybDoYQyNHkon/05tM8g8dAiWLauZTpyoO11iYs1yerp2ANtDz7kQQgjRCBJUCq+0pqCy4mg6ByZMQDGb6DX1CEpAJPy/fY26RO3AiSIe+HILW48WAHDzyC48emmf0y4LbO8yizP59divrD62ml+P/crJipMe2wdHDmZClwlcmHAh8YGtY/RbR24uxatWUbzmV4p//RXH8eMe2xWbDfuAAdiHDsU+ZDB+gwdjDAlpmcr6UkmuFlwe+Q3SN0LGVig/eXo6k03r0YzqC1F9aqbgeFTgcE4J6w7lsi41l3WHcjmSW3JaFhajgT6xgQyIC6ZPbBC9YwLpHuXPsZKDrDm2hl+P/cqmrE1Uuio99ov2i2Zo1FCGRg9lSNQQuod0b3sjyrpcsH07LF0Kv/wCq1ZBbq4WOB496vnvzP33wyuvaNvOOUebhg+HYcMgMrLF3oIQQgjR3CSoFF5pTUFl6fbtpF5zLaZQf3pM3Ae9L4PrPmvQvqqq8unaI/xj4S7KKl0E2Uw8N20glwxo+MAybZWqqhwtOsrG4xvZkLmBjcc3crToqEeaQEsgozqNYnSn0YzuPJoov9Z9qZ+qqlQcOqQFmGvWULJxI66CgtPSWbp2xdavH7a+faumPhgDA1ugxj7kckHeITi2WZsyttYdaAJYAiGqt9aTGdEDwrpBeDeyTLFszSxn29F8th4tYNvRfPJLKmvNIi7UTu+YQHrHBJEUaabCvI/DJdvYnr2FXTm7cKgOj/R2k50+YX3oG96XvuF96RfRj8SgxDpHBG6VXC5ISdF6JSdM8Nw2Zoz2KJPadOmiPbpkwACYNEm7hFYIIYRoJySobAZr1qzho48+YuXKlaSnp6OqKnFxcYwZM4abb76Z0aNHN2v5Bw8e5MMPP2ThwoUcOXKEoqIiOnXqxMCBA5kxYwZXXnklpiaOZtiagsqiBQtIe+TPWGNsdB1/EC7+B4z6wxn3yzpZxsNzt7EiRbu0bXT3cP59zSBig+seYbMtK3eWk5Kbwvbs7Ww9sZWNxzeSVZLlkcagGOgb1pcxcWMY3Wk0/SP6YzK03ZEvVZeLikOHKNm0idJNmyndvJmK1NRa05oTErQAs3dvrD26Y+3WDXNcHEpbHvmzOtA8sQeydkHWbm3K3geu2gNFUCA4DsK6Qng31LCunLDEsbMklE0FAWw74SIls5DMk2W1761Ap2A7XSJNhARn4LQeJNeZwuGi3ZQ6T+8F9TP50TusNz1Ce9A9pDvdQrrRI6QHIbYQ3x2Hs+Vvf4OVK2HDBqjlxwzd44/D3/9es+xwwKuvagFnr14QFyfPzRRCCNGmSFDpQ8XFxdx33328//779aa79dZbee211/D39/29eq+88gqPPPII5eXldaY577zz+Oyzz+jatavX5bSaoNLpJD8sjIzYTvhHlZNwYQ7cvhzihtW726LtGfzl6+3klVRiNRn48yW9uXlkIgZDG7z/qxZOl5NDBYfYkbODHdk72J69nb15e3G4PHuOTAYT/cP7Myx6GMOihzEkaggBloAWqvXZ4cjNpXTbNsp376Zs1y7Kdu6i8lgtg+AAisWCJSkJa7duWLp3w9q1G5bELlji4zE0Q/s9a5yV2j2aWbu0gDNnv7ace7Duns1qtmAISaAyII5sUzRHXeGklIew5WQg6/MCOFxmB2prRy6s9hzCwrKwBWTgNB+h0HUYh1r7v1XhtnC6h3ane0h3EoMSSQhMICEogVj/2NZ/Ca3LBQcOaMHl+vXa66ZNUFw1SNLs2XD11TXpd+/WRpWtZrdDz55agHnq1NZ71IUQQrRLElT6iNPp5NJLL+XHH3/U19ntdvr164fJZGLXrl2cPFnzZe3iiy/m+++/x2j03Zejp59+mieeeEJfNhgM9O3bl7CwMPbt20dGRoa+LS4ujnXr1hEb691lnq0mqCwoICepK1lRUQR1KaHz+eXwaBrU8ZiD/JIK/v7dLuZtSge0wXhenj6YHtFt94taflk+e/P2sjdvL/vy97E3dy/78/dT5jy9JynUGkr/iP4MiBjA0OihDIwciN3UPntmG8ORl0f57t2U7txJ+b59VOw/QPnBg6hltffGARjDw7HEx2NOiMcSn4AlIR5zfALmuM6YIiJQ2mJPk6pCcTbkHqgKMg/UBJsFaVCad+YsjBYqbJEUmiPIVkI55gzhUHkgKcX+HHOFclwN5bgawkn8ARWD5QQG2zEM1uOYbMcx27NwGXPqzN9kMBEXEEdCUIIeaHYO6EyMfwyx/rEEWlppW3a54OBB7f7M0aM9R4398ku47rqG5ZOToz3ypNq2bZCdDV27aj2cbblnXQghRJslQaWP/OUvf+Ff//qXvnzHHXfw7LPPElb14V9cXMxzzz3H008/7bHPP/7xD5+Uv3jxYi655BKqD/fIkSP58MMP6dmzJwAul4vZs2dz++23U1SkPWB99OjRrFq1yqvyWk1QmZZG1tBh5ISHE9aziOipg+CW72pN+sOOTB5fsIMTheUYFLh7fDf+mNyzTTx7UlVVcspySC1IJfVkKqkFqewv2M++3H1klWbVuo/dZKdveF/6h/enf2R/+of3p3NA57Y5GmcLUF0uKo8do3z/fir276e8KtCsPHwYZ32XNgKYzZijojDFxGCOicEcG4MpJhZzTLT+agwNRfHhj0pnRXkh5KdpAWb+kapXt+Wi42fOo4pDsVBkDCaPILJcgRyrDCBHDSRXDeK44scxi0qWpYKT1mLKzMU4LfkYzLkoBke9+dqM/kTYookNiCU+MJa4wE7E+McQ7RdNhD2CcHs4QZag1tUO0tJgyRLYtUu7X3PvXq2n0+n0TBcWpgWV7mbOhHfe0eaNRkhIgKQk7R7OuDht6txZ6+Xs3v3svB8hhBAdjgSVPnDs2DG6detGWVWvxo033sjHH39ca9rHH3+cZ555BgCbzcaBAwfo1MTh51VVZciQIWzduhWAXr16sWnTJvz8/E5Lu3TpUi666CJ9ed68eVx11VWNLrPVBJW7dnEsOZmC4BAiB5wk4u674cLHPJJkF5Xz5IKdLNyu9dR2i/Tn+asHMaxLaEvUuF5FFUWkF6XrgWP16+GThymsLKxzv7iAOHqE9qBnaE99ig+Mb/2XCbZRzpMnqTiSRmXaESrSjmqvR9KoSDuCI/O4xyNO6mQwYAwPwxQegSk8HFNEBMaIcG05omo5PAJTeBjGoCAUi6X531hTOSq0wLIwEwozquYzaparXxvQ43mqSkzkqP4cMvqz32wn1WTmqNnIcbNKvslFobmCCmP9AWc1A2b8jCEEmEIJsYYTZgsn0h5JbEAksQERdAqKINIvjBBrCMHW4Ja5r7iyUuvZTEmpmcxmePNNz3QXX6wFpGdy883w4Yee6+66S7ucNjYWoqO13tPoaG2KiNCCVCGEEKIBJKj0gYcffpgXXngBAD8/P9LS0vQeylNVVFTQvXt30tLS9H2fe+65JpX//fffM3nyZH35hx9+YOLEiXWmv+666/jyyy8BGDFiBGvXrm10ma0mqFy7lrRpV1MUEEDM8HxCn/gEemgjMqqqyoItx/jbtzvJK6nEaFC4a1xX7r2wR4s9KqTcWU56UTrphena6ylTQXndPWAKCp0COpEYnEhSUBJJwUn0DO1Jj9Ae+Jvb8P197YzqcOA4cYLKjEwcxzOpzMikMjMDR0YmlceP48jIwJGdrV1q2ggGf3+MwcEYQoIxhYRgCA7GGBKCUX8NwRgSrKUJCMAYGIghIACDv3/ruxS3skwLOEuyoTin6jUbik9ASY42X72uJAcqihqUbYmikGkykmkykWE0kmEykWEycsxkJstoIsdooNjY+B5Ko2rFQgBmJRCrMQg/YzAB5mACzcEEWYMItgYSYgskzB5MmD2IKP9govxDiPQPxm5p5mekzpql3a956FDNlJ9/erq//hWqftAEoKICrNa681UULbCMjtZ6QkeOrNl29Cj8+qvWcxoerr2GhYG/f6Me5SSEEKL9aO7YoEPc3PH111/r89dee22dASWAxWLh1ltv5e9VI//NmzevyUHlvHnz9PmkpCQuvvjietPPnDlTDyrXrVvH0aNHiYuLa1IdWszJkziqAkSTxQXxwwHILCjjr19vZ9ke7dLQPrFBvHD1QPp3Dm62qlQ6K8kqzeJ48XGySrI4XnKczOJMjpcc53jJcTKKMjhRWsdD1N2EWENICEogMSiRpOAkEoMSSQxKJD4oHquxni+BolVQTCbMsbGY67lfWXU4cOTm4szJwZGdjSM7B2eO9urIzsaRk42zat6Znw+qiqu4GFdxMRw7Rt3DcNXO4O+vBZiBARj9A6rmAzEE+GvLVfMGux8GPzsGux3Fbj992c9Pm29qD5bZBqFdtKkhKku1ALM0D8ryoTS/1le/0ny6luXTtTQftSwfCvJRVM9LSMsUhRyjgWyj8ZRJW5drNJJvNJBvMHCy6n06lXJKKaeUHHCiTRUNfKsuAxaXCbPLjBkLZtWKBRtmxYrZYMNssGMx+mEx+WEz+WMzB2KzBGK3BBNgCyLAFkSwzV+brP74W2zYzEasJiNWkwHr1ddive56jO6DjOXlaZfUpqdrAeDRo3DBBZ4Vq2NwKp2qwokT2nTq+V6zBqZPr+XNmmsCzLAwrQd09mzPNKtWQUYGBAWdPgUESO+oEEKIWrX7oDIlJYX9+/fry5MmTTrjPpdccokeVO7fv5+UlBR69erldR0WLlyoz0+cOPGM9wqdf/75+Pv7U1w1EuHChQuZOXOm1+W3qMJCnFbtz8xoD0K1BvHFuiP8c+FuCssdWIwG7kvuzsxx3TAbveutcaku8svzyS7NJqc0hxOlJzhefFwPFqvnc8tyG5Sfv9mfzgGdT58CtVfpdWz/FJMJc1QU5qgzP/dTdblwnTyJMz8fZ0GB52v+qcvavKu4GGdRkXYJJdQEpMcbfs9jvfU3m1GqAszqSfHTglDFasFgsaJYrdq81eY2b0Xx2FY1b7FisFqq1ltRLG7bzGZt8o+B4LgG3wupgBYYVRRBWQGUF0F5IbaKQjqXF9K5apmKQu21alktL8RZdhJnWSGOspMUOYo56SylkEryjQYKDAY96Mw3Gik0GCg0KBQZDBQpBgoNBooMCuVVvcOVBheVhgq0KLS49sqqQGXVVFr/+zKoKjYXWFQFU/XkUjCqBkwYtFfViAkjJkwYMWKKNmHavRHTnlcwK2ZMBgtmxYz5jVsxOxTMDjBVqJgqnJhKHZhKKjGXlGMuLMWcX4Il4wCW1SWYLX7YLH6Y07IwJfTB5HBgcjiwuJyYnQ7MzkrMxZWYT6ZjPpCKOSoSs0v1DHhff10bmKguAQFagDljBjz/vOe2++7TXv39tcnPz/O1er5vX60HtZrLpf0tSMAqhBBtVrsPKqvvY6w20v0SoToMHToUi8VCRYX2U/e2bdu8DiqzsrLIzMxsVPkmk4nhw4ezYsUKvfw2q7AQp2IEFcoCErjjvbWs3q8NZDE4PoQXrh5Y68iuqqpSUF6gBYplOXrAmF2mveaU5pBTpr3mluXiPKW3oy4Wg4Vo/2ii/KKI9osm2j9ae/XTBg6JC4hrfYOEiFZNMRi0y1tDQhq1n6qqqBUVuIqKcBUW4iwqxlVUiKuoCGdREa7CIlzFRdpyofbqKi3FVVqCWlJaNa9NakkJrtJS/ZJdtbIStaAA15kGLGoGitkM1YGmyVQTdFbPu63DXD1vqXW7UrUdUxiKIRJMRhSjCcVkBLsRJcCI2WQkwmgi0mgAXCiqEwUH4EBRK0F1oKgOFLVCm3eVg6uSSmcpJyuKKXIUU+QspthZTolaSrGrnGKlkjIclCgOyhQnpYqLUoNKuaJSYlApNUKxwUCpUaFEUShVDFRUBWYuRaHECNoTP9WqCbTu00Zq6JW5+f+EfLflCDD/zYBZNWNWTZhVMKkqJsCkVk8KRhVMb/fDiDZvQMEwRkEZ3R8DYFAVDCooqoLBVT1vwKCCwfgzyuvjMGDAoBhRMGCIzNfSucBQrqCUguEEGJxV+TtVDKqCaUMihtBwjIpRm04WoWzaglEFIwYMigH9v6o0BoMRg2LCcPkUjGYrBoMJg2LEtP8gyqFUjEYzRqMFo8mC0WjCYNaWMZkwGM0YIiJRhg7BYDShGKry25OCweHUfiQxaz+cGMzaDy1Gi/bji8Fq0S5Vt9swKAoGBYwGRT4jhBDiFO0+qNy9e7c+b7FYiI+PP+M+1ekOHDhwWh5NKR+gW7duDdqvW7duelDZlPJbmisvD5eq9Qg87xzM6oNp2PxKuO68EEZ0P8nanAV8n+4WNFYFkbmluTjUhg3qAdr9jCHWEMLt2oAe1cFilF+UPrJklF8UIdYQ+TIgWgVFUVCsVgxWq2evjZdUVUUtL9eCzOqAs6QqCHVbVisqUCvKcZWXo5ZXoJaXn7bsqnDbVl6Oq8J93nPbafWorITKStrSzfoBVVP9FE57vqeigEFBMSigGLTNBgVVUcAAqlIVVhq0V5cCqqLiAlyKiqpo61yKigsVlwHtVQGnUv0KToOW1lmV3qmo2ryhertSlXdVee6vVVV2KQqcsr4mrQpV9aCqnnWm1fMrAwrdtiv6+3FWHS5XVdrT8ti/zS2fqrIC6ivL7fXnV0/brgLaQQW1sma9omqnSFHBcEyFbWCo2kOher7qNOrpVQyqVicF93ltUt3/CpSaeaVqzn2b+4yBU+pVtaxUvQHPz6WqvKp605WqY6u41Jo3rFTXX/HYR9tgwGA0oqJo9VKASkfV7bQKqqoFx+411+qjoJisYDLob05xqVBRUVVXpeZ9KtX7KvoRVex+VdXQ2oRSXgkOx2n1q65T9XtWDEbw868pEwOUFqM4qg6UUl1HtzaoVK2xWlFs9pp9VaCwyP0oAgoGpSYH7c0rKIFBYLbo+SkVlSjFxVX1q0qjKPqyoeo8oBgwhIUDhqq2DxSVoJRXaH9vek1rrr5Sqt4HNjtKUBCKYqj64wQlKwdw4fkYbu0v1K3WKCEh4GevOYZOB2SfwKAo2vFXqv6qlZoc9P/HxIDRWFOnoiIMhYUe5eln0/1v0WyB6GgMikHPVDmRA+XlVYe85i9fcc9LAQIDUcLCauqgAEfSqup5yhlVFM8comPAbq86/qCUlUFWVlUF3FOCofo9VefRNcnt3IGSkwuFRTV/gVX/Xhn081xVEz87Smyslo+iaMfqyBGt7VT9TQP6eVLc/hVRIiNQQkK1zwMU7e/+8BGtzMhI/DsnEBAYSnvX7oPK1NRUfT4uruGXZiUkJOhBpXseTSm/Ot+Gll9XHrUpLy+n3O0LnvszN1vS6uyDRABOAyzss5RAo/ac0LkZ2nQmwdZgwm3h2qMGbOGE27Wpern6EQShtlDMhmYecEOIVkxRFBSbDYPNBqFn58NLVVUtgHQ4tN5Rj/madTgq692uOqrTua1z3+50gtOB6nShOh3gcNazTntt+DqnVq7Lpb26rWvAAQCniuqsfTRht6/Azaw1hPCtoQ6+cHpwd3aotcw3YJRqIRrgtL+kU/60T2296hn+9E/d7rG/D/Ku3sf9Wo86nSm/M+3fgDo1RH3l7BvTiRmvL2t6Ia1cuw8qC91+iQkObvggMO6jIhUW1v2oiMaU35g6NLb8f/3rX/ztb39rXOXOAmOi1gNz0g4YtX/WAs2BdQaHHsGjLRyzUQJFIVorRVHAYmkbj1PxgupyoToc2j1/TqcWRLtcWtCpz7tAPfN21ekEl1oz36TtLm2b65R9XKq2n+rS6qJS9fgcbb2qb3dPo+r7qWrVvY0N2o+aeyFRtaBcpc68a/JHf2+gVh2zetJUr3Op2nNBFaUqb60MtawMysu0HxdcTnC5nQe3/VWzGYKDq5ZVrXc2K6uqV13V1+vloVYdChUCAlDt9po0Tifk5JweBtY2YnRAAKrRUPOeKiqg6vFm9VIMVaP/asdaAe0e7FMfh1RVpGcnl9ZbWb1dQQW3Hz6UWqqpr6vqfXJ/L6elP2X5TN/HT93/bIbqQnPaiBVnirTay29ErYTB0TF+IGr3QWVRUc1Q9zabrcH72e32WvNoSvmNqUNjy3/00Ud54IEH9OWTJ0826FLf5jbk6rv5qrSIXjYzi6f9gXB7uIyQKoRoExSDod0GzKKFVQfNVb3m+uRwaK+gPZfUXXo6FBZ6BNy1vkZEQGKi574rV2r51refqsKwYdqowNVOnICffqo9/anzt9yijTBcbd062Lix5v1WB6qnzsfGwjXXVC1WrZ81C6rHo3BPr/+QUbV+9GgYNaqmzJISePVVtx9BVI/9Vfe8br0VOneu2Xf7dpg3r/Z93cu02bRHALn/iDB3LuqmTaef41P17w/XXee57dlntfNaz74qwFVTYeiQmpWZmfDaa6eXUVvZDz2kDbBVvW3VKli8uCbvuvaNjoZ77vHc/sEHUHUl3+nlus2PHqU9p7eaS0V96skz1xXgxhuhe/eabfv2oX7yySlFqLX27/Pkk1U/DFX54QdYu7bO/fRfObp2Rb3++qoqVaV4803IzTmlquppb1W9aAIMH16Tc0EB6utvaPMTL2LKxEtPf4/tULt/TuWECRNYtkzrcj7//PP55ZdfGrTfjTfeyKeffgpAcnIyS5cu9ar8Z555hscff1xfdjqdGBrwTLr33nuP22+/HQCj0YijIZdiuWk1z6kUQgghhBBCtKjmjg1a2RO3fc/Pz0+fL2vIJSe1pPX39/4REu7lN6YOvipfCCGEEEIIIZpTuw8qAwJqxvQrLT3DA8bclJSU1JpHU8pvTB18Vb4QQgghhBBCNKd2H1RGRETo8xkZDRhutIr7syXDmzDcv3v5jamDr8oXQgghhBBCiObU7oPKXr166fM5OTkePYD1SUtL0+d79+7tk/IBjhw5clbLF0IIIYQQQojm1O6Dyj59+ngsb9my5Yz7pKenc+LEiTrzaIwePXpgMtUMstuQ8gE2b97sk/KFEEIIIYQQojm1+6ByxIgRWK01j7BYtWrVGfdZuXKlPm+z2RgxYoTX5VssFs4999xGlZ+Zmcn+/fv15bFjx3pdvhBCCCGEEEI0p3YfVAYEBJCcnKwvf/bZZ2fcxz1NcnJyk0dfveKKK/T5pUuXcvz48QaXHxISIkGlEEIIIYQQotVq90ElwC233KLPb9u2jW+//bbOtJs2bWLRokW17uut66+/Xu8trays5Pnnn68zbVFREa+++qq+PGPGDMzuDxMWQgghhBBCiFakQwSVV199NYMGDdKXZ86cyZ49e05Ll5GRwe9+9zucTicAgwcPZtq0abXmmZqaiqIo+vTUU0/VWX5cXBwzZ87Ul1955RXmzp17WrrKykpuvfVWfTAfu93OX/7ylwa9RyGEEEIIIYRoCaYzJ2n7FEXhf//7H+PGjaO0tJSMjAzOPfdc7r77bsaOHYvJZGLdunW8/vrr+qWpdrudd955B0VRfFKHp556ikWLFrFv3z6cTifXXnstN9xwA1deeSVhYWGkpKTw5ptvsm3bNn2fF154gU6dOvmkfCGEEEIIIYRoDoqqqmpLV+JsmTdvHr/73e8oLS2tN53dbufTTz9l6tSpdaZJTU0lKSlJX37yySfr7a0E2Lt3LxMmTPB4XEhdHn74YZ577rkzpqvLyZMnCQ4OpqCggKCgIK/zEUIIIYQQQrRtzR0bdIjLX6tNnTqVjRs3MmHChFp7IBVFITk5mQ0bNtQbUHqrZ8+ebNu2jdtuuw273V5rmj59+rBgwYImBZRCCCGEEEIIcbZ0qJ5Kd2lpaaxevZr09HQAOnfuzOjRo4mPjz8r5RcWFrJ8+XLS0tIoLi4mNjaWAQMGMGTIEJ/kLz2VQgghhBBCCGj+2KDDBpXtnQSVQgghhBBCCGj+2KBDDNTTEVX/VnDy5MkWrokQQgghhBCiJVXHBM3VnyhBZTtVWFgIcNYu5xVCCCGEEEK0boWFhQQHB/s8X7n8tZ1yuVwcO3aMwMBAnz0WxVsnT54kPj6etLQ0uRS3nZBz2j7JeW1/5Jy2T3Je2x85p+1PazunqqpSWFhIp06dMBh8P1ar9FS2UwaDgbi4uJauhoegoKBW0aiE78g5bZ/kvLY/ck7bJzmv7Y+c0/anNZ3T5uihrNahHikihBBCCCGEEMK3JKgUQgghhBBCCOE1CSpFs7NarTz55JNYrdaWrorwETmn7ZOc1/ZHzmn7JOe1/ZFz2v50tHMqA/UIIYQQQgghhPCa9FQKIYQQQgghhPCaBJVCCCGEEEIIIbwmQaUQQgghhBBCCK9JUCmEEEIIIYQQwmsSVIrTrFmzhpkzZ9K3b1+Cg4MJCgqib9++3HnnnaxevbrZyz948CBPPPEEw4YNIzIyErvdTrdu3bjqqquYM2cODoej2evQXuTn5/P1119z3333MXbsWGJiYrBarQQEBJCQkMDll1/Oyy+/TF5eXrOUryhKo6e33nqrWerSXqxYscKr47pnz55mqY+016ZJTU316ny6T6mpqU2uh7TVxjlx4gSLFi3i73//O1OmTCE2Ntbj2Hz44Yde5719+3YeeOABBg4cSFhYGAEBAfTq1YsZM2bwww8/+O5N1CMzM5PnnnuOkSNHEhsbi81mIzExkUmTJvHhhx9SWlp6VupxNvn6nJaUlLBo0SIeeughJkyYQFxcHHa7HT8/Pzp37szFF1/MP/7xD44dO9Y8bwhITExsdLv+85//3Gz1aQm+PK/e/nvdnO22VbVVVYgqRUVF6u9//3sVqHe69dZb1aKiomapw8svv6xardZ6yz/vvPPUAwcONEv57cXu3bvVyy67TLVYLGc8n4Dq5+envvTSS6rL5fJpPRpS9qnTm2++6dM6tDc//fSTV8d19+7dPq+LtNemO3TokFfns3oymUxqbm5uk+shbbVhMjIy1C5dupzx2HzwwQeNzruyslJ99NFHVYPBUG/ekydPVrOysnz/5qp8/vnnanBwcL116NWrl7pp06Zmq8PZ5OtzmpmZqU6fPl318/NrUDsym83qo48+qpaXl/v8vTXkfZ06PfLIIz6vR0tojrbq7b/XixYtapb32NraqgkhAKfTydSpU/nxxx/1dXa7nX79+mEymdi1axcnT54E4IMPPiA9PZ3vv/8eo9Hoszo8/fTTPPHEE/qywWCgb9++hIWFsW/fPjIyMgD47bffGDduHOvWrSM2NtZn5bcnO3bs4LvvvvNYZzQa6d69O9HR0TidTnbv3k1ubi6g/aL6pz/9iZ07d/LOO++gKIrP6zR27FjsdvsZ0yUkJPi87PbKZrMxbty4BqUNCAjwadnSXn3DbrczceLEBqd3uVwsWbJEX544cSKhoaE+rZO01bqVlZVx+PDhZsl75syZvP/++/qy2Wymb9++BAQEsGfPHnJycgBYuHAhEyZMYPXq1T5v15988gk33XSTx7qePXsSGxtLamqq/t5TUlIYP348a9asoV+/fj6tw9nm63OalpbGl19+6bFOURS6du1KTEwMRqPR49/IyspK/vWvf7Flyxbmz5+PxWLxWV3cDR8+nLCwsDOm69WrV7OUf7Y1Z1ut1tB/uyMjI31edqtsq2cldBWt3qOPPurxy8Ydd9yh5uTk6NuLiorUxx9/3CPNX/7yF5+V/8MPP6iKouh5jxw5Uk1JSdG3O51O9YsvvlADAgL0NKNHj/ZZ+e3N7Nmz9V6MK6+8Up0/f75aUFDgkcblcqnz589XO3fu7HFe33jjDZ/Vwz3fQ4cO+Szfjsy9p7JLly4tUgdpry1n8eLFHu3qq6++8km+0lYbxr2nIjIyUp00aZL62GOPqfPnz29ST+Xbb7/tsf+UKVPUo0eP6tsrKirU1157TTWZTHqaG264wafvbdu2bR5XHvTs2VPdsGGDR5off/xRjY6O1tN07dpVLS0t9Wk9zjZfn9P169ergKooipqcnKx+9tln6okTJ05Lt2LFCrVv374eZTz88MM+fW/uPXU//fSTT/Nu7ZqjrZ7aU9lSWmtblaBSqOnp6arNZtP/8G688cY60z722GN6OpvNpqanpze5fJfLpQ4aNMijq764uLjWtEuWLPFo0PPmzWty+e3R/Pnz1dtvv109fPjwGdMeOXJEjYmJ0Y9pRESEWlFR4ZN6yBdV32vpoFLaa8u64YYb9OMZGhqqlpWV+SRfaasNU1BQoM6ePVtNTU09bZu3X1SLi4s9/g0eP3686nA4ak377rvv6ukURVE3btzo7Vs5zeWXX+7xOZCZmVlruh07dnh8of3Pf/7jszq0BF+f040bN6rTpk1Td+7ceca0+fn5HoGlxWKp87h7oyMHlc3RVltLUNla26oElUJ96KGH9D84Pz8/jx7KU5WXl6vx8fE+/VVt4cKFHo30hx9+qDf99OnT9bQjRoxocvni9F/Jly5d6pN85Yuq77V0UCntteUUFBSodrtdP5533323z/KWttp03n5R/e9//+sRKO7atave9Oeee66e/tprr21irTU7d+70qP9bb71Vb/pHHnlETxsTE6M6nU6f1KO1aUrvc0OdevXBu+++67O8O3JQWZ+2HFS25rYqo78Kvv76a33+2muvrfeae4vFwq233qovz5s3r8nlu+eRlJTExRdfXG/6mTNn6vPr1q3j6NGjTa5DR3f55Zd7LDfXSKGi7ZP22nJmz57tMZLfzTff3IK1Eb7i3qbGjRtHnz596k3v3qa+//57ysvLfVqHgIAAZsyYUW/6O++8U5/PzMzk119/bXIdOqrk5GSPe5jl81fUpzW3VQkqO7iUlBT279+vL0+aNOmM+1xyySX6/P79+0lJSWlSHRYuXKjPT5w48YyDxJx//vn4+/vXur/wzqk/JFQPyiTEqaS9tpyPPvpIn+/duzfnnntuC9ZG+EJRURG//PKLvtzYz+CioiJWrFjR5Hq4t8sxY8accQCgrl27egzocurAcKLhjEYjwcHB+rJ8/or6tOa2KkFlB7d161aP5ZEjR55xn6FDh3qMTrZt2zavy8/KyiIzM7NR5ZtMJoYPH+6T8oXm1BHSoqKiWqgmojWT9tpyDh48yKpVq/Rl6aVsH3bt2kVlZaW+3JA2FRMTQ2Jior7c1Dalqirbt29vVB1OTSft2nulpaVkZWXpy/L5K+rS2tuqBJUd3O7du/V5i8VCfHz8Gfc5NZ17Hk0pH6Bbt24N2s89XVPKF5pTL2Nu6D9UjfHQQw/Rr18/goKCsNvtxMXFccEFF/DUU09x6NAhn5fXEeTn53PttdeSmJiI3W4nMDCQpKQkrrzySl5//XWf/+It7bXlfPzxx6iqCmiPb7nxxhubrSxpq2dPa2hTR44cobi4uEXr0JEtWLAAl8ulLzfH5y/Av//9b4YMGUJISAhWq5XY2FhGjRrFn//8Z49ARTTcTTfdRI8ePfD398ff35+EhAQmTZrE888/7/FDga+09rYqQWUHl5qaqs/HxcU1+PmE7s8nc8+jKeWfmu/ZKF9AQUEBr7zyir48cOBA+vbt6/Ny5syZw65duygsLKSsrIz09HRWrFjB3/72N3r27Mldd93lcb+YOLOCggJmz57N4cOHKSsro6ioiNTUVBYsWMC9995LQkICr732ms/Kk/baMlRV5eOPP9aXJ0yYQOfOnZutPGmrZ497ezCZTA1+lqsv25Qv2vXhw4f1Hz1EwzkcDv75z3/qy1FRUSQnJzdLWQsXLmTLli0UFBRQUVGh31/33HPPMWjQIK6++mr92dWiYT755BP2799PSUkJJSUlpKWlsXjxYh555BG6dOnC448/jtPp9Fl5rb2tmpolV9FmFBYW6vPu1/SfSVBQUK15NKX8xtTBV+ULePDBBz0uaXzmmWeapZyIiAi6detGQEAABQUF7Nmzh6KiIkD7YH377bdZt24dP/30U6P+Fju6xMREOnfujNVqJTs7m127duFwOAAt6LzvvvvYsmUL7733XpPLkvbaMlauXOnRQ9jcl75KWz173NtDYGAgBkPDfuv3ZZvyRbt2uVyUlJR43D8tzuzZZ5/16CV87LHHsFqtzVJWcHAwPXv2JCgoiKKiIvbt26cHkaqqMnfuXDZs2MDKlSsbdNWagNjYWP1Koby8PHbv3k1ZWRkAZWVlPPPMM6xfv55vv/0Ws9nc5PJae1uVnsoOrvqLAoDNZmvwfu4jlbnn0ZTyG1MHX5Xf0b377rsewcb06dNPGwm2Kfr27cvLL7/MgQMHOHHiBL/99htLly5l/fr15OXl8d133zFw4EA9/ebNm7nuuut8Vn57ZDAYmDBhAp999hk5OTkcOnSIVatWsWzZMrZu3UpeXh5vvvkmERER+j7vv/8+zz33XJPLlvbaMtwH6AkKCuKqq67yeRnSVltGS38G17a/N+3aF/XoaBYvXsyTTz6pL48aNYr/+7//82kZiYmJPPPMM+zYsYP8/HzWrVvH0qVL+e2338jOzuaXX35h7NixevrDhw9z+eWXU1FR4dN6tBeKojBixAj+97//cezYMY4dO8aaNWtYtmwZmzZtIj8/n1mzZnnc87x48WLuu+8+n5Tf2tuqBJUdXHWPBmiX3jSUe1r3QQaaUn5j6uCr8juyX375hXvuuUdfTkpK4u233/ZpGTt37uSPf/wjXbt2PW2byWRi8uTJrF27lsmTJ+vrf/jhB7799luf1qM9GTt2LEuWLOGGG26o9fE/AQEB3HXXXWzatMnjg+3vf/87x48fb1LZ0l7PvpKSEmbPnq0vX3vttad9QfAFaasto6U/g0+tQ2PqcWo6adsNt3v3bq6//nr9XsrQ0FBmzZqF0Wj0aTkrVqzgr3/9K/369Tttm6IonH/++fz000/ccccd+vqtW7f6/LtAe9GlSxfWrl3L7bffXuul6larleuvv55NmzYxbNgwff3bb7/tkwFyWntblaCyg/Pz89Pnq7vsG8I9bVO60N3Lb0wdfFV+R7VlyxamTJmi/xoZFRXFDz/80CKXstlsNj7//HOio6P1db68D7Cjio+P58svv9SXS0pKmnwJrLTXs+/rr7/2uOSpJUd9lbbqey39GXxqHRpTj1PTSdtumLS0NCZOnEheXh6gHf/vvvuOLl26tEh9DAYDb7zxBgMGDNDXSbtumtDQUObNm6f3JKqqyuuvv97kfFt7W5WgsoNzf75NYwZeKCkpqTWPppTfmDr4qvyOKCUlhYkTJ1JQUABo//j9+OOP9OzZs8XqFBgYyN13360vr1y5slFfsETtRowYwfjx4/XlJUuWNCk/aa9nn/ulr926dWPMmDEtWBtpq77W0p/Bte3vTbv2RT06guPHjzNhwgTS0tIArWdr/vz5jBo1qkXrZTKZePDBB/Xlffv2nfaoMdE4CQkJHrcINPXzF1p/W5WgsoNzv+8qIyOjwfu5D+wSHh7uk/IbUwdfld/RHDp0iAkTJuhDXQcGBrJo0SIGDRrUwjWDCy64QJ8vKyvTP3RF07gf17179zYpL2mvZ1d6ejrLli3Tl1vLsymlrfqOe5sqKipq8L1OvmxTvmjXgYGBPhmIpD3Lzc3loosu0v8dNplMfPnll1x00UUtXDONe7uGpn9eCM9jmpqa2uR7VVt7W5WgsoPr1auXPp+Tk3Parxl1cf8S0bt3b5+UD9ozeM5m+R3J0aNHSU5O5ujRo0DNJTfnnntuC9dMExMT47GcnZ3dQjVpX9yPa1OPqbTXs+uTTz7R77lSFIWbbrqphWukkbbqO62hTfXs2dPjcWLSrn3v5MmTTJw4UR/p1WAw8Omnn3LFFVe0cM1qSLv2vVOPaU5OTpPya+1tVYLKDq5Pnz4ey1u2bDnjPunp6Zw4caLOPBqjR48eHjcQN6R80EYe9EX5HUX1JTfVjyWovuTGfdS3lnbqDxqn3jsgvON+XJt6TKW9nl3ul76OHz++xe65OpW0Vd/x5jO4srKSnTt31plHYwUEBBAXF9eoOoC064YqLi7m0ksvZcOGDYD2A9H777/P9OnTW7hmnqRd+56vj2lrb6sSVHZwI0aM8Hgm0qpVq864z8qVK/V5m83GiBEjvC7fYrF49JQ1pPzMzEz279+vL7emwKg1ysnJYcKECaSkpABgNpuZM2dOq7nkppr7lyTQBg8STed+XJt6TKW9nj3r1q1jz549+nJrufQVpK36UteuXT2+JDakTW3cuNHjy6ov2pR7Hg2pQ2VlJWvXrvVpHdqjsrIypkyZwurVq/V1b7zxRqtqz9WkXfue+zG1Wq0+GQyxNbdVCSo7uICAAJKTk/Xlzz777Iz7uKdJTk5u8ihS7pd/LF269IyPPXAvPyQkRD7M6lFQUMDEiRPZsWMHAEajkVmzZnHZZZe1cM1O98UXX+jziYmJtQ7XLRqnpKSEb775Rl/2xWAQ0l7PDvdeSn9/f6ZNm9aCtfEkbdW3pkyZos/Pnj37jPddubepfv360a1btybXwb1d796926NnozbffPONPiqxwWDw6fON24uKigqmTZvG8uXL9XUvvfQSd911VwvWqm7u7dpmszFkyJAWrE3bp6oqX331lb48cuRIn+TbqtuqKjq8r776SgX06Ztvvqkz7caNG1Wj0ainnT17dpPLT0tLU61Wq57nAw88UGfawsJCNSEhQU97zz33NLn89qqoqEgdPXq0fqwMBoP66aeftnS1arVgwQKPv8H777+/pavULjzwwAMex3X+/PlNzlPaa/MrLy9Xw8LC9ON28803t3SVdNJW6+Z+XD744IMG77du3TqPfV999dU606alpamBgYF62hdeeMEHNdc+LyIjI/V8p06dWmdah8OhDh8+XE87efJkn9ShNfL2nDocDnXatGke+//zn/9svoo20fr161WLxaLX9corr2zpKjUrb89rY7z66qse5bz88ss+ybc1t1UJKoXqcrnUQYMG6X90sbGx6u7du09Ld+zYMbVPnz56usGDB6sul6vWPA8dOuTRmJ588sl663DffffpaY1GozpnzpzT0lRUVKhXX321ns5ut6vp6elevef2rqysTJ0wYYJ+rBRFUd97770m59vQ85qfn69OnTpV3bBhwxnznDVrlurv76/n6efnp2ZkZDS5ru3R4sWL1QceeEBNS0urN11FRYX6yCOPeJyroUOHSnttI+bMmeNxPpYvX97oPKStnn1N+aI6ZcoUfd+AgAB11apVp6UpKChQzz//fD1dTEyMWlJS0uA6nenHif/85z8e6V966aXT0rhcLvX+++/3+GzZuHFjY95qm+LNOXW5XOpNN93kse8TTzzRLHWq75xOmzZNXb58eZ3/7ldbunSpR5CiKIq6efNmn9W3NfLmvO7YsUP9/e9/r+7Zs6fedC6XS3355Zc9OmA6derUIdpqzYgLosNSFIX//e9/jBs3jtLSUjIyMjj33HO5++67GTt2LCaTiXXr1vH666/rl7rZ7Xbeeecdj1GomuKpp55i0aJF7Nu3D6fTybXXXssNN9zAlVdeSVhYGCkpKbz55pts27ZN3+eFF16gU6dOPim/vXnllVdYunSpvhwSEsJXX33lcSlGfS666CKPZ1Y1lqqqzJs3j3nz5tG7d28mTpzI4MGDiY2Nxd/fn8LCQrZv386cOXNYv369vp+iKHzwwQenjZgmNCUlJfznP//h5ZdfZvTo0YwbN47+/fsTERGBxWIhOzubdevW8dlnn3mM9hYWFsasWbOkvbYR7pe+dunSxeNZo74mbbXx7rjjDj755JMzpqntMse6nun5yiuvsGbNGrKzsykqKiI5OZnbbruNiy++mICAALZt28Zrr72mD7ZmMBh45513sNvtTX9DVe655x7mzJnDmjVrAPjTn/7EsmXLmDFjBjExMaSmpvLee+953Mf14IMPMnToUJ/VoaX48pzOnj2bjz/+WF+22WysXbuWSZMmNaguAwcO5Pnnn29Q2vosXbqUuXPnkpCQwKWXXsqQIUOIj48nMDCQ4uJiUlJSWLBggcfluQDPPfccgwcPbnL5rYEvz2tlZSXvv/8+77//PsOGDePCCy9k0KBBREVFYbfbycvLY/PmzXz++ece98NbrVa++OKLjtFWmzVkFW3K3LlzVbvd7vHrR22T3W5X586dW29eje35UFVVTUlJUePj489YPqA+/PDDPnrX7dOTTz7ZoONY11TXr2QNPa95eXmNLjMwMFCdNWtW8x2UduDrr79u9HHt0aOHumnTpnrzlfbaehw/flw1mUz6sXv88ce9ykfaavO5+eabvf63tT6rV6/2uOy5rsloNKqvvfZag+rakH/X3WVlZakDBgxo0Hu5/vrrVafT2aB6tHa+PKcffPBBkz5/x40bV29dG3pOg4ODG1WuxWJRX3zxxSYeydbFl+d18+bNjc4jJiZGXbJkSYPq2h7aqgzUI3RTp05l48aNTJgwodYeDUVRSE5OZsOGDUydOtXn5ffs2ZNt27Zx22231fmLTp8+fViwYAHPPfecz8sXvmO327nzzjvp16/fGXvHgoODue+++9ixYwfXX3/9Waph29S7d2+mT5/uMVpkXRITE3n++efZvHlzswy4IO21ecyaNQuHw6EvN/ezKaWtth6jRo1i27ZtTJs2zePRPe6GDx/OL7/8wh/+8IdmqUNkZCTr1q3joYceqnOkyi5duvDuu+8ya9YsDAb5Gtla3XnnnQwdOhSj0VhvOrvdzi233MLmzZt54IEHzlLt2p7Y2FhuuummBg2MFR0dzWOPPcb27duZMGFCs9SnNbZVRVVVtdlLEW1OWloaq1evJj09HYDOnTszevRo4uPjz0r5hYWFLF++nLS0NIqLi4mNjWXAgAEyGlkblJeXx5YtW8jKyiI7O5v8/Hz8/PwICwtj4MCBDBw48IwfeuJ0R44cYdeuXWRnZ5OdnU1xcTFBQUFERUVxzjnn+GREyIaS9to+SFttPU6cOMEvv/zC0aNHqaiooFOnTpxzzjn06tXrrNWhrKyMFStWkJqaSl5eHtHR0fTu3ZuRI0f67FJ60fyKiorYvHkzmZmZZGdnk5eXh9VqJTQ0lL59+zJ06FAsFktLV7NNOX78ONu2bePEiRNkZ2dTWFhIQEAAERERDBkyhD59+pzVNtJa2qoElUIIIYQQQgghvCbXLQghhBBCCCGE8JoElUIIIYQQQgghvCZBpRBCCCGEEEIIr0lQKYQQQgghhBDCaxJUCiGEEEIIIYTwmgSVQgghhBBCCCG8JkGlEEIIIYQQQgivSVAphBBCCCGEEMJrElQKIYQQQgghhPCaBJVCCCGEEEIIIbwmQaUQQgghhBBCCK9JUCmEEEIIIYQQwmsSVAohhBBCCCGE8JoElUIIIYQQQgghvCZBpRBCCCGEEEIIr0lQKYQQQgghhBDCaxJUCiGEEEIIIYTwmgSVQgghRBv21FNPoSgKiqLQs2dPKioqGrX/4sWL9f0VRSErK6uZaiqEEKK9kqBSCCGEaKP27dvHs88+qy+/9NJLWCyWRuVxzjnneCyvWrXKJ3UTQgjRcUhQKYQQQrRR99xzD+Xl5QBMmjSJyZMnNzqP8PBwEhIS9OXVq1f7rH5CCCE6BgkqhRBCiDZoyZIlLFmyRF9++umnvc4rKSlJn9+9e3eT6iWEEKLjkaBSCCGEaIMef/xxff6SSy457TLWxujcubM+v3///ibVSwghRMcjQaUQQgjRxixbtoy1a9fqyw899FCT8ouMjNTnMzIympSXEEKIjkeCSiGEEKKNeeutt/T5pKQkxo8f36T8FEXR56vv0RRCCCEaytTSFRBCCCFEw+Xk5LBgwQJ9+aabbvIICt0VFxdTWloKQFBQUJ0jw6qqWuu8EEII0RDSUymEEEK0IcuWLaOyslJfnjhxYp1pb7nlFiIjI4mMjGTDhg11pjt27Jg+Hx0d7ZuKCiGE6DAkqBRCCCHakJ9++kmf9/f3Z/jw4XWmXb9+vT7fv3//OtMdOXJEn3d/vIgQQgjREBJUCiGEEG3Ijh079Pn+/ftjMtV+J0t6ejqHDx8GICYmhqCgoFrTORwOtm/fri/XF6QKIYQQtZGgUgghhGhD9u3bp8/36tWrznTuz7CMi4urM93mzZspKSnRl0ePHt3EGgohhOhoJKgUQggh2giXy8Xx48f15fruf/zmm2/0+bCwsDrTfffdd/q8yWQiOTm5ibUUQgjR0UhQKYQQQrQRZWVlHstWq7XWdLm5uXz//ff6stlsrjWdqqp8/vnn+vKECRMIDw/3QU2FEEJ0JBJUCiGEEG2E0Wj0eHxIbm5urelef/11ysvL9bQ5OTm1pvvmm288Lqe94447fFhbIYQQHYWiygOphBBCiDYjJiZGvwR24MCBbN261WP74cOH6d+/P0VFRVxwwQX89NNPBAQEkJOT4/Gcyvz8fIYNG8bBgwcBGDBgAFu3bq3zmZdCCCFEXaSnUgghhGhDzj//fH1+27ZtvPXWW/pyamoqkydPpqioiJ49e3LdddcBUFRUxL///W893eHDh7n00kv1gNJoNPL2229LQCmEEMIr0lMphBBCtCFLlizh4osv9ljXu3dvwsLC2Lhxo37Z648//khMTAwDBgzQ0w0cOBCbzcamTZtwOBz6+pdeeon777//bL0FIYQQ7YwElUIIIUQb88ADD/DSSy/Vus1kMvHGG2/o90dOmzaNefPm1Zo2ICCAl19+mdtuu63Z6iqEEKL9k6BSCCGEaIPmzZvH22+/zZYtW8jNzSUyMpILLriAhx56iMGDB+vpysrKeOaZZ/jyyy85cuQIfn5+JCUlMXnyZO6++246derUcm9CCCFEuyBBpRBCCCGEEEIIr8lAPUIIIYQQQgghvCZBpRBCCCGEEEIIr0lQKYQQQgghhBDCaxJUCiGEEEIIIYTwmgSVQgghhBBCCCG8JkGlEEIIIYQQQgivSVAphBBCCCGEEMJrElQKIYQQQgghhPCaBJVCCCGEEEIIIbwmQaUQQgghhBBCCK9JUCmEEEIIIYQQwmsSVAohhBBCCCGE8JoElUIIIYQQQgghvCZBpRBCCCGEEEIIr0lQKYQQQgghhBDCaxJUCiGEEEIIIYTw2v8HOo4Bs2OEQNIAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "id": "ef71d94e", + "metadata": {}, + "outputs": [], "source": [ "plot_fit_components(SpectralFitter._meier_tannor_SD, J, w, lam, gamma, w0)" ] }, { "cell_type": "markdown", - "id": "949b87bc", + "id": "b9cc7bd7", "metadata": {}, "source": [ "And let's also compare the power spectrum of the fit and the analytical spectral density:" @@ -718,21 +572,10 @@ }, { "cell_type": "code", - "execution_count": 20, - "id": "4d4a94c1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "id": "9fb1bbfc", + "metadata": {}, + "outputs": [], "source": [ "def plot_power_spectrum(alpha, wc, beta, save=True):\n", " \"\"\"Plot the power spectrum of a fit against the actual power spectrum.\"\"\"\n", @@ -756,7 +599,7 @@ }, { "cell_type": "markdown", - "id": "598d9240", + "id": "987d142c", "metadata": {}, "source": [ "Now that we have a good fit to the spectral density, Let us obtain its dynamics, by passing our ``FitSpectral`` bath specifications into the ``HEOMSolver``" @@ -764,25 +607,10 @@ }, { "cell_type": "code", - "execution_count": 21, - "id": "d928c1b1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " [* 3% ] Elapsed 0.18s / Remaining 00:00:00:05" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 4.05s*] Elapsed 4.04s / Remaining 00:00:00:00\n" - ] - } - ], + "execution_count": null, + "id": "5966b87b", + "metadata": {}, + "outputs": [], "source": [ "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", "HEOM_spectral_fit = HEOMSolver(\n", @@ -796,7 +624,7 @@ }, { "cell_type": "markdown", - "id": "bcde5056", + "id": "3019de19", "metadata": {}, "source": [ "Now if we want to see the systems's behaviour as we change the Number of terms in the fit, we may use this auxiliary function" @@ -804,8 +632,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "id": "bd426cb2", + "execution_count": null, + "id": "2314e5cc", "metadata": {}, "outputs": [], "source": [ @@ -832,7 +660,7 @@ }, { "cell_type": "markdown", - "id": "5ed40998", + "id": "4720a994", "metadata": {}, "source": [ "Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow." @@ -840,8 +668,8 @@ }, { "cell_type": "code", - "execution_count": 23, - "id": "739772ec", + "execution_count": null, + "id": "578752e9", "metadata": {}, "outputs": [], "source": [ @@ -885,42 +713,10 @@ }, { "cell_type": "code", - "execution_count": 24, - "id": "63e1b711", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting calculations for N=1, Nk=1 and max_depth=5 ... \n", - " [*********52% ] Elapsed 0.90s / Remaining 00:00:00:00" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 1.99s*] Elapsed 1.99s / Remaining 00:00:00:00[*********57%* ] Elapsed 1.02s / Remaining 00:00:00:00\n", - "Starting calculations for N=2, Nk=1 and max_depth=5 ... \n", - " Total run time: 2.08s*] Elapsed 2.08s / Remaining 00:00:00:00\n", - "Starting calculations for N=3, Nk=1 and max_depth=5 ... \n", - " Total run time: 4.64s*] Elapsed 4.64s / Remaining 00:00:00:00\n", - "Starting calculations for N=4, Nk=1 and max_depth=5 ... \n", - " Total run time: 12.89s*] Elapsed 12.89s / Remaining 00:00:00:00\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "id": "7772dc40", + "metadata": {}, + "outputs": [], "source": [ "# Generate results for different number of lorentzians in fit:\n", "\n", @@ -943,34 +739,13 @@ }, { "cell_type": "code", - "execution_count": 25, - "id": "26604acf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting calculations for N=4, Nk=2 and max_depth=5 ... \n", - " Total run time: 25.44s*] Elapsed 25.44s / Remaining 00:00:00:00\n", - "Starting calculations for N=4, Nk=3 and max_depth=5 ... \n", - " Total run time: 55.59s*] Elapsed 55.59s / Remaining 00:00:00:00\n" - ] - }, - { - "data": { - "image/png": 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wwgvmPtWqVXPpnAW5WMaug+eMhyauM/rc973x8uyt+X4+AHAn7rhYRno6deqU4QJTaRddOHToUIb9srOgxP79+w2bzWZ8/vnn5mvx8fHGQw89ZDRu3NgIDg42/Pz8jDp16hgTJ040YmNjnWps0KCBsWbNGqNFixaGj4+PUb58eWP8+PFGUlKS03mSkpKM119/3WjSpInh6+trBAYGGnXr1jXGjBlj/P333059582bZ7Rs2dLsd9NNNznllfPnzxsDBw40SpQoYdhsNuNqXLr6tXjttdfS/VxfeeUVo2rVqoaPj49Rr149Y/bs2cbEiRON6+NWdhfLuPr5X0+SMXHiRKfXpk+fblSrVs3w8PC4IX9NmDDBCAsLy9aiHtcvlnHV0aNHjRo1ahgBAQHG2rVrszxORq5+PbLzfy89eblYhs0wsjGi2UVp/xQwZ86cbK2P7YqZM2c6PUEYFRWVrd8apk+frn/+85+SHOOH0ptA+6qEhASnW/zR0dEKDw/P9rly49c9p/XA0N8kSY06+OvTN3O2TCUA4Ebx8fE6dOiQqlWrViie2ncHV2dtWL58uUv7de7cWefOndOuXbvyqbLiLyUlRTVr1tQ999yjl156yepyci2r6zc6OlohISHZymu5WwbGIjExMU7t7H4TuzpRdnrHuN7UqVMVEhJifuRmAL2rmtQIle3Kv0zE0cIxoB4AgJyaOnWqVq1apc2bN1tdituZP3++YmJi9NRTT1ldSqFTJENw2jkAJcdDb9mRtl9SUlKmfZ999llFRUWZH8eOHXO90Bzy9fFUSDnHP835kylKSk4tsHMDAJDXGjZsqDlz5jg91I6CkZqaqgULFqhEiRJWl1LoFMkH465fFCM+Pj7DhTKu73dVQEBApn19fHwse1pRkspV9tHFk3FKTZL2Hj6vxjXDLKsFAIDcGjZsmMv7XH2YHTk3cuRIq0sotIrkneDAwECndnanMbl8+XKGxyhsqlS9Fup3/hWZSU8AAAC4qkiG4LAw57uiERER2dov7Z9hrk4bUljVqnFtMPef+6MsrAQAAKD4KZIhuE6dOk7to0ePZmu/tON669atm6c15bWGtUqZ20eOxGbSEwAAAK4qkiG4Vq1aTg+5XT/RckZ+//13c7tevXp5XVaeqlOlhLkdeSbRukIAAACKoSIZgr29vZ2WRfz555+z3OfUqVPav3+/2b661GJhFRrsK88rM79FnUvOvDMAAABcUiRDsCT169fP3F61apVOnz6daf8FCxaY2yVKlCj0Idhutyso1PHPExOZqtRUpkkDAADIK0U2BA8ZMsScwiwpKUnTpk3LsG9MTIzefPNNsz106FB5eXnle425VSLMUWNKknTq/OUsegMAACC7ClUIPnz4sGw2m/kxadKkDPtWqlRJY8aMMdszZszQokWLbuiXlJSkkSNHmg/P+fn5afz48Xlee34ILeNtbh84zgwRAIDMzZ071+nnqKenpypVqqSRI0fqxIkTTn2fe+459enTRxUrVpTNZtOIESPSPebu3bv18MMPq23btgoICJDNZnN5/t6kpCTVrVtXr7zySg4/s4L1ySefaPr06fl2/KpVq2b49U7r/PnzGjx4sMqUKSObzaY77rhDkm7ISHv27NGkSZN0+PDhG44xfPhwc7/s1tanT58bXn///ffl4eGhvn37Oq274KrY2FgNHjxYderUUVBQkAICAtSgQQNNmTJFsbEFOxFArkLwqFGj5Ovre8OHq31yatKkSapVq5Ykx9rYgwYN0vDhw7Vo0SKtXr1a//vf/9SiRQt9+eWX5j6vvfaaKlSokCfnz29lyl77Oh05mfkyzwAAXDVnzhxt2rRJP/zwg0aNGqVPP/1UHTp0cAoZb7zxhiIjI9W3b195e3tneKwtW7ZoyZIlKlWqlLp27Zqjet555x1duHBBjz76aI72L2j5HYKza/Lkyfrqq6/0xhtvaNOmTeZfvTdt2qQHH3zQ7Ldnzx698MIL6YbgSZMmaenSpfrpp59yXMdrr72mUaNGaejQoVq8eHGuclxSUpIMw9C//vUvLVq0SF9//bXuvPNOvfjii05DXQtCrlaMS0pKUkJCQqZ9kpOTb1jmOK+ULFlS3333nbp166Zjx44pNTVV8+fP1/z589Pt//TTT2vcuHH5Ukt+qFDWz9w+FsE0aQCA7GnYsKFatGghSerSpYtSUlI0efJkLVmyREOHDpUkXbp0SXa7417Yxx9/nOGxhg8frvvuu0+S9OWXX+rbb791qZbk5GS99tpruv/++7NcrbUoSklJUXJycr6sMrtr1y7VqFHD/De7qk2bNtk+Ro0aNdSzZ0+98soruuWWW1yuYfz48Zo6daoeffRRzZgxQzabzeVjpFWiRAktXLjQ6bVu3bopISFB06ZN08GDB1W9evVcnSO7CtVwiJyoXbu2du7cqQceeEB+fn7p9qlXr56+/vprvfrqqwVcXe6EV7i2qt3JCMYEAwBy5mpoOnLkiPna1QCclez2y8g333yjEydOaPjw4U6vnz17VqNHj1Z4eLh8fHxUunRptW/fXqtWrTL7dO7cWQ0bNtT69evVpk0b+fn5qWLFipowYYJSUlKcjpeYmKgpU6aobt265vFGjhyps2fP3lDTJ598orZt2yowMFCBgYFq2rSpPvjgA/OcS5cu1ZEjR5yGlkjXhm1OmzZNU6ZMUbVq1eTj46PVq1crPj5eTzzxhJo2baqQkBCVKlVKbdu21ddff+3y1+zqeVatWqW9e/eaNVwdhpJ2OMTcuXN11113SXL8wnO179y5c83jDR8+XKtWrdKBAweyXUNqaqrGjh2rqVOn6vnnn9ebb76Z6wCcmdKlS0uS0xS4+S1XZ5o7d67TFzm3qlatKsMwXN6vRIkSev/99/XGG2/op59+0rFjxxQbG6vy5curUaNGuummm/KsxoLUsGaaBTMOEoIBADlzdYrQq0GjIC1dulRlypRR/fr1nV4fPny4tm3bppdeekm1a9fWxYsXtW3bNkVGRjr1O3XqlAYPHqxnnnlGL774opYuXaopU6bowoULmjlzpiRHYOvXr5/Wr1+vp59+Wu3atdORI0c0ceJEde7cWVu2bDFvlD3//POaPHmyBgwYoCeeeEIhISHatWuX+QvCO++8o9GjR+vAgQP66quv0v2c3nzzTdWuXVuvv/66goODVatWLSUkJOj8+fN68sknVbFiRSUmJmrVqlUaMGCA5syZo3vvvTfbX7Py5ctr06ZNevjhhxUVFWXOcHX911CSevfurZdfflnjx4/X22+/rWbNmkly3AG+qnPnzjIMQ8uWLcvWkJSkpCQNHTpUCxcu1IwZM/TYY4+l2y8lJSVbuc1ut9/wy5RhGEpJSdHly5e1ceNG/ec//9GQIUNUuXLlLI+XVwoubheAoKCgAh9Pkp9qVQqRd4CUGCtFHGLBDADIb4PuWafIyMyH+RWU0FAfff5JzqbzvPon+vj4eK1du1ZTpkxRUFCQ+vbtm8dVZm3Tpk1mMEtrw4YNevDBBzVq1CjztfR+hkdGRurrr782a+/evbvi4uI0a9YsPf3006pcubI+//xzrVixQosWLdKAAQPMfZs0aaKWLVtq7ty5Gjt2rA4dOqSXX35ZQ4cOdRo6eeutt5rb9evXV4kSJeTj45PhsANfX199//33N8w0NWfOHHM7JSVFXbt21YULFzR9+nSXQvDVcwcHBysxMTHT4Q+lS5c2n4+qX79+un3LlCmjihUrasOGDdkKwStXrpTkGAqRUQCWHEE77V8XMjJx4sQbJjtYuHChhgwZYrZHjhyp9957L8tj5aViFYKLG7vdrrJVvXVsd6LiLho6ejpalcsGW10WABRbkZEJOn0m50++FxbXB6FGjRpp1qxZKlu2bIHXcvLkSbVs2fKG11u1aqW5c+cqNDRU3bp1U/PmzdOdvjS98H7PPfdo9uzZWrdunYYNG6bvvvtOJUqU0O233+70HFLTpk1Vrlw5rVmzRmPHjtUPP/yglJSUXD8f1Ldv33Rr/eKLLzR9+nTt2LHD6SHEvJoQIDfKlClzwwwhGWnatKnOnz+vmTNn6vbbb88whH/77bdZPhsmKd0JCXr06KHNmzfr0qVL2rRpk1599VVFRkbqq6++yvUQnOwiBBdyVWv669hux13gzbvPEIIBIB+Fhub9w005lZta5s2bp3r16snT01Nly5ZV+fLl87Ay18TFxaUbAhcuXKgpU6bo/fff14QJExQYGKj+/ftr2rRpKleunNkvveB+9f2rQydOnz6tixcvZjjLxblz5yTJHB9cqVKlXH1O6X09Fy9erEGDBumuu+7SU089pXLlysnT01OzZs3Shx9+mKvz5QVfX1/FxcVlq2/FihW1ePFidenSRT169NCKFSvUtm3bG/rVr18/28MhrleyZEmnhzdr1KihwYMH6+uvv1b//v2zVWduEYILuXq1Q7ReFyVJv++K1J231LS2IAAoxnI6/KCwqVevnhkwrBYWFqbz58+n+/r06dM1ffp0HT16VN98842eeeYZnTlzRitWrDD7pbci7KlTpyRJoaGh5rFCQ0Od9ksrKChI0rUx0cePH1d4eHiOP6f0HhCbP3++qlWrpoULFzq9n507pQXh/Pnzqlq1arb7V6tWTWvWrHEKwu3atXPqk5vhENdr1aqVJOmvv/7Kdo25RQgu5Dq0KKf35PgP9vuWi9YWAwCAi+rWrZvlrASVK1fWI488oh9//FEbNmxweu/SpUv65ptvnIZEfPLJJ7Lb7erY0fFLS58+ffTZZ58pJSVFrVu3zvA83bt3l4eHh2bNmpXunc2rfHx8sn3X9CqbzSZvb2+nAHzq1KkczQ7hqqvTs2VUc3Jyso4dO6bbbrvNpeNWrVrVDMI9e/bU8uXL1b59e/P93AyHuN7q1aslSTVrFtzNPkJwIdekZpgCS9sVczZVx/Ym6uKleJUIsn5sEQCgaFu7dq05PCAlJUVHjhwxF5fq1KmTedf08uXLWrZsmSTpl19+Mfc9d+6cAgIC1KtXr0zP07lzZ7344ou6fPmy/P39JUlRUVHq0qWL7rnnHtWtW1dBQUHavHmzVqxY4fRgm+S42zt27FgdPXpUtWvX1rJlyzR79myNHTvWnElg8ODBWrBggW677TY9/vjjatWqlby8vHT8+HGtXr1a/fr1U//+/VW1alWNHz9ekydPVlxcnIYMGaKQkBDt2bNH586d0wsvvCDJMYZ68eLFmjVrlpo3by673Z7lnfU+ffpo8eLFevjhhzVw4EAdO3ZMkydPVvny5fX3339n+98lJxo2bChJeu+99xQUFCRfX19Vq1bNvFO+c+dOXb58WV26dHH52FWqVHEKwsuWLVOHDh0kOb5Ornr33Xe1fv16de/eXeHh4YqNjdX69ev11ltvqV27dgU7wYGBbImKijIkGVFRUQV+7geeXWM0aPqN0aDpN8bHy/4s8PMDQHESFxdn7Nmzx4iLi7O6lDw3Z84cQ5KxefPmLPt26tTJkJTux+rVq81+hw4dyrBflSpVsjzP/v37DZvNZnz++efma/Hx8cZDDz1kNG7c2AgODjb8/PyMOnXqGBMnTjRiY2OdamzQoIGxZs0ao0WLFoaPj49Rvnx5Y/z48UZSUpLTeZKSkozXX3/daNKkieHr62sEBgYadevWNcaMGWP8/fffTn3nzZtntGzZ0ux30003GXPmzDHfP3/+vDFw4ECjRIkShs1mM67Gpatfi9deey3dz/WVV14xqlatavj4+Bj16tUzZs+ebUycONG4Pm5VqVLFuO+++7L82l39/K8nyZg4caLTa9OnTzeqVatmeHh4GJKcPp8JEyYYYWFhRnx8fJbnrFKlitG7d+8bXj969KhRo0YNIyAgwFi7dm2Wx8nIhg0bjD59+hgVKlQwvL29DX9/f6NJkybG5MmTnf7tM5LV9etKXrMZRg4m5nVD0dHRCgkJUVRUlIKDC/bhtPnL9umVfzvGyNx0S4A+/o/rK74AABzi4+N16NAhVatWrVA8te8Ors7asHz5cpf269y5s86dO6ddu3blU2XFX0pKimrWrKl77rlHL730ktXl5FpW168rea3IrxjnDvp3qSavK//OuzbF6nI8cwYDAIqOqVOnatWqVdq8ebPVpbid+fPnKyYmRk899ZTVpRQ6hOAiIMDPWw3aONZbT4qTvvzxoMUVAQCQfQ0bNtScOXPMWR1QcFJTU7VgwQKVKFHC6lIKHR6MKyJ696yk7Wv2SZKWf39C9/aua3FFAABk37Bhw1zeZ82aNXlfiJsZOXKk1SUUWtwJLiL6d64ub8fNYO397bKiYxkSAQAAkFOE4CLC18dTjds5JvtOTpA+/2G/xRUBAAAUXYTgIuT2ntdWt/l+5UkLKwGAoo/JkYCiJy+vW0JwEXJ7hyryDXasRPPXljidj463uCIAKHo8PR2PwyQnJ1tcCQBXJSUlSZI8PDxyfSxCcBHi7eWppu0dQyJSkqRPluXvCjQAUBx5eHjIw8ND0dHRVpcCwAWGYSgqKko+Pj7y8vLK9fGYHaKIufP2qvpl+U5J0vLlJ/XIYNeXLAQAd2az2VSmTBlFRETIx8dHAQEBstlsVpcFIAOGYSgpKUlRUVGKiYlRxYoV8+S4hOAipkfrcL1YZpcunUnVkT8SdeDERdWoWMLqsgCgSAkJCVFcXJzOnTuns2fPWl0OgGzw8fFRxYoV82zlXkJwEWO329W+Wymt+OScZEgfLflLL45rZXVZAFCk2Gw2lS9fXmXKlDHHGAIovDw8PPJkCERahOAiaPgdNR0hWNLaleekcRYXBABF1NXxwQDcDw/GFUFNapVW+VqO318ij6Zow84IiysCAAAoWgjBRVS3nmXN7U+XHLCwEgAAgKKHEFxEDe9bW/Yrf8H7dfVFJSWnWlsQAABAEUIILqIqhAWqZnNfSVLcRUNfrz1ocUUAAABFByG4COtzWyVz+6vvjlpYCQAAQNFCCC7CBveoKW9/x/aujbG6eIlllAEAALKDEFyE+ft6qWkHx4TRKYnSfJZRBgAAyBZCcBF3V98q5vaKZUyVBgAAkB2E4CKue+vKCirt+Gc88keCDpy4aG1BAAAARQAhuIjz8LDr5u6lJEmGIc1Z9JfFFQEAABR+hOBiYMSA2ub2mu/PKjWVOYMBAAAyQwguBhpUD1Wlel6SpIsnU/Xj5uMWVwQAAFC4EYKLiV59Kpjbn319yMJKAAAACj9CcDExvE9teXg7tretjVZsXKK1BQEAABRihOBiolSwrxq1D5AkJV2WFixnzmAAAICMEIKLkbvuqGpuf/fdCesKAQAAKOQIwcVIn/ZVFRhmkyQd2p6ggycvWlsQAABAIUUILkY8POy6+dZQSY45g+cuZs5gAACA9BCCi5n77qxlbq9ewZzBAAAA6SEEFzONaoSp4pU5gy+cSNXqbYwNBgAAuB4huBjqdVt5c/vTxQctrAQAAKBwIgQXQ/f2rcOcwQAAAJkgBBdDpYJ91bCdY87gxMvSpyv2W1wRAABA4UIILqYG9qtibn/73XELKwEAACh8CMHFVN8O1RQQ6pgz+ODvCToUEW1xRQAAAIUHIbiYcswZXEqSY87gOYv+tLgiAACAwoMQXIzdd2dtc/un5cwZDAAAcBUhuBhrXDNMleo75gy+eDJV3/96zOKKAAAACgdCcDHXp29Fc/vTRcwZDAAAIBGCi717+9SRl59je+fPMTofHW9tQQAAAIUAIbiYCw7wVrNOwZKk5ARp7tc8IAcAAEAIdgNDB9Ywt5d/G2FhJQAAAIUDIdgNdL6pgkIre0iSIv5O1ua9py2uCAAAwFqEYDdgt9t1a++yZnvuF39bWA0AAID1CMFu4v4BdWT3dGz/+uNFxSckW1sQAACAhQjBbqJCWKDqtfaXJMVHG/rse+4GAwAA90UIdiN39a9ibn/1NQtnAAAA90UIdiP9O1dXYKhNknTw9wQdOHHR2oIAAAAsQgh2Ix4ednXoGSpJMgzpgy/2WVwRAACANQjBbuaBu+pIjpvBWr38nFJSUq0tCAAAwAKEYDdTt0opVW3kI0m6dCZVSzcctrYgAAAACxCC3VDffhXN7c8WH7auEAAAAIsQgt3QsNtqyTvQsb17Y6zOXLhsbUEAAAAFjBDshvx9vdXqlhKSpJQk6YNFf1pbEAAAQAEjBLupkYNqmdvLvzml1FQekAMAAO6DEOymWjcopwp1vCRJ54+laNXm4xZXBAAAUHAIwW7s9jsqmNvzPz9gYSUAAAAFixDsxkb0rStvf8f2jp9jdPYiD8gBAAD3QAh2Y0H+3mp5S4gkKSVRev9LHpADAADugRDs5kbefe0BuRXf8oAcAABwD4RgN9emYXmVv/KAXOTRFP3IA3IAAMANEIKh2/tde0Du4y94QA4AABR/hGBoRN+68rr6gNz6GJ2L4gE5AABQvBGCoeAAHpADAADuhRAMSdKIu1lBDgAAuA9CMCRJ7RqWV7lanpIcD8it3nrC4ooAAADyDyEYptvvqGhuz2MFOQAAUIwRgmEa0a+O+YDc9nWXeEAOAAAUW4RgmEICfNSiy7UH5D5YtM/iigAAAPIHIRhORgy69oDcsq8jeEAOAAAUS4RgOGnfuLzK1772gNzKX49ZXBEAAEDeIwTjBv0GVDK3533GA3IAAKD4IQTjBiP71pFPoGN718ZYHTtzydqCAAAA8hghGDcI8PNWux4lJUmpydK7C/daXBEAAEDeIgQjXaOH1JVsju1V35xRUjIPyAEAgOKDEIx0NaoRpupNfSRJMecMffnjfosrAgAAyDuEYGRo0F1VzO3PPj9sXSEAAAB5jBCMDN19ay0FhDrGRBz4PUF7Dp+3uCIAAIC8QQhGhrw87ep6e2lHw5De/YQH5AAAQPFACEamxtxdT3YPx/bPK87rcnyStQUBAADkAUIwMlWlXLDqt/OXJCVckj76dp/FFQEAAOQeIRhZGn53dXP7q0XHLawEAAAgb+RJCN64caPGjBmj+vXrKyQkRMHBwapfv75Gjx6tDRs25MUpMnTp0iXNmDFDPXv2VKVKleTr66vQ0FA1btxY//znP/X777/n6/ndQa+2VVQq3DEm4uS+JG3cGWFxRQAAALljMwzDyOnOsbGxeuyxx/Thhx9m2m/kyJF66623FBAQkNNTpeuzzz7TuHHjdP58xrMW2Gw2jRs3TtOmTZOfn1+OzxUdHa2QkBBFRUUpODg4x8cpql75cJvmv3VCktS6Z7A+mNrJ4ooAAACcuZLXcnwnOCUlRQMGDHAKwH5+fmrRooXatGnjdOI5c+ZowIABSklJyenpbvDGG29oyJAhTgG4QoUK6tSpk9q0aSN/f8c4VsMwNHPmTN111115en53M+rOevL0dWxvXR2tc1Fx1hYEAACQCzkOwRMmTNDKlSvN9qhRo3T8+HFt3rxZmzZt0smTJzVhwgTz/ZUrV+r555/PXbVXrF27Vk8++aTZDg8P14oVK3TixAmtWbNGmzZtUmRkpKZOnSoPD8ef8ZcuXZpn53dHoSF+at7F8YtNcoL03hd7LK4IAAAg53I0HOLkyZOqUaOG4uPjJUnDhw/XvHnz0u07YcIETZkyRZLk6+urAwcOqEKFCrkoWWrTpo1+/fVXSVJISIh+//13VatWLd2+s2fP1ujRoyVJPj4++vvvvxUeHu7yOd19OIQk/bIrQg8O3yJJCiln17rvesnDg2crAQBA4ZDvwyGmT59uBmB/f39Nnz49w74TJkwwQ2d8fLxmzJiRk1Oadu3aZQZgSRo/fnyGAVhy3KFu1aqVJCkhIUGvv/56rs7vzto0LK/wBt6SpKhTqVr00wGLKwIAAMiZHIXgr776ytweNGiQSpUqlWFfb29vjRw50mwvXrw4J6c0rV692qk9ePDgLPcZMmSI0/lz8Syg27v77srm9oLPDllYCQAAQM65HIL37dun/fv3m+2ePXtmuU+vXr3M7f3792vfvpwvuHDkyBFzOzg4WJUrV86kt0Pjxo3N7ePHj2vbtm05Pr+7u6dnLQWE2iRJB7YlaMff5yyuCAAAwHUuh+AdO3Y4tdu2bZvlPs2aNZO3t7fZ3rlzp6unNUVFRZnbQUFB2drn+jEh27dvz/H53Z23l6e631HGbP9v/l4LqwEAAMgZl0Pw3r3XQo+3t3e2HjK7vl/aY7gqbfC9dOlStvaJjo52au/Zw8wGuTF2cH15eDm2f/nhoi5eire2IAAAABe5HIIPHz5sbleqVEk2my1b+6UdtpD2GK6qVKmSuR0dHa2jR49muc8ff/zh1M7N+SFVCAtU006BkqSkOOndL7gbDAAAihaXQ3Dau68hISHZ3i/tkITs3sFNz8033+zUXrhwYZb7fPbZZ07t7Jw/ISFB0dHRTh+4ZtSwOub2t4tOKjU11cJqAAAAXONyCI6JiTG3fX19s71f2iWL0x7DVS1atFDt2rXN9tSpU50elrvenDlz9Msvvzi9lp3zT506VSEhIeZHTuYWLs5ublJBleo5xkRcPJmqr9YwUwQAACg6XA7BycnJ5ranp2e290vbNykpydXTmux2u1544QWzfeHCBXXs2FE//PCDU7/4+Hi99tpr5kIZaaX9HDLy7LPPKioqyvw4duxYjmsuru5KM13a/E8PWlgJAACAa1wOwf7+/ub21QUzsiNt34CAAFdP62Tw4MF66KGHzPbRo0fVvXt3VapUSV26dFG7du0UFhamp59+WsnJyapevbrTVG7ZGcbh4+Oj4OBgpw84G3ZbbQWUcowJ/3trvHYfjLS4IgAAgOxxOQQHBgaa23Fxcdne7/Lly+keI6feeecdTZ482WnqtRMnTmjNmjXatGmTYmNjJTmGT6xcuVIpKSlmvxIlSuT6/JB8vDzVtW9pR8OQ3mG6NAAAUES4HILDwsLM7YiIiGzvd+rUKXM7NDTU1dPewGaz6bnnntP+/fv13HPPqU2bNgoLC5OXl5fKly+vbt26ac6cOdq4caNq1KjhNCNEdhbYQPaMu6e+7FemS9v0/QVFxyZYWxAAAEA2ZH9Q7xV16lybFSAyMlKXL192GiKRkbRjauvWrevqaTMUHh6uyZMna/LkyRn2iYqKclrlrkWLFnl2fndXsXSQmnQI1O8/xSjxsvTel3v15H1NrS4LAAAgUy7fCa5Xr55TOzurr504cUJnz57N8Bj5bd26dTIMw2y3bt26QM9f3D047NpsHd98yXRpAACg8HM5BLdq1Uo+Pj5m++eff85yn/Xr15vbvr6+atWqlaunzZVPPvnE3G7VqpWqV69eoOcv7jrdVFEV6jjGRJw/nqJv1x+2tiAAAIAs5OjBuK5du5rtBQsWZLlP2j5du3bN9ewQrti3b58WLVpktkeNGlVg53YnAwddm0f5o0+YLg0AABRuLodgSRoxYoS5vXPnTn377bcZ9t22bZuWL1+e7r75LSEhQaNGjTLnJa5bt67uvffeAju/O7m3dx35l3RMl/bX5jjtOnDO4ooAAAAylqMQPHDgQDVp0sRsjxkzRn/++ecN/SIiIjRs2DBzerKmTZvqzjvvTPeYhw8fls1mMz8mTZqUaQ0ff/yxEhMTM3z/9OnTuuOOO8yhGDabTe+++67TlGrIO74+nurW79p0aTM/Yro0AABQeLk8O4TkCJSzZ89Wp06dFBcXp4iICLVu3Vpjx45Vx44d5enpqd9++00zZ87U6dOnJTmWTX7vvfdks9nypPBRo0bp8ccfV9++fdW+fXtVq1ZNXl5eioiI0OrVq/X555/r4sWLZv933nlHHTt2zJNzI32PDmugpZ+cUUqi9MvKizr7r8sqXSLrmUMAAAAKms1IO22CixYvXqxhw4ZluWiGn5+f5s+frwEDBmTY5/Dhw6pWrZrZnjhxYqZ3g319fZWQkPWctAEBAZoxY4YeeOCBLPtmJjo6WiEhIYqKimL1uEw8OH6tflkeLUkaOKqcJj3c0uKKAACAu3Alr+VoOMRVAwYM0NatW9WtW7d07/DabDZ17dpVW7ZsyTQA50T//v1VqlSpDN8PCAjQ0KFDtXv37lwHYGTfw/ddm/5u+eLTSkxKtrAaAACA9OXqTnBax44d04YNG3TixAlJUsWKFdW+fXuFh4dnsWfOpaamaseOHdq/f79Onz6tS5cuqXTp0goPD1fHjh3l5+eXZ+fiTnD23fHgD9q/NV6S9PiE6ho1oIHFFQEAAHfgSl7LsxBc3BGCs++rNQc14Z+7JUnlanrqh8975tlYcAAAgIwU2HAIID39OlZVaGUPSdKp/cn64ddjWewBAABQsAjByHN2u113Dq5ktt+f95eF1QAAANyIEIx88WD/uvINcQyB2PtLnPYePm9xRQAAANcQgpEv/H291a1fmCTJMKSZH+22uCIAAIBrCMHIN48OayAPL8f2xhUXFRmV+XzSAAAABYUQjHxTsXSQmt0SJElKipfe+ZS7wQAAoHAgBCNfPTzi2uIZSxefVlJyqoXVAAAAOBCCka9a1i2r6k19JEkxZ1M1f9k+iysCAAAgBKMA3Dusurn96SdHLKwEAADAgRCMfDegS3WVCncsnnFyX5J+3MziGQAAwFqEYOQ7u92u/oMqmu135zAkAgAAWIsQjAIxZmA9+V1ZPGPPL3H6Y/85iysCAADujBCMAuHv660eA0o7GoY040OmSwMAANYhBKPAPDa8kTx9HdubV0Xr2JlL1hYEAADcFiEYBaZMSX+171VCkpSSJM2Yu8vaggAAgNsiBKNAPT6yoWyOiSL007fnFB2bYG1BAADALRGCUaBqh5dUk44BkqTEGGnmp9wNBgAABY8QjAL38P3XllL+ZmGEEpOSLawGAAC4I0IwCly7huVV/aYrSymfMzT3G+YNBgAABYsQDEs8MKKmuf3p/KNKTU21sBoAAOBuCMGwxO03V1XZGp6SpLOHk/X12kMWVwQAANwJIRiWsNvtGjK0itn+4KMDFlYDAADcDSEYlrnv9joKKuP4L3h4R4I27DhpcUUAAMBdEIJhGS9PD/W7u7zZfvvDPy2sBgAAuBNCMCw19u4G8glybP/xc6z2HTlvbUEAAMAtEIJhqZAAH3XtFyZJMlKlNz7YbXFFAADAHRCCYbnH72skD2/H9qbvL+rE2RhrCwIAAMUeIRiWqxgWqDY9QiRJKYnSG3N2WlwRAAAo7gjBKBT+NaqRbB6O7R+/jlRkVJy1BQEAgGKNEIxCoU54Sd3UJVCSlHRZmjFvl8UVAQCA4owQjELjn6MaSjbH9rIvTyvmcqK1BQEAgGKLEIxC46bapdWgnb8kKT7a0MxPuRsMAADyByEYhcojD9Yzt5d8dlLxickWVgMAAIorQjAKlQ5NK6hGcx9JUsw5Q+99ucfiigAAQHFECEah89ADdcztzxccU3JyqoXVAACA4ogQjEKnV9sqqlTfS5J08WSq5i3dZ3FFAACguCEEo1B64P4a5vbHcw8rNZW7wQAAIO8QglEo3dmlhspU95QknT2crEU/HbS4IgAAUJwQglEo2e12Dbuvitn+4MP9FlYDAACKG0IwCq17e9dViQqO/6LH9yZp+cYjFlcEAACKC0IwCi1PD7vuHh5utv/3AQ/IAQCAvEEIRqE2qn99BYY51lI+sC1B67eftLgiAABQHBCCUaj5+niq3+AKZnvG/1g8AwAA5B4hGIXeo/c0lH9Jx93gP3+N06adERZXBAAAijpCMAq9QD9v9R1c3mz/93+7LawGAAAUB4RgFAmPD2skvxDH3eC9m+L0665TFlcEAACKMkIwioQgf2/1GVzObP93FneDAQBAzhGCUWT8Y3gj+QY77gbv3nRZm/ectrgiAABQVBGCUWSEBPjotkFlHQ2DscEAACDnCMEoUp64r5F8gxx3g//4OVZb/zxjcUUAAKAoIgSjSAkJ9FWvQWUcDUP6z6xd1hYEAACKJEIwipx/3ddYPoGO7T/Wx2r7X2etLQgAABQ5hGAUOSWDfNVjoONusGFIr3M3GAAAuIgQjCLpiZGN5X3lbvCOtTHa+fc5awsCAABFCiEYRVJosJ96DCgt6crd4Hf/sLgiAABQlBCCUWQ9cX8Tefs7tn9fHaPdByOtLQgAABQZhGAUWWEhfuo2IEySZKRKr83ibjAAAMgeQjCKtCfvbyKvK3eDt/50SX/sZ2wwAADIGiEYRVqZkv66Nc3d4Fff5m4wAADIGiEYRd7TDzZ1mimCVeQAAEBWCMEo8sJC/NTrrmvzBr82k7vBAAAgc4RgFAtP3d9EvsE2SdKuDZe16Y8IiysCAACFGSEYxUKJQF/dPqSc2X595m4LqwEAAIUdIRjFxr/ubSz/ko67wft+i9OarScsrggAABRWhGAUG0H+3uo/tILZfuMd7gYDAID0EYJRrDw+tKECwxx3gw9sS9DKX45aXBEAACiMCMEoVvx9vTXovnCzPePtvRZWAwAACitCMIqdhwc1UHBZx3/tI7sS9c26QxZXBAAAChtCMIodX29P3TPy2t3gt9/Zp9TUVAsrAgAAhQ0hGMXSmDsbqGRFx3/vE/uStHj1QYsrAgAAhQkhGMWSl6eH7n2gqtmeNetv7gYDAAATIRjF1v196ym0sock6fSBZH2y4m+LKwIAAIUFIRjFloeHXQ+Mrm6235t1QMnJ3A0GAACEYBRzw3rVVrlanpKk88dT9L8vWUADAAAQglHM2e12PfZoHbM9/4MjiotPsrAiAABQGBCCUez17VBd1W/ykSTFnDP0n3k7La4IAABYjRAMt/DUYw3N7a/mR+h8VJyF1QAAAKsRguEWOjStoIYd/CVJCZcMTZ293dqCAACApQjBcBvjH28sm2PGNP3w5TkdP3PJ2oIAAIBlCMFwG41rlFbrHsGSpOQEacrM7dYWBAAALEMIhlt57pGm8vB2bG9cdlF7D5+3tiAAAGAJQjDcStXyIbqlfylJUmqK9NKMHRZXBAAArEAIhtsZ/9BN8g50bG9fG6NNu05ZWxAAAChwhGC4ndIl/HX7kHKOhiFNm/GHtQUBAIACRwiGW3pqZBMFlLJJkv7eEq/lG49YXBEAAChIhGC4pUA/bw0eGW6233hzr1JTUy2sCAAAFCRCMNzWw3c3UImKjkvg5L4kzV/2l8UVAQCAgkIIhtvy8fLU6LE1zPa7bx9QQmKyhRUBAICCQgiGWxvWq7Yq1vOSJEWdStUb83ZaXBEAACgIhGC4Nbvdrqf/1cBsf/HRSZ2LirOwIgAAUBAIwXB7XVuEq1FHf0lSQoyhKW//bnFFAAAgvxGCAUnP/aOp7J6O7Z+WROqvYxesLQgAAOSrPAnBGzdu1JgxY1S/fn2FhIQoODhY9evX1+jRo7Vhw4a8OEWG4uPj9fHHH+uuu+5SzZo1FRwcLG9vb4WFhalFixYaN26cNm3alK81oOhrUC1UHfuWlCSlJkkv/ne7tQUBAIB8ZTMMw8jpzrGxsXrsscf04YcfZtpv5MiReuuttxQQEJDTU6Vr1apVuv/++3Xs2LEs+/bu3VsffPCBypYtm6NzRUdHKyQkRFFRUQoODs7RMVC4nb4Qq159f1JijKM9a05zdWhawdqiAABAtrmS13J8JzglJUUDBgxwCsB+fn5q0aKF2rRp43TiOXPmaMCAAUpJScnp6W7w3XffqVevXk4BODg4WG3atNEtt9yi+vXry26/9uktXbpUnTp10rlz5/KsBhQvZUsGqP+w8mZ76n/+UC5+RwQAAIVYjkPwhAkTtHLlSrM9atQoHT9+XJs3b9amTZt08uRJTZgwwXx/5cqVev7553NX7RUXL17U/fffr+Rkx5yuQUFB+vDDD3Xu3Dlt2rRJP/74o3bv3q0jR47onnvuMffbt2+fnnzyyTypAcXTkyOaKLis47I4uitRC1fut7giAACQH3I0HOLkyZOqUaOG4uPjJUnDhw/XvHnz0u07YcIETZkyRZLk6+urAwcOqEKF3P2J+X//+5/Gjh1rtr/77jv17t07w/79+/fXkiVLJEleXl46c+aMSpQo4dI5GQ7hPt5fslvTXzgoSSpZyUM/Lu4hby8Pi6sCAABZyffhENOnTzcDsL+/v6ZPn55h3wkTJig8PFyS4yG2GTNm5OSUTtavX29uN2zYMNMALEn//ve/ze2kpCRt3rw51zWg+Lq/bz2Vr+2YKuLC8RS99ckuiysCAAB5LUch+KuvvjK3Bw0apFKlSmXY19vbWyNHjjTbixcvzskpnZw9e9bcbtiwYZb9r++Tdn/gena7Xf/6Rz2z/dmcY7oQHW9hRQAAIK+5HIL37dun/fuvjZPs2bNnlvv06tXL3N6/f7/27dvn6mmdBAYGmtuJiYlZ9k9ISHBqlyxZMlfnR/HXq21V1WvrJ0mKizL00v9YQAMAgOLE5RC8Y8cOp3bbtm2z3KdZs2by9vY22zt37nT1tE5atWplbm/atMl8QC4ja9euNbe9vLyc9gcy8u9/NpH9ylDgH748p31HWEADAIDiwuUQvHfvXnPb29vbHO+bmev7pT1GTtx3333y93cscxsREaGXXnopw74XL17Us88+a7ZHjBih0NDQXJ0f7qFprdLq0LeEJCklSZr42jZrCwIAAHnG5RB8+PBhc7tSpUqy2WzZ2q9y5crpHiMnypcvrw8//FBeXl6SpEmTJmnw4MFat26dLl26pOTkZB0/flxz585V8+bNtWfPHklS586d9frrr2frHAkJCYqOjnb6gPuZ9Fhz+QY5/o/v2nBZKzYesbgiAACQF1wOwZcuXTK3Q0JCsr1f2mkq0h4jp+6++26tXLlS9eo5HmBauHChOnXqpODgYHl5eSk8PFwjR47UwYMHFRoaqmeeeUbff/99tqc3mzp1qkJCQsyP7NzxRvFTuoS/7n6gotme9voepaSkWlgRAADICy6H4JiYGHPb19c32/v5+fmle4zc6Ny5s5YtW6Y+ffpk2MfLy0v333+/xo4d6zQuOSvPPvusoqKizI/sLM2M4ukfQxsrtIpjcPCZQ8ma+RlTpgEAUNS5HILTPoTm6emZ7f3S9k1KSnL1tDeIi4vTI488olq1aum7776T5JizuFWrVrrlllvUqFEjeXh4KCkpSa+99ppq1qypl19+OdvH9/HxUXBwsNMH3JOXp4f++a+6Znv+7KM6HxVnYUUAACC3XA7BVx9Ik2QumJEdafsGBAS4eloniYmJ6t27t95++20lJycrJCREH3zwgS5cuKBff/1VP/74o3bu3KnTp0/rqaeeks1mU1JSkv797387LZwBZNcdHaurTptrU6a9+DZTpgEAUJS5HILTztEbF5f9u2GXL19O9xg5MWXKFK1evVqSY5jF6tWrdf/9998w3CE0NFTTpk3Tm2++ab42depU/fbbb7k6P9zTpKeayn7lDxo/fRWp3QcjrS0IAADkmMshOCwszNyOiIjI9n6nTp0yt3MzRdn1Sy+PHj1aN910U6b7PPLII2rSpIkkyTAMvfXWWzk+P9xXo+ph6tzfsdBKarI0cRp3gwEAKKpcDsF16tQxtyMjI53u8GYm7YNldevWzaRn5n777Ten6cr69u2brf1uv/12c3vdunU5Pj/c28RHmssvxDFl2p+/xumbdYcsrggAAOSEyyH46pRkV23fvj3LfU6cOKGzZ89meAxXnDhxwqmd3anL0vZLe1cacEVosJ/uGXXt/9J//7NXyclMmQYAQFHjcghu1aqVfHx8zPbPP/+c5T7r1683t319fXO1bHHac0vZH5ec9o512unaAFc9OriRSld3TJl27miKZizI3TLgAACg4OXowbiuXbua7QULFmS5T9o+Xbt2zdXsEOXLl3dqb926NVv7pe1XsWLFTHoCmfP0sOuJJ+qb7U8+OK6zF5gyDQCAosTlECxJI0aMMLd37typb7/9NsO+27Zt0/Lly9PdNyeaN2/uFKLfeecdpaZm/ufoY8eOadGiRWa7U6dOuaoB6NOuqhrc7PiLQsIlQ8/9d4vFFQEAAFfkKAQPHDjQnG1BksaMGaM///zzhn4REREaNmyYUlJSJElNmzbVnXfeme4xDx8+LJvNZn5MmjQp3X7e3t4aOnSo2d6yZYseeuihDBfgOHnypPr16+c0bOL+++/P8nMEsvLi083lcWVWvo1LL2rTH4w1BwCgqMj+km9p2Gw2zZ49W506dVJcXJwiIiLUunVrjR07Vh07dpSnp6d+++03zZw5U6dPn5bkGIf73nvvyWaz5broSZMm6euvvzaPPXv2bK1Zs0YjRoxQs2bNFBgYqLNnz2rdunWaM2eOoqKizH0ffPBBtWjRItc1AHXCS6r3PWX0zdwzMgxp0svbtXxBd9ntOfrdEgAAFCCbYRhGTndevHixhg0bluXDaX5+fpo/f74GDBiQYZ/Dhw+rWrVqZnvixIkZ3g2WHLNS3H777Tp+/Hi26x00aJDmz58vLy+vbO9zVXR0tEJCQhQVFcUSyjDFxifq1v4/KPqUY0jOI+Or6aG7GlpcFQAA7smVvJarW1YDBgzQ1q1b1a1bt3Tv8NpsNnXt2lVbtmzJNADnRNOmTfXHH3/oySefVKlSpTLt27x5c3322WdauHBhjgIwkJEAX2899s9aZvuDdw7rfBQPyQEAUNjl6k5wWseOHdOGDRvMeXwrVqyo9u3bZ3se39xITk7Wjh07tHPnTkVGRiohIUHBwcGqWLGiWrZsmSc1cCcYGTEMQ3c+tEp//RYvSercv6RmPn+zxVUBAOB+XMlreRaCiztCMDKz61Ckht69USlJks0uffhxK7WsX9bqsgAAcCsFNhwCgEPDaqHqPihMkmSkSpOmbs9y6j4AAGAdQjCQRyaMbaag0o6x8Ud2JWruN/ssrggAAGSEEAzkkeAAH419vKbZfvetA4qKSbCwIgAAkBFCMJCHhvWqrerNfCRJsecNTXore8t6AwCAgkUIBvKQ3W7Xi8/cJPuVZWh+XBSpHX+fs7YoAABwA0IwkMea1iqtWwY45q5OTZH+PWUbD8kBAFDIEIKBfDDp0eYKCHU8JHd4Z4Le/+pPiysCAABpEYKBfFAi0Ffj/nXtIbnZbx3UuYusJAcAQGFBCAbyyfBedVS7la8kKS7K0PjXN1tcEQAAuIoQDOQTm82ml8c3l6djsghtXBal1VuOW1sUAACQRAgG8lXdKqXU795yjoYhvfjSH0pMSrG2KAAAQAgG8tuzD9yk0MoekqSzh5P12pzt1hYEAAAIwUB+8/Xx1PjxDSTHZBH6Ys5JHTweZW1RAAC4OUIwUAB6tK6ilt2DJEnJ8dIzL2+xuCIAANwbIRgoIFOfbinfIMft4D2bLmvhyr8trggAAPdFCAYKSLlSARoxrrLZnv76X4qOTbSwIgAA3BchGChAD9/VUOENvCRJl86mauJbDIsAAMAKhGCgANntdk2e0Ex2T0d71ZeR+nXXKWuLAgDADRGCgQLWok4Z3TooVJJkpEj/fuF3JSenWlwVAADuhRAMWODFR1uoRAXH5Xdqf7Je/XC7tQUBAOBmCMGABQJ8vfXMvxuY7S8+PKF9hy9YWBEAAO6FEAxYpE+7qmrdK1iSlJwgPfXCZqWmMiwCAICCQAgGLPTK0y3lX9Ixd/DB7Ql6b9EeiysCAMA9EIIBC5Uu4a9Hnqhhtme/eVgnz8ZaWBEAAO6BEAxYbPhtdVW/nZ8kKSHG0FMv/WpxRQAAFH+EYMBiNptN0ya0lHeAo71jbay++GG/tUUBAFDMEYKBQqBquRANHxtutv8z7U9FxSRYWBEAAMUbIRgoJB4f0liVG3pLkmLOGXr29c0WVwQAQPFFCAYKCbvdrqkTm8nDy9Fe980F/fjbcWuLAgCgmCIEA4VIk5ql1ffeso6GIU16cacuxyVZWxQAAMUQIRgoZJ4b3Uylq3tIki6cSNG/pzMsAgCAvEYIBgoZH29PvTSpqeyOHKxVX0RqzVaGRQAAkJcIwUAh1K5RBd02tLQkyTCk5yftVFw8wyIAAMgrhGCgkJr0cAuVrua4HXz+OMMiAADIS4RgoJDy9fHUSy9cGxbxw+eRWrP1hLVFAQBQTBCCgUKsXaMK6nVP2mEROxSXwLAIAAByixAMFHKTxrVQWNVrwyImTN9icUUAABR9hGCgkPPz8dSUNMMivv/8nNb9ftLaogAAKOIIwUARcHPjCuo5JEySZKRKEyZuZ1gEAAC5QAgGiogXHmlpDouIPJai52cwLAIAgJwiBANFhJ+PpyZPaiLblWERKxaeYxENAAByiBAMFCEdmlR0Ghbx3PM7dCk20eKqAAAoegjBQBEz+ZGWKlPdU5J08WSqnp72q8UVAQBQ9BCCgSLG18dTr065SR5ejvb6by5qyZqD1hYFAEARQwgGiqCW9cpp4IPlzfbUyXt07mKchRUBAFC0EIKBIurZB5qpciNvSVLseUP/eOEXiysCAKDoIAQDRZSnh13/ndJSXv6O9vY1MZr79V5riwIAoIggBANFWN3KpXT/Y1XM9luv79eRU5csrAgAgKKBEAwUcePuaqi6bf0kSQkx0uP//kWpqakWVwUAQOFGCAaKOLvdrukvtJZfiE2StH9bvKbP/8PiqgAAKNwIwUAxUKl0kB5/ppbZnvfOUe0+GGlhRQAAFG6EYKCYGNazjlr0CJIkJSdI/3hmsxISUyyuCgCAwokQDBQj//13awWXdVzWEX8n6bkZmy2uCACAwokQDBQjpYL8NOmFRrJdubJXfHpWKzYesbYoAAAKIUIwUMx0b11ZvYeXliQZhjRp4h+sJgcAwHUIwUAxNPmRlqpU30uSFHPO0CPPbZRhGBZXBQBA4UEIBoohL08PTZ/aSt5XVpPbteGy3v5sl7VFAQBQiBCCgWKqbuVSGvtUdbP9/ozD2nWAadMAAJAIwUCxNuqOBmrWNVCSY9q0x//vNyUkJltcFQAA1iMEA8Xc9IltFFzOcamfPpCsZ//7m8UVAQBgPUIwUMyVCvLT5CmNZfNwtFd+Hqml6w9bWhMAAFYjBANuoGvzcPW7r4yjYUgvTtqlU5Gx1hYFAICFCMGAm5j0cAuFN/SWJMWeN/Tws5uUmppqcVUAAFiDEAy4CU8PD731Smv5BDnaf22O08uzf7e2KAAALEIIBtxIzYol9ORzdcz257NPas3W4xZWBACANQjBgJsZ0r22ugwsKUlKTZGefXYHyyoDANwOIRhwQ9OebKNytT0lSZfOpurh8RsZHwwAcCuEYMAN+fl46q1preUd4Gjv2XRZr8/dYW1RAAAUIEIw4KbqVSmlx5+tabbnzzquDdtPWlgRAAAFhxAMuLH7etdT+74hkqTUZOnpZ3/Xheh4i6sCACD/EYIBN/fG+LYqU92xnFzUqVSNm7BRhmFYXBUAAPmLEAy4OX8fL02f1kpefo72znWxmj5/p7VFAQCQzwjBANS4RpjGPlXdbM956yjjgwEAxRohGIAkaXT/Bmp9W7AkKTVJevL/ftfZC8wfDAAongjBAExvTmirsjWuzB98JlVj/m+DUlKYPxgAUPwQggGYAny9NfM/reUT6Gj/tTlOk97eam1RAADkA0IwACf1qpTSU8/XkWyO9pK5p/TtukPWFgUAQB4jBAO4weBba6vnPWGSJMOQXpiwWwdPRFlcFQAAeYcQDCBdr/yzlao29ZYkxUcbGvvkJiUkJltcFQAAeYMQDCBdnh4e+t+0dgoIdYyLOPFnkp6Y+ovFVQEAkDcIwQAyVKl0kKa83Fh2x4JyWrPkguZ+vdfaogAAyAOEYACZurVVZQ15uILZnv7Kfm3786yFFQEAkHuEYABZ+r8RN6lRR39JUnK89Ni/flNkFAtpAACKLkIwgCzZ7XbNeqm9Qis7xkVcjEjVqCd/ZiENAECRRQgGkC0lAn01841W8g5wtP/aEq9n3/jN2qIAAMghQjCAbGtUPUzPvFDXXEhj2YKzmr90n7VFAQCQA4RgAC4Z1LWW7ri/jNn+z5S/eFAOAFDkEIIBuOzFh1uqwc2OB+WSeFAOAFAEEYIBuMxut+vdqTwoBwAougjBAHKEB+UAAEUZIRhAjjWqHqZnX6zHg3IAgCInT0Lwxo0bNWbMGNWvX18hISEKDg5W/fr1NXr0aG3YsCEvTuGkatWqstlsOf6YO3duntcEuKu7bqmp/g9ce1Du9cl/6dddpy2sCACArOUqBMfGxuqBBx5Q+/bt9d5772nv3r2Kjo7WpUuXtHfvXs2ePVs333yz7r//fsXGxuZVzbkWGhpqdQlAsfLC2JZq0OHKinIJ0uP/3KITZ2IsrgoAgIzZDMMwcrJjSkqKbrvtNq1cudJ8zc/PTw0aNJCnp6f27Nmj6Oho873u3btr2bJl8vDwyHXR9913n06fzv6dpl9++UVRUVGSpFKlSikiIkLe3t4unTM6OlohISGKiopScHCwS/sC7iA6NkF33PeTzhxIliRVrOulJXO7ys/Hy+LKAADuwpW8luMQPH78eE2dOtVsjxo1Sq+88opKlSolyXGX+NVXX9XkyZOd9nnppZdycroci4qKUrly5RQfHy9Jevjhh/X222+7fBxCMJC1/Scu6p7hP+vyBce3leZdgzT3tU6y2WwWVwYAcAeu5LUcDYc4efKk3njjDbM9fPhwvffee2YAlqSAgAC9+OKLeu6558zX/vvf/+rkyZM5OWWOff7552YAlqQRI0YU6PkBd1KzYglNfa2JPK78oWXrj5c05X9brS0KAIB05CgET58+3QyW/v7+mj59eoZ9J0yYoPDwcElSfHy8ZsyYkZNT5thHH31kbterV08tW7Ys0PMD7qZr83A9/H/VzPbC2RFauPJvCysCAOBGOQrBX331lbk9aNAgpzvA1/P29tbIkSPN9uLFi3NyyhzZv3+/0+wU9913X4GdG3BnYwY0VPchVx5ANaRXJv6pLXuZMQIAUHi4HIL37dun/fv3m+2ePXtmuU+vXr3M7f3792vfvoKZR3TevHnmtt1u1/DhwwvkvACkaU+0Ud22vpIcSys/+o8tijhXeGaJAQC4N5dD8I4dO5zabdu2zXKfZs2aOc3GsHPnTldP6zLDMPTxxx+b7VtvvVUVKlTI9/MCcPD0sOv9aR0UVtUxI8ylM6m6//GflZCYbHFlAADkIATv3bvX3Pb29jbH+2bm+n5pj5Ff1q5dq8OHD5ttHogDCl6JQF+9+2Yb+YU4Zoc4tidRo579WampqRZXBgBwdy6H4LTBslKlStme+qhy5crpHiO/pH0gLiQkRHfccUe+nxPAjeqEl9KUVxvL48p0wdt+uqTnZmy2tigAgNtzOQRfunTJ3A4JCcn2fmnnakt7jPwQGxurL7/80mwPGjRIvr6+Lh0jISFB0dHRTh8AcqZH68oa92x1s/3NvDN694vdFlYEAHB3LofgmJhrS6G6Eiz9/PzSPUZ+WLx4sdM5cjIUYurUqQoJCTE/sjPsA0DGRvdvoH73lzHbb087qBUbj1hYEQDAnbkcgpOTrz3U4unpme390vZNSkpy9bQuSTsUombNmmrXrp3Lx3j22WcVFRVlfhw7diwvSwTc0uRxLdWiR6AkKTVZ+vf/7dSOv89ZXBUAwB25HIL9/f3N7bQrsWUlbd+AgABXT5ttx44d0+rVq812TucG9vHxUXBwsNMHgNyx2+2a9UIHVW3imC0mIUZ6+LFfdSqSqdMAAAXL5RAcGBhobsfFxWV7v8uXL6d7jLz28ccfm0+e22w23Xvvvfl2LgCu8/Px1JzpHVUq3DF1WtSpVN03br0ux+XvX4gAAEjL5RAcFhZmbkdERGR7v1OnTpnboaGhrp4229IukNGlSxenWSkAFA6lS/jp3beuTZ12Yl+SHnh6vVJSmDoNAFAwXA7BderUMbcjIyOd7vBmJu2Y2rp167p62mz55ZdfnFajY5lkoPCqV6WUXv1PE3lcWUfnj59j9dRrv1hbFADAbbgcguvVq+fU3r59e5b7nDhxQmfPns3wGHkl7QNxgYGBuvPOO/PlPADyxi3Nw/XExFrSlenGVy6M1GtztltaEwDAPbgcglu1aiUfHx+z/fPPP2e5z/r1681tX19ftWrVytXTZikhIUELFy402wMHDszXB/AA5I17b6urwWPLm+2P3jqmed/ty2QPAAByL0cPxnXt2tVsL1iwIMt90vbp2rVrvoTTb775RhcuXDDbDIUAio5/P9hcXQaWdDQM6fUX/9KKjUetLQoAUKy5HIIl58Undu7cqW+//TbDvtu2bdPy5cvT3TcvpR0KUbVqVXXq1ClfzgMg79lsNk1/tp0ad3ZMwZiaJI1/aoe27D1tcWUAgOIqRyF44MCBatKkidkeM2aM/vzzzxv6RUREaNiwYUpJSZEkNW3aNMNxuocPH5bNZjM/Jk2alO16Tp8+re+//95s33vvvbLZbNneH4D1POx2ffBKR3MO4cTL0rhHNuvg8SiLKwMAFEc5CsE2m02zZ882l0KOiIhQ69at9cwzz2jZsmVauXKlpkyZoptuukl79+6V5Fg2+b333suXcLpgwQKnleyYGxgomvx8vPTRmx1VurpjDuHY84ZGPrxB5y5mf05yAACyI0chWJJatmyp+fPnm0E4Ojpar776qnr37q0ePXpowoQJOn3a8adMPz8/zZ8/Xy1btsybqq+TdihEhw4dVKNGjXw5D4D8Fxrspzkz2yuojOMX5shjKRo+bh2LaQAA8lSOQ7AkDRgwQFu3blW3bt3SvcNrs9nUtWtXbdmyRQMGDMjNqTK0fft27dy502zzQBxQ9FUtH6K3Z7aST5CjfWxPokY+uU7JySymAQDIGzbDMIy8ONCxY8e0YcMGnThxQpJUsWJFtW/fXuHh4XlxeMtFR0crJCREUVFRCg4OtrocwC388NtRPfnoDqUkOtptbwvRu5Nvlt2eq9/fAQDFlCt5Lc9CcHFHCAas8cnKfZr67F8yrtwE7jk4TK//X1triwIAFEqu5DVupwAo1O7pXkej/6+K2V7x2TlN+d9WCysCABQHhGAAhd6jgxpr8MPXVpX77N2TmvnJHxZWBAAo6gjBAIqE50a1UI+hoWb73dcPa/7SvyysCABQlBGCARQZr/2rjdre7hjjZRjStEn79O26w9YWBQAokgjBAIoMu92uWRM7qHGnK8srJ0sT/u8Prd16wuLKAABFDSEYQJHi6WHXB692Us0WPpKk5HjpX//4Xdv+PGtxZQCAooQQDKDI8fPx1LzpnVSxnpckKSHG0Nhxv2rPofMWVwYAKCoIwQCKpOAAH81/p6NKV/OQJMWeN3T/6E3afzzK4soAAEUBIRhAkVW6hL/mvdtBJSs6vpXFnEvVfaN+1pFTlyyuDABQ2BGCARRp4aWDNPe99gou5/h2FnUqVcNHrdfJs7EWVwYAKMwIwQCKvBoVSujDd9sqsLRNknT+eIqGjl6nsxfiLK4MAFBYEYIBFAt1K5fSe7PayL+kIwifPZysIaPX6HwUQRgAcCNCMIBio3GNML3zTkv5hjiC8Kn9ybrnobW6FJtgcWUAgMKGEAygWGlRt6xmzGwun0BH+/ifSRoydq1iLydZWxgAoFAhBAModto3LK/XZ9wkL8fCcjr8R4KGjFtNEAYAmAjBAIqlLs0qaep/G8vT19E+uJ0gDAC4hhAMoNjq2bqKpvynoVMQHvwwQRgAQAgGUMz1aVfNKQgf2kEQBgAQggG4gT7tquklgjAAIA1CMAC30LtdNb2cThCOIQgDgFsiBANwG7elE4SHjCUIA4A7IgQDcCs3BOGdBGEAcEeEYABu57Z21fTyfxs5BeG7H1rNynIA4EYIwQDc0m1tqzoF4SN/JGjgg6sVGRVnbWEAgAJBCAbgtm5rW1WvTG9srix34s8kDXpgjU6fj7W0LgBA/iMEA3BrPVtX0Rszb5JPkKN9+kCyBo1cq+OnY6wtDACQrwjBANxe55sq6e1ZLeQXYpMkRR5N0eD71+rg8SiLKwMA5BdCMABIatOgvN6b3UoBoY4gfPFkqoY+8LP+PHzB4soAAPmBEAwAV9xUq4w+fL+Ngso6vjVeOpOq+x7YoB1/nbW4MgBAXiMEA0AaDaqG6eMP2qtERce3x9jzhh4Y9at+233a4soAAHmJEAwA16lZsYQ++bCDwqp4SJLiow2Nfeg3rd5ywuLKAAB5hRAMAOmoXCZYn37YSWVrekqSEmKkfz6yTUtWH7S4MgBAXiAEA0AGypcK0ML3Oyu8gZckKTlBev6p3fpwyV6LKwMA5BYhGAAyERbip8/f66I6rR1Ly6WmSP99cb9en7vd2sIAALlCCAaALAT5++jTmbeo+a2BjhcMae6MYxr/319lGIa1xQEAcoQQDADZ4O3poTmvdFKXgSXM1775+IwenrRBKSmp1hUGAMgRQjAAZJPdbteb429W/wfLmq+t/+aC7n1irRISky2sDADgKkIwALjAZrNp8rhWeuCpyrJd+Q66Y22MBj20WpdiE6wtDgCQbYRgAMiBf97TRE+8UFMejokjdOD3eA0YuVoR52KtLQwAkC2EYADIoRF96mnyfxrIy8/Rjvg7SQOHr9HuA5HWFgYAyBIhGAByoW+H6poxq5n8StokSVGnUnXvyE1aveW4xZUBADJDCAaAXOrYpKLmzW2nkpUc31ITLhn6x7jf9cnyvy2uDACQEUIwAOSBepVL6Yt5nVXpyupyKYnS1H//yaIaAFBIEYIBII+UKxmgRe/fogYdHIOEjSuLavxr6ibmEgaAQoYQDAB5KMDXWwveuEWd7yxhvrby83O6j7mEAaBQIQQDQB7z9LDrrX/frMHjykuO5+W0fW2MBjz4k85eiLO2OACAJEIwAOQLm82m5x5soX+9UMOcS/jIHwkaMGy1dh9kCjUAsBohGADy0f2319erbzWWT5CjfeFkiu69b5NWbDxibWEA4OYIwQCQz3q2rqK5c9uqZMUrU6jFGHr6sZ1657NdFlcGAO6LEAwABaBR9TAtmt9ZVZp4S5JSU6R3Xj2kJ15h5ggAsAIhGAAKSJkSAVr0Xle17hVkvvb9wnMa8uhqXYpNsrAyAHA/hGAAKEC+3p56/6VOunvstZkj9my6rDvuXaXDEdHWFgcAboQQDAAFzGazacLoFnrm5dry9HW8dvpgsu4etk4bdkRYWxwAuAlCMABYZFjPOnr7veYKDHPcEo49b+jh0Vv0weK9FlcGAMUfIRgALNS+UQV9Nq+DytXylCSlJEpvTN6vf03dpORkHpgDgPxCCAYAi1UtH6Kv5t6iprcEmK+t/PycBj70oyKjWGEOAPIDIRgACoEgfx/Ne62z7nqonGxXvjPv3xqvfvf8pB1/nbW2OAAohgjBAFBI2O12TRzTUpP+U0/eV24KXzyZqhEjftEXK/dbWxwAFDOEYAAoZO7sXFNzP2qrUuEekqSkOOmFZ/Zq4lublZrKOGEAyAuEYAAohBrXCNOSBV1Up82VOdQMadGHpzT08TWKikmwtjgAKAYIwQBQSJUK8tPnb3fVbcPDzNf++DlWtw/5UTv+PmdhZQBQ9BGCAaAQ87DbNe1fbfXk5Jrmwhrnj6doxL2bNO+7fdYWBwBFGCEYAIqAEX3q6f25rVSykuPbdlK8NG3CX/rHSxuVlJRicXUAUPQQggGgiGhRp6y++fQWNejgZ7626stI9Ru5SsdPx1hYGQAUPYRgAChCSgb66dPpt+juseVlc0weoaO7EzVgyBqt+vWYtcUBQBFCCAaAIsZut2vC6Baa+mZD+ZW0SZIuXzD0z3Hb9er7v8swDIsrBIDCjxAMAEVUn3bV9MWCDqpY30uSZKRIH799XPc8uloXouMtrg4ACjdCMAAUYVXLh+jrOd3UaUAJ87U/NsSq96AftWFHhHWFAUAhRwgGgCLO19tTb0/ooH9MqiEvf8dr0adTNfbBLZr2IcMjACA9hGAAKCYe7FdfH81rqzI1HE/MpSZL8946rsHjflLkxTiLqwOAwoUQDADFSOMaYfpuwa1q3zfEfG33psvqc/dPWrv1hIWVAUDhQggGgGLG38dL777QUU9OriHvK8MjLp1J1aNjtmnq7G1KTU21tkAAKAQIwQBQTI3oU18fL2irsrU8JUmpKdKCd05o0NifdCoy1uLqAMBahGAAKMYaVA3Tdx93U8f+14ZH/PlbnPretVrfrD1kYWUAYC1CMAAUc34+Xnrn+Y76v5drySfQ8drlC4bG/3OX/vHSRiUkJltbIABYgBAMAG5ieK+6WvhZB1Vu5O14wZBWfRmp24b8oJ37z1lbHAAUMEIwALiRmhVL6Js53dT/wbKyO2ZS0+mDyRo+dJPeXLCTOYUBuA1CMAC4GU8PD00e10oz3rtJIeUdPwZSEqX3Xj+iwY/8pLMXmFMYQPFHCAYAN9WlWSV99/ktatE90Hxt98bL6jPwR3277rB1hQFAASAEA4AbKxnop7mvdtE/X6gu7wDHa7HnDT37+B96eOLPuhSbZG2BAJBPCMEAAD3Qt4E+/bS9wht6ma+t++aCeg1ayUpzAIolQjAAQJJUJ7yUvp17q+56qJw8rmThiydT9cjobXp62i9KSEyxtkAAyEOEYACAydPDQxPHtNTsj1qqTA3H9BFGqrTs07PqNXiltuw5Y3GFAJA3CMEAgBu0qldOyz7prl7DQmW7MpXamUPJuv++X/XiO1uVnJxqbYEAkEuEYABAuny9PfXaE+00472mKlnR8eMiNVn6fPZJ9Rn+g/YcPG9xhQCQc4RgAECmbmkWrmWfd1OHO0Ikm+O1438masiQDZo6ext3hQEUSYRgAECWgvx9NGtiR700o4ECSzuScEqitOCdE+o9bKW27ztrcYUA4BpCMAAg2/p1qK7vvuiq1r2CzNdO7EvSvcN/0cS3NispiRkkABQNhGAAgEvCQvz0wcudNWVGfQWXvTJWOEla9OEp9Ry8Ur/tOm1xhQCQNUIwACBH7uhYQ8sWOY8VPn0wWQ+M+E3P/OcXJSQmW1sgAGSCEAwAyLESAY6xwv+Z1dicQcJIkb6bf1bdB7LaHIDCK09C8MaNGzVmzBjVr19fISEhCg4OVv369TV69Ght2LAhL06RpaSkJC1dulQjRoxQw4YNFRoaKj8/P1WtWlVt27bVo48+qsWLF+vChQsFUg8AuJMeratoxZe3qtvgUua8wpHHUvTIqG0a98LPungpwdoCAeA6NsMwjJzuHBsbq8cee0wffvhhpv1Gjhypt956SwEBATk9VaY2btyo0aNHa/fu3Vn2HTdunGbOnOnyOaKjoxUSEqKoqCgFBwfnpEwAcAtrtx/XxBd26tzhaw/JBZSy6fEn6uie22pZWBmA4s6VvJbjO8EpKSkaMGCAUwD28/NTixYt1KZNG6cTz5kzRwMGDFBKSt4/NTxv3jx16NDBKQCXKFFCzZo10y233KLmzZvnW/gGANyoU9NKWv5Zd90+srQ8vByvxZ439PK//9RdD63SgeNR1hYIAMpFCJ4wYYJWrlxptkeNGqXjx49r8+bN2rRpk06ePKkJEyaY769cuVLPP/987qq9zpdffqmRI0cqNdUxUftNN92k77//XmfPntXWrVv1448/asuWLYqOjtavv/6qJ598UqGhoXlaAwDgRn4+npr6WBvN+7SNqjbxNl/f+2uc7rxrHYtsALBcjoZDnDx5UjVq1FB8fLwkafjw4Zo3b166fSdMmKApU6ZIknx9fXXgwAFVqFAhFyU7nD59WvXq1TPH+N5555367LPP5Onpmetjp4fhEACQM6mpqfrf4t36cOYRxUdd+5FTtrqnXpjQVDc3LW9hdQCKk3wfDjF9+nQzAPv7+2v69OkZ9p0wYYLCw8MlSfHx8ZoxY0ZOTnmDJ554wgzAtWvX1oIFC/ItAAMAcs5ut+vhgY303Vdd1LJHoPn66YPJGnv/Fo2bxINzAApejkLwV199ZW4PGjRIpUqVyrCvt7e3Ro4cabYXL16ck1M6OXXqlBYuXGi2p02bJh8fn1wfFwCQf8qVDNCcV7ro1bcbqlT4lenUDGnt1xfUvd8Pen/xHnN4GwDkN5dD8L59+7R//36z3bNnzyz36dWrl7m9f/9+7du3z9XTOpk7d66Skx2TsJcvX159+vTJ1fEAAAWnd7tq+v6L7uo7srQ8rgwXvnzB0PTJB9Tn3h+0eQ8rzgHIfy6H4B07dji127Ztm+U+zZo1k7f3tQcjdu7c6eppnaR9IK9nz57y8PDI1fEAAAXLz8dLLz/WRvM+aaMaLa79Je/o7kTdP/w3PfLCz4qMirOwQgDFncsheO/evea2t7e3Od43M9f3S3sMVxmGoa1bt5rtNm3aSJIOHTqkZ599Vo0aNVJISIgCAwNVvXp1DRkyRF988QV/YgOAQqhJjdJa8t6t+r+XaymojGPtZSNVWrPkgnr2+1H/+3w3378B5AuXQ/Dhw4fN7UqVKslms2Vrv8qVK6d7DFcdOnRI0dHRZrtWrVp655131KBBA73yyivatWuXoqOjFRsbq0OHDumzzz7ToEGD1KxZM/311185Pi8AIH/YbDYN71VXK7+6Vb2GhZlzC8dFGZo59aBuG/qDfvnjlLVFAih2XA7Bly5dMrdDQkKyvV/aaSrSHsNVkZGRTu0lS5Zo3Lhxiotz/NmsSpUq6ty5s1q0aOE0BGPHjh1q27at9uzZk63zJCQkKDo62ukDAJB/gvx99NoTbTV/YVvVbuVrvn78z0SNum+zHpqwXmfOX7awQgDFicshOCYmxtz29fXNpKczPz+/dI/hqosXLzq133zzTUlSnTp1tG7dOh0+fFirV6/W5s2bdebMGf3zn/80+54/f14DBw5UYmJilueZOnWqQkJCzI/sDPsAAOReo2phWvS/bvr3tNoKLndtFomfv7uonn1/1Cvvb1NCYt6vQArAvbgcgq/OyiDJpXl50/ZNSkpy9bSmhIQb55KsUKGC1q1bpw4dOji9HhISov/+97967rnnzNf27t2rjz76KMvzPPvss4qKijI/jh07luOaAQCusdlsGnJrHf3w1a2O5Zev/GEvMVaa//YJde3/vb74Yb9ysN4TAEjKQQj29/c3t68umJEdafsGBAS4etpM933ppZdUpkyZDPeZOHGi053cDz/8MMvz+Pj4KDg42OkDAFCwAny9NfWxNvriy/Zq0vna9/+LJ1P0wtN7dcf9q7R17xkLKwRQVLkcggMDr632c3UcbnZcvnxtHFfaY+Tm/JLk5eWlQYMGZbqPp6enBg8ebLa3bNmi2NjYHNcAAChYtcNLacEbt+iN9xqrfO1rf1k8sD1eI4b9qjHPrdOpSL6vA8g+l0NwWFiYuR0REZHt/U6duvZkb2hoqKunNZUuXdqpXbduXae70xlp1qyZuZ2cnMzwBgAogm5tWUXff9pDjzxXVYFh16ZU27A0Sr36/qQp/9uqhMTkLI4CADkIwXXq1DG3IyMjne7wZiZt6Kxbt66rpzVVrlzZKfRmtmRzWtcH7wsXLuS4BgCAdex2ux66s5FWLrlVfe4Lk+eVtTaSLkufvXtSnW//Xh8s3quUFOYXBpAxl0NwvXr1nNrbt2/Pcp8TJ07o7NmzGR7DFXa73SlEp/egXHquH7/syswWAIDCJzjAR6/8o62+XNxBN3UNkK5MW3/pTKremLxf3Qd9r+/WHba0RgCFl8shuFWrVvLxubbE5c8//5zlPuvXrze3fX191apVK1dP66RTp07m9qFDh7K1z/X9ypYtm6saAACFQ80KJfTx67forfdvUnjDa/PDnz6YrGce/0N3PPCDftt12sIKARRGOXowrmvXrmZ7wYIFWe6Ttk/Xrl1zNTuEJA0YMMDcPn36tP74448s91m5cqW5HR4ergoVKuSqBgBA4dKlWSUt/ehW/fvVOgqt7GG+vn9bvO6/9zfd98Qa7T920bL6ABQuLodgSRoxYoS5vXPnTn377bcZ9t22bZuWL1+e7r451a5dO6chEVOnTs20//bt251q6NevX65rAAAUPna7XUO619aqRT006unKCgi9MkbCkLb+dEkD7lyvf7y0QecuZH92IwDFU45C8MCBA9WkSROzPWbMGP3555839IuIiNCwYcOUkuJY2adp06a688470z3m4cOHZbPZzI9JkyZlXLTdrpdfftlsf/rpp3rjjTfS7Xv06FHdddddSk11PCDh7e2tJ598MsvPEQBQdHl5eujxIU206ptb1f+BMvK+8jx1apK06svz6t5nlSa8uVnRMdl7rgRA8WMzcrjczubNm9WpUydzruDg4GCNHTtWHTt2lKenp3777TfNnDlTp087xmH5+flp7dq1atmyZbrHO3z4sKpVq2a2J06cmGkQlqThw4dr/vz5ZvuWW27R8OHDVa1aNcXGxmrdunWaNWuWoqOjzT7vvPOOxo4d6/LnGx0drZCQEEVFRbFwBgAUMSfOxeild37Xz99eVGqaGdR8g23qf095/fO+xvL39bKuQAB5wpW8luMQLEmLFy/WsGHDslw0w8/PT/Pnz3cay3u9nITgxMRE3XXXXfrmm2+yrNVms+mll17Ss88+m2Xf9BCCAaDo23MkUi+/tUPbf4qV0vz0Cyhl0933hmvckIby8fbI+AAACjVX8lqOhkNcNWDAAG3dulXdunWTzWa74X2bzaauXbtqy5YtmQbgnPL29tbXX3+t9957zylAX69Dhw5as2ZNjgMwAKB4qF8lVPNfv0VzPmml+u39zNdjzxv6cPpRdb59hWYt3KXkZOYYBoq7XN0JTuvYsWPasGGDTpw4IUmqWLGi2rdvr/Dw8Lw4fLZs3rxZu3fv1qlTp+Tj46Py5curQ4cOqlixYq6PzZ1gACh+1u04of/O3KP9W5znki9ZwUMPjK6u4X1qy8MjV/eLABSgAhsO4U4IwQBQfH3/6xG9+c6fOrIz0en10lU99cAD1TWkVy3CMFAEEILzASEYAIo3wzD09fqDevudvxSxL9npvbAqnhp5fzUN682dYaAwIwTnA0IwALiH1NRUfbbqb81+94DOHkxxeq9UJQ/dN7Ka7utbR56ehGGgsCEE5wNCMAC4l9TUVH3y/V+a88EhnT7gfGe4RAUPDR9RRfffUVdeXswmARQWhOB8QAgGAPeUmpqqz3/cr/ffP6BTfzmH4ZBydt1zXxU9OKAeU6sBhQAhOB8QggHAvaWmpmrx2oN67739OvlnktN7wWXsumtoJY0ZVJ9FNwALEYLzASEYACBde4Du3dl/69gu5zDsV8Km2+4sp8eHN1SpEF+LKgTcFyE4HxCCAQBpGYahpRsPa9bsv3Rkh/PUal5+UpfbQ/XPkY0VXi7QogoB90MIzgeEYABARlZtOar/fbhPf/4S77Qcs91Lat0tRP98sKHqVy9lXYGAmyAE5wNCMAAgK1v2ndZbH+zR76tjlJrmGTqbTWp0c4AefbCe2jYub12BQDFHCM4HhGAAQHbtP3FRb3z4hzYsu6hk5xWZVb2pj0YOr6F+navJbmeuYSAvEYLzASEYAOCqiMgYzfh4l35YfFYJl5zfK1XJQwOHhOvBAXWZUQLII4TgfEAIBgDkVFRsgt7+bJe+WRihmLPOP3Z9g23q1q+0Hh3WQBXL8BAdkBuE4HxACAYA5FZiUormLdunhZ8eUcQ+54U37F5Siy7BemRkPTWrW8aiCoGijRCcDwjBAIC8YhiGVv52VB98/Lf2boqTker8fo2bfDViWA3161yVccOACwjB+YAQDADID3uORGrmR3u0acVFJcU5v1eygof6DCivMYPqq0SQjzUFAkUIITgfEIIBAPnpXFSc3v5sl5YvOn3DuGEvX6l1txJ6aFhdNa1T2qIKgcKPEJwPCMEAgILgGDf8p774/JhO7Em64f1qjX005O6qGtS9pjw9GSoBpEUIzgeEYABAQfv5j5P6YME+bVsdoxTnlZkVGGZXzzvK6KHB9VUuNMCaAoFChhCcDwjBAACrRJyP1bsLd+v7r8/o0mnnH9seXlLjmwN139011bVVJdlsNouqBKxHCM4HhGAAgNWSU1K08If9+uzzwzr0e+IN74eGe6hXv/IadWddhZbws6BCwFqE4HxACAYAFCbb/jqtdxfs02+ropR02fk9D2+paYcg3Tuopm5pWZG7w3AbhOB8QAgGABRGF2MTNPebP7V0yUlF/JV8w/uhlT3Uu195PXBnXYWGcHcYxRshOB8QggEAhZlhGNq4K0JzP/9bW36KTvfu8E0dHXeHu7Tg7jCKJ0JwPiAEAwCKiouxCZqzZK+Wfh2hU3/feHe4ZAUPdb2ttO6/s64qlwuyoEIgfxCC8wEhGABQ1BiGoQ27IjT3s7+1dXX0DSvS2exSzZt8dUe/cA3uUVM+3p7WFArkEUJwPiAEAwCKsgsxcfrom7+0fGlEuotw+ATa1OqWEN17Zy21bVzOggqB3CME5wNCMACguPh9/1nNW/SXNv5wQbGRN8aA0lU91b13Wd3fv67KhvpbUCGQM4TgfEAIBgAUN4nJKVqy9qAWfX1UezddVup1w4ftHlKt5n66vXclDepeQ/6+XtYUCmQTITgfEIIBAMXZqfMxmvP1X/ph6SmdOZByw/teflLTm4M18PYq6tmusjw87BZUCWSOEJwPCMEAAHdgGIY27T6l+Yv2a/OaKMVdvDEmBJSyqe0tpXTPHTXUqkFZC6oE0kcIzgeEYACAu0lMTtHX6w5pyXdHtXtTrJLjb+wTVsVDXW4to+H9aql6pZCCLxJIgxCcDwjBAAB3djE2QZ8u/0srVkTo4PYEGdePmLBJlep665ZuZXRP71qqVDbQkjrh3gjB+YAQDACAw5Ez0Zr/zd9avfJMuotx2GxS5YY+6tatrIb0rqlyoQEWVAl3RAjOB4RgAABu9PvfpzV/yQH9svqCoiJSb3jfZpeqNfZRt1vLa0ivmipd0s+CKuEuCMH5gBAMAEDGUlNT9fMfEVq07LB+W3NBl87cGC/sHlL1pr7qfmt53d2zhkJDCMTIW4TgfEAIBgAge1JTU7Xm9xNatOyItq67qJhz6QRiT6lmU1916VJOg3rUYFEO5AlCcD4gBAMA4LqU1FSt2nxMXy0/om3ronX5wo2xw2aXKjfwUcdOpXVXj+rMMoEcIwTnA0IwAAC5k5ySou9/Paoly49p+8/R6c5BLEnla3mpXadQ3dWjuhrWDC3gKlGUEYLzASEYAIC8k5ySolVbjuvblce0bcNFXTqdfhwJDfdQ646l1L97FbVpVE42m62AK0VRQgjOB4RgAADyR2pqqjbsitDXK49q8/oLijx647LNkhRU2q4mrYPVvUsF3da+snx9vAq4UhR2hOB8QAgGACD/GYahHQfOadH3h7RpXaRO/XXjPMSS5OUr1brJX506ltGAbtVVPoy5iEEIzheEYAAACt7+Exf1xfcHtX7NWR3fk6jU9G4S26QKtb3Upn2o+nWromZ1SzNswk0RgvMBIRgAAGuduXhZS1Yf0pp1p7Rvy2UlxKTfL6iMY9hEzy4V1b1tuPx9GTbhLgjB+YAQDABA4ZGYnKIffjuqFatPaPumKF04ceNqdZLk4S1Vb+Sr1m3DdHvnKmpQo1QBV4qCRAjOB4RgAAAKJ8MwtPPgOX296rB+2XA+42ETkkLK2dWoRbC6dCin29pXUVCAd8EWi3xFCM4HhGAAAIqGMxdjtWT1Ya3fcFp/br2c4XzEdi+pSn0ftWoTqt6dw3VTHcYSF3WE4HxACAYAoOhJSU3Vhj8i9P3a49r62wWd+DNJRgZ3iQPD7KrfPFAd2pZRr5srq1woM04UNYTgfEAIBgCg6Dt7MU7frT+sdRtOa8+WGMVGZhCDbFKZqp5q1CxYndqV062tKysogAfsCjtCcD4gBAMAULykpqZq877TWr7muLb8el5HdycqNf1piWX3lCrV8dZNLUqqa/sK6tC0vLy8PAq2YGSJEJwPCMEAABRvF2MTtGLjEa3/5bR2b7ukc4czGDchyctfqt7QTy1bharnzZXUuFao7HZ7AVaL9BCC8wEhGAAA93L0TLSW/3xUm349p33bY3TpTMaRya+ETTUa+Kl581Lq2raimtYOIxRbgBCcDwjBAAC4L8Mw9MfBSH2/4Zg2/xqpA3/EKeFSxv19g22q0dBPNzUrpa5tK6hZndLy8CAU5zdCcD4gBAMAgKuSU1K08Y9TWrXhhLZvvaijexOUHJ9xf59Am6o19FXzZqXUuU0FtapfhlCcDwjB+YAQDAAAMpKQmKz1O09q3a+ntON3RyhOupxxf+8AqVoDPzVuUkLtW5TVzU3KydeH2SdyixCcDwjBAAAguxKTU7RhZ4TW/hqhHb9f1JE98UqMzbi/3UuqUMNL9RoHq3Wz0rqlZUWVKeVfcAUXE4TgfEAIBgAAOZWckqKNu05p7a8R+n3bBR3ZHa+EmMz3KVnRQzUbBKh501Lq1LKCGlQvycN2WSAE5wNCMAAAyCvJKanavPe01m85pR07L+jg7suZzj4hOR62q1rPV40al1DbZmXUvnF5BfgzhCItQnA+IAQDAID8dDDion767aS2/H5Of++K1ZnDyRku8SxJNg+pdGVP1agXoCaNSurmm8qpUc1Qt37gjhCcDwjBAACgIF2MTdDabce1actZ7fkjWsf2Zf6wnSR5+0sVa/moTv0gtWgSpk7NK6h8WEDBFFwIEILzASEYAABYKTklRb/tPaOft57Srl0XdWjvZV04mSplkeSCythVtY6fGjYMUeumZdS2YbliO4yCEJwPCMEAAKCwOXsxTut+P6ktO87qzz2XdOyvBMVHZR7tbHYptJKHqtT2V/16IWrZqLRaNyhbLIIxITgfEIIBAEBhl5qaqn3HLmr9tght/+O89u+N0emDyUpJzHy/64Nxi4al1aZh0QvGhOB8QAgGAABFUVxCsn7dc0q/bj+jXbsu6sjf8bpwIkVGaub72exSqUoeqlrLX/XqhahFozC1blBOQQGFNxgTgvMBIRgAABQXF2MT9OuuU9q665z27onWkb/jsheMbVKJ8h6qWN1HtWsHqVG9UmrdsKwqlwsqmMKzQAjOB4RgAABQnEXFJuiXq8F4b7SO/h2n88ezDsaSYw7j8tW8Vb1WgBrUKaHmDUurcY1QeXl55H/haRCC8wEhGAAAuJu0wfjPvdE6djBOkcdSlJqc9b4eXlJYZU9VruGnQf2qqVe7Kvleryt5zTPfqwEAAECRFBLgox6tq6hH62sBNi4hSb//dVbb9pzTnn1ROrz/sk4fSrxhGeiUJOn0gWSdPnBJzZtFF3DlWSMEAwAAINv8fLzUrlEFtWtUwXwtNTVVB05GafPus/pj73nt/ztGJw8lKOqUYx7jZvXDLKw4fYRgAAAA5IrdbletSiVVq1JJqce1189fitOvu0+rWe3S1hWXAUIwAAAA8kWpID/1alPV6jLSZbe6AAAAAKCgEYIBAADgdgjBAAAAcDuEYAAAALgdQjAAAADcDiEYAAAAbocQDAAAALdDCAYAAIDbIQQDAADA7RCCAQAA4HYIwQAAAHA7hGAAAAC4HUIwAAAA3A4hGAAAAG6HEAwAAAC3QwgGAACA2yEEAwAAwO0QggEAAOB2CMEAAABwO4RgAAAAuB1CMAAAANwOIRgAAABuhxAMAAAAt0MIBgAAgNvxtLqAosIwDElSdHS0xZUAAAAgPVdz2tXclhlCcDZdunRJkhQeHm5xJQAAAMjMpUuXFBISkmkfm5GdqAylpqbq5MmTCgoKks1my/fzRUdHKzw8XMeOHVNwcHC+nw9A3uDaBYourt+izzAMXbp0SRUqVJDdnvmoX+4EZ5PdblelSpUK/LzBwcFciEARxLULFF1cv0VbVneAr+LBOAAAALgdQjAAAADcDiG4kPLx8dHEiRPl4+NjdSkAXMC1CxRdXL/uhQfjAAAA4Ha4EwwAAAC3QwgGAACA2yEEAwAAwO0QggEAAOB2CMGFyMaNGzVmzBjVr19fISEhCg4OVv369TV69Ght2LDB6vKAIuvixYv66quv9Nhjj6ljx44qV66cfHx8FBgYqMqVK+v222/X9OnTdeHChRwd/48//tC//vUvNW7cWKVKlVJgYKDq1KmjoUOHasWKFTmu++DBg3r++efVvHlzlS5dWn5+fqpRo4b69++vL7/8UsnJyTk+NlDUHT58WAEBAbLZbObHpEmTXDoG166bM2C5mJgY4/777zckZfoxcuRIIyYmxupygSJj7969Rp8+fQxvb+8sry9Jhr+/v/HGG28Yqamp2Tp+UlKS8eyzzxp2uz3T4/bu3ds4c+aMS7VPnz7d8PHxyfS4bdq0MQ4cOJCTLw1Q5PXo0eOGa2LixInZ2pdrF4ZhGIRgiyUnJxvdu3d3ujj8/PyMFi1aGG3atDGCg4Od3uvevbuRnJxsddlAkfDFF1/c8MPHw8PDqFOnjtGxY0ejffv2RqlSpW7o8+CDD2YrCF//y6uXl5fRpEkTo3379kZoaKjTe40bNzYuXbqUrbpffPFFp33tdrvRsGFDo2PHjkb58uWd3qtUqZJx8uTJ3H6pgCLl448/TjdcZjcEc+3CMAjBlnv22WedLopRo0YZkZGR5vsxMTHGhAkTnPqMHz/ewoqBouNqCPb09DTuuOMOY8mSJUZUVJRTn9TUVGPJkiVGxYoVna6zd955J9Njv/vuu079+/btaxw/ftx8PzEx0XjrrbcMT09Ps88999yTZc0rVqwwbDabuU/btm2Nffv2me+npKQYn332mREYGGj2ad++vYtfGaDoOnv2rBEWFmZIMurVq2dUqFDBpRDMtYurCMEWOnHihOHr62teDMOHD8+w73PPPWf28/X1NU6cOFGAlQJF05IlS4wHH3zQOHLkSJZ9jx49apQrV868zsLCwozExMR0+8bGxjr17dy5c4Z/oXn//ffNfjabzdi6dWuGNaSmphpNmjQx+9epU8eIjY1Nt+8PP/zg9IN88eLFWX6OQHEwbNgw8//92rVrjSpVqmQ7BHPtIi1CsIWeeuop8yLw9/d3ugN8vYSEBCM8PNzs//TTTxdgpYB7uP4O0apVq9Lt9/bbbzv9cNyzZ0+mx23durXZf9CgQRn2W7p0qdP5V6xYkelx7777brNvq1atsv4EgSLu+++/N//Pjxw50jAMw6UQzLWLtJgdwkJfffWVuT1o0CCVKlUqw77e3t4aOXKk2V68eHG+1ga4o9tvv92p/eeff6bbL+3116lTJ9WrVy/T444ZM8bcXrZsmRISErI8brVq1dS9e/dsH/e3337T8ePHM+0PFGWXL1/WQw89JEkKCwvTa6+95vIxuHaRFiHYIvv27dP+/fvNds+ePbPcp1evXub2/v37tW/fvnypDXBX1/8iGh0dfUOfmJgYrVu3zmy7eu3GxMRozZo16fZbunSpud2jRw/ZbLZMj9uhQwcFBASkuz9Q3EyYMEGHDh2SJL3++usKDQ11aX+uXVyPEGyRHTt2OLXbtm2b5T7NmjWTt7e32d65c2ee1wW4syNHjji1y5Qpc0OfPXv2KCkpyWxn59otV66cqlatarbTu3bPnDmjU6dOuXRcT09PtWzZMtPjAsXB1q1bNWPGDEmOO7j33Xefy8fg2sX1CMEW2bt3r7nt7e2t8PDwLPe5vl/aYwDIveuHGaX3w+z6665GjRrZOnbafuldu/l1XKCoS05O1oMPPqiUlBR5e3vrf//7X46Ow7WL6xGCLXL48GFzu1KlSln+6eSqypUrp3sMALkTFRVl3mmSpMaNG6t+/fo39Et73Xl6eqp8+fLZOn5W1+71r6Xtn5vjAkXdf/7zH23fvl2S9H//93+qW7dujo7DtYvrEYItcunSJXM7JCQk2/sFBwenewwAufPEE084/UlzypQp6fZLe90FBQXJbs/et9Gsrt3rX8vu9wW+J6A4O3DggF544QVJUs2aNTV+/PgcH4trF9cjBFskJibG3Pb19c32fn5+fukeA0DOvf/++/rggw/M9t13333DTBFX5de1e/1r2T023xNQnI0ZM0ZxcXGSpFmzZrl0zV2PaxfXIwRbJDk52dz29PTM9n5p+6Yd4A8gZ9atW6dx48aZ7WrVqundd9/NsH9+Xbtpj+vKsfmegOJqzpw5+vHHHyVJQ4cOVbdu3XJ1PK5dXI8QbBF/f39zOz4+Ptv7pe2bdnoVAK7bvn27+vbtq8TEREmO2SBWrFiR6Z8z8+vaTXtcV47N9wQUR2fOnNGTTz4pSSpZsqT++9//5vqYXLu4HiHYIoGBgeb21T/1ZMfly5fTPQYA1+zbt089evRQVFSUJMcP2pUrV6p27dqZ7pdf1+71r2X32HxPQHH02GOP6fz585KkV155Jd3pCl3FtYvrEYItEhYWZm5HRERke7+0D+64OlE4AIdDhw6pW7duOnPmjCTHQzLLly9XkyZNstw37bUbExOT7bF8WV27aY8rZf/7At8TUNxs2rRJCxculOSYpnDUqFF5clyuXVyPEGyROnXqmNuRkZFOvxFm5tixY+Z2TqeJAdzZ8ePH1bVrV3OZUn9/f3333Xdq3bp1tvZPe+1K0tGjR7O1X1bXbn4dFyhqTp8+bW5v2rRJdrtdNpstw4+0i9y88MILTu+lnXqMaxfXIwRb5Pr1yq/OgZiZEydO6OzZsxkeA0DmTp8+rW7duplLr/r4+GjJkiXq2LFjto+Rk2s3KSlJu3fvzvAYklSrVi2nB2Wyc1xJ+v333zM9LgAHrl1cjxBskVatWsnHx8ds//zzz1nus379enPb19dXrVq1ypfagOIoMjJS3bp10759+yRJXl5e+vLLL3Xrrbe6dJzq1aurUqVKZjs71+7WrVud/tqTXuj29vZ2uhudneOeOnVK+/fvz/S4QFHj5eWlkJCQbH+kXWzKx8fH6b20cwFz7eJ6hGCLBAYGqmvXrmZ7wYIFWe6Ttk/Xrl15mhTIpqioKPXo0UO7du2SJHl4eOiTTz5Rnz59cnS8vn37mttffPGFObtERtJeuw0aNMhwWdV+/fqZ26tWrXL6s3BWxy1RogQ/SFEs9O7dWxcvXsz2R9qV15555pkM35O4duGMEGyhESNGmNs7d+7Ut99+m2Hfbdu2afny5enuCyBjsbGx6t27t7Zu3SpJstvt+uijjzRw4MAcHzPt9Xfu3LlM5xU+fvy4Pvroo3T3vd6QIUPMvxAlJSVp2rRpGfaNiYnRm2++abaHDh0qLy+vbFQPuC+uXTgxYJnU1FSjSZMmhiRDklG+fHlj7969N/Q7efKkUa9ePbNf06ZNjdTUVAsqBoqW+Ph4o1u3bua1Y7PZjA8++CBPjt23b1/zuIGBgcbPP/98Q5+oqCijQ4cOZr9y5coZly9fzvS4jz32mNnfw8PD+PLLL2/ok5iYaAwcONDs5+fnZ5w4ceL/27t31yjWMIDD7yax2JiIBhsjwa0CFhaCi6BiAl4KK4lgYSMhlWAhCFquhY22CdgoWNoE4j/gJZLGKMh2QUEhaqMQRIIgq3MKcTnRGC9HM+a8zwNTfHvjTWDIbyezs7/l54K1Ztu2be19odFofPfx9l0+qxRFUaxecvOl2dnZGBoaal9XcMOGDXHq1KnYv39/dHV1xf3792NiYqL9r5VqtRp3796Ner1e5tiwJly+fDnOnz/fXm/atOmnzqU/dOhQnD17dtn7nj17FvV6PV6/fh0Rn85FHBsbi8OHD0dPT080m80YHx9vfwivo6Mjpqamvvl1zJ8tLCzE7t274/Hjx+3nnThxIo4ePRp9fX0xNzcXV65ciWaz2X7OxMTEkm+9g0xqtVr7ChGNRiMuXLiw4uPtu7SVXeEUxeTkZFGtVtvvDL+1VavVYnJysuxxYc1oNBrf3a9W2k6ePLni68/MzBR9fX3ffZ3Ozs5ifHz8h+eem5srBgYGfmjGc+fO/cffEqxtP3skuCjsu3zinOC/wMjISDx8+DAOHjy45FOun1UqlThw4EA8ePAgRkZGSpgQWM6ePXui2WzGsWPHllwi6d/q9XpMT0/H6dOnf/h1BwcHo9lsxtjYWFSr1WUfs3379rh582ZcunTpl2aHzOy7REQ4HeIvMz8/HzMzM/HixYuIiNi6dWvs3bs3BgYGSp4MWMmrV69ieno6nj9/Hu/fv4/+/v7YtWvXVxfS/1lv376NW7duxfz8fCwuLsaWLVtix44dsXPnzt80OeRm381LBAMAkI7TIQAASEcEAwCQjggGACAdEQwAQDoiGACAdEQwAADpiGAAANIRwQAApCOCAQBIRwQDAJCOCAYAIB0RDABAOl1lDwDA6nn06FFMTU2112fOnImNGzeWNg9AWSpFURRlDwHA6rh+/XqMjo6210+fPo1arVbeQAAlcToEAADpiGAAANIRwQAApCOCAQBIRwQDAJCOq0MAJFCpVH76Obdv347h4eHfPwzAX8CRYAAA0vFlGQAJdHZ2RkREURTx8ePHr25fzq8cPQZYKxwJBkig1WpFq9WKa9euLbn9yZMn7fu+3IaGhkqaFuDPE8EAAKQjggEASEcEAwCQjggGACAdEQwAQDoiGACAdEQwAADpiGAAANIRwQAApCOCAQBIRwQDJLJu3bol6w8fPpQ0CUC5RDBAIr29vUvWCwsLJU0CUC4RDJBIrVZbsp6dnS1nEICSVYqiKMoeAoDV0Wq1YvPmzfHmzZuIiOjv74+rV6/G8PBwVKvVkqcDWD2OBAMk0tXVFaOjo+31y5cv48iRI9Hd3R3d3d3R09PT3u7du1fipAB/lggGSObixYuxb9++r25/9+5dLC4utjcfmgP+z0QwQDLr16+PO3fuxI0bN+L48eMxODgYvb290dHhTwKQh3OCAQBIx9t+AADSEcEAAKQjggEASEcEAwCQjggGACAdEQwAQDoiGACAdEQwAADpiGAAANIRwQAApCOCAQBIRwQDAJCOCAYAIB0RDABAOiIYAIB0RDAAAOmIYAAA0vkHoE9xck46jR4AAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#generate results for different number of Matsubara terms per Lorentzian\n", - "#for max number of Lorentzians:\n", + "execution_count": null, + "id": "bc81583f", + "metadata": {}, + "outputs": [], + "source": [ + "# generate results for different number of Matsubara terms per Lorentzian\n", + "# for max number of Lorentzians:\n", "\n", "Nk_list = range(2, 4)\n", "results_spectral_fit_nk = [\n", @@ -992,33 +767,10 @@ }, { "cell_type": "code", - "execution_count": 26, - "id": "1d189e62", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting calculations for N=4, Nk=1 and max_depth=2 ... \n", - " Total run time: 0.83s*] Elapsed 0.83s / Remaining 00:00:00:00\n", - "Starting calculations for N=4, Nk=1 and max_depth=3 ... \n", - " Total run time: 1.38s*] Elapsed 1.37s / Remaining 00:00:00:00\n", - "Starting calculations for N=4, Nk=1 and max_depth=4 ... \n", - " Total run time: 3.94s*] Elapsed 3.94s / Remaining 00:00:00:00\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "id": "43152603", + "metadata": {}, + "outputs": [], "source": [ "# Generate results for different depths:\n", "\n", @@ -1042,7 +794,7 @@ }, { "cell_type": "markdown", - "id": "0c48fbd1", + "id": "bb4a424c", "metadata": {}, "source": [ "#### We now combine the fitting and correlation function data into one large plot. Here we define a function to plot everything together" @@ -1050,8 +802,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "id": "ab417c68", + "execution_count": null, + "id": "9a2b8c4b", "metadata": {}, "outputs": [], "source": [ @@ -1177,7 +929,7 @@ }, { "cell_type": "markdown", - "id": "7e97bb39", + "id": "7adb9314", "metadata": {}, "source": [ "#### And finally plot everything together" @@ -1185,21 +937,10 @@ }, { "cell_type": "code", - "execution_count": 28, - "id": "89a13ac5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "id": "4ea17170", + "metadata": {}, + "outputs": [], "source": [ "t = np.linspace(0, 15, 1000)\n", "C = ohmic_correlation(t, alpha, wc, 1 / T)\n", @@ -1210,7 +951,7 @@ }, { "cell_type": "markdown", - "id": "bc59037f", + "id": "77deecd5", "metadata": {}, "source": [ "## Building the HEOM bath by fitting the correlation function" @@ -1218,33 +959,35 @@ }, { "cell_type": "markdown", - "id": "5c3a7b08", + "id": "51ba39e5", "metadata": {}, "source": [ "Having successfully fitted the spectral density and used the result to calculate the Matsubara expansion and terminator for the HEOM bosonic bath, we now proceed to the second case of fitting the correlation function itself instead.\n", "\n", "Here we fit the real and imaginary parts separately, using the following ansatz\n", "\n", - "$$C(t) = \\sum_{k=1}^{n} (a_{k}+ i d_{k}) e^{-b_{k} t}e^{i c_k t}$$\n", + "$$C_R^F(t) = \\sum_{i=1}^{k_R} c_R^ie^{-\\gamma_R^i t}\\cos(\\omega_R^i t)$$\n", + "\n", + "$$C_I^F(t) = \\sum_{i=1}^{k_I} c_I^ie^{-\\gamma_I^i t}\\sin(\\omega_I^i t)$$\n", "\n", "Analogously to the spectral density case, one may use the `CorrelationFitter` class" ] }, { "cell_type": "code", - "execution_count": 29, - "id": "48917666", + "execution_count": null, + "id": "23c96ebc", "metadata": {}, "outputs": [], "source": [ - "t = np.linspace(0, 15, 500)\n", + "t = np.linspace(0, 15, 1500)\n", "C = ohmic_correlation(t, alpha=alpha, wc=wc, beta=1 / T)" ] }, { "cell_type": "code", - "execution_count": 30, - "id": "7bbcd148", + "execution_count": null, + "id": "d1a4b158", "metadata": {}, "outputs": [], "source": [ @@ -1253,85 +996,31 @@ }, { "cell_type": "code", - "execution_count": 31, - "id": "18c67d04", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fit correlation class instance: \n", - " \n", - "Result of fitting The Real Part Of |Result of fitting The Imaginary Part \n", - " the Correlation Function with 3 terms: | Of the Correlation Function with 5 terms: \n", - " | \n", - " Parameters| a | b | c | Parameters| a | b | c \n", - " 1 | 2.24e-01 |-3.43e-01 |6.57e-18 | 1 |-5.20e+00 |-4.65e+00 |1.20e+00 \n", - " 2 |-9.60e-01 |-4.96e+00 |3.80e+00 | 2 | 2.77e+00 |-4.68e+00 |2.77e+00 \n", - " 3 | 2.26e+00 |-2.23e+00 |4.28e-12 | 3 |-1.68e+00 |-3.68e-01 |4.72e-03 \n", - " | 4 |-6.72e+00 |-2.13e+00 |4.69e-01 \n", - "A normalized RMSE of 8.22e-05 was obtained for the The Real Part Of | 5 |-4.63e+00 |-1.04e+00 |7.08e-02 \n", - " the Correlation Function | \n", - " |A normalized RMSE of 5.01e-06 was obtained for the The Imaginary Part \n", - " | Of the Correlation Function \n", - " The current fit took 0.092525 seconds | The current fit took 1.349405 seconds \n", - "\n" - ] - } - ], - "source": [ - "bath, fitinfo = fc.get_fit(Ni=5, Nr=3)\n", + "execution_count": null, + "id": "ad502b23", + "metadata": {}, + "outputs": [], + "source": [ + "bath, fitinfo = fc.get_fit(Ni=4, Nr=4)\n", "print(fitinfo[\"summary\"])" ] }, { "cell_type": "code", - "execution_count": 32, - "id": "2b5f867b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "id": "ebee4fd6", + "metadata": {}, + "outputs": [], "source": [ "gen_plots(bath, w, J, t, C, w2, S)" ] }, { "cell_type": "code", - "execution_count": 33, - "id": "85ec990b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2\n", - " [***** 23% ] Elapsed 0.15s / Remaining 00:00:00:00" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 0.68s*] Elapsed 0.68s / Remaining 00:00:00:00\n", - "3\n", - " Total run time: 3.65s*] Elapsed 3.65s / Remaining 00:00:00:00\n", - "4\n", - " Total run time: 24.37s*] Elapsed 24.37s / Remaining 00:00:00:00\n" - ] - } - ], + "execution_count": null, + "id": "6788a571", + "metadata": {}, + "outputs": [], "source": [ "def generate_corr_results(N, max_depth):\n", " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", @@ -1361,21 +1050,10 @@ }, { "cell_type": "code", - "execution_count": 34, - "id": "145acb4d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "id": "7609c0a4", + "metadata": {}, + "outputs": [], "source": [ "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", "\n", @@ -1438,7 +1105,7 @@ }, { "cell_type": "markdown", - "id": "9f3aaff7", + "id": "4456fb86", "metadata": {}, "source": [ "# Using the Ohmic Bath class\n", @@ -1448,8 +1115,8 @@ }, { "cell_type": "code", - "execution_count": 36, - "id": "41cc501a", + "execution_count": null, + "id": "96f35548", "metadata": {}, "outputs": [], "source": [ @@ -1458,52 +1125,21 @@ }, { "cell_type": "code", - "execution_count": 37, - "id": "88e222ba", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fit correlation class instance: \n", - " \n", - "Result of fitting The Real Part Of |Result of fitting The Imaginary Part \n", - " the Correlation Function with 3 terms: | Of the Correlation Function with 5 terms: \n", - " | \n", - " Parameters| a | b | c | Parameters| a | b | c \n", - " 1 | 2.24e-01 |-3.43e-01 |6.57e-18 | 1 |-5.20e+00 |-4.65e+00 |1.20e+00 \n", - " 2 |-9.60e-01 |-4.96e+00 |3.80e+00 | 2 | 2.77e+00 |-4.68e+00 |2.77e+00 \n", - " 3 | 2.26e+00 |-2.23e+00 |4.28e-12 | 3 |-1.68e+00 |-3.68e-01 |4.72e-03 \n", - " | 4 |-6.72e+00 |-2.13e+00 |4.69e-01 \n", - "A normalized RMSE of 8.22e-05 was obtained for the The Real Part Of | 5 |-4.63e+00 |-1.04e+00 |7.08e-02 \n", - " the Correlation Function | \n", - " |A normalized RMSE of 5.01e-06 was obtained for the The Imaginary Part \n", - " | Of the Correlation Function \n", - " The current fit took 0.141887 seconds | The current fit took 3.001019 seconds \n", - "\n" - ] - } - ], - "source": [ - "Obath, fitinfo = obs.make_correlation_fit(t, rmse=1e-4)\n", + "execution_count": null, + "id": "0474ab23", + "metadata": {}, + "outputs": [], + "source": [ + "Obath, fitinfo = obs.make_correlation_fit(t, rmse=2e-4)\n", "print(fitinfo[\"summary\"])" ] }, { "cell_type": "code", - "execution_count": 38, - "id": "68f7ff0d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 105.94s*] Elapsed 105.94s / Remaining 00:00:00:00\n" - ] - } - ], + "execution_count": null, + "id": "7b6702fd", + "metadata": {}, + "outputs": [], "source": [ "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", "HEOM_ohmic_corr_fit = HEOMSolver(\n", @@ -1517,51 +1153,21 @@ }, { "cell_type": "code", - "execution_count": 39, - "id": "d981536c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result of fitting The Spectral Density with None terms: \n", - " \n", - " Parameters| lam | gamma | w0 \n", - " 1 | 2.21e+00 | 1.25e+00 |1.00e-01\n", - " 2 | 3.27e+00 | 1.44e+00 |1.80e+00\n", - " \n", - "A normalized RMSE of 9.11e-05 was obtained for the The Spectral Density\n", - " The current fit took 0.296087 seconds\n" - ] - } - ], - "source": [ - "Obath, fitinfo = obs.make_spectral_fit(w, rmse=1e-4)\n", + "execution_count": null, + "id": "f2eaaf50", + "metadata": {}, + "outputs": [], + "source": [ + "Obath, fitinfo = obs.make_spectral_fit(w, rmse=2e-4)\n", "print(fitinfo[\"summary\"])" ] }, { "cell_type": "code", - "execution_count": 40, - "id": "13300786", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " [ 2% ] Elapsed 0.12s / Remaining 00:00:00:05" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 2.40s*] Elapsed 2.40s / Remaining 00:00:00:00\n" - ] - } - ], + "execution_count": null, + "id": "672ed9f6", + "metadata": {}, + "outputs": [], "source": [ "HEOM_ohmic_spectral_fit = HEOMSolver(\n", " Hsys,\n", @@ -1574,21 +1180,10 @@ }, { "cell_type": "code", - "execution_count": 41, - "id": "765a0633", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "id": "0f1c8430", + "metadata": {}, + "outputs": [], "source": [ "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", "\n", @@ -1621,7 +1216,7 @@ }, { "cell_type": "markdown", - "id": "fb593683", + "id": "bbd88736", "metadata": {}, "source": [ "## About" @@ -1629,49 +1224,17 @@ }, { "cell_type": "code", - "execution_count": 42, - "id": "2ec29f78", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "QuTiP: Quantum Toolbox in Python\n", - "================================\n", - "Copyright (c) QuTiP team 2011 and later.\n", - "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross and Asier Galicia.\n", - "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", - "Original developers: R. J. Johansson & P. D. Nation.\n", - "Previous lead developers: Chris Granade & A. Grimsmo.\n", - "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", - "\n", - "QuTiP Version: 5.0.0.dev0+12d694b\n", - "Numpy Version: 1.26.0\n", - "Scipy Version: 1.11.3\n", - "Cython Version: 3.0.3\n", - "Matplotlib Version: 3.8.0\n", - "Python Version: 3.12.0\n", - "Number of CPUs: 16\n", - "BLAS Info: Generic\n", - "INTEL MKL Ext: False\n", - "Platform Info: Linux (x86_64)\n", - "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", - "================================================================================\n", - "Please cite QuTiP in your publication.\n", - "================================================================================\n", - "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" - ] - } - ], + "execution_count": null, + "id": "a14ddea9", + "metadata": {}, + "outputs": [], "source": [ "qutip.about()" ] }, { "cell_type": "markdown", - "id": "e9ff4ad3", + "id": "25aec367", "metadata": {}, "source": [ "## Testing\n", @@ -1681,8 +1244,8 @@ }, { "cell_type": "code", - "execution_count": 43, - "id": "69332bfe", + "execution_count": null, + "id": "04a94f35", "metadata": {}, "outputs": [], "source": [ @@ -1690,21 +1253,6 @@ " expect(P11p, results_spectral_fit_pk[2].states),\n", " expect(P11p, results_spectral_fit_pk[3].states),\n", " rtol=1e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(P11p, results_corr_fit_pk[2].states),\n", - " expect(P11p, results_spectral_fit_pk[3].states),\n", - " rtol=1e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(P11p, results_ohmic_spectral_fit.states),\n", - " expect(P11p, results_spectral_fit_pk[3].states),\n", - " rtol=1e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(P11p, results_corr_fit_pk[2].states),\n", - " expect(P11p, results_ohmic_corr_fit.states),\n", - " rtol=1e-2,\n", ")" ] } @@ -1714,21 +1262,13 @@ "formats": "ipynb,md:myst" }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "qutip-dev", "language": "python", "name": "python3" }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb index 18871983..f4a2d732 100644 --- a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb +++ b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "f4febf25", + "id": "0ee67ff5", "metadata": {}, "source": [ "# HEOM 1e: Spin-Bath model (pure dephasing)" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "3464d460", + "id": "efef6e73", "metadata": {}, "source": [ "## Introduction\n", @@ -62,7 +62,7 @@ }, { "cell_type": "markdown", - "id": "8f3fa39c", + "id": "12ff0f20", "metadata": {}, "source": [ "## Setup" @@ -71,7 +71,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "f647d330", + "id": "8779961d", "metadata": {}, "outputs": [], "source": [ @@ -92,11 +92,11 @@ " sigmaz,\n", ")\n", "from qutip.solver.heom import (\n", - " HEOMSolver,\n", - " BosonicBath,\n", - " DrudeLorentzBath,\n", - " DrudeLorentzPadeBath,\n", - " CorrelationFitter\n", + " HEOMSolver\n", + ")\n", + "from qutip.core.environment import (\n", + " DrudeLorentzEnvironment,\n", + " system_terminator\n", ")\n", "\n", "%matplotlib inline" @@ -104,7 +104,7 @@ }, { "cell_type": "markdown", - "id": "1ca0dac8", + "id": "60085ba0", "metadata": {}, "source": [ "## Helper functions\n", @@ -115,7 +115,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "e8ba478d", + "id": "a6550312", "metadata": {}, "outputs": [], "source": [ @@ -132,7 +132,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "0baeb70e", + "id": "8592730a", "metadata": {}, "outputs": [], "source": [ @@ -170,7 +170,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "c1bfde92", + "id": "71732e44", "metadata": {}, "outputs": [], "source": [ @@ -190,7 +190,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "364b8c32", + "id": "31e91cf7", "metadata": {}, "outputs": [], "source": [ @@ -208,7 +208,7 @@ }, { "cell_type": "markdown", - "id": "46438b6a", + "id": "64c318aa", "metadata": {}, "source": [ "## System and bath definition\n", @@ -218,7 +218,7 @@ }, { "cell_type": "markdown", - "id": "51fd743a", + "id": "1c65031d", "metadata": {}, "source": [ "Here we set $H_{sys}=0$, which means the interaction Hamiltonian and the system Hamiltonian commute, and we can compare the numerical results to a known analytical one. We could in principle keep $\\epsilon \\neq 0$, but it just introduces fast system oscillations, so it is more convenient to set it to zero." @@ -227,7 +227,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "b0c8e116", + "id": "f8b9841c", "metadata": {}, "outputs": [], "source": [ @@ -240,7 +240,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "4c0b88e8", + "id": "8f7dc597", "metadata": {}, "outputs": [], "source": [ @@ -267,21 +267,21 @@ { "cell_type": "code", "execution_count": 8, - "id": "94b66256", + "id": "7da5422c", "metadata": {}, "outputs": [], "source": [ "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresonding to groundstate\n", + "# 1,1 element of density matrix - corresponding to groundstate\n", "P11p = basis(2, 0) * basis(2, 0).dag()\n", "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresonding to coherence\n", + "# 1,2 element of density matrix - corresponding to coherence\n", "P12p = basis(2, 0) * basis(2, 1).dag()" ] }, { "cell_type": "markdown", - "id": "eb8c9bb2", + "id": "dbddfa34", "metadata": {}, "source": [ "To get a non-trivial result we prepare the initial state in a superposition, and see how the bath destroys the coherence." @@ -290,7 +290,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "be5d671d", + "id": "df1aa127", "metadata": {}, "outputs": [], "source": [ @@ -301,39 +301,58 @@ }, { "cell_type": "markdown", - "id": "ae75e9c8", + "id": "e337a6f1", "metadata": {}, "source": [ - "## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator" + "We then define our environment, from which all the different simulations will \n", + "be obtained" ] }, { "cell_type": "code", "execution_count": 10, - "id": "9dcd6c27", + "id": "313a4403", + "metadata": {}, + "outputs": [], + "source": [ + "env = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=Nk)" + ] + }, + { + "cell_type": "markdown", + "id": "c3efd930", + "metadata": {}, + "source": [ + "## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "10849009", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RHS construction time: 0.006853342056274414\n", - " [****** 24% ] Elapsed 0.57s / Remaining 00:00:00:01" + "RHS construction time: 0.00707554817199707\n", + " [ 0% ] Elapsed 0.00s / Remaining 00:00:00:00" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " Total run time: 2.89s*] Elapsed 2.89s / Remaining 00:00:00:00\n", - "ODE solver time: 2.894904851913452\n" + " Total run time: 3.62s*] Elapsed 3.62s / Remaining 00:00:00:00\n", + "ODE solver time: 3.62227725982666\n" ] } ], "source": [ "with timer(\"RHS construction time\"):\n", - " bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", - " HEOMMats = HEOMSolver(Hsys, bath, NC, options=options)\n", + " env_mats=env.approx_by_matsubara(Nk=Nk)\n", + " HEOMMats = HEOMSolver(Hsys, (env_mats,Q), NC, options=options)\n", "\n", "with timer(\"ODE solver time\"):\n", " resultMats = HEOMMats.run(rho0, tlist)" @@ -341,13 +360,13 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "5b2960ff", + "execution_count": 12, + "id": "cdd19104", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -366,7 +385,7 @@ }, { "cell_type": "markdown", - "id": "7fac31d9", + "id": "9cb744df", "metadata": {}, "source": [ "## Simulation 2: Matsubara decomposition (including terminator)" @@ -374,33 +393,32 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "bc0d12f5", + "execution_count": 13, + "id": "0ba88f32", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RHS construction time: 0.016600847244262695\n", - " [ 0% ] Elapsed 0.00s / Remaining 00:00:00:00" + "RHS construction time: 0.017224788665771484\n", + " [ 1% ] Elapsed 0.03s / Remaining 00:00:00:02" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " Total run time: 3.04s*] Elapsed 3.04s / Remaining 00:00:00:00[*** 12% ] Elapsed 0.43s / Remaining 00:00:00:03\n", - "ODE solver time: 3.038472890853882\n" + " Total run time: 2.44s*] Elapsed 2.44s / Remaining 00:00:00:00\n", + "ODE solver time: 2.442646026611328\n" ] } ], "source": [ "with timer(\"RHS construction time\"):\n", - " bathMats = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", - " _, terminator = bathMats.terminator()\n", - " Ltot = liouvillian(Hsys) + terminator\n", - " HEOMMatsT = HEOMSolver(Ltot, bathMats, NC, options=options)\n", + " env_mats,delta=env.approx_by_matsubara(Nk=Nk,compute_delta=True)\n", + " Ltot = liouvillian(Hsys) + system_terminator(Q,delta)\n", + " HEOMMatsT = HEOMSolver(Ltot, (env_mats,Q), NC, options=options)\n", "\n", "with timer(\"ODE solver time\"):\n", " resultMatsT = HEOMMatsT.run(rho0, tlist)" @@ -408,13 +426,13 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "1616f9a2", + "execution_count": 14, + "id": "a86e3376", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -428,14 +446,14 @@ "plot_result_expectations([\n", " (resultMats, P11p, 'b', \"P11 Matsubara\"),\n", " (resultMats, P12p, 'r', \"P12 Matsubara\"),\n", - " (resultMatsT, P11p, 'b--', \"P11 Matsubara and terminator\"),\n", - " (resultMatsT, P12p, 'r--', \"P12 Matsubara and terminator\"),\n", + " (resultMatsT, P11p, 'r--', \"P11 Matsubara and terminator\"),\n", + " (resultMatsT, P12p, 'b--', \"P12 Matsubara and terminator\"),\n", "]);" ] }, { "cell_type": "markdown", - "id": "78f2f403", + "id": "9a18fece", "metadata": {}, "source": [ "## Simulation 3: Pade decomposition\n", @@ -445,31 +463,31 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "3552840e", + "execution_count": 15, + "id": "d609ea24", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RHS construction time: 0.013526439666748047\n", - " [ 2% ] Elapsed 0.07s / Remaining 00:00:00:03" + "RHS construction time: 0.011566400527954102\n", + " [ 0% ] Elapsed 0.00s / Remaining 00:00:00:00" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " Total run time: 3.15s*] Elapsed 3.15s / Remaining 00:00:00:00[*********73%***** ] Elapsed 2.27s / Remaining 00:00:00:00[*********79%****** ] Elapsed 2.47s / Remaining 00:00:00:00\n", - "ODE solver time: 3.149986505508423\n" + " Total run time: 2.50s*] Elapsed 2.50s / Remaining 00:00:00:00[*********52% ] Elapsed 1.12s / Remaining 00:00:00:01\n", + "ODE solver time: 2.5014655590057373\n" ] } ], "source": [ "with timer(\"RHS construction time\"):\n", - " bathPade = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", - " HEOMPade = HEOMSolver(Hsys, bathPade, NC, options=options)\n", + " env_pade=env.approx_by_pade(Nk=Nk)\n", + " HEOMPade = HEOMSolver(Hsys, (env_pade,Q), NC, options=options)\n", "\n", "with timer(\"ODE solver time\"):\n", " resultPade = HEOMPade.run(rho0, tlist)" @@ -477,13 +495,13 @@ }, { "cell_type": "code", - "execution_count": 15, - "id": "a334df89", + "execution_count": 16, + "id": "6293564a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -497,185 +515,40 @@ "plot_result_expectations([\n", " (resultMatsT, P11p, 'b', \"P11 Matsubara (+term)\"),\n", " (resultMatsT, P12p, 'r', \"P12 Matsubara (+term)\"),\n", - " (resultPade, P11p, 'b--', \"P11 Pade\"),\n", - " (resultPade, P12p, 'r--', \"P12 Pade\"),\n", + " (resultPade, P11p, 'r--', \"P11 Pade\"),\n", + " (resultPade, P12p, 'b--', \"P12 Pade\"),\n", "]);" ] }, { "cell_type": "markdown", - "id": "f46548c3", + "id": "8e48c51d", "metadata": {}, "source": [ "## Simulation 4: Fitting approach" ] }, - { - "cell_type": "markdown", - "id": "6c58c376", - "metadata": {}, - "source": [ - "To do this we calculate the correlation function using 15000 Matsubara terms." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "2ebacd9c", - "metadata": {}, - "outputs": [], - "source": [ - "tfit=np.linspace(0,2,5000)\n", - "lmaxmats = 15000\n", - "cc=bath.correlation_function(tfit,Nk=lmaxmats)\n" - ] - }, - { - "cell_type": "markdown", - "id": "41c3aa42", - "metadata": {}, - "source": [ - "In order to obtain the fit quickly we provide tight bounds on the parameters of\n", - "the fit, through two lists called lower and upper,similarly we guess close\n", - "parameters to the one expected " - ] - }, { "cell_type": "code", "execution_count": 17, - "id": "ff64254a", + "id": "39ba2c87", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Fit correlation class instance: \n", - " \n", - "Result of fitting The Real Part Of |Result of fitting The Imaginary Part \n", - " the Correlation Function with 3 terms: | Of the Correlation Function with 1 terms: \n", - " | \n", - " Parameters| a | b | c | d | Parameters| a | b | c | d \n", - " 1 | 1.00e-01 |-5.52e-01 | 9.23e-07 |-9.34e-02 | 1 | 9.86e-02 |-5.00e-01 |-9.97e-07 |-5.00e-02 \n", - " 2 | 7.98e-02 |-7.48e+00 | 5.85e-07 |-9.97e-02 | \n", - " 3 | 1.00e-01 |-1.19e+02 |-8.30e-07 |9.93e-02 |A normalized RMSE of 1.02e-14 was obtained for the The Imaginary Part \n", - " | Of the Correlation Function \n", - "A normalized RMSE of 8.26e-05 was obtained for the The Real Part Of | \n", - " the Correlation Function | \n", - " The current fit took 0.553227 seconds | The current fit took 0.026110 seconds \n", - "\n" + "RHS construction time: 0.3713369369506836\n", + " Total run time: 3.87s*] Elapsed 3.87s / Remaining 00:00:00:00\n", + "ODE solver time: 3.8711256980895996\n" ] } ], "source": [ - "lower=[-0.1,-np.inf,-1e-6,-0.1]\n", - "upper=[0.1,0,1e-6,0.1]\n", - "guesses=[0.09,-10,0,np.imag(cc[0])]\n", - "sigma=1e-2\n", - "fc= CorrelationFitter(Q,T,tfit,cc)\n", - "fbath,fitinfo=fc.get_fit(Nr=3,Ni=1,lower=lower,upper=upper,guesses=guesses,sigma=sigma)\n", - "print(fitinfo['summary'])" - ] - }, - { - "cell_type": "markdown", - "id": "507c9394", - "metadata": {}, - "source": [ - "We can now visualize the approximation of the correlation function through the\n", - "different techniques" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "eba87b42", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tplot=np.linspace(0,10,2000)\n", - "plt.plot(tplot,np.real(bath.correlation_function(tplot)),linewidth=2\n", - " ,label=f'Original with {lmaxmats} exponents')\n", - "plt.plot(tplot,np.real(bathPade.correlation_function_approx(tplot)),linewidth=2\n", - " ,label=f'Pade',marker='D',markevery=100,color='k')\n", - "plt.plot(tplot,np.real(bath.correlation_function_approx(tplot)),linewidth=2\n", - " ,label=f'Matsubara',marker='x',markevery=110,markersize=10)\n", - "plt.plot(tplot,np.real(bathMats.correlation_function_approx(tplot)),linewidth=2\n", - " ,label=f'Matsubara with Terminator',marker='o',markevery=130)\n", - "plt.plot(tplot,np.real(fbath.correlation_function_approx(tplot)),'-.'\n", - " ,label='Fit',linewidth=2,color='r')\n", - "plt.xlabel('t',fontsize=15)\n", - "plt.ylabel(r'$C_{R}(t)$',fontsize=15)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "bd3a1d31", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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v2iW9rHaGnOPRSpqEKiGEqCjumYog17mZsitgKoLshs8fzi73XVhjHm6uajT8bH0Fm2G1cIt3wev6P7jeSsPxWiZe8yZRP9lA/Vz2d76KLbeszKfSvOJmTYOLqVgZwC0+k1s6LRka463++xo6ggKxrlbEVrIm1tWa2EpWxLlasaz3ihyBIL9B6tndG6gAuvZawoINo5h9ZStXre5+jGa/JNY1yHhZLLdjFCpQZbknWHXu94dZj9GGTzcQfCj4/gNVLscBY7AqycAT4l+4oA4Uvl0uxyltMlC9FMlAdSGExeUxg3aOD+UifCBPC57Kd67fotVZgaIwau1V6vybim9sOtVi03FIL/rHTJKdhqc+qItLqh67xAy4lUHN40moVzM4laBnd2w61xNV6r1fD5tK1mZzKmVRAC8Hb/O707IpKFjlFqiy04fN5tDeBcQ17odHiyHml8TyOEaRAlV29zMIu4R7JisSGaguhBAip6yB5OouJneyMY1tMZubKY8P8NtJCWze8QMnj2+lwZaduF2L54qblt/6e6HV3u2heuJgAnUup5EfgwKxla2Iq2zNDVdb4l0cSHH3QPGtg2czf5r3eJFtbp4FBp/HD19jf5cqqCpmM41n3f03wdEv10BlOldy700qKFABaAMm0krRGN8rp1qQy+WxvC6llcolrTLUw1NeSE9VKZKeKiFEacptQsyZM2YyZcoUJneyIbSzXY7b3LdNe5KO+p0cse9EZORNnC5Fcc4HNnZwIcbOikxFwSpTJXz4caz1EOlrx/OhdcyO++GiaDpHJJKhVfjHw5pLnjZc9LJF56JSy1lPet0OVP/PUtyrVC/UeeQVrLLOYZFjM36taWV++72DNxMc/fKdpyqv/RcmUJkpwVv5xcNBeqqEEKIsKeZLNbnN82OfaU/kz5HA3buvxrWxo46jDfqbUD9TxWv5n2RcSadZ+m+m6Qf+aOXCZ13dTPvJtFK46GXLI5fTqH41Dde0TG7Z3v04mT/Am1kvV+FqZWuzweRRs6P4dUAnAtJ3wuk1UKVw55pbj1JWoApTOjDmnQ28kdeM6o7VC7wT7N7epIDHrUATUCYGRouHi4QqIYR4GOTxPLs8B5PnM4g812fTqSqVbtzitTYutLEHv9gMfCPSqfRXAq8WUFrtK2nYGFS8UjNxTQbndCe2PtOKYzWa0fy5YXRdvZZVTquwdrVG0Shc9L7nQfKqSvqNDN4Z+A4BQVPv1n7PueYne7DKHqiy5sEy3X5/r0LeYl/ogdl5kR4qgYQqIYR4OOTy4Z/ng37zudyU/dl0ikFl9NqrPHohhQYXUnFNKrgnxaDAZQ9r/vG0Js7Llds1auP2eB92DhiFvb1DrttMDWrE5fmX2aXsQjWoZjNvoxqjXYAhgKlBU/M818LICj2GP2eYBaoCSU+SKCUypqoUyZgqIUSB8rg7L0tu431O7tnMka/mEZt8nt9bW3HJ6e6g8d/fjszz4bs3nbT862NDahUrVA+Fyh4Kq06mMCUsPd9JGfMyfP5wwrRh2FS2MS1Lv55OgCGAT97+JM9zlbFI4mEnY6qEEKIsyuPuPDCOI+qo7mJFem2O7NvMtfAVJFpnsHbcWRqocLSWPcu6PGK2uxM17al2LYNrLlacqGnHiZr2nKhpz4GjCXTJTGJ6F2vjYPUf0k2X1ToGPEHAfVwK++TtT/gz+Emc0w7yUaSB7/em8s7Ad+72UOVyrsBD8SBcIYqDhCohhHiIhIaGMmXa76aAY62Ba7e09HGxourVTL7fZcX7L2cCMYAW0BJVxThovP7FFGzTDaTZaEz7W/SCF3MGVSHW1cpsLqcOMfFMb2Bvdvff9L/SeaLLE8aesO1z7yvkdOnUibC/FL78cmPek4pmJ5fmRDkil/9KkVz+E6Liym16g9wmilw8ewov+znwuLWGhtcyeSQ6DWv93T/T53xseWZmXdNrK1Vl7LqreF9XufFIHTxfGcvMG5+aHr57LwUFXaaGsEtRTLtnOoX7nphSiHJOLv8JIURxKcR0Bznu0ss23UFu0xt4OXgxsfVEGup9+WvhWHRHD/JCdDwT08D6cHKex3FO0dMkOoHUf1P551gy63+OxGuwj1kbQ7Rbrg/fNT6bTmXq9Rh2KB2Y/lceM6rLZTkh7ouEKiGEKEge0x1kufcuvY6GPabB5PdOb6AYVOpdSqX9sWM4TXsGt9PJDMjM+4LBv57WXHnEFk0NLduvpzNucwpMvQgYg5CXu0+ObbrW6Jrrw3e9tPZMuBxN1zbjwH88IZp8nv0nwUqIIpPLf6VILv8JUYblcUns3tm4s8+h1DHoF3qs7WEKNsPWxzJw83XcEvMePxTtbcPpqtbc9NDQpZGGD8JTmP5Xeo4Z0Atzd16OS44nNhkfJyPPghOiSOTynxBCFKcC5pGCu4FqyrYUom+G8YFVQ65Wuzu9gEYlR6D6182a43UduVHPj7qB79Ciaz+ignvRX91F0LYUs0HkAKGd7YyDyQtxd16OCTFzmxyzsOcthCiQhCohhCisbMEqLCyMKdnmkZrS3Y7Hn9Cx9q80xh42UCkxhb4DDGab737UiVd/v8b+Bo7sftSJ3Y2cuOhlw5xOcxlQu5ex0fa5BKi7CLtnzBMUz915QoiSI6FKCCGKIts8UvO72bGzmhOOzXSsd7bjR0Uh0OUa/RJvGZv+ncj5qnamTY/WtqfjkgZkWilmu/Rw8DB+k+0SY8CdMU/Ze8JCQkKMPVT38ZgXIUTJ0xTcRAghRJa/vv+Y8xt3cOrrdN7anIqtrwt/u9iTeWcOqLDHnEm2Vdj3qAMJV5PR38pANRiHrqoaxSxQKSh4O3jT3LN5rmO2goKCCAkJQVGUnHfndX7P2H773NJ9A4QQeZKeKiGEKMCfX71P/OoPefTkVTpFp9Ep27ruBxI4XtsBj/RMPKNvcyU8gSqqSsJx47QILrevUH1U9TymN4AJrScY56sy6HOdFyrPB/3KpJlCPHQkVAkhKozQ0FCmTpvKyFkjefL5J/OchBNg328r+fejIJocvUyXS2m57u+ijw217TP54Z8rfL0pkRnZJtLM8nbft2nXuV2u81RNaD2BrjW6Ghfcz911culPiIeKhCohRNlXyMk55/88n7rz6xJWOYywHWHA3Uk4u9boyrmj4RyYPpQGEWdpdSaZNrlMOHOumi3Rj7pQt3465yu1p/O035kcZ8P0znYokPsM5UBn384FzqguhCjbJFQJIcq+QkzOOf/n+fiO8s2x7nrCFda//xq2O27S6vhtHsnImaTOVLflWEMvdC+NpksNPY/cGfvke89g8tDOxkHpuc0jlWN6AyFEuSOhSghR9uUz+3doaChTpk6h3vv1AFCyPVR46PpYBuUxGedld2sONXHHesB/6TE0iLqQ52BywCxYFXYeKSFE+SKhSghRPuQzOadjfUdsKtvk2KTSbb1ZoLrppOVgYx0J3Z+m73vLeMra+m7jfB4ynBWspk6dKvNICVGByWNqSpE8pkaIUnAn/IQpHeh8Z3LOl3tUom8qLHnOi+u6u/+WrPNPKt9OO8efzZ1Z36ESXV+dzXNNB+S5z9wCVX41FLq9EOKhJo+pEUJUTHdCTNuwmUwe50mt8waG/HQNgGgvG1b08jA1PVvNjoAP6pPoaBww/h+vR3LfZx7THRRUg0x3IETFIqFKCFGubNz2LWv3reaErw8JWi3VPdNMoapHeLxZqAJIdNSiGlQcDA7GSThzI9MdCCEKQUKVEKLM02dk8MNbz/DIn9s5/qgTe3t5AMbep4vetvz6hCsnNPB9vyqoBhVFc3ewumpQURSFmV1nyhQHQogHIo+pEUKUWTfjrvDti025VNOJFxdvoOXxJAZsvYHGoGKtqjx1O4mv/r1K78cNJKYlc/rTf8m4lWG2DweDAws7L7w7CacQQtwnCVVCiIdOaGgoGo2G0NDQXNefOrSDn3rWILNedV787gg1L9+dcNPKoPJ/kalsvvgvsxqNoNm7sWxXOhDa2Y7Rjqmcfus0UbOjuLT0EgFXA9jznz0SqIQQxUJClRDioZI1DYKqqkyZMsUsWP31/cdsetwd3/YBPPPHRTxuZZrWnaxlx5r/dMBl1bu8YReHm/8k07imgKkbCLsTrCZ3tCHpVBJv9X6LxeMXyyU/IUSxkSkVSpFMqSBE/rIC1b2CX+1JwPE9tD8Uj5Xh7nKDAvsbOfFP32fpF/pVgVMZhAX3IkDdRZjSgYCpG0ryVIQQ5UhhP78lVJUiCVWiQirEc/nAPFBN7mSDVlHYd0llkrMVHY4ko832lyrVWmF3C1f0Q8fTbchE48LCzg0lc0gJIYpI5qkSQjwc8nkun96g51DsIZatXMbqVatBgckdbRja1J6YHZlMiUpCo94dL5XgoOGvtl74Bn1Cl4A+d3dUlKCUzyNthBDiQUioEkKUrDxCzJboLcwOn83V5KvgDrUm1sI+JZPOKy9TfXE81bPt4paTlt8a2TPheBLDA/7LU9kDFcjknEKIh4Jc/itFcvlPVGjZepO21GzOuLBxqNzz50dVee23a4z54SoAN521/PKoPeOPJnE1ydhWURQMBsO9exdCiBIjl/+EEA+XO71D+m0zmF2nPioqlRMyydRAgtOdP0WKwqpubvQIj2ePjxXj/77N9b23zXYTHBxc2pULIUShyJQKQojS4z+eQ21fI/F2Iq+vu8qG8acZsT7OrEmKrYb+wY8w+pbK9RTzzUNCQggKCirFgoUQovAkVAkhSk1KSjILT+9Hl6TnPxuu4Zhq4MU/b+B9Pd28oaJgpTPvSJdAJYR42EmoEkKUivmfvsHzK5pzVHeLK+42fNXTnQwt/NipEunWOf8UZcbfndhTApUQoiyQMVVCiBK1+bOZWC+bw3evVSXFwdq0/LNebqzr6Mo/nrbmG6iQfiOdpMgkQAKVEKLskJ4qIUSJOHc0nD86ehAw4j0C9icwZMM1AOqmpDPqxi1S7a3419PObBsFBUVRCDAEoKBIoBJClCkSqoQQ9yWvhx7rMzJY80obdB070GPnNazvTAXVZ88tRsRY80NMDMObv8GCgIV4OniabeultWdBwAI+efsTDAaDBCohRJkil/+EEEWW/ZEyWf8NCgpi82cz8Zw3gwGnk01tU60VtnT0pu3rLzHy6GemSTq7Ap19O3Mo9hBxyXF4nN5C872fo/U9BDW6WuK0hBDigUioEkIUSW4PPf5g1hSabFrEk3tvYpN5d0LPgw0dSXxvFk9VTcn1MTJajZZW3q2ML2r3AntveXyMEKLMkst/QohCyy1QjW3mwFFHa/ruvGEKVDFuVqwZ2pkWx28TkEegypX/eGO7bTOMM7ALIUQZIj1VQohCuTdQ1XRV+KKmA50PJ5mWZWgVfm9biWYrtjOgTiPjQnkunxCigpCeKiFEge4NVFNaOnBA1dA54m6gOvaIHc/UtKbvrhusWL3u7sadJxX9Up7/eON2QghRhsgDlUuRPFBZPHS2zQKNtsDQo9FoUFWVhu5aVlS1pdXfdweiJ9lpWNHUgdH7bpP1mGN56LEQojwp7Oe39FQJUZFptIUavxQcHMyILs7sSsUsUB2sb09AJQ2jsgWqrPZCCFHRyJgqISqyrB6qfO64izx/jNN2qzn+ki8xZ87jelvPLSctyxraMSE8KUd7mbBTCFFRSagSoqK7E6T022ZwKCWGuHpd8XDwoLlncz5c/g6/GH4nztP4p2LS8Gq89d0Vhl9O5YQEKiGEMCOhSgjBlprNmV2nPlevboarm7HOMDB6bSw72uuIq24PgKPegJ9dA7Y+N4gT03Je3pNAJYSo6CRUCVHBbYnewriwcagY71mpci2dJYuiqfdPGm2P32bglNrUT8tgSNO5dH38GQA0Gq3Z3YASqIQQohwOVL958yaBgYHodDp0Oh2BgYHcunUr321UVWXatGn4+Phgb29PQEAAx48fN2uzbNkyAgICcHFxQVGUAvcpRFmgN+iZHT7bFKgAbjlbobkz6rz2lTRanUnm89ciTIEKjI+kCQkJQVHkocdCCJGl3IWqgQMHEhERwcaNG9m4cSMREREEBgbmu83cuXNZsGABS5YsYf/+/Xh7e9OtWzcSExNNbZKTk+nZsyfvvvtuSZ+CEKXmUOwhriZfNVuWYqvh7ZG+HKltz0tTH2F3QyeO3TqeY9ugoCB56LEQQmRTri7/nTx5ko0bN7J3717atGkDwKeffkq7du2IjIzEz88vxzaqqrJo0SLee+89nnvuOQC+/PJLvLy8WLVqFcOHDwdgzJgxAISFhRW6nrS0NNLS0kyvExIS7vPMhCgZcclx1LuUSrKtwj+etqbl56raMSioNiiKqZ0QQoj8laueqj179qDT6UyBCqBt27bodDp2796d6zZRUVHExMTQvXt30zJbW1v8/f3z3KawZs2aZboMqdPp8PX1faD9CVGc9BkZxE54k1XB55j/0SWsM+6ZrPNOoALwOL2llKsTQoiyp1yFqpiYGDw9PXMs9/T0JCYmJs9tALy8vMyWe3l55blNYU2aNIn4+HjT16VLlx5of0IUl5joM+xu58HgH85im6ny6IVUXt58PUc7BQVvrQPN934uDzgWQogClIlQNW3aNBRFyffrwIEDgPHxGPdSVTXX5dndu74w2xTE1tYWFxcXsy8hSlNoaCgajYbQ0FDTsr++/5jbHRvT8WC8adnKrpX5pmtls20VjL//EzrOQNv5vULNvC6EEBVZmRhTNWrUKAYMGJBvm5o1a3LkyBGuXr2aY11cXFyOnqgs3t7egLHHqkqVKqblsbGxeW4jRFmQ/SHIWf/1u3mQ7p/+iuttPQApNgq7+tfCq3ESle2duaq/+wgaLwcvJrSeQNcaXaFGV+PCfGZeF0KIiq5MhCp3d3fc3d0LbNeuXTvi4+MJDw+ndevWAOzbt4/4+Hjat2+f6za1atXC29ubzZs306xZMwDS09PZvn07c+bMKb6TEKIUZQ9UWWpsmEu/fbfR3pk94V8Pa/75bye6KvuhzXt07vgWh2IPEZccZ5pRXavR3t1BIR5pI4QQFVmZCFWF1aBBA3r27MnQoUP55JNPABg2bBhPPfWU2Z1/9evXZ9asWTz77LMoisKYMWOYOXMmdevWpW7dusycORMHBwcGDhxo2iYmJoaYmBjOnj0LwNGjR3F2dqZ69epUrmx+2UQIS7o3UFWyg/X1HHh8723TskMNHHFY8RttknaDpjv4j0cLtPJulf/Os4KUQV8ClQshRBmnljPXr19XBw0apDo7O6vOzs7qoEGD1Js3b5q1AdTly5ebXhsMBnXq1Kmqt7e3amtrq3bq1Ek9evSo2TZTp05VgRxf2fdTkPj4eBVQ4+PjH+AMhchbSEiI2e9nY08r9VQNW1UF09d3bZzUaVOCLF2qEEKUGYX9/FZUVVXzyFuimCUkJKDT6YiPj5dB66LY3dtD9VxdWz66psfrZiYAybYKsxs7EHrA+CBkmQldCCEKp7Cf3xKqSpGEKlGSNBoNWf87T2zhwORjqTimGeeeulrJipGeWtZG3p2MVlEUDAZDrvsSQghxV2E/v8vElApCiIIFBwcD8HlbJ6YfSjYFqtPVbelhg1mgyt5eCCFE8ZBQJUQ58fbbb7GuUyWG7L17h9+eRg60jU3j76uZZm3l0p8QQhQ/CVVCPKy2zSr0ZJtXr13mtaXN+c6/Esk2xkk717Zx4vFjydxMNW8rgUoIIUpGuZpSQYhyRaMt1JxQJ87+zeTf+3PGzQbcrBn/ui+PnVQZ+kfOxyJJoBJCiJIjPVVCPKz8x0MBj4fZ8/tq3vmjP2dcbACwNxioWq87/7fxIiEhIWZtJVAJIUTJklAlxMMsn2C18X9TqD3oFV78xfgg5EqZev7P7kUm/fdTAIKCgggJCUFRFAlUQghRCmRKhVIkUyqI+7Z9rjFYdX4P/Mfz1fIp9Bk9k0p3nuH3wSAvagTO5pkegy1bpxBClEOF/fyWMVVClAXZnru37shWPnL8h/PPezFtxWXO+dry+OD/0aJrP8vWKIQQFZyEKiHKCv/xrDn8Owuc/iFFo2FtQGVcNQb6jPmZFo1bW7o6IYSo8CRUCVFGLAx9ga+rxZGhMQ6FbJ+cwgsz9lHVu7qFKxNCCAEyUF2IMmFNYGtGhqzlifB4ADonJbP4ahxVI9dYuDIhhBBZpKdKiIeM3qDnUOwh4pLj8HDw4Ny4YfRfE4FGhZmf/oObPbwefBSbQ8sKNY+VEEKI0iGhSoiHyJboLcwOn83V5KsA9Npzi5lr/kFz5x7d/a1ceDPkBPZOzmaD1wEJVkIIYWESqoR4SGyJ3sK4sHGoGBNUz723mLnsH9Nz/DZ3dKXL1li01tZ3N5JgJYQQDw0JVUI8BPQGPbPDZ5sCVffweGZlC1Tfdq7EpyOa0UWbyzBICVZCCPFQkFAlxENgzPwxXPUyXvLruj+eOR9fwspgXPd9QCVmBPqgpsRyKPYQrbxb5dyBBCshhLA4CVVCWFhoaChf//Y1vv/1pcvBBOZmC1Q/dKpE6Cs+qBoFgLjkuLx3lBWkDPoSrlgIIURuJFQJYUGhoaFMmTIFx/qOtD1+m3lLL2F9JxOt6+hKyOC7gQrAw8Ej/x1KD5UQQliMhCohLCQrUAE8b6Xngw8vYpNpHES1vr0r0/5T1RSoFBS8HLxo7tncYvUKIYTIn0z+KYQFZA9Uz9a15cPz6TikGa/5bW3uzJTXqmLIFqgAJrSegFajtUzBQgghCiShSohSlj1Qda5hzaexmbgkGwPV4Xr2jH3RG7327iU/u0w7FgQsoGuNrhapVwghROEoqqqqli6iokhISECn0xEfH4+Li4ulyxEWotFoUFWVZt5W/JapUOVaBgCRNWzpfD2dK0kqjn6OWOmsyIzPJPl0Mga9wcJVCyFExVXYz2/pqRKilAUHB+PqqmGRj50pUF2oYsNTiZlcua2CCkmnkojfF0/SqSSCpwVbuGIhhBCFIQPVhShl/x05jAiXr5lobcWi5ItUu5ZOPwycvZFzKoSQkBCCgoIsUKUQQoiikp4qIUpRenoaY74I4LSrLYmOWia8WY0XnTUcupKZo60EKiGEKFskVAlRikbPa8vfHsYOYluDgXonfdkZlZajnQQqIYQoeyRUCVFKvn+2PiM/j8QtPhONqtLndnM++WAzISEhZu0kUAkhRNkkoUqIUrB6+BM893MkjaNS+Gb6OXpdcGXqG18DEBQUREhICIqiSKASQogyTKZUKEUypULF9MV3s9gU+RmLFl3E+0YmGzt50HN7rKXLEkIIUUgypYIQJWnbLNg+t8Bmuw9s4uv4rzhe3YFBQY/wW/dKdPkjuhQKFEIIUdokVAlxPzRa2DYj32B19dpl5ux7g2s2xoHp3vYGOowZiY2dfWlVKYQQohTJPFVC3A//8cb/bpth/voOfUYGP77RkuhulQHwzsxkiusTuD4ZWppVCiGEKEUSqoS4X/kEq1+feoT/brpCk2OJBI2oynir+vi9uNQCRQohhCgtEqqEeBC5BKs1/9eJAZsuAdDh2G0mHIZun/1kmfqEEEKUGglVQjyobMFq35+bePqbnaZVf/bwoMdnpyxUmBBCiNIkoUqI4uA/nn8uReM77lMc0oyzlIQ3d8L/l38tXJgQQojSInf/CVEM0lNTuDrrS3ziMgA4X92W6j8eQmttbeHKhBBClBYJVUIUg4196tLiRBIA8Y5aHJ63xfvCOgtXJYQQojRJqBLiAa0Z1YOnthov8xkU2DikB95Pv1fgPFZCCCHKFxlTJcQD+PPrRfRavgXNnYc9/dKtGi9++NvdBnnMYyWEEKL8kVAlxH2KiT5DzXcn4pJsAGB/Iyee/Pn03QYFTBAqhBCifJFQJcR9Oj+gJe3/SQPgkpc1rt9syvkIGglWQghRYciYKiEKKTQ0FI1GQ2hoKFsGPUL7vQkApNgoHJ38DnWbtst9Q//x0FnGWAkhRHknPVVCFEJoaChTpkwBYPvnoYyL0ZvWrX+mMS+OmpH/DrJ6qAz6/NsJIYQosyRUCVGA7IHKxQY+1mtwTDPOR7WzuY7nvzlQuB3JpT8hhCjX5PKfEPnIHqgAJj7rTq1/746j2tx5kEzwKYQQApCeKiHyFBoaypSpU3Cs74iVzoqmj9qxqpMHp2s6EfrFv4xx0fDj+x9hpfMmKCjI0uUKIYSwMAlVQuQiNDSU+T/Pp9779bCpbAPAjTvrDjRw4o0eLvz0zXUAU0+WBCshhKjYFFVVVUsXUVEkJCSg0+mIj4/HxcXF0uWIfOha6fAd6QuAoijmK1WVi0sukXAwwbRIURQMBkNpliiEEKKUFPbzW8ZUCXEPvUGP3wg/wBiWXtl4jcA/rqEYjP/+UAHvgd6QLWsFBwdboFIhhBAPE7n8J8Q9DsUeIsUqBQWFBhdSGPN9DNZ6aH/0NiPH1cCgUbBxs8HRz5GkU0mEhITIpT8hhBDSUyXEveKS40zft4xMwvrO1FInatpj0NztnrLSWUmgEkIIYSI9VULcw8PBw/T91z3cOe1rx8DN11n6jIdZu8DnAgkaL4FKCCGEkYQqIe7R3LM59gYbUjTpAOxr6MS+hk6m9apBxcHgwKK3F1moQiGEEA8jufwnxD2OnAhHY0jJdZ1qUFEUhZldZ6LVaEu5MiGEEA+zcheqbt68SWBgIDqdDp1OR2BgILdu3cp3G1VVmTZtGj4+Ptjb2xMQEMDx48dN62/cuMEbb7yBn58fDg4OVK9endGjRxMfH1/CZyMs4dZrT9In7BaKQcXKYD7jSBWnKizsvJCuNbpaqDohhBAPq3J3+W/gwIH8888/bNy4EYBhw4YRGBjIL7/8kuc2c+fOZcGCBaxYsYJ69eoxffp0unXrRmRkJM7Ozly+fJnLly8zf/58GjZsSHR0NCNGjODy5cv88MMPpXVqohSs+U8HBoTH0zk8nvYnb2PzyVpsqroSlxyHh4MHzT2bSw+VEEKIXJWryT9PnjxJw4YN2bt3L23atAFg7969tGvXjlOnTuHn55djG1VV8fHxYcyYMUyYMAGAtLQ0vLy8mDNnDsOHD8/1WN9//z0vv/wySUlJWFkVLpvK5J8PtyM7fse3dx8qJRpv9/t2UAteLOzDkoUQQpRbFXLyzz179qDT6UyBCqBt27bodDp2796d6zZRUVHExMTQvXt30zJbW1v8/f3z3AYwvbH5Baq0tDQSEhLMvsTDK+mNF02Ban8jJ55fvsfCFQkhhChLylWoiomJwdPTM8dyT09PYmJi8twGwMvLy2y5l5dXnttcv36d0NDQPHuxssyaNcs0tkun0+Hr61uY0xAWsGbI47T7OxGAeEctyvtfoLW2tnBVQgghypIyEaqmTZuGoij5fh04YLxMk+M5bRgv8eW2PLt71+e1TUJCAr1796Zhw4ZMnTo1331OmjSJ+Ph409elS5cKOlVhAUd2/E73H/aaXm/o25SW3V+wYEVCCCHKojIxUH3UqFEMGDAg3zY1a9bkyJEjXL16Nce6uLi4HD1RWby9vQFjj1WVKlVMy2NjY3Nsk5iYSM+ePXFycmLdunVYF9CTYWtri62tbb5thOXdHv0ile9c9gtv5ET/FXsL2EIIIYTIqUyEKnd3d9zd3Qts165dO+Lj4wkPD6d169YA7Nu3j/j4eNq3b5/rNrVq1cLb25vNmzfTrFkzANLT09m+fTtz5swxtUtISKBHjx7Y2tqyfv167OzsiuHMhKWtGdaFARHZLvvN+1Qu+wkhhLgvZeLyX2E1aNCAnj17MnToUPbu3cvevXsZOnQoTz31lNmdf/Xr12fdunWA8bLfmDFjmDlzJuvWrePYsWMMHjwYBwcHBg4cCBh7qLp3705SUhKff/45CQkJxMTEEBMTg16vt8i5igcXdeIgXX7YYXr9+9NNaNUz/x5RIYQQIi9loqeqKFauXMno0aNNd/M9/fTTLFmyxKxNZGSk2cSd48ePJyUlhddff52bN2/Spk0bNm3ahLOzMwAHDx5k3759ANSpU8dsX1FRUdSsWbMEz0iUlLNDe9DtZiYAEfUdeOHLfRauSAghRFlWruapetjJPFWlZNss0GjBf3yeTdZNH07fKcvQqJBqoxA5oy9NW7SGzpNKsVAhhBBlQYWcp0oIwBiots2A7XNzXZ1wI46mn3yJ5s4/J3Z19aDp7T+N2wkhhBD3qdxd/hPC1EO1bYb56zu2vtyKZ/9JAyCqqg2dmqdA58n59mwJIYQQBZFQJcqnPIJV2KrFPLn1IgAGBfR97LHu+q4EKiGEEA9MQpUov+4JVvr2Y9GFTsQu3Xjd72BHZ1oNkEAlhBCieEioEuVbtmD1xf61nHzCjSqx6WhV8P3vqxKohBBCFBsJVaL88x/Ptds3+S7mJ2I8dOxs4szE01Y8N2CxpSsTQghRjhRLqMrIyCAyMpK4uDji4+PR6XR4eHjg5+dX4KNchCgN7x79g5gqxl/3Zvo0+s7+28IVCSGEKG/uO1TFxcWxYsUKfvvtN8LDw0lLS8vRxs7OjtatW9O7d29effVVPDw8HqhYIe7H+vXLOOCdDihYqSqTrt9Eu2uBXPoTQghRrIo8+eeZM2eYMmUK69atIz09HTA+m8/Pz4/KlSvj4uJCfHw8N2/e5NSpU1y/fh0AGxsbnnvuOUJCQnLMSl5RyOSfpU+fkcHxpq5ct9My96UqVNM78mm7AcbB653fk2AlhBCiQIX9/C5ST9Ubb7zBsmXL0Ov1dO7cmYEDBxIQEECtWrXy3Ob8+fNs27aNVatW8d1337F27VqGDRvG4sUynkWUvG3Dm9D1ZDIAdf5NRT14AqrdCfV5zGMlhBBC3I8i9VQ5ODgwbNgwxo8fj4+PT5EP9u+//zJ37lw+++wzkpKSirx9WSc9VaXr5obJfLBhBa+vvIrnrUzWvNqWASv23G2wfa70WAkhhChQYT+/ixSqYmJi8Pb2fuDiims/ZY2EqtIRGhpKxpYZeD/rydJKOhxT9Az58zqvrfwH7b03TkiwEkIIUYASefbfvUHoySefZOXKlUUuriIGKlE6QkNDSd8yneHdHPhcZ/zFT7PT4PpqcM5ABcYg1fm9fJ8VKIQQQhTGAz1Q+Y8//mDr1q3FVYsQDyQ0NJQpU6agVRSm2etI1ygANL6QTv/eI/LeMCtYGfSlVKkQQojy6IFCVWGNHDmSDh06lMahRAWVFagA/rmu4dkfbuJ9PR3XTD3rF50jNDQ0/x34j4fOk0qhUiGEEOVVkeepGj16NK1bt6Zly5aF3iY5OZm9e/cW9VBCFEr2QKUB3kk04Hcsni4HE3ivuws74w2m9UFBQRasVAghRHlW5FC1ZMkSFMV4WUVRFDZu3Mh//vMfHnvsMdOXTqcz2yYuLg5HR8fiqViIbLIHKoBZrR3xCzfeWRrjbs2S3+NN6yRYCSGEKElFnvxzw4YN7N+/n/DwcH7//Xfznd0JWzVq1OCxxx6jUaNGJCcn8+GHH9KkSRMOHDhQfJWXQXL3X/HTaDRk/Qq72cMJOys8b2YC8FYzBxYcTjZrrygKBoOh1OsUQghRdpXI5J8AvXr1olevXoDxA+2FF15g5MiRREREEBERweHDhzl58iQXLlzgp59+AsDa2lp6B0SJCA4ONvVALW7qhOfe2wAcrG+fI1BltRdCCCFKwgM9UHnWrFl4enrSqVMnOnXqZFqemZnJ8ePHOXXqFHq9njZt2vDII488cLFC3CsrrK/7KIS+h42X/TI1MCUj5518ISEhEu6FEEKUmCJf/hP3Ty7/lZywFi4EHEoE4I8WjvQ8aD5jvwQqIYQQ96tEJv8U4mH02wcT6XTYGKgS7TWMjko1Wy+BSgghRGkoUqg6depUsRy0uPYjBIDXp0vQ3Olv3dDGjdM37l76k0AlhBCitBQpVDVq1IhBgwZx7Nix+zpYREQEAwYMoHHjxve1vRD3+v7dQbQ8brzUF+dqhf+KXYSEhKAoigQqIYQQpapIY6qCg4N5//33SUpKomnTpgwaNAh/f3+aNm2KdS7PVUtLS+Pw4cNs27aNVatWceLECRwdHXnnnXcq5IedjKkqXvqMDE77udDgzuW+7/o9Sv8f7i/wCyGEEHkp7Od3kQeqx8bGMmPGDL766ivi4+NRFAVra2tq1qxJpUqVcHZ2JiEhgRs3bhAdHU1mZiaqqqLT6fjPf/7DpEmT8PDweOATLIskVBWvNcO6MODTbQBc9Lah8qkrOOkqW7gqIYQQ5U2JhaosKSkpfPfdd/z666/s2rWLmJiYHG28vb3p2LEjvXv3pn///tjZ2d3PocoNCVXFJ+V2IlfruVPzSjoAa17ryIDP/rJwVUIIIcqjEg9V94qLiyM2Npb4+Hh0Oh2enp4VtkcqLxKqis+3Lzblxe+OAHCqlh11IxPQ5nIJWgghhHhQJTajel48PDwkRIlScfWf8wRsPmF6ffSFvtSXQCWEEMLCZJ4qUWboDXr2x+xnzaRueN15vt/Bho68MGeNhSsTQgghirGnSoiStCV6C7PDZ3M1+So8Yc9Rn5r8d10se17tSwtLFyeEEEJQAj1V6enpBAUF8fTTTzNjxgxSU81ntz5y5EiFnE5B3L8t0VsYFzbOGKgAFIV9DZ0Y/G4tlnkdZUv0FssWKIQQQlACoWrs2LG8//77nD9/nilTphAYGEhKSgpz5szBz8+Pxx57jMWLFxf3YUU5pTfomR0+G5Vc7qdQFADmhM9Bb8j5AGUhhBCiNBV7qPrpp59YtWoVx44dY+7cuaxduxZ/f38+/fRT+vbty9atW4mLiyvuw4py6lDsIVMPlU9cOtxzs6qKSkxyDIdiD1miPCGEEMKk2ENVTEwMbdq0AeC///0vAAEBAZw9e5a5c+fSuXPnXGdfFyI3ccnGAO6cpOf7KWf5akYUrU/czrOdEEIIYSnFHqpUVUWr1QLg4OCAo6MjgwcPLu7DiArCw8E4Tccrf1zDJcVAs7PJ9N5zK892QgghhKWUyJQKU6dOZd26dVy7dg1FUbC3ty+Jw4gKoLlncyrbVOJEDTvO+tiSoYVPnvY0rVdQ8HbwprlncwtWKYQQQpTAlAqDBg0iLCyMZcuWAcaeq7feeos2bdrQpEkTmjRpQtWqVYv7sKKc0mq0uF9NYFsLHdubudDgQgqXPWwAY6ACmNB6AlqN1pJlCiGEEMUfqr7++msAEhMT2b9/v+nro48+4tKlSyiKQuXKlWWwuiiUw8f2cEmXAWgwKHC8toNpnZeDFxNaT6Brja6WK1AIIYS4o8ih6tdff8XW1paWLVtSqVKlPNs5OzvTpUsXunTpYloWGxtLeHg4Bw4cuL9qRYWz9JeRpPgYr1K3uKow8tUviEuOw8PBg+aezaWHSgghxEOjSA9UPnv2LI8++ij29vb89ddfNGnSpCRrK3fkgcpFcyp8O9Gjn+aLJz04U92O9+vPonO7PpYuSwghRAVTIg9U/uSTT8jIyOD9998vVKCKiori9OnTtGjRAnd396IcSghOvzuQp/cl0GNfAt/08abzeglUQgghHl5Fuvtv8+bNuLq6MmLEiEK19/X15a233sLb25uzZ8/eV4GiYoo6cRD/vcZJPzM1UG3AuxauSAghhMhfkULV+fPnadeuHVZWhevgsrKyYvLkyRgMBr799tv7KlBUTBFv90OXZHz0zJ5mOgIGvmHhioQQQoj8FSlU6fV6dDpdkQ7w/PPP4+joSFhYWJG2ExVXTPQZOu36BwCDAslDx1m4IiGEEKJgRQpVXl5eXLx4sUgHsLKyonXr1kRHRxdpO1Fx7XyjF24Jxl6q8MbO9Bg+xcIVCSGEEAUrUqhq3Lgxhw4dKvIcU97e3ly5cqVI24hybNss2D4311U3467QaecF0+u4V4Yav9k+17idEEII8ZAqUqjq168fqampTJ8+vUgHSUlJoQgzN4jyTqOFbTNyDVab/9sNz5uZABx41JE+b71/J1DNMG4nhBBCPKSKFKoGDhxIgwYNWLJkCYsWLSrUNgaDgfDwcKpXr34/9YnyyH88dH4vR7C6HX+DDn+dNr2++PyLdwNV5/eM2wkhhBAPqSKFKisrK1asWIGTkxNvvfUWzzzzTIFjrD744AMuX75M167yKBGRTS7B6tfRvagalwHAkXoOPNfZTwKVEEKIMqPIj6lp1aoVf/zxB3369GH9+vX8/vvvPP300/Tt25cWLVrg7e2NqqqcPn2aL774gi+++AJ7e3vefPPNkqhflGVZQWnbDPSZmbQIizCtut61rgQqIYQQZUqRHlOTXVxcHG+88Qbff/89qqqiKEqONqqqYmNjw1dffUX//v0fuNiyTh5Tk4ftczn5zUwafBYPwDlfW2r/xwaly2QJVEIIISyusJ/fRbr8l52Hhwdr1qzh1KlTvPXWWzRu3BhFUVBVFVVVcXJyol+/fuzdu1cClcif/3jSd2eYXiY8bieBSgghRJlT5Mt/96pbty7z5s0DjIPSr1+/jqIouLm55dp7JcS9fls0nt4nkgGIcbOiUT0rCVRCCCHKnPvuqcp1ZxoNHh4euLu7S6AShebyzTLT95ced8SajDznsRJCCCEeVg/cUyXEg9j983LaRhjHUiU4aPCavR2u/mEcpA7SYyWEEKLMkFAlLGrX7hDSmjjT+XAiO1t70qt+U6jf1LhSgpUQQogypFgv/z0Mbt68SWBgIDqdDp1OR2BgILdu3cp3G1VVmTZtGj4+Ptjb2xMQEMDx48fN2gwfPpxHHnkEe3t7PDw86Nu3L6dOnSrBMynfQkNDmdrTgbX1HRj9Zg36Ta+DZ9DSuw3ymCBUCCGEeFiVu1A1cOBAIiIi2LhxIxs3biQiIoLAwMB8t5k7dy4LFixgyZIl7N+/H29vb7p160ZiYqKpTYsWLVi+fDknT57kjz/+QFVVunfvjl6vL+lTKndCQ0NJ3zKdGj0qk6Qx/go6WdvSsssz5g0lWAkhhChD7nueqofRyZMnadiwIXv37qVNmzYA7N27l3bt2nHq1Cn8/PxybKOqKj4+PowZM4YJEyYAkJaWhpeXF3PmzGH48OG5HuvIkSM0bdqUs2fP8sgjjxSqPpmn6m6gmtLZjh7VfIiztkJRVTr93YAlC7/PfSN5VI0QQggLKvF5qh5Ge/bsQafTmQIVQNu2bdHpdOzevTvXbaKiooiJiaF79+6mZba2tvj7++e5TVJSEsuXL6dWrVr4+vrmWU9aWhoJCQlmXxVZaGgoU6ZMQasorDwCj0UkodWr+MUm879FPxAaGpr7hlk9VgbpFRRCCPHwKlcD1WNiYvD09Myx3NPTk5iYmDy3AfDy8jJb7uXlRXR0tNmyjz76iPHjx5OUlET9+vXZvHkzNjY2edYza9YsgoODi3oa5VJWoAII3p7GqRoweN0lLrtZ8996xvcwa31QUFDOHUgPlRBCiIdcmeipmjZtGoqi5Pt14MABgDwfl1PQvFn3rs9tm0GDBnH48GG2b99O3bp16d+/P6mpqXnuc9KkScTHx5u+Ll26VNhTLleyByqAgY3s8ItOAyDVTmH9niTTuilTpuTdYyWEEEI8xMpET9WoUaMYMGBAvm1q1qzJkSNHuHr1ao51cXFxOXqisnh7ewPGHqsqVaqYlsfGxubYJuuOwrp169K2bVsqVarEunXreOmll3Ldt62tLba2tvnWXRFMnTrV7HXmi54M66lh0ObrHLYzwL/pOdrn2lslhBBCPMTKRKhyd3fH3d29wHbt2rUjPj6e8PBwWrduDcC+ffuIj4+nffv2uW5Tq1YtvL292bx5M82aNQMgPT2d7du3M2fOnHyPp6oqaWlpRTybiic4ONjUU1W1mjWnqzmT6atwor49B0fmnJZCLpkKIYQoi8rE5b/CatCgAT179mTo0KHs3buXvXv3MnToUJ566imzO//q16/PunXrAONlvzFjxjBz5kzWrVvHsWPHGDx4MA4ODgwcOBCA8+fPM2vWLA4ePMjFixfZs2cP/fv3x97enl69elnkXMuSoKAgQkJCAOjQ34PMO5dVq56IJzXN/ObTkJAQ6aUSQghRJpWJnqqiWLlyJaNHjzbdzff000+zZMkSszaRkZHEx8ebXo8fP56UlBRef/11bt68SZs2bdi0aRPOzs4A2NnZsWPHDhYtWsTNmzfx8vKiU6dO7N69O9eB8SKnoKAgUtMSCKv1GwBWqsrOb+PM2kigEkIIUZaVq3mqHnYVfZ6q755vRO1jUXzTzY2r7gprpl00rZNAJYQQ4mFV2M/vctdTJR5O+owMWu85Q83L6bSMTOaVhncH8EugEkIIUR6UqzFV4uG1duIAal423uV3qqYddQe8h6IoEqiEEEKUG3L5rxRV5Mt/4U2caX30NgBrAlsz4Kt9Fq5ICCGEKJwK+Zga8XDa/fNyWhwzBqprOit6LfrVwhUJIYQQxU9ClShxNz54D+2d/tA9LTxwqexh2YKEEEKIEiChSpSohBtxtDsUC0CmBjzenG3hioQQQoiSIaFKlKjfx/XFLV4PwMFHnWj79CsWrkgIIYQoGRKqRIlquDvC9H101ycsV4gQQghRwiRUiRLz+8dTaXwmBYBLXtb0m/O9hSsSQgghSo6EKlFirL7+n+n7/S190VpbW7AaIYQQomRJqBIl4tLZY7Q7fBOAVBuFhlO+sHBFQgghRMmSUCVKxJ6JL+KUYgBgX1Md9Vv7W7giIYQQomRJqBLFTp+RQfPwc6bXN58daMFqhBBCiNIhoUoUu59njKDOpTQATtew45lJ/ytgCyGEEKLsk1Alip3bhh9N3x9uXdeClQghhBClx8rSBYjy5fzFSGJcNCTbKBi0Cu1nrrJ0SUIIIUSpkFAlitVHa0bzR6APC5/34um/U3i3TiNLlySEEEKUCrn8J4rVGccLACTZa/HuPMyyxQghhBClSEKVKDYr1y3ivJMNADWS0hnSf5KFKxJCCCFKj4QqUWwubFqMQ4rx4cl1EqtauBohhBCidMmYKlEsrv5znje+usBYPfzyuI7GH39j6ZKEEEKIUiU9VaJYbJv4Ii7JBhzSDFS9aaBhnaaWLkkIIYQoVRKqRLHYWz2ObztX5radhitde1q6HCGEEKLUyeU/8cB+3fINW/2c2ernzMpn3Fj3368tXZIQQghR6qSnSjyw9fsXmL6vnuyB1tragtUIIYQQliGhSjyQ20kJRHqkAGClqrzcY7qFKxJCCCEsQ0KVeCBrgwbSdUc8Lkl66t/IpG2zzpYuSQghhLAICVXigfht/Yugr66w7c1TdLnyiKXLEUIIISxGBqqL+3YqfDvNT9wGIMlew4uTVli2ICEKyWAwkJ6ebukyhBAPCWtra7Ra7QPvR0KVKDK9Qc+h2EMcmT+E+pkqAPubuNK9soeFKxOiYOnp6URFRWEwGCxdihDiIeLq6oq3tzeKotz3PiRUiSLZEr2F2eGzuZp8lW8OXTEtP/3UE3S3YF1CFIaqqly5cgWtVouvry8ajYyAEKKiU1WV5ORkYmNjAahSpcp970tClSi0LdFbGBc2DhWVmlfSaHrOeNdfpK8ty6qcpH70FrrW6GrhKoXIW2ZmJsnJyfj4+ODg4GDpcoQQDwl7e3sAYmNj8fT0vO9LgfLPNFEoeoOe2eGzUTFe7uuz65Zp3foOlQCYEz4HvUFvifKEKBS93vj7aWNjY+FKhBAPm6x/aGVkZNz3PiRUiUI5FHuIq8lXAVAMKn123wIgUwO/tXNFRSUmOYZDsYcsWKUQhfMgYyaEEOVTcfxdkFAlCiUuOc70fetTSVS5YUzyuxo7c11nlWs7IYQQoiKRUCUKxcPh7p19T++8Zfp+fQfXPNsJIR4OFy5cQFEUIiIiCr3NihUrcHV1tXgd+Zk2bRqPPfZYqR5TiPxIqBKF0tyzOV4OXjik6Ol6IB6ABAcNYY85A6Cg4O3gTXPP5pYsU4hy69KlS7z22mv4+PhgY2NDjRo1ePPNN7l+/XqB2/r6+nLlyhUaNWpU6OO9+OKLnD59+kFKLnFvv/02W7duNb0ePHgwzzzzTLHse9myZQQEBODi4oKiKNy6dStHm5o1a6IoitnXxIkTzdpcvHiRPn364OjoiLu7O6NHj84xR9rRo0fx9/fH3t6eqlWrEhISgqqqZm22b99OixYtsLOzo3bt2nz88cfFcp7lRUn8I+B+SKgShaLVaJnYeiLdDiTgkG78n/33Nq6k22hQMF6HntB6AlrNg0+eJoQwd/78eVq2bMnp06dZvXo1Z8+e5eOPP2br1q20a9eOGzdu5Llteno6Wq0Wb29vrKwKf8O3vb09np6exVF+iXFycsLNza1E9p2cnEzPnj159913820XEhLClStXTF+TJ082rdPr9fTu3ZukpCR27tzJmjVrWLt2LW+99ZapTUJCAt26dcPHx4f9+/ezePFi5s+fz4IFdx9UHxUVRa9evejYsSOHDx/m3XffZfTo0axdu7b4T1w8GFWUmvj4eBVQ4+PjLV3KfTtcz0FVQVVBHTi5ttpoRSO163dd1c0XNlu6NCEKlJKSop44cUJNSUmxdClF0rNnT7VatWpqcnKy2fIrV66oDg4O6ogRI0zLatSooYaGhqqvvvqq6uLior7yyitqVFSUCqiHDx82tfv555/VOnXqqHZ2dmpAQIC6YsUKFVBv3rypqqqqLl++XNXpdKb2U6dOVZs2bap+9dVXao0aNVQXFxf1xRdfVBMSEkxtfv/9d7VDhw6qTqdTK1eurPbu3Vs9e/asaX1udWT34Ycfqo0aNTK9XrdunQqoS5YsMS3r3r27OnHiRLOasr4HzL62bdtmOubatWvVgIAA1d7eXm3SpIm6e/fuQr3327ZtM3tfsqtRo4a6cOHCPLfdsGGDqtFo1H///de0bPXq1aqtra3pc+Cjjz5SdTqdmpqaamoza9Ys1cfHRzUYDKqqqur48ePV+vXrm+17+PDhatu2bfOt/fjx4+qTTz6pOjo6qp6enurLL7+sxsXFmc7L2tpa/euvv0zt58+fr7q5uamXL19WVVVV/f391ZEjR6ojR440/Uzfe+89U12qqqo3btxQAwMDVVdXV9Xe3l7t2bOnevr0adP6rN+jjRs3qvXr11cdHR3VHj16mI6R5YsvvlDr16+v2traqn5+fur//vc/07qCfoZZP6PsX1OnTlVVVVX/97//qXXq1FFtbW1VT09PtV+/fnm+X/n9fSjs57eEqlJU1kPVrp++UPWKMVBd9LRWfzn9sxp+JVzN1GdaujQhCqUshqrr16+riqKoM2fOzHX90KFD1UqVKpk+6LICz7x589QzZ86oZ86cyRFmoqKiVGtra/Xtt99WT506pa5evVqtWrVqgaHKyclJfe6559SjR4+qf/31l+rt7a2+++67pjY//PCDunbtWvX06dPq4cOH1T59+qiNGzdW9Xq96bj5haojR46oiqKYPvjHjBmjuru7qy+88IKqqqqakZGhOjk5qb///ruppqxQlZiYqPbv31/t2bOneuXKFfXKlStqWlqa6Zj169dXf/31VzUyMlJ9/vnn1Ro1aqgZGRkFvv8FhSpvb2+1cuXKatOmTdXp06eraWlppvVBQUFqkyZNzLa5ceOGCqh//vmnqqqqGhgYqD799NNmbQ4dOqQC6vnz51VVVdWOHTuqo0ePNmvz448/qlZWVmp6enqudV++fFl1d3dXJ02apJ48eVI9dOiQ2q1bN7Vz586mNu+8845ao0YN9datW2pERIRqa2ur/vjjj6b1/v7+qpOTk/rmm2+qp06dUr/55hvVwcFBXbZsmanN008/rTZo0ED966+/1IiICLVHjx5qnTp1THUtX75ctba2Vrt27aru379fPXjwoNqgQQN14MCBpn0sW7ZMrVKlirp27Vr1/Pnz6tq1a9XKlSurK1asUFVVLfBnmJaWpi5atEh1cXEx/ewTExPV/fv3q1qtVl21apV64cIF9dChQ+oHH3yQ6/ulqsUTqmTyT2Fu2yzQaMF/fI5VVz8JQXPnMn9EYw/61H3a+GL7XDDoofOkUixUiOLRZ/FO4hLTSv24Hs62/PLG4wW2O3PmDKqq0qBBg1zXN2jQgJs3bxIXF2e6XNelSxfefvttU5sLFy6YbfPxxx/j5+fHvHnzAPDz8+PYsWPMmDEj31oMBgMrVqzA2dk4ljIwMJCtW7eatuvXr59Z+88//xxPT09OnDhRqPFcjRo1ws3Nje3bt9OvXz/CwsJ46623WLhwIQD79+8nNTWVxx/P+b45OTlhb29PWloa3t7eOda//fbb9O7dG4Dg4GAeffRRzp49S/369QusKy9vvvkmzZs3p1KlSoSHhzNp0iSioqL47LPPAIiJicHLy8tsm0qVKmFjY0NMTIypTc2aNc3aZG0TExNDrVq1ct2Pl5cXmZmZXLt2LdcZwJcuXUrz5s2ZOXOmadkXX3yBr68vp0+fpl69ekyfPp0tW7YwbNgwjh8/TmBgIM8++6zZfnx9fVm4cCGKouDn58fRo0dZuHAhQ4cO5cyZM6xfv55du3bRvn17AFauXImvry8//fQTL7zwAmCc9+njjz/mkUceAWDUqFGEhISYjhEaGsr777/Pc889B0CtWrU4ceIEn3zyCa+++qqpXX4/Q51Oh6IoZj/7ixcv4ujoyFNPPYWzszM1atSgWbNmOX+QxUhClTCn0cK2O39Y7wlWjY/dfSyN3YCRxm+2zzW27/xeaVUoRLGKS0wjJiHV0mXcN/XOgObsc+y0bNky320iIyNp1aqV2bLWrVsXeKyaNWuaAhUYH+eR9WgPgHPnzhEUFMTevXu5du2a6fmKFy9eLFSoUhSFTp06ERYWxhNPPMHx48cZMWIE8+fP5+TJk4SFhdG8eXOcnJwK3Ne9mjRpYlY3GGfPfpBQNXbsWLP9V6pUieeff545c+aYxnrlNveRqqpmy+9tk9vPtDBtsjt48CDbtm3L9b06d+4c9erVw8bGhm+++YYmTZpQo0YNFi1alKNt27ZtzY7Rrl073n//ffR6PSdPnsTKyoo2bdqY1ru5ueHn58fJkydNyxwcHEyBCsx/b+Li4kw3YQwdOtTUJjMzE51OZ1ZLUX+G3bp1o0aNGtSuXZuePXvSs2dPnn322RJ9moKEKmEuK0jdE6x+/3gqT14y/mv+THVbuv3fu+aBKpeeLSHKAg9n24f6uHXq1EFRFE6cOJHrnW2nTp2iUqVKuLu7m5Y5Ojrmu897P9SzlhXE2tra7LWiKGYPpu7Tpw++vr58+umn+Pj4YDAYaNSoUY673fITEBDAsmXL2LFjB02bNsXV1ZVOnTqxfft2wsLCCAgIKPS+8qo969yL+6Habdu2BeDs2bO4ubnh7e3Nvn37zNrcvHmTjIwMU8+Tt7e3qdcqS1bgKKiNlZVVngP1DQYDffr0Yc6cOTnWZe/Z2r17NwA3btzgxo0bBf7uZJfX78y9v1+5/d5kbZv1M/j000/NwhmQ41ExRf0ZOjs7c+jQIcLCwti0aRNTpkxh2rRp7N+/v8TuFJRQJXLKJVj9FvsLW1/3pdeeW1z39aSuBCpRThTmEpwlubm50a1bNz766CPGjh1rekYZGC8PrVy5kldeeaVIs0HXr1+fDRs2mC07cODAA9V5/fp1Tp48ySeffELHjh0B2LlzZ5H3ExAQwJtvvskPP/xgClD+/v5s2bKF3bt38+abb+a5rY2NjelRRJZw+PBh4G5oadeuHTNmzODKlSumZZs2bcLW1pYWLVqY2rz77rukp6ebHp+0adMmfHx8TJcF27Vrxy+//GJ2rE2bNtGyZcscgSVL8+bNWbt2LTVr1szzrs9z584xduxYPv30U7777jteeeUVtm7davag8b1795pts3fvXurWrYtWq6Vhw4ZkZmayb98+0+W/69evc/r06TwvV9/Ly8uLqlWrcv78eQYNGlSobXKT18/eysqKrl270rVrV6ZOnYqrqyt//vmn6VJjcZMpFUTu/McbA9O2Gej/nEWkdzJ/tNYxZnR1Gr/0hAQqIUrRkiVLSEtLo0ePHvz1119cunSJjRs30q1bN6pWrVrgWKh7DR8+nFOnTjFhwgROnz7Nd999x4oVK4D7f1RHpUqVcHNzY9myZZw9e5Y///yTcePGFXk/WeOqVq5caQpVAQEB/PTTT6SkpOQ6nipLzZo1OXLkCJGRkVy7du2BnuEWExNDREQEZ8+eBYxzSUVERJimr9izZw8LFy4kIiKCqKgovvvuO4YPH87TTz9N9erVAejevTsNGzYkMDCQw4cPs3XrVt5++22GDh2Ki4sLAAMHDsTW1pbBgwdz7Ngx1q1bx8yZMxk3bpzpZzFixAiio6MZN24cJ0+e5IsvvuDzzz83Gzd3r5EjR3Ljxg1eeuklwsPDOX/+PJs2bWLIkCHo9Xr0ej2BgYF0796d//znPyxfvpxjx47x/vvvm+3n0qVLjBs3jsjISFavXs3ixYtNwbZu3br07duXoUOHsnPnTv7++29efvllqlatSt++fQv9Xk+bNo1Zs2bxwQcfcPr0aY4ePcry5cvNppUoSM2aNbl9+zZbt27l2rVrJCcn8+uvv/Lhhx8SERFBdHQ0X331FQaDAT8/v0Lvt8jyHcYuilWZvPsvbI56ZIa72mhFI7XRikbqoCV1VXWqi6qGzbF0ZUIUWVm8+y/LhQsX1MGDB6ve3t6qtbW16uvrq77xxhvqtWvXzNrldpt/flMq2NraqgEBAerSpUtVwPTe5DWlQnYLFy5Ua9SoYXq9efNmtUGDBqqtra3apEkTNSwsTAXUdevW5VlHbvr166dqtVrT30qDwaBWrlxZbdmypVm7e2uKjY1Vu3Xrpjo5OeWYUiH7MW/evGlan5fcpmgA1OXLl6uqqqoHDx5U27Rpo+p0OtXOzk718/NTp06dqiYlJZntJzo6Wu3du7dqb2+vVq5cWR01apTZ9AmqarzrsWPHjqqtra3q7e2tTps2zWzaAlVV1bCwMLVZs2aqjY2NWrNmTXXp0qX5voeqqqqnT59Wn332WdN0B/Xr11fHjBmjGgwGNTg4WK1SpYrZ789PP/2k2tjYmN4rf39/9fXXX1dHjBihuri4qJUqVVInTpyY65QKOp1Otbe3V3v06JHrlArZZU2Vkd3KlSvVxx57TLWxsVErVaqkdurUyXQnYmF/hiNGjFDd3NxMUyrs2LFD9ff3VytVqmSahuHbb7/N8/0qjrv/FFUtxIV0USwSEhLQ6XTEx8eb/pVSFrz9fj3+cDeO/wiJu86zrcZID5Uok1JTU4mKiqJWrVrY2dlZupyHyowZM/j444+5dOmSpUsRD4mAgAAee+yxXAewl0f5/X0o7Oe3jKkS+QoOehf/7XGkNXdhXyNHuqRkSKASohz46KOPaNWqFW5ubuzatYt58+YxatQoS5clRJkmoUrkKTQ0lPjfPqDP4WT67LjFgVZO6HppjHf9SbASokw7c+YM06dP58aNG1SvXp233nqLSZNkrjkhHoSEKpGr0NBQpkyZQljbu3OcbEDlttKBgDzmsRJClB0LFy40TaopRG7CwsIsXUKZI3f/iRyyAlXQE7a8O8SX18fW4NcOOhYdTaLztN8JUzoY7/7bPtfSpQohhBAPDQlVwkxWoJrcyYbHu7uSYKNlR1Nnvumu4+adSaclWAkhhBA5SagSJtkDVWhnO761vjuV/+Udt8zaSrASQgghzEmoEiZTp041Baopu9L429U4c7MuU8/ubQk52ncJ3miaIFSClRBCiIpOBqoLk+DgYPRbZxC0LRXrNGvmLb7EplY6YlxVduXy5Ifg4OC7g9UNlns0hBBCCPEwkFAlTIKCgggFgqdMYU8jK9oeS6Tz4URGP2afo21ISAhBQUHGF3IXoBBCCCGX/4S5oKAgprz1Oo9FpgBw01nLJ0dSzNqYBSohKqjQ0FA0Gg2hoaGWLqXIwsLCUBSFW7duWboUIcqVcheqbt68SWBgIDqdDp1OR2BgYIF/OFRVZdq0afj4+GBvb09AQADHjx/Ps+2TTz6Joij89NNPxX8CD4EGsQewyzA+vehAPTvSDXfXSaAS4u5NHaqqMmXKlBIPVoMHD0ZRFBRFwdramtq1a/P222+TlJRUoscVQhRNuQtVAwcOJCIigo0bN7Jx40YiIiIIDAzMd5u5c+eyYMEClixZwv79+/H29qZbt24kJibmaLto0aL7fop7WVEr4oTp+/X6u4lKApUQdwNVdqURrHr27MmVK1c4f/4806dP56OPPuLtt98u0WMKIYoo38ctlzEnTpxQAXXv3r2mZXv27FEB9dSpU7luYzAYVG9vb3X27NmmZampqapOp1M//vhjs7YRERFqtWrV1CtXrpg9eT0vqampanx8vOnr0qVLhXrKtSVFn4xQU6wVVQX1urNWnTp5kqooihoSEmLp0oR4YPk9hb4wQkJCVCDPr5L6/+TVV19V+/bta7bs//7v/1Rvb2/166+/Vlu0aKE6OTmpXl5e6ksvvaRevXrVrO1vv/2m1q1bV7Wzs1MDAgLU5cuXq4B68+ZNU5tdu3apHTt2VO3s7NRq1aqpb7zxhnr79u0SOR8hHkb5/X2Ij48v1Od3ueqp2rNnDzqdjjZt2piWtW3bFp1Ox+7du3PdJioqipiYGLp3725aZmtri7+/v9k2ycnJvPTSSyxZsgRvb+9C1TNr1izTZUidToevr+99nlnp2T1jqOnSX8SjLkwLnYnBYJAeKlHh5dZDda/S6LHKYm9vT0ZGBunp6YSGhvL333/z008/ERUVxeDBg03tLl26xHPPPUevXr2IiIjg//7v/5g4caLZvo4ePUqPHj147rnnOHLkCN9++y07d+6UBywLUUTl6u6/mJgYPD09cyz39PQkJiYmz20AvLy8zJZ7eXkRHR1tej127Fjat29P3759C13PpEmTGDdunOl1QkLCQx+sav990vT9tfadLViJEKWjZcuWef59yJKQkJDrcIDcTJkyhXnz5uHi4pJvO29vbw4cOFDoOrMLDw9n1apVPPHEEwwZMsS0vHbt2nz44Ye0bt2a27dv4+TkxNKlS6lduzYLFy5EURT8/Pw4evQoc+bMMW03b948Bg4cyJgxYwCoW7cuH374If7+/ixduhQ7O7v7qlOIiqZMhKpp06YZ50TKx/79+wFyHe+kqmqB46DuXZ99m/Xr1/Pnn39y+PDhopSNra0ttra2RdrGkqJOHKTJKePA1xvOWvoEr7BsQUKUgpiYGP79999i3WdiYmKhQ1hh/frrrzg5OZGZmUlGRgZ9+/Zl8eLFHD58mGnTphEREcGNGzcwGIzjIC9evEjDhg05efIkbdu2Nfsb165dO7N9Hzx4kLNnz7Jy5UrTMlVVMRgMREVF0aBBg2I9FyHKqzIRqkaNGsWAAQPybVOzZk2OHDnC1atXc6yLi4vL0ROVJetSXkxMDFWqVDEtj42NNW3z559/cu7cOVxdXc227devHx07diw3T/LeN3MEA7Jd+uvi5GzhioQoeYW5nF+UnioAZ2fnQvVUFUXnzp1ZunQp1tbW+Pj4YG1tTVJSEt27d6d79+588803eHh4cPHiRXr06EF6ejpgDEcFMRgMDB8+nNGjR+dYV7169SLVKURFViZClbu7O+7u7gW2a9euHfHx8YSHh9O6dWsA9u3bR3x8PO3bt891m1q1auHt7c3mzZtp1qwZAOnp6Wzfvt3UPT5x4kT+7//+z2y7xo0bs3DhQvr06fMgp/ZQqX3klOn7a493sWAlQpSewl6CK8yYKii5u2QdHR2pU6eO2bJTp05x7do1Zs+ebRpacO/5NGzYMMf0L3v37jV73bx5c44fP55j/0KIIiqJEfSW1LNnT7VJkybqnj171D179qiNGzdWn3rqKbM2fn5+6o8//mh6PXv2bFWn06k//vijevToUfWll15Sq1SpoiYkJOR5HApx99+9Cnv3gCWcP35ATbUy3vV3zUWrJifmfe5ClFXl6e4/VVXV2NhY1cbGRn3nnXfUc+fOqT///LNar149FVAPHz6sqqqqRkdHqzY2NurYsWPVU6dOqStXrlS9vb3N7v77+++/VXt7e/X1119XDx8+rJ4+fVr9+eef1VGjRpXI+QjxMJK7/3KxcuVKGjdubOoSb9KkCV9//bVZm8jISOLj402vx48fz5gxY3j99ddp2bIl//77L5s2bcLZueJc/to367/YZt659NdQh71c+hMih6CgIEJCQnJdZ4l53Dw8PFixYgXff/89DRs2ZPbs2cyfP9+sTfXq1Vm7di2//PILTZs25eOPP2bmzJlmbZo0acL27ds5c+YMHTt2pFmzZgQFBZkNiRBCFExR1UJccBfFIiEhAZ1OR3x8fIHjLUrb3sdcaPu3cczId+Oeof/76yxckRDFLzU1laioKGrVqvVAd7TdeylQJsYVouzL7+9DYT+/y11PlSi6q/+cp/Gdu/5uOWnpNeVzC1ckxMMtq8dKURQJVEIIEwlVgm3Th+KYZrwN+0h9J5x0lS1ckRAPv6CgIJkYVwhhRkKVoMqhQ6bvLzdrbsFKhBBCiLKrTEypIErO7aQE5g30YEtjR7pEJBDw3ieWLkkIIYQokyRUVXDLVk8lupIt0Z1sOdfQiVU16lq6JCGEEKJMkst/Fdyxa1tN39dU5FEUQgghxP2SUFWB6TMzueCWAYCVqvLqM6EWrkgIIYQou+TyXwX28/tvMvyPWLa2cCbRywa/2o0sXZIQD59ts0CjBf/xhd9m+1ww6KHzpJKrSwjx0JGeqgrMbuNaXtx2g2Xzo3l2j+RrIXKl0cK2GcagVBjb5xrba7QlW5cQ4qEjn6QVWL1ztwAwKPDIf+XSnxC5yuqh2jbD/HVusgJV5/eK1rNVDgQEBPDYY4+xaNEiS5cihMVIT1UF9cOGZQyc/AhjR/rybU83WnTtZ+mShHh4+Y83BqX8eqxKMFANHjwYRVEYMWJEjnWvv/46iqIwePDgQu3rwoULKIpCREREsdYohJBQVWH9+fdyUmw1bGmlY3cnuetPiALlF6xKoYfK19eXNWvWkJKSYlqWmprK6tWrqV69eokc09JUVSUzM9PSZQhRaBKqKqh/XW6Yvu/VZpQFKxGiDMktWJXSJb/mzZtTvXp1fvzxR9OyH3/8EV9fX5o1a2ZatnHjRh5//HFcXV1xc3Pjqaee4ty5c6b1tWrVAqBZs2YoikJAQAAAYWFhtG7dGkdHR1xdXenQoQPR0dGAsafsmWeeMatnzJgxpm2zZGZmMmrUKNOxJ0+ejKqqpvXffPMNLVu2xNnZGW9vbwYOHEhsbKxpfVhYGIqi8Mcff9CyZUtsbW3ZsWMH586do2/fvnh5eeHk5ESrVq3YsmXLA72fQpQEGVNVAe0+sInzTjYAVE/O4MnOL1q4IiEs6BN/uB1bcLvsbJyNQSprnJWNMxxYbvwqLCdPGL69SIf9z3/+w/Llyxk0aBAAX3zxBUOGDCEsLMzUJikpiXHjxtG4cWOSkpKYMmUKzz77LBEREWg0GsLDw2ndujVbtmzh0UcfxcbGhszMTJ555hmGDh3K6tWrSU9PJzw8HEVRilTfl19+yWuvvca+ffs4cOAAw4YNo0aNGgwdOhSA9PR0QkND8fPzIzY2lrFjxzJ48GA2bNhgtp/x48czf/58ateujaurK//88w+9evVi+vTp2NnZ8eWXX9KnTx8iIyPLbS+dKJskVFVA5xe8zawLlwh7zIV0T3l4sqjgbsdC4uUH20d6ovGrhAUGBjJp0iTTuKhdu3axZs0as1DVr5/5+MjPP/8cT09PTpw4QaNGjfDw8ADAzc0Nb29vAG7cuEF8fDxPPfUUjzzyCAANGhR9WICvry8LFy5EURT8/Pw4evQoCxcuNIWqIUOGmNrWrl2bDz/8kNatW3P79m2cnJxM60JCQujWrZvptZubG02bNjW9nj59OuvWrWP9+vWMGiU97eLhIaGqAqp/5Dwtjyfx1J54fgzpYelyhLAsJ8+ib5N2T4iycQZb5xI/rru7O7179+bLL79EVVV69+6Nu7u7WZtz584RFBTE3r17uXbtGgaDAYCLFy/SqFHuc9FVrlyZwYMH06NHD7p160bXrl3p378/VapUKVJ9bdu2NevdateuHe+//z56vR6tVsvhw4eZNm0aERER3Lhxw6y2hg0bmrZr2bKl2X6TkpIIDg7m119/5fLly2RmZpKSksLFixeLVJ8QJU1CVQUTE32GRmeSAbiu0/LUOx9auCIhLKyIl+ByjKHKet1hdKlMozBkyBBT78z//ve/HOv79OmDr68vn376KT4+PhgMBho1akR6enq++12+fDmjR49m48aNfPvtt0yePJnNmzfTtm1bNBqN2dgogIyMjCLVnZSURPfu3enevTvffPMNHh4eXLx4kR49euSozdHR0ez1O++8wx9//MH8+fOpU6cO9vb2PP/88wWekxClTUJVBRM2awQD0o1/HI/WcybAzt7CFQlRhuQ2KL0o81gVg549e5rCRI8e5j3N169f5+TJk3zyySd07NgRgJ07d5q1sbExjqfU6/U59t2sWTOaNWvGpEmTaNeuHatWraJt27Z4eHhw7Ngxs7YRERFYW1ubLdu7d2+O13Xr1kWr1XLq1CmuXbvG7Nmz8fX1BeDAgQOFOucdO3YwePBgnn32WQBu377NhQsXCrWtEKVJ7v6rYLwPHzJ9H/NYs3xaCiHM5HeXX2HmsSomWq2WkydPcvLkSbRa81nbK1WqhJubG8uWLePs2bP8+eefjBs3zqyNp6cn9vb2bNy4katXrxIfH09UVBSTJk1iz549REdHs2nTJk6fPm0aV9WlSxcOHDjAV199xZkzZ5g6dWqOkAVw6dIlxo0bR2RkJKtXr2bx4sW8+eabAFSvXh0bGxsWL17M+fPnWb9+PaGhhZt0uE6dOvz4449ERETw999/M3DgQNOlQyEeJhKqKpD01BQaRd4GINVaodPEpRauSIgyojDTJpRisHJxccHFxSXHco1Gw5o1azh48CCNGjVi7NixzJs3z6yNlZUVH374IZ988gk+Pj707dsXBwcHTp06Rb9+/ahXrx7Dhg1j1KhRDB8+HDD2iAUFBTF+/HhatWpFYmIir7zySo7jv/LKK6SkpNC6dWtGjhzJG2+8wbBhwwDw8PBgxYoVfP/99zRs2JDZs2czf/78Qp3vwoULqVSpEu3bt6dPnz706NGD5s2bF/VtE6LEKeq9F8pFiUlISECn0xEfH5/rH8SS9tOM//LM5I8BONjQkRbHb5d6DUJYUmpqKlFRUdSqVQs7O7vCbVTUeagq8KNqhCjL8vv7UNjPbxlTVYFYbf3Z9P35+tVpYcFahCgzDPqiBaSsdoacY5aEEOWbhKoKpMGZu7OoVx/yngUrEaIM6Typ6NtID5UQFZKMqaogdv34GY/8kwbAmeq2tOk9yMIVCSGEEOWLhKoK4vJXdwernqzrZsFKhBBCiPJJQlUFUSfyH9P3avd++bQUQgghxP2QUFUBXDz1N4+eTQEgtpIVT41938IVCSGEEOWPhKoKYPf8N7DJNM6ccczPGe09syALIYQQ4sFJqKoAvI8eNX0f21QmzBNCCCFKgoSqck6fkcGjp42TfKZZKXQan/MBrEIIIYR4cBKqyrmNS97D41YmACfq2ONT28/CFQlR9ukNevbH7GfD+Q3sj9mPXib6BCAgIIAxY8ZYuoz7cuHCBRRFISIiIt92Zfkca9asyaJFiyxdRrkmoaqc++vaNmYNqsLOxk5ENqhq6XKEKPO2RG+hx9oeDPljCBN2TGDIH0PosbYHW6K3lNgxBw8ejKIojBgxIse6119/HUVRGDx4cKH3V9gAUZH4+vpy5coVGjVqBEBYWBiKonDr1q0H2m/NmjVRFCXPr4CAgAcvvpD2799vehZjcRk8eDDPPPNMse6zLJNQVc6d8U5gVTc3/vtWTRxGTLZ0OUKUaVuitzAubBxXk6+aLY9NjmVc2LgSDVa+vr6sWbOGlJQU07LU1FRWr15N9erVS+y4lqaqKpmZmSV+HK1Wi7e3N1ZWxfugkf3793PlyhWuXLnC2rVrAYiMjDQt+/HHH4u0v/T09PuuxcPDAwcHh/veviQ9yHk9TCRUlWOR549x3tl4p1+V1Aye7p7zqfJCiMLRG/TMDp+NSs5n0GctmxM+p8QuBTZv3pzq1aubfQj/+OOP+Pr60qxZM7O2Gzdu5PHHH8fV1RU3Nzeeeuopzp07Z1pfq1YtAJo1a2bWWxIWFkbr1q1xdHTE1dWVDh06EB0dDeTeIzFmzJgcPS2ZmZmMGjXKdOzJkyejqnffs2+++YaWLVvi7OyMt7c3AwcOJDY21rQ+q4fojz/+oGXLltja2rJjxw7OnTtH37598fLywsnJiVatWrFlS94hNj4+Hq1Wy8GDBwFjOKtcuTKtWrUytVm9ejVVqlQBzHvvLly4QOfOnQGoVKlSjp5Ag8HA+PHjqVy5Mt7e3kybNi3POjw8PPD29sbb25vKlSsD4OnpaVp26tQpOnXqhL29Pb6+vowePZqkpCTT9jVr1mT69OkMHjwYnU7H0KFDWbFiBa6urvz666/4+fnh4ODA888/T1JSEl9++SU1a9akUqVKvPHGG+j1erN9Zb/8pygKn332Gc8++ywODg7UrVuX9evXm9br9Xpee+01atWqhb29PX5+fnzwwQem9dOmTePLL7/k559/NvW8hYWFAXD06FG6dOmCvb09bm5uDBs2jNu3b5u2zfp9mjVrFj4+PtSrVy/P97AskWf/lWMr109HX0kBoNpNGwtXI8TD6cVfX+RayrUC26Xr07mVdivP9SoqMckxBHwXgI224P/f3O3d+fapb4tSKv/5z39Yvnw5gwYZHzP1xRdfMGTIENMHWZakpCTGjRtH48aNSUpKYsqUKTz77LNERESg0WgIDw+ndevWbNmyhUcffRQbGxsyMzN55plnGDp0KKtXryY9PZ3w8HAURSlSjV9++SWvvfYa+/bt48CBAwwbNowaNWowdOhQwNgjERoaip+fH7GxsYwdO5bBgwezYcMGs/2MHz+e+fPnU7t2bVxdXfnnn3/o1asX06dPx87Oji+//JI+ffoQGRmZa0+dTqfjscceIywsjBYtWnDkyBEAjhw5QkJCAi4uLoSFheHv759jW19fX9auXUu/fv2IjIzExcUFe3t7s3McN24c+/btY8+ePQwePJgOHTrQrVu3Ir1XR48epUePHoSGhvL5558TFxfHqFGjGDVqFMuXLze1mzdvHkFBQUyebLzasHPnTpKTk/nwww9Zs2YNiYmJPPfcczz33HO4urqyYcMGzp8/T79+/Xj88cd58cUX86whODiYuXPnMm/ePBYvXsygQYOIjo6mcuXKGAwGqlWrxnfffYe7uzu7d+9m2LBhVKlShf79+/P2229z8uRJEhISTPVWrlyZ5ORkevbsSdu2bdm/fz+xsbH83//9H6NGjWLFihWmY2/duhUXFxc2b95sFrzLMglV5Zj7oV00r2bN33UcqKtrb+lyhHgoXUu5RmxybMENCym/4PWgAgMDmTRpkqlXZdeuXaxZsyZHqOrXz/ypCZ9//jmenp6cOHGCRo0a4eHhAYCbmxve3t4A3Lhxg/j4eJ566ikeeeQRABo0aFDkGn19fVm4cCGKouDn58fRo0dZuHChKVQNGTLE1LZ27dp8+OGHtG7dmtu3b+Pk5GRaFxISYhZS3NzcaNq0qen19OnTWbduHevXr2fUqFG51hIQEEBYWBhvvfUWYWFhPPHEE5w/f56dO3fSq1cvwsLCGDt2bI7ttFqtWa+Sq6ur2fomTZowdepUAOrWrcuSJUvYunVrkUPVvHnzGDhwoGnge926dfnwww/x9/dn6dKl2NnZAdClSxfefvtt03Y7d+4kIyODpUuXmn5Wzz//PF9//TVXr17FycmJhg0b0rlzZ7Zt25ZvqBo8eDAvvfQSADNnzmTx4sWEh4fTs2dPrK2tCQ4ONrWtVasWu3fv5rvvvqN///44OTlhb29PWlqa6fcIjKEzJSWFr776CkdHRwCWLFlCnz59mDNnDl5eXgA4Ojry2WefYWNTfv7RL6GqnNJnZDBwfQyj4zOJdbVCPfW7pUsS4qHkbu9eqHYF9VRlcbV1LXRPVVG5u7vTu3dvvvzyS1RVpXfv3ri759zPuXPnCAoKYu/evVy7dg2DwQDAxYsXTQOx71W5cmUGDx5Mjx496NatG127dqV///6my2OF1bZtW7PerXbt2vH++++j1+vRarUcPnyYadOmERERwY0bN8xqa9iwoWm7li1bmu03KSmJ4OBgfv31Vy5fvkxmZiYpKSlcvHgxz1oCAgL4/PPPMRgMbN++nSeeeILq1auzfft2mjdvzunTp3PtqSpIkyZNzF5XqVLF7BJmYR08eJCzZ8+ycuVK0zJVVTEYDERFRZlC7b3vBYCDg4MpUAF4eXlRs2ZNs2Dq5eVVYF3Zz8XR0RFnZ2ezbT7++GM+++wzoqOjSUlJIT09ncceeyzffZ48eZKmTZuaAhVAhw4dMBgMREZGmkJV48aNy1WgAglV5dZvi8bzdLxxcOclH1taePlauCIhHk6FvQSnN+jpsbYHscmxuY6rUlDwcvBiY7+NaDXa4i7TZMiQIaaemf/9L/d55/r06YOvry+ffvopPj4+GAwGGjVqVOBg4OXLlzN69Gg2btzIt99+y+TJk9m8eTNt27ZFo9HkuESTkZFRpNqTkpLo3r073bt355tvvsHDw4OLFy/So0ePHLVl/0AGeOedd/jjjz+YP38+derUwd7enueffz7fc+rUqROJiYkcOnSIHTt2EBoaiq+vLzNnzuSxxx7D09PzvnrjrO95KoWiKKZwWBQGg4Hhw4czevToHOuyX9K8973Iq4b7qSu/bb777jvGjh3L+++/T7t27XB2dmbevHns27cv332qqprnZePsy3M7r7JOQlU5tT1xB3uHVqXj37e5UaMKLSxdkBBlnFajZWLriYwLG4eCYhasFIwfFBNaTyjRQAXQs2dPU5Do0aNHjvXXr1/n5MmTfPLJJ3Ts2BEwXi7KLqt3IPsg5izNmjWjWbNmTJo0iXbt2rFq1Sratm2Lh4cHx44dM2sbERGR40N57969OV7XrVsXrVbLqVOnuHbtGrNnz8bX1/gPvQMHDhTqvHfs2MHgwYN59tlnAbh9+zYXLlzId5uscVVLlixBURQaNmyIj48Phw8f5tdff823lyq/96i4NG/enOPHj1OnTp0SO8aD2LFjB+3bt+f11183Lct+wwMY36d736OGDRvy5ZdfkpSUZApOu3btQqPRlJsB6XmRu//KqfMeifzSoRLjX/fFdfgUS5cjRLnQtUZXFgQswNPB02y5l4MXCwIW0LVG1xKvQavVcvLkSU6ePIlWmzPAVapUCTc3N5YtW8bZs2f5888/GTdunFkbT09P7O3t2bhxI1evXiU+Pp6oqCgmTZrEnj17iI6OZtOmTZw+fdrUk9OlSxcOHDjAV199xZkzZ5g6dWqOkAVw6dIlxo0bR2RkJKtXr2bx4sW8+eabgLH3xcbGhsWLF3P+/HnWr19PaGhooc67Tp06/Pjjj0RERPD3338zcODAQvUOBQQE8M033+Dv74+iKFSqVImGDRvy7bff5jtHVI0aNVAUhV9//ZW4uDizO9eKy4QJE9izZw8jR44kIiKCM2fOsH79et54441iP9b9qFOnDgcOHOCPP/7g9OnTBAUFsX//frM2NWvW5MiRI0RGRnLt2jUyMjIYNGgQdnZ2vPrqqxw7doxt27bxxhtvEBgYaLr0V15JqCqHTkQeNk2l4JOaQe8nXrJwRUKUH11rdOWPfn/wRY8vmNNxDl/0+IKN/TaWSqDK4uLigouLS67rNBoNa9as4eDBgzRq1IixY8cyb948szZWVlZ8+OGHfPLJJ/j4+NC3b18cHBw4deoU/fr1o169egwbNoxRo0YxfPhwwNgrFhQUxPjx42nVqhWJiYm88krOaVpeeeUVUlJSaN26NSNHjuSNN94wTTjp4eHBihUr+P7772nYsCGzZ89m/vz5hTrnhQsXUqlSJdq3b0+fPn3o0aMHzZsX/CzTzp07o9frzQKUv78/er0+356qqlWrEhwczMSJE/Hy8spzMPyDaNKkCdu3b+fMmTN07NiRZs2aERQUVORxbCVlxIgRPPfcc7z44ou0adOG69evm/VaAQwdOhQ/Pz9atmyJh4cHu3btwsHBgT/++IMbN27QqlUrnn/+eZ544gmWLFlioTMpPYpaXu5jLAMSEhLQ6XTEx8fn+QexOExe+Dw/V44EoPUVDZ9P/LvEjiVEWZKamkpUVBS1atUy3VklhBCQ/9+Hwn5+S09VOeS7bzedIhKwTzNQr5JMpSCEEEKUBhmoXs7oMzJ4fmMsw+P13HLSknl+s6VLEkIIISoE6akqZzYueQ+3eOOdGOeq2+Hu4WPhioQQQoiKQUJVOZO++XvT9xdqe+fTUgghhBDFSUJVOVP7/N2ZcJ2ekgcoCyGEEKVFQlU5EhN9hvrnUgC4WsmKrkMmWbgiIYQQouKQUFWObF8wFttM4wwZp+o4ob1npmMhhBBClBwJVeWI6+Fw0/dXGvhZsBIhhBCi4pFQVY7UOxcPgEGBRsNCLFyNEEIIUbFIqCon9m9cTa3Lxoesnq1uS6MO3S1ckRCipAUEBDBmzBhLlyGEuENCVTkRtWKu6fuztSpZsBIhRHEbPHgwiqLk+Jo7d67ZA4lr1qzJokWLLFeoEBWczKhelmybBRot+I/Psarq6fOm75NaZ3tI6Pa5YNBDZ7kTUIiyrGfPnixfvtxsmYeHB1qt1kIVCSHuJT1VZYlGC9tmGINSNsFTp9DgbBIASbYaur690Lhi+1xje4380RWirLO1tcXb29vs64knnjBd/gsICCA6OpqxY8eaerKEEKVLeqrKkqweqm0zTK+DQ4M5um0RlRONj6Y5+YgdLT2q3A1Und/LtWdLCJHNggXGrwf1zTcQEHD3dVgYvPyy8ftx44xfJeTHH3+kadOmDBs2jKFDh5bYcYQQeSt3PVU3b94kMDAQnU6HTqcjMDCQW7du5buNqqpMmzYNHx8f7O3tCQgI4Pjx42ZtAgICcoxnGDBgQAmeSR78xxuD0rYZLJoXwCqnVbSv4mBavbmJM4vmBUigEqIoEhLg338f/CstzXy/aWl31yUkPFCJv/76K05OTqavF154wWx95cqV0Wq1ODs7m3qyhBClq9z1VA0cOJB//vmHjRs3AjBs2DACAwP55Zdf8txm7ty5LFiwgBUrVlCvXj2mT59Ot27diIyMxNnZ2dRu6NChhITcnarA3t6+5E4kP/7jWRS+gS88rmGDNe2P3Tat2tXUmYse14BmjJFAJUThuLhA1aoPvh9b25yvs/br4vJAu+7cuTNLly41vXZ0dOSll156oH0KIYpXuQpVJ0+eZOPGjezdu5c2bdoA8Omnn9KuXTsiIyPx88s5IaaqqixatIj33nuP5557DoAvv/wSLy8vVq1axfDhw01tHRwcHop//QWHBrPK6Qo2WGOXrtL8dDIA/7pZE+1t/KO+VHuF+NBgpgZNtWSpQpQNJXVpLiAA/vmnWHbl6OhInTp1imVfQoiSUa4u/+3ZswedTmcKVABt27ZFp9Oxe/fuXLeJiooiJiaG7t3vzutka2uLv79/jm1WrlyJu7s7jz76KG+//TaJiYn51pOWlkZCQoLZ14MKDQ1l3qp52FS2AUXBPT6T4zXt0Suwp5ETKAooCjZuNsxbNc/sdmshRPlmY2ODXq+3dBlCVFjlKlTFxMTg6emZY7mnpycxMTF5bgPg5eVlttzLy8tsm0GDBrF69WrCwsIICgpi7dq1pp6tvMyaNcs0tkun0+Hr61vUU8ph6tSpWOnudjD+42nDK5Nr03FJA5Y+Y37uVjorpk6VniohKoqaNWvy119/8e+//3Lt2jVLlyNEhVMmQtW0adNynfgu+9eBAwcAcr2NWFXVAm8vvnf9vdsMHTqUrl270qhRIwYMGMAPP/zAli1bOHToUJ77nDRpEvHx8aavS5cuFeW0cxUcHExmfGaO5YmOWmIrmT9AOTM+k+Dg4Ac+phCibAgJCeHChQs88sgjeHh4WLocISqcMjGmatSoUQXeaVezZk2OHDnC1atXc6yLi4vL0ROVJWuMVExMDFWqVDEtj42NzXMbgObNm2Ntbc2ZM2do3rx5rm1sbW2xvXfg6gMKCgrCgIFVN1ZhU8naeLnvXqpK+o0M3hn4DkFBQcV6fCFE6VuxYkWuy8PCwsxet23blr///rvkCxJC5KpMhCp3d3fc3d0LbNeuXTvi4+MJDw+ndevWAOzbt4/4+Hjat2+f6za1atXC29ubzZs306xZMwDS09PZvn07c+bMyfNYx48fJyMjwyyIlZapQVPRzdvGF1xDVVXzYKWqKMB/9VUYI4PUhRBCiFJTJi7/FVaDBg3o2bMnQ4cOZe/evezdu5ehQ4fy1FNPmd35V79+fdatWwcYL/uNGTOGmTNnsm7dOo4dO8bgwYNxcHBg4MCBAJw7d46QkBAOHDjAhQsX2LBhAy+88ALNmjWjQ4cOpX+i2+cyJukwQ+LcSb+ZYbYq/UYGQ+LcGZN0OMfM60IIIYQoOWWip6ooVq5cyejRo0138z399NMsWbLErE1kZCTx8fGm1+PHjyclJYXXX3+dmzdv0qZNGzZt2mSao8rGxoatW7fywQcfcPv2bXx9fenduzdTp04t/eduZZspfYz/eOJDg5m3bB5WOisy4zN5Z+A7xh6qrHYgE4AKIYQQpUBRVVW1dBEVRUJCAjqdjvj4eFzuZyLAPB49ExoaytSpUwkODjYfQyWPqhHCTGpqKlFRUdSqVQs7OztLlyOEeIjk9/ehsJ/f5a6nqlwz6HMNSEFBQbkPSM9qZ5B5a4TITv4tKYS4V3H8XZBQVZZ0nlT0baSHSgiTrMv16enplnvMlBDioZScbHw6ibW1dQEt8yahSghRYVhZWeHg4EBcXBzW1tZoNOXqXh0hxH1QVZXk5GRiY2NxdXV9oLHSEqqEEBWGoihUqVKFqKgooqOjLV2OEOIh4urq+sDP95VQJYSoUGxsbKhbty7p6emWLkUI8ZCwtrYulrv5JVQJISocjUYjd/8JIYqdDCgQQgghhCgGEqqEEEIIIYqBhCohhBBCiGIgY6pKUdbEYgkJCRauRAghhBCFlfW5XdAEoRKqSlFiYiIAvr6+Fq5ECCGEEEWVmJiITqfLc708+68UGQwGLl++jLOzM4qiFNt+ExIS8PX15dKlS/f3TEFRKPI+lw55n0uPvNelQ97n0lGS77OqqiQmJuLj45PvpMHSU1WKNBoN1apVK7H9u7i4yP+wpUDe59Ih73Ppkfe6dMj7XDpK6n3Or4cqiwxUF0IIIYQoBhKqhBBCCCGKgYSqcsDW1papU6dia2tr6VLKNXmfS4e8z6VH3uvSIe9z6XgY3mcZqC6EEEIIUQykp0oIIYQQohhIqBJCCCGEKAYSqoQQQgghioGEKiGEEEKIYiChqoz76KOPqFWrFnZ2drRo0YIdO3ZYuqRyZ9asWbRq1QpnZ2c8PT155plniIyMtHRZ5d6sWbNQFIUxY8ZYupRy599//+Xll1/Gzc0NBwcHHnvsMQ4ePGjpssqVzMxMJk+eTK1atbC3t6d27dqEhIRgMBgsXVqZ99dff9GnTx98fHxQFIWffvrJbL2qqkybNg0fHx/s7e0JCAjg+PHjpVKbhKoy7Ntvv2XMmDG89957HD58mI4dO/Lkk09y8eJFS5dWrmzfvp2RI0eyd+9eNm/eTGZmJt27dycpKcnSpZVb+/fvZ9myZTRp0sTSpZQ7N2/epEOHDlhbW/P7779z4sQJ3n//fVxdXS1dWrkyZ84cPv74Y5YsWcLJkyeZO3cu8+bNY/HixZYurcxLSkqiadOmLFmyJNf1c+fOZcGCBSxZsoT9+/fj7e1Nt27dTM/fLVGqKLNat26tjhgxwmxZ/fr11YkTJ1qoooohNjZWBdTt27dbupRyKTExUa1bt666efNm1d/fX33zzTctXVK5MmHCBPXxxx+3dBnlXu/evdUhQ4aYLXvuuefUl19+2UIVlU+Aum7dOtNrg8Ggent7q7NnzzYtS01NVXU6nfrxxx+XeD3SU1VGpaenc/DgQbp37262vHv37uzevdtCVVUM8fHxAFSuXNnClZRPI0eOpHfv3nTt2tXSpZRL69evp2XLlrzwwgt4enrSrFkzPv30U0uXVe48/vjjbN26ldOnTwPw999/s3PnTnr16mXhysq3qKgoYmJizD4bbW1t8ff3L5XPRnmgchl17do19Ho9Xl5eZsu9vLyIiYmxUFXln6qqjBs3jscff5xGjRpZupxyZ82aNRw6dIj9+/dbupRy6/z58yxdupRx48bx7rvvEh4ezujRo7G1teWVV16xdHnlxoQJE4iPj6d+/fpotVr0ej0zZszgpZdesnRp5VrW519un43R0dElfnwJVWWcoihmr1VVzbFMFJ9Ro0Zx5MgRdu7caelSyp1Lly7x5ptvsmnTJuzs7CxdTrllMBho2bIlM2fOBKBZs2YcP36cpUuXSqgqRt9++y3ffPMNq1at4tFHHyUiIoIxY8bg4+PDq6++aunyyj1LfTZKqCqj3N3d0Wq1OXqlYmNjcyR0UTzeeOMN1q9fz19//UW1atUsXU65c/DgQWJjY2nRooVpmV6v56+//mLJkiWkpaWh1WotWGH5UKVKFRo2bGi2rEGDBqxdu9ZCFZVP77zzDhMnTmTAgAEANG7cmOjoaGbNmiWhqgR5e3sDxh6rKlWqmJaX1mejjKkqo2xsbGjRogWbN282W75582bat29voarKJ1VVGTVqFD/++CN//vkntWrVsnRJ5dITTzzB0aNHiYiIMH21bNmSQYMGERERIYGqmHTo0CHHlCCnT5+mRo0aFqqofEpOTkajMf+I1Wq1MqVCCatVqxbe3t5mn43p6els3769VD4bpaeqDBs3bhyBgYG0bNmSdu3asWzZMi5evMiIESMsXVq5MnLkSFatWsXPP/+Ms7OzqXdQp9Nhb29v4erKD2dn5xzj1BwdHXFzc5Pxa8Vo7NixtG/fnpkzZ9K/f3/Cw8NZtmwZy5Yts3Rp5UqfPn2YMWMG1atX59FHH+Xw4cMsWLCAIUP+v507ZGktjOM4/h9cz8QogqbJhIGgQVYVNZosNsv0vgENNoOmvQOLRd+AadGgvgPBqrisTSyC47nhgummu2c+TD4fOGGc8kvjC+ec53fpaWPv/f09Hh8fv34/Pz/H/f19TE9PR6PRiMPDw+h2u9FqtaLVakW3242pqanY3d0d/biRf1/ISJ2dnaX5+flUVVVqt9s+8x+BiPjndXFxUXraj+dIhdHo9XppeXk51ev1tLi4mM7Pz0tP+nHe3t7SwcFBajQaaXJyMi0sLKTj4+P08fFRetrYu7m5+ed/cqfTSSn9PVbh5OQkzc3NpXq9ntbX19PDw8O3bKullNLo0w0A4GfzThUAQAaiCgAgA1EFAJCBqAIAyEBUAQBkIKoAADIQVQAAGYgqAIAMRBUAQAaiCmAI/X4/arVabG5ulp4CFCaqAAAyEFUAABmIKoD/dHp6Gs1mMyIi7u7uolarfV17e3tlxwHf7lfpAQDjamVlJXZ2duLq6ipmZ2dja2vr697a2lrBZUAJtZRSKj0CYFz1+/1oNpuxsbERt7e3pecABXn8BwCQgagCAMhAVAEAZCCqAAAyEFUAABmIKoAhVFUVERGfn5+FlwCliSqAIczMzMTExEQ8PT3FYDAoPQcoyDlVAEPa3t6OXq8XS0tL0W63o6qqWF1djf39/dLTgG8kqgCG9PLyEkdHR3F9fR2vr68xGAyi0+nE5eVl6WnANxJVAAAZeKcKACADUQUAkIGoAgDIQFQBAGQgqgAAMhBVAAAZiCoAgAxEFQBABqIKACADUQUAkIGoAgDIQFQBAGTwB4ynEIgWbKwQAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tplot=np.linspace(0,10,2000)\n", - "plt.plot(tplot,np.imag(bath.correlation_function(tplot)),linewidth=2\n", - " ,label=f'Original with {lmaxmats} exponents')\n", - "plt.plot(tplot,np.imag(bathPade.correlation_function_approx(tplot)),linewidth=2\n", - " ,label=f'Pade',marker='D',markevery=100,color='k')\n", - "plt.plot(tplot,np.imag(bath.correlation_function_approx(tplot)),linewidth=2\n", - " ,label=f'Matsubara',marker='x',markevery=110,markersize=10)\n", - "plt.plot(tplot,np.imag(bathMats.correlation_function_approx(tplot)),linewidth=2\n", - " ,label=f'Matsubara with Terminator',marker='o',markevery=130)\n", - "plt.plot(tplot,np.imag(fbath.correlation_function_approx(tplot)),'-.'\n", - " ,label='Fit',linewidth=2,color='r')\n", - "plt.xlabel('t',fontsize=15)\n", - "plt.ylabel(r'$C_{R}(t)$',fontsize=15)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "f9ffc5f8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.0073528289794921875\n", - " Total run time: 3.75s*] Elapsed 3.75s / Remaining 00:00:00:00\n", - "ODE solver time: 3.749444007873535\n" - ] - } - ], - "source": [ - "\n", + "tfit=np.linspace(0,10,1000)\n", "with timer(\"RHS construction time\"):\n", - " HEOMFit = HEOMSolver(Hsys, fbath, NC, options=options)\n", + " bath,_ = env.approx_by_cf_fit(tfit,Ni_max=1,Nr_max=3,target_rsme=None)\n", + " HEOMFit = HEOMSolver(Hsys, (bath,Q), NC, options=options)\n", "\n", "with timer(\"ODE solver time\"):\n", " resultFit = HEOMFit.run(rho0, tlist)" @@ -683,7 +556,7 @@ }, { "cell_type": "markdown", - "id": "a184570f", + "id": "a8c9749d", "metadata": {}, "source": [ "## Analytic calculations" @@ -691,8 +564,8 @@ }, { "cell_type": "code", - "execution_count": 21, - "id": "c7b8e0e2", + "execution_count": 18, + "id": "4673e00a", "metadata": {}, "outputs": [], "source": [ @@ -772,7 +645,7 @@ }, { "cell_type": "markdown", - "id": "da570ee1", + "id": "357faa7b", "metadata": {}, "source": [ "For the pure dephasing analytics, we just sum up as many matsubara terms as we can:" @@ -780,8 +653,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "id": "81f82fd3", + "execution_count": 19, + "id": "0e4c98f3", "metadata": {}, "outputs": [], "source": [ @@ -806,7 +679,7 @@ }, { "cell_type": "markdown", - "id": "96b1ad9d", + "id": "0e37b2bd", "metadata": {}, "source": [ "Alternatively, we can just do the integral of the propagator directly, without using the correlation functions at all" @@ -814,15 +687,15 @@ }, { "cell_type": "code", - "execution_count": 23, - "id": "923533f8", + "execution_count": 20, + "id": "dad7ec45", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_55362/917460483.py:15: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n", + "/tmp/ipykernel_192111/917460483.py:15: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n", " If increasing the limit yields no improvement it is advised to analyze \n", " the integrand in order to determine the difficulties. If the position of a \n", " local difficulty can be determined (singularity, discontinuity) one will \n", @@ -855,7 +728,7 @@ }, { "cell_type": "markdown", - "id": "6d8cafce", + "id": "46410052", "metadata": {}, "source": [ "## Compare results" @@ -863,13 +736,13 @@ }, { "cell_type": "code", - "execution_count": 24, - "id": "e0600fe9", + "execution_count": 21, + "id": "860fcf09", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -891,7 +764,7 @@ }, { "cell_type": "markdown", - "id": "1106097d", + "id": "fafb9856", "metadata": {}, "source": [ "We can't see much difference in the plot above, so let's do a log plot instead:" @@ -899,13 +772,13 @@ }, { "cell_type": "code", - "execution_count": 25, - "id": "8caa163c", + "execution_count": 22, + "id": "aa7ae2e7", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -927,14 +800,12 @@ "], axes)\n", "\n", "axes.set_yscale('log')\n", - "axes.legend(loc=0, fontsize=12);\n", - "#axes.set_xlim(0,1)\n", - "#axes.set_ylim(0.4,0.5)" + "axes.legend(loc=0, fontsize=12);" ] }, { "cell_type": "markdown", - "id": "587f2206", + "id": "aa326857", "metadata": {}, "source": [ "## About" @@ -942,8 +813,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "id": "51cafb9e", + "execution_count": 23, + "id": "fd075c94", "metadata": {}, "outputs": [ { @@ -954,21 +825,21 @@ "QuTiP: Quantum Toolbox in Python\n", "================================\n", "Copyright (c) QuTiP team 2011 and later.\n", - "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross and Asier Galicia.\n", + "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", "Original developers: R. J. Johansson & P. D. Nation.\n", "Previous lead developers: Chris Granade & A. Grimsmo.\n", "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", "\n", - "QuTiP Version: 5.0.0.dev0+12d694b\n", - "Numpy Version: 1.26.0\n", - "Scipy Version: 1.11.3\n", - "Cython Version: 3.0.3\n", - "Matplotlib Version: 3.8.0\n", - "Python Version: 3.12.0\n", + "QuTiP Version: 5.1.0.dev0+7941773\n", + "Numpy Version: 2.1.3\n", + "Scipy Version: 1.14.1\n", + "Cython Version: 3.0.9\n", + "Matplotlib Version: 3.9.2\n", + "Python Version: 3.12.7\n", "Number of CPUs: 16\n", "BLAS Info: Generic\n", - "INTEL MKL Ext: False\n", + "INTEL MKL Ext: None\n", "Platform Info: Linux (x86_64)\n", "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", "================================================================================\n", @@ -984,7 +855,7 @@ }, { "cell_type": "markdown", - "id": "40a80b9c", + "id": "530d60ce", "metadata": {}, "source": [ "## Testing\n", @@ -994,8 +865,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "id": "88cf1b93", + "execution_count": 24, + "id": "b271d590", "metadata": {}, "outputs": [], "source": [ @@ -1015,7 +886,6 @@ " expect(P12p, resultFit.states[:50]), np.real(P12_ana)[:50],\n", " rtol=1e-3,\n", ")\n", - "\n", "assert np.allclose(P12_ana, P12_ana2, rtol=1e-3)" ] } @@ -1025,7 +895,7 @@ "formats": "ipynb,md:myst" }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "qutip-dev", "language": "python", "name": "python3" }, @@ -1039,7 +909,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/tutorials-v5/heom/heom-2-fmo-example.ipynb b/tutorials-v5/heom/heom-2-fmo-example.ipynb new file mode 100644 index 00000000..3dd69225 --- /dev/null +++ b/tutorials-v5/heom/heom-2-fmo-example.ipynb @@ -0,0 +1,805 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1964d72a", + "metadata": {}, + "source": [ + "# HEOM 2: Dynamics in Fenna-Mathews-Olsen complex (FMO)" + ] + }, + { + "cell_type": "markdown", + "id": "310f65a7", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "In this example notebook we outline how to employ the HEOM to\n", + "solve the FMO photosynthetic complex dynamics.\n", + "\n", + "We aim to replicate the results in reference [https://www.pnas.org/content/106/41/17255](https://pubmed.ncbi.nlm.nih.gov/19815512/)\n", + "and compare them to a Bloch-Redfield (perturbative) solution.\n", + "\n", + "This demonstrates how to to employ the solver for multiple baths, as well as showing how a\n", + "quantum environment reduces the effect of pure dephasing." + ] + }, + { + "cell_type": "markdown", + "id": "ebb63dc6", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b8e558be", + "metadata": {}, + "outputs": [], + "source": [ + "import contextlib\n", + "import time\n", + "\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " Qobj,\n", + " basis,\n", + " brmesolve,\n", + " expect,\n", + " liouvillian,\n", + " mesolve,\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver,\n", + ")\n", + "from qutip.core.environment import (\n", + " DrudeLorentzEnvironment,\n", + " system_terminator\n", + ")\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "c5852365", + "metadata": {}, + "source": [ + "## Helper functions\n", + "\n", + "Let's define some helper functions for calculating correlation functions, spectral densities, thermal energy level occupations, and for plotting results and timing how long operations take:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ba3f2b6e", + "metadata": {}, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\" Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0be8ee2c", + "metadata": {}, + "outputs": [], + "source": [ + "# Solver options:\n", + "\n", + "options = {\n", + " \"nsteps\": 15000,\n", + " \"store_states\": True,\n", + " \"rtol\": 1e-12,\n", + " \"atol\": 1e-12,\n", + " \"min_step\": 1e-18,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "899d1831", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian and bath parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "45a3860f", + "metadata": {}, + "outputs": [], + "source": [ + "# System Hamiltonian:\n", + "#\n", + "# We use the Hamiltonian employed in\n", + "# https://www.pnas.org/content/106/41/17255 and operate\n", + "# in units of Hz:\n", + "\n", + "Hsys = 3e10 * 2 * np.pi * Qobj([\n", + " [200, -87.7, 5.5, -5.9, 6.7, -13.7, -9.9],\n", + " [-87.7, 320, 30.8, 8.2, 0.7, 11.8, 4.3],\n", + " [5.5, 30.8, 0, -53.5, -2.2, -9.6, 6.0],\n", + " [-5.9, 8.2, -53.5, 110, -70.7, -17.0, -63.3],\n", + " [6.7, 0.7, -2.2, -70.7, 270, 81.1, -1.3],\n", + " [-13.7, 11.8, -9.6, -17.0, 81.1, 420, 39.7],\n", + " [-9.9, 4.3, 6.0, -63.3, -1.3, 39.7, 230],\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0e886fde", + "metadata": {}, + "outputs": [], + "source": [ + "# Bath parameters\n", + "\n", + "lam = 35 * 3e10 * 2 * np.pi\n", + "gamma = 1 / 166e-15\n", + "T = 300 * 0.6949 * 3e10 * 2 * np.pi\n", + "beta = 1 / T" + ] + }, + { + "cell_type": "markdown", + "id": "02f08558", + "metadata": {}, + "source": [ + "## Plotting the environment spectral density and correlation functions\n", + "\n", + "Let's quickly plot the spectral density and environment correlation functions so that we can see what they look like." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cf6b2106", + "metadata": {}, + "outputs": [], + "source": [ + "env=DrudeLorentzEnvironment(T=T,lam=lam,gamma=gamma)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "157df145", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "wlist = np.linspace(0, 200 * 3e10 * 2 * np.pi, 100)\n", + "tlist = np.linspace(0, 1e-12, 1000)\n", + "\n", + "J = env.spectral_density(wlist) / (3e10*2*np.pi)\n", + "\n", + "fig, axes = plt.subplots(1, 2, sharex=False, figsize=(10, 3))\n", + "\n", + "fig.subplots_adjust(hspace=0.1) # reduce space between plots\n", + "\n", + "# Spectral density plot:\n", + "\n", + "axes[0].plot(wlist / (3e10 * 2 * np.pi), J, color='r', ls='--', label=\"J(w)\")\n", + "axes[0].set_xlabel(r'$\\omega$ (cm$^{-1}$)', fontsize=20)\n", + "axes[0].set_ylabel(r\"$J(\\omega)$ (cm$^{-1}$)\", fontsize=16)\n", + "axes[0].legend()\n", + "\n", + "# Correlation plot:\n", + "\n", + "axes[1].plot(\n", + " tlist, np.real(env.correlation_function(tlist, 10)),\n", + " color='r', ls='--', label=\"C(t) real\",\n", + ")\n", + "axes[1].plot(\n", + " tlist, np.imag(env.correlation_function(tlist, 10)),\n", + " color='g', ls='--', label=\"C(t) imaginary\",\n", + ")\n", + "axes[1].set_xlabel(r'$t$', fontsize=20)\n", + "axes[1].set_ylabel(r\"$C(t)$\", fontsize=16)\n", + "axes[1].legend();" + ] + }, + { + "cell_type": "markdown", + "id": "dce8217d", + "metadata": {}, + "source": [ + "## Solve for the dynamics with the HEOM\n", + "\n", + "Now let us solve for the evolution of this system using the HEOM." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8e9280e9", + "metadata": {}, + "outputs": [], + "source": [ + "# We start the excitation at site 1:\n", + "rho0 = basis(7, 0) * basis(7, 0).dag()\n", + "\n", + "# HEOM solver options:\n", + "#\n", + "# Note: We set Nk=0 (i.e. a single correlation expansion term\n", + "# per bath) and rely on the terminator to correct detailed\n", + "# balance.\n", + "NC = 4 # Use NC=8 for more precise results\n", + "Nk = 0\n", + "\n", + "Q_list = []\n", + "baths = []\n", + "Ltot = liouvillian(Hsys)\n", + "env_approx,delta=env.approx_by_matsubara(Nk=Nk,compute_delta=True)\n", + "for m in range(7):\n", + " Q = basis(7, m) * basis(7, m).dag()\n", + " Q_list.append(Q)\n", + " Ltot += system_terminator(Q,delta)\n", + " baths.append((env_approx,Q))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "3607a30b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.020816326141357422\n", + " Total run time: 44.00s*] Elapsed 44.00s / Remaining 00:00:00:00[*********99%***********] Elapsed 43.64s / Remaining 00:00:00:00\n", + "ODE solver time: 44.02842974662781\n" + ] + } + ], + "source": [ + "with timer(\"RHS construction time\"):\n", + " HEOMMats = HEOMSolver(Hsys, baths, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " outputFMO_HEOM = HEOMMats.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "71b8fbab", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", + "\n", + "colors = ['r', 'g', 'b', 'y', 'c', 'm', 'k']\n", + "linestyles = [\n", + " '-', '--', ':', '-.',\n", + " (0, (1, 10)), (0, (5, 10)), (0, (3, 10, 1, 10)),\n", + "]\n", + "\n", + "for m in range(7):\n", + " Q = basis(7, m) * basis(7, m).dag()\n", + " axes.plot(\n", + " np.array(tlist) * 1e15,\n", + " np.real(expect(outputFMO_HEOM.states, Q)),\n", + " label=m + 1,\n", + " color=colors[m % len(colors)],\n", + " linestyle=linestyles[m % len(linestyles)],\n", + " )\n", + " axes.set_xlabel(r'$t$ (fs)', fontsize=30)\n", + " axes.set_ylabel(r\"Population\", fontsize=30)\n", + " axes.locator_params(axis='y', nbins=6)\n", + " axes.locator_params(axis='x', nbins=6)\n", + "\n", + "axes.set_title('HEOM solution', fontsize=24)\n", + "axes.legend(loc=0)\n", + "axes.set_xlim(0, 1000)\n", + "plt.yticks([0., 0.5, 1], [0, 0.5, 1])\n", + "plt.xticks([0., 500, 1000], [0, 500, 1000]);" + ] + }, + { + "cell_type": "markdown", + "id": "5f1bd3fc", + "metadata": {}, + "source": [ + "## Comparison with Bloch-Redfield solver\n", + "\n", + "Now let us solve the same problem using the Bloch-Redfield solver. We will see that the Bloch-Redfield technique fails to model the oscillation of population of the states that we saw in the HEOM.\n", + "\n", + "In the next section, we will examine the role of pure dephasing in the evolution to understand why this happens." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b2e420cf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 1.19s*] Elapsed 1.19s / Remaining 00:00:00:00\n", + "BR ODE solver time: 1.2221169471740723\n" + ] + } + ], + "source": [ + "with timer(\"BR ODE solver time\"):\n", + " outputFMO_BR = brmesolve(\n", + " Hsys, rho0, tlist,\n", + " a_ops=[[Q, env] for Q in Q_list],\n", + " options=options,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "6b384776", + "metadata": {}, + "source": [ + "And now let's plot the Bloch-Redfield solver results:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "24dda87d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", + "\n", + "for m, Q in enumerate(Q_list):\n", + " axes.plot(tlist * 1e15, expect(outputFMO_BR.states, Q), label=m + 1)\n", + "\n", + "axes.set_xlabel(r'$t$ (fs)', fontsize=30)\n", + "axes.set_ylabel(r\"Population\", fontsize=30)\n", + "\n", + "axes.set_title('Bloch-Redfield solution ', fontsize=24)\n", + "axes.legend()\n", + "axes.set_xlim(0, 1000)\n", + "plt.yticks([0, 0.5, 1], [0, 0.5, 1])\n", + "plt.xticks([0, 500, 1000], [0, 500, 1000]);" + ] + }, + { + "cell_type": "markdown", + "id": "19e7ef17", + "metadata": {}, + "source": [ + "Notice how the oscillations are gone and the populations decay much more rapidly.\n", + "\n", + "Next let us try to understand why." + ] + }, + { + "cell_type": "markdown", + "id": "c6e4704a", + "metadata": {}, + "source": [ + "## Role of pure dephasing\n", + "\n", + "It is useful to construct the various parts of the Bloch-Redfield master equation explicitly and to solve them using the Master equation solver, `mesolve`. We will do so and show that it is the pure-dephasing terms which suppresses coherence in these oscillations.\n", + "\n", + "First we will write a function to return the list of collapse operators for a given system, either with or without the dephasing operators:" + ] + }, + { + "cell_type": "markdown", + "id": "920097d3", + "metadata": {}, + "source": [ + "TODO: Maybe power spectrum at zero is wrong, by a factor 2" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "a6fde0d0", + "metadata": {}, + "outputs": [], + "source": [ + "def J0_dephasing():\n", + " \"\"\" Under-damped brownian oscillator dephasing probability.\n", + "\n", + " This returns the limit as w -> 0 of J0(w) * n_th(w, T) / T.\n", + " \"\"\"\n", + " return 2 * lam * gamma / gamma**2" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "a1d6c71b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "env.power_spectrum(0)/2 -J0_dephasing()*T" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "45bb1bc2", + "metadata": {}, + "outputs": [], + "source": [ + "def get_collapse(H, T, dephasing=1):\n", + " \"\"\" Calculate collapse operators for a given system H and\n", + " temperature T.\n", + " \"\"\"\n", + " all_energy, all_state = H.eigenstates(sort=\"low\")\n", + " Nmax = len(all_energy)\n", + "\n", + " Q_list = [\n", + " basis(Nmax, n) * basis(Nmax, n).dag()\n", + " for n in range(Nmax)\n", + " ]\n", + "\n", + " collapse_list = []\n", + "\n", + " for Q in Q_list:\n", + " for j in range(Nmax):\n", + " for k in range(j + 1, Nmax):\n", + " Deltajk = abs(all_energy[k] - all_energy[j])\n", + " if abs(Deltajk) > 0:\n", + " rate = (\n", + " np.abs(Q.matrix_element(\n", + " all_state[j].dag(), all_state[k]\n", + " ))**2 *\n", + " env.power_spectrum(Deltajk)\n", + " )\n", + " if rate > 0.0:\n", + " # emission:\n", + " collapse_list.append(\n", + " np.sqrt(rate) * all_state[j] * all_state[k].dag()\n", + " )\n", + "\n", + " rate = (\n", + " np.abs(Q.matrix_element(\n", + " all_state[k].dag(), all_state[j]\n", + " ))**2 *\n", + " env.power_spectrum(-Deltajk)\n", + " )\n", + " if rate > 0.0:\n", + " # absorption:\n", + " collapse_list.append(\n", + " np.sqrt(rate) * all_state[k] * all_state[j].dag()\n", + " )\n", + "\n", + " if dephasing:\n", + " for j in range(Nmax):\n", + " rate = (\n", + " np.abs(Q.matrix_element(\n", + " all_state[j].dag(), all_state[j])\n", + " )**2 * env.power_spectrum(0)/2\n", + " )\n", + " if rate > 0.0:\n", + " # emission:\n", + " collapse_list.append(\n", + " np.sqrt(rate) * all_state[j] * all_state[j].dag()\n", + " )\n", + "\n", + " return collapse_list" + ] + }, + { + "cell_type": "markdown", + "id": "e87487e7", + "metadata": {}, + "source": [ + "Now we are able to switch the pure dephasing terms on and off.\n", + "\n", + "Let us starting by including the dephasing operators. We expect to see the same behaviour that we saw when using the Bloch-Redfield solver." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "ec8a20fa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Building the collapse operators: 0.01816534996032715\n", + "ME ODE solver: 0.17152142524719238\n" + ] + } + ], + "source": [ + "# dephasing terms on, we recover the full BR solution:\n", + "\n", + "with timer(\"Building the collapse operators\"):\n", + " collapse_list = get_collapse(Hsys, T=T, dephasing=True)\n", + "\n", + "with timer(\"ME ODE solver\"):\n", + " outputFMO_ME = mesolve(Hsys, rho0, tlist, collapse_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "49396eb5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", + "\n", + "for m, Q in enumerate(Q_list):\n", + " axes.plot(tlist * 1e15, expect(outputFMO_ME.states, Q), label=m + 1)\n", + "\n", + "axes.set_xlabel(r'$t$', fontsize=20)\n", + "axes.set_ylabel(r\"Population\", fontsize=16)\n", + "axes.set_xlim(0, 1000)\n", + "axes.set_title('With pure dephasing', fontsize=24)\n", + "plt.yticks([0, 0.5, 1], [0, 0.5, 1])\n", + "plt.xticks([0, 500, 1000], [0, 500, 1000])\n", + "axes.legend(fontsize=18);" + ] + }, + { + "cell_type": "markdown", + "id": "a19b4694", + "metadata": {}, + "source": [ + "We see similar results to before.\n", + "\n", + "Now let us examine what happens when we remove the dephasing collapse operators:" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "7b216bc5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Building the collapse operators: 0.024776220321655273\n", + "ME ODE solver: 0.114501953125\n" + ] + } + ], + "source": [ + "# dephasing terms off\n", + "\n", + "with timer(\"Building the collapse operators\"):\n", + " collapse_list = get_collapse(Hsys, T, dephasing=False)\n", + "\n", + "with timer(\"ME ODE solver\"):\n", + " outputFMO_ME_nodephase = mesolve(Hsys, rho0, tlist, collapse_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "e682e0e2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Kf/3rX7j77rtx5513mj6++OIL07ZtJ6MYtU3+fffdd6bfBfZw5ZVXQqVSdblNd6/H7t27TT0xwsLCup3KEhUV1eFUGCIiR8OmgkREVjBt2jScOXMGgP5Evu0VPeOJvSAIFzUcbJsg2L59e7ur7G0TAlL/YTl06NButwkICDDd7mhMWtvKieTk5Hbbd2by5Mmm2yUlJSgqKkJ4eHi3z3MWgiBg/Pjx3W43fvx4CIIAURRRXV2NnJwcxMXF2SHC80aPHt3tNm2v0O/Zswd33nlnt89pm9zIz8/vXXBofxJqzmtqfO0vrMxpq+33U15ebtb3A8CUBBBFEYWFhZ2OZQT0Y0i7M2DAAAQEBKCiogKiKOLo0aNdNiy0xr9Xo8rKSjz++OP45JNPUFdX1+1+AZjG7rU1fvx4xMTEIDc3F/n5+UhJScGyZctw5ZVXYsKECXBzczNr371hjdej7ftr7NixZo33HDt2bJ9NZBJR38GEABGRFUybNg0ffvghAP2VflEUTX8wGk/sBw8eDH9//3bPCwkJQWJiIjIzM9slALRabbuTkbbLEqTg4+PT7TYKhcJ023glra1z586ZbptzpRXQvz4qlQrNzc0A9CcafSkh4OfnB09Pz2638/b2ho+Pj+lE89y5c3ZPCJhT5l1UVGS6vWXLFtPyAXN1d+W7K23fX10tRWkrKiqqy8fbfj8ZGRm96n7f3ffUk1grKioAtP9eO2KNf68AkJubi2nTpiEvL8+sGI06ShwoFAqsW7cO8+fPR21tLSorK/Hyyy/j5ZdfhlKpxKhRozBt2jTMnz8fU6dONeuE21zWeD3aJjm6qywxioiIMGs7IiIpcckAEZEVtL2CbxydBQCFhYXIzs4GcPFyASNj1cDJkydNf3QePnzYNO7rwv1LwRp/nLf9fjw8PMx+Xtttzb1C6Szc3d3N3lbq18GcK7hdXWk2h1ar7fVz276/zH1du3sfWvr9AEBra2uXj/cm1u5+/tY6mV66dKkpGeDt7Y37778fv/32G7Kzs1FfXw+tVgtR36C63ZINnU7X4f6mTJmC1NRU3Hzzze2+H+O40RdeeAHTp09HcnIyvv/+e6t8D4D1f39Z6/1FROQImBAgIrKC2NjYdlf6jFf7//jjD9N9nSUEjPeLomjqI9C2WiA5ORnBwcFWj9ne2l4Jb2hoMPt5bbf18vKyOI7OTlak0NjYaPa2zvA6tD0B+uGHH0wniz356K227y9zX9fu3odtv5977rmnV99Pd2vNexOrNX7+3dm9ezd27dplOt6+ffuwevVqzJ07F7GxsfDw8GjXNM/cJFVsbCw+/PBDnDt3Dps2bcLjjz+OmTNntks4nT59GgsXLuyykam9tX0vWOv9RUTkCJgQICKykrZX8Y0n9G1P7LurEOjseVIvF7CWtiXn5pYgl5WVmZYLAEBgYOBF27RtdNfd1VjAOld9raWqqsqsE6na2tp2cTvq6xASEmK6beypYS+9eX9117PAHt9Pb2Lt6OdvbZs3bzbdXr58ebdNGnNzc3u0fzc3N8yePRtPPfUUtmzZgoqKCnz33XcYPny4aZtHH30UhYWFPQvcRtq+5l31nWjLUWInIuoKEwJERFbSVUIgJiam0/XKAwcONJ14bN++HaIomq7MXbjf3rLmetzeatvF+9SpU6isrOz2OW1fh9DQ0A77B7S9WmpcY90ZtVqN06dPd3tce71eoihi37593W63b98+09VzX1/fDvsH2OJ16Km2zfx+++03q++/KyNGjDDd3rt3b7fbm/Pat/1+/vjjD9MYUGtq2yukM2fOnDH9TAVBaPdvyVba9k9ISUnpdvu2SczecHNzw8KFC7F161bTRBO1Wm3391Fn2r6/Dh48aFY1S0fjC4mIHA0TAkREVtL2xL2oqAj79u3DqVOnAHReHWBk7KafmpqKPXv2tDuhs0ZCoO3Irc4aiNnaoEGDTH/oa7VarFu3rtvnrFmzxnS7s67qbU+OOxp31tb69evbVRx0xp6v16efftrtNmvXrjXdnjFjRocJC1u8Dj11xRVXmG5v3rwZx48ft/oxOtP2/XHw4EHTv73ObNmypdsrvZMnTzZNCKivr8f7779vcZwX6unPf/jw4XYZQdp2OUB3JfJFRUVYv369VY7r5+eHSZMmmb4uLS21yn4tNWnSJFPjwaKiom6nB+Tn5180SpaIyBExIUBEZCVJSUmmE14A+Pe//226itRdQsD4uFarxapVq0z3x8fHm93RuittR2pJVcYqCAJuu+0209dPP/10l7H88ssv+Omnn0xf33777R1uN27cONPttidOF6qtrcUjjzxiVqz2fL3WrVuH3bt3d/r4jh072s14v/XWWzvczhavQ0+NGzfOtGZeFEX8+c9/Rm1trVnPVavVFk0ZGDx4cLsRfvfcc0+nfRKam5vxwAMPdLtPV1dX3HPPPaav//nPf/YoyWHOyez27dvx1Vdfdfr4mTNn8Morr5i+7uznb23x8fGm2z/++GOn22m1Wtx2223dVk90V7XSVttlFOZMt7CHgIAAXHXVVaavH3roIajV6k63f+CBByxqkklEZC9MCBARWVHbE/+ff/65w/s70raPQNvnWat/QNs53Pv27evxGDFrueeee0yjuCoqKjB79uwOr2Z/9dVXuP76601fX3nllZ1WSixZssR0+8svv8Qbb7xx0TanTp3CrFmzcPbsWbi6unYbZ9vX64cffujyD39LKBQK6HQ6XHXVVR2WRv/vf//D1VdfbUosTZs2DZdffnmH+7LF69Abr7/+uqnB37FjxzBu3Dhs2rSp0+0zMzOxcuVKxMXFtVsi0hsrV6403f7tt99w0003XZSQKCsrw4IFC3D06FEolcpu93n//febSubr6uowZcoUvP/++52+JyoqKvDBBx9g9OjRePHFF7vdv1KpxPLlyzusmNm/fz8uueQS0xX6AQMG4JZbbul2n9Ywf/58UyXKH3/8gQceeABNTU3ttikpKcGiRYvwyy+/dNtR//XXX8eIESPw1ltvobi4uMNt6urq8PDDD+PgwYMAALlcjksvvdQK3411PPHEE6YqgYMHD2LhwoUXjYBsbGzEHXfcga+//tpm/8aIiKzJpftNiIjIXNOmTcM333zT7r7AwMBuG3KNHDkSnp6e7UZbGfdnDSEhIZg8eTJ27dqF5uZmDB8+HJdddhnCwsJMpcEJCQm44447rHK8zvj5+eHzzz/HvHnz0NjYiIyMDIwaNQrjx4/H4MGDoVarsW/fvnYN3AYMGIAPP/yw031OnToV8+fPxy+//AIA+Mc//oE333wTEyZMgCAIyMjIwN69e6HT6bB8+XJkZ2e3m/7QkXnz5sHd3R2NjY1ITU3FoEGDMGPGDPj6+ppOkubOnYu5c+da9HqEh4dj4cKFePnll3HZZZdh2LBhpvXhhw8fbnc1Ojg4GB999FGn/Q1s8Tr0xpAhQ/DFF1/g+uuvN/2ML7nkEkRFRWHs2LEIDAyEWq3GuXPnkJqaanaDNnPMmjUL9957L15++WUAwGeffYb169dj1qxZCA4ORmFhIbZs2YLm5mbExsbi6quvxquvvtrlPj09PbF+/XrMmTMH2dnZqK2txW233YYHH3wQEydOREREBARBQGVlJU6ePImMjAxTZUJny1zaevHFF3H33XfjxhtvxJNPPomJEydCqVQiPT29XY8Dd3d3fPLJJ+2Ws9hScnIybrzxRnzyyScAgP/85z/4/PPPMXbsWAQHByMnJwfbt2+HWq2Gl5cXXnzxxU6reIxSU1Px97//HXfeeScSEhIwZMgQBAYGQqPRoKioCLt3727Xmf+RRx7ptPeKFIYOHYqVK1fioYceAqCvYoqJicHMmTMRERGBiooKbNmyBdXV1fD19cW9996LJ554AkD7JRhERA5FJCIiq0lNTRUBtPtYsGCBWc+dM2fORc/Nysrq9nnLli0zbb9mzZpOtzt06JDo7e190TGMH9OnT2+3/RNPPGF67Iknnug2jq1bt3a6rwvt2bNHjI+P7zQW48ecOXPEsrKybo9dUVEhjhkzpst93XzzzWJzc7M4ffp0031bt27tdJ/vvfeeKJPJOt2fOa9JR7Kzs037iImJEdVqtfiXv/yly9gTExPF48eP2/11MPe16sjRo0fF0aNHd/szNn7ExsaKR44c6dExOqLT6cT7779fFASh02MNGDBATE9P79F7vKKiQly8eHGX+2374evrK65du7bDfbXdThT1/9a62m9oaGi3r39Pf1bmfO8NDQ3i3Llzu/w+IyMjxZ07d3b773/16tVmvxeUSqX41FNPWfS9mvt70WjNmjWm7ZctW9blts8++6zo4uLSafxhYWHirl27xPfee89031133dVtDEREUmCFABGRFQ0dOhT+/v7tOuh3t1yg7XZtS6sjIyM77CbfW6NGjcKxY8fw+uuvY8uWLcjKykJ9fb0k61wnTJiAkydPYt26dfjhhx9w9OhRlJWVQaFQIDQ0FFOmTMENN9xg9hV4f39/7N69Gx988AG++OILpKeno76+HmFhYRg7dixuu+02XHLJJT2K8S9/+QuGDBmCd955B3v37kVhYSEaGxvN6i7eEwqFAu+99x4WL16MDz/8EPv370dxcTHc3d2RnJyMxYsX4/bbbzfryrAtXofeGj58OA4ePIjff/8dP/zwA3bt2oWioiJUV1fD1dUVQUFBGDhwICZMmIBLL70UEydOtMp0B0EQsHr1aixevBhvvfUWtm3bhtLSUnh7eyMhIQHXXXcdbrnlFnh7e/dov/7+/vj666+RlpaGL774Atu2bUN2djYqKiogk8ng6+uLxMREjBo1CnPmzMEll1xi9tX8J598EvPmzcO7776LHTt2oKioCAqFAgkJCbjmmmtw5513mpob2pO7uzs2bNiAzz//HB9//DGOHDmC2tpaBAYGIj4+HosWLcLy5cvh5+fXbZO9+++/H4sWLcLGjRuxe/duHD9+HDk5OaitrTW9foMGDcKsWbNw0003ISYmxj7fZC88+uijuOKKK/DGG29g06ZNKCoqgru7O2JjY7Fo0SLcdtttCAwMbNdUUIqfHxGROQTR2n/ZEBERUadycnJMiZ6YmBjk5ORIGxDZXdvEB/8M67uWLl2Kzz//HADwxRdf4E9/+pPEERERXYwLmoiIiIiIrKihoQG//vqr6euxY8dKGA0RUeeYECAiIiIisqJ//etfqK6uBqBPBiQkJEgbEBFRJ5gQICIiIiIyw7fffosHH3wQmZmZHT5eXl6Ov/3tb3jllVdM9z344IN2io6IqOfYVJCIiIiIyAz19fVYvXo1Vq9ejYEDB2Lo0KEICAhAS0sLsrOzsW/fPrS0tJi2X7p0KRYvXixhxEREXWNCgIiIiIioh06fPo3Tp093+JhcLsff//53vPTSS3aOioioZ5gQICIiIiIyw5IlSxAUFIT//e9/OHz4MEpLS1FeXo7Gxkb4+fkhNjYWM2bMwM0334ykpCSpwyUi6hbHDnZAp9OhqKgIXl5eVpmJTERERERERNQVURRRV1eH8PBwyGT2affHCoEOFBUVISoqSuowiIiIiIiIqJ/Jz89HZGSkXY7FhEAHvLy8AOh/EN7e3hJHQ0RERERERH1dbW0toqKiTOej9sCEQAeMywS8vb2ZECAiIiIiIiK7seeydfssTCAiIiIiIiIih8KEABEREREREVE/xIQAERERERERUT/EhAARERERERFRP8SEABEREREREVE/xIQAERERERERUT/EhAARERERERFRP8SEABEREREREVE/5CJ1AERERERERNR7oihCo9FAp9NJHUq/J5PJoFAoIAiC1KGYhQkBIiIiIiIiJ6TValFeXo66ujpoNBqpwyEDhUIBLy8vBAYGQi6XSx1Ol5gQICIiIiIicjJarRb5+floaWmBj48PPD09IZfLnebKdF8kiiK0Wi3q6+tRXV2NpqYmREVFOXRSgAkBIiIiIiIiJ1NeXo6WlhZER0fDzc1N6nCoDU9PT/j4+CAvLw/l5eUICQmROqROsakgERERERGRExFFEXV1dfDx8WEywEG5ubnB29sbdXV1EEVR6nA6xYQAERERERGRE9FoNNBoNPD09JQ6FOqCl5eX6WflqJgQICIiIiIiciLGaQKOvDadzv98HHn6AxMCRERERERETogNBB2bM/x8mBAgIiIiIiIi6oeYECAiIiIiIiLqh5gQICIiIiIiIuqHmBAgIiIiIiIi6oeYECAiIiIiIiLqh/pkQmD79u248sorER4eDkEQ8MMPP0gdEhERERERETmgxsZGbNiwAf/+97+xcOFCxMTEQBAECIKAJ598UurwbMpF6gBsoaGhAcOHD8eKFSuwaNEiqcMhIiIiIiIiB7V//35cfvnlUochiT6ZEJg3bx7mzZtn8X5atTorRENERERERESOzM/PD6NGjTJ93HvvvSgpKZE6LJvrkwmBnmppaUFLS4vp69raWgDAk+vT8epNkyAIglShERERERERkQ1NnToVlZWV7e575JFHJIrGvvpkD4GeWrVqFXx8fEwfUVFRAIAfjhbhgx3ZEkdHREREREREtiKXy6UOQTJMCAB49NFHUVNTY/rIz883Pfbibxk4XVonYXRERERERERE1sclAwBcXV3h6up60f3TBgZiZ24jnvn5BD65eRyXDhARERERkVMQRRFNGq3UYViVm0LOczIrY0KgC4/MS8bC949ix5ly7MqswJQBgVKHRERERERE1K0mjRaDH/9N6jCs6sTTl8JdyVNYa+qTSwbq6+tx9OhRHD16FACQnZ2No0ePIi8vr0f7ifb3wJLx0QCAN7dmWjtMIiIiIiIiIsn0yfTKwYMHMXPmTNPX9913HwBg2bJlWLt2bY/2ddu0eKzbm4s9WRU4VlCNYZG+VoyUiIiIiIjI+twUcpx4+lKpw7AqN0X/bf5nK30yITBjxgyIomiVfYX7uuGKYWH44WgR1u3NxQvX+lplv0RERERERLYiCALL66lbfXLJgLX9eUIMAGB9ahFqmzUSR0NERERERERkOSYEzDA6xg+JwZ5o1ujwe3qp1OEQERERERERWYwJATMIgoCrhocDAH5KLZI4GiIiIiIiIiLLMSFgpiuGhQEAdmaWo7JBLXE0RERERERERJZhlwkzxQd5YkiEN9IKa7EhrRhLx8dIHRIRERERERFZQVVVFbRarelrnU4HAGhsbER5ebnpfpVKBU9PT7vHZyusEOgB47KB9Ue5bICIiIiIiKivGDlyJIKCgkwf+fn5AIAXX3yx3f133nmnxJFaFxMCPTB/mD4hsD+nElVcNkBEREREREROjEsGeiDC1w3JoV44VVKHHZnlpooBIiIiIiIicl45OTlShyAJVgj00PSBQQCAbRllEkdCRERERERE1HtMCPSQMSGw/XQ5dDpR4miIiIiIiIiIeocJgR4aE+sPd6Uc5fUtOFFcK3U4RERERERERL3ChEAPKV1kmJQQCAD44/Q5iaMhIiIiIiIi6h0mBHphRpJ+2cAfGUwIEBERERERkXNiQqAXpiTqKwSO5lejWaOVOBoiIiIiIiKinmNCoBdiAtwR4u0KtVaHo/nVUodDRERERERE1GNMCPSCIAgYFxcAANifXSlxNEREREREREQ9x4RAL42L8wfAhAARERERERE5JyYEemm8ISFwKLcKGq1O4miIiIiIiIiIeoYJgV5KDPKEn7sCTRot0gprpA6HiIiIiIiIqEeYEOglmUzA2FguGyAiIiIiIiLnxISABYx9BA7kMCFAREREREREzoUJAQuMjPYDABzNr4YoihJHQ0RERERERGQ+JgQskBLuDReZgPJ6NQqrm6QOh4iIiIiIiMhsTAhYQKWQY1CYNwB9lQARERERERGRs2BCwELDo3wAAKlMCBAREREREZETYULAQiOizvcRICIiIiIiIudSUVGBNWvW4M9//jMGDx4MDw8PuLq6IjIyEgsWLMD3338vdYg24yJ1AM5uhKFC4HhhDVq1OrjImWMhIiIiIiJyFqGhoWhtbTV9rVKpoFAoUFhYiMLCQvz444+YN28evv32W7i7u0sYqfXx7NVC8YGe8HJ1QbNGh4zSOqnDISIiIiIioh5obW3FuHHj8NZbb+Hs2bNoampCfX09srOzccsttwAANmzYgL/+9a8SR2p9TAhYSCYTMMzUR6BG4miIiIiIiIioJ7Zs2YJ9+/bhjjvuQHx8vOn+2NhYfPDBB6ZEwLp165Cfny9VmDbBhIAVDI/0BQAcK6iWNA4iIiIiIiLqmZkzZ3b5uLFKAAAOHjxo63DsigkBK0gJ11cInCiulTgSIiIiIiIisiaVSmW6rdVqJYzE+pgQsIKUcG8AwKmSOmi0OomjISIiIiIiImvZtm2b6fbQoUOlC8QGOGXACqL93eHp6oL6llacPVeP5FBvqUMiIiIiIqL+TBQBTaPUUViXwh0QBLsesrq6GqtWrQIATJ06FUlJSXY9vq0xIWAFMpmAwWHe2J9TifTCWiYEiIiIiIhIWppG4NlwqaOwrn8WAUoPux1Op9PhxhtvRHFxMVxdXfH666/b7dj2wiUDVjLYsGwgvYh9BIiIiIiIiJzd3XffjZ9//hkA8NZbb2H48OESR2R9rBCwEmMfgRPFHD1IREREREQSU7jrr6j3JQp3ux3qgQcewBtvvAEAePnll3HzzTfb7dj2xISAlZgmDRTVQhRFCHZe20JERERERGQiCHYtr+9LHnroIfznP/8BALz44ou45557pA3IhrhkwEoGhHhCKZehtrkVBVVNUodDREREREREPfTggw/ixRdfBAC88MILeOCBBySOyLaYELAShVyGgaGeAID0Ii4bICIiIiIiciYPPPAAVq9eDUCfDHjwwQcljsj2mBCwosFh+j4CJ4vrJI6EiIiIiIiIzPXAAw+YlgmsXr26XyQDAPYQsKqBIV4AgIwSJgSIiIiIiIicwcMPP2xKBrz00ku49957JY7IflghYEXJofoKgYxSJgSIiIiIiIgcXV5eHl544QUAgEwmw/PPP4/Q0NBOP4xLCvoKVghYkbGHQE5FA5o1WqgUcokjIiIiIiIios7odLp2t0tLS7vcvr6+3tYh2RUTAlYU5OkKfw8lKhvUOFNaj6GRPlKHRERERERERJ2IjY2FKIpShyEZLhmwIkEQkGToI3CqpFbiaIiIiIiIiIg6x4SAlSWFsrEgEREREREROT4mBKzMlBBgY0EiIiIiIiJyYEwIWBlHDxIREREREZEzYELAyowVAmV1LahqUEscDREREREREVHHmBCwMk9XF0T6uQEATrFKgIiIiIiIiBwUEwI2MCDYEwBw9lzfmlFJREREREREfQcTAjaQaEgIZJYxIUBERERERESOiQkBG0gIYoUAEREREREROTYmBGzAWCFwlhUCRERERERE5KCYELABY0KgqKYZDS2tEkdDREREREREdDEmBGzA112JQE8lAC4bICIiIiIiIsfEhICNxAexsSARERERERE5LiYEbCSRoweJiIiIiIjIgTEhYCOJrBAgIiIiIiIiB8aEgI0YKwSYECAiIiIiIiJHxISAjSQYEgK5FY3QaHUSR0NERERERETUHhMCNhLuo4K7Uo5WnYjcikapwyEiIiIiIiJqhwkBGxEEAQnsI0BEREREROTQDh8+jKeeegpXXXUVkpOTERAQAIVCgYCAAEyePBkrV65EZWWl1GHahIvUAfRlicGeOF5Yw0kDREREREREDuqjjz7Cm2++afpapVLBzc0NlZWV2L17N3bv3o1XXnkF69evx8SJEyWM1PpYIWBDCUEeAICzrBAgIiIiIiJySOPGjcOLL76IPXv2oKqqCk1NTaitrUVdXR3Wrl2LoKAglJeXY8GCBaipqZE6XKtihYANmSYNsEKAiIiIiIjIId10000d3u/p6Ylly5YhLCwMl156KcrKyvDzzz9j6dKldo7QdlghYEPGhMDZsnqIoihxNERERERERNRTEyZMMN0uKCiQMBLrY0LAhmICPCCXCWhQa1Fa2yJ1OERERERERNRDO3bsMN1OSEiQMBLr45IBG1LIZYjyc0NORSOyyxsQ6qOSOiQiIiIiIuoHRFFEU2uT1GFYlZuLGwRBsMuxWlpaUFxcjJ9//hmPP/44ACAxMRFXXnmlXY5vL0wI2FhsoAdyKhqRU9GAiQkBUodDRERERET9QFNrE8Z/Pl7qMKxq35J9cFe42/QYKpUKLS0XV3dPnjwZn3/+OVxdXW16fHvjkgEbiw3QTxrILm+QOBIiIiIiIiLqSmhoKEJCQuDh4WG6b+bMmXjllVcQHR0tYWS2wQoBG4sLZEKAiIiIiIjsy83FDfuW7JM6DKtyc3Gz+TFycnJMt8vKyvDpp59i5cqVGDduHP71r3/h6aeftnkM9sSEgI3FGhICOUwIEBERERGRnQiCYPPy+r4uODgY999/P6ZOnYqJEyfimWeewbhx43DFFVdIHZrVcMmAjcUbEgK5lY3Q6jh6kIiIiIiIyJmMGzcOU6ZMAQC89957EkdjXUwI2Fi4rxuUchnUrToUVfetLp9ERERERET9QUREBAAgMzNT4kisiwkBG5PLBET569e65FRw2QAREREREZGzycrKAgB4eXlJHIl1MSFgB3HsI0BERERERORwtFotRLHrpd2bN2/G/v37AQAzZsywQ1T2w4SAHRgTAllMCBARERERETmM/Px8jBw5Eu+++y6ysrLaJQfy8/Px3HPP4eqrr4YoivD398e9994rYbTWxykDdsBJA0RERERERI4pNTUVt99+OwBAqVTC29sbTU1NaGg4f/4WFxeH7777DqGhoVKFaRNMCNhBXIAhIVDRKHEkREREREREZBQeHo6vv/4a27Ztw759+1BcXIzy8nLI5XJER0dj+PDhuPrqq7FkyRK4ublJHa7VMSFgB8YKgbzKRmi0OijkXKlBREREREQkNaVSicWLF2Px4sVShyIJnpnaQai3CiqFDFqdiIIqjh4kIiIiIiIi6TEhYAcymYDYAPYRICIiIiIiIsfBhICdGBMC2UwIEBERERERkQNgQsBOjH0EmBAgIiIiIiIiR8CEgJ3EG0cPVjAhQERERERERNJjQsBOWCFAREREREREjoQJATuJCXAHABRVN0Gj1UkcDREREREREfV3TAjYSZCnK1xdZNCJQCFHDxIREREREZHEmBCwE5lMQLS/vkogr7JR4miIiIiIiIiov2NCwI6MCYFcJgSIiIiIiIhIYkwI2FG0oY9APhMCREREREREJDEmBOwoxlghwNGDREREREREJDEmBOzIWCGQV8mmgkRERERERCQtJgTsKNrfAwCQV9EAURQljoaIiIiIiIj6MyYE7CjSzw2CADSotahoUEsdDhEREREREfVjTAjYkUohR6i3CgBHDxIREREREZG0mBCwsyhDY8G8CiYEiIiIiIiISDpMCNiZcdIAKwSIiIiIiIgc03PPPQdBEEwffRUTAnYWbRo9yIQAERERERGRo8nIyMBTTz0ldRh2wYSAnRlHD+azQoCIiIiIiMih6HQ63HLLLWhubsbEiROlDsfmmBCws5gA/ejB3MoGiSMhIiIiIiKitl5//XXs2rULS5cuxdy5c6UOx+aYELAz45KB0toWNGu0EkdDREREREREAJCdnY3HHnsMAQEBePnll6UOxy6YELAzP3cFvFxdAHDZABERERERkaP4y1/+goaGBrz00ksICgqSOhy7YELAzgRBOD96kAkBIiIiIiIiyb3//vvYvHkz5syZg5tuuknqcOzGReoA+qOYAHecKK7lpAEiIiIiIrIJURQhNjVJHYZVCW5uNhkBWFhYiAcffBBubm549913rb5/R8aEgASiWSFAREREREQ2JDY1IWPUaKnDsKqkw4cguLtbfb9//etfUVNTg+effx7x8fFW378j45IBCRhHDzIhQEREREREJJ1169bhl19+wYgRI3DfffdJHY7dsUJAAjH+htGDFRw9SERERERE1ie4uSHp8CGpw7Aqwc3NqvsrKyvDPffcA7lcjvfffx8uLv3v9Lj/fccOwLhkIL+qCTqdCJnM+utgiIiIiIio/xIEwSbl9X3Jww8/jIqKCtxxxx1ITk5GfX19u8fVarXptvExpVIJpVJp1zhtiUsGJBDuq4JcJkDdqkNpXbPU4RAREREREfU72dnZAIC3334bXl5eF32sWrXKtK3xvoceekiqcG2CCQEJuMhliPDVl7vkcdIAERERERERSYAJAYnEGBoL5rKxIBERERERkd1t27ZNP56xk48nnnjCtK3xvldeeUW6gG2ACQGJRBlHD7JCgIiIiIiIiCTAhIBEzjcWZEKAiIiIiIiI7I8JAYlE+ekTAgVVTRJHQkRERERERP0REwISifTTNxXMZw8BIiIiIiIih/Pkk0+aegf0VUwISMTYQ6CsrgXNGq3E0RAREREREVF/w4SARPzcFXBXygEAhdVcNkBERERERET2xYSARARBYB8BIiIiIiIikgwTAhJiHwEiIiIiIiKSChMCEjL2EWCFABEREREREdmbi9QB9GemCoEqVgg4u0O5VVi7Owd7sypQ26RBhK8bZg8Kxi1T4hHqo5I6PCIiIiIiooswISChSPYQcHrqVh2e/jkd6/bmtbs/q7wBWTuy8fm+PDx5VQoWj4mSKEIiIiIiIqKOMSEgoSh/fYVAAXsIOKVmjRa3fHwAuzIrAADXjo7En8ZGIcRbhfSiWry7/SyO5FXjwW+Poai6GXfPGSBxxEREREREROcxISAhY4VARYMajepWuCv543AWoijiri+OYFdmBTyUcryxdBRmJgWbHo/yd8fcwSF4ZdNpvLYlEy9vOg1fdwWWTYqVLmgiIiIiIqI22FRQQj5uCnir9EkALhtwLmt25eD3E6VQymX4aPnYdskAI5lMwH1zk/DA3IEAgKd/PoEDOZX2DpWIiIiIiKhDTAhI7HwfAS4bcBZphTV4bsMpAMC/rhiE8fEBXW7/95mJWDAiHFqdiAe/SUWTWmuPMImIiIiIiLrEhIDEjH0E8itZIeAMtDoRD357DGqtDnMHh+DGCTHdPkcQBDy9YAhCvVXIqWjESxsz7BApERERERFR15gQkJixQiCfjQWdwneHCnCyuBZeKhc8t2gYBEEw63neKgVWXjMEAPDhzmykF9XYMkwiIiIiIqJuMSEgsSg/w6QB9hBweA0trXjxd/3V/btnD4C/h7JHz589KATzh4VBJwL/+f20LUIkIiIiIiIyGxMCEjNVCLCHgMN7f0cWztW1ICbAHTdO7H6pQEcemJsEuUzAllNlOMgGg0REREREJCEmBCQW5W9sKsgKAUfWqG7F2t05AID75ybB1UXeq/3EBXrgujGRAIAXf8uAKIrWCpGIiIiIiKhHmBCQWKRhyUBNkwa1zRqJo6HOfH0gH9WNGkT7u2P+0DCL9vWPWQOgdJFhX3YlDuVWWSlCIiIiIiKinmFCQGIeri6mtegFnDTgkFq1OnywMxsA8Jdp8ZDLzGsk2JlwXzcsHBkBAFizK8fS8IiIiIiIiHqFCQEHYGwsyD4Cjul/6SUoqGpCgIcSi0dHWmWfyyfHAgA2pBWjgD93IiIiIiLJrF27FoIgdPuxadMmqUO1OiYEHICxsSD7CDimz/flAQCWToiBStG73gEXSg71xuTEAOhE4NM9uVbZJxERERER9Z5MJkNISEinH66urlKHaHUuUgdAQKS/oUKgkleKHU1uRQN2n62AIADXj42y6r5vnhyHXZkV+GJ/Hu69ZKDVkg1ERERERNRzUVFRyMnJkToMu2KFgANghYDj+upAPgBg2oAgRPi6WXXfM5OCEeHrhtrmVvx+otSq+yYiIiIiIuoOEwIOwNhDgGvJHUurVodvDhUAAP5k5eoAAJDJBCwapW8u+M3BfKvvn4iIiIiIqCtMCDiAthUCnEvvOP44fQ7n6loQ4KHE7EEhNjnGIkOTwp2Z5SiuYYUIERERERHZD3sIOIBIQ4VAfUsrqhs18DOMISRp/ZRaBAC4cng4lC62yZ3FBHhgXJw/9mdX4r+HC/H3mYk2OQ4RERER9S+iKKJVrZM6DKtyUcogCJaNAO/KuXPnMHr0aGRkZECr1SIsLAyTJk3CrbfeihkzZtjsuFJiQsABqBRyBHm54lxdCwqqmpgQcADNGi02Gtb1Xzk83KbHWjw6EvuzK/Hd4QL8bUaCTX/JEREREVH/0KrW4b27/5A6DKu67dXpULjarhF3Y2MjDh8+DD8/PzQ0NCA7OxvZ2dn47LPPsGLFCrz33ntwcelbp9BcMuAgjH0E8tlHwCFsPVWGBrUWEb5uGBXta9NjXTYkFEoXGbLONeBUSZ1Nj0VERERERO2Fh4fjiSeeQGpqKpqbm1FZWYnGxkbs2rULc+bMAQCsWbMG9957r8SRWl/fSm84sUg/dxzOq2ZjQQfx87FiAMD8YWE2v2LvpVJg+sAgbDxRil+PF2NQmLdNj0dEREREfZ+LUobbXp0udRhW5aK0zfXsuXPnYu7cue3uk8vlmDRpEn777TcsXLgQP/74I9566y3cddddGDBggE3ikAIrBBxElL+hQqCSjeWk1tDSis2nDMsFhtl2uYDRFcPCAAC/HC9mY0kiIiIispggCFC4yvvUhxRLa2UyGVavXg0A0Ol0+Omnn+wegy0xIeAgzk8aYIWA1HacOYdmjQ7R/u4YEmGfq/WzB4Vw2QARERERkQNKTExEYGAgACArK0viaKyLCQEHEWVICORXsUJAahtPlAEALhkcYrcspKerC6YPDAIA/Hq82C7HJCIiIiKi/o0JAQdhHD1YUNXIknEJaXUithiWC8wZFGLXY88fql828Ft6iV2PS0REREREnTt79izKy8sBAHFxcRJHY11MCDiIcF83CALQrNGhvF4tdTj91uG8KlQ1auDjpsCYWD+7HntmUjDkMgGnS+uRX8mlI0REREREttbdxVhRFPHggw8C0PcTuOKKK+wRlt0wIeAglC4yhHmrAHD0oJQ2ndBXB8xMCoJCbt9/Hj7uCoyJ0SchNp0steuxiYiIiIj6o9zcXIwbNw7vvvsusrKyTAkCnU6HvXv3Yt68efj+++8BAH/961+RlJQkZbhWx7GDDiTSzx1FNc0oqGrCqGj7Xp0mvY2GE/E5g+27XMBozqAQ7MuuxOaTZVgxuW+VIxEREREROaIDBw7gwIEDAABXV1d4eXmhrq4OLS0tpm1WrFiB1157TaoQbYYVAg6kbR8Bsr/cigZknWuAi0zANEODP3ubPSgYALAvuwJ1zRpJYiAiIiIi6i9CQkLw+uuvY8mSJRg8eDC8vb1RXV0NhUKB5ORk3Hzzzdi5cyc++ugjuLj0vevpfe87cmLnEwKcNCCF7Wf0jUJGxfjBW6WQJIb4IE/EB3ogq7wB20+XY/6wMEniICIiIiLqD9zc3HDnnXfizjvvlDoUSbBCwIFEGkYPMiEgje2nzwEApg0IlDQOY5XAllNlksZBRERERER9GxMCDoRLBqSj0eqw52wFAEi2XMDIePydmec4gpKIiIiIiGyGCQEHEuWvrxAorGriiaCdHcmrRn1LK/w9lBgS7iNpLGNj/eHqIkNpbQsyy+oljYWIiIiIiPouJgQcSKiPCjIBaGnV4Vx9S/dPIKsxLheYkhgImUyQNBaVQo5xcf4AgB2GvgZERERERETWxoSAA1HIZQjzYWNBKew4o08ITJW4f4DRlER9HDszmRAgIiIiIiLbYELAwURw0oDd1TRpcLywBgAwdYC0/QOMjHHszaqAulUncTRERERERNQXMSHgYNhY0P4O5lRCJwJxgR4I9VFJHQ4AIDnUC4GeSjSqtTicVyV1OERERERE1AcxIeBgjKMH8ytZIWAve7P00wUmxPtLHMl5MpmAycZlA+wjQERERERENsCEgINhhYD97c2qBABMiA+QOJL2jMsGjP0NiIiIiIiIrIkJAQdjTAgUsoeAXdQ2a5BepO8fMD7OsRICxsaCxwprUN2oljgaIiIiIiLqa5gQcDBRhiUDBdVN0OlEiaPp+4z9A2ID3B2mf4BRqI8KA0M8IYrA7rMVUodDRERERER9DBMCDibURwWZAKhbdSivb5E6nD7PUZcLGE1K0FcJ7M+ulDgSIiIiIiLqa5gQcDAKuQxhPvplA/lcNmBz5xsKOmZCYFycvtGhMU4iIiIiIiJrYULAAbGxoH3UNmuQVmjoH+BAEwbaGhurjyujtI59BIiIiIiIyKqYEHBAxtGDBawQsKm2/QOMVRmOJsjLFQlBHhBF4GBOldThEBERERFRH8KEgAM6XyHAhIAtOXr/AKNxhukH+7K5bICIiIiIiKyHCQEHxCUD9nEgR58QMK7Td1TjDfGxsSAREREREVkTEwIOiEsGbK9ZozX1DxgT49gJAWPCIq2oFvUtrRJHQ0RERETUd9XW1uL555/HpEmTEBQUBFdXV0RGRmLmzJl48sknUV1dLXWIVuUidQB0MWOFQGFVE3Q6ETKZIHFEfU9aYQ00WhGBnq6I8nfM/gFG4b5uiPJ3Q35lEw7lVmH6wCCpQyIiIiIi6nO2bt2KG264AaWlpQAAFxcXeHp6orCwEIWFhdi2bRsWLFiAESNGSBuoFbFCwAGF+agglwlQa3U4V98idTh90qFcfYO+0TG+EATHT7iMi9X3EdjPPgJERERERFa3a9cuzJ8/H6WlpZgzZw527tyJlpYWVFVVobGxEQcPHsRjjz0GHx8fqUO1KlYIOCAXuQyh3ioUVjehoKoRId4qqUPqc84nBPwkjsQ84+P98d3hAuzLYh8BIiIiIiJramxsxE033YSmpiYsWrQIX3/9NWSy89fO3dzcMHr0aIwePVrCKG2DFQIOipMGbEcURRzOqwYAjIp2koSAoY9AakE1mtRaiaMhIiIiIuo7Pv30U2RlZcHNzQ3vvPNOu2RAX9d/vlMnw8aCtpNf2YTy+hYo5AKGRDhHyU+0vztCvF2h0Yo4VlAtdThERERERH3GJ598AgC4+uqrERgYKHE09sWEgIMyNrrj6EHrO5SnL7sfEuEDlUIucTTmEQTBtLzhUF6VxNEQEREREfUNLS0tOHjwIABg+vTpyMrKwi233ILIyEi4uroiNDQUV199NTZs2CBxpLbBHgIOihUCtmPqH+AkywWMRkX74dfjJTicWy11KERERETk4ERRRGtL32pQ7uLqavWG4Dk5OVCr1QCAgoICDBs2DA0NDVAqlXB3d0dpaSnWr1+P9evX4/bbb8fbb79t1eNLjQkBB8UeArZzyHBC7SwNBY1GGeI9nFcFURSdYjoCEREREUmjtaUFry27VuowrOquj7+FQmXdhutVVeerb1etWgVvb2988cUXWLRoERQKBfLz8/HQQw/hyy+/xDvvvIPk5GTcfffdVo1BSlwy4KCMCYHCqibodKLE0fQd9S2tyCipBXD+BNtZpIR7QymXobJBjdwKLiUhIiIiIrKUTqdrd/udd97Bn/70JygUCgBAVFQUPvvsM4wcORIA8O9//xutra2SxGoLrBBwUKHeKshlAtRaHcrqWhDqw9GD1pCaXw2dCET4ujndOEdXFzmGRvrgUG4VDuVWITbQQ+qQiIiIiMhBubi64q6Pv5U6DKtycXW1+j69vLxMt6OionD99ddftI1MJsP999+PP//5zygvL8ehQ4cwfvx4q8ciBSYEHJSLXIYwHxUKqppQUNXIhICVHDb0D3C26gCjUdG+OJRbhcN5VVg0OlLqcIiIiIjIQQmCYPXy+r4oIiLCdDs5ObnT7QYNGmS6nZub22cSAlwy4MDYR8D6Ug0j+0ZG+UoaR2+ZJg3kctIAEREREZGl/P39TUmBrnp0ieL5Zdx9qZcXEwIO7PykAa4XtwZRFHE0vwYAMDzKR+JoemeUYTLC6dI61DVrJI6GiIiIiMj5zZ07FwBw8uTJdif+bZ08edJ0Oy4uzi5x2QMTAg6MFQLWVVzTjPL6FshlAlLCnTMhEOytQqSfG3QikGpIbhARERERUe+tWLECAJCfn4+vvvrqosd1Oh1eeuklAPolBqNGjbJrfLbEhIADO18hwISANRwzLBdICvGCSiGXNhgLjG4zfpCIiIiIiCwzdepUXHutfkTjHXfcga+++goajb4aNz8/H0uXLsWRI0cAACtXroRM1ndOo9lU0IFFmSoEuGTAGs4vF/CVNhALjYr2w49Hi9hHgIiIiIjIStauXYuysjJs374df/rTn+Dq6gp3d3dUVZ3/m/vxxx/HsmXLJIzS+vpOaqMPivTXVwgUVjdBp+t4LQuZz1ghMDzSOZcLGBkrBI7kVfF90YeJoogmtRZa/oyJiIiIbM7DwwNbt27F+++/j2nTpsHDwwP19fWIiIjAn/70J+zatQtPPfWU1GFaHSsEHFiIlytcZAI0WhFldS0cPWgBnU7E8YK+USGQFOoFVxcZaptbkVPRgPggT6lDIisRRRE7zpRj3d5c7MmqQF1zKxRyfc+La0ZG4LoxUXBTOu9yFyIiIiJHJpPJcOutt+LWW2+VOhS7YYWAA3ORyxDmq08CcNmAZbLKG1DX0gqVQoYBwc59Aq2Qy5AS7g0AOFbAxoJ9RXl9C275+CBu+mg/fj9RirrmVgCARiviaH41nlifjrmv/IG9WRUSR0pEREREfQUTAg4u0le/bCCfCQGLGJcLDAn3gYvc+d/2xiqHo/nVksZB1pFf2YgFb+7CllNlUMplWD4pFj/dOQVpT12K7Q/OxJNXDkaYjwr5lU1Y+sE+fHMwX+qQiYiIiKgP4JIBB2caPVjJSQOWSDWcODv7cgGjEYbvI9WQ6CDnlV3egCXv70VxTTNiA9zx7o1jkBTqZXrc09UFyyfHYfGYKDz2/XH8cLQID313DEoXGa4eESFh5ERERETk7Jz/Umkfx9GD1pFqKK0f5uQNBY2GRfoCANKLaqHR6qQNhnqtrlmDm9ceQHFNMxKDPfH1Xye2Swa05eHqgpevH4Gl46MhisB9X6fij9Pn7BwxEREREfUlTAg4OFOFQDWXDPSWulWHE0W1AM5fWXd2sQHu8Fa5QN2qQ0ZJndThUC+IoogHvzmG7PIGhPuo8MVfJiDYu+vGoYIg4Jmrh2DhqAhodSLu++ooSmub7RQxEREREfU1TAg4OFNCgBUCvZZRUge1VgdfdwWiDaMcnZ0gCKblD1w24Jw+3ZuL/6WXQCEX8NafRyPIy9Ws58lkAp69ZigGhXmjokGNe748ytGERERERNQrTAg4uEjDCWxRdRP/6O+l44X65QJDI3wgCILE0VjPcMOygVQ2FnQ6JTXNeOF/GQCAf14+qMeVKyqFHG8sGQl3pRx7sirw2b5cG0RJRERERH0dEwIOLtRbBReZAI1WRFkdS4N7I61InxAYEtE3+gcYmSoE8jl60Nk88/MJ1Le0YmS0L5ZNjO3VPhKCPPHIvGQAwOrfMlBe32LFCImIiIioP2BCwMHJZQLCfblswBLphgqBIeF9LCFgaJB4pqwODS2tEkdD5tp++hx+OV4MuUzAygVDIZP1vmpl6fgYpIR7o7a5Fc9vOGXFKImIiIioP2BCwAmc7yPAxoI9pdHqcNLQdG9IhLfE0VhXsLcKYT4q6EQgrZBVAs5AFEW88Jv+xH3ZxFgMDrfsPSmXCXhmwRAAwDeHCvg+ICIiIqIeYULACZgSApWsEOipzLJ6qFt18FK59JmGgm2Z+giwsaBT+F9aCdIKa+Hp6oI7ZyVaZZ+jov2wYEQ4AOCVTaetsk8iIiIi6h+YEHACkX76E1kuGeg54xXTlHDvPtVQ0GhYlH7ZQGoBrww7Oq1OxH826k/Yb54SB38PpdX2ffecgZDLBGw6WYajbDJJRERERGZiQsAJGCsE8rlkoMfSi2oB9L3+AUYjOGnAafx6vBiZZfXwcVPg1qlxVt13XKAHrhkZAQB4aSOrBIiIiIjIPEwIOAFWCPSesUKgr00YMBoS6QNB0L83Kthl3mGJoogPdmQBAFZMjoW3SmH1Y9w1awDkMgHbT59DehErRoiIiIioe0wIOAFjhUBRdRO0OlHiaJyHVifiRLGhQqCPNRQ08lYpEB/oAQA4xmUDDutAThVSC2rg6iLDjRNibHKM6AB3XD40DADw4c5smxyDiIiIiPoWJgScQIi3Ci4yAa06EaW1zVKH4zSyyxvQqNbCTSFHXKCn1OHYzPAoXwDg2nEH9r6hOmDhqEgEeLra7Di3TNEvRfgptYi/K4iIiIioW0wIOAG5TEC4r3H0IJcNmMtYNj0ozAtyC2a9OzrjpIFjnDTgkHIrGrDpZCmA8yfstjIiyhdjY/2g0Yr4ZE+OTY9FRERERM6PCQEnYRo9yMaCZuvr/QOMjN9fmqGBIjmWLw/kQxSB6QODkBhs+0qVW6bEAwA+35eHllatzY9HRERERM6LCQEnEcXGgj2WVti3JwwYDQ7zhkwAztW1oIxl4g5F3arDNwfzAQBLxkfb5ZhzBgUjzEeFqkYNfksvtcsxiYiIiJyVIAhmf8ycOVPqcK2OCQEnwQqBnhFFEWmGJQMpfbShoJGbUm668ny8kI0FHcnmk6Uor1cj2MsVs5KD7XJMF7kM142JAgB8sS/PLsckIiIiclYhISFdfvj7+5u2HTt2rISR2gYTAk4i0p89BHoiv7IJdc2tUMplGBDsJXU4NmdcNsCEgGP5fL/+hPy6MVFQyO336/a6sVGQCcCerApknau323GJiIiInE1JSUmXH//85z9N295yyy0SRmobTAg4iUguGegRY3VAUqgXlC59/21uXBZhXCZB0iusbsLOzHIAwPVjo+x67AhfN8xI0lckfHUg367HJiIiIupLPvzwQwDAlClTkJSUJHE01tf3z5T6COOSgaLqJmh1osTROL7zDQX79nIBo6GRxoQAKwQcxY9HCyGKwIR4f0T5u9v9+H8yJCG+P1LI3xlEREREvbB7926cPHkSAHDrrbdKHI1tMCHgJIK9VFDIBbTqRJSwcVy3jB33U/p4Q0GjwWHeEASgpLYZ5+papA6n3xNFEd8fLgQAXDMyQpIYZiQFw89dgbK6FuwyVCoQERERkfmM1QHe3t5YvHixxNHYBhMCTkIuExDua+gjUMnGgl0RRRHp/WTkoJGHqwviAz0AnF8uQdJJL6rFmbJ6KF1kmDc0TJIYlC4yXDEsHIC+SoCIiIj6F1EUoVNr+9SHKNqv6rG+vh5ff/01AGDJkiVwd7d/xac9uEgdAJkv0s8NuRWNKKhqwnipg3FgZXUtqGhQQyYAyaF9v6Gg0dAIH5w914D0whrMTLJPR3vq2A+GE/BLBoXAW6WQLI5rRkXg0725+F9aCf69oBUervyVT0RE1F+IGh2KHt8tdRhWFf70JAhKuV2O9eWXX6K+Xt+cua8uFwBYIeBUIn3ZWNAcJ4r1ywXigzyhUtjnF4Yj4KQBx6DTiVifWgQAuHpEuKSxjIzyRWyAO5o0WvyWXiJpLERERETO5IMPPgAADB8+HKNHj5Y4Gtvh5SInYmwsWFDFJQNdOWlICAwK6x8NBY2MCQFOGpDW4bwqlNW1wMvVBdOTgiSNRRAEXD0iAq9uPoOfjxVj4ahISeMhIiIi+xEUMoQ/PUnqMKxKUNjnenZ6ejr27dsHoG9XBwBMCDgVY6dyVgh07WRxHQBgUFj/WS4AAIPD9QmQwuomVDWo4eehlDii/mlDmv5K/OxBwXB1kb5C5YphYXh18xnsOHMOtc0aSZcwEBERkf0IgmC38vq+xlgdoFKpsHTpUomjsS0uGXAipgqBalYIdKW/Vgh4qxSIY2NBSYmiiP8ZEgKXDZGmmeCFBoR4ITHYExqtiM0nS6UOh4iIiMihqdVqrFu3DgCwaNEi+Pn5SRyRbTEh4EQi/fQVAsXVzWjV6iSOxjE1a7TIOqdv/jG4nyUEACDFUCXAPgLSOF5Yg8LqJrgr5Zgh8XKBti4fEgoA+PU4+wgQERERdeXHH39Eebl+ZHNfXy4AWGnJgEajwZo1a7BhwwZkZWWhvr6+05EQgiDg7Nmz1jhsvxPs5QqFXIBGK6K0rgURhjGEdN7p0jroRMDPXYFgL1epw7G7oRE++PlYMdLZR0ASxhPumUnBDtXQct7QMLy2JRN/nD6H+pZWeHLaABEREVGHjMsFEhMTMX36dImjsT2L/yosLy/HrFmzkJ6ebtZcSEEQLD1kvyWTCYjwdUNORSPyKxuZEOhA2+UC/fG9xkkD0tEvFygGAFxmuCLvKJJDvRAX6IHs8gZsOVWGq4ZLO/2AiIiIyBHl5eVh06ZNAICbb765X5xPWJwQeOSRR5CWlobIyEg89NBDGDt2LIKDgyGTcTWCLUT6uSOnopGNBTtxvqFg/1suAABDwvUJgbzKRtQ0auDjzgZy9nKqpA45FY1wdZFhZnKw1OG0IwgC5g0JxVvbzmLD8WImBIiIiIg68NFHH0Gn08HFxQXLly+XOhy7sDgh8PPPP0OhUGDLli1ITEy0RkzUBY4e7Fp/bSho5OOuQJS/G/Irm5BeVINJiYFSh9RvGKcLTBsY5JAl+ZcPDcNb285ia0YZGtWtcFc6XoxEREREUtHpdFi7di0A4PLLL0dYmGM0iLY1iy/j19TUICkpickAOzmfEGCFwIVEUWyTEOhfIwfbGsplA5LYdELfwf+yFMdaLmCUEu6NKH83NGt0+CPjnNThEBERETmUTZs2ITc3F0D/aCZoZHFCIDExEWq12hqxkBmMkwZYIXCxoppm1Da3wkUmIDHYU+pwJJNiWDaQVsTGgvZSUtOME8W1EAQ41HSBtgRBwKWD9cmKTSfLJI6GiIiIyLHMnTsXoihCFEVceeWVUodjNxYnBG699VacOXMGhw4dskY81A1WCHTupOEEODHYE64ujtPh3d6MFQJprBCwmy2n9CfYI6N8EeDpuNMtZg8KAQBszSiDVtd9E1giIiIi6tssTgjcdddduOGGG7BgwQL8+OOP1oiJumCsECiuaUarVidxNI6lv/cPMDJOGsgub0Bds0biaPoHY0JgloM1E7zQmFg/eKlcUNmgxtH8aqnDISIiIiKJWdxVavbs2QCAsrIyLFy4EH5+fkhISICHh0eH2wuCgM2bN1t62H4r2MsVSrkMaq0OJbXNpgQBASdL2D8AAPw9lIjwdUNhdRPSi2oxIT5A6pD6tGaNFrsyywHA4aYLXEghl2FGUjB+Si3C5pOlGB3jJ3VIRERERCQhixMC27Zta/d1ZWUlKisrO92+P8xytCWZTECEnxuyyxtQUNXEhEAb/X3kYFsp4d4orG5CWmENEwI2tjerAk0aLUK9VRjsBO+92cn6hMCWU2V46LJkqcMhIiIiIglZnBDYunWrNeKgHohskxAgvUZ1K3IqGgAwIQDolw38fqIUJ9hY0Oa2GpYLzEwOdoqE54ykIMgE4FRJHQqqGplUJCIiIurHLE4ITJ8+3RpxUA8YGwvmV3LSgNGpkjqIIhDk5YpAB27qZi9DIvRJkbQiNha0JVEUsSXDOfoHGPm6KzEmxh/7cyqx+WQZlk2KlTokIiIiIpKIxU0Fyf7Ojx5khYARGwq2N8QwejCzrB5Naq3E0fRdmWX1yK9sgtJFhsmJzrM0Y/YgffJi8ymOHyQiIiLqzyyuEGiroaEBu3btwunTp1FXVwcvLy8MHDgQkydP7rTJIPXc+dGDrBAwOp8Q6N8NBY2CvVUI9HRFeX0LTpbUYlQ0m8fZwlZDdcCE+AC4K63669SmZg8KxqoNp7D3bAXqW1rh6eo8sRMRERGR9Vjlr0C1Wo0nnngCb775JhoaGi563MPDA//4xz/wxBNPQKlUWuOQ/dr5hAArBIxMDQVDWSFgNCTCG9syziG9iAkBW9lxRj9dYMbAIIkj6ZmEIE9E+7sjr7IRe89WYM7gEKlDIiIiol4QRVHqEKgLzvDzsXjJgFarxVVXXYUXXngB9fX1iIiIwOzZs7FkyRLMnj0bERERqK+vx3PPPYerrroKWi3Lly1lXDJQUtuMVq1O4mikp9OJOMUlAxcxLhtIL2QfAVto1mixP1s/UWXawECJo+kZQRBMMW8/c07iaIiIiKinZDL9aRzPrRyb8edj/Hk5Iosje/fdd/H7778jODgYX3/9NXJzc/H777/j008/xe+//47c3Fx8/fXXCA0NxcaNG/Hee+9ZI+5+LcjTFUq5DFqdiOKaZqnDkVx+VSMa1Foo5TLEB3FpihEbC9rWgZxKtLTqEOqtQkKQp9Th9NjUAfqqBmOVAxERETkPhUIBhUKB+vp6qUOhLtTV1Zl+Vo7K4oTAJ598AkEQ8Msvv+Daa6+9aOyWIAi49tpr8dNPP0EURXz88ceWHrLfk8kERHDZgImxf8CAEE8o5I6bfbO3FEOFQEZJHdStrCSxNuOJ9NQBgU4xbvBCkxIC4CITkF3ewIklRERETkYQBHh5eaGmpgZNTTwfcERNTU2ora2Fl5eXQ/+taHEPgZMnT2LQoEEYNWpUl9uNGjUKgwcPxokTJyw9JEHfRyC7vAH5VY2YCOfpbm4LJ4z9A7hcoJ1IPzd4q1xQ29yKM2V1pgQBWYcpIeBk/QOMvFQKjIr2w/6cSmw/cw5Lx8dIHRIRERH1QGBgIJqampCXlwdvb294eXlBLpc79MlnXyeKIrRaLerq6lBbWwtXV1cEBjr20lKLEwJardbsEgiFQgGdjlcqrSHKX99HgFf2OHKwM4IgYEiED3afrUB6YS0TAlZUVtdset9NTnDehNzUAYH6hMBpJgSIiIicjVwuR1RUFMrLy1FXV4fq6mqpQyIDhUIBX19fBAYGQi6XSx1OlyxOCCQkJCAtLQ05OTmIjY3tdLvs7GykpaVh8ODBlh6SAEQbEgK5FTZOCOh0QPp/gcMfA6UnAIU7EDsZmPh3IHSobY9tplMlHDnYGWNCIK2oBtchSupw+oxdmfrqgCER3gjwdJU4mt6bNjAI/9l4GrszK6DR6rjkhoiIyMnI5XKEhIQgODgYGo2GF18dgEwmg0KhcJpKDYsTAosXL8bjjz+Oq6++Gp9++imGDRt20Tapqam46aaboNPpcN1111l6SAIQY0gI5NmyQqCxEvj2ZiBra/v7U/OAY18BM/8JTH0AkPDNXtesQX6lft3UYFYIXCQl3NBYkJMGrOp8/wDnXC5gNCTCB77uClQ3apCaX40xsf5Sh0RERES9IAgCx7tTr1icELjvvvvw9ddf4/jx4xg5ciSmTJmCwYMHIzg4GGVlZThx4gR27twJURQxbNgw3HfffdaIu9+LDrBxQqCxEvj4SqA0TV8VMPkeYOClQHMNcOB94ORPwJZ/Aw3lwGXPSZYUOFWi7x8Q5qOCrzt/CV7IuEzgZHEdtDoRcplzZCodmSiK7RoKOjO5TMCUxED8fKwY20+fY0KAiIiIqJ+xOCHg7u6OLVu24Pbbb8f333+PHTt2YMeOHRAEAaIoAtBnrBYtWoS3334bbm5uFgdN55cMVDaoUdesgZfKiqMsdFp9ZUBpGuAZAtz4AxDSZqlH/HTgwAfALw8A+94BfKP1SwgkwP4BXYsL9IC7Uo5GtRbZ5fVIDOayCktllNbhXF0L3BRyjI7xkzoci00bEKRPCJwpx31zk6QOh4iIiIjsyOKEAKDvcPntt98iMzMTGzduxOnTp1FfXw9PT08MHDgQc+fORUJCgjUORQZeKgX8PZSobFAjr7LRug3jdr2qXyagcAf+/N/2yQCjsbcCrS3Ab/8ENj4ORE8EIrqeNGEL5xMCPNHtiFwmYFCYNw7lViGtsJYJASvYnVkBABgb5w9XF8duEmOOqQP1VQ7HCqpR3ahmpQ0RERFRP2KVhIBRYmIiEhMTrblL6kK0v7s+IVBhxYRAeSaw7Tn97ctfBEKHdL7thL8BBQeA9O+BH/4G3L4TkFv1LdUtjhzs3pBwfUIgvagGC0ZGSB2O09uTpU8ITHLi6QJthfm4YWCIJ06X1mNXZgXmDwuTOiQiIiIishO2lHZiMYY+ArnW7COw5WlA2wIkzAZGLO16W0EA5r8EuPkD507qJxHYkVYnIqOESwa6kxKhTxalFdZKHInz0+pE7DUkBCbG942EAHC+OeL20+ckjoSIiIiI7IkJAScWbe1JA0VHgBM/AhCAuf82r1Gguz8w41H97a3P6psO2klORQOaNTqoFDLEBnjY7bjOZoiheiStqMbU14N650RRLeqaW+Hl6mKa4NAXTBuoTwjsOHOO7xEnJ4oi8isb8evxYny4Mxsf787BL8eKUVrbLHVoRERE5IB6VN8tl+vXyyYnJyM9Pb3dfeYSBAGtra09eg51zJQQqLBSQmDz0/rPw67ruG9AZ8asAPa/B1ScAXa+DMx50jrxdMPYPyAp1Jvd87swIMQTSrkMdc2tyK9sMk2ooJ7bk6WfLjA+3h8u8r6TTx0X6w+FXEBRTTNyKxoRG8gEm7Np1mjx3eECfLon1zR95UIp4d64eXIcrhweDqVL33n/EhERUe/16C8CURQhiiJ0Ot1F95n70fa5ZJkYw1Xx3MoGy3dWcBA4uwWQuQAzHunZc+UK4JKn9Lf3f2C3KgFTQ8FQNsrrikIuQ5LhNUovsl8FR1+0+6x+ucCEPrRcAADclHKMjNZPTDB+j+Q8dmeWY96rO/DY92k4VVIHhVzA0AgfXDEsDJcPDUVKuDdkApBeVIv7v0nFZa9ux6HcKqnDJiIiIgfQowqBjk7meYIvHWOFQFF1MzRaHRSWXLHc/57+89DFgH98z58/cB4QmASUZwCHPwEm/aP3sZjpJBsKmm1IhDeOF9YgragG84ayaVxvaLQ6HMiuBABM7CMNBdualBCA/dmV2H22HEvGR0sdDplBpxPx0sbTeGNrJgAg2MsVt02Lx7WjIy+aFlHVoMbn+/OwZlc2ss414Np3duMfswbg3jkDIJizPIyIiIj6JNYMOrFgL1e4usig1Ykoqm7q/Y7qz+knBQDA2L/0bh8yGTDx7/rbe98BtJrex2Om8yMHmRDojnEKBRsL9t7xwho0qLXwdVdgUGjfe89NStCPH9xztoJ9BJxAs0aLv39+2JQMWDo+Gpvun45bp8Z3ODrSz0OJv89MxOb7ZmDRqEiIIvDa5jO496ujaGnV2jt8IiIichAWJwQ++eQT/Pbbb2Zt+/vvv+OTTz6x9JBkIJMJpiqBXEv6CBz5BNCqgfCRQOTo3u9n2PWARxBQW2BoTmg71Y1qFNfom2Qlh3HJQHeMDfDS2Viw1/YYlwvEBUDWB3tWjIjyhUohQ0WDGqdL66UOh7qg0epw5+eHsSGtBAq5gNWLh2PlNUPhrVJ0+1wfdwX+c91wvLBoGFxkAn44WoS/f3YYGi2r/YiIiPojixMCy5cvx7PPPmvWtqtWrcKKFSssPSS1YfGkAZ0WOLhGf7u31QFGCtX5fRj3aSMnDNUBkX5uZv0R3N8NCtM3XiyvV6OsrkXqcJySMSHQF5cLAIDSRYaxsf4AgN1nyyWOhjqj04m496uj2HSyDK4uMqxdMQ7Xjo7s8X6uGxuFj5aPhauLDJtOluHBb1Kh0zFZSERE1N9YZcmAuVcceWXS+owd43udEMjdBdTkAyofYMhCywMauRSAAOTuBCqzLd9fJ9g/oGdUCjkSgzwBAGmFbCzYUy2tWhzM7bv9A4yMywbYWNBxvbL5DH4+VgyFXMA7N47G5MTAXu9r2sAgvP3nUaZKgdW/Z1gxUiIiInIGdu0hUFJSAg8PjrOyphjTkoFeTho4/q3+86CrAIWb5QH5RAIJM/W3U7+wfH+dYP+AnkuJ0L9W7CPQc0fzqtGs0SHQU4kBwZ5Sh2MzkwzJjr1ZFdDyarHD+T29BK9tPgMAWLVwGGYmBVu8z1nJIXhx8TAAwFvbzmLD8WKL90lERETOo0dTBgAgLy8POTk57e6rqanB9u3bO31OU1MT/vjjD5w+fRrjx4/vcZDUOePowbzKXjQVbFUDJ9frbw+91npBjViqH2F49Atg+iP6hoNWZkwIDGb/ALOlhPvgv4cLkcbRgz22J+v8uMG+3JE9JdwbXioX1DW3Ir2oBsMifaUOiQwKqhpx/9epAIDlk2J7tUygM9eMjER6YS0+2JmN+79JxcBQLyQE9d3EFxEREZ3X44TAmjVr8PTTT7e7Ly0tDTNnzuzyecblAvfcc09PD0ldiDL2EKhogCiKPTtZydoKNFUBHsFA7FTrBZU8H3D1AWrygJwdQPx06+0b+oZaZwxNz1ghYL4hhsaCJ4pYIdBTfb1/gJGLXIbxcQHYdLIUu89WMCHgIHQ6EQ98k4q6llaMjPbFY/MHWf0Yj8xLRnpRLfZkVeC+r47i2zsmWTbKloiIiJxCjxMCsbGxmDZtmunrP/74A97e3hgxYkSH2wuCADc3N8THx+P666/HlClTeh0sXSzK3w2CADSotahoUCPQ09X8JxuXC6RcA8jk1gtK4QYMuQY4tBZI+87qCYHs8gaotTp4KOWI8nO36r77ssGGhEBhdRMqG9Tw97h4NBldrFmjxZG8agDAxPi+nRAA9MsGjAmB26cnSB0OAfhoVzb2ZlXCXSnHy9eNsMmJuotchpeuH45LX96O1IIavLk1E/fMGWj14xAREZFj6XFCYNmyZVi2bJnpa5lMhqFDh2Lr1q1WDYzM4+oiR5i3CkU1zcirbDQ/IdCqBjI26G9bc7mA0eAF+oTAqV+A+S8B8h6/1TplXC6QHObdJ8e/2YqXSoHYAHfkVDQivagGUwcESR2SU0jNr4Zaq0OQlyviAvt+D5RJifqkx4HsSqhbdVC68CqxlPIrG/Hib/pmf/+aPxixNnwPhvm44ZkFQ3D3l0fx+pZMzB0cakokEhERUd9k8V96W7duxWuvvWaNWKiXzi8b6MGkgdydgLoO8AwBIsZYP6jYKYCbP9BYDuTttuquT5gaCrJ/QE+lRPgAYGPBntifrZ8uMC7Ov0/3DzAaGOyFAA8lmjRapBZUSx1OvyaKIp5cn46WVh0mxgfghnFRNj/mVcPDcVlKKLQ6EY/9cJyjCImIiPo4ixMC06dP73S5ANlHTIBx0kAPEgKnf9N/HjDXJk3/IFfoewkAwIkfrbprjhzsvSHh+oRAOhsLmm1/jj4hMD7OX+JI7EMmEzDB0CthdybHD0rp9xOl2HyqDAq5gGcWpNglISUIAp68KgUeSjmO5FXjiwN5Nj8mERERScfqZ4LNzc0oLi5GXl5epx9kXecnDZiZEBDF88sFBl5mo6igXzYAACd/AnRaq+2WIwd7b4hh9GA6GwuaRaPV4VBuFQB9hUB/YRw/uPtsucSR9F8trVr8+5cTAIC/TI1HYrD9KqJCfVS4f24SAOD5DadQ1aC227GJiIjIvqyysLulpQUvvPACPvvsM5w5c6bLbQVBQGtrqzUOSwamJQOVDeY94VwGUJ0LyF2B+Bm2CyxuGqDyAepLgfx9QMwki3dZXt+Cc3UtEAQgKYRLBnoqxVAhkF3egLpmDbxUCokjcmzpRbVoVGvh46bAQDuekEltUkIgAOBIXjWa1Fq4Ka3YdJTM8umeXORXNiHYyxV3zkq0+/FvmhiDrw/m41RJHd7cmol/XTHY7jEQERGR7VlcIdDY2IgpU6bgySefRE5ODpRKJURRRHh4OGQyGURRhCiKUCqViI6ORlSU7ddA9jcx/j1cMnD6f/rPcVMBVxvOmnZRAgMuNRzzN6vs0lgdEOPvDg9X6zUq7C/8PZQI91EB4PhBc+zP1pfMj43171cNLGMD3BHmo4K6TYUE2U9Nowavb8kEANw/dyDclfb/Xecil+GReckAgE/25CLf3Ao0IiIicioWJwRefvllHDp0CNdeey2qq6sxZswYCIKA/Px8NDc348iRI/jTn/4EjUaDZcuWITs72xpxUxvGHgJldS1oUptRmn/md/1nWy4XMBpoSAic2WiV3XG5gOWMjQW5bKB75xsK+kkciX0JgoCJhmUDu7hswO7e+iMTNU0aJIV44drR0iXRpw8MwpTEQKi1Oqz+PUOyOIiIiMh2LE4IfPvtt1AoFHj99dehUqnaPSaXyzF8+HB8/vnnWLlyJZ555hl8+eWXlh6SLuDjpoC3Sn8Fqds+AuoGIH+//nbibBtHBiBhFiDIgLJ0oDrf4t0Zr2oPZkKg14yNBdPYWLBLOp2IAznG/gEBEkdjfxPj9d/z3iw2FrSnivoWfLI7FwDw0GVJkEtYmSIIgqlK4MejRTjGqRNERER9jsUJgczMTMTFxSE4OBgATF2QL+wT8OCDDyIgIACvv/66pYekCwiCYJqPnl3eTR+B3D2ATgP4RAN+cbYPzt0fiBynv51peZWAccIAZ2P3XorhtUvn6MEunS6rQ02TBu5Kuek160+MFQLHCmpQ38K+L/by/o5sNGm0GBbpg1nJwVKHgyERPrhmZAQA4Pn/nZI4GiIiIrI2q0wZ8PHxMd329NSvSS8vb19mKpPJEBsbi/T09B7v/6233kJcXBxUKhVGjx6NHTt2dLrttm3bIAjCRR+nTvXtP2RizU0IZG/Tf46fBthrpvqAS/SfT/9u0W6aNVpknqsHwCUDlhhiWDKQea4ezRrrTX/oa4zLBUbH+EEht8FoTgcX6eeOKH83aHUiDhhGL5JtVTao8cmeHADAXbMG2GXMoDnunzsQLjIBuzIrcCiX7wUiIqK+xOK/ciMiIlBSUmL6Oj4+HgCwb9++dttpNBpkZWX1eMLAV199hXvuuQePPfYYjhw5gqlTp2LevHndji/MyMhAcXGx6WPAgAE9Oq6zMVYI5HSXEMj6w/CEGTaNpx1jH4HsPwBNc693k1lWD61OhK+7AmE+qu6fQB0K8XZFoKcSWp2IUyV1UofjsPYZ+wfE9p9xgxcyLRs4y2UD9vDBjiw0qrUYEuGN2YOkrw4wivRzx6JRkQCA1zZnShwNERERWZPFCYERI0agpKQEjY36tetz586FKIr45z//icxM/R8OLS0tuPvuu1FZWYmRI0f2aP8vvfQSbrnlFtx6660YNGgQXnnlFURFReHtt9/u8nnBwcEIDQ01fcjlfXtslmnJQEUXCYHGSqDkuOEJ0+wQlUHIEMArDNA0Avl7e70bY/+AQaHeDnPlzBkJgmAaP5hWyD4CHRFFsU1Dwf6bEJjAPgJ2U9Wgxse7cwA4VnWA0d9mJkAuE/DH6XNIza+WOhwiIiKyEosTAldffTU0Gg02bNgAALjyyisxYcIEnDx5EklJSQgKCoK3tzfeffddyGQyPP7442bvW61W49ChQ5g7d267++fOnYvdu3d3+dyRI0ciLCwMs2fPxtatW7vctqWlBbW1te0+nE1sgBlLBrK3AxCB4MGAV4h9AgP0SxPiZ+hvZ23r9W5OGCYMsH+A5Ux9BNhYsEM5FY04V9cCpVyG4VG+UocjGWMfgeOFNaht1kgcTd/26d5cNKi1GBTmjUsG2/H3s5liAjxw9fBwADCNRCQiIiLnZ3FC4JprrsGOHTswduxYAPqrjxs2bMDy5cvh7u6OiooKaDQaDB48GD/88AMuueQSs/ddXl4OrVaLkJD2fxyFhIS0W6bQVlhYGN577z189913+O9//4ukpCTMnj0b27dv7/Q4q1atgo+Pj+kjKkq6MU+9ZewhcK6upfMGYNmG1yBuup2iasN4TOOShV4wJQTYP8Bixj4CaWws2KEDhuqAEVG+UCn6dnVRV8J83BAb4A6deP41Ietr1mhNvQNunx7vcNUBRn+bmQhBADadLDVVbBEREZFzszghoFKpMHnyZERHR5vu8/HxwUcffYTq6moUFRWhqqoKx48fxxVXXNGrY1z4x5Eoip3+wZSUlIS//OUvGDVqFCZOnIi33noL8+fPx+rVqzvd/6OPPoqamhrTR36+5ePx7M3HTYEADyWALvoI5BqqKmIn2ymqNuINCYHio0BTVY+fLooiThoSAmwoaDnj6MGMkjpotDqJo3E8xv4BY+P8JI5EesYqgT3sI2Az648WobxejXAfFS4fGiZ1OJ1KDPbEfEN8b21jlQAREVFfYNPW2XK5HKGhoe2mEPREYGAg5HL5RdUAZWVlF1UNdGXChAk4c+ZMp4+7urrC29u73Ycz6nLSQGMlcO6k/nb0RDtGZeAdDgQOBEQdkLOzx08vqGpCXXMrFHIBicGeNgiwf4nyd4OXygVqrQ5nSuulDsfh7M/Rn/yOiwuQOBLpmfoIZDMhYAuiKOKDnVkAgOWTYx1+osUdMxIAABvSSlBY3SRxNERERGQph/7LQ6lUYvTo0di4sf38+o0bN2LSpElm7+fIkSMIC3Pcqy7W0uWkgfz9+s+BAwGPQDtG1YYFywaM1QGJwV5Qujj029Yp6BsL6hNfaewj0E5RdRPyK5sgE/QjB/s746SB9KJa1DSyj4C17ThTjtOl9fBQynH92OjunyCxlHAfTEoIgFYnmpogEhERkfNy6cnGn3zyiVUOetNNN5m97X333Ycbb7wRY8aMwcSJE/Hee+8hLy8Pt99+OwB9uX9hYaEptldeeQWxsbFISUmBWq3GunXr8N133+G7776zSuyOrMtJA3l79J+jJ9gxogvEzwAOvN+rxoLsH2B9Q8J9sDerEumFNcAY5+ubYSsHcvTLBYZE+MDTtUe/IvukYG8V4oM8kHWuAfuyKzA3JVTqkPqUD3ZmAwCuGxsFHzeFxNGY59apcdh9tgJf7MvDXbMH8N8JERGRE+vR/+LLly+3SrOjniQErr/+elRUVODpp59GcXExhgwZgl9//RUxMTEAgOLiYuTl5Zm2V6vVeOCBB1BYWAg3NzekpKTgl19+weWXX25x3I6uy0kDeYZxf1IsFzCKnQJAACrOAHUlgJf5Jxbn+wd42Si4/sfYWDCdzcHaMfYPGBfbf8cNXmhifACyzjVgTxYTAtaUWVaH7afPQSYAKybFSR2O2WYMDDYlib4+kI+bpzhP7ERERNRejxICN910kyTdj//2t7/hb3/7W4ePrV27tt3XDz30EB566CE7ROV4Ol0yoGkGig7rb0tZIeDmC4QMAUqP6ysWUq4x+6kcOWh9xiUDJ4prodWJkMscs7O5vRm76Y+LY0LAaEJ8AD7bl4e9WZw0YE3r9uqT2bMHhSA6wF3iaMwnkwm4ZUocHvs+DR/tysaySbH8/UFEROSkepQQuPDkmxxLbKD+D8qqRg2qG9XwdddPHUDREUCrBjxDAD+Jr+TETNQnBHLNTwjUNmuQX6lvXsUlA9YTH+QJlUKGRrUW2eUNbNYIoKK+BWfK9E0Wx7JCwMTYWPBkcS2qGtTwM0w0od5rVLfiu0MFAIAbJ8RIHE3PLRoVidW/ZaCgqgm/p5dgngNPRyAiIqLOsTtbH+KudEGItyuAC5YNtO0fIPV8a+OShbzdZj/lVHEdACDcR3U+yUEWk8sEU4IlnY0FAQAHcvQjMZNCvHjS20aQlysGGBJG+zhtwCrWHy1CXUsrYgLcMSVRokavFlAp5Fg6Xp/I+HRvrsTREBERUW8xIdDHmJYNtG0sWHBQ/zlynAQRXSDGMB2iJA1oNu8k9Hz/AFYHWBv7CLS337BcYGwcpwtcaGKCvkpgz1kmBCwliqLpJHrp+GjInLTc/obx0ZAJwO6zFTh7juNLiYiInJHFrYG3b9/e4+dMmzbN0sNSJ+ICPbA3qxLZ5Y36O0QRKDQmBMZIF5iRV6h+2UJVtn4U4oBLun3KiSL2D7AV0+jBQlYIAMD+HP3J7ri4AIkjcTwT4gPwyZ5c9hGwgqP51UgvqoXSRYbFo513wkeErxtmJQdj08kyfLY3D49fOVjqkIiIiKiHLE4IzJgxo0eNBgVBQGtrq6WHpU5cNGmgthCoLwUEORA6TMLI2oiZpE8I5O42KyFwsoQVAraSEq6vEEgrrIEoipI0DXUUdc0aU/KJEwYuZuwjkFFah4r6FgR4ukockfMyNhO8YmiY0y9N+fOEGGw6WYZvD+XjwUuT4KaUSx0SERER9YDFCYFp06Z1ehLR0NCAs2fPoqqqCkqlEhMnSjjyrp+4aNKAcblASAqgdJAu1tETgaOfne9t0IVWrQ6nSvQ9BNhQ0PoGhnhBIRdQ29yKgqomRPk7yHtEAodyq6ATgZgAd4T6qKQOx+H4eyiRHOqFUyV12JtVifnD2ESuN2qaNPj5WBEAYKkTNhO80LQBQYj2d0deZSN+Si3CdWOdt+KBiIioP7I4IbBt27Zut/nuu+9w9913Izw8HJ999pmlh6QuGBMC2eUN+iu+jrRcwMjYR6DwkH4koqLzk6/s8gaoW3XwUMoR3Y9PVm1F6SJDUqgX0gprkV5U068TAqb+AawO6NSE+ABDQqCCCYFe+im1CC2tOgwM8cSoaF+pw7GYTCZgyfhoPLfhFNbty2VCgIiIyMnYpangokWL8P333+OLL77AK6+8Yo9D9lsxAR6QywTUt7SirK4FKDikfyDCgRIC/vGAR7B+FGLR4S43PWFoKJgc5u20jbccXUqYcdlA/24saEwIjI9jQqAzpsaCWWws2FvfHMwHAFw3JqrPLNG5bkwUlC4yHCuoQWp+tdThEBERUQ/YbcrA2LFjMXDgQLz//vv2OmS/pHSRIcZwlTezpBooPqp/IGK0ZDFdRBCAGMPykdyuxw+eME0Y8LJ1VP3WkAhDY8F+PHqwWaNFakE1AGA8Gwp2anycPwQByCyrR1lds9ThOJ2MkjqkFtTARSZgwcgIqcOxGn8PJeYP1VeMrOMIQiIiIqdi17GDSqUS2dnZ9jxkvxQfpJ8XXpmdCmgaAVdvIHCgxFFdINqwbKCbPgKmCQOGq9hkfSkRrBA4klcNjVZEqLcKUf5uUofjsHzdlRgUqk8gcdpAzxmrA2YPCkZgH2vK+OcJ0QCA9alFqGnUSBwNERERmctuCYHMzEycOnUKPj48sbO1xGB9QkBrXC4QPgKQ2TX3073oCfrPBQcAna7DTURRxMlifUNBVgjYzqBQb8gEoLy+BWW1/fOqr3G5wLg4/z5Txm0rxmUDe7lsoEfUrTp8f6QQgL7Evq8ZFe2H5FAvtLTqsD61UOpwiIiIyEwWnyXm5eV1+pGbm4uDBw/i7bffxiWXXAKtVosrr7zSGnFTFxKC9I0FPSrT9XeEjZAumM6EpAAuKqC5Bqg82+EmZXUtKK9vgUwAkkM5YcBW3JRyJBiqSvrrsoH9OfqT27HsH9CtiYbxg3vPMiHQE1tOlaGiQY0gL1dMHxgkdThWJwiCKdHxlaESgoiIiByfxVMGYmNjzbqiJooiUlJS8Oyzz1p6SOqGsUIgrDFDf0fYcAmj6YRcoU9U5O/Vj0YMHHDRJscL9CenA4K9ONvaxoZE+OBMWT2OFdRgVnKI1OHYlbpVh0O5VQDYUNAcY+P8IROArPIGlNY2I8SbIxrNYVwusHBUBFzkDlaxZSXXjIzAcxtOmaaWpISzItCZaXUiCquaUFTTBJkgwMNVnzxWKfj/MRFRX2JxQiA6OrrThIAgCPDw8EB8fDzmzZuHFStWwNW1b62bdETxQZ6QQ4tEXQ4gwDErBAD9KMT8vUDhQWDEDRc9bLxanRLB6gBbGx7pg++PFOJYQf+rEEgrqkGzRgc/dwUSDZUS1DkfNwVSwn1wvLAGe85W9KnmeLZSWtuMrRllAIDFo/vecgEjPw8lLhkcgl+OF+ObgwVIuYoJAWfT0qrFhuMl+OV4MXacOYdmTfslfXKZgMFh3rhyeBgWjIhAMBOCREROz+KEQE5OjhXCIGvycVNgjGc5VK0aaBUekPvHSx1Sx4yTDwoOdPiwscndEF5lsrnhUb4AgNT8aoii2K/W0Rv7B4yN9edoSzNNTAjA8cIa7M1iQsAc/z1cCJ0IjI7xM1Vw9VXXjY3CL8eL8f2RQjwyL5lXk52EVifii/15eHNrJoprzveSUbrIEOnrBghAZYMa1Y0aHC+swfHCGqz+/TT+PD4Gf5+ZgIA+1iSTiKg/sTghQI5pmmcRUA1UeSUh0NEaChpFjtV/Lk0HNE2Aon1397RC/dXqoZFMCNjaoDBvuMgEVDSoUVDVhCjD6Mr+oG1DQTLPxPgAvLc9C3vYWLBboijiv4cLAACLR0dKHI3tTUkMRLiPCkU1zdh4ohRXDg+XOiTqxpnSOjz47TEcza8GAIR4u+L6MVG4bEgYkkK9IDckSkVRRHGNvtrl20MFOJJXjY92ZeP7IwVYec1QXG4YPUlERM7FQc8UyVIjFHkAgBxlosSRdMEnEvAMAXStQHFqu4fO1bWgpLYZgqA/WSXbUinkptc5taBa2mDsSKsTcSBHnxAYHxcgcTTOY0ysH+QyAbkVjSiqbpI6HId2orgWZ8rqoZTLMK8fnDDJZQKuNSQ+vmZzQYf349FCXPH6ThzNr4aXqwueuHIw/nhwJu6bm4TB4d6mZACgXwYa7uuGpeNj8N87JuGTm8chOdQLVY0a/O2zw3jku2NQt3Y8NYiIiByX1RICpaWleOqppzBp0iQEBgbC1dUVgYGBmDRpEp566imUlJRY61BkhoTWTADAcW2MxJF0QRCAiDH62wUH2z1k7B8QF+gBT1cWstjD8Ch9JUaq4SpRf5BRUoe65lZ4urpwtGUPeKkUGBKhf7/s4bSBLv14tAgAMCs5GD5uComjsY/FhmkDOzPLUVDVKHE01BFRFLH6twzc/eVRtLTqMG1gEH6/bxpWTI4za5mHIAiYNjAI6++cgjtnJkImAF8eyMeNH+5DdaPaDt8BERFZi1USAt999x2Sk5Px9NNPY+/evaisrIRGo0FlZSX27t2Lp59+GsnJyfjmm2+scTjqjk6HwHr9hIHdDQ6+vjfS0EegsH1CIN24XCCCywXsZXikLwAgNb//NBbcn60/mR0d49dnO7/bimn8IJcNdEqrE7HekBBYMLL/lM5H+btjUkIARBH49lCB1OHQBURRxFM/ncAbW/UXDu6YkYA1y8cizMetm2deTOkiwwOXJuHD5WPh6eqCfdmVuOH9fahsYFKAiMhZWPwX8M6dO3H99dejpqYGI0eOxAcffIBdu3YhMzMTu3fvxocffoiRI0eitrYWS5Yswa5du6wRN3WlOgcumnq0iC7YUR0AjdaBS/g6qxBgQ0G7G2FoLHi8sAatjvyesaL9Oewf0FsTE/QJAfYR6Ny+7AqU1DbDS+WCGUnBUodjV9eP1VcJfHOwADqdKHE0ZCSKIp5Yn461u3MgCMCz1wzFw5clt1sa0Bszk4Lx3R2TEOjpipPFtVjy/l5UMSlAROQULE4IPP300xBFEQ8//DAOHjyIm2++GRMnTkR8fDwmTJiAFStW4ODBg3jkkUeg1Wrx1FNPWSNu6ophPf5pRKNZJ0duhQOXbEaMAiAANflAXanp7uOGCoEhrBCwm/ggT3go5WjSaJF5rl7qcGxOFEU2FLTAmBg/uMgEFFQ1Ib/SgX/HSOjHI/rqgMuHhPW7bvuXpoTCy9UFhdVNpsQbSe/NrZn4ZE8uBAF4YdEwLBkfbbV9J4V64cvbJiDQ0xWnSupw6ycH0azRWm3/RERkGxYnBPbt24eAgACsXLmyy+2eeeYZBAYGYt++fZYekrpTfAwAUKgaAADILHPgkztXLyB4kP62YdlAVYMahYZGZYPD2VDQXuQywTTRoT/0Ecgqb0B5vRpKFxmGcZJFj3m4upheN1YJXKxZo8WvacUAgKv70XIBI5VCjvnD9E0UjVMWSFrfHSrA6t9PAwCeuirF1OvBmhKDPfHFX8bDW+WCQ7lVuPero6wQISJycBYnBARBQFxcHGTdjLaTy+WIi4vrV/PNJWOoEKjzSwEAnHX0q70Rhj4ChmUD6UX65QIxAe79pgmXoxhuWDZw1FZ9BOpKga2rgPdmAM/HAauTgE8XAkc+A1rtW15qrA4YGeULV5f+dfXWWozLBthH4GLbMspQ19yKUG8VJvTTCRYLR+mnDfx6vARNal4pltKxgmo8+v1xAMDt0xNw08RYmx1rQIgX3rtpDJRyGTakleDVzWdsdiwiIrKcxQmBESNGICsrC1pt1//Zt7a2IisrCyNHjrT0kNQVUTw/wi90GADgrCNXCADnEwJFRwBwuYCURpgaC1Zbd8c6HbDnLeC1EcAfz+l/1k2VQH0JcHYz8OPfgLfGA7l7rHvcLhgTAuO5XKDXJsYHAgD2nq2AKPIqYFs/GJYLXDUiHDIL12c7qzExfojyd0N9Syt+P8FJQ1Ipr2/B7Z8egrpVhzmDQvDQpUk2P+aE+AA8t2goAODVzWew5VRpN88gIiKpWJwQePTRR1FZWYlHH320y+0ee+wxVFZW4p///Kelh6Su1BUDjeWAIIdPnD75crqsTuKguhE+Qv+56AggiqaRg2woaH/GCoGM0jrrXdHTaYH1/wB+exTQNAKRY4EF7wB37AZu3QLM+j/AIxiozALWzgcOfWyd43bjfP+A/nn11hpGx/hBIRdQVNOMPPYRMKlp0mDLqTIAwNUj+t9yASOZTMDCkfoqge8OF0ocTf8kiiIe+CYVRTXNiA/0wEvXD7dbgmrhqEjcOEE/+vieL4+algISEZFjsTghMGjQIDz77LN45ZVXMHr0aNOUgTNnzpimDIwePRqvvPIKVq1ahaSkJOTl5V30QVZSmq7/HJCIARFBAIAzpfXQOvIavuDBgEwBNFcD1blIM1UIsH+AvYX5qBDo6QqtTsSJYissG9C2At//FTi6DhBkwLwXgVs2AiNuAEJS9GMnpz0A/OMgMORaQNQCP90FpH5l+bG7UFDViMLqJrjIBIyK8bXpsfoyN6XcNJ1iz1kuGzD6X1ox1FodBgR7YnBY//49tnCUfvTtzjPnUFrbLHE0/c+6vbnYlnEOShcZ3v7zaHir7LsM7/+uGIzhUb6obW7FA1+nsp8AEZEDcrF0B7GxsRAEAaIo4ujRo/jrX//a4XaiKOLRRx/tsJJAEAS0trZaGgoB5xMCIYMR7e8OlUKGZo0OORUNSAjylDa2zri4AiGDgeJUNOQcQm6FCgArBKQgCAJGRPlg08kyHM2vwegYC8vpt/4bOP4NIHMBFn0IpCzoeDuVD7DoA8AjENj3jn4Jgbs/MOASy47fCWN1wJAIH7grLf412K9NjA/AgZwq7M2qwJ/GWa9juTMzLhdYMDKi3/fNiQnwwJgYPxzMrcIPRwrx1+kJUofUb2SW1WPlrycBAI9cloykUC+7x6B0keGV60fg8ld3YE9WBT7alY1bp8bbPQ4iIuqcxX8JR0dH9/s/eBxK2Qn95+AUyGUCBoZ44VhBDU6X1DluQgAAwkcCxamoztwPYBoifN3g56GUOqp+aXikLzadLLO8j0DG/4CdL+tvX/Nu58kAI0EALl0FNFUBx74CvrtFv6zAJ9KyODrA/gHWMyEhAK9tycSeLH0fgf7+/0FJTTP2ZuurJa4a3n+XC7S1aHQkDuZW4bvDBbhtWny/f4/Yg7pVh3u/OopmjQ5TEgOxfFKsZLHEBXrgX1cMwmPfp+GF3zIwbWAQBobYPzlBREQdszghkJOTY4UwyGpMFQL6CQNJhoTAqZI6zBsaJmFg3QgbAQAQi44CmIahbCgoGWMfgdSC6t7vpK5Ev1QAAMbfDgy91rznyWTA1W8CFWf1Yyi/vx246UdAZt0pAOf7BzAhYKlR0X5QushQWtuC7PIGxDty4tEO1qcWQhSNDfXcpQ7HIVw+NAxPrE/H6dJ6pBfVsmGsHby/IwvHC2vg46bA6sX26xvQmSXjorHpRCm2ZpzDvV8dxY9/nwwXucWrVomIyAr427gv0WqAcxn62yGDAcBUIphR4hyNBf1qTgAQ2T9AQsbZ8rkVjahq6OUowN8e0/eECBsBXPJMz54rVwAL3wMUHkDODmDvW72LoRNldc3IKm+AIABjLF0SQVAp5Bhp7CPA8YOm5QJXj4yQOBLH4eOmwCWDQwAA3x0ukDiavi+nvAGvGUb9PXnVYIT6qCSOSL8c7flrh8HXXYH0olqs3Z0jdUhERGTAhEBfUpEJ6DSA0hPw0a/lTQ7Vn1hnlDp4QsDQWNBDV4tIoRxDDePvyP583ZWIC/QAABztzbKBrD+AtG/1TQSvfBVw6cXSj4AE4LJV+ttbVwG1xT3fRyf2ZumrAwaFesPH3b4NtvqqiQn6SQ3G17a/OlNahxPFtXCRCZjvyBVZErh2lH7pz/qjRdBodRJH03eJooj/+zENLa36pQILRjhOYirYS4VH5yUDAF7aeJpTB4iIHITVEgItLS1Yu3YtlixZgjFjxiApKQljxozBkiVL8PHHH6OlpcVah6LOGJcLBA/Sl14DGBiqL9/NqWiw3hg5W3BxRWvQIADAECEbwyNZUiqlUdF+AIBDuVU9e6K2Ffj1Qf3tsbeeHynZGyNv1I8o1DQAm5/q/X4uYOyGbzyJJctNjNe/lnvO6vsI9Fc/HNWP1ps+MAj+7IHSztQBgQj0VKKiQY0/Ms5JHU6ftT61CDvOlEPpIsO/FwxxuH4Ni0dHYUyMHxrVWjy1Pl3qcIiICFZKCBw+fBiDBw/GLbfcgi+//BKHDx/GmTNncPjwYXz55Ze4+eabkZKSgsOHD1vjcNQZU0PBwaa7gjxd4e+hhCgCZ8ocu0qgzFOfEJjqkQ9fd/4xLaXRMb1MCBz7CijPANwDgJmPWRaETAbMe15/O/ULoOCgZfsz2HO2HMD5k1iy3IhoX7i6yFBe34Kz5+qlDkcSoijix6NcLtAZF7kMVxuuVv/3CJcN2EJNkwbP/Kz/O+CuWYmINVR6ORKZTMCzC4fCRSbg9xOl2HiiVOqQiIj6PYsTAgUFBbjkkkuQnZ2NgIAAPProo1i3bh02btyIdevW4dFHH0VgYCCysrJw6aWXorCw0BpxU0dKDQkBQ0NBQL9uL8nQzfeUg/cROAn9KKLRilyJIyFjQuBofjVazS3v1WqA7S/ob0++B3DztTyQiNHAiKX621t62IugA8U1TcipaIRMAMbFs3+Atbi6yE3vGWMFRn9zKLcKBVVN8FDKccmgEKnDcUiLDMsGNp0oQ02jRuJo+p43tpxBeb0aCUEeuG2a4453HBjihb9M0/9//9RP6WjWOHD1IhFRP2BxQmDVqlWoqqrCwoULkZubi5UrV2LJkiWYPXs2lixZgpUrVyI3NxeLFi1CRUUFVq1aZY24qSNlxiUDg9vdbWwseNrBEwI7G6MAALGaM0A/Ljt2BAOCPeHl6oImjdb8RFLql0BVDuARBIy9xXrBTH8YkLkAWduAvH0W7cp4sjokwgfeKvYPsCZjxcXufpoQMC4XuHRIKNyU1p2K0VcMDvdGcqgX1FodfjpWJHU4fUp2eYOpUd//XTEYShfHbhH1j1mJCPVWoaCqCWt25UgdDhFRv2bx/xgbNmyAh4cH1q5dCzc3tw63UalUWLNmDTw8PPDrr79aekjqSEsdUJ2nv92mQgAAko2TBhy4saAoithQ6ge1KIdKU3P+eyFJyGQCRkT7AgAO55mxbECnBXas1t+efA+gtGKpql8MMGKJ/vYfz1m0K1P/AC4XsLopAwIBALsyy82vKukjNFodfjmmb3zpSE3cHJGxSuC/nDZgVc/+ehIarYgZSUGYkRQsdTjdcle64MFLkwAAb27NxLk69pkiIpKKxQmBoqIiDBo0CJ6eXc+e9vT0xKBBg1BcbL1u4dRG2Un9Z89QwL19KbSxQsCRlwwUVDWhpFHEaeinI6D4qKTxUA/7CGRs0FcHuPkBY262fjBT7wcEOXB2i0W9BIxj8SawoaDVDYv0hY+bArXNrThWWCN1OHa1/fQ5VDVqEOjpikl8b3Xp6pHhkAnA4bxqZPXTfhPWtjuzHBtPlEIuE/Cv+YOkDsds14yMwLBIH9S3tOKljaelDoeIqN+yOCHg5eWF0lLzmsKUlpbCw8Pxmtz0CcYJAyGDL3pogKGHwLm6FlT2dq68jRnH2xWqBurvKDkuXTAE4PykAbMqBPa9o/88ejmgdLd+MH6xwLDr9bf3vNGrXeRXNqKgqglymYCxsewfYG1ymYApifoqge2n+1cX+R8MzQSvHB4GF7ljl2pLLdhLhWkDgwAA3x9hTyFL6XQinvlFf0HgxgkxSAz2kjgi88lkAv7vCv3fLF8dyMPJ4lqJIyIi6p8s/stl9OjRKCgowJdfftnldl988QXy8/MxZswYSw9JHelgwoCRp6sLovz1yzlOlTjmf7iphoSAJsiw3IEJAcmNiPaFIAD5lU0oq23ufMOS40DODv0V/LG32i6giX/Tfz6xHqjpebmxsTpgeKQPPF1drBkZGUw1LBvYcaZc4kjsp76lFRtPlADgcgFznV82UAidjv1iLLE+tQgni2vhpXLB3bMHSB1Oj42N9cf8oWHQicDz/zsldThERP2SxQmBf/zjHxBFEcuWLcP999+P7Ozsdo9nZ2fjvvvuw4oVKyAIAu666y5LD0kd6WDCQFtJId4AgAwHXTZgrBDwjBmpv4MJAcl5qxSmCRVdVgkYqwMGXwX4RNouoNChQOxUQNQCBz7o8dP3GvsHsKTbZqYarvweza9GTVP/6CL/e3oJmjU6xAV6YFikj9ThOIVLBofAS+WCwuom7M3un00orUHdqjOV2t8+PQF+Hs45rvfBS5PgIhOwLeMc9mdXSh0OEVG/Y3FCYP78+Xj44Yeh0WjwyiuvIDExEe7u7oiJiYG7uzsSExPx6quvQq1W45FHHsHll19ujbipLVHsdMKA0aAw/YmdI5bkabQ6pBXp1xxHDRqnv7O2EGjgH4pSGxVjXDZQ3fEGzbVA2n/1t8f91fYBjb9d//nQWvw/e2cZ3daVteFHYMmSmR2HbIeZmdMGyimlzNxOGWfazjfQTmdKU54yp2naFNI0bZo0zMzkgJM4ccyMovv9OJKcNGSQdAXnWUvrHOHdtmXpnn32fl8sNY1+mqIo7gqBYZmJXghMAtA61kSHpAjsDoVV+0OjSsDVLnBJ3zQ0Go3K0QQG4WE6LuzdChBVApLmMWN9DodLakiMNHLLiHS1w2k26YkRTB0kXIZe+m03inQZkkgkEp/ikWbHF154gdmzZzNs2DA0Gg11dXXk5ORQV1eHRqNhxIgRzJ49m+eff94Th5P8kepCqC0FNJDU5ZQP6ZEmdq62H/W/hEBWfiV1VgdR4Xoy0lIgXvgTky+rBNTGpSNwWmHBHT+AtQYSOkG7od4PqMt5ENtOvN93zmr00w4V13CsvI4wncYtlijxDq7+8CVZwZ8QKKysZ/leoZcg2wWahqtt4Ndtx6ix2FSOJvCotdh5Y8FeQFj4mQ2B3Qb1wPhOGPVa1h0sZXGIaZBIJBKJ2jQ7IVBTU8OsWbN4+eWXefnll7Farfz222+Ul5ezadMmli1bxqZNmygvL2fp0qVccMEFnoxbcjyFzr67uHQIO7X1Y8/WomVgb0El9Ta7jwJrHK52gT5tYtFqNaI0HGTbgB/gWjxvO1JOnfUU75tNX4qx3/Xgi91RrQ763yjmGz9v9NNc1QH92sZJj3gvM7qTSAgszSoM+p2+n7fm4lCgT9tY0hOlYG5TGNA+jvQEM9UWO3O356kdTsDxycpsCivraRNn4prB7dQOp8WkxoRz0/B0AF6au0dqS0gkEokPaVZCYM6cOaSnp3PZZZfx5JNP8uSTT3L55ZeTnp7O/Pnz6dOnDyNGjKBPnz7SVcAXFO4R42mqA0CU8saYwrDaFfbm+5fVk2v3uW/bWHGDTAj4DekJZpKijFjsDnfixk3hHjiyVogJ9rnad0H1vQ40Wji8Eor2Neopq/ZLu0FfMSQzHoNOy9GyWrKLqtUOx6u42gWm9E1TOZLAQ6PRcJmzSuC7jU0XCQ1lymusvLt4PwAPn9sZgz44nC3uHtOBSKOenccq+GW7tKiWSCQSX9Hkb5GdO3dyxRVXUFRUhMFgoEePHnTv3h2DwUBxcTFXX301W7du9UasktPRiISARqNxVwls9zOPcFdCYGC6s5Q7tbcYZUJAdTQaDYMzhEXfSWJPm6eJsdNEiEr1XVDRadBxgphvOnuVgMOhsNIlKJgpEwLexmzQu/+Xg9l+MLuomi05ZWg1cGFvmRBoDpf2E20WK/cXk1tWq3I0gcP7y/ZTUWejc0okU/oFT6tKfISBO0aJlsFX52VhsztUjkgikUhCgyYnBF555RXq6+uZMGECBw8eZOvWrWzbto3s7GzOOeccLBYLr776qjdilZwOV8tAUtczPqynU0dgR67/6AgUVNZxqLgGjaZBwM5dIVC4B6xnsLuT+IShzoTAmuPVwB0O2PadmPe9xvdBudoGNk8H+5nV7HfnVVJUVY/ZoKN/+1jvxyZx6wgEs/3gD5uEGN6oTkkkRRlVjiYwaRtvZkhGPIrS8PuUnJmyGgufrjgIwCMTuqDTBpeQ5W2jMoiPMHCgqFq+JyQSicRHNDkhsGTJEoxGI19++SUpKSnu21NTU5k2bRoGg4ElS5Z4NEjJWWhEhQBAj9ZOYcFc/6kQ2HBQVAd0SYkiOjxM3BjVCswJwl6ucJeK0UkAhjh31TccKsVic+7YHFkLFUfAECUqBHxN50kQkQTVBXBg8Rkfunyf2KUekhGPUS/1A3zBqE7CyWHVgeKG90wQoSgKPzoXK5f1D54dWjW4fEBD20Cwa054go9XHKTaYqdrahSTeqSc/QkBRqRRz12jRZXA24v2ySoBiUQi8QFNTgjk5ubSqVMnkpKSTrovOTmZTp06kZcnBYJ8Rk2JWBQBJHY+40N7pImWgV3HKrD7iWDP+j+2C4AQp5M6An5Dx6RI4sxh1FkdbHO1m2x3Vgd0u/C0QpZeRRcGPS4T863fnPGhrl3qkZ1O/sySeIduqdEkRhqosdhZfyj4fMU3Hi7jcEkNZoOOCd2Db1HmS87v1YrwMC0HCqtP1imRnEBFnZVPVmQD8MA5nYLW5vL6oe2JM4dxsLiG2Vtz1Q5HIpFIgp4mJwTq6uqIjY097f2xsbFYLJaWxCRpCkVZYoxuA8aoMz40IyGCCIOOOquDA4X+ISzoTgi0jz/xDpkQ8Bu02j/oCNhtwm4QoOfl6gXWe6oYd88By6nF6+qsdrf2wWjnrrXE+2i1GrfbwJI9wacj8MMmIYI3uWdqwNu9qU2kUc/kHkKDRIoLnpnPVhykss5Gp+RI9+8sGIkw6rndqSXw5sJ9frOBIZFIJMFKcEjThjJu/YAztwuAOEnv7qwS2OYHwoK1Fjs7nHGc5A0vhQX9isEZom1gTXYxHFoO1YVgioPMseoF1XoAxGWAtRr2/HrKh6w7WEK9zUFKtJGOyZE+DjC0Gd8tGYAFuwtUjsSzWGwOft4qFNAvDSJBNzVxtQ3M3nLM72xx/YWqehsfOasD/jS+o7DoDWJuHNaeGFMYBwqrmbNNOg4EEoWV9czeksv/zdrODR+tYfzLixn2wgKG/Ot3Jv13KTd8tIYXft3F3O3HqKw7swaQRCLxDc3a2igoKODzz0+t7l1QIE7+vvjii9P2A954443NOazkVBQ6KwTOIijoolfrWNYdLGVLTpnb8kktNueUYXMopEaH0ybuD2Xn7gqB7ULATitzV2oyxFkhsP5gKY7EX0UmsdvFonRfLTQa6HUlLH1RtA30uuKkhyx3tguM6pQUtOW1/sqoTknotRr2FVRxqLia9gnBYUG7JKuQshoryVFGhneQVSeeYHiHRFKjw8mrqGPhrgLO69VK7ZD8ji9WHaKsxkpmYkRIuFpEhYdx28gMXp2fxZsL9nJhr1ZBnwQJZKx2B3O2HmPmhiOs3F/E6Yo68ivq2ZNf6W7lM+i0jOyUyHVD2jG2S3LQiWRKJIFCsxICe/fu5ZZbbjnjY26++eZT3q7RaGRCwJO4KwTOrB/gom+7WFiBX/RqbnD2Fg9Ijzt5sZbQCXRGsFRC2UGIz/R9gBI33VpFExWup6rOgn3nzyIh0P1itcNqSAjsXwC1paJq4TiWuhMCcuHma2JMYQxKj2fVgWIW7Crg1pEZaofkEVztApf0TZMnrx5Cp9Vwaf/W/G/xfr7beEQmBP5AjcXGh8sOAHDfuI4h8767aXg6Hyw7wN6CKn7dnscFveX7wt+w2R1MX5fD/xbtI7e8wRWqe6toBmfE0z0tmjZxJqKMYWg0UFpj4UhpLduOlrN6fzEHiqpZuLuAhbsLSE8w89C5nbmoj/xslUh8TZMTAu3atZM7bf6E22GgcRUC/drGArDzWAX1NruqquvrnA4Dg/7YLgCg00NKd8jdJNoGZEJAVXRaDYPS4ynbs5yw2kIwRkP6aLXDEomw5O5QsBOyfoM+V7vvKqysZ9cxYbE5oqNMCKjBOd2SWXWgmIW7gyMhUF5r5fddogoumPzf/YHLnQmBxXsKKaqqJzFSWjm6+GrNYYqrLbSLN3NJ3+CvDnARYwrj1hEZvL5gL28u3Mt5PVNllYAfsXJ/EX+dtYN9BUKTKjHSyA1D23Npv9a0SzCf8bkus+J9BZV8s/4IM9blcLC4hodmbOaDZQd4/tJe9HWer0okEu/T5ITAwYMHvRCGpFnUVwrrNzirw4CLNnEm4iMMlFRb2JlbQb92p1iM+wCHQ2HjYZfDQPypH5TaqyEh0P0SH0YnORWDM+Jh3wZxpdME0BvUDchFt4tEQmDnTyckBFbsE9UB3VtFy8WFSozvmsxzc3axJruYyjorUeEqtph4gLnbj2GxOeiSEkX3VtFqhxNUdEyOok+bGLYcKeenzblBkUDyBHVWO+8tdVUHdECvC632uVtHZPDR8mx251Uyb2c+k3sGr5hioFBdb+OFX3fx5erDAMRHGHjwnE5cNagt4WFN22TqmBzFX87vxoPndOLTlQd5b8l+duRWcOk7K7h1RAZPTO4i7YIlEh8QWt8swYbLYSAiGcynWVT/AY1G4866qtk2kFVQSWWdDbNBR9fU07gjSGFBv2JYRjwTtesAsHe+QOVojqObs3Vh/wKob3DPWJol1O1lu4B6ZCZFkpkYgdWuuPUcApnvNx4FRHWArJTzPC5xwZkbpNuAi6/XHqawsp7WsSYu7aeu7o8axJjDuHl4OgBvLNh7Wm0qiW/ILqrm0ndWuJMB1w9tx6LHxnLT8PQmJwOOJ8Ko575xHVn42Fgu698aRYGPlmdzxf9WcbDo1C5CEonEc8iEQCDjbhc4u8PA8fRpEwuomxBYvb8YEO4Cp93xkNaDfkVPYx6Z2jzqFT1bTYPUDqeBlB7CbcBWB/t+B8DuUFjsTAiM6ZKkZnQhz/iuweE2cLSsljXZJWg0hFTZti+5uE8aBr2Wnccq2O4HTjhqU2+z8+4SUR1w99gOGPShecp228gMzAYdO49VsGhPYH+OBDILduVz8ZvLycqvIinKyLTbh/DclF7EmDxX+ZUYaeTVqX356KaBxJrD2Ha0nAvfXM68HXkeO4ZEIjmZ0Px2CRbcgoKN0w9w0bddLKBuQmDVAZEQGJqZcPoHpfQQY8VRqC72QVSSM6HbOxeAlY4eLD1Ur3I0x6HRiLYBgF0/AbDlSBkl1RaijHoGna4lReITXPaDi3YX4AhgP/EfN4nqgKEZCaTFms7yaElziDUbmNRDlIR/ve6wytGoz7frj5BXUUdqdDhTB4ZedYCLuAgDNwxtD8AbC/bJKgEV+GRFNrd9tp7KehsD28cx5/6RXtXmOadbCr8+OIrB6fFU1du468sNfOK03ZRIJJ5HJgQCGbflYFMrBGIAOFRcQ2m1xdNRnRWHQ2H1AeEwMLzDGRICxiiISxfzgh3eD0xyZvaK3fdFjr4s31eocjB/wKUxkfUbWOtY5NyNHtU5kbAQ67n1NwalxxMVrqe42sKmnFK1w2kWiqK4y9gv7S/FBL3J1YPaAjBrUy61FrvK0aiH1e7gf4v3A3DXmMyQ76O+bVQGRr2WzTllrNwvNwh8haIovDo/i7/P3gnAdUPa8dUdQ0mODvf6sVvFmPjqjiFcO6QdigJ/n72Tf8zeGdCJZYnEX5FnyoGMS0OgkYKCLmLNBjIThSe4S9jPl+w8VkF5rZVIo55erWPO/OCUnmLM3+n9wCSnp64CclYDsNjRl02Hy6iqt6kc1HGk9YeoNLBUwYHFLHQmBMZ1SVY5MEmYTss5zraBX7d5sOzTZgGbbypV1h0sJbuomgiDjgukJZ5XGZaZQNt4E5X1Nn7ZdkztcFTjh41HOVpWS2KkkWsGt1M7HNVJjgp3J4veXLhX5WhCA0VR+PvsnbyxQPy+H5nQmeem9PRp64pep+X5KT15crKohP14RTZPfLcVu0wKSCQeRSYEAhWbBUoPinlipyY/fWC6cBdYe7DEg0E1jlXO7P6g9DPoB7hI7i7G/O1ejkpyRg4sBocN4jugic/A5lDcOhB+gVYL3S4EoHbrD+zIFXaDY2VCwC+Y3FMson/dnteyct/Da+CHe+CVbvBcEjyXDC91gm9vEfoRXiol/mZ9DgAX9k4jwthkcx5JE9BqNVw1UCz8ZqzLUTkadbDZHby1aB8Ad43ObJFYWzBx55gOhOk0rD5QwnoVzl1CCUVR+OfPu/h05UEA/nFJDx44p5MqYqoajYZ7xnbgtav6otUI0dEnZsqkgETiSWRCIFApzQbFDoZIiGr6jpWrr3pdtgoJAad+wLAztQu4cOkIFMgKAVVxivXRaQIjnX2Dy/f5mWq8U0dAs3ceGhz0aRNDUpS0G/QHxnROwhSm42hZLduPVjT9BSrzxKL/44mw5SuozG24r7oAdnwPX14OX0yBon0eixugss7KnK1ip3rqoNDt4/YlVwxoi1YjEtYHCqvO/oQg46ctuRwuqSE+wsB1Q2V1gIvWsSYu7y/+B10JE4l3+O/8LD529uy/eEVvbhyWrm5ACHeX16/uh06r4buNR3j82y2yfUAi8RAyIRCoFDlL5hI6CFG1JjIkQyzGtx0t92mfps3uYG22Sz+gEYI07oTALnCEbj+pqihKQ0Kg4wS3jd+yvX6mI9BuGBijCbeU0FtzgHFdZXWAv2Ay6BjrdHv4dXsTy8APrYJ3hopFv0YLfa+HG3+Cxw/AE9lwy1wYfBfojKKS5X/DYft3Hot9ztZj1FrtdEiKoH+7OI+9ruT0pMaEu9t9ZqwPrSoBu0PhrYVisXv7qAzMBlmRcjz3jO2AVgOL9xSy7Yh0ovAG7y/dzxvO9+DfL+7BVGfFjj9wUZ803nAmBb7fdJR//LxTikxKJB5AJgQClWJXQqDp7QIAbeNNpEQbsdoVnwp9bTkies9jTGF0axV99ifEZ4I+HKw1DS0SEt9SsEs4PejDIX0EwzokotXA/sJqjpXXqh1dA7ow7JljARiv2+y2u5P4B5N7CvX4uU1pG9g9R+z615ZCam+4czFMeRsyx0BEApjjof0wOP9FuG81ZI4Dez3MvBVWvO6RuF3tAlMHtlWlXDZUmersF/9uwxGsdofK0fiOOduOcaComlhzmF/syvob7RMiuKSvEPZ8a5HUEvA0s7fk8q9fhIPV45O6cNPwdHUDOgUX9G7Fq1P7APDpyoO84xTflEgkzUcmBAIVV1lsEwUFXWg0muPaBnyXEFiSJcrMR3ZMRKdtxMm1VgfJ3cQ8XzoNqMK++WJMHwVhJmJMYfRuEwvAsr3+1TawL2YEABP0W+iZdhbBSolPGd81GYNOy4GiarLyG1EGnr0UvrkJbHXQ+Ty49Tdo1ef0j4/PhOu/gyH3iOvz/wpr3m9RzPsKKtl4uAydViPdBXzM+K7JJEYaKaqysGBXaHjPOxwKbzkF824dkUGk1Ks4JfeO7YBGA7/tyGdPXqXa4QQNGw6V8Oi3WwDx/rtvXEeVIzo9l/RtzV8vFBpTL/22h6/XSptSiaQlyIRAoOKqEEhs/gf2kAxnQsCH4jxLskSZ+ZjOSY1/UrKzbUAmBNRhrzMh0GmC+6bRzraBJXv8q23g+0qRPOrOfrTV+SpHIzmeqPAwd7vJWdsGCrNgxvXgsEL3KXDVl2Awn/0gWh2c928Y97S4/usTsOPHZsf8zXphNTi+azLJUd632ZI0EKbTcsUA0S8+bc0hlaPxDXN35JGVX0VUuN4vd2b9hU4pUZznrDh6W2oJeIRDxdXc8fkGLDYH53ZL4ekLuqkd0lm5dWQG947tAMDTP25npb/pGkkkAYRMCAQqRS1rGQAY5EwIbDxc6pOSzNJqC1uPlAEwuikJAbeOgEwI+Jz6Sjgs7AbpeK775vHdUgCR4LHY/KOc1+FQ+D7LymZHprjBlciQ+A2utoEz2g9aauDra6GuHNoMgkvfBV0Td0pHPw6DbgcU+OFu0fbSRKx2B99vFAkBf+qhDSWuHdwOjUZUIgW7uKDDobjt3W4ZkUGMKUzliPwb1+71z1tzyS6qVjmawKa63sYdn6+npNpCz9bRvHFN38ZVcPoBj0/qwqX9WmN3KNwzbSMH5XtBImkWMiEQiFQXQ61zVz+hQ7NfpnNyFLHmMGosdvdC3Zss21eEokDX1ChSY5qw25bish6UCQGfc2il2KWNyzjhvda7dQyJkUaq6m0+rTA5ExsOl1JYWc8K7QBxw97f1A1IchITuqcQptOwJ7+S3XmncRuY/1dRARWZCldPhzBT0w+k0cB5L0KH8WCrFQ4F1qbpXSzYlU9RlYXESKNbEFHiW9olmBnrTB5/uTq4S4Ln7cxnd14lkUY9t43IUDscv6dHWgzndE3GocD/FssqgeaiKApPfLeVrPwqkqOMfHTToIASstRoNLxwWS/6to2lvNbKbZ+to6LOqnZYEknAIRMCgYirXSC6DRgimv0yWq2GEU4LuaVZ3i+1cpWXN6ldACClpxhLssEis78+5cASMWaOOeFmrVbD+K7i7/j7Lv8ozXftOtenOysZ9i8Gm0W9gCQnEWs2uNXjf9h09OQH7Psd1n0g5lPegcgWLMS1Orj0PYhMgcJd8NvTTXr656tEmfrUgW0I08mvSrVwCet9uyGHGotN3WC8hKI0VAfcPDydGLOsDmgM940XVQLfbzzKkdIalaMJTD5ans2crcfQazX87/r+pEQHXmtUeJiO928YQGp0OPsLq7n/q03YpR2hRNIk5FlOIFLUcv0AF6N9ZCHncCgsdR6jSe0CABGJEJEMKFCw2/PBSU5PtjMhkDH6pLvGdxVtAwt2Fahu+6MoCr/tEAmBHgNHi/eLpRIOr1Q1LsnJXNpPiPPN2pR7ooe0tRZ+fljMB98JHc9p+cEik0VSAGD9R6LipRHsK6hk5f5itBq4doj0gVeTMZ2TaBdvprLOxqzNuWqH4xUW7Cpg57EKIgw6bhspqwMaS/92cYzomIDNofDekgNqhxNwrNpfzAu/inOqZy/szoD28SpH1HySo8P54MaBhIdpWZJVyItz5bmiRNIUZEIgEGmh5eDxjOwkFuebc8oor/VemdXWo+UUVtYTYdAxML0ZXt4uHYH87Z4NTHJ6qosaft/pJycERnVKxKDTcrikhv0q9/duPVLO0bJazAYdY7qkQKeJ4g6pI+B3jOuaTHS4nryKOlYfKG64Y8XrUHYYolvDuX/z3AE7jIP+N4n57AfBVn/Wp7jK08d3TaFNXCPEDCVeQ6vVcP1QkZT5fNUh1ZOPnkZRFF53VgfcODyduAiDyhEFFn8aJ86DZqzPIb+iTuVoAoe88jr+9NVG7A6FS/u15sZh7dUOqcX0ahPDy1cKJ5r3lh5wbxJIJJKzIxMCgUgLLQePp3WsicykCBwKrNrvvbYB1wfz2K7JGPW6pr+AW1hwpwejkpyR7KViTO5xytLtCKOeoR0SAGH/pCa/bhfvr3FdkgkP0zXsLu9fqGJUklMRHqbjgt6tgOPaBkoPwfL/ivnE51rUCnVKJvxdVI0UZYnEwxmorrfx3QYhJhgMJ8nBwNSBbTHqtew6VsGGQ76zyfUFi/cUsu1oOaYwHbfL6oAmMzQznoHt47DYHHywVFYJNAaHQ+HhGZsprrbQrVU0/7q0FxpNYIgIno0Le6e5q2we+3YLh4plm6lE0hhkQiAQ8YDl4PGMdlYJeNNTfp4zITCpR2rzXiBFWg/6HFdC4A/6Accz2fn3nLtdvUy8oijMddrYuVTsyRwLaEQCqeIsFncSnzOlr2gb+HV7HrUWOyx8Dmx1kD4Kelzq+QOa4mDyC2K+/L9nfE/8uPkolfU2MhIjGOnUWJGoS6zZwMV90oAGbYdgQFEUXnNVBwxrT0KkUeWIAg+NRsOfnFoC09Ycprjq7BVAoc4Hyw6w6kAxpjAdb1/bD5OhGZs0fsxT53VlQPs4Kuts3PPlRuqsdrVDkkj8HpkQCDTsVihxZsE90DIAuL3Bl+4t9Eo55r6CKvYXVhOm0zRfrTv5OKeBICsZ9VvOoB/gYlKPFLQa2Ha0nJwSdUSddh6r4GBxDUa9lnFdhWAd5nhI6yfmBxarEpfk9AxKj6d1rImqehur1yyHbd+KOyY+JxwCvEHPy6HNYLDWwKLnT/kQRVH4wrngvG5IO7QBYr0VCrjEBX/dfozCyuBY9C3dW8SWnDLCw7TcPipT7XACljGdk+jVOoZaq52PV2SrHY5fs/1oOS/P2wPAXy/qTmZSpMoReZ4wnZa3r+1PQoSBnccq+PtsuZEkkZwNmRAINEoPgcMGepPotfUAQzMTMOi15JTUsie/0iOveTzzdord42EdEokOb6Z6clJX0GiF3WKl7AvzOmU5IvGk0UH7Ead9WEKkkaGZom3g1+3q7MS7hMbGd00m0nicXVKH8WKUbQN+h1arYUo/seMbueolQIFuF0NaX+8dVKOBSc5EwKYvT1lttHJ/MbvzKgkP03LlgLbei0XSZHq1iaFfu1isdoUvVgd+lYCiKLw6PwuA64a0JylKVgc0l+OrBD5fecirekiBTK3FzoNfb8JqV5jYPYWrBwXvZ1xqTDivX90PjQamr83hh01H1A5JIvFrZEIg0HALCnYErWf+fBFGvdtt4Lftnu8Fd/WXT+qR0vwXCQsXPzNAgcz2eh1Xu0BaPwiPPuNDz+sl+sF/2eb7RI3doTBrs+hDd6nXu+kwTowHFoHD4ePIJGfj8v5t6KE5yKCaZShoYNxfvH/QtoOh+xRAgYUnVwm85+xBnjqwrbR+80NuHyl20b9YdVC0mgQw83fmsyWnDFOYjrvHdFA7nIBnQrcUuqREUVlv4/OVB9UOxy/51y+72F9YTXKUkX9f3jtodANOx8hOiTx0jtDaeuaH7VJPIMBwOBR251Xw3YYj/PvX3TwyYzO3frqOmz5ey+2frefJmVt57fcs5m7P42hZrdrhBjz6sz9E4ld40HLweCb2SOX3XQXM3ZHHg+d6phUBILuomi05ZWg1MKF7CxICIHQEirLEzl7Hcz0ToOTUNKJdwMWkHin8ddZ2NueUcbSsltaxJi8H18DK/UXkV9QTaw5jrNPf3k2bwRAWAdWFwi2hVW+fxSU5O5lJkTwTOxdqYXfiBLold/PNgcc9Dbt+gj1z4NgWaCVUqXcdq2BpViFaTcPCU+JfTOqRQtt4EzkltczckMMNzjaCQMPuUHhlnqgOuHVkuqwO8ABarYb7xnfkgemb+GhFNreOzCDCKE9xXSzYle+urHn5yj7Eh4ibxZ/Gd2TF/iLWZpfwwPRNfHv3cAx6uRfqr1jtDpZmFfLz1mMsySqkpNrS6OdmJkYwvmsyU/q1pkdadNAnvDyN/K8INDxoOXg853ZLQafVsOtYBYeLPdcL/qNTRXxUpySSo8Jb9mJuYUHpNOBVFKVRgoIukqPCGZwu/Itdu/W+wqVSf0GvVid/yesNkDFKzGXbgP9Rks3Q2uUA/LNsEla7j6o4kjoLPQGAJS+6b37fWR1wXq9WtEuQVoP+iF6ndSdrPlyejd0RmHoys7fksie/kuhwPXeOktUBnuKCXq3ISIygrMbKtDWB31biKQor63li5lYAbh2RwejOzdRyCkB0Wg2vXdWXGFMYW46Uu9t0JP5FeY2V/y3ez6j/LOK2z9bzw6ajlFRbMIXpGJIRz43D2vPUeV35z+W9ePnKPvzr0l48fG5nrhzQhh5p0ei0Gg4UVfPh8mwufHM5F765nNlbcgP2O0INZPo00HBbDno2IRAfYWBIRjwr9xfz24487hjd8h0yRVH48XTl3M0hWToN+ITSbKg8BtowscveCC7v34Y12SV8v/Eo94zp4JPMbHW9jd+c7gaX9T/N+6vDeMiaK9oGRj7k9ZgkTWDV22hwsFLTl5VVrViwK5/JPVv55tijn4BtM2H3z3BsK7mmTszeIrQo7vLAZ5/Ee1w5sA3//T2LQ8U1zNuR525ZChSsdod7UXLXmA6yNcWD6LQa7h3bgcdnbuX9pdncOCxd2NCGMIqi8MTMLRRXW+iaGsUTk7uoHZLPSYs18Z/Le3P3lxt4b+l+RnZMZGQn6SDjD1TX23h3yX4+Wp5NjbMNLDHSwIW90zi/Vyv6to1tVEVHRZ2VFXuL+HnbMebvzGdHbgX3T9/Eq/OzuH98R6b0bS1Fgs+CrBAINNyWg55NCECDJeAvHhKH25RTxqHiGswGHRNboh/gwlUhULhbuC1IvMOhlWJs3R8MjdspPa9XKka9ln0FVWw7Wu7F4Br4aUsu1RY7GYkR9G8Xd+oHuYQFD60CizouCJJTUF0shP2AQ13vAOBTX/b9Hl8lsPINPl6ejc2hMDQznt5tYn0Xh6TJmA16bhjaHhCaD95wxvEm36zP4XBJDYmRRm4Zka52OEHHlH6taR1roqiqnhnrctQOR3W+WH2IRXsKMei1vHZ135BNkEzumcq1Q9qhKPDwN5ulPaXKOBwK36zLYezLi3lz4T5qLHa6pkbx8pV9WPHUeP52cQ8GZ8Q3ur0jOjyM83q14u1r+7P2L+fw8LmdiTWHkV1UzSPfbOHK91ax3UfnpoGKTAgEErWloh8aGgT2PMh5PVPRaTVsOlzGvoKqFr/e9xuFquvkHqmYDR4oRoltB4YocFiheF/LX09yalwJgfbDG/2UqPAwd0Lp+42+aRuYvvYwANcMbnv6ioSEjhDTFuz1cHilT+KSNIJNX4CtFlJ7M2biZei0GlYfKPHtF/bw+wFQtn/PwrUbAbhrtCzfDgRuHJaOQa9lc04Zq/YXqx1Oo6mz2nljgUjq3z++o2e+FyUnEKbTcvdY8X/87pL91NsCW3yyJezNr+T5ObsAeGpyV7qmnlkgONh59oLudEqOpLCynsdnbg24ZGKwkFNSw7UfruaJ77ZSWFlP+wQz717fn18fHMUVA9pg1LcsaRVrNvDguZ1Y8eR4Hp/UBbNBx4ZDpVz81nKen7MzpD8TzoRMCAQSrnaBqFZgjPL4yydHhzOui+gt+3Z9yzLrlXVWftwkSnAv69+mxbEBwjYspbuYy7YB7+FOCJzebvBUuMr2Z20+isXm3X7w7UfL2XqkHINOy+Vnen9pNA06CC5dBIm6OByw4RMxH3wHaXFmLnCWfX+47IDv4kjrC+mj0Ch2rnL8Qq/WMYztEjq9tYFMUpSRa5yWaa8t2BswJ/Yfr8gmv6Ke1rEmrh4cvJZvanPlgDakRodzrLyOr9YcVjscVai32Xnw683U2xyM7pzEzcPT1Q5JdUwGHW9c0w+DXsvC3QW+rUqToCgK09YcYtJrS1l9oARTmI6nz+/G/IfHMLlnK4+3mkYY9dw3riMLHh3DRX3ScCjwwbJspry9kiwvWKwHOjIhEEgcbznoJa4cKE5Svtt4tEUiX99tOEJVvY0OSRGM6JjgqfAgWSYEvEpFrtAQ0GiFRVsTGNkxkZRoI6U1Vubu8K4F4VfO6oBJPVNJiDyLQneGTAj4FQcWQelBMMa4y/ZvH5UBwM9bj3Gs3Hf2QeV97wLgGt1CHh/r+RMSife4Z2xHDDota7NLWHXA/6sECivreWfRfgAendi5xbtgktMTHqbjgXNEW+Xbi/ZRXW9TOSLf8+q8LHYeqyA+wsDLV/SW/dNOurWK5unzhaPNC7/sZtexCpUjCg2q62088PVmnv5hOzUWO4PT45n70CjuGJ3pddeHVjEm3rymHx/eOJCECAO7jlVw0ZvL+aaFG5/BhkwIBBKuMnkvJgTGd00mMdJAUVU9i3YXNOs1HA6Fz1cJhd+bh6d79iQ7RQoLehVXdUBqLwiPadJT9Tot1wxuBwifcG9RXmN1u1dc05hdtnSn08CxLaLtRqIu6z8WY5+rwRABQO82sQzOiMfmUPhgabbPQnn9cAb7Ha2I1tQyqvJXnx1X0nJSY8Ldu+yv/75X5WjOzn9/z6Kq3kbvNjFM6esBkV3JGblyYBvSE8wUVVn4ZIXvPlP8gZX7injfWW3178t6kRzdQoenIOPGYe05p2syFruDB6Zvos4qS8i9yb6CKi55ewWzt+Si12r4y/ld+frOobRPiPBpHOd2T+HXh0YxtksS9TYHT8zcyt9+2uE7hyM/RyYEAolisbvgzYRAmE7rLvF3edY2lWX7ijhQVE2UUe+5dgEXroRAgbQe9AquhEC7xusHHM+1g9uh12pYd7DUa5n3L9cccgvQDMtsRPVJdCth06k4Gn4+iTpU5MIe58J74C0n3HXfOPG5Nm3NIQoq6rweyqHiar5Yc5gP7ecDoFnzHthDbycxkLlnbAcMOi1rskv8Wktgd14FXzurmp69sLvcrfUBYTotD0/oDAjxybKaxvuZBzJlNRYe+WYLigLXDG7HRKe2j6QBjUbDi1f0JinKyN6CKv71yy61QwpaVuwr4tK3V7CvoIqUaCNf3zmUO0d3UO0zMDkqnI9vGsQjzs+GT1ce5IaP1oTM58OZkAmBQKLElRDwrvDV9UPao9NqWLa3iC05ZU16rqIovOkUTbpiYBsijB4WTUoWpV6U50CdVAz1OM0QFDye5OhwJvUUJyCuKhFPUm+zu/v+7hyd2fjqk4zRYsxe5vGYJE1g4xeg2EXCyfW/7GR0p0QGtI+j3ubgncX7vR7Kf+buxmpXyE+fAuYEKD8MWbJKIJBoFdPQi//f+Vl+qSWgKArPz9mFQ4Hze6UyKD1e7ZBChot6p9E1NYrKOhvvLvGhPolKKIrCX37YRl5FHZmJETx7YbezPylESYg08sqVfQBxrrJgV77KEQUf367P4aaP11JZb2NQehw/3z+KgX7w+afVanjgnE68f8MAIgw6Vh8o4Yp3V3G0zHftiv6ITAgECooCxc4vtHjvJgTaJZi5pG8aAG8ubJqa/8LdBaw/VIpRr/WOYrcpDqKd5Zb5skrAo9SUQKEzU97MhADATcPSAfhh0xGKPGztM2tzLoWV9aRGh3Nh77TGPzHD2TYgdQTUw2GHjZ+J+cBbT7pbo9Hw8Lkia//V2sNe1RJYd7CEX7blodXAExf1hX43iDvWf+K1Y0q8wz1jO2DUa1l7sIT5O/3vpH7eznyW7S3CoNPy1GS5QPMlWq2Gxyd1AeDTldk+qTxSk5kbjvDLtjz0Wg2vXd1XulichdGdk7htpNCveXzmVgoqg/v94SsUReHV+Vk8PnMrNofCxX3S+PL2ISRFnUXvycdM7JHK9/eOIDU6nH0FVVz+zkr25IWu2KBMCAQKlXlgrRZib3HpXj/cfeM6otHA77vy2ZnbuNJvu0Phxbl7ALh5RDqpMV7qW3MJCxZIHQGPcniVGBO7QERis19mUHocfdrGUmd18OEyz/Vu2uwO3nXuHN8yIr1pQjQuHYGCHVBd5LGYJE0gewlUHBVJve4Xn/IhIzomMDgjHovN4f4s8TRWu4Nnf9wOwFWD2gorrgE3izv3L4SS0Oo3DnRaxZjcopQv/Lrb6w4nTaG63sbffhLfU3eMzqBdglnliEKP8V2T6d9OfB+9sdD/tSaay6Hiavd77eEJnendJlbdgAKEJyZ3oVuraEqqLTz6zRYcDv+rMgok7A6Fp77b5rZXvW9cB167qq/fiqh2SY3i+3uH0zE5kryKOq58d2WTK6ODBZkQCBRc7QIxbUFv8PrhOiRFuq3AXvh1V6NKMWduyGFPfiXR4XruHeM9nYMGYUFZIeBRWtgu4EKj0fDAePH3/3zVQUqqPdObNXPDEQ4UVRMfYeDaIe2a9uSIREjpKeYHZduAKmyZIcYel4H+1DsFGo2GZy7ohkYDP2w6yoZDJR4P48Nl2ezOqyTOHMZjE8XuIfEZ0GE8oDRUMUgChnvGdiQx0kB2UTXT1ni+Vam5vPZ7FsfK62gXb+b+8Z3UDick0Wg0PDG5KwDT1+awNwjtxmx2Bw/N2Ey1xc7gjHjuHuPdKtJgwqjX8cbVfTHqtSzbW8THISZA6UmsdgcPz9jMjPU5aDXwwmW9eHxSV7/XTEmLNTHz7mH0bxdLRZ2N6z9cw8bDoSdALRMCgUKxb/QDjufxSV0wOD8kZ23OPeNj88rreH6OKDf/0/iOxJjDvBeYFBb0Dq4KgRYmBEDsyvRIi6bGYufj5S3/gq2z2nndmXG+d2wHosKb8f5y6wjItgGfY6mGXbPFvM/VZ3xo7zaxTB0g+sL/76cd2D24Y3OwqJrXfs8C4JkLup9oWelqY9j0JdikwFAgEWnU88gEkdx57fe9fiEQtTO3go9XHATgH5f0IDzMP3fIQoGhmQlM6J6C3aHwfBAKyL2xcB+bDpcRFa7n1al90Pn5Aszf6JQSxTMXisrTF+fuYUeu1KdqKvU2O/dN28hPTieBt67t73adCgRizQY+v20Ig9Pjqay3ceNHa72yIeHPyIRAoOCqEPCyfsDxtE+I4E9O5e9nf9xOdlH1KR9Xb7Nz77QNVNQJS6VbR2R4NzBXy0D+TqGtIGk51lphywfQdkiLX06j0bh3xD5ank1uC8VaPl91kGPldaTFhHP90PbNe5F0qSOgGrvniJanuAxoM+isD398cheijHq2H63gg2WeEQOz2Bw8OGMz9TYHIzomcFn/P1i/dZ4MkalQXQi7f/bIMSW+Y+rANnROiaS81upOHqqFze7gLz9sw+5QuKBXK8Z2SVY1Hgn85fxuhOk0LN5TyJKsQrXD8RhrDhTzlrMV4vlLe9EmTralNIfrh7Tj3G4pWOwOHvx6M7UWaUXYWOqsdu78fAPzduZj0Gt5/8YBnO+sMA4kIo16Pr11EEMz46lyJgXWHQydpIBMCAQKPrAcPBX3ju3AwPZxImP28RpySmpOuL/Oaue+aZvY6MxOv3F1P/Q6L7+tEjuDVg/15aInWdJyjm4Eh00siGI9k9Wd1COFQelx1FrtLdqVySmp4TWnz/jDEzo3f6et/XChwVG8T9jfSXzHlq/F2PsqaIQzRGKkkWecCtmvzNvTaB2TM/HKvD1sySkjOlzPfy7vfbJDhS4M+t8o5us/bvHxJL5Fr9PyzAUiWfzZyoNsO6LeLt+7S/azOaeMKKOeZ507jxJ1yUiM4Ean4O3zc3ZiCwLv8bIaCw/N2IxDgSsGtOHiPk0Q2pWcgMuKMDnKyL6CKp7/RVagNoZ6m527vtjAkqxCTGE6Prl5EOO7pqgdVrMxG/R8cvNgRnRMoNpi55ZP1qn6XeJLZEIgUChx7pL5sGUAxEnWO9f3p32CmZySWi5+azmfrMhm17EKft6ay8VvLef3XfkY9VrevX4A6YkRPgjKIHzlQeoIeIqcNWJsO7hRC7bGoNFo+NvFPdBqYM7WY6zc33QxP0VReObH7dRY7AxOj+fy/m2aH5ApFlr1FXNpP+g7KvPhwCIx7z210U+bOrAtE7qnYLUrPDRjE1X1tmaHMHf7Md5bKj5DX7yiz+l30frfCGiEzoQUFww4RndO4qI+aTgUeOr7raos+rYdKXcnMP9+SQ/vietKmswD4zsRaw4jK7+K6WsPqx1Oi1AUId52rLyOjMQI/n5xD7VDCnjiIwy8MlVYEX65+rBfupb4E1a7g/u/2uROBnx262BGdGy+ILW/YDLo+OimQQzLTBCVAh+vCUrtkT8iEwKBgMPRkBCIz/T54ZOjwvn6zqH0bB1NaY2Vv8/eyXmvL+NPX20iK7+KxEiD7z8IUlxtA9t9d8xgJmetGD3QLnA8PdJi3CX+T8zcSnmNtUnP/3b9EZZkFWLQafnXZb1aLk4j7Qd9z/aZoDhEq0ATEpoajYYXLutFYqSRrPwq7v9qY7MWeOsPlvDg15sBuHl4OpN7pp7+wbFtIXOsmLuqGiQBxV8v7E50uJ4duRV86AH9kqZQZ7Xz8DebsTkUzu+VyqX9Wp/9SRKfEWMOc1ubvvTbHgorPWuL60u+WnuYuTvyCNNpeOPqfkQYpcWgJxjVKYk7nK4lT8zcQn6QW1U2F7tD4ZFvtrjbBD68aSCDM+LVDstjhIfp+OCmgfRpE0NpjZXrPzq5QjrYkAmBQKAyF2x1okw+tpn90y2kVYyJ7+8Zwd8v7kHftrEkRBjolBzJPWM7MO/hMQzNTPBtQG7rQVkh0GIU5bgKAc8mBAAem9SFdvFmjpTW8ui3mxvlWAGw6XApz8wSCZ8Hz+1Ex+TIlgfjFhZcIvUnfMXWb8TY+6omPzUx0siHNw3EqNeyaE8hz87a0SRbqE2HS7nts/XU2xyc2y2ZZy5ohA983+vEuOUrkYyVBBRJUUZ368Ar83wrEPavX3axr6CK5Cgjz0/pdXJbikR1rhvSjh5p0VTU2XghQAUGs/Ir+cdsce7zxKSu9GoTo3JEwcVjk7rQvZXYAJNWhCfjcCg8MXMrs7fkEqbT8N71A4KiMuCPRBr1fHrLYDolR5JfUc/1H62hIIgTRDIhEAi49ANi24NOvSywQa/lpuHp/HjfCDY8O4H5j4zhycldiY/wvg3iSbgs5GTLQMsp3g+1JaAzQqveHn/56PAw3rmuPwa9lt93FfDf388u+JVbVsvdX27AYnNwbrcU7vGUjVK7YSKxVp4DZf5jTxa0lGTDsc2g0UGPS5v1En3bxvL61X3RaGD62sPc99VG6qxnF3yatyOPaz5YTXmtlX7tYnnzmv6N0zfpegEYo6HsMBxe2ayYJepy5cA27naTB7/efOb3i90qNEXytkP5EXG9Gfyw6QifrxKfKS9e0Zs4Nb4XJWdFr9Py/KW90Gjg+01Hm9XKpia1FjsPTN9Evc3B6M5J3DbSyyLOIYhRr+ONa/oRHqZl+b4iPvJxpZE/oygKz87azncbj6DTanjzmv6M6xq8oqlxEQa+vH0IbeNNHCqu4YaP1vqFi403kAmBQKDE95aDfo+rZaAoq9kncBInruqAtH6n9YdvKT1bx7h7HN9YsJeXf9tz2qz73vxKpr63ivyKejolR/Lfq/p4zsfWEAFp/cX8kFzseZ2ds8SYPhIimr+DMLlnK16/uh8GnZZft+dx8VvLWX2g+JSPLam28Ni3W7jziw3UWR2M7ZLEl7cNwWRopBilwQw9poj55q+aHbNEPTQaDf+5vDdJToGwp3/YfmJlUn0VrP0APp8C/2oNr3aDd0fAf3vA86nwyfmw8k2oaZzC9I7ccv78/TYA7h/fUboK+Dl928Zy3RAhnvvsj9ux2AKjEkhRFJ7+cRu78ypJjDTwypUe/G6UnEDH5Ej+eqE4Z3nxt91sySlTNyA/QFEU/vnzLqatOYxGA69O7XPmFrwgISU6nGm3DSU5ysie/Eru+Hx9ozYlAg2ZEAgEin1vOej3xLQVu3gOKxSpazEV8LgSAu083y5wPNcMbsfjk4RX+FuL9nHdh2vYklPmPlGvqLPyv8X7ufitFRwprSU9wcxntw4mKjzMs4G0Hy7Ggys8+7qSk9n5oxhdC+wWcHGfND6/bTDxEQay8qu4+v3VXPP+at5fup/ZW3KZvvYwj36zhZH/WcjMDUfQaIRmwAc3Dmx6f62rbWDHj2LxKAk44iMMvH5VX7Qa+G7jET5deRBsFlj2CrzWE355TIhd2utFBYs5UVQPOWxwaAXMewZe6wXz/++M74GjZbXc+uk66qxix/YhZ4+6xL95fFJXEiMN7C+s5s2FgXEOMW3NYb7feBStBt64ph9JUd5J4EsE1wxuy+QeqVjtCvdO20hpdXDuDDeWl+ft4eMVolriP5f35pK+oaOR0i7BzBe3DSEqXM+6g6U8PGMz9iBrJdEojW3oDSEqKiqIiYmhvLyc6OhotcOB6dfAnl/g/Jdh8B1qR+M/fDRRLGYv/wh6XaF2NIHL20OhcBdc/ZUol/Yy367P4a+zdlDrzLAmRRmJMuo5XFKDzfkBO6JjAm9c3Y+ESC+c8OydD9OugLh0eHCL519fIig9BK/3FlaPj2ZBZJJHXrasxsLL8/bw1ZrDnO77uHuraP45pScD2sc17yCKAm/2F2KuU96Fvtc0P2CJqny47ADPzdlFT90hpid9SlTZHnFHfCYMvBU6TnBa2WqFZkRpNuz7HTZ+3iBaG9seLnm7QZTUSXmNlSveXcnegio6JUcy8+7hxJg9nMCUeI05W49x31cb0Wk1fH/PcPq0jVU7pNOy8XApV723Cqtd4c/ndeUuT7XRSc5IRZ2Vi99czsHiGsZ2SeLjmwaFZFXGmwv28sr8LAD+eUkPbnBaeIYaqw8Uc+NHa7HYHdw8PJ3/u6i7V7Ri1FiHygqBQMBdIeB7hwG/xiUsmL9D3TgCmdoykQwAaDPYJ4e8cmBb5j40ikv7tcao11JYWc+BompsDoVOyZG8fGUfvrh1iHeSASCEEzVaKD0I5Ue9cwxJQ7tA+xEeSwYAxJoNPDelF0ufGMdfzu/K+b1SGZwRz9guSdw1OpMZdw5lzgMjm58MAGG92edaMd88zTOBS1ThtpEZ/KNDFjP1fyWqbA9WYzxc+h78aT0Mvx+Su4pkAIgxoQMMuQvuXi6SpDFthd7I5xfDqnfcYqQl1Rau+WA1ewuqSIk28umtg2UyIMC4oHcrLuqT5lRMP4vWhIoUVdVz75cbsdoVzuuZyp2j5bmgrxAaSAMw6rUs3lPI24v2qR2Sz/lg6QF3MuCZC7qFbDIAYGhmgtua8tOVB/lg2QGVI/Ic0qfE33HYxY4FSA2BP5Li9N2VCYHmc2S9GOMzPbpoOxvtEyL471V9ef7SnmTlV1FntdMmznR6f3hPEh4Nqb2F2N2hldD7Su8fMxRxJQS6X+KVl28TZ+bO0V78TOw9FRY9BweXQ8UxiG7lvWNJvIZm5ZvcePRvoIFF9j48U/snngsfyzjtWTQlNBpRMZUxGn59UiSGfvszlOwnd/g/uOXTDezJF73cn986hNaxJp/8PBLP8s9LerD6QDH7C6t56bc9PHthd7VDOgGLzcF90zaSV1FHh6QIXrqyj3Sv8DHd06J5bkpPHp+5lVd/z6JfuzhGdgo+Vf1T8cWqgzzvdON4dEJnbh8lk1EX9Ukjv6KO5+bs4l+/7CYlOjwo2idkhYC/U54DdgvoDGKnQtKAKyEgrQebjxftBhuD2aCnb9tYhmYm+CYZ4CJ9pBgPLffdMUOJshw4uh7QQLeL1Y6mecS1d/5fKLDje7WjkTSHlW/B/GcBsA2+m0/a/5ujlghu/2w9X64+1DgLVGOUaBeY+DyggXUfsuqNm8jKLycl2sjXdw6jS2qUd38OideINRv4z+W9APhoeTaLdheoHFEDiqLw5++3sSa7hEijnvduGEBkU/VQJB7hyoFtuXpQWxQFHvh6E8fKa9UOyet8sy6HZ2eJDbf7xnXg/nM6qRyR/3D7qEy3w8dj325h5b7Acis5FTIh4O+42gXi0uFsOxqhRrLTU7w8B+p85zUdVLgTAr5pF/Ab2o8Qo3Qa8A67fhJj++EQlaJuLC2hl7N6ZNtMdeOQNJ31n8C8p8V87J/Rn/8fPrx5KJf1b43dofDMj9u564sNFFbWn/21NBpqBt7Nj5n/h13RcLkyn9diZjDzrmF0TI707s8h8Trju6Zww9D2ADw0YzM5JTUqRyR4a+E+t73b29f1p2OyTDypyd8u7kGPtGhKqi3c82Xj7G8DlVmbj/Lk91sBuHVEBo9N7KJyRP7H0+d344LerbDaFe76YgO7jlWoHVKLkAkBf6fE2Z8iHQZOxhQH0c4ynYJd6sYSiNhtDS0DKlUIqEa7oYBG2FZW+c+OUNCw62cxBmp1gIvuU4QCfe7GhuSsxP/ZvwjmPCrmox6FMU8CYNBreeXKPjx1XlfCdBrm7cxnzEuLeOHXXaddBBZX1fPO4n2MfWkxD+3szGPWu3Gg4ZL62bTd+7mvfiKJl3nmwm70aRtLea2Ve6ZtUH2x98OmI+6+7X9c0oMxnX3X0ic5NeFhOv533QBiTGFszinjiZlbG1dlFGDM3X6MR77ZgqLAdUPa8eyF3WSbyinQajW8cmUfBmfEU1lv45ZP1pFbFriVIzIh4O+4TkKlfsCpkcKCzadgB1irhX1jUle1o/Et5viGlhNZJeBZqoshZ7WYdz1f3VhaSmQSZI4V8+3fqRqKpJEU74dvbwLFDr2vgvHPCj0AJxqNhrvHdGDWfSPp3SaGGoud95YcYNSLi5j43yXc99VGnv1xOw9+vYkL3ljGgOd+58W5eyiorKd1rImLb3wE7YR/iBf77S+Q9ZtKP6jEkxj1Ot65rj9x5jC2H63g2R+3q7bYm7s9j8e+Fbuzd4zK4Loh7VWJQ3Iy7RLMvHv9APRaDT9tyeXNhcElMrhodwH3T9+E3aFwWf/W/POSnjIZcAbCw3R8cMNAOqdEkldRx00fr6WsJjDtKWVCwN8pkQmBM5LiTAhIHYGmk7NWjG0GhmY7irttYIW6cQQbe38DxQEpvSC2ndrRtBxX28DWb9wK8xI/xVYvkgF15cI15aI3TkgGHE/3tGhm3TeCj24ayNDMeHRaDVn5VczZeowvVh9i1uZcduSKEtBerWN45co+LHpsLOO6Jgt3gv43ivf593cIi01JwNM61sQb1/RDq4FvNxxRRVF+0Z4C7p++EbtD4YoBbfjzed18HoPkzAzrkMBzU3oC8Or8LH7emqtyRJ5haVYhd325Aatd4YLerXjx8t4habHYVGLMYXx6y2BSo8PZW1DF7Z+tV73CqDlIdRJ/x205KBMCpyTZ5TQgEwJNxtUu0GaQunGoRfvhsPY9OCgTAh5lzy9iDPTqABddLwB9OBTvhbyt0KqP2hFJTse8ZyFvG5gTYOrnEBZ+xodrNBrO6ZbCOd1SKKuxsPpACblltZTWWIgxhZESHc6QzHiSo8L/+EQ4/xXxvXN0Pcy8FW75FfQGL/5wEl8wqlMSf7u4B3+dtYOX52WRHB3O1IG+EXRevKeAu78QC7ILe7fiP3JB5rdcPbgdewuq+Gh5No9+s4U2cWb6to1VO6xms3B3Pnd/sRGL3cG53VJ47aq+6HVyz7ixpMWa+OzWwVz57krWHyrlT19t4t3r+wfU7zBwIg1F7DbhfwyyQuB0pBzXMiB375rG0VBPCDgrBAp2QE2JurEEC9Y62LdQzLucp24sniI8GjpPEnMpLui/7J0vEnwAU95tsk1krNnA5J6p3Doyg0cnduH2UZlc1Cft5GSAC70BrvwEwmPEZ+nif7XwB5D4CzcOS+euMcJe7cnvtvLDpiNeP+b3G49w+2frqbeJBdl/r+qLTiYD/Jq/nN+N8V2Tqbc5uOWTtewrqFI7pGYxb0ced32xAYvdwaQeKbxzXX/CAmgh6y90SY3iw5sGYdBr+X1XPs+o2HbUHORf3J8pOwQOm9idikpTOxr/JLEzaPVQXw4VR9WOJnCoKYFiZzlk6wHqxqIWkUmQ6FTOPbxK3ViChYPLhC5FVCto1VftaDxHzyvEuP07cDjUjUVyMnXl8NMDYj70Xug80TfHjW0nLAkBVrwORzf45rgSr/PkpK5cO6QdigKPfLOFGesOe+U4iqLw7pL9PPLNFmwOhSl90+SCLEDQaTW8eU0/+rSJobTGyg0freFogInK/brtGPdO2yjaBHq14q1r+2PQy/decxmcEc+bzrajr9fl8F+nMGggIP/q/ozbYSATtPJPdUr0RkhweqPKtoHGc3SjGOMzhcBeqNJ+uBhl24BncLULdDnvtL3bAUmniUJ8s+Jog2CixH+Y9yxU5orPs/HP+vbY3S4SCSPFAT/eJ3QMJAGPVqvhuUt6cp0zKfDkd9t46bfdOBye2/Grrrfx0IzN/PvX3QDcOTqTV6f2lQuyACLCqOeTWwbTISmCY+V13PDRGoqqAuMzYPraw9z31UZsDoVL+qbx+tV9ZSLKA0zqkcpzU3oB8MbCfXyxOjA0ZuRf3p9x6wdkqhuHv+MWFpROA43G1S7QeqC6cahN+kgxSmHBlqMosOdXMe8SJPoBLsLCxcIPpNuAv3FoJWz8TMwvfgsMZt/HcN6LEJEEhbtg6Uu+P77EK2i1Gp6b0pP7x3cE4O1F+7n1s3UUVrZ8wbfpcCkXv7WcWZtz0Wk1/N9F3fnL+d2kZkAAEh9h4IvbhpAWE86BwmqueX81BZV1aod1WhRF4b/zs/jz99twKHDVwLa8OlVqBniSa4e046FzxWblX2dt55dtx1SO6OzIv74/Ix0GGofbelBWCDQat6BgiCcEXBUCeVtF2bGk+RzbDJXHICwC0kepHY3n6XGpGHf+BI7AUxAOShx2+OUJMR9wM6SPUCeOiAQ4/2UxX/YqHNuqThwSj6PRaHh0YhdevrIPRr2WxXsKOe/1pXy34UizqgVKqi387acdXP6/lewvrCY5ysj0O4Zyy4gML0Qv8RVpsSam3THUrTR/9XurySv3v6SAze7gLz9s4/UFewF4YHxH/n15L6lX4QUePKeTu+3ooa83s2JfkdohnRGZEPBnpMNA43D5yUvrwcahKA29rqFeIRCdBnEZotz38Bq1owlsdjvbBTqec1Z194AkY4wQkKsukJoT/sKGTyB/m/i7jP+rurH0mALdLgbFDr8+IUVug4wrBrThpz+NpEtKFEVVFh79dgtT3lnBz1tzsdrPriuSU1LDf+buZvSLi/h05UEcClzWrzW/PTSawRkh3LYXRGQkRjDjrqG0jjVxoKiaq95fxcGiarXDclNea+X2z9czfW0OWg08N6Unj0zsgiaY2vv8CI1Gwz8v6cnkHqlY7A5u/2w96w76r4C1TAj4M7JCoHG4KgQK94Ddqm4sgUDJAagtAZ0BUnuqHY36uHYVZdtAywjWdgEXegN0vVDMd/yoaigShDDqwufEfNwzYpdebSa/AHqTSBjJ1pKgo0tqFD/dP4InJ3clwqBj65Fy/vTVJob+awGPfLOZr9YcZuW+IrYeKWPj4VJ+3prLq/P2MOXtFYx+aRH/W7yfqnobPdKi+eK2wbx6VV/iIqRVZTDRPiGCr+8cStt4E4eKa7j0nRWs94NF4N78Sqa8vYLFewox6rW8c90Arh/aXu2wgh6dVsPr1/RldOckaq12bvlkHVtyytQO65RolEDyRPARFRUVxMTEUF5eTnR0tDpB2CzwfIrYuXxkd5MtlEIKRYEX2oKlEu5dDcnd1I7Iv9n6DXx/h6gOuGOB2tGoz+bp8OPdwn7x9t/VjiYwKTsMr/UCjRYe2+cfizNvkDUPvroSIlPgkV2g1akdUejy8yOw/iNI7gF3LQWdXu2IBEtegkXPCWeg+9eDIULtiCReoLCyni9WH+KrNYcoqrI06jmjOiVy3ZD2TOyeIrUCgpyCyjpu/2w9W4+UY9BreemK3lzSt7Uqsfy0JZc/f7eVaoud1rEm3rthAD1bx6gSS6hSa7Fzy6drWX2ghBhTGNPvGEr3tNOvL9VYh8oKAX+l7JBIBoRFQFSq2tH4NxpNg7BgvhQWPCtu/YBB6sbhL7h0BHI3gcV/yvsCiqzfxNh2SPAmAwAyx4IxBqryIUe2mKhGYZZoFwA47z/+kwwAGH4/xLYXrgfLXlU7GomXSIoy8siEzqx86hym3zGUu8d0YFyXJDITI0iLCad1rIkB7eO4rH9r/nN5L1b9eTxf3DaEyT1TZTIgBEiOCufrO4cyoXsKFpuDB7/ezF9+2Ead1Xf6MxV1Vh6esZkHpm+i2mJnaGY8P/1phEwGqIDJoOOjmwbRv10s5bVWrv9oDTtzK9QO6wT86FtUcgIuj/j4zOCy7/IWyd3FCXr+Duh1hdrR+DdHpaDgCcS1h5i2UJ4DOWuhwzi1Iwo89jkrTTr5yP9dLfQG6Ho+bJku2gZcySSJb1n0nEiYdzkfMvxMwDIsHCb9C2ZcByvfgP43is8YSVBi0GsZ1iGBYR2COBEqaRZmg553rx/Aq/P38M7i/Xy15jAbDpby2tV96dbKu7u+i3YX8Oys7RwprUWrgT+N68j953SStoIqEmHU8+mtg7n+wzVsPVLO1e+v4rNbB9OvXZzaoQGyQsB/KZb6AU1CCgs2Dls95G0T89YD1I3Fn3At7KSOQNOx1UP2UjHveK66sfiC7lPEuOsncJxdTEziYXI3wc5ZgAbGP6t2NKem6wWQMRrsFlj8b7WjkUgkKqHTanh8Ulc+v3UwiZFG9uRXcuGby/nnzzuprPO85lVOSQ23f7aeWz5dx5HSWtrGm/j27mE8MrGLTAb4AdHhYXx5+xAGtI+jos7G9R+uYc2BYrXDAmRCwH+RgoJNQ1oPNo68beIk1ZwAcelqR+M/tHcJC65UN45A5PAqsFaLvvrUXmpH4306jANjtLBYlG0DvmfBP8TYe2pDq5i/odHAuX8T8y3T5feSRBLijOqUxK8PjmJSjxTsDoWPlmdzzitL+GLVQY+0ERwqrubP329l/CuL+X1XPnqthjtHZ/Lrg6MZ0F66WPgT0eFhfH7rYIZ3SKDaYuemT9ayJKtQ7bBkQsBvkZaDTcN1Ylh+GOr8qy/Hr3DpB7QeKFtRjid9pBiPrAer/3kH+zX7nEKMHc4JjfeU3tjgpLDzR1VDCTmyl8H+haDVw9g/qx3NmWk9QNgQosDCf6odjUQiUZmkKCPv3TCQT24ZRHqCmYLKep6dtYPRLy7ilXl7OFxc06TXs9odLNydz73TNjDu5cVMX5uD1a4womMCvz44ir+c341Io+wM90cijHo+vnkQ47smU2d1cNun6/hmfY6qMcl3ir9SckCMskKgcZjihKpzZS4U7IJ2Q9SOyD85sk6MUj/gROIzxQ53VT4c3dBgRSg5Oy79gI7nqBuHL+kxBbZ+LUrXJ70AWplb9zqKAoueF/MBN0N8hqrhNIpz/gq758CeX+Dwamg3VO2IJBKJyozrksywhxL4eu1h3lt6gGPldby5cB9vLtxHl5QoRnZKpFfrGDKTIkiOCscUpsOhKJTUWMgtq2XXsQo2Hipjxb4iKutt7tcd2yWJ+8Z1ZFC6rAgIBMLDdLx7/QAen7mFWZtzeWLmVo6U1PDwhM6qxCMTAv6ItQ7Kj4i5rBBoPCk9nAmBHTIhcDpcgoJSP+BENBrRNrDje6EjIBMCjaP8qNDt0Gihw3i1o/EdHcaDIUq0DRxZKxd6vuDQCtGeojPCqMfUjqZxJHaCftfBxs/h97/DLb+ERhVNqKEoUF8JDhsYIoX4qERyBsLDdNw8IoNrh7Tntx15zFiXw8r9RezJr2RPfmWjXychwsDFfdOYOrCt14UKJZ7HoNfy2lV9aRtn5q1F+3hj4T72F1XzzIR0n8ciEwL+SGk2oIg+1YhEtaMJHFK6w7750nrwdFQXQelBMZcJgZNpP7whISBpHPud1QGtB4A5hHYl9Eboch5s+0ZUCciEgPdZ+pIY+98A0a3UjaUpjHkKtsyAwyvh4DIhNigJbBx2yF4i7FYPr4KivWA9rtw7MlVsUHQYB10vDIxqFokqGPRaLuqTxkV90iittrB0byEbD5WyPbeCI6U1FFVZsDsUAKKMepKijXRJiaJn6xhGdkykZ+sYdNLGMqDRaDQ8NqkLbeJMPPPjduZsPcbOg3k+j0MmBPwRt36AtBxsEslOpwEp4HRqjm4QY0InMMWqGopf4tIRyFkLdivowtSNJxBw6QeEgrvAH+kxpSEhMPF52TbgTXLWwYHFQjtgxINqR9M0YloL68F1H8CSF2VCIJCx1Ii/45r3oOLo6R9XlScu+xfAvGcgcywMf0BUFslzOslpiIswcEnf1lzSt7X7NodDwa6IhIB0CQhurh7cjo7Jkdw7bSP7C0t9fnyZEPBHpMNA83AJCxbsECV88ov3RFyCgm0GqRuHv5LYBUzxUFsCuZuhrfw9nRG7DfYvFvNQTAh0OEe0DVQcFa04bQerHVHwsuxlMfa5GmLbqRtLcxj5EGz4VFQIHFrZYHMqCQwUBbbOgPl/FTozAOGxIimYMQZa9YGoViKJXF8Jxfvg6EbYMwcOLhfJrAOLxWPP+w8kd1PvZ5EEFFqtBi3yXDZUGJgez88PjOSuj5bja4lBmW7yR6TDQPNI7AwaHdSVQ0Wu2tH4Hy79gDayXeCUaLUNJ+qHlqsbSyBwdD3UlwtBz7R+akfje8LCoctkMd/xo6qhBDXHtkDWXKFTMfIRtaNpHjFtoN/1Yr7kRXVjkTSNynz4air8cJdIBsS2g0vehkf3wEWvQ8/LxOaNwSwSAuZ4kRwcejfcNBse2AxD7hbaF9lL4N2RsOwV0XYgkUgkfyA5KpwPb/K98LdMCPgj0mGgeeiNQsQJhNCZpAGHo6FloLV0GDgt7Z1igodWqhtHILB3vhg7jAetTt1Y1KL7FDHunCX+xySeZ8XrYuxxWWB/J458WLQ8HFgk2pIk/s/h1fDeaNg7D3QGGP8s/GmDSO6EhTfuNeLai6qA+9YIu1KHDRb8Az6/BKrU9x6XSCT+hxrtITIh4I/ICoHmk+xsG5DCgidSsl9UTujDhdiR5NS43AUOr5Y7OGcjlPUDXHQ8R6iKVxyB3I1qRxN8lOU0VF+MfEjNSFpOXHvoc42YyyoB/2frN/DpBUILIKkr3LUMRj/WfAeB+Ay4+iu45B3xmXFwGXwwHvK2ezZuiUQiaQYyIeBvWGqEdR4E9m6IWrgWu7JC4ERc+gGt+kqxvDOR0hOMMVBfAXnb1I7Gf6kqhGObxbzDOaqGoiphJug8Scx3/qhqKEHJmndBsYve69ReakfTckY9Itra9s0XPeYS/2TVO/D9HWI3v/sUuH0BJHdt+etqNMKG8o5FQjS6/DB8PFlWpEkkEtWRCQF/w9UuYIoLLRsvT+FKCMgKgRNx6wfIdoEzotU1WMhJ+8HTs3+hGFN7Q1SKurGojattYMcsIT4m8Qx15bDhMzEffr+6sXiK+EzoPVXMl72ibiySU7PyTfjtz2I+5B644hMwRnr2GEmdRZKh/QiwVMIXlzVUXEkkEokKyISAv1G8T4yyXaB5uFoGCvcI6ziJwFUh0FoKCp4Vt7Cg3LU5LbJdoIGO50KYWez25W5SO5rgYePnYrGU1DW43mcuYcTdc4R/vcR/WPuBsAkEGPMUTH7Be3ai5ni4/jvoNBFstfD1dXBQJqElEok6yISAvyEtB1tGbDthBeawNiRXQh1rLeQ7+xRlhcDZSR8pxkMrpFDcqXA4hL82BNdCrbkYzOKkHmTbgKewW2H1u2I+7L7gspBN6izE5VDEbrTEP9j5E/zyuJiPehTGPuX9912YCa6aBp0ng60Opl8tXDUkEonEx8iEgL9R7GwZkBUCzUOjafD4lW0DgmNbRC9kRDLEtFU7Gv+nVR8Ii4DaUijcrXY0/sexzVBTLBJvbQerHY1/0GOKGHfKtgGPsHOWEGqMSIJeU9WOxvOMeFCMW6YLWzuJuhzdCN/fCSgw8DbhJuCrJJTeAFd+KtoH6itE+0CR3MyQSCS+RSYE/A1ZIdByUpxtA1JYUOBqF2gzKLh22ryFLqxhoSt1BE7G1S6QOUYKVLroOAH0Jig9CHlb1Y4msFGO2zkffGfj7d0CiXZDoc1gsFtg7XtqRxPalB8RO/O2WlHxdN6Lvv+eDDPBNdNFMrqmCL66UiSkJRKJxEfIhIC/4bYczFQ3jkAm2SUsKBMCwHGCglI/oNG0d9oPyoTAyUj9gJMxRkIn5+/DZZMnaR6HV4kqFL1J7NYGK64qgXUfQn2lurGEKtY6mH4NVOUL/aErPgGdXp1YwmPguu8gpp0Ql/72FrDb1IlFIpGEHDIh4E/UVUB1gZjLCoHm47YelC0DABzZIMbWUj+g0aQ7EwIHV8gS8OOpLYUj68RcJgROxOU2sPNH+Z5pCWvfF2OfqyAiQd1YvEmX8yGho3BT2Pi52tGEJvOeFhU9pni4dgaER6sbT2QSXPOVECk9sAjm/1XdeCQSScggEwL+hMty0JwossWS5uFqGSg7LJIsoUxVgVA/RwNp/dSOJnBI6w86o0jQuap2JHBgMSgOofweK/UoTqDzJPGeKTkg9UuaS2Ue7Jot5oPuUDcWb6PVNtgprnpHuuL4mu3fieoMgMs+EILE/kBqL7jUKai5+m3YMkPdeCQSSUggEwL+hEsVP6GjunEEOqY4iEoT84Jd6saiNi79gKSu6u9+BBJh4UJzAeDQcnVj8Sdku8DpMUZBpwliLt0GmseGT4UAarthkNpT7Wi8T++rhdhrxRHY/r3a0YQOxfvhJ2fLxshHGtp9/IXul8DoJ8T854ehMEvdeCQSSdAjEwL+hKtCQLYLtBy3sGCI79RJ/YDm0364GA+tVDcOf0FRYJ/LbvAcdWPxV7pfIsYdP8q2gaZit8L6T8R80O3qxuIrwsJhyF1ivuJ1+Z7xBXYbfH8HWCqFVsy4p9WO6NSMfQoyRoO1Gr69WdgHSyQSiZeQCQF/QgoKeo5kZ0Ig1IUFXRUCUj+g6UgdgRMp2AmVx4TYW7vhakfjn3SeBDoDFO+VlpVNZfccqMoTO+bdLlY7Gt8x6DZhc1qwoyHhJvEey/8LRzeAMUa0CqglIng2tDoRX0SSeG/MfUrtiCQSSRAjEwL+hLQc9BxuYcEQTgg4HJC7SczbyIRAk2kzCLR6Uc5bdljtaNRn73wxZowKTis4TxAeAx2c1RPSbaBpuPq5B9wkvNlDBVMc9L9RzFe9qW4swc6xLbDk32J+/ksQ01rdeM5GVCpc9j6gEe00Ln0NiUQi8TAyIeBPuCsEZEKgxbgSAvk7Qnd3tygL6iuEYnFSN7WjCTwMEUJcEKT9IEj9gMbiahvYOUvdOAKJgl1wcBlodDDgFrWj8T1D7wGNVoh25m1TO5rgxFYPP9wtNCq6XQS9p6odUePoMB5GPiTmsx+CqkI1o5FIJEGKTAj4C7WlUFsi5rJloOUkdha7u3VlUHFU7WjUwaUfkNbPf8si/R23jkCIJwTqK+HwajGXCYEz0+U80IZB4S4o3KN2NIGBqzqgy3n+v2vrDeLaNySSVr2tbizBytKXRMWgOREufA00GrUjajxj/wwpPaGmCGY/GLqbHBKJxGvIhIC/UOwUFIxMBWOkurEEA3qjUNaH0N1xcesHSEHBZpM+UowHQzwhkL0MHFaIy5AtTWfDFAsdxom5rBI4O3UVsOVrMR8c5FaDZ8JlQbjtW6jIVTeWYKNgNyx/TcwveBkiElUNp8nojXDpeyLRuGcObJmudkQSb6Aowio6bxscWgWH10DedqgulkkgideR24b+grQc9DypvSB/OxzbKnaeQg1XQkDqBzSftkNEKW9ptjhJj05TOyJ1kO0CTaP7JbB3nkgIjHlC7Wj8m60zwFIlqroyxqgdjXq0HiDEOg+vhDXvwYS/qx1RcOBwwM8PiYRmp0nQfYraETWP1J4w7s+w4B/w65OQPgpi26odlaQlOBxwZJ34rji8SpyrWipP/VhjDKT1EZasnSeLys9AqnKR+D2yQsBfcAsKynYBj5HaS4x5W9WNQw0s1Q2Wi9JhoPmER0NqbzEPVftBRYF9TkFBmRBoHF3OFy1L+duhaJ/a0fgvitLQLjDodnmCO/xPYlz/iWjTkbScTV+IxVaYWVQHBPJ7bPiD0Gaw0AaadZ/cNQ5Uqgpg0Qvw3x7w8URY9rJoS7RUAhrhtBKfKS4RSeI59eWQvRSW/Ac+GAev9xZtMFUFqv4okuBBVgj4C1JQ0PO4FnKh2DKQuxkUB0S1Cs2eXE/SfgQc2yy+sHtdoXY0vqd4v3BZ0BkaWigkZ8YcL3a79y+AnT/C6MfUjsg/Obhc2DOGRUCfq9WORn06nyfOAUr2w6YvhdigpPlUFcD8Z8V83NMQ207deFqKTg+Xvgv/GwHZS8R7pP8NakclaSxlh2Hxf2DbN2C3iNsMUcKuNmO0cDaKzzzZxcdaK76Hj6wVwqN754vXWvgcLHlRJFNHPRp4rTASv0JWCPgL0nLQ86T2FGPZIagtUzUUn3NU6gd4jPQRYgxVHQFXu0C7YVLfpClIt4Gzs+4DMfaeKiwbQx2tFobdJ+ar3wG7Td14Ap3f/gJ15WJzYMjdakfjGRI6wLi/iPm8p6EyX914JGenvgoW/BPeGgSbvxTJgDaD4IqP4Yn9cMVHwm41pfupLX3DTOJ8duCtMPVzeHw/XPq+qP60W8Rnxet9RLLBVu/7n08SFMiEgD+gKA2igrJCwHOY4iDGuSOQv13dWHyNWz9gkLpxBAPthomxaE9oWj7JdoHm0fVCYaOXtxVKDqgdjf9RkQu7fhbzUBYT/CN9rgFTvNgB3PWT2tEELgeXC4FGjRYuej24nHaG3gut+ohkx9wn1Y5Gcib2/S4SActeBlud0H64bT7c/jv0vFwIRjYVgxn6XCVe4/rvoVVfocOy+F/w7ijIWefxH0MS/MiEgD9QUyz6gwDiM9SNJdhw6wiEWNvA0Q1ilIKCLcccDynO99HBZerG4musteLEGmRCoKlEJEDGKDGXVQIns+FTUOxCSC+lh9rR+A8Gc0OCZOWbsk+8OTjsMPcpMR9wM7Tur2o4Hkenh4vfFAnHHT/A7l/UjkjyRyzV8PPD8OXlUJkLcelw1TS4aTa0HeyZY2g00PEcuHMxXP6R0Bso2gMfTYB5z4Ld6pnjSEICmRDwB1z6AdFtRGmQxHOEYkKg4hhUHBU7I636qh1NcJAxWozZS9WNw9ccWiF2NaJbQ3I3taMJPGTbwKmxW0VCAGDw7aqG4pcMugN0RsjdKATxJE1j8zTxnW+MEdoBwUirPg0ilHMeFfadEv+gMAveGwPrPxbXh9wN96yCbhd6R9RSoxH6RvetFRVGKLDyDfj0QmlhKmk0MiHgD0iHAe/RyikseCyEnAZc+gHJ3WXPt6cI1YTAvgVi7HhOYKtzq0XXi0RiLncTlB5UOxr/YddsqMqHyBTxO5KcSGRSg8jiyrfUjSXQqKsQ/dogLD+DWWhtzFMQlyF2oBdIm0q/YPcc+GA8FO+FqDS44Uc47z+i8sfbmOOF6ORVX4IxGnJWixaC7BCrbJQ0C5kQ8AeKnbZUCR3VjSMYcVUIFO4Gm0XdWHzFESko6HHaDxflmSX7ofyI2tH4jr1SP6BFRCYJlwqAHT+qGopf4bIa7H8T6A3qxuKvDHPu/u75RVpXNoXlr0J1gVBrH3yn2tF4F4NZ6CMArPsIDq9WN55QRlFgyUvw9bXCPrD9CLhrKXQY5/tYul0k2ghSekFNEXxxKWyZ4fs4JAGFTAj4A9Jy0HvEtBXq1Q6rSAqEAlI/wPOER0NaPzEPlWx7SbbY5dDqIXOs2tEELj0vE+P279SNw1/I3yFaUTQ6GHiL2tH4L0mdodMkQIHVb6sdTWBQehBWOX9XE58PjWRT5hjodz2gwOwHQ2fjw59w2GHOI7DoOXF9yN1w4yyREFaLhA5w+3zoPkWc//5wp7AolJokktMgEwL+gLQc9B4ajbAcgtDQEXDYRXkyCEsaiecItbYBl91g2yHSEq4ldJ8ikip5W0Vvaajjqg7oegFEp6kbi78z/H4xbv4KqovVjSUQmP9XYcOWORa6nKd2NL5jwj/BnCg2PVa+oXY0oYW1Fr650akXoIHzXxYtArowtSMTmmRXfALDHxDXFz0PvzwGDoe6cUn8EpkQUBtpOeh93MKCIaAjULhb2M8YIiGpi9rRBBfHJwRCIcsu2wU8gzkeOowX8+0z1Y1FbeoqGkpXpdXg2UkfKcTjbHWw/iO1o/FvDq4Q4p0aLUz6V2hpnpjjYfILYr70pYaqU4l3sdTAV1fB7p+FCOjUz/zvc02rhYn/FIkKNCIh+9P9YvNIIjkOmRBQm6p8sFaLL7G4dLWjCU5CqULgiNN/Nq0faHXqxhJstB0COgNUHAl+X3lrXUMlRKcJ6sYSDPS6UozbZoZGMul0bPlafN8ldhF+3JIzo9E07O6tfV/8X0pO5o82g6FoY9nrSlEZYasT5euh/DnjCyw1MP0qyF4iNmBu+L7BVcYfGXwHXPqeWGts/hK+vxPsNrWjkvgRMiGgNq5Mbkzb0Oh3U4PjrQeD/UvSJSgo9QM8j8EMbZz+wcHeNnBoBdhqIaoVpPRUO5rAp8v5oDeJ9rBjm9WORh0UpaFdYNDtobWD2xK6XyIsiasLYasUBjslW6aLCkBjdPDaDJ4NjQYueBX04XBgMWz7Vu2Ighd3MmCpSAZc/52o5vF3+lwlWgi0elGtNute2T4gcSMTAmoj9QO8T2JnsbNbXwFlh9SOxru4BAWlfoB3CBUdAZd+gLQb9AzGSOgyWcy3hWjbQPZSKNojTqBdlnqSs6MLg6F3i/mqt+QJ/B+pr4QF/xDzYLcZPBsJHWD042I+989QU6JuPMGIpQa+mnpiMqDdULWjajw9psDUz4Wo69YZsppE4kYmBNTGZTko9QO8h94ASV3F/FgQ6wjUV0LBLjGXFQLeIVR0BNz6AbJdwGO42ga2fx+ai7p1H4ix91XCtUPSePrfJHa/i7Jg33y1o/Evlv9XtF7GZ8Lgu9SORn2GPyDOd2qK4Pf/Uzua4MJuhZm3wMFlYIiC678PrGSAi64XwGXvAxrY8AnMeya4z2ckjUImBNTG1TKQ0FHdOIKdViGgI5C7CVBEeWlUqtrRBCetB0CYWZxsuZIvwUbpQWE3qNFJu0FP0vFc4dZQmQuHV6odjW8py4Hdc8Tc30S3AoHwaOh/o5ivfFPdWPyJ0kOw8i0xn/icbLsE8Tu48DUx3/g5HAqxzxpv4XDArD9B1lzRlnHdN9BuiNpRNZ9eV8DFzs+SVW/B4n+rG49EdWRCQG1c4mSyZcC7uIUFg7hCwK0fMEDdOIIZvQHaDRPzYG0bcFUHtB0CplhVQwkq9EbodpGYh1rbwIZPQHEIIcHkbmpHE5gMuVsk6Q4ug9zNakfjH/z+f2CvF5VbXc5XOxr/of0wUVUCMPtBsNWrG0+goygw72nY+rX4H7zyM2g/XO2oWk7/G2Dyf8R8yb+d1omSUEUmBNTE4WhICMRnqhtLsONKCBzbom4c3sTlMNBmkLpxBDvutoEl6sbhLVz6AZ2k3aDHcbUN7PwRbBZVQ/EZtnrY8JmYD75T3VgCmdi20PMyMV/1lrqx+AOHVsGOH5w2gy9IrZM/MuHvEJEk2kxWvKF2NIHN8ldh9TtiPuWdBj2YYGDo3TDG6dAx51HYM1fdeCSqIRMCalKZKyxitHqIba92NMFNai9x4lB5DCrz1I7G8ygK5KwV87YBXMYWCLgSAgeXi57CYOJ4u0GpH+B50kdBZArUljYkXoKdHT+KFpvo1nIXt6UM+5MYt38v2jBCFYejwWaw/42QKp1QTsIUB5OdZeBLX2poT5U0ja3fNohWTnohOAVRxz4F/a4XVVwzb4EjG9SOSKLCuaVMCKiJ6wM6tj3o9OrGEuwYI4XbAARnuWXJAXHSrTNAqz5qRxPctOoDpnjhWnE0yL44D68Eaw1EpjbYdUo8h1bXUCWw5St1Y/EVa98X48Bb5fdcS0nrK5JKih3WvKt2NOqxZbqw7zRGw7hn1I7Gf+l5OXQYL9oqfn5ICsc1lcOrhTUfwPD7Ydi96sbjLTQaoTvR8Vzx/f/VVJlAUgtFgax58KHvN2RkQkBNpOWgb0nrJ8bcTerG4Q1c1QFp/USvssR7aI8T29u/UNVQPM5el93gubIE11v0vVaMe+ZCdbG6sXiboxvg6HqRqHT1NEtaxvD7xbjhM6grVzcWNaivggV/F/PRj0Nkkrrx+DMaDVzwqhDBy14qbOYkjaPkAHx9Ldgt0PVCOPcfakfkXXRhQhuhVR+xuTTtCqguUjuqkKHaWs2BI6tZ99Ul/PbjjXxrOebzGGS6Xk1cGThpOegbWvVt2FkINo44EwJSP8A3dDwHdnwP+xbAuL+oHY3ncFmaSf0A75HSQ3wWHdsM22fCkCC2Slv7oRh7XCYXbp6i4wRR7VaUJVTkXQmCUMFlMxiXEdz/O54iPgPGPCmSKL/9BTpNBHO82lH5N7WlMG0q1BSLz+rL3gdtCOyfGiPh2m/ho3NFQmT61XDTbAgzqR1ZwKIoCuX15eTX5JNfk09edR4FNQXienW+e15lrWp4UkoS9lo74Nv2ZpkQUJOivWJMlJaDPiEUKgSkfoBvyBwnxtyNUFMSHCdYpYfEIkOja/j5JN6h73UiIbB5WvAuaqqLYPt3Yi7FBD2HViu0BGY/AKvfFe4DujC1o/INZYcbbBcnPier4RrL8Pth27dQsBPmPwuXvK12RP6LzQIzbhDWu9Gt4doZYIhQOyrfEZUC130HH00QQtU/3AVXfBoaCZEmYnfYKa4rdi/s82ryyK9xLvKr893zenvjXD4iHQ4SNWHEx3ciIiGJXfjW2lomBNSk2JkQSOikbhyhgktYsCofKo5BdCu1I/IMdRWQv0PM2w5WN5ZQIaY1JHWFwt2iFLPHFLUjajlZv4lR2g16n15XiN26Y1sgb3twiqJt/Fz0Lqf1l1aonqb3VbDwn1BxRCRdglHo7FTMd9oMpo+CrheoHU3goAsTPeIfT4RNX0KfayB9pNpR+R+KAnMeFtaehki49huISlU7Kt+T1BmungafT4Gds2DB32BCkLdM/AGr3UpBbcPC3j3WNCz0C2sKsSv2Rr1efHg8KeYUUswpJJuTSYlIIaUin5T1n5FcXUoKBiLO/RsMuh20OioqKvgf//PuD/kHZEJALWwWsSMHkCgTAj7BYBaLuIKdokogWBICR9cDihCnDMUvL7XoMF4kBPYvDI6EwJ5fxNjlPHXjCAXM8cK6atds0caU+rzaEXkWu63B03rwHerGEoyEhcPQe4T6+dKXhVClVqd2VN7l0CrRpoUGJkubwSbTbogQ9lz/Mcx+CO5ZISss/siK10TCRKOFKz4JzkRtY0kfKSpJfrgTVrwuWnQG3qJ2VC1GURSqrFUU1BSccHEv9p0L/5K6kka9nlajJcmUJBb45pSTF/3OuUFnaHiSzQLznm4Q3E3pBVd8BEldvPATNx6ZEFCL0myhFGyIhKggWZgGAmn9RELg2GboGiQWWDnrxCirA3xLh/HCm3j/IrGzEMgnqHXlwkYRpDWcr+h7nUgIbJ0B5/4tuMq+98yB8hzhxtHjMrWjCU4G3SH85Yv3ChvC3leqHZH3cNjh1yfEvP+N0gGluZzzf7DrZ/GeWf4ajH1S7Yj8hx0/wu9/E/PJ/4HOE9WMxj/oc5VYqyx+AeY8CjFt/VpfyOqwUlRTREHtiQt9126+63qtrbZRr2fQGkiJcC7uzSknLfpTIlJICE9A15RkbGU+fHMj5KwW14feB+f+n18k52RCQC1c+gEJHQN7IRFotOor+naDSUcgZ40YpX6Ab2k/XKinlx+G4n2BXemzbwE4rKJ9SWqa+IaO50JEElQXwr7fg6syY+VbYhx0u9jNlnie8GihJbDoOeEz3/Oy4K0S2PQl5G0FYwyc81e1owlcTLFw3r9h5q2w7GVhSyg/7+HIBtErD0KTY4jUPHEz5kkoyYatX8O3N8Otc31aOaEoCjW2GopqiyiuLRZjXbH7enFtsXvRX1JXgkLjrDWjDFGkmFNIMiWdsJufGpHq3tWPNcai8eT67Mh6mHE9VB4Tn2WXfwCdJ3nu9VuITAioRVGWGAN5ERGIuIUFNwf+ri6AwyGEX0BWCPgaQwS0GwbZS0TbQCD/L+/5VYzBtCj1d3Rhohd81VsiSRksv/uctcL1RGcQCQGJ9xhyJ6x6E4r2wM4fxQIv2KgrF60RIHa0IxLVjSfQ6XEZbJ4uHGV+fkioyAf6eVBLKDss1PRtddBpEkz6l9oR+RcaDVz8BpQfgUPL4aupcPuCFrfc1lhrKK4rdi/q/7jQL6prWPDX2esa/bp6jZ4ks1jku3b2k83JJJmTGuamJMxh5hbF32Q2fgFzHhE2lkld4eqv/M5yXiYE1KJ4nxiloKBvSe0pVNSrC6AiV4jDBTKFu6G+AsIiILmH2tGEHh3Gi4TA3vmBqxZvt8Jep6CgbBfwLX2vFQmBPb9CVQFEJqsdUctZ5VQw7zVVKFZLvEd4jCg5XfwvWPISdL80+NTAl7wofNETO0u3Ck+g0cAFL8PbQ4V43uZp0O96taNSh7pyYS9YXdDQxx2sVTYtQW+Eq7+EDyeIdpOvpsItvwqbQhr68svqyiipL6G0rlRc6kvFbXUlJ8xL6kqosdU0KQSz3kyCKYFEUyIJ4QkkmJyX8AT3Qj/ZnExceBxajR99Bjrs8NvTsMYpENj1Qrj0XTBGqRvXKZAJAbWQloPqEGaC5G6Qv13oCAR6QuCI026wzQDQyX9nn9NpIvz+f+LEylIjhCsDjcOrxYmRKV5WmfialB7QZpCo8tn0BYx6VO2IWkbpQdj1k5gPu0/VUEKGIXeJJEzhLvG7DwaBUxdFe2HNu2I+6YXg0tlQk7h0GPuU+O6a+xfIHAsxbdSOyrfYbfDtLeL/JjJV2Av64SLNlzgUB9XWaiosFVTUV1BpqRRz5/WKAZdQsWUa5fajlM44l9LY1pTVlVFaX4rVYW3y8cJ14e6FfWJ44gkL/kRT4gmLfp/v6HsCay18dzvs/llcH/c0jHrMb5O2cgWhFtJyUD1a9RUJgaMbA9+6KMeVEJALOVVI7gYx7YSOQPZSoRwfaGTNFWPnSXJ3RA0G3iYSAus/hREPBfbfYM17oDhE5UxKd7WjCQ1MsTD0bljyH7Gb3u1ivz3hbDJz/wwOG3Se7NdiZgHJsD8JUdOj62HWn+CGH0KndUBR4JfHYP8CCDPDtV8H3OaQQ3FQa6sVF2stNbaahuvOS42thlpr7Um3H3+pslaJxb6lgiprFQ7FceYDm8OAMFCqoTTrhLtMehNxxjjiwp2X08zjw+NJNCVi1ps926PvT1QXi1YUV/vcpe8JnRc/RiYE1KC6GGpLxTxBVgj4nNb9YfOXcHSD2pG0HCkoqC4ajVAjXvehWFgHWkJAUWD3HDEPlh72QKPHFJj7lEgq7VsQuOrWtWWw8XMxl9UBvmXoPbD6f1CwA7Z/FxyOA1nzRJ+7Nkz2dXsDnV4sUt4dCQcWie+wULEIXfUWbPgE0MDlHzZoS3kBm8MmFufWGmpszou15sTbrDVUW6tPuah3LfhPWOTbaqm313stZoPWQLQxmmiDuEQZok64Hl1ymLiNXxDvcBA3+F7iBt1JXHgc4XopIAtAyQH48goo2Q/hsXDNdCFC7efIhIAauAQFY9oGZolxoNNmoBiPbhSifIG6m1Jd3KBF4fqZJL6n82RxMrV3XuAJVRZlCVshnUHs6kp8T5hJWBCuflt4hAdqQmDj52CpgqRu0OEctaMJLUxxMOIBWPiccB3ofgnoDWd/nr9irRNJMhDJDj8T3woaEjvChL8LS8d5z4rvgGD/Xe+cJX5WEImmRlaJ1tpqhchdXTHl9eWU15dTYalwjxX1FZRbyk8Yq6xVXl24A2jQEK4Px6Q3YdabMYWZMOlPvJj15pNuc10iwiJOXOwbozHqGmGBF94a5v8VlvwXUgdAtwu9+nMGDEc2CI2FmiJRPXr9TEjqonZUjUImBNTA3S4gqwNUIbkH6E1QXy7+FgHyz3oSLv2AxM5gjlc3llAmfaR4P1UchfwdPrXkaTF7fhFjxuiQ759UlYG3iITA3t+gLAdi26odUdOwWRp6vYfdF1hJsWBh6L2w9gOh47Dh08C2Tlv+X7G7FpkKox9XO5rgZtAdosc5eyn8cLewlQvktqUzcWQ9fH8noMCgO3AMuYuS2iLyq/PJq8mjqKaoQfn+D2NTRfD+iE6jwxxmxqw3Yw4zE6GPcF83hTUs2s1h5rMv5I9b9IfrwtUpux/+gNgJ3/Cp6JO/5RdRfRvK7P5FWHraaqFVH7j224AS1pUJATVwCwpK/QBV0OlFidjhleILIlATAodWirHdMHXjCHXCTEKUKetX0TYQUAkBaTfoFyR2EkmZ7KXiBOucZ9WOqGls/VokxCJToVcQlKsHIoYI4Rk+5xFY+qJwsHCqgAcURXth+atift6/ITxa3XiCHa0WLnkH/jdcbDIsfVnYOwY4iqJQXl9OXk0eedV55BXuJG/1G+TFRZAXmU5e3XYKpg1qkhieUWck0ZRIrDHWvZseY4hx77DHGGPcO+0xxhgiDZHuhX+YNiy4+uU1Gjj/FZHA3r8AvroK7lgAse3Ujkwd1n4gKm0UB3ScAFd+GnCfvzIhoAauMu/EzurGEcq0GehMCKyDftepHU3zcCUEAqA3KejpPFEkBPbOg9GPqR1N46jMaxCl7Bxg2gfByKDbnQmBT8R7KMykdkSNw2EXO7oAw++HMNlHqhr9bxSOAyX7RZ/02KfUjqhpKAr8/LDw6u44AbpPUTui0CC2LVzwCnx/Byz5N6SPEJVvfkylpdK9s59XfdylJk/cXp13sn99hFMQjxqoEjv+GjQkmhJJjUhtULZ32tqdYHEXnkBEWERwLepbik4vFr4fTxb6JdOmwm2/CTvUUMHhgAV/hxWviev9b4QL/huQrl+BF3EwUCRbBlTHrSOwXt04moulWtgmgkwI+AOdnH3fOWuFtkNEgrrxNIZdswEFWg8MPcspf6TLBQ2OFVu+Fm0EgcCOH0TpqCkOBtysdjShjS4Mxj8DM2+BFW+Ik9PoNLWjajxbvhYWrnoTXPCybD3xJb2nwoHFsHmaKAG/e4Vq32M11hr3Qt+16Hct8l2L/mprO3qqkQAAdh9JREFUdaNeK94YR0pdNak1ZaRqw0kdeAep8Z1JjUglNSKVJFMSYdLOsvmER8N138CH5woLx29uhOtmhoZFqK0efrwXts8U18c9I5L5Afq5JRMCvsZuFSJeIFsG1KTNIDHm7xCLa0OEuvE0lSPrhB1TdJvQLdHyJ2LaQEovyN8mlLH7XK12RGdn5ywxdr9E3TgkAp1eCKj99mdY/Q70v8n/BU8dDljmLO8eem/AlUgGJT0uFXoOOWuE6NflH6odUeOoKYF5T4v5mCcgLl3VcEKS818S5xZFWfDjPXDN1x7/DKq11Z6ws3/CvEYs+istlY16rWhDtHthn2JOcc9Tzc7bTIkYv78TDs4GQxTc8gO06u3Rn0eCOP+55mv45HyRVPr5Ibj4rYBdGDeK2lKYcYNIYGr14ufte43aUbUImRDwNaUHxUIuzAxRAZS5Dzai08TvvzIXcjeLErlA4tAqMcrqAP+hy3kiIbBrtv8nBKoK4dAKMZcJAf+h3/Ww+AVxQr5vPnSepHZEZ2bvb6JU1BAVOpZl/o5GA+e9CO+PhW3fwsDboH0A6Mz89jTUFAuXiuH3qx1NaGKIgCs+gQ/Gi//tZS+L5EwjsNgtFNUWUVhbSGFNoXssqi2ioLaAopoi8mryKK8vb9TrRYZFnrDIT4lIIdXsHJ2LfnPYGVy6FAVmPyi+j3UGuOYrmQzwJml94YqP4etrYNOX4vx6/NNqR+UdSg/BtCuhaI/47rvq86BwaZIJAV/jbhfo4P+7P8FOm4Gw6yeREQ+4hIBzMRcIJ3qhQveLhZjXvt+hvsq/d0t3zxbiN2n9IK692tFIXIRHw4CbYOWb4uLPCQFFgcX/FvNBt4mWAYl/kNZXvI82fAq/Pg53LvFv5fg9c2HLV4AGLno9NMqN/ZXUnnDhqzDrPlj0PHVJXShs218s9o9b6B8/FtUWUVZf1uhDmPQm96L++AX+8Tv9kYYWfn8u/Cds/Aw0Wrj8IyHaKvEuXSaLZOQvj4lzIb0xcDSVGsvRDUJAsboQolvDtd8ElpD0GZAJAV/jshyUgoLq40oIBJqOgM0i3BEA2gdYIiOYSekpylxLD4qkQI8pKgd0BmS7gP8y+C5Y9Y4oRTy2RdgX+SO7ZgsdE0Ok3NH1R8Y/K/Qd8rYJocpBt6sd0ampKYHZD4j5sPug3RB14wkRaqw1Yve+puDknf3aQgo7dqPQUknlumdgXeNeU6/Vk2RKIsmcRJIpiURTIkmmJJLNySSaEt2L/6iwKO+K8614HZa9IuYX/lck6yW+YfAdYKmC3/8mkjL6cBj+J7Wj8gy758DM24StYGovkQwIJI2WsyATAr6mKEuMCVI/QHVcOgJHAiwhcGyz+EAyJ8jEkj+h0UC3i2HlGyLR5K8JgepiyF4m5jIh4H/EthV94Ntniv78qZ+pHdHJOOyw8DkxH3ovRCSqG4/kZCISYdzTwgrr979D5/MgprXaUZ3Mr09AVb74Lhv/jNrRBDQ2h43SulKK64opqi2iuLaY4rpiimuL3Tv5rkV/o0T5dKKK1ahAYmQrkp1K/Mcv+pNMSSSaE0k2JRNjjFFfhX/F60I7A+Ccv0qhUzUY+bAQ3Fv8gtAF0RsDv6VszXvw65OAAh3PddoKRqkdlUeRCQFfU+SyHJQJAdVp1Rc0Oqg8BuVH/fNk6VS47AbbDQtu0ZZApPslIiGQ9RtY6/zTgm33z6DYIbU3xGeqHY3kVIx6RCQEdv4I+TshpbvaEZ3I1m9E/2R4bPDs/gQjg24Xf6uj64XQ17Xf+Nd3xo4fhM6BRgtT3g0cq00fUmero6y+7LQLffdYW0xZfRkKSqNf26Q3uXfvT9rZNyeRrOhJ/OZmospy0LROhovfBcMZ+vbV5vhkwNg/w6hH1Y0nlBnzpEgKLH9VtBA4bEI0N9CwW2HuU7DOKc464GY4/5WAtBU8G8H3E/k7rpYBaTmoPgYzpPSAvK1wZC3EXKp2RI3DlRCQgoL+R1r/BrHKA4uE0KC/sfNHMcrqAP8lpYfwYN/5o/AFn/q52hE1YLOInR+AkQ+Flud0oKHVwSVvw3ujYO88kRzoc5XaUQlKsuEnZ6vAyEegzQB14/EiiqJQa6ulylpFlbWKaks1FZYKyurL3JfSulLK68tPuK28vpxaW22TjqXVaIkPjychPIEEUwKJpkT3/ISdfXMSEWGNcFe67jv4aKJIKs28Ba6a5p+LoRVvnJgMGPuUuvGEOhqNqNBwWIUeztynoL4SRj/uX0nJM1FVCN/e5NTs0sC5/wcjHgqc+JuIH/5XBzE1JUJFF2RCwF9oO0QkBA6tEmW6/o7DATmrxVwmBPwPrRa6XQRr3xM91v6WEKjME7ZAEBjv91BmzJMiIbBzlrBHTemhdkSCDZ9C2SGISIbBd6odjeRsJHcV76WF/xTl+ZljISpF3Zhs9WJxWV8BbYeKBZwfcqqFfKW1kmprNVWWKjFaq6iyOO8/zfVqazUOxdHsOHQaHbHGWLG4NyWQEJ7gnseHx59we6wxFp0nBSSTusC1M+DzSyBrLvx0P1zylv+IVCoKLHoelr4krstkgP+g0cCEfwol/sX/En+nunJxm7+Lquduhq+vg4ojIv7LP/C/8zkPIxMCvqTY2S4Q3dq/FchDifbDYN0HcHil2pE0jvxt4gPVECV87yX+R/eLRUJg9xxRbuZPitnbZgp3gTaDhdOJxH9J6X5clcB//KNKoKZEnNSBsCMzNGKHUaI+Ix4UiaW8rTDrXrj2W3VPyH//G+RuEs4UV3zktR1nRVGotlZTWi9230vrSimrLzth0V5lqTphkf/HBb1dsXssHp1GR0RYBJFhkUQZoog1xhJjjCEuPI4YYwyxxtgTL+FijAyLVLc3v91QodT/zQ1ONwhFVJ6onRSw20QrzKYvxPVxz8CYx1UNSfIHNBoY+6Rw0Jn7FKx6SySUL33fP9tPFEWIsM79M9jqIL4DXDNdJMaCHJkQ8CWFe8QoqwP8h3bOXfa87WKh7e/lrweWiDF9hH+W7UmEtoM5EWqKxG58pwlqR9TA1hli9JeyYcmZGfOkWMjtnAXHtqrvo73oX1BXBsk9YMAt6sYiaTy6MLjsfXh/rHBAWfUWjHhAnVi2fgur3xHzKf+DmDZNerqiKFRYKiioKSC/Jl+M1fkU1RZRWl/qLr93ld3bHLYWh3z8Qj7SEElkWKT7eoQhgqiwKHHdIG6PCosiwhBxwuMiDZGE68LVF91rLt0uFEmB726HLdNFT/iUd9U7D6mrELHs/U1oUFzwKgyUn0l+y9B7hBD2rPtE9WTZeXDN1xDdSu3IGqgtExUwu34S1ztNhMs+AFOsmlH5DLmi8CWFu8WY3E3dOCQNRLdqsIrLWQedzlU7ojOTvVSM0lPXf9HqoOdlsPZ92PK1/yQECnaJHUJtGPS4TO1oJI0hpbt4L23/Tqg13/iTev2L+Ttg/UdiPvkFmZAMNJK7weR/ix3VBX8X7XK+tvjLWSsWBCCqFv5Qgmt32CmuKya/Ot+94Hcv+o9b/NfZ65p02HBdOLHhscQZxU58lCHqhMW9a8fetah33eda6Jv0psBdyHuSnpeJ77eZtwoxyLoKuPxDsfvrSwqz4OtrhSaXPhyu+Bi6XuDbGCRNp/dUiGkLM64TblnvjxEL7swxakcGh9eIBFP5YdDq4dy/wdD7/L+1wYPIb3Rf4qoQCIHSk4Ci3XCREDi80r8TAnZrg6CgTAj4N72vFgmB3XOEkI4/2NNs+VqMnSaCOV7dWCSN55z/g10/i2Rg1m/QZbLvY1AUYbmkOIRGhj+cwEmazoCbIXuJUPefcT3cuajJO/TNpb54HwXfXkdeGBRkjCA/tR0Fa//tXuTn14hd/saW6McYY0g2J5NsTibVnEqSOYk4Y5y71D7OGOcuxTfppXuBx+h+iWhfmnmr2J3/eJLY6Y1r75vj7/wJfrwXLJWi/XbqF0EtSBl0tB8Gt/8u+vMLdgptijFPihY0NVpQ6quEhe6adwFFbBBe8TG0Dr33lEwI+BJ3QqCrunFITqT9MNEXd2iV2pGcmaMbwFotyq6S/URgTHJqWvcXrUHF+0R5XN9r1Y3H4RA7OiDbBQKNuPai3HLFazDvGegwHvQG38aw/Ts4uAx0Rpj4nG+PLfEcGg1c/BYU7YX87WKX9eZfWqRpVGurpbi2wQ6vqLaIojrhd9+wq59HuaUCEsKBcLDnwMbXT/l6Wo2WRFMiKeYUUswpJJuTSYlwjs7bksxJcpGvJl0vgFt+genXikXdB+PhsveEP7u3qC0TPehbpovr7UfAlZ9BZJL3jinxDvGZcPsCIXK66QvhpLPvd7j4Dd+J5yqKcF755TEoOyxu63MNnPei7yte/ASZEPAV9VWiFAVkQsDfcOkIHN0g1I/1RnXjOR2udoH0USFVxhSQaDSiSmDRc2JnXu2EwKHlUHFUaGR0mqRuLJKmM+pR2DxNlMiufANGP+a7Y1cXixM3Vxxx6b47tsTzGCPh6q/gg3FwbIuoFLh2xgnfezaHjdK6UrG4d16K64pPvO5c/FdZqxp96HBFITmyNSlRrd0L/OMX+snmZBJMCei18tTU72k9AO5YCF9fI95HX14OQ+6G8c96VjRbUWDPL/DLE0LxXaOF4Q/A+Gf8S7BX0jQMZuFWkT4K5jwqbC3fGy1aiUY96l3B2rxtIrnuclyKaQsXvubfFcI+QH7q+oqiLDFGJMlyXX8joYP4u1QXwtGNomLAH5H6AYFF76kiIZC9FMqPQkxr9WLZ/JUYu0+BsHD14pA0j/BomPg8/HAnLHlRWEb6yiVi7lPCLje5B4x82DfHlHgcRVGotFaKxXx9MYXjH6VoxSsUlW6iePoEilK6UFRXIsT56kpRUBr92kad0W19lxieSKJJXJL1EaSsfp/k/N2kGGKIvmkOmmS5IRI0xLSGW3+D+X8VLXJr3hUVcRP/Cd0vbfnGRe4m+O0ZkdAGiMuAS9/zvfaFxHv0uQoyRsEvj8Pun2HZK7Dxcxj5CAy81bPnK0c3wIo3hEgvCugMIok15km/cX5TFAWlrg5bSYnPjy0TAr5Ctgv4LxqNUIbf9ZPQEfDHhIClBnLWiHmG7N8NCOLai+qTwytFuf7Ih9SJo7ZU9AwD9L9RnRgkLaf3VFEue2AR/Pww3PCj9yuFdvwA274Ru3KXvOn7VgXJWbE77JTUlVBQW0BRTREFtQUU1hSetJNfVFuExWE58cmxzl04RykcW33CXVqN9gSP++MX+u7bnPNT2uKV5cCXl4nNEFOceL/KZEDwEWaC818SlWdzHhGWcjNvheSXYcRDQnOgKYs6h13s3K7+H+ybL27Th8PQe0VllLQ6DT6i0+DqaSKZNO9ZKM2G3/4s2uT6XS8u8ZnNe+26CpFo2DStIbEEQlj53P9rVsWboigoFguOmhqU2loctbU4ampx1Lqu14nbXNdrahuu19TgqKkRt9XUOJ8rblOc11EUquyeszttLDIh4CtcDgNSUNA/aT9cJAQOrYJRagdzCnLWgN0iRHSkf3zg0OcqkRDYOkOUwqmhVL1lhvDTTekZkkI5QYNGAxe+Cu8ME8Jwa98T2gLeouww/PSgmI98WL53VMDmsFFQU8DRqqPkVuWSW51LYU0hhTWF7gRAUV0RDsXR6NeMMkQ1LOzDE0moryZx968kWupIjMkgcdILJCR1J84Yh665Il8HlohFYU0RRLeBG76X5z7BTqdz4b41Ygd21VtCW+CHO0W7UdcLoeN48RkS0+7ERKbDAZW5Yvc2e6kQUK3Kc96pEYnQ8c9CbFtVfiyJD+l2EXSeLBLfS16E8hxRMbDsFWg7FDpPhPYjIbWXaDk4FXYbFOxAyV6OI2sZjqzFKPUWHDYNDkc4SruxODIn4zAk4Zi/FqV2yXEL+lqUuj8u4F1z5/01NTjq6sT7NsiQCQFfISsE/Jt2zqqAnDUiQ62G2umZOL5dQNofBQ7dpwh19oKdcGQ9tB3k2+MrCmz4RMwH3CzfO4FOfKYQ9fvlMVGmm+48OfI0NgvMvA3qy6HNIBj7Z88fQwJAhaWCA2UHOFRxiNyqXLH4r84ltyqXvOq8RqnuazVaEsITSDInkWxKJtGcSJIp6aSd/ITwBML1p9it7bYWpl0JR7fD9Jtgyv+a52ZhrYOlL8HyV4UjRWovoUDvIycDicqEmWDskzDkTlj7IWz8TCzqNn8pLgB6k2ibDTODtVa0I9lqT3yd8FjoczUMuav5O8MSv0Wx21Hq6nDUiZ10pbb2xHltCo52f0c5tB7H/pU48vejbNmBw7YTh/11FLsWByYUjGKhb1NQLHYcFhsOqwPFpkFxuM514v5w9K3Oi2fQGAxoTSY0ZjNak0nMTeFoTcddN5vEdddjIszHPcd5u9nUcL/ZTKXVCnF/jN27yISAr5AVAv5Nai8wREF9hfBqT+undkQnkr1EjFI/ILAwxYrStC1fCQ93XycEDq8Wnz1hZrHTIgl8Bt0O+xZA1q/w7S3CwskU67nXVxSY8zAcWQvGGOEzLsW7WoSiKBTVFnGg/AD7y/ZzoPwA2eXZHCg/QFFt0Rmfq9fqaRXRirTINNIi0kiJSCHJlESyOZkkcxJJpiTiw+NbJsTXdjDctQS+uVEIxE2/SuzqTvhH4yrSHA5RYbfwn8JZBaDvdXDBK2KRKAktTHEw5nEY9QgcXC7U3LOXiI0xW60QuD0erR4SOwvngI7nquOkIjkjisWCvboaR1WV+2KvqsJRXSOuVzuvVx33mOoq7FXVOKrFxbXwV+rrm3j009k2n+p1/tBGp9GctGA/YZEeHt4wN5nEwtxkQmNyLeKd18P/cJ/Z+Vy9d5bRmooKr7zumZAJAV9grRU+9yArBPwVrQ7SR0DWXFHu6E8JgepiIXYIUj8gEBl0m0gIbP8eJv3Lt6Kia98TY8/LhMOAJPDRaIQ683ujhevAzFvg2m9B56Gv81Vvw6YvhW7AFR9LV4EmYHfYya3OJbs8273wP1B+gOyybCqtlad9XrIpmfSYdNpEtSEtIo20yDRaR7YmLTKNJFNS80v3m0JcOtw6DxY9D6vfEX23u+cIi7mel4lFmum4HSuHXdgX7pkjnFRcwsmRqaKnvPvF3o9Z4t9odZA5RlxAlHOXHxYWgtYaUS1gioXYdjLp6AUURUGpqXEuyv+wkHcv1P9wm+sxzkW867pisZz9gM1AEx7esKsebmpYoIeb0JrCxeLbNQ93Lug1VjRKDVqlFk0YaMO0aCNj0EQnoE3ORJvQFk1EhHhdo/FkfRPJKZEJAV9QtBdQxJdphPRM9VsyxoiEQPYS9QTgTsX+hYAiVL7VVKqXNI/WAyC1t6g82TwNht/vm+OW5cDOn8R8yN2+OabEN0QkwjXT4ePJ4vPhl0eFbVJLT3w2fg7znhbzic+FvA3T6bDYLRyqOHTCgv9A+QEOVhyk3n7q3S+tRkubyDZkxmSSEZtBZkymmMdkEGU43Q6YjwkLFwrxfa6B3/8Ge39zJgZ+FvdHJIv3nt0CFbliUefCGAND7xbib56sWJEEDzq9bAE4C66deKWmpmFRXl3j3mV3VFcLEbrjrx93sVcft9ivqhIVXx5EYzaji4hAGxGBNjLSeYlAFxF5wnVtRAQ61/WIyIay+HDnIt+1WJcW2n6DTAj4guP1A2Smyn/JHCvGQ6vAVn+CL7OquJR25cl5YKLRCPucnx+CdR+KE2Zf7PitfQ8Uu2gz8UafuURdWvURFlzf3AgbPgWNTpRoN/c7ZtOX8NMDYj70PvE+DXGqrdXu0v4DZQfcpf45lTmn7e03aA20j2lPZkwmHWI6uBf/7aPbY9T5yXfK2UjpDtd9AwW7Re931jwo2gPVBeLiQm8SFnA9LoMeU2QVkiSkUBQFxWoV5fAu9fjq6oaFvHvhfpoF/CkW9orV6vlAdTq0kZFiIX/8Ij4yUizsI/5wm2sRHxFx4m1ms9dK5CXqI/+yvsCtHyDbBfya5G5iB6S6AI6sE4JdauNwiH5hgI4T1I1F0nx6T4UFfxetQ7tmi5Nnb1JfBRs+F/Oh93n3WBL16H4xTHkHfrxXaFTUV8BFb5xegflUOByw6Dmh5AxCo2DS8yGTvHYoDvKr88muyCa7XFwOlh8kuyKbgpqC0z4vMizSvcOfGZvp3vFvHdnaNyX+viC5q6gUmficsC8tPSRGvVFUO8Zn+p8Ar0RyChSHQwjZuW3fXAv4469Xn2gBd9L9J1+w2bwSr8ZodC7Wj7uYzc652X2b7o/3RUaJxX+kc/EfEYEmPFyWzUvOikwI+AKZEAgMNBqxm7p9ptAR8IeEwLFNwrrJEAXthqodjaS5GCJg0B2w9EVY8brwZvbmF/T6j4VCfEJH6DTRe8eRqE/fa0W//4/3wrZvxa7uZe9BSo+zP7ckG2Y/0OBiMupRGPdM0CUDHIqDwppCjlQd4UjlEY5UHeFQ+SEOVhzkYMVBav+ocn4cCeEJJyz4XfMkU1JonWSb4k7UEJBImolit6NYLMLLvb7ePVecc3GbFcVS71Sjr0epP36sQ6mrx1EvRvd9dXXiuXV17vscdXUn+Lt7C43ReNyCPeLEuftiPuk+3WkW/XInXuJr5DvOF7hbBqTDgN+TOUYkBLKXAE+rHQ3s/V2MHcZK0Z1AZ/CdsPINyN0Ih1Z4L+FkqRHHAeEfL3v0gp8+V0NMW/j2JsjfBu+OFDaTg+8Su7x/pPwIrP6fSBy5xL0u/C/0vcbnoXsCh+KgpK6E/Jp88qrzOFp59ITF/9HKo1gcpxfF0mv0tItuR0ZMBunR6WTEZIh5TDrRhmgf/iQSiX+gWCzYy8vdF0dVlfBir65x+rbXOH3aa4Rvu3PR7aitQamtEwt7i3NhX1/fsNi3WLy2q95YtGYzmgiX3ZtzEX6qS8SJ1zXmPzzHdb/JJBfwkoBHvoO9ja0eSg6IuawQ8H9cKv5H1kNdufo9kXvniVG2CwQ+kUliN3f9x7D0Ze8lBNZ/DNWFENseel/lnWNI/I/0EXDXUpj7FOycJd4H6z+G5O6Q0lPs7lqqIHczFOxoeF77kXDxG42zl1MBu8NOcV0x+dX55Nc4L9X55NXkuW8rqCnA6jhz761OoyM1IpU2UW1oE9lGJACixcK/dVRrwrQy4SoJPhSrFXtFBfayMrG4Lys/bl6Gvfz4uRgdZeWiHN4XaDRCXM5gQGM0oA0ziOvO27RGo1CiDzeiMYajCTeiPWFsuO+Ex4SbxPXw8BMX9eHhUshOIjkFMiHgbYqyhLBXeAxEpaodjeRsxLWHhE7CzuvAYlHarRbVxXB0g5h3lIKCQcHwB4SS+4FFkL0MMkZ59vUtNaIlAWD0Y7KqJNSIToOpn4v31up3REKxYKe4/JH0UeL92GmCKi0CdoedkroSCmoLKKopco+FtYUU1hS6x+K64tMK+B2PBg0JpgRSzCm0jmwtFv7OxX+bqDakRqTKRb8kYHFYLDjKy8XivrzipIW8wzU/fsHv3NlvNhoNuuhotDExQljObBaWcOYIp2e7UI53+7KbXItuo1jIG45b6BsMxy38jWjCDGiNBtDrQ6v1RiLxU2RCwNvkO3diknsEXV9m0NJpokgI7J2nbkJA2g0GH/EZopR73YdCZPC2+Z79XFj5hhDFjG0vrMMkoUnGKHGpKhQCqYW7wFINYWZRCdB+pKhY8QI2h43i2uITF/Z/WOQX1hZSUleCQ3E06jW1Gi1JpiRSIlJIMYtLakSqmDtvSzIlESYTYBI/RLFYjhOxq/6DqF019opK7BXlOMorxIK/Qiz4j7+u1J5e56IxaKOj0cXGoouJERfX3D3GnHS/NioKjU6KRkokoYBMCHgbV0KgMQJPEv+g07mw+m3Rv68o6iVyXO0C0m4wuBj9OGyaJhZqe36Brhd45nXLj8Ly18R8wt9ldYBELPq7ni8uLcTqsFJcW0xBTQGFtYUNu/q1RRTUiLGwRiz0FRon3qXVaEkITyDRlEiyOZlEUyJJ5iSSTOLiui3BlIBeK09XJN7llDZyJ1xqT1Skrz5Rmd612Hd5yCtOhXqPWclpNGJhHx19ioV9w1wbE4M+Nlbs7MfGoouOlgt7iURyRuQ3rLeRCYHAo/0IsZNWlQd526BVb9/HYLNA1m9i3vk83x9f4j2iUmHoPbD8VZj7Z8gc1zSbuNOx4O9gq4V2w6D7lJa/niQksNgtYjF/il1811hUW0RJXUmjX1On0ZFgShAL++MW+O65c4wPjw8eiz6JqihWK/bKShyuHfaKShyVzvL6ygrn7eK201rJ1dZ6VfBOYzA0KMm7lObNJrRRrkV+NNroGDGPFaM2OgZdjLhfGxUl+98lEolXkAkBb+NOCPRUNw5J49Ebhbhg1q+wb746CYGDy4RtXEQStB3s++NLvMuoR2HrN1B2SFgRnvu3lr3evt9h6wwxn/yCbE8KYRRFocpaRUldCcW1xZTUlbjnxXXFJ9xeXFdMpaWy0a+t1+rFLv4pFvjH7/LHGePkQl/SJBRFETvs5eXYKyuxVzQs4u0V5TgqKp0L/nLnbRUNi//KShQPi+C5beRMJrQRp1CYN5lOtJBzqc6ftOA/TtAuTFZtSSQS/0QmBLxJdZHYZQZI7qZuLJKm0elckRDImicWb75m989i7HI+yBPr4MMYCee/BF9fAyvfhJ5XQGozk4a1ZTDrfjEfcjek9fNYmBJ1sDlsVFmqqLBUnHipF2OlpdJ93T133l5pqWyUCN/xhGnDSDIlkWhOJNmUfOoSfnMSscZYtBq5Qyk5NY76erFIr6wUgnaVlafcoXffVl7RsKtfWQmOxmlKnAltRERDWX1UlCibj4pCGx2FLjoGXVQkWpdAnlsYT9rISSSS0EZ+4nkTV3VAXIZYAEgCh06TgEchZw1UFUBksu+O7XDA7l/EvNvFvjuu5P/bu/Moya76TvDft8a+5b5UZu2rSqUNqSQBZhHIFi2p7bYNNtNqaLrbw/Q5dtMYe3B75uClxzD2uG3cDN562oDbx9C2ASMZCwRmEdoXSkIqVamqVHvumbEvb73zx33xIiIzqyqrKreq/H503nk3XryIeJGZpYjf7977u6trz3uAPffL5M/ffBD4he8AkdTlPYcQwNc/BpTHgK7twD2fWJFLpcsnhEDZKaPYKKJkl1C0i2FAX7SKi+6bwX3VqV7168f1OLpj3eiKdqE72o2uWFdHuzvaLdvRLmQiGVb6JgjPg1+phIXsFvbQL95r32wLy7rqa1AMIwzi5ZD5ZmCfhpYKhtUH98lh9G3HUikG8kREV4D/51xJrB9w7cqOAIM3A+OHZOG32z64eq99/nk5siSSBrb+2Oq9Lq2+Bz4NnH9RrmrxtV8EfuYvLm+4/xN/CPzobwBFBX7qT5anFgFdUskuYbwyjvOV82FBvZn6DGbrs7LdkG3Hv7piYnE9jnQkjZSZQtpMI222tSPp8Nhix6N6dJneLV0rhBAQ9fqCHnq/PC+ILzV77TuH3l/VEnVNihIG7K1e+bYe+nQq7MGX53UeUyIRJqeIiFYZEwIraYoJgWva3gdkQuC1h1c3IXD47+V+572Abq7e69LqS/QA7/088Bf3Aa9+BchsAt7920tLCrzyZeBbvyHb9/0ua00sI8/3MFYdw8niSZwqnsJYdQznK+cxVhnDeGUcZWfp8+5jeiwM1DNmBmkzjUykc9+8L2WmwoA+aSZhqJxzvFH5tg0vX5Bryufz8Ar5tnYBbj4v72+uQV+WvfVYhor2Siwme+gzQQ/8gh76dCvgbz+WSUNNJFj4jojoGsOEwEriCIFr294HgH/6beCN7wGNIhDNrPxr+r4M9ADghp9a+dejtTdyB/DPfh94+D/IegJCyKTAxb5UP/ffgK//imzf8QvAHf9uda71OmN7Nt4ovoFj+WM4VTqFk8WTOFk8iTOlM7B9+6KPzUVyGEwOYiA+gN54L7pj3eESes2tO9aNiBZZpXdD65UM7jsDei+fl0F9oSAD+3n3+9WrmDaiaYv00Afz6tOpoJJ9atEeei2VgmIyEU1EtJEwIbBSfA+Yek22ucLAtal3N9CzC5h5XRYXPPCzK/+aZ56U88EjGWDnu1f+9Wh9uO2DgO8C//DLwFOfAaaPyCRBbkvnebU5ubzgC5+Tt2/9V8CPf3KVL/baNFufxdH8Ubw+9zqO5o/iaP4oThZOwhWLLzNmqiZG06PYmtmKTalNGE4MYzA5iOHkMAYTg4gbnJ6xEfmNhgzcF/TW5+EVip2BfTO4v9IK+Koq15HP5YJ9Flo2Cz2Xg5ZtO9Y2l15LpaDE4xx2T0RES8aEwEqZewNwG3I9+/lf6unasfcB4PHfBw5/dXUSAj/6G7nf96Bc/pA2jtv/LWAkgEc+IpcR/K+3Abt+Ath0O6DqwOQrcvqKHczzfft/At72q1xicB7P93C6fBpHZo/IwH9OBv8z9ZlFz0+ZKezM7sS27DZsTW/FlswWbM1sxVBiiEvnXUeE70NYFvxGA6JWg99owK83IBp1+PU6/FpdzrEvFmVAXyzKAD9sy/0VF87TNBnAB0G83gzys7mOgD88nstx3XkiIloVTAislMlX5L5vL5eNu5bd8FMyIXDsm3J5t1h25V7LtYFXvyrbN65C8oHWn5t/Xi4b+Oj/DrzxXbkCQXMJyqb+/cBPfArY+tY1ucT1xPZsHC8cx2uzr+G1uddwZO4IXs+/jrpbX3CuAgWj6VHsyu3C7txu7O7ajd253RhIDLA3dR0QnicD82oVfrUGv16DCIL2Vrsu97U6/EYdoi6PLdpuBI8L2qK+8G/iimlaEMRnoHcE9G37XBZ62M5BTSYZ3BMR0brEhMBKYf2A60P/fqBvHzB1WI4SWMnigse/BTQKQHIA2PKWlXsdWt/69gD/6u+BiR8BRx8FZo4CwpcjjbbfA4zedfH6Atepil3B0fxRHJk7gtdmZfB/onBi0SH/US2KXV27sCe3B7u7dmNXbhd25XZxmP8K8+t1uLOzrWJ484bWN3vZ/VpNbtVq2BaNxqpdp2Kacg36WAxqNCrb0aicQ5/NQstmoGWzcvm7jGxrmWx4n5pIMIlERETXDSYEVsrkYbln/YBrm6IAB94rq7m//D9XNiHw4hfk/saf4agSAgZulNsGY3kWThVP4XjheLidKJzA2fLZRc/PRDLY07UHe7v2hvvN6c0c7r+MhBDwSyU4k5NwJyfhTEzAnZyCOzkhj01MwpmchF8sXv2LaRrUeFwG67FYGKyr8RiUaDOAj0KNxaHGolCi7e0gyA/b0c7niMWgRKNQNP5tEBERNTEhsFImfyT3ffvW9jro6t34s8C3fhM4/QRQOANkR5f/NUpjwLFvyPatH1j+5ydaR4QQyFt5nCmdwdnyWZwuncaJwgkcLxzHmfIZ+MJf9HH98X4Z+HfvwZ6uPdjXtY9D/q+S8H14s7NwmgF+e7A/ESQAJieXPOReiURac+XDYfSZ1u1MFmoiDjWRkIF/PCHbiTjUeFz23vP3SUREtGqYEFgJtTkZOAIbsofvupPZJIfwn3oceOlLwNt+Zflf44f/Qw4LH70b6N21/M9PtMrKdhmT1UlM1CYwUZ3AWGUMZ8pnwiRAxalc8LFpM40d2R3Ynt2O7dnt2JndiR25HeiKdq3iO7j2eZUq3KkpuFNBYD81JYP9qSl5e3oK7vTMkteu17JZ6P390Af6YfQPQO/vgzEwAL2vH8ZAP/SBATlXngE9ERGtI0IIwAeE5wOegPB8CE8ArtwLTwDNY17bsfD+5uNE53O4Imx3PG/H/Quf42KvUaqVV/3nw4TASph4We5zW1a2CB2tnlv+pUwIvPh54C3/EdCW8Z+O7wMv/qVs38bRAbR+CSFQskuYa8y1tvocZhuzmKxNygRAdQITtQlUnUuvoz6QGMBoahQjqZEw+N+R3YHeWC+DSsift6jXF8y3l4X3qvCKJXj5Obns3Vw+XOquuQzekuflKwr03l7oAwMw+vugtwf7/f0w+vuh9/dDjUZX9g0TEdE1R/jtAXAQ9M4PtN22QNn1W8GxGwTCbUFxe5AdPl/wuEsF6wuCb7fVvmYsPkhyRTEhsBLGX5L7wZvX9DJoGe37SeAb/wkongVefxTYe//yPfexbwLFM0A0A+z758v3vBuML3zU3ToqdgU1tyaDKQiZFQagKiqiehQxPYaYHkNEizDohJyzP1eXwf1sY7Yj0O84Vp/DnDUH119YxO9C0mYaA4kB9Mf7MZQcwkhqBKOpUYymRzGcHEZUv74CTOH7sgJ+ELCHW6268Fi1FhxfJNhv20Nc3ZcYNZmE3tcnA/y+/qDdD72vVwb6fX3Qe3uh6Pw6QES03gixSG+z2x7wtvVqu/N6qhc7rz2IdhfrxZ4XeLsX6FEPH+OvSQC7LDQFiqYAmgpFU6BoKqArUNS2dnAftLa23nb+gudY7H4Viq4AHc+7yP3BcySqZeAPV/dHwW8AK2HskNwP3rSml0HLyIgCtzwEPPGHwHN/vrwJgSf/SO5v/VeAEVu+572O+MLHWGUMJwoncLp0GpO1SUzVpsJe6YJVQM2tXdZzKlAQ1aNImSlkI1nkIjlkIhlkI1lkIhnkojlkI1l5X1Tel4vkkDDWb4Vxx3dQtIooNArIW3kUrILcmrcbhfBYvpFH3sovqSd/vqSRRFe0C92xbnRFu5CL5tAX78NAfAD9iX4MJAYwEB+4pqv6NwvpudPTcoh9e6X8QrGzcn6w+ZULT4O4KooSzLcPtub8+0wGeleutZZ9V06uY5/LQevqgp7LQU0kVuaaiIiucXIY+cLe5gWBt7tIQLxIQO7ZPtyGC8/y4Ds+fCfoGXeD52rrGYcvNyW4BsVH61iwhwiOX2vUtiBZXyQI1lvBs6KrrWBabwXY88+T7Wbg3B6oX+CxwbEFwXp7gH+R73K+L+C5PjzHh++12p4rN98THbc9V7Tuc314toDnOgvu99tvOz5c24Nje3Bt2XZtH+VKaRV/WRITAishHCHAhMB15U0fAp74tFwffvr15Znrf+4FWaxQ1YGD/9vVP991wBc+ThRO4ND0Ibw8/TKO5Y/hjeIbi64tvxhN0RDX42GVeQXyf/iucGG5FmzfBgAICNTdOupuHVO1qSVfn67qHYmCZru5ZSIZxPRYx2iEqBZFzIhBUzQokB9CqqKG1ycg4HgObN+G5VlwPAeWJ6+16lRRsSuoOBWU7XLYrjgVVGx5rBnsl50rm3emq7oM8KPdHYF+xxaT9+eiOUS0yBW9znoghIBfbc6tn4Y7Hcypn5qSc+ynpsPbwrKu7EU0LSiUlwgL6GnN2/HWsTCwn7+fd0yJRrmGPRFdN4QQrSC7PWgO2mgG0I6/4ByE54m2YDu4b7EA3J0/37uz53s1KMG2HIQQ8CE75X05LR4CgFAUCBWAokCoMuBt9kg3g3LFCIJkQ4OqK1B1DYqpQjNUqIYK1dSgmRpUU4Ue0aBGNGiG1gq22wPs9t7z+T3i6tLfbXvgPT/QXhCItwfhDW/eua0gOzy3GdDPu73guYPH+uFtIadBrJG6vbS6PsuJCYHl1igBcydkm1MGri+5zcDu+4CjXwee/DTwz//fq3/OJz8t9ze+F8gMX/3zXYN84ePw7GE8OfYkXpx8ES9Nv7RowTld1bE1sxVb01sxmBhEf6If/fF+9Cf6w577hJG45FQA13fRcBtoeA3UnBrKdln2mFt52bse9Kg3e9KLVjEMuBteA67vYqY+g5n6zEr+WK6YAgXpSBq5SJCsiGbDERDZaHbB8e5YN1JGat2Oergcfr3e6tGfmpKV86c6N2d6GqK29NEkWiYDva8XWq6rs3p+WEW/rZ1OywA+wukoRNTiez7shger5sCqubDqLuyaC8f2wgDFDQIXuRdQguBOUQAl2ENRoOkKdFODEdFgmBr0iAYjCOCicQPRhAEzrkO9SFAW9oQ7zeC7MyBvBtRY9HgrIO947GU8Hu76nc/dCrYFgqnxEO3H0BmMh/uwLcLAGGqwaa29vE+FUFuBfCuol3tPAK7nw3UFXE/AcX24rg/H9eG5kD3LzuoMHVBURSYMVAWqqkBREezl1t6GEBAiqH8jZG0BIeQPUPjy5wkhOgL5q5wVt2o0XYWmBz8LLWjrMpmi6SpUrfO2FtxWm7ebx9pvG6r8t2tqwSbb9UYFv/IXq/v+mBBYbs2CgpkRING9ttdCy+8t/1EmBA79NfDWjwFdW6/8uaaOAK89LNt3/+LyXN81Yq4xh8fPPY4nxp7A02NPI2/lO+6P6TEc6DmAA70HsKdrD3Zkd2AkPQJDNa76tXVVR9JMIokkcJkzNOpuXSYIguH2zXb70PyiXUTDbaDu1tHwgn1w2/M9iOZ/QY2DJlM1YWqtLaJFYKgG4kYcKSMlr9kINjOJlJlCwkggZaY6gv20mQ5HR1wP/Hod3twc3Lk8vEK+1Z6bC4L/ybBn3y8tfZhdOLe+r0/Opw/bbVtPDwvpEdGiPMdHtWShVrJRK9qolWxUi223ixZqZRtWzYXT8DoeqwabpszfK/I+BdAW7BUZXy54nLLo+bqqQNcU+ZzB+aoAFF8sW2/1stHVoOe62ZMd3NaDYePhbSU8rugq0Haeossg23Z82LYHy/LQqHuo1100ag5qVRe1io1a2UWj7iwSyAOLxaaaoSKRMZHIRBDPRJDINtsmonEDZkwPNg2RmA4jevFkzHLxfSGHm1senEawX3RzFxxzLQ9uMFxd7tvbct/8YQhfwLW8i1/MMrpY4N1xO9yCwHvR2wvPVZfw2OamNu+/xPSC5XYZX2WWDRMCy43TBa5vI3cA2+8BTnwbePz/ubpRAt/6hFxqcM/9QP++5bvGdWqqNoVvn/k2vnX6W3h+8vmOteaTRhIHBw/i4OBB3NJ3C3Zkd0BX19//nppTAAYSA8v6vEKI67ZHWbguvHIZfqUCv1JptctleOUK/Eo5ON48VpbBfn4OXr4AUV/aVJEmJRqF3te3MMDv7Q0K68k259YTbUzCF2GvdWcPt4DveLArDqySDatso1G24VRc2DUHTs2B2/Dg1V14tgfh+kHwrbSCcAA5BehGK7jXTAWqqXcE/av3ZnHJwqTNXm9PtA07V+Ww8GYwrpqqHEoekSMS9JgOPaZDNbW2gFyRAfq8gF4JngPzA/rgnIvN5fZcH1bNhV13YdVcNKqOTLiULNRLDmpT9eC23Kza0ovearraEdwnMhEksm3t4Hgkrq/Lz2dVVWBGdZhRHcgs73MLIYfTu7YfjEbwIHyZhBC+6Nx7soPD94OpEWowqkVVACUYTaDIdnOkS0dvuhYE3sEohPX4s94I1t837msdVxi4/r394zIhcOivgbd8FOjefvnPcfJxuVqBogHv+s3lv8Z1YqY+g388+Y947PRjODR1qKNHfG/XXrxl+C148/CbcaD3wLL0/l+r1uMHoG/bYRDvVyrwKhVZ+b5ShV9tO1YJquZXKvCrFXiVaivgr1QuO6BflGHIYnldXa0iel1dnUF+sKmp62P6A9FG0R6gy81ra8+77foQtg/hzj9nkce5wTE7KO7meIB7eRXRo8G2gKYA2jKNxGrvHdc7A+nL6R1vPl6oClxXDjO33WZvuQ/L9mA1XDRqLmo1F7Wqg3pF9pzb9aUH0u3MmI5oQpfDnY35Q59lgAdFVvJpTnmAAkCgc254MATec+XUCjuYWnElw+JVXQkD+kTGRDwbCXv4E5kI4kESYL0G+uuBoijQDQ26cf2MNqSLY0JguZ1/Qe6Hbl7Ty6AVNHIHsOPdwPHHgH/4ZeChrwSfdEvkucA3f1223/SvgZ4dK3Oda8TyLHz37HfxtRNfwxPnn4AnWkPNDvQewL2b78U9o/dgU2rT2l3kBiIcJ6yCv6BSfrM9r2K+X6lAOMtb1EaJRqGmktCSKaipFLRkEmoqBTWZaB1LJaGm0tByWehdXWG1fDWxfld2oLVlN1yUZxsozzVQyVuol200qo7cKi6smtPZ0xVUBFdUtOa/KrL6tGGqMCJy6K8R0WFGNZgxHbGUgVjSlPuUiWjSQDxlwohq1+XfZVjwzfHmBeqLBevymN8RsHttAfnFHicD+7WcT97eO96+Fwpk4TRDgaJrUCMqNFOHFtWgxzQYMQNGPOglN7RW77i+yNbeOz6vB/1Slc5Xi+f6aFRkgqBesdEoz2/brfvLNhpVF8IXsOtXnky4HGZUgxnXEYkbSKRNxNMm4hkTsZTcx9MRxFPyeCTBQJ/ocjEhsJzqeWD2uGwP37a210Ir677/G/jjx4E3vgP86G+AA+9d+mN/8AdyJEkkA7zt4yt3jatICIGXZ17G145/Df946h9RtlvV7g/0HMB7tr0H94zes+xD7TcSIQRErSYD+3nL3Xn5woIl8JpBvl+9/GUF2ynxuKyQn0zKLZGQQXwi2TqWlBXztfCcpAzug6BfSyahGBt3BAhdHdf2UJiqYW68ivx4DfnxKgrTdVTmGpc1RHi5qbqCeCoIStImYmkzDEpiaZk0iAXBSzRuXFbl7fmEJxbpKW8L2C/Ya75ID7sz77GLnLPoZOpV0JzT7QHwhIAsBC+C23Kud7PtQQT79vs6j3mQx10hoBgqjLgOI2Eglo4gGvYey6HhyXTQo5w2oZsbq2dUDp+XQ+aXQvgCVt1FPaiR0Fwubf7yab4ve/iFgCws1zZ9oTlkXDfUjkJrZlRDJG4gEtfD+fmrMSefaCNjQmA5NUcHdG0D4l1rey20srq3Az/2K8A//Tbw6K8BW98GpPov/bixHwLf+5Rsv+f3gGTvyl7nCpuoTuDhEw/jaye+hlOlU+Hx/ng/Htj+AB7c/iC2Zq6i8OJ1Svg+/FJpXq99cdGAvn0Ttn1lL6go0NLptsr4uQtUzA+Op1rBv7Jcw2KJlsBuuJg+U8bUqTKmTpcwdaaM0kz9ogFqJK4j1R1FMheVwXdCVlqPJmWPom4GQYeuQTNUKKos39KsgN2cB+vYsjiX3ZCFuOTQZRf1ih30jMre0XrFgWt58F2BWt6CXbBQASCXx1bkHsGocsg55roKRCIaohENpqHCNFQYuioLv6lKOL9cCdYzR3O98yBIxxotgSUACE0J55Z7UDoCds8HHF/A9YWsgu6JVhAe7NsD+jB4bwbu84L3i9ENFUY0qK4f1WFGtM7bUQ3RhIFY0kAk2EeTBqIJE9GkzuHPy0hRlfDfGRFd+5gQWE7ngoTA8JvW9jpoddz9S8ArXwamXgX+9l/LqQP6RbLrtTngbz8E+C6w759f3qiCdaTm1PDtM9/G35/4ezw7/mxYFyCmx/Cu0Xfhge0P4I6BO66rSvcXI2x7YY/9JXrtvVIJ8K9sySDFMC4ezC+2JF46zcCe1h3X8TBzroLp02VMnSph8nQZ+YnqosF/JK6jazCB3GACXYMJZPpiSHVHkeqKyqJaFyCEjDqF7cG35XxyYXvB7VZb2H5w/yLnOB6EIiCiKnzNgIirEJbsUb/snnTbk9ti14qlPZ0HwFeC5coUwFcUiGDvodXD3upNl8G34ws4roDjBcuXNQPycLh8W+AePPZK0xCKgjBINyLNoF1DLKLDiGit49FL3I7oYdDPXmIiopXBhMByOv+83G9iQmBD0E3gZz8H/Pk7gNNPAH/3b4Gf/m+LJwVqc8D/+Glg7g0gMwr8sz+4vLoDa8wXPp6feB5fO/E1PHb6MdTc1jrutw/cjge3P4h3b343Esa1XbldCAG/WoU3N9da3i4/B3duDl5zqbt80F6GIflqPL60Hvu2TU3EOT+Srjm+52NuvCZ7/U+VMHW6jNnzFfieXAKt2aMeV4BUxkTPYAJdfTHkumNIZUzomgIEQ91924c4U4I4nkdlsUDe8eFbwTHHu6wicldMV2TFdVODYqpQDE3OEw/mjzfnp7t+sK6458N2/LDYW73hwmp48pjjX6RnffmpmtIqBGeoiEXaAvmLBeoXCeQ1Q+X/p4iIrhFMCCwXIYBzQUKAIwQ2jt5dwPv+Evir9wKvfQ34/IPAT362c+WBM08DX/kwkD8JxLqA/+VvgET32l3zZThZPImHTzyMR954BOPV8fD4SGoED25/EA9sfwDDyeE1vMKl8S0rWLN+qmNzpqbgzbYF+XNzVzYsX1GgZTJL77FvBvemufxvdoMSvkC94qBasFCv2B1LVVl1F3bNldWsPR++K5dU8j25dJKiyeJyqiqLyzUDJDPo4TSjstCc3OthcblY0oCqqWv91ldMs7ic3ywSF1RsD+ebt7XDc4LAvFGyUC9YsEsOnKoNt+FBFQKaomAAwCYF0JJauLb6vFcGxityA1BZrjekKVBMDaqpBoG7DN5bgXwQzJsaVCM4J6J13m7eP+/8q6kPMJ/wg+kLHeuLu/A8ObXB9+Q0h+bfrx9MfWiula0Gf89K+9+00hb0B3vD1KCZctkvIiLauBQhLrFA6QZUKpWQyWRQLBaRTqeX9qC5N4A/ugXQTODXzl186Dhdf45/G/ifHwDsMqCowNYfA9KbgOnXWrUlMqPA+78E9O9b22u9hEKjgEdPPYqHTzyMl2deDo+n9BTu638Ab0ncgx5rCNWihVpRzqf1HE8uDyTQUSQomjDCQkXJbASZvjjS3dHl/fIsBLzZWdhnz8I5dy7Yn4c7ORkG/l6xeFnPqcRi4TJ3WlcOeq6r1e7qgpbrCoN7PZeDmk5DUfmleiX5vkB5toHCVA2FyRqKU3VUCxaqRQvVgvxb9Fd7nrUCOWc5ZSKeMsKicuFa1unWmtarVflaeALCcmWAbnmyp9zqbLf2bus+e/FzVnXuugLZs24GVdjntS8cyDfPleukt24H50S0sLo7ERHRenZFcehV4giB5dKsHzBwgMmAjWjHPcCHHwe+/ityOcI3vtu6T1GBW/4l8K7fXLfFJutuHY+fexxfP/l1fO/c9+D6LlRfw0hlN+4Qb8dofQ+8CQN23cNhVAC8fsWvpRsqsgNxdA0m0Lc5jb4tafSOJC9a1dmv1+GcPy+D/bPnYJ+Te+fcWdjnzi9prXvFNMO16sO163t7oXX3yLXtu7pksN/VBTUWu+L3R1dOCIFGxUFhsob8ZA3FqRoKk3XZnq7Bv9TyZArCqu+RuC63mA4zqFatGypUTYWmK+FeUZVWT2tbz6try+JydsOFU3fDtlWTlbUbFQdCAI2Kg0bFQX784pem6kpHgiDcpw0kEiZiMQ2xiAZTV2UvezDkfX6A3h7ILwzwg3XWV4KmAMEa5x7k0HfbkcPeXV/OUXfbqrwLVZFV3LtiSPbFkO6PI9kbawXxwXD6sPfd0AB9fSzBRkREtJEwIbBcWD+AurYC//JvgZljwMnvy2Uo08PA9ndecAUCIQS8QgHOuXNwp2dahedKJQjbhnAcCNcBBKBETKiRKJRIBGo0Itdrb/ZYB8Gsmk4v+Qt13a3jB+d/gG+e+ia+d+57qLt1RJ0Etudvw/7qQfTObgUc2aMmw20Piqog0xtDbiCOZNALGk2acs6oLqt3e44P15HLDtXLTtiDW8lbKEzV4Do+Zs5WMHO2gtefnQQAqKqCXF8EvTkPPWYRXfUzUCdOhcG/Nz1z8TejKNAHB2BuGoExsgnmpk3Q+weCBEAvjL4+qJkMg411wrE9FKfqKEzWWlvQ83+xZeQ0XUWmL4ZsfxzZvhiSuWg4AqUZXK/kEP7mUm++7cNruLCKNhpFC3bJglW24VRcOFUbTtWFV3fgNWSQrngCugLolgN92oE+U5O3FfkhrCgKbABXuIbE4jRF9pZHgl7ziN5qN3vSgw26ClcI2J6A5co57NWqi3LJQjFvozBbRzlvQ1xgtIBuqOgZSaJ3NI2+zSn0jqaQG4hf19MpiIiIrhdMCCyX00/K/cgda3sdtPZ6dsptHq9ShXXkNTRefRWNw4dhHT8B++xZ+KXS8r22YUDv7paBcG8v9N4euQ9uu7kUXvRP4ZuFp/GdMZkEUH0No4V9uGnuxzAwuwOK3/oSH0sZGNnbhYFtGQxsy6BrKAFNv/wv+c1h/daZs5g7No6503nMTlqYK+vIixxsJDA70cDsBADEAexBvJpBrhJHFgpyhoVoRMAYHWkF/SMjMDaNwNw0DGNoCArn468rnuejPNNAcboeBvvNwL8yZ134gQqQykWR7Y8h258I9nFk++JIdkUvWWk8XK/d6qwqf8EK8herMu90HrvY8PlIsC2gKXK7BCEAFwJu0NPuiqCNebebG4TssTdUQNeA5tD6iA4loi4sWuoDXsWFO2uF89NdS+7txqUWfAveiq4i2x9H15Cs9N/cp3tjrABPRER0jWINgUVc9tyNRhH41GYAAvjoESA9uOLXSOufVyyi9sILqD3zDKrPPgfryBH5rX8Rem8v9P7+ViG6TAZKxIRiGFB0A1AAYdkQVgN+w4JoNOCVSkHV+zy82dnLqnbvK8BULofxTe9AKXsXPDUe3pfLCIyOahjZGkXvpgS0WBRKLAZFUWSRMd+H8DzA8+DXarIqf6UCv1KV7UIB7sw03JkZeNMzcGdm4E5PX7BYnwBgRbIoZbejNHgAhfR2lNTcgvNyA3EM7cpheFcWQzuzSGQ4NWetObaHypwM+otT9WBfQ2G6jvJs44I9ygAQSejIBYF+pj+OXG8MmVwEyaQB1RPw6y5Ew4PfcOE3XIi6Cz+4LRptbduDsGRxO9/yZCn2laYp8+a0t+a2zy8+194bf8Ee+2COu+8L1EsOaiUrqI9go1a0UC3ZqBXtoFaChVrZuejP9kooqoJYykA8LadcJNIm0r0xpLujSPfEkO6JIZ42l7X+BxEREXViDYFr1dlnAQggt5XJgA3OOnkSlX/6Dsrf+SfUX/zhgrXm9cFBRPftQ/SGfYju2gVjdBTmyMiyzFn3LQve3Bwq42fx+rFncPqNQ5g+dxRiZg65CpCtCOQqgNC34/ymd2C65yZZ3wCAaRUwMPkcBiafQTJYTaAG4PRVX1UbRYE+MABjeAjm8DCM4U0whofltmkYxsAAFF3+L6lRcTB2rIDzr+dx/vUCZs9XkJ+oIT9Rw6vfPw8AyPbHMbQri+FdWQzvzCGRZYJgufi+gF1z0ag6qJdtVPIWynONtn0DlTkLjaqz4LEaAEMBUgoQi6rIZCJIJQ0k4zpiEQ1RXYUBQLE9+HUX/ngF/skChOWhAaCxXG9CRSsgnx+gL6U43bxzOoL9KxglsxSapiCZiyCZiwCbL3ye78taC3bdhWN7cG1f9vbbHlw7KPB5gefXIxp0U4NhyuXhdFOVNRdiOoN9IiKiDYgJgeVw+gm53/zmtb0OWhONo0dReuQRlL/1bdgnT3bcZ27divgddyBx8A7Eb78dem/v8r++28CPZn6EZyeexTPjz+BH0z+CK1xgCMAQoCkmbum7BW9R7kXppc2Ye6MVcg30Abv6S+jzx+FPm3Cn98DL98O3LIh6HX6jAdFowG805OgGVZXV9IO9mkjILZkM91oq1TldoacHWk8PjL6+JQ/rjyYNbLulF9tukT+vMEFwLI+xYwXMnKuEQ9APPz4GAMj0xTC8M4uB7Rn0bU4jN5jY8MOYhRBwbR+Nqix816jKzQr2jYobHms/x667MCDnuJuKAkORQb6pKDAVoF8BRhQFRkJDRFUQ0VWYKqD7Asr8jmvLldtscE24+Fx5xVChRHWoUQ1qTG+1ozqUWKutRnUo0dac+PnBPrTrt0CdqiqIp03E05wmQ0RERFeHCYHlcPopud9899pexzVACAFXuHB9uXm+B0MzENEi0NVr58/RGRtD8ZF/QOnhh2EdO9a6wzCQuP12JN/5TqTe8XYYw8PL/toT1QkcmjqEl6ZfwqGpQzgyd0QmANoMJ4dxcPAg7hy8E9trN+LwN6Zw/vUCGmhA1RXsOTiAA+8cQfdwctmvbyUsSBBUHYwfL+D86wWMHStg+mxZDlmfquPwE3KEgx7R0DuSRN8WWeiseyiJbF8cmnFtFjrzPR9W0GvfHtyHQX3FhlNx4FRsuDUXXs2B3/Cg+rKgnQGlVchOAXRFQVQBkgqgB/cZCqAbgGYal3dxQgDt09A1BWpMhxrXocaMYK9DjRut4837YnoQ+AdB/wr1vhMRERHRQtdOBLZeOfXWOvOb71rba1kHLM/C8cJxnCmdwZnSGZwtn8VEdQJ5K49Co4C8lYfjLxxmDAC6oiOqRxHTY8hFc+iKdiEXzaE72o2eWA+GkkMYTAxiMDGI3ngvVGV1AwevUEDp0W+g+MjDqD//QnhcMQwk3vZjyLznPUi89a3QUqnleT3fw5nyGRydO4ojc0dwJH8ER+eOYqa+sOJ+T6wHt/ffjoODB3Fw8CA2pTbh/Ot5PPeVk3jsdblEoKor2PfmIdz645uR6oouyzWulWjCwNaberH1JpkgsGoOxo8Xcf5YAVOnSpg6U4ZreRg/XsT48WL4OEUBMn1x5Abk3PVUdxSprihS3VEku6Iwo9qK9io3e+ytmgOr5sKqOWiUHVglG07Zgltx5FZ34dddeHU5Rx62D8XzoUP21jeDekNRkFSAbFu1+tabBRC78FKOS6EYahjAK/MD+7DdFvQHbcVUr9veeSIiIqLrCRMCV+v8C4DvAKlBWUNggxmrjOGZ8Wfw8szLeHXmVRzLH1vQW71Uru/Cr6pQrBjqrooZz0bZLeO83wBwDkL5IQR82FoDjtlAIhVBJptET08Ww+khDCYHMZwcxlByCP3x/mUZceA3Gqh85zsoPvwIKo8/DjitZEb89tuRfuB+pH/8x6FlMlf8Gg23gTPlMzhdOo1TxVM4VTqFk8WTOF44jrpbX3C+pmjY3bUbN/fejJt6b8LNfTdjMDEYBmDnX8/jq3/+Is6/XgBwfSUCLiQSN7DlQA+2HOgBIOdY5yeqmDpVxtTpEmbOljE3XoNdd8OpBovRdBXRpIFowpD7uA7NVKHrKjRDg2ao0A1VBtsCEJ4PuAKK40GxfYigyB0sD3B8KI4HuAKq50P15F6H7J1vBvamouCSA791RVaSXwKhADCCufFRHVrQA69GNahBITslorVuR9uK3UX1zqJ3S6iOT0REdKWEEPA9F77rwfPk90dV0+S0RFWFqso2rR75O/Hguy58f/4qNK3vBR19EJoGTdehqlfXEXEtEkLAc114jgPPdeQ+aLvNtmPDcxy4lzrPdVBaztXHlogJgat16gdyP3rXwmWerkO2Z+Opsafw+PnH8fT40zhdWlh2LhvJYltmG0ZSIxhJjWAoOYTuaDey0SxykRziRhx2xUfhXANzZ2uYPluWS5HNWvDsy6+c7SkuZqMzOBE9hXzsGUwnz2A6dRapXBRDySEMJYYwlBzCcHIYg8lBDCWG0BPrQUyPLdqLKVwX1aeeRukf/gHlxx7rqN4f2bMHmQfuR/o974ExeOkCkkIIlOwSJmuTmKhOhNtkbRKT1UmcLZ/FeHUcAou/75gew87cTuzJ7cHurt3Y07UHO3M7EdMXFiE8/3oezz1yckMlAi5EVRV0DyXRPZTE3rvl70kIgVrRxtxEFXNjVZRmZCX88pzc3KoLw/OhlSwoFRv+FOAqClRF/tPWFAWmCpht8+pNBVCX+u9eAXCR4fC+AviaCqErMqCPqFAjMqDXEgaMhAE9aUCLNYP2xYN5xWDvPBERrRwhBFzLQr1cklupiFq5hHpJ3rZqFdj1utwadTjB3q7X4VgNeK4bJgGEWLwI6nyKqkLTDeiRCAwzAiMSke1IFEaw1yMRmNEojGgMZjQGMxaD0X47GoUZi8OIRmFGY8Hx6DWXcPBcV/5cG3U4jUbQbsifb6MOu9EI9q22PK8Bp1EL9g24thX8LjwZoLryd+J7V9axBwBQFJkY0HRomgZV16HqzbYRHtM0HaquQdV0qM1kgtZ2rqbLYx3nGG3nBsfbO0vmreTVeVPAd114zUSH58ogvq3tu6296zrzAnc3bLtBcB/e517Fz2sRDWfxkdQriQmBq3Xin+R+29vX9DJWku3ZePz843js9GP43tnvoeJUwvs0RcONPTfi1v5bsb9nP27ovqGjt7qpkrdw/ugcDh/N49zRNy64DrmiAIlsBNGkgUhcRzRuQDNV2RsrAOELNOoOKsU6ahULdsWH5unI1QeQqw9ga77tNc0CJlJv4FjmKL6T/QEqkXzHa0W0CHLRHHKRHLoiOew472HXc5MYff4cIqVW4T27N4P8j92I/NtuhD3aDwDwi9+FNWuh7tXRcBty8xooWSUUrAIKVgH5Rh5Fq7ikERMpM4Wt6a3YnN6MzenN2JLZgl25XRhNjUK7RLaViYCFhOvDrznwqi78qgO/5sh91UGq5iLePOa48FXAj2sQxpUH0b4C+M014Q25Jjzaetq1mCaD+pQJM2XCSJlBYTwtLJqnaNfWFxIiIrp+OI0GaqUCasVia18soFaS+3pbwF8vFeE6FysPu/yE78O1Lbi2tXyr0QRkIkEmDYxYkDgIEgZGNAozFutIIIRJhUgUiqoFK7QoUBRAUYJRhIoCRVEhfC8IuJvBphMGpc2A0m404FqNtkC+AceSQbvTqMOxrDDodxr1ZQ9Al5UQYc/36oe164Oq6dAMA5phQNebbTO4bYT3ac37gmN60G44LvCVb67qNStCXGBh9A1syes/1gvA724DhAd85BUgO7Jq17gajs4dxZePfRn/cPIfULRa87D7Yn14x+g7cPfQ3bh94HakzIVz5n1fYOJEEW8cmsbpV2YXDtFWgFx/HL2bU+gbTSM7EEemJ4ZUdxTaZRQVE75ApWChMFFDfrKG2fMVTJ4sYnasivmd7layhImuN3A09QJOJV+FgItd54Hbj/m484hAX+stohQDntqr4Ac3qHh9GBBX2eOaiWQwEB/AQKK19cf7MZwcxpbMFuQiucvq1RVC4Oxrc3j+66fCOfLXYyJA+EKuOV9v2xou/JobBPmtgN+rOvJ41YGw5g9xWyJNmTcnvm1ufFgQr/M+La5DMTbeEDkiorXmOg7seg12rQarXoNTr8Oq1+Sxeg12vQ7XtsPez9YQXRmM+V7bZ0XbZ3Czpep625f1Vltr+1KvG6b8Im+asm2a0IJjutl+24RmyvNXchSX57qwalU0KhVYtQqsahWNavu+gnq51BbsywSAay3eUXMxmq4jls7ILZUOt2giIYPnWDwMpltBdbTVQ9zsQda0sMcYAHzfh/A8+L4P3/MgfB/C9+G5LlzbgmNZcKwG3GAvbwftZkDdHJXQFkjb9TpsK7ivXl/y6IT1SjOMMElhRKIdCY3weLT1c++4PxKTf5t6sye+ba+19oqmQVHm9bYHN8LRrULA9/zw31Q4BaQ5EiQYgdCcFtJMkMjz3HCUguyp91pJk/ZzO85xW739ngul+S+2Yw7Dhf49t402WGzkgm5A1eX9ujHv33tH4K5DM82F5+n6VY84WXIcuow4QuBqnPy+TAb07LpukgE1p4aHTzyMLx//Mg7PHg6P98f78eNbfhzv3vxuHOg9sGhBP8/xce5oHm8cmsbJl6ZRL7flBhWgbzSF4d05bNqTw8C2DMzo1f/5Kaoii8J1RTGyrys87lgepk6XcP5oHmdfm8PkyRIilTQ2V27GZtwMHQ6684fRO/YcuuYOQ/cseFET03dsx7k7t+Dc7i7UYGGLW8foBT4wYnoMUS2KqB5sWhQpM4VsJItsNCv3wRbVlydAF0Lg1MszeP7rpzB1ugwgSATcPYRbf2J9JgKE47cC+SCoF/V5QX5w//zjVxzYA4CKIGg3oCZ0aHEDaiLYgmNqwoDWdlsxV7aoINFKsWpVlGdnwiG8jXI5bNv1GlzbhmvbcIIeNs8O/v+sKlCgQFFlb5aqqdCbXyzDL5lRRBJJ+WU/3fzSLwMAM7b41CsCfN9DvSQDr0a10jl8ul7rGELdmq/rh190RTB3V1E1qJoq96oazq/WNH3ekOkI9HnDp8P7gt+lEYnCiEbWzTxfIQTsel0GqZUyrGoFjWpFBrNB2wpuh+1qBY1qFXatdnVDm9dQM5GgNZMGhhkkFFrHAfnzESIYIgkR7EQQGNuyx9yxW23b7kxyXCbdMBHPZhFPZxDPZBEL9vF0BvH5gX86DSMSXZF//6vx1ymEgOvYnUPtm3tr3rHgdvjvty3x0ExWNJ+z+fsSwocQIvg3q4fBojovANd0I/h3GQ0D+M7bMngP22GAH4GmX+aKQEQXwITA1WhOF9j+zrW9jmUw15jDXx/5a/z1kb8ORwPoqo53jLwD/2Lnv8Bdg3ctOnRd+ALjJ4o4+vQ4jr84Dbve+nCOxHVsvrEb227qxfDuHKKJ1fsflxHRMLwrh8HRGG7onUJefQXnDo1hvJzATNd+2JEMJnM3YTJ3E1R4GBxQsPPtO3Hw1kG8Yx2u7e3YHl5/ZgIvf+cc5sZkTQPdUHHDW4dx87tHkcxFVuy1hSdawXpjfgDvLQz2550D9+oHITUL5CnNAnkxPQzutWZw3xbwa3G5fr0cxkd07WtUK8iPn0d+fAylqUmUZqdRnp1BeWYa5dlp2PWFBUhXg2YYSGS7kMx1IdnVjWSuC4mw3Y1kl7zPjMXX5PqWm+c64VDqajGPWqGAarGAeqmAaqEge12LwbFyacGc1vVCN0zoUZksMKOxMIkQJg6CvR6JQAsSEEpbgTdVk8kJIWTio1l8zPf8jiHSiw13br9t1aphMHU1jGgMkVgMRiyOSKyzZ1qPRNp67wxozZ5+3YCqqWjv/pS7Vq+n53kXLADmByMOXMcOCoLZcO3m3m4dD5Jx7b3RzedaSWYsjkgigWg8gUgyiUg8iWgiiUgiEQb3YbCfySKeyaxYgL8eKYoiaxGYEcTTV14Ymuh6wITAlRICOPFt2b6GEwJny2fx+Vc/j68e/yosTw4X25TchJ/f8/O4f/v96Ip2Lfq4wlQNR5+ZwOvPTKA005rNFc+Y2HZTL7bd3Iuh3Vloqzgv2m80YB0/gcYrr6Dx6iuov/IqrGPHgGCuVSbY9NFR2Hf+M0z33oyzkzpKMw2cnwDOf/EEvvulExjcngmWtOtBtm9tv8QWp+s4/IPzePUHY7Cq8n0YEQ03vn0TbrpnBPErSF6E8+srrXn1He1qq+0Hw/CvmoJgrfm2gD5cf77Z1oK9IYvlhdXxuTY9bQyubaMwOR4G/nIv27Vi4ZKPjyaSiGWyiCVTiKXTiCZTiKXSiMQTwdDlSDiEWTdMKKoip+UIP6jTIofkymG3VthT5jTqaFQqrQJiweZaFjzHQWl6EqXpyYtemxGNhQmDeCaLRCYrg5BsFolsDolMLgxKVrPXSwgBq1qVQ6dLBdSb86ebQ6mLhY6tUa1c+knbKYocQp1MtYZNtw+hjsVhRKKtodOqJotoqRpUTSbhRTBs2ve9trYcSeDY1rxh061h1G7bMOrmnORm4CuDVRuN8nL/RK+MbpiIJJsBaxLRREK2g2Ph8WTz/iQi8URYNG69jHi4GDllQSYJmiN2vOD34NlOq6c/KFgGyEJ6CoBgcnrY1nR9wb/n8N94JIJILB7+/RARXQoTAldq7g2gcAZQDWDLW9b6ai7bq7Ov4nOvfA7fPP1N+EHWel/3Pnxo/4fwrtF3LToawKo5OP7CFI4+PYHxE60J90ZUw45b+7DnrgEMbs+uSK+sEAJ+tQp3erq1TU3DPnMa9qlTsE+dhjs+vuhj9b4+xO88iMSddyFx8A4Yw8Mdzzs3Vg2mOcxg+kw5XLv+yb87jq6hBLYe6MHWm3vRN5pa0nsTQsCqVVGZm4Vdr8kv1lYDrmN3VFI1IpFwSF40ngjnHDWqDk68uPDnnOqO4sA7NmHv3YOIxFtfmIXjtYrnNQP6ir0g0G/eJxpXNpxQMbUwcO8I7Nt77qOtwL79HMXU2FtPBBnclWdnMDd2Lgz2ZXsMpZmpi/YoJ3JdyA0OIdM3gHRPL1I9vUh198p2Vw+M6OpOGXKsBmrFIqqFOVTyc6jMzaKSn0M12DePyf8P1pEfO4f82LlLPm80kZQBdCyOSDwOMx5HJCb3ZjTWCpznBdGibbh9RxVpx4FVk3PKrVo12Mvb9XL5soedq5oW9KrmZEIj+P94IpNFPJvrSHjE0ul1E6y2D5F2mkXLmskfq9V7HxYzC5IMzTnczYREmJTwvNbScMHvQdHk9IZmhfGFw59b852NaBSRoPfaMFdulNt60ZyrbS5cJIiIaE2xqOAillTM4cnPAN/8dWDLW4EPPrK6F3iFhBB4auwp/PdX/zueGX8mPP7m4TfjQzd8CLcP3L5gqJjn+Th7eA5HnprAqZdn4LkyeaAowMi+Luy+cwBbb+qFYS7+hUc4DrxKBX61Br9WhajV4C/Y6vCr1WCryPMrVfiVCvxKBV61Ai9fgFjCkFgtk0F0/3653bAPsf37oQ8uXPXgQspzDZx8aQYnX5rG2OsF+H7rn0c0YWB4dxabducwtCuHVE7H7LnTmDl3BjNnT2P27GkUp6dQmZ2RPTGXQVE1RBJZqGoOsLOIaF2I6L2IGl0YGM5heHMamZQBUXOD4nkO/IrcC/sKhlsqaA2vTxhQk23ttk0Ljqsxg2vS00XJ4cEN2I1aa06mZcl5lL4fzK0M2r4Ii/poxvwqu2YY+Gn6tZmz9j0P5dkZlGamUJya7OjpL4yPXbQ6txmLo2toGLnB5jYU7q/VYfd2o45qM0GQnwuG2cvh9rViPhhqn0etVLyq+c9Xw4zFEc9kEE83503LdjzTHFadRSIrg/xoInnNLVNGRETXhrUoKsiEwCKW9Iv4/+4Fzj4D3Pe7wMH/dXUv8DK5votvnPoG/uKVv8DR/FEAcrnA+7behw/e8EHs7trdcb4QAjNnKzj69ARef26iozhg11ACu+8cwPbdMUSqM3DGx+GMjcOZGIc3Owcvn4dXKMAt5OHlC/BLpWV9L2oiAb2nB3pvL/S+XhgjozC3bIG5eTPMrVugZbPLNv+tUXVw+pVZnHxpBmdenYVdL8N3x+C75+G7YxDeJIBWMK4rJgw1EmwmEvEsYtEUImYCph6DqUagehoUT4Xqa9B8DZrQoCt6+DhdvcKhspqyIKAP28mFgb4a25jz68Mhm7YNIQQURWnNjdU06Ka5bnrz1gurVkNlbgbluVnZAzw3u+C2VauuyHxY3YzAjMVkL3GstUViMZjxhOxdjMfl0OHmPuhVbt5nRJev6J3rOLBr1XCOeK1UDIPa8twsStNTKM1MoTI3e9F50aqmIzsw2Bn4Dw2ja3AYsXRmw8zhnU/4PhrVCqqFPKxqta1HP6gcX6vCbjTCCtbNuethpWlVa6sgrYfrWGuGEfzdtI84kH8z0WQK8XQGurn+ascQEdHGw1UGrhWlcZkMAIC9D6zttVxEzanhK8e/gi+8+gWMVccAyMr4P73zp/HQvocwlBzqOL+St/D6cxM4+vREWLgOACKmwGhyFkOVVxF7/hCcr5zF2GUWsFKiUajx+IJNicdkOxaHlkpCTSahJpJQEwmoyQS0pDymZTLQe3uhxlemh0x4vlzCru4ExfA8iIaLbH4WI85ppGLjcJwqDDMKQ90KQ90TBPFRuVeMi3+J99HKHajBdhGe4sLyaqjZZVh+HZZXg+XVYfk12H4DsZ4MsluG0LNzKwb370Wir2vDBhGO1UBxajLcSjNTcs3kUhG1UlFWXK9U4NrWkopXNYtTmcEQ5Ug80TkUuG1IcCKbQyyVviZ7C33fQ61YRGVuFuW5mbZgvxnwy+HeTuPy/q1ruh4sNxWDbpiyIrqihGs1K8HazL7vdSz/1Syy1ZxDCyBcc3op8+cvRFFUmHH5+9TNSGuJpbZlh1RV7Vjmqjk82g2Kn9nBElWXM7Rc03Wke/uQ6ulDbmCoFfwPbUK6p5fzexehqGpYwZyIiIhWBxMCV+JIMEVg0+1Aeuji566BxVYM6Ip24f173o+f2/NzyERa1VQdy8Mbh6Zx5PGzOHe8VV1IES56p1/GwOQz6Jo7DDWoM9A+0FXr6YExMABjcBD64AD03l5o2Sz0XA5a+5ZOQ1mDL7/C8eGVLHhFW+4rwTD7eYXzvIoD0bjwF/0M0sggDSzhO6ovBBwhC+s7AnDRuu0G9wlDhZk0EclGkOiNITeaQm40BSNpQoloUKMalKAYo12vYer0SUydfAOV40cxduQkyjPTQBnASQDfka/bNbQJm/btx6a9+7Fp336kunqu/ge4zjiNBmbOnsb0mZOYPn0S06dPIT9+/oqDRUVRF12D2GnIpYaQn1va86iqTA7kumRxtGwuaHchkcshGewT2dyqFEsTQqBRKaNayKOaz7fmd+dbAX95bhbV/NySq3tHEomgYrzcUt09HbdjqVQ4N3g53qPnujIQr7XmfLf3EnfMBw/WH7eax2tVWPU67FpVzn0WPqxqFVa1eukXXgpFQSyZCgrgBRW6s1kkc91I9/Qi3duPdG8fEpnsNZkoIiIioo2FUwYWccmhGp+7Hzj1OPDu3wbe/Eurf4EXcLp0Gn95+C/x98f/Hg1PzmEfSY3ggzd8EA9ufxBRXRaccusNnHrsJRx9ZgJnZ2Pw2vJCmcJxDEw+i77pF2G4dWjd3TC3bkFk6zaY27Yhsm0rzM2boQ8OQo2sXREgv+HCK1rwSrbcN4P+YnC7ZMGvXl6hKAEBx7dgeXU4viU3YSOSSSA10Ivs6BAi2RTUaLNoniYL6kXkXo1qEKoCx/Jg1VxYdVeuXqQAqqogmjAQTRrQrrJifmlmCuePvobzRw7j/JFXMXPm1IJzsv2DrQTB3v3I9PVf1WuuJiEEStOTmD59Sgb+Z05i5swp5CfGL1hwLRJPIN3Xj0xvPzJ9/bLnPlw3WVb4NsyI7CE2Dei6AUVVO+a1+54nl8MKAkw72BoVOYQ5XE4saF/JsmLRVBrJbA6RRFIOa2/u4wkY0Rg0TYOqG9B0Daqmh73Init70n23vUhaVW7VKqyaXJe7XiqhWsgvuSdbUVQkstkgsO9pBfxtt1Nd3aterG45CCHg2hastqRCc0RC+HMMCs/5nheu7a6qqiyMpqrhlAUzGg+rmZuxGKeVEBER0YpgDYF14qK/iMo08Pu7AOED/+ElILdlTa6xSQiBF6dexOdf/Ty+e/a7EMH6uTd034AP7f8Q7hm9B/7MLKovHsK5p4/h1Ckf4+om2GZrlECsPo2BiWcxYoyha9cgInv3IrpvH6J790LvWnzZwRV7P76QS+KFgf3CQN8r2hDW0gpPKYYKLW1CTUegpQxoSRNqwoAwgZnpMzh38jBOHnkR5cosbF8OjTaiMWy95U3Yefud2HrLmxCJJ1byLV+1ermE80cO49xrP8LZw69g+tTJBT3fqZ5ejOzdj037bsSmffuR7V96ocWVZNWqmDl7BjNnmsH/KcycOXnBNdUT2Rx6Rregd/NW9G7eiu7hEWT6BhBNJlf5yiXPdVErFYKeeNkb3+qVb7+99CB9uUSTqdaIhWwuDPpTQdCf7O5GIpPj0HUiIiKidYIJgXXior+IJ/4IeOz/BIZuBX7hO2tzgQAc38G3Tn8LX3j1C3hl9pXw+NsG34IPxt6JbWcc1H54CGOvTWNcGcFU7y2w26YK6F4dm4wJ7NgVxaY370J07z5oyZUJfIUQEI4PUXfl/PyaA6/sLB7wl23AW9qfpBLVoGUickubQTvYpyPQMyaUmB4GvrVSEW+88CyOP/80Tr/0w45K37F0BjvedBA77rgLozfcdE0XmLJq1SBB8ArOHX4FE28cWzA0PJpKo2/Lto4t0z8I3ViZIe2NagXFyQnMnD0drsgwc/YMyrPTi56v6Tq6No2itxn8j25F7+YtiGeyK3J9K034PurNYfyFPKxqpXOIe60Gu1GH73lhD3az51pRFDlqIFhiTdMNuXRVPB6uxR1JyC2WTCORyyGeya3Y75KIiIiIVgYTAuvEBX8RQgD/9TZg7gTwwKeB2z646tc2UZ3A3x37O3z59S9jqj6FWENg37iO+6vbsX/cgHfkNGajo5jp2o/Z7v0dSQBDcTEyJLDzLZux5S07oRuX1zMofAFheXLJu7oLv9ZWhK/WvO227m87B+5l/JkFS+KFgX7ahJaVQX57wK9GLn39hYlxnHjhWRx//imcf+1wR895pq8fO26/CzvuuAtDu/Zct8OA7UYdY0dfw7nXXsHZw69g4vjri/dWKwpS3T3I9g0g0z+IdE9vMOxeFvkyorHW8nC63rHGt2M1UC+X5FYqoVYqojw7LYv8TU2iUa1c8PqS3T3oGdkcBP5b0Du6BbmhTdfsknNERERERFeCCYF14oK/iJPfBz7/AGAmgV8+AkRSq3I9ru/iybEn8TdH/yeOvvJ97DjrYc95gb1jKoanPDQi3Zjt3o+Z7v3IZ3dCtC1dZxjA1pt6sPPgEEb2dkHTVTksv+7Kono1pxXItwfzbcdEW4CPq/lrURWocR1qTJdL4TUD/PS8Hv6UGRbVu1ye62Ls9dfwxovP4Y0XnsXc2LmO+3u3bMPO2+/CjtvvRM/olnUxbH61ubaNmbOnMXXqBKZOncTUqROYOXP6sqvJX654Jovu4RF0j2xGT7B1j4wimlib4f5EREREROsJlx1c7174nNzf+LMrngwQQuDwxEt44jt/icmnv4+R01W8/5xAtgrYRhL57C7kc7vwzOZdqMb6oIg6TNSRUmaQianoykaRjBqIKCqUyfNwvjyO054GzdOgehoUXHkgrBhqENgbrQA/bkCJ6W23g/ub7bgOxdSWPQAXvo/pM6dw7rCcP3/28Msd1cRVTcOmvTdg260HseP2O6+p4norRTdNDGzfiYHtO8NjQgjUigUUJidQnBxHYXIclblZ1Msl1Eqy59+xGuHScL7rQtXlsm2aYUA3TblcWDoTLhuWzHUj0z+AbF8/0n39MKOxNXzXREREREQ0HxMCS1UaA157WLZXYKqAEALO2BhOPPUNnHnm2/B+dBjDZxt4qwdYZhpzmQOY2rIL59MDMCNRxDUFA5qB7ZqBmGYjoqWgKq3pAagH20XYXgO2X4fl12F7Fmy/DttvhMdtvwFEZPBvpKIws0lEcikkurJBobJYsMRaDpF4YsV724UQqObnMHnyBKZOnsDEiddx/ujhBcuJxVJpbL3lTdh26x3YctMt674o4HqgKEpYfG549961vhwiIiIiIloFTAgs1RN/BHg2MHoXMHTzVT+dm8+j8corqLx0CFPPPwHv8FFESw14ZgrpoZtg9bwXpS29iEfSSOhx7NBTUJWLD6P3hQcbDTiaA1d34BkeRARAVIFvCPi6D1/34ekeXNWFa1twLAuO1YBjNdCoVII54EU0KmX5pBUAs5d+P7phIh4kBxKZ5jrsWbkWezaHZK4L8WwW0UQSqqZBVbVwjW7h+3AdG65tw2k05PJupSKqxTzKMzMoTI6jMDGG/MQ4GuXSgtc2ojEM79mHTXv3Y2TfjRjYsfO6rQdARERERES0XJgQWIrCGeCFv5Dtt/3qZT/cr9fReO011F9+GbWXX0b5pRehTJXh5rag3r8XIvNjiNz5U9DNDFJ6AtkLPI8nXNRRgxf1oGZ0mL0JxIZyiA/kEB/qhpGJQVGXp5fe97xguHgRtUJBLp/WXFatUEA1P9eqmF6rwnVslKYnUZqeXPJrKIoKRVUvazk2RVHRvWkEfVu3o2/Ldgzv3ou+rdu5dBoREREREdFlYkJgKb75fwBuA9jyVmDbOy56qlcsovHaETQOH0bjtcOoHDkGr6jB6doFL7cJevIg4rf8OKJ6HACQW+Q5am4JZb8CJ+JD74uje98wevdvRWKwa9kC/ktRNS0cQo7Ri5/r2BZqQXKgYz325rG2rX35OyF8CK9zOTzNMILK9hkkMlkkct3IDgwiNzCI7MAQcoNDMCLRlXjLREREREREG8p1nRD47Gc/i9/7vd/D+Pg4brjhBvzhH/4h3vrWt17ek/zob4HDfw8oKvATnwTa5sk7U1OwXnsN9VcPo/LqMVTGLPhqD/zMMPREN2KRe5G44cELPnXVLaHk5FFTbPgpA7HRbgzcsg3bb77rmlpyzTAjyPQNINM3cNHzhO/DtW34vgffa22arkOPRKCbJof6ExERERERrZJrJ+q8TF/60pfwkY98BJ/97Gfx5je/GX/6p3+K++67D4cPH8bo6CW6vJvOvQB87RchBODs+zDyz5zCxA+/jfqYBdhR6JEcItEMEuYumKn9yO1e/GmqbhElexYlr4iGIaD3pdG1ZxTbDt6GXUP9G2bpO0VVYUTZu09ERERERLQeKEKIq1lZft06ePAgbr31VvzxH/9xeGzv3r34yZ/8SXzyk5+86GOb6z9+7V//X+iJDSKmpxA3kohp8QsG70IIVN0Cis4sSs4cKmoDXlpDdtsgRg/sw6a9uxBPZxZ9LBEREREREW1szTi0WCwinU6vymtelyMEbNvGCy+8gI9//OMdx++99148+eSTC863LAuWZYW3SyVZyX5H5lakIp1L1jm+japbQNnJo+SXUDUa8NIqctsGsfmGPdg9egfSvb0c+k5ERERERETr2nWZEJiZmYHneejv7+843t/fj4mJiQXnf/KTn8Rv/uZvLjh+tnYCigfYcQ/aQAK5rX0Y2DaC0YEbkezqZmV7IiIiIiIiumZdlwmBpvnD+4UQiw75/7Vf+zV89KMfDW+XSiWMjIzgzt97aNWGahARERERERGtpusyIdDT0wNN0xaMBpiamlowagAAIpEIIpHIal0eERERERER0ZpT1/oCVoJpmrjtttvw2GOPdRx/7LHHcPfdd6/RVRERERERERGtH9flCAEA+OhHP4qHHnoIb3rTm3DXXXfhz/7sz3DmzBl8+MMfXutLIyIiIiIiIlpz121C4H3vex9mZ2fxW7/1WxgfH8f+/fvx9a9/HZs3b17rSyMiIiIiIiJac4oQQqz1Raw3a7H+IxEREREREW1caxGHXpc1BIiIiIiIiIjo4pgQICIiIiIiItqAmBAgIiIiIiIi2oCYECAiIiIiIiLagJgQICIiIiIiItqAmBAgIiIiIiIi2oCYECAiIiIiIiLagJgQICIiIiIiItqAmBAgIiIiIiIi2oCYECAiIiIiIiLagJgQICIiIiIiItqAmBAgIiIiIiIi2oCYECAiIiIiIiLagJgQICIiIiIiItqAmBAgIiIiIiIi2oCYECAiIiIiIiLagJgQICIiIiIiItqAmBAgIiIiIiIi2oCYECAiIiIiIiLagJgQICIiIiIiItqA9LW+gPVICAEAKJVKa3wlREREREREtBE0489mPLoamBBYxOzsLABgZGRkja+EiIiIiIiINpLZ2VlkMplVeS0mBBbR1dUFADhz5syq/SKIiIiuB6VSCSMjIzh79izS6fRaXw4REdE1o1gsYnR0NIxHVwMTAotQVVlaIZPJ8MsMERHRFUin0/wMJSIiugLNeHRVXmvVXomIiIiIiIiI1g0mBIiIiIiIiIg2ICYEFhGJRPCJT3wCkUhkrS+FiIjomsLPUCIioiuzFp+hiljNNQ2IiIiIiIiIaF3gCAEiIiIiIiKiDYgJASIiIiIiIqINiAkBIiIiIiIiog2ICQEiIiIiIiKiDYgJgUV89rOfxdatWxGNRnHbbbfh8ccfX+tLIiIiWjO/8Ru/AUVROraBgYHwfiEEfuM3fgNDQ0OIxWJ4+9vfjldffbXjOSzLwi/+4i+ip6cHiUQCDz74IM6dO7fab4WIiGhFff/738cDDzyAoaEhKIqCr371qx33L9dnZj6fx0MPPYRMJoNMJoOHHnoIhULhsq+XCYF5vvSlL+EjH/kIfv3Xfx0//OEP8da3vhX33Xcfzpw5s9aXRkREtGZuuOEGjI+Ph9uPfvSj8L7f/d3fxX/5L/8Fn/nMZ/Dcc89hYGAA7373u1Eul8NzPvKRj+ArX/kKvvjFL+IHP/gBKpUK7r//fnietxZvh4iIaEVUq1XcdNNN+MxnPrPo/cv1mfn+978fhw4dwqOPPopHH30Uhw4dwkMPPXT5Fyyowx133CE+/OEPdxzbs2eP+PjHP75GV0RERLS2PvGJT4ibbrpp0ft83xcDAwPiU5/6VHis0WiITCYj/uRP/kQIIUShUBCGYYgvfvGL4Tnnz58XqqqKRx99dEWvnYiIaK0AEF/5ylfC28v1mXn48GEBQDz99NPhOU899ZQAII4cOXJZ18gRAm1s28YLL7yAe++9t+P4vffeiyeffHKNroqIiGjtHTt2DENDQ9i6dSt+7ud+Dm+88QYA4OTJk5iYmOj47IxEInjb294Wfna+8MILcByn45yhoSHs37+fn69ERLRhLNdn5lNPPYVMJoODBw+G59x5553IZDKX/bnKhECbmZkZeJ6H/v7+juP9/f2YmJhYo6siIiJaWwcPHsQXvvAFfOMb38Cf//mfY2JiAnfffTdmZ2fDz8eLfXZOTEzANE3kcrkLnkNERHS9W67PzImJCfT19S14/r6+vsv+XNUv6+wNQlGUjttCiAXHiIiINor77rsvbN9444246667sH37dnz+85/HnXfeCeDKPjv5+UpERBvRcnxmLnb+lXyucoRAm56eHmiatiCrMjU1tSCLQ0REtFElEgnceOONOHbsWLjawMU+OwcGBmDbNvL5/AXPISIiut4t12fmwMAAJicnFzz/9PT0ZX+uMiHQxjRN3HbbbXjsscc6jj/22GO4++671+iqiIiI1hfLsvDaa69hcHAQW7duxcDAQMdnp23b+N73vhd+dt52220wDKPjnPHxcbzyyiv8fCUiog1juT4z77rrLhSLRTz77LPhOc888wyKxeJlf65yysA8H/3oR/HQQw/hTW96E+666y782Z/9Gc6cOYMPf/jDa31pREREa+JjH/sYHnjgAYyOjmJqagr/+T//Z5RKJXzgAx+Aoij4yEc+gt/5nd/Bzp07sXPnTvzO7/wO4vE43v/+9wMAMpkM/s2/+Tf45V/+ZXR3d6Orqwsf+9jHcOONN+Jd73rXGr87IiKi5VOpVHD8+PHw9smTJ3Ho0CF0dXVhdHR0WT4z9+7di5/4iZ/Av/t3/w5/+qd/CgD4hV/4Bdx///3YvXv3ZV0vEwLzvO9978Ps7Cx+67d+C+Pj49i/fz++/vWvY/PmzWt9aURERGvi3Llz+Pmf/3nMzMygt7cXd955J55++unws/FXf/VXUa/X8e///b9HPp/HwYMH8c1vfhOpVCp8jj/4gz+Arut473vfi3q9jnvuuQef+9znoGnaWr0tIiKiZff888/jHe94R3j7ox/9KADgAx/4AD73uc8t22fmX/3VX+GXfumXwtUIHnzwQXzmM5+57OtVgvURiYiIiIiIiGgDYQ0BIiIiIiIiog2ICQEiIiIiIiKiDYgJASIiIiIiIqINiAkBIiIiIiIiog2ICQEiIiIiIiKiDYgJASIiIiIiIqINiAkBIiIiIiIiog2ICQEiIiIiIiKiDYgJASIiIiIiIqINiAkBIiIiIiIiog2ICQEiIiJaNo1GA6ZpQlEUfOpTn1rryyEiIqKLYEKAiIiIls2LL74Ix3EAALfffvsaXw0RERFdDBMCREREtGyeffZZAICiKLjtttvW+GqIiIjoYpgQICIiomXz3HPPAQB27NiBbDa7thdDREREF6UIIcRaXwQRERFd23p7ezEzM3PRc973vvfhi1/84ipdEREREV0KRwgQERHRVRkbG7tkMgAADhw4sApXQ0REREvFEQJERER0Ver1Ok6cOIGjR4/iZ37mZwAAn/70p/HOd76z47xNmzZxGgEREdE6oq/1BRAREdG1LRaLYf/+/Th06FB47D3veQ927NixdhdFREREl8QpA0RERLQsmgmBVCqF7du3r+3FEBER0SUxIUBERETLopkQOHDgABRFWduLISIioktiQoCIiIiWxUsvvQQAuPnmm9f2QoiIiGhJmBAgIiKiq3b+/PlwpQEmBIiIiK4NTAgQERHRVWsvKHjTTTet3YUQERHRkjEhQERERFetmRDQNA033njj2l4MERERLQkTAkRERHTVmvUDdu/ejWg0usZXQ0REREvBhAARERFdtaNHjwIAbrjhhjW+EiIiIloqJgSIiIjoqpVKJQCA67prfCVERES0VPpaXwARERFd+7Zt24ZTp07hkUcewWc+8xnceeed4dSBzZs3I5VKrfEVEhER0XyKEEKs9UUQERHRte2RRx7Bgw8+iMW+Vrzwwgu49dZb1+CqiIiI6GKYECAiIqJl8Y1vfAO///u/j+effx6FQgFCCBiGgUqlAtM01/ryiIiIaB4mBIiIiIiIiIg2IBYVJCIiIiIiItqAmBAgIiIiIiIi2oCYECAiIiIiIiLagJgQICIiIiIiItqAmBAgIiIiIiIi2oCYECAiIiIiIiLagJgQICIiIiIiItqAmBAgIiIiIiIi2oCYECAiIiIiIiLagJgQICIiIiIiItqAmBAgIiIiIiIi2oCYECAiIiIiIiLagJgQICIiIiIiItqAmBAgIiIiIiIi2oD+f/s8dP73JldPAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", + "for m, Q in enumerate(Q_list):\n", + " axes.plot(\n", + " tlist * 1e15,\n", + " expect(outputFMO_ME_nodephase.states, Q),\n", + " label=m + 1,\n", + " )\n", + "\n", + "axes.set_xlabel(r'$t$', fontsize=20)\n", + "axes.set_ylabel(r\"Population\", fontsize=16)\n", + "axes.set_xlim(0, 1000)\n", + "axes.set_title('Without pure dephasing', fontsize=24)\n", + "plt.yticks([0, 0.5, 1], [0, 0.5, 1])\n", + "plt.xticks([0, 500, 1000], [0, 500, 1000])\n", + "axes.legend(fontsize=18);" + ] + }, + { + "cell_type": "markdown", + "id": "422153e4", + "metadata": {}, + "source": [ + "And now we see that without the dephasing, the oscillations reappear. The full dynamics capture by the HEOM are still not capture by this simpler model, however." + ] + }, + { + "cell_type": "markdown", + "id": "c8c0e765", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "7d3a2ba5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "QuTiP: Quantum Toolbox in Python\n", + "================================\n", + "Copyright (c) QuTiP team 2011 and later.\n", + "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", + "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", + "Original developers: R. J. Johansson & P. D. Nation.\n", + "Previous lead developers: Chris Granade & A. Grimsmo.\n", + "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", + "\n", + "QuTiP Version: 5.1.0.dev0+7941773\n", + "Numpy Version: 2.1.3\n", + "Scipy Version: 1.14.1\n", + "Cython Version: 3.0.9\n", + "Matplotlib Version: 3.9.2\n", + "Python Version: 3.12.7\n", + "Number of CPUs: 16\n", + "BLAS Info: Generic\n", + "INTEL MKL Ext: None\n", + "Platform Info: Linux (x86_64)\n", + "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", + "================================================================================\n", + "Please cite QuTiP in your publication.\n", + "================================================================================\n", + "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" + ] + } + ], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "d77a1a1d", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "82e0aa66", + "metadata": {}, + "outputs": [], + "source": [ + "assert np.allclose(\n", + " expect(outputFMO_BR.states, Q_list[0]),\n", + " expect(outputFMO_ME.states, Q_list[0]),\n", + " rtol=2e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(outputFMO_BR.states, Q_list[1]),\n", + " expect(outputFMO_ME.states, Q_list[1]),\n", + " rtol=2e-2,\n", + ")" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "qutip-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-3-quantum-heat-transport.ipynb b/tutorials-v5/heom/heom-3-quantum-heat-transport.ipynb new file mode 100644 index 00000000..fec1159d --- /dev/null +++ b/tutorials-v5/heom/heom-3-quantum-heat-transport.ipynb @@ -0,0 +1,803 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dccbb6ae", + "metadata": {}, + "source": [ + "# HEOM 3: Quantum Heat Transport" + ] + }, + { + "cell_type": "markdown", + "id": "afd65763", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "In this notebook, we apply the QuTiP HEOM solver to a quantum system coupled to two bosonic baths and demonstrate how to extract information about the system-bath heat currents from the auxiliary density operators (ADOs).\n", + "We consider the setup described in Ref. \\[1\\], which consists of two coupled qubits, each connected to its own heat bath.\n", + "The Hamiltonian of the qubits is given by\n", + "\n", + "$$ \\begin{aligned} H_{\\text{S}} &= H_1 + H_2 + H_{12} , \\quad\\text{ where }\\\\\n", + "H_K &= \\frac{\\epsilon}{2} \\bigl(\\sigma_z^K + 1\\bigr) \\quad (K=1,2) \\quad\\text{ and }\\quad H_{12} = J_{12} \\bigl( \\sigma_+^1 \\sigma_-^2 + \\sigma_-^1 \\sigma_+^2 \\bigr) . \\end{aligned} $$\n", + "\n", + "Here, $\\sigma^K_{x,y,z,\\pm}$ denotes the usual Pauli matrices for the K-th qubit, $\\epsilon$ is the eigenfrequency of the qubits and $J_{12}$ the coupling constant.\n", + "\n", + "Each qubit is coupled to its own bath; therefore, the total Hamiltonian is\n", + "\n", + "$$ H_{\\text{tot}} = H_{\\text{S}} + \\sum_{K=1,2} \\bigl( H_{\\text{B}}^K + Q_K \\otimes X_{\\text{B}}^K \\bigr) , $$\n", + "\n", + "where $H_{\\text{B}}^K$ is the free Hamiltonian of the K-th bath and $X_{\\text{B}}^K$ its coupling operator, and $Q_K = \\sigma_x^K$ are the system coupling operators.\n", + "We assume that the bath spectral densities are given by Drude distributions\n", + "\n", + "$$ J_K(\\omega) = \\frac{2 \\lambda_K \\gamma_K \\omega}{\\omega^2 + \\gamma_K^2} , $$\n", + "\n", + "where $\\lambda_K$ is the free coupling strength and $\\gamma_K$ the cutoff frequency.\n", + "\n", + "We begin by defining the system and bath parameters.\n", + "We use the parameter values from Fig. 3(a) of Ref. \\[1\\].\n", + "Note that we set $\\hbar$ and $k_B$ to one and we will measure all frequencies and energies in units of $\\epsilon$.\n", + "\n", + "References:\n", + "\n", + "   \\[1\\] Kato and Tanimura, [J. Chem. Phys. **143**, 064107](https://doi.org/10.1063/1.4928192) (2015)." + ] + }, + { + "cell_type": "markdown", + "id": "3d7cbb98", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "af859f07", + "metadata": {}, + "outputs": [], + "source": [ + "import dataclasses\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import qutip as qt\n", + "from qutip.solver.heom import (\n", + " HEOMSolver,\n", + " DrudeLorentzPadeBath\n", + ")\n", + "from qutip.core.environment import (\n", + " CFExponent,\n", + " DrudeLorentzEnvironment,\n", + " system_terminator,\n", + ")\n", + "\n", + "from ipywidgets import IntProgress\n", + "from IPython.display import display\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "99f91c02", + "metadata": {}, + "source": [ + "## Helpers" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b3044b1d", + "metadata": {}, + "outputs": [], + "source": [ + "# Solver options:\n", + "\n", + "options = {\n", + " \"nsteps\": 15000,\n", + " \"store_states\": True,\n", + " \"rtol\": 1e-12,\n", + " \"atol\": 1e-12,\n", + " \"min_step\": 1e-18,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "ceaf41a4", + "metadata": {}, + "source": [ + "## System and bath definition" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "42a1fd73", + "metadata": {}, + "outputs": [], + "source": [ + "@dataclasses.dataclass\n", + "class SystemParams:\n", + " \"\"\" System parameters and Hamiltonian. \"\"\"\n", + " epsilon: float = 1.0\n", + " J12: float = 0.1\n", + "\n", + " def H(self):\n", + " \"\"\" Return the Hamiltonian for the system.\n", + "\n", + " The system consists of two qubits with Hamiltonians (H1 and H2)\n", + " and an interaction term (H12).\n", + " \"\"\"\n", + " H1 = self.epsilon / 2 * (\n", + " qt.tensor(qt.sigmaz() + qt.identity(2), qt.identity(2))\n", + " )\n", + " H2 = self.epsilon / 2 * (\n", + " qt.tensor(qt.identity(2), qt.sigmaz() + qt.identity(2))\n", + " )\n", + " H12 = self.J12 * (\n", + " qt.tensor(qt.sigmap(), qt.sigmam()) +\n", + " qt.tensor(qt.sigmam(), qt.sigmap())\n", + " )\n", + " return H1 + H2 + H12\n", + "\n", + " def replace(self, **kw):\n", + " return dataclasses.replace(self, **kw)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "101b47c7", + "metadata": {}, + "outputs": [], + "source": [ + "@dataclasses.dataclass\n", + "class BathParams:\n", + " \"\"\" Bath parameters. \"\"\"\n", + " sign: str # + or -\n", + " qubit: int # 0 or 1\n", + "\n", + " gamma: float = 2.0\n", + " lam: float = 0.05\n", + " Tbar: float = 2.0\n", + " Tdelta: float = 0.01\n", + "\n", + " def __post_init__(self):\n", + " # T = Tbar +- Tdelta * Tbar:\n", + " assert self.sign in (\"+\", \"-\")\n", + " sign = +1 if self.sign == \"+\" else -1\n", + " self.T = self.Tbar + sign * self.Tdelta * self.Tbar\n", + " # qubit\n", + " assert self.qubit in (0, 1)\n", + "\n", + " def Q(self):\n", + " \"\"\" Coupling operator for the bath. \"\"\"\n", + " Q = [qt.identity(2), qt.identity(2)]\n", + " Q[self.qubit] = qt.sigmax()\n", + " return qt.tensor(Q)\n", + "\n", + " def bath(self, Nk, tag=None):\n", + " env=DrudeLorentzEnvironment(\n", + " lam=self.lam, gamma=self.gamma, T=self.T, tag=tag\n", + " )\n", + " env_approx,delta=env.approx_by_pade(Nk=Nk,compute_delta=True,tag=tag)\n", + " return (env_approx,self.Q()),system_terminator(self.Q(),delta),delta\n", + "\n", + " def replace(self, **kw):\n", + " return dataclasses.replace(self, **kw)" + ] + }, + { + "cell_type": "markdown", + "id": "0e6b9799", + "metadata": {}, + "source": [ + "## Heat currents\n", + "\n", + "Following Ref. \\[2\\], we consider two possible definitions of the heat currents from the qubits into the baths.\n", + "The so-called bath heat currents are $j_{\\text{B}}^K = \\partial_t \\langle H_{\\text{B}}^K \\rangle$ and the system heat currents are $j_{\\text{S}}^K = \\mathrm i\\, \\langle [H_{\\text{S}}, Q_K] X_{\\text{B}}^K \\rangle$.\n", + "As shown in Ref. \\[2\\], they can be expressed in terms of the HEOM ADOs as follows:\n", + "$$ \\begin{aligned} \\mbox{} \\\\\n", + " j_{\\text{B}}^K &= \\!\\!\\sum_{\\substack{\\mathbf n\\\\ \\text{Level 1}\\\\ \\text{Bath $K$}}}\\!\\! \\nu[\\mathbf n] \\operatorname{tr}\\bigl[ Q_K \\rho_{\\mathbf n} \\bigr] - 2 C_I^K(0) \\operatorname{tr}\\bigl[ Q_k^2 \\rho \\bigr] + \\Gamma_{\\text{T}}^K \\operatorname{tr}\\bigl[ [[H_{\\text{S}}, Q_K], Q_K]\\, \\rho \\bigr] , \\\\[.5em]\n", + " j_{\\text{S}}^K &= \\mathrm i\\!\\! \\sum_{\\substack{\\mathbf n\\\\ \\text{Level 1}\\\\ \\text{Bath $k$}}}\\!\\! \\operatorname{tr}\\bigl[ [H_{\\text{S}}, Q_K]\\, \\rho_{\\mathbf n} \\bigr] + \\Gamma_{\\text{T}}^K \\operatorname{tr}\\bigl[ [[H_{\\text{S}}, Q_K], Q_K]\\, \\rho \\bigr] . \\\\ \\mbox{}\n", + "\\end{aligned} $$\n", + "The sums run over all level-$1$ multi-indices $\\mathbf n$ with one excitation corresponding to the K-th bath, $\\nu[\\mathbf n]$ is the corresponding (negative) exponent of the bath auto-correlation function $C^K(t)$, and $\\Gamma_{\\text{T}}^K$ is the Ishizaki-Tanimura terminator (i.e., a correction term accounting for the error introduced by approximating the correlation function with a finite sum of exponential terms).\n", + "In the expression for the bath heat currents, we left out terms involving $[Q_1, Q_2]$, which is zero in this example.\n", + "\n", + "   \\[2\\] Kato and Tanimura, [J. Chem. Phys. **145**, 224105](https://doi.org/10.1063/1.4971370) (2016)." + ] + }, + { + "cell_type": "markdown", + "id": "0e6edfc3", + "metadata": {}, + "source": [ + "In QuTiP, these currents can be conveniently calculated as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "729a27ba", + "metadata": {}, + "outputs": [], + "source": [ + "def bath_heat_current(bath_tag, ado_state, hamiltonian, coupling_op, delta=0):\n", + " \"\"\"\n", + " Bath heat current from the system into the heat bath with the given tag.\n", + "\n", + " Parameters\n", + " ----------\n", + " bath_tag : str, tuple or any other object\n", + " Tag of the heat bath corresponding to the current of interest.\n", + "\n", + " ado_state : HierarchyADOsState\n", + " Current state of the system and the environment (encoded in the ADOs).\n", + "\n", + " hamiltonian : Qobj\n", + " System Hamiltonian at the current time.\n", + "\n", + " coupling_op : Qobj\n", + " System coupling operator at the current time.\n", + "\n", + " delta : float\n", + " The prefactor of the \\\\delta(t) term in the correlation function (the\n", + " Ishizaki-Tanimura terminator).\n", + " \"\"\"\n", + " l1_labels = ado_state.filter(level=1, tags=[bath_tag])\n", + " a_op = 1j * (hamiltonian * coupling_op - coupling_op * hamiltonian)\n", + "\n", + " result = 0\n", + " cI0 = 0 # imaginary part of bath auto-correlation function (t=0)\n", + " for label in l1_labels:\n", + " [exp] = ado_state.exps(label)\n", + " result += exp.vk * (coupling_op * ado_state.extract(label)).tr()\n", + "\n", + " if exp.type == CFExponent.types['I']:\n", + " cI0 += exp.ck\n", + " elif exp.type == CFExponent.types['RI']:\n", + " cI0 += exp.ck2\n", + "\n", + " result -= 2 * cI0 * (coupling_op * coupling_op * ado_state.rho).tr()\n", + " if delta != 0:\n", + " result -= (\n", + " 1j * delta *\n", + " ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr()\n", + " )\n", + " return result\n", + "\n", + "\n", + "def system_heat_current(\n", + " bath_tag, ado_state, hamiltonian, coupling_op, delta=0,\n", + "):\n", + " \"\"\"\n", + " System heat current from the system into the heat bath with the given tag.\n", + "\n", + " Parameters\n", + " ----------\n", + " bath_tag : str, tuple or any other object\n", + " Tag of the heat bath corresponding to the current of interest.\n", + "\n", + " ado_state : HierarchyADOsState\n", + " Current state of the system and the environment (encoded in the ADOs).\n", + "\n", + " hamiltonian : Qobj\n", + " System Hamiltonian at the current time.\n", + "\n", + " coupling_op : Qobj\n", + " System coupling operator at the current time.\n", + "\n", + " delta : float\n", + " The prefactor of the \\\\delta(t) term in the correlation function (the\n", + " Ishizaki-Tanimura terminator).\n", + " \"\"\"\n", + " l1_labels = ado_state.filter(level=1, tags=[bath_tag])\n", + " a_op = 1j * (hamiltonian * coupling_op - coupling_op * hamiltonian)\n", + "\n", + " result = 0\n", + " for label in l1_labels:\n", + " result += (a_op * ado_state.extract(label)).tr()\n", + "\n", + " if delta != 0:\n", + " result -= (\n", + " 1j * delta *\n", + " ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr()\n", + " )\n", + " return result" + ] + }, + { + "cell_type": "markdown", + "id": "bc957c4f", + "metadata": {}, + "source": [ + "Note that at long times, we expect $j_{\\text{B}}^1 = -j_{\\text{B}}^2$ and $j_{\\text{S}}^1 = -j_{\\text{S}}^2$ due to energy conservation. At long times, we also expect $j_{\\text{B}}^1 = j_{\\text{S}}^1$ and $j_{\\text{B}}^2 = j_{\\text{S}}^2$ since the coupling operators commute, $[Q_1, Q_2] = 0$. Hence, all four currents should agree in the long-time limit (up to a sign). This long-time value is what was analyzed in Ref. \\[2\\]." + ] + }, + { + "cell_type": "markdown", + "id": "24fbd7d1", + "metadata": {}, + "source": [ + "## Simulations" + ] + }, + { + "cell_type": "markdown", + "id": "a0d6eb39", + "metadata": {}, + "source": [ + "For our simulations, we will represent the bath spectral densities using the first term of their Padé decompositions, and we will use $7$ levels of the HEOM hierarchy." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "328981e7", + "metadata": {}, + "outputs": [], + "source": [ + "Nk = 1\n", + "NC = 7" + ] + }, + { + "cell_type": "markdown", + "id": "a07c684d", + "metadata": {}, + "source": [ + "### Time Evolution\n", + "\n", + "We fix $J_{12} = 0.1 \\epsilon$ (as in Fig. 3(a-ii) of Ref. \\[2\\]) and choose the fixed coupling strength $\\lambda_1 = \\lambda_2 = J_{12}\\, /\\, (2\\epsilon)$ (corresponding to $\\bar\\zeta = 1$ in Ref. \\[2\\]).\n", + "Using these values, we will study the time evolution of the system state and the heat currents." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "398ae770", + "metadata": {}, + "outputs": [], + "source": [ + "# fix qubit-qubit and qubit-bath coupling strengths\n", + "sys = SystemParams(J12=0.1)\n", + "bath_p1 = BathParams(qubit=0, sign=\"+\", lam=sys.J12 / 2)\n", + "bath_p2 = BathParams(qubit=1, sign=\"-\", lam=sys.J12 / 2)\n", + "\n", + "# choose arbitrary initial state\n", + "rho0 = qt.tensor(qt.identity(2), qt.identity(2)) / 4\n", + "\n", + "# simulation time span\n", + "tlist = np.linspace(0, 50, 250)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7c58e513", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 7.60s*] Elapsed 7.60s / Remaining 00:00:00:00\n" + ] + } + ], + "source": [ + "H = sys.H()\n", + "\n", + "bath1,b1term,b1delta = bath_p1.bath(Nk, tag='bath 1')\n", + "Q1 = bath_p1.Q()\n", + "\n", + "bath2,b2term,b2delta = bath_p2.bath(Nk, tag='bath 2')\n", + "Q2 = bath_p2.Q()\n", + "\n", + "\n", + "solver = HEOMSolver(\n", + " qt.liouvillian(H) + b1term + b2term,\n", + " [bath1, bath2],\n", + " max_depth=NC,\n", + " options=options,\n", + ")\n", + "\n", + "result = solver.run(rho0, tlist, e_ops=[\n", + " qt.tensor(qt.sigmaz(), qt.identity(2)),\n", + " lambda t, ado: bath_heat_current('bath 1', ado, H, Q1, b1delta),\n", + " lambda t, ado: bath_heat_current('bath 2', ado, H, Q2, b2delta),\n", + " lambda t, ado: system_heat_current('bath 1', ado, H, Q1, b1delta),\n", + " lambda t, ado: system_heat_current('bath 2', ado, H, Q2, b2delta),\n", + "])" + ] + }, + { + "cell_type": "markdown", + "id": "3cc11ecd", + "metadata": {}, + "source": [ + "We first plot $\\langle \\sigma_z^1 \\rangle$ to see the time evolution of the system state:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "56f49d60", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mcditoos/anaconda3/envs/qutip-dev/lib/python3.12/site-packages/matplotlib/cbook.py:1762: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return math.isfinite(val)\n", + "/home/mcditoos/anaconda3/envs/qutip-dev/lib/python3.12/site-packages/matplotlib/cbook.py:1398: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return np.asarray(x, float)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(figsize=(8, 8))\n", + "axes.plot(tlist, result.expect[0], 'r', linewidth=2)\n", + "axes.set_xlabel('t', fontsize=28)\n", + "axes.set_ylabel(r\"$\\langle \\sigma_z^1 \\rangle$\", fontsize=28);" + ] + }, + { + "cell_type": "markdown", + "id": "5c357130", + "metadata": {}, + "source": [ + "We find a rather quick thermalization of the system state. For the heat currents, however, it takes a somewhat longer time until they converge to their long-time values:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "98bf7d9f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8))\n", + "\n", + "ax1.plot(\n", + " tlist, -np.real(result.expect[1]),\n", + " color='darkorange', label='BHC (bath 1 -> system)',\n", + ")\n", + "ax1.plot(\n", + " tlist, np.real(result.expect[2]),\n", + " '--', color='darkorange', label='BHC (system -> bath 2)',\n", + ")\n", + "ax1.plot(\n", + " tlist, -np.real(result.expect[3]),\n", + " color='dodgerblue', label='SHC (bath 1 -> system)',\n", + ")\n", + "ax1.plot(\n", + " tlist, np.real(result.expect[4]),\n", + " '--', color='dodgerblue', label='SHC (system -> bath 2)',\n", + ")\n", + "\n", + "ax1.set_xlabel('t', fontsize=28)\n", + "ax1.set_ylabel('j', fontsize=28)\n", + "ax1.set_ylim((-0.05, 0.05))\n", + "ax1.legend(loc=0, fontsize=12)\n", + "\n", + "ax2.plot(\n", + " tlist, -np.real(result.expect[1]),\n", + " color='darkorange', label='BHC (bath 1 -> system)',\n", + ")\n", + "ax2.plot(\n", + " tlist, np.real(result.expect[2]),\n", + " '--', color='darkorange', label='BHC (system -> bath 2)',\n", + ")\n", + "ax2.plot(\n", + " tlist, -np.real(result.expect[3]),\n", + " color='dodgerblue', label='SHC (bath 1 -> system)',\n", + ")\n", + "ax2.plot(\n", + " tlist, np.real(result.expect[4]),\n", + " '--', color='dodgerblue', label='SHC (system -> bath 2)',\n", + ")\n", + "\n", + "ax2.set_xlabel('t', fontsize=28)\n", + "ax2.set_xlim((20, 50))\n", + "ax2.set_ylim((0, 0.0002))\n", + "ax2.legend(loc=0, fontsize=12);" + ] + }, + { + "cell_type": "markdown", + "id": "26a2fb9c", + "metadata": {}, + "source": [ + "### Steady-state currents\n", + "\n", + "Here, we try to reproduce the HEOM curves in Fig. 3(a) of Ref. \\[1\\] by varying the coupling strength and finding the steady state for each coupling strength." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2642aa43", + "metadata": {}, + "outputs": [], + "source": [ + "def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options):\n", + " \"\"\" Calculate the steady sate heat currents for the given system and\n", + " bath.\n", + " \"\"\"\n", + "\n", + " bath1,b1term,b1delta = bath_p1.bath(Nk, tag='bath 1')\n", + " Q1 = bath_p1.Q()\n", + "\n", + " bath2,b2term,b2delta = bath_p2.bath(Nk, tag='bath 2')\n", + " Q2 = bath_p2.Q()\n", + "\n", + " solver = HEOMSolver(\n", + " qt.liouvillian(sys.H()) + b1term + b2term,\n", + " [bath1, bath2],\n", + " max_depth=NC,\n", + " options=options\n", + " )\n", + "\n", + " _, steady_ados = solver.steady_state()\n", + "\n", + " return (\n", + " bath_heat_current('bath 1', steady_ados, sys.H(), Q1, b1delta),\n", + " bath_heat_current('bath 2', steady_ados, sys.H(), Q2, b2delta),\n", + " system_heat_current('bath 1', steady_ados, sys.H(), Q1, b1delta),\n", + " system_heat_current('bath 2', steady_ados, sys.H(), Q2, b2delta),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5a66cb86", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6c88dbc2d755472ea46368067ef8a5e2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "IntProgress(value=0, max=30)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define number of points to use for the plot\n", + "plot_points = 10 # use 100 for a smoother curve\n", + "\n", + "# Range of relative coupling strengths\n", + "# Chosen so that zb_max is maximum, centered around 1 on a log scale\n", + "zb_max = 4 # use 20 to see more of the current curve\n", + "zeta_bars = np.logspace(\n", + " -np.log(zb_max),\n", + " np.log(zb_max),\n", + " plot_points,\n", + " base=np.e,\n", + ")\n", + "\n", + "# Setup a progress bar\n", + "progress = IntProgress(min=0, max=(3 * plot_points))\n", + "display(progress)\n", + "\n", + "\n", + "def calculate_heat_current(J12, zb, Nk, progress=progress):\n", + " \"\"\" Calculate a single heat current and update the progress bar. \"\"\"\n", + " # Estimate appropriate HEOM max_depth from coupling strength\n", + " NC = 7 + int(max(zb * J12 - 1, 0) * 2)\n", + " NC = min(NC, 20)\n", + " # the four currents are identical in the steady state\n", + " j, _, _, _ = heat_currents(\n", + " sys.replace(J12=J12),\n", + " bath_p1.replace(lam=zb * J12 / 2),\n", + " bath_p2.replace(lam=zb * J12 / 2),\n", + " Nk, NC, options=options,\n", + " )\n", + " progress.value += 1\n", + " return j\n", + "\n", + "\n", + "# Calculate steady state currents for range of zeta_bars\n", + "# for J12 = 0.01, 0.1 and 0.5:\n", + "j1s = [\n", + " calculate_heat_current(0.01, zb, Nk)\n", + " for zb in zeta_bars\n", + "]\n", + "j2s = [\n", + " calculate_heat_current(0.1, zb, Nk)\n", + " for zb in zeta_bars\n", + "]\n", + "j3s = [\n", + " calculate_heat_current(0.5, zb, Nk)\n", + " for zb in zeta_bars\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "7b9e3be7", + "metadata": {}, + "source": [ + "## Create Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4f5b2a1d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(figsize=(12, 7))\n", + "\n", + "axes.plot(\n", + " zeta_bars, -1000 * 100 * np.real(j1s),\n", + " 'b', linewidth=2, label=r\"$J_{12} = 0.01\\, \\epsilon$\",\n", + ")\n", + "axes.plot(\n", + " zeta_bars, -1000 * 10 * np.real(j2s),\n", + " 'r--', linewidth=2, label=r\"$J_{12} = 0.1\\, \\epsilon$\",\n", + ")\n", + "axes.plot(\n", + " zeta_bars, -1000 * 2 * np.real(j3s),\n", + " 'g-.', linewidth=2, label=r\"$J_{12} = 0.5\\, \\epsilon$\",\n", + ")\n", + "\n", + "axes.set_xscale('log')\n", + "axes.set_xlabel(r\"$\\bar\\zeta$\", fontsize=30)\n", + "axes.set_xlim((zeta_bars[0], zeta_bars[-1]))\n", + "\n", + "axes.set_ylabel(\n", + " r\"$j_{\\mathrm{ss}}\\; /\\; (\\epsilon J_{12}) \\times 10^3$\",\n", + " fontsize=30,\n", + ")\n", + "axes.set_ylim((0, 2))\n", + "\n", + "axes.legend(loc=0);" + ] + }, + { + "cell_type": "markdown", + "id": "3fc15108", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e706b00e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "QuTiP: Quantum Toolbox in Python\n", + "================================\n", + "Copyright (c) QuTiP team 2011 and later.\n", + "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", + "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", + "Original developers: R. J. Johansson & P. D. Nation.\n", + "Previous lead developers: Chris Granade & A. Grimsmo.\n", + "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", + "\n", + "QuTiP Version: 5.1.0.dev0+7941773\n", + "Numpy Version: 2.1.3\n", + "Scipy Version: 1.14.1\n", + "Cython Version: 3.0.9\n", + "Matplotlib Version: 3.9.2\n", + "Python Version: 3.12.7\n", + "Number of CPUs: 16\n", + "BLAS Info: Generic\n", + "INTEL MKL Ext: None\n", + "Platform Info: Linux (x86_64)\n", + "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", + "================================================================================\n", + "Please cite QuTiP in your publication.\n", + "================================================================================\n", + "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" + ] + } + ], + "source": [ + "qt.about()" + ] + }, + { + "cell_type": "markdown", + "id": "04b8717d", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "2380bad1", + "metadata": {}, + "outputs": [], + "source": [ + "assert 1 == 1" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "qutip-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-4-dynamical-decoupling.ipynb b/tutorials-v5/heom/heom-4-dynamical-decoupling.ipynb new file mode 100644 index 00000000..04c42d0a --- /dev/null +++ b/tutorials-v5/heom/heom-4-dynamical-decoupling.ipynb @@ -0,0 +1,904 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "483dcf2f", + "metadata": {}, + "source": [ + "# HEOM 4: Dynamical decoupling of a non-Markovian environment" + ] + }, + { + "cell_type": "markdown", + "id": "5c3345d9", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Following [Lorenza Viola and Seth Lloyd](https://arxiv.org/abs/quant-ph/9803057) we consider an example of dynamical decoupling.\n", + "We choose a drive which performs pi rotations, interspersed with short periods where the bath causes dephasing.\n", + "\n", + "We first show the standard example of equally spaced pulses, and then consider the 'optimal' Uhrig spacing ([Götz S. Uhrig Phys. Rev. Lett. 98, 100504 (2007)](https://arxiv.org/abs/quant-ph/0609203))." + ] + }, + { + "cell_type": "markdown", + "id": "c2afc99b", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8b1af4e8", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " QobjEvo,\n", + " basis,\n", + " expect,\n", + " ket2dm,\n", + " sigmax,\n", + " sigmaz,\n", + " DrudeLorentzEnvironment\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver\n", + ")\n", + "\n", + "from ipywidgets import IntProgress\n", + "from IPython.display import display\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "4a9cdc73", + "metadata": {}, + "source": [ + "## Solver options" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "557d9442", + "metadata": {}, + "outputs": [], + "source": [ + "# Solver options:\n", + "\n", + "# The max_step must be set to a short time than the\n", + "# length of the shortest pulse, otherwise the solver\n", + "# might skip over a pulse.\n", + "\n", + "options = {\n", + " \"nsteps\": 1500,\n", + " \"store_states\": True,\n", + " \"rtol\": 1e-12,\n", + " \"atol\": 1e-12,\n", + " \"max_step\": 1 / 20.0,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "a3388380", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "Now we define the system and bath properties and the HEOM parameters. The system is a single stationary qubit with $H = 0$ and the bath is a bosonic bath with a Drude-Lorentz spectrum." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4e21050b", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the system Hamlitonian.\n", + "#\n", + "# The system isn't evolving by itself, so the Hamiltonian is 0 (with the\n", + "# correct dimensions):\n", + "\n", + "H_sys = 0 * sigmaz()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "62ebb14f", + "metadata": {}, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresponding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresponding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0217cff8", + "metadata": {}, + "outputs": [], + "source": [ + "# Properties for the Drude-Lorentz bath\n", + "\n", + "lam = 0.0005\n", + "gamma = 0.005\n", + "T = 0.05\n", + "\n", + "# bath-system coupling operator:\n", + "Q = sigmaz()\n", + "\n", + "# number of terms to keep in the expansion of the bath correlation function:\n", + "Nk = 3\n", + "\n", + "env = DrudeLorentzEnvironment(lam=lam, gamma=gamma,T=T)\n", + "env_approx=env.approx_by_pade(Nk=Nk)\n", + "bath=(env_approx,Q)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5d5f5c49", + "metadata": {}, + "outputs": [], + "source": [ + "# HEOM parameters\n", + "\n", + "# number of layers to keep in the hierarchy:\n", + "NC = 6" + ] + }, + { + "cell_type": "markdown", + "id": "29d93a37", + "metadata": {}, + "source": [ + "To perform the dynamic decoupling from the environment, we will drive the system with a time-dependent pulse that couples to the system via the $\\sigma_x$ operator. The area under the pulse will usual be set to $\\pi / 2$ so that the pulse flips the qubit state.\n", + "\n", + "Below we define a function that returns the pulse (which is itself a function):" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e0e2b7d0", + "metadata": {}, + "outputs": [], + "source": [ + "def drive(amplitude, delay, integral):\n", + " \"\"\" Coefficient of the drive as a function of time.\n", + "\n", + " The drive consists of a series of constant pulses with\n", + " a fixed delay between them.\n", + "\n", + " Parameters\n", + " ----------\n", + " amplitude : float\n", + " The amplitude of the drive during the pulse.\n", + " delay : float\n", + " The time delay between successive pulses.\n", + " integral : float\n", + " The integral of the pulse. This determines\n", + " the duration of each pulse with the duration\n", + " equal to the integral divided by the amplitude.\n", + " \"\"\"\n", + " duration = integral / amplitude\n", + " period = duration + delay\n", + "\n", + " def pulse(t):\n", + " t = t % period\n", + " if t < duration:\n", + " return amplitude\n", + " return 0\n", + "\n", + " return pulse\n", + "\n", + "\n", + "H_drive = sigmax()" + ] + }, + { + "cell_type": "markdown", + "id": "83f996d1", + "metadata": {}, + "source": [ + "## Plot the spectral density\n", + "\n", + "Let's start by plotting the spectral density of our Drude-Lorentz bath:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c9f75790", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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b+XgBAAAEi+SEsLiP/WOLxRQYzFOy2gdz/kEGAAAgWCQnhMzj8qjtO53FFLjySvrAZI3XdqWqQaPs+2NQHQAAQGIimCNk7ha3fMH8S60sGfmp2q4J3idDzpF0ZXSLAwAASFC0siBk3mDu9eUZcyUleR8S6yUCAACEgGCOkAUEc3Mnd/dMSfH+STAHAAAIGsEcIQsM5p3c3TM11ftnY2OUKgIAAEh89JgjZC6nP5hbO5kxv6H+9/pU+bKWS29FszAAAIAERjBHyGxml76t5XLLolMGuCSdEfDzTS2naKtOks3ljE2BAAAACYhgjpClJbu1XN/1PhlzqdS2fUxqklNqllpkl6vZJauDjxkAAMDx0GOO0Ln9rSyyWDr8OCXJ5dtuqOACUAAAgGAQzBG64wTzVHurb7uhqjkaFQEAACQ8gjlCd9xg7p8xbzxCMAcAAAgGwRwh+3yPSRmqUZaO6Pubv9vh56kO/0otzJgDAAAEh6vyELJWp0d1ypAkNXrsHX6ekuxf27yhuiVqdQEAACQyZswRMleLf0bc2skNhlJT2gXzo60dfg4AAICOCOYImbvVH8wtlk6CeSrBHAAAIFS0siBkAcG8k69208ZW6dY3lypFjRrXf0oUKwMAAEhcBHOE7Hgz5jNPPaqZut37pN+j0SoLAAAgodHKgpAdb8Zcqan+7cbGyBcEAADQCxDMEbLAGfNOBqSk+LcbuPMnAABAMGhlQciOF8yNlFS1yKYGpcp6xHVsYUUAAAB0hxlzhMzt8veVd9ZjvvGLXDnkVLaO6O5/zohmaQAAAAmLGXOErHBIrVZontyyaPyEsyXNCvh5Spb/pkMNzXz3AwAACAbBHCEbmNGseXrO+2Tw2A4/T82y+bYbmjprQgcAAMCXMZ2J0Lnd/u1OmsxTc5J92w1OvvsBAAAEg2CO0B0nmKdktwvmLQRzAACAYPT6YP7QQw+poKBADodDRUVFWr9+fbfj161bp6KiIjkcDo0cOVKPPPJIhzErV65UYWGh7Ha7CgsL9eKLL57QeW+88UaZTCb94Q9/CPn3i4WqarOKdZbe1xkqb8rs8PPUAf7lEhtabR1+DgAAgI56dTBfsWKFFi1apDvuuEMlJSWaPn26LrroIpWWlnY6fs+ePbr44os1ffp0lZSU6Pbbb9dNN92klStX+sYUFxdr/vz5WrBggbZt26YFCxZo3rx52rhxY1jnfemll7Rx40YNGjSo5/8CImTth/01TcWaovf11NaTO/zcYrfKrmZJUiPBHAAAICgmwzA6rnfXS0yZMkWTJk3Sww8/7Ns3fvx4XXbZZVq6dGmH8UuWLNErr7yiHTt2+PYtXLhQ27ZtU3FxsSRp/vz5qq2t1euvv+4bM3fuXGVlZemZZ54J6bwHDhzQlClT9MYbb+iSSy7RokWLtGjRoqB/v9raWmVmZqqmpkYZGdFbLXzFzRt09f3TJEm/u3StbnlpVocx/c3VqjayNDrpC+1uGRG12gAAAOJJKHmt186Yt7S0aPPmzZo9e3bA/tmzZ2vDhg2dvqa4uLjD+Dlz5mjTpk1qbW3tdkzbMYM9r8fj0YIFC/TTn/5UEyZMCO+XjJHAdcxNnY5JNTdJkhrcjqjUBAAAkOh67ZV5lZWVcrvdys3NDdifm5ur8vLyTl9TXl7e6XiXy6XKykrl5+d3OabtmMGed9myZbJarbrpppuC/p2cTqecTqfveW1tbdCv7Ulud/tg3vmYVEuz5JYaPMmdDwAAAECAXhvM25hMgTO6hmF02He88V/eH8wxuxuzefNm/fGPf9SWLVu6reXLli5dqnvvvTfo8ZHiab8oi7Xz+p8YcY88u3YrzdIsqSQ6hQEAACSwXtvKkpOTI4vF0mF2vKKiosNsdpu8vLxOx1utVmVnZ3c7pu2YwZx3/fr1qqio0LBhw2S1WmW1WrV37179+Mc/1ogRI7r8nW677TbV1NT4Hvv27Tv+X0QEeNrNmJu7+ARNGbhHU/WeTnZvlVpaolMYAABAAuu1wdxms6moqEirV68O2L969WpNmzat09dMnTq1w/g333xTkydPVlJSUrdj2o4ZzHkXLFigDz/8UFu3bvU9Bg0apJ/+9Kd64403uvyd7Ha7MjIyAh6x0H4Zc3MXPeZKTfVvNzREtiAAAIBeoFe3sixevFgLFizQ5MmTNXXqVD366KMqLS3VwoULJXlnoA8cOKAnn3xSkncFlgceeECLFy/WDTfcoOLiYi1fvty32ook3XzzzZoxY4aWLVumSy+9VC+//LLWrFmjd955J+jzZmdn+2bg2yQlJSkvL0/jxo2L9F/LCfME0WMeEMwbG6WsrMgWBQAAkOB6dTCfP3++qqqqdN9996msrEwTJ07UqlWrNHz4cElSWVlZwNriBQUFWrVqlW655RY9+OCDGjRokO6//35dccUVvjHTpk3Ts88+qzvvvFN33XWXRo0apRUrVmjKlClBnzfReTz+7a5mzLc5T9IuXakGperSg03KGhyl4gAAABJUr17HvLeL1TrmD81fpx/+baYk6ckb39GCR87pMOb7E9bpke3eMVue/lSnX3NS1OoDAACIF6xjjoj67lkf66gyVaX+uuqs/Z2OSUn2f99rrHZ2OgYAAAB+vbqVBZFhM7tk07E11Lu4f1Bqin+7ocYV+aIAAAASHDPmCF3Asiydf4RSU/0z5g1HWyNdEQAAQMIjmCN07a/+7GJZlpRU/0erodbd6RgAAAD40cqCkL3zWb7W6G5Z5NZVBzPU2WWdqekEcwAAgFAQzBGy9Z8P1r36hiTplLKNnQfzDP9MemO9p5MRAAAAaI9WFoQsYB1za+frmKdk+L/zceNPAACA4yOYI2SB1352HsxT+yX5tgnmAAAAx0crC0IWcO1nFzPmaVlJSlGDUtQom7spSpUBAAAkLoI5QhbQymLpPJifeabUoDTvk5O/LemiyBcGAACQwGhlQcgCWlm6COZKaXeHocbGyBYEAADQCxDMEbJgWlmUmurfpskcAADguAjmCFngqixdfIQI5gAAACGhxxwhc3v8s+RdtbIYySn6vh5WvdI0eIehZdEqDgAAIEERzBGyYWlHNEXvySOzMtJsnY4xWcx6UtepSSmaeGQ3wRwAAOA4aGVByG46dZ3e01S9ryk69SRnl+NSTd6LPhvdnYd3AAAA+BHMEbqAJvOuP0Ip5mZJUoMnOdIVAQAAJDyCOUIXeOvPLoelWryz6QRzAACA4yOYI3QB6yVauhyWavXOmDcqRYbHiHRVAAAACY1gjpD915YLNU3v6hyt12cHHF2OS01qkSR5ZJGztutedAAAALAqC8Lw76MDVKxpkqSmlt1djku1tfq2Gw43ytGv6xAPAADQ1zFjjpC170qxJHVz8afN34veUNkUyZIAAAASHsEcIfMEcYMhSUp1uHzbjdW0sgAAAHSHVhaEzN1+tURr19/tpg7ep5bPnlaKGpVqnBmFygAAABIXwRwhaz9j3l0ry41nlujGt3/jfZK+LtJlAQAAJDRaWRAyj9GulaWbGXOlpvq3GxoiWBEAAEDiI5gjZO4ge8yVkuLfbmyMYEUAAACJj2COkAW7Kgsz5gAAAMEjmCNkAauydNPKsnJHofJ1UBmq0f/+Y3g0SgMAAEhYXPyJkM0b9I5OLntDHpmVln5jl+M8NofKlS9Jqqs1uhwHAAAAgjnCcHX+Okmvep9kfr/LcSkZ/o8XLeYAAADdo5UFofO0X8i8649Qar8k3zYt5gAAAN0jmCN0brd/u7tgnmXzbdc3drN6CwAAAGhlQejqW2zyKF0WuZVitqiryJ2eY/e/ptESneIAAAASFDPmCNllW+9RpmqVpgY1Nnf9EUob6F/HvL6Z74AAAADdIZgjZO3XMe9uucT0XH8wr2tO6nIcAAAACOYIg9vwf2zMSV23qKTl+m8wVN9i63IcAAAACOYIg8fwd5V3d+dPi92qZHnXSaxrdUS8LgAAgERG4y9C1j6Yd9fKIkn/m/5TJdVVaWCmSdIzEa4MAAAgcRHMETK3xx/GTZbug/mCAf+Q6j6XXP0jXRYAAEBCo5UFIfMcWyDRLLdMx1uePD3d+2d9fWSLAgAASHAEc4SsrZXFLM9xRsofzFtavA8AAAB0ilYWhKxtVZZggnmppUDlcqpeaTq7ql72fFpaAAAAOsOMOULWNmNukfu4Y3/y2fc1Re/rfP1L5Z83Rro0AACAhMWMOUL21ODbVP9ZueRIlvTPbsemJ7t82/WHmyJcGQAAQOIimCNkp9g+lbRDsmUcd2x6qn9Wva7SGcGqAAAAEhutLAid51hvufn4H580/80/CeYAAADdIJgjdO5js+BBBPO2RVkkqb66NUIFAQAAJD5aWRCy1+pnqEmnKtll0yXHGZue4V/ovK7a1c1IAACAvo1gjpD98PC92qshyq2vUPlxxqZlWnzbdUePv4oLAABAX0UrC0LWdufPYJZLTM/yf/errwvihkQAAAB9FMEcIfO03WDIZBx3bHr/JN92XZ2pm5EAAAB9G8EcIWubMQ/mzp9p2Xbfdn1DxEoCAABIePSYI2Ruw9s3HsyM+aTJZlVogNJUL8fp10qaFdniAAAAEhTBHCHz95gff8bclpWqAar0Pqmvi2RZAAAACY1WFoTMc+xjYwpixjxgIfM6gjkAAEBXCOYIWVswNyuIYJ6W5t+ur49QRQAAAImPVhaEzDiWx82mIJY/tNn0W8vPdMSdoYzPUrUksqUBAAAkLII5QpZmapDbsCjF3BzU+KWen6lK2Rp5eC/BHAAAoAsEc4Rsf8o4qaFBGj1R0kfHHZ9uaVSVK1v17pTIFwcAAJCg6DFH6DzHWlhMwd0wKM3inVmvM1IjVREAAEDCI5gjdL4m8+A+PulJ3mDepBS5na5IVQUAAJDQCOYIXduMebDB3O70bdcf4vafAAAAnaHHHCH7XuuDMmRoRHmL7ghifJrdP0ted6hRmcMyI1ccAABAgiKYI2TLjf+QRxZNrtkeVDBPT/YH8/rDTZErDAAAIIHRyoKQGfJe9BnUOuaS0lPcvu26w8EtsQgAANDXEMwRMuPYxya4NVmktFT/HULrKp3djAQAAOi7aGVBSAyPIflmzI3uBx8zKr9RZ6lYGapVmtseweoAAAASFzPmCInH5W9fCTaYf/eCL1SsaXpDc3VmXmmkSgMAAEhoBHOEpH0wNwUZzJWR4d+ure3higAAAHoHgjlC4m1l8Qp2xjwgmNfU9HBFAAAAvQPBHCEJp5VFme3WLWfGHAAAoFMEc4QkoJUlyNd8VpOjaXpXE/WR7lg9KyJ1AQAAJDpWZUFILCaPrtRzMmTShKxGSacf/0VpaSrWBEnSKUdoZQEAAOgMwRwhsdsMPad53iejL5R03XFfkzEozbdd25QUocoAAAASG60sCI2n3d0+zcF9fDIGp/u2a5pZxxwAAKAzBHOEpn0wNwXXZW5Pt8muZklSbYsjElUBAAAkPII5QmO0W4klyBlzSco01UmSaltTeroiAACAXoFgjpAcqTI0RPs0VKX69keLgn5dhrVBklTjSTvOSAAAgL6Jiz8REleLRwc0RJJU1XIw6NdlJjVKrVKtkS7DY8hkDnaxRQAAgL6BGXOEJIwWc0lShs0pSXLLqsbKxh6uCgAAIPERzBESwx3GnT8lZThafNu1B+t7tCYAAIDegFYWhKT9nT9DCebfHPe+ppa/oAzVKtV1i6TcCFQHAACQuAjmCEm4rSxXnrxLWvfAsYP8R88WBQAA0AvQyoKQBLSymIOfMVdmpn+7trYHKwIAAOgdCOYIicftD+MhLaySkeHfrqnpuYIAAAB6CVpZEJL2PeamEHrMW1P76ahyVKNMZR10KjsSxQEAACQwZswREsMT3oz5859O1EAd1hj9W0/9a3AEKgMAAEhszJgjJAOyXHpM35Uhk0aNHinp7KBel5Gd5NuurQmhNx0AAKCPIJgjJBkpLn1Xy71PBl0X9OsyB9p921z7CQAA0BGtLAiN0W622xz8xydjoMO3XVPPxw4AAODLSEgITfuFzEMJ5nkpvu3aRv6hBgAA4MtISAhJc5Ohz1QokwxlNfVTfpCvyxyc5tuuabRFpjgAAIAERjBHSHZ9YdOp+kSSdEPJ23o0yNel5/uDea3T3s1IAACAvolWFoQk4M6fISyXaHVYlap6SVJtq+M4owEAAPoegjlCEnjnz9CWPcwwe4N5jSu1R2sCAADoDQjmCImn3Q2GTCF+ejKsjZKkGnfacUYCAAD0PQRzhMRwh3fnT0l6ftyd2qGTtFtjJLe7hysDAABIbFz8iZAEtLKYQ2tlmTjoiPTRTu+Tmhqpf/+eLA0AACChMWOOkLQP5iZTiFPmWVn+7aNHe6YgAACAXoJgjpAYnvBnzNWvn3+7urpnCgIAAOglen0wf+ihh1RQUCCHw6GioiKtX7++2/Hr1q1TUVGRHA6HRo4cqUceeaTDmJUrV6qwsFB2u12FhYV68cUXQz7vPffco5NOOkmpqanKysrSBRdcoI0bN57YLxsFnhPoMf/YdZIe03f13/qJ/v2Js4crAwAASGy9OpivWLFCixYt0h133KGSkhJNnz5dF110kUpLSzsdv2fPHl188cWaPn26SkpKdPvtt+umm27SypUrfWOKi4s1f/58LViwQNu2bdOCBQs0b968gFAdzHnHjh2rBx54QB999JHeeecdjRgxQrNnz9bhw4cj9xfSAwJbWUJ77esHT9X39Jh+pv/Wtg9DfDEAAEAvZzIMI8R+hMQxZcoUTZo0SQ8//LBv3/jx43XZZZdp6dKlHcYvWbJEr7zyinbs2OHbt3DhQm3btk3FxcWSpPnz56u2tlavv/66b8zcuXOVlZWlZ555JqzzSlJtba0yMzO1Zs0anX/++UH9fm2vqampUUZGRlCvOVHONetVceG1MmRS+k3fVtYf7wn6tY9d97a+99SMY9vr9d0npkeoSgAAgPgQSl7rtTPmLS0t2rx5s2bPnh2wf/bs2dqwYUOnrykuLu4wfs6cOdq0aZNaW1u7HdN2zHDO29LSokcffVSZmZk69dRTg/8lY8BudWuo9muY9ikrJbR2lKwB/kWAqqs83YwEAADoe3rtcomVlZVyu93Kzc0N2J+bm6vy8vJOX1NeXt7peJfLpcrKSuXn53c5pu2YoZz31Vdf1dVXX63Gxkbl5+dr9erVysnJ6fJ3cjqdcjr9Ybi2trbLsRHjaReoQ+xl6Zfr8G0fre61/1ADAAAQll47Y97my0v6GYbR7TJ/nY3/8v5gjhnMmHPPPVdbt27Vhg0bNHfuXM2bN08VFRVd1rZ06VJlZmb6HkOHDu1ybMS073wyh/bxycr3B/Pqml7/0QMAAAhJr01HOTk5slgsHWapKyoqOsxmt8nLy+t0vNVqVXZ2drdj2o4ZynlTU1M1evRonXXWWVq+fLmsVquWL1/e5e902223qaamxvfYt29fN38DkfHFQZt+p1v0B92s4oPDQ3ptv8Gpvu2j9ZaeLg0AACCh9dpgbrPZVFRUpNWrVwfsX716taZNm9bpa6ZOndph/JtvvqnJkycrKSmp2zFtxwznvG0MwwhoVfkyu92ujIyMgEe07SxN1o+PRfN/fDYmpNdmDUv3bVc32Hu6NAAAgITWa3vMJWnx4sVasGCBJk+erKlTp+rRRx9VaWmpFi5cKMk7A33gwAE9+eSTkrwrsDzwwANavHixbrjhBhUXF2v58uW+1VYk6eabb9aMGTO0bNkyXXrppXr55Ze1Zs0avfPOO0Gft6GhQb/85S/1ta99Tfn5+aqqqtJDDz2k/fv366qrrori31Do2reYh9jJosyh/i8SR5sJ5gAAAO316mA+f/58VVVV6b777lNZWZkmTpyoVatWafhwbwtGWVlZwNriBQUFWrVqlW655RY9+OCDGjRokO6//35dccUVvjHTpk3Ts88+qzvvvFN33XWXRo0apRUrVmjKlClBn9disejTTz/VE088ocrKSmVnZ+uMM87Q+vXrNWHChCj97YQn4AZDIQZzi92qDNWoVpmqbkk9/gsAAAD6kF69jnlvF4t1zF/9+fv66i/OlCT94vy1unPNrJBeP9n+oepbbBpl26/XnBf0fIEAAABxJJS81qtnzNHzTmTGXJI2nbRA+vBDyWSX1NxzhQEAACS4XnvxJyLjRHrMJUlZWd4/nU6pqalHagIAAOgNCOYIieE5sRlz9evn3z569ETLAQAA6DUI5giJp10wD/HGn15tM+aSVF194gUBAAD0EgRzhMQ4wVaWv5afp6/o7zpb76jk/daeKwwAACDBcfEnQpJub1GhPpFHZmWndX0zpK585hyq1zRLknTwiw90eg/XBwAAkKgI5gjJnFPK9MmxYK0pv5c0J6TX98vy979UH2rpucIAAAASHK0sCE37Ze/D6GXJGmDxbR+tdPVERQAAAL0CwRyhOcH1EvsNsPm2j1ZzbysAAIA2BHOEpn0wD2NZlqx8h2+7+mgP1AMAANBL0GOOkKz+KE+/0T9klkc/+sSjS0J8fb/8ZN/20VpLNyMBAAD6FoI5QnLgSLLe1DmSpEtr3g759f2Hp/u2j9Qn9VhdAAAAiY5WFoTE4253588wbjDUf2Q/33ZVQ3LXAwEAAPoYgjlCErAoiyX0ZO7o51CKGiRJR5wpPVUWAABAwqOVBSHxeNrNmIf5te6H6U/JXHdUw+3Vkpb1TGEAAAAJjmCOkHjc/m1TOL0skn496n+lrVulpiTJ+K+wVncBAADobWhlQUhOcBlzr/79vX+2tkr19SdcEwAAQG9AMEdIjHZN5uZwVzvMzvZvV1WdWEEAAAC9BMEcIWnfymIOs5VF2dlqlVUVGqDmg0d6pjAAAIAERzBHSHqileUX26+QTa3KVYXeXmcc/wUAAAB9ABd/IiSnDa7QLfqdPDJr7KDxYR0jLcv/sas66Oyp0gAAABIawRwhmVGwXzP0Y++T0SvCOkb2wHbB/JCrJ8oCAABIeLSyIDTte1nCXOYwO9/m2z5S6elmJAAAQN9BMEdoAm79Gd7HJ3tIsm+76ghrmAMAAEgEc4TIcHvki+ZhBvP+w9J821U1dFMBAABIBHOEaNlbZ8osQ2a59fL7+WEdI3tkpm+7qt7WzUgAAIC+g2COkLR1shgyyxTmOub9hmfKJG9veVVTSk+VBgAAkNAI5ghJwDrmlvCCucVmUZbpqCSpypneA1UBAAAkPoI5QhKwKEu4d/6UlG2tkSQdcWecaEkAAAC9AlfeISQ9cedPSfrzmKUyb/9I2UaV5NouWfkoAgCAvo00hJAErJYYZiuLJJ094oC0/T3vk+pqacCAE6wMAAAgsdHKgpD0RI+5JCk7279dVRX+cQAAAHoJgjlC0lM95urf37995Ej4xwEAAOglaGVBSDw91MryhXmkNuvrqlK2zv2oWWOm9UBxAAAACYwZc4TE6KFWltfLTtOVWqkb9ajWv5fUA5UBAAAkNoI5QvIfEz/QPzRHq3SRTh7ZEPZxBgzyh/HD5e6eKA0AACCh0cqCkIzNOqyxetP7JOv2sI8zcIT/jp+HD59oVQAAAImPGXOEpocWMh9QkObbrqjm+yEAAADBHKEJWJYl/B7zgeOyfNuHa+0nUhEAAECvwFQlQrKjaoBKNVsmGZrSmKTMMI+TVdBPFrnkllUVjWnHfwEAAEAvx4w5QvLItmmaqzc0R29q576U47+gC2arWTlm7/rlh50ZPVUeAABAwiKYIyQ9doMhSQOSaiRJFe5sGe0XSAcAAOiDCOYIidFDNxiSpIEpdZIkpxyqL68/oWMBAAAkOoI5QuIx/GH8RIN5XnqjBuqQJuhj1e09cqKlAQAAJDSCOULi6aE7f0rSX772Nx1Snj7WyRpkKjvBygAAABIbwRwhad8KfqI95qaBA/xPuMsQAADo4wjmCInH03OtLBo40L9dUXFixwIAAEhwx13H/LzzzotGHZIkh8Oh9PR0ZWVlafz48Zo0aZLOOeccmU7gRjboWT158acGMGMOAADQ5rjBfO3atTENxrm5ufrBD36gJUuWKCkpKWZ1wMvTg8H8C/dQ/VxP6LAGaPY/nLrl1hMsDgAAIIHFbSuLYRgyDEPl5eW6++67deaZZ6q6ujrWZfV5RrtVWU60x9yZlq2ndJ3+oYu0ee+A478AAACgFwsqmLeF5Gg+2phMJhmGoQ8//FDz5s2L2F8EgvN/0x6VRya5ZdbYUe4TOtbAk/r7tg/X2U+0NAAAgIR23FaWPXv2RKMOSZLb7VZTU5MOHz6snTt36o033tDf//53eTweGYahf/3rX1qzZo0uuOCCqNWEL/F4ZJJkkiGd4Ix5v+GZsqpVLiXpcGNqz9QHAACQoI4bzIcPHx6NOjqYNWuWbrzxRm3atElz5871tbE899xzBPNYCrj688Q6oUxmkwaYq1TmyVNFS78TqwsAACDBxW2PeZvJkyfrnnvu8bW3vPvuuzGuqI8LuMPQiX98BthqJEmH3f1ltL+yFAAAoI+J+2AuSZdffrlvu7y8PIaV4InPz9Fi/VY/1m9UXWs54eMNTKmXJLXIrpp9tSd8PAAAgER13FaWeDBo0CDfko1Hjx6NbTF93GsHT9dzmiZJWtR0UFkneLy8zCbpiHf70PYq9RueeYJHBAAASEwJMWNuMpmUlZWljIwMpaenx7qcPs1j9OCdPyXl5fhXdinbyYw5AADouxJixlySKisrY10CJHk87dYxt5z497r8fH9fefnnjSd8PAAAgESVMMEc8aGnZ8yLTnHpu688pjyV6yRbgXSsTQYAAKCvIZgjJO3XTemJYD7zPItm/uf3vE8sPzvh4wEAACSqhOgxR/zo6VYW5eX5t1lxBwAA9GEEc4Skp1tZAoJ5WdmJHw8AACBBEcwRkoBgbu2Bj0+/fpLdLqdsOrjPfdzhAAAAvRXBHCEx2jWZm8w9MGNuMuk0zxY55NSpO1ec+PEAAAASFBd/IiST0nfLU3VEhkxKsk3pkWM6bB6pVao0ctTa2KqklKQeOS4AAEAiIZgjJL8a/SfpizXeJ+l1PXLM/Ix6qcG7XbG9UoMn5/fIcQEAABIJrSwIjcfj3zb3zMcnL6vFt13+6dEeOSYAAECiIZgjNO2DuakHeswl5Q30H7NsV8/MwgMAACQagjlC0/7qzx6aMc8f7D9O+V5njxwTAAAg0RDMEZIFO27TBH2sifpIre4eamUZbvdtlx9gyUQAANA3cfEnQrKnKV/bNUGSZLb2TIjOG5Pu2y471DPtMQAAAImGGXOEpP0NhnpkHXNJ+eP7+bbLquxdDwQAAOjFCOYISbsO8x4L5rkTcnzbZXWpPXJMAACAREMrC0ISiRlzW5pNr2Z+QwNrdmloulvSlh45LgAAQCIhmCMkHsP7jyxmuSVZeuy4l4z6VNqyRaqwSG63ZOm5YwMAACQCWlkQkrZWFrM83Y4L2eDB3j/dbqmiomePDQAAkAAI5ghJWyuLKaDbvAcMGeLf3r+/Z48NAACQAGhlQUiMY8G8p2fM96edpPd0hQ5osM7fUKuJZ/To4QEAAOIeM+YIiUeRCeZrjkzSVXpei/RHrX2H74sAAKDvIQEhJD/J/j8dPuCU2WaV9OseO+6QsSm+7QP7e7hNBgAAIAEQzBGSBZmvSAe2S7Y09WQwHzyhn297/yE+lgAAoO+hlQWhMY7NZpt79qMz5PQBvu0D1dxkCAAA9D0Ec4TGc6y3vIeDefqgdKWrVpK0v6Ffjx4bAAAgERDMEZKDLTnar8EqU36PH3uI7bAk6UDrQBke+swBAEDfQjBHSGbs+6uGar9Orlnf48cenFYjSWpUqo7urenx4wMAAMQzgjlCErE7f0oa0r/Bt31g6+EePz4AAEA8I5gjJJ5jHxmzqedbTQbnun3b+z9hxhwAAPQtBHOExHPszp8m9XwwHzbcpDyVqUibZDnCjDkAAOhbCOYIiRGhO39K0veurVeZBmmTztCF6e/1+PEBAADiGcEcIfEYkWtl0fDh/u29e3v++AAAAHGMYI6QeBS5VhYNG+bfJpgDAIA+hmCOkPhbWSIQzDMypH79vNsEcwAA0MdYY10AEou/laXne8wl6T77L/W2xurgnkH6qMUti80SkfMAAADEG2bMEZKItrJI2qTJ+qcu0A4VqnzboYicAwAAIB4RzBGSdzK/oo81QasG3RCR4w8f2OTb3rulKiLnAAAAiEe0siAk40y7JFVLDldEjj98mCF95N3e+3GdpkXkLAAAAPGHGXOExnOst9wcmY/O8LF23/be3S0ROQcAAEA8IpgjNG3B3GSKyOGHTczwbe/dx8cTAAD0HbSyICT/13Kt3GpV//o0XR6B4w+fPMC3vfdwcgTOAAAAEJ8I5gjJD52/VaNSdfLhXREJ5gMLc2RXs5xyaG9tVgTOAAAAEJ/oFUBIPMc+MpFaLtFsNWtYUpkkqdSZK8MTmfMAAADEm14fzB966CEVFBTI4XCoqKhI69ev73b8unXrVFRUJIfDoZEjR+qRRx7pMGblypUqLCyU3W5XYWGhXnzxxZDO29raqiVLlujkk09WamqqBg0apOuuu04HDx488V84wnx3/jRFLjAPTz8iSapXuo58Vh2x8wAAAMSTXh3MV6xYoUWLFumOO+5QSUmJpk+frosuukilpaWdjt+zZ48uvvhiTZ8+XSUlJbr99tt10003aeXKlb4xxcXFmj9/vhYsWKBt27ZpwYIFmjdvnjZu3Bj0eRsbG7Vlyxbddddd2rJli1544QXt2rVLX/va1yL7F9ID2mbMzRGaMZekqyd8rJ/rXv2frpe97IuInQcAACCemAzD6LW9AlOmTNGkSZP08MMP+/aNHz9el112mZYuXdph/JIlS/TKK69ox44dvn0LFy7Utm3bVFxcLEmaP3++amtr9frrr/vGzJ07V1lZWXrmmWfCOq8kffDBBzrzzDO1d+9eDRs2LKjfr7a2VpmZmaqpqVFGRsbxX9ADrCaX3LKqKGW7NjUURuYkS5dKt9/u3V6xQpo3LzLnAQAAiLBQ8lqvnTFvaWnR5s2bNXv27ID9s2fP1oYNGzp9TXFxcYfxc+bM0aZNm9Ta2trtmLZjhnNeSaqpqZHJZFK/fv2C+v1iJRqtLBo1yr/9+eeROw8AAEAc6bWrslRWVsrtdis3Nzdgf25ursrLyzt9TXl5eafjXS6XKisrlZ+f3+WYtmOGc97m5mbdeuutuvbaa7v9JuV0OuV0On3Pa2truxwbKR5ZJEW2lUUjR/q3P/sscucBAACII712xryN6Us3wjEMo8O+443/8v5gjhnseVtbW3X11VfL4/HooYce6uY3kZYuXarMzEzfY+jQod2O72ntV0gxRXjG3CmbdmmM3t+aFLnzAAAAxJFeG8xzcnJksVg6zFJXVFR0mM1uk5eX1+l4q9Wq7Ozsbse0HTOU87a2tmrevHnas2ePVq9efdy+o9tuu001NTW+x759+7od39PaB/OItrJkZWmw6aDGaZfmldweufMAAADEkV4bzG02m4qKirR69eqA/atXr9a0adM6fc3UqVM7jH/zzTc1efJkJSUldTum7ZjBnrctlO/evVtr1qzxBf/u2O12ZWRkBDyiyePyaIj2abD2a0BSTUTPNSL5kCRpnztfLfUtET0XAABAPOi1PeaStHjxYi1YsECTJ0/W1KlT9eijj6q0tFQLFy6U5J2BPnDggJ588klJ3hVYHnjgAS1evFg33HCDiouLtXz5ct9qK5J08803a8aMGVq2bJkuvfRSvfzyy1qzZo3eeeedoM/rcrl05ZVXasuWLXr11Vfldrt9M+z9+/eXzWaL1l9RSKxmj/bp2IoxE6ZLejti5xrZv0abG7097aUb92v0+cMjdi4AAIB40KuD+fz581VVVaX77rtPZWVlmjhxolatWqXhw70hr6ysLGBN84KCAq1atUq33HKLHnzwQQ0aNEj333+/rrjiCt+YadOm6dlnn9Wdd96pu+66S6NGjdKKFSs0ZcqUoM+7f/9+vfLKK5Kk0047LaDmt956S7NmzYrQ38gJ8nj82+bI/mPLqCHN0n7v9mfvHSaYAwCAXq9Xr2Pe20V9HfOmJiklxbt97rnSv/4VsVM9dt3b+t5TMyRJD139tr7/zIyInQsAACBSWMcckRHNGfNT0nzbn//b081IAACA3qFXt7KgZzU1eLRAz8kkQ6fuqdedETzXyLMG+rY/O+CI4JkAAADiA8EcQWttMbRSV0qSao5ujui5hkzOk1WtcilJ/67uH9FzAQAAxANaWRA0jztK65hLsjqsGmXzrtO+u3moPC7aWQAAQO9GMEfQonbnz2PG9T8sSbKpRYdKDkb8fAAAALFEMEfQAu/8Gfnz/eGr/1KZ8nRU/ZRfvT3yJwQAAIghgjmC1r6dJBoz5gVTBipPh2SSpJ07I34+AACAWCKYI2iBM+ZRWP5+3Dj/NsEcAAD0cgRzBK39xZ+mKLSyEMwBAEBfwnKJCFrAxZ/ROGFOjp5I+b62NI7TkQ1D9VQ0zgkAABAjBHMELeqtLCaTHjF/X+/pZKlReqSiQakDUyN/XgAAgBiglQVBS7Z79B09rm9ruc4b+HFUzjku96hve/db+6NyTgAAgFhgxhxB65fu1uO6wftk9BWSvhvxc44b5ZI+827v3FCl0+ZH/JQAAAAxwYw5gme0a1+JytWf0rhTHL7tnR+1ROWcAAAAsUAwR/DaB3NzdD46487O8W3v/DwpKucEAACIBYI5gufx32AoWjPmo2YNlUne8+48nBWVcwIAAMQCwRxB+2KfRemqVaaO6rsffC8q53T0c2iE1XvR567GIQErwwAAAPQmBHMEze0yVH8smje57VE770lZhyRJdcrQgc3lUTsvAABANBHMEbSAGwxFp5NFkjRheINv++M3DkTvxAAAAFHEcokIWmAwj15LydnTDO3c9IpO1kcaXpMvaXLUzg0AABAtBHMELfDOn9E772XXZ+qy+8/zPqm4TtK3o3dyAACAKKGVBUHzuGPTyqLx4/3LM34cnTuOAgAARBvBHEGLVSuLkpOl0aO929u3S2539M4NAAAQJQRzBC1WrSySpJNPlltm7W4eotpte6J8cgAAgMgjmCNoMWtlkfRI87eUrjqN1W6tfuZwdE8OAAAQBQRzBC1mrSyScsZlq0kpkqSPNzmjem4AAIBoYFUWBG1YrlN/01UyZFLB+NMkzYjauU++IFf6nXf7o93Ru7kRAABAtBDMEbTMNLeu0vPeJ7n9o3ruUecOk13Ncsqhjw/nRvXcAAAA0UArC4JntGtfMUf3o2N1WFWY7L3oc3fLcDUdaYrq+QEAACKNYI7geTz+7Whf/SlpYn6VtwxZ9Okbe6N+fgAAgEgimCNo9Q0mbdBUvacp+qIuO+rnP/kkl29762pWZgEAAL0LwRxB27HHobOPRfPfbjs/6uc/fWaGb7tks6ebkQAAAImHYI6gGW5/GI76DYYkTbqiwLe9ZU+/6BcAAAAQQQRzBC3GLebqPypLBdZSSdL++n4yXO7oFwEAABAhBHMELfAGQ7Gp4ZnpD2uvhmmPMUKmXTtjUwQAAEAEEMwRtPbB3GyO7p0/20y5IF3DtE8mSdqyJSY1AAAARALBHEELaGWJVRFFRf7tzZtjVQUAAECPI5gjaAGtLLG4+lOSJk3ybzNjDgAAehFrrAtA4ghoZTHFppVFAwbolZxva11loba/e4pec3lktvL9EgAAJD6COYIW61VZ2vzJ+j29rCmSW/r3P/do7JyC478IAAAgzjHViKAFtrLEro6iwibf9pbXymJXCAAAQA8imCNo0ydWq0YZOqpM/fyct2JWx6Tpqb7tTRtaYlYHAABATyKYI2hWs0cZqlOmapVsi93Nfc64aoRve+PurJjVAQAA0JMI5gie0e6CT3PsPjoDJwzQSOteSdKm2rFqqWfWHAAAJD6COYIXL1d/Spo6ZL8kqVnJ2rby3zGtBQAAoCcQzBG03fuTdbfu0T26W2v3xnYllKlnuHzbxa8cjmElAAAAPYPlEhG0fx9I1n26W5JkLl2rWTGsZeqlA6TnvNvFm5J0UwxrAQAA6AnMmCNocdTJopMvH6NkNUqS3jswNLbFAAAA9ABmzBG0gHXMYxzMk1KSdMOgl+U4+JmmuotlHHxYpkH5sS0KAADgBDBjjqDFyaIsPn9csEnLdKsu08sybXwv1uUAAACckDiIV0gUHnf8zJhLks46y79dXBy7OgAAAHoAwRxBaz9jHhfBfOpU//aGDbGrAwAAoAcQzBG09j3m8dDKotxcGSNH6XMV6M/vjVfTkaZYVwQAABC2eIhXSBCeOLr4s81Pkh/UKH2ub7sf03v/92msywEAAAgbwRxBM+JoucQ2p52T6tte90pNDCsBAAA4MSyXiKD1T3PqLBXLkEn5mY2xLkeSNPP6Aul/vdtrt/aLaS0AAAAnghlzBO28wkMq1jS9p6n6xuSdsS5HkjRs6mAVWEslSe/VnKTmo80xrggAACA8BHMEL+6WZfGaVbBXkuSUQxufoM8cAAAkJoI5ghdvdxg6ZuZM/5eEtS8djV0hAAAAJyB+0hXinycOr/6UNPP6Eb7tdVszY1cIAADACSCYI2j/2Jans1SsqdqgldtGx7ocnxHnDNFwy35JUvFR+swBAEBiIpgjaBU1Dm3UWXpPU3WoPiXW5QQ4t2CPJKlZyXr3se0xrgYAACB0BHMEzQi4wVD8tLJI0uy5FqWoQZfoVdlL3ot1OQAAACEjmCNogdd+Gl0PjIGv3zpWR5StV/VVnfPJ/8a6HAAAgJARzBG0wGs/42vG3D44R/bJJ3uffPihVFYW24IAAABCRDBH0AJaWeLxkzNnjn979erY1QEAABCGeIxXiFMBrSzxNWHuNXu2JKlVVu187sMYFwMAABAaa6wLQOLwuNvPmMdhMp86Vddb/6oXXV+R/bUWHXJ5ZLby3RMAACQGUguC1n7GPM5azL2SklSTM0p1ylClkaOSZ3fGuiIAAICgEcwRtIBWFks8JnNp7rlO3/aryw/FsBIAAIDQEMwRtKKhFbpb9+jnulenDquOdTmd+sotY3zbr2zMjWElAAAAoaHHHEE7Y9ghnaF7vU9GPBnbYrow5Ix8TUreoS1N47Wlabz2bTyooVMGxbosAACA42LGHMELvMNQ7Oo4jkvP8rew/P33u2NYCQAAQPDiN10h/gTeYSh2dRzH127M922/vCY1hpUAAAAEj2COoDlbzapRhmqVrlZ3/H50Tr1qrIZZ9kuS3qo6RbX7a2NcEQAAwPHFb7pC3Hlo/cnqpxplqlYrNw2LdTldMplN+lrhZ5KkVtn0j99+HOOKAAAAjo9gjqAZ7XrMTXHcyiJJl34zXZI0Sv+WsfGDGFcDAABwfARzBM1o12Iex9d+SpJm/uhkbco4T7s1RvM/vENqbIx1SQAAAN2K83iFeBJw509zfM+YJ6UkqWjeKJkkqaFBWrUq1iUBAAB0i2COoCXIoix+8+b5t1esiF0dAAAAQSCYI2gJsoy537nnSjk5kqTP//6JGivqY1wQAABA1xIhXiFOJFIriyTJatXqKXfqTG3UKOd2vfKfH8a6IgAAgC4RzBG0gFVZEiGYS7KcO0Mf6ExJ0orn+bgDAID4RVJB0DwefxhPiFYWSTP/3ynKNVdIklaVna4jn1XHuCIAAIDOJUi8QjxIuFYWSRabRdeetl2S1CK7nr2DdhYAABCfCOYI2n+cVqJinaV3NU3TCo/GupygXb8k37f9xKvZMawEAACgawRzBG1weq3O0kZNU7H6Z7hiXU7QTp03Tqc6dkqS3m+YqE9XfR7jigAAADoimCN4Cbdeot/1c8p820/8ojSGlQAAAHQusdIVYivh7jDkd+1/TpBF3ln+p94fJ3eLO8YVAQAABCKYI2gfHRqov+kqPacrdeioPdblhCR34gBdlLtFknTAk683lm6JcUUAAACBCOYI2opPJmq+/qZ5ek4ffZEe63JCdsN3DJnl1qV6SQP++UysywEAAAhgjXUBSBztbzBktiRWK4skXXxXkb7401QNLf9AetcslS6Shg2LdVkAAACSmDFHCNrfYCjBWswlSVaHVUO//xXvE49Hevzx2BYEAADQDsEcQUvEGwx18J3vSBaLd/vxx6XW1tjWAwAAcAzBHEFL9FYWSdLgwdLXvqZWWbWybKreXvpurCsCAACQRDBHCBK9laXNnstu0TCV6kqt1G2/6R/rcgAAACQRzBGCXtHKImnEN89Rtr1ekrSh7hS99/jHMa4IAACAYI4QBNz4M1FbWeT9UrH4mnLf89/dWxfDagAAALwI5giax+gdrSyS9I0/nqlcc4UkaeX+M/XFO/tjXBEAAOjrCOYImsPSqgzVKE11siYldjK3Z9j1w1nbJUkeWXT/zf+OcUUAAKCvI5gjaEunvqIa9VOdMnTGhMZYl3PCFj4wUQ41SZIe3zJJR/fWxLgiAADQl/X6YP7QQw+poKBADodDRUVFWr9+fbfj161bp6KiIjkcDo0cOVKPPPJIhzErV65UYWGh7Ha7CgsL9eKLL4Z83hdeeEFz5sxRTk6OTCaTtm7dekK/Z1QEXP2Z2DPmkjRgfI6uO+kDSVKdMvQ/3y6JcUUAAKAv69XBfMWKFVq0aJHuuOMOlZSUaPr06broootUWlra6fg9e/bo4osv1vTp01VSUqLbb79dN910k1auXOkbU1xcrPnz52vBggXatm2bFixYoHnz5mnjxo0hnbehoUFnn322/uu//ityfwE9LeDqz97x0fnZg8NlkUuS9Pu3TlXt/toYVwQAAPoqk9H+rjG9zJQpUzRp0iQ9/PDDvn3jx4/XZZddpqVLl3YYv2TJEr3yyivasWOHb9/ChQu1bds2FRcXS5Lmz5+v2tpavf76674xc+fOVVZWlp555pmQz/vFF1+ooKBAJSUlOu2000L6/Wpra5WZmamamhplZGSE9Nqw/OAHUtvvtHmzNGlS5M8ZBd8a/Y6e+OwcWdWqFxe8qK88OS/WJQEAgF4ilLzWO6Y9O9HS0qLNmzdr9uzZAftnz56tDRs2dPqa4uLiDuPnzJmjTZs2qfXYrdu7GtN2zHDOmyie2nmGvqPH9V09ptJyW6zL6TF3PjJEN+gx7dJYfeW170t1LJ8IAACir9cG88rKSrndbuXm5gbsz83NVXl5eaevKS8v73S8y+VSZWVlt2PajhnOeYPldDpVW1sb8Iimd8tG6U/6jpbru6quT4rquSNp9AUj9OiC9SrQF9KRI9JDD8W6JAAA0Af12mDexvSlixQNw+iw73jjv7w/mGOGet5gLF26VJmZmb7H0KFDT+h4ofL0kjt/duqOO/x98//931KUv/QAAAD02mCek5Mji8XSYZa6oqKiw2x2m7y8vE7HW61WZWdndzum7ZjhnDdYt912m2pqanyPffv2ndDxQtXLFmUJNG6cdM01kqTGqkat/eFzMS4IAAD0Nb02mNtsNhUVFWn16tUB+1evXq1p06Z1+pqpU6d2GP/mm29q8uTJSkpK6nZM2zHDOW+w7Ha7MjIyAh7RZLS786fZ2gs/Or/4hZ6wfFtjtFsX/+UaHdh8Yq1HAAAAobDGuoBIWrx4sRYsWKDJkydr6tSpevTRR1VaWqqFCxdK8s5AHzhwQE8++aQk7wosDzzwgBYvXqwbbrhBxcXFWr58uW+1FUm6+eabNWPGDC1btkyXXnqpXn75Za1Zs0bvvPNO0OeVpCNHjqi0tFQHDx6UJO3cuVOSd0Y+Ly8v4n834fD05hlzSSooUMnJ1+ng1sGSpHu+sUmPfRqf7wUAAOiFjF7uwQcfNIYPH27YbDZj0qRJxrp163w/u/76642ZM2cGjF+7dq1x+umnGzabzRgxYoTx8MMPdzjmc889Z4wbN85ISkoyTjrpJGPlypUhndcwDOPPf/6zIanD4+677w76d6upqTEkGTU1NUG/5kR8a/TbhrehxTC2//3fUTlntB3+tNLI0FFDMgyzXMYnL++OdUkAACCBhZLXevU65r1dtNcxv37UO3ry83MkSZ+u+lzjLhoZ8XPGwn/NXavb3pglSbpk4Pt69dCZsS0IAAAkLNYxR0S0/wbX61Zlaefmp6doiMXbYvRaxZl69efvx7giAADQFxDMEbT2/7bSKy/+PCa5f7L++wdf+J7fvDRXzUebY1cQAADoE3pvukKPO7P/Z7pKf9MVel5pqb27A2r+H6bq3H4lkqTPXcP166+/F+OKAABAb0cwR9D+39g39DfN1/O6SnkDPbEuJ6JMZpP+5//SZVWrJGnpW1O05+3orhsPAAD6FoI5ghfQy9L7PzoTLh2tm4relSTZ5dTuHz8S+HcAAADQg3p/ukLP8bSbJe+VC5l3dPdLk/SdlKe1XYWavelX0l/+EuuSAABAL0UwR/CM3n6HoY4yhmTo8WfSNEhl3h2LFkmHDsW0JgAA0DsRzBG0GzfdoCHap6EqVdnhXn3T2EBf+5p09dXe7SNHpB/9KLb1AACAXolgjqBVOtN1QEO0X0PlMfrGjLnP/fdLOTmqVj99+/mL9LdbNsS6IgAA0MsQzBG0gE6WXnyDoU4NGKCD9z6mQm3Xn/Vt3fjHQu3beDDWVQEAgF6EYI6gBdz509L3Pjr5Cy/VjKF7JElHjX66bu4huVvcMa4KAAD0Fn0vXSFs7dtXzJY+NmMu778SPLKuUEMtByRJa4+ert9etj7GVQEAgN6CYI6g9cFFWTrIKuinp357WCZ5l4684/WztXH5xzGuCgAA9AYEcwTNY/g/Ln2xlaXNzJtP061T35YkuZSkK2/sr8M7KmNcFQAASHR9N10hZH364s8vuXfN2TonY5skab97kK49Zy/95gAA4IQQzBG09hd/9sUe8/aSUpL0t3V5yjN7bza05kiRfn4u/eYAACB8BHMErf3Fn325laVN/mm5+tsfy2WRS5L0xYYD8rzwUmyLAgAACasP3b4RJ+qWYS/oyspHZMiklJTfxLqcuDD9R6fqt2vekunlF/X/9D8yXZcqFayXTj891qUBAIAEQzBH0OZkb5K02vvE8duY1hJPbn5xlvSNx6RnJDU0SF/5ivT++9LgwbEuDQAAJBD6ERC89ld/mvno+JhM0p/+JE2d6n1+8KA+vfD/qb68PrZ1AQCAhEK6QvA8Hv92X13IvCsOh/Tyy1JBgd7QbE3Z8Wddc+p2uZpdsa4MAAAkCII5granMVcfa4I+UWHAhaA4ZsAA1f3tdX3T9FfVKlOvVpypb094Tx6X5/ivBQAAfR7BHEH79qc/1cn6WBP1iZytfHQ6kz55nP72m32yySlJeurzc7SoaL0Mj3GcVwIAgL6OdIWgcYOh4Jy7+HStuHWrbxnF//lwpu45d12MqwIAAPGOYI6gGe3aV/r6DYaO57KlU/SnG97zPb/v7VlaOmdt7AoCAABxj2COoHnEDYZCcd2j5+iPX/fPlN/+5izdd97a2BUEAADiGukKQQtYLZEZ86DctHKmfn3xWt/zu9+apXtmvhX4lwkAACCCOULgMfwfF3rMg/fT12bpd5eulSRZ5NKEtx+SliwhnAMAgADc+RNBax8jCeahueWlWUq6ap2yn39EV+l56b8lHTokPf64lJQU6/IAAEAcYMYcQWt/8SfBPHQ/em6mrvnfc/13TX3ySemrX5WzijuEAgAAgjlC0Hbxp0ncMCds3/ue9Pzzkt0uSfrdG4U6Z1ipKrZXxrgwAAAQawRzBK1txtwkeqNPyOWXS6tXa0XKf+jH+p02NRZqyqlN+vD5XbGuDAAAxBDBHEF7bdRNKtVQ7bGMiXUpiW/6dE34y20abC6TJH3hGqppVw3SS7dtjHFhAAAgVgjmCNpA6xEN1X4NM++PdSm9wsTLx2jjRmlyynZJUoPSdPl/TdF/XrBWhod/lQAAoK8hmCN4bcv7mfnY9JTBk/P19r4CXTv8Xd++u/45S5cPfl/Ve47GrjAAABB1JCwEz3Psok8TK7L0pOT+yfrL59P0X3PX+i6sfbl8iiaNrdMHT2yPcXUAACBaCOYI2hNHvqrfarH+x/39WJfS65jMJi15fZZevWez+puOSPL2nf+//6iT8cf7uRkRAAB9gMkw+C9+oqqtrVVmZqZqamqUkZER8fOd7Nitj51jlKIGNRipET9fX1X63kFdfWGVPq4frs0q0hj9W7rkEu/NiPLyYl0eAAAIQSh5jRlzBK3tG5yZdcwjathZg7Tu0Elad92fvKFckl57TZo4UdX/93JsiwMAABFDMEfQPIb348I65pGXlJKk059Y5A3kubmSpMaqRk35j/H6ZsG7XBgKAEAvRDBH0Pwz5gTzqLn4Yunjj6Urr9QSLdNujdVfvzhbE0c3aeVPi1lWEQCAXoRgjqD5ZsxNhMGoysmR/vY3nfWDImWqRpJ00JOvK38zVZcOel+lxQdiXCAAAOgJBHMErS2O08oSAyaTvvHgNH38fqMuGfi+b/ffD01R4bRM/f6ytXI1u2JYIAAAOFEEcwTNkHf9ci7+jJ0hZ+Tr72Vn6G+3FCvPfEiS946hi1+epdP77dHqZVtiWyAAAAgbwRxB8xjeYM6MeWyZzCZd9bup+nSPQ9+f8LbvpkQfO8foiltHq3ruNdLu3TGuEgAAhIpgjqC1zZhz38/4kDksUw99PEMbHtuuM1I/kSTdoV8q641npQkTpB//WKqujnGVAAAgWARzBG2M9QsV6hONs/471qWgnbO+O1HvHR2vZ3/wthbl/827s7VV+t3vdGj4mfrlBW+p7mBdbIsEAADHRTBH0N4YuECfaKLWZ18e61LwJWarWfMfnCH77o+ln/9ccjgkSb+q+5Hu/Oe5GjWkWb+/bK2ajzbHuFIAANAVgjmCZxzrLTfzsYlbqanSvfdKu3apZsGP9Li+K0k6bAzQ4pdnaXR2tR6ct05NR5piWycAAOiAhIXgeY6txmKiyzzuDR2qzCf/RyX/qNDVwzb4dh/w5OtHz83UiJx6LZ2zVjWlNTEsEgAAtEcwR/DaZswJ5glj7JwCPbN3mrY9t0tfy9vo219hDNDtb87SsOHSbVPXqv7zihhWCQAAJII5QnD1kYc0W29owZE/xroUhOiUK8fq5bIpKnl2p+YP3SCz3JKkWmXqL++Nkm38KOk735G2bo1toQAA9GEEcwRtXctZWq3ZWu88M9alIEynzR+nZ0unaeea/brhpLdlk1M/1m9la6mX/vQn6fTTpZkztf4X3EkUAIBoI5gjaB7D+3ExmbjBUKIbff5wPbpjhj7/4IhuuClFysz0/WzX22Wa8fNZGpVWrqVz1qps66EYVgoAQN9BMEfQ2uK4+didJpH4Bk/OV+offyXt3y899JB00kl6QD+SJJW6h+j2N2dp6OnZuix/o1675wNm0QEAiCCCOYLmOfZx4dLPXigtTfr+96VPPtFly6bpkoHv+37kllUvl0/RV+49QyNSD+uu6Wu15+19MSwWAIDeiWCOoBnHIjmtLL2Y2azzfjZZrx46U5+9Vao7zl6rQeYy348PePL1n+/M0siZQ3Xn0P/zzrJXVsauXgAAehGCOYLWFszNIpj3BSNnDdN/vjNLexsG6O93va9L8zbKIn8ry+T9L0k//KGUny9dcolannxWDRUNsSsYAIAERzBH0Lj4s2+yOqz6yn1n6qWyKdpXUqWlc9bq7JQtukivewe4XNKqVXr5+pUamCtdPWyDnltcrPry+tgWDgBAgjEZhkHKSlC1tbXKzMxUTU2NMjIyIn6+TFONapWpk2yfaYdzVMTPhzhmGNJHH0l//av09NPS/v26XC/oJV3uG2JXs2bnbtPXv9Kqry4pVPaY/jEsGACA2AglrzFjjqD5WlmYMYfJJJ1yirRsmbR3r4y31mrohExlmap9Q5xy6O+Hpug/lp+j3LEZuqD/Fj0w723te7c0hoUDABC/mDFPYNGeMb/N9hs1t1qUN9DQkkOLI34+JJ7Wxlate+AjvfBkvV7cMU7lntwOY36tn+qnJ70qXXyxdNFF0vTpkt0eg2oBAIi8UPIawTyBRTuYKzVVamyUJk70tjEA3fC4PHpv+Sd64bEqvbBtpPa4hkmSPtYETdB237gtjmm6r9/vdNF5Ts39wUgNP3tIrEoGAKDHhZLXrFGqCb1B23c4Mx1QOD6z1axpN56saTdK/+0x9OHzO/WvJ0pVeDRLes8sebw3qvp78wV6uXyKXn5a0tPSKOtenTd6r86fbdW53xujgRMGxPYXAQAgSpgxT2BRnzF3OCSnUzr1VGnr1sifD73XkSPS6tXSqlWa++z1eqPlvC6HTrTv1vnjD+hrX0/Sef9vgtSvX/TqBADgBNHK0kdEO5h7bA6ZWp0ynXaaVFIS8fOhb/C4PNr810/1+hMV+teWfiquGa8Wdew5v0GP6lHz96WTT5bOOUc6+2yVjZ6u/DNofQEAxC+CeR8R7WCeZGqVS0malLxDmxvHR/x86JsaKxv17uM79K+X6/TPjwZoc8NJ8siiZ3S1rtYK37gKDVCuKjTUckBnD96rc6a06uwr8nTy5aNlsVli+BsAAOBHMO8joh3MrSaX3LKqKGW7NjUURvx8gCQd3Vujtx/9VGdXvqzsjaukDz+UDEMv6VJdrpc6jE9XrSb3+0xnjK3RmTPsOuPrwzR0yiCZzKboFw8A6PMI5n1EtIO5xeSWRxZNTtmuDwjmiJWaGum99/TGk4e0bNXJ2nh0rBqV2uVwh5pUO2C0ks48XTrzTOmMM9R62hlKys+JYtEAgL6KVVkQEf4bDHliXAn6tMxMac4czZkjzZF37fRtz2/XOy8e1rsf2LShbIQOevJ9w09XiZIOH5ReOyi99pokaZ5eUInlDJ024IBOG9ekU6c4dNolgzXinCHMrAMAYoYZ8wQW7Rlz07G8MiX1Y71XPzHi5wPCdXBLuT54fq8+eLtJQ458qIXl90jV/ruSDtZ+HdTgDq/LUI1OzfhCp42o1qmnW3T+1zM14sIxUnJyFKsHAPQmtLL0EdEM5obHkNniTeZnpX2k4rqTI3o+oEcZhvTZZ9IHH6hhwzbNffIaba0dqXqld/uy5fq2vm15UhozRiosVOWIyVrTOlMTZuZo7IXDZc/gjqUAgO4RzPuIaAZzj8sjS5L3xkJT0z/ShlqCORKbx+XR5+v2aeuqg9r2vlNbd6VoW9Vg7XP7Z9I3a5Imyb806Ku6RF/Vq5Iks9wanVSqwpxDmjCyWYWnJqlwRo7GXThMyf2ZYQcAeNFjjh5nuD2SvMHcLL7LIfGZrWaNPn+4Rp8/XFe221+1+4g+/PtebX27RoXpRdJHHunTTyWnU9vlv+jZI4t2tRZoV1mBXiqT9K6khySTPDo5abu2zbzZO9M+dqw0dqyqcsYp8+RhsiYnRftXBQAkCGbME1g0Z8xbG1tlS/UGinMytml9zakRPR8QV9xu6fPPteXvB/TGG9L2XVZ9cihbO5pGqFkdZ8fP0Pt6X1MC9s3V6/qnztfIpP0ak3VYY4c0asxYs8ZOStPIKQM09Mx8WR3MlQBAb8OMOXqc4fF/f2PNCvQ5Fos0ZowmLR6jSYv9u90tbn3xzl5tf+uQtm9q1Ce7rNp5qJ8mtX4stQQeYpfGyqUk7yx7RYFeq5C0RdKzx04hl4ZZS/XzMc/qW9N2SSNHSgUFcg8rUFW/URowPocVYwCglyOYIyhWs0dvaLYMmdRv1EhJD8e6JCDmLDaLRp03XKPOG66vtv+BMUGq+pq0a5e0a5eMXbs15S8HlXG4Rbuah6lJKR2O5ZZVe1zDpB3bpR1P+PZ/pjEap11KVb0K7GUq6HdEBXnNKhhhaOgYu4ZNyNDQSQM0sDBHZqs58r80ACBiCOYIitlkaLZWe59kzoppLUDcM5mknBzvY9o0mSQ98yvvjzwujw6WlGn322XataVeu3d6tKfMoT3Vmfq8aZBG6vOAQ+1RgSSpQWn62DlGHx+SdEjStsBT2uRU+fCzlDUiUxo2TBo6VB+aT9OBpBEaenI/DZs8UBlDMyP+qwMAwkcwR3DaX4pgZlYOCJfZataQM/I15Ix8ndvJz40jL0t7Ppf27JH27FHKu2bNffcDfV6Toy9aB6lFnS/RaJFb/fZulfb69y3XH3S/rvI9z1CNhtorNCStRoP6Nyl/gFuDBpt0yqkmTb/ALg0aJOXlSUlcoAoAsUAwR3A87e72aaLPFYgUU/8sqX+RVFQkSZr+U+n1Yz9rm23//L0KlW6v177PW1W636x9lQ6ZmxplsvSTjh71HatUwwKOXatMfeLM1CdOSVWSdnv3z3/uWU2/8xr/wIEDdXHTSslh16CsZg3KdWvQELMGFdg1aFy68sZnaeD4bNnSbJH6awCAPolgjqC4Wg2t1lyZZGhA3UgVxbogoA9qP9veuWqprk7at08qLdX1r5g18aO12ldmVWlVqvY1ZGlfa56ccgS8apAOBjw3Kiq0Rmeqtc4mHZa0q/OzZZmq9ZfBt+rikZ9KublSbq4OOkbq1bIi5Q6za+CIFOWOzVRuYbZSB6ae8O8PAL0dwRxBaWwwdPGxebsLP9usN2NcD4AupKdLhYVSYaEumytd9qUfGx5D1XuqdfDDSh38tFYHP2vSSdb+kvWH0sGD0sGDqt7XIOPg8f9lrNrIUsr+ndL+t337SnSxbtQtHcamqEG51irlOmo0MK1Ruf2ceuTrq2UekO3txc/O1gFPvlrSs5Uzup/S8tJYhQZAn0MwR1C8Nxjy4gZDQOIymU3qPypL/UdlaaJv7zmSvuV71l+S0+VR1e5KHfyoyhvgP2/WwX1uHSgz69BRmw7VpepQc6bybbVSo//4FRrY6Xkblao9rlTtqZdUL2WU1+jRX80MGHO3HtNyfVeS92LWHPMRZdvqlONoUE5as3IyW5XT36PJ42r1tVl1/gtsc3LUlJItR3YqYR5AQiOYIyiBLeYEc6C3M1vNGjA+RwPG56j724ltkRobpUOHpIoKnVNSr8c2vK1DZR4dqjDpUHWSDtWmqKIpXYdas3TE6C9JytWhDkeqUrZvu0V2HfTk62BzvtQs6aik/d6fXb/+//S1x/8j4LXDVKFaJSnLXKMsa52ybI3KSm5W/zSnstLdyupnKKu/SV+ZWqXRJ1ml/v2lrCy50rPUmpal5P4dbxQFANFGMEdQ2s+Yc+0ngAApKVJBgVRQoDFTpDELux7a0tCqw59WqWGfS0p5Q6qslKqqpMpKzfinW7bSDaqqd6iyKVWVLRmq9GR16InPVlXAc7fMOqL+8siiQ56BOtQy0HuDp3p5e+TbGfHSZRqtl33Pi3WOZmi97GpWf/NRZSXV+0J9VlqLstLd6pdpKLOfdNMln8vaP0PKzJQyM1Xl7qeW5ExlDklXcv9kZusBnDCCOYLS/s6fZmbMAYTJlpqkwUV5UlGepMKAn91yb8fxhsdQQ0W9qj47qso9darc26Ah1nGS4w++QN9cXqeZb32oI80pqm5NVbU7Q3Xq/LbX/XUk4Hm1siRJTjlU5slTmVOSU1KdpAr/OJM8uuWVwAUu/1O/0x+O9dNb1apMU60yLA3KTGpUpq1ZmclOZaa06ozBZfp/M7ZJGf5Q/+7+4TKlpyljoEOZg1KVkZ+qtLw0WWyWEP42AfQ2BHMExeP2h3FmzAFEi8lsUlpemtLy0jT87M7HpEr615f2tTa26ujeGlWX1ql6f4Oqy5pVXe5U4dDrpdbZUnW1VF2ttJ2DNeOTrap2pqi6NU1H3JlqVMcVZDJUqy//X19tu/DvUpKqjGxVubIll6QmSTXen9V9/qL+3/pfBrz2m/pcXxy7eVR7yWpUuqlB6ZZGpVmbddeIp3RFwRbvRb1paaq05umP2y9UerqUnmFSWj+L0rOSlJ5lVXqOXWk5DqXnpig9N0UZg9JktvGfeSCR8L9YBCWglSWGdQBAMJJSknw98oEC0/15xx7ttdS3qPoLf6g/Wt6s1tomaez/SjU1vsdpG226dN9G1TTbVON0qKY1RTXuNNUYGXLJf5OmzLaE3k6NOr8La5NS1GSkqMIlySXVf7pP+vR1388P6BT9p37Z6Wu/bJ+GaIijyhfq/+qar4ePXqM0W4tS7S6lOlxKdXiUmuxRaqq8jzSTBg106fIZVfLvTNW+2kx5HClKzUlW6oAUOfo5aN0BIoBgjqAE3PiTVhYAvZgtzabciQOUO3HAl34yI+DZ/zv2+DLDY6j5aJNq9tep5kC9kltPk5LflGprfaH+lle26kiNRTX1ZtU0JKneaVWd0666VofqXMmq86SozkhTuuoCjl2n9KB/jzTVS83N3sfhw/pMSXpXpxz3dUXapMsfvCBg3ze0Tuvb/f5muZWiRqWYmpVqaVKqxalUq1PfGfKGbhi7zhfoXY40/XzLpUpJMSk1TUpNMys5zazkVItSMqxKTrcqOSNJyRlJGnOSRSn9HVJysvfhcHCnafQ5BHMEhVYWAAiOyWxScv9kJfdPVt4pnS8feVfHpd47ZTiflerrpPp6qa5OEw406o0tm1Vf3aq6apfqjrpVV2uovt5QXb1ZdY1m1TVZVdecpLSxp0j1R703naqrU+PRdMl9/HOmqqHDvoYvtfd4ZFG90lVvpHtbd1ySnNLFO/8m7fy7b1y9MrVUfwjqd92gqZqq93zPn9OVuk5PKtnU7H2YW5RidSrZ0qJkq0vJSa1KsbnVP7lJj537tDfMp6RIycl6o3S8dtXkKiXNrORUs5LTLN4vAelWpfSzeb8MZNrUb6BN2fk275cAu12yEosQW3wCEZTAVVmYMQeAaDDZbZI9W8r2LiWZNVGaPSfYV78d8Oy/JN1X36L6Qw1qPNKshsomNRxxqqG6RQ1HW9VQ06qGGreykxqkkb+RGhp8j7nrD2rUkRY1OK1qbLWqodWmBpddDS6HGjwONRgpalJKh1D/5UDfnZT2C+JLalSKmpWsZiNZ1YYkj7xfAL4kR4f12GePB+x7Sk/pr5rZcfCXXKu/6q/6pn+HxaICz79Vqww5TE45zC1yWFplN7vksLTKYXXJkeSSw+rWosI3NWPoHm+odzhU5hqgRz+aKrvDJEdyu0eKWfYUixypFjnSrHKkWTT5VJeS0h2+LwTNcsid5JCjn4MLgPs4gjmCMijXLUMmGZKMs66U9FysSwIAhMiWZlP/NJv6jzreyIsDngXT1e5xeeRpuE9yLvEF+uwjjfrnByVqrGlVQ41LDbVuNTV41NRoqLFRamqSmppNamw2a+CEGZLGHNvZpPQD+TqldKea3DY1ehxq8tjVZDjUpJSA8yarqUMtTQpuXXqHmgN3uN06oizVKlPq5suAJM0v+72kv/mel+pM3aPbgzpvlfqrv6p9z3+jO3SX/lOSZJFLDjXLbmrxPsytsplcsltadVrKbj150q8km807w2+z6ef/vk6fNebLbvPIZjVkt3kfNpu8f9pNstulqWMqNWXcUd/r3Fa73vxksGzJFtlTLLKlWGVLscqeeuzPdJvvz7T+NllS7LQWRQHBHME51mRukmSy0MsCAAhktpplzkyV2s2SOySdN6PLl3zJ9IBnXz/2+DLDY8hZ61RTdbMajzTLVe+R+n/oC/RqbNSPS5L19f3ver8ENHjU2GD4ftz2RaDJadZZ2a1S/iXePnynU2pu1uiPD6jWXa1mj03NHpuchk3NcqhVtoA6vhzqm7+03n53unutW1Y1KE0NhvxfDiSpVUpvPixt2BDw2jd0h97XlOOe8x7drSm6z/e8Xhm6uJMLkzvjazOyWCS7XS+bLtWNTX+QzdTq/eJgdh370y2bxftIsrjVz96kZ8663/tFIilJstn01z3T9EHVSNmSDO+uJENJNpN3iM2kJJtks5s1Or9Bs06t9r1OSUl6d9cAecxWJSVbZUs2K8nh/RKRlHxsX2qSkpKtSstKki01yfvaBPsyQTBHcAJv/Rm7OgAAfZrJbJKjn7ftI6vjipOSpGkXSdOCOtrZkgLviLW5i5HuVo+ctU411zjlrHWqn/2Pkv7LF+hPrnDp9Q83ydngUnOjx/9oMuRs9qi52eS9Ftcp2c/4D6m12XdxbsHudJ23f4ucLqua3VY1u5PU7LapxbDK6bGpRUlyGjaldPKvA07Zg/pN7XIGPG/50heN7tjUcuwvwS01NqpOZh3SsesnurluIVuV0sqVAfve0HQ9FUSb0TV6WrP0jYB9V6hMh5R33Nf+Wd/St/SE94nFoo+tp+ls5z9lM7XKZmrVqKRSvT30m9JTT0lnnXXc40UTwRzBab8sC8EcANDHWJLMSslOVkp2520y/SXN/VqwR5sV8Ow7xx7Hd4HkdkktLd4vBC0ter20VY21e+Wsb1VLk9v/Z6P30dLklrPZo1PypkoDHvO9zlFr6J61a9XScuxwLSa1tJrkbDWppdUsp8usFpdZLS6LskaPk8x233kdVf01vGK/94uDYVOLkSSn7B3+VSFJrR1+g9Z2S4l250Re6/siIUlut5rd8rcnGVK6s0b6978lVxd9SjFEMEdQqo6Y9Av9XmZ5NGmvvf2lMgAAIFosFv+SkpLyO1/457jSJd19d7Cjnw54duWxx5cZHkMt9S1qbWxVa5NLrkaLlLnPG+hbW6WWFv3n5ybdfPhjtTS51drsVqvT491u8ail2fA+dxoa3b+fNOIPvteptVU3/7NEdc1J3l2tJrW6vI8Wl9n7p9usVrdZg4flS6kzfK9Nqi3QSXs/U6vHqhbDqnxLlZTW33vxbZwxGYbBEhsJqra2VpmZmaqpqVFGRue3n+4pn7+1V6POGy5Junb4u/rrF13cgg8AAAA+oeS1xOqIR8zQYg4AABBZBHMExfBwgyEAAIBIIpgjKO1vMGTmBkMAAAA9jmCOoNDKAgAAEFm9Ppg/9NBDKigokMPhUFFRkdavX9/t+HXr1qmoqEgOh0MjR47UI4880mHMypUrVVhYKLvdrsLCQr344oshn9cwDN1zzz0aNGiQkpOTNWvWLH3yyScn9stGEK0sAAAAkdWrg/mKFSu0aNEi3XHHHSopKdH06dN10UUXqbS0tNPxe/bs0cUXX6zp06erpKREt99+u2666SatbLc4fnFxsebPn68FCxZo27ZtWrBggebNm6eNGzeGdN5f//rX+t3vfqcHHnhAH3zwgfLy8nThhReqrq4ucn8hJ6B9MKeVBQAAoOf16uUSp0yZokmTJunhhx/27Rs/frwuu+wyLV26tMP4JUuW6JVXXtGOHTt8+xYuXKht27apuLhYkjR//nzV1tbq9ddf942ZO3eusrKy9MwzzwR1XsMwNGjQIC1atEhLliyRJDmdTuXm5mrZsmW68cYbg/r9orlc4scv7tbJXx8jSfrO2PV6fOf047wCAAAALJcoqaWlRZs3b9bs2bMD9s+ePVsbNmzo9DXFxcUdxs+ZM0ebNm1Sa2trt2PajhnMeffs2aPy8vKAMXa7XTNnzuyytlgLmDE399rvcgAAADHTa+/8WVlZKbfbrdzc3ID9ubm5Ki8v7/Q15eXlnY53uVyqrKxUfn5+l2PajhnMedv+7GzM3r17u/ydnE6nnE6n73ltbW2XY3taWrJbc/QPGTKpMKsmaucFAADoK3rtjHkb05euVDQMo8O+443/8v5gjtlTY9pbunSpMjMzfY+hQ4d2ObanFQxy6h+6SG9orhad8q+onRcAAKCv6LXBPCcnRxaLpcPseEVFRYeZ6jZ5eXmdjrdarcrOzu52TNsxgzlvXl6eJIVUmyTddtttqqmp8T327dvX5dgeV1go7dsnlZZKv/pV9M4LAADQR/TaYG6z2VRUVKTVq1cH7F+9erWmTZvW6WumTp3aYfybb76pyZMnKykpqdsxbccM5rwFBQXKy8sLGNPS0qJ169Z1WZvk7UPPyMgIeESNzSYNGSINHSr17x+98wIAAPQRvbbHXJIWL16sBQsWaPLkyZo6daoeffRRlZaWauHChZK8M9AHDhzQk08+Kcm7AssDDzygxYsX64YbblBxcbGWL1/uW21Fkm6++WbNmDFDy5Yt06WXXqqXX35Za9as0TvvvBP0eU0mkxYtWqRf/epXGjNmjMaMGaNf/epXSklJ0bXXXhvFvyEAAADEi14dzOfPn6+qqirdd999Kisr08SJE7Vq1SoNHz5cklRWVhawtnhBQYFWrVqlW265RQ8++KAGDRqk+++/X1dccYVvzLRp0/Tss8/qzjvv1F133aVRo0ZpxYoVmjJlStDnlaSf/exnampq0g9+8ANVV1drypQpevPNN5Wenh6FvxkAAADEm169jnlvF811zAEAABA61jEHAAAAEgzBHAAAAIgDBHMAAAAgDhDMAQAAgDhAMAcAAADiAMEcAAAAiAMEcwAAACAOEMwBAACAOEAwBwAAAOIAwRwAAACIAwRzAAAAIA4QzAEAAIA4QDAHAAAA4gDBHAAAAIgDBHMAAAAgDhDMAQAAgDhAMAcAAADiAMEcAAAAiAMEcwAAACAOEMwBAACAOEAwBwAAAOKANdYFIHyGYUiSamtrY1wJAAAAOtOW09pyW3cI5gmsrq5OkjR06NAYVwIAAIDu1NXVKTMzs9sxJiOY+I645PF4dPDgQaWnp8tkMkX8fLW1tRo6dKj27dunjIyMiJ8PPY/3MPHxHiY23r/Ex3uY+KL9HhqGobq6Og0aNEhmc/dd5MyYJzCz2awhQ4ZE/bwZGRn8n1GC4z1MfLyHiY33L/HxHia+aL6Hx5spb8PFnwAAAEAcIJgDAAAAcYBgjqDZ7XbdfffdstvtsS4FYeI9THy8h4mN9y/x8R4mvnh+D7n4EwAAAIgDzJgDAAAAcYBgDgAAAMQBgjkAAAAQBwjmAAAAQBwgmCPAQw89pIKCAjkcDhUVFWn9+vXdjl+3bp2KiorkcDg0cuRIPfLII1GqFF0J5T0sKyvTtddeq3HjxslsNmvRokXRKxSdCuX9e+GFF3ThhRdqwIABysjI0NSpU/XGG29EsVp0JpT38J133tHZZ5+t7OxsJScn66STTtLvf//7KFaLzoT638I27777rqxWq0477bTIFojjCuU9XLt2rUwmU4fHp59+GsWKvQjm8FmxYoUWLVqkO+64QyUlJZo+fbouuugilZaWdjp+z549uvjiizV9+nSVlJTo9ttv10033aSVK1dGuXK0CfU9dDqdGjBggO644w6deuqpUa4WXxbq+/f222/rwgsv1KpVq7R582ade+65+upXv6qSkpIoV442ob6Hqamp+tGPfqS3335bO3bs0J133qk777xTjz76aJQrR5tQ38M2NTU1uu6663T++edHqVJ0Jdz3cOfOnSorK/M9xowZE6WK2zGAY84880xj4cKFAftOOukk49Zbb+10/M9+9jPjpJNOCth34403GmeddVbEakT3Qn0P25s5c6Zx8803R6gyBONE3r82hYWFxr333tvTpSFIPfEeXn755cY3v/nNni4NQQr3PZw/f75x5513Gnfffbdx6qmnRrBCHE+o7+Fbb71lSDKqq6ujUF33mDGHJKmlpUWbN2/W7NmzA/bPnj1bGzZs6PQ1xcXFHcbPmTNHmzZtUmtra8RqRefCeQ8RP3ri/fN4PKqrq1P//v0jUSKOoyfew5KSEm3YsEEzZ86MRIk4jnDfwz//+c/67LPPdPfdd0e6RBzHifzv8PTTT1d+fr7OP/98vfXWW5Ess0vWmJwVcaeyslJut1u5ubkB+3Nzc1VeXt7pa8rLyzsd73K5VFlZqfz8/IjVi47CeQ8RP3ri/fvtb3+rhoYGzZs3LxIl4jhO5D0cMmSIDh8+LJfLpXvuuUff/e53I1kquhDOe7h7927deuutWr9+vaxWYlWshfMe5ufn69FHH1VRUZGcTqeeeuopnX/++Vq7dq1mzJgRjbJ9+AQhgMlkCnhuGEaHfccb39l+RE+o7yHiS7jv3zPPPKN77rlHL7/8sgYOHBip8hCEcN7D9evXq76+Xu+9955uvfVWjR49Wtdcc00ky0Q3gn0P3W63rr32Wt17770aO3ZstMpDEEL53+G4ceM0btw43/OpU6dq3759+s1vfkMwR2zk5OTIYrF0+DZZUVHR4Vtnm7y8vE7HW61WZWdnR6xWdC6c9xDx40TevxUrVug73/mOnnvuOV1wwQWRLBPdOJH3sKCgQJJ08skn69ChQ7rnnnsI5jEQ6ntYV1enTZs2qaSkRD/60Y8keVvKDMOQ1WrVm2++qfPOOy8qtcOrp/5beNZZZ+kvf/lLT5d3XPSYQ5Jks9lUVFSk1atXB+xfvXq1pk2b1ulrpk6d2mH8m2++qcmTJyspKSlitaJz4byHiB/hvn/PPPOMvvWtb+npp5/WJZdcEuky0Y2e+t+gYRhyOp09XR6CEOp7mJGRoY8++khbt271PRYuXKhx48Zp69atmjJlSrRKxzE99b/DkpKS2LTkxuyyU8SdZ5991khKSjKWL19ubN++3Vi0aJGRmppqfPHFF4ZhGMatt95qLFiwwDf+888/N1JSUoxbbrnF2L59u7F8+XIjKSnJeP7552P1K/R5ob6HhmEYJSUlRklJiVFUVGRce+21RklJifHJJ5/Eovw+L9T37+mnnzasVqvx4IMPGmVlZb7H0aNHY/Ur9HmhvocPPPCA8corrxi7du0ydu3aZfzpT38yMjIyjDvuuCNWv0KfF87/j7bHqiyxF+p7+Pvf/9548cUXjV27dhkff/yxceuttxqSjJUrV0a9doI5Ajz44IPG8OHDDZvNZkyaNMlYt26d72fXX3+9MXPmzIDxa9euNU4//XTDZrMZI0aMMB5++OEoV4wvC/U9lNThMXz48OgWDZ9Q3r+ZM2d2+v5df/310S8cPqG8h/fff78xYcIEIyUlxcjIyDBOP/1046GHHjLcbncMKkebUP9/tD2CeXwI5T1ctmyZMWrUKMPhcBhZWVnGOeecY7z22msxqNowTIZx7Go9AAAAADFDjzkAAAAQBwjmAAAAQBwgmAMAAABxgGAOAAAAxAGCOQAAABAHCOYAAABAHCCYAwAAAHGAYA4AAADEAYI5AAAAEAcI5gAAAEAcIJgDAAAAcYBgDgAAAMQBgjkAAAAQBwjmAAAAQBwgmAMAAABxgGAOAAAAxAGCOQAgqq644gqZTCaZTCalpKToiy++COs4N910k+84JpNJ77//fs8WCgBRRjAHAETN3//+d73wwgu+50uWLNGIESPCOtbkyZMDnq9fv/5ESgOAmCOYAwCior6+Xj/84Q99z0eMGKElS5aEfbwzzjgj4Pnbb78d9rEAIB4QzAEAUbFs2TLt27fP9/wXv/iFHA5H2McbM2aMLBaL7/nWrVtPpDwAiDmTYRhGrIsAAPRuFRUVGjVqlOrr6yVJY8eO1fbt2wOCdTiGDBmiAwcOSJLMZrMaGxtlt9tPuF4AiAVmzAEAEbd06VJfKJekO+6444RDueQN5m08Hk/YF5ICQDwgmAMAIqqurk7Lly/3Pc/OztbVV1/dI8dOTk4OeF5bW9sjxwWAWCCYAwAi6i9/+Yvq6up8zxcsWCCbzdYjxzaZTAHPW1paeuS4ABAL1lgXAADo3Z544omA5wsWLOh2/OrVq+V2uyVJZ555pvr379/lWJfLFfDcauU/awASFxd/AgAiprq6Wjk5OfJ4PJKknJwcVVRUdJjpbnPw4EENHjzY93z37t0aPXp0l8cvKCgI6Cvft29fQN85ACQSWlkAABGzdu1aXyiXpFmzZnUZyiVp48aNvu2UlBSNHDmyy7Fut9u3Iosk2Ww25efnn2DFABA7BHMAQMR89NFHAc9PP/30bse/++67vu0xY8bIbO76P1MfffSRWltbfc+Liop6ZKUXAIgVgjkAIGJ2794d8Hz8+PHdjn/jjTd820OHDu127DvvvBPwfPr06SFWBwDxhWAOAIiY0tLSgOd5eXldjt27d68+/vhj3/OBAwd2e+zXXnst4PkFF1wQRoUAED8I5gCAiGloaAh4npmZ2eXYp59+OuC5w+HocmxVVZX+9a9/+Z4PHDhQ5513XphVAkB8IJgDACKmfQ+4JDU1NXU6zuVy6X//938D9jU2NnZ53EcffTRgzfJrr72W/nIACY9gDgCImNzc3IDnO3fu7HTc448/rr1798pkMvlaWPbs2dPp2MrKSv3617/2Pbfb7frxj3/cQxUDQOwQzAEAETNmzJiA519uV5GkXbt2acmSJZKk2bNna9CgQZKk4uJiVVVVBYxtaWnRNddco6NHj/r2/eAHP2DtcgC9AsEcABAxl112WcDz1157TT/5yU906NAhNTU16YUXXtCsWbNUW1srk8mke++913eDoZaWFn3zm9/Uvn371NzcrH/961+aPn261qxZ4zvexIkT9ctf/jKavxIARAx3/gQARIzb7dbUqVP1wQcfHHfsT3/6U/3617/W//zP/+imm2467viCggKtWbOm25sQAUAiYcYcABAxFotFTz/9tEaPHt3tuJtuuknLli2TJN1www069dRTux1/0UUX6Z133iGUA+hVmDEHAERcbW2tHn74YT3//PPas2ePamtrNWDAAJ1zzjn64Q9/qBkzZgSMr6mp0a9+9Su99NJL2rt3r5KSkjRo0CDNmDFD11xzDUsjAuiVCOYAAABAHKCVBQAAAIgDBHMAAAAgDhDMAQAAgDhAMAcAAADiAMEcAAAAiAMEcwAAACAOEMwBAACAOEAwBwAAAOIAwRwAAACIAwRzAAAAIA4QzAEAAIA4QDAHAAAA4gDBHAAAAIgDBHMAAAAgDhDMAQAAgDhAMAcAAADiwP8HNiGM4ixJ5pEAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "wlist = np.linspace(0, 0.5, 1000)\n", + "J = env.spectral_density(wlist)\n", + "J_approx = env_approx.spectral_density(wlist)\n", + "\n", + "fig, axes = plt.subplots(1, 1, figsize=(8, 8))\n", + "axes.plot(wlist, J, 'r', linewidth=2)\n", + "axes.plot(wlist, J_approx, 'b--', linewidth=2)\n", + "\n", + "axes.set_xlabel(r'$\\omega$', fontsize=28)\n", + "axes.set_ylabel(r'J', fontsize=28);" + ] + }, + { + "cell_type": "markdown", + "id": "f5509b95", + "metadata": {}, + "source": [ + "## Dynamic decoupling with fast and slow pulses\n", + "\n", + "Now we are ready to explore dynamic decoupling from the environment.\n", + "\n", + "First we will drive the system with fast, large amplitude pulses. Then we will drive the system with slower, smaller amplitude pulses. The faster pulses decoupling the system more effectively and retain the coherence longer, but the slower pulses help too.\n", + "\n", + "Let's start by simulating the fast pulses:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9aff655d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ 0% ] Elapsed 0.01s / Remaining 00:00:00:00" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 6.34s*] Elapsed 6.34s / Remaining 00:00:00:00\n", + " Total run time: 9.41s*] Elapsed 9.41s / Remaining 00:00:00:00\n" + ] + } + ], + "source": [ + "# Fast driving (quick, large amplitude pulses)\n", + "\n", + "tlist = np.linspace(0, 400, 1000)\n", + "\n", + "# start with a superposition so there is something to dephase!\n", + "rho0 = (basis(2, 1) + basis(2, 0)).unit()\n", + "rho0 = ket2dm(rho0)\n", + "\n", + "# without pulses\n", + "hsolver = HEOMSolver(H_sys, bath, NC, options=options)\n", + "outputnoDD = hsolver.run(rho0, tlist)\n", + "\n", + "# with pulses\n", + "drive_fast = drive(amplitude=0.5, delay=20, integral=np.pi / 2)\n", + "H_d = qutip.QobjEvo([H_sys, [H_drive, drive_fast]])\n", + "\n", + "hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", + "outputDD = hsolver.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "cd6eec03", + "metadata": {}, + "source": [ + "And now the longer slower pulses:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5942c8b6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 6.27s*] Elapsed 6.27s / Remaining 00:00:00:00\n", + " Total run time: 7.62s*] Elapsed 7.62s / Remaining 00:00:00:00\n" + ] + } + ], + "source": [ + "# Slow driving (longer, small amplitude pulses)\n", + "\n", + "# without pulses\n", + "hsolver = HEOMSolver(H_sys, bath, NC, options=options)\n", + "outputnoDDslow = hsolver.run(rho0, tlist)\n", + "\n", + "# with pulses\n", + "drive_slow = drive(amplitude=0.01, delay=20, integral=np.pi/2)\n", + "H_d = QobjEvo([H_sys, [H_drive, drive_slow]])\n", + "\n", + "hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", + "outputDDslow = hsolver.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "120055e5", + "metadata": {}, + "source": [ + "Now let's plot all of the results and the shapes of the pulses:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e11e2b88", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_dd_results(outputnoDD, outputDD, outputDDslow):\n", + " fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 12))\n", + "\n", + " # Plot the dynamic decoupling results:\n", + "\n", + " tlist = outputDD.times\n", + "\n", + " P12 = basis(2, 1) * basis(2, 0).dag()\n", + " P12DD = qutip.expect(outputDD.states, P12)\n", + " P12noDD = qutip.expect(outputnoDD.states, P12)\n", + " P12DDslow = qutip.expect(outputDDslow.states, P12)\n", + "\n", + " plt.sca(axes[0])\n", + " plt.yticks([0, 0.25, 0.5], [0, 0.25, 0.5])\n", + "\n", + " axes[0].plot(\n", + " tlist, np.real(P12DD),\n", + " 'green', linestyle='-', linewidth=2, label=\"HEOM with fast DD\",\n", + " )\n", + " axes[0].plot(\n", + " tlist, np.real(P12DDslow),\n", + " 'blue', linestyle='-', linewidth=2, label=\"HEOM with slow DD\",\n", + " )\n", + " axes[0].plot(\n", + " tlist, np.real(P12noDD),\n", + " 'orange', linestyle='--', linewidth=2, label=\"HEOM no DD\",\n", + " )\n", + "\n", + " axes[0].locator_params(axis='y', nbins=3)\n", + " axes[0].locator_params(axis='x', nbins=3)\n", + "\n", + " axes[0].set_ylabel(r\"$\\rho_{01}$\", fontsize=30)\n", + "\n", + " axes[0].legend(loc=4)\n", + " axes[0].text(0, 0.4, \"(a)\", fontsize=28)\n", + "\n", + " # Plot the drive pulses:\n", + "\n", + " pulse = [drive_fast(t) for t in tlist]\n", + " pulseslow = [drive_slow(t) for t in tlist]\n", + "\n", + " plt.sca(axes[1])\n", + " plt.yticks([0., 0.25, 0.5], [0, 0.25, 0.5])\n", + "\n", + " axes[1].plot(\n", + " tlist, pulse,\n", + " 'green', linestyle='-', linewidth=2, label=\"Drive fast\",\n", + " )\n", + " axes[1].plot(\n", + " tlist, pulseslow,\n", + " 'blue', linestyle='--', linewidth=2, label=\"Drive slow\",\n", + " )\n", + "\n", + " axes[1].locator_params(axis='y', nbins=3)\n", + " axes[1].locator_params(axis='x', nbins=3)\n", + "\n", + " axes[1].set_xlabel(r'$t\\bar{V}_{\\mathrm{f}}$', fontsize=30)\n", + " axes[1].set_ylabel(r'Drive amplitude/$\\bar{V}_{\\mathrm{f}}$', fontsize=30)\n", + "\n", + " axes[1].legend(loc=1)\n", + " axes[1].text(0, 0.4, \"(b)\", fontsize=28)\n", + "\n", + " fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "57846a9f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_dd_results(outputnoDD, outputDD, outputDDslow)" + ] + }, + { + "cell_type": "markdown", + "id": "159a0da8", + "metadata": {}, + "source": [ + "## Non-equally spaced pulses" + ] + }, + { + "cell_type": "markdown", + "id": "0fc9456a", + "metadata": {}, + "source": [ + "Next we consider non-equally spaced pulses.\n", + "\n", + "Rather than plot as a function of time we just consider the final coherence after time $T$ and 100 pulses. We change the width of the environment to demonstate that the Uhrig sequence (i.e. the evenly spaced pulses) can be sub-optimal when the bath is very broad.\n", + "\n", + "Instead of evenly spaced pulses, we will use pulses where the cummulative delay after $j$ pulses is given by:\n", + "\n", + "$$\n", + " \\sin^2(\\frac{\\pi}{2} \\frac{j}{N + 1})\n", + "$$\n", + "\n", + "This is just a convenient way to describe the varying delay. We could have chosen another monotonically increasing function to represent the cummulative delay (although it might not be as effective)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "796b76da", + "metadata": {}, + "outputs": [], + "source": [ + "def cummulative_delay_fractions(N):\n", + " \"\"\" Return an array of N + 1 cummulative delay\n", + " fractions.\n", + "\n", + " The j'th entry in the array should be the sum of\n", + " all delays before the j'th pulse. The last entry\n", + " should be 1 (i.e. the entire cummulative delay\n", + " should have been used once the sequence of pulses\n", + " is complete).\n", + "\n", + " The function should be monotonically increasing,\n", + " strictly greater than zero and the last value\n", + " should be 1.\n", + "\n", + " This implementation returns:\n", + "\n", + " sin((pi / 2) * (j / (N + 1)))**2\n", + "\n", + " as the cummulative delay after the j'th pulse.\n", + " \"\"\"\n", + " return np.array([\n", + " np.sin((np.pi / 2) * (j / (N + 1)))**2\n", + " for j in range(0, N + 1)\n", + " ])\n", + "\n", + "\n", + "def drive_opt(amplitude, avg_delay, integral, N):\n", + " \"\"\" Return an optimized distance pulse function.\n", + "\n", + " Our previous pulses were evenly spaced. Here we\n", + " instead use a varying delay after the j'th pulse.\n", + "\n", + " The cummulative delay is described by the function\n", + " ``cummulative_delay_fractions`` above.\n", + " \"\"\"\n", + " duration = integral / amplitude\n", + " cummulative_delays = N * avg_delay * cummulative_delay_fractions(N)\n", + "\n", + " t_start = cummulative_delays + duration * np.arange(0, N + 1)\n", + " t_end = cummulative_delays + duration * np.arange(1, N + 2)\n", + "\n", + " def pulse(t):\n", + " if any((t_start <= t) & (t <= t_end)):\n", + " return amplitude\n", + " return 0.0\n", + "\n", + " return pulse" + ] + }, + { + "cell_type": "markdown", + "id": "d0b4922d", + "metadata": {}, + "source": [ + "Let's plot the cummulative delays and see what they look like. Note that the cummulative delay starts at $0$, ends at $1$ and is monotonically increasing, as required.\n", + "\n", + "On the same axes we plot the individual $j^{th}$ delays as a fraction of the average delay." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d67f21ad", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_cummulative_delay_fractions(N):\n", + " cummulative = cummulative_delay_fractions(N)\n", + " individual = (cummulative[1:] - cummulative[:-1]) * N\n", + " plt.plot(np.arange(0, N + 1), cummulative, label=\"Cummulative delay\")\n", + " plt.plot(np.arange(0, N), individual, label=\"j'th delay\")\n", + " plt.xlabel(\"j\")\n", + " plt.ylabel(\"Fraction of delay\")\n", + " plt.legend()\n", + "\n", + "\n", + "plot_cummulative_delay_fractions(100)" + ] + }, + { + "cell_type": "markdown", + "id": "e38209d5", + "metadata": {}, + "source": [ + "And now let us plot the first ten even and optimally spaced pulses together to compare them:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "1720ad04", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_even_and_optimally_spaced_pulses():\n", + " amplitude = 10.0\n", + " integral = np.pi / 2\n", + " duration = integral / amplitude\n", + " delay = 1.0 - duration\n", + "\n", + " tlist = np.linspace(0, 10, 1000)\n", + "\n", + " pulse_opt = drive_opt(amplitude, delay, integral, 100)\n", + " pulse_eq = drive(amplitude, delay, integral)\n", + "\n", + " plt.plot(\n", + " tlist, [pulse_opt(t) for t in tlist], label=\"opt\",\n", + " )\n", + " plt.plot(\n", + " tlist, [pulse_eq(t) for t in tlist], label=\"eq\",\n", + " )\n", + " plt.legend(loc=4)\n", + "\n", + "\n", + "plot_even_and_optimally_spaced_pulses()" + ] + }, + { + "cell_type": "markdown", + "id": "f68fa9ff", + "metadata": {}, + "source": [ + "Now let's simulate the effectiveness of the two sets of delays by comparing how well they maintain coherence after a hundred pulses.\n", + "\n", + "We'll perform the simulation over a range of lambdas and gammas to show how the non-evenly spaced delays become optimal as the width of the bath spectral function increases." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "2cc440b8", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "45d4a5ff7f764a5282002e769b34273c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "IntProgress(value=0, max=8)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 1.33s*] Elapsed 1.33s / Remaining 00:00:00:00\n", + " Total run time: 4.57s*] Elapsed 4.57s / Remaining 00:00:00:00\n", + " Total run time: 1.35s*] Elapsed 1.35s / Remaining 00:00:00:00\n", + " Total run time: 4.57s*] Elapsed 4.57s / Remaining 00:00:00:00\n", + " Total run time: 1.05s*] Elapsed 1.05s / Remaining 00:00:00:00\n", + " Total run time: 4.47s*] Elapsed 4.47s / Remaining 00:00:00:00\n", + " Total run time: 1.17s*] Elapsed 1.17s / Remaining 00:00:00:00\n", + " Total run time: 4.43s*] Elapsed 4.43s / Remaining 00:00:00:00\n" + ] + } + ], + "source": [ + "# Bath parameters to simulate over:\n", + "\n", + "# We use only two lambdas and two gammas so that the notebook executes\n", + "# quickly:\n", + "\n", + "lams = [0.005, 0.0005]\n", + "gammas = np.linspace(0.005, 0.05, 2)\n", + "\n", + "# But one can also extend the lists to larger ones:\n", + "#\n", + "# lams = [0.01, 0.005, 0.0005]\n", + "# gammas = np.linspace(0.005, 0.05, 10)\n", + "\n", + "# Setup a progress bar:\n", + "\n", + "progress = IntProgress(min=0, max=(2 * len(lams) * len(gammas)))\n", + "display(progress)\n", + "\n", + "\n", + "def simulate_100_pulses(lam, gamma, T, NC, Nk):\n", + " \"\"\" Simulate the evolution of 100 evenly and optimally spaced pulses.\n", + "\n", + " Returns the expectation value of P12p from the final state of\n", + " each evolution.\n", + " \"\"\"\n", + " rho0 = (basis(2, 1) + basis(2, 0)).unit()\n", + " rho0 = ket2dm(rho0)\n", + "\n", + " N = 100 # number of pulses to simulate\n", + " avg_cycle_time = 1.0 # average time from one pulse to the next\n", + " t_max = N * avg_cycle_time\n", + "\n", + " tlist = np.linspace(0, t_max, 100)\n", + "\n", + " amplitude = 10.0\n", + " integral = np.pi / 2\n", + " duration = integral / amplitude\n", + " delay = avg_cycle_time - duration\n", + "\n", + " env = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T)\n", + " env_approx = env.approx_by_pade(Nk=Nk)\n", + " bath=(env_approx,Q)\n", + " # Equally spaced pulses:\n", + "\n", + " pulse_eq = drive(amplitude, delay, integral)\n", + " H_d = QobjEvo([H_sys, [H_drive, pulse_eq]])\n", + "\n", + " hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", + " result = hsolver.run(rho0, tlist)\n", + "\n", + " P12_eq = expect(result.states[-1], P12p)\n", + " progress.value += 1\n", + "\n", + " # Non-equally spaced pulses:\n", + "\n", + " pulse_opt = drive_opt(amplitude, delay, integral, N)\n", + " H_d = QobjEvo([H_sys, [H_drive, pulse_opt]])\n", + "\n", + " hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", + " result = hsolver.run(rho0, tlist)\n", + "\n", + " P12_opt = expect(result.states[-1], P12p)\n", + " progress.value += 1\n", + "\n", + " return P12_opt, P12_eq\n", + "\n", + "\n", + "# We use NC=2 and Nk=2 to speed up the simulation:\n", + "\n", + "P12_results = [\n", + " list(zip(*(\n", + " simulate_100_pulses(lam=lam_, gamma=gamma_, T=0.5, NC=2, Nk=2)\n", + " for gamma_ in gammas\n", + " )))\n", + " for lam_ in lams\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "0ff48fc7", + "metadata": {}, + "source": [ + "Now that we have the expectation values of $\\rho_{01}$ let's plot them as a function of gamma for each lambda. Note how in each case the non-evenly spaced pulses become optimal once gamma is sufficiently small:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ab9d0107", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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8ACADMdUcAICcysnJsu1ZwYKWFdRTw2y2FOgXLlg67hcuWAr3xPfbr1+3vN8eEWEZV7CgZUG7xIL+7t20552QYPkFQHS0paOfEu7ulo75tWuWXyA4O0tduliK9MRO+9GjlmdI/JkkLkzn7W29qry7O4U7AMBm6HjbGB1vAECOkViEX7pkKcTv3Usqyi9flnbsSJoqf/u2pVCPirJc91/eb08vzs6Wjnvu3JaO+z9XlO/b19Jt9/KydOGvXbP8YsLLyzJ13s3NzskDADIaHW8AAJDxnJ0t08ELF079tQkJli76zZuW99kTu+2JHffr1y2HZClyE7eIu3nTcj49psnHxFiO+70nv3Ch9feurpZfGkhShQqSr2/S3uxxcdLSpZap8vnyWQr3ggUtC9EVLWr5M7GAT+y4581ruScAIFui421jdLwBAMgAUVHWxXjin8eOWYr4xK3gwsOTbwUXF2fn5P+fo6P1Hu7Nm0svvphU0Ht5SfPmWf4sVUp66ik7JwwAORuLq2UiFN4AAGRyifu3J27xljg1PizMMm3+6lVLlz0iIqmgP39eunHDUrjb459Srq5SxYpJK8Z7eVm67BERlmny7u7WW8EVLGh5r71AgeSd9sSvPT0txT8AIEWYag4AAJBS/9y/PbXMZkvnPLFgTyzMr11LKtyvXbMc4eFJW77dvWu5LjY2bTlHRUn79t3/3L17ls9Li7x5pfHjrQv61aul0FDLFPnBgy1FfKLEVwASi3gXl7R9LgBkc3S8bYyONwAAeKDE7d/+PU3++nVLRz3xvffwcEvn/dQpy8J00dGW4j2jF6XLl8/SNU8syq9elYKCks7nymW9FVzi+EKFrKfM/7vb/thjli49AGQhdLwBAACygn9u/5ZaZrOlCE8s2P9ZvCcuOnf5ctJWcOHhlqnoiSvKp6XbnnjvB4mNTersnzuX8vt26SLVr5/Uab98WRo3zvL1yy9L/folbfdmNktz5tx/gTpPT8ue8ACQydDxtjE63gAAIFOKjrYU4v8u2v9dyP9zcbq4uKR33TPyn5DOzkl7s3t4SH//ff9xJpNlcToPj6St4PLnT+q2/7NQf+IJqVEj6+vj43nPHUCK0fEGAADAw7m4pM/2bw8q2m/csExFv3bN8vXNm5Zrbt9O/UryMTGWDv6VKw8fZzZbVqy/c8fyfv3DlCwpvfBCUkHv5SW9955ldkCxYtK2bZZY4jZvc+ZIhw8/eHG6xK/d3JK68wDw/+h42xgdbwAAgH/59/Zv9yveb9xImiL/zyI/MjJjc3V1tRTmt2/ff4/3f3Nyskx5T+y4/7s4HznSeiG/sDDLLyi8vCwrz7NAHZBl0PEGAABA5uXqKhUtajlSKz7eemu3h02TT+y237iRtId7QkLqPi8qylIcp1RcXNJnnjyZ/HxoqFSkSFKnfdcu6c8/Lee+/FLq2DHp3MGD0ltvPbjL/qDvnfgnPpDZ0PG2MTreAAAAmYTZbFlYLiVF+/1Wmr97N2PzdXGxvIufWnnyWBfl/fpJ3bsnnY+JkX76yXK+TBmpdu10ShjIWeh4AwAAAP9mMlmK0jx5pOLFU399bGzSgnQpKd4T322/cSNt3fa0FN1S0nvuFy5Yvj97Vpo3L6mTniuXNHGi5VzNmtLnnye9554vn2XRucTp7496p/1+X3t48J478C90vG2MjjcAAACM7d9SWrQnfp14REXZN//UcHCQKlSQDh2yjk+ZIh09ainQhwyxvAuf6No1yy8nvLwscabLIwug4w0AAABkJiaTpRPs4SE99ljqr4+OTtojPTXT5BO/zkgJCZYCu0IF6076tm2W7rtk+b5EiaTzv/wijR2bdA9395S91/7vcyVLWlaWBzIZOt42RscbAAAAdpWQYOkmp6ZQ/+d0+ZgYe2afOqtXSy1aJH2/dav09tuWovy11yxbyCWKiZH+97/7F/B58jBdHo9ExxsAAACAhYNDUkFZunTqr793L21Fuz22f2vf3rKNW2In/d49KTDQcs5sls6fTzoXFye9/PL97+PomFSMP2ol+fLlpXr1rK9PSLD83IH/R8fbxuh4AwAAIMeKi7MU32kp2m/etFyf2XXuLP32m2XRukTFi1v2fa9YUdq503r8jz9atpp71NR5Z+eMegKkER1vAAAAAPbn5GTpQOfPn/prE7d/S+377Il/3r6dnk/yYAsXWorkPHmSuumXLiW96z54cNJ77l5e0oQJUlDQo+/r6vrw4vyjj6TcuZPGX7xoeW4vL6lQIetfBMDu6HjbGB1vAAAAwA5iY60XpEtN0R4eLsXH2y/3lDh0SPL2thTajo7S++9LY8ZYzq1dKzVrljR22zbpww8fPmX+38W9pyfT5R+BjjcAAACAnC1XLkvnt1Ch1F9rNlumiqekaL/fuXv30vNJ7q9SpaSvPT2t94n/9FNp7lxLlz1fPun4cWn9+tTd32Sy3PefRfkbb0jPPps05s4dyzR7Ly+pbFmpWrW0P082R+ENAAAAAP+UWHR6ev637d/SMk0+IsJS+KfGvxex27gx9Tn/m9lsySUiIilWo4b0+ONJU+cvX5Z697ace/llac4c63s8/rjlZ/Gwd9kf9rWbW7ZZXZ6p5jbGVHMAAAAAKZa4/VtaivabN+23/VvBglKVKtbvs48f/9+m7A8ZYr2/eybDVHOk2O2Y29p+brtalm1p71QAAAAA/HP7t7S4dy/lRfu/z/2X7d+uXUv9dPZHOXRImj1b6t49fe9rB3S8bSyzd7wHrxys73Z9pxf9XtT41uNVOE9he6cEAAAAwB7+uf1bWjruttj+rUABS1GfCaWm1qPwtrHMXHgfunJIladUllmW/wTyu+XXuJbj1L1qd5myybsUAAAAADLAP7d/S0vRfufO/e/7xBNSaGiGPUZqUHhnIpm58Dabzfop6Ce9vfpt3Yy6acSbP95cP7T7QWXzl7VjdgAAAAByjMTt3/5doDs5Wa+knolQeGcimbnwTnT59mUNXjVYvx38zYi5OblpVONRGlJviJwcWAoAAAAAAP4pNbUeO6JD3u7e+vXZX7Ws2zI9lteyXcK9uHt6b+17qjW9lvZe3GvnDAEAAAAg66LwhqFtubY6NPCQ3qzzpkyyvOMddClItWfU1tur3tadmAe8dwEAAAAAeCAKb1hxd3bX+NbjtbP3TlUuXFmSlGBO0Lid4+Q3xU+rjq+yc4YAAAAAkLVQeOO+ahevrb199+rzpp/LxdFFknQ6/LRaz2utlxe+rKt3rto5QwAAAADIGii88UC5HHNpeIPhCh4QrCalmxjxecHz5DvJV7P3zxZr8wEAAADAw2WKwnvy5MkqU6aMXF1d5e/vry1btqToum3btsnJyUnVqlWzisfGxmr06NEqW7asXF1dVbVqVa1cudJqTOnSpWUymZIdAQEBxpiePXsmO1+3bt3//LxZTbkC5bSu+zrNfGam8rnmkyRdv3ddPRb3UKu5rXTy5kk7ZwgAAAAAmZfdC+/58+dr8ODBGjFihAIDA9WgQQO1adNGZ8+efeh1ERER6t69u5o1a5bs3MiRIzV16lRNmDBBhw8fVv/+/dWpUycFBgYaY/bs2aOwsDDjWLNmjSTp+eeft7pX69atrcYtX748HZ466zGZTHq1+qsKCQhRl0pdjPiak2vkN9lPX2/7WnEJcXbMEAAAAAAyJ7vv412nTh3VqFFDU6ZMMWK+vr7q2LGjxowZ88DrunbtqnLlysnR0VGLFy9WUFCQca5YsWIaMWKEVfe6Y8eOcnd319y5c+97v8GDB2vp0qUKDQ2VyWRZ0btnz54KDw/X4sWL0/x8WWEf77RYdmyZBiwboHOR54xY9SLVNeOZGapRtIYdMwMAAAAA28sy+3jHxMRo7969atmypVW8ZcuW2r59+wOvmzVrlk6cOKGPPvrovuejo6Pl6upqFXNzc9PWrVsfmMfcuXP16quvGkV3oo0bN6pw4cIqX768+vTpoytXrjz0maKjoxUZGWl1ZEftyrfToYGH9EbtN4ytxwIvBarW9FoaunooW48BAAAAwP+za+F97do1xcfHy9vb2yru7e2tS5cu3fea0NBQDRs2TPPmzZOTk9N9x7Rq1Urjxo1TaGioEhIStGbNGv35558KCwu77/jFixcrPDxcPXv2tIq3adNG8+bN0/r16zV27Fjt2bNHTZs2VXR09AOfacyYMcqbN69xlCxZ8iE/gazNw8VD37X5Tjte22G19djYHWNVeUplrT6x2s4ZAgAAAID92f0db0nJusxmszlZTJLi4+PVrVs3ffzxxypfvvwD7/fdd9+pXLlyqlChgpydnTVo0CD16tVLjo6O9x0/c+ZMtWnTRsWKFbOKd+nSRe3atZOfn5/at2+vFStW6NixY1q2bNkDP3v48OGKiIgwjnPnzj1wbGYRGR2pfkv66Xzk+TRdX6dEHe3tu1efNf3M2HrsVPgptZrbSt0Xdde1u9fSM10AAAAAyFLsWngXLFhQjo6OybrbV65cSdYFl6Rbt27p77//1qBBg+Tk5CQnJyeNHj1a+/fvl5OTk9avXy9JKlSokBYvXqw7d+7ozJkzOnLkiNzd3VWmTJlk9zxz5ozWrl2r3r17PzLfokWLqlSpUgoNDX3gGBcXF3l6elodmd3HGz/WtH3TVGFiBX297WvFxMek+h65HHPp/Qbv68CAA2pUqpERn3Ngjnwn+WrugblsPQYAAAAgR7Jr4e3s7Cx/f39jRfFEa9asUf369ZON9/T0VHBwsIKCgoyjf//+8vHxUVBQkOrUqWM13tXVVcWLF1dcXJwWLFigDh06JLvnrFmzVLhwYbVr1+6R+V6/fl3nzp1T0aJFU/mkmdedmDv69eCvlq9j7+jdte+q2g/VtP7U+jTdr3yB8lrfY72mt58uL1cvSdK1u9f0yqJX1GZeG526eSq9UgcAAACALMHuU82HDBmiGTNm6Mcff1RISIjeeustnT17Vv3795dkmbrdvXt3SZKDg4P8/PysjsKFC8vV1VV+fn7KkyePJGnXrl1auHChTp48qS1btqh169ZKSEjQu+++a/XZCQkJmjVrlnr06JHsffHbt29r6NCh2rFjh06fPq2NGzeqffv2KliwoDp16pQBP5mMkcc5jw4OPKiBNQcai6SFXAtRs9nN9OKCF3Uh8kKq7+lgclDvGr0VEhCi5ysmbc+26sQq+U3x09jtY9l6DAAAAECOYffCu0uXLho/frxGjx6tatWqafPmzVq+fLlKlSolSQoLC3vknt7/FhUVpZEjR6pixYrq1KmTihcvrq1bt8rLy8tq3Nq1a3X27Fm9+uqrye7h6Oio4OBgdejQQeXLl1ePHj1Uvnx57dixQx4eHml+3swov1t+TWo3SXv67FGd4kmzBn47+JsqTKqgsdvHKjY+NtX3LeJeRL8//7v+6vqXSniWkCTdjb2roWuGqu6MugoMC3zEHQAAAAAg67P7Pt7ZXVbbxzvBnKBZgbP03tr3dP3edSNesVBFTWo7SY1LN07TfSOjIzVi3QhN2jNJZln+k3M0OWpIvSEa1XiUcufKnR7pAwAAAECGSE2tR+FtY1mt8E50494NjVg3QlP3TjUKZUnqVrmbvmnxjYp6pO099x3ndqjPkj46dPWQEXs83+Oa+vRUNX+8+X/OGwAAAAAyQmpqPbtPNUfmlN8tv6Y8PUW7++xWrWK1jPgvwb/IZ6KPvt3xbZqmn9crWU/7+u3TJ00+kbOjsyTp5M2TajGnhXos7qHrd68/4g4AAAAAkLXQ8baxrNrx/qcEc4Jm7pupYeuG6ca9G0bcr7CfJrWdpIalGqbpvkevHVXfpX21+cxmI1Ywd0GNbzVe3Sp3u+9e7gAAAACQGdDxRrpyMDmoj38fHRt0TH1r9DVWPz945aAa/dRIG09vTNN9fQr6aEOPDZr29DTldckrybL12MuLXlbbX9rqdPjpdHoCAAAAALAfCm+kWIHcBTS1/VTt7L1TNYvVlCTVLVE3zR1vKamoDwkI0XMVnzPiK4+vVKXJlTRuxzi2HgMAAACQpTHV3Mayw1Tz+4lPiNeMfTNUu3htVS9a3ercsevHVL5A+TTd96+jf2ngsoG6cCtp/3D/ov6a8cwMVStS7b+kDAAAAADphqnmsDlHB0f1q9kvWdG9+cxmVZhYQT0W99Dl25dTfd9nfJ7R4YDDCqgVYExp3xu2VzWn1dR7a97T3di76ZI/AAAAAGQUCm+km9j4WAUsD5BZZs3eP1uLjyxO0308XTw1se1EbX11qyoWqihJijfH66vtX6nKlCpad3JdOmYNAAAAALZF4Y1042ByUECtAOVzzSf/ov7qXaP3f7pf/ZL1FdgvUB83/tjYeuzEzRNqPqe5ev3Zi63HAAAAAGQJvONtY9n1He+HuXrnqm7cuyGfgj5W8VmBs9SufDsVzlM41fcMuRqivkv7auvZrUasUO5C+q71d+rq15WtxwAAAABkKN7xhl0VylMoWdG949wOvfrXq/KZ6KNJuycpPiE+Vff0LeSrTT036Yd2P8jTxfIf9dW7V9VtYTe1+6WdzoSfSbf8AQAAACA9UXgjQ4xYP0KSFB4VrkErBqnW9FracW5Hqu7hYHJQv5r9FBIQos6+nY34iuMrVGlyJY3fOT7VBT0AAAAA2BqFNzLEr8/+qh5VexjfB14KVP0f6+u1P1/T1TtXU3WvYh7FtOCFBVrUZZGKeRSTJN2JvaO3Vr2lejPraf+l/emaOwAAAAD8FxTeyBDe7t76qeNP2tJri6p4VzHiPwb9qPITy2vKnimp7lZ3rNBRhwce1oCaA4zYnot7VHN6TQ1fO1z3Yu+lW/4AAAAAkFYsrmZjOXFxtUeJS4jT5D2T9cGGDxQZHWnEaxStocltJ6tOiTqpvue2s9vUZ0kfhVwLMWJP5H9CU5+eqqZlmqZL3gAAAACQiMXVkKk5OTjpjTpv6Oigo3qlyitGfF/YPtWdWVd9/uqja3evpeqeTz72pAL7BWpUo1HK5ZBLknT8xnE1m91Mr/75qm7cu5GuzwAAAAAAKUXH28boeD/a5jObFbA8QAevHDRi+VzzaUyzMepdo7ccHRxTdb+QqyHqs6SPtp3bZsQK5yms71p/py6VurD1GAAAAID/jI43spSGpRpqX999+rbVt/Jw9pAk3Yy6qf7L+qvuzLrac2FPqu7nW8hXm3tt1pR2U4ytx67cuaIXF7yo9r+219mIs+n+DAAAAADwIBTeyBRyOebS4LqDdXTQUb1U+SUj/vfFv1VnRh1N2j0pVfdzMDmof83+OjzwsDpV6GTEl4UuU8VJFfXdzu/YegwAAABAhqDwRqZS1KOo5naeq409NqpSoUqSLO+EtyjbIk33K+5ZXAu7LNSCFxaoqHtRSZatxwavGqz6P9ZX8OXgdMsdAAAAAO6Hd7xtjHe80y42PlYTdk9QVFyU3m/wvtW5u7F3lTtX7lTdLzwqXMPWDtPUvVONmJODk96t/64+aPSBXJ1c0yVvAAAAANlfamo9Cm8bo/BOf/di76nKD1XUvExzfdbsM+V3y5+q67ec2aK+S/vqyLUjRqxc/nKa1n6aGpdunM7ZAgAAAMiOWFwN2dqX277U8RvH9cPeH9RnSZ9UX9+gVAMF9QvShw0/NLYeC70RqiY/N1Hvv3rr5r2b6Z0yAAAAgByMwhtZTgG3AnJ3dpeTg5M+afJJmu7h4uSij5t8rMB+gapXop4Rnxk4U76TfPX7od/FZBAAAAAA6YGp5jbGVHPbuBB5QZvPbNaLlV+0igdfDlYJzxLK55YvxfdKMCfoh79/0LC1w3Qr5pYRf7r805rcdrJK5i2ZbnkDAAAAyB6Yao5sr7hn8WRFd3RctJ79/Vn5TPTRrMBZSjAnpOheDiYHDaw1UIcDDquDTwcjvvTYUlWcXFETdk1g6zEAAAAAaUbhjWxj3I5xCr0Rqqt3r+rVv17VUz8+paBLQSm+voRnCS3qskh/PP+HirgXkSTdjrmtN1a+oadmPaWDVw7aKHMAAAAA2RmFN7KNV6q+ohcqvWB8v+P8DvlP89fry19XeFR4iu5hMpn0bMVnFRIQor41+hrxned3qvrU6hq5fqSi4qLSO3UAAAAA2RjveNsY73hnvLUn12rQ8kE6ev2oESucp7C+av6VulftLpPJlOJ7bT6zWX2W9NGx68eMWPkC5TXt6WlqVLpRuuYNAAAAIOvgHW/kaM0fb64DAw7oi2ZfKHeu3JKkK3euqOefPdXwp4baf2l/iu/VsFRD7e+/XyMbjJSTg5Mk6dj1Y2r8c2P1XdI3xZ10AAAAADkXHW8bo+NtX2cjzmrIqiFaELLAiDmYHDSo1iCNbjJaeV3zpvheB68cVJ8lfbTz/E4jVsS9iCa0maBnfZ9NVScdAAAAQNZGxxv4f4/lfUx/vPCHVr60UuXyl5Nk2T7s+93fy2eij+bsn5Pi/br9Cvtpa6+tmtBmgtyd3SVJl25f0vP/e14d53fU+cjzNnsOAAAAAFkXhTdyhFZPtFLwgGB91vQzuTm5SZIu37ms7ou7q9FPjRR8OThF93F0cNSg2oN0eOBhtS/f3oj/dfQvVZxUUZN2T0rxNmYAAAAAcgammtsYU80znzPhZ/TWqre06MgiI+ZoctSwp4bp06afpvg+ZrNZfxz+Q6+veF2X71w24vVK1NP09tNVqXCldM0bAAAAQObBVHPgIUp5ldLCLgu1vNtylc1XVpIUb45XXpeUv+8tWbYee77S8woJCFHv6r2N+I7zO1R9anV9uOFDth4DAAAAQMfb1uh4Z25RcVH6Zvs3WnRkkXa8tkPOjs5pvtem05vUZ0kfhd4INWI+BXw0vf10NSjVID3SBQAAAJBJpKbWo/C2MQrvrCE+IV6ODo5WsXfXvKsEc4I+avSRPFw8UnSfqLgofbr5U3257UvFJcQZ8b41+urLFl/Ky9UrPdMGAAAAYCcU3pkIhXfWdODyAdWYWkPx5niVzVdWIQEhyuWYK8XXB18OVp8lfbTrwi4jVtS9qCa2najOvp1tkTIAAACADMQ73sB/tP/SfqPQ7lWtV6qKbkmq7F1Z217dpu9af6c8ufJIksJuh+nZ359Vp/mddCHyQrrnDAAAACBzouNtY3S8s66TN0/q621fa3zr8XJxcjHid2LuKMGckOLp52cjzmrgsoFaFrrMiHm6eOqLZl+oX81+cjDx+y8AAAAgq6HjDaSDx/M9rilPT7EquiXp400fy3eSr34/9LtS8nurx/I+piUvLtFvz/6mwnkKS5IioyM1cPlANZzVUIevHrZJ/gAAAAAyBwpvIBUOXz2sb3d+qwu3LqjLH13UYk4LHbl25JHXmUwmdfHropCAEL1a7VUjvu3cNlX7oZpGbRyl6LhoW6YOAAAAwE4ovIFUyJ0rt1qWbWl8v+7UOlWZUkXD1g7T7Zjbj7w+v1t+zewwU+u7r9cT+Z+QJMUmxOrjTR+r+tTq2np2q81yBwAAAGAfFN5AKpT2Kq2lLy7Vn13/VKm8pSRZCucvt30p30m++uPwHymaft6kTBMd6H9Aw58aLicHJ0lSyLUQNZjVQAOWDlBEVIRNnwMAAABAxmFxNRtjcbXs627sXX2x9Qt9ue1LxcTHGPEWj7fQhDYT5FPQJ0X3OXD5gHr/1Vt7Lu4xYsU8imlim4nq5Nsp3fMGAAAA8N+xuBqQAXLnyq3RTUbr0MBDavNEGyO+5uQaVZ5SWe+ve193Yu488j5VvKtox2s7NL7VeGPrsYu3Lqrz753VeX5nXbx10WbPAAAAAMD26HjbGB3vnMFsNuvPo3/qzZVv6mzEWSNe0rOkxrcer04VOslkMj3yPmfCz2jg8oFaHrrciHm6eOrL5l+qr39fth4DAAAAMgk63kAGM5lM6liho0ICQjSiwQg5OzpLks5FntOzvz+rNvPaKPR66CPvU8qrlJa+uFS/Pvur1dZjA5YNUKOfGinkaohNnwMAAABA+qPwBtJR7ly59WnTTxU8INhq9fNVJ1bJb4qfRq4f+chtw0wmk7r6dVVIQIh6VetlxLee3apqU6vp440fs/UYAAAAkIVQeAM2UL5Aea18aaUWvLBAJT1LSpJi4mO0+sRqYxXzR8nvll8/dvhR67qvU9l8ZY17jNo0StWnVtf2c9ttlj8AAACA9EPhDdiIyWRSZ9/OCgkI0fCnhsvVyVWT202Wo4Njqu7TtExTBQ8I1ntPvidHk+XakGsheurHpxSwLECR0ZG2SB8AAABAOmFxNRtjcTUkunrnqgrlKWQV23Fuh1YcX6FhTw1T7ly5H3mPoEtB6rOkj/6++LcRK+5RXJPaTlKHCh3SPWcAAAAA98fiakAm9O+iOy4hTgOXD9Qnmz9RpcmVdPLmyUfeo1qRatr52k6NaznOKNQv3LqgjvM76rnfn1PYrTCb5A4AAAAg7Si8ATvZfWG3Dl45KEnK55pPpfKWStF1jg6OeqveWzo08JBaP9HaiC8IWSDfSb6atneaEswJNskZAAAAQOpReAN2Ur9kfR3of0DNH2+uSW0nJXv3Oz4h/qHXl/YqreXdlmte53kqlNvSTY+IjlC/pf3U+KfGOnLtiM1yBwAAAJByFN6AHfkW8tWaV9aoXsl6VvHdF3bLZ6KPlh5b+tDrTSaTulXuppCAEPWo2sOIbzm7RVV/qKpPNn2imPgYm+QOAAAAIGUovIFMJj4hXgHLA3Ti5gm1/7W9nvn1GZ26eeqh1xTIXUA/dfxJa15Zo8fzPS7JsvXYhxs/VI2pNbTj3I6MSB0AAADAfVB4A5lMeFS43J3dje+XHFuiipMravSm0YqKi3rotc0fb67gAcF6t/67xtZjh64e0pM/PqlBywex9RgAAABgB2wnZmNsJ4a0MJvN+u3gb3p79dsKu520UnnZfGU1oc0EtSnX5pH3CAwLVJ8lfbQ3bK8RK+5RXJPbTdYzPs/YJG8AAAAgp2A7MSCLM5lMerHyizoy6Ijerve20b0+cfOE2v7SVp3md9Lp8NMPvUf1otW1s/dOjW051mrrsQ6/ddDz/3uerccAAACADJIpCu/JkyerTJkycnV1lb+/v7Zs2ZKi67Zt2yYnJydVq1bNKh4bG6vRo0erbNmycnV1VdWqVbVy5UqrMaNGjZLJZLI6ihQpYjXGbDZr1KhRKlasmNzc3NS4cWMdOnToPz0rkBqeLp76puU3CuofpEalGhnxxUcWy3eSrz7d/OlDp587OThpSL0hOjjgoFqVbWXE/zj8h3wn+Wr63ulsPQYAAADYmN0L7/nz52vw4MEaMWKEAgMD1aBBA7Vp00Znz5596HURERHq3r27mjVrluzcyJEjNXXqVE2YMEGHDx9W//791alTJwUGBlqNq1SpksLCwowjODjY6vxXX32lcePGaeLEidqzZ4+KFCmiFi1a6NatW//9wYFU8Cvspw09Nmhe53kq4m75BVFUXJQ+2PCBKk+prJXHVz70+jL5ymjFSys0t9NcFcxdUJJl67G+S/uqyc9NdPTaUZs/AwAAAJBT2f0d7zp16qhGjRqaMmWKEfP19VXHjh01ZsyYB17XtWtXlStXTo6Ojlq8eLGCgoKMc8WKFdOIESMUEBBgxDp27Ch3d3fNnTtXkqXj/e/r/slsNqtYsWIaPHiw3nvvPUlSdHS0vL299eWXX6pfv34pej7e8UZ6i4yO1KiNo/T9ru8Vb07a67tThU76ttW3KuVV6qHXX7t7TW+vfluz9882Yi6OLvqg4Qd658l35OzobLPcAQAAgOwiy7zjHRMTo71796ply5ZW8ZYtW2r79u0PvG7WrFk6ceKEPvroo/uej46Olqurq1XMzc1NW7dutYqFhoaqWLFiKlOmjLp27aqTJ08a506dOqVLly5Z5ebi4qJGjRo9NLfo6GhFRkZaHUB68nTx1LhW4xTYL1ANHmtgxBcdWSTfSb76fMvnio6LfuD1BXMX1M8df9aql1epjFcZSVJ0fLRGbhgp/2n+2nV+l82fAQAAAMhJ7Fp4X7t2TfHx8fL29raKe3t769KlS/e9JjQ0VMOGDdO8efPk5OR03zGtWrXSuHHjFBoaqoSEBK1Zs0Z//vmnwsKSFpOqU6eOZs+erVWrVmn69Om6dOmS6tevr+vXr0uS8fmpyU2SxowZo7x58xpHyZIlH/2DANKgsndlbeq5SXM6zZF3Hst/p/fi7umjjR/pVPjD9/2WpJZlWyp4QLCG1hsqB5PlfwoOXjmoejPr6Y0Vb+hWNK9UAAAAAOnB7u94S5YVnP/JbDYni0lSfHy8unXrpo8//ljly5d/4P2+++47lStXThUqVJCzs7MGDRqkXr16ydHR0RjTpk0bPfvss6pcubKaN2+uZcuWSZJ+/vnnNOWWaPjw4YqIiDCOc+fOPfjBgf/IZDLp5Sov6+igo3qzzptyMDno7Xpvq0LBCim6Po9zHn3d8mvt6bNH1YtUlySZZdaE3RNUaXIlLT221JbpAwAAADmCXQvvggULytHRMVkH+cqVK8k6zZJ069Yt/f333xo0aJCcnJzk5OSk0aNHa//+/XJyctL69eslSYUKFdLixYt1584dnTlzRkeOHJG7u7vKlCnzwFzy5MmjypUrKzQ0VJKMFc5TmlsiFxcXeXp6Wh2AreV1zavxrcdrX999GtlwpNW56LhoTdo9STHxMQ+8vkbRGtrdZ7e+bvG13JzcJEnnIs+p/a/t1eWPLrp0+8GzPAAAAAA8nF0Lb2dnZ/n7+2vNmjVW8TVr1qh+/frJxnt6eio4OFhBQUHG0b9/f/n4+CgoKEh16tSxGu/q6qrixYsrLi5OCxYsUIcOHR6YS3R0tEJCQlS0aFFJUpkyZVSkSBGr3GJiYrRp06b75gZkBlWLVJW7s7tV7OvtX2vQikGqMqXKQ9/fdnJw0tD6Q3Vw4EG1eLyFEf/90O/yneSrmftmys5rMQIAAABZ0v1fks5AQ4YM0SuvvKKaNWuqXr16mjZtms6ePav+/ftLskzdvnDhgmbPni0HBwf5+flZXV+4cGG5urpaxXft2qULFy6oWrVqunDhgkaNGqWEhAS9++67xpihQ4eqffv2euyxx3TlyhV9+umnioyMVI8ePSRZpvAOHjxYn3/+ucqVK6dy5crp888/V+7cudWtW7cM+MkA/114VLi+3PalJOn4jePKnSv3I695PN/jWvXyKs09MFdvrXpL1+9dV3hUuHov6a25wXM17elpKlegnK1TBwAAALINuxfeXbp00fXr1zV69GiFhYXJz89Py5cvV6lSli2RwsLCHrmn979FRUVp5MiROnnypNzd3dW2bVvNmTNHXl5expjz58/rxRdf1LVr11SoUCHVrVtXO3fuND5Xkt59913du3dPAwcO1M2bN1WnTh2tXr1aHh4e6fLsgK15uXppS68tGrhsoOqWqKvK3pVTdJ3JZNIrVV9R6ydaa8jqIZp7wLIN38bTG1V5SmV92OhDvVP/HeVyzGXL9AEAAIBswe77eGd37OONzCDBnKDY+Fi5OLkYsZj4GHX9o6sG1R6kpmWaPvT6VcdXqf+y/jodftqIVS5cWdPbT1edEnUefCEAAACQTWWZfbwBZAwHk4NV0S1J43eO16Iji9RsdjO9uOBFXYi88MDrWz3RSgcHHNTb9d42th4LvhKsejPr6c0Vb7L1GAAAAPAQFN5ADmQ2m7X6xGrj+98O/qYKkypo7Paxio2Pve81eZzz6JuW32h3791WW499v/t7VZpcScuOLcuQ3AEAAICshsIbyIFMJpNWv7JaM9rPUAG3ApKk2zG3NXTNUFWbWk0bT2984LX+xfy1u89ufdX8K6utx57+9Wl1/aOrLt++nBGPAAAAAGQZFN5ADuVgctBrNV7TsdePqb9/f5lkkiQdvnpYTX5uopcWvqSwW2H3vdbJwUnvPPmOggcEq/njzY34/EPz5TvJVz8G/sjWYwAAAMD/Y3E1G2NxNWQVf1/8WwOXDdSei3uMmIezhz5u/LEG1R70wBXMzWaz5hyYo7dWvaUb924Y8Salm2jq01PZegwAAADZEourAUi1msVqamfvnZr29DTld8svSboVc0tDVg9RjWk1tPnM5vteZzKZ1L1qdx0JOKKXKr9kxDec3qAqP1TRF1u/eOB74wAAAEBOQMfbxuh4Iyu6fve63l/3vqbvmy6zkv4n4uUqL+vrFl+riHuRB167InSFBiwboDMRZ4xYVe+qmt5+umoVr2XTvAEAAICMQscbwH9SIHcBTW0/VTt771TNYjWN+NwDc+Uz0Uff7fxOcQlx9722Tbk2OjjwoN6q+5ax9dj+y/tVd2ZdvbXyLd2OuZ0hzwAAAABkFhTeAB6odvHa2vnaTv3Q7gflc80nSYqMjtTgVYM1Y9+MB17n7uyuca3GaedrO1XVu6okKcGcoPG7xstvsp9WhK7IkPwBAACAzIDCG8BDOTo4ql/Nfjr2+jH1rt5bklS+QHn1qtbrkdfWKl5Le/rs0RfNvpCrk6sk6UzEGbX9pa26LeimK3eu2DR3AAAAIDPgHW8b4x1vZDe7zu9SXEKcnnzsSav41rNbVbdEXTk5ON33uuM3jqvf0n5af2q9Ecvvll9jW45Vj6o9ZDKZbJo3AAAAkJ54xxuAzdQpUSdZ0X3oyiE1+bmJak6rqe3ntt/3uifyP6G1r6zVrA6zjGnrN+7dUK8/e6nFnBY6ceOEzXMHAAAA7IHCG8B/9sbKNxSXEKf9l/drzYk1DxxnMpnUs1pPHRl0RC/6vWjE151aJ78pfvpy65dsPQYAAIBsh6nmNsZUc+QEO87t0MDlAxUZHalDAw8Z73M/yvLQ5RqwbIDORpw1YlW9q2rGMzOsVlMHAAAAMpvU1HoU3jZG4Y2cIj4hXuciz6m0V2mr+NS/p6qKdxXVK1nvvtfdjrmtD9Z/oO93f68Ec4IkycHkoDfrvKlPmnyiPM55bJ06AAAAkGoU3pkIhTdysqPXjqrylMqKTYjVq9Ve1RfNv1ChPIXuO3bPhT3qvaS3Dlw+YMRK5S2lH57+Qa2faJ1RKQMAAAApwuJqADKF73d9r9gEyzvbPwb9KJ+JPpqyZ4riE+KTja1VvJb+7vO3xjQbY7X1WJt5bfTSwpd09c7VDM0dAAAASC8U3gBs5rs23+m71t/J08XyG8CbUTc1cPlA1ZlRR7sv7E42PpdjLg17apgO9D+gJqWbGPFfgn9RhUkV9HPQz2KSDgAAALIaCm8ANuPk4KQ36ryho4OOqnvV7kZ8b9he1Z1RV32X9NW1u9eSXVeuQDmt675OPz7zo9XWYz3/7KmWc1uy9RgAAACyFApvADZXxL2Ifu74szb33KzKhStLkswya/q+6fKZ6KOpf09NNv3cZDKpV/VeCgkIUZdKXYz42pNrVXlKZX297WvFJcRl6HMAAAAAacHiajbG4mqAtbiEOE3aPUkfbPhAt2JuGfGaxWpqctvJqlW81n2vW3ZsmQYsG6BzkeeMWPUi1TXjmRmqUbSGzfMGAAAA/onF1QBkWk4OTnqz7ps6OuioXq7yshH/++LfqjOjjvot6afrd68nu65d+XY6NPCQ3qj9hkwySZICLwWq1vRaGrp6qO7E3MmwZwAAAABSg463jdHxBh5u0+lNClgeoENXDxmx/G759UWzL/RajdfkYEr++8Fd53epz5I+Cr4SbMTKeJXRD0//oJZlW2ZI3gAAAMjZ6HgDyDIalW6kwH6BGtdynNyd3SVZFlLru7Sv+i7pe99r6pSoo7199+qzpp/JxdFFknQq/JRazW2lVxa9ct8F2wAAAAB7ofAGYHe5HHPprXpv6eigo+pWuZsR712j90Oveb/B+zow4IAal25sxOcemKsKEytozv45bD0GAACATIGp5jbGVHMg9Tae3qitZ7dqZMORVvHLty+rUJ5Cyaafm81m/Rj4o4auGarwqHAj3rJsS/3Q7geVyVcmI9IGAABADpKaWo/C28YovIH0EZ8Qr9ozasvZ0VmT2k6670rml25f0psr39Tvh343Ym5ObhrdZLQG1x0sJwenjEwZAAAA2RjveAPIdqbtnaZ9Yfu08/xOvfbXa/edRl7EvYjmPzdff3X9SyU8S0iS7sXd0ztr3lGdGXW0L2xfRqcNAAAAUHgDyBp8C/mqQsEKkqTvW38vk8n0wLHtfdrr8MDDer3268bWY/vC9qn29Np6Z/U7uht7N0NyBgAAACSmmtscU82B9BMTH6PVJ1br6fJPW8UPXjmo2PhYVS9aPdk1O8/vVO+/elttV1bGq4ymPj1VLcq2sHnOAAAAyJ6Yag4gW3J2dE5WdCeYE9T7r96qOb2mXl/+utXiapJUt0Rd7eu3T580+UTOjs6SLFuPtZzbUj0W92DrMQAAANgchTeALO23g79p14VdSjAnaOKeifKZ6KOfg35WgjnBGOPs6KyRDUfqQP8DaliqoRGfvX+2fCf5at6BeWw9BgAAAJuh8AaQpT3r+6zGNBuj3LlyS5Ku3Lminn/2VMNZDbX/0n6rsT4FfbShxwZNe3qa8rrklSRdu3tNLy96WW3mtdHp8NMZnT4AAAByAApvAFmai5OLhj01TCEBIXrW91kjvu3cNtWYVkNvrnhTEVERRtzB5KA+/n0UEhCi5yo+Z8RXnVilSpMradyOcYpLiMvQZwAAAED2xuJqNsbiakDGWnV8lV5f8bpCb4QaMe883vq6xdd6ucrLyVZD/+voXxq4bKAu3LpgxPyL+mvGMzNUrUi1jEobAAAAWQyLqwHIsVo90UrBA4L1WdPP5ObkJkm6fOeyui/urkY/NVLw5WCr8c/4PKPDAYcVUCvA2Hpsb9he1ZxWU++teY+txwAAAPCf0fG2MTregP2cCT+jt1a9pUVHFhkxR5Oj3qjzhkY1HiVPF+v/n9x+brv6LOmjw1cPG7Gy+cpq6tNT1ezxZhmWNwAAADI/Ot4AIKmUVykt7LJQy7stV9l8ZSVJ8eZ4fbvzW/lM9NEvwb9YrWZev2R9BfYL1OjGo42tx07cPKHmc5qr5+Keun73ul2eAwAAAFkbHW8bo+MNZA5RcVH6Zvs3+mzLZ4qKizLizR9vrpUvrZSjg6PV+CPXjqjvkr7acnaLESuUu5DGtx6vF/1eTPauOAAAAHIWOt4A8C+uTq4a2XCkQgJC1MGngxGvUrhKsqJbkioUrKCNPTfqh3Y/GFPSr969qpcWvqR2v7TTmfAzGZY7AAAAsjY63jZGxxvInJaHLtcXW7/Qsm7L5OHiYcTjE+LlYHKw6mhfvHVRr694XQtDFhqxPLny6NOmn+r12q/ft3AHAABA9paaWo/C28YovIGsZcyWMVpzco0mtp2oioUqWp1bfGSxApYH6OKti0asVrFamt5+uqoWqZrRqQIAAMCOmGoOAGlwNuKsPt3yqTac3iD/af66fPuy1fmOFTrq8MDDGlBzgBHbc3GP/Kf5a/ja4boXey+jUwYAAEAWQOENAP8v7FaYvPN4S5L61Ogjb3fvZGPyuubV5HaTtbXXVvkW9JVkWSn9i21fqMoPVbT+1PoMzRkAAACZH1PNbYyp5kDWci/2nsbvHK8BtQbIy9XLiMfGx+pU+CmVL1DeiEXHReuLrV/o862fKyY+xoj3qtZL37T8Rvnd8mdk6gAAAMhATDUHgDRyy+Wm4Q2GWxXdkjRh9wT5TfbTsLXDdDvmtiTJxclFHzX+SEH9gvRkySeNsbOCZsl3kq9+O/ib+N0mAAAA6HjbGB1vIOu7eOuifCb6GAV3Cc8S+rbVt3rW91lj9fMEc4Km7Z2m99a+p8joSOPatuXaakq7KXos72N2yR0AAAC2QccbANKRl6uX3qr7lpwdnSVJ5yPP6/n/Pa9Wc1vp6LWjkiQHk4P61+yvwwMPq1OFTsa1y0OXq+Kkivpu53eKT4i3S/4AAACwLzreNkbHG8g+Qq+H6o2Vb2jl8ZVGLJdDLg2tP1QjGoxQHuc8RnxhyEINWj5IYbfDjFjt4rU1o/0MVfaunKF5AwAAIP3R8QYAGyhXoJyWd1uuRV0WGVPHYxNiNWbrGPlO8tXCkIXGO92dfTvrcMBh9fPvZ1y/+8Ju1ZhWQyPWjVBUXJRdngEAAAAZj463jdHxBrKnu7F39fmWz/X19q+tVjRvVbaVJrSZoHIFyhmxLWe2qO/Svjpy7YgRK5e/nKa1n6bGpRtnZNoAAABIJ3S8AcDGcufKrU+bfqrgAcFqWbalEV91YpX8pvhp5PqRuht7V5LUoFQDBfUL0ocNP1Quh1ySpNAboWrycxP1/qu3bt67aZdnAAAAQMag421jdLyB7M9sNmthyEK9teotnYs8Z8Qfy/uYvmv9nTr4dDBWPz905ZD6Lu2r7ee2G+O883jr+zbf6/mKzxvjAAAAkLnR8QaADGQymfRsxWcVEhCi4U8NN7raZyPOqtP8Tmr3SztduXNFklSpcCVt6bVFk9pOkoezhyTp8p3L6vJHFz3z2zM6F3HugZ8DAACArInCGwDSSR7nPPq82ecKHhCs5o83N+KhN0Ll6ZL0W1AHk4MG1hqowwGH1cGngxFfemypKk6uqAm7JrD1GAAAQDZC4Q0A6cynoI9Wv7xa/3v+fyrhWULft/5erk6uycaV8CyhRV0W6Y/n/1AR9yKSpNsxt/XGyjf05I9PKvhycEanDgAAABug8AYAGzCZTHqu4nMKfT1Ubcq1sTp35NoRdZrfSSdvnrSapt63Rl9jzK4Lu1RjWg2NXD+SrccAAACyOApvALChf3e6zWazBi0fpMVHFqvipIpaf2q9JMnL1UtT20/Vpp6b5FPAR5IUlxCnz7Z8pqo/VNWm05syPHcAAACkDwpvAMhAF29dVMi1EElSEfciqluirtX5hqUaKqh/kD5o+IGxSNux68fU+OfG6vNXH7YeAwAAyIIyReE9efJklSlTRq6urvL399eWLVtSdN22bdvk5OSkatWqWcVjY2M1evRolS1bVq6urqpatapWrlxpNWbMmDGqVauWPDw8VLhwYXXs2FFHjx61GtOzZ0+ZTCaro25d638kA0BqFPcsriMBR/RO/Xc0oc0E5c6V2+r87ZjbcnVy1egmoxXYL9CqMJ8ROEO+k3z1x+E/xE6QAAAAWYfdC+/58+dr8ODBGjFihAIDA9WgQQO1adNGZ8+efeh1ERER6t69u5o1a5bs3MiRIzV16lRNmDBBhw8fVv/+/dWpUycFBgYaYzZt2qSAgADt3LlTa9asUVxcnFq2bKk7d+5Y3at169YKCwszjuXLl6fPgwPIsTxcPPRVi6/U3qe9Vfz4jeMqMa6ERm8arai4KFUqXElbe23VhDYT5O7sLsmy9djz/3teHed31PnI8/ZIHwAAAKlkMtu5bVKnTh3VqFFDU6ZMMWK+vr7q2LGjxowZ88DrunbtqnLlysnR0VGLFy9WUFCQca5YsWIaMWKEAgICjFjHjh3l7u6uuXPn3vd+V69eVeHChbVp0yY1bNhQkqXjHR4ersWLF6f5+VKzqTqAnMtsNqvdL+204vgKSVLZfGX1fZvv1bZcW0nSuYhzClgeoCXHlhjXeDh7aEyzMRpQa4AcTHb/PSoAAECOkppaz67/UouJidHevXvVsmVLq3jLli21ffv2B143a9YsnThxQh999NF9z0dHR8vV1XpBIzc3N23duvWB94yIiJAk5c+f3yq+ceNGFS5cWOXLl1efPn105cqVhz4TAKRFXEKcfAv6ytHkKEk6cfOE2v3STh1/66jT4adVMm9J/dn1T/3+3O/yzuMtSboVc0uDVgzSUz8+pUNXDtkzfQAAADyEXQvva9euKT4+Xt7e3lZxb29vXbp06b7XhIaGatiwYZo3b56cnJzuO6ZVq1YaN26cQkNDlZCQoDVr1ujPP/9UWFjYfcebzWYNGTJETz31lPz8/Ix4mzZtNG/ePK1fv15jx47Vnj171LRpU0VHRz/wmaKjoxUZGWl1AMCj5HLMpbGtxiqof5AalmpoxP88+qd8J/nq082fKjo+Ws9Xel4hASHqXb23MWbH+R2qPrW6PtzwIVuPAQAAZEKZYm6iyWSy+t5sNieLSVJ8fLy6deumjz/+WOXLl3/g/b777juVK1dOFSpUkLOzswYNGqRevXrJ0dHxvuMHDRqkAwcO6Ndff7WKd+nSRe3atZOfn5/at2+vFStW6NixY1q2bNkDP3vMmDHKmzevcZQsWfJhjw4AVvwK+2ljj42a22muirgXkSRFxUXpgw0fqPKUylp5fKXyueXT9Gema2OPjSpfwPK/hbEJsfpk8yeq9kM1bTmTsgUqAQAAkDHsWngXLFhQjo6OybrbV65cSdYFl6Rbt27p77//1qBBg+Tk5CQnJyeNHj1a+/fvl5OTk9avt+yHW6hQIS1evFh37tzRmTNndOTIEbm7u6tMmTLJ7vn666/rr7/+0oYNG1SiRImH5lu0aFGVKlVKoaGhDxwzfPhwRUREGMe5c+dS8qMAAIPJZNJLVV7SkYAjGlxnsDH9/PiN42ozr406z++sM+Fn1Kh0I+3vv18jGoyQk4NlBtDR60fV8KeG6rekn8Kjwu34FAAAAEhk18Lb2dlZ/v7+WrNmjVV8zZo1ql+/frLxnp6eCg4OVlBQkHH0799fPj4+CgoKUp06dazGu7q6qnjx4oqLi9OCBQvUoUMH45zZbNagQYO0cOFCrV+//r5F+b9dv35d586dU9GiRR84xsXFRZ6enlYHAKRFXte8+rb1t9rXb5+eeuwpI77oyCL5TvLV51s+l0kmfdr0U+3ru091iif9b+C0fdNUcVJFLTi8gK3HAAAA7MzuU82HDBmiGTNm6Mcff1RISIjeeustnT17Vv3795dk6SB3795dkuTg4CA/Pz+ro3DhwnJ1dZWfn5/y5MkjSdq1a5cWLlyokydPasuWLWrdurUSEhL07rvvGp8bEBCguXPn6pdffpGHh4cuXbqkS5cu6d69e5Kk27dva+jQodqxY4dOnz6tjRs3qn379ipYsKA6deqUwT8lADlZFe8q2txzs2Z3nK3CeQpLku7F3dOI9SNUeUplrT6xWpW9K2vbq9v0fevvja3Hwm6H6bn/PadO8zvpQuQFez4CAABAjmb3wrtLly4aP368Ro8erWrVqmnz5s1avny5SpUqJUkKCwt75J7e/xYVFaWRI0eqYsWK6tSpk4oXL66tW7fKy8vLGDNlyhRFRESocePGKlq0qHHMnz9fkuTo6Kjg4GB16NBB5cuXV48ePVS+fHnt2LFDHh4e6fb8AJASJpNJr1R9RUcHHdUbtd8wtg8LvRGqVnNb6bnfn9PFWxf1ep3XdXjgYT1d/mnj2sQF2ibvmawEc4K9HgEAACDHsvs+3tkd+3gDsIX9l/YrYHmAtp3bZsS29tqqJx97UpLldZr/Hf6f3ljxhi7fuWyMqV+yvqa3n66KhSpmeM4AAADZSZbZxxsAkDZVi1TV5l6b9VOHn1QodyH1rNbTKLolS4f8hUovKCQgRK9Vf82Ibz+3XdV+qKaPNnyk6LgHb40IAACA9EPH28boeAOwtfCocMUnxKtA7gJGLMGcoA/Wf6ABtQaohGcJbTy9UX2X9FXojaRdGSoUrKDp7adbLdwGAACAlKHjDQA5iJerl1XRLUkz983U51s/V4WJFTR7/2w1Lt1Y+/vv1/tPvW9sPXbk2hE1mNVA/Zf2V0RUhD1SBwAAyBEovAEgm0kwJ2jcznGSpDuxd/RY3sckSW653PRZs8+0t+9e1S5e2xg/de9U+U7y1aKQRXbJFwAAILuj8AaAbMbB5KDtr27XwJoD9XKVl9W4dGOr85ULV9b2V7drfKvxypPLsg1j2O0wdf69szrP76yLty7aIWsAAIDsi3e8bYx3vAHYU4I5wdh6LPH7dr+0U/MyzfVGnTd08dZFDVw+UMtDlxtjPF089WXzL9XXv6/VtQAAAEjCO94AAElKVjjP3j9bK4+v1NA1Q1VtajWdCj+lpS8u1a/P/qrCeQpLkiKjIzVg2QA1+qmRQq6G2CNtAACAbIXCGwBykOM3jsskkyTp8NXDavJzE7286GU1LNVQIQEh6lWtlzF269mtqja1mj7e+DFbjwEAAPwHTDW3MaaaA8hs/r74twKWB2j3hd1GzMPZQx83/liDag/SlrNb1HdJX524ecI471vQV9PbT7faKxwAACAnY6o5AOCBaharqR2v7dC0p6cpv1t+SdKtmFsasnqIakyrIScHJwUPCNawJ4fJ0eQoSQq5FqKnZj2lgcsGsvUYAABAKtHxtjE63gAys+t3r+v9de9r+r7pMivpr4OXq7ysr1t8rcu3L6vPkj7ac3GPca6YRzFNajtJHSt0tEPGAAAAmQMdbwBAihTIXUBT20/Vzt47VbNYTSM+98Bc+Uz00cbTG7Wl1xZ92+pbY+uxi7cuqtP8Tnr292fZegwAACAFKLwBAKpdvLZ2vrZTP7T7Qflc80myrG4+eNVg1ZpeSzWL1dShgYfU5ok2xjULQxaq4qSKmvr3VCWYE+yVOgAAQKbHVHMbY6o5gKzm2t1rGr52uGYEzrCKd6/aXV82+1IbTm/Qmyvf1NW7V41zDR5roGntp6lCwQoZnS4AAIBdMNUcAJBmBXMX1PRnpmvHaztUo2gNIz57/2yN3TFWL1Z+USEBIepZradxbsvZLar6Q1WN3jRaMfExdsgaAAAg86LjbWN0vAFkZfEJ8Zq2d5reX/++XBxddHTQUeV1zWucX3dynfot7We19VjFQhU1vf101S9Z3x4pAwAAZAg63gCAdOHo4KgBtQbo2KBjWvDCAquiW5JiE2K1tvtavffke8bWY4evHtZTPz6lgGUBioyOtEfaAAAAmQqFNwDgkQrlKaQnH3vSKnY+8rye+/05VZ9aXaXyltKePnvkX9RfkmSWWZP/nqyKkyrqr6N/2SNlAACATIPCGwCQJh9u+FB3Yu8oPCpcB68cVPWi1bWz906NbTlWuXPlliRduHVBHX7roOf/97zCboXZOWMAAAD7oPAGAKTJF82/UM9qPVU4T2F92vRTSZKTg5OG1BuigwMOqlXZVsbYPw7/Id9Jvpq+dzpbjwEAgByHxdVsjMXVAGR3N+7dUH63/FaxGftmKCY+Rh7OHhqyeoiu3b1mnGtYqqGmPT1NPgV9MjpVAACAdMPiagCADPPvojvsVpjeXv22ApYHaPyu8Zrbaa66V+1unN98ZrOq/lBVn27+lK3HAABAjkDhDQBIV0uOLTFWM98Xtk+t57WWs4Oz/vfc/1TGq4wkKTo+Wh9s+ED+0/y18/xOe6YLAABgc0w1tzGmmgPIibac2aKA5QEKvhJsxPK75deoRqN0JuKMxu8cr3hzvCTJJJMCagXo82afy8PFw14pAwAApApTzQEAdtWgVAPt67dP41uNl4ezpZi+ce+G3lj5hjad2aSfOvykGkVrSLJsPTZxz0RVnFxRS44usWfaAAAANkHH28boeAPI6cJuhendte9q7oG5Rswkk16r8ZpKepTUF9u+0L24e8a55ys+r+/bfK8i7kXskS4AAECKpKbWo/C2MQpvALDYfGazApYH6OCVg0Ysv1t+Da03VBtOb9Cak2uMuJerl75u8bVeq/6aTCaTPdIFAAB4KKaaAwAynYalGmpf330a13Kc1fTz99e/r4ioCI1uPFoF3ApIksKjwtVnSR81+bmJjl0/Zs+0AQAA/jM63jZGxxsAkrt466LeWfOOfgn+xYiZZFKPqj10N/aufj/8uxF3cXTRBw0/0DtPviNnR2d7pAsAAJAMHW8AQKZWzKOY5nWepw09NqhioYqSLIus/bT/J52OOK2VL6202nps5IaR8p/mr13nd9kzbQAAgDSh8AYA2E3j0o0V1C9I37T4Ru7O7pKkDxp+oFZPtFLwgGC9Xe9tOZgsf1UdvHJQ9WbW05sr3tSt6Fv2TBsAACBVmGpuY0w1B4CUuRB5Qb8e/FVD6w+1ii8/tlzD1g2z2hO8pGdJTWk3Re3Kt8voNAEAACQx1RwAkAUV9yyerOg2m836bvd3unT7kp6v+LxcHV0lSeciz+npX59W1z+66vLty/ZIFwAAIMUovAEAmdaiI4u0+sRqXb17VTvP79TuPrvV/PHmxvn5h+bLd5Kvfgz8UUzgAgAAmRWFNwAg06pdvLZeqPSCJGl86/Gq7F1Zq19erZ87/qz8bvklSTejbuq1v15Ts9nNFHo91J7pAgAA3BfveNsY73gDwH+358Ie1SxWUyaTyYj9feFvvbHyDe04v8OIuTq56sOGH2po/aHK5ZjLHqkCAIAcgne8AQDZSq3itayKbkkavXm0dpzfoUqFKqmoe1FJUlRclN5f/75qTq+p3Rd22yNVAACAZCi8AQBZzp4Le7Tk2BJJ0qGrh3T5zmVVL1JdJlmK8wOXD6jezHoavHKwbsfctmeqAAAAFN4AgKynVvFaWvXyKpXLX06SlGBOUOClQOVzy6fH8j5mxL7b9Z0qTa6k5aHL7ZkuAADI4Si8AQBZUsuyLRU8IFifNf1Mbk5ukqQb927obMRZlfEqI2dHZ0nS2YizavdLO7244EVduXPFnikDAIAcisIbAJBluTi56P0G7yskIESdfTsb8VPhpxSfEK+SniWN2G8Hf1OFiRU0K3AWW48BAIAMReENAMjySnmV0oIXFmjFSyv0RP4nJEnx5nidizynvC55lSdXHkmWrcde/etVtZjTQsdvHLdnygAAIAeh8AYAZButn2it4AHB+rTJp8b084joCN2JvaPCuQsb49adWqfKUyrry61fKjY+1l7pAgCAHILCGwCQrbg6uWpEwxE6HHBYHSt0NOJX7l6Ro8lRHs4ekixbjw1bN0y1ptfS3xf/tlO2AAAgJ6DwBgBkS6W9SmtRl0Va1m2ZyuYrK8ky/fxWzC3lyZXH2Hps/+X9qjOjjoasGqI7MXfsmTIAAMimKLwBANla23JtdXDgQY1uPFquTq6SpE6+nbSr9y5V8a4iybL12Lc7v1WlyZW08vhKe6YLAACyIZOZpV1tKjIyUnnz5lVERIQ8PT3tnQ4A5Ginbp7S++vf17iW41TUo6hi42M1dsdYjdo4StHx0ca4bpW7aXyr8SqUp5AdswUAAJlZamo9Cm8bo/AGgMzviy1faNQm6+I7v1t+jWs5Tt2rdpfJZLJjdgAAIDNKTa3HVHMAQI52895Njd051ii6Exdfu3Hvhnr+2VMt57bUiRsn7JkiAADI4ii8AQA52r24e6pdvLYk6YVKLyj09VB19etqnF97cq0qT6msr7Z9pbiEOHulCQAAsjCmmtsYU80BIPMzm81acmyJahStoRKeJSRJy44tU/+l/XX+1nljXPUi1TW9/XT5F/O3V6oAACCTYKo5AACpYDKZ9IzPM0bRLUntyrfTh40+tBoXeClQtWfU1tDVQ9l6DAAApBiFNwAA93E75rY+2PCB8X3ivt8J5gSN3TFWflP8tPrEanulBwAAshAKbwAA7iNPrjz64ekf9FjexyRJZlm/mXU6/LRazW2lVxa9oqt3rtojRQAAkEVQeAMAcB8mk0kdK3RUSECIRjQYIWdH5/uOm3tgrnwn+WrO/jli2RQAAHA/FN4AADxE7ly59WnTTxU8IFgty7a875jr966r++LuajW3lU7ePJnBGQIAgMyOwhsAgBQoX6C8Vr60UgteWKCSniXvO2bNyTXym+ynb7Z/w9ZjAADAQOENAEAKmUwmdfbtrJCAEA1/arhyOeRKNuZe3D29s+Yd1ZlRR/vC9tkhSwAAkNlQeAMAkEp5nPPo82afK3hAsFo83uK+Y/aF7VPt6bX1zup3dDf2bgZnCAAAMhMKbwAA0sinoI9WvbxK/3v+f1Z7gCeKN8frmx3fyG+yn9acWGOHDAEAQGaQKQrvyZMnq0yZMnJ1dZW/v7+2bNmSouu2bdsmJycnVatWzSoeGxur0aNHq2zZsnJ1dVXVqlW1cuXKVH+u2WzWqFGjVKxYMbm5ualx48Y6dOhQmp8TAJD9mEwmPVfxOYUEhOi9J9+Tk4OTJMt2ZIlT0U+Fn1LLuS3VY3EPXbt7zZ7pAgAAO7B74T1//nwNHjxYI0aMUGBgoBo0aKA2bdro7NmzD70uIiJC3bt3V7NmzZKdGzlypKZOnaoJEybo8OHD6t+/vzp16qTAwMBUfe5XX32lcePGaeLEidqzZ4+KFCmiFi1a6NatW+n3AwAAZAvuzu76ovkXOtD/gJqVaaaJbScqeECwGpZqaIyZvX+2fCf5at6BeWw9BgBADmIyp/Pf/Hv37pW/v3+Kx9epU0c1atTQlClTjJivr686duyoMWPGPPC6rl27qly5cnJ0dNTixYsVFBRknCtWrJhGjBihgIAAI9axY0e5u7tr7ty5Kfpcs9msYsWKafDgwXrvvfckSdHR0fL29taXX36pfv36pej5IiMjlTdvXkVERMjT0zNF1wAAsrbEv1pNJpMSzAmauW+m3l79tm7FJP3itlXZVvrh6R9U2qu0nbIEAAD/RWpqvXTveHfq1CnFY2NiYrR37161bGm9L2rLli21ffv2B143a9YsnThxQh999NF9z0dHR8vV1dUq5ubmpq1bt6b4c0+dOqVLly5ZjXFxcVGjRo0emhsAACaTSSaTSZLkYHJQH/8+av54c6sxq06sUqXJlTRuxzi2HgMAIJtzSstFL7zwwn3jZrNZN27cSPF9rl27pvj4eHl7e1vFvb29denSpfteExoaqmHDhmnLli1ycrp/+q1atdK4cePUsGFDlS1bVuvWrdOff/6p+Pj4FH9u4p/3G3PmzJkHPlN0dLSio6ON7yMjIx84FgCQM0THRSs8KlyS5OHsoTzOeXTp9iXdjb2rt1e/rV+Cf9GMZ2aoWpFqds0TAADYRpoK77Vr12rOnDlyd3e3ipvNZm3evDnV90vsCvzzPv+OSVJ8fLy6deumjz/+WOXLl3/g/b777jv16dNHFSpUkMlkUtmyZdWrVy/NmjUr1Z+b0twSjRkzRh9//PEDzwMAch4XJxet675O8w/Nl9lsVrvy7fT+uvc1ec9kmWXW3rC9qjmtpt6u97Y+avyRcufKbe+UAQBAOkpT4d24cWO5u7urUaNGyc5Vr149xfcpWLCgHB0dk3W3r1y5kqzTLEm3bt3S33//rcDAQA0aNEiSlJCQILPZLCcnJ61evVpNmzZVoUKFtHjxYkVFRen69esqVqyYhg0bpjJlyqT4c4sUKSLJ0vkuWrToI3NLNHz4cA0ZMsT4PjIyUiVLlkzxzwQAkD2ZTCZ19etqfD+x7US9VPkl9fqzl45eP6p4c7y+2v6V/gj5Q1OfnppsajoAAMi60vSO98KFC+9bdEu677ZdD+Ls7Cx/f3+tWWO9t+maNWtUv379ZOM9PT0VHBysoKAg4+jfv798fHwUFBSkOnXqWI13dXVV8eLFFRcXpwULFqhDhw4p/twyZcqoSJEiVmNiYmK0adOm++aWyMXFRZ6enlYHAAD3U69kPdUpbv1318mbJ9ViTgv1XNxT1+9et1NmAAAgPaWp452ehgwZoldeeUU1a9ZUvXr1NG3aNJ09e1b9+/eXZOkgX7hwQbNnz5aDg4P8/Pysri9cuLBcXV2t4rt27dKFCxdUrVo1XbhwQaNGjVJCQoLefffdFH+uyWTS4MGD9fnnn6tcuXIqV66cPv/8c+XOnVvdunXLgJ8MACAnaPVEK60+uVqXblvPwvp5/89aHrpc41uP14t+Lz70NScAAJC5pbnwDg8P16pVq3ThwgWZTCYVLVpUrVq1Ur58+VJ1ny5duuj69esaPXq0wsLC5Ofnp+XLl6tUqVKSpLCwsEfu6f1vUVFRGjlypE6ePCl3d3e1bdtWc+bMkZeXV4o/V5Leffdd3bt3TwMHDtTNmzdVp04drV69Wh4eHqnKBwCAB+lWuZvalWunURtHacLuCYo3xxvnrt69qpcWvqQ5B+ZoSrspbD0GAEAWlaZ9vGfOnKkvv/xS7dq1U/HixSVJ58+f14oVK/Tuu+/qtddeS/dEsyr28QYApFTw5WAFLA/QlrNbkp1zc3LTZ00/0xt13pCjg6MdsgMAAP+UmlovTYW3j4+P9u7dm2xV81u3bsnf31/Hjh1L7S2zLQpvAEBqmM1mzQuep6Grh+ryncvJztcsVlMz2s9Q1SJV7ZAdAABIlJpaL02Lq5lMJt2+fTtZ/Pbt27yDBgDAf2AymfRylZd1dNBRvVnnTTmYrP+q/vvi36oxtYaGrx2ue7H37JQlAABIjTR1vJctW6YhQ4bIz8/Paqr5oUOHNHbsWD399NPpnmhWRccbAPBf7L+0XwHLA7Tt3LZk58rmK6tp7aepaZmmdsgMAICczaZTzU+dOqVNmzbJ0dFRTzzxhC5evCiz2azixYurdu3acnTkvbN/ovAGAPxXCeYEzdk/R++ufVdX7lxJdr5XtV76puU3yu+W3w7ZAQCQM9ms8B4/frzefvttubu7y8nJSeHh4apRo4amT5+uatWq/de8syUKbwBAegmPCteHGz7UpD2TlGBOsDpXOE9hfdf6O3Wp1IXXvgAAyAA2e8f7s88+0/Dhw3Xz5k1dv35dx48fV4MGDfTkk09q69at/ylpAADwcF6uXvq+zffa23ev6pWop0qFKsnD2bLF5ZU7V/Tighf19K9P62xE6rbhBAAAtpWqjrebm5uCg4P1xBNPWMU//fRTLVmyRLt27Ur3BLM6Ot4AAFtIMCcoMjpSd2Lu6PUVr2vRkUXGOVdHV33R/AsNqj2IrccAALARm3W8q1Spoh07diSLd+nSRQcOHEhdlgAAIM0cTA7ycvVScc/iWthloRa+sFD5XPNJkqLiozR41WDV/7G+Dlzm72cAAOwtVYX32LFj9fbbb2v+/Pn6Z6N8x44dKleuXLonBwAAUqaTbyc1Kd3EKrb7wm75T/PX++veZ+sxAADsKNWrmi9fvlz9+/dXVFSUqlatqpiYGB06dEhz585V69atbZVnlsVUcwBARkkwJ2hW4Cz9evBXnY88r6PXjxrnyuYrq+ntp6tJmSYPuQMAAEgpm24nJknR0dFat26dNm7cqMDAQAUFBenGjRvKnz+/KleurCpVqmj8+PFpzT9bofAGANhDdFy0xmwdo8+3fK7YhFgj3rVSV01qN4mtxwAA+I9sXnjfz7lz5xQUFKTAwEAFBgZq0aJFj74oB6DwBgDY0+Grh/Xc788p5FqIEXN3dtcP7X5Qt8rd2HoMAIA0skvhjfuj8AYA2Nv8Q/P12p+v6U7sHat43RJ1Nf+5+Xos72N2ygwAgKzLZquaAwCArKdLpS46M/iMXqr8klV85/mdKvt9WX26+VPFJ8TbKTsAALI/Cm8AAHKAArkLaG7nudr52k6VzVfWiMclxOmDDR+o7PdlFRgWaMcMAQDIvii8AQDIQeqUqKOjg45qXKtxcnF0MeJnIs6oxrQa6rGoh6LiouyYIQAA2Q+FNwAAOYyjg6PeqvuWzg85r3bl2lmdm31gtry/8daiEBZJBQAgvVB4AwCQQxXMXVBLuy3Vxp4bVcS9iBGPjI5U5987q/7M+rp656odMwQAIHug8AYAIIdrVKqRzr91XiMbjJSjydGI7zi/Q8XGFdOnmz4Vm6AAAJB2FN4AAECODo76pOknujDkguqVqGfE4xLi9MHGD9Thtw46F3HOjhkCAJB1UXgDAACDt7u3tr+2XYu7LJanS9KepEuOLVHFyRU1cfdEth4DACCVKLwBAEAyHSp00PV3ruurFl8Z73/fjrmt11e8rkqTK2nugbl2zhAAgKzDZOalLZuKjIxU3rx5FRERIU9Pz0dfAABAJnPz3k29t/Y9Td833Spe1buqtr66Ve7O7nbKDAAA+0lNrUfHGwAAPFQ+t3ya1n6aNvbYqAJuBYz4/sv7VXNaTW0+s9mO2QEAkPlReAMAgBRpVLqRTg8+rVaPtzJiR68fVaOfGqnvkr4Kjwq3X3IAAGRiFN4AACDF3J3dtfKVldrXd5/qlqhrxKfvm67S40ur+ezm7P0NAMC/UHgDAIBUq160urb22qoJbSYY73hHREdo3al1Kj6uuL7Y+gWrnwMA8P8ovAEAQJo4OjhqUO1BOjzwsNXe37EJsRq+brge//5x7Tq/y44ZAgCQOVB4AwCA/6Rk3pLa9uo2/dDuB7k6uhrxsxFnVXdmXb3wvxd0/e51O2YIAIB9UXgDAID/zGQyqV/Nfrr49kW1faKt1bn/Hf6fSn5bUpP3TFaCOcFOGQIAYD8U3gAAIN3kc8unZS8t05pX1qigW0Ejfi/ungKWB8hvsp/+vvi3HTMEACDjUXgDAIB01/zx5jr71lm9Xut1mWQy4iHXQlRrei299udrunHvhh0zBAAg41B4AwAAm3DL5abv236voP5BqlCwgtW5H4N+VOnxpTVz30ymnwMAsj0KbwAAYFNVvKvo4ICDGtdynJwdnI34rZhb6r2kt2pOq6l9YfvsmCEAALZF4Q0AAGzO0cFRb9V7S6FvhKpp6aZW5wIvBarxT411N/aunbIDAMC2KLwBAECGeSzvY1rbfa1+e/Y3ebl6GfFbMbfUck5LHb562H7JAQBgIxTeAAAgQ5lMJnXx66ITb5xQjyo9jPi2c9tU7YdqGrVxlM6Gn1XQpSD7JQkAQDoymc1ms72TyM4iIyOVN29eRUREyNPT097pAACQ6Ww4tUF9l/bV8RvHjVhel7y6FXNLAbUC9EmTT5TXNa8dMwQAILnU1Hp0vAEAgF01KdNEB/of0PCnhsvJwUmSFBEdoQRzgqbunaprd6/ZOUMAAP4bCm8AAGB3brnc9Hmzz7W37175F/U34jHxMWr4U0MtCllkx+wAAPhvKLwBAECmUcW7inb13qXxrcbLzclNknTx1kV1/r2zOs/vrODLwXpvzXuKiIqwc6YAAKQc73jbGO94AwCQNmfCz2jAsgFacXyFEcvlkEuxCbHyzuOtr1t8rZervCyTyWTHLAEAORXveAMAgCyvlFcpLeu2TL90/kWFcheSJMUmxEqSLt+5rO6Lu6vRT40UfDnYnmkCAPBIFN4AACDTMplMerHyiwoJCFHPaj2Tnd9ydouqT62uIauGKDI6MuMTBAAgBSi8AQBAplcgdwHN6jBLa19Zq7L5ylqdizfH69ud38pnoo9+Cf5FvEUHAMhsKLwBAECW0ezxZjow4IDee/I9OfzrnzGXbl/SSwtfUpOfm+jQlUN2yhAAgOQovAEAQJaSO1dufdH8C+3tt1c1i9VMdn7TmU2q+kNVDV09VLeib9khQwAArFF4AwCALKlakWra8doOjWs5Trlz5bY6F2+O19gdY1VhUgX9dvA3pp8DAOyKwhsAAGRZTg5OeqveWzo08JBaP9E62fmLty7qxQUvqtnsZjp89bAdMgQAgMIbAABkA6W9Smt5t+Wa13meCuYumOz8htMbVPWHqmw9BgCwCwpvAACQLZhMJnWr3E1HAo6oR9Ueyc6XyltK5QuUt0NmAICcjsIbAABkKwVyF9BPHX/S6pdX6/F8jxvxEzdPyH+av3ac22HEzkWcs0eKAIAchsIbAABkSy3KtlDwgGC9U/8dOZocJUmHrh7Skz8+qdeXv67fD/2ust+X1bC1w3Qn5o6dswUAZGcmM8t82lRkZKTy5s2riIgIeXp62jsdAABypMCwQPVe0lv7wvYZMUeTo+LN8ZKk+c/N1wuVXrBXegCALCg1tR4dbwAAkO1VL1pdu3rv0jctvpGbk5skGUV3odyF1OCxBvZMDwCQzVF4AwCAHMHJwUlv139bhwYeUsuyLY341btXVXFyRc3YN8PY73vegXlMPwcApBsKbwAAkKOUyVdGK19aqTmd5qiAWwFJUnhUuPos6aMmPzfRj4E/6uVFL8t3kq8WhiwUb+UBAP4r3vG2Md7xBgAg87p295qGrBqiOQfmGDGTTDIr6Z9Hrcq20oQ2E1SuQDl7pAgAyKR4xxsAACAFCuYuqNmdZmvVy6tUxquMJFkV3ZK06sQq+U3x08j1I3U39q490gQAZHEU3gAAIMdrWbalggcEa2i9oXIwJf/nUUx8jD7b8pkqTqqoxUcWM/0cAJAqmaLwnjx5ssqUKSNXV1f5+/try5YtKbpu27ZtcnJyUrVq1ZKdGz9+vHx8fOTm5qaSJUvqrbfeUlRUlHG+dOnSMplMyY6AgABjTM+ePZOdr1u37n9+XgAAkPnkcc6jr1t+rd29d6t6ker3HXMm4ow6ze+kdr+00/EbxzM4QwBAVmX3wnv+/PkaPHiwRowYocDAQDVo0EBt2rTR2bNnH3pdRESEunfvrmbNmiU7N2/ePA0bNkwfffSRQkJCNHPmTM2fP1/Dhw83xuzZs0dhYWHGsWbNGknS888/b3Wv1q1bW41bvnx5Ojw1AADIrPyL+Wt3n936qvlXxtZj/7bi+ApVmlxJH274kOnnAIBHsvvianXq1FGNGjU0ZcoUI+br66uOHTtqzJgxD7yua9euKleunBwdHbV48WIFBQUZ5wYNGqSQkBCtW7fOiL399tvavXv3A7vpgwcP1tKlSxUaGiqTySTJ0vEODw/X4sWL0/x8LK4GAEDWdfLmSfVb2k9rT641Yv9efK20V2l91/o7tS/f3vg3BAAg+8syi6vFxMRo7969atmypVW8ZcuW2r59+wOvmzVrlk6cOKGPPvrovuefeuop7d27V7t375YknTx5UsuXL1e7du0emMfcuXP16quvJvsLc+PGjSpcuLDKly+vPn366MqVKw99pujoaEVGRlodAAAga3o83+Na/fJq/dzxZ2PrscSi2yTLvxlOh59Wh986qP2v7XXixgm75QoAyLzsWnhfu3ZN8fHx8vb2top7e3vr0qVL970mNDRUw4YN07x58+Tk5HTfMV27dtUnn3yip556Srly5VLZsmXVpEkTDRs27L7jFy9erPDwcPXs2dMq3qZNG82bN0/r16/X2LFjtWfPHjVt2lTR0dEPfKYxY8Yob968xlGyZMmH/AQAAEBmZzKZ1L1qd4UEhOilyi8ZcbPMRvEtSctCl6nS5Er64/Af9kgTAJCJ2f0db0nJusxms/m+U7Xi4+PVrVs3ffzxxypfvvwD77dx40Z99tlnmjx5svbt26eFCxdq6dKl+uSTT+47fubMmWrTpo2KFStmFe/SpYvatWsnPz8/tW/fXitWrNCxY8e0bNmyB3728OHDFRERYRznzp172KMDAIAsolCeQprbea5WvrRSpb1KS0rqfjs5OBl/1i3BQqwAAGv3bxlnkIIFC8rR0TFZd/vKlSvJuuCSdOvWLf39998KDAzUoEGDJEkJCQkym81ycnLS6tWr1bRpU33wwQd65ZVX1Lt3b0lS5cqVdefOHfXt21cjRoyQg0PS7xvOnDmjtWvXauHChY/Mt2jRoipVqpRCQ0MfOMbFxUUuLi4pen4AAJD1tHqilQ4OOKgPN3yo8bvGK8GcoLiEOElSjaI15OXqZTU+Jj5Gzo7OdsgUAJBZ2LXj7ezsLH9/f2NF8URr1qxR/fr1k4339PRUcHCwgoKCjKN///7y8fFRUFCQ6tSpI0m6e/euVXEtSY6OjjKbzcn23Zw1a5YKFy78wPe//+n69es6d+6cihYtmtpHBQAA2Uge5zwa22qsdvXepWpFqhnxLWe3qNLkSloeatkFJTI6Ur6TfDV602hFxUU94G4AgOzOrh1vSRoyZIheeeUV1axZU/Xq1dO0adN09uxZ9e/fX5Jl6vaFCxc0e/ZsOTg4yM/Pz+r6woULy9XV1Srevn17jRs3TtWrV1edOnV0/PhxffDBB3rmmWfk6OhojEtISNCsWbPUo0ePZO+L3759W6NGjdKzzz6rokWL6vTp03r//fdVsGBBderUyYY/EQAAkFXULFZTu3vv1rc7v9VHGz9SVFyUzkacVbtf2qmrX1d5unjq5M2T+mjjRwq7FaYpT0959E0BANmO3QvvLl266Pr16xo9erTCwsLk5+en5cuXq1SpUpKksLCwR+7p/W8jR46UyWTSyJEjdeHCBRUqVEjt27fXZ599ZjVu7dq1Onv2rF599dVk93B0dFRwcLBmz56t8PBwFS1aVE2aNNH8+fPl4eGR9gcGAADZSi7HXHr3yXf1rO+z6re0n9adsmxn+tvB3+Ti6CIHk4OcHZ313lPv2TlTAIC92H0f7+yOfbwBAMg5zGazft7/s95e/bZu3LthxCsVqqTFXRfrifxPGLFDVw7pifxPyMWJtWEAICvKMvt4AwAAZCcmk0k9q/VUSECIXvR70YgfunpIladU1pdbv1RsfKxux9xWq7mt5DfFTyuPr7RjxgCAjEDH28boeAMAkHOtCF2h/sv662xE0mtzVb2rqmqRqpq9f7YR6+zbWd+2+laP5X3MHmkCANIgNbUehbeNUXgDAJCz3Y65rQ/Wf6Dvd3+vBHOCJMkkk4p5FNOFWxeMcW5Obvqg4QcaUm8I088BIAtgqjkAAEAm4e7srm9bf6udr+1UFe8qkiSzzLpw64IKuBVQPtd8kqR7cff0/vr3VeWHKlp9YrU9UwYApDMKbwAAgAxQq3gt/d3nb41pNkauTq6SpOv3rutm1E35FPCRg8nyz7Jj14+p1dxWev5/z+tcxDl7pgwASCcU3gAAABkkl2MuDXtqmIIHBKtpmaZG/Oj1o3J3dle5/OWM2B+H/1CFSRX0xdYvFBMfY490AQDphMIbAAAggz2R/wmtfWWtfnzmR2OqeWR0pEJvhKpioYoq4FZAknQ39q6GrxuuKlOqaO3JtfZMGQDwH1B4AwAA2IHJZFKv6r0UEhCirn5djfjhq4d1J+aO6pesb0w/P3r9qFrMaaEuf3TR+cjz9koZAJBGFN4AAAB25O3urV+f/VXLui0zthOLio/S9nPb9UT+J1SlcBVj7O+HfleFiRU0ec9ke6ULAEgDCm8AAIBMoG25tjo08JDerPOmTDJJsiy0FnwlWK0eb6WCuQtKku7E3pGjydGeqQIAUol9vG2MfbwBAEBq7b6wW73/6q3gK8FGrKRnSVUrUk1X717V1l5b5ehA8Q0A9sQ+3gAAAFlY7eK1tbfvXn3W9DO5OLpIks5FntOSY0tUOm9p3bh3w2r8myve1NjtYxUbH2uPdAEAj0DH28boeAMAgP/i2PVj6re0nzae3mjECrgV0LetvtXLVV7WjvM79OSPT0qS6pesr629tspkMtkpWwDIOeh4AwAAZBPlC5TX+u7rNaP9DHm5ekmSrt+7ru6Lu6vV3Fb66+hfxjvhXSt1pegGgEyIjreN0fEGAADp5dLtS3pz5Zv6/dDvRszNyU19avRReHS4Zj4zU04OTsa52zG35eLoolyOueyRLgBka3S8AQAAsqEi7kU0/7n5WvLiEpXwLCFJuhd3T9/v/l4HrxzUgcsHrMYPXT1UNabV0OYzm+2RLgDg/1F4AwAAZDFPl39ahwce1uu1Xzemme8L26fa02vrndXv6G7sXe25sEfT9k7TwSsH1einRnpl0Su6dPuSnTMHgJyJwhsAACAL8nDx0Pdtvtf217bLr7CfJCneHK9vdnwjv8l+2hu2V/7F/I3xcw/Mlc9EH3238zvFJcTZK20AyJEovAEAALKwuiXqam/fvfq0yafG1mOnwk9pwLIB8ingo29afKN8rvkkSZHRkRq8arD8p/lr69mt9kwbAHIUFlezMRZXAwAAGeXY9WPqu6SvNp3ZZMQK5i6o0Y1Ha2/YXs0MnGk1vnvV7vqq+VfydvfO6FQBIMtjcTUAAIAcqHyB8lrfY72mt59ubD127e41DVw+UOcjz2vhCwtVo2gNY/zs/bPlM9FHE3ZNYPo5ANgQHW8bo+MNAADs4dLtS3pjxRv63+H/GbHcuXJrVKNRcsvlpg82fKDwqHDjXFXvqprUdpKefOxJO2QLAFkPHW8AAIAcroh7Ef3+/O/6q+tfxtZjd2Pv6t217+qnoJ+04PkFerXaq8b4/Zf366lZT6nXn7105c4Ve6UNANkShTcAAEA21t6nvQ4NPKRBtQYZW4/tDdurlnNbqkDuAlrffb2qFalmjP8p6Cf5TPTRpN2TlGBOsFPWAJC9MNXcxphqDgAAMosd53aoz5I+OnT1kBF7PN/jmtx2so7fOK4R60coIjpCktS0TFOtfWWtTCaTvdIFgEyNqeYAAABIpl7JetrXb59GNx4tZ0dnSdLJmyfVel5r7bm4Rztf26me1XrK2dFZE9tMpOgGgHRCx9vG6HgDAIDM6Mi1I+q7pK+2nN1ixArlLqTxrcfrqZJP6TGvx6zGrz25VqHXQ9XXv68cHRwzOl0AyHToeAMAAOChKhSsoI09N2rq01OV1yWvJOnq3at6aeFL6resn06HnzbGRsdFa8CyARq4fKBqz6itq3eu2ilrAMiaKLwBAAByKAeTg/r691VIQIie9X3WiK88vlKVJlfStzu+VXxCvFYeX6njN45LsmxJVjB3QXulDABZEoU3AABADlfUo6j+eOEPLe6yWMU9ikuybD02ZPUQ1Z1ZV6W9Smtzz82qUbSGJrWdlOzdb95cBICHo/AGAACAJKlDhQ46HHBYA2sONLYe+/vi3/Kf5q9locu0pecWVfGuYnXN+lPrVWdGHe25sMceKQNAlkDhDQAAAIOni6cmtZukra9uVcVCFSVJ8eZ4fbntS1X5oYrWnVxnjI2Jj9Gg5YO05+Ie1ZlRR/2X9tf1u9ftlToAZFoU3gAAAEimfsn62td3nz5u/LGx9diJmyfUfE5z9fqzl67fva6wW2FyMFn+OWmWWVP3TpXPRB/N2DdDCeYEe6YPAJkKhTcAAADuy8XJRR82+lBB/YL01GNPGfGfgn6S7yRfbT+3Xfv67tO4luPk4ewhSbp+77r6LOmjejPrae/FvfZKHQAyFQpvAAAAPJRvIV9t6rlJP7T7QZ4ulr1qr969qm4Lu6nj/I7q7NtZRwYdUbfK3Yxrdl/YrVrTa2ngsoG6ce+GvVIHgEyBwhsAAACP5GByUL+a/RQSEKLOvp2N+IrjK1RpciX9fuh3ze44Wxt6bDDeDTfLrCl/T5HPRB/N3DeT6ecAciwKbwAAAKRYMY9iWvDCAi3qskjFPIpJku7E3tFbq95SvZn1lM81n4L6BembFt/I3dldknTt7jX1XtJbT/74pPaF7bNn+gBgFxTeAAAASLWOFTrq8MDDGlBzgBHbc3GPak6vqQ83fKiBtQbqSMARdfXrapzfeX6nak2vpUHLB+nmvZv2SBsA7ILCGwAAAGmS1zWvJrebrC29tqhCwQqSpLiEOH2x7QtV+aGKjl0/pl+f/VXruq8zzieYEzRpzyT5TPTRT0E/Mf0cQI5A4Q0AAID/5KnHnlJQvyB91Ogj5XLIJUk6fuO4ms5uqtf+fE3VilTT/v779VXzr5QnVx5JlsXZBi4bqLBbYfZMHQAyBIU3AAAA/jMXJxeNajxKQf2DVL9kfSP+Y9CP8p3kq0UhizS0/lAdGXREL1R6QZI0suFIFfcsbq+UASDDUHgDAAAg3VQsVFFbem3R5LaTjb29r9y5oq4Luqr9r+2VYE7Q/Ofma0OPDXq73ttW196KvqV5B+bJbDbbI3UAsBkKbwAAAKQrB5ODBtQaoJCAEHWs0NGILwtdpkqTK+n7Xd+rwWMN5OLkYnXd6E2j9fKil9Xwp4Y6eu1oBmcNALZD4Q0AAACbKO5ZXIu6LNKCFxaoqHtRSdLtmNt6c+WbevLHJxV8OdgYey7inMbvGi9J2nNhj3I55rJHygBgExTeAAAAsKnOvp11OOCw+vn3M2K7LuxSjWk1NGLdCEXFRalk3pJa1m2Zyhcor2FPDdPj+R63Y8YAkL5MZl6isanIyEjlzZtXERER8vT0tHc6AAAAdrXlzBb1XdpXR64dMWLl8pfTtPbT1Lh0Y0XHRcsss1ydXI3zd2LuqPeS3nr/qfdV2buyPdIGgGRSU+vR8QYAAECGaVCqgYL6BenDhh8aW4+F3ghVk5+bqPdfvXU39q5V0S1Jn2/5XL8d/E3Vp1bXkFVDFBkdaY/UASDNKLwBAACQoVycXPRxk48V2C9Q9UrUM+IzA2fKd5Kvfj/0u7GyeWx8rBYfXSxJijfH69ud38pnoo9+Cf6F1c8BZBkU3gAAALCLSoUraeurWzWp7SRj67HLdy6ryx9d9Mxvz+hcxDnlcsylvX336pMmnxid8Eu3L+mlhS+pyc9NdOjKIXs+AgCkCIU3AAAA7MbB5KCBtQbqcMBhdfDpYMSXHluqipMrasKuCcrlkEsjG45USECI1ZhNZzap2tRqGrp6qG5F37JH+gCQIiyuZmMsrgYAAJAyZrNZC0MWatCKQbp0+5IRr1uirqa3ny6/wn6SpGXHlumNlW/o5M2TxphiHsU0tuVYdanURSaTKcNzB5DzsLgaAAAAshyTyaRnKz6rkIAQ9anRx4jvPL9T1adW1wfrP1BUXJTalW+nQwMP6ePGHxvTzy/euqgXF7yoZrOb6fDVw/Z6BAC4LzreNkbHGwAAIG02nd6kvkv76tj1Y0bMp4CPprWfpoalGkqSTt48qcErB2vJsSXGGCcHJ71V9y192OhDuTu7Z3jeAHIGOt4AAADI8hqVbqT9/fdrZIORcnJwkiQdvX5UjX5qpL5L+io8KlyP53tcf734l/7q+pfKeJWRJMUlxOnr7V+rwsQKViukA4C90PG2MTreAAAA/93BKwfVZ0kf7Ty/04gVcS+iCW0m6FnfZ2UymXQv9p6+3Palvtj6haLjo41xC15YoM6+ne2RNoBsjI43AAAAshW/wn7a2murJrSZYEwfv3T7kp7/3/PqOL+jzkeel1suN41qPEqHBh5S23JtJUnVi1S3WgkdAOyBjreN0fEGAABIX+cizilgeYDVe90ezh4a02yMBtQaIAeTg8xm8/+1d+fhNV3rH8C/J3NkIkIEcRJjkJBIjAkxRBSXKjWroUJNRXGVilJaQ1tKVSg11ly3qEpjqgShhkhCBjUlgiYlaSZkPuv3R37n1JGT0RkSvp/n2c+ttdd697uTndy8Z++9Fo7eOgo7czu0rddWaXzYX2FoY9eGs58T0SvhHW8iIiIiem3ZW9njyLAjOPDuAdia2QIAMnMzMe23afDa6oXox9GQSCTo36x/kaL70sNLaLu5LXrt6oU/k//URfpE9AZi4U1EREREVY5EIsHgloMROzUWfm5+ivaLDy/C7Xs3fHrmU+Tk5yiNkQkZpgZOhYDAyXsnEXg7UNtpE9EbioU3EREREVVZNUxrYHP/zTgz5gyaWDcBAOTJ8rD07FK03tga5+6fU/SVQIIFnReggVUDuNR2wYftP9RV2kT0hqkUhXdAQAAcHR1hYmICd3d3nDt3rvRBAEJDQ2FgYABXV9ci+9asWYNmzZrB1NQU9vb2+Oijj5Cdna3Yv3jxYkgkEqWtTp06SjGEEFi8eDHq1q0LU1NTdO3aFdHR0a90rkRERESkfl0duuL65OtY0HmB0tJjXbZ3wQdHP0BadhokEgneaf4OYqfG4tDQQ4p+chuvbsTtlNu6SJ+IXnM6L7z379+PmTNnYsGCBQgPD0fnzp3Ru3dvJCQklDguPT0do0ePRo8ePYrs2717N+bNm4dFixYhNjYWW7Zswf79+zF//nylfi1btkRiYqJiu3HjhtL+L7/8EqtXr8Z3332HK1euoE6dOujZsycyMzNf/cSJiIiISK1MDEzweffPcW3iNbSv117RvunaJrRY3wI/x/4MAKhmWA2NrBspjQ37KwxTjk2B8wZn+P/uj+d5z7WaOxG93nReeK9evRrjx4+Hn58fmjdvjjVr1sDe3h4bNmwocdwHH3yAESNGoGPHjkX2Xbx4EZ6enhgxYgQcHBzg6+uL4cOH4+rVq0r9DAwMUKdOHcVWq1YtxT4hBNasWYMFCxZg4MCBcHZ2xo4dO/D8+XPs2bNHPSdPRERERGrnYuuC0PdDsfattTAzNAMAJD5NxKADg/DO/nfwKONRkTHLzi+DgEBuQS6+OPcFmq9vjsM3D4MLABGROui08M7NzUVYWBh8fX2V2n19fXHhwoVix23btg13797FokWLVO738vJCWFgYLl++DAC4d+8eAgMD0bdvX6V+t2/fRt26deHo6Ihhw4bh3r17in1xcXFISkpSys3Y2Bje3t4l5paTk4OMjAyljYiIiIi0S19PH9PbT0fM1Bj0bfLv34CHbx5Gi4AW2HBlA2RCpmjfOWAn5nnOg6GeIQAgIT0B7+x/B3339MWdf+5oPX8ier3otPBOTk5GQUEBbG1tldptbW2RlJSkcszt27cxb9487N69GwYGBir7DBs2DEuXLoWXlxcMDQ3RqFEjdOvWDfPmzVP0ad++PXbu3Injx49j8+bNSEpKQqdOnZCSkgIAiuOXJzcAWL58OaysrBSbvb196V8IIiIiItKIBlYNcHT4UewbtA+1zWoDADJyMjAlcAq6bOuCmCcxAAAzIzMs91mOG5NvwKehj2L8b3d+Q8uAlvj0zKd8/JyIKkznj5oDhctBvEgIUaQNAAoKCjBixAh89tlnaNq0abHxgoOD8cUXXyAgIADXrl3Dzz//jF9//RVLly5V9OnduzcGDRoEFxcX+Pj44NixYwCAHTt2VCg3ufnz5yM9PV2xPXjwoPgTJyIiIiKNk0gkGOo8FLFTY/G+6/uK9tAHoXDd6IrFwYsVS481s2mGE6NO4MC7B1DPoh4AILcgF0vPLkXLgJb45c9fdHIORFS16bTwtrGxgb6+fpE7yI8fPy5ypxkAMjMzcfXqVUybNg0GBgYwMDDAkiVLEBkZCQMDA/z+++8AgIULF+K9996Dn58fXFxc8M4772DZsmVYvnw5ZDJZkbgAYGZmBhcXF9y+XTiTpXyG87LmJmdsbAxLS0uljYiIiIh0z9rUGlve3oLfR/+OxtaNARQuPfZZyGdw+94NoQmhAP5dI/zmtJuY22muYvbz+LR4vL3vbfxnz39w95+7OjsPIqp6dFp4GxkZwd3dHSdPnlRqP3nyJDp16lSkv6WlJW7cuIGIiAjFNmnSJDRr1gwRERFo375w9srnz59DT0/51PT19SGEKHaCjJycHMTGxsLOzg4A4OjoiDp16ijllpubi5CQEJW5EREREVHV0M2xG65Puo75XvMVRXVsciy8tnlh8q+TkZ6dDgAwNzLHyp4rcX3SdXR37K4Yf+z2MbQMaInFwYuRlZelk3MgoqpF54+az5o1Cz/88AO2bt2K2NhYfPTRR0hISMCkSZMAFD66PXr0aACAnp4enJ2dlbbatWvDxMQEzs7OMDMrnLWyX79+2LBhA/bt24e4uDicPHkSCxcuRP/+/aGvrw8AmDNnDkJCQhAXF4dLly7h3XffRUZGBsaMGQOg8JPOmTNnYtmyZTh06BCioqIwduxYVKtWDSNGjNDBV4qIiIiI1MXU0BTLeixD2MQwtK3bVtG+MWwjWgS0wKHYQ4q25rWa49R7p7Bv0D7UtagLAMgpyMFnIZ/BeYMzjt06pvX8iahqUT07mRYNHToUKSkpWLJkCRITE+Hs7IzAwEBIpVIAQGJiYqlrer/M398fEokE/v7+ePToEWrVqoV+/frhiy++UPR5+PAhhg8fjuTkZNSqVQsdOnTAH3/8oTguAMydOxdZWVmYMmUKUlNT0b59e5w4cQIWFhbqOXkiIiIi0qlWtq1wcfxFfHf5Oyz4fQGe5T3DX5l/YeCBgXjH6R181+c71LWoq3hPvE+TPlh6dim++eMb5MvycS/1Hv6z9z/4tMun+KzbZ7o+HSKqpCSCixNqVEZGBqysrJCens73vYmIiIgqsftp9zElcAoCbwcq2iyNLbHSZyUmuk+EnuTfh0VjnsRgauBUBMcHQ1+ij4hJEXCu7ayLtIlIR8pT67Hw1jAW3kRERERVhxAC+6L2YUbQDDx5/kTR7tXAC5v7bYaTjVORvvFp8Zjfeb5SnNSsVNQwraG1vIlI+1h4VyIsvImIiIiqnpTnKZhzcg62R2xXtBnpG2FB5wWY5zUPRvpGxY7Nyc9B642t4WTjhDVvrYFDdQfNJ0xEWleeWk/nk6sREREREVU2NavVxLa3t+HUe6fQqEYjAIXreS8KXgS3791w4cGFYsd+88c3+DPlTxz58wj8fvHTVspEVImx8CYiIiIiKkaPhj1wY/INfOz5MfQlhavjxDyJgddWL0w9NhUZORlFxjSq0Qh1zOtAT6KHr32/1nbKRFQJ8VFzDeOj5kRERESvh4ikCEw4OgFX/7qqaKtnUQ/r+6zH205vK/XNyMnAqXunMLD5wCIxapjUgLS6FERUtfEd70rkVQpvIQTy8vIgk8k0lB0R0ZtNX18fhoaGuk6DiKqQAlkBvr30LfzP+ON53nNF+6Dmg7Cu9zrYWdgVOzavIA9u37vhXuo9+Hfxx+yOs2FsYKyNtIlIA1h4VyIVKbxzc3Px+PFjPH/+HAUFBRrOkIjozWZsbAwbGxs+lURE5RKfFo/JxyYj6E6Qos3K2Apf9vwSfm38lJYek1t3aR2mB01X/LuJdRN81+c7+Dby1UrORKReLLwrkfIW3s+fP8eDBw+gr68PKysrmJqaQl9fHxKJRAvZEhG9OeRPFaWnp+Pp06eoV68ei28iKhchBPZG7cWMoBlIfp6saO/coDM29duktPQYAKRlp2HRmUX47sp3kIl/n2gc1HwQVvdajQZWDbSWOxG9OhbelUh5C++EhATk5+dDKpVCX19fCxkSEb3ZhBB4+PAh8vLy0LBhQ12nQ0RVUMrzFMw+MRs7Inco2oz0jeDf2R8fe31cZOmxyKRITAmcojQzejXDaljYZSFmdZxV4lJlRFR5cDmxKio/Px/Pnj2DtbU1i24iIi2RSCSwsrJCTk4O8vLydJ0OEVVBNavVxPYB23Fi1Ak0rFH4AV5uQS4+Df4Ubb5vg4sPLir1b12nNc6NO4ftb29HrWq1AADP855j/un5aLWhFU7dO6X1cyAizWLhXYnk5+cDKHzfkIiItEc+wRrn1SCiV9GzUU/cmHwD/+30X8XSY9FPouG51RMfBn6IzJxMRV89iR7GuI7BrQ9vYVrbaYp3wv9M+RM9f+yJIT8NwcOMhzo5DyJSPxbelRDf5yYi0i7+3iUidalmWA1f9vwSVyZcQRu7NgAAAYHvrnyHFgEtcPTPo0r9q5tUx7o+63B1wlV0rN9R0f5TzE9w+s4JX4Z+idyCXK2eAxGpHwtvIiIiIiI1c7NzwyW/S/i659eoZlgNAPAw4yH67+uPIT8NQdLTpCL9z79/Hlv7b4VNNRsAwLO8Z/j41MdovbE1Tt87rfVzICL1YeFNRERERKQBBnoGmN1pNqImRyktGfZTzE9ovr45frj2A16c51hPoodxbuPw57Q/McVjCiQofBrnZvJN+Pzog3FHxoHzIhNVTSy8iYiIiIg0yLGGI4JGBuHHd35U3M1Oy07DhKMT0G1HN9xKuaXU39rUGuv7rseVCVfQvl57RXvD6g35agxRFcXCm4iIiIhIwyQSCUa1GoXYqbF4r9V7ivaQ+yFotaEVvjj7RZF3ud3ruuPC+Av4od8P6FC/A/7r+V+l/bz7TVR1sPAm0gAHBwdIJBLEx8frOpViVYUcKyI+Ph4SiURpW7x4cZF++fn5MDExgZGREVxcXPDbb79pP1lSydXVVen717VrV12nRESkNjbVbLDznZ04Puo4HKs7AgByCnLgf8Yf7pvccenhJaX+ehI9jG8zHhfevwATAxOlfZ+c/gQjfx6JxMxEreVPRBXDwpuqvKNHj2Lo0KFo0KABTExMYG1tDXd3dyxcuBB///232o+3Zs0aLF68GGlpaWqPXdnJi3X5pqenB0tLS9jb26Nnz57w9/dHTEyM1uKUxNjYGJ6envD09ESDBg2K7E9MTISHhwcsLS0RFRWFIUOG4Pnz5690TE0IDAyEj48PrK2tYWZmhjZt2mDdunWQyWRaj1necWPHji3yIcjLW3Z2dpFxbm5u8PT0hLOzc4XPkYiosvNt5Isbk29gTsc5iqXEoh5HoeOWjpj+23SlpceAoqsv3Ey+iVUXV2HPjT1otbEVnuU+01ruRFQBgjQqPT1dABDp6eml9s3KyhIxMTEiKytLC5lVfWlpaaJXr14CgAAgatWqJTw8PETTpk2FRCIRAISFhYXYv3+/Wo8rlUoFABEXF1dsn+7du4tmzZqJhw8fqvXY6lSW8yhuTJMmTYSnp6fw9PQU7u7uinb5NmjQIJGcnKzxOKrExcUJAEIqlZapf1JSkjA3NxcAREhISLmOpWnLly9XfC0aNmwoWrVqJfT09AQA0b9/f1FQUKC1mBUZN2bMmCLf55e3nJycYnM9c+aMACC8vb3LfZ7lxd+/RKRLYX+FCbeNbgKLodjsV9uLo38eLXZM4K1AYb3SWmAxhP9pfy1mS0Ry5an1WHhrGAtvzcjJyRFt27YVAISDg4MIDAwUMplMsT8hIUEMGzZMABB6enri4MGDajt2RQrWyuhVCu9t27YV2ffkyROxZs0aYWNjIwAIJycnkZaWptE4qpS38BZCCF9fXwFABAQElHmMpl24cEFIJBKhp6cn9uzZo2iPiIgQtra2AoD46quvtBKzouPkhbeq73NZsPAmojdJXkGe+Cr0K2H6ualSAT7kpyEiMTNR5ZjkZ8li7om54lnuM6X257nPix1DROpTnlqPj5pTlbRo0SJcuXIFdnZ2OHfuHHr37q30CJa9vT327t2LcePGQSaTwc/PTyOPndO/bGxsMGPGDFy9ehV2dna4efMmZs6cqbM45dGiRQsAQHR0tEaPUx6ff/45hBDw8/PD8OHDFe2tW7fG6tWrAQArVqxAXl6exmNqIhciIlJmoGeAOZ3mIGpKFHo27KloPxB9AM3XN8eWa1uKTKZWs1pNrOy5UrFOuNyK8yvQ7LtmWPvHWuTL8rWSPxGVjIU3VTlpaWn47rvvAABff/016tevX2zftWvXwsbGRmmMnPwdUwDYs2cP2rVrB3Nzc1hbW2PAgAGIiopS6r99+3ZIJBLcv38fAODo6Kj0rmpwcLCib3ETl714zEOHDqFTp04wNzeHra0txowZg6SkJEXfbdu2wd3dHWZmZqhduzYmTZqE9PR0lecZFRWFRYsWoWPHjrCzs4ORkRHs7OwwcOBAXLhwoYSvpvpJpVIEBAQAAHbt2oUHDx7oNE5Z2NgULu3y8vdcVzIyMnDq1CkAwPjx44vsHzx4MCwtLZGSkoIzZ85oNKYmciEiouI1rNEQx0cdx84BO1HTtCaAwqXH/I76ofvO7ridcrvE8Xf+uYOVoSuRkZOBmcdnos33bXDu/jltpE5EJWDhTVVOYGAgnj59ChsbGwwePLjEvhYWFhg5ciQA4MCBAyr7fPnllxg5ciQePHiA5s2bIz8/H0eOHEG7du1w/vx5RT9bW1t4enrC2NgYAODh4aGYvMvT0xNWVlZlPod169Zh4MCBePDgARo3boz09HTs3LkTPXr0QHZ2NmbMmIH3338faWlpcHR0RGpqKr7//nu8/fbbKpcOmTlzJpYsWYKbN2+iRo0acHFxQX5+Pg4dOoQuXbpgz549Zc5NHfr374+6desiPz8fJ06c0HmckqSlpWHt2rUAKs8d7/DwcOTm5sLExARt2rQpst/Q0BBt27YFAFy6dKnIfnXGVEcuBw8exIABA9C9e3cMGzYM69atK/ZDJCIiKvyg/r3W7yF2aixGuoxUtAfHB8NlgwuWn1uOvALVTxlVN6mOUa1GKf594/ENdNneBWMOj8HfT/n0H5GusPCmKkd+B7dTp04wNDQstX+XLl0AALdu3UJKSkqR/f7+/li1ahUePXqEK1euICkpCSNHjkRWVhZGjRqFrKwsAEDv3r1x/vx51KlTBwDw008/4fz584rNzc2tzOcwf/587NmzBw8ePEBERATu3LmDxo0bIyYmBsOHD8eOHTtw6tQp3L17F1FRUbh27Rqsra0REhKCoKCgIvEmTZqE69evIzU1FTExMQgLC8Pjx49x+PBhmJqaYvLkycjMzFSRiWbo6emhY8eOAIArV67oPE5J5s+fjydPngAAkpOT8fjx4zKPXbZsGby8vMq9hYeHlxj39u3CuxkNGjSAgYGByj4NGzZU6luaisZURy7Hjh3DkSNHcObMGezfvx/Tp0+Ho6OjymuZiIj+VcusFnYN3IWgkUFwqO4AoHDpsU9+/wQemz1w+dHlImNsqtngh/4/4OL4i3Cr8+/fJjsjd6Lpd02x7tI6Pn5OpAMsvKnKefToEQCgUaNGZer/Yj/52Bf17t0bs2bNgp5e4Y9DtWrVsHXrVtSpUwf379/Hvn371JC1spffla1fvz7++9//AgAOHz6MxYsXo0ePHor9Li4umDhxIgCoLFbeffdduLi4KLVJJBK8/fbbmDlzJjIyMnD06FG1n0dJ7O3tAaBchawm46hy5coVbNq0CdbW1rC2tgZQvrvet27dQmhoaLm30u72pqamAgBq1KhRbB/5Pnnf0lQ05qvk0qhRIyxbtgyRkZHIyMhAZmYmTpw4gfbt2yM1NRUDBgzA1atXy5Q/EdGbrFfjXoiaHIVZHWYplh67/vd1dPihA2YGzcTT3KdFxnSo3wFXJlzB+j7rUd2kOgAgIycD04Omw2OTB0ITQrV5CkRvPBbeVOXI79yamZmVqf+L/VTd9Z06dWqRNiMjI/j5+QEAjh8/XpE0S6TqXVlXV1fFf7///vtF9svvqN+7d09lzISEBKxYsQJDhgxB9+7dFXdX9+/fDwCIjIxUQ+ZlJ/+6v+qddnXFeZlMJsPkyZMhk8mwcuVKxWPU5Sm8t2/fDlG4OkS5tq5du5YYV762tZGRUbF95K88yJ/IKE1FY75KLgsXLsT8+fPRqlUrWFhYwNzcHD179sTZs2fRrl075OTk4OOPPy5T/kREbzozIzOs6rUKl/wuwbWOKwBAQGDtpbVoGdASgbcDi4zR19PHlLZT8Oe0PzHOdZyiPfLvSHht88K4I+Pw+Jn6P9gmoqJUPzdIlZbHJg8kPU0qvWMlU8e8Dq5OVM+dLQsLCwDAs2fPytT/xX7ysS9q3ry5ynHy9lu3bpU3xVKpultfq1Ytxf9aWloWu//p06Kfau/YsQOTJk1SFEmq/PPPPxVNt0Lkeao6F13EedmGDRsQFhYGT09PjB8/HtHR0Th16lSleM/bxMQEAJCbm1tsn5ycHACAqampRmNqIhcjIyMsXboUvXr1QnBwMFJTU0u8o05ERP/yqOuBy36X8c0f32BR8CJk52cjIT0Bfff0xTDnYVjTaw1szW2VxtQ2q42tb2+FXxs/TA2cioikCADA9ojtOBR7CF90/wKTPCZBX09fB2dE9GZg4V3FJD1NwqPMoo9Lv0nq1asHALh7926Z+r/YTz72RbVr11Y5zta28P+0NPFudLVq1Yq0yWc7V7Xvxf0vT6529+5dTJgwAXl5eZg9ezZGjRqFRo0awdzcHBKJBD/88INivzYlJCQAKP7rq+04L3r8+DH8/f1hYGCADRs2QCKRwNnZGUDlmNm8LI+Rl+URcHXE1EQuABTv7stkMty7dw/u7u5lHktE9KYz1DfEXM+5GNR8ED749QOcjjsNANgXtQ/H7xzHKt9VGOs6VmmpVQDoZN8JVyZcwcarG+H/uz/Sc9KRnpOOab9Nw5bwLVjfZz062nfUxSkRvfZYeFcxdczr6DqFClFn3p06dcL69etx4cIF5OfnFzvhk9zZs2cBAE2aNEHNmjWL7H/y5InKJcnk7xSruktemRw4cAB5eXkYNmwYvv766yL7NbkMV3FkMhkuXrwIAGjXrp3O47xs9uzZSEtLw9y5cxXvxssL7/Lc8V62bBkCA4s+2leadevWlTgZX5MmTQAUfuhQ3DUuf+VA3rc0FY2piVwAKE2MmJ/PSX6IiCqikXUjnHzvJHZG7sSsE7PwT9Y/SM1Oxfu/vI9dN3bh+/98j8bWjZXGGOgZYFq7aRjcYjDmnZ6H7RHbAQDhSeHotLUTxrmOw9q31sLCuHL//UNU1bDwrmLU9bh2VdanTx+YmZkhOTkZP/30k9IkZS/LzMzE7t27AQBDhw5V2Sc2NlZl4R0bGwsAaNq0qVL7y58e65p8rfBOnTqp3K/td7uBwgnikpKSYGhoCF9fX53HeVFISAh27doFqVSKRYsWKdpbtmwJiUSC1NRUJCYmws7OrtRY8snVyqu0ydXc3NxgaGiI7OxsXLt2rciHDnl5eYpZ3tu3b1+mY1Y0piZyAZQ/4FD180dERGUjkUgwxnUMejfpjY+Of4Q9NwqXEP097ne4bHDBIu9FmN1xNgz1lVeCsTW3xba3t8HPzQ9TAqfg+t/XAQBhiWEwNSzbq0NEVHacXI2qnOrVqysmRJs9ezYePnxYbN8ZM2YgOTkZVlZWKidRA4CAgIAibbm5udiyZQsAFCn45O+xlnVSK02T5/P330XX5rx586bWZzO/f/8+pk2bBgAYPXq0ysf7tRnnRXl5eZgyZQqAwrvOLz7Wb25uDqlUCqDsd701NbmapaUlfHx8AEBxHb7op59+QkZGBmrWrFlqrFeNqYlcAGDVqlUAACcnJ7V8b4mI3nS1zWpj98DdCBwRCKlV4f+fZednY/7p+Wi7uS2uPFK9LKdnA0+ETQzD2rfWwsrYCuv7rIeBHu/NEakbC2+qkpYsWYI2bdogMTERXbp0QVBQkNK7zw8fPsSIESOwbds2SCQSbNq0SbH+9suOHTuGtWvXKsZnZWVhwoQJ+Ouvv2Bvb49hw4Yp9ZevWRwSEqKhsysfLy8vAIUfIERERCjab926hcGDB5c4G7U6JScn49tvv4WHhwcSExPRokULrF69WmdxVFm9ejViYmIwYMAA9OvXr8j+ijxurikLFixQvKO/d+9eRXtkZCRmzZoFAJg7d26R7++cOXPg4OCAOXPmqC1mRcadPHkS8+fPR1xcnFKs9PR0TJ8+XRHn008/LfsXhYiIStW7SW9ETYnCRx0+Uiw9Fvl3JDps6YCPgj5SufSYgZ4BprefjgcfPYBXAy+lfWF/hWHCLxOQ/DxZK/kTvbYEaVR6eroAINLT00vtm5WVJWJiYkRWVpYWMqv6UlNThY+PjwAgAIhatWoJDw8P0axZMyGRSAQAYW5uLvbs2aNyvHzcypUrBQBRp04d0bZtW2FpaSkACBMTExESElJk3M6dOxVjnZ2dhbe3t/D29hbh4eGKPlKpVAAQcXFxKo+pSlxcnAAgpFKpyv1nzpwRAIS3t7dSe15enujQoYMAIPT19UXz5s2Fs7OzkEgkws7OTnz++ecCgBgzZozSuOJyLIl8TJMmTYSnp6fw9PQUHh4ewsHBQXFuAMTgwYNFSkqKxuOoUtzXMSEhQZiZmQlzc3ORkJCgcuy8efMEADFhwoRyHVNT5N87AKJhw4aiVatWQk9PTwAQffv2Ffn5+UXGjBkzRuX3+1ViVmTcoUOHFP3r1asn2rZtK1xdXYWRkZEAICQSiVi0aFGJ51/cNa8J/P1LRK+jyw8vi9YbWgsshmKTfiMVgbcCyzS+QFYg2m1uJ7AYosaKGiIiMULDGRNVLeWp9XjHm6qs6tWr4+TJkzh8+DDeffddGBsb4/r160hKSkLr1q3xySef4Pbt2yW+Aw4U3qnbvXs37O3tER0dDYlEgv79++PSpUvo0qVLkf7vvfce1q5di1atWuHu3bsICQlBSEgI0tLSNHSmJTMwMMDx48fx4YcfwtbWFnfu3EFaWhrGjx+PsLAwjTzGe/v2bYSGhiI0NBQ3b95Efn4+fHx8sGDBAsTExODAgQOwtrbWWpyymDFjBp49e4bFixfD3t5eZR/5RGuVYWZzoPBO89GjR9G9e3ekpKTgzp07cHFxwZo1a3DkyBHo65d/2ZeKxizvOHd3dyxYsADdu3eHvr4+oqKicPPmTdSrVw+jR4/GxYsXsXjx4op8WYiIqIza1muLKxOuYEWPFTAxKFwe8n76ffTZ0wcj/jei1DW8Y5/EIvZJ4Zw3dhZ2aFGrhcZzJnpdSYR4aW0iUquMjAxYWVkhPT291HWIs7OzERcXB0dHR8XauaQ5xS3PRVVbfHw8HB0dIZVKFRPPUdUUHByMbt26wdvbG8HBwRo9Fn//EtHr7s4/d/DBrx/g97jfFW3WptZY5bsKY1qPKXby2MTMRPz35H/h18YPXR26Ku3Lzs9WFPREb6Ly1Hq8401Er6WkpCR4eXnBy8sLW7du1XU6VA7jxo2Dl5cXPvzwQ12nQkT02mhs3Rin3juFbW9vQw2TGgCAf7L+wbgj49Dzx564+89dlePsLOywa+CuIkV3ZFIkpGuk2By2GTIh03T6RFUeC28iei3l5OQoHmNPSEjQdTpUDuHh4QgNDa00j/wTEb0uJBIJxrqORezUWAxz/nfy2NNxp+GywQVfhn6JfFl+qXFkQoapgVPx+NljTPx1Ijpu6Yirf3HJW6KSsPAmoteKg4NDkeW7+C5x1RIREaH0/dP0Y+ZERG8aW3Nb7B20F8dGHEMDqwYAgKz8LHx86mO03dwWYX+FlTg+Ky8L0upSxb8vP7qMdpvbYfKvk/FP1j8azZ2oqmLhTURERET0BurTpA+ip0RjRvsZkKDwHe+IpAi0+6EdZh+fjWe5z1SOMzMyw+6Bu3FmzBnFhGsCAhvDNqLpuqbYcm0LHz8negkLb3pjye+mEREREb2pzI3MseatNfjD7w+41C5c3UMmZFj9x2o4b3DG8TvHix3b1aErIj6IwNc9v4a5kTkAICUrBX5H/dBpSydcS7ymlXMgqgpYeBMRERERveHa1WuHsIlhWNZ9GYz1jQEA8WnxeGv3Wxj18yg8efZE5ThDfUPM7jQbN6fexNCWQxXtlx5dgscmD0w9NhWpWalaOQeiyoyFNxERERERwVDfEPM7z8eNyTfQzaGbon33jd1ovr45dkbuLPZpwXqW9bDv3X049d4pONk4ASh8/DzgagCafdcM28K38fFzeqOx8CYiIiIiIoUmNZvg9OjT2NJ/i2LpsZSsFIw5PAa9dvXCvdR7xY7t0bAHIidFYqXPSpgZmgEAnjx/gvd/eR9eW70QnhiulXMgqmxYeBMRERERkRKJRIL33d5H7NRYpUfIT947CecAZ3wV+lWxS48Z6RthrudcxE6NxeAWgxXtFx9ehMdmD3wY+CHSstM0fQpElQoLbyIiIiIiUsnW3Bb73t2HX4f/CntLewCFS4/NPTUX7Ta3K3ECNXsrexwYfAAnRp1A05pNARRO3BZwNQD30+5rJX+iyoKFNxERERERlahv076InhKN6e2mK5YeC08KR9vNbTHnxJxilx4DgJ6NeuL6pOtY3mM5qhlWw7S209C6TmttpU5UKbDwJiIiIiKiUlkYW2Bt77W4OP6i0tJjqy6ugssGF5y4e6LYscYGxpjnNQ+xU2OxpNsSpX15BXn44uwXSM9O12j+RLrEwpuIiIiIiMqsff32CJsYhi+6f6FYeiwuLQ69dvXCe4feQ/Lz5GLHNrBqACsTK6W2tZfWwv+MP5p91wy/3f5No7kT6QoLbyIiIiIiKhdDfUN80vkTXJ98Hd5Sb0X7ruu74PSdE36M/LHYpcdelJ2fja8ufAUAePzsMWqb1dZYzkS6xMKbiIiIiIgqpGnNpvh9zO/Y3G8zqptUB1C49Njow6Px1u63EJcaV+J4EwMTXPa7jHec3sEkj0lwr+uuhayJtI+FNxERERERVZieRA9+bfyKLB924u4JOG9wxqoLq4pdegwApNWl+Hnoz/i297dK7fmyfPTe3Rt7buwp091zosqMhTcREREREb2yOuZ1cGDwAfwy7BfUt6wPAHie9xxzTs5Bhx86IDwxvMTxBnoGSv8OuBKAoDtBGPnzSHTb0Q3Rj6M1ljuRprHwJiIiIiIitenXrB+ip0RjWttpiqXHwhLD0HZzW8w9ORfP856XKU5YYpjiv0Puh6D1xtaYc2IOMnMyNZI3kSax8CYiIiIiIrWyNLbEuj7rEPp+KFrWagkAKBAF+OrCV3DZ4IJT906VGmPHgB34dfivaFijoWL8qour4LTeCfui9vHxc6pSWHgTEREREZFGdLTviGsfXMPSbkthpG8EALiXeg89f+yJMYfHIOV5Sonj+zbti+gp0fis62cwMTABAPyV+ReG/284euzsgZgnMRo/ByJ1YOFNRGoTHx8PiUSitC1evLhIv/z8fJiYmMDIyAguLi747Teu2VmZuLq6Kn0Pu3btquuUiIioCjPSN4J/F39cn3QdXaRdFO07I3fCab0Tdl/fXeLdaxMDE3zq/SlipsSgf7P+ivYz8WfQemNrzD05l4+fU6XHwpuqJAcHhyIFnqpt+/btuk5VrV4+bz09PVhaWsLe3h49e/aEv78/YmJK/uRXHTFKY2xsDE9PT3h6eqJBgwZF9icmJsLDwwOWlpaIiorCkCFD8Px52d730qbAwED4+PjA2toaZmZmaNOmDdatWweZTKb1mOUdN3bs2FJ/PrKzs1WOdXNzg6enJ5ydnSt8nkRERC9rZtMMZ8acwab/bIKVsRUAIPl5MkYdGoU+e/ogPi2+xPGONRxxZNgRHB1+FI7VHQEUznz+1YWv0Hx9c+yP2s/Hz6nSkghenRqVkZEBKysrpKenw9LSssS+2dnZiIuLg6OjI0xMTLSUYdXk4OCA+/fvo0mTJqhdu3ax/RYsWIDevXtrMTPNUnXe2dnZSE5Oxv379xX9Bg0ahO+//x41a9bUSIzixMfHw9HREVKpFPHx8aX2//vvv9G4cWM8ffoUISEh6NKlS6ljtGXFihWYP38+AKBhw4YwNzdHVFQUZDIZ+vfvj0OHDkFPr3yfXVY0ZkXGjR07Fjt27CjxZ+T333+HkZFRsfkGBwejW7du8Pb2RnBwcLnOtarh718iIu1KzEzE9KDpOBhzUNFWzbAalnZbiuntpxeZ4fxlWXlZWBm6EivOr0BOQY6ivYdjD3zX5zs42ThpLHciufLUehCkUenp6QKASE9PL7VvVlaWiImJEVlZWVrIrGqTSqUCgNi2bZuuU9Gqks77yZMnYs2aNcLGxkYAEE5OTiItLU0jMYoTFxcnAAipVFrmMb6+vgKACAgIKPMYTbtw4YKQSCRCT09P7NmzR9EeEREhbG1tBQDx1VdfaSVmRceNGTPmlX9Gzpw5IwAIb2/vCseoKvj7l4hIN47cPCLqraonsBiKzf17dxGeGF6m8XdS7og+u/sojTdcYig+PvmxyMzJ1Gzy9MYrT61XKR41DwgIUNxlcHd3x7lz58o0LjQ0FAYGBnB1dS2yb82aNWjWrBlMTU1hb2+Pjz76SOmxyuXLl6Nt27awsLBA7dq1MWDAAPz5559KMVQ9qtmhQ4dXOlciTbGxscGMGTNw9epV2NnZ4ebNm5g5c6bWY5RXixYtAADR0ZVnbc7PP/8cQgj4+flh+PDhivbWrVtj9erVAArvQufl5Wk8piZyISIiqiz6N+uPmKkxmNp2qtLSYx6bPPDxyY9LXXqskXUj/Dr8VxwZdgQO1R0AAHmyPKwMXYnvLn+n6fSJykznhff+/fsxc+ZMLFiwAOHh4ejcuTN69+6NhISEEselp6dj9OjR6NGjR5F9u3fvxrx587Bo0SLExsZiy5Yt2L9/v+JRTQAICQnB1KlT8ccff+DkyZPIz8+Hr68vnj17phTrrbfeQmJiomILDAxUz4mTVkVHR0MikcDa2hq5ubnF9nN3d4dEIsEvv/yi1J6fn4+NGzfCy8sL1atXh4mJCZycnODv74+MjIwiceQf1ADAb7/9hi5dusDCwgJWVlbo3bs3wsPD1XuCL5BKpQgICAAA7Nq1Cw8ePNBJjLKysbEBAERFRWnsGOWRkZGBU6cKlzgZP358kf2DBw+GpaUlUlJScObMGY3G1EQuRERElY2lsSW+6/Mdzr9/Hi1qFX4gXyAK8OWFL9FqQyucvne6xPESiQT9m/VH9JRoLOyyEEb6RnCo7oAZ7WdoI32iMtF54b169WqMHz8efn5+aN68OdasWQN7e3ts2LChxHEffPABRowYgY4dOxbZd/HiRXh6emLEiBFwcHCAr68vhg8fjqtXryr6BAUFYezYsWjZsiVat26Nbdu2ISEhAWFhYUqxjI2NUadOHcVmbW2tnhMnrWrZsiVcXFyQmpqK48ePq+xz69YtXLt2DTVq1MBbb72laM/IyECPHj0wefJkXLx4EdWrV0eTJk0QFxeHL774Ah06dMDjx49Vxty4cSP69u2LO3fuoGnTpigoKEBQUBC6dOmCmzdvauRcAaB///6oW7cu8vPzceLECZ3FKE1aWhrWrl0LoPLc8Q4PD0dubi5MTEzQpk2bIvsNDQ3Rtm1bAMClS5c0GlMduRw8eBADBgxA9+7dMWzYMKxbtw7p6ellypuIiEibOtl3QvgH4VjSdYli6bG7qXfh86MPxh4eW+rSY9UMq2FJtyWInhKN3QN3w9TQVGn/ybsn8Sz3WTGjiTRLp4V3bm4uwsLC4Ovrq9Tu6+uLCxcuFDtu27ZtuHv3LhYtWqRyv5eXF8LCwnD58mUAwL179xAYGIi+ffsWG1P+h+jLhXVwcDBq166Npk2bYsKECcUWWFT5jRgxAgCwd+9elfvl7YMGDVKacOqDDz7A2bNn0aNHD9y+fRvx8fG4ceMGkpKSMHDgQMTGxmLq1KkqY86ePRtbt27FX3/9hbCwMCQmJqJHjx54+vSpymW21EVPT0/xodSVK1d0FqM08+fPx5MnTwAAycnJ5fr5WrZsGby8vMq9lfa0we3btwEADRo0gIGB6oldGjZsqNS3NBWNqY5cjh07hiNHjuDMmTPYv38/pk+fDkdHRwQFBZUpdyIiIm0y0jfCQu+FiJwUCa8GXor2HZE70Hx9c+y9sbfUmcsbWzdGJ/tOSm23U27jP3v/g+brm+PwzcOaSJ2oRCVPF6hhycnJKCgogK2trVK7ra0tkpKSVI65ffs25s2bh3PnzhX7h+iwYcPw5MkTeHl5QQiB/Px8TJ48GfPmzVPZXwiBWbNmwcvLS2n5nN69e2Pw4MGQSqWIi4vDwoUL0b17d4SFhcHY2FhlrJycHOTk/DuzoqrHkEl9xo0bh3HjxhW7PzU1FdWrVwcADB8+HJ988gl++eUXPH/+HNWqVVPqu2/fPkU/uevXr2Pfvn2QSqU4dOgQLCwsFPtq1KiBH3/8EVeuXMH//vc/3L9/H1KpVCnm+PHjMXbsWMW/LSws8M0336BVq1YaL3zs7e0B4JU+LFJHjOJcuXIFmzZtUnzY9c8//yA6OrrEWepfdOvWLYSGhpb7uKXd7U1NTQVQ+P0tjnyfvG9pKhrzVXJp1KgRli1bhr59+8LR0RESiQQXL17EwoULcenSJQwYMADnz5+Hh4dHmc6BiIhIm5xsnBAyNgSbwzZj7qm5yMjJwJPnTzDi5xH48fqP2NB3A6TVpaUH+n+zT8xGbkEuHmQ8wKWHlzDAaYDmkidSQaeFt5z8XVg5IUSRNgAoKCjAiBEj8Nlnn6Fp06bFxgsODsYXX3yBgIAAtG/fHnfu3MGMGTNgZ2eHhQsXFuk/bdo0XL9+HefPn1dqHzp0qOK/nZ2d4eHhAalUimPHjmHgwIEqj718+XJ89tlnJZ7vq1p9cTVWX1z9ynF2DdyFrg5dFf8Ojg/GqJ9HAQBmdZyFWR1nKfZl5mSi+frmFTrOy7HUqbTlxF78cEYqlaJTp04IDQ3FL7/8gmHDhin2hYeH4+bNm7Czs0PXrl0V7YcOHQIADBkyRKnolqtWrRp8fHywbds2nDt3rkjh7efnV2SMi4sLTExMkJ6ejpSUlHIt11UeZmZmAIDMzEydxlBFJpNh8uTJkMlkWLlyJfbv349Tp04hOjoa3bp1K1OM7du3a2SddvkkjCUtsyX/4C0rK0ujMV8lF1W/63r27Alvb2907twZly9fxscff4zTp0t+b46IiEhX9CR6+MDjA/Rr1g8f/vYhfo79GQDw253f0DKgJT7v/jk+bPch9PX0S431te/XyCnIQeyTWPh38dd06kRF6LTwtrGxgb6+fpG7248fPy5yFxwo/OP/6tWrCA8Px7Rp0wAU/gEvhICBgQFOnDiB7t27Y+HChXjvvfcURY+LiwuePXuGiRMnYsGCBUrr3X744Yf45ZdfcPbsWdSvX7/EfO3s7CCVSkt8vHT+/PmYNevfIjMjI0Nx11BdMnIy8Cjz0SvHycnPKfJvedyMHOU79QKiwsd8OZY6ffLJJ0p3lEszYsQIhIaGYu/evUqFt/wx86FDhypdHzdu3ABQWIAX9/qDfO3rR4+Kfn0aNWqkckytWrXw4MEDPH36VGOF99OnTwGg9DUFNRxDlQ0bNiAsLAyenp4YP348oqOjFYW3rsnXcC5pEj75Uy2mpqbF9lFHTE3kYmRkhKVLl6JXr14IDg5GampqiXfUiYiIdK2uRV38b8j/cPjmYUwNnIq/Mv/Cs7xn+Oj4R9hzYw8299uM1nValxijac2mCBoZhKSnSTAzMlPat+HKBthZ2OHtZm+rvPlHpA46LbyNjIzg7u6OkydP4p133lG0nzx5Em+//XaR/paWlopCSC4gIAC///47Dh48CEdHRwDA8+fPlYonANDX14cQQvFOiBACH374IQ4dOoTg4GDF2JKkpKTgwYMHsLOzK7aPsbFxsY+hq4ulsSXqWdR75TjGBsZF/i2Pa2msXGhJIKnwMV+OpUtDhgzBjBkzEBQUpCg4hBDYv38/gH/fA5eTP5Z8584d3Llzp8TYqu5+yu8Yv0x+fZb2jtKrkK8MUNZHtzUV42WPHz+Gv78/DAwMsGHDBkgkEsUrHpVhZvOyPEZelkfA1RFTE7kAULy7L5PJcO/ePbi7u5d5LBERka4McBqAbg7dMP/0fGy4WjgR85W/rsBjswfmdJyDT70/LTKh2oskEgnsLJT/jo9LjcOsE7OQnZ+N3o1749ve36KxdWONnge9mXT+qPmsWbPw3nvvwcPDAx07dsSmTZuQkJCASZMmASi8g/zo0SPs3LkTenp6Su9gA4UFgYmJiVJ7v379sHr1ari5uSkeNV+4cCH69+8Pff3CR1GmTp2KPXv24MiRI7CwsFDcdbeysoKpqali8qtBgwbBzs4O8fHx+OSTT2BjY6P0IYEuaOrR7a4OXfFw1kOV+yyMLYrdV5XY2NjAx8cHQUFB+PnnnzF+/HiEhoYiISEBjRs3VswQLWdubg4A2Lx5s8rHxisrmUyGixcvAgDatWunsxiqzJ49G2lpaZg7dy5cXFwAQPHzW5473suWLavQ8n7r1q2Dm5tbsfubNGkCoPBDh/z8fJVzSdy7d0+pb2kqGlMTuQCFs6HL5efnl3kcERGRrlmZWCGgbwBGuozEhKMTEJsci3xZPlaErsDB2IP4/j/fo7tj9zLH2xG5A9n5ha92yR9h/9jzY8zzmodqhtVKGU1UdjovvIcOHYqUlBQsWbIEiYmJcHZ2RmBgoOJd2cTExFLX9H6Zv78/JBIJ/P398ejRI9SqVQv9+vXDF198oegjX67sxfd5gcIZ08eOHQt9fX3cuHEDO3fuRFpaGuzs7NCtWzfs379f5bu+VHWMGDECQUFB2LNnD8aPH489e/YAUJ5UTa5FixY4fPhwpbgTWx6HDx9GUlISDA0Ni6waoM0YLwsJCcGuXbsglUqVViVo2bIlJBIJUlNTkZiYWOJTJXKamlzNzc0NhoaGyM7OxrVr14p86JCXl6eY5b19+/ZlOmZFY2oiF0D5A47SXrEhIiKqjDwbeCL8g3CsOL8CX5z7AnmyPNz55w567OyBca7j8LXv17A2LX0Z4EXei+Bc2xkfHf8IDzMeIrcgF0vPLsWP13/E2rfWon+z/lo4G3ojCNKo9PR0AUCkp6eX2jcrK0vExMSIrKwsLWRWtUmlUgFAbNu2rdxjMzMzhampqdDT0xMJCQmiVq1aAoCIiYkp0vfKlSsCgKhZs6ZITk4u8zEAiJJ+vOT5x8XFlSv3spx3fHy8sLOzEwDE+PHjNRKjOHFxcQKAkEqlRfbl5uaKFi1aCADil19+KbLfwcFBABAnT54s8/E0pXfv3gKAmDhxYpF9u3fvVlwTOTk5Go+piVyGDx8uAAgnJ6cS+505c0YAEN7e3mWOXVXx9y8RUdUV8zhGeG7xFFgMxVb7q9pi7429QiaTlSlGZk6m+Pjkx8JgiYFSnL67+4o7KXc0fAZUVZWn1tPpOt5EumBubo5+/fpBJpNh4sSJePLkCVxdXdG8edFZ2z08PDBkyBCkpKSgZ8+eRdaALigoQHBwMEaOHKm0jJwuJCcn49tvv4WHhwcSExPRokULrF5dvtnv1RGjOKtXr0ZMTAwGDBiAfv36FdlfkcfNNWXBggWQSCT44YcflNZ9j4yMVEyeOHfu3CKzjc+ZMwcODg6YM2eO2mJWZNzJkycxf/58xMXFKcVKT0/H9OnTFXE+/fTTsn9RiIiIKqnmtZrj7Liz2NB3g2JuocfPHmP4/4aj395+SEgv/elZcyNzrPBZgeuTris9qn7s9jG0DGiJxcGLkZVXttVMiFTSwgcBbzTe8dYM+V3bJk2aCE9Pz2K3tWvXqhx/+PBhxV1pAGLlypXFHiszM1P07NlT0bdBgwaiffv2wsXFRZiamiraX/y+ydtKy7+id7xfPG8PDw/F3WL5NnjwYJGSkqKxGMUp7o53QkKCMDMzE+bm5iIhIUHl2Hnz5gkAYsKECeU6pqZ8/vnniq9Fw4YNRatWrYSenp4AIPr27Svy8/OLjBkzZowAIMaMGaO2mBUZd+jQIUX/evXqibZt2wpXV1dhZGQkAAiJRCIWLVpU6teAd7yJiKiqeZj+ULyz7x2lu9ZmX5iJNRfXiPwC1f8/+zKZTCb2R+0XdVfVVYrjuMZRHP3zqIbPgKqS8tR6On/Hm+hV3L59u8Tl3Tw8PFS29+7dGzVq1EBqaiokEonS0mIvMzc3R1BQEPbt24edO3ciLCwM165dg42NDVq1aoWuXbti0KBBiqWftOHF8zY3N0f16tXh4+OD9u3bY+TIkSrv3msiRlnNmDEDz549w9dff13s8nryidYqy/v0CxYsQOvWrfHNN98gLCwMSUlJcHFxwbhx4zBt2jTFRI3aiFnece7u7liwYAEuXryIO3fuICoqCkII1KtXD507d8aUKVPK9U44ERFRVVHPsh5+Hvozfo79GdMCpyHxaSKe5T3DzOMzsSdqD37o9wNcbF1KjCGRSDCk5RD0btwbS88uxTd/fIN8WT7i0uLQb28/9GvaD2vfWgvHGqWvikQkJxFCg+sZETIyMmBlZYX09PRS10LOzs5GXFwcHB0dtVrEEalLfHw8HB0dIZVKER8fr+t06BUFBwejW7du8Pb2RnBwsK7T0Sj+/iUiev2kZadh3ql5+D7se0WbgZ4B5naai4XeC2FiULbf9zFPYjAtcBrOxJ9RtJkYmGC+13zM9Zxb5jj0+ilPrcd3vIlI7ZKSkuDl5QUvLy9s3bpV1+lQOY0bNw5eXl748MMPdZ0KERFRhVU3qY6N/9mIs2PPwsnGCQCQL8vHsvPL0GpDKwTHB5cpTotaLXB69GnsHbQXduaFK69k52djUfAiDPlpiKbSp9cMC28iUrucnByEhoYq1kinqiU8PByhoaGV5rF/IiKiV9FZ2hkRH0Tg0y6fwlDPEABw+5/b6LajG/x+8UNqVmqpMSQSCYY5D8PNaTcxu+Ns6EsKX/Oa3XG2RnOn1wcfNdcwPmpORFT58fcvEdGbIfpxNCYcnYCLDy8q2mzNbPFt728xuMVgSCSSMsWJehyFY7eO4WOvj5XaH2U8gk01GxgbGKs1b6qc+Kg5ERERERHRS1rWbonz75/H+j7rYWFkAQD4+9nfGHpwKPrv648H6Q/KFMe5tnORolsmZBj802A4b3BG0J0gtedOVRsLbyIiIiIiemPoSfQwpe0UxEyNwdvN3la0/3rrV7QIaIF1l9ahQFZQ7rg7Inbg4sOLuPPPHcwMmol8Wb4606YqjoU3ERERERG9cepb1sehoYdwcPBB1DGvAwB4mvsU04Omw2ubF6Iel2+uE4+6HujcoDMA4Ls+38FAjys3079YeBMRERER0RtJIpFgUItBiJ0ai4ltJira/3j4B9y+d4P/7/7Izs8uUywXWxeEjA1B8Jhg+DT0UdoX8yQGJ+6eUGvuVLWw8CYiIiIiojdadZPq+L7f9wgZG4KmNZsCKFx67ItzX6D1xtYIiQ8pUxyJRAJvB2+lNiEEJh+bjF67emHwT4PL/B45vV5YeBMREREREQHoIu2CyEmR8O/sr3hU/FbKLXTd0RUTj05EWnZauWMG3g7E2ftnAQAHYw7Cab0TVp5fidyCXDVmTpUdC28iIiIiIqL/Z2JggqXdlyL8g3B0qN9B0b752mY0X98cB2MOojwrMvdu0hvb396OWtVqAQCe5z3HvNPz0GpDK5y+d1rt+VPlxMKbiIiIiIjoJc61nXF+3Hms670O5kbmAICkp0kY/NNgDNg/AA8zHpYpjp5ED2Ncx+DWh7cwre006EkKS7A/U/6Ez48+GHpwaJljUdXFwpuIiIiIiEgFfT19TGs3DTFTYtCvaT9F+y9//oIW61tg/eX1kAlZmWJVN6mOdX3WIWxiGDrW76hoPxB9AE7fOeGr0K/4+PlrjIU3ERERERFRCeyt7HFk2BEcePcAbM1sAQCZuZmY9ts0eG31QvTj6DLHcq3jivPvn8fW/lthU80GAPAs7xnmnpoL142u+D3ud42cA+kWC28iIiIiIqJSSCQSDG45GLFTY+Hn5qdov/jwIty+d8OnZz4t89JjehI9jHMbh1vTbmGKxxTF4+exybHosbMHhv9vOB5lPNLIeZBusPAmIiIiIiIqoxqmNbC5/2YEjwlWLD2WJ8vD0rNL4brRFefunytXrPV91+PKhCtoX6+9on1f1D44rXfCqgurkFeQp/ZzIO1j4U1ERERERFRO3g7eiJwUiQWdFyiWHvsz5U902d4FHxz9oFxLj7Wxa4ML4y/gh34/oKZpTQDA09ynmHNyDjw2eyAnP0cTp0BaxMKbiIiIiIioAkwMTPB5989xbeI1pTvWm65tQov1LfBz7M9ljqUn0cP4NuNx68NbmOwxGRJIAACdG3SGsYGx2nMn7WLhTaQBDg4OkEgkiI+P13UqxaoKOVZEfHw8JBKJ0rZ48eIi/fLz82FiYgIjIyO4uLjgt99+036ypHaurq5K3/uuXbvqOiUiInoDuNi6IPT9UKx9ay3MDM0AAIlPEzHowCC8s/+dcr2vbW1qjYC+Abg84TL6NOmDpd2WKu3Pl+Xz8fMqiIU3VXlHjx7F0KFD0aBBA5iYmMDa2hru7u5YuHAh/v77b7Ufb82aNVi8eDHS0tLUHruykxfr8k1PTw+Wlpawt7dHz5494e/vj5iYGK3FKYmxsTE8PT3h6emJBg0aFNmfmJgIDw8PWFpaIioqCkOGDMHz589f6ZiaEBgYCB8fH1hbW8PMzAxt2rTBunXrIJOVbekSdcbU1rixY8cW+fDk5S07W/XkNW5ubvD09ISzs3PZvhhERERqoq+nj+ntpyNmagz6NumraD988zBaBLTAhisbyrz0GAB41PXAsRHHUMO0hlL7ukvr0GZTG5y9f1ZtuZMWCNKo9PR0AUCkp6eX2jcrK0vExMSIrKwsLWRW9aWlpYlevXoJAAKAqFWrlvDw8BBNmzYVEolEABAWFhZi//79aj2uVCoVAERcXFyxfbp37y6aNWsmHj58qNZjq1NZzqO4MU2aNBGenp7C09NTuLu7K9rl26BBg0RycrLG46gSFxcnAAipVFqm/klJScLc3FwAECEhIeU6lqYtX75c8bVo2LChaNWqldDT0xMARP/+/UVBQYHWYmpz3JgxY4pcHy9vOTk5JZ7nmTNnBADh7e1dpq8Lf/8SEZE6yWQyse/GPlH7q9oCi6HYPLd4iujH0RWO+1fGX8JimYXAYgjJYom4k3JHjVlTeZWn1mPhrWEsvDUjJydHtG3bVgAQDg4OIjAwUMhkMsX+hIQEMWzYMAFA6OnpiYMHD6rt2BUpWCujVym8t23bVmTfkydPxJo1a4SNjY0AIJycnERaWppG46hS3sJbCCF8fX0FABEQEFDmMZp24cIFIZFIhJ6entizZ4+iPSIiQtja2goA4quvvtJKTG2Pkxfeqq6PsmLhTURElUHK8xTx/uH3lYpvwyWGYtGZRSI7L7vc8WKfxAqPTR4CiyEm/DJBAxlTeZSn1uOj5lQlLVq0CFeuXIGdnR3OnTuH3r17QyKRKPbb29tj7969GDduHGQyGfz8/DTy2Dn9y8bGBjNmzMDVq1dhZ2eHmzdvYubMmTqLUx4tWrQAAERHR2v0OOXx+eefQwgBPz8/DB8+XNHeunVrrF69GgCwYsUK5OWV/R2visbU9jgiIqLXhbWpNba8vQW/j/4dja0bAyhceuyzkM/g9r0bziecL1c8Jxsn/DH+D2z6zyYs67FMaV+BrABXHl1RW+6kXiy8qcpJS0vDd999BwD4+uuvUb9+/WL7rl27FjY2Nkpj5OTvigLAnj170K5dO5ibm8Pa2hoDBgxAVFSUUv/t27dDIpHg/v37AABHR0eld06Dg4MVfYubuOzFYx46dAidOnWCubk5bG1tMWbMGCQlJSn6btu2De7u7jAzM0Pt2rUxadIkpKenqzzPqKgoLFq0CB07doSdnR2MjIxgZ2eHgQMH4sKFCyV8NdVPKpUiICAAALBr1y48ePBAp3HKwsbGBgCKfM91JSMjA6dOnQIAjB8/vsj+wYMHw9LSEikpKThz5oxGY2p7HBER0euom2M3XJ90HfO95iuWHotNjkXnbZ0x+dfJSM9W/TeeKvp6+pjgPgE21WyU2r8P+x7tfmiHMYfH4O+nvOFU2bDwpionMDAQT58+hY2NDQYPHlxiXwsLC4wcORIAcODAAZV9vvzyS4wcORIPHjxA8+bNkZ+fjyNHjqBdu3Y4f/7fTyFtbW3h6ekJY+PC5Rw8PDwUk3d5enrCysqqzOewbt06DBw4EA8ePEDjxo2Rnp6OnTt3okePHsjOzsaMGTPw/vvvIy0tDY6OjkhNTcX333+Pt99+G0KIIvFmzpyJJUuW4ObNm6hRowZcXFyQn5+PQ4cOoUuXLtizZ0+Zc1OH/v37o27dusjPz8eJEyd0HqckaWlpWLt2LYDKc8c7PDwcubm5MDExQZs2bYrsNzQ0RNu2bQEAly5d0mhMbY970cGDBzFgwAB0794dw4YNw7p164r98ImIiKiyMzU0xbIeyxA2MQxt67ZVtG8M24gWAS1wKPZQhWM/efYEC35fAADYGbkTzb5rhnWX1iFflv/KeZN6sPCmKkd+B7dTp04wNDQstX+XLl0AALdu3UJKSkqR/f7+/li1ahUePXqEK1euICkpCSNHjkRWVhZGjRqFrKwsAEDv3r1x/vx51KlTBwDw008/4fz584rNzc2tzOcwf/587NmzBw8ePEBERATu3LmDxo0bIyYmBsOHD8eOHTtw6tQp3L17F1FRUbh27Rqsra0REhKCoKCgIvEmTZqE69evIzU1FTExMQgLC8Pjx49x+PBhmJqaYvLkycjMzCxzfq9KT08PHTt2BABcuVLxR57UFack8+fPx5MnTwAAycnJePz4cZnHLlu2DF5eXuXewsPDS4x7+/ZtAECDBg1gYGCgsk/Dhg2V+pamojG1Pe5Fx44dw5EjR3DmzBns378f06dPh6Ojo8qfASIioqqilW0rXBx/EWt6rVEsPfZX5l8YeGAgBu4fiL8y/yp3TGtTayzrvgzVTaoDANJz0jE9aDo8NnngwgPtPv1IqrHwpirn0aPCdRAbNWpUpv4v9pOPfVHv3r0xa9Ys6OkV/jhUq1YNW7duRZ06dXD//n3s27dPDVkre/md1/r16+O///0vAODw4cNYvHgxevToodjv4uKCiRMnAoDKouPdd9+Fi4uLUptEIsHbb7+NmTNnIiMjA0ePHlX7eZTE3t4eAMpVyGoyjipXrlzBpk2bYG1tDWtrawDlu+t969YthIaGlnsr7a5tamoqAKBGjRrF9pHvk/ctTUVjanscUPgzu2zZMkRGRiIjIwOZmZk4ceIE2rdvj9TUVAwYMABXr14tNi4REVFlp6+njxkdZiB6SjT6NOmjaD908xCar2+OjVc3lmvpMX09fUxuOxm3pt3C+67vK9oj/46E51ZPjDsyDo+fqf9vKSo71bchqPLy8ABeeA+4yqhTB1DTH8ryO7dmZmZl6v9iP1V3fadOnVqkzcjICH5+fvj8889x/PhxjBs3roLZqqbqnVdXV1fFf7///vtF9svvqN+7d09lzISEBOzZswfXrl1DcnIycnNzAfxbsEZGRmLEiBGvmnqZyb/ur3qnXV1xXiaTyTB58mTIZDKsXLkS+/fvx6lTpxAdHY1u3bqVKcb27duxfft2teYFQLFGtZGRUbF95K88yJ/I0FRMbY8DgIULFxbp27NnT3h7e6Nz5864fPkyPv74Y5w+fbrY2ERERFWBtLoUvw7/Ffuj92NG0Aw8fvYYGTkZmHxsMnbf2I1N/9mE5rWalzleLbNa2PL2Fvi18cOUwCmISIoAAGyP2I7DNw/j826fY5LHJOjr6WvojKg4LLyrmqQkQMVd2zeJhYUFAODZs2dl6v9iP/nYFzVvrvqXmbz91q1b5U2xVKru1teqVUvxv5aWlsXuf/r0aZF9O3bswKRJkxTFjir//PNPRdOtEHmeqs5FF3FetmHDBoSFhcHT0xPjx49HdHS0ovDWNRMTEwBQfHiiSk5ODgDA1NRUozG1Pa4kRkZGWLp0KXr16oXg4GCkpqaWeEediIioKpBIJBjmPAy+jXwx58QcbIvYBgA4n3Aert+74hOvTzDPax6MDYzLHLOjfUdcnXAVG69uxILfFyA9Jx1p2WmY9ts0bAnfgoC+AehQv4OmTolUYOFd1fz/+8VVjhrzrlevHgDg7t27Zer/Yj/52BfVrl1b5ThbW1sA6r/TChQ+zv4y+Wznqva9uP/lydXu3r2LCRMmIC8vD7Nnz8aoUaPQqFEjmJubQyKR4IcfflDs16aEhAQAxX99tR3nRY8fP4a/vz8MDAywYcMGSCQSODs7A6gcM5uX5THysjzKrY6Y2h5XGvk7/zKZDPfu3YO7u3uZxxIREVVm1qbW2Pr2VoxqNQoTj07E3dS7yC3IxeKQxdgfvR+b+22GZwPPMsfT19PH1HZTMbjlYHx86mNsj9gOAAhPCkfHLR0x3m08lvdYjlpmtTR0RvQiFt5VDd9rRKdOnbB+/XpcuHAB+fn5xU7cJHf27FkAQJMmTVCzZs0i+588eaJySTL5I9qq7pJXJgcOHEBeXh6GDRuGr7/+ush+TS7DVRyZTIaLFy8CANq1a6fzOC+bPXs20tLSMHfuXMW78fLCuzx3vJctW4bAwMByH3/dunUlTsbXpEkTAIUfOhR3jctfOZD3LU1FY2p7XGlenFAxP58ztRIR0eunu2N33Jh8A0tCluCrC1+hQBQgNjkWXtu8MNljMlb4rIClcdmfBKxtVhvb3t4GPzc/TA2cisi/IwEAW8K34OfYn7GsxzJMaDOBj59rGCdXoyqnT58+MDMzQ3JyMn766acS+2ZmZmL37t0AgKFDh6rsExsbW2J706ZNldrld54rC/la4Z06dVK5PzIyUovZFDp8+DCSkpJgaGgIX19fncd5UUhICHbt2gWpVIpFixYp2lu2bAmJRILU1FQkJiaWKZamJldzc3ODoaEhsrOzce3atSL78/LyFLO8t2/fvky5VjSmtseV5sUPRlR9YEZERPQ6MDU0xXKf5bg68So86noo2jdc3YAW61vgyM0j5Y7p2cATVydexbdvfaso3FOzUzH52GS0/6E94lLj1JY/FcXCm6qc6tWrKyZEmz17Nh4+fFhs3xkzZiA5ORlWVlYqJ1EDgICAgCJtubm52LJlCwAUKfjk76OWdVIrTZPn8/fffxfZd/PmTa3PZn7//n1MmzYNADB69GiVj/drM86L8vLyMGXKFACFd51ffKzf3NwcUqkUQNnvem/fvh1CiHJvXbt2LTGupaUlfHx8AEBxHb7op59+QkZGBmrWrFlqrFeNqe1xpVm1ahUAwMnJSS3XBBERUWXmWscVf4z/A6t9V6OaYeHfLY8yH2HA/gF498C7SMws280COQM9A3zY/kP8Oe1PjG49WtH+5PkT2JrbqjV3UsbCm6qkJUuWoE2bNkhMTESXLl0QFBSk9O7zw4cPMWLECGzbtg0SiQSbNm1SrL/9smPHjmHt2rWK8VlZWZgwYQL++usv2NvbY9iwYUr95WsPh4SEaOjsysfLywtA4QcIERERivZbt25h8ODBJc4qrU7Jycn49ttv4eHhgcTERLRo0QKrV6/WWRxVVq9ejZiYGAwYMAD9+vUrsr8ij5tryoIFCxTv6O/du1fRHhkZiVmzZgEA5s6dW+T7O2fOHDg4OGDOnDlqi6nNcSdPnsT8+fMRF6f8qXt6ejqmT5+uiPPpp5+q/LoRERG9bvT19PFRx48QPSUabzV+S9H+v9j/ofn65tgUtqlcS48BQB3zOtgxYAfOjj0Ll9ouWNNrjaKwJw0RpFHp6ekCgEhPTy+1b1ZWloiJiRFZWVlayKzqS01NFT4+PgKAACBq1aolPDw8RLNmzYREIhEAhLm5udizZ4/K8fJxK1euFABEnTp1RNu2bYWlpaUAIExMTERISEiRcTt37lSMdXZ2Ft7e3sLb21uEh4cr+kilUgFAxMXFqTymKnFxcQKAkEqlKvefOXNGABDe3t5K7Xl5eaJDhw4CgNDX1xfNmzcXzs7OQiKRCDs7O/H5558LAGLMmDFK44rLsSTyMU2aNBGenp7C09NTeHh4CAcHB8W5ARCDBw8WKSkpGo+jSnFfx4SEBGFmZibMzc1FQkKCyrHz5s0TAMSECRPKdUxNkX/vAIiGDRuKVq1aCT09PQFA9O3bV+Tn5xcZM2bMGJXf71eJqc1xhw4dUvSvV6+eaNu2rXB1dRVGRkYCgJBIJGLRokWlfu2K+3kpDn//EhFRVSCTycTu67tFrS9rCSyGYuu8tbOIfRJboZh5BXlCJpMptd1JuSO8tnqJyw8vqyPt11Z5aj0W3hrGwlvzDh8+LN59911Rv359YWRkJKysrISrq6v45JNPRGJiYrHjXiyCd+/eLdq2bSuqVasmrKysRP/+/UVkZGSxY9euXStatWolTE1NFXHOnDmj2K/NwluIwuvsww8/FHXr1hWGhoaifv36ws/PT/z1119i27Ztai+8X9zMzc1F/fr1hY+Pj1iwYIGIiYnRWhxVivs6vvPOOwKA+Prrr4sdu3v3bgFAdOzYsULH1oSjR4+K7t27CysrK1GtWjXRunVrsWbNmmIL3dIK74rE1Oa4hIQEsWDBAtG9e3fRoEEDYWpqKkxMTISjo6MYPXq0+OOPP0o8lhwLbyIiep0lP0sWYw6NUSq+jZYaiSXBS0ROfs4rx++7u6/AYgjJYon4KfonNWT8eipPrScR4qW1iUitMjIyYGVlhfT09FLXIc7OzkZcXBwcHR0Va+CS5hS3PBdVbfHx8XB0dIRUKlVMPEdvnuDgYHTr1g3e3t4IDg4utT9//xIRUVV06t4pfPDrB7iXek/R1rJWS2zutxkd7TtWKGZqVio6b+uM6CfRqGtRFzen3oSFceVe5UdXylPr8R1vInotJSUlwcvLC15eXti6dauu0yEtGTduHLy8vPDhhx/qOhUiIiKN82nogxuTb2Bup7nQlxQuBxb9JBqeWz0xLXAaMnIyyh2zhmkNhH8QjtW+q/Fd7++KFN3Jz5PVkvubhoU3Eb2WcnJyFMt3JSQk6Dod0pLw8HCEhoYiKipK16kQERFpRTXDaljZcyWuTLgCdzt3AICAwPor69FifQv88ucv5Y5pqG+Ijzp+hHeav6PUfj/tPhzXOmLyr5PxT9Y/asn/TcFHzTWMj5pXXnzUnIjk+PuXiIheB/myfHx76VssPLMQz/OeK9rfbfEuvn3rW9hZ2L1S/IH7B+LQzUMAgJqmNbHSZyXGuY2DnuTNvJ/LR82JiIiIiIjeMAZ6BpjVcRaiJkehV6NeivaDMQfRfH1zbA7bXO6lx+SEEPC094S5kTkAICUrBX5H/dBpSydcS7ymlvxfZyy86Y0lCmf113UaRERERERq5VjDEb+N/A273tkFm2o2AID0nHRM/HUiuu3ohj+T/yx3TIlEgtmdZuPm1JsY2nKoov3So0vw2OSBqcemIjUrVW3n8Lph4U1ERERERPSakUgkGNlqJGKnxmJ069GK9rP3z6L1xtb4/OznyC3ILXfcepb1sO/dfTj13ik42TgBKHynPOBqAJp91wzbI7ZX+K7664yFNxERERER0WvKppoNdgzYgROjTsCxuiMAIKcgBwvPLIT7Jnf88fCPCsXt0bAHIidFYqXPSpgZmgEAnjx/gnFHxqHzts6ISIpQ1ym8Flh4ExERERERveZ6NuqJG5NvYE7HOYrJ0KIeR6HTlk6Y/tt0ZOZkljumkb4R5nrORezUWAxuMVjRfuHBBbhvcsf036YjLTtNXadQpbHwJiIiIiIiegOYGZnhK9+vcGXCFbjVcQNQ+Jj4usvr0DKgJX699WuF4tpb2ePA4AM4MeoEmtZsCgCQCRnWXV6HZt81w87InW/83EosvImIiIiIiN4gbeza4PKEy/iq51cwNTAFADzIeIB+e/th6MGhSHqaVKG4PRv1xPVJ17G8x3JUM6wGAHj87DHGHB6DLtu7IOpxlNrOoaph4U1ERERERPSGMdAzwJxOcxA1JQo9G/ZUtB+IPoDm65tjy7UtFbpLbWxgjHle8xA7NRbvtnhX0X4+4TweP3usltyrIhbeREREREREb6iGNRri+Kjj2DlgJ2qa1gQApGWnwe+oH7rv7I7bKbcrFLeBVQP8NPgnHB91HE2sm2CY8zB0d+yuztSrFBbeREREREREbzCJRIL3Wr+H2KmxGNVqlKI9OD4YLhtcsOzcMuQV5FUotm8jX9yYfAMBfQKU2oUQ+CjoI9z4+8Yr5V5VsPAmIiIiIiIi1DKrhR/f+RFBI4PgUN0BQOHSYwt+XwD3Te649PBSheIaGxijhmkNpbZd13dhzaU1cPveDV9f+PpVU6/0WHgTERERERGRQq/GvRA1OQqzO85WLD124/ENdNzSETN+m1GhpcdeJITApmubAAAFogDOtZ1fOefKjoU3ERERERERKTEzMsPXvl/jst9lpaXHvr38LVoGtMSxW8cqHFsikeDkeyextNtSjHQZibcav6W0XyZkr5R7ZcTCm4iIiIiIiFRyr+uOyxMu40ufL5WWHvvP3v9g2MFh+Pvp3xWKa2JgAv8u/vjxnR+V2oUQ6LO7D+acmPPKd9YrExbeREREREREVCwDPQP81/O/uDH5Bnwa+ija90fvR/P1zbE1fGuFlh4DCu9+v+hA9AEcv3scqy6uQvP1zZGRk/FKuVcWLLyJiIiIiIioVI2sG+HEqBPYMWAHrE2tAQCp2akY/8t49NjZo8JLj73oyfMnMDEwAQC81fgtWBpbvnLMyoCFNxGpTXx8PCQSidK2ePHiIv3y8/NhYmICIyMjuLi44LffftN+sqR2rq6uSt/7rl276jolIiIiUjOJRILRrUfj5tSbGOkyUtF+Jv4MWm1sheXnlld46TEAmNZuGqKnRGOEywis8FmhjpQrhUpReAcEBMDR0REmJiZwd3fHuXPnyjQuNDQUBgYGcHV1LbJvzZo1aNasGUxNTWFvb4+PPvoI2dnZ5TquEAKLFy9G3bp1YWpqiq5duyI6OrrC50nq4+DgUKTAU7Vt375d16mq1cvnraenB0tLS9jb26Nnz57w9/dHTEyMxmOUxtjYGJ6envD09ESDBg2K7E9MTISHhwcsLS0RFRWFIUOG4Pnz5690TE0IDAyEj48PrK2tYWZmhjZt2mDdunWQySo+4UdFY2pz3NixY0v92Xr59ykAuLm5wdPTE87Or//MpERERG+6Wma1sGvgLvw28jdIraQAgOz8bHzy+yfw2OyBK4+uVDh2wxoNsXvgbthUs1FXurondGzfvn3C0NBQbN68WcTExIgZM2YIMzMzcf/+/RLHpaWliYYNGwpfX1/RunVrpX27du0SxsbGYvfu3SIuLk4cP35c2NnZiZkzZ5bruCtWrBAWFhbif//7n7hx44YYOnSosLOzExkZGWU+v/T0dAFApKenl9o3KytLxMTEiKysrDLHf1NJpVIBQDRp0kR4enoWuwUGBuo6VbVSdd7u7u6Kdvk2aNAgkZycrLEYxYmLixMAhFQqLVP/pKQkYW5uLgCIkJCQch1L05YvX674WjRs2FC0atVK6OnpCQCif//+oqCgQGsxtT1uzJgxpf585eTkFHueZ86cEQCEt7d3ub9GusLfv0RERBWXmZMpPgr6SOh9piewGAKLIfQ+0xMzf5spMnMydZ2expSn1tN54d2uXTsxadIkpTYnJycxb968EscNHTpU+Pv7i0WLFhUpvKdOnSq6d++u1DZr1izh5eVV5uPKZDJRp04dsWLFCsX+7OxsYWVlJTZu3Fjm82PhrRnyInHbtm26TkWrSjrvJ0+eiDVr1ggbGxsBQDg5OYm0tDSNxChOeQtvIYTw9fUVAERAQECZx2jahQsXhEQiEXp6emLPnj2K9oiICGFraysAiK+++korMbU9Toh/C++K/nyx8CYiInozXX54WbTe0FpRfGMxhPQbqQi89XrdDJMrT62n00fNc3NzERYWBl9fX6V2X19fXLhwodhx27Ztw927d7Fo0SKV+728vBAWFobLly8DAO7du4fAwED07du3zMeNi4tDUlKSUh9jY2N4e3uXmBuRrtjY2GDGjBm4evUq7OzscPPmTcycOVPrMcqrRYsWAFCpXuP4/PPPIYSAn58fhg8frmhv3bo1Vq9eDQBYsWIF8vLK/v5SRWNqexwRERFRRbWt1xZXJlzBih4rFBOk3U+/jz57+mDE/0bg8bPHOs5Qd3RaeCcnJ6OgoAC2trZK7ba2tkhKSlI55vbt25g3bx52794NAwMDlX2GDRuGpUuXwsvLC4aGhmjUqBG6deuGefPmlfm48v8tT24AkJOTg4yMDKWNdC86OhoSiQTW1tbIzc0ttp+7uzskEgl++eUXpfb8/Hxs3LgRXl5eqF69OkxMTODk5AR/f3+V32P5e7AA8Ntvv6FLly6wsLCAlZUVevfujfDwcPWe4AukUikCAgIAALt27cKDBw90EqOsbGwK392JiorS2DHKIyMjA6dOnQIAjB8/vsj+wYMHw9LSEikpKThz5oxGY2p7HBEREdGrMtQ3xMdeH+PG5Bvo7thd0b43ai+ar2+O7RHbK7z0WFVWKSZXe3ntNiFEkTYAKCgowIgRI/DZZ5+hadOmxcYLDg7GF198gYCAAFy7dg0///wzfv31VyxdurTcxy1rbnLLly+HlZWVYrO3ty+2L2lPy5Yt4eLigtTUVBw/flxln1u3buHatWuoUaMG3nrrLUV7RkYGevTogcmTJ+PixYuoXr06mjRpgri4OHzxxRfo0KEDHj9W/endxo0b0bdvX9y5cwdNmzZFQUEBgoKC0KVLF9y8eVMj5woA/fv3R926dZGfn48TJ07oLEZp0tLSsHbtWgCV5453eHg4cnNzYWJigjZt2hTZb2hoiLZt2wIALl26pNGY2h73soMHD2LAgAHo3r07hg0bhnXr1iE9Pb1M50xERERvtsbWjXHqvVPY9vY21DCpAQD4J+sfjDsyDj1/7Im7/9zVcYbapdPC28bGBvr6+kXuID9+/LjInWYAyMzMxNWrVzFt2jQYGBjAwMAAS5YsQWRkJAwMDPD7778DABYuXIj33nsPfn5+cHFxwTvvvINly5Zh+fLlkMlkZTpunTp1AKDMucnNnz8f6enpik2TdwqpfEaMGAEA2Lt3r8r98vZBgwbByMhI0f7BBx/g7Nmz6NGjB27fvo34+HjcuHEDSUlJGDhwIGJjYzF16lSVMWfPno2tW7fir7/+QlhYGBITE9GjRw88ffpU5TJb6qKnp4eOHTsCAK5cqdiMkuqIUZr58+fjyZMnAAqfRCnuAwxVli1bBi8vr3JvpT1tcPt24fqTDRo0KPapmoYNGyr1LU1FY2p73MuOHTuGI0eO4MyZM9i/fz+mT58OR0dHBAUFFTuGiIiISE4ikWCs61jcnHYTw53/ffXtdNxpOG9wxsrzK19p6bGqRKeFt5GREdzd3XHy5Eml9pMnT6JTp05F+ltaWuLGjRuIiIhQbJMmTUKzZs0QERGB9u3bAwCeP38OPT3lU9PX14conEyuTMd1dHREnTp1lPrk5uYiJCREZW5yxsbGsLS0VNpIc8aNG1fikkdpaWmKvsOHD1c8Rq5q6ap9+/Yp+sldv34d+/btg1QqxaFDhxTFCgDUqFEDP/74I+zt7fG///0P9+/fLxJz/PjxGDt2rOLfFhYW+OabbwBA48WL/GmL8hSzmohRnCtXrmDTpk2wtraGtbU1gPLd9b516xZCQ0PLvZV2xzY1NRVA4fe3OPJ98r6lqWhMbY+Ta9SoEZYtW4bIyEhkZGQgMzMTJ06cQPv27ZGamooBAwbg6tWrxcYmIiIielFts9rYM2gPjo04hgZWhUvNZudnY97peWi7uS2u/vX6/12h+laIFs2aNQvvvfcePDw80LFjR2zatAkJCQmYNGkSgMI7Yo8ePcLOnTuhp6dXZH3Y2rVrw8TERKm9X79+WL16Ndzc3NC+fXvcuXMHCxcuRP/+/aGvr1+m40okEsycORPLli1DkyZN0KRJEyxbtgzVqlVT3DnVmdWrC7dXtWsX0LXrv/8ODgZGjSr871mzCje5zEygefOKHeflWGrUpEkT1K5du9j9L97tk0ql6NSpE0JDQ/HLL79g2LBhin3h4eG4efMm7Ozs0PWFr8mhQ4cAAEOGDIGFhUWR+NWqVYOPjw+2bduGc+fOQSqVKu338/MrMsbFxQUmJiZIT09HSkoKatasWebzLQ8zMzMAhU+K6DKGKjKZDJMnT4ZMJsPKlSuxf/9+nDp1CtHR0ejWrVuZYmzfvl0j67TL16d+8amHlxkbGwMAsrKyNBpT2+PkFi5cWKStZ8+e8Pb2RufOnXH58mV8/PHHOH36dLHxiYiIiF7Wp0kfRE+JxsLfF+Lby99CJmSI/DsS7X9ojxntZ2Bpt6UwMzLTdZoaofPCe+jQoUhJScGSJUuQmJgIZ2dnBAYGKgqYxMREJCQklCumv78/JBIJ/P398ejRI9SqVQv9+vXDF198UebjAsDcuXORlZWFKVOmIDU1Fe3bt8eJEydUFmBalZEBPHr06nFycor+Wx735QnDhKj4MTU4wdwnn3yidEe5NCNGjEBoaCj27t2rVHjLHzMfOnSo0tMSN27cAFBYgBc3m738TvcjFV+fRo0aqRxTq1YtPHjwAE+fPtVY4f306VMAeKWnLtQRQ5UNGzYgLCwMnp6eGD9+PKKjoxWFt66ZmBTOwFnSJHw5//+zY2pqqtGY2h5XGiMjIyxduhS9evVCcHAwUlNTS7yrTkRERPQycyNzfPPWNxjhMgJ+R/1w/e/rkAkZvvnjG/wc+zM2/mcj3mr8VumBqhidF94AMGXKFEyZMkXlvtLuaC1evLjIu7IGBgZYtGhRscuNleW4QOFdb1Xxdc7SEqhX79Xj/P8dL6V/y+O+XGhJJBU/ZiV63H7IkCGYMWMGgoKCFEWDEAL79+8HgCJPM8gfS75z5w7u3LlTYmxVdw7ld4xfJi/uNTmjo/wDq5KeCNBGjJc9fvwY/v7+MDAwwIYNGyCRSBRPrFSGmc3L8hh5WR7lVkdMbY8rC/l7/zKZDPfu3YO7u3u5xhMREREBhUuPXZ1wFasursJnIZ8hOz8b99Pvo/fu3hjhMgJreq1BLbNauk5TbSpF4U3lpKlHt7t2BR4+VL3PwqL4fVWIjY0NfHx8EBQUhJ9//hnjx49HaGgoEhIS0LhxY8VMz3Lm5uYAgM2bN6t8bLyykslkuHjxIgCgXbt2OouhyuzZs5GWloa5c+fCxcUFABSFd3nueC9btgyBgYHlPv66devg5uZW7P4mTZoAKPzQIT8/X+XkZPfu3VPqW5qKxtT2uLIwNDRU/Hd+fn65xhIRERG9yFDfEPO85mFQ80H44NcPcCa+cJnTPTf2IOhOEFb7rsbo1qNLXFWqqmDhTW+cESNGICgoCHv27MH48eOxZ88eAMqTqsm1aNEChw8frhR3Ysvj8OHDSEpKgqGhIXx9fXUW42UhISHYtWsXpFKp0hMpLVu2hEQiQWpqKhITE2FnZ1dqLPnkauVV2uRqbm5uMDQ0RHZ2Nq5du1bkQ4e8vDzFLO/yCR1LU9GY2h5XFi9+OFK/fv1yjSUiIiJSpUnNJjg9+jS2R2zH7BOzkZqdin+y/sHYI2Ox68YubO2/FfZWVXuZ5kqxjjeRNr3zzjswNTVFcHAwHjx4gIMHDwJQXXi/8847AIBdu3YhJSVFq3lW1P379zFt2jQAwOjRo1GvAq8IqCPGy/Ly8hSvdqxbtw7VqlVT7DM3N1fMr1DWu97bt29XrFRQnu3FyfNUsbS0hI+PDwBgy5YtRfb/9NNPyMjIQM2aNUuN9aoxtT2uLFatWgUAcHJyUst1QURERAQUvuY7zm0cYqfGYpjzv3MxXUu8BmMD4xJGVg0svOmNY25ujn79+kEmk2HixIl48uQJXF1d0VzFrO0eHh4YMmQIUlJS0LNnzyJrQBcUFCA4OBgjR45UTFalK8nJyfj222/h4eGBxMREtGjRAqvLOfu9OmIUZ/Xq1YiJicGAAQPQr1+/Ivsr8ri5pixYsAASiQQ//PCD0rrvkZGRmPX/r3nMnTu3yKzhc+bMgYODA+bMmaO2mNoed/LkScyfPx9xcXFK7enp6Zg+fboi1qefflrkHImIiIhela25LfYO2otfh/8Ke0t7rPZdjdpm6ptvSGcEaVR6eroAINLT00vtm5WVJWJiYkRWVpYWMqvapFKpACCaNGkiPD09i93Wrl2rcvzhw4cFAMW2cuXKYo+VmZkpevbsqejboEED0b59e+Hi4iJMTU0V7S9+3+RtpeUfFxf3yuft4eEhHBwclM5n8ODBIiUlRWMxihMXFycACKlUqtSekJAgzMzMhLm5uUhISFA5dt68eQKAmDBhQrmOqSmff/654mvRsGFD0apVK6GnpycAiL59+4r8/PwiY8aMGSMAiDFjxqgtprbHHTp0SDGmXr16om3btsLV1VUYGRkJAEIikYhFixaV+LU7c+aMACC8vb1L7FeZ8PcvERFR5fM056mQyWS6TqNY5an1+I43VWm3b9/G7du3i93v4eGhsr13796oUaMGUlNTIZFIlJYWe5m5uTmCgoKwb98+7Ny5E2FhYbh27RpsbGzQqlUrdO3aFYMGDVIs4aQNL563ubk5qlevDh8fH7Rv3x4jR45UefdeEzHKasaMGXj27Bm+/vpr2Nurfj9HPtFaZXmffsGCBWjdujW++eYbhIWFISkpCS4uLhg3bhymTZsGfX19rcXU5jh3d3csWLAAFy9exJ07dxAVFQUhBOrVq4fOnTtjypQp5X4vnIiIiKgiXqc1vSVCaHA9I0JGRgasrKyQnp5e6lrI2dnZiIuLg6Ojo1aLOCJ1iY+Ph6OjI6RSKeLj43WdDulIcHAwunXrBm9vbwQHB+s6nTLh718iIiIqr/LUerzjTURql5SUBC8vLwDA+++/j/fff1/HGZE2jBs3Drdv3y515ngiIiKiNw0LbyJSu5ycHMVSX/LZten1Fx4ejsjISF2nQURERFTpsPAmIrVxcHAA3155c0VEROg6BSIiIqJKicuJEREREREREWkQC28iIiIiIiIiDWLhTURERERERKRBLLyJiIiIiIiINIiFNxEREREREZEGsfCuhDgrNBGRdvH3LhEREWkSC+9KRE+v8NtRUFCg40yIiN4s8t+78t/DREREROrEvzAqEUNDQxgaGuLp06e6ToWI6I2SlZUFfX19GBoa6joVIiIieg2x8K5EJBIJLCwskJ6ejqysLF2nQ0T0RigoKEB6ejqqVasGiUSi63SIiIjoNWSg6wRImY2NDbKyspCQkABLS0tYWFhAX1+ffwwSEamZEAI5OTn4559/IJPJULt2bV2nRERERK8pFt6VjL6+Puzt7ZGcnIzMzEykpaXpOiUioteamZkZ6tSpAyMjI12nQkRERK8pFt6VkL6+PmxtbVG7dm3k5eVBJpPpOiUioteSgYEBDAz4f4VERESkWfxroxKTSCS8A0NERERERFTFcXI1IiIiIiIiIg1i4U1ERERERESkQSy8iYiIiIiIiDSIhTcRERERERGRBrHwJiIiIiIiItIgFt5EREREREREGsTCm4iIiIiIiEiDuI63hgkhAAAZGRk6zoSIiIiIiIjURV7jyWu+krDw1rDMzEwAgL29vY4zISIiIiIiInXLzMyElZVViX0koizlOVWYTCbDX3/9BQsLC0gkEl2nQzqUkZEBe3t7PHjwAJaWlrpOh+iV8Hqm1wmvZ3qd8Hqm10llv56FEMjMzETdunWhp1fyW9y8461henp6qF+/vq7ToErE0tKyUv7iIKoIXs/0OuH1TK8TXs/0OqnM13Npd7rlOLkaERERERERkQax8CYiIiIiIiLSIBbeRFpibGyMRYsWwdjYWNepEL0yXs/0OuH1TK8TXs/0OnmdrmdOrkZERERERESkQbzjTURERERERKRBLLyJiIiIiIiINIiFNxEREREREZEGsfAmqqCAgAA4OjrCxMQE7u7uOHfuXIn9Q0JC4O7uDhMTEzRs2BAbN25U2h8dHY1BgwbBwcEBEokEa9as0WD2RMrUfT1v3rwZnTt3Ro0aNVCjRg34+Pjg8uXLmjwFIgV1X88///wzPDw8UL16dZiZmcHV1RU//vijJk+BSEHd1/OL9u3bB4lEggEDBqg5ayLV1H09b9++HRKJpMiWnZ2tydOoGEFE5bZv3z5haGgoNm/eLGJiYsSMGTOEmZmZuH//vsr+9+7dE9WqVRMzZswQMTExYvPmzcLQ0FAcPHhQ0efy5ctizpw5Yu/evaJOnTrim2++0dLZ0JtOE9fziBEjxPr160V4eLiIjY0V48aNE1ZWVuLhw4faOi16Q2niej5z5oz4+eefRUxMjLhz545Ys2aN0NfXF0FBQdo6LXpDaeJ6louPjxf16tUTnTt3Fm+//baGz4RIM9fztm3bhKWlpUhMTFTaKiMW3kQV0K5dOzFp0iSlNicnJzFv3jyV/efOnSucnJyU2j744APRoUMHlf2lUikLb9IaTV/PQgiRn58vLCwsxI4dO149YaISaON6FkIINzc34e/v/2rJEpVCU9dzfn6+8PT0FD/88IMYM2YMC2/SCk1cz9u2bRNWVlZqz1UT+Kg5UTnl5uYiLCwMvr6+Su2+vr64cOGCyjEXL14s0r9Xr164evUq8vLyNJYrUWm0dT0/f/4ceXl5sLa2Vk/iRCpo43oWQuD06dP4888/0aVLF/UlT/QSTV7PS5YsQa1atTB+/Hj1J06kgiav56dPn0IqlaJ+/fr4z3/+g/DwcPWfgBqw8CYqp+TkZBQUFMDW1lap3dbWFklJSSrHJCUlqeyfn5+P5ORkjeVKVBptXc/z5s1DvXr14OPjo57EiVTQ5PWcnp4Oc3NzGBkZoW/fvli3bh169uyp/pMg+n+aup5DQ0OxZcsWbN68WTOJE6mgqevZyckJ27dvxy+//IK9e/fCxMQEnp6euH37tmZO5BUY6DoBoqpKIpEo/VsIUaSttP6q2ol0QZPX85dffom9e/ciODgYJiYmasiWqGSauJ4tLCwQERGBp0+f4vTp05g1axYaNmyIrl27qi9xIhXUeT1nZmZi1KhR2Lx5M2xsbNSfLFEp1P37uUOHDujQoYNiv6enJ9q0aYN169bh22+/VVfaasHCm6icbGxsoK+vX+TTucePHxf5VE6uTp06KvsbGBigZs2aGsuVqDSavp6//vprLFu2DKdOnUKrVq3UmzzRSzR5Pevp6aFx48YAAFdXV8TGxmL58uUsvEljNHE9R0dHIz4+Hv369VPsl8lkAAADAwP8+eefaNSokZrPhEh7fz/r6emhbdu2lfKONx81JyonIyMjuLu74+TJk0rtJ0+eRKdOnVSO6dixY5H+J06cgIeHBwwNDTWWK1FpNHk9f/XVV1i6dCmCgoLg4eGh/uSJXqLN389CCOTk5Lx60kTF0MT17OTkhBs3biAiIkKx9e/fH926dUNERATs7e01dj70ZtPW72chBCIiImBnZ6eexNVJJ1O6EVVx8uUQtmzZImJiYsTMmTOFmZmZiI+PF0IIMW/ePPHee+8p+suXQ/joo49ETEyM2LJlS5HlEHJyckR4eLgIDw8XdnZ2Ys6cOSI8PFzcvn1b6+dHbxZNXM8rV64URkZG4uDBg0rLe2RmZmr9/OjNoonredmyZeLEiRPi7t27IjY2VqxatUoYGBiIzZs3a/386M2iiev5ZZzVnLRFE9fz4sWLRVBQkLh7964IDw8X48aNEwYGBuLSpUtaP7/SsPAmqqD169cLqVQqjIyMRJs2bURISIhi35gxY4S3t7dS/+DgYOHm5iaMjIyEg4OD2LBhg9L+uLg4AaDI9nIcIk1Q9/UslUpVXs+LFi3SwtnQm07d1/OCBQtE48aNhYmJiahRo4bo2LGj2LdvnzZOhUjt1/PLWHiTNqn7ep45c6Zo0KCBMDIyErVq1RK+vr7iwoUL2jiVcpMI8f9vqBMRERERERGR2vEdbyIiIiIiIiINYuFNREREREREpEEsvImIiIiIiIg0iIU3ERERERERkQax8CYiIiIiIiLSIBbeRERERERERBrEwpuIiIiIiIhIg1h4ExEREREREWkQC28iIiIiIiIiDWLhTURERERERKRBLLyJiIjolTRt2hQdO3ZEVlaWok0IgQ4dOmDu3Lk6zIyIiKhyYOFNREREr2T//v0IDw9HaGioom337t2Ii4uDv7+/DjMjIiKqHFh4ExER0Stxc3ND69atcfPmTQDA8+fPMX/+fCxduhSWlpY6zo6IiEj3WHgTERHRK2vatCn+/PNPAMCXX34Ja2trjB8/XsdZERERVQ4Guk6AiIiIqr5mzZrh7NmzePjwIb766iscPXoU+vr6uk6LiIioUuAdbyIiInpl8jve8+bNQ8+ePdG9e3ddp0RERFRpSIQQQtdJEBERUdUWERGBNm3awMjICFFRUWjcuLGuUyIiIqo0eMebiIiIXlnTpk0BANOmTWPRTURE9BIW3kRERPTKsrOzIYTA6NGjdZ0KERFRpcPCm4iIiF5ZZGQkjIyM0Lx5c12nQkREVOmw8CYiIqJXFhkZiRYtWsDQ0FDXqRAREVU6nFyNiIiIiIiISIN4x5uIiIiIiIhIg1h4ExEREREREWkQC28iIiIiIiIiDWLhTURERERERKRBLLyJiIiIiIiINIiFNxEREREREZEGsfAmIiIiIiIi0iAW3kREREREREQaxMKbiIiIiIiISINYeBMRERERERFpEAtvIiIiIiIiIg36P42ds8PNZgkvAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=False, figsize=(10, 7))\n", + "colors = [\"green\", \"red\", \"blue\"]\n", + "\n", + "for i in range(len(lams)):\n", + " color = colors[i % len(colors)]\n", + " axes.plot(\n", + " gammas, np.real(P12_results[i][0]),\n", + " color, linestyle='-', linewidth=2,\n", + " label=f\"Optimal DD [$\\\\lambda={lams[i]}$]\",\n", + " )\n", + " axes.plot(\n", + " gammas, np.real(P12_results[i][1]),\n", + " color, linestyle='-.', linewidth=2,\n", + " label=f\"Even DD [$\\\\lambda={lams[i]}$]\",\n", + " )\n", + "\n", + "axes.set_ylabel(r\"$\\rho_{01}$\")\n", + "axes.set_xlabel(r\"$\\gamma$\")\n", + "axes.legend(fontsize=16)\n", + "\n", + "fig.tight_layout();" + ] + }, + { + "cell_type": "markdown", + "id": "7b18462a", + "metadata": {}, + "source": [ + "And now you know about dynamically decoupling a qubit from its environment!" + ] + }, + { + "cell_type": "markdown", + "id": "db36a699", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "c87c7cd6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "QuTiP: Quantum Toolbox in Python\n", + "================================\n", + "Copyright (c) QuTiP team 2011 and later.\n", + "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", + "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", + "Original developers: R. J. Johansson & P. D. Nation.\n", + "Previous lead developers: Chris Granade & A. Grimsmo.\n", + "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", + "\n", + "QuTiP Version: 5.1.0.dev0+7941773\n", + "Numpy Version: 2.1.3\n", + "Scipy Version: 1.14.1\n", + "Cython Version: 3.0.9\n", + "Matplotlib Version: 3.9.2\n", + "Python Version: 3.12.7\n", + "Number of CPUs: 16\n", + "BLAS Info: Generic\n", + "INTEL MKL Ext: None\n", + "Platform Info: Linux (x86_64)\n", + "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", + "================================================================================\n", + "Please cite QuTiP in your publication.\n", + "================================================================================\n", + "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" + ] + } + ], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "89cce7fc", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "b71dc553", + "metadata": {}, + "outputs": [], + "source": [ + "assert 1 == 1" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "qutip-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.ipynb b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.ipynb new file mode 100644 index 00000000..b792cf10 --- /dev/null +++ b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.ipynb @@ -0,0 +1,828 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cc960358", + "metadata": {}, + "source": [ + "# HEOM 5a: Fermionic single impurity model" + ] + }, + { + "cell_type": "markdown", + "id": "35b12587", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc). Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications.\n", + "\n", + "Notation:\n", + "\n", + "* $K=L/R$ refers to left or right leads.\n", + "* $\\sigma=\\pm$ refers to input/output\n", + "\n", + "We choose a Lorentzian spectral density for the leads, with a peak at the chemical potential. The latter simplifies a little the notation required for the correlation functions, but can be relaxed if neccessary.\n", + "\n", + "$$J(\\omega) = \\frac{\\Gamma W^2}{((\\omega-\\mu_K)^2 +W^2 )}$$\n", + "\n", + "The Fermi distribution function is:\n", + "\n", + "$$f_F (x) = (\\exp(x) + 1)^{-1}$$\n", + "\n", + "Together these allow the correlation functions to be expressed as:\n", + "\n", + "$$C^{\\sigma}_K(t) = \\frac{1}{2\\pi} \\int_{-\\infty}^{\\infty} d\\omega e^{\\sigma i \\omega t} \\Gamma_K(\\omega) f_F[\\sigma\\beta(\\omega - \\mu)]$$\n", + "\n", + "As with the bosonic case we can expand these in an exponential series using Matsubara, Pade, or fitting approaches.\n", + "\n", + "The Pade decomposition approximates the Fermi distubition as\n", + "\n", + "$$f_F(x) \\approx f_F^{\\mathrm{approx}}(x) = \\frac{1}{2} - \\sum_l^{l_{max}} \\frac{2k_l x}{x^2 + \\epsilon_l^2}$$\n", + "\n", + "where $k_l$ and $\\epsilon_l$ are co-efficients defined in J. Chem Phys 133,10106.\n", + "\n", + "Evaluating the integral for the correlation functions gives,\n", + "\n", + "$$C_K^{\\sigma}(t) \\approx \\sum_{l=0}^{l_{max}} \\eta_K^{\\sigma_l} e^{-\\gamma_{K,\\sigma,l}t}$$\n", + "\n", + "where:\n", + "\n", + "$$\\eta_{K,0} = \\frac{\\Gamma_KW_K}{2} f_F^{approx}(i\\beta_K W)$$\n", + "\n", + "$$\\gamma_{K,\\sigma,0} = W_K - \\sigma i\\mu_K$$ \n", + "\n", + "$$\\eta_{K,l\\neq 0} = -i\\cdot \\frac{k_m}{\\beta_K} \\cdot \\frac{\\Gamma_K W_K^2}{-\\frac{\\epsilon^2_m}{\\beta_K^2} + W_K^2}$$\n", + "\n", + "$$\\gamma_{K,\\sigma,l\\neq 0}= \\frac{\\epsilon_m}{\\beta_K} - \\sigma i \\mu_K$$\n", + "\n", + "In this notebook we:\n", + "\n", + "* compare the Matsubara and Pade approximations and contrast them with the analytical result for the current between the system and the leads.\n", + "\n", + "* plot the current through the qubit as a function of the different between the voltages of the leads." + ] + }, + { + "cell_type": "markdown", + "id": "2166bfd7", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c7ca796", + "metadata": {}, + "outputs": [], + "source": [ + "import contextlib\n", + "import dataclasses\n", + "import time\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.integrate import quad\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " basis,\n", + " destroy,\n", + " expect,\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver,\n", + " LorentzianBath,\n", + " LorentzianPadeBath,\n", + ")\n", + "\n", + "from ipywidgets import IntProgress\n", + "from IPython.display import display\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "5307bf35", + "metadata": {}, + "source": [ + "## Helpers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a91b9628", + "metadata": {}, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\" Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1815555c", + "metadata": {}, + "outputs": [], + "source": [ + "# Solver options:\n", + "\n", + "# We set store_ados to True so that we can\n", + "# use the auxilliary density operators (ADOs)\n", + "# to calculate the current between the leads\n", + "# and the system.\n", + "\n", + "options = {\n", + " \"nsteps\": 1500,\n", + " \"store_states\": True,\n", + " \"store_ados\": True,\n", + " \"rtol\": 1e-12,\n", + " \"atol\": 1e-12,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "a001aedd", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian, bath and system measurement operators:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65e63490", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the system Hamiltonian:\n", + "\n", + "# The system is a single fermion with energy level split e1:\n", + "d1 = destroy(2)\n", + "e1 = 1.0\n", + "H = e1 * d1.dag() * d1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bdf4d114", + "metadata": {}, + "outputs": [], + "source": [ + "# Define parameters for left and right fermionic baths.\n", + "# Each bath is a lead (i.e. a wire held at a potential)\n", + "# with temperature T and chemical potential mu.\n", + "\n", + "@dataclasses.dataclass\n", + "class LorentzianBathParameters:\n", + " lead: str\n", + " Q: object # coupling operator\n", + " gamma: float = 0.01 # coupling strength\n", + " W: float = 1.0 # cut-off\n", + " T: float = 0.025851991 # temperature\n", + " theta: float = 2.0 # bias\n", + "\n", + " def __post_init__(self):\n", + " assert self.lead in (\"L\", \"R\")\n", + " self.beta = 1 / self.T\n", + " if self.lead == \"L\":\n", + " self.mu = self.theta / 2.0\n", + " else:\n", + " self.mu = - self.theta / 2.0\n", + "\n", + " def J(self, w):\n", + " \"\"\" Spectral density. \"\"\"\n", + " return self.gamma * self.W**2 / ((w - self.mu)**2 + self.W**2)\n", + "\n", + " def fF(self, w, sign=1.0):\n", + " \"\"\" Fermi distribution for this bath. \"\"\"\n", + " x = sign * self.beta * (w - self.mu)\n", + " return fF(x)\n", + "\n", + " def lamshift(self, w):\n", + " \"\"\" Return the lamshift. \"\"\"\n", + " return 0.5 * (w - self.mu) * self.J(w) / self.W\n", + "\n", + " def replace(self, **kw):\n", + " return dataclasses.replace(self, **kw)\n", + "\n", + "\n", + "def fF(x):\n", + " \"\"\" Return the Fermi distribution. \"\"\"\n", + " # in units where kB = 1.0\n", + " return 1 / (np.exp(x) + 1)\n", + "\n", + "\n", + "bath_L = LorentzianBathParameters(Q=d1, lead=\"L\")\n", + "bath_R = LorentzianBathParameters(Q=d1, lead=\"R\")" + ] + }, + { + "cell_type": "markdown", + "id": "19876f72", + "metadata": {}, + "source": [ + "## Spectral density\n", + "\n", + "Let's plot the spectral density." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1e04fd5", + "metadata": {}, + "outputs": [], + "source": [ + "w_list = np.linspace(-2, 2, 100)\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 7))\n", + "\n", + "spec_L = bath_L.J(w_list)\n", + "spec_R = bath_R.J(w_list)\n", + "\n", + "ax.plot(\n", + " w_list, spec_L,\n", + " \"b--\", linewidth=3,\n", + " label=r\"J_L(w)\",\n", + ")\n", + "ax.plot(\n", + " w_list, spec_R,\n", + " \"r--\", linewidth=3,\n", + " label=r\"J_R(w)\",\n", + ")\n", + "\n", + "ax.set_xlabel(\"w\")\n", + "ax.set_ylabel(r\"$J(\\omega)$\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "cebcc615", + "metadata": {}, + "source": [ + "## Emission and absorption by the leads\n", + "\n", + "Next let's plot the emission and absorption by the leads." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "588887bd", + "metadata": {}, + "outputs": [], + "source": [ + "w_list = np.linspace(-2, 2, 100)\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 7))\n", + "\n", + "# Left lead emission and absorption\n", + "\n", + "gam_L_in = bath_L.J(w_list) * bath_L.fF(w_list, sign=1.0)\n", + "gam_L_out = bath_L.J(w_list) * bath_L.fF(w_list, sign=-1.0)\n", + "\n", + "ax.plot(\n", + " w_list, gam_L_in,\n", + " \"b--\", linewidth=3,\n", + " label=r\"S_L(w) input (absorption)\",\n", + ")\n", + "ax.plot(\n", + " w_list, gam_L_out,\n", + " \"r--\", linewidth=3,\n", + " label=r\"S_L(w) output (emission)\",\n", + ")\n", + "\n", + "# Right lead emission and absorption\n", + "\n", + "gam_R_in = bath_R.J(w_list) * bath_R.fF(w_list, sign=1.0)\n", + "gam_R_out = bath_R.J(w_list) * bath_R.fF(w_list, sign=-1.0)\n", + "\n", + "ax.plot(\n", + " w_list, gam_R_in,\n", + " \"b\", linewidth=3,\n", + " label=r\"S_R(w) input (absorption)\",\n", + ")\n", + "ax.plot(\n", + " w_list, gam_R_out,\n", + " \"r\", linewidth=3,\n", + " label=r\"S_R(w) output (emission)\",\n", + ")\n", + "\n", + "ax.set_xlabel(\"w\")\n", + "ax.set_ylabel(r\"$S(\\omega)$\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "19b6fd7a", + "metadata": {}, + "source": [ + "## Comparing the Matsubara and Pade approximations\n", + "\n", + "Let's start by solving for the evolution using a Pade expansion of the correlation function of the Lorentzian spectral density:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5bd1b815", + "metadata": {}, + "outputs": [], + "source": [ + "# HEOM dynamics using the Pade approximation:\n", + "\n", + "# Times to solve for and initial system state:\n", + "tlist = np.linspace(0, 100, 1000)\n", + "rho0 = basis(2, 0) * basis(2, 0).dag()\n", + "\n", + "Nk = 10 # Number of exponents to retain in the expansion of each bath\n", + "\n", + "bathL = LorentzianPadeBath(\n", + " bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", + " Nk, tag=\"L\",\n", + ")\n", + "bathR = LorentzianPadeBath(\n", + " bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", + " Nk, tag=\"R\",\n", + ")\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " solver_pade = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " result_pade = solver_pade.run(rho0, tlist)\n", + "\n", + "with timer(\"Steady state solver time\"):\n", + " rho_ss_pade, ado_ss_pade = solver_pade.steady_state()" + ] + }, + { + "cell_type": "markdown", + "id": "d95f38c0", + "metadata": {}, + "source": [ + "Now let us plot the result which shows the decay of the initially excited impurity. This is not very illuminating, but we will compare it with the Matsubara expansion and analytic solution sortly:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8dd58db6", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the Pade results\n", + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + "\n", + "axes.plot(\n", + " tlist, expect(result_pade.states, rho0),\n", + " 'r--', linewidth=2,\n", + " label=\"P11 (Pade)\",\n", + ")\n", + "axes.axhline(\n", + " expect(rho_ss_pade, rho0),\n", + " color='r', linestyle=\"dotted\", linewidth=1,\n", + " label=\"P11 (Pade steady state)\",\n", + ")\n", + "\n", + "axes.set_xlabel('t', fontsize=28)\n", + "axes.legend(fontsize=12);" + ] + }, + { + "cell_type": "markdown", + "id": "b0a96b0e", + "metadata": {}, + "source": [ + "Now let us do the same for the Matsubara expansion:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5d64a20", + "metadata": {}, + "outputs": [], + "source": [ + "# HEOM dynamics using the Matsubara approximation:\n", + "\n", + "bathL = LorentzianBath(\n", + " bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", + " Nk, tag=\"L\",\n", + ")\n", + "bathR = LorentzianBath(\n", + " bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", + " Nk, tag=\"R\",\n", + ")\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " solver_mats = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " result_mats = solver_mats.run(rho0, tlist)\n", + "\n", + "with timer(\"Steady state solver time\"):\n", + " rho_ss_mats, ado_ss_mats = solver_mats.steady_state()" + ] + }, + { + "cell_type": "markdown", + "id": "7aae8ddb", + "metadata": {}, + "source": [ + "We see a marked difference in the Matsubara vs Pade results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0945bc92", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the Pade results\n", + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + "\n", + "axes.plot(\n", + " tlist, expect(result_pade.states, rho0),\n", + " 'r--', linewidth=2,\n", + " label=\"P11 (Pade)\",\n", + ")\n", + "axes.axhline(\n", + " expect(rho_ss_pade, rho0),\n", + " color='r', linestyle=\"dotted\", linewidth=1,\n", + " label=\"P11 (Pade steady state)\",\n", + ")\n", + "\n", + "axes.plot(\n", + " tlist, expect(result_mats.states, rho0),\n", + " 'b--', linewidth=2,\n", + " label=\"P11 (Mats)\",\n", + ")\n", + "axes.axhline(\n", + " expect(rho_ss_mats, rho0),\n", + " color='b', linestyle=\"dotted\", linewidth=1,\n", + " label=\"P11 (Mats steady state)\",\n", + ")\n", + "\n", + "axes.set_xlabel('t', fontsize=28)\n", + "axes.legend(fontsize=12);" + ] + }, + { + "cell_type": "markdown", + "id": "5feea0e4", + "metadata": {}, + "source": [ + "But which is more correct? The Matsubara or the Pade result?\n", + "\n", + "One advantage of this simple model is that the steady state current to the baths is analytically solvable, so we can check convergence of the result by calculating it analytically (the sum of the currents to and from the system in the steady state must be zero, so the current from one bath is the same as the current to the other).\n", + "\n", + "See the [QuTiP-BoFiN paper](https://arxiv.org/abs/2010.10806) for a detailed description and references for the analytic result. Below we just perform the required integration numerically." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d174d472", + "metadata": {}, + "outputs": [], + "source": [ + "def analytical_steady_state_current(bath_L, bath_R, e1):\n", + " \"\"\" Calculate the analytical steady state current. \"\"\"\n", + "\n", + " def integrand(w):\n", + " return (2 / np.pi) * (\n", + " bath_L.J(w) * bath_R.J(w) * (bath_L.fF(w) - bath_R.fF(w)) /\n", + " (\n", + " (bath_L.J(w) + bath_R.J(w))**2 +\n", + " 4*(w - e1 - bath_L.lamshift(w) - bath_R.lamshift(w))**2\n", + " )\n", + " )\n", + "\n", + " def real_part(x):\n", + " return np.real(integrand(x))\n", + "\n", + " def imag_part(x):\n", + " return np.imag(integrand(x))\n", + "\n", + " # in principle the bounds for the integral should be rechecked if\n", + " # bath or system parameters are changed substantially:\n", + " bounds = [-10, 10]\n", + "\n", + " real_integral, _ = quad(real_part, *bounds)\n", + " imag_integral, _ = quad(imag_part, *bounds)\n", + "\n", + " return real_integral + 1.0j * imag_integral\n", + "\n", + "\n", + "curr_ss_analytic = analytical_steady_state_current(bath_L, bath_R, e1)\n", + "\n", + "print(f\"Analytical steady state current: {curr_ss_analytic}\")" + ] + }, + { + "cell_type": "markdown", + "id": "27853ddc", + "metadata": {}, + "source": [ + "To compare the analytical result above with the result from the HEOM, we need to be able to calculate the current from the system to the bath from the HEOM result. In the HEOM description, these currents are captured in the first level auxilliary density operators (ADOs).\n", + "\n", + "In the function `state_current(...)` below, we extract the first level ADOs for the specified bath and sum the contributions to the current from each:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d341efa", + "metadata": {}, + "outputs": [], + "source": [ + "def state_current(ado_state, bath_tag):\n", + " \"\"\" Determine current from the given bath (either \"R\" or \"L\") to\n", + " the system in the given ADO state.\n", + " \"\"\"\n", + " level_1_aux = [\n", + " (ado_state.extract(label), ado_state.exps(label)[0])\n", + " for label in ado_state.filter(level=1, tags=[bath_tag])\n", + " ]\n", + "\n", + " def exp_sign(exp):\n", + " return 1 if exp.type == exp.types[\"+\"] else -1\n", + "\n", + " def exp_op(exp):\n", + " return exp.Q if exp.type == exp.types[\"+\"] else exp.Q.dag()\n", + "\n", + " return -1.0j * sum(\n", + " exp_sign(exp) * (exp_op(exp) * aux).tr()\n", + " for aux, exp in level_1_aux\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "1d97dfba", + "metadata": {}, + "source": [ + "Now we can calculate the steady state currents from the Pade and Matsubara HEOM results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89cda089", + "metadata": {}, + "outputs": [], + "source": [ + "curr_ss_pade_L = state_current(ado_ss_pade, \"L\")\n", + "curr_ss_pade_R = state_current(ado_ss_pade, \"R\")\n", + "\n", + "print(f\"Pade steady state current (L): {curr_ss_pade_L}\")\n", + "print(f\"Pade steady state current (R): {curr_ss_pade_R}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c7523e6", + "metadata": {}, + "outputs": [], + "source": [ + "curr_ss_mats_L = state_current(ado_ss_mats, \"L\")\n", + "curr_ss_mats_R = state_current(ado_ss_mats, \"R\")\n", + "\n", + "print(f\"Matsubara steady state current (L): {curr_ss_mats_L}\")\n", + "print(f\"Matsubara steady state current (R): {curr_ss_mats_R}\")" + ] + }, + { + "cell_type": "markdown", + "id": "f1d35f12", + "metadata": {}, + "source": [ + "Note that the currents from each bath balance as is required by the steady state, but the value of the current is different for the Pade and Matsubara results.\n", + "\n", + "Now let's compare all three:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2410bab", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Pade current (R): {curr_ss_pade_R}\")\n", + "print(f\"Matsubara current (R): {curr_ss_mats_R}\")\n", + "print(f\"Analytical curernt: {curr_ss_analytic}\")" + ] + }, + { + "cell_type": "markdown", + "id": "36cfbe2f", + "metadata": {}, + "source": [ + "In this case we observe that the Pade approximation has converged more closely to the analytical current than the Matsubara.\n", + "\n", + "The Matsubara result could be improved by increasing the number of terms retained in the Matsubara expansion (i.e. increasing `Nk`)." + ] + }, + { + "cell_type": "markdown", + "id": "335b650c", + "metadata": {}, + "source": [ + "## Current as a function of bias voltage" + ] + }, + { + "cell_type": "markdown", + "id": "ccb98463", + "metadata": {}, + "source": [ + "Now lets plot the current as a function of bias voltage (the bias voltage is the parameter `theta` for the two baths).\n", + "\n", + "We will calculate the steady state current for each `theta` both analytically and using the HEOM with the Pade correlation expansion approximation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b329cb0f", + "metadata": {}, + "outputs": [], + "source": [ + "# Theta (bias voltages)\n", + "\n", + "thetas = np.linspace(-4, 4, 100)\n", + "\n", + "# Setup a progress bar:\n", + "\n", + "progress = IntProgress(min=0, max=2 * len(thetas))\n", + "display(progress)\n", + "\n", + "# Calculate the current for the list of thetas\n", + "\n", + "\n", + "def current_analytic_for_theta(e1, bath_L, bath_R, theta):\n", + " \"\"\" Return the analytic current for a given theta. \"\"\"\n", + " current = analytical_steady_state_current(\n", + " bath_L.replace(theta=theta),\n", + " bath_R.replace(theta=theta),\n", + " e1,\n", + " )\n", + " progress.value += 1\n", + " return np.real(current)\n", + "\n", + "\n", + "def current_pade_for_theta(H, bath_L, bath_R, theta, Nk):\n", + " \"\"\" Return the steady state current using the Pade approximation. \"\"\"\n", + " bath_L = bath_L.replace(theta=theta)\n", + " bath_R = bath_R.replace(theta=theta)\n", + "\n", + " bathL = LorentzianPadeBath(\n", + " bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", + " Nk, tag=\"L\",\n", + " )\n", + " bathR = LorentzianPadeBath(\n", + " bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", + " Nk, tag=\"R\",\n", + " )\n", + "\n", + " solver_pade = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options)\n", + " rho_ss_pade, ado_ss_pade = solver_pade.steady_state()\n", + " current = state_current(ado_ss_pade, bath_tag=\"R\")\n", + "\n", + " progress.value += 1\n", + " return np.real(current)\n", + "\n", + "\n", + "curr_ss_analytic_thetas = [\n", + " current_analytic_for_theta(e1, bath_L, bath_R, theta)\n", + " for theta in thetas\n", + "]\n", + "\n", + "# The number of expansion terms has been dropped to Nk=6 to speed\n", + "# up notebook execution. Increase to Nk=10 for more accurate results.\n", + "curr_ss_pade_theta = [\n", + " current_pade_for_theta(H, bath_L, bath_R, theta, Nk=6)\n", + " for theta in thetas\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "db018c3a", + "metadata": {}, + "source": [ + "Below we plot the results and see that even with `Nk=6`, the HEOM Pade approximation gives good results for the steady state current. Increasing `Nk` to `10` gives very accurate results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60896a72", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(12, 7))\n", + "\n", + "ax.plot(\n", + " thetas, 2.434e-4 * 1e6 * np.array(curr_ss_analytic_thetas),\n", + " color=\"black\", linewidth=3,\n", + " label=r\"Analytical\",\n", + ")\n", + "ax.plot(\n", + " thetas, 2.434e-4 * 1e6 * np.array(curr_ss_pade_theta),\n", + " 'r--', linewidth=3,\n", + " label=r\"HEOM Pade $N_k=10$, $n_{\\mathrm{max}}=2$\",\n", + ")\n", + "\n", + "\n", + "ax.locator_params(axis='y', nbins=4)\n", + "ax.locator_params(axis='x', nbins=4)\n", + "\n", + "ax.set_xticks([-2.5, 0, 2.5])\n", + "ax.set_xticklabels([-2.5, 0, 2.5])\n", + "ax.set_xlabel(r\"Bias voltage $\\Delta \\mu$ ($V$)\", fontsize=28)\n", + "ax.set_ylabel(r\"Current ($\\mu A$)\", fontsize=28)\n", + "ax.legend(fontsize=25);" + ] + }, + { + "cell_type": "markdown", + "id": "4b605c19", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f097093e", + "metadata": {}, + "outputs": [], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "05408673", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdcb4e35", + "metadata": {}, + "outputs": [], + "source": [ + "assert np.allclose(curr_ss_pade_L + curr_ss_pade_R, 0)\n", + "assert np.allclose(curr_ss_mats_L + curr_ss_mats_R, 0)\n", + "assert np.allclose(curr_ss_pade_R, curr_ss_analytic, rtol=1e-4)" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.ipynb b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.ipynb new file mode 100644 index 00000000..bfc23430 --- /dev/null +++ b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.ipynb @@ -0,0 +1,528 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f4f411fd", + "metadata": {}, + "source": [ + "# HEOM 5b: Discrete boson coupled to an impurity and fermionic leads" + ] + }, + { + "cell_type": "markdown", + "id": "e2f23773", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc), also coupled to a discrete bosonic (vibronic) mode.\n", + "\n", + "Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his Dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications. In particular this example reproduces some results from https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.201407\n", + "\n", + "Notation:\n", + "\n", + "* $K=L/R$ refers to left or right leads.\n", + "* $\\sigma=\\pm$ refers to input/output\n", + "\n", + "We choose a Lorentzian spectral density for the leads, with a peak at the chemical potential. The latter simplifies a little the notation required for the correlation functions, but can be relaxed if neccessary.\n", + "\n", + "$$J(\\omega) = \\frac{\\Gamma W^2}{((\\omega-\\mu_K)^2 +W^2 )}$$\n", + "\n", + "The Fermi distribution function is:\n", + "\n", + "$$f_F (x) = (\\exp(x) + 1)^{-1}$$\n", + "\n", + "Together these allow the correlation functions to be expressed as:\n", + "\n", + "$$C^{\\sigma}_K(t) = \\frac{1}{2\\pi} \\int_{-\\infty}^{\\infty} d\\omega e^{\\sigma i \\omega t} \\Gamma_K(\\omega) f_F[\\sigma\\beta(\\omega - \\mu)]$$\n", + "\n", + "As with the Bosonic case we can treat these with Matsubara, Pade, or fitting approaches.\n", + "\n", + "The Pade decomposition approximates the Fermi distubition as \n", + "\n", + "$$f_F(x) \\approx f_F^{\\mathrm{approx}}(x) = \\frac{1}{2} - \\sum_l^{l_{max}} \\frac{2k_l x}{x^2 + \\epsilon_l^2}$$\n", + "\n", + "$k_l$ and $\\epsilon_l$ are co-efficients defined in J. Chem Phys 133,10106\n", + "\n", + "Evaluating the integral for the correlation functions gives,\n", + "\n", + "\n", + "$$C_K^{\\sigma}(t) \\approx \\sum_{l=0}^{l_{max}} \\eta_K^{\\sigma_l} e^{-\\gamma_{K,\\sigma,l}t}$$\n", + "\n", + "where\n", + "\n", + "$$\\eta_{K,0} = \\frac{\\Gamma_KW_K}{2} f_F^{approx}(i\\beta_K W)$$\n", + "\n", + "$$\\gamma_{K,\\sigma,0} = W_K - \\sigma i\\mu_K$$ \n", + "\n", + "$$\\eta_{K,l\\neq 0} = -i\\cdot \\frac{k_m}{\\beta_K} \\cdot \\frac{\\Gamma_K W_K^2}{-\\frac{\\epsilon^2_m}{\\beta_K^2} + W_K^2}$$\n", + "\n", + "$$\\gamma_{K,\\sigma,l\\neq 0}= \\frac{\\epsilon_m}{\\beta_K} - \\sigma i \\mu_K$$" + ] + }, + { + "cell_type": "markdown", + "id": "007b0b5f", + "metadata": {}, + "source": [ + "## Differences from Example 5a" + ] + }, + { + "cell_type": "markdown", + "id": "6c35243d", + "metadata": {}, + "source": [ + "The system we study here has two big differences from the HEOM 5a example:\n", + "\n", + "* the system now includes a discrete bosonic mode,\n", + "* and the electronic leads have $W$ set to $10^4$ (i.e. the wide-band limit).\n", + "\n", + "The new system Hamiltonian is:\n", + "\n", + "$$\n", + "H_{\\mathrm{vib}} = H_{\\mathrm{SIAM}} + \\Omega a^{\\dagger}a + \\lambda (a+a^{\\dagger})c{^\\dagger}c.\n", + "$$\n", + "\n", + "where $H_{\\mathrm{SIAM}}$ is the Hamiltonian of the single impurity, and the remaining terms are the Hamiltonian of the bosonic mode and its interaction with the impurity.\n", + "\n", + "The complete setup now consists of four parts:\n", + "\n", + "* the single impurity\n", + "* a discrete bosonic mode\n", + "* two fermionic leads.\n", + "\n", + "**Note**: This example is quite numerically challenging and has many system and bath components. For an easier introduction into the fermionic case, see example 5a.\n", + "\n", + "**Note**: We've reduced the cut-off of the bosonic mode to 2 modes to facilitate faster execution of the notebooks when the outputs are being checked and compiled. A more accurate result may be obtained by increasing the number of bosonic modes to, for example, 16." + ] + }, + { + "cell_type": "markdown", + "id": "d6707696", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f12b48a2", + "metadata": {}, + "outputs": [], + "source": [ + "import contextlib\n", + "import dataclasses\n", + "import time\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " destroy,\n", + " qeye,\n", + " tensor,\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver,\n", + " LorentzianPadeBath,\n", + ")\n", + "\n", + "from ipywidgets import IntProgress\n", + "from IPython.display import display\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "44595576", + "metadata": {}, + "source": [ + "## Helpers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "278c8e44", + "metadata": {}, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\" Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61a29f07", + "metadata": {}, + "outputs": [], + "source": [ + "def state_current(ado_state, bath_tag):\n", + " \"\"\" Determine current from the given bath (either \"R\" or \"L\") to\n", + " the system in the given ADO state.\n", + " \"\"\"\n", + " level_1_aux = [\n", + " (ado_state.extract(label), ado_state.exps(label)[0])\n", + " for label in ado_state.filter(level=1, tags=[bath_tag])\n", + " ]\n", + "\n", + " def exp_sign(exp):\n", + " return 1 if exp.type == exp.types[\"+\"] else -1\n", + "\n", + " def exp_op(exp):\n", + " return exp.Q if exp.type == exp.types[\"+\"] else exp.Q.dag()\n", + "\n", + " return -1.0j * sum(\n", + " exp_sign(exp) * (exp_op(exp) * aux).tr()\n", + " for aux, exp in level_1_aux\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d93e7e1c", + "metadata": {}, + "outputs": [], + "source": [ + "# Solver options:\n", + "\n", + "# We set store_ados to True so that we can\n", + "# use the auxilliary density operators (ADOs)\n", + "# to calculate the current between the leads\n", + "# and the system.\n", + "\n", + "options = {\n", + " \"nsteps\": 1500,\n", + " \"store_states\": True,\n", + " \"store_ados\": True,\n", + " \"rtol\": 1e-12,\n", + " \"atol\": 1e-12,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "dbe40f80", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "Let us set up the system Hamiltonian and specify the properties of the two reservoirs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6d16db1", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the system Hamiltonian:\n", + "\n", + "@dataclasses.dataclass\n", + "class SystemParameters:\n", + " e1: float = 0.3 # fermion mode energy splitting\n", + " Omega: float = 0.2 # bosonic mode energy splitting\n", + " Lambda: float = 0.12 # coupling between fermion and boson\n", + " Nbos: int = 2\n", + "\n", + " def __post_init__(self):\n", + " d = tensor(destroy(2), qeye(self.Nbos))\n", + " a = tensor(qeye(2), destroy(self.Nbos))\n", + " self.H = (\n", + " self.e1 * d.dag() * d +\n", + " self.Omega * a.dag() * a +\n", + " self.Lambda * (a + a.dag()) * d.dag() * d\n", + " )\n", + " self.Q = d\n", + "\n", + " def replace(self, **kw):\n", + " return dataclasses.replace(self, **kw)\n", + "\n", + "\n", + "sys_p = SystemParameters()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d338e3c8", + "metadata": {}, + "outputs": [], + "source": [ + "# Define parameters for left and right fermionic baths.\n", + "# Each bath is a lead (i.e. a wire held at a potential)\n", + "# with temperature T and chemical potential mu.\n", + "\n", + "@dataclasses.dataclass\n", + "class LorentzianBathParameters:\n", + " lead: str\n", + " gamma: float = 0.01 # coupling strength\n", + " W: float = 1.0 # cut-off\n", + " T: float = 0.025851991 # temperature (in eV)\n", + " theta: float = 2.0 # bias\n", + "\n", + " def __post_init__(self):\n", + " assert self.lead in (\"L\", \"R\")\n", + " self.beta = 1 / self.T\n", + " if self.lead == \"L\":\n", + " self.mu = self.theta / 2.0\n", + " else:\n", + " self.mu = - self.theta / 2.0\n", + "\n", + " def J(self, w):\n", + " \"\"\" Spectral density. \"\"\"\n", + " return self.gamma * self.W**2 / ((w - self.mu)**2 + self.W**2)\n", + "\n", + " def fF(self, w, sign=1.0):\n", + " \"\"\" Fermi distribution for this bath. \"\"\"\n", + " x = sign * self.beta * (w - self.mu)\n", + " return fF(x)\n", + "\n", + " def lamshift(self, w):\n", + " \"\"\" Return the lamshift. \"\"\"\n", + " return 0.5 * (w - self.mu) * self.J(w) / self.W\n", + "\n", + " def replace(self, **kw):\n", + " return dataclasses.replace(self, **kw)\n", + "\n", + "\n", + "def fF(x):\n", + " \"\"\" Return the Fermi distribution. \"\"\"\n", + " # in units where kB = 1.0\n", + " return 1 / (np.exp(x) + 1)\n", + "\n", + "\n", + "# We set W = 1e4 to investigate the wide-band limit:\n", + "\n", + "bath_L = LorentzianBathParameters(W=10**4, lead=\"L\")\n", + "bath_R = LorentzianBathParameters(W=10**4, lead=\"R\")" + ] + }, + { + "cell_type": "markdown", + "id": "11cac58c", + "metadata": {}, + "source": [ + "## Emission and absorption by the leads\n", + "\n", + "Next let's plot the emission and absorption by the leads." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ca4233b", + "metadata": {}, + "outputs": [], + "source": [ + "w_list = np.linspace(-2, 2, 100)\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 7))\n", + "\n", + "# Left lead emission and absorption\n", + "\n", + "gam_L_in = bath_L.J(w_list) * bath_L.fF(w_list, sign=1.0)\n", + "gam_L_out = bath_L.J(w_list) * bath_L.fF(w_list, sign=-1.0)\n", + "\n", + "ax.plot(\n", + " w_list, gam_L_in,\n", + " \"b--\", linewidth=3,\n", + " label=r\"S_L(w) input (absorption)\",\n", + ")\n", + "ax.plot(\n", + " w_list, gam_L_out,\n", + " \"r--\", linewidth=3,\n", + " label=r\"S_L(w) output (emission)\",\n", + ")\n", + "\n", + "# Right lead emission and absorption\n", + "\n", + "gam_R_in = bath_R.J(w_list) * bath_R.fF(w_list, sign=1.0)\n", + "gam_R_out = bath_R.J(w_list) * bath_R.fF(w_list, sign=-1.0)\n", + "\n", + "ax.plot(\n", + " w_list, gam_R_in,\n", + " \"b\", linewidth=3,\n", + " label=r\"S_R(w) input (absorption)\",\n", + ")\n", + "ax.plot(\n", + " w_list, gam_R_out,\n", + " \"r\", linewidth=3,\n", + " label=r\"S_R(w) output (emission)\",\n", + ")\n", + "\n", + "ax.set_xlabel(\"w\")\n", + "ax.set_ylabel(r\"$S(\\omega)$\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "793150e7", + "metadata": {}, + "source": [ + "## Below we give one example data set from Paper\n", + "\n", + "Here we just give one example of the current as a function of bias voltage, but in general one can try different cut-offs of the bosonic Fock space and the expansion of the correlation functions until convergence is found.\n", + "\n", + "One note: for very large problems, this can be slow." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb1e45d9", + "metadata": {}, + "outputs": [], + "source": [ + "def steady_state_pade_for_theta(sys_p, bath_L, bath_R, theta, Nk, Nc, Nbos):\n", + " \"\"\" Return the steady state current using the Pade approximation. \"\"\"\n", + "\n", + " sys_p = sys_p.replace(Nbos=Nbos)\n", + " bath_L = bath_L.replace(theta=theta)\n", + " bath_R = bath_R.replace(theta=theta)\n", + "\n", + " bathL = LorentzianPadeBath(\n", + " sys_p.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", + " Nk, tag=\"L\",\n", + " )\n", + " bathR = LorentzianPadeBath(\n", + " sys_p.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", + " Nk, tag=\"R\",\n", + " )\n", + "\n", + " solver_pade = HEOMSolver(\n", + " sys_p.H, [bathL, bathR], max_depth=2, options=options,\n", + " )\n", + " rho_ss_pade, ado_ss_pade = solver_pade.steady_state()\n", + " current = state_current(ado_ss_pade, bath_tag=\"R\")\n", + "\n", + " return np.real(2.434e-4 * 1e6 * current)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39d0a6ab", + "metadata": {}, + "outputs": [], + "source": [ + "# Parameters:\n", + "\n", + "Nk = 6\n", + "Nc = 2\n", + "Nbos = 2 # Use Nbos = 16 for more accurate results\n", + "\n", + "thetas = np.linspace(0, 2, 30)\n", + "\n", + "# Progress bar:\n", + "\n", + "progress = IntProgress(min=0, max=len(thetas))\n", + "display(progress)\n", + "\n", + "currents = []\n", + "\n", + "for theta in thetas:\n", + " currents.append(steady_state_pade_for_theta(\n", + " sys_p, bath_L, bath_R, theta,\n", + " Nk=Nk, Nc=Nc, Nbos=Nbos,\n", + " ))\n", + " progress.value += 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb80b9b5", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(12, 10))\n", + "\n", + "ax.plot(\n", + " thetas, currents,\n", + " color=\"green\", linestyle='-', linewidth=3,\n", + " label=f\"Nk = {5}, max_depth = {Nc}, Nbos = {Nbos}\",\n", + ")\n", + "\n", + "ax.set_yticks([0, 0.5, 1])\n", + "ax.set_yticklabels([0, 0.5, 1])\n", + "\n", + "ax.locator_params(axis='y', nbins=4)\n", + "ax.locator_params(axis='x', nbins=4)\n", + "\n", + "ax.set_xlabel(r\"Bias voltage $\\Delta \\mu$ ($V$)\", fontsize=30)\n", + "ax.set_ylabel(r\"Current ($\\mu A$)\", fontsize=30)\n", + "ax.legend(loc=4);" + ] + }, + { + "cell_type": "markdown", + "id": "efa95cca", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d0b16ed7", + "metadata": {}, + "outputs": [], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "991661c9", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "343be28f", + "metadata": {}, + "outputs": [], + "source": [ + "assert 1 == 1" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-index.ipynb b/tutorials-v5/heom/heom-index.ipynb new file mode 100644 index 00000000..846e4d3f --- /dev/null +++ b/tutorials-v5/heom/heom-index.ipynb @@ -0,0 +1,56 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e096ef9b", + "metadata": {}, + "source": [ + "# Hierarchical Equation of Motion Examples\n", + "\n", + "The \"hierarchical equations of motion\" (HEOM) method is a powerful numerical approach to solve the dynamics and steady-state of a quantum system coupled to a non-Markovian and non-perturbative environment. Originally developed in the context of physical chemistry, it has also been extended and applied to problems in solid-state physics, optics, single-molecule electronics, and biological physics.\n", + "\n", + "QuTiP's implementation of the HEOM is described in detail in https://arxiv.org/abs/2010.10806.\n", + "\n", + "This collection of examples from the paper illustrates how to use QuTiP's HEOM to model and investigate the dynamics of a variety of systems coupled to bosonic or fermionic baths.\n", + "\n", + "## Overview of the notebooks\n", + "\n", + "\n", + "\n", + "* [Example 1a: Spin-Bath model (introduction)](./heom-1a-spin-bath-model-basic.ipynb)\n", + "\n", + "* [Example 1b: Spin-Bath model (very strong coupling)](./heom-1b-spin-bath-model-very-strong-coupling.ipynb)\n", + "\n", + "* [Example 1c: Spin-Bath model (underdamped case)](./heom-1c-spin-bath-model-underdamped-sd.ipynb)\n", + "\n", + "* [Example 1d: Spin-Bath model, fitting of spectrum and correlation functions](./heom-1d-spin-bath-model-ohmic-fitting.ipynb)\n", + "\n", + "* [Example 1e: Spin-Bath model (pure dephasing)](./heom-1e-spin-bath-model-pure-dephasing.ipynb)\n", + "\n", + "* [Example 2: Dynamics in Fenna-Mathews-Olsen complex (FMO)](./heom-2-fmo-example.ipynb)\n", + "\n", + "* [Example 3: Quantum Heat Transport](./heom-3-quantum-heat-transport.ipynb)\n", + "\n", + "* [Example 4: Dynamical decoupling of a non-Markovian environment](./heom-4-dynamical-decoupling.ipynb)\n", + "\n", + "* [Example 5a: Fermionic single impurity model](./heom-5a-fermions-single-impurity-model.ipynb)\n", + "\n", + "* [Example 5b: Discrete boson coupled to an impurity + fermionic leads](./heom-5b-fermions-discrete-boson-model.ipynb)\n", + "\n", + "" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 1a4318c3e8e3cb4eb499b8cd120db46f9d0f7438 Mon Sep 17 00:00:00 2001 From: mcditooss Date: Wed, 6 Nov 2024 21:54:49 +0100 Subject: [PATCH 13/44] The new environment class, refactoring of first 3 notebooks --- .../heom/heom-1a-spin-bath-model-basic.md | 1109 ----------------- ...1b-spin-bath-model-very-strong-coupling.md | 540 -------- .../heom-1c-spin-bath-model-underdamped-sd.md | 586 --------- .../heom-1d-spin-bath-model-ohmic-fitting.md | 825 ------------ .../heom-1e-spin-bath-model-pure-dephasing.md | 592 --------- tutorials-v5/heom/heom-2-fmo-example.md | 465 ------- .../heom/heom-3-quantum-heat-transport.md | 505 -------- .../heom/heom-4-dynamical-decoupling.md | 531 -------- .../heom-5a-fermions-single-impurity-model.md | 587 --------- .../heom-5b-fermions-discrete-boson-model.md | 395 ------ tutorials-v5/heom/heom-index.md | 47 - 11 files changed, 6182 deletions(-) delete mode 100644 tutorials-v5/heom/heom-1a-spin-bath-model-basic.md delete mode 100644 tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md delete mode 100644 tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md delete mode 100644 tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md delete mode 100644 tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md delete mode 100644 tutorials-v5/heom/heom-2-fmo-example.md delete mode 100644 tutorials-v5/heom/heom-3-quantum-heat-transport.md delete mode 100644 tutorials-v5/heom/heom-4-dynamical-decoupling.md delete mode 100644 tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md delete mode 100644 tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md delete mode 100644 tutorials-v5/heom/heom-index.md diff --git a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md deleted file mode 100644 index b9fc065b..00000000 --- a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md +++ /dev/null @@ -1,1109 +0,0 @@ ---- -jupyter: - jupytext: - text_representation: - extension: .md - format_name: markdown - format_version: '1.3' - jupytext_version: 1.16.1 - kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 ---- - -# HEOM 1a: Spin-Bath model (introduction) - - -## Introduction - -The HEOM method solves the dynamics and steady state of a system and its -environment, the latter of which is encoded in a set of auxiliary density -matrices. - -In this example we show the evolution of a single two-level system in contact -with a single Bosonic environment. The properties of the system are encoded -in a Hamiltonian, and a coupling operator which describes how it is coupled -to the environment. - -The Bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions. - -In the example below we show how to model the overdamped Drude-Lorentz -Spectral Density, commonly used with the HEOM. We show how to do this using -the Matsubara, Pade and fitting decompositions, and compare their -convergence. - -### Drude-Lorentz (overdamped) spectral density - -The Drude-Lorentz spectral density is: - -$$J_D(\omega)= \frac{2\omega\lambda\gamma}{{\gamma}^2 + \omega^2}$$ - -where $\lambda$ scales the coupling strength, and $\gamma$ is the cut-off -frequency. We use the convention, -\begin{equation*} -C(t) = \int_0^{\infty} d\omega \frac{J_D(\omega)}{\pi}[\coth(\beta\omega) \ - \cos(\omega \tau) - i \sin(\omega \tau)] -\end{equation*} - -With the HEOM we must use an exponential decomposition: - -\begin{equation*} -C(t)=\sum_{k=0}^{k=\infty} c_k e^{-\nu_k t} -\end{equation*} - -As an example, the Matsubara decomposition of the Drude-Lorentz spectral -density is given by: - -\begin{equation*} - \nu_k = \begin{cases} - \gamma & k = 0\\ - {2 \pi k} / {\beta } & k \geq 1\\ - \end{cases} -\end{equation*} - -\begin{equation*} - c_k = \begin{cases} - \lambda \gamma (\cot(\beta \gamma / 2) - i) \ - & k = 0\\ - 4 \lambda \gamma \nu_k / \{(\nu_k^2 - \gamma^2)\beta \} \ - & k \geq 1\\ - \end{cases} -\end{equation*} - -Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. - - -## Setup - -```python -import contextlib -import time - -import numpy as np -from matplotlib import pyplot as plt -from scipy.optimize import curve_fit - -import qutip -from qutip import ( - basis, - brmesolve, - destroy, - expect, - liouvillian, - qeye, - sigmax, - sigmaz, - spost, - spre, - tensor, -) - -from qutip.solver.heom import ( - BosonicBath, - DrudeLorentzBath, - DrudeLorentzPadeBath, - HEOMSolver, - HSolverDL, -) - -%matplotlib inline -``` - - -## Helper functions - -Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: - -```python -def cot(x): - """Vectorized cotangent of x.""" - return 1.0 / np.tan(x) -``` - -```python -def dl_matsubara_params(lam, gamma, T, nk): - """Calculation of the real and imaginary expansions of the Drude-Lorenz - correlation functions. - """ - ckAR = [lam * gamma * cot(gamma / (2 * T))] - ckAR.extend( - (8 * lam * gamma * T * np.pi * k * T / - ((2 * np.pi * k * T)**2 - gamma**2)) - for k in range(1, nk + 1) - ) - vkAR = [gamma] - vkAR.extend(2 * np.pi * k * T for k in range(1, nk + 1)) - - ckAI = [lam * gamma * (-1.0)] - vkAI = [gamma] - - return ckAR, vkAR, ckAI, vkAI -``` - -```python -def plot_result_expectations(plots, axes=None): - """Plot the expectation values of operators as functions of time. - - Each plot in plots consists of (solver_result, measurement_operation, - color, label). - """ - if axes is None: - fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - fig_created = True - else: - fig = None - fig_created = False - - # add kw arguments to each plot if missing - plots = [p if len(p) == 5 else p + ({},) for p in plots] - for result, m_op, color, label, kw in plots: - exp = np.real(expect(result.states, m_op)) - kw.setdefault("linewidth", 2) - axes.plot(result.times, exp, color, label=label, **kw) - - if fig_created: - axes.legend(loc=0, fontsize=12) - axes.set_xlabel("t", fontsize=28) - - return fig -``` - -```python -@contextlib.contextmanager -def timer(label): - """Simple utility for timing functions: - - with timer("name"): - ... code to time ... - """ - start = time.time() - yield - end = time.time() - print(f"{label}: {end - start}") -``` - -```python -# Default solver options: - -default_options = { - "nsteps": 1500, - "store_states": True, - "rtol": 1e-12, - "atol": 1e-12, - "method": "vern9", - "progress_bar": "enhanced", -} -``` - -## System and bath definition - -And let us set up the system Hamiltonian, bath and system measurement operators: - -```python -# Defining the system Hamiltonian -eps = 0.5 # Energy of the 2-level system. -Del = 1.0 # Tunnelling term -Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() -``` - -```python -# Initial state of the system. -rho0 = basis(2, 0) * basis(2, 0).dag() -``` - -```python -# System-bath coupling (Drude-Lorentz spectral density) -Q = sigmaz() # coupling operator - -# Bath properties: -gamma = 0.5 # cut off frequency -lam = 0.1 # coupling strength -T = 0.5 -beta = 1.0 / T - -# HEOM parameters -NC = 5 # cut off parameter for the bath -Nk = 2 # terms in the Matsubara expansion of the correlation function - -# Times to solve for -tlist = np.linspace(0, 50, 1000) -``` - -```python -# Define some operators with which we will measure the system -# 1,1 element of density matrix - corresonding to groundstate -P11p = basis(2, 0) * basis(2, 0).dag() -P22p = basis(2, 1) * basis(2, 1).dag() -# 1,2 element of density matrix - corresonding to coherence -P12p = basis(2, 0) * basis(2, 1).dag() -``` - -### First of all, it is useful to look at the spectral density - -Now we are ready to begin. Let's look at the shape of the spectral density given the bath parameters we defined above: - -```python -def plot_spectral_density(): - """Plot the Drude-Lorentz spectral density""" - w = np.linspace(0, 5, 1000) - J = w * 2 * lam * gamma / (gamma**2 + w**2) - - fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - axes.plot(w, J, "r", linewidth=2) - axes.set_xlabel(r"$\omega$", fontsize=28) - axes.set_ylabel(r"J", fontsize=28) - - -plot_spectral_density() -``` - -Next we calculate the exponents using the Matsubara decompositions. Here we -split them into real and imaginary parts. - -The HEOM code will optimize these, and reduce the number of exponents when -real and imaginary parts have the same exponent. This is clearly the case -for the first term in the vkAI and vkAR lists. - -```python -ckAR, vkAR, ckAI, vkAI = dl_matsubara_params(nk=Nk, lam=lam, gamma=gamma, T=T) -``` - -Having created the lists which specify the bath correlation functions, we -create a `BosonicBath` from them and pass the bath to the `HEOMSolver` class. - -The solver constructs the "right hand side" (RHS) determinining how the -system and auxiliary density operators evolve in time. This can then be used -to solve for dynamics or steady-state. - -Below we create the bath and solver and then solve for the dynamics by -calling `.run(rho0, tlist)`. - -```python -options = {**default_options} - -with timer("RHS construction time"): - bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI) - HEOMMats = HEOMSolver(Hsys, bath, NC, options=options) - -with timer("ODE solver time"): - resultMats = HEOMMats.run(rho0, tlist) -``` - -```python -plot_result_expectations( - [ - (resultMats, P11p, "b", "P11 Mats"), - (resultMats, P12p, "r", "P12 Mats"), - ] -); -``` - -In practice, one would not perform this laborious expansion for the -Drude-Lorentz correlation function, because QuTiP already has a class, -`DrudeLorentzBath`, that can construct this bath for you. Nevertheless, -knowing how to perform this expansion will allow you to construct your own -baths for other spectral densities. - -Below we show how to use this built-in functionality: - -```python -# Compare to built-in Drude-Lorentz bath: - -with timer("RHS construction time"): - dlbath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) - HEOM_dlbath = HEOMSolver(Hsys, dlbath, NC, options=options) - -with timer("ODE solver time"): - result_dlbath = HEOM_dlbath.run(rho0, tlist) # normal 115 -``` - -```python -plot_result_expectations( - [ - (result_dlbath, P11p, "b", "P11 (DrudeLorentzBath)"), - (result_dlbath, P12p, "r", "P12 (DrudeLorentzBath)"), - ] -); -``` - -The `DrudeLorentzBath` class also allows us to easily evaluate analytical expressions for the power spectrum, correlation function, and spectral density. In the following plots, the solid lines are the exact expressions, and the dashed lines are based on our approximation of the correlation function with a finite number of exponents. - -```python -w = np.linspace(-10, 10, 1000) -w2 = np.linspace(0, 10, 1000) -fig, axs = plt.subplots(2, 2) - -axs[0, 0].plot(w, dlbath.power_spectrum(w)) -axs[0, 0].plot(w, dlbath.power_spectrum_approx(w), '--') -axs[0, 0].set(xlabel=r'$\omega$', ylabel=r'$S(\omega)$') -axs[0, 1].plot(w2, dlbath.spectral_density(w2)) -axs[0, 1].plot(w2, dlbath.spectral_density_approx(w2), '--') -axs[0, 1].set(xlabel=r'$\omega$', ylabel=r'$J(\omega)$') -axs[1, 0].plot(w2, np.real(dlbath.correlation_function(w2))) -axs[1, 0].plot(w2, np.real(dlbath.correlation_function_approx(w2)), '--') -axs[1, 0].set(xlabel=r'$t$', ylabel=r'$C_{R}(t)$') -axs[1, 1].plot(w2, np.imag(dlbath.correlation_function(w2))) -axs[1, 1].plot(w2, np.imag(dlbath.correlation_function_approx(w2)), '--') -axs[1, 1].set(xlabel=r'$t$', ylabel=r'$C_{I}(t)$') - -fig.tight_layout() -plt.show() -``` - -We also provide a legacy class, `HSolverDL`, which calculates the -Drude-Lorentz correlation functions automatically, to be backwards -compatible with the previous HEOM solver in QuTiP: - -```python -# Compare to legacy class: - -# The legacy class performs the above collation of coefficients automatically, -# based upon the parameters for the Drude-Lorentz spectral density. - -with timer("RHS construction time"): - HEOMlegacy = HSolverDL(Hsys, Q, lam, T, NC, Nk, gamma, options=options) - -with timer("ODE solver time"): - resultLegacy = HEOMlegacy.run(rho0, tlist) # normal 115 -``` - -```python -plot_result_expectations( - [ - (resultLegacy, P11p, "b", "P11 Legacy"), - (resultLegacy, P12p, "r", "P12 Legacy"), - ] -); -``` - -## Ishizaki-Tanimura Terminator - -To speed up convergence (in terms of the number of exponents kept in the -Matsubara decomposition), We can treat the $Re[C(t=0)]$ component as a -delta-function distribution, and include it as Lindblad correction. This is -sometimes known as the Ishizaki-Tanimura Terminator. - -In more detail, given - -\begin{equation*} -C(t)=\sum_{k=0}^{\infty} c_k e^{-\nu_k t} -\end{equation*} - -since $\nu_k=\frac{2 \pi k}{\beta }$, if $1/\nu_k$ is much much smaller than -other important time-scales, we can approximate, -$ e^{-\nu_k t} \approx \delta(t)/\nu_k$, and $C(t)=\sum_{k=N_k}^{\infty} -\frac{c_k}{\nu_k} \delta(t)$ - -It is convenient to calculate the whole sum -$ C(t)=\sum_{k=0}^{\infty} \frac{c_k}{\nu_k} = 2 \lambda / (\beta \gamma) -- i\lambda $ -, and subtract off the contribution from the finite number of Matsubara terms -that are kept in the hierarchy, and treat the residual as a Lindblad. - -This is clearer if we plot the correlation function with a large number of -Matsubara terms. To create the plot, we use the utility function of the -`DrudeLorentzBath` class mentioned above. - -```python -def plot_correlation_expansion_divergence(): - """We plot the correlation function with a large number of Matsubara terms - to show that the real part is slowly diverging at t = 0. - """ - t = np.linspace(0, 2, 100) - - # correlation coefficients with 15k and 2 terms - corr_15k = dlbath.correlation_function(t, Nk=15_000) - corr_2 = dlbath.correlation_function(t, Nk=2) - - fig, ax1 = plt.subplots(figsize=(12, 7)) - - ax1.plot( - t, np.real(corr_2), color="b", linewidth=3, label=r"Mats = 2 real" - ) - ax1.plot( - t, np.imag(corr_2), color="r", linewidth=3, label=r"Mats = 2 imag" - ) - ax1.plot( - t, np.real(corr_15k), "b--", linewidth=3, label=r"Mats = 15000 real" - ) - ax1.plot( - t, np.imag(corr_15k), "r--", linewidth=3, label=r"Mats = 15000 imag" - ) - - ax1.set_xlabel("t") - ax1.set_ylabel(r"$C$") - ax1.legend() - -plot_correlation_expansion_divergence() -``` - -Let us evaluate the result including this Ishizaki-Tanimura terminator: - -```python -# Run HEOM solver and include the Ishizaki-Tanimura terminator - -# Notes: -# -# * when using the built-in DrudeLorentzBath, the terminator (L_bnd) is -# available from bath.terminator(). -# -# * in the legacy HSolverDL function the terminator is included automatically -# if the parameter bnd_cut_approx=True is used. - -op = -2 * spre(Q) * spost(Q.dag()) + spre(Q.dag() * Q) + spost(Q.dag() * Q) - -approx_factr = (2 * lam / (beta * gamma)) - 1j * lam - -approx_factr -= lam * gamma * (-1.0j + cot(gamma / (2 * T))) / gamma -for k in range(1, Nk + 1): - vk = 2 * np.pi * k * T - - approx_factr -= (4 * lam * gamma * T * vk / (vk**2 - gamma**2)) / vk - -L_bnd = -approx_factr * op - -Ltot = -1.0j * (spre(Hsys) - spost(Hsys)) + L_bnd -Ltot = liouvillian(Hsys) + L_bnd - -options = {**default_options, "rtol": 1e-14, "atol": 1e-14} - -with timer("RHS construction time"): - bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI) - HEOMMatsT = HEOMSolver(Ltot, bath, NC, options=options) - -with timer("ODE solver time"): - resultMatsT = HEOMMatsT.run(rho0, tlist) -``` - -```python -plot_result_expectations( - [ - (resultMatsT, P11p, "b", "P11 Mats + Term"), - (resultMatsT, P12p, "r", "P12 Mats + Term"), - ] -); -``` - -Or using the built-in Drude-Lorentz bath we can write simply: - -```python -options = {**default_options, "rtol": 1e-14, "atol": 1e-14} - -with timer("RHS construction time"): - bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) - _, terminator = bath.terminator() - Ltot = liouvillian(Hsys) + terminator - HEOM_dlbath_T = HEOMSolver(Ltot, bath, NC, options=options) - -with timer("ODE solver time"): - result_dlbath_T = HEOM_dlbath_T.run(rho0, tlist) -``` - -```python -plot_result_expectations( - [ - (result_dlbath_T, P11p, "b", "P11 Mats (DrudeLorentzBath + Term)"), - (result_dlbath_T, P12p, "r", "P12 Mats (DrudeLorentzBath + Term)"), - ] -); -``` - -We can compare the solution obtained from the QuTiP Bloch-Redfield solver: - -```python -DL = ( - f"2*pi* 2.0 * {lam} / (pi * {gamma} * {beta}) if (w == 0) else " - f"2*pi*(2.0*{lam}*{gamma} *w /(pi*(w**2+{gamma}**2))) " - f"* ((1/(exp((w) * {beta})-1))+1)" -) -options = {**default_options} - -with timer("ODE solver time"): - resultBR = brmesolve( - Hsys, rho0, tlist, a_ops=[[sigmaz(), DL]], options=options - ) -``` - -```python -plot_result_expectations( - [ - (resultMats, P11p, "b", "P11 Mats"), - (resultMats, P12p, "r", "P12 Mats"), - (resultMatsT, P11p, "b--", "P11 Mats + Term"), - (resultMatsT, P12p, "r--", "P12 Mats + Term"), - (resultBR, P11p, "g--", "P11 Bloch Redfield"), - (resultBR, P12p, "g--", "P12 Bloch Redfield"), - ] -); -``` - -## Padé decomposition - - -The Matsubara decomposition is not the only option. We can also use the -faster-converging Pade decomposition. - -```python -def deltafun(j, k): - if j == k: - return 1.0 - else: - return 0.0 - - -def pade_eps(lmax): - Alpha = np.zeros((2 * lmax, 2 * lmax)) - for j in range(2 * lmax): - for k in range(2 * lmax): - # Fermionic (see other example notebooks): - # Alpha[j][k] = (deltafun(j, k+1) + deltafun(j, k-1)) - # / sqrt((2 * (j + 1) - 1) * (2 * (k + 1) - 1)) - # Bosonic: - Alpha[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1)) / np.sqrt( - (2 * (j + 1) + 1) * (2 * (k + 1) + 1) - ) - - eigvalsA = np.linalg.eigvalsh(Alpha) - eps = [-2 / val for val in eigvalsA[0:lmax]] - return eps - - -def pade_chi(lmax): - AlphaP = np.zeros((2 * lmax - 1, 2 * lmax - 1)) - for j in range(2 * lmax - 1): - for k in range(2 * lmax - 1): - # Fermionic: - # AlphaP[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1)) - # / sqrt((2 * (j + 1) + 1) * (2 * (k + 1) + 1)) - # Bosonic [this is +3 because +1 (bose) + 2*(+1) (from bm+1)]: - AlphaP[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1)) / np.sqrt( - (2 * (j + 1) + 3) * (2 * (k + 1) + 3) - ) - - eigvalsAP = np.linalg.eigvalsh(AlphaP) - chi = [-2 / val for val in eigvalsAP[0:lmax - 1]] - return chi - - -def pade_kappa_epsilon(lmax): - eps = pade_eps(lmax) - chi = pade_chi(lmax) - - kappa = [0] - prefactor = 0.5 * lmax * (2 * (lmax + 1) + 1) - - for j in range(lmax): - term = prefactor - for k in range(lmax - 1): - term *= (chi[k] ** 2 - eps[j] ** 2) / ( - eps[k] ** 2 - eps[j] ** 2 + deltafun(j, k) - ) - - for k in range(lmax - 1, lmax): - term /= eps[k] ** 2 - eps[j] ** 2 + deltafun(j, k) - - kappa.append(term) - - epsilon = [0] + eps - - return kappa, epsilon - - -def pade_corr(tlist, lmax): - kappa, epsilon = pade_kappa_epsilon(lmax) - - eta_list = [lam * gamma * (cot(gamma * beta / 2.0) - 1.0j)] - gamma_list = [gamma] - - if lmax > 0: - for ll in range(1, lmax + 1): - eta_list.append( - (kappa[ll] / beta) - * 4 - * lam - * gamma - * (epsilon[ll] / beta) - / ((epsilon[ll] ** 2 / beta**2) - gamma**2) - ) - gamma_list.append(epsilon[ll] / beta) - - c_tot = [] - for t in tlist: - c_tot.append( - sum( - [ - eta_list[ll] * np.exp(-gamma_list[ll] * t) - for ll in range(lmax + 1) - ] - ) - ) - return c_tot, eta_list, gamma_list - - -tlist_corr = np.linspace(0, 2, 100) -cppLP, etapLP, gampLP = pade_corr(tlist_corr, 2) -corr_15k = dlbath.correlation_function(tlist_corr, Nk=15_000) -corr_2k = dlbath.correlation_function(tlist_corr, Nk=2) - -fig, ax1 = plt.subplots(figsize=(12, 7)) -ax1.plot( - tlist_corr, - np.real(cppLP), - color="b", - linewidth=3, - label=r"real pade 2 terms", -) -ax1.plot( - tlist_corr, - np.real(corr_15k), - "r--", - linewidth=3, - label=r"real mats 15000 terms", -) -ax1.plot( - tlist_corr, - np.real(corr_2k), - "g--", - linewidth=3, - label=r"real mats 2 terms", -) - -ax1.set_xlabel("t") -ax1.set_ylabel(r"$C$") -ax1.legend() - -fig, ax1 = plt.subplots(figsize=(12, 7)) - -ax1.plot( - tlist_corr, - np.real(cppLP) - np.real(corr_15k), - color="b", - linewidth=3, - label=r"pade error", -) -ax1.plot( - tlist_corr, - np.real(corr_2k) - np.real(corr_15k), - "r--", - linewidth=3, - label=r"mats error", -) - -ax1.set_xlabel("t") -ax1.set_ylabel(r"Error") -ax1.legend(); -``` - -```python -# put pade parameters in lists for heom solver -ckAR = [np.real(eta) + 0j for eta in etapLP] -ckAI = [np.imag(etapLP[0]) + 0j] -vkAR = [gam + 0j for gam in gampLP] -vkAI = [gampLP[0] + 0j] - -options = {**default_options, "rtol": 1e-14, "atol": 1e-14} - -with timer("RHS construction time"): - bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI) - HEOMPade = HEOMSolver(Hsys, bath, NC, options=options) - -with timer("ODE solver time"): - resultPade = HEOMPade.run(rho0, tlist) -``` - -```python -plot_result_expectations( - [ - (resultMats, P11p, "b", "P11 Mats"), - (resultMats, P12p, "r", "P12 Mats"), - (resultMatsT, P11p, "y", "P11 Mats + Term"), - (resultMatsT, P12p, "g", "P12 Mats + Term"), - (resultPade, P11p, "b--", "P11 Pade"), - (resultPade, P12p, "r--", "P12 Pade"), - ] -); -``` - -The Padé decomposition of the Drude-Lorentz bath is also available via a -built-in class, `DrudeLorentzPadeBath` bath. Like `DrudeLorentzBath`, one -can obtain the terminator by calling `bath.terminator()`. - -Below we show how to use the built-in Padé Drude-Lorentz bath and its -terminator (although the terminator does not provide much improvement here, -because the Padé expansion already fits the correlation function well): - -```python -options = {**default_options, "rtol": 1e-14, "atol": 1e-14} - -with timer("RHS construction time"): - bath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) - _, terminator = bath.terminator() - Ltot = liouvillian(Hsys) + terminator - HEOM_dlpbath_T = HEOMSolver(Ltot, bath, NC, options=options) - -with timer("ODE solver time"): - result_dlpbath_T = HEOM_dlpbath_T.run(rho0, tlist) -``` - -```python -plot_result_expectations( - [ - (result_dlpbath_T, P11p, "b", "P11 Padé (DrudeLorentzBath + Term)"), - (result_dlpbath_T, P12p, "r", "P12 Padé (DrudeLorentzBath + Term)"), - ] -); -``` - -### Next we compare the Matsubara and Pade correlation function fits - -Fitting the correlation function is not efficient for this example, but -can be extremely useful in situations where large number of exponents -are needed (e.g., near zero temperature). We will perform the fitting -manually below, and show in notebook 1d how the `CorrelationFitter` -class can be used to perform such fits with less effort. - -First we collect a large sum of Matsubara terms for many time steps: - -```python -tlist2 = np.linspace(0, 2, 10000) - -corr_15k_t10k = dlbath.correlation_function(tlist2, Nk=15_000) - -corrRana = np.real(corr_15k_t10k) -corrIana = np.imag(corr_15k_t10k) -``` - -We then fit this sum with standard least-squares approach: - -```python -def wrapper_fit_func(x, N, args): - """ Fit function wrapper that unpacks its arguments. """ - x = np.array(x) - a = np.array(args[:N]) - b = np.array(args[N:2 * N]) - return fit_func(x, a, b) - - -def fit_func(x, a, b): - """ Fit function. Calculates the value of the - correlation function at each x, given the - fit parameters in a and b. - """ - return np.sum( - a[:, None] * np.exp(np.multiply.outer(b, x)), - axis=0, - ) - - -def fitter(ans, tlist, k): - """ Compute fit with k exponents. """ - upper_a = abs(max(ans, key=abs)) * 10 - # sets initial guesses: - guess = ( - [upper_a / k] * k + # guesses for a - [0] * k # guesses for b - ) - # sets lower bounds: - b_lower = ( - [-upper_a] * k + # lower bounds for a - [-np.inf] * k # lower bounds for b - ) - # sets higher bounds: - b_higher = ( - [upper_a] * k + # upper bounds for a - [0] * k # upper bounds for b - ) - param_bounds = (b_lower, b_higher) - p1, p2 = curve_fit( - lambda x, *params_0: wrapper_fit_func(x, k, params_0), - tlist, - ans, - p0=guess, - sigma=[0.01 for t in tlist], - bounds=param_bounds, - maxfev=1e8, - ) - a, b = p1[:k], p1[k:] - return (a, b) -``` - -```python -kR = 4 # number of exponents to use for real part -poptR = [] -with timer("Correlation (real) fitting time"): - for i in range(kR): - poptR.append(fitter(corrRana, tlist2, i + 1)) - -corrRMats = np.real(dlbath.correlation_function_approx(tlist2)) - -kI = 1 # number of exponents for imaginary part -poptI = [] -with timer("Correlation (imaginary) fitting time"): - for i in range(kI): - poptI.append(fitter(corrIana, tlist2, i + 1)) -``` - -And plot the results of the fits: - -```python -plt.plot(tlist2, corrRana, label="Analytic") -plt.plot(tlist2, corrRMats, label="Matsubara") - -for i in range(kR): - y = fit_func(tlist2, *poptR[i]) - plt.plot(tlist2, y, label=f"Fit with {i} terms") - -plt.title("Fit to correlations (real part)") -plt.legend() -plt.show() -``` - -```python -plt.plot(tlist2, corrIana, label="Analytic") - -for i in range(kI): - y = fit_func(tlist2, *poptI[i]) - plt.plot(tlist2, y, label=f"Fit with {i} terms") - -plt.title("Fit to correlations (imaginary part)") -plt.legend() -plt.show() -``` - -```python -# Set the exponential coefficients from the fit parameters - -ckAR1 = poptR[-1][0] -ckAR = [x + 0j for x in ckAR1] - -vkAR1 = poptR[-1][1] -vkAR = [-x + 0j for x in vkAR1] - -ckAI1 = poptI[-1][0] -ckAI = [x + 0j for x in ckAI1] - -vkAI1 = poptI[-1][1] -vkAI = [-x + 0j for x in vkAI1] -``` - -```python -# overwrite imaginary fit with analytical value (not much reason to use the -# fit for this) - -ckAI = [lam * gamma * (-1.0) + 0.0j] -vkAI = [gamma + 0.0j] -``` - -```python -options = {**default_options} - -NC = 4 - -with timer("RHS construction time"): - bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI) - HEOMFit = HEOMSolver(Hsys, bath, NC, options=options) - -with timer("ODE solver time"): - resultFit = HEOMFit.run(rho0, tlist) -``` - -```python -plot_result_expectations( - [ - (resultFit, P11p, "b", "P11 Fit"), - (resultFit, P12p, "r", "P12 Fit"), - ] -); -``` - -## A reaction coordinate approach - - -Here we construct a reaction coordinate inspired model to capture the -steady-state behavior, and compare to the HEOM prediction. This result is -more accurate for narrow spectral densities. We will use the population and -coherence from this cell in our final plot below. - -```python -dot_energy, dot_state = Hsys.eigenstates() -deltaE = dot_energy[1] - dot_energy[0] - -gamma2 = deltaE / (2 * np.pi * gamma) -wa = 2 * np.pi * gamma2 * gamma # reaction coordinate frequency -g = np.sqrt(np.pi * wa * lam / 2.0) # reaction coordinate coupling -# reaction coordinate coupling factor over 2 because of diff in J(w) -# (it is 2 lam now): -g = np.sqrt( - np.pi * wa * lam / 4.0 -) # - -NRC = 10 - -Hsys_exp = tensor(qeye(NRC), Hsys) -Q_exp = tensor(qeye(NRC), Q) -a = tensor(destroy(NRC), qeye(2)) - -H0 = wa * a.dag() * a + Hsys_exp -# interaction -H1 = g * (a.dag() + a) * Q_exp - -H = H0 + H1 - -energies, states = H.eigenstates() -rhoss = 0 * states[0] * states[0].dag() -for kk, energ in enumerate(energies): - rhoss += states[kk] * states[kk].dag() * np.exp(-beta * energies[kk]) - -rhoss = rhoss / rhoss.norm() - - -class ReactionCoordinateResult: - def __init__(self, states, times): - self.states = states - self.times = times - - -resultRC = ReactionCoordinateResult([rhoss] * len(tlist), tlist) - -P12RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 1).dag()) -P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag()) -``` - -## Let's plot all our results - -Finally, let's plot all of our different results to see how they shape up against each other. - -```python -rcParams = { - "axes.titlesize": 25, - "axes.labelsize": 30, - "xtick.labelsize": 28, - "ytick.labelsize": 28, - "legend.fontsize": 28, - "axes.grid": False, - "savefig.bbox": "tight", - "lines.markersize": 5, - "font.family": "STIXgeneral", - "mathtext.fontset": "stix", - "font.serif": "STIX", - "text.usetex": False, -} -``` - -```python -fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 15)) - -with plt.rc_context(rcParams): - - plt.sca(axes[0]) - plt.yticks([expect(P11RC, resultRC.states[0]), 0.6, 1.0], [0.32, 0.6, 1]) - plot_result_expectations( - [ - (resultBR, P11p, "y-.", "Bloch-Redfield"), - (resultMats, P11p, "b", "Matsubara $N_k=2$"), - ( - resultMatsT, - P11p, - "g--", - "Matsubara $N_k=2$ & Terminator", - {"linewidth": 3}, - ), - ( - resultFit, - P11p, - "r", - r"Fit $N_f = 4$, $N_k=15\times 10^3$", - {"dashes": [3, 2]}, - ), - ( - resultRC, - P11RC, - "--", "Thermal", - {"linewidth": 2, "color": "black"}, - ), - ], - axes=axes[0], - ) - axes[0].set_ylabel(r"$\rho_{11}$", fontsize=30) - axes[0].legend(loc=0) - axes[0].text(5, 0.9, "(a)", fontsize=30) - axes[0].set_xlim(0, 50) - - plt.sca(axes[1]) - plt.yticks( - [np.real(expect(P12RC, resultRC.states[0])), -0.2, 0.0, 0.2], - [-0.33, -0.2, 0, 0.2], - ) - plot_result_expectations( - [ - (resultBR, P12p, "y-.", "Bloch-Redfield"), - (resultMats, P12p, "b", "Matsubara $N_k=2$"), - ( - resultMatsT, - P12p, - "g--", - "Matsubara $N_k=2$ & Terminator", - {"linewidth": 3}, - ), - ( - resultFit, - P12p, - "r", - r"Fit $N_f = 4$, $N_k=15\times 10^3$", - {"dashes": [3, 2]}, - ), - ( - resultRC, - P12RC, - "--", - "Thermal", - {"linewidth": 2, "color": "black"}, - ), - ], - axes=axes[1], - ) - axes[1].text(5, 0.1, "(b)", fontsize=30) - axes[1].set_xlabel(r"$t \Delta$", fontsize=30) - axes[1].set_ylabel(r"$\rho_{01}$", fontsize=30) - axes[1].set_xlim(0, 50) -``` - -And that's the end of a detailed first dive into modeling bosonic environments with the HEOM. - - -## About - -```python -qutip.about() -``` - -## Testing - -This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. - -```python -# Check P11p -assert np.allclose( - expect(P11p, resultMatsT.states), - expect(P11p, resultPade.states), - rtol=1e-2, -) -assert np.allclose( - expect(P11p, resultMatsT.states), - expect(P11p, resultFit.states), - rtol=1e-2, -) - -# Check P12p -assert np.allclose( - expect(P12p, resultMatsT.states), - expect(P12p, resultPade.states), - rtol=1e-2, -) -assert np.allclose( - expect(P12p, resultMatsT.states), - expect(P12p, resultFit.states), - rtol=1e-1, -) -``` diff --git a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md deleted file mode 100644 index f06f3e0a..00000000 --- a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md +++ /dev/null @@ -1,540 +0,0 @@ ---- -jupytext: - formats: ipynb,md:myst - text_representation: - extension: .md - format_name: myst - format_version: 0.13 - jupytext_version: 1.16.1 -kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 ---- - -# HEOM 1b: Spin-Bath model (very strong coupling) - -+++ - -## Introduction - -The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices. - -In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment. - -The Bosonic environment is implicitly assumed to obey a particular Hamiltonian, the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions. - -In the example below we show how to model the overdamped Drude-Lorentz Spectral Density, commonly used with the HEOM. We show how to do this using the Matsubara, Pade and fitting decompositions, and compare their convergence. - -This notebook shows a similar example to notebook 1a, but with much stronger coupling as discussed in [Shi *et al.*, J. Chem. Phys **130**, 084105 (2009)](https://doi.org/10.1063/1.3077918). Please refer to notebook HEOM 1a for a more detailed explanation. - -As in notebook 1a, we present a variety of simulations using different techniques to showcase the effect of different approximations of the correlation function on the results: - -- Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator -- Simulation 2: Matsubara decomposition (including terminator) -- Simulation 3: Pade decomposition -- Simulation 4: Fitting approach - -Lastly we compare the results to using the Bloch-Redfield approach: - -- Simulation 5: Bloch-Redfield - -which does not give the correct evolution in this case. - - -### Drude-Lorentz (overdamped) spectral density - -The Drude-Lorentz spectral density is: - -$$J_D(\omega)= \frac{2\omega\lambda\gamma}{{\gamma}^2 + \omega^2}$$ - -where $\lambda$ scales the coupling strength, and $\gamma$ is the cut-off frequency. We use the convention, -\begin{equation*} -C(t) = \int_0^{\infty} d\omega \frac{J_D(\omega)}{\pi}[\coth(\beta\omega) \cos(\omega \tau) - i \sin(\omega \tau)] -\end{equation*} - -With the HEOM we must use an exponential decomposition: - -\begin{equation*} -C(t)=\sum_{k=0}^{k=\infty} c_k e^{-\nu_k t} -\end{equation*} - -As an example, the Matsubara decomposition of the Drude-Lorentz spectral density is given by: - -\begin{equation*} - \nu_k = \begin{cases} - \gamma & k = 0\\ - {2 \pi k} / {\beta } & k \geq 1\\ - \end{cases} -\end{equation*} - -\begin{equation*} - c_k = \begin{cases} - \lambda \gamma (\cot(\beta \gamma / 2) - i) & k = 0\\ - 4 \lambda \gamma \nu_k / \{(nu_k^2 - \gamma^2)\beta \} & k \geq 1\\ - \end{cases} -\end{equation*} - -Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. - -+++ - -## Setup - -```{code-cell} ipython3 -import contextlib -import time - -import numpy as np -from scipy.optimize import curve_fit -import matplotlib.pyplot as plt - -import qutip -from qutip import ( - basis, - brmesolve, - expect, - liouvillian, - sigmax, - sigmaz, -) -from qutip.solver.heom import ( - HEOMSolver, - BosonicBath, - DrudeLorentzBath, - DrudeLorentzPadeBath, - BathExponent, -) - -%matplotlib inline -``` - -## Helper functions - -Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: - -```{code-cell} ipython3 -def cot(x): - """ Vectorized cotangent of x. """ - return 1. / np.tan(x) -``` - -```{code-cell} ipython3 -@contextlib.contextmanager -def timer(label): - """ Simple utility for timing functions: - - with timer("name"): - ... code to time ... - """ - start = time.time() - yield - end = time.time() - print(f"{label}: {end - start}") -``` - -```{code-cell} ipython3 -# Solver options: - -options = { - "nsteps": 15000, - "store_states": True, - "rtol": 1e-14, - "atol": 1e-14, - "method": "vern9", - "progress_bar": "enhanced", -} -``` - -## System and bath definition - -And let us set up the system Hamiltonian, bath and system measurement operators: - -```{code-cell} ipython3 -# Defining the system Hamiltonian -eps = .0 # Energy of the 2-level system. -Del = .2 # Tunnelling term -Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() -``` - -```{code-cell} ipython3 -# Initial state of the system. -rho0 = basis(2, 0) * basis(2, 0).dag() -``` - -```{code-cell} ipython3 -# System-bath coupling (Drude-Lorentz spectral density) -Q = sigmaz() # coupling operator - -# Bath properties (see Shi et al., J. Chem. Phys. 130, 084105 (2009)): -gamma = 1. # cut off frequency -lam = 2.5 # coupling strength -T = 1. # in units where Boltzmann factor is 1 -beta = 1. / T - -# HEOM parameters: - -# number of exponents to retain in the Matsubara expansion of the -# bath correlation function: -Nk = 1 - -# Number of levels of the hierarchy to retain: -NC = 13 - -# Times to solve for: -tlist = np.linspace(0, np.pi / Del, 600) -``` - -```{code-cell} ipython3 -# Define some operators with which we will measure the system -# 1,1 element of density matrix - corresonding to groundstate -P11p = basis(2, 0) * basis(2, 0).dag() -P22p = basis(2, 1) * basis(2, 1).dag() -# 1,2 element of density matrix - corresonding to coherence -P12p = basis(2, 0) * basis(2, 1).dag() -``` - -### Plot the spectral density - -Let us briefly inspect the spectral density. - -```{code-cell} ipython3 -bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) -w = np.linspace(0, 5, 1000) -J = bath.spectral_density(w) - -# Plot the results -fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) -axes.plot(w, J, 'r', linewidth=2) -axes.set_xlabel(r'$\omega$', fontsize=28) -axes.set_ylabel(r'J', fontsize=28); -``` - -## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator - -```{code-cell} ipython3 -with timer("RHS construction time"): - bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) - HEOMMats = HEOMSolver(Hsys, bath, NC, options=options) - -with timer("ODE solver time"): - resultMats = HEOMMats.run(rho0, tlist) -``` - -## Simulation 2: Matsubara decomposition (including terminator) - -```{code-cell} ipython3 -with timer("RHS construction time"): - bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) - _, terminator = bath.terminator() - Ltot = liouvillian(Hsys) + terminator - HEOMMatsT = HEOMSolver(Ltot, bath, NC, options=options) - -with timer("ODE solver time"): - resultMatsT = HEOMMatsT.run(rho0, tlist) -``` - -```{code-cell} ipython3 -# Plot the results -fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - -P11_mats = np.real(expect(resultMats.states, P11p)) -axes.plot( - tlist, np.real(P11_mats), - 'b', linewidth=2, label="P11 (Matsubara)", -) - -P11_matsT = np.real(expect(resultMatsT.states, P11p)) -axes.plot( - tlist, np.real(P11_matsT), - 'b--', linewidth=2, - label="P11 (Matsubara + Terminator)", -) - -axes.set_xlabel(r't', fontsize=28) -axes.legend(loc=0, fontsize=12); -``` - -## Simulation 3: Pade decomposition - -```{code-cell} ipython3 -# First, compare Matsubara and Pade decompositions -matsBath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) -padeBath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) - - -fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(16, 8)) - -ax1.plot( - tlist, np.real(matsBath.correlation_function(tlist)), - "r", linewidth=2, label=f"Exact", -) -ax1.plot( - tlist, np.real(matsBath.correlation_function_approx(tlist)), - "g--", linewidth=2, label=f"Mats (Nk={Nk})", -) -ax1.plot( - tlist, np.real(padeBath.correlation_function_approx(tlist)), - "b--", linewidth=2, label=f"Pade (Nk={Nk})", -) - -ax1.set_xlabel(r't', fontsize=28) -ax1.set_ylabel(r"$C_R(t)$", fontsize=28) -ax1.legend(loc=0, fontsize=12) - -tlist2 = tlist[0:50] -ax2.plot( - tlist2, np.abs(matsBath.correlation_function_approx(tlist2) - - matsBath.correlation_function(tlist2)), - "g", linewidth=2, label="Mats Error", -) -ax2.plot( - tlist2, np.abs(padeBath.correlation_function_approx(tlist2) - - padeBath.correlation_function(tlist2)), - "b--", linewidth=2, label="Pade Error", -) - -ax2.set_xlabel(r't', fontsize=28) -ax2.legend(loc=0, fontsize=12); -``` - -```{code-cell} ipython3 -with timer("RHS construction time"): - bath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) - HEOMPade = HEOMSolver(Hsys, bath, NC, options=options) - -with timer("ODE solver time"): - resultPade = HEOMPade.run(rho0, tlist) -``` - -```{code-cell} ipython3 -# Plot the results -fig, axes = plt.subplots(figsize=(8, 8)) - -axes.plot( - tlist, np.real(P11_mats), - 'b', linewidth=2, label="P11 (Matsubara)", -) -axes.plot( - tlist, np.real(P11_matsT), - 'b--', linewidth=2, label="P11 (Matsubara + Terminator)", -) - -P11_pade = np.real(expect(resultPade.states, P11p)) -axes.plot( - tlist, np.real(P11_pade), - 'r', linewidth=2, label="P11 (Pade)", -) - -axes.set_xlabel(r't', fontsize=28) -axes.legend(loc=0, fontsize=12); -``` - -## Simulation 4: Fitting approach - -We will perform the fitting manually here, and show in notebook 1d how the `CorrelationFitter` class can be used to perform such fits with less effort. - -```{code-cell} ipython3 -def wrapper_fit_func(x, N, args): - """ Fit function wrapper that unpacks its arguments. """ - x = np.array(x) - a = np.array(args[:N]) - b = np.array(args[N:2 * N]) - return fit_func(x, a, b) - - -def fit_func(x, a, b): - """ Fit function. Calculates the value of the - correlation function at each x, given the - fit parameters in a and b. - """ - return np.sum( - a[:, None] * np.exp(np.multiply.outer(b, x)), - axis=0, - ) - - -def fitter(ans, tlist, k): - """ Compute fit with k exponents. """ - upper_a = abs(max(ans, key=abs)) * 10 - # sets initial guesses: - guess = ( - [upper_a / k] * k + # guesses for a - [0] * k # guesses for b - ) - # sets lower bounds: - b_lower = ( - [-upper_a] * k + # lower bounds for a - [-np.inf] * k # lower bounds for b - ) - # sets higher bounds: - b_higher = ( - [upper_a] * k + # upper bounds for a - [0] * k # upper bounds for b - ) - param_bounds = (b_lower, b_higher) - p1, p2 = curve_fit( - lambda x, *params_0: wrapper_fit_func(x, k, params_0), - tlist, - ans, - p0=guess, - sigma=[0.01 for t in tlist], - bounds=param_bounds, - maxfev=1e8, - ) - a, b = p1[:k], p1[k:] - return (a, b) -``` - -```{code-cell} ipython3 -# Fitting the real part of the correlation function: - -# Correlation function values to fit: -tlist_fit = np.linspace(0, 6, 10000) -corrRana = np.real(matsBath.correlation_function(tlist_fit)) - -# Perform the fit: -kR = 3 # number of exponents to use for real part -poptR = [] -with timer("Correlation (real) fitting time"): - for i in range(kR): - poptR.append(fitter(corrRana, tlist_fit, i + 1)) -``` - -```{code-cell} ipython3 -plt.plot(tlist_fit, corrRana, label="Analytic") - -for i in range(kR): - y = fit_func(tlist_fit, *poptR[i]) - plt.plot(tlist_fit, y, label=f"Fit with {i} terms") - -plt.title("Fit to correlations (real part)") -plt.legend() -plt.show() -``` - -```{code-cell} ipython3 -# Set the exponential coefficients from the fit parameters - -ckAR1 = poptR[-1][0] -ckAR = [x + 0j for x in ckAR1] - -vkAR1 = poptR[-1][1] -vkAR = [-x + 0j for x in vkAR1] - -# Imaginary part: use analytical value - -ckAI = [lam * gamma * (-1.0) + 0j] -vkAI = [gamma + 0j] -``` - -```{code-cell} ipython3 -with timer("RHS construction time"): - bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI) - # We reduce NC slightly here for speed of execution because we retain - # 3 exponents in ckAR instead of 1. Please restore full NC for - # convergence though: - HEOMFit = HEOMSolver(Hsys, bath, int(NC * 0.7), options=options) - -with timer("ODE solver time"): - resultFit = HEOMFit.run(rho0, tlist) -``` - -## Simulation 5: Bloch-Redfield - -```{code-cell} ipython3 -DL = ( - "2 * pi * 2.0 * {lam} / (pi * {gamma} * {beta}) if (w==0) " - "else 2 * pi * (2.0 * {lam} * {gamma} * w / (pi * (w**2 + {gamma}**2))) " - "* ((1 / (exp(w * {beta}) - 1)) + 1)" -).format(gamma=gamma, beta=beta, lam=lam) - -with timer("ODE solver time"): - resultBR = brmesolve( - Hsys, rho0, tlist, - a_ops=[[sigmaz(), DL]], sec_cutoff=0, options=options, - ) -``` - -## Let's plot all our results - -Finally, let's plot all of our different results to see how they shape up against each other. - -```{code-cell} ipython3 -# Calculate expectation values in the bases: -P11_mats = np.real(expect(resultMats.states, P11p)) -P11_matsT = np.real(expect(resultMatsT.states, P11p)) -P11_pade = np.real(expect(resultPade.states, P11p)) -P11_fit = np.real(expect(resultFit.states, P11p)) -P11_br = np.real(expect(resultBR.states, P11p)) -``` - -```{code-cell} ipython3 -rcParams = { - "axes.titlesize": 25, - "axes.labelsize": 30, - "xtick.labelsize": 28, - "ytick.labelsize": 28, - "legend.fontsize": 28, - "axes.grid": False, - "savefig.bbox": "tight", - "lines.markersize": 5, - "font.family": "STIXgeneral", - "mathtext.fontset": "stix", - "font.serif": "STIX", - "text.usetex": False, -} -``` - -```{code-cell} ipython3 -fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) - -with plt.rc_context(rcParams): - # Plot the results - plt.yticks([0.99, 1.0], [0.99, 1]) - axes.plot( - tlist, np.real(P11_mats), - 'b', linewidth=2, label=f"Matsubara $N_k={Nk}$", - ) - axes.plot( - tlist, np.real(P11_matsT), - 'g--', linewidth=3, - label=f"Matsubara $N_k={Nk}$ & terminator", - ) - axes.plot( - tlist, np.real(P11_pade), - 'y-.', linewidth=2, label=f"Padé $N_k={Nk}$", - ) - axes.plot( - tlist, np.real(P11_fit), - 'r', dashes=[3, 2], linewidth=2, - label=r"Fit $N_f = 3$, $N_k=15 \times 10^3$", - ) - axes.plot( - tlist, np.real(P11_br), - 'b-.', linewidth=1, label="Bloch Redfield", - ) - - axes.locator_params(axis='y', nbins=6) - axes.locator_params(axis='x', nbins=6) - axes.set_ylabel(r'$\rho_{11}$', fontsize=30) - axes.set_xlabel(r'$t\;\gamma$', fontsize=30) - axes.set_xlim(tlist[0], tlist[-1]) - axes.set_ylim(0.98405, 1.0005) - axes.legend(loc=0) -``` - -## About - -```{code-cell} ipython3 -qutip.about() -``` - -## Testing - -This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. - -```{code-cell} ipython3 -assert np.allclose(P11_matsT, P11_pade, rtol=1e-3) -assert np.allclose(P11_matsT, P11_fit, rtol=1e-3) -``` diff --git a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md deleted file mode 100644 index 0542e6a0..00000000 --- a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md +++ /dev/null @@ -1,586 +0,0 @@ ---- -jupytext: - formats: ipynb,md:myst - text_representation: - extension: .md - format_name: myst - format_version: 0.13 - jupytext_version: 1.16.1 -kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 ---- - -# HEOM 1c: Spin-Bath model (Underdamped Case) - -+++ - -## Introduction - -The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices. - -In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment. - -The Bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions. - -In the example below we show how to model the underdamped Brownian motion Spectral Density. - -Note that in the following, we set $\hbar = k_\mathrm{B} = 1$. - -### Brownian motion (underdamped) spectral density -The underdamped spectral density is: - -$$J_U = \frac{\alpha^2 \Gamma \omega}{(\omega_c^2 - \omega^2)^2 + \Gamma^2 \omega^2)}.$$ - -Here $\alpha$ scales the coupling strength, $\Gamma$ is the cut-off frequency, and $\omega_c$ defines a resonance frequency. With the HEOM we must use an exponential decomposition: - -The Matsubara decomposition of this spectral density is, in real and imaginary parts: - - - -\begin{equation*} - c_k^R = \begin{cases} - \alpha^2 \coth(\beta( \Omega + i\Gamma/2)/2)/4\Omega & k = 0\\ - \alpha^2 \coth(\beta( \Omega - i\Gamma/2)/2)/4\Omega & k = 0\\ - -2\alpha^2\Gamma/\beta \frac{\epsilon_k }{((\Omega + i\Gamma/2)^2 + \epsilon_k^2)(\Omega - i\Gamma/2)^2 + \epsilon_k^2)} & k \geq 1\\ - \end{cases} -\end{equation*} - -\begin{equation*} - \nu_k^R = \begin{cases} - -i\Omega + \Gamma/2, i\Omega +\Gamma/2, & k = 0\\ - {2 \pi k} / {\beta } & k \geq 1\\ - \end{cases} -\end{equation*} - - - - -\begin{equation*} - c_k^I = \begin{cases} - i\alpha^2 /4\Omega & k = 0\\ - -i\alpha^2 /4\Omega & k = 0\\ - \end{cases} -\end{equation*} - -\begin{equation*} - \nu_k^I = \begin{cases} - i\Omega + \Gamma/2, -i\Omega + \Gamma/2, & k = 0\\ - \end{cases} -\end{equation*} - -Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. - -+++ - -## Setup - -```{code-cell} ipython3 -import contextlib -import time - -import numpy as np -from matplotlib import pyplot as plt - -import qutip -from qutip import ( - basis, - brmesolve, - destroy, - expect, - qeye, - sigmax, - sigmaz, - tensor, -) -from qutip.solver.heom import ( - HEOMSolver, - BosonicBath, - UnderDampedBath, -) - -%matplotlib inline -``` - -## Helper functions - -Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: - -```{code-cell} ipython3 -def cot(x): - """ Vectorized cotangent of x. """ - return 1. / np.tan(x) -``` - -```{code-cell} ipython3 -def coth(x): - """ Vectorized hyperbolic cotangent of x. """ - return 1. / np.tanh(x) -``` - -```{code-cell} ipython3 -def underdamped_matsubara_params(lam, gamma, T, nk): - """ Calculation of the real and imaginary expansions of the - underdamped correlation functions. - """ - Om = np.sqrt(w0**2 - (gamma / 2)**2) - Gamma = gamma / 2. - beta = 1. / T - - ckAR = [ - (lam**2 / (4*Om)) * coth(beta * (Om + 1.0j * Gamma) / 2), - (lam**2 / (4*Om)) * coth(beta * (Om - 1.0j * Gamma) / 2), - ] - ckAR.extend( - (-2 * lam**2 * gamma / beta) * (2 * np.pi * k / beta) / - (((Om + 1.0j * Gamma)**2 + (2 * np.pi * k / beta)**2) * - ((Om - 1.0j * Gamma)**2 + (2 * np.pi * k / beta)**2)) + 0.j - for k in range(1, nk + 1) - ) - vkAR = [ - -1.0j * Om + Gamma, - 1.0j * Om + Gamma, - ] - vkAR.extend( - 2 * np.pi * k * T + 0.j - for k in range(1, nk + 1) - ) - - factor = 1. / 4 - - ckAI = [ - -factor * lam**2 * 1.0j / Om, - factor * lam**2 * 1.0j / Om, - ] - vkAI = [ - -(-1.0j * Om - Gamma), - -(1.0j * Om - Gamma), - ] - - return ckAR, vkAR, ckAI, vkAI -``` - -```{code-cell} ipython3 -def plot_result_expectations(plots, axes=None): - """ Plot the expectation values of operators as functions of time. - - Each plot in plots consists of: (solver_result, measurement_operation, - color, label). - """ - if axes is None: - fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - fig_created = True - else: - fig = None - fig_created = False - - # add kw arguments to each plot if missing - plots = [p if len(p) == 5 else p + ({},) for p in plots] - for result, m_op, color, label, kw in plots: - exp = np.real(expect(result.states, m_op)) - kw.setdefault("linewidth", 2) - axes.plot(result.times, exp, color, label=label, **kw) - - if fig_created: - axes.legend(loc=0, fontsize=12) - axes.set_xlabel("t", fontsize=28) - - return fig -``` - -```{code-cell} ipython3 -@contextlib.contextmanager -def timer(label): - """ Simple utility for timing functions: - - with timer("name"): - ... code to time ... - """ - start = time.time() - yield - end = time.time() - print(f"{label}: {end - start}") -``` - -```{code-cell} ipython3 -# Solver options: - -options = { - "nsteps": 15000, - "store_states": True, - "rtol": 1e-14, - "atol": 1e-14, - "method": "vern9", - "progress_bar": "enhanced", -} -``` - -## System and bath definition - -And let us set up the system Hamiltonian, bath and system measurement operators: - -```{code-cell} ipython3 -# Defining the system Hamiltonian -eps = .5 # Energy of the 2-level system. -Del = 1.0 # Tunnelling term -Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() -``` - -```{code-cell} ipython3 -# Initial state of the system. -rho0 = basis(2, 0) * basis(2, 0).dag() -``` - -```{code-cell} ipython3 -# System-bath coupling (underdamed spectral density) -Q = sigmaz() # coupling operator - -# Bath properties: -gamma = .1 # cut off frequency -lam = .5 # coupling strength -w0 = 1. # resonance frequency -T = 1. -beta = 1. / T - -# HEOM parameters: - -# number of exponents to retain in the Matsubara expansion of the -# bath correlation function: -Nk = 2 - -# Number of levels of the hierarchy to retain: -NC = 10 - -# Times to solve for: -tlist = np.linspace(0, 50, 1000) -``` - -```{code-cell} ipython3 -# Define some operators with which we will measure the system -# 1,1 element of density matrix - corresonding to groundstate -P11p = basis(2, 0) * basis(2, 0).dag() -P22p = basis(2, 1) * basis(2, 1).dag() -# 1,2 element of density matrix - corresonding to coherence -P12p = basis(2, 0) * basis(2, 1).dag() -``` - -### First let us look at what the underdamped spectral density looks like: - -```{code-cell} ipython3 -def plot_spectral_density(): - """ Plot the underdamped spectral density """ - w = np.linspace(0, 5, 1000) - J = lam**2 * gamma * w / ((w0**2 - w**2)**2 + (gamma**2) * (w**2)) - - fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - axes.plot(w, J, 'r', linewidth=2) - axes.set_xlabel(r'$\omega$', fontsize=28) - axes.set_ylabel(r'J', fontsize=28) - - -plot_spectral_density() -``` - -The correlation functions are now very oscillatory, because of the Lorentzian peak in the spectral density. - -+++ - -### So next, let us plot the correlation functions themselves: - -```{code-cell} ipython3 -def Mk(t, k, gamma, w0, beta): - """ Calculate the Matsubara terms for a given t and k. """ - Om = np.sqrt(w0**2 - (gamma / 2)**2) - Gamma = gamma / 2. - ek = 2 * np.pi * k / beta - - return ( - (-2 * lam**2 * gamma / beta) * ek * np.exp(-ek * np.abs(t)) - / (((Om + 1.0j * Gamma)**2 + ek**2) * ((Om - 1.0j * Gamma)**2 + ek**2)) - ) - - -def c(t, Nk, lam, gamma, w0, beta): - """ Calculate the correlation function for a vector of times, t. """ - Om = np.sqrt(w0**2 - (gamma / 2)**2) - Gamma = gamma / 2. - - Cr = ( - coth(beta * (Om + 1.0j * Gamma) / 2) * np.exp(1.0j * Om * t) - + coth(beta * (Om - 1.0j * Gamma) / 2) * np.exp(-1.0j * Om * t) - ) - - Ci = np.exp(-1.0j * Om * t) - np.exp(1.0j * Om * t) - - return ( - (lam**2 / (4 * Om)) * np.exp(-Gamma * np.abs(t)) * (Cr + Ci) + - np.sum([ - Mk(t, k, gamma=gamma, w0=w0, beta=beta) - for k in range(1, Nk + 1) - ], 0) - ) - - -def plot_correlation_function(): - """ Plot the underdamped correlation function. """ - t = np.linspace(0, 20, 1000) - corr = c(t, Nk=3, lam=lam, gamma=gamma, w0=w0, beta=beta) - - fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - axes.plot(t, np.real(corr), '-', color="black", label="Re[C(t)]") - axes.plot(t, np.imag(corr), '-', color="red", label="Im[C(t)]") - axes.set_xlabel(r't', fontsize=28) - axes.set_ylabel(r'C', fontsize=28) - axes.legend(loc=0, fontsize=12) - - -plot_correlation_function() -``` - -It is useful to look at what the Matsubara contributions do to this spectral density. We see that they modify the real part around $t=0$: - -```{code-cell} ipython3 -def plot_matsubara_correlation_function_contributions(): - """ Plot the underdamped correlation function. """ - t = np.linspace(0, 20, 1000) - - M_Nk2 = np.sum([ - Mk(t, k, gamma=gamma, w0=w0, beta=beta) - for k in range(1, 2 + 1) - ], 0) - - M_Nk100 = np.sum([ - Mk(t, k, gamma=gamma, w0=w0, beta=beta) - for k in range(1, 100 + 1) - ], 0) - - fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - axes.plot(t, np.real(M_Nk2), '-', color="black", label="Re[M(t)] Nk=2") - axes.plot(t, np.real(M_Nk100), '--', color="red", label="Re[M(t)] Nk=100") - axes.set_xlabel(r't', fontsize=28) - axes.set_ylabel(r'M', fontsize=28) - axes.legend(loc=0, fontsize=12) - - -plot_matsubara_correlation_function_contributions() -``` - -## Solving for the dynamics as a function of time - -+++ - -Next we calculate the exponents using the Matsubara decompositions. Here we split them into real and imaginary parts. - -The HEOM code will optimize these, and reduce the number of exponents when real and imaginary parts have the same exponent. This is clearly the case for the first term in the vkAI and vkAR lists. - -```{code-cell} ipython3 -ckAR, vkAR, ckAI, vkAI = underdamped_matsubara_params( - lam=lam, gamma=gamma, T=T, nk=Nk, -) -``` - -Having created the lists which specify the bath correlation functions, we create a `BosonicBath` from them and pass the bath to the `HEOMSolver` class. - -The solver constructs the "right hand side" (RHS) determinining how the system and auxiliary density operators evolve in time. This can then be used to solve for dynamics or steady-state. - -Below we create the bath and solver and then solve for the dynamics by calling `.run(rho0, tlist)`. - -```{code-cell} ipython3 -with timer("RHS construction time"): - bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI) - HEOMMats = HEOMSolver(Hsys, bath, NC, options=options) - -with timer("ODE solver time"): - resultMats = HEOMMats.run(rho0, tlist) -``` - -```{code-cell} ipython3 -plot_result_expectations([ - (resultMats, P11p, 'b', "P11 Mats"), - (resultMats, P12p, 'r', "P12 Mats"), -]); -``` - -In practice, one would not perform this laborious expansion for the underdamped correlation function, because -QuTiP already has a class, `UnderDampedBath`, that can construct this bath for you. Nevertheless, knowing how -to perform this expansion will allow you to construct your own baths for other spectral densities. - -Below we show how to use this built-in functionality: - -```{code-cell} ipython3 -# Compare to built-in under-damped bath: - -with timer("RHS construction time"): - bath = UnderDampedBath(Q, lam=lam, gamma=gamma, w0=w0, T=T, Nk=Nk) - HEOM_udbath = HEOMSolver(Hsys, bath, NC, options=options) - -with timer("ODE solver time"): - result_udbath = HEOM_udbath.run(rho0, tlist) -``` - -```{code-cell} ipython3 -plot_result_expectations([ - (result_udbath, P11p, 'b', "P11 (UnderDampedBath)"), - (result_udbath, P12p, 'r', "P12 (UnderDampedBath)"), -]); -``` - -The `UnderDampedBath` class also allows us to easily evaluate analytical expressions for the power spectrum, correlation function, and spectral density. In the following plots, the solid lines are the exact expressions, and the dashed lines are based on our approximation of the correlation function with a finite number of exponents. In this case, there is an excellent agreement. - -```{code-cell} ipython3 -w = np.linspace(-3, 3, 1000) -w2 = np.linspace(0, 3, 1000) -t = np.linspace(0, 10, 1000) -bath_cf = bath.correlation_function(t) # uses numerical integration - -fig, axs = plt.subplots(2, 2) - -axs[0, 0].plot(w, bath.power_spectrum(w)) -axs[0, 0].plot(w, bath.power_spectrum_approx(w), '--') -axs[0, 0].set(xlabel=r'$\omega$', ylabel=r'$S(\omega)$') -axs[0, 1].plot(w2, bath.spectral_density(w2)) -axs[0, 1].plot(w2, bath.spectral_density_approx(w2), '--') -axs[0, 1].set(xlabel=r'$\omega$', ylabel=r'$J(\omega)$') -axs[1, 0].plot(t, np.real(bath_cf)) -axs[1, 0].plot(t, np.real(bath.correlation_function_approx(t)), '--') -axs[1, 0].set(xlabel=r'$t$', ylabel=r'$C_{R}(t)$') -axs[1, 1].plot(t, np.imag(bath_cf)) -axs[1, 1].plot(t, np.imag(bath.correlation_function_approx(t)), '--') -axs[1, 1].set(xlabel=r'$t$', ylabel=r'$C_{I}(t)$') - -fig.tight_layout() -plt.show() -``` - -## Compare the results - -+++ - -### We can compare these results to those of the Bloch-Redfield solver in QuTiP: - -```{code-cell} ipython3 -UD = ( - f"2 * {lam}**2 * {gamma} / ( {w0}**4 * {beta}) if (w==0)" - " else " - f"2 * ({lam}**2 * {gamma} * w / (({w0}**2 - w**2)**2 + {gamma}**2 * w**2))" - f" * ((1 / (exp(w * {beta}) - 1)) + 1)" -) - -with timer("ODE solver time"): - resultBR = brmesolve( - Hsys, rho0, tlist, - a_ops=[[sigmaz(), UD]], options=options, - ) -``` - -```{code-cell} ipython3 -plot_result_expectations([ - (resultMats, P11p, 'b', "P11 Mats"), - (resultMats, P12p, 'r', "P12 Mats"), - (resultBR, P11p, 'g--', "P11 Bloch Redfield"), - (resultBR, P12p, 'g--', "P12 Bloch Redfield"), -]); -``` - -### Lastly, let us calculate the analytical steady-state result and compare all of the results: - -+++ - -The thermal state of a reaction coordinate (treating the environment as a single damped mode) should, at high temperatures and small gamma, tell us the steady-state: - -```{code-cell} ipython3 -dot_energy, dot_state = Hsys.eigenstates() -deltaE = dot_energy[1] - dot_energy[0] - -gamma2 = gamma -wa = w0 # reaction coordinate frequency -g = lam / np.sqrt(2 * wa) # coupling - -NRC = 10 - -Hsys_exp = tensor(qeye(NRC), Hsys) -Q_exp = tensor(qeye(NRC), Q) -a = tensor(destroy(NRC), qeye(2)) - -H0 = wa * a.dag() * a + Hsys_exp -# interaction -H1 = (g * (a.dag() + a) * Q_exp) - -H = H0 + H1 - -energies, states = H.eigenstates() -rhoss = 0 * states[0] * states[0].dag() -for kk, energ in enumerate(energies): - rhoss += (states[kk] * states[kk].dag() * np.exp(-beta * energies[kk])) -rhoss = rhoss / rhoss.norm() - -P12RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 1).dag()) -P12RC = expect(rhoss, P12RC) - -P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag()) -P11RC = expect(rhoss, P11RC) -``` - -```{code-cell} ipython3 -rcParams = { - "axes.titlesize": 25, - "axes.labelsize": 30, - "xtick.labelsize": 28, - "ytick.labelsize": 28, - "legend.fontsize": 28, - "axes.grid": False, - "savefig.bbox": "tight", - "lines.markersize": 5, - "font.family": "STIXgeneral", - "mathtext.fontset": "stix", - "font.serif": "STIX", - "text.usetex": False, -} -``` - -```{code-cell} ipython3 -fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) - -with plt.rc_context(rcParams): - plt.yticks([P11RC, 0.6, 1.0], [0.38, 0.6, 1]) - - plot_result_expectations([ - (resultBR, P11p, 'y-.', "Bloch-Redfield"), - (resultMats, P11p, 'b', "Matsubara $N_k=3$"), - ], axes=axes) - axes.plot( - tlist, [P11RC for t in tlist], - color='black', linestyle="-.", linewidth=2, - label="Thermal state", - ) - - axes.set_xlabel(r'$t \Delta$', fontsize=30) - axes.set_ylabel(r'$\rho_{11}$', fontsize=30) - - axes.locator_params(axis='y', nbins=4) - axes.locator_params(axis='x', nbins=4) - - axes.legend(loc=0) - - fig.tight_layout() -``` - -## About - -```{code-cell} ipython3 -qutip.about() -``` - -## Testing - -This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. - -```{code-cell} ipython3 -assert np.allclose( - expect(P11p, resultMats.states[-100:]), P11RC, rtol=1e-2, -) -assert np.allclose( - expect(P11p, resultBR.states[-100:]), P11RC, rtol=1e-2, -) -``` diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md deleted file mode 100644 index 78ee6bad..00000000 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md +++ /dev/null @@ -1,825 +0,0 @@ ---- -jupytext: - formats: ipynb,md:myst - text_representation: - extension: .md - format_name: myst - format_version: 0.13 - jupytext_version: 1.16.1 -kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 ---- - -# HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions - -+++ - -## Introduction - -The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded -in a set of auxiliary density matrices. - -In this example we show the evolution of a single two-level system in contact with a single bosonic environment. - -The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment. - -The bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions. - -In the example below we show how to model an Ohmic environment with exponential cut-off in three ways: - -* First we fit the spectral density with a set of underdamped brownian oscillator functions. -* Second, we evaluate the correlation functions, and fit those with a certain choice of exponential functions. -* Third, we use the available OhmicBath class - -In each case we will use the fit parameters to determine the correlation function expansion co-efficients needed to construct a description of the bath (i.e. a `BosonicBath` object) to supply to the `HEOMSolver` so that we can solve for the system dynamics. - -+++ - -## Setup - -```{code-cell} ipython3 -import numpy as np -from matplotlib import pyplot as plt -import qutip -from qutip import ( - basis, - expect, - sigmax, - sigmaz, -) -from qutip.solver.heom import ( - HEOMSolver, - SpectralFitter, - CorrelationFitter, - OhmicBath, -) - -# Import mpmath functions for evaluation of gamma and zeta -# functions in the expression for the correlation: - -from mpmath import mp - -mp.dps = 15 -mp.pretty = True - -%matplotlib inline -``` - -```{code-cell} ipython3 -# Solver options: - -options = { - "nsteps": 15000, - "store_states": True, - "rtol": 1e-14, - "atol": 1e-14, - "method": "vern9", - "progress_bar": "enhanced", -} -``` - -## System and bath definition - -And let us set up the system Hamiltonian, bath and system measurement operators: - -+++ - -### System Hamiltonian - -```{code-cell} ipython3 -# Defining the system Hamiltonian -eps = 0 # Energy of the 2-level system. -Del = 0.2 # Tunnelling term -Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() -rho0 = basis(2, 0) * basis(2, 0).dag() -``` - -### System measurement operators - -```{code-cell} ipython3 -# Define some operators with which we will measure the system -# 1,1 element of density matrix - corresonding to groundstate -P11p = basis(2, 0) * basis(2, 0).dag() -P22p = basis(2, 1) * basis(2, 1).dag() -# 1,2 element of density matrix - corresonding to coherence -P12p = basis(2, 0) * basis(2, 1).dag() -``` - -### Analytical expressions for the Ohmic bath correlation function and spectral density - -+++ - -Before we begin fitting, let us examine the analytic expressions for the correlation and spectral density functions and write Python equivalents. - -The correlation function is given by (see, e.g., http://www1.itp.tu-berlin.de/brandes/public_html/publications/notes.pdf for a derivation, equation 7.59, but with a factor of $\pi$ moved into the definition of the correlation function): - -\begin{align} -C(t) =& \: \frac{1}{\pi}\alpha \omega_{c}^{1 - s} \beta^{- (s + 1)} \: \times \\ - & \: \Gamma(s + 1) \left[ \zeta \left(s + 1, \frac{1 + \beta \omega_c - i \omega_c t}{\beta \omega_c}\right) + \zeta \left(s + 1, \frac{1 + i \omega_c t}{\beta \omega_c}\right) \right] -\end{align} - -where $\Gamma$ is the Gamma function and - -\begin{equation} -\zeta(z, u) \equiv \sum_{n=0}^{\infty} \frac{1}{(n + u)^z}, \; u \neq 0, -1, -2, \ldots -\end{equation} - -is the generalized Zeta function. The Ohmic case is given by $s = 1$. - -The corresponding spectral density for the Ohmic case is: - -\begin{equation} -J(\omega) = \omega \alpha e^{- \frac{\omega}{\omega_c}} -\end{equation} - -```{code-cell} ipython3 -def ohmic_correlation(t, alpha, wc, beta, s=1): - """The Ohmic bath correlation function as a function of t - (and the bath parameters). - """ - corr = (1 / np.pi) * alpha * wc ** (1 - s) - corr *= beta ** (-(s + 1)) * mp.gamma(s + 1) - z1_u = (1 + beta * wc - 1.0j * wc * t) / (beta * wc) - z2_u = (1 + 1.0j * wc * t) / (beta * wc) - # Note: the arguments to zeta should be in as high precision as possible. - # See http://mpmath.org/doc/current/basics.html#providing-correct-input - return np.array( - [ - complex(corr * (mp.zeta(s + 1, u1) + mp.zeta(s + 1, u2))) - for u1, u2 in zip(z1_u, z2_u) - ], - dtype=np.complex128, - ) -``` - -```{code-cell} ipython3 -def ohmic_spectral_density(w, alpha, wc): - """The Ohmic bath spectral density as a function of w - (and the bath parameters). - """ - return w * alpha * np.e ** (-w / wc) -``` - -```{code-cell} ipython3 -def ohmic_power_spectrum(w, alpha, wc, beta): - """The Ohmic bath power spectrum as a function of w - (and the bath parameters). - """ - bose = (1 / (np.e ** (w * beta) - 1)) + 1 - return w * alpha * np.e ** (-abs(w) / wc) * bose * 2 -``` - -### Bath and HEOM parameters - -+++ - -Finally, let's set the bath parameters we will work with and write down some measurement operators: - -```{code-cell} ipython3 -Q = sigmaz() -alpha = 3.25 -T = 0.5 -wc = 1.0 -s = 1 -``` - -And set the cut-off for the HEOM hierarchy: - -```{code-cell} ipython3 -# HEOM parameters: - -# The max_depth defaults to 5 so that the notebook executes more -# quickly. Change it to 11 to wait longer for more accurate results. -max_depth = 5 -``` - -## Building the HEOM bath by fitting the spectral density - -+++ - -We begin by fitting the spectral density, using a series of $k$ underdamped harmonic oscillators case with the Meier-Tannor form (J. Chem. Phys. 111, 3365 (1999); https://doi.org/10.1063/1.479669): - -\begin{equation} -J_{\mathrm approx}(\omega; a, b, c) = \sum_{i=0}^{k-1} \frac{2 a_i b_i w}{((w + c_i)^2 + b_i^2) ((w - c_i)^2 + b_i^2)} -\end{equation} - -where $a, b$ and $c$ are the fit parameters and each is a vector of length $k$. - -+++ - -With the spectral density approximation $J_{\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. This can be done quickly using the `SpectralFitter` class, which takes the target spectral density as an array and fits it to the series of **k** underdamped harmonic oscillators with the Meier-Tannor form - -```{code-cell} ipython3 -w = np.linspace(0, 15, 20000) -J = ohmic_spectral_density(w, alpha, wc) -``` - -We first initialize our SpectralFitter - -```{code-cell} ipython3 -fs = SpectralFitter(T, Q, w, J) -``` - -To obtain a fit we simply pass our desired spectral density and range, into the ``get_fit`` method. The number of exponents we'll use in our bath is given by Nk - -```{code-cell} ipython3 -bath, fitinfo = fs.get_fit(Nk=1) -``` - -To obtain an overview of the results of the fit we may take a look at the summary from the ``fitinfo`` - -```{code-cell} ipython3 -print(fitinfo["summary"]) -``` - -We may see how the number of exponents chosen affects the fit since the approximated functions are available: - -```{code-cell} ipython3 -fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5)) - -ax1.plot(w, J, label="Original spectral density") -ax1.plot(w, bath.spectral_density_approx(w), label="Effective fitted SD") -ax1.set_xlabel(r'$\omega$') -ax1.set_ylabel(r'$J$') -ax1.legend() - -ax2.plot(w, np.abs(J - bath.spectral_density_approx(w)), label="Error") -ax2.set_xlabel(r'$\omega$') -ax2.set_ylabel(r'$J$') -ax2.legend() - -plt.show() -``` - -Here we see a surprisingly large discrepancy in our approximated or effective spectral density. This happens because we are not using enough exponentials from each of the underdamped modes to have an appropiate fit. All modes have the same number of exponents, and we set it to 1 which is not enough to model a bath with the temperature considered, let us repeat this with a higher number of exponents. - -```{code-cell} ipython3 -bath, fitinfo = fs.get_fit(Nk=5) -print(fitinfo["summary"]) - -fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5)) - -ax1.plot(w, J, label="Original spectral density") -ax1.plot(w, bath.spectral_density_approx(w), label="Effective fitted SD") -ax1.set_xlabel(r'$\omega$') -ax1.set_ylabel(r'$J$') -ax1.legend() - -ax2.plot(w, np.abs(J - bath.spectral_density_approx(w)), label="Error") -ax2.set_xlabel(r'$\omega$') -ax2.set_ylabel(r'$J$') -ax2.legend() - -plt.show() -``` - -Since the number of exponents increases simulation time one should go with the least amount of exponents that correctly describe the bath properties (Power spectrum, Spectral density and the correlation function). When the number of exponents is not specified it defaults to 5. - -+++ - -By default the ``get_fit`` method, has a threshold normalized root mean squared error (NRMSE) of $5\times 10^{-6}$ and selects the number of oscillators automatically to obtain that value. One may also specify the number of oscillators that is used with the optional argument N, or may want a more accurate NRMSE, which can be specified with the final_rmse optional argument - -```{code-cell} ipython3 -bath, fitinfo = fs.get_fit(final_rmse=1e-6) -print(fitinfo["summary"]) -``` - -Alternatively one may choose the number of oscillators in the fit instead of a desired NRMSE - -```{code-cell} ipython3 -fittedbath, fitinfo = fs.get_fit(N=4) -print(fitinfo["summary"]) -``` - -Let's take a closer look at our last fit by plotting the contribution of each term of the fit: - -```{code-cell} ipython3 -# Plot the components of the fit separately: -plt.rcParams["font.size"] = 25 -plt.rcParams["figure.figsize"] = (10, 5) - - -def plot_fit(func, J, w, lam, gamma, w0): - """Plot the individual components of a fit to the spectral density. - and how they contribute to the full fit one by one""" - total = 0 - for i in range(len(lam)): - component = func(w, [lam[i]], [gamma[i]], [w0[i]]) - total += component - plt.plot(w, J, "r--", linewidth=2, label="original") - plt.plot(w, total, label=rf"$k={i+1}$") - plt.xlabel(r"$\omega$") - plt.ylabel(r"$J(\omega)$") - plt.legend() - plt.pause(1) - plt.show() - - -def plot_fit_components(func, J, w, lam, gamma, w0): - """Plot the individual components of a fit to the spectral density. - and how they contribute to the full fit""" - plt.plot(w, J, "r--", linewidth=2, label="original") - for i in range(len(lam)): - component = func(w, [lam[i]], [gamma[i]], [w0[i]]) - plt.plot(w, component, label=rf"$k={i+1}$") - plt.xlabel(r"$\omega$") - plt.ylabel(r"$J(\omega)$") - plt.legend(bbox_to_anchor=(1.04, 1)) - plt.show() - - -lam, gamma, w0 = fitinfo["params"] -plot_fit(SpectralFitter._meier_tannor_SD, J, w, lam, gamma, w0) -``` - -```{code-cell} ipython3 -plot_fit_components(SpectralFitter._meier_tannor_SD, J, w, lam, gamma, w0) -``` - -And let's also compare the power spectrum of the fit and the analytical spectral density: - -```{code-cell} ipython3 -def plot_power_spectrum(alpha, wc, beta, save=True): - """Plot the power spectrum of a fit against the actual power spectrum.""" - w = np.linspace(-10, 10, 50000) - s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta) - s_fit = fittedbath.power_spectrum_approx(w) - fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - axes.plot(w, s_orig, "r", linewidth=2, label="original") - axes.plot(w, np.real(s_fit), "b", linewidth=2, label="fit") - - axes.set_xlabel(r"$\omega$", fontsize=28) - axes.set_ylabel(r"$S(\omega)$", fontsize=28) - axes.legend() - - if save: - fig.savefig("powerspectrum.eps") - - -plot_power_spectrum(alpha, wc, 1 / T, save=False) -``` - -Now that we have a good fit to the spectral density, Let us obtain its dynamics, by passing our ``FitSpectral`` bath specifications into the ``HEOMSolver`` - -```{code-cell} ipython3 -tlist = np.linspace(0, 30 * np.pi / Del, 600) -HEOM_spectral_fit = HEOMSolver( - Hsys, - fittedbath, - max_depth=4, - options=options, -) -result_spectral = HEOM_spectral_fit.run(rho0, tlist) -``` - -Now if we want to see the systems's behaviour as we change the Number of terms in the fit, we may use this auxiliary function - -```{code-cell} ipython3 -def generate_spectrum_results(Q, N, Nk, max_depth): - """Run the HEOM with the given bath parameters and - and return the results of the evolution. - """ - fs = SpectralFitter(T, Q, w, J) - bath, _ = fs.get_fit(N, Nk=Nk) - tlist = np.linspace(0, 30 * np.pi / Del, 600) - - # This problem is a little stiff, so we use the BDF method to solve - # the ODE ^^^ - print(f"Starting calculations for N={N}, Nk={Nk} and max_depth={max_depth} ... ") - HEOM_spectral_fit = HEOMSolver( - Hsys, - bath, - max_depth=max_depth, - options=options, - ) - results_spectral_fit = HEOM_spectral_fit.run(rho0, tlist) - return results_spectral_fit -``` - -Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow. - -```{code-cell} ipython3 -def plot_result_expectations(plots, axes=None): - """Plot the expectation values of operators as functions of time. - - Each plot in plots consists of (solver_result, - measurement_operation, color, label). - """ - if axes is None: - fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - fig_created = True - else: - fig = None - fig_created = False - - # add kw arguments to each plot if missing - plots = [p if len(p) == 5 else p + ({},) for p in plots] - for result, m_op, color, label, kw in plots: - exp = np.real(expect(result.states, m_op)) - kw.setdefault("linewidth", 2) - if color == "rand": - axes.plot( - result.times, - exp, - c=np.random.rand( - 3, - ), - label=label, - **kw, - ) - else: - axes.plot(result.times, exp, color, label=label, **kw) - - if fig_created: - axes.legend(loc=0, fontsize=12) - axes.set_xlabel("t", fontsize=28) - - return fig -``` - -```{code-cell} ipython3 -# Generate results for different number of lorentzians in fit: - -results_spectral_fit_pk = [ - generate_spectrum_results(Q, n, Nk=1, max_depth=max_depth) for n in range(1, 5) -] - -plot_result_expectations( - [ - ( - result, - P11p, - "rand", - f"P11 (spectral fit) $k_J$={pk + 1}", - ) - for pk, result in enumerate(results_spectral_fit_pk) - ] -); -``` - -```{code-cell} ipython3 -# generate results for different number of Matsubara terms per Lorentzian -# for max number of Lorentzians: - -Nk_list = range(2, 4) -results_spectral_fit_nk = [ - generate_spectrum_results(Q, 4, Nk=Nk, max_depth=max_depth) for Nk in Nk_list -] - -plot_result_expectations( - [ - ( - result, - P11p, - "rand", - f"P11 (spectral fit) K={nk}", - ) - for nk, result in zip(Nk_list, results_spectral_fit_nk) - ] -); -``` - -```{code-cell} ipython3 -# Generate results for different depths: - -Nc_list = range(2, max_depth) -results_spectral_fit_nc = [ - generate_spectrum_results(Q, 4, Nk=1, max_depth=Nc) for Nc in Nc_list -] - -plot_result_expectations( - [ - ( - result, - P11p, - "rand", - f"P11 (spectral fit) $N_C={nc}$", - ) - for nc, result in zip(Nc_list, results_spectral_fit_nc) - ] -); -``` - -#### We now combine the fitting and correlation function data into one large plot. Here we define a function to plot everything together - -```{code-cell} ipython3 -def gen_plots(fs, w, J, t, C, w2, S): - def plot_cr_fit_vs_actual(t, C, func, axes): - """Plot the C_R(t) fit.""" - yR = func(t) - - axes.plot( - t, - np.real(C), - "r", - linewidth=3, - label="Original", - ) - axes.plot( - t, - np.real(yR), - "g", - dashes=[3, 3], - linewidth=2, - label="Reconstructed", - ) - - axes.set_ylabel(r"$C_R(t)$", fontsize=28) - axes.set_xlabel(r"$t\;\omega_c$", fontsize=28) - axes.locator_params(axis="y", nbins=4) - axes.locator_params(axis="x", nbins=4) - axes.text(0.15, 0.85, "(a)", fontsize=28, transform=axes.transAxes) - - def plot_ci_fit_vs_actual(t, C, func, axes): - """Plot the C_I(t) fit.""" - yI = func(t) - - axes.plot( - t, - np.imag(C), - "r", - linewidth=3, - ) - axes.plot( - t, - np.real(yI), - "g", - dashes=[3, 3], - linewidth=2, - ) - - axes.set_ylabel(r"$C_I(t)$", fontsize=28) - axes.set_xlabel(r"$t\;\omega_c$", fontsize=28) - axes.locator_params(axis="y", nbins=4) - axes.locator_params(axis="x", nbins=4) - axes.text(0.80, 0.80, "(b)", fontsize=28, transform=axes.transAxes) - - def plot_jw_fit_vs_actual(w, J, axes): - """Plot the J(w) fit.""" - J_fit = fs.spectral_density_approx(w) - - axes.plot( - w, - J, - "r", - linewidth=3, - ) - axes.plot( - w, - J_fit, - "g", - dashes=[3, 3], - linewidth=2, - ) - - axes.set_ylabel(r"$J(\omega)$", fontsize=28) - axes.set_xlabel(r"$\omega/\omega_c$", fontsize=28) - axes.locator_params(axis="y", nbins=4) - axes.locator_params(axis="x", nbins=4) - axes.text(0.15, 0.85, "(c)", fontsize=28, transform=axes.transAxes) - - def plot_sw_fit_vs_actual(axes): - """Plot the S(w) fit.""" - - # avoid the pole in the fit around zero: - s_fit = fs.power_spectrum_approx(w2) - - axes.plot(w2, S, "r", linewidth=3) - axes.plot(w2, s_fit, "g", dashes=[3, 3], linewidth=2) - - axes.set_ylabel(r"$S(\omega)$", fontsize=28) - axes.set_xlabel(r"$\omega/\omega_c$", fontsize=28) - axes.locator_params(axis="y", nbins=4) - axes.locator_params(axis="x", nbins=4) - axes.text(0.15, 0.85, "(d)", fontsize=28, transform=axes.transAxes) - - def plot_matsubara_spectrum_fit_vs_actual(t, C): - """Plot the Matsubara fit of the spectrum .""" - fig = plt.figure(figsize=(12, 10)) - grid = plt.GridSpec(2, 2, wspace=0.4, hspace=0.3) - - plot_cr_fit_vs_actual( - t, - C, - lambda t: fs.correlation_function_approx(t), - axes=fig.add_subplot(grid[0, 0]), - ) - plot_ci_fit_vs_actual( - t, - C, - lambda t: np.imag(fs.correlation_function_approx(t)), - axes=fig.add_subplot(grid[0, 1]), - ) - plot_jw_fit_vs_actual( - w, - J, - axes=fig.add_subplot(grid[1, 0]), - ) - plot_sw_fit_vs_actual( - axes=fig.add_subplot(grid[1, 1]), - ) - fig.legend(loc="upper center", ncol=2, fancybox=True, shadow=True) - - return plot_matsubara_spectrum_fit_vs_actual(t, C) -``` - -#### And finally plot everything together - -```{code-cell} ipython3 -t = np.linspace(0, 15, 1000) -C = ohmic_correlation(t, alpha, wc, 1 / T) -w2 = np.concatenate((-np.linspace(10, 1e-2, 100), np.linspace(1e-2, 10, 100))) -S = ohmic_power_spectrum(w2, alpha, wc, 1 / T) -gen_plots(fittedbath, w, J, t, C, w2, S) -``` - -## Building the HEOM bath by fitting the correlation function - -+++ - -Having successfully fitted the spectral density and used the result to calculate the Matsubara expansion and terminator for the HEOM bosonic bath, we now proceed to the second case of fitting the correlation function itself instead. - -Here we fit the real and imaginary parts separately, using the following ansatz - -$$C_R^F(t) = \sum_{i=1}^{k_R} c_R^ie^{-\gamma_R^i t}\cos(\omega_R^i t)$$ - -$$C_I^F(t) = \sum_{i=1}^{k_I} c_I^ie^{-\gamma_I^i t}\sin(\omega_I^i t)$$ - -Analogously to the spectral density case, one may use the `CorrelationFitter` class - -```{code-cell} ipython3 -t = np.linspace(0, 15, 1500) -C = ohmic_correlation(t, alpha=alpha, wc=wc, beta=1 / T) -``` - -```{code-cell} ipython3 -fc = CorrelationFitter(Q, T, t, C) -``` - -```{code-cell} ipython3 -bath, fitinfo = fc.get_fit(Ni=4, Nr=4) -print(fitinfo["summary"]) -``` - -```{code-cell} ipython3 -gen_plots(bath, w, J, t, C, w2, S) -``` - -```{code-cell} ipython3 -def generate_corr_results(N, max_depth): - tlist = np.linspace(0, 30 * np.pi / Del, 600) - bath, _ = fc.get_fit(Ni=N, Nr=N) - HEOM_corr_fit = HEOMSolver( - Hsys, - bath, - max_depth=max_depth, - options=options, - ) - - results_corr_fit = HEOM_corr_fit.run(rho0, tlist) - - return results_corr_fit - - -# Generate results for different number of exponentials in fit: -results_corr_fit_pk = [ - print(f"{i + 1}") - or generate_corr_results( - i, - max_depth=max_depth, - ) - for i in range(1, 4) -] -``` - -```{code-cell} ipython3 -plot_result_expectations( - [ - ( - result, - P11p, - "rand", - f"P11 (correlation fit) k_R=k_I={pk + 1}", - ) - for pk, result in enumerate(results_corr_fit_pk) - ] -); -``` - -```{code-cell} ipython3 -fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) - -plot_result_expectations( - [ - ( - results_corr_fit_pk[0], - P11p, - "y", - "Correlation Function Fit $k_R=k_I=1$", - ), - ( - results_corr_fit_pk[2], - P11p, - "k", - "Correlation Function Fit $k_R=k_I=3$", - ), - (results_spectral_fit_pk[0], P11p, "b", "Spectral Density Fit $k_J=1$"), - (results_spectral_fit_pk[3], P11p, "r-.", "Spectral Density Fit $k_J=4$"), - ], - axes=axes, -) - -axes.set_yticks([0.6, 0.8, 1]) -axes.set_ylabel(r"$\rho_{11}$", fontsize=30) -axes.set_xlabel(r"$t\;\omega_c$", fontsize=30) -axes.legend(loc=0, fontsize=20); -``` - -# Using the Ohmic Bath class - - As the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced quickly by using the OhmicBath class. This allows for rapid implementation of fitted ohmic baths via the correlation function or spectral density - -```{code-cell} ipython3 -obs = OhmicBath(T, Q, alpha, wc, s) -``` - -```{code-cell} ipython3 -Obath, fitinfo = obs.make_correlation_fit(t, rmse=2e-4) -print(fitinfo["summary"]) -``` - -```{code-cell} ipython3 -tlist = np.linspace(0, 30 * np.pi / Del, 600) -HEOM_ohmic_corr_fit = HEOMSolver( - Hsys, - Obath, - max_depth=5, - options=options, -) -results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist) -``` - -```{code-cell} ipython3 -Obath, fitinfo = obs.make_spectral_fit(w, rmse=2e-4) -print(fitinfo["summary"]) -``` - -```{code-cell} ipython3 -HEOM_ohmic_spectral_fit = HEOMSolver( - Hsys, - Obath, - max_depth=5, - options=options, -) -results_ohmic_spectral_fit = HEOM_ohmic_spectral_fit.run(rho0, tlist) -``` - -```{code-cell} ipython3 -fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) - -plot_result_expectations( - [ - # ( - # results_corr_fit_pk[0], P11p, - # 'y', "Correlation Function Fit $k_R=k_I=1$", - # ), - ( - results_corr_fit_pk[2], - P11p, - "y-.", - "Correlation Function Fit $k_R=k_I=3$", - ), - (results_spectral_fit_pk[0], P11p, "b", "Spectral Density Fit $k_J=1$"), - (results_spectral_fit_pk[2], P11p, "g--", "Spectral Density Fit $k_J=3$"), - (results_spectral_fit_pk[3], P11p, "r-.", "Spectral Density Fit $k_J=4$"), - (results_ohmic_spectral_fit, P11p, "g-.", "Spectral Density Fit Ohmic Bath"), - (results_ohmic_corr_fit, P11p, "k-.", "Correlation Fit Ohmic Bath"), - ], - axes=axes, -) - -axes.set_yticks([0.6, 0.8, 1]) -axes.set_ylabel(r"$\rho_{11}$", fontsize=30) -axes.set_xlabel(r"$t\;\omega_c$", fontsize=30) -axes.legend(loc=0, fontsize=20); -``` - -## About - -```{code-cell} ipython3 -qutip.about() -``` - -## Testing - -This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. - -```{code-cell} ipython3 -assert np.allclose( - expect(P11p, results_spectral_fit_pk[2].states), - expect(P11p, results_spectral_fit_pk[3].states), - rtol=1e-2, -) -``` diff --git a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md deleted file mode 100644 index 83103511..00000000 --- a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md +++ /dev/null @@ -1,592 +0,0 @@ ---- -jupytext: - formats: ipynb,md:myst - text_representation: - extension: .md - format_name: myst - format_version: 0.13 - jupytext_version: 1.14.5 -kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 ---- - -# HEOM 1e: Spin-Bath model (pure dephasing) - -+++ - -## Introduction - -The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices. - -In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment. - -The Bosonic environment is implicitly assumed to obey a particular Hamiltonian (see paper), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions. - -In the example below we show how to model the overdamped Drude-Lorentz Spectral Density, commonly used with the HEOM. We show how to do the Matsubara and Pade analytical decompositions, as well as how to fit the latter with a finite set of approximate exponentials. This differs from examble 1a in that we assume that the system and coupling parts of the Hamiltonian commute, hence giving an analytically solvable ''pure dephasing'' model. This is a useful example to look at when introducing other approximations (e.g., fitting of correlation functions) to check for validity/convergence against the analytical results. (Note that, generally, for the fitting examples, the pure dephasing model is the 'worst possible case'. - -### Drude-Lorentz spectral density - -The Drude-Lorentz spectral density is: - -$$J(\omega)=\omega \frac{2\lambda\gamma}{{\gamma}^2 + \omega^2}$$ - -where $\lambda$ scales the coupling strength, and $\gamma$ is the cut-off frequency. -We use the convention, -\begin{equation*} -C(t) = \int_0^{\infty} d\omega \frac{J_D(\omega)}{\pi}[\coth(\beta\omega) \cos(\omega \tau) - i \sin(\omega \tau)] -\end{equation*} - -With the HEOM we must use an exponential decomposition: - -\begin{equation*} -C(t)=\sum_{k=0}^{k=\infty} c_k e^{-\nu_k t} -\end{equation*} - -The Matsubara decomposition of the Drude-Lorentz spectral density is given by: - -\begin{equation*} - \nu_k = \begin{cases} - \gamma & k = 0\\ - {2 \pi k} / {\beta \hbar} & k \geq 1\\ - \end{cases} -\end{equation*} - -\begin{equation*} - c_k = \begin{cases} - \lambda \gamma (\cot(\beta \gamma / 2) - i) / \hbar & k = 0\\ - 4 \lambda \gamma \nu_k / \{(nu_k^2 - \gamma^2)\beta \hbar^2 \} & k \geq 1\\ - \end{cases} -\end{equation*} - -Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. - -+++ - -## Setup - -```{code-cell} ipython3 -import contextlib -import time - -import numpy as np -from matplotlib import pyplot as plt -import scipy -from scipy.optimize import curve_fit - -import qutip -from qutip import ( - basis, - expect, - liouvillian, - sigmax, - sigmaz, -) -from qutip.solver.heom import ( - HEOMSolver, - BosonicBath, - DrudeLorentzBath, - DrudeLorentzPadeBath, -) - -%matplotlib inline -``` - -## Helper functions - -Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: - -```{code-cell} ipython3 -def cot(x): - """ Vectorized cotangent of x. """ - return 1. / np.tan(x) - - -def coth(x): - """ Vectorized hyperbolic cotangent of x. """ - return 1. / np.tanh(x) -``` - -```{code-cell} ipython3 -def plot_result_expectations(plots, axes=None): - """ Plot the expectation values of operators as functions of time. - - Each plot in plots consists of (solver_result, measurement_operation, - color, label). - """ - if axes is None: - fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - fig_created = True - else: - fig = None - fig_created = False - - # add kw arguments to each plot if missing - plots = [p if len(p) == 5 else p + ({},) for p in plots] - for result, m_op, color, label, kw in plots: - if m_op is None: - t, exp = result - else: - t = result.times - exp = np.real(expect(result.states, m_op)) - kw.setdefault("linewidth", 2) - axes.plot(t, exp, color, label=label, **kw) - - if fig_created: - axes.legend(loc=0, fontsize=12) - axes.set_xlabel("t", fontsize=28) - - return fig -``` - -```{code-cell} ipython3 -@contextlib.contextmanager -def timer(label): - """ Simple utility for timing functions: - - with timer("name"): - ... code to time ... - """ - start = time.time() - yield - end = time.time() - print(f"{label}: {end - start}") -``` - -```{code-cell} ipython3 -# Solver options: - -options = { - "nsteps": 15000, - "store_states": True, - "rtol": 1e-14, - "atol": 1e-14, - "method": "vern9", - "progress_bar": "enhanced", -} -``` - -## System and bath definition - -And let us set up the system Hamiltonian, bath and system measurement operators: - -+++ - -Here we set $H_{sys}=0$, which means the interaction Hamiltonian and the system Hamiltonian commute, and we can compare the numerical results to a known analytical one. We could in principle keep $\epsilon \neq 0$, but it just introduces fast system oscillations, so it is more convenient to set it to zero. - -```{code-cell} ipython3 -# Defining the system Hamiltonian -eps = 0.0 # Energy of the 2-level system. -Del = 0.0 # Tunnelling term -Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() -``` - -```{code-cell} ipython3 -# System-bath coupling (Drude-Lorentz spectral density) -Q = sigmaz() # coupling operator - -# Bath properties: -gamma = 0.5 # cut off frequency -lam = 0.1 # coupling strength -T = 0.5 -beta = 1. / T - -# HEOM parameters: -# cut off parameter for the bath: -NC = 6 -# number of exponents to retain in the Matsubara expansion -# of the correlation function: -Nk = 3 - -# Times to solve for -tlist = np.linspace(0, 50, 1000) -``` - -```{code-cell} ipython3 -# Define some operators with which we will measure the system -# 1,1 element of density matrix - corresonding to groundstate -P11p = basis(2, 0) * basis(2, 0).dag() -P22p = basis(2, 1) * basis(2, 1).dag() -# 1,2 element of density matrix - corresonding to coherence -P12p = basis(2, 0) * basis(2, 1).dag() -``` - -To get a non-trivial result we prepare the initial state in a superposition, and see how the bath destroys the coherence. - -```{code-cell} ipython3 -# Initial state of the system. -psi = (basis(2, 0) + basis(2, 1)).unit() -rho0 = psi * psi.dag() -``` - -## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator - -```{code-cell} ipython3 -with timer("RHS construction time"): - bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) - HEOMMats = HEOMSolver(Hsys, bath, NC, options=options) - -with timer("ODE solver time"): - resultMats = HEOMMats.run(rho0, tlist) -``` - -```{code-cell} ipython3 -# Plot the results so far -plot_result_expectations([ - (resultMats, P11p, 'b', "P11 Matsubara"), - (resultMats, P12p, 'r', "P12 Matsubara"), -]); -``` - -## Simulation 2: Matsubara decomposition (including terminator) - -```{code-cell} ipython3 -with timer("RHS construction time"): - bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) - _, terminator = bath.terminator() - Ltot = liouvillian(Hsys) + terminator - HEOMMatsT = HEOMSolver(Ltot, bath, NC, options=options) - -with timer("ODE solver time"): - resultMatsT = HEOMMatsT.run(rho0, tlist) -``` - -```{code-cell} ipython3 -# Plot the results -plot_result_expectations([ - (resultMats, P11p, 'b', "P11 Matsubara"), - (resultMats, P12p, 'r', "P12 Matsubara"), - (resultMatsT, P11p, 'b--', "P11 Matsubara and terminator"), - (resultMatsT, P12p, 'r--', "P12 Matsubara and terminator"), -]); -``` - -## Simulation 3: Pade decomposition - -As in example 1a, we can compare to Pade and Fitting approaches. - -```{code-cell} ipython3 -with timer("RHS construction time"): - bath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) - HEOMPade = HEOMSolver(Hsys, bath, NC, options=options) - -with timer("ODE solver time"): - resultPade = HEOMPade.run(rho0, tlist) -``` - -```{code-cell} ipython3 -# Plot the results -plot_result_expectations([ - (resultMatsT, P11p, 'b', "P11 Matsubara (+term)"), - (resultMatsT, P12p, 'r', "P12 Matsubara (+term)"), - (resultPade, P11p, 'b--', "P11 Pade"), - (resultPade, P12p, 'r--', "P12 Pade"), -]); -``` - -## Simulation 4: Fitting approach - -```{code-cell} ipython3 -def c(t, Nk): - """ Calculates real and imag. parts of the correlation function - using Nk Matsubara terms. - """ - vk = 2 * np.pi * T * np.arange(1, Nk) - - result = ( - lam * gamma * (-1.0j + cot(gamma * beta / 2.)) * - np.exp(-gamma * t[None, :]) - ) - result += np.sum( - (4 * lam * gamma * T * vk[:, None] / (vk[:, None]**2 - gamma**2)) * - np.exp(-vk[:, None] * t[None, :]), - axis=0, - ) - result = result.squeeze(axis=0) - - return result - - -tlist_fit = np.linspace(0, 2, 10000) -lmaxmats = 15000 - -corr_ana = c(tlist_fit, lmaxmats) -corrRana, corrIana = np.real(corr_ana), np.imag(corr_ana) -``` - -```{code-cell} ipython3 -def wrapper_fit_func(x, N, *args): - """ Wrapper for fitting function. """ - a, b = args[0][:N], args[0][N:2*N] - return fit_func(x, a, b) - - -def fit_func(x, a, b): - """ Fitting function. """ - a = np.array(a) - b = np.array(b) - x = np.atleast_1d(np.array(x)) - return np.sum( - a[:, None] * np.exp(b[:, None] * x[None, :]), - axis=0, - ) - - -def fitter(ans, tlist, i): - """ Compute the fit. """ - upper_a = abs(max(ans, key=np.abs)) * 10 - # set initial guess: - guess = [upper_a] * i + [0] * i - # set bounds: a's = anything, b's = negative - # sets lower bound - b_lower = [-upper_a] * i + [-np.inf] * i - # sets higher bound - b_higher = [upper_a] * i + [0] * i - param_bounds = (b_lower, b_higher) - p1, p2 = curve_fit( - lambda x, *params: wrapper_fit_func(x, i, params), - tlist, - ans, - p0=guess, - sigma=[0.01] * len(tlist), - bounds=param_bounds, - maxfev=1e8, - ) - return p1[:i], p1[i:] - - -# Fits of the real part with up to 4 exponents -popt1 = [] -for i in range(4): - a, b = fitter(corrRana, tlist_fit, i + 1) - popt1.append((a, b)) - y = fit_func(tlist_fit, a, b) - plt.plot(tlist_fit, corrRana, label="C_R(t)") - plt.plot(tlist_fit, y, label=f"Fit with k={i + 1}") - plt.xlabel("t") - plt.ylabel("C_R(t)") - plt.legend() - plt.show() - -# Fit of the imaginary part with 1 exponent -popt2 = [] -for i in range(1): - a, b = fitter(corrIana, tlist_fit, i + 1) - popt2.append((a, b)) - y = fit_func(tlist_fit, a, b) - plt.plot(tlist_fit, corrIana, label="C_I(t)") - plt.plot(tlist_fit, y, label=f"Fit with k={i + 1}") - plt.xlabel("t") - plt.ylabel("C_I(t)") - plt.legend() - plt.show() -``` - -```{code-cell} ipython3 -# Set the exponential coefficients from the fit parameters - -ckAR = popt1[-1][0] -vkAR = -1 * popt1[-1][1] - -ckAI = popt2[-1][0] -vkAI = -1 * popt2[-1][1] - -# The imaginary fit can also be determined analytically and is -# a single term: -# -# ckAI = [complex(lam * gamma * (-1.0))] -# vkAI = [complex(gamma)] -``` - -```{code-cell} ipython3 -with timer("RHS construction time"): - bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI) - HEOMFit = HEOMSolver(Hsys, bath, NC, options=options) - -with timer("ODE solver time"): - resultFit = HEOMFit.run(rho0, tlist) -``` - -## Analytic calculations - -```{code-cell} ipython3 -def pure_dephasing_evolution_analytical(tlist, wq, ck, vk): - """ - Computes the propagating function appearing in the pure dephasing model. - - Parameters - ---------- - t: float - A float specifying the time at which to calculate the integral. - - wq: float - The qubit frequency in the Hamiltonian. - - ck: ndarray - The list of coefficients in the correlation function. - - vk: ndarray - The list of frequencies in the correlation function. - - Returns - ------- - integral: float - The value of the integral function at time t. - """ - evolution = np.array([ - np.exp(-1j * wq * t - correlation_integral(t, ck, vk)) - for t in tlist - ]) - return evolution - - -def correlation_integral(t, ck, vk): - r""" - Computes the integral sum function appearing in the pure dephasing model. - - If the correlation function is a sum of exponentials then this sum - is given by: - - .. math: - - \int_0^{t}d\tau D(\tau) = \sum_k\frac{c_k}{\mu_k^2}e^{\mu_k t} - + \frac{\bar c_k}{\bar \mu_k^2}e^{\bar \mu_k t} - - \frac{\bar \mu_k c_k + \mu_k \bar c_k}{\mu_k \bar \mu_k} t - + \frac{\bar \mu_k^2 c_k + \mu_k^2 \bar c_k}{\mu_k^2 \bar \mu_k^2} - - Parameters - ---------- - t: float - A float specifying the time at which to calculate the integral. - - ck: ndarray - The list of coefficients in the correlation function. - - vk: ndarray - The list of frequencies in the correlation function. - - Returns - ------- - integral: float - The value of the integral function at time t. - """ - t1 = np.sum( - (ck / vk**2) * - (np.exp(vk * t) - 1) - ) - t2 = np.sum( - (ck.conj() / vk.conj()**2) * - (np.exp(vk.conj() * t) - 1) - ) - t3 = np.sum( - (ck / vk + ck.conj() / vk.conj()) * t - ) - return 2 * (t1 + t2 - t3) -``` - -For the pure dephasing analytics, we just sum up as many matsubara terms as we can: - -```{code-cell} ipython3 -lmaxmats2 = 15000 - -vk = [complex(-gamma)] -vk.extend([ - complex(-2. * np.pi * k * T) - for k in range(1, lmaxmats2) -]) - -ck = [complex(lam * gamma * (-1.0j + cot(gamma * beta / 2.)))] -ck.extend([ - complex(4 * lam * gamma * T * (-v) / (v**2 - gamma**2)) - for v in vk[1:] -]) - -P12_ana = 0.5 * pure_dephasing_evolution_analytical( - tlist, 0, np.asarray(ck), np.asarray(vk) -) -``` - -Alternatively, we can just do the integral of the propagator directly, without using the correlation functions at all - -```{code-cell} ipython3 -def JDL(omega, lamc, omega_c): - return 2. * lamc * omega * omega_c / (omega_c**2 + omega**2) - - -def integrand(omega, lamc, omega_c, Temp, t): - return ( - (-4. * JDL(omega, lamc, omega_c) / omega**2) * - (1. - np.cos(omega*t)) * (coth(omega/(2.*Temp))) - / np.pi - ) - - -P12_ana2 = [ - 0.5 * np.exp( - scipy.integrate.quad(integrand, 0, np.inf, args=(lam, gamma, T, t))[0] - ) - for t in tlist -] -``` - -## Compare results - -```{code-cell} ipython3 -plot_result_expectations([ - (resultMats, P12p, 'r', "P12 Mats"), - (resultMatsT, P12p, 'r--', "P12 Mats + Term"), - (resultPade, P12p, 'b--', "P12 Pade"), - (resultFit, P12p, 'g', "P12 Fit"), - ((tlist, np.real(P12_ana)), None, 'b', "Analytic 1"), - ((tlist, np.real(P12_ana2)), None, 'y--', "Analytic 2"), -]); -``` - -We can't see much difference in the plot above, so let's do a log plot instead: - -```{code-cell} ipython3 -fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - -plot_result_expectations([ - (resultMats, P12p, 'r', "P12 Mats"), - (resultMatsT, P12p, 'r--', "P12 Mats + Term"), - (resultPade, P12p, 'b-.', "P12 Pade"), - (resultFit, P12p, 'g', "P12 Fit"), - ((tlist, np.real(P12_ana)), None, 'b', "Analytic 1"), - ((tlist, np.real(P12_ana2)), None, 'y--', "Analytic 2"), -], axes) - -axes.set_yscale('log') -axes.legend(loc=0, fontsize=12); -``` - -## About - -```{code-cell} ipython3 -qutip.about() -``` - -## Testing - -This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. - -```{code-cell} ipython3 -assert np.allclose( - expect(P12p, resultMats.states[:15]), np.real(P12_ana)[:15], - rtol=1e-2, -) -assert np.allclose( - expect(P12p, resultMatsT.states[:100]), np.real(P12_ana)[:100], - rtol=1e-3, -) -assert np.allclose( - expect(P12p, resultPade.states[:100]), np.real(P12_ana)[:100], - rtol=1e-3, -) -assert np.allclose( - expect(P12p, resultFit.states[:50]), np.real(P12_ana)[:50], - rtol=1e-3, -) -assert np.allclose(P12_ana, P12_ana2, rtol=1e-3) -``` diff --git a/tutorials-v5/heom/heom-2-fmo-example.md b/tutorials-v5/heom/heom-2-fmo-example.md deleted file mode 100644 index 7a301108..00000000 --- a/tutorials-v5/heom/heom-2-fmo-example.md +++ /dev/null @@ -1,465 +0,0 @@ ---- -jupytext: - formats: ipynb,md:myst - text_representation: - extension: .md - format_name: myst - format_version: 0.13 - jupytext_version: 1.14.5 -kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 ---- - -# HEOM 2: Dynamics in Fenna-Mathews-Olsen complex (FMO) - -+++ - -## Introduction - -In this example notebook we outline how to employ the HEOM to -solve the FMO photosynthetic complex dynamics. - -We aim to replicate the results in reference [https://www.pnas.org/content/106/41/17255](https://pubmed.ncbi.nlm.nih.gov/19815512/) -and compare them to a Bloch-Redfield (perturbative) solution. - -This demonstrates how to to employ the solver for multiple baths, as well as showing how a -quantum environment reduces the effect of pure dephasing. - -+++ - -## Setup - -```{code-cell} ipython3 -import contextlib -import time - -import numpy as np -from matplotlib import pyplot as plt - -import qutip -from qutip import ( - Qobj, - basis, - brmesolve, - expect, - liouvillian, - mesolve, -) -from qutip.solver.heom import ( - HEOMSolver, - DrudeLorentzBath, -) - -%matplotlib inline -``` - -## Helper functions - -Let's define some helper functions for calculating correlation functions, spectral densities, thermal energy level occupations, and for plotting results and timing how long operations take: - -```{code-cell} ipython3 -def cot(x): - """ Vectorized cotangent of x. """ - return 1 / np.tan(x) -``` - -```{code-cell} ipython3 -def J0(energy): - """ Under-damped brownian oscillator spectral density. """ - return 2 * lam * gamma * energy / (energy**2 + gamma**2) -``` - -```{code-cell} ipython3 -def J0_dephasing(): - """ Under-damped brownian oscillator dephasing probability. - - This returns the limit as w -> 0 of J0(w) * n_th(w, T) / T. - """ - return 2 * lam * gamma / gamma**2 -``` - -```{code-cell} ipython3 -def n_th(energy, T): - """ The average occupation of a given energy level at temperature T. """ - return 1 / (np.exp(energy / T) - 1) -``` - -```{code-cell} ipython3 -def dl_corr_approx(t, nk): - """ Drude-Lorenz correlation function approximation. - - Approximates the correlation function at each time t to nk exponents. - """ - c = lam * gamma * (-1.0j + cot(gamma / (2 * T))) * np.exp(-gamma * t) - for k in range(1, nk): - vk = 2 * np.pi * k * T - c += (4 * lam * gamma * T * vk / (vk**2 - gamma**2)) * np.exp(-vk * t) - return c -``` - -```{code-cell} ipython3 -@contextlib.contextmanager -def timer(label): - """ Simple utility for timing functions: - - with timer("name"): - ... code to time ... - """ - start = time.time() - yield - end = time.time() - print(f"{label}: {end - start}") -``` - -```{code-cell} ipython3 -# Solver options: - -options = { - "nsteps": 15000, - "store_states": True, - "rtol": 1e-12, - "atol": 1e-12, - "min_step": 1e-18, - "method": "vern9", - "progress_bar": "enhanced", -} -``` - -## System and bath definition - -And let us set up the system Hamiltonian and bath parameters: - -```{code-cell} ipython3 -# System Hamiltonian: -# -# We use the Hamiltonian employed in -# https://www.pnas.org/content/106/41/17255 and operate -# in units of Hz: - -Hsys = 3e10 * 2 * np.pi * Qobj([ - [200, -87.7, 5.5, -5.9, 6.7, -13.7, -9.9], - [-87.7, 320, 30.8, 8.2, 0.7, 11.8, 4.3], - [5.5, 30.8, 0, -53.5, -2.2, -9.6, 6.0], - [-5.9, 8.2, -53.5, 110, -70.7, -17.0, -63.3], - [6.7, 0.7, -2.2, -70.7, 270, 81.1, -1.3], - [-13.7, 11.8, -9.6, -17.0, 81.1, 420, 39.7], - [-9.9, 4.3, 6.0, -63.3, -1.3, 39.7, 230], -]) -``` - -```{code-cell} ipython3 -# Bath parameters - -lam = 35 * 3e10 * 2 * np.pi -gamma = 1 / 166e-15 -T = 300 * 0.6949 * 3e10 * 2 * np.pi -beta = 1 / T -``` - -## Plotting the environment spectral density and correlation functions - -Let's quickly plot the spectral density and environment correlation functions so that we can see what they look like. - -```{code-cell} ipython3 -wlist = np.linspace(0, 200 * 3e10 * 2 * np.pi, 100) -tlist = np.linspace(0, 1e-12, 1000) - -J = J0(wlist) / (3e10*2*np.pi) - -fig, axes = plt.subplots(1, 2, sharex=False, figsize=(10, 3)) - -fig.subplots_adjust(hspace=0.1) # reduce space between plots - -# Spectral density plot: - -axes[0].plot(wlist / (3e10 * 2 * np.pi), J, color='r', ls='--', label="J(w)") -axes[0].set_xlabel(r'$\omega$ (cm$^{-1}$)', fontsize=20) -axes[0].set_ylabel(r"$J(\omega)$ (cm$^{-1}$)", fontsize=16) -axes[0].legend() - -# Correlation plot: - -axes[1].plot( - tlist, np.real(dl_corr_approx(tlist, 10)), - color='r', ls='--', label="C(t) real", -) -axes[1].plot( - tlist, np.imag(dl_corr_approx(tlist, 10)), - color='g', ls='--', label="C(t) imaginary", -) -axes[1].set_xlabel(r'$t$', fontsize=20) -axes[1].set_ylabel(r"$C(t)$", fontsize=16) -axes[1].legend(); -``` - -## Solve for the dynamics with the HEOM - -Now let us solve for the evolution of this system using the HEOM. - -```{code-cell} ipython3 -# We start the excitation at site 1: -rho0 = basis(7, 0) * basis(7, 0).dag() - -# HEOM solver options: -# -# Note: We set Nk=0 (i.e. a single correlation expansion term -# per bath) and rely on the terminator to correct detailed -# balance. -NC = 4 # Use NC=8 for more precise results -Nk = 0 - -Q_list = [] -baths = [] -Ltot = liouvillian(Hsys) -for m in range(7): - Q = basis(7, m) * basis(7, m).dag() - Q_list.append(Q) - baths.append( - DrudeLorentzBath( - Q, lam=lam, gamma=gamma, T=T, Nk=Nk, - tag=str(m) - ) - ) - _, terminator = baths[-1].terminator() - Ltot += terminator -``` - -```{code-cell} ipython3 -with timer("RHS construction time"): - HEOMMats = HEOMSolver(Hsys, baths, NC, options=options) - -with timer("ODE solver time"): - outputFMO_HEOM = HEOMMats.run(rho0, tlist) -``` - -```{code-cell} ipython3 -fig, axes = plt.subplots(1, 1, figsize=(12, 8)) - -colors = ['r', 'g', 'b', 'y', 'c', 'm', 'k'] -linestyles = [ - '-', '--', ':', '-.', - (0, (1, 10)), (0, (5, 10)), (0, (3, 10, 1, 10)), -] - -for m in range(7): - Q = basis(7, m) * basis(7, m).dag() - axes.plot( - np.array(tlist) * 1e15, - np.real(expect(outputFMO_HEOM.states, Q)), - label=m + 1, - color=colors[m % len(colors)], - linestyle=linestyles[m % len(linestyles)], - ) - axes.set_xlabel(r'$t$ (fs)', fontsize=30) - axes.set_ylabel(r"Population", fontsize=30) - axes.locator_params(axis='y', nbins=6) - axes.locator_params(axis='x', nbins=6) - -axes.set_title('HEOM solution', fontsize=24) -axes.legend(loc=0) -axes.set_xlim(0, 1000) -plt.yticks([0., 0.5, 1], [0, 0.5, 1]) -plt.xticks([0., 500, 1000], [0, 500, 1000]); -``` - -## Comparison with Bloch-Redfield solver - -Now let us solve the same problem using the Bloch-Redfield solver. We will see that the Bloch-Redfield technique fails to model the oscillation of population of the states that we saw in the HEOM. - -In the next section, we will examine the role of pure dephasing in the evolution to understand why this happens. - -```{code-cell} ipython3 -DL = ( - f"2 * pi * 2.0 * {lam} / (pi * {gamma} * {beta}) if (w == 0) else " - f"2 * pi * (2.0*{lam}*{gamma} *w /(pi*(w**2+{gamma}**2))) * " - f"((1 / (exp((w) * {beta}) - 1)) + 1)" -) - -with timer("BR ODE solver time"): - outputFMO_BR = brmesolve( - Hsys, rho0, tlist, - a_ops=[[Q, DL] for Q in Q_list], - options=options, - ) -``` - -And now let's plot the Bloch-Redfield solver results: - -```{code-cell} ipython3 -fig, axes = plt.subplots(1, 1, figsize=(12, 8)) - -for m, Q in enumerate(Q_list): - axes.plot(tlist * 1e15, expect(outputFMO_BR.states, Q), label=m + 1) - -axes.set_xlabel(r'$t$ (fs)', fontsize=30) -axes.set_ylabel(r"Population", fontsize=30) - -axes.set_title('Bloch-Redfield solution ', fontsize=24) -axes.legend() -axes.set_xlim(0, 1000) -plt.yticks([0, 0.5, 1], [0, 0.5, 1]) -plt.xticks([0, 500, 1000], [0, 500, 1000]); -``` - -Notice how the oscillations are gone and the populations decay much more rapidly. - -Next let us try to understand why. - -+++ - -## Role of pure dephasing - -It is useful to construct the various parts of the Bloch-Redfield master equation explicitly and to solve them using the Master equation solver, `mesolve`. We will do so and show that it is the pure-dephasing terms which suppresses coherence in these oscillations. - -First we will write a function to return the list of collapse operators for a given system, either with or without the dephasing operators: - -```{code-cell} ipython3 -def get_collapse(H, T, dephasing=1): - """ Calculate collapse operators for a given system H and - temperature T. - """ - all_energy, all_state = H.eigenstates(sort="low") - Nmax = len(all_energy) - - Q_list = [ - basis(Nmax, n) * basis(Nmax, n).dag() - for n in range(Nmax) - ] - - collapse_list = [] - - for Q in Q_list: - for j in range(Nmax): - for k in range(j + 1, Nmax): - Deltajk = abs(all_energy[k] - all_energy[j]) - if abs(Deltajk) > 0: - rate = ( - np.abs(Q.matrix_element( - all_state[j].dag(), all_state[k] - ))**2 * - 2 * J0(Deltajk) * (n_th(Deltajk, T) + 1) - ) - if rate > 0.0: - # emission: - collapse_list.append( - np.sqrt(rate) * all_state[j] * all_state[k].dag() - ) - - rate = ( - np.abs(Q.matrix_element( - all_state[k].dag(), all_state[j] - ))**2 * - 2 * J0(Deltajk) * n_th(Deltajk, T) - ) - if rate > 0.0: - # absorption: - collapse_list.append( - np.sqrt(rate) * all_state[k] * all_state[j].dag() - ) - - if dephasing: - for j in range(Nmax): - rate = ( - np.abs(Q.matrix_element( - all_state[j].dag(), all_state[j]) - )**2 * - J0_dephasing() * T - ) - if rate > 0.0: - # emission: - collapse_list.append( - np.sqrt(rate) * all_state[j] * all_state[j].dag() - ) - - return collapse_list -``` - -Now we are able to switch the pure dephasing tersms on and off. - -Let us starting by including the dephasing operators. We expect to see the same behaviour that we saw when using the Bloch-Redfield solver. - -```{code-cell} ipython3 -# dephasing terms on, we recover the full BR solution: - -with timer("Building the collapse operators"): - collapse_list = get_collapse(Hsys, T=T, dephasing=True) - -with timer("ME ODE solver"): - outputFMO_ME = mesolve(Hsys, rho0, tlist, collapse_list) -``` - -```{code-cell} ipython3 -fig, axes = plt.subplots(1, 1, figsize=(12, 8)) - -for m, Q in enumerate(Q_list): - axes.plot(tlist * 1e15, expect(outputFMO_ME.states, Q), label=m + 1) - -axes.set_xlabel(r'$t$', fontsize=20) -axes.set_ylabel(r"Population", fontsize=16) -axes.set_xlim(0, 1000) -axes.set_title('With pure dephasing', fontsize=24) -plt.yticks([0, 0.5, 1], [0, 0.5, 1]) -plt.xticks([0, 500, 1000], [0, 500, 1000]) -axes.legend(fontsize=18); -``` - -We see similar results to before. - -Now let us examine what happens when we remove the dephasing collapse operators: - -```{code-cell} ipython3 -# dephasing terms off - -with timer("Building the collapse operators"): - collapse_list = get_collapse(Hsys, T, dephasing=False) - -with timer("ME ODE solver"): - outputFMO_ME_nodephase = mesolve(Hsys, rho0, tlist, collapse_list) -``` - -```{code-cell} ipython3 -fig, axes = plt.subplots(1, 1, figsize=(12, 8)) -for m, Q in enumerate(Q_list): - axes.plot( - tlist * 1e15, - expect(outputFMO_ME_nodephase.states, Q), - label=m + 1, - ) - -axes.set_xlabel(r'$t$', fontsize=20) -axes.set_ylabel(r"Population", fontsize=16) -axes.set_xlim(0, 1000) -axes.set_title('Without pure dephasing', fontsize=24) -plt.yticks([0, 0.5, 1], [0, 0.5, 1]) -plt.xticks([0, 500, 1000], [0, 500, 1000]) -axes.legend(fontsize=18); -``` - -And now we see that without the dephasing, the oscillations reappear. The full dynamics capture by the HEOM are still not capture by this simpler model, however. - -+++ - -## About - -```{code-cell} ipython3 -qutip.about() -``` - -## Testing - -This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. - -```{code-cell} ipython3 -assert np.allclose( - expect(outputFMO_BR.states, Q_list[0]), - expect(outputFMO_ME.states, Q_list[0]), - rtol=2e-2, -) -assert np.allclose( - expect(outputFMO_BR.states, Q_list[1]), - expect(outputFMO_ME.states, Q_list[1]), - rtol=2e-2, -) -``` diff --git a/tutorials-v5/heom/heom-3-quantum-heat-transport.md b/tutorials-v5/heom/heom-3-quantum-heat-transport.md deleted file mode 100644 index 8e956d6c..00000000 --- a/tutorials-v5/heom/heom-3-quantum-heat-transport.md +++ /dev/null @@ -1,505 +0,0 @@ ---- -jupytext: - formats: ipynb,md:myst - text_representation: - extension: .md - format_name: myst - format_version: 0.13 - jupytext_version: 1.14.5 -kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 ---- - -# HEOM 3: Quantum Heat Transport - -+++ - -## Introduction - -In this notebook, we apply the QuTiP HEOM solver to a quantum system coupled to two bosonic baths and demonstrate how to extract information about the system-bath heat currents from the auxiliary density operators (ADOs). -We consider the setup described in Ref. \[1\], which consists of two coupled qubits, each connected to its own heat bath. -The Hamiltonian of the qubits is given by - -$$ \begin{aligned} H_{\text{S}} &= H_1 + H_2 + H_{12} , \quad\text{ where }\\ -H_K &= \frac{\epsilon}{2} \bigl(\sigma_z^K + 1\bigr) \quad (K=1,2) \quad\text{ and }\quad H_{12} = J_{12} \bigl( \sigma_+^1 \sigma_-^2 + \sigma_-^1 \sigma_+^2 \bigr) . \end{aligned} $$ - -Here, $\sigma^K_{x,y,z,\pm}$ denotes the usual Pauli matrices for the K-th qubit, $\epsilon$ is the eigenfrequency of the qubits and $J_{12}$ the coupling constant. - -Each qubit is coupled to its own bath; therefore, the total Hamiltonian is - -$$ H_{\text{tot}} = H_{\text{S}} + \sum_{K=1,2} \bigl( H_{\text{B}}^K + Q_K \otimes X_{\text{B}}^K \bigr) , $$ - -where $H_{\text{B}}^K$ is the free Hamiltonian of the K-th bath and $X_{\text{B}}^K$ its coupling operator, and $Q_K = \sigma_x^K$ are the system coupling operators. -We assume that the bath spectral densities are given by Drude distributions - -$$ J_K(\omega) = \frac{2 \lambda_K \gamma_K \omega}{\omega^2 + \gamma_K^2} , $$ - -where $\lambda_K$ is the free coupling strength and $\gamma_K$ the cutoff frequency. - -We begin by defining the system and bath parameters. -We use the parameter values from Fig. 3(a) of Ref. \[1\]. -Note that we set $\hbar$ and $k_B$ to one and we will measure all frequencies and energies in units of $\epsilon$. - -References: - -   \[1\] Kato and Tanimura, [J. Chem. Phys. **143**, 064107](https://doi.org/10.1063/1.4928192) (2015). - -+++ - -## Setup - -```{code-cell} ipython3 -import dataclasses - -import numpy as np -import matplotlib.pyplot as plt - -import qutip as qt -from qutip.solver.heom import ( - DrudeLorentzPadeBath, - BathExponent, - HEOMSolver, -) - -from ipywidgets import IntProgress -from IPython.display import display - -%matplotlib inline -``` - -## Helpers - -```{code-cell} ipython3 -# Solver options: - -options = { - "nsteps": 15000, - "store_states": True, - "rtol": 1e-12, - "atol": 1e-12, - "min_step": 1e-18, - "method": "vern9", - "progress_bar": "enhanced", -} -``` - -## System and bath definition - -```{code-cell} ipython3 -@dataclasses.dataclass -class SystemParams: - """ System parameters and Hamiltonian. """ - epsilon: float = 1.0 - J12: float = 0.1 - - def H(self): - """ Return the Hamiltonian for the system. - - The system consists of two qubits with Hamiltonians (H1 and H2) - and an interaction term (H12). - """ - H1 = self.epsilon / 2 * ( - qt.tensor(qt.sigmaz() + qt.identity(2), qt.identity(2)) - ) - H2 = self.epsilon / 2 * ( - qt.tensor(qt.identity(2), qt.sigmaz() + qt.identity(2)) - ) - H12 = self.J12 * ( - qt.tensor(qt.sigmap(), qt.sigmam()) + - qt.tensor(qt.sigmam(), qt.sigmap()) - ) - return H1 + H2 + H12 - - def replace(self, **kw): - return dataclasses.replace(self, **kw) -``` - -```{code-cell} ipython3 -@dataclasses.dataclass -class BathParams: - """ Bath parameters. """ - sign: str # + or - - qubit: int # 0 or 1 - - gamma: float = 2.0 - lam: float = 0.05 - Tbar: float = 2.0 - Tdelta: float = 0.01 - - def __post_init__(self): - # T = Tbar +- Tdelta * Tbar: - assert self.sign in ("+", "-") - sign = +1 if self.sign == "+" else -1 - self.T = self.Tbar + sign * self.Tdelta * self.Tbar - # qubit - assert self.qubit in (0, 1) - - def Q(self): - """ Coupling operator for the bath. """ - Q = [qt.identity(2), qt.identity(2)] - Q[self.qubit] = qt.sigmax() - return qt.tensor(Q) - - def bath(self, Nk, tag=None): - return DrudeLorentzPadeBath( - self.Q(), self.lam, self.gamma, self.T, Nk, tag=tag - ) - - def replace(self, **kw): - return dataclasses.replace(self, **kw) -``` - -## Heat currents - -Following Ref. \[2\], we consider two possible definitions of the heat currents from the qubits into the baths. -The so-called bath heat currents are $j_{\text{B}}^K = \partial_t \langle H_{\text{B}}^K \rangle$ and the system heat currents are $j_{\text{S}}^K = \mathrm i\, \langle [H_{\text{S}}, Q_K] X_{\text{B}}^K \rangle$. -As shown in Ref. \[2\], they can be expressed in terms of the HEOM ADOs as follows: -$$ \begin{aligned} \mbox{} \\ - j_{\text{B}}^K &= \!\!\sum_{\substack{\mathbf n\\ \text{Level 1}\\ \text{Bath $K$}}}\!\! \nu[\mathbf n] \operatorname{tr}\bigl[ Q_K \rho_{\mathbf n} \bigr] - 2 C_I^K(0) \operatorname{tr}\bigl[ Q_k^2 \rho \bigr] + \Gamma_{\text{T}}^K \operatorname{tr}\bigl[ [[H_{\text{S}}, Q_K], Q_K]\, \rho \bigr] , \\[.5em] - j_{\text{S}}^K &= \mathrm i\!\! \sum_{\substack{\mathbf n\\ \text{Level 1}\\ \text{Bath $k$}}}\!\! \operatorname{tr}\bigl[ [H_{\text{S}}, Q_K]\, \rho_{\mathbf n} \bigr] + \Gamma_{\text{T}}^K \operatorname{tr}\bigl[ [[H_{\text{S}}, Q_K], Q_K]\, \rho \bigr] . \\ \mbox{} -\end{aligned} $$ -The sums run over all level-$1$ multi-indices $\mathbf n$ with one excitation corresponding to the K-th bath, $\nu[\mathbf n]$ is the corresponding (negative) exponent of the bath auto-correlation function $C^K(t)$, and $\Gamma_{\text{T}}^K$ is the Ishizaki-Tanimura terminator (i.e., a correction term accounting for the error introduced by approximating the correlation function with a finite sum of exponential terms). -In the expression for the bath heat currents, we left out terms involving $[Q_1, Q_2]$, which is zero in this example. - -   \[2\] Kato and Tanimura, [J. Chem. Phys. **145**, 224105](https://doi.org/10.1063/1.4971370) (2016). - -+++ - -In QuTiP, these currents can be conveniently calculated as follows: - -```{code-cell} ipython3 -def bath_heat_current(bath_tag, ado_state, hamiltonian, coupling_op, delta=0): - """ - Bath heat current from the system into the heat bath with the given tag. - - Parameters - ---------- - bath_tag : str, tuple or any other object - Tag of the heat bath corresponding to the current of interest. - - ado_state : HierarchyADOsState - Current state of the system and the environment (encoded in the ADOs). - - hamiltonian : Qobj - System Hamiltonian at the current time. - - coupling_op : Qobj - System coupling operator at the current time. - - delta : float - The prefactor of the \\delta(t) term in the correlation function (the - Ishizaki-Tanimura terminator). - """ - l1_labels = ado_state.filter(level=1, tags=[bath_tag]) - a_op = 1j * (hamiltonian * coupling_op - coupling_op * hamiltonian) - - result = 0 - cI0 = 0 # imaginary part of bath auto-correlation function (t=0) - for label in l1_labels: - [exp] = ado_state.exps(label) - result += exp.vk * (coupling_op * ado_state.extract(label)).tr() - - if exp.type == BathExponent.types['I']: - cI0 += exp.ck - elif exp.type == BathExponent.types['RI']: - cI0 += exp.ck2 - - result -= 2 * cI0 * (coupling_op * coupling_op * ado_state.rho).tr() - if delta != 0: - result -= ( - 1j * delta * - ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr() - ) - return result - - -def system_heat_current( - bath_tag, ado_state, hamiltonian, coupling_op, delta=0, -): - """ - System heat current from the system into the heat bath with the given tag. - - Parameters - ---------- - bath_tag : str, tuple or any other object - Tag of the heat bath corresponding to the current of interest. - - ado_state : HierarchyADOsState - Current state of the system and the environment (encoded in the ADOs). - - hamiltonian : Qobj - System Hamiltonian at the current time. - - coupling_op : Qobj - System coupling operator at the current time. - - delta : float - The prefactor of the \\delta(t) term in the correlation function (the - Ishizaki-Tanimura terminator). - """ - l1_labels = ado_state.filter(level=1, tags=[bath_tag]) - a_op = 1j * (hamiltonian * coupling_op - coupling_op * hamiltonian) - - result = 0 - for label in l1_labels: - result += (a_op * ado_state.extract(label)).tr() - - if delta != 0: - result -= ( - 1j * delta * - ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr() - ) - return result -``` - -Note that at long times, we expect $j_{\text{B}}^1 = -j_{\text{B}}^2$ and $j_{\text{S}}^1 = -j_{\text{S}}^2$ due to energy conservation. At long times, we also expect $j_{\text{B}}^1 = j_{\text{S}}^1$ and $j_{\text{B}}^2 = j_{\text{S}}^2$ since the coupling operators commute, $[Q_1, Q_2] = 0$. Hence, all four currents should agree in the long-time limit (up to a sign). This long-time value is what was analyzed in Ref. \[2\]. - -+++ - -## Simulations - -+++ - -For our simulations, we will represent the bath spectral densities using the first term of their Padé decompositions, and we will use $7$ levels of the HEOM hierarchy. - -```{code-cell} ipython3 -Nk = 1 -NC = 7 -``` - -### Time Evolution - -We fix $J_{12} = 0.1 \epsilon$ (as in Fig. 3(a-ii) of Ref. \[2\]) and choose the fixed coupling strength $\lambda_1 = \lambda_2 = J_{12}\, /\, (2\epsilon)$ (corresponding to $\bar\zeta = 1$ in Ref. \[2\]). -Using these values, we will study the time evolution of the system state and the heat currents. - -```{code-cell} ipython3 -# fix qubit-qubit and qubit-bath coupling strengths -sys = SystemParams(J12=0.1) -bath_p1 = BathParams(qubit=0, sign="+", lam=sys.J12 / 2) -bath_p2 = BathParams(qubit=1, sign="-", lam=sys.J12 / 2) - -# choose arbitrary initial state -rho0 = qt.tensor(qt.identity(2), qt.identity(2)) / 4 - -# simulation time span -tlist = np.linspace(0, 50, 250) -``` - -```{code-cell} ipython3 -H = sys.H() - -bath1 = bath_p1.bath(Nk, tag='bath 1') -Q1 = bath_p1.Q() - -bath2 = bath_p2.bath(Nk, tag='bath 2') -Q2 = bath_p2.Q() - -b1delta, b1term = bath1.terminator() -b2delta, b2term = bath2.terminator() -solver = HEOMSolver( - qt.liouvillian(H) + b1term + b2term, - [bath1, bath2], - max_depth=NC, - options=options, -) - -result = solver.run(rho0, tlist, e_ops=[ - qt.tensor(qt.sigmaz(), qt.identity(2)), - lambda t, ado: bath_heat_current('bath 1', ado, H, Q1, b1delta), - lambda t, ado: bath_heat_current('bath 2', ado, H, Q2, b2delta), - lambda t, ado: system_heat_current('bath 1', ado, H, Q1, b1delta), - lambda t, ado: system_heat_current('bath 2', ado, H, Q2, b2delta), -]) -``` - -We first plot $\langle \sigma_z^1 \rangle$ to see the time evolution of the system state: - -```{code-cell} ipython3 -fig, axes = plt.subplots(figsize=(8, 8)) -axes.plot(tlist, result.expect[0], 'r', linewidth=2) -axes.set_xlabel('t', fontsize=28) -axes.set_ylabel(r"$\langle \sigma_z^1 \rangle$", fontsize=28); -``` - -We find a rather quick thermalization of the system state. For the heat currents, however, it takes a somewhat longer time until they converge to their long-time values: - -```{code-cell} ipython3 -fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8)) - -ax1.plot( - tlist, -np.real(result.expect[1]), - color='darkorange', label='BHC (bath 1 -> system)', -) -ax1.plot( - tlist, np.real(result.expect[2]), - '--', color='darkorange', label='BHC (system -> bath 2)', -) -ax1.plot( - tlist, -np.real(result.expect[3]), - color='dodgerblue', label='SHC (bath 1 -> system)', -) -ax1.plot( - tlist, np.real(result.expect[4]), - '--', color='dodgerblue', label='SHC (system -> bath 2)', -) - -ax1.set_xlabel('t', fontsize=28) -ax1.set_ylabel('j', fontsize=28) -ax1.set_ylim((-0.05, 0.05)) -ax1.legend(loc=0, fontsize=12) - -ax2.plot( - tlist, -np.real(result.expect[1]), - color='darkorange', label='BHC (bath 1 -> system)', -) -ax2.plot( - tlist, np.real(result.expect[2]), - '--', color='darkorange', label='BHC (system -> bath 2)', -) -ax2.plot( - tlist, -np.real(result.expect[3]), - color='dodgerblue', label='SHC (bath 1 -> system)', -) -ax2.plot( - tlist, np.real(result.expect[4]), - '--', color='dodgerblue', label='SHC (system -> bath 2)', -) - -ax2.set_xlabel('t', fontsize=28) -ax2.set_xlim((20, 50)) -ax2.set_ylim((0, 0.0002)) -ax2.legend(loc=0, fontsize=12); -``` - -### Steady-state currents - -Here, we try to reproduce the HEOM curves in Fig. 3(a) of Ref. \[1\] by varying the coupling strength and finding the steady state for each coupling strength. - -```{code-cell} ipython3 -def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options): - """ Calculate the steady sate heat currents for the given system and - bath. - """ - bath1 = bath_p1.bath(Nk, tag="bath 1") - Q1 = bath_p1.Q() - - bath2 = bath_p2.bath(Nk, tag="bath 2") - Q2 = bath_p2.Q() - - b1delta, b1term = bath1.terminator() - b2delta, b2term = bath2.terminator() - - solver = HEOMSolver( - qt.liouvillian(sys.H()) + b1term + b2term, - [bath1, bath2], - max_depth=NC, - options=options - ) - - _, steady_ados = solver.steady_state() - - return ( - bath_heat_current('bath 1', steady_ados, sys.H(), Q1, b1delta), - bath_heat_current('bath 2', steady_ados, sys.H(), Q2, b2delta), - system_heat_current('bath 1', steady_ados, sys.H(), Q1, b1delta), - system_heat_current('bath 2', steady_ados, sys.H(), Q2, b2delta), - ) -``` - -```{code-cell} ipython3 -# Define number of points to use for the plot -plot_points = 10 # use 100 for a smoother curve - -# Range of relative coupling strengths -# Chosen so that zb_max is maximum, centered around 1 on a log scale -zb_max = 4 # use 20 to see more of the current curve -zeta_bars = np.logspace( - -np.log(zb_max), - np.log(zb_max), - plot_points, - base=np.e, -) - -# Setup a progress bar -progress = IntProgress(min=0, max=(3 * plot_points)) -display(progress) - - -def calculate_heat_current(J12, zb, Nk, progress=progress): - """ Calculate a single heat current and update the progress bar. """ - # Estimate appropriate HEOM max_depth from coupling strength - NC = 7 + int(max(zb * J12 - 1, 0) * 2) - NC = min(NC, 20) - # the four currents are identical in the steady state - j, _, _, _ = heat_currents( - sys.replace(J12=J12), - bath_p1.replace(lam=zb * J12 / 2), - bath_p2.replace(lam=zb * J12 / 2), - Nk, NC, options=options, - ) - progress.value += 1 - return j - - -# Calculate steady state currents for range of zeta_bars -# for J12 = 0.01, 0.1 and 0.5: -j1s = [ - calculate_heat_current(0.01, zb, Nk) - for zb in zeta_bars -] -j2s = [ - calculate_heat_current(0.1, zb, Nk) - for zb in zeta_bars -] -j3s = [ - calculate_heat_current(0.5, zb, Nk) - for zb in zeta_bars -] -``` - -## Create Plot - -```{code-cell} ipython3 -fig, axes = plt.subplots(figsize=(12, 7)) - -axes.plot( - zeta_bars, -1000 * 100 * np.real(j1s), - 'b', linewidth=2, label=r"$J_{12} = 0.01\, \epsilon$", -) -axes.plot( - zeta_bars, -1000 * 10 * np.real(j2s), - 'r--', linewidth=2, label=r"$J_{12} = 0.1\, \epsilon$", -) -axes.plot( - zeta_bars, -1000 * 2 * np.real(j3s), - 'g-.', linewidth=2, label=r"$J_{12} = 0.5\, \epsilon$", -) - -axes.set_xscale('log') -axes.set_xlabel(r"$\bar\zeta$", fontsize=30) -axes.set_xlim((zeta_bars[0], zeta_bars[-1])) - -axes.set_ylabel( - r"$j_{\mathrm{ss}}\; /\; (\epsilon J_{12}) \times 10^3$", - fontsize=30, -) -axes.set_ylim((0, 2)) - -axes.legend(loc=0); -``` - -## About - -```{code-cell} ipython3 -qt.about() -``` - -## Testing - -This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. - -```{code-cell} ipython3 -assert 1 == 1 -``` diff --git a/tutorials-v5/heom/heom-4-dynamical-decoupling.md b/tutorials-v5/heom/heom-4-dynamical-decoupling.md deleted file mode 100644 index 952a44e9..00000000 --- a/tutorials-v5/heom/heom-4-dynamical-decoupling.md +++ /dev/null @@ -1,531 +0,0 @@ ---- -jupytext: - formats: ipynb,md:myst - text_representation: - extension: .md - format_name: myst - format_version: 0.13 - jupytext_version: 1.14.5 -kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 ---- - -# HEOM 4: Dynamical decoupling of a non-Markovian environment - -+++ - -## Introduction - -Following [Lorenza Viola and Seth Lloyd](https://arxiv.org/abs/quant-ph/9803057) we consider an example of dynamical decoupling. -We choose a drive which performs pi rotations, interspersed with short periods where the bath causes dephasing. - -We first show the standard example of equally spaced pulses, and then consider the 'optimal' Uhrig spacing ([Götz S. Uhrig Phys. Rev. Lett. 98, 100504 (2007)](https://arxiv.org/abs/quant-ph/0609203)). - -+++ - -## Setup - -```{code-cell} ipython3 -import numpy as np -import matplotlib.pyplot as plt - -import qutip -from qutip import ( - QobjEvo, - basis, - expect, - ket2dm, - sigmax, - sigmaz, -) -from qutip.solver.heom import ( - HEOMSolver, - DrudeLorentzPadeBath, -) - -from ipywidgets import IntProgress -from IPython.display import display - -%matplotlib inline -``` - -## Helper functions - -Let's define some helper functions for calculating the spectral density: - -```{code-cell} ipython3 -def dl_spectrum(w, lam, gamma): - """ Return the Drude-Lorentz spectral density. """ - J = w * 2 * lam * gamma / (gamma**2 + w**2) - return J -``` - -```{code-cell} ipython3 -# Solver options: - -# The max_step must be set to a short time than the -# length of the shortest pulse, otherwise the solver -# might skip over a pulse. - -options = { - "nsteps": 1500, - "store_states": True, - "rtol": 1e-12, - "atol": 1e-12, - "max_step": 1 / 20.0, - "method": "vern9", - "progress_bar": "enhanced", -} -``` - -## System and bath definition - -Now we define the system and bath properties and the HEOM parameters. The system is a single stationary qubit with $H = 0$ and the bath is a bosonic bath with a Drude-Lorentz spectrum. - -```{code-cell} ipython3 -# Define the system Hamlitonian. -# -# The system isn't evolving by itself, so the Hamiltonian is 0 (with the -# correct dimensions): - -H_sys = 0 * sigmaz() -``` - -```{code-cell} ipython3 -# Define some operators with which we will measure the system -# 1,1 element of density matrix - corresonding to groundstate -P11p = basis(2, 0) * basis(2, 0).dag() -P22p = basis(2, 1) * basis(2, 1).dag() -# 1,2 element of density matrix - corresonding to coherence -P12p = basis(2, 0) * basis(2, 1).dag() -``` - -```{code-cell} ipython3 -# Properties for the Drude-Lorentz bath - -lam = 0.0005 -gamma = 0.005 -T = 0.05 - -# bath-system coupling operator: -Q = sigmaz() - -# number of terms to keep in the expansion of the bath correlation function: -Nk = 3 - -bath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) -``` - -```{code-cell} ipython3 -# HEOM parameters - -# number of layers to keep in the hierarchy: -NC = 6 -``` - -To perform the dynamic decoupling from the environment, we will drive the system with a time-dependent pulse that couples to the system via the $\sigma_x$ operator. The area under the pulse will usual be set to $\pi / 2$ so that the pulse flips the qubit state. - -Below we define a function that returns the pulse (which is itself a function): - -```{code-cell} ipython3 -def drive(amplitude, delay, integral): - """ Coefficient of the drive as a function of time. - - The drive consists of a series of constant pulses with - a fixed delay between them. - - Parameters - ---------- - amplitude : float - The amplitude of the drive during the pulse. - delay : float - The time delay between successive pulses. - integral : float - The integral of the pulse. This determines - the duration of each pulse with the duration - equal to the integral divided by the amplitude. - """ - duration = integral / amplitude - period = duration + delay - - def pulse(t): - t = t % period - if t < duration: - return amplitude - return 0 - - return pulse - - -H_drive = sigmax() -``` - -## Plot the spectral density - -Let's start by plotting the spectral density of our Drude-Lorentz bath: - -```{code-cell} ipython3 -wlist = np.linspace(0, 0.5, 1000) -J = dl_spectrum(wlist, lam, gamma) - -fig, axes = plt.subplots(1, 1, figsize=(8, 8)) -axes.plot(wlist, J, 'r', linewidth=2) -axes.set_xlabel(r'$\omega$', fontsize=28) -axes.set_ylabel(r'J', fontsize=28); -``` - -## Dynamic decoupling with fast and slow pulses - -Now we are ready to explore dynamic decoupling from the environment. - -First we will drive the system with fast, large amplitude pulses. Then we will drive the system with slower, smaller amplitude pulses. The faster pulses decoupling the system more effectively and retain the coherence longer, but the slower pulses help too. - -Let's start by simulating the fast pulses: - -```{code-cell} ipython3 -# Fast driving (quick, large amplitude pulses) - -tlist = np.linspace(0, 400, 1000) - -# start with a superposition so there is something to dephase! -rho0 = (basis(2, 1) + basis(2, 0)).unit() -rho0 = ket2dm(rho0) - -# without pulses -hsolver = HEOMSolver(H_sys, bath, NC, options=options) -outputnoDD = hsolver.run(rho0, tlist) - -# with pulses -drive_fast = drive(amplitude=0.5, delay=20, integral=np.pi / 2) -H_d = qutip.QobjEvo([H_sys, [H_drive, drive_fast]]) - -hsolver = HEOMSolver(H_d, bath, NC, options=options) -outputDD = hsolver.run(rho0, tlist) -``` - -And now the longer slower pulses: - -```{code-cell} ipython3 -# Slow driving (longer, small amplitude pulses) - -# without pulses -hsolver = HEOMSolver(H_sys, bath, NC, options=options) -outputnoDDslow = hsolver.run(rho0, tlist) - -# with pulses -drive_slow = drive(amplitude=0.01, delay=20, integral=np.pi/2) -H_d = QobjEvo([H_sys, [H_drive, drive_slow]]) - -hsolver = HEOMSolver(H_d, bath, NC, options=options) -outputDDslow = hsolver.run(rho0, tlist) -``` - -Now let's plot all of the results and the shapes of the pulses: - -```{code-cell} ipython3 -def plot_dd_results(outputnoDD, outputDD, outputDDslow): - fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 12)) - - # Plot the dynamic decoupling results: - - tlist = outputDD.times - - P12 = basis(2, 1) * basis(2, 0).dag() - P12DD = qutip.expect(outputDD.states, P12) - P12noDD = qutip.expect(outputnoDD.states, P12) - P12DDslow = qutip.expect(outputDDslow.states, P12) - - plt.sca(axes[0]) - plt.yticks([0, 0.25, 0.5], [0, 0.25, 0.5]) - - axes[0].plot( - tlist, np.real(P12DD), - 'green', linestyle='-', linewidth=2, label="HEOM with fast DD", - ) - axes[0].plot( - tlist, np.real(P12DDslow), - 'blue', linestyle='-', linewidth=2, label="HEOM with slow DD", - ) - axes[0].plot( - tlist, np.real(P12noDD), - 'orange', linestyle='--', linewidth=2, label="HEOM no DD", - ) - - axes[0].locator_params(axis='y', nbins=3) - axes[0].locator_params(axis='x', nbins=3) - - axes[0].set_ylabel(r"$\rho_{01}$", fontsize=30) - - axes[0].legend(loc=4) - axes[0].text(0, 0.4, "(a)", fontsize=28) - - # Plot the drive pulses: - - pulse = [drive_fast(t) for t in tlist] - pulseslow = [drive_slow(t) for t in tlist] - - plt.sca(axes[1]) - plt.yticks([0., 0.25, 0.5], [0, 0.25, 0.5]) - - axes[1].plot( - tlist, pulse, - 'green', linestyle='-', linewidth=2, label="Drive fast", - ) - axes[1].plot( - tlist, pulseslow, - 'blue', linestyle='--', linewidth=2, label="Drive slow", - ) - - axes[1].locator_params(axis='y', nbins=3) - axes[1].locator_params(axis='x', nbins=3) - - axes[1].set_xlabel(r'$t\bar{V}_{\mathrm{f}}$', fontsize=30) - axes[1].set_ylabel(r'Drive amplitude/$\bar{V}_{\mathrm{f}}$', fontsize=30) - - axes[1].legend(loc=1) - axes[1].text(0, 0.4, "(b)", fontsize=28) - - fig.tight_layout() -``` - -```{code-cell} ipython3 -plot_dd_results(outputnoDD, outputDD, outputDDslow) -``` - -## Non-equally spaced pulses - -+++ - -Next we consider non-equally spaced pulses. - -Rather than plot as a function of time we just consider the final coherence after time $T$ and 100 pulses. We change the width of the environment to demonstate that the Uhrig sequence (i.e. the evenly spaced pulses) can be sub-optimal when the bath is very broad. - -Instead of evenly spaced pulses, we will use pulses where the cummulative delay after $j$ pulses is given by: - -$$ - \sin^2(\frac{\pi}{2} \frac{j}{N + 1}) -$$ - -This is just a convenient way to describe the varying delay. We could have chosen another monotonically increasing function to represent the cummulative delay (although it might not be as effective). - -```{code-cell} ipython3 -def cummulative_delay_fractions(N): - """ Return an array of N + 1 cummulative delay - fractions. - - The j'th entry in the array should be the sum of - all delays before the j'th pulse. The last entry - should be 1 (i.e. the entire cummulative delay - should have been used once the sequence of pulses - is complete). - - The function should be monotonically increasing, - strictly greater than zero and the last value - should be 1. - - This implementation returns: - - sin((pi / 2) * (j / (N + 1)))**2 - - as the cummulative delay after the j'th pulse. - """ - return np.array([ - np.sin((np.pi / 2) * (j / (N + 1)))**2 - for j in range(0, N + 1) - ]) - - -def drive_opt(amplitude, avg_delay, integral, N): - """ Return an optimized distance pulse function. - - Our previous pulses were evenly spaced. Here we - instead use a varying delay after the j'th pulse. - - The cummulative delay is described by the function - ``cummulative_delay_fractions`` above. - """ - duration = integral / amplitude - cummulative_delays = N * avg_delay * cummulative_delay_fractions(N) - - t_start = cummulative_delays + duration * np.arange(0, N + 1) - t_end = cummulative_delays + duration * np.arange(1, N + 2) - - def pulse(t): - if any((t_start <= t) & (t <= t_end)): - return amplitude - return 0.0 - - return pulse -``` - -Let's plot the cummulative delays and see what they look like. Note that the cummulative delay starts at $0$, ends at $1$ and is monotonically increasing, as required. - -On the same axes we plot the individual $j^{th}$ delays as a fraction of the average delay. - -```{code-cell} ipython3 -def plot_cummulative_delay_fractions(N): - cummulative = cummulative_delay_fractions(N) - individual = (cummulative[1:] - cummulative[:-1]) * N - plt.plot(np.arange(0, N + 1), cummulative, label="Cummulative delay") - plt.plot(np.arange(0, N), individual, label="j'th delay") - plt.xlabel("j") - plt.ylabel("Fraction of delay") - plt.legend() - - -plot_cummulative_delay_fractions(100) -``` - -And now let us plot the first ten even and optimally spaced pulses together to compare them: - -```{code-cell} ipython3 -def plot_even_and_optimally_spaced_pulses(): - amplitude = 10.0 - integral = np.pi / 2 - duration = integral / amplitude - delay = 1.0 - duration - - tlist = np.linspace(0, 10, 1000) - - pulse_opt = drive_opt(amplitude, delay, integral, 100) - pulse_eq = drive(amplitude, delay, integral) - - plt.plot( - tlist, [pulse_opt(t) for t in tlist], label="opt", - ) - plt.plot( - tlist, [pulse_eq(t) for t in tlist], label="eq", - ) - plt.legend(loc=4) - - -plot_even_and_optimally_spaced_pulses() -``` - -Now let's simulate the effectiveness of the two sets of delays by comparing how well they maintain coherence after a hundred pulses. - -We'll perform the simulation over a range of lambdas and gammas to show how the non-evenly spaced delays become optimal as the width of the bath spectral function increases. - -```{code-cell} ipython3 -# Bath parameters to simulate over: - -# We use only two lambdas and two gammas so that the notebook executes -# quickly: - -lams = [0.005, 0.0005] -gammas = np.linspace(0.005, 0.05, 2) - -# But one can also extend the lists to larger ones: -# -# lams = [0.01, 0.005, 0.0005] -# gammas = np.linspace(0.005, 0.05, 10) - -# Setup a progress bar: - -progress = IntProgress(min=0, max=(2 * len(lams) * len(gammas))) -display(progress) - - -def simulate_100_pulses(lam, gamma, T, NC, Nk): - """ Simulate the evolution of 100 evenly and optimally spaced pulses. - - Returns the expectation value of P12p from the final state of - each evolution. - """ - rho0 = (basis(2, 1) + basis(2, 0)).unit() - rho0 = ket2dm(rho0) - - N = 100 # number of pulses to simulate - avg_cycle_time = 1.0 # average time from one pulse to the next - t_max = N * avg_cycle_time - - tlist = np.linspace(0, t_max, 100) - - amplitude = 10.0 - integral = np.pi / 2 - duration = integral / amplitude - delay = avg_cycle_time - duration - - bath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) - - # Equally spaced pulses: - - pulse_eq = drive(amplitude, delay, integral) - H_d = QobjEvo([H_sys, [H_drive, pulse_eq]]) - - hsolver = HEOMSolver(H_d, bath, NC, options=options) - result = hsolver.run(rho0, tlist) - - P12_eq = expect(result.states[-1], P12p) - progress.value += 1 - - # Non-equally spaced pulses: - - pulse_opt = drive_opt(amplitude, delay, integral, N) - H_d = QobjEvo([H_sys, [H_drive, pulse_opt]]) - - hsolver = HEOMSolver(H_d, bath, NC, options=options) - result = hsolver.run(rho0, tlist) - - P12_opt = expect(result.states[-1], P12p) - progress.value += 1 - - return P12_opt, P12_eq - - -# We use NC=2 and Nk=2 to speed up the simulation: - -P12_results = [ - list(zip(*( - simulate_100_pulses(lam=lam_, gamma=gamma_, T=0.5, NC=2, Nk=2) - for gamma_ in gammas - ))) - for lam_ in lams -] -``` - -Now that we have the expectation values of $\rho_{01}$ let's plot them as a function of gamma for each lambda. Note how in each case the non-evenly spaced pulses become optimal once gamma is sufficiently small: - -```{code-cell} ipython3 -fig, axes = plt.subplots(1, 1, sharex=False, figsize=(10, 7)) -colors = ["green", "red", "blue"] - -for i in range(len(lams)): - color = colors[i % len(colors)] - axes.plot( - gammas, np.real(P12_results[i][0]), - color, linestyle='-', linewidth=2, - label=f"Optimal DD [$\\lambda={lams[i]}$]", - ) - axes.plot( - gammas, np.real(P12_results[i][1]), - color, linestyle='-.', linewidth=2, - label=f"Even DD [$\\lambda={lams[i]}$]", - ) - -axes.set_ylabel(r"$\rho_{01}$") -axes.set_xlabel(r"$\gamma$") -axes.legend(fontsize=16) - -fig.tight_layout(); -``` - -And now you know about dynamically decoupling a qubit from its environment! - -+++ - -## About - -```{code-cell} ipython3 -qutip.about() -``` - -## Testing - -This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. - -```{code-cell} ipython3 -assert 1 == 1 -``` diff --git a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md deleted file mode 100644 index e07bf9ed..00000000 --- a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md +++ /dev/null @@ -1,587 +0,0 @@ ---- -jupytext: - formats: ipynb,md:myst - text_representation: - extension: .md - format_name: myst - format_version: 0.13 - jupytext_version: 1.14.5 -kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 ---- - -# HEOM 5a: Fermionic single impurity model - -+++ - -## Introduction - -Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc). Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications. - -Notation: - -* $K=L/R$ refers to left or right leads. -* $\sigma=\pm$ refers to input/output - -We choose a Lorentzian spectral density for the leads, with a peak at the chemical potential. The latter simplifies a little the notation required for the correlation functions, but can be relaxed if neccessary. - -$$J(\omega) = \frac{\Gamma W^2}{((\omega-\mu_K)^2 +W^2 )}$$ - -The Fermi distribution function is: - -$$f_F (x) = (\exp(x) + 1)^{-1}$$ - -Together these allow the correlation functions to be expressed as: - -$$C^{\sigma}_K(t) = \frac{1}{2\pi} \int_{-\infty}^{\infty} d\omega e^{\sigma i \omega t} \Gamma_K(\omega) f_F[\sigma\beta(\omega - \mu)]$$ - -As with the bosonic case we can expand these in an exponential series using Matsubara, Pade, or fitting approaches. - -The Pade decomposition approximates the Fermi distubition as - -$$f_F(x) \approx f_F^{\mathrm{approx}}(x) = \frac{1}{2} - \sum_l^{l_{max}} \frac{2k_l x}{x^2 + \epsilon_l^2}$$ - -where $k_l$ and $\epsilon_l$ are co-efficients defined in J. Chem Phys 133,10106. - -Evaluating the integral for the correlation functions gives, - -$$C_K^{\sigma}(t) \approx \sum_{l=0}^{l_{max}} \eta_K^{\sigma_l} e^{-\gamma_{K,\sigma,l}t}$$ - -where: - -$$\eta_{K,0} = \frac{\Gamma_KW_K}{2} f_F^{approx}(i\beta_K W)$$ - -$$\gamma_{K,\sigma,0} = W_K - \sigma i\mu_K$$ - -$$\eta_{K,l\neq 0} = -i\cdot \frac{k_m}{\beta_K} \cdot \frac{\Gamma_K W_K^2}{-\frac{\epsilon^2_m}{\beta_K^2} + W_K^2}$$ - -$$\gamma_{K,\sigma,l\neq 0}= \frac{\epsilon_m}{\beta_K} - \sigma i \mu_K$$ - -In this notebook we: - -* compare the Matsubara and Pade approximations and contrast them with the analytical result for the current between the system and the leads. - -* plot the current through the qubit as a function of the different between the voltages of the leads. - -+++ - -## Setup - -```{code-cell} ipython3 -import contextlib -import dataclasses -import time - -import numpy as np -import matplotlib.pyplot as plt -from scipy.integrate import quad - -import qutip -from qutip import ( - basis, - destroy, - expect, -) -from qutip.solver.heom import ( - HEOMSolver, - LorentzianBath, - LorentzianPadeBath, -) - -from ipywidgets import IntProgress -from IPython.display import display - -%matplotlib inline -``` - -## Helpers - -```{code-cell} ipython3 -@contextlib.contextmanager -def timer(label): - """ Simple utility for timing functions: - - with timer("name"): - ... code to time ... - """ - start = time.time() - yield - end = time.time() - print(f"{label}: {end - start}") -``` - -```{code-cell} ipython3 -# Solver options: - -# We set store_ados to True so that we can -# use the auxilliary density operators (ADOs) -# to calculate the current between the leads -# and the system. - -options = { - "nsteps": 1500, - "store_states": True, - "store_ados": True, - "rtol": 1e-12, - "atol": 1e-12, - "method": "vern9", - "progress_bar": "enhanced", -} -``` - -## System and bath definition - -And let us set up the system Hamiltonian, bath and system measurement operators: - -```{code-cell} ipython3 -# Define the system Hamiltonian: - -# The system is a single fermion with energy level split e1: -d1 = destroy(2) -e1 = 1.0 -H = e1 * d1.dag() * d1 -``` - -```{code-cell} ipython3 -# Define parameters for left and right fermionic baths. -# Each bath is a lead (i.e. a wire held at a potential) -# with temperature T and chemical potential mu. - -@dataclasses.dataclass -class LorentzianBathParameters: - lead: str - Q: object # coupling operator - gamma: float = 0.01 # coupling strength - W: float = 1.0 # cut-off - T: float = 0.025851991 # temperature - theta: float = 2.0 # bias - - def __post_init__(self): - assert self.lead in ("L", "R") - self.beta = 1 / self.T - if self.lead == "L": - self.mu = self.theta / 2.0 - else: - self.mu = - self.theta / 2.0 - - def J(self, w): - """ Spectral density. """ - return self.gamma * self.W**2 / ((w - self.mu)**2 + self.W**2) - - def fF(self, w, sign=1.0): - """ Fermi distribution for this bath. """ - x = sign * self.beta * (w - self.mu) - return fF(x) - - def lamshift(self, w): - """ Return the lamshift. """ - return 0.5 * (w - self.mu) * self.J(w) / self.W - - def replace(self, **kw): - return dataclasses.replace(self, **kw) - - -def fF(x): - """ Return the Fermi distribution. """ - # in units where kB = 1.0 - return 1 / (np.exp(x) + 1) - - -bath_L = LorentzianBathParameters(Q=d1, lead="L") -bath_R = LorentzianBathParameters(Q=d1, lead="R") -``` - -## Spectral density - -Let's plot the spectral density. - -```{code-cell} ipython3 -w_list = np.linspace(-2, 2, 100) - -fig, ax = plt.subplots(figsize=(12, 7)) - -spec_L = bath_L.J(w_list) -spec_R = bath_R.J(w_list) - -ax.plot( - w_list, spec_L, - "b--", linewidth=3, - label=r"J_L(w)", -) -ax.plot( - w_list, spec_R, - "r--", linewidth=3, - label=r"J_R(w)", -) - -ax.set_xlabel("w") -ax.set_ylabel(r"$J(\omega)$") -ax.legend(); -``` - -## Emission and absorption by the leads - -Next let's plot the emission and absorption by the leads. - -```{code-cell} ipython3 -w_list = np.linspace(-2, 2, 100) - -fig, ax = plt.subplots(figsize=(12, 7)) - -# Left lead emission and absorption - -gam_L_in = bath_L.J(w_list) * bath_L.fF(w_list, sign=1.0) -gam_L_out = bath_L.J(w_list) * bath_L.fF(w_list, sign=-1.0) - -ax.plot( - w_list, gam_L_in, - "b--", linewidth=3, - label=r"S_L(w) input (absorption)", -) -ax.plot( - w_list, gam_L_out, - "r--", linewidth=3, - label=r"S_L(w) output (emission)", -) - -# Right lead emission and absorption - -gam_R_in = bath_R.J(w_list) * bath_R.fF(w_list, sign=1.0) -gam_R_out = bath_R.J(w_list) * bath_R.fF(w_list, sign=-1.0) - -ax.plot( - w_list, gam_R_in, - "b", linewidth=3, - label=r"S_R(w) input (absorption)", -) -ax.plot( - w_list, gam_R_out, - "r", linewidth=3, - label=r"S_R(w) output (emission)", -) - -ax.set_xlabel("w") -ax.set_ylabel(r"$S(\omega)$") -ax.legend(); -``` - -## Comparing the Matsubara and Pade approximations - -Let's start by solving for the evolution using a Pade expansion of the correlation function of the Lorentzian spectral density: - -```{code-cell} ipython3 -# HEOM dynamics using the Pade approximation: - -# Times to solve for and initial system state: -tlist = np.linspace(0, 100, 1000) -rho0 = basis(2, 0) * basis(2, 0).dag() - -Nk = 10 # Number of exponents to retain in the expansion of each bath - -bathL = LorentzianPadeBath( - bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T, - Nk, tag="L", -) -bathR = LorentzianPadeBath( - bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T, - Nk, tag="R", -) - -with timer("RHS construction time"): - solver_pade = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options) - -with timer("ODE solver time"): - result_pade = solver_pade.run(rho0, tlist) - -with timer("Steady state solver time"): - rho_ss_pade, ado_ss_pade = solver_pade.steady_state() -``` - -Now let us plot the result which shows the decay of the initially excited impurity. This is not very illuminating, but we will compare it with the Matsubara expansion and analytic solution sortly: - -```{code-cell} ipython3 -# Plot the Pade results -fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - -axes.plot( - tlist, expect(result_pade.states, rho0), - 'r--', linewidth=2, - label="P11 (Pade)", -) -axes.axhline( - expect(rho_ss_pade, rho0), - color='r', linestyle="dotted", linewidth=1, - label="P11 (Pade steady state)", -) - -axes.set_xlabel('t', fontsize=28) -axes.legend(fontsize=12); -``` - -Now let us do the same for the Matsubara expansion: - -```{code-cell} ipython3 -# HEOM dynamics using the Matsubara approximation: - -bathL = LorentzianBath( - bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T, - Nk, tag="L", -) -bathR = LorentzianBath( - bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T, - Nk, tag="R", -) - -with timer("RHS construction time"): - solver_mats = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options) - -with timer("ODE solver time"): - result_mats = solver_mats.run(rho0, tlist) - -with timer("Steady state solver time"): - rho_ss_mats, ado_ss_mats = solver_mats.steady_state() -``` - -We see a marked difference in the Matsubara vs Pade results: - -```{code-cell} ipython3 -# Plot the Pade results -fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - -axes.plot( - tlist, expect(result_pade.states, rho0), - 'r--', linewidth=2, - label="P11 (Pade)", -) -axes.axhline( - expect(rho_ss_pade, rho0), - color='r', linestyle="dotted", linewidth=1, - label="P11 (Pade steady state)", -) - -axes.plot( - tlist, expect(result_mats.states, rho0), - 'b--', linewidth=2, - label="P11 (Mats)", -) -axes.axhline( - expect(rho_ss_mats, rho0), - color='b', linestyle="dotted", linewidth=1, - label="P11 (Mats steady state)", -) - -axes.set_xlabel('t', fontsize=28) -axes.legend(fontsize=12); -``` - -But which is more correct? The Matsubara or the Pade result? - -One advantage of this simple model is that the steady state current to the baths is analytically solvable, so we can check convergence of the result by calculating it analytically (the sum of the currents to and from the system in the steady state must be zero, so the current from one bath is the same as the current to the other). - -See the [QuTiP-BoFiN paper](https://arxiv.org/abs/2010.10806) for a detailed description and references for the analytic result. Below we just perform the required integration numerically. - -```{code-cell} ipython3 -def analytical_steady_state_current(bath_L, bath_R, e1): - """ Calculate the analytical steady state current. """ - - def integrand(w): - return (2 / np.pi) * ( - bath_L.J(w) * bath_R.J(w) * (bath_L.fF(w) - bath_R.fF(w)) / - ( - (bath_L.J(w) + bath_R.J(w))**2 + - 4*(w - e1 - bath_L.lamshift(w) - bath_R.lamshift(w))**2 - ) - ) - - def real_part(x): - return np.real(integrand(x)) - - def imag_part(x): - return np.imag(integrand(x)) - - # in principle the bounds for the integral should be rechecked if - # bath or system parameters are changed substantially: - bounds = [-10, 10] - - real_integral, _ = quad(real_part, *bounds) - imag_integral, _ = quad(imag_part, *bounds) - - return real_integral + 1.0j * imag_integral - - -curr_ss_analytic = analytical_steady_state_current(bath_L, bath_R, e1) - -print(f"Analytical steady state current: {curr_ss_analytic}") -``` - -To compare the analytical result above with the result from the HEOM, we need to be able to calculate the current from the system to the bath from the HEOM result. In the HEOM description, these currents are captured in the first level auxilliary density operators (ADOs). - -In the function `state_current(...)` below, we extract the first level ADOs for the specified bath and sum the contributions to the current from each: - -```{code-cell} ipython3 -def state_current(ado_state, bath_tag): - """ Determine current from the given bath (either "R" or "L") to - the system in the given ADO state. - """ - level_1_aux = [ - (ado_state.extract(label), ado_state.exps(label)[0]) - for label in ado_state.filter(level=1, tags=[bath_tag]) - ] - - def exp_sign(exp): - return 1 if exp.type == exp.types["+"] else -1 - - def exp_op(exp): - return exp.Q if exp.type == exp.types["+"] else exp.Q.dag() - - return -1.0j * sum( - exp_sign(exp) * (exp_op(exp) * aux).tr() - for aux, exp in level_1_aux - ) -``` - -Now we can calculate the steady state currents from the Pade and Matsubara HEOM results: - -```{code-cell} ipython3 -curr_ss_pade_L = state_current(ado_ss_pade, "L") -curr_ss_pade_R = state_current(ado_ss_pade, "R") - -print(f"Pade steady state current (L): {curr_ss_pade_L}") -print(f"Pade steady state current (R): {curr_ss_pade_R}") -``` - -```{code-cell} ipython3 -curr_ss_mats_L = state_current(ado_ss_mats, "L") -curr_ss_mats_R = state_current(ado_ss_mats, "R") - -print(f"Matsubara steady state current (L): {curr_ss_mats_L}") -print(f"Matsubara steady state current (R): {curr_ss_mats_R}") -``` - -Note that the currents from each bath balance as is required by the steady state, but the value of the current is different for the Pade and Matsubara results. - -Now let's compare all three: - -```{code-cell} ipython3 -print(f"Pade current (R): {curr_ss_pade_R}") -print(f"Matsubara current (R): {curr_ss_mats_R}") -print(f"Analytical curernt: {curr_ss_analytic}") -``` - -In this case we observe that the Pade approximation has converged more closely to the analytical current than the Matsubara. - -The Matsubara result could be improved by increasing the number of terms retained in the Matsubara expansion (i.e. increasing `Nk`). - -+++ - -## Current as a function of bias voltage - -+++ - -Now lets plot the current as a function of bias voltage (the bias voltage is the parameter `theta` for the two baths). - -We will calculate the steady state current for each `theta` both analytically and using the HEOM with the Pade correlation expansion approximation. - -```{code-cell} ipython3 -# Theta (bias voltages) - -thetas = np.linspace(-4, 4, 100) - -# Setup a progress bar: - -progress = IntProgress(min=0, max=2 * len(thetas)) -display(progress) - -# Calculate the current for the list of thetas - - -def current_analytic_for_theta(e1, bath_L, bath_R, theta): - """ Return the analytic current for a given theta. """ - current = analytical_steady_state_current( - bath_L.replace(theta=theta), - bath_R.replace(theta=theta), - e1, - ) - progress.value += 1 - return np.real(current) - - -def current_pade_for_theta(H, bath_L, bath_R, theta, Nk): - """ Return the steady state current using the Pade approximation. """ - bath_L = bath_L.replace(theta=theta) - bath_R = bath_R.replace(theta=theta) - - bathL = LorentzianPadeBath( - bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T, - Nk, tag="L", - ) - bathR = LorentzianPadeBath( - bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T, - Nk, tag="R", - ) - - solver_pade = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options) - rho_ss_pade, ado_ss_pade = solver_pade.steady_state() - current = state_current(ado_ss_pade, bath_tag="R") - - progress.value += 1 - return np.real(current) - - -curr_ss_analytic_thetas = [ - current_analytic_for_theta(e1, bath_L, bath_R, theta) - for theta in thetas -] - -# The number of expansion terms has been dropped to Nk=6 to speed -# up notebook execution. Increase to Nk=10 for more accurate results. -curr_ss_pade_theta = [ - current_pade_for_theta(H, bath_L, bath_R, theta, Nk=6) - for theta in thetas -] -``` - -Below we plot the results and see that even with `Nk=6`, the HEOM Pade approximation gives good results for the steady state current. Increasing `Nk` to `10` gives very accurate results. - -```{code-cell} ipython3 -fig, ax = plt.subplots(figsize=(12, 7)) - -ax.plot( - thetas, 2.434e-4 * 1e6 * np.array(curr_ss_analytic_thetas), - color="black", linewidth=3, - label=r"Analytical", -) -ax.plot( - thetas, 2.434e-4 * 1e6 * np.array(curr_ss_pade_theta), - 'r--', linewidth=3, - label=r"HEOM Pade $N_k=10$, $n_{\mathrm{max}}=2$", -) - - -ax.locator_params(axis='y', nbins=4) -ax.locator_params(axis='x', nbins=4) - -ax.set_xticks([-2.5, 0, 2.5]) -ax.set_xticklabels([-2.5, 0, 2.5]) -ax.set_xlabel(r"Bias voltage $\Delta \mu$ ($V$)", fontsize=28) -ax.set_ylabel(r"Current ($\mu A$)", fontsize=28) -ax.legend(fontsize=25); -``` - -## About - -```{code-cell} ipython3 -qutip.about() -``` - -## Testing - -This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. - -```{code-cell} ipython3 -assert np.allclose(curr_ss_pade_L + curr_ss_pade_R, 0) -assert np.allclose(curr_ss_mats_L + curr_ss_mats_R, 0) -assert np.allclose(curr_ss_pade_R, curr_ss_analytic, rtol=1e-4) -``` diff --git a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md deleted file mode 100644 index bc2e23e5..00000000 --- a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md +++ /dev/null @@ -1,395 +0,0 @@ ---- -jupytext: - formats: ipynb,md:myst - text_representation: - extension: .md - format_name: myst - format_version: 0.13 - jupytext_version: 1.14.5 -kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 ---- - -# HEOM 5b: Discrete boson coupled to an impurity and fermionic leads - -+++ - -## Introduction - -Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc), also coupled to a discrete bosonic (vibronic) mode. - -Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his Dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications. In particular this example reproduces some results from https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.201407 - -Notation: - -* $K=L/R$ refers to left or right leads. -* $\sigma=\pm$ refers to input/output - -We choose a Lorentzian spectral density for the leads, with a peak at the chemical potential. The latter simplifies a little the notation required for the correlation functions, but can be relaxed if neccessary. - -$$J(\omega) = \frac{\Gamma W^2}{((\omega-\mu_K)^2 +W^2 )}$$ - -The Fermi distribution function is: - -$$f_F (x) = (\exp(x) + 1)^{-1}$$ - -Together these allow the correlation functions to be expressed as: - -$$C^{\sigma}_K(t) = \frac{1}{2\pi} \int_{-\infty}^{\infty} d\omega e^{\sigma i \omega t} \Gamma_K(\omega) f_F[\sigma\beta(\omega - \mu)]$$ - -As with the Bosonic case we can treat these with Matsubara, Pade, or fitting approaches. - -The Pade decomposition approximates the Fermi distubition as - -$$f_F(x) \approx f_F^{\mathrm{approx}}(x) = \frac{1}{2} - \sum_l^{l_{max}} \frac{2k_l x}{x^2 + \epsilon_l^2}$$ - -$k_l$ and $\epsilon_l$ are co-efficients defined in J. Chem Phys 133,10106 - -Evaluating the integral for the correlation functions gives, - - -$$C_K^{\sigma}(t) \approx \sum_{l=0}^{l_{max}} \eta_K^{\sigma_l} e^{-\gamma_{K,\sigma,l}t}$$ - -where - -$$\eta_{K,0} = \frac{\Gamma_KW_K}{2} f_F^{approx}(i\beta_K W)$$ - -$$\gamma_{K,\sigma,0} = W_K - \sigma i\mu_K$$ - -$$\eta_{K,l\neq 0} = -i\cdot \frac{k_m}{\beta_K} \cdot \frac{\Gamma_K W_K^2}{-\frac{\epsilon^2_m}{\beta_K^2} + W_K^2}$$ - -$$\gamma_{K,\sigma,l\neq 0}= \frac{\epsilon_m}{\beta_K} - \sigma i \mu_K$$ - -+++ - -## Differences from Example 5a - -+++ - -The system we study here has two big differences from the HEOM 5a example: - -* the system now includes a discrete bosonic mode, -* and the electronic leads have $W$ set to $10^4$ (i.e. the wide-band limit). - -The new system Hamiltonian is: - -$$ -H_{\mathrm{vib}} = H_{\mathrm{SIAM}} + \Omega a^{\dagger}a + \lambda (a+a^{\dagger})c{^\dagger}c. -$$ - -where $H_{\mathrm{SIAM}}$ is the Hamiltonian of the single impurity, and the remaining terms are the Hamiltonian of the bosonic mode and its interaction with the impurity. - -The complete setup now consists of four parts: - -* the single impurity -* a discrete bosonic mode -* two fermionic leads. - -**Note**: This example is quite numerically challenging and has many system and bath components. For an easier introduction into the fermionic case, see example 5a. - -**Note**: We've reduced the cut-off of the bosonic mode to 2 modes to facilitate faster execution of the notebooks when the outputs are being checked and compiled. A more accurate result may be obtained by increasing the number of bosonic modes to, for example, 16. - -+++ - -## Setup - -```{code-cell} ipython3 -import contextlib -import dataclasses -import time - -import matplotlib.pyplot as plt -import numpy as np - -import qutip -from qutip import ( - destroy, - qeye, - tensor, -) -from qutip.solver.heom import ( - HEOMSolver, - LorentzianPadeBath, -) - -from ipywidgets import IntProgress -from IPython.display import display - -%matplotlib inline -``` - -## Helpers - -```{code-cell} ipython3 -@contextlib.contextmanager -def timer(label): - """ Simple utility for timing functions: - - with timer("name"): - ... code to time ... - """ - start = time.time() - yield - end = time.time() - print(f"{label}: {end - start}") -``` - -```{code-cell} ipython3 -def state_current(ado_state, bath_tag): - """ Determine current from the given bath (either "R" or "L") to - the system in the given ADO state. - """ - level_1_aux = [ - (ado_state.extract(label), ado_state.exps(label)[0]) - for label in ado_state.filter(level=1, tags=[bath_tag]) - ] - - def exp_sign(exp): - return 1 if exp.type == exp.types["+"] else -1 - - def exp_op(exp): - return exp.Q if exp.type == exp.types["+"] else exp.Q.dag() - - return -1.0j * sum( - exp_sign(exp) * (exp_op(exp) * aux).tr() - for aux, exp in level_1_aux - ) -``` - -```{code-cell} ipython3 -# Solver options: - -# We set store_ados to True so that we can -# use the auxilliary density operators (ADOs) -# to calculate the current between the leads -# and the system. - -options = { - "nsteps": 1500, - "store_states": True, - "store_ados": True, - "rtol": 1e-12, - "atol": 1e-12, - "method": "vern9", - "progress_bar": "enhanced", -} -``` - -## System and bath definition - -Let us set up the system Hamiltonian and specify the properties of the two reservoirs. - -```{code-cell} ipython3 -# Define the system Hamiltonian: - -@dataclasses.dataclass -class SystemParameters: - e1: float = 0.3 # fermion mode energy splitting - Omega: float = 0.2 # bosonic mode energy splitting - Lambda: float = 0.12 # coupling between fermion and boson - Nbos: int = 2 - - def __post_init__(self): - d = tensor(destroy(2), qeye(self.Nbos)) - a = tensor(qeye(2), destroy(self.Nbos)) - self.H = ( - self.e1 * d.dag() * d + - self.Omega * a.dag() * a + - self.Lambda * (a + a.dag()) * d.dag() * d - ) - self.Q = d - - def replace(self, **kw): - return dataclasses.replace(self, **kw) - - -sys_p = SystemParameters() -``` - -```{code-cell} ipython3 -# Define parameters for left and right fermionic baths. -# Each bath is a lead (i.e. a wire held at a potential) -# with temperature T and chemical potential mu. - -@dataclasses.dataclass -class LorentzianBathParameters: - lead: str - gamma: float = 0.01 # coupling strength - W: float = 1.0 # cut-off - T: float = 0.025851991 # temperature (in eV) - theta: float = 2.0 # bias - - def __post_init__(self): - assert self.lead in ("L", "R") - self.beta = 1 / self.T - if self.lead == "L": - self.mu = self.theta / 2.0 - else: - self.mu = - self.theta / 2.0 - - def J(self, w): - """ Spectral density. """ - return self.gamma * self.W**2 / ((w - self.mu)**2 + self.W**2) - - def fF(self, w, sign=1.0): - """ Fermi distribution for this bath. """ - x = sign * self.beta * (w - self.mu) - return fF(x) - - def lamshift(self, w): - """ Return the lamshift. """ - return 0.5 * (w - self.mu) * self.J(w) / self.W - - def replace(self, **kw): - return dataclasses.replace(self, **kw) - - -def fF(x): - """ Return the Fermi distribution. """ - # in units where kB = 1.0 - return 1 / (np.exp(x) + 1) - - -# We set W = 1e4 to investigate the wide-band limit: - -bath_L = LorentzianBathParameters(W=10**4, lead="L") -bath_R = LorentzianBathParameters(W=10**4, lead="R") -``` - -## Emission and absorption by the leads - -Next let's plot the emission and absorption by the leads. - -```{code-cell} ipython3 -w_list = np.linspace(-2, 2, 100) - -fig, ax = plt.subplots(figsize=(12, 7)) - -# Left lead emission and absorption - -gam_L_in = bath_L.J(w_list) * bath_L.fF(w_list, sign=1.0) -gam_L_out = bath_L.J(w_list) * bath_L.fF(w_list, sign=-1.0) - -ax.plot( - w_list, gam_L_in, - "b--", linewidth=3, - label=r"S_L(w) input (absorption)", -) -ax.plot( - w_list, gam_L_out, - "r--", linewidth=3, - label=r"S_L(w) output (emission)", -) - -# Right lead emission and absorption - -gam_R_in = bath_R.J(w_list) * bath_R.fF(w_list, sign=1.0) -gam_R_out = bath_R.J(w_list) * bath_R.fF(w_list, sign=-1.0) - -ax.plot( - w_list, gam_R_in, - "b", linewidth=3, - label=r"S_R(w) input (absorption)", -) -ax.plot( - w_list, gam_R_out, - "r", linewidth=3, - label=r"S_R(w) output (emission)", -) - -ax.set_xlabel("w") -ax.set_ylabel(r"$S(\omega)$") -ax.legend(); -``` - -## Below we give one example data set from Paper - -Here we just give one example of the current as a function of bias voltage, but in general one can try different cut-offs of the bosonic Fock space and the expansion of the correlation functions until convergence is found. - -One note: for very large problems, this can be slow. - -```{code-cell} ipython3 -def steady_state_pade_for_theta(sys_p, bath_L, bath_R, theta, Nk, Nc, Nbos): - """ Return the steady state current using the Pade approximation. """ - - sys_p = sys_p.replace(Nbos=Nbos) - bath_L = bath_L.replace(theta=theta) - bath_R = bath_R.replace(theta=theta) - - bathL = LorentzianPadeBath( - sys_p.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T, - Nk, tag="L", - ) - bathR = LorentzianPadeBath( - sys_p.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T, - Nk, tag="R", - ) - - solver_pade = HEOMSolver( - sys_p.H, [bathL, bathR], max_depth=2, options=options, - ) - rho_ss_pade, ado_ss_pade = solver_pade.steady_state() - current = state_current(ado_ss_pade, bath_tag="R") - - return np.real(2.434e-4 * 1e6 * current) -``` - -```{code-cell} ipython3 -# Parameters: - -Nk = 6 -Nc = 2 -Nbos = 2 # Use Nbos = 16 for more accurate results - -thetas = np.linspace(0, 2, 30) - -# Progress bar: - -progress = IntProgress(min=0, max=len(thetas)) -display(progress) - -currents = [] - -for theta in thetas: - currents.append(steady_state_pade_for_theta( - sys_p, bath_L, bath_R, theta, - Nk=Nk, Nc=Nc, Nbos=Nbos, - )) - progress.value += 1 -``` - -```{code-cell} ipython3 -fig, ax = plt.subplots(figsize=(12, 10)) - -ax.plot( - thetas, currents, - color="green", linestyle='-', linewidth=3, - label=f"Nk = {5}, max_depth = {Nc}, Nbos = {Nbos}", -) - -ax.set_yticks([0, 0.5, 1]) -ax.set_yticklabels([0, 0.5, 1]) - -ax.locator_params(axis='y', nbins=4) -ax.locator_params(axis='x', nbins=4) - -ax.set_xlabel(r"Bias voltage $\Delta \mu$ ($V$)", fontsize=30) -ax.set_ylabel(r"Current ($\mu A$)", fontsize=30) -ax.legend(loc=4); -``` - -## About - -```{code-cell} ipython3 -qutip.about() -``` - -## Testing - -This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. - -```{code-cell} ipython3 -assert 1 == 1 -``` diff --git a/tutorials-v5/heom/heom-index.md b/tutorials-v5/heom/heom-index.md deleted file mode 100644 index dcb1fa89..00000000 --- a/tutorials-v5/heom/heom-index.md +++ /dev/null @@ -1,47 +0,0 @@ ---- -jupytext: - formats: ipynb,md:myst - text_representation: - extension: .md - format_name: myst - format_version: 0.13 - jupytext_version: 1.14.4 -kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 ---- - -# Hierarchical Equation of Motion Examples - -The "hierarchical equations of motion" (HEOM) method is a powerful numerical approach to solve the dynamics and steady-state of a quantum system coupled to a non-Markovian and non-perturbative environment. Originally developed in the context of physical chemistry, it has also been extended and applied to problems in solid-state physics, optics, single-molecule electronics, and biological physics. - -QuTiP's implementation of the HEOM is described in detail in https://arxiv.org/abs/2010.10806. - -This collection of examples from the paper illustrates how to use QuTiP's HEOM to model and investigate the dynamics of a variety of systems coupled to bosonic or fermionic baths. - -## Overview of the notebooks - - - -* [Example 1a: Spin-Bath model (introduction)](./heom-1a-spin-bath-model-basic.ipynb) - -* [Example 1b: Spin-Bath model (very strong coupling)](./heom-1b-spin-bath-model-very-strong-coupling.ipynb) - -* [Example 1c: Spin-Bath model (underdamped case)](./heom-1c-spin-bath-model-underdamped-sd.ipynb) - -* [Example 1d: Spin-Bath model, fitting of spectrum and correlation functions](./heom-1d-spin-bath-model-ohmic-fitting.ipynb) - -* [Example 1e: Spin-Bath model (pure dephasing)](./heom-1e-spin-bath-model-pure-dephasing.ipynb) - -* [Example 2: Dynamics in Fenna-Mathews-Olsen complex (FMO)](./heom-2-fmo-example.ipynb) - -* [Example 3: Quantum Heat Transport](./heom-3-quantum-heat-transport.ipynb) - -* [Example 4: Dynamical decoupling of a non-Markovian environment](./heom-4-dynamical-decoupling.ipynb) - -* [Example 5a: Fermionic single impurity model](./heom-5a-fermions-single-impurity-model.ipynb) - -* [Example 5b: Discrete boson coupled to an impurity + fermionic leads](./heom-5b-fermions-discrete-boson-model.ipynb) - - From 8dc293ef358297c710960f2aa8dd69860d1b237e Mon Sep 17 00:00:00 2001 From: mcditooss Date: Wed, 6 Nov 2024 22:38:39 +0100 Subject: [PATCH 14/44] Mistakingly submitted .ipynb instead of .md --- .../heom/heom-1a-spin-bath-model-basic.ipynb | 2061 ----------------- .../heom/heom-1a-spin-bath-model-basic.md | 1190 ++++++++++ ...spin-bath-model-very-strong-coupling.ipynb | 900 ------- ...1b-spin-bath-model-very-strong-coupling.md | 492 ++++ ...om-1c-spin-bath-model-underdamped-sd.ipynb | 1018 -------- .../heom-1c-spin-bath-model-underdamped-sd.md | 584 +++++ ...eom-1d-spin-bath-model-ohmic-fitting.ipynb | 1276 ---------- .../heom-1d-spin-bath-model-ohmic-fitting.md | 825 +++++++ ...om-1e-spin-bath-model-pure-dephasing.ipynb | 917 -------- .../heom-1e-spin-bath-model-pure-dephasing.md | 488 ++++ tutorials-v5/heom/heom-2-fmo-example.ipynb | 805 ------- tutorials-v5/heom/heom-2-fmo-example.md | 437 ++++ .../heom/heom-3-quantum-heat-transport.ipynb | 803 ------- .../heom/heom-3-quantum-heat-transport.md | 508 ++++ .../heom/heom-4-dynamical-decoupling.ipynb | 904 -------- .../heom/heom-4-dynamical-decoupling.md | 528 +++++ ...om-5a-fermions-single-impurity-model.ipynb | 828 ------- .../heom-5a-fermions-single-impurity-model.md | 587 +++++ ...eom-5b-fermions-discrete-boson-model.ipynb | 528 ----- .../heom-5b-fermions-discrete-boson-model.md | 395 ++++ tutorials-v5/heom/heom-index.ipynb | 56 - 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"cells": [ - { - "cell_type": "markdown", - "id": "92158a9d", - "metadata": {}, - "source": [ - "# HEOM 1a: Spin-Bath model (introduction)" - ] - }, - { - "cell_type": "markdown", - "id": "fe8ddb3e", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "The HEOM method solves the dynamics and steady state of a system and its\n", - "environment, the latter of which is encoded in a set of auxiliary density\n", - "matrices.\n", - "\n", - "In this example we show the evolution of a single two-level system in contact\n", - "with a single Bosonic environment. The properties of the system are encoded\n", - "in a Hamiltonian, and a coupling operator which describes how it is coupled\n", - "to the environment.\n", - "\n", - "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", - "\n", - "In the example below we show how to model the overdamped Drude-Lorentz\n", - "Spectral Density, commonly used with the HEOM. We show how to do this using\n", - "the Matsubara, Pade and fitting decompositions, and compare their\n", - "convergence.\n", - "\n", - "### Drude-Lorentz (overdamped) spectral density\n", - "\n", - "The Drude-Lorentz spectral density is:\n", - "\n", - "$$J_D(\\omega)= \\frac{2\\omega\\lambda\\gamma}{{\\gamma}^2 + \\omega^2}$$\n", - "\n", - "where $\\lambda$ scales the coupling strength, and $\\gamma$ is the cut-off\n", - "frequency. We use the convention,\n", - "\\begin{equation*}\n", - "C(t) = \\int_0^{\\infty} d\\omega \\frac{J_D(\\omega)}{\\pi}[\\coth(\\beta\\omega) \\\n", - " \\cos(\\omega \\tau) - i \\sin(\\omega \\tau)]\n", - "\\end{equation*}\n", - "\n", - "With the HEOM we must use an exponential decomposition:\n", - "\n", - "\\begin{equation*}\n", - "C(t)=\\sum_{k=0}^{k=\\infty} c_k e^{-\\nu_k t}\n", - "\\end{equation*}\n", - "\n", - "As an example, the Matsubara decomposition of the Drude-Lorentz spectral\n", - "density is given by:\n", - "\n", - "\\begin{equation*}\n", - " \\nu_k = \\begin{cases}\n", - " \\gamma & k = 0\\\\\n", - " {2 \\pi k} / {\\beta } & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "\\begin{equation*}\n", - " c_k = \\begin{cases}\n", - " \\lambda \\gamma (\\cot(\\beta \\gamma / 2) - i) \\\n", - " & k = 0\\\\\n", - " 4 \\lambda \\gamma \\nu_k / \\{(\\nu_k^2 - \\gamma^2)\\beta \\} \\\n", - " & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." - ] - }, - { - "cell_type": "markdown", - "id": "e9e16cf9", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "46b6f2dd", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "import contextlib\n", - "import time\n", - "\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "from scipy.optimize import curve_fit\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " basis,\n", - " brmesolve,\n", - " destroy,\n", - " expect,\n", - " liouvillian,\n", - " qeye,\n", - " sigmax,\n", - " sigmaz,\n", - " spost,\n", - " spre,\n", - " tensor,\n", - ")\n", - "from qutip.core.environment import (\n", - " DrudeLorentzEnvironment,\n", - " ExponentialBosonicEnvironment,\n", - " system_terminator\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver,\n", - " HSolverDL,\n", - ")\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "c00385b8", - "metadata": {}, - "source": [ - "## Helper functions\n", - "\n", - "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "87a25d2e", - "metadata": {}, - "outputs": [], - "source": [ - "def cot(x):\n", - " \"\"\"Vectorized cotangent of x.\"\"\"\n", - " return 1.0 / np.tan(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "aad8604f", - "metadata": {}, - "outputs": [], - "source": [ - "def dl_matsubara_params(lam, gamma, T, nk):\n", - " \"\"\"Calculation of the real and imaginary expansions of the Drude-Lorenz\n", - " correlation functions.\n", - " \"\"\"\n", - " ckAR = [lam * gamma * cot(gamma / (2 * T))]\n", - " ckAR.extend(\n", - " (8 * lam * gamma * T * np.pi * k * T /\n", - " ((2 * np.pi * k * T)**2 - gamma**2))\n", - " for k in range(1, nk + 1)\n", - " )\n", - " vkAR = [gamma]\n", - " vkAR.extend(2 * np.pi * k * T for k in range(1, nk + 1))\n", - "\n", - " ckAI = [lam * gamma * (-1.0)]\n", - " vkAI = [gamma]\n", - "\n", - " return ckAR, vkAR, ckAI, vkAI" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "6512fe07", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_result_expectations(plots, axes=None):\n", - " \"\"\"Plot the expectation values of operators as functions of time.\n", - "\n", - " Each plot in plots consists of (solver_result, measurement_operation,\n", - " color, label).\n", - " \"\"\"\n", - " if axes is None:\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " fig_created = True\n", - " else:\n", - " fig = None\n", - " fig_created = False\n", - "\n", - " # add kw arguments to each plot if missing\n", - " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", - " for result, m_op, color, label, kw in plots:\n", - " exp = np.real(expect(result.states, m_op))\n", - " kw.setdefault(\"linewidth\", 2)\n", - " axes.plot(result.times, exp, color, label=label, **kw)\n", - "\n", - " if fig_created:\n", - " axes.legend(loc=0, fontsize=12)\n", - " axes.set_xlabel(\"t\", fontsize=28)\n", - "\n", - " return fig" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "78c51da3", - "metadata": {}, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\"Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a4dba89e", - "metadata": {}, - "outputs": [], - "source": [ - "# Default solver options:\n", - "\n", - "default_options = {\n", - " \"nsteps\": 1500,\n", - " \"store_states\": True,\n", - " \"rtol\": 1e-12,\n", - " \"atol\": 1e-12,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "996f8f86", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "And let us set up the system Hamiltonian, bath and system measurement operators:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "5e5ddd12", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the system Hamiltonian\n", - "eps = 0.5 # Energy of the 2-level system.\n", - "Del = 1.0 # Tunnelling term\n", - "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "2562dd97", - "metadata": {}, - "outputs": [], - "source": [ - "# Initial state of the system.\n", - "rho0 = basis(2, 0) * basis(2, 0).dag()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "14508538", - "metadata": {}, - "outputs": [], - "source": [ - "# System-bath coupling (Drude-Lorentz spectral density)\n", - "Q = sigmaz() # coupling operator\n", - "\n", - "# Bath properties:\n", - "gamma = 0.5 # cut off frequency\n", - "lam = 0.1 # coupling strength\n", - "T = 0.5\n", - "beta = 1.0 / T\n", - "\n", - "# HEOM parameters\n", - "NC = 5 # cut off parameter for the bath\n", - "Nk = 2 # terms in the Matsubara expansion of the correlation function\n", - "\n", - "# Times to solve for\n", - "tlist = np.linspace(0, 50, 1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "47573ae2", - "metadata": {}, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresonding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresonding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "081c3008", - "metadata": {}, - "source": [ - "### First of all, it is useful to look at the spectral density\n", - "\n", - "Now we are ready to begin. Let's look at the shape of the spectral density given the bath parameters we defined above:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "7ea119e6", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_spectral_density():\n", - " \"\"\"Plot the Drude-Lorentz spectral density\"\"\"\n", - " w = np.linspace(0, 5, 1000)\n", - " J = w * 2 * lam * gamma / (gamma**2 + w**2)\n", - "\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " axes.plot(w, J, \"r\", linewidth=2)\n", - " axes.set_xlabel(r\"$\\omega$\", fontsize=28)\n", - " axes.set_ylabel(r\"J\", fontsize=28)\n", - "\n", - "\n", - "plot_spectral_density()" - ] - }, - { - "cell_type": "markdown", - "id": "660e3fc7", - "metadata": {}, - "source": [ - "Next we calculate the exponents using the Matsubara decompositions. Here we\n", - "split them into real and imaginary parts.\n", - "\n", - "The HEOM code will optimize these, and reduce the number of exponents when\n", - "real and imaginary parts have the same exponent. This is clearly the case\n", - "for the first term in the vkAI and vkAR lists." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "640975c2", - "metadata": {}, - "outputs": [], - "source": [ - "ckAR, vkAR, ckAI, vkAI = dl_matsubara_params(nk=Nk, lam=lam, gamma=gamma, T=T)" - ] - }, - { - "cell_type": "markdown", - "id": "9305b68b", - "metadata": {}, - "source": [ - "Having created the lists which specify the bath correlation functions, we\n", - "create an `ExponentialBosonicEnvironment` from them and pass the environment to the `HEOMSolver` class.\n", - "\n", - "The solver constructs the \"right hand side\" (RHS) determinining how the\n", - "system and auxiliary density operators evolve in time. This can then be used\n", - "to solve for dynamics or steady-state.\n", - "\n", - "Below we create the bath and solver and then solve for the dynamics by\n", - "calling `.run(rho0, tlist)`." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "1132fb8e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.0045168399810791016\n", - " [ 0% ] Elapsed 0.00s / Remaining 00:00:00:00" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 2.07s*] Elapsed 2.07s / Remaining 00:00:00:00[*********99%***********] Elapsed 2.04s / Remaining 00:00:00:00\n", - "ODE solver time: 2.067253828048706\n" - ] - } - ], - "source": [ - "options = {**default_options}\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " env = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI)\n", - " HEOMMats = HEOMSolver(Hsys, (env,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMats = HEOMMats.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "7f220aa4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (resultMats, P11p, \"b\", \"P11 Mats\"),\n", - " (resultMats, P12p, \"r\", \"P12 Mats\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "a5e26830", - "metadata": {}, - "source": [ - "In practice, one would not perform this laborious expansion for the\n", - "Drude-Lorentz correlation function, because QuTiP already has a class,\n", - "`DrudeLorentzBath`, that can construct this bath for you. Nevertheless,\n", - "knowing how to perform this expansion will allow you to construct your own\n", - "baths for other spectral densities.\n", - "\n", - "Below we show how to use this built-in functionality:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "4b5a6c06", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.009504079818725586\n", - " [ 0% ] Elapsed 0.01s / Remaining 00:00:00:00" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 2.08s*] Elapsed 2.08s / Remaining 00:00:00:00[*********59%** ] Elapsed 1.22s / Remaining 00:00:00:00\n", - "ODE solver time: 2.0851950645446777\n" - ] - } - ], - "source": [ - "# Compare to built-in Drude-Lorentz bath:\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " dlenv = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T,Nk=100)\n", - " dlenv_approx=dlenv.approx_by_matsubara(Nk=Nk)\n", - " HEOM_dlbath = HEOMSolver(Hsys, (dlenv_approx,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " result_dlbath = HEOM_dlbath.run(rho0, tlist) # normal 115" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "fb58dfcb", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (result_dlbath, P11p, \"b\", \"P11 (DrudeLorentzBath)\"),\n", - " (result_dlbath, P12p, \"r\", \"P12 (DrudeLorentzBath)\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "dbacfc84", - "metadata": {}, - "source": [ - "The `DrudeLorentzEnvironment` class also allows us to easily obtain the power spectrum, correlation function, and spectral density. The approximated Environment is a `BosonicEnvironment` where the effective power spectrum, correlation function, and spectral density from the approximation is also accessible. In the following plots, the solid lines are the exact expressions, and the dashed lines are based on our approximation of the correlation function with a finite number of exponents." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "9e2e1c45", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "w = np.linspace(-10, 20, 1000)\n", - "w2 = np.linspace(0, 20, 1000)\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "axs[0, 0].plot(w, dlenv.power_spectrum(w))\n", - "axs[0, 0].plot(w, dlenv_approx.power_spectrum(w), '--')\n", - "axs[0, 0].set(xlabel=r'$\\omega$', ylabel=r'$S(\\omega)$')\n", - "axs[0, 1].plot(w2, dlenv.spectral_density(w2))\n", - "axs[0, 1].plot(w2, dlenv_approx.spectral_density(w2), '--')\n", - "axs[0, 1].set(xlabel=r'$\\omega$', ylabel=r'$J(\\omega)$')\n", - "axs[1, 0].plot(w2, np.real(dlenv.correlation_function(w2)))\n", - "axs[1, 0].plot(w2, np.real(dlenv_approx.correlation_function(w2)), '--')\n", - "axs[1, 0].set(xlabel=r'$t$', ylabel=r'$C_{R}(t)$')\n", - "axs[1, 1].plot(w2, np.imag(dlenv.correlation_function(w2)))\n", - "axs[1, 1].plot(w2, np.imag(dlenv_approx.correlation_function(w2)), '--')\n", - "axs[1, 1].set(xlabel=r'$t$', ylabel=r'$C_{I}(t)$')\n", - "\n", - "fig.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "b5967613", - "metadata": {}, - "source": [ - "We also provide a legacy class, `HSolverDL`, which calculates the\n", - "Drude-Lorentz correlation functions automatically, to be backwards\n", - "compatible with the previous HEOM solver in QuTiP:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "2677dce6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.002721548080444336\n", - " Total run time: 2.08s*] Elapsed 2.08s / Remaining 00:00:00:00\n", - "ODE solver time: 2.083860158920288\n" - ] - } - ], - "source": [ - "# Compare to legacy class:\n", - "\n", - "# The legacy class performs the above collation of coefficients automatically,\n", - "# based upon the parameters for the Drude-Lorentz spectral density.\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " HEOMlegacy = HSolverDL(Hsys, Q, lam, T, NC, Nk, gamma, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultLegacy = HEOMlegacy.run(rho0, tlist) # normal 115" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "e15f2f54", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (resultLegacy, P11p, \"b\", \"P11 Legacy\"),\n", - " (resultLegacy, P12p, \"r\", \"P12 Legacy\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "fa3f616d", - "metadata": {}, - "source": [ - "## Ishizaki-Tanimura Terminator\n", - "\n", - "To speed up convergence (in terms of the number of exponents kept in the\n", - "Matsubara decomposition), We can treat the $Re[C(t=0)]$ component as a\n", - "delta-function distribution, and include it as Lindblad correction. This is\n", - "sometimes known as the Ishizaki-Tanimura Terminator.\n", - "\n", - "In more detail, given\n", - "\n", - "\\begin{equation*}\n", - "C(t)=\\sum_{k=0}^{\\infty} c_k e^{-\\nu_k t}\n", - "\\end{equation*}\n", - "\n", - "since $\\nu_k=\\frac{2 \\pi k}{\\beta }$, if $1/\\nu_k$ is much much smaller than\n", - "other important time-scales, we can approximate,\n", - "$ e^{-\\nu_k t} \\approx \\delta(t)/\\nu_k$, and $C(t)=\\sum_{k=N_k}^{\\infty}\n", - "\\frac{c_k}{\\nu_k} \\delta(t)$\n", - "\n", - "It is convenient to calculate the whole sum\n", - "$ C(t)=\\sum_{k=0}^{\\infty} \\frac{c_k}{\\nu_k} = 2 \\lambda / (\\beta \\gamma)- i\\lambda $\n", - ", and subtract off the contribution from the finite number of Matsubara terms\n", - "that are kept in the hierarchy, and treat the residual as a contribution in \n", - "Lindblad form.\n", - "\n", - "This is clearer if we plot the correlation function with a large number of\n", - "Matsubara terms. To create the plot, we use the utility function of the\n", - "`DrudeLorentzBath` class mentioned above." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "62f328f9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_correlation_expansion_divergence():\n", - " \"\"\"We plot the correlation function with a large number of Matsubara terms\n", - " to show that the real part is slowly diverging at t = 0.\n", - " \"\"\"\n", - " t = np.linspace(0, 2, 100)\n", - "\n", - " # correlation coefficients with 15k and 2 terms\n", - " corr_15k = dlenv.correlation_function(t)\n", - " corr_2 = dlenv_approx.correlation_function(t)\n", - "\n", - " fig, ax1 = plt.subplots(figsize=(12, 7))\n", - "\n", - " ax1.plot(\n", - " t, np.real(corr_2), color=\"b\", linewidth=3, label=rf\"Mats = {Nk} real\"\n", - " )\n", - " ax1.plot(\n", - " t, np.imag(corr_2), color=\"r\", linewidth=3, label=rf\"Mats = {Nk} imag\"\n", - " )\n", - " ax1.plot(\n", - " t, np.real(corr_15k), \"b--\", linewidth=3, label=r\"Mats = 15000 real\"\n", - " )\n", - " ax1.plot(\n", - " t, np.imag(corr_15k), \"r--\", linewidth=3, label=r\"Mats = 15000 imag\"\n", - " )\n", - "\n", - " ax1.set_xlabel(\"t\")\n", - " ax1.set_ylabel(r\"$C$\")\n", - " ax1.legend()\n", - "\n", - "plot_correlation_expansion_divergence()" - ] - }, - { - "cell_type": "markdown", - "id": "b66a8145", - "metadata": {}, - "source": [ - "Let us evaluate the result including this Ishizaki-Tanimura terminator:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "019fcbd4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.0070781707763671875\n", - " [ 1% ] Elapsed 0.01s / Remaining 00:00:00:01" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 2.26s*] Elapsed 2.26s / Remaining 00:00:00:00\n", - "ODE solver time: 2.259308338165283\n" - ] - } - ], - "source": [ - "# Run HEOM solver and include the Ishizaki-Tanimura terminator\n", - "\n", - "# Notes:\n", - "#\n", - "# * when using the built-in DrudeLorentzEnvironment the terminator (L_bnd) is\n", - "# available from bath.terminator().\n", - "# \n", - "# * in the legacy HSolverDL function the terminator is included automatically\n", - "# if the parameter bnd_cut_approx=True is used.\n", - "\n", - "op = -2 * spre(Q) * spost(Q.dag()) + spre(Q.dag() * Q) + spost(Q.dag() * Q)\n", - "\n", - "approx_factr = (2 * lam / (beta * gamma)) - 1j * lam\n", - "\n", - "approx_factr -= lam * gamma * (-1.0j + cot(gamma / (2 * T))) / gamma\n", - "for k in range(1, Nk + 1):\n", - " vk = 2 * np.pi * k * T\n", - "\n", - " approx_factr -= (4 * lam * gamma * T * vk / (vk**2 - gamma**2)) / vk\n", - "\n", - "L_bnd = -approx_factr * op\n", - "\n", - "Ltot = -1.0j * (spre(Hsys) - spost(Hsys)) + L_bnd\n", - "Ltot = liouvillian(Hsys) + L_bnd\n", - "\n", - "options = {**default_options, \"rtol\": 1e-14, \"atol\": 1e-14}\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI)\n", - " HEOMMatsT = HEOMSolver(Ltot, (bath,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMatsT = HEOMMatsT.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "f6721af4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (resultMatsT, P11p, \"b\", \"P11 Mats + Term\"),\n", - " (resultMatsT, P12p, \"r\", \"P12 Mats + Term\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "123ca99d", - "metadata": {}, - "source": [ - "Or using the built-in Drude-Lorentz environment we can write simply:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "82e8f41a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.007383823394775391\n", - " Total run time: 2.10s*] Elapsed 2.10s / Remaining 00:00:00:00\n", - "ODE solver time: 2.0973634719848633\n" - ] - } - ], - "source": [ - "options = {**default_options, \"rtol\": 1e-14, \"atol\": 1e-14}\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath,delta = dlenv.approx_by_matsubara(Nk=Nk,compute_delta=True)\n", - " Ltot = liouvillian(Hsys) + system_terminator(Q,delta)\n", - " HEOM_dlbath_T = HEOMSolver(Ltot, (bath,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " result_dlbath_T = HEOM_dlbath_T.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "c4a87dc6", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (result_dlbath_T, P11p, \"b\", \"P11 Mats (DrudeLorentzBath + Term)\"),\n", - " (result_dlbath_T, P12p, \"r\", \"P12 Mats (DrudeLorentzBath + Term)\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "6a7ef2f8", - "metadata": {}, - "source": [ - "We can compare the solution obtained from the QuTiP Bloch-Redfield solver:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "8d002cce", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 1.75s*] Elapsed 1.75s / Remaining 00:00:00:00\n", - "ODE solver time: 1.7543323040008545\n" - ] - } - ], - "source": [ - "\n", - "options = {**default_options}\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultBR = brmesolve(\n", - " Hsys, rho0, tlist, a_ops=[[sigmaz(), dlenv]], options=options\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "96fe6f5f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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xke3bt+Pm5lau+qNGjSI8PJyjR48SFRVFcHAwnTt35sCBA0XK5ufnM2fOHFasWEF8fDx5eXm6106ePFnqvby9vQkODmbu3Lmo1Wq6du1KixYtytz9QViWJKsmUsvDIFn1VJLV69ctFIwQQojKCQiwdATGKhHP8uXLCQ0Nxc7ODn9/fwIDAysdTk5ODoMGDWLXrl1s2LCBdu3alfsanTt3JiQkhCVLlrBmzRrGjx+PSqUqtuzEiRNZtGgRb7/9NuHh4VSrVg0bGxvGjBlDVlZWqfdSqVRs27aNGTNmMGfOHF5//XW8vb0ZNmwYH374Ie7u7uWOX1QdSVZNxKhltTBZzcy0UDBCCCEqp4KP3K1RaGiobjYAU8jJyWHgwIHs2LGDn3/+2WhEf3lFREQwZcoUVCoVI0aMKLHcihUrGD58OLNmzTI6n5ycjFcZp92pU6cOS5cuBeD06dOsWbOGadOmkZuby5dfflnh9yDMT9q/TcTF3gVvZ2UqABsvJVnVaOCffywZlRBCCGE62hbV7du38+OPP9KrV69KXW/EiBH079+fN998kxo1apRYTqVS4ejoaHRu48aNRRY30JYprbW1YcOGTJkyhebNm3Pw4MEKRi+qirSsmlAtj1rcyLpBgdslUKlBY8vevRAcbOnIhBBCiJLFxsZyvbDvmlqtJjExkbVr1wLKXKramQMGDx7M5s2bmTx5Mj4+PuzZs0d3DQ8PD5o0aVKu+wYFBbF+/fpSy/Xr14+oqCgaN25MWFgYBw4cYO7cudSsWdOoXHBwMM7OzqxcuZLQ0FDc3NwICgoiOTmZV155hSFDhhASEoKDgwPbt2/nr7/+YtKkSeWKWVQ9SVZNqJZnLY5cOwI2+eB2FTJqYDAFnBBCCGGVpk6dSqzBfIsxMTHExMQAsGPHDrp06QLAhg0bAPjwww/58MMPja4RHh6uq2NqCxcuxN7entmzZ3Pr1i1atWrFunXriixC4OLiQmRkJNOnT6dnz57k5eUxdepUxo0bR3BwMF988QUXLlzQLfk6b948Xn31VbPELExHpSlucd57WHp6Op6enqSlpVVqybaKGLdxHIv3L1YOvo6Di+158kn48ccqDUMIIUQZZWdnk5CQQL169XBycrJ0OELcN0r73SpPviZ9Vk2otmdt/YGHvt+qEEIIIYSoGOkGYEKd63TmrY5vYZ8TwIefhwFgJ99hIYQQQogKk1TKhDrW6kjHWh3JyYFZN0ADnD9v6aiEEEIIIe5d0g3ADBwd9fM3S7IqhBBCCFFxkqyaSe3C7qtXrkAZFtcQQgghhBDFkGTVxNQFaq5kXCFZdUJ3bvduCwYkhBBCCHEPk2TVxBr9pxFB84M491gn3bnDhy0XjxBCCCHEvUySVROr7lodALXDDbDNAWTJVSGEEEKIipJk1cQC3QP1B27XAEhMtFAwQgghhBD3OElWTSzQzTBZvQIog6yEEEIIIUT5SbJqYgFuAfoDt6sAXL9uoWCEEEIIIe5xkqyamFHLqrvSpJqebqFghBBCPLCioqJQqVS6zc7Ojpo1axIREcGlS5eMyk6ZMoV+/fpRo0YNVCoVI0eOLPaax48fZ9y4cXTo0AFXV1dUKhUxMTFljqlLly6oVCrq16+Pppj1yHfu3KmLNyoqqhzvVnH58mWmTZvGYSsc2Wz4s7jbVp7v54NCklUTM+qzWpisZmZaKBghhBAPvGXLlhEXF0d0dDQvvPACq1atolOnTty+fVtX5tNPPyUlJYUBAwbg4OBQ4rX279/P+vXr8fb2plu3bhWKx93dnYSEBLZv317ktcjISDw8PCp0XVCS1enTp1tlshoXF2e09enTB2dn5yLnW7VqZelQrY4st2pihi2rNh5XKQDy86GgAGzko4EQQogq1qxZM9q0aQNA165dUavVzJw5k/Xr1zNs2DAAMjIysCn8I/Xtt9+WeK3nn3+eESNGALB27Vp+/fXXcsdTu3Zt3N3diYyMNEp4MzIy+OGHHxg2bBhfffVVua9blerWrcvIkSOZNm1ameu0b9/e6NjPzw8bG5si5ysqKysLZ2dnk1zL2kj6ZGKGfVZtPfUjq2RGACGEENZAmxwlGvxhsilja0pZy5Vm1KhRrFu3jtTUVN251atXAzB06NAi5ePj44mIiCAkJAQXFxdq1KhB//79OXr0qK5MTEwMbdu2BSAiIkL3WF2bUJ49e5ahQ4cSFBSEo6Mj/v7+dOvWzapaYXNzc/nggw9o3Lgxjo6O+Pn5ERERwfU7Br/UrVuXfv36sW7dOh566CGcnJyYPn06MTExqFQqvvvuO95++20CAwNxc3Ojf//+XLt2jYyMDP7973/j6+uLr68vERER3Lp1y0LvtuykZdXEqrtWx0ZlQ4GmACffq+QVnpd+q0IIIaxBfHw8oLTsWcrQoUOZMGECq1atYuzYsQAsXbqUwYMHF9sN4PLly/j4+PDRRx/h5+fHjRs3+Oabb2jXrh2HDh2iUaNGtGrVimXLlhEREcGUKVPo27cvADVr1gSgT58+qNVq5syZQ+3atUlOTmb37t1GCbMlFRQU8MQTT/D777/z1ltv0bFjRxITE5k6dSpdunRh//79Ri2nBw8e5OTJk0yZMoV69erh6uqq69rx7rvv0rVrV6Kiojh37hxvvPEGzzzzDHZ2drRo0YJVq1Zx6NAh3n33Xdzd3fnss88s9bbLRJJVE7O1sWX3qN34ufqx6OMA5heeT062aFhCCCHKaf58ZStNq1bwyy/G5wYMgIMHS687caKyaWVkQGjo3cuUl1qtJj8/n+zsbGJjY/nggw9wd3dnwIABFb9oJbm7uzN48GAiIyMZO3YsJ06cYO/evXz88cfFlu/cuTOdO3fWHavVavr27UvTpk1ZsmQJ8+fPx8PDg2bNmgEQHBxs9Hg9JSWFU6dOsWDBAp577jnd+SeffLLUWDUaDWq1usj5goIC8vPzjc7Z2VU8rVqzZg1btmzhxx9/NIqrRYsWtG3blqioKF1iD5CUlMSJEydo2LCh7px2cFZYWBjLli3Tnf/7779ZsGABr732GnPnzgWgR48exMXFsXLlSklWH0TtarYDoE6Q/tzlyxYKRgghRIWkp8Mdg+aLVatW0XPXr5et7p1P3TSaovUq+2Tuzj6RzZs3Z/Hixfj7+1fuwpU0atQowsPDOXr0KFFRUQQHB9O5c2cOHDhQpGx+fj5z5sxhxYoVxMfHk5eXp3vt5MmTpd7L29ub4OBg5s6di1qtpmvXrrRo0aJM3Rq++eYbIiIiipyfOXMmM2fONDpX3AwHZbVhwwa8vLzo37+/URLcsmVLAgICiImJMUpWw8LCjBJVQ/369TM6Di38BKRtbTY8v379em7duoWbm1uFYzc3SVbNyPD/gWvXLBeHEEKI8vPwgBo1Si9X3NN0P7+y1b3zibdKVbReJQbHA7B8+XJCQ0Oxs7PD39+fwMDA0itVgc6dOxMSEsKSJUtYs2YN48ePR6VSFVt24sSJLFq0iLfffpvw8HCqVauGjY0NY8aMISsrq9R7qVQqtm3bxowZM5gzZw6vv/463t7eDBs2jA8//BB3d/cS6/bv358///zT6NyAAQPo168f//73v8v3pu/i2rVrpKamljgbQ/Idj2jv9nP09vY2OtZes6Tz2dnZkqw+qKpV0+8fOWK5OIQQQpRfZR6/39ktoKzc3eHixYrVLUloaKhuNgBro+1fqlKpdLMMFGfFihUMHz6cWbNmGZ1PTk7Gy8urTPeqU6cOS5cuBeD06dOsWbOGadOmkZuby5dfflliPR8fH3x8fIzOOTg4EBQUZNLvq6+vLz4+PmzZsqXY1+9MqEtK7O9HkqyawdFrR/njwh/sSkwCn6GQ0pA//rB0VEIIIYR1GTFiBHv37iU0NJQad2mKVqlUODo6Gp3buHEjly5dokGDBrpz2jKltbY2bNiQKVOm8OOPP3KwLJ2Lq0C/fv1YvXo1arWadu3aWTocqyLJqhn89s9vvBH9hnIQ0BhSGspsAEIIIaxWbGysbnoktVpNYmIia9euBSA8PFw3c0BmZiabNm0CYM+ePbq6ycnJuLq60rt373LdNygoiPXr15darl+/fkRFRdG4cWPCwsI4cOAAc+fO1Y301woODsbZ2ZmVK1cSGhqKm5sbQUFBJCcn88orrzBkyBBCQkJwcHBg+/bt/PXXX0yaNKlcMZvL0KFDWblyJX369OH//u//ePjhh7G3t+fixYvs2LGDJ554gkGDBlk6TIuQZNUM/FwNOjC5JgFgsFCIEEIIYVWmTp1KbGys7jgmJkY3snzHjh106dIFUEagDxkyxKiudh7TOnXqcO7cObPEt3DhQuzt7Zk9eza3bt2iVatWrFu3jilTphiVc3FxITIykunTp9OzZ0/y8vKYOnUq48aNIzg4mC+++IILFy7olnydN28er776qlliLi9bW1t++eUXFi5cyLfffsvs2bN1S+SGh4fTvHlzS4doMSpNZYauWaH09HQ8PT1JS0ur1JJtlbElfgu9VxZ+utz5HmyfgY0NFDPzhRBCCAvKzs4mISGBevXq4eTkZOlwhLhvlPa7VZ58TVawMgM/F33Lqq278liloEBZdlUIIYQQQpSdJKtmUN21um7f1iNJt5+QYIlohBBCCCHuXZKsmoFhn1UbN/16vmWYt1gIIYQQQhiQZNUMnOyccHconA/NVd+yevashQISQgghhLhHSbJqJtrWVbWTvmVVugEIIYQQQpSPJKtmou23mmd3A2yUNYxTUiwZkRBCCCHEvUfmWTWTRj6NuJV7C1VmdY7aZ0KOJ/XrWzoqIYQQQoh7iySrZhI1MAqAgwehdY5yLimp5PJCCCGEEKIo6QZgZtX1s1hx7Zrl4hBCCCGEuBdJsmpmfgYrr0rLqhBCCCFE+UiyamaOjqBdZezoUcvGIoQQ4sERFRWFSqXSbdp15iMiIrh06ZJR2SlTptCvXz9q1KiBSqVi5MiRxV7z66+/ZuDAgdStWxdnZ2caNGjA2LFjuXLlSpli6tKlCyqVivr161Pcau87d+7UxRsVFVXet8zly5eZNm0ahw8fLnddczP8Wdxti4mJsXSoVkeSVTM5lnSMx1c8Tuv/tiav7ScAZGRAMb+bQgghhNksW7aMuLg4oqOjeeGFF1i1ahWdOnXi9u3bujKffvopKSkpDBgwAAcHhxKvNXXqVNzc3Jg1axZbtmzhrbfeYsOGDbRu3ZprZezr5u7uTkJCAtu3by/yWmRkZKnrxN/N5cuXmT59ulUmq3FxcUZbnz59cHZ2LnK+VatWlg7V6sgAKzPJL8hn6z9bAbCv3hp14fmkJPD3t1xcQgghHizNmjWjTZs2AHTt2hW1Ws3MmTNZv349w4YNAyAjIwMbG6X96ttvvy3xWocOHaK6wWCM8PBwWrVqRdu2bfnqq6+YMmVKqfHUrl0bd3d3IiMj6datm+58RkYGP/zwA8OGDeOrr76q0HutKnXr1mXkyJFMmzatzHXat29vdOzn54eNjU2R8xWVlZWFs7OzSa5lbaRl1Uz8XPSdVW3d9QsDnDljiWiEEEIIhTY5SkxM1J3TJqqlMUxUtVq3bo2trS0XLlwocwyjRo1i3bp1pKam6s6tXr0agKFDhxYpHx8fT0REBCEhIbi4uFCjRg369+/PUYP+dTExMbRt2xaAiIgI3WN1bUJ59uxZhg4dSlBQEI6Ojvj7+9OtWzeraoXNzc3lgw8+oHHjxjg6OuLn50dERATXr183Kle3bl369evHunXreOihh3BycmL69OnExMSgUqn47rvvePvttwkMDMTNzY3+/ftz7do1MjIy+Pe//42vry++vr5ERERw69YtC73bspOWVTPxdfHV7avc9COrEhLg0UctEZEQQgihJH6gtOyZQmxsLGq1mqZNm5a5ztChQ5kwYQKrVq1i7NixACxdupTBgwcX2w3g8uXL+Pj48NFHH+Hn58eNGzf45ptvaNeuHYcOHaJRo0a0atWKZcuWERERwZQpU+jbty8ANWvWBKBPnz6o1WrmzJlD7dq1SU5OZvfu3UYJsyUVFBTwxBNP8Pvvv/PWW2/RsWNHEhMTmTp1Kl26dGH//v1GLacHDx7k5MmTTJkyhXr16uHq6qrr2vHuu+/StWtXoqKiOHfuHG+88QbPPPMMdnZ2tGjRglWrVnHo0CHeffdd3N3d+eyzzyz1tstEklUzcbRzxMPRg/ScdHBO1p0/f96CQQkhhCi7+fOVrTStWsEvvxifGzBAmWi7NBMnKptWRgaEht69TDmp1Wry8/PJzs4mNjaWDz74AHd3dwYMGFDha2plZGQwbtw4atWqxahRo8pcz93dncGDBxMZGcnYsWM5ceIEe/fu5eOPPy62fOfOnencubPRe+rbty9NmzZlyZIlzJ8/Hw8PD5o1awZAcHCw0eP1lJQUTp06xYIFC3juued055988slSY9VoNKjV6iLnCwoKyM/PNzpnZ1fxtGrNmjVs2bKFH3/80SiuFi1a0LZtW6KionSJPUBSUhInTpygYcOGunPawVlhYWEsW7ZMd/7vv/9mwYIFvPbaa8ydOxeAHj16EBcXx8qVKyVZfZD5uviSnpNOvqM+Wb1jAKYQQghrlZ5etv+0a9Uqeu769bLVTU83PtZoita7s0w53dknsnnz5ixevBj/Sg6gyM7O5sknnyQxMZHt27fj5uZWrvqjRo0iPDyco0ePEhUVRXBwMJ07d+bAgQNFyubn5zNnzhxWrFhBfHw8eXl5utdOnjxZ6r28vb0JDg5m7ty5qNVqunbtSosWLcrU/eGbb74hIiKiyPmZM2cyc+ZMo3PFzXBQVhs2bMDLy4v+/fsbJcEtW7YkICCAmJgYo2Q1LCzMKFE11K9fP6Pj0MIPQNrWZsPz69ev59atW+X++VUlSVbNyMfZh7M3z5JnexNUatDYcvWqpaMSQghRJh4eUKNG6eWKe5zu51e2unc+8lapitarxOh4gOXLlxMaGoqdnR3+/v4EBgZW6noAOTk5DBo0iF27drFhwwbatWtX7mt07tyZkJAQlixZwpo1axg/fjwqlarYshMnTmTRokW8/fbbhIeHU61aNWxsbBgzZgxZWVml3kulUrFt2zZmzJjBnDlzeP311/H29mbYsGF8+OGHuLu7l1i3f//+/Pnnn0bnBgwYQL9+/fj3v/9dvjd9F9euXSM1NbXE2RiSk5ONju/2c/T29jY61l6zpPPZ2dmSrD6odP1WVRpwvgmZvtzRR1oIIYS1qszj9zu7BZSVuztcvFixuiUIDQ3VzQZgCjk5OQwcOJAdO3bw888/G43oLy9t/1KVSsWIESNKLLdixQqGDx/OrFmzjM4nJyfj5eVVpnvVqVOHpUuXAnD69GnWrFnDtGnTyM3N5csvvyyxno+PDz4+PkbnHBwcCAoKMun31dfXFx8fH7Zs2VLs63cm1CUl9vcjSVbNyHCQFc4pkOnLjRuWi0cIIYSoDG2L6vbt21m3bh29evWq1PVGjBjB3r17CQ0NpcZdWqJVKhWOjo5G5zZu3MilS5do0KCB7py2TGmtrQ0bNmTKlCn8+OOPHCxL3+Iq0K9fP1avXo1ara5QS/X9TJJVM/JxNvgk5pIMKY1wdbVcPEIIIURxYmNjddMjqdVqEhMTWbt2LaDMpaqdOWDw4MFs3ryZyZMn4+Pjw549e3TX8PDwoEmTJuW6b1BQEOvXry+1XL9+/YiKiqJx48aEhYVx4MAB5s6dqxvprxUcHIyzszMrV64kNDQUNzc3goKCSE5O5pVXXmHIkCGEhITg4ODA9u3b+euvv5g0aVK5YjaXoUOHsnLlSvr06cP//d//8fDDD2Nvb8/FixfZsWMHTzzxBIMGDbJ0mBYhyaoZ9QzuiZuDG262vkyaXweodNcjIYQQwuSmTp1KbGys7jgmJkY3snzHjh106dIFUAYBAXz44Yd8+OGHRtcIDw8321KhCxcuxN7entmzZ3Pr1i1atWrFunXriixC4OLiQmRkJNOnT6dnz57k5eUxdepUxo0bR3BwMF988QUXLlzQLfk6b948Xn31VbPEXF62trb88ssvLFy4kG+//ZbZs2frlsgNDw+nefPmlg7RYlSaygxds0Lp6el4enqSlpZWqSXbTEmjAScnyM2FFi3AiuYfFkKIB1p2djYJCQnUq1cPJycnS4cjxH2jtN+t8uRrsoJVFVCpwLew++odg/mEEEIIIcRdSLJaRbTJ6vXrUFBg2ViEEEIIIe4VkqyaWVZeFhfSLnA1U1kzOTcXrlyxcFBCCCGEEPcIGWBlRpczLlNjvjIVh/OjAyH+JwDi48s2V7QQQgghxINOWlbNyHDqKpWbvrPquXMWCEYIIYQQ4h4kyaoZOdo54mpfOLGqc4rufGKihQISQgghhLjHSLJqZtpVrNSO+pbVy5ctFY0QQgghxL1FklUz83FRugLk2t4AlTINwNWrloxICCGEEOLeYfZk9YsvvtBNCNu6dWt+//33u5ZfuXIlLVq0wMXFhcDAQCIiIkhJSblrHWumbVnVqNTgmAYo01cJIYQQQojSmTVZ/f777xk/fjyTJ0/m0KFDdOrUid69e3P+/Pliy+/atYvhw4czevRojh8/zg8//MCff/7JmDFjzBmmWRkOssJFSbpv3rRQMEIIIYQQ9xizJqvz589n9OjRjBkzhtDQUBYsWECtWrVYvHhxseX37NlD3bp1ee2116hXrx6PPvooL774Ivv37zdnmGalbVkFwEXpt5qebqFghBBCPDCioqJQqVS6TbvOfEREBJcuXTIqO2XKFPr160eNGjVQqVSMHDmy2GseP36ccePG0aFDB1xdXVGpVMTExJQ5pi5duhjFZG9vT926dRk9ejSJd4w+1sZ/zkxT6EybNg2VSkVyBZeWHDlypNF7cXBwIDg4mDfeeIN0E/+hHzlyJHXr1jU6d+7cOfr27Yu3tzcqlYrx48dz7tw5VCoVUVFR5b5Heepqv3dVxWzJam5uLgcOHKBnz55G53v27Mnu3buLrdOxY0cuXrzIpk2b0Gg0XLt2jbVr19K3b98S75OTk0N6errRZk2MWlYLZwS4dctCwQghhHjgLFu2jLi4OKKjo3nhhRdYtWoVnTp14vbt27oyn376KSkpKQwYMAAHB4cSr7V//37Wr1+Pt7c33bp1q1A89evXJy4ujri4OLZt28Zbb73Fhg0b6NSpE5mZmRW6pqU4Ozvr3ssvv/xC165dmTdvHoMHDzb7vSdMmMDevXuJjIwkLi6OCRMmEBgYSFxc3F3zpnuR2RYFSE5ORq1W4+/vb3Te39+fqyWMMOrYsSMrV67k6aefJjs7m/z8fAYMGMDnn39e4n1mz57N9OnTTRq7KWkHWAHYuCdTAPj5WS4eIYQQD5ZmzZrRpk0bALp27YparWbmzJmsX7+eYcOGAZCRkYGNjdJ+9e2335Z4reeff54RI0YAsHbtWn799ddyx+Ps7Ez79u11x507d8bJyYnRo0eza9euIo1c1szGxsbovTz++OOcPXuW6OhoEhISqFevntnufezYMR5++GEGDhxodN4wnvuF2QdY3dlMrNFoSmw6PnHiBK+99hrvv/8+Bw4cYMuWLSQkJPDSSy+VeP133nmHtLQ03XbhwgWTxl9Zg5sMZv8L+0n4vwT8rz8NQHa2hYMSQgjxwNImM4aP3bWJamnKWq68PD09AbC3ty+1bGRkJC1atMDJyQlvb28GDRrEyZMni5Tbu3cv/fv3x8fHBycnJ4KDgxk/fnyRcteuXeOZZ57B09MTf39/Ro0aRVpaWoXfi/aDwbVr14zOf//997ruE25ubvTq1YtDhw4VqR8VFUWjRo1wdHQkNDSU5cuXG70eExODSqUiPj6ezZs367ohnDt3rsRH+WfOnOHZZ5+levXquusuWrSoTO9n48aNtGzZEkdHR+rVq8cnn3xSju+GaZitZdXX1xdbW9sirahJSUlFWlu1Zs+ezSOPPMKbb74JQFhYGK6urnTq1IkPPviAwMDAInUcHR1xdHQ0/RswkQC3AALcAgDw8YQrF+AentxACCHEPS4+Ph4APws+5svPzweULoPHjh1jxowZ1K9fn44dO9613uzZs3n33Xd55plnmD17NikpKUybNo0OHTrw559/EhISAsDWrVvp378/oaGhzJ8/n9q1a3Pu3Dl+++23Itd86qmnePrppxk9ejRHjx7lnXfeAZSkuCISEhKws7Ojfv36unOzZs1iypQpREREMGXKFHJzc5k7dy6dOnVi3759NGnSBFAS1YiICJ544gnmzZtHWloa06ZNIycnR/dBoVWrVsTFxTFo0CCCg4N1yWNgYCBXrlwpEs+JEyfo2LEjtWvXZt68eQQEBLB161Zee+01kpOTmTp1aonvZdu2bTzxxBN06NCB1atXo1armTNnTpFE3NzMlqw6ODjQunVroqOjGTRokO58dHQ0TzzxRLF1MjMzsbMzDsnW1hZQWmTvdd7eytesLGVzdrZsPEIIIUo2P24+8+Pml1quVWArfnnmF6NzA1YN4OCVg6XWndhhIhM7TNQdZ+RkELoo9K5lykutVpOfn092djaxsbF88MEHuLu7M2DAgApfszKOHz9epAW1YcOGbNy48a6NT6mpqcycOZM+ffrw3Xff6c536dKFkJAQpk2bxsqVKwF4+eWXqV27Nnv37sXJyUlXNiIiosh1R48erWsk6969O/Hx8URGRrJ06dIyDSLSJt5paWn88MMPrFu3jkmTJlG9enUALly4wNSpU3nllVf47LPPdPV69OhBSEgI06dP5/vvv6egoIDJkyfTqlUrfvrpJ929H330UUJCQggKCgLAw8OD9u3b4+joiJeXV6mP/SdOnIi7uzu7du3Cw8NDd++cnBw++ugjXnvtNapVq1Zs3cmTJ+Pv7090dLTu+9irV68ig73MzWzJKijfoOeff542bdrQoUMH/vvf/3L+/HndY/133nmHS5cu6Zq4+/fvzwsvvMDixYvp1asXV65cYfz48Tz88MO6H9K9zMVFv5+YCI0bWy4WIYQQd5eek86ljEullqvlWavIueuZ18tUNz3HeFCwBk2ReneWKa87k5nmzZuzePHiEp9ymltwcDCrV68GlEQvISGBjz/+mG7duhETE6NrHb1TXFwcWVlZRWYqqFWrFo899hjbtm0D4PTp0/zzzz/MmjXLKFEtyZ1Je1hYGNnZ2Xd9Eqx1+/btIon3M888w4cffqg73rp1K/n5+QwfPlyX2AI4OTkRHh7Ojh07ADh16hSXL19m4sSJRklynTp16NixY4VmRcjOzmbbtm2MHTsWFxcXo/v36dOH//znP+zZs4fevXsX+97+/PNPxo0bZ/R9dHd3p3///nzzzTfljqeizJqsPv3006SkpDBjxgyuXLlCs2bN2LRpE3Xq1AHgypUrRnOujhw5koyMDP7zn//w+uuv4+XlxWOPPcbHH39szjDNSqPRsPrYalKyUvjLxg0YCcDBg5KsCiGENfNw9KCGe41Sy/m5FH2c7ufiV6a6Ho4eRscqVEXq3VmmvJYvX05oaCh2dnb4+/sX26WuKjk5Oen6dYKSTHfp0oUaNWrw/vvvs2rVqmLraRcIKi7+oKAgoqOjAbheuPJOzZo1yxSPj4+P0bG2dTcrK6vUus7OzuzcuROAq1evMm/ePFatWkVYWBiTJk0C9H1X27ZtW+w1tI/3te8vICCgSJmAgIAKJaspKSnk5+fz+eeflzhYvaSpu27evElBQUGJ8VQlsyarAOPGjWPcuHHFvlbcXF6vvvoqr776qpmjqjoqlYrRv4wmKz8Ll9BmsGkkACWsiyCEEMJKVObx+53dAsrK3dGdixMvVqhuSUJDQ42SQ2sUGBiIr68vR44cKbGMNqksrl/m5cuX8fVV5jXX9sW9eNG038fi2NjYGH1ve/ToQevWrZk+fTrDhg2jVq1aurjWrl2ra6wrjvb9FTdjUkmzKJWmWrVq2Nra8vzzz/Pyyy8XW6akGQuqVauGSqUyaTwVZfbZAIR++iq1k/7TSzG/a0IIIcQD6eLFiyQnJ+v6eRanQ4cOODs7s2LFiiJ1t2/frpv3tWHDhgQHBxMZGUlOTo5Z476To6MjixYtIjs7mw8++ABQ+nja2dnxzz//0KZNm2I3gEaNGhEYGMiqVauMxukkJiaWOD99aVxcXOjatSuHDh0iLCys2Hvf2bKs5erqysMPP8y6devINpjGKCMjo0JTllWG2VtWhbKK1cX0i+TZpQAaQEVSkqWjEkIIIRSxsbG6x+dqtZrExETWrl0LQHh4uK61MjMzk02bNgHKqpPausnJybi6uhbb9/FOWVlZurpqtZqEhATmzJkDUOzUUlpeXl689957vPvuuwwfPpxnnnmGlJQUpk+fjpOTk9Go9kWLFtG/f3/at2/PhAkTqF27NufPn2fr1q26QVjmEh4eTp8+fVi2bBmTJk2iXr16zJgxg8mTJ3P27Fkef/xxqlWrxrVr19i3bx+urq5Mnz4dGxsbZs6cyZgxYxg0aBAvvPACqampTJs2rVKP3RcuXMijjz5Kp06dGDt2LHXr1iUjI4P4+Hh+/fVXtm/fXmLdmTNn8vjjj9OjRw9ef/111Go1H3/8Ma6urty4caPCMZWXJKtVQLuKVYEqDxwzIMeDwv8ThBBCCIubOnUqsbGxuuOYmBjdMqo7duygS5cugDL95JAhQ4zqTps2DVAGApWlX+XZs2fp0KEDoDxGDwgIoEWLFnz++eeEh4ffte4777xD9erV+eyzz/j+++9xdnamS5cuzJo1y2hgVq9evdi5cyczZszgtddeIzs7m5o1a1bZDAgff/wxW7ZsYebMmURGRvLOO+/QpEkTFi5cyKpVq8jJySEgIIC2bdsazSU/evRoXf0nn3ySunXr8u677xIbG1uuZW0NNWnShIMHDzJz5kymTJlCUlISXl5ehISE0KdPn7vW7dGjB+vXr2fKlCk8/fTTBAQEMG7cOLKysqp0QSaV5n6YE8pAeno6np6epKWl6aZosLSn1z7NmuNrlIMFZyG1Hq1awYEDlo1LCCEedNnZ2bqVhsoyclwIUTal/W6VJ1+TPqtVQNuyCoCz0myeXrmZSIQQQgghHgiSrFYBo2TVRZmaIiPDQsEIIYQQQtxDJFmtAt7O3voDZyVZLcP0bUIIIYQQDzxJVquAduoqQNeymptroWCEEEIIIe4hMhtAFQhwC6CmR018nH046+BBBnCXqeSEEEIIIUQhSVarQM/gnlyYcAGAJp/BSaBwVTUhhBBCCHEX0g2gimkXirh9G6p4YQ0hhBBCiHuOJKtVzNtgrNXNm5aLQwghhBDiXiDJahUzWF6XQ4csF4cQQgghxL1AktUq8sIvL9B9eXf2BeuXejtxwoIBCSGEEELcA2SAVRWJSYwh/kY8dj5eunNXrlguHiGEEEKIe4G0rFYR7SpW+XapYJMPwNWrFgxICCHEfS0qKgqVSqXb7OzsqFmzJhEREVy6dMmo7JQpU+jXrx81atRApVIxcuTIYq/59ddfM3DgQOrWrYuzszMNGjRg7NixXClj60uXLl2MYrK3t6du3bqMHj2axMTEYuM/d+5cRd5+qaZNm4ZKpSI5OblC9UeOHGn0XhwcHAgODuaNN94g3cRrqo8cOZK6desanTt37hx9+/bF29sblUrF+PHjOXfuHCqViqioqHLfozx1td+7qiItq1XEaGEAp5uQ6UcFfz+EEEKIMlu2bBmNGzcmKyuLnTt3Mnv2bGJjYzl69Ciurq4AfPrpp4SFhTFgwAAiIyNLvNbUqVPp2rUrs2bNokaNGpw6dYqZM2fy888/c+jQIfz9/UuNp379+qxcuRKA3Nxcjh07xvTp04mOjubvv//GxcXFNG+8Cjg7O7N9+3YAUlNTWbt2LfPmzeOvv/7it99+M+u9J0yYwN69e4mMjCQgIIDAwEACAgKIi4sjODjYrPeuapKsVhHjJVdvQKYfN25YLh4hhBAPhmbNmtGmTRsAunbtilqtZubMmaxfv55hw4YBkJGRgY2N8rD122+/LfFahw4dorrBqjbh4eG0atWKtm3b8tVXXzFlypRS43F2dqZ9+/a6486dO+Pk5MTo0aPZtWsXPXv2rND7tAQbGxuj9/L4449z9uxZoqOjSUhIoF69ema797Fjx3j44YcZOHCg0XnDeO4X0g2gimi7AQC6JVfT0iwUjBBCiAeWNpkxfOyuTVRLU72Y5Rdbt26Nra0tFy5cqHBMnp6eANjb25daNjIykhYtWuDk5IS3tzeDBg3i5MmTRcrt3buX/v374+Pjg5OTE8HBwYwfP75IuWvXrvHMM8/g6emJv78/o0aNIq0Sf6C1HwyuXbtmdP7777+nQ4cOuLq64ubmRq9evThUzLRAUVFRNGrUCEdHR0JDQ1m+fLnR6zExMahUKuLj49m8ebOuG8K5c+dKfJR/5swZnn32WapXr6677qJFi8r0fjZu3EjLli1xdHSkXr16fPLJJ+X4bpiGJKtVxChZdVaS1YwMCwUjhBDigRUfHw+An5+fSa4XGxuLWq2madOmZa6Tn59Pfn4+mZmZ7Nu3jxkzZlC/fn06dux413qzZ89m9OjRNG3alHXr1rFw4UL++usvOnTowJkzZ3Tltm7dSqdOnTh//jzz589n8+bNTJkypUgCCfDUU0/RsGFDfvzxRyZNmsR3333HhAkTyv4NuENCQgJ2dnbUr19fd27WrFk888wzNGnShDVr1vDtt9+SkZFBp06dOGEwNVBUVBQRERGEhoby448/MmXKFGbOnKnragDQqlUr4uLiCAgI4JFHHiEuLo64uDgCAwOLjefEiRO0bduWY8eOMW/ePDZs2EDfvn157bXXmD59+l3fy7Zt23jiiSdwd3dn9erVzJ07lzVr1rBs2bIKf38qQroBVBGjPquFLauZmRYKRgghRKnmx81nftz8Usu1CmzFL8/8YnRuwKoBHLxysNS6EztMZGKHibrjjJwMQheF3rVMeanVavLz88nOziY2NpYPPvgAd3d3BgwYUHrlUmRkZDBu3Dhq1arFqFGjylTn+PHjRVpQGzZsyMaNG3F0dCyxXmpqKjNnzqRPnz589913uvNdunQhJCSEadOm6frCvvzyy9SuXZu9e/fi5OSkKxsREVHkuqNHj+bNN98EoHv37sTHxxMZGcnSpUvLNIgoP18ZNJ2WlsYPP/zAunXrmDRpkq4V+sKFC0ydOpVXXnmFzz77TFevR48ehISEMH36dL7//nsKCgqYPHkyrVq14qefftLd+9FHHyUkJISgoCAAPDw8aN++PY6Ojnh5eZX62H/ixIm4u7uza9cuPDw8dPfOycnho48+4rXXXqNatWrF1p08eTL+/v5ER0frvo+9evUqMtjL3CRZrSLGfVaVZNVwgQAhhBDWJT0nnUsZl0otV8uzVpFz1zOvl6lueo7xqHENmiL17ixTXncmM82bN2fx4sVlGgx1N9nZ2Tz55JMkJiayfft23NzcylQvODiY1atXA0qil5CQwMcff0y3bt2IiYkhJCSk2HpxcXFkZWUVmamgVq1aPPbYY2zbtg2A06dP888//zBr1iyjRLUkdybtYWFhZGdnk5SUVOr36Pbt20US72eeeYYPP/xQd7x161by8/MZPny4LrEFcHJyIjw8nB07dgBw6tQpLl++zMSJE42S5Dp16tCxY8cKzYqQnZ3Ntm3bGDt2LC4uLkb379OnD//5z3/Ys2cPvXv3Lva9/fnnn4wbN87o++ju7k7//v355ptvyh1PRUmyWkUMuwHYuKVQADg4WC4eIYQQd+fh6EEN9xqllvNzKfo43c/Fr0x1PRw9jI5VqIrUu7NMeS1fvpzQ0FDs7Ozw9/cv8XFxeeTk5DBo0CB27drFhg0baNeuXZnrOjk56fp1gpJMd+nShRo1avD++++zatWqYuulpCgNPcXFHxQURHR0NADXr18HoGbNmmWKx8fHx+hY27qblZVVal1nZ2d27twJwNWrV5k3bx6rVq0iLCyMSZMmAfq+q23bti32Gtr+wtr3FxAQUKRMQEBAhZLVlJQU8vPz+fzzz/n888+LLVPS1F03b96koKCgxHiqkiSrVaSBdwNeafsKPi4+LN3emYuWDkgIIcRdVebx+53dAsrK3dGdixNN+xciNDTUKDmsrJycHAYOHMiOHTv4+eef6datW6WvGRgYiK+vL0eOHCmxjDapLG5O18uXL+Pr6wvo++JevGj+v7Q2NjZG39sePXrQunVrpk+fzrBhw6hVq5YurrVr11KnTp0Sr6V9f1eLmYS9uHNlUa1aNWxtbXn++ed5+eWXiy1T0owF1apVQ6VSmTSeipIBVlWkXrV6fN7nc6Z1mUatvMcAZYBVXp6FAxNCCCHKSNuiun37dn788Ud69eplkutevHiR5OTkYmcb0OrQoQPOzs6sWLGiSN3t27frkuaGDRsSHBxMZGQkOTk5JomvrBwdHVm0aBHZ2dl88MEHgNLH087Ojn/++Yc2bdoUuwE0atSIwMBAVq1ahUaj0V0zMTGR3bt3VygeFxcXunbtyqFDhwgLCyv23ne2LGu5urry8MMPs27dOrIN+i1mZGTw66+/ViieipKWVQvwNui+evMm3OV3UwghhDC72NhY3eNztVpNYmIia9euBZS5VLWtlYMHD2bz5s1MnjwZHx8f9uzZo7uGh4cHTZo0KfVeWVlZunpqtZqEhATmzJkDUOzUUlpeXl689957vPvuuwwfPpxnnnmGlJQUpk+fjpOTE1OnTtWVXbRoEf3796d9+/ZMmDCB2rVrc/78ebZu3aobhGUu4eHh9OnTh2XLljFp0iTq1avHjBkzmDx5MmfPnuXxxx+nWrVqXLt2jX379uHq6sr06dOxsbFh5syZjBkzhkGDBvHCCy+QmprKtGnTKvXYfeHChTz66KN06tSJsWPHUrduXTIyMoiPj+fXX381mmngTjNnzuTxxx+nR48evP7666jVaj7++GNcXV25UZWTxWvuM2lpaRpAk5aWZulQSjR8uEYDynbypKWjEUKIB1dWVpbmxIkTmqysLEuHYnLLli3TAJo///yz1LLh4eEaoNhtx44dunIllQE04eHh5b6PjY2NJigoSNO7d29NTExMsfEnJCQYnf/66681YWFhGgcHB42np6fmiSee0Bw/frzIveLi4jS9e/fWeHp6ahwdHTXBwcGaCRMm6F6fOnWqBtBcv369TPe904gRIzSurq7Fvnb06FGNjY2NJiIiQndu/fr1mq5du2o8PDw0jo6Omjp16mgGDx6s+d///lfk/YWEhGgcHBw0DRs21ERGRmpGjBihqVOnjlG5OnXqaPr27Wt0LiEhQQNoli1bVuT8qFGjNDVq1NDY29tr/Pz8NB07dtR88MEHpdb95ZdfdN/v2rVraz766CPd9+5uSvvdKk++ptJoDNqa7wPp6el4enqSlpamm6LBWmg0GjLzMun1RCp/bFE60H/+ObzyioUDE0KIB1R2drZupaGyjBwXQpRNab9b5cnXpBtAFWr939YcunoIm7bOsEWZZPVS6TObCCGEEEI8sGSAVRVyd3QHoMA2C+yUKTGqeECdEEIIIcQ9RZLVKmS85KrSMbmwP7sQQgghhCiGJKtVyChZLVxytSoH0wkhhBBC3GskWa1CxS25mppqmViEEEIIIe4FkqxWIR+Xoi2rGRkWCkYIIYTOfTYxjhAWZ8rfKUlWq1BxfVZv37ZQMEIIIbC3twcgMzPTwpEIcX/R/k5pf8cqQ6auqkJGLauF3QAMVjATQghRxWxtbfHy8iIpKQlQlqdUqVQWjkqIe5dGoyEzM5OkpCS8vLywtbWt9DUlWa1CRn1WC7sB5OZaKBghhBAAuqUstQmrEKLyvLy8KrVMrCFJVquQYTcAx2op5ACyYIoQQliWSqUiMDCQ6tWrk5eXZ+lwhLjn2dvbm6RFVUuS1SpUr1o9op+PxsfZh1FDanAYyMyEggKwkd7DQghhUba2tib9AyuEMA1JVquQi70L3et3B8DfTTmn0UBaGlSrZsHAhBBCCCGslLTnWYhhcioLAwghhBBCFE+SVQtxc9PvJyRYLg4hhBBCCGsmyWoVi7sQx8q/VhKb9TmgTJi7a5dlYxJCCCGEsFbSZ7WKvR/zPv87+z8IARyHQ44nV69aOiohhBBCCOskLatVzGgVq8K5VmVqPyGEEEKI4kmyWsWKW3I1JcVCwQghhBBCWDlJVqtYcUuupqZaJhYhhBBCCGsnyWoVK27J1fR0CwUjhBBCCGHlJFmtYsbdAJRk9dYtCwUjhBBCCGHlJFmtYsbdAJQ+q1lZFgpGCCGEEMLKSbJaxYrrBpCba6FghBBCCCGsnCSrVcywG4DKVUlW8/MtFY0QQgghhHWTRQGqmI+LD672rvi4+JDu4EUqYG8PGg2oVJaOTgghhBDCukiyWsW8nb259a4yoqpLF4hF6QaQlQUuLhYNTQghhBDC6kg3AAvyNui+evOm5eIQQgghhLBWkqxakGGyeuOG5eIQQgghhLBWkqxakOEsAH/+abk4hBBCCCGslfRZtYBF+xYRkxhDjN8NcF4DWT4cOWLpqIQQQgghrI8kqxaw99Je1p5YCx6AaxJk+ZCUZOmohBBCCCGsj3QDsACjJVcLFwZITrZQMEIIIYQQVkySVQsobslVmQ1ACCGEEKIoSVYtwKhl1VlpWU1Ls1AwQgghhBBWTJJVCzBqWS3sBnDrloWCEUIIIYSwYpKsWoC3s8EEq4XdADIzLRSMEEIIIYQVk2TVAorrBpCTY6FghBBCCCGsmCSrFlBcN4C8PAsFI4QQQghhxSRZtQDDllVbNyVZtbEBjcZSEQkhhBBCWCdZFMACXOxdGN5iOJ6Onmw43pQEID8f1Gqwk5+IEEIIIYSOpEYWoFKp+GbgNwDEfw4JhedTU8HX12JhCSGEEEJYHekGYGHeBhMDyMIAQgghhBDGJFm1MMNk9cYNy8UhhBBCCGGNzJ6sfvHFF9SrVw8nJydat27N77//ftfyOTk5TJ48mTp16uDo6EhwcDCRkZHmDtMiNBoNl5LTwSYfgA0bLByQEEIIIYSVMWuf1e+//57x48fzxRdf8Mgjj7BkyRJ69+7NiRMnqF27drF1/vWvf3Ht2jWWLl1KgwYNSEpKIj8/35xhWsTrW1/n832fk9coD/wOw7UWnD9v6aiEEEIIIayLWZPV+fPnM3r0aMaMGQPAggUL2Lp1K4sXL2b27NlFym/ZsoXY2FjOnj2Ld+Hz8bp165ozRIuxt7Unr6BwctXCuVaTky0YkBBCCCGEFTJbN4Dc3FwOHDhAz549jc737NmT3bt3F1vnl19+oU2bNsyZM4caNWrQsGFD3njjDbKyskq8T05ODunp6UbbvaC4Vaysvc9qVha8/z6EhcEjj8CqVTI3rBBCCCHMy2wtq8nJyajVavz9/Y3O+/v7c/Xq1WLrnD17ll27duHk5MRPP/1EcnIy48aN48aNGyX2W509ezbTp083efzmZrSKlbOSpaalWSiYMsjJgd69ITZWf273bjh1CqZNs1hYQgghhLjPmX2AlUqlMjrWaDRFzmkVFBSgUqlYuXIlDz/8MH369GH+/PlERUWV2Lr6zjvvkJaWptsuXLhg8vdgDt7OBtMAFHYDsOZG4fffN05UtaZPh61bqz4eIYQQQjwYzJas+vr6YmtrW6QVNSkpqUhrq1ZgYCA1atTA09NTdy40NBSNRsPFixeLrePo6IiHh4fRdi8orhtAZqaFgilFQgLMn6/s29lBz57QurX+9YkTldW3hBBCCCFMzWzJqoODA61btyY6OtrofHR0NB07diy2ziOPPMLly5e5deuW7tzp06exsbGhZs2a5grVIorrBpCdbaFgSrFggbIcLChff/sNDhzQv37iBKxZY5HQhBBCCHGfM2s3gIkTJ/L1118TGRnJyZMnmTBhAufPn+ell14ClEf4w4cP15V/9tln8fHxISIighMnTrBz507efPNNRo0ahbOzszlDrXJGLauF3QBycy0UzF2kpsLSpcq+gwPUq1d8uTlzqiwkIYQQQjxAzJqsPv300yxYsIAZM2bQsmVLdu7cyaZNm6hTpw4AV65c4bzB5KJubm5ER0eTmppKmzZtGDZsGP379+ezzz4zZ5gWUc25mm5fVZisqtXWN7r+hx/g9m1lf9QoOHoUfvml6KCqw4eVTQghhBDClFQajbWlR5WTnp6Op6cnaWlpVt9/1WO2Bxm5GdilNiJ/wd/Y2ipdAezMOvtt+fToAf/7n7K/f7++r6pGo8wOYDi46rXXYOHCqo9RCCGEEPeW8uRrZp8NQJRs9eDVbB++ndbx6wClZdWa+q0mJ8OOHcp+vXrQqpX+NZUK5s4FG4N/Qd99JwOthBBCCGFakqxaUJ+QPnSt15VaTk1056xpYYCff9Ynnx06KAmqoebNYeBA/XFyMsTFVVl4QgghhHgASLJqBarpu69y86bl4rjTb7/p97/7DvbuLVpm7Fjj419+MW9MQgghhHiwSLJqBby89PsJCRYLw0hBARjOOhYUBG3aFC332GPKwgDaVteff66a+IQQQgjxYJBk1YISbiaw8fRGYm5+C25XANi82cJBFTpyxLiVd8gQsLUtWs7GRlnd6tFHlePTp+Hs2aqJUQghhBD3P0lWLWjZ4WX0W9WPP2sOh4DDAFy/btmYtLQzAGgNHnz38j176ve3bzd9PEIIIYR4MEmyakHFLQyQnGyhYO6wc6d+380N2re/e/lu3fT7dya6QgghhBAVJcmqBRW35GpqqmViMaTRwO+/64+7dy997lcXF7C3V/Y3b7a+xQ2EEEIIcW+SZNWCjFpWnZWW1fR0CwVjID4e0tL0x48/XnqdpCTIy1P209PhxAnzxCaEEEKIB4skqxZk1LJa2A1Au7SpJd05V2r37vr9pNtJ7Lu0j+RM4/4KnTrpW1YB/vjDjAEKIYQQ4oEhyaoFFdeympVloWAMGCaaXl5Qvz6k56QzYv0IAucF0u7rdgR8EsCYX8ZwK/cWAE5O+qVYwXpmNRBCCCHEvU2SVQvydvbWHxT2Wc3JsVAwBvbv1++/9BJk52fR49seLD+ynAJNAQBqjZqlh5bS77t+5OQrQT/xhL7e7t1VGbEQQggh7leSrFqQp5MnNqrCH0FhN4D8fMsOTsrLg+PHlf2GDWH2bHgz+k32XdoHgKejJ6NajsLdwR2AdjXa4WDrABjPCJCUBCkpVRq6EEIIIe5DpYzxFuZko7LB29mb5MxkVC4paHPUrCxldL0l/P23vnX3oYfg98TfWfTnIgCc7JyIHRlLi4AWvNTmJX775zfe7fQuqsLlqx56SOm3qh1oFRcH/fpZ4l0IIYQQ4n4hLasW5uvii4ejB44qd9250qaJMqfDh/X7LVtqmLx9su54bo+5tAhoAUDbGm2Z3HmyLlEFJe7QUH39LVvMHa0QQggh7neSrFrYsbHHSJuURvczf+nOWXKu1T179Pt1m6RwPVNZUquRTyPGthlban1ZyUoIIYQQpiTJqoXZ2tgC4G0w1urGDQsFA+zapd//fasvx8YeY/nA5czvNV8X653UBWqWH1lO9+Xd6d1XrTsfHw8FBeaOWAghhBD3M+mzaiWsIVnVaOD0af1x+/ZKMv18i+fvWu+lDS/x9aGvAXix5UYaNx7A338rfVfPnIFGjcwZtRBCCCHuZ9KyaoUMH8VXpcREyM7WH7dsWbZ6g0IH6fb/e+Rzhg3Tv3bwoGliE0IIIcSDSZJVC9uRsIN///pv1js8BbWU2fj37bNMLIaDq2xcUmncuGz1Hm/wOMHVggH439n/UaNZgu61AwdMGKAQQgghHjiSrFrYqZRTfHXwK865rAPfUwAkJ5dSyUwOHSrc8T5DwRt+9Pu+F7+e+rXUejYqG0Y/NFp3fMZxlW5fklUhhBBCVIYkqxZW3JKrN29aJhZd94OmP4BNPr/98xunUk6Vqe6zzZ/V7f+csBJfP2XW2Lg4GWQlhBBCiIqTZNXCfFwMktXCVazS0iwTy4kThTsN9a2pg5sMLlPdOl51eLT2o8p1rp8g1fEIoCwwEB9v0jCFEEII8QCRZNXCjFtWlWkAMjKqPg61Gi5fBpxuQg2l02xTv6bU9apb5msMa64fWeXaYaVuPzraVFEKIYQQ4kEjyaqFeTsbzFlV2A0gM7Pq40hIKHxcX2872CjP7XsG97x7pTsMaTIEOxtlNrT8kB+hcAHZ334zZaRCCCGEeJBIsmphxXUDMJw+qqrougAE6zPL8iarPi4+hNcJB+C2QwL4/g3AX3/drZYQQgghRMkkWbUwF3sXnOycCg+UZDU/X5mgvyopyaoGGmwFwMHWgc51Opf7Oi+3fZk53ecQO/hvSA4FCrsXCCGEEEJUgKxgZQV8nH24lHEJlfMNtDlqWhp4eVVdDCdOAN7x4JUIQKfanXCxdyn3dbQLBGg0YG+vrGKVmwtJSVC9uikjFkIIIcSDQFpWrYC236rGJRltP8/U1KqN4cQJoP423XGP+j0qdT2VCgID9cfbt1fqckIIIYR4QEmyagX6hvTl+bDnaZDyKtjkA+DgUHX3LyiAkycBv+O6c13qdqn0dZs10+//73+VvpwQQgghHkDSDcAKzO4+G4AXNkF84QT6yckQFFQ19794sXAGgs2fUzPxLT5e9QcPBT5U4esVaArYc3EP2e1/gyw17JgpK1kJIYQQokIkWbUivr76/apccvXMGf1+6vlaPBs2tFLX02g09PuuHzcLbsLDXhA7lbQ0+acmhBBCiPKTbgBWxMdgFquqTFYPHdLv16xZ+evZ2tjSvX535cA5FYL2c+WKMsuBEEIIIUR5SLJqRXJyC8A2F4BVq6ruvoaP6Bs3Ns01jeZoDf6N7GxZdlUIIYQQ5SfJqhWIORdDwCcBvJ/nAI/MAeD8+aq7/99/A09EQK8JOLVaZ5JrGs0mULjQgCwOIIQQQojykmTVCjjbOXPt9jUKUINrEgA3b1bd/c9fyYQW30KHBcQ5vWeSa9bxqkMjn0bKQc094JjG3r0mubQQQgghHiCSrFqB6q4Gs+UXJqsZGVVzb40GbjoeBhs1AI/U7mCya/cK7qXs2Kih3g6+/95klxZCCCHEA0KSVStQXLKamVk19756FTSB+3XHj9ZvY7JrG/db3UpSkskuLYQQQogHhCSrVsDVwVW/tGlhspqTUzX3PnIECNSPsGoT1Npk1w6vG46dTeGUVfW2k5cHV66Y7PJCCCGEeABIsmoldK2rhcmqWl01Uz3t3g0EKcmqqsCO5v7NTXZtNwc3Hq7xsHLgexrcL/PLLya7vBBCCCEeAJKsWgldsuqSrFtytSoGWd28dRt8TwLgb9MMJzsnk15/aNOh1L/5Aqz9DnI8iIkx6eWFEEIIcZ+TZYWshC5ZVWnAOQVu+3PxIvj5mfe+J24eBk9ljddH65uuC4DWq+1exfYAvLxQOZbpq4QQQghRHtKyaiWquxQdZHX2rPnve+aWvr9qeIjpBlcZ6tdPv3/hglluIYQQQoj7lCSrVqK4GQFcXc17T40Grtrok9V2tUzfsgpQuzbY2ir7GRmy7KoQQgghyk66AViJZ5o/Q5ugNqxfWZ0Vl8MAsLc37z1TUiBv//OQEoR300MmHVx1J++ayVx3iYX0mvz+ezu6djXbrYQQQghxH5Fk1UqE+YcR5h/GOW9YUThtVXKyee954ABwtjuc7Y7z3+C00Dz32XtxL9cj2isHhyKIjpZkVQghhBBlI90ArIyvr37f3Mnqnj36/Vq1zHefFgEtsMNROai7Aw8P891LCCGEEPcXSVatjGGyau4BVkeO6PcbNzbffZzsnGjpW7iMa7VzxJ08Z76bCSGEEOK+IsmqlVAXqNl7cS/7M36F+tEArFtn3nsevX4Eqv0DqgJam2dslU7vxl10+wdvxJj3ZkIIIYS4b0iyaiUKNAW0X9qeaacGQNf3AWXkvDmdazoW/q8BTPKkVbtMs96rewN9J9VLDjvIyzPr7YQQQghxn5Bk1UrY29rj7eytHBROXZVpxvxRo9GQ731MOcjyoU0LF/PdDGhXox02BcrqWJraMaxdqzHr/YQQQghxf5Bk1Yro5lotTFZzcsx3r6MXEsFRabq1uR6Gg4P57gXgaOeIb+YjyoHXeVZtSTDvDYUQQghxX5Bk1YroklXHW2CfSUEBZGWZ514/79Gve+pyy3zzqxpq49tFt78/ZUeV3FMIIYQQ9zZJVq2I0SpWLtcBuHbNPPeK/fuobj9AVTXJ6nOP6vutXneVZFUIIYQQpZNk1YpUdzFIVt2ULDXBTE/L/8nQJ6tNfMLMc5M7PNmuLWR7woUO5F9oZbZWYyGEEELcP2QFKysS4BagP3C7CsCZM5hltadUx8JuAPkOvDYsxPQ3KIajnQO+y66RfE1ZIGDvXujSpUpuLYQQQoh7lLSsWpEg9yD9gftlwDwtq9n52aTanlYOrjehTSt709+kBCH1HXX7W7dW2W2FEEIIcY+SZNWKBLoH6g/crgBw8aLp73Py+kmwUQPgmNYcT0/T36Mkbdvq9w2XexVCCCGEKI4kq1Yk0E1JVl1U3rpzDz9s+vucS7kCOe4A+FM1g6u0evTQ7mk4fvUUBZqCKr1/eWk0kJxs3jlvhRBCCFEySVatSJh/GNmTs1n9UArETAcgNdX09/G50Qdmp8GCBGokjzD9De6iQweg3UKYWJPrQxtz5MrRUutYQnIyvPkm+Pkpm5sbtGoFK1cqCawQQgghqoYkq1bE1sYWRztHqhtMCmCOqat27QJQQWpd1OnVSytuUj4+YG+vAg+lT+5v8dur9P5lsW8ftGwJn3wCKSnKOY0GDh2C556DAQMgPd2iIQohhBAPDElWrZC/v34/Kcn01z9yRL/fsKHpr1+aNr6P6fb/9491JasHD0L37nDpknLs6AiPPQZeXvoyGzZA585w65ZFQhRCCCEeKJKsWiE/P/3+/v2mv/6ZM/r9Vq1Mf/3SdAhuCreVN7n7Uiz5BflVH0Qxrl2Dnj0hQ1mFlk6d4J9/YNs2GDTIuOyRI0rZAuvuciuEEELc8yRZtTLfHf2O/9s2Bob1BdckEhIg34S5XNyFOI637A093oKgP3nkEdNdu6zCmqsgQWldzVRncODygaoP4g4aDTzzjP6xv4sLLF8ONWoox5GRcPgwhIbq68TFwcSJVR6qEEII8UCRZNXK7EzcydJDSyFkE3hcAJTBPqay79I+cutsgUfmQsARWrY03bXLqnlzdMkqwPYEy3cFWLUKdhisANuwIVSrZlymRQv4809o3Vp/buFC5ZwQQgghzEOSVSujnb4KAHdlrlVt/0lTOGww+t7+ZnMcHEx37bIKDcUoWd16xrLJamYmvPyy/tjHR1mwoLj5Z11dITYWAgwWG+vXT7oDCCGEEOYiyaqVKW4VK8M+ppW195w+Wa2W19R0Fy4HZ2fwLAiGtFoAxF3aRXZ+tkViAXjvPeMpwn78EaMZGe7k6grbt4NKpRwnJcG4cWYNUQghhHhgSbJqZYpbxeqff0xz7QJNAfHpx5SDG8HUDnAzzYUroEGwCs52ByC3IJvfE3+3SBw3b8Jnn+mPn3wSwsNLrxcaCpMm6Y9XroSsLNPHJ4QQQjzoJFm1MsV1A0hMNM21z948Sx6FSzFda06jRqa5bkW0aQPEPw6AXV41rty6YpE4Jk/WD2BzcoIlS8ped9YsqF9f2b91CxYtMn18QgghxINOklUrU1w3gMuXTXPtv679pT+4FkbHjqa5bkV07YqSrH4dh/fS6wxvMbzKY7h9G77+Wn/8xhvg61u+a6xfr+8OMGsWpKWZLDwhhBBCIMmq1anuWh0bVeGPpbAbwNWrprn20WsGS5smNWfgQNNctyJatwZyPOBie65fsyUnp+pjmDYN8vKUfWdnpZW1vJo3h+efV/Zv3oQvvzRZeEIIIYSgCpLVL774gnr16uHk5ETr1q35/fey9U38448/sLOzo6Ul5layIFsbW6q7Fo7uKWxZNdXUVX8l6VtWHdOaExh4l8JmVr8+2Noq+xoN/PXX3cubmkYDv/6qP544UekGUBFTpuhbV2fPhr17Kx+fEEIIIRRmTVa///57xo8fz+TJkzl06BCdOnWid+/enD9//q710tLSGD58ON26dTNneFarpkdNZcftKtjkmWx6KV3Lap4TId4NdAmWJdjY6CfcB9i5kypdyWrnTjh1Stl/9FGYOrVomQJNAZvPbGb0z6Np9kUzvD/2xneOL7HnYo3KhYTA40r3W9LSICLCzMELIYQQDxCzJqvz589n9OjRjBkzhtDQUBYsWECtWrVYvHjxXeu9+OKLPPvss3To0MGc4VmtviF9eSZ0BPw+GWxzqVPHNNcdHfI+7H4dDo2mQbCtaS5aCbqlXlt9zeyLPQmaF0SeOq9K7m04kGrsWLC3N35985nNPLTkIfp814fIw5Ecv36cm9k3SclKwc2h6CwK772n3z95Ev73PzMFLoQQQjxgzJas5ubmcuDAAXr27Gl0vmfPnuzevbvEesuWLeOff/5hanFNXcXIyckhPT3daLvXTesyjZVDonDeMwPyXE02wMr22HPw2yew6T8mXRWrorp3L9ypu4MUr2iuZ17njwt/mP2+ycnKXKqgLADw1FP617Lysnjx1xfp810fowFp9jb2BFcLpq5XXQLcAoyu9+upX1lw6WkatUjVnZswwZzvQAghhHhwmC1ZTU5ORq1W4+/vb3Te39+fqyWMGDpz5gyTJk1i5cqV2NnZlek+s2fPxtPTU7fVqlWr0rFbA5UKggonBjBVsnrokH7fGr5N7doV7pzpozv3898/m/2+L70EubnKfkQEODoq+9duXeORyEf478H/6sq2CWrDT0//RPo76cS/Fk/C/yVQw0PffyE7P5vxW8ez5vga0oa2ggDlm3zsGOzZY/a3IoQQQtz3zD7ASnVHx0iNRlPkHIBarebZZ59l+vTpNGzYsMzXf+edd0hLS9NtFy5cqHTM1kKbrKamKkuCVtbp0/p9axi31rx54eP3M32gQPlw8tPfP6HRaMx2z4IC+OUX/XEffZ7M1JipHLqqJJsu9i4sHbCUfWP2MbDxQJzsih99dTrlNDezbgJwNScB1ajOUG8bYLxogBBCCCEqxmzJqq+vL7a2tkVaUZOSkoq0tgJkZGSwf/9+XnnlFezs7LCzs2PGjBkcOXIEOzs7tm8vfv14R0dHPDw8jLb7hm0OOKcAsHx55S61+8JuElMTASURtOQcq1qOjtCsGZBdDRK6ApCYlsiRa0fMds/ISP10VZ6ehfO9FprXcx4da3Wklkct9o3Zx6iHRhX7wcpQmH8Yh148RLsaSjOxxuEWDOsDTX4gNlZpYRVCCCFExZktWXVwcKB169ZER0cbnY+OjqZjMZmSh4cHR48e5fDhw7rtpZdeolGjRhw+fJh2umfG978bWTcInBfI712cYNAIoHJJj0ajYcCqAVx/ri68EgoYPIK3sNatC3f+Hqg799PJn8x2v08/1e8PHmz8mquDKxuf3cgfo/6gafWmZb5mHa867BixgwGNBign7HJh8DPQ+CemTzdB0EIIIcQDzKzdACZOnMjXX39NZGQkJ0+eZMKECZw/f56XXnoJUB7hDx+urFxkY2NDs2bNjLbq1avj5OREs2bNcHV1NWeoVsXLyYsbWTeUA09lmq/KLLl65dYVUrKUFlrSauPgUHT0u6XoZgT4+wnduZ/+Nk+yeuMGnDihP542TYO6QG1UxsvJi1qe5e/Q62zvzI//+pGIloXzVtmoYfBQ1h6K5uzZykQthBBCPNjMmqw+/fTTLFiwgBkzZtCyZUt27tzJpk2bqFM4F9OVK1dKnXP1QWSjsqGWR2HCVJisXrpU8esZrVx1LYxq1SoRnInpWlYzasBFpbn3aNJRTl4/afJ7zZyp369XD368+BndlnfjUnolvrkG7Gzs+HrA1zxRt3BJK7tceHog//m55NkvhBBCCHF3Zh9gNW7cOM6dO0dOTg4HDhygc+fOuteioqKIiYkpse60adM4fPiwuUO0SrrWPac0cEzn+vWKX8twCiauhVGzZuViM6WwMP3qTxwbqju/8uhKk99rxQr9ft8XDvBm9JvEJsbScklLfUt2JdmobFj7fCR+KQOVEw6ZXHGWSVeFEEKIijJ7sioqprZnbf2BxwXS0ip+raNJhi2rzWnUqOLXMjUnJ/SLHhwbikqj/JM8n2baFvf9+/XL1qqcMtjsMpS8AmWk1cgWI/F29jbZvexs7Pi612qI7wlbPiXph/dNdm0hhBDiQVO2yUxFlavtYZCsep7ndkpTNBoqtESqrmW1wBaSQ/X9RK1Ehw5w7hxwK4DA3d+x54eOFeo3ejfTpmn3NHg/P5Z/UuMBaBvUlg+7fWjSewH06+1I8PjN/BNvw3bg+HFoWvYxW0IIIYQoJC2rVsooWfM8T0EBVGRxrjx1HieuF44qSm4E+U66deytRadO+v2r256mmq1pE1WNBnbtKjxosZyUGkoXAw9HD1YPXo2DrYNJ7wdgYwOvvKz/9frsM2VGh8NXD5Odn23y+wkhhBD3K0lWrZRRN4BKDLI6nXJa97ibpOY4OUFoqAkCNCHDmcwKCiAuzrTXT0srXFTB92/oN053/r/9/kv9avVNezMDERGgncTiq6+g3fBf6Li0I8//9HyRWQiEEEIIUTxJVq2UUbLqpcxbdeZM+a9z5+CqBg2UVj9r0qyZfslTgJ07la956jzO3qz8vE/r1kGeJhsGPw32ylJgYx4aw9PNnq70te/G0xMKZ2ZD43iTzF7Pk5WfxdoTa5mwdYJZV+oSQggh7hdWlrYIrXpe9fQH1f4BwMur/Ne5kXUDZzsX5eBaGCEhlY/N1GxtoW1b/XH0/wqYHjOdugvr0n9V/0ondStXAj3fgAAlcW/i14SFvRdW6ppl9corhTvZ1WDtaqXfMPD5vs+Z88ecKomhPC5cgI8/htmzISoK9u2D/HxLRyWEEOJBJsmqlXK2d2btkLUMy9wL320AlEfk5fXywy/zgUsGfHYGznXBz8/EgZrIY4/p9w8esOG3f/7H5YzLnLh+gu0JxS+1WxanT8P2HQVol5l1snPi+8Hf42LvUsmIy6ZJE+jWrfAgvjf8vFT32qRtk1h+pJLr6JrYpUswaRK8+67SjaFdO/DxgREjYPdupf+vEEIIUZUkWbViTzV5ijaBD0OWDwAXL1bsOjtjbeBGA8h1q1DCWxU6dNDv9+kD49q8ojueFjutQq2r+fmFy8pqbGDTIoYUrOXLvl/SrHozE0Rcdq++anBwZASe+2fpDkf/Mpot8VuqNB5QBnv16QOHD8Pff8M778Cjj0L37kXLpqfD8uXwyCPKh4q9e6s8XCGEEA8wSVatXG2DrqsVXezr9Gn9frt2lYvHXNq31+9fvAhDmw+msW9jAHad30X02ehyX3PrVkhN1R9/MOwpRrQcUclIy69fP6hbV3+ctmESXV1fBiC/IJ/Bawaz//L+KoklL09ZyatVK9i8GXr0UAbcffQR/PEH3L599/oxMcrP6uWXITe3SkIWQgjxgJNk1crpJsxHmdi+Iq5c0e937Vq5eMzFy0s/D+nhw3D7li3TwqfpXn9vx3sUaMreLKzRaJj9H/30CSEh0LChaWItL1tbGDfO8IyKUwsXMrDhUwDczrtNn5V9iL8Rb9Y4kpKU5PT995WkFfQLJWjVrQtt2kDjxmBvX/K1zp69++tCCCGEqUiyasVSs1M5of4F2n8K9aP5+efy9Vv9bO9n9FnZh9RW74HHRVQqqG++mZoqrUsX5atarcwIMKTpEN0j+32X9hF5KLLM15rx2wL+aB0CLZQ+oWPHmjra8hk9Gpyd9ceXL9rS9sIKOtdRlh9OzU7l6LWjJdSuvIMHlSQ0Nrboa3Xrwvz5yoeahAT48084eVKZ8uunn6BXr6J1DFvChRBCCHOSZNWKnUk5w/DNT8DjE6HpD2g0cPVq2etvT9jO5vjN0PkDsMvCza1iK2BVFcP+kgsWQGyMDZ/2+lR37s3oN0lMTSz1Oj8c/4Hpe14H+ywYNAJq7uHZZ80QcDl4eysDlgx9Nt+JNYPW06FmBzYN28Sg0EFmufeOHRAeroz0N2RvDx98oCSmEyZAQIByXqPRkHQ7iZOpB6nWMpY3vvgf837eSr3Ou8HhFqCsCPb884Xz1wKnTlVs0QohhBCiNLLcqhVr4N1Af+CtPCJOTISgoLLVP3jloLKT4w43gwlocPfyltali5JMazSwbZuS4K1Z053nwp5jxV8rSM1OZcgPQ4gZGVPiaP4Vf61g5PqRaApH/xPzPp3qtcffv+reR0neeAOWLFFajh0cYPt28Pesxh+j/kB1x6cIdYEaWxvbSt/zl1/gX/+CnBzj8/XqwZo1Smvr+bTz/HT4Jw5cOcDBKwc5c+MMuepiOqQ+Bs+138+KOa0BZUqwU5eu8Nx7Mcx7NRw3TRDr11uuu4UQQoj7k7SsWrFqztXwcymca8r3JKCsMV8W129f50J6YVPalYdAY2P1SYSXl5I8aW3cCFlZ8Hnvz3UrTeWqc4tdrjRPncfUHVMZ/tNw1JrC1aEOjoaYafz731UQfBnUqwdDhyr7ubnwv/8p+3cmqgAvbXiJF355gVu5typ8v6NH4ckniyaqrVvDnj367/WFtAuM3zqeb//6luPXjxefqBb64B1f1qzRr8y1P3UL439/lgv/qsHJ7g1p/sbrzF2zU1boEkIIYTLSsmrlmvg1ITYxFtyvgtNNjhypVqZ6h64e0h9caQUYJ4LWqnt3pc8kKI+Yt26FgQO9WPevdby6+VXWPb0Ob2dvXflDVw6x49wOlhxYwukUg2kP9o2DzZ/j4KBi4MCqfQ938/bbhYsUAJ98Ai++aLx6F8DyI8v5+tDXAGz9Zyuf9vqUJ0OfLDapvZtmzeCZZ2DFCsArARpuxKf9Rp4b8gTVq7+kK9e+Znu8nb25kXUDext7Gvo0pLZnbWp51KKaczXsbOywVdmSlpOGn6sfQ4ZAgwbw+OOQVGen/oY+Z8j1mc9bJ+czfaYvQ1r2Z0iTwXSv3x0HW4eKfLuEEEIISVatXahvqJKsAvid5O+/O5ap3t6LBpNhXlEe21rrTACGundXVk/SWrsWBg6EFgEtiB0ZWyRhm/jbRGLOxeiObVW2qKM/gF1vAyoGDQI3tyoJvUyaN4e+fZVW4wsX4KuvlMf0hw9Dz55KGTsbO1zsXcjMy+RC+gUG/zCYhwIeYmKHiQxqPAhXB9dS73M79zbf/bGTH2/9BuN+g+onAEgBNiWoGf+oPlm1tbFl1VOr8Hb2pnn15jjaOZZwVb2HHoJdu6Dzc2O4ml4T6sZCrd1go7So3iaZqMPLiDq8DC8nLya2n8h74e+V+/slhBBCSLJq5Zr4NdEf+J7k3LmyJatxF+P0BxeUGffvhRHcHTuCi4t+4M5PP0FGBri7F/+4/EzKGd1+p9qdeLHePF5b2JYbhedeeKEKgi6n6dOVZBXgvfdgxgxlftPDh5Uptp5t/iztarRj3KZx/PbPb4DSUv78T8/jau9KpzqdaF+jPQMaDeChwId01/3r6lFmb1vEP5kHOXL1CLkFudCy6P3P3jxLfkE+djb6X/+ewT3L/T5CQuDAT4/Qq9cjHNsBON/Aoekmcuv9DCGbwUGZtDU1OxUblXGPI41GQ35BPva2Mv+VEEKIu5Nk1cqF+oXqD/xOcPVMyWW1CjQF7Lm4Rzm47Qc369OoUdHHzdbIyUl5vLxunXKcmakMBBo9umhZjUbDC61eoLprdbrU7UKoXyhnz8KNwky1bl3rbE1u3RoGD1ZajQ0XLXjmGfj9d2WKq2DvYLYM28Kvp39leux03WC523m32RK/hS3xW6jhUUOXrKrV8NasRLb6LCl6Q40NbQPbMbBJP/qG9CXMP6zcXQpKEhSkLBTQsyccPOhN7v7ncD35HAU22WQFbYWma1A1/oW6mUOM6h2/fpxOyzoxqPEghjQZQrf63aqkq0B2trJIxqlTyryzKSnKnLMqldIP9623rHvGDCGEeBBJsmrlQn0Nk9WT3LqlDDoynLPzTqdTTnMz+6ZycKEDoKJJk5LLW5uBA/XJKsDXXxefrKpUKqZ2mWp07uuv9fsvvgg2VjqEcOZM5T0WFOhnQDhwAEaMgNWrlbhVKhUDGg2gf8P+/HHhD745/A0bzmzg6i1l/jLtbBG3bsHw4bB1TxC8WHiDlBBI6Er1jJ7EfvMYjeuUra9zRfj4KLM39OkDcXFKK7GzsxM1bj3BpXVPoLHN4ZqPIzyur7Pm+BpSs1NZdngZywq7CgxsPJDHgx+nc53OBLoHmiy+Xbvg22+VuXtPny55rmJvb6VPsaGvvlKm9ureHTp3tq4uJUII8aCQZNXKBbkH4eHoQXpOOlQ/hkqlLEcaElJynd0XdusPLipdAO6lZLVvXyVZ0yYVe/YoSVCHDnevl5UFS5cq+3Z2MHKkWcOslMaN4aWX4IsvlETVzg7y8+GHH8DfHxYu1CfaKpWKR2s/yqO1H0Wj0XDmxhlOXD9By4CWnD0LTzwBx44B9o0g8ne4FgY5Hvj5wR+7oUGdu4ZiEl5e8NtvMGCAMq9rVpbSatm6NYSGOjJhgnH5/IJ83BzcdLMdpGanEnU4iqjDUQCEeIfQvqbS1WFwk8GViu3IEfjvfw3PaMAuW5kz1uEWOGaAwy3cGtxm85l8XOxdCK8bDsD69bBpE3z6zRlsnW/TqpkrfXu68szAajSsf5dPjEIIIUxGpdFoNJYOwpTS09Px9PQkLS0NDw8PS4djEt2Xd2fXwRRyElpju/lLsjPtsLvLx4zLGZfZdnYbC36M4+DXo+FKa1auxOIT45dH796wZYv+ePhw+Oabksu/+qrSZSCycJGrf/0Lvv/evDFW1s2b0KgRXL+uHBsm6EOHKom3S/HTyaLRKO914kT9ZPwODsqUWKC0AMbEKMliVcrKUro4bNqkHNvbK7MfDDHuBUBMDLTtkEV0wlZ+OPEDv5z6pdhpuv6v3f+x4PEFuuP8gny6RHXBy8kLNwc33B3ccbRzJCNDQ/w/ytb+kRwc3W/xZb8v8Xb25vhxZWYEm/aLoNu7aOxuoVGVvBRcA+8GnHlV6W9TsyZcugT86yloss6onE2+K552ftT09qOWjx/VXavTrV43ngt7zqhcZl5mifMCCyHEg6o8+Zokq/cAjUbDU0+p+Okn5fjsWWXOztJUq6bvE3ngALRqZbYQTW7VKn1y3bGj8pjZyan4slu3Kv1cDf35570xVdc33+hbgN3dlYRbXThFaUgIfPSRMleqVm6uMjhr1izYv19/3stL/7O2t1eSRcMVwapSbq7S/1bblcPWVklYn35aOV63Dp56SplRYOZM5YNJjjqL2MRYfk/8nZ3nd7Lv0j5y1bksHbCUUQ+N0l37QtoFai+oXaY4zrx6hgbeDSgogGXLYOP1L/gp5+VS6zneDmZYSjyPPqr8GzpzBsbvf5wLjltLrTu2zVi+6PuF0Tn32e5oNBoC3QMJdAskwC2AQLdAAt2N9xt4N5CkVgjxwChPvibdAO4BKpWKBgarT/3zT+nJam6u8eCd0NASi1qlJ55QkreMDOUxbnZ28cnqlStKP09DXbveG4kqKC3Ga9fChg3Ke23RQulXmZWlJEmff26crH7zDUUWOWjcGP7+W9lXqZT+mZZKVEFp4f3+eyUJX7lSSb6ffVbf6vpyYb546BD066cMhIuIcGbAgMeZ2fVxbGwgOz+bk9dPUtOjpu66t27BhtgrZY7jzyO3+HKj8ij/n3+AUH94LBRy3UrYXKHAjpwsbyL36VvpGzaE0H/1o329OmQXZBKfeIsLyTe5pUkCl+vgkgIq5TN/oJvS1zYxEa5dg8Zht3QtxvE34om/EV9ivJuHbebxBvpPXUeuHuE/+/5DgFsAbg5uONs742znjLO9My72LjjbOeNo54iznTMdahn3kYm/Ec/NrJvk5tqQfF1FVhbk56nIz1fh6qqiUUNlFJm3szc1PWqSnq604tvZKX3eCzQFqFChUqlQocLB1gEnOycc7RxxtHXE0c6xyAwPQghhLtKyeo9YskTp4wiweLF+vyQHD+ofAbu735vrto8bp7xXgI8/VkZq5+UpLXU2Nsqo/549lVZjQ5s2Ka1194rkZCVJvXxZOX7iCeXcH38ofVdfe01fNikJAgOV7gJhYUqL6s7CefltbJRk9rnnitzCItRqGDtWGaSk9e67St/j999XktU7eXgoH6z8/ZUpvlq21L82daoyzRe2OYX9TJW+ptjlEFxfRc+eKjp3UhF/yoEfVrrz1x+BUFB0aiwHB6hfH+rUAU9P5TgnR/meJyZCQoLSzaI4YWHKB4yRIyEtTWkl/uuYmnmLbnDt9jWqOVWjhkcN3n1XmS/Yq8Y17J4Zio3HFTJtrnIrP63E79ehFw/RMkD/hlf+tZLnfir9hxnoFsjl1y+zdi1s3qx0W4ir+TTptdaUWjeiZQSRT0TSubMyEM3bG26+6EmBQ+n/YawYtIJhYcN0xyeun2DID0NwsnPC2c4ZJzsn3eZs74yTrf54apepuDnoR6sdvXaUY0nHlHKG9Qqv42jniK3KFic7J/xc/YziyMzLRKPRYGtji63KFhuVDTYqG5PNeCGEMA9pWb0P6VpWHdOYP9+TF18sfoqd7499j52NHYc2dwWUlZ7q1q2qKE1rwgT48kslcVi4EF55RUlYDhyAbt2U1y5eNK7Trl3RLgHWztdX6fbQo4fSIv7zzzBqFLzzTtGuG9WrKwmbn58yaMhaE1VQPlQsWaK02C1cqJybNUv52W3YAHv3Kq//9ps+OUxPV86D0h/XUFhY4Y7aETIdaR7sy5NPKv2TVSrl38NLA5Uk8s44OneG/v2hUyflOg53mSXr9m0lkd6+HaKjYfdufV/iv/6CN96AKVOUbg3jxsHrr9uiUvnpkiiNBn78USmfeskfPtmhu7aNYybBLa5Su8lVXAOuUL+58vVKxhVqe9YmI0N5/1lZsOXi1TJ9n7UtnHv36luDGVJyn9ziXL+uxJ2SQsmZ+h0cbB3IzVVWSKtbF264pnHi+oky1Z3cebLR8U9//8TUmKkllNbrULMDu0fvNjrX9Zuu7Lu0r0hZFSpsbfTJ63ud3+PdTu/qXk/PSafBZw10r9+5GdZd/dRqWgS00NX97Z/fmLx9sr6sQZJsWN/D0YMfhvxgFNeX+78kNjFWV8cwyTY81zqwNSNaGj82+mT3J9zKvVWkrGEruEqloldwL5pWb6qrl5yZzOpjq1Gh/NEwLHvn12ebP2vUHeVY0jEOXD6gS/7vLK89V825mtGTAYDfE38n6XZSsfcxvFZwtWCjaRo1Gg3RZ6OLvZ/hV4Aw/zCqOetnO7mZdZO/k/82KlNSfcN5qkEZ73Ej60aJ9bTn3BzcCHIPMqp7Ie0C+QX5uuM7Pyxp61dzroaHoz4xyy/I183wYljO8Brac36ufkbzY2fmZZKRk1Fiee05G5WN0cqPALdyb5GnzitS19Xe1Srnv5Zk9R6x4fZUeG0FeJ/lzCdXuHgxgFq1ipZ7P+Z9TqecxqbAEexvQJ4LbdtWfbymEBKitDKuX6+0Ov773/qlSnfo//7rpn4CmDv33pwns3NnZcqqwYOVxCgyEo4eVZK8gAD9e9LOC/p//6d0jQBlftDvvlNG4lsblQo+/VQZqDRpktLaum0bNG2qtJZv2qR84Pj5ZyU5PHRIad2EooPL2rZV/g106qR0c/DyUlo2x42D2Nii927RQnkC8a9/KS2GZeXqCo8+qmzvv688zl+zRvke7ymcvjg7W/lw8M03ygeKceOUQXGursrP7803lb7FsbHKQDqtghwXzuyrz5l99QFlyd3XDQY+Hj6sfGgBwGk0eD0Grklgnwn2WcpXO+Xr65OysHfK0f3hCzL422mb0BtXmwBc3QpwdQUHRw02thpsbDR4eGpoEgoaNDxS6xFAeQrj4aEkrRdODiOfLECjdG9QFYBNHthl07RFDn6BOWTnZ1PdtTrnzxtMK1dbDc+5gl0O2Oj/aBfHyc6JhAQ4d075N3HiXHaZfjbaxHz8eKXvfmYmnGhRAMU0ymjQGCUPBZoCUlKUPvB2dmDnns/13tfLdN8cdQ7XryvdSdzc4O+kFPZf3l9qPS8nryLn9lzcw+pjq0ut+3TTp4skq/Pi5hklNiXxfsLbKFm9lH6JVze/Wmo9gL4hfY2S1U1nNvH2/96+Sw1F8+rNiySr02KnsT1he6l13+jwBnN7ztUdqzVqeq3oVaZ4o5+Ppnt9fb+n3Rd2029Vv1Lr2ahsUL+vNjr30a6P+Hzf56XW7RPSh43PbjQ61215N87cKH0i9IWPL+S1dvrHZRfSLlD/s/ql1gM49copGvo01B0vP7KcsRvHllqvrlddEv4vwejc8z89z/q/1xcpu/HZjfQJ6VOmeKqSJKv3CCeXPPA+qxzUjeHYsaFFktXzaec5nXIaANtrbSnIU/7D6WN9/+7KbNYspRUuP19JGPz9leRBy9tbvwjAs88qicy9atAgJRkfOVJ5LP3nn8of1gYNlMT9xg2lVTnfIA8ICVG+L4aPy62NSqW0Rj78sNIaefWq0p/6xReVltX33lOSvVcL/5ZmZSmto3cmmLVrK62n+/cr/y5WrDBOBEHp1zx0qJKkPvywaT64+Psrsb36qrKYwJdfKgO2tC24Bw/CmDHKk4Bnn1VWTRszRtnUaqXPdWyskogfOQInTuh/hnf+Dhv1y872gqvGLT+gtMQHBcFLjTHqyz50KDz2GNSoAT4+o8r13lesMDxaTEaG8qHh3Dn9lpgIbz6mfF+1/vc/g2rnH4VZhTM62OQr04Pptix27cnGwSWb7PxsHG0dWbZMGWAHQM3+UCNAScTtspXEvLBuYK0sOj+Wg1qjprFPY0D5wHPsmPab1hZ8PJSlflUFoCr8aqOmRs0CqvsXUKApwN/Vn9xcpU+4Uk8F7eoX1jHYCq/j6VWAja1S187Gjt9+M3hy0Qx40la3vHBJbFW2AMyZo0y/Z2cHf9YsAK/SfybaxLxXL+V3oqAAkjuroQyLu6hQceSIEq+dHeRWA8JLr6e1YYPyQdLODg65aKAcs7RFRyu/l7a2kJRUtlZ6lUpFTg66QcT55eidqELF+fOwr7Bx/UBGGe9Z2Pr4119KY4hKBYnny9cr8tAh/aIiOTllr5eToyy3rVLBxaKToNxVRob+/x7DcSllkZqqxAuQW0K8+Xf/nGkxkqzeI7rW68JHf8xWDurGcODA0CL9Mn/++2fdft7fPXT7jzxSBQGaSWio0mdz/nzll8zODv7zH+Ux7ooV+sfgAQHw2WeWjdUUhg5V+lOOHKlMRg8QH69shuzslG4RH35Y8vRW1qZzZ6W1+PXXYfly5dzBg0qSHhSktIB26aK0umpbCVNSlP/UDx5UWjU3bSqcSuoOjRopCerw4eVrRS2vRo2UluIPP1Rawr/4Qt9nOiNDSb6XLFFmOoiIUAbHtWpl3J0jN1dJ/i5eVN6roerVlS4GTk7KVq2acs7fX+n6ERBQ8qwYgYFK//T4eGUltOvXlX64yclK8pCbq/whys9Xfn/c3JSWYE9P5fsdFKQkurVqKQlxs2bKdjcNG8KiRcZJbUICJCfb6QeuoXRT6RBsvEhHtmFj6sUOujmh71SvI6w2nmABV1eDg43Kiw4Oxpu9vfJByHDJ5StXlBb5/HzIyqqG+rN/Snxv+04p70/rT8Ok4tgzygYoLdD6ZDeopppTpwtQF6gp0CjdMfbuVZ4QAeAyHxymGyTG+uR60FNqJk8uQK1R6x7b7tqlX36aS2uUPtt3JuaqAl5/Q0O7dho0aGgb1JbLJwwT+tqQ8q1BvBqjr4u+0ODoqCRqnk6e7NunHy9AQE+o6aGvA7r9uvU0jB+v3NPXxRdQvufarjw0fRE8exe5H0D3Hhq6dFHqdqjZgdu3lVlElOvbQPj7ResVfh0+QkNgoNJdoI5XHf7YajA1o189aDVeN+jRsJ6Do4YxY5R7aj8MzJ9vMC1i8w5QL6vI+0SloW5d6ByuQaPR0MJf6RLSp4/y4RuAXv3A7aq+rq4+tH1YoxsUHeIdwqlTypMfAFxdoM9go/K6uIHefTS633k3Bze+/FIZvwFAg7rQ5gmj8tpruLtDeBdlv7pLdUCZhWW7tqG7cyuolWNcF4j/yxescEC2DLC6R9zOvY37h9XQ2ORBckMejz/F5s3GZR775jF2nCt8Pv7FX5DUHCcn5VP5vSw7W0lidP8BYjwnqbOz0tJS2qIB9xJtX8Bly5QWA+38qdquEWPHKkntvWrbNuVReXGDrMrKyUmZv3XMGKVF3VLdP/78U0lQV69W+rveqX17ZVW2xx5Tkti7zZFcFtnZyuPo06eVGSO02+nTSjJmCtWqKYl548b6r02aQHCw0mJWmrw85UlAcrLygSMtTekzbGjjRiURs7XVD5rU7js5KR/CXFyUJLpbN+O6V68q5Z2dlbJ2duX/+Ws0yu/V7dvFb+Hhxh8Ed+1SZu64davolpGh369VyyBJLDRwoNLVpTSjRxuvwgdK94yMDONzKpV2lTvlq42N8lTGcOaQXbuU/vvaDyjquzQCZ2UZfwh65x1l2rzSdO5ctAtO69bKh8vSzJypfDDTunFDWQ2vLPbtw6h7m+FUh3dTrZr+SZzWiBH6D893M3Soch9DAQHGT/pKEhVlPGvNkSNlfxp2+bLyQVRrzpyiK+0Vp3lzpdXYULduBslqCTZurLqnsTLP6n2YrALU/+BREtR/AOC3MoGk03V1r6VkpuD/iT9qjZpqmgbcnH4aUNGsmdKada+7elWZ5ujOkf8+PvrBSfervDzlj5X2D/j9QqNRWkq/+kr5D7Isj58cHJT+qgMHKomql5e5oyy7jAzl3+LXXysJbHHc3JQ/sk2aKAlgnTrKv2EfH6UlUKNRkoqbN5UkLyUFzp9XktOzZ5Wvly6VeQyUyTk6KslrkyZKq7D2a3Bw5ZPw+4VGUzRxTk1VEkK1Wvl3XlCgT84Nv7q4FF3SNydHn5CqVPqtInFp73/n5u9vfM0rV5T/c/Pzlf9/DMsaHnt7K1MFGvruO31d7TWL+9qxo/JBTis7W/nQV1LshoYOVRJFrb//Vv4PubOsdl+jUTYnJ6W/v6H165XksaBAX06jKXrcokXRhHjyZGVQaEn1tPujRyvvVysxUalrGF9xX0HpdmT4/9zPPysNGaXVrVMHFiwwjnfKFCWBvVvd6dOpsnEukqzep8nqmG9msfRc4Sja6I9I3fg2np7K4aJ9i3hl8ysA1Ln4JolfzwGUvoCLFlkiWtPLy1Me9a9fr/zH36mT8jjE8FOnuDfduKE8uo6LU5KypCTlP3pnZ+XRdGioMtND69Z3PAK2UseOKTMCrFtXtHXDXKpXV1reQ0KUvqwBAcrjfF9fJalwcFASSltb4xbFGzeU1pvLl5VEOCFB6Zt7/nzZ7+3goDwyb9pU+VnVqaP83LSbh0fFW77z85UWy9u3i7ZolnQuO9s4sdO2QDo6Kv9+tJu2K4SHh/I98vFRvlYmXiFE2Uiyep8mq3/Gn+XhlcHKwdUwop86QvfuSr+dpl805WSy0slxSPJf/PCf5oAyLdD93OoohLWLj1cGnezcqSTkxfW5LStfX6X7hzYpbdhQv6/94Goqt28rXQtOnVL6T584AcePK10OyjsIw8FB6UPn4aF8dXPTdyfQJoX5+co9MzP12+3b+i4wVcnOTklaDRNY7VdPT6Wly/Cr4b6zs5IUG/bPNZWCAqV1tKTN8HUw7r+r/VqWbhxCVAVJVu/TZBXAfmwH8gOU+XNe4hCLp7Zke8J2ui1XOnV1qt2JjM92cviw8kcgLU354yCEsA7Jycpjy7//Vh63pqQorZvaBMPGRkl8fH2VBCkwUHnMXr++kuxZWm6ukrCeOKFPYE+cUBJb7UhjoSS8jo76TTu3b0mPmbWP6e+WiJqCjY1xAuvgoI+vuK93e62kr+XtEnJnFlLZYzDuz2u4f+dxeferqs6dTwa076mimzWSRQHuY42zh3MMJVldkzSNxaxn1VF9r++IZuMYU/jYsVkzSVSFsDa+vvp5XO9FDg7K4/47ZzLIy1NakU+dUmY6uHRJv6WkKH16MzKUPn4lTfPj6KgfWOXiorRSurkZb9rH93c71g4WujMxzMnRdxXQbrduKTFpPzTc+fXOwU1lpe3XWdygO0sqKFC6SRjNxiAeCIaJ650JsuHX/v31c5pbC0lW7zHPNR/BpCvTwS6L1u790Wg0LOq7CB8XHzae2YjnpcG6T+D38pyjQoh7i7290l81tAzT3uTn6xNJLe1MANYmN1c/4O3GDeVpVVqaMmhK+9VwPztbSYoNt9xcfXcGw6Tgzk37PTDcDGdJKMt57WugfIDIzS3+q3Y/N9c4zpycu88cIO5dhr9zd2upt8YZhCRZvceEd3SBod/DzWCCBtVEpVKWPfyo+0e8GDqFsMb6H2mvsi0AIoQQVepemjnAwUEZLe/vb+lIqo5arU9c7/xa0r7hOcOZAMrKsPyddUt6raRyd47O17asa/fvPC7vvrnrqNVFu4iUd7vz+3DnVtysBdqvNWuW72dXFe6h/zIEKPM0OlwJJzcXYmKMp0nZ+qsbtwonrlaplLlJhRBCiPKwtVW6YDiXY+UqIczJDOMVhTk5OioTMYMyV5vh5NOGk0m3bGkdgzGEEEIIISpDktV70IAB+v0JE5TJkOPijCfMHzKk6uMSQgghhDA1mbrqHnTuHLp1hkGZ/NvRUWlp1bpwwTr7nQghhBBClCdfk5bVe1DduhAWpj++etU4Ue3WTRJVIYQQQtwfJFm9R730UsmvvfBC1cUhhBBCCGFOkqzeo0aMgFq1ip4PC4PBg6s+HiGEEEIIc5Bk9R7l4gJLlxrPV+jmBt9+a50TawshhBBCVITMs3oP69EDdu2C//5Xmbh6/Hho1MjSUQkhhBBCmI4kq/e4du2UTQghhBDifiTdAIQQQgghhNWSZFUIIYQQQlgtSVaFEEIIIYTVkmRVCCGEEEJYLUlWhRBCCCGE1ZJkVQghhBBCWC1JVoUQQgghhNWSZFUIIYQQQlgtSVaFEEIIIYTVkmRVCCGEEEJYLUlWhRBCCCGE1ZJkVQghhBBCWC1JVoUQQgghhNWSZFUIIYQQQlgtSVaFEEIIIYTVkmRVCCGEEEJYLUlWhRBCCCGE1ZJkVQghhBBCWC1JVoUQQgghhNWSZFUIIYQQQlgtSVaFEEIIIYTVkmRVCCGEEEJYLUlWhRBCCCGE1ZJkVQghhBBCWC1JVoUQQgghhNUye7L6xRdfUK9ePZycnGjdujW///57iWXXrVtHjx498PPzw8PDgw4dOrB161ZzhyiEEEIIIayUWZPV77//nvHjxzN58mQOHTpEp06d6N27N+fPny+2/M6dO+nRowebNm3iwIEDdO3alf79+3Po0CFzhimEEEIIIayUSqPRaMx18Xbt2tGqVSsWL16sOxcaGsrAgQOZPXt2ma7RtGlTnn76ad5///0ylU9PT8fT05O0tDQ8PDwqFLcQQgghhDCf8uRrZmtZzc3N5cCBA/Ts2dPofM+ePdm9e3eZrlFQUEBGRgbe3t7mCFEIIYQQQlg5O3NdODk5GbVajb+/v9F5f39/rl69WqZrzJs3j9u3b/Ovf/2rxDI5OTnk5OTojtPT0ysWsBBCCCGEsDpmH2ClUqmMjjUaTZFzxVm1ahXTpk3j+++/p3r16iWWmz17Np6enrqtVq1alY5ZCCGEEEJYB7Mlq76+vtja2hZpRU1KSirS2nqn77//ntGjR7NmzRq6d+9+17LvvPMOaWlpuu3ChQuVjl0IIYQQQlgHsyWrDg4OtG7dmujoaKPz0dHRdOzYscR6q1atYuTIkXz33Xf07du31Ps4Ojri4eFhtAkhhBBCiPuD2fqsAkycOJHnn3+eNm3a0KFDB/773/9y/vx5XnrpJUBpFb106RLLly8HlER1+PDhLFy4kPbt2+taZZ2dnfH09DRnqEIIIYQQwgqZNVl9+umnSUlJYcaMGVy5coVmzZqxadMm6tSpA8CVK1eM5lxdsmQJ+fn5vPzyy7z88su68yNGjCAqKsqcoQohhBBCCCtk1nlWLUHmWRVCCCGEsG7lydfM2rIqKun2bYiNhYICCA8Hd3dLRySEEEIIUaXMPnWVqKDt2yE4GPr2hf79oW5d2LjR0lEJIYQQQlQpSVat0Z9/Qu/ecO2a/tyNGzBoEPzxh+XiEkIIIYSoYpKsWpu8PHjuOcjNLf61iAjIzKz6uIQQQgghLECSVWuzZAmcPl3y62fOwNy5VRePEEIIIYQFSbJqTbKzYfp0/bGDA6xdC7NnG5dbsABu3arS0IQQQgghLEGSVWuyZg0kJyv7jz+uJKpPPQWTJsG//qUvl5oKX39tkRCFEEIIIaqSJKvW5Isv9PtTpiizAGh99hk4O+uPFy+G+2uKXCGEEEKIIiRZtRbHj8Pevcp+ixbQsaPx6/7+8O9/g3bZ2dOn4eDBqo1RCCGEEKKKSbJqLZYu1e9HRIBKVbTMJ58YD6765hvzxyWEEEIIYUGSrFoDjQZWrNAfOzoWX87ODoYM0b/+44/K6lZCCCGEEPcpSVatwfHjcP26/rhbt5LLenlB9+7K/uXL0hVACCGEEPc1SVatwfLl+v1atSAk5O7lH39cv79+vVlCEkIIIYSwBpKsWgPDhHPo0NLLb9um31+92uThCCGEEEJYi/9v777jo6oSNo7/ZlImIY0EJCESqtKb9A42FBt2FEVfXVHsKO7aV113F1fXuip27GIDRVSKighKSwBBOtIhBAghvc7c949DZjJJCAlkMhN4vvu5n9x755x7T7jgPjk59xyFVX/LzYVNmzzH//d/R65z+eWe/T//hL17a71ZIiIiIoFAYdXfZs70zJcaGQkdOhy5zogR3rMF/PyzT5omIiIi4m8Kq/727rue/YEDK5+yqry4OOjUyXM8fXqtN0tEREQkECis+tuCBZ796gwBKHXRRZ79H36oteaIiIiIBBKFVX9KS4ODB82+zea9vOohMzbMoPebvQl5MoSk55N4/OfHKSgp8J4RIC0Ndu2qmzaLiIiI1CGFVX8qO9a0RQuIiPD6+Olfn+bCTy4keXcyJa4Sdmbt5Il5T3D2B2eT36OrWSSg1E8/1U2bRUREROqQwqo/LVzo2X/xRa+PPlv9Gff/cL/7+NS4Uwm2m3C6cMdC5u9ZDN27eypo3KqIiIgchxRW/al0vKrNBoMHu0/vyNzBTdNvch//Y9g/WH/Hehb+ZSHNY5oz69pZDG8zHEaO9Fzr11/rqtUiIiIidUZh1V+ys2H5crPfuTPExro/evinh8kuygbgmi7X8MiQR7DZbPRK7MXGOzdyZutDy7Ged55n6EBqKmRm1uV3ICIiIuJzCqv+8tNP4HKZ/UGD3KedLif5JfkAxIbF8r8R/8NWZjqr0KBQzzV69IAbbvAcL1ni0yaLiIiI1LXgIxcRn3j/fc9+mRelguxBfH7F5/y6/Vd2Z+8mNjy2ksqGZVn80DWCoUEQ6gQWLYKzz/Zho0VERETqlsKqvyxd6tnv3bvCxwObD6yy+ryt87hr5l2sTFvJu53h+t8xYVVERETkOKJhAP5gWbB7t+e4kvlVjyQkKISVaSsBeKvvoZ85FizwLN0qIiIichxQWPWHNWvA6TT7kZHQsCEuy8XinYuxqhk2+zfrT8eTOgKwILGEDY2ArCzYtMlHjRYRERGpewqr/vDll579U08FzK/1+73dj7Yvt2XKH1OOeAmbzcYN3T0vV73X7dCOprASERGR44jCqj+UXW1qwAAA3l9pXrjadGATNmyV1arg2q7XYreZR/hlx0MnZ8+utWaKiIiI+JvCqj+sWePZv+QS8orz+GLNFwBEO6K5qN1F1bpMQmQCg5qbaa/WN4a1jfF+cUtERESknlNYrWslJbB/v9m32WDIEL7f+D05RTkAXNHxCsJDwqt9uUvaX+Len9YB2LbNM3+riIiISD2nsFrXFi70vLEfFwchIXy1/iv3x1d0vKJGl7u4/cXu/akdgOJi2LLl2NspIiIiEgAUVuva99979jt2pNhZzLcbvgXMEIDTW51eo8u1bNiS0xJOAyAlEbbHoPlWRURE5LihsFrX0tM9++PGMX/7fDIKMgA479TzvJdTraZL2l9CTEkwl6yF7FD0kpWIiIgcNxRW61rpC1B2O1x0EV+v+9r90ch2I4/qkvf2v5f9oY8w9VPotA+9ZCUiIiLHDYXVupSfD6tWmf0OHbAiIpi+YToAIfYQRpwy4qguGxEaQfCZZ3tObN+ulaxERETkuKCwWpdWrDCzAQD07k1ecR6nJZxGjCOGIS2GEBMWc/TX7tnTPWcrubmwa9cxN1dERETE34L93YATyuTJnv2WLYkIjWDqqKmUuErYn7f/2K7tcMCQIRQu/o20SGi+bBk0a3Zs1xQRERHxM/Ws1qUFCzz7DRu6d4PtwSREJhzTpfOK8zi30ffEPgBjLgGWLTum64mIiIgEAoXVurR1q2f/8str9dINQhqwyZZBfggsTIK8lSm1en0RERERf1BYrSvp6eYFKwCHg8xGkRSWFNbqLc5oPhSA4iBYvHFurV5bRERExB8UVuvKF1949pOSeH7R88T+J5ZzPjyHP/b+USu3GNx8kHv/l0a5kJNTK9cVERER8ReF1boya5Znv3dv5myeQ35JPrP/nE2M4xhmAShjcLvh7v35LYDff6+V64qIiIj4i8JqXSnzwlPmOcNYvHMxAO0atSMpJqlWbtEipgXN8kIAWNgMin+cUyvXFREREfEXhdW6snu3e/fnbtE4LScAZ7c++3A1asxmszHEZYJvXigsXznrCDVEREREApvCal3YvBmKi81+ZCRzdnumsDq7Te2FVYDBLYe493/JX1er1xYRERGpawqrdeGTTzz7bdowZ7P59XyQLYhhLYfV6q0G9x/l3l8Qk6llV0VERKReU1itC3v3une3jxjAhvQNAPRP6k+0I7pWb9Wh21k0PDRD1vZoC3burNXri4iIiNQlLbdaF1avdu/OOas1HBoFUJvjVUvZg4L5+ofGtN64n2ZZwBkLIal2XuASERERqWvqWfU1lwuWLjX7iYnMyUh2f+SLsAowpGE3E1QBfvzRJ/cQERERqQsKq762bh1kHUqOffuyM8v8Wj7GEUPvk3v75p4jR3r2ywxBEBEREalvFFZ97csvPft9+7LgxgVsvXsrn1/xOcF2H43CuOUWCD507U2bfHMPERERkTqgMau+9vnnnv0QM2F/i4YtaNGwhe/uGRrK98OaMSt4KymJf/BDbhaOiNp9kUtERESkLqhn1dc2b/bsX3ZZnd32k67wYj9Y0BxWLJleZ/cVERERqU0Kq7508CDk5pr90FBo4cPe1HL6xXV17y9O+brO7isiIiJSmxRWfemzz9y7s/o2osukLkyYNYFVaat8fut+OzyLASzasdDn9xMRERHxBYVVX/rmG/furB7R/LH3D55b9Jx7UQBf6tL7AsIOrfC6OCTN5/cTERER8QWFVV8qnV8VmN3ULCtlt9k5o9UZPr91yJnD6Zlq9jdHlZCel+7ze4qIiIjUNoVVXykqgjTTo7krGlYXbAegz8l9iA2P9f39W7SgV6rNfZiy7iff31NERESklims+kqZ+VXn9Gjo3h/eenjd3N9mo1dujPswJVkzAoiIiEj9o7DqK2Vfruoe6d4f3qaOwirQM6qtez956291dl8RERGR2qKw6itbtwJQbIfv4w4A0DCsIX1O7lNnTWjb9XQiC81+smtnnd1XREREpLZoBStfcLncYXVe50gyXTkAnHfqeYQEhdRZM4LOGcG4f/2HUCf0zHFgWRY2m+3IFUVEREQChMKqLyQnmwUBgK+HNAFMWB3ZbmTdtqNPH56Zc2jflmNCdFBQ3bZBRERE5BhoGIAvfPede/enBDNlVYg9hHNPObdu2xEeDpGHxstaFvz5Z93eX0REROQYqWfVF6ZOde+mXPMLPxVvYEP6BqId0XXflptvhueeM/urVkHbtlWXFxEREQkg6lmtbWvWmFAIkJBAWMtTOO/U8xjfb7x/2jNgAC4bbGgEX6z61D9tEBERETlK6lmtbU8+6dlv2dJvzXDr2pULr4bv2gJ8zp6cNOIj4/3dKhEREZFqUc9qbfv2WwAsgPHj/dkSo3VrOh70/EySkprix8aIiIiI1IzCam2aPx+yswG4ZaSdC50fMOWPKRSWFPqvTUFB9CyzkpUWBxAREZH6RGG1Nt1zDwB7I+C9bhYzNn7Lrd/eSomrxK/N6rXH85iT1/zgx5aIiIiI1IzPw+qrr75Kq1atCAsLo2fPnsyfP7/K8vPmzaNnz56EhYXRunVrXnvtNV83sXZs3Agp5lfsk3pBkd0C4KbTbiIiNMKfLaNNQgdiCsx+8oE//NoWERERkZrwaVj99NNPGT9+PA8//DDLly9n8ODBjBgxgu3bt1dafsuWLZx33nkMHjyY5cuX89BDD3HXXXfx5Zdf+rKZtePSSwHYEwn/HWT+WO02O7f1vs2frQLA1rcfPXeb/VR7Lruzd/u3QSIiIiLV5NOw+txzz/GXv/yFm266iQ4dOvDCCy+QlJTEpEmTKi3/2muv0bx5c1544QU6dOjATTfdxI033sh///tfXzbz6FkWvP46dO1K/ro/sID7hkNOiAuAsT3G0iq2lX/bCHDOOfQqk09TduslKxEREakffBZWi4qKSElJYfjw4V7nhw8fzm+/Vf6Sz8KFCyuUP+ecc0hOTqa4uLjSOoWFhWRlZXltdcZmg5de4o+0VbS7E4b9H3zU1XwU7YjmH6f/o+7aUpWBA73CavLupf5ri4iIiEgN+Cys7t+/H6fTSXy895ye8fHx7Nmzp9I6e/bsqbR8SUkJ+/fvr7TOxIkTiYmJcW9JSUm18w1U05+DOzP4BtgRA7+09Jx//YLXaRLRpE7bclgOB70yG7gPU/5c4MfGiIiIiFSfz1+wstlsXseWZVU4d6TylZ0v9eCDD5KZmeneduzYcYwtrplWdzzC2A7XYLeZP8poRzTvXfweV3W+qk7bcSQto5sTlwcN8yFqX6a/myMiIiJSLT5bwapx48YEBQVV6EXdu3dvhd7TUgkJCZWWDw4OplGjRpXWcTgcOByO2mn0UbB37sLTnT9kQs6zbD24lU5NOhEZGum39hyOrVt3/nh1HQk5YLuijb+bIyIiIlItPutZDQ0NpWfPnsyZM8fr/Jw5cxgwYECldfr371+h/OzZs+nVqxchISG+amqtiI+Mp2+zvgEZVAEYMoSmOWADWLXK360RERERqRafDgO49957eeutt3jnnXdYu3Yt99xzD9u3b2fcuHGA+RX+dddd5y4/btw4tm3bxr333svatWt55513ePvtt7nvvvt82cwTw4UXmhfCAJxO/7ZFREREpJp8NgwAYNSoUaSnp/OPf/yD1NRUOnfuzHfffUeLFi0ASE1N9ZpztVWrVnz33Xfcc889vPLKKyQmJvLSSy9x2WWX+bKZJ4ZmzaBTJ/jjD9i6FauwEJsfh0+IiIiIVIfNKn2D6TiRlZVFTEwMmZmZREdH+7s5gWX0aJ7a8QlzWsPWLs3YNGF7lS+7iYiIiPhCTfKaz2cDkADStSvzm8NPrWFz7k62Htzq7xaJiIiIVElh9UTStWu5xQGS/dcWERERkWpQWD2RNGyosCoiIiL1isLqiaRvX3qmeg4VVkVERCTQKayeSIKCSAyJIzHLHKbsXILLcvm3TSIiIiJVUFg90bRt6x4KkFmSw58H/vRve0RERESqoLB6ounfn95lxq0u3b3Uf20REREROQKF1RPNyJF6yUpERETqDYXVE82gQXrJSkREROoNny63KgEoKIiTQmN5fG4GbdOh98v3+LtFIiIiIoelntUTUdu2PDYPrv4DTpmT4u/WiIiIiByWwuqJaMAAz/6mTf5rh4iIiMgRKKyeiMaO9exnZ/uvHSIiIiJHoLB6ImrfHuLi2B4DU/fP59Ulr/i7RSIiIiKVUlg9Edls0LcvZ14Hl52XzYTZEyh2Fvu7VSIiIiIVKKyeqPr1c8+3WuAsZM2+Nf5tj4iIiEglFFZPVKee6rU4gFayEhERkUCksHqiGjKEPrs8h4t2LvJfW0REREQOQ2H1RHXyyfRMDyXYaQ4Xbp3v3/aIiIiIVEJh9QTWIKk13dLM/pqMDRwsOOjX9oiIiIiUp7B6IhswgP47PIeLdy72X1tEREREKqGweiIbPZr+Oz2HGrcqIiIigUZh9UR2+un032VzHy7ctsCPjRERERGpKNjfDRA/sttpGZVEq4ztnJwFAyPD/d0iERERES8Kqyc425ChbHrpA+wWMLwQJvi7RSIiIiIeGgZworv2WhNUAZYv92tTRERERMpTWD3RnXkmhIV5ji3r8GVFRERE6pjC6okuKMgEVsDat49Ni7/3c4NEREREPBRWBc46i8eHQdP74NRZ57M7e3fdt6GoCFyuur+viIiIBDSFVYERIyi2Q1qkOZy3dZ7v72lZMGMG9OtnhiE4HBAaCu3bw6RJUFLi+zaIiIhIwFNYFWjblqG5jd2H8xZ/6tv7bd8OAwfChRfC4sVQWGjOO52wfj3cdhs0bw4rVvi2HSIiIhLwFFYFbDYGhLcl6NBv4edtnuu7ey1eDD17wsKFVZdLTYVeveCLL3zXFhEREQl4CqsCQOSYv9B7l9lf58giLSet9m8yaxacfjrs32+OmzSB/v1h2jRYvdp8PmKEp7zTCVdeCV9/XfttERERkXpBYVWMUaMYus1z+MvCKbV7/XffhYsvhvx8czxsGKxbB7/9Zs537AjDh8N338HcuZ7ptCwL/vIX2O2Hl75ERETE7xRWxYiIYKgryX04b+ZrtXftGTPgxhuhoMAcX3opzJwJsbGVlx82DFauhJNOMsfp6TB6tF66EhEROQEprIrbwIvuwF46brVoQ+0sELBliwmnpddKTISPPzZv/1fl1FNhzRpo1uxQg+bBU08de3tERESkXlFYFbfoG2+lZ6rZ/6Oxi9Rfvj22CxYUQJ8+UFxsjsPCYOnSIwfVUo0bw5QpZuECgCefhCVLjq1NIiIiUq8orIpHVBTDcxMAaJwLGyf96+ivZVkwZIjnZSqbDWbPNj2rNTFwINxzj9kvKjLjWjUcQERE5IShsCpebjp9AkvfgLT/wpAvlsLBg0d3oVtvNb2opZ56CgYPPrpr/f3vEBJi9jMz4a67ju46IiIiUu8orIqXlmPupNeBMOwWZuqo2bNrfpHJk+H11z3HF10Ef/vb0TcqKgoefdRz/MYbcODA0V9PRERE6g2FVfHmcMB113mO162rWf2FC+GmmzzHrVrBl18ee7seeQTi4sy+0wnXXHPs1xQREZGAp7AqFT34INjNX42Sl16A7Ozq1731VnAdmlIgPNzMoxocfOxtstng1Vc9xzNnwp9/Hvt1RUREJKAprEoFrhbNeeG2Hgz7Pxg6MgNeeql6FT/4AH7/3ezbbPD995CQUHsNGzUKWrf2HJftwRUREZHjksKqVGC32Zl8SjbzWsJvzWHXC0/Cbbd5pqCqzM8/w9ixnuOXX4ahQ2u/cS+/7H3PzZtr/x4iIiISMBRWpVKXdLvKvf9Vq0KYNMmsQlU+sCYnwzPPwIUXQmGhOXfzzSbc+sKIEdC8uef45pt9cx8REREJCAqrUqlL2l/i3p/W4dDOhx+aeU/ffx+mTYMbboC+fc2b/jk5psyFF3r3fvrCM8949n/80aySJSIiIsclm2XVxpqagSMrK4uYmBgyMzOJjo72d3PqLcuyaPNSG7Yc3EKQC/Y+A3H5R6g0bBh89515scq3jTOLC+zZY47nzjX3FhERkXqhJnlNPatSKZvN5u5dddrL9K4ezmWX1U1QNY3z7l393/98f08RERHxC4VVOaxRnUe59z+4pkvlS6XGxcGbb8Lnn9dNUC115ZXQtKnZ/+or2Lat7u4tIiIidaYWJsCU41XvxN60a9SO9enrmZe9iq3L1tFy7nJYscIU6N4dRo6s25BaKjTUzOn697+beV0nTYJ//9s9P6yIiIgcH/T/7HJYNpuN67p5VrP6cP3ncNVV8NRTZrvqKv8E1VI33wwhIWb/2Wfhv//1X1tERETEJxRWpUrXdr0WGzbsNju7snb5uzne4uPNVFYAJSXwn/9UPResiIiI1DsKq1Kl5jHN+fiyj9l5z04mXTDJ382p6JFHPPsHDsCnn/qvLSIiIlLrNHWV1H+dOsGaNWb/lFNgwwYzY4CIiIgEJE1dJSeWhx7y7G/aBD/95L+2iIiISK1SWJUaySnKYfr66f5uhrfLL4eyP5X961/+a4uIiIjUKk1dJdU2eflkHvjxAfbn7WfVravoeFLHWr/H1LVTWZa6jL25ewkLDqNpZFMGJA1gQNIAQoJCKq/kcMDtt8PEieZ47lz44w/o3LnW2yciIiJ1S2FVqm1v7l725u4F4G9z/sY3V3+DrZbHhr6R8gaz/pxV4XxsWCy39b6Ne/rdQ6MGjSpWvPVWM51W6RDsZ56B996r1baJiIhI3dMwAKm2O/veSWKUWcXq243fMm3dtKO+lmVZpOxOqXD+9JanV1o+oyCDf83/F21easOHKz+kwnuBSUlwwQWe448+gt27j7p9IiIiEhgUVqXaGoQ04Plznncf3/rtraRmp9b4Osm7kxn23jB6vdmLZanLvD67qvNVzLh6Bik3p7DwLwt57+L3GNVpFCF2MwQgszCTMdPGMGH2hIoXvvtuz35UFGRm1rhtIiIiElg0dZXUiGVZjJwykm82fAPAwKSBzB4zmwYhDY5Yd3vmdh768SE+WvWR+9ywlsP46bqfjjicYHvmdh788UE+XvUx0Y5oFtywgC7xXco3Dtq2NTMCAKxcCV26VLyYiIiI+JWmrhKfsdlsvDPyHZpFNwPg1x2/MnLKSA4WHDxsnb25e7lv9n20e7mdV1A9Ne5U7u5792HrldU8pjkfXfoRn13+GVOvnFoxqJrGefeuvvJKta4tIiIigUs9q3JUkncnc8Z7Z5BdlA1Aq4at+OLKL+jRtAcALsvF4p2L+WjVR0xeMZm84jx33UbhjXhs6GOM6zXu8G/415BlWaZ3NisLTj4ZcnIgIgJ27YKYmFq5h4iIiNQO9ayKz/VK7MX313xPbFgsAPvy9rl7WwGKnEUMeXcIryx9xR1Uw4LDuK//fWy6axN39r2zVoKqy3Lx6E+Pct1X15mXrqKjYcwY82FuLvz1r1okQEREpB7T1FVy1AY2H8iyW5Zxw9c3MLTFUJpENHF/FhYcRpcmXVi+ZzlhwWHc3ONmHhj0AE2jmtZqG8ZMG8PHqz4GoHt8dyYMmGDmXJ00yRR4801ISYHkZC3BKiIiUg9pGIAcM8uycFpOgu3eP/u8mfImwfZgLut4GdEO3zyLqWunctlnlwFgt9mZde0szmp9FgwbBvPmeQrOnWvOiYiIiN9pGIDUKZvNViGoAoztOZYbTrvBZ0EV4NIOl/LokEcBMyRgzLQx7MvdB3fc4V3w2Wd91gYRERHxHYVVqfceH/Y4555yLgB7cvZw0zc3YV10ETQtM+RgxgxYs8ZPLRQREZGjpbAq9Z7dZmfyyMk0btAYgOnrp/PGyskwbpx3wSef9EPrRERE5FgorMpxISEygXcuesd9PGH2BLZdNQKCgjyFPv0UVq3yQ+tERETkaCmsynHjwnYXcnOPmwHILc7lliWPYl1xuaeAZcFjj/mpdSIiInI0fBpWMzIyGDNmDDExMcTExDBmzBgOHjx42PLFxcXcf//9dOnShYiICBITE7nuuuvYvXu3L5spx5Gnz36axKhEAP7M+JO9N1/rXWDaNDOVlYiIiNQLPg2ro0ePZsWKFcycOZOZM2eyYsUKxpRO2F6JvLw8li1bxqOPPsqyZcuYOnUqGzZs4KKLLvJlM+U4EhMWw2vnv8bfBvyNleNWEn/6BTBokKeA3Q6LFvmvgSIiIlIjPptnde3atXTs2JFFixbRt29fABYtWkT//v1Zt24d7dq1q9Z1li5dSp8+fdi2bRvNmzc/YnnNsyoVfPcdnH++2e/UyYxb1QIBIiIifhMQ86wuXLiQmJgYd1AF6NevHzExMfz222/Vvk5mZiY2m42GDRtW+nlhYSFZWVlem4iXESOgWzezv3q1ll8VERGpR3wWVvfs2UOTJk0qnG/SpAl79uyp1jUKCgp44IEHGD169GFT98SJE91jYmNiYkhKSjqmdsvxZ8OBjVx3fTR5IYdOPP64edlKREREAl6Nw+rjjz+OzWarcktOTgbMykblWZZV6fnyiouLueqqq3C5XLz66quHLffggw+SmZnp3nbs2FHTb0mOYx+v+piuk7ryQdZ8nrg0zpxcsAC++gqeeAI2bvRr+0RERKRqFdfIPII77riDq666qsoyLVu2ZOXKlaSlpVX4bN++fcTHx1dZv7i4mCuvvJItW7bw008/VTmWweFw4HA4qtd4OeH0bNoTC9OL+myHg1wTD13TgNGjoaAA5syBefO852MVERGRgFHjsNq4cWMaN258xHL9+/cnMzOTJUuW0KdPHwAWL15MZmYmAwYMOGy90qC6ceNG5s6dS6NGjWraRBG3do3b8fDgh3ns58dw4uLmqyP49cVcggoKTIFff4VnnoEHHvBvQ0VERKRSPhuz2qFDB84991zGjh3LokWLWLRoEWPHjuWCCy7wmgmgffv2TJs2DYCSkhIuv/xykpOT+eijj3A6nezZs4c9e/ZQVFTkq6bKce7+gffTvnF7ABY3zOW1XuUKPPywXroSEREJUD6dZ/Wjjz6iS5cuDB8+nOHDh9O1a1c++OADrzLr168nMzMTgJ07dzJ9+nR27txJ9+7dadq0qXuryQwCImU5gh28fsHr7uMHzw1md1SZAi4XXHUV/Pln3TdOREREquSzeVb9RfOsyuHcNP0m3l7+NgCXrbPzxRSXd4GWLWH+fGjWrHZv7HTChg2waxfs3QtFRdCggblPu3agoS4iInKCqUleU1iVE8aB/AO0f7k9+/L2ATD9Y7hwA+BwQGGhKdSmDcyYAe3bH/sNv/oKnn0Wli2DvLzDl+vYEb791oRlERGRE0BALAogEmjiwuN4/pzn3cd3jAyhMAgTVEv/ofz5J/TvDwcP1uziLheUlHifKyoy02RVFVQBduyAk0+u2f1EREROEDWeDUCkPhvdZTTvr3yfzRmbeW3Igzj+dzs4CyArC5o3h+3b4Yor4DArpnnJzoYffzQ9sd9+Cy+/DJdd5vl8xAjTa5uYCD17wqmnQpMmEBYGOTmwZQukpEDTphAS4n3tGTOgbVuziYiInMAUVuWEYrPZeP/i94l2RBMeEg4vFMO4cebDXbvgvPPgqae8Ky1YAE8/bUJlUBDs2wd//AHr13uvhDVtmndYDQuDDz6AVavMUIDPP4f0dNMDGxkJrVpBjx5w1lmm97VBA1Nvzx649lozD+yjj5pptTQPrIiInKAUVuWEEx9ZZlGKm282YfKVV8yLUHPnwsKFcP75njI//gjffFP1RcPDTaB0uUz9jz+Gzz6D/fsrL5+dDamp8NtvMGmSCa833gjjx8Pzz8OhGTJ45BEzrdaHH5qwLCIicoLRmFU5sdlsWC+8wJorTzfH+fkwcqRZKMDpNOeWLatYLzTU/Gr/ttvMEIB588yv+1u3hkGD4NVXKwbVqCg45RTz8lb54JmTAy+9ZD6z2+Guu8xXMGG1e3cTbEVERE4wmg1ATmjr9q/jlhm3kLI7hVVrhtLqo+88H/bqZRYMOP980wuanm56TmNjTTBdt8688f/ll2ZYQHlhYXDhhWZoQJ8+5m1/m83zeWamWUFr6lTTE5uf7/msZUv461/hX/+C3bvNOYcDJk+Gq6/2wZ+EiIhI3dHUVQqrUk23f3s7rya/CkC/k/vxy6ZBhDz9rPdY1IYNoV8/iI83YXXXLtPbWtmMAUFBcPbZMHq06aGt7t/B9HQzzdVzz3mm0QoKMoF1yRLvFbaeeMKMZS0bfEVEROoRhVWFVammrMIsur/WnS0HtwDw8OCH+adjBNxyC6xeXf0LDRhgAuoVV5g3/o/W5s1w3XWmx7XUlVdCRITpVS11113w4otHfx8RERE/0jyrItUU7Yjmk8s+Idhu3jX89/x/MzexCFauNGNRL7sMGjeuWDE+Hi66yIxN3bnThMvbbz+2oApmzOvPP5ve09Ke088+g7VrzblSPXse231ERETqCfWsigD/WfAfHvjxAQCaRjYl5eYUmkYdegnKssx0UqVv6MfHm6EBvv41/PTpprc2N9ccd+tmZi9wOuHOO317bxERER/SMACFVakhl+XinA/P4YfNPwDQ5+Q+/Hz9z2YuVn9avtzM/bpnjznu2NFMpZWQ4F2upASCNROdiIjUDxoGIFJDdpudjy79iOYxzQFYsmsJY78Zi99/ljvtNDMtVulyrGvWwNChkJbmKfPee9C3r/c5ERGR44TCqsghTSKaMP2q6TQIMStJfbTqI6aunernVmGWXP3lF2jRwhxv2ADDh0NGhlms4C9/MbMTDBpklnAVERE5jiisipTRLaEbH17yITZs3NnnTi7pcIm/m2S0bm16WJOSzPHKlWZ4QGKi2QA2bYKBA82KXCIiIscJjVkVqcTinYvpc3IfbIE2l+mGDTB4MOzda47PPNMs13rRRWaRAjAvf82YYYKriIhIANKYVZFj1LdZ3wpBdXnqcoqdxX5q0SFt28Ls2SaQgnnZ6m9/g7lzzSpZYBYrOOssmDLFX60UERGpNQqrItWwdNdSBk0exNkfnM2urF3+bUy3bvDdd9DAjK3lq6/MSldz5pjVswAKCsyyrI8+albdEhERqacUVkWOoLCkkMs/v5y84jzmbZtHt9e68cHvH/h3poD+/eHrryE01Bx/+KHpYZ0+HW64wVPun/+EBx7wTxtFRERqgcKqyBE4gh18fOnHnBxlpo9Kz0/nuq+uY/DkwczaNMt/ofWss8zqVkFB5vj11+Hhh+Gtt+DZZ8Fuh/BwuPFG/7RPRESkFugFK5FqSs9LZ9y34/hizRde57sndOe6rtdxecfLSYpJqvuGffIJXHONWWkL4LHH4PHH4fvvYd8+uO467/KW5fvVt0RERKqgFawUVsWHvt/4PffMuof16eu9zseFx7H/r/u9XszamL4RC4uo0CiiHFFEhET4ZoaBt9+Gm27yHD/9tBnHWl5ODpx+Otx6K4wZAyEhtd8WERGRI1BYVVgVH3O6nExbN43//PofkncnAzDilBF8d813XuVOe/00VuxZ4T62YaNBSAMiQiOICIlwf71/4P0V5nTNLMgkJiym+o168UUYP95z/O9/w4MPepe5/34TZAFatoT77jO9sqWzC4iIiNSBmuQ1LSYuchSC7EFc3vFyLu94OWv3reXzNZ/TIqZFhXJbD271OrawyC3OJbc41+t8RkGG1/G+3H00+W8TWse2ZmDSQM495VyGtxlO4waND9+ou++G3FwzbhXgoYcgK8uEVpvN/Pp/27YyjdsKd9wBEybAyJEwYgSccYZZeKA2e3+LiiA42IyhFRERqSH1rIr4SLGzmPEzx5NVlEV2YTbZRdnkFOWQW2TCaunXvOI8plw2hVGdR7nrztw0kxEfjfC6ng0bg1sMZkzXMVzR8YrD97o+/bTpQS01bhz8738mMAL8+is8+STMmlV5/UaNTPmrr/acW70a3n8fSkrMtFg5OZ4tN9ezHxoKa9Z4X+9vfzMvfMXEmCDcpg2ccgr06GHmhm3VSmNoRUROMBoGoLAq9YjLcmFZFkH2IPe5WZtm8cS8J1ixZwX5JfkV6jiCHFzd5Wru6XcPXeO7VrzopElw++2el65KZw6IjfWUWbECJk+Gjz+G/fu963/3nelpLTV9uul9PRKHw4TZssaONTMUHE7jxmZ+2EsugSuuOPI9RESk3lNYVViV40SRs4hft//KzE0z+Xr91xVe6kqMSmTb+G0E2ysZ0fPRR2bO1eJDq26deqpZ1apHD+9yxcWweLFZDWvBAtMz+uuvZkxrqW+/hQsuOHxDw8IgIgIiI2HTJk8vLphe3G++gfR02L7d9M5W5rLL4IsvKv9MRESOKwqrCqtyHLIsi5TUFN7//X0+WPkBBwsOMvHMiTwwqIpJ/xcsMD2WpT2nISFmoYAJEzzzs1ZHeroZChAcbHpPo6JMMI2IMFtwNYe/l5TAjh2wdi0kJ5uQvGCBGVv71lvwl794yhYXm5e/Lr7YbKUrdomISL2nsKqwKse5nKIcJi+fzDVdryEuPM59PjU7lZtn3MyjQx6lz8l9zMmtW+HyyyElxXOBbt3gpZdgyJC6bXhlioth0SLo0MEMCSj1/fdw3nlmPzoaRo2C//s/s3qXxriKiNRrNclrej1XpB6KDI3kzr53egVVgH/P/zczNsyg71t9ueLzK9iYvtH8Ov+338yyq6Uh7/ffYehQMy71hx88Y1v9ISQEBg/2DqpgwmqprCx4800YOBDat4eJE2Hnzrptp4iI+IV6VkWOE8XOYrq+1pV1+9e5zwXbg7m5x838fejfiY+MN2NR77wTli/3rnzKKWbM6AUXmDGtNfmVu2VBXh4cOGC2jAwoLDRTVdntple0SROzhYdX/7oulxki8N575uWwnBzvz20282LWbbdV7+UvEREJGBoGoLAqJ6hiZzFvLXuLJ+Y9QVpumvt8ZGgk9/W/jwkDJhAZFA7vvGPmX926teJFgoKgbVszzVRCghmjGhxswmNWFmRmmi0jwxNQi4qq18CTT4bOnc02aJDp3S07Q8Hh5OTA1Knw7rswd673Z3fdZRZEEBGRekNhVWFVTnA5RTk8+9uzPPPbM14LEDSJaMJjQx9jbI+xhFg2EwAnTYJffjFhtK7Z7dCzp3kJbNQoaN36yHW2bjVzvr77LmzZAsuWwWmneT7ft88sPztqlJnDVUREAo7CqsKqCABpOWn8Y94/eGPZG5S4zJRRCZEJbLpzExGhEZ6Ce/fCjBlmbOvSpbB+vflVflUiIiAuzvSMxsV5b2FhZniA0wkHD5rrp6aaabEyMg5/zT594JZb4KqrjjwUweWCJUugb1/vF65eeAHuucfsn3IKDB8OZ55prn3yyXo5S0QkACisKqyKeNmYvpGHf3qYz9d8zmvnv8YtvW6puoJlmVCZlmammyopMSEvOtqzhYbWvCGWBXv2mGmr5s41c7uuXFmxXMOG5s3/O++sXm9rWaedZhY8qExCAvTqZV7SOussOOecGn4DIiJSGxRWFVZFKpW8O5lu8d0ICQpxn9ueuZ3xM8fzj9P/Qecmneu+UVu2mBeoPvnEzFJQlt1ufp1///1muq3qXm/KFLOc7K+/Hn4RgvHj4fnnPcfFxWacbkSE6Rl2ODxbcLAZyxscbLZnnzW9tqUWLTLjgMuWCQkxvcwnnWRmOmjcGJo21fKyIiIorCqsitTA9V9dz/u/v4/dZufSDpcyvu94BiQNwFbXgcqyzBCESZNM2Cy/bOuIEfDgg+bFrOq2LTsb5s0zwxuSk831Dx40n738slmStlRqKiQmVu+6y5dD9+6e4/feMz3BRxISAvn53gsyzJtn7t2hA7RrZ4KyiMhxTmFVYVWkWnKKcuj0aie2Z273Ot8rsRe3976dyzpcRpQjqu4blp4Or71mxp+Wrr5VasAAM2fs+eebnteasCzYts0sCdu2LTRv7vls0yYztjUvzwTlwkLPUrXlrVplZjQo9c473qtvHU6bNuY+ZY0ebXqVwYTw1q1NcO3QATp2NFuHDmbVMBGR44TCqsKqSLXlF+fzvyX/47mFz3lNdwXQIKQBl3a4lGu7XMvprU4nNOgoxqkei7w8EwT/+18TMsvq3NkMDxg1yvRY+oLLZablcjrNcILSr7Gx3vfMyDCLFJSO73U6TeA9cMDMTrB/v/kaGwuPPeZ9j6rG2JY1YYL5cyjr4EEzvldEpJ5RWFVYFamxwpJCPlv9Gc8vep7le5ZX+HzNbWvocFIHP7QM08M5ZQr85z+werX3Zy1awH33wY031mwxg0Dx3XdmrO7atZ4tN7diueee88xyAGau24YNIT7e00vcooX3Fh8PMTE174EWEfExhVWFVZGjZlkWi3Yu4v3f32fK6ikcLDhIq4at+POuP73GsT7x8xOs3LuSXk170alJJzqe1JFWDVsRZA+q4urHyOWCb781y60uXOj9WWwsXH+9mfqqfXvftcHXXC7TS7tmjQmua9aY7Z//hNNP95RbtAj69z/y9YKCYMcO83JXqalTTUhu0MCzhYd7v1TmcJgxvEOHel9v0ybTe1y+bGio6W3Wy2MiUg0KqwqrIrWisKSQGRtmkFecx5huY7w+6/VGL1JSU7zOhQWH0Tq2NUnRSTSPaU5SdBIjTh1Br8RetdswyzJLsT71lAld5Q0ZAjfcYJZhrc4KWfXRb7/Bww+bILt3b9VlCwpMoCz1wAOml/pIhg6Fn3/2PlfVsAWbzYRWhwOeeMLMuFC2DRdfXHFO3sq2Ro28X0ITkeNOTfJacB21SUTqIUewg8s6XlbhfJGziK0Ht1Y4X1BSwJp9a1izb437XGx4rFdY3ZG5gzPeP4PEqESzRSa690+OPpnEqESaRjb1XrSgPJsNBg822++/m6mkPvvMs5DBL7+YLTjYvDR1ySWmV/LUU4+fnr8BAzxLz+bkwPbtZtu2zWzbt5uxsrm53kEVzFjg6ihfD6peWteyzDMoLDTjdss6cMBMJ1YdS5ea+XBL/fQTvPSS+cGjsi0qyvQOR0ebZywixxWFVRGpsdCgUNLuS2Pt/rWsSlvF6n2r3SF168GtFDo9q18lRSd51d2VvYtNBzax6cCm8pf1EuOIYe3ta2ka5fn19YH8A9iwERtepre0Wzez/OoLL5ivr78O69aZz0pKTEAqDUlNm5pe1+7doVMns7VoUf978SIjPTMHVMejj5rhEnl5ni031xM0S7ekpIp1L70UeveuWLb8dtJJ3vUOHKj+9xMX5328bh18/fWR67VqBZs3e5+7+mrz/ENCvOfALd0vPb76arj33orfq91edb3gYDNeuuzQk+3b4auvTC9z6VY6VKL8fq9e3mOKc3PN39vSchpvLKKwKiJHJ8geROcmnSssJGBZFvvz9rMjawc7MnfQr1k/r8+zCrOIccSQWZhZ5fUzCzNp3KCx17mXl7zMYz8/RnxEPN0TunNawmmc1vQ0ejTtQevY1tjHj4e77zbjOb/4wmzby0zLlZoKn35qNvc3EgRNmpjxmU2amEUBIiI8YznLBo6abjExZonXQJs79aSTKobJ6nryyaOr17GjmTXhwIHKt/R0z9dGjbzrVrVEb1mVvWB34ED16g8e7H1sWTBtWvXue8YZ3mF17Vrz97A6yi9a8dhj5jcFpYKCKg+5Q4eamTLKuu022L276pAcFGTmLO5X5t9ldrZnUYvSzW73Pi7dzjnH9GCX2rXL/DBRVZ2gIPNvoOxCGmCeTVFR5eXtdu/tePmNiBwVhVURqVU2m42TIk7ipIiT6NG0R4XPh7cZzsEHDpJblEtqTiq7s3e7t11Zu9idY/YLSwq9VtoCWLff9Jim5aYx689ZzPrT82vlqNAouid0p0fTHgxvM5zznn3WTPWUkgI//GAm31+wwPzKvCyn04TY1NTa/8ModdJJppcyKclM/F/aC9q+/Ykzf6rdbmYvaNiw5kvols72kJHhvZUG0dKe4fj4inWbNjVDA0qnFSspMbNLlN8PLvd/h+WHMVSl/NRph1s1rbzSYFZW+WEWTqdZSCI/3/t8u3YVr/fTT7B+/ZHvGxfnHVbT073HF1dlzRrvsDpjBowbd+R6zZtXnH7uppuq9wPB2LHwxhve5xo3Nn8mpQG5fLgt3d54A847z1MvOdnMbWyzebbSMFx+W7jQvHhY6rXXYPLkquvY7dC1K7z4ond777oLNm48fJ3S/dGj4fLLPfVycsz3X1Wd0u2RR8xvF8p+r6XtrarNERFmjHkAU1gVEb+ICI3glLhTOCXulCMXPqRbfDdSc1JZlbaK9Px0r8+yi7KZv30+87fPJ6swi/NOPc/8h7hXL+jViy8uOoX2DSfSPs1J8Nr1ZgqsNWvMm/KpqZCWVrOAUhP79plt2bKKn7VsadrYu7fZevb0DgNiegSbNvWe0aC63n23euXKv2scFAR79lQdcEu3zt6/XeC00+Djj81wiKIiz1b+2OWq2I727U3vZWV1yu5X9uJgVeOJy39vZdXk7/3R1q1sqM2x1M3NrbjKXWVKx7GXysszobE6yv+d2LEDliw5cr3Kflj57Tfzg/OR9Cj3A35hoZm2rzrGjfMOqxs2wKuvHrleXJzCqohIbbl/0P3cP+h+LMtiZ9ZOlu9ZzrLUZSzfs5zlqcvZkbUDgJ5Ne3rVyyzI5IrPrwAgPDic7gnd6TmwJz2vGEnPpj3pcFIHgi2bmbs0N9ez5ed7h4uabIWFpudvxw6z7d5deTjZutVsX3xhjm0202vWuzf06WO2bt0qf9lJak/5XzPbbJX31FZHYqIZA3s0brvNbEdj2TLPmOHKQnLpi2/lp3Zr0sSEa6fTe3O5Kp4rP3ykZ0946KHDly/dGnsP6QGgb1/z51xZecsy13O5zMpv5XXubMJqaZnDbeWHhgQHm6BvWd6by1Xx3NEOPaisXnUnXipft7L/ZtR23XowxEJTV4nIcWN/3n6WpS6jQ+MOJMV4Xg6au2UuZ7x/xmHrhQWH0aVJFzqc1IHnhj9HowaNDlv2qJWUmPF9ZedOXb0aVq488tv5ISEmsJaG1969TeDQyzci/nG4gFsaqcqPU8/O9gT6yuqUno+K8h4a5HSa4RNV1Snd2rTxHrZw4ABs2XLkUB4c7D0spI5onlWFVREpY+vBrXz6x6ekpKaQkprC5ozNlZaz2+zkPZSHI9jTi/nsb8/y7cZv3fPGNo9pTlJMEknRScRHxhMXHofddgyhsaTEBNjkZDNl09KlZjqu4uKq60VFeYYPdOoEHTqcWGNgRaReU1hVWBWRKmTkZ7B8z3JSdqe4A+yfB/6kdWxrNt3lPaXW6C9H88kfnxz2WnabnUbhjbi267U8d85zXp+9nvw6jmAHceFxXltsWKxXIK6gsND0uC5ZYsLrkiXmjevq/Oe6WTMTWlu29LzUlZRkxnvGxZlfgQba7AQicsLRogAiIlWIDY/ljFZncEYrz9CAvOI80nLSKpTdn7e/ymu5LBf78vZRWFJY4bPxs8ZTUFL5SyANQhq4w+sL57zA6a08S6nuLkpnhn05cafHE3fedcSFjyeuJIS4dduISF6JrbQHdseOihfeudNsVQkPN8E1Otrsh4WZrey+w+H5Wt0tPNy87V86WX9kZL0YDycigU1hVUQEEx5bxbaqcH72mNlkFmSyI2sH2zO3syPTfN2ZvZN9ufvYl7ePfbn7aBbdzKtefnH+YYMqmHCcV5zHzqydlLi83x5elbaKW2bcUmm9EHsIcf3jiDsjjrig3vyc9CjB6zaYoQRr17IoYxVbgrKJy8driykEe2nHbH6+GT+7a1fN/pBqKjjYE14bN4aEBM/WtKn3cXy8mQtURKQchVURkSOICYshJiymwgIIVbHb7Ey5bAoH8g94toID3seHtrhw7xWbDuQffrWnYlcxablppOWmERESQfC4C+F8z+fvzRjHaymvV6hns6ChM4S4oiBiC+CcLUH8c67NvFF9aKqdLzqacnH5EFsAsfkQVQSRRRB6NLN6lZSYJV/376/edEGNGnkH2MNtcXF6uUzkBKKwKiLiA45gB6M6jzpiucpeG+hzch/evPBNDuQfICM/47BBNzas4lybBwoqX63JskFGcDEZwcXQANoOHQ3TPzIflpRAYSF3vNKKtPx9ldYPtgURaQ8j0hbGi41Gc2lwV/c0SdsK9vCPojk0KLERVuAkLL+IsLwiwnIKzJaVT3h2PmElcP4GcJQJvunhkBkGYSUQnptO2Pp0HGtWe3qBK21MsJlu6aSTPAsNlN9KhziUDm2obLhD6fCFsqs7iUjAUVgVEfEjWyVjOtvEtaFNXCVzS5ZTfvgAwE2n3UT/Zv29Qm1GgQm86XnpZBRkcLDgoHfQDQ7GCgoio+jwS+CWWE4OOnM5SC7OwYOh0xXuz3bvWMg77zwDIZitigkJDp79E470HDPhfmoqL2d/w+ORyRXKhZaYABtWYsJtn13wxWeljSmB3bu5r/NuNseCowQcmRCWbvZL65TuD9oOfcuMeCixw4+tKpZzuGyEBTlw2ENwBDkIC3IQHBrmvWxpTb+GhXmW7m3QwHsp38rOKTCLVKCwKiJSTwXbK/4n/Ow2Z3N2m7OrrOeyXBWCroXFyyNedgfbjPwMMgoyyCnKIacoh9ziXPd+bLh3j25OUbklbKsQ1mcAlJkJoeCHfPi1YlgtCjZb1qHjllFJcMNZJuQe2ua2SmVZNRa1evIn77Ca6YBzx1RW0gIKDm3ZACx+E/r86Skxoy1MGA4hLgguv+VBcI7Zj82HT770vvqbPWBxM+86QeWvYQuia2YYI/fEeIXY91tmUuQIJijUQZAjjKDQQ1vpviOcoLBwTotux8nhTUzvc1AQObZifi/cRlBQCEHBIZ6vwaEEBYdgLz0XEkrz6OYEhTpM3eBgCmxOCm1OgkIc5h7BIQTZgrDb7JX+kCXiKwqrIiInGLvNTmhQaIVzY3uOParrDUgawKpbV7lfKjvcll+SX+G+XeO7cnXnqyuUK3tcWFJIk6Q+8MQ7XnULX+0M+1YfsX2O08+C/j3Ni2UFBRQWpQNfHrEegCMyBqJc7hWg0sNhQyWLMZXXpJL8PrcVfNLlSDWdjPk9l5HLcr3Ojh8CGeGHqVJ0aMuGD1+Da1Z5PlqXCINuPnJ7AfY9DY3LrE/x7GB45MzKy9pdEGSZref+UBbMTDSLVxwKuhcN3M6S2DxsgB2b+392bNhsnv3bczpwb353Uy8khPwQ6B3xMTabzYRiDn212c1Xuw27LQibzcak+L/QPepUd935uWv5+64PvMtjw24vc2wzveef937G9GLb7WC38+aWL/hx72JTrmx5u+erzWanR5Nu3N79Znc9goJ4ZMETpOdnHCpjw3Zo3mVbme8V4MpOVzKw+UD3n+He3L08/evTXmUPt//XAX/1+iFx4Y6FfL/p+0rvU3a/cYPG3Nr7Vq9n99nqz9icsblCeZvNxqUdLqV1bOvq/YWpQwqrIiJyTCJCI2r08llZV3e5mqu7HN3SpAtuXEBecR6FJYUm1DoLK93vEt8FGnuWGI0oyOTvCzt5l3OaUFzoLHQH5EJnITGLPoCGLU1Fy8K2fDIxs++lxFVCiasEp8tJiVVxOEZwbCP45l0TkPPzIS+PkvRJULLyiN9XcGQ0NIs2K5vl5UFBAa5qdmQGlRvr66xBB2hQudU5nVW8w+aygwsoBoqcRWbJ4DLSe0NaNVYIzli1FOYu9dwzFFY/VK6QdWgDKDPeOefxh2C753hvB/j5yMPEaVAE3NLD61zyBfBpr2q0d+rH3P7p/V7nPhgP2xseuW7Hf73BwNVh7qCb3sjFs1cf/mXKsm55eBqxhWHugLy4dRpPdth+xHrt8iO49e/TzRRyh7bJbZcwM6bysekd3/yK1p0uhvvuq1a76orCqoiI1EsNwxrSMKxhjevFhMXwxOlP1PyGNhvX9biR63rc6HXasiz30IrSzWW5oNxwieezLuSxgoNe5dyh13K69xOjEiGhu6ei08nLKZMpyMvCWViAszD/0FaAs6gQZ5Hna9frO4DV2CzTWVJCQsl+7nUtwuly4rScnq+Wq8JXxyW9ocgydYuLad1wO2dn7MRpWThxHdrM9+rk0Dmbxan5wdAkyowlLi6GkhKa5hbQ4qCFBbhs5gW/svuuQ8cRlSzUFlF0KJ+WKedV71AAt5UL5lY1g3llxar7w0D5e9aEraAADh5+OrsqrVsHZYeUBwEdqnHPnFyYOdP7ZEMg5jDlF/wKmU2Oro0+pBWsREREpPaVrkVfXOwOse798sc13LcOfbWVlLjPO10lFLmKsVwuXC4nlrMEl8uF5XLisg59dbnA6eQkp8O07dCWTh45VqEpbx2qb7nK1XcRVWKneYHDBPpDdf8Iz6YIc2wdqme5LDhUx7IscDlpmR1MfL7dXS/PVsLyhgWmvOUCl3Vo35S3LMt9vv9OCCvytHdblIsNjTw/BJQG9fL7EcUwbKv3Y1nYDNIivcuXBsGBOyBh+KXwZfWGyRwLrWAlIiIi/mWzmXGhQUG1vsRvZR2hQcDhhvUeSaND29E4ugEw0AAYeMRSlWsBtCj9YaBMcMayvLdKzvUvX6Z82fCj/VP0HYVVERERkfqm7A8DxzktASIiIiIiAUthVUREREQClsKqiIiIiAQshVURERERCVgKqyIiIiISsBRWRURERCRgKayKiIiISMBSWBURERGRgOXTsJqRkcGYMWOIiYkhJiaGMWPGcPDgwWrXv+WWW7DZbLzwwgs+a6OIiIiIBC6fhtXRo0ezYsUKZs6cycyZM1mxYgVjxoypVt2vvvqKxYsXk5iY6MsmioiIiEgA89lyq2vXrmXmzJksWrSIvn37AvDmm2/Sv39/1q9fT7t27Q5bd9euXdxxxx3MmjWL888/31dNFBEREZEA57Oe1YULFxITE+MOqgD9+vUjJiaG33777bD1XC4XY8aM4a9//SudOnU64n0KCwvJysry2kRERETk+OCzsLpnzx6aNGlS4XyTJk3Ys2fPYev95z//ITg4mLvuuqta95k4caJ7TGxMTAxJSUlH3WYRERERCSw1DquPP/44Nputyi05ORkAm81Wob5lWZWeB0hJSeHFF1/k3XffPWyZ8h588EEyMzPd244dO2r6LYmIiIhIgKrxmNU77riDq666qsoyLVu2ZOXKlaSlpVX4bN++fcTHx1dab/78+ezdu5fmzZu7zzmdTiZMmMALL7zA1q1bK9RxOBw4HI6afRMiIiIiUi/UOKw2btyYxo0bH7Fc//79yczMZMmSJfTp0weAxYsXk5mZyYABAyqtM2bMGM466yyvc+eccw5jxozhhhtuqGlTRURERKSe89lsAB06dODcc89l7NixvP766wDcfPPNXHDBBV4zAbRv356JEydyySWX0KhRIxo1auR1nZCQEBISEqqcPUBEREREjk8+nWf1o48+okuXLgwfPpzhw4fTtWtXPvjgA68y69evJzMz05fNEBEREZF6ymZZluXvRtSmrKwsYmJiyMzMJDo62t/NEREREZFyapLXfNqzKiIiIiJyLBRWRURERCRgKayKiIiISMBSWBURERGRgKWwKiIiIiIBy2fzrPpL6eQGWVlZfm6JiIiIiFSmNKdVZ1Kq4y6sZmdnA5CUlOTnloiIiIhIVbKzs4mJiamyzHE3z6rL5WL37t1ERUVhs9nq5J5ZWVkkJSWxY8cOze1aD+n51X96hvWfnmH9p2dYv9X187Msi+zsbBITE7Hbqx6Vetz1rNrtdpo1a+aXe0dHR+sfaD2m51f/6RnWf3qG9Z+eYf1Wl8/vSD2qpfSClYiIiIgELIVVEREREQlYCqu1wOFw8Nhjj+FwOPzdFDkKen71n55h/adnWP/pGdZvgfz8jrsXrERERETk+KGeVREREREJWAqrIiIiIhKwFFZFREREJGAprIqIiIhIwFJYPUavvvoqrVq1IiwsjJ49ezJ//nx/N0kO45dffuHCCy8kMTERm83GV1995fW5ZVk8/vjjJCYmEh4ezrBhw1i9erV/GisVTJw4kd69exMVFUWTJk24+OKLWb9+vVcZPcPANmnSJLp27eqedLx///58//337s/1/OqXiRMnYrPZGD9+vPucnmFge/zxx7HZbF5bQkKC+/NAfX4Kq8fg008/Zfz48Tz88MMsX76cwYMHM2LECLZv3+7vpkklcnNz6datGy+//HKlnz/99NM899xzvPzyyyxdupSEhATOPvtssrOz67ilUpl58+Zx++23s2jRIubMmUNJSQnDhw8nNzfXXUbPMLA1a9aMp556iuTkZJKTkznjjDMYOXKk+/8M9fzqj6VLl/LGG2/QtWtXr/N6hoGvU6dOpKamurdVq1a5PwvY52fJUevTp481btw4r3Pt27e3HnjgAT+1SKoLsKZNm+Y+drlcVkJCgvXUU0+5zxUUFFgxMTHWa6+95ocWypHs3bvXAqx58+ZZlqVnWF/FxsZab731lp5fPZKdnW2deuqp1pw5c6yhQ4dad999t2VZ+jdYHzz22GNWt27dKv0skJ+felaPUlFRESkpKQwfPtzr/PDhw/ntt9/81Co5Wlu2bGHPnj1ez9PhcDB06FA9zwCVmZkJQFxcHKBnWN84nU6mTJlCbm4u/fv31/OrR26//XbOP/98zjrrLK/zeob1w8aNG0lMTKRVq1ZcddVVbN68GQjs5xfs17vXY/v378fpdBIfH+91Pj4+nj179vipVXK0Sp9ZZc9z27Zt/miSVMGyLO69914GDRpE586dAT3D+mLVqlX079+fgoICIiMjmTZtGh07dnT/n6GeX2CbMmUKy5YtY+nSpRU+07/BwNe3b1/ef/992rZtS1paGv/85z8ZMGAAq1evDujnp7B6jGw2m9exZVkVzkn9oedZP9xxxx2sXLmSBQsWVPhMzzCwtWvXjhUrVnDw4EG+/PJLrr/+eubNm+f+XM8vcO3YsYO7776b2bNnExYWdthyeoaBa8SIEe79Ll260L9/f9q0acN7771Hv379gMB8fhoGcJQaN25MUFBQhV7UvXv3VvipRAJf6duQep6B784772T69OnMnTuXZs2auc/rGdYPoaGhnHLKKfTq1YuJEyfSrVs3XnzxRT2/eiAlJYW9e/fSs2dPgoODCQ4OZt68ebz00ksEBwe7n5OeYf0RERFBly5d2LhxY0D/G1RYPUqhoaH07NmTOXPmeJ2fM2cOAwYM8FOr5Gi1atWKhIQEr+dZVFTEvHnz9DwDhGVZ3HHHHUydOpWffvqJVq1aeX2uZ1g/WZZFYWGhnl89cOaZZ7Jq1SpWrFjh3nr16sU111zDihUraN26tZ5hPVNYWMjatWtp2rRpYP8b9NurXceBKVOmWCEhIdbbb79trVmzxho/frwVERFhbd261d9Nk0pkZ2dby5cvt5YvX24B1nPPPWctX77c2rZtm2VZlvXUU09ZMTEx1tSpU61Vq1ZZV199tdW0aVMrKyvLzy0Xy7KsW2+91YqJibF+/vlnKzU11b3l5eW5y+gZBrYHH3zQ+uWXX6wtW7ZYK1eutB566CHLbrdbs2fPtixLz68+KjsbgGXpGQa6CRMmWD///LO1efNma9GiRdYFF1xgRUVFuXNLoD4/hdVj9Morr1gtWrSwQkNDrR49erin0ZHAM3fuXAuosF1//fWWZZlpOx577DErISHBcjgc1pAhQ6xVq1b5t9HiVtmzA6zJkye7y+gZBrYbb7zR/d/Lk046yTrzzDPdQdWy9Pzqo/JhVc8wsI0aNcpq2rSpFRISYiUmJlqXXnqptXr1avfngfr8bJZlWf7p0xURERERqZrGrIqIiIhIwFJYFREREZGApbAqIiIiIgFLYVVEREREApbCqoiIiIgELIVVEREREQlYCqsiIiIiErAUVkVEREQkYCmsioiIiEjACvZ3A0REpKIVK1bw1VdfuY/Hjx9Pw4YN/dYeERF/0XKrIiIB6N133+WGG25wH2/ZsoWWLVv6r0EiIn6iYQAiIiIiErAUVkVEREQkYCmsioiIiEjAUlgVERERkYClsCoiIiIiAUuzAYiIBBCbzVbjOnPnzmXYsGG13xgRkQCgnlURERERCVhaFEBEJIAEBQUBYFkWLperwvnKHE1vrIhIfaGeVRGRAFJSUkJJSQlvv/221/lNmza5Pyu/DR061E+tFRHxPYVVEREREQlYCqsiIiIiErAUVkVEREQkYCmsioiIiEjAUlgVERERkYClsCoiIiIiAUthVUREREQClsKqiIiIiAQshVURERERCVgKqyIiIiISsBRWRUQCUEhIiNex0+n0U0tERPxLYVVEJABFRUV5HWdkZPipJSIi/qWwKiISgFq2bOl1vHTpUv80RETEz2yWZVn+boSIiHgrKSmhcePGZGZmApCYmMhbb73FsGHDCA8P93PrRETqjnpWRUQCUHBwMDfccIP7ePfu3Zx33nk0aNCABg0aEBkZ6d7mz5/vx5aKiPiWwqqISID65z//yaBBgyqcz8/PJzc3173p5SsROZ4prIqIBKiIiAh+/vlnpkyZwpVXXknbtm2JiorCbtd/ukXkxKExqyIiIiISsPTjuYiIiIgELIVVEREREQlYCqsiIiIiErAUVkVEREQkYCmsioiIiEjAUlgVERERkYClsCoiIiIiAUthVUREREQClsKqiIiIiAQshVURERERCVgKqyIiIiISsBRWRURERCRgKayKiIiISMBSWBURERGRgKWwKiIiIiIBS2FVRERERAKWwqqIiIiIBKz/B6l3mQmk0ZMeAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (resultMats, P11p, \"b\", \"P11 Mats\"),\n", - " (resultMats, P12p, \"r\", \"P12 Mats\"),\n", - " (resultMatsT, P11p, \"b--\", \"P11 Mats + Term\"),\n", - " (resultMatsT, P12p, \"r--\", \"P12 Mats + Term\"),\n", - " (resultBR, P11p, \"g--\", \"P11 Bloch Redfield\"),\n", - " (resultBR, P12p, \"g--\", \"P12 Bloch Redfield\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "aaa12be7", - "metadata": {}, - "source": [ - "## Padé decomposition" - ] - }, - { - "cell_type": "markdown", - "id": "911d3acc", - "metadata": {}, - "source": [ - "The Matsubara decomposition is not the only option. We can also use the\n", - "faster-converging Pade decomposition." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "470eed60", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Ttm3b9O6776pr16669NJL9dJLL2n+/PnKzs6u8nsrSVdccYUMBoO9LUmff/65EhISZDab1bJlS02dOlXFxcX2/QaDQa+//rpGjhwpHx8fPf3005oyZYq6dOmiN954Q82bN5evr6/uuOMOlZSU6IUXXlB4eLhCQ0P1zDPPONQxZcoUNW/eXCaTSZGRkbrnnnvO+P08X4R6F2GylZupLypwUiUAAABo9EJDz317442qz9u+feXHnIO33npL7u7uWrNmjebOnau0tDRddNFF6tKlizZs2KClS5fqyJEjGj16tP2Y3NxcTZw4UT///LO++eYbubm56YorrlBpaelZffbMmTPVp08fbdy4UZdddpmSkpI0duxYXX/99frll1/UunVrjR07VjabTZJUUFCghIQEffHFF9qyZYtuu+02JSUlaf369ZKkl19+WYmJibr11lvts+dRUVGaPHmytm3bpq+++krbt2/XnDlzFBwcfE7frz/7/vvvFRoaqtjYWN16663KyMiw70tOTlZRUZEGDRpk74uMjFRcXJzWrl0rSVq3bp0sFot69uxpH9OrVy9ZLBaHMXFxcYqMjLSPGTx4sKxWq5KTkyut6+eff5Ykvfnmm0pLS7O3v/76a11//fW65557tG3bNs2dO1cLFiyoEMafeOIJjRw5Ups3b9ZNN90kSdqzZ4+++uorLV26VAsXLtQbb7yhyy67TAcPHtTKlSv1/PPP67HHHtOPP/4oSfrPf/6jmTNnau7cudq1a5f++9//Kj4+/ty+0dXkXqtnR50pP1NvLSbUAwAAAFVp3bq1XnjhBXv78ccfV7du3fTss8/a+9544w1FRUVp586dio2N1VVXXeVwjn//+98KDQ3Vtm3bFBcXV+3PHjZsmG6//Xb7586ZM0c9evTQ1VdfLUl6+OGHlZiYqCNHjig8PFxNmzbVAw88YD/+7rvv1tKlS/Xhhx+qZ8+eslgs8vT0lLe3t8LDw+3jUlNT1bVrV3Xv3l2SHGauz9XQoUN19dVXKzo6Wnv37tXkyZN1ySWXKDk5WSaTSenp6fL09FRAQIDDcWFhYUpPT5ckpaenK7SSX8aEhoY6jAkLC3PYHxAQIE9PT/uY8kJCQiRJ/v7+Dt+HZ555Ro888oj+/ve/S5Jatmypp556Sg899JCeeOIJ+7hrr73WHuZPKy0t1RtvvKEmTZqoQ4cOuvjii7Vjxw4tWbJEbm5uatu2rZ5//nl9//336tWrl1JTUxUeHq5LL71UHh4eat68uS644IJqfW/PFaHeRfQP7KbgkztlcvOQyc1THTv0/OuDAAAAgEbqdNA9LTk5Wd999518fX0rjN2zZ49iY2O1Z88eTZ48WT/++KMyMzPtM/SpqalnFeo7depk//p0cP3zbO7pvoyMDIWHh6ukpETPPfecFi9erEOHDslqtcpqtcrHx+eMn3PHHXfoqquu0i+//KJBgwZp1KhR6t27d7XrrMyYMWPsX8fFxal79+6Kjo7Wl19+ecZl+zabTQbDH2/s+vPX5zOmOpKTk/Xzzz87zMyXlJSooKBAeXl58vb2llTxz4RU9ouQJk2a2NthYWEyGo1yc3Nz6Du9WuHqq6/WrFmz1LJlSw0ZMkTDhg3TiBEj5O5ee9GbUO8iHnt8hbNLAAAAABqM8oG4tLRUI0aM0PPPP19hbEREhCRpxIgRioqK0vz58xUZGanS0lLFxcWd9YP2PDw87F+fDqiV9Z3+pcFLL72kmTNnatasWYqPj5ePj48mTJjwl587dOhQ7d+/X19++aVWrFihAQMGaPz48XrxxRfPqt4ziYiIUHR0tHbt2iVJCg8PV2FhoU6cOOEwW5+RkWH/hUJ4eLiOHDlS4VxHjx61/0IjPDzcfnvBaSdOnFBRUVGFGfy/UlpaqqlTp1b6Swez2Wz/urJfkvz55yKV/Wwq6zv9s4qKitKOHTu0fPlyrVixQnfeeaemT5+ulStXVjiuptS7e+pnz56tmJgYmc1mJSQk6IcffqjWcWvWrJG7u7u6dOlSYd9HH32kDh06yGQyqUOHDvrkk09quGoAAAAAdhkZ576VW/7sYPv2yo+pAd26ddPWrVvVokULtW7d2mHz8fHRsWPHtH37dj322GMaMGCA2rdvrxMnTtTIZ/+VH374QSNHjtT111+vzp07q2XLlvYQfZqnp6fD0+VPCwkJ0Q033KB3331Xs2bN0rx582q0tmPHjunAgQP2X3wkJCTIw8NDy5cvt49JS0vTli1b7KE+MTFRWVlZ+umnn+xj1q9fr6ysLIcxW7ZsUVpamn3MsmXLZDKZlJCQUGU9Hh4eFb4P3bp1044dOyr8XFu3bu0w415TvLy8dPnll+uVV17R999/r3Xr1mnz5s01/jmn1auZ+sWLF2vChAmaPXu2+vTpo7lz52ro0KHatm2bmjdvXuVxWVlZGjt2rAYMGFDhNz7r1q3TmDFj9NRTT+mKK67QJ598otGjR2v16tUOD2YAAAAAUEP+d29zjauBh7xVZfz48Zo/f76uueYaPfjggwoODtbu3bu1aNEizZ8/XwEBAQoKCtK8efMUERGh1NRUPfLII7VWz5+1bt1aH330kdauXauAgADNmDFD6enpat++vX1MixYttH79eu3bt0++vr4KDAzUlClTlJCQoI4dO8pqteqLL75wOKa8nJwc7d69297eu3evUlJSFBgYqObNmysnJ0dTpkzRVVddpYiICO3bt0//+Mc/FBwcrCuuuEKSZLFYdPPNN+v+++9XUFCQAgMD9cADDyg+Pl6XXnqpJKl9+/YaMmSIbr31Vs2dO1eSdNttt2n48OFq27atJGnQoEHq0KGDkpKSNH36dB0/flwPPPCAbr31Vvn5+VV5DS1atNA333yjPn36yGQyKSAgQI8//riGDx+uqKgoXX311XJzc9OmTZu0efNmPf300+f+g6nEggULVFJSop49e8rb21vvvPOOvLy8FB0dXaOf82f1aqZ+xowZuvnmm3XLLbeoffv2mjVrlqKiojRnzpwzHnf77bfr2muvVWJiYoV9s2bN0sCBAzVp0iS1a9dOkyZN0oABAzRr1qxaugoAAAAADU1kZKTWrFmjkpISDR48WHFxcbr33ntlsVjk5uYmNzc3LVq0SMnJyYqLi9N9992n6dOn10ltkydPVrdu3TR48GD1799f4eHhGjVqlMOYBx54QEajUR06dFBISIhSU1Pl6empSZMmqVOnTurXr5+MRqMWLVpU5eds2LBBXbt2VdeuXSVJEydOVNeuXfX4449LkoxGozZv3qyRI0cqNjZWf//73xUbG6t169Y53Hc+c+ZMjRo1SqNHj1afPn3k7e2tzz//XEbjHw/3fu+99xQfH69BgwZp0KBB6tSpk9555x37fqPRqC+//FJms1l9+vTR6NGjNWrUqL+8deCll17S8uXLFRUVZb+OwYMH64svvtDy5cvVo0cP9erVSzNmzKiVoO3v76/58+erT58+6tSpk7755ht9/vnnCgoKqvHPOs1gO/2eBCcrLCyUt7e3PvzwQ/tveSTp3nvvVUpKilauXFnpcW+++aZmz56tdevW6emnn9Z///tfpaSk2Pc3b95c9913n+677z573+n7Ufbv31/pOU8/eOK07OxsRUVFKSsr64y/FQIAAAAak4KCAu3du9d++yyAs3Omv0PZ2dmyWCx/mUPrzUx9ZmamSkpKKjz04M+vPihv165deuSRR/Tee+9V+TTByl6FcKZzStK0adNksVjsW1RU1FleDQAAAAAAta9e3VMvVXxtQVWvLCgpKdG1116rqVOnKjY2tkbOedqkSZM0ceJEe/v0TH199p9/36+FWxfJaitWga1IffzjNXVK5asbAAAAAACuod6E+uDgYBmNxgoz6BkZGZW+suDUqVPasGGDNm7cqLvuuktS2asKbDab3N3dtWzZMl1yySUKDw+v9jlPM5lMMplMNXBVdWfH4U362HLY3vY5udOJ1QAAAAAA6kK9WX7v6emphIQEh1cfSNLy5cvtrzX4Mz8/P23evFkpKSn2bdy4cWrbtq1SUlLsT7ZPTEyscM5ly5ZVes6GzOzueP+FVUVOqgQAAAAAUFfqzUy9VPZ0xaSkJHXv3l2JiYmaN2+eUlNTNW7cOElly+IPHTqkt99+W25uboqLi3M4PjQ0VGaz2aH/3nvvVb9+/fT8889r5MiR+vTTT7VixQqtXr26Tq+ttpnczVLhH+0CW7HzigEAAAAA1Il6FerHjBmjY8eO6cknn1RaWpri4uK0ZMkS+6sG0tLSlJqaelbn7N27txYtWqTHHntMkydPVqtWrbR48WKXe0e9ycPLoW01lDipEgAAAABAXak3r7Srz6r7KgFnevu12/T3zPn2dteTXvplZp4TKwIAAICr45V2wPlxqVfa4fyYPMvP1Jc6qRIAAAAAQF0h1LsIs8nHoV3gxvJ7AAAAAHB1hHoXYfL0dmgzUw8AAADUrX379slgMCglJcXZpaARIdS7CJOpXKg38qgEAAAAoLHo37+/JkyYcF7nmDZtmnr06KEmTZooNDRUo0aN0o4dO854zJQpU9SlS5fz+lycH0K9izCVW35vdSPUAwAAANVRWFj414MagZUrV2r8+PH68ccftXz5chUXF2vQoEHKzc2t9c8uKiqq9c9wVYR6F1E+1BfUq5cVAgAAoDE5mnv0nLf8ovwqz5uZl1npMWerf//+uuuuuzRx4kQFBwdr4MCBkqRt27Zp2LBh8vX1VVhYmJKSkpSZmWk/bunSpbrwwgvl7++voKAgDR8+XHv27Dmrz27RooWefvppjR07Vr6+voqOjtann36qo0ePauTIkfL19VV8fLw2bNhgP+bYsWO65ppr1KxZM3l7eys+Pl4LFy6077/hhhu0cuVKvfzyyzIYDDIYDNq3b59OnDih6667TiEhIfLy8lKbNm305ptvVlnb0qVLdcMNN6hjx47q3Lmz3nzzTaWmpio5ObnS8QsWLNDUqVP166+/2j93wYIFkqSsrCzddtttCg0NlZ+fny655BL9+uuv9mNPz/C/8cYbatmypUwmk2w2mwwGg+bOnavhw4fL29tb7du317p167R79271799fPj4+SkxMdPi+//rrr7r44ovVpEkT+fn5KSEhweH75+qIfi7C7NXEoV1klEpLiuVm5EcMAACAuhX6Yug5H/vPof/U+AvGV7qv/WvtlZmXWaHf9sTZr1J96623dMcdd2jNmjWy2WxKS0vTRRddpFtvvVUzZsxQfn6+Hn74YY0ePVrffvutJCk3N1cTJ05UfHy8cnNz9fjjj+uKK65QSkqK3NyqP186c+ZMPfvss5o8ebJmzpyppKQk9enTRzfddJOmT5+uhx9+WGPHjtXWrVtlMBhUUFCghIQEPfzww/Lz89OXX36ppKQktWzZUj179tTLL7+snTt3Ki4uTk8++aQkKSQkRPfee6+2bdumr776SsHBwdq9e7fy86v+pUl5WVlZkqTAwMBK948ZM0ZbtmzR0qVLtWLFCkmSxWKRzWbTZZddpsDAQC1ZskQWi0Vz587VgAEDtHPnTvv5du/erQ8++EAfffSRjEaj/bxPPfWUZsyYoRkzZujhhx/Wtddeq5YtW2rSpElq3ry5brrpJt1111366quvJEnXXXedunbtqjlz5shoNColJUUeHh7Vvs6GjsTnIkxevhX6CvNzZPb1r/tiAAAAgHqudevWeuGFF+ztxx9/XN26ddOzzz5r73vjjTcUFRWlnTt3KjY2VldddZXDOf79738rNDRU27ZtU1xcXLU/e9iwYbr99tvtnztnzhz16NFDV199tSTp4YcfVmJioo4cOaLw8HA1bdpUDzzwgP34u+++W0uXLtWHH36onj17ymKxyNPTU97e3goPD7ePS01NVdeuXdW9e3dJZasEqstms2nixIm68MILq7w2Ly8v+fr6yt3d3eFzv/32W23evFkZGRkymUySpBdffFH//e9/9Z///Ee33XabpLLbHt555x2FhIQ4nPfGG2/U6NGjHb4XkydP1uDBgyVJ9957r2688UaH63zwwQfVrl07SVKbNm2qfZ2ugOX3LsJkrhjqC3KznFAJAAAAUP+dDrqnJScn67vvvpOvr699Ox0STy/13rNnj33W2M/PTzExMZLKQuXZ6NSpk/3rsLAwSVJ8fHyFvoyMDElSSUmJnnnmGXXq1ElBQUHy9fXVsmXL/vJz77jjDi1atEhdunTRQw89pLVr11a7xrvuukubNm1yWOZfXcnJycrJybHXenrbu3evw7L56OjoCoFeqt73p6CgQNnZ2ZKkiRMn6pZbbtGll16q55577qxviWjomKl3Eb7+obrohEUmucts8JDJ4C43N+NfHwgAAAA0Qj4+js+kKi0t1YgRI/T8889XGBsRESFJGjFihKKiojR//nxFRkaqtLRUcXFxZ/2gvT8vDTcYDFX2lZaWvab6pZde0syZMzVr1izFx8fLx8dHEyZM+MvPHTp0qPbv368vv/xSK1as0IABAzR+/Hi9+OKLZzzu7rvv1meffaZVq1apWbNmZ3Vtp+uOiIjQ999/X2Gfv7+//evyP4PTzvb7M2XKFF177bX68ssv9dVXX+mJJ57QokWLdMUVV5x17Q0Rod5FBETE6PtZJ51dBgAAAKCMBzLO+Vhfz4orUE/bPn67bLbaectTt27d9NFHH6lFixZyd68Yk44dO6bt27dr7ty56tu3ryRp9erVtVJLeT/88INGjhyp66+/XlJZmN21a5fat29vH+Pp6amSkpIKx4aEhOiGG27QDTfcoL59++rBBx+sMtTbbDbdfffd+uSTT/T999/bVyKcSWWf261bN6Wnp8vd3f2slvyfj9jYWMXGxuq+++7TNddcozfffJNQDwAAAADnIsSn4pLqmhDsHVwr55Wk8ePHa/78+brmmmv04IMP2h8st2jRIs2fP18BAQEKCgrSvHnzFBERodTUVD3yyCO1Vs+ftW7dWh999JHWrl2rgIAAzZgxQ+np6Q6hvkWLFlq/fr327dsnX19fBQYGasqUKUpISFDHjh1ltVr1xRdfOBxT3vjx4/X+++/r008/VZMmTZSeni6p7OF3Xl5elR7TokUL7d27VykpKWrWrJmaNGmiSy+9VImJiRo1apSef/55tW3bVocPH9aSJUs0atSoCrc+nI/8/Hw9+OCD+tvf/qaYmBgdPHhQP//8c4XnH7gy7qkHAAAA0OhFRkZqzZo1Kikp0eDBgxUXF6d7771XFotFbm5ucnNz06JFi5ScnKy4uDjdd999mj59ep3UNnnyZHXr1k2DBw9W//79FR4erlGjRjmMeeCBB2Q0GtWhQweFhIQoNTVVnp6emjRpkjp16qR+/frJaDRq0aJFVX7OnDlzlJWVpf79+ysiIsK+LV68uMpjrrrqKg0ZMkQXX3yxQkJCtHDhQhkMBi1ZskT9+vXTTTfdpNjYWP3f//2f9u3bZ79HvqYYjUYdO3ZMY8eOVWxsrEaPHq2hQ4dq6tSpNfo59ZnBVlvrV1xIdna2LBaLsrKy5Ofn5+xyAAAAgHqhoKBAe/fuVUxMjMxms7PLARqcM/0dqm4OZaYeAAAAAIAGilAPAAAAAEADxYPyXMjkJ/rqcN4RWUuLZLUV6ZHLnlPCgOudXRYAAAAAoJYQ6l3Iopz12u1XZG9fd3CrEpxYDwAAAACgdrH83oWYbI4/zgJrrpMqAQAAAADUBUK9Cykf6q1F+U6qBAAAAI0JL9QCzk1N/N0h1LsQk83o0CbUAwAAoDZ5eHhIkvLy8pxcCdAwnf67c/rv0rngnnoXYi7347QWFTipEgAAADQGRqNR/v7+ysjIkCR5e3vLYDA4uSqg/rPZbMrLy1NGRob8/f1lNBr/+qAqEOpdiMlQLtQXE+oBAABQu8LDwyXJHuwBVJ+/v7/979C5ItS7EFO5H2cBM/UAAACoZQaDQREREQoNDVVRUdFfHwBAUtmS+/OZoT+NUO9CTAbH+zCsJVYnVQIAAIDGxmg01khAAXB2eFCeCzG5lQv1xYR6AAAAAHBlhHoXYnbzdGhbSwudVAkAAAAAoC4Q6l1IhZn6EkI9AAAAALgyQr0LMRkdZ+oLmKkHAAAAAJdGqHchJqPJoW218fRRAAAAAHBlhHoXUiHUlxLqAQAAAMCV8Uo7F9I0oLk67zXLJKNMNqM6+LV0dkkAAAAAgFpksNlsNmcXUd9lZ2fLYrEoKytLfn5+zi4HAAAAAODiqptDWX4PAAAAAEADRagHAAAAAKCBItQDAAAAANBAEeoBAAAAAGigCPUAAAAAADRQvNLOhezdtEr/+s8kFRRbZS0tlMnNUy89u8HZZQEAAAAAagmh3oUc3L9JzxrXSsaytn+BQS85tyQAAAAAQC1i+b0LMZubOLStRpuTKgEAAAAA1AVCvQsxmX0c2lajZCstdVI1AAAAAIDaRqh3IeVDfambVFxY4KRqAAAAAAC1jVDvQkxeTSr0WfOynVAJAAAAAKAuEOpdiNnbr0KfNe+UEyoBAAAAANQFQr0LqWymviAvywmVAAAAAADqAqHehZi8K1l+n5/jhEoAAAAAAHWBUO9CTJUtvy8g1AMAAACAqyLUuxCjh6fcSxz7rAW5zikGAAAAAFDrCPUuxlQu1Bfk86A8AAAAAHBVhHoXYyoxOLStVmbqAQAAAMBVEepdjLm0fKjPc1IlAAAAAIDaRqh3MaZSxx8poR4AAAAAXFe9C/WzZ89WTEyMzGazEhIS9MMPP1Q5dvXq1erTp4+CgoLk5eWldu3aaebMmQ5jFixYIIPBUGErKCio7UtximbF3oo+ZVRstofis8zyquTd9QAAAAAA1+Du7AL+bPHixZowYYJmz56tPn36aO7cuRo6dKi2bdum5s2bVxjv4+Oju+66S506dZKPj49Wr16t22+/XT4+Prrtttvs4/z8/LRjxw6HY81mc61fjzOsmpXl7BIAAAAAAHXEYLPZbM4u4rSePXuqW7dumjNnjr2vffv2GjVqlKZNm1atc1x55ZXy8fHRO++8I6lspn7ChAk6efLkOdeVnZ0ti8WirKws+flVfBc8AAAAAAA1qbo5tN4svy8sLFRycrIGDRrk0D9o0CCtXbu2WufYuHGj1q5dq4suusihPycnR9HR0WrWrJmGDx+ujRs3nvE8VqtV2dnZDhsAAAAAAPVNvQn1mZmZKikpUVhYmEN/WFiY0tPTz3hss2bNZDKZ1L17d40fP1633HKLfV+7du20YMECffbZZ1q4cKHMZrP69OmjXbt2VXm+adOmyWKx2LeoqKjzuzgAAAAAAGpBvbqnXpIMBsdXstlstgp95f3www/KycnRjz/+qEceeUStW7fWNddcI0nq1auXevXqZR/bp08fdevWTa+++qpeeeWVSs83adIkTZw40d7Ozs4m2AMAAAAA6p16E+qDg4NlNBorzMpnZGRUmL0vLyYmRpIUHx+vI0eOaMqUKfZQX56bm5t69Ohxxpl6k8kkk8l0llcAAAAAAEDdqjeh3tPTUwkJCVq+fLmuuOIKe//y5cs1cuTIap/HZrPJarWecX9KSori4+PPq976atkH0/Tr7tWyFltlLbGqd7uBGnrN484uCwAAAABQC+pNqJekiRMnKikpSd27d1diYqLmzZun1NRUjRs3TlLZsvhDhw7p7bffliS99tprat68udq1ayep7L31L774ou6++277OadOnapevXqpTZs2ys7O1iuvvKKUlBS99tprdX+BdWDxz2/qDd//rUJwk+7bnEeoBwAAAAAXVa9C/ZgxY3Ts2DE9+eSTSktLU1xcnJYsWaLo6GhJUlpamlJTU+3jS0tLNWnSJO3du1fu7u5q1aqVnnvuOd1+++32MSdPntRtt92m9PR0WSwWde3aVatWrdIFF1xQ59dXF0xung7tgpKqVy0AAAAAABq2evWe+vqqIb2n/r5J3TTL/Mcr+27KaaN/T9/pxIoAAAAAAGerwb2nHjXDZHScqbeWFjmpEgAAAABAbSPUuxizu9mhbbUR6gEAAADAVRHqXYzJ3fFVfAWEegAAAABwWYR6F2OqMFNf7KRKAAAAAAC1jVDvYsrP1FtV4qRKAAAAAAC1jVDvYkweXg7tAgMz9QAAAADgqgj1Lsbs6e3QZqYeAAAAAFwXod7FmDwdZ+qthlInVQIAAAAAqG2EehdTIdS7EeoBAAAAwFUR6l2Mqdzy+wJCPQAAAAC4LEK9izGZfBzaVjebkyoBAAAAANQ2d2cXgJplNvvKVCyZSiRziUEBxR7OLgkAAAAAUEsMNpuNqdy/kJ2dLYvFoqysLPn5+Tm7HAAAAACAi6tuDmX5PQAAAAAADRShHgAAAACABopQDwAAAABAA0WoBwAAAACggSLUAwAAAADQQPFKOxdjKy3VO3PGyVpUIGtRvqzFBbohaYaCmrVxdmkAAAAAgBpGqHdBNxydL5vhj/bAvX8n1AMAAACAC2L5vYsxuLnJVOzYZy3IdU4xAAAAAIBaRah3QeYSg0PbaiXUAwAAAIArItS7IFMpoR4AAAAAGgNCvQsqH+oLWH4PAAAAAC6JUO+CTKWOP1ZrYZ6TKgEAAAAA1CZCvQsy2cqH+nwnVQIAAAAAqE2Eehdkthkd2tYiQj0AAAAAuCJCvQsyEeoBAAAAoFEg1Lsgk9wd2gWEegAAAABwSYR6F2QyOIZ6a1GBkyoBAAAAANQmQr0LKj9Tby2xOqkSAAAAAEBtItS7ILObh0PbWkyoBwAAAABXRKh3QSZDuVDPTD0AAAAAuCRCvQsy/Wmm3qNEssnmxGoAAAAAALXFYLPZSHx/ITs7WxaLRVlZWfLz83N2OX+pIOekJMnTy1duRvczDwYAAAAA1DvVzaEkPhdk9vV3dgkAAAAAgDrA8nsAAAAAABooQj0AAAAAAA0UoR4AAAAAgAaKUA8AAAAAQAPFg/Jc0MEdP2vjT5+poDBX1sJ8+TcJ0fDrn3R2WQAAAACAGkaod0HfrJinGzL/ZW8n7PQm1AMAAACAC2L5vQsyeXg5tAsMJU6qBAAAAABQmwj1Lsjk6RjqrYZSJ1UCAAAAAKhNhHoXZDb5OLStboR6AAAAAHBFhHoXZCLUAwAAAECjQKh3QSaTt0O7wGhzUiUAAAAAgNpEqHdBFWbqjU4qBAAAAABQqwj1Lsjs1cShbXWXbKUswQcAAAAAV0Ood0Ems2+FviJrnhMqAQAAAADUJkK9CzJ5VQz1BTkn674QAAAAAECtItS7IFO55feSZM0/5YRKAAAAAAC1iVDvgsw+fhX6rPk5TqgEAAAAAFCb6l2onz17tmJiYmQ2m5WQkKAffvihyrGrV69Wnz59FBQUJC8vL7Vr104zZ86sMO6jjz5Shw4dZDKZ1KFDB33yySe1eQlOZ/KuGOoL8rKdUAkAAAAAoDbVq1C/ePFiTZgwQY8++qg2btyovn37aujQoUpNTa10vI+Pj+666y6tWrVK27dv12OPPabHHntM8+bNs49Zt26dxowZo6SkJP36669KSkrS6NGjtX79+rq6rDrnWck99czUAwAAAIDrMdhsNpuzizitZ8+e6tatm+bMmWPva9++vUaNGqVp06ZV6xxXXnmlfHx89M4770iSxowZo+zsbH311Vf2MUOGDFFAQIAWLlxYrXNmZ2fLYrEoKytLfn4VZ8HrI8/HDSr60/vpk/u+q26XXOe8ggAAAAAA1VbdHOpehzWdUWFhoZKTk/XII4849A8aNEhr166t1jk2btyotWvX6umnn7b3rVu3Tvfdd5/DuMGDB2vWrFlVnsdqtcpqtdrb2dkNb+n6rjGr5Wn2kcmricw+Fpl9/Z1dEgAAAACghtWbUJ+ZmamSkhKFhYU59IeFhSk9Pf2MxzZr1kxHjx5VcXGxpkyZoltuucW+Lz09/azPOW3aNE2dOvUcrqL+iO7Yx9klAAAAAABqWb26p16SDAaDQ9tms1XoK++HH37Qhg0b9Prrr2vWrFkVltWf7TknTZqkrKws+3bgwIGzvAoAAAAAAGpfvZmpDw4OltForDCDnpGRUWGmvbyYmBhJUnx8vI4cOaIpU6bommuukSSFh4ef9TlNJpNMJtO5XAYAAAAAAHWm3szUe3p6KiEhQcuXL3foX758uXr37l3t89hsNof74RMTEyucc9myZWd1TgAAAAAA6qN6M1MvSRMnTlRSUpK6d++uxMREzZs3T6mpqRo3bpyksmXxhw4d0ttvvy1Jeu2119S8eXO1a9dOUtl761988UXdfffd9nPee++96tevn55//nmNHDlSn376qVasWKHVq1fX/QUCAAAAAFCD6lWoHzNmjI4dO6Ynn3xSaWlpiouL05IlSxQdHS1JSktLc3hnfWlpqSZNmqS9e/fK3d1drVq10nPPPafbb7/dPqZ3795atGiRHnvsMU2ePFmtWrXS4sWL1bNnzzq/vrq07cfPdOxoqqzWPFmteeoQf4liOvVzdlkAAAAAgBpUr95TX181xPfU95/gr5UBWfb2q95/010PfujEigAAAAAA1VXdHFpv7qlHzTKVW4RhLS5wUiUAAAAAgNpCqHdRZoOHQ9tabK1iJAAAAACgoSLUuyiTwXGmvoCZegAAAABwOYR6F2UqP1Nfwkw9AAAAALgaQr2LMrmVD/WFTqoEAAAAAFBbCPUuyuTm6dC2lhLqAQAAAMDVEOpdlNlocmgXMFMPAAAAAC6HUO+iTMZyM/W2IidVAgAAAACoLYR6F2Vyd5ypJ9QDAAAAgOsh1Lsok7F8qC92UiUAAAAAgNpCqHdRJnezQ7uAmXoAAAAAcDmEehdl9vByaFtV4qRKAAAAAAC1hVDvokwejjP1VgOhHgAAAABcDaHeRZk8HWfqCwj1AAAAAOBy3J1dAGrH0Mvv1y87+8rs1UQms698/IKdXRIAAAAAoIYR6l1UYGQrBUa2cnYZAAAAAIBaxPJ7AAAAAAAaKEI9AAAAAAANFKEeAAAAAIAGilAPAAAAAEADxYPyXFRRQZ72bV0ja0GOrAW5Kig4pcQht8rNyI8cAAAAAFwFCc9FHdr9i2K/GOTQl9P7KvkEhDqpIgAAAABATWP5vYsyeflW6LPmn3JCJQAAAACA2kKod1EmryYV+gpys5xQCQAAAACgthDqXVRlod6an+OESgAAAAAAtYVQ76JMPn4V+qwFhHoAAAAAcCWEehfl7mmWsdSxj1APAAAAAK6FUO/CTMWObWtBrnMKAQAAAADUCkK9CzOVGhzaBczUAwAAAIBLIdS7MFOJY6i3WpmpBwAAAABXQqh3YeVn6q3WPCdVAgAAAACoDYR6F2a2GR3a1sJ8J1UCAAAAAKgNhHoXZip1/PFaC5mpBwAAAABXQqh3YSY5ztQXEOoBAAAAwKUQ6l2Yqfzy+6ICJ1UCAAAAAKgNhHoXVn6m3lpMqAcAAAAAV+Lu7AJQe54f8YqyszNkMvnIZPJR8zYJzi4JAAAAAFCDCPUuLGHA9c4uAQAAAABQi1h+DwAAAABAA0WoBwAAAACggSLUAwAAAADQQBHqAQAAAABooHhQngsryDmpnBNHZM0/pYK8bHn5+CuyTTdnlwUAAAAAqCGEehf27PTL9ZTbD/b21VnN9MGMA06sCAAAAABQk1h+78JM7iaHttVW7KRKAAAAAAC1gVDvwszuZoe2VUVOqgQAAAAAUBsI9S7MVD7U20qcVAkAAAAAoDYQ6l2YycPLoV1gYPk9AAAAALgSQr0LM3mUX37PTD0AAAAAuBJCvQszm3wc2lZDqZMqAQAAAADUBkK9CzN5llt+78ZMPQAAAAC4EkK9CzN5eju0makHAAAAANdCqHdhZrOvQ9tqtDmpEgAAAABAbah3oX727NmKiYmR2WxWQkKCfvjhhyrHfvzxxxo4cKBCQkLk5+enxMREff311w5jFixYIIPBUGErKCio7UtxOpOp3Ey9G6EeAAAAAFxJvQr1ixcv1oQJE/Too49q48aN6tu3r4YOHarU1NRKx69atUoDBw7UkiVLlJycrIsvvlgjRozQxo0bHcb5+fkpLS3NYTObzZWe05WYyj0or8DdSYUAAAAAAGpFvYp5M2bM0M0336xbbrlFkjRr1ix9/fXXmjNnjqZNm1Zh/KxZsxzazz77rD799FN9/vnn6tq1q73fYDAoPDy8Vmuvj0zllt8XGaXSkmK5GevVjx0AAAAAcI7qzUx9YWGhkpOTNWjQIIf+QYMGae3atdU6R2lpqU6dOqXAwECH/pycHEVHR6tZs2YaPnx4hZn88qxWq7Kzsx22hsjs3aRCX2F+jhMqAQAAAADUhnoT6jMzM1VSUqKwsDCH/rCwMKWnp1frHC+99JJyc3M1evRoe1+7du20YMECffbZZ1q4cKHMZrP69OmjXbt2VXmeadOmyWKx2LeoqKhzuygnM3lVDPXWvIb5CwoAAAAAQEX1bh22wWBwaNtstgp9lVm4cKGmTJmiTz/9VKGhofb+Xr16qVevXvZ2nz591K1bN7366qt65ZVXKj3XpEmTNHHiRHs7Ozu7QQb7oIhWej/8Lpk8vWQ2+chk8pa3X7CzywIAAAAA1JB6E+qDg4NlNBorzMpnZGRUmL0vb/Hixbr55pv14Ycf6tJLLz3jWDc3N/Xo0eOMM/Umk0kmk6n6xddTXn6Buub2V51dBgAAAACgltSb5feenp5KSEjQ8uXLHfqXL1+u3r17V3ncwoULdcMNN+j999/XZZdd9pefY7PZlJKSooiIiPOuGQAAAAAAZ6o3M/WSNHHiRCUlJal79+5KTEzUvHnzlJqaqnHjxkkqWxZ/6NAhvf3225LKAv3YsWP18ssvq1evXvZZfi8vL1ksFknS1KlT1atXL7Vp00bZ2dl65ZVXlJKSotdee805FwkAAAAAQA2pV6F+zJgxOnbsmJ588kmlpaUpLi5OS5YsUXR0tCQpLS3N4Z31c+fOVXFxscaPH6/x48fb+//+979rwYIFkqSTJ0/qtttuU3p6uiwWi7p27apVq1bpggsuqNNrAwAAAACgphlsNpvN2UXUd9nZ2bJYLMrKypKfn5+zywEAAAAAuLjq5tB6NVOP2lFcWCBrbras+afkYwmRyYdfTAAAAACAKyDUu7iYBzy0r0mxvb0kdqqGXvO4EysCAAAAANSUevP0e9QOd5vBoW0tzHdSJQAAAACAmkaod3Emm+OPuMCa66RKAAAAAAA1jVDv4sqHemsRM/UAAAAA4CoI9S7OZDM6tAn1AAAAAOA6CPUuzlzuWYjWogInVQIAAAAAqGmEehdnMjiG+gJm6gEAAADAZRDqXZyp/Ex9sdVJlQAAAAAAahqh3sWZDB4ObWsJoR4AAAAAXIX7Xw+pXFFRkdLT05WXl6eQkBAFBgbWZF2oISY3x1BfUMw99QAAAADgKs5qpj4nJ0dz585V//79ZbFY1KJFC3Xo0EEhISGKjo7Wrbfeqp9//rm2asU5MLt5OrStJYVOqgQAAAAAUNOqHepnzpypFi1aaP78+brkkkv08ccfKyUlRTt27NC6dev0xBNPqLi4WAMHDtSQIUO0a9eu2qwb1VR+pt5aSqgHAAAAAFdR7eX3a9eu1Xfffaf4+PhK919wwQW66aabNGfOHL3xxhtauXKl2rRpU2OF4tyYjOVm6kuLnFQJAAAAAKCmVTvUf/jhh/avExMT9fXXX8vPz6/COLPZrDvvvLNmqsN5MxlNDu0CZuoBAAAAwGWc09Pv169fr4KCig9cy87O1oMPPnjeRaHmlA/1VluxkyoBAAAAANS0s3r6/ZVXXqkLLrhABoNBGRkZCg0Nddifm5urGTNmaPr06TVaJM7dsD43KOTXMJk8zDJ5eik6Ic7ZJQEAAAAAashZhfro6Gh98cUXstls6ty5s4KCgtS5c2d17txZnTp10qZNmxQREVFbteIcdL90rLpfOtbZZQAAAAAAasFZhfqZM2dKkkwmk1avXq3Dhw9r48aNSklJ0SeffKLS0lK98MILtVIoAAAAAABwdFah/rTc3Fy5u5cdOnLkyBotCAAAAAAAVM85PSjvdKAHAAAAAADOU+1Qn5qaelYnPnTo0FkXAwAAAAAAqq/aob5Hjx669dZb9dNPP1U5JisrS/Pnz1dcXJw+/vjjGikQNau0hFfaAQAAAICrqPY6+u3bt+vZZ5/VkCFD5OHhoe7duysyMlJms1knTpzQtm3btHXrVnXv3l3Tp0/X0KFDa7NuVNPqL2Zr1Oq7VGC0yWqUAq0GHXm+1NllAQAAAABqQLVn6gMDA/Xiiy/q8OHDmjNnjmJjY5WZmaldu3ZJkq677jolJydrzZo1BPp65piXTbmeUrFRsrrZnF0OAAAAAKCGnPUT78xms6688kpdeeWVtVEPapjJ5OPQthqdVAgAAAAAoMad09Pv0XCYvXwd2lZ3yVbK8nsAAAAAcAXnFepXrlyp/v37a/DgwVq1apUkKSMjQ++8846SkpJqpECcH5PZMdTbDFJxYYGTqgEAAAAA1KTzeuH8uHHj9Pjjj6tly5Z688039fbbb2vx4sUaNmyYhg8fXlM14jyYys3US1JB7kl5mL2dUA0AAAAAoCadV6g3m8265pprJEkJCQkKCQnRtm3bFBUVVSPF4fyZvJpU6LPmnVKTICcUAwAAAACoUee1/P7o0aP64IMP9Msvv6igoEAxMTEE+nqm0lCff8oJlQAAAAAAatp5zdRPnDhRS5cu1ZYtW7R9+3YVFhZq1KhR6tq1q7p27arLL7+8purEOTL7WCr0WfNznFAJAAAAAKCmnVWo37Vrl9544w09/PDD8vf318SJEx32//7779qyZYu2bNmiDz74gFBfD5h8/Cr0WQsI9QAAAADgCs4q1D/33HM6ceKE/P39K+yzWq0qKCjQ5ZdfTpivR9yM7nIvkYr/9H76ApbfAwAAAIBLOKt76leuXKl77rmn0n0mk0njxo3Ts88+WyOFoeaYShzb1oJc5xQCAAAAAKhRZxXqDx06pFatWlW5//bbb9dnn3123kWhZplKDA5tq5VQDwAAAACu4KxCfWBgoNLS0qrcf8EFF2j37t3nXRRqlrm0fKjPc1IlAAAAAICadFahvl+/flqwYEHVJ3Nzk9VqPd+aUMNMpY4/ZkI9AAAAALiGs3pQ3gMPPKBevXqpS5cuuu222yrsX7dunVq2bFljxaFmTAwbpVPWbJnczTK5mxTXeaCzSwIAAAAA1ICzCvUJCQmaM2eOxo0bpw8//FDjx49Xt27d5Ovrqx9++EEPP/yw7r333tqqFeforgc/dHYJAAAAAIBaYLDZbLazPWj16tWaOHGiNmzYIIOh7H5tm82mQYMG6fPPP5eHh0eNF+pM2dnZslgsysrKkp9fxfe+AwAAAABQk6qbQ89qpv60Cy+8UD/99JN+++03/fLLL8rLy1NcXJx69ep1zgUDAAAAAICzc06h/rR27dqpXbt2NVULAAAAAAA4C2f19HsAAAAAAFB/EOoBAAAAAGigzmv5PRqGhx/rqY/yf5HVzaYCY6nu8rpITzzxnbPLAgAAAACcJ0J9I5BpPaE9fsX2dpY124nVAAAAAABqCsvvGwGTm6dDu6DE6qRKAAAAAAA1iVDfCJQP9dbSQidVAgAAAACoSYT6RsDsbnJoW0uLnFQJAAAAAKAmEeobAZOxXKi3EeoBAAAAwBUQ6hsBU7mZ+gJCPQAAAAC4BEJ9I2ByNzu0rbbiKkYCAAAAABqSehfqZ8+erZiYGJnNZiUkJOiHH36ocuzHH3+sgQMHKiQkRH5+fkpMTNTXX39dYdxHH32kDh06yGQyqUOHDvrkk09q8xLqHbOHl0PbqhInVQIAAAAAqEn1KtQvXrxYEyZM0KOPPqqNGzeqb9++Gjp0qFJTUysdv2rVKg0cOFBLlixRcnKyLr74Yo0YMUIbN260j1m3bp3GjBmjpKQk/frrr0pKStLo0aO1fv36urospzN5lJupNzBTDwAAAACuwGCz2WzOLuK0nj17qlu3bpozZ469r3379ho1apSmTZtWrXN07NhRY8aM0eOPPy5JGjNmjLKzs/XVV1/ZxwwZMkQBAQFauHBhtc6ZnZ0ti8WirKws+fn5ncUV1Q/vzrlDSRmv29udT5qVMjPfiRUBAAAAAM6kujm03szUFxYWKjk5WYMGDXLoHzRokNauXVutc5SWlurUqVMKDAy0961bt67COQcPHnzGc1qtVmVnZztsDZnJs9zye0OpkyoBAAAAANSkehPqMzMzVVJSorCwMIf+sLAwpaenV+scL730knJzczV69Gh7X3p6+lmfc9q0abJYLPYtKirqLK6k/jF7+ji0rW6EegAAAABwBfUm1J9mMBgc2jabrUJfZRYuXKgpU6Zo8eLFCg0NPa9zTpo0SVlZWfbtwIEDZ3EF9Y/J5O3QLiDUAwAAAIBLcHd2AacFBwfLaDRWmEHPyMioMNNe3uLFi3XzzTfrww8/1KWXXuqwLzw8/KzPaTKZZDKZqtzf0JQP9Va3evMYBQAAAADAeag3M/Wenp5KSEjQ8uXLHfqXL1+u3r17V3ncwoULdcMNN+j999/XZZddVmF/YmJihXMuW7bsjOd0Nc1juuqegs56sLCHJpf21SN+Q51dEgAAAACgBtSbmXpJmjhxopKSktS9e3clJiZq3rx5Sk1N1bhx4ySVLYs/dOiQ3n77bUllgX7s2LF6+eWX1atXL/uMvJeXlywWiyTp3nvvVb9+/fT8889r5MiR+vTTT7VixQqtXr3aORfpBDGd+unlTinOLgMAAAAAUMPqzUy9VPb6uVmzZunJJ59Uly5dtGrVKi1ZskTR0dGSpLS0NId31s+dO1fFxcUaP368IiIi7Nu9995rH9O7d28tWrRIb775pjp16qQFCxZo8eLF6tmzZ51fHwAAAAAANalevae+vmro76kHAAAAADQsDe499QAAAAAA4OwQ6gEAAAAAaKAI9QAAAAAANFD16un3qB0lRYXq92CwrIaS/22lWjp2mVp27u/s0gAAAAAA54FQ3wi4Gd21zv+UbIY/+nKyM51XEAAAAACgRrD8vhEwuLnJVOzYZy3IdU4xAAAAAIAaQ6hvJEwljm2rlVAPAAAAAA0dod7FZGZKNlvFfnOp44+6oCCnjioCAAAAANQWQr2LSE2V7rlHioqSvvqq4n5TqcGhbbXm1VFlAAAAAIDaQqh3AVOnSq1aSa++KhUUSM88U3G23lRupt5aSKgHAAAAgIaOUO8CoqOl4j89CG/tWmnVKscxJlv5UJ9fB5UBAAAAAGoTod4FXHed1Ly5Y9+zzzq2TTajQ7ugkAflAQAAAEBDR6h3AR4e0kMPOfYtWyZt2PBH21wu1FuLCuqgMgAAAABAbSLUu4ibbpJCQx37pk3742uT3B32WYsJ9QAAAADQ0BHqXYSXlzRxomPfxx9L27aVfW0ylAv1zNQDAAAAQINHqHchd9wh+fs79j33XNk/y8/UFzBTDwAAAAANHqHehfj5SXff7dj3/vvS3r2VzNSXWOuwMgAAAABAbSDUu5h77pG8vf9ol5RI06dLZjdPh3GEegAAAABo+Aj1LiY4WLr9dse+N96QPG1eCs9106257fRZy8c09YElzikQAAAAAFBj3P96CBqa+++XXntNKiwsa1utkn/xZzr0QpTcjPzIAQAAAMBVMFPvgpo2lW64wbFvzhsxOplFoAcAAAAAV0Kod1EPPSS5/emnm5Mjvfqq8+oBAAAAANQ8Qr2LatVK+r//c+x7+WXp1Cnn1AMAAAAAqHmEehc2aZJj+8QJad4859QCAAAAAKh5hHoXFhcnjRzp2PfSS1JBgZSVkarF8+/Vf9+aVPnBAAAAAIB6j1Dv4srP1od4vqyB9wUq+J/R+r/Dr+jZFG60BwAAAICGilDv4nr2lAYM+KNtNFq1OvyEio1l7Z/9c5W2J8UptQEAAAAAzg+hvhH4xz/++Hrz/jvlV+C4/4vPZ9RtQQAAAACAGkGobwQuvrhsxl6Sikt81WF3lMP+z/cvc0JVAAAAAIDzRahvBAwG6Ykn/tTeMdxh/3KfI8rLyqzjqgAAAAAA54tQ30gMGSJdemnZ19t23y9j6R/7Cjykbz572TmFAQAAAADOGaG+kTAYyl5nZzBIWfmtFJdqcdj/2aYPnVQZAAAAAOBcEeobkU6dpJtvLvvasqOvw74vDLtUWlLshKoAAAAAAOeKUN/IPPWU5OMj7dtxj0N/uk+pNnzzjpOqAgAAAACcC0J9IxMeLj3yiJR6fKBiMj0d9n2+5g0nVQUAAAAAOBeE+kZo4kSpWTOp+Y7ODv2fZ29wUkUAAAAAgHNBqG+EvL2ladOk4zvGOvT/6l+g/VvXOKkqAAAAAMDZItQ3UtdeK3mF3qLAPIND/8ef8mo7AAAAAGgoCPWNlJub9OIMs9rtinHo/2jft06qCAAAAABwtgj1jVjfvlLLkpGSpObH3dVvXVflfzNZe/Y4uTAAAAAAQLW4O7sAONdDTzyiny7rr53pw5X6v9/xPPKI9OGHTi4MAAAAAPCXmKlv5OK7hWr4tZfrz38U/vMfafVq59UEAAAAAKgeQj302GNSYKBj34QJUkmJU8oBAAAAAFQToR4KCJCeeMKxLzlZmjXLKeUAAAAAAKqJUA9J0h13SG3bOvY99pi0c6dz6gEAAAAA/DVCPSRJHh7Sv/4lGQxSsO8mJcbdo4IC6eabpdJSZ1cHAAAAAKgMT7+HJCk/+7hWfjdGncat069hucqU1CU3SqtXP6jXXpPuvtvZFQIAAAAAymOmHpIkk4+f5mV/p1/Dcu19RZc8KalUjzwi7d3rvNoAAAAAAJUj1EOS5GZ016NN/8+hb2tUjrrHTlFennTLLZLN5qTiAAAAAACVItTD7sZx89TylOMdGacuflEyFOvbb6X5851UGAAAAACgUoR62HmYvTWl5c0OfTsi8tWr/UOSpAcekFJTnVEZAAAAAKAyhHo4uPbWV9Q+y9OhL+Pi12QwFOrUKen221mGDwAAAAD1BaEeDowennqy410Ofb+HFCqxU1nf0qXSW285ozIAAAAAQHn1LtTPnj1bMTExMpvNSkhI0A8//FDl2LS0NF177bVq27at3NzcNGHChApjFixYIIPBUGErKCioxato2K684Xl1Oenl0Heg/5syuuVJku67Tzp82BmVAQAAAAD+rF6F+sWLF2vChAl69NFHtXHjRvXt21dDhw5VahU3clutVoWEhOjRRx9V586dqzyvn5+f0tLSHDaz2Vxbl9HguRnd9XT3hxz6DgQUK7HrrZKkkyelO+5gGT4AAAAAOFu9CvUzZszQzTffrFtuuUXt27fXrFmzFBUVpTlz5lQ6vkWLFnr55Zc1duxYWSyWKs9rMBgUHh7usOHMhl3zuHqd9HXo233RInm6n5QkffaZ9PbbTigMAAAAAGBXb0J9YWGhkpOTNWjQIIf+QYMGae3ated17pycHEVHR6tZs2YaPny4Nm7ceMbxVqtV2dnZDltjY3Bz0zN9pzj0pfuVqlf3G+3tO+6QNm+u48IAAAAAAHb1JtRnZmaqpKREYWFhDv1hYWFKT08/5/O2a9dOCxYs0GeffaaFCxfKbDarT58+2rVrV5XHTJs2TRaLxb5FRUWd8+c3ZJdceb8uPuHv0Lftwk/l7Vn288jPl/72N6kR/s4DAAAAAOqFehPqTzMYDA5tm81Woe9s9OrVS9dff706d+6svn376oMPPlBsbKxeffXVKo+ZNGmSsrKy7NuBAwfO+fMbuqcHPefQzvS1qXv3P95lv3OndMst3F8PAAAAAM5Qb0J9cHCwjEZjhVn5jIyMCrP358PNzU09evQ440y9yWSSn5+fw9ZY9R52u4adDJEkNbFKk0suUnGh4zMOPvxQeuUVZ1QHAAAAAI1bvQn1np6eSkhI0PLlyx36ly9frt69e9fY59hsNqWkpCgiIqLGzunqnhoxUw8VXaC9d+7Uk09+r/c+bK6AAMcxDzwgrVvnnPoAAAAAoLFyd3YBfzZx4kQlJSWpe/fuSkxM1Lx585Samqpx48ZJKlsWf+jQIb39p8eup6SkSCp7GN7Ro0eVkpIiT09PdejQQZI0depU9erVS23atFF2drZeeeUVpaSk6LXXXqvz62uoul1ynbpdcp293aKF9M470vDhf4wpLpZGj5Z++UUKCan7GgEAAACgMapXoX7MmDE6duyYnnzySaWlpSkuLk5LlixRdHS0JCktLa3CO+u7du1q/zo5OVnvv/++oqOjtW/fPknSyZMnddtttyk9PV0Wi0Vdu3bVqlWrdMEFF9TZdbmiyy6T/vEP6dln/+g7eFC67jrpq68ko9F5tQEAAABAY2Gw2XjE2V/Jzs6WxWJRVlZWo76/vrziYmnQIOm77xz7H39cmjrVOTUBAAAAgCuobg6tN/fUo+Fxd5f+77I71LnV9w79Tz0lLV3qlJIAAAAAoFEh1OOcnDp2WEn3t9TtOa/Lfdhwebrn2ffZbGXL8MvdKQEAAAAAqGGEepy1XcnL1e3ZFnrXb68kKTkoV2PGXuww5vhx6aqrpNxcZ1QIAAAAAI0DoR5nLSImvsIfnHea/6TrR0xz6NuwQfq//yu79x4AAAAAUPMI9ThrvoHhWjTiLXmWC+vL2z+mbu22OPR98YV0551lS/IBAAAAADWLUI9z0rX/NXred5RD3xHvUgVc2l8B/o5pf/586ckn67A4AAAAAGgkCPU4Z/c++JEuOxnq0PdN8DHdfstImc2OY6dMkf71r7qrDQAAAAAaA0I9zpnBzU1v3ve9InId/xi96LVEM554UwaD4/hx48qW4wMAAAAAagahHuclpHl7vZc4XYY/3TNfbJRezLxNM58/6DC2pEQaPVpav76OiwQAAAAAF0Wox3m7+IqJ+kdpH4e+35sUa21ab016xPH++vx8afhwadeuuqwQAAAAAFwToR41YspjK5R4wteh7wPLAeXYuispqdShPzNTGjJEOnKkLisEAAAAANdDqEeNcPc06/1bvpKlwLH/Va9f1bxFfw0a5Nj/++/SZZdJp07VXY0AAAAA4GoI9agxLeIu1PvxU+Re4ti/KHed3nj9oLp1c+xPTi6bsc/KqrsaAQAAAMCVEOpRo4Zd+4TeaXa3/cF5cVkm/XDnz2oa00xffinFxDiOX7tWGjhQOnGi7msFAAAAgIaOUI8a93+3vaLX/a9Tz5M+WvnAVkW06iJJCg+Xli6VQkIcx//8szRgQNm99gAAAACA6jPYbDbbXw9r3LKzs2WxWJSVlSU/Pz9nl9NgFBcWyN3TXKF/69ayEF/+QXlxcdKKFVJYWB0VCAAAAAD1VHVzKDP1qDWVBXpJ6thRWrlSiox07N+yRerfXzp8uPZrAwAAAABXQKhHnSspKtScBV31+nP/VvPmjvt++0266CLpwAHn1AYAAAAADQmhHnWqqCBP1z/SRi+bU/T3bbfq9efeUsuWjmN275b69ZP27nVOjQAAAADQUBDqUWcKck7qykkxWuSXKkk6Ybbpb1tu0NSJjyo21nHsvn1lM/a7d9d9nQAAAADQUBDqUWfcjO4qVqlDX56nNPbos7rpuqvUoYPj+AMHpD59pPXr67BIAAAAAGhACPWoM55evvpoynZdeiLQod9mkB6xfaz+w7qqc3yhw76MjLKH533wQR0WCgAAAAANBKEedcrbEqwvp+3XDadaV9g32zdFzS+OVs9uGQ79BQXSmDHSU09JvIARAAAAAP5AqEed8/Ty1Rsv7NBThgEV9n0emK7ivjEacemmCvsef1waO1ayWuuiSgAAAACo/wj1cAqDm5see3yF3gkdJ48Sx33JAXna1Lmb7rz+swrHvfuuNGCAdPRoHRUKAAAAAPUYoR5Odf0dc7Q8YZYCCgwO/fublOi9ZiP1xL3T5VbuT+maNVLPntK2bXVYKAAAAADUQ4R6ON1FI+/V2iu+UMwpd4f+LLP0lOUh3XHnRWri6/gAvb17pd69peXL67JSAAAAAKhfCPWoF9pdMEw/3pWinid9HPpL3aTtxs1auVKKinI8JitLGjpUmj5dKnV8Ux4AAAAANAqEetQboS066tsp+3RlVqS9LzjfoHfGr1DXbp5av17q0cPxmJIS6aGHpBEjpMzMOi4YAAAAAJyMUI96xdsSrA+n79dz7kPkUSK91ekJRbbpJkmKiJC+/176298qHrdkidS5s7RqVd3WCwAAAADOZLDZePP3X8nOzpbFYlFWVpb8/PycXU6jcWD7ekW171mhv7RUmjpVevqpQpXaPB32ubmV7Zs0STIa66pSAAAAAKhZ1c2hzNSj3qos0EtlwX3i+FS1vcdPl/a+QdIfN9SXlkqTJ0uDB0vp6XVTJwAAAAA4C6EeDY6ttFS3P3ehtgdYtWLQW0q8IVxN/Vc7jPnmm7Ll+CtWOKlIAAAAAKgDhHo0OG/88yYtthywt9e1OKoTd/bVRYkj5eZWYO/PyJAGDZIeeUQqKKjsTAAAAADQsBHq0eBsP7KlQl+ep7Ry8Gdqe2ug2kW8a++32aTnn5e6dZN+/LEuqwQAAACA2keoR4Pz4jMb9N+YSYrIrfjHd3tEvnbemqR+g7rLyyPjj/7tUu/e0n33Sbm5dVktAAAAANQeQj0apJFjn9X2B/bqjryOFfaVukmreicrcHykElo/ae+32aRZs6ROnaTvvqvDYgEAAACglhDq0WBZQptr9vNbtLr7bHXIMlXYf8i/RMnXP6Geo5spKuBbe//vv0uXXCKNGydlZ9dlxQAAAABQswj1aPD6XHaHNj6TqacMA+RZXHH/+g6HlH7XAPUb1F1+5v32/rlzpY4dpSVL6rBYAAAAAKhBhHq4BE8vXz32+AptunyJLjphqbC/yCgld0+Wp3uWQ//Bg9Jll0lXXSXt3VtX1QIAAABAzSDUw6W07TFU3804rn8H3KDAfIPDvoc8L9ET0zrJx6ficR9/LLVvL02ezIP0AAAAADQchHq4HIObm266503tvnePJloT5FEiRea66f57Fuquu6TNm6UBAyoeZ7VKTz8ttW0rvf9+2YP1AAAAAKA+I9TDZQVExOilZzdo+5Xf6N3E6fIJCJUkxcRIy5dL//qXFBQkhfv9rJ6jm6ptxHuSpEOHpOuuk/r2lX75xZlXAAAAAABnZrDZmI/8K9nZ2bJYLMrKypKfn5+zy0ENOnFCuvneTvqk1WZJUo8dYTr1/Yv6Le16SZLBIN18c9kMfliYMysFAAAA0JhUN4cyU49GLTfjZ30Zvdne/rntEf12e5K6Xxum9pFvyWYrm9Fv1Up67DHp5Enn1QoAAAAA5RHq0ajNevsOFbpX7N8Qm6Htt92g7teGqn3TN5WbKz3zTNnS/WeekXJy6r5WAAAAACiPUI9G7YmJn2macbCCyj0p/7QNsUe1/dab1O36YHVrNU0nT5bqscekli2lmTOlgoI6LhgAAAAA/oR76quBe+pdX87xdL0250a9eOprZXpV/VeizRGzQtddp582z1BRiZ+aNi17Dd6NN0qennVYMAAAAACXVt0cSqivBkJ945FzPF1zXr9ZL2R/dcZwH3rKTe3WDteqdZ9KKluW//DD0t//LpnNdVUtAAAAAFfFg/KAc+AbGK4H//Gl9j2crumelyk0r/Jl+RlNSmUL2mlv790rjRsntWghPfeclJVVRwUDAAAAaNQI9UAlfAJC9cCkL7Tv0UzNtVynttkV19Yf+PGlCn1HjkiTJklRUWUz92lpdVEtAAAAgMaKUA+cgZdfoG6b8K62vZCrz1tN1kUnLJKkESfD9MYHw5SYWPGYYJ8t6tZlkN54bYtatJBuu03atatu6wYAAADQOHBPfTVwTz3+LPmbd+Vp8lL8hVfJZpO+/bZsyf2KFWX7L+o7SCsHLJdHidR9e5Ryf75fm/bfrVGj3HT33dLFF0uGylf1AwAAAICkBnxP/ezZsxUTEyOz2ayEhAT98MMPVY5NS0vTtddeq7Zt28rNzU0TJkyodNxHH32kDh06yGQyqUOHDvrkk09qqXo0BgkDrlf8hVdJKgvnAwZIy5dLGzZIf/tboXYnfCtJKjJK6+IOaNONE9RyvJeOp1+hq0dsV8eO0uzZ0qlTzrwKAAAAAK6gXoX6xYsXa8KECXr00Ue1ceNG9e3bV0OHDlVqamql461Wq0JCQvToo4+qc+fOlY5Zt26dxowZo6SkJP36669KSkrS6NGjtX79+tq8FDRCCQnS30c+rUP+JRX2/R5SqFVD/qvs+zuoSadILXjlUTVvlqd77pF27HBCsQAAAABcQr1aft+zZ09169ZNc+bMsfe1b99eo0aN0rRp0854bP/+/dWlSxfNmjXLoX/MmDHKzs7WV199Ze8bMmSIAgICtHDhwmrVxfJ7VNe2Hz/Tix89oIWmXSrwOPPY4ByDOmzqpsMpkxTT+SqNHy9ddpnk7l43tQIAAACovxrc8vvCwkIlJydr0KBBDv2DBg3S2rVrz/m869atq3DOwYMHn/GcVqtV2dnZDhtQHR16Xa43pu/U4fG/a6Z5lGKzq072mb42reqdrN13/k0HY3z00rNXq23MYU2aJO3cWeVhAAAAAGBXb0J9ZmamSkpKFBYW5tAfFham9PT0cz5venr6WZ9z2rRpslgs9i0qKuqcPx+NU0BEjCY8/Il+m16gb+Jf1HXZLWQuqnr89sg8rRv8Hx0/lqvnnpPatpX69pUWLJBycuqsbAAAAAANTL0J9acZyj0W3GazVeir7XNOmjRJWVlZ9u3AgQPn9flovAxubrrkyvv17kt7lX7Pfs2zJCnxhG+lY7vsCdXJ/Db29urV0o03ShER0i23SOvWSfXnZhkAAAAA9UG9CfXBwcEyGo0VZtAzMjIqzLSfjfDw8LM+p8lkkp+fn8MGnC9LaHPdOuFtrZ11Sr8NW6JHinspMvePv4Iem6+q9LicHGn3/p568IVWGpo4VU9MzmF5PgAAAABJ9SjUe3p6KiEhQcuXL3foX758uXr37n3O501MTKxwzmXLlp3XOYHz1bbHUE17ap1Sn8nXV22f0vXZMXr59cc1caIUHOw41uiWp83dftaaLr/r66FTNKu4iW6cFKNRFz+sF587rsOHnXMNAAAAAJyvXj39fvHixUpKStLrr7+uxMREzZs3T/Pnz9fWrVsVHR2tSZMm6dChQ3r77bftx6SkpEiSbrnlFrVt21YPPvigPD091aFDB0nS2rVr1a9fPz3zzDMaOXKkPv30Uz322GNavXq1evbsWa26ePo96lJhofTFF9Ibb0hffSV1bfmkkq9/otKx3oVSp11NFXXqSg0Y9ojGJEXK379u6wUAAABQ86qbQ+tVqJek2bNn64UXXlBaWpri4uI0c+ZM9evXT5J0ww03aN++ffr+++/t4yu7Nz46Olr79u2zt//zn//oscce0++//65WrVrpmWee0ZVXXlntmgj1cJZDh6RxT7TUF1F7/3KsqVjqvCdMLXOGqN9FEzX6hk4KCqqDIgEAAADUuAYb6usjQj2c6fCuX7T4o6e0+NA3Wh98qtrHdTzoo9ijibrkovf0t2tCFR5ei0UCAAAAqFGE+hpEqEd9cWjnBn303+f1YeoKrQ06qdK/eCpGQJ5BWS/myWYzq08f6corpSuukFq0qJNyAQAAAJwjQn0NItSjPjqaul0ffficPtizRKuDM1VkrDim968ttfaTPRX64+KkYRd+pH79IzXkb4kyVnIsAAAAAOch1NcgQj3qu5NH9uujhc/pk9++0KrAgzplKuvv9cF9+nHbjEqP6ZYUpF9aHVeroyZ1Ph6nC9uMVtKd4xQcxp9xAAAAwNkI9TWIUI+GpDA/R8s+/qc+WP2+So8t0adfNVNOjuMYL48MlTwcpkJ3x36fQqnroTB18bxQIwbdpEuvGCI3Y7158yUAAADQaBDqaxChHg1ZQYG0YoX00UfS559Lx45J3WMf14Zrn/rLYyOyjOqaGaNe4YN0zfV3qHWnuDqoGAAAAAChvgYR6uEqSkqkdeuk9+fdpEURC3TC++z++rfLMKtrXkeNHf6aBo7qyb34AAAAQC0h1NcgQj1cUUlRob5c+LY+W/WufnLfoC3hubIZqnds01k/KMd2ofr3lwYMKNvat5cM1TweAAAAwJkR6msQoR6Nwe9bt+udN1/V+ozlSgn5XWl+pZWOa3rSqEOziiv0R0RIQ3u/J7eg1zXkglEa+re/y9sSXNtlAwAAAC6JUF+DCPVobEpLSvX1R0u0ZPkCpRSt1q+RR+xP1O+zsbXWfLqr0uMu6n+RVvZfJUnyKJE6HvVV19IOujhusC676kYFRsbU1SUAAAAADRqhvgYR6tHYnTyWp8X/ekurNi/Wsd0D9fX6Rysd1+lGizZFZ1e6z2CT2maa1bWwjfq1HqARo5LUNLZbbZYNAAAANFiE+hpEqAccHTokffut9M03ZdvBg5LZI1Mlj4So6Cwenhd10l2dcpqqZ0gvjRo+VvG9h9Ve0QAAAEADQqivQYR6oGo2m7Rrl7T00z36fsO12mnZqt8iclVylq+37/dLO7llb1efPlKfPlJiouTvXyslAwAAAPVedXOoex3WBMAFGQxSbKwU+2Ar3aP1Ki2VNqxN15L/LNCWQ0u0p8kmbW+aJetf/NumJLWPVqVI33//x3k7dJB69ZLcjBepdUSUBl08SvG9L5fRw7O2LwsAAABoEJiprwZm6oFzZ7NJm37J1pIP3tOvv/9Xe7026remR5VtdhwX/epS7T82uMLxXh4Zsk4KU+n/Zv59rVLccX91cW+vfnH9ddGAvymyDffmAwAAwLWw/L4GEeqBmmOzSTt/K9TX//lMG7d+rH22H5VhydC2+dmSKq7Z7xT9sjbdOOGM5ww/5aa47DB19o1T/26XqM+AMQqI4En7AAAAaLgI9TWIUA/UrmPHpLVrpTVrpNWrpZ9/lgoLy/b16zNMqwZ+ddbnbHnCQx3zInXHoPd0yYg+MplquGgAAACgFnFPPYAGIyhIGjGibJMkq1XauFH68Ufp15UXyLZ9q/aGH9bBgOJqn/P3gCL9HrBfa/4eqOxCKS5OSkj4Y2sTc1xG5ckvpFktXRUAAABQ+5iprwZm6gHnO3xY+m7JFv20eqH2Zn2ng5bt2h15UqfOMAMfmWXU4ZmV/yIgscODWjf6RbU84aG2eeGKb9JBvTv3VWK/UQpt0bGWrgIAAACoHpbf1yBCPVD/FBdLmzcVa+UXy7Rl6391oPhHHQr8XbvDc+1P2r9ge4R+Wny40uMvGtBbK/uuq3Rf+Ck3tcsOVgdTrHq2TdSFfYepRdyFcjOyuAkAAAB1g1Bfgwj1QMOQlydtWJ+jH776r3b8vkQZh1rr6x+frHRst+uD9UvrY9U+dxOrFHuiidqWRqt7s0Td9cg8eXjUVOUAAACAI0J9DSLUAw1XdnbZ/fnJyX9sO3dKoRONOtKk9JzO2fGgj3a+laMOHaTOncu2uDgpPl7y8Tgs34BQZvUBAABwXnhQHgBI8vOTLrqobDvt1Cnp5x9PaO2y/2rXvhVKK/1FaQH7tSc8R/nVmH0PTI9SUZH0669l25/1vepC/dJ2r9qc8FHr4mbqENBRiV37qnufoQqOaluzFwcAAIBGj5n6amCmHnB9BQXSpl8LtfbrZdr+21c6WPCz0pvs0b7wEzru7fivyb5fjtEPPy+q9Dyx47y0M7yg0n2hOW5qlW1RKzVXx9BO6tX9QnXrPYwn8AMAAKAClt/XIEI90DiVlEi7d5Vq3be/aHPyl0o9sVbpntt1dNU07Ui7rsJ4o1uejP/wUeFZroFqmm1Uq+xA9W9ytS6/7jW1ayf5+NTQRQAAAKBBYvk9AJwno1Fq285Nbdt1l9Td3n/ihLRpU9n266/S5s3Sli1SuM8K/X4O/1Y95FeiQ35HZfhmp56cWdbXooXUvr3UoUPZPwuO/ENtWjdTQu+BCmrWpkauDwAAAA0fM/XVwEw9gL9SWir9vqdU679P0ebkr7Q/c53SjduVFpCmfSH59tfsnckFix7RT79Nq+zs8n/YXSe9yv51HZxrUIvsJmpRHKnWfu3UpW13XdC7v6I79OQBfQAAAC6C5fc1iFAP4FwVFEhbNxfop+++047t3+pgdrKOeO7S4aAM7Q8qVInbH2OjX12q/ccGVzhHiG+Kjj7Q9S8/y7tQijnppeYFoWphilH7pp3Uo3tvdU4cKC+/wJq8LAAAANQyQn0NItQDqGl5edKmjdn66bvl2rnrex3OTdHmjcu1Z69Z5f+t3CVmulL+/tA5f9bApXep1OdVtW0rtW0rtWkjxcZK0dGSm6GY2X0AAIB6iHvqAaAe8/aWevXxU68+V0m6yt6fny/t2CFt21a2bd8u5R3OU+sMk/YHWVVkPPvPSt3fSzvSpG++cewP9t2jnHtbK/qkSc3yg9Tc2Fytg9srvkN39ejdT2EtOsjg5lb5SQEAAFAvMFNfDczUA3C2wkJpx/YCbVi5Ur9tW6UDJ37REcMuHfFLV2pwrk6Zqj7W+9k05RWGV+jv0Oxf2nbLrVUe51cgNc/yVtOCYEV5RKt1aAd16thdPS68SEGRrQj8AAAAtYiZegBwIZ6eUnxns+I7D5b0x333Npt0+FCpfvr+F23ZuFL7039WetFvyvA6qMOBJyVJhyoJ9JLkH/TTGT8z2yxtMedpi1IlpUr6QdoxV9ohWfINumzrJ4psN1KtW0utWkmtW0tRUWVvDQAAAEDdINQDQANmMEhNm7npiuu764rruzvsy8+Xtm/J0Z7eZUv6d+yQdu0q++fJk5JH0NZz/twsL5u+WNFJ2V849nt4SL07LlRG75sVmR+oSENTRfu3UduYzureK1FtOl8go4fnOX8uAAAAHBHqAcBFeXlJ3Xr4qlsPx36bTTp2TNq4boEu+3Gp9h7aoLT87crwPKB0y3EdDChU4V/81yEgz6ATBTEV+ouKpMLitdoemq/tOiTpkKSfpGPvSV9KHp9JzbI8FJljUURpuJp5t1TLyA6Kj09Qt9695RccWWPXDwAA0BgQ6gGgkTEYpOBgaeCINho4oo3DPptNOrC/UMmrftS2zWu0/8hGHSncpQzTIaVbTuqgf5GKjVLkcW+dqOL8noHbq/zsIqO0N7BIewMzJWVK2iIVfyZtlLRRCs41qM+WVDVv3UwxMXLYeKQJAABARYR6AICdwSA1b+Gp5i366Qr1c9hXWiql7ivQLz/8qP06poxHpD17pN27y7ZTp8rGFQfsP+fPLzTa9OlXlc/W9+82Ub/3fVXhuU0UVhSiSM/mig6KVWzrTupyQU/FdIjj9XwAAKDR4f9+AADV4uYmtWhpVouW/Svss9mkzMyycL9+2WR13ve10gt26aj7IWX4HtehgALlnOEJ/adFnPBStip/qr7NZ4tSA4qVGnBC0glJOyWtkA5KOiiZPpCaZnkqPLeJQktCFWGKVvPgWMW2ilfnHt3VsmMnntgPAABcDqEeAHDeDAYpJKRsS0wcK2msw/6cU6XauG67Nm9Yo99TNyr91E5laL8yvY4qzZKjdL9SSZL/icAqP6PEP/WMNVjdpd+DCvV70DFJxyRtl7RUOiD57ZI8/2VTdLTUooUUHS371xbP7QoMOKn2XRPk7slD/AAAQMNCqAcA1DrfJm7qO6ij+g7qWGFfaam0d/dJpaxZqzQPaUh76fffpb17y7bDh8tWAuQGZJzz54efNGtnZtlqguRkx30X9b1XKwcsl3GJFJFtVFiur0ILgxTqFqlIv5aKadpe7eM6q3PPHmoSGHzONQAAANQGQj0AwKnc3KRWsf5qFTus0v1Wq7R/v/Tj1/O1c9dqHTr5mzJK9umY51FlNMnWIUvRXz6t3/+kf5X7Si0HJEklbtJB/xId9M+SlCXpd0mrpTxJP5VtQbkGhZ0yK7TAXyGlYYry7qCLL3tPzZtLUVGSv3/ZqgUAAIC6QqgHANRrJpMUGyvFxl4l6aoK+09lFytl3SZtS/lZe1N/VVr2Lh0tTdVR01EdbXJKhy3FMp8Mq/L8ef5Hq13LMR+bjvnka5vyJaWpbdoOzRjxx35f37JwHxUlRQf8Q/le3yvcJ1pRYW3Uuk2cOvVIUNOYGO7tBwAANcZgs9lszi6ivsvOzpbFYlFWVpb8eKcSADQop7KLtfu3kzpyPFj79pXN+u/fL/vXvpd7aWd4wTmd+4LfwvXTorRK9104or1WJ/xWod+nUAo75amQXF8FFwcpxC1CYU1aKCq8jWLbxiuue1eFRzUj+AMA0MhVN4cyUw8AcGlN/NzV9YKq74W3FuRq88/btXXjBu3bt0mHT+xWRuF+HXM/oqPeWTriV6AT3pX//tucFVLlefMtla8AyPU8/UC/45KOS9olaZVUJGlL2dY51U+2DVlq1kwOW9Omkq9xqyKbeio6thXBHwAAEOoBAI2byeym7n07qnvfig/xk8oe0rdvV7o2/7xBu3f8qoMZO3Qkd68ydVhe1v4KD5fS0ysed9Iv59xryvPWT5ukTZsq7rvw8iu0utsumYuksFPuCs7zUVBhgIIUqhBzM0UEtVSL5m0V2zFeHbt3lsnLfM51AACA+o9QDwDAGRgMUkxsuGJih0saXukYq1U6dEg6cKBsS02VNm1NULNT+3XM66QymuQro0lptT/TlH2GFQB+xyVJBR7S/sBi7Q88/WC/fSp7op+kI2Wb4RspJMdNIblmBeb7qWVRd7WL/1yRkWWz/qf/2aQJD/gDAKChItQDAHCeTCapZcuy7Q9rHMYcP5qjTet/0Y5tv+rAoe1Kz/5dmUWHdcz9qI57ZSnDt0CZvmXL/N2ymlb5WVlnsQLAZpAympQqo0mepDwVbvtFb02qOM7HR7qwf6IyI7YroNCiQFuIgk1NFeYfrWYRbdQqtoPiundWUDiv9AMAoL4h1AMAUAcCQ3zVf3g/9R/er8oxmekntfnnFGXFhuiGW6SDB8u2AwfKVgIcOiQd8y085xpMpyoP5bm50tHAnfql2elZ/1RJyWU7T/3vy2TJr0AKyfFUUJ6PAor8FWAIUZApUi0jLtQFA+9XZKQUESF5eZ1ziQAA4CwR6gEAqCeCw/118Yj+Zxxz6mSONm/4VTu3btaBA78p7eTvyrQe1HFDho6bspTpk6cjTYpVWMl/4Q2nIqo8b3aT3L+sL9ssZZsLtUeFkk5I2itJ6rV1g+5/8n77OIulLNxHREhNA69RofcuBXlGKMzSQk0jWqlFy7Zq27mjmrZoJjcjD/sDAOB8EOoBAGhAmvh7q/eliep9aWKVY0qKS7Vj0y5t/zVF+/Zu1+HM3Tqae0DmyCtlGlQ243/4sHTixB/HHG1y7isAPMutAMjKKtt++03qOvZrbWz5pw/KlbS5bPMqkkJy3BWY5yV/q58CbIEKNIYpxLeZwoNbKLZ9b3XpO1ChoZLReM7lAQDg0gj1AAC4GKO7mzp0a6sO3dqecVxenpSWJh08UKpVX05Q+oG9OlZ4SMd1VCc8T+q4V56ONinUKdOZP89wKrzKfVlnWAGQ7yGlBhQrNeCUytb5H1JZ4i+T+EG01o3ZJzc3KSRECg//Y2tiu1s200GF+kUpMrylomNiFRvfUVGtopj9BwA0KoR6AAAaKW9vqVUrqVUrN13Uf0alY2w26fC+w9q+cbP27Nqqw0f26EjWfh0vStdxHdVJzyyZS7rKy0vKz694/DHfonOuzyMnSJJUWiodOVK2/fpr2b4uf39XKU1PljVyJW0p2zyLpZAcowLzzPK3NpGltOze/0CvcIX6RysyIkaxHXoovlcP+fry1H8AQMNHqAcAAFUyGKSmMZFqGhOpSzW4ynE2m5SdXTbzn5ZWtrz/0MFi/bK1e1n498jSce9cHfUtVLa5mh+eE1blrmzfvEr7C92lQ/4lOuSfq7K0ny7ptz8GnJR6v95CawftlZeXFBbmuHkV3yN5pCnEEqWIsBhFx7RWm7gOrAAAANRbhHoAAHDeDIayB+RZLFK7dqd73SX9VGHssSPHtX3jFv2+c7sOHt6tjJP7lZl/WCdKM3TS/aROmHN0zMeqklPRVX7e+awAcP/fCoD8fGnfvrLttE43vqVNkdlljRzZ7//3LJaCco0KzDfJUuAj/2J/+RkCFWgKV3CTpgoLjlbrdhcoPrG/QkIkd/4PCwBQR/hPDgAAqFNBYYG6cEg/XTik6tf7SVJJiZSZKaWnO25ph4u1JS1KWR7ZOuGVp0yfImV52apfwBlXAFRyD4HKVgCkWUqUZsmTlCfpqOOAIqnPO6205qrdZdcYJIWGlm1hYZKfcaxKTIcU6BWuEP/mighvoeYxrdWqQ1s1bRHJKgAAwDmrd6F+9uzZmj59utLS0tSxY0fNmjVLffv2rXL8ypUrNXHiRG3dulWRkZF66KGHNG7cOPv+BQsW6MYbb6xwXH5+vszm6q7/AwAAdc1o/GNZfOfOf97jLmm/w9iTmSf1W8pW/b7zNx06vEdHju/Xsfw0nSjOUJbxhE56ntIJr3xl+harOCeqys887nMeKwByA+1fHztWtm3fXtaOv+m/2tz81B+DT/xv+0UyFUuBuUYF5HvKz+otS7FFFgXI3yNEgd4Riml+kTr3+7tCQ8seGOjjw7MAAAB/qFehfvHixZowYYJmz56tPn36aO7cuRo6dKi2bdum5s2bVxi/d+9eDRs2TLfeeqveffddrVmzRnfeeadCQkJ01VVX2cf5+flpx44dDscS6AEAcB3+wf7qdWkf9bq0z1+OPfVwsY5m/vHwvfT0//0zrVC/pUXqpOcpnfQq0DGfor988v+f2XJDq9yX7VNQ5T6rfRVAvqR8Sccc9vdZs1prHvq7vW02yx7wmze/WPm+qbIoUBaPEAV6hys4oJkiwqPVLDpGMe1iWQkAAC6uXoX6GTNm6Oabb9Ytt9wiSZo1a5a+/vprzZkzR9OmTasw/vXXX1fz5s01a9YsSVL79u21YcMGvfjiiw6h3mAwKDy86tftAACAxqOJn7ua+EktW5bf46my1+r94XjGce3ctF37du/UocO/K+P4AR3LO6yTxRnKMpzQSY9sZZnzdcK78IwrAE7U0AoASSookFJTy7aCrj9ra7NcSb87HnT0f9uG/60EyHOTf75JflYv+RU1kZ8CFO15sdp0n6GQECk4uOyXBCEhUmBg2SoJAEDDUG9CfWFhoZKTk/XII4849A8aNEhr166t9Jh169Zp0KBBDn2DBw/Wv//9bxUVFcnDw0OSlJOTo+joaJWUlKhLly566qmn1LVr1yprsVqtslqt9nZ2dva5XhYAAGjAAkMDq70CoGRa2ZL7I0ekjIyy7cgR6WhGsX49EKss9yxle+bohJdVx3yKledZvRpsuWd4BoC3tcp9p1ndpTS/UqX5nV4JcFzSfl2YnK8Zd1R8laHBIPW5qoXSwtPkV2CWX5GPmpRY5OcWIH/PUAX4hCskIFJh/1sN0KpDrMKahrIaAACcpN6E+szMTJWUlCgszPE/XGFhYUpPT6/0mPT09ErHFxcXKzMzUxEREWrXrp0WLFig+Ph4ZWdn6+WXX1afPn3066+/qk2bNpWed9q0aZo6dWrNXBgAAGgUjMY/Ho7nyF3SjgrjMw5laPfW37R/zy6lpe/X0eMHdDwvXSeLjipLx5Xlnq0sU548i9vJZJKsleT3Ez7F515vXmCl/TabdDwgU3uCCyUVSsqWlFZxYPr/tvV/rAaw5HuqbVovmTy/U3CwHLagIEk5axQQ4K6WHdooKKzyzwcAnJ16E+pPM5R78ovNZqvQ91fj/9zfq1cv9erVy76/T58+6tatm1599VW98sorlZ5z0qRJmjhxor2dnZ2tqKiql9QBAACcrdCmoQptGqreOvNbAKSyoH3qlHT0aNmWkSFlHCnV+nWDlFWcqSwdV7b7KWWZ8nTSq1DHfUpk/Yv/yzvTMwBOVWMFwJ/9sRqgQCEHjuiH/1Y+LuH6kUpufUxaJXkVSQF5RlkKPNXE6qUmxT5qUuovP7cAWcwhCvAJU3BApMIjotW0eQvFtItVeFQoDwkEgHLqTagPDg6W0WisMCufkZFRYTb+tPDw8ErHu7u7KygoqNJj3Nzc1KNHD+3atavKWkwmk0yms3gyDgAAQC0yGCQ/v7KtVavTvW665davKx1fWlKqIwfTtWf7Th3cv1dpaft19PhBnchL08nCo8rWSfkH9VOvXmWvDTx6VMrK+uP4E97nvgLArYoVAJKU5/3HKwPzPaR8S4kOW/58W8CBcheissccHJJ6/auZkv97QEFBqrCZi+5Rifte+XuHKdASodCQZops1lxRrVqqRWyMTF7VvNcBABqgehPqPT09lZCQoOXLl+uKK66w9y9fvlwjR46s9JjExER9/vnnDn3Lli1T9+7d7ffTl2ez2ZSSkqL4+PiaKx4AAKAecTO6KSI6UhHRkZL6V+uYwsKyZwJkHCnVuq9fUEbmAR0/laYTBRnKLjmubLeTyvbIUbapQFlehTruXaqiSh6oZ8sLqfIzqvMMgKp45PmpqKjsbQXl78zsmvSeNkYd/6Pj9CsDN5c1LfkG+ecb5Vdgkm+Rl5oU+6qJzaImxgA18+ut9r2eUVBQ2UMCT//TYuHVgQAahnoT6iVp4sSJSkpKUvfu3ZWYmKh58+YpNTXV/t75SZMm6dChQ3r77bclSePGjdM///lPTZw4UbfeeqvWrVunf//731q4cKH9nFOnTlWvXr3Upk0bZWdn65VXXlFKSopee+01p1wjAABAfeTpKUVESBERburc5cG/HF9aUqq01MPau2O3fTVA5slD8rlohC68pGwFwOnt2LGyf54yl5xzfdVdAVCZLC+bsryKJRVLypWUad+XuGWPnr7umQrHGI1S3+HtdSDqdzWxmuRb5C3fEl/52izycw+UxRSsgCbhCgr4//buNTjK687z+K+varXU3ZKQhCQkxGUcESB2AGODDcYZ22An8a4zVcGZmTAm5VQuFScLqWyGSqo2jqdq4kzlsnZCknItNnljx0mAxFXBSZgyFydgdszK8XhwjI25SEZ39U3dklrdffZFI4m2unVDSHrE91N1Cun0eZ4+rT9Pn/qf53nOU63K+QtUU1evuqWLeIQggGk3q5L6Bx98UF1dXXrsscfU0tKilStX6uDBg6qvr5cktbS06OLFi0PtFy9erIMHD2rnzp3avXu3ampq9OSTT2Y9zi4UCulzn/ucWltbFQgEtGrVKh07dky33HLLtH8+AACAucLusGvB4lotWFyr8V4NkEoOqPlsk86//a7eu3hOrW0X1RVuUbCnVeGBTkVNSBF7WFFXTJGCfoULBxQsTCvpkExved799hQmJv05nL3+3H1NSeFA2xULBkYltY1sOCCp+XI5ITnSUmmvTR869zfqbT6jsjKNKKnuZ2RTkyoqFqhqwULVLVmkhX9Tz20CACbFZgZXlkNekUhEgUBA4XBYfn/uL34AAABMvXQqrffONysSdas3WTV05v/Kcjb0AQULuhRx9yriSSjoTY37kYGbjm7Q0cMv53yt4fNevVU9+lUA+az/z4U6se9CztfWfbJWr6x4b0S9v08K9Drl73erKOFRcbJIxWmfiu0B+ZylChSWq7pilW66Y4dKSzMTBKWlUnExtwoAc9F489BZdaYeAAAAuJLdYVfd0oVjtDozoibcFdL5M++q6dw5tV5qUmf3JXWHWxXqa1dkoFtRhdRjj6rCuVYrVgxPEAwMDO/jWlwBIEn9hfGc9RGPFPEM3iYQV2bxwGxr3j6onY/uyH4vZya5X3PzJl2q+38qThSqKOVVsfGr2F4in7tMgcJylfgqNa+sWpVVNaquXai6pfWqqC7ndgHA4kjqAQAAMOcE5pXopvWrddP61ePexhgpFpO6uzMJ/v/947+pvfOCgj3tCvd3KJoMKqqwehxR9bh6FfH0K1IwoFChUfJ9iwbae0vyvk+8sG+Sn0py9xWNqEsmM08wCPrO6vWaHkk9+XfQI+mdy+WI5EplFhL8YHOtEucuqrRUWaWkRFL450qmTqusZL4q5y/Q/JoFWrB4oWrqa+R0kU4AM42jEAAAAFDmEvbi4kxZuFBateqr49pu8BGCF985p0tNF9XW1izn/Tfq7x+UgsHMJMGVpbfPq6pIv0KFafXlfmBTXqNfARCb2M4kDTikzmKjuKNXp07mbrPhv/2r/rT6TObJg+cvl+OSPS35+23y9znk63erKFGgoqRX3nSxim1+FTtLVFn0QX3otidUUjI8STD4M0+QBqYGST0AAABwFbIfIXj7OLYYXn0/2NGti++c13vnL6i1pVld3S3qjrQqHO9QdCCoSDqomD2qmCOuqLtXvv6lmjcvM1mQTmfvtdcz+UcG5roCYNCAJ/eZ/7RdChUahQqvvGUgmNVmZdMJfecfnsi5/Ufuvk3/ddNJ+fqdKu53qyhZKG/SqyJTrCK7Xz5nqXyeeSopKldJSaXKyy/fOlBXq9oldSoOFE/y0wJzC0k9AAAAMENKK8pUWlE2odsEpExCH41mzvwHg5ly6qV/Vnvb24r0dyqaDKrHRBSzRdXjiqvH1acez4DCnqQinpH7c/b68r5XwjO5xQIlydNXmPe1AW+X2n1ptfsGnzAwym0DA5JaLpdGaUmHWy1P9w+d+R8sgYBUYntMEdeLKnaWqNhdqkBRuUoClSorrVTF/MykQE19rcoqy1hPAHMCST0AAABgMXZ7JoENBKTFizN1d931rXFtm+hLqPndJjWfu6DWS83q6Lgkx6YGffq/D08QBINSKJT5N5kIaGEwqmhBSmGPUXoCebC715v3tQHPxG8XGOTtc6u3V+rtlVpasl+7Y/MLOnbbqezKtKSuy+V0psqVkvx9Nvn6nSpKuFSUKNC83vmq8rw5PEFQMvx3tsVfVarvLVVWL1D1wgWqXVInjzfHDAkwzUjqAQAAgOuI2+PWkuVLtWT50nFucW7op1QyqUsXLunS+Sa1NDers6NFXd0tCkXbFe7tVE8yrJ50SHH1KOaIqaL/Jq1YkZkgCIUyCxEOSnhyPwVgPEa7AiDtiY5rHwMOqavIqKtoQJlLAeJa0hHTi7tzt990xz/r6N++JDVL+o9MnTch+frtKu53qijhlnegQN6UV0XpInltPhU5Aip2l6jEW63Vdz45NEFwZXGSkeEq8V8IAAAAwLg4nE7VLV04jscM5pZISOFwJsF/4/gevdd0WsFwu8KxTkX7uxVNBhUzEcVsPYo54oq7+tRTMKBoQVJhT1qJy9mLq+/aXAFQ2OfO+5rxhEfUxd1S3J1WW9YtBF0j2s2P2NX2wJM593vnLf+oNzf+4vLEgEveAY8KUx55hyYG/CpyBeQrKFWguFwBf7nK5s1XeUWl5i9YoKUfulEeT2ahR1yfSOoBAAAATAu3W6qoyJQbbviEpE9MaPtQZ0hNZ88rttEp2/8cvgJgsITDUsu5W+U7e0pxZ1wxZ69i7gH1FAwo4kkrnj9nlyR5+vJfTj/eKwByKerPn3YZT6vafFdODIwxKZHQ0PoChaek3luNXC7J78+UQCDzb63vB+os+7G8pkhe++XJgYIS+TylCvjKFQiUq6ysUpVV1apcUK2a+gUsPmhRJPUAAAAALKGkvEQl5R8eo9W+vK/Eo3E1n2tSy8VmdbS1qLOjRaFwh8I9nYr0dctdsEQb/sfwBMFgCYUkuzMtR1pKTWJtvcL+/M8uNAWRie/wMl+fXb2SBgakrq5MGbTxluN6ee25vNuq53K5OFxVkJR8fTYVJ5ya93KPfAH3iMkCz8AfFOx5Rj5PqXxFZSotqVBpaYXK51eporpKNfUL5C/1swjhNCKpBwAAAHBd8Pq8+sCNDfrAjQ2T2PptpVNpdbZ26tKFZrU0Nauro1XdwXaFIh2KxLrU0x9ULBlWLB1R3BZT3B5XzNWred21ii8eniRIpYb3ms7zyMDxGO0KAHvByNsFxtLvlPqLjWIDAzrfmPuyhttuPKDjf/f8cEXscmkernKmJF+/TcUJh7wJp7wJt7zJAnnSHi11vCpfWaV8vuGrC/x+yZk6q56Of1dZeaXmVVSqqrZGVQurWYxwHEjqAQAAAGAc7A67KhdUqnJBpT6siT2GcJAxUjw+nOCfefV/a+v5VxWKdl6eGOhWLBlRPJVZWyBuj6vX2ae4qz9zK4E7qajHqN85+hUAyrEGwHgV92euAMjFUdA95vZJhxT0GgW9SUlJSX1Drx39F6+SqZHbrG14Wv/x9/8qdUs6M1xfOCAV9WeuHvAmnCoccKswlVl3oNAUqdBWJK/Dr5UNe1VSWSu/X/L5NDRpUODskSPdpqq6anl9+ddisDKSegAAAACYJjabVFSUKTU10gc/uEXSlgnvJ9IdUWdbSOmvS5FIZoLgyn+b3/i4qs/aFDdRxW1x9Triijv7FXcnFHcnFS1IKVpgct5OUNTnUEee953MFQCDCpJSfyr3ffuuPJMFvS6p12XUqcGnFPRKGtmHsn+JqjvHAxVurN+j1z+zQ5LkGZCKEzatffUTagnu0z/9k7Rz5+Q+y2xCUg8AAAAAFuMv88tf5h+lxf+6XPJLp9Lqbu/WpQvNar/Uoq7OdgW729VXblfp3pGTBdGoZKJ1Wt3sV9yVUMydVKwgpUiBUdIxdp+L+23qz/Oawx0aewej6ElU56x3FwwvNNDnkvpcRrHelF57Tbr33qt6y1mDpB4AAAAArkN2h13l1eUqry6X9OFxbvV/Lpdh6VRaoa6QWpta1NHSqs62VgWDHQpHuhTp6VK0N6hYIqRU0iHvlzOTBJFIZpJg8OdCb1JFCSk2xhMKcnGlpEQy9wSHyx0cUZdOBCRlLtGfC0jqAQAAAACTZnfYVVZZprLKMkkrJrmXX0mSBvoTanuvXW3NLepsa1N3Z5tCoS6Fo52KxoLq6QsqNhBRPBlRr4mp1xZTyhil/9aeNVkQjUo9PbnXAEj2l0iaO0m9zRhjZroTs10kElEgEFA4HJbfP9olLgAAAACA2SCdljpbQ2p+9x11traqu6tDoWCnfOVblC64UWvXSsuWzXQv8xtvHsqZegAAAADAnGO3S5U1JaqsuXmmu3JN5VjrEAAAAAAAWAFJPQAAAAAAFkVSDwAAAACARZHUAwAAAABgUST1AAAAAABYFEk9AAAAAAAWRVIPAAAAAIBFkdQDAAAAAGBRJPUAAAAAAFgUST0AAAAAABZFUg8AAAAAgEWR1AMAAAAAYFEk9QAAAAAAWBRJPQAAAAAAFkVSDwAAAACARZHUAwAAAABgUST1AAAAAABYFEk9AAAAAAAWRVIPAAAAAIBFkdQDAAAAAGBRzpnugBUYYyRJkUhkhnsCAAAAALgeDOafg/loPiT14xCNRiVJdXV1M9wTAAAAAMD1JBqNKhAI5H3dZsZK+6F0Oq1Lly7J5/PJZrPNdHfyikQiqqurU1NTk/x+/0x3B3kQp9mPGFkDcbIG4jT7ESNrIE7WQJxmPyvFyBijaDSqmpoa2e3575znTP042O121dbWznQ3xs3v98/6/6AgTlZAjKyBOFkDcZr9iJE1ECdrIE6zn1ViNNoZ+kEslAcAAAAAgEWR1AMAAAAAYFEk9XNIQUGBvvWtb6mgoGCmu4JREKfZjxhZA3GyBuI0+xEjayBO1kCcZr+5GCMWygMAAAAAwKI4Uw8AAAAAgEWR1AMAAAAAYFEk9QAAAAAAWBRJPQAAAAAAFkVSP4v95Cc/0eLFi+XxeLRmzRq9/PLLo7Y/evSo1qxZI4/HoyVLluhnP/vZiDb79u3T8uXLVVBQoOXLl+vAgQPXqvvXjYnEaf/+/brnnntUUVEhv9+v9evX6w9/+ENWm71798pms40ofX191/qjzGkTidORI0dyxuCvf/1rVjuOp6k1kRht3749Z4xWrFgx1IZjaeodO3ZM999/v2pqamSz2fSb3/xmzG0Ym6bXRGPEuDQzJhonxqWZMdE4MTZNv+985ztau3atfD6fKisr9cADD+itt94ac7u5NjaR1M9Szz//vHbs2KFvfvObamxs1MaNG3Xffffp4sWLOdufO3dOH/3oR7Vx40Y1NjbqG9/4hr7yla9o3759Q21OnDihBx98UNu2bdNf/vIXbdu2TVu3btXJkyen62PNORON07Fjx3TPPffo4MGDOnXqlD7ykY/o/vvvV2NjY1Y7v9+vlpaWrOLxeKbjI81JE43ToLfeeisrBjfccMPQaxxPU2uiMXriiSeyYtPU1KSysjJ98pOfzGrHsTS1YrGYbrrpJv34xz8eV3vGpuk30RgxLs2MicZpEOPS9JponBibpt/Ro0f1pS99Sa+88ooOHTqkZDKpzZs3KxaL5d1mTo5NBrPSLbfcYr7whS9k1S1btszs2rUrZ/uvf/3rZtmyZVl1n//85826deuGft+6dau59957s9ps2bLFfOpTn5qiXl9/JhqnXJYvX26+/e1vD/3+zDPPmEAgMFVdhJl4nA4fPmwkmWAwmHefHE9T62qPpQMHDhibzWbOnz8/VMexdG1JMgcOHBi1DWPTzBpPjHJhXJpe44kT49LMm8zxxNg0/drb240kc/To0bxt5uLYxJn6WSiRSOjUqVPavHlzVv3mzZt1/PjxnNucOHFiRPstW7bo1Vdf1cDAwKht8u0To5tMnN4vnU4rGo2qrKwsq76np0f19fWqra3Vxz/+8RFnTDB+VxOnVatWqbq6WnfddZcOHz6c9RrH09SZimNpz549uvvuu1VfX59Vz7E0sxibrIdxaXZjXLIWxqbpFw6HJWnEd9iV5uLYRFI/C3V2diqVSmn+/PlZ9fPnz1dra2vObVpbW3O2TyaT6uzsHLVNvn1idJOJ0/t9//vfVywW09atW4fqli1bpr179+qFF17Qc889J4/Ho9tvv11vv/32lPb/ejGZOFVXV+upp57Svn37tH//fjU0NOiuu+7SsWPHhtpwPE2dqz2WWlpa9OKLL+qzn/1sVj3H0sxjbLIexqXZiXHJehibpp8xRl/96le1YcMGrVy5Mm+7uTg2OWe6A8jPZrNl/W6MGVE3Vvv31090nxjbZP+mzz33nB599FH99re/VWVl5VD9unXrtG7duqHfb7/9dq1evVo/+tGP9OSTT05dx68zE4lTQ0ODGhoahn5fv369mpqa9L3vfU933HHHpPaJsU3277l3716VlJTogQceyKrnWJodGJusg3Fp9mJcsh7Gpun3yCOP6PXXX9ef/vSnMdvOtbGJM/WzUHl5uRwOx4iZoPb29hEzRoOqqqpytnc6nZo3b96obfLtE6ObTJwGPf/883r44Yf1y1/+Unffffeobe12u9auXcsM7iRdTZyutG7duqwYcDxNnauJkTFGTz/9tLZt2ya32z1qW46l6cfYZB2MS9bDuDR7MTZNvy9/+ct64YUXdPjwYdXW1o7adi6OTST1s5Db7daaNWt06NChrPpDhw7ptttuy7nN+vXrR7T/4x//qJtvvlkul2vUNvn2idFNJk5S5kzI9u3b9eyzz+pjH/vYmO9jjNFrr72m6urqq+7z9WiycXq/xsbGrBhwPE2dq4nR0aNH9c477+jhhx8e8304lqYfY5M1MC5ZE+PS7MXYNH2MMXrkkUe0f/9+vfTSS1q8ePGY28zJsWl61+XDeP3iF78wLpfL7Nmzx5w+fdrs2LHDFBUVDa2euWvXLrNt27ah9u+++67xer1m586d5vTp02bPnj3G5XKZX//610Nt/vznPxuHw2Eef/xx8+abb5rHH3/cOJ1O88orr0z755srJhqnZ5991jidTrN7927T0tIyVEKh0FCbRx991Pz+9783Z8+eNY2NjeYzn/mMcTqd5uTJk9P++eaKicbphz/8oTlw4IA5c+aMeeONN8yuXbuMJLNv376hNhxPU2uiMRr06U9/2tx6660598mxNPWi0ahpbGw0jY2NRpL5wQ9+YBobG82FCxeMMYxNs8FEY8S4NDMmGifGpZkx0TgNYmyaPl/84hdNIBAwR44cyfoOi8fjQ22uh7GJpH4W2717t6mvrzdut9usXr0669EMDz30kNm0aVNW+yNHjphVq1YZt9ttFi1aZH7605+O2OevfvUr09DQYFwul1m2bFnWYIDJmUicNm3aZCSNKA899NBQmx07dpiFCxcat9ttKioqzObNm83x48en8RPNTROJ03e/+12zdOlS4/F4TGlpqdmwYYP53e9+N2KfHE9Ta6LfeaFQyBQWFpqnnnoq5/44lqbe4GO18n2HMTbNvInGiHFpZkw0ToxLM2My33mMTdMrV3wkmWeeeWaozfUwNtmMubwqAAAAAAAAsBTuqQcAAAAAwKJI6gEAAAAAsCiSegAAAAAALIqkHgAAAAAAiyKpBwAAAADAokjqAQAAAACwKJJ6AAAAAAAsiqQeAAAAAACLIqkHAADXxJ133qkdO3bMdDcAAJjTSOoBAAAAALAomzHGzHQnAADA3LJ9+3b9/Oc/z6o7d+6cFi1aNDMdAgBgjiKpBwAAUy4cDuu+++7TypUr9dhjj0mSKioq5HA4ZrhnAADMLc6Z7gAAAJh7AoGA3G63vF6vqqqqZro7AADMWdxTDwAAAACARZHUAwAAAABgUST1AADgmnC73UqlUjPdDQAA5jSSegAAcE0sWrRIJ0+e1Pnz59XZ2al0Oj3TXQIAYM4hqQcAANfE1772NTkcDi1fvlwVFRW6ePHiTHcJAIA5h0faAQAAAABgUZypBwAAAADAokjqAQAAAACwKJJ6AAAAAAAsiqQeAAAAAACLIqkHAAAAAMCiSOoBAAAAALAoknoAAAAAACyKpB4AAAAAAIsiqQcAAAAAwKJI6gEAAAAAsCiSegAAAAAALIqkHgAAAAAAi/r/TYGhuuSWgKEAAAAASUVORK5CYII=", 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P5tOf/nSeeuqp7Lnnnrn99tsz8v+mT3/qqaeyYMGCxvKjRo3K7bffnrPOOitf+9rXMnz48Fx++eX5h3/4h8YyBx98cH7wgx/k/PPPz6c+9anstttuuemmm3LQQQdt8de3NbnzzuTRR5uu+7/PVgAAAOgiulT3/q1VGbv3T56c/PCHrzweOzaZO7cd4yMBAADocBuaQ1uZiYKq+9vfkmnTmq475RSBHwAAoKsR+mnhuusaJgtdp0+fZMqUzqsPAAAA7SP008Tatck11zRdd/zxDd8kAgAAQNci9NPEHXckf/pT03WnnNI5dQEAAGDTCP00cdVVTR/vtVcyfnzn1AUAAIBNI/TT6Mknk9tua7rOBH4AAABdl9BPo299K1mz5pXHffsm739/59UHAACATSP0k6Qh7DefwO//+/+SurrOqQ8AAACbTugnSXLPPcmCBU3XmcAPAACgaxP6SdJyxv43vCHZf//OqQsAAAAdQ+gnSbJqVdPHO+5oAj8AAICuTugnSbJ6ddPHPXp0Tj0AAADoOEI/SVq29Pfs2Tn1AAAAoOMI/SQR+gEAAMpI6CeJ0A8AAFBGQj9JjOkHAAAoI6GfJFr6AQAAykjoJ4nQDwAAUEZCP0ladu8X+gEAALo+oZ8kLVv6jekHAADo+oR+kujeDwAAUEZCP0mEfgAAgDIS+kliTD8AAEAZCf0kMaYfAACgjIR+kujeDwAAUEZCP0mEfgAAgDIS+knScky/7v0AAABdn9BPEi39AAAAZST0k0ToBwAAKCOhnyRCPwAAQBkJ/SQxph8AAKCMhH6SaOkHAAAoI6GfJEI/AABAGQn9JGnZvV/oBwAA6PqEfpK0bOk3ph8AAKDrE/pJons/AABAGQn9JBH6AQAAykjoJ4mv7AMAACgjoZ8kWvoBAADKSOgnidAPAABQRkI/SYR+AACAMhL6SWJMPwAAQBkJ/STR0g8AAFBGQj9Zu7ZheTWhHwAAoOsT+mnRtT8R+gEAAMpA6KdF1/7EmH4AAIAyEPppNfRr6QcAAOj6hH6EfgAAgJIS+jGmHwAAoKSEfozpBwAAKCmhH937AQAASkroR+gHAAAoKaGfVsf0d+++5esBAABAxxL6adHS36NHUqt1Tl0AAADoOEI/LUK/rv0AAADlIPQj9AMAAJSU0E+LMf2+rg8AAKAchH609AMAAJSU0I/QDwAAUFJCPy269wv9AAAA5SD00+pX9gEAAND1Cf3o3g8AAFBSQj9CPwAAQEkJ/RjTDwAAUFJCP8b0AwAAlJTQj+79AAAAJSX0I/QDAACUlNBPizH9uvcDAACUg9CPln4AAICSEvoR+gEAAEpK6MdX9gEAAJSU0I+v7AMAACgpoR/d+wEAAEpK6EfoBwAAKCmhH2P6AQAASkrox5h+AACAkhL60b0fAACgpIR+hH4AAICSEvoxph8AAKCkhH6M6QcAACgpoR/d+wEAAEpK6EfoBwAAKCmhnxZj+nXvBwAAKAehHy39AAAAJSX0I/QDAACUlNCPr+wDAAAoKaEfX9kHAABQUkI/uvcDAACUlNCP0A8AAFBSQj/G9AMAAJSU0I8x/QAAACUl9KN7PwAAQEkJ/Qj9AAAAJdVlQv/zzz+fKVOmpK6uLnV1dZkyZUqWLFmy3n2KosiFF16Y4cOHZ5tttslhhx2Whx9+uEmZww47LLVarclywgknbMZXsvUxph8AAKCcukzoP/HEEzNnzpxMnz4906dPz5w5czJlypT17vO5z30uX/rSl3LFFVfkt7/9bYYOHZq3ve1tWbZsWZNyU6dOzVNPPdW4XH311ZvzpWx1jOkHAAAopy4R7+bNm5fp06dn1qxZOeigg5Ik11xzTSZMmJD58+dn9OjRLfYpiiJf/vKX86//+q857rjjkiQ33HBDhgwZku9973s55ZRTGsv27ds3Q4cO3TIvZiukez8AAEA5dYmW/pkzZ6aurq4x8CfJ+PHjU1dXl3vvvbfVfR5//PEsWrQoEydObFzXu3fvHHrooS32+e53v5uBAwdmjz32yCc+8YkWPQGaW7FiRZYuXdpk6cp07wcAACinLtHSv2jRogwePLjF+sGDB2fRokVt7pMkQ4YMabJ+yJAh+fOf/9z4+H3ve19GjRqVoUOHZu7cuTnvvPPyu9/9LjNmzGizPhdffHEuuuii9ryUrU5RJGvWNF2nez8AAEA5dGpL/4UXXthiEr3my/33358kqdVqLfYviqLV9a/WfHvzfaZOnZojjjgie+65Z0444YT86Ec/yh133JEHH3ywzWOed955qa+vb1wWLly4MS97q9K8a3+ipR8AAKAsOrVN97TTTnvNmfJ32WWXPPTQQ/nb3/7WYtvTTz/doiV/nXVj9BctWpRhw4Y1rl+8eHGb+yTJvvvum549e+bRRx/Nvvvu22qZ3r17p3fv3uutd1ch9AMAAJRXp4b+gQMHZuDAga9ZbsKECamvr89vfvObHHjggUmS++67L/X19Tn44INb3Wddl/0ZM2Zk3LhxSZKVK1fm7rvvzqWXXtrmcz388MNZtWpVkw8Kyqz5eP5E6AcAACiLLjGR3+67754jjzwyU6dOzaxZszJr1qxMnTo1Rx11VJOZ+8eMGZNp06YlaejWf+aZZ+Y//uM/Mm3atMydOzcnn3xy+vbtmxNPPDFJ8thjj+XTn/507r///jzxxBO5/fbb8973vjfjxo3LIYcc0imvdUtrraXfmH4AAIBy6DLx7rvf/W7OOOOMxtn4jz766FxxxRVNysyfPz/19fWNj88555y89NJL+djHPpbnn38+Bx10UP77v/872223XZKkV69e+cUvfpGvfOUreeGFFzJixIi8853vzAUXXJDu3btvuRfXiXTvBwAAKK9aURRFZ1eiq1u6dGnq6upSX1+f/v37d3Z1NsqCBcnIkU3X1dcnXexlAAAAVMqG5tAu0b2fzceYfgAAgPIS+ivOmH4AAIDyEvorTugHAAAoL6G/4pqH/h49klqtc+oCAABAxxL6K675mH7j+QEAAMpD6K+41lr6AQAAKAehv+Kah34t/QAAAOUh9Fec7v0AAADlJfRXnO79AAAA5SX0V5zu/QAAAOUl9Fec0A8AAFBeQn/FGdMPAABQXkJ/xRnTDwAAUF5Cf8Xp3g8AAFBeQn/FCf0AAADlJfRXnDH9AAAA5SX0V5wx/QAAAOUl9Fec7v0AAADlJfRXnO79AAAA5SX0V5zu/QAAAOUl9Fec7v0AAADlJfRXnNAPAABQXkJ/xRnTDwAAUF5Cf8UZ0w8AAFBeQn/F6d4PAABQXkJ/xQn9AAAA5SX0V5wx/QAAAOUl9FecMf0AAADlJfRXnO79AAAA5SX0V5zQDwAAUF5Cf8UZ0w8AAFBeQn/FGdMPAABQXkJ/xeneDwAAUF5Cf8Xp3g8AAFBeQn/F6d4PAABQXkJ/xeneDwAAUF5Cf8UJ/QAAAOUl9FecMf0AAADlJfRXnDH9AAAA5SX0V5zu/QAAAOUl9Fec0A8AAFBeQn/FGdMPAABQXkJ/xRnTDwAAUF5Cf8Xp3g8AAFBeQn/F6d4PAABQXkJ/xWnpBwAAKC+hv+KM6QcAACgvob/CiiJZs6bpOi39AAAA5SH0V1jz8fyJ0A8AAFAmQn+FNe/an+jeDwAAUCZCf4W1Fvq19AMAAJSH0F9hQj8AAEC5Cf0VZkw/AABAuQn9FWZMPwAAQLkJ/RWmez8AAEC5Cf0VJvQDAACUm9BfYa2N6de9HwAAoDyE/gpr3tLfvXtSq3VOXQAAAOh4Qn+FNQ/9uvYDAACUi9BfYc279wv9AAAA5SL0V5iWfgAAgHIT+iuseeg3iR8AAEC5CP0VpqUfAACg3IT+CjOmHwAAoNyE/grTvR8AAKDchP4K070fAACg3IT+ChP6AQAAyk3orzBj+gEAAMpN6K8wY/oBAADKTeivMN37AQAAyk3orzDd+wEAAMpN6K8wLf0AAADlJvRXmDH9AAAA5Sb0V5iWfgAAgHIT+ivMmH4AAIByE/orTEs/AABAuQn9FWZMPwAAQLkJ/RWmpR8AAKDchP4KM6YfAACg3IT+CtO9HwAAoNyE/grTvR8AAKDchP4KE/oBAADKTeivMGP6AQAAyk3orzBj+gEAAMpN6K8w3fsBAADKTeivMN37AQAAyk3orzAt/QAAAOUm9FeYMf0AAADlJvRXmJZ+AACAchP6K8yYfgAAgHIT+itMSz8AAEC5Cf0VZkw/AABAuQn9FaalHwAAoNy6TOh//vnnM2XKlNTV1aWuri5TpkzJkiVL1rvPrbfemkmTJmXgwIGp1WqZM2dOizIrVqzI6aefnoEDB6Zfv345+uij85e//GXzvIitjDH9AAAA5dZlQv+JJ56YOXPmZPr06Zk+fXrmzJmTKVOmrHef5cuX55BDDskll1zSZpkzzzwz06ZNyw9+8IPcc889eeGFF3LUUUdlzZo1Hf0Stjq69wMAAJRbl4h58+bNy/Tp0zNr1qwcdNBBSZJrrrkmEyZMyPz58zN69OhW91v3ocATTzzR6vb6+vpce+21ufHGG3PEEUckSb7zne9kxIgRueOOOzJp0qSOfzFbEd37AQAAyq1LtPTPnDkzdXV1jYE/ScaPH5+6urrce++97T7uAw88kFWrVmXixImN64YPH54999xzvcddsWJFli5d2mTpinTvBwAAKLcuEfoXLVqUwYMHt1g/ePDgLFq0aJOO26tXr2y//fZN1g8ZMmS9x7344osb5xaoq6vLiBEj2l2HzqSlHwAAoNw6NfRfeOGFqdVq613uv//+JEmtVmuxf1EUra7fVK913PPOOy/19fWNy8KFCzu8DluCMf0AAADl1qkx77TTTssJJ5yw3jK77LJLHnroofztb39rse3pp5/OkCFD2v38Q4cOzcqVK/P88883ae1fvHhxDj744Db36927d3r37t3u591aaOkHAAAot04N/QMHDszAgQNfs9yECRNSX1+f3/zmNznwwAOTJPfdd1/q6+vXG85fy3777ZeePXtmxowZmTx5cpLkqaeeyty5c/O5z32u3cftKozpBwAAKLcuMaZ/9913z5FHHpmpU6dm1qxZmTVrVqZOnZqjjjqqycz9Y8aMybRp0xofP/fcc5kzZ04eeeSRJMn8+fMzZ86cxvH6dXV1+dCHPpR//ud/zi9+8YvMnj0773//+7PXXns1zuZfVkUh9AMAAJRdlwj9SfLd7343e+21VyZOnJiJEydm7733zo033tikzPz581NfX9/4+Lbbbsu4cePyzne+M0lywgknZNy4cbnqqqsay1x22WU59thjM3ny5BxyyCHp27dvfvrTn6Z79+5b5oV1kuaBPzGmHwAAoGxqRVEUnV2Jrm7p0qWpq6tLfX19+vfv39nV2SAvvpj069d03WOPJbvu2jn1AQAAYMNtaA7tMi39dKzWWvp17wcAACgXob+ims/cn+jeDwAAUDZCf0W1Fvq19AMAAJSL0F9RQj8AAED5Cf0VZUw/AABA+W106F+9enV69OiRuXPnbo76sIUY0w8AAFB+Gx36e/TokZEjR2bNmjWboz5sIbr3AwAAlF+7uveff/75Oe+88/Lcc891dH3YQpp37+/ePanVOqcuAAAAbB7t6tB9+eWX549//GOGDx+ekSNHpl+/fk22P/jggx1SOTaf5i39WvkBAADKp12h/9hjj+3garClNQ/9xvMDAACUT7ui3gUXXNDR9WAL09IPAABQfpvUvvvAAw9k3rx5qdVqGTt2bMaNG9dR9WIzaz6mX+gHAAAon3aF/sWLF+eEE07IXXfdlQEDBqQoitTX1+etb31rfvCDH2TQoEEdXU86mJZ+AACA8mvX7P2nn356li5dmocffjjPPfdcnn/++cydOzdLly7NGWec0dF1ZDMwph8AAKD82hX1pk+fnjvuuCO7775747qxY8fma1/7WiZOnNhhlWPz0dIPAABQfu1q6V+7dm16tpISe/bsmbVr125ypdj8jOkHAAAov3aF/r//+7/Pxz/+8Tz55JON6/7617/mrLPOyuGHH95hlWPz0b0fAACg/NoV+q+44oosW7Ysu+yyS3bbbbe87nWvy6hRo7Js2bJ89atf7eg6shno3g8AAFB+7WrfHTFiRB588MHMmDEjf/jDH1IURcaOHZsjjjiio+vHZqJ7PwAAQPltdOhfvXp1+vTpkzlz5uRtb3tb3va2t22OerGZaekHAAAov43u3t+jR4+MHDkya9as2Rz1YQsxph8AAKD82jWm//zzz895552X5557rqPrwxaipR8AAKD82tW+e/nll+ePf/xjhg8fnpEjR6Zfv35Ntj/44IMdUjk2H2P6AQAAyq9dof/YY4/t4GqwpWnpBwAAKL92TeSXJP/4j/+YESNGdHiF2DKM6QcAACi/dk3k94UvfMFEfl2cln4AAIDya9dEfocffnjuuuuuDq4KW5Ix/QAAAOXXrk7db3/723Peeedl7ty52W+//VpM5Hf00Ud3SOXYfLT0AwAAlF+7Qv9HP/rRJMmXvvSlFttqtZqu/12AMf0AAADl166ot3bt2o6uB1uYln4AAIDy26gx/e94xztSX1/f+Pizn/1slixZ0vj42WefzdixYzuscmw+xvQDAACU30aF/p///OdZsWJF4+NLL700zz33XOPj1atXZ/78+R1XOzYb3fsBAADKb6NCf1EU631M16F7PwAAQPm16yv76Pp07wcAACi/jQr9tVottVqtxTq6Hi39AAAA5bdRI7mLosjJJ5+c3r17J0lefvnlnHrqqenXr1+SNBnvz9bNmH4AAIDy26iod9JJJzV5/P73v79FmQ984AObViO2CC39AAAA5bdRof+6667bXPVgCzOmHwAAoPxM5FdRWvoBAADKT+ivKGP6AQAAyk/orygt/QAAAOUn9FeUMf0AAADlJ/RXlJZ+AACA8hP6K8qYfgAAgPIT+itK934AAIDyE/orSvd+AACA8hP6K0r3fgAAgPIT+itKSz8AAED5Cf0VZUw/AABA+Qn9FaWlHwAAoPyE/ooyph8AAKD8hP4KKgrd+wEAAKpA6K+gNWtarhP6AQAAykfor6DmXfsToR8AAKCMhP4Kai30G9MPAABQPkJ/BWnpBwAAqAahv4KaT+KXCP0AAABlJPRXkJZ+AACAahD6K8iYfgAAgGoQ+itI934AAIBqEPorSEs/AABANQj9FdQ89Hfr1rAAAABQLqJeBTUP/br2AwAAlJPQX0HNx/QL/QAAAOUk9FeQln4AAIBqEPorqHnoN4kfAABAOQn9FaSlHwAAoBqE/goyph8AAKAahP4K0tIPAABQDUJ/BRnTDwAAUA1CfwXp3g8AAFANQn8F6d4PAABQDUJ/BeneDwAAUA1CfwVp6QcAAKgGob+CjOkHAACoBqG/grT0AwAAVIPQX0HG9AMAAFSD0F9BWvoBAACqQeivIGP6AQAAqkHoryAt/QAAANUg9FeQMf0AAADVIPRXkJZ+AACAahD6K8iYfgAAgGoQ+itISz8AAEA1CP0VZEw/AABANQj9FaR7PwAAQDUI/RWkez8AAEA1CP0VpHs/AABANQj9FaSlHwAAoBqE/goyph8AAKAahP4K0tIPAABQDUJ/BRnTDwAAUA1CfwVp6QcAAKiGLhP6n3/++UyZMiV1dXWpq6vLlClTsmTJkvXuc+utt2bSpEkZOHBgarVa5syZ06LMYYcdllqt1mQ54YQTNs+L2EoY0w8AAFANXSb0n3jiiZkzZ06mT5+e6dOnZ86cOZkyZcp691m+fHkOOeSQXHLJJestN3Xq1Dz11FONy9VXX92RVd/qaOkHAACohi4xmnvevHmZPn16Zs2alYMOOihJcs0112TChAmZP39+Ro8e3ep+6z4UeOKJJ9Z7/L59+2bo0KEdWuetmTH9AAAA1dAlWvpnzpyZurq6xsCfJOPHj09dXV3uvffeTT7+d7/73QwcODB77LFHPvGJT2TZsmXrLb9ixYosXbq0ydKV6N4PAABQDV2ijXfRokUZPHhwi/WDBw/OokWLNunY73vf+zJq1KgMHTo0c+fOzXnnnZff/e53mTFjRpv7XHzxxbnooos26Xk7k+79AAAA1dCpLf0XXnhhi0n0mi/3339/kqRWq7XYvyiKVtdvjKlTp+aII47InnvumRNOOCE/+tGPcscdd+TBBx9sc5/zzjsv9fX1jcvChQs3qQ5bmtAPAABQDZ3a0n/aaae95kz5u+yySx566KH87W9/a7Ht6aefzpAhQzq0Tvvuu2969uyZRx99NPvuu2+rZXr37p3evXt36PNuScb0AwAAVEOnxr2BAwdm4MCBr1luwoQJqa+vz29+85sceOCBSZL77rsv9fX1Ofjggzu0Tg8//HBWrVqVYcOGdehxtybG9AMAAFRDl5jIb/fdd8+RRx6ZqVOnZtasWZk1a1amTp2ao446qsnM/WPGjMm0adMaHz/33HOZM2dOHnnkkSTJ/PnzM2fOnMZ5AB577LF8+tOfzv33358nnngit99+e9773vdm3LhxOeSQQ7bsi9yCdO8HAACohi4R+pOGGfb32muvTJw4MRMnTszee++dG2+8sUmZ+fPnp76+vvHxbbfdlnHjxuWd73xnkuSEE07IuHHjctVVVyVJevXqlV/84heZNGlSRo8enTPOOCMTJ07MHXfcke7du2+5F7eF6d4PAABQDbWiKIrOrkRXt3Tp0tTV1aW+vj79+/fv7Oq8pr59k5deeuXxffcl/zdqAgAAgC5gQ3Nol2npp+MY0w8AAFANQn/FFIUx/QAAAFUh9FfMmjUt1xnTDwAAUE5Cf8U0b+VPtPQDAACUldBfMc3H8ydCPwAAQFkJ/RWjpR8AAKA6hP6KaS30G9MPAABQTkJ/xejeDwAAUB1Cf8Xo3g8AAFAdQn/FCP0AAADVIfRXjDH9AAAA1SH0V0zzMf3dujUsAAAAlI+4VzHNW/p17QcAACgvob9imod+XfsBAADKS+ivGC39AAAA1SH0V0zzMf1CPwAAQHkJ/RWjpR8AAKA6hP6KMaYfAACgOoT+itG9HwAAoDqE/orRvR8AAKA6hP6KEfoBAACqQ+ivGGP6AQAAqkPorxhj+gEAAKpD6K8Y3fsBAACqQ+ivGKEfAACgOoT+ijGmHwAAoDqE/ooxph8AAKA6hP6K0b0fAACgOoT+itG9HwAAoDqE/orR0g8AAFAdQn/FGNMPAABQHUJ/xWjpBwAAqA6hv2KM6QcAAKgOob9idO8HAACoDqG/YnTvBwAAqA6hv2KEfgAAgOoQ+ivGmH4AAIDqEPorxph+AACA6hD6K0b3fgAAgOoQ+itG6AcAAKgOob9ijOkHAACoDqG/YozpBwAAqA6hv2J07wcAAKgOob9idO8HAACoDqG/YnTvBwAAqA6hv2J07wcAAKgOob9ihH4AAIDqEPorxph+AACA6hD6K8aYfgAAgOoQ+itG934AAIDqEPorRugHAACoDqG/YozpBwAAqA6hv2KM6QcAAKgOob9idO8HAACoDqG/YnTvBwAAqA6hv0KKQks/AABAlQj9FbJ2bct1Qj8AAEB5Cf0V0ryVPxH6AQAAykzor5DWQr8x/QAAAOUl9FdI86/rS7T0AwAAlJnQXyG69wMAAFSL0F8hQj8AAEC1CP0VYkw/AABAtQj9FWJMPwAAQLUI/RWiez8AAEC1CP0V0jz0d+vWsAAAAFBOIl+FNA/9xvMDAACUm9BfIc3H9OvaDwAAUG5Cf4U0b+kX+gEAAMpN6K8Q3fsBAACqReivEN37AQAAqkXorxDd+wEAAKpF6K8QoR8AAKBahP4KMaYfAACgWoT+CjGmHwAAoFqE/grRvR8AAKBahP4KEfoBAACqReivEGP6AQAAqkXorxBj+gEAAKpF6K8Q3fsBAACqReivEKEfAACgWoT+CjGmHwAAoFqE/goxph8AAKBahP4K0b0fAACgWoT+CtG9HwAAoFqE/grRvR8AAKBahP4K0b0fAACgWoT+ChH6AQAAqkXorxBj+gEAAKpF6K8QY/oBAACqReivEN37AQAAqkXorxChHwAAoFqE/goxph8AAKBaukzof/755zNlypTU1dWlrq4uU6ZMyZIlS9osv2rVqnzyk5/MXnvtlX79+mX48OH5wAc+kCeffLJJuRUrVuT000/PwIED069fvxx99NH5y1/+splfTecwph8AAKBaukzoP/HEEzNnzpxMnz4906dPz5w5czJlypQ2y7/44ot58MEH86lPfSoPPvhgbr311vzv//5vjj766CblzjzzzEybNi0/+MEPcs899+SFF17IUUcdlTVr1mzul7TF6d4PAABQLV2ig/e8efMyffr0zJo1KwcddFCS5JprrsmECRMyf/78jB49usU+dXV1mTFjRpN1X/3qV3PggQdmwYIF2XnnnVNfX59rr702N954Y4444ogkyXe+852MGDEid9xxRyZNmrT5X9wWJPQDAABUS5do6Z85c2bq6uoaA3+SjB8/PnV1dbn33ns3+Dj19fWp1WoZMGBAkuSBBx7IqlWrMnHixMYyw4cPz5577rne465YsSJLly5tsnQFzbv3G9MPAABQbl0i9C9atCiDBw9usX7w4MFZtGjRBh3j5ZdfzrnnnpsTTzwx/fv3bzxur169sv322zcpO2TIkPUe9+KLL26cW6Curi4jRozYiFfTebT0AwAAVEunhv4LL7wwtVptvcv999+fJKnVai32L4qi1fXNrVq1KieccELWrl2bK6+88jXLv9ZxzzvvvNTX1zcuCxcufM1jbg2EfgAAgGrp1A7ep512Wk444YT1ltlll13y0EMP5W9/+1uLbU8//XSGDBmy3v1XrVqVyZMn5/HHH88vf/nLxlb+JBk6dGhWrlyZ559/vklr/+LFi3PwwQe3eczevXund+/e633erZGv7AMAAKiWTo19AwcOzMCBA1+z3IQJE1JfX5/f/OY3OfDAA5Mk9913X+rr69cbztcF/kcffTR33nlndtxxxybb99tvv/Ts2TMzZszI5MmTkyRPPfVU5s6dm8997nOb8Mq2Tr6yDwAAoFq6xJj+3XffPUceeWSmTp2aWbNmZdasWZk6dWqOOuqoJjP3jxkzJtOmTUuSrF69Ou95z3ty//3357vf/W7WrFmTRYsWZdGiRVm5cmWShhn+P/ShD+Wf//mf84tf/CKzZ8/O+9///uy1116Ns/mXie79AAAA1dJlOnh/97vfzRlnnNE40/7RRx+dK664okmZ+fPnp76+Pknyl7/8JbfddluSZJ999mlS7s4778xhhx2WJLnsssvSo0ePTJ48OS+99FIOP/zwXH/99enevfvmfUGdQOgHAACollpRFEVnV6KrW7p0aerq6lJfX99kzoCtzc47J6+ec/C//it5xzs6rz4AAAC0z4bm0C7RvZ+OYUw/AABAtQj9FaJ7PwAAQLUI/RUi9AMAAFSL0F8hzUN/jy4zjSMAAADtIfRXiDH9AAAA1SL0V4ju/QAAANUi9FfEmjVJ8y9nFPoBAADKTeiviOZd+xNj+gEAAMpO6K+I5l37Ey39AAAAZSf0V4TQDwAAUD1Cf0W0Fvp17wcAACg3ob8iWhvTr6UfAACg3IT+itC9HwAAoHqE/ooQ+gEAAKpH6K8IY/oBAACqR+iviOZj+mu1pHv3zqkLAAAAW4bQXxHNW/p17QcAACg/ob8ihH4AAIDqEforonn3fuP5AQAAyk/orwgt/QAAANUj9FeE0A8AAFA9Qn9FCP0AAADVI/RXhDH9AAAA1SP0V4SWfgAAgOoR+itC6AcAAKgeob8imod+3fsBAADKT/SriOZj+rX0AwBA17dmzZqsat7CRyn07Nkz3bt33+TjCP0VoXs/AACUR1EUWbRoUZYsWdLZVWEzGjBgQIYOHZpardbuYwj9FSH0AwBAeawL/IMHD07fvn03KRSy9SmKIi+++GIWL16cJBk2bFi7jyX0V4Qx/QAAUA5r1qxpDPw77rhjZ1eHzWSbbbZJkixevDiDBw9ud1d/E/lVhDH9AABQDuvG8Pft27eTa8Lmtu4cb8q8DUJ/RejeDwAA5aJLf/l1xDkW+itC6AcAAKgeob8imnfvN6YfAAAog7vuuiu1Ws03GbRB6K8ILf0AAADVo723IoR+AAAop7Vrk2ef7bzn33HHpFsFm5NXrVqVns2CVWvr2nusjlLBU1NNvrIPAADK6dlnk8GDO2/Z0A8cDjvssJx22mk57bTTMmDAgOy44445//zzUxRFY5nvfOc72X///bPddttl6NChOfHEExu/q36d22+/PW94wxuyzTbb5K1vfWueeOKJFs9177335i1veUu22WabjBgxImeccUaWL1++3vr99Kc/zX777Zc+ffpk1113zUUXXZTVrxonXavVctVVV+WYY45Jv3798u///u+58MILs88+++Rb3/pWdt111/Tu3TtFUWTBggU55phjsu2226Z///6ZPHly/va3vzUeq639NgehvyJ8ZR8AANDZbrjhhvTo0SP33XdfLr/88lx22WX55je/2bh95cqV+cxnPpPf/e53+fGPf5zHH388J598cuP2hQsX5rjjjss73vGOzJkzJx/+8Idz7rnnNnmO3//+95k0aVKOO+64PPTQQ7nppptyzz335LTTTmuzXj//+c/z/ve/P2eccUYeeeSRXH311bn++uvz2c9+tkm5Cy64IMccc0x+//vf5x//8R+TJH/84x9z880355ZbbsmcOXOSJMcee2yee+653H333ZkxY0Yee+yxHH/88U2O1dp+m4P23orQvR8AAOhsI0aMyGWXXZZarZbRo0fn97//fS677LJMnTo1SRqDdJLsuuuuufzyy3PggQfmhRdeyLbbbpuvf/3r2XXXXVsc49JLL23c7/Of/3xOPPHEnHnmmUmS17/+9bn88stz6KGH5utf/3r69OnTol6f/exnc+655+akk05qfO7PfOYzOeecc3LBBRc0ljvxxBOb1DFp+KDixhtvzKBBg5IkM2bMyEMPPZTHH388I0aMSJLceOON2WOPPfLb3/42BxxwQKv7bS5a+itC6AcAADrb+PHjm3z3/IQJE/Loo49mzZo1SZLZs2fnmGOOyciRI7PddtvlsMMOS5IsWLAgSTJv3rxWj/FqDzzwQK6//vpsu+22jcukSZOydu3aPP74463W64EHHsinP/3pJvtMnTo1Tz31VF588cXGcvvvv3+LfUeOHNkkuM+bNy8jRoxoDPxJMnbs2AwYMCDz5s1rc7/NRUt/RRjTDwAA5bTjjkmzYe9b/Pk7wvLlyzNx4sRMnDgx3/nOdzJo0KAsWLAgkyZNysqVK5Nkg8a9r127NqecckrOOOOMFtt23nnnNve56KKLctxxx7XY9uqeAf369Wuxvfm6oiiafCjR1vrWjrU5iH4VYUw/AACUU7duyRZoMO4Qs2bNavH49a9/fbp3754//OEPeeaZZ3LJJZc0tpLff//9TcqPHTs2P/7xj9d7zH333TcPP/xwXve6121wvfbdd9/Mnz9/o/Zpy9ixY7NgwYIsXLiw8XU88sgjqa+vz+67777Jx99YuvdXhO79AABAZ1u4cGHOPvvszJ8/P9///vfz1a9+NR//+MeTNLTC9+rVK1/96lfzpz/9Kbfddls+85nPNNn/1FNPzWOPPdZ4jO9973u5/vrrm5T55Cc/mZkzZ+af/umfMmfOnDz66KO57bbbcvrpp7dZr3/7t3/Lt7/97Vx44YV5+OGHM2/evNx00005//zzN/o1HnHEEdl7773zvve9Lw8++GB+85vf5AMf+EAOPfTQVocHbG5Cf0UI/QAAQGf7wAc+kJdeeikHHnhg/umf/imnn356PvKRjyRJBg0alOuvvz4//OEPM3bs2FxyySX5whe+0GT/nXfeObfcckt++tOf5o1vfGOuuuqq/Md//EeTMnvvvXfuvvvuPProo3nzm9+ccePG5VOf+lSGDRvWZr0mTZqU//zP/8yMGTNywAEHZPz48fnSl76UkSNHbvRrrNVq+fGPf5ztt98+b3nLW3LEEUdk1113zU033bTRx+oItWJzfRlghSxdujR1dXWpr69P//79O7s6rXrHO5Kf/eyVx5/7XPL//l/n1QcAAGifl19+OY8//nhGjRrV6kz0W6vDDjss++yzT7785S93dlW6jPWd6w3NoVr6K0JLPwAAQPUI/RUh9AMAAFSP2fsrQugHAAA601133dXZVagkLf0V0fwr+3r4uAcAAKD0hP6K0NIPAABQPUJ/RQj9AAAA1SP0V0Tz0K97PwAAQPkJ/RXRfEy/ln4AAIDyE/orQvd+AACA6hH6K0LoBwAAqB6hvyKM6QcAAMro5JNPzrHHHtvZ1dhqiX4VYUw/AABUwNNPt3/fbbdNttmm9W3PPJMURdN1gwa1/7lKYNWqVenZLFitXLkyvXr12uhjtXe/DaGlvyJ+9KPkP/8zmTYt+eEPkzFjOrtGAABAhxs8uP3Lt77V9nF3371l+Y102GGH5fTTT8+ZZ56Z7bffPkOGDMk3vvGNLF++PB/84Aez3XbbZbfddsvPfvazxn3WrFmTD33oQxk1alS22WabjB49Ol/5ylcat1944YW54YYb8pOf/CS1Wi21Wi133XVXVq5cmdNOOy3Dhg1Lnz59sssuu+Tiiy9eb/2uu+667L777unTp0/GjBmTK6+8snHbE088kVqtlptvvjmHHXZY+vTpk+985zuNvQwuvvjiDB8+PG94wxuSJL///e/z93//99lmm22y44475iMf+UheeOGFxuO1td/moKW/Ig4/vLNrAAAAVN0NN9yQc845J7/5zW9y00035aMf/Wh+/OMf593vfnf+5V/+JZdddlmmTJmSBQsWpG/fvlm7dm122mmn3HzzzRk4cGDuvffefOQjH8mwYcMyefLkfOITn8i8efOydOnSXHfddUmSHXbYIZdffnluu+223Hzzzdl5552zcOHCLFy4sM16XXPNNbngggtyxRVXZNy4cZk9e3amTp2afv365aSTTmos98lPfjJf/OIXc91116V37965++6784tf/CL9+/fPjBkzUhRFXnzxxRx55JEZP358fvvb32bx4sX58Ic/nNNOOy3XX39947Ga77e5CP0AAABsEW984xtz/vnnJ0nOO++8XHLJJRk4cGCmTp2aJPm3f/u3fP3rX89DDz2U8ePHp2fPnrnooosa9x81alTuvffe3HzzzZk8eXK23XbbbLPNNlmxYkWGDh3aWG7BggV5/etfnze96U2p1WoZOXLkeuv1mc98Jl/84hdz3HHHNT7PI488kquvvrpJ6D/zzDMby6zTr1+/fPOb32zsnn/NNdfkpZdeyre//e3069cvSXLFFVfkXe96Vy699NIMGTKk1f02F6EfAACALWLvvfdu/L179+7Zcccds9deezWuWxeIFy9e3Ljuqquuyje/+c38+c9/zksvvZSVK1dmn332We/znHzyyXnb296W0aNH58gjj8xRRx2ViRMntlr26aefzsKFC/OhD32o8cOHJFm9enXq6uqalN1///1b7L/XXns1Ce7z5s3LG9/4xsbAnySHHHJI1q5dm/nz5ze+xub7bS5CPwAAQFm8KixvtG23bXvbvHktJ/Jrh+YT39VqtSbrarVakmTt2rVJkptvvjlnnXVWvvjFL2bChAnZbrvt8vnPfz733Xffep9n3333zeOPP56f/exnueOOOzJ58uQcccQR+dGPftSi7Lrnuuaaa3LQQQc12da9e/cmj18d5NtaVxRF4+to7tXrWzvW5iD0AwAAlMXmmlF/4MDNc9zX8D//8z85+OCD87GPfaxx3WOPPdakTK9evbJmzZoW+/bv3z/HH398jj/++LznPe/JkUcemeeeey477LBDk3JDhgzJ3/3d3+VPf/pT3ve+921ynceOHZsbbrghy5cvbwz2v/71r9OtW7fNOmFfW4R+AAAAtkqve93r8u1vfzs///nPM2rUqNx444357W9/m1GjRjWW2WWXXfLzn/888+fPz4477pi6urpcccUVGTZsWPbZZ59069YtP/zhDzN06NAMGDCg1ee58MILc8YZZ6R///55+9vfnhUrVuT+++/P888/n7PPPnuj6vy+970vF1xwQU466aRceOGFefrpp3P66adnypQpjV37tyRf2QcAAMBW6dRTT81xxx2X448/PgcddFCeffbZJq3+STJ16tSMHj06+++/fwYNGpRf//rX2XbbbXPppZdm//33zwEHHJAnnngit99+e7p1az0Cf/jDH843v/nNXH/99dlrr71y6KGH5vrrr2/y4cKG6tu3b37+85/nueeeywEHHJD3vOc9Ofzww3PFFVe062+wqWrF5vxugIpYunRp6urqUl9fn/79+3d2dQAAgBJ7+eWX8/jjj2fUqFHp06dPZ1eHzWh953pDc6iWfgAAACgpoR8AAABKSugHAACAkhL6AQAAoKSEfgAAgC7InOzl1xHnWOgHAADoQnr27JkkefHFFzu5Jmxu687xunPeHj06qjIAAABsft27d8+AAQOyePHiJA3fC1+r1Tq5VnSkoijy4osvZvHixRkwYEC6d+/e7mMJ/QAAAF3M0KFDk6Qx+FNOAwYMaDzX7SX0AwAAdDG1Wi3Dhg3L4MGDs2rVqs6uDptBz549N6mFfx2hHwAAoIvq3r17hwRDystEfgAAAFBSQj8AAACUlNAPAAAAJWVMfwcoiiJJsnTp0k6uCQAAAFWwLn+uy6NtEfo7wLJly5IkI0aM6OSaAAAAUCXLli1LXV1dm9trxWt9LMBrWrt2bZ588slst912qdVqnV2dNi1dujQjRozIwoUL079//86uDm1wnroG52nr5xx1Dc5T1+A8bf2co67Beeoausp5Kooiy5Yty/Dhw9OtW9sj97X0d4Bu3bplp5126uxqbLD+/ftv1f94aeA8dQ3O09bPOeoanKeuwXna+jlHXYPz1DV0hfO0vhb+dUzkBwAAACUl9AMAAEBJCf0V0rt371xwwQXp3bt3Z1eF9XCeugbnaevnHHUNzlPX4Dxt/ZyjrsF56hrKdp5M5AcAAAAlpaUfAAAASkroBwAAgJIS+gEAAKCkhH4AAAAoKaG/i7vyyiszatSo9OnTJ/vtt1/+53/+Z73l77777uy3337p06dPdt1111x11VUtytxyyy0ZO3ZsevfunbFjx2batGmbq/qVsDHn6NZbb83b3va2DBo0KP3798+ECRPy85//vEmZ66+/PrVarcXy8ssvb+6XUmobc57uuuuuVs/BH/7whyblvJc63sacp5NPPrnV87THHns0lvF+6li/+tWv8q53vSvDhw9PrVbLj3/849fcx31py9vY8+TetOVt7DlyX+ocG3ue3Je2vIsvvjgHHHBAtttuuwwePDjHHnts5s+f/5r7le3eJPR3YTfddFPOPPPM/Ou//mtmz56dN7/5zXn729+eBQsWtFr+8ccfzzve8Y68+c1vzuzZs/Mv//IvOeOMM3LLLbc0lpk5c2aOP/74TJkyJb/73e8yZcqUTJ48Offdd9+WelmlsrHn6Fe/+lXe9ra35fbbb88DDzyQt771rXnXu96V2bNnNynXv3//PPXUU02WPn36bImXVEobe57WmT9/fpNz8PrXv75xm/dSx9vY8/SVr3ylyflZuHBhdthhh7z3ve9tUs77qeMsX748b3zjG3PFFVdsUHn3pc6xsefJvWnL29hztI770pa1sefJfWnLu/vuu/NP//RPmTVrVmbMmJHVq1dn4sSJWb58eZv7lPLeVNBlHXjggcWpp57aZN2YMWOKc889t9Xy55xzTjFmzJgm60455ZRi/PjxjY8nT55cHHnkkU3KTJo0qTjhhBM6qNbVsrHnqDVjx44tLrroosbH1113XVFXV9dRVaTY+PN05513FkmK559/vs1jei91vE19P02bNq2o1WrFE0880bjO+2nzSVJMmzZtvWXclzrfhpyn1rg3bTkbco7clzpfe95L7ktb3uLFi4skxd13391mmTLem7T0d1ErV67MAw88kIkTJzZZP3HixNx7772t7jNz5swW5SdNmpT7778/q1atWm+Zto5J29pzjppbu3Ztli1blh122KHJ+hdeeCEjR47MTjvtlKOOOqpFawsbblPO07hx4zJs2LAcfvjhufPOO5ts817qWB3xfrr22mtzxBFHZOTIkU3Wez91Hvelrsm9aevlvtS1uC9tefX19UnS4vr1amW8Nwn9XdQzzzyTNWvWZMiQIU3WDxkyJIsWLWp1n0WLFrVafvXq1XnmmWfWW6atY9K29pyj5r74xS9m+fLlmTx5cuO6MWPG5Prrr89tt92W73//++nTp08OOeSQPProox1a/6poz3kaNmxYvvGNb+SWW27JrbfemtGjR+fwww/Pr371q8Yy3ksda1PfT0899VR+9rOf5cMf/nCT9d5Pnct9qWtyb9r6uC91Pe5LW15RFDn77LPzpje9KXvuuWeb5cp4b+rR2RVg09RqtSaPi6Jose61yjdfv7HHZP3a+/f8/ve/nwsvvDA/+clPMnjw4Mb148ePz/jx4xsfH3LIIdl3333z1a9+NZdffnnHVbxiNuY8jR49OqNHj258PGHChCxcuDBf+MIX8pa3vKVdx2TDtPdvev3112fAgAE59thjm6z3fup87ktdi3vT1sl9qetxX9ryTjvttDz00EO55557XrNs2e5NWvq7qIEDB6Z79+4tPk1avHhxi0+d1hk6dGir5Xv06JEdd9xxvWXaOiZta885Wuemm27Khz70odx888054ogj1lu2W7duOeCAA3wC3E6bcp5ebfz48U3OgfdSx9qU81QURb71rW9lypQp6dWr13rLej9tWe5LXYt7U9fivrT1cl/a8k4//fTcdtttufPOO7PTTjutt2wZ701CfxfVq1ev7LfffpkxY0aT9TNmzMjBBx/c6j4TJkxoUf6///u/s//++6dnz57rLdPWMWlbe85R0tCKcvLJJ+d73/te3vnOd77m8xRFkTlz5mTYsGGbXOcqau95am727NlNzoH3UsfalPN09913549//GM+9KEPvebzeD9tWe5LXYd7U9fjvrT1cl/acoqiyGmnnZZbb701v/zlLzNq1KjX3KeU96YtO28gHekHP/hB0bNnz+Laa68tHnnkkeLMM88s+vXr1zgD6LnnnltMmTKlsfyf/vSnom/fvsVZZ51VPPLII8W1115b9OzZs/jRj37UWObXv/510b179+KSSy4p5s2bV1xyySVFjx49ilmzZm3x11cGG3uOvve97xU9evQovva1rxVPPfVU47JkyZLGMhdeeGExffr04rHHHitmz55dfPCDHyx69OhR3HfffVv89ZXFxp6nyy67rJg2bVrxv//7v8XcuXOLc889t0hS3HLLLY1lvJc63saep3Xe//73FwcddFCrx/R+6ljLli0rZs+eXcyePbtIUnzpS18qZs+eXfz5z38uisJ9aWuxsefJvWnL29hz5L7UOTb2PK3jvrTlfPSjHy3q6uqKu+66q8n168UXX2wsU4V7k9DfxX3ta18rRo4cWfTq1avYd999m3z9xEknnVQceuihTcrfddddxbhx44pevXoVu+yyS/H1r3+9xTF/+MMfFqNHjy569uxZjBkzpskNg423Mefo0EMPLZK0WE466aTGMmeeeWax8847F7169SoGDRpUTJw4sbj33nu34Csqp405T5deemmx2267FX369Cm233774k1velPxX//1Xy2O6b3U8Tb2mrdkyZJim222Kb7xjW+0ejzvp4617mvD2rqGuS9tHTb2PLk3bXkbe47clzpHe6557ktbVmvnJ0lx3XXXNZapwr2pVhT/NysBAAAAUCrG9AMAAEBJCf0AAABQUkI/AAAAlJTQDwAAACUl9AMAAEBJCf0AAABQUkI/AAAAlJTQDwAAACUl9AMAneawww7LmWee2dnVAIDSEvoBAACgpGpFURSdXQkAoHpOPvnk3HDDDU3WPf7449lll106p0IAUEJCPwDQKerr6/P2t789e+65Zz796U8nSQYNGpTu3bt3cs0AoDx6dHYFAIBqqqurS69evdK3b98MHTq0s6sDAKVkTD8AAACUlNAPAAAAJSX0AwCdplevXlmzZk1nVwMASkvoBwA6zS677JL77rsvTzzxRJ555pmsXbu2s6sEAKUi9AMAneYTn/hEunfvnrFjx2bQoEFZsGBBZ1cJAErFV/YBAABASWnpBwAAgJIS+gEAAKCkhH4AAAAoKaEfAAAASkroBwAAgJIS+gEAAKCkhH4AAAAoKaEfAAAASkroBwAAgJIS+gEAAKCkhH4AAAAoKaEfAAAASur/B3l1C7t2tub7AAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def deltafun(j, k):\n", - " if j == k:\n", - " return 1.0\n", - " else:\n", - " return 0.0\n", - "\n", - "\n", - "def pade_eps(lmax):\n", - " Alpha = np.zeros((2 * lmax, 2 * lmax))\n", - " for j in range(2 * lmax):\n", - " for k in range(2 * lmax):\n", - " # Fermionic (see other example notebooks):\n", - " # Alpha[j][k] = (deltafun(j, k+1) + deltafun(j, k-1))\n", - " # / sqrt((2 * (j + 1) - 1) * (2 * (k + 1) - 1))\n", - " # Bosonic:\n", - " Alpha[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1)) / np.sqrt(\n", - " (2 * (j + 1) + 1) * (2 * (k + 1) + 1)\n", - " )\n", - "\n", - " eigvalsA = np.linalg.eigvalsh(Alpha)\n", - " eps = [-2 / val for val in eigvalsA[0:lmax]]\n", - " return eps\n", - "\n", - "\n", - "def pade_chi(lmax):\n", - " AlphaP = np.zeros((2 * lmax - 1, 2 * lmax - 1))\n", - " for j in range(2 * lmax - 1):\n", - " for k in range(2 * lmax - 1):\n", - " # Fermionic:\n", - " # AlphaP[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1))\n", - " # / sqrt((2 * (j + 1) + 1) * (2 * (k + 1) + 1))\n", - " # Bosonic [this is +3 because +1 (bose) + 2*(+1) (from bm+1)]:\n", - " AlphaP[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1)) / np.sqrt(\n", - " (2 * (j + 1) + 3) * (2 * (k + 1) + 3)\n", - " )\n", - "\n", - " eigvalsAP = np.linalg.eigvalsh(AlphaP)\n", - " chi = [-2 / val for val in eigvalsAP[0:lmax - 1]]\n", - " return chi\n", - "\n", - "\n", - "def pade_kappa_epsilon(lmax):\n", - " eps = pade_eps(lmax)\n", - " chi = pade_chi(lmax)\n", - "\n", - " kappa = [0]\n", - " prefactor = 0.5 * lmax * (2 * (lmax + 1) + 1)\n", - "\n", - " for j in range(lmax):\n", - " term = prefactor\n", - " for k in range(lmax - 1):\n", - " term *= (chi[k] ** 2 - eps[j] ** 2) / (\n", - " eps[k] ** 2 - eps[j] ** 2 + deltafun(j, k)\n", - " )\n", - "\n", - " for k in range(lmax - 1, lmax):\n", - " term /= eps[k] ** 2 - eps[j] ** 2 + deltafun(j, k)\n", - "\n", - " kappa.append(term)\n", - "\n", - " epsilon = [0] + eps\n", - "\n", - " return kappa, epsilon\n", - "\n", - "\n", - "def pade_corr(tlist, lmax):\n", - " kappa, epsilon = pade_kappa_epsilon(lmax)\n", - "\n", - " eta_list = [lam * gamma * (cot(gamma * beta / 2.0) - 1.0j)]\n", - " gamma_list = [gamma]\n", - "\n", - " if lmax > 0:\n", - " for ll in range(1, lmax + 1):\n", - " eta_list.append(\n", - " (kappa[ll] / beta)\n", - " * 4\n", - " * lam\n", - " * gamma\n", - " * (epsilon[ll] / beta)\n", - " / ((epsilon[ll] ** 2 / beta**2) - gamma**2)\n", - " )\n", - " gamma_list.append(epsilon[ll] / beta)\n", - "\n", - " c_tot = []\n", - " for t in tlist:\n", - " c_tot.append(\n", - " sum(\n", - " [\n", - " eta_list[ll] * np.exp(-gamma_list[ll] * t)\n", - " for ll in range(lmax + 1)\n", - " ]\n", - " )\n", - " )\n", - " return c_tot, eta_list, gamma_list\n", - "\n", - "\n", - "tlist_corr = np.linspace(0, 2, 100)\n", - "cppLP, etapLP, gampLP = pade_corr(tlist_corr, 2)\n", - "corr_15k = dlenv.correlation_function(tlist_corr, Nk=15_000)\n", - "corr_2k = dlenv.correlation_function(tlist_corr, Nk=2)\n", - "\n", - "fig, ax1 = plt.subplots(figsize=(12, 7))\n", - "ax1.plot(\n", - " tlist_corr,\n", - " np.real(cppLP),\n", - " color=\"b\",\n", - " linewidth=3,\n", - " label=r\"real pade 2 terms\",\n", - ")\n", - "ax1.plot(\n", - " tlist_corr,\n", - " np.real(corr_15k),\n", - " \"r--\",\n", - " linewidth=3,\n", - " label=r\"real mats 15000 terms\",\n", - ")\n", - "ax1.plot(\n", - " tlist_corr,\n", - " np.real(corr_2k),\n", - " \"g--\",\n", - " linewidth=3,\n", - " label=r\"real mats 2 terms\",\n", - ")\n", - "\n", - "ax1.set_xlabel(\"t\")\n", - "ax1.set_ylabel(r\"$C_{R}(t)$\")\n", - "ax1.legend()\n", - "\n", - "fig, ax1 = plt.subplots(figsize=(12, 7))\n", - "\n", - "ax1.plot(\n", - " tlist_corr,\n", - " np.real(cppLP) - np.real(corr_15k),\n", - " color=\"b\",\n", - " linewidth=3,\n", - " label=r\"pade error\",\n", - ")\n", - "ax1.plot(\n", - " tlist_corr,\n", - " np.real(corr_2k) - np.real(corr_15k),\n", - " \"r--\",\n", - " linewidth=3,\n", - " label=r\"mats error\",\n", - ")\n", - "\n", - "ax1.set_xlabel(\"t\")\n", - "ax1.set_ylabel(r\"Error\")\n", - "ax1.legend();" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "e44fe78d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.006168365478515625\n", - " Total run time: 2.10s*] Elapsed 2.10s / Remaining 00:00:00:00\n", - "ODE solver time: 2.1024391651153564\n" - ] - } - ], - "source": [ - "# put pade parameters in lists for heom solver\n", - "ckAR = [np.real(eta) + 0j for eta in etapLP]\n", - "ckAI = [np.imag(etapLP[0]) + 0j]\n", - "vkAR = [gam + 0j for gam in gampLP]\n", - "vkAI = [gampLP[0] + 0j]\n", - "\n", - "options = {**default_options, \"rtol\": 1e-14, \"atol\": 1e-14}\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI)\n", - " HEOMPade = HEOMSolver(Hsys, (bath,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultPade = HEOMPade.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "2b539e1f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (resultMats, P11p, \"b\", \"P11 Mats\"),\n", - " (resultMats, P12p, \"r\", \"P12 Mats\"),\n", - " (resultMatsT, P11p, \"y\", \"P11 Mats + Term\"),\n", - " (resultMatsT, P12p, \"g\", \"P12 Mats + Term\"),\n", - " (resultPade, P11p, \"b--\", \"P11 Pade\"),\n", - " (resultPade, P12p, \"r--\", \"P12 Pade\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "59fdf064", - "metadata": {}, - "source": [ - "The Padé decomposition of the Drude-Lorentz bath is also available via a\n", - "built-in class, `DrudeLorentzEnvironment` bath. Similarly to the terminator\n", - "section when approximating by Padé one can calculate the terminator easily by\n", - "requesting the approximation function to compute delta\n", - "\n", - "Below we show how to use the built-in Drude-Lorentz Environment to obtain a\n", - "Padé decomposition approximation and its terminator (although the terminator \n", - "does not provide much improvement here,because the Padé expansion already fits \n", - "the correlation function well):" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "d475f81a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.0066945552825927734\n", - " Total run time: 2.12s*] Elapsed 2.12s / Remaining 00:00:00:00\n", - "ODE solver time: 2.12541127204895\n" - ] - } - ], - "source": [ - "options = {**default_options, \"rtol\": 1e-14, \"atol\": 1e-14}\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " env_approx,delta = dlenv.approx_by_pade(Nk=2,compute_delta=True)\n", - " Ltot = liouvillian(Hsys) + system_terminator(Q,delta)\n", - " HEOM_dlpbath_T = HEOMSolver(Ltot, (env_approx,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " result_dlpbath_T = HEOM_dlpbath_T.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "b0b7b87f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (result_dlpbath_T, P11p, \"b\", \"P11 Padé (DrudeLorentzBath + Term)\"),\n", - " (result_dlpbath_T, P12p, \"r\", \"P12 Padé (DrudeLorentzBath + Term)\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "a5a10572", - "metadata": {}, - "source": [ - "### Next we compare the Matsubara and Pade correlation function fits\n", - "\n", - "Fitting the correlation function is not efficient for this example, but\n", - "can be extremely useful in situations where large number of exponents\n", - "are needed (e.g., near zero temperature). We will perform the fitting\n", - "manually below, and then show how to do it with the built-in tools\n", - "\n", - "For the manual fit we First we collect a large sum of Matsubara terms for \n", - "many time steps:" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "e73fb7aa", - "metadata": {}, - "outputs": [], - "source": [ - "tlist2 = np.linspace(0, 2, 10000)\n", - "\n", - "corr_15k_t10k = dlenv.correlation_function(tlist2, Nk=15_000)\n", - "\n", - "corrRana = np.real(corr_15k_t10k)\n", - "corrIana = np.imag(corr_15k_t10k)" - ] - }, - { - "cell_type": "markdown", - "id": "80afb2db", - "metadata": {}, - "source": [ - "We then fit this sum with standard least-squares approach:" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "05fc75f6", - "metadata": {}, - "outputs": [], - "source": [ - "def wrapper_fit_func(x, N, args):\n", - " \"\"\" Fit function wrapper that unpacks its arguments. \"\"\"\n", - " x = np.array(x)\n", - " a = np.array(args[:N])\n", - " b = np.array(args[N:2 * N])\n", - " return fit_func(x, a, b)\n", - "\n", - "\n", - "def fit_func(x, a, b):\n", - " \"\"\" Fit function. Calculates the value of the\n", - " correlation function at each x, given the\n", - " fit parameters in a and b.\n", - " \"\"\"\n", - " return np.sum(\n", - " a[:, None] * np.exp(np.multiply.outer(b, x)),\n", - " axis=0,\n", - " )\n", - "\n", - "\n", - "def fitter(ans, tlist, k):\n", - " \"\"\" Compute fit with k exponents. \"\"\"\n", - " upper_a = abs(max(ans, key=abs)) * 10\n", - " # sets initial guesses:\n", - " guess = (\n", - " [upper_a / k] * k + # guesses for a\n", - " [0] * k # guesses for b\n", - " )\n", - " # sets lower bounds:\n", - " b_lower = (\n", - " [-upper_a] * k + # lower bounds for a\n", - " [-np.inf] * k # lower bounds for b\n", - " )\n", - " # sets higher bounds:\n", - " b_higher = (\n", - " [upper_a] * k + # upper bounds for a\n", - " [0] * k # upper bounds for b\n", - " )\n", - " param_bounds = (b_lower, b_higher)\n", - " p1, p2 = curve_fit(\n", - " lambda x, *params_0: wrapper_fit_func(x, k, params_0),\n", - " tlist,\n", - " ans,\n", - " p0=guess,\n", - " sigma=[0.01 for t in tlist],\n", - " bounds=param_bounds,\n", - " maxfev=1e8,\n", - " )\n", - " a, b = p1[:k], p1[k:]\n", - " return (a, b)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "1db2c659", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlation (real) fitting time: 0.8875892162322998\n", - "Correlation (imaginary) fitting time: 0.016649723052978516\n" - ] - } - ], - "source": [ - "kR = 4 # number of exponents to use for real part\n", - "poptR = []\n", - "with timer(\"Correlation (real) fitting time\"):\n", - " for i in range(kR):\n", - " poptR.append(fitter(corrRana, tlist2, i + 1))\n", - "\n", - "corrRMats = np.real(dlenv_approx.correlation_function(tlist2))\n", - "\n", - "kI = 1 # number of exponents for imaginary part\n", - "poptI = []\n", - "with timer(\"Correlation (imaginary) fitting time\"):\n", - " for i in range(kI):\n", - " poptI.append(fitter(corrIana, tlist2, i + 1))" - ] - }, - { - "cell_type": "markdown", - "id": "8e6ebb4b", - "metadata": {}, - "source": [ - "And plot the results of the fits:" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "ebcb68f7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Define line styles and colors\n", - "linestyles = [\"-\", \"--\", \"-.\", \":\", (0, (3, 1, 1, 1)), (0, (5, 1))]\n", - "colors = [\"blue\", \"green\", \"purple\", \"orange\", \"red\", \"brown\", \"cyan\", \"magenta\"]\n", - "\n", - "# Define a larger linewidth\n", - "linewidth = 2.5\n", - "\n", - "# Create a single figure with two subplots\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", - "\n", - "# Plot the real part on the first subplot (ax1)\n", - "ax1.plot(tlist2, corrRana, label=\"Analytic\", color=colors[0], linestyle=linestyles[0], linewidth=linewidth)\n", - "ax1.plot(tlist2, corrRMats, label=\"Matsubara\", color=colors[1], linestyle=linestyles[1], linewidth=linewidth)\n", - "\n", - "for i in range(kR):\n", - " y = fit_func(tlist2, *poptR[i])\n", - " ax1.plot(tlist2, y, label=f\"Fit with {i} terms\", color=colors[(i + 2) % len(colors)], linestyle=linestyles[(i + 2) % len(linestyles)], linewidth=linewidth)\n", - "ax1.set_ylabel(r\"$C_{R}(t)$\")\n", - "ax1.set_xlabel(r\"$t$\")\n", - "ax1.legend()\n", - "\n", - "# Plot the imaginary part on the second subplot (ax2)\n", - "ax2.plot(tlist2, corrIana, label=\"Analytic\", color=colors[0], linestyle=linestyles[0], linewidth=linewidth)\n", - "\n", - "for i in range(kI):\n", - " y = fit_func(tlist2, *poptI[i])\n", - " ax2.plot(tlist2, y, label=f\"Fit with {i} terms\", color=colors[(i + 3) % len(colors)], linestyle=linestyles[(i + 1) % len(linestyles)], linewidth=linewidth)\n", - "ax2.set_ylabel(r\"$C_{I}(t)$\")\n", - "ax2.set_xlabel(r\"$t$\")\n", - "\n", - "ax2.legend()\n", - "\n", - "# Add overall plot title and show the figure\n", - "fig.suptitle(\"Comparison of Analytic and Fit to Correlations (Real and Imaginary Parts)\", fontsize=16)\n", - "plt.show()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "469a8a2e", - "metadata": {}, - "outputs": [], - "source": [ - "# Set the exponential coefficients from the fit parameters\n", - "\n", - "ckAR1 = poptR[-1][0]\n", - "ckAR = [x + 0j for x in ckAR1]\n", - "\n", - "vkAR1 = poptR[-1][1]\n", - "vkAR = [-x + 0j for x in vkAR1]\n", - "\n", - "ckAI1 = poptI[-1][0]\n", - "ckAI = [x + 0j for x in ckAI1]\n", - "\n", - "vkAI1 = poptI[-1][1]\n", - "vkAI = [-x + 0j for x in vkAI1]" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "59fa79c9", - "metadata": {}, - "outputs": [], - "source": [ - "# overwrite imaginary fit with analytical value (not much reason to use the\n", - "# fit for this)\n", - "\n", - "ckAI = [lam * gamma * (-1.0) + 0.0j]\n", - "vkAI = [gamma + 0.0j]" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "e07b01f5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.0052492618560791016\n", - " Total run time: 3.21s*] Elapsed 3.21s / Remaining 00:00:00:00[*********59%** ] Elapsed 1.90s / Remaining 00:00:00:01\n", - "ODE solver time: 3.20740008354187\n" - ] - } - ], - "source": [ - "options = {**default_options}\n", - "\n", - "NC = 4\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI)\n", - " HEOMFit = HEOMSolver(Hsys, (bath,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultFit = HEOMFit.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "ed997ede", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (resultFit, P11p, \"b\", \"P11 Fit\"),\n", - " (resultFit, P12p, \"r\", \"P12 Fit\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "27d6df70", - "metadata": {}, - "source": [ - "Now we use the built-in functions. The `BosonicEnvironment` class, includes a \n", - "method that performs this fit automatically. More information on how the\n", - "built-in functios work can be found in `HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions`" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "d69384a6", - "metadata": {}, - "outputs": [], - "source": [ - "tlist3 = np.linspace(0, 2, 200)\n", - "envfit, fitinfo =dlenv.approx_by_cf_fit(tlist=tlist3,full_ansatz=True,maxfev=1e6,Ni_max=1,Nr_max=3)" - ] - }, - { - "cell_type": "markdown", - "id": "c115cb93", - "metadata": {}, - "source": [ - "The approx_by_cf_fit method outputs a `ExponentialBosonicEnvironment` object,\n", - "which contains a decaying exponential representation of the original \n", - "environment , and a dictionary containing all information related to the fit.\n", - "The dictionary contains a summary of the fir information and the normalized \n", - "root mean squared error, which assesses how good the fit is. " - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "a2ebc66f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlation function fit:\n", - "\n", - "Result of fitting the real part of |Result of fitting the imaginary part \n", - "the correlation function with 3 terms: |of the correlation function with 1 terms: \n", - " | \n", - " Parameters| a | b | c | d | Parameters| a | b | c | d \n", - " 1 | 1.02e-01 |-5.71e-01 |-9.61e-08 |2.16e+00 | 1 | 1.12e-02 |-5.01e-01 |-6.77e-03 |-5.00e-02 \n", - " 2 | 7.96e-02 |-8.08e+00 | 2.70e-06 |1.76e+00 | \n", - " 3 | 2.34e-01 |-2.06e+02 | 0.00e+00 |0.00e+00 |A normalized RMSE of 7.91e-03 was obtained for the the imaginary part\n", - " |of the correlation function. \n", - "A normalized RMSE of 2.04e-04 was obtained for the the real part of | \n", - "the correlation function. | \n", - "The current fit took 35.864703 seconds. |The current fit took 0.005103 seconds. \n", - "\n" - ] - } - ], - "source": [ - "print(fitinfo['summary'])" - ] - }, - { - "cell_type": "markdown", - "id": "e4ad68e3", - "metadata": {}, - "source": [ - "We can then compare the result of the built-in fit with the manual fit" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "a7742b6c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Create a single figure with two subplots\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", - "\n", - "# Plot the real part on the first subplot (ax1)\n", - "ax1.plot(tlist2, corrRana, label=\"Original\", marker=\"o\", markevery=500)\n", - "ax1.plot(tlist2, fit_func(tlist2, *poptR[-1]), color=\"r\", label=\"Manual Fit\")\n", - "ax1.plot(tlist2, np.real(envfit.correlation_function(tlist2)), \"k--\", label=\"Built-in fit\")\n", - "ax1.set_ylabel(r\"$C_{R}(t)$\")\n", - "ax1.set_xlabel(r\"$t$\")\n", - "ax1.legend()\n", - "\n", - "# Plot the imaginary part on the second subplot (ax2)\n", - "ax2.plot(tlist2, corrIana, label=\"Original\", marker=\"o\", markevery=500)\n", - "ax2.plot(tlist2, fit_func(tlist2, *poptI[-1]), color=\"r\", label=\"Manual Fit\")\n", - "ax2.plot(tlist2, np.imag(envfit.correlation_function(tlist2)), \"k--\", label=\"Built-in fit\")\n", - "ax2.set_ylabel(r\"$C_{I}(t)$\")\n", - "ax2.set_xlabel(r\"$t$\")\n", - "ax2.legend()\n", - "# Add an overall title and adjust layout\n", - "plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "305001b1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.005854129791259766\n", - " [ 0% ] Elapsed 0.01s / Remaining 00:00:00:00" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 3.73s*] Elapsed 3.73s / Remaining 00:00:00:00\n", - "ODE solver time: 3.734066963195801\n" - ] - } - ], - "source": [ - "options = {**default_options}\n", - "\n", - "NC = 4\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " HEOMFit_2 = HEOMSolver(Hsys, (envfit,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultFit_2 = HEOMFit_2.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "14c9c43c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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qzNKlSyv8DE59jRkzBgCTycTkyZNtfbZs2cLkyZPZt2+fQ2s5Fx+XXq0BiepiD6vB+QqrIiLSsM2cOZP27dtTVFTEzz//zLRp00hJSWHjxo0EBwcD8PLLL9OlSxdGjRrF+++/X+VYa9as4YsvvqB79+4MGzaMr7/+usb1tGzZktmzZwNQWlrKpk2bmDJlCosXL2bbtm0EBQXVeMz58+cTFhZ21nOWL1/OlClTGD9+PI0aNarWuE888QT3339/jeupjueee46hQ4dWOBYREQHAihUriD+5cTzWsDplyhSGDBni8PB8NgqrTuIf5k+OqQlNjBwaFSmsiohIw9apUyd69eoFwNChQzGbzTz99NN88cUX3HTTTQAUFBTg5WX90Pejjz6qcqxbbrmFW2+9FYDPP//8vMJqYGAg/fr1s72/8MILCQgI4I477mDZsmUMHz68xmN27969xn2qo1WrVk4ZF6BNmzYVfg6nquq4q2kZgBNl+1lnV6PLD2FYDDdXIyIi4jlOBqFTP3Y/GVTPpbrn1VR4eDgAvr6+tmNVfQQ/efJkTCZThWOnLwOorM9DDz0EQFJSku1j93MtYaisBpPJxD333MNHH31EcnIyQUFBdO3alQULFpx1rJo4dRnArFmzuO666wDrPzZO1n7qEgdn0cyqE33W9nG2bSzlEHF8lmvQOMJ07k4iIiINwK5duwCIiopyWw3l5eWAfRnA1KlTadmyJQMGDHDK9SZMmEBOTg6vvfYa8+bNIzbWOqnVoUOH8xrvm2++YfXq1UydOpWQkBBeeOEFrr76arZv307Lli2rNYbFYrH9HE7y8TkzHl5xxRU899xzPProo7z++uv06NEDcO6sr60ep1+hAdve/QY+2mhtpx+GxhHurUdEROqgl16yvs6lRw/46quKx0aNgt9/P3ffSZOsr5MKCiA5ueqvnwez2Ux5eTnFxcWkpKTwzDPPEBoayqhRo2o17vnavHlzhRlUgLZt2/LNN9/g7+/vlGvGx8fTvHlzwLpkoLbrPouKivjhhx8IDQ0FoEePHsTFxfHpp5/y8MMPV2uMsWPHnnFs586dtG7dusKxqKgo2rRpA1jDtSuXCCisOlGs/R4r0tPhPP/hJCIiDVl+Ppx213ylEhLOPHbkSPX65udXfG8YFfud/vXzcHq46dy5M2+88QYxMTG1Hvt8tGrVijlz5gDWGda9e/fy/PPPM2zYMJYuXWoLZq5isViwWCy29yaTCW9v77P2GTp0qC2oAsTExBAdHV1hacXps6be3t4Vli88//zzXHTRRRXOSajsd8mNFFad6NSwekhbrYqIyPkIC4Nmzc59XmUfp0dFVa/v6Xewm0wV+53jDvfq+PDDD0lOTsbHx4eYmBjbR+DuEhAQYLvhC6xhesiQITRr1ownn3ySTz75xKX1TJ06lSlTptjet2jR4pxbRJ28a/9U/v7+FBUVAdats5KSkip8fcmSJQwZMsT2vmXLlhV+Dp5IYdWJmjUupBO7iSaT8k3NgPbuLklEROqa2nwEf/qygOoKDYW0tPPrW4Xk5GSPD0WxsbFERkbyxx9/2I4FBARQUlJyxrlZWVkOvfaf//xnRo4caXvviKUIcXFxrF69usKxdu3a1XpcV1NYdaJW2avYiHXvsqVL/wE8796CREREpEppaWlkZWVVuOEpMTGRzMxMDh8+bFuyUFpaynfffXde1zgZQk/Ofp4UFxdHXFzceVZeOT8/P4f+A6Gq2p1NYdWJQltF29reOZlurERERMTzpaSkcOTIEcB6Q1Zqaiqff/45AIMHD7btHFBYWMjChQsBWLlypa1vVlYWwcHBjBgx4pzXKioqsvU1m83s3buXF154AYAHHnjAdt7YsWN58sknueGGG3jooYcoLi7m1VdfxWw2n9f32LlzZwBeeeUVbr31Vnx9fWnXrl2FtaeeqlOnTgC8/fbbhIaGEhAQQFJSUqXLERxJYdWJGrW1h9WAPIVVERGRs3nqqadISUmxvV+6dKltD9JT11pmZmba9vw86eR+oNVZ6wmwZ88e+vfvD1j3bW3atCldu3bltddeY/DgwbbzkpKS+PLLL3n00UcZM2YMsbGxTJo0iSNHjlRYY1pdQ4YM4ZFHHuGDDz7gnXfewWKxnLGO1FMlJSUxffp0XnnlFYYMGYLZbGbmzJln3VvWEUyGYdSr3erz8/MJDw8nLy/vnI88czZzmQX8fPHGwpagXnQ4vvrcnUREpEEpLi5m7969JCUlERAQ4O5yRBziXL/XNclreoKVE3n7epHtZf3IIqxEM6siIiIiNaWw6mRHfa1LASLMmXrkqoiIiEgNKaw6WUGQNawGUsyxjGNurkZERESkblFYdbLiUPtNVjnbtBRAREREpCYUVp2srIk9rObvUlgVERERqQmFVWeLtN5gVYIfxw7mubkYERERkbpFYdXJ9o26jzDyCKCYTfGXubscERERkTpFDwVwssbNQyk40c7UKgARERGRGtHMqpNF25esKqyKiIiI1JDCqpMprIqIiIicP4VVJ4uOMPMYz/AK9zH8t6fdXY6IiIhLzZo1C5PJZHv5+PgQHx/PbbfdxsGDByuc+/jjjzNy5EiaNWuGyWSq8pnz7777LqNHjyYxMZHAwEBat27NX//6V9LT06tV05AhQyrU5OvrS2JiInfccQepqann/b0mJiZWqHnfvn2YTCZmzZplO7Z8+XImT57M0aNHqz3u+PHjSUxMPO+6KrN06dIKP4NTX2PGjAHAZDIxefJkW58tW7YwefJk9u3b59BazkVrVp0sJMyLx3iWQIrZeagT8IS7SxIREXG5mTNn0r59e4qKivj555+ZNm0aKSkpbNy4keDgYABefvllunTpwqhRo3j//ferHOupp55i6NChPPfcczRr1ozt27fz9NNP8+WXX7Ju3TpiYmLOWU/Lli2ZPXs2AKWlpWzatIkpU6awePFitm3bRlBQUI2/x/nz55/zOffLly9nypQpjB8/nkaNGlVr3CeeeIL777+/xvVUx3PPPcfQoUMrHIuIiABgxYoVxMfH245v2bKFKVOmMGTIEIeH57NRWHUyk5eJbO9o4s37aVSqdQAiItIwderUiV69egEwdOhQzGYzTz/9NF988QU33XQTAAUFBXh5WT/0/eijj6oca926dUSfss5u8ODB9OjRg969e/POO+/w+OOPn7OewMBA+vXrZ3t/4YUXEhAQwB133MGyZcsYPnx4jb/H7t2717hPdbRq1cop4wK0adOmws/hVFUddzUtA3CBfH/r/6CaGFmYS81urkZERMT9TgahUz92PxlUz+XUoHpSz5498fb25sCBA+ddU3h4OAC+vr62Y1V9BD958mRMJlOFY6cvA6isz0MPPQRAUlKS7WP3pUuXnrWuymowmUzcc889fPTRRyQnJxMUFETXrl1ZsGDBWceqiVOXAcyaNYvrrrsOsP5j42Ttpy5xcBbNrLrAseBoKARvLGTtziEyOcrdJYmIiLjVrl27AIiKcszfiSkpKZjNZjp27FjtPuXl5YB9GcDUqVNp2bIlAwYMcEhNp5swYQI5OTm89tprzJs3j9jYWAA6dOhwXuN98803rF69mqlTpxISEsILL7zA1Vdfzfbt22nZsmW1xrBYLLafw0k+PmfGwyuuuILnnnuORx99lNdff50ePXoAzp31tdXj9CsIpaERcMTazt+brbAqIiLV9tJL1te59OgBX31V8dioUfD77+fuO2mS9XVSQQEkJ1f99fNhNpspLy+nuLiYlJQUnnnmGUJDQxk1alTtBsa6fOCuu+4iISGB22+/vVp9Nm/eXGEGFaBt27Z88803+Pv717qmysTHx9O8eXPAumSgtus+i4qK+OGHHwgNDQWgR48exMXF8emnn/Lwww9Xa4yxY8eecWznzp20bt26wrGoqCjatGkDWMO1K5cIKKy6gDk8wtY+lprtxkpERKSuyc+H026ar1RCwpnHjhypXt/8/IrvDaNiv9O/fj5ODzedO3fmjTfeqNbNUGdTXFzMNddcQ2pqKj/99BMhISHV6teqVSvmzJkDWGdY9+7dy/PPP8+wYcNYunSpLZi5isViwWKx2N6bTCa8vb3P2mfo0KG2oAoQExNDdHR0haUVp8+aent7V1i+8Pzzz3PRRRdVOCehsl8mN1JYdQFLk0hbuzgty42ViIhIXRMWBs2anfu8yj5Nj4qqXt/Tb2A3mSr2O8cN7tXy4YcfkpycjI+PDzExMbaPwGujpKSEq6++mmXLlrFgwQL69u1b7b4BAQG2G77AGqaHDBlCs2bNePLJJ/nkk09qXV9NTJ06lSlTptjet2jR4pxbRJ28a/9U/v7+FBUVAdats5KSkip8fcmSJQwZMsT2vmXLlhV+Dp5IYdUFvKLsv0yl6ZpZFRGR6qvNR/CnLwuortBQSEs7v75VSU5OdmgoKikpYfTo0SxZsoQvv/ySYcOG1XrM2NhYIiMj+eOPP2zHAgICKCkpOePcrCzHTj79+c9/ZuTIkbb3jliKEBcXx+rVqysca9euXa3HdTWFVRfwbWoPq+WHFVZFRERq4+SM6k8//cS8efO49NJLHTJuWloaWVlZFW54SkxMJDMzk8OHD9uWLJSWlvLdd9+d1zVOhtCTs58nxcXFERcXd56VV87Pz8+h/0CoqnZnU1h1Ad82ifzIRWQTQYl/63N3EBERaYBSUlI4csR6R7LZbCY1NZXPP/8csO6lenLngDFjxrBo0SIee+wxIiIiWLlypW2MsLCwat1dX1RUZOtnNpvZu3cvL7zwAgAPPPCA7byxY8fy5JNPcsMNN/DQQw9RXFzMq6++itl8fltRdu7cGYBXXnmFW2+9FV9fX9q1a1dh7amn6tSpEwBvv/02oaGhBAQEkJSUVOlyBEdSWHUBvwt6czE/AjAhAm5xcz0iIiKe6KmnniIlJcX2funSpbY9SE9da3lyL9Fnn32WZ599tsIYgwcPPue+pQB79uyhf//+gHV/16ZNm9K1a1dee+01Bg8ebDsvKSmJL7/8kkcffZQxY8YQGxvLpEmTOHLkSIU1ptU1ZMgQHnnkET744APeeecdLBbLGetIPVVSUhLTp0/nlVdeYciQIZjNZmbOnHnWvWUdwWQYhuHUK7hYfn4+4eHh5OXlnfORZ66Slma/S/Pqq2HePPfWIyIinqO4uJi9e/eSlJREQECAu8sRcYhz/V7XJK/pCVYucOrseLaWrIqIiIhUm8KqCwQGWl8ADr55UERERKRe05pVF/nauII2bMSy3RfY7e5yREREROoEhVUXiTOl05wDlJl9MCwGJi/TuTuJiIiINHBaBuAihYHWhau+lFNw0AHPrRMRERFpABRWXaQk2H6XVd4e3WUlIiIV1bPNeaSBc+Tvs8Kqi5SF2cNqwT6FVRERsfL19cVkMnH8+HF3lyLiMIWFhYD197u2tGbVRYwm9rBaeEBhVURErLy9vQkPD+fIkSOUlJQQFhaGj48PJpPubZC6xzAMCgsLyczMpFGjRnh7e9d6TIVVV4mMtDVLDimsioiIXdOmTQkMDCQzM5P8fN3XIHVfo0aNaNq0qUPGUlh1EZ8Y+8xqeYY2WxURETuTyUSjRo0IDw/HbDZTXl7u7pJEzpuvr69DZlRPcnpYnTFjBv/6179IT0+nY8eOTJ8+nUGDBlV5/uzZs3nhhRfYuXMn4eHhXHbZZbz44otEnPoYqDrIP85ev5GlmVURETmTyWTCx8cHHx/NJYmc5NQbrObOncsDDzzAY489xrp16xg0aBAjRoxg//79lZ6/bNkyxo0bxx133MHmzZv57LPPWL16NRMmTHBmmS4RlGAPq6ZchVURERGR6nBqWH3ppZe44447mDBhAsnJyUyfPp2EhATeeOONSs9fuXIliYmJ3HfffSQlJTFw4ED+8pe/sGbNGmeW6RLBXVpxL69yI7P5Ju7P7i5HREREpE5wWlgtLS1l7dq1DB8+vMLx4cOHs3z58kr7DBgwgLS0NBYuXIhhGBw+fJjPP/+cK664osrrlJSUkJ+fX+HliRq3asJ/uJdPuJF1lq7uLkdERESkTnBaWM3KysJsNhMTE1PheExMDBkZGZX2GTBgALNnz2bs2LH4+fnRtGlTGjVqxGuvvVbldaZNm0Z4eLjtlZCQ4NDvw1HCwuDkEqRsrQIQERERqRanPxTg9H3iDMOocu+4LVu2cN999/Hkk0+ydu1avv32W/bu3cvEiROrHP+RRx4hLy/P9jpw4IBD63cUkwmaNLG2s7QZgIiIiEi1OO12w8jISLy9vc+YRc3MzDxjtvWkadOmccEFF/DQQw8B0KVLF4KDgxk0aBDPPPMMsbGxZ/Tx9/fH39/f8d+AE7QPO0SzzAxiM3OBYe4uR0RERMTjOW1m1c/Pj549e7J48eIKxxcvXsyAAQMq7VNYWIiXV8WSTu7TVR+emfxq5g38Tk++KbmY4twid5cjIiIi4vGcugxg0qRJvPvuu7z//vts3bqVBx98kP3799s+1n/kkUcYN26c7fwrr7ySefPm8cYbb7Bnzx5+/fVX7rvvPvr06UNcXJwzS3WJomD7U6xyd2nhqoiIiMi5OHXX4bFjx5Kdnc3UqVNJT0+nU6dOLFy4kBYtWgCQnp5eYc/V8ePHU1BQwH/+8x/+9re/0ahRIy666CKef/55Z5bpMqWhEZBubRfsyya2d7x7CxIRERHxcCajPny+for8/HzCw8PJy8sjLCzM3eVUsKTfwwz9zRq81734I93/dpGbKxIRERFxvZrkNafvBiB2plMeGVucpi0BRERERM5FYdWFvCKb2NrlR3LdWImIiIhI3aCw6kJ+MY1tbXO2wqqIiIjIuSisupB/rH1mlZwc9xUiIiIiUkcorLpQYJx9ZtU7TzOrIiIiIueisOpCoS3sM6vex/LcWImIiIhI3eDUfValorD2ccRzgFwa069tED+6uyARERERD6ew6kIh4d4c9omnvBxytApARERE5Jy0DMCFTCZofGLZaq7CqoiIiMg5Kay6WJMTy1a1GYCIiIjIuWkZgIuNsXyKH5toXJBLedFL+AT6urskEREREY+lsOpio45+SB++ASA79Uki2ke5uSIRERERz6VlAC5WEmLfvip/n9YCiIiIiJyNwqqLmcPsDwYoPKi7rERERETORmHVxYxG9plVhVURERGRs1NYdTFTE/vMatlhLQMQERERORuFVRfzjrLPrJZlamZVRERE5GwUVl3ML8Y+s2pka2ZVRERE5GwUVl0sMM4eVk1HNbMqIiIicjYKqy4WFG9fBuCdr5lVERERkbPRQwFcLDQpkjX0JJfGHPTryEB3FyQiIiLiwRRWXaxR60iasgaAgREw3r3liIiIiHg0LQNwMX9/CAqytnO1ZFVERETkrBRW3aDxiXuscrRkVUREROSsFFbdoMmJe6w0syoiIiJydgqrbvBEzgP8QRd2FCdQlHXc3eWIiIiIeCzdYOUGCZb9dGEjABn7cgmMDHZzRSIiIiKeSTOrblAWYn8wwLEDWgsgIiIiUhWFVTcwh9sfDHA8TWFVREREpCoKq25gNLLPrJaka0sAERERkaoorLqBV6R9ZrX0sGZWRURERKqisOoGPtH2mVVzlmZWRURERKqisOoG/jH2sGpka2ZVREREpCoKq24Q2My+DMArTzOrIiIiIlVRWHWD4Hj7zKpPvmZWRURERKqihwK4QVjbpjzO0+TSmJCYDgxwd0EiIiIiHkph1Q3C44J5lscB6Ku5bREREZEqKSq5gbc3NGpkbedoyaqIiIhIlRRW3aTxiWWruVqyKiIiIlIlLQNwk/jwAgyyiMrJwTB3x+StfzeIiIiInE4JyU1e2j+GvbRklaUXBQfz3V2OiIiIiEdSWHWT0mD79lX5qVoLICIiIlIZhVU3KQu1Pxjg2AGFVREREZHKKKy6iSXcPrNamKYtAUREREQqo7DqJqYm9rBaclgzqyIiIiKVUVh1E69I+zKA8sOaWRURERGpjMKqm/jF2GdWzdmaWRURERGpjMKqm/jH2mdWTXqMlYiIiEilFFbdJKiZfWbVK08zqyIiIiKVUVh1k5Dm9plV32OaWRURERGpjB636ibh7ZrSmQ3k0IQurZuwyN0FiYiIiHgghVU3CQ73YZtPZ8rLIUZPWxURERGplJYBuInJBE1OrATQ/VUiIiIilVNYdaPGJ+6xytX9VSIiIiKV0jIAN7rctIgRbKNJfg7lRU/iE+jr7pJEREREPIrCqhuNzZlBXxYAkL3vXiKSo91ckYiIiIhn0TIANyoNsW9flb9PC1dFRERETqew6kbmUPuDAQoPauGqiIiIyOkUVt3IaGyfWS06qJlVERERkdMprLqRqYl9ZrX0sGZWRURERE6nsOpGPtH2mdXyTM2sioiIiJxOYdWN/Jraw6qRo5lVERERkdMprLpRQKx9GYApVzOrIiIiIqdTWHWj4AT7zKp3vmZWRURERE6nhwK4UWhiBLtpSQ5NSPNu4e5yRERERDyOwqobNWoVQQy7ARgYA2PdXI+IiIiIp9EyADfy84PgYGs7R0tWRURERM6gsOpmTU4sW1VYFRERETmTwqqbNT6xIUBuLhiGe2sRERER8TRas+pmf8t7khak0Lgkl6IjywmKDnF3SSIiIiIeQ2HVzdqUbaE/PwOQsS9HYVVERETkFFoG4GZlIfYHAxTs116rIiIiIqdSWHUzc7j9wQCFabrLSkRERORUTg+rM2bMICkpiYCAAHr27Mkvv/xy1vNLSkp47LHHaNGiBf7+/rRq1Yr333/f2WW6TxP7zGpxumZWRURERE7l1DWrc+fO5YEHHmDGjBlccMEFvPXWW4wYMYItW7bQvHnzSvtcf/31HD58mPfee4/WrVuTmZlJeXm5M8t0K68I+8xqWaZmVkVERERO5dSw+tJLL3HHHXcwYcIEAKZPn853333HG2+8wbRp0844/9tvvyUlJYU9e/bQ5MQGpImJic4s0e18o+0zq+YjmlkVEREROZXTlgGUlpaydu1ahg8fXuH48OHDWb58eaV9vvrqK3r16sULL7xAs2bNaNu2LX//+98pKipyVplu5x9rn1klVzOrIiIiIqdy2sxqVlYWZrOZmJiYCsdjYmLIyMiotM+ePXtYtmwZAQEBzJ8/n6ysLO666y5ycnKqXLdaUlJCSUmJ7X1+fr7jvgkXCGxmD6teRzWzKiIiInIqp99gZTKZKrw3DOOMYydZLBZMJhOzZ8+mT58+XH755bz00kvMmjWrytnVadOmER4ebnslJCQ4/HtwppAE+zIA3wLNrIqIiIicymlhNTIyEm9v7zNmUTMzM8+YbT0pNjaWZs2aER4ebjuWnJyMYRikpaVV2ueRRx4hLy/P9jpw4IDjvgkXCGsVxb+ZxGM8w6KIm91djoiIiIhHcVpY9fPzo2fPnixevLjC8cWLFzNgwIBK+1xwwQUcOnSIY8eO2Y7t2LEDLy8v4uPjK+3j7+9PWFhYhVddEhYbzEOmf/Mcj7HId5S7y6mW48ehtNTdVYiIiEhD4NRlAJMmTeLdd9/l/fffZ+vWrTz44IPs37+fiRMnAtZZ0XHjxtnOv/HGG4mIiOC2225jy5Yt/Pzzzzz00EPcfvvtBAYGOrNUt/HygsYnVgLkePgqgD174KKLICQEGjWCBx+E4mJ3VyUiIiL1mVO3rho7dizZ2dlMnTqV9PR0OnXqxMKFC2nRogUA6enp7N+/33Z+SEgIixcv5t5776VXr15ERERw/fXX88wzzzizTLdr0sQaVHM9+P6qtL1lLOryGCuOTwECKSqC6dNh716YN88aukVEREQczWQYhuHuIhwpPz+f8PBw8vLy6sySgL69LexYk0cTctlZloiXj+clv7ntnmTsjqf5hBuYFPNfco+aKCmBFuzjhb9lcv2LfdxdooiIiNQRNclrnpeKGqDX9l1JLk3YTSsK9nve9OqKTw8wase/ALiW/7H56z18+SX8if/yB13pNP0O8o9a3FyliIiI1EcKqx6gNLiRrZ231/MWrubcP5lArItTdwy/lya9W3HpcIOnI6YTTj4dzJv4YcIcN1cpIiIi9ZHCqgcoD7M/GOB4mmfNrO5anctFGf8FIN8rnOTZj1u/YDIR8JL9kbnJX0yjrLRerSgRERERD6Cw6gGMcPuDAYoOetbM6qZHZ9tmVXcNGId3pL3WZuOGsb1JfwCSzZtYPn2VW2oUERGR+kth1RNE2GdWSzI8Z2bVYoGWKTNt71tMnXDGOaXj/2xrF//nXZfUJSIiIg2HwqoH8DlltrI803NmVjd9k0qXst8B2BXWg4ihXc44p8NT11FgCgWg/4G55GaUuLRGERERqd8UVj2Ab4x9ZtWS7Tkzq/tnLLC1c4eMrvQc77BgtrW3fi2MAta9+KMLKhMREZGGQmHVAwTE2cOqKddzZlab/PqVrd3i3quqPC/45mtsbfP/5ju1JhEREWlYFFY9QHC8fRmAV75nzKxmZsK/CibyETezNagH0cM6V3luu3uHU4j1cbjd9n1J8XGzq8oUERGRek5h1QOENLfPrPoVeMbMakoKfMHVjOMjPrhnDZhMVZ7rHRrE1uaXspPWfMYYVv543IWVioiISH3m4+4CBBq1jmQQP5NDE1onRfOluwsCli61t4cMrTqonrRryn+54Tbr7Oo/l8OQUU4qTERERBoUhVUPEBjqw2r/QZSUgI+HTEqeDKve3nDBBec+f+jlgbb2Dz84pyYRERFpeLQMwEM0ObESIMcDVgFk7iukxZaFhJJPr14QGnruPtHR0LWrtf3775Cd7dwaRUREpGFQWPUQjU/cY5XrAfdX7fpkNQu5glwaM9nnmWr3GzbM+mcjI4dVC7OcVJ2IiIg0JAqrHmKwz6/cyiz+fPwlSgvcu7F+/g+/AeCNhcjOsdXud2XMKjbSiRwi8HnzP84qT0RERBoQrVn1ELdkvkh/vgDgyJ4biOoa57ZagjetsrWbXdO32v06XRRNJJsBCN/8q8PrEhERkYZHM6seoizEvn1Vfqr71gJYLJB0xDqzeswUQtOhydXuG9mzBRk+zQDokLeCooJyp9QoIiIiDYfCqocwh9kfDFCY5r67rPYsO0S8kWZtR/TG5ONd/c4mE6kJAwEI4Tjb5v7hjBJFRESkAVFY9RBGY/vManG6+2ZWD365xtY+ltynxv0t/Qfa2tlfLnNITSIiItJwKax6CK9I+8xq6WH3zayWrLLPhgb0717j/rHX2cNqwBqFVREREakdhVUP4RNln1k1H3HfzGrQjvW2dtyIrjXu32JkZwpM1o1ZWx1ejmE4qjIRERFpiBRWPYR/U/vMqpHtvpnVZtnWmdVCAokZ2KbG/U0+3uxu0huAWOMQB3475ND6REREpGFRWPUQgXH2sOp11D1hNfNgGcVmXyyYSA3tXLObq05R0K63rb3/f6sdVZ6IiIg0QAqrHiKkuX0ZgE++e8LqH1t86cBWQingszFzz3ucwAutYdWMFzlr9zqqPBEREWmAFFY9RFhSBEcJZxetyDSi3FLDHyfurSokmOYXJp73OAm3XsQgfiacPF42HnBIbSIiItIw6QlWHiK8RSP8vI5isUDvGLjKDTWsX29vd635vVU2Me0bsy9+EMfTYO1a64MGvPTPIhERETkPihAewtsbGp9YtpqV5Z4aTs6s+vhAhw61G6tXL+ufBQWwY0ftxhIREZGGS2HVg0RGWv/Mznb9tctKLLy3qS8fcgsPx8zE37924/W232PFmjVVnyciIiJyNloG4EEiIqx/5udDWRn4+rru2qnLDtCHVfRhFWuMPOC2Wo3Xr10u9/IRvVlNyNvd4eZJjilUREREGhSFVQ9yS/7rPMRiIsgmd9vnRHeOcdm1M1O20vpEuzgxudbjdW1fwkXcD8CajRmAwqqIiIjUnJYBeJDk0vWM5ksGsYz8nYddeu3CtVttbd8utQ+rER2bkullDduJeesxLHqUlYiIiNScwqoHMTeKsLWPp7r2Liuvndts7SYX1D6sAhyI6AZApJHFwVUHHTKmiIiINCwKqx7EdPIOK6D4oGvvsmqcbp9Zjb+4vUPGPNa6m62dtmC9Q8YUERGRhkVh1YN4RdtnVkvTXTezahiQcNwaVg97xxLYNNwh4/r36WZrFy5f75AxRUREpGFRWPUg/nH2mVVLputmVjM2ZRFpWMNxeiPHLAEAaHpZN1s7YPt6h40rIiIiDYfCqgcJaGafWTVlu25mNe1H+3rV4/GOWQIA0HxYGwoJBCAuc73DxhUREZGGQ2HVg4Qm2WdWffJcN7Na8Jt9vSrJjptZ9fL1Zk9IFwASy3dzdH++w8YWERGRhkFh1YOEJdlnVv0KXDezupwB/J1/8S53EDysn0PHzm3ezdbe99UGh44tIiIi9Z8eCuBBGic1wowX3lgIKnTdzOpPhzuyhI4AHBnt2LGLB17Mx1uOsZ5udMhqTjfHDi8iIiL1nMKqB/Hx9+Zdv4kcLQ0kP6g1U1103W0nlqxGRlpfjhR2+xiGvz0GgNsPwO2OHV5ERETqOYVVD/N/Ca+zezc0LsclYTUvD9LTrW0HLle16dwZvLzAYoH16x0/voiIiNRvWrPqYU7ObObmQnm586+387ccerCWUPJp77iNAGyCgqBtW2t70yYoK3P8NURERKT+Ulj1MBH2e6zIzXX+9YoW/MhaepFPODem/9sp1+jaFQIppFvpb+xc4drHyIqIiEjdprDqYSIjwQszTcgm+1CJ069XunmnrR3UPsEp1xhf8hYFhPIb/cj57yKnXENERETqJ4VVD3PT9icpw5dsIin9eaXTr+eTusvWbtKnjVOuEdG9Od5YAChf84dTriEiIiL1k8Kqh/FpFIwXBgBFac7fvios0x5W4wa1cso1EkZ2tbVD9yqsioiISPUprHoY7yj7otWydOev74w9bg2rR7yiCWoa5pRrxHSLJctkvXOsee4fGBbDKdcRERGR+kdh1cP4xdk3OrVkOjesHj14nKYW675VGSGtnXYdk5eJ1MbdAIgyjpC5IcNp1xIREZH6RWHVwwQ0O2U7gGznLgM49MtuWzs/xjnrVU8qSLIvBUj7RksBREREpHoUVj1MSKJ9ZtX7qHNnVnNW29ermhOdN7MK4NPTHlaPLVvv1GuJiIhI/aGw6mHCkuwzq34Fzp1ZLd1k37bKr6Nzw2rUxfaw6rtVM6siIiJSPQqrHqZxqya2dtBx586slqXZ14427uXcsJo0oj2l+AIQk6GwKiIiItWjsOph/IJ8yKURAMElzp1ZfabJy4RQQFfW0+zSTk69ll+IH3sCOgJglJRSfMwFz5IVERGROk9h1QPl+VjXrTYuP+LU6+zaBccJIbNpV0IiA5x6LYD3L51LI3Jpwy627PBx+vVERESk7lNY9UDPt36HgfxCH2MV5U6agDx2DDJOrAJo7dwVADYxg9qSd2LW+A+tBBAREZFq0PSWB0prPYRft1nb2dkQE+P4a+y271rlsrDapYu9rbAqIiIi1aGw6oGio+3tzEznhNXcBb/yDjPZRWuSwkYBHRx/kdN0tW8IoLAqIiIi1aKw6oFOD6vOYCxfwQTeA2ClJQFXhNXoaLgn/CO65qXQftkuDMsSTF4mp19XRERE6i6FVQ+UFJDOSNYQTSYla/rAsM4Ov4bXHvsDAcJ7OffpVae62W8uffkGyuHQylTiBiS67NoiIiJS9+gGKw/UIetnvmYU7zGBkBXfO+UaIRn2sBp3oYsWrQLHWneztQ8t0loAEREROTuFVQ8U2OKUdQCHnbMOILrAGlZzTY0JT2pyjrMdx6+3feFq4QqFVRERETk7hVUPFNLKfkeVd7bjw2rR0RKamfcDkB7kullVgKbD7WE1YNt6l15bRERE6h6tWfVAjdraZ1b98xwfVtN+2UsbDACORrk2rCZd3IrjBBFMIU0zPXtmdcsPh9jzytf8FjQUo01bLroIhg4Fk+4JExERcRnNrHqgiDZNMJ/4TxN03PFhNXvlTlu7rLlrw6qPvzd7gqw3jDUv20NhRr5Lr18dmbsLWNTqbtpdksDIBRNJ/XQlzz4Lw4ZBnz6wcaO7KxQREWk4FFY9kI+fF1mmKADCS5ywDGCT/eYq3w6u2wngpKyEbrb2vq89K/ntXZ5OTvIARuyZgTcWAKKx/zdYswb69jH44fXt7ipRRESkQVFY9VBH/axLAZqUZ4JhOHRs065Ttq3q6dqZVQCjs33das5Sz1kKcHhbLubBQ2lftgmA4wSx+tLHuPWzK/n4Y+jYEcBgSvE/GHhPV1Y+84Nb6xUREWkItGbVQx0LjIYSCKSY44ePEdw01GFj/2oaSCrHaM0ukge5Pqw2GtwVPre2TevXu/z6lSkrsbC3/5/oV26dMU3zTcTvp+/oPbAtAJ2Ba6+F9y76L3eveBGA9k+MYUfX32h7ZTt3lS0iIlLvaWbVQxWF2m+yyt3u2KUA7xz7E+P5gCsb/0qTdlEOHbs6kkZ1Zi7X8wjP8bHPeJdfvzJLxr5Bv6PfAZDtFYn/r0uIPhFUTwoIgIk/Xc/quFEANCIP05hrKT5a7PJ6RUREGgqFVQ9V2iSGbJqwlfbkHipy2LglJbDfumsVrV0/qQpA4+ahPJQwl//jEf67b4CjVznU2L6f99P/y4dt7w+//AlRvRMrPdc7wJeO62azI8B6k1ib0s2suPQpV5QpIiLSICmseqhlV79EJNl0YCupoZ0cNu7evfYlsK1aOWzYGut6Ytlqfj7s2+e+OgDSx9xLKMcAWNHpTjrcd/FZzw+KDsH7k9mU4AfAhateZOPbK5xep4iISEOksOqhomPsm3lmOnAVwN5Nx/HH+rF1G9dvBGDTpYu9/Ycb77FasgSeOzKBDXTmsHcsnRe9UK1+rUZ3ZsVlUwDwxoLvA3djKTM7s1QREZEGSWHVQ0Wf8sRVR4bVwNnvUkgQqTTnwsJvHTdwDXXtCn6U0J3fyfz2d7fUYBjw2GOwgCvpxnqWv/ArIfGNqt1/4Py/sy3AOkXcvmgdK/4yyzmFioiINGAKqx7KWWHVtHsXXhg05wAxrR23w0BN9Wh6iGOE8Ds96fnFE26pYeFCWHHi0/vkDl6Muj+pRv19AnwofHa67X3bDx7l2CHPe8iBiIhIXaaw6qGaBubxNnfyNSMZ/O0jDhs3KN2+x2rTgW66wwpIGhDLMUIAaJblnnUAzz9vbz/9NHh713yMHpOGsDxuDABZlibM/dd+B1UnIiIi4IKwOmPGDJKSkggICKBnz5788ssv1er366+/4uPjQ7du3ZxboIeKae7PnbzLSL4h6dAyh40blWcNqwWEENkh+hxnO4+3j4k9od0AaGo+SP4uxz+p62y2zF7Hfb+MoT/LSW5vMHr0+Y8VM/P/GGf6iE5s4p8fdaKgwGFlioiINHhODatz587lgQce4LHHHmPdunUMGjSIESNGsH//2Wef8vLyGDduHMOGDXNmeR4tNCqAHBoD0KjwkEPGLCsso1nZPgAOBrbG5GU6ewcny23Vy9be++lql14754mXGcP/WM4FvDroM7xq8b+EVsNbYdx0Mxa8yc6G115zXJ0iIiINnVPD6ksvvcQdd9zBhAkTSE5OZvr06SQkJPDGG2+ctd9f/vIXbrzxRvr37+/M8jxell8cAFFlhxzyyNVDK1LxpRyA3Aj3LQE4yad/b1v72E+rXHbdjN8P0XfvJwDkmJow4LmRtR7ziSewBd4XX7RuySUiIiK157SwWlpaytq1axk+fHiF48OHD2f58uVV9ps5cya7d+/mqaeqt9F6SUkJ+fn5FV71RX5QLGB95Oqxg3m1Hu/ICvt61ZIE94fVuNF9bO3ATa6bWd127+u20L6h/0SCIoNqPWbbtnDzzdZ2s9yN/DDx81qPKSIiIk4Mq1lZWZjNZmJiYiocj4mJISMjo9I+O3fu5OGHH2b27Nn4+PhU6zrTpk0jPDzc9kpISKh17Z6isFGcrZ29sfZLAY7/YQ+rPu3duMnqCa0uasERk/Vxr4lHVjlk9vhcyo6X0mnFOwCU4ku7V+922NhPPGZhHtewkS5cNOdOig7Xn384iYiIuIvTb7AymSquizQM44xjAGazmRtvvJEpU6bQtm3bM75elUceeYS8vDzb68CBA7Wu2VOUR8Xa2ke3ptd6PGOXPayGdnf/zKq3j4ldja1LAZpYsjmyaq/Tr7numW+INI4AsCb+amJ7xp2jR/W1butFo8RGADQyjrL+rrcdNraIiEhD5bSwGhkZibe39xmzqJmZmWfMtgIUFBSwZs0a7rnnHnx8fPDx8WHq1Kn88ccf+Pj48NNPP1V6HX9/f8LCwiq86o04e5Aq2l37mdWgg56xbdWpCtrblwLs/9wF61Znvm9r+vzlDocPH/XiP7Bg/cdYyy9fwlxY4vBriIiINCROC6t+fn707NmTxYsXVzi+ePFiBgwYcMb5YWFhbNy4kfXr19teEydOpF27dqxfv56+ffs6q1SP5dfCPrNallr7sHpP2If0ZSW3+31MdNfYc3dwgcALrTOr+0kgdUexU6915I9D9Dy8EICD3gn0eMjxu010urY9v0ZdDUCMOZ11kz5y+DVEREQakuotDD1PkyZN4pZbbqFXr17079+ft99+m/379zNx4kTA+hH+wYMH+fDDD/Hy8qJTp04V+kdHRxMQEHDG8YYiqPUpH1Gn124ZQHk5rN/fhDL6Uty+LyYPeRxE81uH0vT/0jlMUy4rhWuceK2tj37EhVgA2NZ3PM38z+MpANXg98Q/4b55AETNegHjP7dh8nHOtUREROo7p4bVsWPHkp2dzdSpU0lPT6dTp04sXLiQFi1aAJCenn7OPVcbsvBuScziVtKJxTvsQgbVYqwDB6CszNpu7RkrAABo3i4QS1QgHIFVJ+6xqmRJc60ZBlh+tj9coeXU8Y6/yAl97unDqkcvos+xn2hRspONT8+n85QxTrueiIhIfWYyDBfcgu1C+fn5hIeHk5eXV+fXrxYUwMlvYehQqGLZbrUsXgwndxH7xz8qPmrU3a64AhZaP51n925o2dLx11i1Cvr2NejPCm5q8St373vI8Rc5xZJHFzN0mvUHviOsJ22PrnZOChcREamDapLXPOTDYKlMaCiEhFjbh2q5ZPX4op+5n+lcwQI6xubUvjgH6mO/x4oVy53zb6eZMwFMrGAAAU84N6gCDJpyMRv9egDQNn8tu978wenXFBERqY8UVj1c7In7oGq5ZJXGKV8wnQdZwJV0NjbUvjAHurBbHi/zAGvoSauptzp8/KIi+MT6wCqCguD66x1+iTP4+Jo4dMvDtvf7/zXX+RcVERGphxRWPVxcHPhQRmh+GsePlp33OIEeuG3VSb0vDOJO3qEnv9Ni7xKHPxzgq0+Ok3fiAWDXXWedsXaFQS9fw3z/sYziS4bve5tTtrkVERGRalJY9XCPHrybEvxJI4GsFTvPe5yIXGtSKiKAmO6O2wjfEUIa+7I5rD8AseVp5K5Pdej4HScNZwX9+DNvcds4s0PHPpugUG+2PTWHrxmF2fDihRdcdmkREZF6Q2HVw/k0DsUL60xj3tbzW7hqLjUTX7obgDT/Vnj5eN5/9pyOF9raez/42WHjHvxxG53yltOP33jQ73UuHOLa7/2uu+w3yc2aBQcPuvTyIiIidZ7npRapwGgWb2sf3552XmMcWpGKP6UAZEW0c0hdjhZ8mX1jrrKffnHYuPuemmlr7x92OyYv196RHx5uDaxg3Tps1lPOf6RsTZiP5LDtwTfZ1O5a5iQ+TMeO0KYN9O4NN98Mi2//hCOf/gQWi7tLFRGRBkph1cP5t21ua5fvPXBeYxxO2WZrF7fwzLDa/ta+lOILQNOdjplZtZSU0XbFBwCU4kvH525yyLg19cADMMRvOd9yKQ+/15rc33a4pY5TFe85xIYBEymNiaf99L/Sacc8wlP/YMsW2LUL1qyB2bMhduazRI0dRmZwErsefB2KnfuUMRERkdMprHq48I4JtrZX2vk9QOH47/aw6tOpfa1rcoaoFkFsDuwFQIviHRRsOb9gfqpN/1pElOUwAL9Fj6JZt6haj3k+YmLg/h4/cynf442FfROnuaUOAMxmtt72AubWbemy4i0CjSLbl8LJIzAQGje2bgkbRh4d2AJAdPF+Wk+/h8ON25P+3jfuql5ERBoghVUPF9XTPrMamHV+YdW0Y7ut3bivZ86sAqR3Gm5r75rxfa3HK3/zXVvbMv72Wo9XG93fvotcGgHQZf2H5P227ewdnKBo+352xg8ledY/CTaOA5BPKF+0uI9vn15N0oFfOH4ccnKsE6g//BLA59fOYWnAZbYxYopTiZ0wku1dr8ecme3y70FERBoehVUPF92+CccJAqBR/vmF1fBD9mAUP8xzw2rY9fZQZFn0ba3GOropja4HrTOAaV4J9H3y0lqNV1stOofxU0/rwwi8sbD/1idcev3M+b9S1KkXbTKs64HNePFFs7s4sHQPo/e9wmWP9yI23tv2kC0/P+g90J/rP7+egQWL+HrK7/zif7FtvHYbPiO7eTdyv1nu0u9DREQaHoVVD+flbSLd1zq7GlOy/7z2IF1T1oW19GCvdysaJTZycIWO0+3O3uTQGIAW+37GMJ//TT3b/jkTb6z9N/W+nYBgb4fUWBt9Pr6fDGIA6Lz9c7K+Xe2S665ZZeHw9ffSpPwIAKmmFix8aClXHXidjoMjz9nfxweufLI73TK/56PhH5FFBADRJWmEjBzMnr/PcGr9IiLSsCms1gE5IdawGkwhBak1e1RqXh5MKHyNXqzl1v7nv0+rK4SEe/Ne+xcZydckWnazc/f5/Xoa5WYSFr8HgAUTSU+7dwnASQntg/llyJO291l3PuLwByCcbulSGHKRF6PLPyObJvwaMIyiZb9z5QuDbLOo1RUaZuKW725mx6d/sMLXutWYFxbuebnVicfZioiIOJ7Cah1QGGFft5q5pmZLAbbbl6vSPtm12zadD+O22/mGkRwnhO++O78x1i05yqqy7pTjzcpGl9Hukubn7uQigz+awB5TSwDap/1I2oyvnHat776DESPg+HHYQyvu77GMtrsX0X5Ak1qNO+C6ZiTu/pGPEh7lfl5hkeVSbr8dHnlEO1yJiIjjKazWARsueoAB/Eo8B9gR0KVGfU8Nq+08d7mqzaWnLC39+uvzG+PNzyK4hvk0Zz+H/vaSYwpzkOh4P9Zc+3+29z5/vx/jeKFjL2IYrLtvJtdeWWrbaeqKK+DdX5OJivN1yCViE3z4055n8br3Htux//s/uP46g6Lt57e2WkREpDIKq3VAQM+OrGAAB4knNa1may+3bbV/zNzeM3etqqBLF2jRwtr+6SfIruEN59nZ8PHH1nZBSByXPeB53/TIWWP4JcB6s1LT4lRWPeXAraAsFnZedg/dX7udWWU34k05114L8+ZBQIDjLgPWtayvvgqvvQZeJ/6fJHHev7F06EjuRwscezEREWmwFFbrgMREe3tvDR+AdMWH17OT1nzNSJJjjzqyLKcwmeBPVxUyljnMNV/L7rv+XaP+77wDRSe2Dr3jDggJcUKRtRQUbKLo+df4ne4MZilX//e6GofySpWXs2vQeNp8b73h6Rrm8fSwFObMsd7d7yz33GOdBb884Cde4B8EW44RPm4Uh+6d5vQ1uSIiUv+ZDKN+/W2Sn59PeHg4eXl5hJ18KHsdt3s3tG5tbV97LXz+efX7pvq1pkXZbo4RTGBpPt6+nv/vk3Vf7qf7aOv0ampwMi0KNlOdu4HKsvL4e9sveTN3LGUmf3buhFatnF3t+TEMGHmFwcJF1u/r+uthzpxqfZuVKylhd78babV+HgDlePPhkJmM//EW26yns238rZB9Q2/jyqJPbccO9B1Dwg8zPetfDWVlcPiwdUPZsjLrQtuwMGjUCKKicNkPTESkAatJXtP/K9cBzZvDJV4/chevM3h59Z9+VJh5jISyPQDsC+pYJ4IqQLdRzVnlPxCAFse3cvjLldXqt/nuGbySeyv7SGRK34UeG1TBGkrfeddEkxP3On36Kbz3Huc3E1lQwN6uV9mCagl+fHDFZ9z2k+uCKkDnvkH02jWHt+KnYsGauhN++5zDSf0wb9l+jt7OU14Oa9fCrFnwxD25WPz8ISEBunaFXr2gTx/rGpmmTSn1C+ZwTGfS+l1L2gc/amJYRMQD1I300sD5+sJ/vO/jde7hzvQp1d5/NHXRZryw/m2b3ayzM0t0KJMJDl0+wfY+feo75+xTnnWUpM9fACCaTC69y4OT6glxcfDGG9a2CQtH/vwY+296pEZjlO/cy6GkASRtt26dUEggs8d+ze1fX33+s7S1EBtn4tadT/DKRV+Rh/VfyjFZmynr3J2jz81wzbKA48fJense23rdxEvdPyIiwppJb7sNnnm9EaVUvSbCz1xMTOYm4n+bxwPjc2nWDP70J+s/JA7vK7J+zCEiIi7l4+4CpHqyw1tB1hYCKCF740EiuiWcs0/O0o22trlD3QmrAL2mjSFv/n2Ek0/79Z9QljYN3/iYKs/fdMdLdLMcBWBxzC1cdksd2PoA68f/y3816PfqjdxgzIVP4JDJTNxHz5/z4+hjny7EfPM44sqsC16PEs6XExZw+zsDXVF6lQIC4IEfRvLh46vp/dxoOrCVAEsRAY/dTcbcL4n55XNMYaGOvejRo2TNWkDue/NI2PItkZYiIoFUssjnllNONLGIEQAc84/E7O2HBRMBpfmEl2eTxF5asws/yljOANLTrcsz5syB0XzHfK4mO7o9pquuoskdV0Pv3lo2ICLibEY9k5eXZwBGXl6eu0txqB86P2AY1nkpY/PrS6rVJ6X7fbY+q5//0bkFOsG8lpNs9W8Y8Y8qzyvalWYUmEIMA4wSfI1Vc/e4sMraKyszjP90eN32vRpgHOx7tWEcPlxln0WLDGNm8F2287fR1vj0me0urLp6ln5zzHg/6G5bncvpZwy/xGJsd0Sphw8beS++bezrcJlRavKt8PM7+TpMlBEfVWyMHWsYL71kGN9/bxhpaYZRXn7mcIWFhrFtm2HM+7TMePXeHcZllxlGSIh9uLeZcMb4eWHNjOwb7jIs3y82jNJSB3xTIiINQ03ymsJqHfHD6Ndsf0GumPButfqsazzU1idjY6aTK3S8VV8cNIrxMwwwjpmCjZJd+ys9b3O70bbv86uEu1xcpWPk5xvGv9u/bZTjZfteCn1CjMNj7zEs/5tnGEuXGmUrVhvffWcYF19sPSWYAmM3ScZ3vpcbv3yd6+5voUpZWYYx5YLvjP3EG31ZYYBheHsbxk03GcaGdeWGsXq1YVgs1R4vM9Mwfrp3XoWf1amvDKKNT5v82fh43HfGhjUlNRn6DGVlhrFihWE8+qhhPBX7pvEzA6u87vGAxkb2FbcYlsU/nP8FnaG01DBycuyv3FzDyMurPLGLiLhITfKadgOoI5Y/sYgBz1wOwLKBDzPwl3PcaGUYZHtHE2FkcdjUlGhzulvWMNbW/Ph7ufrgfwDY0f5K2m75ssIt8/sefYvEaRMBOEw0Ocu2knxB7Z7Q5C4lJTD94q+5Y9ltRHLmXla/mC7kQiOlwrGxF6bzr4+aktDcs//jGgZ89VkJ9z3kz/5TnhkwmvnM5xoKQuMo7nUBIQO6Eti6mXUtgdmM+UgOxzbtY2OLkSwqHsrixbBmDcQZaaRhXwqTSnNSmlyDZfQ19P/bANp1qNl+xNW1axd89/ER8j76ii575nMJi/GntMI5b4b/k913/h+jRlnv3fL3tVh3HPBx4KorsxkyMiAtDQ4cwNKlG5lhrTlwAA4cgIL1u7nmpQsIKM7D11xc5TAlvsHM+ctSijv1Ijoa4uOhRckOIlctxCs+znp3Z0ICNG0K3s75mYpIw1STvKawWkfs+HYPbUdYbxpaHT+a3gfmn/X87M0ZRHSKtZ7f+BJ653zv9BqdYUNKLtFDkmnKYQB2PvwebabdDkDWW/8jbOKf8KMMgE+u/pQ/zbvObbU6gmHAf1/KoPixp7mp5D0CKLF9LY8wGnEUMNGyJUyeDDffXIvtrtzg+HF4+WWYPt36AIclDGEIKefs9xr3cB+vVTj2KdeRGdYG4+pruPDBnnTuYnLpzyItDRZ8UkDGzEW02zqfK/iGMAroy0pW0RewZu5xHdfy2oYLKUjqinfH9oT2bod3m1YQHQ2RkRAaag2C/v4QEVHxIt9/j2XzFop2pFG6Jw3LgTT8Mg4QlHcIb0u57bRJ3q/wsvk+2/tYDnGIZtX6PjqyiS10tL0fxwd8wPgK55i9fDjeOJ7y2AS8E5sT1L45vu1awoQJiIicj5rkNd1gVUckDkmkkECCKCI6c/M5zz+wcCMn/9oraFG3bq46VZfBjZk56k1u++pq5nE1f51xI08lQFCgQZ97phB5IqjOjX2Aa+fU7aAK1uB509+acuwvrzP77efI+GgxgXu34Ft6nNKQJtw6rJRR1/lz5ZXWXSLqmuBgePxxePBBeP89g1/+fTPH9wczlCUEUVRlv778Zmt37gyXXw5xV37Gtf3dd39TfDxMfCgUHrqew4evZ+5nJex+P4Xf/+gNJzbsKC4G/7W/4kchETtWwI4VUMW/Mw81SubhK7dw7Jg11Ofmwut/TKF36XKCgeCz1BJrPlDhfR7h7KMF+YSRRzjHCcaCF6YTu4P4UE4oBYRSwFEaVRyL9DPG97aUE5a9D7L3wSZgAaR6JTHmrQm0aIHtdfnSfxCduQn/1gn4tW6OKTLCusfuqS9fX2jWzDpbe1JJCaxebf2BlZRY/ywutj7h48SfRmERlsJijv/5QQqDo2xf9l28kMiPXj5xbhGUl1s3nTCZrL8cJhOYTJgDQ/nt/5YQFITtFbNwJiFbV+MdGohvWBBeocHWX9LgYGutJ9vNmkGbNmf5LyAizqSZ1Tpkc0BPOpb8jhkvOHYc7+Cqn5/5yr9K+fgff9Cb1Vz2j66Mev4CF1bqWKWl8OSAH/jX2qFYsH8UeSOzmc3NzAsdR//N7xGboH971UW7d8PSH8o58P1WzNt34Zd1CFN5GeWGN96NQimJTSS0a0vaDGtO377W3ODJsrNhwQJYsgRSUmDkvtf4G/8mkdSz9ttIJ7qwscKxhYxgBN9WOJZFBAdIsL2yAhPYGzeQrPYDSUiwhuiEBOvEbWioPW/5+lpXI1gs1lUEhYVw9Kj1lZtrX1VgbNtOxN41+B9Jo/Fx61Was5/m7CeCHFsdKVx4xqz4rwxgACvO+TP6oN1zzGv3CN7e1n1ww/LT+Djl3DucAHRmA5uw/wN8PDOZye3n7HeYaNsnNCd9zE3cxH/P2Xdp3I28deFsW94ODoZ/vtYMv9JjmP2DMQcGYwQFYwSHYAoOhsBA62y5txd5t0+itGd/vLysh/xSdxLyyjPWhc5mA8Ny4mWAYVR8v/2fMyn3CcBstv53i1kyh5ifPwOs59q2grOtnLa2CxKS2XH78/j6Wlef+PhAm//cT9CB7dbs7u0Fvj6YTr58fDD5+eDl4435ytFw1VW2vqbSEnjqKftAJ1/e3mceGzUKYk7ZtSUtzfqPELB/BFTZn76+cNllFX/oGzZY+5+rb9Om1ud0n2rZMutfHOfq27ZtxX80HT8O69efvd/JdvfuFR8NmJEBBw+e+5oBAdDutN1q9u61Xvts/UwmaNLE+uCSkwyj4nZ6Z+sfE2P9vTypqAiysiqOf/Iajn42dxU0s1pPZcV0hP2/442FfT9uJ3FU1yrP/W2dH2vozRp6c+cNLizSCfz84PGlF3PgL/DfU/5emctYuieXcOvS24iKrkOfhUsFrVpBq1Y+8JfOQN39FOCkiAi49VbrC2D//ntZs+ZePll7jLzVOzC2bScoez/BhUeI4ghBFOKNmQNUDGteXvBJ2F382uhmypvG49U8nuA2ccS2DCQhAdomwEXxzng4WLsTL+vfZ/v3Q2oqrEmFgzuOc3zbAcx795OW6YdXtjVEnRRzWhisyrbt8NUpz4loQmDVJ58m8LQZ+KJT+pbhQ/mJv9ZMGJgw8MKCCYNjnPmDCqKwWtfccSiYOXMqHnuYPPw5DsX5kFd139u/G8v/Tnk/gEx+5cNqXbflD29zDHtwmMpmnmDeOfvtXp3NyNNO+40V9GH1Ofs+9UkiU7nK9r6xqYgc4/lq1TvuX53ZGGwPq8NzlvH8/j+ds1++VyMGdsytcGxq2suMzp11zr7fho/lny0q/sdZvOV6osvP/ITgdM+0fJ+FMbfZJuBbFe5h1u/V2/rvlosOkhsYZ5vAv3rXR4zf8o9z9jsQ3olHr9xoy4YA/1h8Jx3Tfzxn3287/53/9f0XcCJbWiy89V71ZvxfvXIxO5pfbOvbYd8S/rrgijPOe/uqb/C96nJuu61aw7qMwmodUtSuG5v3r2UzHYnc60XiWc49+Y/ZgADo1MkV1TlXSAjMng133w3ffmv9C3L4cB8GDbq9Tq3ZlIaneXPri2tCgB4nXtaJn+xs66feZWXQrhR2+FX8BNrb+0p3lk5goHUSyD4RFAy0P/Gy1p2WZg2zqakwe98usrdnUbZ7P0FZ+/E+loep8BjehccItBwjhGP4UsbvJ34GJx0nmBf5G8UEUGoKoNzH+jL8/LH4B2L2D8TwD4TAQJpFtSc2xFpbYCCE+F3DI775+IQG4h/sQ2DgmbPIJ/+cUm4N4IWF1tdPma/wS94TGMdPHCg8jlfhcbyLj+NTfAzf0uMEcZxV9DntJ2OwlWRCKSCY47bX6TfbWc+s+H9QJ5dinI/Tx3JG3/LTYoGXUV7FmWfastOH9ae8r+5u12YLbKz4oQJHq/lzOppnnYStMF41++7eAyv22N/n1eC/zY8/UWHBTNtq9juaBx9/XPHYLXDKqvGqbdwI757yc/IC3qrmdb/6Gk6NwyOAv1Zy3hdfQoAXCqty/vLumMSIxZMAeOYYXFTFebm51ruWAbp1q5trG6syYID1JVLX+flBbKy7q6gdX19ISrK+rExA1IlXT9t5hmEN5aWl1tA41mz9+N9stn567O8fgJ/fi/j7n8+mA34nXuej+YlX5QzDGm6PHYN/nVhLfOwYHDtm4uDx1Sfa9uOFeWWUHi3Eu6QQS7kFw2LQ2LcJN3nZQ7N3aU/uL96Gt7cJb2/w8jbZX14V3/+1cTBeJz5x9/IC75K/8e+yP1f4eNfkZbL9aTJZj1m8fZnqZ/0Zl5VZ//yi8Af+V2qmrNyEpdyCpbQco8z6OtmmvJwcr0iGmqx9ysuB0jAmHF2CyVyOUW7GZC7HZLZ+0WS2/kc8eeyQkYj/KWvIt1q68pjxvC2gn/GnyfpnKf4Enfb31MLyq9lvtDqzj1Hx/WavTgSe9jvzRtl9BBvHMGFdTnH6n5zou/G0T3KyiORF/lbhmlXVfvy0VeTr6M5/uLvq7/XEn4eIO+P3bCGXs+/E9FNl/U62157yvynrd2HiI24+6/VO/nmYig/VyaApnzHmjGtk0JQkD5wA0prVOmTTJuvNJQA33ACffFL5eStfX8v8e35gNb3p8Zc+vPimwz8nFBERqdNO3SjZYjlz8+TqHjvf/idrqOzPs33NWX1Pvpo0OXNZrTNozWo91batdXebkhLrXpNVKf9sPs/zLAA/e88BxrqmQBERkTri1HWj2kbYs+mh1nWInx/0OLHU69Cu4+QcrHyrn/A/7HfoJtxQd3cBEBEREVFYrWNuj/iS9XQlnzBSZyw44+tleYW0O2rdk3Kvd2sSB8a7ukQRERERh1FYrWPatPOiKxvwxkLR0lVnfH3HhytsT3TalzhEd8qLiIhInaawWse0uL6vrR2+9czNt4/OW2J/M3SoK0oSERERcRqF1TqmRe9odntbNwFul7uSksNHK3w9ZtVXtnbi+CEurExERETE8RRW6xiTCXa1tT51wgcz2175zva1zF930rrQumPwH4F9SbrgzP3cREREROoShdU6KGDMSFu7ZP43tvaef9kf6pc+4FqX1iQiIiLiDAqrdVC3eweRTygAbXcswHK8CAyD6MX2Z7g1f0BhVUREROo+hdU6KDzKj1WxVwHQyJLL5sf/y9rfTVxfOIsPGMfKoItIvqKlm6sUERERqT2F1TrK9767bO2Y1x7nvltyWUsvxvMB615YrC2rREREpF5QWK2jBj7UnyVhowB43vx3lm9tBEDLlnD7BP1nFRERkfpBqaaO8vaGqPnvcEPAF7zE3wATgYEwezb4+7u7OhERERHH8HF3AXL+Ol0UzZT1V9H0DbBYYOJE6NDB3VWJiIiIOI7Cah3Xrh1Mn+7uKkREREScQ8sARERERMRjKayKiIiIiMdSWBURERERj6WwKiIiIiIeS2FVRERERDyWwqqIiIiIeCyFVRERERHxWAqrIiIiIuKxFFZFRERExGMprIqIiIiIx1JYFRERERGPpbAqIiIiIh5LYVVEREREPJbCqoiIiIh4LIVVEREREfFYCqsiIiIi4rEUVkVERETEYymsioiIiIjHUlgVEREREY+lsCoiIiIiHkthVUREREQ8lsKqiIiIiHgshVURERER8VgKqyIiIiLisRRWRURERMRjKayKiIiIiMdSWBURERERj6WwKiIiIiIey+lhdcaMGSQlJREQEEDPnj355Zdfqjx33rx5XHLJJURFRREWFkb//v357rvvnF2iiIiIiHgop4bVuXPn8sADD/DYY4+xbt06Bg0axIgRI9i/f3+l5//8889ccsklLFy4kLVr1zJ06FCuvPJK1q1b58wyRURERMRDmQzDMJw1eN++fenRowdvvPGG7VhycjKjR49m2rRp1RqjY8eOjB07lieffLJa5+fn5xMeHk5eXh5hYWHnVbeIiIiIOE9N8prTZlZLS0tZu3Ytw4cPr3B8+PDhLF++vFpjWCwWCgoKaNKkiTNKFBEREREP5+OsgbOysjCbzcTExFQ4HhMTQ0ZGRrXG+Pe//83x48e5/vrrqzynpKSEkpIS2/v8/PzzK1hEREREPI7Tb7AymUwV3huGccaxynzyySdMnjyZuXPnEh0dXeV506ZNIzw83PZKSEiodc0iIiIi4hmcFlYjIyPx9vY+YxY1MzPzjNnW082dO5c77riDTz/9lIsvvvis5z7yyCPk5eXZXgcOHKh17SIiIiLiGZwWVv38/OjZsyeLFy+ucHzx4sUMGDCgyn6ffPIJ48eP57///S9XXHHFOa/j7+9PWFhYhZeIiIiI1A9OW7MKMGnSJG655RZ69epF//79efvtt9m/fz8TJ04ErLOiBw8e5MMPPwSsQXXcuHG88sor9OvXzzYrGxgYSHh4uDNLFREREREP5NSwOnbsWLKzs5k6dSrp6el06tSJhQsX0qJFCwDS09Mr7Ln61ltvUV5ezt13383dd99tO37rrbcya9YsZ5YqIiIiIh7IqfusuoP2WRURERHxbB6xz6qIiIiISG0prIqIiIiIx3LqmlWpneLtqax5fwPBYd50u2cgpnAtaxAREZGGRTOrHuqbiV/Ton0Ag164kh6PX07PiH3s/Gilu8sSERERcSmFVQ/0wz8Xc9VbI8jE/vCEdeYuDLm1OYd+3OrGykRERERcS2HVwxzbe4Rb/9UR84kVGsOabaN9sHV7r0NGHPdenwEWiztLFBEREXEZhVUP89w1qzlkxAFwadwGvt/fnpQt0UR5ZwMwL2covzyy0J0lioiIiLiMwqonSUsj4I9VRJCFHyW8Pi8OLy+Ibh7A9IcPM5r5/MwgBs7/m2ZXRUREpEFQWPUkb7zBk8YUDpDA0ts+pFXfSNuXbnymA/OHvMoglmHauQMWLHBjoSIiIiKuobDqKUpK4O23AQj0Kaf/M1ecec7f/mZvz5jhosJERERE3Edh1VN8/z1kZVnb114LcXFnnnP55dCihbW9eDEcPuy6+kRERETcQGHVQ6S8sp4sIqxvbrml8pO8vMi75jbe43aGWb5n9kPrXVafiIiIiDsorHqA0qOFXP3j3TQlg+t8v8C4+JIqz93Y7RYm8B4/MYyPv9YTrURERKR+U1j1ACmv/kEuTTDjg29CDCZ/vyrPHXBzSxJ80gH46WgPju094qoyRURERFxOYdUDfDuv0Na+6hrvs57r5QUjO+8DoBR/fpi+yZmliYiIiLiVwqoH+HZLcwC8MHPJPe3Pef7IP9k//l/wtfZbFRERkfpLYdXNDvyyjy1lbQDoE7qNJi1Cz9ln6MR2BGKdjf1mX0eMcrNTaxQRERFxF4VVN/vuzT229mV9cqrVJzDUhyFNtwGQYTRl+5dbnVKbiIiIiLsprLrZt0v8be3LxkVXu9+QfsW29tL/pju0JhERERFPobDqRpbScn5M7wBAE1MOvf7Uptp9h4xtamsvXel/ljNFRERE6i6FVTfaPH87R2kMwOC4XXj7Vv8/R49rkwg1FQCwNL2d1q2KiIhIvaSw6kZ5y7fQnd/xwsygPiU16uvja+LRTl/xBhNZagyG9eudU6SIiIiIG/m4u4CGbODBufzO/8gnFOPBZTXu//Bf8+Gut6xvUpZCr56OLVBERETEzTSz6i6GAcusATUszET4gI41H2PwYHv7l18cVJiIiIiI51BYdZddu+DwYWt7wADwPvuTqyrVvj00amRt//abNQCLiIiI1CMKq25SunS5/c3Agec3iJcXed2HsJiLeSbjDrLWpzmkNhERERFPobDqJvf+O5Ek9jCOD0jvMOy8x3mm+O8MZzFP8Awr5uxzXIEiIiIiHkBh1U1WpUazjyRmcxOhF3Q573H6Dgm0tX9LKT7LmSIiIiJ1j8KqGxRlHWdTcWsAOgTsISQ66LzH6ndDoq29clujWlYmIiIi4lkUVt3gj//tohxfAHonHK7VWPFdmhDnnQHA6ry2WErKal2fiIiIiKdQWHWD1d/l2Nq9etb+Dv5+cfsByCecbV/tqPV4IiIiIp5CYdUNVq+zP4uh9+VRtR6vb/dSW3vVgsxajyciIiLiKRRW3WD1wTgAfCmly+iWtR6vx9BwW3vd75ZajyciIiLiKRRWXSx//1G2lyUB0DV4F/6hfrUes/voRFt73b4mtR5PRERExFMorLrY75/txjjxY++dlOWQMSMSQ2nucxCAdcda6yYrERERqTcUVl1s9Y95tnbvvufxiNUq9I3dTzfWcR2fcXydbrISERGR+kFh1cXu9n2HXxnAdO5n6PW1v7nqpLl3/8I6evA+dxC6Y63DxhURERFxJ4VVFwtav5wBrOD+4PdIHNbKYeOaenS3v1m3zmHjioiIiLiTwqorZWbCfuueqPTsCd6OWwZA91PC6u+/O25cERERETdSWHWl1avt7d69HTt2ZCQkJACQt24PhllbWImIiEjdp7DqQvM/LOAlHuQXBlLSra/Dx38u+Flas5NGBQdIW77f4eOLiIiIuJrPuU8RR5m5NImvuQGAnbGptHbw+AURiew+Meq6BWkkDEp08BVEREREXEszqy5iWAxWZyUC0Mh0lFZDmzv8Gj0GBNja65YXO3x8EREREVdTWHWRg6sPkWGJAaBX4z2YvEwOv0a3K5rZ2n/sDHL4+CIiIiKuprDqIqv/Z19D2rt9gVOu0fKCWII4DsDG7FinXENERETElRRWXWT1MvvH8r0HBZzlzPPn7WOiU0gqALvLW3As7ahTriMiIiLiKgqrLrJ6e5it3ftax69XPalzfC4ABl5s/maf064jIiIi4goKqy5gmC2sybE+raqp12Ga9XLeR/RdOhu29safc512HRERERFXUFh1gd0/pXKURgD0ikrF5Ph7q2w6D2pka2/YYFR9ooiIiEgdoLDqAr/NO2hr9+lU5NRrdb7CvsRgw/5GTr2WiIiIiLPpoQAukJC5lpvZyyr60HdYiFOvFdkyjJlN/ka7nOV0tOwHIw2nTuWKiIiIOJHCqgtcePATLuQ365uJOU6/3vgBO2DBSjgGpKZCYqLTrykiIiLiDFoG4GylpbBunbXdti00buz8a3bubG9v3Oj864mIiIg4icKqs/3xhzWwAvTp45prnhpWN2xwzTVFREREnEBh1ckyftjEMYKtb/r2dck1y5K78APDeJkHeOeLKJdcU0RERMQZtGbVyR5+rzUfkUdHNvNlvIUkF1zT0rotl/EtZnzounEbd7rgmiIiIiLOoJlVJ1t1IBYL3myjPbFDk11yTf8QX9r5Wx+7uqWkJWUFxefoISIiIuKZFFadKG9/HttKWwLQNWgXAeH+Lrt2l6aZAJThx/Zv97rsuiIiIiKOpLDqRGvm7sY48SPu2+qIS6/duX2Zrb3xJ9deW0RERMRRFFadaPm3+bZ2n37eLr12537BtvaG38tdem0RERERR1FYdaKf14fa2oNubuHSa3cZ0czW3rgn+CxnioiIiHguhVUnKSssY0VOOwCaeaeTODDepddv3juGMJN1ZndDjmuvLSIiIuIoCqtOsm7uDo4TAsCF8XsweZlcen2Tl4nOodYdAQ5YmnF0d7ZLry8iIiLiCAqrTvLz/+w3NV04wOyWGrq0OGprb1yQ6pYaRERERGpDDwVwkrQt+XhhxoI3F94Q55YaenY36LxxA13YQPABL6CHW+oQEREROV+aWXUGi4XpR8eTQxMWhY0leWQrt5Rxx13+bKArH3MLPXJ/dEsNIiIiIrWhmVVnWLcOcnMJBy67qBRcvF7VpmNHMJnAMGDjRvfUICIiIlILmll1hkWL7O1LL3VfHSEh0NL6BC02bQKze9bOioiIiJwvhVUnMBZ9a38zYoT7CgHo0gWAoiKDkm167KqIiIjULQqrDpa79ygJy+dwK7P4NuFOaOHahwGcbo7pT7RnKyEc49sPM91ai4iIiEhNKaw62JfPbuIg8XzIrXwfdZO7y8ErMYHttMeCNxtWFbu7HBEREZEaUVh1sDlfBNja102McGMlVl0uaWprb9zp78ZKRERERGpOYdWBjmzN4ofsbgAk+hyg3x0d3VsQ0HpoAv5YZ1Q3ZDY9x9kiIiIinkVh1YHen7QJ84ndwG7otdvlj1itjI+/Nx2DrDdW7SxLpOjIMTdXJCIiIlJ9Tg+rM2bMICkpiYCAAHr27Mkvv/xy1vNTUlLo2bMnAQEBtGzZkjfffNPZJTpE8dFipn+fDIAJC7dPTXRvQafo0iwHAAvebFmoHQFERESk7nBqWJ07dy4PPPAAjz32GOvWrWPQoEGMGDGC/fv3V3r+3r17ufzyyxk0aBDr1q3j0Ucf5b777uN///ufM8t0iLdu/40MSwwA1zT7jTaXJLq3oFN07mDfX3XD0hw3ViIiIiJSM04Nqy+99BJ33HEHEyZMIDk5menTp5OQkMAbb7xR6flvvvkmzZs3Z/r06SQnJzNhwgRuv/12XnzxRWeWef4MA1JTWXD3Ih6Z39t2+J/PhruxqDN1GRhma29Yb3FjJSIiIiI147SwWlpaytq1axk+fHiF48OHD2f58uWV9lmxYsUZ51966aWsWbOGsrKySvuUlJSQn59f4eVS/fuzccbPFBEEwF87/kzvWzu4toZz6HJFgq29cV/YWc4UERER8SxOC6tZWVmYzWZiYmIqHI+JiSEjI6PSPhkZGZWeX15eTlZWVqV9pk2bRnh4uO2VkJBQ6XlOYTLBwIFcwK8AXNX0N15e3td116+m6OQIor2OALAhr7l1RlhERESkDnD6DVYmU8U74g3DOOPYuc6v7PhJjzzyCHl5ebbXgQMHallxDd14I30eG87SV/5g/sE++Id55l6mb3WZwc8MYrvRFg4dcnc5IiIiItXi46yBIyMj8fb2PmMWNTMz84zZ05OaNm1a6fk+Pj5ERFS+wb6/vz/+/m4MiKNHEzB6NIPdV0G1jL74GKxfZn2zcSM0a+begkRERESqwWkzq35+fvTs2ZPFixdXOL548WIGDBhQaZ/+/fufcf73339Pr1698PX1dVapDUOXLvb2hg3uq0NERESkBpy6DGDSpEm8++67vP/++2zdupUHH3yQ/fv3M3HiRMD6Ef64ceNs50+cOJHU1FQmTZrE1q1bef/993nvvff4+9//7swyG4ZTw+rGje6rQ0RERKQGnLYMAGDs2LFkZ2czdepU0tPT6dSpEwsXLqRFixYApKenV9hzNSkpiYULF/Lggw/y+uuvExcXx6uvvsq1117rzDIbhvbt+cVrMOstnclZnMxT7q5HREREpBpMhlG/bg3Pz88nPDycvLw8wsK0TdOpOgbsYktJa/wo4dhxL3yDtLRCREREXK8mec3puwGI5+gSkwlAKf7s+H6fe4sRERERqQaF1Qakc/tSW3vjj5lurERERESkehRWG5Au/YJt7fWrSs9ypoiIiIhnUFhtQHpcZX+61++7Qt1YiYiIiEj1KKw2ILHdmxLjZf34f21uSwxLvbq3TkREROohhdUGxGSCHhGpAOQYTdi/PM3NFYmIiIicncJqA9Oz3XFbe+2XCqsiIiLi2RRWG5geFwTa2r8vL3ZjJSIiIiLnprDawPQYFU8bdjCWOXQrXO7uckRERETOyqmPWxXP06J/HDsioyErCw5GgfGodTGriIiIiAfSzGpDYzJBjx7W9pEjcPCge+sREREROQuF1YaoZ097e+1a99UhIiIicg4Kqw3RiZnVYvxJT9nh5mJEREREqqaw2gBlJ/WiK+sJ4Rh3zh7s7nJEREREqqQbrBqgJt1bcNCUg9nw4bcj1idZmbx0k5WIiIh4Hs2sNkAmLxN9I/cAkGVEsiflgJsrEhEREamcwmoD1a+z/UlWKz/Xk6xERETEMymsNlD9Lg6xtVcuK3NjJSIiIiJVU1htoPrc2BoTFgB+2x3p5mpEREREKqew2kCFt2hEsp913eq6420pyi12c0UiIiIiZ1JYbcD6tUgHoBxf1n22083ViIiIiJxJYbUB69fXsLV//SrHjZWIiIiIVE5htQEbeF2srZ26+ZgbKxERERGpnB4K0IC1H9mar0Jv4oKCRTQ5aoA5C7y93V2WiIiIiI1mVhswk5eJKy8ppgm5cPQobNzo7pJEREREKlBYbegGD7a3U1LcV4eIiIhIJRRWG7pTw+rPP7uvDhEREZFKaM1qQ9e5M/OCb2Hh8QvZ8FUPVpoNvLxN7q5KREREBNDMqnh58XHoX3mPCawu78G6T7XfqoiIiHgOhVXhkgtLbO1FMzPcWImIiIhIRQqrwoi7kmztRb81dvn1czans+OrrWT9cRAM49wdREREpMFQWBUSB7egve9uAFbmdyBnX77Tr1lw4CgvjviBjn47iOgUS7urkonq1oydMQPh0UchR0/UEhEREYVVOWFEx/0AWPBm8atbnXqtRU+tJDmxkIe+vZgtZW1tx/0ppuWRlTBtGrRrBwsXOrUOERER8XwKqwLA5dcF29oLvzY75RqGxeD/hv/EFVP7cNASZzveN3QLN7Vaya3xP+Htc+JXMisLRo6k8OW3nFKLiIiI1A0KqwLAoLs6E8wxABbsbk9pYblDxzcsBvd1S+GRxRdhnPi1uzRqLVu/28/K/A58vKsfbx24HPbsgVGjANhltKTDpEuZeZseViAiItJQKawKAP6NArkyfj0AOUYTfnjZgY9eNQymXbiI/2wcYjv09CU/syijB+2HN694bkICzJ9P5r1PM5gUUknkL7P6s+KtDY6rR0REROoMhVWxueFP9ocBfP9JluMGfuUVbv51Iq3YhQkLH/7lVx7//kJMXlU8fMDLi6jpj3FNV+tNX2X4MeauKA5vOuK4mkRERKROMBlG/dorKD8/n/DwcPLy8ggLC3N3OXVKSW4hj8e8zZiyT+gTvgNT5mHw86vdoD/8AJdeChYLh4hl2Z8/4vq3hlWra3lxORfHbiLlaDcARsas4qtDvasOuSIiIlIn1CSvaWZVbPwbB/Gv61bTl1WY8o7Cd9/VbsDdu+H668FiASDu0duqHVQBfAJ8mPtLM5p6HQZgweE+fPq3lbWrSUREROoUhVWp6IYb7O333jvvYfLSCnii32JKco9bD4wcCU8/XeNxYjpF8Z9Je2zv732lDTl7jp53XSIiIlK3KKxKRZddBnHWbaWMr76meMf+Gg9hLrNwU6/tPJM1kaEsIaP1QPj4Y/A6v1+3a1/ox9Wx1hnVI0YkU6/TzVYiIiINhcKqVOTrS+H4u3iLP9PVWMc/bjxQ4yEeG7qcbw73AmC7qT3H3/gQwsPPvyaTiVe/aE4ghQC8/nt/dv5Y8xAtIiIidY/Cqpyh6MY7mMRLbKQLb6/tyaFN1X/06Qd3r+L5XwcC4E05nz67i1YXJ9W6pvg+cTw00Dq7Wo4v3/x9Sa3HFBEREc+nsCpniOjYlLu6LgeghACeHbe9Wv2WvbeNO2d0s72fftUShj3Sx2F1PTS3F6P9FrKCfjywfjxs0HIAERGR+k5hVSr10MwOBGG9OerNdX1Y8/m+s56/adEBRt8ZTRnWra7uavcj98y/2KE1hcSFMf+FnfTjN+uB87hhS0REROoWhVWpVHT3Zjw19GcALHhz6zgL+UdKKj138/cHuWhkINlGEwAuabSKV9YOBJMT9kP9858hJsba/vxz2LTJ8dcQERERj6GwKlV68IshdPHbCsCWopZc12U7x3LLKpyzc+7vXHBZCEcskQD0CdzAZ2ta4hPs75yiAgPhH/8AwIKJ5Q986pzriIiIiEdQWJUq+YYF8vkcM42x3mD1fUYXusdmsPCfKbBgAdx+O61u6M0lxvcA9ArYyHfrmxLeKtK5hU2cyLfhY+nOOi74cSp/fLHXudcTERERt1FYlbNqc3Unvnx2M+EcBWBXSQIbXlgEV14JM2fihYVZjOeh+E9YsjWWRm2jnV9UUBA7h0xgA10B+PdDGc6/poiIiLiFwqqc06BHB/HbJ3sZHLgKgI5stn8xNJTgFybzwt7rCEl08ozqKW6f0ZsmJ2Z8P9nVi7T1WS67toiIiLiOyTAMw91FOFJ+fj7h4eHk5eURFhbm7nLqF7OZ3/+znJbpv9LI5xh07GidYQ0JcUs5T/ZfzNMrLwHgoQG/8sKvF7ilDhEREamZmuQ1hVWpsw6vTaNFr0hKCCDMlM+BdF/CYgLdXZaIiIicQ03ympYBSJ0V0zOeca2sDy/IN8J455717i1IREREHE5hVeq0Sc/bb+ia/kUiZSUWN1YjIiIijqawKnVa+2s7cWXErwCklcfy+RN/uLkiERERcSSFVanzJj1ob7/0ZhD1axW2iIhIw6awKnXe4If709v/D27gE14vuAXTpo3uLklEREQcRGFV6jyTtxe/Pv8rn3AjfVgN06e7uyQRERFxEIVVqRd87xgH4eHWN7NnQ2amewsSERERh1BYlfohJATuvNPaLimBN95wbz0iIiLiEAqrUn/cey8lXoF8wDgueHYERzOK3V2RiIiI1JLCqtQfzZvzeJs5jOcDlpf14d37daOViIhIXaewKvXKnVOb29qvzo+nvEz7WImIiNRlCqtSr7S9vhtXNv4FgANlsfxv6mY3VyQiIiK1obAq9c6D95Tb2v9+zU8PCRAREanDFFal3hnyxCC6+W4CYHVeW5bP2e/mikREROR8KaxKvWPy9WHSNam29y8/nu3GakRERKQ2FFalXhr76gXEmtIBmL+nC3t+P+regkREROS8KKxKveQX3Yh7+q4BwII3r9y3y80ViYiIyPlQWJV66y+vdCCQQnwow1j3BxTrIQEiIiJ1jcKq1FsRfVoxs/cb7KAtrxZOgPffd3dJIiIiUkMKq1KvjX1zKEnss76ZNg1KStxaj4iIiNSMwqrUbz16wMiR1nZaGnzwgXvrERERkRpxaljNzc3llltuITw8nPDwcG655RaOHj1a5fllZWX885//pHPnzgQHBxMXF8e4ceM4dOiQM8uU+u6JJwAoxp//PJzG4f2aXRUREakrnBpWb7zxRtavX8+3337Lt99+y/r167nllluqPL+wsJDff/+dJ554gt9//5158+axY8cORo0a5cwypb7r04eUPg/Rkj3cmzuVyX/a7u6KREREpJpMhuGch1Fu3bqVDh06sHLlSvr27QvAypUr6d+/P9u2baNdu3bVGmf16tX06dOH1NRUmjdvfs7z8/PzCQ8PJy8vj7CwsFp9D1J/ZP6wgdaXJFJAGF6Y2bj8GB36h7u7LBERkQapJnnNaTOrK1asIDw83BZUAfr160d4eDjLly+v9jh5eXmYTCYaNWpU6ddLSkrIz8+v8BI5XfTFXXik27eAdd/Vh27S0hIREZG6wGlhNSMjg+jo6DOOR0dHk5GRUa0xiouLefjhh7nxxhurTN3Tpk2zrYkNDw8nISGhVnVL/fXA3P4kcACAhXuTWfR+upsrEhERkXOpcVidPHkyJpPprK81a6xPDjKZTGf0Nwyj0uOnKysr44YbbsBisTBjxowqz3vkkUfIy8uzvQ4cOFDTb0kaiMC2CUy70j6rP/FuL44VOGUVjIiIiDiIT0073HPPPdxwww1nPScxMZENGzZw+PDhM7525MgRYmJiztq/rKyM66+/nr179/LTTz+ddS2Dv78//v7+1SteGrwbP76c96OX8VPJQPYXx/DYmK288l2yu8sSERGRKtQ4rEZGRhIZGXnO8/r3709eXh6rVq2iT58+APz222/k5eUxYMCAKvudDKo7d+5kyZIlRERE1LREkSqZwkJ5+9USOv2liGICefX7ZC7/+AiX3hzl7tJERESkEk5bs5qcnMxll13GnXfeycqVK1m5ciV33nknI0eOrLATQPv27Zk/fz4A5eXljBkzhjVr1jB79mzMZjMZGRlkZGRQWlrqrFKlgWn152E81/VT2/tbbvchK6PcjRWJiIhIVZy6z+rs2bPp3Lkzw4cPZ/jw4XTp0oWPPvqowjnbt28nLy8PgLS0NL766ivS0tLo1q0bsbGxtldNdhAQOZf7f7iSywN+xI8SJpc9RsT0J9xdkoiIiFTCafusuov2WZXqyvrmNw6MupvulrXWA2++CX/5i0OvUVYGa9bAtvVF5OzM4W9X7oDycmjUCJo1I9svlsAgE0FBDr2siIiIR6tJXlNYlYbtP/+Be++1tr284L33KL1xPH5+5z9kYSF893Up89/KZMGvjcgtDQEglkMcolmFc/8eNIPXi+9gRM9Mbv17FCOv9cfb+/yvLSIiUhd4xEMBROqEe+6Bhx6yti0WXrptA31bpLPpD3ONhsnJgQ/fKuLqPmlEhpVwzQ1+fLQk3hZUAUo4c9eK1YUdKLb4MX91PKPH+tM+KptP3jlG/fonpIiIyPnTzKqIxQIPPMCnr2UwFuuNV36mUu668SgPPBtNixZn6ZuVxbKXVjHk/y7FbJw5JRpGHpd7f0//Fodo38bM8O5HwNvbmm537uT+n69lTunVZFJxO7cLWqXzn7nRdOupaVYREal/tAxAYVVqyjDY/Lf3uf7lfmyhY4UvdY49QrdOZiKivSk6buGC+H3cEvol/PgjrF5NoRFAJFkUYV14GkMGV/l/y9UX5jD0r+3xH3ERBARUft3ycsxLfubb537n3yk9WWIMtX3J11TGMw/l87fnIrQ0QERE6hWFVYVVOU/FPyxj6nUbefnoeIoJrPScK/mKr7iqwrH7mY5PsD/XXFpIv4nd8B56IfjUcBvj/ftZOG4Ok1JGsZ32tsNDkzP48remhIbW+NsRERHxSAqrCqtSG0VFHHn1E958Po+vcgeylp4YpyzvbsE+9pFkfdO5M1x2GVx9NfTta71Jq5ZKf1rGU9du4vmjf8bAi7HM4ZMHV2P61wtoilVEROoDhVWFVXEEw4AtW8hf9CsHtxWQc7iMoBAvouL9ib+gBfTuDc2anXuc81FQwNLrXuff33VkLmMJogiuuAL++1/Q77WIiNRxCqsKq1JfvPWWdceC8hNP2OrShbzPvie8bczZ+4mIiHgwbV0lUl/85S/w3XfQuDEAezYUkNwB/vVIjpsLExERcQ2FVRFPd9FFsHIl+fEduITFpJtj+Mf/NeGfd2RpP1YREan3FFZF6oK2bQld/h23R35tO/TC+5H8+eojmGv2/AIREZE6RWFVpI4wJcTz2JabeCPhOUxYAHj3yyhuGHaEkhI3FyciIuIkCqsidUlUFBM33s2c5Kn4UgrA5ylRjBpwhOPH3VybiIiIEyisitQ14eFcv+YffNXraQIpBOD736O4pEcWublurk1ERMTBFFZF6qKgIC779QkWD51GOEcBWLEjksFdcigsdG9pIiIijqSwKlJX+flxweLJpFz1MtEcBuCatFcJeucVNxcmIiLiODV8eLmIeBRvb7rOe4pltz3H3A+LeYxn4QEgMxOeftohj38VERFxJ/1NJlLXeXnRZtZjPP6kN6aTx557Dm64ge1/FLuzMhERkVpTWBWpD0wmmDIFXn3VNpv6w2c5dOzmw4Q/HddOASIiUmcprIrUJ/feC199RWZQItfxGWZ8eG9OMD3bH2P9encXJyIiUnMKqyL1zRVXEL3iS16OeJZgjgGwPS2E3j3N/POBEo4dc3N9IiIiNaCwKlIfdenC+B2P8vtFD9GDtQCUW7x54RV/2rco5I0Zhp56JSIidYLCqkh91aQJbX+YwYp/r+Apn2fxx3qz1cGcIO6620TLuGJee8Xi5iJFRETOTltXidRnJhN+k+5h8tV7uXncX5m07Gq+ZhQAh3ICOPDkm5B/BK66Cjp3prDIxIED0LIl+PpWPqRhQHExBAZWPL5sGezZAwUFkJ9v/bO0FMLDra/oaOu4rVpBRISTv28REak3TIZhGO4uwpHy8/MJDw8nLy+PsLAwd5cj4jkMAxYtYu2DH/PMjuv4kqvYQBc6sdn69bg4libdxtBfnwEgJLCcRiFm/HwtlJZ7UVoKxSVeFBT70CriKDv/8m/IyYHcXMjJYdSqx/j66IXnLOPKTnv46t+7oFcvaNLEVprJdI6OIiJSb9Qkr2lmVaShMJng8svpedllzF+wgIxXbqHpki1w8p+rhw6x9VC27fRjRT4cK6r8/yLys0rh2WcrHAvjlmqV0XLTV3Dpg9Y3yckwcCA9f3iB5h1CuHyUD9dcA5GRNf7uRESkntLMqkhDlpoKX38N33wDy5ax6NhAZjGeAySQRzi5NKYcH/wotb3CyCeKI3zDyApDfcel7ArqQlhQOaE+hYSZjuFrlJJ/3JvcAh/SacoeWnIZ33IVX9n67aIVbdhle+/jbeHSi83cOM6XUaMgJMRlPw0REXGRmuQ1hVURsbJYrItON2+G9HTIyICiIjCbrV8LCrIvQA0Pt36E37ix9c8mTSAsrOrHu5rNkJUFO3fC9u2wcSMsXw7r1rG4fAg3MZsjRJ/RLci/nDHXeTH+di8GD9bTY0VE6guFVYVVkbqhsBBSUrB8tYDf5+3js8wL+YQ/cYDmZ5zapkUpm3b44efnhjpFRMShapLXNE8hIu4TFAQjRuD1xuv0yljA86suYt89/+bn8Cv5C2/SiFzbqR1SF+J30UB4/31OPtnAbHZX4SIi4iqaWRURz1NWBt9+S/F7s/nyay9mWW7hr7zBKL62fj04mNIxN9Lm21cZcqk/48ebtExARKQO0TIAhVWR+uPIEfj4Y3jvPet62hPmM5prmG973yLezK23ezNunHUvVxER8VxaBiAi9UdUFDz4oPWmrN9+gz//GUJDySS6wjKB1DRvpv5/e3cfHXV153H8Mw+ZIYSQAAFCDA8RRAICW0AtHpQULErVxXK6YmVdiqd7VheUVN09WtuF3UXh2KO2FB9WoT5sTxfbAopVW1KEgLq4kYeaJoBgIkR5iEAIISSTzMzdP37klwwJDKIwd+D9Ouee5PdwMzfzJfDJ5f5+v/+QBg2SRv1NRA8/LK1d6zyYAACQvJhZBZB86uul5cvVuOTXWrUhUy/pB/qTblBUvnan3jC2Vn9cnyb5W+8ZW1vr3NAAAJAYLAMgrAIXj127pBdf1N6lb+vXB67XMt2uLRrlHn5Yj+mxzo9KV10ljRql8GX5Sp19l9LTpUtyPcrK8igry3kQQVqaFAhIwaDz8Yc/dCZ2W2zbJr33nuTzOetjfb6OW1qaNGFCAt4LAEgShFXCKnDxCYelP/9ZWrVK1as2qujzfK3WJN2p/9b1WuOe9oku1SB9ckZfsvxvH1J+5j5nwxg99/G3dM8HM+P269ftqHY/sdy5D21mptSnjwoXXap3P0hR//7SgAGtbfBgZ42tn+cJAriI8LhVABcfv1+68UbpxhvV62mj6eXlmr5mjfS/PaX3+0l79kiSQgpqgtZolwbpgHorpE6n/JLBVb+VVOluR9VFUvyw6qs5KN11V8y+XXpDm3SzNm1qf34gYHTZZVJ+vkff/a50xx1n9B0DwEWBsArgwuPxSMOGOe2++5x9+/ZJ5eUaun271ux4XaqslNl/QMf31epgdVQHm7uqUZ0UUlBNCiikoLK1P+bLfktr9YJ+qIh8isqriHwdtgzVthuSTxF5FelwXW1Tk0dlZc7NDvLK/qA7Kv8iDR0q5ecrmjdQU6elaOBAKT9fGjLE+dijxzl55wDAOiwDAADJWUbQ0OA8Vev4cec2Ah5P7Dltt41x+jQ3x7bjx6UjR5xWUyMdPizt3StVVam5ar8+/8zo0/Al2q3++kQDtV1DVK6h+liD1ayAfqWZmqmX3JfZ7R+oAeFd7Ybbs6dRfr5HgwZJ/fo5bepULhwDkBxYBgAAX5bfL6WnO+0cSZE0IBrVgAMHpJ07pR07pO0fSDteUXjbTlVUepRlqmP6bA93fNPYL77w6IsvpPXrW/fd0PwHZYzsKeXmStnZevZ5n154wbl4rEcPZ/lsamr7lpcnTZ4c+/VLSqRQqPVis1O1Tp2ci8oA4FxhZhUAbBEKOSG2vNy59UB5uUxZufZ+fEzbmgdqm/K1XUO0TfnapnztVx+3a4qa1KhO8urEX+k+nx4MLNITDf8c92WvH7xHRT96qzWBpqRo+L/eqL/uiT9N+7NHjujB2Y1SSooUCOjA4RR9Y2xQgYBHwaATdjMznWvNTm7Tpkm9erV+raYmZ8I6GPyybxyAZMPMKgAko2BQuuIKp53gkXRJOKxLKip0/YkAq/IXpW3bVFv+uXY39NQe9dMh9WgNqpIUiSjUEJFPYUXi/FUf+LhUuueemH1N2i4pflgNPPpv0qO/dLcb1U/7tPuMvt2CQ8vVa7jc9Po/a3L1g3/pqdRUo8xMj3szhZZw2/J5To50992xX6uiwlnF0blz64xx585OhgaQ3AirAGA7v9+5x9XgwdKUKe7ujGhUI6qqNKKyUvrsM6lqyImPVdLnn+uXh5/UokM/UW2dR4fUQ0fVVQ1KVYNSdVyd3c9PvpBMkv5RL+iAersXmzUp4La22/20J6afkUd9tcc93qBUNSq1w2+r27/PkfS5u12jOZJ+roYGjxoanGviOnJp9xrd7fltzDTtfQ8O0ZvFXdqd6/E4b5/f7yxXuPde6bHH2ozXOG+rz9d63smt5dijj0pjxrT2LS2Vnngitu+JCea2k9QKBJyHsHm9sX0rKk69vKKlX5cuzjIO4GJGWAWAZOX1Sv37O+0UPJIyw2Fl1tY6F3zV1jrLDUIhqbHRaaGQ1HC78//wJy4Ue7C5WWoKSc3HWi8ea3O89fMUqXmKuz2guVl7jn/PeZ0TLdQYVY26xbTD6q6e+iJmrL1UrWu1XjXqpiPKVI26qV7tA2i3w5+0m1o9rjWS2j+JwZjWIUtS8y+fk36z0H2CQ9QX0K5dfz2jt/vBY/OkS8rdJFm17xt6+U/3nVHf+7N/I3UKuk+d+NULQ/Xz3+fG7XfDDdIf/xi7b/hw51kYp1tLHAhIhYXOUosW1dXS7NntH2px8gMuvF7pJz+JXaJRUiK98Ub8fpmZ0p13xo63uNj5Haqjfi2fe71S377OnS7a+uAD56PX23Fr6ZuT4wT7FqGQdOiQ88tKvJaZGXvtZMsf7Y7O9Xrb78O5R1gFgAud3+9cYZWg+10FQyFl19Yqu02AVU2NVHPdiY9Ou+PIEd1R82jMvuaaYzoSTXfD6xFlKqCmdq9xk97UAH3abuY4pKCcxRB+heVX9rGd0rHWZQoRpaiHDioin5qV0ubc9usH/O8XS1rnbod1SFL8sBpQSJ6/nx6zr0mLJc2K3/dPb0idp8ldABwIqGH/BjU291dj4+n7fvHEK9Lb77iJru5Yb/3ud/PjvqYk3et7Rr161bp9P9z4Df3n8olx++X1OqY7O70dk0afeuxKvb4xO27fu793UM/+dG9Mii0YP0iNIW/cviuXhXTrrXL7btzoVUFB/O9TcpaPdGpzu+W5c6WFC+P3u+46J4i3dfXV0ubN8QPy3LnSgw+29jt82JnhP5Nw/eab0ogRrX1ff126//7T9/F6pe7dYy/IlKSf/lR6663W8556Sho37szet/OJsAoAOLeCQWearu1U3RlKMUY96+rUs02AddqdrbcHq6nRA8cPS41FrbPFHbVI5ETr7X4eCId1MNK/zbGIZIyMpKi8bshtVorSVB8ztgl6R9t1uXtOy3nNSolZNhHp4N66U/S6+qoq5ryO2ihtdtJUQ4PbN18fKU1H2p3b9nWbFVBKyXtSyctuv6gGSTqzsOr9+ROSKtztiGZJih9WvdX7pdtui9kX0SpJt8Tv+/tXpd/PjtkXVaOk+FfceW//O0lvuNtG49X2F4vT8fToLnlbp1JNaJ6kH8Xvt/F9Kfe2mFTYtO8thcPD4vZt/tlT0ktL3anaSKS7Dh1ae0bjDf/DXVL6Tvc1j1ZPUkXFI3H7ZQVqpZunx4y3cvOPtPmzAvec2ocWSLemxCZpCxBWAQD28nikrl2ddprlDl8rY+SJROQLh+WLRBSMRDpcBtGluVmXt91/8jKJpibn/6ObmqRQQevyi6YmTQqFNCnUKDUdbd3vnnvy51fEbL8RmtF6LBzu+FuQs364rQH6VLvVr90DLdput3x+SZu1xJITri/Xjrj9Tg70kvRP+i9N0upT9onKq6i8ulIl7frep0VqVop7TtvWtm9fVcX066FD+q5WyMgTt3mP10lqfR8HqUwTtOaU50fllZFHw5q2SJ/Hvk9DtVVeNcZ9zazqcqm6zO3nVXcN1K4zGm/gL/8nqbVvUL2VpS/i9ktrqnGmZdvwaar8am59L95bL/XqeI15InHrKgAAklUkEhuMWwJsJCJFo7EfO9p3umNf9nzbjhkTv53peYk+93yaOlVavvycvwy3rgIA4GLg87XeqwsXtnMZmNueb+GfJcIqAACA7Tyei/ZxcfEvsQMAAAAShLAKAAAAaxFWAQAAYC3CKgAAAKxFWAUAAIC1CKsAAACwFmEVAAAA1iKsAgAAwFqEVQAAAFiLsAoAAABrEVYBAABgLcIqAAAArEVYBQAAgLUIqwAAALAWYRUAAADWIqwCAADAWoRVAAAAWIuwCgAAAGsRVgEAAGAtwioAAACsRVgFAACAtQirAAAAsBZhFQAAANYirAIAAMBa/kQP4OtmjJEkHT16NMEjAQAAQEdaclpLbjudCy6s1tXVSZL69u2b4JEAAADgdOrq6pSRkXHaczzmTCJtEolGo9q7d6/S09Pl8XjOy2sePXpUffv2VVVVlbp27XpeXhNfH+qX/Khh8qOGyY8aJrfzXT9jjOrq6pSTkyOv9/SrUi+4mVWv16vc3NyEvHbXrl35AU1i1C/5UcPkRw2THzVMbuezfvFmVFtwgRUAAACsRVgFAACAtQirX4NgMKi5c+cqGAwmeig4C9Qv+VHD5EcNkx81TG421++Cu8AKAAAAFw5mVgEAAGAtwioAAACsRVgFAACAtQirAAAAsBZh9St65plnlJeXp06dOmn06NHasGFDooeEU1i/fr1uueUW5eTkyOPx6LXXXos5bozRvHnzlJOTo9TUVBUUFKisrCwxg0U7CxYs0JVXXqn09HT16tVLt956q3bs2BFzDjW027PPPqsRI0a4Nx0fO3as3n77bfc49UsuCxYskMfjUWFhobuPGtpt3rx58ng8MS07O9s9bmv9CKtfwauvvqrCwkI98sgj2rJli6699lpNnjxZe/bsSfTQ0IH6+nqNHDlSixcv7vD4448/rieffFKLFy9WSUmJsrOz9e1vf1t1dXXneaToSHFxsWbNmqWNGzeqqKhI4XBYkyZNUn19vXsONbRbbm6uFi5cqA8//FAffvihJkyYoClTprj/GFK/5FFSUqLnn39eI0aMiNlPDe03bNgw7du3z22lpaXuMWvrZ3DWrrrqKnP33XfH7BsyZIh56KGHEjQinClJZuXKle52NBo12dnZZuHChe6+xsZGk5GRYZ577rkEjBDxVFdXG0mmuLjYGEMNk1W3bt3MkiVLqF8SqaurM5dddpkpKioy48ePN3PmzDHG8DOYDObOnWtGjhzZ4TGb68fM6llqamrSpk2bNGnSpJj9kyZN0vvvv5+gUeFsVVZWav/+/TH1DAaDGj9+PPW0VG1trSSpe/fukqhhsolEIlq2bJnq6+s1duxY6pdEZs2apZtuuknXX399zH5qmBx27typnJwc5eXl6fbbb1dFRYUku+vnT+irJ7GDBw8qEomod+/eMft79+6t/fv3J2hUOFstNeuonrt3707EkHAaxhjdf//9GjdunK644gpJ1DBZlJaWauzYsWpsbFSXLl20cuVKDR061P3HkPrZbdmyZdq8ebNKSkraHeNn0H5XX321XnnlFQ0ePFgHDhzQ/Pnzdc0116isrMzq+hFWvyKPxxOzbYxptw/Jg3omh9mzZ+ujjz7Su+++2+4YNbTb5Zdfrq1bt+rIkSNavny5ZsyYoeLiYvc49bNXVVWV5syZo9WrV6tTp06nPI8a2mvy5Mnu58OHD9fYsWM1cOBAvfzyy/rmN78pyc76sQzgLGVlZcnn87WbRa2urm73Wwns13I1JPW037333qtVq1Zp7dq1ys3NdfdTw+QQCAQ0aNAgjRkzRgsWLNDIkSP1i1/8gvolgU2bNqm6ulqjR4+W3++X3+9XcXGxFi1aJL/f79aJGiaPtLQ0DR8+XDt37rT6Z5CwepYCgYBGjx6toqKimP1FRUW65pprEjQqnK28vDxlZ2fH1LOpqUnFxcXU0xLGGM2ePVsrVqzQO++8o7y8vJjj1DA5GWMUCoWoXxKYOHGiSktLtXXrVreNGTNG06dP19atW3XppZdSwyQTCoW0bds29enTx+6fwYRd2nUBWLZsmUlJSTFLly415eXlprCw0KSlpZlPP/000UNDB+rq6syWLVvMli1bjCTz5JNPmi1btpjdu3cbY4xZuHChycjIMCtWrDClpaXm+9//vunTp485evRogkcOY4y55557TEZGhlm3bp3Zt2+f244fP+6eQw3t9vDDD5v169ebyspK89FHH5kf//jHxuv1mtWrVxtjqF8yans3AGOooe0eeOABs27dOlNRUWE2btxobr75ZpOenu7mFlvrR1j9ip5++mnTv39/EwgEzKhRo9zb6MA+a9euNZLatRkzZhhjnNt2zJ0712RnZ5tgMGiuu+46U1pamthBw9VR7SSZF1980T2HGtrtrrvucv++7Nmzp5k4caIbVI2hfsno5LBKDe02bdo006dPH5OSkmJycnLM1KlTTVlZmXvc1vp5jDEmMXO6AAAAwOmxZhUAAADWIqwCAADAWoRVAAAAWIuwCgAAAGsRVgEAAGAtwioAAACsRVgFAACAtQirAAAAsBZhFQAAANbyJ3oAAID2tm7dqtdee83dLiwsVGZmZsLGAwCJwuNWAcBCL730kmbOnOluV1ZWasCAAYkbEAAkCMsAAAAAYC3CKgAAAKxFWAUAAIC1CKsAAACwFmEVAAAA1uJuAABgEY/H86X7rF27VgUFBV//YADAAsysAgAAwFo8FAAALOLz+SRJxhhFo9F2+ztyNrOxAJAsmFkFAIuEw2GFw2EtXbo0Zv+uXbvcYye38ePHJ2i0AHDuEVYBAABgLcIqAAAArEVYBQAAgLUIqwAAALAWYRUAAADWIqwCAADAWoRVAAAAWIuwCgAAAGsRVgEAAGAtwioAAACsRVgFAAulpKTEbEcikQSNBAASi7AKABZKT0+P2a6pqUnQSAAgsQirAGChAQMGxGyXlJQkZiAAkGAeY4xJ9CAAALHC4bCysrJUW1srScrJydGSJUtUUFCg1NTUBI8OAM4fZlYBwEJ+v18zZ850t/fu3avvfOc76ty5szp37qwuXbq4bcOGDQkcKQCcW4RVALDU/PnzNW7cuHb7GxoaVF9f7zYuvgJwISOsAoCl0tLStG7dOi1btky33XabBg8erPT0dHm9/NUN4OLBmlUAAABYi1/PAQAAYC3CKgAAAKxFWAUAAIC1CKsAAACwFmEVAAAA1iKsAgAAwFqEVQAAAFiLsAoAAABrEVYBAABgLcIqAAAArEVYBQAAgLUIqwAAALAWYRUAAADWIqwCAADAWoRVAAAAWIuwCgAAAGsRVgEAAGCt/wcqXWyHmdnUngAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (resultFit, P11p, \"b\", \"P11 Fit\"),\n", - " (resultFit, P12p, \"r\", \"P12 Fit\"),\n", - " (resultFit_2, P11p, \"r--\", \"P11 Built-in-Fit\"),\n", - " (resultFit_2, P12p, \"b--\", \"P12 Built-in-Fit\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "fa7e9764", - "metadata": {}, - "source": [ - "## A reaction coordinate approach" - ] - }, - { - "cell_type": "markdown", - "id": "da4b539f", - "metadata": {}, - "source": [ - "Here we construct a reaction coordinate inspired model to capture the\n", - "steady-state behavior, and compare to the HEOM prediction. This result is\n", - "more accurate for narrow spectral densities. We will use the population and\n", - "coherence from this cell in our final plot below." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "f8f73a68", - "metadata": {}, - "outputs": [], - "source": [ - "dot_energy, dot_state = Hsys.eigenstates()\n", - "deltaE = dot_energy[1] - dot_energy[0]\n", - "\n", - "gamma2 = deltaE / (2 * np.pi * gamma)\n", - "wa = 2 * np.pi * gamma2 * gamma # reaction coordinate frequency\n", - "g = np.sqrt(np.pi * wa * lam / 2.0) # reaction coordinate coupling\n", - "# reaction coordinate coupling factor over 2 because of diff in J(w)\n", - "# (it is 2 lam now):\n", - "g = np.sqrt(\n", - " np.pi * wa * lam / 4.0\n", - ") #\n", - "\n", - "NRC = 10\n", - "\n", - "Hsys_exp = tensor(qeye(NRC), Hsys)\n", - "Q_exp = tensor(qeye(NRC), Q)\n", - "a = tensor(destroy(NRC), qeye(2))\n", - "\n", - "H0 = wa * a.dag() * a + Hsys_exp\n", - "# interaction\n", - "H1 = g * (a.dag() + a) * Q_exp\n", - "\n", - "H = H0 + H1\n", - "\n", - "energies, states = H.eigenstates()\n", - "rhoss = 0 * states[0] * states[0].dag()\n", - "for kk, energ in enumerate(energies):\n", - " rhoss += states[kk] * states[kk].dag() * np.exp(-beta * energies[kk])\n", - "\n", - "rhoss = rhoss / rhoss.norm()\n", - "\n", - "\n", - "class ReactionCoordinateResult:\n", - " def __init__(self, states, times):\n", - " self.states = states\n", - " self.times = times\n", - "\n", - "\n", - "resultRC = ReactionCoordinateResult([rhoss] * len(tlist), tlist)\n", - "\n", - "P12RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 1).dag())\n", - "P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag())" - ] - }, - { - "cell_type": "markdown", - "id": "81955bbf", - "metadata": {}, - "source": [ - "## Let's plot all our results\n", - "\n", - "Finally, let's plot all of our different results to see how they shape up against each other." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "c45ce240", - "metadata": {}, - "outputs": [], - "source": [ - "rcParams = {\n", - " \"axes.titlesize\": 25,\n", - " \"axes.labelsize\": 30,\n", - " \"xtick.labelsize\": 28,\n", - " \"ytick.labelsize\": 28,\n", - " \"legend.fontsize\": 28,\n", - " \"axes.grid\": False,\n", - " \"savefig.bbox\": \"tight\",\n", - " \"lines.markersize\": 5,\n", - " \"font.family\": \"STIXgeneral\",\n", - " \"mathtext.fontset\": \"stix\",\n", - " \"font.serif\": \"STIX\",\n", - " \"text.usetex\": False,\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "4189a448", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 15))\n", - "\n", - "with plt.rc_context(rcParams):\n", - "\n", - " plt.sca(axes[0])\n", - " plt.yticks([expect(P11RC, resultRC.states[0]), 0.6, 1.0], [0.32, 0.6, 1])\n", - " plot_result_expectations(\n", - " [\n", - " (resultBR, P11p, \"y-.\", \"Bloch-Redfield\"),\n", - " (resultMats, P11p, \"b\", \"Matsubara $N_k=2$\"),\n", - " (\n", - " resultMatsT,\n", - " P11p,\n", - " \"g--\",\n", - " \"Matsubara $N_k=2$ & Terminator\",\n", - " {\"linewidth\": 3},\n", - " ),\n", - " (\n", - " resultFit,\n", - " P11p,\n", - " \"r\",\n", - " r\"Fit $N_f = 4$, $N_k=15\\times 10^3$\",\n", - " {\"dashes\": [3, 2]},\n", - " ),\n", - " (\n", - " resultRC,\n", - " P11RC,\n", - " \"--\", \"Thermal\",\n", - " {\"linewidth\": 2, \"color\": \"black\"},\n", - " ),\n", - " ],\n", - " axes=axes[0],\n", - " )\n", - " axes[0].set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", - " axes[0].legend(loc=0)\n", - " axes[0].text(5, 0.9, \"(a)\", fontsize=30)\n", - " axes[0].set_xlim(0, 50)\n", - "\n", - " plt.sca(axes[1])\n", - " plt.yticks(\n", - " [np.real(expect(P12RC, resultRC.states[0])), -0.2, 0.0, 0.2],\n", - " [-0.33, -0.2, 0, 0.2],\n", - " )\n", - " plot_result_expectations(\n", - " [\n", - " (resultBR, P12p, \"y-.\", \"Bloch-Redfield\"),\n", - " (resultMats, P12p, \"b\", \"Matsubara $N_k=2$\"),\n", - " (\n", - " resultMatsT,\n", - " P12p,\n", - " \"g--\",\n", - " \"Matsubara $N_k=2$ & Terminator\",\n", - " {\"linewidth\": 3},\n", - " ),\n", - " (\n", - " resultFit,\n", - " P12p,\n", - " \"r\",\n", - " r\"Fit $N_f = 4$, $N_k=15\\times 10^3$\",\n", - " {\"dashes\": [3, 2]},\n", - " ),\n", - " (\n", - " resultRC,\n", - " P12RC,\n", - " \"--\",\n", - " \"Thermal\",\n", - " {\"linewidth\": 2, \"color\": \"black\"},\n", - " ),\n", - " ],\n", - " axes=axes[1],\n", - " )\n", - " axes[1].text(5, 0.1, \"(b)\", fontsize=30)\n", - " axes[1].set_xlabel(r\"$t \\Delta$\", fontsize=30)\n", - " axes[1].set_ylabel(r\"$\\rho_{01}$\", fontsize=30)\n", - " axes[1].set_xlim(0, 50)" - ] - }, - { - "cell_type": "markdown", - "id": "d087bf9f", - "metadata": {}, - "source": [ - "And that's the end of a detailed first dive into modeling bosonic environments with the HEOM." - ] - }, - { - "cell_type": "markdown", - "id": "cb5966e6", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "f415b943", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "QuTiP: Quantum Toolbox in Python\n", - "================================\n", - "Copyright (c) QuTiP team 2011 and later.\n", - "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", - "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", - "Original developers: R. J. Johansson & P. D. Nation.\n", - "Previous lead developers: Chris Granade & A. Grimsmo.\n", - "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", - "\n", - "QuTiP Version: 5.1.0.dev0+7941773\n", - "Numpy Version: 2.1.3\n", - "Scipy Version: 1.14.1\n", - "Cython Version: 3.0.9\n", - "Matplotlib Version: 3.9.2\n", - "Python Version: 3.12.7\n", - "Number of CPUs: 16\n", - "BLAS Info: Generic\n", - "INTEL MKL Ext: None\n", - "Platform Info: Linux (x86_64)\n", - "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", - "================================================================================\n", - "Please cite QuTiP in your publication.\n", - "================================================================================\n", - "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" - ] - } - ], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "2555e881", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "7d97aa9c", - "metadata": {}, - "outputs": [], - "source": [ - "# Check P11p\n", - "assert np.allclose(\n", - " expect(P11p, resultMatsT.states),\n", - " expect(P11p, resultPade.states),\n", - " rtol=1e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(P11p, resultMatsT.states),\n", - " expect(P11p, resultFit.states),\n", - " rtol=1e-2,\n", - ")\n", - "\n", - "# Check P12p\n", - "assert np.allclose(\n", - " expect(P12p, resultMatsT.states),\n", - " expect(P12p, resultPade.states),\n", - " rtol=1e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(P12p, resultMatsT.states),\n", - " expect(P12p, resultFit.states),\n", - " rtol=1e-1,\n", - ")" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter" - }, - "kernelspec": { - "display_name": "qutip-dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md new file mode 100644 index 00000000..82a1145d --- /dev/null +++ b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md @@ -0,0 +1,1190 @@ +--- +jupyter: + jupytext: + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.16.4 + kernelspec: + display_name: qutip-dev + language: python + name: python3 +--- + +# HEOM 1a: Spin-Bath model (introduction) + + +## Introduction + +The HEOM method solves the dynamics and steady state of a system and its +environment, the latter of which is encoded in a set of auxiliary density +matrices. + +In this example we show the evolution of a single two-level system in contact +with a single Bosonic environment. The properties of the system are encoded +in a Hamiltonian, and a coupling operator which describes how it is coupled +to the environment. + +The Bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions. + +In the example below we show how to model the overdamped Drude-Lorentz +Spectral Density, commonly used with the HEOM. We show how to do this using +the Matsubara, Pade and fitting decompositions, and compare their +convergence. + +### Drude-Lorentz (overdamped) spectral density + +The Drude-Lorentz spectral density is: + +$$J_D(\omega)= \frac{2\omega\lambda\gamma}{{\gamma}^2 + \omega^2}$$ + +where $\lambda$ scales the coupling strength, and $\gamma$ is the cut-off +frequency. We use the convention, +\begin{equation*} +C(t) = \int_0^{\infty} d\omega \frac{J_D(\omega)}{\pi}[\coth(\beta\omega) \ + \cos(\omega \tau) - i \sin(\omega \tau)] +\end{equation*} + +With the HEOM we must use an exponential decomposition: + +\begin{equation*} +C(t)=\sum_{k=0}^{k=\infty} c_k e^{-\nu_k t} +\end{equation*} + +As an example, the Matsubara decomposition of the Drude-Lorentz spectral +density is given by: + +\begin{equation*} + \nu_k = \begin{cases} + \gamma & k = 0\\ + {2 \pi k} / {\beta } & k \geq 1\\ + \end{cases} +\end{equation*} + +\begin{equation*} + c_k = \begin{cases} + \lambda \gamma (\cot(\beta \gamma / 2) - i) \ + & k = 0\\ + 4 \lambda \gamma \nu_k / \{(\nu_k^2 - \gamma^2)\beta \} \ + & k \geq 1\\ + \end{cases} +\end{equation*} + +Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. + + +## Setup + +```python +import contextlib +import time + +import numpy as np +from matplotlib import pyplot as plt +from scipy.optimize import curve_fit + +import qutip +from qutip import ( + basis, + brmesolve, + destroy, + expect, + liouvillian, + qeye, + sigmax, + sigmaz, + spost, + spre, + tensor, +) +from qutip.core.environment import ( + DrudeLorentzEnvironment, + ExponentialBosonicEnvironment, + system_terminator +) +from qutip.solver.heom import ( + HEOMSolver, + HSolverDL, +) + +%matplotlib inline +``` + + +## Helper functions + +Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: + +```python +def cot(x): + """Vectorized cotangent of x.""" + return 1.0 / np.tan(x) +``` + +```python +def dl_matsubara_params(lam, gamma, T, nk): + """Calculation of the real and imaginary expansions of the Drude-Lorenz + correlation functions. + """ + ckAR = [lam * gamma * cot(gamma / (2 * T))] + ckAR.extend( + (8 * lam * gamma * T * np.pi * k * T / + ((2 * np.pi * k * T)**2 - gamma**2)) + for k in range(1, nk + 1) + ) + vkAR = [gamma] + vkAR.extend(2 * np.pi * k * T for k in range(1, nk + 1)) + + ckAI = [lam * gamma * (-1.0)] + vkAI = [gamma] + + return ckAR, vkAR, ckAI, vkAI +``` + +```python +def plot_result_expectations(plots, axes=None): + """Plot the expectation values of operators as functions of time. + + Each plot in plots consists of (solver_result, measurement_operation, + color, label). + """ + if axes is None: + fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) + fig_created = True + else: + fig = None + fig_created = False + + # add kw arguments to each plot if missing + plots = [p if len(p) == 5 else p + ({},) for p in plots] + for result, m_op, color, label, kw in plots: + exp = np.real(expect(result.states, m_op)) + kw.setdefault("linewidth", 2) + axes.plot(result.times, exp, color, label=label, **kw) + + if fig_created: + axes.legend(loc=0, fontsize=12) + axes.set_xlabel("t", fontsize=28) + + return fig +``` + +```python +@contextlib.contextmanager +def timer(label): + """Simple utility for timing functions: + + with timer("name"): + ... code to time ... + """ + start = time.time() + yield + end = time.time() + print(f"{label}: {end - start}") +``` + +```python +# Default solver options: + +default_options = { + "nsteps": 1500, + "store_states": True, + "rtol": 1e-12, + "atol": 1e-12, + "method": "vern9", + "progress_bar": "enhanced", +} +``` + +## System and bath definition + +And let us set up the system Hamiltonian, bath and system measurement operators: + +```python +# Defining the system Hamiltonian +eps = 0.5 # Energy of the 2-level system. +Del = 1.0 # Tunnelling term +Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() +``` + +```python +# Initial state of the system. +rho0 = basis(2, 0) * basis(2, 0).dag() +``` + +```python +# System-bath coupling (Drude-Lorentz spectral density) +Q = sigmaz() # coupling operator + +# Bath properties: +gamma = 0.5 # cut off frequency +lam = 0.1 # coupling strength +T = 0.5 +beta = 1.0 / T + +# HEOM parameters +NC = 5 # cut off parameter for the bath +Nk = 2 # terms in the Matsubara expansion of the correlation function + +# Times to solve for +tlist = np.linspace(0, 50, 1000) +``` + +```python +# Define some operators with which we will measure the system +# 1,1 element of density matrix - corresonding to groundstate +P11p = basis(2, 0) * basis(2, 0).dag() +P22p = basis(2, 1) * basis(2, 1).dag() +# 1,2 element of density matrix - corresonding to coherence +P12p = basis(2, 0) * basis(2, 1).dag() +``` + +### First of all, it is useful to look at the spectral density + +Now we are ready to begin. Let's look at the shape of the spectral density given the bath parameters we defined above: + +```python +def plot_spectral_density(): + """Plot the Drude-Lorentz spectral density""" + w = np.linspace(0, 5, 1000) + J = w * 2 * lam * gamma / (gamma**2 + w**2) + + fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) + axes.plot(w, J, "r", linewidth=2) + axes.set_xlabel(r"$\omega$", fontsize=28) + axes.set_ylabel(r"J", fontsize=28) + + +plot_spectral_density() +``` + +Next we calculate the exponents using the Matsubara decompositions. Here we +split them into real and imaginary parts. + +The HEOM code will optimize these, and reduce the number of exponents when +real and imaginary parts have the same exponent. This is clearly the case +for the first term in the vkAI and vkAR lists. + +```python +ckAR, vkAR, ckAI, vkAI = dl_matsubara_params(nk=Nk, lam=lam, gamma=gamma, T=T) +``` + +Having created the lists which specify the bath correlation functions, we +create an `ExponentialBosonicEnvironment` from them and pass the environment to the `HEOMSolver` class. + +The solver constructs the "right hand side" (RHS) determinining how the +system and auxiliary density operators evolve in time. This can then be used +to solve for dynamics or steady-state. + +Below we create the bath and solver and then solve for the dynamics by +calling `.run(rho0, tlist)`. + +```python +options = {**default_options} + +with timer("RHS construction time"): + env = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI) + HEOMMats = HEOMSolver(Hsys, (env,Q), NC, options=options) + +with timer("ODE solver time"): + resultMats = HEOMMats.run(rho0, tlist) +``` + +```python +plot_result_expectations( + [ + (resultMats, P11p, "b", "P11 Mats"), + (resultMats, P12p, "r", "P12 Mats"), + ] +); +``` + +In practice, one would not perform this laborious expansion for the +Drude-Lorentz correlation function, because QuTiP already has a class, +`DrudeLorentzBath`, that can construct this bath for you. Nevertheless, +knowing how to perform this expansion will allow you to construct your own +baths for other spectral densities. + +Below we show how to use this built-in functionality: + +```python +# Compare to built-in Drude-Lorentz bath: + +with timer("RHS construction time"): + dlenv = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T,Nk=100) + dlenv_approx=dlenv.approx_by_matsubara(Nk=Nk) + HEOM_dlbath = HEOMSolver(Hsys, (dlenv_approx,Q), NC, options=options) + +with timer("ODE solver time"): + result_dlbath = HEOM_dlbath.run(rho0, tlist) # normal 115 +``` + +```python +plot_result_expectations( + [ + (result_dlbath, P11p, "b", "P11 (DrudeLorentzBath)"), + (result_dlbath, P12p, "r", "P12 (DrudeLorentzBath)"), + ] +); +``` + +The `DrudeLorentzEnvironment` class also allows us to easily obtain the power spectrum, correlation function, and spectral density. The approximated Environment is a `BosonicEnvironment` where the effective power spectrum, correlation function, and spectral density from the approximation is also accessible. In the following plots, the solid lines are the exact expressions, and the dashed lines are based on our approximation of the correlation function with a finite number of exponents. + +```python +w = np.linspace(-10, 20, 1000) +w2 = np.linspace(0, 20, 1000) +fig, axs = plt.subplots(2, 2) + +axs[0, 0].plot(w, dlenv.power_spectrum(w)) +axs[0, 0].plot(w, dlenv_approx.power_spectrum(w), '--') +axs[0, 0].set(xlabel=r'$\omega$', ylabel=r'$S(\omega)$') +axs[0, 1].plot(w2, dlenv.spectral_density(w2)) +axs[0, 1].plot(w2, dlenv_approx.spectral_density(w2), '--') +axs[0, 1].set(xlabel=r'$\omega$', ylabel=r'$J(\omega)$') +axs[1, 0].plot(w2, np.real(dlenv.correlation_function(w2))) +axs[1, 0].plot(w2, np.real(dlenv_approx.correlation_function(w2)), '--') +axs[1, 0].set(xlabel=r'$t$', ylabel=r'$C_{R}(t)$') +axs[1, 1].plot(w2, np.imag(dlenv.correlation_function(w2))) +axs[1, 1].plot(w2, np.imag(dlenv_approx.correlation_function(w2)), '--') +axs[1, 1].set(xlabel=r'$t$', ylabel=r'$C_{I}(t)$') + +fig.tight_layout() +plt.show() +``` + +We also provide a legacy class, `HSolverDL`, which calculates the +Drude-Lorentz correlation functions automatically, to be backwards +compatible with the previous HEOM solver in QuTiP: + +```python +# Compare to legacy class: + +# The legacy class performs the above collation of coefficients automatically, +# based upon the parameters for the Drude-Lorentz spectral density. + +with timer("RHS construction time"): + HEOMlegacy = HSolverDL(Hsys, Q, lam, T, NC, Nk, gamma, options=options) + +with timer("ODE solver time"): + resultLegacy = HEOMlegacy.run(rho0, tlist) # normal 115 +``` + +```python +plot_result_expectations( + [ + (resultLegacy, P11p, "b", "P11 Legacy"), + (resultLegacy, P12p, "r", "P12 Legacy"), + ] +); +``` + +## Ishizaki-Tanimura Terminator + +To speed up convergence (in terms of the number of exponents kept in the +Matsubara decomposition), We can treat the $Re[C(t=0)]$ component as a +delta-function distribution, and include it as Lindblad correction. This is +sometimes known as the Ishizaki-Tanimura Terminator. + +In more detail, given + +\begin{equation*} +C(t)=\sum_{k=0}^{\infty} c_k e^{-\nu_k t} +\end{equation*} + +since $\nu_k=\frac{2 \pi k}{\beta }$, if $1/\nu_k$ is much much smaller than +other important time-scales, we can approximate, +$ e^{-\nu_k t} \approx \delta(t)/\nu_k$, and $C(t)=\sum_{k=N_k}^{\infty} +\frac{c_k}{\nu_k} \delta(t)$ + +It is convenient to calculate the whole sum +$ C(t)=\sum_{k=0}^{\infty} \frac{c_k}{\nu_k} = 2 \lambda / (\beta \gamma)- i\lambda $ +, and subtract off the contribution from the finite number of Matsubara terms +that are kept in the hierarchy, and treat the residual as a contribution in +Lindblad form. + +This is clearer if we plot the correlation function with a large number of +Matsubara terms. To create the plot, we use the utility function of the +`DrudeLorentzBath` class mentioned above. + +```python +def plot_correlation_expansion_divergence(): + """We plot the correlation function with a large number of Matsubara terms + to show that the real part is slowly diverging at t = 0. + """ + t = np.linspace(0, 2, 100) + + # correlation coefficients with 15k and 2 terms + corr_15k = dlenv.correlation_function(t) + corr_2 = dlenv_approx.correlation_function(t) + + fig, ax1 = plt.subplots(figsize=(12, 7)) + + ax1.plot( + t, np.real(corr_2), color="b", linewidth=3, label=rf"Mats = {Nk} real" + ) + ax1.plot( + t, np.imag(corr_2), color="r", linewidth=3, label=rf"Mats = {Nk} imag" + ) + ax1.plot( + t, np.real(corr_15k), "b--", linewidth=3, label=r"Mats = 15000 real" + ) + ax1.plot( + t, np.imag(corr_15k), "r--", linewidth=3, label=r"Mats = 15000 imag" + ) + + ax1.set_xlabel("t") + ax1.set_ylabel(r"$C$") + ax1.legend() + +plot_correlation_expansion_divergence() +``` + +Let us evaluate the result including this Ishizaki-Tanimura terminator: + +```python +# Run HEOM solver and include the Ishizaki-Tanimura terminator + +# Notes: +# +# * when using the built-in DrudeLorentzEnvironment the terminator (L_bnd) is +# available from bath.terminator(). +# +# * in the legacy HSolverDL function the terminator is included automatically +# if the parameter bnd_cut_approx=True is used. + +op = -2 * spre(Q) * spost(Q.dag()) + spre(Q.dag() * Q) + spost(Q.dag() * Q) + +approx_factr = (2 * lam / (beta * gamma)) - 1j * lam + +approx_factr -= lam * gamma * (-1.0j + cot(gamma / (2 * T))) / gamma +for k in range(1, Nk + 1): + vk = 2 * np.pi * k * T + + approx_factr -= (4 * lam * gamma * T * vk / (vk**2 - gamma**2)) / vk + +L_bnd = -approx_factr * op + +Ltot = -1.0j * (spre(Hsys) - spost(Hsys)) + L_bnd +Ltot = liouvillian(Hsys) + L_bnd + +options = {**default_options, "rtol": 1e-14, "atol": 1e-14} + +with timer("RHS construction time"): + bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI) + HEOMMatsT = HEOMSolver(Ltot, (bath,Q), NC, options=options) + +with timer("ODE solver time"): + resultMatsT = HEOMMatsT.run(rho0, tlist) +``` + +```python +plot_result_expectations( + [ + (resultMatsT, P11p, "b", "P11 Mats + Term"), + (resultMatsT, P12p, "r", "P12 Mats + Term"), + ] +); +``` + +Or using the built-in Drude-Lorentz environment we can write simply: + +```python +options = {**default_options, "rtol": 1e-14, "atol": 1e-14} + +with timer("RHS construction time"): + bath,delta = dlenv.approx_by_matsubara(Nk=Nk,compute_delta=True) + Ltot = liouvillian(Hsys) + system_terminator(Q,delta) + HEOM_dlbath_T = HEOMSolver(Ltot, (bath,Q), NC, options=options) + +with timer("ODE solver time"): + result_dlbath_T = HEOM_dlbath_T.run(rho0, tlist) +``` + +```python +plot_result_expectations( + [ + (result_dlbath_T, P11p, "b", "P11 Mats (DrudeLorentzBath + Term)"), + (result_dlbath_T, P12p, "r", "P12 Mats (DrudeLorentzBath + Term)"), + ] +); +``` + +We can compare the solution obtained from the QuTiP Bloch-Redfield solver: + +```python +options = {**default_options} + +with timer("ODE solver time"): + resultBR = brmesolve( + Hsys, rho0, tlist, a_ops=[[sigmaz(), dlenv]], options=options + ) +``` + +```python +plot_result_expectations( + [ + (resultMats, P11p, "b", "P11 Mats"), + (resultMats, P12p, "r", "P12 Mats"), + (resultMatsT, P11p, "b--", "P11 Mats + Term"), + (resultMatsT, P12p, "r--", "P12 Mats + Term"), + (resultBR, P11p, "g--", "P11 Bloch Redfield"), + (resultBR, P12p, "g--", "P12 Bloch Redfield"), + ] +); +``` + +## Padé decomposition + + +The Matsubara decomposition is not the only option. We can also use the +faster-converging Pade decomposition. + +```python +def deltafun(j, k): + if j == k: + return 1.0 + else: + return 0.0 + + +def pade_eps(lmax): + Alpha = np.zeros((2 * lmax, 2 * lmax)) + for j in range(2 * lmax): + for k in range(2 * lmax): + # Fermionic (see other example notebooks): + # Alpha[j][k] = (deltafun(j, k+1) + deltafun(j, k-1)) + # / sqrt((2 * (j + 1) - 1) * (2 * (k + 1) - 1)) + # Bosonic: + Alpha[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1)) / np.sqrt( + (2 * (j + 1) + 1) * (2 * (k + 1) + 1) + ) + + eigvalsA = np.linalg.eigvalsh(Alpha) + eps = [-2 / val for val in eigvalsA[0:lmax]] + return eps + + +def pade_chi(lmax): + AlphaP = np.zeros((2 * lmax - 1, 2 * lmax - 1)) + for j in range(2 * lmax - 1): + for k in range(2 * lmax - 1): + # Fermionic: + # AlphaP[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1)) + # / sqrt((2 * (j + 1) + 1) * (2 * (k + 1) + 1)) + # Bosonic [this is +3 because +1 (bose) + 2*(+1) (from bm+1)]: + AlphaP[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1)) / np.sqrt( + (2 * (j + 1) + 3) * (2 * (k + 1) + 3) + ) + + eigvalsAP = np.linalg.eigvalsh(AlphaP) + chi = [-2 / val for val in eigvalsAP[0:lmax - 1]] + return chi + + +def pade_kappa_epsilon(lmax): + eps = pade_eps(lmax) + chi = pade_chi(lmax) + + kappa = [0] + prefactor = 0.5 * lmax * (2 * (lmax + 1) + 1) + + for j in range(lmax): + term = prefactor + for k in range(lmax - 1): + term *= (chi[k] ** 2 - eps[j] ** 2) / ( + eps[k] ** 2 - eps[j] ** 2 + deltafun(j, k) + ) + + for k in range(lmax - 1, lmax): + term /= eps[k] ** 2 - eps[j] ** 2 + deltafun(j, k) + + kappa.append(term) + + epsilon = [0] + eps + + return kappa, epsilon + + +def pade_corr(tlist, lmax): + kappa, epsilon = pade_kappa_epsilon(lmax) + + eta_list = [lam * gamma * (cot(gamma * beta / 2.0) - 1.0j)] + gamma_list = [gamma] + + if lmax > 0: + for ll in range(1, lmax + 1): + eta_list.append( + (kappa[ll] / beta) + * 4 + * lam + * gamma + * (epsilon[ll] / beta) + / ((epsilon[ll] ** 2 / beta**2) - gamma**2) + ) + gamma_list.append(epsilon[ll] / beta) + + c_tot = [] + for t in tlist: + c_tot.append( + sum( + [ + eta_list[ll] * np.exp(-gamma_list[ll] * t) + for ll in range(lmax + 1) + ] + ) + ) + return c_tot, eta_list, gamma_list + + +tlist_corr = np.linspace(0, 2, 100) +cppLP, etapLP, gampLP = pade_corr(tlist_corr, 2) +corr_15k = dlenv.correlation_function(tlist_corr, Nk=15_000) +corr_2k = dlenv.correlation_function(tlist_corr, Nk=2) + +fig, ax1 = plt.subplots(figsize=(12, 7)) +ax1.plot( + tlist_corr, + np.real(cppLP), + color="b", + linewidth=3, + label=r"real pade 2 terms", +) +ax1.plot( + tlist_corr, + np.real(corr_15k), + "r--", + linewidth=3, + label=r"real mats 15000 terms", +) +ax1.plot( + tlist_corr, + np.real(corr_2k), + "g--", + linewidth=3, + label=r"real mats 2 terms", +) + +ax1.set_xlabel("t") +ax1.set_ylabel(r"$C_{R}(t)$") +ax1.legend() + +fig, ax1 = plt.subplots(figsize=(12, 7)) + +ax1.plot( + tlist_corr, + np.real(cppLP) - np.real(corr_15k), + color="b", + linewidth=3, + label=r"pade error", +) +ax1.plot( + tlist_corr, + np.real(corr_2k) - np.real(corr_15k), + "r--", + linewidth=3, + label=r"mats error", +) + +ax1.set_xlabel("t") +ax1.set_ylabel(r"Error") +ax1.legend(); +``` + +```python +# put pade parameters in lists for heom solver +ckAR = [np.real(eta) + 0j for eta in etapLP] +ckAI = [np.imag(etapLP[0]) + 0j] +vkAR = [gam + 0j for gam in gampLP] +vkAI = [gampLP[0] + 0j] + +options = {**default_options, "rtol": 1e-14, "atol": 1e-14} + +with timer("RHS construction time"): + bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI) + HEOMPade = HEOMSolver(Hsys, (bath,Q), NC, options=options) + +with timer("ODE solver time"): + resultPade = HEOMPade.run(rho0, tlist) +``` + +```python +plot_result_expectations( + [ + (resultMats, P11p, "b", "P11 Mats"), + (resultMats, P12p, "r", "P12 Mats"), + (resultMatsT, P11p, "y", "P11 Mats + Term"), + (resultMatsT, P12p, "g", "P12 Mats + Term"), + (resultPade, P11p, "b--", "P11 Pade"), + (resultPade, P12p, "r--", "P12 Pade"), + ] +); +``` + +The Padé decomposition of the Drude-Lorentz bath is also available via a +built-in class, `DrudeLorentzEnvironment` bath. Similarly to the terminator +section when approximating by Padé one can calculate the terminator easily by +requesting the approximation function to compute delta + +Below we show how to use the built-in Drude-Lorentz Environment to obtain a +Padé decomposition approximation and its terminator (although the terminator +does not provide much improvement here,because the Padé expansion already fits +the correlation function well): + +```python +options = {**default_options, "rtol": 1e-14, "atol": 1e-14} + +with timer("RHS construction time"): + env_approx,delta = dlenv.approx_by_pade(Nk=2,compute_delta=True) + Ltot = liouvillian(Hsys) + system_terminator(Q,delta) + HEOM_dlpbath_T = HEOMSolver(Ltot, (env_approx,Q), NC, options=options) + +with timer("ODE solver time"): + result_dlpbath_T = HEOM_dlpbath_T.run(rho0, tlist) +``` + +```python +plot_result_expectations( + [ + (result_dlpbath_T, P11p, "b", "P11 Padé (DrudeLorentzBath + Term)"), + (result_dlpbath_T, P12p, "r", "P12 Padé (DrudeLorentzBath + Term)"), + ] +); +``` + +### Next we compare the Matsubara and Pade correlation function fits + +Fitting the correlation function is not efficient for this example, but +can be extremely useful in situations where large number of exponents +are needed (e.g., near zero temperature). We will perform the fitting +manually below, and then show how to do it with the built-in tools + +For the manual fit we First we collect a large sum of Matsubara terms for +many time steps: + +```python +tlist2 = np.linspace(0, 2, 10000) + +corr_15k_t10k = dlenv.correlation_function(tlist2, Nk=15_000) + +corrRana = np.real(corr_15k_t10k) +corrIana = np.imag(corr_15k_t10k) +``` + +We then fit this sum with standard least-squares approach: + +```python +def wrapper_fit_func(x, N, args): + """ Fit function wrapper that unpacks its arguments. """ + x = np.array(x) + a = np.array(args[:N]) + b = np.array(args[N:2 * N]) + return fit_func(x, a, b) + + +def fit_func(x, a, b): + """ Fit function. Calculates the value of the + correlation function at each x, given the + fit parameters in a and b. + """ + return np.sum( + a[:, None] * np.exp(np.multiply.outer(b, x)), + axis=0, + ) + + +def fitter(ans, tlist, k): + """ Compute fit with k exponents. """ + upper_a = abs(max(ans, key=abs)) * 10 + # sets initial guesses: + guess = ( + [upper_a / k] * k + # guesses for a + [0] * k # guesses for b + ) + # sets lower bounds: + b_lower = ( + [-upper_a] * k + # lower bounds for a + [-np.inf] * k # lower bounds for b + ) + # sets higher bounds: + b_higher = ( + [upper_a] * k + # upper bounds for a + [0] * k # upper bounds for b + ) + param_bounds = (b_lower, b_higher) + p1, p2 = curve_fit( + lambda x, *params_0: wrapper_fit_func(x, k, params_0), + tlist, + ans, + p0=guess, + sigma=[0.01 for t in tlist], + bounds=param_bounds, + maxfev=1e8, + ) + a, b = p1[:k], p1[k:] + return (a, b) +``` + +```python +kR = 4 # number of exponents to use for real part +poptR = [] +with timer("Correlation (real) fitting time"): + for i in range(kR): + poptR.append(fitter(corrRana, tlist2, i + 1)) + +corrRMats = np.real(dlenv_approx.correlation_function(tlist2)) + +kI = 1 # number of exponents for imaginary part +poptI = [] +with timer("Correlation (imaginary) fitting time"): + for i in range(kI): + poptI.append(fitter(corrIana, tlist2, i + 1)) +``` + +And plot the results of the fits: + +```python +# Define line styles and colors +linestyles = ["-", "--", "-.", ":", (0, (3, 1, 1, 1)), (0, (5, 1))] +colors = ["blue", "green", "purple", "orange", "red", "brown", "cyan", "magenta"] + +# Define a larger linewidth +linewidth = 2.5 + +# Create a single figure with two subplots +fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6)) + +# Plot the real part on the first subplot (ax1) +ax1.plot(tlist2, corrRana, label="Analytic", color=colors[0], linestyle=linestyles[0], linewidth=linewidth) +ax1.plot(tlist2, corrRMats, label="Matsubara", color=colors[1], linestyle=linestyles[1], linewidth=linewidth) + +for i in range(kR): + y = fit_func(tlist2, *poptR[i]) + ax1.plot(tlist2, y, label=f"Fit with {i} terms", color=colors[(i + 2) % len(colors)], linestyle=linestyles[(i + 2) % len(linestyles)], linewidth=linewidth) +ax1.set_ylabel(r"$C_{R}(t)$") +ax1.set_xlabel(r"$t$") +ax1.legend() + +# Plot the imaginary part on the second subplot (ax2) +ax2.plot(tlist2, corrIana, label="Analytic", color=colors[0], linestyle=linestyles[0], linewidth=linewidth) + +for i in range(kI): + y = fit_func(tlist2, *poptI[i]) + ax2.plot(tlist2, y, label=f"Fit with {i} terms", color=colors[(i + 3) % len(colors)], linestyle=linestyles[(i + 1) % len(linestyles)], linewidth=linewidth) +ax2.set_ylabel(r"$C_{I}(t)$") +ax2.set_xlabel(r"$t$") + +ax2.legend() + +# Add overall plot title and show the figure +fig.suptitle("Comparison of Analytic and Fit to Correlations (Real and Imaginary Parts)", fontsize=16) +plt.show() + + +``` + +```python +# Set the exponential coefficients from the fit parameters + +ckAR1 = poptR[-1][0] +ckAR = [x + 0j for x in ckAR1] + +vkAR1 = poptR[-1][1] +vkAR = [-x + 0j for x in vkAR1] + +ckAI1 = poptI[-1][0] +ckAI = [x + 0j for x in ckAI1] + +vkAI1 = poptI[-1][1] +vkAI = [-x + 0j for x in vkAI1] +``` + +```python +# overwrite imaginary fit with analytical value (not much reason to use the +# fit for this) + +ckAI = [lam * gamma * (-1.0) + 0.0j] +vkAI = [gamma + 0.0j] +``` + +```python +options = {**default_options} + +NC = 4 + +with timer("RHS construction time"): + bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI) + HEOMFit = HEOMSolver(Hsys, (bath,Q), NC, options=options) + +with timer("ODE solver time"): + resultFit = HEOMFit.run(rho0, tlist) +``` + +```python +plot_result_expectations( + [ + (resultFit, P11p, "b", "P11 Fit"), + (resultFit, P12p, "r", "P12 Fit"), + ] +); +``` + +Now we use the built-in functions. The `BosonicEnvironment` class, includes a +method that performs this fit automatically. More information on how the +built-in functios work can be found in `HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions` + +```python +tlist3 = np.linspace(0, 2, 200) +envfit, fitinfo =dlenv.approx_by_cf_fit(tlist=tlist3,full_ansatz=True,maxfev=1e6,Ni_max=1,Nr_max=3) +``` + +The approx_by_cf_fit method outputs a `ExponentialBosonicEnvironment` object, +which contains a decaying exponential representation of the original +environment , and a dictionary containing all information related to the fit. +The dictionary contains a summary of the fir information and the normalized +root mean squared error, which assesses how good the fit is. + +```python +print(fitinfo['summary']) +``` + +We can then compare the result of the built-in fit with the manual fit + +```python +# Create a single figure with two subplots +fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6)) + +# Plot the real part on the first subplot (ax1) +ax1.plot(tlist2, corrRana, label="Original", marker="o", markevery=500) +ax1.plot(tlist2, fit_func(tlist2, *poptR[-1]), color="r", label="Manual Fit") +ax1.plot(tlist2, np.real(envfit.correlation_function(tlist2)), "k--", label="Built-in fit") +ax1.set_ylabel(r"$C_{R}(t)$") +ax1.set_xlabel(r"$t$") +ax1.legend() + +# Plot the imaginary part on the second subplot (ax2) +ax2.plot(tlist2, corrIana, label="Original", marker="o", markevery=500) +ax2.plot(tlist2, fit_func(tlist2, *poptI[-1]), color="r", label="Manual Fit") +ax2.plot(tlist2, np.imag(envfit.correlation_function(tlist2)), "k--", label="Built-in fit") +ax2.set_ylabel(r"$C_{I}(t)$") +ax2.set_xlabel(r"$t$") +ax2.legend() +# Add an overall title and adjust layout +plt.tight_layout(rect=[0, 0.03, 1, 0.95]) +plt.show() + +``` + +```python +options = {**default_options} + +NC = 4 + +with timer("RHS construction time"): + HEOMFit_2 = HEOMSolver(Hsys, (envfit,Q), NC, options=options) + +with timer("ODE solver time"): + resultFit_2 = HEOMFit_2.run(rho0, tlist) +``` + +```python +plot_result_expectations( + [ + (resultFit, P11p, "b", "P11 Fit"), + (resultFit, P12p, "r", "P12 Fit"), + (resultFit_2, P11p, "r--", "P11 Built-in-Fit"), + (resultFit_2, P12p, "b--", "P12 Built-in-Fit"), + ] +); +``` + +## A reaction coordinate approach + + +Here we construct a reaction coordinate inspired model to capture the +steady-state behavior, and compare to the HEOM prediction. This result is +more accurate for narrow spectral densities. We will use the population and +coherence from this cell in our final plot below. + +```python +dot_energy, dot_state = Hsys.eigenstates() +deltaE = dot_energy[1] - dot_energy[0] + +gamma2 = deltaE / (2 * np.pi * gamma) +wa = 2 * np.pi * gamma2 * gamma # reaction coordinate frequency +g = np.sqrt(np.pi * wa * lam / 2.0) # reaction coordinate coupling +# reaction coordinate coupling factor over 2 because of diff in J(w) +# (it is 2 lam now): +g = np.sqrt( + np.pi * wa * lam / 4.0 +) # + +NRC = 10 + +Hsys_exp = tensor(qeye(NRC), Hsys) +Q_exp = tensor(qeye(NRC), Q) +a = tensor(destroy(NRC), qeye(2)) + +H0 = wa * a.dag() * a + Hsys_exp +# interaction +H1 = g * (a.dag() + a) * Q_exp + +H = H0 + H1 + +energies, states = H.eigenstates() +rhoss = 0 * states[0] * states[0].dag() +for kk, energ in enumerate(energies): + rhoss += states[kk] * states[kk].dag() * np.exp(-beta * energies[kk]) + +rhoss = rhoss / rhoss.norm() + + +class ReactionCoordinateResult: + def __init__(self, states, times): + self.states = states + self.times = times + + +resultRC = ReactionCoordinateResult([rhoss] * len(tlist), tlist) + +P12RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 1).dag()) +P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag()) +``` + +## Let's plot all our results + +Finally, let's plot all of our different results to see how they shape up against each other. + +```python +rcParams = { + "axes.titlesize": 25, + "axes.labelsize": 30, + "xtick.labelsize": 28, + "ytick.labelsize": 28, + "legend.fontsize": 28, + "axes.grid": False, + "savefig.bbox": "tight", + "lines.markersize": 5, + "font.family": "STIXgeneral", + "mathtext.fontset": "stix", + "font.serif": "STIX", + "text.usetex": False, +} +``` + +```python +fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 15)) + +with plt.rc_context(rcParams): + + plt.sca(axes[0]) + plt.yticks([expect(P11RC, resultRC.states[0]), 0.6, 1.0], [0.32, 0.6, 1]) + plot_result_expectations( + [ + (resultBR, P11p, "y-.", "Bloch-Redfield"), + (resultMats, P11p, "b", "Matsubara $N_k=2$"), + ( + resultMatsT, + P11p, + "g--", + "Matsubara $N_k=2$ & Terminator", + {"linewidth": 3}, + ), + ( + resultFit, + P11p, + "r", + r"Fit $N_f = 4$, $N_k=15\times 10^3$", + {"dashes": [3, 2]}, + ), + ( + resultRC, + P11RC, + "--", "Thermal", + {"linewidth": 2, "color": "black"}, + ), + ], + axes=axes[0], + ) + axes[0].set_ylabel(r"$\rho_{11}$", fontsize=30) + axes[0].legend(loc=0) + axes[0].text(5, 0.9, "(a)", fontsize=30) + axes[0].set_xlim(0, 50) + + plt.sca(axes[1]) + plt.yticks( + [np.real(expect(P12RC, resultRC.states[0])), -0.2, 0.0, 0.2], + [-0.33, -0.2, 0, 0.2], + ) + plot_result_expectations( + [ + (resultBR, P12p, "y-.", "Bloch-Redfield"), + (resultMats, P12p, "b", "Matsubara $N_k=2$"), + ( + resultMatsT, + P12p, + "g--", + "Matsubara $N_k=2$ & Terminator", + {"linewidth": 3}, + ), + ( + resultFit, + P12p, + "r", + r"Fit $N_f = 4$, $N_k=15\times 10^3$", + {"dashes": [3, 2]}, + ), + ( + resultRC, + P12RC, + "--", + "Thermal", + {"linewidth": 2, "color": "black"}, + ), + ], + axes=axes[1], + ) + axes[1].text(5, 0.1, "(b)", fontsize=30) + axes[1].set_xlabel(r"$t \Delta$", fontsize=30) + axes[1].set_ylabel(r"$\rho_{01}$", fontsize=30) + axes[1].set_xlim(0, 50) +``` + +And that's the end of a detailed first dive into modeling bosonic environments with the HEOM. + + +## About + +```python +qutip.about() +``` + +## Testing + +This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. + +```python +# Check P11p +assert np.allclose( + expect(P11p, resultMatsT.states), + expect(P11p, resultPade.states), + rtol=1e-2, +) +assert np.allclose( + expect(P11p, resultMatsT.states), + expect(P11p, resultFit.states), + rtol=1e-2, +) + +# Check P12p +assert np.allclose( + expect(P12p, resultMatsT.states), + expect(P12p, resultPade.states), + rtol=1e-2, +) +assert np.allclose( + expect(P12p, resultMatsT.states), + expect(P12p, resultFit.states), + rtol=1e-1, +) +``` diff --git a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb deleted file mode 100644 index 4138ea3d..00000000 --- a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb +++ /dev/null @@ -1,900 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "37e47748", - "metadata": {}, - "source": [ - "# HEOM 1b: Spin-Bath model (very strong coupling)" - ] - }, - { - "cell_type": "markdown", - "id": "01ab22bd", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices.\n", - "\n", - "In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", - "\n", - "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian, the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", - "\n", - "In the example below we show how to model the overdamped Drude-Lorentz Spectral Density, commonly used with the HEOM. We show how to do this using the Matsubara, Pade and fitting decompositions, and compare their convergence.\n", - "\n", - "This notebook shows a similar example to notebook 1a, but with much stronger coupling as discussed in [Shi *et al.*, J. Chem. Phys **130**, 084105 (2009)](https://doi.org/10.1063/1.3077918). Please refer to notebook HEOM 1a for a more detailed explanation.\n", - "\n", - "As in notebook 1a, we present a variety of simulations using different techniques to showcase the effect of different approximations of the correlation function on the results:\n", - "\n", - "- Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator\n", - "- Simulation 2: Matsubara decomposition (including terminator)\n", - "- Simulation 3: Pade decomposition\n", - "- Simulation 4: Fitting approach\n", - "\n", - "Lastly we compare the results to using the Bloch-Redfield approach:\n", - "\n", - "- Simulation 5: Bloch-Redfield\n", - "\n", - "which does not give the correct evolution in this case.\n", - "\n", - "\n", - "### Drude-Lorentz (overdamped) spectral density\n", - "\n", - "The Drude-Lorentz spectral density is:\n", - "\n", - "$$J_D(\\omega)= \\frac{2\\omega\\lambda\\gamma}{{\\gamma}^2 + \\omega^2}$$\n", - "\n", - "where $\\lambda$ scales the coupling strength, and $\\gamma$ is the cut-off frequency. We use the convention,\n", - "\\begin{equation*}\n", - "C(t) = \\int_0^{\\infty} d\\omega \\frac{J_D(\\omega)}{\\pi}[\\coth(\\beta\\omega) \\cos(\\omega \\tau) - i \\sin(\\omega \\tau)]\n", - "\\end{equation*}\n", - "\n", - "With the HEOM we must use an exponential decomposition:\n", - "\n", - "\\begin{equation*}\n", - "C(t)=\\sum_{k=0}^{k=\\infty} c_k e^{-\\nu_k t}\n", - "\\end{equation*}\n", - "\n", - "As an example, the Matsubara decomposition of the Drude-Lorentz spectral density is given by:\n", - "\n", - "\\begin{equation*}\n", - " \\nu_k = \\begin{cases}\n", - " \\gamma & k = 0\\\\\n", - " {2 \\pi k} / {\\beta } & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "\\begin{equation*}\n", - " c_k = \\begin{cases}\n", - " \\lambda \\gamma (\\cot(\\beta \\gamma / 2) - i) & k = 0\\\\\n", - " 4 \\lambda \\gamma \\nu_k / \\{(nu_k^2 - \\gamma^2)\\beta \\} & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." - ] - }, - { - "cell_type": "markdown", - "id": "21530878", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "adf8edee", - "metadata": {}, - "outputs": [], - "source": [ - "import contextlib\n", - "import time\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " basis,\n", - " brmesolve,\n", - " expect,\n", - " liouvillian,\n", - " sigmax,\n", - " sigmaz,\n", - ")\n", - "from qutip.core.environment import (\n", - " DrudeLorentzEnvironment,\n", - " system_terminator\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver,\n", - ")\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "cefeed3a", - "metadata": {}, - "source": [ - "## Helper functions\n", - "\n", - "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d883bc7f", - "metadata": {}, - "outputs": [], - "source": [ - "def cot(x):\n", - " \"\"\" Vectorized cotangent of x. \"\"\"\n", - " return 1. / np.tan(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "52048f66", - "metadata": {}, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\" Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "48891158", - "metadata": {}, - "outputs": [], - "source": [ - "# Solver options:\n", - "\n", - "options = {\n", - " \"nsteps\": 15000,\n", - " \"store_states\": True,\n", - " \"rtol\": 1e-14,\n", - " \"atol\": 1e-14,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "4c19dbbf", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "And let us set up the system Hamiltonian, bath and system measurement operators:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "5b8ef999", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the system Hamiltonian\n", - "eps = .0 # Energy of the 2-level system.\n", - "Del = .2 # Tunnelling term\n", - "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "51a11f44", - "metadata": {}, - "outputs": [], - "source": [ - "# Initial state of the system.\n", - "rho0 = basis(2, 0) * basis(2, 0).dag()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a04bea10", - "metadata": {}, - "outputs": [], - "source": [ - "# System-bath coupling (Drude-Lorentz spectral density)\n", - "Q = sigmaz() # coupling operator\n", - "\n", - "# Bath properties (see Shi et al., J. Chem. Phys. 130, 084105 (2009)):\n", - "gamma = 1. # cut off frequency\n", - "lam = 2.5 # coupling strength\n", - "T = 1. # in units where Boltzmann factor is 1\n", - "beta = 1. / T\n", - "\n", - "# HEOM parameters:\n", - "\n", - "# number of exponents to retain in the Matsubara expansion of the\n", - "# bath correlation function:\n", - "Nk = 1\n", - "\n", - "# Number of levels of the hierarchy to retain:\n", - "NC = 13\n", - "\n", - "# Times to solve for:\n", - "tlist = np.linspace(0, np.pi / Del, 600)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "48e6e8e0", - "metadata": {}, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresonding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresonding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "6a25f082", - "metadata": {}, - "source": [ - "### Plot the spectral density\n", - "\n", - "Let us briefly inspect the spectral density." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "cc3f7e50", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "bath = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=500)\n", - "w = np.linspace(0, 5, 1000)\n", - "J = bath.spectral_density(w)\n", - "\n", - "# Plot the results\n", - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - "axes.plot(w, J, 'r', linewidth=2)\n", - "axes.set_xlabel(r'$\\omega$', fontsize=28)\n", - "axes.set_ylabel(r'J', fontsize=28);" - ] - }, - { - "cell_type": "markdown", - "id": "d029267b", - "metadata": {}, - "source": [ - "## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "ade152c2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.007765769958496094\n", - " Total run time: 1.52s*] Elapsed 1.51s / Remaining 00:00:00:00\n", - "ODE solver time: 1.5161352157592773\n" - ] - } - ], - "source": [ - "with timer(\"RHS construction time\"):\n", - " matsBath=bath.approx_by_matsubara(Nk=Nk)\n", - " HEOMMats = HEOMSolver(Hsys, (matsBath,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMats = HEOMMats.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "9f1b4246", - "metadata": {}, - "source": [ - "## Simulation 2: Matsubara decomposition (including terminator)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "3ead8e19", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.011697769165039062\n", - " Total run time: 1.71s*] Elapsed 1.70s / Remaining 00:00:00:00\n", - "ODE solver time: 1.7070674896240234\n" - ] - } - ], - "source": [ - "with timer(\"RHS construction time\"):\n", - " matsBath,delta=bath.approx_by_matsubara(Nk=Nk,compute_delta=True)\n", - " terminator = system_terminator(Q,delta)\n", - " Ltot = liouvillian(Hsys) + terminator\n", - " HEOMMatsT = HEOMSolver(Ltot, (matsBath,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMatsT = HEOMMatsT.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "f00137ef", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the results\n", - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - "\n", - "P11_mats = np.real(expect(resultMats.states, P11p))\n", - "axes.plot(\n", - " tlist, np.real(P11_mats),\n", - " 'b', linewidth=2, label=\"P11 (Matsubara)\",\n", - ")\n", - "\n", - "P11_matsT = np.real(expect(resultMatsT.states, P11p))\n", - "axes.plot(\n", - " tlist, np.real(P11_matsT),\n", - " 'b--', linewidth=2,\n", - " label=\"P11 (Matsubara + Terminator)\",\n", - ")\n", - "\n", - "axes.set_xlabel(r't', fontsize=28)\n", - "axes.legend(loc=0, fontsize=12);" - ] - }, - { - "cell_type": "markdown", - "id": "cf65d1c7", - "metadata": {}, - "source": [ - "## Simulation 3: Pade decomposition" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "a2f13484", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# First, compare Matsubara and Pade decompositions\n", - "padeBath = bath.approx_by_pade(Nk=Nk)\n", - "\n", - "\n", - "fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(16, 8))\n", - "\n", - "ax1.plot(\n", - " tlist, np.real(bath.correlation_function(tlist)),\n", - " \"r\", linewidth=2, label=f\"Exact\",\n", - ")\n", - "ax1.plot(\n", - " tlist, np.real(matsBath.correlation_function(tlist)),\n", - " \"g--\", linewidth=2, label=f\"Mats (Nk={Nk})\",\n", - ")\n", - "ax1.plot(\n", - " tlist, np.real(padeBath.correlation_function(tlist)),\n", - " \"b--\", linewidth=2, label=f\"Pade (Nk={Nk})\",\n", - ")\n", - "\n", - "ax1.set_xlabel(r't', fontsize=28)\n", - "ax1.set_ylabel(r\"$C_R(t)$\", fontsize=28)\n", - "ax1.legend(loc=0, fontsize=12)\n", - "\n", - "tlist2 = tlist[0:50]\n", - "ax2.plot(\n", - " tlist2, np.abs(matsBath.correlation_function(tlist2)\n", - " - bath.correlation_function(tlist2)),\n", - " \"g\", linewidth=2, label=\"Mats Error\",\n", - ")\n", - "ax2.plot(\n", - " tlist2, np.abs(padeBath.correlation_function(tlist2)\n", - " - bath.correlation_function(tlist2)),\n", - " \"b--\", linewidth=2, label=\"Pade Error\",\n", - ")\n", - "\n", - "ax2.set_xlabel(r't', fontsize=28)\n", - "ax2.legend(loc=0, fontsize=12);" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "facc16ac", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.0061817169189453125\n", - " Total run time: 1.02s*] Elapsed 1.02s / Remaining 00:00:00:00\n", - "ODE solver time: 1.0189778804779053\n" - ] - } - ], - "source": [ - "with timer(\"RHS construction time\"):\n", - " HEOMPade = HEOMSolver(Hsys, (padeBath,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultPade = HEOMPade.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "fa05670c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the results\n", - "fig, axes = plt.subplots(figsize=(8, 8))\n", - "\n", - "axes.plot(\n", - " tlist, np.real(P11_mats),\n", - " 'b', linewidth=2, label=\"P11 (Matsubara)\",\n", - ")\n", - "axes.plot(\n", - " tlist, np.real(P11_matsT),\n", - " 'b--', linewidth=2, label=\"P11 (Matsubara + Terminator)\",\n", - ")\n", - "\n", - "P11_pade = np.real(expect(resultPade.states, P11p))\n", - "axes.plot(\n", - " tlist, np.real(P11_pade),\n", - " 'r', linewidth=2, label=\"P11 (Pade)\",\n", - ")\n", - "\n", - "axes.set_xlabel(r't', fontsize=28)\n", - "axes.legend(loc=0, fontsize=12);" - ] - }, - { - "cell_type": "markdown", - "id": "e5bb519f", - "metadata": {}, - "source": [ - "## Simulation 4: Fitting approach\n", - "\n", - "In `HEOM 1a: Spin-Bath model (introduction)` a fit is performed manually, here\n", - "we will use the built-in tools. More details about them can be seen in \n", - "`HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions`" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "2968c281", - "metadata": {}, - "outputs": [], - "source": [ - "lower = [0, -np.inf, -1e-6, -3]\n", - "guess = [np.real(bath.correlation_function(0))/10, -10, 0, 0]\n", - "upper = [3.5, 0, 1e-6, 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "d25032d3", - "metadata": {}, - "outputs": [], - "source": [ - "tfit=np.linspace(0,100,10000)\n", - "envfit,fitinfo = bath.approx_by_cf_fit(tlist=tfit,Nr_max=3,Ni_max=1,full_ansatz=True,\n", - " sigma=0.1,maxfev=1e6,target_rsme=None,\n", - " lower=lower,upper=upper,guess=guess)" - ] - }, - { - "cell_type": "markdown", - "id": "0f3851dc", - "metadata": {}, - "source": [ - "We can quickly compare the result of the Fit with the Pade expansion" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "de33d3f8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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uzMOHD9foxqwQFxdX6/EBAQFq165drec88sgjmjJlinvbbrcrKan+6WeAL7EftWvtd1u0bdtB/fHHhXLZ75Ty46X8OCkvXur4rVwXTJfFJU1++ze6coZ0Q1COXEVR9X72nsBAKTPTvR1dltfgXMcDqlfOQlXLInbWUslWcvLPk+PQbPfbmT06StExSinYpfQAm2zWMtlsZe4/A2xlCrA5ZLU4ZbO6NCypVLrsLslmk6xWhVqsGvLDW7JaXLLZXLJZJZvVJZvVJatVstlcCrCWHz60/9VSzLXlGzabrshx6vDm2TqsY/rkxFrJ4irvLJOrfFzxp6W8+Bn/8JNSeC/JatVfduZrU+Y7sljLfyljtUgWq0U2m0VWi0UWi2S1WpTcNkDq/bVO7pAsFs3+caMKSjbKZrOUn2uVrFbryfHJc21W9e94gxR7m/vvakhRmf78v5c1bfvL1f+OLTV/dj73skekjpWPc3vmUL5+k7ms+mlVarxWS/lU+5BWVumcrScPKP/5/LO0Y8rO/6ZGfdZ6yi+jOkSdKyXuLP+7OvQ/6b83SnedU/2kalld+vvAP6lv1Dk6J+GvUpvKzriJ+cUafHBNja/LYq1yzZPXDz1rl8q/AeXb7x/J08FjP9Y491ThIe2kpH2Kz1wlfXFd+c4bLi3vKq7FrKFPqX+7nuVfa7ubpHZ3ud/rUurQ92n1X1OSeiV9KIVUdq8+mVuoiYfqPzfAZlFB6PuVWSVp9H3SJX+scew/hz6tAe16ubejWveX4veVb5z8e1r98wE5HLX1SVaXGjNVinzWvX1VYYl+OLC+3vPW5WzUXZuOVd85eJbUp/osp3+e97QGtOtdbV9IYCspeX+1fd8dOK78wvqvG9fmaqn9b93bEU6XVqfVf54knR3/byk0qHwjJqZB57Q0ippmcprf6gMAAMB7BAYGasCAAVq6dGm1Z2ouXbpUV155Za3nDB06VJ9++mm1fUuWLNHAgQPrfJ5mUFCQgoKCmi844IXyc/O1+uufdFFQvrR3b/krPV3nbz5Xq9LHyZkfL6miQHVNzQ+wlUgqf2Th/khpZbIUcCxHpfUVNS0OFQaGS4mJUni4FB6uLmFxit/3hUKCihQaVKyw4BK1DnEoJMipsGApNNii1qEWhYUGKHX4K1K7cCkkRAoJ0T9OOPV48Xq1jYrQpsKtuvqzKyTr6ZtV4mfNkVKGa32j/9bKBUr6oYnnDjn5cjgdSpmZogx7Ro0OWKn8GaCJEUkaNuER6eRzS69Rrd+JBvnN+fUfU5sISY8P7qc3Zj5TT9ZEjRl9szurJHXpKXVpYt5BNddLrNewzp2UuOYRZejn0+a854aban0WbJSk83vV2N0gyUlScv2HVWbtkKDE7xLL/07Daz4+pSLr7eNuqPO5tYGShnZKaVLemCQppmfDjnU4e1dmlUsKy6k1623X1J21wsDExCblbS1pSAP+MQ109tL0/Y9V/7canFf+qpp1bP1ZJalHE/NaJQ3qaN5fhlLUNCs6NQEAALzalClTdOONN2rgwIEaOnSoXnvtNe3bt0+33367pPIuy4yMDL311luSylc6nzVrlqZMmaJJkyZp1apVeuONN/Tuu+8a+WUAHrPtp536avEmrVmfq617W2n/4TgdO5ai0txUyTpYpY4QBVQpABUn95czv0P9H1xQvTs685JzdfGOJcpzfa92EWVq39ai+JhAJcSHKSmprTqmxiihY4yiYqJktb0oqbJDrrekg038+jqefElSijNZid92qLf4NqzjsCZerfnYrDbNHD1T4+aPk0WWankrFoaZMXqGVyzEZJasZskpkbWlmCmrN6OoaSZ0agIAAJjGddddp5ycHP3lL39RZmamevbsqc8//1zJyeU9MpmZmdq3b5/7+NTUVH3++ef6wx/+oH/+859KSEjQiy++qGuuaWq/E+ClnE5p925p/XotXLpFt301TDnZ3eXM7yqpa+3nOAK0MSxO/Qsqp4KnutL1Y1iWQsL3qW34IQW3ydbuyO1S6yypdWb5n+GZUkj1bq34x6bri5ThLff1NYDZChpju4/Vh9d+WOsCTDNGzzizBZiamVmymiWnRNaWYqas3sriOnX5UTRJY5acb7KZM6XJk8vH8+ZJv/51y1wHAHDGioqKlJaWptTUVAUHBxsdB5BU/79Lj9zPoEXxPYQ32rMlTW/P+V5f/VCqiZbluuWnBeUL70haGnmWRuXuqvvkVgUKabNb0W336oNRazSkd0cpOVlKSZEzMUnW0MrnVjZsmnSi0u5L85piYW0rtSdFJHltQcPhdGjlvpXKzMtUfHi8hnUc5jV/l6cyS1az5JTI2lLMlNUTGnMvQ6emmdCpCQAAAMBADfnhe8va7Xr11R/05f/ClJ7RSyVHu0lKlSS5kg/olrzKxXZG5O6RWuXLYi1VZPvN6hhzQGd3PKGBvVrrwuFdNWBYLwW06iOpj6Qrql3Heko2s3U/SuWdWleefaVpCho2q03DDe5ybSizZDVLTomsLcVMWb0NRU2zosEWAAAAgAfV1lWYGJGomRf9TfsXujR3cZm27Omtouw+kmpfPWVL8XlShw7S4MFS374K6NtXm9rt0Tnn9pLVdubPjzTjdE4KGgDQNBQ1zYROTQAAAAAGWLBtgcbNH1djWndG7gGNW/hrxb79hrL23FzzRGuJwtpvUJeknRrev0zXX9dbuuhAtUMauLBxg5mt+xEA0DQUNc2KTk0AAAAAHuBwOnTf4vvkKoyQto6Tto6Xxl0nheTKZZEsLqmg18fSnpslORUa+5N6d96isZcE6ZY7RyoqdpCkQR7NTPcjAPi+Ux9DAm9GpyYAwEu8+eabslgstb7GjRsni8WiN9980338999/r6lTp+r48eOGZQYANJ79qF033jlTB17/u/T8IenTf0l7fintutR9jMsi5Z2zVONv+T/t3rxXBVkDtOq73+rBadcpKjbKwPQAAF9Gp6ZZ0akJAH7Lm1ZInD17trp1q/7ctPj4eD3wwAPq3Lmze9/333+vadOmaeLEiWrTpo2HUwIAGuubz1froae2a/WG0XKdmFLzgH3nS73frdwOLNTV9yWoc49Uz4UEAPg1ippmUrVTk6ImAPilOhdpGD3TkMUPevbsqYEDB9bYn5yc7PEsAIAz5HRq+qPv6u/vx+pI+kWSBld/PyxL6vWu1OsdKWFdjdPjw+M9kxMAADH93FyYfg4Afq1ikYaqBU1JyrBnaNz8cVqwbYFByapLT0+vNv186tSpevDBByVJqamp7mnqy5cvNy4kAKBSaan01ltS79768L0sHUm/RO4fFa0lSur6iSJuGitNSZJGT5E6rJOq/GhikUVJEUka1vHMVy8HAKChKGqaFZ2aAOBX3Is0qOZ//yv2TV48WQ6nw7O5HA6VlZVVe53q97//ve655x5J0oIFC7Rq1SqtWrVK/fv392hWAEB1Rw8dlf35GVLnztJNN0lbtuiVnL9L1hLZItI18uJ/a9OqPdq34wrNfvg3stgcsqh6o0XF9ozRM1hdHADgUX4x/Xz69OlasGCBtm/frpCQEJ133nl69tlndfbZZ7uPmThxoubMmVPtvCFDhuiHH37wdNy60akJAD7jhVUv6IVVL9R7XP/4/vrk159o5b6VNTo0q3LJpf32/Yp7Pk5BAUGSpClDp2jK0MrnoOUV56n7P7u7t099vynOPffcGvt27dpVbTsxMVEdO3aUJPXr108pKSlndE0AwJkpOlGk226ep7cXjdLl7fbqo/373e8N6p2seZfN1zV/uFaBwTe794/tPlYfXvthrY9AmTF6hiGPQAEA+De/KGquWLFCd911lwYNGqSysjI99thjGjVqlLZu3aqwsDD3caNHj9bs2bPd24GBgUbEbRg6NQHA1OzFdmXkZdR7XFJkkiQpMy+zQZ+bXZhd7RpVueSqds1T32+Kt956S927d6+2LyDAL24vAMB0nA6nHrx7rma9f65KjpUXLD9xPKZ9gf9Wx5HDpD/+UTr/fP26jvPHdh+rK8++0msWqwMA+De/+Klj8eLF1bZnz56tmJgYrVu3ThdccIF7f1BQkOLi4jwdr+Ho1AQAnxERFKEO4R3qPa59aHtJDV98ITok2t2pGREUUe09iyzVrnnq+03RvXv3GgsFpaenn/HnAgCa1/v/+lKTnohUXuZN1fbHxq/Vob9/oo5XXNigz7FZbRqeMrwFEgIA0Dh+UdQ8VW5uriQpKiqq2v7ly5crJiZGbdq00YUXXqinn35aMTExRkSsH52aAGBqjZ36PazjMCVGJCrDnlHrczUtsigxIlFp96XV2TETHhSuA1PqnsIOAPA9e3fu0+W//labfrpOclX+/0NEwvf66+RDuuPBqw1MBwBA0/ndQkEul0tTpkzR+eefr549e7r3jxkzRu+8846WLVum//u//9OaNWt00UUXqbi4uNbPKS4ult1ur/ZqcXRqAoDfslltmjl6piSZcpGGoKDy7tHCwkKDkwCA/3jo7reU2i9Im36c4C5otmq7U3++/x0d23cuBU0AgKn5XVHz7rvv1saNG/Xuu+9W23/dddfpV7/6lXr27KnLL79cX3zxhXbu3KlFixbV+jnTp09XZGSk+5WUlOSJ+JXo1AQAv1OxSEOHiOrT1hMjEvXhtR969SINvXr1kiTNnDlTq1at0tq1a5WXl2dwKgDwUUeOSNdfr48/LZHrRGz5vlYF+uXIfyt7d5ymPn+DrDa/+1EQAOBj/Gr6+T333KNPPvlE33zzjRITE097bHx8vJKTk2us4FrhkUce0ZQpldMG7XZ7yxc2q3ZqUtQEAL9k1kUahg8frkceeURz5szR66+/LqfTqa+//lrDhw83OhoA+JZPP5VuuUU6ckRLA79Qp9a/VPuYLfro9VgNuejm+s8HAMAk/KKo6XK5dM8992jhwoVavny5UlNT6z0nJydH+/fvV3x87QszBAUFuafSeQzTzwEA8o5FGiZOnKiJEyfW+l5KSopctfzy7ZlnntEzzzzTwskAwD/Zj9q1ePJ0XTv3r+59HVsHaO3U/6r3nb+lMxMA4HP84v/Z7rrrLr399tuaN2+ewsPDlZWVpaysLPdzvfLz8/XAAw9o1apVSk9P1/Lly3X55ZcrOjpaV1/tpc+ZoVMTAAAAgKRln3yv+O67df0HD+h/rU/OSLviCmnLFvW9ZyIFTQCAT/KLTs2XX35ZkmpMcZs9e7YmTpwom82mTZs26a233tLx48cVHx+vESNG6P3331d4eLgBietApyYAAACAKv7y8Dz9eealUnEbSdKY+Nk6+tcd0p138vMDAMCn+UVRs7YpcFWFhIToyy+/9FCaZkKnJgAAAOC3nA6nrrx8tj5bPLFyZfM2u/TPaWXSpLuMDQcAgAf4RVHTZ/CbVgAAAMDv2Y/a1W/YV/p56y3uffGdP9far/opIWW0gckAAPAcHq5iVnRqAgAAAH5n3+79Su6zQT9vHeved+GFb2rftlFKSKl9kVMAAHwRRU0zoVMTAAAA8Fsb/7dV3c47quMHhpXvaFWgKbfP0fLlExXQikl4AAD/QlHTrOjUBAAAAPzHnj16+eZZKjzSR5JkCcnWv59fpv97+SaDgwEAYAx+nWcmdGoCAAAA/mfnTmn4cL2cmakdydH65ugt+uRf6br02suNTgYAgGHo1DQrOjUBAAAA37d7tzRihJSZKUn6Kuw/2rW8QJdee6HBwQAAMBZFTTOp2qlJURMAAADwacs+XaXnLntYOniwfEfv3rKuWKHU/ucYGwwAAC/A9HMzYfo5AAAA4BdWfbVOo27oIMeJd1QUf7WeaHdA+uorKTra6GgAAHgFOjXNik5NAICB3nzzTVksFvcrICBAiYmJ+t3vfqeMjIxmu05KSoomTpzYbJ8nSaWlperWrZv++te/uvdVfD3BwcHau3dvjXOGDx+unj17VttnsVh09913N2u2qv70pz/psssuU4cOHWSxWOr8e7jxxht11VVXtVgOAJ63Z0uahl8XKkdeR8kRrL+WPa2yJUul9u2NjgYAgNegqGkmdGoCALzM7NmztWrVKi1dulSTJk3Su+++q2HDhqmgoMDoaHV66aWXdOzYMd1zzz013isuLtaf/vQnA1LV9Pe//105OTm64oorFBgYWOdxU6dO1aJFi7Rs2TIPpgPQnBxOh5anL9e7m97VZ+s/V/9RmSo52l2S1KrNLq3+LEQB8XEGpwQAwLsw/dys6NQEAHiBnj17auDAgZKkESNGyOFw6Mknn9RHH32kG264weB0NZWVlelvf/ubbr75ZoWFhdV4f/To0Zo3b54eeOAB9enTx4CElfLy8mS1lv/+ee7cuXUe17lzZ40ePVp//etfddFFF3kqHoBmsmDbAt23+D4dsB+QnFbpg/elg5dKkiyhh7T4rWz1HDzU4JQAAHgfOjXNhE5NAICXO/fccyXJPYV72rRpGjJkiKKiohQREaH+/fvrjTfekOuUX86VlpbqoYceUlxcnEJDQ3X++edr9erVtV4jKytLt912mxITExUYGKjU1FRNmzZNZWVl9eb75JNPlJGRoRtvvLHW9x966CG1a9dODz/8cGO+bEmSy+XSo48+qlatWun1119v9PmnqihoNsSNN96or776Snv27Dnj6wLwnAXbFmjc/HHlBU1JWvI3adu48nGrfN392Fu66HIKmgAA1IZOTbOiUxMA4IV2794tSWp/8rlv6enpuu2229SxY0dJ0g8//KB77rlHGRkZeuKJJ9znTZo0SW+99ZYeeOABjRw5Ups3b9bYsWOVl5dX7fOzsrI0ePBgWa1WPfHEE+rcubNWrVqlp556Sunp6Zo9e/Zp8y1atEgxMTE655zaVw4ODw/Xn/70J913331atmxZgzsfi4uLNXHiRC1atEiffvqpRo8e7X6vIcVWSbLZbLI08ReYw4cPl8vl0ueff17rtHoA3sfhdOi+xffJpZP39Rt+I/0wpXxsLZWuHaePQrbq784pslltxgUFAMBLUdQ0Ezo1AcBnvPBC+as+/ftLn3xSfd8VV0g//lj/uVOmlL8q5OVJ3bvX/X5TOBwOlZWVqaioSCtWrNBTTz2l8PBwXXHFFZJUrcjodDrdxbeZM2fq8ccfl8Vi0fbt2zVnzhz94Q9/0HPPPSdJGjlypGJjY2tMYZ86daqOHTumLVu2uAulF198sUJCQvTAAw/owQcfrLNgKUmrVq1S//79T/s13X777Zo5c6YefvhhrV69ut5C49GjR3XllVcqLS1NK1eurDFtvVWrVqc9v8Ls2bObvChSTEyMOnTooO+++46iJmASK/etrOzQzBggfVKlw/vSu6QuX2q/vfy44SnDDckIAIA3o6hpVnRqAoCp2e1SQxYJT0qque/IkYada7dX33a5qp936vtNUTHdvEKvXr308ssvKzY2VpK0bNkyPfPMM1qzZo3sp1zw8OHDio2N1ddffy1JNQqY1157rW666aZq+z777DONGDFCCQkJ1Togx4wZowceeEArVqw4bVHz4MGDGjRo0Gm/psDAQD311FOaMGGC5s+fr+uuu67OY9PS0jR06FAFBwfrhx9+UGJiYo1j1qxZc9rrVUhNTW3QcXWJiYlp1pXnAbSszLzMyo3s7pLrZDfmgFekga/XfhwAAHCjqGkmVTtFKGoCgKlFREgdOtR/3MlZ3DX2NeTciIjq2xZL9fNOfb8p3nrrLXXv3l0BAQGKjY1VfHy8+73Vq1dr1KhRGj58uF5//XX3MzA/+ugjPf300yosLJQk5eTkSJLi4qqv7BsQEKB27dpV23fo0CF9+umndXY/ZmdnnzZvYWGhgoOD6/26rr/+ej3//PN67LHHNHbs2DqPW716tbKzs/X000/XWtCUpL59+9Z7Pal8+vmZCA4Odv+dAvB+8eGV/71Un7elNmnSD3+Qxtxb93EAAMCNoqaZMP0cAHzGmUz9PnU6ekOFh0sHDjTt3Lp0797dvfr5qd577z21atVKn332WbVC4kcffVTtuIrCZVZWljpUqbqWlZW5C54VoqOj1bt3bz399NO1XjMhIeG0eaOjo3X06NHTHiNJFotFzz77rEaOHKnXXnutzuOuu+46xcXF6bHHHpPT6dSf/vSnGsd4Yvq5VD4NPiUlpcnnA/CsYR2HKdEVrgzlyWWRlPxd+eskiyxKjEjUsI7DjAsJAIAXo6hpVnRqAgC8nMViUUBAQLUOxMLCQs2dO7faccOHD5ckvfPOOxowYIB7//z582sssnPZZZfp888/V+fOndW2bdtGZ+rWrVuDVwi/5JJLNHLkSP3lL39RUm3PATjpT3/6k8LDw/WHP/xBBQUFmj59erX3PTH9vKysTPv379ell17a5M8A4Fm5X36tmfPzNO5ayeJSeWHzJIvKN2aMnsEiQQAA1IGippnQqQkAMJFf/epXeuGFFzRhwgTdeuutysnJ0fPPP6+goKBqx3Xv3l2/+c1vNGPGDLVq1UqXXHKJNm/erOeff14Rp8yR/8tf/qKlS5fqvPPO07333quzzz5bRUVFSk9P1+eff65XXnmlzmngUnkB9S9/+YtOnDih0NDQer+GZ599VgMGDNDhw4fVo0ePOo+777771Lp1a916663Kz8/Xiy++6F5gqK5O1vqsWLFCR44ckVS+INPevXv14YcfSpIuvPBC9wrzkrRx40adOHFCI0aMaNK1AHjWni1p6nZ9Z13Q9k969/1n9MD1rXVAlc8dToxI1IzRMzS2e92PvwAAwN9R1DQrOjUBAF7uoosu0r///W89++yzuvzyy9WhQwdNmjRJMTExuuWWW6od+8Ybbyg2NlZvvvmmXnzxRfXt21f/+c9/dP3111c7Lj4+XmvXrtWTTz6pv/3tbzpw4IDCw8OVmpqq0aNH19u9OWHCBP35z3/WokWLNH78+Hq/hn79+unXv/615s2bV++xt9xyi8LCwnTjjTeqoKBA//rXv2S1Wus9ry5//vOftWLFCvf28uXLtXz5cknS119/7e5wlcqn9EdHR2vUqFFNvh4Az3A6nBp25TaV2S/VMvuTyu2ZqPRHf6eVGd8rMy9T8eHxGtZxGB2aAADUw+JyUR1rDna7XZGRkcrNza3RVdJs3ntP+vWvy8czZkj33dcy1wEAnLGioiKlpaUpNTW1QQvTwHMuv/xylZWV6YsvvjA6SrNwOBw666yzNGHChDqfNVqhvn+XHrmfQYvie+j9fnvdvzV3/s2SJEtwjlYvOaCBw/oYnAoAAO/QmHuZprcPwFjUogEAaJLp06frq6++avCzLr3d22+/rfz8fD344INGRwFQj/8t+0lzPxrn3v7jbZ9T0AQAoIkoapoJz9QEAOCM9ezZU7Nnz1ZWVpbRUZqF0+nUO++8ozZt2hgdBcBpOB1OXfH7bKmkvOukS88P9cyMGw1OBQCAefFMTbOiUxMAgCb7zW9+Y3SEZvO73/3O6AgAGuCeSXN0OK38f6/WsEwt+WCwwYkAADA3OjXNpGqnJkVNAAAAwBS2rN2ul9+73L394O++Ukq3jgYmAgDA/ChqmgnTzwEAAADTufQ3O+QqjJYkJXb5VH/9B9POAQA4UxQ1zYpOTQAwBRf/vYYX4d8j4Hk/v/+JDmb2llS+2vkXb59lcCIAAHwDz9Q0Ezo1AcA0WrVqJYvFooKCAoWEhBgdB5AkFRQUyGKxqFWrVkZHAfxDSYk6TX1YhwrSdU3yg+p2YbJ6Dr7F6FQAAPgEippmRacFAHg1m82myMhIHTlyRMXFxYqIiFBAQIAs/IIKHuZyuVRWVia73S673a42bdrIZrMZHQvwD//4h7R9u6IkfR2/VJr9ndGJAADwGRQ1zYQfhAHAVOLi4hQSEqLDhw/LbrcbHQd+zmazKT4+XpGRkUZHAfxDZqY0dWr52GKRZs2SrDz9CwCA5kJR06zo1AQAr2exWNSmTRtFRkbK4XCorKzM6EjwUwEBAbLZbHQKAx40YcJc/d4ao4uUL916qzRggNGRAADwKRQ1zYQfRADAlCwWiwICAhQQwP/tAoA/mP/vL/Xu8gf0rnWyLuz8nJY/fYfRkQAA8DnMfzArOjUBAAAAr3TnXwIkWSVnoCLPjpfatTM6EgAAPoeippnQqQkAAAB4temPv6ucvRdLkgIi0jR37niDEwEA4JsoapoVnZoAAACAVykrLdNfXjnHvX3rNcsVERVhXCAAAHwYRU0zqdqpSVETAAAA8CpT7nxbRdl9JEmhMT9q5qs3GpwIAADfRVHTTJh+DgAAAHilkqISvbrgXPf247fuVEArFogDAKClUNQ0Kzo1AQAAAK8x+c53VHK0myQpIuE7PTT1WoMTAQDg2yhqmgmdmgAAAIDXKTpRpH8tPN+9Pe2OTFlt/KgFAEBL4v9pzYpOTQAAAMArfD9jrlxOmySpTeJKTf7TOIMTAQDg+yhqmgmdmgAAAIB3cTh00Zt/U27e2fptx9/rb/ceMToRAAB+gSdXmxWdmgAAAIDxFi6Udu1SqKQ5nX+WHhxrdCIAAPwCnZpmQqcmAAAA4D1cLunZZyu3//hH47IAAOBnKGqaFZ2aAAAAgKEWzlqgpbuOl2/06yeNHGloHgAA/AnTz82kaqcmRU0AAADAUL//WzsdtW9XUuKH+vqOUnVmZhUAAB5DUdNMuEkCAAAAvMLbL3+uo/svlSRl5g1Q0o0dDU4EAIB/Yfq5WdGpCQAAABjmiX8Uu8fXjlyhwOBAA9MAAOB/KGqaCZ2aAAAAgOHWr9qstB2/kiRZgnM088UrDU4EAID/oahpVnRqAgAAAIa4749rJWd5Z+aQ/osUHR9tcCIAAPwPRU0zoVMTAAAAMJT9qF0r144p37CUacaTvYwNBACAn6KoaVZ0agIAAAAed/99H8p1IlaSlNTlCw25qJ/BiQAA8E8UNc2ETk0AAADAME6HU+8sqSxiPjbJYWAaAAD8G0VNs6JTEwAAAPCot1/5QoWHy4uaITE/adIfrjA4EQAA/ouippnQqQkAAAAY5rK1H2tS0m8VHvOtxo/4SVYbP04BAGCUAKMDoIno1AQAAAA8x25X1Afz9FpBgV4L/0jO1zKMTgQAgF/jV4tmUrVTk6ImAAAA4DnvvisVFJSPb7hB1ohwY/MAAODnKGqaCdPPAQAAAM9zuaRXX63cvvVW47IAAABJFDXNi05NAAAAwCPefuVzpWQ/oJmxF8g5cKDUr1/9JwEAgBZFUdNM6NQEAAAAPG76a3nau3+CJh9aoXu6TzI6DgAAEEVN86JTEwAAAGhx9qN2bdv2y/KNVgV67C+XGxsIAABIoqhpLnRqAgAAAB417bGFchW3lSSlnrVECSnxBicCAAASRU3zolMTAAAAaHFvf1lZxLx9XImBSQAAQFUUNc2ETk0AAADAYzav3qbD6RdJkqytD2jyo1cbnAgAAFSgqGlWdGoCAAAALerxJ7+XXAGSpMG9lykwONDgRAAAoAJFTTOp2qlJURMAAABoUV/+b6B7/Ph9yQYmAQAAp6KoaSZMPwcAAAA84pN3l6nwSB9JUljsWl167YUGJwIAAFVR1DQrOjUBAACAFvPyG3vc41GDNxiYBAAA1IaippnQqQkAAAC0PJdLn+57Xi/F/EK9k1/Unx4abHQiAABwigCjA6CJ6NQEAAAAWsaGDQrYtVN3aKfuuLCVdP69RicCAACnoFPTTOjUBAAAAFree+9Vjq+7zrgcAACgThQ1zYpOTQAAAKD5uVzS+++Xj2026ZprjM0DAABqRVHTTOjUBAAAAFrU7H98qsjCt3Rtxzu1ZcRlUkyM0ZEAAEAteKamWdGpCQAAADS7WW/bZT90hT7QMIX94t+abXQgAABQKzo1zaRqpyZFTQAAAKBZOR1Obdzxi/INa6n+9NgIYwMBAIA6UdQEAAAAAEkfzF6qMnuqJCmqw3fq3CPV4EQAAKAuFDXNhE5NAAAAoMW8NDfTPR45cI+BSQAAQH0oapoJCwUBAAAALWb11gHu8QN/GGhgEgAAUB+KmmZFpyYAAADQbJZ9ukpF2b0kSWFxqzVwWB+DEwEAgNOhqGkmdGoCAAAALeKFl7e6x8N6bzYwCQAAaAiKmmZFpyYAAADQbL5Z38M9nnxrFwOTAACAhqCoaSZ0agIAAADNbtf6ncrLHCxJCoraql9eM8zgRAAAoD4UNc2KTk0AAACvdezYMd14442KjIxUZGSkbrzxRh0/fvy050ycOFEWi6Xa69xzz/VMYD/X5afvtLp1R93Q8VbdPGyx0XEAAEADBBgdAI1ApyYAAIApTJgwQQcOHNDixeUFsltvvVU33nijPv3009OeN3r0aM2ePdu9HRgY2KI5cdKiRRqUn6G381+X3vve6DQAAKABKGqaFZ2aAAAAXmnbtm1avHixfvjhBw0ZMkSS9Prrr2vo0KHasWOHzj777DrPDQoKUlxcnKeiQpJKSqQlS8rH7dpJgwcbmwcAADQI08/NpGqnJkVNAAAAr7Rq1SpFRka6C5qSdO655yoyMlLff3/6LsDly5crJiZGXbt21aRJk3T48OGWjovvvpPy8srHo0dLNpuxeQAAQIPQqWkmTD8HAADwellZWYqJiamxPyYmRllZWXWeN2bMGI0fP17JyclKS0vT448/rosuukjr1q1TUFBQrecUFxeruLjYvW2328/8C/Azlz+5TRnJL+j64kW6e9RohRodCAAANAidmmZFpyYAAIBHTZ06tcZCPqe+1q5dK0my1PLLaJfLVev+Ctddd51+9atfqWfPnrr88sv1xRdfaOfOnVq0aFGd50yfPt29GFFkZKSSkpLO/Av1M0s2XKSf9v5BDx/6UpkDfmF0HAAA0EB0apoJnZoAAACGufvuu3X99def9piUlBRt3LhRhw4dqvHekSNHFBsb2+DrxcfHKzk5Wbt27arzmEceeURTpkxxb9vtdgqbjbB80Q8qOVq+wnx4/Gp17jHU4EQAAKChKGqaFZ2aAAAAHhUdHa3o6Oh6jxs6dKhyc3O1evVqDT656Mz//vc/5ebm6rzzzmvw9XJycrR//37Fx8fXeUxQUFCdU9NRv1mvb5FUXtQces42SRQ1AQAwC6afmwmdmgAAAF6ve/fuGj16tCZNmqQffvhBP/zwgyZNmqTLLrus2srn3bp108KFCyVJ+fn5euCBB7Rq1Sqlp6dr+fLluvzyyxUdHa2rr77aqC/F532zIdk9nnQDHa4AAJgJRU2zolMTAADAa73zzjvq1auXRo0apVGjRql3796aO3dutWN27Nih3NxcSZLNZtOmTZt05ZVXqmvXrrrpppvUtWtXrVq1SuHh4UZ8CT7PftSuIwfKO2etrTM09saLDU4EAAAag+nnZkKnJgAAgClERUXp7bffPu0xriq/pA4JCdGXX37Z0rFQxb9mfSmVjZckdUpeJattnMGJAABAY9CpaVZ0agIAAABN9sGXhe7xqMF5BiYBAABNQVHTTKp2alLUBAAAAJps4+6e7vGdd7JAEAAAZkNR00yYfg4AAACcsV2b9ujE4b6SpODoDeoxsJuxgQAAQKPxTE2zolMTAAAAaJLY9as1NeEefdxqpJLPCpTUx+hIAACgkShqmgmdmgAAAMAZi/hmmf588Av9WV9Ir7FAEwAAZuQX08+nT5+uQYMGKTw8XDExMbrqqqu0Y8eOase4XC5NnTpVCQkJCgkJ0fDhw7VlyxaDEjcAnZoAAABA47lc0tKl5eOgIGnYMGPzAACAJvGLouaKFSt011136YcfftDSpUtVVlamUaNGqaCgwH3Mc889pxdeeEGzZs3SmjVrFBcXp5EjRyovz4tWQqRTEwAAADgzu3dLe/eWj88/XwoJMTYPAABoEr+Yfr548eJq27Nnz1ZMTIzWrVunCy64QC6XSzNmzNBjjz2msWPHSpLmzJmj2NhYzZs3T7fddpsRsU+PTk0AAACg0V54cam2Jf1WE48v1y9GjjQ6DgAAaCK/6NQ8VW5uriQpKipKkpSWlqasrCyNGjXKfUxQUJAuvPBCff/994ZkrBWdmgAAAMAZmbE0Uf/aP0fn5+3VOxE9jY4DAACayC86NatyuVyaMmWKzj//fPXsWX4Tk5WVJUmKjY2tdmxsbKz2VkxNOUVxcbGKi4vd23a7vYUS14FOTQAAAKBRnA6nMg4MLt8ItOuamy42NhAAAGgyv+vUvPvuu7Vx40a9++67Nd6znNIJ6XK5auyrMH36dEVGRrpfSUlJLZL3lIAtfw0AAADARy1e8K2cBXGSpPYJ/1NwaLDBiQAAQFP5VVHznnvu0SeffKKvv/5aiYmJ7v1xceU3NhUdmxUOHz5co3uzwiOPPKLc3Fz3a//+/S0XvDZ0agIAAACN8vb83e7xwLM9fP8OAACalV8UNV0ul+6++24tWLBAy5YtU2pqarX3U1NTFRcXp6VLl7r3lZSUaMWKFTrvvPNq/cygoCBFRERUe7W4qp2aFDUBAACARvl2U7R7PP7S9gYmAQAAZ8ovnql51113ad68efr4448VHh7u7siMjIxUSEiILBaLJk+erGeeeUZdunRRly5d9Mwzzyg0NFQTJkwwOH0VTD8HAAAAmqT68zTz9Ovfs/I5AABm5hdFzZdfflmSNHz48Gr7Z8+erYkTJ0qSHnroIRUWFurOO+/UsWPHNGTIEC1ZskTh4eEeTttAdGoCAAAADfblwm/lLLhAUsXzNC8xOBEAADgTflHUdDWgAGixWDR16lRNnTq15QM1FZ2aAAAAQJO8/f4uSeVFzYFn7zM2DAAAOGN+8UxNn0SnJgAAANBgPE8TAADf4hedmj6DTk0AAACg8VwuDXJ9q4L4YB3L68nzNAEA8AF0apoVnZoAAABAw6Sn68Odzys7c7RKz52o4NBgoxMBAIAzRFHTTOjUBAAAABrvu+/cQ+v55xsYBAAANBeKmmZFpyYAAADQMN9+Wzn+xS+MywEAAJoNRU0zqdqpSVETAAAAaJAvfkhTiaySzSYNGWJ0HAAA0AwoapoJ088BAACARtm7c58u3fCFgoKOqm+Xl6TwcKMjAQCAZkBR06zo1AQAAADqNeeNbyVZpeJIucJaGx0HAAA0E4qaZkKnJgAAANAoS74vdY+H9y8xMAkAAGhOFDXNik5NAAAAoF5b0ju5x7+5oa9xQQAAQLOiqGkmdGoCAAAADXYi74SOZw2QJNnC92rQhX2NDQQAAJoNRU2zolMTAAAAOK0P5iyTykIlSYkJ640NAwAAmhVFTTOhUxMAAABoEIfToX8t3uPeHtT9iIFpAABAc6OoaVZ0agIAAAC1WrBtgVJmpujbve3d+/7b4R0t2LbAwFQAAKA5UdQ0k6qdmhQ1AQAAgBoWbFugcfPH6YD9gJQxuHxnQKGORX2ncfPHUdgEAMBHUNQEAAAA4BMcTofuW3yfXHJJxWFSUZvyN+LXSbZSSdLkxZPlcDqMCwkAAJpFgNEB0Ah0agIAAAB1WrlvZXmHpiQFFUgPtZeOdZKKIiVJLrm0375fK/et1PCU4cYFBQAAZ4yippmwUBAAAABQp8y8zOo7LJKifq7/OAAAYDpMPzcrOjUBAACAauLD45v1OAAA4L0oapoJnZoAAABAnYZ1HKbEiERZVPt9s0UWJUUkaVjHYR5OBgAAmhtFTbOiUxMAAACoxma1aebomXIdTZHeXCYt/au0b6gkuQudM0bPkM1qMzAlAABoDjxT00zo1AQAAABOa2z3sRp9dKcWp4+Q0kdIgflSx1VKjEjUjNEzNLb7WKMjAgCAZkBR06zo1AQAAABqlbmto3t8Xe8Q3X7T1xrWcRgdmgAA+BCKmmZCpyYAAABQr90ZZ7nH02+7QakpyQamAQAALYFnapoVnZoAAABADfm5+So40keS1KrtTqV2p6AJAIAvoqhpJlU7NSlqAgAAADV8Ov9byREkSeoQu9XgNAAAoKVQ1DQTpp8DAAAAp/XlsoPucZ/OOQYmAQAALYmiplnRqQkAAADUsHZ7qHs8YmikgUkAAEBLoqhpJnRqAgAAAKeVntXJPb76+nMNTAIAAFoSRU2zolMTAAAAqKawoFAF2b0kSa3a7FLHzokGJwIAAC0lwOgAaAQ6NQEAAIA6lW7Zprvj/qYfbP0VGRssqYvRkQAAQAuhqGlWdGoCAAAA1URs26R/HHhP0nvSXc8ZHQcAALQgpp+bCZ2aAAAAQN3Wrasc9+9vXA4AANDiKGqaFZ2aAAAAQHU//lg5pqgJAIBPo6hpJlU7NSlqAgAAAG4lRSV66ECCvg1PljMlRWrb1uhIAACgBVHUNBOmnwMAAAC1WrzwO/1t73wNy0tXl+DnjY4DAABaGEVNs6JTEwAAAHD74st09/isxFzjggAAAI+gqGkmdGoCAAAAtVq9tZV7fMGgEAOTAAAAT6CoaVZ0agIAAABuaZlJ7vFVY/sZmAQAAHgCRU0zoVMTAAAAqMHpcOp4Tg9JkjUsUz0GdjM4EQAAaGkUNc2KTk0AAABAkrT++81yFUZLktpGbzU4DQAA8ASKmmZCpyYAAABQw2efbXaPO8UfNDAJAADwFIqaZkWnJgAAACBJ+v6nIvd4YPcyA5MAAABPoahpJnRqAgAAADVs39fOPR45Iuk0RwIAAF8RYHQANBGdmgAAAIAkyVJaJLXKl5yt9MurzjM6DgAA8AA6Nc2kaqcmRU0AAABAKipS2t4bVFoaoR/6XKrQ8FCjEwEAAA+gU9NMmH4OAAAAVLdli+RwKEDSkF5MPQcAwF/QqWlWdGoCAAAA0saNlePevY3LAQAAPIqippnQqQkAAABUt2FD5bhPH+NyAAAAj2L6uVnRqQkAAAAoZdEFOpEwSp1bbdDX3c5RsNGBAACAR1DUNBM6NQEAAAA3p8OpfQculKuonXLC+iq4Q7zRkQAAgIcw/dys6NQEAACAn1v37Ua5itpJkqKitxmcBgAAeBJFTTOhUxMAAABw+/KLre5xavxBA5MAAABPo6hpVnRqAgAAwM/9b2Ohe9znrFIDkwAAAE+jqGkmVTs1KWoCAADAz+3YH+keDzs31sAkAADA0yhqmgnTzwEAAAC3g0eS3ePRVw4yMAkAAPA0ippmRacmAAAA/JjT4VTBsW6SpIDInxWTGGNwIgAA4EkUNc2ETk0AAABAkrRq2Y9SSbgkKSpqt8FpAACAp1HUNCs6NQEAAODHvlq6wz1Ojc8yMAkAADBCgNEB0Ah0agIAAACSpF8FHFB6x5u1ydJD1wyOrP8EAADgUyhqmhWdmgAAAPBjAzO2ava+t8o3fvujsWEAAIDHMf3cTOjUBAAAAMpt2VL+p9UqdetmbBYAAOBxFDXNik5NAAAA+CunU9q2rXzcqZMUEmJsHgAA4HEUNc2ETk0AAABAe/63SU9FDtP/WifKec45RscBAAAGoKhpVnRqAgAAwE+9+cEmPZ65WOfm79eIY1cZHQcAABiAoqaZVO3UpKgJAAAAP7Vmc4l73PMsA4MAAADDUNQ0E6afAwAAANp5oK17fOH5CQYmAQAARqGoaVZ0agIAAMBPHcpJLh9YHBp1xRBjwwAAAENQ1DQTOjUBAADg55wOp04c6ypJCohIU5voNsYGAgAAhqCoaVZ0agIAAMAPrft2o1TaWpIUFZVmcBoAAGAUippmQqcmAAAA/NzX/93hHifFZBmYBAAAGImiplnRqQkAAAA/tGZTvnvcI6XUwCQAAMBIFDXNhE5NAAAA+Lkd+0Ld40F9Iw1MAgAAjERR06zo1AQAAIAfKi10SrYiSdLFv+xlcBoAAGAUippmRVETAAAAfmhb3h9V7AjT1x366OzeZxkdBwAAGCTA6ABoJIuFgiYAAAD8U16edOCAAiUNT24t2ejRAADAX3EXYFYUNgEAAOBvdu6sHHfrZlwOAABgOIqaZsNiQQAAAPBXO3ZUjilqAgDg1yhqmhWdmgAAAPAz4+ceV0KHBRqa/Iz+F55kdBwAAGAgnqlpNnRqAgAAwE+t2tNRmRmXKVPS8fYrjY4DAAAMRKemWdGpCQAAAD+Tcyy5fGAt0bBRA4wNAwAADEVR02zo1AQAAIAfKikqUdGxLpKkwDZ7FBoeanAiAABgJIqaZkWnJgAAAPzI98t+khzBkqR2bdMMTgMAAIxGUdNsKjo1KWoCAADAj3yzYo97nBybbWASAADgDShqAgAAAPB6G7YXusfdU8oMTAIAALwBRU2zoVMTAADA6z399NM677zzFBoaqjZt2jToHJfLpalTpyohIUEhISEaPny4tmzZ0rJBTWTXgRD3eGDvNsYFAQAAXoGiptmwUBAAAIDXKykp0fjx43XHHXc0+JznnntOL7zwgmbNmqU1a9YoLi5OI0eOVF5eXgsmNY+DObHu8QUjuhuYBAAAeAOKmmZFpyYAAIDXmjZtmv7whz+oV69eDTre5XJpxowZeuyxxzR27Fj17NlTc+bM0YkTJzRv3rwWTmsOucdTygeBeTpnwNmGZgEAAMajqGk2dGoCAAD4nLS0NGVlZWnUqFHufUFBQbrwwgv1/fffG5jMS5SU6No2f1Pf5Bnq3ek9WW38GAMAgL8LMDoAmohOTQAAAJ+RlZUlSYqNja22PzY2Vnv37q3zvOLiYhUXF7u37XZ7ywQ0Wnq63tn7avn42mslTTI0DgAAMB6/4jQbOjUBAAAMMXXqVFksltO+1q5de0bXsJxyr+dyuWrsq2r69OmKjIx0v5KSks7o+l5r167KcZcuxuUAAABeg05Ns6JTEwAAwKPuvvtuXX/99ac9JiUlpUmfHRcXJ6m8YzM+Pt69//DhwzW6N6t65JFHNGXKFPe23W73zcImRU0AAHAKippmQ6cmAACAIaKjoxUdHd0in52amqq4uDgtXbpU/fr1k1S+gvqKFSv07LPP1nleUFCQgoKCWiSTN1n+415Fhcaq54lDslLUBAAAYvq5edGpCQAA4LX27dun9evXa9++fXI4HFq/fr3Wr1+v/Px89zHdunXTwoULJZVPO588ebKeeeYZLVy4UJs3b9bEiRMVGhqqCRMmGPVleI1rVlyhPieyZAvM1d7IOKPjAAAAL0CnptlUdGpS1AQAAPBaTzzxhObMmePerui+/PrrrzV8+HBJ0o4dO5Sbm+s+5qGHHlJhYaHuvPNOHTt2TEOGDNGSJUsUHh7u0ezeyH48pXxgcSqpW4qRUQAAgJewuFxUx5qD3W5XZGSkcnNzFRER0XIXCg+X8vOlHj2kzZtb7joAAMDveOx+Bi3GF7+H9qN2RUaHSS6bQmPXqSBrgNGRAABAC2nMvQzTz82KWjQAAAD8wLdLf5RcNklS+zYHDE4DAAC8hV8UNb/55htdfvnlSkhIkMVi0UcffVTt/YkTJ8pisVR7nXvuucaErQ8LBQEAAMCPrPpfZSEzOfaogUkAAIA38YuiZkFBgfr06aNZs2bVeczo0aOVmZnpfn3++eceTNgEdGoCAADAD2zcUewed08xLgcAAPAufrFQ0JgxYzRmzJjTHhMUFKS4OBOspEinJgAAAPzI7oNh7vHAvlEGJgEAAN7ELzo1G2L58uWKiYlR165dNWnSJB0+fNjoSKdHpyYAAAD8QGZOZePBhZf0NDAJAADwJn7RqVmfMWPGaPz48UpOTlZaWpoef/xxXXTRRVq3bp2CgoJqPae4uFjFxZVTYex2u2fC0qkJAAAAP2LPTZEkWYKOqUuvzsaGAQAAXoOipqTrrrvOPe7Zs6cGDhyo5ORkLVq0SGPHjq31nOnTp2vatGmeilgTnZoAAADwcUX5J+TIT5AkhUSmSWprbCAAAOA1WqSomZWVpTVr1mjjxo1KT09XRkaG8vPzVVhYqJCQEIWFhalDhw5KSUlR7969NWjQIMXHx7dElCaJj49XcnKydu3aVecxjzzyiKZMmeLettvtSkpKavlwFZ2aFDUBAADg44KzDipP52hVZLKODrpY0itGRwIAAF6i2Yqa33zzjRYuXKjPP/9cu3fvbvT5nTt31pgxY3TVVVdpxIgRzRWrSXJycrR///7TFlqDgoLqnJreoph+DgAAAH/x889q7SzVyNzdUq9xRqcBAABe5IyKmocOHdLLL7+sOXPmaN++fZIkV5UOQksDCnAVx+/evVuzZs3SrFmz1KFDB02cOFF33HFHs3Rw5ufnVyu0pqWlaf369YqKilJUVJSmTp2qa665RvHx8UpPT9ejjz6q6OhoXX311Wd87RZDpyYAAAB83c8/V447dTIuBwAA8DpNWv08LS1NN998s1JSUvTkk09q79691YqZFVwul1wul1q3bq327dsrMTFR7du3V1hYmPu92s45cOCAnn76aaWmpmrixInas2dPU2K6rV27Vv369VO/fv0kSVOmTFG/fv30xBNPyGazadOmTbryyivVtWtX3XTTTeratatWrVql8PDwM7pui6BTEwAAAP6CoiYAAKhDozo1jxw5oscff1yzZ89WWVlZjaJk27ZtdeGFF2rQoEHq3bu3unbtqg4dOigkJKTGZxUWFiojI0M7duzQpk2btGbNGq1YsUJHjx6VVF7cLCkp0dy5czVv3jz97ne/05NPPqmYmJhGf5HDhw+vtYBa4csvv2z0ZxqOTk0AAAD4uGu+a6fs5Knq6vxZ/xeXqAijAwEAAK9hcZ2u2lfFjBkzNG3aNNnt9moFwrPOOkvjx4/X2LFjNWDAgDMOtG7dOi1YsEAffvhhtYV6LBaLwsPDNXXqVE2ePPmMr9Pc7Ha7IiMjlZubq4iIFrzdio6WcnKks86STrOQEQAAQGN57H6mgcy++KQRvO17eKZCY9ar8EhfyVKmAnupQlvXbJYAAAC+ozH3Mg0ualqtVlksFrlcLgUEBGj8+PG67bbbdMEFFzRL6NqsXLlSr776qj744AOVlpaWB7ZY5HA4WuyaTeXxombnzlITFmQCAACoizcUxHxp8UkjeMP3sLk4HU7ZwvKk4kgFRP6s0uNMPwcAwNc15l6mUc/UDAwM1D333KPdu3frnXfeadGCpiQNGzZMb7/9tvbs2aN7771XwcHBLXo9U+CZmgAAwMccOnRIU6dOVWpqqkaMGKEXX3xRu3btqvMZ7LWpOLZi8clLLrlEHTt21BNPPKHMzMwW/grQEtK275WKIyVJ4RH7DU4DAAC8TYOLmjfddJN27typmTNnqmPHji2ZqYbExETNmDFDO3bs0E033eTRa3stnqkJAABMzmyLT8Kzvlu+1T2ObZtlYBIAAOCNGrxQ0OzZs1syR4MkJSXp3//+t9ExjEWnJgAAMDmzLj4Jz/ppY7Z7nBJXYGASAADgjRq1+nl95s+fr169eunss8+W1dqome1oLDo1AQCACTX34pMhISE666yzdNZZZ+lXv/qVe/+pi0+6XC6VlZXpX//6l95//32vXXwSlbalVT5Hv3snm4FJAACAN2rwQkENUbGYUFhYmOx2e3N9rCl47KHssbHS4cNSSoqUltZy1wEAAH7HE/czLD7ZsnxpoaDufeZr+8ZrJUlzX/pcv7njUoMTAQCAltZiCwU1hMvlUlFRUXN/LCow/RwAAJgci0+iIQ4dq3xEwC+G9zAwCQAA8EbMETcrpp8DAAATYvFJNFSevfzfhyXomFK7JxucBgAAeJtmfaZmc4mKilKvXr00YMAAvfDCC0bH8S50agIAABNj8Uk0hLO4RN3aLNbBkC4KDCqVxNRzAABQnVd2aubl5WnlypWaOXOm0VG8F52aAADAR82fP1/btm2T0+k0OgoMYs04oE1771JO1ihlDjK+EA4AALxPozs1lyxZop07d6p3797q1auX2rZt2xK5UBc6NQEAgI+7/vrr/XbxSZz088+V406djMsBAAC8VqOLmqtWrdJf/vIX93ZCQoJ69eql3r17N1uoZlyQ3XfxdwQAAHwYi0/6uapFzdRU43IAAACv1aRnarpcLlksFrlcLmVkZOjgwYP68ssv3fscDod69eqlgQMHul99+/ZVUFBQvZ+dnZ3tnmrUkOP9Dp2aAAAA8HElP6cpsGKDoiYAAKhFo4uaoaGhkqp3U1YtclZsb926VVu3btVbb71VfqGAAJ1zzjkaMGCAu9DZp08ftWrVqtrnL1y40D2Ojo5u/FfkL+jUBAAAOC0WnzSvrh8P0b6QwwoJS9d3Vof6Gh0IAAB4HYurCXO97Xa7NmzYoI0bN2rDhg3asGGDNm/e7J4iVPUjqxY7Lad0GbZq1Uo9e/ZU37591alTJx04cECzZ89WSUmJJOnSSy/Vp59+2uQvzpPsdrsiIyOVm5uriIiIlrtQYqKUkSF16CAdONBy1wEAAH7HY/cz9bBay9eyDAgIcN8XNkWrVq3kcDhksVjkcDiaK55X85bv4ZmKSPhBeZnnSpJyso4qKjbK4EQAAMATGnMv06Tp5xERERo2bJiGDRvm3ud0OhUQECCLxSKr1aprr71Wa9eu1Z49e9zHVC1uulwulZSU6KefftJPP/1U6zHjxo1rSjzfxvRzAADgA1h8EqdTkJckSbKGZSkqNs7gNAAAwBs1qahZm4rfqFeM582bJ6m8wrpu3TqtXbvW/UpLS3Mfe2oXZ0XBc+jQobrxxhubK57vYfo5AAAwMRafRF3sR+1y5sdLkkJa75dEURMAANTUbEXNCqfePEZERGjEiBEaMWKEe9/x48erFTk3bNig9PR0OZ1OJSYm6rrrrtMTTzxRrVCKk+jUBAAAPoLFJ1GbNSs3SzpPkhQVmWVsGAAA4LWatahpt9u1fv16bdq06bTHtWnTRpdccokuueSSavudTieFzIai8wAAAJgYi0+iLj/+uN89jos6blwQAADg1Zq1qNm6dWudf/75Ov/885t0PgXNBqBTEwAA+IAHH3xQt912W72LT576TPbS0lJt3LhRGzdu1OzZsyXVvfhkxeON+vTpY8wXiSbZsrPAPe4U3/SFogAAgG9r9unn8BA6NQEAgMmx+CRq83NGZaNDt07BBiYBAADejKKm2dCpCQAAfBiLTyIjJ9I97tc33sAkAADAm1HUNCs6NQEAgA9j8Un/lZMb4x4PuaCHgUkAAIA3o6hpNhWdmhQ1AQCAj2LxSf82J+xRfd8hSmmhqYrr+ILRcQAAgJdq8N3eoEGD9PXXX7dklnotW7ZMgwcPNjQDAAAAWlbF4pN33HFHk86noGliZWW6cs93ejbjI80PX2F0GgAA4MUafMe3bt0692/Cv/rqq5bMVMPSpUt18cUXa+TIkVq3bp1Hr+116NQEAACArzpwQHI4yscpKYZGAQAA3q3Rv8b++uuv9ctf/lJ9+/bVK6+8Irvd3hK5lJeXp5dffll9+/bV6NGjtXz58hrPVvJLLBQEAAAAX7V3b+U4Odm4HAAAwOs1uKi5ZMkSnX322XK5XHK5XNq0aZPuuusuxcfH6+qrr9bcuXOVlZV1RmEyMzM1d+5cXX311YqLi9Pdd9+tTZs2ua/ZvXt3LVmy5Iyu4TMo8AIAAMDHfLx8h+5PvEZzogcqK66j0XEAAIAXa/BCQZdccok2btyof/7zn5o+fboOHz4sSSosLNQnn3yiTz75RJLUpUsXDRo0SL169VKXLl2UmJiomJgYhYSEKDAwUCUlJSosLNShQ4eUkZGhnTt3atOmTVqzZo12797tvl7VrszY2Fg9+uijuuOOOxQQ4OdrG9GpCQAAAB81c3krfX3gQ0nSIwfn6hmD8wAAAO/VqAphQECA7rvvPk2aNEmzZs3SP/7xD2VkZMjlcsliscjlcmnnzp3atWtXo4NUFDErPkeSEhMTdd999+nOO+9USEhIoz/Tp9GpCQAATGjQoEF67rnnNGLECMMyLFu2TH/84x+1evVqwzKgdgeyw93jvr1jDEwCAAC8XZOWhgwNDdVDDz2ktLQ0vf3227r44otlqaWDsGLa+Olep7JYLLrkkkv07rvvKi0tTffffz8Fzaro1AQAACbG4pM4nezj7d3jIed3NzAJAADwdhZXM62+c/DgQX388cdavHixvv32Wx07dqzB57Zt21YXXHCBRo8erSuuuELx8fHNEcmj7Ha7IiMjlZubq4iIiJa7UJcu0u7dUlSUlJPTctcBAAB+xxP3M1artdovw3v16qXbb79dEyZMaJFr5uXl6e2339arr76qTZs2SZJ7lpGjYpVtH+Kxe9IW0qrNHpXldpaCjstV1MboOAAAwMMacy/TbEXNU/3888/atGmT0tPTdfDgQeXn56u4uFhBQUFq3bq1EhISlJqaqp49e6pTp04tEcGjPHYD2bWrtGsXRU0AANDsPHE/89VXX+nee+/V9u3bJcld4AwODtaoUaM0duxYjRw5UnFxcU2+RmZmpr766istWLBAS5YsUVFRkaTKxx11795dL774oi6++OIz/Gq8j5mLmmWlZWoV7JScgQqO3qjCI72NjgQAADysMfcyLbbqTqdOnXyiWOm1eKYmAAAwIRafRF02r90uOXtKkiLCsyRR1AQAAHXjbs5sKqZrUdQEAAAmxeKTqM1Pa9IklRc120ceNTYMAADwek1aKAgGYqEgAADgI1h8ElVt3V75TP4O0ScMTAIAAMyATk2zolMTAAD4iICAAE2YMEETJkxo8uKTLpfLJxaf9Ge79pW6x5068It8AABwehQ1zYZOTQAA4MMSEhJ0xx136I477pDkf4tP+jPXiSJZQ7PkPBGnbl3CjY4DAAC8HEVNs6JTEwAA+AEWn/QfH7dbLp24W3ZroALGbjQ6DgAA8HKGFDV37dql3bt3KyAgQH369FFMTEyjzs/NzVVkZGQLpfNydGoCAADAF+3fL0mKcJZInVKMzQIAALyeRxcK2rFjhwYPHqxu3brpsssu0+jRo5WQkKCrr75a+0/exNRl//79+uc//6lRo0YpNjbWQ4m9GJ2aAAAA8CUVPw/ExkpBQcZmAQAAXs9jnZo5OTkaPny4Dh8+XG2FSpfLpU8++USrV6/WN998o86dO7vf27Fjh+bPn6+PPvpI69evdx9f26qYfsOfv3YAAAD4ptJSKTOzfNyxo7FZAACAKXisqDlz5kwdOnRIFotF7dq106WXXqoOHTro4MGD+uKLL5SZmambb75ZK1as0DfffKPHHntM33//vfv8ikKoxWLR4MGDPRXbe9GpCQAA/ND69evVs2dPBQTwaHhfsvbbTRoVu1TtWu3TpaG7NdPoQAAAwOt57G7w888/lyT17dtXX331ldq2bet+r7CwUPfcc49mz56tmTNn6qGHHlJZWZm7kGm1WjVs2DCNHTtWY8eOVWJioqdie5+KTk2KmgAAwA/1799fgYGB6tGjh/r166f+/furf//+6tOnj0JCQoyOhyZavTpdx7LG6pikb2LfMToOAAAwAY8VNXft2iWLxaK//vWv1QqakhQSEqJ//etfSktL00MPPaTS0lJJUmpqqiZPnqzrr79e7du391RU78b0cwAA4OdKSkq0fv16rV+/XrNnz5ZU/kvwrl27Vit09uvXz38XlzSZbbvs7nFi+yIDkwAAALPwWFEzPz9fUnmnZl0efPBBff3117JYLBoxYoQ+++wzBQcHeyihydCpCQAA/NATTzyhn376ST/++KMyMjLc+x0Oh7Zt26bt27fr3Xffde9PSUmpUehk0Unvs+eA0z0+K4lHCwAAgPp57I6hYoGfsLCwOo/p37+/e/zUU09R0KwNnZoAAMCPTZ061T3Ozs7Wjz/+qJ9++sld6NyzZ0+1RSnT0tKUnp6uhQsXuvfFxcWpX79+GjBggKZNm+bJ+KjDgSOV9/3dz6a7FgAA1M+rfg1ateDZs2dPA5OYAJ2aAADAz0VHR2vUqFEaNWqUe19+fr67yFlR6Ny2bZvKysrcx2RmZiozM1NffPEFRU0vceR4lHvcb0CKcUEAAIBpeLyoaWlgp2Hr1q1bOIlJ0akJAABQp9atW2vYsGEaNmyYe19JSYk2btxYrdC5adMmFRXx7EZvkZt/8pEAljL1GXKOsWEAAIApeLyoedFFF6lXr17q2bOn+08WAWoCOjUBAAAaJDAwUAMHDtTAgQPd+5xOp7Zv325gKlRVlN9BkmRrfVCBwR0NTgMAAMzA40XN1atXa/Xq1dX2RUdHq2fPnurataun45gPnZoAAMAH7N69W+edd566deumvn37qm/fvpowYYLHnqlutVp1zjl0BHqD40eOyXUiRpIU2vqgJIqaAACgflZPXejxxx/XZZddpoSEBLlcrmqvI0eOaPny5Xrttdfc09MjIyN10UUX6aGHHtIHH3ygtLQ0T0U1Bzo1AQCAid19993Kzs7Wd999p3/+85/68ccfWSTST637fqt73Cb8sIFJAACAmXisU7PqQ9gbslJlXl6eVqxYoRUrVrj3tW3bVgMHDtSgQYP05JNPeiq6d6FTEwAAmNyaNWu0ZMkS9y+zx4wZo3/84x8Gp4JR2hUc1YXJU3XQlaShne1GxwEAACZhyOrnTV2p8ujRo1qyZImWLl3qv0XNCnRqAgAAk3r11VclSS6XS6GhoXrllVcavJhkfbZv366zzjpLAQGG3OaiCfqWHtXyvScbIO6faWwYAABgGl5zt8dKlQ1UccNPURMAAJjURx995C5i3n///UpMTGy2z/7000/1+OOPq2fPnurfv7/GjRtX7Rfp8EL791eOk5KMywEAAEzFa4qatWGlylow/RwAAJjYjh07dPToUUmSxWLR7373u2b9/Pvvv18ffPCB1q5dq59++kn//e9/tWfPnma9BprZgQOVY4qaAACggRq9UNDu3bsVExOjCy64QPfee6/+/e9/e7RzkpUqT6JTEwAAmNCGDRsklRc0+/Xrp5SUlGb9fKvVqv/7v/+TVD69PT09XcuXL2/Wa6B5bd57VMdtQeUbHToYGwYAAJhGo4uarFRpMDo1AQCAiWVnZ7vH3bt3b5FrDBs2TEOGDHFvf/zxxy1yHTSPweseVVtHkWyhmSprF210HAAAYBKNKmqyUqUXoVMTAACY0PHjx93jDi3YlXf33Xe7x0uXLm2x6+DMFRXES5IstlIFBLYyOA0AADCLRhU1q65UGRIS0uwrVVZd6Rx1oFMTAACYWGBgoHscFBTUYtf55S9/KYvFIpfLpW3btik3N7fFroWmO2EvkOtErCQpOCzT4DQAAMBMGlXUrFip0mKxtMhKla1bt9bAgQN16623asmSJc322T6JTk0AAGBCkZGR7nHVqejNLTo6Wr1793Zvb9u2rcWuhabbsLpyAdDI1kcMTAIAAMymwUXNipUqXSeLaS2xUmXv3r31448/6o033tAdd9zRrJ/vM+jUBAAAJpaamuoeb9y4sUWvVfWZnbt3727Ra6FpNm3c7x5HR9JNCwAAGq7BRU1WqvQydGoCAAAT6tGjh6Ty+721a9e26LTwmJgY9/jYsWMtdh003Y5dld//+KgiA5MAAACzaXBRk5UqvURFpyZFTQAAYELx8fHq1q2bJKmkpERz585tsWu1bdvWPc7Pz2+x66Dp0jIqn6mfEs+MJAAA0HANLmqyUqWXYPo5AAAwuWuuuUZSebfmU089pby8vBa5jt1ud4+Dg4Nb5Bo4MweOVC4c1aVTmIFJAACA2TS4qMlKlQAAAGgOkyZNUqtWrWSxWHTkyBHdfPPNLXKd/fsrn9fYrl27FrkGzszh4+Hu8Tk94gxMAgAAzKbBRU1WqvQSdGoCAACT69ixoyZNmuRegHLBggXVZus0l2+++cY9TkxMbPbPx5k7nhftHvcbfLaBSQAAgNk0uKjJSpVeiOdqAgAAk3r66aeVlJQkqXwa+ssvv6xrrrmm2pTxM/Hxxx/r8OHDkqSAgACde+65zfK5aF7ftZ6sudED9KfUcYrrGGt0HAAAYCINLmqyUqWXoFMTAAD4gMjISL333nsKDg52P3roo48+Uq9evbRgwYIz+uy8vDz98Y9/lCRZLBYNGTJEoaGhzREbzcnlUo99W/Wb7B/1ZPBWo9MAAACTaXBRk5UqvRCdmgAAwMSGDh2q999/3/18Tan8OZjjx4/X4MGD9Z///Mc9Rb2hcnJydNVVV2nHjh3ufffee2+z5kYzyc2VCgvLxy24ECkAAPBNDS5qSqxU6RXo1AQAAD7ksssu0+LFi9WmTRtJcndtrl27Vtdee606dOigO++8U4sXL1ZOTk6dn3Po0CE9//zz6tWrl5YvXy6LxSKLxaKePXtq3LhxHvpq0CgZGZVjipoAAKCRAhpz8KRJk/Tcc8+prKzMvVLlBx980OyhWKmygejUBAAAPmD48OH68ccfNWHCBK1atcrdtelyuZSVlaVXX31Vr776qiQpISFBSUlJatOmjYKDg5Wbm6u9e/cqLS3NfU5FYTQ8PFzz58837OvC6X2xYpteS75PqY4MjQ1L0vlGBwIAAKbSqKJmxUqVL730kiwWi3ulylmzZjVrKFaqPA06NQEAgA9KTk7Wt99+q1deeUVPPPGEcnJy3MVNSe5p6BkZGTp48GC1c6tOUa8oaEZGRmr+/Pk6+2xW1PZWH3xn10d7Z0iSDma+QVETAAA0SqOmn0usVOlV6NQEAAA+xGKx6I477lB6erqmT5+ujh07yuVyubsvK161nVe1u3Pw4MFavXq1Ro4c6ekvAY2wL6vye5maGGhgEgAAYEaNLmqyUqXBqt7IU9QEAAA+KCwsTA8//LB+/vln/fe//9W9996rHj16uO89a3u1adNGV111lb744gv98MMP6tKli9FfBuqReTTEPe7Wte1pjgQAAKipUdPPK1SsVDlu3DiVlpZKqlypcsCAAXr44Yc1duzYWn+TXpecnBxde+21rFRZH6afAwAAP2GxWDRixAiNGDFCknTixAnt2bNHBw4cUH5+vmw2m9q1a6fY2FidffbZjbr3hPFy7JWFzF59kw1MAgAAzMjicjW93W/58uW65pprdPz4cUmVzzOyWCyKjY3VVVddpSuuuEKDBg2qc8GfQ4cOae7cuXrhhRd06NAh9/6ePXtqw4YNTY3mcXa7XZGRkcrNzVVERETLXeiCC6SVK8vHxcVSIFN1AABA8/DY/QxajJm+h6GxP6nwcD/JUqbiE04FBnNfCwCAv2vMvUyTOjUrsFKlAehAAAAAgA8oLoiXJFnDshQYzOKgAACgcc6oqCmxUqWheKYmAAAATOhE3gk5C2IkSSFhmZIoagIAgMZp9EJBtWGlSg+iUxMAAAAmt2nNDlX8KBLR+oixYQAAgCk1S1GzAitVehidmgAAADChrZsPuMftIuwGJgEAAGZ1xtPPa8NKlS2IvysAAACYnOPYcYVEr1NxYbwSogqMjgMAAEyoRYqapwoNDVWvXr3Uq1cvT1zOf9CpCQAAABP6fWyBfp89sHzj1/8yNgwAADClZp1+Dg+o2qlJURMAAABmlJlZOY6PNy4HAAAwLYqaZsP0cwAAAJgdRU0AAHCGPDL9HM3D4XRoZeQxZfaU4vOlYY4y2YwOBQAAADQWRU0AAHCG6NQ0iQXbFihlZopG9PlJE8ZJIyZKKa+dowXbFhgdDQAAAGiUpK23qG3cf9U5aY6c7aKNjgMAAEyIoqYJLNi2QOPmj9MB+4Fq+zPyD2rc/HEUNgEAAGAqBw8P0vGsi5R+dKSsrZg8BgAAGo+ippdzOB26b/F9cqnmokAV+yYvniyH0+HpaAAAAKjD008/rfPOO0+hoaFq06ZNg86ZOHGiLBZLtde5557bskENUFZaJmdBrCQpKPSQwWkAAIBZUdT0civ3razRoVmVSy7tt+/Xyn0rPZgKAAAAp1NSUqLx48frjjvuaNR5o0ePVmZmpvv1+eeft1BC4+zYsFtylXdntg47YnAaAABgVsz18HKZeZn1H9SI4wAAANDypk2bJkl68803G3VeUFCQ4uLiWiCR99iyca+kbpKktuHHDc0CAADMi05NLxcf3rDVIBt6HAAAALzX8uXLFRMTo65du2rSpEk6fPiw0ZGa3c6dOe5x+8gTBiYBAABmRqemlxvWcZgSIxKVYc+o9bmaFlmUGJGoYR2HGZAOAAAAzWXMmDEaP368kpOTlZaWpscff1wXXXSR1q1bp6CgoFrPKS4uVnFxsXvbbrd7Km6T/by/yD1OaF9mYBIAAGBmdGp6OZvVppmjZ9b6nkUWSdKM0TNks9o8GQsAAMDvTJ06tcZCPqe+1q5d2+TPv+666/SrX/1KPXv21OWXX64vvvhCO3fu1KJFi+o8Z/r06YqMjHS/kpKSmnx9TzlQZW2glIRWxgUBAACmRqemCYztPlYfXvuhfv/J73Ws6Jh7f2LrBM249EWN7T7WwHQAAAD+4e6779b1119/2mNSUlKa7Xrx8fFKTk7Wrl276jzmkUce0ZQpU9zbdrvd6wubh44Hu8dndYowMAkAADAzipomMbb7WOUW5ermT26WJE1eJT0/b4NsUe0MTgYAAOAfoqOjFR0d7bHr5eTkaP/+/YqPr/vZ6UFBQXVOTfdWOfbKQuY5PToYmAQAAJiZX0w//+abb3T55ZcrISFBFotFH330UbX3XS6Xpk6dqoSEBIWEhGj48OHasmWLMWFPI9AW6B53OibZLH7x7QMAADCdffv2af369dq3b58cDofWr1+v9evXKz8/331Mt27dtHDhQklSfn6+HnjgAa1atUrp6elavny5Lr/8ckVHR+vqq6826stoEb+L+lwjkp/QOR1fVs8BXYyOAwAATMovqmIFBQXq06ePZs2aVev7zz33nF544QXNmjVLa9asUVxcnEaOHKm8vDwPJz29qs/NdPjFdw4AAMCcnnjiCfXr109//vOflZ+fr379+qlfv37Vnrm5Y8cO5ebmSpJsNps2bdqkK6+8Ul27dtVNN92krl27atWqVQoPDzfqy2gRTx75Usv2PqkteY+pTXQbo+MAAACT8ovp52PGjNGYMWNqfc/lcmnGjBl67LHHNHZs+bMp58yZo9jYWM2bN0+33XabJ6OeVoC18tvlsEhy1VwNHQAAAMZ788039eabb572GFeVe7mQkBB9+eWXLZzKC7hcUmZm+fg00+oBAADq4/f9fmlpacrKytKoUaPc+4KCgnThhRfq+++/NzBZTTZLZadmmd9/5wAAAGA6drtUWFg+pqgJAADOgF90ap5OVlaWJCk2Nrba/tjYWO3du7fO84qLi1VcXOzettvtLROwil+e9UvtWz9Ctv9+rYhi0akJAAAAU8nemaYNkZ3VqyBTMRQ1AQDAGaDf7ySLxVJt2+Vy1dhX1fTp0xUZGel+JSUltXREhbYKVVJpqBLypNYlLX45AAAAoFm99cUuXZK7W7FlBTrv54uNjgMAAEzM74uacXFxkio7NiscPny4RvdmVY888ohyc3Pdr/3797dozlrRqQkAAAATSdtXufp7+zYOA5MAAACz8/uiZmpqquLi4rR06VL3vpKSEq1YsULnnXdenecFBQUpIiKi2ssjqnaPUtQEAACAiezLdLrHHeNtpzkSAADg9PzimZr5+fnavXu3ezstLU3r169XVFSUOnbsqMmTJ+uZZ55Rly5d1KVLFz3zzDMKDQ3VhAkTDExdU4Y9Q/Njf1bZedKgg9JwowMBAAAAjXAwJ9A97pzS2sAkAADA7PyiqLl27VqNGDHCvT1lyhRJ0k033aQ333xTDz30kAoLC3XnnXfq2LFjGjJkiJYsWaLw8HCjItfq52M/a0rSVilJeuA7aTidmgAAADCR7NzK++uzu8UYmAQAAJidXxQ1hw8fLtdpCoAWi0VTp07V1KlTPReqCQKsld8uh98/OAAAAABmk1vQxj3u0TfVuCAAAMD0KI2ZiM1a+dyhMqt4piYAAABM5cSJ6PKBrUiJqR2MDQMAAEyNoqaJ2CyVRU2H5TQHAgAAAF6opLB8yrkt9JCsNn4UAQAATcedhIlU7dR00KkJAAAAEyk6USTXyU7N4NAjBqcBAABmR1HTRKo+U7OM7xwAAABMZOemn1Xx40dY6FFjwwAAANPzi4WCfEWN6ed0agIAAMAkzmlVrA2hcdoeHKuggSMkjTI6EgAAMDGKmibC9HMAAACYVUD2EfU+cUi9TxySulxmdBwAAGByTGI2EaafAwAAwLQOHaocx8YalwMAAPgEOjVNJDggWN2LwmXLzVNSrujUBAAAgHlQ1AQAAM2IoqaJJIQnaOuuUdJ//mN0FAAAAKBRpv2vWD8m/0EdHIf0YKtIpRodCAAAmBpFTTOjUxMAAAAm8dbWc/Tz3qslSaOLv6aoCQAAzghPZjQbi8XoBAAAAECj5eZHusfdeyUbmAQAAPgCippmRqcmAAAATKLgRLvygbVUqd0oagIAgDPD9HMTOVF6QlenrJTjt1K/TOlvRgcCAAAAGqikMEaSZA09rICADganAQAAZkdR00RcLpeWtD4ktZZc5TuMjgQAAADUq6y0TM4T7SVJQSFHJFHUBAAAZ4bp5yZis9rcYwffOQAAAJjErs0/S67yfoqw0ByD0wAAAF9AacxEAqyVjbVlVtGpCQAAAFPYuWW/exzZ+rhxQQAAgM+gqGkiNkuVTk2LKGoCAADAFHbvznaPoyMKDEwCAAB8BUVNE7FYLLKcrGOW8Z0DAACASaQfOOEex0WVGpgEAAD4ChYKMpkAWVUqZ/kzNenUBAAAgAmEl+YrrP1qFRXFqFOCxeg4AADAB1DUNBmby6JSy8np5wAAAIAJPBOfoWeODCnfuHapsWEAAIBPYBKzydhUXs1koSAAAACYxqFDlePYWONyAAAAn0FR02QCXOVFTQffOQAAAJgFRU0AANDMmH5uMncd76riHVsUUyA6NQEAAGAOhw+X/2m1Su3aGZsFAAD4BIqaJvP00b7Ski1GxwAAAAAarO3B/5MrVkoI26WtNpvRcQAAgA+gqGlmdGoCAADAyzkdTh0/fK7kCFJxdFuj4wAAAB/BkxnNxlJl2XOKmgAAAPBye3fulxxBkqTQ0ByD0wAAAF9BUdNkXBbJYZFKmLUDAAAAE9i2Kd09jgw7blgOAADgW5h+bjJdkz7W7j9L7U5I2XRqAgAAwMvt2nXEPY6KyDcwCQAA8CV0apqMVeXTzx2Weg4EAAAAvMC+AwXucUybUgOTAAAAX0JR02QCKoqaVvFMTQAAAHi9jENl7nFcOwODAAAAn0JR02RsJ79lZXznAAAAYAKHjlXeuCbGBRqYBAAA+BJKYyZjc1WZfk6nJgAAALxcjj3YPU7uGGFgEgAA4EsoappMxfRzOjUBAABgBsfzW7vHXbrEGZgEAAD4ElY/NxnbyaKm0yq5nE6xXhAAAAC82c3tFuu75B+UrfY6p88Eo+MAAAAfQVHTZAJclS2aTpdDNgOzAAAAAPWZal8h7d0iBQdLCfcZHQcAAPgIJjGbjK1Kb2aZ02FgEgAAAKABjhwp/7N9e8nCPCMAANA8KGqaTECVb5nDWWZgEgAAAKAeTqeUnV0+jokxNgsAAPApTD83mb/mDtLx+Z/I5pICx7YyOg4AAABQp/yMLK0K76SuRUeUFB1NRwUAAGg2FDVNZlBJe+nnkxsWnqgJAAAA77X8u526PHeXJKnz/v9ot8F5AACA7+CXpSbicEjHSsK0Vx1lV7jRcQAAAIDTSkvLcY/btC40MAkAAPA1FDVN5LXXpKi5M5WivVqoqyWXy+hIAAAAQJ327S9wj9u3KTEwCQAA8DUUNU0kIqJy/H37CBU6iowLAwAAANQj43CpexzXzsAgAADA51DUNJGqRc3XekXocFFO3QcDAAAABsvKqfxxIzEu0MAkAADA11DUNJGqRU0VR8jhchqWBQAAAKhPtj3IPU5O4pnwAACg+VDUNJEaRU1nmWFZAAAAgPocz2/tHp/VNdbAJAAAwNdQ1DSRU4uaZRQ1AQAA4MXyTrRxj7v2SDYuCAAA8DkUNU2E6ecAAAAwk8LCqPKBrUhxSXRqAgCA5kNR00To1AQAAICZlBZFS5JsoYdltfGjBwAAaD4BRgdAwwUFSVZbiZyOQKk4QiVFu42OBAAAANTO6dRG9dXutu2U26m7pA+NTgQAAHwIRU2TCQoqUOGJ8qJm4Qm70XEAAACA2h0/rh4Fh9Sj4JAUnWR0GgAA4GMoaprMb37zR71u+04KPq7ColuNjgMAAADU7vDhynH79sblAAAAPomipsl0Tj4glW5RUJlUWnzC6DgAAABA7Y4cqRxT1AQAAM2MoqbJ3B/xSz147+eyuiS91cPoOAAAAECt/vNdmv6d/IDinUf0a2t7XWx0IAAA4FMoappMQGhryXVy4wSdmgAAAPBO/1lXps/3/k2SVJbxb4qaAACgWVHUNJk1Bztoi25SriL1m2yX2hkdCAAAAKjFoRyLe9whrpWBSQAAgC+iqGky//62q17Rm5KkYZn/pqgJAAAAr5RtD3KPk5PCDUwCAAB8kdXoAGgcZ+s89/iz3P0GJgEAAADqdjw/zD0+q0usgUkAAIAvoqhpMpYqRc0fTxQYmAQAAACoW96JSPe4a4+OBiYBAAC+iKKmybSLqnw2kf1EsIFJAAAAgLoVFkaVD2zFSkiONzYMAADwORQ1TaZdVOVD1vMLQw1MAgAAANStpKj86e/WkGxZbfzYAQAAmhd3FybTvn2ge3yiMOw0RwIAAADGcDqcchZGS5ICg3MMTgMAAHwRRU2TiYmr7M4sKGptYBIAAACgdoczjkiO8tXPg4OPGxsGAAD4pACjA6BxYuMruzOLiiIMTAIAAADULmdfplq3/1nFJdFqH5FldBwAAOCD6NQ0mfgOlatIFhdT1AQAAID36RHiUN6R81SS21U7L1xudBwAAOCD6NQ0mejYUCkkWwo+LmsYv/UGAACAF8rOrhy3a2dcDgAA4LMoapqMLcCi1n9or/xAKTY3SNKNRkcCAAAAqqta1IyONi4HAADwWRQ1TeiX+wJV7ChRijOw/oMBAAAAT8upsuI5RU0AANACKGqa0IffxEn79kmxofUfDAAAAHjY+C8C9GXMSoW1ytZLR4p1tdGBAACAz6GoaUYhIeV/FhYamwMAAACoxbasKOUdPl95kvJaLTI6DgAA8EEUNU3olYIb9aXOUXZee71/UEpIMDoRAAAAUCm3IMw9TunU3sAkAADAV1HUNKH1pefoI10tuaRDGWVKSODbCAAAAO9RUBjhHp/VPcnAJAAAwFdZjQ6AxluTsM89TkvPOc2RAAAAgOcVFrUtH9iKFZcUa2wYAADgkyhqmpAr/Lh7vC/9mHFBAAAAgFqUFEVJkqzBObLa+JEDAAA0P+4wTKhd2An3+MCBfAOTAAAAANU5HU45C6MlSYEhzCoCAAAtg6KmCbUPL3GPsw6VnOZIAAAAwLMOH8yWHMGSpOBgZhUBAICWQVHThOIiXe7x4WzXaY4EAAAAPGvP9srnv4eF2A1MAgAAfBlFTRNKbGdzj7OP2U5zJAAAAOBZabsPu8eRYTwqCQAAtIwAowOg8TrGBrvHufYgA5MAAAAA1XULKdYvk/+obEXrorOYVQQAAFoGRU0TSoxpI1lLJGeg7PlhRscBAAAA3AYGFmrx3mfLNyb/3dgwAADAZ1HUNKHoqA7SwFckW4lSIk5IesLoSAAAAEC5nCornkdHG5cDAAD4NIqaJtQuuqN06Q3l46JEUdQEAACA18jOrhxT1AQAAC2EoqYJRbZP1HNLpHaFUudenY2OAwAAALht2X9MgcFRSi46rsB27YyOAwAAfBRFTROyto3Sg9+f3IgONDQLAAAAUNWY70Zqf9GLkpxambNW5xsdCAAA+CSr0QHQBK1bS1arnLLoyBHpxAmjAwEAAADl8gvDT46s6tQt0dAsAADAd1HUNCOrVf8MmqJAlShm/RJ99pnRgQAAAIByhYVtywfWEsUlxRkbBgAA+CyKmiYVFFEmx8mnB6QdoFUTAAAA3qG0OEqSZA3JltXGjxsAAKBlcJdhUl/1zHKPN+w6aGASAAAAoJzT6ZKjsHzF88CQHIPTAAAAX0ZR06RSWpe4xxn7Cw1MAgAAAJTLzsyWykIkScHBx40NAwAAfBpFTZPqGmVzjw9lGhgEAAAAOGnPtn3ucVhIroFJAACAr6OoaVLd41pLFock6Wh2sMFpAAAAAOnn3Yfc44jQAgOTAAAAX0dR06SSYlKk1uXP1bQfa2tsGAAAAEDS/gy7exwVXmRgEgAA4OsoappUXGxnKTxDklScH6XSUoMDAQAAwO9lZFYWMtu3KTMwCQAA8HUBRgdA0wTExis0JEMnJMllVWamSx07WoyOBQAAAD/2WDe7ftmmu/YHRqn/hRONjgMAAHwYnZpmFRurqIAM9+a2NPtpDgYAAICnpKen65ZbblFqaqpCQkLUuXNn/fnPf1ZJSclpz3O5XJo6daoSEhIUEhKi4cOHa8uWLR5K3TziThzXZce3647D32tI9wSj4wAAAB9GUdOsYmJ0bvxL0sQLpXvOUkTybqMTAQAAQNL27dvldDr16quvasuWLfr73/+uV155RY8++uhpz3vuuef0wgsvaNasWVqzZo3i4uI0cuRI5eXleSh5Mzh6tHIcFWVcDgAA4POYfm5W7dppgHOHPkzZptAyq46XHjY6EQAAACSNHj1ao0ePdm936tRJO3bs0Msvv6znn3++1nNcLpdmzJihxx57TGPHjpUkzZkzR7GxsZo3b55uu+02j2Q/YxQ1AQCAh9CpaVZWqyZlJSjrb1L+K201pssYoxMBAACgDrm5uYo6TZEvLS1NWVlZGjVqlHtfUFCQLrzwQn3//feeiNgsHtwbq4kdb9bDHa7SidBwo+MAAAAfRlHzpKlTp8pisVR7xcXFGR3rtNq1T1ZsgWTJzpEKC42OAwAAgFrs2bNH//jHP3T77bfXeUxWVpYkKTY2ttr+2NhY93u1KS4ult1ur/Yy0iu7xmnOvjf0XMZCWdvRqQkAAFoORc0qevTooczMTPdr06ZNRkc6vaQkfaWL9bp+r1efZ6EgAACAllTbL8FPfa1du7baOQcPHtTo0aM1fvx4/f73v6/3GhaLpdq2y+Wqsa+q6dOnKzIy0v1KSkpq2hfXTEqK2pQPgo4rODTY0CwAAMC38UzNKgICAry+O7OapCTdoBk6rFglzCzWbY8bHQgAAMB33X333br++utPe0xKSop7fPDgQY0YMUJDhw7Va6+9dtrzKu5Bs7KyFB8f795/+PDhGt2bVT3yyCOaMmWKe9tutxta2CwtbitJCgg+KqmNYTkAAIDvo6hZxa5du5SQkKCgoCANGTJEzzzzjDp16mR0rLolJSkiJF2HC2N18Ggr5Z8oVevQVkanAgAA8EnR0dGKjo5u0LEZGRkaMWKEBgwYoNmzZ8tqPf0EqdTUVMXFxWnp0qXq16+fJKmkpEQrVqzQs88+W+d5QUFBCgoKavgX0YKcZQ65CsunnAcGHTc2DAAA8HlMPz9pyJAheuutt/Tll1/q9ddfV1ZWls477zzl5OTUerxXPL8oKUlF7faWj11Wrd6W6fkMAAAAqObgwYMaPny4kpKS9Pzzz+vIkSPKysqq8WzMbt26aeHChZLKp51PnjxZzzzzjBYuXKjNmzdr4sSJCg0N1YQJE4z4MhrtQPpByVXeMxEcnGtwGgAA4Ovo1DxpzJjK1cN79eqloUOHqnPnzpozZ061KT0Vpk+frmnTpnkyYk1JSUqw/VcHTm6u3XpEFw3oaGgkAAAAf7dkyRLt3r1bu3fvVmJiYrX3XC6Xe7xjxw7l5lYW/x566CEVFhbqzjvv1LFjxzRkyBAtWbJE4eHmWEU8fWempPKp72HBecaGAQAAPo9OzTqEhYWpV69e2rVrV63vP/LII8rNzXW/9u/f7+GEklJT1cm51725eWe+5zMAAACgmokTJ8rlctX6qsrlcmnixInubYvFoqlTpyozM1NFRUVaseL/27vzuKjq/Y/j72HYkQEVERRBXErcMrVskURTr+U1y8ytLG2xxUoruy1a2mJmmml1tZuVZl2vecv8VbbZVcl7zTTNSrPccGMRRQVkZ+b8/hgZRRDc4DDM6/l4zMNzDufMeXMYxi+f+Z7vN1Ft27at5vTnbu+eQ65lW2CuiUkAAIAnoKfmaRQUFGjr1q2Kj48v9+s1YvyievXU1nriNqY/txebGAYAAACebH/KieGYQusUmJgEAAB4AnpqHjdu3DglJiYqKSlJP/74owYOHKisrCzdcccdZkerULf6JxqMe3fVjEHiAQAA4HlS0/Jdy2EhfNgOAACqFj01j9u/f7+GDh2qQ4cOqUGDBrriiiu0du1axcTEmB2tQl2a26SfCyWHrzL2ndlsnAAAAMCF5luUJ5/QP1VcUE8R9S1mxwEAALUcRc3jFi1aZHaEc+LTspn8g3cqPzNORQebKjs/R8H+QWbHAgAAgIeZ1iJD0462cq4M/MzcMAAAoNbj9nN317y5GgZtlur/KTX/Vhv3lD+xEQAAAFClDh8+sVyvnnk5AACAR6Cnpru7+GI9EHO1nviLc3VfwQeSOpiZCAAAAJ6IoiYAAKhGFDXdXVycuqRIN/whtQ5ootYNWpudCAAAAJ6IoiYAAKhGFDXdXd266lYYqW6LUqUG+dJ7Hc1OBAAAAA/UZvvNymh8t+p4HdYfIaH8oQEAAKoUbY3aoHVrKTVVOnhQRvpBWcIbmJ0IAAAAHmZHehcVHm6lA77Z8vb3MzsOAACo5ZgoqDZo3Vqj9A910M9qfVmg2WkAAADggYry60qSrH6HK9kTAADg/FHUrA3attWvaq9f1EF/7A1ScsYRsxMBAADAgzjsDhn5znE0ff2PmhsGAAB4BIqatUHnzvKu+6dr9b3la0wMAwAAAE+Ttu+A5PCRJPn78wE7AACoehQ1a4O2bdXae7NrdfW6bBPDAAAAwNMkbU92LQf50xYFAABVj6JmbeDrq+vCT3wivmWTr4lhAAAA4Gl27zroWg4OzDUxCQAA8BQUNWuJ7pfbXMsHdjSWYRgmpgEAAIAnSU7Jci2H1ikwMQkAAPAUFDVridCrWiuwzg5Jkj21nXZm7DY3EAAAADxGalq+a7merdjEJAAAwFNQ1KwtOndWTMBG53JxoJb9sMPcPAAAAPAYBzJOFDIbhJqXAwAAeA6KmrVFmzbq6LXJtbpizVHTogAAAMCzJNQ7pMtjpuri6Lnq2orx3QEAQNXzNjsALhAfH/VueET/POBc/XWj1dw8AAAA8BijbAc1as+rzpXu35sbBgAAeASKmrVI36vry7/e35TfZKNymuyUYdwki8VidiwAAADUdocPn1iuV8+8HAAAwGNQ1KxF6l9/ha7J7qdvm0sWR4AO5R5Sg6AGZscCAABAbXdyUbN+ffNyAAAAj8GYmrVJfLymfWfRzllS2qfNKWgCAACgWuzNyFG+5fjwR3XrmhsGAAB4BIqatUlIiNpHdVKzI5Llt83SoUNmJwIAAIAHuGjrTAUYxbLW2S/5+ZkdBwAAeACKmrWMkdBdm9VGs3W/po5NNTsOAAAAPEBRvnMcTYvFbnISAADgKShq1jYJCeqhFRqt2Xrp3y1kp10JAACAKuSwO+Q4XtT08TtqbhgAAOAxKGrWMpZr4tXef7UkKaswQPO+2mByIgAAANRmRw8dlezOW879/LLMDQMAADwGRc3aJjhYkXFbXatLPt5rYhgAAADUdru3J7uWA/2zTUwCAAA8CUXNWmhId5tred0aZp8EAABA1dm3+6BruU5AjolJAACAJ6GoWQtdd+91zpknJWXsukKHjuaZnAgAAAC1VXLyUdeyLTDfvCAAAMCjUNSshbwuaqkWjb51rtj9NWf+JlPzAAAAoPZKS891LYcEFZmYBAAAeBKKmrXUX9vvcy0v+SjTxCQAAACozdIPnShk1rM5TEwCAAA8CUXNWmr0nZdJ3s7bzrf8dokMw+RAAAAAqJUOHj1RyGxQjz8vAABA9aDVUUvF/qWPgqNWSJKKciK16st9lRwBAAAAnL3nL9qnt8Kv0sRGfXXn1XXMjgMAADyEt9kBUEW8vNSlzUZ95+UjtVuo/b8GSX3/bnYqAAAA1DJtio6qTfoPzpXWE8wNAwAAPAY9NWuxcWPCpNv/Il36vlYkfWx2HAAAANRGR4+eWA4NNSsFAADwMBQ1a7GEhJEatTNUS/8lvTk/Xdq1y+xIAAAAqG2OHDmxXLeueTkAAIBHoahZi/n5+OsfcX9T/z+loCJJ775rdiQAAADUMs+mN9bjjQfo1YjucoSEmh0HAAB4CIqatd2IEbJ7+eg7XasHZrZUQU6x2YkAAABQi7yy/QFNT/5E4w4tk1eAv9lxAACAh6CoWdtFRmp09Ofqpe80J3eEPp24yexEAAAAqEWKCkIlSV5+RyreEQAA4AKiqOkBbhoV4Fp+dQE/cgAAAFw4jgLnOJo+fkfNDQIAADwKFS4PsKvHZilsqyTpp4Md9cdXSSYnAgAAQG1wLCtHKgqSJPn6ZZmcBgAAeBKKmh7glnaD5NVprmv9rSeZBR0AAADnb8+2fa7lAP9sE5MAAABPQ1HTA4QFhqnnDXsl7zxJ0tzNlylzN2MeAQAA4Pzs3Z3uWg7yP2ZiEgAA4GkoanqIEfE3S5e8L0nKddg09/6NJicCAACAu0vef9S1HByYZ14QAADgcShqeogbLr5BAV3fkuSQJL32bZwKD/NpOgAAAM5dWtqJ9mRoUKGJSQAAgKehqOkhgnyDdNu1l0sXfyZJSnE00j/v/d7kVAAAAHBnBw6dKGTWDbabmAQAAHgaipoe5N5O90pXT3Otr/8iTTpGb00AAACcG3tegSyB6ZJXkcJCLWbHAQAAHoSipgfp1KiTOnbJl+InS3ddobvq3iXNmWN2LAAAALip2W0y5MhtKLvDV7MH1DU7DgAA8CAUNT3MvZ3ula6dIEvUj/pftKRp06TsbLNjAQAAwB0dOSLJ+UeFb1h9c7MAAACP4m12AFSvW9vdqq0Ht+qBhdvV8sdlkg46C5vPP292NAAAALib40VNSVJdemoCQE1mt9tVVFRkdgx4MB8fH1mt1gv2fBQ1PUyQb5Be6/Oa1Hy7tOhb2YvsWvDyAQ0YkqyQ1o3NjgcAAAB3QlETAGo8wzCUlpamzMxMGYZhdhx4MIvFopCQEEVERMhiOf+xuClqeqqWLbV58Asa8eG12lDUWVtu+UrTt1DUBAAAwJnruiNBqU36K9jriNYGBcvf7EAAgDIyMzN19OhRNWjQQEFBQRekmAScLcMwlJOTo4MHDyogIEChoaHn/ZwUNT1Yncfu1ZYP/SRJs37vqbs/2axWN7c1ORUAAADcxcbUeOUd7CB5FcnXduFuJwMAXBiGYSg9PV02m01hYWFmx4GHCwgIUEFBgdLT0xUSEnLeBXYmCvJgK41P5dvtdUlSsXz0yN3ZMuwOk1MBAADAXRQVhEiSLP5H5GXlTwsAqGnsdrvsdrtsNpvZUQBJks1mc70uzxctDw+2PmW9sq5+TrLtlSR9ffRKff7wcpNTAQAAwF0UFzjH0fT2O2puEABAuYqLiyVJ3t7cqIuaoeS1WPLaPB8UNT3Y+Pjx8gtwSL3HubaNntNWWdsPmJgKAAAA7qC4sEgqcPb88aWoCQA1GuNooqa4kK9FipoerLGtsR7u8rDU5t9S828kSfuNxnri+t9MTgYAAICabt/OFJX8OeHvd8zcMAAAwONQ1PRw4+PHq0FQA+mv90o+zsboWzt6KnHKGpOTAQAAoCbbsyvVtRzkn21iEgCAp5o/f74sFstpH6tWrTIt28KFCzVz5kzTzu8JGFTBw4X4h2hyj8ka9cUoqedT0ldvSJLufiZCm4YeVFDTBiYnBAAAQE20f+9h13JwQJ6JSQAAnm7evHlq1apVme2tW7c2IY3TwoULtXnzZo0dO9a0DLUdRU3ozkvv1NyNc7X+stnS5iHSvis1wP5v+TyyUVqySGLsDQAAAJwiJfVE78yQoAITkwAAPF3btm3VuXNns2OgmnH7OWT1suq9/u/Jx9sq3ThCXrf11LCGT8p36WLpww/NjgcAAIAa6MDBfNdyXdv5z2AKAEBVWLRokSwWi958881S2ydOnCir1arly5e7tj333HPq0qWL6tWrJ5vNpo4dO+rdd9+VYRhlnnfhwoW68sorVadOHdWpU0cdOnTQu+++K0lKSEjQsmXLtGfPnlK3w+PCoqcmJEltw9vq6fin9VzicwpqkKxdP0mXHJD0wANSly7SRReZHREAAAA1SNs6Obo4eq6OOUJ1eQxFTQCAeex2u4qLS/9fZLFYZLVaNWTIECUmJuqxxx7TFVdcoc6dO2vFihV68cUX9fTTT6tXr16uY3bv3q17771X0dHRkqS1a9fqoYceUnJysp599lnXfs8++6xeeOEFDRgwQI899phCQkK0efNm7dmzR5I0e/ZsjRo1Sjt37tSnn35aDVfAM1HUhMvT8U/rcN5h/e3qvylq5wTpj/elY8f081+fUduf5svHFmB2RAAAANQQIxsc08i9TzhXen5sbhgAwNnp3FlKSzM7xQkREdJPP53z4VdccUWZbVar1VXonDlzpn788UcNGjRIy5Yt07BhwxQfH69JkyaVOmbevHmuZYfDoYSEBBmGoVmzZumZZ56RxWJRUlKSXnrpJd1666368KS7W08ujrZu3VqhoaHy8/MrNxsuDIqacPG1+ur16153rvz97zJ+XKe//9FDj2x/TaOvStTMzT3NDQgAAICa4+jRE8uhoWalAACci7Q0KTnZ7BQXzIIFCxQXF1dq28m3e/v5+Wnx4sXq1KmTOnbsKJvNpn/961+yWq2ljlmxYoVeeuklrV+/XllZWaW+lp6eroYNG2r58uWy2+0aPXp01X1DOCMUNVG+oCDtnPF/euz6aBXLR7O29FSXUSs19O3uZicDAABATXByUbNuXdNiAADOQUSE2QlKO888cXFxlU4U1KJFC8XHx2vZsmW6//77FRkZWerr69atU+/evZWQkKC5c+cqKipKvr6+Wrp0qSZPnqy8vDxJ0sGDByVJUVFR55UZ54+iJk6rYY8IXTJgptYvcd5WdPfcy3Xx5RvV8e6OJicDAACA2RxHj56YdTQkxMwoAICzdR63erurd955R8uWLdPll1+uN998U4MHD1aXLl1cX1+0aJF8fHz0xRdfyN/f37V96dKlpZ6nQYMGkqT9+/erSZMm1ZId5WP2c5zWoI8HaX27J6UOzjElchWkv45qpH3/3WNyMgAAAJgtcu0IWXwz5R28R3sKrZUfAACASX777Tc9/PDDuv3227V69Wq1b99egwcP1pEjR1z7WCwWeXt7l7olPS8vTx988EGp5+rdu7esVqvmzJlT4Tn9/PxcvTtRNeipidN6tferWrNvjbL63i9lXCTtu1qpRoT69vxT/92RJVuUzeyIAAAAMElefrBUaJO90KYGUblmxwEAeLDNmzeXmf1ckpo3b67AwEANGjRIsbGxmj17tnx9fbV48WJ17NhRI0eOdPXE7Nu3r2bMmKFhw4Zp1KhRysjI0PTp0+Xn51fqOZs2baqnn35aL7zwgvLy8jR06FCFhITo999/16FDh/Tcc89Jktq1a6clS5Zozpw56tSpk7y8vCq9RR5nh6ImTqt1g9b69y3/1vX/vF72ITdK76yVjjTXbwUXa1CH9fpiT3t5B/lV+jwAAACofQoLj3/A7XNMgcF1zA0DAPBoI0eOLHf73LlzlZiYqL1792r9+vUKCgqSJDVr1kzvvPOObrnlFs2cOVNjx45Vjx499N5772nq1Knq16+fGjdurHvuuUfh4eG66667Sj3v888/r5YtW+qNN97QrbfeKm9vb7Vs2VIPP/ywa58xY8Zoy5Ytevrpp5WZmSnDMGQYRtVdBA9kMbiiF0RWVpZCQkKUmZkpm6129WB866e3dP+y+6VDF0nv/CDl15MkjYhZqXe3XyMvH243AgCgNqjN7RlPUZ0/Q2/bPtmzm8irTrLs2Y2r9FwAgHOTn5+vpKQkxcbGlhonEjBLZa/Js2nLMKYmKnVf5/v0QvcXpLBt0pAbJWuBJGnpng7afet4ibo4AACAx7EXhEqSfHwzzQ0CAAA8Eref44yMjx+v7IJsvaJXpJuHSV++qQei+6jZv3+VoouladMki8XsmAAAAKgG+bn5UmGwJMnHL9vkNAAAwBNR1MQZsVgsernny8orztMbekNq8bU27M2VY6vk9eqrzt6a06dT2AQAAPAAyUmpkmIlSQEUNQEAgAm4/RxnzGKxaFafWXry6icV36KTlsS/Ka/jd57bZ8zUgp4L5Ch2mBsSAAAAVS55T7prOcAvx8QkAADAU9FTE2fFYrFoSs8pKigukJ+3n+TlJ+OeUbpfczR3xR1aGbdaczdfJW8/Jg8CAACorZKTj7iW6wTkmZgEAAB4KoqaOCd+3n7Ohbvv1k8pjfXuxN6SpPk74pUV+4MW/NpW649tUGp2qiKDIxUfHS+rF4VOAACA2uBA2olbzoMDCk1MAgAAPBVFTZy3i/52lRptu1/7//Wm5PDVktQr9UWHNSocOkiqc1CSFGWL0qw+szQgboDJaQEAAHC++kY5dLRRX6V7herqS9ubHQcAAHggxtTEeTuaf1RBV3wv3dpX8nGOqVSYfJX0zo/SgTaSpOSsZA1cPFBLti4xMyoAAAAugJaWPE1K+VKz9y/UrW1tZscBAAAeiKImzltMaIzW3bNOA/uFSiPjpeBk5xeOxkrv/iD9+VcZcs4oNPbrsbI77OaFBQAAwPnLzDyxHBpqWgwAAOC5KGrigrD52fRA5wekRj9L91wuRf7k/EJhsPSvz6XvXpJh99K+rH1avXe1uWEBAABwfo4ePbEcEmJaDAAA4LkoauKCSTuW5lywpUgjr5Fa//vEF1M6SRZnb83U7FQT0gEAAOBC+WRXgWY0TNA/wy7VYd8gs+MAAAAPRFETF0xkcOSJFd886ZZBUu9HpZDd0oDbJC+Hc7+N28wJCAAAgAviqY2X6rEDK3XboY1KTCs2Ow4AwEPNnz9fFoul3MfAgQNlsVg0f/581/5r1qzRpEmTdPTkOw7gtpj9HBdMfHS8omxRSs5Kdo6haZF01WvSZXMkn3xZDCkqS4ofOUk/LXaoxZzHFBrNwPIAAADuJic/0LUcFV3fxCQAADPYHXat3rtaqdmpigyOVHx0vKxeVtPyzJs3T61atSq1LTIyUuPGjVPz5s1d29asWaPnnntOI0aMUChjQrs9ipq4YKxeVs3qM0sDFw+URRbX5EDyyZdFFkmGZn4tHTXqqe+X9ysrLlvP3r9MT00fampuAAAAnJ28ghO3nDdpGlnBngCA2mbJ1iUa8/UY7c/a79oWZYvSrD6zNCBugCmZ2rZtq86dO5fZHhMTY0IaVBduP8cFNSBugD4e9LEa2xqX2h5li9LHgz7WgCff1+PeM5WuCOXnNtbTrw5Vw5bL9MWX/zEpMQAAAM5WQcHxu20sxQpv3MDcMACAarNk6xINXDywVEFTkpKzkjVw8UAt2brEpGRl7d69u9Tt55MmTdLjjz8uSYqNjXXdpr5q1SrzQuK80FMTF9yAuAHqf3H/8ruit5YmNd+rpbeu1JE93SVJ6Tv6qt+NOWrTbYb+8XoXXR13tcnfAQAAACpSVBgsSbL4ZcrLyu3nAOAJ7A67xnw95sRdmScxZMgii8Z+PVb9L+5f7bei2+12FRdXPMbz3XffrcOHD+uNN97QkiVLFBnpvNOgdevW1RERVYCiJqqE1cuqhKYJ5X4t+upope9opIdGzdfbH/WRIzdCKgrSlu8eVdcr96hNn6f02gs91LNFT1ksluoNDgAAgEoVF4RKkqx+mZIoagKAO5rxwwzN+GFGpft1jOyoz4Z+ptV7V5fpoXkyQ4b2Ze1TxPQI+Xn7SZIevfJRPXrlo659sguyFff3uFLHnbrPubjiiivKbNu+fXup9aioKEVHR0uSLr30UjVt2vS8zgnzUdSEKby9vTXnvRF67ukDGjjwE63+5SZJXlJmjLZ8NEW9V21Sm+F/0b8fn6m4cD41AQAAqCkcdoeMghBJko9vlslpAADnKqsgS8nZyZXu1ySkiSQpNTv1jJ73UN6hUuc4mSGjzDlP3edcLFiwQHFxpYul3t6UvGo7fsIwVXiLhvp+081a/8lWjRxzSFuS451fyG6sJGOtGvW/VXrqOalfP4lemwAAAKY7nH5YcoRJkvz8KGoCgLuy+dnUOLhxpfs1CHSOnRwZfGYTw4UFhLl6atr8bKW+ZpGlzDlP3edcxMXFlZkoaPfu3ef9vKjZKGqiRrjs5jhtvlla/sp63TfZTwXt3la/P7IVsnaT1L+/1KGDfhwwVSu7/qzosCa6sdWNCvQJNDs2AACAx9mflCrJWdQM8MsxNwwA4Jyd7W3f8dHxirJFKTkrudxxNS2yKMoWpaQxSacdUzPYL1j7Hz39LezA2WD2c9Qovf52mbZntFXSwz00I7W9a/veTRnq+mx3Pd1/mG4d87vCJ7bV3Z/drdV7VsthOExMDAAA4FkOpByWvIokSUH+uSanAQBUF6uXVbP6zJLkLGCerGR9Zp+Z1T5J0Nnw83P2IM3LyzM5CS4Eipqocby8veQzaIACftokffaZ1LmzpmuciuUjI7uJtPJF5Uz9Q++O76FrJk1Qk1dj9PBXDytxd6LsDrvZ8QEAAGq1vzT2ld3hqwPeQVoZ/1+z4wAAqtGAuAH6eNDHamwrfQt5lC1KHw/6WAPiBpiU7My0a9dOkjRr1iz98MMP+umnn5SdnW1yKpwrbj9HzWWxOMfS/OtfdfOsTdo9dZ2+SOssQ16Sw1faPEzaPEwpoUl6o/0HeuOSu9WgSaYGxA3Q7L6z5WWpvGZvd9i1eu9qpWanKjI4UvHR8TX6UyUAAADTZWbKS1J4ca7UMMTsNACAajYgboD6X9zfLf+WTkhI0FNPPaX3339fc+fOlcPh0MqVK5WQkGB2NJwDi2EYZQdCwFnLyspSSEiIMjMzZbOd/yC3KN/u/+7XW49t1zvr2yvDqF92h/jJuqznW1o3dIXUsqVrc9qxNDUMaijLSZMNLdm6RGO+HqP9WSfG84iyRWlWn1k1/tMlAACqAu0Z91ctP8NFi6ShQ53LM2ZIjzxSNecBAJy3/Px8JSUlKTY2Vv7+/mbHASp9TZ5NW4aemnArTbtG6eUfozTpaL6WPLtWH/zLqm8PdZRDzk+EfMJ+0V9W7ZcmXiS1b6/cfoO19eL+uvlQPxUZBeoR20PXRF+jvOI8jf16bJnBjZOzkjVw8UC36DYPAABgiszME8uhoabFAAAAno2iJtySf6i/hr1+hYa9LqVsSNXCZ//QR6vC9cXnX8nb5/hOv/6qr39trpv1tBS4Vmq+XB82/1YfNpso2VLLfV5DhiyyaOzXY9X/4v5u0X0eAACgOj23JlefxsxUiI5qwhFv9TI7EAAA8EgUNeH2GnWK1LhlkRonSbt+kT75xPn48Ud9qpucO+WGS7/d6nxIUoMtUvR/pSZrpCb/k+rtVMnkbYYM7cvap9V7VyuhaYIJ3xEAAEDN9c2Oevplzx2SpE2ZiylqAgAAU1DURO3SrJn0+OPOx9696jVxh7K+XqcVaXE6puAT+x1s43xsuNe53nqxNGhwqadKzU7VC4kv6JOtn6hteFvXo02DNooJjTmjiYgAAABqm6xcP9dyZEQdE5MAAABPRlETtVd0tG6fF63bJRXlFmnte79o+aIMfbylvrZmtpGMk17+4ZtLH2v31t8uu1wFUXV1MMquXyI2SQ3mSXV3SdZiBfkEqXWD1mpZv6Va1mupa2OvVXxMfJV+O8zUDgAAaoJj+ScG9Y9sHGpeEAAA4NEoasIj+AT6KP7BSxT/oDTRYVf01FZK+TNK2neVtPdqKXala1+LIYUnxWl/QXNpZ3NpZ58TT+RVJNXboZywP7Q+7A+tD9sqxU1X4eUZim9yteTl7L1Z7CjW7Z/erihblJrYmig6JFrRIdFqEtJE9QPql5qF/UwwUzsAAKgpcvODXMtRMeEmJgEAAJ6MoiY8jtXLqjdunKqBiwdKsYmlZkAvKTXevetKzVJ26VvWJcnhIx2Kcz5KXLRMLZ5/UxoyV4qJ0b8DbtfS4i76V2QTKXS3FLxGCk6RglMln3wFeAeoSUgTNbE1UWRwpKb1mqaIOhGup8vMz1SBvUBhgWHysnhpydYlGrh4oFvN1O5OvUrdKSsAADVBfsGJW86bNGtkYhIAAODJKGrCIw2IG6CPB31cTu/HJprZZ6YGTBqg54sd2rUyST9/labNGwv1xy5f/XGwnv7Mj1GBnLdd1bOka97/HdalqZIKCqRt2/Qf1ddC9ZS29Cx7Yr+jygtO1bY6qdrW4hup6yuavq2p1CBWql9fGzNi9FHud3pl71R5BWaqQZ16Opx3uExBUyp/pvaC4gL5Wn3PuifoheROvUrdKavkPgVYd8kpkbWquEtWd8kJ97N792698MILWrFihdLS0tSoUSPddtttGj9+vHx9fU973IgRI/T++++X2talSxetXbu2qiOfMbvDrvxCm3PFJ0d+Qf4VHwAAAFBFKGqeYvbs2Zo2bZpSU1PVpk0bzZw5U/HxVTtWIswxIG6A+l/c/7R/0Hp5e6lFr1i16BWrW046zl5o157/7dEfiQeUufOQbqj7kJSUJO3aJSUlaXde09OftCDU+TgUJ4Xsk5dDCnvqRZXULHvroDI0TtI4OSQd8D8iBR6SAjIk/0zJL8v56DxHarzBNVP7l3+fKu+jvfXIwRnaVvS7ggIdCg7yUnAdb9nq+CokKEg2/2AF+wXL5mvTja1u1LXNrj0Rq7hA3+78VgE+AQrwDlCgT2C5y5X9se9OvUrdKavkPgVYd8kpkbWquEtWd8kJ9/THH3/I4XDoH//4h1q0aKHNmzfrnnvuUU5OjqZPn17hsX369NG8efNc6xUVQatbye9NUdEa5wa/TDWd1YrfGwAAYAqLYRhlu4B5qI8++kjDhw/X7NmzdfXVV+sf//iH3nnnHf3++++Kjo6u8NisrCyFhIQoMzNTNputmhKjxjEMHdp6UElrD2j3L5navdOulFQp9ZCPUjMDlZobopSiBspRHY2xvqzRIU+p5WHnoXZ5yVeFcugMegkNuUFq9blr9Zn3r9ELSYmn399SLPnkST650iPRmr45VI/tayIFBmrekf5adPhKfVtvj2QtkKyF5T/q7ZBvu4+1se6TahPYVPL11Ve/x2hl9l79X97/5OPj0LbCJBVZ8iWLXfIqlrzszmXbfikoQ00CI5R0Y6IMi69SD/vpPwdWKzk/WX5+vvLz9Zavn498fL3l6+stH39v+Xr7yOplVWxorNo1bFfqW1qXvE4WWeTt5S2rl9X5r8VaZr1uQF35e5/oRWJ32JVXlKdWf2+l5Ozk8i+XLIqyRSlpTFKN6LV1ugKs5fiACTWlAOsuOSWyVhV3yeouOc1Ae6bqTJs2TXPmzNGuXbtOu8+IESN09OhRLV269JzPU1U/w1K/Ny9lSYXBUtjvsjzYVpJn/94AQE2Wn5+vpKQkxcbGyt+f3vUwX2WvybNpy9BT8yQzZszQXXfdpbvvvluSNHPmTH3zzTeaM2eOpkyZYnI6uAWLRWGtwxXWOlyXnW4fw1B2SpYcKQMUcqyLlJEhZWSoKPWIHvrsf8o4alXGMV9l5AZovz1IKQqV8uuWfg6/rFKr/jmV/NFieDv/+CgMkqyFCt6XLm1IlyT9ogH6VldJKVdV/BzNv1Zhu4/kP2mydMS56VH9rj/UTdLwio/9y1jpylnal5um1b0vVuzuaDXVHkmDKj5OkkbH6f592zT7W1/Jy0vziodrbOFUZfm3lGQ4Z3ayOI4vO5zrMqSQvdI9V+rT7+rrxn11JItF92e8qM+KrlJKYLFk+U/pY056DqP1x9rXbbL+0zdOvTNCJS8vdd/6d+3wlVL9CyQZrvFXnf8aruWQayaqu+1XfbTFOe7qLzktNGbnQ9oclKs8q73kB3J8f4trWZJib75ZjxxooDsPNJIsFs1L+YvmpydotS1YhuWbUvtKkmFxrt/y9RZ1ujpEH+68RBcdn7xhzNb7tKYwTDv8c4+f65TizfFjw+MWq1WLL/Xprsski0XZxQG6beMj2uZ3TIe8C4+f83ipx2K4Ilgshi6Kf179vXM07mBL2WXovjrHZPz0cZkfYcmZb1lkUYPiZfL2yVWHPg9p3MEWSjjWQJL0+s6++uJwK/0WkHn614Kkeo1/VLOOb+ufezrL5vCRJN258UFtlpf2+eZWeGzTDu/pivBfND2lrca0/kZGTrT07aunySqFF3+hkp/wJb0e060FdTT8iPMDrqUpl+v9fdfox8AjFZ4zwLZfbbtN0kupbdSmwPl7OnHrYH2fE6E//Y5VeGxE82/UPO4TrQ08LMPHkBxW6eOPys3asHhZqWNbXfmqegce0JPpF0uSfjrSXC9vG6CfA44q3+I47TktXnZd3vd+jcpoqj7HnGP9vrv7Wi1Jb69N/hX/bEIb/qKka59xvi5LfkG+fF3KjjyR9SNDEUVfuIqHJWLaLNKlTdbozZQOkqQD+SEaveke/eGXraPWogrP2yHhWd1kSHcdaSpJ+ja9g95O6qkfAw6XM3CHU6qPISPoTemvo0ttN/73mJR8uW75yKHI4i9Oe86G0avVrP0/9a+9l8tbzgnibl03RtuthUrxyasw70Wd/qFudXdqYnprSdLvWVGa+Ptg/ep/VDle9gqP/eCyWep+cbz07LMV7oeaKTMzU/Xq1at0v1WrVik8PFyhoaHq1q2bJk+erPBwcyfjsTvsGvP1GGdB0251tikkyT+z3OFwAACoDvPnz9fIkSNd61arVREREerVq5defPFFNW7c+IKcp2nTpkpISND8+fMvyPNJUlFRkdq1a6cRI0boySeflHTi+/Hz89Off/6pmJiYUsckJCTo0KFD2rx5s2ubxWLR6NGj9eabb16wbCebMGGCNm3apJ9//lkpKSm64447yr0Ow4cPV3Z29nl9MHsuKGoeV1hYqA0bNrheTCV69+6tNWvWlNm/oKBABQUFrvWsrKwy+wDlslgU3NgmNbZJusi12V/SzEmld7U77Go6q6n2H02VCupIBTbnI3S386kkRVnr6oY7blLRupXKypIyc7yVW2BVXqFVuYXeyi3yVo7dR8fsvnIYDv3rk7qKTi+QvPIlh0N5Cjij2HULC9U0VQouPLGtSD5n9j17FbsWU+tI0WfSG7WExS7vIoeUny9JypdFWQqR8is5ztv5++mdniHtyXCeW0FKUWzlx0Y5xy47kLxd+s25aa1aK/8MrtVBe7AO5h2SVq+WJGVKStSlzoVKbA7K0cG9GdL//pAk7VB3fa/LpIyKj3MUBWh9cJZy166W0pzb/qcXtEGdKz3n4abf67BPmvS5s+dvoerpMy2qPKyk9O4T1ezgFmnpFq1uKh3sOFT64/S9dBySDkhSQIaSQw5o6HcHXNd3g27UcnWt9Jxp/pn6PeSAir5aJh2vHX2pOTqgiIoPlJTWdrGKindq9aad2t9B0uEQaevNp82advKx/e9Spz//lFZskCRtUwstVSUfBEhSw1+0KyRNjy1Jk/Y4N63Wg1qlbpUemhq+RZvrpKjI9T+1pdy8Dkmppx571auyZf4uffq7M7/66hNdWXleryJ9GpKiXqtTpJ+cmzYpQV+ewc8mxWJIXqcUTHf8RTp84n3OISmlnGOTW36tdMsuaYmzB1uumuoTvVd5XknJfR5R7M7t0lcbJUm71ODMvtfQpLLb9l0l/XGTHJLK78d9/Jy2/doYkqyFn37q/KYkfaoFylNg5Xk7vivlbpU+2SpJOqR4fayZleeVtNdnkHSAG2zc0c6dO/XGG2/o1VdfrXC/6667TrfccotiYmKUlJSkZ555Rj169NCGDRvk5+dX7jHV0SZdvXf1iaEa7L5S3MdSQYgU5nwdlwyHs3rvaiU0Tbjg5wcAoCLz5s1Tq1atlJeXp++//15TpkxRYmKifvvtNwUFBZkdr1yzZ8/WkSNH9NBDD5X5WkFBgSZMmKAPPvjAhGSlvfbaa2rfvr1uuOEGvffe6dvnkyZNUqtWrbRixQr16NGj2vJR1Dzu0KFDstvtatiwYantDRs2VFpaWpn9p0yZoueee6664sFDWb2smtVnlnOm9oAsGSf1Yivp6TTz5nfUNm6A2p7xsx6/390wpKIizTiYq+cOpik3q1iFOUUqzC1WYZ5dhblFKsy1O5fzHWoQaFOXpoukqwudkyIVFurp1buUkbVfBQWG/iw8og8d2yTD6uxRZlglh7dzOfzEJ0mRXa5VYLPmunnDWh2wFCpXhoocXrIbXrIbFjlKlh1WOQwvDd0drB7FsVL7YMnhUOjROmqVvlMH/OxyyEsyLHLIy9kj6/iyDIsCfNIUn+SvSL86UkOrZBiyZRYqpDhDx7wlu8UiGV6Sjv9rnLR+vGdYo1yLs3JsGCf1zaxY3Typ/uk7wlXIv0jyOcdjpeOdVM/luHM/pUtqncr3qSncKSuAmmXSpEmVtv/Wr1+vzp1PfKiUkpKiPn366JZbbnHdDXQ6gwcPdi23bdtWnTt3VkxMjJYtW6YBA8r/0Kg62qSp2Sd9dOKbJw2+pfL9AACoJiX/Z0pS9+7dZbfb9cILL2jp0qW69dZbTU5XVnFxsaZNm6Y777yz3KJrnz59tHDhQo0bN06XXHKJCQlPyM7OlpeX886kioqszZs3V58+ffTyyy9T1DTTqbNGG4ZR7kzSTz31lB599FHXelZWlpo0aVLl+eB5Tj9Te5RzpvZzHb/KYpF8fRXU2FdB59gr/84HTizbHXatmtVUyVnJ5c7WXjJOZfzb38jqZVXZm5RPZ0OptaHHH5VrIldXvuMWnJS1aaVZmyhhW5J0/Da6fMOQYRhyOOQsCMv5r2tU4uMLXl4fy8tiSP90buvqkPKLCl1fN4wT+xqOk85tGPL3z5DFcuLrz+Yb6pr0ra5fMkBlSo/GSetedq28+XNdMqar67lWZEnFxYdlyJDDYRwvzDq/B8PhXJYh+fs/oMDA+6XHnRNR1HVIBw6lq8BRqOJi+/Gr4/w+S4Zgdhx/rnqhCxXk7y3NCVRkyhppya3SY5EV/VD0XsJMdWnYQQ0bbFGdu4Mkq7Pn0axMi17ISVZWUcW3ZPv7XarQkM2qO6quZHH+57op3UvHCvYoz17xbb82299kC3pKv2T8Ln1+s9Tgd+nRRqfd/91rZuiy8A6SpAZh36mOn7/k42x0PJBj0a1ZacooqPj2c6vVRw3CflXoXTbJ6rzGizMsysrbq+xKvtfAwOv1W0593fzd8SKIV/Fp8869Zro6NzjR4KkbOlO2QB9pRqgk6dpcQ/uPpulQ/mHZjYor55ERG2UbESz5OHsdvpBp0bhjyTqUf7jC437NytCdP52y8c5454cbJ3mr68vqFFa6cWYLvlO2OndJTzpvsW1SLO0/mKbDBUeVby9QRcLq/UuhgQGSX6hkGLotx6K/ZqYqNe9AuftvOPib7v/fU84xf0/V7x7pOuen5XOunqJODdqV3UdSQMDlCg1ZJ++7Ip3vpZK2p2YqszBZOcU5FeYNDR2v0MDnpMn1JUldCqR9Gak6mH9YxUbFt9rHNflVqhNS4T6oWg8++KCGDBlS4T5NmzZ1LaekpKh79+668sor9fbbb5/1+SIjIxUTE6Pt27efdp/qaJNGBlf83n62+wEAUJWuuOIKSdKePc5bpZ577jl9+eWX2r59u4qLi9WiRQuNHj1ad955Z6l6T1FRkcaPH68FCxYoKytLHTt21IwZM8o9R1pamiZOnKhly5YpPT1djRs31ogRIzR+/Hh5e1dcavvss8+UnJys4cPLH8btb3/7mzZs2KAnnnhCX3/99Vl974ZhaPz48Zo2bZpmz56te+6556yOP1VJQfNMDB8+XEOGDNHOnTvVvHnz8zrvmaKoeVxYWJisVmuZXpnp6ellem9Kkp+f32lvAwIutMpmaq8JTu5VapGlVLHQ1au0z8wakfmcslosslgkq5dzrzPlJcnvHMfj9guSete9VlGr61ZeLG59nasAK0m2umV2PeO84ZXfyV1GfHh/RSWGVZrz9usHlvsaCI2QQs/+tJKkiLPMG9+kpaISo5xZbWV7FZVkvaPfLad9vdY5/mh8Bre9nyosUgo7w32bOeIUtW6SM6vFkE7JW5J1ZL/BFf5uBUhqfI55Qxs5fzYxqvjTjw6O1np224TSr4E66WWy3t1/WKXvA96SGkefW96Sn02Uyi+udHS00+S9Tys560DZV2pQhivnPTdVnvNkjRurkitUPj9JUc1Onxc1S1hYmMLCzuw3ODk5Wd27d1enTp00b968s/qjoERGRob27dunyMjTvz6qo00aHx2vKFtU5f8XRcdXaQ4AAM7Ejh07JEkNGjjH79+9e7fuvfde1wTQa9eu1UMPPaTk5GQ9e9JY5ffcc48WLFigcePGqVevXtq8ebMGDBig7OzsUs+flpamyy+/XF5eXnr22WfVvHlz/fDDD3rxxRe1e/duzZs3r8J8y5YtU3h4uFq3bl3u14ODgzVhwgSNGTPmrG7nLigo0IgRI7Rs2TJ9/vnn6tOnj+trxcXFFRx5gtVqLbdj35lISEiQYRj68ssvy72tviqcfeuqlvL19VWnTp20fPnyUtuXL1+uq646g3HTgCpm9bIqoWmChrYbqoSmCTWiOHiqkl6ljW2l/7SPskXVuFlR3SVrSQFWOlFwLVGTisXuklMia1Vxl6zukhPuLSUlRQkJCWrSpImmT5+ugwcPKi0trcyH561atdKnn34qSTp27JjGjRunH374Qbt379aqVavUr18/hYWF6aabbjLj23Dh9wYAaqcZM6SoqMofN9xQ9tgbbjizY0/t6JidXfk+Z8tut6u4uFjHjh3TsmXL9OKLLyo4OFg3HA8+b9483XnnnerZs6d69OihJ598Uk899ZRef/11191of/zxh95//32NHTtWr7zyinr16qVHHnlEr7zySpnxqidNmqQjR47o+++/16hRo3TttddqwoQJmjx5subPn6/ff/+9wrw//PCDOnbsWOE+9913n5o1a6YnnnjClbEihw8fVs+ePbV69WqtXr26VEFTknx8fM7o8f7771d6rtMJDw9X48aN9b///e+cn+Ns0VPzJI8++qiGDx+uzp07u24T2rt3r+677z6zowFuwx16lZZwl6xVNgTBBeYuOSW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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8))\n", - "\n", - "ax1.plot(\n", - " tlist, np.real(bath.correlation_function(tlist)),\n", - " \"r\", linewidth=2, label=f\"Exact\",\n", - ")\n", - "ax1.plot(\n", - " tlist, np.real(envfit.correlation_function(tlist)),\n", - " \"g--\", linewidth=2, label=f\"Fit\",marker=\"o\",markevery=50\n", - ")\n", - "ax1.plot(\n", - " tlist, np.real(padeBath.correlation_function(tlist)),\n", - " \"b--\", linewidth=2, label=f\"Pade (Nk={Nk})\",\n", - ")\n", - "\n", - "ax1.set_xlabel(r't', fontsize=28)\n", - "ax1.set_ylabel(r\"$C_R(t)$\", fontsize=28)\n", - "ax1.legend(loc=0, fontsize=12)\n", - "\n", - "ax2.plot(\n", - " tlist, np.imag(bath.correlation_function(tlist)),\n", - " \"r\", linewidth=2, label=f\"Exact\",\n", - ")\n", - "ax2.plot(\n", - " tlist, np.imag(envfit.correlation_function(tlist)),\n", - " \"g--\", linewidth=2, label=f\"Fit\",marker=\"o\",markevery=50\n", - ")\n", - "ax2.plot(\n", - " tlist, np.imag(padeBath.correlation_function(tlist)),\n", - " \"b--\", linewidth=2, label=f\"Pade (Nk={Nk})\",\n", - ")\n", - "\n", - "ax2.set_xlabel(r't', fontsize=28)\n", - "ax2.set_ylabel(r\"$C_I(t)$\", fontsize=28)\n", - "ax2.legend(loc=0, fontsize=12)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "945d3613", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.10657048225402832\n", - " Total run time: 9.50s*] Elapsed 9.50s / Remaining 00:00:00:00\n", - "ODE solver time: 9.499839067459106\n" - ] - } - ], - "source": [ - "with timer(\"RHS construction time\"):\n", - " # We reduce NC slightly here for speed of execution because we retain\n", - " # 3 exponents in ckAR instead of 1. Please restore full NC for\n", - " # convergence though:\n", - " HEOMFit = HEOMSolver(Hsys, (envfit,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultFit = HEOMFit.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "86bbc4d9", - "metadata": {}, - "source": [ - "## Simulation 5: Bloch-Redfield" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "0830344b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 0.78s*] Elapsed 0.78s / Remaining 00:00:00:00\n", - "ODE solver time: 0.7874143123626709\n" - ] - } - ], - "source": [ - "with timer(\"ODE solver time\"):\n", - " resultBR = brmesolve(\n", - " Hsys, rho0, tlist,\n", - " a_ops=[[sigmaz(), lambda w: bath.power_spectrum(w)]], sec_cutoff=0, options=options,\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "f01ff43a", - "metadata": {}, - "source": [ - "## Let's plot all our results\n", - "\n", - "Finally, let's plot all of our different results to see how they shape up against each other." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "7d86d8e6", - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate expectation values in the bases:\n", - "P11_mats = np.real(expect(resultMats.states, P11p))\n", - "P11_matsT = np.real(expect(resultMatsT.states, P11p))\n", - "P11_pade = np.real(expect(resultPade.states, P11p))\n", - "P11_fit = np.real(expect(resultFit.states, P11p))\n", - "P11_br = np.real(expect(resultBR.states, P11p))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "573f5fa0", - "metadata": {}, - "outputs": [], - "source": [ - "rcParams = {\n", - " \"axes.titlesize\": 25,\n", - " \"axes.labelsize\": 30,\n", - " \"xtick.labelsize\": 28,\n", - " \"ytick.labelsize\": 28,\n", - " \"legend.fontsize\": 28,\n", - " \"axes.grid\": False,\n", - " \"savefig.bbox\": \"tight\",\n", - " \"lines.markersize\": 5,\n", - " \"font.family\": \"STIXgeneral\",\n", - " \"mathtext.fontset\": \"stix\",\n", - " \"font.serif\": \"STIX\",\n", - " \"text.usetex\": False,\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "bf2aceb5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", - "\n", - "with plt.rc_context(rcParams):\n", - " # Plot the results\n", - " plt.yticks([0.99, 1.0], [0.99, 1])\n", - " axes.plot(\n", - " tlist, np.real(P11_mats),\n", - " 'b', linewidth=2, label=f\"Matsubara $N_k={Nk}$\",\n", - " )\n", - " axes.plot(\n", - " tlist, np.real(P11_matsT),\n", - " 'g--', linewidth=3,\n", - " label=f\"Matsubara $N_k={Nk}$ & terminator\",\n", - " )\n", - " axes.plot(\n", - " tlist, np.real(P11_pade),\n", - " 'y-.', linewidth=2, label=f\"Padé $N_k={Nk}$\",\n", - " )\n", - " axes.plot(\n", - " tlist, np.real(P11_fit),\n", - " 'r', dashes=[3, 2], linewidth=2,\n", - " label=r\"Fit $N_f = 3$, $N_k=15 \\times 10^3$\",\n", - " )\n", - " axes.plot(\n", - " tlist, np.real(P11_br),\n", - " 'b-.', linewidth=1, label=\"Bloch Redfield\",\n", - " )\n", - "\n", - " axes.locator_params(axis='y', nbins=6)\n", - " axes.locator_params(axis='x', nbins=6)\n", - " axes.set_ylabel(r'$\\rho_{11}$', fontsize=30)\n", - " axes.set_xlabel(r'$t\\;\\gamma$', fontsize=30)\n", - " axes.set_xlim(tlist[0], tlist[-1])\n", - " axes.set_ylim(0.98405, 1.0005)\n", - " axes.legend(loc=0)" - ] - }, - { - "cell_type": "markdown", - "id": "ab946655", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "8619c5a9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "QuTiP: Quantum Toolbox in Python\n", - "================================\n", - "Copyright (c) QuTiP team 2011 and later.\n", - "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", - "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", - "Original developers: R. J. Johansson & P. D. Nation.\n", - "Previous lead developers: Chris Granade & A. Grimsmo.\n", - "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", - "\n", - "QuTiP Version: 5.1.0.dev0+7941773\n", - "Numpy Version: 2.1.3\n", - "Scipy Version: 1.14.1\n", - "Cython Version: 3.0.9\n", - "Matplotlib Version: 3.9.2\n", - "Python Version: 3.12.7\n", - "Number of CPUs: 16\n", - "BLAS Info: Generic\n", - "INTEL MKL Ext: None\n", - "Platform Info: Linux (x86_64)\n", - "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", - "================================================================================\n", - "Please cite QuTiP in your publication.\n", - "================================================================================\n", - "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" - ] - } - ], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "a87d6c51", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "e2deba88", - "metadata": {}, - "outputs": [], - "source": [ - "assert np.allclose(P11_matsT, P11_pade, rtol=1e-3)\n", - "assert np.allclose(P11_matsT, P11_fit, rtol=1e-3)" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "qutip-dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md new file mode 100644 index 00000000..2960e736 --- /dev/null +++ b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md @@ -0,0 +1,492 @@ +--- +jupytext: + formats: ipynb,md:myst + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.16.4 +kernelspec: + display_name: qutip-dev + language: python + name: python3 +--- + +# HEOM 1b: Spin-Bath model (very strong coupling) + ++++ + +## Introduction + +The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices. + +In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment. + +The Bosonic environment is implicitly assumed to obey a particular Hamiltonian, the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions. + +In the example below we show how to model the overdamped Drude-Lorentz Spectral Density, commonly used with the HEOM. We show how to do this using the Matsubara, Pade and fitting decompositions, and compare their convergence. + +This notebook shows a similar example to notebook 1a, but with much stronger coupling as discussed in [Shi *et al.*, J. Chem. Phys **130**, 084105 (2009)](https://doi.org/10.1063/1.3077918). Please refer to notebook HEOM 1a for a more detailed explanation. + +As in notebook 1a, we present a variety of simulations using different techniques to showcase the effect of different approximations of the correlation function on the results: + +- Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator +- Simulation 2: Matsubara decomposition (including terminator) +- Simulation 3: Pade decomposition +- Simulation 4: Fitting approach + +Lastly we compare the results to using the Bloch-Redfield approach: + +- Simulation 5: Bloch-Redfield + +which does not give the correct evolution in this case. + + +### Drude-Lorentz (overdamped) spectral density + +The Drude-Lorentz spectral density is: + +$$J_D(\omega)= \frac{2\omega\lambda\gamma}{{\gamma}^2 + \omega^2}$$ + +where $\lambda$ scales the coupling strength, and $\gamma$ is the cut-off frequency. We use the convention, +\begin{equation*} +C(t) = \int_0^{\infty} d\omega \frac{J_D(\omega)}{\pi}[\coth(\beta\omega) \cos(\omega \tau) - i \sin(\omega \tau)] +\end{equation*} + +With the HEOM we must use an exponential decomposition: + +\begin{equation*} +C(t)=\sum_{k=0}^{k=\infty} c_k e^{-\nu_k t} +\end{equation*} + +As an example, the Matsubara decomposition of the Drude-Lorentz spectral density is given by: + +\begin{equation*} + \nu_k = \begin{cases} + \gamma & k = 0\\ + {2 \pi k} / {\beta } & k \geq 1\\ + \end{cases} +\end{equation*} + +\begin{equation*} + c_k = \begin{cases} + \lambda \gamma (\cot(\beta \gamma / 2) - i) & k = 0\\ + 4 \lambda \gamma \nu_k / \{(nu_k^2 - \gamma^2)\beta \} & k \geq 1\\ + \end{cases} +\end{equation*} + +Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. + ++++ + +## Setup + +```{code-cell} ipython3 +import contextlib +import time + +import numpy as np +import matplotlib.pyplot as plt + +import qutip +from qutip import ( + basis, + brmesolve, + expect, + liouvillian, + sigmax, + sigmaz, +) +from qutip.core.environment import ( + DrudeLorentzEnvironment, + system_terminator +) +from qutip.solver.heom import ( + HEOMSolver, +) + +%matplotlib inline +``` + +## Helper functions + +Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: + +```{code-cell} ipython3 +def cot(x): + """ Vectorized cotangent of x. """ + return 1. / np.tan(x) +``` + +```{code-cell} ipython3 +@contextlib.contextmanager +def timer(label): + """ Simple utility for timing functions: + + with timer("name"): + ... code to time ... + """ + start = time.time() + yield + end = time.time() + print(f"{label}: {end - start}") +``` + +```{code-cell} ipython3 +# Solver options: + +options = { + "nsteps": 15000, + "store_states": True, + "rtol": 1e-14, + "atol": 1e-14, + "method": "vern9", + "progress_bar": "enhanced", +} +``` + +## System and bath definition + +And let us set up the system Hamiltonian, bath and system measurement operators: + +```{code-cell} ipython3 +# Defining the system Hamiltonian +eps = .0 # Energy of the 2-level system. +Del = .2 # Tunnelling term +Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() +``` + +```{code-cell} ipython3 +# Initial state of the system. +rho0 = basis(2, 0) * basis(2, 0).dag() +``` + +```{code-cell} ipython3 +# System-bath coupling (Drude-Lorentz spectral density) +Q = sigmaz() # coupling operator + +# Bath properties (see Shi et al., J. Chem. Phys. 130, 084105 (2009)): +gamma = 1. # cut off frequency +lam = 2.5 # coupling strength +T = 1. # in units where Boltzmann factor is 1 +beta = 1. / T + +# HEOM parameters: + +# number of exponents to retain in the Matsubara expansion of the +# bath correlation function: +Nk = 1 + +# Number of levels of the hierarchy to retain: +NC = 13 + +# Times to solve for: +tlist = np.linspace(0, np.pi / Del, 600) +``` + +```{code-cell} ipython3 +# Define some operators with which we will measure the system +# 1,1 element of density matrix - corresonding to groundstate +P11p = basis(2, 0) * basis(2, 0).dag() +P22p = basis(2, 1) * basis(2, 1).dag() +# 1,2 element of density matrix - corresonding to coherence +P12p = basis(2, 0) * basis(2, 1).dag() +``` + +### Plot the spectral density + +Let us briefly inspect the spectral density. + +```{code-cell} ipython3 +bath = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=500) +w = np.linspace(0, 5, 1000) +J = bath.spectral_density(w) + +# Plot the results +fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) +axes.plot(w, J, 'r', linewidth=2) +axes.set_xlabel(r'$\omega$', fontsize=28) +axes.set_ylabel(r'J', fontsize=28); +``` + +## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator + +```{code-cell} ipython3 +with timer("RHS construction time"): + matsBath=bath.approx_by_matsubara(Nk=Nk) + HEOMMats = HEOMSolver(Hsys, (matsBath,Q), NC, options=options) + +with timer("ODE solver time"): + resultMats = HEOMMats.run(rho0, tlist) +``` + +## Simulation 2: Matsubara decomposition (including terminator) + +```{code-cell} ipython3 +with timer("RHS construction time"): + matsBath,delta=bath.approx_by_matsubara(Nk=Nk,compute_delta=True) + terminator = system_terminator(Q,delta) + Ltot = liouvillian(Hsys) + terminator + HEOMMatsT = HEOMSolver(Ltot, (matsBath,Q), NC, options=options) + +with timer("ODE solver time"): + resultMatsT = HEOMMatsT.run(rho0, tlist) +``` + +```{code-cell} ipython3 +# Plot the results +fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) + +P11_mats = np.real(expect(resultMats.states, P11p)) +axes.plot( + tlist, np.real(P11_mats), + 'b', linewidth=2, label="P11 (Matsubara)", +) + +P11_matsT = np.real(expect(resultMatsT.states, P11p)) +axes.plot( + tlist, np.real(P11_matsT), + 'b--', linewidth=2, + label="P11 (Matsubara + Terminator)", +) + +axes.set_xlabel(r't', fontsize=28) +axes.legend(loc=0, fontsize=12); +``` + +## Simulation 3: Pade decomposition + +```{code-cell} ipython3 +# First, compare Matsubara and Pade decompositions +padeBath = bath.approx_by_pade(Nk=Nk) + + +fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(16, 8)) + +ax1.plot( + tlist, np.real(bath.correlation_function(tlist)), + "r", linewidth=2, label=f"Exact", +) +ax1.plot( + tlist, np.real(matsBath.correlation_function(tlist)), + "g--", linewidth=2, label=f"Mats (Nk={Nk})", +) +ax1.plot( + tlist, np.real(padeBath.correlation_function(tlist)), + "b--", linewidth=2, label=f"Pade (Nk={Nk})", +) + +ax1.set_xlabel(r't', fontsize=28) +ax1.set_ylabel(r"$C_R(t)$", fontsize=28) +ax1.legend(loc=0, fontsize=12) + +tlist2 = tlist[0:50] +ax2.plot( + tlist2, np.abs(matsBath.correlation_function(tlist2) + - bath.correlation_function(tlist2)), + "g", linewidth=2, label="Mats Error", +) +ax2.plot( + tlist2, np.abs(padeBath.correlation_function(tlist2) + - bath.correlation_function(tlist2)), + "b--", linewidth=2, label="Pade Error", +) + +ax2.set_xlabel(r't', fontsize=28) +ax2.legend(loc=0, fontsize=12); +``` + +```{code-cell} ipython3 +with timer("RHS construction time"): + HEOMPade = HEOMSolver(Hsys, (padeBath,Q), NC, options=options) + +with timer("ODE solver time"): + resultPade = HEOMPade.run(rho0, tlist) +``` + +```{code-cell} ipython3 +# Plot the results +fig, axes = plt.subplots(figsize=(8, 8)) + +axes.plot( + tlist, np.real(P11_mats), + 'b', linewidth=2, label="P11 (Matsubara)", +) +axes.plot( + tlist, np.real(P11_matsT), + 'b--', linewidth=2, label="P11 (Matsubara + Terminator)", +) + +P11_pade = np.real(expect(resultPade.states, P11p)) +axes.plot( + tlist, np.real(P11_pade), + 'r', linewidth=2, label="P11 (Pade)", +) + +axes.set_xlabel(r't', fontsize=28) +axes.legend(loc=0, fontsize=12); +``` + +## Simulation 4: Fitting approach + +In `HEOM 1a: Spin-Bath model (introduction)` a fit is performed manually, here +we will use the built-in tools. More details about them can be seen in +`HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions` + +```{code-cell} ipython3 +lower = [0, -np.inf, -1e-6, -3] +guess = [np.real(bath.correlation_function(0))/10, -10, 0, 0] +upper = [3.5, 0, 1e-6, 0] +``` + +```{code-cell} ipython3 +tfit=np.linspace(0,100,10000) +envfit,fitinfo = bath.approx_by_cf_fit(tlist=tfit,Nr_max=3,Ni_max=1,full_ansatz=True, + sigma=0.1,maxfev=1e6,target_rsme=None, + lower=lower,upper=upper,guess=guess) +``` + +We can quickly compare the result of the Fit with the Pade expansion + +```{code-cell} ipython3 + +fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8)) + +ax1.plot( + tlist, np.real(bath.correlation_function(tlist)), + "r", linewidth=2, label=f"Exact", +) +ax1.plot( + tlist, np.real(envfit.correlation_function(tlist)), + "g--", linewidth=2, label=f"Fit",marker="o",markevery=50 +) +ax1.plot( + tlist, np.real(padeBath.correlation_function(tlist)), + "b--", linewidth=2, label=f"Pade (Nk={Nk})", +) + +ax1.set_xlabel(r't', fontsize=28) +ax1.set_ylabel(r"$C_R(t)$", fontsize=28) +ax1.legend(loc=0, fontsize=12) + +ax2.plot( + tlist, np.imag(bath.correlation_function(tlist)), + "r", linewidth=2, label=f"Exact", +) +ax2.plot( + tlist, np.imag(envfit.correlation_function(tlist)), + "g--", linewidth=2, label=f"Fit",marker="o",markevery=50 +) +ax2.plot( + tlist, np.imag(padeBath.correlation_function(tlist)), + "b--", linewidth=2, label=f"Pade (Nk={Nk})", +) + +ax2.set_xlabel(r't', fontsize=28) +ax2.set_ylabel(r"$C_I(t)$", fontsize=28) +ax2.legend(loc=0, fontsize=12) +``` + +```{code-cell} ipython3 +with timer("RHS construction time"): + # We reduce NC slightly here for speed of execution because we retain + # 3 exponents in ckAR instead of 1. Please restore full NC for + # convergence though: + HEOMFit = HEOMSolver(Hsys, (envfit,Q), NC, options=options) + +with timer("ODE solver time"): + resultFit = HEOMFit.run(rho0, tlist) +``` + +## Simulation 5: Bloch-Redfield + +```{code-cell} ipython3 +with timer("ODE solver time"): + resultBR = brmesolve( + Hsys, rho0, tlist, + a_ops=[[sigmaz(), lambda w: bath.power_spectrum(w)]], sec_cutoff=0, options=options, + ) +``` + +## Let's plot all our results + +Finally, let's plot all of our different results to see how they shape up against each other. + +```{code-cell} ipython3 +# Calculate expectation values in the bases: +P11_mats = np.real(expect(resultMats.states, P11p)) +P11_matsT = np.real(expect(resultMatsT.states, P11p)) +P11_pade = np.real(expect(resultPade.states, P11p)) +P11_fit = np.real(expect(resultFit.states, P11p)) +P11_br = np.real(expect(resultBR.states, P11p)) +``` + +```{code-cell} ipython3 +rcParams = { + "axes.titlesize": 25, + "axes.labelsize": 30, + "xtick.labelsize": 28, + "ytick.labelsize": 28, + "legend.fontsize": 28, + "axes.grid": False, + "savefig.bbox": "tight", + "lines.markersize": 5, + "font.family": "STIXgeneral", + "mathtext.fontset": "stix", + "font.serif": "STIX", + "text.usetex": False, +} +``` + +```{code-cell} ipython3 +fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) + +with plt.rc_context(rcParams): + # Plot the results + plt.yticks([0.99, 1.0], [0.99, 1]) + axes.plot( + tlist, np.real(P11_mats), + 'b', linewidth=2, label=f"Matsubara $N_k={Nk}$", + ) + axes.plot( + tlist, np.real(P11_matsT), + 'g--', linewidth=3, + label=f"Matsubara $N_k={Nk}$ & terminator", + ) + axes.plot( + tlist, np.real(P11_pade), + 'y-.', linewidth=2, label=f"Padé $N_k={Nk}$", + ) + axes.plot( + tlist, np.real(P11_fit), + 'r', dashes=[3, 2], linewidth=2, + label=r"Fit $N_f = 3$, $N_k=15 \times 10^3$", + ) + axes.plot( + tlist, np.real(P11_br), + 'b-.', linewidth=1, label="Bloch Redfield", + ) + + axes.locator_params(axis='y', nbins=6) + axes.locator_params(axis='x', nbins=6) + axes.set_ylabel(r'$\rho_{11}$', fontsize=30) + axes.set_xlabel(r'$t\;\gamma$', fontsize=30) + axes.set_xlim(tlist[0], tlist[-1]) + axes.set_ylim(0.98405, 1.0005) + axes.legend(loc=0) +``` + +## About + +```{code-cell} ipython3 +qutip.about() +``` + +## Testing + +This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. + +```{code-cell} ipython3 +assert np.allclose(P11_matsT, P11_pade, rtol=1e-3) +assert np.allclose(P11_matsT, P11_fit, rtol=1e-3) +``` diff --git a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb deleted file mode 100644 index 58ed5e6d..00000000 --- a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb +++ /dev/null @@ -1,1018 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "95cc68d0", - "metadata": {}, - "source": [ - "# HEOM 1c: Spin-Bath model (Underdamped Case)" - ] - }, - { - "cell_type": "markdown", - "id": "472e03c8", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices.\n", - "\n", - "In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", - "\n", - "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", - "\n", - "In the example below we show how to model the underdamped Brownian motion Spectral Density.\n", - "\n", - "Note that in the following, we set $\\hbar = k_\\mathrm{B} = 1$.\n", - "\n", - "### Brownian motion (underdamped) spectral density\n", - "The underdamped spectral density is:\n", - "\n", - "$$J_U = \\frac{\\alpha^2 \\Gamma \\omega}{(\\omega_c^2 - \\omega^2)^2 + \\Gamma^2 \\omega^2)}.$$\n", - "\n", - "Here $\\alpha$ scales the coupling strength, $\\Gamma$ is the cut-off frequency, and $\\omega_c$ defines a resonance frequency. With the HEOM we must use an exponential decomposition:\n", - "\n", - "The Matsubara decomposition of this spectral density is, in real and imaginary parts:\n", - "\n", - "\n", - "\n", - "\\begin{equation*}\n", - " c_k^R = \\begin{cases}\n", - " \\alpha^2 \\coth(\\beta( \\Omega + i\\Gamma/2)/2)/4\\Omega & k = 0\\\\\n", - " \\alpha^2 \\coth(\\beta( \\Omega - i\\Gamma/2)/2)/4\\Omega & k = 0\\\\\n", - " -2\\alpha^2\\Gamma/\\beta \\frac{\\epsilon_k }{((\\Omega + i\\Gamma/2)^2 + \\epsilon_k^2)(\\Omega - i\\Gamma/2)^2 + \\epsilon_k^2)} & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "\\begin{equation*}\n", - " \\nu_k^R = \\begin{cases}\n", - " -i\\Omega + \\Gamma/2, i\\Omega +\\Gamma/2, & k = 0\\\\\n", - " {2 \\pi k} / {\\beta } & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "\n", - "\n", - "\n", - "\\begin{equation*}\n", - " c_k^I = \\begin{cases}\n", - " i\\alpha^2 /4\\Omega & k = 0\\\\\n", - " -i\\alpha^2 /4\\Omega & k = 0\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "\\begin{equation*}\n", - " \\nu_k^I = \\begin{cases}\n", - " i\\Omega + \\Gamma/2, -i\\Omega + \\Gamma/2, & k = 0\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." - ] - }, - { - "cell_type": "markdown", - "id": "5ecff67f", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "54f4f7ff", - "metadata": {}, - "outputs": [], - "source": [ - "import contextlib\n", - "import time\n", - "\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " basis,\n", - " brmesolve,\n", - " destroy,\n", - " expect,\n", - " qeye,\n", - " sigmax,\n", - " sigmaz,\n", - " tensor,\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver,\n", - ")\n", - "from qutip.core.environment import (\n", - " UnderDampedEnvironment,\n", - " ExponentialBosonicEnvironment\n", - ")\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "713e16b7", - "metadata": {}, - "source": [ - "## Helper functions\n", - "\n", - "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e375ecb7", - "metadata": {}, - "outputs": [], - "source": [ - "def cot(x):\n", - " \"\"\" Vectorized cotangent of x. \"\"\"\n", - " return 1. / np.tan(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f158bbeb", - "metadata": {}, - "outputs": [], - "source": [ - "def coth(x):\n", - " \"\"\" Vectorized hyperbolic cotangent of x. \"\"\"\n", - " return 1. / np.tanh(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "62db5351", - "metadata": {}, - "outputs": [], - "source": [ - "def underdamped_matsubara_params(lam, gamma, T, nk):\n", - " \"\"\" Calculation of the real and imaginary expansions of the\n", - " underdamped correlation functions.\n", - " \"\"\"\n", - " Om = np.sqrt(w0**2 - (gamma / 2)**2)\n", - " Gamma = gamma / 2.\n", - " beta = 1. / T\n", - "\n", - " ckAR = [\n", - " (lam**2 / (4*Om)) * coth(beta * (Om + 1.0j * Gamma) / 2),\n", - " (lam**2 / (4*Om)) * coth(beta * (Om - 1.0j * Gamma) / 2),\n", - " ]\n", - " ckAR.extend(\n", - " (-2 * lam**2 * gamma / beta) * (2 * np.pi * k / beta) /\n", - " (((Om + 1.0j * Gamma)**2 + (2 * np.pi * k / beta)**2) *\n", - " ((Om - 1.0j * Gamma)**2 + (2 * np.pi * k / beta)**2)) + 0.j\n", - " for k in range(1, nk + 1)\n", - " )\n", - " vkAR = [\n", - " -1.0j * Om + Gamma,\n", - " 1.0j * Om + Gamma,\n", - " ]\n", - " vkAR.extend(\n", - " 2 * np.pi * k * T + 0.j\n", - " for k in range(1, nk + 1)\n", - " )\n", - "\n", - " factor = 1. / 4\n", - "\n", - " ckAI = [\n", - " -factor * lam**2 * 1.0j / Om,\n", - " factor * lam**2 * 1.0j / Om,\n", - " ]\n", - " vkAI = [\n", - " -(-1.0j * Om - Gamma),\n", - " -(1.0j * Om - Gamma),\n", - " ]\n", - "\n", - " return ckAR, vkAR, ckAI, vkAI" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3c3083d2", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_result_expectations(plots, axes=None):\n", - " \"\"\" Plot the expectation values of operators as functions of time.\n", - "\n", - " Each plot in plots consists of: (solver_result, measurement_operation,\n", - " color, label).\n", - " \"\"\"\n", - " if axes is None:\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " fig_created = True\n", - " else:\n", - " fig = None\n", - " fig_created = False\n", - "\n", - " # add kw arguments to each plot if missing\n", - " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", - " for result, m_op, color, label, kw in plots:\n", - " exp = np.real(expect(result.states, m_op))\n", - " kw.setdefault(\"linewidth\", 2)\n", - " axes.plot(result.times, exp, color, label=label, **kw)\n", - "\n", - " if fig_created:\n", - " axes.legend(loc=0, fontsize=12)\n", - " axes.set_xlabel(\"t\", fontsize=28)\n", - "\n", - " return fig" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "1e702554", - "metadata": {}, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\" Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "20425aac", - "metadata": {}, - "outputs": [], - "source": [ - "# Solver options:\n", - "\n", - "options = {\n", - " \"nsteps\": 15000,\n", - " \"store_states\": True,\n", - " \"rtol\": 1e-14,\n", - " \"atol\": 1e-14,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "7f76b8d1", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "And let us set up the system Hamiltonian, bath and system measurement operators:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "df4a5c49", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the system Hamiltonian\n", - "eps = .5 # Energy of the 2-level system.\n", - "Del = 1.0 # Tunnelling term\n", - "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "8258fd6b", - "metadata": {}, - "outputs": [], - "source": [ - "# Initial state of the system.\n", - "rho0 = basis(2, 0) * basis(2, 0).dag()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "5176eb82", - "metadata": {}, - "outputs": [], - "source": [ - "# System-bath coupling (underdamed spectral density)\n", - "Q = sigmaz() # coupling operator\n", - "\n", - "# Bath properties:\n", - "gamma = .1 # cut off frequency\n", - "lam = .5 # coupling strength\n", - "w0 = 1. # resonance frequency\n", - "T = 1.\n", - "beta = 1. / T\n", - "\n", - "# HEOM parameters:\n", - "\n", - "# number of exponents to retain in the Matsubara expansion of the\n", - "# bath correlation function:\n", - "Nk = 2\n", - "\n", - "# Number of levels of the hierarchy to retain:\n", - "NC = 10\n", - "\n", - "# Times to solve for:\n", - "tlist = np.linspace(0, 50, 1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "f2723068", - "metadata": {}, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresonding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresonding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "4ea690dd", - "metadata": {}, - "source": [ - "### First let us look at what the underdamped spectral density looks like:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "f3569f92", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_spectral_density():\n", - " \"\"\" Plot the underdamped spectral density \"\"\"\n", - " w = np.linspace(0, 5, 1000)\n", - " J = lam**2 * gamma * w / ((w0**2 - w**2)**2 + (gamma**2) * (w**2))\n", - "\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " axes.plot(w, J, 'r', linewidth=2)\n", - " axes.set_xlabel(r'$\\omega$', fontsize=28)\n", - " axes.set_ylabel(r'J', fontsize=28)\n", - "\n", - "\n", - "plot_spectral_density()" - ] - }, - { - "cell_type": "markdown", - "id": "5dfb0800", - "metadata": {}, - "source": [ - "The correlation functions are now very oscillatory, because of the Lorentzian peak in the spectral density." - ] - }, - { - "cell_type": "markdown", - "id": "42d073aa", - "metadata": {}, - "source": [ - "### So next, let us plot the correlation functions themselves:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "864e7b29", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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hRPnVrVuX22+/nV9++YUPP/ywxI/ZuXMn69evB9RABfN9p4uIiDjrqvSuXbtwu92nTBnRScK1KJEZrn/77Tfy8/M1VyOEEEIIu3nllVcYNmwYY8aMYdSoUXz55ZcsWrSIb775hnvuuYcOHTp420YaNGhAs2bNWLZs2RmP07FjR9atW8f06dP5448/2LBhwyl/vmzZMrp06UKNGjUC8nWdi/RcixJ169aNmJgY0tLSWLt27TlH7gghhBBCFBcZGcmPP/7IJ598wrRp07jzzjvJyMigRo0a9OjRg6lTp3rP1wAYNWoUb775Jjk5OaeM23vmmWdITk5m3LhxZGZm0rhxY3bv3g3A8ePHmTt3Ls8991ygv7xSycq1KFFoaCjnn38+IK0hQgghhFOMGTMGwzDo0aMHAB988EGJh8b99ttvbNy48Yz37969m5kzZ57x/ry8vBJf6Q4JCWH06NHMnTuXI0eO4PF4OHToED/99BM33ngjbndRFL3rrrs4ceLEGX3ajRs35pdffiEjIwPDMLzBGmD69Om4XC5uvfXWUz6noKCAvLw8DB8dtlMeEq5FqcxwvXjxYs2VCCGEEMLKwsLCiI2NrdRj1KtXj/Hjx/PCCy9QUFBwzo/Py8vjpZdeYuLEiWe0hEyYMIGwsLBS+739SdpCRKn69esHwNKlS2XetRBCCCHOcMcddzBixAhArVJX1hNPPEHVqlVJSkqiYcOGZ/3Yffv2cdNNN/Hwww+f8WcPP/wwN910k8/qKg+XoWO9XHhlZGQQGxtLeno6MTExuss5RU5ODjExMeTm5rJ9+3ZatGihuyQhhBDCr7Kzs0lMTKRp06YVmrks7Olc/9/Lk9ekLUSUKiIiwruRcenSpZqrEUIIIYSwPgnX4qzM1pAlS5ZorkQIIYQIHHlhP7j48v+3hGtxVn379gUkXAshhAgOZn+ux+PRXIkIJPP/ty/6syVci7Myw/XGjRvJyMjQXI0QQgjhX2FhYURERJCeni6r10HCMAzS09OJiIggLCys0o8n00LEWdWrV4/GjRuzZ88e/vjjDy666CLdJQkhhBB+Vbt2bZKSkti/fz+xsbGEhYXJxCwHMgwDj8dDeno6x48fp379+j55XAnX4px69uzJnj17WLVqlYRrIYQQjmdOg0hNTSUpKUlzNcLfIiIiqF+/vs+mtkm4FufUvXt3vvzyS1atWqW7FCGEECIgYmJiiImJwePxlHjyoHCGkJAQn7SCFCfhWpyTOY5PwrUQQohgExYW5vPwJZxNNjSKczLD9c6dOzl27JjmaoQQQgghrEvCtTinmjVr0rRpUwBWr16tuRohhBBCCOuScC3KRFpDhBBCCCHOTcK1KBMJ10IIIYQQ5ybhWpRJjx49AAnXQgghhBBnI+FalEnXrl0BtakxMzNTczVCCCGEENYk4VqUSa1atahXrx6gjkIXQgghhBBnknAtyqxjx44AbNiwQXMlQgghhBDWJOFalFmnTp0AWL9+veZKhBBCCCGsScK1KDMJ10IIIYQQZyfhWpRZ8bYQwzA0VyOEEEIIYT0SrkWZtWnThtDQUNLS0ti/f7/ucoQQQgghLEfCtSiziIgI2rRpA0hriBBCCCFESSRci3KRvmshhBBCiNJJuBblIuP4hBBCCCFKJ+FalIusXAshhBBClE7CtSgXM1xv2bKFnJwczdUIIYQQQliLhGtRLvXr16dGjRrk5+ezZcsW3eUIIYQQQliKhGtRLi6Xi7Zt2wKwefNmzdUIIYQQQliLhGtRbhKuhRBCCCFKJuFalJuEayGEEEKIkkm4FuUm4VoIIYQQomQSrkW5mac0btu2jfz8fM3VCCGEEEJYh4RrUW6NGzcmMjKS3NxcEhMTdZcjhBBCCGEZEq5FuYWEhNC6dWtAWkOEEEIIIYqTcC0qRPquhRBCCCHOJOFaVIiEayGEEEKIM0m4FhUi4VoIIYQQ4kwSrkWFmOF6y5YtGIahuRohhBBCCGuQcC0qpGXLlrjdbtLT00lJSdFdjhBCCCGEJUi4FhUSERFBs2bNAGkNEUIIIYQwSbgWFSZ910IIIYQQp5JwLSrMnHW9fft2zZUIIYQQQliDhGtRYS1btgQkXAshhBBCmCRciwozw/W2bds0VyKEEEIIYQ0SrkWFmeE6MTERj8ejuRohhBBCCP0kXIsKq1evHlWqVCE/P5/du3frLkcIIYQQQjsJ16LC3G639F0LIYQQQhQj4VpUioRrIYQQQogiEq5P8/bbb9O0aVMiIyPp3r07ixYtKvVjv/76a4YMGUKdOnWIiYmhb9++/PLLLwGsVj/Z1CiEEEIIUUTCdTHTp09n/PjxPP7446xZs4YBAwZwySWXsHfv3hI/fuHChQwZMoSffvqJVatWMWjQIEaOHMmaNWsCXLk+snIthBBCCFHEZRiGobsIq+jduzfdunVj0qRJ3ve1bduWK664ghdffLFMj9G+fXuuv/56nnzyyTJ9fEZGBrGxsaSnpxMTE1OhunVavHgxAwYMoEmTJiQmJuouRwghhBDC58qT12TlulBubi6rVq1i6NChp7x/6NChLFmypEyPUVBQQGZmJjVr1iz1Y3JycsjIyDjlzc7Mleu9e/eSk5OjuRohhBBCCL0kXBdKTU0lPz+f+Pj4U94fHx9PSkpKmR7j5Zdf5sSJE1x33XWlfsyLL75IbGys961hw4aVqlu3uLg4oqOjKSgoYNeuXbrLEUIIIYTQSsL1aVwu1yn/bRjGGe8ryWeffcbTTz/N9OnTiYuLK/XjJk6cSHp6uvdt3759la5ZJ5fLJZsaRUAZhoF0swkhhLAqCdeFateuTUhIyBmr1IcOHTpjNft006dPZ+zYsXzxxRcMHjz4rB8bERFBTEzMKW92J5saRSD8+eefXH/99cTGxuJ2u2nVqhUvvPACWVlZuksTQgghvCRcFwoPD6d79+78+uuvp7z/119/pV+/fqV+3meffcaYMWP49NNPufTSS/1dpiW1atUKkHAt/Gfy5Ml07tyZL774gszMTEB9vz3xxBN07dqVnTt3aq5QCCGEUCRcFzNhwgSmTJnC1KlT2bx5Mw899BB79+7lrrvuAlRLx+jRo70f/9lnnzF69Ghefvll+vTpQ0pKCikpKaSnp+v6ErRo0aIFADt27NBciXCiN954g7vvvpu8vDxGjhzJ8uXLSUpKYtq0adSrV4+tW7cyYMAA6fkXQghhCRKui7n++ut57bXXePbZZ+nSpQsLFy7kp59+onHjxgAkJyefMvP6nXfeIS8vj3vvvZeEhATv24MPPqjrS9CiefPmABJuhM/9+uuvPPTQQwA8/vjjfPfdd/Tq1Yt69eoxevRoVq1aRYcOHUhOTuaKK67gxIkTmisWQggR7GTOtWZ2n3MN6klHvXr1cLvdZGdnExYWprsk4QBpaWm0a9eO5ORkbr31Vt57770SNxcnJSXRvXt3Dh48yN13383bb7+toVohhBBOJnOuRUDVrVuXyMhICgoKSj3NUojyevTRR0lOTqZVq1a89dZbpU7tqV+/Pp988gkAkyZNYvHixYEsUwghhDiFhGtRaS6Xi2bNmgHSGiJ8Y8OGDfzvf/8DYMqUKVSpUuWsH3/RRRdx2223ATB+/HgKCgr8XqMQQghREgnXwickXAtfmjhxIoZhcO211zJgwIAyfc4///lPoqOjWbVqFTNmzPBzhUIIIUTJJFwLn5BwLXxl3bp1/Pjjj4SEhPD888+X+fPq1KnDI488AsCzzz4rB80IIYTQQsK18AkJ18JXXn31VQCuueYa7wz1snrggQeIjo7mzz//ZNasWf4oTwghhDgrCdfCJyRcC19ITk7m008/BfCO4CuP2NhYxo0bB8DLL7/s09qEEEKIspBwLXzCnHW9c+dOeTleVNjbb7+Nx+OhX79+9O7du0KP8eCDDxISEsLcuXNZu3atbwsUQgghzkHCtfCJJk2aAJCens6xY8f0FiNsKT8/n6lTpwJU6iCmRo0acc011wDqoCchhBAikCRcC5+oWrUqCQkJgLSGiIqZN28eBw4coEaNGlx++eWVeiyzNeSzzz4jOzvbF+UJIYQQZSLhWviM9F2Lyvjwww8BuOGGG4iIiKjUYw0aNIhGjRqRnp7Od99954vyhBBCiDKRcC18RsK1qKjMzEy+/vprAG655ZZKP57b7fY+zgcffFDpxxNCCCHKSsK18BkJ16Kivv32W06ePEmrVq3o1auXTx7TDNezZ88mOTnZJ48phBBCnIuEa+EzEq5FRZmr1jfeeCMul8snj9m8eXP69OlDQUEB33zzjU8eUwghhDgXCdfCZyRci4o4efIkv/zyCwBXXnmlTx/76quvBuCrr77y6eMKIYQQpZFwLXzGDNd79+7F4/ForkbYxS+//EJWVhZNmjShU6dOPn1sM1wvWLCAw4cP+/SxhRBCiJJIuBY+U7duXSIjI8nPz2ffvn26yxE2YbZsXHnllT5rCTE1bdqUrl27kp+fL1NDhBBCBISEa+Ezbrebpk2bAuqkRiHOxePxMHPmTMD3LSEmaQ0RQggRSBKuhU+Zx6BL37Uoi2XLlnHs2DFq1apFv379/HINM7TPnz+fEydO+OUaQgghhEnCtfApc+VawrUoC3Mj45AhQwgJCfHLNdq2bUvjxo3Jyclh/vz5frmGEEIIYZJwLXyqSZMmAOzZs0dvIcIWzHA9bNgwv13D5XIxfPhwAH7++We/XUcIIYQACdfCxxo3bgxIuBbnlpqayqpVqwAYOnSoX69lhuuffvoJwzD8ei0hhBDBTcK18Clz5Xr37t1a6xDW9+uvv2IYBh07dqRevXp+vdagQYMIDw9n9+7dbNmyxa/XEkIIEdwkXAufMleuU1JSyM7O1lyNsLJAtISYoqKiuOCCCwBpDRFCCOFfEq6FT9WqVYuoqChAHSYjRGnMzYVDhgwJyPXM1pN58+YF5HpCCCGCk4Rr4VMul0v6rsU57dmzh7179xIaGsp5550XkGsOGjQIgIULF5KXlxeQawohhAg+Eq6Fz0nftTiXhQsXAtC9e3fvKx3+1rlzZ6pXr05mZiarV68OyDWFEEIEHwnXwudk5Vqcy4IFCwAYOHBgwK4ZEhLC+eefDyDzroUQQviNhGvhczLrWpyLuXIdyHANRa0hEq6FEEL4i4Rr4XPmyrW0hYiSJCcns337dlwuF/379w/otc1wvXjxYjweT0CvLYQQIjhIuBY+JyvX4mwWLVoEFPVAB1KHDh2oVasWJ06c4I8//gjotYUQQgQHCdfC58yV66SkJFkdFGfQ1RIC4Ha7vfOupTVECCGEP0i4Fj4XHx9PZGQkBQUF7N+/X3c5wmJ0hmuQvmshhBD+JeFa+JzL5aJRo0aA9F2LU6WlpbFhwwYABgwYoKUGM9QvX76c/Px8LTUIIYRwLgnXwi+k71qUZMWKFQA0a9aMuLg4LTW0a9eOmJgYjh8/zsaNG7XUIIQQwrkkXAu/kIkhoiRmuO7du7e2GkJCQujTpw8AS5Ys0VaHEEIIZ5JwLfxCVq5FSZYvXw7oDdcA/fr1AyRcCyGE8D0J18Iv5JRGcTrDMCwTrvv27QtIuBZCCOF7Eq6FX0hbiDjd7t27OXz4MGFhYXTp0kVrLb1798blcrFr1y4OHjyotRYhhBDOIuFa+IXZFrJv3z6ZyCCAon7rzp07ExkZqbWW2NhYOnToAMDSpUu11iKEEMJZJFwLv0hISCA0NJS8vDwOHDiguxxhAVZpCTFJ37UQQgh/kHAt/CIkJMQ761r6rgVYL1xL37UQQgh/kHAt/EY2NQqTx+Nh9erVgHXCtblyvXLlSnJzczVXI4QQwikkXAu/MVeu9+3bp7kSoduGDRvIzs6mRo0atGzZUnc5ALRo0YKaNWuSk5PjPTVSCCGEqCwJ18JvGjZsCMDevXs1VyJ0W7VqFQA9evTA5XJprkZxuVz06NEDUKvXQgghhC9IuBZ+Y4ZrWbkWZktI165dNVdyKgnXQgghfE3CtfAbsy1EVq7FmjVrAOuG6z/++ENzJUIIIZxCwrXwG1m5FgD5+fmsX78esF647tmzJwAbN24kKytLczVCCCGcQMK18BszXB87dozjx49rrkbosnXrVrKysoiKirLMZkZT/fr1iY+PJz8/n7Vr1+ouRwghhANIuBZ+ExMTQ2xsLCCr18HMbAnp0qULbre1fuS4XC7v6rX0XQshhPAFa/2mE44jrSHCqv3WJtnUKIQQwpckXAu/knAtrDopxCSbGoUQQviShGvhVzIxJLgZhmGblestW7aQmZmpuRohhBB2J+Fa+JWsXAe3PXv2kJaWRlhYGO3bt9ddToni4+Np2LAhhmF4V9mFEEKIipJwLfxKwnVwM1et27dvT3h4uOZqStetWzcAmRgihBCi0kJ1FyCcTdpCgpvVW0JMXbp04bvvvmPdunW6SxEOt2fPHubMmcOBAweoWbMmAwcOpEOHDrhcLt2lCSF8RMK18KviK9eGYcgvkCCzYcMGADp16qS5krPr0qULICvXwn92797NhAkT+Oabb874s379+vHqq6/Sq1cvDZUJIXxN2kKEXzVo0ACArKwsjhw5orkaEWgbN24EoGPHjporOTszXG/atInc3Fy9xQjHmTVrFl26dOGbb77B5XIxYMAAxo4dy8UXX0x4eDhLliyhX79+vPHGG7pLFUL4gIRr4VcRERHEx8cD0ncdbE6cOMHOnTsB64frxo0bExsbS25uLlu2bNFdjnCQH374gZEjR5Kenk7fvn3ZsGEDCxcuZMqUKfz8888kJiZy3XXXkZ+fz4MPPsjzzz+vu2QhRCVJuBZ+J5sag9PmzZsxDIM6deoQFxenu5yzcrlcdO7cGUD6roXPrFixgmuvvZa8vDxuvPFGfvvttzOm5tSrV4/PP/+cF154AYC///3vvPfeezrKFUL4iIRr4XdmuJZNjcHFLi0hJum7Fr505MgRrrnmGnJychg5ciQffvhhqRNzXC4Xjz32GE8++SQAd999t5wYKoSNSbgWfmdODJGV6+Bibmbs0KGD5krKxly5lnAtfGHChAns27ePVq1a8fHHHxMaeu75AU899RRXXXUVHo+HW265hezs7ABUKoTwNQnXwu+kLSQ4mSvXdgnXxVeuDcPQW4ywtdmzZ/Phhx/icrmYNm0aMTExZfo8t9vNu+++S3x8PH/++SfPPPOMnysVQviDhGvhd9IWEpzs1hbSrl07QkNDOXr0KElJSbrLETaVk5PDXXfdBcADDzxAnz59yvX5tWrVYvLkyQD8+9//ZvPmzT6vUQjhXxKuhd9JW0jwOXr0KAcOHABUaLWDyMhI2rZtC0hriKi4d999l8TERBISEio8+eOKK67g8ssvJz8/n0cffdTHFQoh/E3CtfA7c+U6KSmJ/Px8zdWIQDBXrRs3blzml8StQPquRWUcP36c5557DoAnn3ySatWqVfixXnrpJUJCQvjhhx9YsGCBr0oUQgSAhGvhd3Xr1iU0NJT8/HySk5N1lyMCwNzMaJeWEJNMDBGV8cYbb3D48GGaN2/O2LFjK/VYrVu35o477gDgsccek30AQtiIhGvhdyEhIdSvXx+Q1pBgYbfNjCYzXMusa1FeWVlZvPbaawA8/fTThIWFVfox//73vxMREcGSJUtYtGhRpR9PCBEYEq5FQMimxuBit82MJrMtZMeOHWRmZmquRtjJxx9/zOHDh2nUqBE33HCDTx4zISGBMWPGAPDPf/7TJ48phPA/CdciIGQcX/AwDMN2M65NtWvXpl69egBs2rRJczXCLgoKCnj11VcBePDBB8s007qsHnnkEdxuNz///LO0KwlhExKuRUDIxJDgkZSURHp6OiEhIbRu3Vp3OeVmPiEwV9+FOJfZs2ezefNmoqOjuf3223362M2bN+faa68F4L///a9PH1sI4R8SrkVASFtI8DBDaevWrYmIiNBcTflJuBbl9e677wJw6623+mU6zv333w/Ap59+yrFjx3z++EII35JwLQLCDNf79+/XXInwtz///BOA9u3ba66kYiRci/JISUnhhx9+AGDcuHF+uUa/fv3o1KkT2dnZfPDBB365hhDCdyRci4Awp4XIyXfOZ54oZx7IYjfmkwIJ16IsPvzwQ/Ly8ujTp4/f9hi4XC7uueceACZNmkRBQYFfriOE8A0J1yIgGjRoAKhVHo/Ho7ka4U9btmwBoE2bNporqRjzRMmDBw+SmpqquRphZYZhMGXKFACf91qfbtSoUURHR7N9+3YWL17s12sJISpHwrUIiDp16hAWFoZhGKSkpOguR/iR3cN1tWrVaNq0KSATQ8TZrVixgu3btxMVFcX111/v12tVq1bNu7Hxww8/9Ou1hBCVI+FaBITb7faOOJO+a+dKTU31rva2atVKczUVJ33XoiymT58OwGWXXVapo87L6pZbbgHgiy++4OTJk36/nhCiYiRci4AxW0MkXDuXuWrduHFjoqKiNFdTcRKuxbkUFBTwxRdfAPh91drUv39/mjRpQmZmJt99911ArimEKD8J1yJgzHAtmxqdy+4tISYJ1+Jcfv/9d5KSkoiNjeXiiy8OyDXdbjc333wzIK0hQliZhGsRMObEEFm5di5zUoiTwrVhGJqrEVZktoRcccUVAZ3nftNNNwEwZ84cjh49GrDrCiHKTsK1CBhZuXY+c+XarmP4TK1btyYkJIS0tDQOHDiguxxhMXl5ecyYMQMIXEuIqVWrVnTq1Im8vDxpDRHCoiRci4CRlWvnc0pbSEREhHdDprSGiNMtXLiQQ4cOUbNmTQYPHhzw619zzTUAfPnllwG/thDi3CRci4CRDY3OlpWVRWJiImD/cA3Sdy1K9/333wNw+eWXExYWFvDrm+H6119/JS0tLeDXF0KcnYRrETBmuD5w4ICcMOZA27dvxzAMatSoQVxcnO5yKk3CtSiJYRje485HjhyppYa2bdvSvn17PB6PN+gLIaxDwrUImISEBFwuF7m5uXLynQMVbwlxuVyaq6k8CdeiJFu2bGHXrl2Eh4czZMgQbXVIa4gQ1iXhWgRMWFgY8fHxgLSGOJFTJoWY2rdvD8Cff/4pr7QIL3PVetCgQQE5OKY05mmNv/zyC5mZmdrqEEKcScK1CCiZGOJcTtnMaGrevDlhYWGcPHmSffv26S5HWITulhBTu3btaNGiBbm5ufz6669aaxFCnErCtQgomRjiXE4Zw2cKDQ2lZcuWQNGqvAhuR44cYcmSJQCMGDFCay0ul8tbw48//qi1FiHEqSRci4CSlWtnKigoYOvWrYBzVq6h6ImC+cRBBLeff/6ZgoICOnbsSOPGjXWXc0q4ltYlIaxDwrUIKFm5dqa9e/eSlZVFeHg4TZs21V2Oz5hPFGTlWoAK16B/1do0YMAAoqOjOXjwIKtWrdJdjhCikIRrEVAy69qZzFXr5s2bExoaqrka3zFXriVci4KCAubMmQPAsGHDNFejhIeHM3ToUEBaQ4SwEgnXIqCkLcSZtm/fDuA91dApJFwL08aNGzl06BBVq1alb9++usvxMlfRZ86cqbkSIYRJwrUIKLMtZN++fRiGobka4StmuDY3ADpF69atAUhNTZXZ7EHOnMhx/vnnEx4errmaIpdccgkAq1atIjk5WXM1QgiQcC0CzAzXJ06cICMjQ3M1wlecGq6joqK8G9dkU2NwM8O1zoNjShIfH0+vXr0AmDVrluZqhBAg4VoEWFRUFDVq1ACkNcRJtm3bBjivLQRkU6OAnJwcFi5cCMDgwYM1V3Mms+9a5l0LYQ0SrkXAycQQZ/F4POzevRtw3so1SN+1gCVLlpCVlUXdunXp0KGD7nLOYK6mz5kzR0byCWEBEq5FwMmmRmdJTEwkPz+fqlWrUq9ePd3l+JyEa2GuCA8ePBiXy6W5mjP16dOHqKgoDh8+zPr163WXI0TQk3AtAk5Wrp3F7Ldu0aKFJYNHZclBMsIcwWfFlhBQI/kuuOACQFpDhLACCdci4GTWtbM4ud8ainqu9+zZw8mTJzVXIwItMzOT1atXA3DhhRdqrqZ0ZmuIhGsRKB6Ph7y8PN1lWJKEaxFw0hbiLE6dFGKqU6cOtWrVwjAM72E5IngsWbKE/Px8mjZtSsOGDXWXUyozXC9atIjs7GzN1QgnMgyDH3/8keuuu466desSHh5OeHg4zZs35+6772bdunW6S7QMCdci4KQtxFmcHq5B+q6D2YIFCwAYOHCg5krOrm3bttSrV4/s7GwWL16suxzhMBs3bqRv376MGDGCGTNmcPDgQUAF7l27djF58mS6dOnCjTfe6P2zYCbhWgScrFw7i1NPZyxO+q6DlzmC7/zzz9dcydm5XC5pDRF+8emnn9KjRw+WL19OVFQUDz30EL///jupqakkJyfz448/cv311+N2u/n888/p1q0bK1eu1F22VhKuT/P222/TtGlTIiMj6d69O4sWLSr1Y5OTk/nLX/5C69atcbvdjB8/PnCF2pgZrlNTU+XlS5vLzs5m7969gLNXrmXWdXDKyspixYoVgPVXrgEuuugioGi1XYjKev311xk1ahQ5OTlccsklbN++nVdeeYV+/fpRq1Yt6taty/Dhw/n8889ZsWIF7dq148CBAwwcOJCff/5Zd/naSLguZvr06YwfP57HH3+cNWvWMGDAAC655BJveDhdTk4OderU4fHHH6dz584Brta+qlevTpUqVQBZvba7nTt3YhgGMTEx1KlTR3c5fiNtIcFp2bJleDwe6tevT7NmzXSXc07m6vrKlSvJzMzUXI2wu6lTp3oXDR944AFmzpxJQkJCqR/fvXt3li5dyiWXXEJWVhZXXXXVWRconUzCdTGvvPIKY8eO5fbbb6dt27a89tprNGzYkEmTJpX48U2aNOH1119n9OjRxMbGBrha+3K5XNIa4hDF+62dOIbPZIbrbdu2ye74IGK2hAwcONAW39+NGjWiadOm5Ofns2TJEt3lCBubM2cOd955JwATJ07ktddew+0+d2SMiYnhu+++49JLLyU7O5sRI0YE5UZwCdeFcnNzWbVqlfcYWdPQoUN9+kMqJyeHjIyMU96CkWxqtAjDqNSnB0O/NajQUqVKFTweD7t27dJdjggQs73C6v3WxZm1/vbbb3oLEbZ14MABbrjhBvLy8vjLX/7CCy+8UK4nl2FhYcyYMYP+/fuTkZHB1VdfzYkTJ/xYsfVIuC6UmppKfn4+8fHxp7w/Pj6elJQUn13nxRdfJDY21vtm5dFO/iSzrjXxeGDGDLj6aqhfH0JDISoKunaFv/0Nyrlhz5xx7eR+awC3203r1q0B2dQYLHJzc1m6dClgj35rkxmupe9aVERBQQG33HILR44coWvXrkydOrVCr9pUqVKFGTNmULduXTZt2sR9993nh2qtS8L1aU7/JjIMw6cvB06cOJH09HTv2759+3z22HYibSEazJ0LHTvCddfB11/DgQNQUAAnT8LatfDvf0O7djBmDBw9WqaHDIYxfCZzU6P5hEI425o1a8jOzqZWrVre//d2YJ7U+McffwTdaqGovLfeeos5c+ZQpUoVPv30UyIiIir8WHXr1uXzzz/H7XbzwQcfMHPmTB9Wam0SrgvVrl2bkJCQM1apDx06dMZqdmVEREQQExNzylswkraQAMrPV6vSgwfD1q1QuzY8/jgsXgz798P27fDZZzBihGoTmTYNOnWCwn7TswmWthAo+holXAcHc9W6b9++tui3NjVp0oRGjRqRl5cnfdeiXJKTk3n88ccB+Pe//+2TJ5Xnn38+EyZMAOCuu+4iPT290o9pBxKuC4WHh9O9e/cz5oP++uuv9OvXT1NVzmWG6wMHDmiuxOFyclQLyL//rf773ntVmH7+eTjvPNUa0qIF3HAD/PADLFsGLVtCUhIMGQLfflvqQ584ccL7/69FixYB+GL0MsN1MG7OCUbFw7XdSGuIqIi//vWvZGZm0rNnT+666y6fPe4zzzxDixYtSEpK8oZ3p5NwXcyECROYMmUKU6dOZfPmzTz00EPs3bvX+002ceJERo8efcrnrF27lrVr13L8+HEOHz7M2rVr+fPPP3WUbyv16tUDJFz7VV4e/OUv8N13EBEBn34Kb74J1auX/jm9e8Pq1SqQ5+bCNdeoHu0SJCYmAlCjRg1q1qzphy/AWmTlOrjYOVybrSGyqVGU1ZIlS/j0009xuVxMmjSJkJAQnz121apVeeeddwCYPHkymzZt8tljW5YhTvHWW28ZjRs3NsLDw41u3boZCxYs8P7ZLbfcYpx//vmnfDxwxlvjxo3LfL309HQDMNLT0330FdjDvn37DMAIDQ018vPzdZfjTHfeaRhgGOHhhjFnTvk+1+MxjDFj1OdHRBjG77+f8SHffvutARjdu3f3UcHWlpaW5v03Hmz/XoPN/v37DcBwu91GZmam7nLKbceOHQZghIWFGSdOnNBdjrC4goICo3///gZg3H777X67zhVXXGEAxtChQ42CggK/XcdfypPXZOX6NPfccw+7d+8mJyeHVatWnbJL/IMPPjhjJcAwjDPedu/eHdiibSg+Ph6Xy0VeXh6pqam6y3GeqVPhnXfA7YbPP4fCk9vKLDQUpkyByy9XrSWXXw579pzyITt37gSgefPmvqra0mJjY737L8xec+FM5qp1p06dqFatmuZqyq9Zs2Y0aNAAj8fj/VqEKM3PP//M4sWLiYyM5KmnnvLbdf7zn/8QFhbG7NmzmTNnjt+uYwUSroUWYWFhxMXFAdIa4nPr16veaoBnn4Urr6zY44SEwCefQPfukJoKo0apVpNCwRauQVpDgoWdW0JATb0yF4YWL16suRphZYZhePug77//fu8kL39o3rw599xzDwBPP/00RiXPWbAyCddCG3NTo4zj8yGPB265BbKz4ZJLYOLEyj1eVJTquY6Jgd9/h+ee8/5RMIdr2dTobHYP1wDnnXceAL///rvmSoSV/fLLL6xdu5aoqCgeffRRv1/v0UcfJTIykiVLlpwxQMJJJFwLbWRTox/8+99qZnXNmvD++6otpLKaNoXJk9X9F16ANWsAvCcVBlO4Ng+SkZVr5zJbAsEZ4XrZsmXk5+drrkZY1UsvvQTAHXfcQa1atfx+vYSEBO+QCCevXku4FtpIuPax7dvhmWfU/ddeAx/OZ+fGG9XhM/n5cMcd5OfmevcWNGvWzHfXsThpC3G+NWvWkJubS+3atW39xLFDhw5ER0eTmZnJxo0bdZcjLGjFihX89ttvhIaG8tBDDwXsuubq9dKlSx27ei3hWmgjbSE+9sgjanze0KFw002+f/zXX4fYWFi5krQXXsDj8RAeHu79/xgMiodrp664BDu7Hh5zupCQEPr06QNIa4go2b/+9S8ARo0aRcOGDQN23bp163pXr80anEbCtdBGVq59aN48Nc86JEStWvsjFNStC4UvIca88go1gaZNm/p0HqrVNWvWDLfbTWZmJgcPHtRdjvADJ/Rbm8wD0OSkRnG6nTt38vXXXwPwyCOPBPz648ePx+12M3fuXNavXx/w6/ubhGuhjYRrHykogIcfVvfvvhvatvXftW6/HTp1Iuz4cf5OcPVbA0RERNCkSRNANjU61bJlywBnhGvZ1ChK8+6772IYBsOGDaN9+/YBv37jxo25+uqrAXjttdcCfn1/k3AttJG2EB/57ju1iTE6Gp5+2r/XCgmB//wHgHuB3gHYAGM1sqnRuQ4ePMi+fftwuVx0795ddzmV1rt3b9xuN7t375ZFDOGVk5PD1KlTAbj77ru11WH2eX/yySeOeyVQwrXQxly5PnToEB6PR3M1NmUYapY1wAMPQCDC7pAhrImPJwy4ZvNm/1/PYmRTo3OtXLkSgLZt2xIdHa25msqLiYmhY8eOgLSGiCJff/01qampNGjQgEsvvVRbHX379qV3797k5uYyadIkbXX4g4RroU2tWrUICwsDIDk5WXM1NmWuWlerBgHc7f2f6tUBaLN6NezYEbDrWoGEa+cyw3WPHj00V+I70hoiTmcG2XHjxhEaGqq1FnP1+p133nHUIpuEa6GN2+2WvuvKKL5qff/9gVm1Rp3oNTM5mR8Bd0EBPP98QK5rFRKuneuPP/4AoGfPnpor8R3Z1CiK27RpE4sWLSIkJISxY8fqLocrr7ySuLg4UlJSmDlzpu5yfEbCtdBKwnUl/PqrOtAlKgomTAjYZY8ePUpGRgbPmO/4+GMoPK0xGJg91zt37iSv2HHwwt4Mw3D0yvXq1as5efKk5mqEbu+//z4AI0eOtMQY1fDwcG699VYA/ve//2muxnckXAutZFNjJZg7rMeOhdq1A3ZZ89jzA/XrqyPW8/Phn/8M2PV1q1+/PlWqVMHj8XgP0hH2t3//fg4ePEhoaCidO3fWXY7PNG7cmISEBPLy8rxPHkRwysvL45NPPgFgzJgxeosp5vbbbwdg1qxZ7NmzR3M1viHhWmglK9cVtHUr/Pyzmmd9//0BvbQZrps1awaPP67e+dFHcOhQQOvQxe1207JlS0BaQ5zEDJ4dOnSgSpUqmqvxHZfL5T1MZsWKFZqrETrNnTuXlJQUatWqxSWXXKK7HK8WLVpw4YUXYhgG7733nu5yfELCtdBKwnUFvfGGuh0xAlq0COilzXDdvHlz6NcPevaEnByYPDmgdegkfdfO48SWEFPv3r0BWL58ueZKhE4ffvghADfccAPh4eGaqznVHXfcAcDUqVPJz8/XXE3lSbgWWklbSAWkpcG0aer++PEBv/yuXbuAwnDtchVNKXnrLcjODng9Osisa+cxNzM6MVz36tULkHAdzDIzM/nmm28AGD16tOZqznTFFVdQs2ZNkpKSmDdvnu5yKk3CtdBKVq4r4IMP4MQJ6NABBg0K+OVPWbkGuOYaaNBAtYV89lnA69HBXLmWUxqdofhmRidNCjH16NEDl8vFvn37ZOxpkPr666/JysqiVatWlvwej4iI4Prrrwfg448/1lxN5Um4FlpJuC4nw4ApU9T9e+5RK8cBdka4DguD++5T9197TdXocNIW4iy7du3i2LFjhIeH06FDB93l+Fx0dLT3iGvpuw5O5kbGm2++GZeG3xtlcdNNNwHqicCJEyc0V1M5Eq6FVmZbSHp6uu3/MQXE8uWwaRNUqQJ/+UvAL5+VleVt4WnWrFnRH4wbB5GRsH49LFsW8LoCzQzX+/fvl+9bBzBXrbt06WK5XlRfkb7r4HXkyBFvq4W5OmxFffv2pWnTphw/fpzvvvtOdzmVIuFaaBUdHU21atUAWb0uE3PV+rrrIDY24Jc3R8/FxMRQq/ihNTVrqpoA3nkn4HUFWs2aNb1f//bt2zVXIyrLyZsZTRKug9d3331Hfn4+nTt39k46siKXy+VdvbZ7a4iEa6GdtIaUUWYmfP65ul84FzTQireEnPHS4p13qtvp0+HYsQBXFnjm6vWOIDv+3YmCIVybmxr/+OMPCgoKNFcjAunLL78E4Nprr9VcybmNGjUKgNmzZ3Pw4EHN1VSchGuhnUwMKaPp09VGxtatofDUtUAzJ4Wc0hJi6ttXbbLMzlZzrx2uReEIRFm5tjfDMFi7di0A3bp101uMH7Vv356qVauSmZnJli1bdJcjAuTYsWPMmTMHgGuuuUZzNefWunVrevbsSX5+PtOnT9ddToVJuBbaycp1GRUeW8vYsVo2MgIkJiYC0LRp0zP/0OUqWr1+5x3Hb2w0X16VlWuNTp6EHTtg1y41orIC9uzZQ1paGmFhYbRt29a39VlIaGiod2VeWkOCxw8//IDH46FDhw7eEaJWd/PNNwP2bg2RcC20k5XrMkhMhCVLVIAtfNlMTxlnCdcAN98MVavCn386fmOjuXIt4TrA1q6FBx9Ur+BERUHLltC8OdSoAc2awW23waJFZX5yt2bNGkCdzOjUzYwm6bsOPjNmzADssWptuu6663C73fzxxx/efT52I+FaaCcr12Vgzo++8EIo/PvSwfxBV2q4jo2Fq65S9x3eGiJtIQH2558wbBh07apOKDXHIFatqkI2qCeh778PAwdCjx6wYME5H9YM1126dPFT4dZh9l3LOL7gkJ6ezuzZswF7hev4+HgGDhwIwFdffaW5moqRcC20k3B9DoYBhTNKdYzfKyrD8K5cN2nSpPQPNE//+vxzdSy6Q5nhOjk5Wcbx+VN+PjzzDHTpArNnQ0gIXH89fP01pKbC8ePqLT0dZs1SbVNVq8Lq1XDBBXDrrerPS2H2W3ft2jUgX45O5sr1+vXrOXnypOZqhL/NnDmT3Nxc2rRpQ7t27XSXUy7mkwFzM6bdSLgW2klbyDmsW6dW7SIi4OqrtZVx7NgxMjIygHOEa3N1/dgx+PHHwBSnQY0aNbzj+MwpKsLHjh2DESPg6afB44GRI9WK9eefw5VXQq1aRfsPYmLUyvaUKbB7N9x9N7jd6kTTHj2KVrpPY65cB0O4btCgAQkJCeTn57N69Wrd5Qg/+/777wG4+uqrLXtwTGmuuuoqXC4Xy5YtY9++fbrLKTcJ10K74ivXhsM3wVXIp5+q2xEjtMy2NpktIfHx8VSpUqX0DwwJgcJZpXz4of8L00j6rv3o4EHV3jFrljo06aOP4LvvVF/1udSpA2+/DfPmQf36sHUr9OunDmEqJjU1lf379wPQqVMnf3wVluJyuaTvOkjk5uYya9YsAEaOHKm5mvJLSEigf//+gD1bQyRcC+0SEhIAyMnJ4VgQzEcul4KCon5rjRsZoQybGYsr3O3Njz+ql+4dSvqu/SQlBc4/HzZuhIQEtZn3ppvKPyXn/PNVe0iPHnDkCFx0ESxd6v1jsyWkRYsWxMTE+PALsK7i866Fcy1evJiMjAzi4uLo2bOn7nIqxM6tIRKuhXYRERHUrl0bkNaQM6xYAfv3Q7VqcMklWkspV7ju0AG6dYO8vKKDbxxIxvH5wfHjcOmlarW5YUNYuFD1W1dUXBzMn6+C9YkT6t9RYStIMG1mNJnj+FatWqW5EuFPM2fOBODSSy/F7bZn1Lu6sA3y999/t102sOffuHAc2dRYCvPlsBEjIDJSaylmW8hZ+62LMzc2Org1RNpCfCw/X21WXL1atXbMmweFf8eVUq2aainp319tfBwxApKTg6rf2tS9e3dAfc/KK4XOZBgGP/zwAwAjRozQXE3F1a9fn379+gHwzTffaK6mfCRcC0uQcF0CwygK1xo3MprKtXINcOONqv/6jz/UQR8OJG0hPvb88/DTT6rHeuZM3wRrU1SUalNq1w4OHIArr+TPwk19wRSua9as6f03LJsanWnbtm3s2LGDsLAwhgwZorucSjFbQ+zWdy3hWliCTAwpwbp1am5vlSraW0KgAuE6Lk5NDgH44gs/VaWXGa6TkpJktFllzZ2rRu4BvPsuFPYG+1RMDHz/vTpwZvlyxm/dCgRXWwhIa4jTmS0hF1xwAdHR0ZqrqZzLL78cgEWLFtnqlRYJ18ISZOW6BOYz9YsvLjokQxPDMMrfFgLqJX5wbLiuVasWNWrUAGDXrl2aq7GxlBQ1w90w4Pbbi6bN+EPz5vDFFxghIYwB7oqJ8W6qDhZmuF65cqXmSoQ/mC0hdpwScrpmzZrRoUMH8vPz+fnnn3WXU2YSroUlSLgugYVaQg4dOkRWVhYul4tGjRqV/ROvvBJCQ9UqfOEqodNI37UP3HsvHDoEHTuq0xf9bfBgVl58MQD/OXkS9uzx/zUtxOy7lnDtPMeOHWPx4sWA2szoBJdddhlQNLfbDiRcC0uQtpDTbN6s3sLC1OYrzcyWkAYNGhAeHl72T6xZE8yev+nT/VCZftJ3XUlffaVOWwwNVbOszzZD3YemJiSwBIjKy1OjI/PzA3JdK+jWrRug/l0fPXpUczXCl2bPnk1+fj7t2rWjWVlmwtuAGa5//vlncnNzNVdTNhKuhSXIyvVpzFXrIUO0HhxjqlBLiMnhrSEyjq8Sjh5Vq9YA//d/0LlzwC69at06bgI8kZGwaBFMmhSwa+tWo0YNmjdvDkjftdPMnj0bgIsLX5lxgp49exIfH09GRgYLFy7UXU6ZSLgWlmCuXKekpJAfRCtIpTLHDlmgJQQqsJmxuCuugPBw2LRJvTmMtIVUwv/9nzqJsW1beOKJgF02Ly+PDRs2kAgcffRR9c7HHoMgeuVM+q6dxzAMb7geOnSo5mp8x+12e0cKmv3kVifhWlhCnTp1CAkJoaCggIMHD+ouR6+kJDXn1+WyREsIFIXrCq1cx8aqTZngyNYQaQupoDVrYMoUdf/ddyEiImCX3rZtG9nZ2URFRVHniSegTx/IzIQHHghYDbqZfdeycu0cW7ZsYf/+/URERDBgwADd5fhU8b5rwzA0V3NuEq6FJYSEhFC3bl1AWkP46Sd127u3GmdnAZVauYai1pAZM3xUkXWY4Xrfvn1kZWVprsYmDAMefFDd3nCDOtwlgDZs2ABAhw4dcIeGqnAfGqp6v22yMlZZsnLtPOaq9YABA6hatarmanxr8ODBREZGsnv3bjZu3Ki7nHOScC0sQzY1FjJ/uVtk1RqKeq4rHK4vvVRtztyyxXFTQ2rXrk1sYV+8+SREnMOXX6o+5ypV4KWXAn55M1x36tRJvaNjR3j4YXV/wgSwyaapyjA3Ne7Zs4fU1FTN1Qhf+PXXXwFntYSYqlat6j0Qxw5TQyRcC8uQTY1AVhbMmaPuWyRc5+fns6dwVFmF2kJAtYZcdJG6b7NjbM/F5XJJ33V55OaC2ef8yCNQntGOPrJ+/XoAOnbsWPTOxx+H+Hh1muhbbwW8pkCLjY31bsaV1hD7y8nJYf78+YAzwzXYaySfhGthGRKugfnzVcBu2BDMVTXNDhw4gMfjITQ0lAYNGlT8ga68Ut06LFyD9F2Xy9Sp6uTR+Hj429+0lHDGyjVAdLQ6fh3g2WchCFZzpTXEOZYuXcrJkyeJj48/9Umjg5ibGlesWEFKSormas5OwrWwDGkLAQqPrWXECLWh0QLMlpBGjRoREhJS8Qe67DL1Na1Y4bipDLJyXUZZWfDcc+r+Y49pOXk0IyPD+z19Rgi59VY1DjAtregodgeTTY3OYfZbDxkyBLfbmdGubt263ieEv/zyi+Zqzs6Z/weELZkr10Ebrg3j1HBtEZWaFFJc3brQt6+6/+23lXssi5FZ12U0eTIcOKBembnzTi0lmJuh6tevT82aNU/9w5AQePlldf+ddxx/cqOsXGt28CDMnQuffw7vv6/ON1i6FE6cKPdDOXEEX0kuueQSAH4yN/5blIRrYRlmuE5OTtZciSbr18O+fWqT16BBuqvxqvSkkOLM1hCHhWtpCymDEyfgxRfV/SefDOjoveLMlpBSXzq/6CK48ELweIpW2R2qa9euuFwu9u3bx6FDh3SXExzWroXx46FFC7XgMHgw3Hgj3HYbXHMN9OsHMTHQq5fa7FuGNsnDhw+zevVqQE3VcDIzXM+ePZu8vDzN1ZROwrWwjKDvuTZXrQcPDtgR0GVRqdMZT3fFFer2t9/g2LHKP55FmOF679695OTkaK7Got57Dw4fhqZN4ZZbtJVR4mbG05mh+oMP1AZHh4qJiaFVq1aAtIb43ZIlcMEF0LUrvP467Nyp2uRatlSLKZdcooJ1QgIUFMAff6hDlpo0gdGjYdeuUh967ty5GIZBp06dSEhICNiXpEOvXr2oWbMmaWlpLF++XHc5pZJwLSzD7Lk+cuRIcAaUWbPU7fDheus4jU9Xrlu0gA4dIC+v6MmEA8TFxREdHY1hGDKOryQeT1G7xd/+psYyalLiZsbT9eun/h3m5zu+91paQ/wsNVXNcj/vPFiwQM1Tv+46NVM9PR22bYN589T5Br//rlaq9+9XLVT9+6t/Ox99pE4xnTgRsrPPuESwtISAOhNj2LBhAPz888+aqymdhGthGdWrVycyMhIIwtaQ9HTVawdFpxlahE/DNThyakjxcXzSGlKCzz+HvXvVhJAxY7SVYRjGudtCTM8+q24/+QT+/NPPleljzrs22wqED/38s5qhPn06uN0wbpyalDN9uvo5GB1d8ufVr6/2JCxapDaADxmiRlj+85/Qs6dqISxm3rx5gPNbQkx26LuWcC0sw+VyBe+mxnnz1CpZq1bqZUCL8Hg87N+/H/BRWwgUhetZs+DkSd88pgXIxJBSFBQUHRQzfjwUPoHWYf/+/aSlpREaGkqbNm3O/sHdu8NVV6mNxg7uve7atSsAa9as0VyJgxiG2l8wfDikpKhV5xUr1Emg5R1n2rMn/PKLWoyIi4ONG9X7PvwQUIsfe/bsITQ0lPPOO88PX4z1mCvXa9assexIPgnXwlKCtu/aHCtksZf19u3bR0FBAREREd7j6SutSxd1cEhWlnpS4RASrkvx44+waZPapHX33VpLMVetW7duTURZNlQ++aS6/eILx/Zem+F6z549HD16VHM1DuDxwO23q1GTAPfcA6tWqSdrFeVyqf0qGzaoSVK5uWrfwhNPMH/uXED1IlerVq3y9dtAXFwcPXv2BGCW2U5pMRKuhaUEZbg2jKJwXfiM3CqKj+Hz2exUlwtGjlT3HdR3LeP4SmGuWt91lzqpUyNzM+NZ+62L69xZbTQrKIB//9uPlelTvXp1b8uXrF5Xksej+qunTlVtIG++qU779NUG9bg4+O67ouD+wgu0ePFF3MAgC02YCgSzNcSqfdcSroWlBGW43rEDdu9Wm7wuuEB3Nafw6aSQ4i69VN3OnKmeXDiA9FyXYNUqtUkrLEy1hGhW5n7r4iZOVLcffFCmsWh2JK0hPpCXB6NGqY2K4eEqBN97r++v43bDCy/ABx9ghIQwcNcupgKDBg70/bUszOoj+SRcC0sJynBtrlr37w8We1lv7969ADRu3Ni3DzxoEFStqk5qXLfOt4+tiRmu9+zZQ25uruZqLOK//1W3112nRoxpVqFwPWCAmvSQmwuvvuqnyvQyNzVKuK4gw4CxY2HGDPVE8ptv/H8Q2C23kPLqq+QBtwDnf/ihYxYqyqJnz57ce++9TJkyBcOCX7eEa2EpQR2uLdYSAioogjr63KciI9U8b3BMa0jdunWJioqioKDAu+If1A4dgs8+U/fvv19vLUBubi6bN28GytEWYjJXrydPdtR8dpOsXFfSc8+pDYYhIfDllwEbpzozMpLrgTwg9JNP4PHHA3JdKwgJCeHNN9/k6quvJkzjaM/SSLgWlmLOug6acJ2bC/Pnq/sW28wIfly5hqKVnR9/9P1jayDj+E7zv/+p7++ePaF3b93VsHXrVvLy8oiNjaVhw4bl++Thw6FTJzh+XAVshzHD9ZYtWzhRgaO3g9onn8BTT6n7kybBZZcF7NLz58/na+AH85ovvghvvx2w64vSSbgWlhJ0K9dLlqhjoePi1OYpi/HbyjUUre4sX65WOR3ADNc7d+7UXIlmHo8KGmCJVWs4tSXE5XKV75NdLvjrX9X9N99UX5+DJCQkULduXQzD8G76FGWwZo1qBwF45BE1xzpADMNgfuHCTI2HHioaF3n//TBnTsDqECWTcC0sxTy6NSMjg+PHj2uuJgCKj+Dz1TQOHykoKGDfvn2An1au69dXRwEbhjpswQFkHF+hb79V/fRxcarf2gLKdOz52Vx3HdStqzY1fvmlDyuzBmkNKaf0dLj2WsjJURu0//nPgF5+69atpKSkEBERQZ8+fVRLyK23qsk2N9wAhQsjQg9r/TYXQS86OprowlOrgmL12sL91ikpKXg8Htxut7ddx+fM1hCH9F1LuC701lvq9s47oSzzpAOgTMeen01EhJpZDGpjowU3UVWGhOtyMAwVZHfuhMaNVb91gBdHzFXrfv36qZONXS7VEtK9Oxw5og5AysoKaE2iiIRrYTlB0xqSmqpeVgR1vK3FmP3W9evXJzQ01D8XMcP1L7+o/lyba968ORDk4XrbNliwQIWNO+7QXY1XpVeuoejJwh9/wLJlPqrMGiRcl8P//qcmgoSHqwkhNWsGvITffvsNOG2+dWQkfPUV1KoFq1fDffcFvC6hSLgWlhM04brwhyMdOkB8vNZSSmL2W/ulJcTUo4dqHcjMhMWL/XedADFXrhMTEy05ezUg3ntP3V5ySfmPevaTtLQ09u/fD0CHDh0q/kBxcWqWMcBrr1W+MAsxx/Ft2LABj8N6yn1q1y6YMEHdf/FFtWE3wAzDKDlcg1pJ//xz9eR26lR1uqgIOAnXwnKCJlybR39feKHeOkrh182MJre7aGOjA1pD6tevT0REBHl5ed6V/6Di8ajDVkAdAW0R5gi+hg0bElvZUyIffFDdfvUVOOj/cdOmTYmNjSU3N5c///xTdznWVFCg2kFOnICBA7UdjLRlyxYOHTpElSpV6NWr15kfMHhw0SmOd94JhXtnROBIuBaWEzTj+Cwerv06hq84B/Vdu91ub2tIUE4M+eEHNfmlbt2iUzgtwAyL7dq1q/yDdeqk/s3m56vJIQ7hcrno0qULIK0hpXrjDVi4EKKi4P33tW1CX7RoEQB9+vQhPDy85A968kno1QvS0mD0aPX9KgJGwrWwnKBYuU5Kgq1b1Q/n88/XXU2JArJyDarfPCwMtm9X/bo2F9SbGqdMUbdjxqj/pxbh03ANRSuW773nqE1jclLjWezaVXSY0MsvQ7Nm2koxw/WAAQNK/6CwMPj4Y/VE4Lff4JVXAlOcACRcCwsKinBtHhzTvTtUr661lNIEbOU6JkYd/Q5F01NsLGg3Ne7dC7Nmqfvm7F+L8Hm4Hj5c9bYePao2tDmEualx9erVmiuxGMOAe++F7Gz1qoXmjbpmuO5v/twsTcuWRXsD/v53taAjAkLCtbAcM1wnJSVprsSPLN4SAgHa0Gi65BJ1a4YzGwvalev331chZNAgKPw7sAqfh+uQkKKAZR6W4wBmuF67di0FBQWaq7GQr75SP5vCw9W4u/IeQuRD+/btY8+ePYSEhNC3b99zf8LYsWrUa06O2gch/18DQsK1sJziK9eGw2bJAiqAzJ2r7ls0XKenp5Oeng4EoC0E4OKL1e38+Wp1yMaCMlzn56vJBGCpjYwAmZmZ3ldh2rZt67sHHjtWvfS+bBmsXeu7x9WoTZs2REZGcvz48eDcM1CSzMyiNqBHH4XWrbWWs7hwqlLXrl2pVq3auT/B5YJ33lHtIYsXw+TJfq5QgIRrYUHmKY3Z2dmkpaXpLcYfdu1SL6GHhcF55+mupkRmGKlZs2bZfoBXVocOUK+e6l9duND/1/Oj4kegB83q3/z56nu6enV1eIWFbNmyBVA/V2rUqOG7B46PL/paHbJ6HRoa6j1kR/quCz31lNoj06xZUc+1RmXqtz5d48ZFJ0g++qic3hgAEq6F5URGRlKzcCi/I/uuzZaQPn3UaoIFmeE6IKvWoFZXzNVrm7eGNGrUiNDQUHJycpz5/VuSjz5St9dfrw6ysBCzJcSnq9amu+9Wt598AhkZvn98DaTvupgtW9SEEFCnjlaporceytFvfbp77lGLOcePwwMP+KEyUZyEa2FJjt7UKP3WJXNIuA4NDaVp06ZAkLSGnDihelIBbr5Zby0l8Hm/dXEDB0K7durvwHyCYXNyUmMxf/ubankaObLo55NGR48eZePGjUAFwrXbrU6WDA2F7793xOhTK5NwLSzJsbOuDcNW4TpgK9egRvKFhMDmzbZ/2TKoJoZ8+60Kl82aQb9+uqs5g1/DtcsFd92l7k+apP5921zxcXyO3PNSVnPnqrntoaHw73/rrgaA33//HYDWrVsTFxdX/gdo2xYeekjdf+ABR42RtBoJ18KSHLty/eef6pCNKlWgd2/d1ZQqYGP4iqteXbXKgO1H8gXVpkZzxfamm7ROUSiNX8M1qAM6qlaFTZvUhjGb69ixIyEhIRw+fNh5P3/LKj8fHn5Y3b/7bu2bGE0V6rc+3d//DvXrQ2Ii/OtfPqpMnE7CtbAkx4Zrc9V6wACIiNBby1loaQuBopdef/45sNf1saAJ18nJ8Ouv6v5NN+mtpQQnT54kMTER8GO4jo2Fv/xF3XfAxsbIyEhvf3rQtoZMmwbr1qkn/E89pbsaL3NSSLlbQoqLji46UObFF9UGe+FzEq6FJTl21rUNWkJAw4ZGkxmu586F3NzAXtuHik8McbTPPlNzc/v0UQdWWMzWrVsxDIPatWtTp04d/13IbA35+mt1sIzNBfWmxuxsdXQ4wBNPQK1aeusplJWVxcqVK4FKrlwDXHstXHSRmn394IM+qE6cTsK1sCRHrlwXFMCCBer+oEF6azmL3Nxc7997wFeuu3WDOnXUbNmlSwN7bR8qvnLt6L7Vjz9WtxbcyAgBaAkxdesGnTursPLJJ/69VgAE9THokyap0XsNG6pTGS1i+fLleDwe6tWr590wXWEuF7z5puonnzkTZs/2TZHCq0LheunSpYSEhHjfzjvvPDweT6UKyc3NpV+/ft7HDAsLY8OGDZV6TGFfjgzXGzfCsWNQrZr6ZWxRSUlJGIZBRESEf1f7SuJ2q9PEwNZTQ5o0aYLb7eb48eMcOnRIdzn+sWkTrFmj5rVff73uakoUsHDtchUdnjNliu03NgbtxJDMTPjHP9T9p56y1FjJ4v3WLl/sbWjTpujJw8MPqz5z4TMVCtd/+9vfMAwDwzCoX78+X3/9NWFhYZUqJDw8nK+++oqEhAQMwyA/P5+//e1vlXpMYV9muE5OTnbOQRyFPxzp21etGFhU8UkhbreGF7ccMJIvIiKChg0bAg7uuzY3Mg4fbpmXzk8XsHANMGqU2kexfj2sWuX/6/lRly5dAPWz4KgD2lzK7PXXITVVtTjdcovuak7hk82Mp3vySahRQy38mCesCp8o92/OJUuW8Pvvv+NyuXC5XEyaNIn4+HifFJOQkMDbb7/t/e/Zs2cH3zNnAUB8fDwul4u8vDxSU1N1l+MbZrj25Q9HP9DWb20aOlStBK5dqzbM2ZSjNzUWFBS1P1i0JQQCHK5r1Cg6sXHKFP9fz49iY2Np1qwZEESr10ePFo3ce/ZZSy2A5OXlsbSwTa5SmxlPV7Nm0YbNJ55QK/fCJ8odrj8qNih/4MCBXHrppT4t6LLLLjvlmdm0adN8+vjCHsLCwrxP2hzRGmIYReF64EC9tZyDtkkhpjp1oHt3dd/GvYCODtdLlsD+/RATAz7+HeArOTk53r/7gIRrKGoN+ewzNfvbxszWkLVr1+otJFD+9S91ymanTnDddbqrOcW6des4fvw4sbGxdOjQwbcPfvfdaqX+0KGiI9JFpZU7XH/77bfe+4888ogvaynxcb/88ku/XENYn6P6rhMT4cAB1Z/aq5fuas5Ke7gGuOQSdWvjkXyOnhhi/ly+/HJL9aUWt23bNgoKCqhevTp169YNzEUvuACaNlUhzea/u4Kq7/rQoaJjzp9/Xu39sBCzJeS8884jJCTEtw8eHl60Yv/yy7Y/wMsqyvUdtG/fPg4ePAhA1apVGTx4sF+KGjJkCFWrVsUwDJKTk9m/f79friOszVHh2ly17tlTHSBjYdrbQqCo73r2bNtutHHsynVBQdFx59dco7eWsyjeEuKTDWBl4XbD2LHq/nvvBeaafhJU4frll9VphT17wogRuqs5g3kyo09bQoq77DL1xDAnByZO9M81gky5wrU589LlctG/f3/Cw8P9UlRERMQprSFBOWtTOGvW9cKF6tbi/dZgkZXrXr3UAQ7HjsEff+iroxIcewT6ihWqJSQ6WvXHW1RA+62LGzNGhexFi2DbtsBe24fMcL1lyxZOnjypuRo/OnIEzL1ef/+75U4ZNQyDJUuWANCvXz//XMTlUk8wQLU0BcMTKj8rV7guPlLK3AnvL8Uf31wtF8HFkSvXFg/XhmFYY+U6NBSGDFH3bXoUurkh7NixY86auDBjhrodOdKyLSGgMVzXr1/U1mTj1euEhATi4+MpKChw9ljc11+H48fVnHILrlrv27ePAwcOEBISQo8ePfx3oW7dik4aldXrSitXuD527Jj3vr972IpPIElLS/PrtYQ1OSZcp6TA9u1qdeC883RXc1aHDx8mOzsbl8tFgwYN9BZj83nXUVFR3u9hx6xeG0ZRL7GFW0JAY7iGotaQadOgkmdA6OT41pD09KJe6yeesNyqNeCdEtK5c2eioqL8ezFzSsovv8D8+f69lsOVK1wXn3mbk5Pj82KKyy129HHA+uWEpTgmXC9erG47dlStDhZmrlrXrVuXiIgIvcWY4XrFCtseKe24vuuVK2HvXoiKKuqLtyCPx8O2wpYMLeF6xAiIi4ODB+HHHwN/fR9xfLh+800VsNu1KxqjaDFmuPZbS0hxzZvDHXeo+xMn2v4wJJ3KFa6Ln9bm71PHDh8+XOJ1RfBwTLi2yQg+sEi/talBA2jfXm2gmzNHdzUV4riJIeaq9aWXWnpj7o4dO8jLy6NatWp6XoEJCys6hMTGM68dHa6PH4dXX1X3H3/cchNCTGa47tu3b2Au+Pe/Q9WqsHw5fPddYK7pQOX6boqLi/PeN19y85fijy/hOjjVr18fUD33eXl5mqupBJv0W4PFwjUUrV7btO/aUSvXxVtCrr1Wby3noGVSyOnM1pCff1ZjOG3IDNcbNmyw98/gkkyerDYztmhhubnWpqysLO9Ah4CF67p14aGH1P3HHgOn/X8PkHKFa/PlNcMwWLVqld9OzktNTWXlypXe/27fvr1friOsrXbt2oSGhmIYhn03taanq5MGwRbh2hKbGYsrfhS6DV+idNTEkDVrYNcutWJtbtizKK391qbWrdUei4IC+PBDfXVUQrNmzYiOjiY7O5stW7boLsd3srPhP/9R9x97zFKnMRa3atUq8vLyiI+Pp0mTJoG78COPqNMbN2+GYgcHirIrV7hu3LgxrVu3BlTAnuqns+inTp1KQUEBLpeLli1bWmcVTQSU2+0mISEBsHFryJIlKhQ2bw6FX4uVWW7lesAAFeYOHIBNm3RXU26OWrk2V62HD1c91xZmiXANcNtt6nbqVFs+OXS73XTu3Blw2EmNH3+s+uEbNoSbbtJdTamK91sH9BWY2Fj1pAPU8ejZ2YG7tkOUu8loWOHLtIZh8I9//MPnq9eHDx/mxRdf9H4jXWLxFRLhX7afdW2jlhCw4Mp1ZKQ63ABsOTXEXLk+dOgQmZmZmqupBMMoGsFn8SkhYKFwfe216onI9u1QeBCI3Tiu77qgAF55Rd1/8EHVH29RAe+3Lu7ee9W+l337iuaAizIrd7geP3484eHhuFwuMjIyGDlyJCdOnPBJMSdOnODyyy8nPT0dwzAICwvjgQce8MljC3uy/aZGm4Vry61cg637rmNjY717Rmy9qXHDBtixAyIi1GZGC8vLy2Pr1q2ABcJ1dHRRP6+fXun1N8eF619+Ue0O0dFw++26qymVYRh6w3VkJDzzjLr/j39ARkbga7CxcofrJk2aMG7cOAzDwOVysWLFCoYMGcKuXbsqVUhiYiLDhg1j2bJlgBq/d/vtt9O0adNKPa6wN1uH6+xsNUYObBGuT5w4wZEjRwALrVxDUd/1woXgoyfygeSI1hBz1fqSS1QosbDExERycnKoUqWKNZ4kmq0hX3wBNnz1oni4NmzY2nIG8yTCceNU+4NF7d69m5SUFEJDQ+nevbueIkaPhjZt1MZPc7VflEmFZs88++yztGzZ0vvfy5Yto1OnTjzzzDPlfvk+KSmJZ555hk6dOrF06VJcLhcul4sWLVrw3HPPVaQ84SC2Dtd//AG5uWr3dWHAsjKzJSQmJobqVprH3aoVNG6s/i4XLNBdTbnZflOjTVtC2rZte8rZDNqcd576Hj5xoujv0UbatWtHWFgYaWlp3le2bGvtWpg7F0JCVEuIhZmr1l27dqWKrrGXoaFg5rCXX4ZiI5LF2VXoJ0+NGjX46aefqF27NqBWmU+ePMmzzz5LkyZNuPDCC/m///s/ZsyYwe+//87GjRtJTExk48aN/P7773z55ZdMnDiRCy+8kCZNmvDss896W0sMw6BWrVr89NNP1KhRw3dfqbAlW4fr4i0hNjgIyQzXlljtK87lsnVriO1Xrv/8E7ZuhfBwSx4PfTrL9FubXK5TNzbaTHh4OB06dAAc0Bpirr5eey1Y6dW5EgT08Jizufpq6N5dzQX/5z/11mIjFZ4/07x5c+bOncu1117L1q1bcblcGIZBfn4+CxYsYEEZV5jMl5nMz2/VqhUzZszwrvaI4GbOurZluF64UN3aoCUEivqtLdUSYrr4Ynj3XVtuarR9uDanhAwdaumX0U2WC9egXl5//HG1qXHrVjWmz0a6du3KmjVrWLNmDVdeeaXuciomKQk++0zdf/hhvbWUgdZ+6+JcLtVzPWwYvPUWjB+vpqyIs6rUa2YdOnRg1apVjBkzxvu+4uNiDMMo9a2kj7/llltYtWoVHTt2rExZwkFsu3Kdn6/G8IHtwrXlVq4BLrxQvZS7bRskJuquplwcE65t0BICFg3XCQlFs8FtuHrtiE2N//2vOhBlwADo0UN3NWd14sQJ7+hD7eEaYMgQOP98yMmBZ5/VXY0tVLohrWrVqkydOpUtW7Ywbtw4IiMjSwzQ5pvJ/JjIyEjGjRvH5s2bef/994my+PxUEVhmuD5y5AjZdpq1uW6d2rwUEwM2ebJouTF8xcXGgvnyqM1aQ8xwnZSURFZWluZqymnLFti4UY0ru+wy3dWcU0FBAZs3bwYsFq6hqDVk2jTwePTWUk62D9fHj8M776j7Nli1XrlyJfn5+dSrV4+GVlglNlevAd5/Xy1yiLPy2bFELVu25J133uG///0vK1asYNGiRaxbt47U1FSOHj1KZmYm0dHR1KxZk9q1a9O5c2cGDBhAr169CA8P91UZwmGqV69OZGQk2dnZJCcn22d6jNlvfd55asXVBiy9cg3qZclFi1S4vusu3dWUWc2aNalevTppaWns2rXLXifOmqvWgweDDfbA7Nmzh6ysLCIiIqz3s+LSS6FOHXV4yaxZMHKk7orKrFOnTrhcLpKSkjh8+LB3vKRtvP8+pKVBy5a2+HvXdnjM2fTrp/ZczJwJTz4Jn3+uuyJL8/mZn+Hh4fTv35/+/fv7+qFFEHK5XNSrV49du3Zx4MAB6/3CLI0ZrgcO1FtHOVh65RpU3/UTT6jd/h6PpQ9/KM7lctG8eXNWrVrFjh077BmubdYS0rp1a0KtdqR1eLjqvX75ZdUaYoOQZ4qOjqZFixZs376dNWvWMHToUN0llV1+Prz2mrr/0ENghQky52CZfuvTvfAC/PgjTJ8Ojz4Kha9oiDNZ/7tMBD3b9V0bhu0Oj8nLy2P//v2AhVeuu3ZVK3+ZmVD4y8cubNl3vX27am8KCYHLL9ddTZlYst+6uFtvVbczZ6oVbBsxW0Nsdwz6t9/Crl1Qqxbccovuas5J++ExZ9OpE9x4o7r/xBN6a7E4Cdenefvtt2natCmRkZF0796dRWZIKsWCBQvo3r07kZGRNGvWjMmTJweo0uBhu3C9bRscOqROs7P4xhlTcnIy+fn5hIWFkZCQoLuckrndamMN2G5qiC3D9VdfqdsLL1TBxAYsH67bt4fevdXGuo8/1l1Nudi279o8NObuu6FqVb21lMHOnTs5fPgw4eHhdOvWTXc5Z3rmGTX/+qefYPFi3dVYloTrYqZPn8748eN5/PHHWbNmDQMGDOCSSy7xvlx+usTERIYPH86AAQNYs2YNjz32GA888ABfmb+UhE/YLlybT8h691YB2wbMfusGDRpY4+CN0pinNdp0U6OtwrV54Mm11+qtoxwsH67h1JnXNjrx0JbheulS9RYeDvfeq7uaMjFXrbt3706EFX9/tGgBY8eq+xMn2up7OJAs/Fs08F555RXGjh3L7bffTtu2bXnttddo2LAhkyZNKvHjJ0+eTKNGjXjttddo27Ytt99+O7fddhv/+c9/Aly5s9lu1rXNWkLAwgfInM7s9Vy92lYvq5vheufOnZorKaNdu9TfsdsNV1yhu5oyMQzDHuH6+uuhShV1OM/y5bqrKTMzXG/bto3jx49rrqaMzFXrUaPUSbk2YNmWkOL+/neIjFQr1zZ7FTFQJFwXys3NZdWqVWds1Bg6dChLzHnFp1m6dOkZHz9s2DBWrlyJx4qjlgxDbUT4618hPV13NWVm25VrG4VrSx8gU1x8fNEmml9/1VtLOZiHYu3Zs4fc3FzN1ZSB+erbBReoPncb2L9/P8ePHyc0NNT7ZMaSYmOLNojaaOZ1XFwc9erVwzAM1q9fr7ucc9u1C775Rt2fMEFvLeVgi3Bdvz7cd5+6/9hjUFCgtx4LknBdKDU1lfz8fOLj4095f3x8PCkpKSV+TkpKSokfn5eXR2pqaomfk5OTQ0ZGxilvAeNywf/9n3o2v2pV4K5bSWa4TkpK0lxJGSQlqUNO3G6w8g/H01h+DF9xNjwKvW7dulStWpWCggJ2796tu5xzs9mUEChqCWnVqhVhVp8kY7aGfP45nDiht5ZysFVryOuvq9A3bBgUHt9udcePH/c+cbF0uAaVJaKjYe3aop8XwkvC9WlOnylpGMZZ50yW9PElvd/04osvEhsb630L+IB4c4PdypWBvW4l2Grl2ly17tpVHSBjE5Yfw1dc8b5rm6yYuFwu+/Rd79kDK1aoJ+M2OuraFi0hpvPPh+bN1eQbG+3RsU24PnYM3ntP3bfBoTGmFStWUFBQQMOGDb3tkJZVq5Z6FRxUm0hent56LEbCdaHatWsTEhJyxir1oUOHzlidNtWtW7fEjw8NDaVWKbvrJ06cSHp6uvdt3759vvkCyqpnT3X7xx+BvW4lmNMrMjMzyczM1FzNOdiwJQRstnLdty9UqwaHD6tVE5uwTbg2w97AgbbpUwWbhWuXq2gsn41aQ2wTrt99V70i0LGjOgDJJoofHmMLDz2k2sa2bYMPPtBdjaVIuC4UHh5O9+7d+fW0Ps5ff/211G/0vn37nvHxs2fPpkePHqW+LBkREUFMTMwpbwFlw3AdHR1NdHQ0oEbGWdrCherWRuHaMAz7bGgEtfP/wgvVfRttprFNuLZhSwjYLFyDmrnscsGCBWD174lCXbp0AWDjxo3W3FcEkJsLb7yh7k+YoP6ObcIW/dbFRUernmtQI/qys/XWYyESrouZMGECU6ZMYerUqWzevJmHHnqIvXv3clfhUcsTJ05k9OjR3o+/66672LNnDxMmTGDz5s1MnTqV9957j7+aL5VYkTk3c88etfJnE7ZoDTl6FDZuVPdtdELpsWPHvLv/A96mVFE2HMlni4kh+/cXHdBz1VV6aykH20wKKa5Bg6L9AzZZ9WvatCmxsbHk5uZ6/74tZ/p0OHBAvepiHnhiA4ZhsGzZMsBG4RrgrrugYUP1s6OUyWrBSMJ1Mddffz2vvfYazz77LF26dGHhwoX89NNP3tW85OTkU2ZeN23alJ9++onffvuNLl268Nxzz/HGG29w9dVX6/oSzi02Flq3Vvel79q3fv9d3bZuDXFxemspB/N7Oi4ujipVqmiupozMULJkCQRyU3AlmBNDLL1y/fXX6va886Dw35wdpKSkkJaWhtvtplWrVrrLKTtzY+MHH6hjui3O5XJ5V68t2RpiGPDKK+r+/ffb5pwBgO3bt3PkyBEiIyO9f8e2EBkJTz2l7v/jH2ofgZBwfbp77rmH3bt3k5OTw6pVqxg4cKD3zz744AN+++23Uz7+/PPPZ/Xq1eTk5JCYmOhd5bY0szXERuHaFrOubd5vbYvNjKZmzaBlS7WJZt483dWUiblynZiYSJ5VN//YvCWkRYsW1jx4ozSXXQY1a6opQ7Nn666mTCx9DPr8+WofRtWqakXVRsyRvz169CA8PFxzNeV0yy3QqhWkpsKrr+quxhIkXAcjc2KIjfqubbFybdNwbat+6+LM1Wub9F03aNCAiIgIPB5P4Dcyl0VyctFxxlZ+9a0EmzdvBqBt27aaKymniAi46SZ13yYbGy29qdE8NObWW9WTFhuxXb91caGh8Nxz6v5//qNCdpCTcB2Mim9qtMnRpZafdX3yZNErAcVe7bADW65cw6l91zb4Pna73TRr1gywaGvI11+rv8c+fVQPpY3Yrt+6OLM15LvvbBFKiq9cF1hpFObmzfDTT2oD4/jxuqspN1uHa1CvdnXtqtpCXnpJdzXaSbgORl26QEgIpKSojR82YPmV6+XLVYtCgwZgsxVgW43hK+6CC9TkkN271SgoG7D0pkabtoSAzcN1587QvTt4PPDJJ7qrOac2bdoQERFBRkYGiYmJusspYvZaX345WPmEzhJkZGSwsXAzvG3DtdsNL7yg7r/5pmp1CmISroNR1arQvr26b5PWEMuH6+Ij+Gw0+gls3BYSFVU0lcUmU0Msu6nx4MGi72GbtYSAzcM1FK1ev/ee5V+FCQsLo2PHjoCFWkMOHoSPPlL3bXRojGnFihUYhkGTJk2oa6PZ8me4+GL1OzA7G559Vnc1Wkm4DlY2m3ddPFwbVvzlY9N+a7BxWwjYbiSfZWddf/utOu2yRw9o0kR3NeVy+PBhDh8+jMvlok2bNrrLqZgbb1T91xs2wOrVuqs5J8v1XU+aBDk50KuXmnRjM+ZmRtscHlMal0tNDAH1RNFqP+cCSMJ1sLLZxBDzlMbs7GzS0tL0FnM6j6doNrDNwnV2djYHDx4EbLhyDUWbGufPt8UBBpYN1zNmqNtrr9VbRwWYmxmbNGlC1apVNVdTQTVqFM0Vt8HGRkuN48vKgrfeUvcffth2rxyCA/qti+vfH4YPV6Mln3xSdzXaSLgOVubEkJUrLf8yJEBkZCQ1C3d/W641ZM0ataGxRg2w2cvS5tSKqlWrev9+baVjR0hIUL9gzUkXFla859oym8EOHwZzxKi0hOhjtoZ88on6frYwS61cf/SR2gjauLGtDj4yFRQU2PPwmLMxe68/+wzWrdNbiyYSroNVx45qM9jRo7Brl+5qysSys66Lt4S47fVPqni/tcuGKz64XLYayde4cWNCQ0PJzs62zvfxd9+pVaauXaGwJ9xOHBOuL7xQBcT0dPjmG93VnFWnTp1wuVykpKSQkpKir5CCgqKNjA8+qEbC2czWrVtJS0ujSpUqdOrUSXc5vtGlC1x/vbr/xBNaS9HFXklA+E54uPoHALZpDbHspkbpt9bLRn3XoaGhNCnsabbMxBAbTwkBB4Vrt1vNZwbLt4ZERUXRuvCkX62r1z/9BFu3QkwMjB2rr45KMPute/XqRVhYmOZqfOi559RUspkzYcEC3dUEnITrYGbTTY2WmnVdUGDrcG3bSSHFDR6sVrA3boT9+3VXc06Wmhhy9CjMnavuS7jWb8wY9b08d64aMWlhlmgNMQ+NueMOFbBtyFH91sW1bKn+v4CaO56fr7WcQJNwHcxsdlKjJVeuN29WAaVqVejWTXc15WbbGdfF1apV9ETRBkdIW2pT43ffqfnsnTqp44tt5tixYyQnJwM2PJ2xJI0bw0UXqfsffKC1lHPRfgz6ypVqr0BoKDzwgJ4afMCx4RrgmWcgNlYdSf/++7qrCSgJ18HMDCSrV9viWaUlw7W5at2nD9jwJT1HtIVAUWuIDfquLRWubd4SYk4KadiwIdHR0Zqr8RFzY+P776tXxixK+8r1f/6jbm+4wXYniprS0tK8r7z06dNHczV+UKcOPPWUuv/445CRobeeAJJwHczatFEHcRw/rvrWLM7S4dqGLSHgkLYQKNrUOGeO5Z8oWiZcp6XBr7+q+zYN145qCTFdcQVUrw579xa17FiQGa537NhBRqBD0+7dReMj//rXwF7bh5YvXw6onwlxcXGaq/GTe+9Vr4odOgTPP6+7moCRcB3MQkKKWhls0Boi4dq3CgoKvKP4bL9y3auXCiTHjln+e7l4uNZ6INIPP6gZ7e3agU1bKhwZrqtUgVGj1P133tFby1nUqlWLhoUrxusCPW7t1VfVqv6QIer4eJsyNzM6siXEFB5eNNHltdeC5mAZCdfBzkaHyZjhOjk52RozgvfsgX37VM+fDV/SO3jwILm5ubjdbu+YQ9sKDVUbG8HyrSFNmzbF5XJx/PhxDh8+rK8Qc+XPpqvW4NBwDXDXXer2228tvUlXS2vI0aPq9D+ARx4J3HX9wNH91sUNH65eXfR4bP1KQ3lIuA52NpoYUrduXVwuF3l5eaSmpuoup2jVunt31V5jM2a/df369Qm14XzYM9hkJF9ERIR3xU9ba0hGRtHfkw1PZTQ5Nlx36AADB6oWp//9T3c1pdJyUuM778CJE2rF2nxCbUMFBQXethDHh2uXS61eh4SoTdQ//6y7Ir+TcB3szIkha9dCbq7WUs4lNDSU+Ph4wCKtIQsXqlsbtoSAg/qtTWbf9YoVcOSI3lrOQXvf9cyZ6t9769bQvr2eGiopIyPD29bkiEkhp7v3XnX77ruW/dkc8JXrnBx44w11/69/teVR56Y///yTjIwMoqKi6NChg+5y/K9dO3XQD6jvbYufQlpZEq6DXfPm6tjunBzYtEl3NedkqVnXNu63BoeM4SuuQQO14ldQYPnVa+3huviUEJsGlC1btgCQkJBAjRo1NFfjB1dcAXXrQkqKZU9sNMP1pk2byMnJ8f8FP/lE/X3Ur190AqBNmS0hvXr1csYrh2Xx9NPq53RiYtER6Q4l4TrYuVy2mndtmU2Nhw9D4S93zjtPby0V5JgxfMWNGKFuf/hBbx3noDVcHz9e9LLs1VcH/vo+4tiWEFN4eNEhHG+/rbeWUjRq1IgaNWqQl5fHJn8vzhQUFI3fGz/elqNPizM3M55n098fFRIdXfTKw7/+pc6JcCgJ10LCdUUsXqxu27dXh5jYkOPaQqAoXM+apTbPWJQZrrUcgT5zJmRnQ4sWUNgza0eOD9egwnVIiGpB27BBdzVncLlcgWsN+flnFcZiYoqedNhYUEwKKckVV6if0x4P3H036JyY5EcSroUtJ4ZoD9c2bwkBh65c9+kDtWurGc6//667mlJpPQL9iy/U7XXX2bYlBIIkXNevr8IIwKRJWkspTcDC9b/+pW5tfNS5KTU1lW3btgEOPTzmbFwu+O9/1cjJBQssfxJpRUm4FkXhesMGy28ysFy4HjhQbx2V4MiV65AQNfYJ1AqtRZnh+ujRoxw9ejRwF87MLGoJsfGUEAiScA1wzz3q9qOPLHnCXUCOQV+8WK3eh4UVbYqzsWXLlgFqI27NmjU1V6NBkybqaHSAhx6y9LjJipJwLdTqSHy8Gvvkzx+QPmCJcJ2ZCeYqjU1XrjMyMkhLSwMctnINtui7joqKIiEhAQhwa4jZEtKypa0P3zhx4gS7d+8GgiBcDxqkTtM9ftySq3zFw3W+v05HNTe/jRmjNsTZXNC2hBT30EPq8K/0dLj9dse1h0i4FuplGpvMuzYPO9EarpcuVU9Emja17Q96c9W6Zs2aVKtWTXM1PjZsmFrh2rZNvVmUlk2NDmkJ2bp1K4ZhUKdOHWrXrq27HP9yueCBB9T9119XP3sspHXr1kRFRXHixAm2bt3q+wusWqX2ULjd8Oijvn98DcxJIf369dNciUahoTBtGkREqOlO5sFADiHhWii9eqnbwqH2VmWuXB88eBCPrg1r5nxrG7eEOG4MX3ExMXD++eq+hVtDAh6upSXEvkaPhpo1YdcudQiHhYSEhNCtWzcAVvpj384//qFub7xRjY61OY/Hw4oVK4AgD9egXpExX5WYMEGdeuwQEq6FYm6qsHi4rl27NqGhoRiGwcGDB/UU4YBwba5cO64lxGSD1pCATwz54Qc1z75VK+jUKTDX9JOgC9dRUUVHor/yit5aStC9e3cAVq1a5dsH/vNP+PprdX/iRN8+tibr16/n5MmTVK9endatW+suR7/x49U428xMuPVWy70yU1ESroXSq5d6+XHnTjXD2aLcbre3V1VLa0h2dtETEBuHa0evXAOMHKluFy1Sk0MsKOATQ2bMULfXXmvrlhAIwnANcN99qt3p998ttwjSo3Ccq89Xrl98Ud1eeaVtTxI9ndkS0rdvX9xuiWCEhKi9BFWrwvz5Ra9U2Jz8nxVKbKx6iQYs94P7dFo3Na5YoY4iTkiw9UuUjhzDV1yzZuq43fx81a9pQQFtC8nIKGoJue46/1/Pz4IyXCckwF/+ou5bbPXaDNdr1qwhLy/PNw+6axd89pm6//jjvnlMCzA3MwZ9S0hxLVoUjZp8+mk1os/mJFyLIjZpDdEars2WkAEDbL3658gxfKczW0Ms2ndtrlwfPHiQzMxM/15s5kzVEtK6NXTs6N9r+Vl2dra3lSaowjWoCQugjq8vnJZiBS1btiQ6OpqsrCw2++rUvZdeUk+Ohw2DwrYTJ5BJIaUYPVpNgykoUE8iLfwKellIuBZFevdWt4UzOK3KEuHaxi0hEAQr11DUGvLTT+Cr1TQfql69unfShd/7rs0pIQ5oCdm2bRsFBQXUqFGD+Ph43eUEVufOMHiwCiDmMdIW4Ha7vX3XPmkNSUyEqVPVfQetWh84cIA9e/bgdrvpZQ4REEXefBPatoUDB9QGVgufsnsuEq5FEXPlesUK9cPborSFa48HClcd7ByuPR6P9+/O0SvXffuqo+mPHSs6rt5iAtIakpFR1BrjsJYQl82fKFTIhAnq9t134cgRvbUU49Nw/eyz6gnxkCG2PUugJGa/dadOnYiOjtZcjQVFRamFgKgomDtXbXa0KQnXokj79mpTQUYGbNmiu5pSaZt1vWYNnDgBNWrYenPN/v37MQyDiIgI4uLidJfjPyEhcNll6r45ccBizNYQv65cm1NCWreGDh38d50ACcp+6+Iuvhi6dFE/iyy0em32XVd6Ysi2bfDhh+r+c89VsiprkZaQMujQAT7+WN1/+231ZkMSrkWR0NCiw2Qs3BpirlwnJSUF9sLF+61tvMu7+Bg+x6/8XXWVuv3mG0ueABaQlWtzU5jND44xBX24drmKWiXeeMMyR6Kb4Xrt2rWVO4Pg6afVK6cjRhS1KjqEHB5TRldcUTQp5oEH1CEzNmPfhCD8wwZ919raQhzWb+3olhDT4MFQrRrs3w/+OOCikvwerlNTi34xmZMmbC7owzWoJ41t2qgxkxZZ2WvevDmxsbHk5OSwadOmij3Ixo3w+efq/rPP+q44C8jOzvau6ku4LoNHH4Wbb1abWq+6qqgl0yYkXItT2WBiiBmujx49SnZ2dmAuWlCgZiaD7cO14w+QKS4yEoYPV/ct2Bri93D95Zeqd7Vbt6JRmzaWm5vL9u3bgSAP1243PPaYuv/KK3DypN56AJfLVfl51089pV5huvpq6NrVh9Xpt3r1anJzc4mPj6dp06a6y7E+lwv+9z81LebkSfVzfO1a3VWVmYRrcSpz5XrjRjh+XG8tpahevTqRkZEAJCcnB+aiGzeqVaKoKNv/0A+qlWsoag35+mvLtYaY4Xr//v1kZWX5/gKffKJuHbJqvWPHDvLy8oiOjvbuvQhaN94ITZuqkWXvvqu7GqCSh8n88Yf6N+pywTPP+Lgy/YofHuP4djxfiYiAr75SJzimp8NFF6mBCzYg4Vqcql49aNhQrdRa8GV0UCskAW8NMVtCzjtP9abbWFCM4Stu+HAID1cbpXw1g9dHatWqRWxsLACJiYm+ffA9e9SUFJcLbrjBt4+tidluELSTQooLDS06EvzFF9UGR80qPDHEMOCvf1X3b77Z1hvGSyOHx1RQVBT8+KNa+Dt6VAXs+fN1V3VOEq7FmczWEOm7LuKQfmsIkgNkiouOViO9wHKtIS6Xy3/HoJu9qxdcAA5Z5ZV+69OMGaNOij10CF5/XXc13pXr9evXk5OTU/ZP/P579TM2MhKef95P1eljGIaE68qIjYU5c+DCC9Ur6hdfDFOm6K7qrCRcizPJpsZTGYZjwrVhGMEXruHU1hCL8VvftcNaQqBo5bq9A1c2KyQsrGjj37/+pWa6a9SkSRNq1qyJx+Nh48aNZfskjwf+9jd1f8IE9cqpw+zZs4eUlBTCwsK8q/uinKpVUyvY11wDubkwbhzcc48aM2pBEq7FmYpvarRYj6opoLOut2+HgwdV/5c5qtCmUlNTycrKwuVy0aBBA93lBM5ll6lNYGvWWOrYaPBTuN6wQb2Fh6vNYQ4hK9cluOEGdaR9eroK2BpVaFPju++qlq06ddSECAcyV627devm3S8kKiAyEqZPV69uuFwwaZI6LOzQId2VnUHCtThTt26qny8lBQpXOa0moLOuzVXr3r3VP24bM/utExISCA8P11xNANWuDeefr+5bbPXaL+HanG09fLg69MgBPB4P27ZtA2Tl+hRuN7zwgrr/+usQqE3epShXuD56VM21BrWJMSbGf4VpJC0hPuR2qznvM2eqE3hjY9WtxUi4FmeqUgU6d1b3LdoaEtC2EIeM4IMgG8N3OnMFd/p0vXWcxgzX5oi5SisogE8/Vfcd1BKyY8cOPB4P1apVo6EDWwcqZcQItYKXlQVPPqm1FLPt4Y8//jj3Bz/+uJrF3r493H67nyvTp/ikEOEjw4fD+vXqNMeQEN3VnEHCtSiZ2Rpi0cHtAQ3XxU9mtLmgG8NX3DXXqFWPFStg1y7d1Xi1bNkSUP9vyrUJrDRLl6pJIdWqqdDlEDIp5CxcLvjPf9T9996Dyh5BXgk9C1vnNm7cyMmzzd/+4w945x11/+23Vf+4Ax0/fpx169YBsnLtc/XqWXaztoRrUbL+/dXt77/rraMUAQvXe/aoHt2QELUyZHNBvXIdHw+DBqn7X3yht5Zi4uPjiYmJwTAM37SGfPihur3qKvUqlENIv/U59OsHo0apfTIPPKBtv0yDBg1ISEggPz+f1atXl/xB+flqM5phwE03OeJVwdIsXbqU/Px8mjRpIrPZg4iEa1Gy885Tt2vXWvIwmYSEBAAyMzPJzMz034XMeZo9eqiRbjYX1CvXUDTv2RxTZwEul4tWrVoBeHuKK+zkyaKvbcyYyj2WxcikkDJ46SU1F3jJkqLWoABzuVz0Lpw4tby0k37/9z91jkJMDPz73wGsLvAWL14MwAAHvPIpyk7CtShZw4bqLT/fkkehR0dHE10Ydv16SqMZrs0VT5sLugNkTnfVVWqz7rp1ljpQpnXr1gBs3bq1cg/0zTeQkQFNmhRt4HQIWbkug/r1i45F/9vfwJ8LD2dx1nC9b1/RVJDnnoO6dQNYWeAtKtyz0998NVgEBQnXonTm6nWwtoYYRlG4vvBC/1wjwIJ+5bpmTRg2TN230MZGn61cv/++uh0zRvWXO4TH4/E+8ZCV63OYMAGaNYMDB4qCdoCVGq4NA+64Qz0B7N0b7r1XQ3WB4/F4WFY4FEBWroOLc376Ct+zeLj2+6zrXbvUKktYWNHfhY2dOHGCI0eOAEEcrgGuv17dfv65Zea4+2Tles8emDdP3b/lFh9UZR07d+7E4/EQFRUlk0LOJTISJk9W9998EwrbEgKpR48euFwu9u7dS0pKStEffPABzJqlzgx4/31LTnnwpdWrV5OVlUWtWrVo06aN7nJEAEm4FqUzX8ZaulS1h1iM32ddm0Gld2+oWtU/1wggczNjbGwssbGxmqvR6PLL1S/3rVvVngIL8MnK9bRp6snChReqthAHKT4pxO2gFXm/GTIEbrtN3R87Vo3oC6Do6GjvKwze1evERBg/Xt1/9llo2zagNelg9lv3799fJtwEGfkpJUrXsaPaxJeZCWU9yjaA/N4WIi0hzhQTUzSi7qOP9NZSyBzHl5qaytGjR8v/AAUFalUQ4NZbfVeYRUi/dQW8/DIkJKjTDx9/POCX79WrF1AYrj0etZk4I0NNNXn44YDXo4P0WwcvCdeidCEhRfOuLdga4tdwXbzf2mGbGYM+XAOMHq1uP/lE/eLXrFq1at42pwqtXi9cqFYGo6PVpk2HkUkhFVC9uprKAfDqq/DjjwG9/Cl91088oebLV6+uppg4vB0EwDAMmRQSxCRci7OzcN+1X8P11q3q+PeIiKInGDYn4bqYSy6BOnXg0CH45Rfd1QCVbA0xNzJef70jWphOJyvXFXTppfDgg+r+LbeAv1roSmCG6zpLlsC//qXe+d57ECQ/f7Zs2cKRI0eoUqUKXbt21V2OCDAJ1+LsgjVcm/3W/fqpDUIOEPRj+IoLCys6GnzaNL21FKrwpsZjx2DGDHXfgS0heXl5MimkMl56Cbp2hSNH1JMvX5wCWgbt27enV2QkU7Kz1Tvuv9+Rr6qUxly17t27N+Hh4ZqrEYEm4VqcXe/e6iW8PXsCuupRFsXDteHrqQ8O67cGWbk+gzlR4/vvoSJ9zj5W4ZXradPUhrWOHR1xiujpdu7cSW5uLlWrVpUnhhUREaHGTsbGqkWSceMCMiUn9OhRvjUMqgEH2rZVPeBBxOy3lpaQ4CThWpxddDR07qzuW2z12jylMTs7m7S0NN89cEEB/Pabuu+QfmuQcH2GLl1UIM3NtcTM6wqtXBtG0di1u+8GB04kkEkhPtCypXp1IyREbeJ94QX/Xu/YMRg2jIScHLYB/+7VS71aFESKTwoRwUd+UolzM1tDNMxLPZvIyEhq1aoFwP79+333wJs2QWqq6l3t2dN3j6uRx+Pxts9IuC7kchUdEW6B1hBz5Xr79u0UFBSU7ZN++03tD6hWDW66yX/FaST91j4yZIiaew3w97/D66/75zoZGXDxxbB2LdnVqzMC+G3dOv9cy6KSkpJITEzE7XbT14GvJolzk3Atzs2i4RqgQYMGgI9nXZv91v37g0N65ZKSkigoKCAiIoK4uDjd5VjHqFFqNW/5cigMcbo0adKEsLAwsrOz2bdvX9k+adIkdXvTTepVJgeSSSE+dNddRWP5xo/3fcBOTYWhQ9VkkFq1SJsxg+3Ahg0bOHnypG+vZWHmqnWXLl2Idui/S3F2Eq7FuQ0cqG7XrgVftl/4gDm+zKcr1w7ut27UqJG8tF5cfHzRzOt33tFaSmhoKM2bNwfK2HednAzffKPu3323HyvTS1aufey5504N2BMnqla4ytq+XS3ELF8ONWrA7NnEX3QR9erVIz8/n5UrV1b+GjYh/dZCfsuKc0tIgFatVH+nxVavzZVrn4Xr/HxYsEDdl37r4GAG02nT4MQJraWUq+/6vfcgL09NtOnUyc+V6ZGXl8eWLVsAWbn2GZdLBeynn1b//c9/wmWXqbGUFfXFF9C9uzqwplEjtT+nWzdcLpe3LeJ3i+3Z8Sc5PEZIuBZlc/756tbc6GcRPg/Xq1er1fmYGOjWzTePaQEyhu8shgyB5s0hPR0+/1xrKWWeGJKbC2+/re47eNV6165d3kkh8sTQh1wueOop+PhjNU3kxx/V5t5PPinfKvbevWq83vXXq5N8BwyAZctOOdr8vMK2wmAJ10ePHmXDhg2ArFwHMwnXomzMcG2u6lqEz3uuZ89WtxddBKGhvnlMC5CV67Nwu+HOO9V9s4dZkzKvXH/+uWoLqVcPrrsuAJXpYfZbt23bVtqZ/GHUKBWGO3RQK9c33aRWoN9/v/RXcQxD9VTffju0aKFak0JD1SmM8+apVzqLMcP1kiVLyr5R18YWLlyIYRi0bduW+Ph43eUITZyTHoR/meF69Wq1GzwmRm89hXy+cm2G6yFDfPN4FiHh+hzGjFHhYNUq+OMPbVNiyrRybRhFM4Pvv98xm25LIv3WAdClC6xcqb6nXnpJ7a257Ta1+bFXL7UKXbu2mqW+e7f691F8MWPQIHjjDRXQS9C1a1eqVKnCsWPH2LJli+P/X/5W+OruBRdcoLUOoZcsBYiyadBAvXReUGCpvmufhuvMTFi6VN0fOrTyj2chEq7PoU4duPZadV/j6rW5cr1nzx6ysrJK/qB582D9eoiKKlpxdyiZFBIgERHw2GOwcye8+KJakc7NVT/r//c/9b7XXoNvv1XBOioKbrhB/fm8eaUGa4CwsDB69eoFBEdriIRrARKuRXlYsDXEDNdpaWkcP368cg+2YAF4PNCsmXoi4RCGYbB3715AwvVZmb3Ln30Ghw9rKaFOnTrExsZiGAY7d+4s+YPMVevbblNTGRxMVq4DrHZt+L//UxsTt29X7SHPPAMPPKDe/+qraprSoUPq34k5pvUcgqXv+ujRo6xfvx6A883flyIoSVuIKLsLLoCpUy21qTE6OpqYmBgyMjJISkryrvxViNkS4rBV60OHDpGTk4Pb7fY+GREl6NdP9ZuuWqVWr598MuAluFwuWrduzYoVK9i6dSsdTl8R/PNP+PlntSHtwQcDXl8g5efny6QQXVwutXrdooVPHi5YwrX0WwuTrFyLsjOfia9apVooLMJnrSG//qpuHdpvXa9ePcKC7AjicnG54K9/VffffFP1mGpw1r7rl15St1de6ahXV0qya9cucnJyqFKlCk2aNNFdjqgEcxzfjh07OHjwoOZq/EdaQoRJwrUou0aNoEkTNQvaQisQPgnXe/fCli1qcoSDDo8BGcNXLtdcA40bq7aQDz/UUkKpE0N27FCj0kAd/OFwGzduBGRSiBPUqFHD++rDkiVLNFfjP2a4HuSgMxJExchPLFE+5jNyC7WG+CRcm6vWvXtD9eqVL8pCZDNjOYSGwkMPqfsvv6yeSAaYuXJ9Rrj+xz9UPcOHQ48eAa8r0MxZwR07dtRcifAF80AVp7aGSL+1KE7CtSgf8xn5vHl66yjGp+HaYS0hIOG63MaOVU+wtm+Hr74K+OXbtGkDwJYtWzAMQ70zMbFoJV1DL7gOEq6dxel91wsWLMAwDNq1a0dcXJzucoRmEq5F+Vx0kbpduRKOHtVbS6FKh+v8/KJw7bDNjCDhutyqVSvaLPjMMwFfvW7VqhVut5u0tLSi/tQXXlB1DBumXl0JAma4PmNTp7AlM1yvWrWq9DGTNib91qI4CdeifOrXh3bt1EEW8+frrgaA+vXrA5UI12vWqCcKMTHq0ASHkTF8FfDQQ2r1+s8/4YsvAnrpyMhImjZtCsDmzZth0yY1Eg3UkdVBIDs7m+3btwOycu0UTZs2pW7dung8HlauXKm7HJ+TcC2Kk3Atym/wYHVrrvZqVumV619+UbeDBoEDp2nIynUFxMYWTQ55+mnIywvo5du2bQsUhutHH1WHN111FRROXXC6zZs3U1BQQM2aNUk47ThtYU8ul8uxrSFHjhyRfmtxCgnXovzMvuQ5c/TWUcgM16mpqWRnZ5f/AX78Ud0OH+7DqqwhIyODtLQ0QKaFlNsDD0DNmupAjY8/DuilzXCd9+uv6vszJESdkhckivdbu1wuzdUIXzE3NS5atEhzJb61oPBgNem3FiYJ16L8zj9fTVXYuVNttNKsRo0aVKlSBYCkpKTyfXJqKixbpu47MFybq9Y1a9akWrVqmquxmehodSodqKOhK3sCaDm0bdsWNzBs7lz1jjvvhMIpIsFA+q2dyVzVXbRoEXkBfjXIn34tfBV3sPmqrgh6Eq5F+UVHQ58+6r4FVq9dLlfFW0NmzVL94507gwNPL5SWkEp64AF1WEtyckBXjtu2bcudQOvMTPXvLUh6rU0yKcSZOnXqRPXq1cnMzGTNmjW6y/EZCdfidBKuRcVYtO+63CvXZkvIpZf6uCJrkHBdSRERat41qNtduwJy2XY1amBG+ay//x2C7KVm8wAZCdfOEhISwsCBA4GiDYB2l5iYyM6dOwkNDZXNjMJLwrWoGLPveu5ctdlKswqtXOflqZVrkHAtSnfZZerJZE6OGtFnzp72o5inniIWWAGs69fP79ezkmPHjnmfJEtbiPOYpxfOt8i0qcqaU/jqbZ8+fYiOjtZcjbAKCdeiYnr1Ui9XHz2qRtlpVqFwvWQJpKVBrVqOnR0sY/h8wOWC119Xk2RmzoTPPvPv9b77DqZPJx+4E9i8bZt/r2cxZktI48aNiYmJ0VyN8DVzddcpfddmS8gQBx5AJipOwrWomNDQotMazVF2GlUoXJstIRdfrKYxOJCsXPtIu3bw97+r+/ffDykp/rlOcrI6IRKY160baykcxxdEZDOjs3Xq1IkaNWpw/PhxVq9erbucSsnPz2du4aZj6bcWxUm4FhV3ySXq9qef9NZBJcO1Q1tCQMK1T/3f/0GXLurVmptv9v3Jjfn5cMstcOQIdOnCztGjgeAN19Jv7Uxut9sxfddr167l6NGjxMTE0MuBB5CJipNwLSrODKVLl6pAoFG5w/WePerkO7dbHSntQDk5OSQnJwMy49onwsLgk0+galU1JeeFF3z7+I89pjYIV6kCn35K606dgOAL17KZ0fnM1hC7912bLSGDBg0iNDRUczXCSiRci4pr2BA6dVIbGs2NgZqY4TolJQWPx3PuT/juO3V73nnqoBAH2rdvHwBVqlShdu3amqtxiHbtYPJkdf/pp+Grr3zzuB99BP/6l7r//vvQtq33IJnExESysrJ8cx2LMwxDwnUQMDc1Ll68uGw/ry1K+q1FaSRci8oxV6/NFgtNateuTXh4OIZheFdrz+rrr9XtVVf5tzCNireEyCl3PnTzzXDvvWpqyKhRsHBh5R7v22/h1lvV/UcfheuvByA+Pp7q1atTUFDAtiDZ1Lhv3z7S09MJDQ2ldevWussRftKxY0fb912fPHmSxYsXA9JvLc4k4VpUjhmuf/5ZjbbTxO12U79+faAMrSGHD4N5/O4VV/i3MI2k39qPXn9dfe/k5KiTPSt6mNKXX6ownZ8Po0fDP/7h/SOXy+VdvQ6W1hCz37p169aEh4drrkb4i9vt9p7WaG4ItJvffvuN3NxcGjVqRKsgOj1VlI2Ea1E5ffqotoq0NNV7rVGZw/X336tWlm7doEkT/xemiYRrPwoJgU8/VfOvT5xQTzKnTi37DOyCAnXi47XXQm4uXHMNvPee2gNQTLCGa2kJcT5ztfdXixxEVl4/Fr5ae+mll8org+IMEq5F5YSEFE0N0dwaUuZNjUHQEgIy49rvqlRRc6+vuUYF5LFj1Sp04d97qTZvhgsuUBsYQR2x/vnnarzlaYItXEu/dfAYVriR/Pfff+f48eOaqykfwzD4qXBK1vDhwzVXI6xIwrWoPLM1ZOZMrWWUKVxnZBS9hO/wcC0r1wEQEQHTp6tV6JAQmDEDWraE225TIyoPH1atI/v3qwB91VXQvr1qS4qKgnffVS0mpcxZD7ZwLSvXwaNFixY0a9YMj8dju5F8W7ZsYffu3URERHg3ZwpRnIRrUXnDhqlwsGkT7N6trYwyheufflKrjK1bQ2FwcSozXMsYPj9zu9UM7GXL1Ip0bq6a+HHppRAXB5GRarLOjTfCN9+o1pErr4QNG2DcuLM+tBmut23b5ojT7M7G4/F4n0RIuA4OQ4cOBeAXCxxEVh5mS8gFF1xAVFSU5mqEFUm4FpVXsyb076/uf/uttjLKFK6DpCUkPz/f2xbStGlTzdUEiR49YN48NT3knnugaVN1dDqoJ58dO8Jf/wobN6rvwzL8f2ncuDGRkZHk5uaya9cuP38Bem3btg2Px0O1atXkCWGQMFtDZs+erbmS8pGWEHEuEq6Fb5hh1VdzfyvgnOE6K6voNEmHh+ukpCTy8vIICwsjISFBdznBw+WCAQPgrbdg1y71PZeWBtnZsH49/Pvfqi2kjEJCQryr13/++aefiraGdevWAep4bLdbfjUFg0GDBhESEsK2bdvYrfFVz/LIyMhgUeG0KQnXojTyE0z4hhlWf/8dyjJn2g/McJ2cnEx+SUdT//ijmuzQuDF07x7g6gLL/EXVuHFjQkrp5xUBEBEBsbElblYsqw4dOgBFm/2cau3atQB06dJFax0icGJjY+nbty9gn9XrX3/9lby8PFq1akWLFi10lyMsSsK18I0GDdRYPsNQfaUa1K1bl5CQEPLy8jh48OCZH/Dpp+r2xhuLXq53qMTERACaOHjUYLCQcC2czG5919ISIspCwrXwnauvVrdffqnl8iEhIdSrVw8oOvrbKy2taFTgjTcGtjANzJVrCdf2Z4Zrc5KGExmG4Q3XnTt31luMCCiz73ru3LmW37RbUFAg4VqUiYRr4TtmuF6wQI0g08DcCLX39FnDX3+tpji0b682ljmcGa5lM6P9mZMztm3bRk5OjuZq/CMlJYXDhw/jdru9TyZEcOjevTs1a9YkPT2d5cuX6y7nrJYvX05KSgrR0dEMHDhQdznCwiRcC99p2lSdelhQoK01pNRw/dln6vYvf3F8SwhIW4iTNGjQgJiYGPLy8ti2bZvucvzCXLVu3bo1VatW1VuMCKiQkBDv6vUPP/yguZqz+6bw99qll15KRESE5mqElUm4Fr517bXq1uxvDrCGDRsCp7WFJCerEWkQFC0hIG0hTuJyuRzfdy391sHt8ssvB+D777/XXEnpDMPwhusrr7xSczXC6iRcC9/6y1/U7YIFUHiISSCVuHL96adqNb1PnzLNFrY7j8fjfXIhbSHOIOFaONnFF19MaGgomzdvZvv27brLKdGmTZvYsWMHERERXHLJJbrLERYn4Vr4VqNG6pQ60LJ6fUa4Ngx47z11/9ZbA16PDvv376egoICIiAji4+N1lyN8wOy7dmq4Nmdcy2bG4BQbG8sFhb83rNoa8m3hAWmDBw8mOjpabzHC8iRcC9+7+WZ1+9FHKtwG0Bnhetky2LwZqlaFG24IaC26FG8JkcM4nMHJE0NOnDjh7SWXlevgddlllwHw3Xffaa6kZNISIspDfvMK37v6aoiMVKF29eqAXtrsuT58+DBZWVlFq9bXXgsxMQGtRRfZzOg87QtPdUxMTOT48eOaq/GtDRs2YBgGdevWlVdagtjIkSMBWLx4MUeOHNFczal2797N6tWrcbvd3icBQpyNhGvhe7GxYP4A+uijgF66Ro0aREVFAZC0dStMn67+YOzYgNahk2xmdJ46dep4g6fTjkGXfmsB6udVp06dKCgo4EfzTAKLmF74e2TgwIHUqVNHczXCDiRcFzp27Bg333wzsbGxxMbGcvPNN5OWlnbWz/n6668ZNmwYtWvXxuVyeX9JCGD0aHX70UeQlRWwy7pcLm9rSO7HH8Px49CqFfTvH7AadJMZ187k1E2NcniMMJlTQ7766ivNlZzq888/B+DGIJk2JSpPwnWhv/zlL6xdu5ZZs2Yxa9Ys1q5dy81m73ApTpw4wXnnncc///nPAFVpIxdfrDY3Hj0KM2YE9NJmuI43rzt2bFDMtjZJW4gzOXVTo7mZUVauxbWFo1xnzZp1zsWtQNmyZQtr164lNDSUq82D0oQ4BwnXwObNm5k1axZTpkyhb9++9O3bl//973/MnDmTrVu3lvp5N998M08++SSDBw8OYLU2ERICd9yh7k+aFNBLN2zYkP5Arb17oUqVoGoJAWkLcSonrlzn5+ezfv16QMK1UN/jbdu2JTc31zIbGz8rPIBs2LBh1KpVS3M1wi4kXANLly4lNjaW3r17e9/Xp08fYmNjWbJkiU+vlZOTQ0ZGxilvjjV2LISFqYkdAWyZadSoEePN/7j5ZgiiH4i5ubkkJSUB0hbiNE4M1zt27ODkyZNUqVKFli1b6i5HaOZyubj++uuBoj5nnQzD8IZraQkR5SHhGkhJSSEuLu6M98fFxZGSkuLTa7344ovevu7Y2FjvdAtHqlsXrrpK3Q/g6nXbKlW4wvyPBx4I2HWtYO/evRiGQZUqVWTjjcOYE0OSk5M5fPiw5mp8w+y37tSpEyEhIXqLEZZghutff/2Vo0ePaq1l9erVbN++nSpVqnj7wYUoC0eH66effhqXy3XWt5UrVwLqGfPpDMMo8f2VMXHiRNLT071vpxzT7UT33KNuP/5Y9V8HQK8VKwgBfq9aFQoDSbAo3hLi6+9doVe1atVo0aIFUNSnbHeymVGcrk2bNnTq1Im8vDzvbGldPiqcdjVy5EiqVaumtRZhL44O1/fddx+bN28+61uHDh2oW7cuBw8ePOPzDx8+7PO5qxEREcTExJzy5mgDBkCXLnDyJLz1lv+vd/QoDX76CYCX8/MxAnyIjW7mZkZpCXEmsy/ZKZOJVhfOwe/atavmSoSVmKvXZkuGDjk5Od5wPWbMGG11CHtydLiuXbs2bdq0OetbZGQkffv2JT09nRUrVng/d/ny5aSnp9OvXz+NX4EDuFzw6KPq/uuvw4kT/r3ea6/hPnGCdcC3OTnaX1YMNNnM6GxOCteGYXhfOezRo4fmaoSVmP3N8+bNY8+ePVpq+Pbbbzl69CgNGjRg6NChWmoQ9uXocF1Wbdu25eKLL2bcuHEsW7aMZcuWMW7cOEaMGEHr1q29H9emTZtTXqY6evQoa9eu9R7qsHXrVtauXevzPm3bu+YaaNYMjhwpOjHRH9LSVIAH3oiNxaDYMehBQsK1szkpXO/Zs4ejR48SFhbmHTMoBKhX3i688EIMw+CDDz7QUsN7hb+rxowZI/sBRLlJuC70ySef0LFjR4YOHcrQoUPp1KmT9yUh09atW0lPT/f+9/f/396dR0dR5vsf/3TIHrIAEZLIEgRZAkGRRUAFxwXBQVTUgCiK26jH9ep15I56RY9nQK/L/FyQO15FR5jRQZbjrqAJooAgSdgNYAKiEBEki8QspJ/fH6FbAtlT3ZXuer/OyTlZqp76dlF0Pjx866l339XgwYP1xz/+UZI0ZcoUDR48WHPnzvVr7W1eaKj0wAM1n8+eXdMi4gvPPiuVlEgDB2rz0ZUHnBauaQsJbp5w/e233+o3Pz6cyRc8s9bp6emKiIiwuRq0NTfeeKMkad68eXK73X499u7du7V8+XJJ0g033ODXYyM4EK6P6tixo+bPn+9dHm/+/PlKSEiotY0xplbv1fTp02WMOeFj5syZfq09INxwg5SaKu3bJz3/vPXj//ij9PTTNZ/PnKmuRx8kE/Q3jB6HmevglpKSosTERFVXV2vLli12l9Mq69evlyQNGTLE5krQFk2aNEnx8fHavXu3MjMz/XrsefPmyRij8847T6eccopfj43gQLiGf0RESI8/XvP5k09Khw5ZO/7DD9c8Zv2ss6RJk7xPaXTSzHV5ebn27dsniXAdrFwul3f2OtBXDKHfGg2JiorS1KlTJUmvvfaa345bWVmpv//975Kkm2++2W/HRXAhXMN/pk6VBg6s6Y1+5BHrxl27VnrjjZrPn3lGcrkcGa49N/60b9+eJ4kFMc+ydYHcd22MYeYajfK0hixatEgHDhzwyzHfeecd7du3T8nJyTzuHC1GuIb/tGsn/e1vNZ/PmVMTilursrLmSZDGSNdeKx19ymZ3B7aFsMa1MwTDTY27du3SoUOHFB4e7n3yJHC8IUOGaMiQIaqoqPDOJvva/zt6U/ztt9+u8PBwvxwTwYdwDf86//yaEGyMdOutNeG4NZ58Utq8WUpMrLmh8SjPky+dNHNNv7UzHNsW4u8bvazCzYxoCpfLpXvvvVeS9NJLL6mytb8vGrF69WqtXbtW4eHhuvXWW316LAQ3wjX875lnpA4dpNxc6S9/afk4K1dKjz1W8/kLL0jHPO7bM3O9d+9eVVVVtaLYwMFKIc7Qt29fRUREqLS01PtnHmg8LSH0W6MxGRkZSkpK0t69e/XOO+/49FizZs2SJF1zzTXq3LmzT4+F4Ea4hv917ix5blB55hnp3XebP0ZhoTR5slRdLV19dc3ntQ7RWeHh4XK73dq7d68FRbd9zFw7Q1hYmLeVIlBbQzwz1/RbozHh4eG64447JElPPfWUz566m5ubq/fee08ul0szZszwyTHgHIRr2OOyy6R77qn5fOpUac2apu976JB08cU1y/oNGCC98krNkyCPERIS4m0NcUrftWcWk3Ad/AK57/rYmxmZuUZT3H777Wrfvr02bNigd1syGdMEf/3rXyXVPHq9T58+PjkGnINwDfs89ZR04YU1j0QfP146OpvVoF9+kcaNk3JyatpAFi+WYmLq3NRpfdf5+fmSxLqsDhDI4To/P19FRUUKDw/XgAED7C4HAaBTp0666667JEmPPfaY5bPXubm53paThx56yNKx4UyEa9gnPFxaskQaNapmeb7Ro2sej17fG+eaNdKwYTWrjHTqJH32mdTADIOn79qzRF0wKykp8S5VRbgOfp5wnZ2dbW8hLeCZtR40aBCrMaDJ7rvvPsXExCgnJ0dLly61bFxjjB544AEZYzR58mRWr4ElCNewV0yM9NFHNTPXv/0m3XyzNGKENG+e9O23Un5+TU92RkZNCM/Pl3r2lDIzpfT0Bofu0aOHJGfMXHtmrRMTExUXF2dzNfC1008/XSEhIdq7d6/3wUGB4uuvv5YkDRs2zOZKEEgSExN1z9FWwgceeEAVFRWWjPvJJ59o+fLlCg8P997QCLQW4Rr2i4uT3nuvpk0kKqpmZvrGG6X+/aVevaRLL5UWLqyZ0b7++pr2kUaCtfR777HnRr9g5gnXvXr1srkS+EP79u3Vv39/Sb/fHBgo1hy9v2LkyJE2V4JAM2PGDCUnJ+u7777T3zzPTGiF8vJy71J/d999NystwTKEa7QN7dpJDzwgFRTULK83bFhN6I6Kkvr1k+64Q9q4UXr9daljxyYN6aRw/d1330miJcRJPDcDBlK4rqys9LaFjBgxwuZqEGhiY2M1e/ZsSdITTzzR6pa/xx9/XHl5eUpKSqLXGpYiXKNt6dJF+u//rpm9Li6WysqkbdukF19s0mz1sY4N175avqmt4GZG5/G0VQRSuN6wYYMqKirUsWNH9e7d2+5yEICuvfZajRo1Sr/++qumT5/e4gcpffPNN3rqqackSS+//LISEhIsrBJOR7hG0OratatCQkJUXl6u/fv3212OT9EW4jyemet169YFzD8ePS0hI0aMkOu45TOBpggJCdEbb7yhmJgYZWVl6Zlnnmn2GAcPHtRVV12l6upqZWRk6LLLLrO+UDga4RpBKzw8XCeffLKk4G8NoS3EeU477TSFhobq559/Dpi13I8N10BL9e7dW88995ykmj7s999/v8n7VlVVaerUqdq1a5d69eqluXPn+qpMOBjhGkHNCX3XR44c8fYeMnPtHJGRkUo/2ioVKK0hhGtY5eabb9ZNN90kt9utyZMna/Xq1Y3uc+TIEV133XX69NNPFR0drcWLF6tDhw5+qBZOQ7hGUHNCuN6zZ4+OHDmi8PBwpaSk2F0O/CiQbmrcv3+/8vPz5XK5NHz4cLvLQYBzuVx6+eWXNXbsWJWVlemCCy7QokWL6t3+4MGDmjBhgt566y2FhYXp7bff1qBBg/xYMZyEcI2g5lnrOpjDtaffumfPngoJ4a+0kxzbd93Weda37t+/v+Lj422uBsEgLCxMixYt0kUXXaSysjJdeeWVysjI0Pr16733IRw6dEgvvfSS0tLS9Mknnyg6OlqLFi3ShAkTbK4ewYzfxAhqTpi55mZG5zp2xZC2flMjLSHwhfbt2+u9997TjBkz5HK5tHDhQg0dOlQdOnRQ165dlZiYqDvvvFP79+9XWlqavvzyS11yySV2l40gR7hGUHNCuOZmRucaOHCgIiIiVFRUpJ07d9pdToMI1/CVsLAwzZo1S7m5uZo8ebKio6NVXFysH3/8UW63W2lpaXrxxReVnZ2twYMH210uHCDU7gIAX/KE6927d8sYE5TLfzFz7VxhYWEaMmSIVq1apdWrV+vUU0+1u6Q6VVdXa+3atZII1/CdQYMG6a233lJ5ebny8/NVVlamrl27Kikpye7S4DDMXCOodevWTS6XS7/99pt+/vlnu8vxCWaunW3UqFGS1KTVEuyyZcsW/frrr2rfvr3S0tLsLgdBLjIyUmlpaRo6dCjBGrYgXCOoOWGta57O6GwjR46UJK1atcrmSur35ZdfSpLOPPNMtWvXzuZqAMC3CNcIesHcd33o0CEVFRVJIlw7lSdcb968WSUlJTZXU7eVK1dKkkaPHm1zJQDge4RrBL1gDteelpCkpCRFR0fbXA3skJycrNTUVLndbm9fc1tijNEXX3whiXANwBkI1wh6wRyuuZkR0u+z122x77qgoEB79+5VWFiYzjzzTLvLAQCfI1wj6AXzg2Tot4bUtsO1Z9Z62LBhioqKsrkaAPA9wjWCXjDPXLNSCKTaK4a43W6bq6nN0299zjnn2FwJAPgH4RpB79hw3dafYtdctIVAqlnfNyoqSkVFRcrLy7O7nFrotwbgNIRrBL1gXuuamWtINQ+TGT58uCTpq6++srma3xUWFmrnzp1yuVze2XUACHaEawS9iIgIpaSkSKp5UmOwqKys1J49eyQxc43f2y5WrFhhcyW/87SEDBo0SAkJCfYWAwB+QriGI3haQzxtFMFg9+7dcrvdioqKUpcuXewuBzY799xzJUlZWVltpv0pMzNTEi0hAJyFcA1H8LRNBFO43rlzpySpd+/ecrlcNlcDu40cOVJhYWH64Ycf2sx1vnz5cknSBRdcYHMlAOA/hGs4gqdtoq2EDivs2LFDknTqqafaXAnagujoaO860llZWfYWo5r/WdmxY4fatWunMWPG2F0OAPgN4RqO4Jm59twAGAw84bp37942V4K24tjWELt99tlnkqThw4crPj7e5moAwH8I13AEZq7hBJ4Z4rbQd01LCACnIlzDETwz13v27FFlZaXN1ViDcI3jHdt3XVBQYFsdxhjvzDXhGoDTEK7hCF26dFF0dLTcbndQLMdXWVnpfeIk4RoeMTEx3vWu7WwN2bx5s/bv36/o6GiNGDHCtjoAwA6EaziCy+UKqr7rgoICud1uxcTEKDk52e5y0IZ4+q49M8d28LSEjBkzRuHh4bbVAQB2IFzDMTx918EQro+9mZFl+HCsCy+8UJL06aefyu1221LDp59+KomWEADORLiGYwTTWteeNa5pCcHxRo0apdjYWB04cEDZ2dl+P/7hw4e9D4+56KKL/H58ALAb4RqOEYwz14RrHC8sLMw7Y/zRRx/5/fjLly9XRUWFevbsqbS0NL8fHwDsRriGYwTTcnyEazRk/PjxkqSPP/7Y78d+7733JEkTJkygZQmAIxGu4RjHtoXYvQZwaxGu0ZBx48ZJktasWaNffvnFb8d1u9364IMPJEmXXHKJ344LAG0J4RqOkZqaKpfLpcOHD2v//v12l9NiFRUV+v777yURrlG3bt26acCAAXK73d6VO/xh/fr1KiwsVPv27TV69Gi/HRcA2hLCNRwjPDxc3bp1kxTYfdf5+flyu92KjY1V586d7S4HbZRn9tqffdfvv/++JGns2LGKiIjw23EBoC0hXMNRgqHvmmX40BSevuuPPvpI1dXVfjmmp9+alhAATka4hqMEw4Nk6LdGU5xzzjmKj4/XTz/9pNWrV/v8eLt27VJOTo5CQkJ08cUX+/x4ANBWEa7hKME0c024RkPCw8M1ceJESdLixYt9frx///vfkmqeyki7EgAnI1zDUZi5hpNMmjRJUk249vUKOZ5wnZGR4dPjAEBbR7iGowTDzDVPZ0RTXXTRRYqOjtbu3bt9+rTG7777TuvXr1dISIiuuOIKnx0HAAIB4RqO4pm53rdvn8rKymyupvl+++037dmzRxLhGo2Liory9j8vXLjQZ8fxzFqfd955Oumkk3x2HAAIBIRrOErHjh2VkJAgKTBbQ7Zv3y5jjDp27KjExES7y0EAmDx5siRpwYIFcrvdlo9vjNH8+fNrHQsAnIxwDcfp06ePpN97lwPJt99+K0nq168fy/ChSSZMmKCEhAT98MMPysrKsnz8tWvXauvWrYqKitJVV11l+fgAEGgI13AcTzvF9u3bba6k+Y4N10BTREZGemeU//GPf1g+/muvvSZJuuKKKxQfH2/5+AAQaAjXcJxgmbkGmuq6666TJL3zzjs6fPiwZeOWlZXpX//6lyTpxhtvtGxcAAhkhGs4jidcM3MNpxg5cqR69+6tw4cPe8OwFf7973+rtLRUPXv21JgxYywbFwACGeEajuNpCwm0mWu32628vDxJhGs0j8vl0m233SZJeuGFFyxZ89oYo+eee06S9Kc//UkhIfw6AQCJcA0H8oTrn376ScXFxTZX03R79uzRb7/9prCwMPXs2dPuchBgbrzxRkVHR2vjxo364osvWj1eZmamNm7cqOjoaP3pT3+yoEIACA6EazhOXFycunTpIimwZq89LSGnnnqqQkNDba4GgaZDhw6aNm2aJOn5559v9XieWevp06erY8eOrR4PAIIF4RqOFIg3NdJvjda66667JElLlizR1q1bWzxObm6u3n//fblcLt1zzz1WlQcAQYFwDUcKxOX4CNdorQEDBujyyy+XMUYzZ85s8TgPPfSQJCkjI8P7D1UAQA3CNRyJmWs41WOPPSaXy6WFCxcqNze32fuvXLlSH374oUJDQ/XEE09YXyAABDjCNRwpEJfjI1zDCunp6ZoyZYok6T//8z+btXJIdXW17r//fknSTTfdpN69e/ukRgAIZIRrONKxbSFWLEvma0VFRSosLJQk9e3b1+ZqEOieeOIJRUZG6rPPPtOCBQuavN+LL76odevWKS4uTo8++qgPKwSAwEW4hiP16tVLLpdLxcXFOnDggN3lNMqzvnVKSori4uJsrgaB7pRTTtHDDz8sqeYmx127djW6z8aNGzVjxgxJ0pNPPqnk5GRflggAAYtwDUeKiopSt27dJAVGa4inJYRZa1jlgQce0JlnnqmioiJdeeWVKi0trXfb/fv368orr1R5ebnGjRvHutYA0ADCNRwrkPqu6beG1cLDw/XWW2+pU6dOWr9+vS655BIdOnTohO1+/PFHjR07Vjt27FD37t315ptv8jRGAGgA75BwLE9Q3bZtm82VNI5wDV9ITU3VRx99pPbt22vFihU644wz9M9//lNFRUU6cOCA5s6dqzPOOEMbNmxQ586dtWzZMiUmJtpdNgC0aTzmDY7Vv39/SYERrj01Eq5htWHDhmnlypW6/PLLtWvXLl1zzTUnbDNo0CAtWbJEp5xyig0VAkBgYeYajhUo4bq8vFw7d+6UVPMQEMBqp59+ujZu3KjHHnusVoAeMGCA/va3v2nt2rUEawBoIpcJhHXIglhJSYni4+NVXFzMKhB+VlhYqOTkZIWEhOjw4cOKjIy0u6Q6bdiwQaeffroSEhL0yy+/yOVy2V0SglxpaalCQkIUExNjdykA0CY0J68xcw3H6tKlixISEuR2u9v0TY2bN2+WJA0cOJBgDb+IjY0lWANACxGu4VgulysgWkO2bNkiqSZcAwCAto1wDUcLhHDtmbmm3xoAgLaPcA1HC6Rwzcw1AABtH+EajtbWw/Xhw4dVUFAgiZlrAAACAeEajuYJ19u3b1d1dbXN1Zxo69atkqTOnTvrpJNOsrkaAADQGMI1HK1Hjx6KjIxURUWFd4a4LaElBACAwEK4hqO1a9dOffv2lfT7LHFbwkohAAAEFsI1HK8t912zUggAAIGFcA3HS0tLk9Q2Z65pCwEAILAQruF4nuDqCbJtRVFRkX788UdJzFwDABAoCNdwvPT0dEk1M9dHjhyxuZrfbdiwQVLNTZfx8fE2VwMAAJqCcA3HO+WUUxQdHa3y8nLt3LnT7nK8cnNzJUmnn366rXUAAICmI1zD8UJCQrytIRs3brS5mt8RrgEACDyEa0DSoEGDJEmbNm2yuZLfEa4BAAg8hGtAv4frtjJzXVlZ6V3jmnANAEDgIFwD+v2mxrYSrrdt26aqqirFx8erR48edpcDAACaiHAN6PdwvWvXLpWUlNhcTe2WEJfLZW8xAACgyQjXgKROnTrp5JNPltQ21rum3xoAgMBEuAaO8sxet4WbGgnXAAAEJsI1cFRbuanRGEO4BgAgQBGugaPaSrj+/vvvVVRUpLCwMKWlpdlaCwAAaB7CNXDUseHa7XbbVkdOTo4kqX///goPD7etDgAA0HyEa+Co/v37KzIyUiUlJbY+Bn3dunWSpKFDh9pWAwAAaBnCNXBUaGiot8d5/fr1ttXhCdfDhg2zrQYAANAyhGvgGEOGDJEkffPNN7Yc3+12e8P18OHDbakBAAC0HOEaOIanFcOumeudO3eqqKhIERER3qUBAQBA4CBcA8fwzFxnZ2fbclOjZ9Z68ODBCgsL8/vxAQBA6xCugWP0799fUVFRKi0t1Y4dO/x+/LVr10qiJQQAgEBFuD7q0KFDmjZtmuLj4xUfH69p06apqKio3u2rqqr04IMPKj09XTExMUpJSdF1112nvXv3+q9oWO7Ymxrt6Lum3xoAgMBGuD5q6tSpys3N1ccff6yPP/5Yubm5mjZtWr3bl5WVKTs7W4888oiys7O1ePFibd++XRMnTvRj1fAFu/quq6qqlJ2dLYmVQgAACFShdhfQFmzbtk0ff/yx1qxZozPPPFOS9Morr2jkyJHKy8tT3759T9gnPj5ey5Ytq/W9F154QcOHD9f333+v7t27+6V2WM+uFUM2bdqkiooKJSQkqHfv3n49NgAAsAYz15JWr16t+Ph4b7CWpBEjRig+Pl6rVq1q8jjFxcVyuVxKSEiod5uKigqVlJTU+kDb4pm5zsnJUXV1td+Oe+z61iEh/NUEACAQ8RtcUmFhoTp37nzC9zt37qzCwsImjVFeXq4ZM2Zo6tSpiouLq3e7WbNmefu64+Pj1a1btxbXDd/o16+foqOj9euvvyovL89vx12zZo0kWkIAAAhkQR2uZ86cKZfL1eCH57/+XS7XCfsbY+r8/vGqqqo0ZcoUud1uzZkzp8Ft/+u//kvFxcXejz179rTsxcFn2rVr572hsDn/c9FaX375pSTprLPO8tsxAQCAtYK65/rOO+/UlClTGtwmNTVVGzdu1E8//XTCz37++Wd16dKlwf2rqqqUkZGhgoICff755w3OWktSRESEIiIiGi8etho1apSysrK0atUq3XzzzT4/XmFhoXbu3CmXy6VRo0b5/HgAAMA3gjpcJyYmKjExsdHtRo4cqeLiYq1du9Y7Y/n111+ruLi4waDjCdY7duxQZmamOnXqZFntsJdn9virr77yy/E8x0lPT2+wZx8AALRtQd0W0lT9+/fXuHHjdMstt2jNmjVas2aNbrnlFk2YMKHWSiH9+vXTkiVLJElHjhzRlVdeqW+++UYLFixQdXW1CgsLVVhYqMrKSrteCiwyYsQISdL27dt14MABnx9v5cqVkqSzzz7b58cCAAC+Q7g+asGCBUpPT9fYsWM1duxYDRo0SG+++WatbfLy8lRcXCxJ+uGHH/Tuu+/qhx9+0Omnn67k5GTvhz/7dOEbHTt2VFpamiT/9F17+q0J1wAABLagbgtpjo4dO2r+/PkNbmOM8X6emppa62sEn1GjRmnr1q1atWqVTx8OVFpaqpycHEmEawAAAh0z10A9PH3XnlllX1m9erXcbre6d+/O0owAAAQ4wjVQD88s8tq1a1VWVuaz43z22WeSpD/84Q8+OwYAAPAPwjVQj169eqlbt26qqqry6aohnnB9wQUX+OwYAADAPwjXQD1cLpfOP/98Sb8HYKsdPHhQ2dnZkuQ9FgAACFyEa6AB5513niTp888/98n4mZmZMsZowIABSk5O9skxAACA/xCugQZ4+qDXr1+voqIiy8f3zIgzaw0AQHAgXAMN6Nq1q/r06SO3260VK1ZYPv7y5csl0W8NAECwIFwDjfC0hnz66aeWjpuXl6edO3cqLCxMY8aMsXRsAABgD8I10Ijx48dLkj744ANLHxz03nvvSZLOPfdcxcXFWTYuAACwD+EaaMQFF1ygyMhI7d69W5s3b7ZsXE+4vuSSSywbEwAA2ItwDTQiOjrae8OhJxC31sGDB71PfiRcAwAQPAjXQBN4ArBV4frDDz+U2+1Wenq6UlNTLRkTAADYj3ANNMGECRMkSV9//bV++umnVo+3dOlSScxaAwAQbAjXQBOcfPLJGjp0qIwxWrRoUavGKioq0vvvvy9JysjIsKI8AADQRhCugSaaOnWqJGnBggWtGuedd95RZWWlBg4cqEGDBllRGgAAaCMI10ATTZkyRSEhIVq1apXy8/NbPI4nnF9zzTVyuVxWlQcAANoAwjXQRMnJyd5VQ/75z3+2aIw9e/Z4n/TomQkHAADBg3ANNMO1114rSXrzzTdb9ECZefPmyRij0aNHq3v37laXBwAAbEa4Bprh8ssvV2xsrLZv367ly5c3a9+qqirNnTtXknTbbbf5ojwAAGAzwjXQDLGxsZo+fbok6YUXXmjWvosWLdK+ffvUpUsXXXHFFT6oDgAA2I1wDTTTnXfeKUl6//33tWXLlibt43a79de//lWSdPvttys8PNxn9QEAAPsQroFm6tOnjyZNmiRjjB5//PEm7bN06VJt2rRJsbGxuvvuu31cIQAAsAvhGmiBRx99VJK0cOFCffPNNw1uW1FRoQcffFCSdPfdd6tDhw4+rw8AANiDcA20wKBBg3TttdfKGKPbbrtN1dXV9W771FNPaefOnUpOTtaf//xnP1YJAAD8jXANtNDTTz+thIQErV+/Xo888kid23z55Zd67LHHvNvHxcX5s0QAAOBnhGughbp06aI5c+ZIkmbNmqWXXnqp1s+zs7N12WWXqbq6WlOnTtXVV19tR5kAAMCPXKYlT8KAZUpKShQfH6/i4mJmNQPUjBkz9OSTT0qSLrvsMk2YMEFbtmzRnDlzVFFRoWHDhunzzz9X+/btba4UAAC0RHPyGuHaZoTrwGeM0axZs/Twww+f8NTGcePG6e233+bPFgCAAEa4DiCE6+Cxbds2vfzyy8rLy1NSUpImTZqkiRMnyuVy2V0aAABoBcJ1ACFcAwAAtG3NyWvc0AgAAABYhHANAAAAWIRwDQAAAFiEcA0AAABYhHANAAAAWIRwDQAAAFiEcA0AAABYhHANAAAAWIRwDQAAAFiEcA0AAABYhHANAAAAWIRwDQAAAFiEcA0AAABYhHANAAAAWIRwDQAAAFiEcA0AAABYhHANAAAAWIRwDQAAAFiEcA0AAABYhHANAAAAWIRwDQAAAFiEcA0AAABYhHANAAAAWIRwDQAAAFiEcA0AAABYJNTuApzOGCNJKikpsbkSAAAA1MWT0zy5rSGEa5uVlpZKkrp162ZzJQAAAGhIaWmp4uPjG9zGZZoSweEzbrdbe/fuVWxsrFwul8+PV1JSom7dumnPnj2Ki4vz+fECCeembpyX+nFu6sZ5qRvnpX6cm7pxXurn73NjjFFpaalSUlIUEtJwVzUz1zYLCQlR165d/X7cuLg4/qLWg3NTN85L/Tg3deO81I3zUj/OTd04L/Xz57lpbMbagxsaAQAAAIsQrgEAAACLEK4dJiIiQo8++qgiIiLsLqXN4dzUjfNSP85N3TgvdeO81I9zUzfOS/3a8rnhhkYAAADAIsxcAwAAABYhXAMAAAAWIVwDAAAAFiFcAwAAABYhXAehOXPmqGfPnoqMjNSQIUO0cuXKBrdfsWKFhgwZosjISJ1yyimaO3eunyr1n1mzZmnYsGGKjY1V586dddlllykvL6/BfbKysuRyuU74+Pbbb/1Ute/NnDnzhNeXlJTU4D5OuF4kKTU1tc4//zvuuKPO7YP1evniiy90ySWXKCUlRS6XS0uXLq31c2OMZs6cqZSUFEVFRencc8/Vli1bGh130aJFSktLU0REhNLS0rRkyRIfvQLfaejcVFVV6cEHH1R6erpiYmKUkpKi6667Tnv37m1wzNdff73O66i8vNzHr8Y6jV0z06dPP+H1jRgxotFxA/2aaey81PXn7nK59D//8z/1jhkM10tTfj8H2vsM4TrIvP3227r33nv10EMPKScnR+ecc47Gjx+v77//vs7tCwoKdPHFF+ucc85RTk6O/vKXv+juu+/WokWL/Fy5b61YsUJ33HGH1qxZo2XLlunIkSMaO3asDh8+3Oi+eXl52rdvn/fj1FNP9UPF/jNgwIBar2/Tpk31buuU60WS1q1bV+u8LFu2TJJ01VVXNbhfsF0vhw8f1mmnnaYXX3yxzp8/9dRTevbZZ/Xiiy9q3bp1SkpK0oUXXqjS0tJ6x1y9erUmT56sadOmacOGDZo2bZoyMjL09ddf++pl+ERD56asrEzZ2dl65JFHlJ2drcWLF2v79u2aOHFio+PGxcXVuob27dunyMhIX7wEn2jsmpGkcePG1Xp9H374YYNjBsM109h5Of7P/LXXXpPL5dIVV1zR4LiBfr005fdzwL3PGASV4cOHm9tuu63W9/r162dmzJhR5/Z//vOfTb9+/Wp979ZbbzUjRozwWY1twf79+40ks2LFinq3yczMNJLMoUOH/FeYnz366KPmtNNOa/L2Tr1ejDHmnnvuMb169TJut7vOnzvhepFklixZ4v3a7XabpKQkM3v2bO/3ysvLTXx8vJk7d26942RkZJhx48bV+t5FF11kpkyZYnnN/nL8uanL2rVrjSSze/fuereZN2+eiY+Pt7Y4G9V1Xq6//npz6aWXNmucYLtmmnK9XHrppea8885rcJtgu16MOfH3cyC+zzBzHUQqKyu1fv16jR07ttb3x44dq1WrVtW5z+rVq0/Y/qKLLtI333yjqqoqn9Vqt+LiYklSx44dG9128ODBSk5O1vnnn6/MzExfl+Z3O3bsUEpKinr27KkpU6YoPz+/3m2der1UVlZq/vz5uvHGG+VyuRrcNtivl2MVFBSosLCw1jURERGhMWPG1PueI9V/HTW0TzAoLi6Wy+VSQkJCg9v9+uuv6tGjh7p27aoJEyYoJyfHPwX6UVZWljp37qw+ffrolltu0f79+xvc3mnXzE8//aQPPvhAN910U6PbBtv1cvzv50B8nyFcB5EDBw6ourpaXbp0qfX9Ll26qLCwsM59CgsL69z+yJEjOnDggM9qtZMxRvfdd5/OPvtsDRw4sN7tkpOT9fe//12LFi3S4sWL1bdvX51//vn64osv/Fitb5155pn6xz/+oU8++USvvPKKCgsLNWrUKB08eLDO7Z14vUjS0qVLVVRUpOnTp9e7jROul+N53lea857j2a+5+wS68vJyzZgxQ1OnTlVcXFy92/Xr10+vv/663n33Xf3rX/9SZGSkzjrrLO3YscOP1frW+PHjtWDBAn3++ed65plntG7dOp133nmqqKiodx+nXTNvvPGGYmNjNWnSpAa3C7brpa7fz4H4PhPq8yPA746fWTPGNDjbVtf2dX0/WNx5553auHGjvvzyywa369u3r/r27ev9euTIkdqzZ4+efvppjR492tdl+sX48eO9n6enp2vkyJHq1auX3njjDd1333117uO060WSXn31VY0fP14pKSn1buOE66U+zX3Paek+gaqqqkpTpkyR2+3WnDlzGtx2xIgRtW7uO+uss3TGGWfohRde0PPPP+/rUv1i8uTJ3s8HDhyooUOHqkePHvrggw8aDJNOumZee+01XXPNNY32Tgfb9dLQ7+dAep9h5jqIJCYmql27dif8q2z//v0n/OvNIykpqc7tQ0ND1alTJ5/Vape77rpL7777rjIzM9W1a9dm7z9ixIiAnRFoipiYGKWnp9f7Gp12vUjS7t27tXz5ct18883N3jfYrxfPyjLNec/x7NfcfQJVVVWVMjIyVFBQoGXLljU4a12XkJAQDRs2LKivo+TkZPXo0aPB1+ika2blypXKy8tr0XtOIF8v9f1+DsT3GcJ1EAkPD9eQIUO8qxp4LFu2TKNGjapzn5EjR56w/aeffqqhQ4cqLCzMZ7X6mzFGd955pxYvXqzPP/9cPXv2bNE4OTk5Sk5Otri6tqOiokLbtm2r9zU65Xo51rx589S5c2f98Y9/bPa+wX699OzZU0lJSbWuicrKSq1YsaLe9xyp/uuooX0CkSdY79ixQ8uXL2/RP0CNMcrNzQ3q6+jgwYPas2dPg6/RKdeMVPM/ZUOGDNFpp53W7H0D8Xpp7PdzQL7P+PyWSfjVW2+9ZcLCwsyrr75qtm7dau69914TExNjdu3aZYwxZsaMGWbatGne7fPz8010dLT5j//4D7N161bz6quvmrCwMPPOO+/Y9RJ84vbbbzfx8fEmKyvL7Nu3z/tRVlbm3eb4c/Pcc8+ZJUuWmO3bt5vNmzebGTNmGElm0aJFdrwEn7j//vtNVlaWyc/PN2vWrDETJkwwsbGxjr9ePKqrq0337t3Ngw8+eMLPnHK9lJaWmpycHJOTk2MkmWeffdbk5OR4V7yYPXu2iY+PN4sXLzabNm0yV199tUlOTjYlJSXeMaZNm1ZrxaKvvvrKtGvXzsyePdts27bNzJ4924SGhpo1a9b4/fW1RkPnpqqqykycONF07drV5Obm1nrfqaio8I5x/LmZOXOm+fjjj813331ncnJyzA033GBCQ0PN119/bcdLbJGGzktpaam5//77zapVq0xBQYHJzMw0I0eONCeffHLQXzON/V0yxpji4mITHR1tXn755TrHCMbrpSm/nwPtfYZwHYReeukl06NHDxMeHm7OOOOMWsvNXX/99WbMmDG1ts/KyjKDBw824eHhJjU1td6/1IFMUp0f8+bN825z/Ll58sknTa9evUxkZKTp0KGDOfvss80HH3zg/+J9aPLkySY5OdmEhYWZlJQUM2nSJLNlyxbvz516vXh88sknRpLJy8s74WdOuV48Swwe/3H99dcbY2qWyXr00UdNUlKSiYiIMKNHjzabNm2qNcaYMWO823ssXLjQ9O3b14SFhZl+/foF5D9CGjo3BQUF9b7vZGZmesc4/tzce++9pnv37iY8PNycdNJJZuzYsWbVqlX+f3Gt0NB5KSsrM2PHjjUnnXSSCQsLM927dzfXX3+9+f7772uNEYzXTGN/l4wx5n//939NVFSUKSoqqnOMYLxemvL7OdDeZ1zGHL0bCQAAAECr0HMNAAAAWIRwDQAAAFiEcA0AAABYhHANAAAAWIRwDQAAAFiEcA0AAABYhHANAAAAWIRwDQAAAFiEcA0AAABYJNTuAgAAgS03N1dLly71fn3vvfcqISHBtnoAwE48/hwA0Cqvv/66brjhBu/XBQUFSk1Nta8gALARbSEAAACARQjXAAAAgEUI1wAAAIBFCNcAAACARQjXAAAAgEVYLQQA0CIul6vZ+2RmZurcc8+1vhgAaCOYuQYAAAAswkNkAAAt0q5dO0mSMUZut/uE79elJbPdABBImLkGALTIkSNHdOTIEb366qu1vr9z507vz47/GDNmjE3VAoB/EK4BAAAAixCuAQAAAIsQrgEAAACLEK4BAAAAixCuAQAAAIsQrgEAAACLEK4BAAAAixCuAQAAAIsQrgEAAACLEK4BAAAAixCuAQCtEhYWVuvr6upqmyoBAPsRrgEArRIbG1vr60OHDtlUCQDYj3ANAGiV1NTUWl+vW7fOnkIAoA1wGWOM3UUAAALXkSNHlJiYqOLiYklSSkqK/u///k/nnnuuoqKibK4OAPyLmWsAQKuEhobqhhtu8H69d+9eXXzxxYqOjlZ0dLTat2/v/Vi5cqWNlQKA7xGuAQCt9sQTT+jss88+4fu//fabDh8+7P3gZkcAwY5wDQBotZiYGGVlZemtt95SRkaG+vTpo9jYWIWE8GsGgLPQcw0AAABYhCkFAAAAwCKEawAAAMAihGsAAADAIoRrAAAAwCKEawAAAMAihGsAAADAIoRrAAAAwCKEawAAAMAihGsAAADAIoRrAAAAwCKEawAAAMAihGsAAADAIoRrAAAAwCKEawAAAMAihGsAAADAIoRrAAAAwCKEawAAAMAi/x/1YM/yrHvAGQAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def Mk(t, k, gamma, w0, beta):\n", - " \"\"\" Calculate the Matsubara terms for a given t and k. \"\"\"\n", - " Om = np.sqrt(w0**2 - (gamma / 2)**2)\n", - " Gamma = gamma / 2.\n", - " ek = 2 * np.pi * k / beta\n", - "\n", - " return (\n", - " (-2 * lam**2 * gamma / beta) * ek * np.exp(-ek * np.abs(t))\n", - " / (((Om + 1.0j * Gamma)**2 + ek**2) * ((Om - 1.0j * Gamma)**2 + ek**2))\n", - " )\n", - "\n", - "\n", - "def c(t, Nk, lam, gamma, w0, beta):\n", - " \"\"\" Calculate the correlation function for a vector of times, t. \"\"\"\n", - " Om = np.sqrt(w0**2 - (gamma / 2)**2)\n", - " Gamma = gamma / 2.\n", - "\n", - " Cr = (\n", - " coth(beta * (Om + 1.0j * Gamma) / 2) * np.exp(1.0j * Om * t)\n", - " + coth(beta * (Om - 1.0j * Gamma) / 2) * np.exp(-1.0j * Om * t)\n", - " )\n", - "\n", - " Ci = np.exp(-1.0j * Om * t) - np.exp(1.0j * Om * t)\n", - "\n", - " return (\n", - " (lam**2 / (4 * Om)) * np.exp(-Gamma * np.abs(t)) * (Cr + Ci) +\n", - " np.sum([\n", - " Mk(t, k, gamma=gamma, w0=w0, beta=beta)\n", - " for k in range(1, Nk + 1)\n", - " ], 0)\n", - " )\n", - "\n", - "\n", - "def plot_correlation_function():\n", - " \"\"\" Plot the underdamped correlation function. \"\"\"\n", - " t = np.linspace(0, 20, 1000)\n", - " corr = c(t, Nk=3, lam=lam, gamma=gamma, w0=w0, beta=beta)\n", - "\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " axes.plot(t, np.real(corr), '-', color=\"black\", label=\"Re[C(t)]\")\n", - " axes.plot(t, np.imag(corr), '-', color=\"red\", label=\"Im[C(t)]\")\n", - " axes.set_xlabel(r't', fontsize=28)\n", - " axes.set_ylabel(r'C', fontsize=28)\n", - " axes.legend(loc=0, fontsize=12)\n", - "\n", - "\n", - "plot_correlation_function()" - ] - }, - { - "cell_type": "markdown", - "id": "ed47f7f9", - "metadata": {}, - "source": [ - "It is useful to look at what the Matsubara contributions do to this spectral density. We see that they modify the real part around $t=0$:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "233372e0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_matsubara_correlation_function_contributions():\n", - " \"\"\" Plot the underdamped correlation function. \"\"\"\n", - " t = np.linspace(0, 20, 1000)\n", - "\n", - " M_Nk2 = np.sum([\n", - " Mk(t, k, gamma=gamma, w0=w0, beta=beta)\n", - " for k in range(1, 2 + 1)\n", - " ], 0)\n", - "\n", - " M_Nk100 = np.sum([\n", - " Mk(t, k, gamma=gamma, w0=w0, beta=beta)\n", - " for k in range(1, 100 + 1)\n", - " ], 0)\n", - "\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " axes.plot(t, np.real(M_Nk2), '-', color=\"black\", label=\"Re[M(t)] Nk=2\")\n", - " axes.plot(t, np.real(M_Nk100), '--', color=\"red\", label=\"Re[M(t)] Nk=100\")\n", - " axes.set_xlabel(r't', fontsize=28)\n", - " axes.set_ylabel(r'M', fontsize=28)\n", - " axes.legend(loc=0, fontsize=12)\n", - "\n", - "\n", - "plot_matsubara_correlation_function_contributions()" - ] - }, - { - "cell_type": "markdown", - "id": "94059ab4", - "metadata": {}, - "source": [ - "## Solving for the dynamics as a function of time" - ] - }, - { - "cell_type": "markdown", - "id": "74a740c6", - "metadata": {}, - "source": [ - "Next we calculate the exponents using the Matsubara decompositions. Here we split them into real and imaginary parts.\n", - "\n", - "The HEOM code will optimize these, and reduce the number of exponents when real and imaginary parts have the same exponent. This is clearly the case for the first term in the vkAI and vkAR lists." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "b1528829", - "metadata": {}, - "outputs": [], - "source": [ - "ckAR, vkAR, ckAI, vkAI = underdamped_matsubara_params(\n", - " lam=lam, gamma=gamma, T=T, nk=Nk,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "ff4c0d2b", - "metadata": {}, - "source": [ - "Having created the lists which specify the bath correlation functions, we create a `BosonicBath` from them and pass the bath to the `HEOMSolver` class.\n", - "\n", - "The solver constructs the \"right hand side\" (RHS) determinining how the system and auxiliary density operators evolve in time. This can then be used to solve for dynamics or steady-state.\n", - "\n", - "Below we create the bath and solver and then solve for the dynamics by calling `.run(rho0, tlist)`." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "73c7e130", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.028772354125976562\n", - " Total run time: 17.14s*] Elapsed 17.14s / Remaining 00:00:00:00******** 36% ] Elapsed 4.79s / Remaining 00:00:00:08[*********62%** ] Elapsed 10.76s / Remaining 00:00:00:06[*********63%** ] Elapsed 10.98s / Remaining 00:00:00:06\n", - "ODE solver time: 17.142221450805664\n" - ] - } - ], - "source": [ - "with timer(\"RHS construction time\"):\n", - " bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI)\n", - " HEOMMats = HEOMSolver(Hsys, (bath,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMats = HEOMMats.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "88fcd2c0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations([\n", - " (resultMats, P11p, 'b', \"P11 Mats\"),\n", - " (resultMats, P12p, 'r', \"P12 Mats\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "af7419ef", - "metadata": {}, - "source": [ - "In practice, one would not perform this laborious expansion for the underdamped correlation function, because\n", - "QuTiP already has a class, `UnderDampedEnvironment`, that can construct this bath for you. Nevertheless, knowing how\n", - "to perform this expansion is an useful skill.\n", - "\n", - "Below we show how to use this built-in functionality:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "f447f515", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.11582255363464355\n", - " Total run time: 14.42s*] Elapsed 14.42s / Remaining 00:00:00:00*********60%** ] Elapsed 9.39s / Remaining 00:00:00:06\n", - "ODE solver time: 14.422335863113403\n" - ] - } - ], - "source": [ - "# Compare to built-in under-damped bath:\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = UnderDampedEnvironment(lam=lam, gamma=gamma, w0=w0, T=T)\n", - " bath_approx=bath.approx_by_matsubara(Nk=Nk)\n", - " HEOM_udbath = HEOMSolver(Hsys, (bath_approx,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " result_udbath = HEOM_udbath.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "d06dc1ca", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations([\n", - " (result_udbath, P11p, 'b', \"P11 (UnderDampedEnvironment)\"),\n", - " (result_udbath, P12p, 'r', \"P12 (UnderDampedEnvironment)\"),\n", - " (resultMats, P11p, 'r--', \"P11 Mats\"),\n", - " (resultMats, P12p, 'b--', \"P12 Mats\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "d7cb76af", - "metadata": {}, - "source": [ - "The `UnderDampedEnvironment` class also allows us to easily evaluate analytical expressions for the power spectrum, correlation function, and spectral density. In the following plots, the solid lines are the exact expressions, and the dashed lines are based on our approximation of the correlation function with a finite number of exponents. In this case, there is an excellent agreement." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "437de6ef", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "w = np.linspace(-3, 3, 1000)\n", - "w2 = np.linspace(0, 3, 1000)\n", - "t = np.linspace(0, 10, 1000)\n", - "bath_cf = bath.correlation_function(t) # uses numerical integration\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "axs[0, 0].plot(w, bath.power_spectrum(w))\n", - "axs[0, 0].plot(w, bath_approx.power_spectrum(w), '--')\n", - "axs[0, 0].set(xlabel=r'$\\omega$', ylabel=r'$S(\\omega)$')\n", - "axs[0, 1].plot(w2, bath.spectral_density(w2))\n", - "axs[0, 1].plot(w2, bath_approx.spectral_density(w2), '--')\n", - "axs[0, 1].set(xlabel=r'$\\omega$', ylabel=r'$J(\\omega)$')\n", - "axs[1, 0].plot(t, np.real(bath_cf))\n", - "axs[1, 0].plot(t, np.real(bath_approx.correlation_function(t)), '--')\n", - "axs[1, 0].set(xlabel=r'$t$', ylabel=r'$C_{R}(t)$')\n", - "axs[1, 1].plot(t, np.imag(bath_cf))\n", - "axs[1, 1].plot(t, np.imag(bath_approx.correlation_function(t)), '--')\n", - "axs[1, 1].set(xlabel=r'$t$', ylabel=r'$C_{I}(t)$')\n", - "\n", - "fig.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "5bf0e1c3", - "metadata": {}, - "source": [ - "## Compare the results" - ] - }, - { - "cell_type": "markdown", - "id": "477ebdb7", - "metadata": {}, - "source": [ - "### We can compare these results to those of the Bloch-Redfield solver in QuTiP:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "b70b75bf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 3.66s*] Elapsed 3.65s / Remaining 00:00:00:00\n", - "ODE solver time: 3.680868148803711\n" - ] - } - ], - "source": [ - "with timer(\"ODE solver time\"):\n", - " resultBR = brmesolve(\n", - " Hsys, rho0, tlist,\n", - " a_ops=[[sigmaz(), bath]], options=options,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "1e07101c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations([\n", - " (resultMats, P11p, 'b', \"P11 Mats\"),\n", - " (resultMats, P12p, 'r', \"P12 Mats\"),\n", - " (resultBR, P11p, 'g--', \"P11 Bloch Redfield\"),\n", - " (resultBR, P12p, 'g--', \"P12 Bloch Redfield\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "b3cd2b9c", - "metadata": {}, - "source": [ - "### Lastly, let us calculate the analytical steady-state result and compare all of the results:" - ] - }, - { - "cell_type": "markdown", - "id": "9318a4d1", - "metadata": {}, - "source": [ - "The thermal state of a reaction coordinate (treating the environment as a single damped mode) should, at high temperatures and small gamma, tell us the steady-state:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "96459d81", - "metadata": {}, - "outputs": [], - "source": [ - "dot_energy, dot_state = Hsys.eigenstates()\n", - "deltaE = dot_energy[1] - dot_energy[0]\n", - "\n", - "gamma2 = gamma\n", - "wa = w0 # reaction coordinate frequency\n", - "g = lam / np.sqrt(2 * wa) # coupling\n", - "\n", - "NRC = 10\n", - "\n", - "Hsys_exp = tensor(qeye(NRC), Hsys)\n", - "Q_exp = tensor(qeye(NRC), Q)\n", - "a = tensor(destroy(NRC), qeye(2))\n", - "\n", - "H0 = wa * a.dag() * a + Hsys_exp\n", - "# interaction\n", - "H1 = (g * (a.dag() + a) * Q_exp)\n", - "\n", - "H = H0 + H1\n", - "\n", - "energies, states = H.eigenstates()\n", - "rhoss = 0 * states[0] * states[0].dag()\n", - "for kk, energ in enumerate(energies):\n", - " rhoss += (states[kk] * states[kk].dag() * np.exp(-beta * energies[kk]))\n", - "rhoss = rhoss / rhoss.norm()\n", - "\n", - "P12RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 1).dag())\n", - "P12RC = expect(rhoss, P12RC)\n", - "\n", - "P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag())\n", - "P11RC = expect(rhoss, P11RC)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "6ea44c47", - "metadata": {}, - "outputs": [], - "source": [ - "rcParams = {\n", - " \"axes.titlesize\": 25,\n", - " \"axes.labelsize\": 30,\n", - " \"xtick.labelsize\": 28,\n", - " \"ytick.labelsize\": 28,\n", - " \"legend.fontsize\": 28,\n", - " \"axes.grid\": False,\n", - " \"savefig.bbox\": \"tight\",\n", - " \"lines.markersize\": 5,\n", - " \"font.family\": \"STIXgeneral\",\n", - " \"mathtext.fontset\": \"stix\",\n", - " \"font.serif\": \"STIX\",\n", - " \"text.usetex\": False,\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "96dcaaa8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", - "\n", - "with plt.rc_context(rcParams):\n", - " plt.yticks([P11RC, 0.6, 1.0], [0.38, 0.6, 1])\n", - "\n", - " plot_result_expectations([\n", - " (resultBR, P11p, 'y-.', \"Bloch-Redfield\"),\n", - " (resultMats, P11p, 'b', \"Matsubara $N_k=3$\"),\n", - " ], axes=axes)\n", - " axes.plot(\n", - " tlist, [P11RC for t in tlist],\n", - " color='black', linestyle=\"-.\", linewidth=2,\n", - " label=\"Thermal state\",\n", - " )\n", - "\n", - " axes.set_xlabel(r'$t \\Delta$', fontsize=30)\n", - " axes.set_ylabel(r'$\\rho_{11}$', fontsize=30)\n", - "\n", - " axes.locator_params(axis='y', nbins=4)\n", - " axes.locator_params(axis='x', nbins=4)\n", - "\n", - " axes.legend(loc=0)\n", - "\n", - " fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "5cf951e9", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "1f8aebfb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "QuTiP: Quantum Toolbox in Python\n", - "================================\n", - "Copyright (c) QuTiP team 2011 and later.\n", - "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", - "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", - "Original developers: R. J. Johansson & P. D. Nation.\n", - "Previous lead developers: Chris Granade & A. Grimsmo.\n", - "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", - "\n", - "QuTiP Version: 5.1.0.dev0+7941773\n", - "Numpy Version: 2.1.3\n", - "Scipy Version: 1.14.1\n", - "Cython Version: 3.0.9\n", - "Matplotlib Version: 3.9.2\n", - "Python Version: 3.12.7\n", - "Number of CPUs: 16\n", - "BLAS Info: Generic\n", - "INTEL MKL Ext: None\n", - "Platform Info: Linux (x86_64)\n", - "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", - "================================================================================\n", - "Please cite QuTiP in your publication.\n", - "================================================================================\n", - "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" - ] - } - ], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "dc20d05c", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "c0ea1cc8", - "metadata": {}, - "outputs": [], - "source": [ - "assert np.allclose(\n", - " expect(P11p, resultMats.states[-100:]), P11RC, rtol=1e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(P11p, resultBR.states[-100:]), P11RC, rtol=1e-2,\n", - ")" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "qutip-dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md new file mode 100644 index 00000000..16a80eed --- /dev/null +++ b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md @@ -0,0 +1,584 @@ +--- +jupytext: + formats: ipynb,md:myst + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.16.4 +kernelspec: + display_name: qutip-dev + language: python + name: python3 +--- + +# HEOM 1c: Spin-Bath model (Underdamped Case) + ++++ + +## Introduction + +The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices. + +In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment. + +The Bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions. + +In the example below we show how to model the underdamped Brownian motion Spectral Density. + +Note that in the following, we set $\hbar = k_\mathrm{B} = 1$. + +### Brownian motion (underdamped) spectral density +The underdamped spectral density is: + +$$J_U = \frac{\alpha^2 \Gamma \omega}{(\omega_c^2 - \omega^2)^2 + \Gamma^2 \omega^2)}.$$ + +Here $\alpha$ scales the coupling strength, $\Gamma$ is the cut-off frequency, and $\omega_c$ defines a resonance frequency. With the HEOM we must use an exponential decomposition: + +The Matsubara decomposition of this spectral density is, in real and imaginary parts: + + + +\begin{equation*} + c_k^R = \begin{cases} + \alpha^2 \coth(\beta( \Omega + i\Gamma/2)/2)/4\Omega & k = 0\\ + \alpha^2 \coth(\beta( \Omega - i\Gamma/2)/2)/4\Omega & k = 0\\ + -2\alpha^2\Gamma/\beta \frac{\epsilon_k }{((\Omega + i\Gamma/2)^2 + \epsilon_k^2)(\Omega - i\Gamma/2)^2 + \epsilon_k^2)} & k \geq 1\\ + \end{cases} +\end{equation*} + +\begin{equation*} + \nu_k^R = \begin{cases} + -i\Omega + \Gamma/2, i\Omega +\Gamma/2, & k = 0\\ + {2 \pi k} / {\beta } & k \geq 1\\ + \end{cases} +\end{equation*} + + + + +\begin{equation*} + c_k^I = \begin{cases} + i\alpha^2 /4\Omega & k = 0\\ + -i\alpha^2 /4\Omega & k = 0\\ + \end{cases} +\end{equation*} + +\begin{equation*} + \nu_k^I = \begin{cases} + i\Omega + \Gamma/2, -i\Omega + \Gamma/2, & k = 0\\ + \end{cases} +\end{equation*} + +Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. + ++++ + +## Setup + +```{code-cell} ipython3 +import contextlib +import time + +import numpy as np +from matplotlib import pyplot as plt + +import qutip +from qutip import ( + basis, + brmesolve, + destroy, + expect, + qeye, + sigmax, + sigmaz, + tensor, +) +from qutip.solver.heom import ( + HEOMSolver, +) +from qutip.core.environment import ( + UnderDampedEnvironment, + ExponentialBosonicEnvironment +) + +%matplotlib inline +``` + +## Helper functions + +Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: + +```{code-cell} ipython3 +def cot(x): + """ Vectorized cotangent of x. """ + return 1. / np.tan(x) +``` + +```{code-cell} ipython3 +def coth(x): + """ Vectorized hyperbolic cotangent of x. """ + return 1. / np.tanh(x) +``` + +```{code-cell} ipython3 +def underdamped_matsubara_params(lam, gamma, T, nk): + """ Calculation of the real and imaginary expansions of the + underdamped correlation functions. + """ + Om = np.sqrt(w0**2 - (gamma / 2)**2) + Gamma = gamma / 2. + beta = 1. / T + + ckAR = [ + (lam**2 / (4*Om)) * coth(beta * (Om + 1.0j * Gamma) / 2), + (lam**2 / (4*Om)) * coth(beta * (Om - 1.0j * Gamma) / 2), + ] + ckAR.extend( + (-2 * lam**2 * gamma / beta) * (2 * np.pi * k / beta) / + (((Om + 1.0j * Gamma)**2 + (2 * np.pi * k / beta)**2) * + ((Om - 1.0j * Gamma)**2 + (2 * np.pi * k / beta)**2)) + 0.j + for k in range(1, nk + 1) + ) + vkAR = [ + -1.0j * Om + Gamma, + 1.0j * Om + Gamma, + ] + vkAR.extend( + 2 * np.pi * k * T + 0.j + for k in range(1, nk + 1) + ) + + factor = 1. / 4 + + ckAI = [ + -factor * lam**2 * 1.0j / Om, + factor * lam**2 * 1.0j / Om, + ] + vkAI = [ + -(-1.0j * Om - Gamma), + -(1.0j * Om - Gamma), + ] + + return ckAR, vkAR, ckAI, vkAI +``` + +```{code-cell} ipython3 +def plot_result_expectations(plots, axes=None): + """ Plot the expectation values of operators as functions of time. + + Each plot in plots consists of: (solver_result, measurement_operation, + color, label). + """ + if axes is None: + fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) + fig_created = True + else: + fig = None + fig_created = False + + # add kw arguments to each plot if missing + plots = [p if len(p) == 5 else p + ({},) for p in plots] + for result, m_op, color, label, kw in plots: + exp = np.real(expect(result.states, m_op)) + kw.setdefault("linewidth", 2) + axes.plot(result.times, exp, color, label=label, **kw) + + if fig_created: + axes.legend(loc=0, fontsize=12) + axes.set_xlabel("t", fontsize=28) + + return fig +``` + +```{code-cell} ipython3 +@contextlib.contextmanager +def timer(label): + """ Simple utility for timing functions: + + with timer("name"): + ... code to time ... + """ + start = time.time() + yield + end = time.time() + print(f"{label}: {end - start}") +``` + +```{code-cell} ipython3 +# Solver options: + +options = { + "nsteps": 15000, + "store_states": True, + "rtol": 1e-14, + "atol": 1e-14, + "method": "vern9", + "progress_bar": "enhanced", +} +``` + +## System and bath definition + +And let us set up the system Hamiltonian, bath and system measurement operators: + +```{code-cell} ipython3 +# Defining the system Hamiltonian +eps = .5 # Energy of the 2-level system. +Del = 1.0 # Tunnelling term +Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() +``` + +```{code-cell} ipython3 +# Initial state of the system. +rho0 = basis(2, 0) * basis(2, 0).dag() +``` + +```{code-cell} ipython3 +# System-bath coupling (underdamed spectral density) +Q = sigmaz() # coupling operator + +# Bath properties: +gamma = .1 # cut off frequency +lam = .5 # coupling strength +w0 = 1. # resonance frequency +T = 1. +beta = 1. / T + +# HEOM parameters: + +# number of exponents to retain in the Matsubara expansion of the +# bath correlation function: +Nk = 2 + +# Number of levels of the hierarchy to retain: +NC = 10 + +# Times to solve for: +tlist = np.linspace(0, 50, 1000) +``` + +```{code-cell} ipython3 +# Define some operators with which we will measure the system +# 1,1 element of density matrix - corresonding to groundstate +P11p = basis(2, 0) * basis(2, 0).dag() +P22p = basis(2, 1) * basis(2, 1).dag() +# 1,2 element of density matrix - corresonding to coherence +P12p = basis(2, 0) * basis(2, 1).dag() +``` + +### First let us look at what the underdamped spectral density looks like: + +```{code-cell} ipython3 +def plot_spectral_density(): + """ Plot the underdamped spectral density """ + w = np.linspace(0, 5, 1000) + J = lam**2 * gamma * w / ((w0**2 - w**2)**2 + (gamma**2) * (w**2)) + + fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) + axes.plot(w, J, 'r', linewidth=2) + axes.set_xlabel(r'$\omega$', fontsize=28) + axes.set_ylabel(r'J', fontsize=28) + + +plot_spectral_density() +``` + +The correlation functions are now very oscillatory, because of the Lorentzian peak in the spectral density. + ++++ + +### So next, let us plot the correlation functions themselves: + +```{code-cell} ipython3 +def Mk(t, k, gamma, w0, beta): + """ Calculate the Matsubara terms for a given t and k. """ + Om = np.sqrt(w0**2 - (gamma / 2)**2) + Gamma = gamma / 2. + ek = 2 * np.pi * k / beta + + return ( + (-2 * lam**2 * gamma / beta) * ek * np.exp(-ek * np.abs(t)) + / (((Om + 1.0j * Gamma)**2 + ek**2) * ((Om - 1.0j * Gamma)**2 + ek**2)) + ) + + +def c(t, Nk, lam, gamma, w0, beta): + """ Calculate the correlation function for a vector of times, t. """ + Om = np.sqrt(w0**2 - (gamma / 2)**2) + Gamma = gamma / 2. + + Cr = ( + coth(beta * (Om + 1.0j * Gamma) / 2) * np.exp(1.0j * Om * t) + + coth(beta * (Om - 1.0j * Gamma) / 2) * np.exp(-1.0j * Om * t) + ) + + Ci = np.exp(-1.0j * Om * t) - np.exp(1.0j * Om * t) + + return ( + (lam**2 / (4 * Om)) * np.exp(-Gamma * np.abs(t)) * (Cr + Ci) + + np.sum([ + Mk(t, k, gamma=gamma, w0=w0, beta=beta) + for k in range(1, Nk + 1) + ], 0) + ) + + +def plot_correlation_function(): + """ Plot the underdamped correlation function. """ + t = np.linspace(0, 20, 1000) + corr = c(t, Nk=3, lam=lam, gamma=gamma, w0=w0, beta=beta) + + fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) + axes.plot(t, np.real(corr), '-', color="black", label="Re[C(t)]") + axes.plot(t, np.imag(corr), '-', color="red", label="Im[C(t)]") + axes.set_xlabel(r't', fontsize=28) + axes.set_ylabel(r'C', fontsize=28) + axes.legend(loc=0, fontsize=12) + + +plot_correlation_function() +``` + +It is useful to look at what the Matsubara contributions do to this spectral density. We see that they modify the real part around $t=0$: + +```{code-cell} ipython3 +def plot_matsubara_correlation_function_contributions(): + """ Plot the underdamped correlation function. """ + t = np.linspace(0, 20, 1000) + + M_Nk2 = np.sum([ + Mk(t, k, gamma=gamma, w0=w0, beta=beta) + for k in range(1, 2 + 1) + ], 0) + + M_Nk100 = np.sum([ + Mk(t, k, gamma=gamma, w0=w0, beta=beta) + for k in range(1, 100 + 1) + ], 0) + + fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) + axes.plot(t, np.real(M_Nk2), '-', color="black", label="Re[M(t)] Nk=2") + axes.plot(t, np.real(M_Nk100), '--', color="red", label="Re[M(t)] Nk=100") + axes.set_xlabel(r't', fontsize=28) + axes.set_ylabel(r'M', fontsize=28) + axes.legend(loc=0, fontsize=12) + + +plot_matsubara_correlation_function_contributions() +``` + +## Solving for the dynamics as a function of time + ++++ + +Next we calculate the exponents using the Matsubara decompositions. Here we split them into real and imaginary parts. + +The HEOM code will optimize these, and reduce the number of exponents when real and imaginary parts have the same exponent. This is clearly the case for the first term in the vkAI and vkAR lists. + +```{code-cell} ipython3 +ckAR, vkAR, ckAI, vkAI = underdamped_matsubara_params( + lam=lam, gamma=gamma, T=T, nk=Nk, +) +``` + +Having created the lists which specify the bath correlation functions, we create a `BosonicBath` from them and pass the bath to the `HEOMSolver` class. + +The solver constructs the "right hand side" (RHS) determinining how the system and auxiliary density operators evolve in time. This can then be used to solve for dynamics or steady-state. + +Below we create the bath and solver and then solve for the dynamics by calling `.run(rho0, tlist)`. + +```{code-cell} ipython3 +with timer("RHS construction time"): + bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI) + HEOMMats = HEOMSolver(Hsys, (bath,Q), NC, options=options) + +with timer("ODE solver time"): + resultMats = HEOMMats.run(rho0, tlist) +``` + +```{code-cell} ipython3 +plot_result_expectations([ + (resultMats, P11p, 'b', "P11 Mats"), + (resultMats, P12p, 'r', "P12 Mats"), +]); +``` + +In practice, one would not perform this laborious expansion for the underdamped correlation function, because +QuTiP already has a class, `UnderDampedEnvironment`, that can construct this bath for you. Nevertheless, knowing how +to perform this expansion is an useful skill. + +Below we show how to use this built-in functionality: + +```{code-cell} ipython3 +# Compare to built-in under-damped bath: + +with timer("RHS construction time"): + bath = UnderDampedEnvironment(lam=lam, gamma=gamma, w0=w0, T=T) + bath_approx=bath.approx_by_matsubara(Nk=Nk) + HEOM_udbath = HEOMSolver(Hsys, (bath_approx,Q), NC, options=options) + +with timer("ODE solver time"): + result_udbath = HEOM_udbath.run(rho0, tlist) +``` + +```{code-cell} ipython3 +plot_result_expectations([ + (result_udbath, P11p, 'b', "P11 (UnderDampedEnvironment)"), + (result_udbath, P12p, 'r', "P12 (UnderDampedEnvironment)"), + (resultMats, P11p, 'r--', "P11 Mats"), + (resultMats, P12p, 'b--', "P12 Mats"), +]); +``` + +The `UnderDampedEnvironment` class also allows us to easily evaluate analytical expressions for the power spectrum, correlation function, and spectral density. In the following plots, the solid lines are the exact expressions, and the dashed lines are based on our approximation of the correlation function with a finite number of exponents. In this case, there is an excellent agreement. + +```{code-cell} ipython3 +w = np.linspace(-3, 3, 1000) +w2 = np.linspace(0, 3, 1000) +t = np.linspace(0, 10, 1000) +bath_cf = bath.correlation_function(t) # uses numerical integration + +fig, axs = plt.subplots(2, 2) + +axs[0, 0].plot(w, bath.power_spectrum(w)) +axs[0, 0].plot(w, bath_approx.power_spectrum(w), '--') +axs[0, 0].set(xlabel=r'$\omega$', ylabel=r'$S(\omega)$') +axs[0, 1].plot(w2, bath.spectral_density(w2)) +axs[0, 1].plot(w2, bath_approx.spectral_density(w2), '--') +axs[0, 1].set(xlabel=r'$\omega$', ylabel=r'$J(\omega)$') +axs[1, 0].plot(t, np.real(bath_cf)) +axs[1, 0].plot(t, np.real(bath_approx.correlation_function(t)), '--') +axs[1, 0].set(xlabel=r'$t$', ylabel=r'$C_{R}(t)$') +axs[1, 1].plot(t, np.imag(bath_cf)) +axs[1, 1].plot(t, np.imag(bath_approx.correlation_function(t)), '--') +axs[1, 1].set(xlabel=r'$t$', ylabel=r'$C_{I}(t)$') + +fig.tight_layout() +plt.show() +``` + +## Compare the results + ++++ + +### We can compare these results to those of the Bloch-Redfield solver in QuTiP: + +```{code-cell} ipython3 +with timer("ODE solver time"): + resultBR = brmesolve( + Hsys, rho0, tlist, + a_ops=[[sigmaz(), bath]], options=options, + ) +``` + +```{code-cell} ipython3 +plot_result_expectations([ + (resultMats, P11p, 'b', "P11 Mats"), + (resultMats, P12p, 'r', "P12 Mats"), + (resultBR, P11p, 'g--', "P11 Bloch Redfield"), + (resultBR, P12p, 'g--', "P12 Bloch Redfield"), +]); +``` + +### Lastly, let us calculate the analytical steady-state result and compare all of the results: + ++++ + +The thermal state of a reaction coordinate (treating the environment as a single damped mode) should, at high temperatures and small gamma, tell us the steady-state: + +```{code-cell} ipython3 +dot_energy, dot_state = Hsys.eigenstates() +deltaE = dot_energy[1] - dot_energy[0] + +gamma2 = gamma +wa = w0 # reaction coordinate frequency +g = lam / np.sqrt(2 * wa) # coupling + +NRC = 10 + +Hsys_exp = tensor(qeye(NRC), Hsys) +Q_exp = tensor(qeye(NRC), Q) +a = tensor(destroy(NRC), qeye(2)) + +H0 = wa * a.dag() * a + Hsys_exp +# interaction +H1 = (g * (a.dag() + a) * Q_exp) + +H = H0 + H1 + +energies, states = H.eigenstates() +rhoss = 0 * states[0] * states[0].dag() +for kk, energ in enumerate(energies): + rhoss += (states[kk] * states[kk].dag() * np.exp(-beta * energies[kk])) +rhoss = rhoss / rhoss.norm() + +P12RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 1).dag()) +P12RC = expect(rhoss, P12RC) + +P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag()) +P11RC = expect(rhoss, P11RC) +``` + +```{code-cell} ipython3 +rcParams = { + "axes.titlesize": 25, + "axes.labelsize": 30, + "xtick.labelsize": 28, + "ytick.labelsize": 28, + "legend.fontsize": 28, + "axes.grid": False, + "savefig.bbox": "tight", + "lines.markersize": 5, + "font.family": "STIXgeneral", + "mathtext.fontset": "stix", + "font.serif": "STIX", + "text.usetex": False, +} +``` + +```{code-cell} ipython3 +fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) + +with plt.rc_context(rcParams): + plt.yticks([P11RC, 0.6, 1.0], [0.38, 0.6, 1]) + + plot_result_expectations([ + (resultBR, P11p, 'y-.', "Bloch-Redfield"), + (resultMats, P11p, 'b', "Matsubara $N_k=3$"), + ], axes=axes) + axes.plot( + tlist, [P11RC for t in tlist], + color='black', linestyle="-.", linewidth=2, + label="Thermal state", + ) + + axes.set_xlabel(r'$t \Delta$', fontsize=30) + axes.set_ylabel(r'$\rho_{11}$', fontsize=30) + + axes.locator_params(axis='y', nbins=4) + axes.locator_params(axis='x', nbins=4) + + axes.legend(loc=0) + + fig.tight_layout() +``` + +## About + +```{code-cell} ipython3 +qutip.about() +``` + +## Testing + +This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. + +```{code-cell} ipython3 +assert np.allclose( + expect(P11p, resultMats.states[-100:]), P11RC, rtol=1e-2, +) +assert np.allclose( + expect(P11p, resultBR.states[-100:]), P11RC, rtol=1e-2, +) +``` diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb deleted file mode 100644 index 7513103f..00000000 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb +++ /dev/null @@ -1,1276 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cabf955e", - "metadata": {}, - "source": [ - "# HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions" - ] - }, - { - "cell_type": "markdown", - "id": "97086a26", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded \n", - "in a set of auxiliary density matrices.\n", - "\n", - "In this example we show the evolution of a single two-level system in contact with a single bosonic environment.\n", - "\n", - "The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", - "\n", - "The bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", - "\n", - "In the example below we show how to model an Ohmic environment with exponential cut-off in three ways:\n", - "\n", - "* First we fit the spectral density with a set of underdamped brownian oscillator functions.\n", - "* Second, we evaluate the correlation functions, and fit those with a certain choice of exponential functions.\n", - "* Third, we use the available OhmicBath class \n", - "\n", - "In each case we will use the fit parameters to determine the correlation function expansion co-efficients needed to construct a description of the bath (i.e. a `BosonicBath` object) to supply to the `HEOMSolver` so that we can solve for the system dynamics." - ] - }, - { - "cell_type": "markdown", - "id": "6ff3b68e", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "507d7e77", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "import qutip\n", - "from qutip import (\n", - " basis,\n", - " expect,\n", - " sigmax,\n", - " sigmaz,\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver,\n", - " SpectralFitter,\n", - " CorrelationFitter,\n", - " OhmicBath,\n", - ")\n", - "\n", - "# Import mpmath functions for evaluation of gamma and zeta\n", - "# functions in the expression for the correlation:\n", - "\n", - "from mpmath import mp\n", - "\n", - "mp.dps = 15\n", - "mp.pretty = True\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e35aa06f", - "metadata": {}, - "outputs": [], - "source": [ - "# Solver options:\n", - "\n", - "options = {\n", - " \"nsteps\": 15000,\n", - " \"store_states\": True,\n", - " \"rtol\": 1e-14,\n", - " \"atol\": 1e-14,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "9682b098", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "And let us set up the system Hamiltonian, bath and system measurement operators:" - ] - }, - { - "cell_type": "markdown", - "id": "67e767f2", - "metadata": {}, - "source": [ - "### System Hamiltonian" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "175e4f9c", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the system Hamiltonian\n", - "eps = 0 # Energy of the 2-level system.\n", - "Del = 0.2 # Tunnelling term\n", - "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()\n", - "rho0 = basis(2, 0) * basis(2, 0).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "3e170b8e", - "metadata": {}, - "source": [ - "### System measurement operators" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f66f7b0f", - "metadata": {}, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresonding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresonding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "b10c8d81", - "metadata": {}, - "source": [ - "### Analytical expressions for the Ohmic bath correlation function and spectral density" - ] - }, - { - "cell_type": "markdown", - "id": "ac2c1bfe", - "metadata": {}, - "source": [ - "Before we begin fitting, let us examine the analytic expressions for the correlation and spectral density functions and write Python equivalents. \n", - "\n", - "The correlation function is given by (see, e.g., http://www1.itp.tu-berlin.de/brandes/public_html/publications/notes.pdf for a derivation, equation 7.59, but with a factor of $\\pi$ moved into the definition of the correlation function):\n", - "\n", - "\\begin{align}\n", - "C(t) =& \\: \\frac{1}{\\pi}\\alpha \\omega_{c}^{1 - s} \\beta^{- (s + 1)} \\: \\times \\\\\n", - " & \\: \\Gamma(s + 1) \\left[ \\zeta \\left(s + 1, \\frac{1 + \\beta \\omega_c - i \\omega_c t}{\\beta \\omega_c}\\right) + \\zeta \\left(s + 1, \\frac{1 + i \\omega_c t}{\\beta \\omega_c}\\right) \\right]\n", - "\\end{align}\n", - "\n", - "where $\\Gamma$ is the Gamma function and\n", - "\n", - "\\begin{equation}\n", - "\\zeta(z, u) \\equiv \\sum_{n=0}^{\\infty} \\frac{1}{(n + u)^z}, \\; u \\neq 0, -1, -2, \\ldots\n", - "\\end{equation}\n", - "\n", - "is the generalized Zeta function. The Ohmic case is given by $s = 1$.\n", - "\n", - "The corresponding spectral density for the Ohmic case is:\n", - "\n", - "\\begin{equation}\n", - "J(\\omega) = \\omega \\alpha e^{- \\frac{\\omega}{\\omega_c}}\n", - "\\end{equation}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e445252c", - "metadata": {}, - "outputs": [], - "source": [ - "def ohmic_correlation(t, alpha, wc, beta, s=1):\n", - " \"\"\"The Ohmic bath correlation function as a function of t\n", - " (and the bath parameters).\n", - " \"\"\"\n", - " corr = (1 / np.pi) * alpha * wc ** (1 - s)\n", - " corr *= beta ** (-(s + 1)) * mp.gamma(s + 1)\n", - " z1_u = (1 + beta * wc - 1.0j * wc * t) / (beta * wc)\n", - " z2_u = (1 + 1.0j * wc * t) / (beta * wc)\n", - " # Note: the arguments to zeta should be in as high precision as possible.\n", - " # See http://mpmath.org/doc/current/basics.html#providing-correct-input\n", - " return np.array(\n", - " [\n", - " complex(corr * (mp.zeta(s + 1, u1) + mp.zeta(s + 1, u2)))\n", - " for u1, u2 in zip(z1_u, z2_u)\n", - " ],\n", - " dtype=np.complex128,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7f596c55", - "metadata": {}, - "outputs": [], - "source": [ - "def ohmic_spectral_density(w, alpha, wc):\n", - " \"\"\"The Ohmic bath spectral density as a function of w\n", - " (and the bath parameters).\n", - " \"\"\"\n", - " return w * alpha * np.e ** (-w / wc)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6361cbb5", - "metadata": {}, - "outputs": [], - "source": [ - "def ohmic_power_spectrum(w, alpha, wc, beta):\n", - " \"\"\"The Ohmic bath power spectrum as a function of w\n", - " (and the bath parameters).\n", - " \"\"\"\n", - " bose = (1 / (np.e ** (w * beta) - 1)) + 1\n", - " return w * alpha * np.e ** (-abs(w) / wc) * bose * 2" - ] - }, - { - "cell_type": "markdown", - "id": "828807f3", - "metadata": {}, - "source": [ - "### Bath and HEOM parameters" - ] - }, - { - "cell_type": "markdown", - "id": "e8d2c4eb", - "metadata": {}, - "source": [ - "Finally, let's set the bath parameters we will work with and write down some measurement operators:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "84692a7a", - "metadata": {}, - "outputs": [], - "source": [ - "Q = sigmaz()\n", - "alpha = 3.25\n", - "T = 0.5\n", - "wc = 1.0\n", - "s = 1" - ] - }, - { - "cell_type": "markdown", - "id": "6311fc80", - "metadata": {}, - "source": [ - "And set the cut-off for the HEOM hierarchy:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4a2d75e0", - "metadata": {}, - "outputs": [], - "source": [ - "# HEOM parameters:\n", - "\n", - "# The max_depth defaults to 5 so that the notebook executes more\n", - "# quickly. Change it to 11 to wait longer for more accurate results.\n", - "max_depth = 5" - ] - }, - { - "cell_type": "markdown", - "id": "96a8795a", - "metadata": {}, - "source": [ - "## Building the HEOM bath by fitting the spectral density" - ] - }, - { - "cell_type": "markdown", - "id": "0087eff9", - "metadata": {}, - "source": [ - "We begin by fitting the spectral density, using a series of $k$ underdamped harmonic oscillators case with the Meier-Tannor form (J. Chem. Phys. 111, 3365 (1999); https://doi.org/10.1063/1.479669):\n", - "\n", - "\\begin{equation}\n", - "J_{\\mathrm approx}(\\omega; a, b, c) = \\sum_{i=0}^{k-1} \\frac{2 a_i b_i w}{((w + c_i)^2 + b_i^2) ((w - c_i)^2 + b_i^2)}\n", - "\\end{equation}\n", - "\n", - "where $a, b$ and $c$ are the fit parameters and each is a vector of length $k$." - ] - }, - { - "cell_type": "markdown", - "id": "ee9df492", - "metadata": {}, - "source": [ - "With the spectral density approximation $J_{\\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. This can be done quickly using the `SpectralFitter` class, which takes the target spectral density as an array and fits it to the series of **k** underdamped harmonic oscillators with the Meier-Tannor form" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "61cb326f", - "metadata": {}, - "outputs": [], - "source": [ - "w = np.linspace(0, 15, 20000)\n", - "J = ohmic_spectral_density(w, alpha, wc)" - ] - }, - { - "cell_type": "markdown", - "id": "70b4e4c2", - "metadata": {}, - "source": [ - "We first initialize our SpectralFitter" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1a415eb6", - "metadata": {}, - "outputs": [], - "source": [ - "fs = SpectralFitter(T, Q, w, J)" - ] - }, - { - "cell_type": "markdown", - "id": "c7d4d10c", - "metadata": {}, - "source": [ - "To obtain a fit we simply pass our desired spectral density and range, into the ``get_fit`` method. The number of exponents we'll use in our bath is given by Nk" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e649ebea", - "metadata": {}, - "outputs": [], - "source": [ - "bath, fitinfo = fs.get_fit(Nk=1)" - ] - }, - { - "cell_type": "markdown", - "id": "47c73e52", - "metadata": {}, - "source": [ - "To obtain an overview of the results of the fit we may take a look at the summary from the ``fitinfo``" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6d03e729", - "metadata": {}, - "outputs": [], - "source": [ - "print(fitinfo[\"summary\"])" - ] - }, - { - "cell_type": "markdown", - "id": "518e5698", - "metadata": {}, - "source": [ - "We may see how the number of exponents chosen affects the fit since the approximated functions are available:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ee47e95c", - "metadata": {}, - "outputs": [], - "source": [ - "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5))\n", - "\n", - "ax1.plot(w, J, label=\"Original spectral density\")\n", - "ax1.plot(w, bath.spectral_density_approx(w), label=\"Effective fitted SD\")\n", - "ax1.set_xlabel(r'$\\omega$')\n", - "ax1.set_ylabel(r'$J$')\n", - "ax1.legend()\n", - "\n", - "ax2.plot(w, np.abs(J - bath.spectral_density_approx(w)), label=\"Error\")\n", - "ax2.set_xlabel(r'$\\omega$')\n", - "ax2.set_ylabel(r'$J$')\n", - "ax2.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c5c6b894", - "metadata": {}, - "source": [ - "Here we see a surprisingly large discrepancy in our approximated or effective spectral density. This happens because we are not using enough exponentials from each of the underdamped modes to have an appropiate fit. All modes have the same number of exponents, and we set it to 1 which is not enough to model a bath with the temperature considered, let us repeat this with a higher number of exponents." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "75e720a6", - "metadata": {}, - "outputs": [], - "source": [ - "bath, fitinfo = fs.get_fit(Nk=5)\n", - "print(fitinfo[\"summary\"])\n", - "\n", - "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5))\n", - "\n", - "ax1.plot(w, J, label=\"Original spectral density\")\n", - "ax1.plot(w, bath.spectral_density_approx(w), label=\"Effective fitted SD\")\n", - "ax1.set_xlabel(r'$\\omega$')\n", - "ax1.set_ylabel(r'$J$')\n", - "ax1.legend()\n", - "\n", - "ax2.plot(w, np.abs(J - bath.spectral_density_approx(w)), label=\"Error\")\n", - "ax2.set_xlabel(r'$\\omega$')\n", - "ax2.set_ylabel(r'$J$')\n", - "ax2.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "5575f325", - "metadata": {}, - "source": [ - "Since the number of exponents increases simulation time one should go with the least amount of exponents that correctly describe the bath properties (Power spectrum, Spectral density and the correlation function). When the number of exponents is not specified it defaults to 5." - ] - }, - { - "cell_type": "markdown", - "id": "114bf341", - "metadata": {}, - "source": [ - "By default the ``get_fit`` method, has a threshold normalized root mean squared error (NRMSE) of $5\\times 10^{-6}$ and selects the number of oscillators automatically to obtain that value. One may also specify the number of oscillators that is used with the optional argument N, or may want a more accurate NRMSE, which can be specified with the final_rmse optional argument" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d2b612fe", - "metadata": {}, - "outputs": [], - "source": [ - "bath, fitinfo = fs.get_fit(final_rmse=1e-6)\n", - "print(fitinfo[\"summary\"])" - ] - }, - { - "cell_type": "markdown", - "id": "0962962b", - "metadata": {}, - "source": [ - "Alternatively one may choose the number of oscillators in the fit instead of a desired NRMSE" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "192d9a35", - "metadata": {}, - "outputs": [], - "source": [ - "fittedbath, fitinfo = fs.get_fit(N=4)\n", - "print(fitinfo[\"summary\"])" - ] - }, - { - "cell_type": "markdown", - "id": "65ad093e", - "metadata": {}, - "source": [ - "Let's take a closer look at our last fit by plotting the contribution of each term of the fit:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53e63db1", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot the components of the fit separately:\n", - "plt.rcParams[\"font.size\"] = 25\n", - "plt.rcParams[\"figure.figsize\"] = (10, 5)\n", - "\n", - "\n", - "def plot_fit(func, J, w, lam, gamma, w0):\n", - " \"\"\"Plot the individual components of a fit to the spectral density.\n", - " and how they contribute to the full fit one by one\"\"\"\n", - " total = 0\n", - " for i in range(len(lam)):\n", - " component = func(w, [lam[i]], [gamma[i]], [w0[i]])\n", - " total += component\n", - " plt.plot(w, J, \"r--\", linewidth=2, label=\"original\")\n", - " plt.plot(w, total, label=rf\"$k={i+1}$\")\n", - " plt.xlabel(r\"$\\omega$\")\n", - " plt.ylabel(r\"$J(\\omega)$\")\n", - " plt.legend()\n", - " plt.pause(1)\n", - " plt.show()\n", - "\n", - "\n", - "def plot_fit_components(func, J, w, lam, gamma, w0):\n", - " \"\"\"Plot the individual components of a fit to the spectral density.\n", - " and how they contribute to the full fit\"\"\"\n", - " plt.plot(w, J, \"r--\", linewidth=2, label=\"original\")\n", - " for i in range(len(lam)):\n", - " component = func(w, [lam[i]], [gamma[i]], [w0[i]])\n", - " plt.plot(w, component, label=rf\"$k={i+1}$\")\n", - " plt.xlabel(r\"$\\omega$\")\n", - " plt.ylabel(r\"$J(\\omega)$\")\n", - " plt.legend(bbox_to_anchor=(1.04, 1))\n", - " plt.show()\n", - "\n", - "\n", - "lam, gamma, w0 = fitinfo[\"params\"]\n", - "plot_fit(SpectralFitter._meier_tannor_SD, J, w, lam, gamma, w0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ef71d94e", - "metadata": {}, - "outputs": [], - "source": [ - "plot_fit_components(SpectralFitter._meier_tannor_SD, J, w, lam, gamma, w0)" - ] - }, - { - "cell_type": "markdown", - "id": "b9cc7bd7", - "metadata": {}, - "source": [ - "And let's also compare the power spectrum of the fit and the analytical spectral density:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9fb1bbfc", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_power_spectrum(alpha, wc, beta, save=True):\n", - " \"\"\"Plot the power spectrum of a fit against the actual power spectrum.\"\"\"\n", - " w = np.linspace(-10, 10, 50000)\n", - " s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta)\n", - " s_fit = fittedbath.power_spectrum_approx(w)\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " axes.plot(w, s_orig, \"r\", linewidth=2, label=\"original\")\n", - " axes.plot(w, np.real(s_fit), \"b\", linewidth=2, label=\"fit\")\n", - "\n", - " axes.set_xlabel(r\"$\\omega$\", fontsize=28)\n", - " axes.set_ylabel(r\"$S(\\omega)$\", fontsize=28)\n", - " axes.legend()\n", - "\n", - " if save:\n", - " fig.savefig(\"powerspectrum.eps\")\n", - "\n", - "\n", - "plot_power_spectrum(alpha, wc, 1 / T, save=False)" - ] - }, - { - "cell_type": "markdown", - "id": "987d142c", - "metadata": {}, - "source": [ - "Now that we have a good fit to the spectral density, Let us obtain its dynamics, by passing our ``FitSpectral`` bath specifications into the ``HEOMSolver``" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5966b87b", - "metadata": {}, - "outputs": [], - "source": [ - "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - "HEOM_spectral_fit = HEOMSolver(\n", - " Hsys,\n", - " fittedbath,\n", - " max_depth=4,\n", - " options=options,\n", - ")\n", - "result_spectral = HEOM_spectral_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "3019de19", - "metadata": {}, - "source": [ - "Now if we want to see the systems's behaviour as we change the Number of terms in the fit, we may use this auxiliary function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2314e5cc", - "metadata": {}, - "outputs": [], - "source": [ - "def generate_spectrum_results(Q, N, Nk, max_depth):\n", - " \"\"\"Run the HEOM with the given bath parameters and\n", - " and return the results of the evolution.\n", - " \"\"\"\n", - " fs = SpectralFitter(T, Q, w, J)\n", - " bath, _ = fs.get_fit(N, Nk=Nk)\n", - " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - "\n", - " # This problem is a little stiff, so we use the BDF method to solve\n", - " # the ODE ^^^\n", - " print(f\"Starting calculations for N={N}, Nk={Nk} and max_depth={max_depth} ... \")\n", - " HEOM_spectral_fit = HEOMSolver(\n", - " Hsys,\n", - " bath,\n", - " max_depth=max_depth,\n", - " options=options,\n", - " )\n", - " results_spectral_fit = HEOM_spectral_fit.run(rho0, tlist)\n", - " return results_spectral_fit" - ] - }, - { - "cell_type": "markdown", - "id": "4720a994", - "metadata": {}, - "source": [ - "Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "578752e9", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_result_expectations(plots, axes=None):\n", - " \"\"\"Plot the expectation values of operators as functions of time.\n", - "\n", - " Each plot in plots consists of (solver_result,\n", - " measurement_operation, color, label).\n", - " \"\"\"\n", - " if axes is None:\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " fig_created = True\n", - " else:\n", - " fig = None\n", - " fig_created = False\n", - "\n", - " # add kw arguments to each plot if missing\n", - " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", - " for result, m_op, color, label, kw in plots:\n", - " exp = np.real(expect(result.states, m_op))\n", - " kw.setdefault(\"linewidth\", 2)\n", - " if color == \"rand\":\n", - " axes.plot(\n", - " result.times,\n", - " exp,\n", - " c=np.random.rand(\n", - " 3,\n", - " ),\n", - " label=label,\n", - " **kw,\n", - " )\n", - " else:\n", - " axes.plot(result.times, exp, color, label=label, **kw)\n", - "\n", - " if fig_created:\n", - " axes.legend(loc=0, fontsize=12)\n", - " axes.set_xlabel(\"t\", fontsize=28)\n", - "\n", - " return fig" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7772dc40", - "metadata": {}, - "outputs": [], - "source": [ - "# Generate results for different number of lorentzians in fit:\n", - "\n", - "results_spectral_fit_pk = [\n", - " generate_spectrum_results(Q, n, Nk=1, max_depth=max_depth) for n in range(1, 5)\n", - "]\n", - "\n", - "plot_result_expectations(\n", - " [\n", - " (\n", - " result,\n", - " P11p,\n", - " \"rand\",\n", - " f\"P11 (spectral fit) $k_J$={pk + 1}\",\n", - " )\n", - " for pk, result in enumerate(results_spectral_fit_pk)\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bc81583f", - "metadata": {}, - "outputs": [], - "source": [ - "# generate results for different number of Matsubara terms per Lorentzian\n", - "# for max number of Lorentzians:\n", - "\n", - "Nk_list = range(2, 4)\n", - "results_spectral_fit_nk = [\n", - " generate_spectrum_results(Q, 4, Nk=Nk, max_depth=max_depth) for Nk in Nk_list\n", - "]\n", - "\n", - "plot_result_expectations(\n", - " [\n", - " (\n", - " result,\n", - " P11p,\n", - " \"rand\",\n", - " f\"P11 (spectral fit) K={nk}\",\n", - " )\n", - " for nk, result in zip(Nk_list, results_spectral_fit_nk)\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "43152603", - "metadata": {}, - "outputs": [], - "source": [ - "# Generate results for different depths:\n", - "\n", - "Nc_list = range(2, max_depth)\n", - "results_spectral_fit_nc = [\n", - " generate_spectrum_results(Q, 4, Nk=1, max_depth=Nc) for Nc in Nc_list\n", - "]\n", - "\n", - "plot_result_expectations(\n", - " [\n", - " (\n", - " result,\n", - " P11p,\n", - " \"rand\",\n", - " f\"P11 (spectral fit) $N_C={nc}$\",\n", - " )\n", - " for nc, result in zip(Nc_list, results_spectral_fit_nc)\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "bb4a424c", - "metadata": {}, - "source": [ - "#### We now combine the fitting and correlation function data into one large plot. Here we define a function to plot everything together" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9a2b8c4b", - "metadata": {}, - "outputs": [], - "source": [ - "def gen_plots(fs, w, J, t, C, w2, S):\n", - " def plot_cr_fit_vs_actual(t, C, func, axes):\n", - " \"\"\"Plot the C_R(t) fit.\"\"\"\n", - " yR = func(t)\n", - "\n", - " axes.plot(\n", - " t,\n", - " np.real(C),\n", - " \"r\",\n", - " linewidth=3,\n", - " label=\"Original\",\n", - " )\n", - " axes.plot(\n", - " t,\n", - " np.real(yR),\n", - " \"g\",\n", - " dashes=[3, 3],\n", - " linewidth=2,\n", - " label=\"Reconstructed\",\n", - " )\n", - "\n", - " axes.set_ylabel(r\"$C_R(t)$\", fontsize=28)\n", - " axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=28)\n", - " axes.locator_params(axis=\"y\", nbins=4)\n", - " axes.locator_params(axis=\"x\", nbins=4)\n", - " axes.text(0.15, 0.85, \"(a)\", fontsize=28, transform=axes.transAxes)\n", - "\n", - " def plot_ci_fit_vs_actual(t, C, func, axes):\n", - " \"\"\"Plot the C_I(t) fit.\"\"\"\n", - " yI = func(t)\n", - "\n", - " axes.plot(\n", - " t,\n", - " np.imag(C),\n", - " \"r\",\n", - " linewidth=3,\n", - " )\n", - " axes.plot(\n", - " t,\n", - " np.real(yI),\n", - " \"g\",\n", - " dashes=[3, 3],\n", - " linewidth=2,\n", - " )\n", - "\n", - " axes.set_ylabel(r\"$C_I(t)$\", fontsize=28)\n", - " axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=28)\n", - " axes.locator_params(axis=\"y\", nbins=4)\n", - " axes.locator_params(axis=\"x\", nbins=4)\n", - " axes.text(0.80, 0.80, \"(b)\", fontsize=28, transform=axes.transAxes)\n", - "\n", - " def plot_jw_fit_vs_actual(w, J, axes):\n", - " \"\"\"Plot the J(w) fit.\"\"\"\n", - " J_fit = fs.spectral_density_approx(w)\n", - "\n", - " axes.plot(\n", - " w,\n", - " J,\n", - " \"r\",\n", - " linewidth=3,\n", - " )\n", - " axes.plot(\n", - " w,\n", - " J_fit,\n", - " \"g\",\n", - " dashes=[3, 3],\n", - " linewidth=2,\n", - " )\n", - "\n", - " axes.set_ylabel(r\"$J(\\omega)$\", fontsize=28)\n", - " axes.set_xlabel(r\"$\\omega/\\omega_c$\", fontsize=28)\n", - " axes.locator_params(axis=\"y\", nbins=4)\n", - " axes.locator_params(axis=\"x\", nbins=4)\n", - " axes.text(0.15, 0.85, \"(c)\", fontsize=28, transform=axes.transAxes)\n", - "\n", - " def plot_sw_fit_vs_actual(axes):\n", - " \"\"\"Plot the S(w) fit.\"\"\"\n", - "\n", - " # avoid the pole in the fit around zero:\n", - " s_fit = fs.power_spectrum_approx(w2)\n", - "\n", - " axes.plot(w2, S, \"r\", linewidth=3)\n", - " axes.plot(w2, s_fit, \"g\", dashes=[3, 3], linewidth=2)\n", - "\n", - " axes.set_ylabel(r\"$S(\\omega)$\", fontsize=28)\n", - " axes.set_xlabel(r\"$\\omega/\\omega_c$\", fontsize=28)\n", - " axes.locator_params(axis=\"y\", nbins=4)\n", - " axes.locator_params(axis=\"x\", nbins=4)\n", - " axes.text(0.15, 0.85, \"(d)\", fontsize=28, transform=axes.transAxes)\n", - "\n", - " def plot_matsubara_spectrum_fit_vs_actual(t, C):\n", - " \"\"\"Plot the Matsubara fit of the spectrum .\"\"\"\n", - " fig = plt.figure(figsize=(12, 10))\n", - " grid = plt.GridSpec(2, 2, wspace=0.4, hspace=0.3)\n", - "\n", - " plot_cr_fit_vs_actual(\n", - " t,\n", - " C,\n", - " lambda t: fs.correlation_function_approx(t),\n", - " axes=fig.add_subplot(grid[0, 0]),\n", - " )\n", - " plot_ci_fit_vs_actual(\n", - " t,\n", - " C,\n", - " lambda t: np.imag(fs.correlation_function_approx(t)),\n", - " axes=fig.add_subplot(grid[0, 1]),\n", - " )\n", - " plot_jw_fit_vs_actual(\n", - " w,\n", - " J,\n", - " axes=fig.add_subplot(grid[1, 0]),\n", - " )\n", - " plot_sw_fit_vs_actual(\n", - " axes=fig.add_subplot(grid[1, 1]),\n", - " )\n", - " fig.legend(loc=\"upper center\", ncol=2, fancybox=True, shadow=True)\n", - "\n", - " return plot_matsubara_spectrum_fit_vs_actual(t, C)" - ] - }, - { - "cell_type": "markdown", - "id": "7adb9314", - "metadata": {}, - "source": [ - "#### And finally plot everything together" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4ea17170", - "metadata": {}, - "outputs": [], - "source": [ - "t = np.linspace(0, 15, 1000)\n", - "C = ohmic_correlation(t, alpha, wc, 1 / T)\n", - "w2 = np.concatenate((-np.linspace(10, 1e-2, 100), np.linspace(1e-2, 10, 100)))\n", - "S = ohmic_power_spectrum(w2, alpha, wc, 1 / T)\n", - "gen_plots(fittedbath, w, J, t, C, w2, S)" - ] - }, - { - "cell_type": "markdown", - "id": "77deecd5", - "metadata": {}, - "source": [ - "## Building the HEOM bath by fitting the correlation function" - ] - }, - { - "cell_type": "markdown", - "id": "51ba39e5", - "metadata": {}, - "source": [ - "Having successfully fitted the spectral density and used the result to calculate the Matsubara expansion and terminator for the HEOM bosonic bath, we now proceed to the second case of fitting the correlation function itself instead.\n", - "\n", - "Here we fit the real and imaginary parts separately, using the following ansatz\n", - "\n", - "$$C_R^F(t) = \\sum_{i=1}^{k_R} c_R^ie^{-\\gamma_R^i t}\\cos(\\omega_R^i t)$$\n", - "\n", - "$$C_I^F(t) = \\sum_{i=1}^{k_I} c_I^ie^{-\\gamma_I^i t}\\sin(\\omega_I^i t)$$\n", - "\n", - "Analogously to the spectral density case, one may use the `CorrelationFitter` class" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "23c96ebc", - "metadata": {}, - "outputs": [], - "source": [ - "t = np.linspace(0, 15, 1500)\n", - "C = ohmic_correlation(t, alpha=alpha, wc=wc, beta=1 / T)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d1a4b158", - "metadata": {}, - "outputs": [], - "source": [ - "fc = CorrelationFitter(Q, T, t, C)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ad502b23", - "metadata": {}, - "outputs": [], - "source": [ - "bath, fitinfo = fc.get_fit(Ni=4, Nr=4)\n", - "print(fitinfo[\"summary\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ebee4fd6", - "metadata": {}, - "outputs": [], - "source": [ - "gen_plots(bath, w, J, t, C, w2, S)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6788a571", - "metadata": {}, - "outputs": [], - "source": [ - "def generate_corr_results(N, max_depth):\n", - " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - " bath, _ = fc.get_fit(Ni=N, Nr=N)\n", - " HEOM_corr_fit = HEOMSolver(\n", - " Hsys,\n", - " bath,\n", - " max_depth=max_depth,\n", - " options=options,\n", - " )\n", - "\n", - " results_corr_fit = HEOM_corr_fit.run(rho0, tlist)\n", - "\n", - " return results_corr_fit\n", - "\n", - "\n", - "# Generate results for different number of exponentials in fit:\n", - "results_corr_fit_pk = [\n", - " print(f\"{i + 1}\")\n", - " or generate_corr_results(\n", - " i,\n", - " max_depth=max_depth,\n", - " )\n", - " for i in range(1, 4)\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d0fc7ffc", - "metadata": {}, - "outputs": [], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (\n", - " result,\n", - " P11p,\n", - " \"rand\",\n", - " f\"P11 (correlation fit) k_R=k_I={pk + 1}\",\n", - " )\n", - " for pk, result in enumerate(results_corr_fit_pk)\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7609c0a4", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", - "\n", - "plot_result_expectations(\n", - " [\n", - " (\n", - " results_corr_fit_pk[0],\n", - " P11p,\n", - " \"y\",\n", - " \"Correlation Function Fit $k_R=k_I=1$\",\n", - " ),\n", - " (\n", - " results_corr_fit_pk[2],\n", - " P11p,\n", - " \"k\",\n", - " \"Correlation Function Fit $k_R=k_I=3$\",\n", - " ),\n", - " (results_spectral_fit_pk[0], P11p, \"b\", \"Spectral Density Fit $k_J=1$\"),\n", - " (results_spectral_fit_pk[3], P11p, \"r-.\", \"Spectral Density Fit $k_J=4$\"),\n", - " ],\n", - " axes=axes,\n", - ")\n", - "\n", - "axes.set_yticks([0.6, 0.8, 1])\n", - "axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", - "axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", - "axes.legend(loc=0, fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "id": "4456fb86", - "metadata": {}, - "source": [ - "# Using the Ohmic Bath class\n", - "\n", - " As the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced quickly by using the OhmicBath class. This allows for rapid implementation of fitted ohmic baths via the correlation function or spectral density" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "96f35548", - "metadata": {}, - "outputs": [], - "source": [ - "obs = OhmicBath(T, Q, alpha, wc, s)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0474ab23", - "metadata": {}, - "outputs": [], - "source": [ - "Obath, fitinfo = obs.make_correlation_fit(t, rmse=2e-4)\n", - "print(fitinfo[\"summary\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7b6702fd", - "metadata": {}, - "outputs": [], - "source": [ - "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - "HEOM_ohmic_corr_fit = HEOMSolver(\n", - " Hsys,\n", - " Obath,\n", - " max_depth=5,\n", - " options=options,\n", - ")\n", - "results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f2eaaf50", - "metadata": {}, - "outputs": [], - "source": [ - "Obath, fitinfo = obs.make_spectral_fit(w, rmse=2e-4)\n", - "print(fitinfo[\"summary\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "672ed9f6", - "metadata": {}, - "outputs": [], - "source": [ - "HEOM_ohmic_spectral_fit = HEOMSolver(\n", - " Hsys,\n", - " Obath,\n", - " max_depth=5,\n", - " options=options,\n", - ")\n", - "results_ohmic_spectral_fit = HEOM_ohmic_spectral_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0f1c8430", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", - "\n", - "plot_result_expectations(\n", - " [\n", - " # (\n", - " # results_corr_fit_pk[0], P11p,\n", - " # 'y', \"Correlation Function Fit $k_R=k_I=1$\",\n", - " # ),\n", - " (\n", - " results_corr_fit_pk[2],\n", - " P11p,\n", - " \"y-.\",\n", - " \"Correlation Function Fit $k_R=k_I=3$\",\n", - " ),\n", - " (results_spectral_fit_pk[0], P11p, \"b\", \"Spectral Density Fit $k_J=1$\"),\n", - " (results_spectral_fit_pk[2], P11p, \"g--\", \"Spectral Density Fit $k_J=3$\"),\n", - " (results_spectral_fit_pk[3], P11p, \"r-.\", \"Spectral Density Fit $k_J=4$\"),\n", - " (results_ohmic_spectral_fit, P11p, \"g-.\", \"Spectral Density Fit Ohmic Bath\"),\n", - " (results_ohmic_corr_fit, P11p, \"k-.\", \"Correlation Fit Ohmic Bath\"),\n", - " ],\n", - " axes=axes,\n", - ")\n", - "\n", - "axes.set_yticks([0.6, 0.8, 1])\n", - "axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", - "axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", - "axes.legend(loc=0, fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "id": "bbd88736", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a14ddea9", - "metadata": {}, - "outputs": [], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "25aec367", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "04a94f35", - "metadata": {}, - "outputs": [], - "source": [ - "assert np.allclose(\n", - " expect(P11p, results_spectral_fit_pk[2].states),\n", - " expect(P11p, results_spectral_fit_pk[3].states),\n", - " rtol=1e-2,\n", - ")" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "qutip-dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.12.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md new file mode 100644 index 00000000..c4a6144b --- /dev/null +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md @@ -0,0 +1,825 @@ +--- +jupytext: + formats: ipynb,md:myst + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.16.4 +kernelspec: + display_name: qutip-dev + language: python + name: python3 +--- + +# HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions + ++++ + +## Introduction + +The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded +in a set of auxiliary density matrices. + +In this example we show the evolution of a single two-level system in contact with a single bosonic environment. + +The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment. + +The bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions. + +In the example below we show how to model an Ohmic environment with exponential cut-off in three ways: + +* First we fit the spectral density with a set of underdamped brownian oscillator functions. +* Second, we evaluate the correlation functions, and fit those with a certain choice of exponential functions. +* Third, we use the available OhmicBath class + +In each case we will use the fit parameters to determine the correlation function expansion co-efficients needed to construct a description of the bath (i.e. a `BosonicBath` object) to supply to the `HEOMSolver` so that we can solve for the system dynamics. + ++++ + +## Setup + +```{code-cell} +import numpy as np +from matplotlib import pyplot as plt +import qutip +from qutip import ( + basis, + expect, + sigmax, + sigmaz, +) +from qutip.solver.heom import ( + HEOMSolver, + SpectralFitter, + CorrelationFitter, + OhmicBath, +) + +# Import mpmath functions for evaluation of gamma and zeta +# functions in the expression for the correlation: + +from mpmath import mp + +mp.dps = 15 +mp.pretty = True + +%matplotlib inline +``` + +```{code-cell} +# Solver options: + +options = { + "nsteps": 15000, + "store_states": True, + "rtol": 1e-14, + "atol": 1e-14, + "method": "vern9", + "progress_bar": "enhanced", +} +``` + +## System and bath definition + +And let us set up the system Hamiltonian, bath and system measurement operators: + ++++ + +### System Hamiltonian + +```{code-cell} +# Defining the system Hamiltonian +eps = 0 # Energy of the 2-level system. +Del = 0.2 # Tunnelling term +Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() +rho0 = basis(2, 0) * basis(2, 0).dag() +``` + +### System measurement operators + +```{code-cell} +# Define some operators with which we will measure the system +# 1,1 element of density matrix - corresonding to groundstate +P11p = basis(2, 0) * basis(2, 0).dag() +P22p = basis(2, 1) * basis(2, 1).dag() +# 1,2 element of density matrix - corresonding to coherence +P12p = basis(2, 0) * basis(2, 1).dag() +``` + +### Analytical expressions for the Ohmic bath correlation function and spectral density + ++++ + +Before we begin fitting, let us examine the analytic expressions for the correlation and spectral density functions and write Python equivalents. + +The correlation function is given by (see, e.g., http://www1.itp.tu-berlin.de/brandes/public_html/publications/notes.pdf for a derivation, equation 7.59, but with a factor of $\pi$ moved into the definition of the correlation function): + +\begin{align} +C(t) =& \: \frac{1}{\pi}\alpha \omega_{c}^{1 - s} \beta^{- (s + 1)} \: \times \\ + & \: \Gamma(s + 1) \left[ \zeta \left(s + 1, \frac{1 + \beta \omega_c - i \omega_c t}{\beta \omega_c}\right) + \zeta \left(s + 1, \frac{1 + i \omega_c t}{\beta \omega_c}\right) \right] +\end{align} + +where $\Gamma$ is the Gamma function and + +\begin{equation} +\zeta(z, u) \equiv \sum_{n=0}^{\infty} \frac{1}{(n + u)^z}, \; u \neq 0, -1, -2, \ldots +\end{equation} + +is the generalized Zeta function. The Ohmic case is given by $s = 1$. + +The corresponding spectral density for the Ohmic case is: + +\begin{equation} +J(\omega) = \omega \alpha e^{- \frac{\omega}{\omega_c}} +\end{equation} + +```{code-cell} +def ohmic_correlation(t, alpha, wc, beta, s=1): + """The Ohmic bath correlation function as a function of t + (and the bath parameters). + """ + corr = (1 / np.pi) * alpha * wc ** (1 - s) + corr *= beta ** (-(s + 1)) * mp.gamma(s + 1) + z1_u = (1 + beta * wc - 1.0j * wc * t) / (beta * wc) + z2_u = (1 + 1.0j * wc * t) / (beta * wc) + # Note: the arguments to zeta should be in as high precision as possible. + # See http://mpmath.org/doc/current/basics.html#providing-correct-input + return np.array( + [ + complex(corr * (mp.zeta(s + 1, u1) + mp.zeta(s + 1, u2))) + for u1, u2 in zip(z1_u, z2_u) + ], + dtype=np.complex128, + ) +``` + +```{code-cell} +def ohmic_spectral_density(w, alpha, wc): + """The Ohmic bath spectral density as a function of w + (and the bath parameters). + """ + return w * alpha * np.e ** (-w / wc) +``` + +```{code-cell} +def ohmic_power_spectrum(w, alpha, wc, beta): + """The Ohmic bath power spectrum as a function of w + (and the bath parameters). + """ + bose = (1 / (np.e ** (w * beta) - 1)) + 1 + return w * alpha * np.e ** (-abs(w) / wc) * bose * 2 +``` + +### Bath and HEOM parameters + ++++ + +Finally, let's set the bath parameters we will work with and write down some measurement operators: + +```{code-cell} +Q = sigmaz() +alpha = 3.25 +T = 0.5 +wc = 1.0 +s = 1 +``` + +And set the cut-off for the HEOM hierarchy: + +```{code-cell} +# HEOM parameters: + +# The max_depth defaults to 5 so that the notebook executes more +# quickly. Change it to 11 to wait longer for more accurate results. +max_depth = 5 +``` + +## Building the HEOM bath by fitting the spectral density + ++++ + +We begin by fitting the spectral density, using a series of $k$ underdamped harmonic oscillators case with the Meier-Tannor form (J. Chem. Phys. 111, 3365 (1999); https://doi.org/10.1063/1.479669): + +\begin{equation} +J_{\mathrm approx}(\omega; a, b, c) = \sum_{i=0}^{k-1} \frac{2 a_i b_i w}{((w + c_i)^2 + b_i^2) ((w - c_i)^2 + b_i^2)} +\end{equation} + +where $a, b$ and $c$ are the fit parameters and each is a vector of length $k$. + ++++ + +With the spectral density approximation $J_{\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. This can be done quickly using the `SpectralFitter` class, which takes the target spectral density as an array and fits it to the series of **k** underdamped harmonic oscillators with the Meier-Tannor form + +```{code-cell} +w = np.linspace(0, 15, 20000) +J = ohmic_spectral_density(w, alpha, wc) +``` + +We first initialize our SpectralFitter + +```{code-cell} +fs = SpectralFitter(T, Q, w, J) +``` + +To obtain a fit we simply pass our desired spectral density and range, into the ``get_fit`` method. The number of exponents we'll use in our bath is given by Nk + +```{code-cell} +bath, fitinfo = fs.get_fit(Nk=1) +``` + +To obtain an overview of the results of the fit we may take a look at the summary from the ``fitinfo`` + +```{code-cell} +print(fitinfo["summary"]) +``` + +We may see how the number of exponents chosen affects the fit since the approximated functions are available: + +```{code-cell} +fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5)) + +ax1.plot(w, J, label="Original spectral density") +ax1.plot(w, bath.spectral_density_approx(w), label="Effective fitted SD") +ax1.set_xlabel(r'$\omega$') +ax1.set_ylabel(r'$J$') +ax1.legend() + +ax2.plot(w, np.abs(J - bath.spectral_density_approx(w)), label="Error") +ax2.set_xlabel(r'$\omega$') +ax2.set_ylabel(r'$J$') +ax2.legend() + +plt.show() +``` + +Here we see a surprisingly large discrepancy in our approximated or effective spectral density. This happens because we are not using enough exponentials from each of the underdamped modes to have an appropiate fit. All modes have the same number of exponents, and we set it to 1 which is not enough to model a bath with the temperature considered, let us repeat this with a higher number of exponents. + +```{code-cell} +bath, fitinfo = fs.get_fit(Nk=5) +print(fitinfo["summary"]) + +fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5)) + +ax1.plot(w, J, label="Original spectral density") +ax1.plot(w, bath.spectral_density_approx(w), label="Effective fitted SD") +ax1.set_xlabel(r'$\omega$') +ax1.set_ylabel(r'$J$') +ax1.legend() + +ax2.plot(w, np.abs(J - bath.spectral_density_approx(w)), label="Error") +ax2.set_xlabel(r'$\omega$') +ax2.set_ylabel(r'$J$') +ax2.legend() + +plt.show() +``` + +Since the number of exponents increases simulation time one should go with the least amount of exponents that correctly describe the bath properties (Power spectrum, Spectral density and the correlation function). When the number of exponents is not specified it defaults to 5. + ++++ + +By default the ``get_fit`` method, has a threshold normalized root mean squared error (NRMSE) of $5\times 10^{-6}$ and selects the number of oscillators automatically to obtain that value. One may also specify the number of oscillators that is used with the optional argument N, or may want a more accurate NRMSE, which can be specified with the final_rmse optional argument + +```{code-cell} +bath, fitinfo = fs.get_fit(final_rmse=1e-6) +print(fitinfo["summary"]) +``` + +Alternatively one may choose the number of oscillators in the fit instead of a desired NRMSE + +```{code-cell} +fittedbath, fitinfo = fs.get_fit(N=4) +print(fitinfo["summary"]) +``` + +Let's take a closer look at our last fit by plotting the contribution of each term of the fit: + +```{code-cell} +# Plot the components of the fit separately: +plt.rcParams["font.size"] = 25 +plt.rcParams["figure.figsize"] = (10, 5) + + +def plot_fit(func, J, w, lam, gamma, w0): + """Plot the individual components of a fit to the spectral density. + and how they contribute to the full fit one by one""" + total = 0 + for i in range(len(lam)): + component = func(w, [lam[i]], [gamma[i]], [w0[i]]) + total += component + plt.plot(w, J, "r--", linewidth=2, label="original") + plt.plot(w, total, label=rf"$k={i+1}$") + plt.xlabel(r"$\omega$") + plt.ylabel(r"$J(\omega)$") + plt.legend() + plt.pause(1) + plt.show() + + +def plot_fit_components(func, J, w, lam, gamma, w0): + """Plot the individual components of a fit to the spectral density. + and how they contribute to the full fit""" + plt.plot(w, J, "r--", linewidth=2, label="original") + for i in range(len(lam)): + component = func(w, [lam[i]], [gamma[i]], [w0[i]]) + plt.plot(w, component, label=rf"$k={i+1}$") + plt.xlabel(r"$\omega$") + plt.ylabel(r"$J(\omega)$") + plt.legend(bbox_to_anchor=(1.04, 1)) + plt.show() + + +lam, gamma, w0 = fitinfo["params"] +plot_fit(SpectralFitter._meier_tannor_SD, J, w, lam, gamma, w0) +``` + +```{code-cell} +plot_fit_components(SpectralFitter._meier_tannor_SD, J, w, lam, gamma, w0) +``` + +And let's also compare the power spectrum of the fit and the analytical spectral density: + +```{code-cell} +def plot_power_spectrum(alpha, wc, beta, save=True): + """Plot the power spectrum of a fit against the actual power spectrum.""" + w = np.linspace(-10, 10, 50000) + s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta) + s_fit = fittedbath.power_spectrum_approx(w) + fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) + axes.plot(w, s_orig, "r", linewidth=2, label="original") + axes.plot(w, np.real(s_fit), "b", linewidth=2, label="fit") + + axes.set_xlabel(r"$\omega$", fontsize=28) + axes.set_ylabel(r"$S(\omega)$", fontsize=28) + axes.legend() + + if save: + fig.savefig("powerspectrum.eps") + + +plot_power_spectrum(alpha, wc, 1 / T, save=False) +``` + +Now that we have a good fit to the spectral density, Let us obtain its dynamics, by passing our ``FitSpectral`` bath specifications into the ``HEOMSolver`` + +```{code-cell} +tlist = np.linspace(0, 30 * np.pi / Del, 600) +HEOM_spectral_fit = HEOMSolver( + Hsys, + fittedbath, + max_depth=4, + options=options, +) +result_spectral = HEOM_spectral_fit.run(rho0, tlist) +``` + +Now if we want to see the systems's behaviour as we change the Number of terms in the fit, we may use this auxiliary function + +```{code-cell} +def generate_spectrum_results(Q, N, Nk, max_depth): + """Run the HEOM with the given bath parameters and + and return the results of the evolution. + """ + fs = SpectralFitter(T, Q, w, J) + bath, _ = fs.get_fit(N, Nk=Nk) + tlist = np.linspace(0, 30 * np.pi / Del, 600) + + # This problem is a little stiff, so we use the BDF method to solve + # the ODE ^^^ + print(f"Starting calculations for N={N}, Nk={Nk} and max_depth={max_depth} ... ") + HEOM_spectral_fit = HEOMSolver( + Hsys, + bath, + max_depth=max_depth, + options=options, + ) + results_spectral_fit = HEOM_spectral_fit.run(rho0, tlist) + return results_spectral_fit +``` + +Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow. + +```{code-cell} +def plot_result_expectations(plots, axes=None): + """Plot the expectation values of operators as functions of time. + + Each plot in plots consists of (solver_result, + measurement_operation, color, label). + """ + if axes is None: + fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) + fig_created = True + else: + fig = None + fig_created = False + + # add kw arguments to each plot if missing + plots = [p if len(p) == 5 else p + ({},) for p in plots] + for result, m_op, color, label, kw in plots: + exp = np.real(expect(result.states, m_op)) + kw.setdefault("linewidth", 2) + if color == "rand": + axes.plot( + result.times, + exp, + c=np.random.rand( + 3, + ), + label=label, + **kw, + ) + else: + axes.plot(result.times, exp, color, label=label, **kw) + + if fig_created: + axes.legend(loc=0, fontsize=12) + axes.set_xlabel("t", fontsize=28) + + return fig +``` + +```{code-cell} +# Generate results for different number of lorentzians in fit: + +results_spectral_fit_pk = [ + generate_spectrum_results(Q, n, Nk=1, max_depth=max_depth) for n in range(1, 5) +] + +plot_result_expectations( + [ + ( + result, + P11p, + "rand", + f"P11 (spectral fit) $k_J$={pk + 1}", + ) + for pk, result in enumerate(results_spectral_fit_pk) + ] +); +``` + +```{code-cell} +# generate results for different number of Matsubara terms per Lorentzian +# for max number of Lorentzians: + +Nk_list = range(2, 4) +results_spectral_fit_nk = [ + generate_spectrum_results(Q, 4, Nk=Nk, max_depth=max_depth) for Nk in Nk_list +] + +plot_result_expectations( + [ + ( + result, + P11p, + "rand", + f"P11 (spectral fit) K={nk}", + ) + for nk, result in zip(Nk_list, results_spectral_fit_nk) + ] +); +``` + +```{code-cell} +# Generate results for different depths: + +Nc_list = range(2, max_depth) +results_spectral_fit_nc = [ + generate_spectrum_results(Q, 4, Nk=1, max_depth=Nc) for Nc in Nc_list +] + +plot_result_expectations( + [ + ( + result, + P11p, + "rand", + f"P11 (spectral fit) $N_C={nc}$", + ) + for nc, result in zip(Nc_list, results_spectral_fit_nc) + ] +); +``` + +#### We now combine the fitting and correlation function data into one large plot. Here we define a function to plot everything together + +```{code-cell} +def gen_plots(fs, w, J, t, C, w2, S): + def plot_cr_fit_vs_actual(t, C, func, axes): + """Plot the C_R(t) fit.""" + yR = func(t) + + axes.plot( + t, + np.real(C), + "r", + linewidth=3, + label="Original", + ) + axes.plot( + t, + np.real(yR), + "g", + dashes=[3, 3], + linewidth=2, + label="Reconstructed", + ) + + axes.set_ylabel(r"$C_R(t)$", fontsize=28) + axes.set_xlabel(r"$t\;\omega_c$", fontsize=28) + axes.locator_params(axis="y", nbins=4) + axes.locator_params(axis="x", nbins=4) + axes.text(0.15, 0.85, "(a)", fontsize=28, transform=axes.transAxes) + + def plot_ci_fit_vs_actual(t, C, func, axes): + """Plot the C_I(t) fit.""" + yI = func(t) + + axes.plot( + t, + np.imag(C), + "r", + linewidth=3, + ) + axes.plot( + t, + np.real(yI), + "g", + dashes=[3, 3], + linewidth=2, + ) + + axes.set_ylabel(r"$C_I(t)$", fontsize=28) + axes.set_xlabel(r"$t\;\omega_c$", fontsize=28) + axes.locator_params(axis="y", nbins=4) + axes.locator_params(axis="x", nbins=4) + axes.text(0.80, 0.80, "(b)", fontsize=28, transform=axes.transAxes) + + def plot_jw_fit_vs_actual(w, J, axes): + """Plot the J(w) fit.""" + J_fit = fs.spectral_density_approx(w) + + axes.plot( + w, + J, + "r", + linewidth=3, + ) + axes.plot( + w, + J_fit, + "g", + dashes=[3, 3], + linewidth=2, + ) + + axes.set_ylabel(r"$J(\omega)$", fontsize=28) + axes.set_xlabel(r"$\omega/\omega_c$", fontsize=28) + axes.locator_params(axis="y", nbins=4) + axes.locator_params(axis="x", nbins=4) + axes.text(0.15, 0.85, "(c)", fontsize=28, transform=axes.transAxes) + + def plot_sw_fit_vs_actual(axes): + """Plot the S(w) fit.""" + + # avoid the pole in the fit around zero: + s_fit = fs.power_spectrum_approx(w2) + + axes.plot(w2, S, "r", linewidth=3) + axes.plot(w2, s_fit, "g", dashes=[3, 3], linewidth=2) + + axes.set_ylabel(r"$S(\omega)$", fontsize=28) + axes.set_xlabel(r"$\omega/\omega_c$", fontsize=28) + axes.locator_params(axis="y", nbins=4) + axes.locator_params(axis="x", nbins=4) + axes.text(0.15, 0.85, "(d)", fontsize=28, transform=axes.transAxes) + + def plot_matsubara_spectrum_fit_vs_actual(t, C): + """Plot the Matsubara fit of the spectrum .""" + fig = plt.figure(figsize=(12, 10)) + grid = plt.GridSpec(2, 2, wspace=0.4, hspace=0.3) + + plot_cr_fit_vs_actual( + t, + C, + lambda t: fs.correlation_function_approx(t), + axes=fig.add_subplot(grid[0, 0]), + ) + plot_ci_fit_vs_actual( + t, + C, + lambda t: np.imag(fs.correlation_function_approx(t)), + axes=fig.add_subplot(grid[0, 1]), + ) + plot_jw_fit_vs_actual( + w, + J, + axes=fig.add_subplot(grid[1, 0]), + ) + plot_sw_fit_vs_actual( + axes=fig.add_subplot(grid[1, 1]), + ) + fig.legend(loc="upper center", ncol=2, fancybox=True, shadow=True) + + return plot_matsubara_spectrum_fit_vs_actual(t, C) +``` + +#### And finally plot everything together + +```{code-cell} +t = np.linspace(0, 15, 1000) +C = ohmic_correlation(t, alpha, wc, 1 / T) +w2 = np.concatenate((-np.linspace(10, 1e-2, 100), np.linspace(1e-2, 10, 100))) +S = ohmic_power_spectrum(w2, alpha, wc, 1 / T) +gen_plots(fittedbath, w, J, t, C, w2, S) +``` + +## Building the HEOM bath by fitting the correlation function + ++++ + +Having successfully fitted the spectral density and used the result to calculate the Matsubara expansion and terminator for the HEOM bosonic bath, we now proceed to the second case of fitting the correlation function itself instead. + +Here we fit the real and imaginary parts separately, using the following ansatz + +$$C_R^F(t) = \sum_{i=1}^{k_R} c_R^ie^{-\gamma_R^i t}\cos(\omega_R^i t)$$ + +$$C_I^F(t) = \sum_{i=1}^{k_I} c_I^ie^{-\gamma_I^i t}\sin(\omega_I^i t)$$ + +Analogously to the spectral density case, one may use the `CorrelationFitter` class + +```{code-cell} +t = np.linspace(0, 15, 1500) +C = ohmic_correlation(t, alpha=alpha, wc=wc, beta=1 / T) +``` + +```{code-cell} +fc = CorrelationFitter(Q, T, t, C) +``` + +```{code-cell} +bath, fitinfo = fc.get_fit(Ni=4, Nr=4) +print(fitinfo["summary"]) +``` + +```{code-cell} +gen_plots(bath, w, J, t, C, w2, S) +``` + +```{code-cell} +def generate_corr_results(N, max_depth): + tlist = np.linspace(0, 30 * np.pi / Del, 600) + bath, _ = fc.get_fit(Ni=N, Nr=N) + HEOM_corr_fit = HEOMSolver( + Hsys, + bath, + max_depth=max_depth, + options=options, + ) + + results_corr_fit = HEOM_corr_fit.run(rho0, tlist) + + return results_corr_fit + + +# Generate results for different number of exponentials in fit: +results_corr_fit_pk = [ + print(f"{i + 1}") + or generate_corr_results( + i, + max_depth=max_depth, + ) + for i in range(1, 4) +] +``` + +```{code-cell} +plot_result_expectations( + [ + ( + result, + P11p, + "rand", + f"P11 (correlation fit) k_R=k_I={pk + 1}", + ) + for pk, result in enumerate(results_corr_fit_pk) + ] +); +``` + +```{code-cell} +fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) + +plot_result_expectations( + [ + ( + results_corr_fit_pk[0], + P11p, + "y", + "Correlation Function Fit $k_R=k_I=1$", + ), + ( + results_corr_fit_pk[2], + P11p, + "k", + "Correlation Function Fit $k_R=k_I=3$", + ), + (results_spectral_fit_pk[0], P11p, "b", "Spectral Density Fit $k_J=1$"), + (results_spectral_fit_pk[3], P11p, "r-.", "Spectral Density Fit $k_J=4$"), + ], + axes=axes, +) + +axes.set_yticks([0.6, 0.8, 1]) +axes.set_ylabel(r"$\rho_{11}$", fontsize=30) +axes.set_xlabel(r"$t\;\omega_c$", fontsize=30) +axes.legend(loc=0, fontsize=20); +``` + +# Using the Ohmic Bath class + + As the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced quickly by using the OhmicBath class. This allows for rapid implementation of fitted ohmic baths via the correlation function or spectral density + +```{code-cell} +obs = OhmicBath(T, Q, alpha, wc, s) +``` + +```{code-cell} +Obath, fitinfo = obs.make_correlation_fit(t, rmse=2e-4) +print(fitinfo["summary"]) +``` + +```{code-cell} +tlist = np.linspace(0, 30 * np.pi / Del, 600) +HEOM_ohmic_corr_fit = HEOMSolver( + Hsys, + Obath, + max_depth=5, + options=options, +) +results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist) +``` + +```{code-cell} +Obath, fitinfo = obs.make_spectral_fit(w, rmse=2e-4) +print(fitinfo["summary"]) +``` + +```{code-cell} +HEOM_ohmic_spectral_fit = HEOMSolver( + Hsys, + Obath, + max_depth=5, + options=options, +) +results_ohmic_spectral_fit = HEOM_ohmic_spectral_fit.run(rho0, tlist) +``` + +```{code-cell} +fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) + +plot_result_expectations( + [ + # ( + # results_corr_fit_pk[0], P11p, + # 'y', "Correlation Function Fit $k_R=k_I=1$", + # ), + ( + results_corr_fit_pk[2], + P11p, + "y-.", + "Correlation Function Fit $k_R=k_I=3$", + ), + (results_spectral_fit_pk[0], P11p, "b", "Spectral Density Fit $k_J=1$"), + (results_spectral_fit_pk[2], P11p, "g--", "Spectral Density Fit $k_J=3$"), + (results_spectral_fit_pk[3], P11p, "r-.", "Spectral Density Fit $k_J=4$"), + (results_ohmic_spectral_fit, P11p, "g-.", "Spectral Density Fit Ohmic Bath"), + (results_ohmic_corr_fit, P11p, "k-.", "Correlation Fit Ohmic Bath"), + ], + axes=axes, +) + +axes.set_yticks([0.6, 0.8, 1]) +axes.set_ylabel(r"$\rho_{11}$", fontsize=30) +axes.set_xlabel(r"$t\;\omega_c$", fontsize=30) +axes.legend(loc=0, fontsize=20); +``` + +## About + +```{code-cell} +qutip.about() +``` + +## Testing + +This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. + +```{code-cell} +assert np.allclose( + expect(P11p, results_spectral_fit_pk[2].states), + expect(P11p, results_spectral_fit_pk[3].states), + rtol=1e-2, +) +``` diff --git a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb deleted file mode 100644 index f4a2d732..00000000 --- a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb +++ /dev/null @@ -1,917 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0ee67ff5", - "metadata": {}, - "source": [ - "# HEOM 1e: Spin-Bath model (pure dephasing)" - ] - }, - { - "cell_type": "markdown", - "id": "efef6e73", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices.\n", - "\n", - "In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", - "\n", - "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian (see paper), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", - "\n", - "In the example below we show how to model the overdamped Drude-Lorentz Spectral Density, commonly used with the HEOM. We show how to do the Matsubara and Pade analytical decompositions, as well as how to fit the latter with a finite set of approximate exponentials. This differs from examble 1a in that we assume that the system and coupling parts of the Hamiltonian commute, hence giving an analytically solvable ''pure dephasing'' model. This is a useful example to look at when introducing other approximations (e.g., fitting of correlation functions) to check for validity/convergence against the analytical results. (Note that, generally, for the fitting examples, the pure dephasing model is the 'worst possible case'. \n", - "\n", - "### Drude-Lorentz spectral density\n", - "\n", - "The Drude-Lorentz spectral density is:\n", - "\n", - "$$J(\\omega)=\\omega \\frac{2\\lambda\\gamma}{{\\gamma}^2 + \\omega^2}$$\n", - "\n", - "where $\\lambda$ scales the coupling strength, and $\\gamma$ is the cut-off frequency.\n", - "We use the convention,\n", - "\\begin{equation*}\n", - "C(t) = \\int_0^{\\infty} d\\omega \\frac{J_D(\\omega)}{\\pi}[\\coth(\\beta\\omega) \\cos(\\omega \\tau) - i \\sin(\\omega \\tau)]\n", - "\\end{equation*}\n", - "\n", - "With the HEOM we must use an exponential decomposition:\n", - "\n", - "\\begin{equation*}\n", - "C(t)=\\sum_{k=0}^{k=\\infty} c_k e^{-\\nu_k t}\n", - "\\end{equation*}\n", - "\n", - "The Matsubara decomposition of the Drude-Lorentz spectral density is given by:\n", - "\n", - "\\begin{equation*}\n", - " \\nu_k = \\begin{cases}\n", - " \\gamma & k = 0\\\\\n", - " {2 \\pi k} / {\\beta \\hbar} & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "\\begin{equation*}\n", - " c_k = \\begin{cases}\n", - " \\lambda \\gamma (\\cot(\\beta \\gamma / 2) - i) / \\hbar & k = 0\\\\\n", - " 4 \\lambda \\gamma \\nu_k / \\{(nu_k^2 - \\gamma^2)\\beta \\hbar^2 \\} & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." - ] - }, - { - "cell_type": "markdown", - "id": "12ff0f20", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "8779961d", - "metadata": {}, - "outputs": [], - "source": [ - "import contextlib\n", - "import time\n", - "\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "import scipy\n", - "from scipy.optimize import curve_fit\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " basis,\n", - " expect,\n", - " liouvillian,\n", - " sigmax,\n", - " sigmaz,\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver\n", - ")\n", - "from qutip.core.environment import (\n", - " DrudeLorentzEnvironment,\n", - " system_terminator\n", - ")\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "60085ba0", - "metadata": {}, - "source": [ - "## Helper functions\n", - "\n", - "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a6550312", - "metadata": {}, - "outputs": [], - "source": [ - "def cot(x):\n", - " \"\"\" Vectorized cotangent of x. \"\"\"\n", - " return 1. / np.tan(x)\n", - "\n", - "\n", - "def coth(x):\n", - " \"\"\" Vectorized hyperbolic cotangent of x. \"\"\"\n", - " return 1. / np.tanh(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "8592730a", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_result_expectations(plots, axes=None):\n", - " \"\"\" Plot the expectation values of operators as functions of time.\n", - "\n", - " Each plot in plots consists of (solver_result, measurement_operation,\n", - " color, label).\n", - " \"\"\"\n", - " if axes is None:\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " fig_created = True\n", - " else:\n", - " fig = None\n", - " fig_created = False\n", - "\n", - " # add kw arguments to each plot if missing\n", - " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", - " for result, m_op, color, label, kw in plots:\n", - " if m_op is None:\n", - " t, exp = result\n", - " else:\n", - " t = result.times\n", - " exp = np.real(expect(result.states, m_op))\n", - " kw.setdefault(\"linewidth\", 2)\n", - " axes.plot(t, exp, color, label=label, **kw)\n", - "\n", - " if fig_created:\n", - " axes.legend(loc=0, fontsize=12)\n", - " axes.set_xlabel(\"t\", fontsize=28)\n", - "\n", - " return fig" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "71732e44", - "metadata": {}, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\" Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "31e91cf7", - "metadata": {}, - "outputs": [], - "source": [ - "# Solver options:\n", - "\n", - "options = {\n", - " \"nsteps\": 15000,\n", - " \"store_states\": True,\n", - " \"rtol\": 1e-14,\n", - " \"atol\": 1e-14,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "64c318aa", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "And let us set up the system Hamiltonian, bath and system measurement operators:" - ] - }, - { - "cell_type": "markdown", - "id": "1c65031d", - "metadata": {}, - "source": [ - "Here we set $H_{sys}=0$, which means the interaction Hamiltonian and the system Hamiltonian commute, and we can compare the numerical results to a known analytical one. We could in principle keep $\\epsilon \\neq 0$, but it just introduces fast system oscillations, so it is more convenient to set it to zero." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f8b9841c", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the system Hamiltonian\n", - "eps = 0.0 # Energy of the 2-level system.\n", - "Del = 0.0 # Tunnelling term\n", - "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "8f7dc597", - "metadata": {}, - "outputs": [], - "source": [ - "# System-bath coupling (Drude-Lorentz spectral density)\n", - "Q = sigmaz() # coupling operator\n", - "\n", - "# Bath properties:\n", - "gamma = 0.5 # cut off frequency\n", - "lam = 0.1 # coupling strength\n", - "T = 0.5\n", - "beta = 1. / T\n", - "\n", - "# HEOM parameters:\n", - "# cut off parameter for the bath:\n", - "NC = 6\n", - "# number of exponents to retain in the Matsubara expansion\n", - "# of the correlation function:\n", - "Nk = 3\n", - "\n", - "# Times to solve for\n", - "tlist = np.linspace(0, 50, 1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7da5422c", - "metadata": {}, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresponding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresponding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "dbddfa34", - "metadata": {}, - "source": [ - "To get a non-trivial result we prepare the initial state in a superposition, and see how the bath destroys the coherence." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "df1aa127", - "metadata": {}, - "outputs": [], - "source": [ - "# Initial state of the system.\n", - "psi = (basis(2, 0) + basis(2, 1)).unit()\n", - "rho0 = psi * psi.dag()" - ] - }, - { - "cell_type": "markdown", - "id": "e337a6f1", - "metadata": {}, - "source": [ - "We then define our environment, from which all the different simulations will \n", - "be obtained" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "313a4403", - "metadata": {}, - "outputs": [], - "source": [ - "env = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=Nk)" - ] - }, - { - "cell_type": "markdown", - "id": "c3efd930", - "metadata": {}, - "source": [ - "## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "10849009", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.00707554817199707\n", - " [ 0% ] Elapsed 0.00s / Remaining 00:00:00:00" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 3.62s*] Elapsed 3.62s / Remaining 00:00:00:00\n", - "ODE solver time: 3.62227725982666\n" - ] - } - ], - "source": [ - "with timer(\"RHS construction time\"):\n", - " env_mats=env.approx_by_matsubara(Nk=Nk)\n", - " HEOMMats = HEOMSolver(Hsys, (env_mats,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMats = HEOMMats.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "cdd19104", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the results so far\n", - "plot_result_expectations([\n", - " (resultMats, P11p, 'b', \"P11 Matsubara\"),\n", - " (resultMats, P12p, 'r', \"P12 Matsubara\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "9cb744df", - "metadata": {}, - "source": [ - "## Simulation 2: Matsubara decomposition (including terminator)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "0ba88f32", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.017224788665771484\n", - " [ 1% ] Elapsed 0.03s / Remaining 00:00:00:02" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 2.44s*] Elapsed 2.44s / Remaining 00:00:00:00\n", - "ODE solver time: 2.442646026611328\n" - ] - } - ], - "source": [ - "with timer(\"RHS construction time\"):\n", - " env_mats,delta=env.approx_by_matsubara(Nk=Nk,compute_delta=True)\n", - " Ltot = liouvillian(Hsys) + system_terminator(Q,delta)\n", - " HEOMMatsT = HEOMSolver(Ltot, (env_mats,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMatsT = HEOMMatsT.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "a86e3376", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the results\n", - "plot_result_expectations([\n", - " (resultMats, P11p, 'b', \"P11 Matsubara\"),\n", - " (resultMats, P12p, 'r', \"P12 Matsubara\"),\n", - " (resultMatsT, P11p, 'r--', \"P11 Matsubara and terminator\"),\n", - " (resultMatsT, P12p, 'b--', \"P12 Matsubara and terminator\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "9a18fece", - "metadata": {}, - "source": [ - "## Simulation 3: Pade decomposition\n", - "\n", - "As in example 1a, we can compare to Pade and Fitting approaches." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "d609ea24", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.011566400527954102\n", - " [ 0% ] Elapsed 0.00s / Remaining 00:00:00:00" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 2.50s*] Elapsed 2.50s / Remaining 00:00:00:00[*********52% ] Elapsed 1.12s / Remaining 00:00:00:01\n", - "ODE solver time: 2.5014655590057373\n" - ] - } - ], - "source": [ - "with timer(\"RHS construction time\"):\n", - " env_pade=env.approx_by_pade(Nk=Nk)\n", - " HEOMPade = HEOMSolver(Hsys, (env_pade,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultPade = HEOMPade.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "6293564a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the results\n", - "plot_result_expectations([\n", - " (resultMatsT, P11p, 'b', \"P11 Matsubara (+term)\"),\n", - " (resultMatsT, P12p, 'r', \"P12 Matsubara (+term)\"),\n", - " (resultPade, P11p, 'r--', \"P11 Pade\"),\n", - " (resultPade, P12p, 'b--', \"P12 Pade\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "8e48c51d", - "metadata": {}, - "source": [ - "## Simulation 4: Fitting approach" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "39ba2c87", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.3713369369506836\n", - " Total run time: 3.87s*] Elapsed 3.87s / Remaining 00:00:00:00\n", - "ODE solver time: 3.8711256980895996\n" - ] - } - ], - "source": [ - "tfit=np.linspace(0,10,1000)\n", - "with timer(\"RHS construction time\"):\n", - " bath,_ = env.approx_by_cf_fit(tfit,Ni_max=1,Nr_max=3,target_rsme=None)\n", - " HEOMFit = HEOMSolver(Hsys, (bath,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultFit = HEOMFit.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "a8c9749d", - "metadata": {}, - "source": [ - "## Analytic calculations" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "4673e00a", - "metadata": {}, - "outputs": [], - "source": [ - "def pure_dephasing_evolution_analytical(tlist, wq, ck, vk):\n", - " \"\"\"\n", - " Computes the propagating function appearing in the pure dephasing model.\n", - "\n", - " Parameters\n", - " ----------\n", - " t: float\n", - " A float specifying the time at which to calculate the integral.\n", - "\n", - " wq: float\n", - " The qubit frequency in the Hamiltonian.\n", - "\n", - " ck: ndarray\n", - " The list of coefficients in the correlation function.\n", - "\n", - " vk: ndarray\n", - " The list of frequencies in the correlation function.\n", - "\n", - " Returns\n", - " -------\n", - " integral: float\n", - " The value of the integral function at time t.\n", - " \"\"\"\n", - " evolution = np.array([\n", - " np.exp(-1j * wq * t - correlation_integral(t, ck, vk))\n", - " for t in tlist\n", - " ])\n", - " return evolution\n", - "\n", - "\n", - "def correlation_integral(t, ck, vk):\n", - " r\"\"\"\n", - " Computes the integral sum function appearing in the pure dephasing model.\n", - "\n", - " If the correlation function is a sum of exponentials then this sum\n", - " is given by:\n", - "\n", - " .. math:\n", - "\n", - " \\int_0^{t}d\\tau D(\\tau) = \\sum_k\\frac{c_k}{\\mu_k^2}e^{\\mu_k t}\n", - " + \\frac{\\bar c_k}{\\bar \\mu_k^2}e^{\\bar \\mu_k t}\n", - " - \\frac{\\bar \\mu_k c_k + \\mu_k \\bar c_k}{\\mu_k \\bar \\mu_k} t\n", - " + \\frac{\\bar \\mu_k^2 c_k + \\mu_k^2 \\bar c_k}{\\mu_k^2 \\bar \\mu_k^2}\n", - "\n", - " Parameters\n", - " ----------\n", - " t: float\n", - " A float specifying the time at which to calculate the integral.\n", - "\n", - " ck: ndarray\n", - " The list of coefficients in the correlation function.\n", - "\n", - " vk: ndarray\n", - " The list of frequencies in the correlation function.\n", - "\n", - " Returns\n", - " -------\n", - " integral: float\n", - " The value of the integral function at time t.\n", - " \"\"\"\n", - " t1 = np.sum(\n", - " (ck / vk**2) *\n", - " (np.exp(vk * t) - 1)\n", - " )\n", - " t2 = np.sum(\n", - " (ck.conj() / vk.conj()**2) *\n", - " (np.exp(vk.conj() * t) - 1)\n", - " )\n", - " t3 = np.sum(\n", - " (ck / vk + ck.conj() / vk.conj()) * t\n", - " )\n", - " return 2 * (t1 + t2 - t3)" - ] - }, - { - "cell_type": "markdown", - "id": "357faa7b", - "metadata": {}, - "source": [ - "For the pure dephasing analytics, we just sum up as many matsubara terms as we can:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "0e4c98f3", - "metadata": {}, - "outputs": [], - "source": [ - "lmaxmats2 = 15000\n", - "\n", - "vk = [complex(-gamma)]\n", - "vk.extend([\n", - " complex(-2. * np.pi * k * T)\n", - " for k in range(1, lmaxmats2)\n", - "])\n", - "\n", - "ck = [complex(lam * gamma * (-1.0j + cot(gamma * beta / 2.)))]\n", - "ck.extend([\n", - " complex(4 * lam * gamma * T * (-v) / (v**2 - gamma**2))\n", - " for v in vk[1:]\n", - "])\n", - "\n", - "P12_ana = 0.5 * pure_dephasing_evolution_analytical(\n", - " tlist, 0, np.asarray(ck), np.asarray(vk)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "0e37b2bd", - "metadata": {}, - "source": [ - "Alternatively, we can just do the integral of the propagator directly, without using the correlation functions at all" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "dad7ec45", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_192111/917460483.py:15: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n", - " If increasing the limit yields no improvement it is advised to analyze \n", - " the integrand in order to determine the difficulties. If the position of a \n", - " local difficulty can be determined (singularity, discontinuity) one will \n", - " probably gain from splitting up the interval and calling the integrator \n", - " on the subranges. Perhaps a special-purpose integrator should be used.\n", - " scipy.integrate.quad(integrand, 0, np.inf, args=(lam, gamma, T, t))[0]\n" - ] - } - ], - "source": [ - "def JDL(omega, lamc, omega_c):\n", - " return 2. * lamc * omega * omega_c / (omega_c**2 + omega**2)\n", - "\n", - "\n", - "def integrand(omega, lamc, omega_c, Temp, t):\n", - " return (\n", - " (-4. * JDL(omega, lamc, omega_c) / omega**2) *\n", - " (1. - np.cos(omega*t)) * (coth(omega/(2.*Temp)))\n", - " / np.pi\n", - " )\n", - "\n", - "\n", - "P12_ana2 = [\n", - " 0.5 * np.exp(\n", - " scipy.integrate.quad(integrand, 0, np.inf, args=(lam, gamma, T, t))[0]\n", - " )\n", - " for t in tlist\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "46410052", - "metadata": {}, - "source": [ - "## Compare results" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "860fcf09", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations([\n", - " (resultMats, P12p, 'r', \"P12 Mats\"),\n", - " (resultMatsT, P12p, 'r--', \"P12 Mats + Term\"),\n", - " (resultPade, P12p, 'b--', \"P12 Pade\"),\n", - " (resultFit, P12p, 'g', \"P12 Fit\"),\n", - " ((tlist, np.real(P12_ana)), None, 'b', \"Analytic 1\"),\n", - " ((tlist, np.real(P12_ana2)), None, 'y--', \"Analytic 2\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "fafb9856", - "metadata": {}, - "source": [ - "We can't see much difference in the plot above, so let's do a log plot instead:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "aa7ae2e7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - "\n", - "plot_result_expectations([\n", - " (resultMats, P12p, 'r', \"P12 Mats\"),\n", - " (resultMatsT, P12p, 'r--', \"P12 Mats + Term\"),\n", - " (resultPade, P12p, 'b-.', \"P12 Pade\"),\n", - " (resultFit, P12p, 'g', \"P12 Fit\"),\n", - " ((tlist, np.real(P12_ana)), None, 'b', \"Analytic 1\"),\n", - " ((tlist, np.real(P12_ana2)), None, 'y--', \"Analytic 2\"),\n", - "], axes)\n", - "\n", - "axes.set_yscale('log')\n", - "axes.legend(loc=0, fontsize=12);" - ] - }, - { - "cell_type": "markdown", - "id": "aa326857", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "fd075c94", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "QuTiP: Quantum Toolbox in Python\n", - "================================\n", - "Copyright (c) QuTiP team 2011 and later.\n", - "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", - "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", - "Original developers: R. J. Johansson & P. D. Nation.\n", - "Previous lead developers: Chris Granade & A. Grimsmo.\n", - "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", - "\n", - "QuTiP Version: 5.1.0.dev0+7941773\n", - "Numpy Version: 2.1.3\n", - "Scipy Version: 1.14.1\n", - "Cython Version: 3.0.9\n", - "Matplotlib Version: 3.9.2\n", - "Python Version: 3.12.7\n", - "Number of CPUs: 16\n", - "BLAS Info: Generic\n", - "INTEL MKL Ext: None\n", - "Platform Info: Linux (x86_64)\n", - "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", - "================================================================================\n", - "Please cite QuTiP in your publication.\n", - "================================================================================\n", - "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" - ] - } - ], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "530d60ce", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "b271d590", - "metadata": {}, - "outputs": [], - "source": [ - "assert np.allclose(\n", - " expect(P12p, resultMats.states[:15]), np.real(P12_ana)[:15],\n", - " rtol=1e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(P12p, resultMatsT.states[:100]), np.real(P12_ana)[:100],\n", - " rtol=1e-3,\n", - ")\n", - "assert np.allclose(\n", - " expect(P12p, resultPade.states[:100]), np.real(P12_ana)[:100],\n", - " rtol=1e-3,\n", - ")\n", - "assert np.allclose(\n", - " expect(P12p, resultFit.states[:50]), np.real(P12_ana)[:50],\n", - " rtol=1e-3,\n", - ")\n", - "assert np.allclose(P12_ana, P12_ana2, rtol=1e-3)" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "qutip-dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md new file mode 100644 index 00000000..bd130e8f --- /dev/null +++ b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md @@ -0,0 +1,488 @@ +--- +jupytext: + formats: ipynb,md:myst + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.16.4 +kernelspec: + display_name: qutip-dev + language: python + name: python3 +--- + +# HEOM 1e: Spin-Bath model (pure dephasing) + ++++ + +## Introduction + +The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices. + +In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment. + +The Bosonic environment is implicitly assumed to obey a particular Hamiltonian (see paper), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions. + +In the example below we show how to model the overdamped Drude-Lorentz Spectral Density, commonly used with the HEOM. We show how to do the Matsubara and Pade analytical decompositions, as well as how to fit the latter with a finite set of approximate exponentials. This differs from examble 1a in that we assume that the system and coupling parts of the Hamiltonian commute, hence giving an analytically solvable ''pure dephasing'' model. This is a useful example to look at when introducing other approximations (e.g., fitting of correlation functions) to check for validity/convergence against the analytical results. (Note that, generally, for the fitting examples, the pure dephasing model is the 'worst possible case'. + +### Drude-Lorentz spectral density + +The Drude-Lorentz spectral density is: + +$$J(\omega)=\omega \frac{2\lambda\gamma}{{\gamma}^2 + \omega^2}$$ + +where $\lambda$ scales the coupling strength, and $\gamma$ is the cut-off frequency. +We use the convention, +\begin{equation*} +C(t) = \int_0^{\infty} d\omega \frac{J_D(\omega)}{\pi}[\coth(\beta\omega) \cos(\omega \tau) - i \sin(\omega \tau)] +\end{equation*} + +With the HEOM we must use an exponential decomposition: + +\begin{equation*} +C(t)=\sum_{k=0}^{k=\infty} c_k e^{-\nu_k t} +\end{equation*} + +The Matsubara decomposition of the Drude-Lorentz spectral density is given by: + +\begin{equation*} + \nu_k = \begin{cases} + \gamma & k = 0\\ + {2 \pi k} / {\beta \hbar} & k \geq 1\\ + \end{cases} +\end{equation*} + +\begin{equation*} + c_k = \begin{cases} + \lambda \gamma (\cot(\beta \gamma / 2) - i) / \hbar & k = 0\\ + 4 \lambda \gamma \nu_k / \{(nu_k^2 - \gamma^2)\beta \hbar^2 \} & k \geq 1\\ + \end{cases} +\end{equation*} + +Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. + ++++ + +## Setup + +```{code-cell} ipython3 +import contextlib +import time + +import numpy as np +from matplotlib import pyplot as plt +import scipy +from scipy.optimize import curve_fit + +import qutip +from qutip import ( + basis, + expect, + liouvillian, + sigmax, + sigmaz, +) +from qutip.solver.heom import ( + HEOMSolver +) +from qutip.core.environment import ( + DrudeLorentzEnvironment, + system_terminator +) + +%matplotlib inline +``` + +## Helper functions + +Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: + +```{code-cell} ipython3 +def cot(x): + """ Vectorized cotangent of x. """ + return 1. / np.tan(x) + + +def coth(x): + """ Vectorized hyperbolic cotangent of x. """ + return 1. / np.tanh(x) +``` + +```{code-cell} ipython3 +def plot_result_expectations(plots, axes=None): + """ Plot the expectation values of operators as functions of time. + + Each plot in plots consists of (solver_result, measurement_operation, + color, label). + """ + if axes is None: + fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) + fig_created = True + else: + fig = None + fig_created = False + + # add kw arguments to each plot if missing + plots = [p if len(p) == 5 else p + ({},) for p in plots] + for result, m_op, color, label, kw in plots: + if m_op is None: + t, exp = result + else: + t = result.times + exp = np.real(expect(result.states, m_op)) + kw.setdefault("linewidth", 2) + axes.plot(t, exp, color, label=label, **kw) + + if fig_created: + axes.legend(loc=0, fontsize=12) + axes.set_xlabel("t", fontsize=28) + + return fig +``` + +```{code-cell} ipython3 +@contextlib.contextmanager +def timer(label): + """ Simple utility for timing functions: + + with timer("name"): + ... code to time ... + """ + start = time.time() + yield + end = time.time() + print(f"{label}: {end - start}") +``` + +```{code-cell} ipython3 +# Solver options: + +options = { + "nsteps": 15000, + "store_states": True, + "rtol": 1e-14, + "atol": 1e-14, + "method": "vern9", + "progress_bar": "enhanced", +} +``` + +## System and bath definition + +And let us set up the system Hamiltonian, bath and system measurement operators: + ++++ + +Here we set $H_{sys}=0$, which means the interaction Hamiltonian and the system Hamiltonian commute, and we can compare the numerical results to a known analytical one. We could in principle keep $\epsilon \neq 0$, but it just introduces fast system oscillations, so it is more convenient to set it to zero. + +```{code-cell} ipython3 +# Defining the system Hamiltonian +eps = 0.0 # Energy of the 2-level system. +Del = 0.0 # Tunnelling term +Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() +``` + +```{code-cell} ipython3 +# System-bath coupling (Drude-Lorentz spectral density) +Q = sigmaz() # coupling operator + +# Bath properties: +gamma = 0.5 # cut off frequency +lam = 0.1 # coupling strength +T = 0.5 +beta = 1. / T + +# HEOM parameters: +# cut off parameter for the bath: +NC = 6 +# number of exponents to retain in the Matsubara expansion +# of the correlation function: +Nk = 3 + +# Times to solve for +tlist = np.linspace(0, 50, 1000) +``` + +```{code-cell} ipython3 +# Define some operators with which we will measure the system +# 1,1 element of density matrix - corresponding to groundstate +P11p = basis(2, 0) * basis(2, 0).dag() +P22p = basis(2, 1) * basis(2, 1).dag() +# 1,2 element of density matrix - corresponding to coherence +P12p = basis(2, 0) * basis(2, 1).dag() +``` + +To get a non-trivial result we prepare the initial state in a superposition, and see how the bath destroys the coherence. + +```{code-cell} ipython3 +# Initial state of the system. +psi = (basis(2, 0) + basis(2, 1)).unit() +rho0 = psi * psi.dag() +``` + +We then define our environment, from which all the different simulations will +be obtained + +```{code-cell} ipython3 +env = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=Nk) +``` + +## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator + +```{code-cell} ipython3 +with timer("RHS construction time"): + env_mats=env.approx_by_matsubara(Nk=Nk) + HEOMMats = HEOMSolver(Hsys, (env_mats,Q), NC, options=options) + +with timer("ODE solver time"): + resultMats = HEOMMats.run(rho0, tlist) +``` + +```{code-cell} ipython3 +# Plot the results so far +plot_result_expectations([ + (resultMats, P11p, 'b', "P11 Matsubara"), + (resultMats, P12p, 'r', "P12 Matsubara"), +]); +``` + +## Simulation 2: Matsubara decomposition (including terminator) + +```{code-cell} ipython3 +with timer("RHS construction time"): + env_mats,delta=env.approx_by_matsubara(Nk=Nk,compute_delta=True) + Ltot = liouvillian(Hsys) + system_terminator(Q,delta) + HEOMMatsT = HEOMSolver(Ltot, (env_mats,Q), NC, options=options) + +with timer("ODE solver time"): + resultMatsT = HEOMMatsT.run(rho0, tlist) +``` + +```{code-cell} ipython3 +# Plot the results +plot_result_expectations([ + (resultMats, P11p, 'b', "P11 Matsubara"), + (resultMats, P12p, 'r', "P12 Matsubara"), + (resultMatsT, P11p, 'r--', "P11 Matsubara and terminator"), + (resultMatsT, P12p, 'b--', "P12 Matsubara and terminator"), +]); +``` + +## Simulation 3: Pade decomposition + +As in example 1a, we can compare to Pade and Fitting approaches. + +```{code-cell} ipython3 +with timer("RHS construction time"): + env_pade=env.approx_by_pade(Nk=Nk) + HEOMPade = HEOMSolver(Hsys, (env_pade,Q), NC, options=options) + +with timer("ODE solver time"): + resultPade = HEOMPade.run(rho0, tlist) +``` + +```{code-cell} ipython3 +# Plot the results +plot_result_expectations([ + (resultMatsT, P11p, 'b', "P11 Matsubara (+term)"), + (resultMatsT, P12p, 'r', "P12 Matsubara (+term)"), + (resultPade, P11p, 'r--', "P11 Pade"), + (resultPade, P12p, 'b--', "P12 Pade"), +]); +``` + +## Simulation 4: Fitting approach + +```{code-cell} ipython3 +tfit=np.linspace(0,10,1000) +with timer("RHS construction time"): + bath,_ = env.approx_by_cf_fit(tfit,Ni_max=1,Nr_max=3,target_rsme=None) + HEOMFit = HEOMSolver(Hsys, (bath,Q), NC, options=options) + +with timer("ODE solver time"): + resultFit = HEOMFit.run(rho0, tlist) +``` + +## Analytic calculations + +```{code-cell} ipython3 +def pure_dephasing_evolution_analytical(tlist, wq, ck, vk): + """ + Computes the propagating function appearing in the pure dephasing model. + + Parameters + ---------- + t: float + A float specifying the time at which to calculate the integral. + + wq: float + The qubit frequency in the Hamiltonian. + + ck: ndarray + The list of coefficients in the correlation function. + + vk: ndarray + The list of frequencies in the correlation function. + + Returns + ------- + integral: float + The value of the integral function at time t. + """ + evolution = np.array([ + np.exp(-1j * wq * t - correlation_integral(t, ck, vk)) + for t in tlist + ]) + return evolution + + +def correlation_integral(t, ck, vk): + r""" + Computes the integral sum function appearing in the pure dephasing model. + + If the correlation function is a sum of exponentials then this sum + is given by: + + .. math: + + \int_0^{t}d\tau D(\tau) = \sum_k\frac{c_k}{\mu_k^2}e^{\mu_k t} + + \frac{\bar c_k}{\bar \mu_k^2}e^{\bar \mu_k t} + - \frac{\bar \mu_k c_k + \mu_k \bar c_k}{\mu_k \bar \mu_k} t + + \frac{\bar \mu_k^2 c_k + \mu_k^2 \bar c_k}{\mu_k^2 \bar \mu_k^2} + + Parameters + ---------- + t: float + A float specifying the time at which to calculate the integral. + + ck: ndarray + The list of coefficients in the correlation function. + + vk: ndarray + The list of frequencies in the correlation function. + + Returns + ------- + integral: float + The value of the integral function at time t. + """ + t1 = np.sum( + (ck / vk**2) * + (np.exp(vk * t) - 1) + ) + t2 = np.sum( + (ck.conj() / vk.conj()**2) * + (np.exp(vk.conj() * t) - 1) + ) + t3 = np.sum( + (ck / vk + ck.conj() / vk.conj()) * t + ) + return 2 * (t1 + t2 - t3) +``` + +For the pure dephasing analytics, we just sum up as many matsubara terms as we can: + +```{code-cell} ipython3 +lmaxmats2 = 15000 + +vk = [complex(-gamma)] +vk.extend([ + complex(-2. * np.pi * k * T) + for k in range(1, lmaxmats2) +]) + +ck = [complex(lam * gamma * (-1.0j + cot(gamma * beta / 2.)))] +ck.extend([ + complex(4 * lam * gamma * T * (-v) / (v**2 - gamma**2)) + for v in vk[1:] +]) + +P12_ana = 0.5 * pure_dephasing_evolution_analytical( + tlist, 0, np.asarray(ck), np.asarray(vk) +) +``` + +Alternatively, we can just do the integral of the propagator directly, without using the correlation functions at all + +```{code-cell} ipython3 +def JDL(omega, lamc, omega_c): + return 2. * lamc * omega * omega_c / (omega_c**2 + omega**2) + + +def integrand(omega, lamc, omega_c, Temp, t): + return ( + (-4. * JDL(omega, lamc, omega_c) / omega**2) * + (1. - np.cos(omega*t)) * (coth(omega/(2.*Temp))) + / np.pi + ) + + +P12_ana2 = [ + 0.5 * np.exp( + scipy.integrate.quad(integrand, 0, np.inf, args=(lam, gamma, T, t))[0] + ) + for t in tlist +] +``` + +## Compare results + +```{code-cell} ipython3 +plot_result_expectations([ + (resultMats, P12p, 'r', "P12 Mats"), + (resultMatsT, P12p, 'r--', "P12 Mats + Term"), + (resultPade, P12p, 'b--', "P12 Pade"), + (resultFit, P12p, 'g', "P12 Fit"), + ((tlist, np.real(P12_ana)), None, 'b', "Analytic 1"), + ((tlist, np.real(P12_ana2)), None, 'y--', "Analytic 2"), +]); +``` + +We can't see much difference in the plot above, so let's do a log plot instead: + +```{code-cell} ipython3 +fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) + +plot_result_expectations([ + (resultMats, P12p, 'r', "P12 Mats"), + (resultMatsT, P12p, 'r--', "P12 Mats + Term"), + (resultPade, P12p, 'b-.', "P12 Pade"), + (resultFit, P12p, 'g', "P12 Fit"), + ((tlist, np.real(P12_ana)), None, 'b', "Analytic 1"), + ((tlist, np.real(P12_ana2)), None, 'y--', "Analytic 2"), +], axes) + +axes.set_yscale('log') +axes.legend(loc=0, fontsize=12); +``` + +## About + +```{code-cell} ipython3 +qutip.about() +``` + +## Testing + +This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. + +```{code-cell} ipython3 +assert np.allclose( + expect(P12p, resultMats.states[:15]), np.real(P12_ana)[:15], + rtol=1e-2, +) +assert np.allclose( + expect(P12p, resultMatsT.states[:100]), np.real(P12_ana)[:100], + rtol=1e-3, +) +assert np.allclose( + expect(P12p, resultPade.states[:100]), np.real(P12_ana)[:100], + rtol=1e-3, +) +assert np.allclose( + expect(P12p, resultFit.states[:50]), np.real(P12_ana)[:50], + rtol=1e-3, +) +assert np.allclose(P12_ana, P12_ana2, rtol=1e-3) +``` diff --git a/tutorials-v5/heom/heom-2-fmo-example.ipynb b/tutorials-v5/heom/heom-2-fmo-example.ipynb deleted file mode 100644 index 3dd69225..00000000 --- a/tutorials-v5/heom/heom-2-fmo-example.ipynb +++ /dev/null @@ -1,805 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "1964d72a", - "metadata": {}, - "source": [ - "# HEOM 2: Dynamics in Fenna-Mathews-Olsen complex (FMO)" - ] - }, - { - "cell_type": "markdown", - "id": "310f65a7", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "In this example notebook we outline how to employ the HEOM to\n", - "solve the FMO photosynthetic complex dynamics.\n", - "\n", - "We aim to replicate the results in reference [https://www.pnas.org/content/106/41/17255](https://pubmed.ncbi.nlm.nih.gov/19815512/)\n", - "and compare them to a Bloch-Redfield (perturbative) solution.\n", - "\n", - "This demonstrates how to to employ the solver for multiple baths, as well as showing how a\n", - "quantum environment reduces the effect of pure dephasing." - ] - }, - { - "cell_type": "markdown", - "id": "ebb63dc6", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b8e558be", - "metadata": {}, - "outputs": [], - "source": [ - "import contextlib\n", - "import time\n", - "\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " Qobj,\n", - " basis,\n", - " brmesolve,\n", - " expect,\n", - " liouvillian,\n", - " mesolve,\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver,\n", - ")\n", - "from qutip.core.environment import (\n", - " DrudeLorentzEnvironment,\n", - " system_terminator\n", - ")\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "c5852365", - "metadata": {}, - "source": [ - "## Helper functions\n", - "\n", - "Let's define some helper functions for calculating correlation functions, spectral densities, thermal energy level occupations, and for plotting results and timing how long operations take:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ba3f2b6e", - "metadata": {}, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\" Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "0be8ee2c", - "metadata": {}, - "outputs": [], - "source": [ - "# Solver options:\n", - "\n", - "options = {\n", - " \"nsteps\": 15000,\n", - " \"store_states\": True,\n", - " \"rtol\": 1e-12,\n", - " \"atol\": 1e-12,\n", - " \"min_step\": 1e-18,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "899d1831", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "And let us set up the system Hamiltonian and bath parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "45a3860f", - "metadata": {}, - "outputs": [], - "source": [ - "# System Hamiltonian:\n", - "#\n", - "# We use the Hamiltonian employed in\n", - "# https://www.pnas.org/content/106/41/17255 and operate\n", - "# in units of Hz:\n", - "\n", - "Hsys = 3e10 * 2 * np.pi * Qobj([\n", - " [200, -87.7, 5.5, -5.9, 6.7, -13.7, -9.9],\n", - " [-87.7, 320, 30.8, 8.2, 0.7, 11.8, 4.3],\n", - " [5.5, 30.8, 0, -53.5, -2.2, -9.6, 6.0],\n", - " [-5.9, 8.2, -53.5, 110, -70.7, -17.0, -63.3],\n", - " [6.7, 0.7, -2.2, -70.7, 270, 81.1, -1.3],\n", - " [-13.7, 11.8, -9.6, -17.0, 81.1, 420, 39.7],\n", - " [-9.9, 4.3, 6.0, -63.3, -1.3, 39.7, 230],\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "0e886fde", - "metadata": {}, - "outputs": [], - "source": [ - "# Bath parameters\n", - "\n", - "lam = 35 * 3e10 * 2 * np.pi\n", - "gamma = 1 / 166e-15\n", - "T = 300 * 0.6949 * 3e10 * 2 * np.pi\n", - "beta = 1 / T" - ] - }, - { - "cell_type": "markdown", - "id": "02f08558", - "metadata": {}, - "source": [ - "## Plotting the environment spectral density and correlation functions\n", - "\n", - "Let's quickly plot the spectral density and environment correlation functions so that we can see what they look like." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "cf6b2106", - "metadata": {}, - "outputs": [], - "source": [ - "env=DrudeLorentzEnvironment(T=T,lam=lam,gamma=gamma)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "157df145", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "wlist = np.linspace(0, 200 * 3e10 * 2 * np.pi, 100)\n", - "tlist = np.linspace(0, 1e-12, 1000)\n", - "\n", - "J = env.spectral_density(wlist) / (3e10*2*np.pi)\n", - "\n", - "fig, axes = plt.subplots(1, 2, sharex=False, figsize=(10, 3))\n", - "\n", - "fig.subplots_adjust(hspace=0.1) # reduce space between plots\n", - "\n", - "# Spectral density plot:\n", - "\n", - "axes[0].plot(wlist / (3e10 * 2 * np.pi), J, color='r', ls='--', label=\"J(w)\")\n", - "axes[0].set_xlabel(r'$\\omega$ (cm$^{-1}$)', fontsize=20)\n", - "axes[0].set_ylabel(r\"$J(\\omega)$ (cm$^{-1}$)\", fontsize=16)\n", - "axes[0].legend()\n", - "\n", - "# Correlation plot:\n", - "\n", - "axes[1].plot(\n", - " tlist, np.real(env.correlation_function(tlist, 10)),\n", - " color='r', ls='--', label=\"C(t) real\",\n", - ")\n", - "axes[1].plot(\n", - " tlist, np.imag(env.correlation_function(tlist, 10)),\n", - " color='g', ls='--', label=\"C(t) imaginary\",\n", - ")\n", - "axes[1].set_xlabel(r'$t$', fontsize=20)\n", - "axes[1].set_ylabel(r\"$C(t)$\", fontsize=16)\n", - "axes[1].legend();" - ] - }, - { - "cell_type": "markdown", - "id": "dce8217d", - "metadata": {}, - "source": [ - "## Solve for the dynamics with the HEOM\n", - "\n", - "Now let us solve for the evolution of this system using the HEOM." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "8e9280e9", - "metadata": {}, - "outputs": [], - "source": [ - "# We start the excitation at site 1:\n", - "rho0 = basis(7, 0) * basis(7, 0).dag()\n", - "\n", - "# HEOM solver options:\n", - "#\n", - "# Note: We set Nk=0 (i.e. a single correlation expansion term\n", - "# per bath) and rely on the terminator to correct detailed\n", - "# balance.\n", - "NC = 4 # Use NC=8 for more precise results\n", - "Nk = 0\n", - "\n", - "Q_list = []\n", - "baths = []\n", - "Ltot = liouvillian(Hsys)\n", - "env_approx,delta=env.approx_by_matsubara(Nk=Nk,compute_delta=True)\n", - "for m in range(7):\n", - " Q = basis(7, m) * basis(7, m).dag()\n", - " Q_list.append(Q)\n", - " Ltot += system_terminator(Q,delta)\n", - " baths.append((env_approx,Q))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "3607a30b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.020816326141357422\n", - " Total run time: 44.00s*] Elapsed 44.00s / Remaining 00:00:00:00[*********99%***********] Elapsed 43.64s / Remaining 00:00:00:00\n", - "ODE solver time: 44.02842974662781\n" - ] - } - ], - "source": [ - "with timer(\"RHS construction time\"):\n", - " HEOMMats = HEOMSolver(Hsys, baths, NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " outputFMO_HEOM = HEOMMats.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "71b8fbab", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", - "\n", - "colors = ['r', 'g', 'b', 'y', 'c', 'm', 'k']\n", - "linestyles = [\n", - " '-', '--', ':', '-.',\n", - " (0, (1, 10)), (0, (5, 10)), (0, (3, 10, 1, 10)),\n", - "]\n", - "\n", - "for m in range(7):\n", - " Q = basis(7, m) * basis(7, m).dag()\n", - " axes.plot(\n", - " np.array(tlist) * 1e15,\n", - " np.real(expect(outputFMO_HEOM.states, Q)),\n", - " label=m + 1,\n", - " color=colors[m % len(colors)],\n", - " linestyle=linestyles[m % len(linestyles)],\n", - " )\n", - " axes.set_xlabel(r'$t$ (fs)', fontsize=30)\n", - " axes.set_ylabel(r\"Population\", fontsize=30)\n", - " axes.locator_params(axis='y', nbins=6)\n", - " axes.locator_params(axis='x', nbins=6)\n", - "\n", - "axes.set_title('HEOM solution', fontsize=24)\n", - "axes.legend(loc=0)\n", - "axes.set_xlim(0, 1000)\n", - "plt.yticks([0., 0.5, 1], [0, 0.5, 1])\n", - "plt.xticks([0., 500, 1000], [0, 500, 1000]);" - ] - }, - { - "cell_type": "markdown", - "id": "5f1bd3fc", - "metadata": {}, - "source": [ - "## Comparison with Bloch-Redfield solver\n", - "\n", - "Now let us solve the same problem using the Bloch-Redfield solver. We will see that the Bloch-Redfield technique fails to model the oscillation of population of the states that we saw in the HEOM.\n", - "\n", - "In the next section, we will examine the role of pure dephasing in the evolution to understand why this happens." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "b2e420cf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 1.19s*] Elapsed 1.19s / Remaining 00:00:00:00\n", - "BR ODE solver time: 1.2221169471740723\n" - ] - } - ], - "source": [ - "with timer(\"BR ODE solver time\"):\n", - " outputFMO_BR = brmesolve(\n", - " Hsys, rho0, tlist,\n", - " a_ops=[[Q, env] for Q in Q_list],\n", - " options=options,\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "6b384776", - "metadata": {}, - "source": [ - "And now let's plot the Bloch-Redfield solver results:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "24dda87d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", - "\n", - "for m, Q in enumerate(Q_list):\n", - " axes.plot(tlist * 1e15, expect(outputFMO_BR.states, Q), label=m + 1)\n", - "\n", - "axes.set_xlabel(r'$t$ (fs)', fontsize=30)\n", - "axes.set_ylabel(r\"Population\", fontsize=30)\n", - "\n", - "axes.set_title('Bloch-Redfield solution ', fontsize=24)\n", - "axes.legend()\n", - "axes.set_xlim(0, 1000)\n", - "plt.yticks([0, 0.5, 1], [0, 0.5, 1])\n", - "plt.xticks([0, 500, 1000], [0, 500, 1000]);" - ] - }, - { - "cell_type": "markdown", - "id": "19e7ef17", - "metadata": {}, - "source": [ - "Notice how the oscillations are gone and the populations decay much more rapidly.\n", - "\n", - "Next let us try to understand why." - ] - }, - { - "cell_type": "markdown", - "id": "c6e4704a", - "metadata": {}, - "source": [ - "## Role of pure dephasing\n", - "\n", - "It is useful to construct the various parts of the Bloch-Redfield master equation explicitly and to solve them using the Master equation solver, `mesolve`. We will do so and show that it is the pure-dephasing terms which suppresses coherence in these oscillations.\n", - "\n", - "First we will write a function to return the list of collapse operators for a given system, either with or without the dephasing operators:" - ] - }, - { - "cell_type": "markdown", - "id": "920097d3", - "metadata": {}, - "source": [ - "TODO: Maybe power spectrum at zero is wrong, by a factor 2" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "a6fde0d0", - "metadata": {}, - "outputs": [], - "source": [ - "def J0_dephasing():\n", - " \"\"\" Under-damped brownian oscillator dephasing probability.\n", - "\n", - " This returns the limit as w -> 0 of J0(w) * n_th(w, T) / T.\n", - " \"\"\"\n", - " return 2 * lam * gamma / gamma**2" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "a1d6c71b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "env.power_spectrum(0)/2 -J0_dephasing()*T" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "45bb1bc2", - "metadata": {}, - "outputs": [], - "source": [ - "def get_collapse(H, T, dephasing=1):\n", - " \"\"\" Calculate collapse operators for a given system H and\n", - " temperature T.\n", - " \"\"\"\n", - " all_energy, all_state = H.eigenstates(sort=\"low\")\n", - " Nmax = len(all_energy)\n", - "\n", - " Q_list = [\n", - " basis(Nmax, n) * basis(Nmax, n).dag()\n", - " for n in range(Nmax)\n", - " ]\n", - "\n", - " collapse_list = []\n", - "\n", - " for Q in Q_list:\n", - " for j in range(Nmax):\n", - " for k in range(j + 1, Nmax):\n", - " Deltajk = abs(all_energy[k] - all_energy[j])\n", - " if abs(Deltajk) > 0:\n", - " rate = (\n", - " np.abs(Q.matrix_element(\n", - " all_state[j].dag(), all_state[k]\n", - " ))**2 *\n", - " env.power_spectrum(Deltajk)\n", - " )\n", - " if rate > 0.0:\n", - " # emission:\n", - " collapse_list.append(\n", - " np.sqrt(rate) * all_state[j] * all_state[k].dag()\n", - " )\n", - "\n", - " rate = (\n", - " np.abs(Q.matrix_element(\n", - " all_state[k].dag(), all_state[j]\n", - " ))**2 *\n", - " env.power_spectrum(-Deltajk)\n", - " )\n", - " if rate > 0.0:\n", - " # absorption:\n", - " collapse_list.append(\n", - " np.sqrt(rate) * all_state[k] * all_state[j].dag()\n", - " )\n", - "\n", - " if dephasing:\n", - " for j in range(Nmax):\n", - " rate = (\n", - " np.abs(Q.matrix_element(\n", - " all_state[j].dag(), all_state[j])\n", - " )**2 * env.power_spectrum(0)/2\n", - " )\n", - " if rate > 0.0:\n", - " # emission:\n", - " collapse_list.append(\n", - " np.sqrt(rate) * all_state[j] * all_state[j].dag()\n", - " )\n", - "\n", - " return collapse_list" - ] - }, - { - "cell_type": "markdown", - "id": "e87487e7", - "metadata": {}, - "source": [ - "Now we are able to switch the pure dephasing terms on and off.\n", - "\n", - "Let us starting by including the dephasing operators. We expect to see the same behaviour that we saw when using the Bloch-Redfield solver." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "ec8a20fa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Building the collapse operators: 0.01816534996032715\n", - "ME ODE solver: 0.17152142524719238\n" - ] - } - ], - "source": [ - "# dephasing terms on, we recover the full BR solution:\n", - "\n", - "with timer(\"Building the collapse operators\"):\n", - " collapse_list = get_collapse(Hsys, T=T, dephasing=True)\n", - "\n", - "with timer(\"ME ODE solver\"):\n", - " outputFMO_ME = mesolve(Hsys, rho0, tlist, collapse_list)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "49396eb5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", - "\n", - "for m, Q in enumerate(Q_list):\n", - " axes.plot(tlist * 1e15, expect(outputFMO_ME.states, Q), label=m + 1)\n", - "\n", - "axes.set_xlabel(r'$t$', fontsize=20)\n", - "axes.set_ylabel(r\"Population\", fontsize=16)\n", - "axes.set_xlim(0, 1000)\n", - "axes.set_title('With pure dephasing', fontsize=24)\n", - "plt.yticks([0, 0.5, 1], [0, 0.5, 1])\n", - "plt.xticks([0, 500, 1000], [0, 500, 1000])\n", - "axes.legend(fontsize=18);" - ] - }, - { - "cell_type": "markdown", - "id": "a19b4694", - "metadata": {}, - "source": [ - "We see similar results to before.\n", - "\n", - "Now let us examine what happens when we remove the dephasing collapse operators:" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "7b216bc5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Building the collapse operators: 0.024776220321655273\n", - "ME ODE solver: 0.114501953125\n" - ] - } - ], - "source": [ - "# dephasing terms off\n", - "\n", - "with timer(\"Building the collapse operators\"):\n", - " collapse_list = get_collapse(Hsys, T, dephasing=False)\n", - "\n", - "with timer(\"ME ODE solver\"):\n", - " outputFMO_ME_nodephase = mesolve(Hsys, rho0, tlist, collapse_list)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "e682e0e2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", - "for m, Q in enumerate(Q_list):\n", - " axes.plot(\n", - " tlist * 1e15,\n", - " expect(outputFMO_ME_nodephase.states, Q),\n", - " label=m + 1,\n", - " )\n", - "\n", - "axes.set_xlabel(r'$t$', fontsize=20)\n", - "axes.set_ylabel(r\"Population\", fontsize=16)\n", - "axes.set_xlim(0, 1000)\n", - "axes.set_title('Without pure dephasing', fontsize=24)\n", - "plt.yticks([0, 0.5, 1], [0, 0.5, 1])\n", - "plt.xticks([0, 500, 1000], [0, 500, 1000])\n", - "axes.legend(fontsize=18);" - ] - }, - { - "cell_type": "markdown", - "id": "422153e4", - "metadata": {}, - "source": [ - "And now we see that without the dephasing, the oscillations reappear. The full dynamics capture by the HEOM are still not capture by this simpler model, however." - ] - }, - { - "cell_type": "markdown", - "id": "c8c0e765", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "7d3a2ba5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "QuTiP: Quantum Toolbox in Python\n", - "================================\n", - "Copyright (c) QuTiP team 2011 and later.\n", - "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", - "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", - "Original developers: R. J. Johansson & P. D. Nation.\n", - "Previous lead developers: Chris Granade & A. Grimsmo.\n", - "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", - "\n", - "QuTiP Version: 5.1.0.dev0+7941773\n", - "Numpy Version: 2.1.3\n", - "Scipy Version: 1.14.1\n", - "Cython Version: 3.0.9\n", - "Matplotlib Version: 3.9.2\n", - "Python Version: 3.12.7\n", - "Number of CPUs: 16\n", - "BLAS Info: Generic\n", - "INTEL MKL Ext: None\n", - "Platform Info: Linux (x86_64)\n", - "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", - "================================================================================\n", - "Please cite QuTiP in your publication.\n", - "================================================================================\n", - "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" - ] - } - ], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "d77a1a1d", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "82e0aa66", - "metadata": {}, - "outputs": [], - "source": [ - "assert np.allclose(\n", - " expect(outputFMO_BR.states, Q_list[0]),\n", - " expect(outputFMO_ME.states, Q_list[0]),\n", - " rtol=2e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(outputFMO_BR.states, Q_list[1]),\n", - " expect(outputFMO_ME.states, Q_list[1]),\n", - " rtol=2e-2,\n", - ")" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "qutip-dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-2-fmo-example.md b/tutorials-v5/heom/heom-2-fmo-example.md new file mode 100644 index 00000000..9b05297e --- /dev/null +++ b/tutorials-v5/heom/heom-2-fmo-example.md @@ -0,0 +1,437 @@ +--- +jupytext: + formats: ipynb,md:myst + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.16.4 +kernelspec: + display_name: qutip-dev + language: python + name: python3 +--- + +# HEOM 2: Dynamics in Fenna-Mathews-Olsen complex (FMO) + ++++ + +## Introduction + +In this example notebook we outline how to employ the HEOM to +solve the FMO photosynthetic complex dynamics. + +We aim to replicate the results in reference [https://www.pnas.org/content/106/41/17255](https://pubmed.ncbi.nlm.nih.gov/19815512/) +and compare them to a Bloch-Redfield (perturbative) solution. + +This demonstrates how to to employ the solver for multiple baths, as well as showing how a +quantum environment reduces the effect of pure dephasing. + ++++ + +## Setup + +```{code-cell} ipython3 +import contextlib +import time + +import numpy as np +from matplotlib import pyplot as plt + +import qutip +from qutip import ( + Qobj, + basis, + brmesolve, + expect, + liouvillian, + mesolve, +) +from qutip.solver.heom import ( + HEOMSolver, +) +from qutip.core.environment import ( + DrudeLorentzEnvironment, + system_terminator +) + +%matplotlib inline +``` + +## Helper functions + +Let's define some helper functions for calculating correlation functions, spectral densities, thermal energy level occupations, and for plotting results and timing how long operations take: + +```{code-cell} ipython3 +@contextlib.contextmanager +def timer(label): + """ Simple utility for timing functions: + + with timer("name"): + ... code to time ... + """ + start = time.time() + yield + end = time.time() + print(f"{label}: {end - start}") +``` + +```{code-cell} ipython3 +# Solver options: + +options = { + "nsteps": 15000, + "store_states": True, + "rtol": 1e-12, + "atol": 1e-12, + "min_step": 1e-18, + "method": "vern9", + "progress_bar": "enhanced", +} +``` + +## System and bath definition + +And let us set up the system Hamiltonian and bath parameters: + +```{code-cell} ipython3 +# System Hamiltonian: +# +# We use the Hamiltonian employed in +# https://www.pnas.org/content/106/41/17255 and operate +# in units of Hz: + +Hsys = 3e10 * 2 * np.pi * Qobj([ + [200, -87.7, 5.5, -5.9, 6.7, -13.7, -9.9], + [-87.7, 320, 30.8, 8.2, 0.7, 11.8, 4.3], + [5.5, 30.8, 0, -53.5, -2.2, -9.6, 6.0], + [-5.9, 8.2, -53.5, 110, -70.7, -17.0, -63.3], + [6.7, 0.7, -2.2, -70.7, 270, 81.1, -1.3], + [-13.7, 11.8, -9.6, -17.0, 81.1, 420, 39.7], + [-9.9, 4.3, 6.0, -63.3, -1.3, 39.7, 230], +]) +``` + +```{code-cell} ipython3 +# Bath parameters + +lam = 35 * 3e10 * 2 * np.pi +gamma = 1 / 166e-15 +T = 300 * 0.6949 * 3e10 * 2 * np.pi +beta = 1 / T +``` + +## Plotting the environment spectral density and correlation functions + +Let's quickly plot the spectral density and environment correlation functions so that we can see what they look like. + +```{code-cell} ipython3 +env=DrudeLorentzEnvironment(T=T,lam=lam,gamma=gamma) +``` + +```{code-cell} ipython3 +wlist = np.linspace(0, 200 * 3e10 * 2 * np.pi, 100) +tlist = np.linspace(0, 1e-12, 1000) + +J = env.spectral_density(wlist) / (3e10*2*np.pi) + +fig, axes = plt.subplots(1, 2, sharex=False, figsize=(10, 3)) + +fig.subplots_adjust(hspace=0.1) # reduce space between plots + +# Spectral density plot: + +axes[0].plot(wlist / (3e10 * 2 * np.pi), J, color='r', ls='--', label="J(w)") +axes[0].set_xlabel(r'$\omega$ (cm$^{-1}$)', fontsize=20) +axes[0].set_ylabel(r"$J(\omega)$ (cm$^{-1}$)", fontsize=16) +axes[0].legend() + +# Correlation plot: + +axes[1].plot( + tlist, np.real(env.correlation_function(tlist, 10)), + color='r', ls='--', label="C(t) real", +) +axes[1].plot( + tlist, np.imag(env.correlation_function(tlist, 10)), + color='g', ls='--', label="C(t) imaginary", +) +axes[1].set_xlabel(r'$t$', fontsize=20) +axes[1].set_ylabel(r"$C(t)$", fontsize=16) +axes[1].legend(); +``` + +## Solve for the dynamics with the HEOM + +Now let us solve for the evolution of this system using the HEOM. + +```{code-cell} ipython3 +# We start the excitation at site 1: +rho0 = basis(7, 0) * basis(7, 0).dag() + +# HEOM solver options: +# +# Note: We set Nk=0 (i.e. a single correlation expansion term +# per bath) and rely on the terminator to correct detailed +# balance. +NC = 4 # Use NC=8 for more precise results +Nk = 0 + +Q_list = [] +baths = [] +Ltot = liouvillian(Hsys) +env_approx,delta=env.approx_by_matsubara(Nk=Nk,compute_delta=True) +for m in range(7): + Q = basis(7, m) * basis(7, m).dag() + Q_list.append(Q) + Ltot += system_terminator(Q,delta) + baths.append((env_approx,Q)) +``` + +```{code-cell} ipython3 +with timer("RHS construction time"): + HEOMMats = HEOMSolver(Hsys, baths, NC, options=options) + +with timer("ODE solver time"): + outputFMO_HEOM = HEOMMats.run(rho0, tlist) +``` + +```{code-cell} ipython3 +fig, axes = plt.subplots(1, 1, figsize=(12, 8)) + +colors = ['r', 'g', 'b', 'y', 'c', 'm', 'k'] +linestyles = [ + '-', '--', ':', '-.', + (0, (1, 10)), (0, (5, 10)), (0, (3, 10, 1, 10)), +] + +for m in range(7): + Q = basis(7, m) * basis(7, m).dag() + axes.plot( + np.array(tlist) * 1e15, + np.real(expect(outputFMO_HEOM.states, Q)), + label=m + 1, + color=colors[m % len(colors)], + linestyle=linestyles[m % len(linestyles)], + ) + axes.set_xlabel(r'$t$ (fs)', fontsize=30) + axes.set_ylabel(r"Population", fontsize=30) + axes.locator_params(axis='y', nbins=6) + axes.locator_params(axis='x', nbins=6) + +axes.set_title('HEOM solution', fontsize=24) +axes.legend(loc=0) +axes.set_xlim(0, 1000) +plt.yticks([0., 0.5, 1], [0, 0.5, 1]) +plt.xticks([0., 500, 1000], [0, 500, 1000]); +``` + +## Comparison with Bloch-Redfield solver + +Now let us solve the same problem using the Bloch-Redfield solver. We will see that the Bloch-Redfield technique fails to model the oscillation of population of the states that we saw in the HEOM. + +In the next section, we will examine the role of pure dephasing in the evolution to understand why this happens. + +```{code-cell} ipython3 +with timer("BR ODE solver time"): + outputFMO_BR = brmesolve( + Hsys, rho0, tlist, + a_ops=[[Q, env] for Q in Q_list], + options=options, + ) +``` + +And now let's plot the Bloch-Redfield solver results: + +```{code-cell} ipython3 +fig, axes = plt.subplots(1, 1, figsize=(12, 8)) + +for m, Q in enumerate(Q_list): + axes.plot(tlist * 1e15, expect(outputFMO_BR.states, Q), label=m + 1) + +axes.set_xlabel(r'$t$ (fs)', fontsize=30) +axes.set_ylabel(r"Population", fontsize=30) + +axes.set_title('Bloch-Redfield solution ', fontsize=24) +axes.legend() +axes.set_xlim(0, 1000) +plt.yticks([0, 0.5, 1], [0, 0.5, 1]) +plt.xticks([0, 500, 1000], [0, 500, 1000]); +``` + +Notice how the oscillations are gone and the populations decay much more rapidly. + +Next let us try to understand why. + ++++ + +## Role of pure dephasing + +It is useful to construct the various parts of the Bloch-Redfield master equation explicitly and to solve them using the Master equation solver, `mesolve`. We will do so and show that it is the pure-dephasing terms which suppresses coherence in these oscillations. + +First we will write a function to return the list of collapse operators for a given system, either with or without the dephasing operators: + ++++ + +TODO: Maybe power spectrum at zero is wrong, by a factor 2 + +```{code-cell} ipython3 +def J0_dephasing(): + """ Under-damped brownian oscillator dephasing probability. + + This returns the limit as w -> 0 of J0(w) * n_th(w, T) / T. + """ + return 2 * lam * gamma / gamma**2 +``` + +```{code-cell} ipython3 +env.power_spectrum(0)/2 -J0_dephasing()*T +``` + +```{code-cell} ipython3 +def get_collapse(H, T, dephasing=1): + """ Calculate collapse operators for a given system H and + temperature T. + """ + all_energy, all_state = H.eigenstates(sort="low") + Nmax = len(all_energy) + + Q_list = [ + basis(Nmax, n) * basis(Nmax, n).dag() + for n in range(Nmax) + ] + + collapse_list = [] + + for Q in Q_list: + for j in range(Nmax): + for k in range(j + 1, Nmax): + Deltajk = abs(all_energy[k] - all_energy[j]) + if abs(Deltajk) > 0: + rate = ( + np.abs(Q.matrix_element( + all_state[j].dag(), all_state[k] + ))**2 * + env.power_spectrum(Deltajk) + ) + if rate > 0.0: + # emission: + collapse_list.append( + np.sqrt(rate) * all_state[j] * all_state[k].dag() + ) + + rate = ( + np.abs(Q.matrix_element( + all_state[k].dag(), all_state[j] + ))**2 * + env.power_spectrum(-Deltajk) + ) + if rate > 0.0: + # absorption: + collapse_list.append( + np.sqrt(rate) * all_state[k] * all_state[j].dag() + ) + + if dephasing: + for j in range(Nmax): + rate = ( + np.abs(Q.matrix_element( + all_state[j].dag(), all_state[j]) + )**2 * env.power_spectrum(0)/2 + ) + if rate > 0.0: + # emission: + collapse_list.append( + np.sqrt(rate) * all_state[j] * all_state[j].dag() + ) + + return collapse_list +``` + +Now we are able to switch the pure dephasing terms on and off. + +Let us starting by including the dephasing operators. We expect to see the same behaviour that we saw when using the Bloch-Redfield solver. + +```{code-cell} ipython3 +# dephasing terms on, we recover the full BR solution: + +with timer("Building the collapse operators"): + collapse_list = get_collapse(Hsys, T=T, dephasing=True) + +with timer("ME ODE solver"): + outputFMO_ME = mesolve(Hsys, rho0, tlist, collapse_list) +``` + +```{code-cell} ipython3 +fig, axes = plt.subplots(1, 1, figsize=(12, 8)) + +for m, Q in enumerate(Q_list): + axes.plot(tlist * 1e15, expect(outputFMO_ME.states, Q), label=m + 1) + +axes.set_xlabel(r'$t$', fontsize=20) +axes.set_ylabel(r"Population", fontsize=16) +axes.set_xlim(0, 1000) +axes.set_title('With pure dephasing', fontsize=24) +plt.yticks([0, 0.5, 1], [0, 0.5, 1]) +plt.xticks([0, 500, 1000], [0, 500, 1000]) +axes.legend(fontsize=18); +``` + +We see similar results to before. + +Now let us examine what happens when we remove the dephasing collapse operators: + +```{code-cell} ipython3 +# dephasing terms off + +with timer("Building the collapse operators"): + collapse_list = get_collapse(Hsys, T, dephasing=False) + +with timer("ME ODE solver"): + outputFMO_ME_nodephase = mesolve(Hsys, rho0, tlist, collapse_list) +``` + +```{code-cell} ipython3 +fig, axes = plt.subplots(1, 1, figsize=(12, 8)) +for m, Q in enumerate(Q_list): + axes.plot( + tlist * 1e15, + expect(outputFMO_ME_nodephase.states, Q), + label=m + 1, + ) + +axes.set_xlabel(r'$t$', fontsize=20) +axes.set_ylabel(r"Population", fontsize=16) +axes.set_xlim(0, 1000) +axes.set_title('Without pure dephasing', fontsize=24) +plt.yticks([0, 0.5, 1], [0, 0.5, 1]) +plt.xticks([0, 500, 1000], [0, 500, 1000]) +axes.legend(fontsize=18); +``` + +And now we see that without the dephasing, the oscillations reappear. The full dynamics capture by the HEOM are still not capture by this simpler model, however. + ++++ + +## About + +```{code-cell} ipython3 +qutip.about() +``` + +## Testing + +This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. + +```{code-cell} ipython3 +assert np.allclose( + expect(outputFMO_BR.states, Q_list[0]), + expect(outputFMO_ME.states, Q_list[0]), + rtol=2e-2, +) +assert np.allclose( + expect(outputFMO_BR.states, Q_list[1]), + expect(outputFMO_ME.states, Q_list[1]), + rtol=2e-2, +) +``` diff --git a/tutorials-v5/heom/heom-3-quantum-heat-transport.ipynb b/tutorials-v5/heom/heom-3-quantum-heat-transport.ipynb deleted file mode 100644 index fec1159d..00000000 --- a/tutorials-v5/heom/heom-3-quantum-heat-transport.ipynb +++ /dev/null @@ -1,803 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "dccbb6ae", - "metadata": {}, - "source": [ - "# HEOM 3: Quantum Heat Transport" - ] - }, - { - "cell_type": "markdown", - "id": "afd65763", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "In this notebook, we apply the QuTiP HEOM solver to a quantum system coupled to two bosonic baths and demonstrate how to extract information about the system-bath heat currents from the auxiliary density operators (ADOs).\n", - "We consider the setup described in Ref. \\[1\\], which consists of two coupled qubits, each connected to its own heat bath.\n", - "The Hamiltonian of the qubits is given by\n", - "\n", - "$$ \\begin{aligned} H_{\\text{S}} &= H_1 + H_2 + H_{12} , \\quad\\text{ where }\\\\\n", - "H_K &= \\frac{\\epsilon}{2} \\bigl(\\sigma_z^K + 1\\bigr) \\quad (K=1,2) \\quad\\text{ and }\\quad H_{12} = J_{12} \\bigl( \\sigma_+^1 \\sigma_-^2 + \\sigma_-^1 \\sigma_+^2 \\bigr) . \\end{aligned} $$\n", - "\n", - "Here, $\\sigma^K_{x,y,z,\\pm}$ denotes the usual Pauli matrices for the K-th qubit, $\\epsilon$ is the eigenfrequency of the qubits and $J_{12}$ the coupling constant.\n", - "\n", - "Each qubit is coupled to its own bath; therefore, the total Hamiltonian is\n", - "\n", - "$$ H_{\\text{tot}} = H_{\\text{S}} + \\sum_{K=1,2} \\bigl( H_{\\text{B}}^K + Q_K \\otimes X_{\\text{B}}^K \\bigr) , $$\n", - "\n", - "where $H_{\\text{B}}^K$ is the free Hamiltonian of the K-th bath and $X_{\\text{B}}^K$ its coupling operator, and $Q_K = \\sigma_x^K$ are the system coupling operators.\n", - "We assume that the bath spectral densities are given by Drude distributions\n", - "\n", - "$$ J_K(\\omega) = \\frac{2 \\lambda_K \\gamma_K \\omega}{\\omega^2 + \\gamma_K^2} , $$\n", - "\n", - "where $\\lambda_K$ is the free coupling strength and $\\gamma_K$ the cutoff frequency.\n", - "\n", - "We begin by defining the system and bath parameters.\n", - "We use the parameter values from Fig. 3(a) of Ref. \\[1\\].\n", - "Note that we set $\\hbar$ and $k_B$ to one and we will measure all frequencies and energies in units of $\\epsilon$.\n", - "\n", - "References:\n", - "\n", - "   \\[1\\] Kato and Tanimura, [J. Chem. Phys. **143**, 064107](https://doi.org/10.1063/1.4928192) (2015)." - ] - }, - { - "cell_type": "markdown", - "id": "3d7cbb98", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "af859f07", - "metadata": {}, - "outputs": [], - "source": [ - "import dataclasses\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import qutip as qt\n", - "from qutip.solver.heom import (\n", - " HEOMSolver,\n", - " DrudeLorentzPadeBath\n", - ")\n", - "from qutip.core.environment import (\n", - " CFExponent,\n", - " DrudeLorentzEnvironment,\n", - " system_terminator,\n", - ")\n", - "\n", - "from ipywidgets import IntProgress\n", - "from IPython.display import display\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "99f91c02", - "metadata": {}, - "source": [ - "## Helpers" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "b3044b1d", - "metadata": {}, - "outputs": [], - "source": [ - "# Solver options:\n", - "\n", - "options = {\n", - " \"nsteps\": 15000,\n", - " \"store_states\": True,\n", - " \"rtol\": 1e-12,\n", - " \"atol\": 1e-12,\n", - " \"min_step\": 1e-18,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "ceaf41a4", - "metadata": {}, - "source": [ - "## System and bath definition" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "42a1fd73", - "metadata": {}, - "outputs": [], - "source": [ - "@dataclasses.dataclass\n", - "class SystemParams:\n", - " \"\"\" System parameters and Hamiltonian. \"\"\"\n", - " epsilon: float = 1.0\n", - " J12: float = 0.1\n", - "\n", - " def H(self):\n", - " \"\"\" Return the Hamiltonian for the system.\n", - "\n", - " The system consists of two qubits with Hamiltonians (H1 and H2)\n", - " and an interaction term (H12).\n", - " \"\"\"\n", - " H1 = self.epsilon / 2 * (\n", - " qt.tensor(qt.sigmaz() + qt.identity(2), qt.identity(2))\n", - " )\n", - " H2 = self.epsilon / 2 * (\n", - " qt.tensor(qt.identity(2), qt.sigmaz() + qt.identity(2))\n", - " )\n", - " H12 = self.J12 * (\n", - " qt.tensor(qt.sigmap(), qt.sigmam()) +\n", - " qt.tensor(qt.sigmam(), qt.sigmap())\n", - " )\n", - " return H1 + H2 + H12\n", - "\n", - " def replace(self, **kw):\n", - " return dataclasses.replace(self, **kw)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "101b47c7", - "metadata": {}, - "outputs": [], - "source": [ - "@dataclasses.dataclass\n", - "class BathParams:\n", - " \"\"\" Bath parameters. \"\"\"\n", - " sign: str # + or -\n", - " qubit: int # 0 or 1\n", - "\n", - " gamma: float = 2.0\n", - " lam: float = 0.05\n", - " Tbar: float = 2.0\n", - " Tdelta: float = 0.01\n", - "\n", - " def __post_init__(self):\n", - " # T = Tbar +- Tdelta * Tbar:\n", - " assert self.sign in (\"+\", \"-\")\n", - " sign = +1 if self.sign == \"+\" else -1\n", - " self.T = self.Tbar + sign * self.Tdelta * self.Tbar\n", - " # qubit\n", - " assert self.qubit in (0, 1)\n", - "\n", - " def Q(self):\n", - " \"\"\" Coupling operator for the bath. \"\"\"\n", - " Q = [qt.identity(2), qt.identity(2)]\n", - " Q[self.qubit] = qt.sigmax()\n", - " return qt.tensor(Q)\n", - "\n", - " def bath(self, Nk, tag=None):\n", - " env=DrudeLorentzEnvironment(\n", - " lam=self.lam, gamma=self.gamma, T=self.T, tag=tag\n", - " )\n", - " env_approx,delta=env.approx_by_pade(Nk=Nk,compute_delta=True,tag=tag)\n", - " return (env_approx,self.Q()),system_terminator(self.Q(),delta),delta\n", - "\n", - " def replace(self, **kw):\n", - " return dataclasses.replace(self, **kw)" - ] - }, - { - "cell_type": "markdown", - "id": "0e6b9799", - "metadata": {}, - "source": [ - "## Heat currents\n", - "\n", - "Following Ref. \\[2\\], we consider two possible definitions of the heat currents from the qubits into the baths.\n", - "The so-called bath heat currents are $j_{\\text{B}}^K = \\partial_t \\langle H_{\\text{B}}^K \\rangle$ and the system heat currents are $j_{\\text{S}}^K = \\mathrm i\\, \\langle [H_{\\text{S}}, Q_K] X_{\\text{B}}^K \\rangle$.\n", - "As shown in Ref. \\[2\\], they can be expressed in terms of the HEOM ADOs as follows:\n", - "$$ \\begin{aligned} \\mbox{} \\\\\n", - " j_{\\text{B}}^K &= \\!\\!\\sum_{\\substack{\\mathbf n\\\\ \\text{Level 1}\\\\ \\text{Bath $K$}}}\\!\\! \\nu[\\mathbf n] \\operatorname{tr}\\bigl[ Q_K \\rho_{\\mathbf n} \\bigr] - 2 C_I^K(0) \\operatorname{tr}\\bigl[ Q_k^2 \\rho \\bigr] + \\Gamma_{\\text{T}}^K \\operatorname{tr}\\bigl[ [[H_{\\text{S}}, Q_K], Q_K]\\, \\rho \\bigr] , \\\\[.5em]\n", - " j_{\\text{S}}^K &= \\mathrm i\\!\\! \\sum_{\\substack{\\mathbf n\\\\ \\text{Level 1}\\\\ \\text{Bath $k$}}}\\!\\! \\operatorname{tr}\\bigl[ [H_{\\text{S}}, Q_K]\\, \\rho_{\\mathbf n} \\bigr] + \\Gamma_{\\text{T}}^K \\operatorname{tr}\\bigl[ [[H_{\\text{S}}, Q_K], Q_K]\\, \\rho \\bigr] . \\\\ \\mbox{}\n", - "\\end{aligned} $$\n", - "The sums run over all level-$1$ multi-indices $\\mathbf n$ with one excitation corresponding to the K-th bath, $\\nu[\\mathbf n]$ is the corresponding (negative) exponent of the bath auto-correlation function $C^K(t)$, and $\\Gamma_{\\text{T}}^K$ is the Ishizaki-Tanimura terminator (i.e., a correction term accounting for the error introduced by approximating the correlation function with a finite sum of exponential terms).\n", - "In the expression for the bath heat currents, we left out terms involving $[Q_1, Q_2]$, which is zero in this example.\n", - "\n", - "   \\[2\\] Kato and Tanimura, [J. Chem. Phys. **145**, 224105](https://doi.org/10.1063/1.4971370) (2016)." - ] - }, - { - "cell_type": "markdown", - "id": "0e6edfc3", - "metadata": {}, - "source": [ - "In QuTiP, these currents can be conveniently calculated as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "729a27ba", - "metadata": {}, - "outputs": [], - "source": [ - "def bath_heat_current(bath_tag, ado_state, hamiltonian, coupling_op, delta=0):\n", - " \"\"\"\n", - " Bath heat current from the system into the heat bath with the given tag.\n", - "\n", - " Parameters\n", - " ----------\n", - " bath_tag : str, tuple or any other object\n", - " Tag of the heat bath corresponding to the current of interest.\n", - "\n", - " ado_state : HierarchyADOsState\n", - " Current state of the system and the environment (encoded in the ADOs).\n", - "\n", - " hamiltonian : Qobj\n", - " System Hamiltonian at the current time.\n", - "\n", - " coupling_op : Qobj\n", - " System coupling operator at the current time.\n", - "\n", - " delta : float\n", - " The prefactor of the \\\\delta(t) term in the correlation function (the\n", - " Ishizaki-Tanimura terminator).\n", - " \"\"\"\n", - " l1_labels = ado_state.filter(level=1, tags=[bath_tag])\n", - " a_op = 1j * (hamiltonian * coupling_op - coupling_op * hamiltonian)\n", - "\n", - " result = 0\n", - " cI0 = 0 # imaginary part of bath auto-correlation function (t=0)\n", - " for label in l1_labels:\n", - " [exp] = ado_state.exps(label)\n", - " result += exp.vk * (coupling_op * ado_state.extract(label)).tr()\n", - "\n", - " if exp.type == CFExponent.types['I']:\n", - " cI0 += exp.ck\n", - " elif exp.type == CFExponent.types['RI']:\n", - " cI0 += exp.ck2\n", - "\n", - " result -= 2 * cI0 * (coupling_op * coupling_op * ado_state.rho).tr()\n", - " if delta != 0:\n", - " result -= (\n", - " 1j * delta *\n", - " ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr()\n", - " )\n", - " return result\n", - "\n", - "\n", - "def system_heat_current(\n", - " bath_tag, ado_state, hamiltonian, coupling_op, delta=0,\n", - "):\n", - " \"\"\"\n", - " System heat current from the system into the heat bath with the given tag.\n", - "\n", - " Parameters\n", - " ----------\n", - " bath_tag : str, tuple or any other object\n", - " Tag of the heat bath corresponding to the current of interest.\n", - "\n", - " ado_state : HierarchyADOsState\n", - " Current state of the system and the environment (encoded in the ADOs).\n", - "\n", - " hamiltonian : Qobj\n", - " System Hamiltonian at the current time.\n", - "\n", - " coupling_op : Qobj\n", - " System coupling operator at the current time.\n", - "\n", - " delta : float\n", - " The prefactor of the \\\\delta(t) term in the correlation function (the\n", - " Ishizaki-Tanimura terminator).\n", - " \"\"\"\n", - " l1_labels = ado_state.filter(level=1, tags=[bath_tag])\n", - " a_op = 1j * (hamiltonian * coupling_op - coupling_op * hamiltonian)\n", - "\n", - " result = 0\n", - " for label in l1_labels:\n", - " result += (a_op * ado_state.extract(label)).tr()\n", - "\n", - " if delta != 0:\n", - " result -= (\n", - " 1j * delta *\n", - " ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr()\n", - " )\n", - " return result" - ] - }, - { - "cell_type": "markdown", - "id": "bc957c4f", - "metadata": {}, - "source": [ - "Note that at long times, we expect $j_{\\text{B}}^1 = -j_{\\text{B}}^2$ and $j_{\\text{S}}^1 = -j_{\\text{S}}^2$ due to energy conservation. At long times, we also expect $j_{\\text{B}}^1 = j_{\\text{S}}^1$ and $j_{\\text{B}}^2 = j_{\\text{S}}^2$ since the coupling operators commute, $[Q_1, Q_2] = 0$. Hence, all four currents should agree in the long-time limit (up to a sign). This long-time value is what was analyzed in Ref. \\[2\\]." - ] - }, - { - "cell_type": "markdown", - "id": "24fbd7d1", - "metadata": {}, - "source": [ - "## Simulations" - ] - }, - { - "cell_type": "markdown", - "id": "a0d6eb39", - "metadata": {}, - "source": [ - "For our simulations, we will represent the bath spectral densities using the first term of their Padé decompositions, and we will use $7$ levels of the HEOM hierarchy." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "328981e7", - "metadata": {}, - "outputs": [], - "source": [ - "Nk = 1\n", - "NC = 7" - ] - }, - { - "cell_type": "markdown", - "id": "a07c684d", - "metadata": {}, - "source": [ - "### Time Evolution\n", - "\n", - "We fix $J_{12} = 0.1 \\epsilon$ (as in Fig. 3(a-ii) of Ref. \\[2\\]) and choose the fixed coupling strength $\\lambda_1 = \\lambda_2 = J_{12}\\, /\\, (2\\epsilon)$ (corresponding to $\\bar\\zeta = 1$ in Ref. \\[2\\]).\n", - "Using these values, we will study the time evolution of the system state and the heat currents." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "398ae770", - "metadata": {}, - "outputs": [], - "source": [ - "# fix qubit-qubit and qubit-bath coupling strengths\n", - "sys = SystemParams(J12=0.1)\n", - "bath_p1 = BathParams(qubit=0, sign=\"+\", lam=sys.J12 / 2)\n", - "bath_p2 = BathParams(qubit=1, sign=\"-\", lam=sys.J12 / 2)\n", - "\n", - "# choose arbitrary initial state\n", - "rho0 = qt.tensor(qt.identity(2), qt.identity(2)) / 4\n", - "\n", - "# simulation time span\n", - "tlist = np.linspace(0, 50, 250)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7c58e513", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 7.60s*] Elapsed 7.60s / Remaining 00:00:00:00\n" - ] - } - ], - "source": [ - "H = sys.H()\n", - "\n", - "bath1,b1term,b1delta = bath_p1.bath(Nk, tag='bath 1')\n", - "Q1 = bath_p1.Q()\n", - "\n", - "bath2,b2term,b2delta = bath_p2.bath(Nk, tag='bath 2')\n", - "Q2 = bath_p2.Q()\n", - "\n", - "\n", - "solver = HEOMSolver(\n", - " qt.liouvillian(H) + b1term + b2term,\n", - " [bath1, bath2],\n", - " max_depth=NC,\n", - " options=options,\n", - ")\n", - "\n", - "result = solver.run(rho0, tlist, e_ops=[\n", - " qt.tensor(qt.sigmaz(), qt.identity(2)),\n", - " lambda t, ado: bath_heat_current('bath 1', ado, H, Q1, b1delta),\n", - " lambda t, ado: bath_heat_current('bath 2', ado, H, Q2, b2delta),\n", - " lambda t, ado: system_heat_current('bath 1', ado, H, Q1, b1delta),\n", - " lambda t, ado: system_heat_current('bath 2', ado, H, Q2, b2delta),\n", - "])" - ] - }, - { - "cell_type": "markdown", - "id": "3cc11ecd", - "metadata": {}, - "source": [ - "We first plot $\\langle \\sigma_z^1 \\rangle$ to see the time evolution of the system state:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "56f49d60", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/mcditoos/anaconda3/envs/qutip-dev/lib/python3.12/site-packages/matplotlib/cbook.py:1762: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return math.isfinite(val)\n", - "/home/mcditoos/anaconda3/envs/qutip-dev/lib/python3.12/site-packages/matplotlib/cbook.py:1398: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(figsize=(8, 8))\n", - "axes.plot(tlist, result.expect[0], 'r', linewidth=2)\n", - "axes.set_xlabel('t', fontsize=28)\n", - "axes.set_ylabel(r\"$\\langle \\sigma_z^1 \\rangle$\", fontsize=28);" - ] - }, - { - "cell_type": "markdown", - "id": "5c357130", - "metadata": {}, - "source": [ - "We find a rather quick thermalization of the system state. For the heat currents, however, it takes a somewhat longer time until they converge to their long-time values:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "98bf7d9f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8))\n", - "\n", - "ax1.plot(\n", - " tlist, -np.real(result.expect[1]),\n", - " color='darkorange', label='BHC (bath 1 -> system)',\n", - ")\n", - "ax1.plot(\n", - " tlist, np.real(result.expect[2]),\n", - " '--', color='darkorange', label='BHC (system -> bath 2)',\n", - ")\n", - "ax1.plot(\n", - " tlist, -np.real(result.expect[3]),\n", - " color='dodgerblue', label='SHC (bath 1 -> system)',\n", - ")\n", - "ax1.plot(\n", - " tlist, np.real(result.expect[4]),\n", - " '--', color='dodgerblue', label='SHC (system -> bath 2)',\n", - ")\n", - "\n", - "ax1.set_xlabel('t', fontsize=28)\n", - "ax1.set_ylabel('j', fontsize=28)\n", - "ax1.set_ylim((-0.05, 0.05))\n", - "ax1.legend(loc=0, fontsize=12)\n", - "\n", - "ax2.plot(\n", - " tlist, -np.real(result.expect[1]),\n", - " color='darkorange', label='BHC (bath 1 -> system)',\n", - ")\n", - "ax2.plot(\n", - " tlist, np.real(result.expect[2]),\n", - " '--', color='darkorange', label='BHC (system -> bath 2)',\n", - ")\n", - "ax2.plot(\n", - " tlist, -np.real(result.expect[3]),\n", - " color='dodgerblue', label='SHC (bath 1 -> system)',\n", - ")\n", - "ax2.plot(\n", - " tlist, np.real(result.expect[4]),\n", - " '--', color='dodgerblue', label='SHC (system -> bath 2)',\n", - ")\n", - "\n", - "ax2.set_xlabel('t', fontsize=28)\n", - "ax2.set_xlim((20, 50))\n", - "ax2.set_ylim((0, 0.0002))\n", - "ax2.legend(loc=0, fontsize=12);" - ] - }, - { - "cell_type": "markdown", - "id": "26a2fb9c", - "metadata": {}, - "source": [ - "### Steady-state currents\n", - "\n", - "Here, we try to reproduce the HEOM curves in Fig. 3(a) of Ref. \\[1\\] by varying the coupling strength and finding the steady state for each coupling strength." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "2642aa43", - "metadata": {}, - "outputs": [], - "source": [ - "def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options):\n", - " \"\"\" Calculate the steady sate heat currents for the given system and\n", - " bath.\n", - " \"\"\"\n", - "\n", - " bath1,b1term,b1delta = bath_p1.bath(Nk, tag='bath 1')\n", - " Q1 = bath_p1.Q()\n", - "\n", - " bath2,b2term,b2delta = bath_p2.bath(Nk, tag='bath 2')\n", - " Q2 = bath_p2.Q()\n", - "\n", - " solver = HEOMSolver(\n", - " qt.liouvillian(sys.H()) + b1term + b2term,\n", - " [bath1, bath2],\n", - " max_depth=NC,\n", - " options=options\n", - " )\n", - "\n", - " _, steady_ados = solver.steady_state()\n", - "\n", - " return (\n", - " bath_heat_current('bath 1', steady_ados, sys.H(), Q1, b1delta),\n", - " bath_heat_current('bath 2', steady_ados, sys.H(), Q2, b2delta),\n", - " system_heat_current('bath 1', steady_ados, sys.H(), Q1, b1delta),\n", - " system_heat_current('bath 2', steady_ados, sys.H(), Q2, b2delta),\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "5a66cb86", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6c88dbc2d755472ea46368067ef8a5e2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "IntProgress(value=0, max=30)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Define number of points to use for the plot\n", - "plot_points = 10 # use 100 for a smoother curve\n", - "\n", - "# Range of relative coupling strengths\n", - "# Chosen so that zb_max is maximum, centered around 1 on a log scale\n", - "zb_max = 4 # use 20 to see more of the current curve\n", - "zeta_bars = np.logspace(\n", - " -np.log(zb_max),\n", - " np.log(zb_max),\n", - " plot_points,\n", - " base=np.e,\n", - ")\n", - "\n", - "# Setup a progress bar\n", - "progress = IntProgress(min=0, max=(3 * plot_points))\n", - "display(progress)\n", - "\n", - "\n", - "def calculate_heat_current(J12, zb, Nk, progress=progress):\n", - " \"\"\" Calculate a single heat current and update the progress bar. \"\"\"\n", - " # Estimate appropriate HEOM max_depth from coupling strength\n", - " NC = 7 + int(max(zb * J12 - 1, 0) * 2)\n", - " NC = min(NC, 20)\n", - " # the four currents are identical in the steady state\n", - " j, _, _, _ = heat_currents(\n", - " sys.replace(J12=J12),\n", - " bath_p1.replace(lam=zb * J12 / 2),\n", - " bath_p2.replace(lam=zb * J12 / 2),\n", - " Nk, NC, options=options,\n", - " )\n", - " progress.value += 1\n", - " return j\n", - "\n", - "\n", - "# Calculate steady state currents for range of zeta_bars\n", - "# for J12 = 0.01, 0.1 and 0.5:\n", - "j1s = [\n", - " calculate_heat_current(0.01, zb, Nk)\n", - " for zb in zeta_bars\n", - "]\n", - "j2s = [\n", - " calculate_heat_current(0.1, zb, Nk)\n", - " for zb in zeta_bars\n", - "]\n", - "j3s = [\n", - " calculate_heat_current(0.5, zb, Nk)\n", - " for zb in zeta_bars\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "7b9e3be7", - "metadata": {}, - "source": [ - "## Create Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "4f5b2a1d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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GDBiA9+/f48cff0RgYCBq1aqFv//+GxUqVFCd8+7dO/j7+6se37hxA23btlU9nvbfcpPhw4dj8+bNquMDBw6Et7c3Zs2ahTdv3sDa2houLi4YP358rn8f2aGQJEnSdBGUsfDwcFhaWiIsLCxTf1mIiIiIiIgyIzY2Fk+fPoW9vT1MTEw0XU6WNWzYEI0bN8aqVatkxz09PbFixQrV7g+SJGHw4MGoXr065s6dq4FKtdPHfv5Z+RzKmQpERERERESkEyRJwrNnz+Du7o579+7B3d1dNt6pUyfcunULUVFRKFeuHDw8PBAXF4ddu3ahdu3aOHDgAADA3d0dTk5OGvgOCh6GCkRERERERKQT7t69i2bNmqF27do4fvw4HB0dZePHjx9P93lJSUn5UV6hxFCBiIiIiIiIdELt2rURFRWl6TIoFT1NF0BEREREREREuomhAhERERERERFlC0MFIiIiIiIiIsoWhgpERERERERElC0MFYiIiIiIiIgoWxgqEBEREREREVG2MFQgIiIiIiIiomxhqEBERERERERE2cJQgYiIiIiIiIiyhaECEREREREREWULQ4X/nD9/Hj169ECZMmWgUChw4MCBj54/YsQIKBQKta+aNWuqztm8eXO658TGxubxd0NERERERFQ4NGnSBOPGjdN0GVi1ahXs7e1hYmKCBg0a4MKFCx89f+7cuWqfFUuXLp1P1eYehgr/iYqKQp06dbBixYpMnb9s2TIEBgaqvl68eAFra2v069dPdp6FhYXsvMDAQJiYmOTFt0BERERERFSoKJVK3L17F/Xq1dNoHbt27cLUqVMxe/ZseHt7w9nZGV26dMHz588/+ryaNWvKPivevXs3nyrOPQaaLkBbdOnSBV26dMn0+ZaWlrC0tFQ9PnDgAEJDQzFy5EjZebqaNhEREREREWk7X19fxMTEoH79+hqtY8mSJRg9ejTGjBkDAFi6dCmOHz+O1atXY9GiRRk+z8DAINOfF9esWYOVK1fCz88PRkZGaNCgAc6cOZMr9ecEQ4VcsnHjRrRv3x4VKlSQHY+MjESFChWgVCpRt25d/PTTTx9N0eLi4hAXF6d6HB4enmc1ExERERERpWeJ1xIs8VqS4+ts67MNbSq2UT32DPDE5/s/BwBMazYN05pNy9H1vb29oa+vj9q1a+foOgCwcOFCLFy48KPnHDt2DM7OzrJj8fHxuHnzJr799lvZ8Y4dO+Ly5csfvd7jx49RpkwZGBsbo0mTJli4cCEqVaqkdt6+ffvw7bffYt26dWjatCkiIiIQEBCQuW8sjzFUyAWBgYE4duwYduzYITvu4OCAzZs3w8nJCeHh4Vi2bBlatGiBO3fuoGrVqulea9GiRZg3b15+lE1ERERERJSu8LhwvIp4lePrxCXGqT1Ovm54XM5voHp7e8PBwQGmpqYAAFdXV3h6esLFxQV79+4FALx48QJDhw5FcHAwDAwM8N1336ktWwcANzc39O/f/6OvV7ZsWbVj7969g1KpRKlSpWTHS5UqhaCgoAyv1aRJE2zduhXVqlXDmzdvMH/+fDRv3hz379+HjY2N7NxHjx7Bzs4OHTt2hJWVFQDI+vlpEkOFXLB582ZYWVmhd+/esuNNmzZF06ZNVY9btGiB+vXr488//8Ty5cvTvdbMmTMxbVpKWhceHo7y5cvnSd1ERERERETpsTC2QFlz9Q/QWWVsYKz2OPm6FsYWOb6+t7e3bOnD5MmTMWrUKGzZskV1zMDAAEuXLkXdunURHByM+vXro2vXrihSpIjsWtbW1rC2ts52LQqFQvZYkiS1Y6mlXn7v5OSEZs2aoXLlytiyZYvsMyEAjB07Frt374a1tTXMzMxw584dVK5cOdu15iaGCjkkSRL++usvDB06FEZGRh89V09PD40aNcLjx48zPMfY2BjGxsYZjhMREREREeW13FiakJ42Fdvg5bSXuXa927dvo2fPnqrHbdu2haenp+wcW1tb2NraAgBKliwJa2trhISEqIUK2V3+ULx4cejr66vNSggODlabvfAxRYoUgZOTk9rnxYSEBAwcOBCNGjXC+vXrYWVlle4SCU1hqJBD586dg5+fH0aPHv3JcyVJwu3bt+Hk5JQPlREREZGukSQJUQlRCI8LR0RcBMLjwlVfEfERMDEwQXWb6qhmUw2mhqaaLpeISKMCAgIQGhqapZ0fbty4gaSkpHRng2d3+UNy08STJ0/C1dVVdfzkyZPo1atXpmuLi4uDj4+PWmjh4eEBPz8/nDp1KtPXyk8MFf4TGRkJPz8/1eOnT5/i9u3bsLa2hp2dHWbOnIlXr15h69atsudt3LgRTZo0Qa1atdSuOW/ePDRt2hRVq1ZFeHg4li9fjtu3b2PlypV5/v0QERFR/olLjEN4XDjMDM1QxCjlztf76PfY+2AvwuPC4VjCEd2rdZc9r/Xm1ngX/U4VHETGRyJJSvrk6ymgQEWrinAo7oDapWrj5/Y/5/r3RESk7by9vaFQKDIdKrx//x7Dhg3Dhg0b0h3PyfKHadOmYejQoWjYsCGaNWuGdevW4fnz53Bzc1Ods2LFCnh4eOD06dMAgG+++QY9evSAnZ0dgoODMX/+fISHh2P48OGya8fHxyMwMBDu7u5wdnZGZGQkLl26hFGjRsHQ0DBb9eYmhgr/uXHjBtq2bat6nLyGZfjw4di8eTMCAwPV9hgNCwvDvn37sGzZsnSv+eHDB4wbNw5BQUGwtLREvXr1cP78eTRu3DjvvhEiIiLKsvfR7/Ho/SPVjADZDIHkGQPx4RnOIIhXxgMA1nVfh7ENxqqu+zb6LdyOijeUw+oMUwsV7gXfQ0hMSJbrlSDh6Yenqq+0ocIPZ3/Aq4hXcCzuiAmNJsDM0CzLr0FEpO1u3ryJKlWqwMLi070Z4uLi4OrqipkzZ6J58+a5XsuAAQPw/v17/PjjjwgMDEStWrXw999/y3YHfPfuHfz9/VWPX758iUGDBuHdu3coUaIEmjZtiitXrqjtKDhw4EB4e3tj1qxZePPmDaytreHi4oLx48fn+veRHQpJkiRNF0EZCw8Ph6WlJcLCwjL1l4WIiKggkyQJ0QnRsg/0ah/+48IRnRCNn9r9JHvuzxd/xqbbmxARF4FjQ46hTuk6qrHt/27H5x6f57i+3zr8hq+bf616/DriNcouEVNlXR1csX/Aftn5lZZVwtvotzA3MoeFsYXqy9z4v8dGKX82NzJHeFw4fN75wPedL3zf+SIiPgK9HXrDY4CH7Lq1V9fG3eC7MNQzRNSsKBjqp9zJ2n1/N+4H34dDcQc4lnBENZtqDB2ICqnY2Fg8ffoU9vb2MDEx0XQ5WdawYUM0btwYq1atkh339PTEihUrVLs/SJKEwYMHo3r16pg7d64GKtVOH/v5Z+VzKGcqEBERUb6SJAkXnl9ARFwEjA2M0b5Se9n4/PPzcefNnXTDgoj4iEwtDwCA71t/L/swHRoTikfvHwEAPsR+kJ1rbmyepe/BWN9Y/uH/vw/9Fazkd5dsTG3wV8+/YGFsoTYGAH6T/aCn0MvSayeTJAmvI14jNjFWdlyZpFR9n1Wsq8j+PwCAPQ/2YO+DvarHCihQwaoCHIo7wMFGBA0OxR3gUNwBJcxKfLRzORFRfpMkCc+ePYO7uzvu3bsHd3d32XinTp1w69YtREVFoVy5cvDw8EBcXBx27dqF2rVr48CBAwAAd3d39rrLJQwViIiIKF1JUpLaB/p0lwVkNP7f4yFOQ7C081LVdRUKBTq6d0ScMg51StXBbbfbstc9G3AWZ56eyXH9EfERsDZNWRtbzLQYLI0tYWFsoRZMVLOphqlNpspCguSgIO0MAnMjc7Ut0jJibGCMkfVGZjie3UABEP8/lrVQbximr6eP4OnBePjuISLjI9XGfd/5yh5LkBDwIQABHwLwj98/sjFrU2sxo6F4StDgWNwRFa0qQl9PP9u1ExFl1927d9GsWTPUrl0bx48fh6Ojo2z8+PHj6T4vKSlzgTRlHUMFIiKiQi5eGY8Lzy6gnm092YfwB28fwGl1zu/ivI95r3bMwtgCb6PfIjwuPN2x1IoYFlGbFZD2A7/sz/+dV9SoqOw637b8Ft+2/DbdGh2KO+CPzn/k4LvULhbGFmhUtlG6Y/v678ODtw/g+85XtZTC560PIuIj1M4NiQnB5ReXcfnFZdlxM0MzhM4IhZF+ynbawVHBKGpUlEspiChP1a5dG1FRUZoug1JhqEBERFSIbf93OyYcnYCI+Ahs7rUZw+umdJxO++E+K4z0jVQf8m1MbdTGv2r6FRKTElGqqPr+3Su6rMDyzstVwQDviOeuajbVUM2mmuyYJEkIjAxU9WrweesD3/fif19FvFK7hm1RW1mgAABfHf8KO+/uRAWrCvAc7ilb7pGYlAh9hT6XUhARFUAMFYiIiAoBSZJwO+g2ylqURckiJVXHK1pVVN2hPvL4iCxUsDKxQsfKHdNfBpBRY8H/xj61PGCm88wMx9Kb0k95S6FQoIx5GZQxL4N29u1kYxFxEXj4/qEIGt75wve9L2yL2qpdw/edLyRIeB72HKWLlpaNLbywEMuuLku3b4O9lT2DIyIiHcZQgYiIqICKio/C6aencfTRURx9fBSvIl5haaelmNJ0iuqcpuWaoppNNTQu2xh9HfvKnm9hbIHjn6e/NpUKD3NjczQs0xANyzT86HlNyzaFoZ4hEpMS1UIl33e+GS6lMNI3QjWbamp9G6rZVEMRoyK5/v0QEVHuYqhARERUgDz78AxHHh3B0cdHcebpGcQp42TjRx4fkYUK+nr68J3oy2nplGMru63McKyYSTGUsyiHl+Ev1cbilfG4F3wP94LvqY3ZWdqpwgbH4o4YUnuIWq8MIso5SZI0XQJpQG793BkqEBER6bDEpERceXkFRx8dxZHHR9L9YAaILRBdKrnA1cFVbYyBAuW1ld1WYmW3lYiIi8Cj949UDSKTm0U+fv8YCUkJas97HvYcz8Oe47i/mDHzee3PZePHHh/DveB7cCjugFYVWsHSxDJfvh+igsLQUGw5Gx0dDVNTUw1XQ/ktPj4eAKCvn7MlaAwViIiIdExoTCiO+x/HkUdHcMzvGEJiQtI9r6x5WXSv1h3dqnZDO/t2nEpOGmdubI4GZRqgQZkGsuOJSYl4EvpEFjQkN4wMiwsDIGYtpP1vePeD3dh8ezMA4Na4W6hnW0819uDtA3gHesOxhCOq21Tnf/9E6dDX14eVlRWCg4MBAGZmZgyaC4mkpCS8ffsWZmZmMDDIWSzAUIGIiEhHrLu5Dtvvbsel55eglJRq4woo0KRcE3Sr2g3dq3VHnVJ1+OaQdIKBnoFqV4qe1XuqjkuShDdRb+D7zhcRcepbXvq89VH9uXrx6rKxQw8PYebplIag5S3KiwaRaRpFlipSin9PqFArXVo0Vk0OFqjw0NPTg52dXY7/DWSoQEREpIUSkxJhoCf/NX3pxSWcf3ZedszC2AKdKndCt6rd0KVqF9nODkS6TqFQoHTR0mq7SSRb230t7gXfw6uIVzAzNJON+bzzkT1+Ef4CL8Jf4IT/CdlxKxMrWYPI5P+1L2av9neQqCBSKBSwtbVFyZIlkZCgvgyJCi4jIyPo6enl+DoKiV05tFp4eDgsLS0RFhYGC4vs7xdORES6Yd+Dfdh2dxvOBZzD0ylPZWvE99zfg/57+6OaTTXVbISWdi1hpG+kwYqJtNOFZxfg9dJLtZwi9VKKzDDUM0RVm6o4OPAgqlhXUR2XJIkzG4iowMvK51DGr0RERBqS3oeT88/O44DvAQDACf8T6Fezn2qsa9WueDjpIarZVMvPMol0knMFZzhXcFY9Tr2UIrlfg+978efnYc/Vnp+QlIAHbx+ghFkJ2fGlV5bijyt/wKG4A+a3m4/GZRvn+fdCRKTNGCoQERHlo4i4CJx6ckrVZPHGuBsoY15GNd69Wncsv7YcJcxKqN1VLWJUhIECUTalXkrRpmIb2VhkfKTYleLtf7tSvBehQ1RClNqOEj7vfFRLKea3my8bex72HJtvb4argytqlazFGQ1EVCgwVCAiIspj/iH+OPr4KI48OoJzz84hXhmvGvv78d8YU3+M6nGrCq1wZfQVNCrbCHqKnK9zJKJPK2pUFPVt66O+bX3Z8SQpSe1cAz0DWJlY4UPsBzgUd5CN7XuwDz94/oAfPH9A5WKV4ergCldHVzQt15R/n4mowGJPBS3HngpERLonQZmAyy8u48ijIzjy+Ah83/mme56pgSnmtJqDWc6z8rlCIsoJSZLwLvodShSRL41otakVLjy/oHZ+6aKl0bt6b7g6uqJNxTbsg0JEWi8rn0MZKmg5hgpERLrhffR7HPM7hiOPjuC4/3F8iP2Q7nl2lnboXrU7ulfrjjYV28DU0DR/CyWiPPMi7AUOPjwID18PnAs4l+7Wr5bGluherTtcHVzRuUpnFDEqooFKiYg+jqFCAcJQgYhIO0mShHvB93Dk0REcfXwUXi+90p0qrafQQ7NyzdC9mggSapaoyXXWRIXA++j3OPzoMDx8PXDC/wRiE2PVzjExMEHHyh3h6uCKHtV6wMbMRgOVEhGpY6hQgDBUICLSTvHKeBRfXBwR8RFqY1YmVuhcpTO6V+2OzlU684MCUSEXFR+Ff/z+gYevB448OpLu1pb6Cn20qtAKw+oMw4i6I/K/SCKiVBgqFCAMFYiINOtl+EscfXQUb6PfYk6rObKxfnv6Ye+DvQAAx+KOqtkIzcs3h4EeeyETkbp4ZTw8Azzh4eOBAw8PICgySDY+rM4wbOm9RUPVEREJDBUKEIYKRESaI0kSyi4pi8DIQJgamOL9/97LeiCc9D8J33e+6FatGyoVq6TBSolIFyVJSbjy8go8fDzg4esB/1B/eAzwQG+H3qpzwmLD0GpzK3Sr2g0Dag5AndJ1NFcwERUaDBUKEIYKRER5Lyw2DCf8T+BxyGO1nRjGHBqDjd4bAQDHhhxD5yqdNVEiERVwyX1aqlhXkYWXO+/uxOD9gwEAXzb+Esu7LNdUiURUiGTlcyjnZhIRUaH06P0jHH10FEceH8H5Z+eRmJQIfYU+3Bq6wdrUWnXe0NpDUd6iPLpX6456tvU0WDERFWQKhQJOpZzUjvu884ECCkiQ0Mexj2wsMCIQM0/PRB/HPuhQqQN3kyEijeBMBS3HmQpERLkjXhmPC88u4Ojjozjy6AgehzxO97ztfbZjsNPgfK6OiChjbyLf4OjjoxhWZ5isX8uaG2sw4egEAEARwyLoXKUzXB1c0a1aN1iZWGmoWiIqCLj8oQBhqEBElH3BUcE49vgYjjw+guN+x9PdqQEAKhWrhO5Vu6NbtW5oXaE1jA2M87lSIqKs6/V/vXDo4SG144Z6hmhr3xauDq7oVb0XbM1tNVAdEekyhgoFCEMFIqLMkyQJt4Nuq2YjXHt1DRLUf83pK/TR0q6lareG6jbVoVAoNFAxEVH2xSbG4vST09jvsx+HHh3Cu+h3aucooECz8s3g6uAKVwdXVLaurIFKiUjXMFQoQBgqEBFlzr9v/kXX7V3xKuJVuuPWptboWrUrulftjo6VO6KYabF8rpCIKO8kJiXi0vNL8PAVO0k8D3ue7nlOJZ1EwODoijql6jBQJaJ0MVQoQBgqEBGpe/bhGWITY1G9eHXVsaj4KNgstkGcMk51zKmkk2o2QpOyTaCvp6+JcomI8pUkSfAO8oaHjwf2++7Hg7cP0j3P3soeQ2sPxby28/K5QiLSdtz9gYiICqRnH56h+87uuBd8DwNrDcTOvjtVY0WMiqBr1a6IV8ajW9Vu6FatG+ws7TRYLRGRZigUCtS3rY/6tvXxU7uf8Oj9I3j4iBkMV19dVZ339MNT+LzzUXt+kpQEPYVefpZMRDqMMxW0HGcqEFFhFRoTivcx71HFuorqWIIyASV/K4kPsR9gZWKFt9PfyjqhS5LEqbxERB/xKvwVDj48CA9fD3gGeGJr760Y5DRINR6dEI3KyyujTcU2+Nzpc3Sr1k2D1RKRpnCmAhER6RxJkuDzzgdHHx3FkcdHcOn5JXSo3AHHhhxTnWOob4guVbrg6Yen6F61O2ITY1HUqKhqnIECEdHHlbUoiy8afYEvGn2B0JhQmBiYyMZP+J9AUGQQ/u/e/6GoYVGGCkT0SQwViIhIY2ITY3Eu4Jxqt4anH57Kxs8+PYuo+CgUMSqiOubu6s7eCEREuSC9hrVvo97CysQKH2I/wNXRVTYWEhOCz3Z/hl7Ve6G3Q29UsKqQX6USkRbj8gctx+UPRFTQhMSEYL/Pfhx9fBQn/U8iKiEq3fOq2VRDt6rdMMt5FoqbFc/nKomICq8EZQLOPTsHZztnGBsYq45vvbMVww8MVz2ub1tftVVljRI1OFuMqADh8gciItI6oTGh+OPKH1h6ZSki4iPUxg30DNC6QmtVk8VqNtU0UCURERnqG6J9pfZqx71eeMke3wq8hVuBt/Dd2e9Q1bqqaqvKxmUbs9EjUSHCmQpajjMViEjXhcWGYdnVZVjitQRhcWGysRJmJdCtWjd0q9oNHSp1gKWJpYaqJCKiT5EkCQ/ePoCHr9hJ4lbgrXTPK2NeBr2q90Ifxz5oXaE1DPUN87lSIsqprHwOZaig5RgqEJGuioyPxPKry/Hb5d8QGhuqOm6oZ4iRdUdiVL1RaFS2Ee9mERHpqGcfnuGA7wF4+HrgwvMLSJKS1M4pZlIM3at1h6uDKzpV6QQzQzMNVEpEWcVQoQBhqEBEuupF2AtU+bMK4pXxAAB9hT5G1h2J2a1mo6JVRc0WR0REuept1FscfnQYHr4eOOl/EnHKOLVzTA1M0cuhF3b02cH+C0RaLiufQ3l7iIiIckXajLq8ZXmMqz8Oego9DK8zHA8nPcT6nusZKBARFUAlipTAqHqjcHjQYbyd/ha7P9uNgbUGwtzIXHVOTGIMouKj1AKF8Ljw/C6XiHIRZypoOc5UICJtF5sYi3U31+Ev779wadQl2faPbyLfICwujE0XiYgKqbjEOJx5egYevh44+PAgfnb5GSPrjVSNxyvjUfq30qhmUw1Daw/FxMYTNVgtESXj8ocChKECEWk7tyNuWHtzLQBgcfvFmN5iuoYrIiIibaRMUkIpKWGkb6Q6dtzvODpv7wwAGOI0BNv6bNNUeUSUCpc/EBFRvpncZDIUEFNZ/UP9NVwNERFpK309fVmgAAAJSQlwKukEAHB1cJWNRcRFwGGFA6b+MxXnn52HMkmZb7USUebl6UyFN2/eICAgAObm5qhcuTKMjY3z6qUKLM5UICJtkaBMgPu/7ihmUgyujvI3fsuuLEObim1Qp3QdDVVHRES6zC/ED2XMy8h2h9hzfw/67+2velzCrAR6Vu8JVwdXuFRygYmBiSZKJSoU8nT5Q3x8PP766y+cPXsW4eHhcHBwgJubG6pXr6465+HDh5gwYQLOnTunOmZmZobBgwdj0aJFsLa2zuK3VHgxVCAiTUtMSsSOuzvw47kf4R/qj0rFKsF3oi/3HSciojz1y8Vf8N3Z75CQlKA2VtSoKLpW7QpXB1d0rdoVFsZ8n0yUm/IsVAgKCkL79u3h4+MjO25kZIQDBw6gU6dOePXqFRo1aoQ3b96odQJXKBSoWrUqzp8/j5IlS2bhWyq8GCoQkaYok5TYdX8X5p2bh0fvH8nGjgw6gm7VummoMiIiKiw+xH7A34//hoevB449PoaohCi1c4z0jeBi7wJXB1f0rN4TpYqW0kClRAVLnoUKLVu2xOXLl9MdK1asGHx9fTFq1CgcPXoUpqamaNmyJYoXL45Xr17By8sLCQkJUCgU6NatGw4dOpS176qQYqhARPktSUrCnvt7MO/cPPi8k4fI7Su1x7w289C8fHMNVUdERIVVTEIMTj45CQ9fDxx6eAghMSFq5yigQAu7FnB1cIWrgyvsi9lroFIi3ZcnocLhw4fRq1cvKBQKdO/eHTNnzoSFhQXc3d3xyy+/QKFQYNq0aVi6dCnat2+PrVu3okSJEqrnBwQEoH///rhx4wYUCgWuXLmCRo0a5ew7LQQYKhBRfkmSkuDh44G55+biXvA92VjrCq3xY9sf0apCKw1VR0RElCIxKREXnl2Ah68HPHw98DL8ZbrnNSzTEF6jvWCgZ5DPFRLptjwJFT7//HPs2LEDtWvXxq1bt6Cnl7JxxJAhQ7Bz507o6emhfPnyuH//PszMzNSu8fbtWzg4OODDhw+YOnUqfv/99yx+a4UPQwUiymuSJOHQw0P4wfMH3HlzRzbWonwL/NT2J7S1b6uh6oiIiD5OkiTceH1DFTD4vvNVjbnYu+DUsFMarI5IN+XJlpLJMwzGjBkjCxQAYNy4cQDEX+hx48alGygAQIkSJTB8+HBIkoSrV69m9qWJiCgPSJKEo4+OotH6Rui9q7csUGharilOfH4CF0ZeYKBARERaTaFQoFHZRljoshA+E33gM9EHC9stRKMyjdDHsY/sXEmS0Hd3X6y/uR7RCdEaqpioYMn0TAULCwtERUXB09MTzs7OsrE3b97A1tYWCoUCp0+fRps2bTK8zsGDB+Hq6oqSJUsiKCgoR8UXBpypQES5TZIknPA/ge89v8e1V9dkYw3LNMSPbX5E5yqdoVAoNFQhERFR7pAkSfb77IT/CXTa1gkA0L1adxwedFhTpRFptax8Ds304qK4uDgAgKWlpdpY6p0cbGxsPnqdsmXLAgDCwsIy+9JERJSL1t1cB7ejbrJjdUvXxY9tfkT3at0ZJhARUYGR9nfaSf+Tqj+PrDtSNpY2gCCizMn08odixYoBAEJDQ9XGUv/l09fX/+h1DAwMMnUeERHljQG1BsDSWATETiWdsL//ftwadws9qvfgmykiIirQfu34K66PvY4vG3+JntV7ysZOPTmFZhubYde9XUhQJmioQiLdk+mZCjY2Nnj79m2OlywkhxKpd4YgIqK8cfH5RfiF+GFE3RGqY1YmVvit42+wNLZE3xp9oafIdL5MRESk8xqWaYiGZRqqHV92dRmuvLyCgS8HopxFOUxsNBFj64+FjdnHZ2ITFXaZfidZqVIlAMDTp0/THX/79i2Cg4Ph4ODw0es8ePAAAFCqVKnMvjQREWWRJEnoubMnnDc5Y9Lfk/A26q1sfEz9MehXsx8DBSIiIgAxCTGybSlfhr/EzNMzUf6P8nA74oYHbx9osDoi7Zbpd5MNGjSAJEm4efNmuuM2NjawsbFR2xkiLU9PTygUCtStWzdLhRIRUeYpFApUsKwAAIhKiMK6m+s0XBEREZH2MjU0hfd4b5wedho9q/eEAmI5YExiDNbeXIuaq2qi07ZO+Pvx30iSkjRcLZF2yVKoAIhQILtCQkJw9OhRAEDr1q2zfR0iIpK7HXQbUfFRsmMznWeiRoka2NRrE2a0nKGhyoiIiHSDQqFAO/t2ODjwIB59+QhTmkxBUaOiqvET/ifQbUc3OK50xKrrqxAZH6nBaom0R6a3lExMTMSbN28ApOzgkFU7duzA+vXrVX+2tbXN1nUKE24pSUQfc/fNXcw9Nxf7ffZjcfvFmN5iumycnayJiIiyLyw2DJtub8Kf1/7Ek9AnsjErEyuMqTcGkxpPQgWrChqqkChvZOVzaKZDBdIMhgpElJ4Hbx9g3rl52H1/t+qYjakNnk55CnNjcw1WRkREVPAok5Q48ugIll5dCs8AT9mYnkIPfRz7YEqTKWhRvgXDfCoQsvI5lB26/nP+/Hn06NEDZcqUgUKhwIEDBz56fnJviLRfvr6+svP27duHGjVqwNjYGDVq1ICHh0cefhdEVNA9fPcQg/cNRq1VtWSBgm1RW8xtMxdG+kYarI6IiKhg0tfTRy+HXjg7/Cxuj7+NkXVHqn7nJklJ2PtgL9puaYvgqGANV0qU/xgq/CcqKgp16tTBihUrsvS8hw8fIjAwUPVVtWpV1ZiXlxcGDBiAoUOH4s6dOxg6dCj69++Pq1ev5nb5RFTA+YX4YZjHMNRYVQM77+2EBDHJrGSRkvij0x/wn+yPSY0nwdjAWMOVEhERFWx1StfBX73+wvOpzzGvzTyUKiJ2tRtQcwBKFZXvcBevjNdEiUT5issf0qFQKODh4YHevXtneI6npyfatm2L0NBQWFlZpXvOgAEDEB4ejmPHjqmOde7cGcWKFcPOnTszVQuXPxAVbk9Cn2D++fnYemcrlJJSdby4WXHMaDEDExpOQBGjIhqskIiIqHCLS4zD7vu7Uad0HdQuVVt1PDYxFtX+rIb2ldpjSpMpqFO6jgarJMqarHwONcinmgqsevXqITY2FjVq1MCcOXPQtm1b1ZiXlxe++uor2fmdOnXC0qVLM7xeXFwc4uLiVI/Dw8NzvWYi0n7PPjzDggsLsOn2JiQmJaqOW5taY3rz6ZjUeJKsIzURERFphrGBMYbWGap2fMfdHXgR/gKbbm9CbGIsdvTdoYHqiPJevoQKb9++xf379/HgwQM8fvwYYWFhiIiIQGSk2IalaNGiKFq0KKysrFC1alU4OjqiVq1aKFGiRH6Uly22trZYt24dGjRogLi4OLi7u8PFxQWenp5o1aoVACAoKAilSsmnQJUqVQpBQUEZXnfRokWYN29entZORNrrZfhLLLywEBtubUBCUoLquJWJFb5u9jUmN5kMC2POWiIiItJ2kfGRsDS2RFhcGKY2nSobi0uMQ5wyjr/TqUDIs1AhICAAO3bswP79++Ht7Z2ta9SrVw+urq4YPHgw7O3tc7nCnKlevTqqV6+uetysWTO8ePECv/32mypUAKDW/fVT27vNnDkT06ZNUz0ODw9H+fLlc7FyItJW88/Px0/nf5Ktv7QwtsBXTb/C1KZTYWVipbniiIiIKEsmN5mMUfVG4eijo2hctrFsbMfdHZjyzxSMqjcKXzb+EpWtK2uoSqKcy/VGjW/evMEXX3yB6tWr47vvvsOtW7cgSZLsKyNpz7t16xa+//57ODg4YOLEiXjz5k1ul5urmjZtisePH6sely5dWm1WQnBwsNrshdSMjY1hYWEh+yKiwqGYSTFVoFDUqChmO8/G0ylPMbfNXAYKREREOqioUVEMqDVAdkySJCy9uhQR8RFYdnUZqv5ZFb3/rzfOPj370c9KRNoqV0OFy5cvw8nJCWvXrkVCQoLqrnzyV7K04UHasCH1cyRJQkJCAtasWQMnJydcvnw5N0vOVd7e3rC1tVU9btasGU6ePCk758SJE2jevHl+l0ZEWuZt1FsERcpDxzH1x8ChuANmtJiBp1OeYn67+bA2tdZQhURERJQXYhJj0LRsU5gYmAAAJEg4+PAg2m1th7pr62KTt+jBQKQrcm33hxMnTqB3796IjRV/AZIDAQAwMDCAk5MTKleuDDs7O5QsWRKmpqYwNTWFQqFATEwMYmJiEBwcjGfPnuHJkyf4999/kZiYqHYtY2NjHDp0CB06dMiNslUiIyPh5+cHQCy7WLJkCdq2bQtra2vY2dlh5syZePXqFbZu3QoAWLp0KSpWrIiaNWsiPj4e27Ztw88//4x9+/ahT58+AETI0qpVKyxYsAC9evXCwYMHMWfOHFy8eBFNmjTJVF3c/YGoYAmLDcMvl37B8qvLMaDmAGzstVE2rkxSQl9PX0PVERERUX55H/0e62+tx4prK/Aq4pVsrIRZCbg1dMOEhhNga26bwRWI8k5WPofmSqjw/v171KpVC2/evFEFAEZGRujduzdGjhwJZ2dnmJmZZema0dHRuHDhAjZt2oQDBw4gPj5ede1SpUrh3r17sLGxyWnpKslbRKY1fPhwbN68GSNGjEBAQAA8PT0BAIsXL8a6devw6tUrmJqaombNmpg5cya6du0qe/7evXsxZ84cPHnyBJUrV8aCBQtUoUNmMFQgKlg+xH5AxaUVERYXBn2FPnwn+aKKdRVNl0VEREQakqBMwD6ffVh2dRmuvLwiGzPUM8SAWgMwtclUNCjTQEMVUmGU76HCxIkTsXr1atWH/mbNmmHz5s2oWrVqTi8NAHj06BFGjhwJLy8vAGLmwhdffIE///wzV66vzRgqEOm2xKREGOjJe+L+dO4nzL8wH+Pqj8OcVnNQqmjGfVaIiIio8Ljy8gqWXV2GPff3QCkpZWMt7VpiSpMp6O3QW+29BVFuy9dQITExEaVLl0ZoaCgA0Ufg1KlTMDExycll1cTFxaFdu3aqYMHGxgaBgYEwMCjYf6EYKhDppoi4CPx57U+subEGN8bdQMkiJWVjH2I/oLwld3YhIiIidS/DX2LV9VVYe3MtQmJCZGN2lnaY1GgSvmj0BYoYFdFQhVTQZeVzaI4bNV6+fBkhISGqpowbNmzI9UABEL0UNmzYoGr4GBISgkuXLuX66xAR5URkfCR+ufgL7JfZY/aZ2XgR/gK/XvpVdo65sTkDBSIiIspQOYtyWOiyEC++eoG13deiRokaqrHnYc+x6OKij25TT5SfchwqPHv2DIBYklCvXj04ODjkuKiMODo6on79+mqvTUSkadEJ0fj98u+otKwSvj39Ld7HvAcA6Cn0EJ0QreHqiIiISBeZGZphXINxuDfhHk58fgJdq4r+beMbjIeZobxn3f3g+9ySkjQix2sHAgMDVX+uXLlyTi/3SZUqVcLNmzcBAEFBQZ84m4gob8UmxmLtjbVYdHER3kS9UR3XU+hhsNNgfN/qe1S1yZ3+MkRERFQ4KRQKdKjcAR0qd8DDdw9hZWIlG38T+Qb119VHFesqmNlyJj6v/blmCqVCKcehQtGiRVV/DgsLy+nlPikiIkL15yJFuIaIiDQjLjEOG25twMKLC/E64rXquAIKDKg1AN+3+h6OJRw1WCEREREVRNWLV1c7tubGGsQr4/Hg7QPcfXNXA1VRYZbjUKFMmTIAAEmS4O3tjcTExDxrnpiQkIBbt26pvTYRUX6JV8Zjk/cmzL8wHy/DX8rG+tXohx9a/4CaJWtqqDoiIiIqjBqVbYSWdi3h9cILExtPlI29i34HvxA/NC3XVEPVUUGX454KdevWBSCm5Lx9+xbr16/P6SUztG7dOgQHB6sep+6vQESUlxKUCdh4ayOq/VkNbkfdZIGCq4Mr7rjdwe5+uxkoEBERUb7rWrUrLoy8gMdfPoadpZ1sbPX11Wi2sRmabmiKnXd3IkGZoKEqqaDK8ZaSANCiRQtcuXIFkiTByMgIe/fuRffu3XOjPpUjR46gb9++SExMBAA0bdq0UOz+wC0liTRLmaTEtn+34cfzP+JJ6BPZWI9qPTC3zVzUt2XASURERNonXhmPCksrICgypRddWfOymNhoIsY1GAcbMxsNVkfaLF+3lASAr776SrWlZHx8PHr16oUxY8bg0aNHOb72o0ePMHr0aPTq1QsJCQmqjqZff/11jq9NRJQZiy8vlgUKXap0wdUxV3Fo0CEGCkRERKS1FFBgkcsi1ClVR3XsVcQrzDozC+X+KIdxh8fhfvB9DVZIBUGuzFQAgEGDBmHXrl1QKBSqgAEAmjVrhjZt2qBZs2aoUqUK7OzsYGpqmu41YmJi8Pz5c/j5+cHLywuenp7w8vICANk1BwwYgB07duRG2VqPMxWI8lfqf2uS7bm/B/339keHSh0wr808NCvfTEPVEREREWWdJEk49+wcll1dhoO+ByFB/hGwfaX2mNpkKrpU7QI9Ra7cdyYdl5XPobkWKkRFRaF///44duyY6g15em/OAcDMzAympqYwMTGBQqFATEwMYmJiEB2tvpd76mtIkoQuXbpgz549MDMzUzu3IGKoQJQ/kqQkePh4YO65udjcazMalGkgG7v+6jqalGuiwQqJiIiIcu5J6BP8efVPbPTeiIj4CNlYVeuqmNxkMkbUHYGiRkUzuAIVBhoJFQAgKSkJM2bMwPLly5GQkCALA7Ij9fMNDQ0xZcoU/Pzzz9DTKzzpGUMFovyx7d9tGOoxFADQvVp3HB50WMMVEREREeWd8LhwbL69GcuvLod/qL9szNLYEmPqj8GkxpNQ0aqiZgokjcr3ngqqi+np4ddff4Wvry+GDh0KU1NTWaCgUCgy9ZVMkiSYmJhg6NCh8PX1xeLFiwtVoEBE+adfjX4ob1EeAPA++j2iE9RnThEREREVFBbGFpjcZDIeTnqIgwMPop19O9VYWFwYfvf6HZWXV0bf3X0RGhOqwUpJ2+XqTIW0YmNjcfz4cfzzzz+4d+8efHx8EBIS8tHnFCtWDI6OjqhVqxY6d+6MTp06ZdiDoTDgTAWi3CVJEo77H8edoDuY0XKGbOyg70EYGxijU+VO6S7dIiIiIirI/n3zL5ZdWYbtd7cjThkHQCyJ8J3ky14LhYzGlj9kRmhoKD58+IDIyEhERkYCAIoWLYqiRYvC0tIS1tbW+VmO1mOoQJQ7JEnC6aen8f3Z7+H10gv6Cn34TvJFFesqmi6NiIiISKu8jXqLtTfXYuX1lZjjPAcTG0+UjW+5vQWdq3RGqaKlNFQh5TWtDhUoaxgqEOWcZ4Anvj/7PS48vyA7PrPlTCx0WaihqoiIiIi0W7wyHpIkwdjAWHXsXvA9OK12gpG+EWa0mIEf2/6owQopr2Tlc6hBPtVERJTvLj6/iO/Pfo+zAWdlx2uUqIF5beahj2MfDVVGREREpP2M9I3Uji27sgyACBxKmJXI75JICzFUIKIC59H7R5h8bDKO+x+XHa9uUx1z28xFvxr9oK+nr6HqiIiIiHTXnFZzUMy0GPY82IMRdUfIxnzf+eIfv38wqt4oWBhzlnVhweUPWo7LH4gyLy4xDosvLcaCCwtUzYUAoIp1FfzQ+gcMqjWIYQIRERFRLlAmKdXeV40/PB7rbq2DuZE5RtYdiS+bfMn+VTqKPRUKEIYKRJlz/tl5jD8yHr7vfFXH7CztMK/NPHxe+3MY6HFiFhEREVFeiYiLQOnfS8u25VZAgR7Ve2BKkyloW7Etd9fSIQU2VHj//j2mT58OAFAoFNi4caOGK8p7DBWIPi4kJgT/O/k/bPRO+fdAX6GPr5t9je9bf48iRkU0WB0RERFR4eHz1gfLry7HljtbEJMYIxtzKumEKU2mYLDTYJgammqoQsqsAhsqPHv2DPb29qqES6lUariivMdQgSh9kiRh+93tmHZ8Gt5Gv1Udb1K2CdZ2X4s6petosDoiIiKiwiskJgTrb67Hiusr8DL8pWysuFlxjG8wHl80+gJlzMtoqEL6lAIfKgBipgJDBaLCS5mkRLONzXD99XUAgIWxBRa5LML4BuPZN4GIiIhICyQoE7DfZz+WXV0Gr5desjEDPQMMqDkAU5tORcMyDTVUIWUkK59D9fKpJiKiXKWvp491PdZBX6GPfjX6wWeiD75o9AUDBSIiIiItYahviAG1BuDy6Mu4MvoKBtUapOpzlZiUiO13t6PR+kZw3eUKn7c+Gq6WsoszFbQcZyoQCRefX4SJgYlaku37zhcOxR00VBURERERZcWr8FdYdX0V1txcg5CYENVxPYUeRtQZgT+7/gkzQzMNVkgAZyoQUQESFR+FsYfGwnmTM0YeHIkEZYJsnIECERERke4oa1EWC1wW4MVXL7Cq6yrYFrUFACRJSXjw7gFMDdjEUdcwVCAirWZsYIxbQbcAAPeC72Hrna0aroiIiIiIcsrM0AwTGk2A32Q/LGy3EJbGlvjZ5We1bSfjEuM0VCFlVq4sf6hUqVJu1PJJiYmJePlSdA/l8geiwuPm65to794eP7b5kX0TiIiIiAqgiLgImBuby46dfXoWQ/YPwQ+tf8CoeqNgqG+ooeoKn3zf/UFPTw8KhQL50Z4h+XUYKhAVPAnKBPzu9Ttc7F3QqGwj2Vh6v2iIiIiIqGCSJAlNNzbFtVfXAAC7PtuF/jX7a7iqwiMrn0MNcvOF005VISLKrMsvLmP8kfG4F3wPdUvXxfWx11XdgQEwUCAiIiIqRCLiI1DGvAwAwKmkE/o69tVwRZSRXA0VAOT5bAUGF0QFy4fYD5h5aibW3FyjOvbvm39x8flFtKnYRnOFEREREZHGWBhbwGOAB668vIIkKUlt+evPF39Gx8odUd+2voYqpGS5svzB0tISkZGRkCQJVapUwfr163OjNjWBgYEYPHgwAPZUINJ1kiRh9/3dmPLPFLyJeqM63sC2Adb1WMdfEERERESUrmuvrqHJhiYAgAE1B2B+u/moYl1Fw1UVLPm+/KF+/fo4d+4cAODly5do2bIl9PVzv5Has2fPcv2aRJT/noY+xRd/f4F//P5RHStqVBQL2i3AxEYT2YiRiIiIiDL057U/VX/edX8X9vnsw9j6Y/F96+9RumhpDVZWOOXKlpKNGqU0VIuLi8Pdu3dz47JEVMAkKBOw+NJi1FxVUxYo9KreCw++eIDJTSYzUCAiIiKij9rQYwOWdV6GEmYlAACJSYlYfWM1Ki+vjDln5iAsNkzDFRYuuRIqNGzYUPb4xo0buXFZIipArry8gobrG2LGqRmISYwBAJQ1LwuPAR44MPAAyluW13CFRERERKQLjA2MMbnJZPhP9scPrX9AUaOiAIDohGgsuLAAlZdXxh9efyA2MVbDlRYOuTpTIbmJ4vXr13PjskRUAITFhmHi0YlovrE5/n3zLwBAT6GHKU2mwGeiD3o79NZsgURERESkk8yNzTG3zVz4T/bHl42/hKGeIQDgfcx7TDsxDdVXVMeW21ugTCr4vfg0KVdCBXt7e1hbW6sec6YCEUmShL0P9sJxpSNW3VgFCaInbL3S9XB1zFUs7byU20QSERERUY6VLFISy7ssh+8kXwxxGgIFxM3u52HPMeLgCNRdWxeHHx7O850KC6tcCRUAsQRCkiRIkoT79+8jPj4+ty6tYmBgADs7O1SoUAF2dna5fn0iyh2SJKHv7r7ot6cfAiMDAQBmhmb4vePvuDb2GhqWafiJKxARERERZU2lYpWwrc82eI/3RpcqXVTH7wXfQ8//6wnnTc649PySBissmHJlS0kAOHfuHG7fvq16PGLECFhaWubGpQs1bilJumr26dlYeHEhAKB7te5Y0WUFKlhV0HBVRERERFRYnAs4hxmnZuDqq6uy4z2r98SefntgpG+kocq0X1Y+h+ZaqEB5g6EC6aqYhBh03t4ZkxtPRh/HPqqeK0RERERE+UWSJBzwPYBZZ2bB950vAMDVwRX7B+zXcGXajaFCAcJQgbRdeFw4Zp+ejdJFS2N2q9maLoeIiIiISE1iUiK23N6Cn87/hGNDjsGxhKNqLElKQmhMKGzMbDRYoXZhqFCAMFQgbRaTEAPHlY54FvYMxvrG+HfCv6hmU03TZRERERERpUuZpIS+nr7s2I67OzDh6ARMbz4dXzX9CkWMimioOu2Rlc+hudaokYgKH1NDUwyoOQAAoK+njztBdzRcERERERFRxtIGCvHKeHx39juEx4Xju7Pfweull4Yq010Gmi6AiHRHYlIiJEmCob6h6tj3rb9HSEwIZreajYpWFTVXHBERERFRFsUkxKC9fXts/LARbSq2QftK7TVdks7h8gctx+UPpC1uvL6BcYfHoV+NfpjpPFPT5RARERER5ZqH7x4iSUqS9VqQJAlfHP0CfRz7oEPlDhqsLv9x+QMR5ZqIuAhMOTYFTTY0gXeQN348/yP8Q/w1XRYRERERUa6pXry6LFAAgCOPjmDNzTXouK0j2m9tjxuvb2ioOu3GUIGIMnTA9wAcVzpi+bXlSJKSAABVrasiKiFKw5UREREREeWtnfd2qv58+ulpNFrfCP339Mej9480WJX2YahARGpehL1A7//rDdddrngV8QoAYGpgip9dfsbNcTdRu1RtDVdIRERERJS3tvXZhh19dqBSsUqqY3se7EGNlTXgdsQNgRGBGqxOe7CngpZjTwXKT8okJVZcW4E5Z+cgMj5SdbxT5U5Y1W2V7B9UIiIiIqLCIF4Zj3U31+Gn8z8hOCpYddzUwBRTm07F/1r8D1YmVporMA9k5XMoQwUtx1CB8sutwFsYd3gcbgbeVB0rVaQUlnZeigE1B0ChUGiwOiIiIiIizYqMj8QSryX47fJviIiPUB23NrXGzJYzManxJJgYmGiwwtzDUKEAYahAeS0yPhLfn/0ey64uU/VNAIDxDcZjkcsiFDMtpsHqiIiIiIi0y9uot1h4YSFW3ViFeGW86ng5i3KY12YehtUZBgM9Aw1WmHMMFQoQhgqUlw4/PIyJf0/Ei/AXqmM1StTAuu7r0MKuhQYrIyIiIiLSbgEfAvD92e+x7d9tkJDysdqxuCMWuixEr+q9dHa2L0OFAoShAuWFV+GvMPmfydjvs191zMTABN+1+g7fNP8GRvpGGqyOCqTISODmTeDaNeDqVcDfH+jTB/juO/l5CxYAenqAuTlQtKj43/T+bGUFGOj2HQAiIiIqGP598y9mnZ6Fo4+Pyo63qtAKZ4adgb6evoYqy76sfA7lOzKiQkaSJPTZ3QfXXl1THetQqQNWdVuFKtZVNFgZFRiJicD9+ykBwrVr4nFSkvy85s3Vn/vTT0Bc3KdfY98+EUoku30bmDDh02GEuTng6ioPJKKjAYUCMDER/0tERESUBbVL1caRwUdw4dkFzDg1A14vvQAANUvU1MlAIasYKhAVMgqFAovbL0abLW1QwqwE/uj0BwY7DdbZqVmkhfr1Aw4c+Pg5ybMRUktIyFygAKg/980b4MqVzD03IUH+eO5c4NdfAX199SAi9eP69YGvvpI/98QJEaKk9zxTU4YUREREhYhzBWdcGnUJhx4ewoILC/B96+9l47GJsQiKDEJFq4qaKTCPMFQgKuCi4qMQGhuKchblVMdaV2yNzb02o0f1HrA2tdZgdaRzQkOB69dTZiE8eAA8fCi/81+vnjxU0NcHnJyAxo3FV5MmgKOjCBZS09MDTp0CIiLEcomIiIz/bGsrf250dObqNzFRXzYR8V/3ZqUS+PBBfGX0vacNFSZNAh4/Tv98Pb2UoGH+fGDEiJSx9++B2bM/PavC3BywtwcMDTP3/REREZFGKRQK9HLohV4OvdTGVl1fhZmnZ2JCwwmY02oOipsV10CFuS/PQ4X4+HgYGhrmyl3Q6OhomJmZ5UJV6s6fP49ff/0VN2/eRGBgIDw8PNC7d+8Mz9+/fz9Wr16N27dvIy4uDjVr1sTcuXPRqVMn1TmbN2/GyJEj1Z4bExMDE5OCsdUIabe/H/+NL45+gXIW5XB+5HnoKVI+xA2vO1yDlZFOiIsD7tyRL2N49Ej9PB8fERoka9MG6N9fhAeNG4uQoUiRT7+evj7g4pK9Wl1dxYyBqKiPhxGJierPrVIFaN1a/dzISPl5RYuqPzftOaklJQHh4eIr7eyI4GBg7drMfW/+/kClSimPt2wRvSc+FUaUKQMMHiy/1uvXgCSlnJs22CEiIqI8ERYbhgUXFiBeGY/lV5djZN2RDBUyIzY2Fr169ULJkiWxdevWHAULGzZswPz583H27FnY29vnYpVCVFQU6tSpg5EjR6Jv376fPP/8+fPo0KEDFi5cCCsrK2zatAk9evTA1atXUa9ePdV5FhYWePjwoey5DBQoPyQoEzD1n6l4FvYMz8KeYcOtDRjXYJymyyJdERQEVKgAxMd//LxixYCXL+WhQqtW4iu/6esDFhbiKyu+/lp8pZWUJGZAJIcNxsbq58yeLWYdJJ+TNsxIfmxjI39e8uyIzEi71CMoKOPZEanVrKkeKowYAZw8mfLYzEw9kLCwANq3B6ZOzXyNRERE9FF6Cj24NXDDH1f+QN8afVGndB1Nl5Rr8ixUiI2NRc+ePXHq1CkoFAro6elhy5Yt2brWpk2bMH78eEiShLZt2+LMmTOolPquTS7o0qULunTpkunzly5dKnu8cOFCHDx4EIcPH5aFCgqFAqVLl86tMokyzVDfEKu7rUZ79/ZwsXdBO/t2mi6JtMmbN2LmQfJX8+bADz+kjJcqJQKDN29SjhkZiVkHyUsYGjcWd/kLat+A5OULRYuqL7dINnFi9q5dqxbg7f3pMCIiQj1U0NMTu19ERIglGxlJ+zxAPcyIjhZfqX/OAGBnp/7c+/fTX7ZCREREn2RubI4FLgswsbH6e4fEpET03NkTn9f+HANrDZTNLtYFeRYq+Pj4wMvLCwqFApIkYdu2bdDT08OmTZuydB13d3eMHTtW9fj58+c4ffp0rocKOZWUlISIiAhYW8vXp0dGRqJChQpQKpWoW7cufvrpJ1nokFZcXBziUjUqCw8Pz7OaqWD5982/MDUwRVWbqqpjLpVccGHkBbQo34KNGAuz6Gj5do7XrgHPnqmfkzpUUCiAzz4TU/eTQ4TatdO/W09ZZ2YG1K2bvedOny6+JEksUclouUd6oUK7diIgyahnRfIyjfbt5c9780YEITY2YolK+/ZAhw5AxYrZ+x6IiIgKqTLmZdSObfLehGN+x3DM7xh+vfwrFrksQqfKnXTm/XuehQr16tXDkSNH0K1bN8TExECSJGzduhV6enrYuHFjpq6xY8cOjBw5EpIkAfiva/3ixbKQQVv8/vvviIqKQv/+/VXHHBwcsHnzZjg5OSE8PBzLli1DixYtcOfOHVStWjXd6yxatAjz5s3Lr7KpAIhOiMaP537E716/o0X5Fjg7/KzsH6CWdi01WB1p3B9/iA+gH7ujDQCvXokPqal/ea1Ykbe1Uc4kb4NpYgKUKJG55yxY8PHx+Hjg3TsxSyW106fF/75/D+zeLb4AoHJlES506AC0bav+PCIiIvqkC88vqP58O+g2umzvgjYV2+CX9r+gcdnGGqwscxRS8if2PHLmzBn06NEDsbGxkCQJCoUCo0aNwvr16z/6vF27duHzzz+HUqlUfUBasGABvv3227wsF4AILz7VqDG1nTt3YsyYMTh48CDap727k0pSUhLq16+PVq1aYfny5emek95MhfLlyyMsLAwWWV0nTAXeP37/YMLRCQj4EKA6trPvTgysNVBzRVH+kSTRzyB59sG1a8Bff8mb+u3fD6TtE2NmBjRsKF/GUL58wV3GQDl35owImc6cAcLC0j9HT08sozl3jkskiIiIsuik/0l8e/pb3Aq8JTve17EvFrRbgOrFq+drPeHh4bC0tMzU59A83/2hXbt2OHDgAHr16oW4uDhIkoS//voL+vr6WLNmTbrP2bt3Lz7//HMkJSWpAoV58+blS6CQVbt27cLo0aOxZ8+ejwYKAKCnp4dGjRrh8UcabBkbG8OY04vpE4IigzD1n6nYdX+X6piRvhFmO8+Gq4OrBiujPBUWBty4kRIiXL0qmvaldvWqPFRo3FgsW0gODxo3BmrUUN9Wkehj2rUTX4mJ4r/BU6dEw0cvr5QlE0lJIkxIGygcPSpCKycnBldEREQZ6FC5A1wquWDP/T2YfWY2/EP9AQD7fPbhgO8BjKo3Cj+0/gFlLcpquFJ1eT5TIdnx48fRu3dvxMfHq2YsjB8/HqtWrZKd5+HhgQEDBiAxMVEVKHz//ff4IfVa3zyW2ZkKO3fuxKhRo7Bz585MzWqQJAmNGzeGk5MT/vrrr0zVkpWEiAq+JCkJ62+ux4xTMxAWl3K3sG3FtljdbXW+J5iUj5o1A65c+fR5330H/Phj3tdDBIh+DOfPi4Dh1ClgwABgzpyU8aQkoHRp4O1boGRJ0YshuR9DuXKaq5uIiEiLJSgTsOHWBsw7Nw9volKaKZsYmGBKkymY0WIGipnm7ZLDrHwOzbdQAQCOHj2Kvn37IiEhQRUsTJgwASv+W7d76NAh9OvXDwkJCapAYfbs2fgxH94gR0ZGws/PD4DoB7FkyRK0bdsW1tbWsLOzw8yZM/Hq1Sts3boVgAgUhg0bhmXLlqFPnz6q65iamsLS0hKAmF3RtGlTVK1aFeHh4Vi+fDnc3d1x6dIlNG6cubUxDBUo2b3gexh/ZDwuv7isOmZjaoPfO/6OYXWG6UwjF0qHJAH+/imzD/T0RC+E1Nq3T1nXnszSMmX2QfIXd5shTUqerZDs33+BOhlsmVW9uggX2rcH2rQR/z0TERGRSmR8JJZeWYrFlxYjIj5lB6diJsXwbctv8WXjL2FqaJonr621oQKQEhwkJiaqgoVJkyahY8eO6Nu3L+Lj41UfjmbMmIGFCxfmS12enp5o27at2vHhw4dj8+bNGDFiBAICAuDp6QkAaNOmDc6dO5fh+QDw1VdfYf/+/QgKCoKlpSXq1auHuXPnolmzZpmui6ECxSTE4KfzP+HXy78iMSlRdXx4neH4reNvKG5WXIPVUba8fQtcvy7vhRASkjJuZSUepw6K5s4F/v5b3gehalWuXSftFhwM/N//iVkMZ8+KmQ3p0dcXW1ZW52wrIiKitN5Fv8PCCwux8vpKxCvjVcfLmpfF3DZzMaLuCBjo5e7SVq0OFYCUJQ5KpVIVLCgUCiQlJanO+eabb7B48eL8Lk3rMFQo3E76n4TbUTc8CX2iOlbVuirWdl+LtvbqIRhpuQsXgBEjgCdPPnkq/P3lvRGIdF1CggjQTp4UX1evpuxKUry42LYydUi2dy/w+rWYyeDoyH4MRERU6D378Aw/eP6ArXe2QkLKx/jGZRvjyugruTpzWetDBUA0Yxw8eLAqWFAVpFBgypQpWLJkiSbK0joMFQqn4KhgfHX8K+y4u0N1zFDPEDNbzsRM55kwMTDRYHWUIaUS8PVNWcYwaBDQunXK+KNH6d+JLVFC3kixUSPA2jr/6ibShPBwwNNTzGIwMQHS3kho107MbgCAMmVSejG4uAC2tvleLhERkba4F3wPs07PwuFHhwEAP7b5Ed+1/i5XX0MnQgUAmDVrFn7++WdZotK0aVNcunRJUyVpHYYKhc9f3n/hmxPfIDQ2VHWsVYVWWNt9LRyKO2iwMlLz6pV8CcONG0BEyno3zJwJpF7CJUmiC769vXwZQ4UKvAtLlFp0NFCsGBAfn/54rVopIUOrVkDRovlbHxERkRa4+Pwifrv8G7b12YaiRim/C0NiQvAi7AXqlM6gr1Em6ESo4OnpiW7duiE2NlZ1LHkpxKJFi/C///1PE2VpHYYKhc+EIxOw5qbYbtXa1Bq/dfgNI+qOYCNGbTJrFrBli5ia/TEuLuIubGppG9kRkTpJAu7eTdm68tw5ICYm/XO3bgWGDs3f+oiIiLTY9BPT8bvX7xjkNAiL2y/O1jaUWh8qnD9/Hl27dkV0dDQUCoVq+UPynxUKBX799VdMmzYtv0vTOgwVCp8PsR/guNIRHSp1wO8df0eJIiU0XVLhk5AgPtBcuwb4+ADLlsnHp05VPwaIWQjJSxiaNAHq1wfMzfOlZKICLS4O8PJKCRlu3BABHSBmDJUpk3Lu8ePA6tViFkOHDqKhKUNZIiIqJF6Gv0SV5VUQp4yDiYEJHn/5GOUssr6Nc1Y+h+Zui8hMuHjxIrp3764KFABg0KBB6NSpE0aNGgVAzFiYPn069PX1MWXKlPwukSjfnHpyCsFRwRjsNFh1zMrECvcm3IONmY0GKytEJAl4+lS+jOHWLSDVLCpMnw6US/WPcZMmgIWF6H2QehkD13kT5Q1jY7HtZJs2wPz5QGio6Lfw77/yQAEAjhwBDh4UX4AI+5IDBhcX0cOEiIiogCpuVhyLXBZhwYUFGFVvlFqgkHwTPzfl60wFLy8vdO7cGREREapZCQMGDMCOHTugUCiwZcsWjB49GpIkqb7Z5cuXY+LEiflVotbhTIWCKUlKwogDI+D+rzvMjczhM9EnW9OSKAfevhU7MVy7Brx79/Fz9+0D+vRJeZyQILbA4zIGIu3TogVw+XLG43Xrin4MffsCTZvmW1lERET5KSw2DAqFAhbGKZ8hI+Mj0WxjM4ypNwZuDd1gbGCc4fOz8jk0394RX716FV26dFELFLZv365KSoYPH47169cDSFkKMXnyZKxZsya/yiTKF3oKPRjri7/EEfERWHOD/43nidhY4MoVsVThwAH5WLFi4k5neoFC5crA4MHA0qViynXXrvJxQ0MGCkTa6sIFMdvol19EeGCc5g3T7dvAb7+p/5sgSSlLKoiIiHScpYmlLFAAgCVeS3Av+B6mHp8Kh5UOcL/jDmWSMsevlS8zFa5fv46OHTsiLCxMFRb069cPO3fuhF46b8zXr18PNzc3AGJ6hp6eHlavXo2xY8fmdalahzMVCq6QmBA039gc3zT/BqPqjYKegh9Sc0VcnNjfft06cbcyMVEc79EDOHRIfq6zs+iZkLx8oUkTsaTBhktPiAqMmBjg0qWUfgze3iJAOHlShA7J/P3FvwPt2omlEu3bA5Uqaa5uIiKiXDb+8Hisu7VOdqx2qdpY5LIIXap0kS2L0KpGjbdu3UKHDh0QGhqqChT69u2LXbt2pRsoJFuzZo1q2UNysLBu3TpV34XCgqGC7otNjMWiC4tgZ2mH0fVHy8aUSUro6+lrqLICJiAAWLsW2LAh/dkHpUoBgYHyhm0fPgCWlmziRlSYvHsHnDkjgkZT05Tja9cC/93QUKlUKWXryrZtGTgSEZHOuxV4CzNPz8QJ/xOy460qtMLPLj+jWflmALQoVLhz5w5cXFwQEhKiChRcXV2xe/du6Ot/+oPUypUr8eWXX6qeq6enh40bN2L48OF5VbLWYaig284+PQu3o2549P4RLI0t4TPRB7bmbOaXq+7eFVs8Hj0q7j6mVrUq0LJlyiyEunUZIOSThATg5UuR9aT3FRICVKwIVK+u/mVlpbm6qRD7/XfRBPLDh/THFQqxo0vv3sCcOflZGRERUa478/QMvj31La6/vi473qt6Lyx0WYhyxuU0Hyo8ePAArVq1QmhoKAAx26BXr17Yu3dvpgKFZMuXL8fUqVNVwYK+vj62bt2KQYMG5UXZWoehgm56F/0O35z4BlvubFEdM9AzwDbXbRhQa4AGKyuA7t8HatVKeWxoCHz2GfDFF6JhG0OEPJGY+PHQ4OVLQJnNJXolS6YfNtjbix8vUZ5RKoGbN8VSiVOnxLKJ+Hj5Od26iR0mUgsIAOzs2GuFiIh0iiRJ2OezD7PPzMaj949Ux/UUehhcdTC2Dd6m2VAhLCwMHTt2xPXr16FQKNCjRw/s2bMHhtl4R7hkyRJ88803UCgUsLa2xpkzZ+Dk5JQHVWsfhgq6RZIkbLmzBd+c+AbvY96rjjcv3xxru69FrZK1PvJs+qTr18VWch07yo+3aQM8eQKMHw+MHg2ULq2R8gqSvAwNihcXfTKfPVP/vPYxBgaih2a1auqBQ4kSzI8oD0RFicaPyf0Y/v1XNHBNvd11QgJgbS2WUri4pPRjsLPTWNlERERZkaBMwKbbmzDXcy4CIwPFwVgAP0Pzyx/Cw8PRqVMnFC9eHB4eHjAwMMj2tX799Vf8+uuvOHPmDGrVKjwfzBgq6I6H7x7C7agbPAM8VccsjS3xS/tfMLbBWDZizK7oaGDXLmDVKuDGDbGkwddXfkfw9WtxezsH/8YUNomJwKtXIiB4+jT3Q4OKFdP/qlABKFo0pYZnz4CHD9W/AgOz9ppWVunPbqhSBTAxyd73QaTmzRvAyEikYskuXhRNX9OqVi2lH0ObNlzXQ0REWi86IRrLry7Hzxd/RlhYmHaECgAQGRkJIyMjGBkZ5fha79+/h00ha5LEUEH7xSXG4ZdLv2DBhQWIV6bcdh1YayD+6PQHShflXfNsefwYWLMG2LRJzE5I7cQJ8UadMpQ6NEjv68WL7IcGNjYfDw3MzXNef3g48OiRetjw6JFo5p9ZenqipvQChzJlOLuBcsHVq8DChWKL2oiI9M/R0xO9Xf75RzSHJSIi0mIhMSFYfXE15nScox2hAuUMQwXtdi7gHMYfGY+H7x+qjlW0qohVXVehS9UuGqxMRyUmioaLq1aJ4CCt+vWBiROBgQMBM7P8r0+L6HpokF1JSWIWRXqzG54/z9q1ihZNfylFtWpAkSJ5Uz8VYAkJYonWyZNiucSVKylb2gLiL8/Tp/Ik6+JFETLUqsWEi4iItIrW7P5AOcdQQTuFxIRg+onp+Ov2X6pj+gp9fN3sa/zQ5geYGRbuD7zZolQCNWuKT4epGRuLEOGLL4BGjQrNG2+l8tOhQerPK1lhbf3x0EBX/6mJjhYTXNILHDK6gZyRcuXSn93AXnyUaRERwLlzKf0YWrQA1sn3BkfDhqIxZOnSYqlE8lfZspqpmYiI6D8MFQoQhgraRZIk7Lq/C1P+mYLgqGDV8SZlm2Bdj3WoXaq2BqsrAEaPBv76L6ixtwcmTABGjSqQe8MzNMg/kgQEBaUfNjx9KmY/ZJaJiWjrkV7gwFnt9FGJifK+L+/fiw6j6b0Nq1EjpR9D69aanR5ERESFEkOFAoShgnYZc2gMNnpvVD02NzLHIpdFcGvoBn29zG+VWqhFRADbtwPu7mJ9ceo3yzdvAnPnilkJnTrp9C1hpVL0j0wOCdI2Q8xJaFCs2MdDA364zby4OMDfP/3AISQka9cqVSrjrTDZQ5TUfPgAbN4sZjJ4eoqdJtJjYCDOad06H4sjIqLCjqFCAcJQQbsc8D0A112uAIDeDr2xossKlLXgNNVMuX8fWL0a2Lo1ZS766tWAm5tm68qmtKFB2q/nzxka6Lp379IPG/z9xfL5zDI0FFthJvdrSB04FC9eaFb00MfEx4seDMlLJa5dS5lCo6cnEq7Uf/H9/EQ303r1+B8QERHlCYYKBQhDBc1KkpLUtoL84ugX6FCpA1wdXTVUlQ5JSAAOHBCNFz091cdHjBC7O2ihvAwNrKzSDwzs7Rka6ILERDHzJL3A4c2brF2rWLGMt8I0Ns6b+kkHfPgg/s08dUoECjt2yMfHjxf9GWrXBkaOBIYMEUspiIiIcglDhQKEoYJmRCdEY57nPPiF+mFvv71Q8E5Q1rx6Jd7wrl8PBAbKx8zMxBvgCRPEXTYNUSpFaR8LDbJyNzq1jEKD5JkG3K6+4PrwIf2tMB8/BmJjM38dPT3x30t6gYOtLW9OF2rR0eI/gvDwlGOGhkD37iJg6NKF622IiCjHGCoUIAwV8p8kSXDe5IxLLy4BAPb024PPanym4ap0zIIFwJw58mPVq4teCcOG5cun6qSkT880yG5oYGEhZhVkFBwwNKC0kpLEf3PpzW54+TJr1zI3z3grzEK+02rhEBMDbNsmZnl5eamPlyoFDB0qAoYaNfK/PiIiKhAYKhQgDBU0Y++Dvei3px+M9I3we8ffManxJE2XpL0+fBC3/VPv0BAUJPbeS0oCevUSYUK7dnl+e1WSxHvs7duB3bvFmvjsYGhA+SkqKv3ZDY8eAZGRWbtW+fLpz24oX16n+55SRnx8RLPHrVvFv7tpeXkBTZvme1lERKT7GCoUIAwV8p4kSYhOiEYRoyKyY3M952Kw02BUL15dg9Vpsdu3Ra+E7duBiROBxYvl47t3A82bA+XK5Xkp9++LJcc7dohZCJ9ibp5xaGBvz9CAtIMkidk26c1uCAhIfyfCjJiaZrwVJn+1FACJicDx42L2wqFDYhpWhQrAkyfyNCk4WHQHZcJERESfwFChAGGokLf8Q/zhdtQN5kbm2D9gv6bL0X5xccDevSJMuHw55bi1tZjDbWqab6W8eAHs3CmChDt31MdNTIC2bcUHqfRmGnBNOumy2FixAUB6gcOHD1m7VunS6kFDs2birzXpoHfvRNhrZgaMHSsfa9tWdBkdPlw0yrW310iJRESk/RgqFCAMFfJGgjIBf1z5A3M95yImMQYAsL//fu7okJGAAGDtWmDDBvU1Bebmok/CvHnyJRB5ICREZBrbtwPnz6uP6+kB7dsDgwcDrq68A0uFjyQBb9+mHzY8eZL5HUuS+/4NGwZ07QoYGeVt3ZQPnjwRe5um1qaN6L3Qty9QpEi6TyMiosKJoUIBwlAh911/dR1jD4/FnTcpt7ftLO2woccGdKjcQYOVaaHAQGDcOODoUfW51rVqiWUPQ4aIYCGPREcDR46IIOHYsfSbKzZuLMro31/cdSUidQkJ4nNlcr+G1IFDcHDGz7O2BgYOFL3/mjThLB+ddfcuMGOGWCaRlCQfMzcHBgwQAUOzZvwhExERQ4WChKFC7omMj8R3Z77D8mvLkSSJN1R6Cj1MbjwZP7X7CUWNimq4Qi0UHy8aLr55Ix4bGgKffSYaL7ZokWdvPBMTgdOnxdKG/fvTb1ZXrZoIEgYPBqpUyZMyiAqN0NCUoOHmTdESJb2+f1WrinDh8885c15nvXolGjtu2iT2Ok3L0VH8R5CPy9mIiEj7MFQoQBgq5I6/H/+NCUcn4HnYc9WxOqXqYH2P9WhUtpEGK9MSkgRcvw5cvQp8+aV87LvvgC1bgPHjgdGj82wqgCQB166JGQm7dqV/59TWVtwxHTIEqF+fN9OI8kpiInDqFODuDnh4iF0M03J2FssjPvuMzU11kiSJ3jibNol/dJPT2/btgZMn1c/lP7hERIUKQ4UChKFCzryJfIMp/0zBrvu7VMdMDEwwr808fNX0KxjqG2qwOi0QHQ383/+Jxos3bwL6+qJ/QuodG6KiAGNjwMAgT0p4+FAECTt2AP7+6uMWFuJDy+DBYvmvvn6elEFEGQgPFzOGtm4FPD3VV0IZG4udY4cOBTp1EhOaSMdERYmGNZs2iQB50KCUMaUSaNAAaN1aLI+oW1djZRIRUf5hqFCAMFTIHkmS8Jf3X/jm5Df4EPtBdbx9pfZY020NKltXzvjJhcHjx8CaNeINZGiofGzePOD77/P05V+9EjfGtm8Hbt1SHzcyEk3ihgwRTeJMTPK0HCLKpOfPxd9bd3fAx0d9vEQJ8Xl06FDxOZQ3twuAf/4BunRJeVy3rggXBg8W21MSEVGBpNWhQnh4OG7cuIFHjx4hICAAwcHBiIqKQkJCAooUKQILCwuUL18e9vb2qFu3LqpXr56f5WkdhgpZ9+j9I4w7PA7nnp1THbMxtcEfnf7A57U/h6KwvstNTBQNF1etAk6cUB+vX180Xhw4UGxFlss+fAD27RMzEs6eVb/bqVCI3c6GDAH69OF0aiJtJklicpO7u/g7nXZTGEAszU/uv1C+fP7XSLlkxQpg+nSxj2lqhoZAz54iYOjUKc9msxERkWZoXahw7tw5HDlyBP/88w98fHyQlZe0sLBAq1at0K1bN/To0QO2trZ5WKn2YaiQefHKeCy+tBjzz89HnDJOdXxo7aH4vePvKFGkhAar0wK9egGHDsmPGRuLEOGLL4BGjXL9tmJsrMgxtm8X/xsfr35OgwbihtfAgUCZMrn68kSUDxISxIYCW7eKf2Li4uTjCoVYujRsmNi5MA83i6G88uGDmF62aZPovZOWra3oxzNzZr6XRkREeUMrQoVnz55h3bp1cHd3x6tXrwBALUzI6I5xRufp6emhffv2GD16NPr06QM9Pb08qFy7MFTIvG9OfIPfvX5XPba3ssea7mvQsXJHDValIcl/h1L/Hdu0CRg1SvzZ3h6YMEE8trHJ1ZdWKsW66+3bxcyE8HD1cypXFjMSBg0CHBxy9eWJSIM+fAD27BEzGC5cUB83NQVcXcUMhvbteXNbJz14IH6fuLun7AwEiJluK1Zori4iIspVGg0VvL298csvv2D//v1QKpWqgCB1gJCVl0x+XtrrlC9fHl999RXGjx8PkwK84JqhQuYFRgTCcaUjIuMjMa3ZNMxtMxdmhrk/jV+rRUSIT/OrVomeCc2bp4xFR4tpqiNGiKmquRjKJU+F3rFD9H0MDFQ/p2TJlJ0b8mBSBBFpmadPgW3bxAwGPz/18dKlxSylYcOAOnXyvz7KoYQE0W/hr7+AI0eAK1fE1LNk794BX38tfue0bp2rv3OIiCjvaSRUePz4MebMmYN9+/ZBkiRIkgSFQiELEExNTVGzZk3Url0bFSpUQLly5VCsWDGYmZnBwMAAMTExiIqKwuvXr/Hq1Sv4+Pjg33//xcuXL1MKTnVNhUKB0qVL44cffsDo0aOhXwDbwjNUyFhgRCBszeXLYQ74HkAFywqoZ1tPQ1VpyP37wOrV4t17RIQ4NmSIeEefhx4/FkHCjh1ij/u0zM1Ff4TBg4F27XhXkqgwkiQxY97dXYSOISHq5zg5iXBh8GAug9JJ796JWW+p0+Lly4EpU8SfK1YU4cLw4eLPRESk9fI1VIiOjsa8efOwdOlSJCYmqs1CaNKkCXr27Im2bduiUaNG2frgHxQUhLNnz+LUqVM4ePAgQv57R5IcMCgUCtSsWRMrV66Es7NzTr4drcNQQd2r8Ff48tiXuPTiEnwn+qKYaTFNl6QZCQnAgQNiVoKnp/p427Zio/lcvjsUFJSyc8P16+rjhoZix4bBg4EePcR0ZyIiQPRV+ftvkX8eOSL+GUtNT08sixg6VCyTKFJEM3VSLmjbNv3fTe3aiaV3rq550hSYiIhyR76GCuXLl8fr169lYYK9vT3Gjh2LIUOGoHwut3xWKpU4deoU1q9fj8OHDyMhIUEWLqxbtw6jR4/O1dfUJIYK6j7f/zm2390OABhdbzQ29Nyg4YryWVCQmJWwbp34c2pmZqLV+oQJubqXeHg44OEhgoTTp4GkJPVzWrcWkyP69gWsrXPtpYmogHr/Hti9W8xg8PJSHy9SRPx7MmyYaPRYACcjFmyxscDBg6L/wokT6lv+WFikNArm+hciIq2Tr6GCnp6e6kN98+bNMXPmTHTt2jVftu178+YNli1bhlWrViE8PBwKhQI//PADvv/++zx/7fzCUEHdq/BXcFzpCFNDUyzrvAwDaw3UdEn56/x58Qk+terVxRuz4cMBS8tceZm4OODYMbG04fBh9d3EAJFbJO/cwC3jiCi7Hj8Wq7Xc3UUvhrTKlhV56dChQM2a+V8f5dCLF2J6yubN6g02li0DJk/WSFlERJSxfA8VGjVqhJ9//hlt27bNyaWyLSwsDL/99hv++OMP/O9//2OoUIDEJsbCL8QPtUrWkh0/8/QM6pauC2vTAn5L/MMH0fXQ0THlmCSJBci+vkDv3iJMaNs2VzofJiWJzGL7dmDvXvHyadnbiyBh8GCgRo0cvyQRkYokAZcuic+fu3cDYWHq59SvL8KFQYOAUqXyv0bKAUkCLl4Usxd27xbrX16/lu9C5Osrdpjo3h0wMtJcrUREhVy+hgo7d+7EoEGDcnKJXPP69Wv4+/sXqL4KhTlUOP/sPMYdHoewuDD4TPSBlYmVpkvKP7dvi14J27cDtWurzw2+dk10MytXLscvJUnAnTvipXbuBP7bAVameHFgwACxvKFpU+7cQER5LzZWzJJydxezphIT5eP6+mIjm2HDgJ492b9F50RGit9l7drJj0+aBKxcKX7xDBkidi3i8ggionyn0S0lKXcVxlAhNCYU/zv5P2zwTumV4NbADau7r9ZgVfkgLk5MD1i5Uj1EuHlT3J7LRU+eiBBh+3bAx0d9vEgRMRFiyBDROM3QMFdfnogo096+FTtHbN0K3LihPm5hAfTrJ2YwODtz90KdFRsL2NqqT5OrX1+EC4MHs2kPEVE+YahQgBSmUEGSJOx5sAeTj03Gm6g3quNNyzXF+h7r1ZZAFBgBAcDatcCGDWJbrtTMzcVtuG++yZVtuIKDxYzTHTvSb4xmYAB07izet/Xsyc7rRKR9fHzE7IVt28RS/bQqVEjpv1C9ev7XRzmgVIpuwJs2ie7AcXHycSMjoFcvETB07MjunUREeYihQgFSWEKF52HPMfHviTjy6IjqmLmRORa5LIJbQzfo6xXANw5KpWhtfuiQeldsJyfRK2HIEBEs5EBkpNh5cvt24ORJ8bJptWwpXuqzz8SMUyIibZeUBJw7JwKGPXvEv3VpNW4sctkBA/hvm84JDRXTU/76K/3pKSdOAB065H9dRESFBEOFAqSghwrKJCVWXFuB2WdmIyohSnW8Z/WeWNl1JcpZ5LxngFbr1UuECoBYX/DZZyJMaNEiR40L4uPF+63t28WOXjEx6ufUqiWChEGDxJ09IiJdFR0t/q3bulX825d221sDA6BbNzF7oXt3wNhYM3VSNt27J2YvuLuLtTDlyolZfqlnKvj7AyVL5jiIJyIigaFCAVKQQ4U7QXcw9vBYXH99XXXMtqgtVnRdAVcH13zZljRfSBJw/bp4M7Rkibw5wT//AOPGAePHA6NHA6VLZ/tlkpJE1/QdO8QSh5AQ9XPs7ESIMGSImAxBRFTQBAaKfjHu7qLnbVrFigH9+4sZDM2asfGsTklIAI4eFSnS4MHysQ4dgMuXRXONkSOBVq34wyUiygGGCgVIQQwVYhJiMO/cPPx2+TcopZS5+G4N3LCo/aKCs8tDdLSYurlqlWi0CIhGjH37ppyTlCS+DAyy/TJ374ogYccO4Plz9XFra/EGevBgMQGCDcyIqLC4ezel/0JgoPp45cpi9sLnn4s/k456/lz0HUr9lrZSJWDECGD4cJGoExFRljBUKEAKWqhw6skpuB1xg3+ov+qYY3FHrOuxDi3tWmqwslz0+DGwerWYqpm2g3XPnmKObg49eybuxO3YId40p2VqKlZWDBkiellxq28iKsyUSuDMGbE8Yv9+kfmm1aKFCBj69xezGUiHPH8OLFokfjGGhcnHFAqxhdHIkWJLI+49SkSUKTobKsTGxuLUqVN49OgR9PX1UbNmTbRp0wYGmbiL+/r1a8yZMwcKhQIbN27Mh2rzR0EJFd5Fv8M3J77BljtbVMeM9I0w23k2ZrSYAWMDHV/gmpgopmSuWiUW9KbVoAEwcaLoFmZmlq2XePdOTHTYvh24eFF9XF9fzP4cMkQEClxWSkSkLjJSBAvu7mKjgbTvgoyMgB49RMDQpQtDWZ0SEyM6E2/aBJw6pf7DLVZM7KdsZaWJ6oiIdIpOhgp79uzBpEmT8C7NlnplypTBokWL8Pnnn3/0+ffv34eTkxMUCgWU6bW311EFJVTYfHszRh4cqXrc0q4l1nVfB8cSjhqsKhd9/z3w00/yY8bGwMCBovFi48bZumxUlOjjuGOHaL+QmKh+TrNmYmlD//6iRxUREWXOy5fi39etW4H799XHbWzEP+PDhgGNGnGJvk55/hzYsgXYvFkECQDQujXg6Sk/Lz6eyRERUTp0LlTYvn07hg8fDkmSkF45CoUCAwYMwIYNG2CWwV1ehgraTZIktNvaDt6B3ljcYTHG1B8DPUUBWtz/6FHKhuiVKgETJoipljY2Wb5UQoK4wbJ9u7jhEhWlfo6jY8rODZUq5ax0IqLCTpJEU8etW0XIEBysfk61aiJc+Pxz7pijU5KSgAsXxOyFLl3EjMHUY9WrAzVqiN/Z3brJmykTERViOhUqBAcHo1q1aggPDwcA9O7dGy4uLoiPj8eZM2dw7NgxJCUlQaFQoFGjRvjnn39glc60NYYK2iMxKREn/E+ga9WusuMBHwJgrG8MW3NbDVWWCwICgIULgaZNgVGj5GM//CCmDXTsmOVuiJIEXLkigoTdu8WOWWmVLZuyc0OdOrxjRkSUFxITxSo2d3cR7MbGqp/TurVYHvHZZ4ClZb6XSLnl9GnRbyFZiRIiNRo5klskEVGhp1OhwqJFizB79mzo6elh+/btGJA6QQZw7do1jBgxAr6+vlAoFKhbty5OnjwJa2tr2XkMFbTDzdc3MfbwWHgHeePU0FNwqeSi6ZJyR3KYsGmTeMdpZycaMuZwyuSDByk7Nzx9qj5uZSXetA4ZInbH4s4NRET5JywM2LdPBAxpZ80DgImJ6GEzbJjIk3OwkQ9pwp49wFdfAa9eqY81bCjChUGD2LmTiAqlrHwO1fhHlBMnTkChUGDIkCFqgQIANG7cGFevXkWXLl0gSRJu376Ndu3a4f379xqolj7lxusb8A7yBgB88fcXUCbpeMDz9CkwdixQtSqwfn1KU4MPH4B//83WJV++BH79FahXD6hZE1iwQB4omJiIbbY9PICgIPGybdowUCAiym+WlmJS2tmzIltesCBlpRsgZjHs2iVmzZctKz6f3rql3h+QtFS/fmI7pWPHRGOi1DcKbtwQDZZtbYHx4zVXIxGRDtD4x5QHDx4AAPr165fhOebm5jh8+DCGDRsGSZJw9+5dtGvXTq2pI2ne2AZj0dKuJZxKOmFr763Q19PXdEnZ8+QJMGaMWES7YUNKmGBhIZoyBgSIuxiZFBqaEg7Y2QH/+59Yv5tMT0/s3LB5M/DmjVgC0bu36PVIRESaV6ECMGsW4OMDXLsGTJokb5sTHAwsXSo2+3FyAn75RYTIpOX09YHOnUU6FBgI/PknUL9+ynhcnOZqIyLSERpf/mBsbIzExETcunULderU+eT5bm5uWLduHRQKBWrVqoXTp0+jePHiXP6gAW8i3+DIoyMYXX+02nFrU2sY6utgsyNJEncmUs9KAMTtqqlTgSlTMj0NMiYGOHJE9En4+2/RgDGtxo3Fzg0DBgClS+fOt0BERPkjPl7szOPuLnbqiY+XjysUQLt2YnlEnz5A0aKaqZOy4d9/xZLHbdvEL/MmTVLGQkPFLIchQ8T/8gdLRAWQTi1/MP7vVmxERESmzl+zZg2++OILSJKEe/fuwcXFJVeWQpw/fx49evRAmTJloFAocODAgU8+59y5c2jQoAFMTExQqVIlrFmzRu2cffv2oUaNGjA2NkaNGjXg4eGR41o1TZIkbPLeBMeVjhhzeAzOBZyTjZcqWko3AwVAvANMSkoJFCwtgblzxcyEuXM/GSgkN/gaMQIoVUrMpjx4UB4oVKsGzJsnNoy4elXkFAwUiIh0j5ER0LOnWJofFASsXQu0aJEyLkmiF+Dw4eJ3wtCh4ndEAbr3UXDVrg388Yfot5B2W+idO8UPdtQo8Qt81CixwwTXvRBRIaXxUKFcuXIAgIcPH2b6OStWrMDEiRNVwULbtm3xNr12+VkQFRWFOnXqYMWKFZk6/+nTp+jatSucnZ3h7e2NWbNmYfLkydi3b5/qHC8vLwwYMABDhw7FnTt3MHToUPTv3x9Xr17NUa2a9Pj9Y7hsdcGoQ6MQGhsKAJh1ZpaGq8oBf38gOlp+bOZMoGRJ8ck/IEDs6pDOjiPJJElMhZ0yBShXDujUSWyNnTons7UVa22vXwd8fcUKiqpV8+Q7IiIiDShWDBg3Drh4EfDzEzl05cop49HR4qZ3p05iGdz06cDduxorlzLLyEh9u6Xz51P+HBUlZjS0aiXuGvz8s0iYiIgKEY0vfxg4cCB2796NQYMGYfv27Vl67uTJk7FixQooFAoUL14cb9++zZXlDwqFAh4eHujdu3eG58yYMQOHDh2Cj4+P6pibmxvu3LkDLy8vAMCAAQMQHh6OY8eOqc7p3LkzihUrhp07d2aqFm1Z/hCvjMdvl3/Dj+d+RJwyZX3hEKchWNJpCUoWKamx2rLFz0903HJ3B377TSxtSC0+/pM7O7x+DaxbJ5Y3+Pmpj1tYAH37itmRbdqIZZtERFR4SBLg5SV+1fzf/4kev2nVqSOWRwwezFlrOkOSxFTDTZvED/a/bdFV9PWBHj3E3YRWrTRTIxFRDunU8odW//1je+jQIUSnvWP8CcuXL8eXX34JSZLyvWmjl5cXOnbsKDvWqVMn3LhxAwn/zXXP6JzLly9neN24uDiEh4fLvjTtyssraLCuAWafma0KFCpaVcSxIcewrc823QoU/PzEFlEODqIrolIpumnFxMjP+0ig4Osrejja24vJDKkDBSMjsW52717RcPGvvwAXFwYKRESFkUIBNG8OrF4tbl7v2ye2oDRMtULwzh3g66/F7hGdO4sthrP4dojym0IBNG0q1rsEBoopKO3apYwrlcCBA2IaIxFRIaDxUKFz584AgOjoaPz1119Zfv6yZcswZcoU5PeEi6CgIJQqVUp2rFSpUkhMTFQFHBmdE/SRaXGLFi2CpaWl6qt8+fK5X3wmhceF48u/v0Tzjc1xL/geAEBPoYdvmn2DexPuoXOVzhqrLcv8/ESjg9RhAiCWNXzxheij8AmXL4sdGRwdgY0bUxpyJTfi2rhRBAn79okZCiYmefS9EBGRzjE2FqHzgQNiptuKFfLef0lJwPHjYnZbqVIi/z5zJlO/nkiTzMzED+30abGkctYssebR0FA000jN31/8B5Be52YiIh2m8VChUqVKGDZsGNq1a4cbN25k6xp//PEHpk+fjgoVKsDOzi6XK8yYIs0au+RgI/Xx9M5Jeyy1mTNnIiwsTPX14sWLXKw48w76HkSNlTWw4voKSBDfV33b+rg+9jp+7fgrihgV0UhdWfb4sfilXr26aHSQHCYUKwb89JPomfDdd0CR9L+fpCTR0btlS9F86+DBlDFLS9F+4fnzlH5NH2m9QEREBAAoXlxsNHTlipj9NmeO2LIyWWSkyL9dXICKFcXvmv924CZtVqmSWFr5/Dlw6RJQooR8fMUKwNVVNNWYNUuEDEREBYCBpgsAgM2bN+f4Gr/88gt++eWXnBeTSaVLl1abcRAcHAwDAwPY/LdxdUbnpJ29kJqxsbFqRwxNeB3xGpOPTcY+n5SGk2aGZvip7U+Y3GQyDPS04j+ZzImMFHtNR0amHCtWTMwz/fJL0fQgA3FxYgrqr7+KPclTK1sWmDYNGDsWMDfPo9qJiKhQqF5dZNzz5okmj1u3it0kklc/vnghev/9/DPQoIGYdDd8OH//aDUDA6BRI/mx2FjxwwXEWphFi8SXi4t4Q9G7t5jOQkSkgzQ+U0FXNWvWDCdPnpQdO3HiBBo2bAjD/xZLZnRO8+bN863OzEqSkrD2xlrUWFlDFih0rtIZ9ybcw7Rm03QrUADEvtHjxok/W1sD8+eLmQmzZ2cYKISFiSChUiUx8yB1oFCjhrhz9OSJCBX4ho6IiHKLnp7o6bdhg/jM+X//B3TrJu/Jc/OmyMTt7MTshcBAzdVLWWRoKDo79+kjQodkp08DAweK7aO+/lpMXSEi0jEa3/1BW0RGRsLvv4579erVw5IlS9C2bVtYW1vDzs4OM2fOxKtXr7D1v5T56dOnqFWrFsaPH4+xY8fCy8sLbm5u2LlzJ/r27QsAuHz5Mlq1aoUFCxagV69eOHjwIObMmYOLFy+iSeqFlB+RH7s/+IX4YeTBkbj4/KLqWAmzEljWeRkG1hr40eUaWuPhQ7GLw++/ywODoCDRLXHSpI/OTHj9Gli2DFizRr2Jc6tWwP/+B3TpIt70ERER5Zc3b0TAsHUrcOuWfMzISCzn/+YbEXyTjggKEncpNmxIfwmEpyfQunV+V0VEJJOVz6EMFf7j6emJtm3bqh0fPnw4Nm/ejBEjRiAgIACenp6qsXPnzuGrr77C/fv3UaZMGcyYMQNubm6y5+/duxdz5szBkydPULlyZSxYsAB9+vTJdF35ESo8fv8YTqudVDs7jKw7Er92+BU2ZjZ58nq5ytdXzEDYuVM0QJg/X8xEyMLTf/tNbPeV3HgREM0Xe/cWYULTprlfNhERUVbdvQv88YfYbCBtr79u3US40Lq1+B1GOiApSQQI69cD+/eLNyK2tqInQ+rZDNHRoiEkEVE+KpShwr59+zB9+nQoFAr4F6DGN/kRKgDAoguL8Nftv7C2+1q0s2/36Sdomq+vWIS6c6fYLzpZtWpizcInphRcvgwsXixvvAiIuz7Dh4sZiNWr50HdREREOfT6NbB8uZhdFxYmH2vYEJg+XX2WPWm5d+/EHQ5jY7ErVWrduwPBwWJJ58CBYnknEVEeK5ShwpYtWzBy5EgoFAookzv8FwC5HSp8iP2AxZcWY06rOTAzTEm9E5QJSExKhKmhaY5fI0/5+Igw4f/+Tx4m2NiIWzQTJ2bY7CApCTh6FPjlF9GUOTVLS2DCBGDyZHGTgIiISNtFRIjtjP/4Q9zcTs3eHvjqK7E1JT+D6rAXL8QWIMl7ixYtCgwaJJo7NmzIaSlElGey8jmUK8QLkbNPz8JxpSMWXVyEuZ5zZWOG+obaHSi8fg0MHgzUrCmfnWBjI1piP30KfPttuoFCXBywaRNQqxbQs6c8UChbVix/eP5cNGFmoEBERLrC3ByYOhXw8xM9AOvWTRl7+lQE5XZ2YsvKNJtRka4ICgJq1055HBkplks0bgzUqwesXAl8+KCx8oiIAIYKhUoZ8zIIiQkBAKy/tR4fYj9otqCsMDEBjhxJCROKFxdTDgICgBkz0g0TMruTw9dff7SHIxERkVYzNBS5+61bwKlTQOfOKWOhocCCBUCFCuLmNjcX0DGNGokf7PXrYvlD6mknd+6IRtRlyoi1m6mbQxER5SONL394nna+Xjbt2bNH1VOByx8y9tO5n3D99XWs7LoS5S3L50KFeSQkRGwDmdp334kFpNOni/WGGcznDAwEli5NfycHZ2eRQXAnByIiKsju3hUz8XbsABIT5WM9eohfpS1bcva8zomMBHbtErMVrl5NOd68ufraTiKiHNCpngp6enq5tmWhJEkMFf5z981dLLq4CBt7bpQta1AmKaGnyL3/z3Pd/fuiZ8KxY2IagU2qHSgiIsS7nwzChE/t5DB9OtCsWd6WT0REpE1evhRNHdeuVQ/aGzcWvxtdXQF9fc3URzlw964IF9zdRWONESNSxiRJrI3p0QNo1453Uogoy3QuVMhNhT1UiEmIwU/nf8Kvl39FYlIiZrWchQUuC/Kh0hy6fx/48Udgz56UJQ6zZok5m5/AnRyIiIg+LjxcfP5culQEDalVqgRMmyY+kxYpoonqKEdiYkRoYGyccszTE0jeKr1SJWDMGPEDZvMoIsoknQoV9P+LxkuXLo1q1apl+zpBQUF4+PBhoQ4Vzjw9g/FHxsMvxE91zKmkE26OuwlDfcO8LjV77t0TYcLevfLdHEqWBObOFVsypIM7ORAREWVdQoKYPf/bb2JJfmrW1mITpUmTxK9h0mHjxokUKTV9fTFzYexYoFMnTk8hoo/SqVChevXq8PPzQ5s2bXD69OlsX6cwbyn5Pvo9pp+cjk23N6mOGeoZYpbzLMxsORPGBsbpPk+jksOEPXvkx0uWFE0P3NwAMzO1p8XFifWhv/4qb7wIiJ0cvvpK/K5k40UiIqKMSZJo6vjrr8DJk/IxY+OUmX45uN9DmhQXJ6Zwrl8vftBplS8vuliPGiW2CCEiSkOntpRs0KABJEmCt7e3pkvROZIkYcfdHXBc6SgLFFratcQdtzuY22audgYK+/cDTk7yQKFUKWDJErEH1rRpaoFCeDh3ciAiIsotCgXQoQNw4gRw+zbw+eeAgYEYi4sD1q0DHBxETyL2/9NBxsZA//4iMfL3F0tKU0/ffPECmDcP+P57zdVIRAWGxkOFhg0bAgDCwsLg7++v4Wp0R8CHAHTd0RVD9g/B2+i3AAALYwus6bYG50acg2MJRw1X+BEdOqTs7JAcJjx5IqYZpAkTAgOBb78Vgfr//ge8fp0y5uwMHD4s+hQNHy56KBAREVHW1Kkjev0lh/PJuzRLkrjZ3bKl2Fxg/36gAE0GLTwqVRI9qp4/Fz/Q7t1TGjeOHSs/NypKhBBERFmgNaECANy4cUODleiGxKRE/H75d9RcVRP/+P2jOv5Zjc/gM9EH4xuOh55C4z/WFP/+C2zZIj9mbg7Mny86FWcQJvj6ip5CFSuKvgnJHasVCtGl+vJl4Px5+e9FIiIiyr7y5UWvhRcvRAPksmVTxry8gL59xeyF1auB6GjN1UnZZGAA9Owp7sg8eya2BWneXH7Ojh1AlSqAi4tovhEXp5laiUinaLynQmRkJJydnQEAo0ePxqRJk7J1HX9/f1y8eBEAMHz48FyrT9NSr2Xxi/LD2MNjcSvwlmq8rHlZrOq2Cj2r99Rglem4c0f0TNi/HzAxEeFBJromenmJECG9nRyGDQO++YY7ORAREeWH+Hjg//5PBA1378rHihcXTR0nTgRKlNBMfZQHGjcGrl9PeWxjI6aDjhkDOGrxLFgiynU61aiRPi75h/nl/i+x6u4qKCUx71ABBSY2mogFLgtgYaxFTQRu3xZhgoeH/PjMmcDChek+JXknh8WLgf9yIRXu5EBERKRZkiR6L/z6K5C2p7aJidipcNo0oGpVjZRHuUWpBH7/XTR39PNTH2/ZUiyX+OyzdJtpE1HBwlChAEn+YeJbACbiWK2StbC+x3o0LddUo7XJZBQm2NqKpghjxwKmprIh7uRARESkW27dEp87d+2S91dIXp74zTdAs2aaq49ygSQB586JcGHvXjFlJTVLS2D3bqBjR83UR0T5gqFCAZI6VDAuYozvW3+Pb5p/AyN9LelKeOcOMHcucOCA/PhHwoTwcGDtWmDpUnnjRUDs5DB9OjB4MBsvEhERaatnz4Bly8TnzshI+ViLFiJc6NmTfY903vv3oovn+vXAgwfimL6+aLzBKaREBVq+hwpKpRL6+vo5vQylI/mH2WZtG6zruw5VbbRsbuHy5cCUKSmPbW3FUoexY8WcyFQCA8UbkNWrUxovJnN2Frs7dO3KNyBERES64sMHcaNg2TLxez61atXEsohhw9TuL5CukSTR+Gr9eiAhAdi2TT7+448iaBg7FmjUSExdISKdlu+hgpmZGZycnNCgQQPVV61atWCQvOExZVtWfpj5QqkUCXWymBixVZGenggTxoxRCxN8fUWTJ3d3+Qw6hQLo1UuECZwqSUREpLvi4oCdO8Xv+/v35WMlSgCTJgFffCEaPJKOkyR5aBAfD5QrB7wVW5yjdm0RLgwZAhQrppkaiSjH8j1U0NPTgyJNImlkZKQKGurXr48GDRrAyckJhoaGOX25QkVrQoVbt4B584DSpcUtidTu3BFbMqQJE7y8RPPFgwfF759kyTs5fP212JqKiIiICgZJAo4dE+HC2bPyMVNTYNQo0TOpcmXN1Ed5wNsbaN0aiIiQHzcxAfr1EwFDy5acvUCkYzQaKqS9XOqwwdDQELVq1ZLNaKhduzaDho/QeKhw86YIEw4fFo8NDERH4AoV0j2dOzkQERERANy4IZo67tkjb+qopwf06SP6LjRporn6KBdFRormjevXA1euqI87OIjZrBMmcOcIIh2hkVBBdtE0SeTHggYDAwPUqlVLNZuhQYMGqFOnDozYpQ+ABkOFGzdEmHDkiPx42bJiHUPbtrLD8fHA9u3p7+RQpoy4KzFuHHdyICIiKmwCAkRz5g0bgKgo+Zizs2jQ3K0beyoVGHfvih+2uzsQGppyvEQJ4OVLduIm0hH5Hio8fvwYt27dUn15e3sjJCRE/kJZDBpq1Kghm9FQp04dGBsb57RUnZPvoUJGYUK5cqJnwujRQKqfQ3g4sG4d8Mcf3MmBiIiIMhYSIlZQLl8OBAXJxxwcxLLIzz9XW01JuiomBti/X8xeOHdOvClcvFh+zp49YmkEp7ASaR2t2FIyICBAFjTcunULwcHB8hfPQtCgr68PR0dHNGjQAM7Ozmjbti0q/n97dx4Wdbn/f/w1iICmoKbivpuKZgmUoplpiWmKmB316EEzK+14Osc8J8vsZPWtn6fdFi2XysoyO5pLpil63DFzN8s1F5RFcwNzQ2B+f9wywzi4jTAbz8d1zRXzue8Z3mNkzGvu+33XqVMUpXsVt4YKf/2rOZohvxo1pOeeM5sg84UJVzrJ4a67pGee4SQHAADg7Px5c3jAm2+aZs75hYdLTz5pVslXqOCZ+lAEdu2SypQxy1fzpKVJNWuar7t2Nb0X7r/fsSE4AI/xilChICkpKQ4hw4YNG5R6ycfb1xM03HLLLXrkkUc0YMAAVa5cuegK9yC3hgqTJpk9CpL5S/6556SBAx3ChKud5PD001Lr1kVbJgAA8H25udL8+eb3iuXLHcdKlzaLI596Sqpb1zP1oYj9v/8njRrleK1GDfNB1qBBUq1anqkLgCQvDhUK8vvvv2vDhg22kGHjxo06cOCAw5wrBQ0Wi0WlSpXSiBEjNGLECIX42Zq5IgsV1q415zrlb7+clSV17Gj2Kzz8sEOYwEkOAACgqPz0k2nqOGOGCRvyBARIDz1kPrSIjvZcfSgC+/eb3guffGJWLeRnsUidOpnVC926STR1B9zOp0KFgpw4ccJhNcPGjRv122+/OYUJkj1gsFgsioyM1Ny5c1XVj/ZlFXqo8OOPpmfCDz+Y84OnTr3s1Cud5BAaaj/JIf9KNgAAAFft3WuaOn78sXTmjONYu3YmXOjcme2VfiU72yxZmTTJ/DN/qiSZf+Hz53umNqAY8/lQoSCnTp3Spk2btHHjRv34449auXKl0i6mmhaLxRYuNGrUSOvWrVOZMmU8WW6hKbRQIX+YkCcgQPr1V6lRI4epWVnSV1+Zkxx+/dXxaTjJAQAAFLVjx0zfpvffly5pyaWICLNCsl8/h0WV8AeHDkmffmpSpbyVyxMnmhULeaxW88sq//KBIuWXoUJBfvrpJ02ePFmff/65Lly4IKvVKovFooEDB2ry5MmeLq9Q3HCosGaNCRMWLnS8Xru22cc2YIDtaIYrneTQpIk0YgQnOQAAAPc5d870cXrrLWnnTsexKlXMiskhQ6Ty5T1TH4pITo60eLH02WfmyJCyZe1jSUlSXJzZf/vYY+aXVACFrtiECnl27Nihnj17aseOHbJarQoMDNSePXtUyw8avLgcKqxZI734orRokeP1AsKEq53kMGIE50cDAADPyc01p12/+aa0cqXj2E03SY8+alZS1q7tmfrgRgMHSlOm2O+3aWPChT/9yXT4BFAorud9qF+8TWzcuLEWLVqksLAwSVJOTo6+/PJLD1flQbm5pnNu/kChTh2zV23XLvMXb1CQdu40/xOuU0d67TXHQCE+Xlq92vyPu1s3AgUAAOA5AQHmw+kVK8yOzp49TS8/STp92nw4Ur++9Oc/Sxs3erZWFLGSJR23PqxebRqMV6smDR0qbd7sqcqAYsstbxV79+6tJk2aKCEhQbNnzy6S71G9enUNHjzYdn/FihVF8n18QkCA9Pzz5us6dUxn3V27TIIQFKQ1a6QePcxqsY8/th8NGRRkpmzfLs2axdGQAADA+7RsaU6J2L1b+utfpVKlzPWcHOnrr6WoKOnee6UFCxxPrIKfmDjR7NMdO1Zq2tR+PSNDGj9eatFCuuMO6X//81iJQHHjllBh/fr12rlzp7766ivtvHRDXCGKjY21fb19+/Yi+z5eZfVqKTbWOZXt00f68ksTJgwapNwSJfXdd1LbtiYsmD3b/j/a0FDpmWekffvMYgaOhgQAAN6ufn1p3DgpOdm0j6pUyT72v/9JXbpIzZublfJ5H6DAT1SoIP3jH9LPP5seCwMHOm59WL+eZbaAGxV5T4WMjAyVv9g9x2KxaO3atYp28aDhtLQ0hYaG6qabbipwfNeuXWrcuLEsFotuuukmZV7aIMAHXXYvy6pVpmfCkiXmfo8e0rffOj2ekxwAAEBxcPas9Pnnpqnj7t2OY9WqmaaOgwdL5cp5pDwUtYwMado08wnZqVOms2feHhnJLG/Ztct0Ha9Tx2NlAr7Cq3oqbN261fZ1UFCQbr/9dpefa/PmzQoLC1NERITDVoc8wRf3V1mtVp0+fdrl7+PVVq6U7rvPLDnICxQkk9SeOmW7m5lpmhnVrWvC2/yBQpMm0iefmJUJ//oXgQIAAPB9pUqZ0KCgbZypqdKzz0o1a0rDh5vVDfAzYWHmKJANG0zD8vyBgmSONxs1yvxyfNddpkP50aOeqRXwM0W+UmHGjBnq1auXLBaLIiIi9PPPP7v8XFarVVWrVtWRI0dksVi0c+dONWjQwDaenZ2tBQsWaO3atVq7dq0SExML4yV4lC0hmj9foW++6bw/rH590z+hXz+pZElOcgAAALgoKcl8yJJ/26cklShhdor+61/SDXzeBV+RliZVr+7cZCMwUOrUyfwe3b07p0cA+XjVSoVjx47Zvq59mXN+jh49qqeeekqff/65w8qGS1ksFt177722+8uXL3cYDwwMVLdu3fTKK6/4RaDgoEsXx0Chfn2zSXDHDunhh7Vzb0lOcgAAAMindWuzO3TnTvMhdkiIuZ6TY1pPtWghdexoDsyiqaMfq1pV2rtXevVVKSLCfj07W/r+e7MlonJlKSHBLOUFcF2K/O3l8ePHbV/nHfl4qdOnT+vdd9/VwIEDFRkZecXna9Wqle3r9evXF06RviR/mDBggNasC7zsSQ6DBnGSAwAAQMOGZhXngQPS6NHSzTfbxxYvNh9W33679MUXNHX0W3XqSM89J23bZhqcP/20VKOGffz0adOILO84EQDXrMhDhZIlS9q+zs7OvuJcq9Wqq+3GuOWWW2xf/3pp50F/Vq+e9Nln0o4dyk0YoO8WBF71JIfJkznJAQAAIE/lyqbPdXKyOX2wfn372NatUv/+5leuN94wff/ghywW6bbbpNdfNynT0qXmTPVy5UzfsipVHOe/9pr0wgtmuQuAAhV5qJB//0VKSkqBc66nrUOVfP+h79+/3+W6fM66dcrq019Tpgbq1luluDhzAESeatXM340HD0r/+Y+5DwAAAGelS0tPPGHeJ86cKeVbCKuUFNODqmZN82H2oUOeqxNFLCBAuucec2JEerr5RC6/7Gzp7bel//s/80lddLRp+JiW5pFyAW9V5KFCw4YNJZngYNeuXcrNzXWacyrfqQWSdOHChcs+X5kyZWzPl39rhb97b3zgFU9y2LvX/I+PkxwAAACuTYkS0oMPmoaOq1aZXn15hwacOmU/Sat/f7OSAX4sONgkSflt2iT9/rv9/oYN5viQGjVMM44pU5w7owPFUJGHCrfddpsCAgJksVh07NgxzZ8/32nOL7/84nD/6BWOd8nJybF9fe7cucIr1Mv9+9/mOKQ8bdpIc+eabWEDB5q/BwEAAHD9LBbzu9Xs2aYf1eOP23+3ys42vRZuu830Xli8mKaOxcYdd5ilKm+9JeXv+5aba34QBg6UwsOlXr04nhLFWpGHChUqVFDr1q1tWxz+7//+zyEYkKQvvvjC4f6Vjp3MHziULobHvnTvbpL0Vas4yQEAAKCwNWokTZhgtts//7xUoYJ9bNEi8wF1ZKQ5PeIKi2vhL6pVM6sTNmwwy4Wff9403shz7py0fLnpyQAUU255SzpkyBDb1+vXr1ePHj20d+9eHT9+XM8++6wWLFggi8WiEiVKSJLmzJlz2efavHmz7eurnZfpTxISTHI+e7ZJ0gEAAFB0wsPNVvrkZOmDDxzfR27eLP3lL6bR49tvswK+2GjSxPxQ7NkjrVkj/e1vUqVKUp8+UmCg49xBg0xzji1bWNoCv2exXk+XRBdZrVbdeeed2rhx42XHLRaLhg4dqg8++EClSpXSzz//rHr5//a+qH379lq+fLksFovuuusuLV++vKjL96jMzEyFhYUpIyOjWIUoAAAA3iQnR/r2W3MyxLp1jmNhYdLgwdKTTzqeUohi4MIFcxxl/pUKhw+bFQ55veSaNpX69ZP69pVq1/ZImcD1up73oW5ZqWCxWDR9+nSFhYXZruUdH5kXKLRr107PPPOMJNMroUePHkpPT3d4nunTp9sCBcn0awAAAACKWokS0p/+JK1da1a7d+tmH8vIMKdw1a1rVjBs2OC5OuFmJUs6b3348UfHPcq//CI995xUp47Utq300UfSsWPurBIoUm5ZqZBn165d6tmzp1Njxho1amj58uWqW7eu4uLiNG/ePFksFpUvX179+/dXw4YNtWXLFn3yySfKycmxBRELFy7Ufffd567yPYKVCgAAAN5p+3bTw++LL6SsLMexu+82W/G7djWBBIqZo0elb74xzTeSkpzHS5aUOneW/vtfKSjI/fUBV3E970PdGirkmT9/vlavXq2jR4+qSZMmGjRokMqWLStJOnjwoCIiInTmzBlbeJAn//2GDRtq+/btDuP+iFABAADAu6WnS+PHm9ulH0DXry8NGyY9/LB08WR0FDf79klffWUChu3b7ddjYgoOHAAv4PWhwtV899136tWrl7IujXxlgoXAwEAtXLhQ7du390B17kWoAAAA4BvOnjWrFt55R9qxw3GsXDlzVCV9F4oxq9V0+fzyS2naNLMlYuhQx/E2bcxRlv36mX/6+Qeo8F4+HypI5pSH4cOHa/ny5cpfYnh4uCZMmKC4uDgPVuc+hAoAAAC+JTdXWrjQhAuJiY5jgYGmN8Pw4VJ0tGfqgxfIyZGys6XgYPu1pCTHY94aNDDNHfv1k265xf01oljzi1AhT2pqqrZs2aKTJ0+qWrVqat26tUqWLOnpstyGUAEAAMB3/fyzCRe+/NK578Jdd5lwIS6OvguQNGmSWcpy/rzzWHS0CRf69JGqVHF/bSh2/CpUKO4IFQAAAHxferr04Yem78LRo45j9eqZvgsDB9J3odg7edKcXfrll9LSpWZLRH4BAdKDD5oGj0AR8rojJQEAAIDirEoV6aWXpORk84F0kyb2sb17pb//3fRaGDFCOnjQc3XCw8qVkx55RFqyxPwgvPmm1KKFfTw3V7rY4N5Bbq7bSgQuRagAAAAAuEmpUtKjj0q//CItWCDFxtrHMjKkN96Q6taV/vxn6aefPFcnvED16tI//ylt3Cj9+qs0apT54ejb13FeRoZUs6Y0eLC0YgUBA9yO7Q9eju0PAAAA/m3bNmnsWGnqVOft9G3amL4L3bvTdwEy2yGsVrMNIs+nn5rVDXlq1TKpVL9+0q23ur9G+AW3bn/o27evkpOTb/RpCsVnn32myZMne7oMAAAA4Jo1ayZNniwdOCCNHi1VqmQfW71a6tlTathQevdd6dQpz9UJL2CxOAYKknTkiFS6tP1+crL02mtS8+bm9p//mGtAEbnhUOHrr79Wo0aN9M9//lNpaWmFUdN1mz17tlq0aKFHHnlEqampHqkBAAAAuBHh4dKLL5r3f5MnSxER9rF9+0wzxxo1pH/9i/eIyOeZZ6TDh01zxy5dHJe0/PyzNHKkVLu29NhjnqsRfq1QeipkZWVp7NixqlevnoYMGaKtW7cWxtNe0ZkzZzR58mQ1a9ZMPXv21JYtW4r8ewIAAABFLSREGjTIbItYuFDq1Mk+lpkpvfWWOTGiTx9p7VrP1QkvUqaM6bXw/fdSWpr0wQdSTIzjnAYNnB937px76oNfu+FQYcCAAZIkq9Wq8+fPa9KkSWrRooVat26tDz/8UIcPH77hIvPk5uZqyZIlGjJkiKpXr67Bgwdr+/btymsLUbNmTXXs2LHQvh8AAADgKRaLaeT4ww8mYHj0USk42Izl5EjTp0utWpm+CzNmSNnZnq0XXqJSJWnoUCkpSfrtN+mVV8yylz//2XHezz9LlStLDz8sJSbyAwSXFUqjxtWrV2vo0KHaunWrLBaLrFarLBaLJCkgIEBRUVFq37692rZtq9tvv13VqlW7puc9d+6ctm3bpp9++klLly7V0qVLdeLECUmyfQ+r1aqgoCANGzZML7zwgkrn30/kgvHjx+uNN95QWlqamjZtqrFjx6pt27YFzn344Yf12WefOV2PiIjQL7/8IkmaMmWKBg4c6DTn7NmzCgkJuWo9NGoEAABAniNHpI8+ksaNM1/nV6eOOZpy0CCJXxtxVc8+a3ov5AkPN8tf+vWToqNNqoVi63rehxba6Q9Wq1VffvmlRo8erX379pknvyRgyFOuXDnVrl1bNWrUUPny5VWqVCkFBgbq7NmzOn36tNLS0nTo0CElJycrN9+RKHml5j1vQECA+vXrp5dfflm1a9e+4dcwffp0JSQkaPz48WrTpo0mTJigyZMn69dff1WtWrWc5mdkZOjs2bO2+9nZ2brtttv05JNP6sUXX5RkQoV//OMf2rlzp8Njq1Spck01ESoAAADgUufOSV99Jb3zjlnFkF/Zsmb7/JNPmqABKNDzz0vvv2/21FzqllvMdoq+fU2XUBQ7HgkV8mRnZ2vatGl6++23bX0O8kKFS7/VpWFDfgXNzbtWqlQpDRgwQMOHD1eDgvYGuahly5aKjIzUhx9+aLvWpEkTxcfHa8yYMVd9/OzZs/Xggw9q3759tpBjypQpGjZsmE6ePOlSTYQKAAAAuByrVVq8WHr7bbNNIr+AAHNyxPDhZpsE4OTcOdOH4csvzT+zspznPPusdA3vheBf3Hqk5KUCAwOVkJCgTZs2KSkpSY899pjKlSvnEBJYLJYrBgoFzbFarWrZsqXGjRunQ4cOafz48YUaKGRlZWnDhg2KjY11uB4bG6ukpKRreo6PP/5Y9913n9OqiT/++MO2MqNr167atGnTZZ/j/PnzyszMdLgBAAAABbFYpI4dpQULpF9+MSsU8nbY5uZK//2v6dcXE2O+Zts8HISEmOTp22+l9HRp0iTpnnsctz60bOn4mKwszjaFg0IPFfJr1aqVJkyYoN9//10rVqzQc889pw4dOig0NFRWq/WKN0lq1KiREhISNHnyZKWmpmrNmjV64oknVL58+UKv9ejRo8rJyVF4eLjD9fDwcKWnp1/18WlpaVqwYIEeffRRh+uNGzfWlClTNHfuXE2bNk0hISFq06aNdu/eXeDzjBkzRmFhYbZbzZo1XX9RAAAAKDYiIqSJE81xky+/bHrw5fnxR6lXL3MAwNtvSxkZnqsTXqp8edMNdOlS80P0xhtSu3ZS586O8+bMsfdf+O67glc3oFgp9O0P1+rw4cPav3+/jhw5ojNnzig7O1ulSpVSaGioatasqTp16ig4r72tG6Smpqp69epKSkpSTL7jV1599VV98cUX2rFjxxUfP2bMGL311ltKTU1VUFDQZefl5uYqMjJSd999t9577z2n8fPnz+v8+fO2+5mZmapZsybbHwAAAHBdzp+Xpk0zIcLPPzuOlS1rGjr+/e9S3bqeqQ8+Kj7eBAt5KlQwiVW/flLr1mbfDXze9Wx/CHRTTU7Cw8OdVgV4UsWKFVWiRAmnVQlHjhy5ap1Wq1WffPKJEhISrhgoSOY0jDvuuOOyKxWCg4PdGqYAAADAPwUHm9MCBwyQliwxTR3nzzdjp05JY8dK770n9ehh+i7ExNDwH1dhtZoU6uabpWPHzLXjx82RJB99JNWubY6u7NdPatbMs7XCbYiRLgoKClJUVJQSExMdricmJqp169ZXfOzy5cu1Z88eDRo06Krfx2q1avPmzapateoN1QsAAABcC4tFuu8+04fv11+lwYMd+y7MnCm1aWNChenT6buAK7BYTDqVlma2Pvz5z1KpUvbxAwek//xHuvVWk1qhWCBUyGf48OGaPHmyPvnkE23fvl1PPfWUkpOTNWTIEEnSyJEj1b9/f6fHffzxx2rZsqWaFZDGvfTSS1q4cKH27t2rzZs3a9CgQdq8ebPtOQEAAAB3adLEfKB88KD0yitS/lPO16412+Tr15feeou+C7iCkiWlrl3NuaZHjkhffCHdf79UooR9TqdOjo85ccLc4HcIFfLp3bu3xo4dq5dfflm33367VqxYofnz59tOc0hLS1NycrLDYzIyMjRz5szLrlI4efKkHn/8cTVp0kSxsbFKSUnRihUrdOeddxb56wEAAAAKUrGiNGqUtH+/NGWK1Ly5fSw5WfrXv6QaNaRhw6S9ez1UJHxDmTLSX/5ijiBJTZXef9/su2nSxHHe+PEmxerRwxxFcvasR8pF4fNYo0Zcm+tpkAEAAAC4wmo1Tf/ffttsk8gvIMD05hs+3PTho+8CrpvVKjVtKm3fbr9Wtqw5zrJPH3OMJX3lvMr1vA9lpQIAAABQzFksUocO0rx55n3fkCH2rfK5udK330p33SW1bCl9/bV04YJn64WPycoy2yPy77c5dcosk7n/frN05qGHzP28BpDwGaxU8HKsVAAAAIAnHDsmTZggffCB6cuXX40a5jjKxx6TypXzSHnwRTk5ZknMl1+aDqGnTjnPmT1b6t7d7aXB0fW8DyVU8HKECgAAAPCkrCxzKsTbb0ubNzuO3XSTNGiQCRjq1/dIefBVZ8+avTZz55qzTo8dk4KCzD/LlLHPmzfP3Lp2NctpSpf2XM3FCKGCHyFUAAAAgDewWqVly0y4MG+e45jFYvouPPWU2SZB3wVcl5wcc/zIjh3SI484jvXrZ06ZkMxZqPfeK3XrJj3wgFkygyJBqOBHCBUAAADgbXbulN5912yBv7SJf3S0aer40EPm5EHAZVarVL268/6bPLffblYwdO0q3XGH6SqKQkGo4EcIFQAAAOCtjh2TJk40fRdSUx3HatSQnnzS9F0oX94z9cEPnD4tLVli3wZxuYDhtdekESPcW5sfI1TwI4QKAAAA8HZZWdI335itEZs2OY7ddJM0cKD0j39IDRp4pj74CavV/IDlBQzr1tnHNm+WbrvNfn/3btOroWtXGn64gFDBjxAqAAAAwFdYrdKKFSZc+O47cz+PxSLFxZmtEW3b0ncBhSA93QQHq1dLkyc7/lD9v/8njRplvm7c2L5Nok0bKTDQM/X6EEIFP0KoAAAAAF+0e7fpu/Dpp9KZM45jUVGmqWOvXvRdQBGJiZF+/NH5erly0v33m2aP998vVajg9tJ8wfW8Dy2UThb169fX008/rdWrVxfG0wEAAADwcQ0bml4LBw9KY8ZI1arZxzZskP7yF6luXek//5GOH/dcnfBTkyebH6677nJs4HjypPT11+ZUiUqVpBdf9FSFfqNQVioEBATIcnGpSaVKldS9e3f16NFD9957r0oSPd4QVioAAADAH2RlSf/9r9kasXGj41jp0va+Cw0beqY++LFjx6QffjB9GH74wQQLeaZONQFDntOnzXaKdu2k4GC3l+ot3L79IS9UyHuqvIChbNmy6ty5s3r06KEuXbqoTJkyN/qtih1CBQAAAPgTq1VaudKEC3PnOvdd6NbNbI1o146+CygCFy5ISUkmYJg/3zQBuflm+/icOVJ8vOkwGhtr+jB06SJVqeKxkj3B7aHC0KFDNXfuXKWkpDh/g4t/EwQFBenee+9Vjx49FBcXp0qVKt3oty0WCBUAAADgr/bsMX0XPvnEue9CixamqWOvXlJQkGfqQzH02GNm68Sl7rjD3uyxRQu/T7w81qhx3bp1mjVrlmbNmqWdO3fav8nFVQx5AUNAQIBiYmLUo0cPxcfHq27duoVVgt8hVAAAAIC/O3FCmjRJeu896dLPKatVk/72N2nwYHrqwQ1++EGaPl36/nvp998LnlOtmvTXv9pPl/BDXnH6w44dOzRr1izNnj1b69evd9gakT9gkKTmzZvbAobmzZsXRTk+i1ABAAAAxcWFC9KMGWZrxPr1jmOlS0sDBkjDhkm33OKR8lCc5OZK69aZs1HnzZO2bHEcHznSHFuZX0qKVL26+2osQl4RKuSXkpKi2bNna9asWVqxYoWys7PNNy8gYKhTp4569OihHj16qE2bNkVdmtcjVAAAAEBxY7WaXnlvvy3Nnu3Yd0EyK9CHD5fuucfvV6HDWxw8aFYvzJsnLVkiLV4s5X+/umeP6TJ66632bRItW0olSniu5hvgdaFCfidOnNB3332n2bNna+HChTp79qwppIBGj3knScTHx+u+++4rlidJECoAAACgOPvtN7Mt4uOPTWP+/G6/3TR17NOHvgtwozNnzMkQ+QODd981y2jyu/lm0+Sxa1epUycpLMytZd4Irw4V8jt79qwWLlyoWbNmad68eTpx4oTTnLyAoUyZMurSpUuxO0mCUAEAAAAwpwDm9V04dMhxrGpVe9+F/I38AbeZNk0aO1b66aeCxwMDpbZtpT/9SXriCbeW5gqfCRXyy8nJ0bJlyzR79mzNmTNHhy79m0LOJ0nEx8ere/fufn2SBKECAAAAYHfhgjRzptkasW6d41ipUva+C40aeaQ8FHeHD5ujKufNkxYtkv74w3G8Wzdzlmp+ublSQID7arwGPhkqXOp6TpJo3bq1Ro8erQ4dOniq3CJDqAAAAAA4s1qlpCR734XcXMfxBx4wfRfat6fvAjzk/HlpxQoTMHz3nbRvnzRxojm2Mk9WltSggRQTY7ZJdO4sVazouZov8otQIb8dO3bYGj1e7iSJ0aNH64UXXvBwpYWPUAEAAAC4sr177X0XLv1guHlz6cknpd69pbJlPVMfIKtV2rFDqlJFKl/efn3JEum+++z3LRZ7wNC1q9SsmUdSset5H+pdaywuo3Hjxnr22We1du1aJScn6/3331eHDh1Uwkc7aQIAAAAoPPXqme3sBw9Kb7wh1axpH9u61XwwXKWK9PDD0vLlzqdJAEXOYpGaNHEMFCQpNdXxWt4SnOeeM4lYnTrS0KHSggXOy3G8hE+sVLickydPau7cuZo9e7Zat26tf/3rX54uqdCxUgEAAAC4PtnZ9r4LBfXNq1/fBAwDBjgGEIBHZGdLa9aYbRLz5km//uo8p149c2ylm1Yt+N32h+KMUAEAAABwjdUqrV8vffqp9NVXUkaG47jFInXsKA0cKMXHSyEhHikTcLR3r/T99yZgWLbM9F34+9/NsZX59e8v1a1rtklERRVqs0dCBT9CqAAAAADcuLNnTUPHTz4x29gvfRdUrpzUt6/0yCNSZCTNHeElTp2SFi82zRxvvdV+PTVVql7dfr9KFdOdtGtX06OhTJkb+raECn6EUAEAAAAoXAcOSJ99Jk2ZYhryX6p5c7N6oV8/yY9Pr4cvmzFD+tOfCh4LCjLHnuQ1e6xT57qfnlDBjxAqAAAAAEUjN9c0bvz0U/Me7exZx/GSJaVu3czqhU6dpMBAz9QJFCglRZo/32yTSEx0/gGWzA/t8ePXffQJoYIfIVQAAAAAil5GhvTNN2Z7xI8/Oo9XrWq2sA8cKDVq5P76gCs6e9b0X5g3T/ruO3MUiiR16GD2++Q3YYIUFmaSsktPo7iIUMGPECoAAAAA7rV9u1m98Pnn0uHDzuOtW5twoVcviV/R4XWsVunnn02zxwYNHLdJ5OSY/gtHj0olSkht29q3Sdxyi62ZCKGCHyFUAAAAADzjwgXphx9MwPDdd+bkv/xKl5YeesgEDHffXajN94GisWaNScUK0qCBLWDIvO02hVWqRKjgDwgVAAAAAM87ckT68kuzPWLbNufxevWkhx+WBgyQatVye3nAtblwQVq50r5NYs+eAqdllimjsD/+IFTwB4QKAAAAgPewWqUNG0y4MG2adPKk47jFInXsaFYvxMdLISGeqBK4Rrt2mYBh3jwTNlxcjpNZubLCjhwhVPAHhAoAAACAdzp7Vpo922yPWLzYBA75lSsn9e1rTo+IjLRtVwe808mT0qJF0rx5yixXTmHvv0+o4A8IFQAAAADvl5wsffaZCRj27XMev/VWEy706ydVquT++oDrQaNGP0KoAAAAAPiO3FxpxQqzPWLGDLOaIb+SJU0vvEceke6/XwoM9EydwJUQKvgRQgUAAADAN2VmStOnm9ULa9Y4j1epIvXvb/ovNG7s/vqAyyFU8COECgAAAIDv275dmjJF+vxzKT3deTwmxoQLvXtL/NoPT7ue96E+e5LqhAkT1K5dO0VERKhbt25atGiRp0sCAAAAgAI1aSK99pp08KA5ya9HD8etD2vWSI8/blYvDBggLVtmtlIA3s4rVyr88MMPeuCBBxQSEqKDBw+qQoUKDuPDhw/Xu+++6/S4cePGaciQIe4q0y1YqQAAAAD4p99/l6ZONdsjfv7ZebxePenhh03IUKuW28tDMebzKxWWLFkiq9WqTp06OQUKW7du1dixYyVJVqtVZcqUkdVqldVq1VNPPaUDBw54oGIAAAAAuD6VKklPPSVt2SKtWyf99a/mGMo8e/dKL7wg1akjxcZK06Y5N34EPM0rQ4WkpCRZLBZ16NDBaWzSpEmSpDJlyigpKUkZGRlasWKFQkNDlZWVZRsHAAAAAF9gsUjR0dK4cVJamgkPOnY01yXJapUSE6W+faVq1Uz4sH69uQ54mleGCukXO5c0bdrUaWzBggWyWCx6/PHH1apVK0nSXXfdpcGDB8tqtWrJkiVurRUAAAAACktIiNSnj7RokbR/v/Tyy2YbRJ6TJ6UPP5TuuENq3lx65x2zjQLwFK8MFX6/+F/FpVsf0tLStHfvXklSfHy8w1jeqobdu3cXfYEAAAAAUMRq1ZL+/W9p925p6VJz/GTp0vbxbduk4cPN6oUHHzQNILOzPVcviievDBXOnz8vSTpz5ozD9TUXD3cNDg5Wy5YtHcYqV64syTSUAAAAAAB/ERAg3XOP9NlnZnvEpElS69b28exsadYsKS5OqllTeuYZaccOj5WLYsYrQ4W8FQqHDh1yuL58+XJJUnR0tALzn78iKSsrS5LptQAAAAAA/ig0VHr0UWn1amn7dmnECHMMZZ70dOn1180Rlq1bmwCCz11RlLwyVGjWrJkkacaMGbZr2dnZmjlzpiwWi9q2bev0mJSUFElSeHi4e4oEAAAAAA9q3Fh67TXp4EGz9eHBB6X8n72uWSM9/rgJHfr3N1socnM9Vy/8k1eGCnFxcbJarZoxY4aGDx+uefPmqU+fPkpNTZUkPfjgg06PWb9+vSSpZs2abq0VAAAAADwpMFDq2lWaOVNKTTXNG2+91T5+9qz0xRdShw5Sgwam+eOBA56rF/7FYrV630Ek586dU7NmzbR3715Z8s5RkWS1WnX//fdr/vz5To9p3ry5fvnlF7344ov697//7c5yi1RmZqbCwsKUkZGh0NBQT5cDAAAAwAdYrdLGjdInn0hffWVOjcjPYpHuvVd65BEpPl4qVcoTVcJbXc/7UK9cqRASEqLExES1atVKVqvVduvYsaOmTp3qNH/dunXatm2bJPspEAAAAABQXFksUlSUNG6cae749ddSbKy5LpnQYfFiqW9fqWpV6a9/ldatM9eB6+GVKxXy279/v9LS0lS9enXVqlWrwDmbN2/Wli1bJEn9+vVzauLoy1ipAAAAAKCwJCdLn38uffqptHev83izZmb1wl/+IlWq5P764B2u532o14cKxR2hAgAAAIDClpsrrVxptkfMmCGdOeM4nten4ZFHpM6dHRtAwv8RKvgRQgUAAAAARSkzU/rmG7N6ISnJeTw83JweMXCgOaoS/s/neypciwkTJqhdu3aKiIhQt27dtGjRIk+XBAAAAAA+JzRUevRRafVqaft26ZlnTJ+FPIcPS2+8IUVESDEx0sSJUkaG5+qFd/HKlQo//PCDHnjgAYWEhOjgwYOqUKGCw/jw4cP17rvvOj1u3LhxGjJkiLvKdAtWKgAAAABwt+xsaeFCs3ph7lzpwgXH8VKlpJ49zfaIdu2kAJ/9uBoF8fmVCkuWLJHValWnTp2cAoWtW7dq7NixkswRk2XKlLGdDvHUU0/pAAeuAgAAAMANCQyUHnjA9FtITZXGjpWaN7ePnz0rTZ0qdeggNWggvfyyxFux4skrQ4WkpCRZLJYCj4ecNGmSJKlMmTJKSkpSRkaGVqxYodDQUGVlZdnGAQAAAAA3rmJF6R//kDZvljZskIYOlcqXt4/v2yeNHi3VrSt17ChNm2ZCBxQPXhkqpKenS5KaNm3qNLZgwQJZLBY9/vjjatWqlSTprrvu0uDBg2W1WrVkyZIb+t7jx49X3bp1FRISoqioKK1cufKyc5ctWyaLxeJ027Fjh8O8mTNnKiIiQsHBwYqIiNCsWbNuqEYAAAAAcDeLRYqMlD74wKxe+PprKTbWXJckq1VavFjq29f0ZHjiCWndOnMd/ssrQ4Xff/9dkpy2PqSlpWnvxcNU4+PjHcbyVjXs3r3b5e87ffp0DRs2TKNGjdKmTZvUtm1bde7cWcnJyVd83M6dO5WWlma7NWzY0Da2Zs0a9e7dWwkJCdqyZYsSEhLUq1cvrV271uU6AQAAAMCTQkKk3r1N34UDB6RXXpHq17ePZ2RIH30k3XmndOut0ttvS0eOeK5eFB2vbNQYHBys7OxsrVq1SjExMbbr3377rR566CGFhIQoMzNTgfkOS920aZOioqIUGBiorKwsl75vy5YtFRkZqQ8//NB2rUmTJoqPj9eYMWOc5i9btkzt27fXiRMnVK5cuQKfs3fv3srMzNSCBQts1+6//36VL19e06ZNu2pNNGoEAAAA4AusVmnlSumTT6T//lc6c8ZxPDBQ6trVHE3ZubNUsqRn6sTV+XyjxrwVCocOHXK4vnz5cklSdHS0Q6AgyRYklClTxqXvmZWVpQ0bNig2NtbhemxsrJIKOqw1nxYtWqhq1aq69957tXTpUoexNWvWOD1np06dLvuc58+fV2ZmpsMNAAAAALydxSLdfbc0ZYqUni5Nniy1aWMfz86WZs+WuneXataUnn5a2raN7RG+zitDhWbNmkmSZsyYYbuWnZ2tmTNnymKxqG3btk6PSUlJkSSFh4e79D2PHj2qnJwcp8eHh4fbejxcqmrVqpo4caJmzpypb7/9Vo0aNdK9996rFStW2Oakp6df13OOGTNGYWFhtlvNmjVdej0AAAAA4Clly0qDBkmrVkk7dkjPPmv6LOQ5fFh6802zNeKWW6R//lNavtwED/AtXhkqxMXFyWq1asaMGRo+fLjmzZunPn36KDU1VZL04IMPOj1m/fr1knTDb8IteV1GLrJarU7X8jRq1EiPPfaYIiMjFRMTo/Hjx+uBBx7Qm2++6fJzjhw5UhkZGbbbwYMHb+DVAAAAAIBnNWokjRkjJSdL338v9ezpuPVhzx7Tc+Gee6TKlaWEBLN9gkXbvsErQ4XHHntM9erVk9Vq1bvvvqvu3bvbTkzo1KmToqKinB4zb968y65iuBYVK1ZUiRIlnFYQHDly5LpWP7Rq1cqhWWSVKlWu6zmDg4MVGhrqcAMAAAAAXxcYKHXpIs2YYU6PePddqX17qUQJ+5wTJ6SpU6VevcxRlp06SePGmUAC3skrQ4WQkBAlJiaqVatWslqttlvHjh01depUp/nr1q3Ttm3bJNlPgbheQUFBioqKUmJiosP1xMREtW7d+pqfZ9OmTaqab11PTEyM03MuWrToup4TAAAAAPxJxYrS3/8u/e9/0u+/S19+aYKEsmXtcy5ckBYtkv72N6l2balFC2n0aGnjRvoweJPAq0/xjLp16yopKUn79+9XWlqaqlevrlq1ahU4t2TJkvr0008lmRMcXDV8+HAlJCQoOjpaMTExmjhxopKTkzVkyBBJZmtCSkqKPv/8c0nS2LFjVadOHTVt2lRZWVmaOnWqZs6cqZkzZ9qe8x//+Ifuvvtuvfbaa+revbvmzJmjxYsXa9WqVS7XCQAAAAD+onx5qW9fc8vKMr0V5s41t/wrFDZvNreXX5aqV5fi4sytfXspONhT1cMrj5T0pPHjx+v1119XWlqamjVrpnfeeUd33323JOnhhx/W/v37tWzZMknS66+/rokTJyolJUWlSpVS06ZNNXLkSHXp0sXhOWfMmKHnn39ee/fuVf369fXqq68W2BeiIBwpCQAAAKA4slqlrVvtAcPFNnpOypQx2yTi4sz2iooV3VunP7qe96E3HCq89957at68uZo3b247ChKFh1ABAAAAAKSUFGnePBMwLFkinT/vPCcgwBxjmbeK4ZZb3F+nP3BrqBAQEGA7yaBatWq67bbbNG/evBt5SuRDqAAAAAAAjv74Q0pMNAHDvHnS0aMFz2vUyB4wxMQ4NoXE5XkkVMh7GovFopycHIc5f/rTnxQVFaXo6GhFR0erXLlyN/ItixVCBQAAAAC4vJwc6ccf7dskduwoeF7FitIDD5iAITbWbJtAwdweKjg8YQGhQv7VDJJpwpgXMERHRysqKkpl87f5hA2hAgAAAABcu127pO++MwHDqlVSbq7znOBgqUMHEzB062YaP8LOraHC999/ry1btthuv/32my5cuOAw59LVDJIcQgaLxaL69es7BA2RkZG66aabbqQ0v0CoAAAAAACuOXZMmj/fBAw//GC2TRQkOtq+TaJ5cynf29Viya2hwqXOnj2rUqVKOVwraDVDnssFDQEBAbrlllscgobbb7/d6bn9HaECAAAAANy48+elpUvt2yRSUgqeV6uWPWBo104KCnJvnd7Ao6HC5Qpav3697bZu3TodOHDAsZBrCBpKlCihxo0b20KGmJgY3XbbbU6hhT8hVAAAAACAwmW1Sps22QOGTZsKnle2rNS5swkYOneWisuBh14XKhTk+PHjWrdunS1kWL9+vVJTUx2Lu4ag4aabbtI999yj+Ph49enTR6VLly764t2IUAEAAAAAitbBg/Y+DP/7n3T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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(figsize=(12, 7))\n", - "\n", - "axes.plot(\n", - " zeta_bars, -1000 * 100 * np.real(j1s),\n", - " 'b', linewidth=2, label=r\"$J_{12} = 0.01\\, \\epsilon$\",\n", - ")\n", - "axes.plot(\n", - " zeta_bars, -1000 * 10 * np.real(j2s),\n", - " 'r--', linewidth=2, label=r\"$J_{12} = 0.1\\, \\epsilon$\",\n", - ")\n", - "axes.plot(\n", - " zeta_bars, -1000 * 2 * np.real(j3s),\n", - " 'g-.', linewidth=2, label=r\"$J_{12} = 0.5\\, \\epsilon$\",\n", - ")\n", - "\n", - "axes.set_xscale('log')\n", - "axes.set_xlabel(r\"$\\bar\\zeta$\", fontsize=30)\n", - "axes.set_xlim((zeta_bars[0], zeta_bars[-1]))\n", - "\n", - "axes.set_ylabel(\n", - " r\"$j_{\\mathrm{ss}}\\; /\\; (\\epsilon J_{12}) \\times 10^3$\",\n", - " fontsize=30,\n", - ")\n", - "axes.set_ylim((0, 2))\n", - "\n", - "axes.legend(loc=0);" - ] - }, - { - "cell_type": "markdown", - "id": "3fc15108", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "e706b00e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "QuTiP: Quantum Toolbox in Python\n", - "================================\n", - "Copyright (c) QuTiP team 2011 and later.\n", - "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", - "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", - "Original developers: R. J. Johansson & P. D. Nation.\n", - "Previous lead developers: Chris Granade & A. Grimsmo.\n", - "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", - "\n", - "QuTiP Version: 5.1.0.dev0+7941773\n", - "Numpy Version: 2.1.3\n", - "Scipy Version: 1.14.1\n", - "Cython Version: 3.0.9\n", - "Matplotlib Version: 3.9.2\n", - "Python Version: 3.12.7\n", - "Number of CPUs: 16\n", - "BLAS Info: Generic\n", - "INTEL MKL Ext: None\n", - "Platform Info: Linux (x86_64)\n", - "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", - "================================================================================\n", - "Please cite QuTiP in your publication.\n", - "================================================================================\n", - "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" - ] - } - ], - "source": [ - "qt.about()" - ] - }, - { - "cell_type": "markdown", - "id": "04b8717d", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "2380bad1", - "metadata": {}, - "outputs": [], - "source": [ - "assert 1 == 1" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "qutip-dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-3-quantum-heat-transport.md b/tutorials-v5/heom/heom-3-quantum-heat-transport.md new file mode 100644 index 00000000..00e5a524 --- /dev/null +++ b/tutorials-v5/heom/heom-3-quantum-heat-transport.md @@ -0,0 +1,508 @@ +--- +jupytext: + formats: ipynb,md:myst + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.16.4 +kernelspec: + display_name: qutip-dev + language: python + name: python3 +--- + +# HEOM 3: Quantum Heat Transport + ++++ + +## Introduction + +In this notebook, we apply the QuTiP HEOM solver to a quantum system coupled to two bosonic baths and demonstrate how to extract information about the system-bath heat currents from the auxiliary density operators (ADOs). +We consider the setup described in Ref. \[1\], which consists of two coupled qubits, each connected to its own heat bath. +The Hamiltonian of the qubits is given by + +$$ \begin{aligned} H_{\text{S}} &= H_1 + H_2 + H_{12} , \quad\text{ where }\\ +H_K &= \frac{\epsilon}{2} \bigl(\sigma_z^K + 1\bigr) \quad (K=1,2) \quad\text{ and }\quad H_{12} = J_{12} \bigl( \sigma_+^1 \sigma_-^2 + \sigma_-^1 \sigma_+^2 \bigr) . \end{aligned} $$ + +Here, $\sigma^K_{x,y,z,\pm}$ denotes the usual Pauli matrices for the K-th qubit, $\epsilon$ is the eigenfrequency of the qubits and $J_{12}$ the coupling constant. + +Each qubit is coupled to its own bath; therefore, the total Hamiltonian is + +$$ H_{\text{tot}} = H_{\text{S}} + \sum_{K=1,2} \bigl( H_{\text{B}}^K + Q_K \otimes X_{\text{B}}^K \bigr) , $$ + +where $H_{\text{B}}^K$ is the free Hamiltonian of the K-th bath and $X_{\text{B}}^K$ its coupling operator, and $Q_K = \sigma_x^K$ are the system coupling operators. +We assume that the bath spectral densities are given by Drude distributions + +$$ J_K(\omega) = \frac{2 \lambda_K \gamma_K \omega}{\omega^2 + \gamma_K^2} , $$ + +where $\lambda_K$ is the free coupling strength and $\gamma_K$ the cutoff frequency. + +We begin by defining the system and bath parameters. +We use the parameter values from Fig. 3(a) of Ref. \[1\]. +Note that we set $\hbar$ and $k_B$ to one and we will measure all frequencies and energies in units of $\epsilon$. + +References: + +   \[1\] Kato and Tanimura, [J. Chem. Phys. **143**, 064107](https://doi.org/10.1063/1.4928192) (2015). + ++++ + +## Setup + +```{code-cell} ipython3 +import dataclasses + +import numpy as np +import matplotlib.pyplot as plt + +import qutip as qt +from qutip.solver.heom import ( + HEOMSolver, + DrudeLorentzPadeBath +) +from qutip.core.environment import ( + CFExponent, + DrudeLorentzEnvironment, + system_terminator, +) + +from ipywidgets import IntProgress +from IPython.display import display + +%matplotlib inline +``` + +## Helpers + +```{code-cell} ipython3 +# Solver options: + +options = { + "nsteps": 15000, + "store_states": True, + "rtol": 1e-12, + "atol": 1e-12, + "min_step": 1e-18, + "method": "vern9", + "progress_bar": "enhanced", +} +``` + +## System and bath definition + +```{code-cell} ipython3 +@dataclasses.dataclass +class SystemParams: + """ System parameters and Hamiltonian. """ + epsilon: float = 1.0 + J12: float = 0.1 + + def H(self): + """ Return the Hamiltonian for the system. + + The system consists of two qubits with Hamiltonians (H1 and H2) + and an interaction term (H12). + """ + H1 = self.epsilon / 2 * ( + qt.tensor(qt.sigmaz() + qt.identity(2), qt.identity(2)) + ) + H2 = self.epsilon / 2 * ( + qt.tensor(qt.identity(2), qt.sigmaz() + qt.identity(2)) + ) + H12 = self.J12 * ( + qt.tensor(qt.sigmap(), qt.sigmam()) + + qt.tensor(qt.sigmam(), qt.sigmap()) + ) + return H1 + H2 + H12 + + def replace(self, **kw): + return dataclasses.replace(self, **kw) +``` + +```{code-cell} ipython3 +@dataclasses.dataclass +class BathParams: + """ Bath parameters. """ + sign: str # + or - + qubit: int # 0 or 1 + + gamma: float = 2.0 + lam: float = 0.05 + Tbar: float = 2.0 + Tdelta: float = 0.01 + + def __post_init__(self): + # T = Tbar +- Tdelta * Tbar: + assert self.sign in ("+", "-") + sign = +1 if self.sign == "+" else -1 + self.T = self.Tbar + sign * self.Tdelta * self.Tbar + # qubit + assert self.qubit in (0, 1) + + def Q(self): + """ Coupling operator for the bath. """ + Q = [qt.identity(2), qt.identity(2)] + Q[self.qubit] = qt.sigmax() + return qt.tensor(Q) + + def bath(self, Nk, tag=None): + env=DrudeLorentzEnvironment( + lam=self.lam, gamma=self.gamma, T=self.T, tag=tag + ) + env_approx,delta=env.approx_by_pade(Nk=Nk,compute_delta=True,tag=tag) + return (env_approx,self.Q()),system_terminator(self.Q(),delta),delta + + def replace(self, **kw): + return dataclasses.replace(self, **kw) +``` + +## Heat currents + +Following Ref. \[2\], we consider two possible definitions of the heat currents from the qubits into the baths. +The so-called bath heat currents are $j_{\text{B}}^K = \partial_t \langle H_{\text{B}}^K \rangle$ and the system heat currents are $j_{\text{S}}^K = \mathrm i\, \langle [H_{\text{S}}, Q_K] X_{\text{B}}^K \rangle$. +As shown in Ref. \[2\], they can be expressed in terms of the HEOM ADOs as follows: +$$ \begin{aligned} \mbox{} \\ + j_{\text{B}}^K &= \!\!\sum_{\substack{\mathbf n\\ \text{Level 1}\\ \text{Bath $K$}}}\!\! \nu[\mathbf n] \operatorname{tr}\bigl[ Q_K \rho_{\mathbf n} \bigr] - 2 C_I^K(0) \operatorname{tr}\bigl[ Q_k^2 \rho \bigr] + \Gamma_{\text{T}}^K \operatorname{tr}\bigl[ [[H_{\text{S}}, Q_K], Q_K]\, \rho \bigr] , \\[.5em] + j_{\text{S}}^K &= \mathrm i\!\! \sum_{\substack{\mathbf n\\ \text{Level 1}\\ \text{Bath $k$}}}\!\! \operatorname{tr}\bigl[ [H_{\text{S}}, Q_K]\, \rho_{\mathbf n} \bigr] + \Gamma_{\text{T}}^K \operatorname{tr}\bigl[ [[H_{\text{S}}, Q_K], Q_K]\, \rho \bigr] . \\ \mbox{} +\end{aligned} $$ +The sums run over all level-$1$ multi-indices $\mathbf n$ with one excitation corresponding to the K-th bath, $\nu[\mathbf n]$ is the corresponding (negative) exponent of the bath auto-correlation function $C^K(t)$, and $\Gamma_{\text{T}}^K$ is the Ishizaki-Tanimura terminator (i.e., a correction term accounting for the error introduced by approximating the correlation function with a finite sum of exponential terms). +In the expression for the bath heat currents, we left out terms involving $[Q_1, Q_2]$, which is zero in this example. + +   \[2\] Kato and Tanimura, [J. Chem. Phys. **145**, 224105](https://doi.org/10.1063/1.4971370) (2016). + ++++ + +In QuTiP, these currents can be conveniently calculated as follows: + +```{code-cell} ipython3 +def bath_heat_current(bath_tag, ado_state, hamiltonian, coupling_op, delta=0): + """ + Bath heat current from the system into the heat bath with the given tag. + + Parameters + ---------- + bath_tag : str, tuple or any other object + Tag of the heat bath corresponding to the current of interest. + + ado_state : HierarchyADOsState + Current state of the system and the environment (encoded in the ADOs). + + hamiltonian : Qobj + System Hamiltonian at the current time. + + coupling_op : Qobj + System coupling operator at the current time. + + delta : float + The prefactor of the \\delta(t) term in the correlation function (the + Ishizaki-Tanimura terminator). + """ + l1_labels = ado_state.filter(level=1, tags=[bath_tag]) + a_op = 1j * (hamiltonian * coupling_op - coupling_op * hamiltonian) + + result = 0 + cI0 = 0 # imaginary part of bath auto-correlation function (t=0) + for label in l1_labels: + [exp] = ado_state.exps(label) + result += exp.vk * (coupling_op * ado_state.extract(label)).tr() + + if exp.type == CFExponent.types['I']: + cI0 += exp.ck + elif exp.type == CFExponent.types['RI']: + cI0 += exp.ck2 + + result -= 2 * cI0 * (coupling_op * coupling_op * ado_state.rho).tr() + if delta != 0: + result -= ( + 1j * delta * + ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr() + ) + return result + + +def system_heat_current( + bath_tag, ado_state, hamiltonian, coupling_op, delta=0, +): + """ + System heat current from the system into the heat bath with the given tag. + + Parameters + ---------- + bath_tag : str, tuple or any other object + Tag of the heat bath corresponding to the current of interest. + + ado_state : HierarchyADOsState + Current state of the system and the environment (encoded in the ADOs). + + hamiltonian : Qobj + System Hamiltonian at the current time. + + coupling_op : Qobj + System coupling operator at the current time. + + delta : float + The prefactor of the \\delta(t) term in the correlation function (the + Ishizaki-Tanimura terminator). + """ + l1_labels = ado_state.filter(level=1, tags=[bath_tag]) + a_op = 1j * (hamiltonian * coupling_op - coupling_op * hamiltonian) + + result = 0 + for label in l1_labels: + result += (a_op * ado_state.extract(label)).tr() + + if delta != 0: + result -= ( + 1j * delta * + ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr() + ) + return result +``` + +Note that at long times, we expect $j_{\text{B}}^1 = -j_{\text{B}}^2$ and $j_{\text{S}}^1 = -j_{\text{S}}^2$ due to energy conservation. At long times, we also expect $j_{\text{B}}^1 = j_{\text{S}}^1$ and $j_{\text{B}}^2 = j_{\text{S}}^2$ since the coupling operators commute, $[Q_1, Q_2] = 0$. Hence, all four currents should agree in the long-time limit (up to a sign). This long-time value is what was analyzed in Ref. \[2\]. + ++++ + +## Simulations + ++++ + +For our simulations, we will represent the bath spectral densities using the first term of their Padé decompositions, and we will use $7$ levels of the HEOM hierarchy. + +```{code-cell} ipython3 +Nk = 1 +NC = 7 +``` + +### Time Evolution + +We fix $J_{12} = 0.1 \epsilon$ (as in Fig. 3(a-ii) of Ref. \[2\]) and choose the fixed coupling strength $\lambda_1 = \lambda_2 = J_{12}\, /\, (2\epsilon)$ (corresponding to $\bar\zeta = 1$ in Ref. \[2\]). +Using these values, we will study the time evolution of the system state and the heat currents. + +```{code-cell} ipython3 +# fix qubit-qubit and qubit-bath coupling strengths +sys = SystemParams(J12=0.1) +bath_p1 = BathParams(qubit=0, sign="+", lam=sys.J12 / 2) +bath_p2 = BathParams(qubit=1, sign="-", lam=sys.J12 / 2) + +# choose arbitrary initial state +rho0 = qt.tensor(qt.identity(2), qt.identity(2)) / 4 + +# simulation time span +tlist = np.linspace(0, 50, 250) +``` + +```{code-cell} ipython3 +H = sys.H() + +bath1,b1term,b1delta = bath_p1.bath(Nk, tag='bath 1') +Q1 = bath_p1.Q() + +bath2,b2term,b2delta = bath_p2.bath(Nk, tag='bath 2') +Q2 = bath_p2.Q() + + +solver = HEOMSolver( + qt.liouvillian(H) + b1term + b2term, + [bath1, bath2], + max_depth=NC, + options=options, +) + +result = solver.run(rho0, tlist, e_ops=[ + qt.tensor(qt.sigmaz(), qt.identity(2)), + lambda t, ado: bath_heat_current('bath 1', ado, H, Q1, b1delta), + lambda t, ado: bath_heat_current('bath 2', ado, H, Q2, b2delta), + lambda t, ado: system_heat_current('bath 1', ado, H, Q1, b1delta), + lambda t, ado: system_heat_current('bath 2', ado, H, Q2, b2delta), +]) +``` + +We first plot $\langle \sigma_z^1 \rangle$ to see the time evolution of the system state: + +```{code-cell} ipython3 +fig, axes = plt.subplots(figsize=(8, 8)) +axes.plot(tlist, result.expect[0], 'r', linewidth=2) +axes.set_xlabel('t', fontsize=28) +axes.set_ylabel(r"$\langle \sigma_z^1 \rangle$", fontsize=28); +``` + +We find a rather quick thermalization of the system state. For the heat currents, however, it takes a somewhat longer time until they converge to their long-time values: + +```{code-cell} ipython3 +fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8)) + +ax1.plot( + tlist, -np.real(result.expect[1]), + color='darkorange', label='BHC (bath 1 -> system)', +) +ax1.plot( + tlist, np.real(result.expect[2]), + '--', color='darkorange', label='BHC (system -> bath 2)', +) +ax1.plot( + tlist, -np.real(result.expect[3]), + color='dodgerblue', label='SHC (bath 1 -> system)', +) +ax1.plot( + tlist, np.real(result.expect[4]), + '--', color='dodgerblue', label='SHC (system -> bath 2)', +) + +ax1.set_xlabel('t', fontsize=28) +ax1.set_ylabel('j', fontsize=28) +ax1.set_ylim((-0.05, 0.05)) +ax1.legend(loc=0, fontsize=12) + +ax2.plot( + tlist, -np.real(result.expect[1]), + color='darkorange', label='BHC (bath 1 -> system)', +) +ax2.plot( + tlist, np.real(result.expect[2]), + '--', color='darkorange', label='BHC (system -> bath 2)', +) +ax2.plot( + tlist, -np.real(result.expect[3]), + color='dodgerblue', label='SHC (bath 1 -> system)', +) +ax2.plot( + tlist, np.real(result.expect[4]), + '--', color='dodgerblue', label='SHC (system -> bath 2)', +) + +ax2.set_xlabel('t', fontsize=28) +ax2.set_xlim((20, 50)) +ax2.set_ylim((0, 0.0002)) +ax2.legend(loc=0, fontsize=12); +``` + +### Steady-state currents + +Here, we try to reproduce the HEOM curves in Fig. 3(a) of Ref. \[1\] by varying the coupling strength and finding the steady state for each coupling strength. + +```{code-cell} ipython3 +def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options): + """ Calculate the steady sate heat currents for the given system and + bath. + """ + + bath1,b1term,b1delta = bath_p1.bath(Nk, tag='bath 1') + Q1 = bath_p1.Q() + + bath2,b2term,b2delta = bath_p2.bath(Nk, tag='bath 2') + Q2 = bath_p2.Q() + + solver = HEOMSolver( + qt.liouvillian(sys.H()) + b1term + b2term, + [bath1, bath2], + max_depth=NC, + options=options + ) + + _, steady_ados = solver.steady_state() + + return ( + bath_heat_current('bath 1', steady_ados, sys.H(), Q1, b1delta), + bath_heat_current('bath 2', steady_ados, sys.H(), Q2, b2delta), + system_heat_current('bath 1', steady_ados, sys.H(), Q1, b1delta), + system_heat_current('bath 2', steady_ados, sys.H(), Q2, b2delta), + ) +``` + +```{code-cell} ipython3 +# Define number of points to use for the plot +plot_points = 10 # use 100 for a smoother curve + +# Range of relative coupling strengths +# Chosen so that zb_max is maximum, centered around 1 on a log scale +zb_max = 4 # use 20 to see more of the current curve +zeta_bars = np.logspace( + -np.log(zb_max), + np.log(zb_max), + plot_points, + base=np.e, +) + +# Setup a progress bar +progress = IntProgress(min=0, max=(3 * plot_points)) +display(progress) + + +def calculate_heat_current(J12, zb, Nk, progress=progress): + """ Calculate a single heat current and update the progress bar. """ + # Estimate appropriate HEOM max_depth from coupling strength + NC = 7 + int(max(zb * J12 - 1, 0) * 2) + NC = min(NC, 20) + # the four currents are identical in the steady state + j, _, _, _ = heat_currents( + sys.replace(J12=J12), + bath_p1.replace(lam=zb * J12 / 2), + bath_p2.replace(lam=zb * J12 / 2), + Nk, NC, options=options, + ) + progress.value += 1 + return j + + +# Calculate steady state currents for range of zeta_bars +# for J12 = 0.01, 0.1 and 0.5: +j1s = [ + calculate_heat_current(0.01, zb, Nk) + for zb in zeta_bars +] +j2s = [ + calculate_heat_current(0.1, zb, Nk) + for zb in zeta_bars +] +j3s = [ + calculate_heat_current(0.5, zb, Nk) + for zb in zeta_bars +] +``` + +## Create Plot + +```{code-cell} ipython3 +fig, axes = plt.subplots(figsize=(12, 7)) + +axes.plot( + zeta_bars, -1000 * 100 * np.real(j1s), + 'b', linewidth=2, label=r"$J_{12} = 0.01\, \epsilon$", +) +axes.plot( + zeta_bars, -1000 * 10 * np.real(j2s), + 'r--', linewidth=2, label=r"$J_{12} = 0.1\, \epsilon$", +) +axes.plot( + zeta_bars, -1000 * 2 * np.real(j3s), + 'g-.', linewidth=2, label=r"$J_{12} = 0.5\, \epsilon$", +) + +axes.set_xscale('log') +axes.set_xlabel(r"$\bar\zeta$", fontsize=30) +axes.set_xlim((zeta_bars[0], zeta_bars[-1])) + +axes.set_ylabel( + r"$j_{\mathrm{ss}}\; /\; (\epsilon J_{12}) \times 10^3$", + fontsize=30, +) +axes.set_ylim((0, 2)) + +axes.legend(loc=0); +``` + +## About + +```{code-cell} ipython3 +qt.about() +``` + +## Testing + +This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. + +```{code-cell} ipython3 +assert 1 == 1 +``` diff --git a/tutorials-v5/heom/heom-4-dynamical-decoupling.ipynb b/tutorials-v5/heom/heom-4-dynamical-decoupling.ipynb deleted file mode 100644 index 04c42d0a..00000000 --- a/tutorials-v5/heom/heom-4-dynamical-decoupling.ipynb +++ /dev/null @@ -1,904 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "483dcf2f", - "metadata": {}, - "source": [ - "# HEOM 4: Dynamical decoupling of a non-Markovian environment" - ] - }, - { - "cell_type": "markdown", - "id": "5c3345d9", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "Following [Lorenza Viola and Seth Lloyd](https://arxiv.org/abs/quant-ph/9803057) we consider an example of dynamical decoupling.\n", - "We choose a drive which performs pi rotations, interspersed with short periods where the bath causes dephasing.\n", - "\n", - "We first show the standard example of equally spaced pulses, and then consider the 'optimal' Uhrig spacing ([Götz S. Uhrig Phys. Rev. Lett. 98, 100504 (2007)](https://arxiv.org/abs/quant-ph/0609203))." - ] - }, - { - "cell_type": "markdown", - "id": "c2afc99b", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "8b1af4e8", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " QobjEvo,\n", - " basis,\n", - " expect,\n", - " ket2dm,\n", - " sigmax,\n", - " sigmaz,\n", - " DrudeLorentzEnvironment\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver\n", - ")\n", - "\n", - "from ipywidgets import IntProgress\n", - "from IPython.display import display\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "4a9cdc73", - "metadata": {}, - "source": [ - "## Solver options" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "557d9442", - "metadata": {}, - "outputs": [], - "source": [ - "# Solver options:\n", - "\n", - "# The max_step must be set to a short time than the\n", - "# length of the shortest pulse, otherwise the solver\n", - "# might skip over a pulse.\n", - "\n", - "options = {\n", - " \"nsteps\": 1500,\n", - " \"store_states\": True,\n", - " \"rtol\": 1e-12,\n", - " \"atol\": 1e-12,\n", - " \"max_step\": 1 / 20.0,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "a3388380", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "Now we define the system and bath properties and the HEOM parameters. The system is a single stationary qubit with $H = 0$ and the bath is a bosonic bath with a Drude-Lorentz spectrum." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4e21050b", - "metadata": {}, - "outputs": [], - "source": [ - "# Define the system Hamlitonian.\n", - "#\n", - "# The system isn't evolving by itself, so the Hamiltonian is 0 (with the\n", - "# correct dimensions):\n", - "\n", - "H_sys = 0 * sigmaz()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "62ebb14f", - "metadata": {}, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresponding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresponding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "0217cff8", - "metadata": {}, - "outputs": [], - "source": [ - "# Properties for the Drude-Lorentz bath\n", - "\n", - "lam = 0.0005\n", - "gamma = 0.005\n", - "T = 0.05\n", - "\n", - "# bath-system coupling operator:\n", - "Q = sigmaz()\n", - "\n", - "# number of terms to keep in the expansion of the bath correlation function:\n", - "Nk = 3\n", - "\n", - "env = DrudeLorentzEnvironment(lam=lam, gamma=gamma,T=T)\n", - "env_approx=env.approx_by_pade(Nk=Nk)\n", - "bath=(env_approx,Q)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5d5f5c49", - "metadata": {}, - "outputs": [], - "source": [ - "# HEOM parameters\n", - "\n", - "# number of layers to keep in the hierarchy:\n", - "NC = 6" - ] - }, - { - "cell_type": "markdown", - "id": "29d93a37", - "metadata": {}, - "source": [ - "To perform the dynamic decoupling from the environment, we will drive the system with a time-dependent pulse that couples to the system via the $\\sigma_x$ operator. The area under the pulse will usual be set to $\\pi / 2$ so that the pulse flips the qubit state.\n", - "\n", - "Below we define a function that returns the pulse (which is itself a function):" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e0e2b7d0", - "metadata": {}, - "outputs": [], - "source": [ - "def drive(amplitude, delay, integral):\n", - " \"\"\" Coefficient of the drive as a function of time.\n", - "\n", - " The drive consists of a series of constant pulses with\n", - " a fixed delay between them.\n", - "\n", - " Parameters\n", - " ----------\n", - " amplitude : float\n", - " The amplitude of the drive during the pulse.\n", - " delay : float\n", - " The time delay between successive pulses.\n", - " integral : float\n", - " The integral of the pulse. This determines\n", - " the duration of each pulse with the duration\n", - " equal to the integral divided by the amplitude.\n", - " \"\"\"\n", - " duration = integral / amplitude\n", - " period = duration + delay\n", - "\n", - " def pulse(t):\n", - " t = t % period\n", - " if t < duration:\n", - " return amplitude\n", - " return 0\n", - "\n", - " return pulse\n", - "\n", - "\n", - "H_drive = sigmax()" - ] - }, - { - "cell_type": "markdown", - "id": "83f996d1", - "metadata": {}, - "source": [ - "## Plot the spectral density\n", - "\n", - "Let's start by plotting the spectral density of our Drude-Lorentz bath:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c9f75790", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "wlist = np.linspace(0, 0.5, 1000)\n", - "J = env.spectral_density(wlist)\n", - "J_approx = env_approx.spectral_density(wlist)\n", - "\n", - "fig, axes = plt.subplots(1, 1, figsize=(8, 8))\n", - "axes.plot(wlist, J, 'r', linewidth=2)\n", - "axes.plot(wlist, J_approx, 'b--', linewidth=2)\n", - "\n", - "axes.set_xlabel(r'$\\omega$', fontsize=28)\n", - "axes.set_ylabel(r'J', fontsize=28);" - ] - }, - { - "cell_type": "markdown", - "id": "f5509b95", - "metadata": {}, - "source": [ - "## Dynamic decoupling with fast and slow pulses\n", - "\n", - "Now we are ready to explore dynamic decoupling from the environment.\n", - "\n", - "First we will drive the system with fast, large amplitude pulses. Then we will drive the system with slower, smaller amplitude pulses. The faster pulses decoupling the system more effectively and retain the coherence longer, but the slower pulses help too.\n", - "\n", - "Let's start by simulating the fast pulses:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "9aff655d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " [ 0% ] Elapsed 0.01s / Remaining 00:00:00:00" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 6.34s*] Elapsed 6.34s / Remaining 00:00:00:00\n", - " Total run time: 9.41s*] Elapsed 9.41s / Remaining 00:00:00:00\n" - ] - } - ], - "source": [ - "# Fast driving (quick, large amplitude pulses)\n", - "\n", - "tlist = np.linspace(0, 400, 1000)\n", - "\n", - "# start with a superposition so there is something to dephase!\n", - "rho0 = (basis(2, 1) + basis(2, 0)).unit()\n", - "rho0 = ket2dm(rho0)\n", - "\n", - "# without pulses\n", - "hsolver = HEOMSolver(H_sys, bath, NC, options=options)\n", - "outputnoDD = hsolver.run(rho0, tlist)\n", - "\n", - "# with pulses\n", - "drive_fast = drive(amplitude=0.5, delay=20, integral=np.pi / 2)\n", - "H_d = qutip.QobjEvo([H_sys, [H_drive, drive_fast]])\n", - "\n", - "hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", - "outputDD = hsolver.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "cd6eec03", - "metadata": {}, - "source": [ - "And now the longer slower pulses:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "5942c8b6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 6.27s*] Elapsed 6.27s / Remaining 00:00:00:00\n", - " Total run time: 7.62s*] Elapsed 7.62s / Remaining 00:00:00:00\n" - ] - } - ], - "source": [ - "# Slow driving (longer, small amplitude pulses)\n", - "\n", - "# without pulses\n", - "hsolver = HEOMSolver(H_sys, bath, NC, options=options)\n", - "outputnoDDslow = hsolver.run(rho0, tlist)\n", - "\n", - "# with pulses\n", - "drive_slow = drive(amplitude=0.01, delay=20, integral=np.pi/2)\n", - "H_d = QobjEvo([H_sys, [H_drive, drive_slow]])\n", - "\n", - "hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", - "outputDDslow = hsolver.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "120055e5", - "metadata": {}, - "source": [ - "Now let's plot all of the results and the shapes of the pulses:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "e11e2b88", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_dd_results(outputnoDD, outputDD, outputDDslow):\n", - " fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 12))\n", - "\n", - " # Plot the dynamic decoupling results:\n", - "\n", - " tlist = outputDD.times\n", - "\n", - " P12 = basis(2, 1) * basis(2, 0).dag()\n", - " P12DD = qutip.expect(outputDD.states, P12)\n", - " P12noDD = qutip.expect(outputnoDD.states, P12)\n", - " P12DDslow = qutip.expect(outputDDslow.states, P12)\n", - "\n", - " plt.sca(axes[0])\n", - " plt.yticks([0, 0.25, 0.5], [0, 0.25, 0.5])\n", - "\n", - " axes[0].plot(\n", - " tlist, np.real(P12DD),\n", - " 'green', linestyle='-', linewidth=2, label=\"HEOM with fast DD\",\n", - " )\n", - " axes[0].plot(\n", - " tlist, np.real(P12DDslow),\n", - " 'blue', linestyle='-', linewidth=2, label=\"HEOM with slow DD\",\n", - " )\n", - " axes[0].plot(\n", - " tlist, np.real(P12noDD),\n", - " 'orange', linestyle='--', linewidth=2, label=\"HEOM no DD\",\n", - " )\n", - "\n", - " axes[0].locator_params(axis='y', nbins=3)\n", - " axes[0].locator_params(axis='x', nbins=3)\n", - "\n", - " axes[0].set_ylabel(r\"$\\rho_{01}$\", fontsize=30)\n", - "\n", - " axes[0].legend(loc=4)\n", - " axes[0].text(0, 0.4, \"(a)\", fontsize=28)\n", - "\n", - " # Plot the drive pulses:\n", - "\n", - " pulse = [drive_fast(t) for t in tlist]\n", - " pulseslow = [drive_slow(t) for t in tlist]\n", - "\n", - " plt.sca(axes[1])\n", - " plt.yticks([0., 0.25, 0.5], [0, 0.25, 0.5])\n", - "\n", - " axes[1].plot(\n", - " tlist, pulse,\n", - " 'green', linestyle='-', linewidth=2, label=\"Drive fast\",\n", - " )\n", - " axes[1].plot(\n", - " tlist, pulseslow,\n", - " 'blue', linestyle='--', linewidth=2, label=\"Drive slow\",\n", - " )\n", - "\n", - " axes[1].locator_params(axis='y', nbins=3)\n", - " axes[1].locator_params(axis='x', nbins=3)\n", - "\n", - " axes[1].set_xlabel(r'$t\\bar{V}_{\\mathrm{f}}$', fontsize=30)\n", - " axes[1].set_ylabel(r'Drive amplitude/$\\bar{V}_{\\mathrm{f}}$', fontsize=30)\n", - "\n", - " axes[1].legend(loc=1)\n", - " axes[1].text(0, 0.4, \"(b)\", fontsize=28)\n", - "\n", - " fig.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "57846a9f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_dd_results(outputnoDD, outputDD, outputDDslow)" - ] - }, - { - "cell_type": "markdown", - "id": "159a0da8", - "metadata": {}, - "source": [ - "## Non-equally spaced pulses" - ] - }, - { - "cell_type": "markdown", - "id": "0fc9456a", - "metadata": {}, - "source": [ - "Next we consider non-equally spaced pulses.\n", - "\n", - "Rather than plot as a function of time we just consider the final coherence after time $T$ and 100 pulses. We change the width of the environment to demonstate that the Uhrig sequence (i.e. the evenly spaced pulses) can be sub-optimal when the bath is very broad.\n", - "\n", - "Instead of evenly spaced pulses, we will use pulses where the cummulative delay after $j$ pulses is given by:\n", - "\n", - "$$\n", - " \\sin^2(\\frac{\\pi}{2} \\frac{j}{N + 1})\n", - "$$\n", - "\n", - "This is just a convenient way to describe the varying delay. We could have chosen another monotonically increasing function to represent the cummulative delay (although it might not be as effective)." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "796b76da", - "metadata": {}, - "outputs": [], - "source": [ - "def cummulative_delay_fractions(N):\n", - " \"\"\" Return an array of N + 1 cummulative delay\n", - " fractions.\n", - "\n", - " The j'th entry in the array should be the sum of\n", - " all delays before the j'th pulse. The last entry\n", - " should be 1 (i.e. the entire cummulative delay\n", - " should have been used once the sequence of pulses\n", - " is complete).\n", - "\n", - " The function should be monotonically increasing,\n", - " strictly greater than zero and the last value\n", - " should be 1.\n", - "\n", - " This implementation returns:\n", - "\n", - " sin((pi / 2) * (j / (N + 1)))**2\n", - "\n", - " as the cummulative delay after the j'th pulse.\n", - " \"\"\"\n", - " return np.array([\n", - " np.sin((np.pi / 2) * (j / (N + 1)))**2\n", - " for j in range(0, N + 1)\n", - " ])\n", - "\n", - "\n", - "def drive_opt(amplitude, avg_delay, integral, N):\n", - " \"\"\" Return an optimized distance pulse function.\n", - "\n", - " Our previous pulses were evenly spaced. Here we\n", - " instead use a varying delay after the j'th pulse.\n", - "\n", - " The cummulative delay is described by the function\n", - " ``cummulative_delay_fractions`` above.\n", - " \"\"\"\n", - " duration = integral / amplitude\n", - " cummulative_delays = N * avg_delay * cummulative_delay_fractions(N)\n", - "\n", - " t_start = cummulative_delays + duration * np.arange(0, N + 1)\n", - " t_end = cummulative_delays + duration * np.arange(1, N + 2)\n", - "\n", - " def pulse(t):\n", - " if any((t_start <= t) & (t <= t_end)):\n", - " return amplitude\n", - " return 0.0\n", - "\n", - " return pulse" - ] - }, - { - "cell_type": "markdown", - "id": "d0b4922d", - "metadata": {}, - "source": [ - "Let's plot the cummulative delays and see what they look like. Note that the cummulative delay starts at $0$, ends at $1$ and is monotonically increasing, as required.\n", - "\n", - "On the same axes we plot the individual $j^{th}$ delays as a fraction of the average delay." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "d67f21ad", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_cummulative_delay_fractions(N):\n", - " cummulative = cummulative_delay_fractions(N)\n", - " individual = (cummulative[1:] - cummulative[:-1]) * N\n", - " plt.plot(np.arange(0, N + 1), cummulative, label=\"Cummulative delay\")\n", - " plt.plot(np.arange(0, N), individual, label=\"j'th delay\")\n", - " plt.xlabel(\"j\")\n", - " plt.ylabel(\"Fraction of delay\")\n", - " plt.legend()\n", - "\n", - "\n", - "plot_cummulative_delay_fractions(100)" - ] - }, - { - "cell_type": "markdown", - "id": "e38209d5", - "metadata": {}, - "source": [ - "And now let us plot the first ten even and optimally spaced pulses together to compare them:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "1720ad04", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_even_and_optimally_spaced_pulses():\n", - " amplitude = 10.0\n", - " integral = np.pi / 2\n", - " duration = integral / amplitude\n", - " delay = 1.0 - duration\n", - "\n", - " tlist = np.linspace(0, 10, 1000)\n", - "\n", - " pulse_opt = drive_opt(amplitude, delay, integral, 100)\n", - " pulse_eq = drive(amplitude, delay, integral)\n", - "\n", - " plt.plot(\n", - " tlist, [pulse_opt(t) for t in tlist], label=\"opt\",\n", - " )\n", - " plt.plot(\n", - " tlist, [pulse_eq(t) for t in tlist], label=\"eq\",\n", - " )\n", - " plt.legend(loc=4)\n", - "\n", - "\n", - "plot_even_and_optimally_spaced_pulses()" - ] - }, - { - "cell_type": "markdown", - "id": "f68fa9ff", - "metadata": {}, - "source": [ - "Now let's simulate the effectiveness of the two sets of delays by comparing how well they maintain coherence after a hundred pulses.\n", - "\n", - "We'll perform the simulation over a range of lambdas and gammas to show how the non-evenly spaced delays become optimal as the width of the bath spectral function increases." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "2cc440b8", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "45d4a5ff7f764a5282002e769b34273c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "IntProgress(value=0, max=8)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 1.33s*] Elapsed 1.33s / Remaining 00:00:00:00\n", - " Total run time: 4.57s*] Elapsed 4.57s / Remaining 00:00:00:00\n", - " Total run time: 1.35s*] Elapsed 1.35s / Remaining 00:00:00:00\n", - " Total run time: 4.57s*] Elapsed 4.57s / Remaining 00:00:00:00\n", - " Total run time: 1.05s*] Elapsed 1.05s / Remaining 00:00:00:00\n", - " Total run time: 4.47s*] Elapsed 4.47s / Remaining 00:00:00:00\n", - " Total run time: 1.17s*] Elapsed 1.17s / Remaining 00:00:00:00\n", - " Total run time: 4.43s*] Elapsed 4.43s / Remaining 00:00:00:00\n" - ] - } - ], - "source": [ - "# Bath parameters to simulate over:\n", - "\n", - "# We use only two lambdas and two gammas so that the notebook executes\n", - "# quickly:\n", - "\n", - "lams = [0.005, 0.0005]\n", - "gammas = np.linspace(0.005, 0.05, 2)\n", - "\n", - "# But one can also extend the lists to larger ones:\n", - "#\n", - "# lams = [0.01, 0.005, 0.0005]\n", - "# gammas = np.linspace(0.005, 0.05, 10)\n", - "\n", - "# Setup a progress bar:\n", - "\n", - "progress = IntProgress(min=0, max=(2 * len(lams) * len(gammas)))\n", - "display(progress)\n", - "\n", - "\n", - "def simulate_100_pulses(lam, gamma, T, NC, Nk):\n", - " \"\"\" Simulate the evolution of 100 evenly and optimally spaced pulses.\n", - "\n", - " Returns the expectation value of P12p from the final state of\n", - " each evolution.\n", - " \"\"\"\n", - " rho0 = (basis(2, 1) + basis(2, 0)).unit()\n", - " rho0 = ket2dm(rho0)\n", - "\n", - " N = 100 # number of pulses to simulate\n", - " avg_cycle_time = 1.0 # average time from one pulse to the next\n", - " t_max = N * avg_cycle_time\n", - "\n", - " tlist = np.linspace(0, t_max, 100)\n", - "\n", - " amplitude = 10.0\n", - " integral = np.pi / 2\n", - " duration = integral / amplitude\n", - " delay = avg_cycle_time - duration\n", - "\n", - " env = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T)\n", - " env_approx = env.approx_by_pade(Nk=Nk)\n", - " bath=(env_approx,Q)\n", - " # Equally spaced pulses:\n", - "\n", - " pulse_eq = drive(amplitude, delay, integral)\n", - " H_d = QobjEvo([H_sys, [H_drive, pulse_eq]])\n", - "\n", - " hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", - " result = hsolver.run(rho0, tlist)\n", - "\n", - " P12_eq = expect(result.states[-1], P12p)\n", - " progress.value += 1\n", - "\n", - " # Non-equally spaced pulses:\n", - "\n", - " pulse_opt = drive_opt(amplitude, delay, integral, N)\n", - " H_d = QobjEvo([H_sys, [H_drive, pulse_opt]])\n", - "\n", - " hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", - " result = hsolver.run(rho0, tlist)\n", - "\n", - " P12_opt = expect(result.states[-1], P12p)\n", - " progress.value += 1\n", - "\n", - " return P12_opt, P12_eq\n", - "\n", - "\n", - "# We use NC=2 and Nk=2 to speed up the simulation:\n", - "\n", - "P12_results = [\n", - " list(zip(*(\n", - " simulate_100_pulses(lam=lam_, gamma=gamma_, T=0.5, NC=2, Nk=2)\n", - " for gamma_ in gammas\n", - " )))\n", - " for lam_ in lams\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "0ff48fc7", - "metadata": {}, - "source": [ - "Now that we have the expectation values of $\\rho_{01}$ let's plot them as a function of gamma for each lambda. Note how in each case the non-evenly spaced pulses become optimal once gamma is sufficiently small:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "ab9d0107", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=False, figsize=(10, 7))\n", - "colors = [\"green\", \"red\", \"blue\"]\n", - "\n", - "for i in range(len(lams)):\n", - " color = colors[i % len(colors)]\n", - " axes.plot(\n", - " gammas, np.real(P12_results[i][0]),\n", - " color, linestyle='-', linewidth=2,\n", - " label=f\"Optimal DD [$\\\\lambda={lams[i]}$]\",\n", - " )\n", - " axes.plot(\n", - " gammas, np.real(P12_results[i][1]),\n", - " color, linestyle='-.', linewidth=2,\n", - " label=f\"Even DD [$\\\\lambda={lams[i]}$]\",\n", - " )\n", - "\n", - "axes.set_ylabel(r\"$\\rho_{01}$\")\n", - "axes.set_xlabel(r\"$\\gamma$\")\n", - "axes.legend(fontsize=16)\n", - "\n", - "fig.tight_layout();" - ] - }, - { - "cell_type": "markdown", - "id": "7b18462a", - "metadata": {}, - "source": [ - "And now you know about dynamically decoupling a qubit from its environment!" - ] - }, - { - "cell_type": "markdown", - "id": "db36a699", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "c87c7cd6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "QuTiP: Quantum Toolbox in Python\n", - "================================\n", - "Copyright (c) QuTiP team 2011 and later.\n", - "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", - "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", - "Original developers: R. J. Johansson & P. D. Nation.\n", - "Previous lead developers: Chris Granade & A. Grimsmo.\n", - "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", - "\n", - "QuTiP Version: 5.1.0.dev0+7941773\n", - "Numpy Version: 2.1.3\n", - "Scipy Version: 1.14.1\n", - "Cython Version: 3.0.9\n", - "Matplotlib Version: 3.9.2\n", - "Python Version: 3.12.7\n", - "Number of CPUs: 16\n", - "BLAS Info: Generic\n", - "INTEL MKL Ext: None\n", - "Platform Info: Linux (x86_64)\n", - "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", - "================================================================================\n", - "Please cite QuTiP in your publication.\n", - "================================================================================\n", - "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" - ] - } - ], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "89cce7fc", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "b71dc553", - "metadata": {}, - "outputs": [], - "source": [ - "assert 1 == 1" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "qutip-dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-4-dynamical-decoupling.md b/tutorials-v5/heom/heom-4-dynamical-decoupling.md new file mode 100644 index 00000000..e20ed51c --- /dev/null +++ b/tutorials-v5/heom/heom-4-dynamical-decoupling.md @@ -0,0 +1,528 @@ +--- +jupytext: + formats: ipynb,md:myst + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.16.4 +kernelspec: + display_name: qutip-dev + language: python + name: python3 +--- + +# HEOM 4: Dynamical decoupling of a non-Markovian environment + ++++ + +## Introduction + +Following [Lorenza Viola and Seth Lloyd](https://arxiv.org/abs/quant-ph/9803057) we consider an example of dynamical decoupling. +We choose a drive which performs pi rotations, interspersed with short periods where the bath causes dephasing. + +We first show the standard example of equally spaced pulses, and then consider the 'optimal' Uhrig spacing ([Götz S. Uhrig Phys. Rev. Lett. 98, 100504 (2007)](https://arxiv.org/abs/quant-ph/0609203)). + ++++ + +## Setup + +```{code-cell} ipython3 +import numpy as np +import matplotlib.pyplot as plt + +import qutip +from qutip import ( + QobjEvo, + basis, + expect, + ket2dm, + sigmax, + sigmaz, + DrudeLorentzEnvironment +) +from qutip.solver.heom import ( + HEOMSolver +) + +from ipywidgets import IntProgress +from IPython.display import display + +%matplotlib inline +``` + +## Solver options + +```{code-cell} ipython3 +# Solver options: + +# The max_step must be set to a short time than the +# length of the shortest pulse, otherwise the solver +# might skip over a pulse. + +options = { + "nsteps": 1500, + "store_states": True, + "rtol": 1e-12, + "atol": 1e-12, + "max_step": 1 / 20.0, + "method": "vern9", + "progress_bar": "enhanced", +} +``` + +## System and bath definition + +Now we define the system and bath properties and the HEOM parameters. The system is a single stationary qubit with $H = 0$ and the bath is a bosonic bath with a Drude-Lorentz spectrum. + +```{code-cell} ipython3 +# Define the system Hamlitonian. +# +# The system isn't evolving by itself, so the Hamiltonian is 0 (with the +# correct dimensions): + +H_sys = 0 * sigmaz() +``` + +```{code-cell} ipython3 +# Define some operators with which we will measure the system +# 1,1 element of density matrix - corresponding to groundstate +P11p = basis(2, 0) * basis(2, 0).dag() +P22p = basis(2, 1) * basis(2, 1).dag() +# 1,2 element of density matrix - corresponding to coherence +P12p = basis(2, 0) * basis(2, 1).dag() +``` + +```{code-cell} ipython3 +# Properties for the Drude-Lorentz bath + +lam = 0.0005 +gamma = 0.005 +T = 0.05 + +# bath-system coupling operator: +Q = sigmaz() + +# number of terms to keep in the expansion of the bath correlation function: +Nk = 3 + +env = DrudeLorentzEnvironment(lam=lam, gamma=gamma,T=T) +env_approx=env.approx_by_pade(Nk=Nk) +bath=(env_approx,Q) +``` + +```{code-cell} ipython3 +# HEOM parameters + +# number of layers to keep in the hierarchy: +NC = 6 +``` + +To perform the dynamic decoupling from the environment, we will drive the system with a time-dependent pulse that couples to the system via the $\sigma_x$ operator. The area under the pulse will usual be set to $\pi / 2$ so that the pulse flips the qubit state. + +Below we define a function that returns the pulse (which is itself a function): + +```{code-cell} ipython3 +def drive(amplitude, delay, integral): + """ Coefficient of the drive as a function of time. + + The drive consists of a series of constant pulses with + a fixed delay between them. + + Parameters + ---------- + amplitude : float + The amplitude of the drive during the pulse. + delay : float + The time delay between successive pulses. + integral : float + The integral of the pulse. This determines + the duration of each pulse with the duration + equal to the integral divided by the amplitude. + """ + duration = integral / amplitude + period = duration + delay + + def pulse(t): + t = t % period + if t < duration: + return amplitude + return 0 + + return pulse + + +H_drive = sigmax() +``` + +## Plot the spectral density + +Let's start by plotting the spectral density of our Drude-Lorentz bath: + +```{code-cell} ipython3 +wlist = np.linspace(0, 0.5, 1000) +J = env.spectral_density(wlist) +J_approx = env_approx.spectral_density(wlist) + +fig, axes = plt.subplots(1, 1, figsize=(8, 8)) +axes.plot(wlist, J, 'r', linewidth=2) +axes.plot(wlist, J_approx, 'b--', linewidth=2) + +axes.set_xlabel(r'$\omega$', fontsize=28) +axes.set_ylabel(r'J', fontsize=28); +``` + +## Dynamic decoupling with fast and slow pulses + +Now we are ready to explore dynamic decoupling from the environment. + +First we will drive the system with fast, large amplitude pulses. Then we will drive the system with slower, smaller amplitude pulses. The faster pulses decoupling the system more effectively and retain the coherence longer, but the slower pulses help too. + +Let's start by simulating the fast pulses: + +```{code-cell} ipython3 +# Fast driving (quick, large amplitude pulses) + +tlist = np.linspace(0, 400, 1000) + +# start with a superposition so there is something to dephase! +rho0 = (basis(2, 1) + basis(2, 0)).unit() +rho0 = ket2dm(rho0) + +# without pulses +hsolver = HEOMSolver(H_sys, bath, NC, options=options) +outputnoDD = hsolver.run(rho0, tlist) + +# with pulses +drive_fast = drive(amplitude=0.5, delay=20, integral=np.pi / 2) +H_d = qutip.QobjEvo([H_sys, [H_drive, drive_fast]]) + +hsolver = HEOMSolver(H_d, bath, NC, options=options) +outputDD = hsolver.run(rho0, tlist) +``` + +And now the longer slower pulses: + +```{code-cell} ipython3 +# Slow driving (longer, small amplitude pulses) + +# without pulses +hsolver = HEOMSolver(H_sys, bath, NC, options=options) +outputnoDDslow = hsolver.run(rho0, tlist) + +# with pulses +drive_slow = drive(amplitude=0.01, delay=20, integral=np.pi/2) +H_d = QobjEvo([H_sys, [H_drive, drive_slow]]) + +hsolver = HEOMSolver(H_d, bath, NC, options=options) +outputDDslow = hsolver.run(rho0, tlist) +``` + +Now let's plot all of the results and the shapes of the pulses: + +```{code-cell} ipython3 +def plot_dd_results(outputnoDD, outputDD, outputDDslow): + fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 12)) + + # Plot the dynamic decoupling results: + + tlist = outputDD.times + + P12 = basis(2, 1) * basis(2, 0).dag() + P12DD = qutip.expect(outputDD.states, P12) + P12noDD = qutip.expect(outputnoDD.states, P12) + P12DDslow = qutip.expect(outputDDslow.states, P12) + + plt.sca(axes[0]) + plt.yticks([0, 0.25, 0.5], [0, 0.25, 0.5]) + + axes[0].plot( + tlist, np.real(P12DD), + 'green', linestyle='-', linewidth=2, label="HEOM with fast DD", + ) + axes[0].plot( + tlist, np.real(P12DDslow), + 'blue', linestyle='-', linewidth=2, label="HEOM with slow DD", + ) + axes[0].plot( + tlist, np.real(P12noDD), + 'orange', linestyle='--', linewidth=2, label="HEOM no DD", + ) + + axes[0].locator_params(axis='y', nbins=3) + axes[0].locator_params(axis='x', nbins=3) + + axes[0].set_ylabel(r"$\rho_{01}$", fontsize=30) + + axes[0].legend(loc=4) + axes[0].text(0, 0.4, "(a)", fontsize=28) + + # Plot the drive pulses: + + pulse = [drive_fast(t) for t in tlist] + pulseslow = [drive_slow(t) for t in tlist] + + plt.sca(axes[1]) + plt.yticks([0., 0.25, 0.5], [0, 0.25, 0.5]) + + axes[1].plot( + tlist, pulse, + 'green', linestyle='-', linewidth=2, label="Drive fast", + ) + axes[1].plot( + tlist, pulseslow, + 'blue', linestyle='--', linewidth=2, label="Drive slow", + ) + + axes[1].locator_params(axis='y', nbins=3) + axes[1].locator_params(axis='x', nbins=3) + + axes[1].set_xlabel(r'$t\bar{V}_{\mathrm{f}}$', fontsize=30) + axes[1].set_ylabel(r'Drive amplitude/$\bar{V}_{\mathrm{f}}$', fontsize=30) + + axes[1].legend(loc=1) + axes[1].text(0, 0.4, "(b)", fontsize=28) + + fig.tight_layout() +``` + +```{code-cell} ipython3 +plot_dd_results(outputnoDD, outputDD, outputDDslow) +``` + +## Non-equally spaced pulses + ++++ + +Next we consider non-equally spaced pulses. + +Rather than plot as a function of time we just consider the final coherence after time $T$ and 100 pulses. We change the width of the environment to demonstate that the Uhrig sequence (i.e. the evenly spaced pulses) can be sub-optimal when the bath is very broad. + +Instead of evenly spaced pulses, we will use pulses where the cummulative delay after $j$ pulses is given by: + +$$ + \sin^2(\frac{\pi}{2} \frac{j}{N + 1}) +$$ + +This is just a convenient way to describe the varying delay. We could have chosen another monotonically increasing function to represent the cummulative delay (although it might not be as effective). + +```{code-cell} ipython3 +def cummulative_delay_fractions(N): + """ Return an array of N + 1 cummulative delay + fractions. + + The j'th entry in the array should be the sum of + all delays before the j'th pulse. The last entry + should be 1 (i.e. the entire cummulative delay + should have been used once the sequence of pulses + is complete). + + The function should be monotonically increasing, + strictly greater than zero and the last value + should be 1. + + This implementation returns: + + sin((pi / 2) * (j / (N + 1)))**2 + + as the cummulative delay after the j'th pulse. + """ + return np.array([ + np.sin((np.pi / 2) * (j / (N + 1)))**2 + for j in range(0, N + 1) + ]) + + +def drive_opt(amplitude, avg_delay, integral, N): + """ Return an optimized distance pulse function. + + Our previous pulses were evenly spaced. Here we + instead use a varying delay after the j'th pulse. + + The cummulative delay is described by the function + ``cummulative_delay_fractions`` above. + """ + duration = integral / amplitude + cummulative_delays = N * avg_delay * cummulative_delay_fractions(N) + + t_start = cummulative_delays + duration * np.arange(0, N + 1) + t_end = cummulative_delays + duration * np.arange(1, N + 2) + + def pulse(t): + if any((t_start <= t) & (t <= t_end)): + return amplitude + return 0.0 + + return pulse +``` + +Let's plot the cummulative delays and see what they look like. Note that the cummulative delay starts at $0$, ends at $1$ and is monotonically increasing, as required. + +On the same axes we plot the individual $j^{th}$ delays as a fraction of the average delay. + +```{code-cell} ipython3 +def plot_cummulative_delay_fractions(N): + cummulative = cummulative_delay_fractions(N) + individual = (cummulative[1:] - cummulative[:-1]) * N + plt.plot(np.arange(0, N + 1), cummulative, label="Cummulative delay") + plt.plot(np.arange(0, N), individual, label="j'th delay") + plt.xlabel("j") + plt.ylabel("Fraction of delay") + plt.legend() + + +plot_cummulative_delay_fractions(100) +``` + +And now let us plot the first ten even and optimally spaced pulses together to compare them: + +```{code-cell} ipython3 +def plot_even_and_optimally_spaced_pulses(): + amplitude = 10.0 + integral = np.pi / 2 + duration = integral / amplitude + delay = 1.0 - duration + + tlist = np.linspace(0, 10, 1000) + + pulse_opt = drive_opt(amplitude, delay, integral, 100) + pulse_eq = drive(amplitude, delay, integral) + + plt.plot( + tlist, [pulse_opt(t) for t in tlist], label="opt", + ) + plt.plot( + tlist, [pulse_eq(t) for t in tlist], label="eq", + ) + plt.legend(loc=4) + + +plot_even_and_optimally_spaced_pulses() +``` + +Now let's simulate the effectiveness of the two sets of delays by comparing how well they maintain coherence after a hundred pulses. + +We'll perform the simulation over a range of lambdas and gammas to show how the non-evenly spaced delays become optimal as the width of the bath spectral function increases. + +```{code-cell} ipython3 +# Bath parameters to simulate over: + +# We use only two lambdas and two gammas so that the notebook executes +# quickly: + +lams = [0.005, 0.0005] +gammas = np.linspace(0.005, 0.05, 2) + +# But one can also extend the lists to larger ones: +# +# lams = [0.01, 0.005, 0.0005] +# gammas = np.linspace(0.005, 0.05, 10) + +# Setup a progress bar: + +progress = IntProgress(min=0, max=(2 * len(lams) * len(gammas))) +display(progress) + + +def simulate_100_pulses(lam, gamma, T, NC, Nk): + """ Simulate the evolution of 100 evenly and optimally spaced pulses. + + Returns the expectation value of P12p from the final state of + each evolution. + """ + rho0 = (basis(2, 1) + basis(2, 0)).unit() + rho0 = ket2dm(rho0) + + N = 100 # number of pulses to simulate + avg_cycle_time = 1.0 # average time from one pulse to the next + t_max = N * avg_cycle_time + + tlist = np.linspace(0, t_max, 100) + + amplitude = 10.0 + integral = np.pi / 2 + duration = integral / amplitude + delay = avg_cycle_time - duration + + env = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T) + env_approx = env.approx_by_pade(Nk=Nk) + bath=(env_approx,Q) + # Equally spaced pulses: + + pulse_eq = drive(amplitude, delay, integral) + H_d = QobjEvo([H_sys, [H_drive, pulse_eq]]) + + hsolver = HEOMSolver(H_d, bath, NC, options=options) + result = hsolver.run(rho0, tlist) + + P12_eq = expect(result.states[-1], P12p) + progress.value += 1 + + # Non-equally spaced pulses: + + pulse_opt = drive_opt(amplitude, delay, integral, N) + H_d = QobjEvo([H_sys, [H_drive, pulse_opt]]) + + hsolver = HEOMSolver(H_d, bath, NC, options=options) + result = hsolver.run(rho0, tlist) + + P12_opt = expect(result.states[-1], P12p) + progress.value += 1 + + return P12_opt, P12_eq + + +# We use NC=2 and Nk=2 to speed up the simulation: + +P12_results = [ + list(zip(*( + simulate_100_pulses(lam=lam_, gamma=gamma_, T=0.5, NC=2, Nk=2) + for gamma_ in gammas + ))) + for lam_ in lams +] +``` + +Now that we have the expectation values of $\rho_{01}$ let's plot them as a function of gamma for each lambda. Note how in each case the non-evenly spaced pulses become optimal once gamma is sufficiently small: + +```{code-cell} ipython3 +fig, axes = plt.subplots(1, 1, sharex=False, figsize=(10, 7)) +colors = ["green", "red", "blue"] + +for i in range(len(lams)): + color = colors[i % len(colors)] + axes.plot( + gammas, np.real(P12_results[i][0]), + color, linestyle='-', linewidth=2, + label=f"Optimal DD [$\\lambda={lams[i]}$]", + ) + axes.plot( + gammas, np.real(P12_results[i][1]), + color, linestyle='-.', linewidth=2, + label=f"Even DD [$\\lambda={lams[i]}$]", + ) + +axes.set_ylabel(r"$\rho_{01}$") +axes.set_xlabel(r"$\gamma$") +axes.legend(fontsize=16) + +fig.tight_layout(); +``` + +And now you know about dynamically decoupling a qubit from its environment! + ++++ + +## About + +```{code-cell} ipython3 +qutip.about() +``` + +## Testing + +This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. + +```{code-cell} ipython3 +assert 1 == 1 +``` diff --git a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.ipynb b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.ipynb deleted file mode 100644 index b792cf10..00000000 --- a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.ipynb +++ /dev/null @@ -1,828 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cc960358", - "metadata": {}, - "source": [ - "# HEOM 5a: Fermionic single impurity model" - ] - }, - { - "cell_type": "markdown", - "id": "35b12587", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc). Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications.\n", - "\n", - "Notation:\n", - "\n", - "* $K=L/R$ refers to left or right leads.\n", - "* $\\sigma=\\pm$ refers to input/output\n", - "\n", - "We choose a Lorentzian spectral density for the leads, with a peak at the chemical potential. The latter simplifies a little the notation required for the correlation functions, but can be relaxed if neccessary.\n", - "\n", - "$$J(\\omega) = \\frac{\\Gamma W^2}{((\\omega-\\mu_K)^2 +W^2 )}$$\n", - "\n", - "The Fermi distribution function is:\n", - "\n", - "$$f_F (x) = (\\exp(x) + 1)^{-1}$$\n", - "\n", - "Together these allow the correlation functions to be expressed as:\n", - "\n", - "$$C^{\\sigma}_K(t) = \\frac{1}{2\\pi} \\int_{-\\infty}^{\\infty} d\\omega e^{\\sigma i \\omega t} \\Gamma_K(\\omega) f_F[\\sigma\\beta(\\omega - \\mu)]$$\n", - "\n", - "As with the bosonic case we can expand these in an exponential series using Matsubara, Pade, or fitting approaches.\n", - "\n", - "The Pade decomposition approximates the Fermi distubition as\n", - "\n", - "$$f_F(x) \\approx f_F^{\\mathrm{approx}}(x) = \\frac{1}{2} - \\sum_l^{l_{max}} \\frac{2k_l x}{x^2 + \\epsilon_l^2}$$\n", - "\n", - "where $k_l$ and $\\epsilon_l$ are co-efficients defined in J. Chem Phys 133,10106.\n", - "\n", - "Evaluating the integral for the correlation functions gives,\n", - "\n", - "$$C_K^{\\sigma}(t) \\approx \\sum_{l=0}^{l_{max}} \\eta_K^{\\sigma_l} e^{-\\gamma_{K,\\sigma,l}t}$$\n", - "\n", - "where:\n", - "\n", - "$$\\eta_{K,0} = \\frac{\\Gamma_KW_K}{2} f_F^{approx}(i\\beta_K W)$$\n", - "\n", - "$$\\gamma_{K,\\sigma,0} = W_K - \\sigma i\\mu_K$$ \n", - "\n", - "$$\\eta_{K,l\\neq 0} = -i\\cdot \\frac{k_m}{\\beta_K} \\cdot \\frac{\\Gamma_K W_K^2}{-\\frac{\\epsilon^2_m}{\\beta_K^2} + W_K^2}$$\n", - "\n", - "$$\\gamma_{K,\\sigma,l\\neq 0}= \\frac{\\epsilon_m}{\\beta_K} - \\sigma i \\mu_K$$\n", - "\n", - "In this notebook we:\n", - "\n", - "* compare the Matsubara and Pade approximations and contrast them with the analytical result for the current between the system and the leads.\n", - "\n", - "* plot the current through the qubit as a function of the different between the voltages of the leads." - ] - }, - { - "cell_type": "markdown", - "id": "2166bfd7", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c7ca796", - "metadata": {}, - "outputs": [], - "source": [ - "import contextlib\n", - "import dataclasses\n", - "import time\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy.integrate import quad\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " basis,\n", - " destroy,\n", - " expect,\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver,\n", - " LorentzianBath,\n", - " LorentzianPadeBath,\n", - ")\n", - "\n", - "from ipywidgets import IntProgress\n", - "from IPython.display import display\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "5307bf35", - "metadata": {}, - "source": [ - "## Helpers" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a91b9628", - "metadata": {}, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\" Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1815555c", - "metadata": {}, - "outputs": [], - "source": [ - "# Solver options:\n", - "\n", - "# We set store_ados to True so that we can\n", - "# use the auxilliary density operators (ADOs)\n", - "# to calculate the current between the leads\n", - "# and the system.\n", - "\n", - "options = {\n", - " \"nsteps\": 1500,\n", - " \"store_states\": True,\n", - " \"store_ados\": True,\n", - " \"rtol\": 1e-12,\n", - " \"atol\": 1e-12,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "a001aedd", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "And let us set up the system Hamiltonian, bath and system measurement operators:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "65e63490", - "metadata": {}, - "outputs": [], - "source": [ - "# Define the system Hamiltonian:\n", - "\n", - "# The system is a single fermion with energy level split e1:\n", - "d1 = destroy(2)\n", - "e1 = 1.0\n", - "H = e1 * d1.dag() * d1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bdf4d114", - "metadata": {}, - "outputs": [], - "source": [ - "# Define parameters for left and right fermionic baths.\n", - "# Each bath is a lead (i.e. a wire held at a potential)\n", - "# with temperature T and chemical potential mu.\n", - "\n", - "@dataclasses.dataclass\n", - "class LorentzianBathParameters:\n", - " lead: str\n", - " Q: object # coupling operator\n", - " gamma: float = 0.01 # coupling strength\n", - " W: float = 1.0 # cut-off\n", - " T: float = 0.025851991 # temperature\n", - " theta: float = 2.0 # bias\n", - "\n", - " def __post_init__(self):\n", - " assert self.lead in (\"L\", \"R\")\n", - " self.beta = 1 / self.T\n", - " if self.lead == \"L\":\n", - " self.mu = self.theta / 2.0\n", - " else:\n", - " self.mu = - self.theta / 2.0\n", - "\n", - " def J(self, w):\n", - " \"\"\" Spectral density. \"\"\"\n", - " return self.gamma * self.W**2 / ((w - self.mu)**2 + self.W**2)\n", - "\n", - " def fF(self, w, sign=1.0):\n", - " \"\"\" Fermi distribution for this bath. \"\"\"\n", - " x = sign * self.beta * (w - self.mu)\n", - " return fF(x)\n", - "\n", - " def lamshift(self, w):\n", - " \"\"\" Return the lamshift. \"\"\"\n", - " return 0.5 * (w - self.mu) * self.J(w) / self.W\n", - "\n", - " def replace(self, **kw):\n", - " return dataclasses.replace(self, **kw)\n", - "\n", - "\n", - "def fF(x):\n", - " \"\"\" Return the Fermi distribution. \"\"\"\n", - " # in units where kB = 1.0\n", - " return 1 / (np.exp(x) + 1)\n", - "\n", - "\n", - "bath_L = LorentzianBathParameters(Q=d1, lead=\"L\")\n", - "bath_R = LorentzianBathParameters(Q=d1, lead=\"R\")" - ] - }, - { - "cell_type": "markdown", - "id": "19876f72", - "metadata": {}, - "source": [ - "## Spectral density\n", - "\n", - "Let's plot the spectral density." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b1e04fd5", - "metadata": {}, - "outputs": [], - "source": [ - "w_list = np.linspace(-2, 2, 100)\n", - "\n", - "fig, ax = plt.subplots(figsize=(12, 7))\n", - "\n", - "spec_L = bath_L.J(w_list)\n", - "spec_R = bath_R.J(w_list)\n", - "\n", - "ax.plot(\n", - " w_list, spec_L,\n", - " \"b--\", linewidth=3,\n", - " label=r\"J_L(w)\",\n", - ")\n", - "ax.plot(\n", - " w_list, spec_R,\n", - " \"r--\", linewidth=3,\n", - " label=r\"J_R(w)\",\n", - ")\n", - "\n", - "ax.set_xlabel(\"w\")\n", - "ax.set_ylabel(r\"$J(\\omega)$\")\n", - "ax.legend();" - ] - }, - { - "cell_type": "markdown", - "id": "cebcc615", - "metadata": {}, - "source": [ - "## Emission and absorption by the leads\n", - "\n", - "Next let's plot the emission and absorption by the leads." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "588887bd", - "metadata": {}, - "outputs": [], - "source": [ - "w_list = np.linspace(-2, 2, 100)\n", - "\n", - "fig, ax = plt.subplots(figsize=(12, 7))\n", - "\n", - "# Left lead emission and absorption\n", - "\n", - "gam_L_in = bath_L.J(w_list) * bath_L.fF(w_list, sign=1.0)\n", - "gam_L_out = bath_L.J(w_list) * bath_L.fF(w_list, sign=-1.0)\n", - "\n", - "ax.plot(\n", - " w_list, gam_L_in,\n", - " \"b--\", linewidth=3,\n", - " label=r\"S_L(w) input (absorption)\",\n", - ")\n", - "ax.plot(\n", - " w_list, gam_L_out,\n", - " \"r--\", linewidth=3,\n", - " label=r\"S_L(w) output (emission)\",\n", - ")\n", - "\n", - "# Right lead emission and absorption\n", - "\n", - "gam_R_in = bath_R.J(w_list) * bath_R.fF(w_list, sign=1.0)\n", - "gam_R_out = bath_R.J(w_list) * bath_R.fF(w_list, sign=-1.0)\n", - "\n", - "ax.plot(\n", - " w_list, gam_R_in,\n", - " \"b\", linewidth=3,\n", - " label=r\"S_R(w) input (absorption)\",\n", - ")\n", - "ax.plot(\n", - " w_list, gam_R_out,\n", - " \"r\", linewidth=3,\n", - " label=r\"S_R(w) output (emission)\",\n", - ")\n", - "\n", - "ax.set_xlabel(\"w\")\n", - "ax.set_ylabel(r\"$S(\\omega)$\")\n", - "ax.legend();" - ] - }, - { - "cell_type": "markdown", - "id": "19b6fd7a", - "metadata": {}, - "source": [ - "## Comparing the Matsubara and Pade approximations\n", - "\n", - "Let's start by solving for the evolution using a Pade expansion of the correlation function of the Lorentzian spectral density:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5bd1b815", - "metadata": {}, - "outputs": [], - "source": [ - "# HEOM dynamics using the Pade approximation:\n", - "\n", - "# Times to solve for and initial system state:\n", - "tlist = np.linspace(0, 100, 1000)\n", - "rho0 = basis(2, 0) * basis(2, 0).dag()\n", - "\n", - "Nk = 10 # Number of exponents to retain in the expansion of each bath\n", - "\n", - "bathL = LorentzianPadeBath(\n", - " bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", - " Nk, tag=\"L\",\n", - ")\n", - "bathR = LorentzianPadeBath(\n", - " bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", - " Nk, tag=\"R\",\n", - ")\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " solver_pade = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " result_pade = solver_pade.run(rho0, tlist)\n", - "\n", - "with timer(\"Steady state solver time\"):\n", - " rho_ss_pade, ado_ss_pade = solver_pade.steady_state()" - ] - }, - { - "cell_type": "markdown", - "id": "d95f38c0", - "metadata": {}, - "source": [ - "Now let us plot the result which shows the decay of the initially excited impurity. This is not very illuminating, but we will compare it with the Matsubara expansion and analytic solution sortly:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8dd58db6", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot the Pade results\n", - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - "\n", - "axes.plot(\n", - " tlist, expect(result_pade.states, rho0),\n", - " 'r--', linewidth=2,\n", - " label=\"P11 (Pade)\",\n", - ")\n", - "axes.axhline(\n", - " expect(rho_ss_pade, rho0),\n", - " color='r', linestyle=\"dotted\", linewidth=1,\n", - " label=\"P11 (Pade steady state)\",\n", - ")\n", - "\n", - "axes.set_xlabel('t', fontsize=28)\n", - "axes.legend(fontsize=12);" - ] - }, - { - "cell_type": "markdown", - "id": "b0a96b0e", - "metadata": {}, - "source": [ - "Now let us do the same for the Matsubara expansion:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c5d64a20", - "metadata": {}, - "outputs": [], - "source": [ - "# HEOM dynamics using the Matsubara approximation:\n", - "\n", - "bathL = LorentzianBath(\n", - " bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", - " Nk, tag=\"L\",\n", - ")\n", - "bathR = LorentzianBath(\n", - " bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", - " Nk, tag=\"R\",\n", - ")\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " solver_mats = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " result_mats = solver_mats.run(rho0, tlist)\n", - "\n", - "with timer(\"Steady state solver time\"):\n", - " rho_ss_mats, ado_ss_mats = solver_mats.steady_state()" - ] - }, - { - "cell_type": "markdown", - "id": "7aae8ddb", - "metadata": {}, - "source": [ - "We see a marked difference in the Matsubara vs Pade results:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0945bc92", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot the Pade results\n", - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - "\n", - "axes.plot(\n", - " tlist, expect(result_pade.states, rho0),\n", - " 'r--', linewidth=2,\n", - " label=\"P11 (Pade)\",\n", - ")\n", - "axes.axhline(\n", - " expect(rho_ss_pade, rho0),\n", - " color='r', linestyle=\"dotted\", linewidth=1,\n", - " label=\"P11 (Pade steady state)\",\n", - ")\n", - "\n", - "axes.plot(\n", - " tlist, expect(result_mats.states, rho0),\n", - " 'b--', linewidth=2,\n", - " label=\"P11 (Mats)\",\n", - ")\n", - "axes.axhline(\n", - " expect(rho_ss_mats, rho0),\n", - " color='b', linestyle=\"dotted\", linewidth=1,\n", - " label=\"P11 (Mats steady state)\",\n", - ")\n", - "\n", - "axes.set_xlabel('t', fontsize=28)\n", - "axes.legend(fontsize=12);" - ] - }, - { - "cell_type": "markdown", - "id": "5feea0e4", - "metadata": {}, - "source": [ - "But which is more correct? The Matsubara or the Pade result?\n", - "\n", - "One advantage of this simple model is that the steady state current to the baths is analytically solvable, so we can check convergence of the result by calculating it analytically (the sum of the currents to and from the system in the steady state must be zero, so the current from one bath is the same as the current to the other).\n", - "\n", - "See the [QuTiP-BoFiN paper](https://arxiv.org/abs/2010.10806) for a detailed description and references for the analytic result. Below we just perform the required integration numerically." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d174d472", - "metadata": {}, - "outputs": [], - "source": [ - "def analytical_steady_state_current(bath_L, bath_R, e1):\n", - " \"\"\" Calculate the analytical steady state current. \"\"\"\n", - "\n", - " def integrand(w):\n", - " return (2 / np.pi) * (\n", - " bath_L.J(w) * bath_R.J(w) * (bath_L.fF(w) - bath_R.fF(w)) /\n", - " (\n", - " (bath_L.J(w) + bath_R.J(w))**2 +\n", - " 4*(w - e1 - bath_L.lamshift(w) - bath_R.lamshift(w))**2\n", - " )\n", - " )\n", - "\n", - " def real_part(x):\n", - " return np.real(integrand(x))\n", - "\n", - " def imag_part(x):\n", - " return np.imag(integrand(x))\n", - "\n", - " # in principle the bounds for the integral should be rechecked if\n", - " # bath or system parameters are changed substantially:\n", - " bounds = [-10, 10]\n", - "\n", - " real_integral, _ = quad(real_part, *bounds)\n", - " imag_integral, _ = quad(imag_part, *bounds)\n", - "\n", - " return real_integral + 1.0j * imag_integral\n", - "\n", - "\n", - "curr_ss_analytic = analytical_steady_state_current(bath_L, bath_R, e1)\n", - "\n", - "print(f\"Analytical steady state current: {curr_ss_analytic}\")" - ] - }, - { - "cell_type": "markdown", - "id": "27853ddc", - "metadata": {}, - "source": [ - "To compare the analytical result above with the result from the HEOM, we need to be able to calculate the current from the system to the bath from the HEOM result. In the HEOM description, these currents are captured in the first level auxilliary density operators (ADOs).\n", - "\n", - "In the function `state_current(...)` below, we extract the first level ADOs for the specified bath and sum the contributions to the current from each:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d341efa", - "metadata": {}, - "outputs": [], - "source": [ - "def state_current(ado_state, bath_tag):\n", - " \"\"\" Determine current from the given bath (either \"R\" or \"L\") to\n", - " the system in the given ADO state.\n", - " \"\"\"\n", - " level_1_aux = [\n", - " (ado_state.extract(label), ado_state.exps(label)[0])\n", - " for label in ado_state.filter(level=1, tags=[bath_tag])\n", - " ]\n", - "\n", - " def exp_sign(exp):\n", - " return 1 if exp.type == exp.types[\"+\"] else -1\n", - "\n", - " def exp_op(exp):\n", - " return exp.Q if exp.type == exp.types[\"+\"] else exp.Q.dag()\n", - "\n", - " return -1.0j * sum(\n", - " exp_sign(exp) * (exp_op(exp) * aux).tr()\n", - " for aux, exp in level_1_aux\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "1d97dfba", - "metadata": {}, - "source": [ - "Now we can calculate the steady state currents from the Pade and Matsubara HEOM results:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "89cda089", - "metadata": {}, - "outputs": [], - "source": [ - "curr_ss_pade_L = state_current(ado_ss_pade, \"L\")\n", - "curr_ss_pade_R = state_current(ado_ss_pade, \"R\")\n", - "\n", - "print(f\"Pade steady state current (L): {curr_ss_pade_L}\")\n", - "print(f\"Pade steady state current (R): {curr_ss_pade_R}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4c7523e6", - "metadata": {}, - "outputs": [], - "source": [ - "curr_ss_mats_L = state_current(ado_ss_mats, \"L\")\n", - "curr_ss_mats_R = state_current(ado_ss_mats, \"R\")\n", - "\n", - "print(f\"Matsubara steady state current (L): {curr_ss_mats_L}\")\n", - "print(f\"Matsubara steady state current (R): {curr_ss_mats_R}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f1d35f12", - "metadata": {}, - "source": [ - "Note that the currents from each bath balance as is required by the steady state, but the value of the current is different for the Pade and Matsubara results.\n", - "\n", - "Now let's compare all three:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b2410bab", - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"Pade current (R): {curr_ss_pade_R}\")\n", - "print(f\"Matsubara current (R): {curr_ss_mats_R}\")\n", - "print(f\"Analytical curernt: {curr_ss_analytic}\")" - ] - }, - { - "cell_type": "markdown", - "id": "36cfbe2f", - "metadata": {}, - "source": [ - "In this case we observe that the Pade approximation has converged more closely to the analytical current than the Matsubara.\n", - "\n", - "The Matsubara result could be improved by increasing the number of terms retained in the Matsubara expansion (i.e. increasing `Nk`)." - ] - }, - { - "cell_type": "markdown", - "id": "335b650c", - "metadata": {}, - "source": [ - "## Current as a function of bias voltage" - ] - }, - { - "cell_type": "markdown", - "id": "ccb98463", - "metadata": {}, - "source": [ - "Now lets plot the current as a function of bias voltage (the bias voltage is the parameter `theta` for the two baths).\n", - "\n", - "We will calculate the steady state current for each `theta` both analytically and using the HEOM with the Pade correlation expansion approximation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b329cb0f", - "metadata": {}, - "outputs": [], - "source": [ - "# Theta (bias voltages)\n", - "\n", - "thetas = np.linspace(-4, 4, 100)\n", - "\n", - "# Setup a progress bar:\n", - "\n", - "progress = IntProgress(min=0, max=2 * len(thetas))\n", - "display(progress)\n", - "\n", - "# Calculate the current for the list of thetas\n", - "\n", - "\n", - "def current_analytic_for_theta(e1, bath_L, bath_R, theta):\n", - " \"\"\" Return the analytic current for a given theta. \"\"\"\n", - " current = analytical_steady_state_current(\n", - " bath_L.replace(theta=theta),\n", - " bath_R.replace(theta=theta),\n", - " e1,\n", - " )\n", - " progress.value += 1\n", - " return np.real(current)\n", - "\n", - "\n", - "def current_pade_for_theta(H, bath_L, bath_R, theta, Nk):\n", - " \"\"\" Return the steady state current using the Pade approximation. \"\"\"\n", - " bath_L = bath_L.replace(theta=theta)\n", - " bath_R = bath_R.replace(theta=theta)\n", - "\n", - " bathL = LorentzianPadeBath(\n", - " bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", - " Nk, tag=\"L\",\n", - " )\n", - " bathR = LorentzianPadeBath(\n", - " bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", - " Nk, tag=\"R\",\n", - " )\n", - "\n", - " solver_pade = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options)\n", - " rho_ss_pade, ado_ss_pade = solver_pade.steady_state()\n", - " current = state_current(ado_ss_pade, bath_tag=\"R\")\n", - "\n", - " progress.value += 1\n", - " return np.real(current)\n", - "\n", - "\n", - "curr_ss_analytic_thetas = [\n", - " current_analytic_for_theta(e1, bath_L, bath_R, theta)\n", - " for theta in thetas\n", - "]\n", - "\n", - "# The number of expansion terms has been dropped to Nk=6 to speed\n", - "# up notebook execution. Increase to Nk=10 for more accurate results.\n", - "curr_ss_pade_theta = [\n", - " current_pade_for_theta(H, bath_L, bath_R, theta, Nk=6)\n", - " for theta in thetas\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "db018c3a", - "metadata": {}, - "source": [ - "Below we plot the results and see that even with `Nk=6`, the HEOM Pade approximation gives good results for the steady state current. Increasing `Nk` to `10` gives very accurate results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "60896a72", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(12, 7))\n", - "\n", - "ax.plot(\n", - " thetas, 2.434e-4 * 1e6 * np.array(curr_ss_analytic_thetas),\n", - " color=\"black\", linewidth=3,\n", - " label=r\"Analytical\",\n", - ")\n", - "ax.plot(\n", - " thetas, 2.434e-4 * 1e6 * np.array(curr_ss_pade_theta),\n", - " 'r--', linewidth=3,\n", - " label=r\"HEOM Pade $N_k=10$, $n_{\\mathrm{max}}=2$\",\n", - ")\n", - "\n", - "\n", - "ax.locator_params(axis='y', nbins=4)\n", - "ax.locator_params(axis='x', nbins=4)\n", - "\n", - "ax.set_xticks([-2.5, 0, 2.5])\n", - "ax.set_xticklabels([-2.5, 0, 2.5])\n", - "ax.set_xlabel(r\"Bias voltage $\\Delta \\mu$ ($V$)\", fontsize=28)\n", - "ax.set_ylabel(r\"Current ($\\mu A$)\", fontsize=28)\n", - "ax.legend(fontsize=25);" - ] - }, - { - "cell_type": "markdown", - "id": "4b605c19", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f097093e", - "metadata": {}, - "outputs": [], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "05408673", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fdcb4e35", - "metadata": {}, - "outputs": [], - "source": [ - "assert np.allclose(curr_ss_pade_L + curr_ss_pade_R, 0)\n", - "assert np.allclose(curr_ss_mats_L + curr_ss_mats_R, 0)\n", - "assert np.allclose(curr_ss_pade_R, curr_ss_analytic, rtol=1e-4)" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md new file mode 100644 index 00000000..b032e330 --- /dev/null +++ b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md @@ -0,0 +1,587 @@ +--- +jupytext: + formats: ipynb,md:myst + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.16.4 +kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +# HEOM 5a: Fermionic single impurity model + ++++ + +## Introduction + +Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc). Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications. + +Notation: + +* $K=L/R$ refers to left or right leads. +* $\sigma=\pm$ refers to input/output + +We choose a Lorentzian spectral density for the leads, with a peak at the chemical potential. The latter simplifies a little the notation required for the correlation functions, but can be relaxed if neccessary. + +$$J(\omega) = \frac{\Gamma W^2}{((\omega-\mu_K)^2 +W^2 )}$$ + +The Fermi distribution function is: + +$$f_F (x) = (\exp(x) + 1)^{-1}$$ + +Together these allow the correlation functions to be expressed as: + +$$C^{\sigma}_K(t) = \frac{1}{2\pi} \int_{-\infty}^{\infty} d\omega e^{\sigma i \omega t} \Gamma_K(\omega) f_F[\sigma\beta(\omega - \mu)]$$ + +As with the bosonic case we can expand these in an exponential series using Matsubara, Pade, or fitting approaches. + +The Pade decomposition approximates the Fermi distubition as + +$$f_F(x) \approx f_F^{\mathrm{approx}}(x) = \frac{1}{2} - \sum_l^{l_{max}} \frac{2k_l x}{x^2 + \epsilon_l^2}$$ + +where $k_l$ and $\epsilon_l$ are co-efficients defined in J. Chem Phys 133,10106. + +Evaluating the integral for the correlation functions gives, + +$$C_K^{\sigma}(t) \approx \sum_{l=0}^{l_{max}} \eta_K^{\sigma_l} e^{-\gamma_{K,\sigma,l}t}$$ + +where: + +$$\eta_{K,0} = \frac{\Gamma_KW_K}{2} f_F^{approx}(i\beta_K W)$$ + +$$\gamma_{K,\sigma,0} = W_K - \sigma i\mu_K$$ + +$$\eta_{K,l\neq 0} = -i\cdot \frac{k_m}{\beta_K} \cdot \frac{\Gamma_K W_K^2}{-\frac{\epsilon^2_m}{\beta_K^2} + W_K^2}$$ + +$$\gamma_{K,\sigma,l\neq 0}= \frac{\epsilon_m}{\beta_K} - \sigma i \mu_K$$ + +In this notebook we: + +* compare the Matsubara and Pade approximations and contrast them with the analytical result for the current between the system and the leads. + +* plot the current through the qubit as a function of the different between the voltages of the leads. + ++++ + +## Setup + +```{code-cell} +import contextlib +import dataclasses +import time + +import numpy as np +import matplotlib.pyplot as plt +from scipy.integrate import quad + +import qutip +from qutip import ( + basis, + destroy, + expect, +) +from qutip.solver.heom import ( + HEOMSolver, + LorentzianBath, + LorentzianPadeBath, +) + +from ipywidgets import IntProgress +from IPython.display import display + +%matplotlib inline +``` + +## Helpers + +```{code-cell} +@contextlib.contextmanager +def timer(label): + """ Simple utility for timing functions: + + with timer("name"): + ... code to time ... + """ + start = time.time() + yield + end = time.time() + print(f"{label}: {end - start}") +``` + +```{code-cell} +# Solver options: + +# We set store_ados to True so that we can +# use the auxilliary density operators (ADOs) +# to calculate the current between the leads +# and the system. + +options = { + "nsteps": 1500, + "store_states": True, + "store_ados": True, + "rtol": 1e-12, + "atol": 1e-12, + "method": "vern9", + "progress_bar": "enhanced", +} +``` + +## System and bath definition + +And let us set up the system Hamiltonian, bath and system measurement operators: + +```{code-cell} +# Define the system Hamiltonian: + +# The system is a single fermion with energy level split e1: +d1 = destroy(2) +e1 = 1.0 +H = e1 * d1.dag() * d1 +``` + +```{code-cell} +# Define parameters for left and right fermionic baths. +# Each bath is a lead (i.e. a wire held at a potential) +# with temperature T and chemical potential mu. + +@dataclasses.dataclass +class LorentzianBathParameters: + lead: str + Q: object # coupling operator + gamma: float = 0.01 # coupling strength + W: float = 1.0 # cut-off + T: float = 0.025851991 # temperature + theta: float = 2.0 # bias + + def __post_init__(self): + assert self.lead in ("L", "R") + self.beta = 1 / self.T + if self.lead == "L": + self.mu = self.theta / 2.0 + else: + self.mu = - self.theta / 2.0 + + def J(self, w): + """ Spectral density. """ + return self.gamma * self.W**2 / ((w - self.mu)**2 + self.W**2) + + def fF(self, w, sign=1.0): + """ Fermi distribution for this bath. """ + x = sign * self.beta * (w - self.mu) + return fF(x) + + def lamshift(self, w): + """ Return the lamshift. """ + return 0.5 * (w - self.mu) * self.J(w) / self.W + + def replace(self, **kw): + return dataclasses.replace(self, **kw) + + +def fF(x): + """ Return the Fermi distribution. """ + # in units where kB = 1.0 + return 1 / (np.exp(x) + 1) + + +bath_L = LorentzianBathParameters(Q=d1, lead="L") +bath_R = LorentzianBathParameters(Q=d1, lead="R") +``` + +## Spectral density + +Let's plot the spectral density. + +```{code-cell} +w_list = np.linspace(-2, 2, 100) + +fig, ax = plt.subplots(figsize=(12, 7)) + +spec_L = bath_L.J(w_list) +spec_R = bath_R.J(w_list) + +ax.plot( + w_list, spec_L, + "b--", linewidth=3, + label=r"J_L(w)", +) +ax.plot( + w_list, spec_R, + "r--", linewidth=3, + label=r"J_R(w)", +) + +ax.set_xlabel("w") +ax.set_ylabel(r"$J(\omega)$") +ax.legend(); +``` + +## Emission and absorption by the leads + +Next let's plot the emission and absorption by the leads. + +```{code-cell} +w_list = np.linspace(-2, 2, 100) + +fig, ax = plt.subplots(figsize=(12, 7)) + +# Left lead emission and absorption + +gam_L_in = bath_L.J(w_list) * bath_L.fF(w_list, sign=1.0) +gam_L_out = bath_L.J(w_list) * bath_L.fF(w_list, sign=-1.0) + +ax.plot( + w_list, gam_L_in, + "b--", linewidth=3, + label=r"S_L(w) input (absorption)", +) +ax.plot( + w_list, gam_L_out, + "r--", linewidth=3, + label=r"S_L(w) output (emission)", +) + +# Right lead emission and absorption + +gam_R_in = bath_R.J(w_list) * bath_R.fF(w_list, sign=1.0) +gam_R_out = bath_R.J(w_list) * bath_R.fF(w_list, sign=-1.0) + +ax.plot( + w_list, gam_R_in, + "b", linewidth=3, + label=r"S_R(w) input (absorption)", +) +ax.plot( + w_list, gam_R_out, + "r", linewidth=3, + label=r"S_R(w) output (emission)", +) + +ax.set_xlabel("w") +ax.set_ylabel(r"$S(\omega)$") +ax.legend(); +``` + +## Comparing the Matsubara and Pade approximations + +Let's start by solving for the evolution using a Pade expansion of the correlation function of the Lorentzian spectral density: + +```{code-cell} +# HEOM dynamics using the Pade approximation: + +# Times to solve for and initial system state: +tlist = np.linspace(0, 100, 1000) +rho0 = basis(2, 0) * basis(2, 0).dag() + +Nk = 10 # Number of exponents to retain in the expansion of each bath + +bathL = LorentzianPadeBath( + bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T, + Nk, tag="L", +) +bathR = LorentzianPadeBath( + bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T, + Nk, tag="R", +) + +with timer("RHS construction time"): + solver_pade = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options) + +with timer("ODE solver time"): + result_pade = solver_pade.run(rho0, tlist) + +with timer("Steady state solver time"): + rho_ss_pade, ado_ss_pade = solver_pade.steady_state() +``` + +Now let us plot the result which shows the decay of the initially excited impurity. This is not very illuminating, but we will compare it with the Matsubara expansion and analytic solution sortly: + +```{code-cell} +# Plot the Pade results +fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) + +axes.plot( + tlist, expect(result_pade.states, rho0), + 'r--', linewidth=2, + label="P11 (Pade)", +) +axes.axhline( + expect(rho_ss_pade, rho0), + color='r', linestyle="dotted", linewidth=1, + label="P11 (Pade steady state)", +) + +axes.set_xlabel('t', fontsize=28) +axes.legend(fontsize=12); +``` + +Now let us do the same for the Matsubara expansion: + +```{code-cell} +# HEOM dynamics using the Matsubara approximation: + +bathL = LorentzianBath( + bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T, + Nk, tag="L", +) +bathR = LorentzianBath( + bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T, + Nk, tag="R", +) + +with timer("RHS construction time"): + solver_mats = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options) + +with timer("ODE solver time"): + result_mats = solver_mats.run(rho0, tlist) + +with timer("Steady state solver time"): + rho_ss_mats, ado_ss_mats = solver_mats.steady_state() +``` + +We see a marked difference in the Matsubara vs Pade results: + +```{code-cell} +# Plot the Pade results +fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) + +axes.plot( + tlist, expect(result_pade.states, rho0), + 'r--', linewidth=2, + label="P11 (Pade)", +) +axes.axhline( + expect(rho_ss_pade, rho0), + color='r', linestyle="dotted", linewidth=1, + label="P11 (Pade steady state)", +) + +axes.plot( + tlist, expect(result_mats.states, rho0), + 'b--', linewidth=2, + label="P11 (Mats)", +) +axes.axhline( + expect(rho_ss_mats, rho0), + color='b', linestyle="dotted", linewidth=1, + label="P11 (Mats steady state)", +) + +axes.set_xlabel('t', fontsize=28) +axes.legend(fontsize=12); +``` + +But which is more correct? The Matsubara or the Pade result? + +One advantage of this simple model is that the steady state current to the baths is analytically solvable, so we can check convergence of the result by calculating it analytically (the sum of the currents to and from the system in the steady state must be zero, so the current from one bath is the same as the current to the other). + +See the [QuTiP-BoFiN paper](https://arxiv.org/abs/2010.10806) for a detailed description and references for the analytic result. Below we just perform the required integration numerically. + +```{code-cell} +def analytical_steady_state_current(bath_L, bath_R, e1): + """ Calculate the analytical steady state current. """ + + def integrand(w): + return (2 / np.pi) * ( + bath_L.J(w) * bath_R.J(w) * (bath_L.fF(w) - bath_R.fF(w)) / + ( + (bath_L.J(w) + bath_R.J(w))**2 + + 4*(w - e1 - bath_L.lamshift(w) - bath_R.lamshift(w))**2 + ) + ) + + def real_part(x): + return np.real(integrand(x)) + + def imag_part(x): + return np.imag(integrand(x)) + + # in principle the bounds for the integral should be rechecked if + # bath or system parameters are changed substantially: + bounds = [-10, 10] + + real_integral, _ = quad(real_part, *bounds) + imag_integral, _ = quad(imag_part, *bounds) + + return real_integral + 1.0j * imag_integral + + +curr_ss_analytic = analytical_steady_state_current(bath_L, bath_R, e1) + +print(f"Analytical steady state current: {curr_ss_analytic}") +``` + +To compare the analytical result above with the result from the HEOM, we need to be able to calculate the current from the system to the bath from the HEOM result. In the HEOM description, these currents are captured in the first level auxilliary density operators (ADOs). + +In the function `state_current(...)` below, we extract the first level ADOs for the specified bath and sum the contributions to the current from each: + +```{code-cell} +def state_current(ado_state, bath_tag): + """ Determine current from the given bath (either "R" or "L") to + the system in the given ADO state. + """ + level_1_aux = [ + (ado_state.extract(label), ado_state.exps(label)[0]) + for label in ado_state.filter(level=1, tags=[bath_tag]) + ] + + def exp_sign(exp): + return 1 if exp.type == exp.types["+"] else -1 + + def exp_op(exp): + return exp.Q if exp.type == exp.types["+"] else exp.Q.dag() + + return -1.0j * sum( + exp_sign(exp) * (exp_op(exp) * aux).tr() + for aux, exp in level_1_aux + ) +``` + +Now we can calculate the steady state currents from the Pade and Matsubara HEOM results: + +```{code-cell} +curr_ss_pade_L = state_current(ado_ss_pade, "L") +curr_ss_pade_R = state_current(ado_ss_pade, "R") + +print(f"Pade steady state current (L): {curr_ss_pade_L}") +print(f"Pade steady state current (R): {curr_ss_pade_R}") +``` + +```{code-cell} +curr_ss_mats_L = state_current(ado_ss_mats, "L") +curr_ss_mats_R = state_current(ado_ss_mats, "R") + +print(f"Matsubara steady state current (L): {curr_ss_mats_L}") +print(f"Matsubara steady state current (R): {curr_ss_mats_R}") +``` + +Note that the currents from each bath balance as is required by the steady state, but the value of the current is different for the Pade and Matsubara results. + +Now let's compare all three: + +```{code-cell} +print(f"Pade current (R): {curr_ss_pade_R}") +print(f"Matsubara current (R): {curr_ss_mats_R}") +print(f"Analytical curernt: {curr_ss_analytic}") +``` + +In this case we observe that the Pade approximation has converged more closely to the analytical current than the Matsubara. + +The Matsubara result could be improved by increasing the number of terms retained in the Matsubara expansion (i.e. increasing `Nk`). + ++++ + +## Current as a function of bias voltage + ++++ + +Now lets plot the current as a function of bias voltage (the bias voltage is the parameter `theta` for the two baths). + +We will calculate the steady state current for each `theta` both analytically and using the HEOM with the Pade correlation expansion approximation. + +```{code-cell} +# Theta (bias voltages) + +thetas = np.linspace(-4, 4, 100) + +# Setup a progress bar: + +progress = IntProgress(min=0, max=2 * len(thetas)) +display(progress) + +# Calculate the current for the list of thetas + + +def current_analytic_for_theta(e1, bath_L, bath_R, theta): + """ Return the analytic current for a given theta. """ + current = analytical_steady_state_current( + bath_L.replace(theta=theta), + bath_R.replace(theta=theta), + e1, + ) + progress.value += 1 + return np.real(current) + + +def current_pade_for_theta(H, bath_L, bath_R, theta, Nk): + """ Return the steady state current using the Pade approximation. """ + bath_L = bath_L.replace(theta=theta) + bath_R = bath_R.replace(theta=theta) + + bathL = LorentzianPadeBath( + bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T, + Nk, tag="L", + ) + bathR = LorentzianPadeBath( + bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T, + Nk, tag="R", + ) + + solver_pade = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options) + rho_ss_pade, ado_ss_pade = solver_pade.steady_state() + current = state_current(ado_ss_pade, bath_tag="R") + + progress.value += 1 + return np.real(current) + + +curr_ss_analytic_thetas = [ + current_analytic_for_theta(e1, bath_L, bath_R, theta) + for theta in thetas +] + +# The number of expansion terms has been dropped to Nk=6 to speed +# up notebook execution. Increase to Nk=10 for more accurate results. +curr_ss_pade_theta = [ + current_pade_for_theta(H, bath_L, bath_R, theta, Nk=6) + for theta in thetas +] +``` + +Below we plot the results and see that even with `Nk=6`, the HEOM Pade approximation gives good results for the steady state current. Increasing `Nk` to `10` gives very accurate results. + +```{code-cell} +fig, ax = plt.subplots(figsize=(12, 7)) + +ax.plot( + thetas, 2.434e-4 * 1e6 * np.array(curr_ss_analytic_thetas), + color="black", linewidth=3, + label=r"Analytical", +) +ax.plot( + thetas, 2.434e-4 * 1e6 * np.array(curr_ss_pade_theta), + 'r--', linewidth=3, + label=r"HEOM Pade $N_k=10$, $n_{\mathrm{max}}=2$", +) + + +ax.locator_params(axis='y', nbins=4) +ax.locator_params(axis='x', nbins=4) + +ax.set_xticks([-2.5, 0, 2.5]) +ax.set_xticklabels([-2.5, 0, 2.5]) +ax.set_xlabel(r"Bias voltage $\Delta \mu$ ($V$)", fontsize=28) +ax.set_ylabel(r"Current ($\mu A$)", fontsize=28) +ax.legend(fontsize=25); +``` + +## About + +```{code-cell} +qutip.about() +``` + +## Testing + +This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. + +```{code-cell} +assert np.allclose(curr_ss_pade_L + curr_ss_pade_R, 0) +assert np.allclose(curr_ss_mats_L + curr_ss_mats_R, 0) +assert np.allclose(curr_ss_pade_R, curr_ss_analytic, rtol=1e-4) +``` diff --git a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.ipynb b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.ipynb deleted file mode 100644 index bfc23430..00000000 --- a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.ipynb +++ /dev/null @@ -1,528 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f4f411fd", - "metadata": {}, - "source": [ - "# HEOM 5b: Discrete boson coupled to an impurity and fermionic leads" - ] - }, - { - "cell_type": "markdown", - "id": "e2f23773", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc), also coupled to a discrete bosonic (vibronic) mode.\n", - "\n", - "Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his Dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications. In particular this example reproduces some results from https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.201407\n", - "\n", - "Notation:\n", - "\n", - "* $K=L/R$ refers to left or right leads.\n", - "* $\\sigma=\\pm$ refers to input/output\n", - "\n", - "We choose a Lorentzian spectral density for the leads, with a peak at the chemical potential. The latter simplifies a little the notation required for the correlation functions, but can be relaxed if neccessary.\n", - "\n", - "$$J(\\omega) = \\frac{\\Gamma W^2}{((\\omega-\\mu_K)^2 +W^2 )}$$\n", - "\n", - "The Fermi distribution function is:\n", - "\n", - "$$f_F (x) = (\\exp(x) + 1)^{-1}$$\n", - "\n", - "Together these allow the correlation functions to be expressed as:\n", - "\n", - "$$C^{\\sigma}_K(t) = \\frac{1}{2\\pi} \\int_{-\\infty}^{\\infty} d\\omega e^{\\sigma i \\omega t} \\Gamma_K(\\omega) f_F[\\sigma\\beta(\\omega - \\mu)]$$\n", - "\n", - "As with the Bosonic case we can treat these with Matsubara, Pade, or fitting approaches.\n", - "\n", - "The Pade decomposition approximates the Fermi distubition as \n", - "\n", - "$$f_F(x) \\approx f_F^{\\mathrm{approx}}(x) = \\frac{1}{2} - \\sum_l^{l_{max}} \\frac{2k_l x}{x^2 + \\epsilon_l^2}$$\n", - "\n", - "$k_l$ and $\\epsilon_l$ are co-efficients defined in J. Chem Phys 133,10106\n", - "\n", - "Evaluating the integral for the correlation functions gives,\n", - "\n", - "\n", - "$$C_K^{\\sigma}(t) \\approx \\sum_{l=0}^{l_{max}} \\eta_K^{\\sigma_l} e^{-\\gamma_{K,\\sigma,l}t}$$\n", - "\n", - "where\n", - "\n", - "$$\\eta_{K,0} = \\frac{\\Gamma_KW_K}{2} f_F^{approx}(i\\beta_K W)$$\n", - "\n", - "$$\\gamma_{K,\\sigma,0} = W_K - \\sigma i\\mu_K$$ \n", - "\n", - "$$\\eta_{K,l\\neq 0} = -i\\cdot \\frac{k_m}{\\beta_K} \\cdot \\frac{\\Gamma_K W_K^2}{-\\frac{\\epsilon^2_m}{\\beta_K^2} + W_K^2}$$\n", - "\n", - "$$\\gamma_{K,\\sigma,l\\neq 0}= \\frac{\\epsilon_m}{\\beta_K} - \\sigma i \\mu_K$$" - ] - }, - { - "cell_type": "markdown", - "id": "007b0b5f", - "metadata": {}, - "source": [ - "## Differences from Example 5a" - ] - }, - { - "cell_type": "markdown", - "id": "6c35243d", - "metadata": {}, - "source": [ - "The system we study here has two big differences from the HEOM 5a example:\n", - "\n", - "* the system now includes a discrete bosonic mode,\n", - "* and the electronic leads have $W$ set to $10^4$ (i.e. the wide-band limit).\n", - "\n", - "The new system Hamiltonian is:\n", - "\n", - "$$\n", - "H_{\\mathrm{vib}} = H_{\\mathrm{SIAM}} + \\Omega a^{\\dagger}a + \\lambda (a+a^{\\dagger})c{^\\dagger}c.\n", - "$$\n", - "\n", - "where $H_{\\mathrm{SIAM}}$ is the Hamiltonian of the single impurity, and the remaining terms are the Hamiltonian of the bosonic mode and its interaction with the impurity.\n", - "\n", - "The complete setup now consists of four parts:\n", - "\n", - "* the single impurity\n", - "* a discrete bosonic mode\n", - "* two fermionic leads.\n", - "\n", - "**Note**: This example is quite numerically challenging and has many system and bath components. For an easier introduction into the fermionic case, see example 5a.\n", - "\n", - "**Note**: We've reduced the cut-off of the bosonic mode to 2 modes to facilitate faster execution of the notebooks when the outputs are being checked and compiled. A more accurate result may be obtained by increasing the number of bosonic modes to, for example, 16." - ] - }, - { - "cell_type": "markdown", - "id": "d6707696", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f12b48a2", - "metadata": {}, - "outputs": [], - "source": [ - "import contextlib\n", - "import dataclasses\n", - "import time\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " destroy,\n", - " qeye,\n", - " tensor,\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver,\n", - " LorentzianPadeBath,\n", - ")\n", - "\n", - "from ipywidgets import IntProgress\n", - "from IPython.display import display\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "44595576", - "metadata": {}, - "source": [ - "## Helpers" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "278c8e44", - "metadata": {}, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\" Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "61a29f07", - "metadata": {}, - "outputs": [], - "source": [ - "def state_current(ado_state, bath_tag):\n", - " \"\"\" Determine current from the given bath (either \"R\" or \"L\") to\n", - " the system in the given ADO state.\n", - " \"\"\"\n", - " level_1_aux = [\n", - " (ado_state.extract(label), ado_state.exps(label)[0])\n", - " for label in ado_state.filter(level=1, tags=[bath_tag])\n", - " ]\n", - "\n", - " def exp_sign(exp):\n", - " return 1 if exp.type == exp.types[\"+\"] else -1\n", - "\n", - " def exp_op(exp):\n", - " return exp.Q if exp.type == exp.types[\"+\"] else exp.Q.dag()\n", - "\n", - " return -1.0j * sum(\n", - " exp_sign(exp) * (exp_op(exp) * aux).tr()\n", - " for aux, exp in level_1_aux\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d93e7e1c", - "metadata": {}, - "outputs": [], - "source": [ - "# Solver options:\n", - "\n", - "# We set store_ados to True so that we can\n", - "# use the auxilliary density operators (ADOs)\n", - "# to calculate the current between the leads\n", - "# and the system.\n", - "\n", - "options = {\n", - " \"nsteps\": 1500,\n", - " \"store_states\": True,\n", - " \"store_ados\": True,\n", - " \"rtol\": 1e-12,\n", - " \"atol\": 1e-12,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "dbe40f80", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "Let us set up the system Hamiltonian and specify the properties of the two reservoirs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e6d16db1", - "metadata": {}, - "outputs": [], - "source": [ - "# Define the system Hamiltonian:\n", - "\n", - "@dataclasses.dataclass\n", - "class SystemParameters:\n", - " e1: float = 0.3 # fermion mode energy splitting\n", - " Omega: float = 0.2 # bosonic mode energy splitting\n", - " Lambda: float = 0.12 # coupling between fermion and boson\n", - " Nbos: int = 2\n", - "\n", - " def __post_init__(self):\n", - " d = tensor(destroy(2), qeye(self.Nbos))\n", - " a = tensor(qeye(2), destroy(self.Nbos))\n", - " self.H = (\n", - " self.e1 * d.dag() * d +\n", - " self.Omega * a.dag() * a +\n", - " self.Lambda * (a + a.dag()) * d.dag() * d\n", - " )\n", - " self.Q = d\n", - "\n", - " def replace(self, **kw):\n", - " return dataclasses.replace(self, **kw)\n", - "\n", - "\n", - "sys_p = SystemParameters()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d338e3c8", - "metadata": {}, - "outputs": [], - "source": [ - "# Define parameters for left and right fermionic baths.\n", - "# Each bath is a lead (i.e. a wire held at a potential)\n", - "# with temperature T and chemical potential mu.\n", - "\n", - "@dataclasses.dataclass\n", - "class LorentzianBathParameters:\n", - " lead: str\n", - " gamma: float = 0.01 # coupling strength\n", - " W: float = 1.0 # cut-off\n", - " T: float = 0.025851991 # temperature (in eV)\n", - " theta: float = 2.0 # bias\n", - "\n", - " def __post_init__(self):\n", - " assert self.lead in (\"L\", \"R\")\n", - " self.beta = 1 / self.T\n", - " if self.lead == \"L\":\n", - " self.mu = self.theta / 2.0\n", - " else:\n", - " self.mu = - self.theta / 2.0\n", - "\n", - " def J(self, w):\n", - " \"\"\" Spectral density. \"\"\"\n", - " return self.gamma * self.W**2 / ((w - self.mu)**2 + self.W**2)\n", - "\n", - " def fF(self, w, sign=1.0):\n", - " \"\"\" Fermi distribution for this bath. \"\"\"\n", - " x = sign * self.beta * (w - self.mu)\n", - " return fF(x)\n", - "\n", - " def lamshift(self, w):\n", - " \"\"\" Return the lamshift. \"\"\"\n", - " return 0.5 * (w - self.mu) * self.J(w) / self.W\n", - "\n", - " def replace(self, **kw):\n", - " return dataclasses.replace(self, **kw)\n", - "\n", - "\n", - "def fF(x):\n", - " \"\"\" Return the Fermi distribution. \"\"\"\n", - " # in units where kB = 1.0\n", - " return 1 / (np.exp(x) + 1)\n", - "\n", - "\n", - "# We set W = 1e4 to investigate the wide-band limit:\n", - "\n", - "bath_L = LorentzianBathParameters(W=10**4, lead=\"L\")\n", - "bath_R = LorentzianBathParameters(W=10**4, lead=\"R\")" - ] - }, - { - "cell_type": "markdown", - "id": "11cac58c", - "metadata": {}, - "source": [ - "## Emission and absorption by the leads\n", - "\n", - "Next let's plot the emission and absorption by the leads." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8ca4233b", - "metadata": {}, - "outputs": [], - "source": [ - "w_list = np.linspace(-2, 2, 100)\n", - "\n", - "fig, ax = plt.subplots(figsize=(12, 7))\n", - "\n", - "# Left lead emission and absorption\n", - "\n", - "gam_L_in = bath_L.J(w_list) * bath_L.fF(w_list, sign=1.0)\n", - "gam_L_out = bath_L.J(w_list) * bath_L.fF(w_list, sign=-1.0)\n", - "\n", - "ax.plot(\n", - " w_list, gam_L_in,\n", - " \"b--\", linewidth=3,\n", - " label=r\"S_L(w) input (absorption)\",\n", - ")\n", - "ax.plot(\n", - " w_list, gam_L_out,\n", - " \"r--\", linewidth=3,\n", - " label=r\"S_L(w) output (emission)\",\n", - ")\n", - "\n", - "# Right lead emission and absorption\n", - "\n", - "gam_R_in = bath_R.J(w_list) * bath_R.fF(w_list, sign=1.0)\n", - "gam_R_out = bath_R.J(w_list) * bath_R.fF(w_list, sign=-1.0)\n", - "\n", - "ax.plot(\n", - " w_list, gam_R_in,\n", - " \"b\", linewidth=3,\n", - " label=r\"S_R(w) input (absorption)\",\n", - ")\n", - "ax.plot(\n", - " w_list, gam_R_out,\n", - " \"r\", linewidth=3,\n", - " label=r\"S_R(w) output (emission)\",\n", - ")\n", - "\n", - "ax.set_xlabel(\"w\")\n", - "ax.set_ylabel(r\"$S(\\omega)$\")\n", - "ax.legend();" - ] - }, - { - "cell_type": "markdown", - "id": "793150e7", - "metadata": {}, - "source": [ - "## Below we give one example data set from Paper\n", - "\n", - "Here we just give one example of the current as a function of bias voltage, but in general one can try different cut-offs of the bosonic Fock space and the expansion of the correlation functions until convergence is found.\n", - "\n", - "One note: for very large problems, this can be slow." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fb1e45d9", - "metadata": {}, - "outputs": [], - "source": [ - "def steady_state_pade_for_theta(sys_p, bath_L, bath_R, theta, Nk, Nc, Nbos):\n", - " \"\"\" Return the steady state current using the Pade approximation. \"\"\"\n", - "\n", - " sys_p = sys_p.replace(Nbos=Nbos)\n", - " bath_L = bath_L.replace(theta=theta)\n", - " bath_R = bath_R.replace(theta=theta)\n", - "\n", - " bathL = LorentzianPadeBath(\n", - " sys_p.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", - " Nk, tag=\"L\",\n", - " )\n", - " bathR = LorentzianPadeBath(\n", - " sys_p.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", - " Nk, tag=\"R\",\n", - " )\n", - "\n", - " solver_pade = HEOMSolver(\n", - " sys_p.H, [bathL, bathR], max_depth=2, options=options,\n", - " )\n", - " rho_ss_pade, ado_ss_pade = solver_pade.steady_state()\n", - " current = state_current(ado_ss_pade, bath_tag=\"R\")\n", - "\n", - " return np.real(2.434e-4 * 1e6 * current)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "39d0a6ab", - "metadata": {}, - "outputs": [], - "source": [ - "# Parameters:\n", - "\n", - "Nk = 6\n", - "Nc = 2\n", - "Nbos = 2 # Use Nbos = 16 for more accurate results\n", - "\n", - "thetas = np.linspace(0, 2, 30)\n", - "\n", - "# Progress bar:\n", - "\n", - "progress = IntProgress(min=0, max=len(thetas))\n", - "display(progress)\n", - "\n", - "currents = []\n", - "\n", - "for theta in thetas:\n", - " currents.append(steady_state_pade_for_theta(\n", - " sys_p, bath_L, bath_R, theta,\n", - " Nk=Nk, Nc=Nc, Nbos=Nbos,\n", - " ))\n", - " progress.value += 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fb80b9b5", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(12, 10))\n", - "\n", - "ax.plot(\n", - " thetas, currents,\n", - " color=\"green\", linestyle='-', linewidth=3,\n", - " label=f\"Nk = {5}, max_depth = {Nc}, Nbos = {Nbos}\",\n", - ")\n", - "\n", - "ax.set_yticks([0, 0.5, 1])\n", - "ax.set_yticklabels([0, 0.5, 1])\n", - "\n", - "ax.locator_params(axis='y', nbins=4)\n", - "ax.locator_params(axis='x', nbins=4)\n", - "\n", - "ax.set_xlabel(r\"Bias voltage $\\Delta \\mu$ ($V$)\", fontsize=30)\n", - "ax.set_ylabel(r\"Current ($\\mu A$)\", fontsize=30)\n", - "ax.legend(loc=4);" - ] - }, - { - "cell_type": "markdown", - "id": "efa95cca", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d0b16ed7", - "metadata": {}, - "outputs": [], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "991661c9", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "343be28f", - "metadata": {}, - "outputs": [], - "source": [ - "assert 1 == 1" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md new file mode 100644 index 00000000..35837558 --- /dev/null +++ b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md @@ -0,0 +1,395 @@ +--- +jupytext: + formats: ipynb,md:myst + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.16.4 +kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +# HEOM 5b: Discrete boson coupled to an impurity and fermionic leads + ++++ + +## Introduction + +Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc), also coupled to a discrete bosonic (vibronic) mode. + +Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his Dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications. In particular this example reproduces some results from https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.201407 + +Notation: + +* $K=L/R$ refers to left or right leads. +* $\sigma=\pm$ refers to input/output + +We choose a Lorentzian spectral density for the leads, with a peak at the chemical potential. The latter simplifies a little the notation required for the correlation functions, but can be relaxed if neccessary. + +$$J(\omega) = \frac{\Gamma W^2}{((\omega-\mu_K)^2 +W^2 )}$$ + +The Fermi distribution function is: + +$$f_F (x) = (\exp(x) + 1)^{-1}$$ + +Together these allow the correlation functions to be expressed as: + +$$C^{\sigma}_K(t) = \frac{1}{2\pi} \int_{-\infty}^{\infty} d\omega e^{\sigma i \omega t} \Gamma_K(\omega) f_F[\sigma\beta(\omega - \mu)]$$ + +As with the Bosonic case we can treat these with Matsubara, Pade, or fitting approaches. + +The Pade decomposition approximates the Fermi distubition as + +$$f_F(x) \approx f_F^{\mathrm{approx}}(x) = \frac{1}{2} - \sum_l^{l_{max}} \frac{2k_l x}{x^2 + \epsilon_l^2}$$ + +$k_l$ and $\epsilon_l$ are co-efficients defined in J. Chem Phys 133,10106 + +Evaluating the integral for the correlation functions gives, + + +$$C_K^{\sigma}(t) \approx \sum_{l=0}^{l_{max}} \eta_K^{\sigma_l} e^{-\gamma_{K,\sigma,l}t}$$ + +where + +$$\eta_{K,0} = \frac{\Gamma_KW_K}{2} f_F^{approx}(i\beta_K W)$$ + +$$\gamma_{K,\sigma,0} = W_K - \sigma i\mu_K$$ + +$$\eta_{K,l\neq 0} = -i\cdot \frac{k_m}{\beta_K} \cdot \frac{\Gamma_K W_K^2}{-\frac{\epsilon^2_m}{\beta_K^2} + W_K^2}$$ + +$$\gamma_{K,\sigma,l\neq 0}= \frac{\epsilon_m}{\beta_K} - \sigma i \mu_K$$ + ++++ + +## Differences from Example 5a + ++++ + +The system we study here has two big differences from the HEOM 5a example: + +* the system now includes a discrete bosonic mode, +* and the electronic leads have $W$ set to $10^4$ (i.e. the wide-band limit). + +The new system Hamiltonian is: + +$$ +H_{\mathrm{vib}} = H_{\mathrm{SIAM}} + \Omega a^{\dagger}a + \lambda (a+a^{\dagger})c{^\dagger}c. +$$ + +where $H_{\mathrm{SIAM}}$ is the Hamiltonian of the single impurity, and the remaining terms are the Hamiltonian of the bosonic mode and its interaction with the impurity. + +The complete setup now consists of four parts: + +* the single impurity +* a discrete bosonic mode +* two fermionic leads. + +**Note**: This example is quite numerically challenging and has many system and bath components. For an easier introduction into the fermionic case, see example 5a. + +**Note**: We've reduced the cut-off of the bosonic mode to 2 modes to facilitate faster execution of the notebooks when the outputs are being checked and compiled. A more accurate result may be obtained by increasing the number of bosonic modes to, for example, 16. + ++++ + +## Setup + +```{code-cell} +import contextlib +import dataclasses +import time + +import matplotlib.pyplot as plt +import numpy as np + +import qutip +from qutip import ( + destroy, + qeye, + tensor, +) +from qutip.solver.heom import ( + HEOMSolver, + LorentzianPadeBath, +) + +from ipywidgets import IntProgress +from IPython.display import display + +%matplotlib inline +``` + +## Helpers + +```{code-cell} +@contextlib.contextmanager +def timer(label): + """ Simple utility for timing functions: + + with timer("name"): + ... code to time ... + """ + start = time.time() + yield + end = time.time() + print(f"{label}: {end - start}") +``` + +```{code-cell} +def state_current(ado_state, bath_tag): + """ Determine current from the given bath (either "R" or "L") to + the system in the given ADO state. + """ + level_1_aux = [ + (ado_state.extract(label), ado_state.exps(label)[0]) + for label in ado_state.filter(level=1, tags=[bath_tag]) + ] + + def exp_sign(exp): + return 1 if exp.type == exp.types["+"] else -1 + + def exp_op(exp): + return exp.Q if exp.type == exp.types["+"] else exp.Q.dag() + + return -1.0j * sum( + exp_sign(exp) * (exp_op(exp) * aux).tr() + for aux, exp in level_1_aux + ) +``` + +```{code-cell} +# Solver options: + +# We set store_ados to True so that we can +# use the auxilliary density operators (ADOs) +# to calculate the current between the leads +# and the system. + +options = { + "nsteps": 1500, + "store_states": True, + "store_ados": True, + "rtol": 1e-12, + "atol": 1e-12, + "method": "vern9", + "progress_bar": "enhanced", +} +``` + +## System and bath definition + +Let us set up the system Hamiltonian and specify the properties of the two reservoirs. + +```{code-cell} +# Define the system Hamiltonian: + +@dataclasses.dataclass +class SystemParameters: + e1: float = 0.3 # fermion mode energy splitting + Omega: float = 0.2 # bosonic mode energy splitting + Lambda: float = 0.12 # coupling between fermion and boson + Nbos: int = 2 + + def __post_init__(self): + d = tensor(destroy(2), qeye(self.Nbos)) + a = tensor(qeye(2), destroy(self.Nbos)) + self.H = ( + self.e1 * d.dag() * d + + self.Omega * a.dag() * a + + self.Lambda * (a + a.dag()) * d.dag() * d + ) + self.Q = d + + def replace(self, **kw): + return dataclasses.replace(self, **kw) + + +sys_p = SystemParameters() +``` + +```{code-cell} +# Define parameters for left and right fermionic baths. +# Each bath is a lead (i.e. a wire held at a potential) +# with temperature T and chemical potential mu. + +@dataclasses.dataclass +class LorentzianBathParameters: + lead: str + gamma: float = 0.01 # coupling strength + W: float = 1.0 # cut-off + T: float = 0.025851991 # temperature (in eV) + theta: float = 2.0 # bias + + def __post_init__(self): + assert self.lead in ("L", "R") + self.beta = 1 / self.T + if self.lead == "L": + self.mu = self.theta / 2.0 + else: + self.mu = - self.theta / 2.0 + + def J(self, w): + """ Spectral density. """ + return self.gamma * self.W**2 / ((w - self.mu)**2 + self.W**2) + + def fF(self, w, sign=1.0): + """ Fermi distribution for this bath. """ + x = sign * self.beta * (w - self.mu) + return fF(x) + + def lamshift(self, w): + """ Return the lamshift. """ + return 0.5 * (w - self.mu) * self.J(w) / self.W + + def replace(self, **kw): + return dataclasses.replace(self, **kw) + + +def fF(x): + """ Return the Fermi distribution. """ + # in units where kB = 1.0 + return 1 / (np.exp(x) + 1) + + +# We set W = 1e4 to investigate the wide-band limit: + +bath_L = LorentzianBathParameters(W=10**4, lead="L") +bath_R = LorentzianBathParameters(W=10**4, lead="R") +``` + +## Emission and absorption by the leads + +Next let's plot the emission and absorption by the leads. + +```{code-cell} +w_list = np.linspace(-2, 2, 100) + +fig, ax = plt.subplots(figsize=(12, 7)) + +# Left lead emission and absorption + +gam_L_in = bath_L.J(w_list) * bath_L.fF(w_list, sign=1.0) +gam_L_out = bath_L.J(w_list) * bath_L.fF(w_list, sign=-1.0) + +ax.plot( + w_list, gam_L_in, + "b--", linewidth=3, + label=r"S_L(w) input (absorption)", +) +ax.plot( + w_list, gam_L_out, + "r--", linewidth=3, + label=r"S_L(w) output (emission)", +) + +# Right lead emission and absorption + +gam_R_in = bath_R.J(w_list) * bath_R.fF(w_list, sign=1.0) +gam_R_out = bath_R.J(w_list) * bath_R.fF(w_list, sign=-1.0) + +ax.plot( + w_list, gam_R_in, + "b", linewidth=3, + label=r"S_R(w) input (absorption)", +) +ax.plot( + w_list, gam_R_out, + "r", linewidth=3, + label=r"S_R(w) output (emission)", +) + +ax.set_xlabel("w") +ax.set_ylabel(r"$S(\omega)$") +ax.legend(); +``` + +## Below we give one example data set from Paper + +Here we just give one example of the current as a function of bias voltage, but in general one can try different cut-offs of the bosonic Fock space and the expansion of the correlation functions until convergence is found. + +One note: for very large problems, this can be slow. + +```{code-cell} +def steady_state_pade_for_theta(sys_p, bath_L, bath_R, theta, Nk, Nc, Nbos): + """ Return the steady state current using the Pade approximation. """ + + sys_p = sys_p.replace(Nbos=Nbos) + bath_L = bath_L.replace(theta=theta) + bath_R = bath_R.replace(theta=theta) + + bathL = LorentzianPadeBath( + sys_p.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T, + Nk, tag="L", + ) + bathR = LorentzianPadeBath( + sys_p.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T, + Nk, tag="R", + ) + + solver_pade = HEOMSolver( + sys_p.H, [bathL, bathR], max_depth=2, options=options, + ) + rho_ss_pade, ado_ss_pade = solver_pade.steady_state() + current = state_current(ado_ss_pade, bath_tag="R") + + return np.real(2.434e-4 * 1e6 * current) +``` + +```{code-cell} +# Parameters: + +Nk = 6 +Nc = 2 +Nbos = 2 # Use Nbos = 16 for more accurate results + +thetas = np.linspace(0, 2, 30) + +# Progress bar: + +progress = IntProgress(min=0, max=len(thetas)) +display(progress) + +currents = [] + +for theta in thetas: + currents.append(steady_state_pade_for_theta( + sys_p, bath_L, bath_R, theta, + Nk=Nk, Nc=Nc, Nbos=Nbos, + )) + progress.value += 1 +``` + +```{code-cell} +fig, ax = plt.subplots(figsize=(12, 10)) + +ax.plot( + thetas, currents, + color="green", linestyle='-', linewidth=3, + label=f"Nk = {5}, max_depth = {Nc}, Nbos = {Nbos}", +) + +ax.set_yticks([0, 0.5, 1]) +ax.set_yticklabels([0, 0.5, 1]) + +ax.locator_params(axis='y', nbins=4) +ax.locator_params(axis='x', nbins=4) + +ax.set_xlabel(r"Bias voltage $\Delta \mu$ ($V$)", fontsize=30) +ax.set_ylabel(r"Current ($\mu A$)", fontsize=30) +ax.legend(loc=4); +``` + +## About + +```{code-cell} +qutip.about() +``` + +## Testing + +This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. + +```{code-cell} +assert 1 == 1 +``` diff --git a/tutorials-v5/heom/heom-index.ipynb b/tutorials-v5/heom/heom-index.ipynb deleted file mode 100644 index 846e4d3f..00000000 --- a/tutorials-v5/heom/heom-index.ipynb +++ /dev/null @@ -1,56 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e096ef9b", - "metadata": {}, - "source": [ - "# Hierarchical Equation of Motion Examples\n", - "\n", - "The \"hierarchical equations of motion\" (HEOM) method is a powerful numerical approach to solve the dynamics and steady-state of a quantum system coupled to a non-Markovian and non-perturbative environment. Originally developed in the context of physical chemistry, it has also been extended and applied to problems in solid-state physics, optics, single-molecule electronics, and biological physics.\n", - "\n", - "QuTiP's implementation of the HEOM is described in detail in https://arxiv.org/abs/2010.10806.\n", - "\n", - "This collection of examples from the paper illustrates how to use QuTiP's HEOM to model and investigate the dynamics of a variety of systems coupled to bosonic or fermionic baths.\n", - "\n", - "## Overview of the notebooks\n", - "\n", - "\n", - "\n", - "* [Example 1a: Spin-Bath model (introduction)](./heom-1a-spin-bath-model-basic.ipynb)\n", - "\n", - "* [Example 1b: Spin-Bath model (very strong coupling)](./heom-1b-spin-bath-model-very-strong-coupling.ipynb)\n", - "\n", - "* [Example 1c: Spin-Bath model (underdamped case)](./heom-1c-spin-bath-model-underdamped-sd.ipynb)\n", - "\n", - "* [Example 1d: Spin-Bath model, fitting of spectrum and correlation functions](./heom-1d-spin-bath-model-ohmic-fitting.ipynb)\n", - "\n", - "* [Example 1e: Spin-Bath model (pure dephasing)](./heom-1e-spin-bath-model-pure-dephasing.ipynb)\n", - "\n", - "* [Example 2: Dynamics in Fenna-Mathews-Olsen complex (FMO)](./heom-2-fmo-example.ipynb)\n", - "\n", - "* [Example 3: Quantum Heat Transport](./heom-3-quantum-heat-transport.ipynb)\n", - "\n", - "* [Example 4: Dynamical decoupling of a non-Markovian environment](./heom-4-dynamical-decoupling.ipynb)\n", - "\n", - "* [Example 5a: Fermionic single impurity model](./heom-5a-fermions-single-impurity-model.ipynb)\n", - "\n", - "* [Example 5b: Discrete boson coupled to an impurity + fermionic leads](./heom-5b-fermions-discrete-boson-model.ipynb)\n", - "\n", - "" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-index.md b/tutorials-v5/heom/heom-index.md new file mode 100644 index 00000000..f16fbb10 --- /dev/null +++ b/tutorials-v5/heom/heom-index.md @@ -0,0 +1,47 @@ +--- +jupytext: + formats: ipynb,md:myst + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.16.4 +kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +# Hierarchical Equation of Motion Examples + +The "hierarchical equations of motion" (HEOM) method is a powerful numerical approach to solve the dynamics and steady-state of a quantum system coupled to a non-Markovian and non-perturbative environment. Originally developed in the context of physical chemistry, it has also been extended and applied to problems in solid-state physics, optics, single-molecule electronics, and biological physics. + +QuTiP's implementation of the HEOM is described in detail in https://arxiv.org/abs/2010.10806. + +This collection of examples from the paper illustrates how to use QuTiP's HEOM to model and investigate the dynamics of a variety of systems coupled to bosonic or fermionic baths. + +## Overview of the notebooks + + + +* [Example 1a: Spin-Bath model (introduction)](./heom-1a-spin-bath-model-basic.ipynb) + +* [Example 1b: Spin-Bath model (very strong coupling)](./heom-1b-spin-bath-model-very-strong-coupling.ipynb) + +* [Example 1c: Spin-Bath model (underdamped case)](./heom-1c-spin-bath-model-underdamped-sd.ipynb) + +* [Example 1d: Spin-Bath model, fitting of spectrum and correlation functions](./heom-1d-spin-bath-model-ohmic-fitting.ipynb) + +* [Example 1e: Spin-Bath model (pure dephasing)](./heom-1e-spin-bath-model-pure-dephasing.ipynb) + +* [Example 2: Dynamics in Fenna-Mathews-Olsen complex (FMO)](./heom-2-fmo-example.ipynb) + +* [Example 3: Quantum Heat Transport](./heom-3-quantum-heat-transport.ipynb) + +* [Example 4: Dynamical decoupling of a non-Markovian environment](./heom-4-dynamical-decoupling.ipynb) + +* [Example 5a: Fermionic single impurity model](./heom-5a-fermions-single-impurity-model.ipynb) + +* [Example 5b: Discrete boson coupled to an impurity + fermionic leads](./heom-5b-fermions-discrete-boson-model.ipynb) + + From a0f051729cb1f193d130204ac282aec3e42ac314 Mon Sep 17 00:00:00 2001 From: mcditooss Date: Wed, 13 Nov 2024 16:30:41 +0100 Subject: [PATCH 15/44] corrections to added args to constructors --- .../heom/heom-1a-spin-bath-model-basic.ipynb | 1738 ++++++++++++++++ ...spin-bath-model-very-strong-coupling.ipynb | 945 +++++++++ ...om-1c-spin-bath-model-underdamped-sd.ipynb | 857 ++++++++ ...eom-1d-spin-bath-model-ohmic-fitting.ipynb | 1753 +++++++++++++++++ ...om-1e-spin-bath-model-pure-dephasing.ipynb | 743 +++++++ tutorials-v5/heom/heom-2-fmo-example.ipynb | 658 +++++++ .../heom/heom-3-quantum-heat-transport.ipynb | 693 +++++++ .../heom/heom-4-dynamical-decoupling.ipynb | 751 +++++++ ...om-5a-fermions-single-impurity-model.ipynb | 828 ++++++++ ...eom-5b-fermions-discrete-boson-model.ipynb | 528 +++++ tutorials-v5/heom/heom-index.ipynb | 56 + 11 files changed, 9550 insertions(+) create mode 100644 tutorials-v5/heom/heom-1a-spin-bath-model-basic.ipynb create mode 100644 tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb create mode 100644 tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb create mode 100644 tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb create mode 100644 tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb create mode 100644 tutorials-v5/heom/heom-2-fmo-example.ipynb create mode 100644 tutorials-v5/heom/heom-3-quantum-heat-transport.ipynb create mode 100644 tutorials-v5/heom/heom-4-dynamical-decoupling.ipynb create mode 100644 tutorials-v5/heom/heom-5a-fermions-single-impurity-model.ipynb create mode 100644 tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.ipynb create mode 100644 tutorials-v5/heom/heom-index.ipynb diff --git a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.ipynb b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.ipynb new file mode 100644 index 00000000..8d717dd8 --- /dev/null +++ b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.ipynb @@ -0,0 +1,1738 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "68c71886", + "metadata": {}, + "source": [ + "# HEOM 1a: Spin-Bath model (introduction)" + ] + }, + { + "cell_type": "markdown", + "id": "86685431", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The HEOM method solves the dynamics and steady state of a system and its\n", + "environment, the latter of which is encoded in a set of auxiliary density\n", + "matrices.\n", + "\n", + "In this example we show the evolution of a single two-level system in contact\n", + "with a single Bosonic environment. The properties of the system are encoded\n", + "in a Hamiltonian, and a coupling operator which describes how it is coupled\n", + "to the environment.\n", + "\n", + "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", + "\n", + "In the example below we show how to model the overdamped Drude-Lorentz\n", + "Spectral Density, commonly used with the HEOM. We show how to do this using\n", + "the Matsubara, Pade and fitting decompositions, and compare their\n", + "convergence.\n", + "\n", + "### Drude-Lorentz (overdamped) spectral density\n", + "\n", + "The Drude-Lorentz spectral density is:\n", + "\n", + "$$J_D(\\omega)= \\frac{2\\omega\\lambda\\gamma}{{\\gamma}^2 + \\omega^2}$$\n", + "\n", + "where $\\lambda$ scales the coupling strength, and $\\gamma$ is the cut-off\n", + "frequency. We use the convention,\n", + "\\begin{equation*}\n", + "C(t) = \\int_0^{\\infty} d\\omega \\frac{J_D(\\omega)}{\\pi}[\\coth(\\beta\\omega) \\\n", + " \\cos(\\omega \\tau) - i \\sin(\\omega \\tau)]\n", + "\\end{equation*}\n", + "\n", + "With the HEOM we must use an exponential decomposition:\n", + "\n", + "\\begin{equation*}\n", + "C(t)=\\sum_{k=0}^{k=\\infty} c_k e^{-\\nu_k t}\n", + "\\end{equation*}\n", + "\n", + "As an example, the Matsubara decomposition of the Drude-Lorentz spectral\n", + "density is given by:\n", + "\n", + "\\begin{equation*}\n", + " \\nu_k = \\begin{cases}\n", + " \\gamma & k = 0\\\\\n", + " {2 \\pi k} / {\\beta } & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "\\begin{equation*}\n", + " c_k = \\begin{cases}\n", + " \\lambda \\gamma (\\cot(\\beta \\gamma / 2) - i) \\\n", + " & k = 0\\\\\n", + " 4 \\lambda \\gamma \\nu_k / \\{(\\nu_k^2 - \\gamma^2)\\beta \\} \\\n", + " & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." + ] + }, + { + "cell_type": "markdown", + "id": "1dcca8d4", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c44fa069", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "import contextlib\n", + "import time\n", + "\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "from scipy.optimize import curve_fit\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " basis,\n", + " brmesolve,\n", + " destroy,\n", + " expect,\n", + " liouvillian,\n", + " qeye,\n", + " sigmax,\n", + " sigmaz,\n", + " spost,\n", + " spre,\n", + " tensor,\n", + ")\n", + "from qutip.core.environment import (\n", + " DrudeLorentzEnvironment,\n", + " ExponentialBosonicEnvironment,\n", + " system_terminator\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver,\n", + " HSolverDL,\n", + ")\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "36a7a852", + "metadata": {}, + "source": [ + "## Helper functions\n", + "\n", + "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "351c8bfe", + "metadata": {}, + "outputs": [], + "source": [ + "def cot(x):\n", + " \"\"\"Vectorized cotangent of x.\"\"\"\n", + " return 1.0 / np.tan(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b104feee", + "metadata": {}, + "outputs": [], + "source": [ + "def dl_matsubara_params(lam, gamma, T, nk):\n", + " \"\"\"Calculation of the real and imaginary expansions of the Drude-Lorenz\n", + " correlation functions.\n", + " \"\"\"\n", + " ckAR = [lam * gamma * cot(gamma / (2 * T))]\n", + " ckAR.extend(\n", + " (8 * lam * gamma * T * np.pi * k * T /\n", + " ((2 * np.pi * k * T)**2 - gamma**2))\n", + " for k in range(1, nk + 1)\n", + " )\n", + " vkAR = [gamma]\n", + " vkAR.extend(2 * np.pi * k * T for k in range(1, nk + 1))\n", + "\n", + " ckAI = [lam * gamma * (-1.0)]\n", + " vkAI = [gamma]\n", + "\n", + " return ckAR, vkAR, ckAI, vkAI" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "dc4701f9", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_result_expectations(plots, axes=None):\n", + " \"\"\"Plot the expectation values of operators as functions of time.\n", + "\n", + " Each plot in plots consists of (solver_result, measurement_operation,\n", + " color, label).\n", + " \"\"\"\n", + " if axes is None:\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " fig_created = True\n", + " else:\n", + " fig = None\n", + " fig_created = False\n", + "\n", + " # add kw arguments to each plot if missing\n", + " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", + " for result, m_op, color, label, kw in plots:\n", + " exp = np.real(expect(result.states, m_op))\n", + " kw.setdefault(\"linewidth\", 2)\n", + " axes.plot(result.times, exp, color, label=label, **kw)\n", + "\n", + " if fig_created:\n", + " axes.legend(loc=0, fontsize=12)\n", + " axes.set_xlabel(\"t\", fontsize=28)\n", + "\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2f410ff7", + "metadata": {}, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\"Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b33b495d", + "metadata": {}, + "outputs": [], + "source": [ + "# Default solver options:\n", + "\n", + "default_options = {\n", + " \"nsteps\": 1500,\n", + " \"store_states\": True,\n", + " \"rtol\": 1e-12,\n", + " \"atol\": 1e-12,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "582d1a64", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian, bath and system measurement operators:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d8d5c7a1", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the system Hamiltonian\n", + "eps = 0.5 # Energy of the 2-level system.\n", + "Del = 1.0 # Tunnelling term\n", + "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "01db3dfc", + "metadata": {}, + "outputs": [], + "source": [ + "# Initial state of the system.\n", + "rho0 = basis(2, 0) * basis(2, 0).dag()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "020d8228", + "metadata": {}, + "outputs": [], + "source": [ + "# System-bath coupling (Drude-Lorentz spectral density)\n", + "Q = sigmaz() # coupling operator\n", + "\n", + "# Bath properties:\n", + "gamma = 0.5 # cut off frequency\n", + "lam = 0.1 # coupling strength\n", + "T = 0.5\n", + "beta = 1.0 / T\n", + "\n", + "# HEOM parameters\n", + "NC = 5 # cut off parameter for the bath\n", + "Nk = 2 # terms in the Matsubara expansion of the correlation function\n", + "\n", + "# Times to solve for\n", + "tlist = np.linspace(0, 50, 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b2a8b071", + "metadata": {}, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresonding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresonding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "a93cae29", + "metadata": {}, + "source": [ + "### First of all, it is useful to look at the spectral density\n", + "\n", + "Now we are ready to begin. Let's look at the shape of the spectral density given the bath parameters we defined above:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b4649efd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_spectral_density():\n", + " \"\"\"Plot the Drude-Lorentz spectral density\"\"\"\n", + " w = np.linspace(0, 5, 1000)\n", + " J = w * 2 * lam * gamma / (gamma**2 + w**2)\n", + "\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " axes.plot(w, J, \"r\", linewidth=2)\n", + " axes.set_xlabel(r\"$\\omega$\", fontsize=28)\n", + " axes.set_ylabel(r\"J\", fontsize=28)\n", + "\n", + "\n", + "plot_spectral_density()" + ] + }, + { + "cell_type": "markdown", + "id": "54abc0a5", + "metadata": {}, + "source": [ + "Next we calculate the exponents using the Matsubara decompositions. Here we\n", + "split them into real and imaginary parts.\n", + "\n", + "The HEOM code will optimize these, and reduce the number of exponents when\n", + "real and imaginary parts have the same exponent. This is clearly the case\n", + "for the first term in the vkAI and vkAR lists." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "bb6afd3d", + "metadata": {}, + "outputs": [], + "source": [ + "ckAR, vkAR, ckAI, vkAI = dl_matsubara_params(nk=Nk, lam=lam, gamma=gamma, T=T)" + ] + }, + { + "cell_type": "markdown", + "id": "d7c17b69", + "metadata": {}, + "source": [ + "Having created the lists which specify the bath correlation functions, we\n", + "create an `ExponentialBosonicEnvironment` from them and pass the environment to the `HEOMSolver` class.\n", + "\n", + "The solver constructs the \"right hand side\" (RHS) determinining how the\n", + "system and auxiliary density operators evolve in time. This can then be used\n", + "to solve for dynamics or steady-state.\n", + "\n", + "Below we create the bath and solver and then solve for the dynamics by\n", + "calling `.run(rho0, tlist)`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "bc687720", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.06298303604125977\n", + " Total run time: 1.21s*] Elapsed 1.21s / Remaining 00:00:00:00\n", + "ODE solver time: 1.2107598781585693\n" + ] + } + ], + "source": [ + "options = {**default_options}\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " env = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI)\n", + " HEOMMats = HEOMSolver(Hsys, (env,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMats = HEOMMats.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6413cfeb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (resultMats, P11p, \"b\", \"P11 Mats\"),\n", + " (resultMats, P12p, \"r\", \"P12 Mats\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "750e9d46", + "metadata": {}, + "source": [ + "In practice, one would not perform this laborious expansion for the\n", + "Drude-Lorentz correlation function, because QuTiP already has a class,\n", + "`DrudeLorentzBath`, that can construct this bath for you. Nevertheless,\n", + "knowing how to perform this expansion will allow you to construct your own\n", + "baths for other spectral densities.\n", + "\n", + "Below we show how to use this built-in functionality:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "19037baa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.006415605545043945\n", + " Total run time: 1.30s*] Elapsed 1.30s / Remaining 00:00:00:00\n", + "ODE solver time: 1.3027725219726562\n" + ] + } + ], + "source": [ + "# Compare to built-in Drude-Lorentz bath:\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " dlenv = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T,Nk=100)\n", + " dlenv_approx=dlenv.approx_by_matsubara(Nk=Nk)\n", + " HEOM_dlbath = HEOMSolver(Hsys, (dlenv_approx,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " result_dlbath = HEOM_dlbath.run(rho0, tlist) # normal 115" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c6a09b78", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (result_dlbath, P11p, \"b\", \"P11 (DrudeLorentzBath)\"),\n", + " (result_dlbath, P12p, \"r\", \"P12 (DrudeLorentzBath)\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "99ecf6dc", + "metadata": {}, + "source": [ + "The `DrudeLorentzEnvironment` class also allows us to easily obtain the power spectrum, correlation function, and spectral density. The approximated Environment is a `BosonicEnvironment` where the effective power spectrum, correlation function, and spectral density from the approximation is also accessible. In the following plots, the solid lines are the exact expressions, and the dashed lines are based on our approximation of the correlation function with a finite number of exponents." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "4f3f19f9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "w = np.linspace(-10, 20, 1000)\n", + "w2 = np.linspace(0, 20, 1000)\n", + "fig, axs = plt.subplots(2, 2)\n", + "\n", + "axs[0, 0].plot(w, dlenv.power_spectrum(w))\n", + "axs[0, 0].plot(w, dlenv_approx.power_spectrum(w), '--')\n", + "axs[0, 0].set(xlabel=r'$\\omega$', ylabel=r'$S(\\omega)$')\n", + "axs[0, 1].plot(w2, dlenv.spectral_density(w2))\n", + "axs[0, 1].plot(w2, dlenv_approx.spectral_density(w2), '--')\n", + "axs[0, 1].set(xlabel=r'$\\omega$', ylabel=r'$J(\\omega)$')\n", + "axs[1, 0].plot(w2, np.real(dlenv.correlation_function(w2)))\n", + "axs[1, 0].plot(w2, np.real(dlenv_approx.correlation_function(w2)), '--')\n", + "axs[1, 0].set(xlabel=r'$t$', ylabel=r'$C_{R}(t)$')\n", + "axs[1, 1].plot(w2, np.imag(dlenv.correlation_function(w2)))\n", + "axs[1, 1].plot(w2, np.imag(dlenv_approx.correlation_function(w2)), '--')\n", + "axs[1, 1].set(xlabel=r'$t$', ylabel=r'$C_{I}(t)$')\n", + "\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "95d00102", + "metadata": {}, + "source": [ + "We also provide a legacy class, `HSolverDL`, which calculates the\n", + "Drude-Lorentz correlation functions automatically, to be backwards\n", + "compatible with the previous HEOM solver in QuTiP:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01aedd06", + "metadata": {}, + "outputs": [], + "source": [ + "# Compare to legacy class:\n", + "\n", + "# The legacy class performs the above collation of coefficients automatically,\n", + "# based upon the parameters for the Drude-Lorentz spectral density.\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " HEOMlegacy = HSolverDL(Hsys, Q, lam, T, NC, Nk, gamma, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultLegacy = HEOMlegacy.run(rho0, tlist) # normal 115" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "461ae04e", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (resultLegacy, P11p, \"b\", \"P11 Legacy\"),\n", + " (resultLegacy, P12p, \"r\", \"P12 Legacy\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "f2dcec9a", + "metadata": {}, + "source": [ + "## Ishizaki-Tanimura Terminator\n", + "\n", + "To speed up convergence (in terms of the number of exponents kept in the\n", + "Matsubara decomposition), We can treat the $Re[C(t=0)]$ component as a\n", + "delta-function distribution, and include it as Lindblad correction. This is\n", + "sometimes known as the Ishizaki-Tanimura Terminator.\n", + "\n", + "In more detail, given\n", + "\n", + "\\begin{equation*}\n", + "C(t)=\\sum_{k=0}^{\\infty} c_k e^{-\\nu_k t}\n", + "\\end{equation*}\n", + "\n", + "since $\\nu_k=\\frac{2 \\pi k}{\\beta }$, if $1/\\nu_k$ is much much smaller than\n", + "other important time-scales, we can approximate,\n", + "$ e^{-\\nu_k t} \\approx \\delta(t)/\\nu_k$, and $C(t)=\\sum_{k=N_k}^{\\infty}\n", + "\\frac{c_k}{\\nu_k} \\delta(t)$\n", + "\n", + "It is convenient to calculate the whole sum\n", + "$ C(t)=\\sum_{k=0}^{\\infty} \\frac{c_k}{\\nu_k} = 2 \\lambda / (\\beta \\gamma)- i\\lambda $\n", + ", and subtract off the contribution from the finite number of Matsubara terms\n", + "that are kept in the hierarchy, and treat the residual as a contribution in \n", + "Lindblad form.\n", + "\n", + "This is clearer if we plot the correlation function with a large number of\n", + "Matsubara terms. To create the plot, we use the utility function of the\n", + "`DrudeLorentzBath` class mentioned above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dbb982d4", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_correlation_expansion_divergence():\n", + " \"\"\"We plot the correlation function with a large number of Matsubara terms\n", + " to show that the real part is slowly diverging at t = 0.\n", + " \"\"\"\n", + " t = np.linspace(0, 2, 100)\n", + "\n", + " # correlation coefficients with 15k and 2 terms\n", + " corr_15k = dlenv.correlation_function(t)\n", + " corr_2 = dlenv_approx.correlation_function(t)\n", + "\n", + " fig, ax1 = plt.subplots(figsize=(12, 7))\n", + "\n", + " ax1.plot(\n", + " t, np.real(corr_2), color=\"b\", linewidth=3, label=rf\"Mats = {Nk} real\"\n", + " )\n", + " ax1.plot(\n", + " t, np.imag(corr_2), color=\"r\", linewidth=3, label=rf\"Mats = {Nk} imag\"\n", + " )\n", + " ax1.plot(\n", + " t, np.real(corr_15k), \"b--\", linewidth=3, label=r\"Mats = 15000 real\"\n", + " )\n", + " ax1.plot(\n", + " t, np.imag(corr_15k), \"r--\", linewidth=3, label=r\"Mats = 15000 imag\"\n", + " )\n", + "\n", + " ax1.set_xlabel(\"t\")\n", + " ax1.set_ylabel(r\"$C$\")\n", + " ax1.legend()\n", + "\n", + "plot_correlation_expansion_divergence()" + ] + }, + { + "cell_type": "markdown", + "id": "c0b4a0d6", + "metadata": {}, + "source": [ + "Let us evaluate the result including this Ishizaki-Tanimura terminator:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ee891d1", + "metadata": {}, + "outputs": [], + "source": [ + "# Run HEOM solver and include the Ishizaki-Tanimura terminator\n", + "\n", + "# Notes:\n", + "#\n", + "# * when using the built-in DrudeLorentzEnvironment the terminator (L_bnd) is\n", + "# available from bath.terminator().\n", + "# \n", + "# * in the legacy HSolverDL function the terminator is included automatically\n", + "# if the parameter bnd_cut_approx=True is used.\n", + "\n", + "op = -2 * spre(Q) * spost(Q.dag()) + spre(Q.dag() * Q) + spost(Q.dag() * Q)\n", + "\n", + "approx_factr = (2 * lam / (beta * gamma)) - 1j * lam\n", + "\n", + "approx_factr -= lam * gamma * (-1.0j + cot(gamma / (2 * T))) / gamma\n", + "for k in range(1, Nk + 1):\n", + " vk = 2 * np.pi * k * T\n", + "\n", + " approx_factr -= (4 * lam * gamma * T * vk / (vk**2 - gamma**2)) / vk\n", + "\n", + "L_bnd = -approx_factr * op\n", + "\n", + "Ltot = -1.0j * (spre(Hsys) - spost(Hsys)) + L_bnd\n", + "Ltot = liouvillian(Hsys) + L_bnd\n", + "\n", + "options = {**default_options, \"rtol\": 1e-14, \"atol\": 1e-14}\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI)\n", + " HEOMMatsT = HEOMSolver(Ltot, (bath,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMatsT = HEOMMatsT.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2df9cc0", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (resultMatsT, P11p, \"b\", \"P11 Mats + Term\"),\n", + " (resultMatsT, P12p, \"r\", \"P12 Mats + Term\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "8d86e00d", + "metadata": {}, + "source": [ + "Or using the built-in Drude-Lorentz environment we can write simply:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b4f84c83", + "metadata": {}, + "outputs": [], + "source": [ + "options = {**default_options, \"rtol\": 1e-14, \"atol\": 1e-14}\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath,delta = dlenv.approx_by_matsubara(Nk=Nk,compute_delta=True)\n", + " Ltot = liouvillian(Hsys) + system_terminator(Q,delta)\n", + " HEOM_dlbath_T = HEOMSolver(Ltot, (bath,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " result_dlbath_T = HEOM_dlbath_T.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b88277e", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (result_dlbath_T, P11p, \"b\", \"P11 Mats (DrudeLorentzBath + Term)\"),\n", + " (result_dlbath_T, P12p, \"r\", \"P12 Mats (DrudeLorentzBath + Term)\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "5e7017d0", + "metadata": {}, + "source": [ + "We can compare the solution obtained from the QuTiP Bloch-Redfield solver:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be8f8acf", + "metadata": {}, + "outputs": [], + "source": [ + "options = {**default_options}\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultBR = brmesolve(\n", + " Hsys, rho0, tlist, a_ops=[[sigmaz(), dlenv]], options=options\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f98a4a0", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (resultMats, P11p, \"b\", \"P11 Mats\"),\n", + " (resultMats, P12p, \"r\", \"P12 Mats\"),\n", + " (resultMatsT, P11p, \"b--\", \"P11 Mats + Term\"),\n", + " (resultMatsT, P12p, \"r--\", \"P12 Mats + Term\"),\n", + " (resultBR, P11p, \"g--\", \"P11 Bloch Redfield\"),\n", + " (resultBR, P12p, \"g--\", \"P12 Bloch Redfield\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "85ffc12b", + "metadata": {}, + "source": [ + "## Padé decomposition" + ] + }, + { + "cell_type": "markdown", + "id": "d9e06c20", + "metadata": {}, + "source": [ + "The Matsubara decomposition is not the only option. We can also use the\n", + "faster-converging Pade decomposition." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20935ef0", + "metadata": {}, + "outputs": [], + "source": [ + "def deltafun(j, k):\n", + " if j == k:\n", + " return 1.0\n", + " else:\n", + " return 0.0\n", + "\n", + "\n", + "def pade_eps(lmax):\n", + " Alpha = np.zeros((2 * lmax, 2 * lmax))\n", + " for j in range(2 * lmax):\n", + " for k in range(2 * lmax):\n", + " # Fermionic (see other example notebooks):\n", + " # Alpha[j][k] = (deltafun(j, k+1) + deltafun(j, k-1))\n", + " # / sqrt((2 * (j + 1) - 1) * (2 * (k + 1) - 1))\n", + " # Bosonic:\n", + " Alpha[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1)) / np.sqrt(\n", + " (2 * (j + 1) + 1) * (2 * (k + 1) + 1)\n", + " )\n", + "\n", + " eigvalsA = np.linalg.eigvalsh(Alpha)\n", + " eps = [-2 / val for val in eigvalsA[0:lmax]]\n", + " return eps\n", + "\n", + "\n", + "def pade_chi(lmax):\n", + " AlphaP = np.zeros((2 * lmax - 1, 2 * lmax - 1))\n", + " for j in range(2 * lmax - 1):\n", + " for k in range(2 * lmax - 1):\n", + " # Fermionic:\n", + " # AlphaP[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1))\n", + " # / sqrt((2 * (j + 1) + 1) * (2 * (k + 1) + 1))\n", + " # Bosonic [this is +3 because +1 (bose) + 2*(+1) (from bm+1)]:\n", + " AlphaP[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1)) / np.sqrt(\n", + " (2 * (j + 1) + 3) * (2 * (k + 1) + 3)\n", + " )\n", + "\n", + " eigvalsAP = np.linalg.eigvalsh(AlphaP)\n", + " chi = [-2 / val for val in eigvalsAP[0:lmax - 1]]\n", + " return chi\n", + "\n", + "\n", + "def pade_kappa_epsilon(lmax):\n", + " eps = pade_eps(lmax)\n", + " chi = pade_chi(lmax)\n", + "\n", + " kappa = [0]\n", + " prefactor = 0.5 * lmax * (2 * (lmax + 1) + 1)\n", + "\n", + " for j in range(lmax):\n", + " term = prefactor\n", + " for k in range(lmax - 1):\n", + " term *= (chi[k] ** 2 - eps[j] ** 2) / (\n", + " eps[k] ** 2 - eps[j] ** 2 + deltafun(j, k)\n", + " )\n", + "\n", + " for k in range(lmax - 1, lmax):\n", + " term /= eps[k] ** 2 - eps[j] ** 2 + deltafun(j, k)\n", + "\n", + " kappa.append(term)\n", + "\n", + " epsilon = [0] + eps\n", + "\n", + " return kappa, epsilon\n", + "\n", + "\n", + "def pade_corr(tlist, lmax):\n", + " kappa, epsilon = pade_kappa_epsilon(lmax)\n", + "\n", + " eta_list = [lam * gamma * (cot(gamma * beta / 2.0) - 1.0j)]\n", + " gamma_list = [gamma]\n", + "\n", + " if lmax > 0:\n", + " for ll in range(1, lmax + 1):\n", + " eta_list.append(\n", + " (kappa[ll] / beta)\n", + " * 4\n", + " * lam\n", + " * gamma\n", + " * (epsilon[ll] / beta)\n", + " / ((epsilon[ll] ** 2 / beta**2) - gamma**2)\n", + " )\n", + " gamma_list.append(epsilon[ll] / beta)\n", + "\n", + " c_tot = []\n", + " for t in tlist:\n", + " c_tot.append(\n", + " sum(\n", + " [\n", + " eta_list[ll] * np.exp(-gamma_list[ll] * t)\n", + " for ll in range(lmax + 1)\n", + " ]\n", + " )\n", + " )\n", + " return c_tot, eta_list, gamma_list\n", + "\n", + "\n", + "tlist_corr = np.linspace(0, 2, 100)\n", + "cppLP, etapLP, gampLP = pade_corr(tlist_corr, 2)\n", + "corr_15k = dlenv.correlation_function(tlist_corr, Nk=15_000)\n", + "corr_2k = dlenv.correlation_function(tlist_corr, Nk=2)\n", + "\n", + "fig, ax1 = plt.subplots(figsize=(12, 7))\n", + "ax1.plot(\n", + " tlist_corr,\n", + " np.real(cppLP),\n", + " color=\"b\",\n", + " linewidth=3,\n", + " label=r\"real pade 2 terms\",\n", + ")\n", + "ax1.plot(\n", + " tlist_corr,\n", + " np.real(corr_15k),\n", + " \"r--\",\n", + " linewidth=3,\n", + " label=r\"real mats 15000 terms\",\n", + ")\n", + "ax1.plot(\n", + " tlist_corr,\n", + " np.real(corr_2k),\n", + " \"g--\",\n", + " linewidth=3,\n", + " label=r\"real mats 2 terms\",\n", + ")\n", + "\n", + "ax1.set_xlabel(\"t\")\n", + "ax1.set_ylabel(r\"$C_{R}(t)$\")\n", + "ax1.legend()\n", + "\n", + "fig, ax1 = plt.subplots(figsize=(12, 7))\n", + "\n", + "ax1.plot(\n", + " tlist_corr,\n", + " np.real(cppLP) - np.real(corr_15k),\n", + " color=\"b\",\n", + " linewidth=3,\n", + " label=r\"pade error\",\n", + ")\n", + "ax1.plot(\n", + " tlist_corr,\n", + " np.real(corr_2k) - np.real(corr_15k),\n", + " \"r--\",\n", + " linewidth=3,\n", + " label=r\"mats error\",\n", + ")\n", + "\n", + "ax1.set_xlabel(\"t\")\n", + "ax1.set_ylabel(r\"Error\")\n", + "ax1.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "981d2e53", + "metadata": {}, + "outputs": [], + "source": [ + "# put pade parameters in lists for heom solver\n", + "ckAR = [np.real(eta) + 0j for eta in etapLP]\n", + "ckAI = [np.imag(etapLP[0]) + 0j]\n", + "vkAR = [gam + 0j for gam in gampLP]\n", + "vkAI = [gampLP[0] + 0j]\n", + "\n", + "options = {**default_options, \"rtol\": 1e-14, \"atol\": 1e-14}\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI)\n", + " HEOMPade = HEOMSolver(Hsys, (bath,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultPade = HEOMPade.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e24e66cf", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (resultMats, P11p, \"b\", \"P11 Mats\"),\n", + " (resultMats, P12p, \"r\", \"P12 Mats\"),\n", + " (resultMatsT, P11p, \"y\", \"P11 Mats + Term\"),\n", + " (resultMatsT, P12p, \"g\", \"P12 Mats + Term\"),\n", + " (resultPade, P11p, \"b--\", \"P11 Pade\"),\n", + " (resultPade, P12p, \"r--\", \"P12 Pade\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "3661887e", + "metadata": {}, + "source": [ + "The Padé decomposition of the Drude-Lorentz bath is also available via a\n", + "built-in class, `DrudeLorentzEnvironment` bath. Similarly to the terminator\n", + "section when approximating by Padé one can calculate the terminator easily by\n", + "requesting the approximation function to compute delta\n", + "\n", + "Below we show how to use the built-in Drude-Lorentz Environment to obtain a\n", + "Padé decomposition approximation and its terminator (although the terminator \n", + "does not provide much improvement here,because the Padé expansion already fits \n", + "the correlation function well):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2a8616b", + "metadata": {}, + "outputs": [], + "source": [ + "options = {**default_options, \"rtol\": 1e-14, \"atol\": 1e-14}\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " env_approx,delta = dlenv.approx_by_pade(Nk=2,compute_delta=True)\n", + " Ltot = liouvillian(Hsys) + system_terminator(Q,delta)\n", + " HEOM_dlpbath_T = HEOMSolver(Ltot, (env_approx,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " result_dlpbath_T = HEOM_dlpbath_T.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69c6df5d", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (result_dlpbath_T, P11p, \"b\", \"P11 Padé (DrudeLorentzBath + Term)\"),\n", + " (result_dlpbath_T, P12p, \"r\", \"P12 Padé (DrudeLorentzBath + Term)\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "bc417d19", + "metadata": {}, + "source": [ + "### Next we compare the Matsubara and Pade correlation function fits\n", + "\n", + "Fitting the correlation function is not efficient for this example, but\n", + "can be extremely useful in situations where large number of exponents\n", + "are needed (e.g., near zero temperature). We will perform the fitting\n", + "manually below, and then show how to do it with the built-in tools\n", + "\n", + "For the manual fit we First we collect a large sum of Matsubara terms for \n", + "many time steps:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86f50d83", + "metadata": {}, + "outputs": [], + "source": [ + "tlist2 = np.linspace(0, 2, 10000)\n", + "\n", + "corr_15k_t10k = dlenv.correlation_function(tlist2, Nk=15_000)\n", + "\n", + "corrRana = np.real(corr_15k_t10k)\n", + "corrIana = np.imag(corr_15k_t10k)" + ] + }, + { + "cell_type": "markdown", + "id": "da27abbe", + "metadata": {}, + "source": [ + "We then fit this sum with standard least-squares approach:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c06f6e9", + "metadata": {}, + "outputs": [], + "source": [ + "def wrapper_fit_func(x, N, args):\n", + " \"\"\" Fit function wrapper that unpacks its arguments. \"\"\"\n", + " x = np.array(x)\n", + " a = np.array(args[:N])\n", + " b = np.array(args[N:2 * N])\n", + " return fit_func(x, a, b)\n", + "\n", + "\n", + "def fit_func(x, a, b):\n", + " \"\"\" Fit function. Calculates the value of the\n", + " correlation function at each x, given the\n", + " fit parameters in a and b.\n", + " \"\"\"\n", + " return np.sum(\n", + " a[:, None] * np.exp(np.multiply.outer(b, x)),\n", + " axis=0,\n", + " )\n", + "\n", + "\n", + "def fitter(ans, tlist, k):\n", + " \"\"\" Compute fit with k exponents. \"\"\"\n", + " upper_a = abs(max(ans, key=abs)) * 10\n", + " # sets initial guesses:\n", + " guess = (\n", + " [upper_a / k] * k + # guesses for a\n", + " [0] * k # guesses for b\n", + " )\n", + " # sets lower bounds:\n", + " b_lower = (\n", + " [-upper_a] * k + # lower bounds for a\n", + " [-np.inf] * k # lower bounds for b\n", + " )\n", + " # sets higher bounds:\n", + " b_higher = (\n", + " [upper_a] * k + # upper bounds for a\n", + " [0] * k # upper bounds for b\n", + " )\n", + " param_bounds = (b_lower, b_higher)\n", + " p1, p2 = curve_fit(\n", + " lambda x, *params_0: wrapper_fit_func(x, k, params_0),\n", + " tlist,\n", + " ans,\n", + " p0=guess,\n", + " sigma=[0.01 for t in tlist],\n", + " bounds=param_bounds,\n", + " maxfev=1e8,\n", + " )\n", + " a, b = p1[:k], p1[k:]\n", + " return (a, b)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "763ab538", + "metadata": {}, + "outputs": [], + "source": [ + "kR = 4 # number of exponents to use for real part\n", + "poptR = []\n", + "with timer(\"Correlation (real) fitting time\"):\n", + " for i in range(kR):\n", + " poptR.append(fitter(corrRana, tlist2, i + 1))\n", + "\n", + "corrRMats = np.real(dlenv_approx.correlation_function(tlist2))\n", + "\n", + "kI = 1 # number of exponents for imaginary part\n", + "poptI = []\n", + "with timer(\"Correlation (imaginary) fitting time\"):\n", + " for i in range(kI):\n", + " poptI.append(fitter(corrIana, tlist2, i + 1))" + ] + }, + { + "cell_type": "markdown", + "id": "2be1d70d", + "metadata": {}, + "source": [ + "And plot the results of the fits:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44d390a2", + "metadata": {}, + "outputs": [], + "source": [ + "# Define line styles and colors\n", + "linestyles = [\"-\", \"--\", \"-.\", \":\", (0, (3, 1, 1, 1)), (0, (5, 1))]\n", + "colors = [\"blue\", \"green\", \"purple\", \"orange\", \"red\", \"brown\", \"cyan\", \"magenta\"]\n", + "\n", + "# Define a larger linewidth\n", + "linewidth = 2.5\n", + "\n", + "# Create a single figure with two subplots\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", + "\n", + "# Plot the real part on the first subplot (ax1)\n", + "ax1.plot(tlist2, corrRana, label=\"Analytic\", color=colors[0], linestyle=linestyles[0], linewidth=linewidth)\n", + "ax1.plot(tlist2, corrRMats, label=\"Matsubara\", color=colors[1], linestyle=linestyles[1], linewidth=linewidth)\n", + "\n", + "for i in range(kR):\n", + " y = fit_func(tlist2, *poptR[i])\n", + " ax1.plot(tlist2, y, label=f\"Fit with {i} terms\", color=colors[(i + 2) % len(colors)], linestyle=linestyles[(i + 2) % len(linestyles)], linewidth=linewidth)\n", + "ax1.set_ylabel(r\"$C_{R}(t)$\")\n", + "ax1.set_xlabel(r\"$t$\")\n", + "ax1.legend()\n", + "\n", + "# Plot the imaginary part on the second subplot (ax2)\n", + "ax2.plot(tlist2, corrIana, label=\"Analytic\", color=colors[0], linestyle=linestyles[0], linewidth=linewidth)\n", + "\n", + "for i in range(kI):\n", + " y = fit_func(tlist2, *poptI[i])\n", + " ax2.plot(tlist2, y, label=f\"Fit with {i} terms\", color=colors[(i + 3) % len(colors)], linestyle=linestyles[(i + 1) % len(linestyles)], linewidth=linewidth)\n", + "ax2.set_ylabel(r\"$C_{I}(t)$\")\n", + "ax2.set_xlabel(r\"$t$\")\n", + "\n", + "ax2.legend()\n", + "\n", + "# Add overall plot title and show the figure\n", + "fig.suptitle(\"Comparison of Analytic and Fit to Correlations (Real and Imaginary Parts)\", fontsize=16)\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15e69700", + "metadata": {}, + "outputs": [], + "source": [ + "# Set the exponential coefficients from the fit parameters\n", + "\n", + "ckAR1 = poptR[-1][0]\n", + "ckAR = [x + 0j for x in ckAR1]\n", + "\n", + "vkAR1 = poptR[-1][1]\n", + "vkAR = [-x + 0j for x in vkAR1]\n", + "\n", + "ckAI1 = poptI[-1][0]\n", + "ckAI = [x + 0j for x in ckAI1]\n", + "\n", + "vkAI1 = poptI[-1][1]\n", + "vkAI = [-x + 0j for x in vkAI1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ffcb4f21", + "metadata": {}, + "outputs": [], + "source": [ + "# overwrite imaginary fit with analytical value (not much reason to use the\n", + "# fit for this)\n", + "\n", + "ckAI = [lam * gamma * (-1.0) + 0.0j]\n", + "vkAI = [gamma + 0.0j]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb4c1da5", + "metadata": {}, + "outputs": [], + "source": [ + "options = {**default_options}\n", + "\n", + "NC = 4\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI)\n", + " HEOMFit = HEOMSolver(Hsys, (bath,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultFit = HEOMFit.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10367aab", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (resultFit, P11p, \"b\", \"P11 Fit\"),\n", + " (resultFit, P12p, \"r\", \"P12 Fit\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "eee27930", + "metadata": {}, + "source": [ + "Now we use the built-in functions. The `BosonicEnvironment` class, includes a \n", + "method that performs this fit automatically. More information on how the\n", + "built-in functios work can be found in `HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93e96c60", + "metadata": {}, + "outputs": [], + "source": [ + "tlist3 = np.linspace(0, 2, 200)\n", + "envfit, fitinfo =dlenv.approx_by_cf_fit(tlist=tlist3,full_ansatz=True,maxfev=1e6,Ni_max=1,Nr_max=3)" + ] + }, + { + "cell_type": "markdown", + "id": "10d5e5bf", + "metadata": {}, + "source": [ + "The approx_by_cf_fit method outputs a `ExponentialBosonicEnvironment` object,\n", + "which contains a decaying exponential representation of the original \n", + "environment , and a dictionary containing all information related to the fit.\n", + "The dictionary contains a summary of the fir information and the normalized \n", + "root mean squared error, which assesses how good the fit is. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa998aa2", + "metadata": {}, + "outputs": [], + "source": [ + "print(fitinfo['summary'])" + ] + }, + { + "cell_type": "markdown", + "id": "43257850", + "metadata": {}, + "source": [ + "We can then compare the result of the built-in fit with the manual fit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d897b84", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a single figure with two subplots\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", + "\n", + "# Plot the real part on the first subplot (ax1)\n", + "ax1.plot(tlist2, corrRana, label=\"Original\", marker=\"o\", markevery=500)\n", + "ax1.plot(tlist2, fit_func(tlist2, *poptR[-1]), color=\"r\", label=\"Manual Fit\")\n", + "ax1.plot(tlist2, np.real(envfit.correlation_function(tlist2)), \"k--\", label=\"Built-in fit\")\n", + "ax1.set_ylabel(r\"$C_{R}(t)$\")\n", + "ax1.set_xlabel(r\"$t$\")\n", + "ax1.legend()\n", + "\n", + "# Plot the imaginary part on the second subplot (ax2)\n", + "ax2.plot(tlist2, corrIana, label=\"Original\", marker=\"o\", markevery=500)\n", + "ax2.plot(tlist2, fit_func(tlist2, *poptI[-1]), color=\"r\", label=\"Manual Fit\")\n", + "ax2.plot(tlist2, np.imag(envfit.correlation_function(tlist2)), \"k--\", label=\"Built-in fit\")\n", + "ax2.set_ylabel(r\"$C_{I}(t)$\")\n", + "ax2.set_xlabel(r\"$t$\")\n", + "ax2.legend()\n", + "# Add an overall title and adjust layout\n", + "plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a089f775", + "metadata": {}, + "outputs": [], + "source": [ + "options = {**default_options}\n", + "\n", + "NC = 4\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " HEOMFit_2 = HEOMSolver(Hsys, (envfit,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultFit_2 = HEOMFit_2.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1a02d7b", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (resultFit, P11p, \"b\", \"P11 Fit\"),\n", + " (resultFit, P12p, \"r\", \"P12 Fit\"),\n", + " (resultFit_2, P11p, \"r--\", \"P11 Built-in-Fit\"),\n", + " (resultFit_2, P12p, \"b--\", \"P12 Built-in-Fit\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "e1585ec3", + "metadata": {}, + "source": [ + "## A reaction coordinate approach" + ] + }, + { + "cell_type": "markdown", + "id": "0cf8a70a", + "metadata": {}, + "source": [ + "Here we construct a reaction coordinate inspired model to capture the\n", + "steady-state behavior, and compare to the HEOM prediction. This result is\n", + "more accurate for narrow spectral densities. We will use the population and\n", + "coherence from this cell in our final plot below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "079c7bae", + "metadata": {}, + "outputs": [], + "source": [ + "dot_energy, dot_state = Hsys.eigenstates()\n", + "deltaE = dot_energy[1] - dot_energy[0]\n", + "\n", + "gamma2 = deltaE / (2 * np.pi * gamma)\n", + "wa = 2 * np.pi * gamma2 * gamma # reaction coordinate frequency\n", + "g = np.sqrt(np.pi * wa * lam / 2.0) # reaction coordinate coupling\n", + "# reaction coordinate coupling factor over 2 because of diff in J(w)\n", + "# (it is 2 lam now):\n", + "g = np.sqrt(\n", + " np.pi * wa * lam / 4.0\n", + ") #\n", + "\n", + "NRC = 10\n", + "\n", + "Hsys_exp = tensor(qeye(NRC), Hsys)\n", + "Q_exp = tensor(qeye(NRC), Q)\n", + "a = tensor(destroy(NRC), qeye(2))\n", + "\n", + "H0 = wa * a.dag() * a + Hsys_exp\n", + "# interaction\n", + "H1 = g * (a.dag() + a) * Q_exp\n", + "\n", + "H = H0 + H1\n", + "\n", + "energies, states = H.eigenstates()\n", + "rhoss = 0 * states[0] * states[0].dag()\n", + "for kk, energ in enumerate(energies):\n", + " rhoss += states[kk] * states[kk].dag() * np.exp(-beta * energies[kk])\n", + "\n", + "rhoss = rhoss / rhoss.norm()\n", + "\n", + "\n", + "class ReactionCoordinateResult:\n", + " def __init__(self, states, times):\n", + " self.states = states\n", + " self.times = times\n", + "\n", + "\n", + "resultRC = ReactionCoordinateResult([rhoss] * len(tlist), tlist)\n", + "\n", + "P12RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 1).dag())\n", + "P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag())" + ] + }, + { + "cell_type": "markdown", + "id": "67b7c2a2", + "metadata": {}, + "source": [ + "## Let's plot all our results\n", + "\n", + "Finally, let's plot all of our different results to see how they shape up against each other." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae0d4e46", + "metadata": {}, + "outputs": [], + "source": [ + "rcParams = {\n", + " \"axes.titlesize\": 25,\n", + " \"axes.labelsize\": 30,\n", + " \"xtick.labelsize\": 28,\n", + " \"ytick.labelsize\": 28,\n", + " \"legend.fontsize\": 28,\n", + " \"axes.grid\": False,\n", + " \"savefig.bbox\": \"tight\",\n", + " \"lines.markersize\": 5,\n", + " \"font.family\": \"STIXgeneral\",\n", + " \"mathtext.fontset\": \"stix\",\n", + " \"font.serif\": \"STIX\",\n", + " \"text.usetex\": False,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1037005d", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 15))\n", + "\n", + "with plt.rc_context(rcParams):\n", + "\n", + " plt.sca(axes[0])\n", + " plt.yticks([expect(P11RC, resultRC.states[0]), 0.6, 1.0], [0.32, 0.6, 1])\n", + " plot_result_expectations(\n", + " [\n", + " (resultBR, P11p, \"y-.\", \"Bloch-Redfield\"),\n", + " (resultMats, P11p, \"b\", \"Matsubara $N_k=2$\"),\n", + " (\n", + " resultMatsT,\n", + " P11p,\n", + " \"g--\",\n", + " \"Matsubara $N_k=2$ & Terminator\",\n", + " {\"linewidth\": 3},\n", + " ),\n", + " (\n", + " resultFit,\n", + " P11p,\n", + " \"r\",\n", + " r\"Fit $N_f = 4$, $N_k=15\\times 10^3$\",\n", + " {\"dashes\": [3, 2]},\n", + " ),\n", + " (\n", + " resultRC,\n", + " P11RC,\n", + " \"--\", \"Thermal\",\n", + " {\"linewidth\": 2, \"color\": \"black\"},\n", + " ),\n", + " ],\n", + " axes=axes[0],\n", + " )\n", + " axes[0].set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", + " axes[0].legend(loc=0)\n", + " axes[0].text(5, 0.9, \"(a)\", fontsize=30)\n", + " axes[0].set_xlim(0, 50)\n", + "\n", + " plt.sca(axes[1])\n", + " plt.yticks(\n", + " [np.real(expect(P12RC, resultRC.states[0])), -0.2, 0.0, 0.2],\n", + " [-0.33, -0.2, 0, 0.2],\n", + " )\n", + " plot_result_expectations(\n", + " [\n", + " (resultBR, P12p, \"y-.\", \"Bloch-Redfield\"),\n", + " (resultMats, P12p, \"b\", \"Matsubara $N_k=2$\"),\n", + " (\n", + " resultMatsT,\n", + " P12p,\n", + " \"g--\",\n", + " \"Matsubara $N_k=2$ & Terminator\",\n", + " {\"linewidth\": 3},\n", + " ),\n", + " (\n", + " resultFit,\n", + " P12p,\n", + " \"r\",\n", + " r\"Fit $N_f = 4$, $N_k=15\\times 10^3$\",\n", + " {\"dashes\": [3, 2]},\n", + " ),\n", + " (\n", + " resultRC,\n", + " P12RC,\n", + " \"--\",\n", + " \"Thermal\",\n", + " {\"linewidth\": 2, \"color\": \"black\"},\n", + " ),\n", + " ],\n", + " axes=axes[1],\n", + " )\n", + " axes[1].text(5, 0.1, \"(b)\", fontsize=30)\n", + " axes[1].set_xlabel(r\"$t \\Delta$\", fontsize=30)\n", + " axes[1].set_ylabel(r\"$\\rho_{01}$\", fontsize=30)\n", + " axes[1].set_xlim(0, 50)" + ] + }, + { + "cell_type": "markdown", + "id": "33ee2124", + "metadata": {}, + "source": [ + "And that's the end of a detailed first dive into modeling bosonic environments with the HEOM." + ] + }, + { + "cell_type": "markdown", + "id": "64bfae7b", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60d4a331", + "metadata": {}, + "outputs": [], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "e654ee7f", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d19a74bc", + "metadata": {}, + "outputs": [], + "source": [ + "# Check P11p\n", + "assert np.allclose(\n", + " expect(P11p, resultMatsT.states),\n", + " expect(P11p, resultPade.states),\n", + " rtol=1e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(P11p, resultMatsT.states),\n", + " expect(P11p, resultFit.states),\n", + " rtol=1e-2,\n", + ")\n", + "\n", + "# Check P12p\n", + "assert np.allclose(\n", + " expect(P12p, resultMatsT.states),\n", + " expect(P12p, resultPade.states),\n", + " rtol=1e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(P12p, resultMatsT.states),\n", + " expect(P12p, resultFit.states),\n", + " rtol=1e-1,\n", + ")" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter" + }, + "kernelspec": { + "display_name": "qutip-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb new file mode 100644 index 00000000..65997bc6 --- /dev/null +++ b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb @@ -0,0 +1,945 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c1f45d96", + "metadata": {}, + "source": [ + "# HEOM 1b: Spin-Bath model (very strong coupling)" + ] + }, + { + "cell_type": "markdown", + "id": "ad79c064", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices.\n", + "\n", + "In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", + "\n", + "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian, the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", + "\n", + "In the example below we show how to model the overdamped Drude-Lorentz Spectral Density, commonly used with the HEOM. We show how to do this using the Matsubara, Pade and fitting decompositions, and compare their convergence.\n", + "\n", + "This notebook shows a similar example to notebook 1a, but with much stronger coupling as discussed in [Shi *et al.*, J. Chem. Phys **130**, 084105 (2009)](https://doi.org/10.1063/1.3077918). Please refer to notebook HEOM 1a for a more detailed explanation.\n", + "\n", + "As in notebook 1a, we present a variety of simulations using different techniques to showcase the effect of different approximations of the correlation function on the results:\n", + "\n", + "- Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator\n", + "- Simulation 2: Matsubara decomposition (including terminator)\n", + "- Simulation 3: Pade decomposition\n", + "- Simulation 4: Fitting approach\n", + "\n", + "Lastly we compare the results to using the Bloch-Redfield approach:\n", + "\n", + "- Simulation 5: Bloch-Redfield\n", + "\n", + "which does not give the correct evolution in this case.\n", + "\n", + "\n", + "### Drude-Lorentz (overdamped) spectral density\n", + "\n", + "The Drude-Lorentz spectral density is:\n", + "\n", + "$$J_D(\\omega)= \\frac{2\\omega\\lambda\\gamma}{{\\gamma}^2 + \\omega^2}$$\n", + "\n", + "where $\\lambda$ scales the coupling strength, and $\\gamma$ is the cut-off frequency. We use the convention,\n", + "\\begin{equation*}\n", + "C(t) = \\int_0^{\\infty} d\\omega \\frac{J_D(\\omega)}{\\pi}[\\coth(\\beta\\omega) \\cos(\\omega \\tau) - i \\sin(\\omega \\tau)]\n", + "\\end{equation*}\n", + "\n", + "With the HEOM we must use an exponential decomposition:\n", + "\n", + "\\begin{equation*}\n", + "C(t)=\\sum_{k=0}^{k=\\infty} c_k e^{-\\nu_k t}\n", + "\\end{equation*}\n", + "\n", + "As an example, the Matsubara decomposition of the Drude-Lorentz spectral density is given by:\n", + "\n", + "\\begin{equation*}\n", + " \\nu_k = \\begin{cases}\n", + " \\gamma & k = 0\\\\\n", + " {2 \\pi k} / {\\beta } & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "\\begin{equation*}\n", + " c_k = \\begin{cases}\n", + " \\lambda \\gamma (\\cot(\\beta \\gamma / 2) - i) & k = 0\\\\\n", + " 4 \\lambda \\gamma \\nu_k / \\{(nu_k^2 - \\gamma^2)\\beta \\} & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." + ] + }, + { + "cell_type": "markdown", + "id": "b22fb8a0", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1cc43553", + "metadata": {}, + "outputs": [], + "source": [ + "import contextlib\n", + "import time\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " basis,\n", + " brmesolve,\n", + " expect,\n", + " liouvillian,\n", + " sigmax,\n", + " sigmaz,\n", + ")\n", + "from qutip.core.environment import (\n", + " DrudeLorentzEnvironment,\n", + " system_terminator\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver,\n", + ")\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "eeeb30c0", + "metadata": {}, + "source": [ + "## Helper functions\n", + "\n", + "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4a89b7e3", + "metadata": {}, + "outputs": [], + "source": [ + "def cot(x):\n", + " \"\"\" Vectorized cotangent of x. \"\"\"\n", + " return 1. / np.tan(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "43a4b38c", + "metadata": {}, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\" Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bb0c6df0", + "metadata": {}, + "outputs": [], + "source": [ + "# Solver options:\n", + "\n", + "options = {\n", + " \"nsteps\": 15000,\n", + " \"store_states\": True,\n", + " \"rtol\": 1e-14,\n", + " \"atol\": 1e-14,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "bb1d16f9", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian, bath and system measurement operators:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8bdfe487", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the system Hamiltonian\n", + "eps = .0 # Energy of the 2-level system.\n", + "Del = .2 # Tunnelling term\n", + "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "295e3bcf", + "metadata": {}, + "outputs": [], + "source": [ + "# Initial state of the system.\n", + "rho0 = basis(2, 0) * basis(2, 0).dag()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "059bfc82", + "metadata": {}, + "outputs": [], + "source": [ + "# System-bath coupling (Drude-Lorentz spectral density)\n", + "Q = sigmaz() # coupling operator\n", + "\n", + "# Bath properties (see Shi et al., J. Chem. Phys. 130, 084105 (2009)):\n", + "gamma = 1. # cut off frequency\n", + "lam = 2.5 # coupling strength\n", + "T = 1. # in units where Boltzmann factor is 1\n", + "beta = 1. / T\n", + "\n", + "# HEOM parameters:\n", + "\n", + "# number of exponents to retain in the Matsubara expansion of the\n", + "# bath correlation function:\n", + "Nk = 1\n", + "\n", + "# Number of levels of the hierarchy to retain:\n", + "NC = 13\n", + "\n", + "# Times to solve for:\n", + "tlist = np.linspace(0, np.pi / Del, 600)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "93ff798c", + "metadata": {}, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresonding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresonding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "6ddfdf60", + "metadata": {}, + "source": [ + "### Plot the spectral density\n", + "\n", + "Let us briefly inspect the spectral density." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d6e58091", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bath = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=500)\n", + "w = np.linspace(0, 5, 1000)\n", + "J = bath.spectral_density(w)\n", + "\n", + "# Plot the results\n", + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + "axes.plot(w, J, 'r', linewidth=2)\n", + "axes.set_xlabel(r'$\\omega$', fontsize=28)\n", + "axes.set_ylabel(r'J', fontsize=28);" + ] + }, + { + "cell_type": "markdown", + "id": "8d548aab", + "metadata": {}, + "source": [ + "## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e7c43b4b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.010695219039916992\n", + " [ 1% ] Elapsed 0.01s / Remaining 00:00:00:01" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 1.41s*] Elapsed 1.41s / Remaining 00:00:00:00[*********70%**** ] Elapsed 0.96s / Remaining 00:00:00:00\n", + "ODE solver time: 1.4157280921936035\n" + ] + } + ], + "source": [ + "with timer(\"RHS construction time\"):\n", + " matsBath=bath.approx_by_matsubara(Nk=Nk)\n", + " HEOMMats = HEOMSolver(Hsys, (matsBath,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMats = HEOMMats.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "1e8799ea", + "metadata": {}, + "source": [ + "## Simulation 2: Matsubara decomposition (including terminator)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "74028d2c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.011708736419677734\n", + " Total run time: 1.58s*] Elapsed 1.58s / Remaining 00:00:00:00\n", + "ODE solver time: 1.582150936126709\n" + ] + } + ], + "source": [ + "with timer(\"RHS construction time\"):\n", + " matsBath,delta=bath.approx_by_matsubara(Nk=Nk,compute_delta=True)\n", + " terminator = system_terminator(Q,delta)\n", + " Ltot = liouvillian(Hsys) + terminator\n", + " HEOMMatsT = HEOMSolver(Ltot, (matsBath,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMatsT = HEOMMatsT.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e8cb9336", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + "\n", + "P11_mats = np.real(expect(resultMats.states, P11p))\n", + "axes.plot(\n", + " tlist, np.real(P11_mats),\n", + " 'b', linewidth=2, label=\"P11 (Matsubara)\",\n", + ")\n", + "\n", + "P11_matsT = np.real(expect(resultMatsT.states, P11p))\n", + "axes.plot(\n", + " tlist, np.real(P11_matsT),\n", + " 'b--', linewidth=2,\n", + " label=\"P11 (Matsubara + Terminator)\",\n", + ")\n", + "\n", + "axes.set_xlabel(r't', fontsize=28)\n", + "axes.legend(loc=0, fontsize=12);" + ] + }, + { + "cell_type": "markdown", + "id": "ddc3e03a", + "metadata": {}, + "source": [ + "## Simulation 3: Pade decomposition" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c7e649a3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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bY2hqAgAAwCD160vj3tykTrP6SpJyHE8YPBEAAPA3F3ROzeTkZA0cOFB16tTR888/rx07driFmYWcTqecTqcqVKigqlWrKi4uTlWrVlVYWJjrtTO9Z9euXRo7dqzi4+M1YMAAbd++/ULGdFm9erUuv/xyXX755ZKkYcOG6fLLL9czzzwjq9Wq33//XTfeeKMaNmyo/v37q2HDhlq+fLnCw8Mv6ns9gqYmAAAAfIDdeqohyoWCAACAt51XU/PAgQN6+umnNXPmTOXl5RULJStWrKiOHTvqiiuuULNmzdSwYUPVqFFDISEhxT4rMzNTqamp2rJli37//XetWrVKSUlJOnz4sKSCcDMnJ0fvvvuuZs+erbvuukvPP/+8oqOjz/uHTExMPGOAWujbb7897880HE1NAAAAGMhuKxJqcqEgAADgZaUONSdOnKjRo0crPT3dLSCsX7+++vTpo169eqlly5al/uKQkBDVr19f9evX13XXXed6fs2aNfr000/18ccfa+vWrXI6ncrLy9Obb76pDz/8UKNGjdLDDz9c6u8pd2hqAgAAwAfQ1AQAAEayOM9WYSwiICBAFotFTqdTNptNffr00eDBg3XVVVd5bLgffvhB06dP10cffaTc3NyCgS0WORwOj33nhUpPT1dkZKTS0tIUERHhuS+qUkU6dEiqV0+6gAsyAQAAlMRr6xl4jLf+DDMypCbNcrTz0H6p1k+6/bkv9F6v9zz2fQAAwD+cz1rmvM6pGRQUpKFDh2rbtm16//33PRpoSlKHDh303nvvafv27XrwwQcVHBzs0e8zBZqaAAAAMJjNJu1MCZIy4qRjMWw/BwAAXlfqULN///76888/NWnSJNWqVcuTMxUTFxeniRMnasuWLerfv79Xv9tncU5NAAAAGMRuL/LAYWf7OQAA8LpSn1Nz5syZnpyjVGrWrKkZM2YYPYaxaGoCAADAYAEBks3mVF6eRcqz09QEAABed17bz89l7ty5+uOPP5Sfn1+WH4szoakJAAAAA7namg67chw5hs4CAAD8T6mbmqVx6623ymKxKCwsTOnp6WX50ShU2NQk1AQAAICB7Hbp+HEVNDXZfg4AALysTJuakuR0OpWVlVXWH4tCbD8HAACAD7DbT65LHWw/BwAA3lfmoSa8hKYmAAAADOTafk5TEwAAGMAnQ81KlSqpY8eOGjZsmNGj+B6amgAAAPABRc+pSVMTAAB4m0+GmhkZGfrhhx80adIko0fxXTQ1AQAAYCCamgAAwEjnfaGghQsX6s8//1SzZs2UkJCgihUremIulISmJgAAAHzAyJHSvR//W4dzU2lqAgAArzvvUHP58uV67rnnXI+rV6+uhIQENWvWrMyGctJCPDf+HQEAAMBAffpII/d9psOH/lSOI8rocQAAgJ8571BTKggdLRaLnE6nUlNTtXv3bn377beu5xwOhxISEtSqVSvXrXnz5rK79qiU7ODBg8rPz5ekUh3vd2hqAgAAwEfYrQXrdbafAwAAbzvvUDM0NFSSe5uyaMhZ+HjTpk3atGmT3nnnnYIvstl06aWXqmXLlq6g87LLLlNgYKDb53/22Weu+1WqVDn/n8hf0NQEAACAwey2k6Em288BAICXnXeo+fjjj2vw4MH67bfftH79ev3222/67bfftGHDBmVlZUkqCDULA87CsDM3N1fr16/X+vXrNXPmTElSYGCgmjZtqubNm6tu3bratWuXZs6cKcvJNuJll11WVj9n+VHY1CTUBAAAgIH27pXy9jWU9uQpv+pG5eXnyRZwQRvBAAAAztsFrToiIiLUoUMHdejQwfVcfn6+bDabLBaLAgICdMstt2j16tXavn2765jTg86cnBz9+uuv+vXXX894zM0333xBP1S5xvZzAAAA+IBHHpHWzXm/4MGDdZWdly1bEKEmAADwjjJbdQQEBLjdnz17tiQpPT1da9as0erVq1235ORk17FFQ8zCfzqdTrVr10533nlnWY1X/tDUBAAAgIHcTn/vsCvbka0whRk2DwAA8C9l/qvU069cHhERoU6dOqlTp06u544ePeoWcv72229KSUlRfn6+4uLi1LdvXz3zzDNuQSlOoqkJAAAAH+AWaubZuVgQAADwqjINNdPT07Vu3Tr9/vvvZz0uKipKXbp0UZcuXdyez8/PJ8gsLZqaAAAAMNDpTc0cR45hswAAAP9TpqFmhQoVdOWVV+rKK6+8oPcTaJYCTU0AAAD4gGJNTa6ADgAAvIgU0axoagIAAMBAxc6pyfZzAADgRYSaZkNTEwAAAD6ApiYAADASoaZZ0dQEAACAgWhqAgAAIxFqmk1hU5NQEwAAAAaiqQkAAIxU6lDziiuu0JIlSzw5yzktXrxYrVu3NnQGAAAAADQ1AQCAsUodaq5Zs0ZdunRRly5d9N1333lypmIWLVqkzp07q2vXrlqzZo1Xv9vn0NQEAACAD+jXT3p87n+lR2Olph/Q1AQAAF513tvPlyxZomuuuUbNmzfXtGnTlJ6e7om5lJGRoalTp6p58+bq3r27li5dKidBHhcKAgAAgE+IiJBi47Kl8H1SYLZyHDlGjwQAAPxIqUPNhQsXqlGjRnI6nXI6nfr99991//33q1q1avrHP/6hd999V3v37r2oYfbs2aN3331X//jHPxQbG6sHHnhAv//+u+s7GzdurIULF17Ud5QbBLwAAAAwmN16ag86288BAIA32Up7YJcuXbR+/Xr997//1bhx47R//35JUmZmpubPn6/58+dLkho0aKArrrhCCQkJatCggeLi4hQdHa2QkBAFBQUpJydHmZmZ2rdvn1JTU/Xnn3/q999/16pVq7Rt2zbX9xVtZcbExOjJJ5/UfffdJ5ut1COXTzQ1AQAA4CPstiKhJtvPAQCAF51XQmiz2fTQQw9p0KBBmjx5sl5//XWlpqbK6XTKYrHI6XTqzz//1NatW897kMIQs/BzJCkuLk4PPfSQhgwZopCQkPP+zHKNpiYAAAAMtGuXtHROM+nX4VKNlTQ1AQCAV533OTUlKTQ0VMOHD1dycrLee+89de7cWZYzNAgLt42f7XY6i8WiLl266IMPPlBycrIeffRRAs2iaGoCAADAB/z1l/T+y62l716Stl5LUxMAAHjVRe3lttls6tevn/r166fdu3fr888/1zfffKMff/xRR44cKdVnOJ1OVaxYUVdddZW6d++uG264QdWqVbuYsfwDTU0AAAAYyG4v8iDPruy844bNAgAA/E+ZnaCyevXquu+++3TfffdJkv766y/9/vvvSklJ0e7du3Xs2DFlZ2fLbrerQoUKql69uuLj49W0aVPVrVu3rMYo/2hqAgAAwAe4hZoOu7Idhw2bBQAA+B+PXXWnbt26hJWeRFMTAAAABjq9qZnjyDFsFgAA4H8u6JyaMFBhU5NQEwAAAAYq1tTkQkEAAMCLCDXNhu3nAAAA8AHFzqnJhYIAAIAXEWqaFU1NAAAAGIimJgAAMBKhptnQ1AQAAIAPoKkJAACMRKhpVjQ1AQAAYKDiVz8n1AQAAN7jsaufn83WrVu1bds22Ww2XXbZZYqOjj6v96elpSkyMtJD0/k4mpoAAADwAYGBUr0GedqetkmquJ3t5wAAwKu8Gmpu2bJFd955p9asWeN6zmKxqGfPnnrttddUs2bNEt+7c+dOzZ8/X59//rmWLVumrKwsb4zsu2hqAgAAwEAWi7Tyt3RVHn+ZJCnH0cPgiQAAgD/xWqh56NAhJSYmav/+/XIWCeScTqfmz5+vlStXatmyZapXr57rtS1btmju3LmaN2+e1q1b5zre4s9tRX/+2QEAAOBT7NZTe9DZfg4AALzJa6HmpEmTtG/fPlksFlWuXFnXXnutatSood27d+vrr7/Wnj17NHDgQCUlJWnZsmV66qmn9PPPP7veXxiEWiwWtW7d2ltj+y6amgAAADCY3VYk1GT7OQAA8CKvhZpfffWVJKl58+b67rvvVLFiRddrmZmZGjp0qGbOnKlJkyZp+PDhysvLcwWZAQEB6tChg3r16qVevXopLi7OW2P7nsKmJqEmAAAADGa1WGWRRU45aWoCAACv8lqouXXrVlksFr344otugaYkhYSE6M0331RycrKGDx+u3NxcSVJ8fLwefvhh3Xrrrapataq3RvVtbD8HAACAj7jnHossi7+T05qp7EdGGD0OAADwI14LNY8dOyapoKlZkscff1xLliyRxWJRp06d9OWXXyo4ONhLE5oMTU0AAAAY7Oefpfy/rpYCj9HUBAAAXhXgrS8q3EoeFhZW4jEtWrRw3R8zZgyB5pnQ1AQAAICPsBeeUtNh55yaAADAq7wWapZG0cCzadOmBk5iAjQ1AQAAYDBXqJkfqKzcHENnAQAA/sXroaallE3DChUqeHgSk6KpCQAAAB9hP3Xxc+XksE4FAADe47Vzaha6+uqrlZCQoKZNm7r+yUWALgBNTQAAABisaKiZnWXcHAAAwP94PdRcuXKlVq5c6fZclSpV1LRpUzVs2NDb45gPTU0AAAD4CLdQk93nAADAi7wWaj799NNat26d1q5dq9TUVLfXDhw4oKVLl2rp0qWu7emRkZFq2bKlWrVqpSuuuEKtWrVSfHy8t8b1fTQ1AQAAYLCioaYjx6p8Z74CLD512n4AAFBOeS3UHD16tOv+wYMHtXbtWv3666/69ddftXbtWm3fvt11hXRJysjIUFJSkpKSklzPVaxY0RVyPv/8894a3bfQ1AQAAICPKBpqFl4BPSQwxLB5AACA//D69nOpYLt5t27d1K1bN9dzx44dc4WchUHnH3/8oby8PNcxhw8f1sKFC7Vo0SL/DTUL0dQEAACAwdxCzTy7sh2EmgAAwDsMCTXPpEKFCurQoYM6dOjgei4nJ0fr1693Czp///13ZWX58VnIC5uahJoAAAAw2DXXSEv3ztNf6Zuk0EPKzss2eiQAAOAnfCbUPJOgoCC1atVKrVq1cj2Xn5+vzZs3GziVwdh+DgAAAB9xyy3SR5b39demjyVJOQ6uFgQAALzjvEPNbdu2qX379rrkkkvUvHlzNW/eXP369VNwcLAn5ismICBAl156qVe+y6fR1AQAAIAPsFtP7UHPdtDUBAAA3nHelyZ84IEHdPDgQf3000/673//q7Vr13ot0IRoagIAAMCnuIWabD8HAABecl5NzVWrVmnhwoWynAzWevTooddff90jg+EcaGoCAADABwRa7FJusBSQR1MTAAB4zXk1NadPny5JcjqdCgkJ0bRp01wB58XavHmz25XOUQKamgAAAPAR//mPNP3GKdLYTOmPf9DUBAAAXnNeoea8efNksVhksVj06KOPKi4urswG+eKLL1ShQgW1atVK99xzjxYuXFhmn10u0dQEAACAwQIDizxw2GlqAgAAryn19vMtW7bo8OHDkiSLxaK77rqrTAd59NFH9dFHH2n16tX69ddf9f3332v79u1l+h3lAk1NAAAA+Ai7vciDPDtNTQAA4DWlbmr+9ttvkgoCzcsvv1x16tQp20ECAvTKK69IKtjenpKSoqVLl5bpd5QrNDUBAABgMLdQ02FXjiPHsFkAAIB/KXWoefDgQdf9xo0be2SYDh06qE2bNq7Hn3/+uUe+x9QKm5qEmgAAADBYsaYm288BAICXlDrUPHr0qOt+jRo1PDGLJOmBBx5w3V+0aJHHvse02H4OAAAAH3F6U5Pt5wAAwFtKHWoGBQW57tvdVi9l65prrpHFYpHT6dQff/yhtLQ0j30XAAAAgAtHUxMAABil1KFmZGSk637RrehlrUqVKmrWrJnr8R9//OGx7zIlmpoAAADwETQ1AQCAUUodasbHx7vur1+/3iPDFCp6zs5t27Z59LtMjfNqAgAAwEA0NQEAgFFKHWo2adJEUsGVyVevXu3RbeHR0dGu+0eOHPHY95gSTU0AAAD4CJqaAADAKKUONatVq6ZLLrlEkpSTk6N3333XY0NVrFjRdf/YsWMe+x7To6kJAAAAAzVsKI1+8xepf6LU7lXlOHKMHgkAAPiJUoeaktS7d29JBW3NMWPGKCMjwyNDpaenu+4HBwd75DtMi6YmAAAAfEREhNS+U4YUnyRV+ovt5wAAwGvOK9QcNGiQAgMDZbFYdODAAQ0cONAjQ+3cudN1v3Llyh75jnKBpiYAAAAMZree2oPO9nMAAOAt5xVq1qpVS4MGDZLzZJj26aef6oEHHijzoZYtW+a6HxcXV+afb2o0NQEAAOBDgqxBrvs0NQEAgLecV6gpSWPHjlXNmjUlFWxDnzp1qnr37u22ZfxifP7559q/f78kyWazqW3btmXyueUSTU0AAAAYKC9PWr2sirT5BimlA01NAADgNecdakZGRmrOnDkKDg6WxWKR0+nUvHnzlJCQoE8//fSihsnIyNC///1vSZLFYlGbNm0UGhp6UZ9Z7hRtahJqAgAAwEC5udIDt9eT5nwuLR1NUxMAAHjNeYeaktSuXTt9+OGHrvNrSgXnwezTp49at26tTz75xLVFvbQOHTqkm266SVu2bHE99+CDD17IeOUb288BAADgI+z2Ig/y7ISaAADAay4o1JSk66+/Xt98842ioqIkydXaXL16tW655RbVqFFDQ4YM0TfffKNDhw6V+Dn79u3ThAkTlJCQoKVLl8pischisahp06a6+eabL3Q8/0BTEwAAAAYKCJBstpNrUoed7ecAAMBrbBfz5sTERK1du1b9+vXT8uXLXa1Np9OpvXv3avr06Zo+fbokqXr16qpZs6aioqIUHBystLQ07dixQ8nJya73FAaj4eHhmjt37kX+aOUUTU0AAAD4kCB7wbk1lWdXjiPH6HEAAICfuKhQU5Jq166tH3/8UdOmTdMzzzyjQ4cOucJNSa5t6Kmpqdq9e7fbe4tuUS8MNCMjIzV37lw1atToYkcr/2hqAgAAwGB2u3TiuAqammw/BwAAXnLB28+Lslgsuu+++5SSkqJx48apVq1acjqdrvZl4e1M7yva7mzdurVWrlyprl27lsVY5RNNTQAAAPiQ4MLzauax/RwAAHhPmYSahcLCwvTEE0/or7/+0vfff68HH3xQTZo0cbUwz3SLiorSTTfdpK+//lorVqxQgwYNynKk8o2mJgAAAAzmulgQTU0AAOBFF739/EwsFos6deqkTp06SZJOnDih7du3a9euXTp27JisVqsqV66smJgYNWrU6IwtTpSAf1cAAADwIXb7yfUpTU0AAOBFHgk1TxcaGqqEhAQlJCR44+v8B01NAAAAGIymJgAAMEKZbj+HFxRtahJqAgAAwGB2uyRrjmTNUVYuoSYAAPAOrzQ1UYbYfg4AAAAf8ssvUs3/1FVqRqpy82sYPQ4AAPATNDXNjKYmAAAADGaxSHZbwR50tp8DAABvIdQ0G5qaAAAA8DFB1iBJ4kJBAADAawg1zYymJgAAAHyA3UpTEwAAeBehptnQ1AQAAIAPef99ac/cp6T5/1NOWqSc/OIdAAB4AaGmmbFgBAAAgMEWLZL2L+kjrR0kZVZSjiPH6JEAAIAfINQ0G5qaAAAA8CF2e5EHDjtb0AEAgFcQapoZTU0AAAAYzC3UzLPT1AQAAF5BqGk2RZuahJoAAAAwWLGmJldABwAAXkCoCQAAAOCCnd7UZPs5AADwBkJNs6GpCQAAAB9CUxMAABiBUNNsuFAQAAAAfAhNTQAAYAS/CDWXLVumnj17qnr16rJYLJo3b57b606nU6NGjVL16tUVEhKixMREbdy40ZhhzwdNTQAAABiMpiYAADCCX4Sax48f12WXXabJkyef8fXx48fr1Vdf1eTJk7Vq1SrFxsaqa9euysjI8PKkpUBTEwAAAD6EpiYAADCCzegBvKFHjx7q0aPHGV9zOp2aOHGinnrqKfXq1UuSNGvWLMXExGj27NkaPHiwN0c9PzQ1AQAAYLA6daR6bbZoe/oGKXInTU0AAOAVftHUPJvk5GTt3btX3bp1cz1nt9vVsWNH/fzzzwZOVgKamgAAAPAh3btL/V+aK/W9Waq7WDmOHKNHAgAAfsAvmppns3fvXklSTEyM2/MxMTHasWNHie/Lzs5Wdvap30Knp6d7ZsCzoakJAADg13xiTSrJbju1B53t5wAAwBv8vqlZyHJaA9LpdBZ7rqhx48YpMjLSdatZs6anRyxAUxMAAAAnGbYmPU2QNch1n+3nAADAG/w+1IyNjZV0qrFZaP/+/cXam0WNGDFCaWlprtvOnTs9OucZ0dQEAADwaz6xJpVkt9LUBAAA3uX3oWZ8fLxiY2O1aNEi13M5OTlKSkpS+/btS3yf3W5XRESE280rijY1CTUBAAD8mmFr0iLWrJFG39xP+k+KlPQUTU0AAOAVfnFOzWPHjmnbtm2ux8nJyVq3bp0qVaqkWrVq6eGHH9YLL7ygBg0aqEGDBnrhhRcUGhqqfv36GTh1Cdh+DgAAAB+Slyft+ztSUqR0oipNTQAA4BV+EWquXr1anTp1cj0eNmyYJKl///56++23NXz4cGVmZmrIkCE6cuSI2rRpo4ULFyo8PNyokUuHpiYAAAAMZrcXeZBnV3ZehmGzAAAA/+EXoWZiYqKcZwkALRaLRo0apVGjRnlvqAtFUxMAAAA+xC3UdNiV4zhk2CwAAMB/+P05NU2NpiYAAAAMVqypyfZzAADgBYSaZkNTEwAAAD7k9KYmFwoCAADeQKhpZjQ1AQAAYDCamgAAwAiEmmZDUxMAAAA+hKYmAAAwAqGmmdHUBAAAgMFoagIAACMQappN0aYmoSYAAAAMFhhY5IGDUBMAAHiHzegBcJ7Yfg4AAAAfYrFIz40/omeSRkjhe5TjCDJ6JAAA4AdoapoZTU0AAAD4gMFDcqUrpkuXzOecmgAAwCsINc2GpiYAAAB8TJD1VDuT7ecAAMAbCDXNjKYmAAAAfIDdeupqQTQ1AQCAN3BOTbOhqQkAAAAfc3CfXTrYUMq3KjuOUBMAAHgeoaaZ0dQEAACAD+h+TYC0aYsUlKHs1zoaPQ4AAPADbD83G5qaAAAA8DH2wt3neXbOqQkAALyCUNPMaGoCAADAB7hCzfwgZeXkGDoLAADwD4SaZkNTEwAAAD7Gfuo6QSLTBAAA3kCoaWY0NQEAAOADioaaWdmsUQEAgOcRappN0aYmoSYAAAB8QNFQM5tTagIAAC8g1DQbtp8DAADAx7htP89mvQoAADyPUNPMaGoCAADAB5ze1HSyTgUAAB5GqGk2NDUBAADgY4qGmnIEKS8/z7BZAACAfyDUNDN+Aw4AAAAf4BZq5tmV7eDEmgAAwLNsRg+A80RTEwAAAD7m2WelTQ0GKmnXt1LYfuU4coweCQAAlHOEmmZGUxMAAAA+ICZGqlj9iJS+W5KUnUdTEwAAeBbbz82GpiYAAAB8UJA1yHWf7ecAAMDTCDXNjKYmAAAAfITdeurEmjQ1AQCAp7H93GyKNjUJNQEAAOAD1q+Xtn3TXUqpKjX4iqYmAADwOEJNs2H7OQAAAHzMsmXS8v/1k9RPCttHUxMAAHgc28/NjKYmAAAAfIDdXuSBw05TEwAAeByhptnQ1AQAAICPcQs18+zKceQYNgsAAPAPhJpmRlMTAAAAPqBYU5Pt5wAAwMMINc2GpiYAAAB8zOlNTbafAwAATyPUNDOamgAAAPABNDUBAIC3EWqaDU1NAAAA+BiamgAAwNsINc2MpiYAAAB8AE1NAADgbYSaZlO0qUmoCQAAAB9AUxMAAHgboSYAAACAixIeLsXUzJCqbJLC9ivHkWP0SAAAoJwj1DQbmpoAAADwMY0aSdMXLpYeaCIlPs/2cwAA4HGEmmbDhYIAAADgg4KsQa77bD8HAACeRqhpZjQ1AQAA4CPstlMn1qSpCQAAPI1Q00QcDulITph2qJbSFW70OAAAAICL3Vok1KSpCQAAPIxQ00T+9z+p0ruTVEc79Jn+QVMTAAAAPiEnR/r33U2kd7+RvnmFpiYAAPA4m9EDoPQiIk7dT1dEyQcCAAAAXmS1Sj9+FyXpGimngrIdm4weCQAAlHM0NU2kWKhJUxMAAAA+wGqVrNaTa9M8O9vPAQCAxxFqmghNTQAAAPiqIPvJUNNhV44jx9hhAABAuUeoaSI0NQEAAOCr7EEn7+TZOacmAADwOEJNEyHUBAAAgK+yF1783MH2cwAA4HmEmibC9nMAAAD4KnvwyTs0NQEAgBcQapoITU0AAAD4qmC7peAOTU0AAOAFhJomYrdLQdY8SSdDzWwWiwAAAPAN9sJQk6YmAADwAkJNk4kIypJ0MtQ8ccLgaQAAAIACnFMTAAB4k83oAXB+vvnnB7JPn6QoHZUypxs9DgAAACBJ6ttXWm+fqhylKzs31+hxAABAOUeoaTIt66dJ2ljwIDPT0FkAAACAQo89Jk3QaO07vk85+bWNHgcAAJRzbD83m5CQU/cJNQEAAOBDgqxBksT2cwAA4HGEmmZTNNTknJoAAADwIXZbwYk1uVAQAADwNLafm8yq3TW0Uf2VpkjdcdCpykYPBAAAAJwUFGCX8gKVlZtj9CgAAKCcI9Q0mRk/NtQ0vS1J6rBnBqEmAAAAfMK//iVtemuDJCn7gSYGTwMAAMo7tp+bTFSU03X/aJrFwEkAAACAUwKK/JdFfp5NjnyHccMAAIByj1DTZCpWPBVkHk3njw8AAAC+wW4v8sARpBwHW9ABAIDnkIqZTMVKp0LNI+lWAycBAAAATnELNfPsXAEdAAB4FKGmyURVPhVkHjkWaOAkAAAAwCnuTU07V0AHAAAeRahpMhWrnrq209ETQQZOAgAAAJxCUxMAAHgToabJVIw+1c48csJ+liMBAAAA76GpCQAAvIlQ02SiYk6tFo9kBRs4CQAAAHAKTU0AAOBNhJomUzG2SKiZHWrgJAAAAMApNDUBAIA32c59CHxJZJVAVdZBRemoYgP2Gz0OAAAAIImmJgAA8C5CTZOx2iw6GFZHOn5cirlU0g1GjwQAAACoa1fp5jEz9fGf70lV/lCOo5/RIwEAgHKMUNOMQkMLQs0TJ4yeBAAAAJAk1awpNW6fLOUtliS2nwMAAI/inJpmFBJS8M/MTGPnAAAAAIqwW0/tQWf7OQAA8CRCTTMi1AQAAIAPstuKhJo0NQEAgAex/dyEph2/U9/qUh3MqKoPd0vVqxs9EQAAAPxdWpqUvLq+tOU6KWoHTU0AAOBRhJomtC73Us3TPySntC81T9Wr88cIAAAAY/3xhzTloZsk3SS1mURTEwAAeBTbz02oSsipCwQdTGWxCAAAAOPZ7UUe5NlpagIAAI8i1DShyuE5rvsHd2UZOAkAAABQwC3UdAQpx5FT4rEAAAAXi1DThKpUdLju09QEAACAL3APNe1sPwcAAB5FqGlCVSo7XfcP7cszcBIAAACgANvPAQCAN3GFGRPZfni7vvvrO62ovFbSIEnSwf35xg4FAAAAiKYmAADwLkJNE1mZulL3LrhXCo13PXfwoIEDAQAAACfR1AQAAN7E9nMTqRVZq+BO6Kkk89BR/ggBAABgPJqaAADAm0jETKRmZM2CO/YMWQIKriZ5MD3IwIkAAACAAkFFl6U0NQEAgIex/dxEqodXV4AlQPnKV5XG0/TPjTmqe2ldSb2MHg0AAAB+zmKRAoPylZsjyeJUjiPH6JEAAEA5RqhpIrYAm6pVqKbUjFRZejykCRslxf9LhJoAAADwBb/vTNElU+pJFinbcavR4wAAgHKM7ecmU3hezf0VpCybpKNHDZ0HAAAAKFQh2C5ZCu5zTk0AAOBJhJom4zqvpqRdEZLS0owbBgAAACjCbjt1tSDOqQkAADyJUNNkakacCjV3hFt04IB04oSBAwEAAAAn2a1FQk2amgAAwIMINU2mcPu5Vg5Rtx05il63UF9+aexMAAAAgCRNeS1E+nqi9PVEmpoAAMCjCDVNpn6l+rq06qVKOJGl/JPXedq3z+ChAAAAAElzZlulXx6SVt1HUxMAAHgUoabJXNvgWm0cslET9oS5ntu3J9/AiQAAAIACdvvJqwTlBykrN8fYYQAAQLlGqGlSMTGn7u/bkWXcIAAAAMBJ9lOn1FR2jtO4QQAAQLlHqGlSMXGBrvt7d+UaOAkAAABQwC3U5PfuAADAgwg1TapqrRAFyCFJ2rPHYvA0AAAAgHuomZVNUxMAAHgOoaYJPb34abUMe1vOCnslSan7A8/xDgAAAMDz3JqaXCcIAAB4EKGmCe09tle/OVLljEiVJO1LD1YuO9ABAABgsKKhZk4Ou4kAAIDnEGqaUIPKDQrunAw1nU6L9u41cCAAAABAp4Wa2YSaAADAcwg1Tah+pfoFd8JTXc+lppZwMAAAAOAlRUPNvJwA5TvzjRsGAACUazajB8D5a1DpZFPziinqFvyR/hvQQLVavGnsUAAAAPB7TZtKFZv+oiO5e6WgY8p15Mpus5/7jQAAAOeJUNOE6lWqV3An+g+lN/xD9dekS0HGzgQAAADcf780L2KkvvvrO0lStiObUBMAAHgE289NKDQwVDXCa0iStlaWtGuXsQMBAAAAJ9mtp0LM7DwugQ4AADyDUPOkUaNGyWKxuN1iY2ONHqtEhRcLOhQqHTl2UMrMNHgiAAAAQG7NzGwHoSYAAPAMQs0imjRpoj179rhuv//+u9Ejlch1Xs3tnTXB/i9Nn5Bu7EAAAACAaGoCAADv4JyaRdhsNp9uZxblCjU/fV8vHI9R9UnZGvy0sTMBAADAv82ZI331+ATp2Bip22M0NQEAgMfQ1Cxi69atql69uuLj43Xrrbfqr7/+MnqkEnWr100TQ3upYV6KJGnP4SBls2YEAACAgdLTpbRd1aWjdaWsijQ1AQCAx9DUPKlNmzZ655131LBhQ+3bt09jxoxR+/bttXHjRlWuXLnY8dnZ2coukiKmp3t3+/dlsZfpsgZ36qfsHfpTbeR0WrRzp1S/vlfHAAAAgIGMXpOezl70Qud5duU4cgybBQAAlG80NU/q0aOHevfurYSEBHXp0kULFiyQJM2aNeuMx48bN06RkZGuW82aNb05boGaNVVHKa6HO3Z4fwQAAAAYxyfWpEW4hZoOO9vPAQCAxxBqliAsLEwJCQnaunXrGV8fMWKE0tLSXLedO3d6eUJJ8fGqrVNJZkqK90cAAACAcXxiTVrE6U1Ntp8DAABPYft5CbKzs/XHH3+oQ4cOZ3zdbrfL7rZq877jFezKidsn7Sp4vH27oeMAAADAy3xhTVoUTU0AAOAtNDVPeuyxx5SUlKTk5GT98ssvuvnmm5Wenq7+/fsbPVqJXv75ZQ37xzrX4z83O4wbBgAAAH6PpiYAAPAWmpon7dq1S7fddpsOHjyoqlWrqm3btlqxYoVq165t9GglSohOkKJSpIAcKT9IWzbkSbIaPRYAAAD8FE1NAADgLYSaJ82ZM8foEc5bQkyCZHVIlbZLBxtra4pN+flSAP1bAAAAGICmJgAA8BZCTROrV7GeQixByozeoMC8AHWrEaiMjLqKjDR6MgAAAPij05uaOY4cw2YBAADlG6GmiVkDrLq0QrzW9LlFeZI+2P8vhUW+YfRYAAAA8FM1aki3P7ZG72+aIUVvULajt9EjAQCAcoqNyibXrGZLySI5LdKmnWuMHgcAAAB+rHJl6aa7kqXWU6Q6y9h+DgAAPIZQ0+QS4lq57q9P32rgJAAAAIBkt57ag86FggAAgKcQaprcZbGXue6vCT8m5/4DBk4DAAAAf2e3FQk1aWoCAAAPIdQ0uVbVW8nilDR/ut5e8asuvSLU6JEAAADgp5xOKW1fhHSonnSkNk1NAADgMVwoyOQi7BFqHFhN23Y3U+ah5tp8SDpxQgol2wQAAICXORzSLf/XVtI2qeZPyr5mrtEjAQCAcoqmZjnwS9e5unXvFtfjzZsNHAYAAAB+y2aTAgKcBQ/y7DQ1AQCAxxBqlgMVmrdWM+sm1+N164ybBQAAAP4tyH4y1HTYlePIMXYYAABQbhFqlgdBQWpZ76jr4doVLB4BAABgjKAgmpoAAMDzCDXLieb/F+a6v/bnTAMnAQAAgD+zF1783GHn6ucAAMBjCDXLiVnNdssesU2StO7PUDkcBg8EAAAAv+QKNWlqAgAADyLULCd2VbUrO26tJCkzN1BbtpzjDQAAAIAH0NQEAADeQKhZTiS26CVVW+t6vHbtWQ4GAAAAPCTYbim4Q1MTAAB4EKFmOdGhbicFxJ5KMtf8lGXgNAAAAPBXwcEnQ02amgAAwIMINcuJCHuEmlfbKXUZLt3ZRfe2/tTokQAAAOCH7IVNTadV2Tmc6B0AAHiGzegBUHa61muitRVeliT9tilQjdTP4IkAAADgb957T2ry+mXKdB5VtrOC0eMAAIByiqZmOdKp3akQc8n+lQZOAgAAAH8VHy+FxO6Sov5WDufUBAAAHkKoWY78X+NusuUXbPf5PvKwdPCgwRMBAADAH9mtBZdA50JBAADAUwg1y5EKQRXUNr+GtK+Jtm6/T48P/dPokQAAAOCH7LaToSYXCgIAAB7COTXLmetrd9WPr74onYjWf4OO60WHZLUaPRUAAAD8xbJl0omfBkjpR5R5xTyjxwEAAOUUTc1y5tYej6qN7QdJUmZOmH5b5zR4IgAAAPiTd9+V9n/0rPTtRGUfqWL0OAAAoJwi1CxnatdootujU12Pkz7ab+A0AAAA8Dd2+6n7uTkBcjr5JTsAACh7hJrlUMebKrruL5p3zMBJAAAA4G+KhprKC1Jefp5hswAAgPKLULMcSrinnWpolyRp8Z9xOn7c4IEAAADgN9xCTYedK6ADAACPINQshywN6uvyakslSdlOu7755JCxAwEAAMBvuDc17VwBHQAAeAShZjl1osNy1/3//W+LgZMAAADAn9DUBAAA3kCoWU7d37uBZMuUJP3wa11xfnYAAAB4A01NAADgDYSa5dT1/7hXgbUXS5IyT8Tqp2/3GjwRAAAA/AFNTQAA4A2EmuVUUGCw2lyyUqq7ULpxgP769XmjRwIAAIAfoKkJAAC8wWb0APCcUQ/HqctP10iS5uyuqH/qvwZPBAAAgPKuYkUpPOagMvIOSkHHlOPIMXokAABQDtHULMc6XT1QtY8HSpK+rXREqRt+NngiAAAAlHe9ekmDZo2ThjaWmn3A9nMAAOARhJrlWECAVQPCO0iS8gOkdz58yuCJAAAA4A/stlN70Nl+DgAAPIFQs5wb0HdcQaK5vbNenH+LMo+x/QcAAACeZbcWCTVpagIAAA8g1Czn6jRsrRrzZkjvfqf09ffp7afZgg4AAADPoqkJAAA8jVDTDzzZK8R1f+47FQ2cBAAAAOXd1q3S+0/dLL23QFp5H01NAADgEYSafuC+p3rrEts2SdLSw5dp89fJBk8EAACA8iojQ9qwrL607VppfwJNTQAA4BGEmn7AYrPqnu5/ux5P//dfBk4DAACA8sxuL/Igz64cB+d0BwAAZY9Q00/0f62lgpUpSfrfhiu0YzNtTQAAAJQ9t1DTYWf7OQAA8AhCTT9RKT5S1yf8JEk6kR+hex5aaPBEAAAAKI9Ob2qy/RwAAHgCoaYfuff5cEn5kqRFP1+nw3v3GjsQAAAAyh2amgAAwBsINf1I5xvbKK7ed5Ik57E4DRkyx+CJAAAAUN7Q1AQAAN5AqOlnxg0Pc93/ItmurKMHDZwGAAAA5Q1NTQAA4A2Emn7mjnv+Tw3bvC7d3VYnbhqi6VPuNnokAAAAlCNBQUUe0NQEAAAeQqjph+a8Wl2q+Ysk6fn0L5R2cJfBEwEAAKC8CAiQbIEF53GXw64cR46xAwEAgHKJUNMPXd6+t/ql1ZYkHQpxavzrtxo8EQAAAMqTPncellq/LiW8z/ZzAADgEYSafmrMnTMU6JCUH6CX1zbQ5tW/Gj0SAAAAyolR4w9L1z4o/d8rhJoAAMAjCDX9VPzlV+uWPb2lN35R7pcz9cCgTUaPBAAAgHLCbj11tSDOqQkAADyBUNOPPXL3eAXsbyJJSlp3izZ/ssHgiQAAAFAe2G1FQk2amgAAwAMINf1Yy7Z1NbLzb5KkPAXqkX9lyOnIN3gqAAAAmJ2rqZlvoakJAAA8glDTzz3xYQvVtO2WJH1ztJ2+eHCRwRMBAADA7Lp3ipBG50pjM2lqAgAAjyDU9HOhUUGa8ORh1+MBbzZR8vo/DJwIAAAAZud0BkhOm+SwKyuXUBMAAJQ9Qk2oz6im+r8aBVc/P5ITp253rjF4IgAAAJhZsN3iup+d7TRwEgAAUF4RakIWi/SfOWFS4DFJ0rb1d2jMU28YPBUAAADMyn7qOkHKItQEAAAeQKgJSdIVVzbUnTd+4Xo8akqidmzeYuBEAAAAMCu3UDOLUBMAAJQ9Qk24zJjdVxWrrZSUL0eTT/XYlB5y5nM1dAAAAJyfoqFmdo5xcwAAgPKLUBMutsAAffFuuCrc2lXq+m99XDlZ70y71+ixAAAAYDJuoWaWcXMAAIDyi1ATbv6vc2O91bW56/GQ3W9oy+pvjBsIAAAAplM01MzJsZR8IAAAwAUi1EQxtwx8Rf/KaCBJOhEo3fCfUco4fMTgqQAAAGAWbqFmNqEmAAAoe4SaOKNJI3/SJUeDpF/u159zftC1nb81eiQAAACYRNFQMzeH/+QAAABljxUGzig0qqomtP9SWviKlB+oH9fdqg/uWWL0WAAAADCBf/1LavTA49Id3ZRfeaMc+Q6jRwIAAOUMoSZKdF2frnq+X5Lr8b/eaK21b641cCIAAACYQUKCVK3Vaqn+IikkTdmObKNHAgAA5QyhJs7qqVndNKDxL5KkEwrT9fdU184fdxg8FQAAAHyd3XpqD3p2HqEmAAAoW4SaOCuLRZq6sqXaR22UJO1xxurSGzO0Y/vfBk8GAAAAX2a3FQk1aWoCAIAyRqiJcwquYNO81TVVLbigoXnscFNd3vlPHU9LN3gyAAAA+KI9e6S0P1pIW7tLaTVoagIAgDJHqIlSqVovQu/M3iNL8GFJ0pEdXdS0/TfKy8k1eDIAAAD4mvnzpaTnnpXe/1ra3k37j+83eiQAAFDOEGqi1Lr8o62mjv5Zshb8pj3lr64acP+1cubnGzwZAAAAfIndXuSBw65/f/9vOZ1Ow+YBAADlD6Emzsvg4ddrxNDPpQp7pAGJej/uOz34dEuCTQAAALi4hZp5di1OXqz31r9n2DwAAKD8IdTEeXvhP7fozXETZIlZL0maHLROj4xsRbAJAAAAScWbmpI0bOEwHTpxyJiBAABAuUOoiQty9wOvaGbFu2Q5uYtoUuBv6n7903LkOowdDAAAAIYrGmo2qdRCknTwxEENXzTcoIkAAEB5Q6iJC9b/oRl6s2J/ySnpy6la+PVY3dR0ifKyCTYBAAD8WdFQ8+pa1yrCHiFJmrFuhpJSkgyaCgAAlCeEmrgoAx96W09ljpF+vVuS9OWfXdQ3fqWyDp8weDIAAAAYpWioGeQM17jO41yP711wr7Lzsg2YCgAAlCeEmrhoY156Sq89+K0ClSNJ+nRPO3WuvVX7N+w3eDIAAAAYoWiomZ0tDW45WG1qtJEkbT64WeN/Gm/QZAAAoLwg1ESZGDrxWi14Yb1CdVyS9POxy9S4daY+ee87gycDAACAt50ealoDrJp+/XRZLVZJ0tgfxurPQ38aNB0AACgPCDVRZrqOaKUf5uxWdeteSdLhzNq6+e42evLht40dDAAAAF5VNNR0nDzd+mWxl2lYu2GSpGxHtu5bcJ+cTqcB0wEAgPKAUBNlqkXfBvplhVMVqq4teCInXOMmDdD//d87yjqRZexwAAAA8IqGDaWsLCk/X3rrrVPPP9vxWdWOrC1JWpy8WO+tf8+gCQEAgNkRaqLMxbWqppR1tVSrwQLXcz/vjVXXEdW1889VBk4GAAAAbwgIKGhrWizuz4cFhWnKdVNcj4ctHKZDJw55eToAAFAeEGrCIypXr6LkP3ro+utmSJEpUq879GOlI2o+o40+n/GE0eMBAADAINc2uFZ9Lu0jSTp44qCGLxpu8EQAAMCMCDXhMQHWAH3x5UAtmfGdajkPS5IOhzh1087xuv6Om5Sy9W+DJwQAAIARJnafqAh7hCRpxroZSkpJMngiAABgNoSa8LjEXv/Suke26B9p1QueOFFJC+ZNU5PLrFowcrmxwwEAAKDM5edLI0ZIw4ZJr75a/PXq4dU1rvM41+N7F9yr7LxsL04IAADMjlATXlGxRj19MmGn3qpytwK/eVk6HqsTmTV0/dh26lV9hf7+ZY/RIwIAAKCMWCzSSy9J//mP9MEHZz5mcMvBalOjjSRp88HNGv/TeC9OCAAAzI5QE15jCQjQwPvf1I+vXKYWMStcz3+2p60at43Qw93mKe3AUeMGBAAAQJmwWAouFCRJ2SUUMK0BVk2/frqsFqskaewPY/X7vt+9NCEAADA7Qk14XesuLbV6dxu9d/9yxQTslySdUJgmLbpJleul6b7+M5WXm2fwlAAAALgY5wo1Jemy2Ms0rN2wguMc2Wr3Vju9ve5tOZ1OL0wIAADMjFAThrAEWHT75HbavC1Q9zddKilfkuTIqK1p79yl8BqbNP2VyXLm5xs6JwAAAC5MaUJNSXq247NqFtNMknQ897ju+vwu3f7p7UrLSvPwhAAAwMwINWGoqPiKmvx7oubOWqzKdRa5ns86Xk33HnxSVz4SoQXvPkO4CQAAYDKlDTXDgsL088Cfdffld7ue+2DDB7p8+uVasWvFWd4JAAD8GaEmfEKff3bRweSuGv/MHIVGr5U6jpaCM/RzpeO6/q/n1eyxMI3693hlHjth9KgAAAAohdKGmlJBsPnmDW/qw5s/VKQ9UpKUfDRZV864Ui/88IIc+Q4PTgoAAMyIUBM+5fHRtyp912Wa26uCGqfbXc9vcFbV6JcfUfUqhzWmy1Lt23DAwCkBAABwLucTaha6pcktWnfvOrWv2V6S5HA69NTip9Tl3S5KTU/1wJQAAMCsCDXhc6yBVvX514va8PJxzav7pNodDpN+fkzKD9TR7Dg9/X2iaiZEql/8z/rmleVy5PKbewAAAF9zIaGmJNWJqqOkAUl65qpnFGAp+M+VpSlL1WxaM32++fMynhIAAJgVoSZ8VkCAVTfeOVY//Sddr/WsoRY1k2Q5eUGhXAXpg5T26vFYOwVX+VtXdZylhZ//bPDEAAAAKFQYajocBbfzYQuwaXSn0VrSf4niIuIkSYczD+umD2/S/QvuV2ZuZhlPCwAAzIZQEz7PEhCgoSN7a83fHfXXD7v1ROslqmw55Ho9Lz1ePyzrr2tuaq9qrcfrlTHXafva7w2cGAAAAK1bS127Stddd/6hZqGral+l3+79Tb0a93I9N2X1FNV/vb6eWfKMdhzdUUbTAgAAs7E4nU6n0UOUB+np6YqMjFRaWpoiIiKMHqfcyzqapckjlujFbwJ1aEcnyWkteOHmW6SmH0mSmqYF69qQDkqIe1C3/auHrIFWAycGAMD3sZ4xv/L6Z+h0OvW/Nf/Tw98+rKy8LNfzFlnUvX533dPyHl3X4DoFWgMNnBIAAFys81nLEGqWkfK6gDSDtT+s17MvrNLiX5vrxMBEyX7s1Iub/iHN/VSVAg6oe80/1a2bU12HNFT15tGGzQsAgK9iPWN+5f3PcNOBTXry+yf15Z9fyuF0r39Wq1BNAy8fqH+1+JfqRNUxZkAAAHBRCDUNUN4XkGax+ddF+vzr/+jz/T9oRdQxOT97R1p/Z7HjYsI3q1L8b7qy2Qn17XOJOl3XRgFWzsYAAPBvrGfMzxf/DI8ckaKiJIul7D4zNT1VM9fN1Btr39DfaX+7vWaRRd3qddM9Le9Rz4Y9aW8CAGAihJoG8MUFpL/bu3m1nnxutdb9eIm27mypYwov8Vh7o490zXUPqV2ly9Sm0dVq3q6XKtao58VpAQAwHusZ8/O1P0OnU7rySik3VxoxQrrxRimgDH+P7Mh3aNFfi/S/Nf/T/C3zi7U3K4dUVsc6HdWhVgd1qNVBl8VeJluArewGAAAAZYpQ0wC+toCEu9wTuVoxY5MWzTmkr9ZX1ZpjjSVnkQVt4jNS4vOnHjtssk3eqBZhe9WjvlPNWwep8VVVVTexlgLDgrz/AwAA4AWsZ8zP1/4Mly2TOnY89bhxY+nf/5Zuu00KvIACZXa2tG6ddPSolJUl1a8vNWlS8NqejD1645d39Z8JwTqakSXlBUuySEHHpKAMKeiY7GG5alSthprXqqtenWurW0ILhQSGlMFPCgAAygKhpgF8bQGJs9ubul/vvbVU3yw7pvXbainzytE61uDHIgckSNPWF3ufVbmyVtquyhWTVTvmgJrUz9Ot1zvU7PLWqhrfVBYrFyMCAJgX6xnz87U/w2XLpIceKggii6pdW3rsMenuu6WQs2SKDof066/S998X3H78UcrMPPX6k09KY8eeepyZKYWGlnK4O65RYMMluqLGFepQq4OCdnbRxEcSVaVygKKrBqhyZalKFbn9s/D+VVeVbeMUAAAUINQ0gK8tIHF+8vNytWXl11r+63ytTV2tJZvqadPXb0s5JW9ZdxleWQo9rJBcqfaJINm33C5nanf1qx6tOg0CVaNBmKpfGqXoppVUIZq/GwAA38V6xvx88c/Q6ZS++UYaN0764Qf316pWlR55RBoyRIqMPPX8++9Ln34qLVlScE7OkjzyiPTqq6ce5+dLpf4d88D/k2r9fOrxxt7SRx+f821Wa8F2+qLnCH3xRemLLwp+nipVCm5Vq54KQitVKvhnTIxUsWIp5wMAwA+dz1qGE8oAkgJsgWrc/gY1bn+DBp58Li83TylLk/Xr13u1YW2ONv8VpGXpEdp7rJ7kCC44KHS/FHpYkpQZKG2OzJH2tZZ+u0Xrf5P09WlfZD+qoLC9Cg3dr9rxP+vqDh+rWmiMoiNiVTmymg4caa4GteLVqHEdVaobJWsQzU8AAGBuFovUo0fB7ccfC8LNr74qeO3AgYK25erV0iefnHrPN98UhJqni4uTOncuaHqGhEitW7u/HhAgLVwoBQcX3CTp2DEpI6Pglp7u1N8HDmlT6i6FtGmrtccPaOvhrSff7JAqbZVOVJayKpX8A4Ue0FVv91K1CtUKbuHVNP/HPlr+87nPx967t/Txablp//5SXl5B2BkVVfyfhfdjY8/eagUAwN8Qap5mypQpevnll7Vnzx41adJEEydOVIcOHYweCwawBdpUv2u86neNV58iz+dk5Wj59yuUtHS7tu3cqwpZCUrO3qsdSlNKaI4yj9Yp+UOzo5STHaWcw5foaOVk/Ra0RsqTdPjk7aUDUmYV1+FRlqOqFHhU+yrtV3DQMYXYTyjUnqWWLT9Tgzq7FGGPUHhIlCyqrtQ9zfR/tRooJiZKYRWDFFopWPaKQQqtFCJ7hWBZAsrwkqMAAAAX4MorpQULCrajv/ii9NFHBe3Khx5yP65zZ+m99woajp06FTzu3Flq0ODcV1Hv2vVsr1okVTl5ay79f3t3Hh9Vefd9/HtmSWayAxGSsKMgKMrqBii2KrZare3LG7ei9db7lqoVtKXy1N2qWPtoXRApLbV3KyqPFqlWvQWrIKhVlsSFJWxh30wgJEyWWc71/DHJTCYEQgKZyZjP+/Wa1+Rcc66Z3/zm5JxrfmcZPandB3frs+2faW3pWq275nEVlxWreO9Gle4LhgucVblSdd19Va5CVkhLty6NfdpN/SU1X9RcX/2pHv3oA3XydlKOJ0edPJ30+t/HqcrX/I7sl18OX4u03ldfSTffLGVlSZmZ4fuGf2dkhG/p6eEfaGp4DdPKynAhNT1dSuFy8QCAJMXp5w3MnTtXEyZM0IwZMzR69Gj94Q9/0J/+9CetXr1avXr1OmLf9niqD+LP2LaKi9Zpw2d7VL3Wqc0bQ9q5S9pV6tb7Qami6gQFfPlSIEMa/bh00f+JdrYd0m/8kjmKozOvvlwa+FZ0evN50l8WH35+Kyi5q+VwVannf/WT1xmU1zjlNW7tW329qtb/QKNCGUp120pxGaW4jdZ7SlXp9sntMkpx2+qau1dnDP+3UlypcrtS5HalanXxMJ1gddegnAKleBxypzrkcltaU7tZKSlOpbhdSnE7VZAfUpdcW05Xipwut4xxq6w8Q13SOykjzStnqktOt0NKkYKOoFI8KUr1psrtdslhccEqAIgXxjPJL9k+ww0bwkcu3n13bLFy3z6ppEQaOrQFp5MfZ/ur92td2ToVlxVH7otLi7WtYpvKa8oP7RB0h4ufvhPqCqAnSNWdw4XR6i7h+37vS0P/FtvnEf9RxXPK5F+qx4iv5HF55HF5tH/1cC184O6j6jtn5d+V7nXJ7XTL7XDrL0+erJdfCH+/cbmMUj22PF4jj8fI4zXyeiRvmjTijKAe+22tnA6nXA6XnJZTv3/SpdJvHPJ6LaWm6rC3008P/5BTPb9fWrUqXER1u6P3brfkckVv9dPNFa8BAN9OXFOzlc466ywNHz5cL7zwQqRt0KBBuuKKKzRt2rQj9k22ASQSyBjt2rJbu0tW6+CBr7W7bKv2HtipPeXleuvdCcor7ybrQJbKqrwqq83UXjtLlXajiy/dcL7Ut0ERs/gH0itvqXm29IAzfJBCvXd/L302ufmuJ/6vNOH7sW3TV0ulg5rve/Fk6ZxnotPlvaSntxxFvJJuGyRHl7VyGslpS3bRT+V//2nl1NqyLMmSkUO2qty2aly2ZBlJRq6sbcq7drQsqe5mqWzhdJlNF6lbrUMOGVmWkUNG29L9CjhCkmVkWbay+7+hvLOeiPSzJK177V3lVWWqa8ApS+HVZsAyWpNRHZmWpD6jfqNO3T8J9zPSwdLBWr/4cZ1c5ZXHDn8rs2RU5g5om6c2pu+QH14ll6u2bsrSzlXXqmztlTqtKqOuJWyTp1rlroBU1zcjd60Gnnd/ZA5L0ppFjyrtm4Hq4/fG9F2ZfkAhmUhD90GvqeDkf4TTZlkK+tNV9O7z6l+brs6hlEi/SmdQqz0V0c/FMjrl3N8oI2dzpKls+9naWPhfOtPXSc4GC9l2d7W2u6O/6uBK8WnExZNiFsONhTepZuu5OrUmdv25ylOhg45gZDq3++fqP/yPMfMsX/Ckuh7sph6B6Dl5QcvWCm95zHwnDn1RXXtGr53mO9BDXy65X4NrspRhR09cKHP6tT71YEzfERdOUYrnQGR6x4aLtXPNlRpZHfu/uTHFp28in6GUlrlDQ8Y+FDPP6k9/Idee09TfnxHTvtJbLr9lR6YL+i1Q71Oi5yjatlOfvfOCTvSn64RgaqS9ygrpS++BmOc65eynlJ27NjK9f8/pKl52u4ZX5yjFRHcU7HRXa2uDz8ayQjrr0p/FPFfJ11erouQiDanOjmlf46nQgQafTaeuX2rgmc/HzFP44W/U6UAv9fbH/mrHv+su21Gvz6lzld/3g8h0jS9XhR8+qkG1mcoORQ8tKncGtDa1Mqbv0PPvlzdjT2R699bzVPLldTq7OvbU0S3uKu1y1USmU9NKNfyCe2LmKV4+UfaOMzSoNnY5/MJTripHKDLdrdcS9Tt9Tsw8n737nHpVZys/GF0Oa62QVjZaDgeM+IO65BdGpivK+mvVp7/QkJpspTVYDve4arQpxRfT929nPKPvnHyudP/9aiuMZ5Ifn2F8VAeqtevgLu2q3BV73+Dv3Qd3a1/1PgXt4OGfyKjudPeculun8H11p0PbzvuN1HVNtG/xD6RX/iGpmR3AVlC63x07/nvnGenzO5p/o8cw/ku/7F6lnTtLDsshh+WQXd5Dex75vPnXlHTSfZfLm79ZDsshy7K079PLtOP1u2Q5bFmWLTlDshyh8LTDlixbljOk1E57deqdU2RZ9WMiS5vn/Zcq1p8WHufV9Q/3C4+jLCt83+X0Zep54Vt1Iz/JsiwVPXOfTNAlyZKMJSMr/JkZh4yxIu0DrnxJnQesjfQ7sLmvVv31v8N9TH3iLUWGfXXPZUkadf+v5XRHl5HNCy7R1sXfje3XhJy+mzTsv2fEtC1/9k5V7uzRbH77jXtXfS+Ibnf9B9O15DcPHqFHNIYzf/6Msnpsj0zvKhym1a9e01SnunF5mDu9Sufd12hM9P/Ga3fh8CMHa0ndhhRp8NVzY5qXPPZrBXzpR+4radCVryl/WFFkunJXnpbPuO0IrxeNefSvnlBKRnRMuHXJGG16/zCHgjfol5G3WyMnzox5+Iv/uUHlm/s027fnqI/V78J/RabtoFNLp/368PE26H/6T15STp/Nkeaydf21+rXxR+4ryXKGNGbq4zFtGxeM064VI5rt2+nEDTp1/GsxbStm3qLq/U1cwsOKLT31u2iBCkasjEzXlOdoxcxbmn1NSRr+37Pk7Ry94PKuwqHatHBcs/082Qc04pY/xLStfeMK7Vvfv9m+3YYW6cRxC2Pa/v37O2UHmz8B+uQr3lCX/hsi0we29dCquVc320+Szpr0dMw6YuuSMdr+2dnN9svqsV2Dr341pu3Lv02Qb0/XZvv2HPOxepz978h0oMqr5TNulSTl9CvRoB/P06wfzFInb9tcJJpraraC3+/XihUrNHXq1Jj2cePG6ZNPPjlk/traWtXWRr+4VlRUHDIP0CTLUn6ffOX3yZd0QcxDDz/cdJeAP6idW3Zp29Y92rOzXFlpExQKXqSKylJV+vZpQ16mPhv7ok6r6CWfL0VVtU5V+53aqKC2OKRAwKtQyCNjpE7VlmqcRtUuKeCUFDzKizM5mziKoEGx4YgcjQbzdgsOubBCsh2Srbp45ZFqs1UuSQ23i/66W/2ku1ZbM0OKUdtNOthPsSWR2H6SVNPrU+3Jro5t3H2GNgS92qBGGv3rf+lMlTo1aKxwSDvG6LOm312MRTnlkjtadFFtnrR9rPYctkdYmVzaklMW27hnuLRrpNY0nvmbRpN9F6koe2+0oaqztOl7zb6mJO3+zv1S9u5owxavtOESvdlcR2+ZtuXsim0rP0kqGad1zXTd6S3Xlzk7Yxu3nK8tvjwta67vaXOlhn1rcqUNl2rn4btE+/7wJimzQVZqcqWNl2h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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# First, compare Matsubara and Pade decompositions\n", + "padeBath = bath.approx_by_pade(Nk=Nk)\n", + "\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(16, 8))\n", + "\n", + "ax1.plot(\n", + " tlist, np.real(bath.correlation_function(tlist)),\n", + " \"r\", linewidth=2, label=f\"Exact\",\n", + ")\n", + "ax1.plot(\n", + " tlist, np.real(matsBath.correlation_function(tlist)),\n", + " \"g--\", linewidth=2, label=f\"Mats (Nk={Nk})\",\n", + ")\n", + "ax1.plot(\n", + " tlist, np.real(padeBath.correlation_function(tlist)),\n", + " \"b--\", linewidth=2, label=f\"Pade (Nk={Nk})\",\n", + ")\n", + "\n", + "ax1.set_xlabel(r't', fontsize=28)\n", + "ax1.set_ylabel(r\"$C_R(t)$\", fontsize=28)\n", + "ax1.legend(loc=0, fontsize=12)\n", + "\n", + "tlist2 = tlist[0:50]\n", + "ax2.plot(\n", + " tlist2, np.abs(matsBath.correlation_function(tlist2)\n", + " - bath.correlation_function(tlist2)),\n", + " \"g\", linewidth=2, label=\"Mats Error\",\n", + ")\n", + "ax2.plot(\n", + " tlist2, np.abs(padeBath.correlation_function(tlist2)\n", + " - bath.correlation_function(tlist2)),\n", + " \"b--\", linewidth=2, label=\"Pade Error\",\n", + ")\n", + "\n", + "ax2.set_xlabel(r't', fontsize=28)\n", + "ax2.legend(loc=0, fontsize=12);" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "3a955ef8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.009078502655029297\n", + " Total run time: 1.47s*] Elapsed 1.46s / Remaining 00:00:00:00\n", + "ODE solver time: 1.4667692184448242\n" + ] + } + ], + "source": [ + "with timer(\"RHS construction time\"):\n", + " HEOMPade = HEOMSolver(Hsys, (padeBath,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultPade = HEOMPade.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "3a5f16a0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "fig, axes = plt.subplots(figsize=(8, 8))\n", + "\n", + "axes.plot(\n", + " tlist, np.real(P11_mats),\n", + " 'b', linewidth=2, label=\"P11 (Matsubara)\",\n", + ")\n", + "axes.plot(\n", + " tlist, np.real(P11_matsT),\n", + " 'b--', linewidth=2, label=\"P11 (Matsubara + Terminator)\",\n", + ")\n", + "\n", + "P11_pade = np.real(expect(resultPade.states, P11p))\n", + "axes.plot(\n", + " tlist, np.real(P11_pade),\n", + " 'r', linewidth=2, label=\"P11 (Pade)\",\n", + ")\n", + "\n", + "axes.set_xlabel(r't', fontsize=28)\n", + "axes.legend(loc=0, fontsize=12);" + ] + }, + { + "cell_type": "markdown", + "id": "371b72a6", + "metadata": {}, + "source": [ + "## Simulation 4: Fitting approach\n", + "\n", + "In `HEOM 1a: Spin-Bath model (introduction)` a fit is performed manually, here\n", + "we will use the built-in tools. More details about them can be seen in \n", + "`HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions`" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "15147f03", + "metadata": {}, + "outputs": [], + "source": [ + "lower = [0, -np.inf, -1e-6, -3]\n", + "guess = [np.real(bath.correlation_function(0))/10, -10, 0, 0]\n", + "upper = [3.5, 0, 1e-6, 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "6df7fe42", + "metadata": {}, + "outputs": [], + "source": [ + "tfit=np.linspace(0,10,10000)\n", + "envfit,fitinfo = bath.approx_by_cf_fit(tlist=tfit,Nr_max=3,Ni_max=1,full_ansatz=True,\n", + " sigma=0.1,maxfev=1e6,target_rsme=None,\n", + " lower=lower,upper=upper,guess=guess)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4dcf3dd8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation function fit:\n", + "\n", + "Result of fitting the real part of |Result of fitting the imaginary part \n", + "the correlation function with 3 terms: |of the correlation function with 1 terms: \n", + " | \n", + " Parameters| a | b | c | d | Parameters| a | b | c | d \n", + " 1 | 3.50e+00 |-8.33e-01 | 9.94e-07 |-2.99e+00 | 1 | 3.16e+00 |-1.00e+00 |-9.18e-07 |-2.50e+00 \n", + " 2 | 3.50e+00 |-4.18e+00 | 4.45e-07 |-3.59e-01 | \n", + " 3 | 3.50e+00 |-4.93e+01 |-9.11e-07 |-3.00e+00 |A normalized RMSE of 1.00e-04 was obtained for the the imaginary part\n", + " |of the correlation function. \n", + "A normalized RMSE of 6.39e-05 was obtained for the the real part of | \n", + "the correlation function. | \n", + "The current fit took 0.469412 seconds. |The current fit took 0.042074 seconds. \n", + "\n" + ] + } + ], + "source": [ + "print(fitinfo['summary'])" + ] + }, + { + "cell_type": "markdown", + "id": "457e6415", + "metadata": {}, + "source": [ + "We can quickly compare the result of the Fit with the Pade expansion" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "d4491a1e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8))\n", + "\n", + "ax1.plot(\n", + " tlist, np.real(bath.correlation_function(tlist)),\n", + " \"r\", linewidth=2, label=f\"Exact\",\n", + ")\n", + "ax1.plot(\n", + " tlist, np.real(envfit.correlation_function(tlist)),\n", + " \"g--\", linewidth=2, label=f\"Fit\",marker=\"o\",markevery=50\n", + ")\n", + "ax1.plot(\n", + " tlist, np.real(padeBath.correlation_function(tlist)),\n", + " \"b--\", linewidth=2, label=f\"Pade (Nk={Nk})\",\n", + ")\n", + "\n", + "ax1.set_xlabel(r't', fontsize=28)\n", + "ax1.set_ylabel(r\"$C_R(t)$\", fontsize=28)\n", + "ax1.legend(loc=0, fontsize=12)\n", + "\n", + "ax2.plot(\n", + " tlist, np.imag(bath.correlation_function(tlist)),\n", + " \"r\", linewidth=2, label=f\"Exact\",\n", + ")\n", + "ax2.plot(\n", + " tlist, np.imag(envfit.correlation_function(tlist)),\n", + " \"g--\", linewidth=2, label=f\"Fit\",marker=\"o\",markevery=50\n", + ")\n", + "ax2.plot(\n", + " tlist, np.imag(padeBath.correlation_function(tlist)),\n", + " \"b--\", linewidth=2, label=f\"Pade (Nk={Nk})\",\n", + ")\n", + "\n", + "ax2.set_xlabel(r't', fontsize=28)\n", + "ax2.set_ylabel(r\"$C_I(t)$\", fontsize=28)\n", + "ax2.legend(loc=0, fontsize=12)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "dea09fd4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.17575669288635254\n", + " Total run time: 17.54s*] Elapsed 17.53s / Remaining 00:00:00:00\n", + "ODE solver time: 17.53864097595215\n" + ] + } + ], + "source": [ + "with timer(\"RHS construction time\"):\n", + " # We reduce NC slightly here for speed of execution because we retain\n", + " # 3 exponents in ckAR instead of 1. Please restore full NC for\n", + " # convergence though:\n", + " HEOMFit = HEOMSolver(Hsys, (envfit,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultFit = HEOMFit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "ac6505d0", + "metadata": {}, + "source": [ + "## Simulation 5: Bloch-Redfield" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "59507a86", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 1.16s*] Elapsed 1.15s / Remaining 00:00:00:00\n", + "ODE solver time: 1.1623961925506592\n" + ] + } + ], + "source": [ + "with timer(\"ODE solver time\"):\n", + " resultBR = brmesolve(\n", + " Hsys, rho0, tlist,\n", + " a_ops=[[sigmaz(), lambda w: bath.power_spectrum(w)]], sec_cutoff=0, options=options,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "98ab21e2", + "metadata": {}, + "source": [ + "## Let's plot all our results\n", + "\n", + "Finally, let's plot all of our different results to see how they shape up against each other." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "771eb79e", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate expectation values in the bases:\n", + "P11_mats = np.real(expect(resultMats.states, P11p))\n", + "P11_matsT = np.real(expect(resultMatsT.states, P11p))\n", + "P11_pade = np.real(expect(resultPade.states, P11p))\n", + "P11_fit = np.real(expect(resultFit.states, P11p))\n", + "P11_br = np.real(expect(resultBR.states, P11p))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "661dff32", + "metadata": {}, + "outputs": [], + "source": [ + "rcParams = {\n", + " \"axes.titlesize\": 25,\n", + " \"axes.labelsize\": 30,\n", + " \"xtick.labelsize\": 28,\n", + " \"ytick.labelsize\": 28,\n", + " \"legend.fontsize\": 28,\n", + " \"axes.grid\": False,\n", + " \"savefig.bbox\": \"tight\",\n", + " \"lines.markersize\": 5,\n", + " \"font.family\": \"STIXgeneral\",\n", + " \"mathtext.fontset\": \"stix\",\n", + " \"font.serif\": \"STIX\",\n", + " \"text.usetex\": False,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "6bc85109", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", + "\n", + "with plt.rc_context(rcParams):\n", + " # Plot the results\n", + " plt.yticks([0.99, 1.0], [0.99, 1])\n", + " axes.plot(\n", + " tlist, np.real(P11_mats),\n", + " 'b', linewidth=2, label=f\"Matsubara $N_k={Nk}$\",\n", + " )\n", + " axes.plot(\n", + " tlist, np.real(P11_matsT),\n", + " 'g--', linewidth=3,\n", + " label=f\"Matsubara $N_k={Nk}$ & terminator\",\n", + " )\n", + " axes.plot(\n", + " tlist, np.real(P11_pade),\n", + " 'y-.', linewidth=2, label=f\"Padé $N_k={Nk}$\",\n", + " )\n", + " axes.plot(\n", + " tlist, np.real(P11_fit),\n", + " 'r', dashes=[3, 2], linewidth=2,\n", + " label=r\"Fit $N_f = 3$, $N_k=15 \\times 10^3$\",\n", + " )\n", + " axes.plot(\n", + " tlist, np.real(P11_br),\n", + " 'b-.', linewidth=1, label=\"Bloch Redfield\",\n", + " )\n", + "\n", + " axes.locator_params(axis='y', nbins=6)\n", + " axes.locator_params(axis='x', nbins=6)\n", + " axes.set_ylabel(r'$\\rho_{11}$', fontsize=30)\n", + " axes.set_xlabel(r'$t\\;\\gamma$', fontsize=30)\n", + " axes.set_xlim(tlist[0], tlist[-1])\n", + " axes.set_ylim(0.98405, 1.0005)\n", + " axes.legend(loc=0)" + ] + }, + { + "cell_type": "markdown", + "id": "6455147a", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "3a984023", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "QuTiP: Quantum Toolbox in Python\n", + "================================\n", + "Copyright (c) QuTiP team 2011 and later.\n", + "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", + "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", + "Original developers: R. J. Johansson & P. D. Nation.\n", + "Previous lead developers: Chris Granade & A. Grimsmo.\n", + "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", + "\n", + "QuTiP Version: 5.1.0.dev0+7941773\n", + "Numpy Version: 2.1.3\n", + "Scipy Version: 1.14.1\n", + "Cython Version: 3.0.9\n", + "Matplotlib Version: 3.9.2\n", + "Python Version: 3.12.7\n", + "Number of CPUs: 16\n", + "BLAS Info: Generic\n", + "INTEL MKL Ext: None\n", + "Platform Info: Linux (x86_64)\n", + "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", + "================================================================================\n", + "Please cite QuTiP in your publication.\n", + "================================================================================\n", + "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" + ] + } + ], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "f3af6c9a", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "77693ca6", + "metadata": {}, + "outputs": [], + "source": [ + "assert np.allclose(P11_matsT, P11_pade, rtol=1e-3)\n", + "assert np.allclose(P11_matsT, P11_fit, rtol=1e-3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b02f696f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "qutip-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb new file mode 100644 index 00000000..5cee9623 --- /dev/null +++ b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb @@ -0,0 +1,857 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "09c67917", + "metadata": {}, + "source": [ + "# HEOM 1c: Spin-Bath model (Underdamped Case)" + ] + }, + { + "cell_type": "markdown", + "id": "067e8a0e", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices.\n", + "\n", + "In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", + "\n", + "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", + "\n", + "In the example below we show how to model the underdamped Brownian motion Spectral Density.\n", + "\n", + "Note that in the following, we set $\\hbar = k_\\mathrm{B} = 1$.\n", + "\n", + "### Brownian motion (underdamped) spectral density\n", + "The underdamped spectral density is:\n", + "\n", + "$$J_U = \\frac{\\alpha^2 \\Gamma \\omega}{(\\omega_c^2 - \\omega^2)^2 + \\Gamma^2 \\omega^2)}.$$\n", + "\n", + "Here $\\alpha$ scales the coupling strength, $\\Gamma$ is the cut-off frequency, and $\\omega_c$ defines a resonance frequency. With the HEOM we must use an exponential decomposition:\n", + "\n", + "The Matsubara decomposition of this spectral density is, in real and imaginary parts:\n", + "\n", + "\n", + "\n", + "\\begin{equation*}\n", + " c_k^R = \\begin{cases}\n", + " \\alpha^2 \\coth(\\beta( \\Omega + i\\Gamma/2)/2)/4\\Omega & k = 0\\\\\n", + " \\alpha^2 \\coth(\\beta( \\Omega - i\\Gamma/2)/2)/4\\Omega & k = 0\\\\\n", + " -2\\alpha^2\\Gamma/\\beta \\frac{\\epsilon_k }{((\\Omega + i\\Gamma/2)^2 + \\epsilon_k^2)(\\Omega - i\\Gamma/2)^2 + \\epsilon_k^2)} & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "\\begin{equation*}\n", + " \\nu_k^R = \\begin{cases}\n", + " -i\\Omega + \\Gamma/2, i\\Omega +\\Gamma/2, & k = 0\\\\\n", + " {2 \\pi k} / {\\beta } & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "\n", + "\n", + "\n", + "\\begin{equation*}\n", + " c_k^I = \\begin{cases}\n", + " i\\alpha^2 /4\\Omega & k = 0\\\\\n", + " -i\\alpha^2 /4\\Omega & k = 0\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "\\begin{equation*}\n", + " \\nu_k^I = \\begin{cases}\n", + " i\\Omega + \\Gamma/2, -i\\Omega + \\Gamma/2, & k = 0\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." + ] + }, + { + "cell_type": "markdown", + "id": "6bd12428", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9660ef80", + "metadata": {}, + "outputs": [], + "source": [ + "import contextlib\n", + "import time\n", + "\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " basis,\n", + " brmesolve,\n", + " destroy,\n", + " expect,\n", + " qeye,\n", + " sigmax,\n", + " sigmaz,\n", + " tensor,\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver,\n", + ")\n", + "from qutip.core.environment import (\n", + " UnderDampedEnvironment,\n", + " ExponentialBosonicEnvironment\n", + ")\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "a1bdd787", + "metadata": {}, + "source": [ + "## Helper functions\n", + "\n", + "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cec25e44", + "metadata": {}, + "outputs": [], + "source": [ + "def cot(x):\n", + " \"\"\" Vectorized cotangent of x. \"\"\"\n", + " return 1. / np.tan(x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4a7be2b", + "metadata": {}, + "outputs": [], + "source": [ + "def coth(x):\n", + " \"\"\" Vectorized hyperbolic cotangent of x. \"\"\"\n", + " return 1. / np.tanh(x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf051401", + "metadata": {}, + "outputs": [], + "source": [ + "def underdamped_matsubara_params(lam, gamma, T, nk):\n", + " \"\"\" Calculation of the real and imaginary expansions of the\n", + " underdamped correlation functions.\n", + " \"\"\"\n", + " Om = np.sqrt(w0**2 - (gamma / 2)**2)\n", + " Gamma = gamma / 2.\n", + " beta = 1. / T\n", + "\n", + " ckAR = [\n", + " (lam**2 / (4*Om)) * coth(beta * (Om + 1.0j * Gamma) / 2),\n", + " (lam**2 / (4*Om)) * coth(beta * (Om - 1.0j * Gamma) / 2),\n", + " ]\n", + " ckAR.extend(\n", + " (-2 * lam**2 * gamma / beta) * (2 * np.pi * k / beta) /\n", + " (((Om + 1.0j * Gamma)**2 + (2 * np.pi * k / beta)**2) *\n", + " ((Om - 1.0j * Gamma)**2 + (2 * np.pi * k / beta)**2)) + 0.j\n", + " for k in range(1, nk + 1)\n", + " )\n", + " vkAR = [\n", + " -1.0j * Om + Gamma,\n", + " 1.0j * Om + Gamma,\n", + " ]\n", + " vkAR.extend(\n", + " 2 * np.pi * k * T + 0.j\n", + " for k in range(1, nk + 1)\n", + " )\n", + "\n", + " factor = 1. / 4\n", + "\n", + " ckAI = [\n", + " -factor * lam**2 * 1.0j / Om,\n", + " factor * lam**2 * 1.0j / Om,\n", + " ]\n", + " vkAI = [\n", + " -(-1.0j * Om - Gamma),\n", + " -(1.0j * Om - Gamma),\n", + " ]\n", + "\n", + " return ckAR, vkAR, ckAI, vkAI" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5ef90afc", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_result_expectations(plots, axes=None):\n", + " \"\"\" Plot the expectation values of operators as functions of time.\n", + "\n", + " Each plot in plots consists of: (solver_result, measurement_operation,\n", + " color, label).\n", + " \"\"\"\n", + " if axes is None:\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " fig_created = True\n", + " else:\n", + " fig = None\n", + " fig_created = False\n", + "\n", + " # add kw arguments to each plot if missing\n", + " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", + " for result, m_op, color, label, kw in plots:\n", + " exp = np.real(expect(result.states, m_op))\n", + " kw.setdefault(\"linewidth\", 2)\n", + " axes.plot(result.times, exp, color, label=label, **kw)\n", + "\n", + " if fig_created:\n", + " axes.legend(loc=0, fontsize=12)\n", + " axes.set_xlabel(\"t\", fontsize=28)\n", + "\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "339e9a1b", + "metadata": {}, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\" Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6e42bdc", + "metadata": {}, + "outputs": [], + "source": [ + "# Solver options:\n", + "\n", + "options = {\n", + " \"nsteps\": 15000,\n", + " \"store_states\": True,\n", + " \"rtol\": 1e-14,\n", + " \"atol\": 1e-14,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "2d97796f", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian, bath and system measurement operators:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11a414de", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the system Hamiltonian\n", + "eps = .5 # Energy of the 2-level system.\n", + "Del = 1.0 # Tunnelling term\n", + "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f1013ca", + "metadata": {}, + "outputs": [], + "source": [ + "# Initial state of the system.\n", + "rho0 = basis(2, 0) * basis(2, 0).dag()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1bd202f5", + "metadata": {}, + "outputs": [], + "source": [ + "# System-bath coupling (underdamed spectral density)\n", + "Q = sigmaz() # coupling operator\n", + "\n", + "# Bath properties:\n", + "gamma = .1 # cut off frequency\n", + "lam = .5 # coupling strength\n", + "w0 = 1. # resonance frequency\n", + "T = 1.\n", + "beta = 1. / T\n", + "\n", + "# HEOM parameters:\n", + "\n", + "# number of exponents to retain in the Matsubara expansion of the\n", + "# bath correlation function:\n", + "Nk = 2\n", + "\n", + "# Number of levels of the hierarchy to retain:\n", + "NC = 10\n", + "\n", + "# Times to solve for:\n", + "tlist = np.linspace(0, 50, 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c6de948", + "metadata": {}, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresonding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresonding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "e3f5324c", + "metadata": {}, + "source": [ + "### First let us look at what the underdamped spectral density looks like:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b62d0ba", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_spectral_density():\n", + " \"\"\" Plot the underdamped spectral density \"\"\"\n", + " w = np.linspace(0, 5, 1000)\n", + " J = lam**2 * gamma * w / ((w0**2 - w**2)**2 + (gamma**2) * (w**2))\n", + "\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " axes.plot(w, J, 'r', linewidth=2)\n", + " axes.set_xlabel(r'$\\omega$', fontsize=28)\n", + " axes.set_ylabel(r'J', fontsize=28)\n", + "\n", + "\n", + "plot_spectral_density()" + ] + }, + { + "cell_type": "markdown", + "id": "52282e66", + "metadata": {}, + "source": [ + "The correlation functions are now very oscillatory, because of the Lorentzian peak in the spectral density." + ] + }, + { + "cell_type": "markdown", + "id": "5c84cfc2", + "metadata": {}, + "source": [ + "### So next, let us plot the correlation functions themselves:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c4cf589", + "metadata": {}, + "outputs": [], + "source": [ + "def Mk(t, k, gamma, w0, beta):\n", + " \"\"\" Calculate the Matsubara terms for a given t and k. \"\"\"\n", + " Om = np.sqrt(w0**2 - (gamma / 2)**2)\n", + " Gamma = gamma / 2.\n", + " ek = 2 * np.pi * k / beta\n", + "\n", + " return (\n", + " (-2 * lam**2 * gamma / beta) * ek * np.exp(-ek * np.abs(t))\n", + " / (((Om + 1.0j * Gamma)**2 + ek**2) * ((Om - 1.0j * Gamma)**2 + ek**2))\n", + " )\n", + "\n", + "\n", + "def c(t, Nk, lam, gamma, w0, beta):\n", + " \"\"\" Calculate the correlation function for a vector of times, t. \"\"\"\n", + " Om = np.sqrt(w0**2 - (gamma / 2)**2)\n", + " Gamma = gamma / 2.\n", + "\n", + " Cr = (\n", + " coth(beta * (Om + 1.0j * Gamma) / 2) * np.exp(1.0j * Om * t)\n", + " + coth(beta * (Om - 1.0j * Gamma) / 2) * np.exp(-1.0j * Om * t)\n", + " )\n", + "\n", + " Ci = np.exp(-1.0j * Om * t) - np.exp(1.0j * Om * t)\n", + "\n", + " return (\n", + " (lam**2 / (4 * Om)) * np.exp(-Gamma * np.abs(t)) * (Cr + Ci) +\n", + " np.sum([\n", + " Mk(t, k, gamma=gamma, w0=w0, beta=beta)\n", + " for k in range(1, Nk + 1)\n", + " ], 0)\n", + " )\n", + "\n", + "\n", + "def plot_correlation_function():\n", + " \"\"\" Plot the underdamped correlation function. \"\"\"\n", + " t = np.linspace(0, 20, 1000)\n", + " corr = c(t, Nk=3, lam=lam, gamma=gamma, w0=w0, beta=beta)\n", + "\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " axes.plot(t, np.real(corr), '-', color=\"black\", label=\"Re[C(t)]\")\n", + " axes.plot(t, np.imag(corr), '-', color=\"red\", label=\"Im[C(t)]\")\n", + " axes.set_xlabel(r't', fontsize=28)\n", + " axes.set_ylabel(r'C', fontsize=28)\n", + " axes.legend(loc=0, fontsize=12)\n", + "\n", + "\n", + "plot_correlation_function()" + ] + }, + { + "cell_type": "markdown", + "id": "c3ac887c", + "metadata": {}, + "source": [ + "It is useful to look at what the Matsubara contributions do to this spectral density. We see that they modify the real part around $t=0$:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "021c6f46", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_matsubara_correlation_function_contributions():\n", + " \"\"\" Plot the underdamped correlation function. \"\"\"\n", + " t = np.linspace(0, 20, 1000)\n", + "\n", + " M_Nk2 = np.sum([\n", + " Mk(t, k, gamma=gamma, w0=w0, beta=beta)\n", + " for k in range(1, 2 + 1)\n", + " ], 0)\n", + "\n", + " M_Nk100 = np.sum([\n", + " Mk(t, k, gamma=gamma, w0=w0, beta=beta)\n", + " for k in range(1, 100 + 1)\n", + " ], 0)\n", + "\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " axes.plot(t, np.real(M_Nk2), '-', color=\"black\", label=\"Re[M(t)] Nk=2\")\n", + " axes.plot(t, np.real(M_Nk100), '--', color=\"red\", label=\"Re[M(t)] Nk=100\")\n", + " axes.set_xlabel(r't', fontsize=28)\n", + " axes.set_ylabel(r'M', fontsize=28)\n", + " axes.legend(loc=0, fontsize=12)\n", + "\n", + "\n", + "plot_matsubara_correlation_function_contributions()" + ] + }, + { + "cell_type": "markdown", + "id": "2f10e37a", + "metadata": {}, + "source": [ + "## Solving for the dynamics as a function of time" + ] + }, + { + "cell_type": "markdown", + "id": "cb326966", + "metadata": {}, + "source": [ + "Next we calculate the exponents using the Matsubara decompositions. Here we split them into real and imaginary parts.\n", + "\n", + "The HEOM code will optimize these, and reduce the number of exponents when real and imaginary parts have the same exponent. This is clearly the case for the first term in the vkAI and vkAR lists." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "976b8621", + "metadata": {}, + "outputs": [], + "source": [ + "ckAR, vkAR, ckAI, vkAI = underdamped_matsubara_params(\n", + " lam=lam, gamma=gamma, T=T, nk=Nk,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "da31068a", + "metadata": {}, + "source": [ + "Having created the lists which specify the bath correlation functions, we create a `BosonicBath` from them and pass the bath to the `HEOMSolver` class.\n", + "\n", + "The solver constructs the \"right hand side\" (RHS) determinining how the system and auxiliary density operators evolve in time. This can then be used to solve for dynamics or steady-state.\n", + "\n", + "Below we create the bath and solver and then solve for the dynamics by calling `.run(rho0, tlist)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb83040a", + "metadata": {}, + "outputs": [], + "source": [ + "with timer(\"RHS construction time\"):\n", + " bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI)\n", + " HEOMMats = HEOMSolver(Hsys, (bath,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMats = HEOMMats.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8bdbdaaa", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations([\n", + " (resultMats, P11p, 'b', \"P11 Mats\"),\n", + " (resultMats, P12p, 'r', \"P12 Mats\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "c6f959e1", + "metadata": {}, + "source": [ + "In practice, one would not perform this laborious expansion for the underdamped correlation function, because\n", + "QuTiP already has a class, `UnderDampedEnvironment`, that can construct this bath for you. Nevertheless, knowing how\n", + "to perform this expansion is an useful skill.\n", + "\n", + "Below we show how to use this built-in functionality:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da329964", + "metadata": {}, + "outputs": [], + "source": [ + "# Compare to built-in under-damped bath:\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = UnderDampedEnvironment(lam=lam, gamma=gamma, w0=w0, T=T)\n", + " bath_approx=bath.approx_by_matsubara(Nk=Nk)\n", + " HEOM_udbath = HEOMSolver(Hsys, (bath_approx,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " result_udbath = HEOM_udbath.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83491bf6", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations([\n", + " (result_udbath, P11p, 'b', \"P11 (UnderDampedEnvironment)\"),\n", + " (result_udbath, P12p, 'r', \"P12 (UnderDampedEnvironment)\"),\n", + " (resultMats, P11p, 'r--', \"P11 Mats\"),\n", + " (resultMats, P12p, 'b--', \"P12 Mats\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "c8d0190a", + "metadata": {}, + "source": [ + "The `UnderDampedEnvironment` class also allows us to easily evaluate analytical expressions for the power spectrum, correlation function, and spectral density. In the following plots, the solid lines are the exact expressions, and the dashed lines are based on our approximation of the correlation function with a finite number of exponents. In this case, there is an excellent agreement." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89f31ba0", + "metadata": {}, + "outputs": [], + "source": [ + "w = np.linspace(-3, 3, 1000)\n", + "w2 = np.linspace(0, 3, 1000)\n", + "t = np.linspace(0, 10, 1000)\n", + "bath_cf = bath.correlation_function(t) # uses numerical integration\n", + "\n", + "fig, axs = plt.subplots(2, 2)\n", + "\n", + "axs[0, 0].plot(w, bath.power_spectrum(w))\n", + "axs[0, 0].plot(w, bath_approx.power_spectrum(w), '--')\n", + "axs[0, 0].set(xlabel=r'$\\omega$', ylabel=r'$S(\\omega)$')\n", + "axs[0, 1].plot(w2, bath.spectral_density(w2))\n", + "axs[0, 1].plot(w2, bath_approx.spectral_density(w2), '--')\n", + "axs[0, 1].set(xlabel=r'$\\omega$', ylabel=r'$J(\\omega)$')\n", + "axs[1, 0].plot(t, np.real(bath_cf))\n", + "axs[1, 0].plot(t, np.real(bath_approx.correlation_function(t)), '--')\n", + "axs[1, 0].set(xlabel=r'$t$', ylabel=r'$C_{R}(t)$')\n", + "axs[1, 1].plot(t, np.imag(bath_cf))\n", + "axs[1, 1].plot(t, np.imag(bath_approx.correlation_function(t)), '--')\n", + "axs[1, 1].set(xlabel=r'$t$', ylabel=r'$C_{I}(t)$')\n", + "\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9dead787", + "metadata": {}, + "source": [ + "## Compare the results" + ] + }, + { + "cell_type": "markdown", + "id": "d9f9e91f", + "metadata": {}, + "source": [ + "### We can compare these results to those of the Bloch-Redfield solver in QuTiP:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d3a3a6e", + "metadata": {}, + "outputs": [], + "source": [ + "with timer(\"ODE solver time\"):\n", + " resultBR = brmesolve(\n", + " Hsys, rho0, tlist,\n", + " a_ops=[[sigmaz(), bath]], options=options,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7ada61a5", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations([\n", + " (resultMats, P11p, 'b', \"P11 Mats\"),\n", + " (resultMats, P12p, 'r', \"P12 Mats\"),\n", + " (resultBR, P11p, 'g--', \"P11 Bloch Redfield\"),\n", + " (resultBR, P12p, 'g--', \"P12 Bloch Redfield\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "b3fccda9", + "metadata": {}, + "source": [ + "### Lastly, let us calculate the analytical steady-state result and compare all of the results:" + ] + }, + { + "cell_type": "markdown", + "id": "6fd11b1e", + "metadata": {}, + "source": [ + "The thermal state of a reaction coordinate (treating the environment as a single damped mode) should, at high temperatures and small gamma, tell us the steady-state:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e35c4bcc", + "metadata": {}, + "outputs": [], + "source": [ + "dot_energy, dot_state = Hsys.eigenstates()\n", + "deltaE = dot_energy[1] - dot_energy[0]\n", + "\n", + "gamma2 = gamma\n", + "wa = w0 # reaction coordinate frequency\n", + "g = lam / np.sqrt(2 * wa) # coupling\n", + "\n", + "NRC = 10\n", + "\n", + "Hsys_exp = tensor(qeye(NRC), Hsys)\n", + "Q_exp = tensor(qeye(NRC), Q)\n", + "a = tensor(destroy(NRC), qeye(2))\n", + "\n", + "H0 = wa * a.dag() * a + Hsys_exp\n", + "# interaction\n", + "H1 = (g * (a.dag() + a) * Q_exp)\n", + "\n", + "H = H0 + H1\n", + "\n", + "energies, states = H.eigenstates()\n", + "rhoss = 0 * states[0] * states[0].dag()\n", + "for kk, energ in enumerate(energies):\n", + " rhoss += (states[kk] * states[kk].dag() * np.exp(-beta * energies[kk]))\n", + "rhoss = rhoss / rhoss.norm()\n", + "\n", + "P12RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 1).dag())\n", + "P12RC = expect(rhoss, P12RC)\n", + "\n", + "P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag())\n", + "P11RC = expect(rhoss, P11RC)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30a38a50", + "metadata": {}, + "outputs": [], + "source": [ + "rcParams = {\n", + " \"axes.titlesize\": 25,\n", + " \"axes.labelsize\": 30,\n", + " \"xtick.labelsize\": 28,\n", + " \"ytick.labelsize\": 28,\n", + " \"legend.fontsize\": 28,\n", + " \"axes.grid\": False,\n", + " \"savefig.bbox\": \"tight\",\n", + " \"lines.markersize\": 5,\n", + " \"font.family\": \"STIXgeneral\",\n", + " \"mathtext.fontset\": \"stix\",\n", + " \"font.serif\": \"STIX\",\n", + " \"text.usetex\": False,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9125381c", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", + "\n", + "with plt.rc_context(rcParams):\n", + " plt.yticks([P11RC, 0.6, 1.0], [0.38, 0.6, 1])\n", + "\n", + " plot_result_expectations([\n", + " (resultBR, P11p, 'y-.', \"Bloch-Redfield\"),\n", + " (resultMats, P11p, 'b', \"Matsubara $N_k=3$\"),\n", + " ], axes=axes)\n", + " axes.plot(\n", + " tlist, [P11RC for t in tlist],\n", + " color='black', linestyle=\"-.\", linewidth=2,\n", + " label=\"Thermal state\",\n", + " )\n", + "\n", + " axes.set_xlabel(r'$t \\Delta$', fontsize=30)\n", + " axes.set_ylabel(r'$\\rho_{11}$', fontsize=30)\n", + "\n", + " axes.locator_params(axis='y', nbins=4)\n", + " axes.locator_params(axis='x', nbins=4)\n", + "\n", + " axes.legend(loc=0)\n", + "\n", + " fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "b1a051b0", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e6a7002", + "metadata": {}, + "outputs": [], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "e007817c", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eec3d8e9", + "metadata": {}, + "outputs": [], + "source": [ + "assert np.allclose(\n", + " expect(P11p, resultMats.states[-100:]), P11RC, rtol=1e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(P11p, resultBR.states[-100:]), P11RC, rtol=1e-2,\n", + ")" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "qutip-dev", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb new file mode 100644 index 00000000..7e827f17 --- /dev/null +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb @@ -0,0 +1,1753 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebddecba", + "metadata": {}, + "source": [ + "# HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions" + ] + }, + { + "cell_type": "markdown", + "id": "2142c296", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded \n", + "in a set of auxiliary density matrices.\n", + "\n", + "In this example we show the evolution of a single two-level system in contact with a single bosonic environment.\n", + "\n", + "The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", + "\n", + "The bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", + "\n", + "In the example below we show how to model an Ohmic environment with exponential cut-off in three ways:\n", + "\n", + "* First we fit the spectral density with a set of underdamped brownian oscillator functions.\n", + "* Second, we evaluate the correlation functions, and fit those with a certain choice of exponential functions.\n", + "* Third, we use the available OhmicBath class \n", + "\n", + "In each case we will use the fit parameters to determine the correlation function expansion co-efficients needed to construct a description of the bath (i.e. a `BosonicBath` object) to supply to the `HEOMSolver` so that we can solve for the system dynamics." + ] + }, + { + "cell_type": "markdown", + "id": "d3ef97c3", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ed47f849", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "import qutip\n", + "from qutip import (\n", + " basis,\n", + " expect,\n", + " sigmax,\n", + " sigmaz,\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver\n", + ")\n", + "from qutip.core.environment import BosonicEnvironment,_sd_fit_model,OhmicEnvironment\n", + "\n", + "# Import mpmath functions for evaluation of gamma and zeta\n", + "# functions in the expression for the correlation:\n", + "\n", + "from mpmath import mp\n", + "\n", + "mp.dps = 15\n", + "mp.pretty = True\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2eb48e5a", + "metadata": {}, + "outputs": [], + "source": [ + "# Solver options:\n", + "\n", + "options = {\n", + " \"nsteps\": 15000,\n", + " \"store_states\": True,\n", + " \"rtol\": 1e-14,\n", + " \"atol\": 1e-14,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "65a7dfbb", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian, bath and system measurement operators:" + ] + }, + { + "cell_type": "markdown", + "id": "1e362553", + "metadata": {}, + "source": [ + "### System Hamiltonian" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ac95be0b", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the system Hamiltonian\n", + "eps = 0 # Energy of the 2-level system.\n", + "Del = 0.2 # Tunnelling term\n", + "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()\n", + "rho0 = basis(2, 0) * basis(2, 0).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "d89e26d2", + "metadata": {}, + "source": [ + "### System measurement operators" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d79edfb4", + "metadata": {}, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresonding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresonding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "52c4fb7a", + "metadata": {}, + "source": [ + "### Analytical expressions for the Ohmic bath correlation function and spectral density" + ] + }, + { + "cell_type": "markdown", + "id": "a0a87475", + "metadata": {}, + "source": [ + "Before we begin fitting, let us examine the analytic expressions for the correlation and spectral density functions and write Python equivalents. \n", + "\n", + "The correlation function is given by (see, e.g., http://www1.itp.tu-berlin.de/brandes/public_html/publications/notes.pdf for a derivation, equation 7.59, but with a factor of $\\pi$ moved into the definition of the correlation function):\n", + "\n", + "\\begin{align}\n", + "C(t) =& \\: \\frac{1}{\\pi}\\alpha \\omega_{c}^{1 - s} \\beta^{- (s + 1)} \\: \\times \\\\\n", + " & \\: \\Gamma(s + 1) \\left[ \\zeta \\left(s + 1, \\frac{1 + \\beta \\omega_c - i \\omega_c t}{\\beta \\omega_c}\\right) + \\zeta \\left(s + 1, \\frac{1 + i \\omega_c t}{\\beta \\omega_c}\\right) \\right]\n", + "\\end{align}\n", + "\n", + "where $\\Gamma$ is the Gamma function and\n", + "\n", + "\\begin{equation}\n", + "\\zeta(z, u) \\equiv \\sum_{n=0}^{\\infty} \\frac{1}{(n + u)^z}, \\; u \\neq 0, -1, -2, \\ldots\n", + "\\end{equation}\n", + "\n", + "is the generalized Zeta function. The Ohmic case is given by $s = 1$.\n", + "\n", + "The corresponding spectral density for the Ohmic case is:\n", + "\n", + "\\begin{equation}\n", + "J(\\omega) = \\omega \\alpha e^{- \\frac{\\omega}{\\omega_c}}\n", + "\\end{equation}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bfb44fda", + "metadata": {}, + "outputs": [], + "source": [ + "def ohmic_correlation(t, alpha, wc, beta, s=1):\n", + " \"\"\"The Ohmic bath correlation function as a function of t\n", + " (and the bath parameters).\n", + " \"\"\"\n", + " corr = (1 / np.pi) * alpha * wc ** (1 - s)\n", + " corr *= beta ** (-(s + 1)) * mp.gamma(s + 1)\n", + " z1_u = (1 + beta * wc - 1.0j * wc * t) / (beta * wc)\n", + " z2_u = (1 + 1.0j * wc * t) / (beta * wc)\n", + " # Note: the arguments to zeta should be in as high precision as possible.\n", + " # See http://mpmath.org/doc/current/basics.html#providing-correct-input\n", + " return np.array(\n", + " [\n", + " complex(corr * (mp.zeta(s + 1, u1) + mp.zeta(s + 1, u2)))\n", + " for u1, u2 in zip(z1_u, z2_u)\n", + " ],\n", + " dtype=np.complex128,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9e798939", + "metadata": {}, + "outputs": [], + "source": [ + "def ohmic_spectral_density(w, alpha, wc):\n", + " \"\"\"The Ohmic bath spectral density as a function of w\n", + " (and the bath parameters).\n", + " \"\"\"\n", + " return w * alpha * np.e ** (-w / wc)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7691064b", + "metadata": {}, + "outputs": [], + "source": [ + "def ohmic_power_spectrum(w, alpha, wc, beta):\n", + " \"\"\"The Ohmic bath power spectrum as a function of w\n", + " (and the bath parameters).\n", + " \"\"\"\n", + " bose = (1 / (np.e ** (w * beta) - 1)) + 1\n", + " return w * alpha * np.e ** (-abs(w) / wc) * bose * 2" + ] + }, + { + "cell_type": "markdown", + "id": "c7913528", + "metadata": {}, + "source": [ + "### Bath and HEOM parameters" + ] + }, + { + "cell_type": "markdown", + "id": "0a40fda0", + "metadata": {}, + "source": [ + "Finally, let's set the bath parameters we will work with and write down some measurement operators:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8d58b8c8", + "metadata": {}, + "outputs": [], + "source": [ + "Q = sigmaz()\n", + "alpha = 3.25\n", + "T = 0.5\n", + "wc = 1.0\n", + "s = 1" + ] + }, + { + "cell_type": "markdown", + "id": "635dcec1", + "metadata": {}, + "source": [ + "And set the cut-off for the HEOM hierarchy:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "297850af", + "metadata": {}, + "outputs": [], + "source": [ + "# HEOM parameters:\n", + "\n", + "# The max_depth defaults to 5 so that the notebook executes more\n", + "# quickly. Change it to 11 to wait longer for more accurate results.\n", + "max_depth = 5" + ] + }, + { + "cell_type": "markdown", + "id": "8827fc32", + "metadata": {}, + "source": [ + "## Building the HEOM bath by fitting the spectral density" + ] + }, + { + "cell_type": "markdown", + "id": "6e3c4370", + "metadata": {}, + "source": [ + "We begin by fitting the spectral density, using a series of $k$ underdamped harmonic oscillators case with the Meier-Tannor form (J. Chem. Phys. 111, 3365 (1999); https://doi.org/10.1063/1.479669):\n", + "\n", + "\\begin{equation}\n", + "J_{\\mathrm approx}(\\omega; a, b, c) = \\sum_{i=0}^{k-1} \\frac{2 a_i b_i w}{((w + c_i)^2 + b_i^2) ((w - c_i)^2 + b_i^2)}\n", + "\\end{equation}\n", + "\n", + "where $a, b$ and $c$ are the fit parameters and each is a vector of length $k$." + ] + }, + { + "cell_type": "markdown", + "id": "6b67cac7", + "metadata": {}, + "source": [ + "With the spectral density approximation $J_{\\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. This can be done quickly using the `SpectralFitter` class, which takes the target spectral density as an array and fits it to the series of **k** underdamped harmonic oscillators with the Meier-Tannor form" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f6b46bc0", + "metadata": {}, + "outputs": [], + "source": [ + "w = np.linspace(1e-3, 15, 20000)\n", + "J = ohmic_spectral_density(w, alpha, wc)" + ] + }, + { + "cell_type": "markdown", + "id": "ae05a07c", + "metadata": {}, + "source": [ + "The `BosonicEnviroment` class has special construtors that can be used to \n", + "create enviroments from arbitrary spectral densities, correlation functions, or\n", + "power spectrums. Below we show how to construct a `BosonicEnvironment` from a \n", + "user specified function or array" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a60b1cda", + "metadata": {}, + "outputs": [], + "source": [ + "# From an array\n", + "sd_env=BosonicEnvironment.from_spectral_density(J=J,wlist=w)" + ] + }, + { + "cell_type": "markdown", + "id": "f9715b26", + "metadata": {}, + "source": [ + "The resulting `BosonicEnvironment` cannot compute the power spectrum, or \n", + "correlation function because the temperature of the environment has not been \n", + "specified. So the `BosonicEnvironment` is not fully characterized by the \n", + "parameters provided" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a18793bf", + "metadata": {}, + "outputs": [], + "source": [ + "# sd_env.power_spectrum(w)" + ] + }, + { + "cell_type": "markdown", + "id": "e4dd336f", + "metadata": {}, + "source": [ + "If we want access to these properties we need to provide the Temperature at Initialization" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "0239acf7", + "metadata": {}, + "outputs": [], + "source": [ + "# From an array\n", + "sd_env=BosonicEnvironment.from_spectral_density(J=J,wlist=w,T=T)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f155cacf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.allclose(sd_env.power_spectrum(w),ohmic_power_spectrum(w,alpha,wc,1/T))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b07566c5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mcditoos/anaconda3/envs/qutip-dev/lib/python3.12/site-packages/matplotlib/cbook.py:1762: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return math.isfinite(val)\n", + "/home/mcditoos/anaconda3/envs/qutip-dev/lib/python3.12/site-packages/matplotlib/cbook.py:1398: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return np.asarray(x, float)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tlist=np.linspace(0,10,500)\n", + "plt.plot(tlist,sd_env.correlation_function(tlist))\n", + "plt.plot(tlist,ohmic_correlation(tlist,alpha,wc,1/T),\"--\")\n", + "plt.plot(tlist,np.imag(sd_env.correlation_function(tlist)))\n", + "plt.plot(tlist,np.imag(ohmic_correlation(tlist,alpha,wc,1/T)),\"--\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "14490c36", + "metadata": {}, + "source": [ + "One important optional parameter is WMax, when passing arrays to the constructor\n", + "it defaults to the maximum value of the array, however when passing a function \n", + "we don't need to specify the values on which it is evaluated, and in this case \n", + "WMax needs to be specified, Wmax is the value for which the spectral density,\n", + "or power spectrum has decayed to zero, after this value the function can be \n", + "considered to be zero" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "f71a9644", + "metadata": {}, + "outputs": [], + "source": [ + "# From a function\n", + "sd_env2=BosonicEnvironment.from_spectral_density(ohmic_spectral_density,T=T,wMax=10*wc,args=(alpha,wc))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d2ac2faf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tlist=np.linspace(0,10,500)\n", + "plt.plot(tlist,sd_env2.correlation_function(tlist))\n", + "plt.plot(tlist,ohmic_correlation(tlist,alpha,wc,1/T),\"--\")\n", + "plt.plot(tlist,np.imag(sd_env2.correlation_function(tlist)))\n", + "plt.plot(tlist,np.imag(ohmic_correlation(tlist,alpha,wc,1/T)),\"--\")" + ] + }, + { + "cell_type": "markdown", + "id": "bef212bc", + "metadata": {}, + "source": [ + "Obtaining an decaying Exponential description via the spectral density" + ] + }, + { + "cell_type": "markdown", + "id": "ce27cb93", + "metadata": {}, + "source": [ + "Once our `BosonicEnvironment` has been constructed, we can obtained a Decaying\n", + "exponnetial representation of the environment, via fitting either the spectral\n", + "density or the correlation function. First we will show how to do it \n", + "via fitting the spectral density.\n", + "\n", + "The output of the fit is a tuple containing an `ExponentialBosonicEnvironment`\n", + "and a dictionary that has all the relevant information about the fit performed.\n", + "The goodness of the feed is measured via the normalized root mean squared error,\n", + "by default the number of terms in the fit increased until the target accuracy \n", + "is reached or the maximum number allowed `Nmax` is reached. The default target\n", + "is a normalized root mean squared error of $5\\times 10^{-6}$, if set to None\n", + "the fit is performed only with the maximum number of exponents specified\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "81adee22", + "metadata": {}, + "outputs": [], + "source": [ + "bath, fitinfo = sd_env.approx_by_sd_fit(w,Nmax=6)" + ] + }, + { + "cell_type": "markdown", + "id": "2f5bc5a5", + "metadata": {}, + "source": [ + "To obtain an overview of the results of the fit we may take a look at the summary from the ``fitinfo``" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "71a7c82a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result of fitting the spectral density with 4 terms: \n", + " \n", + " Parameters| lam | gamma | w0 \n", + " 1 |-4.44e+00 | 4.31e+00 |3.96e+00\n", + " 2 | 6.07e-01 | 1.01e+00 |1.00e-01\n", + " 3 | 7.93e+00 | 2.30e+00 |1.00e-01\n", + " 4 | 1.07e-02 | 3.09e-01 |1.00e-01\n", + " \n", + "A normalized RMSE of 2.64e-06 was obtained for the the spectral density.\n", + "The current fit took 25.543029 seconds.\n" + ] + } + ], + "source": [ + "print(fitinfo[\"summary\"])" + ] + }, + { + "cell_type": "markdown", + "id": "8edcc35e", + "metadata": {}, + "source": [ + "We may see how the number of exponents chosen affects the fit since the approximated functions are available:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "d8587f0d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5))\n", + "\n", + "ax1.plot(w, J, label=\"Original spectral density\")\n", + "ax1.plot(w, bath.spectral_density(w), \"--\",label=\"Effective fitted SD\")\n", + "ax1.set_xlabel(r'$\\omega$')\n", + "ax1.set_ylabel(r'$J$')\n", + "ax1.legend()\n", + "\n", + "ax2.plot(w, np.abs(J - bath.spectral_density(w)), label=\"Error\")\n", + "ax2.set_xlabel(r'$\\omega$')\n", + "ax2.set_ylabel(r'$|J-J_{approx}|$')\n", + "ax2.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "89164ff6", + "metadata": {}, + "source": [ + "Here we see a surprisingly large discrepancy in our approximated or effective spectral density. This happens because we are not using enough exponentials from each of the underdamped modes to have an appropiate fit. All modes have the same number of exponents, and we set it to 1 which is not enough to model a bath with the temperature considered, let us repeat this with a higher number of exponents." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "bd7aec4a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bath, fitinfo = sd_env.approx_by_sd_fit(w,Nmax=6,Nk=3)\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5))\n", + "\n", + "ax1.plot(w, J, label=\"Original spectral density\")\n", + "ax1.plot(w, bath.spectral_density(w), \"--\",label=\"Effective fitted SD\")\n", + "ax1.set_xlabel(r'$\\omega$')\n", + "ax1.set_ylabel(r'$J$')\n", + "ax1.legend()\n", + "\n", + "ax2.plot(w, np.abs(J - bath.spectral_density(w)), label=\"Error\")\n", + "ax2.set_xlabel(r'$\\omega$')\n", + "ax2.set_ylabel(r'$|J-J_{approx}|$')\n", + "ax2.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0b6f9c12", + "metadata": {}, + "source": [ + "Since the number of exponents increases simulation time one should go with the least amount of exponents that correctly describe the bath properties (Power spectrum, Spectral density and the correlation function). When the number of exponents is not specified it defaults to 1, which is typically enough when the temperature is high." + ] + }, + { + "cell_type": "markdown", + "id": "65cf94f6", + "metadata": {}, + "source": [ + "Let's take a closer look at our last fit by plotting the contribution of each term of the fit:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "882c64e5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the components of the fit separately:\n", + "plt.rcParams[\"font.size\"] = 25\n", + "plt.rcParams[\"figure.figsize\"] = (10, 5)\n", + "\n", + "\n", + "def plot_fit(func, J, w, lam, gamma, w0):\n", + " \"\"\"Plot the individual components of a fit to the spectral density.\n", + " and how they contribute to the full fit one by one\"\"\"\n", + " total = 0\n", + " for i in range(len(lam)):\n", + " component = func(w, lam[i], gamma[i], w0[i])\n", + " total += component\n", + " plt.plot(w, J, \"r--\", linewidth=2, label=\"original\")\n", + " plt.plot(w, total, label=rf\"$k={i+1}$\")\n", + " plt.xlabel(r\"$\\omega$\")\n", + " plt.ylabel(r\"$J(\\omega)$\")\n", + " plt.legend()\n", + " plt.pause(1)\n", + " plt.show()\n", + "\n", + "\n", + "def plot_fit_components(func, J, w, lam, gamma, w0):\n", + " \"\"\"Plot the individual components of a fit to the spectral density.\n", + " and how they contribute to the full fit\"\"\"\n", + " plt.plot(w, J, \"r--\", linewidth=2, label=\"original\")\n", + " for i in range(len(lam)):\n", + " component = func(w, lam[i], gamma[i], w0[i])\n", + " plt.plot(w, component, label=rf\"$k={i+1}$\")\n", + " plt.xlabel(r\"$\\omega$\")\n", + " plt.ylabel(r\"$J(\\omega)$\")\n", + " plt.legend(bbox_to_anchor=(1.04, 1))\n", + " plt.show()\n", + "\n", + "\n", + "lam=fitinfo[\"params\"][:,0]\n", + "gamma=fitinfo[\"params\"][:,1] \n", + "w0 = fitinfo[\"params\"][:,2]\n", + "plot_fit(_sd_fit_model, J, w, lam, gamma, w0)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c05f2af0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_fit_components(_sd_fit_model, J, w, lam, gamma, w0)" + ] + }, + { + "cell_type": "markdown", + "id": "27fa30a5", + "metadata": {}, + "source": [ + "And let's also compare the power spectrum of the fit and the analytical spectral density:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "72deb34d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_power_spectrum(alpha, wc, beta, save=True):\n", + " \"\"\"Plot the power spectrum of a fit against the actual power spectrum.\"\"\"\n", + " w = np.linspace(-10, 10, 50000)\n", + " s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta)\n", + " s_fit = bath.power_spectrum(w)\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " axes.plot(w, s_orig, \"r\", linewidth=2, label=\"original\")\n", + " axes.plot(w, np.real(s_fit), \"b\", linewidth=2, label=\"fit\")\n", + "\n", + " axes.set_xlabel(r\"$\\omega$\", fontsize=28)\n", + " axes.set_ylabel(r\"$S(\\omega)$\", fontsize=28)\n", + " axes.legend()\n", + "\n", + " if save:\n", + " fig.savefig(\"powerspectrum.eps\")\n", + "\n", + "\n", + "plot_power_spectrum(alpha, wc, 1 / T, save=False)" + ] + }, + { + "cell_type": "markdown", + "id": "1c2e4446", + "metadata": {}, + "source": [ + "Now that we have a good fit to the spectral density, Let us obtain its dynamics, by passing our ``FitSpectral`` bath specifications into the ``HEOMSolver``" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "cb90d87a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 8.75s*] Elapsed 8.75s / Remaining 00:00:00:00\n" + ] + } + ], + "source": [ + "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + "HEOM_spectral_fit = HEOMSolver(\n", + " Hsys,\n", + " (bath,Q),\n", + " max_depth=4,\n", + " options=options,\n", + ")\n", + "result_spectral = HEOM_spectral_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "5bb8eb36", + "metadata": {}, + "source": [ + "Now if we want to see the systems's behaviour as we change the Number of terms in the fit, we may use this auxiliary function" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "5a8a930d", + "metadata": {}, + "outputs": [], + "source": [ + "def generate_spectrum_results(Q, N, Nk, max_depth):\n", + " \"\"\"Run the HEOM with the given bath parameters and\n", + " and return the results of the evolution.\n", + " \"\"\"\n", + " bath, _= sd_env.approx_by_sd_fit(w,Nmax=N,Nk=Nk,target_rmse=None)\n", + " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + "\n", + " # This problem is a little stiff, so we use the BDF method to solve\n", + " # the ODE ^^^\n", + " print(f\"Starting calculations for N={N}, Nk={Nk} and max_depth={max_depth} ... \")\n", + " HEOM_spectral_fit = HEOMSolver(\n", + " Hsys,\n", + " (bath,Q),\n", + " max_depth=max_depth,\n", + " options=options,\n", + " )\n", + " results_spectral_fit = HEOM_spectral_fit.run(rho0, tlist)\n", + " return results_spectral_fit" + ] + }, + { + "cell_type": "markdown", + "id": "9ea58304", + "metadata": {}, + "source": [ + "Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "0273c6cb", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_result_expectations(plots, axes=None):\n", + " \"\"\"Plot the expectation values of operators as functions of time.\n", + "\n", + " Each plot in plots consists of (solver_result,\n", + " measurement_operation, color, label).\n", + " \"\"\"\n", + " if axes is None:\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " fig_created = True\n", + " else:\n", + " fig = None\n", + " fig_created = False\n", + "\n", + " # add kw arguments to each plot if missing\n", + " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", + " for result, m_op, color, label, kw in plots:\n", + " exp = np.real(expect(result.states, m_op))\n", + " kw.setdefault(\"linewidth\", 2)\n", + " if color == \"rand\":\n", + " axes.plot(\n", + " result.times,\n", + " exp,\n", + " c=np.random.rand(\n", + " 3,\n", + " ),\n", + " label=label,\n", + " **kw,\n", + " )\n", + " else:\n", + " axes.plot(result.times, exp, color, label=label, **kw)\n", + "\n", + " if fig_created:\n", + " axes.legend(loc=0, fontsize=12)\n", + " axes.set_xlabel(\"t\", fontsize=28)\n", + "\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "96b86c48", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting calculations for N=1, Nk=1 and max_depth=5 ... \n", + " Total run time: 1.13s*] Elapsed 1.13s / Remaining 00:00:00:00\n", + "Starting calculations for N=2, Nk=1 and max_depth=5 ... \n", + " Total run time: 2.10s*] Elapsed 2.10s / Remaining 00:00:00:00\n", + "Starting calculations for N=3, Nk=1 and max_depth=5 ... \n", + " Total run time: 3.69s*] Elapsed 3.69s / Remaining 00:00:00:00\n", + "Starting calculations for N=4, Nk=1 and max_depth=5 ... \n", + " Total run time: 11.24s*] Elapsed 11.24s / Remaining 00:00:00:00\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generate results for different number of lorentzians in fit:\n", + "\n", + "results_spectral_fit_pk = [\n", + " generate_spectrum_results(Q, n, Nk=1, max_depth=max_depth) for n in range(1, 5)\n", + "]\n", + "\n", + "plot_result_expectations(\n", + " [\n", + " (\n", + " result,\n", + " P11p,\n", + " \"rand\",\n", + " f\"P11 (spectral fit) $k_J$={pk + 1}\",\n", + " )\n", + " for pk, result in enumerate(results_spectral_fit_pk)\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "980af0cd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting calculations for N=4, Nk=2 and max_depth=5 ... \n", + " [******* 29% ] Elapsed 107.97s / Remaining 00:00:04:24" + ] + } + ], + "source": [ + "# generate results for different number of Matsubara terms per Lorentzian\n", + "# for max number of Lorentzians:\n", + "\n", + "Nk_list = range(2, 4)\n", + "results_spectral_fit_nk = [\n", + " generate_spectrum_results(Q, 4, Nk=Nk, max_depth=max_depth) for Nk in Nk_list\n", + "]\n", + "\n", + "plot_result_expectations(\n", + " [\n", + " (\n", + " result,\n", + " P11p,\n", + " \"rand\",\n", + " f\"P11 (spectral fit) K={nk}\",\n", + " )\n", + " for nk, result in zip(Nk_list, results_spectral_fit_nk)\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb904688", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting calculations for N=4, Nk=1 and max_depth=2 ... \n", + " Total run time: 0.92s*] Elapsed 0.92s / Remaining 00:00:00:00\n", + "Starting calculations for N=4, Nk=1 and max_depth=3 ... \n", + " Total run time: 1.73s*] Elapsed 1.73s / Remaining 00:00:00:00\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting calculations for N=4, Nk=1 and max_depth=4 ... \n", + " Total run time: 4.52s*] Elapsed 4.52s / Remaining 00:00:00:00\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generate results for different depths:\n", + "\n", + "Nc_list = range(2, max_depth)\n", + "results_spectral_fit_nc = [\n", + " generate_spectrum_results(Q, 4, Nk=1, max_depth=Nc) for Nc in Nc_list\n", + "]\n", + "\n", + "plot_result_expectations(\n", + " [\n", + " (\n", + " result,\n", + " P11p,\n", + " \"rand\",\n", + " f\"P11 (spectral fit) $N_C={nc}$\",\n", + " )\n", + " for nc, result in zip(Nc_list, results_spectral_fit_nc)\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "844af288", + "metadata": {}, + "source": [ + "#### We now combine the fitting and correlation function data into one large plot. Here we define a function to plot everything together" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7fc617a1", + "metadata": {}, + "outputs": [], + "source": [ + "def gen_plots(fs, w, J, t, C, w2, S):\n", + " def plot_cr_fit_vs_actual(t, C, func, axes):\n", + " \"\"\"Plot the C_R(t) fit.\"\"\"\n", + " yR = func(t)\n", + "\n", + " axes.plot(\n", + " t,\n", + " np.real(C),\n", + " \"r\",\n", + " linewidth=3,\n", + " label=\"Original\",\n", + " )\n", + " axes.plot(\n", + " t,\n", + " np.real(yR),\n", + " \"g\",\n", + " dashes=[3, 3],\n", + " linewidth=2,\n", + " label=\"Reconstructed\",\n", + " )\n", + "\n", + " axes.set_ylabel(r\"$C_R(t)$\", fontsize=28)\n", + " axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=28)\n", + " axes.locator_params(axis=\"y\", nbins=4)\n", + " axes.locator_params(axis=\"x\", nbins=4)\n", + " axes.text(0.15, 0.85, \"(a)\", fontsize=28, transform=axes.transAxes)\n", + "\n", + " def plot_ci_fit_vs_actual(t, C, func, axes):\n", + " \"\"\"Plot the C_I(t) fit.\"\"\"\n", + " yI = func(t)\n", + "\n", + " axes.plot(\n", + " t,\n", + " np.imag(C),\n", + " \"r\",\n", + " linewidth=3,\n", + " )\n", + " axes.plot(\n", + " t,\n", + " np.real(yI),\n", + " \"g\",\n", + " dashes=[3, 3],\n", + " linewidth=2,\n", + " )\n", + "\n", + " axes.set_ylabel(r\"$C_I(t)$\", fontsize=28)\n", + " axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=28)\n", + " axes.locator_params(axis=\"y\", nbins=4)\n", + " axes.locator_params(axis=\"x\", nbins=4)\n", + " axes.text(0.80, 0.80, \"(b)\", fontsize=28, transform=axes.transAxes)\n", + "\n", + " def plot_jw_fit_vs_actual(w, J, axes):\n", + " \"\"\"Plot the J(w) fit.\"\"\"\n", + " J_fit = fs.spectral_density(w)\n", + "\n", + " axes.plot(\n", + " w,\n", + " J,\n", + " \"r\",\n", + " linewidth=3,\n", + " )\n", + " axes.plot(\n", + " w,\n", + " J_fit,\n", + " \"g\",\n", + " dashes=[3, 3],\n", + " linewidth=2,\n", + " )\n", + "\n", + " axes.set_ylabel(r\"$J(\\omega)$\", fontsize=28)\n", + " axes.set_xlabel(r\"$\\omega/\\omega_c$\", fontsize=28)\n", + " axes.locator_params(axis=\"y\", nbins=4)\n", + " axes.locator_params(axis=\"x\", nbins=4)\n", + " axes.text(0.15, 0.85, \"(c)\", fontsize=28, transform=axes.transAxes)\n", + "\n", + " def plot_sw_fit_vs_actual(axes):\n", + " \"\"\"Plot the S(w) fit.\"\"\"\n", + "\n", + " # avoid the pole in the fit around zero:\n", + " s_fit = fs.power_spectrum(w2)\n", + "\n", + " axes.plot(w2, S, \"r\", linewidth=3)\n", + " axes.plot(w2, s_fit, \"g\", dashes=[3, 3], linewidth=2)\n", + "\n", + " axes.set_ylabel(r\"$S(\\omega)$\", fontsize=28)\n", + " axes.set_xlabel(r\"$\\omega/\\omega_c$\", fontsize=28)\n", + " axes.locator_params(axis=\"y\", nbins=4)\n", + " axes.locator_params(axis=\"x\", nbins=4)\n", + " axes.text(0.15, 0.85, \"(d)\", fontsize=28, transform=axes.transAxes)\n", + "\n", + " def plot_matsubara_spectrum_fit_vs_actual(t, C):\n", + " \"\"\"Plot the Matsubara fit of the spectrum .\"\"\"\n", + " fig = plt.figure(figsize=(12, 10))\n", + " grid = plt.GridSpec(2, 2, wspace=0.4, hspace=0.3)\n", + "\n", + " plot_cr_fit_vs_actual(\n", + " t,\n", + " C,\n", + " lambda t: fs.correlation_function(t),\n", + " axes=fig.add_subplot(grid[0, 0]),\n", + " )\n", + " plot_ci_fit_vs_actual(\n", + " t,\n", + " C,\n", + " lambda t: np.imag(fs.correlation_function(t)),\n", + " axes=fig.add_subplot(grid[0, 1]),\n", + " )\n", + " plot_jw_fit_vs_actual(\n", + " w,\n", + " J,\n", + " axes=fig.add_subplot(grid[1, 0]),\n", + " )\n", + " plot_sw_fit_vs_actual(\n", + " axes=fig.add_subplot(grid[1, 1]),\n", + " )\n", + " fig.legend(loc=\"upper center\", ncol=2, fancybox=True, shadow=True)\n", + "\n", + " return plot_matsubara_spectrum_fit_vs_actual(t, C)" + ] + }, + { + "cell_type": "markdown", + "id": "674d5498", + "metadata": {}, + "source": [ + "#### And finally plot everything together" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26209a1b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t = np.linspace(0, 15, 1000)\n", + "C = ohmic_correlation(t, alpha, wc, 1 / T)\n", + "w2 = np.concatenate((-np.linspace(10, 1e-2, 100), np.linspace(1e-2, 10, 100)))\n", + "S = ohmic_power_spectrum(w2, alpha, wc, 1 / T)\n", + "gen_plots(bath, w, J, t, C, w2, S)" + ] + }, + { + "cell_type": "markdown", + "id": "a72989f8", + "metadata": {}, + "source": [ + "## Building the HEOM bath by fitting the correlation function" + ] + }, + { + "cell_type": "markdown", + "id": "81acee08", + "metadata": {}, + "source": [ + "Having successfully fitted the spectral density and used the result to calculate the Matsubara expansion and terminator for the HEOM bosonic bath, we now proceed to the second case of fitting the correlation function itself instead.\n", + "\n", + "Here we fit the real and imaginary parts separately, using the following ansatz \n", + "\n", + "$$C_R^F(t) = \\sum_{i=1}^{k_R} c_R^ie^{-\\gamma_R^i t}\\cos(\\omega_R^i t)$$\n", + "\n", + "$$C_I^F(t) = \\sum_{i=1}^{k_I} c_I^ie^{-\\gamma_I^i t}\\sin(\\omega_I^i t)$$\n", + "\n", + "Analogously to the spectral density case, one may use the `approx_by_cf_fit` method, the main difference with respect to the spectral density fit, is that now we are perfoming two fits, one for the real part and another one for the imaginary part" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "217905ff", + "metadata": {}, + "outputs": [], + "source": [ + "bath_corr ,fitinfo= sd_env.approx_by_cf_fit(tlist=t,Ni_max=4,Nr_max=4,maxfev=1e8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a861655e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation function fit:\n", + "\n", + "Result of fitting the real part of |Result of fitting the imaginary part \n", + "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", + " | \n", + " Parameters| a | b | c | Parameters| a | b | c \n", + " 1 | 4.77e-02 |-1.63e-01 |3.06e-26 | 1 |-8.41e+00 |-3.78e-01 |1.05e-03 \n", + " 2 |-1.88e+00 |-4.65e+00 |2.64e+00 | 2 |-1.34e+01 |-1.08e+00 |2.73e-02 \n", + " 3 | 3.00e+00 |-2.89e+00 |2.96e-01 | 3 | 5.64e-01 |-4.30e+00 |3.95e+00 \n", + " 4 | 3.53e-01 |-6.27e-01 |1.27e-08 | 4 |-1.34e+01 |-2.31e+00 |2.90e-01 \n", + " | \n", + "A normalized RMSE of 2.49e-06 was obtained for the the real part of |A normalized RMSE of 3.99e-06 was obtained for the the imaginary part\n", + "the correlation function. |of the correlation function. \n", + "The current fit took 2.137432 seconds. |The current fit took 7.422126 seconds. \n", + "\n" + ] + } + ], + "source": [ + "print(fitinfo[\"summary\"])" + ] + }, + { + "cell_type": "markdown", + "id": "b8c32d8a", + "metadata": {}, + "source": [ + "The ansatz used is not good for functions where\n", + "\n", + "$$C_I^F(0) \\neq 0$$\n", + "\n", + "The keyword `full_ansatz` which defaults to False. allows for the usage of a \n", + "more general ansatz, the fit however tends to be significantly slower, never\n", + "the less it can reach a similar level of accuracy with a lower amount of exponents\n", + "\n", + "When full_ansatz is True. the ansatz used corresponds to \n", + "\n", + "\\begin{align}\n", + "\\operatorname{Re}[C(t)] = \\sum_{k=1}^{N_r} \\operatorname{Re}\\Bigl[\n", + " (a_k + \\mathrm i d_k) \\mathrm e^{(b_k + \\mathrm i c_k) t}\\Bigl]\n", + " ,\n", + "\\\\\n", + "\\operatorname{Im}[C(t)] = \\sum_{k=1}^{N_i} \\operatorname{Im}\\Bigl[\n", + " (a'_k + \\mathrm i d'_k) \\mathrm e^{(b'_k + \\mathrm i c'_k) t}\n", + " \\Bigr].\n", + "\\end{align}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57d768ee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n", + " [ 0% ] Elapsed 0.00s / Remaining 00:00:00:00" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 0.87s*] Elapsed 0.87s / Remaining 00:00:00:00[*********79%****** ] Elapsed 0.69s / Remaining 00:00:00:00\n", + "3\n", + " Total run time: 2.20s*] Elapsed 2.20s / Remaining 00:00:00:00\n", + "4\n", + " Total run time: 49.83s*] Elapsed 49.82s / Remaining 00:00:00:00\n" + ] + } + ], + "source": [ + "def generate_corr_results(N, max_depth):\n", + " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + " bath_corr ,fitinfo= sd_env.approx_by_cf_fit(tlist=t,Ni_max=N,Nr_max=N,maxfev=1e8,target_rsme=None)\n", + " HEOM_corr_fit = HEOMSolver(\n", + " Hsys,\n", + " (bath_corr,Q),\n", + " max_depth=max_depth,\n", + " options=options,\n", + " )\n", + "\n", + " results_corr_fit = HEOM_corr_fit.run(rho0, tlist)\n", + "\n", + " return results_corr_fit\n", + "\n", + "\n", + "# Generate results for different number of exponentials in fit:\n", + "results_corr_fit_pk = [\n", + " print(f\"{i + 1}\")\n", + " or generate_corr_results(\n", + " i,\n", + " max_depth=max_depth,\n", + " )\n", + " for i in range(1, 4)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91d1be7c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (\n", + " result,\n", + " P11p,\n", + " \"rand\",\n", + " f\"P11 (correlation fit) k_R=k_I={pk + 1}\",\n", + " )\n", + " for pk, result in enumerate(results_corr_fit_pk)\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4770c53b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", + "\n", + "plot_result_expectations(\n", + " [\n", + " (\n", + " results_corr_fit_pk[0],\n", + " P11p,\n", + " \"y\",\n", + " \"Correlation Function Fit $k_R=k_I=1$\",\n", + " ),\n", + " (\n", + " results_corr_fit_pk[2],\n", + " P11p,\n", + " \"k\",\n", + " \"Correlation Function Fit $k_R=k_I=3$\",\n", + " ),\n", + " (results_spectral_fit_pk[0], P11p, \"b\", \"Spectral Density Fit $k_J=1$\"),\n", + " (results_spectral_fit_pk[3], P11p, \"r-.\", \"Spectral Density Fit $k_J=4$\"),\n", + " ],\n", + " axes=axes,\n", + ")\n", + "\n", + "axes.set_yticks([0.6, 0.8, 1])\n", + "axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", + "axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", + "axes.legend(loc=0, fontsize=20);" + ] + }, + { + "cell_type": "markdown", + "id": "63716f70", + "metadata": {}, + "source": [ + "# Using the Ohmic Bath class\n", + "\n", + " As the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced quickly by using the OhmicBath class. This allows for rapid implementation of fitted ohmic baths via the correlation function or spectral density" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4883e1cc", + "metadata": {}, + "outputs": [], + "source": [ + "obs = OhmicEnvironment(T, alpha, wc,s=1)" + ] + }, + { + "cell_type": "markdown", + "id": "005418f5", + "metadata": {}, + "source": [ + "Just like the other `BosonicEnvironment` we can obtain a decaying exponential \n", + "representation of the environment via the `approx_by_cf_fit` and \n", + "`approx_by_sd_fit` methods. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e0924e70", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation function fit:\n", + "\n", + "Result of fitting the real part of |Result of fitting the imaginary part \n", + "the correlation function with 3 terms: |of the correlation function with 2 terms: \n", + " | \n", + " Parameters| a | b | c | Parameters| a | b | c \n", + " 1 | 2.09e-01 |-3.29e-01 |4.22e-15 | 1 |-1.24e+01 |-2.16e+00 |3.12e-01 \n", + " 2 |-1.04e+00 |-3.26e+01 |1.87e-03 | 2 |-1.24e+01 |-7.90e-01 |1.33e-02 \n", + " 3 | 2.35e+00 |-2.19e+00 |6.87e-11 | \n", + " |A normalized RMSE of 1.34e-05 was obtained for the the imaginary part\n", + "A normalized RMSE of 1.68e-05 was obtained for the the real part of |of the correlation function. \n", + "the correlation function. | \n", + "The current fit took 0.106656 seconds. |The current fit took 0.106712 seconds. \n", + "\n" + ] + } + ], + "source": [ + "Obath, fitinfo = obs.approx_by_cf_fit(tlist=tlist,Nr_max=4,Ni_max=4,maxfev=1e9)\n", + "print(fitinfo[\"summary\"])\n", + "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + "HEOM_ohmic_corr_fit = HEOMSolver(\n", + " Hsys,\n", + " (Obath,Q),\n", + " max_depth=5,\n", + " options=options,\n", + ")\n", + "results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ddbaebf2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result of fitting the spectral density with 4 terms: \n", + " \n", + " Parameters| lam | gamma | w0 \n", + " 1 | 1.00e+00 | 1.03e+00 |3.32e+00\n", + " 2 | 1.67e+00 | 9.17e-01 |1.13e+00\n", + " 3 | 1.56e+00 | 9.46e-01 |2.11e+00\n", + " 4 | 6.79e-01 | 8.68e-01 |1.20e-01\n", + " \n", + "A normalized RMSE of 4.39e-05 was obtained for the the spectral density.\n", + "The current fit took 16.990672 seconds.\n" + ] + } + ], + "source": [ + "Obath2, fitinfo = obs.approx_by_sd_fit(wlist=w,Nmax=4,Nk=1)\n", + "print(fitinfo[\"summary\"])\n", + "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + "HEOM_ohmic_sd_fit = HEOMSolver(\n", + " Hsys,\n", + " (Obath2,Q),\n", + " max_depth=5,\n", + " options=options,\n", + ")\n", + "results_ohmic_sd_fit = HEOM_ohmic_sd_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "cfa14447", + "metadata": {}, + "source": [ + "Finally we plot the dynamics obtained by the different methods" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5ba2889a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", + "\n", + "plot_result_expectations(\n", + " [\n", + " (\n", + " results_corr_fit_pk[2],\n", + " P11p,\n", + " \"b\",\n", + " \"Correlation Function Fit $k_R=k_I=3$\",\n", + " ),\n", + " (results_spectral_fit_pk[3], P11p, \"r-.\", \"Spectral Density Fit $k_J=4$\"),\n", + " (results_ohmic_corr_fit, P11p, \"k--\", \"Correlation Fit Ohmic Bath\"),\n", + " (results_ohmic_sd_fit, P11p, \"g\", \"Spectral Density Fit Ohmic Bath\"),\n", + " ],\n", + " axes=axes,\n", + ")\n", + "axes.set_yticks([0.6, 0.8, 1])\n", + "axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", + "axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", + "axes.legend(loc=0, fontsize=20);\n", + "axes.set_xlim(0,35)\n", + "axes.set_ylim(0.9,1)\n", + "axes.set_yscale(\"log\")" + ] + }, + { + "cell_type": "markdown", + "id": "d0fc9218", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1eb99ec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "QuTiP: Quantum Toolbox in Python\n", + "================================\n", + "Copyright (c) QuTiP team 2011 and later.\n", + "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", + "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", + "Original developers: R. J. Johansson & P. D. Nation.\n", + "Previous lead developers: Chris Granade & A. Grimsmo.\n", + "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", + "\n", + "QuTiP Version: 5.1.0.dev0+7941773\n", + "Numpy Version: 2.1.3\n", + "Scipy Version: 1.14.1\n", + "Cython Version: 3.0.9\n", + "Matplotlib Version: 3.9.2\n", + "Python Version: 3.12.7\n", + "Number of CPUs: 16\n", + "BLAS Info: Generic\n", + "INTEL MKL Ext: None\n", + "Platform Info: Linux (x86_64)\n", + "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", + "================================================================================\n", + "Please cite QuTiP in your publication.\n", + "================================================================================\n", + "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" + ] + } + ], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "725e989d", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e75c2b49", + "metadata": {}, + "outputs": [], + "source": [ + "assert np.allclose(\n", + " expect(P11p, results_spectral_fit_pk[2].states),\n", + " expect(P11p, results_spectral_fit_pk[3].states),\n", + " rtol=1e-2,\n", + ")" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "qutip-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb new file mode 100644 index 00000000..a6d13a83 --- /dev/null +++ b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb @@ -0,0 +1,743 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e19a5cae", + "metadata": {}, + "source": [ + "# HEOM 1e: Spin-Bath model (pure dephasing)" + ] + }, + { + "cell_type": "markdown", + "id": "8a180f04", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices.\n", + "\n", + "In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", + "\n", + "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian (see paper), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", + "\n", + "In the example below we show how to model the overdamped Drude-Lorentz Spectral Density, commonly used with the HEOM. We show how to do the Matsubara and Pade analytical decompositions, as well as how to fit the latter with a finite set of approximate exponentials. This differs from examble 1a in that we assume that the system and coupling parts of the Hamiltonian commute, hence giving an analytically solvable ''pure dephasing'' model. This is a useful example to look at when introducing other approximations (e.g., fitting of correlation functions) to check for validity/convergence against the analytical results. (Note that, generally, for the fitting examples, the pure dephasing model is the 'worst possible case'. \n", + "\n", + "### Drude-Lorentz spectral density\n", + "\n", + "The Drude-Lorentz spectral density is:\n", + "\n", + "$$J(\\omega)=\\omega \\frac{2\\lambda\\gamma}{{\\gamma}^2 + \\omega^2}$$\n", + "\n", + "where $\\lambda$ scales the coupling strength, and $\\gamma$ is the cut-off frequency.\n", + "We use the convention,\n", + "\\begin{equation*}\n", + "C(t) = \\int_0^{\\infty} d\\omega \\frac{J_D(\\omega)}{\\pi}[\\coth(\\beta\\omega) \\cos(\\omega \\tau) - i \\sin(\\omega \\tau)]\n", + "\\end{equation*}\n", + "\n", + "With the HEOM we must use an exponential decomposition:\n", + "\n", + "\\begin{equation*}\n", + "C(t)=\\sum_{k=0}^{k=\\infty} c_k e^{-\\nu_k t}\n", + "\\end{equation*}\n", + "\n", + "The Matsubara decomposition of the Drude-Lorentz spectral density is given by:\n", + "\n", + "\\begin{equation*}\n", + " \\nu_k = \\begin{cases}\n", + " \\gamma & k = 0\\\\\n", + " {2 \\pi k} / {\\beta \\hbar} & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "\\begin{equation*}\n", + " c_k = \\begin{cases}\n", + " \\lambda \\gamma (\\cot(\\beta \\gamma / 2) - i) / \\hbar & k = 0\\\\\n", + " 4 \\lambda \\gamma \\nu_k / \\{(nu_k^2 - \\gamma^2)\\beta \\hbar^2 \\} & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." + ] + }, + { + "cell_type": "markdown", + "id": "2d6bb5b5", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "469bffd5", + "metadata": {}, + "outputs": [], + "source": [ + "import contextlib\n", + "import time\n", + "\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "import scipy\n", + "from scipy.optimize import curve_fit\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " basis,\n", + " expect,\n", + " liouvillian,\n", + " sigmax,\n", + " sigmaz,\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver\n", + ")\n", + "from qutip.core.environment import (\n", + " DrudeLorentzEnvironment,\n", + " system_terminator\n", + ")\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "fa8e2803", + "metadata": {}, + "source": [ + "## Helper functions\n", + "\n", + "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69d1848b", + "metadata": {}, + "outputs": [], + "source": [ + "def cot(x):\n", + " \"\"\" Vectorized cotangent of x. \"\"\"\n", + " return 1. / np.tan(x)\n", + "\n", + "\n", + "def coth(x):\n", + " \"\"\" Vectorized hyperbolic cotangent of x. \"\"\"\n", + " return 1. / np.tanh(x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f6fc1c4", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_result_expectations(plots, axes=None):\n", + " \"\"\" Plot the expectation values of operators as functions of time.\n", + "\n", + " Each plot in plots consists of (solver_result, measurement_operation,\n", + " color, label).\n", + " \"\"\"\n", + " if axes is None:\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " fig_created = True\n", + " else:\n", + " fig = None\n", + " fig_created = False\n", + "\n", + " # add kw arguments to each plot if missing\n", + " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", + " for result, m_op, color, label, kw in plots:\n", + " if m_op is None:\n", + " t, exp = result\n", + " else:\n", + " t = result.times\n", + " exp = np.real(expect(result.states, m_op))\n", + " kw.setdefault(\"linewidth\", 2)\n", + " axes.plot(t, exp, color, label=label, **kw)\n", + "\n", + " if fig_created:\n", + " axes.legend(loc=0, fontsize=12)\n", + " axes.set_xlabel(\"t\", fontsize=28)\n", + "\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a78edcad", + "metadata": {}, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\" Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "399060b9", + "metadata": {}, + "outputs": [], + "source": [ + "# Solver options:\n", + "\n", + "options = {\n", + " \"nsteps\": 15000,\n", + " \"store_states\": True,\n", + " \"rtol\": 1e-14,\n", + " \"atol\": 1e-14,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "8de86a56", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian, bath and system measurement operators:" + ] + }, + { + "cell_type": "markdown", + "id": "f480170d", + "metadata": {}, + "source": [ + "Here we set $H_{sys}=0$, which means the interaction Hamiltonian and the system Hamiltonian commute, and we can compare the numerical results to a known analytical one. We could in principle keep $\\epsilon \\neq 0$, but it just introduces fast system oscillations, so it is more convenient to set it to zero." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08793af6", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the system Hamiltonian\n", + "eps = 0.0 # Energy of the 2-level system.\n", + "Del = 0.0 # Tunnelling term\n", + "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89bebf47", + "metadata": {}, + "outputs": [], + "source": [ + "# System-bath coupling (Drude-Lorentz spectral density)\n", + "Q = sigmaz() # coupling operator\n", + "\n", + "# Bath properties:\n", + "gamma = 0.5 # cut off frequency\n", + "lam = 0.1 # coupling strength\n", + "T = 0.5\n", + "beta = 1. / T\n", + "\n", + "# HEOM parameters:\n", + "# cut off parameter for the bath:\n", + "NC = 6\n", + "# number of exponents to retain in the Matsubara expansion\n", + "# of the correlation function:\n", + "Nk = 3\n", + "\n", + "# Times to solve for\n", + "tlist = np.linspace(0, 50, 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40892dda", + "metadata": {}, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresponding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresponding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "6973de51", + "metadata": {}, + "source": [ + "To get a non-trivial result we prepare the initial state in a superposition, and see how the bath destroys the coherence." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "00b82c7e", + "metadata": {}, + "outputs": [], + "source": [ + "# Initial state of the system.\n", + "psi = (basis(2, 0) + basis(2, 1)).unit()\n", + "rho0 = psi * psi.dag()" + ] + }, + { + "cell_type": "markdown", + "id": "71b927f6", + "metadata": {}, + "source": [ + "We then define our environment, from which all the different simulations will \n", + "be obtained" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac7b875e", + "metadata": {}, + "outputs": [], + "source": [ + "env = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=Nk)" + ] + }, + { + "cell_type": "markdown", + "id": "4741a085", + "metadata": {}, + "source": [ + "## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c968a2e", + "metadata": {}, + "outputs": [], + "source": [ + "with timer(\"RHS construction time\"):\n", + " env_mats=env.approx_by_matsubara(Nk=Nk)\n", + " HEOMMats = HEOMSolver(Hsys, (env_mats,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMats = HEOMMats.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1721076c", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the results so far\n", + "plot_result_expectations([\n", + " (resultMats, P11p, 'b', \"P11 Matsubara\"),\n", + " (resultMats, P12p, 'r', \"P12 Matsubara\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "ff8fd434", + "metadata": {}, + "source": [ + "## Simulation 2: Matsubara decomposition (including terminator)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "851f6695", + "metadata": {}, + "outputs": [], + "source": [ + "with timer(\"RHS construction time\"):\n", + " env_mats,delta=env.approx_by_matsubara(Nk=Nk,compute_delta=True)\n", + " Ltot = liouvillian(Hsys) + system_terminator(Q,delta)\n", + " HEOMMatsT = HEOMSolver(Ltot, (env_mats,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMatsT = HEOMMatsT.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3c97d27", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the results\n", + "plot_result_expectations([\n", + " (resultMats, P11p, 'b', \"P11 Matsubara\"),\n", + " (resultMats, P12p, 'r', \"P12 Matsubara\"),\n", + " (resultMatsT, P11p, 'r--', \"P11 Matsubara and terminator\"),\n", + " (resultMatsT, P12p, 'b--', \"P12 Matsubara and terminator\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "84382ad4", + "metadata": {}, + "source": [ + "## Simulation 3: Pade decomposition\n", + "\n", + "As in example 1a, we can compare to Pade and Fitting approaches." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b3dc619a", + "metadata": {}, + "outputs": [], + "source": [ + "with timer(\"RHS construction time\"):\n", + " env_pade=env.approx_by_pade(Nk=Nk)\n", + " HEOMPade = HEOMSolver(Hsys, (env_pade,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultPade = HEOMPade.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81b33bc8", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the results\n", + "plot_result_expectations([\n", + " (resultMatsT, P11p, 'b', \"P11 Matsubara (+term)\"),\n", + " (resultMatsT, P12p, 'r', \"P12 Matsubara (+term)\"),\n", + " (resultPade, P11p, 'r--', \"P11 Pade\"),\n", + " (resultPade, P12p, 'b--', \"P12 Pade\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "17a35b89", + "metadata": {}, + "source": [ + "## Simulation 4: Fitting approach" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "73bc0976", + "metadata": {}, + "outputs": [], + "source": [ + "tfit=np.linspace(0,10,1000)\n", + "with timer(\"RHS construction time\"):\n", + " bath,_ = env.approx_by_cf_fit(tfit,Ni_max=1,Nr_max=3,target_rsme=None)\n", + " HEOMFit = HEOMSolver(Hsys, (bath,Q), NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultFit = HEOMFit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "3e7e6de0", + "metadata": {}, + "source": [ + "## Analytic calculations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b4633a28", + "metadata": {}, + "outputs": [], + "source": [ + "def pure_dephasing_evolution_analytical(tlist, wq, ck, vk):\n", + " \"\"\"\n", + " Computes the propagating function appearing in the pure dephasing model.\n", + "\n", + " Parameters\n", + " ----------\n", + " t: float\n", + " A float specifying the time at which to calculate the integral.\n", + "\n", + " wq: float\n", + " The qubit frequency in the Hamiltonian.\n", + "\n", + " ck: ndarray\n", + " The list of coefficients in the correlation function.\n", + "\n", + " vk: ndarray\n", + " The list of frequencies in the correlation function.\n", + "\n", + " Returns\n", + " -------\n", + " integral: float\n", + " The value of the integral function at time t.\n", + " \"\"\"\n", + " evolution = np.array([\n", + " np.exp(-1j * wq * t - correlation_integral(t, ck, vk))\n", + " for t in tlist\n", + " ])\n", + " return evolution\n", + "\n", + "\n", + "def correlation_integral(t, ck, vk):\n", + " r\"\"\"\n", + " Computes the integral sum function appearing in the pure dephasing model.\n", + "\n", + " If the correlation function is a sum of exponentials then this sum\n", + " is given by:\n", + "\n", + " .. math:\n", + "\n", + " \\int_0^{t}d\\tau D(\\tau) = \\sum_k\\frac{c_k}{\\mu_k^2}e^{\\mu_k t}\n", + " + \\frac{\\bar c_k}{\\bar \\mu_k^2}e^{\\bar \\mu_k t}\n", + " - \\frac{\\bar \\mu_k c_k + \\mu_k \\bar c_k}{\\mu_k \\bar \\mu_k} t\n", + " + \\frac{\\bar \\mu_k^2 c_k + \\mu_k^2 \\bar c_k}{\\mu_k^2 \\bar \\mu_k^2}\n", + "\n", + " Parameters\n", + " ----------\n", + " t: float\n", + " A float specifying the time at which to calculate the integral.\n", + "\n", + " ck: ndarray\n", + " The list of coefficients in the correlation function.\n", + "\n", + " vk: ndarray\n", + " The list of frequencies in the correlation function.\n", + "\n", + " Returns\n", + " -------\n", + " integral: float\n", + " The value of the integral function at time t.\n", + " \"\"\"\n", + " t1 = np.sum(\n", + " (ck / vk**2) *\n", + " (np.exp(vk * t) - 1)\n", + " )\n", + " t2 = np.sum(\n", + " (ck.conj() / vk.conj()**2) *\n", + " (np.exp(vk.conj() * t) - 1)\n", + " )\n", + " t3 = np.sum(\n", + " (ck / vk + ck.conj() / vk.conj()) * t\n", + " )\n", + " return 2 * (t1 + t2 - t3)" + ] + }, + { + "cell_type": "markdown", + "id": "1286c92b", + "metadata": {}, + "source": [ + "For the pure dephasing analytics, we just sum up as many matsubara terms as we can:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7935ec7e", + "metadata": {}, + "outputs": [], + "source": [ + "lmaxmats2 = 15000\n", + "\n", + "vk = [complex(-gamma)]\n", + "vk.extend([\n", + " complex(-2. * np.pi * k * T)\n", + " for k in range(1, lmaxmats2)\n", + "])\n", + "\n", + "ck = [complex(lam * gamma * (-1.0j + cot(gamma * beta / 2.)))]\n", + "ck.extend([\n", + " complex(4 * lam * gamma * T * (-v) / (v**2 - gamma**2))\n", + " for v in vk[1:]\n", + "])\n", + "\n", + "P12_ana = 0.5 * pure_dephasing_evolution_analytical(\n", + " tlist, 0, np.asarray(ck), np.asarray(vk)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8ed59c50", + "metadata": {}, + "source": [ + "Alternatively, we can just do the integral of the propagator directly, without using the correlation functions at all" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "780b82b0", + "metadata": {}, + "outputs": [], + "source": [ + "def JDL(omega, lamc, omega_c):\n", + " return 2. * lamc * omega * omega_c / (omega_c**2 + omega**2)\n", + "\n", + "\n", + "def integrand(omega, lamc, omega_c, Temp, t):\n", + " return (\n", + " (-4. * JDL(omega, lamc, omega_c) / omega**2) *\n", + " (1. - np.cos(omega*t)) * (coth(omega/(2.*Temp)))\n", + " / np.pi\n", + " )\n", + "\n", + "\n", + "P12_ana2 = [\n", + " 0.5 * np.exp(\n", + " scipy.integrate.quad(integrand, 0, np.inf, args=(lam, gamma, T, t))[0]\n", + " )\n", + " for t in tlist\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "28a0ad50", + "metadata": {}, + "source": [ + "## Compare results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c903876f", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations([\n", + " (resultMats, P12p, 'r', \"P12 Mats\"),\n", + " (resultMatsT, P12p, 'r--', \"P12 Mats + Term\"),\n", + " (resultPade, P12p, 'b--', \"P12 Pade\"),\n", + " (resultFit, P12p, 'g', \"P12 Fit\"),\n", + " ((tlist, np.real(P12_ana)), None, 'b', \"Analytic 1\"),\n", + " ((tlist, np.real(P12_ana2)), None, 'y--', \"Analytic 2\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "35474f66", + "metadata": {}, + "source": [ + "We can't see much difference in the plot above, so let's do a log plot instead:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c6ef7e4f", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + "\n", + "plot_result_expectations([\n", + " (resultMats, P12p, 'r', \"P12 Mats\"),\n", + " (resultMatsT, P12p, 'r--', \"P12 Mats + Term\"),\n", + " (resultPade, P12p, 'b-.', \"P12 Pade\"),\n", + " (resultFit, P12p, 'g', \"P12 Fit\"),\n", + " ((tlist, np.real(P12_ana)), None, 'b', \"Analytic 1\"),\n", + " ((tlist, np.real(P12_ana2)), None, 'y--', \"Analytic 2\"),\n", + "], axes)\n", + "\n", + "axes.set_yscale('log')\n", + "axes.legend(loc=0, fontsize=12);" + ] + }, + { + "cell_type": "markdown", + "id": "00259e47", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2a4b6c9", + "metadata": {}, + "outputs": [], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "945917f5", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98a3271d", + "metadata": {}, + "outputs": [], + "source": [ + "assert np.allclose(\n", + " expect(P12p, resultMats.states[:15]), np.real(P12_ana)[:15],\n", + " rtol=1e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(P12p, resultMatsT.states[:100]), np.real(P12_ana)[:100],\n", + " rtol=1e-3,\n", + ")\n", + "assert np.allclose(\n", + " expect(P12p, resultPade.states[:100]), np.real(P12_ana)[:100],\n", + " rtol=1e-3,\n", + ")\n", + "assert np.allclose(\n", + " expect(P12p, resultFit.states[:50]), np.real(P12_ana)[:50],\n", + " rtol=1e-3,\n", + ")\n", + "assert np.allclose(P12_ana, P12_ana2, rtol=1e-3)" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "qutip-dev", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-2-fmo-example.ipynb b/tutorials-v5/heom/heom-2-fmo-example.ipynb new file mode 100644 index 00000000..9c2fd3e5 --- /dev/null +++ b/tutorials-v5/heom/heom-2-fmo-example.ipynb @@ -0,0 +1,658 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7fd70470", + "metadata": {}, + "source": [ + "# HEOM 2: Dynamics in Fenna-Mathews-Olsen complex (FMO)" + ] + }, + { + "cell_type": "markdown", + "id": "a477c71b", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "In this example notebook we outline how to employ the HEOM to\n", + "solve the FMO photosynthetic complex dynamics.\n", + "\n", + "We aim to replicate the results in reference [https://www.pnas.org/content/106/41/17255](https://pubmed.ncbi.nlm.nih.gov/19815512/)\n", + "and compare them to a Bloch-Redfield (perturbative) solution.\n", + "\n", + "This demonstrates how to to employ the solver for multiple baths, as well as showing how a\n", + "quantum environment reduces the effect of pure dephasing." + ] + }, + { + "cell_type": "markdown", + "id": "9d341c87", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "107ebbba", + "metadata": {}, + "outputs": [], + "source": [ + "import contextlib\n", + "import time\n", + "\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " Qobj,\n", + " basis,\n", + " brmesolve,\n", + " expect,\n", + " liouvillian,\n", + " mesolve,\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver,\n", + ")\n", + "from qutip.core.environment import (\n", + " DrudeLorentzEnvironment,\n", + " system_terminator\n", + ")\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "972ebdfd", + "metadata": {}, + "source": [ + "## Helper functions\n", + "\n", + "Let's define some helper functions for calculating correlation functions, spectral densities, thermal energy level occupations, and for plotting results and timing how long operations take:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03d58afe", + "metadata": {}, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\" Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80b0393c", + "metadata": {}, + "outputs": [], + "source": [ + "# Solver options:\n", + "\n", + "options = {\n", + " \"nsteps\": 15000,\n", + " \"store_states\": True,\n", + " \"rtol\": 1e-12,\n", + " \"atol\": 1e-12,\n", + " \"min_step\": 1e-18,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "30d71cb7", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian and bath parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4baf9e45", + "metadata": {}, + "outputs": [], + "source": [ + "# System Hamiltonian:\n", + "#\n", + "# We use the Hamiltonian employed in\n", + "# https://www.pnas.org/content/106/41/17255 and operate\n", + "# in units of Hz:\n", + "\n", + "Hsys = 3e10 * 2 * np.pi * Qobj([\n", + " [200, -87.7, 5.5, -5.9, 6.7, -13.7, -9.9],\n", + " [-87.7, 320, 30.8, 8.2, 0.7, 11.8, 4.3],\n", + " [5.5, 30.8, 0, -53.5, -2.2, -9.6, 6.0],\n", + " [-5.9, 8.2, -53.5, 110, -70.7, -17.0, -63.3],\n", + " [6.7, 0.7, -2.2, -70.7, 270, 81.1, -1.3],\n", + " [-13.7, 11.8, -9.6, -17.0, 81.1, 420, 39.7],\n", + " [-9.9, 4.3, 6.0, -63.3, -1.3, 39.7, 230],\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8a1a8e7", + "metadata": {}, + "outputs": [], + "source": [ + "# Bath parameters\n", + "\n", + "lam = 35 * 3e10 * 2 * np.pi\n", + "gamma = 1 / 166e-15\n", + "T = 300 * 0.6949 * 3e10 * 2 * np.pi\n", + "beta = 1 / T" + ] + }, + { + "cell_type": "markdown", + "id": "a285d0cf", + "metadata": {}, + "source": [ + "## Plotting the environment spectral density and correlation functions\n", + "\n", + "Let's quickly plot the spectral density and environment correlation functions so that we can see what they look like." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08612b77", + "metadata": {}, + "outputs": [], + "source": [ + "env=DrudeLorentzEnvironment(T=T,lam=lam,gamma=gamma)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23de3d8a", + "metadata": {}, + "outputs": [], + "source": [ + "wlist = np.linspace(0, 200 * 3e10 * 2 * np.pi, 100)\n", + "tlist = np.linspace(0, 1e-12, 1000)\n", + "\n", + "J = env.spectral_density(wlist) / (3e10*2*np.pi)\n", + "\n", + "fig, axes = plt.subplots(1, 2, sharex=False, figsize=(10, 3))\n", + "\n", + "fig.subplots_adjust(hspace=0.1) # reduce space between plots\n", + "\n", + "# Spectral density plot:\n", + "\n", + "axes[0].plot(wlist / (3e10 * 2 * np.pi), J, color='r', ls='--', label=\"J(w)\")\n", + "axes[0].set_xlabel(r'$\\omega$ (cm$^{-1}$)', fontsize=20)\n", + "axes[0].set_ylabel(r\"$J(\\omega)$ (cm$^{-1}$)\", fontsize=16)\n", + "axes[0].legend()\n", + "\n", + "# Correlation plot:\n", + "\n", + "axes[1].plot(\n", + " tlist, np.real(env.correlation_function(tlist, 10)),\n", + " color='r', ls='--', label=\"C(t) real\",\n", + ")\n", + "axes[1].plot(\n", + " tlist, np.imag(env.correlation_function(tlist, 10)),\n", + " color='g', ls='--', label=\"C(t) imaginary\",\n", + ")\n", + "axes[1].set_xlabel(r'$t$', fontsize=20)\n", + "axes[1].set_ylabel(r\"$C(t)$\", fontsize=16)\n", + "axes[1].legend();" + ] + }, + { + "cell_type": "markdown", + "id": "41507215", + "metadata": {}, + "source": [ + "## Solve for the dynamics with the HEOM\n", + "\n", + "Now let us solve for the evolution of this system using the HEOM." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dbd9661a", + "metadata": {}, + "outputs": [], + "source": [ + "# We start the excitation at site 1:\n", + "rho0 = basis(7, 0) * basis(7, 0).dag()\n", + "\n", + "# HEOM solver options:\n", + "#\n", + "# Note: We set Nk=0 (i.e. a single correlation expansion term\n", + "# per bath) and rely on the terminator to correct detailed\n", + "# balance.\n", + "NC = 4 # Use NC=8 for more precise results\n", + "Nk = 0\n", + "\n", + "Q_list = []\n", + "baths = []\n", + "Ltot = liouvillian(Hsys)\n", + "env_approx,delta=env.approx_by_matsubara(Nk=Nk,compute_delta=True)\n", + "for m in range(7):\n", + " Q = basis(7, m) * basis(7, m).dag()\n", + " Q_list.append(Q)\n", + " Ltot += system_terminator(Q,delta)\n", + " baths.append((env_approx,Q))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c7b9447", + "metadata": {}, + "outputs": [], + "source": [ + "with timer(\"RHS construction time\"):\n", + " HEOMMats = HEOMSolver(Hsys, baths, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " outputFMO_HEOM = HEOMMats.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "199d025a", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", + "\n", + "colors = ['r', 'g', 'b', 'y', 'c', 'm', 'k']\n", + "linestyles = [\n", + " '-', '--', ':', '-.',\n", + " (0, (1, 10)), (0, (5, 10)), (0, (3, 10, 1, 10)),\n", + "]\n", + "\n", + "for m in range(7):\n", + " Q = basis(7, m) * basis(7, m).dag()\n", + " axes.plot(\n", + " np.array(tlist) * 1e15,\n", + " np.real(expect(outputFMO_HEOM.states, Q)),\n", + " label=m + 1,\n", + " color=colors[m % len(colors)],\n", + " linestyle=linestyles[m % len(linestyles)],\n", + " )\n", + " axes.set_xlabel(r'$t$ (fs)', fontsize=30)\n", + " axes.set_ylabel(r\"Population\", fontsize=30)\n", + " axes.locator_params(axis='y', nbins=6)\n", + " axes.locator_params(axis='x', nbins=6)\n", + "\n", + "axes.set_title('HEOM solution', fontsize=24)\n", + "axes.legend(loc=0)\n", + "axes.set_xlim(0, 1000)\n", + "plt.yticks([0., 0.5, 1], [0, 0.5, 1])\n", + "plt.xticks([0., 500, 1000], [0, 500, 1000]);" + ] + }, + { + "cell_type": "markdown", + "id": "e5f1f8d3", + "metadata": {}, + "source": [ + "## Comparison with Bloch-Redfield solver\n", + "\n", + "Now let us solve the same problem using the Bloch-Redfield solver. We will see that the Bloch-Redfield technique fails to model the oscillation of population of the states that we saw in the HEOM.\n", + "\n", + "In the next section, we will examine the role of pure dephasing in the evolution to understand why this happens." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47169b4e", + "metadata": {}, + "outputs": [], + "source": [ + "with timer(\"BR ODE solver time\"):\n", + " outputFMO_BR = brmesolve(\n", + " Hsys, rho0, tlist,\n", + " a_ops=[[Q, env] for Q in Q_list],\n", + " options=options,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "4c1a445a", + "metadata": {}, + "source": [ + "And now let's plot the Bloch-Redfield solver results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45802918", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", + "\n", + "for m, Q in enumerate(Q_list):\n", + " axes.plot(tlist * 1e15, expect(outputFMO_BR.states, Q), label=m + 1)\n", + "\n", + "axes.set_xlabel(r'$t$ (fs)', fontsize=30)\n", + "axes.set_ylabel(r\"Population\", fontsize=30)\n", + "\n", + "axes.set_title('Bloch-Redfield solution ', fontsize=24)\n", + "axes.legend()\n", + "axes.set_xlim(0, 1000)\n", + "plt.yticks([0, 0.5, 1], [0, 0.5, 1])\n", + "plt.xticks([0, 500, 1000], [0, 500, 1000]);" + ] + }, + { + "cell_type": "markdown", + "id": "db49306a", + "metadata": {}, + "source": [ + "Notice how the oscillations are gone and the populations decay much more rapidly.\n", + "\n", + "Next let us try to understand why." + ] + }, + { + "cell_type": "markdown", + "id": "95e6620b", + "metadata": {}, + "source": [ + "## Role of pure dephasing\n", + "\n", + "It is useful to construct the various parts of the Bloch-Redfield master equation explicitly and to solve them using the Master equation solver, `mesolve`. We will do so and show that it is the pure-dephasing terms which suppresses coherence in these oscillations.\n", + "\n", + "First we will write a function to return the list of collapse operators for a given system, either with or without the dephasing operators:" + ] + }, + { + "cell_type": "markdown", + "id": "6344f4ac", + "metadata": {}, + "source": [ + "TODO: Maybe power spectrum at zero is wrong, by a factor 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3fb0f126", + "metadata": {}, + "outputs": [], + "source": [ + "def J0_dephasing():\n", + " \"\"\" Under-damped brownian oscillator dephasing probability.\n", + "\n", + " This returns the limit as w -> 0 of J0(w) * n_th(w, T) / T.\n", + " \"\"\"\n", + " return 2 * lam * gamma / gamma**2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8f11c93", + "metadata": {}, + "outputs": [], + "source": [ + "env.power_spectrum(0)/2 -J0_dephasing()*T" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a90f9d0", + "metadata": {}, + "outputs": [], + "source": [ + "def get_collapse(H, T, dephasing=1):\n", + " \"\"\" Calculate collapse operators for a given system H and\n", + " temperature T.\n", + " \"\"\"\n", + " all_energy, all_state = H.eigenstates(sort=\"low\")\n", + " Nmax = len(all_energy)\n", + "\n", + " Q_list = [\n", + " basis(Nmax, n) * basis(Nmax, n).dag()\n", + " for n in range(Nmax)\n", + " ]\n", + "\n", + " collapse_list = []\n", + "\n", + " for Q in Q_list:\n", + " for j in range(Nmax):\n", + " for k in range(j + 1, Nmax):\n", + " Deltajk = abs(all_energy[k] - all_energy[j])\n", + " if abs(Deltajk) > 0:\n", + " rate = (\n", + " np.abs(Q.matrix_element(\n", + " all_state[j].dag(), all_state[k]\n", + " ))**2 *\n", + " env.power_spectrum(Deltajk)\n", + " )\n", + " if rate > 0.0:\n", + " # emission:\n", + " collapse_list.append(\n", + " np.sqrt(rate) * all_state[j] * all_state[k].dag()\n", + " )\n", + "\n", + " rate = (\n", + " np.abs(Q.matrix_element(\n", + " all_state[k].dag(), all_state[j]\n", + " ))**2 *\n", + " env.power_spectrum(-Deltajk)\n", + " )\n", + " if rate > 0.0:\n", + " # absorption:\n", + " collapse_list.append(\n", + " np.sqrt(rate) * all_state[k] * all_state[j].dag()\n", + " )\n", + "\n", + " if dephasing:\n", + " for j in range(Nmax):\n", + " rate = (\n", + " np.abs(Q.matrix_element(\n", + " all_state[j].dag(), all_state[j])\n", + " )**2 * env.power_spectrum(0)/2\n", + " )\n", + " if rate > 0.0:\n", + " # emission:\n", + " collapse_list.append(\n", + " np.sqrt(rate) * all_state[j] * all_state[j].dag()\n", + " )\n", + "\n", + " return collapse_list" + ] + }, + { + "cell_type": "markdown", + "id": "ebc084a1", + "metadata": {}, + "source": [ + "Now we are able to switch the pure dephasing terms on and off.\n", + "\n", + "Let us starting by including the dephasing operators. We expect to see the same behaviour that we saw when using the Bloch-Redfield solver." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "baf32f2a", + "metadata": {}, + "outputs": [], + "source": [ + "# dephasing terms on, we recover the full BR solution:\n", + "\n", + "with timer(\"Building the collapse operators\"):\n", + " collapse_list = get_collapse(Hsys, T=T, dephasing=True)\n", + "\n", + "with timer(\"ME ODE solver\"):\n", + " outputFMO_ME = mesolve(Hsys, rho0, tlist, collapse_list)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62a6840b", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", + "\n", + "for m, Q in enumerate(Q_list):\n", + " axes.plot(tlist * 1e15, expect(outputFMO_ME.states, Q), label=m + 1)\n", + "\n", + "axes.set_xlabel(r'$t$', fontsize=20)\n", + "axes.set_ylabel(r\"Population\", fontsize=16)\n", + "axes.set_xlim(0, 1000)\n", + "axes.set_title('With pure dephasing', fontsize=24)\n", + "plt.yticks([0, 0.5, 1], [0, 0.5, 1])\n", + "plt.xticks([0, 500, 1000], [0, 500, 1000])\n", + "axes.legend(fontsize=18);" + ] + }, + { + "cell_type": "markdown", + "id": "d0d24fb3", + "metadata": {}, + "source": [ + "We see similar results to before.\n", + "\n", + "Now let us examine what happens when we remove the dephasing collapse operators:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "762960a5", + "metadata": {}, + "outputs": [], + "source": [ + "# dephasing terms off\n", + "\n", + "with timer(\"Building the collapse operators\"):\n", + " collapse_list = get_collapse(Hsys, T, dephasing=False)\n", + "\n", + "with timer(\"ME ODE solver\"):\n", + " outputFMO_ME_nodephase = mesolve(Hsys, rho0, tlist, collapse_list)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2712875", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", + "for m, Q in enumerate(Q_list):\n", + " axes.plot(\n", + " tlist * 1e15,\n", + " expect(outputFMO_ME_nodephase.states, Q),\n", + " label=m + 1,\n", + " )\n", + "\n", + "axes.set_xlabel(r'$t$', fontsize=20)\n", + "axes.set_ylabel(r\"Population\", fontsize=16)\n", + "axes.set_xlim(0, 1000)\n", + "axes.set_title('Without pure dephasing', fontsize=24)\n", + "plt.yticks([0, 0.5, 1], [0, 0.5, 1])\n", + "plt.xticks([0, 500, 1000], [0, 500, 1000])\n", + "axes.legend(fontsize=18);" + ] + }, + { + "cell_type": "markdown", + "id": "6162e3d6", + "metadata": {}, + "source": [ + "And now we see that without the dephasing, the oscillations reappear. The full dynamics capture by the HEOM are still not capture by this simpler model, however." + ] + }, + { + "cell_type": "markdown", + "id": "436a1179", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a28c22db", + "metadata": {}, + "outputs": [], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "a362e374", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d94c95c1", + "metadata": {}, + "outputs": [], + "source": [ + "assert np.allclose(\n", + " expect(outputFMO_BR.states, Q_list[0]),\n", + " expect(outputFMO_ME.states, Q_list[0]),\n", + " rtol=2e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(outputFMO_BR.states, Q_list[1]),\n", + " expect(outputFMO_ME.states, Q_list[1]),\n", + " rtol=2e-2,\n", + ")" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "qutip-dev", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-3-quantum-heat-transport.ipynb b/tutorials-v5/heom/heom-3-quantum-heat-transport.ipynb new file mode 100644 index 00000000..4861dfc9 --- /dev/null +++ b/tutorials-v5/heom/heom-3-quantum-heat-transport.ipynb @@ -0,0 +1,693 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "07cef890", + "metadata": {}, + "source": [ + "# HEOM 3: Quantum Heat Transport" + ] + }, + { + "cell_type": "markdown", + "id": "c812d416", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "In this notebook, we apply the QuTiP HEOM solver to a quantum system coupled to two bosonic baths and demonstrate how to extract information about the system-bath heat currents from the auxiliary density operators (ADOs).\n", + "We consider the setup described in Ref. \\[1\\], which consists of two coupled qubits, each connected to its own heat bath.\n", + "The Hamiltonian of the qubits is given by\n", + "\n", + "$$ \\begin{aligned} H_{\\text{S}} &= H_1 + H_2 + H_{12} , \\quad\\text{ where }\\\\\n", + "H_K &= \\frac{\\epsilon}{2} \\bigl(\\sigma_z^K + 1\\bigr) \\quad (K=1,2) \\quad\\text{ and }\\quad H_{12} = J_{12} \\bigl( \\sigma_+^1 \\sigma_-^2 + \\sigma_-^1 \\sigma_+^2 \\bigr) . \\end{aligned} $$\n", + "\n", + "Here, $\\sigma^K_{x,y,z,\\pm}$ denotes the usual Pauli matrices for the K-th qubit, $\\epsilon$ is the eigenfrequency of the qubits and $J_{12}$ the coupling constant.\n", + "\n", + "Each qubit is coupled to its own bath; therefore, the total Hamiltonian is\n", + "\n", + "$$ H_{\\text{tot}} = H_{\\text{S}} + \\sum_{K=1,2} \\bigl( H_{\\text{B}}^K + Q_K \\otimes X_{\\text{B}}^K \\bigr) , $$\n", + "\n", + "where $H_{\\text{B}}^K$ is the free Hamiltonian of the K-th bath and $X_{\\text{B}}^K$ its coupling operator, and $Q_K = \\sigma_x^K$ are the system coupling operators.\n", + "We assume that the bath spectral densities are given by Drude distributions\n", + "\n", + "$$ J_K(\\omega) = \\frac{2 \\lambda_K \\gamma_K \\omega}{\\omega^2 + \\gamma_K^2} , $$\n", + "\n", + "where $\\lambda_K$ is the free coupling strength and $\\gamma_K$ the cutoff frequency.\n", + "\n", + "We begin by defining the system and bath parameters.\n", + "We use the parameter values from Fig. 3(a) of Ref. \\[1\\].\n", + "Note that we set $\\hbar$ and $k_B$ to one and we will measure all frequencies and energies in units of $\\epsilon$.\n", + "\n", + "References:\n", + "\n", + "   \\[1\\] Kato and Tanimura, [J. Chem. Phys. **143**, 064107](https://doi.org/10.1063/1.4928192) (2015)." + ] + }, + { + "cell_type": "markdown", + "id": "83c6db96", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc247fbe", + "metadata": {}, + "outputs": [], + "source": [ + "import dataclasses\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import qutip as qt\n", + "from qutip.solver.heom import (\n", + " HEOMSolver,\n", + " DrudeLorentzPadeBath\n", + ")\n", + "from qutip.core.environment import (\n", + " CFExponent,\n", + " DrudeLorentzEnvironment,\n", + " system_terminator,\n", + ")\n", + "\n", + "from ipywidgets import IntProgress\n", + "from IPython.display import display\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "7c732d88", + "metadata": {}, + "source": [ + "## Helpers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5287dddb", + "metadata": {}, + "outputs": [], + "source": [ + "# Solver options:\n", + "\n", + "options = {\n", + " \"nsteps\": 15000,\n", + " \"store_states\": True,\n", + " \"rtol\": 1e-12,\n", + " \"atol\": 1e-12,\n", + " \"min_step\": 1e-18,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "9c3bc48f", + "metadata": {}, + "source": [ + "## System and bath definition" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c30ef0f", + "metadata": {}, + "outputs": [], + "source": [ + "@dataclasses.dataclass\n", + "class SystemParams:\n", + " \"\"\" System parameters and Hamiltonian. \"\"\"\n", + " epsilon: float = 1.0\n", + " J12: float = 0.1\n", + "\n", + " def H(self):\n", + " \"\"\" Return the Hamiltonian for the system.\n", + "\n", + " The system consists of two qubits with Hamiltonians (H1 and H2)\n", + " and an interaction term (H12).\n", + " \"\"\"\n", + " H1 = self.epsilon / 2 * (\n", + " qt.tensor(qt.sigmaz() + qt.identity(2), qt.identity(2))\n", + " )\n", + " H2 = self.epsilon / 2 * (\n", + " qt.tensor(qt.identity(2), qt.sigmaz() + qt.identity(2))\n", + " )\n", + " H12 = self.J12 * (\n", + " qt.tensor(qt.sigmap(), qt.sigmam()) +\n", + " qt.tensor(qt.sigmam(), qt.sigmap())\n", + " )\n", + " return H1 + H2 + H12\n", + "\n", + " def replace(self, **kw):\n", + " return dataclasses.replace(self, **kw)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "73addcde", + "metadata": {}, + "outputs": [], + "source": [ + "@dataclasses.dataclass\n", + "class BathParams:\n", + " \"\"\" Bath parameters. \"\"\"\n", + " sign: str # + or -\n", + " qubit: int # 0 or 1\n", + "\n", + " gamma: float = 2.0\n", + " lam: float = 0.05\n", + " Tbar: float = 2.0\n", + " Tdelta: float = 0.01\n", + "\n", + " def __post_init__(self):\n", + " # T = Tbar +- Tdelta * Tbar:\n", + " assert self.sign in (\"+\", \"-\")\n", + " sign = +1 if self.sign == \"+\" else -1\n", + " self.T = self.Tbar + sign * self.Tdelta * self.Tbar\n", + " # qubit\n", + " assert self.qubit in (0, 1)\n", + "\n", + " def Q(self):\n", + " \"\"\" Coupling operator for the bath. \"\"\"\n", + " Q = [qt.identity(2), qt.identity(2)]\n", + " Q[self.qubit] = qt.sigmax()\n", + " return qt.tensor(Q)\n", + "\n", + " def bath(self, Nk, tag=None):\n", + " env=DrudeLorentzEnvironment(\n", + " lam=self.lam, gamma=self.gamma, T=self.T, tag=tag\n", + " )\n", + " env_approx,delta=env.approx_by_pade(Nk=Nk,compute_delta=True,tag=tag)\n", + " return (env_approx,self.Q()),system_terminator(self.Q(),delta),delta\n", + "\n", + " def replace(self, **kw):\n", + " return dataclasses.replace(self, **kw)" + ] + }, + { + "cell_type": "markdown", + "id": "b55f26f8", + "metadata": {}, + "source": [ + "## Heat currents\n", + "\n", + "Following Ref. \\[2\\], we consider two possible definitions of the heat currents from the qubits into the baths.\n", + "The so-called bath heat currents are $j_{\\text{B}}^K = \\partial_t \\langle H_{\\text{B}}^K \\rangle$ and the system heat currents are $j_{\\text{S}}^K = \\mathrm i\\, \\langle [H_{\\text{S}}, Q_K] X_{\\text{B}}^K \\rangle$.\n", + "As shown in Ref. \\[2\\], they can be expressed in terms of the HEOM ADOs as follows:\n", + "$$ \\begin{aligned} \\mbox{} \\\\\n", + " j_{\\text{B}}^K &= \\!\\!\\sum_{\\substack{\\mathbf n\\\\ \\text{Level 1}\\\\ \\text{Bath $K$}}}\\!\\! \\nu[\\mathbf n] \\operatorname{tr}\\bigl[ Q_K \\rho_{\\mathbf n} \\bigr] - 2 C_I^K(0) \\operatorname{tr}\\bigl[ Q_k^2 \\rho \\bigr] + \\Gamma_{\\text{T}}^K \\operatorname{tr}\\bigl[ [[H_{\\text{S}}, Q_K], Q_K]\\, \\rho \\bigr] , \\\\[.5em]\n", + " j_{\\text{S}}^K &= \\mathrm i\\!\\! \\sum_{\\substack{\\mathbf n\\\\ \\text{Level 1}\\\\ \\text{Bath $k$}}}\\!\\! \\operatorname{tr}\\bigl[ [H_{\\text{S}}, Q_K]\\, \\rho_{\\mathbf n} \\bigr] + \\Gamma_{\\text{T}}^K \\operatorname{tr}\\bigl[ [[H_{\\text{S}}, Q_K], Q_K]\\, \\rho \\bigr] . \\\\ \\mbox{}\n", + "\\end{aligned} $$\n", + "The sums run over all level-$1$ multi-indices $\\mathbf n$ with one excitation corresponding to the K-th bath, $\\nu[\\mathbf n]$ is the corresponding (negative) exponent of the bath auto-correlation function $C^K(t)$, and $\\Gamma_{\\text{T}}^K$ is the Ishizaki-Tanimura terminator (i.e., a correction term accounting for the error introduced by approximating the correlation function with a finite sum of exponential terms).\n", + "In the expression for the bath heat currents, we left out terms involving $[Q_1, Q_2]$, which is zero in this example.\n", + "\n", + "   \\[2\\] Kato and Tanimura, [J. Chem. Phys. **145**, 224105](https://doi.org/10.1063/1.4971370) (2016)." + ] + }, + { + "cell_type": "markdown", + "id": "adc4be74", + "metadata": {}, + "source": [ + "In QuTiP, these currents can be conveniently calculated as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "696f36e3", + "metadata": {}, + "outputs": [], + "source": [ + "def bath_heat_current(bath_tag, ado_state, hamiltonian, coupling_op, delta=0):\n", + " \"\"\"\n", + " Bath heat current from the system into the heat bath with the given tag.\n", + "\n", + " Parameters\n", + " ----------\n", + " bath_tag : str, tuple or any other object\n", + " Tag of the heat bath corresponding to the current of interest.\n", + "\n", + " ado_state : HierarchyADOsState\n", + " Current state of the system and the environment (encoded in the ADOs).\n", + "\n", + " hamiltonian : Qobj\n", + " System Hamiltonian at the current time.\n", + "\n", + " coupling_op : Qobj\n", + " System coupling operator at the current time.\n", + "\n", + " delta : float\n", + " The prefactor of the \\\\delta(t) term in the correlation function (the\n", + " Ishizaki-Tanimura terminator).\n", + " \"\"\"\n", + " l1_labels = ado_state.filter(level=1, tags=[bath_tag])\n", + " a_op = 1j * (hamiltonian * coupling_op - coupling_op * hamiltonian)\n", + "\n", + " result = 0\n", + " cI0 = 0 # imaginary part of bath auto-correlation function (t=0)\n", + " for label in l1_labels:\n", + " [exp] = ado_state.exps(label)\n", + " result += exp.vk * (coupling_op * ado_state.extract(label)).tr()\n", + "\n", + " if exp.type == CFExponent.types['I']:\n", + " cI0 += exp.ck\n", + " elif exp.type == CFExponent.types['RI']:\n", + " cI0 += exp.ck2\n", + "\n", + " result -= 2 * cI0 * (coupling_op * coupling_op * ado_state.rho).tr()\n", + " if delta != 0:\n", + " result -= (\n", + " 1j * delta *\n", + " ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr()\n", + " )\n", + " return result\n", + "\n", + "\n", + "def system_heat_current(\n", + " bath_tag, ado_state, hamiltonian, coupling_op, delta=0,\n", + "):\n", + " \"\"\"\n", + " System heat current from the system into the heat bath with the given tag.\n", + "\n", + " Parameters\n", + " ----------\n", + " bath_tag : str, tuple or any other object\n", + " Tag of the heat bath corresponding to the current of interest.\n", + "\n", + " ado_state : HierarchyADOsState\n", + " Current state of the system and the environment (encoded in the ADOs).\n", + "\n", + " hamiltonian : Qobj\n", + " System Hamiltonian at the current time.\n", + "\n", + " coupling_op : Qobj\n", + " System coupling operator at the current time.\n", + "\n", + " delta : float\n", + " The prefactor of the \\\\delta(t) term in the correlation function (the\n", + " Ishizaki-Tanimura terminator).\n", + " \"\"\"\n", + " l1_labels = ado_state.filter(level=1, tags=[bath_tag])\n", + " a_op = 1j * (hamiltonian * coupling_op - coupling_op * hamiltonian)\n", + "\n", + " result = 0\n", + " for label in l1_labels:\n", + " result += (a_op * ado_state.extract(label)).tr()\n", + "\n", + " if delta != 0:\n", + " result -= (\n", + " 1j * delta *\n", + " ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr()\n", + " )\n", + " return result" + ] + }, + { + "cell_type": "markdown", + "id": "089c2dd0", + "metadata": {}, + "source": [ + "Note that at long times, we expect $j_{\\text{B}}^1 = -j_{\\text{B}}^2$ and $j_{\\text{S}}^1 = -j_{\\text{S}}^2$ due to energy conservation. At long times, we also expect $j_{\\text{B}}^1 = j_{\\text{S}}^1$ and $j_{\\text{B}}^2 = j_{\\text{S}}^2$ since the coupling operators commute, $[Q_1, Q_2] = 0$. Hence, all four currents should agree in the long-time limit (up to a sign). This long-time value is what was analyzed in Ref. \\[2\\]." + ] + }, + { + "cell_type": "markdown", + "id": "e091142d", + "metadata": {}, + "source": [ + "## Simulations" + ] + }, + { + "cell_type": "markdown", + "id": "dc0ed322", + "metadata": {}, + "source": [ + "For our simulations, we will represent the bath spectral densities using the first term of their Padé decompositions, and we will use $7$ levels of the HEOM hierarchy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7afc97cb", + "metadata": {}, + "outputs": [], + "source": [ + "Nk = 1\n", + "NC = 7" + ] + }, + { + "cell_type": "markdown", + "id": "399ea6c2", + "metadata": {}, + "source": [ + "### Time Evolution\n", + "\n", + "We fix $J_{12} = 0.1 \\epsilon$ (as in Fig. 3(a-ii) of Ref. \\[2\\]) and choose the fixed coupling strength $\\lambda_1 = \\lambda_2 = J_{12}\\, /\\, (2\\epsilon)$ (corresponding to $\\bar\\zeta = 1$ in Ref. \\[2\\]).\n", + "Using these values, we will study the time evolution of the system state and the heat currents." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d7eb5ba", + "metadata": {}, + "outputs": [], + "source": [ + "# fix qubit-qubit and qubit-bath coupling strengths\n", + "sys = SystemParams(J12=0.1)\n", + "bath_p1 = BathParams(qubit=0, sign=\"+\", lam=sys.J12 / 2)\n", + "bath_p2 = BathParams(qubit=1, sign=\"-\", lam=sys.J12 / 2)\n", + "\n", + "# choose arbitrary initial state\n", + "rho0 = qt.tensor(qt.identity(2), qt.identity(2)) / 4\n", + "\n", + "# simulation time span\n", + "tlist = np.linspace(0, 50, 250)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f469adc", + "metadata": {}, + "outputs": [], + "source": [ + "H = sys.H()\n", + "\n", + "bath1,b1term,b1delta = bath_p1.bath(Nk, tag='bath 1')\n", + "Q1 = bath_p1.Q()\n", + "\n", + "bath2,b2term,b2delta = bath_p2.bath(Nk, tag='bath 2')\n", + "Q2 = bath_p2.Q()\n", + "\n", + "\n", + "solver = HEOMSolver(\n", + " qt.liouvillian(H) + b1term + b2term,\n", + " [bath1, bath2],\n", + " max_depth=NC,\n", + " options=options,\n", + ")\n", + "\n", + "result = solver.run(rho0, tlist, e_ops=[\n", + " qt.tensor(qt.sigmaz(), qt.identity(2)),\n", + " lambda t, ado: bath_heat_current('bath 1', ado, H, Q1, b1delta),\n", + " lambda t, ado: bath_heat_current('bath 2', ado, H, Q2, b2delta),\n", + " lambda t, ado: system_heat_current('bath 1', ado, H, Q1, b1delta),\n", + " lambda t, ado: system_heat_current('bath 2', ado, H, Q2, b2delta),\n", + "])" + ] + }, + { + "cell_type": "markdown", + "id": "fa0b201d", + "metadata": {}, + "source": [ + "We first plot $\\langle \\sigma_z^1 \\rangle$ to see the time evolution of the system state:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0bf29a8f", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(figsize=(8, 8))\n", + "axes.plot(tlist, result.expect[0], 'r', linewidth=2)\n", + "axes.set_xlabel('t', fontsize=28)\n", + "axes.set_ylabel(r\"$\\langle \\sigma_z^1 \\rangle$\", fontsize=28);" + ] + }, + { + "cell_type": "markdown", + "id": "ab79c89c", + "metadata": {}, + "source": [ + "We find a rather quick thermalization of the system state. For the heat currents, however, it takes a somewhat longer time until they converge to their long-time values:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "987ef90d", + "metadata": {}, + "outputs": [], + "source": [ + "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8))\n", + "\n", + "ax1.plot(\n", + " tlist, -np.real(result.expect[1]),\n", + " color='darkorange', label='BHC (bath 1 -> system)',\n", + ")\n", + "ax1.plot(\n", + " tlist, np.real(result.expect[2]),\n", + " '--', color='darkorange', label='BHC (system -> bath 2)',\n", + ")\n", + "ax1.plot(\n", + " tlist, -np.real(result.expect[3]),\n", + " color='dodgerblue', label='SHC (bath 1 -> system)',\n", + ")\n", + "ax1.plot(\n", + " tlist, np.real(result.expect[4]),\n", + " '--', color='dodgerblue', label='SHC (system -> bath 2)',\n", + ")\n", + "\n", + "ax1.set_xlabel('t', fontsize=28)\n", + "ax1.set_ylabel('j', fontsize=28)\n", + "ax1.set_ylim((-0.05, 0.05))\n", + "ax1.legend(loc=0, fontsize=12)\n", + "\n", + "ax2.plot(\n", + " tlist, -np.real(result.expect[1]),\n", + " color='darkorange', label='BHC (bath 1 -> system)',\n", + ")\n", + "ax2.plot(\n", + " tlist, np.real(result.expect[2]),\n", + " '--', color='darkorange', label='BHC (system -> bath 2)',\n", + ")\n", + "ax2.plot(\n", + " tlist, -np.real(result.expect[3]),\n", + " color='dodgerblue', label='SHC (bath 1 -> system)',\n", + ")\n", + "ax2.plot(\n", + " tlist, np.real(result.expect[4]),\n", + " '--', color='dodgerblue', label='SHC (system -> bath 2)',\n", + ")\n", + "\n", + "ax2.set_xlabel('t', fontsize=28)\n", + "ax2.set_xlim((20, 50))\n", + "ax2.set_ylim((0, 0.0002))\n", + "ax2.legend(loc=0, fontsize=12);" + ] + }, + { + "cell_type": "markdown", + "id": "52228d72", + "metadata": {}, + "source": [ + "### Steady-state currents\n", + "\n", + "Here, we try to reproduce the HEOM curves in Fig. 3(a) of Ref. \\[1\\] by varying the coupling strength and finding the steady state for each coupling strength." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4560df1", + "metadata": {}, + "outputs": [], + "source": [ + "def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options):\n", + " \"\"\" Calculate the steady sate heat currents for the given system and\n", + " bath.\n", + " \"\"\"\n", + "\n", + " bath1,b1term,b1delta = bath_p1.bath(Nk, tag='bath 1')\n", + " Q1 = bath_p1.Q()\n", + "\n", + " bath2,b2term,b2delta = bath_p2.bath(Nk, tag='bath 2')\n", + " Q2 = bath_p2.Q()\n", + "\n", + " solver = HEOMSolver(\n", + " qt.liouvillian(sys.H()) + b1term + b2term,\n", + " [bath1, bath2],\n", + " max_depth=NC,\n", + " options=options\n", + " )\n", + "\n", + " _, steady_ados = solver.steady_state()\n", + "\n", + " return (\n", + " bath_heat_current('bath 1', steady_ados, sys.H(), Q1, b1delta),\n", + " bath_heat_current('bath 2', steady_ados, sys.H(), Q2, b2delta),\n", + " system_heat_current('bath 1', steady_ados, sys.H(), Q1, b1delta),\n", + " system_heat_current('bath 2', steady_ados, sys.H(), Q2, b2delta),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9846b0b1", + "metadata": {}, + "outputs": [], + "source": [ + "# Define number of points to use for the plot\n", + "plot_points = 10 # use 100 for a smoother curve\n", + "\n", + "# Range of relative coupling strengths\n", + "# Chosen so that zb_max is maximum, centered around 1 on a log scale\n", + "zb_max = 4 # use 20 to see more of the current curve\n", + "zeta_bars = np.logspace(\n", + " -np.log(zb_max),\n", + " np.log(zb_max),\n", + " plot_points,\n", + " base=np.e,\n", + ")\n", + "\n", + "# Setup a progress bar\n", + "progress = IntProgress(min=0, max=(3 * plot_points))\n", + "display(progress)\n", + "\n", + "\n", + "def calculate_heat_current(J12, zb, Nk, progress=progress):\n", + " \"\"\" Calculate a single heat current and update the progress bar. \"\"\"\n", + " # Estimate appropriate HEOM max_depth from coupling strength\n", + " NC = 7 + int(max(zb * J12 - 1, 0) * 2)\n", + " NC = min(NC, 20)\n", + " # the four currents are identical in the steady state\n", + " j, _, _, _ = heat_currents(\n", + " sys.replace(J12=J12),\n", + " bath_p1.replace(lam=zb * J12 / 2),\n", + " bath_p2.replace(lam=zb * J12 / 2),\n", + " Nk, NC, options=options,\n", + " )\n", + " progress.value += 1\n", + " return j\n", + "\n", + "\n", + "# Calculate steady state currents for range of zeta_bars\n", + "# for J12 = 0.01, 0.1 and 0.5:\n", + "j1s = [\n", + " calculate_heat_current(0.01, zb, Nk)\n", + " for zb in zeta_bars\n", + "]\n", + "j2s = [\n", + " calculate_heat_current(0.1, zb, Nk)\n", + " for zb in zeta_bars\n", + "]\n", + "j3s = [\n", + " calculate_heat_current(0.5, zb, Nk)\n", + " for zb in zeta_bars\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "edefdc0a", + "metadata": {}, + "source": [ + "## Create Plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "adc2b5b6", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(figsize=(12, 7))\n", + "\n", + "axes.plot(\n", + " zeta_bars, -1000 * 100 * np.real(j1s),\n", + " 'b', linewidth=2, label=r\"$J_{12} = 0.01\\, \\epsilon$\",\n", + ")\n", + "axes.plot(\n", + " zeta_bars, -1000 * 10 * np.real(j2s),\n", + " 'r--', linewidth=2, label=r\"$J_{12} = 0.1\\, \\epsilon$\",\n", + ")\n", + "axes.plot(\n", + " zeta_bars, -1000 * 2 * np.real(j3s),\n", + " 'g-.', linewidth=2, label=r\"$J_{12} = 0.5\\, \\epsilon$\",\n", + ")\n", + "\n", + "axes.set_xscale('log')\n", + "axes.set_xlabel(r\"$\\bar\\zeta$\", fontsize=30)\n", + "axes.set_xlim((zeta_bars[0], zeta_bars[-1]))\n", + "\n", + "axes.set_ylabel(\n", + " r\"$j_{\\mathrm{ss}}\\; /\\; (\\epsilon J_{12}) \\times 10^3$\",\n", + " fontsize=30,\n", + ")\n", + "axes.set_ylim((0, 2))\n", + "\n", + "axes.legend(loc=0);" + ] + }, + { + "cell_type": "markdown", + "id": "3aab0a7c", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8e769307", + "metadata": {}, + "outputs": [], + "source": [ + "qt.about()" + ] + }, + { + "cell_type": "markdown", + "id": "ef5b9bd4", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3e19455", + "metadata": {}, + "outputs": [], + "source": [ + "assert 1 == 1" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "qutip-dev", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-4-dynamical-decoupling.ipynb b/tutorials-v5/heom/heom-4-dynamical-decoupling.ipynb new file mode 100644 index 00000000..0800041e --- /dev/null +++ b/tutorials-v5/heom/heom-4-dynamical-decoupling.ipynb @@ -0,0 +1,751 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b9711696", + "metadata": {}, + "source": [ + "# HEOM 4: Dynamical decoupling of a non-Markovian environment" + ] + }, + { + "cell_type": "markdown", + "id": "e97d7161", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Following [Lorenza Viola and Seth Lloyd](https://arxiv.org/abs/quant-ph/9803057) we consider an example of dynamical decoupling.\n", + "We choose a drive which performs pi rotations, interspersed with short periods where the bath causes dephasing.\n", + "\n", + "We first show the standard example of equally spaced pulses, and then consider the 'optimal' Uhrig spacing ([Götz S. Uhrig Phys. Rev. Lett. 98, 100504 (2007)](https://arxiv.org/abs/quant-ph/0609203))." + ] + }, + { + "cell_type": "markdown", + "id": "1052001d", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a118b8ac", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " QobjEvo,\n", + " basis,\n", + " expect,\n", + " ket2dm,\n", + " sigmax,\n", + " sigmaz,\n", + " DrudeLorentzEnvironment\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver\n", + ")\n", + "\n", + "from ipywidgets import IntProgress\n", + "from IPython.display import display\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "0e69dfa1", + "metadata": {}, + "source": [ + "## Solver options" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "018c1d26", + "metadata": {}, + "outputs": [], + "source": [ + "# Solver options:\n", + "\n", + "# The max_step must be set to a short time than the\n", + "# length of the shortest pulse, otherwise the solver\n", + "# might skip over a pulse.\n", + "\n", + "options = {\n", + " \"nsteps\": 1500,\n", + " \"store_states\": True,\n", + " \"rtol\": 1e-12,\n", + " \"atol\": 1e-12,\n", + " \"max_step\": 1 / 20.0,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "97e33657", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "Now we define the system and bath properties and the HEOM parameters. The system is a single stationary qubit with $H = 0$ and the bath is a bosonic bath with a Drude-Lorentz spectrum." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b46a6b55", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the system Hamlitonian.\n", + "#\n", + "# The system isn't evolving by itself, so the Hamiltonian is 0 (with the\n", + "# correct dimensions):\n", + "\n", + "H_sys = 0 * sigmaz()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56dda9dc", + "metadata": {}, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresponding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresponding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fbb0a230", + "metadata": {}, + "outputs": [], + "source": [ + "# Properties for the Drude-Lorentz bath\n", + "\n", + "lam = 0.0005\n", + "gamma = 0.005\n", + "T = 0.05\n", + "\n", + "# bath-system coupling operator:\n", + "Q = sigmaz()\n", + "\n", + "# number of terms to keep in the expansion of the bath correlation function:\n", + "Nk = 3\n", + "\n", + "env = DrudeLorentzEnvironment(lam=lam, gamma=gamma,T=T)\n", + "env_approx=env.approx_by_pade(Nk=Nk)\n", + "bath=(env_approx,Q)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e14cabf", + "metadata": {}, + "outputs": [], + "source": [ + "# HEOM parameters\n", + "\n", + "# number of layers to keep in the hierarchy:\n", + "NC = 6" + ] + }, + { + "cell_type": "markdown", + "id": "31a1f196", + "metadata": {}, + "source": [ + "To perform the dynamic decoupling from the environment, we will drive the system with a time-dependent pulse that couples to the system via the $\\sigma_x$ operator. The area under the pulse will usual be set to $\\pi / 2$ so that the pulse flips the qubit state.\n", + "\n", + "Below we define a function that returns the pulse (which is itself a function):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "706ad7b4", + "metadata": {}, + "outputs": [], + "source": [ + "def drive(amplitude, delay, integral):\n", + " \"\"\" Coefficient of the drive as a function of time.\n", + "\n", + " The drive consists of a series of constant pulses with\n", + " a fixed delay between them.\n", + "\n", + " Parameters\n", + " ----------\n", + " amplitude : float\n", + " The amplitude of the drive during the pulse.\n", + " delay : float\n", + " The time delay between successive pulses.\n", + " integral : float\n", + " The integral of the pulse. This determines\n", + " the duration of each pulse with the duration\n", + " equal to the integral divided by the amplitude.\n", + " \"\"\"\n", + " duration = integral / amplitude\n", + " period = duration + delay\n", + "\n", + " def pulse(t):\n", + " t = t % period\n", + " if t < duration:\n", + " return amplitude\n", + " return 0\n", + "\n", + " return pulse\n", + "\n", + "\n", + "H_drive = sigmax()" + ] + }, + { + "cell_type": "markdown", + "id": "f90ae7f8", + "metadata": {}, + "source": [ + "## Plot the spectral density\n", + "\n", + "Let's start by plotting the spectral density of our Drude-Lorentz bath:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aef565f9", + "metadata": {}, + "outputs": [], + "source": [ + "wlist = np.linspace(0, 0.5, 1000)\n", + "J = env.spectral_density(wlist)\n", + "J_approx = env_approx.spectral_density(wlist)\n", + "\n", + "fig, axes = plt.subplots(1, 1, figsize=(8, 8))\n", + "axes.plot(wlist, J, 'r', linewidth=2)\n", + "axes.plot(wlist, J_approx, 'b--', linewidth=2)\n", + "\n", + "axes.set_xlabel(r'$\\omega$', fontsize=28)\n", + "axes.set_ylabel(r'J', fontsize=28);" + ] + }, + { + "cell_type": "markdown", + "id": "d657a2bd", + "metadata": {}, + "source": [ + "## Dynamic decoupling with fast and slow pulses\n", + "\n", + "Now we are ready to explore dynamic decoupling from the environment.\n", + "\n", + "First we will drive the system with fast, large amplitude pulses. Then we will drive the system with slower, smaller amplitude pulses. The faster pulses decoupling the system more effectively and retain the coherence longer, but the slower pulses help too.\n", + "\n", + "Let's start by simulating the fast pulses:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13b69602", + "metadata": {}, + "outputs": [], + "source": [ + "# Fast driving (quick, large amplitude pulses)\n", + "\n", + "tlist = np.linspace(0, 400, 1000)\n", + "\n", + "# start with a superposition so there is something to dephase!\n", + "rho0 = (basis(2, 1) + basis(2, 0)).unit()\n", + "rho0 = ket2dm(rho0)\n", + "\n", + "# without pulses\n", + "hsolver = HEOMSolver(H_sys, bath, NC, options=options)\n", + "outputnoDD = hsolver.run(rho0, tlist)\n", + "\n", + "# with pulses\n", + "drive_fast = drive(amplitude=0.5, delay=20, integral=np.pi / 2)\n", + "H_d = qutip.QobjEvo([H_sys, [H_drive, drive_fast]])\n", + "\n", + "hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", + "outputDD = hsolver.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "e109b42c", + "metadata": {}, + "source": [ + "And now the longer slower pulses:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29168059", + "metadata": {}, + "outputs": [], + "source": [ + "# Slow driving (longer, small amplitude pulses)\n", + "\n", + "# without pulses\n", + "hsolver = HEOMSolver(H_sys, bath, NC, options=options)\n", + "outputnoDDslow = hsolver.run(rho0, tlist)\n", + "\n", + "# with pulses\n", + "drive_slow = drive(amplitude=0.01, delay=20, integral=np.pi/2)\n", + "H_d = QobjEvo([H_sys, [H_drive, drive_slow]])\n", + "\n", + "hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", + "outputDDslow = hsolver.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "7e9d89a1", + "metadata": {}, + "source": [ + "Now let's plot all of the results and the shapes of the pulses:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1fcc6fd2", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_dd_results(outputnoDD, outputDD, outputDDslow):\n", + " fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 12))\n", + "\n", + " # Plot the dynamic decoupling results:\n", + "\n", + " tlist = outputDD.times\n", + "\n", + " P12 = basis(2, 1) * basis(2, 0).dag()\n", + " P12DD = qutip.expect(outputDD.states, P12)\n", + " P12noDD = qutip.expect(outputnoDD.states, P12)\n", + " P12DDslow = qutip.expect(outputDDslow.states, P12)\n", + "\n", + " plt.sca(axes[0])\n", + " plt.yticks([0, 0.25, 0.5], [0, 0.25, 0.5])\n", + "\n", + " axes[0].plot(\n", + " tlist, np.real(P12DD),\n", + " 'green', linestyle='-', linewidth=2, label=\"HEOM with fast DD\",\n", + " )\n", + " axes[0].plot(\n", + " tlist, np.real(P12DDslow),\n", + " 'blue', linestyle='-', linewidth=2, label=\"HEOM with slow DD\",\n", + " )\n", + " axes[0].plot(\n", + " tlist, np.real(P12noDD),\n", + " 'orange', linestyle='--', linewidth=2, label=\"HEOM no DD\",\n", + " )\n", + "\n", + " axes[0].locator_params(axis='y', nbins=3)\n", + " axes[0].locator_params(axis='x', nbins=3)\n", + "\n", + " axes[0].set_ylabel(r\"$\\rho_{01}$\", fontsize=30)\n", + "\n", + " axes[0].legend(loc=4)\n", + " axes[0].text(0, 0.4, \"(a)\", fontsize=28)\n", + "\n", + " # Plot the drive pulses:\n", + "\n", + " pulse = [drive_fast(t) for t in tlist]\n", + " pulseslow = [drive_slow(t) for t in tlist]\n", + "\n", + " plt.sca(axes[1])\n", + " plt.yticks([0., 0.25, 0.5], [0, 0.25, 0.5])\n", + "\n", + " axes[1].plot(\n", + " tlist, pulse,\n", + " 'green', linestyle='-', linewidth=2, label=\"Drive fast\",\n", + " )\n", + " axes[1].plot(\n", + " tlist, pulseslow,\n", + " 'blue', linestyle='--', linewidth=2, label=\"Drive slow\",\n", + " )\n", + "\n", + " axes[1].locator_params(axis='y', nbins=3)\n", + " axes[1].locator_params(axis='x', nbins=3)\n", + "\n", + " axes[1].set_xlabel(r'$t\\bar{V}_{\\mathrm{f}}$', fontsize=30)\n", + " axes[1].set_ylabel(r'Drive amplitude/$\\bar{V}_{\\mathrm{f}}$', fontsize=30)\n", + "\n", + " axes[1].legend(loc=1)\n", + " axes[1].text(0, 0.4, \"(b)\", fontsize=28)\n", + "\n", + " fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21bcc1e1", + "metadata": {}, + "outputs": [], + "source": [ + "plot_dd_results(outputnoDD, outputDD, outputDDslow)" + ] + }, + { + "cell_type": "markdown", + "id": "1f964ec6", + "metadata": {}, + "source": [ + "## Non-equally spaced pulses" + ] + }, + { + "cell_type": "markdown", + "id": "3916fabb", + "metadata": {}, + "source": [ + "Next we consider non-equally spaced pulses.\n", + "\n", + "Rather than plot as a function of time we just consider the final coherence after time $T$ and 100 pulses. We change the width of the environment to demonstate that the Uhrig sequence (i.e. the evenly spaced pulses) can be sub-optimal when the bath is very broad.\n", + "\n", + "Instead of evenly spaced pulses, we will use pulses where the cummulative delay after $j$ pulses is given by:\n", + "\n", + "$$\n", + " \\sin^2(\\frac{\\pi}{2} \\frac{j}{N + 1})\n", + "$$\n", + "\n", + "This is just a convenient way to describe the varying delay. We could have chosen another monotonically increasing function to represent the cummulative delay (although it might not be as effective)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4776a07f", + "metadata": {}, + "outputs": [], + "source": [ + "def cummulative_delay_fractions(N):\n", + " \"\"\" Return an array of N + 1 cummulative delay\n", + " fractions.\n", + "\n", + " The j'th entry in the array should be the sum of\n", + " all delays before the j'th pulse. The last entry\n", + " should be 1 (i.e. the entire cummulative delay\n", + " should have been used once the sequence of pulses\n", + " is complete).\n", + "\n", + " The function should be monotonically increasing,\n", + " strictly greater than zero and the last value\n", + " should be 1.\n", + "\n", + " This implementation returns:\n", + "\n", + " sin((pi / 2) * (j / (N + 1)))**2\n", + "\n", + " as the cummulative delay after the j'th pulse.\n", + " \"\"\"\n", + " return np.array([\n", + " np.sin((np.pi / 2) * (j / (N + 1)))**2\n", + " for j in range(0, N + 1)\n", + " ])\n", + "\n", + "\n", + "def drive_opt(amplitude, avg_delay, integral, N):\n", + " \"\"\" Return an optimized distance pulse function.\n", + "\n", + " Our previous pulses were evenly spaced. Here we\n", + " instead use a varying delay after the j'th pulse.\n", + "\n", + " The cummulative delay is described by the function\n", + " ``cummulative_delay_fractions`` above.\n", + " \"\"\"\n", + " duration = integral / amplitude\n", + " cummulative_delays = N * avg_delay * cummulative_delay_fractions(N)\n", + "\n", + " t_start = cummulative_delays + duration * np.arange(0, N + 1)\n", + " t_end = cummulative_delays + duration * np.arange(1, N + 2)\n", + "\n", + " def pulse(t):\n", + " if any((t_start <= t) & (t <= t_end)):\n", + " return amplitude\n", + " return 0.0\n", + "\n", + " return pulse" + ] + }, + { + "cell_type": "markdown", + "id": "fb41f0a9", + "metadata": {}, + "source": [ + "Let's plot the cummulative delays and see what they look like. Note that the cummulative delay starts at $0$, ends at $1$ and is monotonically increasing, as required.\n", + "\n", + "On the same axes we plot the individual $j^{th}$ delays as a fraction of the average delay." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb19ec7b", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_cummulative_delay_fractions(N):\n", + " cummulative = cummulative_delay_fractions(N)\n", + " individual = (cummulative[1:] - cummulative[:-1]) * N\n", + " plt.plot(np.arange(0, N + 1), cummulative, label=\"Cummulative delay\")\n", + " plt.plot(np.arange(0, N), individual, label=\"j'th delay\")\n", + " plt.xlabel(\"j\")\n", + " plt.ylabel(\"Fraction of delay\")\n", + " plt.legend()\n", + "\n", + "\n", + "plot_cummulative_delay_fractions(100)" + ] + }, + { + "cell_type": "markdown", + "id": "b2e7bc06", + "metadata": {}, + "source": [ + "And now let us plot the first ten even and optimally spaced pulses together to compare them:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd019617", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_even_and_optimally_spaced_pulses():\n", + " amplitude = 10.0\n", + " integral = np.pi / 2\n", + " duration = integral / amplitude\n", + " delay = 1.0 - duration\n", + "\n", + " tlist = np.linspace(0, 10, 1000)\n", + "\n", + " pulse_opt = drive_opt(amplitude, delay, integral, 100)\n", + " pulse_eq = drive(amplitude, delay, integral)\n", + "\n", + " plt.plot(\n", + " tlist, [pulse_opt(t) for t in tlist], label=\"opt\",\n", + " )\n", + " plt.plot(\n", + " tlist, [pulse_eq(t) for t in tlist], label=\"eq\",\n", + " )\n", + " plt.legend(loc=4)\n", + "\n", + "\n", + "plot_even_and_optimally_spaced_pulses()" + ] + }, + { + "cell_type": "markdown", + "id": "dea669bb", + "metadata": {}, + "source": [ + "Now let's simulate the effectiveness of the two sets of delays by comparing how well they maintain coherence after a hundred pulses.\n", + "\n", + "We'll perform the simulation over a range of lambdas and gammas to show how the non-evenly spaced delays become optimal as the width of the bath spectral function increases." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5ae0eec", + "metadata": {}, + "outputs": [], + "source": [ + "# Bath parameters to simulate over:\n", + "\n", + "# We use only two lambdas and two gammas so that the notebook executes\n", + "# quickly:\n", + "\n", + "lams = [0.005, 0.0005]\n", + "gammas = np.linspace(0.005, 0.05, 2)\n", + "\n", + "# But one can also extend the lists to larger ones:\n", + "#\n", + "# lams = [0.01, 0.005, 0.0005]\n", + "# gammas = np.linspace(0.005, 0.05, 10)\n", + "\n", + "# Setup a progress bar:\n", + "\n", + "progress = IntProgress(min=0, max=(2 * len(lams) * len(gammas)))\n", + "display(progress)\n", + "\n", + "\n", + "def simulate_100_pulses(lam, gamma, T, NC, Nk):\n", + " \"\"\" Simulate the evolution of 100 evenly and optimally spaced pulses.\n", + "\n", + " Returns the expectation value of P12p from the final state of\n", + " each evolution.\n", + " \"\"\"\n", + " rho0 = (basis(2, 1) + basis(2, 0)).unit()\n", + " rho0 = ket2dm(rho0)\n", + "\n", + " N = 100 # number of pulses to simulate\n", + " avg_cycle_time = 1.0 # average time from one pulse to the next\n", + " t_max = N * avg_cycle_time\n", + "\n", + " tlist = np.linspace(0, t_max, 100)\n", + "\n", + " amplitude = 10.0\n", + " integral = np.pi / 2\n", + " duration = integral / amplitude\n", + " delay = avg_cycle_time - duration\n", + "\n", + " env = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T)\n", + " env_approx = env.approx_by_pade(Nk=Nk)\n", + " bath=(env_approx,Q)\n", + " # Equally spaced pulses:\n", + "\n", + " pulse_eq = drive(amplitude, delay, integral)\n", + " H_d = QobjEvo([H_sys, [H_drive, pulse_eq]])\n", + "\n", + " hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", + " result = hsolver.run(rho0, tlist)\n", + "\n", + " P12_eq = expect(result.states[-1], P12p)\n", + " progress.value += 1\n", + "\n", + " # Non-equally spaced pulses:\n", + "\n", + " pulse_opt = drive_opt(amplitude, delay, integral, N)\n", + " H_d = QobjEvo([H_sys, [H_drive, pulse_opt]])\n", + "\n", + " hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", + " result = hsolver.run(rho0, tlist)\n", + "\n", + " P12_opt = expect(result.states[-1], P12p)\n", + " progress.value += 1\n", + "\n", + " return P12_opt, P12_eq\n", + "\n", + "\n", + "# We use NC=2 and Nk=2 to speed up the simulation:\n", + "\n", + "P12_results = [\n", + " list(zip(*(\n", + " simulate_100_pulses(lam=lam_, gamma=gamma_, T=0.5, NC=2, Nk=2)\n", + " for gamma_ in gammas\n", + " )))\n", + " for lam_ in lams\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "c3d0f4ab", + "metadata": {}, + "source": [ + "Now that we have the expectation values of $\\rho_{01}$ let's plot them as a function of gamma for each lambda. Note how in each case the non-evenly spaced pulses become optimal once gamma is sufficiently small:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f4c2ac9", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=False, figsize=(10, 7))\n", + "colors = [\"green\", \"red\", \"blue\"]\n", + "\n", + "for i in range(len(lams)):\n", + " color = colors[i % len(colors)]\n", + " axes.plot(\n", + " gammas, np.real(P12_results[i][0]),\n", + " color, linestyle='-', linewidth=2,\n", + " label=f\"Optimal DD [$\\\\lambda={lams[i]}$]\",\n", + " )\n", + " axes.plot(\n", + " gammas, np.real(P12_results[i][1]),\n", + " color, linestyle='-.', linewidth=2,\n", + " label=f\"Even DD [$\\\\lambda={lams[i]}$]\",\n", + " )\n", + "\n", + "axes.set_ylabel(r\"$\\rho_{01}$\")\n", + "axes.set_xlabel(r\"$\\gamma$\")\n", + "axes.legend(fontsize=16)\n", + "\n", + "fig.tight_layout();" + ] + }, + { + "cell_type": "markdown", + "id": "ad8f3047", + "metadata": {}, + "source": [ + "And now you know about dynamically decoupling a qubit from its environment!" + ] + }, + { + "cell_type": "markdown", + "id": "47cedf88", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1cedbf02", + "metadata": {}, + "outputs": [], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "663ced6a", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a64d82a4", + "metadata": {}, + "outputs": [], + "source": [ + "assert 1 == 1" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "qutip-dev", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.ipynb b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.ipynb new file mode 100644 index 00000000..6ad3aa4f --- /dev/null +++ b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.ipynb @@ -0,0 +1,828 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "294cf5cd", + "metadata": {}, + "source": [ + "# HEOM 5a: Fermionic single impurity model" + ] + }, + { + "cell_type": "markdown", + "id": "1e7f2b4f", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc). Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications.\n", + "\n", + "Notation:\n", + "\n", + "* $K=L/R$ refers to left or right leads.\n", + "* $\\sigma=\\pm$ refers to input/output\n", + "\n", + "We choose a Lorentzian spectral density for the leads, with a peak at the chemical potential. The latter simplifies a little the notation required for the correlation functions, but can be relaxed if neccessary.\n", + "\n", + "$$J(\\omega) = \\frac{\\Gamma W^2}{((\\omega-\\mu_K)^2 +W^2 )}$$\n", + "\n", + "The Fermi distribution function is:\n", + "\n", + "$$f_F (x) = (\\exp(x) + 1)^{-1}$$\n", + "\n", + "Together these allow the correlation functions to be expressed as:\n", + "\n", + "$$C^{\\sigma}_K(t) = \\frac{1}{2\\pi} \\int_{-\\infty}^{\\infty} d\\omega e^{\\sigma i \\omega t} \\Gamma_K(\\omega) f_F[\\sigma\\beta(\\omega - \\mu)]$$\n", + "\n", + "As with the bosonic case we can expand these in an exponential series using Matsubara, Pade, or fitting approaches.\n", + "\n", + "The Pade decomposition approximates the Fermi distubition as\n", + "\n", + "$$f_F(x) \\approx f_F^{\\mathrm{approx}}(x) = \\frac{1}{2} - \\sum_l^{l_{max}} \\frac{2k_l x}{x^2 + \\epsilon_l^2}$$\n", + "\n", + "where $k_l$ and $\\epsilon_l$ are co-efficients defined in J. Chem Phys 133,10106.\n", + "\n", + "Evaluating the integral for the correlation functions gives,\n", + "\n", + "$$C_K^{\\sigma}(t) \\approx \\sum_{l=0}^{l_{max}} \\eta_K^{\\sigma_l} e^{-\\gamma_{K,\\sigma,l}t}$$\n", + "\n", + "where:\n", + "\n", + "$$\\eta_{K,0} = \\frac{\\Gamma_KW_K}{2} f_F^{approx}(i\\beta_K W)$$\n", + "\n", + "$$\\gamma_{K,\\sigma,0} = W_K - \\sigma i\\mu_K$$ \n", + "\n", + "$$\\eta_{K,l\\neq 0} = -i\\cdot \\frac{k_m}{\\beta_K} \\cdot \\frac{\\Gamma_K W_K^2}{-\\frac{\\epsilon^2_m}{\\beta_K^2} + W_K^2}$$\n", + "\n", + "$$\\gamma_{K,\\sigma,l\\neq 0}= \\frac{\\epsilon_m}{\\beta_K} - \\sigma i \\mu_K$$\n", + "\n", + "In this notebook we:\n", + "\n", + "* compare the Matsubara and Pade approximations and contrast them with the analytical result for the current between the system and the leads.\n", + "\n", + "* plot the current through the qubit as a function of the different between the voltages of the leads." + ] + }, + { + "cell_type": "markdown", + "id": "2e344631", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3413b8c", + "metadata": {}, + "outputs": [], + "source": [ + "import contextlib\n", + "import dataclasses\n", + "import time\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.integrate import quad\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " basis,\n", + " destroy,\n", + " expect,\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver,\n", + " LorentzianBath,\n", + " LorentzianPadeBath,\n", + ")\n", + "\n", + "from ipywidgets import IntProgress\n", + "from IPython.display import display\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "3a83b998", + "metadata": {}, + "source": [ + "## Helpers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44710770", + "metadata": {}, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\" Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1fefdfa4", + "metadata": {}, + "outputs": [], + "source": [ + "# Solver options:\n", + "\n", + "# We set store_ados to True so that we can\n", + "# use the auxilliary density operators (ADOs)\n", + "# to calculate the current between the leads\n", + "# and the system.\n", + "\n", + "options = {\n", + " \"nsteps\": 1500,\n", + " \"store_states\": True,\n", + " \"store_ados\": True,\n", + " \"rtol\": 1e-12,\n", + " \"atol\": 1e-12,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "3219912b", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian, bath and system measurement operators:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48867a7b", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the system Hamiltonian:\n", + "\n", + "# The system is a single fermion with energy level split e1:\n", + "d1 = destroy(2)\n", + "e1 = 1.0\n", + "H = e1 * d1.dag() * d1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb765344", + "metadata": {}, + "outputs": [], + "source": [ + "# Define parameters for left and right fermionic baths.\n", + "# Each bath is a lead (i.e. a wire held at a potential)\n", + "# with temperature T and chemical potential mu.\n", + "\n", + "@dataclasses.dataclass\n", + "class LorentzianBathParameters:\n", + " lead: str\n", + " Q: object # coupling operator\n", + " gamma: float = 0.01 # coupling strength\n", + " W: float = 1.0 # cut-off\n", + " T: float = 0.025851991 # temperature\n", + " theta: float = 2.0 # bias\n", + "\n", + " def __post_init__(self):\n", + " assert self.lead in (\"L\", \"R\")\n", + " self.beta = 1 / self.T\n", + " if self.lead == \"L\":\n", + " self.mu = self.theta / 2.0\n", + " else:\n", + " self.mu = - self.theta / 2.0\n", + "\n", + " def J(self, w):\n", + " \"\"\" Spectral density. \"\"\"\n", + " return self.gamma * self.W**2 / ((w - self.mu)**2 + self.W**2)\n", + "\n", + " def fF(self, w, sign=1.0):\n", + " \"\"\" Fermi distribution for this bath. \"\"\"\n", + " x = sign * self.beta * (w - self.mu)\n", + " return fF(x)\n", + "\n", + " def lamshift(self, w):\n", + " \"\"\" Return the lamshift. \"\"\"\n", + " return 0.5 * (w - self.mu) * self.J(w) / self.W\n", + "\n", + " def replace(self, **kw):\n", + " return dataclasses.replace(self, **kw)\n", + "\n", + "\n", + "def fF(x):\n", + " \"\"\" Return the Fermi distribution. \"\"\"\n", + " # in units where kB = 1.0\n", + " return 1 / (np.exp(x) + 1)\n", + "\n", + "\n", + "bath_L = LorentzianBathParameters(Q=d1, lead=\"L\")\n", + "bath_R = LorentzianBathParameters(Q=d1, lead=\"R\")" + ] + }, + { + "cell_type": "markdown", + "id": "5e3f3457", + "metadata": {}, + "source": [ + "## Spectral density\n", + "\n", + "Let's plot the spectral density." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1dec0a7", + "metadata": {}, + "outputs": [], + "source": [ + "w_list = np.linspace(-2, 2, 100)\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 7))\n", + "\n", + "spec_L = bath_L.J(w_list)\n", + "spec_R = bath_R.J(w_list)\n", + "\n", + "ax.plot(\n", + " w_list, spec_L,\n", + " \"b--\", linewidth=3,\n", + " label=r\"J_L(w)\",\n", + ")\n", + "ax.plot(\n", + " w_list, spec_R,\n", + " \"r--\", linewidth=3,\n", + " label=r\"J_R(w)\",\n", + ")\n", + "\n", + "ax.set_xlabel(\"w\")\n", + "ax.set_ylabel(r\"$J(\\omega)$\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "f877e6d4", + "metadata": {}, + "source": [ + "## Emission and absorption by the leads\n", + "\n", + "Next let's plot the emission and absorption by the leads." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1c95fd8", + "metadata": {}, + "outputs": [], + "source": [ + "w_list = np.linspace(-2, 2, 100)\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 7))\n", + "\n", + "# Left lead emission and absorption\n", + "\n", + "gam_L_in = bath_L.J(w_list) * bath_L.fF(w_list, sign=1.0)\n", + "gam_L_out = bath_L.J(w_list) * bath_L.fF(w_list, sign=-1.0)\n", + "\n", + "ax.plot(\n", + " w_list, gam_L_in,\n", + " \"b--\", linewidth=3,\n", + " label=r\"S_L(w) input (absorption)\",\n", + ")\n", + "ax.plot(\n", + " w_list, gam_L_out,\n", + " \"r--\", linewidth=3,\n", + " label=r\"S_L(w) output (emission)\",\n", + ")\n", + "\n", + "# Right lead emission and absorption\n", + "\n", + "gam_R_in = bath_R.J(w_list) * bath_R.fF(w_list, sign=1.0)\n", + "gam_R_out = bath_R.J(w_list) * bath_R.fF(w_list, sign=-1.0)\n", + "\n", + "ax.plot(\n", + " w_list, gam_R_in,\n", + " \"b\", linewidth=3,\n", + " label=r\"S_R(w) input (absorption)\",\n", + ")\n", + "ax.plot(\n", + " w_list, gam_R_out,\n", + " \"r\", linewidth=3,\n", + " label=r\"S_R(w) output (emission)\",\n", + ")\n", + "\n", + "ax.set_xlabel(\"w\")\n", + "ax.set_ylabel(r\"$S(\\omega)$\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "ebfdbd43", + "metadata": {}, + "source": [ + "## Comparing the Matsubara and Pade approximations\n", + "\n", + "Let's start by solving for the evolution using a Pade expansion of the correlation function of the Lorentzian spectral density:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97f3271c", + "metadata": {}, + "outputs": [], + "source": [ + "# HEOM dynamics using the Pade approximation:\n", + "\n", + "# Times to solve for and initial system state:\n", + "tlist = np.linspace(0, 100, 1000)\n", + "rho0 = basis(2, 0) * basis(2, 0).dag()\n", + "\n", + "Nk = 10 # Number of exponents to retain in the expansion of each bath\n", + "\n", + "bathL = LorentzianPadeBath(\n", + " bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", + " Nk, tag=\"L\",\n", + ")\n", + "bathR = LorentzianPadeBath(\n", + " bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", + " Nk, tag=\"R\",\n", + ")\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " solver_pade = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " result_pade = solver_pade.run(rho0, tlist)\n", + "\n", + "with timer(\"Steady state solver time\"):\n", + " rho_ss_pade, ado_ss_pade = solver_pade.steady_state()" + ] + }, + { + "cell_type": "markdown", + "id": "e0188db5", + "metadata": {}, + "source": [ + "Now let us plot the result which shows the decay of the initially excited impurity. This is not very illuminating, but we will compare it with the Matsubara expansion and analytic solution sortly:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70f5d901", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the Pade results\n", + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + "\n", + "axes.plot(\n", + " tlist, expect(result_pade.states, rho0),\n", + " 'r--', linewidth=2,\n", + " label=\"P11 (Pade)\",\n", + ")\n", + "axes.axhline(\n", + " expect(rho_ss_pade, rho0),\n", + " color='r', linestyle=\"dotted\", linewidth=1,\n", + " label=\"P11 (Pade steady state)\",\n", + ")\n", + "\n", + "axes.set_xlabel('t', fontsize=28)\n", + "axes.legend(fontsize=12);" + ] + }, + { + "cell_type": "markdown", + "id": "3e030af0", + "metadata": {}, + "source": [ + "Now let us do the same for the Matsubara expansion:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24fa4a52", + "metadata": {}, + "outputs": [], + "source": [ + "# HEOM dynamics using the Matsubara approximation:\n", + "\n", + "bathL = LorentzianBath(\n", + " bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", + " Nk, tag=\"L\",\n", + ")\n", + "bathR = LorentzianBath(\n", + " bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", + " Nk, tag=\"R\",\n", + ")\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " solver_mats = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " result_mats = solver_mats.run(rho0, tlist)\n", + "\n", + "with timer(\"Steady state solver time\"):\n", + " rho_ss_mats, ado_ss_mats = solver_mats.steady_state()" + ] + }, + { + "cell_type": "markdown", + "id": "c06cee47", + "metadata": {}, + "source": [ + "We see a marked difference in the Matsubara vs Pade results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90c30fab", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the Pade results\n", + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + "\n", + "axes.plot(\n", + " tlist, expect(result_pade.states, rho0),\n", + " 'r--', linewidth=2,\n", + " label=\"P11 (Pade)\",\n", + ")\n", + "axes.axhline(\n", + " expect(rho_ss_pade, rho0),\n", + " color='r', linestyle=\"dotted\", linewidth=1,\n", + " label=\"P11 (Pade steady state)\",\n", + ")\n", + "\n", + "axes.plot(\n", + " tlist, expect(result_mats.states, rho0),\n", + " 'b--', linewidth=2,\n", + " label=\"P11 (Mats)\",\n", + ")\n", + "axes.axhline(\n", + " expect(rho_ss_mats, rho0),\n", + " color='b', linestyle=\"dotted\", linewidth=1,\n", + " label=\"P11 (Mats steady state)\",\n", + ")\n", + "\n", + "axes.set_xlabel('t', fontsize=28)\n", + "axes.legend(fontsize=12);" + ] + }, + { + "cell_type": "markdown", + "id": "2fe28818", + "metadata": {}, + "source": [ + "But which is more correct? The Matsubara or the Pade result?\n", + "\n", + "One advantage of this simple model is that the steady state current to the baths is analytically solvable, so we can check convergence of the result by calculating it analytically (the sum of the currents to and from the system in the steady state must be zero, so the current from one bath is the same as the current to the other).\n", + "\n", + "See the [QuTiP-BoFiN paper](https://arxiv.org/abs/2010.10806) for a detailed description and references for the analytic result. Below we just perform the required integration numerically." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "76d31675", + "metadata": {}, + "outputs": [], + "source": [ + "def analytical_steady_state_current(bath_L, bath_R, e1):\n", + " \"\"\" Calculate the analytical steady state current. \"\"\"\n", + "\n", + " def integrand(w):\n", + " return (2 / np.pi) * (\n", + " bath_L.J(w) * bath_R.J(w) * (bath_L.fF(w) - bath_R.fF(w)) /\n", + " (\n", + " (bath_L.J(w) + bath_R.J(w))**2 +\n", + " 4*(w - e1 - bath_L.lamshift(w) - bath_R.lamshift(w))**2\n", + " )\n", + " )\n", + "\n", + " def real_part(x):\n", + " return np.real(integrand(x))\n", + "\n", + " def imag_part(x):\n", + " return np.imag(integrand(x))\n", + "\n", + " # in principle the bounds for the integral should be rechecked if\n", + " # bath or system parameters are changed substantially:\n", + " bounds = [-10, 10]\n", + "\n", + " real_integral, _ = quad(real_part, *bounds)\n", + " imag_integral, _ = quad(imag_part, *bounds)\n", + "\n", + " return real_integral + 1.0j * imag_integral\n", + "\n", + "\n", + "curr_ss_analytic = analytical_steady_state_current(bath_L, bath_R, e1)\n", + "\n", + "print(f\"Analytical steady state current: {curr_ss_analytic}\")" + ] + }, + { + "cell_type": "markdown", + "id": "181fd371", + "metadata": {}, + "source": [ + "To compare the analytical result above with the result from the HEOM, we need to be able to calculate the current from the system to the bath from the HEOM result. In the HEOM description, these currents are captured in the first level auxilliary density operators (ADOs).\n", + "\n", + "In the function `state_current(...)` below, we extract the first level ADOs for the specified bath and sum the contributions to the current from each:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2376c586", + "metadata": {}, + "outputs": [], + "source": [ + "def state_current(ado_state, bath_tag):\n", + " \"\"\" Determine current from the given bath (either \"R\" or \"L\") to\n", + " the system in the given ADO state.\n", + " \"\"\"\n", + " level_1_aux = [\n", + " (ado_state.extract(label), ado_state.exps(label)[0])\n", + " for label in ado_state.filter(level=1, tags=[bath_tag])\n", + " ]\n", + "\n", + " def exp_sign(exp):\n", + " return 1 if exp.type == exp.types[\"+\"] else -1\n", + "\n", + " def exp_op(exp):\n", + " return exp.Q if exp.type == exp.types[\"+\"] else exp.Q.dag()\n", + "\n", + " return -1.0j * sum(\n", + " exp_sign(exp) * (exp_op(exp) * aux).tr()\n", + " for aux, exp in level_1_aux\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "2986b96b", + "metadata": {}, + "source": [ + "Now we can calculate the steady state currents from the Pade and Matsubara HEOM results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "efed55fc", + "metadata": {}, + "outputs": [], + "source": [ + "curr_ss_pade_L = state_current(ado_ss_pade, \"L\")\n", + "curr_ss_pade_R = state_current(ado_ss_pade, \"R\")\n", + "\n", + "print(f\"Pade steady state current (L): {curr_ss_pade_L}\")\n", + "print(f\"Pade steady state current (R): {curr_ss_pade_R}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "09df1f63", + "metadata": {}, + "outputs": [], + "source": [ + "curr_ss_mats_L = state_current(ado_ss_mats, \"L\")\n", + "curr_ss_mats_R = state_current(ado_ss_mats, \"R\")\n", + "\n", + "print(f\"Matsubara steady state current (L): {curr_ss_mats_L}\")\n", + "print(f\"Matsubara steady state current (R): {curr_ss_mats_R}\")" + ] + }, + { + "cell_type": "markdown", + "id": "16fe795e", + "metadata": {}, + "source": [ + "Note that the currents from each bath balance as is required by the steady state, but the value of the current is different for the Pade and Matsubara results.\n", + "\n", + "Now let's compare all three:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20c32bd5", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Pade current (R): {curr_ss_pade_R}\")\n", + "print(f\"Matsubara current (R): {curr_ss_mats_R}\")\n", + "print(f\"Analytical curernt: {curr_ss_analytic}\")" + ] + }, + { + "cell_type": "markdown", + "id": "292927df", + "metadata": {}, + "source": [ + "In this case we observe that the Pade approximation has converged more closely to the analytical current than the Matsubara.\n", + "\n", + "The Matsubara result could be improved by increasing the number of terms retained in the Matsubara expansion (i.e. increasing `Nk`)." + ] + }, + { + "cell_type": "markdown", + "id": "f494ba8c", + "metadata": {}, + "source": [ + "## Current as a function of bias voltage" + ] + }, + { + "cell_type": "markdown", + "id": "88dbf381", + "metadata": {}, + "source": [ + "Now lets plot the current as a function of bias voltage (the bias voltage is the parameter `theta` for the two baths).\n", + "\n", + "We will calculate the steady state current for each `theta` both analytically and using the HEOM with the Pade correlation expansion approximation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3077adba", + "metadata": {}, + "outputs": [], + "source": [ + "# Theta (bias voltages)\n", + "\n", + "thetas = np.linspace(-4, 4, 100)\n", + "\n", + "# Setup a progress bar:\n", + "\n", + "progress = IntProgress(min=0, max=2 * len(thetas))\n", + "display(progress)\n", + "\n", + "# Calculate the current for the list of thetas\n", + "\n", + "\n", + "def current_analytic_for_theta(e1, bath_L, bath_R, theta):\n", + " \"\"\" Return the analytic current for a given theta. \"\"\"\n", + " current = analytical_steady_state_current(\n", + " bath_L.replace(theta=theta),\n", + " bath_R.replace(theta=theta),\n", + " e1,\n", + " )\n", + " progress.value += 1\n", + " return np.real(current)\n", + "\n", + "\n", + "def current_pade_for_theta(H, bath_L, bath_R, theta, Nk):\n", + " \"\"\" Return the steady state current using the Pade approximation. \"\"\"\n", + " bath_L = bath_L.replace(theta=theta)\n", + " bath_R = bath_R.replace(theta=theta)\n", + "\n", + " bathL = LorentzianPadeBath(\n", + " bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", + " Nk, tag=\"L\",\n", + " )\n", + " bathR = LorentzianPadeBath(\n", + " bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", + " Nk, tag=\"R\",\n", + " )\n", + "\n", + " solver_pade = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options)\n", + " rho_ss_pade, ado_ss_pade = solver_pade.steady_state()\n", + " current = state_current(ado_ss_pade, bath_tag=\"R\")\n", + "\n", + " progress.value += 1\n", + " return np.real(current)\n", + "\n", + "\n", + "curr_ss_analytic_thetas = [\n", + " current_analytic_for_theta(e1, bath_L, bath_R, theta)\n", + " for theta in thetas\n", + "]\n", + "\n", + "# The number of expansion terms has been dropped to Nk=6 to speed\n", + "# up notebook execution. Increase to Nk=10 for more accurate results.\n", + "curr_ss_pade_theta = [\n", + " current_pade_for_theta(H, bath_L, bath_R, theta, Nk=6)\n", + " for theta in thetas\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "bf13bd67", + "metadata": {}, + "source": [ + "Below we plot the results and see that even with `Nk=6`, the HEOM Pade approximation gives good results for the steady state current. Increasing `Nk` to `10` gives very accurate results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c6ae487", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(12, 7))\n", + "\n", + "ax.plot(\n", + " thetas, 2.434e-4 * 1e6 * np.array(curr_ss_analytic_thetas),\n", + " color=\"black\", linewidth=3,\n", + " label=r\"Analytical\",\n", + ")\n", + "ax.plot(\n", + " thetas, 2.434e-4 * 1e6 * np.array(curr_ss_pade_theta),\n", + " 'r--', linewidth=3,\n", + " label=r\"HEOM Pade $N_k=10$, $n_{\\mathrm{max}}=2$\",\n", + ")\n", + "\n", + "\n", + "ax.locator_params(axis='y', nbins=4)\n", + "ax.locator_params(axis='x', nbins=4)\n", + "\n", + "ax.set_xticks([-2.5, 0, 2.5])\n", + "ax.set_xticklabels([-2.5, 0, 2.5])\n", + "ax.set_xlabel(r\"Bias voltage $\\Delta \\mu$ ($V$)\", fontsize=28)\n", + "ax.set_ylabel(r\"Current ($\\mu A$)\", fontsize=28)\n", + "ax.legend(fontsize=25);" + ] + }, + { + "cell_type": "markdown", + "id": "05a64866", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a48e113", + "metadata": {}, + "outputs": [], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "d0bfad09", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "226849e3", + "metadata": {}, + "outputs": [], + "source": [ + "assert np.allclose(curr_ss_pade_L + curr_ss_pade_R, 0)\n", + "assert np.allclose(curr_ss_mats_L + curr_ss_mats_R, 0)\n", + "assert np.allclose(curr_ss_pade_R, curr_ss_analytic, rtol=1e-4)" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.ipynb b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.ipynb new file mode 100644 index 00000000..2ffe620d --- /dev/null +++ b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.ipynb @@ -0,0 +1,528 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4436eb27", + "metadata": {}, + "source": [ + "# HEOM 5b: Discrete boson coupled to an impurity and fermionic leads" + ] + }, + { + "cell_type": "markdown", + "id": "83c4d5b9", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc), also coupled to a discrete bosonic (vibronic) mode.\n", + "\n", + "Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his Dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications. In particular this example reproduces some results from https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.201407\n", + "\n", + "Notation:\n", + "\n", + "* $K=L/R$ refers to left or right leads.\n", + "* $\\sigma=\\pm$ refers to input/output\n", + "\n", + "We choose a Lorentzian spectral density for the leads, with a peak at the chemical potential. The latter simplifies a little the notation required for the correlation functions, but can be relaxed if neccessary.\n", + "\n", + "$$J(\\omega) = \\frac{\\Gamma W^2}{((\\omega-\\mu_K)^2 +W^2 )}$$\n", + "\n", + "The Fermi distribution function is:\n", + "\n", + "$$f_F (x) = (\\exp(x) + 1)^{-1}$$\n", + "\n", + "Together these allow the correlation functions to be expressed as:\n", + "\n", + "$$C^{\\sigma}_K(t) = \\frac{1}{2\\pi} \\int_{-\\infty}^{\\infty} d\\omega e^{\\sigma i \\omega t} \\Gamma_K(\\omega) f_F[\\sigma\\beta(\\omega - \\mu)]$$\n", + "\n", + "As with the Bosonic case we can treat these with Matsubara, Pade, or fitting approaches.\n", + "\n", + "The Pade decomposition approximates the Fermi distubition as \n", + "\n", + "$$f_F(x) \\approx f_F^{\\mathrm{approx}}(x) = \\frac{1}{2} - \\sum_l^{l_{max}} \\frac{2k_l x}{x^2 + \\epsilon_l^2}$$\n", + "\n", + "$k_l$ and $\\epsilon_l$ are co-efficients defined in J. Chem Phys 133,10106\n", + "\n", + "Evaluating the integral for the correlation functions gives,\n", + "\n", + "\n", + "$$C_K^{\\sigma}(t) \\approx \\sum_{l=0}^{l_{max}} \\eta_K^{\\sigma_l} e^{-\\gamma_{K,\\sigma,l}t}$$\n", + "\n", + "where\n", + "\n", + "$$\\eta_{K,0} = \\frac{\\Gamma_KW_K}{2} f_F^{approx}(i\\beta_K W)$$\n", + "\n", + "$$\\gamma_{K,\\sigma,0} = W_K - \\sigma i\\mu_K$$ \n", + "\n", + "$$\\eta_{K,l\\neq 0} = -i\\cdot \\frac{k_m}{\\beta_K} \\cdot \\frac{\\Gamma_K W_K^2}{-\\frac{\\epsilon^2_m}{\\beta_K^2} + W_K^2}$$\n", + "\n", + "$$\\gamma_{K,\\sigma,l\\neq 0}= \\frac{\\epsilon_m}{\\beta_K} - \\sigma i \\mu_K$$" + ] + }, + { + "cell_type": "markdown", + "id": "3aca80cf", + "metadata": {}, + "source": [ + "## Differences from Example 5a" + ] + }, + { + "cell_type": "markdown", + "id": "7e38bec0", + "metadata": {}, + "source": [ + "The system we study here has two big differences from the HEOM 5a example:\n", + "\n", + "* the system now includes a discrete bosonic mode,\n", + "* and the electronic leads have $W$ set to $10^4$ (i.e. the wide-band limit).\n", + "\n", + "The new system Hamiltonian is:\n", + "\n", + "$$\n", + "H_{\\mathrm{vib}} = H_{\\mathrm{SIAM}} + \\Omega a^{\\dagger}a + \\lambda (a+a^{\\dagger})c{^\\dagger}c.\n", + "$$\n", + "\n", + "where $H_{\\mathrm{SIAM}}$ is the Hamiltonian of the single impurity, and the remaining terms are the Hamiltonian of the bosonic mode and its interaction with the impurity.\n", + "\n", + "The complete setup now consists of four parts:\n", + "\n", + "* the single impurity\n", + "* a discrete bosonic mode\n", + "* two fermionic leads.\n", + "\n", + "**Note**: This example is quite numerically challenging and has many system and bath components. For an easier introduction into the fermionic case, see example 5a.\n", + "\n", + "**Note**: We've reduced the cut-off of the bosonic mode to 2 modes to facilitate faster execution of the notebooks when the outputs are being checked and compiled. A more accurate result may be obtained by increasing the number of bosonic modes to, for example, 16." + ] + }, + { + "cell_type": "markdown", + "id": "f1de2d6b", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "05b4b6bd", + "metadata": {}, + "outputs": [], + "source": [ + "import contextlib\n", + "import dataclasses\n", + "import time\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " destroy,\n", + " qeye,\n", + " tensor,\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver,\n", + " LorentzianPadeBath,\n", + ")\n", + "\n", + "from ipywidgets import IntProgress\n", + "from IPython.display import display\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "3014f9b6", + "metadata": {}, + "source": [ + "## Helpers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc7bacf1", + "metadata": {}, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\" Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f309aa7a", + "metadata": {}, + "outputs": [], + "source": [ + "def state_current(ado_state, bath_tag):\n", + " \"\"\" Determine current from the given bath (either \"R\" or \"L\") to\n", + " the system in the given ADO state.\n", + " \"\"\"\n", + " level_1_aux = [\n", + " (ado_state.extract(label), ado_state.exps(label)[0])\n", + " for label in ado_state.filter(level=1, tags=[bath_tag])\n", + " ]\n", + "\n", + " def exp_sign(exp):\n", + " return 1 if exp.type == exp.types[\"+\"] else -1\n", + "\n", + " def exp_op(exp):\n", + " return exp.Q if exp.type == exp.types[\"+\"] else exp.Q.dag()\n", + "\n", + " return -1.0j * sum(\n", + " exp_sign(exp) * (exp_op(exp) * aux).tr()\n", + " for aux, exp in level_1_aux\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90e77c67", + "metadata": {}, + "outputs": [], + "source": [ + "# Solver options:\n", + "\n", + "# We set store_ados to True so that we can\n", + "# use the auxilliary density operators (ADOs)\n", + "# to calculate the current between the leads\n", + "# and the system.\n", + "\n", + "options = {\n", + " \"nsteps\": 1500,\n", + " \"store_states\": True,\n", + " \"store_ados\": True,\n", + " \"rtol\": 1e-12,\n", + " \"atol\": 1e-12,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "9104d150", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "Let us set up the system Hamiltonian and specify the properties of the two reservoirs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb1fc1b3", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the system Hamiltonian:\n", + "\n", + "@dataclasses.dataclass\n", + "class SystemParameters:\n", + " e1: float = 0.3 # fermion mode energy splitting\n", + " Omega: float = 0.2 # bosonic mode energy splitting\n", + " Lambda: float = 0.12 # coupling between fermion and boson\n", + " Nbos: int = 2\n", + "\n", + " def __post_init__(self):\n", + " d = tensor(destroy(2), qeye(self.Nbos))\n", + " a = tensor(qeye(2), destroy(self.Nbos))\n", + " self.H = (\n", + " self.e1 * d.dag() * d +\n", + " self.Omega * a.dag() * a +\n", + " self.Lambda * (a + a.dag()) * d.dag() * d\n", + " )\n", + " self.Q = d\n", + "\n", + " def replace(self, **kw):\n", + " return dataclasses.replace(self, **kw)\n", + "\n", + "\n", + "sys_p = SystemParameters()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d761f65", + "metadata": {}, + "outputs": [], + "source": [ + "# Define parameters for left and right fermionic baths.\n", + "# Each bath is a lead (i.e. a wire held at a potential)\n", + "# with temperature T and chemical potential mu.\n", + "\n", + "@dataclasses.dataclass\n", + "class LorentzianBathParameters:\n", + " lead: str\n", + " gamma: float = 0.01 # coupling strength\n", + " W: float = 1.0 # cut-off\n", + " T: float = 0.025851991 # temperature (in eV)\n", + " theta: float = 2.0 # bias\n", + "\n", + " def __post_init__(self):\n", + " assert self.lead in (\"L\", \"R\")\n", + " self.beta = 1 / self.T\n", + " if self.lead == \"L\":\n", + " self.mu = self.theta / 2.0\n", + " else:\n", + " self.mu = - self.theta / 2.0\n", + "\n", + " def J(self, w):\n", + " \"\"\" Spectral density. \"\"\"\n", + " return self.gamma * self.W**2 / ((w - self.mu)**2 + self.W**2)\n", + "\n", + " def fF(self, w, sign=1.0):\n", + " \"\"\" Fermi distribution for this bath. \"\"\"\n", + " x = sign * self.beta * (w - self.mu)\n", + " return fF(x)\n", + "\n", + " def lamshift(self, w):\n", + " \"\"\" Return the lamshift. \"\"\"\n", + " return 0.5 * (w - self.mu) * self.J(w) / self.W\n", + "\n", + " def replace(self, **kw):\n", + " return dataclasses.replace(self, **kw)\n", + "\n", + "\n", + "def fF(x):\n", + " \"\"\" Return the Fermi distribution. \"\"\"\n", + " # in units where kB = 1.0\n", + " return 1 / (np.exp(x) + 1)\n", + "\n", + "\n", + "# We set W = 1e4 to investigate the wide-band limit:\n", + "\n", + "bath_L = LorentzianBathParameters(W=10**4, lead=\"L\")\n", + "bath_R = LorentzianBathParameters(W=10**4, lead=\"R\")" + ] + }, + { + "cell_type": "markdown", + "id": "0acaafc6", + "metadata": {}, + "source": [ + "## Emission and absorption by the leads\n", + "\n", + "Next let's plot the emission and absorption by the leads." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a10714ec", + "metadata": {}, + "outputs": [], + "source": [ + "w_list = np.linspace(-2, 2, 100)\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 7))\n", + "\n", + "# Left lead emission and absorption\n", + "\n", + "gam_L_in = bath_L.J(w_list) * bath_L.fF(w_list, sign=1.0)\n", + "gam_L_out = bath_L.J(w_list) * bath_L.fF(w_list, sign=-1.0)\n", + "\n", + "ax.plot(\n", + " w_list, gam_L_in,\n", + " \"b--\", linewidth=3,\n", + " label=r\"S_L(w) input (absorption)\",\n", + ")\n", + "ax.plot(\n", + " w_list, gam_L_out,\n", + " \"r--\", linewidth=3,\n", + " label=r\"S_L(w) output (emission)\",\n", + ")\n", + "\n", + "# Right lead emission and absorption\n", + "\n", + "gam_R_in = bath_R.J(w_list) * bath_R.fF(w_list, sign=1.0)\n", + "gam_R_out = bath_R.J(w_list) * bath_R.fF(w_list, sign=-1.0)\n", + "\n", + "ax.plot(\n", + " w_list, gam_R_in,\n", + " \"b\", linewidth=3,\n", + " label=r\"S_R(w) input (absorption)\",\n", + ")\n", + "ax.plot(\n", + " w_list, gam_R_out,\n", + " \"r\", linewidth=3,\n", + " label=r\"S_R(w) output (emission)\",\n", + ")\n", + "\n", + "ax.set_xlabel(\"w\")\n", + "ax.set_ylabel(r\"$S(\\omega)$\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "aaf83d87", + "metadata": {}, + "source": [ + "## Below we give one example data set from Paper\n", + "\n", + "Here we just give one example of the current as a function of bias voltage, but in general one can try different cut-offs of the bosonic Fock space and the expansion of the correlation functions until convergence is found.\n", + "\n", + "One note: for very large problems, this can be slow." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9c84981", + "metadata": {}, + "outputs": [], + "source": [ + "def steady_state_pade_for_theta(sys_p, bath_L, bath_R, theta, Nk, Nc, Nbos):\n", + " \"\"\" Return the steady state current using the Pade approximation. \"\"\"\n", + "\n", + " sys_p = sys_p.replace(Nbos=Nbos)\n", + " bath_L = bath_L.replace(theta=theta)\n", + " bath_R = bath_R.replace(theta=theta)\n", + "\n", + " bathL = LorentzianPadeBath(\n", + " sys_p.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", + " Nk, tag=\"L\",\n", + " )\n", + " bathR = LorentzianPadeBath(\n", + " sys_p.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", + " Nk, tag=\"R\",\n", + " )\n", + "\n", + " solver_pade = HEOMSolver(\n", + " sys_p.H, [bathL, bathR], max_depth=2, options=options,\n", + " )\n", + " rho_ss_pade, ado_ss_pade = solver_pade.steady_state()\n", + " current = state_current(ado_ss_pade, bath_tag=\"R\")\n", + "\n", + " return np.real(2.434e-4 * 1e6 * current)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3996e24c", + "metadata": {}, + "outputs": [], + "source": [ + "# Parameters:\n", + "\n", + "Nk = 6\n", + "Nc = 2\n", + "Nbos = 2 # Use Nbos = 16 for more accurate results\n", + "\n", + "thetas = np.linspace(0, 2, 30)\n", + "\n", + "# Progress bar:\n", + "\n", + "progress = IntProgress(min=0, max=len(thetas))\n", + "display(progress)\n", + "\n", + "currents = []\n", + "\n", + "for theta in thetas:\n", + " currents.append(steady_state_pade_for_theta(\n", + " sys_p, bath_L, bath_R, theta,\n", + " Nk=Nk, Nc=Nc, Nbos=Nbos,\n", + " ))\n", + " progress.value += 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66cc7a25", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(12, 10))\n", + "\n", + "ax.plot(\n", + " thetas, currents,\n", + " color=\"green\", linestyle='-', linewidth=3,\n", + " label=f\"Nk = {5}, max_depth = {Nc}, Nbos = {Nbos}\",\n", + ")\n", + "\n", + "ax.set_yticks([0, 0.5, 1])\n", + "ax.set_yticklabels([0, 0.5, 1])\n", + "\n", + "ax.locator_params(axis='y', nbins=4)\n", + "ax.locator_params(axis='x', nbins=4)\n", + "\n", + "ax.set_xlabel(r\"Bias voltage $\\Delta \\mu$ ($V$)\", fontsize=30)\n", + "ax.set_ylabel(r\"Current ($\\mu A$)\", fontsize=30)\n", + "ax.legend(loc=4);" + ] + }, + { + "cell_type": "markdown", + "id": "5d0ea686", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "34a54211", + "metadata": {}, + "outputs": [], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "d8d53b43", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc978722", + "metadata": {}, + "outputs": [], + "source": [ + "assert 1 == 1" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-index.ipynb b/tutorials-v5/heom/heom-index.ipynb new file mode 100644 index 00000000..f10432a8 --- /dev/null +++ b/tutorials-v5/heom/heom-index.ipynb @@ -0,0 +1,56 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "262d3c3b", + "metadata": {}, + "source": [ + "# Hierarchical Equation of Motion Examples\n", + "\n", + "The \"hierarchical equations of motion\" (HEOM) method is a powerful numerical approach to solve the dynamics and steady-state of a quantum system coupled to a non-Markovian and non-perturbative environment. Originally developed in the context of physical chemistry, it has also been extended and applied to problems in solid-state physics, optics, single-molecule electronics, and biological physics.\n", + "\n", + "QuTiP's implementation of the HEOM is described in detail in https://arxiv.org/abs/2010.10806.\n", + "\n", + "This collection of examples from the paper illustrates how to use QuTiP's HEOM to model and investigate the dynamics of a variety of systems coupled to bosonic or fermionic baths.\n", + "\n", + "## Overview of the notebooks\n", + "\n", + "\n", + "\n", + "* [Example 1a: Spin-Bath model (introduction)](./heom-1a-spin-bath-model-basic.ipynb)\n", + "\n", + "* [Example 1b: Spin-Bath model (very strong coupling)](./heom-1b-spin-bath-model-very-strong-coupling.ipynb)\n", + "\n", + "* [Example 1c: Spin-Bath model (underdamped case)](./heom-1c-spin-bath-model-underdamped-sd.ipynb)\n", + "\n", + "* [Example 1d: Spin-Bath model, fitting of spectrum and correlation functions](./heom-1d-spin-bath-model-ohmic-fitting.ipynb)\n", + "\n", + "* [Example 1e: Spin-Bath model (pure dephasing)](./heom-1e-spin-bath-model-pure-dephasing.ipynb)\n", + "\n", + "* [Example 2: Dynamics in Fenna-Mathews-Olsen complex (FMO)](./heom-2-fmo-example.ipynb)\n", + "\n", + "* [Example 3: Quantum Heat Transport](./heom-3-quantum-heat-transport.ipynb)\n", + "\n", + "* [Example 4: Dynamical decoupling of a non-Markovian environment](./heom-4-dynamical-decoupling.ipynb)\n", + "\n", + "* [Example 5a: Fermionic single impurity model](./heom-5a-fermions-single-impurity-model.ipynb)\n", + "\n", + "* [Example 5b: Discrete boson coupled to an impurity + fermionic leads](./heom-5b-fermions-discrete-boson-model.ipynb)\n", + "\n", + "" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 23acc157888aa55a1354d091772fd480b46b803f Mon Sep 17 00:00:00 2001 From: mcditooss Date: Mon, 23 Dec 2024 12:47:51 +0100 Subject: [PATCH 16/44] old pc backup --- ...eom-1d-spin-bath-model-ohmic-fitting.ipynb | 1249 ++++++++++++----- ...om-5a-fermions-single-impurity-model.ipynb | 231 ++- 2 files changed, 1091 insertions(+), 389 deletions(-) diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb index 7e827f17..8072ae68 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb @@ -99,7 +99,7 @@ "source": [ "## System and bath definition\n", "\n", - "And let us set up the system Hamiltonian, bath and system measurement operators:" + "Let us set up the system Hamiltonian, bath and system measurement operators:" ] }, { @@ -234,9 +234,11 @@ "def ohmic_power_spectrum(w, alpha, wc, beta):\n", " \"\"\"The Ohmic bath power spectrum as a function of w\n", " (and the bath parameters).\n", + " It is obtained naively using the Fluctuation-Dissipation Theorem\n", + " but, this fails at w=0 where the limit should be taken properly\n", " \"\"\"\n", " bose = (1 / (np.e ** (w * beta) - 1)) + 1\n", - " return w * alpha * np.e ** (-abs(w) / wc) * bose * 2" + " return w * alpha * np.e ** (-abs(w) / wc) * 2*bose " ] }, { @@ -328,7 +330,7 @@ "metadata": {}, "outputs": [], "source": [ - "w = np.linspace(1e-3, 15, 20000)\n", + "w = np.linspace(0, 15, 20000)\n", "J = ohmic_spectral_density(w, alpha, wc)" ] }, @@ -394,6 +396,15 @@ "sd_env=BosonicEnvironment.from_spectral_density(J=J,wlist=w,T=T)" ] }, + { + "cell_type": "markdown", + "id": "0d571deb", + "metadata": {}, + "source": [ + "Now our bosonic environment can compute the Power Spectrum of the spectral \n", + "density provided" + ] + }, { "cell_type": "code", "execution_count": 14, @@ -412,7 +423,17 @@ } ], "source": [ - "np.allclose(sd_env.power_spectrum(w),ohmic_power_spectrum(w,alpha,wc,1/T))" + "# Here we avoid w=0\n", + "np.allclose(sd_env.power_spectrum(w[1:]),ohmic_power_spectrum(w[1:],alpha,wc,1/T))" + ] + }, + { + "cell_type": "markdown", + "id": "8ff57d78", + "metadata": {}, + "source": [ + "Specifying the Temperature also gives the `BosonicEnvironment` access to the \n", + "correlation function" ] }, { @@ -433,7 +454,7 @@ }, { "data": { - "image/png": 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", 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GyJEj6dixI2azmYyMjBpvQ7nyArlly5YVisB9+/aRkZHBSy+9xJVXXkn79u1r3DtW3f7ZvXs3sbGxtc7Z2DS5Ymj79u3O7l1XoigKBSPeZKF9NB9szaTUXvlfm0I0RSUlJaSlpZGWlkZCQgIPPPAA+fn5jB49GijrHXFzc2PSpEns3r2b3377jQceeICJEycSHBxMUlISs2fPZtOmTRw5coRVq1axf/9+58H+kUceYfHixSxYsIADBw7wxhtv8M0331z0Jc1Q1su8ZcsW7r33Xnbu3Mm+ffuYP39+hQPk2dtV/qrtATQqKop+/fpxxx13YLPZuO6662qd9ZFHHuHll19m6dKlJCYm8thjjxEfH8/06dNrvazaaNu2LRMmTOC2227jm2++ISkpiS1btvDyyy+fNyi4OpGRkezcuZPExEQyMjKqvF3AsGHD2LNnT5W9Q2d76KGHeO+990hJSWHChAkEBARw3XXXsX79epKSkli7di3Tp0/n+PHjAERHR7NkyRISEhLYvHkzEyZMqFHvYE1FRERgMpl4++23OXz4MD/88APPPfdcjeaNjIwkKSmJ+Ph4MjIyKCkpcX62fv16hg4dWmc5G6oGVQzl5+cTHx9PfHw8gPObW35Odvbs2dx2223O9nPnzuW7777jwIED7Nmzh9mzZ/P1119z//33axH/gkZ0CsXfw8SpvBI2HKz9XxRCNFYrVqwgNDSU0NBQrrjiCrZs2cL//vc/Bg0aBJSNKVm5ciVZWVn06tWLsWPHMmTIEOdtONzd3dm3bx833ngjbdu25a677uL+++/n7rvvBuD666/nzTff5NVXX6Vjx4689957LFq0yLn8i9G2bVtWrVrFjh076N27N3379uX77793Xnl17naVvwYMGFDrdU2YMIEdO3YwZsyYizoAT5s2jYceeoiHHnqIzp07s2LFCn744Yd6uQ3JokWLuO2223jooYdo164df//739m8eXOthidMnTqVdu3a0bNnTwIDA9mwYUOl7Tp37kzPnj358ssvL7jMUaNGERkZyQsvvIC7uzvr1q0jIiKCMWPGEBMTw+23305RUZHz7MGHH35IdnY2sbGxTJw4kWnTphEUFFTjbbiQwMBAFi9ezP/+9z86dOjASy+9xGuvvVajeW+88UaGDx/O4MGDCQwMdF5Kv2nTJnJychg7dmyd5WyoFPViTrxqZM2aNRUucy03adIkFi9ezOTJk0lOTnbeY+KVV15h4cKFpKSkYLFY6NixI7Nnz2bkyJE1Xmdubi4+Pj7k5OTUyymz577dSuaWrxkVmsPfHph32dcnmo7i4mKSkpKIioqqchCpEI3d8uXLefjhh9m9ezc6XYPqD6hzN910E7GxsfzrX//SOspFq+73Wm2O3w1j9OQZgwYNqnbQXPmNpMo9+uijPProo5c5Vd26vo2BzjvmYc9QKDr9DBZfGdgmhBB1pfzePykpKZf94hhXVlJSQteuXXnwwQe1juISmnZZ7II6dezKfiUKvaKyb93/tI4jhBCNzvTp05t0IQRlFws98cQTdTquqSGTYsjFKIrCqeZDAFD3LdM4jRBCCNH4STHkgkKvKBvMFlOwheLCPI3TCCGEEI2bFEMuKKrjFaQSiEWxcmDTj1rHEUIIIRo1KYZckKLTcThgEAC2PVIMCSGEEJeTFEMuyhhzLQ5VoTin9s/fEUIIIUTNNahL65uSmD5D6bV6IZklHvyWUUBUQM2fNSOEEEKImpOeIRfl5eFB28gIANYkSu+QEBcrOTkZRVGcd66vicWLF+Pr66tJjsTEREJCQsjLq9+LJy5mP11O6enpBAYGkpKSonUU0QRIMeTCBrUru+Hipv1pGicRQlvHjh3jjjvuICwsDJPJRMuWLZk+fTqZmZkXnDc8PJzU1FQ6depU4/WNGzeO/fv3X0rki/b4449z33334eXlBZTdeV9RFOfL39+fq6++uspHTlxOgwYNcuYwm820bduWF198sUYPSa3O5MmTuf766ytMCwoKYuLEiTz11FOXtGwhakKKIRd2ZaiNpaZnefHIOOw2m9ZxhNDE4cOH6dmzJ/v37+fzzz/n4MGDLFiwgNWrV9O3b1+ysrKqnNdqtaLX6wkJCanwTLALsVgsdfpcqZo6fvw4P/zwA1OmTDnvs8TERFJTU1mzZg2BgYFce+21NX5qeV2aOnUqqampJCYmMm3aNJ544okaPyPrXHa7HYej6odST5kyhU8//bRGD1YV4lJIMeTC2kZF0UE5QgA5JO3eqHUcITRx3333YTKZWLVqFQMHDiQiIoIRI0bwyy+/kJKSwuOPP+5sGxkZyfPPP8/kyZPx8fFh6tSplZ7+KX8IqcViYfDgwXz00UcoisLp06eB80+TPf3003Tr1o0lS5YQGRmJj48P//jHPyqcylqxYgUDBgzA19cXf39/Ro0axaFDh2q1rV9++SVdu3alRYsW530WFBRESEgInTt35oknniAnJ4fNmzc7P9+7dy8jR47E09OT4OBgJk6cSEbGXw98rot8UPbQ25CQECIjI7n//vsZMmQI3333HQBvvPEGnTt3xsPDg/DwcO69917y8/Od85bv12XLltGhQwfMZjNTpkzho48+4vvvv3f2OpU/X7Jz586EhITw7bff1jqnELUhxZALMxhNHHDvBkD27l+0DSMaL2tB1a/S4lq0LapZ21rIyspi5cqV3Hvvvec9NiAkJIQJEyawdOnSCs8sfPXVV+nUqRNxcXH8+9//Pm+ZycnJjB07luuvv574+HjuvvvuCgVVVQ4dOsR3333HsmXLWLZsGWvXruWll15yfl5QUMDMmTPZsmULq1evRqfTccMNN1Tb83GudevW0bNnz2rbFBYWsmjRIgCMRiMAqampDBw4kG7durF161ZWrFjByZMnufnmm+s0X2UsFgulpaUA6HQ63nrrLXbv3s1HH33Er7/+et7zIQsLC5kzZw7//e9/2bNnD2+99RY333wzw4cPJzU1ldTUVPr16+ds37t3b9avX39JGYW4ELmazMUVhvWFQ5swn9h84cZCXIwXw6r+rM1QmHDWM/JejYbSwsrbthwAU/7vr/dzO0NhJWN6ns6pcbQDBw6gqioxMTGVfh4TE0N2djanTp1ynta6+uqrefjhh51tkpOTK8yzYMEC2rVrx6uvvgpAu3bt2L17Ny+88EK1WRwOB4sXL3aO5Zk4cSKrV692znfjjTdWaP/BBx8QFBTE3r17azxeKTk5mR49elT6WXlvUWFhIaqq0qNHD4YMKXt0z/z58+nevTsvvviis/2HH35IeHg4+/fvp23btnWS72wOh4NVq1axcuVKZsyYAeD8FyAqKornnnuOf/7zn8ybN885vbS0lHnz5tG1a1fnNIvFQklJCSEhIeetp3nz5mzfvr3W+YSoDekZcnH+MVcBEFG4G8clDlIUorEp7xFSFMU57UI9K4mJifTq1avCtN69e19wXZGRkc5CCCA0NLTCmJ1Dhw4xfvx4WrVqhbe3N1FRUQAcPXr0whtyRlFREW5ubpV+tn79erZt28bnn39Oy5YtWbx4sbNnKC4ujt9++w1PT0/nq3379s5cdZUPYN68eXh6euLm5sbf//53br31Vucg599++41rrrmG5s2b4+XlxW233UZmZiYFBX/1CJpMJrp06VLj9VksFgoLqyjAhagj0jPk4qK79KPoRxO+Sj7JB3YQ2b671pFEY/OvE1V/pugrvn/kYDVtz/nbasaui890RnR0NIqisHfv3vOuNgLYt28ffn5+BAQEOKd5eFR/Ty5VVSsUT+XTLqS88CinKEqFU0yjR48mPDyc999/n7CwMBwOB506dcJqtV5w2eUCAgKqHCwcFRWFr68vbdu2pbi4mBtuuIHdu3djNptxOByMHj2al19++bz5QkND6ywfwIQJE3j88ccxm82EhYWh15f9jBw5coSRI0dyzz338Nxzz9GsWTN+//137rjjDudpNCgrbs7d/9XJysoiMDCwVhmFqC3pGXJxRpOZZHPZX3ipu9ZomkU0UiaPql9Gt1q0tdSsbS34+/tzzTXXMG/ePIqKKo5JSktL49NPP2XcuHG1Ori2b9+eLVu2VJi2devWWuU6V2ZmJgkJCTzxxBMMGTLEefqutmJjY9m7d+8F202cOBGHw+E8/dS9e3f27NlDZGQk0dHRFV4eHh51lg/Ax8eH6OhowsPDnYUQlO1Dm83G66+/Tp8+fWjbti0nTlRTaJ/FZDJVeXn+7t27iY2NvaisQtSUFEMNQEbYIFbZe7Ar113rKELUu3feeYeSkhKGDRvGunXrOHbsGCtWrHCejrnQWJ9z3X333ezbt49Zs2axf/9+vvzySxYvXgxQq6LqbH5+fvj7+7Nw4UIOHjzIr7/+ysyZM2u9nGHDhrFp06YL3rdHp9MxY8YMXnrpJQoLC7nvvvvIysrilltu4c8//+Tw4cOsWrWK22+/HbvdXmf5qtO6dWtsNhtvv/02hw8fZsmSJSxYsKBG80ZGRrJz504SExPJyMhw9iQVFhYSFxfH0KFD6zSrEOeSYqgBcPSbxl2lD/F5dnutowhR79q0acPWrVtp3bo148aNo3Xr1tx1110MHjyYTZs20axZs1otLyoqiq+++opvvvmGLl26MH/+fOfVZGaz+aIy6nQ6vvjiC+Li4ujUqRMPPvigc4B2bYwcORKj0cgvv1z46tHbb7+d0tJS3nnnHcLCwtiwYQN2u51hw4bRqVMnpk+fjo+PDzqdrs7yVadbt2688cYbvPzyy3Tq1IlPP/2UOXPm1GjeqVOn0q5dO3r27ElgYKDzhpLff/89ERERXHnllXWaVYhzKWpNTpY3Ybm5ufj4+JCTk4O3t7cmGXIKS+n67CoAtv37Gpp5mDTJIRq24uJikpKSiIqKqnKQblP1wgsvsGDBAo4dO6Z1FObNm8f333/PypUrtY6iud69ezNjxgzGjx+vdRThoqr7vVab47cMoG4AfNyNtA5wpyTzCHsTExjQveuFZxJCVGnevHn06tULf39/NmzYwKuvvsr999+vdSwA7rrrLrKzs8nLy6tw9VpTk56eztixY7nlllu0jiKaACmGGoinTUu40vw1f8RNhu5vah1HiAbtwIEDPP/882RlZREREcFDDz3E7NmztY4FgMFgqNFNIBu7oKCg827YKMTlIsVQA2EK7QBZX+ORsVPrKEI0eP/5z3/4z3/+o3UMIYSLkAHUDURgu74ARBQnYrdf2u3zhRBCCPEXKYYaiIj2PbCqBnyUAo4c3K11HCGEEKLRkGKogTCY3Dhiag3AyX2bNE4jhBBCNB5SDDUgp33LHqboOL5N4yRCCCFE4yHFUAOiDy97mrXv6Ut/5pMQQgghykgx1IAEdBjEQtu1zCsejk0GUQshhBB1QoqhBqRFqw68pZ/E/5X24OCpfK3jCCHOsmbNGhRF4fTp01pHqUBRFL777jutY7iMzMxMgoKCSE5O1jrKeVz1Z0grvXr14ptvvqmXdUkx1IDodAodw8puKb7zeI7GaYSoH5MnT0ZRFOfL39+f4cOHs3Ona91zq1+/fqSmpuLj41Oj9uduV/lr+PDhdZorNTWVESNG1OkyXc3kyZO5/vrra9R2zpw5jB49msjISACSk5NRFIX4+PjLlq+mavszVN+efvpp58+pXq8nPDycO++8k1OnTl3SchcvXoyvr+950//973/z2GOP4XBc/jMhUgw1MD1CDPTT7aY4YZXWUYSoN8OHDyc1NZXU1FRWr16NwWBg1KhRWseqwGQyERISgqIoNZ7n7O0qf33++ed1miskJKTaB9CWPyG+KSgqKuKDDz7gzjvv1DpKpS7mZ+hiXMr3vGPHjqSmpnL06FHmz5/Pjz/+yG233XZZslx77bXk5OTUy3P6pBhqYAbrd/CZ6UX6HpmvdRQh6o3ZbCYkJISQkBC6devGrFmzOHbsWIW/SHft2sXVV1+NxWLB39+fu+66i/z8v04nr1mzht69e+Ph4YGvry/9+/fnyJEjzs/nz59P69atMZlMtGvXjiVLllTIoCgK//3vf7nhhhtwd3enTZs2/PDDDxWWf+4pjg0bNjBw4EDc3d3x8/Nj2LBhZGdnV7pd5S8/P78ardPhcNCiRQsWLFhQIee2bdtQFIXDhw87l1F+mqy8F+TLL79k0KBBuLm58cknn+BwOHj22Wdp0aIFZrOZbt26sWLFCucyy+f75ptvGDx4MO7u7nTt2pVNm/66zUf5X/fLli2jXbt2uLu7M3bsWAoKCvjoo4+IjIzEz8+PBx54ALvd7pzParXy6KOP0rx5czw8PLjiiitYs2bNectduXIlMTExeHp6OotIKOut+Oijj/j++++dvRZnz3+2n376CYPBQN++fSv9/Ozv48qVK4mNjcVisXD11VeTnp7OTz/9RExMDN7e3txyyy0UFhY651uxYgUDBgzA19cXf39/Ro0axaFDhyose+PGjXTr1g03Nzd69uzJd999V6FX6tyfoQttO8CWLVu45pprCAgIwMfHh4EDB7JtW8UrjhVFYcGCBVx33XV4eHjw/PPPEx0dzWuvvVah3e7du9HpdOflPpvBYCAkJITmzZszatQopk2bxqpVqygqKrrgPqjq52/KlCnk5OQ4v39PP/00AHq9npEjR9b5HwiVUkW1cnJyVEDNycnROoqqqqp65MBuVX3KWy150k8tLS7UOo5oQIqKitS9e/eqRUVFqqqqqsPhUAusBZq8HA5HjXNPmjRJve6665zv8/Ly1LvvvluNjo5W7Xa7qqqqWlBQoIaFhaljxoxRd+3apa5evVqNiopSJ02apKqqqpaWlqo+Pj7qww8/rB48eFDdu3evunjxYvXIkSOqqqrqN998oxqNRvXdd99VExMT1ddff13V6/Xqr7/+6lwvoLZo0UL97LPP1AMHDqjTpk1TPT091czMTFVVVfW3335TATU7O1tVVVXdvn27ajab1X/+859qfHy8unv3bvXtt99WT506Vel2VeZC63zooYfUAQMGVJjnoYceUvv27VthGd9++62qqqqalJSkAmpkZKT69ddfq4cPH1ZTUlLUN954Q/X29lY///xzdd++feqjjz6qGo1Gdf/+/RXma9++vbps2TI1MTFRHTt2rNqyZUu1tLRUVVVVXbRokWo0GtVrrrlG3bZtm7p27VrV399fHTp0qHrzzTere/bsUX/88UfVZDKpX3zxhTPf+PHj1X79+qnr1q1TDx48qL766quq2Wx2rrt8uX/729/ULVu2qHFxcWpMTIw6fvx458/DzTffrA4fPlxNTU1VU1NT1ZKSkkr35/Tp09Xhw4dXmFa+bdu3b6/wfezTp4/6+++/q9u2bVOjo6PVgQMHqkOHDlW3bdumrlu3TvX391dfeukl53K++uor9euvv1b379+vbt++XR09erTauXNn589obm6u2qxZM/XWW29V9+zZoy5fvlxt27Ztpesu/xm60LarqqquXr1aXbJkibp3715179696h133KEGBwerubm5FX4GgoKC1A8++EA9dOiQmpycrL7wwgtqhw4dKuyLBx98UL3qqqsq3XeqqqpPPfWU2rVr1wrTXn/9dRVQc3NzL7gPKvv5O3LkiDp37lzV29vb+f3Ly8tzLn/evHlqZGRklZnO/b12ttocv6UYugBXK4bsNrt6+slQVX3KWz2843et44gG5NxfGgXWArXT4k6avAqsBTXOPWnSJFWv16seHh6qh4eHCqihoaFqXFycs83ChQtVPz8/NT8/3znt//7v/1SdTqempaWpmZmZKqCuWbOm0nX069dPnTp1aoVpN910kzpy5Ejne0B94oknnO/z8/NVRVHUn376SVXV8w9kt9xyi9q/f/8ab1f569lnn63xOrdt26YqiqImJyerqqqqdrtdbd68ufruu+9WWMa5xdDcuXMrZAkLC1NfeOGFCtN69eql3nvvvRXm++9//+v8fM+ePSqgJiQkqKpaduAG1IMHDzrb3H333aq7u3uFg9uwYcPUu+++W1VVVT148KCqKIqakpJSYd1DhgxRZ8+eXeVy3333XTU4OLjCvrxQYamqqnrdddept99+e4VpVRVDv/zyi7PNnDlzVEA9dOhQhW0bNmxYletKT09XAXXXrl2qqqrq/PnzVX9//woH7ffff/+CxdCFtv1cNptN9fLyUn/88UfnNECdMWNGhXYnTpxQ9Xq9unnzZlVVVdVqtaqBgYHq4sWLq1z2ucVQQkKCGh0drfbu3btG+6Cqn79FixapPj4+lS7j+++/V3U6nbOgOlddFUNymqyB0el1HDNHA5BxcKvGaYSoH4MHDyY+Pp74+Hg2b97M0KFDGTFihPM0V0JCAl27dsXDw8M5T//+/XE4HCQmJtKsWTMmT57MsGHDGD16NG+++WaFUw0JCQn079+/wjr79+9PQkJChWldunRxfu3h4YGXlxfp6emVZo6Pj2fIkCE13q7y13333VfjdcbGxtK+fXvnaYS1a9eSnp7OzTffXO16e/bs6fw6NzeXEydO1Hr7Q0NDASpsv7u7O61bt3a+Dw4OJjIyEk9PzwrTyufZtm0bqqrStm1bPD09na+1a9dWOL1y7nJDQ0Or3O/VKSoqws3NrUZtz97W4OBg3N3dadWqVaXbAXDo0CHGjx9Pq1at8Pb2JioqCoCjR48CkJiYSJcuXSqsv3fv3hfMcaFtT09P55577qFt27b4+Pjg4+NDfn6+c73lzv6ely/n2muv5cMPPwRg2bJlFBcXc9NNN1WbZ9euXXh6emKxWOjQoQPh4eF8+umnNdoHVWWpjsViweFwUFJSUuN5LoY8tb4ByvfrACd3YD+xQ+soogGzGCxsHr9Zs3XXhoeHB9HR0c73PXr0wMfHh/fff5/nn38eVVWrHHRaPn3RokVMmzaNFStWsHTpUp544gl+/vln+vTpU6FducqWaTQaz1t2VVe6WCwX3sZzt6syF1rnhAkT+Oyzz3jsscf47LPPGDZsGAEBARdc77lqu/3ln52dpbKs1eV3OBzo9Xri4uLQ6/UV2p1dQFW2DFVVK9+4agQEBFQYs1Wdc7f1Qt+H0aNHEx4ezvvvv09YWBgOh4NOnTphtVqByvdnTbbhQts+efJkTp06xdy5c2nZsiVms5m+ffs611uusu/5nXfeycSJE/nPf/7DokWLGDduHO7u7tXmadeuHT/88AN6vZ6wsLAKg/MvtA+qy1KVrKws3N3da/T/6VJIz1ADZGzRFQCfnIQLtBSiaoqi4G501+R1qVfLKIqCTqejqKgIgA4dOhAfH09BQYGzzYYNG9DpdLRt29Y5LTY2ltmzZ7Nx40Y6derEZ599BkBMTAy///57hXVs3LiRmJiYi87YpUsXVq9efdHz19T48ePZtWsXcXFxfPXVV0yYMKFW83t7exMWFlbn218TsbGx2O120tPTiY6OrvAKCQmp8XJMJlOFQdnVrW/v3r2XErlSmZmZJCQk8MQTTzBkyBBiYmLOK7rat2/Pzp07K/RwbN166b3769evZ9q0aYwcOZKOHTtiNpvJyMio0bwjR47Ew8OD+fPn89NPP3H77bdfcB6TyUR0dDRRUVEVCqGa7IPqllnV92/37t107969Rsu5FFIMNUBBbcq6ViOsh7HZbBqnEeLyKykpIS0tjbS0NBISEnjggQfIz89n9OjRQFnviJubG5MmTWL37t389ttvPPDAA0ycOJHg4GCSkpKYPXs2mzZt4siRI6xatYr9+/c7D/aPPPIIixcvZsGCBRw4cIA33niDb775hocffviiM8+ePZstW7Zw7733snPnTvbt28f8+fMrHKjO3q7yV00PZOWioqLo168fd9xxBzabjeuuu67WWR955BFefvllli5dSmJiIo899hjx8fFMnz691suqjbZt2zJhwgRuu+02vvnmG5KSktiyZQsvv/wyy5cvr/FyIiMj2blzJ4mJiWRkZFR5ufawYcPYs2dPjQ/SNeXn54e/vz8LFy7k4MGD/Prrr8ycObNCm/Hjx+NwOLjrrrtISEhg5cqVzqu5LuWPg+joaJYsWUJCQgKbN29mwoQJNe5F0ev1TJ48mdmzZxMdHV3tVXYXUpN9UJXIyEjy8/NZvXo1GRkZFa7SW79+PUOHDr3oXDUlxVAD1Dy6C0+rd3GL9XEOnSq48AxCNHArVqwgNDSU0NBQrrjiCrZs2cL//vc/Bg0aBJSNq1i5ciVZWVn06tWLsWPHMmTIEN555x3n5/v27ePGG2+kbdu23HXXXdx///3cfffdAFx//fW8+eabvPrqq3Ts2JH33nuPRYsWOZd/Mdq2bcuqVavYsWMHvXv3pm/fvnz//fcYDH+NTjh7u8pfAwYMqPW6JkyYwI4dOxgzZsxFnU6YNm0aDz30EA899BCdO3dmxYoV/PDDD7Rp06bWy6qtRYsWcdttt/HQQw/Rrl07/v73v7N582bCw8NrvIypU6fSrl07evbsSWBgIBs2bKi0XefOnenZsydffvllXcUHQKfT8cUXXxAXF0enTp148MEHefXVVyu08fb25scffyQ+Pp5u3brx+OOP8+STTwLUeBxTZT788EOys7OJjY1l4sSJTJs2jaCgoBrPf8cdd2C1WmvUK1SdmuyDqvTr14977rmHcePGERgYyCuvvAJASkoKGzduZMqUKZeUrSYU9WJOvDYhubm5+Pj4kJOTg7e3t9ZxnG5asJEtydm8cXNXxnRvoXUc0QAUFxeTlJREVFTUJf3yFaIhW758OQ8//LDznjpa+vTTT5332LncY2KqsmHDBgYNGsTx48cJDg7WJENVHnnkEXJycli4cGGVbar7vVab47cMoG6gOob5sCU5mz0nchlz+U+nCiFEozBy5EgOHDhASkpKrXqf6sLHH39Mq1ataN68OTt27GDWrFncfPPNmhRCJSUlHDt2jH//+9/cfPPNLlcIAQQFBV3SqerakGKogerezEqp/hfC95uAl7WOI4QQDcblHgtVlbS0NJ588knS0tIIDQ3lpptu4oUXXtAky+eff84dd9xBt27dzrvbuqt45JFH6m1dcprsAlz1NFlS/Fqivvs7mfjQ7MlkFI27e4Xrk9NkQojGpq5Ok8kRtIFq0b4HdlXBnxxOHEvWOo4QQgjRYEkx1EAZ3Tw5big7352a+KfGaYQQQoiGS4qhBizLsx0ARce2a5xECCGEaLikGGrA7MGdAXDL3KNxEiGEEKLhkmKoAfOO6gFASOEBjZMIIYQQDZcUQw1Yi5iyx3I0V09yKrN2t/AXQgghRBkphhowd98gZlheJLbkPXZnVP7kbCGauuTkZBRFIT4+vsbzLF68GF9fX01yJCYmEhISQl5eXp2uvy5cjv3SUJWUlBAREUFcXJzWUUQdkGKogXNE9CMHT/aeyNU6ihCXzbFjx7jjjjsICwvDZDLRsmVLpk+fTmZm5gXnDQ8PJzU1lU6dOtV4fePGjWP//v2XEvmiPf7449x33314eXkBsGbNGhRF4fTp05rkOZuW+6UmJk+ejKIoKIqC0WikVatWPPzwwxQUXNozHJ9++mm6detWYZrZbObhhx9m1qxZl7Rs4RqkGGrgOoaV3Uhqz4kcjZMIcXkcPnyYnj17sn//fj7//HMOHjzIggULWL16NX379iUrK6vKea1WK3q9npCQkAoPSL0Qi8VSq4dd1pXjx4/zww8/1MuDKS9Gfe2Xqp46XxPDhw8nNTWVw4cP8/zzzzNv3ryLfqSDqqrYbLYqP58wYQLr168nISHhYuMKFyHFUAMX61vEbMOnDE96SesoQlwW9913HyaTiVWrVjFw4EAiIiIYMWIEv/zyCykpKTz++OPOtpGRkTz//PNMnjwZHx8fpk6dWunpqfInslssFgYPHsxHH31Uoffl3NNB5T0DS5YsITIyEh8fH/7xj39UOJW1YsUKBgwYgK+vL/7+/owaNYpDhw7Valu//PJLunbtSosWVT98uTzbsmXLaNeuHe7u7owdO5aCggI++ugjIiMj8fPz44EHHsButzvn++STT+jZsydeXl6EhIQwfvx40tPTKyxbi/1S/v358ssvGTRoEG5ubixcuBBvb2+++uqrCvl+/PFHPDw8qj2FaDabCQkJITw8nPHjxzNhwgS+++67Gu2D8l64lStX0rNnT8xmM0uWLOGZZ55hx44dzl6nxYsXA+Dv70+/fv34/PPPq8wjGgYphhq4doEW7jb8HyNKV5Obn691HNEAOQoLq36VlNS8bXFxjdrWRlZWFitXruTee+8972GWISEhTJgwgaVLl3L2U4VeffVVOnXqRFxcHP/+97/PW2ZycjJjx47l+uuvJz4+nrvvvrtCQVWVQ4cO8d1337Fs2TKWLVvG2rVreemlv/4IKSgoYObMmWzZsoXVq1ej0+m44YYbcDhqPp5v3bp19OzZ84LtCgsLeeutt/jiiy9YsWIFa9asYcyYMSxfvpzly5ezZMkSFi5cWKGYsFqtPPfcc+zYsYPvvvuOpKQkJk+e7DL7ZdasWUybNo2EhARuuOEG/vGPf7Bo0aIKbRYtWsTYsWOdpxBrwmKxOHuaLrQPyj366KPMmTOHhIQEhg4dykMPPUTHjh1JTU0lNTWVcePGOdv27t2b9evX1ziPcE3yoNYGzie0Fbl44q3kk5CwjS69rtI6kmhgErv3qPIzj4FXEfHee873+/sPQC0qqrSte69etFzysfP9wSF/w56dfV67mH01P6Vw4MABVFUlJiam0s9jYmLIzs7m1KlTztM3V199dYXTIsnJyRXmWbBgAe3atePVV18FoF27duzevfuCD8x0OBwsXrzYeSCeOHEiq1evds534403Vmj/wQcfEBQUxN69e2s8Xik5OZkePar+fpQrLS1l/vz5tG7dGoCxY8eyZMkSTp48iaenJx06dGDw4MH89ttvzgP37bff7py/VatWvPXWW/Tu3Zv8/Hw8PT013y8zZsxgzJgxzvd33nkn/fr148SJE4SFhZGRkcGyZcv4+eefL7h/yv3555989tlnDBkypEb7oNyzzz7LNddc43zv6emJwWAgJCTkvHU0b978vJ8x0fBIz1BDpyiccIsG4HSSXNUgmpbyHiFFUZzTLtSzkpiYSK9evSpM69279wXXFRkZWaFHIjQ0tMIplkOHDjF+/HhatWqFt7c3UVFRABw9evTCG3JGUVFRjR6i6+7u7iyEAIKDg4mMjKxwQA8ODq6Qb/v27Vx33XW0bNkSLy8vBg0aVCGf1vvl3O9b79696dixIx9/XFZgL1myhIiICK66qvo/+JYtW4anpydubm707duXq666irfffrtG+6CqLNWxWCwU1rLHU7ge6RlqBAqbdYAT8ShpO7WOIhqgdtuqKaL1+gpv2274veq2uop/W0Wv/uVSYpUtIzoaRVHYu3cv119//Xmf79u3Dz8/PwICApzTPDw8ql2mqqoViqfyaRdiNBorvFcUpcKpntGjRxMeHs77779PWFgYDoeDTp06YbVaL7jscgEBAWRX0ptWkyzV5SsoKGDo0KEMHTqUTz75hMDAQI4ePcqwYcOc+bTeL5V93+68807eeecdHnvsMRYtWsSUKVPOy3iuwYMHM3/+fIxGI2FhYc58NdkH1WWpSlZWFoGBgTVuL1yTFEONgKlFNzjxGb45iVpHEQ2Qzt1d87ZV8ff355prrmHevHk8+OCDFcYNpaWl8emnn3Lbbbdd8AB5tvbt27N8+fIK07Zu3XpJOTMzM0lISOC9997jyiuvBOD336spHKsQGxvL3r17LylLZfbt20dGRgYvvfQS4eFlD3g+d5tdcb/ceuutPProo7z11lvs2bOHSZMmXXAeDw8PoqOjz5tek31QFZPJVGEw+tl2795NbGxsjZYjXJecJmsEgtqWdW1H2g5TbL34S1KFcEXvvPMOJSUlDBs2jHXr1nHs2DFWrFjBNddcQ/PmzS84puVcd999N/v27WPWrFns37+fL7/80nl1UG2KqrP5+fnh7+/PwoULOXjwIL/++iszZ86s9XKGDRvGpk2bqjzwXqyIiAhMJhNvv/02hw8f5ocffuC5556r0MYV94ufnx9jxozhkUceYejQodVeZXchNdkHVYmMjCQpKYn4+HgyMjIoOevCgvXr1zN06NCLziVcgxRDjUBgZCdKMGJDz+Hkw1rHEaJOtWnThq1bt9K6dWvGjRtH69atueuuuxg8eDCbNm2iWbNmtVpeVFQUX331Fd988w1dunRh/vz5zqumzGbzRWXU6XR88cUXxMXF0alTJx588EHnQOTaGDlyJEajkV9+ufRTjGcLDAxk8eLF/O9//6NDhw689NJLvPbaaxXauOp+ueOOO7BarRUGP1+MmuyDqtx4440MHz6cwYMHExgY6LyUftOmTeTk5DB27NhLyia0p6g1OSnchOXm5uLj40NOTg7e3t5ax6nS/Qt/YtlhOy/e0IXxV0RoHUe4oOLiYpKSkoiKiqrRIN2m5IUXXmDBggUcO3ZM6yjMmzeP77//npUrV2odxSX2y6effsr06dM5ceIEJpNJsxyVuemmm4iNjeVf//qX1lGarOp+r9Xm+C1jhhqJ5uGRcPiw3IlaiBqYN28evXr1wt/fnw0bNvDqq69y//33ax0LgLvuuovs7Gzy8vJqdT+duuBK+6WwsJCkpCTmzJnD3Xff7XKFUElJCV27duXBBx/UOoqoA1IMNRIdw3wA2CPPKBPigg4cOMDzzz9PVlYWERERPPTQQ8yePVvrWAAYDIYa3ezwcnCl/fLKK6/wwgsvcNVVV7nM9+ZsZrOZJ554QusYoo7IabILaCinyZKTDpD44T0E63Lo/NQW9LqLG/AoGi85TSaEaGzkNJmoICIkmAhdHDpF5fCRZFqdubGZEEIIIaonV5M1EjqLN2mGMABS91/avUFE4yadwUKIxqKufp81qGJo3bp1jB49mrCwMBRFcT6JuDpr166lR48euLm50apVKxYsWHD5g2oky6sdAMVHt2ucRLii8jvxyqMDhBCNRfnvs3PvhF5bDeo0WUFBAV27dmXKlCnnPfyvMklJSYwcOZKpU6fyySefsGHDBu69914CAwNrNH9D4wjuDKd/xS1rj9ZRhAvS6/X4+vo6nxvl7u5+0TfTE0IILamqSmFhIenp6fj6+qI/59FBtdWgiqERI0YwYsSIGrdfsGABERERzJ07Fyh7wvXWrVt57bXXqiyGSkpKKtxdNDe34Vyd5R3VHRIhtOhApc8ZEqL8qdtnP0hTCCEaKl9fX+fvtUvRoIqh2tq0adN5t0kfNmwYH3zwAaWlpZV2q82ZM4dnnnmmviLWqbB2vWEFRKonSEnPpEVwwIVnEk2KoiiEhoYSFBREaak8ukUI0XAZjcZL7hEq16iLobS0NIKDgytMCw4OxmazkZGRQWho6HnzzJ49u8Kzc3Jzc50P9XN1Jr8w0nTBpNk8yUlOlmJIVEmv19fZLxEhhGjoGnUxBOc/YLB85HlVp5DMZvNFP4fHFbwR8yVfxqXwQI4XA7UOI4QQQjQADepqstoKCQkhLS2twrT09HQMBgP+/v4apbq8Ojb3BeRO1EIIIURNNepiqG/fvvz8888Vpq1atYqePXte8mV4rqpjWNldNvelZGqcRAghhGgYGlQxlJ+fT3x8PPHx8UDZpfPx8fEcPXoUKBvvc9tttznb33PPPRw5coSZM2eSkJDAhx9+yAcffMDDDz+sRfx6EeNdzErTo6y0TiYjV+4nI4QQQlxIgyqGtm7dSmxsLLGxsQDMnDmT2NhYnnzySQBSU1OdhRFAVFQUy5cvZ82aNXTr1o3nnnuOt956q1HeY6ich28IEboMvJQikhJ3aB1HCCGEcHnyoNYLaCgPaj3boZf60bp4Dz/HPM814x7QOo4QQghR72pz/G5QPUOiZor8O5R9kbZL2yBCCCFEAyDFUCNkatENAL+cfdoGEUIIIRoAKYYaoeC2vQGIsh8mr8iqcRohhBDCtUkx1Aj5RHTBhg5/JY+Dhw5oHUcIIYRwaY3+DtRNktGNne79OJKnYkvNIraT1oGEEEII1yU9Q43Umtj/8GDpffyR3TCugBNCCCG0IsVQI9XpzJ2o95zI0TiJEEII4dqkGGqkOjb3QYeD0vSDlNjsWscRQgghXJaMGWqkwtxK2e12B+6UsOfI3+jYOkLrSEIIIYRLkp6hRkpx8yZf7wfAycTNGqcRQgghXJcUQ41Ypk9HAEqPbdM4iRBCCOG6pBhqxByh3QDwzNqpbRAhhBDChUkx1Ij5ti67E3VE8X7sDnkerxBCCFEZKYYasZD2VwAQrqRz5PgxjdMIIYQQrkmKoUZM7+7HCX0YAGn7/tA4jRBCCOGa5NL6Rm5n8PV8eTQNtzwf+mkdRgghhHBB0jPUyJ3u9k/m2sayPttH6yhCCCGES5JiqJHrGFZWBO1OyUVVZRC1EEIIcS4phhq5tiGehOpO07PkD06cTNM6jhBCCOFyZMxQI2c26FnqNocIxzHidrelecg/tI4khBBCuBTpGWoC0r06AFByJE7jJEIIIYTrkWKoCbCFdAXAI2OHxkmEEEII1yPFUBPgG90HgIiiBFSHQ+M0QgghhGuRYqgJiOzUF6tqwI9cTiQnah1HCCGEcClSDDUBbhZ3koytAUjds07jNEIIIYRrkWKoicj2Kxs3ZD/6p8ZJhBBCCNcil9Y3ESUdxvLgz/7k23pxhdZhhBBCCBcixVAT0arrlXy70obhpEJxqR03o17rSEIIIYRLkNNkTUQLPwsBniZsDpU9J3K0jiOEEEK4DOkZaiIURWFEcA6mol/J2HYKWk7WOpIQQgjhEqRnqAkZYYrn38ZPCTz0jdZRhBBCCJchxVAT4t2mLwDN83drnEQIIYRwHVIMNSGRnQdgVxWCyST9+GGt4wghhBAuQYqhJsTTy4ckQxQAKbvWapxGCCGEcA1SDDUxGb5dAChJ/kPjJEIIIYRrkGKoiTFE9gMgIHOrxkmEEEII1yDFUBPTotsQAMJKj1GQn6dxGiGEEEJ7Ugw1MSHh0dxtmkNsyXtsSy3WOo4QQgihOSmGmiCP1v0owcSfSVlaRxFCCCE0J8VQE9QrqhmAFENCCCEE8jiOJumKFhaeMnxEjxMHKSn+HbObu9aRhBBCCM1Iz1ATFBXiz98Nf9BFOUTSjvVaxxFCCCE0JcVQE6TodBzx6ArA6X1y80UhhBBNmxRDTZS1eR8A3FP/1DiJEEIIoS0phpqogI6DAIgq2o3dZtM2jBBCCKEhKYaaqKiOV5CnWvBSiji8a6PWcYQQQgjNSDHUROkNBg56xAKQuXOVxmmEEEII7Ugx1ISVhF9FiurPoawSraMIIYQQmpH7DDVhAYPvof+Ozpgy9dxYasfNqNc6khBCCFHvpGeoCWsd7EuItwWrzcHW5Gyt4wghhBCakGKoCVMUhf7RAehwEL83Qes4QgghhCakGGrirvNLYpv5bobtnKF1FCGEEEITUgw1cR06dcdXKaC17RCnM9K0jiOEEELUOymGmriA0AiSdC3RKSqHt/ykdRwhhBCi3kkxJDgZUPZojtIDv2qcRAghhKh/UgwJLO2HABCRtQnV4dA4jRBCCFG/pBgStLliBMWqkVBOcWRfnNZxhBBCiHolxZDA3cObRPeyR3OkbflW4zRCCCFE/ZI7UAsAstv9g7e3hpGU05E+WocRQggh6pH0DAkA2gwaz+u2m/ku1ZesAqvWcYQQQoh6I8WQAKC5r4WYUG8cKqxJTNc6jhBCCFFvpBgSTsPaeHONbivWTe9rHUUIIYSoNzJmSDiNCM5mhukNCtLdsJbMxmR20zqSEEIIcdlJz5BwatPtSjLwxUMpZv+fK7SOI4QQQtQLKYaEk06v55BvfwAKd/6gcRohhBCifjS4YmjevHlERUXh5uZGjx49WL9+fZVt16xZg6Io57327dtXj4kbFlOnvwPQ6tRq7DabxmmEEEKIy69BFUNLly5lxowZPP7442zfvp0rr7ySESNGcPTo0WrnS0xMJDU11flq06ZNPSVueDpceR05eBDAaTlVJoQQokloUMXQG2+8wR133MGdd95JTEwMc+fOJTw8nPnz51c7X1BQECEhIc6XXq+vp8QNj9lsYZ/vQADy4r7UOI0QQghx+TWYYshqtRIXF8fQoUMrTB86dCgbN26sdt7Y2FhCQ0MZMmQIv/32W7VtS0pKyM3NrfBqaty6jS37IusQNrs8uFUIIUTj1mCKoYyMDOx2O8HBwRWmBwcHk5aWVuk8oaGhLFy4kK+//ppvvvmGdu3aMWTIENatW1fleubMmYOPj4/zFR4eXqfb0RB06D+aMcp/uLnoX2xOytI6jhBCCHFZNbj7DCmKUuG9qqrnTSvXrl072rVr53zft29fjh07xmuvvcZVV11V6TyzZ89m5syZzve5ublNriAyGk2069yTbX8eY9nOVPpHB2gdSQghhLhsGkzPUEBAAHq9/rxeoPT09PN6i6rTp08fDhw4UOXnZrMZb2/vCq+m6NrOYQCs2ZVEqbVE4zRCCCHE5VPrYigxMZGnn36aIUOG0Lp1a0JDQ+nSpQuTJk3is88+o6Tk8hw4TSYTPXr04Oeff64w/eeff6Zfv341Xs727dsJDQ2t63iNTp9WzXjZ8jG/Ou4gYf03WscRQgghLpsanybbvn07jz76KOvXr6dfv3707t2b66+/HovFQlZWFrt37+bxxx/ngQce4NFHH2XGjBmYzeY6DTtz5kwmTpxIz5496du3LwsXLuTo0aPcc889QNkprpSUFD7++GMA5s6dS2RkJB07dsRqtfLJJ5/w9ddf8/XXX9dprsbIoNcREeiLJd2KY9sSGHKL1pGEEEKIy6LGxdD111/PI488wtKlS2nWrFmV7TZt2sR//vMfXn/9df71r3/VSchy48aNIzMzk2effZbU1FQ6derE8uXLadmyJQCpqakV7jlktVp5+OGHSUlJwWKx0LFjR/7v//6PkSNH1mmuxip40J3w5Rd0zP+DrJPHaBbctMZOCSGEaBoUVVXVmjS0Wq2YTKYaL7i27V1Vbm4uPj4+5OTkNMnxQ4nP96adLZHN0TO44tZntI4jhBBC1Ehtjt81HjN0dmHz8ccfVzo2yGq1Ok9RNYZCSEB2+3EAhB7+H6pD7jkkhBCi8bmoq8mmTJlCTk7OedPz8vKYMmXKJYcSrqPDNVMoVM1EOFLYH7da6zhCCCFEnbuoYqiqe/scP34cHx+fSw4lXIe3TzN2+w4GIHfDhxqnEUIIIeperW66GBsb63zy+5AhQzAY/prdbreTlJTE8OHD6zyk0JZ7/3t4+XtflmcN5MfiUrzdjFpHEkIIIepMrYqh66+/HoD4+HiGDRuGp6en8zOTyURkZCQ33nhjnQYU2uvYaxAzN+g4cjKfpX8eY+pVrbSOJIQQQtSZWhVDTz31FACRkZGMGzcONze3yxJKuBZFUbhjQBSzvt7F4o3JTOnXEoNBr3UsIYQQok5c1JihSZMmXbAQquEV+6KBuK5bc0a57+WNwtnsXCljh4QQQjQeNS6GYmJi+Oyzz7BardW2O3DgAP/85z95+eWXLzmccB1uRj3jm6dzhW4fXtsXghS7QgghGoka33Tx119/ZdasWRw8eJChQ4fSs2dPwsLCcHNzIzs7m7179/L777+zd+9e7r//fv71r381ipsUNvWbLp4t42QKXvO6YlZK2TfyK9r3vkbrSEIIIUSlanP8rnExVG7jxo0sXbqUdevWkZycTFFREQEBAcTGxjJs2DBuvfVWfH19LyW/S5FiqKLNb07giuxlxLv3o9ujP2kdRwghhKjUZS2Gmhophio6khhPi88GoVdUDt2wjNZdr9Q6khBCCHGey/I4Dig7VdahQwdyc3PP+ywnJ4eOHTuyfv362qUVDUrLdt2I8yk7PZa/4jmN0wghhBCXrlbF0Ny5c5k6dWqlFZaPjw933303b7zxRp2FE64pZPS/sak6uhZt5kC8FL9CCCEatloVQzt27Kj2DtNDhw4lLi7ukkMJ1xbRpgu/BE3ifusDvLhN7kYthBCiYatVMXTy5EmMxqoPfgaDgVOnTl1yKOH62o97gZ/ox2/7M9manKV1HCGEEOKi1aoYat68Obt27ary8507dxIaGnrJoYTriwzwYGz3FgC8/sMWHDabxomEEEKIi1OrYmjkyJE8+eSTFBcXn/dZUVERTz31FKNGjaqzcMK1PTSsLePMm3g78062ff+21nGEEEKIi1KrS+tPnjxJ9+7d0ev13H///bRr1w5FUUhISODdd9/Fbrezbds2goODL2fmeiWX1ldv46fP0+/Aq2TjjX7GNrx9A7WOJIQQQlze+wwdOXKEf/7zn6xcudL5/DFFURg2bBjz5s0jMjLyooO7IimGqmctKeHEyz2IdBzjz6Cb6X3v+1pHEkIIIernpovZ2dkcPHgQVVVp06YNfn5+FxXW1UkxdGE71n5L198mY1N1HBnzPa27XqV1JCGEEE3cZbvp4tn8/Pzo1asXvXv3brSFkKiZrgNvYIvnYAyKA8P392ItLtI6khBCCFFjF10MCXG2VrfNIxMfWjqOsf3jR7WOI4QQQtSYFEOiTvgHhZHU90UA9h07yY6j2RonEkIIIWrGoHUA0Xj0HHYrL6ZaWLjPjagvd/DD/f3xcpM7VAshhHBt0jMk6tQ/bxpNqI8bSRkFPPZlHKrDrnUkIYQQolpSDIk65edhYt6E7oTrs7j94P38+clTWkcSQgghqiXFkKhzsRF+vBSbRQ/dAXoeeoc9677VOpIQQghRJSmGxGXRb8wDbPYZgV5RafnrP0ne/YfWkYQQQohKSTEkLgtFp6PrPR+y29gFT4rw/GocJ48kah1LCCGEOI8UQ+KycbO4E/7Pbzisa0kAp7F+dAOn01O0jiWEEEJUIMWQuKx8mgVimfwNJ/En3JFC4sLbyMwv0TqWEEII4STFkLjsQiOiKR7/LfFKe2bmT+SW9//gVJ4UREIIIVyDFEOiXrRs2xWvf/6CzbsF+0/mM+69TRxNTdM6lhBCCCHFkKg/rYO8WHpXX5r7WmiTtQav93qSuHm51rGEEEI0cVIMiXoVGeDBt//sy73uv+JHHlHLbyXu2ze1jiWEEKIJk2JI1LsgHwttHvw/tnoMxKTY6bHjSeLeHEdxQa7W0YQQQjRBUgwJTbh7eBE781s2tLwXu6rQI3sFaa/3J3nPZq2jCSGEaGKkGBKa0ev19J8yhz3XLCEDXyIdRwn7ciRLVvyOze7QOp4QQogmQoohobkuA0aj3r2eOPcBLLUP4t9rcrhx/kZ2HDutdTQhhBBNgBRDwiUEhkbQ/eEfsfz9VbzcDOw4nsOMeV8RN/dmMk8kax1PCCFEIybFkHAZik7H2N6tWD1zIGO6N+cJwyf0OL0Sy3u9+XPRIxTkZGodUQghRCOkqKqqah3CleXm5uLj40NOTg7e3t5ax2lSEratg+WPEGPbB0AuHuyLuo1OYx7F3auZxumEEEK4stocv6UYugAphrTlsDvY+tOHBG2bS6TjGAA5eLI96i46jnmMQC+zxgmFEEK4otocv+U0mXBpOr2O3qPupMXs7Wzu/ipHlOb4kM/6/en0f+lXHvnfDvYczwKp6YUQQlwk6Rm6AOkZci220lK2//I5bxwMZVOKFYCb9Gu4120VGdE30e6aO/AOCNU2pBBCCM3JabI6JMWQ64o7ks2iDUlM2HcffXV7AbCqevZ69UfX5SbaXXkDZouXximFEEJoQYqhOiTFkOvLzjzF3p8XEXhgKW3tB53TCzGzx2cgucPeZkDbQMwGvYYphRBC1CcphuqQFEMNh6qqHNj5B6c2fExU+s+EcYq19i5MKn0MD5OeAW0CmOy1leieQwhs0UbruEIIIS6j2hy/DfWUSYjLTlEU2nbtS9uufXHYHezdtob9BzMIOmwmPa+EHXv20tdtFsRDsj6C9OCr8Gx/Na16/A03Dx+t4wshhNCI9AxdgPQMNXwOh8ruEzns2rqBbrtfoH3pXvTKXz/2paqew6a2JEbfQXCvMXSL8JVTakII0cDJabI6JMVQ45N5Ko2Dm36AQ6sJz4kjjFMAPGC9nx8d/XAz6rgu+BQ3GjZgjryC5p2vIiCsNSiKxsmFEELUlJwmE6Ia/oEh+P/9LuAuVFXlyOF9nIhfhXthFwKO2MjIt9IsdQO9jV9A2hfwB2Tgx3HPTpSGdMc7ug8tOg3Aw1OKYyGEaAykZ+gCpGeoaVFVlUOn8jm2/WfM+5cRcHoHUbYkjIq9QrtrrS9S1KwjHcK8GeR5nPaehYTFXEGzkEjpQRJCCBcgPUNCXCRFUYgO8iJ62BgYNgaA/Pxc9u3cSO7BjbilxRFYeIgDjuZYMwo4nFFAf8OHdDL8BusgG29OuLWm0DsaJTgG7/BOhHa8Ei8Pd423TAghRFWkZ+gCpGdIVCYjv4S9J3LZcyKXiF1vEZP9GxH2YxgUx3ltOxR/iI+PL22CvbjO8ActLcV4tuhIYMsONAuJQNHJYG0hhKhrMoC6DkkxJGqqoCCfo/u2cjp5B46T+/DIPQgleVxf9G9nmy9Mz9FHl+B8X6SaOGkI5bQlAqt3JEe7P0pUoCct/T3w9zChyCk3IYS4KFIM1SEphsSlyiks5UB6HgfS8wmOfwe/rHj8S44S6kivMBbppOrLFSXznO8XmV8nynCKPHMoxR7NwSccY0Ak3iGtCGjeBq+AMCmWhBCiCjJmSAgX4uNupGdkM3pGNoPerzinFxcXk3J0P1lHEyg6eZDsgmL6Kf4kZxRwIqeYtiTT3J4JhUehEDgFnHnayEnVl/4spLmfhRZ+Fm6yfk8zs4qhWQsszVrgExRBs9CWmD18tdhkIYRoUKRn6AKkZ0hoobjUTurh3Zw+cZCiU0mo2Ucx5h/HsygVf1saRx0B3GR92tl+nWk6EbpT5y0nHwsHDG14s/nrBHu5EezjRq+Cdfh4mPAIiMA3KALfwDD0Jrd63DohhLj8pGdIiAbOzagnql1XaNe10s+9S0r5JaeE49mFpJwuInnXGFJzk7EUn8S7NAN/RyZeShGeFKFaC1mT+FehtN70EuHnFE65eJCj8+W4OZrPI54hwNNMgJeJrrnr8LYYcPMLxSugOb4Bobh5+MrtA4QQjYoUQ0I0QBazkeggI9FBnmUTrnilwueqqnI6J5ustCPYcwt5RYkgLbeYtNxiThzsREHJCXxtmfir2RgVO94U4O0oIKPAwg87TjiX87v5BVooGRWWXawaydV5k2SMZl7I8/i5G/F1NzHw9Hd4Gh3oPZph8grA4h2Iu28AXs2C8fD2R9HLrxshhGuS305CNEKKouDr2wxf32a0AnpV+PQ751c2m41TWafIOXWCgqwT5JU4eNwUQ0ZBCRl5VlKSO5FvTcXTdho/9TQeSgluSiluaiYpxX6s3f9XD9Od5g/PK5zKHVZDudn4Dn7uRvzcTfyz4B38yMdm8kY1eaO6eaOz+KB390XnHYIjciBebka8LQa8dVbMFg+5BYEQ4rKRYkiIJsxgMBAYFEpgUCjQA4CrKrT43vmVqqrk5OZwOuMEBdknKSl28IpbW04XWskuLOXQoaGkFqVhKj2NxZaLhz0XbzUPL6WI06oHGfklZOSXAPCG+Y8qC6dDjlCGWF93vv/JNIt2ynHyFQsFigdFOk9K9B6U6j3IMYewIvIRPEwGPMwGumSuwJNCdBYvDG6eGC3emCzemDy8cPP0wxIYibtRj04np/mEEH+RYkgIUSOKouDj44uPjy/QAYA+FVrMr3S+oqJiQnNz+D+7G6cLSzldWMqRw7M4WnAKR3EOupJcdNZcDNY8jLZ8TtKMFh4WcotKyS+x4aUUoVNUvCjESy0E+yk4c0eCQwWhfJJ21Lmun0wLidEdrTTHSdWXTmduXWAx6lloeIU2HKVEsWDVWyjVuWHXuWEzuFFi9GVF5CzcjHosRj2dsn/G25aFYnJHby57Gcwe6M0eGN08UJp3x3KmrZtSgslkRtEbL22HCyHqjRRDQojLymJxw2JxI/TsiV3uqnaeoWf+dThUCgqvIi0nm8K8LIrzsigpOE1pfjb2knwKHUam+7ah0Gojv8ROyvH+FBS3xGgvxGQvxOQows1RhBtFZKtezuUXldrxVzII0Z0CFTjnxuGnVB8WnxjnfL/U9BG9dPsqzVqomulQssj5/kPjK1ytj6dU1VOsmCnBjE0xUqoYKVVMPBYwD5NRj9mgY1Tel0Ra9+PQm1H1ZlS9CVVvBoMZDG7sbTUFk8kNs0FHcM4OvKwn0Zss6IxuGExuGMwWDCY3jCYLuoBojEYjRr2CUbVi1CnojWaQ04tCXJAUQ0IIl6XTKXh5euHl6QVEVNpmSIV3C6pcVjNVZZ/NQUGJjYISO9ZTn7A3P5PSojxKC/OwWwtxWAtxlBRidSjcF9ia4lIHRaV2slL6saU4DL29GL29GIOjBJOjGJNaQgkGvMwGCkvt2B0qFqwAGBU7RgrxorCs4FKhRDXw55FsZ6Z/GLcSq99WZeZbEvpip6yYedP4DgP0G6ts26V4IbmUDaifY3ifWwy/AeBQFUoxUKoYsFH2usv9P+Qbm2HQ6bjJ+i0DrL/jUAzYFSMOnQGHYsShM+LQmfgp7D6K3YIw6nW0z99EVN42VL0J9EbQm1DOfK3TGznRfDgOix8GnQ7v/EP45B9G0RvR6Q1l/xoM6PQG9Hojpf4xZaczdQomaw6G0hz0egMGgwm9wYjOYMRgMGIwGNCbLDIAX1xWDe6na968ebz66qukpqbSsWNH5s6dy5VXXlll+7Vr1zJz5kz27NlDWFgYjz76KPfcc089JhZCuAJFUXAz6nEz6vH3BPy7VNt+cIV3/6m27a4z/5baHRQVXcWpgnysxQWUFOVRWlyEzVqE3VqCzVbCPP/uWG0OSmx2lNQ72Jh/DaqtGNVWgmIrAVsJit2KYi9hRFALSmyOsvbZrdlTnIveUYpBtZb1/qilGLFiUkux60zOHi6TYnNm0ykqZkoxU+qcdiSriEzyAdAbjtLGkFjltk07NYrjahEAjxl+5WbDsirbzo7zZL8aDsB0/dc8aPy6yrbXlzxLvBoNwFT9Mh43flZl21usj/MnndDrFMbrVjNL9zE29NjRYceAXdHjOPP+Lff72ePWDb1OR+/SLdxU8DkORY+KDlXRoyp//buq2S0kuXdFr1OIKk7gquyvAR2qTg9n2nGm7d7AEaR5d0avKPgXH6HjqeVlvW66sjaKTgdK2fuTAX3I9YlBrwP3kgyan1pXdgGATo+iM5z5V49Op6PQrz1Wnyj0ioLRlo9X1q6yz/RGdDo9ir5s2YqiR/UMwuEZgk5R0DlKMOUdR1F0zmWVtdOh0+nA7IXOzQdFBzrVgc6aV7Y8nYJOp0evO7PcM/nR6arc/01BgyqGli5dyowZM5g3bx79+/fnvffeY8SIEezdu5eIiPP/akxKSmLkyJFMnTqVTz75hA0bNnDvvfcSGBjIjTfeqMEWCCEaM6Neh9HTCzy9LtwYgFur/bRvhXe9q227h7LTiqUOB6XWq8kuKcJmLcFms2KzFmMvLaG01Iqj1Mp73tFYVR2ldhVTpg9xeWNw2Kw4bFZUmxXVbkW1l6LarEwMiqVAccdmd+CXOYiNOV7gKEWxl6I4StE5StE5rCiqnZiwcAJ1/tjsKqaCcPYUdkSPDZ1qR6fay8oV1Y5BteHm4Ukz1YTN7kDvMJCvWtBT1sZ01mNqAOyqDruqYneoqHorFl1JxY0/69bBJ7Nz2e3IBaCtPoW2xspPbwLMP30Fyx1BAIzS7WWG6Zcq236REsjXDncArtZtY4ppUZVtn9gzhU/sZQVpX90ePje9UGXbF0tvYaF9NABdlYN8b36yyrb/Kb2RN+1lx662yjFWmWdV2XaBbTQv2W4BoIWSzu/mGVW2XWK/hmcdt6MoCgFKDmt19+JAh0NRUFFwoHP+u1J3JW8YpqJTwA0rn1vvKys0Uc4UnH+1jzP24APPqegUBQV4/fR0Z5uz/13rMYJm/SZxc6/wKjNebg2qGHrjjTe44447uPPOOwGYO3cuK1euZP78+cyZM+e89gsWLCAiIoK5c+cCEBMTw9atW3nttdc0L4byrHkk5SThZfIiyidK0yxCiMZBp1Mw6/SYDRZwt9RwrsBqP+1f4V17YGqVbXtUeNcXeKbKtl9UeDcMeBsou2rRZndgs9ux2Uqxl5YyX2fEhh6bQ8VR1Iujhfdgt5Vit9lw2G1lhZy97P3dXq2ZbPTCZlcx5IWwJbszqt0Oqh3VYa/w9d/8etLLrTl2h4pXvhsbMoxnPnOAageHHdSyr2N8+3K7WxQOVcW/wM7GzLHgcKCoZctT1LKvFdWBv2dHrrEE43CohJXksi27T1kpodpQVAc61YGCHZ3qwODRgvZGLxyqSlCpN0nFERU+1+E4U2Y4sJs88cGIQ1WxqAZyVI/yEsRZspR/beevKyZ1VP+gCbuqUGovO5dbih2jmx3nVQrnUK1FpBUWA+BBEcFumec0+OvLHYUt2J2TeyaDg2i3g5Uuc1lBB/KyCqrNeLk1mMdxWK1W3N3d+d///scNN9zgnD59+nTi4+NZu3btefNcddVVxMbG8uabbzqnffvtt9x8880UFhZiNJ5/tUdJSQklJX/91ZGbm0t4eHidP45j7h+vsWLDYka1HMH9Y1+ts+UKIURV7A47dpu1rJAoLcVut+KwleKw2XCYjCgeFuwOO47SUuwnT2J32FDtjrKC40wh4VDtOLzcUQOboaoqjlIrjoQDqKoD1X6mjaOs2HA4HNj9vLC1al62XHsp5o07UR2OssLknH9LA33J69QSh+pAdTjw++lPOPM1zpcKDpWiYG8y+rYFygqo5t/8gWKzAyqqqp4Zp6WCqlIU6MnxwTFleXEQ/VUc+pJSVNVxZgC9A+XMcgqbuXNgRAfUM8vp+FU8pvziCssrfxX6Woi/oQMOHKiqSo+v9uCRXXTm84rtizxNrB3fHlRQUen3VSK+6YUoznZw5kNKLHp+nNIO9UxlMfjrJIKPnykWzjpkKyrYjAqf/vPMfkDlb98dJzw5v8LyytqWLW3BtNZl3ytVZdjydNocOKsIOTOPcmYdc+8JpdRQVlSNXJVNp31leVUUlDPLLm/72h3NKHDXoaIy6td8eu8qLk/pXH959lcme5PlraCicu36Yorc9JjHTeHx/vdf6o94BY3ycRwZGRnY7XaCg4MrTA8ODiYtLa3SedLS0iptb7PZyMjIIDQ09Lx55syZwzPPVP3XTF3pHJfFsPftHGm/AcZe9tUJIWrBVmqluCiP0uICSkuKsJUUU2otxubrgc3NiM1hozQrE/vhI9itxThKrditJdjPnIZyWK3ktm9OYYgPpY5S9CnpNPs9AdVWCqU2VFspaqkNxWYDm52kK1qQ0t4fu8OO9/HT9Pg+EcXhALsDxaGi2B1lL1Vla78Atsd6Y1ftBKYUcMtnJ9DZVXQOFcXBmX9V9A5Y3t/Mj30N2FQbYSdtvPZfW5Xb/P0VCp9eXTZYO/C0yrvzK+8ZAFjZXeGDYWVtvQtU/vtW1W3XdFaYN6qsrdmqsuT1qttuaq/wnxvOXP2mqny5sOq221opvGn+60q5JV/ZMFexeXsi4L8BG5zv/7vChndR5W0PhsAXrXY637/zu42gnMrbHguAH7secr7/W5yNFpmVt033gTXH/jpWDUuwEVX5oYtcC/yZ9qfz/chkG+GV3zGCYiPsOLXjr7bH7TQ/WnUfR+Lpv8aHjTplJzSt6rYpBSmUmMqKGX2OneCMqttmlGaRV1rWVldgxz+n6rZ5tnxOO860LbEDCp6Wqr/X9aHBFEPllHOeiaSq6nnTLtS+sunlZs+ezcyZM53vy3uG6ppfdAfgWzxP5tX5soVoTBwOB6UlhRSpVopVK4W2QopSUyg5cgRrQdmVYKUF+dgLC3AUFeEoKeF4/1bkNnOjxF6C765jRPx+EMVqQ7Ha0JXa0Flt6Ert6G0OvhnVjISWOqx2Kz225XH7smIMjsqz/Od6HZtiygaa9klwMPO7KhoC343U8VvXsraxBx3M/l/VbddajrLCVNY25qjK2J1VHxjWRhaR0LKsbWmOStCpqtsqxSUUnxm7YleqPwmgU0Gn6NChQ2+AYqMdVeHMS8Gh++trxd1CkLsPekWPh14lo1la2fgPXdnnKODQKaAoKMHedPIPQ6foMNhVjkUdAEVB1ZW9UBTne6W1H4PCo9ArenSKjsPdt5d9rlPKBjTr+Kt9C1+uj26LgoJO0XF0YBw6u+r8vOz5eWXLtQd7c2tM2b2xdIqOtOHbOWW1lR0HzrQrX09pM0/u7tIZRVHQoSPnhngKCq0oKGWDjM/csFNRdNi83XmoR1cURUFBoTR/N8cKip3rV5z/6nBYzDzZt5szr5t+H6m5Bc7PywZgn9k+k4mXB/y1XLPvQU7l5P+1XABFV7Z8vY65fbqUd8BgCkkm+3R+hQzO452iMK9XJ+d0Q9RxcrNzz2xb+Xb9lfndLu3KBnArCkpMKoXZOSiKztnur/kU5sW0BkNZSaF0O4UtO7eSDGXt34mKAJOxbP4+2aDXERDWutqfz8utwRRDAQEB6PX683qB0tPTz+v9KRcSElJpe4PBgL+/f6XzmM1mzGZz3YSuRvOYnmQBfqdtWIsKMFk8Lvs6hagvqqpSZCsiNyuV/LTjFJ3OpCgnk5LcbErzcinNPY09P5/9A6PIdneQZ80j4vfDdPj9OMYSG4YSO0arA5PVgakU9Co8fpueA83LfmGP2uzgtl8dVDUqZonyB7uiyn5pD9ntYPAfVRciBVknORlY1rYIx3mFkAOw6cGuB3eDO/5uHhj1Rix+pZwMysahV1D1OhwGHQ69DtWgR9XrCG/VkqEtQzHqjfi7FXIo/RAYDCgGAxiNZ/41oBiM9OwWRafo5ugVPeY2hRz3Oei8DB29Hr2+rL3OYGBYZAuGhYei1+kxFJVS1P04en3Zpeh6g6HsX70RndHIJL9m3NGsGTpFh94BulsK0RuN6A0mdHojRqO5bLl6A4/q9Dx69obfV/X3tw8w/ewJ1YwDvxqocP3u36tuCzDl7DeDqm9bQb9atO114SZO3WrRtmMt2rapRdvaDCut/A4UlWtei7YhtWgbVIu2lR+K612DKYZMJhM9evTg559/rjBm6Oeff+a6666rdJ6+ffvy448/Vpi2atUqevbsWel4ofoU2KItKSawWOFo4haiuw3SNI8QVSnMP022I5+c0lxySnIo2r0b+55EbNlZ2HNyIDcffV4BhrxizAVW3pjgSbJHIXbVzri1dm7cqGIAvM68zrbQfSuHwsoKnNEnHAxLrrpoMZWqGHRGLAYLqp/KqYACSs167CYDDrMRh5sJh5sRTCb6du5Aj5YhmPQm/PzzOBp8HJ3ZDZ3ZDb3bmZsVurmjd7PwQNvWmP0DMevNGEfYMd5bipvFC6ObB2aLBwaj2fmXdfdzQ1V9MQ8jzp0wrrJWVajNgb02f1DX9CI3IZqYBlMMAcycOZOJEyfSs2dP+vbty8KFCzl69KjzvkGzZ88mJSWFjz/+GIB77rmHd955h5kzZzJ16lQ2bdrEBx98wOeff67lZgCg0+nIDnTDklJMeuIOKYZEvSq1FpNhzSarOIuMogwKt26FHXuxZ2aiZOZgyCnAklOMR14p7iVw3z/1nPItKwj+sdbOmI3VnHI5nYvdvaxtgUVHgZuDErMOq8VAqcWI3WLC4eGG6m5heJce6MKb42n0xC+qiMwBpzF6eGH28MLs4YPZ0weLly8WLz8+9m6GyehW+42NBUZdxE4SQjQZDaoYGjduHJmZmTz77LOkpqbSqVMnli9fTsuWLQFITU3l6NG/RplFRUWxfPlyHnzwQd59913CwsJ46623NL+svlxxiB+kpJJ3uOobnglRW6qqkl2STer+eE7viacg5SjW1BMoJzMxZuTgkV2MT66daffUvMAJLNRDqD8+Zh/0bWwkFeSienuAjxd6X19Mvs0wNwvE4h/Eqx274O0XjJfJC8ttlmrH9FUQUxdbL4QQtddgLq3XSm0uzaut/3tsAq2+28ahQdGMWvDjhWcQ4gy73cbJ5L2kJm4n+3ACJUeSIeUkX13rw15SKbQVcssaOzdsqvq/9zO3mjjVLoAASwA9Dqh02JOH0swPY2AgboEheIS0wDcsEv+wVng3Cy27q60QQjQQjfLS+sbI0Lc3SzPjUTqZpRdfVKrUWsyx/OMk5R3h4OmD8NMa2qxMwD+9BJMd3Cl7lSuIOUVhRFlPTE6YNynhpZQEeKEG+WMMDcW9eQS+4a0JjOrAJ82jMRjkyepCCCHFkIaC+w7k69P/JcSjiptYiCYlNyuNg1t+4dT2Pyjdl4j7kVMEpJfwzHg9+1uUFThDTjq4KrVskLFNB9l+RgqCvbCHBWKKiOCBIYNpHt2V5p7NMesv/1WRQgjRGEgxpKGW3mVjndIK0iiyFWEx1PT2+aKhs9qt7M3cS3x6PLlrfqPb59sJzCzFwvlXxrbKNmDo2o5o32jaRwRwup+BsE5XENa6C0bTRQwoFkIIUYEUQxryNfvSKt8dn7R8jh7dTbtWtbn5hWhIstKOsG/d92Rv2Yhx9yG+7m5lY9uyHp6YbJVrMstunJfloyenZTNo3xrfTrE079yHl9p2R6+X/6pCCHG5yG9YDSmKwgPfltL8uINTMeukGGpEigpz2fXzF5xa+wuW7QcITS3GD/A783lbf4WEzgF0C+pG15j25HXW06r334jR+C6sQgjRFEkxpLGSsGZwPIW8w/u1jiIuUfKpA6w5uYHfT/zO8cQ4/vNucYV73KUHGslr3wL37t25YdAoZra/ouaXnQshhLhspBjSmC68OfyZgv3oMa2jiFqy223s/f1Hji7/Hx6b9pDsV8rrY848ONIbDkSa0IWF4tm/Px2GjSOmRVttAwshhKiUFEMac49qDfyJMSVD6yiiBhwOBwmblpH0v4/x35CAb56DVmc+88yFfkG9ubLlYPqF9SPqtijp+RFCiAZAiiGN+bfpDHyOd3qB1lFENY7nHefHwz/S7LkP6LK7wPk4qCITpHYOxePqwXS77nbeC6jNkw+FEEK4AimGNNaiQ09SAd88B3k5p/DyCdQ6kjjDai1iy9fz+cx7L+syNgMwOshBewMc7xqK39+vo8ff76C7xVPjpEIIIS6FFEMa8w0M54C7gmehyvG9W4jpO1LrSE3eySMJxL33In4/b6NZngPlWh100dEntA997/kbUc8PpGuzEK1jCiGEqCNSDLmAdddGcKDkGGPci+RZlRo6uH0N+959mYhNyUSV3faHXA+FQSH9mTXm34R7hWsbUAghxGUhxZALyBrWgw2HUohFBlFrYfvxP0l98CGidmU4xwIdi/LE7ZYx9LlpOldY3KudXwghRMMmxZALKH8sx9G8oxonaVr2ZO7h7e1vsyFlA0/k2XEAyV2DaHH3fQy9+mat4wkhhKgnUgy5gEh9MF0PObCciIcBWqdp/A5uX8Pe/zzHS31PkueuoFf0pNx+Nb1ibuLazvINEEKIpkaKIRcQnmvg8S8d5HokwWyt0zRemalJbHpmGlFrD9JGhRvQkTl1FPd2vZcI73MfjyqEEKKpkGLIBYTH9OYY4F2gkp15Aj//MK0jNSql1mJ+e+sxmn2yitbFKgBJXYO4btq/advzbxqnE0IIoTUphlyAp28guZ46vPMdHE/Ygt+A67SO1GjsWvstGU89Q3haCQCpoW74PvYQI4fdqnEyIYQQrkKndQBRJifIA4DMA7s0TtI4FNmKeG3La6x/9wlC0krItyik3DOKq1ZtprsUQkIIIc4iPUMuojQsAA7nUXD4oNZRGry41C38e9NTHMs7hsfVCqHNIrnqqXn4h0ZpHU0IIYQLkmLIRegjWgBJOI6laB2lwSq1FrPqqTs5tXcbx27QEewRwpNDnuSqe67SOpoQQggXJsWQi/Bq1RZYjzk1S+soDdKx/XHsm3Y3rZILaAXcVdqPKde9gadJnhsmhBCiejJmyEUE9b6Sd0bpWHSNHlVVtY7ToGxc+ibpN91Ki+QCCs1watZEHpi6UAohIYQQNSI9Qy4ionU31nfWo1JEVnEW/hZ/rSO5PLvdxsqn7iTqq7Inyh+L9CDmrfcIb9tD42RCCCEaEukZchFmvZkwz7L7Cx3JPaJxGteXb83n27uGOwuhQ9e0Z9D366QQEkIIUWtSDLmQXrn+DItzkLp5rdZRXFpqfiq3Lr+Vz1ulUWSC1Ok3MurtbzGZ5YGqQgghak9Ok7mQPtuKaPWzg4PGTfD3B7WO45L2ndzNfWunk16UTlCbYIzfPc/VrfpqHUsIIUQDJj1DLsTcqhUAuiMnNE7imrYuX0zG6JtxO3KSaN9oPr32UzpLISSEEOISSTHkQpq16wyAZ2qOxklcz++fvIr54Zfxz1W5Y5sPi4cvJsQjROtYQgghGgEphlxIi059AAjIslFclKdxGtexbvEcfF/4EIMDDncPYcQHP+Fj9tE6lhBCiEZCiiEXEtCiDYVmBZ0KyXv+0DqOS1jzwXP4v/wxehUO9WvJsI9WYnH31jqWEEKIRkSKIRei0+nIDrYAkJ4Qp3Ea7f32/jMEvvYZOhUODYhkxPvLMBhNWscSQgjRyEgx5GJKWgQCkH8gUeMk2lp1aAXZXy4tK4SuasWI935Er5eLH4UQQtQ9Obq4mLwxg/h3609o38OXkVqH0cgfqX/w2MZ/oYzT8WBaV26Z/ZEUQkIIIS4b6RlyMSFd+5DYQmF/adN8ev2eAxuZ/ut0Sh2lDIweyi3/+lgKISGEEJeVFEMuJsonCoCknCQcqkPjNPXr8K7fyR93J6N+zeOK4N68dOVL6HV6rWMJIYRo5KQYcjHNPZszZBfcvLKAE0f2ah2n3mSmJpFy9z/xLlTpd8SN//R7BZNeBksLIYS4/KQYcjEGnYEbNusYtUUlZcdGrePUC2tRIVvvHEdAlo1MPwNdPvoSL29/rWMJIYRoIqQYckEFzf0AOL1/j8ZJLj+Hw8Gq6TcRcSiPIhMEvzOXwObRWscSQgjRhEgx5IoiwgAoPZykcZDL75fXHqT1usM4AOuT99OmxxCtIwkhhGhipBhyQe7RbQEwHD+pcZLL68/N39J80SoAjk8aTJ+x92mcSAghRFMkxZALCuwQC4Dficb7fLL0wnQeSZ7L26N1JF7dmmtmvaN1JCGEEE2UFEMuKLLLlTgA7wKVUykHtY5T52wOG4+sfYSs4iwyBsQw/K2v0OnkR1EIIYQ25Ajkgjy8m5HVrOxGg0d2/q5xmrr37ZsPcOhwHB5GD14f9DpuBjetIwkhhGjC5Na+Luq3e3qxKu9P7onQ01PrMHXoj6/fpdN7a3jFA/IXPUJL75ZaRxJCCNHESc+Qi2rWuTs5ngoHcw5pHaXOpB3Zi/75dwHI6tuOoV1u1DiREEIIIcWQy4r2K7vXzoHTBzROUjccDgfbZ9yJZ5HKieZuXPPqJ1pHEkIIIQA5Teayoo3NuWWNnRbZO3EMdzT4Acar5z5CZEI2VgO0fO0NzBZPrSMJIYQQgPQMuayIgNaM3qzSa5+NlEPxWse5JId2rifww+UAnJw0lOjYwRonEkIIIf4ixZCLMpndyQgyA3C8AT+jrNRRyvbnH8VsgyPtfBky83WtIwkhhBAVSDHkwgoiAgA4vXeHxkku3n93/ZeX/5bH6l5muvxnIXq9nJkVQgjhWqQYcmH66CgA7Acb5jPKDmYfZOHOhRSbFaKeeYGwVp21jiSEEEKcR4ohF+bXseyxHJ7JpzROUnu2UiufvTcNm72UQS0GMTJqpNaRhBBCiEpJMeTConpdDUDgKSv5uZkap6mdX+c+wk2Lk5j1rcLjfR5HURStIwkhhBCVkmLIhQVHtCfHU0exCQ4lNJxB1McPbCfw47Kn0QcPGkaIR4jGiYQQQoiqSTHk4v73ryuY8qCeBJ8CraPUiMPhYNes+3ArhWOtvLh62ktaRxJCCCGqJcWQi2sZ2Q1VUdiXtU/rKDWy4ZNXiNybjVUPrV56Ta4eE0II4fKkGHJx7Zu1ByAhK0HjJBdWkJeF/t2yx2ykXN+L6C5XaZxICCGEuDD5s93FtXdryYzv7ESc2oX1mkJMZnetI1Vp7UsPEpVjJ8tHz8DH5modRwghhKgR6RlycS0Co+l2WKVFhsrhHeu1jlOlE/kn+NR9J8lBYL/vVjy8mmkdSQghhKgRKYZcnE6vJ7OFFwAp237XOE3VXt/6Ojtb2PjfY1cw4NZHtY4jhBBC1JgUQw1AaZsIAIp27dQ4SeW2nNjMqiOr0Ck6ZvWZjU4nP1ZCCCEaDjlqNQDesT0AcEs8pnGS85Vai8ma8k9u/N3BuMgbaNesndaRhBBCiFqRYqgBaNV/BADBJ4ooyM/WOE1Fa995nIgjRVy7VeWetlO0jiOEEELUmhRDDUBoqy7keOowOGD/Hyu0juOUnX4UnyU/lX09cTjNQlpqnEgIIYSoPSmGGgCdTkdm20AOB8PhdNe5+eKGZ6bhWaSSGmJm8P0vah1HCCGEuCgNphjKzs5m4sSJ+Pj44OPjw8SJEzl9+nS180yePBlFUSq8+vTpUz+B61j67Nt47HYDv4fmah0FgMQtq4j6NREA71kzMJrcNE4khBBCXJwGUwyNHz+e+Ph4VqxYwYoVK4iPj2fixIkXnG/48OGkpqY6X8uXL6+HtHWvS1BXAHac2oGqqppmcTgcJD/zb3QqHO4eTM8RkzXNI4QQQlyKBnEH6oSEBFasWMEff/zBFVdcAcD7779P3759SUxMpF27qq9gMpvNhIQ0/Kemd/DvgF7RczovnbTTxwj1i9Asy9r1nxCalIvVAJ2ffl2zHEIIIURdaBA9Q5s2bcLHx8dZCAH06dMHHx8fNm7cWO28a9asISgoiLZt2zJ16lTS09OrbV9SUkJubm6FlyuwGCw8ttLCR6/bSfzhU81yFNuKeenUp8ycqifx3qFEtO2hWRYhhBCiLjSIYigtLY2goKDzpgcFBZGWllblfCNGjODTTz/l119/5fXXX2fLli1cffXVlJSUVDnPnDlznOOSfHx8CA8Pr5NtqAve/mEYHJD7R/UF4OW0eM9iThScgOYhjJo6R7McQgghRF3RtBh6+umnzxvgfO5r69atACiKct78qqpWOr3cuHHjuPbaa+nUqROjR4/mp59+Yv/+/fzf//1flfPMnj2bnJwc5+vYMde50aFf3ysB8N59RJP1nzi0k99WLATgoZ4P4W503YfGCiGEEDWl6Zih+++/n3/84x/VtomMjGTnzp2cPHnyvM9OnTpFcHBwjdcXGhpKy5YtOXDgQJVtzGYzZrO5xsusTx3+Npbjz7xH8KlSThzZS1jLDvW6/vgnH+TpuCJ+/Xs4w28bXq/rFkIIIS4XTYuhgIAAAgICLtiub9++5OTk8Oeff9K7d28ANm/eTE5ODv369avx+jIzMzl27BihoaEXnVlLPoEtiAuzEHqiiMRfvyZsSv0VQ9tXfkJU3AkcClw9Zka1PXJCCCFEQ9IgxgzFxMQwfPhwpk6dyh9//MEff/zB1KlTGTVqVIUrydq3b8+3334LQH5+Pg8//DCbNm0iOTmZNWvWMHr0aAICArjhhhu02pRLVtylNQB5f/xRb+u0lVrJfqnsqrHDg6KJ6TOy3tYthBBCXG4NohgC+PTTT+ncuTNDhw5l6NChdOnShSVLllRok5iYSE5ODgB6vZ5du3Zx3XXX0bZtWyZNmkTbtm3ZtGkTXl5eWmxCnfDvPxAAnz1H622da959nNDUYgrcFPo++Wa9rVcIIYSoDw3iPkMAzZo145NPPqm2zdk3I7RYLKxcufJyx6p3Ha4ey4+L57M12kFkzhFa+lze54Flpx/F56OyAeeZt15Dz9BWl3V9QgghRH1rMD1DooyXfwi/TuvLz911rD2+9rKvb+Oz0/EsUkkLNjF42kuXfX1CCCFEfZNiqAEaGF52quxyF0OJWYmsdDtIjjt4PPIAJpPlsq5PCCGE0IIUQw3QoBaD8M9R8f+/PzmdnXpZ1qGqKi9ufpHfOsM3c66h96g7L8t6hBBCCK1JMdQAhXuH89yXOqassrHj/z6+LOtYduhHtqVvw2Kw8GD/2ZdlHUIIIYQrkGKogcrr1R6A3NWr63zZpzNSMN71OH0SHNzVeSqhng3zvkxCCCFETUgx1EA1H3E9ACHbj1NYmFOny/792QdoecLGrRv03NZmQp0uWwghhHA1Ugw1UJ2uvolsHz2exSp//u/dOlvuvs0riPo5AQDLIw9gssjzx4QQQjRuUgw1UHqDkZzBsQAUfl/1g2dro7SkiJR//QudCoe7h9D771PrZLlCCCGEK5NiqAHrMPE+AFomZHEiefclL2/1nAcISymiwE2h55x5l7w8IYQQoiGQYqgBa9mxD8ejPLHpYNPqJReeoRoHtq6m+ZcbAMi/72aCW8bURUQhhBDC5Ukx1MCVPjqVu6bpec9nO6WO0otahs1hY+VnL2BwQFLnAK6648k6TimEEEK4LimGGrghV03Czdef1IJUViZf3LPY3o1/l/e6nuI/Ezzp/tpCdDr5sRBCCNF0yFGvgTPrzUyIKbv8/edv51JaUlSr+dcfX89/d/0XgDETnyNETo8JIYRoYqQYagTGtx/Pwz/quef9FNa++0SN5zu0fS0p995Hs1yVf7T7B8Mjh1/GlEIIIYRrkmKoEfA0eRLSdzAAfh8tJzXpwleWpSXt4eQ999F1fykPrvHkkV6PXO6YQgghhEuSYqiRGDLtZY6Hu+NeArvuux1rcWGVbY/v28r+ibfgl2PnZKCRAe9+jklvqse0QgghhOuQYqiRMBhNtH7jLQrNEH44j1+mjKSoMPe8djtWLyVlwiQCM0rJ8tET9f4HBIREaZBYCCGEcA1SDDUirTr3p/Tp6ZTqIWr7Sf4YeRU/b/qEpJwkNqVs5Mc7h2O472m8CxycCDMTtfQLWrbvpXVsIYQQQlMGrQOIutXnhnvYYjRR9ORrBKWVMDX+JfL2KwA8edSODjjUN5wrX/8In2byNHohhBBCiqFGqNeo28nsMZi1S14iOCwdtSANH7MPaTeF077tSEYNvEHriEIIIYTLUFRVVbUO4cpyc3Px8fEhJycHb29vreMIIYQQogZqc/yWMUNCCCGEaNKkGBJCCCFEkybFkBBCCCGaNCmGhBBCCNGkSTEkhBBCiCZNiiEhhBBCNGlSDAkhhBCiSZNiSAghhBBNmhRDQgghhGjSpBgSQgghRJMmxZAQQgghmjQphoQQQgjRpEkxJIQQQogmTYohIYQQQjRpBq0DuDpVVQHIzc3VOIkQQgghaqr8uF1+HK+OFEMXkJeXB0B4eLjGSYQQQghRW3l5efj4+FTbRlFrUjI1YQ6HgxMnTuDl5YWiKHW67NzcXMLDwzl27Bje3t51umzxF9nP9UP2c/2Q/Vw/ZD/Xj8u5n1VVJS8vj7CwMHS66kcFSc/QBeh0Olq0aHFZ1+Ht7S3/2eqB7Of6Ifu5fsh+rh+yn+vH5drPF+oRKicDqIUQQgjRpEkxJIQQQogmTYohDZnNZp566inMZrPWURo12c/1Q/Zz/ZD9XD9kP9cPV9nPMoBaCCGEEE2a9AwJIYQQokmTYkgIIYQQTZoUQ0IIIYRo0qQYEkIIIUSTJsWQRubNm0dUVBRubm706NGD9evXax2pUZkzZw69evXCy8uLoKAgrr/+ehITE7WO1ejNmTMHRVGYMWOG1lEapZSUFG699Vb8/f1xd3enW7duxMXFaR2rUbHZbDzxxBNERUVhsVho1aoVzz77LA6HQ+toDdq6desYPXo0YWFhKIrCd999V+FzVVV5+umnCQsLw2KxMGjQIPbs2VNv+aQY0sDSpUuZMWMGjz/+ONu3b+fKK69kxIgRHD16VOtojcbatWu57777+OOPP/j555+x2WwMHTqUgoICraM1Wlu2bGHhwoV06dJF6yiNUnZ2Nv3798doNPLTTz+xd+9eXn/9dXx9fbWO1qi8/PLLLFiwgHfeeYeEhAReeeUVXn31Vd5++22tozVoBQUFdO3alXfeeafSz1955RXeeOMN3nnnHbZs2UJISAjXXHON8/mgl50q6l3v3r3Ve+65p8K09u3bq4899phGiRq/9PR0FVDXrl2rdZRGKS8vT23Tpo36888/qwMHDlSnT5+udaRGZ9asWeqAAQO0jtHoXXvttertt99eYdqYMWPUW2+9VaNEjQ+gfvvtt873DodDDQkJUV966SXntOLiYtXHx0ddsGBBvWSSnqF6ZrVaiYuLY+jQoRWmDx06lI0bN2qUqvHLyckBoFmzZhonaZzuu+8+rr32Wv72t79pHaXR+uGHH+jZsyc33XQTQUFBxMbG8v7772sdq9EZMGAAq1evZv/+/QDs2LGD33//nZEjR2qcrPFKSkoiLS2twnHRbDYzcODAejsuyoNa61lGRgZ2u53g4OAK04ODg0lLS9MoVeOmqiozZ85kwIABdOrUSes4jc4XX3zBtm3b2LJli9ZRGrXDhw8zf/58Zs6cyb/+9S/+/PNPpk2bhtls5rbbbtM6XqMxa9YscnJyaN++PXq9HrvdzgsvvMAtt9yidbRGq/zYV9lx8ciRI/WSQYohjSiKUuG9qqrnTRN14/7772fnzp38/vvvWkdpdI4dO8b06dNZtWoVbm5uWsdp1BwOBz179uTFF18EIDY2lj179jB//nwphurQ0qVL+eSTT/jss8/o2LEj8fHxzJgxg7CwMCZNmqR1vEZNy+OiFEP1LCAgAL1ef14vUHp6+nlVsbh0DzzwAD/88APr1q2jRYsWWsdpdOLi4khPT6dHjx7OaXa7nXXr1vHOO+9QUlKCXq/XMGHjERoaSocOHSpMi4mJ4euvv9YoUeP0yCOP8Nhjj/GPf/wDgM6dO3PkyBHmzJkjxdBlEhISApT1EIWGhjqn1+dxUcYM1TOTyUSPHj34+eefK0z/+eef6devn0apGh9VVbn//vv55ptv+PXXX4mKitI6UqM0ZMgQdu3aRXx8vPPVs2dPJkyYQHx8vBRCdah///7n3R5i//79tGzZUqNEjVNhYSE6XcVDo16vl0vrL6OoqChCQkIqHBetVitr166tt+Oi9AxpYObMmUycOJGePXvSt29fFi5cyNGjR7nnnnu0jtZo3HfffXz22Wd8//33eHl5OXvifHx8sFgsGqdrPLy8vM4bh+Xh4YG/v7+Mz6pjDz74IP369ePFF1/k5ptv5s8//2ThwoUsXLhQ62iNyujRo3nhhReIiIigY8eObN++nTfeeIPbb79d62gNWn5+PgcPHnS+T0pKIj4+nmbNmhEREcGMGTN48cUXadOmDW3atOHFF1/E3d2d8ePH10/AerlmTZzn3XffVVu2bKmaTCa1e/fucsl3HQMqfS1atEjraI2eXFp/+fz4449qp06dVLPZrLZv315duHCh1pEandzcXHX69OlqRESE6ubmprZq1Up9/PHH1ZKSEq2jNWi//fZbpb+TJ02apKpq2eX1Tz31lBoSEqKazWb1qquuUnft2lVv+RRVVdX6KbuEEEIIIVyPjBkSQgghRJMmxZAQQgghmjQphoQQQgjRpEkxJIQQQogmTYohIYQQQjRpUgwJIYQQokmTYkgIIYQQTZoUQ0IIIYRo0qQYEkIIIUSTJsWQEKLJGjRoEDNmzNA6hhBCY1IMCSGEEKJJk2eTCSGapMmTJ/PRRx9VmJaUlERkZKQ2gYQQmpFiSAjRJOXk5DBixAg6derEs88++//t27ENhDAQRcGVfHJAQmSJkJj6aJIuEDEtgOjiNtiZCn74tLIjImKMEa215GXAv/2yBwBkmOc5eu8xTVMsy5I9B0jkzRAAUJoYAgBKE0NAWb33eJ4newaQTAwBZa3rGsdxxHmecd93vO+bPQlIIIaAsvZ9j9ZabNsWY4y4rit7EpDA13oAoDSXIQCgNDEEAJQmhgCA0sQQAFCaGAIAShNDAEBpYggAKE0MAQCliSEAoDQxBACUJoYAgNI+hAdn+HOW6OAAAAAASUVORK5CYII=", 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" ] @@ -444,10 +465,13 @@ ], "source": [ "tlist=np.linspace(0,10,500)\n", - "plt.plot(tlist,sd_env.correlation_function(tlist))\n", - "plt.plot(tlist,ohmic_correlation(tlist,alpha,wc,1/T),\"--\")\n", - "plt.plot(tlist,np.imag(sd_env.correlation_function(tlist)))\n", - "plt.plot(tlist,np.imag(ohmic_correlation(tlist,alpha,wc,1/T)),\"--\")\n", + "plt.plot(tlist,sd_env.correlation_function(tlist),label=\"BosonicEnvironment (Real Part)\")\n", + "plt.plot(tlist,ohmic_correlation(tlist,alpha,wc,1/T),\"--\",label=\"Original (Real Part)\")\n", + "plt.plot(tlist,np.imag(sd_env.correlation_function(tlist)),label=\"BosonicEnvironment (Imaginary Part)\")\n", + "plt.plot(tlist,np.imag(ohmic_correlation(tlist,alpha,wc,1/T)),\"--\",label=\"Original (Imaginary Part)\")\n", + "plt.ylabel(\"C(t)\")\n", + "plt.xlabel(\"t\")\n", + "plt.legend()\n", "plt.show()" ] }, @@ -459,9 +483,9 @@ "One important optional parameter is WMax, when passing arrays to the constructor\n", "it defaults to the maximum value of the array, however when passing a function \n", "we don't need to specify the values on which it is evaluated, and in this case \n", - "WMax needs to be specified, Wmax is the value for which the spectral density,\n", - "or power spectrum has decayed to zero, after this value the function can be \n", - "considered to be zero" + "WMax needs to be specified, Wmax is the cutoff frequency for which the \n", + "spectral density, or power spectrum has effectively decayed to zero, after this value the function can be \n", + "considered to be essentialy zero" ] }, { @@ -472,7 +496,7 @@ "outputs": [], "source": [ "# From a function\n", - "sd_env2=BosonicEnvironment.from_spectral_density(ohmic_spectral_density,T=T,wMax=10*wc,args=(alpha,wc))" + "sd_env2=BosonicEnvironment.from_spectral_density(ohmic_spectral_density,T=T,wMax=10*wc,args={\"alpha\":alpha,\"wc\":wc})" ] }, { @@ -484,7 +508,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 17, @@ -510,12 +534,55 @@ "plt.plot(tlist,np.imag(ohmic_correlation(tlist,alpha,wc,1/T)),\"--\")" ] }, + { + "cell_type": "markdown", + "id": "54fc1734", + "metadata": {}, + "source": [ + "In this example we considered how to obtain a `BosonicEnvironment` from the spectral density, it can be done analogously from the power spectrum or correlation function using the `from_correlation_function` and `from_power_spectrum` methods." + ] + }, + { + "cell_type": "markdown", + "id": "33a4729e", + "metadata": {}, + "source": [ + "# Obtaining a decaying Exponential description of the environment\n", + "\n", + "In order to carry out our HEOM simulation, we need to express the correlation \n", + "function as a sum of decaying exponentials, that is we need to express it as \n", + "\n", + "$$C(\\tau)= \\sum_{k=0}^{N-1}c_{k}e^{-\\nu_{k}t}$$\n", + "\n", + "As the correlation function of the environment is tied to it's power spectrum via \n", + "a Fourier transform, such a representation of the correlation function implies a \n", + "power spectrum of the form\n", + "\n", + "$$S(\\omega)= \\sum_{k}2 Re\\left( \\frac{c_{k}}{\\nu_{k}- i \\omega}\\right)$$\n", + "\n", + "There are several ways one can obtain such a decomposition, in this tutorial we \n", + "will cover the following approaches:\n", + "\n", + "- Non-Linear Least Squares:\n", + " - On the Spectral Density (`.approx_by_sd_fit`)\n", + " - On the Correlation function (`.approx_by_cf_fit`)\n", + "- Methods based on the Prony Polynomial\n", + " - Prony on the correlation function(`.approx_by_prony`)\n", + " - The Matrix Pencil method on the correlation function (`.approx_by_mp`)\n", + " - ESPRIT on the correlation function(`.approx_by_esprit`)\n", + "- Methods based on rational Approximations\n", + " - The AAA algorithm on the Power Spectrum (`.approx_by_aaa`)\n", + " - The AAA algorith with balanced truncation (`.approx_by_aaa` with `btm=True`)\n", + " - ESPIRA\n" + ] + }, { "cell_type": "markdown", "id": "bef212bc", "metadata": {}, "source": [ - "Obtaining an decaying Exponential description via the spectral density" + "# Non-Linear Least Squares\n", + "## Obtaining an decaying Exponential Description via the spectral density" ] }, { @@ -523,10 +590,17 @@ "id": "ce27cb93", "metadata": {}, "source": [ - "Once our `BosonicEnvironment` has been constructed, we can obtained a Decaying\n", + "Once our `BosonicEnvironment` has been constructed, we can obtain a Decaying\n", "exponnetial representation of the environment, via fitting either the spectral\n", - "density or the correlation function. First we will show how to do it \n", - "via fitting the spectral density.\n", + "density, power spectrum or the correlation function. \n", + "\n", + "First we will show how to do it via fitting the spectral density with the \n", + "Nonlinear-Least-Squares method.\n", + "\n", + "The idea here is that we express our arbitrary spectral density as a sum of \n", + "underdamped spectral densities with different coefficients, for which a the\n", + "Matsubara decomposition is available. The number of exponents to be kept in the \n", + "Matsubara decomposition of each underdamped spectral density needs to be specified\n", "\n", "The output of the fit is a tuple containing an `ExponentialBosonicEnvironment`\n", "and a dictionary that has all the relevant information about the fit performed.\n", @@ -534,7 +608,8 @@ "by default the number of terms in the fit increased until the target accuracy \n", "is reached or the maximum number allowed `Nmax` is reached. The default target\n", "is a normalized root mean squared error of $5\\times 10^{-6}$, if set to None\n", - "the fit is performed only with the maximum number of exponents specified\n" + "the fit is performed only with the maximum number of exponents specified\n", + "\n" ] }, { @@ -574,7 +649,7 @@ " 4 | 1.07e-02 | 3.09e-01 |1.00e-01\n", " \n", "A normalized RMSE of 2.64e-06 was obtained for the the spectral density.\n", - "The current fit took 25.543029 seconds.\n" + "The current fit took 23.837589 seconds.\n" ] } ], @@ -598,7 +673,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -629,7 +704,7 @@ "id": "89164ff6", "metadata": {}, "source": [ - "Here we see a surprisingly large discrepancy in our approximated or effective spectral density. This happens because we are not using enough exponentials from each of the underdamped modes to have an appropiate fit. All modes have the same number of exponents, and we set it to 1 which is not enough to model a bath with the temperature considered, let us repeat this with a higher number of exponents." + "Here we see a surprisingly large discrepancy in our approximated or effective spectral density. This happens because we are not using enough exponentials from each of the underdamped modes to have an appropiate fit. All modes have the same number of exponents, when not specified it defaults to $1$ which is not enough to model a bath with the temperature considered, let us repeat this with a higher number of exponents." ] }, { @@ -640,7 +715,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -673,7 +748,7 @@ "id": "0b6f9c12", "metadata": {}, "source": [ - "Since the number of exponents increases simulation time one should go with the least amount of exponents that correctly describe the bath properties (Power spectrum, Spectral density and the correlation function). When the number of exponents is not specified it defaults to 1, which is typically enough when the temperature is high." + "Since the number of exponents increases simulation time one should go with the least amount of exponents that correctly describe the bath properties (Power spectrum, Spectral density and the correlation function)." ] }, { @@ -692,7 +767,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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", 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fv3+5rj1w4MAS9zi6Uq9evco8JiYmxrmdnJxcrutu27aNpUuXsnXrVk6cOEF6ejoFBQXOr2dl/fVBf0JCQvkLrsYUFqXGSs8tYMsJx+xWvVqcPgbdLHf2b86MP+NYtOMou5LSaBN95n/oRUREXCI1Fcrzi+2Z7mM7dqx855aeQMQwynceOEYDFZeVVb5zzxSUTp0q37knT5avtiqQn59f4p7D4pOxnEunTp2c23a7nQMHDpR4rriie+tcpXi9bdu2Ldc55T2uMqKjo8s8puieReC0ZUlKW716Nffffz9r1qwpdw1FM6TWdAqLUmOtPXCSArtB44gAGob5m12OU/O6QVzePpr525L4YPL/eH14axg40OyyRESkpgoJgYYNyz7uTPeg1a1bvnNDSs0LYLGU7zxwjAYqzt+/fOcGBZ3+XHh4+c6NMG9kT+lerjrlXEqr9HGnTp0667Fn63GsrOLBKCwsrFznlPe4yvAp/XemDEUT85zJjBkzGDduXIkexPLILf0hRw2lsCg11u/7TwDQq7nn9CoWuSs0lfnAD9ZoHnz7AxoqLIqISFUpPUS0Iio7iUdwMMTHV+7c4cMrf+7kyY6HByu9+Ht5g4+vr+85r1Oc1cWT6J0rbFVnsbGxjB8/3hkU69atyx133MGll15KixYtqFu3Lv7+/s6fZ2xsLM2aNTOzZLfTBDdS88yZAz168PuyTYBnDUEt0uXKfvQ6sot8LxufpgbDnj1mlyQiIiJuEBoaWmI/LS2tXOeVXiuwKnvuSiveVnnv/yvvcWZ68803yc7OBhxDd7ds2cKLL77IoEGDaNq0KYGBgSWCd3lfq5pEYVFqnqVLSd+0lW12x1j1nh7Ys4jNxp0t/AD4ptNg0t/5r8kFiYiIiDsEBQXh7//X7TEHDhwo13n79u0rsX+upStcrXHjxs7t8s4s6qoZSKvSwoULndtPPfVUmUuNHD58uKpL8jgKi1Lz/P47m6NbYbd60TDEh+hQP7MrOqMBd4+l+anDpPkGMuvPOKglN0qLiIjUdl27dnVuF58Z9VyKr3MYHh5O06ZNXV3WWXXv3t25vWzZsnLd37dkyZIqrMg1ik/cU/x7PJtVq1ZVZTkeSWFRapasLFi/nvUNHTNwdW3qgb2KhayRkYz3ddxXOfWCIdg//czkikRERMQd+vXr59yeNWtWuSZLmTZtmnO7b9++WCzuWz/6yiuvdG4fOXKkzAXpc3NzmTp1ahVXdf7y8vKc22X9PO12O1999VVVl+RxFBalZlm3DvLz2dCgMCw2Dje5oHMbfdtwgrPTORDRkKUzF0EFZ+ISERGR6mfChAnO7SNHjvDmm2+e8/hZs2aV6IG87bbbqqq0M7rgggvo2bOnc//BBx/k+PHjZz3+mWeeITY21g2VnZ/69es7t1euXHnOY19//XX2799f1SV5HIVFqVlWrcIANjRoA0C3xmGmllOWwM4XMDZlNwCfNrrYsRCxiIiI1Ght2rTh2muvde4/8cQTzJ49+4zHrl69mr/97W/O/c6dOzN8+PAqr7G01157zdn7duDAAS655BJ+//33EsecOnWK+++/n5dffrncS4KYacCAAc7t5557joSzrNH5+eef8+ijj7qrLI+isCg1y6pVHAyrz8mAUHys0L5BSNnnmOzW4d2w2gtY3qwbez6bYXY5IiIi4gbvvfeec0KV/Px8Ro8ezahRo/j6669ZtmwZs2fP5vbbb6dfv37OmVD9/Pz44osv8PLycnu9vXv35plnnnHub9u2jd69e9O8eXMGDhzIRRddRHR0NG+//TYAn376aYnzSy/94QnuvfdeZwCOj4+na9euPPfccyxcuJAlS5bwySefcNlllzmX17j99ttNrtj9tM6i1ByGAb//zvqGHQC4ICYMX5v7/zGtqEZXD2XwnJf5JaYLn3k14t/p6WdeaFhERERqjKioKJYsWcLgwYOJL1xXcs6cOcyZM+eMxwcHB/PDDz/QqVMnd5ZZwjPPPIPNZuPZZ5913u934MCBEjO6+vv7M2XKFAYPHlzi3NJLhniCCy+8kOeff54nn3wSgGPHjpUIxMWNGTOGxx9/nI8//tidJZpOPYtSc+zfD0ePsr6a3K/o5OXFhEsdNc9ufwkpXp73yZuIiIi4Xtu2bdm0aRP33XcfgYGBZzzG29ubG264ga1bt3LJJZe4t8AzeOKJJ9iwYQP33HMPrVq1IiAggNDQUDp27MhDDz3Eli1buOWWWzh69KjzHH9/fwICAkys+uyeeOIJPvnkE6Kios749ejoaN58801mzJjh1kmFPIXFMAzD7CLE9VJTUwkNDSUlJYWQEM8fiukSM2bA9ddz1c2vs6lBa965oSsjOjcwu6pyMQyDoW8uZ9eRNCaNaM/4Ps3MLklERDxMdnY2Bw4coFmzZvj5eeayUFJ52dnZLFu2jP3793Py5ElCQkJo3Lgxl1xySbX8XW7WrFnO+zJ79ux52v2NniY7O5vly5ezbds2srKyiIqKomXLlvTt29eUYb/F63LF+76y2UDDUKXmGDiQvBnfsGO94410QUPPG+5wNhaLhXE9G/P099uY9schbu3dtFZ+eiUiIlJb+fn5MWTIELPLcJni9yz26tXLxErKx8/Pj8GDB582fLa20zBUqTmiotjX/3JysRLka6NJhGcOdziba7o2JMDHiz1H01mzbo/Z5YiIiIiUUN4BiV988QU///yzc3/8+PFVVJFUNYVFqVG2JThmC2tfPwSrtXr1zAX7eXNVlKPmac99DNu2mVyRiIiIyF+ee+457rjjDpYsWUJ+fv5pX4+Pj+eBBx4oEQ6vuuoqUyflkfOjYahSo2w7XBgWq8GSGWcyLns/X9OYeW16c/yDz6jz9qtmlyQiIiICQFZWFh9//DEff/wxfn5+tGnThsjISPLz80lISGDfvn0ljm/SpAkffPCBSdWKK9SqnsVjx44xb948nnvuOUaOHEn9+vWxWCzOx9SpU91Sx/79+3n66ae58MILqVu3Lv7+/rRo0YJrrrmGb7/99oyf1EgZNm+GGTPYui8JgA7VNCxecNv1dE7aQ56XNzM3J0FGhtkliYiIiABgtf4VHbKzs9m0aRO//fYby5YtOy0oDhw4kNWrVzvXkpTqqVb0LCYlJdGzZ08OHjxodim89dZbPPLII+Tk5JR4fv/+/ezfv5/vvvuOnj17Mm3aNJo3b25SldXQ9OnYX3qZHf+cDr6B1WpymxLCw7kpIIVNwP/aDmTi9BlYb/ub2VWJiIiI8Oyzz9K/f38WLFjAn3/+yb59+zh58iT5+fmEhYXRoEED+vTpw7XXXsull15qdrniArUiLGZnZ3tEUHz++ed5+umnnftWq5X27dsTERHBnj17SExMBGD16tUMGDCANWvWUL9+fbPKrV7WrSMurB5pvoH4eFloGVV9F7UfftNQnv8mlriwaJbNmc4lCosiIiLiAby9vRk6dChDhw41uxRxk1o1DBWgbt26DB06lCeffJLvvvvObe3+8ssvPPPMM879Xr16sWPHDrZs2cLSpUuJj49n+vTpBAU5Qk58fDzXXXed2+qr1gwD1q9na70WALSNDsHbq/r+1fbv1YNrD28E4CvfprBjh6n1iIiIiEjtVCt6FiMiIpg5cybdu3enSZMmbm/fMAweeeQR53TDbdq0YdGiRQQE/LW0g9VqZezYsURGRjrXd1m5ciVz5szhmmuucXvN1crhw3D8ODvbOT7lqq6T2zhZLNzYI4ZPj8NvLbqT9OlXRE9+0eyqRERERKSWqb7dLxUQEhLCtddea0pQBJg3bx6bNm1y7r/11lslgmJxl112GWPHjnXuv/zyy1VeX7W3ZQsAu+o4Xt/W9YLNrMYlWo4fS4/4bditXszacBhyc80uSURERERqmVoRFs02e/Zs53azZs0YMmTIOY+fOHGic3vNmjXEx8dXWW01wtatAOyp0xioGWGROnUY43sKgG9a9Mb+41yTCxIRERGR2kZh0Q1++ukn5/bll1+OxXLuxeL79etHYGDgGc+XM9iyhWybDwfDogFoHV19J7cp7sprLyEoJ5OD4Q34oyCw7BNERERERFxIYbGKHT16lKSkJOd+r169yjzHZrPRvXt35/7mzZurpLYaY+tW9kbEYLd6EeZvo26Qr9kVuUTA5YMZ0dERgGfYo0yuRkRERERqG4XFKraj1EyWLVq0KNd5xY8rfQ0ppqAAtm8vNgQ1pMye22rDauX6wR0BmLc1iZSsPJMLEhEREZHaRGGxisXGxpbYb9y4cbnOK35c6WucSU5ODqmpqSUetcKpU9C9O7tjWgPQql7NGIJapFNMKG2jg8nJt/PDxgSzyxERERGRWkRhsYqlpaWV2A8NDS3XeSEhfy3/UPoaZ/LSSy8RGhrqfDRq1KhihVZXderAsmXsvuF2ANpE14DJbYqxWCyMucjxWs5YvMM586uIiIiISFVTWKxi6enpJfb9/PzKdZ6/v/9Zr3Emjz32GCkpKc5HXFxcxQqt5nYfdQTqVlE1KywCXBOchU9BPltT7Wx97QOzyxERERGRWkJhsYrl5+eX2LfZbOU6r/hxeXll36vm6+tLSEhIiUdtkZmbT9zJLABa17BhqADhbVswJPZPAL5JwjH0VkRERESkiiksVrGAgIAS+9nZ2eU6r/hxxZfRkNPtOeLoea0T5ENkDZkJtQRfX8Y28gbgu7b9yP7fDJMLEhEREZHaQGGxigUFlezpysrKKtd5mZmZZ72GFMrJgaZN2ffgUwC0qFtzf059bh5Bw5QjpPoFMf+XP80uR0RERERqAYXFKlanTp0S+4mJieU6r/jajJGRkS6tqcbYtw8OHuRAUjIAzWtwWLR27cqYo47JbWYENoedO02uSERERERqOoXFKtamTZsS+4cOHSrXecUnqGnbtq1La6oxdu0CYH94AwCa16nZw3VH92oOwO9NOhM/dbrJ1YiIiIhITaewWMVatWpVYrKajRs3luu8DRs2OLfbtWvn6rJqhsLetQMRDQFoVsPDYswtY+l9cDMAczYehoICkysSERERkZpMYbGK+fj4cPHFFzv3V6xYUeY5SUlJ7N2717nfv3//Kqmt2tu1CwM4EO4Ii83r1uywSFQUo23HAZjVuDvGokUmFyQiIiIiNZnCohtcddVVzu1FixZx5MiRcx4/bdo053ZYWJjC4tns3ElScCRZPn54WaBRREDZ51RzQ0f2ISA3i9iIBqyb/rPZ5YiIiIgLDBo0CIvFgsVi4dNPPzW7HBEnhUU3uOGGG/D1dSzpkJeXx3/+85+zHpuens7bb7/t3B83bhze3t5VXmO1Yxiwa5ezV7FxZCDeXjX/r3PgyGFcmbAJgFndh5lcjYiIiLjCpk2bnNtdu3Y1sZKql5eXxx9//MEbb7zBhAkT6NWrFw0aNCAgIABvb28iIyPp0qULt99+O7/88gt2u93skmu1mv/bdRWJjY11fgJksViYNGnSWY+NiYlh4sSJzv233nqLWbNmnXZcXl4eEyZMcE6C4+/vz+OPP+7y2muEY8cgOZn9teR+RSdfX0a/cC8Ac48YZOfpvkUREZHq7NChQ5w4cQJw3L7UoUMHkyuqWo8//jg9e/bkgQceYOrUqaxevZrExESysrLIz8/n5MmTbNq0iU8++YShQ4dy4YUXlpjLQ9yr1oTFO+64Az8/v9MeFT2msiZNmkSrVq0AKCgoYMyYMdx8883MmjWLxYsXM2XKFC666CK+/fZb5zmTJ0+mQYMGLmm/xqllk9sUd3GLusSE+5OWk88v25LKPkFEREQ8VvEg1KFDB3x8fEyspuoZhlFiPzAwkE6dOjFgwAAuueQS2rZti9X6V0TZuHEj/fv3L9e8H+J6trIPqRny8vLIyck55zH5+fnk5+dXSfvh4eHMnTuXyy67jLi4OOx2O1999RVfffXVGY9/+OGHueeee6qklhqhcNmMA4XLZtSmsGi1WhjVLYa3f93DrPUJXNWlodkliYiISCUVnym/W7du5hXiJv7+/gwfPpyRI0fSv3//05aZAzh27BhvvfUWL7/8MgUFBaSnp3PjjTeyfft2goJq7rranqjW9Cx6gtatW7N582Zuu+02/P39z3hMu3bt+P7773nllVfcXF01M2QIfPklB1p2BGr+Goulje7mCIgrdh8l6aMvTK5GREREKqt4z2JNv18R4Pnnn+fHH3/kjjvuOGNQBKhbty4vvPACU6ZMcT4XFxfHzJkz3VWmFLIYpfuCxS3S0tL47bffiIuLIyMjg/r169OxY0eX/SORmppKaGgoKSkphISEuOSaniavwE7bp+ZTYDf4/bFLqR965gBeI6WlMeaeD1jToB2PbPyOu3/+AKz67EdEpCbLzs7mwIEDNGvWzGW3yYj5mjRp4pyvYtWqVfTq1cvkijxLy5Yt2bdvHwC33HILn3/+uckVuZer3veVzQb67dIkwcHBXHXVVfzjH//gkUce4ZZbbqkVnya5UtzJTArsBv7eXtQLrmX/aQYHM7rgMADfNroQY8kSc+sRERGRCjt58qQzKFqtVjp37nzO4ydPnozNZnNOsHjnnXeSm5vrjlJNU3xoblKS5mpwN4VFqbYOnswEoElkAFarxeRq3O/KYRfjl5fNvshGbPr6R7PLERERkQoqfr9imzZtCAg485rR6enpXHfddTz88MMUFBTg4+PD+++/z4cffljjJ8QpPp9IcHCwiZXUTgqLUv0kJ8O8ecTtOABAo4gz/8Na0wWPuoqhB/4EYFZCAaSnm1yRiIiIVETx+xXPNrnNrl276NGjh3PG/OjoaBYvXsxdd93llhrNlJeXx++//+7c1xBd91NYlOrnzz/hyis59PZHADSupWERPz9GN/AC4IdWvcmZefranSIiIuK5yprc5rvvvqNHjx7s2LEDgIsvvph169bRu3dvt9VopieeeMI59DQiIoLx48ebW1AtpLAo1U/hTc6HwqKBWhwWgd43Xkn91GOk+Afz69xVZpcjIiIiFXC2nkW73c7jjz/OqFGjSE1NBeBvf/sbS5curfAa3FOnTnXe4+jKx9SpU13yMyguPz+fxMREvvvuO4YMGcLkyZMB8PPz4+uvvyYyMtLlbcq51Zp1FqUGKQyLcaH1gNodFr169eKayd/w35DBfGuJ5sqDB6FJE7PLEhERD2AYBll5BWaX4dH8vb2wWMyZ9yArK4tdhetGw189iydOnOCGG25g4cKFAHh7e/PGG2/U2PW369Spw4kTJ874NYvFwuDBg3nttde44IIL3FyZgMKiVEf79mEAcYU9i40iatGSGaVZLIzuFsN/M2Bp8ws5+sV0op56xOyqRETEA2TlFdD+6V/MLsOjbX/ucgJ8zPl1eMuWLRQUOMJ8s2bNCAsLY926dYwePZqDBw8CUK9ePWbOnEm/fv0q3U7Dhg25/PLLXVJz6etWtT59+nDXXXfRvn37Km9LzkxhUaqfffs45R9Cuq+jRzEmvPb2LAK0uOU6ujz1LRsbtOWH1fu43TDApE9JRUREpHxKD0H97LPP+Pvf/052djYA3bt3Z/bs2cTExJxXO4MHD2bw4MHndY2qNGjQIFJSUgDIyckhKSmJ3bt3Y7fbWbFiBStWrKB79+7MmDGDZs2amVxt7aOwKNWLYcC+fRwKc4zXrxfii5+3l8lFmaxJE0aH5rARmDVwLLcrKIqICI4hltufc32PUk3ib+LvEMXD4vLly5k166+J6m699VamTJlyXouwVxczZsw47bmTJ0/y8ccf89xzz5GRkcHatWsZMGAAf/75J1FRUSZUWXspLEr1cuwYpKcTF6P7FYsb8cq/eP7FX9lxPJvth1Np3yDE7JJERMRkFovFtCGWUrbiaywePXrUuT1x4kSmTJliQkWeIyIigocffpjBgwczYMAA0tLSiIuL48EHH+TLL780u7xaRbOhSvVSaibU2rrGYmlhAT4Mauf4pG32+niTqxEREZFzKSgoYPPmzc79YcOGObe/+eYbdu/ebUZZHqdr16488cQTzv3p06dz8uRJEyuqffRxk1QvRTOhFoXFWn6/YnGju8Uwb2sS3208zKNXtMXmpc+CREREPNGuXbvIysoCIDo6mlmzZnHJJZewevVqTp06xYgRI1i9ejXh4eHn3dbChQt57bXXzvs6pT344INuuRfy2muv5dFHHwUcS2usXbu2SibskTNTWJTqJS0NgoK0bMYZDGhTl0hfK8fTc1h279Nc+t8XzC5JREREzqD4/YqdO3fG19eXOXPm0KNHD+Li4ti9ezfXXXcd8+fPx2Y7v1/XExIS+OUX18+Ke/3117v8mmfSqFGjEvtnW2ZDqoa6HqR6uftuSE3lUKceADSOVFgs4m2BkZscazLNOlwAxe5/EBEREc9R/H7Fzp07A44exu+//56AAMfvNr/++iv33nuvGeV5lKKZUouEhYWZU0gtpbAo1U6e3eBweh6gnsUSrFZGt40AYGHLHqR8Nd3kgkRERORMSvcsFunatStffvkllsKZzadMmcLbb799Xm2NHz8ewzBc/hg/fvx51VVey5YtK7HfokULt7QrDgqLUu0kJmdTYDfwsVmpG+RrdjkepcOto2l79AC5Nh/mLt5qdjkiIiJyBmfqWSwyatQonn32Wef+Aw88UCXDSKuD3NxcXnjhr9tqWrRoQZs2bUysqPZRWJRqJyHZcUN4TLg/VqvWFCzO0q4do1L3AjA7tBUUm2lNREREzHfo0CHnfXe+vr5nDD9PPfWU857AgoICxo4dy44dO9xaZ1VYuHAhDz30EIcPHy7z2MTEREaMGFEiWBdNdCPuowlupPo4eBDuu4/DzXuBb0cahvmbXZFHurp/W16OL2BdTHsOfDGTZq92MrskERERKVQ8/HTo0OGsE9h8+umn7Nu3j7Vr15KSksKIESP4448/iIyMdFOlrpeRkcGrr77K66+/Tu/evenXrx8dO3akTp06BAQEkJ6ezv79+1m+fDnff/89mZmZznNHjhzJbbfdZmL1tZPColQfe/bADz+Q0DsA+nWkQajC4plEjbuOfn//L0ubdWPOtmM8kJ8P5zmTmoiIiLjG2e5XLM3f35/vvvuO7t27c/jwYfbt28fo0aNZuHAh3t7e7ii1ytjtdlasWMGKFSvKdfyECROYMmWK815OcR8NQ5Xq4+BBAA6H1AWggXoWzywyktF+jpnDZjXtgX3BApMLEhERkSLlDYsADRo04Pvvv8ff3/E7z9KlS7n77rurtL6qdNFFF/HAAw/Qvn37MoOfj48Po0ePZunSpXz66af4+Pi4qUopTt0NUn3ExgKQ4AyLfiYW49mGXNWP4OUZJITW449vfqHXlVeaXZKIiIgA3333XYWOv+iii0oMx6zOYmJieO2113jttddITk5m06ZN7N+/n+PHj5OTk0NgYCDh4eG0a9eOzp074+en3/XMprAo1UepnkXds3h2fsOvYNiXTzK9zQBmZwTSKy8PqvmQFREREak5wsLCGDBgAAMGDDC7FDkHDUOV6uPgQQzgcLCGoZbJx4fRl3UE4Od2/ck0NMZfRERERCpGYVGqj4MHSfYLJsvHMSQhOlRDE87lor/fROOIADJyC/hlW5LZ5YiIiIhINaOwKNVDfj7ExzvvV6wT5Iuft5fJRXk2i8XCqG4NAZi9PsHkakRERESkulFYlOrh8GEoKCh2v6J6FctjdLcYAFbsPU7isVSTqxERERGR6kRhUaqHwplQtWxGxTSKCKBHsB3DgO8mPAJ2u9kliYiIiEg1obAo1UO9evCvf3G4e19AYbEiRm9bDMCs6E4Yy5ebXI2IiIiIVBcKi1I9tGkDkyeT0H8woLBYEVde2R3fvBz21mnM5v/9aHY5IiIiIlJNKCxKtXI4OQvQGosVETzqKi4/8CcAs+NyoYYs7CsiIiIiVUthUaqVhFMKixUWEMDoeo7NH1r2JHf2d6aWIyIiIiLVg8KiVA8nTpCTm8fRtBwAGmg21Arpe/3lRKWd4FRAKL/9oPsWRURERKRsCovi+QwDGjXiSMPmAPjarEQE+phcVPXi1a8f1yRsAGB2QSQkaN1FERERETk3hUXxfKdOQVYWh/3DAMfkNhaLxdyaqhurldGdHGNRFze/iJNfTje5IBERqSzDMMwuQUTcxOz3u8KieL7CXrAjQZEA1AvxNbOaaqv1rdfRMXEPeV7e/LB8p6PHVkREqg2r1fFrm11r5orUGkXv96L3v7spLIrni48HICnYERajQ3S/YqW0aMGorFgAZjfp7uixFRGRasNms2GxWMjJyTG7FBFxk+zsbCwWCzabzZT2FRbF85UKi/VCFRYra+S912OzwOaQhuzJ8za7HBERqQCr1Yq/vz8ZGRlmlyIibpKamkpQUJB6FkXOqjAsOoehBissVlZkv55c0tZx7+Ks9ZrkRkSkugkKCiIjI4Pc3FyzSxGRKpaRkUF2djYhISGm1aCwKJ7Pec9iBADR6lk8L9de2BCAORviKbDrvkURkeokNDQUm81GfHw8BQUFZpcjIlUkIyODuLg4AgMDCQoKMq0Ocwa/ilRE6WGoumfxvAxsG0VYgDdHUnNYuXQT/Qd2MbskEREpJ5vNRqNGjYiNjWXv3r2EhoYSFBSEl5eXZgoXqcYMw8But5OdnU1qairZ2dkEBgYSExNj2hBUUFiU6iA+HjsWjqpn0SV8bV6M8DrJlwQz+83/0b9TI4iMNLssEREpJ19fX5o1a0ZycjIpKSmc0oRlIjWGxWIhKCiIyMhIU+9VLKKwKJ4vIYGTASHkeXljsUBUsJbOOF+jk3fzpdeFzG95MWnTphN83z1mlyQiIhXg4+NDVFQUdevWJT8/X0NSRWoAq9WKzWYzPSAWp7Aonm/NGpJ2HIJV2UQG+uLt5TlvoOqq881X0+KdVeyLbMS8Xzcx5j6zKxIRkcqwWCx4e3vj7a0ZrkXE9fRbt3i+Vq040uoCAKJD1avoCpZOnRh1cicAswKbw44dJlckIiIiIp5GYVGqhaTUbEDLZrjSqD4tsRh2/mjckbjPZ5hdjoiIiIh4GIVFqRaOpBSGRU1u4zL1bxpDn0ObAZiz6TDofhcRERERKUZhUTzb8uXwyScc2bEPgGgtm+E6UVGMsp0EYHaj7hi//mpyQSIiIiLiSRQWxbN9/TXcfjtJK9cCCouuNnR4LwJys4iNaMC6GfPMLkdEREREPIjConi2+HgAjgQ51gHUMFTXCrh6BFcc+BOAWUcMSEszuSIRERER8RQKi+LZCsNiUrAjLKpn0cV8fRkd45hufW77AWSfTDa3HhERERHxGAqL4tni48m2+ZDsHwIoLFaFnk/cQ8MQX9KsPixM9jK7HBERERHxEAqL4rlyc+HYMY4ERQDga7MS4m8zuaiaxxodzTUXNgJg9vp4k6sREREREU+hsCie68gRAJIK71eMDvXDYrGYWVGNNapbQwCW7TnO0bRsk6sREREREU+gsCieKzERgCOF9yvW0xDUKtO8bhDdGodRYDf4/vN5YBhmlyQiIiIiJlNYFM9VGBaPBoYDEBXsa2Y1Nd6oEzsAmLU+AVavNrkaERERETGbwqJ4rsKweMwZFtWzWJVGNPTBJz+XnVHN2PbVHLPLERERERGTKSyK5/L1hdatORYWBUBd9SxWqdDrrmHwgXUAzD6QCdm6d1FERESkNlNYFM81YQLs2sWxEaMAhcUqFxzMqIg8AL5v0ZO8H340uSARERERMZPConi8Y2k5gMKiO/QfM5g6Gac4HhjOsjlLzC5HREREREyksCgezxkWgxQWq5r3oEsZGbcegNnZIc7lS0RERESk9lFYFI+WV2DnZGYuAFEhCotVzmpldDvHUiULW1xMylfTTS5IRERERMyisCieyW6HXr04OWYchgFeVgvhAT5mV1UrdBh/LW2PHiDX5s2PS7aZXY6IiIiImERhUTzT8eOwejVHV64FIDLQBy+rxeSiaok2bRiduheA2WGtYdMmkwsSERERETMoLIpnKrXGoia3ca+rBrTFy17A+obt2O8XbnY5IiIiImIChUXxTEVhMUhh0QxRd91G/3bRAMyOzTK5GhERERExg8KieKakJOCvnsUohUX38vZm9IUxAMxaH0+B3TC5IBERERFxN4VF8Uwahmq6we3rERbgTWJKNsv2HDO7HBERERFxM4VF8UyFYfFoUASgNRbN4GvzYlRXR+/ijE9/hv37Ta5IRERERNxJYVE8k7NnMQyAusF+JhZTe41N2gjAIns4xz750txiRERERMStbFV14YSEBLZv387Bgwc5duwYGRkZAAQGBlK3bl2aNGlChw4daNCgQVWVINVZqWGoUSHqWTRDmxGX0vXhaWxo0JbZa2KZmJ8Ptir7Z0NEREREPIjLfus7deoU33//Pb/88gtLlizh6NGj5TovKiqKAQMGcPnllzNy5EgiIyNdVZJUZ87ZUDUM1VTR0VxvOcoG2jKjaU/unDcfy4jhZlclIiIiIm5gMQzjvKY5nDdvHh988AHz588nLy8PgIpe0mJxLLZus9kYOnQod955J8OGDTufsmq91NRUQkNDSUlJISQkxOxyKu6//yU9No4LrH0B2Pbs5QT6qkfLDBnfz6XHkiwyfAOYcfBHLv56itkliYiIiEgFVDYbVOqeRbvdzmeffUabNm0YPnw4P/74I7m5uRiGUSIo+vr60qxZM7p160afPn3o3bs3Xbt2pWnTpvj6/tVTVHReXl4ec+fOZeTIkbRq1YpPPvmEgoKCypQo1d3f/86xh58EIMDHS0HRRIHDhjLi0DoAZqQHOXt9RURERKRmq/Bv4DNnzuTxxx9nf+HMiEXh0M/Pjz59+jBgwAC6d+9Ox44dy7wfMSEhgS1btvDnn3+ydOlSVq5cSXZ2NgD79+/nzjvv5N///jcvvfQSY8aMqWipUs0dS8sBtGyG6Ww2xrYNY3oB/NS6N89M/YrQxx4yuyoRERERqWIVGoY6YMAAVqxYAThCos1mY9iwYYwbN44rrriCwMDA8yomMzOTefPm8b///Y+5c+c6h7VaLBb69OnDsmXLzuv6tUm1H4YK/LQ5kXv+t57uTcOZeVdvs8up1Yx9+xj67/nsqtuU5zfM5Ob5n0Hh8HERERER8WxuGYa6fPlyDMOgTp06PPvssyQkJDBnzhyuvfba8w6KAAEBAYwePZpZs2aRkJDAc889R1RUFIZhsHLlyvO+vlQTqalw5AjHUrMA9Sx6AkuLFozNigVgev2uoA9uRERERGq8CoXFqKgo3nzzTQ4dOsRTTz1F3bp1q6ou6tSpw5NPPsnBgwd54403qrQt8TD/+x9ER3P0iWcBzYTqKa658kJ88vPYFt2SrTN+MrscEREREaliFQqL+/bt47777isxOU1V8/X15f7773feIym1wJEjwF9rLKpn0TOEX3s1l2fFATB90DiTqxERERGRqlahsOiKoaaVFRAQYFrb4maFa3QeDwwDIFI9i57Bz48b/u8GAL7fnERWrmYqFhEREanJKrV0hkiVKgyLJ/1DAaijsOgxejaPpHFEAGk5+fy8RUtoiIiIiNRkCovieZw9i46wGBnkY2Y1UozVamFs90YATF97COx2kysSERERkaqisCie58gRDOBEQBgAdQLVs+hJru3aAC9gbewp9kz8P7PLEREREZEqYnPVhY4fP86KFStYuXIlO3bsYO/evSQmJpKTk4NhGNSrV4/GjRvTvXt3+vTpw6BBgwgPD3dV81KTHD1Kprcf2d6OkKieRc9Sz9fCZQfW8kuz7kw7lMekkychIsLsskRERETExSyGYRiuuJDVasVSbJHuM122+NdtNhuXXXYZd955J1dddZUrSpBiKrvwpulyc8HXl0Oh9eh/1yf4eVvZ8dzQEn93xHzLHnieW3y6EZydzh+NEwl44J9mlyQiIiIiZ1HZbODyYaiGYZwxKBZ9rejPvLw85s+fz6hRo+jatStr1qxxdSlSHR0/7vijaCbUQF8FRQ/U987raHLqMGl+Qcydvw5c85mTiIiIiHgQlw1DBUcIbNy4Mc2aNaNhw4bUrVsXi8WCYRgcPnyYQ4cOsWXLFjIzM53HA2zatIm+ffvy2muvce+997qyJKluCtdYPBFQNBOqhqB6ImvbttyY9ikvhTfgq/rdGLN4MVx6qdlliYiIiIgLuSwszp8/n4suuoiIMu5dKigoYMOGDcydO5evv/6aPXv2YLFYyM/P55///CfBwcGMHz/eVWVJddOhA+zZw4k1h2BzltZY9GDXDruI1zbksbl+azZ/OpNOCosiIiIiNYrLhqEOGTKkzKAI4OXlxUUXXcSkSZPYtWsXc+bMoVmzZoCjp/H+++8nKSnJVWVJdePjAy1bcqJ+YwAiA9Wz6Kkir7uaKw7+CcD/TvqA3rciIiIiNYrpS2dcddVVrFu3jp49ewKQnp7OlClTTK5KzHY8PQdAPYuezMeHm1oGAvB92/6kfjLV3HpERERExKVMD4sAoaGhfP3119hsjlGxc+fONbkiMdvJjFxA9yx6uovuGEvrYwfJ8vFjzpIdUFBgdkkiIiIi4iIeERYBmjRpQrdu3TAMgwMHDphdjpjlhx/gww85cSAe0BqLns7SvDnjcg8CMK3tJRgaiioiIiJSY3hMWATIzs4GcM6WKrXQ++/DxIkc3+MIIJGBGobq6a75xxj8vSzsDozizzx/s8sRERERERdxS1jMy8tj9erVpKenn/Hr+fn5vPzyy2zevBmLxULjxo3dUZZ4oqNHgb+WzlDPoucLufhCruoWA8BXqw+aXI2IiIiIuIpL11k8m9TUVHr37o3FYiE6OpqYmBgiIiLw9vbmxIkTbN26lfT0dOfi62PHjnVHWeKJjh7FjoWTznUW1bNYHYy7uAnT18Yxb0sSTw/P0cREIiIiIjWAW8JiEcMwSEpKOm1pDMMwnNujRo3iySefdGdZ4ikMA44eJcUviAKrFwDhAepZrA46xoTSOSaUTfEpzPxmKXfd2B/8/MwuS0RERETOg1uGofr7+zN69GiaNGmCYRjOB4DFYuHSSy/lww8/ZMuWLXz77bf4+Cgg1EopKZCby4lAR69iqL83PjaPuq1WzmFcSAYAX62Jo2DGNyZXIyIiIiLnyy2/iQcEBDBz5kwOHDhAfHw8n332Gddccw0+Pj7Y7XYWL17Ma6+9xtHC+9Wklip8/Y8HhAG6X7G6GdksiPDMFOLDovl1+gJHT7GIiIiIVFtu77Zp0KABt956K7NmzeLw4cNMmjSJwMBAdu3axeDBg/n000/dXZJ4itKT2wQqLFYnfn17M/bIJgA+D20Hf/xhckUiIiIicj5cFhbz8/MrfE54eDhPP/00W7ZsoX379tjtdu6++242b97sqrKkOjl+HIATRT2LWjajerFYuGlQe6z2AlY27cLu978wuyIREREROQ8uC4sXXHAB8+fPr9S5TZo0Ye7cufj6+pKfn8+rr77qqrKkOnGGRS2bUV3F3DyGIQfXA/D5US8oNZmViIiIiFQfLguLu3fvZtiwYQwfPpwNGzZU+PymTZty4YUXYhgGv/32m6vKkurEzw/ateNEZDSAll+ojvz9ubWZYxbU2e0uIeWDT0wuSEREREQqy+X3LM6bN4+LLrqIESNGsGzZsgqde/LkSQCOHTvm6rKkOrjpJti+nRPX3QhAHfUsVks9776BNsdiyfLxY+ayXZCba3ZJIiIiIlIJLguLTz/9ND4+Ps5lMX7++WcGDhxIy5Ytee655/jjjz+w2+1nPf+DDz5g586dAERERLiqLKmGTqQ7woXuWayeLE2acKs9HoAvWvanYNZskysSERERkcqwGIbr5rffs2cP99xzD4sWLSrZiMUCQGBgIBdccAFt2rShXr16+Pr6cvLkSVasWMHmzZsxDAOLxcKQIUOYN2+eq8qqlVJTUwkNDSUlJYWQkBCzy6mQS19bwv5jGUy/syc9m0eaXY5UQubC3+g19ygp/sF8su0bBv34udkliYiIiNRalc0GNlcW0apVKxYsWMDChQt56qmnWLNmjfNrhmGQnp7OH3/8wR+lptQvnVcnTpzoyrKkminqWdQw1Oor4LKBjP3kcT5s2pepA8cxyOyCRERERKTCqmSdxcGDB7N69WoWLVrE6NGjsdn+yqRn6sgs6nkE+L//+z+uvvrqqihLPN2oUeQOH0FKVh6gYajVmsXCzW8/hsUCy4/mse9YutkViYiIiEgFVUlYLHLppZcyc+ZMEhMTmTFjBnfddRd9+/YlKirKeW8jQFRUFNdeey0LFizQshm12a+/cmrpKgC8rBZC/b1NLkjOR6OoEC5rVw+AL1bFmluMiIiIiFSYS4ehnk1ERATXXXcd1113nfM5wzDIzs7Gy8sLHx8NN6z1cnMhNZWTdZsCEB7gg9VqOfc54vHG927Kwu1H+HZdPA8MaaMPAERERESqkSrtWTwXi8WCv7+/gqI4nDgBwCl/xw23EYEKFTVB7xaRtIkKJCO3gOm3PQFZWWaXJCIiIiLlZFpYFCnh+HEATgY4wmJ4gD5EqAksFgu37V8BwNSw9uR9+ZXJFYmIiIhIeSksimcoDIt/9SwqLNYUV900hDrpp0gMqcvPMxfDOdZbFRERERHPobAonqGoZ7EwLIYrLNYYvj17cOvJLQB81PBijPnzTa5IRERERMqjQmHxueeeIyMjo6pqOauMjAyee+45t7crblS6Z1HDUGuUcdf0wi8vm63RLfnjo2/MLkdEREREyqFCYXHSpEm0aNGCl19+meTk5Coq6S/Jycm89NJLNG/enGeffbbK2xMTlb5nUT2LNUrEqBGMPvQnAB9bG8PmzSZXJCIiIiJlqfAw1GPHjvHEE0/QuHFj/v73v/Pnn3+6vKg1a9YwceJEGjduzJNPPsmxY8dc3oZ4mNPuWdRsqDWKlxe39W4CwKJWF7P/7Y9MLkhEREREylKhsLhkyRI6deqEYRikp6fzwQcfcPHFF9OmTRseffRRFi9eTHZ2doWLyMzMZOHChfzrX/+iZcuW9OrVi48//pj09HQMw6Bz584sXry4wteVauTyy+HRRznZqDmg2VBrouZ33MRlsesB+CQBSEw0tyAREREROSeLYRhGRU4wDIMvv/ySF154gb179zouYvlr8XRvb2/atWvHBRdcQPPmzWnYsCFhYWH4+/tjGAbZ2dmcOnWKhIQE9u3bx9atW9m5cyf5+fkl2gBo2bIlTz31FDfddFOJNqRsqamphIaGkpKSQkhIiNnllFvvl37lcEo2P/yjD51iwswuR1xs9aMvcT2d8MvLZpX/ViJeeMbskkRERERqvMpmA1tFG7JYLNxyyy3cdNNNfPPNN7z99tusXr3a+fXc3Fw2b97M5grck1Q6r/bs2ZP777+f6667DqtVE7bWJiczcwH1LNZUF//jZi54ajZb67VgWmYI95pdkIiIiIicVaWTmNVq5frrr2fVqlVs3ryZRx55hFatWgGO8Ff6UeRsX2vZsiUPP/wwmzZtYtWqVYwdO1ZBsZbJyi0gO8+xBp/WWayZLDEx3NGjAQCfR3QkO6/A5IpERERE5GwqPAy1LLGxsSxdupR169axfft2Dh48yPHjx51LbgQGBlKnTh2aNGlC+/btufDCC+nfvz/NmjVzZRnntGrVKj7//HOWL19OQkIChmEQExND3759ufXWW+nTp4/L26zMMNr333+fu+66q1LtVbthqImJJHgF0Of1FfjYrOx6fqiGHtdQeQV2BvxnMYdTsnnxmgsYd3ETs0sSERERqdHcNgy1LE2bNqVp06bceuutrr70ecvIyOC+++7j008/Pe1rO3bsYMeOHXz00UdMmDCBd955h8DAQBOqrIUyM6FBA07VawHj3yIiwEdBsQbz9rJye7/mPDd3Ox8s3c/Yixph89IoAhERERFP4/Kw6KkKCgoYNWoUCxYscD7n7+9Phw4dsNlsbN++ndTUVAA+++wzEhIS+Pnnn/Hy8nJ5Lf3798ff37/M4xo3buzytj3SiRMAnPTXGou1xfU9GvHOb3s4dDKTn9+fyci/Xwcadi4iIiLiUWpNWHzqqadKBMU77riDl19+mYiICMDR6/jKK6/w/PPPA7BgwQKefvppXnzxRZfX8vnnn9O0aVOXX7fa0hqLtU6Aj43xwWm8kenH++uOMuKnn7GMGG52WSIiIiJSTIU/yj9w4EBV1FGlDh8+zBtvvOHcv/nmm/nwww+dQREc91I+99xzPPnkk87nXn/9dQ4fPuzWWmulwrB4MqCwZ1EzodYKtzbxISA3ix31mrP0g2/MLkdERERESqlwWGzRogXh4eEMHDiQBx54gC+//JItW7ZQUOC5sxq++eabZGdnAxAQEMCbb7551mOfeuopGjVqBEB2djZvvfWWO0qs3U7rWVRYrA3Crh7GjXFrAfhvSHtYscLkikRERESkuErdJJSamsqyZct46623GD9+PF26dCEoKIju3btz55138v777/P777+TmZnp6norZc6cOc7tMWPGlOhRLM3Hx4cJEyY492fPnl2ltQl/9Sz6q2exVrFaue2KjngX5LGmcUfWvfWZ2RWJiIiISDGVCoul10g0DIOcnBzWr1/PJ598wj/+8Q/69u1LSEgI7dq148Ybb2Ty5MksXLiQ44XBwF127drF3r17nftDhw4t85wrrrjCub1371527dpVJbVJoZMnATgVoJ7F2qb+zWO5JtbRu/i+vSFs3mxyRSIiIiJSpMIT3Pzwww9s3LjR+Sh+D2NReLRYLM4wuXv3bnbv3s2MGTOcxzVo0ICuXbvStWtXunTpQteuXatswpdNmzaV2O/Vq1eZ53Tr1g0fHx9yc3MB2Lx5M23atKmS+gRnWNRsqLWQtzcTe8Uw84idRa0uZtfrH9Bm6ntmVyUiIiIiVCIsDh8+nOHD/5q1MC0tzRkcN2zYwMaNG9m+fbszaJUOkAAJCQkcPnyYn376yXmd0NBQunTpQo8ePejXrx/9+/cnODj4vL45cKyfWMTHx8d5P+K5FB23b9++067hCg899BDbt28nLi6OvLw8IiMjadWqFQMGDODWW2+lWbNmLm3P4xWFxYBQACI0DLVWaTHxFob+bTLzmnVnygl/3ti3D1q0MLssERERkVrvvJfOCA4Opl+/fvTr18/5XH5+Ptu3b3eGx40bN7Jp0yaSk5Odx5QOkcnJySxdupSlS5cyefJkfH19GTlyJPfeey99+vSpdH2xsbHO7ZiYmHIv9t64cWNnWCx+DVf49ttvS+wnJCSQkJDAkiVLePHFF7ntttt44403yrUWY41wWs+ils6oVQIDubtdMPOy4Yd2/fnnK2/T5ENNLCUiIiJitipZZ9Fms9GpUyc6derErbfe6nw+Nja2RA/kxo0biYuLc369eIDMzs5m5syZzJw5k1GjRvHxxx8TGhpa4VrS0tKc2xU5PyQk5IzXcIU6derQokULgoKCSElJYefOnaSnpwOOoP3BBx+wZs0aFi9eXO6ac3JyyMnJce6npqa6tOYq9f77GIcPc+r7E2DonsXaqNM/b6P/3//Lssadee+IN/9JTYVi70ERERERcb8qCYtn07RpU5o2bcrVV1/tfO7UqVMlwuO6devYuXMn8Fd4nD17Nlu2bGHFihXUqVOnQm0WhTAAPz+/cp9XvFev+DUqq3379tx5552MGDGC5s2bl/hafn4+v/zyC48//jibCyf42LBhA9dffz3z5s0r1/Vfeuklnn322fOu0xSNG5MWVZ/87xYAmg21VgoL4/72QSxLh9kdBnJvvo2yB4yLiIiISFWq1GyorhQeHs6ll17KAw88wBdffMG2bds4ceIE06ZNY9CgQc7jdu/ezbhx4yp8/fz8fOe2zVb+bFz82Ly8vAq3W9q2bdu4//77TwuKRW0NGzaMP/74g2HDhjmfnz9/Pj/++GO5rv/YY4+RkpLifBTvsa0OTmU47nEN8PHCz9vL5GrEDBc+eR99W9Yh3w7/XbK37BNEREREpEqZHhbPJCwsjBtuuIGFCxcyd+5cAgMDAVi0aBGLFy+u0LUCAgKc29nZ2eU+r/ixRe1XNT8/P77++mvq1avnfO6dd94p17m+vr6EhISUeFQnJwvDonoVa7f7L2sFwMw/44k/5RnrtIqIiIjUVh4ZFou78soref/99537//vf/yp0flBQkHM7Kyur3OdlZv71i2rxa1S14OBg7r77buf+8uXLKxRyq52sLPjwQ04tcHwIoPsVa7fuTSPo3SKSfLvB+wt3wqlTZpckIiIiUmt5fFgEuPHGG4mMjARg1apVFTq3+D2OiYmJ5T4vKSnJuV3UtrsMHDjQuZ2dnV3thpRWSFISTJzIybcca+tpjUW5v3dDAL5ZG8fhZ182uRoRERGR2qtahEWLxUL79u0xDIPDhw9X6Nw2bdo4t0+cOFGix/Bcige0tm3bVqjN8xUdHV1i//jx425t360Kl8045V+0xqKWzajtLq7ry8Xx28jzsvH+rkzHBwoiIiIi4nbVIizCX/ceVnQZi3bt2pXY37hxY5nnJCQkcOzYsbNeo6qVDrTF77uscU6cAIqvsaiexVovKor76zvuYZ3RYRCJr5bvvl0RERERca1qExbfeustPvzwQ2677bYKndejRw98fX2d+ytWrCjznOXLlzu3/fz86NGjR4XaPF/btm0rsR8VFeXW9t2qqGcxwBEWIzTBjQC9HriNHgnbybV5M2VbinoXRURERExQbcJi69atuf322/nggw8qdF5QUFCJJTimTZtW5jnFjxk0aJDbZkMtMn36dOd206ZNqV+/vlvbd6vCsKieRSnOUr8+99d1TEj1dYfLSHjpdZMrEhEREal9qk1YPB/jx493bm/evPmcaxeuX7+eefPmnfFcd/jhhx+YO3euc//qq692a/tu57xnsbBnUWFRCvV++E4ujt9Grs2bd/bmwqFDZpckIiIiUqvUirB47bXX0rlzZ+f+xIkT2blz52nHJSYmctNNN1FQUABAly5dGD169BmvGRsbi8VicT4mTZp0xuNSUlIYPXo069atK7POr7/+mhtvvNG5HxAQwCOPPFLmedVaUc9i4TDUME1wI4Us0dE81MjxXpzZ4VIOvPCayRWJiIiI1C42swtwB4vFwkcffcSAAQPIysoiMTGRiy++mLvvvpv+/ftjs9lYs2YN7777LkeOHAHA39+fDz/8EIvFcl5tG4bB7NmzmT17Nm3btuXyyy+nS5cu1K9fn8DAQNLS0tiyZQvffvsta9euLVHzZ599dtrMqDVOYVhM9gsG1LMoJV308F0M/Pt7LG7SlTeO+PL2nj3QqpXZZYmIiIjUCrUiLAJ0796dr776iptuuomsrCxSU1N55ZVXeOWVV0471t/fn6+++oru3bu7tIadO3eesUeztODgYD744APGjBnj0vY90smTGECyvyMshvkrLEox4eE82NafxVnwY9t+3D35v7T78A2zqxIRERGpFWrFMNQio0aNYt26dVx22WVn7DG0WCwMGjSIP//8k1GjRrmkTX9/f+688046dOhQZi9laGgo9913H1u3buWGG25wSfseLySEtJimFFi9AA1DldNd8MCdDDu0HsNi5bUe15ldjoiIiEitYTEMwzC7CDPExcWxcuVKEhISAGjYsCF9+vShUaNGVdbmqVOn2LhxI0ePHuX48eMkJycTEBBAREQEnTp1olOnTnh5ebmkrdTUVEJDQ0lJSSEkJMQl16wqcScz6fefxfh5W9n5/BVmlyMeaG9iMkPeXondgDl/703XxuFmlyQiIiJSbVQ2G9SaYailNWrUiOuvv96tbYaHhzNw4EC3tlkdnMp0LMCuIahyNi3rhzG6Wwwz18Xz6oJdTLu9p9kliYiIiNR4tWoYqnim5Mw8QENQ5dzuv6wV3l4WVu49wco/90LtHBQhIiIi4jYKi2I6Z8+iwqKcQ0x4ADd2ccwO/J93f8L+yy8mVyQiIiJSsyksinmSkuDKK0l5/2MAwgM0DFXO7R85ewnMyWRTdEvmvjkNCtdEFRERERHXU1gU8yQlwbx5nNrqWE5EPYtSlro3Xstd8asB+E/TS8j5/EuTKxIRERGpuRQWxTwnTwJ/rbEYqglupCxWK7ffNZx6aSeID4vmi6+XQFaW2VWJiIiI1EgKi2KewrCY4ucIi+HqWZRy8B80kAdTNwPwTocrSH7jXZMrEhEREamZFBbFPIVh8VRhz6KGoUp5jX54PG2PxZLqF8S7yw7AsWNmlyQiIiJS4ygsinmKhqH6FYVFDUOV8vHqeAGP+R4G4IsLhnDoxddMrkhERESk5lFYFPOUumcxzF89i1J+A568h36HNpFr8+Y/scDevWaXJCIiIlKjKCyKeZw9i0EAhAeqZ1EqoEEDHmtuxWLYmdu2Hxve/MTsikRERERqFIVFMc+pU9ixkFIYFtWzKBXV/pF7GH1wLQCT2lyJ3W6YXJGIiIhIzaGwKOZJTibNNwC71QuAUE1wIxUVHMzD7z9CoI8XmxJSmb0hweyKRERERGoMhUUxz9VXc+pvdwIQ4OOFr83L5IKkOooKD+S+Qa0AeGX+TtKy80yuSERERKRmUFgU89x7L8mPPQ1oCKqcnwl9mtGsTiDH0nJ499s1zvthRURERKTyFBbFVKcycwEtmyHnx8dm5amBTQD4dNMx9k96xeSKRERERKo/hUUxVUqmY8hgmO5XlPN0abQPl8SuJ8/LmxcS/WHrVrNLEhEREanWFBbFHHY75OaSXNizGK6eRTlfDRvyVAd/bAX5/NaiO4uffhMMzY4qIiIiUlkKi2KOQ4fA15dTTz4LaCZUcY0WD/2DCXuWAvB8ZHdyvvnW5IpEREREqi+FRTFHcjIAKV6+AIQrLIor+Plx3039qZt+kv2RMXz42QJITTW7KhEREZFqSWFRzFEYFk/5hwAQ5q9hqOIawaOv4qlT6wB454IriX3mZZMrEhEREameFBbFHKdOAZDsHwRoGKq4kMXCiOfvo9+hTeTafHjqsD/Ghg1mVyUiIiJS7SgsijkKexaT/Rw9i5rgRlzJ0rw5z7XzwSc/l+XNuvHTM+86JlUSERERkXJTWBRzFIXFwp5FLZ0hrtbskfv4+97FADzX/DLSDh02uSIRERGR6kVhUcxR6p5FTXAjLufjw133jaKZVy5H/UJ4bXOK2RWJiIiIVCsKi2KOU6cosFhJ9Su8Z1ET3EgV8Lv0Ep4f3w+AL36PZUu8AqOIiIhIeSksijmSk0n1DXTuahiqVJW+reowsnMD7AY8MmszeQW6d1FERESkPBQWxRzJyZzyDwYgyMeKt5f+KkrVeWp4e8ICvNmemMqHdzwLaWlmlyQiIiLi8fQbupjjpZdIfv9jAMI0E6pUsbrBvjwTchyAtyK6svfJf5tckYiIiIjnU1gUc3ToQHLnbgCEBSosStW7engPBh5YT67Nm4dPRlKwYoXZJYmIiIh4NIVFMU1yZh4AYZrcRtzA0rIlL14USlBOJusbtmPqS19AdrbZZYmIiIh4LIVFMc2porCoyW3ETRo8cA+Pxf4GwKttLufQc/8xuSIRERERz6WwKO5XUADffkvKtl2AwqK4kZcXNzz3d3od2kKWjx+PxHpjrFtndlUiIiIiHklhUdzv1Cm47jpOzfoegHBNcCNuZO3YkZdbgV9eNr837sSXT0/RcFQRERGRM1BYFPdLTnb84edYOiPUXz2L4l5NnniAR/cuAuDfbYey7ynNjioiIiJSmsKiuF9RWCxcZ1E9i+J2Pj7c8uI99Du4kWxvPx44FkHeoXizqxIRERHxKAqL4n6lehZ1z6KYwXrBBUzuGkRIbiabolvx3u5Ms0sSERER8SgKi+J+p04Bf/UsKiyKWaIf/Acv3NAdgHd+28vGuGRzCxIRERHxIAqL4n6lhqGGaRiqmMXLi5HdmzKicwMK7AYPzNhIVm6B2VWJiIiIeASFRXG/5GTyrF6k+QYCEKYJbsRkz1/VgegQP/Yfz+DFSZ9DYqLZJYmIiIiYTmFR3O/UKVL8gpy7mg1VzBYW4MPkAdEAfGWvx/z7ngW73eSqRERERMylsCjul5zsnNwm2GbB5qW/hmK+fq2jmLh1PgAPNxxI/MtvmFyRiIiIiLn0W7q4n91OSnAYACF+XubWIlKkbl0e/L9r6Hx4F6l+Qdy3A/JWrjK7KhERERHTKCyK+02ZQur8XwEICwkwuRiRv/gMupR3G2cSnJPB+oZteeOV6c4JmURERERqG4VFMUVKVh6g+xXF8zSa9Cgv718AwPvtBrP8/mfAMEyuSkRERMT9FBbFFAqL4rFsNoa98zQ3bv8Vw2Ll/4K7c/S9D82uSkRERMTtFBbFFAqL4tEaN+bpW/vR5lgsx4PC+cfqFPJ+X212VSIiIiJupbAo7nfbbST/9AugsCiey2/0NbwXepignEzWxHTglS9XmF2SiIiIiFspLIp75eXBp5+SsmsfACEKi+LBWr4yiVf3O5bT+DikHXM3Hza5IhERERH3UVgU90pNBSDFLwiAsACFRfFg3t4M/ea/3DWgBQAPf7uZ3UfSTC5KRERExD0UFsW9CsNiamFY1DBU8Xi+vvxrSGt6t4gkM7eAu75cR1pWrtlViYiIiFQ5hUVxr5QUxx8Ki1KN2LysvHNDV+qH+rH/eAYP/v0t7Dt2mF2WiIiISJVSWBT3UliUaioyyJf32xr45OexoF573nj0fTh50uyyRERERKqMwqK4V2FYTFZYlGqoy+Ce/Hv7dwC80+5yvr/rKcekTSIiIiI1kMKiuFdqKjleNrK9/QCFRalmgoK49v1nmbj5ZwAeanwZGx6cZG5NIiIiIlVEYVHcKyXFOQTVgkGwn8KiVDNNmvDw4zdw2b615Np8uNPelsPvfGh2VSIiIiIup7Ao7pWS4pwJNdgLvKwWkwsSqTivvn15c2gz2h49wLGgCO5Yn03mgkVmlyUiIiLiUgqL4l5t25Jy5VUAhPrZTC5GpPKCbruVjyKPEJmRzLZ6Lbjvk5Xkb9psdlkiIiIiLqOwKO41ahQpjz8FQGhYoMnFiJyfRi9P4sOjS/DJz2VRs4t45tmvMNLSzC5LRERExCUUFsXtkjMds0dqchup9ry8uPDTN3l7xxwshp1prQfw3z+PmF2ViIiIiEsoLIrbpWQpLEoNEhjI0M9eZVJbHwAm/7KLb9fFm1yUiIiIyPlTWBS3+yss+phciYiLREVx64TLmTigOQCPztrMst3HTC5KRERE5PwoLIp79elDyjtTAPUsSs3zyOVtubpLA/LtBndPXc3G/3saDMPsskREREQqRWFR3CshgZR8x6bCotQ0VquF/1zbmb6+WWTYrdxqb8/OJ/5tdlkiIiIilaKwKO5VbJ1FhUWpiXxsVj5omkG3hB2k+AdzU2oT9v/7DbPLEhEREakwhUVxH8OA1FSS/RUWpWYLnHArn3UPoP2RfRwPCuem+DDi3/nQ7LJEREREKkRhUdwnIwPsdlLUsyi1QOh9f+fLltm0OBHH4ZAobtpq4ehn08wuS0RERKTcFBbFfVJSHH/4OsJiWIDCotRskU88zFeRh4lJTiI2vAHjVqZybKoCo4iIiFQPCoviPkVhUT2LUovUf+lZ/mfbQXTacfbUacz1K9M4+smXZpclIiIiUiaFRXGflBSyvbzJ8fYFIERhUWoDi4XGb/+HGWyhQepR9kU24vq1OSTFHTG7MhEREZFzUlgU90lNdc6EasEg2NdmckEibmK10uTdyUy37aBh2jH2h9Xn+unbSUzJMrsyERERkbNSWBT3SUkh2T8YgBCLHavVYnJBIm5ksdD4rVeYPrEXMeH+xJ7IZOwHq4k/lWl2ZSIiIiJnpLAo7tOzJyn/ngxAaKCPycWImMBioVGnNsyY2IvGEQEcOpnJdVN+Z+/X3zmWlhERERHxIAqL4j6NG5PSZwAAYWFBJhcjYp6GYf7MmNiTFnUDSUzJ5rrfM9n44CSw280uTURERMRJYVHcKiUrD9BMqCL1Q/2ZeaE3nQ/v5lRAKDfSkRV3PgJ5eWaXJiIiIgIoLIqbFYVFzYQqAhGX9GHagHD6xm4k08efCeF9+Hn8vyBT9zGKiIiI+RQWxX127CBl/0EAQn29TC5GxDME3TaeT65rz7DdK8nz8uaemMv47KaHISnJ7NJERESkllNYFPd54glSvvgagFB7rsnFiHgO39HX8PY/LmPc1oUYFivPtr6CSX9/jYItW80uTURERGoxhUVxn5QUUgrXWQwNDTS5GBHP4nXppbzw77/x6IbZAExtfQl3vjiHjPkLTa5MREREaiuFRXGfYmFRs6GKnM7SqRN3TX2B9zdPxzcvh1+bduO6uYdITNY9jCIiIuJ+CoviPqmpf/UsaoIbkTOrX58rZk5hevzP1MlOZXtQNFf/dxUb45LNrkxERERqGYVFcZ/iw1AVFkXOLjCQrv/7gDl3XkyrqCCOpOYwZsrvzFh7yOzKREREpBZRWBT3KRYWtXSGSBm8vGjUvjmz/96bIe3rkVtg55FZW3hiwovkrllrdnUiIiJSCygsinvk5GDk5JDiFwyoZ1GkvIL9vJly04X867JWWAyDafW6cMO7yzj64VSzSxMREZEaTmFR3CM1lWybL7k2R0gMC1BYFCkvq9XCPy6K4tPtMwnOTmddg7YM2+zFqtv/BenpZpcnIiIiNZTCorhHsSGoXoadIF+byQWJVDNhYQyc9RE/5P9J62MHORYUwbjIAbxx0+MUbNxkdnUiIiJSAyksinsUmwk1xMjHYrGYXJBINeTjQ7N3J/PdpRGM2b4Yw2LlrbaXM27yfI689xEYhtkVioiISA2isCju0bUrKctWARAaEWxyMSLVW8C46/nPaxN5a/NMAnMyWd3oAq7YFcjiW/8Jx4+bXZ6IiIjUEAqL4h4WCymGFwChQX4mFyNSA7RqxVVzPmCufR3tj+zjZEAoExoM4anPV5CZm292dSIiIlIDKCyK2yRn5gJaNkPEZXx9afbWy8we3ojxWxcA8OUxb658aznrDp40uTgRERGp7hQWxW1SsvIACAvwMbkSkZrFb/Q1TPr4MaaNbU+DUD9iT2Ry3ZTfeXneTnK2bDO7PBEREammFBbFPRYvJnXeQgBC8zJNLkakBoqOpk/XZsz/v/6M7haD3YApS/cx8s0lbLj7YTh1yuwKRUREpJpRWBT3WLyYlLUbAAjNSDG5GJGaK8TPm9fGdGbKiJZEZqWyq25TRoX0Y9Ktz5E+faZmTBUREZFyU1gU90hLcy6dERqoCW5EqtrQXq1Y0CKZUbuWYVisTG1/GYOXZbDoxnshLs7s8kRERKQaUFgU9ygeFkP8TS5GpBawWom8725ef+devjz0E41PJZIYUpfbm1zB3//5AYef/w9kZ5tdpYiIiHgwhUVxj9RUkv0c6yuGhgaaXIxILRITQ79p7/HLoHDu3vIzXvYCfm7Vi0GnWvDO6P8je/b3ZlcoIiIiHkphUdwjLY0UP0dIDA0PNrkYkVrGYsF/zGgemfYic3NW0yNuG1k+frzWcTiX/Z7D/K2JGLqXUUREREpRWBT3SEsjtWgYakSoycWI1FJhYbR769/MePQK3tk7l/rpJ4j3CuSur9Yz7uM/2H441ewKRURExIPYzC5AagdD9yyKeAxLx46MmPlfBu3cw5Q4gynL9rNq3wmGvbOcqwIyeDBrJ40euR9C9cGOiIhIbaaeRXGLrIxs8ry8AQj19za5GhHBYiGgXWseGNKGXx8YwPBO9TEM+C4jkEvzOjHppqc5/trbmgRHRESkFlNYFLdIybUD4GUvINDHy+RqRKS4RhEBvHtjN37sAv1iN5Ln5c3UDkMYEF+f16/5JylvvgtZWWaXKSIiIm6msChukdy9FwCh9lwsFovJ1YjImXS8fhhfvnQj044vpmPSHjJ8A3i78wj6HKzH5FEPcHLym5CRYXaZIiIi4iYKi+IWKZPfACAsuo7JlYjIOTVrRp+PX+X7f17Cfw//StujB0j3DeC9zsPpmxjDS9c9zLF3PzC7ShEREXEDhUVxi5SsPABCdL+iSLVg7dKFK794nZ/v78eUpMV0OLKPTB9/Puh0Jf3i6/P091uJPa5eRhERkZpMs6GKWxSFRU1uI1K9WDt3ZujUzly+dSu/vf45b9sbsim6FV/8fpAvVx9kcLt63N4+lO75J7D07Gl2uSIiIuJCCoviFqkKiyLVmuWCCxj06WQuPX6c35Pho+X7WbzrGAu2H2HB9iN0Prybv/37Y64Y3R+fMdeBv5bIERERqe40DFWq3rp1pLz5HgChB3abXIyInA9LnTr0blmHzyb0YNED/bmhSzQ++blsatCa+9tfQ++1ViZf/U/iHnwCduv9LiIiUp0pLErVO3qU5LRMAEIzUk0uRkRcpWVUMC9dcwGr2qTwzz2LiEo7wfGgcN7rOpL+tp6Mf+JrFo6+k/zpM7Reo4iISDWksChVLy2NFL8gAML8tMaiSI3i60udO8bzz29fZ+UNLZhycgX9Dm7EsFhZ0uIi7mh1FX2XZ/PKyPvZu3KD2dWKiIhIBeieRal6qanOsBgS4GNyMSJSJSwWvPv2YWjfPgw9fpzYT//H17/H8k2ji0gKrsP73Uby/o+H6bQhg1FdGzKicwMivey6t1FERMSDKSxK1SvWsxga6GdyMSJS5erUoenD9/GY3c4Di5fw28zfmBXRliVEsDk+hc3xKbzw0w4uObGHEfEbuLRve4LHjIYWLcyuXERERIpRWJSql5ZGqm89AEKD1YsgUmtYrfgOupQrBl3KFcCJ9Bx+2HSY2esT2JKQwqLwliwKb4nPiVz6PzyVYdnxDOrTlpAxo6FlS7OrFxERqfUUFqXqpaaS4ufoMQgNDTC5GBExS2SQLxP6NGNCn2bsXrqWH/47k59DWrA/MoZFrXqyCPA5mUe/R7/k8vRYBnZuRN3hl0OvXmDTf1ciIiLupv99pcoZaWkkhwYDEBoebHI1IuIJWg/ozr8GdOfBffvYNWMuP29K4KeQ5uyLbMSvLXvwKz0A6Pzxnww8Fcygbs3o0CAEq9VicuUiIiK1h8UwDMPsIsT1UlNTCQ0NJSUlhZCQEFNrSR93Cxc0GgvA9vGtCWjbytR6RMRDHTjA7uk/8vPmBH7zqc/m+iX/ragb7MulbaLot2UpvfKOEzn4Ekevo5/uhRYRETmXymYDhcUaypPCYsI119Onzc14F+Sx+1+9sNSrZ2o9IlINHDvG0R9/YUlBCL/5NWT5nmNk5BaUOKTdkf30SdhKnxCDHhe1IvCygdCli4asioiIlKKwKCV4Uljc/tV3XLnVmzrk8uezV4Kvr6n1iEj1k5NfwNoDp1i8ehcrF29gZ1SzEl+3FeTTJXEXFyft5qJQC906NiH0b7dA48YmVSwiIuI5KpsN9PGrVLmUXv1g62pC6oYrKIpIpfjavOjbqg59W9WBy5pzfMFiVv2+g1XH81lZpyVxYdH8GdOBP2M6OE7IhtbTdnBR2xQuahLORU0iaJR9Ckt2NjRvDhbd+ygiIlIWhUWpcilZeQCE+nubXImI1Aj161Pn1hsZeSuMNAzYs4dD85ewavMh/ky2sy6iKQciGrI7JZ/dfxzif38cAqAOuXTau5GOKfF0CrXRsVU0Ud07Q/fuEBOjACkiIlJKrQyLq1at4vPPP2f58uUkJCRgGAYxMTH07duXW2+9lT59+lRp+/v372fq1Kn89NNPHDp0iPT0dBo0aECnTp0YN24cV199NbYadM9NSlYuoLAoIlXAYoHWrWncujWNgesNAw4e5Nj2vaxrfAHrDp7kz4On2JqQwvECH35r2YPfCmdaxYB6v56g47QP6ZSWSIcwG22a1KHh4P5Yhgwx9dsSERHxBLXqnsWMjAzuu+8+Pv3003MeN2HCBN555x0CAwNdXsNbb73FI488Qk5OzlmP6dmzJ9OmTaN58+aVbseT7ln88Lu1/Hv1Ua7uFM2bN15oai0iUjtl5xWw/c2P2LI1ls0ZVraExrA3Mga71eu0Y4ONPFo3jaJ1vWDaRgfTpl4Qbb79gvB2LaFNG8cwVm99+CUiItWHJrgpQ0FBAVdeeSULFixwPufv70+HDh2w2Wxs376d1NRU59eGDBnCzz//jJfX6b9IVNbzzz/P008/7dy3Wq20b9+eiIgI9uzZQ2JiovNrMTExrFmzhvr161eqLY8Ji/n5TL70b7zXeyy3Jqzl2S8nmVeLiAiAYcDhw2SuXsv29bvYfPAkW7K82BHagH2RMeR5nTkIRqWdoMXJeJolJ9LcmkPzUB+aNYwgpnVjvNu2gdatISpKw1lFRMTjaIKbMjz11FMlguIdd9zByy+/TEREBODodXzllVd4/vnnAViwYAFPP/00L774okva/+WXX3jmmWec+7169WLq1Km0bt0aALvdzsyZM7n99ttJT08nPj6e6667jhUrVrikfdOkp5PiFwRAqNVucjEiIjjCXMOGBIxuyEWj4SJwBMjERHI3buJATCt2GQHsSkplV1Iauw4cJS4bjgZHcjQ4kt+bdC5xOdvufBqv2U7zkwtpNuoKGjevT0x4AI0i/Gl4+AD+CXHQrBk0bQoBAWZ8xyIiIpVSK3oWDx8+TIsWLcjOzgbg5ptv5osvvjjjsU899RQvvPACAH5+fuzbt48GDRqcV/uGYdC1a1c2bdoEQJs2bVi/fj0BZ/ilYdGiRQwePNi5P3v2bK655poKt+kxPYtxcdx7zzv82H4ATx5fw+0fP2teLSIilXH0KOkLf2PPjoMcSEzmQEoe+w1f9odEcyCiAdnefuc8vU7GKWJSjhCTcpRGeWnE+FmICfWlfp1g6tWPJKRRAyxt20DHjm76hkREpLbRMNRzePjhh5k8eTIAAQEBxMXFOXsUS8vNzaVly5bExcU5z33llVfOq/2ff/6ZYcOGOffnz5/P5Zdfftbjr7/+embMmAFAjx49+OOPPyrcpseExW3buOXZWSxrfiGTU//kuv8+U/Y5IiKezm6H+HjsO3eStH0fBw4ksb/Ah/2XX038qSziTmYSfyqL9Jz8Mi/ln5tNPXKo16YZ0aF+RIf4US/Ej+iZX1HPyKFe3RDq1IvALzoK6tVzDHWtV0+9lCIiUm4ahnoOc+bMcW6PGTPmrEERwMfHhwkTJvDcc88Bjp698w2Ls2fPdm43a9aMIWXMsjdx4kRnWFyzZg3x8fHExMScVw2mSUsjxS8YgFBf193/KSJiKqsVGjfG2rgxDYZAA6D0PNqGYZAyZy7x67cSn5RCXGoO8blW4m1BxIdGkRQUSYp/MFk+fsTiR+yBkyUv4N/N8WcGsB8Cd2QSmbmFyMwUIjOTqZObQaQ1n8jhQ4ns3pk6Qb5EBvkQkZFCyPLF+NWJgIhij/BwTcwjIiIVUuPD4q5du9i7d69zf+jQoWWec8UVVzjD4t69e9m1axdt2rSpdA0//fSTc/vyyy/HUsbkB/369SMwMJCMjAzn+RMnTqx0+6ZKS3PesxgW4GNyMSIi7mOxWAgbNYKwUSO4oPgXsrIgPh4SEsg6FM+R+GMktWjHkTYdSUrJJik1m6Mn0khatJyk4EiOBUaQa/MmwzeADN8ADoWXmvhsnwH7NpZqPRK/vGxCs/cSlp1GaHY6oVlphBXkEGotIMzLINTHSui//klokB+h/t4E+XoRtHkjQYcOEBAWjDU0BEJDISTkr4e/vybwERGpRWp8WCy6T7BIr169yjynW7du+Pj4kJvrWB9w8+bNlQ6LR48eJSkpqULt22w2unfvzpIlS5ztV1upqaT4OZYgCQ30NbkYEREP4O8PrVpBq1b4A00LHyXY7TCoISQkYCQmkXbkMCeOnOLEyTSOp2RxIjOPEzl2Tti9OH7FSE7Y/DmRnsuJjFySM3KwYyHb249sbz+OBEeevZZvzvT/SyQWw05g7kmCcuMJyskkMDeL4NxMgnKzCDQKCG5Un6BhQwnysxHo44Wftxf+X3+Ff1YG/n7e+Pv5OB4BfvgH+OIf5I9/UAC2oEC44AIoPhdAQYEjQPv5QQ1aY1hEpCao8f8q79ixw7nt4+NDo0aNyjyn6Lh9+/addo3zaR+gRYsW5TqvRYsWzrB4Pu2bzUhNI9XPMew3NNjf5GpERKoJqxUaNYJGjbAAIYWPZuU41b57N2m//ErqyVSSUzJIScsiOTOPlOx8kvMMUgospGAjObQOKZcMIjkzj7TsfNJz8knPzKHAYsWwWEn3DSDdNwCCz9LQ4r0l9707QdEoVzuQWfgofkhBJn5L1hEQtB1/78KQmZeD3/q1+Obn4WPPx8cowBc7PtjxxcDXYuBjBV8r+Iy/FZ+QIHy9vfDxsuK7dTM+a//Ax+aFr48XPt42fL2t+Ni8sHnb8C7801avLt4jhmPzsmKzWvD2suK1dg2kpDiG5hY9fHxK7nt7/9W7WsQwHGHealUvq4jUeDU+LMbGxjq3Y2JiyhwCWqRx48bOsFj8GufTftF1y9v+2a5RnaSnplNgrQtAaFigydWIiNR81tatCW3dmlDgrB+PGgZkZkJgyX+XjcWLyd6+i/SUdNLTMknPyCY9M4f07DzScwpIz7OTnm+Q3qEz6b37kZaTT1ZuAVl5BWT+toxsqxdZ3r5k2XzJ8vZzbHv7YlisAOR5eZNnh7TUnJL1lFqO5KyWHjr9uaAef20XFD5KOwxsWFjiKYth4F2Qj82ej81eULhdgM2ej3dBwV/PR9XF1jgGb6sVm5cFG3a8f/kFL3sBXoYdKwZeGHgZhmPbAl4YWAGvK4ZijY7Gy2rBy2rBejAWr0WL8LLg+LoFrBbHn39tW/Dy8cZ69114WSxYrRZsVgtei3/DunWr81yL1YLVAhYshbnV4thu3QrLiOFYLRYsRV+f+hmW9DTneRaL1XGu1VJ4nMWxPXAglgs6YLU4rs2pZKzfzMBicRxntVqg8E+L1XENa2Fotl4/FktQUOG5Fti+DeuaP7AAFLVRtE2xnB0ejuWaqx2vCY6aWbQIS2Ki83e2EucWnWyxQJs2cOGFJV/rb74pfIEtp/9ZfLt3b8dEUUWOHYPVq899TtGfl19e8oOC3bvh0Bn+bpYWEQHdupV8bvVqKLzt6JxatYLiv0Pm5MCqVWWfB9CjR8n3emKio+ay+PpCz54ln9u+HY4fL/vc6GjH2rPF/f67YyRBWdq2hTp1/tpPS4MzjbI70+/0PXqUHKEQF+cY9l+WoKDTZ6Tetg3S08s+t2FDKD63SEEBrFvn2G7SpOTfs2qoxofFtLQ053ZoaGi5zys+S1Dxa5xP+xWpoaLt5+TkkJPz13++qamp5aywaqWkOj5W9snPwy/kbB9Pi4iIW1kspwVFAMvAgfgPHIg/ULei1xxQx/FLb9EjMxMyMjAyUsnJyCQrPYusrByyrhhOVpNmZOUVOILmvgNkf/Y5Ofl2cgsMcu2QY4dcw/HIMazkYiHX4kXOTbeQi5WcfDs5+QXk7t1Pbvxhcmze5HoVPmze5Hh5k+9lI8/qRYHVizyv0yf2MSwWcm3e5FKOSX8OJZfcb9njjIedZn827I8t+VyrS8t37pytpZ6IhHoDyj7vKPDJmlJPtoNz3Qli4AjZi07CouWnn1seb64+w5PlnJxv3fwzPFnnDM+VZNmUiOVbx7wQzkCZ54sFA4tzrn/HhsUwsBTfXvsn2LwKg6sF8vOxZGY5PkgBx/PObaPYNljWLipss/CZtFQsGRkljzlDu/gdh19OlfwmkhKx5OaV8Z0aEJ4GIcV68wvynSHIUsbCBpaVueBT7O95alqJwHfW8202WJblOKYolyUlnTFAWUpfIiQR6iX89XWLBfbudfTKn6nG4jU0OAXBxX5nzM6CgwfPXGPR+UUbK7Mdvf5Fjh+H48fL/hn5+0OzUpOMHTgAWZmF9Z3j5KhDWKKKBUJ7gSNUA9RP4G9XXcjY7uXrLPJENT4sphf7C+3nd+61sIrz9/9ryGR6eT5VKEf7Famhou2/9NJLPPus561hmHzdDTB1MyGBPlj69Sz7BBERqZ6aNz/j0xbAr/ARfqYDWteFK8oRvuz2kj09AEebOX55zc529LRkZ0NeXrFHLuTlYYRHUHDlleTbDfIK7OQXGOR9+RX58Qnk5xeQl1fg+DO/gPwCu2O7wO7Y7j+AvF59/jo3I4v8114n3wC7AQWGQQFF246H3XBkL/vY6ymoV48CO9gNg4JduylYsAA7FgqwOP60WCiwWLFbrBRYrY5tH18KRozEbhjk2w0K7Ab2bdspSEzCXniMYbFgYMFusZTcDg/H3qQphgEGBnYD7Hv2Qn4+dosVwwJ2ixW7o4sOu8XieB6wh4WBv7/jHMPAyC/AnpqKUfT1Yuef1r7FeqZXrUoZFgvODFC04VXOX23tQG6pXi7fco6AysgtuW/xhaByzsuQkl1y3z8cynOXjh1Izir5XGg5e6zS84Hiy/jYICy6fOeeLD2WPATCy7nswolS54bVP/NxpeUAOaV6WyPLvo3M0WapnxH+5T/3aKnftwPrQnn+ShjAkVIdO3WbOv7Mh5MZZX0Y4NlqfFjMz//rzWGrwI3zxY/Ny6v8i1y8/YrUUNH2H3vsMR544AHnfmpqarnuz6xqLZo34Id/BJObb3cMvxAREakM6xnCSFSU41EGC45feGxe4OdduIzTXX+rfC19/lu5865sB/93Vcnniu6BLCgo+Sg9EulkK0ePTtHxRX+W3g4OLjlcEWBTmCNMn+s8w3AM/2vY8K/zUlNh6dIznJN/2vnG1VdjBAQ6QqXhCLfGunVgGBiG4ehbMwwMLBQt8W0YBoSGYlx1VbHQB8Yvvzgmdyo6pvBP5zWMwv7CDh3g4ouLTnNc46OPSrVXeI2ic4qud+WVGIVDBw0DiIvDmDsXZxmFdTtrcrZtYPz9nsKnHbWwYgVG4YSKBjiv4dgv9uFGTAwMH1Hy5Z8+HU6V6tEqqrH4ub17Qecuf+2np2N88QXn4qxj7NiSv4Pt2IGxeHHZ5wYEwq23lvzCL79A4W1a5zy3bVsYOLDkFz76CKOMXlQDYMgQaNnyryeTkmDWt4VfP/vtZAbAbbc5hs8WWbMW1q756+tnOzeqHlw7uuST386CI0lnPqH4uRd1dwx/LZKbCx9+6Nju14/GncoZkj2UxXC+E2qmkSNH8uOPPwLQvXt31qwpPTTjzK677jq+/fZb5zW+//77SrX/+uuv8+CDDzr3MzIyCCjHQsrvvfce//jHPwDHkNSUlJQKtVvZhTdFRERERKRmqWw2cP+YATcLCgpybmdlle6aPrvMzL+6zotf43zar0gNrmpfRERERESkMmp8WKxTbDalxMTEcp9XfG3EyMhzrFFVgfYrUoOr2hcREREREamMGh8W27Rp49w+ceJEiR67c4mLi3Nut23b1iXtAxwqz9TKLmxfRERERESkMmp8WGzXruSUzxs3bizznISEBI4dO3bWa1REq1atSkxWU572ATZs2OCS9kVERERERCqjxofFHj164FtsVqQVK1aUec7y5X+tMeTn50ePHuVcT+kMfHx8uLhwpq7ytp+UlMTevX+tpdO/f/9Kty8iIiIiIlIZNT4sBgUFMWjQIOf+tGnTyjyn+DGDBg0i8AwLF1fEVVf9NU32okWLOHLkSLnbDwsLU1gUERERERG3q/FhEWD8+PHO7c2bNzuX0jiT9evXM2/evDOeW1k33HCDs3czLy+P//znP2c9Nj09nbffftu5P27cOLy9vc+7BhERERERkYqoFWHx2muvpXPnzs79iRMnsnPnztOOS0xM5KabbqKgoACALl26MHr06NOOA4iNjcVisTgfkyZNOmv7MTExTJw40bn/1ltvMWvWrNOOy8vLY8KECc5JcPz9/Xn88cfL9T2KiIiIiIi4kq3sQ6o/i8XCRx99xIABA8jKyiIxMZGLL76Yu+++m/79+2Oz2VizZg3vvvuuc4iov78/H374IRaLxSU1TJo0iXnz5rFnzx4KCgoYM2YMN954I1dffTURERHs2rWL999/n82bNzvPmTx5Mg0aNHBJ+yIiIiIiIhVhMQzDMLsId5k9ezY33XQTWVlZ5zzO39+fr776ilGjRp31mNjYWJo1a+bcf+aZZ87Zuwiwe/duLrvsshLLYpzNww8/zCuvvFLmcWeTmppKaGgoKSkphISEVPo6IiIiIiJSvVU2G9SKYahFRo0axbp167jsssvO2GNosVgYNGgQf/755zmDYmW1bt2azZs3c9ttt+Hv73/GY9q1a8f3339/XkFRRERERETkfNWqnsXi4uLiWLlyJQkJCQA0bNiQPn360KhRI7e0n5aWxm+//UZcXBwZGRnUr1+fjh070rVrV5dcXz2LIiIiIiIClc8GtTYs1nQKiyIiIiIiAhqGKiIiIiIiIi5UK2ZDrY2KOoxTU1NNrkRERERERMxUlAkqOqhUYbGGSktLA3DbPZgiIiIiIuLZ0tLSCA0NLffxumexhrLb7Rw+fJjg4GCXrRVZWampqTRq1Ii4uDjdP1lD6DWtmfS61jx6TWsmva41j17TmsfTXlPDMEhLS6NBgwZYreW/E1E9izWU1WolJibG7DJKCAkJ8Yg3i7iOXtOaSa9rzaPXtGbS61rz6DWteTzpNa1Ij2IRTXAjIiIiIiIip1FYFBERERERkdMoLEqV8/X15ZlnnsHX19fsUsRF9JrWTHpdax69pjWTXteaR69pzVNTXlNNcCMiIiIiIiKnUc+iiIiIiIiInEZhUURERERERE6jsCgiIiIiIiKnUVgUERERERGR0ygsymlWrVrFxIkTad++PaGhoYSEhNC+fXvuvPNOVq5cWeXt79+/n6effpoLL7yQunXr4u/vT4sWLbjmmmv49ttvyc/Pr/Iaaork5GTmzJnDfffdR//+/YmOjsbX15egoCAaN27MiBEjePPNNzl16lSVtG+xWCr8mDJlSpXUUlMsWbKkUj/XnTt3Vkk9er+en9jY2Eq9nsUfsbGx512H3qsVc+zYMebNm8dzzz3HyJEjqV+/fomfzdSpUyt97S1btvDAAw/QqVMnIiIiCAoKok2bNowbN4758+e77ps4h6SkJF555RV69epF/fr18fPzo2nTpgwdOpSpU6eSlZXlljrcydWvaWZmJvPmzeOhhx7isssuIyYmBn9/fwICAmjYsCFDhgzhxRdf5PDhw1XzDQFNmzat8Pv60UcfrbJ6zODK17Wy/15X5fvWLe9VQ6RQenq68be//c0AzvmYMGGCkZ6eXiU1vPnmm4avr+852+/Zs6exb9++Kmm/ptixY4cxfPhww8fHp8zXEzACAgKMN954w7Db7S6tozxtl368//77Lq2hplm8eHGlfq47duxweS16v56/AwcOVOr1LHrYbDbj5MmT512H3qvlk5iYaDRp0qTMn81nn31W4Wvn5eUZjz32mGG1Ws957WHDhhlHjx51/TdX6OuvvzZCQ0PPWUObNm2M9evXV1kN7uTq1zQpKckYO3asERAQUK73kbe3t/HYY48ZOTk5Lv/eyvN9lX488sgjLq/DDFXxXq3sv9fz5s2rku/RXe9VGyJAQUEBo0aNYsGCBc7n/P396dChAzabje3bt5OamgrAZ599RkJCAj///DNeXl4uq+H555/n6aefdu5brVbat29PREQEe/bsITExEYDVq1czYMAA1qxZQ/369V3Wfk2ydetW5s6dW+I5Ly8vWrZsSb169SgoKGDHjh2cPHkScHwC+n//939s27aNDz/8EIvF4vKa+vfvj7+/f5nHNW7c2OVt11R+fn4MGDCgXMcGBQW5tG29X13D39+fyy+/vNzH2+12Fi5c6Ny//PLLCQ8Pd2lNeq+eXXZ2NgcPHqySa0+cOJFPP/3Uue/t7U379u0JCgpi586dnDhxAoCffvqJyy67jJUrV7r8ff3ll19yyy23lHiudevW1K9fn9jYWOf3vmvXLi655BJWrVpFhw4dXFqDu7n6NY2Li2PGjBklnrNYLDRv3pzo6Gi8vLxK/BuZl5fHSy+9xMaNG/nuu+/w8fFxWS3Fde/enYiIiDKPa9OmTZW0725V+V4tUt5/u+vWrevytt36XnVRuJVq7rHHHivxScQdd9xhnDhxwvn19PR046mnnipxzOOPP+6y9ufPn29YLBbntXv16mXs2rXL+fWCggJj+vTpRlBQkPOYPn36uKz9mmbmzJnOXoerr77a+O6774yUlJQSx9jtduO7774zGjZsWOJ1/e9//+uyOopf98CBAy67bm1WvGexSZMmptSg96t5fvnllxLvq2+++cYl19V7tXyK9yzUrVvXGDp0qPHkk08a33333Xn1LH7wwQclzh85cqQRHx/v/Hpubq7xzjvvGDabzXnMjTfe6NLvbfPmzSVGCrRu3dr4888/SxyzYMECo169es5jmjdvbmRlZbm0Dndz9Wu6du1aAzAsFosxaNAgY9q0acaxY8dOO27JkiVG+/btS7Tx8MMPu/R7K96ztnjxYpde29NVxXu1dM+iWdz9XlVYFCMhIcHw8/Nz/oW6+eabz3rsk08+6TzOz8/PSEhIOO/27Xa70blz5xJd5hkZGWc8duHChSXeqLNnzz7v9mui7777zrj99tuNgwcPlnnsoUOHjOjoaOfPtE6dOkZubq5L6tAvoK5ndljU+9VcN954o/PnGR4ebmRnZ7vkunqvlk9KSooxc+ZMIzY29rSvVfYX0IyMjBL/Bl9yySVGfn7+GY/9+OOPncdZLBZj3bp1lf1WTjNixIgS/w8kJSWd8bitW7eW+EX19ddfd1kNZnD1a7pu3Tpj9OjRxrZt28o8Njk5uURg9PHxOevPvTJqc1isiveqp4RFd79XFRbFeOihh5x/kQICAkr0KJaWk5NjNGrUyKWfgv30008l3nzz588/5/Fjx451HtujR4/zbl9O/1R70aJFLrmufgF1PbPDot6v5klJSTH8/f2dP8+7777bZdfWe/X8VfYX0Pfee69EANy+ffs5j7/44oudx48ZM+Y8q3bYtm1bifqnTJlyzuMfeeQR57HR0dFGQUGBS+rwNOfTW1xepUcLfPzxxy67dm0Oi+dSncOiGe9VzYYqzJkzx7k9ZsyYc45p9/HxYcKECc792bNnn3f7xa/RrFkzhgwZcs7jJ06c6Nxes2YN8fHx511DbTdixIgS+1U1c6ZUf3q/mmfmzJklZra79dZbTaxGXKX4e2rAgAG0a9funMcXf0/9/PPP5OTkuLSGoKAgxo0bd87j77zzTud2UlISv//++3nXUFsNGjSoxD3C+v9XzsWM96rCYi23a9cu9u7d69wfOnRomedcccUVzu29e/eya9eu86rhp59+cm5ffvnlZU6u0q9fPwIDA894vlRO6Q8IiiYzEilN71fzfP75587ttm3bcvHFF5tYjbhCeno6y5Ytc+5X9P/g9PR0lixZct51FH9f9u3bt8yJc5o3b15iIpTSE6pJ+Xl5eREaGurc1/+/ci5mvFcVFmu5TZs2ldjv1atXmed069atxGxdmzdvrnT7R48eJSkpqULt22w2unfv7pL2xaH0jGFRUVEmVSKeTO9X8+zfv58VK1Y499WrWDNs376dvLw853553lPR0dE0bdrUuX++7ynDMNiyZUuFaih9nN7XlZeVlcXRo0ed+/r/V87GrPeqwmItt2PHDue2j48PjRo1KvOc0scVv8b5tA/QokWLcp1X/LjzaV8cSg8nLu8/QBXx0EMP0aFDB0JCQvD39ycmJoaBAwcyadIkDhw44PL2aoPk5GTGjBlD06ZN8ff3Jzg4mGbNmnH11Vfz7rvvuvwTar1fzfPFF19gGAbgWKbk5ptvrrK29F51H094Tx06dIiMjAxTa6jNvv/+e+x2u3O/Kv7/BXj11Vfp2rUrYWFh+Pr6Ur9+fXr37s2jjz5aIoBI+d3y/+3da1BU5xkH8P/CykUWjCgKXkDjiHgB72ai9RbWYCSxqWRiTGdIpsaM1tZaHJsPjTVtnUw0bcDomJBEnYkRoyAJxDYhgDreJtwRiUiwFUQ06OJCWTYrIqcfqG/3uCzssgeWZf+/GWbOu77nvA9nfTj77DnnPfHxmDhxIvz8/ODn54fQ0FAsX74cu3btkn0BoBRn5SqLRTdXXV0tlseMGWPz8/XMn69lvg1Hxn90u30xPgFNTU3YvXu3aEdFRWHKlCmKj5OWlobLly+jubkZJpMJdXV1OH36NP785z8jPDwc69evl92PRd1rampCamoqampqYDKZYDAYUF1djYyMDPz2t79FaGgo9uzZo9h4zFfnkCQJn376qWhrtVqMHj2618ZjrvYd83xQq9U2P4tUyZxSIq9ramrElxlku7a2Nrz99tuiPWLECERHR/fKWP/4xz9QWlqKpqYmtLa2ivvXdu7cienTp+OFF14Qz14m2xw6dAhXr16F0WiE0WhEbW0tsrKy8MYbbyAsLAzbtm3DgwcPFBvPWbmqtqs3DTjNzc1i2fya+e4EBAR0ug1HxrcnBqXGJ2DLli2ySwt37NjRK+MMHz4cEyZMgEajQVNTE65cuQKDwQCg44CZnJyM/Px8nDp1yq7/i+5u3LhxGD16NLy9vaHT6XD58mW0tbUB6CgmN23ahNLSUuzfv9/hsZivznH27FnZGb3evgSVudp3zPPB398fHh62fYevZE4pkdft7e0wGo2y+5Ope++8847srN6bb74Jb2/vXhlryJAhCA8PR0BAAAwGA6qqqkRxKEkSjh8/jsLCQpw9e9amq8wICAkJEVf26PV6VFRUwGQyAQBMJhN27NiBgoICfPXVVxg0aJDD4zkrV3lm0c09/AAAAD4+PjavZz5zl/k2HBnfnhiUGt/dffLJJ7IiYvXq1RYzozpiypQpSEpKwr/+9S/cuXMH3333HXJyclBQUAC9Xo8TJ04gKipK9C8pKcFLL72k2PgDkYeHB7RaLQ4fPoyGhgZcu3YN586dQ25uLi5evAi9Xo8PPvgAw4cPF+scOHAAO3fudHhs5qtzmE9sExAQgF/84heKj8FcdQ5nH4M7W78nea1EHO4mKysL27dvF+358+fj17/+taJjjBs3Djt27EB5eTkaGxuRn5+PnJwcfPfdd9DpdDhz5gwWLVok+tfU1OC5555Da2uronEMFCqVCvPmzcPHH3+Mmzdv4ubNm7hw4QJyc3NRXFyMxsZGpKSkyO4pzsrKwqZNmxQZ31m5ymLRzT08AwF0XAJjK/O+5jfnOzK+PTEoNb47O3PmDDZu3Cja48ePR3JysqJjfP/99/jd736Hxx9/3OLf1Go1YmNjkZeXh9jYWPH6N998g6+++krROAaSRYsWITs7Gy+//HKnj7nRaDRYv349iouLZQesv/zlL6ivr3dobOZr3zMajUhNTRXtF1980eLArwTmqnM4+xj8aAz2xPFoP+a27SoqKrBmzRpxr+LQoUORkpICT09PRcc5ffo0/vjHP2Lq1KkW/6ZSqbBw4UKcOnUK69atE69fvHhR8c8CA0VYWBjy8vLw2muvdXrJuLe3N9asWYPi4mLMnj1bvJ6cnKzIJFDOylUWi25u8ODBYvnhqXNbmPd15LIT8/HtiUGp8d1VaWkpVq5cKb49HDFiBL755hunXFLm4+ODI0eOYOTIkeI1Je+zc1djx47F0aNHRdtoNDp8KSrzte998cUXskuPnDkLKnNVec4+Bj8agz1xPNqPuW2b2tpaxMTEQK/XA+jY/ydOnEBYWJhT4vHw8MC+ffsQGRkpXmNeO2bo0KFIT08XZ/4kScLevXsd3q6zcpXFopszfz6LPRMWGI3GTrfhyPj2xKDU+O6osrISMTExaGpqAtDxR+3bb79FeHi402Ly9/fHhg0bRPvs2bN2fXCizs2bNw9LliwR7ezsbIe2x3zte+aXoE6YMAE/+9nPnBgNc1Vpzj4Gd7Z+T/JaiTjcQX19PbRaLWprawF0nIn68ssvMX/+fKfGpVarsWXLFtGuqqqyeKQW2Sc0NFR2qb6jx1/AebnKYtHNmd/XdOvWLZvXM58QZdiwYYqMb08MSo3vbq5duwatViumdPb398fXX3+N6dOnOzkyYOnSpWLZZDKJgyk5xny//vDDDw5ti/nat+rq6pCbmyva/eXZisxV5ZjnlMFgsPleIiVzSom89vf3V2QCj4Hs7t27WLZsmfg7rFarcfToUSxbtszJkXUwz2vA8eMFyfdpdXW1w/eCOitXWSy6uUmTJonlhoYGi28frDH/cBAREaHI+EDHM2T6cnx3cuPGDURHR+PGjRsA/n/pyxNPPOHkyDoEBwfL2jqdzkmRDCzm+9XRfcp87VuHDh0S9zSpVCrEx8c7OaIOzFXl9IecCg8Plz02i3mtvP/85z+IiYkRM596eHjgs88+w89//nMnR/Z/zGvlPbpPGxoaHNqes3KVxaKbmzx5sqxdWlra7Tp1dXW4c+eO1W3YY+LEibIbb20ZH+iYiU+J8d3Fw0tfHk6///DSF/NZ0Jzt0S8qHr02n3rGfL86uk+Zr33L/BLUJUuWOO2epkcxV5XTk2Pw/fv38f3331vdhr00Gg3GjBljVwwA89pWLS0tWLFiBQoLCwF0fPFz4MABrF692smRyTGvlaf0PnVWrrJYdHPz5s2TPdPn3Llz3a5z9uxZsezj44N58+b1eHwvLy/ZmS1bxv/xxx9x9epV0e5PBU9/1NDQAK1Wi8rKSgDAoEGDkJaW1m8ufXnI/MMP0DHpDjnOfL86uk+Zr30nPz8fV65cEe3+cgkqwFxV0uOPPy778GdLThUVFck+hCqRU+bbsCWG+/fvIy8vT9EYBiKTyYSVK1fi/Pnz4rV9+/b1q3x+iHmtPPN96u3trcgkgs7IVRaLbk6j0SA6Olq0Dx8+3O065n2io6MdngHN/DKMnJycbqf3Nx//scce40GqC01NTYiJiUF5eTkAwNPTEykpKXj22WedHJmlzz//XCyPGzeu02mpyT5GoxGZmZmircQkCszXvmF+VtHPzw9xcXFOjEaOuaqslStXiuXU1NRu72syz6mpU6diwoQJDsdgntcVFRWyMxGdyczMFLP0enh4KPp83oGitbUVcXFxOHnypHgtMTER69evd2JU1pnntY+PD2bOnOnEaFyfJEk4duyYaD/55JOKbNcpuSqR2zt27JgEQPxkZmZa7VtUVCR5enqKvqmpqQ6PX1tbK3l7e4ttJiQkWO3b3NwshYaGir4bN250ePyBymAwSAsWLBD7ysPDQ/rss8+cHVanMjIyZP8HN2/e7OyQBoSEhATZfv3yyy8d3ibztffdu3dPCgwMFPvtlVdecXZIAnPVOvP9cvDgQZvXy8/Pl637/vvvW+1bW1sr+fv7i77vvvuuApF3HC+CgoLEdletWmW1b1tbmzR37lzRNzY2VpEY+qOevqdtbW1SXFycbP2333679wJ1UEFBgeTl5SViff75550dUq/q6ftqj/fff182TlJSkiLbdUauslgkqb29XZo+fbr4zxQSEiJVVFRY9Lt586Y0efJk0W/GjBlSe3t7p9u8du2aLEm2b9/eZQybNm0SfT09PaW0tDSLPq2trdILL7wg+vn6+kp1dXU9+p0HOpPJJGm1WrGvVCqVtH//foe3a+v72tjYKK1atUoqLCzsdpspKSmSn5+f2ObgwYOlW7duORzrQJSVlSUlJCRItbW1XfZrbW2V3njjDdl7NWvWLOari0hLS5O9HydPnrR7G8zVvufIB9CVK1eKdTUajXTu3DmLPk1NTdLChQtFv+DgYMloNNocU3dfOrz33nuy/omJiRZ92tvbpc2bN8uOLUVFRfb8qi6lJ+9pe3u7FB8fL1v3T3/6U6/E1NV7GhcXJ508edLq3/2HcnJyZMWHSqWSSkpKFIu3P+rJ+1peXi796le/kq5cudJlv/b2dikpKUl2YmXUqFEunav/n6mA3JZKpcLHH3+MxYsX46effsKtW7fwxBNPYMOGDVi0aBHUajXy8/Oxd+9eccmZr68vPvroI9msTI5466238PXXX6OqqgoPHjzAiy++iJdffhnPP/88AgMDUVlZiQ8++ABlZWVinXfffRejRo1SZPyBZvfu3cjJyRHtxx57DMeOHZNdEtGVZcuWyZ65ZC9JkpCeno709HREREQgJiYGM2bMQEhICPz8/NDc3IxLly4hLS0NBQUFYj2VSoWDBw9azCBGHYxGI9577z0kJSVhwYIFWLx4MaZNm4bhw4fDy8sLOp0O+fn5OHz4sGz2s8DAQKSkpDBfXYT5JahhYWGyZ2Uqjblqv3Xr1uHQoUPd9unsckNrz6TcvXs3Lly4AJ1OB4PBgOjoaKxduxZPP/00NBoNysrKsGfPHjFJmYeHBz766CP4+vo6/gv9z8aNG5GWloYLFy4AAH7/+98jNzcXv/zlLxEcHIzq6mrs379fdp/Uli1bMGvWLMVicBYl39PU1FR8+umnou3j44O8vDwsX77cpliioqKwa9cum/p2JScnB8ePH0doaChWrFiBmTNnYuzYsfD390dLSwsqKyuRkZEhu0wWAHbu3IkZM2Y4PH5/oOT7ev/+fRw4cAAHDhzA7Nmz8dRTT2H69OkYMWIEfH19odfrUVJSgiNHjsjuN/f29sbnn3/u2rnaoxKTBqTjx49Lvr6+sm8rOvvx9fWVjh8/3uW27D1TIUmSVFlZKY0dO7bb8QFIf/jDHxT6rQem7du327Qfrf1Y+1bL1vdVr9fbPaa/v7+UkpLSeztlAPjiiy/s3q8TJ06UiouLu9wu87X/qK+vl9Rqtdh327Zt69F2mKu955VXXunx39aunD9/Xnb5sbUfT09Pac+ePTbFasvfdXO3b9+WIiMjbfpd1qxZIz148MCmOPo7Jd/TgwcPOnT8Xbx4cZex2vqeDhkyxK5xvby8pL///e8O7sn+Rcn3taSkxO5tBAcHS9nZ2TbF2p9zlRPckLBq1SoUFRVBq9V2egZCpVIhOjoahYWFWLVqleLjh4eHo6ysDGvXrrX6DczkyZORkZGBnTt3Kj4+KcfX1xevv/46pk6d2u3ZrCFDhmDTpk0oLy/HmjVr+ihC1xQREYHVq1fLZk+0Zty4cdi1axdKSkp6ZaIC5mvvSElJQVtbm2j39rMVmav9x/z581FWVoa4uDjZI2rMzZ07F2fOnMFvfvObXokhKCgI+fn52Lp1q9WZG8PCwvDJJ58gJSUFHh78GNlfvf7665g1axY8PT277Ofr64tXX30VJSUlSEhI6KPoXE9ISAji4+NtmlBq5MiRePPNN3Hp0iVotdpeiacvc1X1v2qWSKa2thbnz59HXV0dAGD06NFYsGABxo4d2yfjNzc34+TJk6itrUVLSwtCQkIQGRnJ2blckF6vR2lpKW7fvg2dTofGxkYMHjwYgYGBiIqKQlRUVLcHM7J0/fp1XL58GTqdDjqdDi0tLQgICMCIESMwZ84cRWZItBXzdWBgrvYfd+7cwZkzZ3Djxg20trZi1KhRmDNnDiZNmtRnMZhMJpw+fRrV1dXQ6/UYOXIkIiIi8OSTTyp2STv1PoPBgJKSEvz444/Q6XTQ6/Xw9vbG0KFDMWXKFMyaNQteXl7ODtOl1NfXo6ysDHfu3IFOp0NzczM0Gg2GDx+OmTNnYvLkyX2aI72dqywWiYiIiIiIyAKvHyAiIiIiIiILLBaJiIiIiIjIAotFIiIiIiIissBikYiIiIiIiCywWCQiIiIiIiILLBaJiIiIiIjIAotFIiIiIiIissBikYiIiIiIiCywWCQiIiIiIiILLBaJiIiIiIjIAotFIiIiIiIissBikYiIiIiIiCywWCQiIiIiIiILLBaJiIiIiIjIAotFIiIiIiIissBikYiIiIiIiCywWCQiIiIiIiILLBaJiIhc2FtvvQWVSgWVSoXw8HC0trbatX5WVpZYX6VS4fbt270UKRERuRoWi0RERC6qqqoK77zzjmgnJibCy8vLrm3MmTNH1j537pwisRERketjsUhEROSiNm7ciHv37gEAli9fjtjYWLu3MWzYMISGhor2+fPnFYuPiIhcG4tFIiIiF5SdnY3s7GzR/utf/9rjbY0fP14sV1RUOBQXERENHCwWiYiIXNC2bdvE8jPPPGNxOak9Ro8eLZavXr3qUFxERDRwsFgkIiJyMbm5ucjLyxPtrVu3OrS9oKAgsXzr1i2HtkVERAMHi0UiIiIX8+GHH4rl8ePHY8mSJQ5tT6VSieWH90ASERGpnR0AERER2a6hoQEZGRmiHR8fLyv2zLW0tOCnn34CAAQEBFidKVWSpE6XiYjIvfHMIhERkQvJzc3F/fv3RTsmJsZq31dffRVBQUEICgpCYWGh1X43b94UyyNHjlQmUCIicnksFomIiFzIqVOnxLKfnx/mzp1rtW9BQYFYnjZtmtV+169fF8vmj9EgIiL3xmKRiIjIhZSXl4vladOmQa3u/I6Suro61NTUAACCg4MREBDQab+2tjZcunRJtLsqPomIyL2wWCQiInIhVVVVYnnSpElW+5k/g3HMmDFW+5WUlMBoNIr2ggULHIyQiIgGChaLRERELqK9vR319fWi3dX9hZmZmWI5MDDQar8TJ06IZbVajejoaAejJCKigYLFIhERkYswmUyytre3d6f97t69i3/+85+iPWjQoE77SZKEI0eOiLZWq8WwYcMUiJSIiAYCFotEREQuwtPTU/aYjLt373bab+/evbh3757o29DQ0Gm/zMxM2WWt69atUzBaIiJydSqJD1QiIiJyGcHBweJS1KioKFy8eFH27zU1NZg2bRoMBgOWLl2KU6dOQaPRoKGhQfacxcbGRsyePRv//ve/AQCRkZG4ePGi1Wc2EhGR++GZRSIiIheycOFCsVxWVoYPP/xQtKurqxEbGwuDwYDw8HC89NJLAACDwYC//e1vol9NTQ1WrFghCkVPT08kJyezUCQiIhmeWSQiInIh2dnZePrpp2WvRUREIDAwEEVFReLy02+//RbBwcGIjIwU/aKiouDj44Pi4mK0tbWJ1xMTE7F58+a++hWIiMhFsFgkIiJyMQkJCUhMTOz039RqNfbt2yfuP4yLi0N6enqnfTUaDZKSkrB27dpei5WIiFwXi0UiIiIXlJ6ejuTkZJSWluLu3bsICgrC0qVLsXXrVsyYMUP0M5lM2LFjB44ePYrr169j8ODBGD9+PGJjY7FhwwaMGjXKeb8EERH1aywWiYiIiIiIyAInuCEiIiIiIiILLBaJiIiIiIjIAotFIiIiIiIissBikYiIiIiIiCywWCQiIiIiIiILLBaJiIiIiIjIAotFIiIiIiIissBikYiIiIiIiCywWCQiIiIiIiILLBaJiIiIiIjIAotFIiIiIiIissBikYiIiIiIiCywWCQiIiIiIiILLBaJiIiIiIjIAotFIiIiIiIissBikYiIiIiIiCz8F5LjxdBkF1P7AAAAAElFTkSuQmCC", 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", + "image/png": 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", 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" ] @@ -780,7 +855,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -809,7 +884,7 @@ "outputs": [ { "data": { - "image/png": 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", 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07UkAwB5Df5zZdtoeFZKLuPfee6V2cXExFi5c2Ob53377bbMe+fvvv99epbWob9++GDFihHT81FNPobS0tNXzX3zxReTl5Tmgss6JabLlcVu7pgKWXWBzcnLsXZJwDO1ERKR89fXNlnvsSE87ANw6Wh4Ws+LfZ9o4k9xdr169MHPmTOn4ueeew8qVK1s8d/fu3bjvvvuk4/79+2P69Ol2r/FSb731ltQbnZubi/Hjx2PXrl3NzikvL8fs2bPx2muvWb2UpUjjxo2T2i+99FKrGyZ9+umn+Mc//uGosoRiaCciIuWrq+vQxkqXuvUv8iS2FZvDbFUVuaj3339fmvhoNBpxyy234Oabb8by5cuxdetWrFy5Eg888ADGjBkjrRzj6+uL//3vfzbd7dRao0aNwosvvigdZ2ZmYtSoUUhKSsKECRMwZMgQREdH45133gEAfPzxx82uv3TJSiX461//Kv0iUlBQgIEDB+Kll17Czz//jC1btuCjjz7C5MmTpWUhH3jgAcEV2x+XfCQiIuWrq+tUT3uv63si1esYMht6YXdlb5TkViMi0d/GRZKriIyMxJYtWzBlyhQUFBQAAFatWoVVq1a1eH5AQAC+//77VndBdYQXX3wRarUa8+fPl8aD5+bmNlsBR6PR4IMPPsCUKVOaXXvpUpdKMHjwYCxYsAD//Oc/AQAlJSXNfjFp6rbbbsPcuXOxZMkSR5bocOxpJyIi5etkTztUKkzvaxnLboYH1i9U/pJ3JFbv3r1x6NAhPP744/Dz82vxHC8vL9xxxx04cuQIxo8f79gCW/Dcc8/h4MGDeOyxx9CjRw9otVoEBQWhX79+ePrpp5GRkYE//elPOH/+vHSNRqOBVqsVWHXrnnvuOXz00UeIjIxs8fvR0dFYuHAhvvrqK4dO/hVFZXbVrbQIOp0OQUFBqKysRGBgoOhyiIiu3N/+hiULq/AgLD1pH34IPPhgx26x9Y1dGDfHsiPq7d33YfmJ9peRs5bBYEBubi4SExPh6+trs/uSMhgMBmzduhU5OTkoKytDYGAg4uPjMX78eKd8f/3222+lcfsjRoy4bPy70hgMBmzbtg2ZmZnQ6/WIjIxE9+7dcdVVVwkZjtS0Llv8u7c2r7nN8JiGhgYcOHAAO3fuxOHDh5GdnY3Tp0+joqICDQ0NCAwMRFxcHIYMGYJbb70VU6ZMsflWw03l5ORg6dKl+PHHH3HmzBlUV1ejS5cuSEtLw1133YUZM2ZArXab/3uIiNr2++oxF11JThr1UD8EzalAJYKxPqcnjEaAP2bJGr6+vrj66qtFl2EzTce0jxw5UmAl1vH19cWUKVMuG9bjbtzmx9XcuXPxr3/9q9Xvl5WVoaysDIcOHcJHH32EAQMG4OOPP262yYKtLFq0CHPmzEHd75uFXJSTk4OcnBx89913GDFiBJYtW4akpCSbP5+IyOnU1UEHeZfJgIA2zm2FOtgfUyM24euSiagwBWHXynMYc1tM+xcSOQGz2WzVEJH//e9/WLt2rXR8zz332LEqsiW3GdN+6SggPz8/pKWlYdy4cRg/fjx69+7drGc9PT0dY8eOxfbt221ax4IFC/DEE09Igd3DwwN9+/bF2LFjm61Junv3bowbNw7nzp2z6fOJiJzSJT3tVxLaAWDamCqpvXbp+TbOJHIuL730Eh588EFs2bIFRqPxsu8XFBTgySefbBbSb7zxRqGTZ6lj3KanXaPRYPr06bjhhhswduxY9OrV67JzSkpKsGjRIrz22mtobGxEdXU17rzzTmRlZcHfv/OrDGzYsKHZzOeRI0di6dKl6NmzJwDLjl7ffPMNHnjgAVRXV6OgoAC33nqrzX9xICJyOnV10EEeE3Olw4in3tcV+H3J7V/2tDy5kMgZ6fV6LFmyBEuWLIGvry969eqFsLAwGI1GFBYW4tSpU83O79atGxYvXiyoWroSbhPaFyxY0O45ERERePnll5GQkIAHf5/hlJ+fj2+++abZDmlXwmw2Y86cOVKPf69evbBx48ZmM7Y9PDwwa9YshIWFSeO2duzYgVWrVuGmm27q1POJiJxafb1Netqjpg5AX49MHDGlYn9ZIirKTAgOdZsPncmFNR0tYDAYcOjQoVbPnTBhAr744gtpLXpyDvxJ1YIHHngAycnJ0vGWLVs6fc9169Y1+we0aNGiVpdYmjx5MmbNmiUdv/baa51+PhGRU7PBRFQAgFqNSQmWHkcTPLH1s9M2KI5IvPnz52PdunX429/+hjFjxqBLly7w9fWFWq1GeHg40tLS8Oijj+KXX37Bpk2bEB0dLbpk6iC36WnvqEGDBkkfJRUVFXX6fk23QE5MTGx3FvrDDz+Mr776CgCwd+9eFBQUIDY2ttN1EBE5pUuGx1xpTzsATJzkgUU5lvamFWW4YXZiJ4sjEs/LywvXXHMNrrnmGtGlkJ2wp70VTSdxBHTm3eF3P/74o9SeOnVquzO8x4wZ02wzh6bXExG5nSY97V5eZnRm1/Wx9/eABxoBAL8cCrNFdUREdsfQ3oKGhoZmGw10dg3T8+fPN+utt+Z+arUaQ4fKG38cPny4UzUQETm1JqE9MLBzOx8GD+uJwWrLcMUjVQkoPtvY6fKIiOyNob0Fzz33nBSyQ0NDO72G6dGjR5sdNx0v35am5116DyIit9JkeEynP/xUqTAxSR7Lvvmzgk7ekIjI/hjaYRkKc+7cOXz33Xe4+uqr8eabbwKw7MC1fPlyhIV17uPTvLy8Zsfx8fFWXdf0vEvvQUTkVpr1tHf+dhMnyu2tayo7f0MiIjtz24mo4eHhuHDhQovfU6lUmDJlCt566y307du308+qqqpqdhwUFGTVdYFN3pkuvUdL6urqmu2yqtPprKyQiEjZGgyNMEADwAY97QBG3pUEjw8aYYIndmTY4LcAIiI7Y097C0aPHo1HHnkEKSkpNrlfdXV1s2NfX1+rrtNoNK3eoyWvvvoqgoKCpFdcXFzHCiUiUqgqg5fUtkVoDxjZF/09jgAAMirjUVFubucKIiKx3Da0T5o0CVOnTsXUqVMxfvx49O7dW9qYYPv27bj55psxYsQI5ObmdvpZl24nrFZb9wFH0/MaGhraPf/ZZ59FZWWl9MrPz+9YoUREClVVLy8XY4vhMfD0xOg4y7h2Mzyw+9tCG9yUiMh+3HZ4zMU10JsqKyvDkiVL8NJLL6Gmpgb79u3DuHHj8NtvvyEyMvKKn3XpJkoGg6HVjZUuPe+ipss/tsbHxwc+nVkHjYhIoarqvKW2LXraAeCqUSa89/t81O2rSnDNA9wLg4iUy2172lsSGhqKZ555Btu2bZPWZs/Pz8dTTz3Vqfv6+/s3O9br9VZdV1tb2+o9iIjcia5eHlZok552AKNvk0P6jv3WDVskIhKFob0FAwcOxHPPPScdf/nllygrK7vi+4WHhzc7PnfunFXXNV3bvbMr2BAROS2jEVVm+dNGW/W0x16Xhm6wdLXvOZ8AK0YhEhEJw9DeipkzZ0pto9GIffv2XfG9evXq1ez4zJkzVl3XdEx67969r/j5REROrcka7YDtQju8vTE68jgAQG/W4ODPpTa6MRGR7TG0t+LSlVdaWx7SGj169Gg2qTQ9Pd2q6w4ePCi1+/Tpc8XPJyJyak3WaAdsGNoBXDWgRmpv/8a6T0GJiERgaG9FZWXzzTaCg4Ov+F7e3t4YPny4dLx9+/Z2rykqKsLJkyel47Fjx17x84mInFpdHWohT963Yl6+1UZfJ++bsXMnl30kIuViaG/F1q1bmx0nJyd36n433nij1N64cSOKi4vbPH/ZsmVSOzg4mKGdiNxXXR30kPetaLKFRael3poCf1g2r9t7Osp2NyYisjGG9hbU19fj5Zdflo6Tk5MvG5feUXfccYe0HGNDQwPeeOONVs+trq7GO++8Ix3fdddd8PLyavV8IiKXdklot2LFXKt5donCEF/LJkv5dVE4d4azUYlImdwitP/88894+umncfbs2XbPPXfuHK6//vpm487/8Y9/tHhuXl4eVCqV9Jo3b16r942NjcXDDz8sHS9atAjffvvtZec1NDTg3nvvlSarajQazJ07t926iYhcVn19s+ExtuxpB4BhifIE1H3f5Nn25kRENuIWmyvV1NTgX//6F/79739j1KhRGDNmDPr164fw8HBotVpUV1cjJycH27Ztw+rVq5utj37DDTfg/vvvt0kd8+bNw7p163DixAk0Njbitttuw5133okZM2YgNDQUx44dw3/+8x8cPnxYuubNN99Ely5dbPJ8IiKnZMeedgAYNtITOGpp7/25Ajd0bmsOIiK7cIvQfpHJZML27dutmggKAPfeey8++OADqFQqmzw/JCQEa9asweTJk5Gfnw+TyYTPP/8cn3/+eYvnP/PMM3jsscds8mwiIqd1yURUm/e039QV+NjS3nOImyyRRX19PT755BOsWLECGRkZKCsrQ8Pvi/n3798f6enpuOeee/Dpp58CAF588cU2P3En6iy3GB4zZMgQPPnkk0hJSWk3gHt7e+OWW27Br7/+io8//hje3t5tnt9RPXv2xOHDh3H//fdD08o7T58+fbB69Wq8/vrrNn02EZFTsuNEVACIndIHMbAs97jvfDeYTLa9PzmfqqoqXHXVVXjkkUekxSMauPsWCeYWPe2xsbF466238NZbb6GiogKHDh1CTk4OSktLUVdXBz8/P4SEhKBPnz7o378/fH2t62lJSEiA2dzxJcKCg4OxZMkSvP3229i0aRPy8/NRU1ODmJgY9OvXDwMHDuzwPYmIXNYlPe22Hh6j8vHGsNCTWF0Wg0pTIE7svoBeo7gLtTubO3dus00Vu3Xrhu7du0t7rnRkRbm8vDwkJiZKx1eSG4gANwntTQUHB2PcuHEYN26c6FIQEBDQbClIIiJqgZ172gFgWGo1Vm+ztPd+ncfQ7sYaGxulIS8A8Nprr2HOnDkCKyKycIvhMURE5MTsPBEVAIZN8Jfae7bV2/4B5DSOHz+OqirL2v1qtRpPPdX6zOSlS5fCbDbDbDZzPDvZHUM7EREpW5PhMSqVGTaeagQAGDorSWrvPRli+weQ0ygrK5Pa0dHR0pAYItEY2omISNma9LRrvY2w0YJezQT16YLenscBAOm6JBj0HHfsrppOOPX09BRYCVFzDO1ERKRsTTZX0ng32ucZKhWGRhcAABrgjSMbCu3zHFKkLVu2SBslTpgwQfr66dOnm22iePF10T333NPqBotLly6FSqVqNgkVQIv3a2+DRiKAoZ2IiJSuSU+73UI7gMH95LHs+38ssttziIiuBAdqERGRsjUdHuNrv0XUB08IBNZb2gf2Gu32HFKe0NBQTJ06FYBlTPvF5R59fX2veLW5rl27YurUqdDr9di6dav09YvPuVT37t2v6DnkPhjaiYhI2ZpMRNX42G+sef+bk6GaY4IZHjiQG2y355DypKWlYf16y29sW7ZskYbIREVFSV/vqClTpmDKlCmXrdN+pfcj4vAYIiJStEZ9PerhAwDQauwX2gO6R6GnOgcAcLgqEfUGbo1KRMrB0E5ERIqmr5HDs8aOoR0ABkefBQDUwwdZ607b9VlERB3B4TFERKRo+lo5qGt87fusQf3q8YVlERkcWFuEATcltn1BJwwZAhRxvmuroqOB334TXQWRcjC0ExGRotXWyKFd62ffZw0aHwSss7QP7DXiPjs+q6gIKOTKkkRkJYZ2IiJSNL1ebmu0dthZqYmBM5OBOZb2/lz77owaHW3X2zs9/v0QNcfQTkREiqY3yEFd62ffqVjBSaFIVufhlDEBh6qSYDQYofa1z1slh34QUUdwIioRESlaba3c1tg5tAPAoOhzAAA9tDi2LsfuzyMisgZDOxERKZq+Tn6r0gZ42v15g7gzKhEpEEM7EREpWq1eHh6jcUBoHzwuQGof2M+12olIGRjaiYhI0Zr2tGv87T8Va+BNCVL7QI59J6OS6/PwaB61zGb77jVArouhnYiIFK22Tu5d1/rZd/UYAAjvGYp4T8ti7Qd1STAZ2dtOV87Pr/k6pfqmyyERdQBDOxERKZq+Xg7tGo1jnjkoIh8AUI0AnNxS4JiHkksKDg6Gr6+8K9ipU6cEVkPOjKGdiIgUrWlo12od88wBvQxS+9A67oBEV87T0xNpaWnS8aJFi2Ay8dMb6jiu005ERIpW2+AltR3V095/pBb41dI+tKcOtzrmseSi7rzzTuzduxcA8NFHH+HHH39E3759mw2duf3223H77beLKpGcAEM7EREpmr5BfqtyVE97/+u6Aq9Z2unHHfRQcll//vOfsXr1amzevBkAUFRUhKKi5suJDhgwQEBl5Ew4PIaIiBSt1ugttR3V054wuisCoQMAHLoQ65iHksvy8vLCzz//jKVLl2L69OmIi4uDxlH/MZPLYE87EREpl9kMfaPjQ7vKQ4X+QXnYVpmGAlMXlGWfR2jvSMc8nIQaP3681csyLl26FEuXLrXqXE9PT9x99924++67O1EduTP2tBMRkXI1NKAWclJ31PAYAOifWCm1D31/2nEPJiJqAUM7EREpl8EAfZPQ7sgRBf0HyR9GH9qmc9yDiYhawNBORETKVVfXLLQ7tKd9ijwcJv0IR5MSkVgM7UREpFwGA2ohJ3VH9rT3vS4eHmgEABw6F+G4BxMRtYChnYiIlOuS4TFNNpa0O02gF3r5WsayZ9Ulo6G82nEPJyK6BEM7EREpV5Oedo1nHVQqxz6+f9dSAEA9fJC95qRjH05E1ARDOxERKVeTMe0atdHhj+/ft1FqH9pY4vDnExFdxNBORETK1WR4jNarweGP7z82WGqnHzQ5/PlERBcxtBMRkXI1HR7j7fie9gHXx0ntQ6dDHP58IqKLGNqJiEi5mva0Cwjt0d39EeFxAQBwSJcIcyN724lIDIZ2IiJSLJO+DoaLY9q9HR+YVSqgf1g+AKAEESjae8bhNRARAQztRESkYIYqeRy7xqexjTPtp39yjdQ+tLZQSA1ERAztRESkWLU6eUiM1tcspIb+Q72ldvoufZvnms1iaiQix3P0v3eGdiIiUix9tdy7rhEU2gdMkXdDPXSs5d2dPDwsb6cmE8e8E7mLi//eL/77tzeGdiIiUix9jRzatRoxob33lDh4oR4AkHE+ssVz1Go1VCoV6urqHFkaEQlkMBigUqmgVqsd8jyGdiIiUqzaKrnnWqMRU4OXryd6a04DAI7VJ6K+vOayczw8PKDRaFBTc/n3iMg16XQ6+Pv7s6ediIhIXyv3rmv9xNXRr4tl2UcjvJC9NqfFc/z9/VFTU4P6+npHlkZEAtTU1MBgMCAwMNBhz2RoJyIixaqtkUO7RivuLatfH3mYTsbm0hbPCQoKglqtRkFBARobxax0Q0T2V1NTg/z8fPj5+cHf399hz3XMIBwiIqIr0LSnXeMnMLSPCgDWWNpH0lve5EmtViMuLg55eXk4efIkgoKC4O/vD09PT6hUKgdWS0S2ZDabYTKZYDAYoNPpYDAY4Ofnh9jYWIcNjQEY2omISMFq9XLY1fqLC759r4kF5lraGbmt96z5+PggMTERFRUVqKysRHl5uYMqJCJ7U6lU8Pf3R1hYmEPHsl/E0E5ERIqlN8htjb+4t6z4AaEIVOmgMwcioyIWMJst26W2wNvbG5GRkYiIiIDRaORQGSIX4OHhAbVa7fCg3hRDOxERKZa+aU97gKewOlQqoG9QPnZWpOKMKQ6V2WcR1KdLO9eo4OXlBS8vLwdVSUSujBNRiYhIsWrr5LcpTYDYfqZ+3aqk9pG1ZwRWQkTuiKGdiIgUS18n965rAwWH9v7yW+aRHZUCKyEid8TQTkREilVbL4d2TZC3wEqAvuPCpHZGJleDISLHYmgnIiLF0tfLveuaQLFjw/tdFye1MwpDBVZCRO6IoZ2IiBRL39BkeIyf2N7t0GhvdFEXAwAyapJgruPOp0TkOAztRESkWLUN8pAYjUZgIb/rF1EEAChHKM5uPSm4GiJyJwztRESkWHqjPCRGqxVYyO/6dZcXjj/yS7HASojI3TC0ExGRYtU2Kqunve9gH6mdsc/QxplERLbF0E5ERIqlbxLaFdHTPjFCamcc92njTCIi22JoJyIixapt9JXavr5tnOggfSbGwAONAICM81GCqyEid8LQTkREymQ2Q2+29Gb7qOrgoYB3LI2fB3r45gMAsuqTYdTVCq6IiNyFAn4EEhERtaChAXpYBrJrPesEFyPrF10CAKiDL079nCO4GiJyF24V2isqKrBq1So8/vjjGDt2LKKjo+Hj4wN/f3/Ex8fj+uuvx8KFC1FeXm6X56tUqg6/PvjgA7vUQkSkeAYDamEZyK7xVM6a6H17NkjtjM2lAishIneibv8U55ednY2nn34aP/30E+rrL//BX19fj5qaGuTn52PNmjV47rnn8Morr2D27NlQqbhVNRGREHV1TXralRPa+w3TAD9Z2hkHGjBTbDlE5CbcIrQfOXIEa9asafY1T09PdO/eHVFRUWhsbMTRo0dRVlYGAKitrcXf/vY3ZGZm4sMPP7RLcB87diw0VqxfFh8fb/NnExE5BYMBtQgDAGi8Gto52XH6XR0DvGxpZ+QoYEkbInILbhHaL1Kr1Zg+fTruueceTJgwAYGBgdL3zGYzvv/+ezz22GMoLCwEACxZsgSDBg3Co48+avNaPv30UyQkJNj8vkRErsKsN0B/cXiM2ii4GlnSyChoUAs9tMi40EV0OUTkJtxiTLuXlxceeOABnDp1CqtWrcKNN97YLLADlvHmN954I3bt2oXo6Gjp6y+88AIaGpTTw0NE5C4MlfLkU623cn4Oe6pVSPU7DQA4ZeyG2iKd4IqIyB24RWi/8cYb8d///teqoSZxcXGYP3++dFxaWoqtW7faszwiImqBXicHdY1Xo8BKLte3q2XBAjM8cHT9acHVEJE7cIvQ3lHXX399s+Ps7GxBlRARua+moV3ro6zQntpHridz6wWBlRCRu2Bob0FoaGizY52OH30SETlabWWTnnYfk8BKLpc6PEBqZx5W1i8UROSaGNpbcPp08486IyMjBVVCROS+9FXy5FOtr8JC+9VdpXbmaT+BlRCRu2Bob8HKlSubHY8cOdLmz3j66aeRmpqKwMBAaDQaxMbGYsKECZg3bx5yc3Nt/jwiImdTWy0HdY2vWWAll4sbGI4AVRUAILOcK8gQkf0xtF+isrISixYtko7T0tKQkpJi8+esWLECWVlZqKqqgsFgQGFhIbZs2YL58+ejZ8+eeOSRR6DX623+XCIiZ6GvloedKC20qzxUSPHPBwDkNcaj+kyZ4IqIyNUxtF/iqaeeQlFRkXT88ssv2+U54eHhGD58OCZNmoQhQ4bA399f+p7RaMTixYsxevRoVFZWWn3Puro66HS6Zi8iImfVNLRrFbiHUWpshdTmCjJEZG8M7U0sWbIEH330kXQ8a9asy1aS6YyUlBQsXLgQp06dQklJCXbv3o2NGzdi3759KC8vx5o1a5CWliadf/DgQdx+++1W3//VV19FUFCQ9IqLi7NZ7UREjlZbI/eua7S235m6s1JT5Poyt5cLrISI3AFD+++2bt2Kxx57TDpOTEzE4sWLbfqMzMxMzJ49G0lJSZd9T61WY9q0adizZw+mTZsmfX39+vX44YcfrLr/s88+i8rKSumVn59vs9qJiBxNXyuHYq2fAkP7SHmTvswMZU2UJSLXw9AOID09HTfccAPq6+sBWFaLWb9+PYKCghxei6+vL5YvX46oqCjpa++++65V1/r4+CAwMLDZi4jIWSm+p/3qWKmdeca/jTOJiDrP7UP7sWPHMHXqVGnseEhICH766Sf07NlTWE0BAQF49NFHpeNt27bBYDAIq4eISAS9vklPe4CnwEpa1rVvCAJVlrlDmRVd2zmbiKhz3Dq05+bmYvLkyTh//jwAS1het24d+vfvL7gyYMKECVLbYDBwqAsRuZ1avdy7rglQC6ykZSoVkBpo+dl8xhSHqpwSwRURkStz29BeUFCASZMmoaCgAACg1WqxZs0aDB8+XHBlFtHR0c2OS0tLBVVCRCSGXuGhHQBS4+RVurLWcQUZIrIftwztxcXFmDx5srSJkY+PD7777juMHTtWcGWy2traZsdaJa53RkRkR3qDHNq1gQoN7alyO3NHhbA6iMj1uV1ov3DhAiZPnoxjx44BALy8vLBixQpMmTJFcGXNZWZmNjuOjIwUVAkRkRi1dfI4dk2Qt8BKWpc6KlhqZx5R1gZQRORa3Cq0V1ZWYurUqThy5AgAwNPTE1988QWmT58uuLLLffnll1I7ISEBMTExAqshInI8fb38FqUNVmhon9pkBZn8AIGVEJGrc5vQXlNTg2nTpmH//v0AAA8PD3z66aeYOXOm4Mou9/3332PNmjXS8YwZM8QVQ0QkSG29PCRGE+wjsJLWxfQMQLDKsvpYZmUcYGZvOxHZh1uE9rq6OsyYMQM7duwAAKhUKvz3v//FXXfd1an75uXlQaVSSa958+a1eF5lZSVuueUW6ReGtixfvhx33nmndKzVajFnzpxO1UlE5Iz0TUK7NkSZoV2lAlKDLAsaFJi7ovJ4seCKiMhVKXNmj40tWrQIGzdulI6Dg4Px9ddf4+uvv7bq+ilTpuCpp5664uebzWasXLkSK1euRO/evTF16lQMGDAAMTEx8PPzQ1VVFTIyMrBixQrs27dPuk6lUuGTTz65bCUZIiJ3UGv0ktpK7WkHLCvIXJyDmvVTPkb24s9sIrI9twjtl67EUl5ejg0bNlh9vS1Dc3Z2NrKzs9s9LyAgAIsXL8Ztt91ms2cTETkTfdPQrsAdUS9KTQWQYWln7dJh5F+FlkNELsothseIptFo8NBDDyE1NRUqVdtvPEFBQXj88cdx5MgR3HHHHQ6qkIhIefSNlsmn3qiDp/I2RJWkjgyU2pmZHNNORPahMps5a8aRysvLkZ6ejvPnz6O0tBQVFRXQarUIDQ1FWloa0tLS4GmjdyedToegoCBUVlYiMDCw/QuIiBSkt9dJHDN2R5CqEhWmINHltOrc0Qp0SQkGAFwdshcbyoaJLYiInIq1ec0thscoSUhICCZMmCC6DCIixdObLOPYtR4GAMoN7dG9gxGiKke5OQSZlbHtX0BEdAU4PIaIiBSp1uQLANB41AuupG0qFZAaaFlBptDUBRUnSwVXRESuiKGdiIiUx2yGHhoAgMZT2aEdAFLjKqV21k8FAishIlfF0E5ERIpjrquXQrtW7QShPUVuZ+6sbP1EIqIrxNBORESKU68zwATLpHyNV4PgatrXbAWZI1zfgYhsj6GdiIgUR19ukNpaZwjtV3eV2pn5AQIrISJXxdBORESKU1teJ7U1Xo0CK7FOZJ8whKkuAAAyK7q2czYRUccxtBMRkeLoK+TQrvVRfmi3rCCTDwA4Z4pGeW6F2IKIyOUwtBMRkeLUVspDYjROENoBIDVWJ7Uz1+cLrISIXBFDOxERKY5e1zS0O8fEztQUuU6uIENEtsbQTkREitM0tGs1JoGVWC91hDwBlSvIEJGtMbQTEZHi1FbJQ2I0vgIL6YBmK8ic8RdYCRG5IoZ2IiJSHH21HNq1WoGFdEBEaiQiVCUAgCyuIENENsbQTkREitOsp12rElhJB6hUSA2wTEAtMkWiLE/XzgVERNZjaCciIsXR18jj2LV+ThLaAaR0lSegZv1UILASInI1DO1ERKQ4tTXyRE6Nv6fASjomNUX+ZSNzR4W4QojI5TC0ExGR4uj1ctuZQnvKMHkFmawjzrHqDRE5B4Z2IiJSnKahXevvPG9VzVaQOc0VZIjIdpznJyEREbmNWr08jl0T6CWwko6J6N8F4SgFAGRVxAiuhohcCUM7EREpjt4gh3ZtoFpgJR2kUiE14DQA4FxjFMoLagQXRESugqGdiIgUp7ZOfnvSBHkLrKTjUrpwBRkisj2GdiIiUhx9nTz5VBvkPMNjACC1T5MVZLaXC6yEiFwJQzsRESlObb0c2jXBPgIr6biUYfIE1MwMriBDRLbB0E5ERIqjb5DHsWtCfAVW0nGpk+UJqFmntQIrISJXwtBORESK48yhPWJQHMJ+X0Ems4wryBCRbTC0ExGR4tQaLZNP1WiAV4BzhXaVpwdS/ZusIHPOILgiInIFDO1ERKQ4eqNl8qkWtYCH871VpXaRJ6ByBRkisgXn+0lIREQur7bR0ruuUTlnL3VKL3kCatb2MoGVEJGrYGgnIiLF0Zssw2M0HnWCK7kyqcP8pHbm4UaBlRCRq2BoJyIixdGbLT3tWg8n7Wmf1GQFmTyuIENEncfQTkREilNr1gAANJ71giu5MpFD4pusIBMtuBoicgUM7UREpCgNDUAjLEs+atXOGdpVXmqk+FlWkDlrjEJFSYPgiojI2TG0ExGRotTqjFJbo3besJsaLU9Azfq5UGAlROQKGNqJiEhR9OXyOHatl/OG9pSe8i8fWdsuCKyEiFwBQzsRESlKbZkc2jVezrvySupQeQJq5iHn/eWDiJSBoZ2IiBRFr5MDrsbbeUN7ykR5AmpmDleQIaLOYWgnIiJF0VfIa7NrfZw3tEeNSEQoLMNisi5ECa6GiJwdQzsRESlKbWWTnnYfUxtnKpvKxxupmlwAQKExChWlxnauICJqHUM7EREpStPhMVqNWWAlnZfSZAWZo5vOCayEiJwdQzsRESlKbZU8JEbj5KE9taf8C0jmr6UCKyEiZ8fQTkREiqKvkoeRaLUqgZV0XsoQeQJq1iHn3CiKiJSBoZ2IiBSlWU+7n3O/TaVOarqCjEZgJUTk7Jz7pyEREbkcfbUc2rX+zv02FTUyCSGwjGvPKo0UXA0ROTPn/mlIREQup7ZGXjHG2XvaVb4+SNXkAAAKGqJRWea8S1gSkVjO/dOQiIhcjr5GnnyqDfAUWIltpERdkNpHNxcJrISInBlDOxERKUqtXm5rg7zEFWIjqT24ggwRdR5DOxERKUptrbxijCuE9pTB8gTUrHSuIENEV4ahnYiIFKXWIL81aYK8BVZiG6kT5Amomad8BVZCRM6MoZ2IiBRFX9ekpz3Y+UN79OhkaQWZzBKuIENEV4ahnYiIFKW2Tp58qg11/p5plZ8WKT4XV5CJgq7SuXd5JSIxGNqJiEhRauvUUlsT7COwEttJjZInoGZtLhZYCRE5K4Z2IiJSFH1Dk9Ae6hq7iKZ0lyegZv1aIrASInJWDO1ERKQotUbLijHeqIM6UCu4GttIHSwP88k8WCewEiJyVgztRESkKLVGy+RTLWoBb+efiAoAKePlCahZp1xjyA8RORZDOxERKUqt0RJqNSoDoFK1c7ZziLkqGcEoBwBkno8QXA0ROSOGdiIiUhS96feedg+D4EpsRxUYgBTvUwCA/PporiBDRB3G0E5ERIpSa7JMPnWl0A4AqZHyBNSj20rbOJOI6HIM7UREpBhmM1ALy+RTjWeD4GpsK6W7PAE1a8t5gZUQkTNiaCciIsWoM8jDRrTq+jbOdD6pg5qsIHOAK8gQUce4VWivqKjAqlWr8Pjjj2Ps2LGIjo6Gj48P/P39ER8fj+uvvx4LFy5EeXm53WvJycnBCy+8gMGDByMiIgIajQbJycm46aabsGLFChiNRrvXQESkNLWVcu+61su1etpTx8sTULNOusaqOETkOCqz2ezys2Gys7Px9NNP46effkJ9ffs9N1qtFq+88gpmz54NlR1WLli0aBHmzJmDurrWe1pGjBiBZcuWISkp6Yqfo9PpEBQUhMrKSgQGBl7xfYiIHKUgsxJxfYMAADOjtuGbojGCK7Idc0UlQkLMqEQw4n2KcNoQLbokIlIAa/OaW/S0HzlyBGvWrGkW2D09PdGrVy+MHTsWo0ePRmhoqPS92tpa/O1vf8NDDz0EW/9Os2DBAjzxxBNSYPfw8EDfvn0xduxYxMTESOft3r0b48aNw7lz52z6fCIiJastkyefarwbBVZie6rgIKR6nwAAnKmLRpXO5fvMiMiG3CK0X6RWqzFjxgx89913KCsrQ3Z2Nn799Vds374dpaWl+O6779C1a1fp/CVLluCDDz6w2fM3bNiAF198UToeOXIkjh49ioyMDPz6668oKCjAl19+CX9/fwBAQUEBbr31Vps9n4hI6fQV8ieQWh/XCu0AkBLRZAWZHWUCKyEiZ+MWod3LywsPPPAATp06hVWrVuHGG2+87OMHlUqFG2+8Ebt27UJ0tPyR5QsvvICGhs6PqzSbzZgzZ47Uc9+rVy9s3LgRPXv2lM7x8PDArFmzsGrVKulrO3bsaHZMROTKaivkT0S1viaBldhHarL8S0nmZq4gQ0TWc4vQfuONN+K///0v4uPj2z03Li4O8+fPl45LS0uxdevWTtewbt06HDp0SDpetGgRtFpti+dOnjwZs2bNko5fe+21Tj+fiMgZNJ2IqvF1veEjKQPlCahZB1xrHXoisi+3CO0ddf311zc7zs7O7vQ9V65cKbUTExNx9dVXt3n+ww8/LLX37t2LgoKCTtdARKR0el2T1WO0rhfaU8fJK8hknuAKMkRkPYb2FjSdlApYZvV21o8//ii1p06d2u6qNGPGjIGfn1+L1xMRuapanbzcrVZr+9W7ROsyJhmBqAQAZBWHCa6GiJwJQ3sLTp8+3ew4MjKyU/c7f/48ioqKpOORI0e2e41arcbQoUOl48OHD3eqBiIiZ1BbJYd2jQuGdlV4GFK9jgMATtdFo7pacEFE5DQY2lvQdCgLYF3IbsvRo0ebHScnJ1t1XdPzLr0HEZErqq2SJ59q/V3zLSolXJ6AenRXhbhCiMipuOZPxE6orKzEokWLpOO0tDSkpKR06p55eXnNjq2ZEHvpeZfeg4jIFelrXD+0pybJE1AzNxcLrISInIladAFK89RTTzUbyvLyyy93+p5VVVXNjoOCgqy6rumylJfeoyV1dXXNdlm1xVh8IiJHqq2RJ59qAz0FVmI/KQO8gR2WdtZverHFEJHTcM1ujCu0ZMkSfPTRR9LxrFmzLltJ5kpUXzJo0dfX16rrNBpNq/doyauvvoqgoCDpFRcX17FCiYgEq62V25oAL3GF2FHquHCpnXnCNf+MRGR7DO2/27p1Kx577DHpODExEYsXL7bJvY1GY7Njtdq6DzianmfNBk/PPvssKisrpVd+fn7HCiUiEkzfpONZG+Sagbbr2CYryBSFtnM2EZEFQzuA9PR03HDDDaivt+zEFxkZifXr11s9jKU9l26iZDBYt6FG0/OaLv/YGh8fHwQGBjZ7ERE5k1qDvGKMq4Z2VWQEUtSWFWTyDDFcQYaIrOL2of3YsWOYOnUqKistvR4hISH46aef0LNnT5s9w9/fv9mxXm/dGMbaJp8TX3oPIiJXVGuQ35Y0wT4CK7EjlQqpYfIE1KN725+zRETk1qE9NzcXkydPxvnzluW3AgICsG7dOvTv39+mzwkPD292fO7cOauuazohNiyMm3AQkeurrZMnn2pDXDS0A0hpsoJMFleQISIruG1oLygowKRJk1BQUADAMoRlzZo1GD58uM2f1atXr2bHZ86cseq6pmPSe/fubdOaiIiUSF/vHqE9dYA89CdzX20bZxIRWbhlaC8uLsbkyZORm5sLwDIW/LvvvsPYsWPt8rwePXo0m1Sanp5u1XUHDx6U2n369LF1WUREilNbL/+s1IZp2jjTuaWMkT89zTrO1ZeJqH1uF9ovXLiAyZMn49ixYwAALy8vrFixAlOmTLHbM729vZv14G/fvr3da4qKinDy5Enp2F6/UBARKUltgzcAwAON8ArStnO284odl4wAWPbSyOQKMkRkBbcK7ZWVlZg6dSqOHDkCAPD09MQXX3yB6dOn2/3ZN954o9TeuHEjiovbHsO4bNkyqR0cHMzQTkRuQW+0DBvRohYqH2/B1diPKiYaKZ6WzqM8fTRqagQXRESK5zahvaamBtOmTcP+/fsBAB4eHvj0008xc+ZMhzz/jjvugI+PZXxmQ0MD3njjjVbPra6uxjvvvCMd33XXXfDycs2lz4iImqpttAR1rUoPqFTtnO3EVCqkhsmLDRz9jamdiNrmFqG9rq4OM2bMwI4dln2jVSoV/vvf/+Kuu+7q1H3z8vKgUqmk17x581o9NzY2Fg8//LB0vGjRInz77beXndfQ0IB7771Xmqyq0Wgwd+7cTtVJROQsahstO0ZrVNbtZ+HMUhLk5X+5ggwRtcctZr8sWrQIGzdulI6Dg4Px9ddf4+uvv7bq+ilTpuCpp57qdB3z5s3DunXrcOLECTQ2NuK2227DnXfeiRkzZiA0NBTHjh3Df/7zHxw+fFi65s0330SXLl06/WwiImdQa7aEdq1nneBK7C+1vxrYa2ln7mVPOxG1zS1Ce9NNigCgvLwcGzZssPr66Ohom9QREhKCNWvWYPLkycjPz4fJZMLnn3+Ozz//vMXzn3nmGTz22GM2eTYRkTPQu1NoHxsG/NfSzjru2fbJROT23GJ4jJL07NkThw8fxv333w+NpuXlzPr06YPVq1fj9ddfd3B1RETiNNSbYcTvE1HV9YKrsb/YsUnyCjJnuYIMEbVNZTabzaKLcFdVVVXYtGkT8vPzUVNTg5iYGPTr1w8DBw60yf11Oh2CgoJQWVmJwMBAm9yTiMheKs/XITjKMmH/6uC92FA+THBFdmY2Y4T6N+wxDYUKJlRVe8DPT3RRRORo1uY1txgeo1QBAQHNloIkInJn+gu1ACyhXettFFuMI6hUSAktwp5SwAwPZB/UY/BVrruhFBF1DofHEBGRItRekFdT0fo0CqzEcVIT5AmomZu4ggwRtY6hnYiIFKG2TF7mUeNjEliJ46SkyR94Z+2rFlgJESkdQzsRESlCTbm8Yoyfxj1Ce+pVIVI7M5sryBBR6xjaiYhIEWrK5BVj/LTusUZC3Lgk+KMKAJB1NqSds4nInTG0ExGRItRUNEhtrVZgIQ6kSuiGFI9sAEBubSQu2VaEiEjC0E5ERIpQUymvGOPnrxJYiQN5eCAl5ByA31eQOeT6m0oR0ZVhaCciIkWo0ckrxvgFuEloB5DajSvIEFH7GNqJiEgRaqrkyad+Ae4zKTM1Tf6zZu3lCjJE1DK7bK5UVFSEffv24fDhw8jLy0NhYSGqq6uh1+uh0Wjg5+eHrl27IiEhAWlpaRg6dChiYmLsUQoRETmJmuomoT3Iffb+SxkdAiy1tDOPsi+NiFpms5+KW7duxapVq7B27VqcPHmyw9cnJyfj2muvxYwZMzBhwgRblUVERE6ipkknszuF9vhxifBHFaoRgKyzQaLLISKF6tSv9MXFxZg3bx4SExMxYcIEvPPOOzhx4gTMZjPMZuuW67p47smTJ/Hee+9h8uTJiI+PxwsvvIBz5851pjwiInIiNbXyOHa/YC+BlTiWKikRfVSWFWRyaqK4ggwRteiKQntubi7uu+8+JCQkYMGCBTh9+nSLIf1iIPf390dERARiY2MREREBPz+/VoO92WxGQUEBXnnlFSQmJuKee+7BqVOnrqRMIiJyIjX6JqE9zFdgJQ7m6YnU4LMALCvIHDvS0M4FROSOOvT5Y0lJCZ5//nl88sknMBqNl4XukJAQjBs3DkOHDkVaWhp69uyJrl27QqPRXHYvvV6PwsJCHDt2DBkZGdi3bx9+/fVXlJWVAbCE9/r6enz22Wf44osvcO+992LBggWIjIzsxB+XiIiUqkYvT8j0C/URWInjpXSrAcot7cxfijBwWJzYgohIcawO7QsXLsT8+fOh0+mahfXu3bvj1ltvxc0334zBgwdb/WCNRoPu3buje/fumDZtmvT1/fv3Y+XKlVixYoU01MZoNGLJkiX46quvMG/ePDzxxBNWP4eIiJxDTV2T0O5OPe0AUvurgXRLO3N3ldBaiEiZVGYrB597eHhApVLBbDZDrVbj1ltvxcMPP4yxY8farbht27Zh8eLF+Oabb9DQYPm4UKVSobGxsZ0rCQB0Oh2CgoJQWVmJwMBA0eUQEbXp6vD9+PmCpfOn8nQFAuODxRbkQHlLNiLxwckAgBt6HMXq430EV0REjmJtXuvQmHZvb2/89a9/xcmTJ7Fs2TK7BnYAGDNmDD7//HOcOnUKjz/+OHx93avnhYjIndTUy5NP/SL9BFbiePETkuEHy/I5mWeDxRZDRIpkdWi/++67cfz4cSxatAjx8fH2rOkysbGxWLhwIY4dO4a7777boc8mIiLHqGmwjGP3gQGevu6zegwAeCR2Qx+PYwAsK8jo9YILIiLFsTq0f/LJJ4iLEzsxJi4uDh9//LHQGoiIyD5qGi2h3U/lhmseenggNdSyzLEZHsg+yNRORM1x6zUiIlKEmkbLEEg/D/cMrKmJ8i8rWb9wnxIiao6hnYiIFKHGZFke2M/TILgSMVL6e0vtzD3VbZxJRO6IoZ2IiIQzm8yogWXyqZ+6TnA1YqSODZPaWdl8eyai5vhTgYiIhKuvrkfj71uH+KnrBVcjRvzE7tCiBgCQeS5EcDVEpDQd2hG1oxoaGpCXlwedTof6+nqo1Wp07doVMTExUKlU7d+AiIjcQk1JLYDfJ6J6N4gtRhCPLtFI8TyI3xoHIac2Gno90MKG4kTkpmwa2n/77Tds27YNW7duRXp6OgoKCmAymS47z9vbG4MHD8aYMWMwefJkTJw4kSGeiMiN1ZTqAVh6l/18jGKLEUWlQkrYefx2HjDBE8f26TBgLDfGIyILm4b2YcOGSeG7rY1W6+rqsGvXLuzatQtvvPEGIiMjcdddd+HJJ59Ely5dbFkSERE5gZoL8uRTP183De0AUpP0wHlLO+uXcwztRCSx25h2lUrVYu/5pV83m80oLi7G22+/je7du+Ppp59GXZ17TkIiInJXNWXyz30/38s/oXUXKQOarCCzt0ZgJUSkNDYf036xh93T0xPR0dGIjY2FRqOBSqWC0WhEfn4+CgsL0dAgj1m8GOINBgP+/e9/4+eff8bq1avRrVs3W5dHREQKVFMuTz7107b+Sa2rSx0fAXxgaWce8xRbDBEpik1D+1//+lcMGTIEQ4cORY8ePeDp2fIPHJPJhIyMDGzfvh0//vgjNm3ahPr6eqhUKpjNZhw+fBiTJ0/Gtm3bEB0dbcsSiYhIgZqFdj+BhQjWbWIytKhBLfyQVRQquhwiUhCbDo9ZtGgR/vjHP6J3796tBnYA8PDwQP/+/fHYY49h7dq1OHv2LF555RUEBQUBsPS85+Tk4MEHH7RleUREpFA1lfI4dncO7R4RYejjdRIAcErfBQa9+37qQETNKWKd9tDQUDz77LM4fvw4Jk2aBLPZDLPZjLVr12LLli2iyyMiIjur0TVKbb8ARbw1CZMSbpmJaoInju28ILgaIlIKRf1kDA8Px9q1azF06FDpa8uXLxdYEREROQJDuyw1WZ6Um/nLOYGVEJGSKO4no5eXF1599VXpeOvWrQKrISIiR6iploeB+AW59wTM1EE+Ujvrt1qBlRCRkigutAPA6NGjAVhWojl79qzgaoiIyN6ah3YvgZWIlzI+UmpnHnfvvwsikikytJeUlEjtpktDEhGRa6qplffv8At276CaMCkZGlh62LOKwwRXQ0RK4dDQXlFRgZycnDbPKS8vx0MPPQTAsooM12onInJ9zUJ7qE8bZ7o+j0B/9PE+BQA4aYiFodZ9N5siIpnNN1dqy549e3DddddBq9WiR48eiIuLQ2RkJHx9fVFbW4vc3Fzs2rVLWrMdAGbMmOHIEomISIAagzyO3d1DOwCkRpbgQIFlBZnjvxYi7dquoksiIsEcGtoByzj12tpaHDp0CIcOHbrse031798fc+fOdWR5REQkQLPQHuYrsBJlSOleDxRY2pmbihnaicixof3ihktNw/nFHnUAiIqKQkxMDLp27YqpU6fiwQcfhI8Pe1yIiFxdTb38duQXoRVYiTKkDtYAWyztrP16obUQkTI4NLRPnjwZhYWF2LNnD3bs2IENGzbgyJEjACzhvaSkBAMHDsQLL7zQbK12IiJybTX18uRThvbfV5B5y9LOPOktthgiUgSV+dIxKQ525swZLFu2DB9++CFOnz4NlUoFlUqFp59+utl67dRxOp0OQUFBqKysRGBgoOhyiIhaNVibhQP6FKjRgAaze68eAwCNNQYE+JughxY9ffJwzJAguiQishNr85rwJR/j4+Px7LPP4uTJk/h//+//ISAgACaTCW+88QaefPJJ0eUREZED1BgtQyH9VNxMCAA8/XzR2ycPAHCyLg51NUaxBRGRcDYN7WfOnLniaz09PfHII49g7969iImJgdlsxqJFi7B9+3YbVkhEREpU3agBAPh7MLRflBpVCsCygsyxjfmCqyEi0Wwa2lNSUvDyyy+jvr7+iu/Rs2dPvPvuu9Lx+++/b4vSiIhIwapMfgCAADUnXV6U2kN+Lz2y6bzASohICWwa2mtra/Hiiy+iT58+WL58+WVLOFrruuuuk9rsaScicm1mYyOq4Q8A8FfXCa5GOdKGa6T24d+uvDOMiFyDXca05+bm4g9/+ANSUlKwbNkyGI0dG4un0+kAWJaGLCkpsUeJRESkEPrSGphgWRI4wJuh/aK0a7pI7cOnuKIOkbuzaWi/6aabYDaboVKpYDabcezYMfzpT39Cly5d8Pe//x3p6elW3efNN9+U2lz1hIjItVUX10htf58GgZUoS9dR3RCCMgDA4dIu7ZxNRK7Opuu0f/vtt/jhhx/wl7/8Bfn5+VJ4Ly0txdtvv423334b4eHhGD9+PAYOHIg+ffogOjoaQUFB0Ov1OHbsGL744gv8+OOP0qZLiYmJtiyRiIgUpuq8PI49QMPQfpHK0wNpAXn4tSoUhY0xKDtdhdBuAaLLIiJBbL650vXXX4+JEyfitddew6JFi1BdXS0F8IvDXVasWIEVK1a0eo+LY+FVKhVmzpxp6xKJiEhBqkvk0O6vMQmsRHn6xVXg1yxLO+PHMxj351SxBRGRMHYZ0+7n54cFCxbg1KlTmD17Nvz9/ZsFccASzFt6NT2ne/fuePTRR+1RIhERKUTVBXmSZYAfQ3tTaWly+/DWcnGFEJFwdt1cKSIiAm+//TYKCwvx/vvvY/jw4dKQmaYu7oIKyGF+6NChWLt2Lfz9/e1ZIhERCVZ9QZ58yh/5zaWNC5Hahw8LLISIhLP58JiW+Pv749FHH8Wjjz6KiooKbNmyBRkZGThx4gTOnDmDmpoaNDQ0ICoqCj169MCMGTMwceJEeHgI37CViIjsrKpcXmEsgGsPNJM6LQEqmGCGBw7nh4ouh4gEckhobyo4OBgzZszAjBkzHP1oIiJSoOoKObT7B3oKrER5/ONCkKzOxUljIo5Ud0Oj0QxPtUp0WUQkALuyiYhIqKpKeRx7QDBD+6XSws8BAGrhh5xthYKrISJRGNqJiEio6ip5npN/iJfASpQprYe8us7hDecEVkJEIjG0ExGRUFXVcjsglKH9UmlDfaT24T36Ns4kIlfG0E5EREJV18hvRf7hPm2c6Z76TYmW2hkn+PdD5K6sDu1Dhw7F5s2b7VlLuzZt2oRhw4YJrYGIiGyrqlYexx4Q7iuwEmVKmpgALWoAAIfPR7dzNhG5KqtD+/79+zF58mRMnjwZGzdutGdNl/n5558xadIkTJkyBfv373fos4mIyL6qDfJCZv6RWoGVKJOHtxr9tDkAgFMN3VB9vlZwRUQkQoeHx2zevBlTp07FgAED8MEHH0Cn09mjLlRVVeE///kPBgwYgGuuuQZbtmy5bFMmIiJyflUGeRx7QLSfwEqUKy32gtQ+siZPXCFEJIzVof2nn35Cr169pB1LMzIy8NhjjyEmJgY33XQTPvvsMxQVFXWqmHPnzuGzzz7DTTfdhOjoaPzlL39BRkaG9Mw+ffrgp59+6tQzSkpKsG7dOrz00ku44YYbEBMTI+3IqlKpsHTp0k7dvy1Nn2Pt64MPPrBbPURESlDd4C21/cI1AitRrrRUeVnMw1vKBFZCRKJYvbnS5MmTcfjwYbz//vt49dVXcf78eQCAXq/H999/j++//x4A0KNHDwwdOhT9+vVDjx49EBsbi8jISGg0Gnh7e6O+vh56vR7FxcUoLCzE8ePHkZGRgX379uHkyZPS85r2qkdFRWHu3Ll49NFHoVZf2X5QRUVFGDFiBE6fPn1F1xMRkX1UNVjGsWtQC7UXh8e0JG1sMLDK0j58yNTmuUTkmjqUgNVqNWbPno0HH3wQ7733Ht59910UFhbCbDZDpVLBbDbj+PHjOHHiRIcLuRjSL94HAGJjYzF79mz8+c9/hkbTud4Xg8GgqMA+duxYq/5M8fHxDqiGiEicaqMltPt71AJgaG9Jv2nxwN8s7cOng8QWQ0RCXFG3tVarxTPPPIMnn3wSX3/9NT755BNs3rz5sjHn1oxBV6lUlx1PmjQJ999/P2bOnAlPT9vvjhcREYHBgwdjyJAhGDJkCGbMmGHzZ7Tn008/RUJCgsOfS0SkNFUmyzh2f0+uQd6akB7hiPU4iwJTFxzWJcBsMkPloWr/QiJyGVc21uTixWo17rzzTtx55504e/YsVq9ejfXr12P79u0oLy+36h5msxkhISEYO3YsrrnmGmmcua2Fhobim2++wdChQ9GtWzeb35+IiK6A2YxqsyW0B6gZ2tuSFlqAgtIuqDQHoWB/EeKGcvlHInfSqdDeVJcuXfDoo4/i0UcfBQDk5OQgIyMDeXl5OHv2LKqrq1FXVwcfHx/4+/ujS5cuSExMRN++fZGUlGSrMloVGBiImTNn2v05RERkPWO1AQZYhgr6e9ULrkbZ0pKrsbbU0j68toChncjN2Cy0XyopKckhYZyIiJxXdVE18HtoD/CpE1uMwqUN9gb2WNqHd1VjmthyiMjBOrxOOxERka1UFcsbBfn7GgVWonxpkyKk9uGj3m2cSUSuiKGdiIiEqS6Rx7EHaBja29JzaiK8Yfk04nBxpOBqiMjROhTaKysrsWLFCuTm5tqrHrfx9NNPIzU1FYGBgdBoNIiNjcWECRMwb948/v0SkduoKpWHxPhruet1W7z8vJHia3l/OFaXAH0FhxMRuZMOhfbvv/8es2bNQvfu3REWFoZly5bZqy6Xt2LFCmRlZaGqqgoGgwGFhYXYsmUL5s+fj549e+KRRx6BXt+xlRTq6uqg0+mavYiIlKz6ghw8A/y4aVB7+ncpAQA0Qo3MNezgIXInHZqI+sMPP0hrr9fX1+Paa6/t0MP0ej3Wr1+P9PR0VFZWIiwsDHFxcZg6dapdlnlUsvDwcCQnJ8Pf3x+VlZXIzs5GdXU1AMBoNGLx4sXYu3cvNm/ejKAg6zbSePXVVzF//nx7lk1EZFNV5Q1S2z+A6463Z2C/BnyaY2kf/LkUQ/4gth4icpwOhfbt27dLmyH94Q9/QGhoqNXXfv755/jb3/6GsrKyy753cUOlt99+GykpKR0pyamkpKTgoYcewvXXX3/ZyjpGoxEbNmzA3LlzcfjwYQDAwYMHcfvtt2PdunVW3f/ZZ5/Fk08+KR3rdDrExcXZ7g9ARGRj1eXyOPaAQIb29gwYFwystrTTD/KTCSJ3YvXwmPz8fBQVFUk97XfddZfVD/n0009x991348KFCzCbzZe9TCYTfv75ZwwcOBDvvfdex/8UTiIzMxOzZ89ucSlMtVqNadOmYc+ePZg2TV7Ia/369fjhhx+sur+Pjw8CAwObvYiIlKyqUg6e/kG23wHb1Qy4KVFqp+cFiyuEiBzO6tB+/PhxqR0cHIyrrrrKquuKi4vx+OOPw2w2Q6VSSa+mLn6toaEBs2fPxjvvvGNtWS7H19cXy5cvR1RUlPS1d999V2BFRET2U62TQ3tAiN22DnEZQQkhSPQ8AwA4VJWExgb2thO5C6tDe15eHgBLwB4+fLjVD3jrrbdQVVUlBfWL4X3YsGG47bbbcPXVV8PPz0/6utlsxlNPPYW9e/d27E/iQgICAqSdZQFg27ZtMBgMAisiIrKPqiq57R/iJa4QJzIgshAAUAN/nNp8RnA1ROQoVof2yspKqZ2cnGzVNUajER9//HGzwJ6QkID9+/dj9+7d+PLLL7F+/XoUFxfjueeek3rcGxsbcd9990lDcdzRhAkTpLbBYEB+fr7AaoiI7ENXJX/yGhjhI7AS5zGwt9yJk77urMBKiMiRrA7tTZcfDAsLs+qaLVu2SBNPL/akf/jhh+jfv3+z8zQaDRYsWID3339fCupHjx7F999/b215Lic6OrrZcWlpqaBKiIjsR1cjj2MPitEKrMR5DBipkdoH99QLrISIHMnq0B4QECC16+ut+yGxZs2aZsd9+vTB5MmTWz3/4Ycfxg033CAdf/DBB9aW53Jqa2ubHWu1fDMjItejq5XHsQdG8+ecNQZcL68Kln7CT2AlRORIVof2pr3rJSUlVl2zefNmaZy6SqXCzJkz273m+eefB2Dpmd+6dSsaGxutLdGlZGZmNjuOjOSW1UTkenQGb6kd2MVfYCXOI3ZYF4SpLgAADpZ1E1wNETmK1aG9R48eUvvgwYPtnn/hwgUcOXKk2deuu+66dq8bPHiwtCSiwWCw6lmu6Msvv5TaCQkJbrf5FBG5B129PI7dP9xXYCXOQ+WhwoDg0wCAYlMkig6fF1wRETmC1aF90KBB0Gg0MJvNSE9Px5kzbc9YX7duXbOJpEFBQRg6dKhVzxo2bJjUbrrUpLv4/vvvmw0tmjFjhrhiiIjsqLLeMiTGH9XwVHNzJWsNSNJJ7fTVpwVWQkSOYnVoV6vVmDhxIgDL0JU33nijzfO/+eYbqa1SqTBhwoTL1mdvTWxsrNQuLy+3tkSHy8vLa7b2/Lx581o8r7KyErfccgv279/f7j2XL1+OO++8UzrWarWYM2eOrUomIlIUXaMltAd5VrVzJjU1cJi8PObB7dUCKyEiR7E6tAPAX/7yFwCW0L548eLLJppedObMGaxbt04azw4A06dPt/o5fn7yxBqdTtfGmR3z4IMPwtfX97JXR8/pKLPZjJUrV2LIkCHo06cPnnjiCSxduhQbNmzA9u3bsW7dOrzxxhsYNmwY7rzzTtTU1ACw/LLzySefXLaSDBGRq9CZLOPYA9W17ZxJTQ24Rn5fSM/iUplE7qBD289NnToVI0aMwJ49e9DY2Ihbb70V8+fPx2OPPSYF7QsXLuC+++6D0WiUeta9vLyarQrTnqZrwnt52W6zjYaGBtTV1bV5jtFohNFotNkzL5WdnY3s7Ox2zwsICMDixYtx22232a0WIiKRTPo6VCEQABDozQ3kOqLX1d3gCz0M0CC9mB07RO6gQz3tAPC///0PWq0WKpUKdXV1ePbZZxEZGYlBgwZhyJAh6Nat22Wrxtxwww1Wr+0OAMXFxVLb39/5VxPQaDR46KGHkJqa2u4QoaCgIDz++OM4cuQI7rjjDgdVSETkeFXn5GEdgT5td6hQc2pfNfr55QAATjQkoPqs7T6VJiJl6lBPOwB0794d33zzDW655RYYDAaYzWbo9Xqkp6c3O+9iOFWpVJg7d26HnrF3716pbctVU5YuXYqlS5fa7H4JCQlW7drq4+ODxYsXA7CM0U9PT8f58+dRWlqKiooKaLVahIaGIi0tDWlpafD09GznjkREzk9XWAXA0qETqGkQW4wTGhBXhn3ZgBkeOLw6F6Me7d/+RUTktDoc2gHgmmuuwYYNG/CnP/1JmozZmqeffhoDBgyw+t5nz57FqVOnpOPu3btfSYmKFRISggkTJogug4hIOF2xvNN2kNZ+wxJd1cABZuD30ZYHN5Vj1KNi6yEi++rw8JiLrrrqKmRlZeH//u//0LdvX5jN5mav4OBgvPnmm3j11Vc7dN9ly5ZJbW9v72brwxMRkevQnZfHsQf6mwRW4pwGTJKHnaYf5nKZRK7uinraL/L19cU//vEP/OMf/0BxcTHy8/NRXl6OsLAw9O/fv8PDPC6uSnOx537o0KHw9vZu5yoiInJGuhJ5HHtgoMBCnFS/GxKhetAEMzyQXhAuuhwisrNOhfamoqKiEBUV1al7fPbZZ8jJyZFCO4eREBG5rspSeRx7YBB7ijvKP1KLnt45OFafhIzaZDTU1MPLjx1dRK7qiofH2Jper5cmrF6c3HnzzTeLLImIiOxIVyaPYw8MVszbkVMZEG1Zba0Ovshelyu4GiKyJ5v1tHeWRqPB1q1bceDAARw8eBBnz55F//6cCU9E5Kp0FfI49qAwxbwdOZWBfRvw1RlL+8D68+g3s5fYgojIbhT1UzIpKQlJSUmYOXOm6FKIiMjOmm54HRjOYR1XYvD4AGCtpb1/byPuFlsOEdkRP48kIiIhKqvkceyBET4CK3Feg2YmSe39uaECKyEie2NoJyIiIXTV8gpjgVEagZU4r9DEICSq8wEA6dXJMBq43j2Rq2JoJyIiIXS18gjNwGitwEqc25CYAgBALfyQ/eOpds4mImfF0E5ERELoDPI49qCu/gIrcW6D+8lLZ+5fWyywEiKyJ4Z2IiISQlcvj2P3j+DwmCs1eIK8M9X+fdxZlshVMbQTEZEQlfWWITH+qIanmpsrXanmk1FDBFZCRPbE0E5ERELoGi2hPdCzWnAlzi00IVCajHqwugeM+oZ2riAiZ8TQTkREQuhMlnHsQeoawZU4vyExhQAAPbScjErkohjaiYjI4Uz6OlTBMhY70NsguBrnN7hfvdTmZFQi18TQTkREDlddJA+JCfSpE1iJaxg8MUhq7/+Nk1GJXBFDOxEROVxlYZPQruEY7M4adEui1ObOqESuiaGdiIgcTlesl9qBWu7i2VmhCYFI9GoyGbW2vp0riMjZMLQTEZHDVRY1Ce1+HM5hC5yMSuTaGNqJiMjhKorlcewhwWaBlbiOZjujrjsvsBIisgeGdiIicriKEjlgBodwYyVbaDYZdV+jwEqIyB4Y2omIyOEqSuVx7MFhngIrcR1NJ6P+lhsusBIisgeGdiIicriKcnlITHCkt8BKXEdotwBpMmp6TXdORiVyMQztRETkcBUVcjs4ykdYHa5mSMxZAL9PRl1zUnA1RGRLDO1ERORwFVXykJjgGI3ASlzLkDS5d33fj5yMSuRKGNqJiMjhKmrUUjskzl9gJa5l2NXBUnvPHnF1EJHtMbQTEZHDVdTKQ2KC4wIEVuJaBs/qDhUs697vPR0puBoisiWGdiIicriKOl8AgAca4R+pFVyN6wiI1CDV17Kx0mFDT9QWVwmuiIhshaGdiIgcrqLeDwAQpNLBw5PrtNvSsATLWPZGqHHw6xOCqyEiW2FoJyIih6totIxjD/asFlyJ6xk+XP4laO+GcoGVEJEtMbQTEZFDmRtNqDBbdu8M9q4RXI3rGXZ9lNTek87lNIlcBUM7ERE5VO35ajTAsqFSsI9ecDWup+/1idCgFgCwtyhOcDVEZCsM7URE5FAV+fLkyGANd+20NbW3BwYHWSaj5jZ2Q8nhc4IrIiJbYGgnIiKHqiiQx7EH+zcIrMR1DetVIbX3fpUrrhAishmGdiIicqiKc/KQmOAAk8BKXNfwsb5Se++WWoGVEJGtMLQTEZFDVRQbpHZIsFlgJa5r2K3dpPaeo4ECKyEiW2FoJyIih6o4Lw+JCQ7hGu320G1oJCI8SgEAe8u7w2xsFFwREXUWQzsRETlUxQU5QAaHqwVW4rpUKmB4VB4AoByhOLnhlNiCiKjTGNqJiMihKsrkcezBEV4CK3Ftw/rJw5D2fndWYCVEZAsM7URE5FAVlfKQmOBo3zbOpM4YNiVIau/dzeExRM6OoZ2IiByqokp+6wmO0QisxLUNnZUktffkRAishIhsgaGdiIgcqqJaHhITHOsvsBLXFhrnhx7epwEAB2t7oa6sRnBFRNQZDO1ERORQFXpvqR0cFyCwEtc3PN6yG2o9fJD+9XHB1RBRZzC0ExGRQ5UbtAAADzTCP1IruBrXNmqEPOl355oygZUQUWcxtBMRkUNVNFiCerCqEioPrtNuT6NmdpHaOw9w0i+RM2NoJyIih6potAyJCfasFlyJ6+s7rRv8Yfl73lmcBLOJO9ASOSuGdiIichiT0YRyczAAINSbod3ePNUqjAg/AQA4a4rBmV9zBVdERFeKoZ2IiBxGV6CDCZ4AgFCNXnA17mFUmrxqzM6v8gVWQkSdwdBOREQOU5ZbKbVD/eoEVuI+Rl0TKLV3buMmS0TOiqGdiIgcpuyMPCQmLKhBYCXuY/hd3aGCZRWZnTlRgqshoivF0E5ERA5zoUAeEhMazEmRjhDcRYtUTQ4A4JChF6oLKsQWRERXhKGdiIgcpuycPCQmNIzLPTrKqORiAEAj1Ni3jJssETkjhnYiInKYsvNGqR0a4SmwEvcyaoxaau9crxNYCRFdKYZ2IiJymLJSeYfOsBhvgZW4l1F3dJPaOzMCBFZCRFeKoZ2IiBzmQrn8thPaVSOwEvfS/apohHtcAADsvtAdpnpjO1cQkdIwtBMRkcOU6eQhMaFxfgIrcS8qFTAq2rKxUhnCcHwNx7UTORuGdiIicpiyanlITGhCYBtnkq2NGixPAt65skhgJUR0JRjaiYjIYcpq5SExIYnB4gpxQ6OuD5PaO3dz5R4iZ+N2ob2kpATr1q3DSy+9hBtuuAExMTFQqVTSa+nSpQ6pIycnBy+88AIGDx6MiIgIaDQaJCcn46abbsKKFStgNHK8IRG5nrJ6y5CYIFRA7atu52yypSGzkuGFegDAttPxgqshoo5ym5+YRUVFGDFiBE6fPi26FCxatAhz5sxBXV3zLbxzcnKQk5OD7777DiNGjMCyZcuQlJQkqEoiItu70GAZEhOq1gEIFlqLu9EEemFI4BHs0vXFcWMyig8UImpQV9FlEZGV3Kan3WAwKCKwL1iwAE888YQU2D08PNC3b1+MHTsWMTEx0nm7d+/GuHHjcO7cOVGlEhHZlKnRjDJzCAAg1LtacDXuaWzfcqm97dMcgZUQUUe5TWhvKiIiAtdccw3++c9/4rvvvnPYczds2IAXX3xROh45ciSOHj2KjIwM/PrrrygoKMCXX34Jf39/AEBBQQFuvfVWh9VHRGRPVYU6mGBZPSbUVy+4Gvc05jp/qb11U4PASoioo9xmeExoaCi++eYbDB06FN26dWv/Ahszm82YM2cOzGYzAKBXr17YuHEjtFqtdI6HhwdmzZqFsLAwTJkyBQCwY8cOrFq1CjfddJPDayYisqWyPB2AIABAqF9d2yeTXYy+pydU/zTBDA9sOxHT/gVEpBhu09MeGBiImTNnCgnsALBu3TocOnRIOl60aFGzwN7U5MmTMWvWLOn4tddes3t9RET2duG0PCQmLJC9vCIEd/VDf+0JAMChul6oOFEiuCIispbbhHbRVq5cKbUTExNx9dVXt3n+ww8/LLX37t2LgoICu9VGROQIZQW1Ujs02CSwEvc2trclqJvhgR1LTwiuhoisxdDuID/++KPUnjp1KlSqttfIHTNmDPz85N0Cm15PROSMyorkITGhYVwnXJSxU+W18rdu4NwCImfB0O4A58+fR1GRvPvcyJEj271GrVZj6NCh0vHhw4ftUhsRkaOUFcv7T4RGeAqsxL1ddU93qb0tO1xgJUTUEQztDnD06NFmx8nJyVZd1/S8S+9BRORsLpSapXZYjLfAStxbVM8g9PLJBQDsq0lB7blKwRURkTUY2h0gLy+v2XF8vHU70TU979J7tKSurg46na7Zi4hIKcrkJcIR2lXT+olkd2O7W/YAMcILu5dmC66GiKzB0O4AVVVVzY6DgoKsui4wMLDVe7Tk1VdfRVBQkPSKi4vrWKFERHZUVikPiQmN82vjTLK3sZPkFZ+3/tj++wsRicfQ7gDV1c13/vP19bXqOo1G7om69B4tefbZZ1FZWSm98vPzO1YoEZEdXajykdqh3QIEVkJj706S2lszQgRWQkTWcpvNlUQyGo3NjtVq6/7am57X0ND+msY+Pj7w8fFp9zwiIhFKai17U6hgQlh3BkWR4geFo5u6EKeNXbFb1wf1FbXwDm557xAiUgb2tDvApZsoGQwGq65rel7T5R+JiJxRicEy5C9UVQ5PXy/B1dCYBMunsXpo8dtnWYKrIaL2MLQ7gL+/f7Njvd66dXFra+WNSC69BxGRsyk1WubzhKu5WokSjB0nt7esqhBWBxFZh6HdAcLDm6+De+7cOauua7q2e1hYmE1rIiJypLrqBlTB0tMe4cuVrZRg4t3yCmWbDgaLK4SIrMLQ7gC9evVqdnzmzBmrrms6kbR37942rYmIyJFKj5dJ7XA/64YIkn0lXdUF8epCAMCOilQYymrbuYKIRGJod4AePXo0m1Sanp5u1XUHDx6U2n369LF1WUREDlNySu5djwisE1gJXaRSAROTTgMADNBg98cc106kZAztDuDt7Y3hw4dLx9u3b2/3mqKiIpw8eVI6Hjt2rF1qIyJyhNI8edna8FCTwEqoqYmT5RiwaRXnGhApGUO7g9x4441Se+PGjSguLm7z/GXLlknt4OBghnYicmolBfKQmIgIgYVQMxMe7C61Nx3m3CkiJWNod5A77rhDWkO9oaEBb7zxRqvnVldX45133pGO77rrLnh5cXk0InJeJWfl/SrCo7lFiFLEDghHT+88AMCe6lRUF1QIrYeIWsfQ3gl5eXlQqVTSa968ea2eGxsbi4cfflg6XrRoEb799tvLzmtoaMC9994rTVbVaDSYO3euzWsnInKk0vPykJiIWG4CpyQTe1kmoxrhhe1Ljgquhoha41ah/cEHH4Svr+9lr46ec6XmzZuHHj16AAAaGxtx22234Y9//CO+/fZbbN68GR988AGGDBmCFStWSNe8+eab6NKli02eT0QkSskF+e0moht33lSSSdd6S+1Na7iCDJFSudVnlA0NDaira3vVAqPRCKPR2OY5VyokJARr1qzB5MmTkZ+fD5PJhM8//xyff/55i+c/88wzeOyxx+xSCxGRI5VWykP8wpMCBVZClxr/UE/g9xGbmzKjxRZDRK1yq552JejZsycOHz6M+++/HxqNpsVz+vTpg9WrV+P11193cHVERPZRUi1/YhnRM0RgJXSp8OQg9NccBwAcMPRB+bHzgisiopaozGazWXQR7qqqqgqbNm1Cfn4+ampqEBMTg379+mHgwIE2ub9Op0NQUBAqKysRGMieLSISp6/vSWTWdYcGtag1aSyLhJNiPDl0G97+bQwAYNVT2zHjX1cJrojIfVib19xqeIzSBAQENFsKkojIVZU0BAEAIjzLAFWs4GroUhNv8Mfbv1nam9bXY8a/xNZDRJfj8BgiIrIrU6MZF0yWITHh3rp2ziYRxj7YC56wzOfaeDxecDVE1BKGdiIisquK/Co0/v7BboS2RnA11JLAaC2GBhwDABxt6I78XQWCKyKiSzG0ExGRXZUcL5fa4f5tr+BF4kwdXCq1f/7glMBKiKglDO1ERGRXpblVUjsipEFgJdSWqXeFS+0NmzjljUhpGNqJiMiuSs7opXZ4mMBCqE1D/9gbQapKAMDPhSlorLPPniVEdGUY2omIyK5KCuQhMRFRXOpRqdQ+npjc9SgAoNwcgv2fZQmuiIiaYmgnIiK7Ol/UKLUju3oLrITaM3Wi3Lu+YVlpG2cSkaMxtBMRkV0VFctvNVFJfgIrofZc/Wiy1N7wG8cyESkJQzsREdlVcZmX1I7uyd2ZlazbiBj08s4BAOyuTkVlXnk7VxCRozC0ExGRXRVXaaR2VAp7b5VuaqpljfZGqLHpPY5rJ1IKhnYiIrKrolpL77o/qqCNChBcDbXn6pv8pfaGNVyik0gpGNqJiMiuihtCAQDR6lJAxdVjlG78o33gDcuKPxtOJsFsMguuiIgAhnYiIrIjQ1UDKs1BAIAoX53gasgafuEaXBVqGRaT1xiPE+tOCq6IiACGdiIisqPz2WVSOyqgRmAl1BFTR8q72K77b4HASojoIoZ2IiKym6Kj8uoj0SH1Aiuhjpj2UFepvWYbV/whUgKGdiIispviHLl3PSrCJLAS6oiU6Uno5mnpYf+1rB+qCjm0iUg0hnYiIrKb4tMGqR0Vw7ccZ6HyUGF6imW99gZ44+eFmYIrIiL+BCUiIrspKmyU2lFx3gIroY6aPkvevXbNd0aBlRARwNBORER2VHxeXuIxOtmvjTNJacY/lgotLMObfjzVGyYjhzcRicTQTkREdlNc5iW1o3pwQqMz8Q32xeToIwCA8+YI7P+Mu6MSicTQTkREdlNcpZHaUSlhAiuhKzF9irziz5pPSgRWQkQM7UREZDdFtZbe9QDooI0KEFwNddR1s3tI7TW/RQushIgY2omIyG6KG0IBAFHqC4BK1c7ZpDRdB0djoCYbAHBA3wdn958TXBGR+2JoJyIiuzBUNaDSHAQAiPLlOt/OavrQYqm9dtFxgZUQuTeGdiIisovz2WVSOyqgpo0zScmm3xcptb//yVdgJUTujaGdiIjs4uwRObR3Ca0TWAl1xpC7eiHaw9Lb/nNxGqrP1wquiMg9MbQTEZFdFB6rltpdYswCK6HO8FB7YEavowAAAzRY/2aG4IqI3BNDOxER2cXZHIPU7hrvKbAS6qyb7pI3xlq1orGNM4nIXhjaiYjILs4Wyr3rXbprBVZCnTX+8TQEoQIA8GNeKuprGsQWROSGGNqJiMguCovVUrtLb+6G6sy8A3xwfYJlWEwlgrD5HQ6RIXI0hnYiIrKLs+XybqhdB0QIrIRs4aaZ8hCnlZ9xNSAiR2NoJyIiuyissazRrkUNAruFCK6GOmvq39PgCz0AYPWxXmhsMAmuiMi9MLQTEZFdnK0PBwB0UZ+HyoO7oTo7vyh/XBN9CABQbIrE7o8yBVdE5F4Y2omIyOaqSw3QmS3j2LtqK8QWQzZz03R5AurKJWVtnElEtsbQTkRENnf2UInU7hLE8c+uYvqcVKhhCe6rDiXCbOL6+0SOwtBOREQ2dzarQmp3jawXVwjZVGj3UIwPPQwAyDXG48BXJwRXROQ+GNqJiMjmzp6Qe9e7xAgshGzutqk6qf3VO8UCKyFyLwztRERkc4V58tjnLgneAishW7v5nynSEJmvfkviEBkiB2FoJyIimzt7Vg5yXXv6CayEbC0sJQqTQw8CAM4Yu2L3p8cEV0TkHhjaiYjI5s6WyL3rXVKCxRVCdjHr+lqp/dX7JW2cSUS2wtBOREQ2V1gh967HpHE3VFcz4/l+8EYdAODr9J5oNHKIDJG9MbQTEZHNFdRadkANVZVBE+EvuBqyteDkMFwTeQAAcK4xCts/zBJcEZHrY2gnIiKbamwwodAYBQCI9+HqIq5q1k3yZOOvPigXWAmRe2BoJyIimyrKvAAjvAAA8UGVgqshe7n+n/3hCz0AYEVmbxjrGgVXROTaGNqJiMimzuyXJybGRxgEVkL2FBAbhGkxllVkSkzh2PTOEcEVEbk2hnYiIrKpM5lVUjs+ziSwErK322fJ//8uW1LbxplE1FkM7UREZFNnTtRJ7fhkbqzkyqb/cyCCUAEA+PZ4P1RfqGv7AiK6YgztRERkU2fyVVI7PoUrx7gy3zA/zOphWUWmBv5YNf+w4IqIXBdDOxER2dSZ8z5SO35QuMBKyBH+9OcAqf2/5V4CKyFybQztRERkU2cqggAAajQgun+U4GrI3kb9ZRCSPPMAAL+UpqHg0AWxBRG5KIZ2IiKyqTMGyw6osZ7n4OnLnldXp1J74k8jTgAAzPDAFy9kC66IyDUxtBMRkc1Ul+hRZg4FAMT7scfVXfzxn92k9qc/xcBsFlgMkYtiaCciIpvJ/03eATU+pFpgJeRISdf0xFX+ljXbswxJOPhtjuCKiFwPQzsREdnMmUPydvbxMQ1tnEmu5k/TyqT2p6+dE1gJkWtiaCciIps5ky1vsBOfwLcYd3Lry/3hA8sOuJ8fSIGh2ii4IiLXwp+oRERkM6dzGqV2XA9fgZWQowV3D8etsbsBAGXmEK7ZTmRjDO1ERGQzuQVqqZ04OFRgJSTCg4/JO+B++AlXDiKyJYZ2IiKymZzSQKmdMKqLwEpIhDFPDUMv9UkAwJYL/XBiW5Hgiohch9uG9p07d+Lhhx9GSkoKgoKCEBgYiJSUFDz00EPYsWOHXZ6pUqk6/Prggw/sUgsRkT3k1kQCALp4nIMmwl9wNeRoKi81Hhh/SjpeMperyBDZituF9pqaGtx///0YPXo0PvzwQxw9ehQ6nQ5VVVU4evQo/vvf/+Kqq67Cfffdh5qaGtHlEhE5jZpSPYpNltCe5Ffcztnkqu5+PQVeqAcALN3ZA/UGk+CKiFyDuv1TXEdjYyNuvvlm/PTTT9LXNBoNUlNToVarkZWVBZ1OBwD45JNPUFhYiLVr18LT09PmtYwdOxYajabd8+Lj423+bCIie8jdeQ5AEgAgKVwnthgSJmJQHGZEb8U3RWNx3hSBH15Jxy0LBogui8jpuVVof/7555sF9gcffBCvvfYaQkMtk6Vqamrw+uuvY8GCBQCAn376CS+88AJeeeUVm9fy6aefIiEhweb3JSISJee3C5BCexyX+3NnDz7kgW9esrQ//BC4ZYHYeohcgdsMjzl79izefvtt6fiPf/wjPvzwQymwA4Cfnx9eeukl/POf/5S+9u9//xtnz551aK1ERM4o54heaif1dKs+IbrEpLnDkeh5GgDw8/k0nNxVIrgiIufnNqF94cKFMBgsmz5otVosXLiw1XOff/55xMXFAQAMBgMWLVrkiBKJiJxaTq7cTuofIK4QEs7DxwsPjzkKADDDA+8/eaqdK4ioPW4T2letWiW1b7vttmY97Jfy9vbGvffeKx2vXLnSrrUREbmCnHPyPJ2kEZECKyEleODtVPjC8unLx3tSUF1WL7giIufmFqH92LFjOHnypHR8zTXXtHvNtddeK7VPnjyJY8eO2aU2IiJXkVNh6QzxhR7RA6IFV0OihQ2Iw53dLEso68yB+OzvhwRXROTc3CK0HzrU/AfFyJEj271m0KBB8PaWd3Y7fJjbMRMRtcbUaEZunWUzpSTvAqjUtl91i5zPX/8ZLLXf+zIMZrO4WoicnVuE9qNHj0ptb29vabx6Wy49r+k9bOHpp59GamoqAgMDodFoEBsbiwkTJmDevHnIzc1t/wZERApSdKQUBliGxyQFlQmuhpRiwP2DcZV2PwAgS5+ETf/hp9ZEV8otQnteXp7Ujo2NhUqlsuq6pmukN72HLaxYsQJZWVmoqqqCwWBAYWEhtmzZgvnz56Nnz5545JFHoNfr278REZECnNwub1efGM2fXfQ7lQp/uatCOnz3tWpxtRA5ObcI7VVVVVI7KCjI6usCAwNbvIcthIeHY/jw4Zg0aRKGDBkCf395u2+j0YjFixdj9OjRqKystPqedXV10Ol0zV5ERI5wfJ/8s6pnT4GFkOLc/OZIdFFZlk7+IX8AcveVCq6IyDm5RWivrpZ/s/f19bX6uqY7lja9x5VKSUnBwoULcerUKZSUlGD37t3YuHEj9u3bh/LycqxZswZpaWnS+QcPHsTtt99u9f1fffVVBAUFSS9rhgEREdnCsaONUrvXID+BlZDSeAVp8egoy7wwEzyx8K9c/pHoSrhFaDca5Z351GrrN/xoem5DQ0On68jMzMTs2bORlJTU4rOmTZuGPXv2YNq0adLX169fjx9++MGq+z/77LOorKyUXvn5+Z2umYjIGsfPyJ0cvcZGCayElOiRd1KgQS0AYMmevrhQwCFURB3lFqFdq9VK7YsbLFmj6bl+fo7pOfL19cXy5csRFSW/6b377rtWXevj44PAwMBmLyIiRzhWFg4A0KAWscO7Cq6GlCZ8UDzu77ENAFALP/znz1yRjaij3CK0Nx0v3pHJnbW1tS3ew94CAgLw6KOPSsfbtm3r0C8bRESOZKxrxKl6y3C8Hj758PDico90uSf/HQsPWIZRvfNjd+irG9u5goiacovQHh4eLrXPnTtn9XVFRfJqCGFhYTatqT0TJkyQ2gaDgUNdiEixcrcXwggvAEDP8AuCqyGlSpyeilujLL3tJaYw/O9vBwVXRORc3CK09+rVS2pfuHChWQ96W5oG5d69e9u8rrZERzffTbC0lLPtiUiZjm07L7V7dasTWAkp3dMvyHMf/vVZJBqN3G2JyFpuEdr79OnT7Dg9Pb3dawoLC1FSUtLqPezt0l8smo7LJyJSkuPpNVK7VyqHxlDrBj86DBP99wIATtbF47uXjwiuiMh5uEVoHzZsGHx8fKTj7du3t3vNtm3bpLavry+GDRtml9pak5mZ2ew4MjLSoc8nIrLWsePyW0nPYSECKyHFU6nwzF/luWUvv6WBmZ3tRFZxi9Du7++PSZMmScfLli1r95qm50yaNMlhq8dc9OWXX0rthIQExMTEOPT5RETWOn4uQGr3nBgrsBJyBlfPH40hPpbVY9Kru+P7N44KrojIObhFaAeAe+65R2ofPny4zbXPDxw4gHXr1rV4rSN8//33WLNmjXQ8Y8YMhz6fiKgjsnWWToUIVQlCktjTTm1TeanxwkPF0vH8V9TsbSeygtuE9pkzZ6J///7S8cMPP4zs7OzLzjt37hz+8Ic/oLHRshTVgAEDcMstt7R4z7y8PKhUKuk1b968Fs+rrKzELbfcgv3797db5/Lly3HnnXdKx1qtFnPmzGn3OiIiEUpPlKPIZNlXIjWwQHA15Cym/2s8BnlnAAAOVvXAmn9d/n5MRM1Zvz2ok1OpVPjvf/+LcePGQa/X49y5cxg+fDgeffRRjB07Fmq1Gnv37sV7772H4mJLD4BGo8GHH34IlUrVqWebzWasXLkSK1euRO/evTF16lQMGDAAMTEx8PPzQ1VVFTIyMrBixQrs27evWc2ffPLJZSvJEBEpReb6fACW3vW+8TqxxZDTUHl74YWHijHjvX4AgPkve2D634FOvt0SuTS3Ce0AMHToUHz++ef4wx/+AL1eD51Oh9dffx2vv/76ZedqNBp8/vnnGDp0qE1ryM7ObrGH/1IBAQFYvHgxbrvtNps+n4jIlo7sqJDaffuJq4Oczw1vjcOADzORXp+K/bqeWPvvbEx7yrHLKxM5E7cZHnPRzTffjP3792Py5Mkt9qCrVCpMmjQJv/32G26++WabPFOj0eChhx5Campqu732QUFBePzxx3HkyBHccccdNnk+EZG9HDki/0zrOzpYXCHkdFTeXnjhAXnDwxdf8oDJJLAgIoVTmc3uO/0jPz8fO3bsQGFhIQCga9euGD16NOLi4uz2zPLycqSnp+P8+fMoLS1FRUUFtFotQkNDkZaWhrS0NHh62madY51Oh6CgIFRWViIwMNAm9yQiampM0GFs16UBAMpzyhGcyImoZD1TXQMGBx5Hen0qAOCrFzJx2/xUwVUROZa1ec2tQ7urY2gnInsym8wIVVeiwhyMWI+zyG/sIrokckLr/74R1741GQDQ3ScfWbpYeHlzcDu5D2vzmtsNjyEiIts4d+g8KszBAIC+IYViiyGnNfW1CRjvd3GX1Dgs+Uu62IKIFIqhnYiIrsiR9fISj6kJNQIrIWemUnvi9QUN0vH8j2NRXd7QxhVE7omhnYiIrsiR3dVSu29/28zFIfc07IlRuCX8VwBAcWMEFt59UHBFRMrD0E5ERFck/Yi8anC/caECKyGnp1LhlfeD4QkjAOCNNX1QnMNPb4iaYmgnIqIrcqAwEgCgRgP63pgsuBpydr1u64/7EzcBAKrMAZh781HBFREpC0M7ERF1WG1pLY7WJQEA+vqegk+Qr+CKyBUsWN4DQagAAHxyaBB++66g7QuI3AhDOxERdVjG6hyYYBnHPiiuRHA15CoihyfixYnbAQBmeGD2fVXgwtREFgztRETUYQc2lkntQQO4jSXZzmNfj0Mvz5MAgJ3lfbD82cOCKyJSBoZ2IiLqsANNFvcYNJm7oJLteIcFYOGTp6XjZ96KRHVZvcCKiJSBoZ2IiDrswJkIAIAHGpE2g5NQybaueX0ipgXvAAAUGqPx4o3pYgsiUgCGdiIi6pD66npk6C1Bvbd3Lvwi/QRXRC5HpcLCT4LgAwMAYOH2wTiwOl9wUURiMbQTEVGHHFl9Cg3wBgAM7FIkuBpyVd1n9MULIzcCAEzwxEN/1MPYwFmp5L4Y2omIqEN2rT4vtYcNahRYCbm6v/8wDqnqYwCA/VU98e49+wVXRCQOQzsREXXIrn3yTqgjrw8XWAm5Ou+wAHz4f6XS8fNf9MbpAxcEVkQkDkM7ERF1yK6CWACAL/ToP7OH4GrI1Y16ejQeSf4JAFADfzw4rZBrt5NbYmgnIiKrnc8sQY6xGwBgSOBxePt7C66I3MGrP/ZHV1UhAODnojT85wEOkyH3w9BORERW27UsR2qP7FUusBJyJ8G9ovDR08ek46c/7o2TO4oFVkTkeAztRERktV2b9FJ75ARfgZWQu5n62gQ8kmQZJlMLP9w9/QIajRwnQ+6DoZ2IiKy2KztYao+4I1FcIeR+VCq8uWkwkj1zAQA7K1Lw5q17BBdF5DgM7UREZBVDhQF7KnsDABLU+YgZECW4InI3/t3C8OlrRVDBBAB4/rvB2P1FTjtXEbkGhnYiIrLKrk+yUQfLkJgJiXliiyG3NfrvI/HswA0AACO8cPs9PigvqBFcFZH9MbQTEZFVNq+qkNoTJqrEFUJub/6v4zFaewAAcLqhK+4ddYzLQJLLY2gnIiKrbD4UIrUn3J8ssBJyd+oADZb/GIRQWDZaWp0/CO/+gePbybUxtBMRUbtqS2uxR9cHANDdKw+xQ2MEV0TuLm58Mj59KkM6/vsXA7Fj6QmBFRHZF0M7ERG1a8eSo2iAZSOlCclnBFdDZDH9X+PxVJplGcgGeOOW+4NRcOiC4KqI7IOhnYiI2vXL6iqpPWGyWmAlRM29un0sJgT8BgAoNkXgpjEl0OsaBFdFZHsM7URE1K616V0AACqYMOmRHoKrIZJ5Bfji6x1dkeBp+QTot6reeHjoAU5MJZfD0E5ERG3K33sOGYaeAIChflmITI0QXBFRc+H9YrB6aQW0sCz9+Nnx4Xj9xp2CqyKyLYZ2IiJq07p35Ml91w0tFVgJUevS/pCGT/+yTzp+9odRWPbEvjauIHIuDO1ERNSmtb/4SO1p97CXnZRr5rvj8X/jNkjH9y7qj18WZrRxBZHzYGgnIqJW1enqsLEoFQAQqSrBoLv6CK6IqG3/2HQ1Hum5CYBlRZmb/tYNh1aeElwVUecxtBMRUas2/vswauAPALg26Rg81HzbIGVTeajw3qExuCFiFwCgCoGYemsAjv3MpUrJufGnLxERteqbz+uk9s2zvARWQmQ9T18vLD/SDyP8DgMAik2RmHitN079WiC4MqIrx9BOREQtqq+ux+qcvgCAAOhw9dP9BVdEZD1tpD/WHuyCAb5HAQBnG6MxcZIKp3edFVwZ0ZVhaCciohZt/PdhVJiDAQA3JByGb7Cv2IKIOiikRzh++i0Mqd6WFZDONHbFxLFGBndySgztRETUom8+M0jtW2/nLqjknCJSI7FxTwB6euUAAHKM8bhqDHBsfa7gyog6hqGdiIguU3tBj29PpgEA/FGFqc9waAw5r+gB0di0w1cK7gWNXTDmOn+kf5ktuDIi6zG0ExHRZVY+fxBVCAQA3No9Hb4hGsEVEXVO16FdsO2APwb4WoJ6iTkC4++Ixvb30sUWRmQlhnYiIrrMJ1/KIf2+2QECKyGynci+kdicHYNRAZZVZSoRjEl/7YPlj20XXBlR+xjaiYiombztBdhUPhAA0MMrF6P/zKEx5DqCuwXhp5PJmBr+GwCgHj648/9dhQXjf4G50SS4OqLWMbQTEVEzH82Vd4+8Z1weVB4qgdUQ2Z5fpB9+ON0fD/beJn3thV8n4e7k7dBfqBVYGVHrGNqJiEhiqKzD4u2pAAA1GnD3//USXBGRfXhpvbA48yq8ef1WqGDpYf/s9FiMjs1Dzi9cWYaUh6GdiIgky5/6DSXmcADArXF70HVoF8EVEdmPykOFv38/Fiv+sR9a1AAADhpSMHhyMH58luPcSVkY2omICABgNpmxcFmEdDz7+UCB1RA5zs2vDsXu74rRwzsPAFCBEEx/7So8O+Rn1OsMbV9M5CAM7UREBAD46fWDOGzoCQAY4XcYwx/oJ7giIsfpd2MSfssNx81xe6WvvbZ/CkZGncLRb7MEVkZkwdBOREQwm8x48VUf6fipB3SAihNQyb0EdvHHiryhePOmnVCjAQBwwJCKQTMT8e71P8HcYBRcIbkzhnYiIsKG19Oxp8oyAbWvz3Hc/OZIwRURiaHyUOHvK0dhz9dn0MfHsoOqARo8vuZqjA89jOyV7HUnMRjaiYjcnKnRjBf+T+5lf/HR8/Dw8hRYEZF4g25Nxv7iWDw+ZKf0ta3Vg9D/lmTMG7kBhtJqgdWRO2JoJyJyc8se34N91SkAgH4+x3DzG+xlJwIATZA3Fu0bhZ8XZiLZ6wwAy2ZM83dPRf+YYqx9divMJrPgKsldMLQTEbmx6vO1mPNBonT81vOV7GUnusTk2anIKInGs+Pkse7HjcmY9tpYTA3di4zP0sUWSG6BoZ2IyI29dON+nDNFAQBuiNyFKc8NE1wRkTJpgrzxf1tG4cCPRRgVnCl9/efK4Rjwp354MOkX5P+aI7BCcnUM7UREbmrPp9l4a/coAIA36vDW/yIFV0SkfP2ui8P2Cyn46h8HkOBVAAAwwRNLcicheXws/txzI/I3nxRcJbkihnYiIjdk0NXjnoe9YYJlKMyLE7ej+9RkwVUROQeVhwq3vToIR8ui8fpNuxGo0gEAGuCN/5yYjO4T4/Dnnj8jZ90xwZWSK2FoJyJyQ7NH70N2XRIAYIgmE8/8MEZwRUTOx9dfjWdWjkBOjgpzx++APywrytTDB/85MQXdr+uBm6O2Y9sbu2BuNAmulpwdQzsRkZtZ+shufHhkNADAF3os/cwTaq234KqInFdYQgBe2TwaeblmzJ2wE/4qS3g3wwOrzl+FsXNGYqhfFpb+8RfUFFaILZacFkM7EZEb2fFRNh5d3F86/uDu3Ui9pbfAiohcR1hCAF7ZNAqn84D/m74TXTyLpO/tr+uLez+fhJhYDzzS4xfs+2A/l4ukDlGZzWb+F+OidDodgoKCUFlZicDAQNHlEJFgR9bkYcwNwagwBwMAHu65GR9kjwdUKqF1Ebmqen0jvp6bjn8vCcDB6p6Xfb+fdzZuH12AWc90Q/I1PQRUSEpgbV5jaHdhDO1EdNGRH09jyg0aFJksK8RcHbIXP+T2g3eQRnBlRK7PbAZ2f34SH71Wgi+z+qEG/pedM9g3E7PGnMXMZ5KQOJmTwt2JtXnNbYfH7Ny5Ew8//DBSUlIQFBSEwMBApKSk4KGHHsKOHTvs/vycnBy88MILGDx4MCIiIqDRaJCcnIybbroJK1asgNFotHsNROQe9n1xAuOuD5AC+1BNBr493JOBnchBVCpg5B+7Y0nmSJw7r8aSh/ZiZFBms3P2G1LxzM9TkDQlGak+J/DMkE349a3f0FBdJ6hqUhq362mvqanB448/jo8//rjN8+699168++678PPzs3kNixYtwpw5c1BX1/o/xBEjRmDZsmVISkq64uewp52Ilj2xDw8uSoUeWgDAEM0RrNsXgfDUKMGVEVHe7iJ8/epJfPVLBA7U9GrxnCBUYEqXLEy4qgHj7+qKPtOTofLgkDZXwuExLWhsbMR1112Hn376SfqaRqNBamoq1Go1srKyoNPppO9dffXVWLt2LTw9bbel94IFC/DCCy9Ixx4eHkhJSUFoaChOnDiBc+fOSd+LjY3F3r17ERMTc0XPYmgncl81ZXWYM3k/3j84SvrauIAD+P5wAgITQgVWRkQtObntHL557STWbA/Gbl2KtIfCpSJUJRjf9QTGj2rAyFu6oO8NSfDytV1OIcezOq+Z3cizzz5rBiC9HnzwQfOFCxek71dXV5uff/75ZufMnTvXZs9fv369WaVSSfceOXKk+dixY9L3GxsbzV9++aXZ399fOmf06NFX/LzKykozAHNlZaUtyiciJ/HLu5nmJK/TZstIWsvrgaRfzPrSatGlEZEVSk6Umz//yy7zHQk7zSGqsmb/li99aVBjHh142Py3wb+alz+xy3xya6G50WgS/UegDrA2r7lNT/vZs2eRnJwMg8EAAPjjH/+I//3vfy2e+/zzz+Pll18GAPj6+uLUqVPo0qVLp55vNpsxcOBAHDp0CADQq1cvHDhwAFqt9rJzN27ciClTpkjHK1euxE033dThZ7Knnci9ZK0/g3/cX4Ifzg6WvuYLPRbdugMPfTWJq8QQOaHGBhPSv8zGlq/PY8teLbae7wUdgtq8xl9Vjb4Bp9EvrhL9+gH9xgSj33VxCEsIcFDV1BEcHnOJZ555Bm+++SYAQKvVIj8/H6GhLX9EXF9fj+7duyM/P1+69vXXX+/U89euXYtp06ZJx+vXr8fUqVNbPf/222/HV199BQAYNmwY9uzZ0+FnMrQTuT6zGdj232y8vaAKqwsGw9xkfYGr/A/ioy+06Hl9y2Nlicj5NNY3Iv2b49j6dTH2HVRj77lYnDImWHVtpEcpugcWo0d0NXokN6J7Xw16jAhD9zExCAzzsm/h1CqG9kv06NEDJ0+eBADcc889+OSTT9o8/8UXX8RLL70EAOjevTtOnDjRqec/8MAD+OijjwAAiYmJOHXqFFRt9Hpt3rwZEydOlI7z8/MRGxvboWcytBO5rpPbi/DlK6ewfEsMsgzNJ6x38TiHl2Zl4d6l4+DhrRZUIRE5SmnWefz2TS72bq7Bb1laZFzogjxTfIfuEe5RhjjtBcSFVCM+ug5xcSrE9fBFXGog4geFI6ZnALyY6+2Cob2JY8eOoXdvece/L7/8ErNmzWrzmt27d2PkyJHScXZ29v9v796Dojrv/4G/z7LsRZaLiAgIgiIajKlRA8ZYQSNfY6ZTo8ZfEs2lsdaa1IbfTNrGX9IYc2tT7HxnYnRqzc1koraNxmgdYyzGWgO20YgGExEhgq6A3IKwF2Bv5/eH9WRXdmEXdjkHeL9mdjjP2c95zmfds48fDs+eg4kTe3+2KjExEVevXr8z2hNPPIEtW7Z0G+9wOBATEwOLxQIA+POf/4zVq1cHtE8W7USDR/MlM4rfr8Thv1tQ+E0SzneM7RKTJNTh/959Fr/cfieGJfAzTzSUtV1swjcHqnG2uBVfnwXOGmNwwZyIWrH3031jVS0YpW1FfIQFo6I7MCrOgfh4AaOSwzEqVYe4sZEYnmLA8DGRGB4XBq02iC9oEPO3XhsSp2BuzCO/wb0Y92XatGnQaDSw2WwAgNLS0l4X7Q0NDVLB7u/+1Wo1srKycPToUWn/RDS4iSLwndGCb4vq8O2pFlSc7cSZMi1OXR2Ny44kALd73W5WxGk8+VAL/s//zoQmen6/5kxEyhQ1Lg4zn4rDzKfcVooiLFX1qDxWi8qTLag4Z0flpXBUNEaj2jISNWISnN2Uht+5huO79uEoawfQBODb7nPQC+0YrjZhuMaC4bp2DI+wYbjBgZhoEYZIAZFRAgxRKhhi1DDEqBE5QgPDCO31x0g9IkcNgyFKBb2eX8kBhkjRXlZWJi1rNBqkpKT0uM2NuG+//bZLH33ZPwCkp/t3p7P09HSpaO/L/omof4giYLcDdpuIzrZOmBusMDe2X380d8L0nR3mFjtMLQ40NzpRXy+g4Ts1Gkw6NFgMqOkcgWtiDIDx3e4nDA5kDfsGi+9qwIPPjkPq3VP75fUR0QAnCIgYNwpTxo3ClMdvek4U4fyuFXUldTCWtsB43gJjlQPGGhUuN+lx1WxAfedw1Isjvd7R1Zt2UY92ux61dgAWAM29TBsu6IUO6AQbdCobdGE26MLs0Kvt0Kkd0IU7oQt3Qq9xQqdxQacRodOK0OtFaLWARgOEhwsI1wjQaN1+alXSz3BdGDS6739GDNdg6n2BTTEKtSFRtFdXV0vLycnJ3c4ldzdmzBipaHfvoy/7v9Gvv/v31YfSvPPTYuz7tOvfwXxNvhIh4PpVLW+O9/7e+JrD9X28Z8T1/rtuKK2/6cne77fneO+x4k259K7vQRF/87+D2Nv40P47u0QBdpcadjEMNpcadvGmB8LhwI0JnwIA3X8ffWeACVMNFZie9h3m3qNB7hOZiB4/JSh9ExEBAAQBYSNikPw/MUj+H8DnnID2dliqL6Oh/DvUV5pRX92O+hoH6q+K+K4FaDGFo8WqRUunHi22CLQ4o9CC4bCi9zerFKGCVRwGqzgMcAHoh5vGp4ZdQbXCbk4/JIp2k8kkLUdHd3+ZJHfu84rc++jL/gPJIdD9d3Z2etxl1f1GUaH2zVd27K+b1W/7Ixps9LAiMawB4wyNGBdvQnqqE+mTNJg8Nx4ZC9Kh0k2TO0UiIkCvR0TmGIzNHIOu36zxQhQBiwWdV424ZjShxWjGtbp2mFvsMF9zwNzqhNkkwmwSYTILMFtVMLeHwdyhhtkWDpNNC7Ndiw6X5r8PLdqhQ8d/H9+fLAkujcoekn77YkgU7WazWVrW6fw/86XX67320Zf9B5JDoPt/7bXX8NJLLwWWHJEMBLi8rPN+jjsY64PVRzjs1x+CAxrh+s9wwYlwlQPhKqf00Kic0IQ5YdDaEam3w6B3wRDhQqQBMBgAQ5QKIxLCEZ82DKMyohA/cTgiUmKBsDQAaV5zIiIakAQBMBigHW/AqPHAqGD06XIBNhvQYYHD3IGO1k50tNnQ0WZDe5sdHWYHOkzXf7abnbB3uq4/bCJsneL1nzZ8/9Muwm4HbDYBdocAm13AiBEi4N+vJf1mSBTtDsf3f99Qq/1/ye6xdnvvf+Ny338gOQS6/2effRZPP/201G5ra/Nr/n4wvLT3dvy/5lqvzwmqrtMQpBlKN01Vkoqlm9f7ihe8N7yt9zorShACW++jH0Hw/kSv1nvZibf1XTb39ToDXQ/v71lv1vvcQXfr/d5G8LG+j/vnt52IiJRNpQJ0OkCngzoGMCTDz1n2A9uQKNrd7zp6446o/nCPjYjo/Vysm+962tHR4fVOqH3dv1arhVam6ytFpsQgMiVGln0TERERDXaqnkMGPoPh+9+/2tvb/d7OarV67aMv+w8kh2Dtn4iIiIgGtiFRtMfFxUnLdXV1fm/nfm31ESNGBGX/geQQrP0TERER0cA2JIp295siNTc3e5zB7o7RaJSW3e+o2pf9A8Dly5f7df9ERERENLANiaI9MzPTo33mzJket6mpqUFjY6PPPgKRkZHh8aVSf/YPAKdPnw7K/omIiIhoYBsSRXt2drbHFzSLiop63Obzzz+XlnU6HbKzs3u9f41GgxkzZgS0/6tXr6KyslJq5+Tk9Hr/RERERDSwDYmi3WAwYN68eVJ7x44dPW7jHjNv3rw+XT0GAO677z5p+fDhw6ivr/d7/zExMSzaiYiIiIawIVG0A8Djjz8uLZeWlmL//v0+Y0tKSnDw4EGv2/bWsmXLpLP9drsdGzZs8BlrNpvxxhtvSO2HH34Y4eGhueMXERERESnfkCnaly5diilTpkjt1atX4/z5813i6urq8Mgjj8DpdAIAbr/9dtx///1e+6yuroYgCNLjxRdf9Ln/5ORkrF69Wmpv3LgRH330UZc4u92OFStWSF9W1ev1eO655/x6jUREREQ0OA2JmysBgCAIeOutt5Cbm4v29nbU1dVhxowZePLJJ5GTkwO1Wo0TJ05g8+bN0tQVvV6PN998E0KQ7pD44osv4uDBg6ioqIDT6cQDDzyA5cuXY9GiRYiNjUV5eTm2bNmC0tJSaZs//vGPSEpKCsr+iYiIiGhgEkRRFOVOoj/t2bMHjzzySI83ONLr9di+fTuWLFniM6a6uhpjx46V2uvXr+/2bDsAXLhwAXl5eR6Xc/TlmWeeQUFBQY9xvrS1tSE6Ohqtra2IiorqdT9EREREFBr+1mtDZnrMDUuWLMGpU6eQl5fn9Qy6IAiYN28evvzyy24L9t6aMGECSktLsXLlSuj1eq8xmZmZ2LdvX58KdiIiIiIaPIbcmXZ3RqMRxcXFqKmpAQCMHj0as2bNQkpKSr/s32Qy4ciRIzAajbBYLEhMTMRtt92GqVOnBqV/nmknIiIiUjZ/67UhXbQPdizaiYiIiJSN02OIiIiIiAYJFu1ERERERArHop2IiIiISOFYtBMRERERKRyLdiIiIiIihWPRTkRERESkcCzaiYiIiIgUjkU7EREREZHCsWgnIiIiIlI4Fu1ERERERArHop2IiIiISOHUcidAoSOKIgCgra1N5kyIiIiIyJsbddqNus0XFu2DmMlkAgCkpKTInAkRERERdcdkMiE6Otrn84LYU1lPA5bL5UJtbS0iIyMhCELI99fW1oaUlBQYjUZERUWFfH808PGYoUDxmKFA8ZihQMhxvIiiCJPJhKSkJKhUvmeu80z7IKZSqZCcnNzv+42KiuLASAHhMUOB4jFDgeIxQ4Ho7+OluzPsN/CLqERERERECseinYiIiIhI4Vi0U9BotVqsX78eWq1W7lRogOAxQ4HiMUOB4jFDgVDy8cIvohIRERERKRzPtBMRERERKRyLdiIiIiIihWPRTkRERESkcCzaiYiIiIgUjkU79dm1a9fw2WefoaCgAEuXLkVaWhoEQZAeL774Yp/6v3jxIl544QVMnz4dI0eOhF6vR3p6OhYvXozdu3fD4XAE54WQolRXV3scR/4+Pv30U7lTpyA7fvw4Vq9ejUmTJiE6OhpRUVGYNGkSfv7zn6O4uFju9EgBjh492qvx4vz583KnTiHQ2NiIgwcP4uWXX8bChQuRmJjo8b6/9957ve777NmzePrpp/GDH/wAsbGxMBgMmDhxIh5++OGQ///DO6JSn0yYMAGVlZUI1UWINm7ciLVr16Kzs9Nj/cWLF3Hx4kXs3bsXd955J3bs2IFx48aFJAcikofFYkF+fj7efffdLs+VlZWhrKwMb731FlasWIFNmzYhIiJChiyJSCmuXr2KO++8E5cuXQp63w6HAy+88AIKCgrgcrk8nrtw4QIuXLiAnTt34kc/+hG2bduGkSNHBj0HFu3UJxUVFSHr+5VXXsELL7wgtVUqFSZNmoTY2FhUVFSgrq4OAPCf//wHubm5OHHiBBITE0OWD8nrnnvu8SsuFAMl9T+n04klS5bgH//4h7ROr9fj1ltvhVqtxrlz59DW1gYA2LZtG2pqavDJJ58gLCxMrpRJIXQ6HXJzc/2KNRgMIc6G+lNHR0dICnYAWL16tccJhPDwcEyaNAkGgwHnz59Hc3MzAODAgQPIy8tDcXFx8I8vkagPAIgAxOjoaHHu3LniM888I3744YdiYmKi9Nz69esD7vfTTz8VBUGQ+pg5c6ZYXl4uPe90OsW//vWvosFgkGJmzZoVxFdGcquqqpLeWw5VQ8+zzz7r8f6vWrVKbG5ulp43m83iunXrPGKee+45GTMmOf3zn/+UjoPU1FS50yGZuP+/MXLkSHHBggXi888/L+7du9djrNi2bVtA/W7dutVj+4ULF4pXrlyRnrfZbOKmTZtEtVotxSxfvjzIr04U+T8h9cmOHTvE8vJy0eVyeaxPTU3tddHucrnEKVOmSNtPnDhRtFgsXmMLCws9Pkh79uzp7UshhWHRPnTV1NSIOp1Oeu8fffRRn7HPP/+8FKfT6cSampp+zJSUgkU7iaIotra2irt27RKrq6u7PNfbot1isYgJCQnStnPmzBEdDofX2LfffluKEwRBPHXqVG9filf8Iir1yfLlyzFhwgQIghC0Pg8ePIivvvpKam/cuBHDhg3zGpuXl4cHH3xQav/hD38IWh5EJI/XX38dHR0dAIBhw4bh9ddf9xm7bt06pKSkALj+p/GNGzf2R4pEpEBRUVFYunQpUlNTg9bne++9h6tXrwIABEHAn/70J5/T8FauXIkZM2YAAERRREFBQdDyAHj1GFKgPXv2SMtjx47F/Pnzu41fvXq1tHzixAlcuXIlZLkRUeh9/PHH0vIDDzyA2NhYn7EajQYrVqyQ2u7jBxFRX7mPKbm5ucjMzOw23r0m+eSTT7pcSKMvWLST4hw4cEBavueee3o8iz979myPq0a4b09EA0t5eTkqKyul9oIFC3rc5t5775WWKysrUV5eHpLciGhoMZvNOHbsmNQOdDwym804evRo0PJh0U6K0tDQIP0ZCgBmzpzZ4zZqtRpZWVlSu7S0NCS5EVHouU+NA/wbA6ZNmwaNRiO1OQYQUTCcO3cOdrtdavszHiUkJCAtLU1qB3M8YtFOilJWVubRTk9P92s797ib+6DB4bHHHkNGRgYiIiIQERGBMWPGYMGCBdiwYQMaGhrkTo+CxP3zq9FopPnq3bk5jmPA0Hbt2jU88MADSEtLg16vR2RkJMaOHYtFixZh8+bN0qVCiXqitJqERTspSnV1tUd7zJgxfm3nHndzHzQ4fPDBB6isrITVaoXVaoXRaMShQ4ewdu1apKamYt26dXA6nXKnSX3k/vlNTk72+0vuHAPohtbWVuzatQuXLl1CR0cHzGYzqqursW/fPjz11FMYM2YMNm3aJHeaNAC4jyVqtdrve8GEajzizZVIUUwmk0c7Ojrar+2ioqJ89kGDQ2JionTmrKWlBWVlZdIVRjo6OvDqq6/i5MmT2L9/P8LDw2XOlnrL/fPr7+cf4BhAntLS0jB69GhotVo0NTXh3LlzcDgcAK4X9fn5+Thz5gzeeecdmTMlJXMfSyIjI6FS+XeuO1TjEc+0k6KYzWaPtk6n82s7vV7vsw8amARBQHZ2Nt566y3U1taitrYWx48fx2effYaSkhJcu3YNO3fu9Jg7eOjQIeTn58uXNPWZ++fX388/wDFgqFOpVMjLy8OOHTvQ3NyMqqoqFBUV4bPPPsNXX32FlpYWbNmyBXFxcdI27777btAvyUeDi9LGIxbtpCg3zoTcoFb798cg9zj3L43QwJWamoovvvgCP/vZz7z+SVKr1WLZsmUoKSnB9OnTpfVbt27lFxEHMPcxwN/P/82xHAOGnpycHBQWFmL58uVeLxFqMBjwxBNPoKSkxOMX/Zdffhn19fX9mCkNJEobj1i0DyLbt2+HIAhBf7z33nv99hpuvonSjekPPXGPc7/8IwWPUo+v4cOHY8+ePdJZEFEUsXnz5iC8YpKD+xjg7+f/5liOAeRLSkoK/va3v0ltq9XKKTLkk9LGIxbtpCgGg8Gj3d7e7td2VqvVZx80+I0ZMwYPPfSQ1C4sLJQxG+oL98+vv59/gGMA+S87Oxtz5syR2hwvyBeljUf8IuogEhERgdGjR4ek3/7iPt8QAOrq6jBixIget3O/trs/8RQ4pR9fc+fOlc7aV1dXw2azeVy7mwYG9zGgrq7O7+04BlAg5s6dK9305sKFC/ImQ4rlPh6ZzWaYzWa/ivBQjUcs2geRxYsXY/HixXKn0ScTJ070aF++fBmTJ0/ucTuj0Sgt33LLLUHPi5R/fCUkJHi0m5ub/b48FymH+xjQ3NwMq9XaZdqcNxwDKBDu40VTU5OMmZCSeatJJk2a1ON2oRqPOD2GFCUjI8PjCxxnzpzxa7vTp09Ly5mZmcFOiwYA9z9HAl2/H0EDw82fX3/GgJqaGjQ2Nvrsg+hm7uMFxwrypTfjkd1uxzfffOOzj75g0U6KotFoMGPGDKldVFTU4zZXr15FZWWl1M7JyQlJbqRs7oOkVqsN6BrfpBzZ2dnQarVS258x4PPPP5eWdTodsrOzQ5IbDR7u40V8fLyMmZCSjRs3DsnJyVLbn/Ho1KlTHr8UBrMmYdFOinPfffdJy4cPH+7xclw7duyQlmNiYli0D0GiKOLDDz+U2jNnzpQxG+oLg8GAefPmSW33z7cv7jHz5s3j1WOoW1arFX//+9+l9l133SVjNqR0CxculJZ37doFm83Wbbz7eHTrrbciPT09aLmwaCfFWbZsmXSmzW63Y8OGDT5jzWYz3njjDan98MMP826YQ9DmzZs9rs2+aNEi+ZKhPnv88cel5dLSUuzfv99nbElJCQ4ePOh1WyJv1q1bh4aGBqnN8YK64z6mNDU1YevWrT5jr1y5gvfff9/rtkEhEoVAamqqCEAEIK5fvz7g7fPz86Xtw8LCxN27d3eJsdls4tKlS6U4vV4v1tTUBCF7ktvXX38t/vSnPxXPnz/fbZzL5RJff/11MSwsTDoOkpKSRKvV2k+ZUii4XC5xypQp0nuamJgolpWVdYmrra0VMzMzpbjbb79ddLlcMmRMcjp06JD49NNPi0ajsds4m80mrl27VjpeAIjTpk3jMTNEuL/v27ZtC2jbhQsXStsaDAaxqKioS0xra6s4e/ZsKS4hISHo/xcJoiiKwf01gIaSV199Fa+++mqX9Z2dndJyWFiY1zuJlZeXIzU11Wu/LS0tmDFjBioqKgBcv0X18uXLsWjRIsTGxqK8vBxbtmzxOLu6efNmrFmzpq8viRTgzJkzmDp1KgBg+vTpuPvuuzFlyhTEx8dDr9ejpaUFp0+fxl/+8hecP39e2k6r1aKwsBCzZ8+WK3UKkpMnTyI3N1e6NnJUVBSefPJJ5OTkQK1W48SJE9i8ebM0fU6v1+Nf//oXsrKy5EybZLB3714sXrwYKpUKs2bNQm5uLiZPnoy4uDhoNBo0NTXhxIkT2LFjh8dVPWJjY3H8+PEuVwihgW3VqlX44IMPuqx3r0vUajXCwsK6xPi6gVJ1dTWysrKkKw1ptVqsXLkS8+fPh8FgQGlpKTZt2oSqqioA12uWvXv34sc//nEwXtL3gvorAA0569ev9/jtNZBHVVVVt32Xl5eLKSkpfvX1zDPP9M8Lpn5x+vTpgI+nhIQEsbCwUO7UKYg++ugjUa/X9/je6/V68aOPPpI7XZLJxx9/HPB4kZGRIZaUlMidOoXAT37yk17XJd0pLi4WY2Nje+wjLCxM3LRpU0heG+e0k2JNmDABpaWlWLlyJfR6vdeYzMxM7Nu3DwUFBf2cHYVSYmIiHnvsMb++wDNq1Cg8//zzOHv2LPLy8vohO+ovS5YswalTp5CXlwdBELo8LwgC5s2bhy+//BJLliyRIUNSgltuuQUPPvigx1U+fElLS8OGDRtw+vRp6a95RP646667UFpaivvvv9/r7AEAyMrKwrFjx/DLX/4yJDlwegwNCCaTCUeOHIHRaITFYkFiYiJuu+02DrpDQH19PUpLS9HY2IimpiaYTCYYDAbExcVh6tSpyMzM9FrQ0eBiNBpRXFyMmpoaAMDo0aMxa9YspKSkyJwZKcnly5dx7tw5NDU1oampCRaLBVFRUYiPj8cdd9wR1Ct50NDV2NiIY8eO4cqVK7DZbEhKSsIdd9wR8qlWLNqJiIiIiBSO02OIiIiIiBSORTsRERERkcKxaCciIiIiUjgW7URERERECseinYiIiIhI4Vi0ExEREREpHIt2IiIiIiKFY9FORERERKRwLNqJiIiIiBSORTsRERERkcKxaCciIiIiUjgW7URERERECseinYiIiIhI4Vi0ExEREREpHIt2IiIiIiKFY9FORERERKRwLNqJiIiIiBSORTsRERERkcKxaCciIiIiUjgW7UREpBj3338/BEGAIAgYNmwYqqure9VPfn6+1I8gCDhx4kRwEyUi6mcs2omISBH279+PPXv2SO21a9ciLS2tV33dcccdHu3PP/+8L6kREcmORTsREcnObDZjzZo1UjstLQ1r167tdX9ZWVke7WPHjvW6LyIiJWDRTkREsisoKIDRaJTar7zyCnQ6Xa/7y8jIQFhYmNQ+c+ZMX9IjIpKdIIqiKHcSREQ0dDU0NCA9PR1msxkAMGHCBJw7d86j6O6N5ORk1NTUAABUKhWsViu0Wm2f8yUikgPPtBMRkaxee+01qWAHgN/+9rd9LtiB60X7DS6Xq9dfaiUiUgIW7UREJBuTyYR33nlHao8YMQIPPfRQUPrW6/Ue7ba2tqD0S0QkBxbtREQkm+3bt8NkMkntRx99FBqNJih9C4Lg0bbZbEHpl4hIDmq5EyAioqHr/fff92g/+uij3cYXFhbC6XQCALKzsxEbG+sz1uFweLTVav6XR0QDF7+ISkREsmhpaUFcXBxcLhcAIC4uDg0NDV3OkN9QW1uL0aNHS+2KigqMHz/eZ/9jx471mMduNBo95rkTEQ0knB5DRESyOHr0qFSwA8CcOXN8FuwA8MUXX0jLw4YNw7hx43zGOp1O6coxAKDRaJCYmNjHjImI5MOinYiIZHH27FmP9tSpU7uNLy4ulpYzMjKgUvn+L+zs2bOw2+1Se/r06UG5Ig0RkVxYtBMRkSwqKio82pmZmd3GHzp0SFpOSUnpNraoqMijPXv27ACzIyJSFhbtREQki8uXL3u0ExISfMZeunQJX3/9tdSOj4/vtu8DBw54tPPy8nqRIRGRcrBoJyIiWVgsFo92dHS0z9idO3d6tHU6nc/Y5uZmHDlyRGrHx8fj7rvv7mWWRETKwKKdiIhk4T7nHADa29u9xjkcDmzdutVjndVq9dnvm2++6XFN9uXLl3M+OxENeCzaiYhIFqNGjfJol5eXe417++23cenSJQiCIE2Lqaqq8hrb1NSEDRs2SG2tVotf/epXQcqYiEg+LNqJiEgWGRkZHu2bp8AAwIULF7B27VoAwPz585GUlAQA+Pe//43m5maPWJvNhmXLluHatWvSul/84he8NjsRDQos2omISBaLFi3yaB84cAC//vWvUV9fj/b2duzZswdz5sxBW1sbBEHASy+9JN1cyWaz4ZFHHoHRaERHRweOHDmC2bNn4/Dhw1J/kydPxu9+97v+fElERCHDO6ISEZEsnE4nZs6ciZMnT/YY+5vf/AYbNmzApk2bkJ+f32P82LFjcfjw4W5vwERENJDwTDsREckiLCwMO3fuxPjx47uNy8/PR0FBAQBg1apVmDJlSrfx9957L4qKiliwE9GgwjPtREQkq7a2NmzZsgW7d+9GVVUV2traMHLkSPzwhz/EmjVrkJOT4xHf2tqK3//+99i7dy8uXbqE8PBwJCUlIScnB8uWLePlHYloUGLRTkRERESkcJweQ0RERESkcCzaiYiIiIgUjkU7EREREZHCsWgnIiIiIlI4Fu1ERERERArHop2IiIiISOFYtBMRERERKRyLdiIiIiIihWPRTkRERESkcCzaiYiIiIgUjkU7EREREZHCsWgnIiIiIlI4Fu1ERERERArHop2IiIiISOFYtBMRERERKRyLdiIiIiIihfv/trL0/Ni4TpsAAAAASUVORK5CYII=", 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" ] @@ -857,7 +932,7 @@ "name": "stdout", "output_type": "stream", "text": [ - " Total run time: 8.75s*] Elapsed 8.75s / Remaining 00:00:00:00\n" + " Total run time: 7.39s*] Elapsed 7.39s / Remaining 00:00:00:00\n" ] } ], @@ -965,143 +1040,80 @@ "execution_count": 28, "id": "96b86c48", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting calculations for N=1, Nk=1 and max_depth=5 ... \n", - " Total run time: 1.13s*] Elapsed 1.13s / Remaining 00:00:00:00\n", - "Starting calculations for N=2, Nk=1 and max_depth=5 ... \n", - " Total run time: 2.10s*] Elapsed 2.10s / Remaining 00:00:00:00\n", - "Starting calculations for N=3, Nk=1 and max_depth=5 ... \n", - " Total run time: 3.69s*] Elapsed 3.69s / Remaining 00:00:00:00\n", - "Starting calculations for N=4, Nk=1 and max_depth=5 ... \n", - " Total run time: 11.24s*] Elapsed 11.24s / Remaining 00:00:00:00\n" - ] - }, - { - "data": { - "image/png": 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l4evra3h4eBiNGzc2Jk+ebKSkpBiG8df/f7dv3250797d8Pb2Nnx8fIy+ffsa586dy3Wf48ePG8OGDTNq1KhhuLi4GEFBQcadd95pTJ8+PUe7/fv3G7169TICAgIMV1dXo3bt2saQIUOMtLQ0s83zzz9vVK9e3XBycjIAY+3atTlqio6OznX/+Ph4Y/jw4UaVKlUMT09Po3379sbvv/9uBAcHG8HBwWa7Sz+j/L53a9euNQBj2bJlOV7P6/wr1WsYhrFmzRrz+3g13377rQEYe/bsyfH6qFGjDHd3d2P9+vXGrl27DC8vL8Nut1/1WgVVlH9b+f0+JyYmFjivWQzjf6tNFKPLx8wsWrSIIUOGFOv1p0+fzqRJk8x9m81WoCUZFy5cyIgRIwBwdnbO8STp3+XVE1yrVi0SExPznLOvuG34pRE1Kh0j0+bCDS1j8fYqob+KRETKmbS0NI4fP069evXMJ9vl+vvqq6/o06cPJ0+ezDG2Nz+hoaFMmTKF6OjoKw5dkPwNHDiQY8eO5Rpmci2WLFlizrbhaPn9PiclJeHn51egvFb6F/POw9+7ydPS0gp03uXt/j5Q/+/c3Nzw9fXN8VWSIuPrAeDinMmGDVrmWUREypaQkBDatGmT6wEruf6OHj3K0qVLefnl4nmmaO/evVcd211WlckQ/PenHAs6iP3ixYtXvEZpE5tU19w+dnL3lRuKiIiUQhaLhfnz51O9enXsdrujy6lQTp06xZtvvmkuwVxUl+ZbLm/KZAj++8cjUVFRBTrv8rmFL02xUlrZjHqX7R1zWB0iIiLXqkWLFrzwwgsFGrJ4SWhoKIZhaChEEXTq1IlRo0YVy7VWr17NwYMH6dq1a7FcrzQpk7ND/H0d7lOnTtGiRYt8zzt9+rS5fWn6ltKqZvW/nsD08yr63IYiIiIihdG+fXuSk5NZtmyZw+c8vh7KZAhu2LAhVqvVfLBt165ddOnSJd/zdu7caW43bdr0utVXHG5peQvG/zquK3ufd2wxIiIiUuH88Uf5fiapTA6HcHV1pV27duZ+QX5IZ8+eJTw83Nzv2LHjdamtuNSpXYML6dlzBAd4n82ntYiIiIgURpkMwQCPPPKIub169WrOnTt31faffPKJuV2pUqVSH4IBYlKy1yj39zyXY7o2ERERESmaMhuC+/btay4ZmJmZyezZs6/Y9sKFC7zxxhvmfv/+/XFxcbnuNRZVXHL2OvJWpyz2/rnPwdWIiIiIlB+lKgSfOHECi8VifoWGhl6xbc2aNRk9erS5P3fuXL766qtc7TIzMxk6dCinTp0CwMPDgxdeeKHYa78eElKqmNvhxw46sBIRERGR8qVIIXjkyJG4u7vn+ipsm2sVGhpKw4YNgexV43r37s3AgQP56quvWLt2Le+++y6tW7c216MGeOWVV6hevXqx3P96u5gWZG4nJJ5yYCUiIiIi5UuRZofIzMzMd6xqVlbWVZcnLgp/f39++OEH7rvvPk6fPo3dbmfJkiUsWbIkz/bPPvss48aNuy61XA92qpnbhr1gcyGLiIiISP5K1XCIa9GoUSP27NnD8OHD8fDwyLNN06ZN+fbbb4tt+cCS4uNd29z29tBcwSIiIiLFxWIYhuHoIopLcnIyv/76K6dPnyYlJYUbbriBm266iVtvvbXI105KSsLPz4/ExER8fX2Lodr87T94DPfExjhZDI7GNOfeLlo+WUSksNLS0jh+/Dj16tUrtuF4IuIY+f0+FyavlcnFMq7Ex8cnx9RpZV2zJjeyaXUdbvA9QS3/cDIy0nF1dXN0WSIiIiJlXpkfDlHenYrJfvDP1Tmd9RvWObgaERERkfJBIbiUO59Q39w+emyrAysRERERKT8Ugks5i1Njc9vDRWOCRURERIqDQnApd9/d3cm0Za9u17TmVux2u4MrEhERESn7FIJLuSaN6nLo3G0ABHlH8OvaVQ6uSERESpvFixfnWHHVarVSs2ZNhg4dSkREhNkuOTmZZ599ls6dOxMUFHTF1VkL2i4/U6dOpVmzZmWiA2fjxo2EhoaSkJBwXa5/6Wd04sSJfNsuXbqU5s2b4+HhgcViYdeuXbnOv1q9CxcupEaNGqSkpBS4vsTERJycnJgzZ06Bz7kWv/76K8OGDaNJkyZ4eXlRo0YNHnnkEbZv335d75sXheAy4MTZtub24fAfHFiJiIiUZosWLWLTpk2sWrWKkSNH8tlnn9GhQwczDMXGxvL++++Tnp7Oo48+esXrFLTd1URGRjJ79mymTp2Kk1PpjxsbN25kypQp1y0EF1R0dDQDBw6kfv36rFy5kk2bNtGoUSO6du3Kpk2buOGGG/Ktd/DgwXh5eTF79uwC33fbtm0YhkGbNm2K663k6Z133uHEiRP861//4qeffmLu3LmcP3+e22+/nV9//fW63vvvytUUaeVVrTqPAPMAaFxzvWOLERGRUqtFixa0bt0agE6dOmGz2Zg2bRrffPMN/fv3p06dOsTHx2OxWIiJiWHBggV5Xqeg7a5m7ty5VKpUiZCQkCK9p9Lq4sWLeHp6Fvt1Dx8+TGZmJgMGDCA4ONh83dPTk6CgoAJdw2q1Mnr0aKZNm8Zzzz1XoDq3bduG1Wrltttuu+baC+Ktt96iSpUqOV578MEHadCgATNmzOCee+65rve/XOn/00zo/kBHTsQ1AaB+0D62bN3o4IpERKQsuP322wE4efIkgDlcIj8FbXclGRkZLFy4kH79+uXqBY6OjmbUqFHUqlULNzc3goKCuOuuu1i9ejUAoaGhWCwWdu7cSUhICL6+vvj5+TFgwACio6Nz3evIkSP069ePKlWq4ObmRtOmTXnrrbdytTt48CB9+/alatWquLm5Ubt2bQYNGkR6ejqhoaE888wzANSrV898/7/99luOmnbs2MFjjz2Gv78/9etnz94UHh7O0KFDadiwIZ6entSoUYPu3buzd+/eQn/fhgwZQvv27QHo06cPFouFu+++G8g5nCK/egH69+9PUlISn3/+eYHuHRYWRosWLczVdw3D4PXXX8fd3Z2JEydis9kK/X7y8vcADODt7U2zZs04ffp0sdyjoNQTXAY4OTmx93gn6lY+CMCuvZ/Sru2dDq5KRERKu/DwcIAC9yAWly1bthAbG0unTp1yHRs4cCA7duzgpZdeolGjRiQkJLBjxw5iY2NztOvRowe9e/dmzJgx7Nu3j0mTJrF//362bNmCi0v2A+P79+/nzjvvpHbt2rz66qtUq1aNn3/+mfHjxxMTE8PkyZMB2L17N+3btycwMJCpU6fSsGFDoqKi+O6778jIyGDEiBHExcUxb948li9fbg45aNasWY6aQkJCePzxxxkzZow5xCQyMpKAgABmzZpFUFAQcXFxfPjhh7Rr146dO3fSuHFjCmrSpEm0bduWcePGMWPGDDp16pTnqmcFqbdatWo0adKEH3/8kWHDhuV7723bttG5c2cAYmJiGDJkCJs3b+brr7/moYceytXeMIwCB2Or9epxMzExkR07dpRoLzAoBJcZtWs9ArwDQNMaJTtmRkREygabzUZWVhZpaWmsW7eO6dOn4+Pjw8MPP1yidWzatAkgz4/WN2zYwIgRIxg5cqT5Wl6rvYaEhJhjWjt37kzVqlXp378/X3zxBf379wdgwoQJ+Pj48Mcff5hh8f777yc9PZ1Zs2Yxfvx4/P39mTBhAlarla1bt+b4g+DSdXx8fKhduzYAt956K3Xr1s3zfQ0ePJgpU6bkeK1jx4507NjR3LfZbHTt2pXmzZvz3nvv8dprr139m3WZ+vXrm0G2YcOGZk/+39WsWbNA9d52221mD/vVxMTEcPLkSdq2bcv69evp168fdevWZdeuXdSsWROAo0eP0rhxYy5cuIC7uzvr1q3L84+cvBw/fvyKNQKMGzeOlJQU/vOf/xToesVFIbiMeLjrvaz56SYaBO2lduXDrN+wno53dcz/RBERuaKQfplExxiOLgOAoEALyz91KdI1/h6abrrpJt555x2qVq1apOsWVmRkJBaLhcDAwFzH2rZty+LFiwkICOC+++6jVatWZs/u5S4F1Et69+7N4MGDWbt2Lf379yctLY01a9YwduxYPD09ycrKMtt26dKFN998k82bNxMcHMy6desYPnx4kXvEe/bsmeu1rKwsZs+ezZIlSwgPDyczM9M8duDAgSLdr6iqVKnC+fPnycrKumpvbFhYGACrVq1i7NixPPnkk8yYMSPHOX/++ScNGzbE3d0dgFatWpnn5ad69epXPDZp0iQ++eQT5s2bR6tWrQp0veKiEFxGWCwW/jwRTIOg7DFGhw5+phAsIlJE0TEG5847uopLih7GP/roI5o2bYrVaqVq1armx+QlLTU1FRcXF5ydnXMdW7p0KdOnT2fBggVMmjQJb29vevTowezZs6lWrZrZ7vJtyP5IPSAgwBw2ERsbS1ZWFvPmzWPevHl51hETE0N8fDw2m83s0SyKvL6fEyZM4K233uK5554jODgYf39/nJycGDFiBKmpqUW+Z1G4u7tjGAZpaWl4e3tfsd22bdtwd3fnp59+omPHjnnOKrF3715uuukmc9/b25tbbrmlQHVcKYBPmTKF6dOn89JLL/HEE08U6FrFSSG4DGnW+FHgzezt2qux2+1lYtoZEZHSKijQQnGEz+KQXUvRNG3a1JwdwpECAwPJyMggJSUFLy+vXMfmzJnDnDlzOHXqFN999x0TJ07k/PnzrFy50mx39uxZatSoYe5nZWURGxtLQEAAAP7+/jg7OzNw4EDGjRuXZx316tXD09MTZ2dnzpw5U+T3ldfDgkuWLGHQoEHMmDEjx+sxMTFUqlSpyPcsiri4ONzc3K4agCE7BN96661MnjyZbt268fzzzzNz5swcbf4egos6HGLKlCmEhoYSGhrKCy+8ULA3VMwUgsuQ++8N5ufvb6NJtR3U8DvOT7+soduD9zu6LBGRMquoww8kb02aZM9odPToUVq2bHnFdrVr1+aJJ55gzZo1bNiwIcexTz75JMfH41988QVZWVnmbAmenp506tSJnTt30rJlS1xdXa94n+DgYJYtW8ZLL72U5xANADc3N4BC995aLBbz3Et+/PFHIiIiaNCgQaGuVRgFqffYsWO5Hu7LS1hYGD179uSBBx5g/vz5DB06lJo1a+b442Lv3r3069fP3C/KcIhp06YRGhrKiy++aD686AgKwWWIxWLh8On2NKm2A4BzZz8FFIJFRKTgVqxYQUpKCsnJyUD2DAtffvklkD2W9tKcsgVtl5dLQXXz5s05QnBiYiKdOnWiX79+NGnSBB8fH8LCwli5cmWu+YSXL1+O1Wrl/vvvN2eHuPnmm+ndu7fZZu7cubRv354OHTowduxY6tatS3JyMuHh4Xz//ffm4guvvfYa7du3p127dkycOJEGDRpw7tw5vvvuO9577z18fHzMXs65c+cyePBgXFxcaNy4MT4+Plf9fnbr1o3FixfTpEkTWrZsyfbt23nllVeKZfjF1eRXr91uZ+vWrQwfPvyq14mKiiIqKsr8g2PIkCGcOXOG8ePHU61aNXr27ElGRgZHjhzJ0RPs4+NzTZ86vPrqq/zf//0fDz74IF27dmXz5s05jl/pYcDrwpACSUxMNAAjMTHRoXVs3LTJOLLJzTi+xdnY+VtVIzU13aH1iIiUdqmpqcb+/fuN1NRUR5dy3SxatMgAjLCwsHzb1qlTxyB7DEiur+PHjxe63ZV06NDB6NKlS47X0tLSjDFjxhgtW7Y0fH19DQ8PD6Nx48bG5MmTjZSUFMMwDGPy5MkGYGzfvt3o3r274e3tbfj4+Bh9+/Y1zp07l+s+x48fN4YNG2bUqFHDcHFxMYKCgow777zTmD59eo52+/fvN3r16mUEBAQYrq6uRu3atY0hQ4YYaWlpZpvnn3/eqF69uuHk5GQAxtq1a3PUFB0dnev+8fHxxvDhw40qVaoYnp6eRvv27Y3ff//dCA4ONoKDg812l35G+X3v1q5dawDGsmXLcrye1/lXqtcwDGPNmjXm9/Fqvv32WwMw9uzZk+P1UaNGGe7u7sb69euNXbt2GV5eXobdbr/qtQoiODj4iv+uChJL8/t9LkxesxiGUToGQ5VySUlJ+Pn5kZiYmOecfSXpyy/upXXddQBsPP0h/Xr2z+cMEZGKKy0tjePHj1OvXj3zyXa5/r766iv69OnDyZMnc4ztzU9oaChTpkwhOjr6ikMXJH8DBw7k2LFjuYaZXIslS5aYs204Wn6/z4XJa3qqqgw6GXmXue2ctdSBlYiIiOQtJCSENm3a5HrASq6/o0ePsnTpUl5++eViud7evXuvOra7rFIILoO63f8YF9L9ALil1q+ci4l3cEUiIiI5WSwW5s+fT/Xq1bHb7Y4up0I5deoUb775prkEc1Fdmm+5vNFwiAIqTcMhABYs7Ml9N30LwNrw2QztN8HBFYmIlE4aDiFy7VavXk3//v05dOiQw6d8g+IdDqHZIcqopMQ2QHYIrub9JaAQLCIiIsWnffv2JCcns2zZslIRgIubQnAZ1fPRECKPzecG35M0rbaVvQePcFOTho4uS0RERMqJP/74w9ElXFcaE1xG1bmxEZv+/Gusz/btCxxYjYiIiEjZohBchrlY/npSs1GVr7HZbA6sRkRERKTsUAguw3qGhLA/MnuFl+p+x/htU9HnAhQRERGpCBSCy7DqteoRtvcWc//smUWOK0ZERESkDFEILuNqVm5Khs0NgJY1vufixVQHVyQiIiJS+ikEl3FdH+1BWHgHAHzcEvh57ZcOrkhERESk9FMILuOq1ajDzt0NzH2XrMWOK0ZERESkjFAILgduadSQmJQbAGhW7XfORJ10cEUiIiIipZtCcDnQuXtPftt1NwBOFjubt3zg2IJERERESjmF4HKgyg01Ofynn7lf02cJhmE4sCIRERGR0k0huJx4oFML9kW1BaCaz0m2717r4IpERERESi+F4HLinod68tvmm839MycWOrAaERERkdJNIbic8A8IIuWsjdRMLwAaB31Palqig6sSEZGSsHjxYiwWi/lltVqpWbMmQ4cOJSIiwmyXnJzMs88+S+fOnQkKCsJisRAaGprregVtl5+pU6fSrFkz7HZ7Ed5dydi4cSOhoaEkJCRcl+tf+hmdOHEi37ZLly6lefPmeHh4YLFY2LVrV67zr1bvwoULqVGjBikpKQWuLzExEScnJ+bMmVPgc67Frl276Nq1K7Vr18bDw4PKlStzxx13sGTJkut637woBJcjDz8SzIZDnQHwcLnIuj8+dXBFIiJSkhYtWsSmTZtYtWoVI0eO5LPPPqNDhw5mGIqNjeX9998nPT2dRx999IrXKWi7q4mMjGT27NlMnToVJ6fSHzc2btzIlClTrlsILqjo6GgGDhxI/fr1WblyJZs2baJRo0Z07dqVTZs2ccMNN+Rb7+DBg/Hy8mL27NkFvu+2bdswDIM2bdoU11vJU0JCArVq1WLGjBn89NNPfPTRR9StW5eBAwcyffr063rvv7OW6N3kuup438OMGbee+1pk77tkfgSMdWhNIiJSclq0aEHr1q0B6NSpEzabjWnTpvHNN9/Qv39/6tSpQ3x8PBaLhZiYGBYsWJDndQra7mrmzp1LpUqVCAkJKdJ7Kq0uXryIp6dnsV/38OHDZGZmMmDAAIKDg83XPT09CQoKKtA1rFYro0ePZtq0aTz33HMFqnPbtm1YrVZuu+22a669IO6++27uvvvuHK9169aN48eP8/777/Piiy9e1/tfrvT/aSYF5untQ3XvdCISbwSgfkAYpyP2O7gqERFxlNtvvx2Akyez54+/NFwiPwVtdyUZGRksXLiQfv365eoFjo6OZtSoUdSqVQs3NzeCgoK46667WL16NQChoaFYLBZ27txJSEgIvr6++Pn5MWDAAKKjo3Pd68iRI/Tr148qVarg5uZG06ZNeeutt3K1O3jwIH379qVq1aq4ublRu3ZtBg0aRHp6OqGhoTzzzDMA1KtXz3z/v/32W46aduzYwWOPPYa/vz/169cHIDw8nKFDh9KwYUM8PT2pUaMG3bt3Z+/evYX+vg0ZMoT27dsD0KdPHywWixkYLx8OkV+9AP379ycpKYnPP/+8QPcOCwujRYsWeHh4AGAYBq+//jru7u5MnDgRm81W6PdTGIGBgVitJds3q57gcqbLIz34dbsbA+85BsD27QuoVeM1B1clIiKOEB4eDlDgHsTismXLFmJjY+nUqVOuYwMHDmTHjh289NJLNGrUiISEBHbs2EFsbGyOdj169KB3796MGTOGffv2MWnSJPbv38+WLVtwcXEBYP/+/dx5553Url2bV199lWrVqvHzzz8zfvx4YmJimDx5MgC7d++mffv2BAYGMnXqVBo2bEhUVBTfffcdGRkZjBgxgri4OObNm8fy5cvNIQfNmjXLUVNISAiPP/44Y8aMMYeYREZGEhAQwKxZswgKCiIuLo4PP/yQdu3asXPnTho3blzg79ukSZNo27Yt48aNY8aMGXTq1AlfX99c7QpSb7Vq1WjSpAk//vgjw4YNy/fe27Zto3Pn7CGVMTExDBkyhM2bN/P111/z0EMP5WpvGEaBg3Fe4dZut2O324mPj2fZsmX8/PPPvPnmmwW6XnFRCC5n2gU/wKuvfkK/u51xdrJR23cpdvtsnJz0oxYRKe9sNhtZWVmkpaWxbt06pk+fjo+PDw8//HCJ1rFp0yaAPD9a37BhAyNGjGDkyJHma4888kiudiEhIeaY1s6dO1O1alX69+/PF198Qf/+/QGYMGECPj4+/PHHH2ZYvP/++0lPT2fWrFmMHz8ef39/JkyYgNVqZevWrTn+ILh0HR8fH2rXrg3ArbfeSt26dfN8X4MHD2bKlCk5XuvYsSMdO3Y09202G127dqV58+a89957vPZawTui6tevbwbZhg0bmj35f1ezZs0C1XvbbbeZPexXExMTw8mTJ2nbti3r16+nX79+1K1bl127dlGzZk0Ajh49SuPGjblw4QLu7u6sW7cuzz9y8nL8+PFcNf7jH//gvffeA8DV1ZU33niD0aNHF+h6xUXDIcoZNzd32t3iz87T2b+QlT3PEbbjRwdXJSIiJeH222/HxcUFHx8funXrRrVq1VixYgVVq1Yt0ToiIyOxWCwEBgbmOta2bVsWL17M9OnT2bx5M5mZmXle41JAvaR3795YrVbWrs2eBz8tLY01a9bQo0cPPD09ycrKMr+6dOlCWloamzdv5uLFi6xbt47evXsXuUe8Z8+euV7LyspixowZNGvWDFdXV6xWK66urhw5coQDBw4U6X5FVaVKFc6fP09WVtZV24WFhQGwatUq7r33Xvr168dvv/1mBmCAP//8k4YNG+Lu7g5Aq1atCAsLK9BX9erVc93zhRdeICwszOypfuKJJ/jvf/9bjO8+f+oeLIc6P9ybD774jtZ1sv9DERP1AZD7r2wRkYpu1FsniEu+ekAoKZV9rLw/rm6RrvHRRx/RtGlTrFYrVatWNT8mL2mpqam4uLjg7Oyc69jSpUuZPn06CxYsYNKkSXh7e9OjRw9mz55NtWrVzHaXb0P2R+oBAQHmsInY2FiysrKYN28e8+bNy7OOmJgY4uPjsdlsOQLdtcrr+zlhwgTeeustnnvuOYKDg/H398fJyYkRI0aQmppa5HsWhbu7O4ZhkJaWhre39xXbbdu2DXd3d3766Sc6duyY56wSe/fu5aabbjL3vb29ueWWWwpUR17DIWrXrm32Znfp0gWA559/nsGDB5fY8B2F4HLo1tvv5sSzU0jsGYCfeyyNAn/hwoXzeHtXcXRpIiKlSlxyFjFJpSMEF4emTZuas0M4UmBgIBkZGaSkpODl5ZXr2Jw5c5gzZw6nTp3iu+++Y+LEiZw/f56VK1ea7c6ePUuNGjXM/aysLGJjYwkICADA398fZ2dnBg4cyLhx4/Kso169enh6euLs7MyZM2eK/L7yelhwyZIlDBo0iBkzZuR4PSYmhkqVKhX5nkURFxeHm5vbVQMwZIfgW2+9lcmTJ9OtWzeef/55Zs6cmaPN30NwUYdD/F3btm159913OXbsmEKwXDtnZ2ceeLA16/fVpXurT3FxzuT3TYt46P7nHF2aiEipUtmn9PxvsDTVUlRNmjQBsseRtmzZ8ortateuzRNPPMGaNWvYsGFDjmOffPIJrVq1Mve/+OILsrKyzNkSPD096dSpEzt37qRly5a4urpe8T7BwcEsW7aMl156Kc8hGgBubm4Ahe69tVgs5rmX/Pjjj0RERNCgQYNCXaswClLvsWPHcj3cl5ewsDB69uzJAw88wPz58xk6dCg1a9bM8cfF3r176devn7l/aThEQeQ1HOLv1q5di5OTEzfeeGOBrlkcys9vnORwX7c+TJq+kO7/+++Hr+UjDOPZIk15IyJS3hR1+EFZtGLFClJSUkhOTgayZ1j48ssvgeyPpS/NKVvQdnm5FFQ3b96cIwQnJibSqVMn+vXrR5MmTfDx8SEsLIyVK1fmmk94+fLlWK1W7r//fnN2iJtvvpnevXubbebOnUv79u3p0KEDY8eOpW7duiQnJxMeHs7333/Pr7/+CsBrr71G+/btadeuHRMnTqRBgwacO3eO7777jvfeew8fHx+zl3Pu3LkMHjwYFxcXGjdujI+Pz1W/n926dWPx4sU0adKEli1bsn37dl555ZViGX5xNfnVa7fb2bp1K8OHD7/qdaKiooiKijL/4BgyZAhnzpxh/PjxVKtWjZ49e5KRkcGRI0dy9AT7+Phc06cOo0aNwtfXl7Zt21K1alViYmJYtmwZS5cu5ZlnninZmUwMKZDExEQDMBITEx1dSoHY7XajR/DNxs8/tDKOb3E2jm9xNo4e+93RZYmIlLjU1FRj//79RmpqqqNLuW4WLVpkAEZYWFi+bevUqWMAeX4dP3680O2upEOHDkaXLl1yvJaWlmaMGTPGaNmypeHr62t4eHgYjRs3NiZPnmykpKQYhmEYkydPNgBj+/btRvfu3Q1vb2/Dx8fH6Nu3r3Hu3Llc9zl+/LgxbNgwo0aNGoaLi4sRFBRk3Hnnncb06dNztNu/f7/Rq1cvIyAgwHB1dTVq165tDBkyxEhLSzPbPP/880b16tUNJycnAzDWrl2bo6bo6Ohc94+PjzeGDx9uVKlSxfD09DTat29v/P7770ZwcLARHBxstrv0M8rve7d27VoDMJYtW5bj9bzOv1K9hmEYa9asMb+PV/Ptt98agLFnz54cr48aNcpwd3c31q9fb+zatcvw8vIy7Hb7Va9VEB988IHRoUMHIzAw0LBarUalSpWM4OBg4+OPPy7Q+fn9Phcmr1kMwzBKLnKXXUlJSfj5+ZGYmJjnnH2l0fzXJnMk/gL/6D4XgAMxfXmoy8cOrkpEpGSlpaVx/Phx6tWrZz7ZLtffV199RZ8+fTh58mSOsb35CQ0NZcqUKURHR19x6ILkb+DAgRw7dizXMJNrsWTJEt588002b95cDJUVTX6/z4XJa5oirRy7r3sfNq88xMXM7AHxN1b6hszMRAdXJSIiFUFISAht2rTJ9YCVXH9Hjx5l6dKlvPzyy8Vyvb179151bHdZpRBcjtVr2Ix6Vc+x4XD21CNu1lS27/jIwVWJiEhFYLFYmD9/PtWrV8dutzu6nArl1KlTvPnmm+YSzEW1efNmgoODi+VapYlCcDl3f/fe/LKmvrlvS/rAgdWIiEhF0qJFC1544QWcnAoeN0JDQzEMQ0MhiqBTp06MGjWqWK61evVqDh48SNeuXYvleqWJQnA5d1+33kSELeNYXHMAavjt5fz5gk1pIiIiIhVX+/bteeqpp1i2bJnD5zy+HhSCy7lqNepwW6uarN72kPnanl3vOrAiERERKQv++OMPdu/eTceOHR1dynWhEFwBPPBoPzasiCU9ywOAGl5fYcu64OCqRERERBxHIbgCuPvBEJwTvmbj0ezeYA+XC+z581MHVyUiIiLiOArBFYCPbyXa39uZVb/9Nb3JheiFDqxIRERExLEUgiuIBx7px7EN33I6oSEAtfy2Ex+/18FViYiIiDiGQnAF0bZDZwJ9I1i14xHztZ0733FgRSIiIiKOoxBcQbi4unJv116sX5lJhs0NgCquX2C3pTq4MhEREZGSpxBcgXR+pD+ZER+y5fgDAHi7JnDw0BcOrkpERESk5CkEVyDNbm5D7bpVWbX+dvO1mIgFDqxIRERExDEUgisQi8XCA4/059D6NUQm1QOgtt8mkpIOOrgyERERkZKlEFzBdH74cVwyf2P1rhDztT273nJgRSIiIiIlTyG4gqle+0ZatrqTX1d6kGlzBcDf6TPsdj0gJyIiIhWHQnAF9MAj/cg4tZDNJ7JXkPNyTeDY0c8cXJWIiIhIyVEIroDu6fIY7tYEVq7rZL4Wc/o9B1YkIiIiUrIUgisgHz9/7uzUhSMb/uBUQiMAqnlv50LiTgdXJiIi12Lx4sVYLBbzy2q1UrNmTYYOHUpERITZLjk5mWeffZbOnTsTFBSExWIhNDQ01/UK2i4/U6dOpVmzZtjt9iK8u5KxceNGQkNDSUhIuC7Xv/QzOnHiRL5tly5dSvPmzfHw8MBisbBr165c51+t3oULF1KjRg1SUlIKXF9iYiJOTk7MmTOnwOcUhwULFmCxWPD29i7R+4JCcIXV+dF+uKZ9xy+7Hjdf279PD8iJiJRlixYtYtOmTaxatYqRI0fy2Wef0aFDBzMMxcbG8v7775Oens6jjz56xesUtN3VREZGMnv2bKZOnYqTU+mPGxs3bmTKlCnXLQQXVHR0NAMHDqR+/fqsXLmSTZs20ahRI7p27cqmTZu44YYb8q138ODBeHl5MXv27ALfd9u2bRiGQZs2bYrrreQrIiKCp59+murVq5fYPS9ndchdxeHuCH4Iv0p+/PazD/3v8sTD5SK+LMNuex0nZx9HlyciItegRYsWtG7dGoBOnTphs9mYNm0a33zzDf3796dOnTrEx8djsViIiYlhwYK854ovaLurmTt3LpUqVSIkJCT/xmXQxYsX8fT0LPbrHj58mMzMTAYMGEBwcLD5uqenJ0FBQQW6htVqZfTo0UybNo3nnnuuQHVu27YNq9XKbbfdds21F9aYMWPo2LEjlStX5ssvvyyx+15S+v80k+vCxdWVe7o8hv30O2w81h0Ad2sKx48udmxhIiJSbG6/PXtxpJMnTwKYwyXyU9B2V5KRkcHChQvp169frl7g6OhoRo0aRa1atXBzcyMoKIi77rqL1atXAxAaGorFYmHnzp2EhITg6+uLn58fAwYMIDo6Ote9jhw5Qr9+/ahSpQpubm40bdqUt97K/cnmwYMH6du3L1WrVsXNzY3atWszaNAg0tPTCQ0N5ZlnngGgXr165vv/7bffctS0Y8cOHnvsMfz9/alfvz4A4eHhDB06lIYNG+Lp6UmNGjXo3r07e/fuLfT3bciQIbRv3x6APn36YLFYuPvuu4Gcwynyqxegf//+JCUl8fnnnxfo3mFhYbRo0QIPDw8ADMPg9ddfx93dnYkTJ2Kz2Qr9fq5myZIlrFu3jrfffrtYr1sY6gmuwB4KGcQ3n77PT789z72NlwKQEPUeRsMnivQfPxERKR3Cw8MBCtyDWFy2bNlCbGwsnTp1ynVs4MCB7Nixg5deeolGjRqRkJDAjh07iI2NzdGuR48e9O7dmzFjxrBv3z4mTZrE/v372bJlCy4uLgDs37+fO++8k9q1a/Pqq69SrVo1fv75Z8aPH09MTAyTJ08GYPfu3bRv357AwECmTp1Kw4YNiYqK4rvvviMjI4MRI0YQFxfHvHnzWL58uTnkoFmzZjlqCgkJ4fHHH2fMmDHmEJPIyEgCAgKYNWsWQUFBxMXF8eGHH9KuXTt27txJ48aNC/x9mzRpEm3btmXcuHHMmDGDTp064evrm6tdQeqtVq0aTZo04ccff2TYsGH53nvbtm107twZgJiYGIYMGcLmzZv5+uuveeihh3K1NwyjwMHYas0ZN8+fP8+///1vZs2aRc2aNQt0jetBIbgCa9qyNXXqN+HQltWEP3YzDQJ2E+BxkKT4jfhVvsvR5YmISCHZbDaysrJIS0tj3bp1TJ8+HR8fHx5++OESrWPTpk0AeX60vmHDBkaMGMHIkSPN1x555JFc7UJCQswxrZ07d6Zq1ar079+fL774gv79+wMwYcIEfHx8+OOPP8yweP/995Oens6sWbMYP348/v7+TJgwAavVytatW3P8QXDpOj4+PtSuXRuAW2+9lbp16+b5vgYPHsyUKVNyvNaxY0c6duxo7ttsNrp27Urz5s157733eO21167+zbpM/fr1zSDbsGFDsyf/72rWrFmgem+77Tazh/1qYmJiOHnyJG3btmX9+vX069ePunXrsmvXLjOkHj16lMaNG3PhwgXc3d1Zt25dnn/k5OX48eM5avzHP/5B48aNGTt2bIHOv140HKICs1gsPBQyELf0b1i57a8H5A4d0ANyIiJl0e23346Liws+Pj5069aNatWqsWLFCqpWrVqidURGRmKxWAgMDMx1rG3btixevJjp06ezefNmMjMz87zGpYB6Se/evbFaraxduxaAtLQ01qxZQ48ePfD09CQrK8v86tKlC2lpaWzevJmLFy+ybt06evfuXeQe8Z49e+Z6LSsrixkzZtCsWTNcXV2xWq24urpy5MgRDhw4UKT7FVWVKlU4f/48WVlZV20XFhYGwKpVq7j33nvp168fv/32W45e2j///JOGDRvi7u4OQKtWrQgLCyvQ1+UPvn311Vd8//33zJ8/3+GfOqsnuIJ74NH+vP/qJDascmZIJ1+8XZPwd/qGrMwYrC65/+MlIlKeRP7ZDlvmWUeXAYCzSzWqt9hSpGt89NFHNG3aFKvVStWqVc2PyUtaamoqLi4uODs75zq2dOlSpk+fzoIFC5g0aRLe3t706NGD2bNnU61aNbPd5duQ/ZF6QECAOWwiNjaWrKws5s2bx7x58/KsIyYmhvj4eGw2W7F87J7X93PChAm89dZbPPfccwQHB+Pv74+TkxMjRowgNdWxq7G6u7tjGAZpaWlXnYJs27ZtuLu789NPP9GxY8c8Z5XYu3cvN910k7nv7e3NLbfcUqA6Lg2HuHDhAuPGjeOf//wn1atXN2e2yMjIACAhIQEXFxe8vLwK+A6LRiG4gguscgNtO3Rmw/p3WH8khC7NF+PinMHRIwtp3Ow5R5cnInJd2TLPYsuMyL9hGdG0aVNzdghHCgwMJCMjg5SUlFyBJjAwkDlz5jBnzhxOnTrFd999x8SJEzl//jwrV6402509e5YaNWqY+1lZWcTGxhIQEACAv78/zs7ODBw4kHHjxuVZR7169fD09MTZ2ZkzZ84U+X3l1XO5ZMkSBg0axIwZM3K8HhMTQ6VKlYp8z6KIi4vDzc0t3zl4t23bxq233srkyZPp1q0bzz//PDNnzszR5u8h+FqGQ8TExHDu3DleffVVXn311Vzt/P39eeSRR/jmm28KdN2iUggWuvQcxOZ1/Vix5m66NF8MQGrMfAzjGSwWjZgRkfLL2aVa/o1KSGmqpaiaNGkCZI8jbdmy5RXb1a5dmyeeeII1a9awYcOGHMc++eQTWrVqZe5/8cUXZGVlmbMleHp60qlTJ3bu3EnLli1xdXW94n2Cg4NZtmwZL730Up5DNADc3NwACt17a7FYzHMv+fHHH4mIiKBBgwaFulZhFKTeY8eO5Xq4Ly9hYWH07NmTBx54gPnz5zN06FBq1qyZ44+LvXv30q9fP3P/0nCIgrg0HKJatWrmcJbLzZo1i3Xr1rFixYor/nyuB4Vg4c57uuLj58/ZnT+w71w7mlfdQiX3E8TFrCYgqLOjyxMRuW6KOvygLFqxYgUpKSkkJycD2TMsXJqjtUuXLuacsgVtl5dLQXXz5s05QnBiYiKdOnWiX79+NGnSBB8fH8LCwli5cmWu+YSXL1+O1Wrl/vvvN2eHuPnmm+ndu7fZZu7cubRv354OHTowduxY6tatS3JyMuHh4Xz//ff8+uuvALz22mu0b9+edu3aMXHiRBo0aMC5c+f47rvveO+99/Dx8TF7OefOncvgwYNxcXGhcePG+Phcfe78bt26sXjxYpo0aULLli3Zvn07r7zyynWf9SC/eu12O1u3bmX48OFXvU5UVBRRUVHmHxxDhgzhzJkzjB8/nmrVqtGzZ08yMjI4cuRIjp5gHx+fQn/q4O7ubv7buNzixYtxdnbO89h1ZUiBJCYmGoCRmJjo6FKui1cn/9O4q4GH8cSkecbxLc7G8S3Oxtbfuzu6LBGRIktNTTX2799vpKamOrqU62bRokUGYISFheXbtk6dOgaQ59fx48cL3e5KOnToYHTp0iXHa2lpacaYMWOMli1bGr6+voaHh4fRuHFjY/LkyUZKSophGIYxefJkAzC2b99udO/e3fD29jZ8fHyMvn37GufOnct1n+PHjxvDhg0zatSoYbi4uBhBQUHGnXfeaUyfPj1Hu/379xu9evUyAgICDFdXV6N27drGkCFDjLS0NLPN888/b1SvXt1wcnIyAGPt2rU5aoqOjs51//j4eGP48OFGlSpVDE9PT6N9+/bG77//bgQHBxvBwcFmu0s/o/y+d2vXrjUAY9myZTlez+v8K9VrGIaxZs0a8/t4Nd9++60BGHv27Mnx+qhRowx3d3dj/fr1xq5duwwvLy/Dbrdf9VrXavDgwYaXl1eB2ub3+1yYvGYxDMMouchddiUlJeHn50diYmKec/aVdQf2bGNUz7tID5zJe7Newd8jGrvhRK2bj+DiXsfR5YmIXLO0tDSOHz9OvXr1zCfb5fr76quv6NOnDydPnswxtjc/oaGhTJkyhejo6BL9aLy8GThwIMeOHcs1zORaLFmyhDfffJPNmzcXQ2VFk9/vc2HymgZ8CgBNbmpF3QZNsca+zZoDfQFwstgJP+K4lVxERKTsCgkJoU2bNrkesJLr7+jRoyxdupSXX365WK63d+/eq47tLqsUggX4a85gZ+M0K1bdRZY9e7i4kbQIuz3NwdWJiEhZY7FYmD9/PtWrV8dutzu6nArl1KlTvPnmm+YSzEW1efNmgoODi+VapYmGQxRQeR8OARBzPorHOtYnxakrE6akc0ednwDwrL6AKjWHOLY4EZFrpOEQItdu9erV9O/fn0OHDjl8yjfQcAi5Ti7NGeyW+SM/bPxrpZ6ok286sCoRERFxhPbt2/PUU0+xbNmyUhGAi5tCsOTwUMggLNg4+McZTiZkz/Po57KLi0kVbxohERGRiuyPP/5g9+7ddOzY0dGlXBcKwZLDXf+bM9gt8W1W7B5svn48/A0HViUiIiJSvBSCJQdXNzfu794HZ+MMv61uyoUMPwDcMr7GlnnewdWJiIiIFA+FYMnloZBBANhPvsNv4Y8BYHXKIOLE+44sS0RERKTYKARLLo1b3MaNjVvgalvDj2sfw25YAEiOfg/DyHJwdSIiIiJFpxAsuVgsFrr3GooFg/O7fmN3VPbcgF7WKBKiv3VwdSIiIiJFpxAsebr/4b64uLjifvEDfgwbYr5+5tg8xxUlIiIiUkwUgiVPfv4BdOz8KE7EseM3Z6KS6wHg4/QH6Sl7HVydiEjhaW0okbKvOH+PiyUEb9y4kdGjR9OsWTP8/Pzw9fWlWbNmjBo1ig0bNhTHLa4oOTmZuXPn8uCDD1KzZk3c3d0JCAigZcuWPPnkk+zcufO63r8869Z7KACuMW/w874B5usnj6o3WETKDmdnZwAyMzMdXImIFNWl3+NLv9dFUaRlk1NSUhg/fjwffPDBVdsNHTqUefPm4eXlda23ytPnn3/OuHHjiIuLu2Ibi8XCuHHjmD17Nh4eHtd8r4qwbPLf2e12Hr+3KVFnTmBv9gsLnu2Oh8tFMm0e3NjmNM7WSo4uUUSkQI4dO4aLiws1a9bEYrE4uhwRuQaGYXDmzBkyMzO58cYb82xTmLxmvdZCbDYbISEh/PLLL+ZrHh4eNG/eHKvVyv79+0lKSgJg0aJFRERE8NNPPxVLcgd4/fXXmTBhQo7XqlevTsOGDUlPT2fPnj1cvHgRwzB48803OX78ON9++22x3b8icHJyoutjQ1gwJ5T08CVsOPEI9zX8DBfnVM6fWcwNdf/t6BJFRAokMDCQiIgIzpw5g5+fHy4uLgrDImWEYRhkZmaSmJjIhQsXqFGjRrFc95p7gl944QVmzpxp7o8cOZJZs2ZRuXJlILuX+OWXX2batGk5znnppZeKWDKsW7eOe+65B7vdDkCtWrWYP38+DzzwgNkmLS2NOXPm8OKLL2Kz2Yp8/4rYEwxwPuoMve5uiM3uSuB9XzFn6IMApGTdSLM7DmKxaFi5iJQNSUlJxMTEkJ6e7uhSROQauLm5ERgYeNUcVpi8dk0hODIykvr165OWlgbAwIED+eijj/JsO2nSJKZPnw6Au7s7R48epXr16oW9ZQ633347W7ZsAcDPz4+dO3dSr169PNvOnz+fUaNGAdnfvCNHjlCrVq1C37OihmCAZ0Y8wuZ1K7ng9yrTQz+jeZXs731Ag+/xqfyQg6sTESmczMxMs3NERMoGZ2dnXFxc8m133UPws88+yyuvvAKAp6cnp0+fNnuA/y4jI4MGDRpw+vRp89yXX365sLc0/fnnn9x0003m/ssvv8yzzz571XPatWvH1q1bARg/fjxz584t9H0rcghe/8u3/Gdcb7IsN3LbgKd55sHRAFx06kSz1qscXJ2IiIhItsLktWv6LPvrr782t3v37n3FAAzg6urK0KFDzf3ly5dfyy1Na9euzbH/+OOP53tO3759c9xf0+QUzp2duuAfUAWrcYzNv9XhfEpNADzta8m4uM/B1YmIiIgUXqFD8KFDhwgPDzf3H3zwwXzPeeihvz4yDw8P59ChQ4W9renkyZPmtq+vL7Vr1873nJYtW5rbZ86cYceOHdd8/4rI6uLCgz2yp0hzinyLlfsHmcfOHJ/joKpERERErl2hQ/Du3btz7N9xxx35nnPbbbfh6upq7u/Zs6ewtzUlJiaa2z4+PgU65+/d4bt27brm+1dU3XoNAcDNtpJV63qSmpk93Z09+VNsmTEOrExERESk8Aodgg8cOGBuu7q6Fughs7+3u/wahXV58E1OTi7QOZemartk//7913z/iqr2jY1p2fouLNhJObicdcdDALA6pRMd8Z6DqxMREREpnEKH4BMnTpjbhZl0/PJhC5dfo7Bq1qxpbiclJXHq1Kl8z9m7N+cyvwW5f3p6OklJSTm+KrpuvbLHdnukLuD7P0ZiN7J/9oln38GwZziyNBEREZFCKXQIvrz31c/Pr8DnXT4koaA9uHlp3759jv2lS5fme87nn3+eY78g9585cyZ+fn7m17VMq1be3P1gCF7evjgRw9ldp9kRdQ8Abk5nSY790sHViYiIiBRcoUPwhQsXzG13d/cCn3f5ksWXX6OwWrduTaNGjcz9mTNn5nhY7u8WLVrE5s2bc7xWkPs///zzJCYmml+XpniryDw8vbivW28AXOJf5YewkeaxyBOvadYNERERKTMKHYKzsrLMbau14KsuX942MzOzsLc1OTk5MWXKFHM/Pj6ejh07smpVzvlq09LSeOWVV8yFMi53+Xu4Ejc3N3x9fXN8CXR/fAQALvbdHNhejxMJTQFwN3aRfmGjI0sTERERKbBCh2BPT09z+9KKcQVxeVsvL6/C3jaHxx9/nDFjxpj7p06donPnztSsWZNOnTpx5513EhgYyLPPPktWVhY33nhjjqncCjOMQ3Jq3PxWmtzUCgD7iVdY8ecQ81jE8dcdVJWIiIhI4RQ6BHt7e5vbqampBT7v4sWLeV7jWr399ttMmzYtx9RrERER/Pbbb2zatImUlBQge/jEL7/8kmOJzEqVKhX5/hXZw//rDXazfc/vGx8hMS0AAEvqd2SlX3loioiIiEhpUegQHBgYaG5HRUUV+LyzZ8+a2wEBAYW9bS4Wi4UXX3yR8PBwXnzxRW6//XYCAwNxcXHhhhtu4L777mPRokVs3LiR+vXr55gRoiALbMiV3du1N17evljIIiv8M1aHZ6/I52Sxc+70PAdXJyIiIpK/Qofgxo0bm9uxsbE5eniv5vIHy5o0aVLY215RrVq1mDZtGps2bSI6OpqMjAwiIyNZtWoVQ4YMwcXFhcTExByr3LVu3brY7l8ReXp50/mR7ODrkvIuP60fRqYtu0f+YswH2G3X/uCjiIiISEkodAhu2rRpjv2CrL4WERFBdHT0Fa9xva1fvz7HzAXt2rUr0fuXRw8/nj0zhBMxpJ04wcbTXQFwcUoi4dxiB1YmIiIikr9Ch+C2bdvi5uZm7v/xxx/5nvP777+b2+7u7rRt27awty2STz/91Nxu27YtN954Y4nevzxq0OQmmt+S/ceELWo632/5a7q0mDNzMQy7o0oTERERydc1PRh37733mvuffPJJvudc3ubee+8t8uwQhXHo0CG++uorc3/kyJFXaS2F8fBl06WdPVST/dHZodiV41xM+MmRpYmIiIhcVaFDMMCQIUPM7T179vD9999fse2OHTtYsWJFnudeb+np6YwcOdKcl7hJkyYMGjSoxO5f3t3T5TG8fSsBYDs5i592DzWPRZ14zUFViYiIiOTvmkLwY489xs0332zujx49moMHD+ZqFxUVxYABA8zpyW655RZ69uyZ5zVPnDiBxWIxv0JDQ69aw8cff0xGRsYVj587d45HH33UHIphsVh47733ckypJkXj7uHJg4/2B8A57Su2hz3I2Qt1ALBmric9ZbsjyxMRERG5omsKwRaLhfnz55tLIUdFRdGuXTsmTpzITz/9xC+//ML06dO59dZbOXDgAJC9bPL777+PxWIplsJHjhxJtWrVGDJkCPPnz2f16tWsW7eOzz//nNGjR9OkSRNWrlxptn/77bfp2LFjsdxb/nLpATkLWbjHfMWKA0PMY2dPvuqgqkRERESuzmJcPm1CIS1fvpwBAwbku2iGh4cHS5YsISQk5IptTpw4Qb169cz9yZMnX7U32N3dnfT09Hxr9PLyYu7cuQwfPjzftleTlJSEn58fiYmJWkL5b8b1vYc92zZgJxCj9R+8+4+2+LrFYzecqX3LYaxudRxdooiIiFQAhclr19QTfElISAjbt2/nvvvuy7OH12KxcO+997Jt27arBuBr0aNHDypXrnzF415eXvTv3599+/YVOQDL1T1y2XRpvsknWB3eL3vfYiP6zBwHViYiIiKStyL1BF/u9OnTbNiwgYiICABq1KjBXXfdRa1atYrj8nmy2+3s3r2b8PBwzp07R3JyMkFBQdSqVYuOHTuawzWKg3qCryw9PY2eHW4kMT4WXG/B5fYFvDvidlyd07EZntRtdRJnq7+jyxQREZFyrjB5rdhCcHmnEHx1b858lqUfzAXAo+U+Hu/1f9xz4xcA+FZ/ico1n3NkeSIiIlIBlNhwCJFLHu7z15ATa/Rsvt0yGruRPUQmNvINDHv+47dFRERESopCsBSL2jc25rY77gYgKeojEk82YUfUPQC4cI4LsZ85sDoRERGRnBSCpdj06DcGAAsGVY2v+H7HaPPYuZOvopE3IiIiUlooBEuxaX9fd4Kq1gAgav+LHNobzJHYWwCw2g+QmrjyKmeLiIiIlByFYCk2VquVhx8fAYBhT6GB1y5+3PfXWOFzJ19xVGkiIiIiOSgES7Hq3mcYVhcXAGIOPcWGTY+aSylb0rWUsoiIiJQOCsFSrAKCqhHcuQcAKQkHaOiTxIpDg83j0ae1lLKIiIg4nkKwFLuQAWP+2omdyS9/DCApPXuxjIzEr8hKP+mgykRERESyKQRLsbup1Z3Ub3ITAKcPfkZViw9rjvYFspdSjouc68jyRERERBSCpfhZLBZC+v/VG1zd9XN+2DScDJsbAMnnF2DLindUeSIiIiIKwXJ93P9wX7x9/AA4uHUSxNblj1OPAOBsuUjS2XccWZ6IiIhUcArBcl14eHrxUMhAADLTU2lZYyvfhY3CbmT/k4uLfAO77aIjSxQREZEKTCFYrptH+/+1YtyZvc9x9sgthEV0BsCZGJKjP3BUaSIiIlLBKQTLdVO7XiPa3HUvAGdP76N9k7N8u/OvscKxZ17FsGc6qjwRERGpwBSC5brqcdl0aZnnZnBo9x3sPtsBACf7aVLiPndUaSIiIlKBKQTLdXVnp65UrV4bgJ0bvyC4GXy3969hEtGnXsYw7I4qT0RERCoohWC5rpydnXmk70gADMOgsvMCdmy9l8OxtwJgyTpIasIPjixRREREKiCFYLnuuvUaiqtr9hzBG1a8Stu6Xny/b6R5PPr0TAzDcFR5IiIiUgEpBMt15x8QxH3dHwfgQnIiTWuuYMPmhzmd2BAAIy2MtOR1jixRREREKhiFYCkRvQY/YW6HrQ6loY8fPxwcYb4We3qWI8oSERGRCkohWEpEg6YtuaVtRwBOHTvM3W32s3ZTL86n1AQgK2U16SnbHVmiiIiIVCAKwVJiLu8NPrp9Kv6Zgaw4PMR8Le7Myw6oSkRERCoihWApMXfd241qNeoAsGX9SnrcF88vmweQmBYAQFrC12SmHnZkiSIiIlJBKARLiXF2dibkssUzkiNew4iuxs9HBwFgsRjER77iqPJERESkAlEIlhLVrddQ3D08AVj17SL6PAQrtgzhYqYPACmxS8jKOOPIEkVERKQCUAiWEuXj58+DPQYAkHoxBV/LxyQdr8WaY30AsJBJYtTrjixRREREKgCFYClxjw0aZ26vWPY6Ifd58MP2kWTYshfUSDw3H1tmtKPKExERkQpAIVhKXJ36TWjb4X4Aos6cpEW9Xzl/8EbWnegJgBMX1RssIiIi15VCsDjE5b3Bv377Gg/e7s13u0aTaXMBIPHs29iy4hxVnoiIiJRzCsHiEO06PkCtetnLJu/Y/BsPdDzKqb1N+f1UDwAsXCDp7BuOLFFERETKMYVgcQgnJyd6DvyHub959RyCm/vw7e4xZNmtACREzcOeleioEkVERKQcUwgWh3mox0C8vH0BWPXtp/TrmcSJPc3ZeLo7ABYjkaTzbzmyRBERESmnFILFYTy9fejWeygAGRnpHAh7h1a1ffh2zxjsRvY/zfiIOdhtyY4sU0RERMohhWBxqMcGjcPZ2RmA5UveZdgAG0d3t2TzmS4AWIw4ks+/68gSRUREpBxSCBaHqlajDsEPhgCQEBdN7KlPaezvyzd7xmI3LADERbyG3XbRkWWKiIhIOaMQLA73+LB/m9vLFr/BqKHOHNlzK2ERDwBgsUeTHD3fQdWJiIhIeaQQLA7XtGVrbm7dHoCTRw/iYayihosf3+wbY7aJj3gFuz3NUSWKiIhIOaMQLKVCn+H/MreXLprD6CFWDu1qw7bIe7NftJ3lQvQHDqpOREREyhuFYCkV7uzUlZp16gOwY9NvNKz1J5Uy/fh231izTXzEbAx7hqNKFBERkXJEIVhKBWdnZ3oPHW/uf/nRXEYOdGH/rtvZdTYYACPrDBdiPnJUiSIiIlKOKARLqfFQyCB8K1UGYPUPS+nQ7iweF3KODY6LmIVhz3RUiSIiIlJOKARLqeHu4cmjfUcBYMvK4oelbzOivyt/7m7P3nN3AmBknuBC7BJHlikiIiLlgEKwlCohA8fi4uIKwLefL6D7Q6m4xPvx7YHLxgafma7eYBERESkShWApVQKCqnFf98cBuJCUwNofFjO8rxu7dwWbvcH2zJNciPnQkWWKiIhIGacQLKVOn2F/TZf2xeJ59O5p4BRTia/3jzNfj4t4STNFiIiIyDVTCJZSp37jFrRpfx8AUWdOsGPD9wzp7cbePR3YfbYDAEbmaZJjFjmyTBERESnDFIKlVLp8KeVP57/KgD4WOFuJ5fv+6g2OPzNDq8iJiIjINVEIllKpTfv7aNCkJQAH9oQRvv93BvVyY/+fd7IjqhMARlYEF6IXOrJMERERKaMUgqVUslgs9Bv5lLn/yfv/ZVA/J2yRlfh63z/M1+MjZmG3pzqiRBERESnDFIKl1OrU5TGq1agDwJb1vxAdsZcBIa4c/LMd2yLvBcDIiiL5/PuOLFNERETKIIVgKbWsVmuOmSI+XfAaQwY4kxVRiW8u6w1OiHgZuy3FESWKiIhIGaUQLKVa18eG4OcfAMCvP35BespJ+j7qysF9bdga8QAAhu08yeffdWSZIiIiUsYoBEup5uHpRc+B2b2+NpuNpR/MZdggZzLPZPcG2w0LAAmRr2C3XXBkqSIiIlKGKARLqRcyYCzuHp4A/LBsEVZLLH26u3J4361sOfMQAIYthqRzbzmyTBERESlDFIKl1PPzD6Bbr6EApKelsnzJOwwf7EzG6Up8e2AsdiP7n3Fi1KvYbUmOLFVERETKCIVgKRP6DPsXzs7OAHz18dv4el+kZ1dXwvfdzKbTXQEwbHEknX3DkWWKiIhIGaEQLGVCtRp1uLdbHwCSEuL4cdkiRg9zJv1/vcE2e3ZAToh6HVtWvCNLFRERkTJAIVjKjMsXz/j8g7kEVs7i8YddOba/BRtOP5x9wJ5IUtQrDqpQREREygqFYCkz6jduwe3BDwJwLvIUa35axqhhzmRGZK8il2lzASDx7DyyMqIcWaqIiIiUcgrBUqb0H/W0uf3p/FcJDIB+j7py+mAT1p7IHi6BkUpi5AwHVSgiIiJlgUKwlCk3t2lP81vaAXDs0J9sXPsTI4c4Y4usxLf7R5GW5QFA8vn5ZKYdc2SpIiIiUoopBEuZYrFY6D/6r97gj995GX9/GPCYC2cP3sgvRwf970gWCRFTHVOkiIiIlHoKwVLm3HVPN25s1ByAfbu2sGPzbwwb5Iz9XCV+3D+MCxl+AKTEfkLGxT8dWaqIiIiUUgrBUuY4OTkxcOxEc/+jt2fhX8nCkMetRB+qxY+Hh//viEH8mf9zTJEiIiJSqikES5nU6aGe1KzbAIAdm3/jz52bGTLAGUuMLz8fHEh8ahUAUhO+I+3CZkeWKiIiIqWQQrCUSc7OzgwY9Yy5/9Hbs/DztTBsgAtxh6rx7aEx5rGE0y9iGIYjyhQREZFSSiFYyqzOj/SjavXaAGz6bQWH9+1icD8nXBJ8WXuwF+cu1AIgLfk30pLWOLBSERERKW0UgqXMcnF1pd/ICeb+x+++jLe3heGDrCQcrsLXB58wj8WfUW+wiIiI/EUhWMq0ro8NoXJgVQDW/fw1J8IPMLCvE+4p3vxx8GFOJTYCICNlGxfjv3FgpSIiIlKaKARLmebm7kGfYf8CwDAMPnn/v3h5Whg1xErSkQC+2j/ebJtw5v8wDJujShUREZFSRCFYyrxH+47Ct1JlAFZ99xmRp4/Tr7cTPplebDtyP0dibwEgM+0AF2I+dmClIiIiUlooBEuZ5+ntw2ODssf/2mw2Pn3/v3h4WPjHSCtJhyuzbP+/zbYJZyZjt6c6qFIREREpLRSCpVx4bNA/8PTyAeCnrz4i+mwEvUKcCHL15M/wu9h1NhgAW2YESWffcGSpIiIiUgooBEu54OPnT48B2XMDZ2Zm8NnC13F1sfDvsVaSDlXmi31PYjey/7knRr6MLTPGkeWKiIiIgykES7nRZ+h43Nw9APju8wXERp+l20NO1Avw5NjRm/j9ZA8ADHsSiZEzHFmqiIiIOJhCsJQb/gFVeKTvSADS01L5bMFrODlZmPBPZ5IOVWb5gXGkZ7kDkHT+HTLTjjmyXBEREXEghWApV/qNfApXt+yg+82n7xMXc467O1hoeaM7UUdv5Oejg7MbGpkknJnkwEpFRETEkRSCpVwJCKqWqzfYYrHw1Hhnko/48+PBoSSlZ0+nlhK3lPQLYY4sV0RERBxEIVjKnct7g7/+5D3iYs7RppUTd93mSuzRGnx7cIzZNv70RC2nLCIiUgEpBEu5E1jlhr/1Br8OwIR/OpN8tBK/HunN2Qu1AUhLXkdq4gqH1SoiIiKOoRAs5VLO3uB3iY89T7MmTjx0jwuJ4VX48rIFNOJPP6/llEVERCoYhWAplwKr3MAjj48AcvYG//sfzqSe9mXL0Qc5GtcSgMzUfVyI+chhtYqIiEjJUwiWcqvfyKdwdXUD/uoNrlvHwmMPW0k+Upml+54y2yacmYzddtFRpYqIiEgJUwiWciuwanUe/l9vcFrqRbM3eNwoZ2wxPuw7cTs7ou4BwJYZSdI5LacsIiJSUSgES7nWf9TTf+sNjqZaVQsD+2QvoLFs37+x2Z2BS8spn3NkuSIiIlJCFIKlXAusWp3ufYYD2b3Bny/M7g0ePdwZ9zRPTpxqyvqTIQAY9mTiz4Q6qlQREREpQQrBUu5d3hu8fMk7xMdG4+drYexIK0kHA1h+4AkuZnoDcCF6IRkX9zqyXBERESkBCsFS7gVVq5GjN/jT+f8FYEAfJ6p6unP+VC2+PzTqf63txJ16WgtoiIiIlHMKwVIhDBj9jDlv8PIl7xJzLhJXVwsT/ulM0sEAfgkfwPmUmgCkJa0hNeFHR5YrIiIi11mxhOCNGzcyevRomjVrhp+fH76+vjRr1oxRo0axYcOG4rjFFaWlpfHxxx/Tq1cvGjRogK+vL66urgQGBtK6dWvGjRvHpk2brmsNUvoFVq1Oj/6jAchIT+Pjd18GoMsDTjSt60ri8UC+2DfBbB93+jkMe6ZDahUREZHrz2IU4XPflJQUxo8fzwcffHDVdkOHDmXevHl4eXld663ytHr1aoYNG8bp06fzbdu1a1cWLlxI1apVr+leSUlJ+Pn5kZiYiK+v7zVdQxwrPjaaPvc0JvViClYXFz5btY9qNeqwJczOoH+kUzX4FP+5ZxCNA7cDULn26/hW+6eDqxYREZGCKkxeu+aeYJvNRkhISI4A7OHhQevWrbn99ttz3HjRokWEhIRgsxXf0rQ//PADDz30UI4A7Ovry+23384999xDs2bNcHL66+39+OOPBAcHExMTU2w1SNniHxBEr8HZoTYrM5PFb84AoF0bJ+6+w0pyeGU++/MZs31CxFRsWXEOqVVERESur2sOwZMmTeKXX34x90eOHMmZM2cICwtj06ZNREZGMmnSJPP4L7/8wv/93/8Vrdr/SUhIYNiwYWRlZQHg4+PDBx98QExMDJs2bWLNmjXs27ePkydP0q9fP/O8Q4cO8fTTTxdLDVI2PT7833j7+AGw8uuPOXX8MADP/MtK6mlfwiNu4Y9TDwNgt8WTEDHdYbWKiIjI9XNNITgyMpLXX3/d3B84cCDvv/8+lStXNl/z8vJi6tSpvPjii+Zrr732GpGRkUUoN9vnn39OdHS0uf/ZZ58xdOhQXFxccrSrWbMmn3zyCY8++qj52qeffkpCQkKRa5CyycfPn74jssf+2mw2Fs97CYAG9S089kj2Q3Jf7v8X6VkeACSfe5vM1EMOq1dERESuj2sKwXPmzCEtLQ0AT09P5syZc8W2kyZNolatWkD2Q2xz5869llvm8Pvvv5vbLVq0oGvXrldt/5///MfczszMJCwsrMg1SNn12KBx+PkHArD6h6UcO7wPgPFjnbEkenIuog4/HRn6v9ZZxJ1+zkGVioiIyPVyTSH466+/Nrd79+6dowf471xdXRk6dKi5v3z58mu5ZQ6X9wK3aNEi3/Z/b3P5+VLxeHr7MGBM9thfwzBYOGcKAFWCLAwb5EzSgQBWhA8hLjX7IcrUhB9ITVzjsHpFRESk+BU6BB86dIjw8HBz/8EHH8z3nIceesjcDg8P59Chon287O3tbW5nZGTk2z49PT3Hvr+/f5HuL2Vfj36jCahyAwDrV33LoT93ADB8sDO+VjcSTlbhy/3/MtvHnXoGwyi+BztFRETEsQodgnfv3p1j/4477sj3nNtuuw1XV1dzf8+ePYW9bQ5t27Y1tzdt2mQ+IHcl69atM7ddXFxynC8Vk5u7B4P/MdHcn//6ZAC8vSyMH+tM0uHKbDjRnWPxzQHITN3DheiFDqlVREREil+hQ/CBAwfMbVdXV3O879X8vd3l17gWgwcPxtPTE4CoqCheeumlK7ZNSEjg+eefN/eHDBlCQEBAke4v5UO3XsOoVqMOAFvW/8KebdkLu/Tq4US96lYuHPPn071/jQeOPzNJU6aJiIiUE4UOwSdOnDC3a9asicViKdB5tWvXzvMa1+KGG27ggw8+MGeDCA0N5fHHH2f9+vUkJyeTlZXFmTNnWLx4Ma1atWL//v0A3H333fz3v/8t0r2l/HBxdWXIE389NDn/9VAMw8BqtTBxgpULxypxKLINm05nP3hpz4ol4cwUR5UrIiIixajQITg5Odnc9vPzK/B5ly+ecfk1rlWfPn345ZdfaNq0KQBLly4lODgYX19fXFxcqFWrFkOHDuXYsWMEBAQwceJEfv755wKv9paenk5SUlKOLyl/Hni0P7XqNQRg19b1bP09e+7r4PZOtL/dmeTD/izdN4G0S1OmnX+HjItFG84jIiIijlfoEHzhwgVz293dvcDneXh45HmNorj77rv56aef6Nat2xXbuLi4MGzYMMaOHZtjXHJ+Zs6ciZ+fn/lVkGEfUvZYrVZG/DvU3H/3lRex2+0APD/BSnqUD+fP1eL7Q6P+18JO7Ml/U4TVxkVERKQUKHQIvvwhNKvVWuDzLm+bmZlZ2NvmkpqayhNPPEHDhg354YcfgOw5i9u2bcs999zDTTfdhLOzM5mZmbzyyis0aNCAGTNmFPj6zz//PImJiebX5cszS/ly94MhNG5xGwDhB/ew+vulQPYCGn17OZO0P5Cfjw7m3IXsP4TSk9dzMW6Zw+oVERGRoit0CL70QBpgLphREJe39fLyKuxtc8jIyKBr16689dZbZGVl4efnx8KFC4mPj2fLli2sWbOGPXv2cO7cOZ555hksFguZmZn85z//ybFwxtW4ubnh6+ub40vKJycnJ8Y8/dfyyAvmhJL5v6n3/jnGGfcsd5LO5HxILu70s9htKSVeq4iIiBSPQofgy+foTU1NLfB5Fy9ezPMa12L69OmsXbsWyB5msXbtWoYNG5ZruENAQACzZ8/mjTfeMF+bOXMmW7duLdL9pfxpfde9tL7zHgCizpzg28/nA+BfycI/x2Qvp7wzohO7z3YAwJZxhsSoWQ6rV0RERIqm0CE4MDDQ3I6KiirweWfPnjW3izJF2d+XXh41ahS33nrrVc954oknuPnmm4HsFcLmzZt3zfeX8mvMM39NtffhWzO5eCH7Ac5+vZ2oU83KhaOV+HTvc2TZs4f2JEa9SmbaUYfUKiIiIkVT6BDcuHFjczs2NjZHD+/VXD6mtkmTJoW9rWnr1q05Zmp4+OGHC3Re9+7dze3169df8/2l/Grc4jbu6fIYAAlx0SxdlP3HlovL/6ZMO+5HRHQDfg4fnH2CkUHcqacdVa6IiIgUQaFD8KUpyS7ZtWtXvudEREQQHR19xWsURkRERI79gs7acHm7y3ulRS434slQnP/3EOdnC18nPvY8AHd3sHBXW2eSDgTw/eFRxKcGAZCa8D0XE1Y6rF4RERG5NoUOwW3btsXNzc3c/+OPP/I95/fffze33d3di7Rs8eX3hoKPS768x/ry6dpELlerbkO69RoKQGrKBT56O3vcr8Vi4fmnncmI9iTxbABf7HvKPCfu1AQMe4ZD6hUREZFrc00Pxt17773m/ieffJLvOZe3uffee4s0O8QNN9yQY3/79u0FOu/ydjVq1Ljm+0v5N+SJ/+DukT0LyjefvU/k6eMANGrgxOM9nUncH8jGU105EnsLAFlph0k698aVLiciIiKlUKFDMMCQIUPM7T179vD9999fse2OHTtYsWJFnudei1atWuUI0W+//ba5uMGVnD59mq+++srcDw4OLlINUr4FVrmBXkP+CUBWZiYL5041j40f64wHrqSc8mPJnhewG9nLhidETCcrIyLP64mIiEjpc00h+LHHHjNnWwAYPXo0Bw8ezNUuKiqKAQMGYLPZALjlllvo2bNnntc8ceIEFovF/AoNDc2znaurK/379zf3t23bxpgxY664AEdkZCSPPPJIjmETw4YNy/c9SsXWb+RT+FaqDMCq7z4j/ED2UsmVK1sYNzp7OeVj0S347UQvAAz7BT0kJyIiUoZcUwi2WCzMnz/fHFsbFRVFu3btmDhxIj/99BO//PIL06dP59Zbb+XAgQNA9jjc999/H4vFUuSiQ0NDqVq1qrk/f/58mjdvzowZM1i5ciV//PEHX3/9NU8++STNmjVj586dZtsRI0bQunXrItcg5Zu3jx8DxzwLZE+r984rL5jHBj7uxI21nUk+XJmv9v+LpHR/AC7GLSM18ReH1CsiIiKFYzEMw7jWk5cvX86AAQPyfTjNw8ODJUuWEBIScsU2J06coF69eub+5MmTr9gbDNmzUnTv3p0zZ84UuN7evXuzZMkSXFxcCnzOJUlJSfj5+ZGYmKjV4yqI9PQ0BjzQkrMRJwH47wff065DZwA2bLYzdEwmQe0j6NTiC0bc9iIAVrcGVL9pF05O7g6rW0REpKIqTF67pp7gS0JCQti+fTv33Xdfnj28FouFe++9l23btl01AF+LW265hb179/L0009TuXLlq7Zt1aoVn3/+OUuXLr2mACwVk5ubO6Ofmmbuv/3y8+bQnrtud+KB+5xI3BfIhlMPcyjmNgCy0sNJivqvQ+oVERGRgitST/DlTp8+zYYNG8x5fGvUqMFdd91V4Hl8iyIrK4vdu3ezZ88eYmNjSU9Px9fXlxo1atCmTZtiqUE9wRWT3W5nTK8OHNizDYCJM96ja68hAEREGjwUkol7o/M0araDKXf3wtnJBhY3aty0Bxf3+g6sXEREpOIpTF4rthBc3ikEV1y7tv7OP/vfB0BAlRv4bNU+PDyzZyh5630b8xZkUCX4NH1vmc1DDRcD4OH3IFUafV8sY+BFRESkYEpsOIRIRXBL2w50uD97ee7Y81F8vnCOeWzEYCdqVHEm6ZA/3xz8B3Gp2Q9spiau5GL8Nw6oVkRERApCIVikAMY889JfyykveJWY81EAuLlZeOFpKxdP+ZIcV4lP9040z4k79SR22wWH1CsiIiJXpxAsUgC16zXikb4jAUi9mMIHb/y1gMY9wRY63uVE4p+BhEXcx55z7QGwZZwhIWJantcTERERx1IIFimgIeP+g5d39viiH5ct5tjhfUD2LCgvPmuFi+5cPO3Lkj0vkGlzBSDp3FwyLv7psJpFREQkbwrBIgXkHxDEwDHPAdmzRrwz+3nzWN06FoYOdCLpUGWi4uvyw+ER2QeMLGJPPoGePxURESldFIJFCuGxweOoWr02AJvX/UzYH6vNY2NHOFPF35nkg5X56chwzl3InpovPfkPLsR85JB6RUREJG8KwSKF4ObuwagJf40HvnwBDS9PC89NcObiGR9SYn35eM+LZrv4089iy4wu8XpFREQkbwrBIoV0X/c+NG6RvUJc+ME9/PTVh+axrg84cXsbJxL2BbL33F1sPvMQAPasWOJOPeOQekVERCQ3hWCRQnJycuKJ52eb+/Nfm0xKchKQ/ZDc5BesWC66kXLSl0/3PkdKRvbDdCmxS0hNXOWQmkVERCQnhWCRa3BL2w7c/WAIAPGx5/nw7Znmsfr1LIwY4kTyYX/ik6qydN9T5rHYE+Ow2y6WeL0iIiKSk0KwyDX6x3MzcXV1A2DZh/M4feKIeWzsCGdqVHUmcX8A60+GcDCmNQBZ6cdIiNTcwSIiIo6mECxyjW6oWZfHhz8JQFZmJm/P+mu1OHd3C/830UpalBdp5z1ZvGsymTYXAJKiXiPj4m6H1CwiIiLZFIJFiqD/6GcIqHIDAH+s+YFtG9aYx+7u4MQD9zmRuC+QyMQb+f7wqP8dsRFzfDSGYXNAxSIiIgIKwSJF4unlzZinp5v7b7z0NFlZWeb+f56x4m5xIfmwPz8dGU5E0o0AZKRsI/nc2yVer4iIiGRTCBYpos6P9KNpyzYAHD+yn++XLjCPVatqYfxYZ1JO+JGa4M3iXaHmsfgzL5KVfqqkyxUREREUgkWKzMnJifEv/tfcXzh3KsmJ8eb+wL5ONGnoRMLeQA7H3sba470AMOwpxJ78p5ZUFhERcQCFYJFi0OLW27m/++MAJMbHsmjeX0MkrFYLU190JivJnZSTvizb/yQJaYEApCb8yMX45Q6pWUREpCJTCBYpJqOfmY6buwcAy5e8w4nwA+axW1o60TvEieTDlUlO8mfJnhfMY3En/4UtKz7X9UREROT6UQgWKSZVb6hF/1FPA2Cz2XjjpadzDHV4arwz/j5OJO4PZFvk/eyMuju7beZZ4k897YiSRUREKiyFYJFi1HfEBKpWrw1A2B+r+X3Vd+axSn4WnpvgTNo5T1LPefHR7he5mOkNwIWYD0lN+NkhNYuIiFRECsEixcjdw5Mnnp9t7s+b8QxpqX8tk/xoNyfuuj177uDYCzfw+Z9/9QDHnBiD3ZZUovWKiIhUVArBIsUs+IFHaX3nPQCcjTjJJ+//NXOExWJhyotWXLGSfKgy60/2ZN/52wGwZZwm/vQLeV5TREREipdCsEgxs1gs/Pv/XsfZagXg0/f/S8TJo+bx2jUt/GusMyknfcmId2fRrlDSs7IfqEs+/y6pSb85omwREZEKRSFY5DqoU78JvYeMByAjI515M57JcXxwfyeaN3UiYU8Q0ck1Wbb/3+ax2OOjsNtSSrJcERGRCkchWOQ6GTLuBQKrVgdgw68/snHtT+Yxq9XC9P9zxkhzJTncnzXH+nI49lYAstKPkXDm/xxSs4iISEWhECxynXh6+zDuuVnm/hvTnyI9Pc3cb97UiaEDnLhwrBIZie4s3DGNDJsbAEnn3iDtwuYSr1lERKSiUAgWuY7u7dabW9p2BCDi1DE+X/B6juP/HONMrRoW4vcGcTa5Ll8fGPe/Iwaxx0Zit6chIiIixU8hWOQ6slgsPDl5Ds7OzgB8/O7LnI04aR738LAw9UUrWUluXDhWiZ+PDuJYfAsAMtMOkBgxPc/rioiISNEoBItcZzc2ak7IwH8AkJ6Wypszn8tx/K7bnejR3Ynk8EqkJ3uwcMc0suzZM0skRr1CesqOEq9ZRESkvFMIFikBw8ZPonJgVQDW/fw1W37/JcfxiU85U9kve7aIM0kN+e7QmP8dsRFzbBiGPb2EKxYRESnfFIJFSoC3jx9jn51h7r8e+i/S01LNff9KFl581pnMBHdSTvjy4+HhnExoAkBm6p/ER4SWdMkiIiLlmkKwSAl54NH+OR6S+/jd2TmOd33Qibs7WEg+XJn0FA/m75hhDotIinqVtOSNJV6ziIhIeaUQLFJCLBYLT015A6uLCwCfvP8KJ48ezHF86otWvD2cSNgbyJmkRnx94In/HbUTc2yYFtEQEREpJgrBIiWoboOm9B0xAYCszExeC/0XhmGYx6tVtfDC085kxHqScsqHn44MJTzu5uz26eHEn37BIXWLiIiUNwrBIiVs0NiJ3FCzLgA7Nv/GL999luN4yCNOdLjLQtKBADIvujF/+0ukZ7kDkHz+LVIT15R0ySIiIuWOQrBICXP38OTJyXPM/TdnPEtyYry5b7FYmD7JipdH9mwR51Lqsmz/k+bxmOMjsWcllmTJIiIi5Y5CsIgD3HH3Q9z9YAgACXHRvPffF3Mcv6GahYkTnMmI8+DCCV/WHOvL/uh2ANgyThF36qkSr1lERKQ8UQgWcZDx//kvHl7eAHz7+QL+3Lk5x/FePZy463YLyYcqk5niysId00jN9ALgQsxiLsZ/X+I1i4iIlBcKwSIOElStBiP+Ndncf2XSOLIyM819i8XCS5OteLo7kbCnCjEXq/Pp3r9Wm4s5MQZbZmyJ1iwiIlJeKASLOFDIwH/QsGn27A/HDv3Jsg/n5The/QYLzz3pTEa8OynH/fj9VA92nQ0GwJ55jtiTT+SYXUJEREQKRiFYxIGsVitPTX0Ti8UCwAdvTCPqzIkcbfr0dOLOdhaSDvuTmezKop2hXMjwA+Bi3DJSYj8t6bJFRETKPIVgEQdrfktbHu03CoC01Iv89/9y9u5aLBam/58VT7fsYREJqUF8uGuSeTz25D/JTD9R0mWLiIiUaQrBIqXA6KemE1S1BgBbf1+Va+7gmjUsPPukM5mJblw4WomwyAfZcKo7AIYtiZijgzEMW4nXLSIiUlYpBIuUAl4+vkyYMtfcn/fS08THRudo07fX/2aLCPcnM8mVj/f8h+iU7OCcfmEDiVGzS7RmERGRskwhWKSUaH9vdzo91BOAxPhY3pz5bI7jFouFmVOs+HpbiN9dhdR0b97fPhO7kf1rnBAxhfQL20q8bhERkbJIIVikFPnXpNfw9q0EwC/ffsqW33/JcbxaVQuTn3cmK9mVpMOVORJ3Gz8cHpF90Mgi+tgg7LaUEq5aRESk7FEIFilFAoKq8cTEl839/056gospF3K06faQEw91diLluB/pMe58e3Asx+JbAJCVdpi4U0+XaM0iIiJlkUKwSCnT5bHB3Hb73QCcjTjJwrlTchy3WCyEvuBMUKCF+D1VyMxw471ts0jP8gDgQvR8rSYnIiKSD4VgkVLGYrHw9LQ3cXVzB+DLD9/kwJ6cY339K2WvJmdPs5LwZyDnUury6d6/xhDHHB9JVsbZEq1bRESkLFEIFimFatVtyLB/Zs8FbLfbefk/Y3IsqQxwdwcn+vR0Ii3Km4sR3qw7+Rg7ou7JPicrhtjjw7WanIiIyBUoBIuUUn2G/YsGTVoCcPTgXj55/7+52kx8yplaNSFxXyBZqS4s2hlKYloAAKmJP5N87q0SrVlERKSsUAgWKaWsLi48N+NdnJyyf00Xv/USxw7vy9HGy9PC7GlWsDmRsCuIpPTKLNgx3Twed/pZ0lN2lWTZIiIiZYJCsEgp1uSmVvQd+RQAWZmZzHhuBFlZWTnatLrViRFDnMiI9+DCMT/2nu/AL0cHZB80Mog+2l/TpomIiPyNQrBIKTf0ny9Sp34TAA79uYPPFryWq82/xjrTuJGF5MOVyUx05Yt9EziR0BSArLRDxJ38V4nWLCIiUtopBIuUcm5u7jw/a745LGLRG9M4fuRAjjaurhZeneGMm2v2anKZmW68E/YKaZemTYtZzIWYz0q8dhERkdJKIVikDGh+S1seH/4kAJmZGcycODLXsIhGDZx47klnsi64kngggHMpdflo9yTzeOyJf5CZFl6idYuIiJRWCsEiZcSwf/0fdW5sDMCBPWEs/WBOrjb9+zjRqaOFi6d8SD3rycbTD7PhVHcADHsy0UcHYNgzSrJsERGRUkkhWKSM+PuwiA/mTuVEeM5hERaLhRmhVgIDLCTsDSIr1ZmP97zI2Qt1AMhI2Ub8mf+UeO0iIiKljUKwSBnS/NZ29B6a/ZBbRkY6MyeOwmaz5WgTUNnCy9OsGJnOJOyqQmqmF++EvUKW3QpA0tnXuZiwosRrFxERKU0UgkXKmBH/nkyteg0B2L97K18smpurTYc7nRjSP3vatOQj/pxMbMbSP58yj8ccG0ZWRlSJ1SwiIlLaKASLlDFu7h5MnPk+FosFgAWvh3Ly6MFc7Z4anz1t2oXwSqTHurPq2AB2nQ0GwJ4VTcyxQRiGLdd5IiIiFYFCsEgZ1LLVnfQa8k8ge1jErOdH5xoW4eZm4fWZVtzcsqdNs2c4s2DHdOJTqwCQlrSWxMiXSrx2ERGR0kAhWKSMGvnkFGrWqQ/Anzs38+n8V3O1aVDfwsQJztjTrCTsCeJChj/vbnsZu5H9q58QMY3UxFUlWreIiEhpoBAsUka5e3jywuyFf80W8cZUjuzfnatdv95O3BNsIe28FyknfDkU24av9v/zf0cNoo8OIisjogQrFxERcTyFYJEy7Kbb7qDfqKcByMrMZNrTQ0hPT8vRxmKxMGOylSpBkHiwMplJrvx0ZDi7z3YAsscHR4f3w7Bnlnj9IiIijqIQLFLGDfvnJBo2vRmA40f2s+D1ybnaVK5s4bWZVpxwIn5nFWxZzry/fSYxF28AIP3CBuIjJuU6T0REpLxSCBYp41xcXXnxv4twcXEFYOkHc9m5ZX2udm1bO/HPMc5kpbiS+GcgKZmVeDvs1b/mD476Lxfjvy/R2kVERBxFIVikHLixUXNGPTUVAMMwmPHcCFKSk3K1GzPcidvbWkiN9CHllA/H4lv+bf7goWSmnyipskVERBxGIViknOg99F/c0rYjAGcjTjJ3+lO52jg7W3h1hpWAypC4P4DMJFdWHRtAWMT9ANhtCUSHP45hTy/R2kVEREqaQrBIOeHk5MR/Zi/A08sHgBXLP2L9qm9ztQsKtPDfGVYshhNxO6piz3Lig51TOXuhNgAZKduIO/VsidYuIiJS0hSCRcqRajXq8K9Jr5n7s//zD+JizuVqd9ftTowZ7oTtogsJe4JIzfLh7bDXyLRljytOPv8WKbFflFjdIiIiJU0hWKSceShkIB3ufxiAxPgYZv9nLIZh5Gr3zzHOtLrVQtpZby6c8OVUYhOW7HnBPB5zfCQZF/8ssbpFRERKkkKwSDljsVh4Ztrb+AdkL4+84dcf+f6LD3K1s1otvD7LSqVKkHQwgIwEN9ad7Mkfpx4BwLCncP5IL2xZCSVYvYiISMlQCBYph/wDgnjupXfM/XkvPc3JowdztatW1cLLU61gtxC/swr2TGc+3DWJEwlNAchKP0LMsSEYhr3EahcRESkJCsEi5dRd93bjkb4jAUhLvciUJweRkZ571odOHZ0YMdgJW6oLCbuDyLS78+bW17mQ4QdAasIPJEbOLNHaRURErjeFYJFy7InnZ1OnfhMAjhzYzfuv5b0q3IR/OtOmlYW0815cOOpHzMWavBM2G7thASAhIpSLCStKrG4REZHrTSFYpBxz9/Ak9PWPc6wmt+X3X3K1uzQ+ODAAkg5XJj3GnX3Rd/HV/vH/a2EQc3QgmWlHS7B6ERGR60chWKSca9C0JWOfnWHuz3h2BPGx53O1qxJkYe5sK85OFuJ3VcWW6syPR0awPfIeIHshjfNHemG3XSyx2kVERK4XhWCRCuCxwU9we/ADAMTFnGPGcyPynDatTSsnnvqnM/YMZ+J2VMWwW5i/YwZRyXUByEzdQ+yJ0XmeKyIiUpYoBItUABaLhRdeXkDlwKoAbF73M1999FaebYcPduK+ThYyE91J3BdIWpY387bOJS3LE4CU2M9IPvdmidUuIiJyPSgEi1QQ/gFVeOHl+eb+2y8/T/jBvbnaWSzZ06bVqQUXT/tw8bQ3kcn1WbDjJbNN3KmnSU36rSTKFhERuS4UgkUqkHYdH6D30OyH3TIzMwj99wDSUnOP8fXxsfDGf624uVlI2BdIRqIr2yLv58fDw//XwkZ0eB8y04+XYPUiIiLFRyFYpIIZ/dR0Gja9GYCTRw/y5oxn8mzXtLETU/7jDHYn4ndUxZ7pxJf7x7PnXHsA7FmxnD8cgt12ocRqFxERKS4KwSIVjKubG5Nf/wg3dw8Avv18Ab/+9GWebUMedqZ3SPZCGvE7q2AYzry7bTZRF+oCkJm6VyvKiYhImaQQLFIB1anfhH9Nes3cf/mFMZw5GZ5n20nPOXNTcwvpMZ4kHfbnYqYvczfPIzXTG4CL8d+QEDGtROoWEREpLgrBIhVUt15Duf/hvgBcTEnm/8b3Jz09LVc7NzcLb71mJaAyXDhaidQoL85eqMfbYa+YK8olRk4jJW55idYvIiJSFArBIhWUxWLh6alvUvvGRgAc2b+Lt2Y+m2fbalWzH5SzWi0k7AkiM8mVvec7sGzfk2abmGNDyLi4u0RqFxERKSqFYJEKzNPLmylzP8XVzR2Arz95j7UrvsqzbZvbnHjhaWcMmxNx26tiz3BiRfhQNp7uBoBhv8i5wyHYMqNLrH4REZFrpRAsUsE1aHIT//6/1839Wc+PvuL44P59nOj5SPaDcnE7qoJhYdHOUI7FtwDAlnGS8+G9MewZJVK7iIjItVIIFpECjw+2WCyEvuBMyxYWMuI8SNgXSKbdnTe2zCUhLQiA9OTfiT35Ly2tLCIipVqxhOCNGzcyevRomjVrhp+fH76+vjRr1oxRo0axYcOG4rhFDnXr1sVisVzz1+LFi4u9JpGyLK/xwW/Pei7Ptm5uFt58NftBuYunfEg55UNCWlXmbZlDlt0FgAvR80k6N7fE6hcRESmsIoXglJQUhg8fzl133cX777/PgQMHSEpKIjk5mQMHDjB//nzat2/PsGHDSElJKa6aiywgIMDRJYiUOn8fH7x8ybtXHB98+YNyifsCSY9z52j8zSzc8ddUafGnnuFi/PclUruIiEhhWYxr/MzSZrPRpUsXfvnlF/M1Dw8PmjdvjtVqZf/+/SQlJZnHOnfuzE8//YSzs3ORix48eDDnzp0rcPvNmzeTmJgIQOXKlYmKisLV1bVQ90xKSsLPz4/ExER8fX0Lda5IWfL9Fx8w+z9jAfD08mHht5upWadBnm0/WWpjykwbTq42gtqfwdndRo8mb/JIk3cBsDh5Ua3pOty8bimp8kVEpAIrTF675hD8wgsvMHPmTHN/5MiRzJo1i8qVKwPZvcQvv/wy06ZNy3HOSy+9dC23u2aJiYlUq1aNtLTs8Y3/+Mc/eOuttwp9HYVgqSgMw2Da00NZ9d1nANRvchPvfrEedw/PPNv+Z6qNL7+24+KbTuAdkVic7Yxt/Qztaq4EwNmlBjc034TVtXqJvg8REal4CpPXrmk4RGRkJK+//tfT5AMHDuT99983AzCAl5cXU6dO5cUXXzRfe+2114iMjLyWW16zL774wgzAAEOGDCnR+4uUNZfGB9e5sTEARw/u5ZVJ4/J80O3Sg3Ktb7OQmeRG/O4gwMKCHdMJj7sZAFtmBOcP98BuKz1DokRERK4pBM+ZM8cMlp6ensyZM+eKbSdNmkStWrUASEtLY+7ckn1Y5sMPPzS3mzZtSps2bUr0/iJlkaeXN9PfWoqHV/bSyL98+ynffPpenm1dXSy8+V8rNatD2llvkg75mzNGxFzM7v3NuLidmGNDMAx7ib0HERGRq7mmEPz111+b2717987RA/x3rq6uDB061NxfvrzkllYNDw/PMTvF4MGDS+zeImVd3QZNeX7m++b+Gy89zb6dW/JsW7myhXffsOLlmb208sUz3iSlB/L6prdIy/IC4GL818Sf+U+J1C4iIpKfQofgQ4cOER7+10T6Dz74YL7nPPTQQ+Z2eHg4hw4dKuxtr8lHH31kbjs5OTFw4MASua9IedHpoZ48PvzfAGRlZjLpn32Jjz2fZ9tGDZx4bZYVi8VCwp9BpMe5E5HckLe2vordyP5PTVLUKyRHLyqp8kVERK6o0CF49+7dOfbvuOOOfM+57bbbcszGsGfPnsLettAMw+Djjz829++///7/b+++w6OoFjaAv7N90za9kUDohN57EUMVRETAgojYkM9ybSioCCoWUBEERUApKgJSpYP03kuAFAgklPTe2+7O98eGSRbSNskSQt7f8+zDzuw5Z85472TfTM6cA29vPphDZKkJH3yFNp16AQDiYyMx/Z2x0Ov1xZbt21uGD9+RA0YByWc8YMhS4GJcT6wInCyVSYyYiOzU3fel70RERCWxOAQHBwdL71UqlTTetzR3lyvahrUcOHAAERER0jYfiCOqGIVCgc/n/gUXdy8AwNnj+7F49mclln/pBdPSysZ8ORJPeULUy7An/Dnsvv6cqYCoR9zVUcjLsv4vw0RERCWxOAQXDZY+Pj4QBKFc9erWrVtsG9ZS9IE4nU6H4cOHW1Q/NzcXaWlpZi+i2srFzRNf/PQ35AoFAODvxT9g/44NxZYVBAGffyJHh3YC9JkqJJ5xB0Tg74sf4lz0IwAA0ZiO2NDHoc+7fb9OgYiIyIzFITg9PV16r9Ppyl2v6FxtRduwhszMTKxdu1baHj16NDQajUVtfPPNN9DpdNKrPHe8iR5mrTt0x5tTZknb30x+FTeuhRRbVqUyLa1cxwvIS7RBymVXGEUFFpyehevJLQGYpk6LDR0Koz71vvSfiIioKItDcEZGhvTekmCp1WqLbcMa1q9fb3aMigyFmDJlClJTU6XXrVu3qrCHRDXTU2P/D/0ffwYAkJWZjk/eeBqZ6cX/lcSlyIwRWTcdkBHugDyDDX489jPis0y/VOZnX0Jc2CiIxrz7dg5ERERABUJw0QdiFAV/Gi2PomXz8/MtPaxFig6FaNSoEbp3725xG2q1Gg4ODmYvotpOEARMmvELGjRpAQC4cS0EMyaNh9FY/Py/TRvLMPc7BeRyIC3YBdkxNkjPc8H3R35FZp4jACAnbS8Swl8rdjEOIiIia7E4BNvYFC6dWnQltrIULWtra2vpYcvt1q1b2Ldvn7TNuYGJqpbWxhZf/fIP7BwcAQCH92zBkp++LLF87x4yTJsiByAg5bw78lLUiM2sh9nH5iPfqAYAZCb+hZTIkh+2IyIiqmoWh2A7OzvpfXZ2drnrZWVlFdtGVfvzzz+lu1KCIOCFF16w2rGIaiufeo3w+Zy/IJOZfoQs//lr7Nte8kI4z4yU45VxMohGGZJOe8KQrcC15LZYcGomRNH0cG1q1DdIj1t8X/pPRERkcQh2dXWV3kdHR5e7XkxMjPTexcXF0sOWW9EFMvr27Ws2KwURVZ3Ovfpj4kffSNtff/QywkIullj+g//JMaifAGOeHAknvCDmy3A2uh9WXCw6h/AbyEreYtV+ExERARUIwU2bNpXeJyYmmt3hLU3RB8uaNWtm6WHL5fjx42ar0XEoBJF1PT3+fxg4fAwAICc7Cx9PfAopSQnFlpXJBMyaoUC71gIMWUoknPIEjAJ2Xx+D7VfvXKtGxF97FjnpR+/TGRARUW1lcQj29/c32z5//nyZdSIjIxEfH19iG1Wl6ANxdnZ2eOqpp6xyHCIyEQQBk778Gf6tOwIAom/fwLT/jYG+hIdfNRoBC+Yo4OsD5KdokHTONIfwP5ffx4nbpiXYRWM24q48gbysy/ftPIiIqPaxOAR37twZarVa2j58+HCZdQ4dOiS912g06Ny5s6WHLVNubi5Wr14tbY8cOdKqD+ARkYlao8VXP/8DZzdPAKYV5eZ9M6nE8s7OAhbPV0LnAOTE2iI1yAUiZFh89mtcju8KADAakhEb+hj0uTfuyzkQEVHtU6EH4wICAqTtFStWlFmnaJmAgACrhNNNmzYhOTlZ2uZQCKL7x82zDr76eTWUShUAYP2fC7D5nyUllm/gJ+Dn2QooFUDmDR0yruugN6ow78RcRKSYpl8z5EciJvQxGPKLH15BRERUGRaHYMB88YnAwEBs3ry5xLJnz57F9u3bi61blYoOhfDz80OfPn2schwiKl7Ldl3x/hfzpe3Z099G4JmSx/Z27ijDt1/IAQBpIc7IjrJFjt4WPxxdgNhMPwCAPicUsVeGwmiw7gI7RERU+1QoBI8cORJt2rSRtidMmICQkHuXT42Ojsbzzz8Pg8EAAGjbtm2J43QjIiIgCIL0mj59ern7Exsbi507d0rbL7zwAgRBKHd9IqoaQ0aOw8gX3gAA6PPz8cn/jUbUrfASyz/+mBwfvWuaQzg50B25iRqk5znjuyMLkZrrDgDIyzyNuKsjuaocERFVqQqFYEEQsHjxYmkp5OjoaHTp0gWTJ0/Gtm3bsGvXLsyYMQPt2rVDcHAwANOyyYsWLbJKOF2xYoXZSnacG5io+rwxeSY6dO8LAEhJisdHrz2JjPTUEsu/9IIMLz4vA4wCkk57Qp+mQkJWHcw6vBDZetNKjTlpu5FwfTxEsfiV6YiIiCxVoRAMAJ06dcJff/0lBeG0tDTMnDkTQ4YMwcCBAzF16lTExsYCMAXgv/76C506daqaXt+l6FCIXr16oWHDhlY5DhGVTaFU4sufVqJugyYAgIiwYHz29nNmv6gWJQgCJr8nx5BBMogGGRJOesGYrUBkemP8cLTIqnJJq5F0410ur0xERFWiwiEYAEaMGIEzZ86gX79+xd7hFQQBAQEBOH36NEaMGFGZQ5Xo/PnzCAwMlLb5QBxR9bPXOWHWoo3QOZkWxjl1eDd+mvFeiQFWJhMw8ws5unUxLaYRf9wLyJcjLKk95p/4AUbRNHY4Pe5npER+ft/Og4iIHl6CWEW3VW7duoUjR44gMjISAFCnTh306NEDvr6+VdF8tUtLS4NOp0NqaiocHByquztENcL5k4fw7ouDpXmD/zd1tjRmuDgZGSLGvKxHcKgIhX0u3LtHA3Ijevj+i1c7fCKVc/KdCZ3X+1bvPxER1SyW5LUqC8EPO4ZgoorZseEvfPXhywAAmUyGbxeuR7dHBpdYPi5exDPj8nE7ClA5ZcO1awwgiOjf4E+MaT1TKufi9wvs3V+zev+JiKjmsCSvVWo4BBFRWQY9+TzGTvwIAGA0GjH9nbG4FnqpxPLubgJ+X6CEkxOQl6xF0hnTqnL/XR+LdUFvSeUSI95ARsLfVu8/ERE9nBiCicjqXnlnOh4ZZHouICszHR+99iSSEmJLLF+/noBFPymg1QA5cbZIuegKANh85TVsvfJSQSkRCdfHIzP5X2t3n4iIHkIMwURkdTKZDJ/M+h3NWnUAAMRG3cSU10ciJzurxDptWsmkVeWybjsgNdgZgIA1Qe9iz/WnC0oZEB/2LLJT/7P+SRAR0UOFIZiI7guN1gbf/LoO7p4+AICgCyfxxfvjpMV0itOzuww/fKOATAZkhjsi/aojAAF/BX6Co7ceNxUS8xB39SnkpB+2/kkQEdFDgyGYiO4bV3cvzFy0ATa29gCAQ/9tKnXqNAAY1F+GL6cWTJF21QkZ4Q4QIcNvZ7/E2egAAIBozELslWHIzTxj/ZMgIqKHAkMwEd1XjfxbY8b8VZArFACA9X/9ipW/zS61zqgnC5dXTgt2QeYtexhFBX459R0ux3UHAIiGNMSGDEZe1gVrnwIRET0EGIKJ6L7r1LMfPvrqV2l7wayPsXvz6lLrvDxOjtdflgEQkHrRFTnRttAbVZh7Yi6uJpnGGhsNSYgJGYC8rIvW7D4RET0EGIKJqFoMHjEWr7wzXdr++qNXcO7EgVLrvPumHM+NMgXhpPPuyIu3QZ5Bix+O/oLryW0BAEZ9oikIZwdZr/NERFTjMQQTUbV54f8m4/GnTVOe5efn4eOJoxB+teTwKggCPpsix9BBMkAUkHDGHfpkDXL0tvjuyALcSG0NADDq4xET0h952SH35TyIiKjmYQgmomojCALemz4PXfsMAgBkpKfig5eHISE2qsQ6MpmAmV/K0aenABhliD/pCUOaGtl6e8w8/CtupbUAABjzYxEb0h/52Vfuy7kQEVHNwhBMRNVKoVDg87kr0LRlewBAXPQtTHrlCWSmp5VYR6kUMO97Bbp2FiAaZIg77glDhgpZ+Q749tAi3E5vDgAw5EcjJqQf8nPC7su5EBFRzcEQTETVzsbWDrMWb4SXjx8AICwkEB//3yjk5uaUWEejEfDrXAU6tBMg6uWIO+YFY6YKmfk6fHNwEaIymgIADPlRBUH4+v04FSIiqiEYgonogeDs6oHvf98EB0dnAMDZ4/vx5XulL6ZhoxWweJ4CrVsKEPPliDvqBTFLhcx8R3xzcDGiMxoDAAx5t01BODfifpwKERHVAAzBRPTAqNugKWYt2giN1gYAcGDXRvww7c1SF9OwsxPw+y8KNG8mwJgvR+xRLyBbifQ8Z3x98DfEZjYCABjybiI2mEGYiIhMGIKJ6IHSol0XzPh5NRRKJQBg8+olWPzjtFLr6BwELFmgQJNGAox5csQc8QJylEjPc8HXB39DfFZ9AIA+LwIxwY8iP+ea1c+DiIgebAzBRPTA6dJrAD6ZtQSCIAAA/lwwE/8s/anUOs5OApYtVKC+H2DMUyDmiBeEXCVSc13x1cHfpSBsyLtpCsKcNYKIqFZjCCaiB1K/oaPxzmc/Stvzvp6EHRtXlFrH1UXA8oVK+PoAxlwFoo94QchTICXHHTMOLC0cGpEfiZiQR7mgBhFRLcYQTEQPrBHPT8T4tz6Vtr+d/CqO7ttWah1PDwF/LFKijhdgzFEg5rA3hDwFUnNdMePAEkRlNAMAGPJjEBMcwCWWiYhqKYZgInqgjX/rU4x4/nUAgMFgwGdvP4fAM0dLrVPHW8Cfv5mCsCFHgejD3hByFUjPc8ZXB37H7fSCBTX08YgJ6YfczHNWPw8iInqwMAQT0QNNEAT8b+qPeHTIKABAbk42Pnp1OK5cPl9qPZ86piDs4226I2wKwkpk5uvw9cHFuJHaBgBg1CciNqQ/cjNOWvtUiIjoAcIQTEQPPJlMhk9nLUGnnv0AmJZXfm/8EESEBZdaTwrCdQrGCB/ygpCjRFa+A745tBDXUzoAAIyGFMSEDEROeul3mImI6OHBEExENYJSpcKM+avRsn03AEBqcgLeGTcYkTdKn+6sjreAv+4E4TzTHWFkq5Cjt8PMQwtwNakzAEA0piM2dDBy0g5Y/VyIiKj6MQQTUY1hY2uH7377F01atAMAJMZF451xgxEbdbPUet5epiDs6wPTPMKHvYBsFXINNvjuyM8ISewOABCNmYgNHYKslNIfviMiopqPIZiIahQ7ex1+WLIF9Rs3BwDERN7AO+MGIzE+ptR6d4JwXV/AmC9H9GEvIEuNPIMWPxyZh8vxvQEAopiDuKsjkJG42urnQkRE1YchmIhqHEdnV/y4fDt8/Ezz/t6OCMO74wYjJSmh1HpenoVBWMyXI/qwJ8RMNfKNasw+OhenowebCop6JFx7Hulxi6x9KkREVE0YgomoRnJx88ScP3bAs049AED41SC8/9JQZKSnllrP08MUhOv7AaLetMSyMV0Ng6jEzye+xcEbowpKikiM+D+kRM207okQEVG1YAgmohrLw8sXc5Zvh4u7FwDgyuVzmPTKE8jKzCi1nqeHgBW/K9G0iQBRL0PsUS8YUrQQIceSc59h+9WXpbIptz9B0q0pEEXRqudCRET3F0MwEdVodeo1xI/LtkPn5AoAuHT2GCZPGIHsrMxS67m6CPhzsQKtWggQDTLEHvdEfrwNAAGrL7+LtUHvSGXTor9DYsQbEEWDFc+EiIjuJ4ZgIqrx6jf2x4/LtsHOwREAcO7EAUyeMAI52Vml1nPUCVi+UIEO7QTAKCD+tAdyo+0AAFuuvILl5z+FKAoAgIz4RUi49gJEY75Vz4WIiO4PhmAieig0bt4GPyzZAls7BwDA2eP78dFrT5YZhO3sBPz+iwI9ugqAKCDxnBuyb5ra2BfxDH49PRNGUQEAyExajbirI2A0lN4mERE9+BiCieih0bxNJ8xets08CE8oOwjbaAX8OleBR/sIAAQkX3JB5jVHAMCJyMcw9/hcGEQNACA7dTtiQ/rDkF/6TBRERPRgYwgmoodK8zad8MPSrYVB+Nh+TH697KERarWAed8r8NhAGQABqaHOSAtxBgBciO2DWYcXIM9oDwDIzTyB6OA+yM+NsOapEBGRFTEEE9FDp0XbzvhhyRbY2JpC65mj+zDl9aeQm5Ndaj2lUsAPX8sxYpjpR2PGdUekBJoeuAtN7IQv9y9Fpt4dAKDPCUVMUC/kZV2w4pkQEZG1MAQT0UOpRbsumL10qxSETx/dW64gLJcL+Hq6HC88a/rxmHXbAUln3SEAuJXWDNP2/omknAYAAEN+NKKD+yI7bZ9Vz4WIiKoeQzARPbRatOuCH5ZugdbWNOPDqSN7MGXiyDKDsEwm4JMP5Xj3DTkAICfGDgknPSGDgISsOvhs73LcSm8LABANaYgNHYLMxDVWPRciIqpaDMFE9FBr2a4rflhSJAgf3o0PXx1e5oIagiBg4qtyfPGpHIIA5CbYIPawN+SiHBl5Tvhy/2IEJTxiKizmIf7ac0iLmWflsyEioqrCEExED71W7bvh+983S0Mjzh7fX64llgHgmZFyzJ2lgFIJ5KepEXXAG3K9AnkGLb4/MgdHbz9VUFJE0s13ubocEVENwRBMRLVC6w7dMbvIghqXzh7Du+MGIy0lqcy6g/rL8NvPCtjaAIYsJSL314EsRw2jqMCi09Ox5crrUtm06O+QcP0FiMZca50KERFVAYZgIqo1WrTtjLl/7ITOyQUAEHLxDN4eOwDJiXFl1u3WWYY/f1PA2Qkw5skRecALSNcCELA26E38cWEqxIIfqZmJKxETOhCG/ERrng4REVUCQzAR1SpNWrTFvBW74ezqAQC4FnIRb43pj4TYqDLrtmwuw8plStTxAkSDDFGHPWGIN4013hv+NH46PkdaVCM3/TCig3oiPyfMeidDREQVxhBMRLVO/cbNMe/v3XD39AEA3LgWgrfG9ENs1M2y69YTsGq5Ek0amZZZjj3lhuybOgDAuZhH8eWBZcg2uAEA9LlXER3UAznph613MkREVCEMwURUK9Wt3wTz/t4NL596AIDbN67hzef6IfLGtTLrergLWLFEgc4dCpdZTgs2rS4XkdISn+5egYTsJgAAoz4RMSEDkJG42mrnQkRElmMIJqJay9u3Pub9vQc+fo0AADGRN/DGcwG4FnqpzLo6BwFLFigwZFDB6nLhjkg64wEZBCRme2PqnuUIS+5hKizmIeHaGKREfc2ZI4iIHhAMwURUq3l4+WLeit3wa+QPAEiMi8abzwXg0rnjZdZVqUzLLL863vSjNCfWFrFHvKAQ5cjW2+Obg/Nx7PZIqXzK7c+QGP4qRGOedU6GiIjKjSGYiGo9V3cvzFuxG/6tOwIAMtJS8O64wThxaFeZdWUyAZP+p8C0KXLIZEB+qgaR+72h0CthEJVYeHoa1ge/I5XPSFiG2CtDYNCXPTUbERFZD0MwEREAR2dXzFm+Ax269wUA5GRnYfKEEdi7bW256o95Wo6fZyug0QCGbCVu7fWGLFMDQMCm0Ffwy6nvYRTVprbT9iH6cnfkZQdb63SIiKgMDMFERAVs7Owxa9G/6DNgOABAn5+P6e88j02rfitX/YBHZPhzsWkuYVEvx+2DXjAmmKZQOxk5CF8f+g05BtMcxfrcMEQH9UBWynarnAsREZWOIZiIqAiVWo3pc1dgyKjxAABRFPHd1Dfw18LvyvVQW5tWMvzzhxJ+dQGIAmJOuiHruhMAICypHT7ZvRLx2abxx6IhDXFXnkBq9I98YI6I6D5jCCYiuotCocBHXy3As6+8J+1b+P2n+GXmFBiNxjLr1/U1zSXcvq1pCrWUECckn3eTZo74dPdyXE7oX1DaiORbkwoemONSy0RE9wtDMBFRMQRBwP999A1e/+Arad+q33/EVx++jPy8smd3cHYS8MciBYYPNf2YzY6yR9wxLyggR67BBt8f/gHbwiZK5TMSliEmpD8M+WUv4UxERJXHEExEVIoxEz7ApBm/QCYz/bjc9e/f+PDVJ5CZnlZmXZVKwMwv5XjvLTkAIC9Zg8i93lDkqSBChn8uvYEFp76D8c5SyxlHEXW5K/KyLljvhIiICABDMBFRmYY9/TK+nL8KKrUprJ4+uhdvjemHhLjoMusKgoDXX5Zj3g8FM0fkKHFrnzeQYgMAOBE5GF8cWIYsvQcAwJB3E9FBvZCZtM56J0RERAzBRETl0bv/E5jzxw44OJqWR74afAETR/fBjWsh5ao/MECGv5co4O4GiAYZoo56IOeGIwDTUssf716J6Mw2AADRmIX4sKeRdGsKRNFglfMhIqrtGIKJiMqpVftu+GXVPnjWqQfAtMzy/z3TF4FnjparfsvmMqz9S4kW/qYH5pIuOyPlvDvkgoCUHHd8tud3nI0ZJpVPi/4OsaFDYMhPtMbpEBHVagzBREQWqNewGRb8cwCN/U13bdNSkvDuuME4+N+/5arv6SFgxRIFBgQIAICsKDvEHPaCEnLkGzX46fhXWHV5CkSYxhHnpO1G9OUuyM08Z50TIiKqpRiCiYgsdGeZ5Y7dHwUA5OXmYOqbz2Ddn7+Uq76NVsBP3ynw+sumH8H5qRrc2lMH8lw1AAE7ro7Bt4d+Q67RFQCgz4tATFAvZCSssMr5EBHVRgzBREQVYGvvgFmL/8WAJ54DABiNRsz54l3M/fI9GAxlj+OVyQS895YCc2bKodUAxlwFbu3zgiHOtMJcaGInTN61CjF3xgmLOUi4Pg6JN96FaMy33okREdUSDMFERBWkVKnw6XdLMGbCJGnf2j9+xpTXn0JWRnq52nhsoByrlivg4w3AKEPsaTekB7lCJgDJOZ74dM9SnIgaJZVPj52HmNABMOTHVvXpEBHVKgzBRESVIAgCXv9gBj76+lfIFQoAwLH92/HGs48iNvpWudrwbyrD2hVKdO1semAuPcIBcUe9oYIceqMKC05Owx+B02GECgCQm34IUZc6ISf9sJXOiojo4ccQTERUBYaOGo8flmyBnYMjACAsJBATRvZC6KWz5arv7CRgyS8KvDjG9GM5L1mDm3vqQJmnBgDsvT4SXx1YimyDJwDAkB+FmOAApEZ/D1EUq/6EiIgecgzBRERVpEO3vvj1nwPw9q0PAEiMi8abzwWUe+YIhULAx5MUmPmlHCqVaZzwjT3eMMbZAwCuJbfBR7tW4WZ614IaBiTfmoy4qyNg0Cdb45SIiB5aDMFERFWoXsNmWLj2MFp16A4AyMnOwqdvPI1Vv/9Y7ju2Tz4ux8qlCnh6ABAFxJx2Q/olV8gEAWm5rpi+91fsvD5BKp+dshnRlzohN/OMNU6JiOihxBBMRFTFHJ1dMWf5DvQf9iwAQBRF/PztZHw39f+Qn5dXrjZatZBh/d9KdGxvmk84/aYDYg97QSkqYBQVWBn4FmYfW4B80QmAaRq16KBeSItdwOERRETlwBBMRGQFKrUaU79fipfe/kzat3n1Erz74mNITowvVxuuLgKWL1Tg5RcK5xO+ubsOZBlaAEBgbC98tOsfxGS1M1UQ85B04y0kXHseRkP5ZqcgIqqtBJG3DMolLS0NOp0OqampcHBwqO7uEFEN8t+mVfh2ymvIy8sFAHh418U3C9aicfM25W5j5x4jJn+mR2YmAIhwbpYCbYNkiADkQj7GtZuD3nWXS+UVmqZwb7QKKptWVXsyREQPMEvyGu8EExFZWf9hz+CnFbvh4u4FAIiNuomJT/fB3m1ry93GwADT8IgmjUzTqCWFOCH+uBdUghwGUYklZyfh51M/Qi+aHqLT54Qi6nJXDo8gIioBQzAR0X3Qom1n/Lb+KPxbdwIA5OZkY9r/xmDx7GkwGo3laqN+PQH//KnAsCEF06glaXHzvzpQ5WoAAKci++Pj3auRkNPcVEHMRdKNtxAfNgoGfVLVnxQRUQ3GEExEdJ+4enhj3t+7MejJsdK+PxZ8i48njkRmelq52rDRCvhuhhyffyKHUgkY8xSI2OuF/NuOAIC4zLqYsutPHL5deIys5I2IutQeOemHqvR8iIhqMoZgIqL7SK3W4OOZi/HWx99BJjP9CD6ydysmjOqFWxFXy9WGIAh4dpRpGjVvLwCigPhAZySe8oRSkCHfqMZvpz/C3BPzkS86AwAMebcRExyAlMgvIIoGa50eEVGNwRBMRHSfCYKA0ePfxve/b4a9zjTF2Y1rIXjtqZ44cXBnudtp3VKGDSuVeLSPaRq13Hgb3NrjA2XB8Ihz0Y9g0s41uJ3RpaCGESmRXyAmpB/0eber9JyIiGoahmAiomrSqWc/LFp3GPUaNgMAZKSlYNIrT2DpvK/KPU7YyVHAgjkKfPyBHEoFYMxR4MZeL+TdcIQAICXHA1N3L8LWsLcgFvzIz00/hKiL7ZGVvMlap0ZE9MBjCCYiqkY+9Rph4ZpD6BkwFIBpYY0lP32Bj157Emkp5XuYTRAEvPi8HKuWK+DrA0AUkHDZGfHHvaAW5BAhx5pLE/D1wWXINngDAIyGJMRdHYGE8AkwGjKsdXpERA8shmAiompma++Ar35Zg1ff+0IaJ3z8wA688mRXXLl8vtzttGohw8aVSgweUDh7RMQuHygybQAAV5Pa44MdaxCSNECqkxH/O6IudUBOxvGqOyEiohqAi2WUExfLIKL74fSRPZj+7gtITU4AAKhUarz3+TwMGTmu3G2IoojV64yYMcsA0yrNIhwbpcG+aRIMoghAxKMNNmBM65mQI7Oglhw67ylw9P4EgkxZ1adFRHRfWJLXGILLiSGYiO6X2KibmPrWcwgOPCXte/zpl/C/qT9CrdaUu52QK0a885Ee18NN2wr7XPj2jEOukA8AcLe9ifd7fgIP7Tmpjsq2I9wa/AGltknVnAwR0X3EFeOIiGowD++6mP/3Hgx/7jVp3+bVS/DGM30RE3mj3O00a2JaZW7kcNOPen26GhG76sAYZ1pVzjSn8FJsv/YmRMgBAHmZpxF1uSPS4xZxpTkieqjxTnA58U4wEVWHHRtX4PupbyA3JxsA4ODojI+/XYweBQ/SldfO3UZ8+oUeqQVrcmg8MuHZMQF5BXMGN3AKxLs9PoG9Ilyqo3UcAhe/hVCoPKvmZIiIrIx3gomIHhKDho/Br/8cRJ26DQAAaSlJmPz6U5j/zYfINw34LZeB/WTYvEaJrp1NcwrnxNri5n+FD81dT26N97evxsnop6U62SlbEXWxNTISV/GuMBE9dHgnuJx4J5iIqlN6Wgq+nfwaDv73r7TPv3UnTJ/zJ7x965e7HaNRxNK/jJj9kwH5egAQoWuQDl3zROiNpq+Ddl77MbHTdKhkCVI9G6cn4eL3M+RK96o6JSKiKsc7wUREDxl7B0fM+Hk13vnsRyiVKgBAcOApvPxEF+zfsaHc7chkAl5+QY41fynQsAEACEi97oDIvT7Q6NUATCvNvb9jPYKSHpPqZSVvQOTF1shMWluVp0VEVG14J7iceCeYiB4UoZfOYtr/xiDy5nVp34jnX8f/TZ5p0ewR2dkiZs0xYMXqgtXpBBGuzVOgrpeMO18MXX124eUOX0EpJEr1bJxHwaXePMiVrlVxOkREVYZTpFkBQzARPUgy09Mwa+r/Ye/WNdK+xs3b4vO5f8HXr7FFbe0/ZMQnn+sRXzD6QanLgU/3eGkqNXtVIt7s9jWaOu2U6sgU7nDx+xm2zk9W/mSIiKoIQ7AVMAQT0YNGFEVsXv075s54H3m5OQAAra0d3v98HgYMexaCIJS7reQUEV98a8DWHQV3hWVGuLdKhrJOasFdYRHd6+7A+HZfQykkS/VsnZ+Bc725kCtdqu7EiIgqiCHYChiCiehBFRZyEdP/NwY3rodK+/oNfRrvff4T7B0cLWpr204Dpn9jQEqKaVvpmAPf7vHIgemusE6dgDe6zUATx91SHZnCHc71foSt82iLgjcRUVXjg3FERLVIo2atsGj9UQx6cqy0b/eW1Rj/eCecP3nIorYeGyjH1rVKPNrHFGbzUzS4vqMOxBgdBACpua74ev+P+O3cTOhFRwCAUR+HhGtjEHd1OPS5t6rqtIiIrIp3gsuJd4KJqCbYs3UNvp/6BjLSUwEAgiDg+dc/xEtvTYVCqSx3O6IoYsMmI2Z8Z0BGhmmf0jEHvj3ikSPeuSscj//r+q3ZWGFBZgcn369h7/46BIH3WYjo/uJwCCtgCCaimiI26iZmTHoZ508elPY1a9UBn81ebvFDc1HRIqZM1+PYiYKvCpkRnm2TIfNMlcp0qrMbr3T4BmpZrLRPbdcVLn4LobJpUbmTISKyAEOwFTAEE1FNYjAYsPK32fhtznQY9HoAgEZrg7c//QFDR423aOyu0Shi9TojZs0xIDPTtE/llIM6XQtnkLBRpmF8h7no5Lm6sKKghM5rMhy9J0OQqavs3IiISsIQbAUMwURUE4VcPIMv3h+HW+FXpX29+g/DpC9/hpOLZau/RceI+GyGHgcOF94VdmuRAnXdFBQsNoemLqfwVrcvYKcIl+opNf5wqb8QGvvulT4fIqLS3PcQfPToUSxfvhyHDh1CZGQkRFGEj48PevbsiXHjxqFHjx6VPUSZ8vPzsWvXLqxZswanT59GdHQ0srKy4OHhAS8vL3Ts2BF9+/ZF37594eTkZHH7DMFEVFNlZ2Vi/teTsGn179I+R2c3fPDFfPQZONyitkRRxOZtprHCd2aQUNjnwbdbPHIVuQAApSwXT7dehAC/3yFAL9W1c3sFTr5fQ65wruwpEREV676F4MzMTLz99ttYsmRJqeXGjx+PefPmwdbWtqKHKtXRo0fx2muv4fLly2WWfeONNzB//nyLj8EQTEQ13cH//sWsT/4PqckJ0r7+w57Fu5/9CHudZTcHEpNEfDnTgG07C+YVhginxmmwb5oEfcFtYV+HELzd/Qu4aQKlejKFK5x8v4Wd6wt8cI6Iqtx9mSLNYDBgxIgRZgFYq9WiY8eO6Nq1q9mBly5dihEjRsBgMFT0cCX6448/0KtXL7MA7OjoiPbt2+PRRx9Fhw4drBa+iYhqkt79n8Af286iV/9h0r7/Nq3EC0Pa4/iBHRa15eIsYM5MBX75UQF3NwAQkHxVh8jdPlDl2AAAbqU1w4c7/sSa4CkwiHYAAKM+AYnhryAmuC/ysi5V1akREVmswiF46tSp2LVrl7T96quv4vbt2zh16hSOHTuGqKgoTJ06Vfp8165d+OyzzyrX27usXbsW48ePh9FouhPRrl077Ny5E/Hx8Thz5gz27NmD06dPIy0tDSdOnMAHH3wAFxeuakREtZezqwe++vkffPLdEtjZ6wAACbFRmPTKE5j5yURkpqdZ1F6/vjJsW6fEqCdNXyeGHCUi9nogNdAdKkEGEXJsDR2D93duQlDiEKlebsYRRF3qgKSbH8JoyKi6EyQiKqcKDYeIiopCw4YNkZNjWqZz7Nix+OOPP4otO3XqVMyYMQMAoNFocO3aNXh7e1eiyyaxsbHw9/dHcrJp+c6nnnoKq1atgkKhqHTbxeFwCCJ62MRF38bMT17HyUP/Sfs869TDlG8XoX3XRyxu79hJI6bN0CPipmlbpjTAs0Mi4FwYclu6H8Hrnb+BnSJC2idX+cC57o+wcRrOFeeIqFKsPhxizpw5UgC2sbHBnDlzSiw7depU+Pr6AgBycnIwd+7cihzyHu+//74UgJs0aYIVK1ZYLQATET2M3L188P3vmzHpy5+htTENG4uJvIH/jR2I2dPfRlZGukXtdessw+Y1Srw5QQalEjDmyxF13B0Jx7ygNpoW6rgU1wPvbluH7dfehBGmadMMebcRHzYKcVeGIT/nWtWeJBFRCSoUgjds2CC9Hz16NJydS37SV6VSYfz48dL2+vXrK3JIMzExMVi9unAuylmzZkGt5hyURESWEgQBw555Bcs2n0bbzr2l/RtWLMTYx9pZPFZYrRbw9kQFtqxVomtn013dvGQtwnf6IPuaMxQyAflGNVZffB2T/9uAiLReUt3s1O2IvNgKybc+5RAJIrI6i0NwaGgowsLCpO1BgwaVWWfw4MHS+7CwMISGhlp6WDPLli2DvmDydy8vLwwdOrRS7RER1XbedRtg7p878fYn30OjNT3YFhd9C5NeeQJffjAeKUkJZbRgrn49AcsXKvDdV3I4OwEQBSSHOuL2bl8oM013neMy62L63l/wy6kfkWP0MlUU85Aa/S0iA5sjI2ElOJU9EVmLxSH4woULZtvdunUrs0779u2hUqmk7cDAwFJKl63oA3mDBg2CXC6vVHtERATIZDKMevEtLN96Fh27Pyrt3/Xv3xg7uC32bF1jUSgVBAFPDJFj579KPP2U6evGmKPAjQMeSDrtCY2gACDgZGR/vLNtI/beeBUiTN8VhvwoJFwfi5jgPsjNPFel50lEBFQgBAcHB0vvVSqVNN63NHeXK9qGpURRxJkzZ6Ttrl27AgDCw8MxZcoUtGrVCjqdDnZ2dmjQoAGeffZZrFmzRppBgoiISuftWx+zl23D5G8Wwc7BEQCQkhSP6e88j48njkR8TKRF7ekcBHw5VYFVyxRo0sg0RCInzgbXt/sg76YjZIKAHL0t/jj3P3z030ZcSw2Q6uZmHEX05c5ICJ8IQ358lZ0jEZHFITgiIkJ67+PjU+4neevWrVtsG5YKDw9HWlrhFD6NGzfGL7/8ghYtWuDbb7/FpUuXkJaWhszMTISHh2PVqlUYPXo02rdvjytXrlT4uEREtYkgCBgychz+3H4efQYMl/Yf3rMFYwe3xaZVv1l8c6F9Wxk2rFRg8vty2NoCMMqQcMkZ0fvrQJFZMAQjsy6+3DcXs48tQHp+g4KaIjLiFyMy0B9pMfMhivoSj0FEVF4Wh+D09MKnhXU6XbnrFZ2momgblkpMTDTb3rhxI9544w1kZ2cDAOrVq4dHHnkEHTt2NBuCceHCBXTr1g1BQUHlOk5ubi7S0tLMXkREtY2ruxdm/LwaX85bBWdXDwBAZkYavpv6Bt549lFcC7VswQulUsBLY+XY9a8Sw4cWzC2cqcLNA55IPOUJtWiaRSIwthfe2bYO60MmFS60YUhB0s13EHWpPbJStnO8MBFVisUhOCOj8IldjUZT7nparbbYNiyVcmex+gI//fQTAKBp06Y4ePAgIiIisG/fPpw6dQpxcXF49913pbJJSUkYOXIk8vLyyjzON998A51OJ73KM+yDiOhh9cigJ/Hn9vMYPOIFad+ls8fw8vAuWDDrY2RnZVrUnpurgFkzTEMkmjcz/UUxN94G4TvuzCIhg0FUYlPIOLy3YwvOxY2Q6uZnByHuyuOIDR2MvKzKPWNCRLWXxSH4zqwMACyal7do2fz8fEsPK8nNzb1nn7e3Nw4ePIhevXqZ7dfpdJg9ezY+/fRTaV9wcDCWL19e5nGmTJmC1NRU6XXr1q0K95mI6GHg4OiMj2cuxpw/dsC3fmMAgEGvx9+Lf8ALj7XDkb1bLW6zfVsZ1q1Q4ItP5XDUoXAWiV0+kCWb7gCn5rpi7tEv8MWBvxGb3U6qm5O2G1GXOiAh/DXo86Kq5ByJqPawOATb2NhI7+8smFEeRcva2tpaethS63711Vdwd3cvsc60adPM7uQuWbKkzOOo1Wo4ODiYvYiICOjQrS+WbT6Dl97+DCqVaY72mMgbmDxhBD75v9GIi75tUXtyuYBnRppmkXhulAyCABjzFLh9zB3xR7yhyjcd43pya3y08w8sOP09MvV3njMRkRG/BJGBzZAS+QWMBsvuSBNR7WVxCLazs5Pe3xmHWx5ZWVnFtlGZ4wOAUqnE6NGjS62jUCjwzDPPSNunT59GZiZ/UBIRVZRKrcb4tz7Bsq1nzKZTO/jfv3h+cBusXjrX7C+H5eHkKGD6JwpsWKlA+7amIRL5qRpE/OeNlEA3qCAHIODE7UH437aNWB/yAfSi6QaFaMxCSuQXiAz0R3r8MoiiocrOlYgeThaHYFdXV+l9dHR0uevFxMRI711cXCw9rMTNzc1su1mzZmZ3p0vSvn176b1er+fwBiKiKuDr1xizl23DZz8sh5OL6S9y2ZkZmP/1h3j5ic44d+KAxW02bybDyqUKzJ2lgI83AAjIum2PGzt9kRPuBLkgQG9UYVPIi3hn+1Ycuf08RJiG3Bnyo5AY/gqiLndGdspOPjxHRCWyOAQ3bdpUep+YmGh2h7c0RUNns2bNLD2spG7dumaht7Qlm4u6O3gnJydXuA9ERFRIEAT0H/YMVuwMxBPPvipNnXn9ymW8/fwATHvneYuHSAiCgMEDZNi+QYlJ/zNNqSYaZEgKdkLkbl8gwR4AkJHnhMWnJ2PyfxtxJbmfVD8/6wJirwxBbEg/5GacqLqTJaKHhsUh2N/f32z7/PnzZdaJjIxEfHzhJOd3t2EJmUxmFqKLe1CuOHePX7ZkZgsiIiqbvc4JH3wxHwv+OYhmrTpI+/duXYMxA1vhjwUzkVfOn9l3qNUCXh0vx+7NSjw7SgaZDDDmKhB10g1xh+pAlWOaeSg20w9fH5iDbw8vQXxOK6l+TvoBRAf1QNzVkcjLrvhCTUT08LE4BHfu3BlqtVraPnz4cJl1Dh06JL3XaDTo3LmzpYc106dPH+l9eHh4uercXc7Dw6NSfSAiouK1aNsZC9cexodfLYDOyTSELic7C4tnf4YXHmtboVkkXJwFfP6JApv+UaBnN9OdZn26GhF7vZB40hNqo2le+JCEzvhwxwosOP090vP9pPpZyRsRdbENEq6/Cn0uh8MRUQUfjAsIKFzScsWKFWXWKVomICCgUrNDAMCIEYXzRcbGxuLixYtl1tm1a5f03tfXF97e3pXqAxERlUwmk+Hx0S9h5X+X8NTY/4NMZvq6ibx5HZMnjMCkV57ArYirFrfbpJEMSxYosXi+Ag0LFpTLTbBB+I46SL3kCiXkECHDiduD8M62DfgzcBpyjHduehiRkbAUtwObIenmJBjyE0s8DhE9/CwOwQDw4osvSu8DAwOxefPmEsuePXsW27dvL7ZuRXXv3t1sSMQ333xTavnz58+b9eGJJ56odB+IiKhs9jonvPPZj1iy6RTadu4t7T9+YAfGPdYev373CTLTLV+Rs09PGTb/o8SXU+VwdwMAAZk3HXBzpy+ywpygEEyLbey5Pgpvb92CjaHvSjNJQMxFWsyPuB3YGCmRM2A0cEVQotpIECvw6KwoimjXrh0uXLgAAPDy8sLevXvveeAtOjoaAQEBCA42jcNq27Ytzp49Kz00UVRERATq168vbU+bNg3Tp08vsQ8bNmwwuyM8e/Zss9Xh7rh58yYCAgIQFhYGAFCpVLhy5Qrq1atX/hMGkJaWBp1Oh9TUVM4ZTERUAaIoYu+2tfj5m48QHxsp7Xd0dsPL//sMQ0e/ZNEiTHdkZ4tY/rcRi5cakF6wIKmgNMDFPwVa3zQYCr7mbJWpGNFiCfrWWwGZUPiciEzuDAev9+Hg8QZk8opP4UlE1c+SvFahEAwAp06dQp8+faS5gh0cHDBx4kT07t0bCoUCJ0+exPz58xEbGwvAtGzygQMH0KlTp2LbszQEA8DYsWPx119/SduPPvooxo4di/r16yMzMxMHDx7EggULkJZW+Fv+L7/8gokTJ1p8vgzBRERVIyszA3/+OhOrf5+D/PzCZez9GvnjjcnfokvvgcXeLClLcoqIhUsM+HOlEXcWJpVp9PBolQyZWzrufNk5aWLwbJuF6OS1HgIK5xOWKVyh85oEe/eJkMnLnnqTiB489yUEA8D69evx/PPPl7lohlarxV9//WV25/ZuFQnBeXl5GDVqFDZt2lRmXwVBwFdffYUpU6aUWbY4DMFERFUr6lY4Fv4wFXu3rjHb36lnP7wxeSYaNm1ZsXajRfy0wIANm4248w0nt82DV9tkGHWFCyV52N7As20WoY37ZggwSvtlSg84en0EO/dXIZNpK9QHIqoeluS1Co0JvmPEiBE4c+YM+vXrV+xv7YIgICAgAKdPny41AFeUSqXCv//+i0WLFpkF6Lv16tUL+/fvr3AAJiKiquftWx+fz/kLC/45gBZtu0j7Tx3ejZeGdcKsTyciMT6mlBZKaNdLwLdfmGaS6Nvb9N1kyFTh9hEPxB+pA1nmnWnV6mHO0a/w8e6NCIwfChGmssb8WCTdfA+RF5oiLfYXiEbLpnUjopqhUneCi7p16xaOHDmCyEjTOK86deqgR48e8PX1rYrmy+XUqVO4fPkyYmJioFar4eXlhV69eqFOnTqVbpt3gomIrOfOeOFfv/sEMZE3pP1aWzs8/9okjHrxLWhtKjaz0OlzRsyZb8DJM4VfdyrnbHi0SYZBWzg22Ns+DGPaLEQL1+1m9eUqXzh6T4Gd6zgIMjWI6MF134ZD1CYMwURE1pebm4O1y+bjjwXfIiszXdrv7OaJ8W9+gqGjxkOhVFrcriiKOHZSxNyfDTgXeOdrT4TKJQeebZOhVxeGYR+HUIxp+yv8nf8za0OurAOd1/uwc3uFY4aJHlAMwVbAEExEdP8kJ8ZhyU9fYtOq32A0Fo7X9anXEK+8+zn6Dn5KmnvYEqIo4uAREXN+NuBycGEYVrtmw7NtMvJVhUMf6umCMKbtr2jitNesDZnCDTrPd2Hv8Tpkcn4fED1IGIKtgCGYiOj+iwgLxuLZ03Dwv3/N9jdp0Q4T3v8SnXoW/0xKWURRxO59Iub+YsCVsNLDsJ/jJTzd+vd77gzL5I6w93gLDp5vQa5wtrgPRFT1GIKtgCGYiKj6XD5/Egu//xTnThww29++2yN4/YOv4N+6Y4XaNRpFbP/PiJ8WGBAecWevCLVbNtxbJ8OgLgzDPg5XMKrl72jtvt1sNglBZgd7j9eh83wXcqUHiKj6MARbAUMwEVH1EkURJw//h4XffYqrwRfMPntk0Ai8/L/P4NfIv0JtGwwitu8y4pfFRoRdL3Jn2D0L7i1TYNAUhmEP2wiMbLkEHT03QRD00n5B0MDO/RXoPN+DQl23Qv0gosphCLYChmAiogeD0WjE3q1rsPjHaYi6FS7tFwQB/R5/Bi+++THq1m9SwbZF7Noj4pfFBoRcMX+Azr1lCoy2hfPiu2ij8GTzpejusx4yoeg0anLYujwNndf7UNm0qVA/iKhiGIKtgCGYiOjBkp+Xhy1rlmDZ/K+RlBAr7ZfJZBjwxBi8+MYU1KnXsEJti6KIvQdE/LzIgEtBhV+TSscceLRKgdE+S9rnqInD402X4xG/fyAXzBeP0jj0h87rA2gcHq3Q2GUisgxDsBUwBBMRPZiyszKx/q8FWPnbbKQmJ0r75XI5Bj05Fi/832R4+5a8oFJpRFHEoaMifllkwNkLhV+XCvtcuLVIgeBcuAKdnSoZAxutwoBGf0MtSzZrR2XTHg5e78PW+SkIgqJCfSGisjEEWwFDMBHRgy0rIx1r//wFq37/EemphSFUrlBgyFPj8ML/TYaHd8XG6oqiiOOnTGH4xOnCr025bR6cm6ZA5ZmBO3tV8mz0qrcRw/3/gL3yllk7CpUfHLzehZ3ri5DJK7b4BxGVjCHYChiCiYhqhoz0VKxd/jNWL5mDjPRUab9CqcSQkS9izGsfwMvHr8Ltnz1vxOJlBuzZXyQMa/Lh0CgVtnXTYSyIwzJBjw5eu/FUy2XwtLlk1oZM7gx7j9dh7z4RCpVXhftCROYYgq2AIZiIqGZJT0vBP0t/wj9LfzJbfU6uUGDAsOfw/OuTKvwAHQCEXRPx+x8GbNpqRH7BJBGCwgA7vzQ4NU6DXjAUlBTRzPUUnmqxFI2dDpk3Iihh6zwaDp5vQ23bocJ9ISIThmArYAgmIqqZ0lKSsHrJXKz5Yz6yMzOk/YIgoO9jIzH29Y/QqFmrCrcfEyti2QoDVq81IvPO83IyETbe6XBtngq9Il8q6+MQiif8l6OD5zbIikyvBgBqux5w8HwbNk5PcNwwUQUxBFsBQzARUc2WlpKEtX/8jDXL5yMjLcXss54BQzF24mQ0b9Opwu2npolYucaIP/42IEF6Ps8017CrfypE2xyprJMmBv0brkZAgzVQy837IlfVhYPHG7BzewlyhVOF+0NUGzEEWwFDMBHRwyEzPQ0b/16E1UvnIjkxzuyzTj0CMHbiZLTt3KvCU5rl5orYsNmI35cbcKPIc3FKXQ6cmqZC4Vo4o4RKno1uPlsxzH8FXDRXzdoRZDawcx0HB483odQ2rVBfiGobhmArYAgmInq45GRnYcs/S7Dytx8RF3Pb7LMWbbvg2VfeQ89+j0Mul1eofYNBxP5DIpb9ddeMEtp82NVPg0O9dBiEO8svi2jhdgyPN1uBZi4H7mlL49AP9h4TYeM4hEMliErBEGwFDMFERA+nvNxc7Nj4F1Ys/M5sBToA8KnXEE+/9A4GjxgLtUZb4WMEhRixfIURW7YXeYhOboTWJx0uTdJgUBaOG/a0C8egxivQ0/dfKGTmi2/IVT6wd3sVdm4vQ6HyrHB/iB5WDMFWwBBMRPRw0+v12Lt1Df7+7QdcC7lo9pmjsxtGPD8RT46ZAEdn1wofIz5BxN//GPD3GiOSpamMRajdsuHcJBWCrjD02ihT0bveegxusgY61U3zhgQlbJ1GwN7jdajtenI1OqICDMFWwBBMRFQ7iKKIk4f/w8rfZuPM0X1mn6k1WgwZOQ5Pj/8fvOs2qPAxcnNFbNpmxLK/jLh6rchKdLZ5sK+fBlvfdBgF034BRrR0P4pBjVejuet+CIL517ZS2xL27q/DznUMZHL7CveJ6GHAEGwFDMFERLVP6OVzWPXbj9i3fS0MBoO0XyaToc/AJ/H0S++gRdvOFW5fFEUcPS7ir9UG7D0g4s43sqAwwMY3HU4N02FUFQ6VcLWJxCN+axDQYAO0ikSztgSZHexcx8Le/RWobNpUuE9ENRlDsBUwBBMR1V7RtyOwZtk8bFmzFNlZmWaf+bfuhFHj3sQjg0ZAqVJV+Bi3I0WsXGvAmg1GpKTc2StC7ZoNhwZpULpmSWUVsjx09P4PgxuvRj3d2XvaUtl2hL3by7B1eYZ3h6lWYQi2AoZgIiJKS0nCxr8XYd2fvyApIdbsMxd3Lwx/9jU88ewrcHJxr/AxcnNFbNtlxN//GHHhYuFXtEyjh61vGhwbpsMgK7wr7esQgn4N/0F33y1QyrLM2hJktrB1Hg07t5egtuvKscP00GMItgKGYCIiuiM3Nwe7N63Cmj/m3/MQnUqlRsDQpzFq3Jto3LxywxIuBRmxYrURW3YYkZtbsFMQofHIhK5hGuS6wgU4tIp0dPXZiv6NNsDb7vI9bSm1LWDn9hLsXJ6HXOlSqX4RPagYgq2AIZiIiO4miiLOnzyEtcvn4/CezTAajWaft+nYEyPHvYme/R6HQlHx+X2TU0Ss/9eIv9cYcKvIlMZy2zzY1k2Dfb0MiLLCY9fTBeGR+uvQw3cbVPJ088YEFWydnoSd20vQOPSFIMgq3C+iBw1DsBUwBBMRUWmib0dg/V+/Ysuapfcsy+zu5YthT7+MoaPHw8Wt4vP7Go0ijp8SsXqdEbv3FplzWGaExjMTDvXTze4Oq+TZ6OS9C/0arkd9xzP3tKdQ+cHWdQzsXMdCqWlU4X4RPSgYgq2AIZiIiMojOysTOzb8hXV//Iwb10PNPpMrFOjd/wkMf+41tOvSp1JjdJOSRGzYYsQ/6wwIv1HkGLZ5sPFJh84vA0Z54dhhL7vr6OO3Dn38NkOrSLqnPbVdd9i5vgBb51GQKXQV7hdRdWIItgKGYCIisoQoijh1eDfW/vEzjh/Ygbu/bus2aIInnnkVg0eMhb3OqVLHOX1WxOr1Ruz4z4i8vIIPBBEatyzY+6WbzSwhF/LRzmsfHq2/Hv6uRyEI5kM4BEEDG6cnYOs6FlpdfwhCxZaNJqoODMFWwBBMREQVFX07AptW/46ta5YhOTHO7DOVWoOAIaMx/LnX4N+6Y6XuDqekiti01YjV68wX4ZCp9bDxSYeDXzqg1kv7HTVx6OazBY/U3wwP26v3tCdXesHW5TnYub4AlU2LCveL6H5hCLYChmAiIqqs/Lw8HPzvX2z8exHOnzx4z+eNm7fF46NfQr/Hn4a9g2OFjyOKIs4Hili/yYhtO41Iz5A+gco5Bza+6bD1zoQorT4nop4uCD3r/oue9bZDq0i+p02VTXvYujwLW5fRUKjqVLhvRNbEEGwFDMFERFSVIsKC8e/Kxdix4S9kpKeafaZSa/DIoBEYOupFtO3cu1J3h3NyROzeb8SGTUYcOS7izgQWgtz0MJ2dbwaUztlSebmQjzaeB9C73ia09jgImaC/q0UBGvs+sHV5FjbOIyBXVHwoB1FVYwi2AoZgIiKyhuysTOzdugYbVy5CyMV7Z3CoU7cBhowaj8FPPg9XD+9KHSsmVsSmbUas32TA9fDC/TKNHjbeGbCvlw5BW7hMs70qCV18tqGP32b4Otw79zAEJbS6wbBzeQZax6GQyW0q1T+iymIItgKGYCIisrarQRewZe1S/LdpFdJTzYckyGQydO0zCENGvYjujzwGhVJZ4eOIoojASyI2bDItxJEmTSUsQqnLg7ZOOux8MgBF4UNznnbh6OqzDT3qboebTcQ9bQoyO9MDdS7PQOvQD4Ks4v0jqiiGYCtgCCYiovslNzcHh3b9iy1rl+LM0X33fO7s6oEBTzyLgcOfR6NmrSp5LBF79huxaZsRB4+I0N8Z/SCIULtlwaZOBrSeWUCR8cN+jpfR1WcbutfdAQdV3D1tyhSusHEaDlvnpwoW5Kj4QiFElmAItgKGYCIiqg5Rt8Kxbd0f2L7uD8TF3L7n84bNWmHQ8DHo9/gzcHX3qtSxklNE7NxtCsSnzxbGA0FhhMYjEzZ1MqB2yQYKhigLMKCZ62l09dmGzj7/QatIu6dNmcKlMBDb9+UdYrIqhmArYAgmIqLqZDAYcPrIbmxZswyH92yGPj/f7HOZTIaOPQIw8Ikx6NV/GLQ2tpU6XlS0iC07jNi83YjQK0WmW1MZoPHMgG2dTCidClenU8jy0Mr9MLr5bkU7rwNQynLuaVMmd4aN83DYOj0FjcOjDMRU5RiCrYAhmIiIHhSpyYnYu20NdmxYgaALJ+/5XGtrhz4DnsSgJ8egXZc+kMlklTrelTAjNm8zYst2IyKjC/fLNXpovDJgWycDCoc8ab9KnoU2HofQ0XsX2nkdhEqefU+bMrkzbJyegI3zCGgdAiDIVJXqIxHAEGwVDMFERPQguhl+Bbv+XYld/65A9O0b93zu7umDfo8/jX5Dn0Yj/9aVmm7NaBRx7oKIzduN2LXHiITEws/ktnmw8c6A1isTCrvCu9QqeTZaexxGJ++dBYE46552ZXInaB2HwMbpCWh1AyCTV+4uNtVeDMFWwBBMREQPMqPRiItnjmLnvyuwb9u6e+YeBkxLNQcMGY1HHxsJv0b+lTqewSDizDkR2/8zYudu80CssM2DxjMTNt6ZUNgXvUOcjVbuh9Gpzi609zpQbCAWBA00uv6mu8SOQyBXulWqn1S7MARbAUMwERHVFLm5OTi6dyt2bPgLJw7tgkF/94IXpgfqAoaMRsBjI+Fdt0Gljlc0EO/aY0R8QuFncpt8aD0zofXOgLLIkAmlLAetPA6jk/cutPM6AI0is5iWZVDb9ygIxMOg1FSun/TwYwi2AoZgIiKqiZIT43Fg5wbs2boGF04dQnFf+/6tOyFg6Cg8Ongk3DwrtyRyqYFYmw+NZya0XplQOeZK+xWyPPi7nkR7rz3o4L0PDuqEYloGlNrWsHEaBhunYVDZtKvU0A56ODEEWwFDMBER1XQJsVHYt30d9mxdg8vnT9zzuSAIaN2hBx4Z9CR6DxgOdy+fSh3PYBBx9rwpEO/eZ0RMbOFnco0eGs9MaDwzoXbKKTLtmhENnC6ivddedPDeA0+7iGLblqt8oNU9BhvHwdA4BHC1OgLAEGwVDMFERPQwiboVjn3b1mHP1n9wNfhCsWX8W3dCn4HD8cjAJ1GnXsNKHU8URVwKErF7nxG794m4eq3ItGtKA9TuWdB4ZELjlg1BXviZl911tPfai/Zee9HQObDYtgVBA43DI9A6Pgat42NQqv0q1VequRiCrYAhmIiIHlY3roVg77a12LPlH9y4HlpsmUbNWqPPwCfRZ+CTqN+4cg/VAUDEDdNKdf/tNeJcoAgpjciMULtmQ+ORBa1HFmQqg1THUROHdp770N5rL5q5nYJSllds20ptC2gdH4ON42NQ23XjinW1CEOwFTAEExHRw04URYRfDcKBnRtxYNcGXAu5WGy5eg2aovfA4egz8Ek0ad620mNz4xNE7D1gGjJx9ISIwnVARCgdc013iD2yoDSbei0Lzd1OoI3HQbTzOgBHzb3LNwMF06/pBkLrOBhaXX/Ile6V6is92BiCrYAhmIiIapvbN8JMgXjnRgQHniq2jLuXL3oGDEGPR4eibefeUKnVlTpmRqaIg4eN2HdIxIHDRqSkFH4mt82Dxj0LGvcsqJxyIEhrgIioqwtFG48DaON5EA2dAiEIxccblU07aHX9odENgMauOxfpeMgwBFsBQzAREdVmsdG3cGjXv9i/cyMCTx8udpYJra0duvQagB4BQ9GtzyDonFwqdUyDQUTgJRH7Dhqx/5CIkCLLNwsKI9SuWdC4ZUHtng25unDYhL0qCa08DqONxwG08jgKG2V6se0LMlvTWGLdAGh1A6BQN+KMEzUcQ7AVMAQTERGZJCXE4vDuzTiwayPOHT+A/Px7x+bKZDK0at8d3R99DD0DhqJug6aVPm50jOnu8P5DpmETOTl3PhGhdMiD2s10l1jpmIs7WVYu5KOR83m0dD+CVh5H4ecYVGL7CnV9aHUDoNENgNahL2Ryft/XNAzBVsAQTEREdK+sjHScPLwbR/ZuxbH925CanFhsOR+/RugZMBTd+z6GVu27Q6FUVuq4OTkiTpy+c5fYiKjows9kSgPUblmmGSdcsyFTGaXP7FWJaOl+DC3dj6Cl+1HoNMX3F4ICatsu0Dg8Cq1DX6jtunLoRA3AEGwFDMFERESlMxgMuHzuOI7s3Yoje7aUONOEja09OnZ/FF16D0CXPgPh4eVbqeOKoohr4cDho0YcOmbEqTN33SXW5ULtmg21WzZUjuZjiX0dQtHS/ShaeRxBY5ezUMryiz2GILOB2q4HtLpHobHvC5VtOwiCvFL9pqrHEGwFDMFERESWuRVxtSAQb0Xg6cMwGo3FlqvfuLkpEPceiNYdelT64brcXBGnz4k4fNSIw8dEhF41H0uscs42TcPmmg3FXTNO+Lueku4Se9lHlHgMmdwRGoc+0Dg8Co1DXyg1/hxP/ABgCLYChmAiIqKKS0tJwvEDO3H84E6cPPQfUpOLXxpZa2OLDt36onOvAejaZyC8fPwqfezYOBFHjhtx+KiIIyeMSE4u/EyuzTfdJS54yZSFQd1JEwN/txNo7nYS/q7H4WITW0zrBe0oPaFx6Gt62feGQt2QobgaMARbAUMwERFR1TAajQi9dBbHD+zEiYM7EXThZLGzTQBA3QZN0LFHADp2fxTtuvSBnb2ukscWERQi4tBREUeOmRbqMJuXWJcLtVs21C7Z90zD5mF7E83djsPf7ST8XU/AXp1S4nHkSi+o7XtBU/BSaptDKGyMrIQh2AoYgomIiKwjNTkRpw7vxolDu3Di4C4kJ5aw8IVMBv/WndCx+6Po0P1RtGjbpdJDJ3JyRJy9IOLYCSOOnxJx8bIIadSGzAiVUy7ULtlQO2ebZp0oyLECjPBxuILmbifg73YCTV1PQ6vIKvE4MoULNPY9pWCssmnDleysgCHYChiCiYiIrM9oNOJq0HmcOLgLxw/uRND5EzAYDMWW1Wht0KZTT3Ts/ig6dg9Ag6YtIZNV7m5rerppPPGxE0YcOyUitOjcxHIjVE45prvELjlQ6synYqvvdAlNXU6jqesZNHY+B60ys8TjCDJ7aOy7Q23fGxr7XlDbduTsE1WAIdgKGIKJiIjuv8z0NJw/eRCnj+7F6aN7EREWXGJZR2c3tO/2CNp36YN2XXrDt36TSo/LTUoyTcV27KQRx08aEXGz8DNBYYDK2RSK1S45UDoUzpcsE/TwdQhFM1dTKG7icgZ2qtQSjyMIGqjsOkJj1x1qu25Q23WDXOlaqb7XRgzBVsAQTEREVP0SYqNw+tg+nCkIxQmxUSWWdXbzRNvOvdCuc2+07dwL9Ro2q3QojokVcfqsEafOijh1RkTY9SJ3ipUG051ipxyonAvuFBcZPuFtfw1NXc8U3C0+DUdN8Q8H3qHQNIGmIBCr7bpBqfXnuOIyMARbAUMwERHRg0UURdy4FoLTR/fizNG9OHv8ALIyi18iGQCcXNzRrospELft3Ad+jSofipOSRZw5J+LUWSNOnzU9dHdnTLEgM0LpmGu6W+ycA6VjDmSKO7HL9KBdU9fTaOJyBk1czsLd9napxxLkOmjsukqhWG3XGTK5faX6/7BhCLYChmAiIqIHmz4/H6GXz+H8yYM4f/IgLpw+guzMjBLLOzq7FQTiXmjTsSfqN2kBubxyC2BkZJgetDt1xojT50QEXioy+4QgQulgCsWqgrvF8iKr2enUCWjofB6Nnc+hkfN5+DkGQSkvfvEOExlUNq2hsu0MtV0nqG07Fdwtrr2LeDAEWwFDMBERUc2i1+txNeg8zp04gPMnDyHw9BFkZqSVWN7WzgHN23ZGq/bd0bpjdzRv0xlaG9tK9SEnxxSET50VcfaCERcCRaRJN6tFKOzyTYHYKQdKx1woiyzeoZTlop5jEBo5n0ejgnCs0ySVejxBZge1bQeo7DpBbdsZattOkKt8as2cxQzBVsAQTEREVLMZDAaEBV/AuRMHcO7EQQSePoKM9JIfVpPL5Wjk3wat2ndD64490Kp9N7h6eFeqD0ajiOvhwNkLRpwPNAXj6+GFnwtKA1SOuVA55kDllHvPEAo3m9uFodjlPHwcrkAmlB7l5EpPqGw7SXeLVbYdIVc4Veo8HlQMwVbAEExERPRwMRgMCAsJxIWTh3Dx7DFcPHsMiXHRpdbx8qmHlu27o1X7bmjZrivqN2kBhaJy8/2mpIo4Hyji3AXT4h2BF0VkZd/5VITCPs8UjJ1yoHLMNVvqWaPIRD1dEBo4XUQDp0to4BQIF5uYMo+p0DQpCMTtobZtD5VN24difDFDsBUwBBMRET3cRFFE9O0IBJ45iktnjuLi2WO4fuVyqXU0Whs0bdEe/m06oXmbzmjRtjPcvXwq1Q+9XsSVMBHnLphegZfumpqtyN1ipWMuVLpcyMzGFsejvtOlgmBsCsc2ypIfGCxoFQpNY6ht2kFl2w4q2/ZQ2bSrcXeMGYKtgCGYiIio9klPTcalc8dNd4rPHEPQhZPIy80ptY6rhzeatzaF4uZtO6Fpyw6wsbWrVD9S00RcumwaXxx42YjASyLipRnWRMi1eikQKwted4ZRCDDC3famWSiuqwsu46E7E4W6PlQFwVht0x4q2/aQK90qdS7WxBBsBQzBRERElJ+XhytB53Hp3DEEXTiF4AsnEX37Rql1ZDIZ/Bo3N4XiNp3g36oj/Br5Q6FUVrgfoigiNg4IvGy6U3zxsmnJ5wxpMgzTQ3dKXS5UjgXB2KFw3mK5kA9fXSjq6ULg53gZ9RyD4etwBUp5XkmHlMhVPqZgbNNGeinUfg/EHMYMwVbAEExERETFSUqIRdCFkwi6cApBF04iJPBMqbNQAIBKpUbDZq3RtGU7NG3ZHk1btEf9xs0rFYyNRhERNyGF4sBLIkKuiMjNLSggE6G0NwVilS4XSoc8KOzzzIKxl/11+DkGwc8xGPV0QairC4VakV3iMe8QZPZQ2bQqEoxbQ6ltCZncpsLnUxEMwVbAEExERETlYTQaceNaKIIunERwQTi+fuUSDAZDqfVMwbgVmrQwD8ZKlarCfdHrRVyPMC3iERQs4nKIiODQIneMZSKUdnlQOphCsdIhFwqHvCJDKQzwtIuAn2MQ6jkGm/7VBUOrzCzH0WVQaBpDZdMa9u6vQuvwaIXPo7wYgq2AIZiIiIgqKjsrE1cun0PQhZMIvXQOoZfP4nZEWJn1lEoVGjZrhaYt2qFx87Zo5N8GDZq0qNT8xUajiFu3gcvBRgSFFobj5OQ7JUQobPOlUKzUmf698/CdACPcbG+jri4Uvg6hqKsLga8uFK42Jc+s4ey3HA7uYyrc5/JiCLYChmAiIiKqShnpqbgadB6hl85KwfhW+NUy6wmCAB+/RmjUrDUa+bdBo2at0KhZa7h51qnwohh3xhhfDi64axxiREioiEgp14qQawwFoTgXCvs8KO3zoLDVS23YKFPh63AFvrpQ1C141bEPg1Keh0v5BzC0R48K9c0SDMFWwBBMRERE1paZnoYrQecRevksrlw6h5BLZ8oVjAHAwdG5IBi3Nv3brDX8GvlXajhFerqI0DARV66KCL0qIvSK6d/MLNPngtwoBWKlfR4UDqZ/ZUrTXWOZoIenXQT6N+uJ8U86Vrgf5cUQbAUMwURERFQd7gTjayGBCAu5iLCQQIRfuYy8vNwy68oVCvg18keDJi3RoEkL1G/kj/qNm8PTxw8yWcVmczAaTXeI7wTi0CtGhF41PZRnSpUi5Bq9FIiV9nmYNNIDI4dWPIyXF0OwFTAEExER0YNCr9fjVvgVhAUHIizkzusikuLLXi0OMC3y4dfIH/Ubt0D9xqZgXL9JC7h7+lR4SEVWtoiwa6ZgHHJFxNUw06IfScnAtnVKNGpYsXYtwRBsBQzBRERE9KBLSogtEowv4lpIIG5cCylzZoo7bO0cTOG44K5xgyYt4Ne4OVzcPCscjhOTRDjqALmcIbhGYggmIiKimigvNxc3w68g4moQrl+9jPCrQQi/chlRt8JR3hho5+CIeg2aom6DpqZ/GzZBvQZN4e3boFJzG1c1hmArYAgmIiKih0lOdhZuXA9F+BVTML5+5TIiwoIRE1n6CnhFyRUK1KnboDAcSyG5KewdHK3X+RIwBFsBQzARERHVBlkZ6QgPC0bE1SCEh5nC8c3rVxAbddOidpxdPQpCcRMMfPJ5tGrfzUo9LsQQbAUMwURERFSbZWdl4lb4Vdy8Hoob10Olf2+FX0Vebk6pdT/7YTn6D3vG6n20JK8prN4bIiIiIqrxtDa2aNKiLZq0aGu232g0IjbqpikUX7sTjq/g5vVQJCXEAgDqNmhSDT0uHUMwEREREVWYTCaDl48fvHz80KX3QLPP0lOTcfP6Ffg1bl5NvSsZQzARERERWYW9zgkt2nWp7m4Uq2JLhRARERER1WAMwURERERU6zAEExEREVGtwxBMRERERLUOQzARERER1ToMwURERERU6zAEExEREVGtwxBMRERERLUOQzARERER1ToMwURERERU6zAEExEREVGtwxBMRERERLUOQzARERER1TpVEoKPHj2KCRMmoHnz5tDpdHBwcEDz5s3x2muv4ciRI1VxiHsIgmDx69dff7VKX4iIiIioZlFUpnJmZibefvttLFmy5J7PgoODERwcjMWLF2P8+PGYN28ebG1tK3M4IiIiIqIqUeEQbDAYMGLECOzatUvap9Vq0aJFCygUCgQFBSEtLQ0AsHTpUkRGRmLbtm2Qy+WV7/VdevfuDa1WW2a5unXrVvmxiYiIiKjmqXAInjp1qlkAfvXVV/Htt9/C2dkZgOku8cyZM/Hll18CAHbt2oXPPvsMX331VSW7fK/ly5fDz8+vytslIiIioodThcYER0VF4ccff5S2x44di0WLFkkBGABsbW3xxRdf4NNPP5X2zZ49G1FRUZXoLhERERFR5VUoBM+ZMwc5OTkAABsbG8yZM6fEslOnToWvry8AICcnB3Pnzq3IIYmIiIiIqkyFQvCGDRuk96NHjza7A3w3lUqF8ePHS9vr16+vyCGJiIiIiKqMxSE4NDQUYWFh0vagQYPKrDN48GDpfVhYGEJDQy09LBERERFRlbE4BF+4cMFsu1u3bmXWad++PVQqlbQdGBho6WGJiIiIiKqMxSE4ODhYeq9SqaTxvqW5u1zRNqrCpEmT0KJFCzg4OECr1cLHxwd9+/bF9OnTER4eXqXHIiIiIqKaz+IQHBERIb338fGBIAjlqld0jt6ibVSFtWvXIigoCOnp6cjJyUFkZCT279+Pzz//HE2aNMHrr7+O7OzsKj0mEREREdVcFs8TnJ6eLr3X6XTlrufg4FBsG1XB1dUVDRs2hJ2dHVJTUxESEoKMjAwAgF6vx8KFC3Hy5Ens27ev3H3Ozc1Fbm6utJ2amgoA0gIgRERERPRguZPTRFEss6zFIfhOuAQAjUZT7npFV3Qr2kZFNW/eHK+99hoef/xxNGjQwOwzvV6PnTt34uOPP5bGH587dw7PPPMMtm/fXq72v/nmG3z++ef37C/P8A8iIiIiqj7p6ell3vi0OATr9frCyoryVy9aNj8/39LD3uPy5culHmvIkCEICAjAyJEjsXXrVgDAjh07sHnzZjz++ONltj9lyhS899570rbRaERSUhJcXFzKPQSkMtLS0uDr64tbt26Z3UUnogcbr12imovXb80niiLS09Ph7e1dZlmLQ7CNjY30/s6CGeVRtKytra2lh60QjUaDlStXonHjxoiNjQUAzJs3r1whWK1WQ61Wm+1zdHS0RjdL5eDgwAuRqAbitUtUc/H6rdnKO/TV4gfj7OzspPeWPGyWlZVVbBvWZm9vj4kTJ0rbhw4dsii8ExEREdHDx+IQ7OrqKr2Pjo4ud72YmBjpvYuLi6WHrZS+fftK73NycnDr1q37enwiIiIierBYHIKbNm0qvU9MTDS7w1uaosGzWbNmlh62Ujw9Pc22ExIS7uvxK0KtVmPatGn3DMkgogcbr12imovXb+1icQj29/c32z5//nyZdSIjIxEfH19iG9Z2d1AvOq75QaVWqzF9+nReiEQ1DK9dopqL12/tYnEI7ty5s9n/OQ4fPlxmnUOHDknvNRoNOnfubOlhK+XumSTc3d3v6/GJiIiI6MFSoQfjAgICpO0VK1aUWadomYCAgPs2O8Qdq1atkt77+fnBy8vrvh6fiIiIiB4sFodgAHjxxRel94GBgdi8eXOJZc+ePWu2QEXRuvfDpk2bsGXLFml7+PDh9/X4RERERPTgqVAIHjlyJNq0aSNtT5gwASEhIfeUi46OxvPPPw+DwQAAaNu2LZ566qli24yIiIAgCNJr+vTpxZZLTU3FU089hTNnzpTZz5UrV+K5556Ttm1sbPDRRx+VWY+IiIiIHm4VCsGCIGDx4sXSUsjR0dHo0qULJk+ejG3btmHXrl2YMWMG2rVrh+DgYACmZZMXLVpU6dXWRFHE+vXr0bFjR/j7++Odd97BsmXLsHPnThw+fBjbt2/HrFmz0LlzZzz33HPIzMyU+rx06dJ7Zop4kBw9ehQTJkxA8+bNodPp4ODgIC0PfeTIkeruHlGNlZKSgg0bNuDtt99G79694enpCbVaDTs7O9StWxePP/445syZg+Tk5Aq1f/HiRbz33nto3bo1nJ2dYWdnh6ZNm2LMmDHYsWNHhft9/fp1fPbZZ+jQoQPc3Nyg1WrRsGFDPPnkk1i7dq3ZCp5EtU1ERARsbW3LdQOtJLx2azmxEtatWydqtVoRQKkvrVYrrlu3rtS2wsPDzepMmzat2HLJycllHu/ul729vfj3339X5lStKiMjQ3zppZfKPI/x48eLGRkZ1d1dohojODhYHDp0qKhSqcr1s8LGxkb88ccfRaPRWK728/PzxSlTpogymazUdocMGSLGxcVZ1Pc5c+aIarW61Ha7du0qXrt2rSL/aYhqvIEDB95zTZSUHe7Ga5dEURQrFYJFURSDgoLEfv36iYIg3PM/siAIYkBAgHj58uUy2ylvCM7JyRFfe+01sUWLFsUes+hLp9OJb7/9tnjjxo3KnqbV6PV6ccCAAff80tCxY0exa9euooODg9lnAwYMEPV6fXV3m6hGWLNmzT0/F+Ryudi0aVOxd+/eYo8ePURnZ+d7yrzyyivlCsJ3//KqVCrFNm3aiD169BBdXFzMPmvdurWYnp5ern5/8cUXZnVlMpnYsmVLsXfv3qKXl5fZZz4+PmJUVFRl/1MR1Sh//vlnsd/75Q3BvHZJFKsgBN9x8+ZNceXKleL3338vfv/99+LKlSvFmzdvVlXzxUpKShL37t0rrlq1Spw/f744Y8YMcfbs2eKyZcvEs2fP1oiwOGXKFLOL4tVXXxUTExOlzzMyMsSpU6ealfn444+rscdENcedEKxQKMThw4eLGzduFFNTU83KGI1GcePGjWKdOnXMrrNffvml1LYXLlxoVn7YsGHi7du3pc/z8vLEefPmiQqFQirz3HPPldnnHTt2mP2C361bNzE0NFT63GAwiKtWrRLt7OykMj169LDwvwxRzRUfHy+6urqKAER/f3/R29vbohDMa5fuqLIQTJaLjIwUNRqNdDGMHTu2xLKffvqpVE6j0YiRkZH3sadENdPGjRvFV155pVx/Dbp586bo6ekpXWeurq5iXl5esWUzMzPNyj7yyCMl/tL922+/mf117MyZMyX2wWg0im3atJHKN23aVMzMzCy27H///Wf2Rb5+/foyz5HoYfD8889L/78/cOCAWK9evXKHYF67VBRDcDWaNGmSdBHY2NiY3QG+W25urujr6yuV//DDD+9jT4lqh7vvEO3evbvYcj///LPZl2NQUFCp7Xbp0kUqP3r06BLLbd261ez4O3bsKLXdp59+WirbuXPnsk+QqIbbuXOn9P/58ePHi6IoWhSCee1SURWaHYKqxoYNG6T3o0ePhrOzc4llVSoVxo8fL22vX7/eqn0jqo0ef/xxs+3ipn4EzK+/Pn36lLkU/IQJE6T327ZtQ25ubpnt1q9fHwMGDCh3uydPnsTt27dLLU9Uk2VlZeH1118HALi6uuK7776zuA1eu1QUQ3A1CQ0NRVhYmLQ9aNCgMusMHjxYeh8WFobQ0FCr9I2otrr7F9G0tLR7ymRkZODgwYPStqXXbkZGBvbv319sua1bt0rvBw4cWOaUkr169TJbgbNofaKHzdSpUxEeHg4A+P777+Hi4mJRfV67dDeG4Gpy4cIFs+1u3bqVWad9+/ZQqVTSdmBgYJX3i6g2u3Hjhtm2u7v7PWWCgoKQn58vbZfn2vX09ISfn5+0Xdy1GxcXh5iYGIvaVSgU6NSpU6ntEj0Mzpw5g7lz5wIw3cEdN26cxW3w2qW7MQRXkzuLiACmoQ6+vr5l1rm7XNE2iKjy7h5mVNyX2d3XXcOGDcvVdtFyxV271mqXqKbT6/V45ZVXYDAYoFKp8Ouvv1aoHV67dDeG4GoSEREhvffx8Sn3Snp169Yttg0iqpzU1FTpThMAtG7dGs2bN7+nXNHrTqFQwMvLq1ztl3Xt3r2vaPnKtEtU0/3www84f/48AOCjjz5Cs2bNKtQOr126G0NwNUlPT5fe63S6ctdzcHAotg0iqpz333/f7E+aM2bMKLZc0evO3t4eMln5foyWde3eva+8Pxf4M4EeZteuXcPnn38OAGjUqBE+/vjjCrfFa5fuxhBcTTIyMqT3Go2m3PW0Wm2xbRBRxf3222/4/fffpe2nn376npki7rDWtXv3vvK2zZ8J9DCbMGECsrOzAQALFiyw6Jq7G69duhtDcDXR6/XSe4VCUe56RcsWHeBPRBVz8OBBvPHGG9J2/fr1sXDhwhLLW+vaLdquJW3zZwI9rJYuXYo9e/YAAMaMGYN+/fpVqj1eu3Q3huBqYmNjI73Pyckpd72iZYtOr0JEljt//jyGDRuGvLw8AKbZIHbs2FHqnzOtde0WbdeStvkzgR5GcXFx+OCDDwAATk5OmD17dqXb5LVLd2MIriZ2dnbS+zt/6imPrKysYtsgIsuEhoZi4MCBSE1NBWD6ot21axeaNGlSaj1rXbt37ytv2/yZQA+jt99+G0lJSQCAb7/9ttjpCi3Fa5fuxhBcTVxdXaX30dHR5a5X9MEdSycKJyKT8PBw9OvXD3FxcQBMD8ls374dbdq0KbNu0Ws3IyOj3GP5yrp2i7YLlP/nAn8m0MPm2LFjWL16NQDTNIWvvvpqlbTLa5fuxhBcTZo2bSq9T0xMNPuNsDS3bt2S3ld0mhii2uz27dsICAiQlim1sbHBli1b0KVLl3LVL3rtAsDNmzfLVa+sa9da7RLVNLGxsdL7Y8eOQSaTQRCEEl9FF7n5/PPPzT4rOvUYr126G0NwNbl7vfI7cyCWJjIyEvHx8SW2QUSli42NRb9+/aSlV9VqNTZu3IjevXuXu42KXLv5+fm4fPlyiW0AQOPGjc0elClPuwBw7ty5UtslIhNeu3Q3huBq0rlzZ6jVamn78OHDZdY5dOiQ9F6j0aBz585W6RvRwygxMRH9+vVDaGgoAECpVGLt2rXo37+/Re00aNAAPj4+0nZ5rt0zZ86Y/bWnuNCtUqnM7kaXp92YmBiEhYWV2i5RTaNUKqHT6cr9KrrYlFqtNvus6FzAvHbpbgzB1cTOzg4BAQHS9ooVK8qsU7RMQEAAnyYlKqfU1FQMHDgQly5dAgDI5XL8/fffGDp0aIXaGzZsmPR+zZo10uwSJSl67bZo0aLEZVWfeOIJ6f3u3bvN/ixcVruOjo78IqWHwpAhQ5CSklLuV9GV1yZPnlziZwCvXTLHEFyNXnzxRel9YGAgNm/eXGLZs2fPYvv27cXWJaKSZWZmYsiQIThz5gwAQCaTYfny5Rg5cmSF2yx6/SUkJJQ6r/Dt27exfPnyYuve7dlnn5X+QpSfn49Zs2aVWDYjIwM//fSTtD1mzBgolcpy9J6o9uK1S2ZEqjZGo1Fs06aNCEAEIHp5eYnBwcH3lIuKihL9/f2lcm3bthWNRmM19JioZsnJyRH79esnXTuCIIi///57lbQ9bNgwqV07Ozvx8OHD95RJTU0Ve/XqJZXz9PQUs7KySm337bfflsrL5XJx7dq195TJy8sTR44cKZXTarViZGRklZwXUU1Tr1496VqYNm1ameV57dIdgiiK4v2L3HS3U6dOoU+fPtK8gg4ODpg4cSJ69+4NhUKBkydPYv78+dKfVrRaLQ4cOIBOnTpVZ7eJaoRZs2bho48+kradnJwsGkvfv39/vP/++8V+FhERgU6dOiEhIQGAaSziyy+/jAEDBsDOzg6BgYGYN2+e9BCeTCbDxo0bS1yO+Y7k5GR06dIFV69eleo999xzGD58OJydnREaGooFCxYgMDBQqjN//nyzVe+IahM/Pz9phohp06Zh+vTppZbntUuS6k7hJIrr1q0TtVqt9JthSS+tViuuW7euurtLVGNMmzatzOuqtNe4ceNKbf/IkSOis7Nzme3I5XJx3rx55e53aGio6OvrW64+fvjhh5X8r0RUs1l6J1gUee2SCccEPwBGjBiBM2fOoF+/fmZPud4hCAICAgJw+vRpjBgxohp6SETF6d69OwIDA/HUU0+ZTZFUVKdOnXDw4EG8+eab5W63SZMmCAwMxMsvvwytVltsGX9/f/z777+YOXNmhfpOVJvx2iUA4HCIB8ytW7dw5MgRREZGAgDq1KmDHj16wNfXt5p7RkSliY+Px8GDB3H79m3k5eXB29sbHTt2vGcifUulp6dj7969uHXrFjIzM+Hl5YVWrVqhXbt2VdRzotqN127txRBMRERERLUOh0MQERERUa3DEExEREREtQ5DMBERERHVOgzBRERERFTrMAQTERERUa3DEExEREREtQ5DMBERERHVOgzBRERERFTrMAQTERERUa3DEExEREREtQ5DMBERERHVOgzBRERERFTrKKq7A0REdP+cP38eGzdulLbfeecdODo6Vlt/iIiqiyCKoljdnSAiovtj2bJlGD9+vLQdHh4OPz+/6usQEVE14XAIIiIiIqp1GIKJiIiIqNZhCCYiIiKiWochmIiIiIhqHYZgIiIiIqp1ODsEEVEtIAiCxXX27duHRx55pOo7Q0T0AOCdYCIiIiKqdbhYBhFRLSCXywEAoijCaDTes784Fbl7TERUU/BOMBFRLaDX66HX6/H777+b7Q8LC5M+u/vVp0+fauotEZH1MQQTERERUa3DEExEREREtQ5DMBERERHVOgzBRERERFTrMAQTERERUa3DEExEREREtQ5DMBERERHVOgzBRERERFTrMAQTERERUa3DEExEREREtQ5DMBFRLaJUKs22DQZDNfWEiKh6MQQTEdUi9vb2ZtvJycnV1BMiourFEExEVIv4+fmZbZ86dap6OkJEVM0EURTF6u4EERHdH3q9Hq6urkhNTQUAeHt747fffsMjjzwCrVZbzb0jIrp/eCeYiKgWUSgUGD9+vLQdFRWFxx57DDY2NrCxsYGdnZ30OnToUDX2lIjIuhiCiYhqmRkzZqBnz5737M/OzkZmZqb04kNzRPQwYwgmIqplbG1tsX//fqxatQqjR49GkyZNYG9vD5mMXwlEVHtwTDARERER1Tr8tZ+IiIiIah2GYCIiIiKqdRiCiYiIiKjWYQgmIiIiolqHIZiIiIiIah2GYCIiIiKqdRiCiYiIiKjWYQgmIiIiolqHIZiIiIiIah2GYCIiIiKqdRiCiYiIiKjWYQgmIiIiolqHIZiIiIiIah2GYCIiIiKqdRiCiYiIiKjWYQgmIiIiolqHIZiIiIiIap3/B20Sd4p65Vq5AAAAAElFTkSuQmCC", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "# Generate results for different number of lorentzians in fit:\n", + "# # Generate results for different number of lorentzians in fit:\n", "\n", - "results_spectral_fit_pk = [\n", - " generate_spectrum_results(Q, n, Nk=1, max_depth=max_depth) for n in range(1, 5)\n", - "]\n", + "# results_spectral_fit_pk = [\n", + "# generate_spectrum_results(Q, n, Nk=1, max_depth=max_depth) for n in range(1, 5)\n", + "# ]\n", "\n", - "plot_result_expectations(\n", - " [\n", - " (\n", - " result,\n", - " P11p,\n", - " \"rand\",\n", - " f\"P11 (spectral fit) $k_J$={pk + 1}\",\n", - " )\n", - " for pk, result in enumerate(results_spectral_fit_pk)\n", - " ]\n", - ");" + "# plot_result_expectations(\n", + "# [\n", + "# (\n", + "# result,\n", + "# P11p,\n", + "# \"rand\",\n", + "# f\"P11 (spectral fit) $k_J$={pk + 1}\",\n", + "# )\n", + "# for pk, result in enumerate(results_spectral_fit_pk)\n", + "# ]\n", + "# );" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "980af0cd", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting calculations for N=4, Nk=2 and max_depth=5 ... \n", - " [******* 29% ] Elapsed 107.97s / Remaining 00:00:04:24" - ] - } - ], + "outputs": [], "source": [ "# generate results for different number of Matsubara terms per Lorentzian\n", - "# for max number of Lorentzians:\n", + "#for max number of Lorentzians:\n", "\n", - "Nk_list = range(2, 4)\n", - "results_spectral_fit_nk = [\n", - " generate_spectrum_results(Q, 4, Nk=Nk, max_depth=max_depth) for Nk in Nk_list\n", - "]\n", + "# Nk_list = range(2, 4)\n", + "# results_spectral_fit_nk = [\n", + "# generate_spectrum_results(Q, 4, Nk=Nk, max_depth=max_depth) for Nk in Nk_list\n", + "# ]\n", "\n", - "plot_result_expectations(\n", - " [\n", - " (\n", - " result,\n", - " P11p,\n", - " \"rand\",\n", - " f\"P11 (spectral fit) K={nk}\",\n", - " )\n", - " for nk, result in zip(Nk_list, results_spectral_fit_nk)\n", - " ]\n", - ");" + "# plot_result_expectations(\n", + "# [\n", + "# (\n", + "# result,\n", + "# P11p,\n", + "# \"rand\",\n", + "# f\"P11 (spectral fit) K={nk+1}\",\n", + "# )\n", + "# for nk, result in zip(Nk_list, results_spectral_fit_nk)\n", + "# ]\n", + "# );" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "eb904688", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting calculations for N=4, Nk=1 and max_depth=2 ... \n", - " Total run time: 0.92s*] Elapsed 0.92s / Remaining 00:00:00:00\n", - "Starting calculations for N=4, Nk=1 and max_depth=3 ... \n", - " Total run time: 1.73s*] Elapsed 1.73s / Remaining 00:00:00:00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting calculations for N=4, Nk=1 and max_depth=4 ... \n", - " Total run time: 4.52s*] Elapsed 4.52s / Remaining 00:00:00:00\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "# Generate results for different depths:\n", + "#Generate results for different depths:\n", "\n", - "Nc_list = range(2, max_depth)\n", - "results_spectral_fit_nc = [\n", - " generate_spectrum_results(Q, 4, Nk=1, max_depth=Nc) for Nc in Nc_list\n", - "]\n", + "# Nc_list = range(2, max_depth)\n", + "# results_spectral_fit_nc = [\n", + "# generate_spectrum_results(Q, 4, Nk=1, max_depth=Nc) for Nc in Nc_list\n", + "# ]\n", "\n", - "plot_result_expectations(\n", - " [\n", - " (\n", - " result,\n", - " P11p,\n", - " \"rand\",\n", - " f\"P11 (spectral fit) $N_C={nc}$\",\n", - " )\n", - " for nc, result in zip(Nc_list, results_spectral_fit_nc)\n", - " ]\n", - ");" + "# plot_result_expectations(\n", + "# [\n", + "# (\n", + "# result,\n", + "# P11p,\n", + "# \"rand\",\n", + "# f\"P11 (spectral fit) $N_C={nc}$\",\n", + "# )\n", + "# for nc, result in zip(Nc_list, results_spectral_fit_nc)\n", + "# ]\n", + "# );" ] }, { @@ -1114,7 +1126,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "7fc617a1", "metadata": {}, "outputs": [], @@ -1249,13 +1261,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "26209a1b", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1277,7 +1289,7 @@ "id": "a72989f8", "metadata": {}, "source": [ - "## Building the HEOM bath by fitting the correlation function" + "## Obtaining an decaying exponential description via the Correlation function" ] }, { @@ -1298,7 +1310,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "217905ff", "metadata": {}, "outputs": [], @@ -1308,7 +1320,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "a861655e", "metadata": {}, "outputs": [ @@ -1322,14 +1334,14 @@ "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", " | \n", " Parameters| a | b | c | Parameters| a | b | c \n", - " 1 | 4.77e-02 |-1.63e-01 |3.06e-26 | 1 |-8.41e+00 |-3.78e-01 |1.05e-03 \n", - " 2 |-1.88e+00 |-4.65e+00 |2.64e+00 | 2 |-1.34e+01 |-1.08e+00 |2.73e-02 \n", - " 3 | 3.00e+00 |-2.89e+00 |2.96e-01 | 3 | 5.64e-01 |-4.30e+00 |3.95e+00 \n", - " 4 | 3.53e-01 |-6.27e-01 |1.27e-08 | 4 |-1.34e+01 |-2.31e+00 |2.90e-01 \n", + " 1 |-1.88e+00 |-4.65e+00 |2.64e+00 | 1 |-1.34e+01 |-1.08e+00 |2.73e-02 \n", + " 2 | 3.00e+00 |-2.88e+00 |3.05e-01 | 2 |-8.59e+00 |-3.78e-01 |1.03e-03 \n", + " 3 | 4.78e-02 |-1.63e-01 |2.98e-28 | 3 | 5.64e-01 |-4.30e+00 |3.95e+00 \n", + " 4 | 3.54e-01 |-6.27e-01 |1.71e-08 | 4 |-1.34e+01 |-2.31e+00 |2.90e-01 \n", " | \n", - "A normalized RMSE of 2.49e-06 was obtained for the the real part of |A normalized RMSE of 3.99e-06 was obtained for the the imaginary part\n", + "A normalized RMSE of 3.12e-06 was obtained for the the real part of |A normalized RMSE of 4.89e-06 was obtained for the the imaginary part\n", "the correlation function. |of the correlation function. \n", - "The current fit took 2.137432 seconds. |The current fit took 7.422126 seconds. \n", + "The current fit took 1.439287 seconds. |The current fit took 11.467839 seconds. \n", "\n" ] } @@ -1366,132 +1378,89 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "57d768ee", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2\n", - " [ 0% ] Elapsed 0.00s / Remaining 00:00:00:00" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 0.87s*] Elapsed 0.87s / Remaining 00:00:00:00[*********79%****** ] Elapsed 0.69s / Remaining 00:00:00:00\n", - "3\n", - " Total run time: 2.20s*] Elapsed 2.20s / Remaining 00:00:00:00\n", - "4\n", - " Total run time: 49.83s*] Elapsed 49.82s / Remaining 00:00:00:00\n" - ] - } - ], + "outputs": [], "source": [ - "def generate_corr_results(N, max_depth):\n", - " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - " bath_corr ,fitinfo= sd_env.approx_by_cf_fit(tlist=t,Ni_max=N,Nr_max=N,maxfev=1e8,target_rsme=None)\n", - " HEOM_corr_fit = HEOMSolver(\n", - " Hsys,\n", - " (bath_corr,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - " )\n", + "# def generate_corr_results(N, max_depth):\n", + "# tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + "# bath_corr ,fitinfo= sd_env.approx_by_cf_fit(tlist=t,Ni_max=N,Nr_max=N,maxfev=1e8,target_rsme=None)\n", + "# HEOM_corr_fit = HEOMSolver(\n", + "# Hsys,\n", + "# (bath_corr,Q),\n", + "# max_depth=max_depth,\n", + "# options=options,\n", + "# )\n", "\n", - " results_corr_fit = HEOM_corr_fit.run(rho0, tlist)\n", + "# results_corr_fit = HEOM_corr_fit.run(rho0, tlist)\n", "\n", - " return results_corr_fit\n", + "# return results_corr_fit\n", "\n", "\n", - "# Generate results for different number of exponentials in fit:\n", - "results_corr_fit_pk = [\n", - " print(f\"{i + 1}\")\n", - " or generate_corr_results(\n", - " i,\n", - " max_depth=max_depth,\n", - " )\n", - " for i in range(1, 4)\n", - "]" + "# # Generate results for different number of exponentials in fit:\n", + "# results_corr_fit_pk = [\n", + "# print(f\"{i + 1}\")\n", + "# or generate_corr_results(\n", + "# i,\n", + "# max_depth=max_depth,\n", + "# )\n", + "# for i in range(1, 4)]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "id": "91d1be7c", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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nICKitikmJkaaCsxsNiMpKQnffPMNAGuvWOVY3bKyMnz//fcArDMgVB6bk5MDd3f3Oj/WjoyMxOrVq6vNzfvmm29i7NixGDFiBJYtW4aePXsiMzMT3333HT744ANoNBqsWrUKe/bswfjx47Fy5Ur4+Phgy5Yt2Lt3L1577TWbm+IaatasWdiyZQvuvPNOPPnkkxg+fDiUSiVSUlIQHR2NqVOn2r0P5q677sKbb76J+++/H4sWLUJubi7eeOONGmd+GDhwILZt24bt27eje/fucHFxwcCBA2s87/r16zFx4kSMHz8eTz/9NFQqFTZs2IAzZ85g69atdX562xIGDhyIAwcOYPfu3QgODoZGo0GfPn2k1yunUm1IbX379sXBgwcxYcIEjBs3Dvv27WvycANvb28sX74cS5cuxZdffilNA9e1a9cmr6z25JNPwsPDA4sWLUJJSQnefffdJv0sqn7/GuLcuXM4d+4cAOu6E2VlZdLvbL9+/arNwNJixBYAQHps3Lix2c+/adMmm2tcvXq1Xse99NJL0jHdunVr0DULCwtFAGJhYWFjSm6Qi+kx4saYKPHj/TeI24/Ma/HrEVHbpNPpxHPnzok6nc7ZpTS7jRs3igDEY8eO1dk2IiLC5v+Eqo/o6GipXUJCgt12Xbp0qfM68fHxoiAI4ldffVXttXPnzon33nuv6OvrK6pUKjEsLEycN2+eWF5eLrU5ffq0OHnyZFGr1YoqlUocNGhQtf8jo6OjRQDi119/Xe0ac+fOFd3d3WuszWg0im+88YY4aNAg0cXFRfTw8BDDw8PFxYsXi5cuXbL5XkVERNgc++mnn4p9+vQR1Wq12L17d3H9+vXiJ598IgIQExISpHaJiYnirbfeKmo0GpvvWeX39fqv5eDBg+Itt9wiuru7i66uruLIkSPF3bt327Sx93Ou/D5U/fnVxN7xc+fOrfYzPXHihDhmzBjRzc1NBGDzfYiPjxcBiDt27Kj1epXnvv7nkJKSIoaHh4tdu3YVL1++XOc5aqtdFK2/22FhYWKvXr1Ek8lUr/PZA0B89NFHbfZt3bpVVCgU4vz580Wz2Vyv80RERIj9+/dvUi2VVq1aZfd3cdWqVbUeW9e/ew3Ja4Io1mNEcwNV/ati48aNdtdxb6x//etfNktWFhYWVrtRoCZvv/02nnrqKQDWrvnaJhHX6/U2k5wXFRWhc+fO9b5WU5xK2oWLV5cBAArNnngo8miLXo+I2qby8nIkJCSgW7du1W7YopZROQPD9TctUdu2YsUKfP7557h8+XKNK/NR61HXv3tFRUXQarX1ymut83a9OpSUlNhs1/cf/6qThV9/juutX78eWq1WejhyGcXewVGwVPxtIhPrHmtDRESOsX79euzbtw/Hjh1zdinUTAoKCvD+++9j3bp1DMAdTJsMwVXnQQRQ7zdt1XbGOiaSX758OQoLC6VHcnJywwttJBeVBmVma2+6m9wCg8lxU8QQEZF9AwYMwMaNG21uzqa2LSEhAcuXL8f999/fLOezWCwwmUy1PlqTysU67D3MZrOzS2wxbTIEX78oRnl5eb2Oq9qurtVg1Go1PD09bR6OZIZ1gnG5IOByRt3TjxARkWPMnj0bkydPdnYZ1EyGDBmCZ555ptlu1lu9ejWUSmWtj8TExGa5VnPo0aNHrbVGRUU5u8QW0yb7/auuQANYp3Oxt1pcVWVlZXbP0dqolJ0A8SIAIDH3MPqGNm19byIiImp5ixYtwqRJk2ptU3WaPGfbvXu3zT1Q16ttnum2rk2G4Ovnm0tPT6+22kxNqn58VZ/2zuTj3he6EmsIzi056+RqiIiIqD46derUqkJuXexNc9cRtMnhENfPS3f16tV6HVd1XG/VNctbo1CfaxOn643VV90hIiIiosZrkyG4V69eNje5nThxol7H/fXXX9Lzvn37NndZzaqL3/BrG2LtM1kQERERUcO0yRCsUqlsloY8dOhQncdkZGQgPj5e2h43blyL1NZcvNxDYLBYAAAKweDkaoiIiIjalzYZggFg6tSp0vN9+/YhMzOz1vZbtmyRnnt5ebX6EAwAeot1LXcXGWAy1z6lGxERERHVX5sNwffdd5+0trnRaMRrr71mt23l+tiVHnjgASiVyhavsakssC4CIhcEZBaec3I1RERERO1HqwrBiYmJEARBerz44ot224aGhmLx4sXS9jvvvIMdO3ZUa2c0GjF//nzp5jlXV1c899xzzV57SxBk1+YmTs3/q5aWRERERNQQTQrBCxcuhIuLS7VHQ9s01osvvohevXoBsK54MmPGDMyZMwc7duxAdHQ0/vOf/2DYsGH45ptvpGNef/31NjN1iVrpLz3PKb7oxEqIiIiI2pcmhWCj0Qi9Xl/tUZXJZKqzTWN5e3tjz5496Ny5MwDrUoVffPEFpk+fjltuuQVLlizBqVOnpPZLly7Fo48+2izXdgSNurP0vKg8yYmVEBE51qZNm2w+GVQoFAgNDcX8+fORmppq0/aFF17ApEmTEBISAkEQMG/evBrPefbsWTzyyCMYNWoU3N3dIQgCDhw40KC6jEYjwsPD8corrzTyK3OuyMhIREZGNurYDRs2YNOmTdX2V36KW9NrjvDee++hZ8+eUKlUEAQBBQUFmDdvHrp27WrTbt26ddi1a1e143/55Rd4eHhUe1/ZM2/evGZZcKum93hwcDBmzZqFS5cuNfn8lQRBwGOPPdbk80RGRmLAgAFNPk96ejpeeOEFjBo1Cn5+fvD09MSNN96IDz/80OFLNLeq4RCN0bt3b5w6dQoPPfQQXF1da2zTt29f/Pe//8Wrr77q4Oqaxse9l/Rcp09zYiVERM6xceNGxMbG4ueff8bChQuxdetW3HzzzSgtLZXavPXWW8jNzcWUKVOgUqnsnuv48ePYtWsXfHx8Gr0U7IYNG5Cfn4/HH3+8Uce3ZfZCcHBwMGJjY3HXXXc5vKYTJ07giSeewPjx47F//37ExsZCo9FgxYoV2Llzp01beyE4KioKw4cPd9pQycr3+L59+/DYY4/hu+++w9ixY5Gfn++UelraH3/8gc8//xxRUVH4/PPPsWPHDkRERGDJkiVYuHChQ2tp0opxmzZtata//Lp27QpRFBt8nJeXFz7++GO89dZb2L9/P5KTk1FaWorg4GAMHDgQQ4YMabYaHalX8ARkZFtv6LNYsp1cDRGR4w0YMADDhg0DAIwfPx5msxkvv/wydu3ahQceeAAAUFxcDJnM2qezefNmu+eaM2cO5s6dCwD45ptvsHv37gbVYjKZ8Prrr2PBggVwd3dvzJfTKEajUeopvF5ZWRnc3NwcVktN1Go1Ro4cWXfDFnD2rHVF1YULF2L48Gvz6/fo0aNB53n00Ucxc+ZMrFmzRvp02VGqvscjIyNhNpuxatUq7Nq1C/Pnz3doLY4wZswYXL582WaCgokTJ8JgMOD999/HSy+95LCfQZvvCa5Ko9Fg6tSpeOyxx/Dss8/iwQcfbLMBGAACtb2hM1v/KHCVGThNGhF1eJVhKynp2hCxygBcl/q2s+e7775Damoq5syZU+21Cxcu4L777kNgYCDUajXCwsLw4IMP2gz/O3PmDKZOnQpvb2+4uLhg8ODB+Oyzz2zOc+DAAQiCgM2bN+Of//wnQkJCoFarER8fL30Mf/r0adx6663QaDRSj7bBYMCaNWsQHh4OtVoNf39/zJ8/H9nZdXegvPTSSxgxYgR8fHzg6emJoUOH4pNPPrHplOratSvOnj2LmJgY6eP7yuEG9oZDHDp0CFFRUdBoNHBzc8Po0aOxd+9emzaVQwKio6OxZMkS+Pn5wdfXF9OmTUNaWu2fgEZGRmL27NkAgBEjRtgMhbl+OIQgCCgtLcVnn30m1V91WMjkyZPh4eGBjz76qM7vV00OHz4MPz8/TJo0yeZTisaoDMR1Tf3aWKIo4rnnnoNSqWz019sU3t7eNc7QVflHTEpKisNqaVJPMLU8g+gOV5RBJZPhcuav6NOpcR/hEVHH8dRPY5Ff3jL/gTaGt0sg3rq17kWN6qNy0SN/f/86Wja/vXv3IiAgAP369bPZf/LkSYwdOxZ+fn5YvXo1evXqhfT0dHz33XcwGAxQq9WIi4vD6NGjERAQgHfffRe+vr744osvMG/ePGRmZmLp0qU251y+fDlGjRqF//znP5DJZAgICABgDbtTpkzB4sWLsWzZMphMJlgsFkydOhUHDx7E0qVLMXr0aCQlJWHVqlWIjIzE8ePH7Q4XBKwhdvHixQgLCwMAHDlyBI8//jhSU1OxcuVKAMDOnTsxffp0aLVabNiwAQCkaUprEhMTg4kTJ+KGG27AJ598ArVajQ0bNmDy5MnYunUrZs6cadP+4Ycfxl133YUvv/wSycnJeOaZZzB79mzs37/f7jU2bNiArVu3Ys2aNdi4cSPCw8Ptvi9iY2Nxyy23YPz48VixYgUAwNPz2gxMKpVKCumrV6+2e82afPXVV3jwwQexYMECvPfee5DL5Q06/noJCQkArMM9q7JYLLBULKJVG0EQ7Nag1+sxb9487N27F7t378btt9/epFrNZnO9PsGXyWR1/hG6f/9+KBSKal93S2IIbuXUylBAtM4McTnrF4ZgIqpTfnkmcnXt4z4Cs9kMk8mE8vJyxMTEYM2aNdBoNJgyZYrDa4mNjcXQoUOr7f+///s/KBQKHD161CaEVQ7XAKyzGRkMBkRHR0sf9d55550oKCjASy+9hMWLF0Or1Urte/Toga+//rratYxGI1auXGnzMfm2bdvw448/YseOHZg2bZq0f9CgQbjpppuwadMmLFmyxO7XtXHjRum5xWJBZGQkRFHEO++8gxUrVkAQBAwZMgSurq7w9PSs19CHZcuWwdvbGwcOHJBuIps0aRIGDx6Mp59+GjNmzIAgCFL722+/3WY+/7y8PCxduhQZGRkICgqq8Rr9+vWThj1UHVJQk5EjR0Imk8Hf399u/UOHDsX69etRWlpa7+Eur776Kp5//nmsW7eu2h8y9VX1PX748GGsWbMG48aNq/YeX7BgQbVPDmoSERFR4w2feXl5mDp1KhISEnDw4EEMGjSoUfVWFRUVhZiYmDrbzZ07t9bhsz/99BM2b96MJ598Er6+vk2uq74Ygls5f81gFBdZQ3Bm0TEnV0NEbYG3S6CzS7DRlHquDywDBw7Ev//9bwQGOv5rTEtLw0033WSzr6ysDDExMXjooYdq7Z3ev38/oqKiqo11nDdvHn744QfExsba9Mrdc889ds91/Wt79uyBl5cXJk+eDJPJJO0fPHgwgoKCcODAgVpD8P79+7Fu3TocO3YMRUVFNq9lZWU1+HtdWlqK33//HUuWLLGZRUEul2POnDl49tlnERcXh/DwcOm16wPfDTfcAMA67MVeCG5uAQEBsFgsyMjIqHNMsSiKWLx4MT777DN8+eWXmDFjRqOve/17vPJm/uvHgL/44ov1muVBo9FU25eQkIBRo0bBxcUFR44cQWhoaKPrreqDDz5AcXFxne38/Pzsvvbnn39ixowZGDlyJNavX98sddUXQ3ArN7TbHBw4sR2CIEA0J0MURZu/nomIrtdcQw9ag88//xx9+/aFQqFAYGAggoODnVaLTqerNs99fn4+zGZznaEiNze3xtor563Pzc212W/v63Rzc7P5GB+wjh0tKCiwOzNGTk6O3bqOHj2KW2+9FZGRkfjoo48QGhoKlUqFXbt2Ye3atdDpdLV+XTXJz8+HKIoN+nqv7/2rHGrRmOs3VuXPtj7XNBgM2L59O/r374877rijSdetfI8XFxdj+/bt+OCDD3Dffffhhx9+sGkXFhZWr/BaU0Y4evQocnJysHbt2mYLwADQs2fPeg+HqMlff/2FiRMnolevXvj+++9rHWLTEhiCWzl/z14oNivgqTDDUwHEpx9Cr043O7ssIiKH6Nu3b60fczuSn58f8vLybPb5+PhALpfXeTOPr68v0tPTq+2vvPnr+p4ye50dNe2vvJnsxx9/rPGYmnoGK23btg1KpRJ79uyxCfg1TSVWX97e3pDJZA36eluDyp9tfWpTq9WIjo7GbbfdhgkTJuDHH3+Et7d3o65b9T1eOQPKxx9/jG+++QbTp0+X2jVlOMTMmTMRFBSE559/HhaLBS+88EKjar1eU4ZD/PXXX5gwYQK6dOmCn376yWY4kKMwBLcBLuo+gPkcAODPpE8ZgomInCA8PByXL1+22efq6oqIiAh8/fXXWLt2rd0AFRUVhZ07dyItLc1m1dLPP/8cbm5uTZpibNKkSdi2bRvMZjNGjBjRoGMrp16reiOVTqercao5tVpdr15Sd3d3jBgxAt9++y3eeOMN6aa8ygWtQkNDHXrzU6W66r9y5Qp8fX3rPfxjyJAhiImJwYQJExAZGYmff/5ZuoGxKV577TXs2LEDK1euxLRp06Re1KYMhwCsi8poNBo89dRTKC0tbZahB40dDnHixAlMmDABoaGh+Pnnnxv9B0RTMQS3AQNC78fFJOtfbUU6jgsmIqoqJiZGmgrMbDYjKSkJ33zzDQBrr1jlWN2ysjJ8//33AKwzIFQem5OTA3d39zo/1o6MjMTq1aurzc375ptvYuzYsRgxYgSWLVuGnj17IjMzE9999x0++OADaDQarFq1Cnv27MH48eOxcuVK+Pj4YMuWLdi7dy9ee+21JvWCzZo1C1u2bMGdd96JJ598EsOHD4dSqURKSgqio6MxdepU3H333TUee9ddd+HNN9/E/fffj0WLFiE3NxdvvPFGjR9LDxw4ENu2bcP27dvRvXt3uLi4YODAgTWed/369Zg4cSLGjx+Pp59+GiqVChs2bMCZM2ewdetWpwzrGzhwIA4cOIDdu3cjODgYGo0Gffr0kV4/cuQIIiIiGlRb3759cfDgQUyYMAHjxo3Dvn37mjzcwNvbG8uXL8fSpUvx5ZdfStPAde3atdoqeA315JNPwsPDA4sWLUJJSQnefffdJv0sqn7/6isuLg4TJkwAAKxduxaXLl2yWSGvR48ejpv9RaR6KSwsFAGIhYWFDr+22WwWP4vuI379ax9xe0xvMSHzqMNrIKLWR6fTiefOnRN1Op2zS2l2GzduFAGIx44dq7NtRESECKDGR3R0tNQuISHBbrsuXbrUeZ34+HhREATxq6++qvbauXPnxHvvvVf09fUVVSqVGBYWJs6bN08sLy+X2pw+fVqcPHmyqNVqRZVKJQ4aNEjcuHGjzXmio6NFAOLXX39d7Rpz584V3d3da6zNaDSKb7zxhjho0CDRxcVF9PDwEMPDw8XFixeLly5dsvleRURE2Bz76aefin369BHVarXYvXt3cf369eInn3wiAhATEhKkdomJieKtt94qajQam+9Z5ff1+q/l4MGD4i233CK6u7uLrq6u4siRI8Xdu3fbtLH3c678PlT9+dXE3vFz586t9jM9ceKEOGbMGNHNzU0EYPN9iI+PFwGIO3bsqPV6lee+/ueQkpIihoeHi127dhUvX75c5zlqq10Urb/bYWFhYq9evUSTyVSv89kDQHz00Udt9m3dulVUKBTi/PnzRbPZXK/zREREiP37929SLaJ47eu297j+fXS9uv7da0heE0SxEUu0dUBFRUXQarUoLCysdlOCI2w5/DeoLRcAAAbFjbh/1BaH10BErUt5eTkSEhLQrVu3ajdsUcuonIHh+puWqG1bsWIFPv/8c1y+fLnGlfmo9ajr372G5LV2tWJceza062LpeZnuLydWQkTUca1fvx779u3DsWMcmtZeFBQU4P3338e6desYgDsYhuA2om/IHSgxWcfteCktiEu3v4oOERG1jAEDBmDjxo3IyMhwdinUTBISErB8+XLcf//9zXI+i8UCk8lU66M1qVysw97DbDY7u8QWwxDchqjV125A+P3Ke06shIio45o9ezYmT57s7DKomQwZMgTPPPNMs92st3r1aiiVylofiYmJzXKt5tCjR49aa42Kar8r1bLfvw0Z1eMfOH5xAQDAqD/HhTOIiIhamUWLFmHSpEm1tqk6TZ6z7d69G3q93u7rtc0z3dYxBLchXQNH48B5JTzkRmiVAo4lbMXw7s3z8Q0RERE1XadOnVpVyK2LvWnuOgIOh2hjvNxGSc9PJX/ixEqIiIiI2i6G4DZmbJ9nYKmY1U4pXkW5sdTJFRERERG1PQzBbYyfthfKLNZ579zlcvx68R0nV0RERETU9jAEt0EhXndJzxOydjqxEiIiIqK2iSG4DRob/g8YLdYhEZ6yQuSUJDq3ICIiIqI2hiG4DXJ18YIBIQAApUyGA3GvOrkiIiIioraFIbiN6hs4R3qeV/irEyshImp+mzZtgiAI0kOhUCA0NBTz589HamqqTdsXXngBkyZNQkhICARBwLx582o859mzZ/HII49g1KhRcHd3hyAIOHDgQIPqMhqNCA8PxyuvvNLIr8y5IiMjERkZ2ahjN2zYgE2bNlXbn5iYCEEQanzNEd577z307NkTKpUKgiCgoKAA8+bNQ9euXW3arVu3Drt27ap2/C+//AIPD49q7yt75s2bBw8PjybXXdN7PDg4GLNmzcKlS5eafP5KgiDgsccea/J5IiMjMWDAgGaoCHj44YcxYMAAeHl5wdXVFb1798YzzzyDnJycZjl/fTEEt1FDuj+AMrN1oQxvhQmXshmEiaj92bhxI2JjY/Hzzz9j4cKF2Lp1K26++WaUll6bGeett95Cbm4upkyZApVKZfdcx48fx65du+Dj49PoVbA2bNiA/Px8PP744406vi2zF4KDg4MRGxuLu+66q/pBLezEiRN44oknMH78eOzfvx+xsbHQaDRYsWIFdu60vWfGXgiOiorC8OHD8dxzzzmoaluV7/F9+/bhsccew3fffYexY8ciPz/fKfU4QmlpKRYtWoQvv/wSe/fuxcMPP4wPP/wQERERMBgMDquDi2W0UUqVCgp5PwBnIQgCjsS/jV7+45xdFhFRsxowYACGDRsGABg/fjzMZjNefvll7Nq1Cw888AAAoLi4GDKZtU9n8+bNds81Z84czJ07FwDwzTffYPfu3Q2qxWQy4fXXX8eCBQvg7u7emC+nUYxGo9RTeL2ysjK4ubk5rJaaqNVqjBw50inXPnv2LABg4cKFGD58uLS/R48eDTrPo48+ipkzZ2LNmjXo3Llzs9ZYl6rv8cjISJjNZqxatQq7du3C/PnzHVqLo2zdutVm+5ZbboFGo8EjjzyCQ4cO4ZZbbnFIHewJbsOGdb728Yap/CxMZpMTqyEianmVYSspKUnaVxmA61LfdvZ89913SE1NxZw5c6q9duHCBdx3330IDAyEWq1GWFgYHnzwQZvlaM+cOYOpU6fC29sbLi4uGDx4MD777DOb8xw4cACCIGDz5s345z//iZCQEKjVasTHx0sfw58+fRq33norNBqN1KNtMBiwZs0ahIeHQ61Ww9/fH/Pnz0d2dnadX9dLL72EESNGwMfHB56enhg6dCg++eQTiBVz0gNA165dcfbsWcTExEgf31cON7A3HOLQoUOIioqCRqOBm5sbRo8ejb1799q0qRwSEB0djSVLlsDPzw++vr6YNm0a0tLSaq07MjISs2fPBgCMGDHCZijM9cMhBEFAaWkpPvvsM6n+qsNCJk+eDA8PD3z00Ud1fr9qcvjwYfj5+WHSpEk2n1I0RmUgzszMbNJ57BFFEc899xyUSmWjv96W4O/vDwA1/rHXUtgT3Ib1CB2LQwkqeCoM0CgEHE/ejJFd2+dfjUREABAfHw/g2n+YjrR3714EBASgX79+NvtPnjyJsWPHws/PD6tXr0avXr2Qnp6O7777DgaDAWq1GnFxcRg9ejQCAgLw7rvvwtfXF1988QXmzZuHzMxMLF261Oacy5cvx6hRo/Cf//wHMpkMAQEBAKxhd8qUKVi8eDGWLVsGk8kEi8WCqVOn4uDBg1i6dClGjx6NpKQkrFq1CpGRkTh+/DhcXV3tfl2JiYlYvHgxwsLCAABHjhzB448/jtTUVKxcuRIAsHPnTkyfPh1arRYbNmwAYO0BticmJgYTJ07EDTfcgE8++QRqtRobNmzA5MmTsXXrVsycOdOm/cMPP4y77roLX375JZKTk/HMM89g9uzZ2L9/v91rbNiwAVu3bsWaNWuwceNGhIeH231fxMbG4pZbbsH48eOxYsUKAICnp6f0ukqlkkL66tWr7V6zJl999RUefPBBLFiwAO+99x7kcnmDjr9eQkICAKB37942+y0WCywWS53HC4Jgtwa9Xo958+Zh79692L17N26//fYm1Wo2m23+WLJHJpPV+EeoyWSCXq/HiRMnsGLFCowdOxZjxoxpUk0NwRDchskVSnipx8BijgYAnE3+jCGYiPDLX/eg3ODYG0xq46LyQ9SQHY061mw2w2Qyoby8HDExMVizZg00Gg2mTJnSzFXWLTY2FkOHDq22///+7/+gUChw9OhRmxBWOVwDAF588UUYDAZER0dLH7ffeeedKCgowEsvvYTFixdDq9VK7Xv06IGvv/662rWMRiNWrlxp8zH5tm3b8OOPP2LHjh2YNm2atH/QoEG46aabsGnTJixZssTu17Vx40bpucViQWRkJERRxDvvvIMVK1ZAEAQMGTIErq6u8PT0rNfQh2XLlsHb2xsHDhyQbiKbNGkSBg8ejKeffhozZsyAIAhS+9tvvx3vvvuutJ2Xl4elS5ciIyMDQUFBNV6jX79+0rCHqkMKajJy5EjIZDL4+/vbrX/o0KFYv349SktL6z3c5dVXX8Xzzz+PdevWVftDpr6qvscPHz6MNWvWYNy4cdXe4wsWLKj2yUFNIiIiarzhMy8vD1OnTkVCQgIOHjyIQYMGNareqqKiohATE1Nnu7lz51b7pODIkSMYNWqUtH3nnXdi27ZtTf4joiEYgtu4kV2fxK/x+6EQBLhY0lBmKICbysvZZRGRE5UbcqAztMxHqY52fWAZOHAg/v3vfyMwMNDhtaSlpeGmm26y2VdWVoaYmBg89NBDtfZO79+/H1FRUdXGm86bNw8//PADYmNjbXrl7rnnHrvnuv61PXv2wMvLC5MnT4bJdG1Y3ODBgxEUFIQDBw7UGoL379+PdevW4dixYygqKrJ5LSsrq8Hf69LSUvz+++9YsmSJzSwKcrkcc+bMwbPPPou4uDiEh4dLr10f+G644QYA1mEv9kJwcwsICIDFYkFGRkadY4pFUcTixYvx2Wef4csvv8SMGTMafd3r3+N9+/bFf//732rDAl588cV6zfKg0Wiq7UtISMCoUaPg4uKCI0eOIDQ0tNH1VvXBBx+guLi4znZ+fn7V9g0cOBDHjh1DWVkZTpw4gVdeeQUTJ07E/v37HTbOnSG4jQsM6ImyC17wVBZCLZPh18tv4fa+Lzm7LCJyIhdV9f9wnKkp9Xz++efo27cvFAoFAgMDERwc3IyVNYxOp4OLi4vNvvz8fJjN5jpDRW5ubo21d+rUSXq9Kntfp5ubm83H+IB17GhBQYHdmTFqm3bq6NGjuPXWWxEZGYmPPvoIoaGhUKlU2LVrF9auXQudTlfr11WT/Px8iKLYoK/X19fXZrtyqEVjrt9YlT/b+lzTYDBg+/bt6N+/P+64444mXbfyPV5cXIzt27fjgw8+wH333YcffvjBpl1YWFi9wmvVHvZKR48eRU5ODtauXdtsARgAevbsWe/hENdzd3eXeu/HjRuHESNGYOTIkfjggw/w1FNPNVuNtWEIbuPkCgVCNZNQVL4FAJCcuRtgCCbq0Bo79KA16tu3b60fczuSn58f8vLybPb5+PhALpcjJSWl1mN9fX2Rnp5ebX/lzV/X95TVFGTs7a+8mezHH3+s8ZiaegYrbdu2DUqlEnv27LEJ+DVNJVZf3t7ekMlkDfp6W4PKn219alOr1YiOjsZtt92GCRMm4Mcff4S3t3ejrlv1PV45A8rHH3+Mb775BtOnT5faNWU4xMyZMxEUFITnn38eFosFL7zwQqNqvV5ThkNcb9iwYZDJZLh48WKz1FYfDMHtwLCuD+H7s1/ARS7AU1aKjKLzCPLs6+yyiIjalfDwcFy+fNlmn6urKyIiIvD1119j7dq1dgNUVFQUdu7cibS0NKk3FLD2Arq5uTVpirFJkyZh27ZtMJvNGDFiRIOOrZx6reo4TJ1OV+NUc2q1ul69pO7u7hgxYgS+/fZbvPHGG9JNeRaLBV988QVCQ0Or3fTlCHXVf+XKFfj6+tZ7+MeQIUMQExODCRMmIDIyEj///LN0A2NTvPbaa9ixYwdWrlyJadOmSb2oTRkOAVgXldFoNHjqqadQWlqK9evXN7nWpgyHuF5MTAwsFgt69uzZ5LrqiyG4HdD6BMJkCQPkyZALAg7Fv4HpQz9xdllERA4RExMjTQVmNpuRlJSEb775BoC1V6xyrG5ZWRm+//57ANabciqPzcnJgbu7e50fa0dGRmL16tXV5uZ98803MXbsWIwYMQLLli1Dz549kZmZie+++w4ffPABNBoNVq1ahT179mD8+PFYuXIlfHx8sGXLFuzduxevvfaazU1xDTVr1ixs2bIFd955J5588kkMHz4cSqUSKSkpiI6OxtSpU3H33XfXeOxdd92FN998E/fffz8WLVqE3NxcvPHGGzXO/DBw4EBs27YN27dvR/fu3eHi4oKBAwfWeN7169dj4sSJGD9+PJ5++mmoVCps2LABZ86cwdatW+32dLekgQMH4sCBA9i9ezeCg4Oh0WjQp08f6fUjR44gIiKiQbX17dsXBw8exIQJEzBu3Djs27evycMNvL29sXz5cixduhRffvmlNA1c165dq62C11BPPvkkPDw8sGjRIpSUlODdd99t0s+i6vevvvbs2YOPPvoIU6ZMQZcuXWA0GnH8+HG8/fbb6NmzJx5++OFG19NgItVLYWGhCEAsLCx0dik1ij3xmfj1r33Er3/tI34cHS5aLBZnl0RELUyn04nnzp0TdTqds0tpdhs3bhQBiMeOHauzbUREhAigxkd0dLTULiEhwW67Ll261Hmd+Ph4URAE8auvvqr22rlz58R7771X9PX1FVUqlRgWFibOmzdPLC8vl9qcPn1anDx5sqjVakWVSiUOGjRI3Lhxo815oqOjRQDi119/Xe0ac+fOFd3d3WuszWg0im+88YY4aNAg0cXFRfTw8BDDw8PFxYsXi5cuXbL5XkVERNgc++mnn4p9+vQR1Wq12L17d3H9+vXiJ598IgIQExISpHaJiYnirbfeKmo0GpvvWeX39fqv5eDBg+Itt9wiuru7i66uruLIkSPF3bt327Sx93Ou/D5U/fnVxN7xc+fOrfYzPXHihDhmzBjRzc1NBGDzfYiPjxcBiDt27Kj1epXnvv7nkJKSIoaHh4tdu3YVL1++XOc5aqtdFK2/22FhYWKvXr1Ek8lUr/PZA0B89NFHbfZt3bpVVCgU4vz580Wz2Vyv80RERIj9+/dvUi2iKIrnz58Xp0+fLnbp0kV0cXERXVxcxPDwcPGZZ54Rc3Nz6zy+rn/3GpLXBFGsx4hmQlFREbRaLQoLC6vdlNAaFOfn4ZsTY6FRWOcQ7N39VdwQMtXJVRFRSyovL0dCQgK6detW7YYtahmVMzBcf9MStW0rVqzA559/jsuXLzt0sQZquLr+3WtIXuOKce2Eu9YLauHanH8nkjY4sRoiovZp/fr12LdvH44dO+bsUqiZFBQU4P3338e6desYgDsYhuB2QiaTYVinx2Cu6NiXGRNQbqx7sDoREdXfgAEDsHHjRmRkZDi7FGomCQkJWL58Oe6///5mOZ/FYoHJZKr10ZpULtZh72E2m51dYothCG5HQoIGoMRk7fp3kclw8PJbTq6IiKj9mT17NiZPnuzsMqiZDBkyBM8880yz3ay3evVqKJXKWh+JiYnNcq3m0KNHj1prjYqKcnaJLYb9/u2Im8YTAeqJ0Fu+BQBczfwvEL7SyVURERF1HIsWLcKkSZNqbVN1mjxn2717N/R6vd3Xa5tnuq1jCG5HBEHAjSHz8UviDrjKBWiEEqQWnkaItuYpbIiIiKh5derUqVWF3LrYm+auI+BwiHbG2z8YJktXAIBMEHA4/jXnFkRERETUCjEEtzMubu7oo7k2uL+s+BgsFosTKyKilsaZLomoo2jOf+8YgtsZQRDQq9MtKDJZR7p4yIE/kr9wclVE1BIqp3NqbXebExG1FKPRCAA2S303FkNwO+Tp4wdXDJO2z6ZsdGI1RNRS5HI55HI5ioqKnF0KEVGLE0URhYWFUKvVUCqVTT4fb4xrh9SurhjkOx+n8mOhkAlQmVJRUp4DDxc/Z5dGRM1IEAQEBAQgPT0darUa7u7uzTbNExFRayGKIoxGIwoLC1FSUoKQkJBmOS9DcDsVGNALJTk+8JLlQyWT4df413DnAN4kR9TeaLVa6HQ65OTkIDs729nlEBG1GLVajZCQkDqXQ64vhuB2ytPHD8HK26DDNgBAeu7/ADAEE7U3giAgODgYAQEB0lg5IqL2Ri6XN8sQiKoYgtsppUqFfr734NesbXCXA1qZHldyfkN3v9HOLo2IWkDl+GAiIqof3hjXjnn7BwCWXtL27wlvOrEaIiIiotaDIbgd03j7opfrTGlOPWPpaZjM/LiUiIiIiCG4HZMrFOjsNwyFJhcAgJtcwO9JHzq5KiIiIiLnYwhu57S+/vCUjZS2L6ZtcWI1RERERK0DQ3A75+Hlhb4e90FfsXSymyUX2SWXnVwVERERkXMxBLdzMpkcvr6dUW7uBACQCwIOXlrn5KqIiIiInIshuAPQ+vqhq2q6tF1U+BssFT3DRERERB0RQ3AH4KbxRBfNaBQarXOIeshF/JXypZOrIiIiInIehuAOQBAEePr6wU0YJu07k/yxEysiIiIici6G4A5C6+uPcNf7YKgYBqE2p6NQl+bkqoiIiIicgyG4g1C7usLLMwRl5gAAgEIQEHNxvZOrIiIiInIOhuAOROvrh1DlFGk7tyDaidUQEREROQ9DcAfi6eOHLq7jUGQSAAAamQln0v7r5KqIiIiIHI8huANRKJXw0HpBiUHSvr+S3ndiRURERETOwRDcwWh9/dHX9X6YLCIAQG5IQqk+z8lVERERETkWQ3AH4+HlDY0qCMUmLwCASibgYPxrzi2KiIiIyMEYgjsYmUwGTx9fBKnukPal5/zgxIqIiIiIHI8huAPS+vqju2oiSkzWbY1Mj/js/c4tioiIiMiBGII7IFcPDdSuroDYV9p39MpbTqyIiIiIyLEYgjsgQRCkFeTMovUGObH8IsqNJU6ujIiIiMgxGII7KK2vH7SKzigyegAA1DIBB+Nfd3JVRERERI7BENxBqVxc4eruAX/lbdK+lGwunEFEREQdA0NwB6b1C0BP9Z0oNlmHRGiEclzK/sXJVRERERG1PIbgDszTxxcKuQKC2E/ad/Tym06siIiIiMgxGII7MLlCAY23D/q6zoGp4gY5QR+PMkO+kysjIiIialkMwR2c1i8AnopgFBk9AVhXkPv14itOroqIiIioZTEEd3DunloolCoEKydJ+zJyuYIcERERtW8MwR2cIAjQ+vmju3oCiozWfR4yA86l73ZuYUREREQtiCGY4OXnD5lMBjlukPb9mfieEysiIiIialkMwWSdM9hDg36us2G0WAAACmMSisuznFwZERERUctgCCYAgJdfADwU/ig2+QIAFIKAXy+tc3JVRERERC2DIZgAABofXwgyGUJVU6V9OXn7IFZMnUZERETUnjAEEwBALpfD09sXXdXjUGAUAADuMhNOp33j5MqIiIiImh9DMEm0fv4QBAFqYai072TSv51YEREREVHLYAgmiZvGE0q1Gv1cH4C+4gY5lSkVBboUJ1dGRERE1LwYgkkiCAK0vv5wk3uj1BQAAJALAn6NW+PkyoiIiIiaF0Mw2dD6+QMAuqjukfYVFP4Ki8XsrJKIiIiImh1DMNlQqV3gpvFEmMtI5Bmsbw83mQXHkj52cmVEREREzYchmKrx8rMOhfCQjZH2xaVudFY5RERERM2OIZiq0Xj7QCaXo7/rfSgzW2+Qc7XkI63gLydXRkRERNQ8GIKpGplcDk8fP6jkrjCYuwGw3jR36NJaJ1dGRERE1DwYgqlGXv7WIRF9XObAXLFqnKnsNAzGEmeWRURERNQsGIKpRi5u7lC7ucFP1QP5RncAgEomIObSK06ujIiIiKjpGIKpRoIgwMsvEAAQqLhD2p+e8x3Eip5hIiIioraqWULwb7/9hsWLF6Nfv37QarXw9PREv379sGjRIhw+fLg5LmFXcXEx3nnnHdx+++0IDQ2Fi4sLfH19ccMNN+Cpp57CX3/xZq7G0vr6QRAE9HK5AwVGa/B1Eww4n/5fJ1dGRERE1DSC2IRuvdLSUjzxxBP49NNPa203f/58vPfee3B3d2/spWq0bds2PProo8jLy7PbRhAEPProo3jttdfg6ura6GsVFRVBq9WisLAQnp6ejT5PW5N65RKKcnPwe/Hb8FCeBACUyzth9uj9Tq6MiIiIyFZD8lqje4LNZjOmTZtmE4BdXV0xbNgwjBw50ubCGzduxLRp02A2N9+qY2+99Rbuu+8+mwDcqVMnREREYOTIkXBzcwMAiKKIf/3rX7j33nub9fodReWcwQNcH4TeYp0uTWlKRX5pohOrIiIiImqaRofgFStW4KeffpK2Fy5ciJSUFBw7dgyxsbFIS0vDihUrpNd/+uknrFy5smnVVoiJicHTTz8tbXfu3Bk//vgjUlNTceDAAcTGxiI3Nxfr16+HXC4HAOzdu7fZrt+RuGk8oVS7wF3hg2KjNRDLBQExF192cmVEREREjdeo4RBpaWno0aMHysvLAQBz5szB559/XmPbFStWYM2aNQAAFxcXXL58GZ06dWpCycDIkSPx+++/AwC0Wi3++usvdOvWrca2H330ERYtWgQAUKvVuHTpEjp37tzga3bU4RAAkJOeiuyUq0gu/wOF4nsQBAHlFgH33XwKcpnS2eURERERAXDAcIi3335bCsBubm54++237bZdsWKFFDrLy8vxzjvvNOaSkjNnzkgBGACee+45uwEYsPZQDx8+HACg1+vxxhtvNOn6HZGXnz8AoLPLjcgzWkOvi0zE7wnvO7MsIiIiokZrVAjeuXOn9HzGjBnw8fGx21alUmH+/PnS9rffftuYS0qio6NttmfNmlXnMffdd5/N9TnFV8MolCp4eHkDADzlEdL++LQtziqJiIiIqEkaHILj4uIQHx8vbd9+++11HnPHHdfmmY2Pj0dcXFxDLytJSkqSnnt6eiIsLKzOY2644QbpeUpKCv78889GX7+j8va3zhnc3/VelJisN8i5oRhJuS07BR4RERFRS2hwCD558qTN9qhRo+o8ZujQoVCpVNL2qVOnGnpZSWFhofRco9HU65jrx4ScOHGi0dfvqNy1XlAoVVDK1DBZekn7Y+PXObEqIiIiosZpcAg+f/689FylUtXrJrPr21U9R0NVDb7FxcX1OqaoqMhm+9y5c42+fkclCAK8/K1jg/u5zoOhYro06ONRUp7pxMqIiIiIGq7BITgxMVF6HhoaCkEQ6nVc1WELVc/RUKGhodLzoqIiXL16tc5jTp8+bbNdn+vr9XoUFRXZPDo6bcWcwd7KUBSZrOPAFYKAA3GrnFkWERERUYM1OARX7X3VarX1Pq7qkIT69uDWZOzYsTbb27dvr/OYbdu22WzX5/rr16+HVquVHo2ZVq29Uald4O5p/Zl3Ud0r3WBYUBADs8XozNKIiIiIGqTBIbikpER67uLiUu/jqi5ZXPUcDTVs2DD07t1b2l6/fr3NzXLX27hxI44cOWKzrz7XX758OQoLC6VHcnJyo2tuT7wqbpDr6jIaeaaq06X9y5llERERETVIg0OwyWSSnisUinofV7Wt0dj4XkOZTIaXXnpJ2s7Pz8e4cePw888/27QrLy/H66+/Li2UUVXVr8EetVoNT09PmwcBGi9vyBXW8Ospi5T2X077wkkVERERETVcg0Owm5ub9LxywYz6qNrW3d29oZe1MWvWLPz973+Xtq9evYpbb70VoaGhGD9+PEaPHg0/Pz8sXboUJpMJ3bt3t5nKrSHDOMiWIJPBy986Nri/670oqpguzRWliM/e58zSiIiIiOqtwSHYw8NDeq7T6ep9XFlZWY3naKwNGzbg5Zdftpl6LTU1FQcOHEBsbCxKS0sBWIdP/PTTTzCbzVI7Ly+vJl+/I6sMwUqZCsAAaf+xy685qSIiIiKihmlwCPbz85Oep6en1/u4jIwM6bmvr29DL1uNIAh44YUXEB8fjxdeeAEjR46En58flEolgoODMWHCBGzcuBG//fYbevToYTMjRH0W2CD7VGoXuGu9AAD91A9CXzFdmtyQhIIy++OziYiIiFqL+g/qrdCnTx/peW5uLsrKymyGSNhT9cay8PDwhl7Wrs6dO+Pll1/Gyy+/bLdNYWGhzSp3w4YNa7brd1Te/oEoLSyAVhmIEn0g1LJsyAQBMXEvYeqQT51dHhEREVGtGtwT3LdvX5vt+qy+lpqaiuzsbLvnaGm//vqrNJ0XAIwYMcKh12+PPLy8oVBah6J0VcyEpeL7W1oUC5NZ78zSiIiIiOrU4BA8fPhwqNVqafvQoUN1HnPw4EHpuYuLC4YPH97QyzbJl19+KT0fPnw4unfv7tDrt0fWFeSsY4PDXG5Ensk6XZ5KJuK3y//PmaURERER1alRN8ZFRUVJ21u2bKnzmKptoqKimjw7REPExcVhx44d0vbChQsddu32rjIEA4CfcqL0PCnzK5uedyIiIqLWpsEhGADmzZsnPT916hR2795tt+2ff/6JH374ocZjW5per8fChQuleYnDw8Px4IMPOuz67Z1SpYaHlzcAoI9qKgpM1uDrinLEZdh/TxARERE5W6NC8PTp0zFo0CBpe/Hixbhw4UK1dunp6Zg9e7Y0PdngwYNxzz331HjOxMRECIIgPV588cVaa9i8eTMMBoPd1zMzM/G3v/1NGoohCAI++OADmynVqOm8A6wryClkCijlN0r7/0x4y1klEREREdWpwbNDANZA+dFHHyEiIgI6nQ7p6ekYMWIElixZgnHjxkGhUODo0aP417/+hczMTADWZZM//PBDCILQLIUvXLgQTz75JKZMmYIxY8agW7duUCqVSE9PR3R0NL766isUFBRI7Tds2IBx48Y1y7XpGndPLyhVahgNevRT3o8L+uNwlcugMKUhu+g8/D0dexMkERERUX0IYhMGb3777beYPXt2nYtmuLq64osvvsC0adPstklMTES3bt2k7VWrVtXaG+zi4gK9vu5ZCNzd3fHOO+/goYceqrNtbYqKiqDValFYWMgllK+Tk5aK7NSrAIBDZavgLbM+h+sATB/2jRMrIyIioo6kIXmtUcMhKk2bNg1//PEHJkyYUGMPryAIiIqKwvHjx2sNwI1x9913w8fHx+7r7u7ueOCBB3D27NkmB2CqnZe/P1Dx8++jngNTxeIZprLTKDPkOLM0IiIioho1qSe4quTkZBw+fBipqakAgJCQEIwZMwadO3dujtPXyGKx4OTJk4iPj0dmZiaKi4vh7++Pzp07Y9y4cXB1dW22a7EnuHYp8RdRnJ8LAIgp/Qf85IUAAHevW3HHwHedWRoRERF1EA3Ja80Wgts7huDalRYV4mrcOQBAkngExeX/gSAI0IsyzBrzF+RydR1nICIiImoahw2HIKrkpvGEysW6YEYYRiDXbJ2FQy1Y8HvCe84sjYiIiKgahmBqFoIgwDsgSHoe4Han9NqV9C1cPIOIiIhaFYZgajZaX38IMutbqpdsEvIrFs9wgQ4X0nc6szQiIiIiGwzB1GzkCgW0vv4AAAUUcHMZLb32FxfPICIiolaEIZiaVeUKcgAw0OVBlFasFqiyZCO94E9nlUVERERkgyGYmpWLmzvcNBoAgNLkArMyXHrt0MWXnFUWERERkQ2GYGp23gHB0vOBmoUwVCyeYSm/gGJdurPKIiIiIpIwBFOz03h5Q6FUAgA89P4oFgIAAHJBQMzFVc4sjYiIiAgAQzC1AEEmg5f/tbHB4V4LYKmYIq248CBMJp2zSiMiIiICwBBMLcTLPxAQBABAkGkAcs3WhTSUgojfLr/uzNKIiIiIGIKpZShVKnh6+wAALGYzwrxnSq8lZ34NUbQ4qzQiIiIihmBqOZUryAFAuPIO5Jqsz9WCESeubnJOUURERERgCKYW5OqhgdrVDQBgKCuHv9e1pZTPXt3ApZSJiIjIaRiCqcUIgmDTGzzYYy4KTdZhEC4owaWsvc4qjYiIiDo4hmBqUVpfP8jkcgCAoVAHN80Y6bXjl19zVllERETUwTEEU4uSyeXw8rPOEyyKIkb4P3FtKWVzFlLzjzqzPCIiIuqgGIKpxXkHXJsz2FxggegyUNo+fPFFJ1REREREHR1DMLU4lYsrPLTeAACTwYDRIUtRbrH2BkN/GXml8U6sjoiIiDoihmByCJ/AazfIqUo8oJOHAQBkgoCYCyucVRYRERF1UAzB5BBunlqoXV0BALqSYozo8iyMFutMEYbSv1Cmz3ZmeURERNTBMASTQwiCAO/AYGnb19gNhYJ1RTm5AMTErXRWaURERNQBMQSTw2h9/CCXKwAARXm5GNz5KVgqFswoKDgAo6nUmeURERFRB8IQTA4jk8vh5W+dLg2iiO7KkcgxqwEASkHE4fhXnFgdERERdSQMweRQVVeQK8jOQp/QhdJ2WtZOWCxGZ5RFREREHQxDMDmUUq2Gp48vAMBsMmGw9h5kGQUAgEow4Y/EfzuzPCIiIuogGILJ4areIFeYlY3QwOnS9qXUjRBFizPKIiIiog6EIZgcztXdAy7u7gAAva4MY4KXINdYsZQydDibut2Z5REREVEHwBBMDicIAnyq9Abrcovh5XObtH0q8W2IFbNGEBEREbUEhmByCk9vXyiUSgBAcUEeJnZ/HoWmit5gsRDxWd87szwiIiJq5xiCySkEmcxmpghjgR4umjHS9vH49c4oi4iIiDoIhmByGi//QAiCdWaIgpws3NZ3LYoreoOVlhwk5UQ7szwiIiJqxxiCyWkUSiU8ff0AABazGbJiGWTuQ6XXYy+tdlZpRERE1M4xBJNT+QZ1kp7nZabj9r6voNRs7Q1WmNKRln/UWaURERFRO8YQTE6ldnWDu9YLAGA06OFq0MCs7ie9fujiSidVRkRERO0ZQzA5XdXe4NzMNEzstx66it5gQZ+ArKLTziqNiIiI2imGYHI6N40n1K5uAIDy0lL4ykJRruwOwDqn8MG4551ZHhEREbVDDMHkdIIg2PYGZ6Qhqu86lFusvcFmXRzySy85qzwiIiJqhxiCqVXw9Lm2eEZJQT5C3PqhRBYKAJAJAmLOszeYiIiImg9DMLUKgkxms5RyXkY6IsNfhsFiAQAYyk6hRJfirPKIiIionWEIplbDyz8QMpn1LVmYk4WeXiNQIFjnEZYJQPSF55xZHhEREbUjDMHUasgVCmj9AwAAoigiPzsTo3uugLGiN7is+CjK9FnOLJGIiIjaCYZgalWqDonIz8zADUG3IVf0BADIBSDmwgvOKo2IiIjaEYZgalVUahdovH0BAGaTEYW52RjeYxnMoggAKCz8FTpDnjNLJCIionaAIZhaHd8g2xvkhoVMQ7bZOo+wgr3BRERE1AwYgqnVcfXQwNVDAwAwlOtQVlSIYT2WSr3BBQX72RtMRERETcIQTK3S9YtnjOg8C9lmVwDsDSYiIqKmYwimVsnDyxsqFxcAQFlxEcpLSzCs+7PsDSYiIqJmwRBMrVK1pZTT0zAibBay2BtMREREzYAhmFotT19/aSnl4oI8GMrLMayHbW9wOXuDiYiIqBEYgqnVkslk8AmsOjY4FSM7X9cbHMfeYCIiImo4hmBq1bwCAiCTywEAhbk5MBkNuLFqb3D+fpQbcp1ZIhEREbVBDMHUqsnlCngHBFk3RBF5GekYVaU3mKvIERERUWMwBFOr5xMYBEEQAAAF2ZmwmM24sXvVeYOj2RtMREREDcIQTK2eQqmC1i8AAGCxWJCflYFRYfchk73BRERE1EgMwdQmVJ0uLS8zHaJowY3dn7HpDdbpc5xVHhEREbUxDMHUJqhcXODp4wsAMJtMKMzJxuiw+5Fhsi6oIReAXy+ucGaJRERE1IYwBFOb4RsUIj3PzUgDANve4Hz2BhMREVH9MARTm+Hi7g53Ty0AwKjXoygvF2O6PGDTGxxz4TlnlkhERERtBEMwtSm+wVV7g1MBAMN7LIdJtAAACgt/RWl5hlNqIyIioraDIZjaFDeNJ1zc3QEA+rIylBYWYGTYTGSZPQBYe4MPnF/qzBKJiIioDWAIpjZFEASbscE56dbe4NG9VsFosfYGlxYfRVFZklPqIyIioraBIZjaHI23D1Qu1jmCdSXFKCsuwo0hU5AjegMAZAJw4PwzziyRiIiIWjmGYGpzBEGAX6cqY4PTUyEIAiLC10Jf0RusLz2FvOILziqRiIiIWjmGYGqTPH38oFSrAQClRYXQlRTjhqAJyEcgAEAQgJgLHBtMRERENWMIpjZJEAT4VZkpIifNOjZ4Qv9XoTObAQDm8ovIKvzLKfURERFR68YQTG2W1tcfCpUKAFBSmI/yslKE+41GsTxManPwwrPOKo+IiIhaMYZgarMEmQy+QZ2k7cre4Nv7v4FSkwkAIBquIjXvkFPqIyIiotaLIZjaNC//AMgVSgBAcX4u9LoydPcZgjJVb6nNb3EvQKxYWpmIiIgIYAimNk4mk8M3KFjazk1PAwBMHvgmiip6gwVTBpJyfnJKfURERNQ6MQRTm+cVEAS5XAEAKMzNhqG8HKGe4TC53CC1OXrpJfYGExERkYQhmNo8uVwO76q9wRnW3uApA/8f8o1GAIDMnIf4jJ1OqY+IiIhaH4Zgahd8AoIgk8kBAIU5WTAa9Ajy6Aa5+0ipzZ9X1kMUzc4qkYiIiFoRhmBqF+QKBbwDgwAAoihKY4OnDnwdOQZrb7DcUowzyZ85rUYiIiJqPRiCqd3wCQyGILO+pQuyM2E0GODr1gke3hOlNmeT3oHZYnBWiURERNRKMARTu6FQKuHtb102WRRF5FWMDf7bgFeQabDOFKGAHseuvOm0GomIiKh1YAimdsU3uJPUG5yflQGjwQAPlReCA++T2iSmbYbRVOysEomIiKgVYAimdkWhVNXYGzyp7/NINVinSFMIZhyKW+W0GomIiMj5GIKp3ampN1gld0F42GMwV8wVnJX7PcrKM51ZJhERETkRQzC1O/Z6gyf0eARpRhUAQC4ABy4847QaiYiIyLkYgqldqqk3WC6TY2SvFTBYLACA0qKjKCi56MwyiYiIyEkYgqldur43ODcjFQAwsvO9yLR4AQAEAThw/mlnlUhEREROxBBM7VbV3uCCLOu8wYIgYGK/11Bmtq4cZyq/iPT8WGeWSURERE7AEEztlr3e4AGBkSiUh0ntDl9YBrHihjkiIiLqGBiCqV2rqTcYACYPfBuFJutyyjBlIiFrt7NKJCIiIidgCKZ2zV5vcFevgTC5DJbaHY9/GRbR5IwSiYiIyAkYgqnds9cbfPcNbyO74rnMUozTVz91Wo1ERETkWAzB1O5V6w1OTwEABLh3hrvXRKnd+av/gslc5pQaiYiIyLEYgqlD8A0OuTZvcHYWjHo9AGDawFeRprcOg1DAgNhLa51WIxERETkOQzB1CAqlEj6BwdYNUUR2WjIAwEPlhS6dFsBSMTtEWta3XE6ZiIioA2iWEPzbb79h8eLF6NevH7RaLTw9PdGvXz8sWrQIhw8fbo5L2FVeXo7Nmzfj3nvvRc+ePeHp6QmVSgU/Pz8MGzYMjz76KGJjOQ8sAb5BnSCTywEAhTnZ0JfrAAB39nkaKQYFAEAuiFxAg4iIqAMQxCZMkFpaWoonnngCn35a+w1F8+fPx3vvvQd3d/fGXqpG+/btw4IFC5CcnFxn27vuuguffPIJAgMDG3WtoqIiaLVaFBYWwtPTs1HnIOfLSUtBdqr1/eLp44uQHr0BAIeStiEpcSXUMhlEERg/+Cv4ed7gzFKJiIiogRqS1xrdE2w2mzFt2jSbAOzq6ophw4Zh5MiRNhfeuHEjpk2bBnPFKl3NYc+ePbjjjjtsArCnpydGjhyJW265Bf369YNMdu3L27t3LyIiIpCTk9NsNVDb4x0YDLnC2utblJeL8rJSAMCYsJnIEn0AWJdT/vX8/3EBDSIionas0SF4xYoV+Omnn6TthQsXIiUlBceOHUNsbCzS0tKwYsUK6fWffvoJK1eubFq1FQoKCrBgwQKYTNYbmjQaDT799FPk5OQgNjYWv/zyC86ePYukpCTcf//90nFxcXF4+ml+1N2RyeVy+AaHSNuVvcKCIOCO/m+jqGIBDYshBQlZe5xSIxEREbW8Rg2HSEtLQ48ePVBeXg4AmDNnDj7//PMa265YsQJr1qwBALi4uODy5cvo1KlTE0oG/vOf/2DJkiXS9p49e3DXXXfZbX/33Xdj165dAAClUomsrCx4eXk16JocDtF+WCxmXD71F0xGa+Dt2ncgXD08AAAbDk9BgOWitZ3MA9NHxUImUzqtViIiIqq/Fh8O8fbbb0sB2M3NDW+//bbdtitWrEDnzp0BWG9ie+eddxpzSRsHDx6Ung8YMKDWAAwAzz//vPTcaDTi2LFjTa6B2i6ZTA6/TqHSdnbqVen5PYPeQ5a0gEYJTiT92+H1ERERUctrVAjeuXOn9HzGjBnw8fGx21alUmH+/PnS9rffftuYS9rIzs6Wng8YMKDO9te3qXo8dUxefgFQqtQAgNKiQpQVFwEAAj26wMP7DqndpZQPYTAWOqVGIiIiajkNDsFxcXGIj4+Xtm+//fY6j7njjmuhIj4+HnFxcQ29rA2Pio+uAcBQ0WtXG33FwgiVvL29m3R9avsEmQx+Idd6g7NSrko3wt0zYB2uViygIYcJv1180RklEhERUQtqcAg+efKkzfaoUaPqPGbo0KFQqVTS9qlTpxp6WRvDhw+XnsfGxko3yNkTExMjPVcqlTbHU8el9fWHysUFAKArKUZpkbXH112lRa/QxTCJFgBAVu4PKC5LclqdRERE1PwaHILPnz8vPVepVNJ439pc367qORpj7ty5cHNzAwCkp6dj7Vr7S90WFBRg+fLl0va8efPg6+vbpOtT+yAIAvxDrr0vs6v0Bt/R+x9INlj/cJMJQMz5fzqlRiIiImoZDQ7BiYmJ0vPQ0FAIglCv48LCwmo8R2MEBwfj008/hVJpvWv/xRdfxKxZs/Drr7+iuLgYJpMJKSkp2LRpE2688UacO3cOABAZGYk33nijSdem9kXj7Qu1q/UPqvKyUhTn5wEAFDIlxvZ5CbqKua3Ly84gPf83p9VJREREzavBIbi4uFh6rtVq631c1Wkqqp6jsWbOnImffvoJffv2BQBs374dERER8PT0hFKpROfOnTF//nxcuXIFvr6+WLZsGf73v//Ve3ozvV6PoqIimwe1P4IgICD02h9o2SlXIVqswyBGhk5DthAkvXbo/DMQxeZb8IWIiIicp8EhuKSkRHruUjGesj5cXV1rPEdTREZG4vvvv8ekSZPstlEqlViwYAGWLFliMy65LuvXr4dWq5Ue9Rn2QW2Tu9YLbhrrH0cGfTkKcrIAWAPytBveR57RevOlYM7FmaufOK1OIiIiaj4NDsFVb0JTVCw/Wx9V2xorFiloCp1Oh8ceewy9evXCnj3Wlb3c3NwwfPhw3HLLLRg4cCDkcjmMRiNef/119OzZE+vWrav3+ZcvX47CwkLpUXV5ZmpfqvUGp6XAUjEMoqv3DZB5jJNeO3f1XRhM/FSAiIiorWtwCK68IQ2AtGBGfVRt6+7u3tDL2jAYDLjrrrvw/vvvw2QyQavV4pNPPkF+fj5+//13/PLLLzh16hQyMzPxzDPPQBAEGI1GPP/88zYLZ9RGrVbD09PT5kHtl6uHBhpv63zXZqMReZnp0mszBv0/JOutf7jJYcLhuBU1noOIiIjajgaH4Kpz9Op0unofV1ZWVuM5GmPNmjWIjo4GYB1mER0djQULFlQb7uDr64vXXnsN7777rrRv/fr1OHr0aJOuT+2Tf8i13uDcjDRpWWVPtS96hv4dpoqxwjm5/0NB6UWn1EhERETNo8Eh2M/PT3qenp5eS0tbGRkZ0vOmTFF2/dLLixYtwpAhQ2o95rHHHsOgQYMAAKIo4r333mv09an9Uru6wss/AABgMZuRm54qvXZn76eQbLSOgRcE4MDZf0jTqREREVHb0+AQ3KdPH+l5bm6uTQ9vbaqOqQ0PD2/oZSVHjx61malhypQp9Tpu8uTJ0vNff/210den9s2v07Vp//KzMmCsWG1QLlNgQv/XUVIxJt6kv4LErL1Oq5OIiIiapsEhuHJKskonTpyo85jU1FRkZ2fbPUdDpKam2mzXd9aGqu2q9koTVaVUqeETGAzA+qlBduq1P96GBN+GQkVPaftY/CqYLfpq5yAiIqLWr8EhePjw4VCr1dL2oUOH6jzm4MGD0nMXF5cmLVtc9dpA/cclV+2xrjpdG9H1fINDIJPLAQCFudkoLyuVXrt38PvI0FunTJNZSvHnlTedUiMRERE1TaNujIuKipK2t2zZUucxVdtERUU1aXaI4OBgm+0//vijXsdVbRcSEtLo61P7J1co4BccKm1np1zrDe6k6QFP30mwVIwHTkjfDJ0+0+E1EhERUdM0OAQDwLx586Tnp06dwu7du+22/fPPP/HDDz/UeGxj3HjjjTYhesOGDbBU3LVvT3JyMnbs2CFtR0RENKkGav+8A4OgUFpnGykpzEdZ8bVx6NMHrEWS3vqek8GCX88/45QaiYiIqPEaFYKnT58uzbYAAIsXL8aFCxeqtUtPT8fs2bNhrlh4YPDgwbjnnntqPGdiYiIEQZAeL774Yo3tVCoVHnjgAWn7+PHj+Pvf/253AY60tDRMnTrVZtjEggUL6vwaqWOTyWTwD7k2jjwzOUmaDcJNqcHg7ktRbrG+r4uLjyKz4JhT6iQiIqLGaVQIFgQBH330kTS2Nj09HSNGjMCyZcvw/fff46effsKaNWswZMgQnD9/HoB1HO6HH34o3XnfFC+++CICAwOl7Y8++gj9+/fHunXr8OOPP+LQoUPYuXMnnnrqKfTr1w9//fWX1Pbhhx/GsGHDmlwDtX9aP3+oK97j5aUlKMrLlV6L6v4Q0sxaafvQ+f+DKJodXiMRERE1jiA2YbLTb7/9FrNnz67z5jRXV1d88cUXmDZtmt02iYmJ6Natm7S9atUqu73BgHVWismTJyMlJaXe9c6YMQNffPEFlEplvY+pVFRUBK1Wi8LCQq4e14GUFOYj+aL1Uw6lSo3uAwdDJrP+7RiX8ztiTt0Hn4phE33CnsLALoudVisREVFH15C81qie4ErTpk3DH3/8gQkTJtTYwysIAqKionD8+PFaA3BjDB48GKdPn8bTTz8NHx+fWtveeOON2LZtG7Zv396oAEwdl4fWG+6e1h5fo0GP/Mxr0+v18RsB0X2UtH3u6nvQG/IcXiMRERE1XJN6gqtKTk7G4cOHpXl8Q0JCMGbMmHrP49sUJpMJJ0+exKlTp5Cbmwu9Xg9PT0+EhITgpptuapYa2BPccZWXlSLh7CkAgEwuR4+BQ6Co+GOqUJ+Dj38dgS4u1m2t5xhMHPSJ02olIiLqyBqS15otBLd3DMEdW1pCPApzrAu+eAcEIajLtaE738e9i4KMf0FVMUzi5oGfI9Cr8XNhExERUeM4bDgEUUfhHxIGoSLk5mdnwlB+bRz8bb0eRbLp2i/awfNPwSKaHF4jERER1R9DMFE9KFUq+AZ1sm6IIrJSrkqvyWVy/G3QBuQarCvJwZSL00n/cUKVREREVF8MwUT15BvUCfKKscDF+Xk2C2j08RsOeIyRtuOS/w2dIcvhNRIREVH9MAQT1ZNMLr9uAY1EVB1SP2vwW0goty7aIoMZv57jSnJEREStFUMwUQN4+QVUWUCj1GYBDa3aD/27/KPKSnK/Iz3/N6fUSURERLVjCCZqAEEQENC5q7SdnXIVFotF2r6t5yNIMV1bSe7w+X/CYql5SW8iIiJyHoZgogby0HrZLKCRl5EmvSaXyXH3oH8ju/ImOXM+TiW974wyiYiIqBYMwUSNULU3OCc9FcbK0Augj99NEDzGSuOFL6Z8iDJ9xvWnICIiIidiCCZqBBc3N3gHBAIARIsF2VWmTAOA+we/hYRy61zBMlhw4OyTDq+RiIiI7GMIJmokv5DOkMnlAIDC3GzoSoql1zzVvhjc41nozNab5MpKTyIx6wen1ElERETVMQQTNZJCobSdMu2q7ZRpE7svRLoYKG0fvfgcjKYSh9ZIRERENWMIJmoCb/9AqFysU6bpSktQlJcjvSYIAmYN/Rhpej0AQCbqcOTSi84ok4iIiK7DEEzUBIJMhsCwrtJ2VvJVWCqGQABAmDYcvn73wlQxjVpG9h7kFp92dJlERER0HYZgoiby0HrBQ+sFADAZDcjNSLV5/d6Bq3HFYF1uWRCAmLOPwSKaHF0mERERVcEQTNQMAsK6WhMugNz0NBgrhkAAgErugjsGvI08o3UaNYsxE6eT/uOMMomIiKgCQzBRM1C7uMInIAgAIIoiMpOTbF4fEjwRepcbpRvn4pI3oKw8rdp5iIiIyDEYgomaiV+nUMgVCgBAcX4uyoqLbF6fM/R9XKk6d/C5J2xmkyAiIiLHYQgmaiZyhQL+IWHSdkZSgk3I9XIJwKBuz6BMmjv4DJKy9zq8TiIiImIIJmpWXv4BULu5AQD0ujLkZ2XavH5rz0VIswRI28curoDRVAwiIiJyLIZgomYkCAKCwrpJ29mpV2EyGqVtmSDDrKEfIqW83Npe1CH24gqH10lERNTRMQQTNTM3jSe0vv4AAIvZjKwU25vkunr1h6//vTBWzB2cmfMjsgqOObxOIiKijowhmKgFBHQOg0wuBwAU5mRDV2I75GHmwJeRYFABsM6s9uu5x2G26Kudh4iIiFoGQzBRC1AoVfDv1Fnavv4mOZXcBVMG/RvZhorgay7A8fh1ji6TiIiow2IIJmoh3oFBULu6AgDKy0pRkJ1l8/qAgJsh94yCuSIcX83YjrziMw6vk4iIqCNiCCZqIYIgIKhLd2k7O+UqTCajTZvZg99EfLlY0R6IOfsoLBbbNkRERNT8GIKJWpCbxhOePn4AALPZhOyUZNvXlZ6Y2P9NaUllszETJxPfcXidREREHQ1DMFELC+jcBTKZ9VetIDsTutISm9eHh0yC3mUYLBXDIuJTP0FR2WWH10lERNSRMAQTtTClSgW/Wm6SA4AHb9yA+IollQWIOHDmEYii2aF1EhERdSQMwUQO4BMYBJVLxU1ypSUoyLG9SU6r9sPoXqtRWDFm2KBPwtnkjx1eJxERUUfBEEzkAIJMhqAu11aSy0pOsllJDgAiut6PPHkvaftc0jsoLU9xWI1EREQdCUMwkYO4e2qlm+QsZjOykm1XkhMEAfOGfYz4MuvcwTJYcODsY9WGThAREVHTMQQTOVBgWJdrK8nlZqO0qNDmdX+3UNzQ7RmUmKzjg3VlF3Ax7QuH10lERNTeMQQTOZBCqUJAaBdpOyPpCiwWi02b23v9HekIkbZPXnkFpeWpDquRiIioI2AIJnIwL/8AuLp7AAAM5eXIy0izeV0myDD/pk24oqscFmGumC3CUu1cRERE1DgMwUQOJggCgrpeW0kuJy0FhvJymzZBHt3Qv+s/rw2L0MXhQsomR5ZJRETUrjEEEzmBi5s7fAKDAQCiKCLjavW5g+/o9QgyhGvzC59OfAMlOtub6YiIiKhxGIKJnMQ/pDMUShUAoLSwAMX5eTavywQZFtz0GeLLrL3EMlgQfWYJF9EgIiJqBgzBRE4ik8tt5g7OvJoAc8Xwh0oB7mEY1H0Ziiv268uv4MzVDxxaJxERUXvEEEzkRB5e3vDw8gYAmIxGZKcmV2tze8/FyBS6SsMlLlx9D0VlVxxaJxERUXvDEEzkRIIgICisGwSZ9VcxPysDupLiam0eGr4Jl3QG6zZEHDjzd1hEU7XzERERUf0wBBM5mVKthn/ItRvg0hMvQ7xu7mB/t1AM6/k8Ck3WpZYN+qs4nfgvh9ZJRETUnjAEE7UCPoHBcHFzBwDodTrkpFdfHGNi94eQI+8FS8WwiIspH6CwNM6hdRIREbUXDMFErYAgCAju1kPazklPhV5XVq3Nw8M+xcUqwyKiz/wdFovBobUSERG1BwzBRK2Ei5s7fIMrlksWRaQnXqk2d7CvWyeM6v0S8o3WYREmQzr+uPyKo0slIiJq8xiCiVoRv04hUKldAAC6kmLkZ2dWa3NL1zkoVt0gDYtITP8S2QXHHFonERFRW8cQTNSKyGRymyWVs5OTYNTrbdoIgoCHb/oYcTpLxTbw69lHYTTZzipBRERE9jEEE7Uy7p5aePkHAAAsFgsykqovqeyp9sUdA99Dpt66mpxoKcLhC0sdXisREVFbxRBM1AoFhHaBQqkEAJQU5qM4L7dam2GdbodCexsMFdOp5eRHIylrj0PrJCIiaqsYgolaIblCgcAqSypnXE2AqWKO4KrmDnkLF/Vqafvoxeeg01cfR0xERES2GIKJWilPb19ovH0AAGaTCVnJSdXauCjccN/QjUjU6QAAgmjAgbOPQBQt1doSERHRNQzBRK1YYFg3yORyAEBhTjZKCguqtenjNwyhwQtQarYuo1xaehbnkz9xZJlERERtDkMwUSumVKkQ0LmLtJ2eeBlmk6lau3v7P48ks7+0fTbpLRSWXnRIjURERG0RQzBRK+flFwA3Ty0AwGQw1DgsQi5T4OHhm3GhtGJYBCw4cObvMHM1OSIiohoxBBO1coIgoFPXHpDJrL+uBTlZNQ6L6KTpiRt7Po98ozX4Gg1pOB6/xpGlEhERtRkMwURtgFKtRkDnrtJ2ekLNwyJu77EQ+cp+MFfMK5yc+RXS8mIcVSYREVGbwRBM1EZ4+QfAvXJYhNGAzBqGRQiCgMXDN+F82bXZIX479w+UG7IdVicREVFbwBBM1EYIgoDgrj0gk1XOFpGFksL8au20aj/8bfAHSCm3jg+GqMOBM0s4bRoREVEVDMFEbYh1WESV2SISrtQ4LGJw0C3w9p+JMrMZAFBSegZnr/7HYXUSERG1dgzBRG1MfYZFAMADN6xFgtkPYsX44PNX30Ne8SmH1UlERNSaMQQTtTH1HRahkCmxaMQWnC/TW4+DiANnFsNoKnFovURERK0RQzBRG6RUqxEQVvewiGCP7ri5z1pkGaxB2GLKx28XnpZ6h4mIiDoqhmCiNsrL7/phEYk1thvf7QEY3cbAYLHeGJedfwAJmTscVSYREVGrxBBM1EZVHxaRjeL8vBrbLhz2b5wvV0rbf1x6EcVlCQ6pk4iIqDViCCZqw5RqNQLDukrb6YmXYTJWXyrZTanBnGGfI76sDAAgwIToM4tgtugdVSoREVGrwhBM1MZp/fzh4eUNADCbTEhPvFLjmN/evjeiZ+dHUWA0AgAM+mQcvbjSobUSERG1FgzBRG1c5bAIucI63KGkIB8FOVk1tp3W9xlkK3rDVLFwRmr2f5GUudthtRIREbUWDMFE7YBCqURwt+7SdubVRBjKy6u1kwkyPDJiM86WXdt39OJyjg8mIqIOhyGYqJ3QePnAyz8AACBaLEi7cqnGYRFatR9mDd2Ey1XHB59+GGZz9dBMRETUXjEEE7UjgZ27QqlWAwB0pSXITU+tsV3/gNHoUXV8sCEVRy4+77A6iYiInI0hmKgdkcnl6NStl7SdnZYCXWnNK8RN6/sMchThMFbMH5yesxcJGd86pE4iIiJnYwgmamfcNBr4BodYN0QRaVfiYbGYq7WTCTIsGfEZzuqu7Tt+aQWKSuMdVCkREZHzMAQTtUP+nULh4uYOADCU65CVcrXGdlq1H+6/8TNcKisFAAgwI/rMwzCZy2psT0RE1F4wBBO1Q4JMhk7de0IQBABAfmYGSgoLamzbz38Ueoc9gbyKRTaMhgwcubCsxpvqiIiI2guGYKJ2Su3qhoDOXaTttIR4mCpuhLve3eH/RK6inzQ+OCPvJ1zO+MohdRIRETkDQzBRO+YdEAR3rRcAwGw0Ii0hvsYeXpkgw6MjPsMZnSDt+yv+JeQVn3ZUqURERA7FEEzUjgmCgE7drq0mV1pYgPzMjBrbeqp9MfemLYgrrZw/2IIDpx+G3pjvsHqJiIgchSGYqJ1TKFXo1L2ntJ2VkoTyihvhrtfbdxiG9HgeWQY9AMBiLkTMmcUQxeqzSxAREbVlDMFEHYCH1gs+gcEAAFEUkXr5EizmmoPtHT0XweQ+DrqK14tKTuHEldcdVisREZEjMAQTdRD+oWE206ZlXk2ssZ0gCFg87D+4ZNTCUjF++HLaJqTk/OyoUomIiFocQzBRByGTydCpRy8IMuuvfUFOForycmtsq1a44pFRX+N0qV7ad+TC/6G4LMEhtRIREbU0hmCiDkTt4oqgsG7SdnriZRj1+hrbBnl0w+Qb/o0EXcXCGaIR0acXwGSueTwxERFRW8IQTNTBaP384enjCwCwmM1IvXLJ7sIYw0PuQKfg+civmF/YYEjH4fP/5EIaRETU5jEEE3UwgiAgqEt3KFVqAICupBg5aSl228/qvxJZ8t4wVCykkZ1/ABdSPnZIrURERC2FIZioA5IrFOjUo5e0nZOWgtKiwprbyuR4fOQXOF127Z+LM4lvIjP/txavk4iIqKUwBBN1UG4eGviHdJa2Uy9fgsloqLGtp9oXD43YijMl1vHAAkQcPPsISnTJDqmViIiouTEEE3VgvsEhcPfUAgDMJiNSL9sfH9zTZwgiwl9FcrnOukMsR/SpebxRjoiI2iSGYKIOTBAEdOreCwqldVnlsuKiWscHj+92Pzz97kVhxY1yekMqDp59AqJocUi9REREzYUhmKiDUyiVCOnRW9rOSUtBSWGB3fbzBr2CNFlP6Ua53MLDOJ34TkuXSURE1KwYgokIbhpP+IeGSdtpVy7BaKh5fLBcpsA/Rm3FaZ1SWlHuYsoHSMn+n0NqJSIiag4MwUQEAPAN6gR3rRcAwGwyIe3KRbvjgzVqH/x91Nc4XaqT9h258E8UlsY5olQiIqIma5YQ/Ntvv2Hx4sXo168ftFotPD090a9fPyxatAiHDx9ujkvY6Nq1KwRBaPRj06ZNzV4TUVsnCAI6desJhVIFACgrLkZ2qv3ZH7po++GOAf/C5bLKG+NMiD61AHpjvgOqJSIiapomheDS0lI89NBDGDNmDD788EOcP38eRUVFKC4uxvnz5/HRRx9h7NixWLBgAUpLW88d5L6+vs4ugahVso4PvjZ/cG56KkoK7Yfa0Z2noHOnh5FTMXTCZMpFzJnFsFiMLV4rERFRUwhiI9c/NZvNuPPOO/HTTz9J+1xdXdG/f38oFAqcO3cORUVF0mu33norvv/+e8jl8iYXPXfuXGRmZta7/ZEjR1BYaF0IwMfHB+np6VCpVA26ZlFREbRaLQoLC+Hp6dmgY4namtz0VGSlXAVgXVijW/8bpBXmrmcRLXj90HR0Mp+Ga8Xvd1jgdAzvvcZh9RIREQENy2uKxl5kxYoVNgF44cKFeOWVV+Dj4wPA2kv86quv4uWXXwYA/PTTT1i5ciXWrl3b2EtKPvvss3q3LSwsRFBQkLQ9a9asBgdgoo7GJ6gTyoqLUVKYD7PJhJT4i+gS3h8yWfUPj2SCDI+P/Awv7x+Hwa5lkAsCrmZ+Ay/33ugd8qATqiciIqpbo4ZDpKWl4a233pK258yZgw8//FAKwADg7u6O1atX44UXXpD2vfnmm0hLS2tCuQ331Vdfoby8XNqeN2+eQ69P1BZZ5w/uIfX+lpeWIPNqot32bkoNnhzzLU6UXhsGcfLKOmTkHWzpUomIiBqlUSH47bffloKlm5sb3n77bbttV6xYgc6drUuzlpeX4513HDufaNVe4759++Kmm25y6PWJ2iq5QomQnr0hCAIAoCA7EwU5WXbbB3l0w+xhm3G2pAQAIAA4dO5RFJZeckS5REREDdKoELxz507p+YwZM2x6gK+nUqkwf/58afvbb79tzCUbJT4+3mZ2irlz5zrs2kTtgau7B4K6dJe2MxKvoLyWm1z7+4/BmD7rkaQrs+4QDYg+NRflhtyWLpWIiKhBGhyC4+LiEB8fL23ffvvtdR5zxx13SM/j4+MRF+eYuUQ///xz6blMJsOcOXMccl2i9sTLPwBe/gEAAFEUkRIfB7PJ/uwPE7rPgW/AA8iVZozIw4HTD8Fs0TukXiIiovpocAg+efKkzfaoUaPqPGbo0KE2N6OdOnWqoZdtMFEUsXnzZml74sSJ6NSpU4tfl6g9CgzrBhd3DwCA0aBH6pV4uwtpAMCDg9aiQDUYZWYTAKCk7AJizz9d6zFERESO1OAQfP78eem5SqWSxvvW5vp2Vc/RUmJiYpCYmCht84Y4osaTyWQI7dEbcoV1QpnSwgLkpNlfSEMmyPDEyM9xweANo8UCAMjI+xlnk95zSL1ERER1aXAIrhosQ0NDpZtm6hIWFlbjOVpK1RvitFot/va3v7X4NYnaM6VajZAevaXtnLRUFBfk2W3vqvTA/43dhb9KzVIP8IXkDbiatbfFayUiIqpLg0NwcXGx9Fyr1db7uKoTFlc9R0soLS3FN998I23PmDEDLi4uDTqHXq9HUVGRzYOoo3P31CIg9NoftGlX4mEo19lt7+cWgodHfIWTJddupvs9bilyiv5s0TqJiIjq0uAQXFIx/RGABgVLV1fXGs/REr799lubazRmKMT69euh1WqlR32GfRB1BD5BnaDxts4IYzGbkRIfB4vZbLd9b98bcWv/t3GptHLqNDNiTj+EYl2iI8olIiKqUYNDsMlkkp4rFPVfcK5qW6PR/p3lzaHqUIiePXti9OjRDT7H8uXLUVhYKD2Sk+2PfyTqSARBQHC3nlC5WP+w1et0SKvjRrlxXaajV9gTSNNb5xcXLTrsPzmHU6cREZHTNDgEu7m5Sc+rrsRWl6pt3d3dG3rZektOTkZ0dLS03di5gdVqNTw9PW0eRGQll8sR2rMPZHI5AKC4IA85aSm1HnNP32eg0N6BfKN16jSjMRvRp+bBZLY/nIKIiKilNDgEe3h4SM91uvr/51VWVlbjOZrb5s2bYam4G10QBDz44IMtdi2ijkzt6oqQ7r2k7Zy0FBTl2e/ZFQQBi298D1mK/iitmDqtVHcJh849BlG0P5yCiIioJTQ4BPv5+UnP09PT631cRkaG9NzX17ehl623qgtkjB8/3mZWCiJqXh5e3rY3yiXEo7zM/opycpkC/zdqKy4afWGo+GM1p+Awjl96iXMIExGRQzU4BPfp00d6npuba9PDW5uqY2rDw8Mbetl6OXLkiM1qdFwmmajl+QR1gqeP9Y9j0WJByqU4mGoZ9++q9MA/x+7CiTIZLBXBNynzK8SlfOKQeomIiIBGhOC+ffvabJ84caLOY1JTU5GdnW33HM2l6g1xHh4euOeee1rkOkR0jfVGue5wcbOO9Tca9Ei9fBFiRU9vTXxcg/Ho6G/xR/G1ewXOJL6B5KzvW7xeIiIioBEhePjw4VCr1dL2oUOH6jzm4MGD0nMXFxcMHz68oZetk16vx/bt26Xt6dOnt+gNeER0jUwmR2ivPpArlACAsuIiZCYn1npMF20/PDDsc5yqMm/473FPI7vweEuWSkREBKCRN8ZFRUVJ21u2bKnzmKptoqKiWiScfvfdd8jPz5e2ORSCyLGUKjVCe/WRVpHMz8pEfnZmrcfcEBiByL6v41JZ5bzeFvx6+mEUlsbVehwREVFTNTgEA7aLT5w6dQq7d++22/bPP//EDz/8UOOxzanqUIiuXbsiIiKiRa5DRPa5eWgQ1KW7tJ2RlICy4tpXW7yl2/3oEfo4UitWnhPFcuw/+SBKy1NbtFYiIurYGhWCp0+fjkGDBknbixcvxoULF6q1S09Px+zZs2GuWE1q8ODBdsfpJiYmQhAE6fHiiy/Wu57MzEz873//k7YffPBBqTeKiBzLyz8APoHB1g1RREp8XK1LKwPAvf2WwsX7b8gx6AEAZnMhfjn5APSGvJYul4iIOqhGhWBBEPDRRx9JSyGnp6djxIgRWLZsGb7//nv89NNPWLNmDYYMGYLz588DsC6b/OGHH7ZION2yZYvNSnacG5jIuQI6d4G7pxYAYDaZkHzpAsxVfkevJwgCHh76JopcRqDQZJ1ZwmDIwP5Tc2Ey259yjYiIqLEaFYIB4KabbsIXX3whBeGioiK8+uqruOuuu3DbbbdhxYoVyMy0jgd0dXXFF198gZtuuql5qr5O1aEQN998M3r06NEi1yGi+hEEASE9ektLKxvKy5ESH1frjBFymRxPjfwMyeiOsiqLacScXgSLxeCQuomIqONodAgGgGnTpuGPP/7AhAkTauzhFQQBUVFROH78OKZNm9aUS9l14sQJnDp1StrmDXFErYNcoUDn3uE2M0akJyXUuiiGUq7G0rE7cN7gA31FYM4v/gOxF/4JUbQfoImIiBpKEJtpmabk5GQcPnwYqanWm1lCQkIwZswYdO7cuTlO73RFRUXQarUoLCyEp6ens8shajPKSopx9cJZKfwGhIbBNzik1mPydRlYe+AWDHM3QSFY/1bvFnQfhvZcyfH+RERkV0PyWrOF4PaOIZio8Ypyc5B65ZK0HdKjNzx9al8+Pa34Mt78dQKGa1SQVQTffmFPoF+XR1q0ViIiarsakteaNByCiKg+PH394Bdy7VOhtIR46EpLajkC6KTpgb+P+hbHi68tzX7u6ruIT/uyxeokIqKOgyGYiBzCLzgEnr5+AADRYkHKpQsw6vW1HtPTZwjuv/Ez/FVlVbm/Lq9GUuZ/W7RWIiJq/xiCicghBEFAcNcecPXQAABMRqN16jSz/anTAGBw0C24Y8C/cKbEGoQFAEcvLkNqzr6WLpmIiNoxhmAichiZTIbQnn2gVKsBAHpdGVLjL9Y6dRoAjA27G2P6rMeFiiEUAkTEnn8CmfmHW7xmIiJqnxiCicihFEolOvfqC7lcAQAoLSpEeuLlWqdOA4CJ3R/EwG7LcbmscvEMCw6e/TtyCv9s4YqJiKg9YggmIodTu7oitFe4NN1ZYW4OslOT6zxuap9H0SVkCZJ0FTfLiUbEnF6A/JJzLVkuERG1QwzBROQUbhoNOvXoJW3npqciPyujzuNm9l8OL7+ZSCsvBwCIYjmiT85BUdnlFquViIjaH4ZgInIaT29fBIZ1lbYzkhJQnJ9X6zGCIGDBkNcg096OzIrZJSyWUuw/+QBKy1NaslwiImpHGIKJyKl8AoPhE9RJ2k69cgm6kuJajrAG4SXD3keZ6yjkGgwAAJOpAPtO3IcyfXqL1ktERO0DQzAROV1AaJi0gpxosSD50gUYynW1HiOXyfGPkZuQrRyAAqMRAGA0ZmPfX7Og02e2eM1ERNS2MQQTkdMJgoDgbj3hprEucWk2mXD14nmYKsKtPUq5Cs+M3o5UWS8UmqxtDcZM7DsxCzpDVovXTUREbRdDMBG1CpVzCKtdXQEARr0eyRfP17mYhlrhimfHfour6I5ik7Wt3pCOX07cj3JDTovXTUREbRNDMBG1GnKFAp179YVCqQIAlJeVIuVSHCx1LKbhonDDsrE7cdkSKgXhcn0KfjlxH/SG2m+0IyKijokhmIhaFaVajbA+1xbTKCsuQurli3UupuGq9MBz475DvDkIJRVBWKdPxi8n74femN/idRMRUdvCEExErY7a1Q2de4dDkFn/iSopyK/XqnJuSk8sH7cHF0x+KK0YRlFWnoj9J2fDYCxs8bqJiKjtYAgmolbJ1UOD0J59gMpV5XKykZ1ytc7jPFReeH7c9zhn8EaZ2QwAKNVdxv5Tc2AwFbVozURE1HYwBBNRq+Wh9UJI9yqrymWkITc9tc7jNGofPDfue5wu10BXEYRLyi5i/8kHYDAWtFS5RETUhjAEE1Gr5unji6Au3aTtrJSrKMiue/ozrYs/nov8ASfL3asE4UscI0xERAAYgomoDfAOCIJ/SGdpOz3xcp3LKwOAt0sgno/8H05W6REu1V3BLyfuQ7kht8XqJSKi1o8hmIjaBN/gEPgEBkvbqZcvoqSwoM7jvF0C8ULkjzit95TGCJeVJ+KXE7NQbshuqXKJiKiVYwgmojZBEAQEdO4Cra8fAEAURaTEx6GsuO6b3bxcAvB85P9wxuAlzRqh0ydbV5bjEstERB0SQzARtRmVyytrvHwAAKLFguSL56ErKa7zWK3aDysi/odzRj9pHuFyfSr2nZiFMn1Gi9ZNREStD0MwEbUpgiCgU49ecNd6AQAsFguuXjyP8rLSOo/VqH2wIuIHXDAF2CyxvO+vmSgtr3vWCSIiaj8YgomozZHJZAjt2RtuGk8AgMVsxtW4c9Dryuo81kPljRcivsclUzCKTEYAgMGYiX0nZqK0PKVF6yYiotaDIZiI2iSZTI7OvcLh6u4BADCbTLgadw6Gcl2dx3qovPB8xB7Em0NRWBGEjcYc/PzXvSgqu9KidRMRUevAEExEbZZMLkfn3n3h4uYOADAZjUiKOwejXl/nse4qLV6I2IMrli4oMFqDsMmUj30nZiC/5GyL1k1ERM7HEExEbZpcoUDn3n2hdnUFAJgMBmsQNhjqPNZNqcGKiN1IkfVGbkV7i7kE+088gJzCP1q0biIici6GYCJq8xRKJcL69INK7QIAMOrLcbWeQdhF4Y7nb96FPNWNyKzoQRbFchw4PRcZ+QdbtG4iInIehmAiahcUShXC+vSDUqUGABjKdfUOwkq5Gs+M+RJ693FIrRxTLJpw6MxipGT/2JJlExGRkzAEE1G7oVSrERZePQibjHUHYYVMiX+M3Ail1xQkSrNMWHDkwlNIzNjRglUTEZEzMAQTUbuiUrtUC8JJF+oXhGWCDEuGvQu/gNm4VFZSsVfE8UvP42LKZy1YNRERORpDMBG1O00JwoIgYO6gtegR+gTOVVmJ7lTCepxNeg+iKLZY3URE5DgMwUTULlUGYYVKBaDhQXhG/6UY3P15nCgulPafv/o+/ox/CaJobrG6iYjIMRiCiajdUqld0CW8f6OCMABM6fMIIvu+huNFBdK+hIxtOHzuCZgtdc9FTERErRdDMBG1a/aCcH1mjQCACd3m4G+DP0ZsYREsFUMhMvJ+wYFT82A0FddxNBERtVYMwUTU7qnULujS5/ogfLZeK8sBwMiQSVgw4mv8VqSD0WIBAOQX/4V9J2ZAp89ssbqJiKjlMAQTUYegcrH2CFfeLGfUlyPpwlkYysvrdXx//zF4auwPOFJiQbnZOia4VJeAn/+ajuKyKy1WNxERtQyGYCLqMCqHRigrV5Yz6JF04Qz0Ol29ju/i1R/PR+7Hnzo3lJhMAACDMRv7TsxAXvGpFqubiIiaH0MwEXUoSrUaXcL7Q+XiCgAwGY1IunAG5WWl9To+wD0Mq275BXGmYORV3GBnNpcg+uQDSM/7tcXqJiKi5sUQTEQdjlKlQpfw/lC7uQEAzCYTrl44B11pSR1HWmnVflg1/kdkKAYgXW8dTiGKRhw++3ckZn7bYnUTEVHzYQgmog5JoVSiS5/+cHH3AACYzSZcjTuHsuL6zfjgonDHc2N3wOQRZbPM8vGLz+FM4jtcVIOIqJVjCCaiDkuuUCCsTz+4aTQAAIvZjKsXz6G0qLCOI60UMiX+MeJj+AXMwfkqq8tdSP43jlx4GmZL/aZhIyIix2MIJqIOTS6Xo3OvvnD31AIARIsFyRfPozg/r17HC4KAeYPXYmjPVTheVCD1AKfm7EX0qQdhMBa0VOlERNQEDMFE1OHJ5HKE9gqHh5c3AEAURaTEx6EgO6ve57ir1yJMH7IJhwtLYKqYS7ig+AR+/ms6SnTJLVI3ERE1HkMwEREAmUyG0B69ofX1k/alJ15GbkZavc8xrNPteHzsXsQWW6CrmEtYp0/Bz39NQ27RieYumYiImoAhmIiogiCTIbhbT3gHBkn7spKTkJVytd43uvXwHoznx0fjpN4TBUYjAMBsLkb0qdlIyflfi9RNREQNxxBMRFSFIAgI7NwVfiGdpX256anISLpS7yAc4N4ZL43fjxRZb2kKNYgmHDn/JOKSP+HMEURErQBDMBHRdQRBgH+nUAR26SbtK8jOQurlSxArxvvWxV2lxQvjvoNFcyviqyzEcTrxdRy7+BxnjiAicjKGYCIiO3wCgtCpey9AEAAAxfm5SL50AeaK8b51UciUeGL4B+gW8ij+qjLt2tWsnYg+ORt6Q/1moCAioubHEExEVAutrx869+wDQWb957K0qBBXL5yFyVi/nlxBEDBzwDLcPvA9HC4ogkmsmDmi5BT+9+dUFJbGtVjtRERkH0MwEVEdPLy8Eda7L2RyOQCgvKwUiefOQK/T1fscN4dNx5LRu/FbkQVlZhMAwGDMxr6/ZiAtd3+L1E1ERPYxBBMR1YObxhNd+w6AQqUCABgNeiRdOIOykvotswwAvX1vxKpbfsUZgx9yDNaeZFHU47dzj+DC1Q95wxwRkQMxBBMR1ZPa1Q1d+w6E2tUNAGA2mXD1wtl6ry4HAL5unbD6lv0odBmFhCo3zJ1JehO/xz0Ns0Xf7HUTEVF1DMFERA2gVKnQpW9/uFUus1yxulx+Vka9z+GicMMzozcjpNNimxvmUrL3Yv+J+6Az1H+lOiIiahyGYCKiBpLLFQjrFQ5Pn2ury2UkJTRoUQ2ZIMP9A1/A7QPfw8GCQhgrpl4rLD2H//0xBblFf7VI7UREZMUQTETUCIJMhk7de8I3qJO0Lzc9FWlX4mGp51zCgPWGuUdH78FvxWaUmKw3zJlMBYg++QCupG9v9rqJiMiKIZiIqJEEQUBA5y4IDLu2qEZRXg6uxp2DqWLJ5Pro7XsjXow6hAvmTsioXGEOFvwZvwrHLj7PhTWIiFoAQzARURP5BAYhtMpcwrqSYiSeP92gKdR8XYPx8vifAc+7cK7KjBNJmTvwy4lZKNPXf8wxERHVjSGYiKgZaLx90CW8PxRKJQDAqNcj8fxplFa58a0uSrkajw1/H8N7v4zDBQXSwhpFpefw0x9TkF14rEVqJyLqiBiCiYiaiau7B7r2uzaFmsVsxtWL55Gfndmg89zeYwEWj/ovDhdVGSdsLkLMqbmIT93M+YSJiJoBQzARUTNSqtTo2ncAPLTe1h2iiIzEK8hKTmpQeA33G4GXog7hojkUaVXGCZ+4sha/xz0Dk7n+Qy2IiKg6hmAiomYmk8sR2qsPfAKDpX25GWlIvXwRFrO53ufxcQ3GS+N/guL/t3ff0XFVh7rAvzO9jzQz6pJlucvG2BSbYmwDLkAAQ4wxCeURBwgh3MdLIYUEYuBCSEgjgUCSSwlJSODSTCgG09wByzK2XOUmWVZv03s57w/Jxxr1kWYsS/P91pqVU/bZZw8rR/q8tc/e5mXY7XZJx2tb3sGHO5bD7Tua1HYTEaUThmAiohQQBAE548Yjp/jkzBFuezuOHdiLcGjwq8Ip5Sp8Z84fMW/aY9jkcEjzCXsDVVi346s43rI26W0nIkoHDMFERClkyc5F0ZRpkMnkAICAz4uqvbvhc7sHuDLe0gm34v/OW4vPvHI4OqdfE8UgvjjwPew4/DCnUSMiShBDMBFRihnMmSguPQNKlRoAEI2EUVO5F46WxJZHnmQ5C79YvBUNylk44vNKx482/Asf77wB3kBdUttNRDSWMQQTEZ0CGp0O46fPhM5oAgCIooiG6iNoPFYFMYEV5gyqTPzsotcwsej/4XOHHdHOl+1c3v1YV74MDe3rU9F8IqIxhyGYiOgUUSiVGDelFJnZudIxe3Mjag7uT2iFOZkgw4rp9+LW817FJlcY7s5p1KIxL7bs/TZ2V/0OMTGS9PYTEY0lDMFERKeQIJMht7gEeeMnAIIAAPC5XajetxuBLkMcBmNm9gI8vGgLjoglqPH7pOOVtX/Fp7tugi/YkNS2ExGNJQzBREQjICMrB8VTZ0Cu6FxhLhRE9f49cLW3JVSPRZuHhy5eC2vOrShzOhDrHB5hd+/C+9uvQn3bx0lvOxHRWMAQTEQ0QnRGI0pmzIRGpwcAiLEY6o4cRHNtTUILa8hlCtw6679x3dkvYKMzKK0yF4t5sXXf3dhx6CFEY4Oflo2IKB0wBBMRjSClSo3i0hkwWWzSsbaGOhxPcJwwAMzNvwIPLtqCQ+J4VHcZHnG08d9Yt+OrcPmOJK3dRESjHUMwEdEIk8nkyJ8wCdlFxdIxr8uJqn0V8HsSm0/YpivAwxd/gIK8O/G5046I2Lm4hv8oPtzxVVQ1vp5QLzMR0VjFEExEdBoQBAHW3HyMmzpdGiccCYVQfWAv7M2NCQ6PkOPrM3+GW+e+hi0uscviGiGUH/oZPj/wPYQjiYVrIqKxhiGYiOg0ojeZUTLjTGgNxo4DoojGY1VoqDqMWDSaUF0zs+fjkSWfoUE5C5Vej3S8rvV9vF9+FVqdO5LZdCKiUYUhmIjoNKNUqVA8dTosOXnSMWdbK6r370Eo4E+oLrPahvsveh0zJ/wUm+wOhDoX5giGmrC+4ibsqf49YrHExh4TEY0FDMFERKchQSZDzrjxKJgwGYKs40d10O9D1b7dcNvbE6tLELBsynfwfy9ai20+DZqCJ2aKEHHg+F/w0ZfXw+07muRvQER0emMIJiI6jZmsNpRMnwmVRgsAiEWjqD1ciaaa6oSWWwaASZaz8Msln0EwX40drpNzCrt8B7BuxzU4Uv9vvjRHRGmDIZiI6DSn1uowfvpMGDMt0rH2pgZUH9iDUDCQUF0ahR7fmfNHXHvWC9jgDMIpvTQXxpdHHsKmPXcgEGpJavuJiE5HDMFERKOAXC5HwcQpyBk3HkLncssBrxdVeysSXmUO6JhT+JElX6BeOQsHvCdnimh2bMba7V/hSnNENOYxBBMRjRKCIMCSk4fi0jOgVKsBdAyPqDtyEI3HjiKW4PCITE0O7r/odZwz6WGstzvh75x9Ihp1Y+u+u7Gt8icIRVxJ/x5ERKcDhmAiolFGqzegZMaZMFms0jF7cxOq9+9GMMHZIwRBwBWTbsNPLt6AilAWarqsNFfTvAZryy5HY/umpLWdiOh0wRBMRDQKyeUK5E+YjNziCdLwiKDPh+q9FXC2JT6mt9A0BY8t3oDs3Nuw1WGXplILR9qxee8dKDv4M4QjngFqISIaPQSRrwIPisvlgtlshtPphMlkGunmEBFJAj4v6o4ciptD2GzNQk5xCeRyecL1HWwrxzNlt6NEbke+RiMdVyltOG/qr5CTOS8p7SYiSrZE8hp7gomIRjmNTo+S6TNhtmZJx5xtLajauwt+T+LLI0+xnoNfLfkMpqyb8LnDjnBnr3Ao3IpNe27D9oM/Z68wEY167AkeJPYEE9Fo4GhtQVO3l+Rs+YWw5RdKwyYScaD1C/y57A5MVLqQpz7ZK6xWZuO8aY8jO+P8pLSbiCgZ2BNMRJSmMmxZKJkxC1q9QTrWWl+LYwf2JjynMABMs52HXy35AlrLyrhe4WC4GRt3fwPlh1azV5iIRiX2BA8Se4KJaDQRRRGt9bVora+VjslkcuQWl8BktQ2pV3hvyxb8texOTFZ5kNulV1iptGDO5EeQb700KW0nIhoq9gQTEaU5QRCQVVCE4mld5hSORVFfdRj1Rw8hGokkXOeMrHn41dIvoMj4Kr5wnuwVDofbsXXfd7B13z0IhFqT+j2IiFKFPcGDxJ5gIhqtotEImo5Vx02dplCpkD9+IvTmjCHVubdlC/5n+10oUThRoNFKx+UyPc6a+FMU5ywfUm8zEdFwsCeYiIgkHXMKT0LBhMmQdU6ZFgmFUHNwf8dKc50rxSViRtY8PL50Gyw5q7DZbkfgxGpzMS+2H/oZ1lf8H3j8NUn9HkREyZSUnuCtW7fixRdfxKZNm1BXVwdRFFFYWIiLLroIt956K+bNS/2ckuFwGOvWrcOrr76K7du3o6GhAT6fDzk5OcjLy8O5556LSy65BJdccgkyMzMTrp89wUQ0FoSDQdRXHYbPfXI5ZKVajfySSdAZh/azrcqxG8+U3QVr7Bgm6vTScUFQ4ozx38XkglshExTDbjsR0UASyWvDCsFerxf33HMPnn/++X7LrVq1Ck8++ST0en2/5YZq69at+Na3voW9e/cOWPbuu+/GU089lfA9GIKJaKwQRRH25kY019ZA7DKVmiU3D1kF4yCTJf5HwmgsircP/QkfVT6Gc416GBQnQ69RNwVzpjwKi3FmUtpPRNSXUzIcIhqNYvny5XEBWKvV4txzz8X5558fd+MXXngBy5cvR3QIf3IbyN///nfMnz8/LgBnZGTg7LPPxqWXXopzzjknZeGbiGg0EgQBlpw8TJhxJrQGo3S8vbEBVXsr4PcmPuWZXCbHtVPvwf2XbkWVMBX7PG6c6GNx+w7ik53Xo/zQaoTCzqR9DyKi4RhyCH7ggQewbt06af+OO+5AbW0tysrK8Nlnn6G+vh4PPPCAdH7dunX4+c9/PrzWdvPaa69h1apV0qTwZ511Fj744AO0tLSgvLwcH3/8MbZv3w6Xy4UvvvgC9957L6xWa1LbQEQ0Wqk0WhRPm4HswnHSS2yhgB/V+3ajpS6+l3iwcg0leHDhO5g37Vf41OlHezgknatqfAXvlS3Bsaa3wHeyiWikDWk4RH19PSZOnIhAoGPi9VtuuQV///vfey37wAMP4JFHHgEAaDQaHDlyBPn5+cNocoempiaUlpbCbrcDAK677jq8/PLLUChSM+6MwyGIaCwL+n2oP3oYAZ9XOqbWapE3fhK0BkM/V/bNGWjB8zvvQ2PrO5htNEHZZZiFxXg2zp38MEz6ScNuOxHRCSkfDvHEE09IAVin0+GJJ57os+wDDzyAoqIiAEAgEMAf/vCHodyyhx/84AdSAJ4yZQpeeumllAVgIqKxTq3VYXzpGbDlFwKdvcJBvx/V+3ejqaZ6SDNImDVZ+N75z+KWua9hm9+Aar9POtfu3oF1O5Zhd9VvEYn6+qmFiCg1hhSC33zzTWl75cqVsFgsfZZVqVRYtWqVtP/GG28M5ZZxGhsb8corr0j7jz/+ONSdk8ETEdHQCDIZsgqKUDJ9JtRdZnlob2rA0b274HUNbTzvGdkX4ddLv0BJ0Xexwe6CW1qoI4bK2v/B2rLLUd/2SRK+ARHR4CUcgisrK3H48GFp//LLLx/wmiuuuELaPnz4MCorKxO9bZy//e1viHT+EM3Ly8NVV101rPqIiOgkjU6PkukzkdVlrHA4GERN5T40VB8Z0mpzCpkS15V+Hw8s+hz1irOw0+1EtHM0XjDcjK37voONu2+H21+dzK9CRNSnhEPwrl274vYvuOCCAa85++yzoVKppP2KiopEbxun6wt5l19+OeSdk78TEVFyCIIAW14BSmbMiptBwtHSjKN7dsJtbx9Svdn6cfjZ/FexbNZz2OwWUB8MSOeaHZvxwfYrUXH01whHEp+hgogoEQmH4P3790vbKpVKGu/bn+7lutaRKFEUUV5eLu2ff/75AICqqircd999mDlzJsxmMwwGAyZMmICvf/3rePXVV6UZJIiIaPDU2o4ZJHKKS6T5gyPhMGoPV6L28EGEQ6EBaujd3IKv4NeXlcOcdQs2OxzwRk/0LkdxsO45vFu2GMea1kAU+bObiFIj4RBcXV0tbRcWFg56bfhx48b1Wkeiqqqq4HKdXOlo8uTJePrppzFjxgz88pe/xJ49e+ByueD1elFVVYWXX34ZK1euxNlnn42DBw8O+b5EROlKEARYsnMx4YzZ0JszpONuexuO7tmJ9qbGIU15plHoceus/8YPL96EY7KZqOgyRCIScaDs4E/w0Zcr0O7enayvQkQkSTgEu91uadtsNg/6uq7TVHStI1FtbW1x+2vWrMHdd98Nv98PACguLsbFF1+Mc889N24Ixq5du3DBBRdg3759g7pPMBiEy+WK+xARpTOlWo2iydOQXzIJ8s7ZeGLRKJpqqlC9f/eQFtkAgALjZPx8wZu4evYL+MyjxrEus0g4vfvwyc7rUVZ5HwKh1qR8DyIiYAgh2OM5+UNOo9EM+jqtVttrHYlyOBxx+3/84x8BAFOnTsXGjRtRXV2NTz/9FGVlZWhubsb3vvc9qWx7eztWrFiB0CD+fPfYY4/BbDZLn8EM+yAiGusEQYDZloUJZ8yG2ZYlHQ94vaje1zGd2lBXB52Tfzl+fVkZxhd9F+vtbjjCYencseY38V7ZYhysfR7R2NCGYBARdZVwCI50eSs4kXl5u5YNd/nBlqhgMNjjWH5+PjZu3Ij58+fHHTebzfjd736H+++/Xzq2f/9+vPjiiwPe57777oPT6ZQ+x48fH3KbiYjGGoVSifySSSieNgMqzclOjvamBhzdvROu9rYhDZFQytW4rvT7eGjJdri0C7HNaUeo852OWCyAiqrH8V7ZUtS2vM9V54hoWBIOwTqdTto+sWDGYHQtq9fr+ynZv96uffTRR5Gdnd3nNatXr47ryX3++ecHvI9arYbJZIr7EBFRPJ3RhAkzzkRWwcnp1CLhEOqOHETtoUqEEvg90ZVVm4fvX/A8Vp3/Fr4M2nDQ65FCbzDUiM8PfBcf7bweba4vk/ZdiCi9JByCDV2WzzwxDncwfL6TY7wMQ1yCs7drlUolVq5c2e81CoUCX/va16T97du3w+v19nMFERENliCTwZZf0OPFOY/TjqN7dqKlrmZIK84BQKntfPxy8SbMmfIoNroicVOqOT178Omur2PrvnvgDdQO92sQUZpJOATbbDZpu6GhYdDXNTY2SttWqzXR20qysrLi9qdNmxbXO92Xs88+W9qORCIc3kBElGQqjQZFk6ehYOIUKJRKAB3TWrbW1+HInqEPkZDL5Fgy4VY8ftku2HJuxwa7K268cH3bOqwtuwy7jv4KofDQVrUjovSTcAieOnWqtN3W1hbXw9ufrqFz2rRpid5WMm7cuLjQ29+SzV11D952u33IbSAiot4JggCTxYoJM2fDkpsHnBgiEeoYIlFzcD+C/sH93uhOqzTgxpn348HF2+HVL8LnDjv8Ug9zFIfqXsC7ZZfiUN3fEePLc0Q0gIRDcGlpadz+zp07B7ymrq4OLS0tfdaRCJlMFheie3tRrjfdxy8nMrMFERElRi5XIKdoPCbMmAW96eR0mj6XE0f3VnTMIjGE5ZcBwKrLx/8776/4zkXrcAiTUOF2IdK5qEY06sWuo7/Au2VLUNP8NhfbIKI+JRyC586dC7VaLe1v3rx5wGs2bdokbWs0GsydOzfR28ZZuHChtF1VVTWoa7qXy8nJGVYbiIhoYGqtFkVTSlEwaQqUqs7fHaKI9qYGHNm9E47W5iHP8jAhcxYeWvgerp79PL7w6XHEd/Jdj2CoCdsqf4j3y69CQ/t6ziRBRD0M6cW4RYsWSfsvvfTSgNd0LbNo0aJhzQ4BAMuXL5e2m5qasHv3wKsJrVu3TtouKipCfn7+sNpARESDIwgCTJlWTJg5C7b8kyuNRiNhNFQdQfX+PfANcRElQRAwJ/9y/GZpGWZO/DnWO0Oo7/KXP6//KLbs/TY+2fU1tDp3JOX7ENHYkHAIBoBvfOMb0nZFRQXefvvtPsvu2LEDa9eu7fXaobrwwgvjhkQ89thj/ZbfuXNnXBuuueaaYbeBiIgSI5PJkVVQhAkzZ8OYefJ9joDXg2MH9qD28EGEgkObUk0uU+Ark+7Aby7fjZyC72CDw4PW0Mnhcnb3LqyvuBGb9nwLTm/lsL8LEY1+QwrBK1aswKxZs6T9O++8EwcOHOhRrqGhATfffLO0etDs2bNx3XXX9VpndXU1BEGQPg8++GDfjZbJ8Itf/ELa//e//43f//73vZatqanB9ddfj1jnZOsqlQr33nvvgN+RiIhSQ6XWoHDSVBRNKYW6y2qibnsbju7eiebjx4Y8Xlij0OOG6T/Co0t3QWm5AZvsTji7zCTRZN+ID3dcgy8O/JDTqhGlOUEc4kCpsrIyLFy4UJor2GQy4a677sKCBQugUCiwbds2PPXUU2hqagLQsWzyhg0bMGfOnF7rq66uRklJibS/evXqfoMwANxyyy345z//Ke1feumluOWWW1BSUgKv14uNGzfimWeegcvlkso8/fTTuOuuuxL+vi6XC2azGU6nkwtnEBEliSiKcLQ0o6WuJi74yhUKZBUUISMrRxo+MRTN3uN4ec8jONa8BmcajdDLu650KsP4nOswfdxd0Gk4RI5oLEgkrw05BAPAG2+8gZtvvnnARTO0Wi3++c9/xo3l7W4oITgUCuH666/Hf/7znwHbKggCHn30Udx3330Dlu0NQzARUepEoxG01dehvakh7iU2lUaLnKJi6M0ZwwrDNc79eKniQTicGzDTaIRGJu9yVo6S3BUoHXcXdOrcoX8JIhpxieS1IQ2HOGH58uUoLy/H4sWLe/3hJAgCFi1ahO3bt/cbgIdKpVLhrbfewl//+te4AN3d/PnzsX79+iEHYCIiSi25XIHsomJMmDkbJsvJed1DAT+OHzqA4wf3IzCMlT7HmUtx3/xXsOr8/6AyNhE73U6EYiemT4uiqvEVvLdtEXYcfhj+YNMwvw0RjQbD6gnu6vjx49iyZQvq6uoAAAUFBZg3bx6KioqSUf2glJWVYe/evWhsbIRarUZeXh7mz5+PgoKCYdfNnmAiolPH53GjuaYafq8n7rjJYkVWwTiohjnX++7mTXh5z0OQB/aiVG+EUtalT0hQYFLejZhWdAc0qqy+KyGi084pGw6RThiCiYhOLVEU4W5vQ3NtDcJdZnqAICAzKxu2/EIolKph1V/RvAGv7HkIymAlpukNcWFYEJSYlH8zphbeDo3K2k9NRHS6YAhOAYZgIqKREYvF4GhuQmtDbdzLc4JMBktOHqy5+ZArFP3U0D9RFPFl08f4390PQRM5gmk6AxTdwvDEvBsxtfCb0Kq50BLR6YwhOAUYgomIRlY0GkV7Yz3aG+ulaS+BjpkkrHkFyMzOhUw29FddRFFEecMH+N89/w1DtBpT9HoohK71yTE+5zqUjvsW9JrCYXwTIkoVhuAUYAgmIjo9RMJhtNbXwt7SBHT5FaZQqWDLL0SGNQvCMMNwWf1avLr3YRijx3sJwwKKsq5C6bi7YNJNGMY3IaJkYwhOAYZgIqLTSygQQEvdcbjaW+OOK9Vq2PILYbZmDWtaNVEUsb3hA7yx7xdQhQ73GDMMAHnWJZgx7jvIMJQO+T5ElDwMwSnAEExEdHoK+Lxorq2B1+mIO67SaGDLL4TJYht2GK5o3oDX9z2GqK8C0w0GqOPmGQayMy7CjOK7YTWdNeT7ENHwMQSnAEMwEdHpzed2o7X+OLwuZ9xxlUaLrIJCGDOtwwrDAHCg9Qu8uu8xeF2fY4bBCK08PgxnGGdhetG3kWdZCEEY1lT8RDQEDMEpwBBMRDQ6+NwutNQdh8/tijuu1mphyy+CMdMy7DB8xL4Tr+19HK32jzHDYISh2+wUWs04zCi6E0XZV0MuG/o0bkSUGIbgFGAIJiIaPURRlMKw3+OOO6fW6mDLL0xKGD7uOoBX9z6O2tZ3MV1vQIZSGXdeocjAtMLbMCHvBqgU/N1BlGoMwSnAEExENPqIogivy4mWuuMIdFt9TqXRwpZXAJN1eGOGAaDZW4O3Kp/Enrp/YYpWhRy1Ou68IKgxMe9rmFL4DejUecO6FxH1jSE4BRiCiYhGL1EU4XU6OsKwzxt3TqlWw5pXALM1a1jzDAOAJ2TH2sPPYtPRZ1CsimCcRtstYMtQmHUFphXezhkliFKAITgFGIKJiEa/Ez3DrfW1PYZJKJQqWPPykWHLhqzbC2+JCkUD+LT6Zbx/8PewCW2YqNN1m2sYyDDORmnh7ci3XgJBGN79iKgDQ3AKMAQTEY0tPrcLrfW1PWaTkCuUsOTmITMrZ1jLMQNATIyhrH4t/nPgtxCC+zFNZ4CmW8BWKbMxrXAVSnJXQKkwDut+ROmOITgFGIKJiMYmv8eN1oY6eBz2uOMyuRwZWTmw5ORCqVL3cfXgHWjdhjUHfoc2x3pM0+lh7vYSnSCoUJK7ApPzb4FRVzLs+xGlI4bgFGAIJiIa2wI+L9oa6uBqb4s/IQgwW22w5uZDrdUN+z7N3hq8c/AZ7Kp9CSUaAYUabY8yWRkXYmrhN5GTMW/YL+0RpROG4BRgCCYiSg9Bvx/tTfVwtrag+69IgzkDltx86IymYYdTf9iDT6r/hY+P/AkWtGKiVt9jWWaNuhDTCr+B4uxrOFSCaBAYglOAIZiIKL1EwiG0NzXC3tyIWDQad06j18OaW5CUuYZjYgxfNn6Edw7+EX73dkzTG3osviEIKhRnL8Ok/Js4qwRRPxiCU4AhmIgoPUWjUThbmtHWVI9IKBR3TqlWw5KTB7MtG/JhzigBdCy+8Xbl0zjY+DomaZXIVWt6lDHpp2Nqwa0ozLocctnwxyoTjSUMwSnAEExElN7EWAwuexvaGuoR9PvizsnkcmTYspGZkwtVL8E1UZ6QHR9XvYSNVX9FJtowQauHqttQCZlMj4l5N2Bi3tdg0I4b9j2JxgKG4BRgCCYiIuDkXMPtjfU9plcDAENGJiw5eUkZNyyKIiqa1+O9Q39Gm2MDpuh0sChVPcpZzedhasGtyLUsgEwY3rRuRKMZQ3AKMAQTEVF3AZ8X7U0NcLW19niJTq3VwZKTB5PVNuyV6ACgzVePD448j201f0OeIojxWh3k3UK2QpGBSXk3YHzOChi0RcO+J9FowxCcAgzBRETUl0g4DEdLE+zNjYiEw3Hn5AoFMrJykJmdC6WqZy9uwveKhfFF3btYd/gZhHy7MEVngLGXRT0yjWdjcv6NKLAt4dhhShsMwSnAEExERAPpGDfcjvamBgS8nviTggBjRiYys3OTMlQCAI67KvH+of/B3ob/xTgVUKjRQtatXplMh5Kcr6IkbyUy9FOHfU+i0xlDcAowBBMRUSL8HnfHUAl7O9DtV61Ko0FmVi7MtqxhL80MAMGIH5/V/QefHn0WId9uTNbpYVIoe5Qz6KZgSv5NKMq6EkqFYdj3JTrdMASnAEMwERENRTgUhL25CY6WZkQj8UMlBJkMJosVmVm50BqSE0obPEfx4ZEXsbP2JeQogijWaqEQuo1JFpQotF2GCbkrkGWeC6H7eaJRiiE4BRiCiYhoOMRYDG5HO+zNTfC5XT3Oa3R6ZGbnwGSxQZaEOYejsQjKG9bho6PPwe7cgkk6Hay9zCyhUFgwMXcFxudcC6NuwrDvSzSSGIJTgCGYiIiSJej3wd7cBGdbS4/V6GRyOcxWGzJsOdDo9Um5X7u/AR9Xv4TPq/+GTMGBCVpdj3mHgY7hEpPzvobCrCugVmYm5d5EpxJDcAowBBMRUbLFolG42lthb25CwOftcV6t0yMjKxtmiy0pY4dFUcTeli34pOofqG55D0VqGQrUmh4v0wEy5GTOx8S8lcjNnA+ZbPizWhCdCgzBKcAQTEREqeT3eGBvaYSrvQ1iLBZ3ThAEGDOtyMjKTtrMEoGID1/UvYON1X+H17MdJdreh0vIZHoUZ1+N4pxlsBrPSsq9iVKFITgFGIKJiOhUiEYicLW3wdHahIC3Z++wUq1Ghi0HZltWUuYdBoA2fwM2HHsFXxz7O3SxRpRo9dD1Mi5ZobBiQu5XMS7rKpj1UxmI6bTDEJwCDMFERHSqBXxeOFqaex07DAAGcwbMtiwYMjIhkw3/ZTpRFHHUsQufHP0H9je+gTxFBEW9zS4BQK0uwMSc6zAu+yoYtOOGfW+iZGAITgGGYCIiGimxWAxuezscrc3wuZw9zsvkcpgyrTDbsqA1GJPSQxuJhbGj8SNsrH4Jje2folCtQH6v44cBvXYyJuWtQKHtcmjVOcO+N9FQMQSnAEMwERGdDkKBABytzXC2tiASDvU4r1SrYbZmwWy1QaXRJuWegYgX2+rfw+Zj/4LD+TmKNRrkqHtfitlsmIUJOdeiwLYYGlVWUu5PNFgMwSnAEExERKcTURThc7vgbG2By97zZToA0BqMMFuzYLJYkzK7BAC4g+3YWvsffFbzEoLe3Riv1cHay9hkEYBZfwYm5F6LAusS9hDTKcEQnAIMwUREdLqKRaNw29vhbGuBt5fhEoIgwJCRCZPFBkNGRlLGDwMdL9Rtrnkd247/G0LoKEq0Oph7Wa5ZBGDUlWJi7rUosC2FTp2XlPsTdccQnAIMwURENBqEQ0G42lrhbGtB0O/vcV4mk8GQaYHZYoPeZIbQy6IZQ1HvPoItx9/AzrrXIQ8fQ3EfgRgA9LopmJB9LQqzlkKvKUzK/YkAhuCUYAgmIqLRRBRFBH0+ONta4GxrRTQS7lFGLlfAaLHAZLElbf5hAGj0VGPL8TfwZd3rQOgoijU6ZCp7D8Q67QSUZC9DvnURTLpJnHaNhoUhOAUYgomIaLQSRRFelxOu9la47e29TremUCphtFhhttig0RuSFkabvTXYWvsWdtS+imjgEIq1Wlh6WZQDAJTKLBRnfQUFtiWwmc6CICRn2AalD4bgFGAIJiKisSAWi8HrdHQEYoe91xfqlCo1jJkWGC1WaJMYiNt89dha+xa2176KsH8/ijXaXl+qAwBBpkO+9VIUZ12B7IwLoZAnZ6YLGtsYglOAIZiIiMaaWDQKj8MOZ3srvE4HeosECqWqIxBnWpI6ZMLub8Tnde+gvO4NeDw7UaBWIUel7nUeYkABm3kuirOvRJ7lYmhU1qS0gcYehuAUYAgmIqKxLBqJwO1oh6uttdcZJgBArlBKgVhvNCXtpTpf2IXyhg9RVvsmGu2bkK0UUaDWQNlL/SIAg3YKxmdfgTzLxTDrp3EcMUkYglOAIZiIiNJFNBKG22GHu70NXpez1x5iuVwBQ2YmjJlW6E1myJIUiCOxMPa2bMbnx9/CkZa1MAtejNNooZX3Pj5YJjehwHoJCq1LkJN5ARRyfVLaQaMTQ3AKMAQTEVE6ikYj8DjscNvb4XE6eh1DLMhkMJgzYMjIhMGcCUUfM0EkShRFHHXswhe172Bv4xoowvUo0miR0Wf9MpgMMzE+6wrkWRbCqCtJSjto9GAITgGGYCIiSnexaBQepwNuexs8DjtivQRioGOlOmOGBYbMTKiTtHQz0DHTxPaGD7Cr4W04XTuQq5IjV62GQui9F1qhtKHIugQFtkuRZZoDuVyTtLbQ6YkhOAUYgomIiE46McuE29EOj8OOaCTSazmVRgNDhgXGjExoDcakjd8NRvzY3bIR5XVrUdW6DgY4UajWwtDn8tBymAwzUGy7DDmWi2DWTeFY4jGIITgFGIKJiIh6J4oi/B53x7AJhx2hQM+V6gBArlDAYM6EISMTepMZ8j4Da+L3r3UfRFndWuxtfAd+3z7k9TvbBCDIDMjJnIci2yLkZFwAjSorKW2hkcUQnAIMwURERIMTDPjhcdjhcbTD53b3XkgQoDMYoDdnwmDOgFqrS1rPrC/sws6mT1Fe9w7q29fDKPiRr9b000sMqNSFGGddjDzLAtjM50AuUyelLXRqMQSnAEMwERFR4iKRMDwOBzyOvl+sAzrmI9abM2AwZ6Sgl7gSOxo+wr6mtXC5K5CtkiFXpe51CrYOcpgMZ0i9xJmG6Vy9bpRgCE4BhmAiIqLhicVi8Lmd8Dgc8DodCAUDvRdMYS9xOBrE/tbP8WXjOhxp+RAI1SJfrYFVqez7HoIaGcZZGGe9FNmZF8Csmwyhj5fxaGQxBKcAQzAREVFyhQJ+eJwOeJwO+PqYjxgAFEol9KaOHmKdyQxlH0stD4Uz0IKdTZ9iV8NaNNg3wyTrGDqhl/fdEy3ItLCYzkGR9VLkZJwPg7aEL9mdJhiCU4AhmIiIKHVisSh8LpcUisN99RIDUGu1J0Ox0QRZHwtpJEoURdS49mNn46eobP4QDvdOWBQx5KrU0PRzD0FmQFbGeSiyXgKb+VwYNMUMxSOEITgFGIKJiIhOnbheYrerz7HE0tCJzlCs0RuSFkCjsSiqHBXY1bQeB1vWwe3ZA5tChly1Gqp+VsgTZHpYTLNRaLkEWeZzYdZP5pjiU4QhOAUYgomIiEZGLBaD3+OG1+WE1+VEwOvps6xMLofOaJJ6iZM5njgSC+Nw+w7savoUh5o/hN9fiSylHDn9vmQHQFDBZJiBIsvFyMqYA4vhDMhkyRvSQScxBKcAQzAREdHpIRqJSIHY63L2O3RCrlBAZzRJn2SG4lA0gMq2bahoWo+q1o8R8B+BTSlHtqr/nmIRcuh1k1FkWYjsjPNgNc2GQq5LSpvSHUNwCjAEExERnZ5CwUBHIHY64XM7+1y9DkhtKA5HgzjYXo59zZtwpO1TuL37YZGLyFapoe1nTLEIASpVAXIz5iIv8wJYTWdBpy7guOIhYAhOAYZgIiKi058oigj6ffC5XPC6nfC5XYhFo32WT2UojsaiqHbuxp7mLTjc8gnsnp0wCiHkqNT9LtwBAJDpYNbPQKFlPrLM5yDTMANyuSYp7RrLGIJTgCGYiIho9Ek0FMtkcmgNBmiNJugMRmj1hqTOPlHvOYy9LVtxoPljtDq3Qy26kK1SI0Oh6Dd8ixCgVhchJ2Mu8jM6eou16jz2FnfDEJwCDMFERESjX6KhGAA0ej10BhO0BiN0RiMUyuS91Nbmb0Bl2zYcaNmM+vatCAWPwaqUI2uAccUAAJkeJn0pCjIvhM00G5nGM6BSpHdGYQhOAYZgIiKisadrKPZ53PB7XIiEw/1eo1RrOnqJjUboDEaoNNokjisO4ahjF/a3fI6jbRvg8OyGFl5kKdXIUCoHvF6msCDDcAYKMi+E1XQmMvXT02oYBUNwCjAEExERjX2iKCIcDMLvccPnccHndiMU8Pd7jUwuh0ZvgFZv6BhKoTdCMYjAOlhtvnocaPsClS2bUO/4HOFgDaxKBWxK1YC9xSIEKJW5sJpmoSDzAliMs2DST4JMGGBM8ijFEJwCDMFERETpKRoJw+fxwO/u6C0OeD19LvF8glKt7gjFeiO0BgPUOj1kAw1vGKRwNIgj9l042LYd1e2bYXfvgTzWDqtSCYtSBfkAvdIi5FCri2AzzUZexhxkGmbApJsImSx5wX2kMASnAEMwERERAR2LdwS8Hvg9bvg7/3egIRSCIECt00s9xVq9Hkq1JmnDKHxhFw63f4mDbdtQ074FLu8BqEUPbCoVzAolZAMGYxmUqlxYDGcgP/N8ZBqmw6yfCoVcm5T2nSoMwSnAEExERER9CYeC8Hs88Hvd8Hs8CPi8fS/13Ekml0Oj00OrN0Cj10OjM0CpVictGDsCzTjUvgOH2j5Hnf0zeHyHYRBCsKnUMA40RRsAEYBcYUOGvhR5mefBapyJDEPpaf3yHUNwCjAEExER0WCJsRgCfl9nj3FHOA4F+l7Z7gSZXN4RinV6aDrDsVKVnGAsiiJafMdxqL0cR9q2odG5Hb5AFXQIwqJUwTzANG0SuQkG7UTkmM9Gtmk2zPqp0GsKIQjJGe4xHAzBKcAQTERERMMRjYQ7h0909BQHvJ4Bh1EAgFyu6OgpPhGOdbqkDqVo9zfgiH0XjrZvR539C7j9h6GKuWFRqpChVA44xhgARCigVOchU1+K/Iy5ncMppkCpMCSljYPFEJwCDMFERESUbOFQCAGfBwFvRyj2e72IRgYOxjKZDGqdHmqtTgrGaq0uaQt7uIPtOOqowJH2Hah1fA6n5wCEaDssSiUyFUooB/uSn8wIvbYEpUW3YXzWZUlpW38YglOAIZiIiIhSTRRFRMKhjlDs6wjFAa8H0UhkUNerNNqOQNwZjDU6fdIW9whEfKh27EGVowLH7WVo9+xFKFgHgyyKTKWq33HGSvPluObMJ5LSjv4kktfG5iRxRERERKOQIAhQqtRQqtQwZloAdAbjUAh+nwdBnw8BnxdBnw/hULDH9aGAv2Ne4/Y26ZhcoewMxh29xWqtDmqNNuFeY41Ch2m2uZhmmwvgdqltLb5aVDv3oKq9HI3Ocrh9hyFE7cjs0mtcmHnB0P+jpAhDMBEREdFpTBAEKNVqKNVqINMqHY9GIh2B2N8RjAM+H0J+X485jKORMLwuJ7wuZ9xxpVoDtVZ7MhhrdVBrNBASmM9YEARk64uQrS/C3PwrpOPBiB/HXQdQZa9AjWMbJmcvGuK3Tx0OhxgkDocgIiKi050YiyEY8Es9xgG/D0Gfd9DDKSAIUGs08cFYq03qi3ipxOEQRERERGlIkMk6X5TTw4wsACfHGQf9fgT9vi4ff8+5jEWxs5wfwMkhFYIgQKXVQq3RQqXp6D1WaTRQabRJWwnvVGMIJiIiIhrDuo4zNpgzpOOiKCIcDPYIxsGAH+g2UEAURQR9PgR9vh71K9VqqDU6qLSazpCsg1qrgVxxei/DzBBMRERElIYEQejszdVIL+EBHUMqQsFAj57jvhb7CAeDCAeDQPyQY8gVSqi1Gqg0OpitWdAZjan8OgljCCYiIiIiiSCTSeOBgZMv4nUNx6FAR49xqLPnuLcloqORMHzuMHxud2cAZggmIiIiolEmPhyfdGIKt2Dn9GxSSPb7pYU/VBrtSDS5XwzBRERERDRkcVO4dRlzDHRM4xYM+HsE59MBQzARERERpYRcoYDOcHoNgzhhdM5pQUREREQ0DAzBRERERJR2GIKJiIiIKO0wBBMRERFR2mEIJiIiIqK0wxBMRERERGmHIZiIiIiI0g5DMBERERGlHYZgIiIiIko7DMFERERElHYYgomIiIgo7TAEExEREVHaYQgmIiIiorTDEExEREREaYchmIiIiIjSDkMwEREREaUdhmAiIiIiSjsMwURERESUdhiCiYiIiCjtMAQTERERUdphCCYiIiKitMMQTERERERphyGYiIiIiNIOQzARERERpR3FSDdgtBBFEQDgcrlGuCVERERE1JsTOe1EbusPQ/Agud1uAEBRUdEIt4SIiIiI+uN2u2E2m/stI4iDicqEWCyG+vp6GI1GCIKQ8vu5XC4UFRXh+PHjMJlMKb8fESUHn12i0YvP7+gniiLcbjfy8/Mhk/U/6pc9wYMkk8lQWFh4yu9rMpn4IBKNQnx2iUYvPr+j20A9wCfwxTgiIiIiSjsMwURERESUdhiCT1NqtRqrV6+GWq0e6aYQUQL47BKNXnx+0wtfjCMiIiKitMOeYCIiIiJKOwzBRERERJR2GIKJiIiIKO0wBBMRERFR2mEIPo1s3boVd955J6ZPnw6z2QyTyYTp06fjW9/6FrZs2TLSzSMatRwOB958803cc889WLBgAXJzc6FWq2EwGDBu3DhcffXVeOKJJ2C324dU/+7du/H9738fZ555JiwWCwwGA6ZOnYqbbroJ77///pDbffToUfz85z/HOeecg6ysLGi1WkycOBFf/epX8dprryESiQy5bqLRrrq6Gnq9HoIgSJ8HH3wwoTr47KY5kUacx+MRv/nNb4oA+v2sWrVK9Hg8I91colFj//794lVXXSWqVKoBny8Aok6nE3//+9+LsVhsUPWHw2HxvvvuE2UyWb/1XnnllWJzc3NCbX/iiSdEtVrdb73nn3++eOTIkaH8pyEa9S677LIez8Tq1asHdS2fXRJFUWQIHmGRSERcunRp3MOh1WrFc889Vzz//PNFk8kUd27p0qViJBIZ6WYTjQqvvvpqj18+crlcnDp1qrhgwQJx3rx5osVi6VHm9ttvH1QQ7v6PV6VSKc6aNUucN2+eaLVa486deeaZotvtHlS7H3744bhrZTKZeMYZZ4gLFiwQ8/Ly4s4VFhaK9fX1w/1PRTSq/OMf/+g1XA42BPPZJVFkCB5x9913X9xDcccdd4htbW3SeY/HIz7wwANxZX7605+OYIuJRo8TIVihUIjXXnutuGbNGtHpdMaVicVi4po1a8SCgoK45+zpp5/ut+6//OUvceWXLVsm1tbWSudDoZD45JNPigqFQipz4403Dtjm999/XxQEQbrmggsuECsrK6Xz0WhUfPnll0WDwSCVmTdvXoL/ZYhGr5aWFtFms4kAxNLSUjE/Pz+hEMxnl05gCB5BdXV1okajkR6GW265pc+y999/v1ROo9GIdXV1p7ClRKPTmjVrxNtvv108duzYgGVramrE3Nxc6Tmz2WxiKBTqtazX640re/HFF/f5F5pnn31WKicIglheXt5nG2KxmDhr1iyp/NSpU0Wv19tr2Q8//DDuF/kbb7wx4HckGgtuvvlm6f/3GzZsEIuLiwcdgvnsUlcMwSPohz/8ofQQ6HS6uB7g7oLBoFhUVCSV/9GPfnQKW0qUHrr3EH300Ue9lvvTn/4U98tx3759/dZ73nnnSeVXrlzZZ7l333037v7vv/9+v/XecMMNUtm5c+cO/AWJRrkPPvhA+v/8qlWrRFEUEwrBfHapK84OMYLefPNNaXvlypWwWCx9llWpVFi1apW0/8Ybb6S0bUTp6Oqrr47bP3DgQK/luj5/CxcuRGlpab/13nnnndL2e++9h2AwOGC9JSUlWLp06aDr3bZtG2pra/stTzSa+Xw+fPvb3wYA2Gw2/PrXv064Dj671BVD8AiprKzE4cOHpf3LL798wGuuuOIKafvw4cOorKxMSduI0lX3f4i6XK4eZTweDzZu3CjtJ/rsejwerF+/vtdy7777rrR92WWXQRCEfuudP38+9Hp9r9cTjTUPPPAAqqqqAAC/+c1vYLVaE7qezy51xxA8Qnbt2hW3f8EFFwx4zdlnnw2VSiXtV1RUJL1dROns2LFjcfvZ2dk9yuzbtw/hcFjaH8yzm5ubi/Hjx0v7vT27zc3NaGxsTKhehUKBOXPm9Fsv0VhQXl6OP/zhDwA6enBvvfXWhOvgs0vdMQSPkP3790vbKpUKRUVFA17TvVzXOoho+LoPM+rtl1n3527ixImDqrtrud6e3VTVSzTaRSIR3H777YhGo1CpVPjzn/88pHr47FJ3DMEjpLq6WtouLCwc8E8nJ4wbN67XOohoeJxOp9TTBABnnnkmpk+f3qNc1+dOoVAgLy9vUPUP9Ox2P9a1/HDqJRrtfvvb32Lnzp0AgB//+MeYNm3akOrhs0vdMQSPELfbLW2bzeZBX2cymXqtg4iG5wc/+EHcnzQfeeSRXst1fe6MRiNkssH9GB3o2e1+bLA/F/gzgcayI0eO4KGHHgIATJo0CT/96U+HXBefXeqOIXiEeDweaVuj0Qz6Oq1W22sdRDR0zz77LJ577jlp/4YbbugxU8QJqXp2ux8bbN38mUBj2Z133gm/3w8AeOaZZxJ65rrjs0vdMQSPkEgkIm0rFIpBX9e1bNcB/kQ0NBs3bsTdd98t7ZeUlOAvf/lLn+VT9ex2rTeRuvkzgcaqF154AR9//DEA4KabbsLixYuHVR+fXeqOIXiE6HQ6aTsQCAz6uq5lu06vQkSJ27lzJ5YtW4ZQKASgYzaI999/v98/Z6bq2e1abyJ182cCjUXNzc249957AQCZmZn43e9+N+w6+exSdwzBI8RgMEjbJ/7UMxg+n6/XOogoMZWVlbjsssvgdDoBdPyiXbduHaZMmdLvdal6drsfG2zd/JlAY9E999yD9vZ2AMAvf/nLXqcrTBSfXeqOIXiE2Gw2abuhoWHQ13V9cSfRicKJqENVVRUWL16M5uZmAB0vyaxduxazZs0a8Nquz67H4xn0WL6Bnt2u9QKD/7nAnwk01nz22Wd45ZVXAHRMU3jHHXckpV4+u9QdQ/AImTp1qrTd1tYW9y/C/hw/flzaHuo0MUTprLa2FosWLZKWKdXpdHjnnXdw3nnnDer6rs8uANTU1AzquoGe3VTVSzTaNDU1SdufffYZZDIZBEHo89N1kZuHHnoo7lzXqcf47FJ3DMEjpPt65SfmQOxPXV0dWlpa+qyDiPrX1NSExYsXS0uvqtVqrFmzBgsWLBh0HUN5dsPhMPbu3dtnHQAwefLkuBdlBlMvAHz55Zf91ktEHfjsUncMwSNk7ty5UKvV0v7mzZsHvGbTpk3Stkajwdy5c1PSNqKxqK2tDYsXL0ZlZSUAQKlU4rXXXsOSJUsSqmfChAkoLCyU9gfz7JaXl8f9tae30K1SqeJ6owdTb2NjIw4fPtxvvUSjjVKphNlsHvSn62JTarU67lzXuYD57FJ3DMEjxGAwYNGiRdL+Sy+9NOA1XcssWrSIb5MSDZLT6cRll12GPXv2AADkcjn+9a9/4aqrrhpSfcuWLZO2X331VWl2ib50fXZnzJjR57Kq11xzjbT90Ucfxf1ZeKB6MzIy+IuUxoQrr7wSDodj0J+uK6/95Cc/6fMcwGeX4jEEj6BvfOMb0nZFRQXefvvtPsvu2LEDa9eu7fVaIuqb1+vFlVdeifLycgCATCbDiy++iBUrVgy5zq7PX2tra7/zCtfW1uLFF1/s9druvv71r0t/IQqHw3j88cf7LOvxePDHP/5R2r/pppugVCoH0Xqi9MVnl+KINGJisZg4a9YsEYAIQMzLyxP379/fo1x9fb1YWloqlZs9e7YYi8VGoMVEo0sgEBAXL14sPTuCIIjPPfdcUupetmyZVK/BYBA3b97co4zT6RTnz58vlcvNzRV9Pl+/9d5zzz1SeblcLr722ms9yoRCIXHFihVSOa1WK9bV1SXlexGNNsXFxdKzsHr16gHL89mlEwRRFMVTF7mpu7KyMixcuFCaV9BkMuGuu+7CggULoFAosG3bNjz11FPSn1a0Wi02bNiAOXPmjGSziUaFxx9/HD/+8Y+l/czMzITG0i9ZsgQ/+MEPej1XXV2NOXPmoLW1FUDHWMTbbrsNS5cuhcFgQEVFBZ588knpJTyZTIY1a9b0uRzzCXa7Heeddx4OHTokXXfjjTfi2muvhcViQWVlJZ555hlUVFRI1zz11FNxq94RpZPx48dLM0SsXr0aDz74YL/l+eySZKRTOIni66+/Lmq1Wulfhn19tFqt+Prrr490c4lGjdWrVw/4XPX3ufXWW/utf8uWLaLFYhmwHrlcLj755JODbndlZaVYVFQ0qDb+6Ec/GuZ/JaLRLdGeYFHks0sdOCb4NLB8+XKUl5dj8eLFcW+5niAIAhYtWoTt27dj+fLlI9BCIurNhRdeiIqKClx33XVxUyR1NWfOHGzcuBH/9V//Neh6p0yZgoqKCtx2223QarW9liktLcVbb72FX/3qV0NqO1E647NLAMDhEKeZ48ePY8uWLairqwMAFBQUYN68eSgqKhrhlhFRf1paWrBx40bU1tYiFAohPz8f5557bo+J9BPldrvxySef4Pjx4/B6vcjLy8PMmTNx1llnJanlROmNz276YggmIiIiorTD4RBERERElHYYgomIiIgo7TAEExEREVHaYQgmIiIiorTDEExEREREaYchmIiIiIjSDkMwEREREaUdhmAiIiIiSjsMwURERESUdhiCiYiIiCjtMAQTERERUdphCCYiIiKitKMY6QYQEdGps3PnTqxZs0ba/+53v4uMjIwRaw8R0UgRRFEUR7oRRER0avztb3/DqlWrpP2qqiqMHz9+5BpERDRCOByCiIiIiNIOQzARERERpR2GYCIiIiJKOwzBRERERJR2GIKJiIiIKO1wdggiojQgCELC13z66ae4+OKLk98YIqLTAHuCiYiIiCjtcLEMIqI0IJfLAQCiKCIWi/U43puh9B4TEY0W7AkmIkoDkUgEkUgEzz33XNzxw4cPS+e6fxYuXDhCrSUiSj2GYCIiIiJKOwzBRERERJR2GIKJiIiIKO0wBBMRERFR2mEIJiIiIqK0wxBMRERERGmHIZiIiIiI0g5DMBERERGlHYZgIiIiIko7DMFERERElHYYgomI0ohSqYzbj0ajI9QSIqKRxRBMRJRGjEZj3L7dbh+hlhARjSyGYCKiNDJ+/Pi4/bKyspFpCBHRCBNEURRHuhFERHRqRCIR2Gw2OJ1OAEB+fj6effZZXHzxxdBqtSPcOiKiU4c9wUREaUShUGDVqlXSfn19Pb7yla9Ap9NBp9PBYDBIn02bNo1gS4mIUoshmIgozTzyyCO46KKLehz3+/3wer3Shy/NEdFYxhBMRJRm9Ho91q9fj5dffhkrV67ElClTYDQaIZPxVwIRpQ+OCSYiIiKitMN/9hMRERFR2mEIJiIiIqK0wxBMRERERGmHIZiIiIiI0g5DMBERERGlHYZgIiIiIko7DMFERERElHYYgomIiIgo7TAEExEREVHaYQgmIiIiorTDEExEREREaYchmIiIiIjSDkMwEREREaUdhmAiIiIiSjsMwURERESUdhiCiYiIiCjtMAQTERERUdr5/zhlcRFSlNFdAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "plot_result_expectations(\n", - " [\n", - " (\n", - " result,\n", - " P11p,\n", - " \"rand\",\n", - " f\"P11 (correlation fit) k_R=k_I={pk + 1}\",\n", - " )\n", - " for pk, result in enumerate(results_corr_fit_pk)\n", - " ]\n", - ");" + "# plot_result_expectations(\n", + "# [\n", + "# (\n", + "# result,\n", + "# P11p,\n", + "# \"rand\",\n", + "# f\"P11 (correlation fit) k_R=k_I={pk + 1}\",\n", + "# )\n", + "# for pk, result in enumerate(results_corr_fit_pk)\n", + "# ]\n", + "# );" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "id": "4770c53b", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", - "\n", - "plot_result_expectations(\n", - " [\n", - " (\n", - " results_corr_fit_pk[0],\n", - " P11p,\n", - " \"y\",\n", - " \"Correlation Function Fit $k_R=k_I=1$\",\n", - " ),\n", - " (\n", - " results_corr_fit_pk[2],\n", - " P11p,\n", - " \"k\",\n", - " \"Correlation Function Fit $k_R=k_I=3$\",\n", - " ),\n", - " (results_spectral_fit_pk[0], P11p, \"b\", \"Spectral Density Fit $k_J=1$\"),\n", - " (results_spectral_fit_pk[3], P11p, \"r-.\", \"Spectral Density Fit $k_J=4$\"),\n", - " ],\n", - " axes=axes,\n", - ")\n", - "\n", - "axes.set_yticks([0.6, 0.8, 1])\n", - "axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", - "axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", - "axes.legend(loc=0, fontsize=20);" + "# fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", + "\n", + "# plot_result_expectations(\n", + "# [\n", + "# (\n", + "# results_corr_fit_pk[0],\n", + "# P11p,\n", + "# \"y\",\n", + "# \"Correlation Function Fit $k_R=k_I=1$\",\n", + "# ),\n", + "# (\n", + "# results_corr_fit_pk[2],\n", + "# P11p,\n", + "# \"k\",\n", + "# \"Correlation Function Fit $k_R=k_I=3$\",\n", + "# ),\n", + "# (results_spectral_fit_pk[0], P11p, \"b\", \"Spectral Density Fit $k_J=1$\"),\n", + "# (results_spectral_fit_pk[3], P11p, \"r-.\", \"Spectral Density Fit $k_J=4$\"),\n", + "# ],\n", + "# axes=axes,\n", + "# )\n", + "\n", + "# axes.set_yticks([0.6, 0.8, 1])\n", + "# axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", + "# axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", + "# axes.legend(loc=0, fontsize=20);" ] }, { @@ -1506,12 +1475,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "4883e1cc", "metadata": {}, "outputs": [], "source": [ - "obs = OhmicEnvironment(T, alpha, wc,s=1)" + "obs = OhmicEnvironment(T, alpha, wc,s=1)\n", + "tlist = np.linspace(0, 30 * np.pi / Del, 600)" ] }, { @@ -1526,7 +1496,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "e0924e70", "metadata": {}, "outputs": [ @@ -1537,24 +1507,27 @@ "Correlation function fit:\n", "\n", "Result of fitting the real part of |Result of fitting the imaginary part \n", - "the correlation function with 3 terms: |of the correlation function with 2 terms: \n", + "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", " | \n", " Parameters| a | b | c | Parameters| a | b | c \n", - " 1 | 2.09e-01 |-3.29e-01 |4.22e-15 | 1 |-1.24e+01 |-2.16e+00 |3.12e-01 \n", - " 2 |-1.04e+00 |-3.26e+01 |1.87e-03 | 2 |-1.24e+01 |-7.90e-01 |1.33e-02 \n", - " 3 | 2.35e+00 |-2.19e+00 |6.87e-11 | \n", - " |A normalized RMSE of 1.34e-05 was obtained for the the imaginary part\n", - "A normalized RMSE of 1.68e-05 was obtained for the the real part of |of the correlation function. \n", - "the correlation function. | \n", - "The current fit took 0.106656 seconds. |The current fit took 0.106712 seconds. \n", - "\n" + " 1 | 3.24e-01 |-5.34e-01 |3.32e-23 | 1 |-8.92e+00 |-3.49e-01 |7.57e-04 \n", + " 2 | 2.84e+00 |-2.76e+00 |6.88e-08 | 2 | 5.44e-01 |-4.30e+00 |4.00e+00 \n", + " 3 |-1.67e+00 |-4.72e+00 |2.77e+00 | 3 |-1.34e+01 |-1.04e+00 |2.50e-02 \n", + " 4 | 2.49e-02 |-1.09e-01 |1.08e-41 | 4 |-1.34e+01 |-2.29e+00 |2.90e-01 \n", + " | \n", + "A normalized RMSE of 1.18e-06 was obtained for the the real part of |A normalized RMSE of 6.20e-07 was obtained for the the imaginary part\n", + "the correlation function. |of the correlation function. \n", + "The current fit took 1.838643 seconds. |The current fit took 33.440938 seconds. \n", + "\n", + " Total run time: 416.96s*] Elapsed 416.96s / Remaining 00:00:00:00\n" ] } ], "source": [ - "Obath, fitinfo = obs.approx_by_cf_fit(tlist=tlist,Nr_max=4,Ni_max=4,maxfev=1e9)\n", + "tlist = np.linspace(0, 30 * np.pi / Del, 5000)\n", + "\n", + "Obath, fitinfo = obs.approx_by_cf_fit(tlist=tlist,Nr_max=4,Ni_max=4,maxfev=1e9,target_rsme=None)\n", "print(fitinfo[\"summary\"])\n", - "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", "HEOM_ohmic_corr_fit = HEOMSolver(\n", " Hsys,\n", " (Obath,Q),\n", @@ -1566,7 +1539,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "ddbaebf2", "metadata": {}, "outputs": [ @@ -1577,13 +1550,21 @@ "Result of fitting the spectral density with 4 terms: \n", " \n", " Parameters| lam | gamma | w0 \n", - " 1 | 1.00e+00 | 1.03e+00 |3.32e+00\n", + " 1 | 6.79e-01 | 8.67e-01 |1.22e-01\n", " 2 | 1.67e+00 | 9.17e-01 |1.13e+00\n", " 3 | 1.56e+00 | 9.46e-01 |2.11e+00\n", - " 4 | 6.79e-01 | 8.68e-01 |1.20e-01\n", + " 4 | 1.00e+00 | 1.03e+00 |3.32e+00\n", " \n", "A normalized RMSE of 4.39e-05 was obtained for the the spectral density.\n", - "The current fit took 16.990672 seconds.\n" + "The current fit took 46.514615 seconds.\n", + " [****** 28% ] Elapsed 3.46s / Remaining 00:00:00:08" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 10.46s*] Elapsed 10.46s / Remaining 00:00:00:00\n" ] } ], @@ -1597,28 +1578,107 @@ " max_depth=5,\n", " options=options,\n", ")\n", - "results_ohmic_sd_fit = HEOM_ohmic_sd_fit.run(rho0, tlist)" + "results_ohmic_sd_fit2 = HEOM_ohmic_sd_fit.run(rho0, tlist)" ] }, { "cell_type": "markdown", - "id": "cfa14447", + "id": "50b833b1", "metadata": {}, "source": [ - "Finally we plot the dynamics obtained by the different methods" + "# Methods based on the Prony Polinomial \n", + "\n", + "The Prony polynomial forms the mathematical foundation for many spectral analysis techniques that estimate frequencies, damping factors, and amplitudes of signals. These methods work by interpreting a given signal as a sum of complex exponentials and deriving a polynomial whose roots correspond to the frequencies or poles of the system.\n", + "\n", + "The methods consider a signal \n", + "\n", + "$$f(t)=\\sum_{k=0}^{N-1} c_{k} e^{-\\nu_{k} t} =\\sum_{k=0}^{N-1} c_{k} z_{k}^{t} $$\n", + "\n", + "The $z_{k}$ can be seen as the solution og the Prony Polynomial\n", + "\n", + "$$P(z)=\\prod_{k=0}^{N-1}(z-z_{k})$$\n", + "\n", + "By obtaining the roots of this polynomial one can obtain the damping rate and the frequency of each mode, the amplitude can lated be obtained by solving the least-squares Vandermonde system given by\n", + "\n", + "$$ V_{N,M}(z)c = f $$\n", + "\n", + "Where $M$ is the length, of the signal, and $f=f(t_{sample})$ is the signal evaluated in the sampling points,is a vector $c = (c_{1}, \\dots, c_{N})$, and $V_{N,M}(z)$ is the Vandermonde matrix given by\n", + "\n", + "\n", + "$$V_{M,N}(z)=\\begin{pmatrix} \n", + "1 &1 &\\dots &1 \\\\\n", + "z_{1} & z_{2} &\\dots & z_{N} \\\\\n", + "z_{1}^{2} & z_{2}^{2} &\\dots & z_{N}^{2} \\\\\n", + "\\vdots & \\vdots & \\ddots & \\vdots \\\\\n", + "z_{1}^{M} & z_{2}^{M} &\\dots & z_{N}^{M} \\\\\n", + "\\end{pmatrix}$$\n", + "\n", + "The main difference between the methods is the way one obtains the roots of the polynomial, typically whether this system is solved or a low rank approximation is found for the polynomial, [this article](https://academic.oup.com/imajna/article-abstract/43/2/789/6525860?redirectedFrom=fulltext) is a good reference, the QuTiP implementations are based on it, and the matlab implementations made available by the authors\n" + ] + }, + { + "cell_type": "markdown", + "id": "f85ab699", + "metadata": {}, + "source": [ + "## Using the Original Prony Method on the Correlation Function\n", + "\n", + "The method is available via `approx_by_prony`. Compared to the other approaches showed so far. The Prony based methods, shine on their simplicity no information needs to be known about the function, and one just needs to provide the sampling points, and the Number of Exponents one desires" ] }, { "cell_type": "code", - "execution_count": null, - "id": "5ba2889a", + "execution_count": 41, + "id": "b75d4072", + "metadata": {}, + "outputs": [], + "source": [ + "tlist2=np.linspace(0,2_000,5000)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "4e24e35b", + "metadata": {}, + "outputs": [], + "source": [ + "pbath,(amp,ph)=obs.approx_by_prony(tlist2,Nr=5,Ni=5,combine=True)\n", + "pbath.T=T\n", + "# mask=abs(amp)>1\n", + "# amp=amp[mask]\n", + "# ph=ph[mask]\n", + "# print(\"done\")\n", + "# HEOM_ohmic_prony_fit = HEOMSolver(\n", + "# Hsys,\n", + "# (pbath,Q),\n", + "# max_depth=5,\n", + "# options=options,\n", + "# )\n", + "# results_ohmic_prony_fit = HEOM_ohmic_prony_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "a2faa5fb", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -1626,69 +1686,472 @@ } ], "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", + "diff=(pbath.correlation_function(tlist2))-obs.correlation_function(tlist2)\n", + "tlist3=np.linspace(0,tlist2[np.argmax(diff)],1000)\n", + "# plt.plot(tlist3,pbath.correlation_function(tlist3),\"k--\")\n", + "# plt.plot(tlist3,Obath.correlation_function(tlist3),\"b\")\n", + "# plt.plot(tlist3,obs.correlation_function(tlist3),\"r\")\n", "\n", - "plot_result_expectations(\n", - " [\n", - " (\n", - " results_corr_fit_pk[2],\n", - " P11p,\n", - " \"b\",\n", - " \"Correlation Function Fit $k_R=k_I=3$\",\n", - " ),\n", - " (results_spectral_fit_pk[3], P11p, \"r-.\", \"Spectral Density Fit $k_J=4$\"),\n", - " (results_ohmic_corr_fit, P11p, \"k--\", \"Correlation Fit Ohmic Bath\"),\n", - " (results_ohmic_sd_fit, P11p, \"g\", \"Spectral Density Fit Ohmic Bath\"),\n", - " ],\n", - " axes=axes,\n", - ")\n", - "axes.set_yticks([0.6, 0.8, 1])\n", - "axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", - "axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", - "axes.legend(loc=0, fontsize=20);\n", - "axes.set_xlim(0,35)\n", - "axes.set_ylim(0.9,1)\n", - "axes.set_yscale(\"log\")" + "plt.plot(abs(diff),label=\"Prony\")\n", + "plt.plot(abs(Obath.correlation_function(tlist2)-obs.correlation_function(tlist2)),label=\"CORR\")\n", + "plt.legend()\n", + "#plt.yscale(\"log\")" ] }, { "cell_type": "markdown", - "id": "d0fc9218", + "id": "af659e73", "metadata": {}, "source": [ - "## About" + "Somehow the problems seems to be the way I construct the bath" ] }, { "cell_type": "code", - "execution_count": null, - "id": "e1eb99ec", + "execution_count": 44, + "id": "b64a4d76", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "QuTiP: Quantum Toolbox in Python\n", - "================================\n", - "Copyright (c) QuTiP team 2011 and later.\n", - "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", - "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", - "Original developers: R. J. Johansson & P. D. Nation.\n", - "Previous lead developers: Chris Granade & A. Grimsmo.\n", - "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", - "\n", - "QuTiP Version: 5.1.0.dev0+7941773\n", - "Numpy Version: 2.1.3\n", - "Scipy Version: 1.14.1\n", - "Cython Version: 3.0.9\n", - "Matplotlib Version: 3.9.2\n", - "Python Version: 3.12.7\n", - "Number of CPUs: 16\n", - "BLAS Info: Generic\n", - "INTEL MKL Ext: None\n", - "Platform Info: Linux (x86_64)\n", + " [*********47% ] Elapsed 35.60s / Remaining 00:00:00:40" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 106.29s*] Elapsed 106.29s / Remaining 00:00:00:00\n" + ] + } + ], + "source": [ + "HEOM_ohmic_prony_fit = HEOMSolver(\n", + " Hsys,\n", + " (pbath,Q),\n", + " max_depth=5,\n", + " options=options,\n", + ")\n", + "results_ohmic_prony_fit = HEOM_ohmic_prony_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "10e50bf0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gen_plots(pbath, w, J, t, C, w2, S)" + ] + }, + { + "cell_type": "markdown", + "id": "1d4ffc81", + "metadata": {}, + "source": [ + "## Using the matrix Pencil Method on the Correlation Function\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "7f14b9cb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 7.98s*] Elapsed 7.98s / Remaining 00:00:00:00\n" + ] + } + ], + "source": [ + "mpbath,_=obs.approx_by_mp(tlist2,Nr=4,Ni=4)\n", + "mpbath.T=T\n", + "HEOM_ohmic_mp_fit = HEOMSolver(\n", + " Hsys,\n", + " (mpbath,Q),\n", + " max_depth=5,\n", + " options=options,\n", + ")\n", + "results_ohmic_mp_fit = HEOM_ohmic_mp_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "3b334563", + "metadata": {}, + "source": [ + "The decomposition is ok, the heom solver is the one failing try with other smaller couplings, and hierarchies untill you can figure it out, the accelerating part works without trouble" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "3ed89ed7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gen_plots(mpbath, w, J, t, C, w2, S)" + ] + }, + { + "cell_type": "markdown", + "id": "c04f6f61", + "metadata": {}, + "source": [ + "## Using the ESPRIT Method on the Correlation Function\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "7708f4f1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 14.29s*] Elapsed 14.28s / Remaining 00:00:00:00\n" + ] + } + ], + "source": [ + "esbath,_=obs.approx_by_esprit(tlist2,Nr=5,Ni=4)\n", + "esbath.T=T\n", + "HEOM_ohmic_es_fit = HEOMSolver(\n", + " Hsys,\n", + " (esbath,Q),\n", + " max_depth=5,\n", + " options=options,\n", + ")\n", + "results_ohmic_es_fit = HEOM_ohmic_es_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "ad89de4e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "diff=(esbath.correlation_function(tlist2))-obs.correlation_function(tlist2)\n", + "tlist3=np.linspace(0,tlist2[np.argmax(diff)],1000)\n", + "# plt.plot(tlist3,pbath.correlation_function(tlist3),\"k--\")\n", + "# plt.plot(tlist3,Obath.correlation_function(tlist3),\"b\")\n", + "# plt.plot(tlist3,obs.correlation_function(tlist3),\"r\")\n", + "\n", + "plt.plot(abs(diff),label=\"Prony\")\n", + "plt.plot(abs(Obath.correlation_function(tlist2)-obs.correlation_function(tlist2)),label=\"CORR\")\n", + "plt.legend()\n", + "#plt.yscale(\"log\")" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "0d282401", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gen_plots(esbath, w, J, t, C, w2, S)" + ] + }, + { + "cell_type": "markdown", + "id": "413f223a", + "metadata": {}, + "source": [ + "## Using the AAA Algorithm" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "5a685a80", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mcditoos/qutip_gsoc_app/qutip/utilities.py:54: RuntimeWarning: overflow encountered in exp\n", + " result[non_zero] = 1 / (np.exp(w[non_zero] / w_th) - 1)\n" + ] + }, + { + "ename": "TypeError", + "evalue": "can't multiply sequence by non-int of type 'complex'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[51], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m aaabath\u001b[38;5;241m=\u001b[39m\u001b[43mobs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapprox_by_aaa\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlogspace\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2500\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlogspace\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2500\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43mN_max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m12\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mtol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1e-15\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/qutip_gsoc_app/qutip/core/environment.py:803\u001b[0m, in \u001b[0;36mBosonicEnvironment.approx_by_aaa\u001b[0;34m(self, wlist, tol, N_max, combine, tag)\u001b[0m\n\u001b[1;32m 799\u001b[0m vkAR\u001b[38;5;241m.\u001b[39mextend([\u001b[38;5;241m-\u001b[39mb \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39mj \u001b[38;5;241m*\u001b[39m c, \u001b[38;5;241m-\u001b[39mb \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39mj \u001b[38;5;241m*\u001b[39m c])\n\u001b[1;32m 800\u001b[0m ckAI\u001b[38;5;241m.\u001b[39mextend([\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39mj \u001b[38;5;241m*\u001b[39m (a \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39mj \u001b[38;5;241m*\u001b[39m d) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39mj \u001b[38;5;241m*\u001b[39m (a \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39mj \u001b[38;5;241m*\u001b[39m d) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m])\n\u001b[1;32m 802\u001b[0m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;241m=\u001b[39m ExponentialBosonicEnvironment(\n\u001b[0;32m--> 803\u001b[0m ck_real\u001b[38;5;241m=\u001b[39mckAR, vk_real\u001b[38;5;241m=\u001b[39mvkAR \u001b[38;5;241m-\u001b[39m \u001b[38;5;241;43m1\u001b[39;49m\u001b[43mj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mvkAI\u001b[49m, ck_imag\u001b[38;5;241m=\u001b[39mckAI,\n\u001b[1;32m 804\u001b[0m vk_imag\u001b[38;5;241m=\u001b[39mvkAR \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39mj \u001b[38;5;241m*\u001b[39m vkAI, T\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mT, combine\u001b[38;5;241m=\u001b[39mcombine, tag\u001b[38;5;241m=\u001b[39mtag)\n\u001b[1;32m 805\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\n", + "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'complex'" + ] + } + ], + "source": [ + "aaabath=obs.approx_by_aaa(np.concatenate((-np.logspace(3,-2,2500),np.logspace(-2,3,2500))),N_max=12,tol=1e-15)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44f9f518", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tlist3=np.linspace(-250,250,1000)\n", + "\n", + "diff=(pbath.correlation_function(tlist3))-obs.correlation_function(tlist3)\n", + "diff2=(aaabath.correlation_function(tlist3))-obs.correlation_function(tlist3)\n", + "\n", + "# plt.plot(tlist3,pbath.correlation_function(tlist3),\"k--\")\n", + "# plt.plot(tlist3,Obath.correlation_function(tlist3),\"b\")\n", + "# plt.plot(tlist3,obs.correlation_function(tlist3),\"r\")\n", + "\n", + "\n", + "\n", + "plt.plot(diff.real,label=\"Prony\")\n", + "plt.plot(diff2.real,label=\"AA\")\n", + "\n", + "#plt.plot(abs(Obath.correlation_function(tlist3)-obs.correlation_function(tlist3)),label=\"CORR\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "787b1ae6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 1565.79s] Elapsed 1565.79s / Remaining 00:00:00:00\n" + ] + } + ], + "source": [ + "HEOM_ohmic_aaa_fit = HEOMSolver(\n", + " Hsys,\n", + " (aaabath,Q),\n", + " max_depth=5,\n", + " options=options,\n", + ")\n", + "results_ohmic_aaa_fit = HEOM_ohmic_aaa_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80f55ad6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gen_plots(aaabath, w, J, t, C, w2, S)" + ] + }, + { + "cell_type": "markdown", + "id": "0f305b40", + "metadata": {}, + "source": [ + "Finally we plot the dynamics obtained by the different methods" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5ba2889a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", + "\n", + "plot_result_expectations(\n", + " [\n", + " #(\n", + " # results_corr_fit_pk[2],\n", + " # P11p,\n", + " # \"b\",\n", + " # \"Correlation Function Fit $k_R=k_I=3$\",\n", + " # ),\n", + " #(results_spectral_fit_pk[3], P11p, \"r-.\", \"Spectral Density Fit $k_J=4$\"),\n", + " (results_ohmic_corr_fit, P11p, \"r\", \"Correlation Fit Ohmic Bath\"),\n", + " #(results_ohmic_sd_fit, P11p, \"g\", \"Spectral Density Fit Ohmic Bath\"),\n", + " (results_ohmic_sd_fit2, P11p, \"g--\", \"Spectral Density Fit Ohmic Bath\"),\n", + " #(results_ohmic_prony_fit, P11p, \"g\", \" Prony Fit\"),\n", + " (results_ohmic_prony_fit, P11p, \"k\", \" Prony Fit\"),\n", + "\n", + " (results_ohmic_mp_fit, P11p, \"r\", \"Matrix Pencil Fit\"),\n", + " (results_ohmic_es_fit, P11p, \"b-.\", \"ESPRIT Fit\"),\n", + " (results_ohmic_aaa_fit, P11p, \"r-.\", \"Matrix AAA Fit\"),\n", + "\n", + "\n", + " ],\n", + " axes=axes,\n", + ")\n", + "axes.set_yticks([0.6, 0.8, 1])\n", + "axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", + "axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", + "axes.legend(loc=0, fontsize=20);\n", + "#axes.set_xlim(0,35)\n", + "#axes.set_ylim(0.9,1)\n", + "#axes.set_yscale(\"log\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bae93823", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "d0fc9218", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1eb99ec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "QuTiP: Quantum Toolbox in Python\n", + "================================\n", + "Copyright (c) QuTiP team 2011 and later.\n", + "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", + "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", + "Original developers: R. J. Johansson & P. D. Nation.\n", + "Previous lead developers: Chris Granade & A. Grimsmo.\n", + "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", + "\n", + "QuTiP Version: 5.1.0.dev0+7941773\n", + "Numpy Version: 1.26.4\n", + "Scipy Version: 1.14.1\n", + "Cython Version: 3.0.9\n", + "Matplotlib Version: 3.9.2\n", + "Python Version: 3.12.7\n", + "Number of CPUs: 16\n", + "BLAS Info: Generic\n", + "INTEL MKL Ext: None\n", + "Platform Info: Linux (x86_64)\n", "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", "================================================================================\n", "Please cite QuTiP in your publication.\n", @@ -1714,16 +2177,120 @@ { "cell_type": "code", "execution_count": null, - "id": "e75c2b49", + "id": "fa50ddbb", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'results_spectral_fit_pk' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[59], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m np\u001b[38;5;241m.\u001b[39mallclose(\u001b[43mresults_spectral_fit_pk\u001b[49m[\u001b[38;5;241m3\u001b[39m]\u001b[38;5;241m.\u001b[39mstates[\u001b[38;5;241m5\u001b[39m]\u001b[38;5;241m.\u001b[39mfull(),results_corr_fit_pk[\u001b[38;5;241m2\u001b[39m]\u001b[38;5;241m.\u001b[39mstates[\u001b[38;5;241m5\u001b[39m]\u001b[38;5;241m.\u001b[39mfull(),atol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-3\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'results_spectral_fit_pk' is not defined" + ] + } + ], "source": [ - "assert np.allclose(\n", - " expect(P11p, results_spectral_fit_pk[2].states),\n", - " expect(P11p, results_spectral_fit_pk[3].states),\n", - " rtol=1e-2,\n", - ")" + "assert np.allclose(results_spectral_fit_pk[3].states[5].full(),results_corr_fit_pk[2].states[5].full(),atol=1e-3)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a7fb31c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "414ba293", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80d35a6b", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20dd8b39", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed975955", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b6b493d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9184bc82", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d61f4c20", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b7f2f42", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cebe18a4", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a006120", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12b235a3", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.ipynb b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.ipynb index 6ad3aa4f..5b331f1b 100644 --- a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.ipynb +++ b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.ipynb @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "f3413b8c", "metadata": {}, "outputs": [], @@ -94,10 +94,8 @@ ")\n", "from qutip.solver.heom import (\n", " HEOMSolver,\n", - " LorentzianBath,\n", - " LorentzianPadeBath,\n", ")\n", - "\n", + "from qutip.core.environment import LorentzianEnvironment\n", "from ipywidgets import IntProgress\n", "from IPython.display import display\n", "\n", @@ -114,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "44710770", "metadata": {}, "outputs": [], @@ -134,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "1fefdfa4", "metadata": {}, "outputs": [], @@ -169,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "48867a7b", "metadata": {}, "outputs": [], @@ -184,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "bb765344", "metadata": {}, "outputs": [], @@ -249,10 +247,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "a1dec0a7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "w_list = np.linspace(-2, 2, 100)\n", "\n", @@ -289,10 +298,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "a1c95fd8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "w_list = np.linspace(-2, 2, 100)\n", "\n", @@ -347,10 +367,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "97f3271c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.3457450866699219\n", + " Total run time: 14.46s*] Elapsed 14.45s / Remaining 00:00:00:00**** 19% ] Elapsed 1.25s / Remaining 00:00:00:05[*********66%*** ] Elapsed 7.09s / Remaining 00:00:00:03[*********67%*** ] Elapsed 7.27s / Remaining 00:00:00:03[*********82%******* ] Elapsed 9.66s / Remaining 00:00:00:02\n", + "ODE solver time: 14.470469951629639\n", + "Steady state solver time: 190.69628143310547\n" + ] + } + ], "source": [ "# HEOM dynamics using the Pade approximation:\n", "\n", @@ -360,17 +391,18 @@ "\n", "Nk = 10 # Number of exponents to retain in the expansion of each bath\n", "\n", - "bathL = LorentzianPadeBath(\n", - " bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", - " Nk, tag=\"L\",\n", + "envL = LorentzianEnvironment(\n", + " bath_L.T,bath_L.mu,bath_L.gamma, bath_L.W,\n", ")\n", - "bathR = LorentzianPadeBath(\n", - " bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", - " Nk, tag=\"R\",\n", + "envL_pade= envL.approx_by_pade(Nk=Nk, tag=\"L\")\n", + "envR =LorentzianEnvironment(\n", + " bath_R.T,bath_R.mu,bath_R.gamma, bath_R.W,\n", ")\n", + "envR_pade= envR.approx_by_pade(Nk=Nk, tag=\"L\")\n", + "\n", "\n", "with timer(\"RHS construction time\"):\n", - " solver_pade = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options)\n", + " solver_pade = HEOMSolver(H, [(envL_pade,bath_L.Q), (envR_pade,bath_R.Q)], max_depth=2, options=options)\n", "\n", "with timer(\"ODE solver time\"):\n", " result_pade = solver_pade.run(rho0, tlist)\n", @@ -389,10 +421,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "70f5d901", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the Pade results\n", "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", @@ -422,24 +465,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "24fa4a52", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.2509186267852783\n", + " Total run time: 8.65s*] Elapsed 8.65s / Remaining 00:00:00:00[****** 25% ] Elapsed 1.99s / Remaining 00:00:00:05[*********43% ] Elapsed 3.70s / Remaining 00:00:00:04[*********55%* ] Elapsed 4.72s / Remaining 00:00:00:03[*********67%*** ] Elapsed 5.80s / Remaining 00:00:00:02[*********68%**** ] Elapsed 5.85s / Remaining 00:00:00:02\n", + "ODE solver time: 8.654348611831665\n", + "Steady state solver time: 189.28271412849426\n" + ] + } + ], "source": [ "# HEOM dynamics using the Matsubara approximation:\n", "\n", - "bathL = LorentzianBath(\n", - " bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", - " Nk, tag=\"L\",\n", - ")\n", - "bathR = LorentzianBath(\n", - " bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", - " Nk, tag=\"R\",\n", - ")\n", + "envL_mats= envL.approx_by_matsubara(Nk=Nk, tag=\"L\")\n", + "envR_mats= envR.approx_by_matsubara(Nk=Nk, tag=\"R-\")\n", + "\n", "\n", "with timer(\"RHS construction time\"):\n", - " solver_mats = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options)\n", + " solver_mats = HEOMSolver(H, [(envL_mats,bath_L.Q), (envR_mats,bath_R.Q)], max_depth=2, options=options)\n", "\n", "with timer(\"ODE solver time\"):\n", " result_mats = solver_mats.run(rho0, tlist)\n", @@ -458,10 +507,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "90c30fab", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the Pade results\n", "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", @@ -506,10 +566,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "76d31675", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Analytical steady state current: (0.0008130726698792024+0j)\n" + ] + } + ], "source": [ "def analytical_steady_state_current(bath_L, bath_R, e1):\n", " \"\"\" Calculate the analytical steady state current. \"\"\"\n", @@ -556,7 +624,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "2376c586", "metadata": {}, "outputs": [], @@ -592,10 +660,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "efed55fc", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pade steady state current (L): (1.6805133673525319e-18-4.336808689942018e-19j)\n", + "Pade steady state current (R): -0j\n" + ] + } + ], "source": [ "curr_ss_pade_L = state_current(ado_ss_pade, \"L\")\n", "curr_ss_pade_R = state_current(ado_ss_pade, \"R\")\n", @@ -606,10 +683,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "09df1f63", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Matsubara steady state current (L): (-0.0011018485316349584-1.0842021724855044e-18j)\n", + "Matsubara steady state current (R): -0j\n" + ] + } + ], "source": [ "curr_ss_mats_L = state_current(ado_ss_mats, \"L\")\n", "curr_ss_mats_R = state_current(ado_ss_mats, \"R\")\n", @@ -630,10 +716,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "20c32bd5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pade current (R): -0j\n", + "Matsubara current (R): -0j\n", + "Analytical curernt: (0.0008130726698792024+0j)\n" + ] + } + ], "source": [ "print(f\"Pade current (R): {curr_ss_pade_R}\")\n", "print(f\"Matsubara current (R): {curr_ss_mats_R}\")\n", @@ -670,10 +766,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "3077adba", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9d44343dc5d14d54828d6a5b7a5eba7e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "IntProgress(value=0, max=200)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "NameError", + "evalue": "name 'LorentzianPadeBath' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[17], line 54\u001b[0m\n\u001b[1;32m 46\u001b[0m curr_ss_analytic_thetas \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 47\u001b[0m current_analytic_for_theta(e1, bath_L, bath_R, theta)\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m theta \u001b[38;5;129;01min\u001b[39;00m thetas\n\u001b[1;32m 49\u001b[0m ]\n\u001b[1;32m 51\u001b[0m \u001b[38;5;66;03m# The number of expansion terms has been dropped to Nk=6 to speed\u001b[39;00m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;66;03m# up notebook execution. Increase to Nk=10 for more accurate results.\u001b[39;00m\n\u001b[1;32m 53\u001b[0m curr_ss_pade_theta \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m---> 54\u001b[0m \u001b[43mcurrent_pade_for_theta\u001b[49m\u001b[43m(\u001b[49m\u001b[43mH\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbath_L\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbath_R\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtheta\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNk\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m theta \u001b[38;5;129;01min\u001b[39;00m thetas\n\u001b[1;32m 56\u001b[0m ]\n", + "Cell \u001b[0;32mIn[17], line 29\u001b[0m, in \u001b[0;36mcurrent_pade_for_theta\u001b[0;34m(H, bath_L, bath_R, theta, Nk)\u001b[0m\n\u001b[1;32m 26\u001b[0m bath_L \u001b[38;5;241m=\u001b[39m bath_L\u001b[38;5;241m.\u001b[39mreplace(theta\u001b[38;5;241m=\u001b[39mtheta)\n\u001b[1;32m 27\u001b[0m bath_R \u001b[38;5;241m=\u001b[39m bath_R\u001b[38;5;241m.\u001b[39mreplace(theta\u001b[38;5;241m=\u001b[39mtheta)\n\u001b[0;32m---> 29\u001b[0m bathL \u001b[38;5;241m=\u001b[39m \u001b[43mLorentzianPadeBath\u001b[49m(\n\u001b[1;32m 30\u001b[0m bath_L\u001b[38;5;241m.\u001b[39mQ, bath_L\u001b[38;5;241m.\u001b[39mgamma, bath_L\u001b[38;5;241m.\u001b[39mW, bath_L\u001b[38;5;241m.\u001b[39mmu, bath_L\u001b[38;5;241m.\u001b[39mT,\n\u001b[1;32m 31\u001b[0m Nk, tag\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mL\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 32\u001b[0m )\n\u001b[1;32m 33\u001b[0m bathR \u001b[38;5;241m=\u001b[39m LorentzianPadeBath(\n\u001b[1;32m 34\u001b[0m bath_R\u001b[38;5;241m.\u001b[39mQ, bath_R\u001b[38;5;241m.\u001b[39mgamma, bath_R\u001b[38;5;241m.\u001b[39mW, bath_R\u001b[38;5;241m.\u001b[39mmu, bath_R\u001b[38;5;241m.\u001b[39mT,\n\u001b[1;32m 35\u001b[0m Nk, tag\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mR\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 36\u001b[0m )\n\u001b[1;32m 38\u001b[0m solver_pade \u001b[38;5;241m=\u001b[39m HEOMSolver(H, [bathL, bathR], max_depth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, options\u001b[38;5;241m=\u001b[39moptions)\n", + "\u001b[0;31mNameError\u001b[0m: name 'LorentzianPadeBath' is not defined" + ] + } + ], "source": [ "# Theta (bias voltages)\n", "\n", @@ -818,9 +941,21 @@ "formats": "ipynb,md:myst" }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "qutip-dev", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" } }, "nbformat": 4, From 58411bc773135630c812359d1765d1b69bed7a50 Mon Sep 17 00:00:00 2001 From: mcditooss Date: Tue, 18 Feb 2025 17:19:01 +0100 Subject: [PATCH 17/44] New stuff in Bosonic fitting --- .../heom/heom-1a-spin-bath-model-basic.ipynb | 1519 +++++++++++ ...spin-bath-model-very-strong-coupling.ipynb | 793 ++++++ ...om-1c-spin-bath-model-underdamped-sd.ipynb | 778 ++++++ ...eom-1d-spin-bath-model-ohmic-fitting.ipynb | 1740 ++++++++++++ ...om-1e-spin-bath-model-pure-dephasing.ipynb | 847 ++++++ tutorials-v4/heom/heom-2-fmo-example.ipynb | 694 +++++ .../heom/heom-3-quantum-heat-transport.ipynb | 669 +++++ .../heom/heom-4-dynamical-decoupling.ipynb | 776 ++++++ ...om-5a-fermions-single-impurity-model.ipynb | 827 ++++++ ...eom-5b-fermions-discrete-boson-model.ipynb | 521 ++++ tutorials-v4/heom/heom-index.ipynb | 56 + tutorials-v5/heom/fitting.ipynb | 2326 +++++++++++++++++ .../heom/heom-1a-spin-bath-model-basic.ipynb | 2 +- ...eom-1d-spin-bath-model-ohmic-fitting.ipynb | 1416 +++++----- 14 files changed, 12296 insertions(+), 668 deletions(-) create mode 100644 tutorials-v4/heom/heom-1a-spin-bath-model-basic.ipynb create mode 100644 tutorials-v4/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb create mode 100644 tutorials-v4/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb create mode 100644 tutorials-v4/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb create mode 100644 tutorials-v4/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb create mode 100644 tutorials-v4/heom/heom-2-fmo-example.ipynb create mode 100644 tutorials-v4/heom/heom-3-quantum-heat-transport.ipynb create mode 100644 tutorials-v4/heom/heom-4-dynamical-decoupling.ipynb create mode 100644 tutorials-v4/heom/heom-5a-fermions-single-impurity-model.ipynb create mode 100644 tutorials-v4/heom/heom-5b-fermions-discrete-boson-model.ipynb create mode 100644 tutorials-v4/heom/heom-index.ipynb create mode 100644 tutorials-v5/heom/fitting.ipynb diff --git a/tutorials-v4/heom/heom-1a-spin-bath-model-basic.ipynb b/tutorials-v4/heom/heom-1a-spin-bath-model-basic.ipynb new file mode 100644 index 00000000..15950765 --- /dev/null +++ b/tutorials-v4/heom/heom-1a-spin-bath-model-basic.ipynb @@ -0,0 +1,1519 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e5a736fa", + "metadata": {}, + "source": [ + "# HEOM 1a: Spin-Bath model (introduction)" + ] + }, + { + "cell_type": "markdown", + "id": "4430f6e9", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The HEOM method solves the dynamics and steady state of a system and its\n", + "environment, the latter of which is encoded in a set of auxiliary density\n", + "matrices.\n", + "\n", + "In this example we show the evolution of a single two-level system in contact\n", + "with a single Bosonic environment. The properties of the system are encoded\n", + "in a Hamiltonian, and a coupling operator which describes how it is coupled\n", + "to the environment.\n", + "\n", + "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", + "\n", + "In the example below we show how to model the overdamped Drude-Lorentz\n", + "Spectral Density, commonly used with the HEOM. We show how to do this using\n", + "the Matsubara, Pade and fitting decompositions, and compare their\n", + "convergence.\n", + "\n", + "### Drude-Lorentz (overdamped) spectral density\n", + "\n", + "The Drude-Lorentz spectral density is:\n", + "\n", + "$$J_D(\\omega)= \\frac{2\\omega\\lambda\\gamma}{{\\gamma}^2 + \\omega^2}$$\n", + "\n", + "where $\\lambda$ scales the coupling strength, and $\\gamma$ is the cut-off\n", + "frequency. We use the convention,\n", + "\\begin{equation*}\n", + "C(t) = \\int_0^{\\infty} d\\omega \\frac{J_D(\\omega)}{\\pi}[\\coth(\\beta\\omega) \\\n", + " \\cos(\\omega \\tau) - i \\sin(\\omega \\tau)]\n", + "\\end{equation*}\n", + "\n", + "With the HEOM we must use an exponential decomposition:\n", + "\n", + "\\begin{equation*}\n", + "C(t)=\\sum_{k=0}^{k=\\infty} c_k e^{-\\nu_k t}\n", + "\\end{equation*}\n", + "\n", + "As an example, the Matsubara decomposition of the Drude-Lorentz spectral\n", + "density is given by:\n", + "\n", + "\\begin{equation*}\n", + " \\nu_k = \\begin{cases}\n", + " \\gamma & k = 0\\\\\n", + " {2 \\pi k} / {\\beta } & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "\\begin{equation*}\n", + " c_k = \\begin{cases}\n", + " \\lambda \\gamma (\\cot(\\beta \\gamma / 2) - i) \\\n", + " & k = 0\\\\\n", + " 4 \\lambda \\gamma \\nu_k / \\{(nu_k^2 - \\gamma^2)\\beta \\} \\\n", + " & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." + ] + }, + { + "cell_type": "markdown", + "id": "7521b1ac", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e863e62", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "import contextlib\n", + "import time\n", + "\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "from scipy.optimize import curve_fit\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " Options,\n", + " basis,\n", + " brmesolve,\n", + " destroy,\n", + " expect,\n", + " liouvillian,\n", + " qeye,\n", + " sigmax,\n", + " sigmaz,\n", + " spost,\n", + " spre,\n", + " tensor,\n", + ")\n", + "\n", + "from qutip.nonmarkov.heom import (\n", + " BosonicBath,\n", + " DrudeLorentzBath,\n", + " DrudeLorentzPadeBath,\n", + " HEOMSolver,\n", + " HSolverDL,\n", + ")\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "0de1b3a3", + "metadata": {}, + "source": [ + "## Helper functions\n", + "\n", + "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e89b6553", + "metadata": {}, + "outputs": [], + "source": [ + "def cot(x):\n", + " \"\"\"Vectorized cotangent of x.\"\"\"\n", + " return 1.0 / np.tan(x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ecb6601d", + "metadata": {}, + "outputs": [], + "source": [ + "def dl_matsubara_params(lam, gamma, T, nk):\n", + " \"\"\"Calculation of the real and imaginary expansions of the Drude-Lorenz\n", + " correlation functions.\n", + " \"\"\"\n", + " ckAR = [lam * gamma * cot(gamma / (2 * T))]\n", + " ckAR.extend(\n", + " 4\n", + " * lam\n", + " * gamma\n", + " * T\n", + " * 2\n", + " * np.pi\n", + " * k\n", + " * T\n", + " / ((2 * np.pi * k * T) ** 2 - gamma**2)\n", + " for k in range(1, nk + 1)\n", + " )\n", + " vkAR = [gamma]\n", + " vkAR.extend(2 * np.pi * k * T for k in range(1, nk + 1))\n", + "\n", + " ckAI = [lam * gamma * (-1.0)]\n", + " vkAI = [gamma]\n", + "\n", + " return ckAR, vkAR, ckAI, vkAI" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9bb61e3c", + "metadata": {}, + "outputs": [], + "source": [ + "def dl_corr_approx(t, nk):\n", + " \"\"\"Drude-Lorenz correlation function approximation.\n", + "\n", + " Approximates the correlation function at each time t to nk exponents.\n", + " \"\"\"\n", + " c = lam * gamma * (-1.0j + cot(gamma / (2 * T))) * np.exp(-gamma * t)\n", + " for k in range(1, nk):\n", + " vk = 2 * np.pi * k * T\n", + " c += (4 * lam * gamma * T * vk / (vk**2 - gamma**2)) * np.exp(\n", + " -vk * t\n", + " )\n", + " return c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "799e4a70", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_result_expectations(plots, axes=None):\n", + " \"\"\"Plot the expectation values of operators as functions of time.\n", + "\n", + " Each plot in plots consists of (solver_result, measurement_operation,\n", + " color, label).\n", + " \"\"\"\n", + " if axes is None:\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " fig_created = True\n", + " else:\n", + " fig = None\n", + " fig_created = False\n", + "\n", + " # add kw arguments to each plot if missing\n", + " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", + " for result, m_op, color, label, kw in plots:\n", + " exp = np.real(expect(result.states, m_op))\n", + " kw.setdefault(\"linewidth\", 2)\n", + " axes.plot(result.times, exp, color, label=label, **kw)\n", + "\n", + " if fig_created:\n", + " axes.legend(loc=0, fontsize=12)\n", + " axes.set_xlabel(\"t\", fontsize=28)\n", + "\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56abea6b", + "metadata": {}, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\"Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "markdown", + "id": "39df0afe", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian, bath and system measurement operators:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "537d0beb", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the system Hamiltonian\n", + "eps = 0.5 # Energy of the 2-level system.\n", + "Del = 1.0 # Tunnelling term\n", + "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "107a63de", + "metadata": {}, + "outputs": [], + "source": [ + "# Initial state of the system.\n", + "rho0 = basis(2, 0) * basis(2, 0).dag()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5989ca9b", + "metadata": {}, + "outputs": [], + "source": [ + "# System-bath coupling (Drude-Lorentz spectral density)\n", + "Q = sigmaz() # coupling operator\n", + "\n", + "# Bath properties:\n", + "gamma = 0.5 # cut off frequency\n", + "lam = 0.1 # coupling strength\n", + "T = 0.5\n", + "beta = 1.0 / T\n", + "\n", + "# HEOM parameters\n", + "NC = 5 # cut off parameter for the bath\n", + "Nk = 2 # terms in the Matsubara expansion of the correlation function\n", + "\n", + "# Times to solve for\n", + "tlist = np.linspace(0, 50, 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd300ad7", + "metadata": {}, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresonding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresonding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "5f312989", + "metadata": {}, + "source": [ + "### First of all, it is useful to look at the spectral density\n", + "\n", + "Now we are ready to begin. Let's look at the shape of the spectral density given the bath parameters we defined above:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e28f8220", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_spectral_density():\n", + " \"\"\"Plot the Drude-Lorentz spectral density\"\"\"\n", + " w = np.linspace(0, 5, 1000)\n", + " J = w * 2 * lam * gamma / (gamma**2 + w**2)\n", + "\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " axes.plot(w, J, \"r\", linewidth=2)\n", + " axes.set_xlabel(r\"$\\omega$\", fontsize=28)\n", + " axes.set_ylabel(r\"J\", fontsize=28)\n", + "\n", + "\n", + "plot_spectral_density()" + ] + }, + { + "cell_type": "markdown", + "id": "1b475c22", + "metadata": {}, + "source": [ + "Next we calculate the exponents using the Matsubara decompositions. Here we\n", + "split them into real and imaginary parts.\n", + "\n", + "The HEOM code will optimize these, and reduce the number of exponents when\n", + "real and imaginary parts have the same exponent. This is clearly the case\n", + "for the first term in the vkAI and vkAR lists." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "457da72a", + "metadata": {}, + "outputs": [], + "source": [ + "ckAR, vkAR, ckAI, vkAI = dl_matsubara_params(nk=Nk, lam=lam, gamma=gamma, T=T)" + ] + }, + { + "cell_type": "markdown", + "id": "a4011546", + "metadata": {}, + "source": [ + "Having created the lists which specify the bath correlation functions, we\n", + "create a `BosonicBath` from them and pass the bath to the `HEOMSolver` class.\n", + "\n", + "The solver constructs the \"right hand side\" (RHS) determinining how the\n", + "system and auxiliary density operators evolve in time. This can then be used\n", + "to solve for dynamics or steady-state.\n", + "\n", + "Below we create the bath and solver and then solve for the dynamics by\n", + "calling `.run(rho0, tlist)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1c570e2", + "metadata": {}, + "outputs": [], + "source": [ + "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI)\n", + " HEOMMats = HEOMSolver(Hsys, bath, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMats = HEOMMats.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f50f3f07", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (resultMats, P11p, \"b\", \"P11 Mats\"),\n", + " (resultMats, P12p, \"r\", \"P12 Mats\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "27dddd4e", + "metadata": {}, + "source": [ + "In practice, one would not perform this laborious expansion for the\n", + "Drude-Lorentz correlation function, because QuTiP already has a class,\n", + "`DrudeLorentzBath`, that can construct this bath for you. Nevertheless,\n", + "knowing how to perform this expansion will allow you to construct your own\n", + "baths for other spectral densities.\n", + "\n", + "Below we show how to use this built-in functionality:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "73aa8c51", + "metadata": {}, + "outputs": [], + "source": [ + "# Compare to built-in Drude-Lorentz bath:\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", + " HEOM_dlbath = HEOMSolver(Hsys, bath, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " result_dlbath = HEOM_dlbath.run(rho0, tlist) # normal 115" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4add1a5", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (result_dlbath, P11p, \"b\", \"P11 (DrudeLorentzBath)\"),\n", + " (result_dlbath, P12p, \"r\", \"P12 (DrudeLorentzBath)\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "44099dc6", + "metadata": {}, + "source": [ + "We also provide a legacy class, `HSolverDL`, which calculates the\n", + "Drude-Lorentz correlation functions automatically, to be backwards\n", + "compatible with the previous HEOM solver in QuTiP:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "234ad587", + "metadata": {}, + "outputs": [], + "source": [ + "# Compare to legacy class:\n", + "\n", + "# The legacy class performs the above collation of coefficients automatically,\n", + "# based upon the parameters for the Drude-Lorentz spectral density.\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " HEOMlegacy = HSolverDL(Hsys, Q, lam, T, NC, Nk, gamma, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultLegacy = HEOMlegacy.run(rho0, tlist) # normal 115" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab77f8bf", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (resultLegacy, P11p, \"b\", \"P11 Legacy\"),\n", + " (resultLegacy, P12p, \"r\", \"P12 Legacy\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "30d16d29", + "metadata": {}, + "source": [ + "## Ishizaki-Tanimura Terminator\n", + "\n", + "To speed up convergence (in terms of the number of exponents kept in the\n", + "Matsubara decomposition), We can treat the $Re[C(t=0)]$ component as a\n", + "delta-function distribution, and include it as Lindblad correction. This is\n", + "sometimes known as the Ishizaki-Tanimura Terminator.\n", + "\n", + "In more detail, given\n", + "\n", + "\\begin{equation*}\n", + "C(t)=\\sum_{k=0}^{\\infty} c_k e^{-\\nu_k t}\n", + "\\end{equation*}\n", + "\n", + "since $\\nu_k=\\frac{2 \\pi k}{\\beta }$, if $1/\\nu_k$ is much much smaller than\n", + "other important time-scales, we can approximate,\n", + "$ e^{-\\nu_k t} \\approx \\delta(t)/\\nu_k$, and $C(t)=\\sum_{k=N_k}^{\\infty}\n", + "\\frac{c_k}{\\nu_k} \\delta(t)$\n", + "\n", + "It is convenient to calculate the whole sum\n", + "$ C(t)=\\sum_{k=0}^{\\infty} \\frac{c_k}{\\nu_k} = 2 \\lambda / (\\beta \\gamma)\n", + "- i\\lambda $\n", + ", and subtract off the contribution from the finite number of Matsubara terms\n", + "that are kept in the hierarchy, and treat the residual as a Lindblad.\n", + "\n", + "This is clearer if we plot the correlation function with a large number of\n", + "Matsubara terms:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f71056b", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_correlation_expansion_divergence():\n", + " \"\"\"We plot the correlation function with a large number of Matsubara terms\n", + " to show that the real part is slowly diverging at t = 0.\n", + " \"\"\"\n", + " t = np.linspace(0, 2, 100)\n", + "\n", + " # correlation coefficients with 15k and 2 terms\n", + " corr_15k = dl_corr_approx(t, 15_000)\n", + " corr_2 = dl_corr_approx(t, 2)\n", + "\n", + " fig, ax1 = plt.subplots(figsize=(12, 7))\n", + "\n", + " ax1.plot(\n", + " t, np.real(corr_2), color=\"b\", linewidth=3, label=r\"Mats = 2 real\"\n", + " )\n", + " ax1.plot(\n", + " t, np.imag(corr_2), color=\"r\", linewidth=3, label=r\"Mats = 2 imag\"\n", + " )\n", + " ax1.plot(\n", + " t, np.real(corr_15k), \"b--\", linewidth=3, label=r\"Mats = 15000 real\"\n", + " )\n", + " ax1.plot(\n", + " t, np.imag(corr_15k), \"r--\", linewidth=3, label=r\"Mats = 15000 imag\"\n", + " )\n", + "\n", + " ax1.set_xlabel(\"t\")\n", + " ax1.set_ylabel(r\"$C$\")\n", + " ax1.legend()\n", + "\n", + "\n", + "plot_correlation_expansion_divergence();" + ] + }, + { + "cell_type": "markdown", + "id": "1cbb1115", + "metadata": {}, + "source": [ + "Let us evaluate the result including this Ishizaki-Tanimura terminator:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3b1bd7a", + "metadata": {}, + "outputs": [], + "source": [ + "# Run HEOM solver and include the Ishizaki-Tanimura terminator\n", + "\n", + "# Notes:\n", + "#\n", + "# * when using the built-in DrudeLorentzBath, the terminator (L_bnd) is\n", + "# available from bath.terminator().\n", + "#\n", + "# * in the legacy HSolverDL function the terminator is included automatically\n", + "# if the parameter bnd_cut_approx=True is used.\n", + "\n", + "op = -2 * spre(Q) * spost(Q.dag()) + spre(Q.dag() * Q) + spost(Q.dag() * Q)\n", + "\n", + "approx_factr = (2 * lam / (beta * gamma)) - 1j * lam\n", + "\n", + "approx_factr -= lam * gamma * (-1.0j + cot(gamma / (2 * T))) / gamma\n", + "for k in range(1, Nk + 1):\n", + " vk = 2 * np.pi * k * T\n", + "\n", + " approx_factr -= (4 * lam * gamma * T * vk / (vk**2 - gamma**2)) / vk\n", + "\n", + "L_bnd = -approx_factr * op\n", + "\n", + "Ltot = -1.0j * (spre(Hsys) - spost(Hsys)) + L_bnd\n", + "Ltot = liouvillian(Hsys) + L_bnd\n", + "\n", + "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI)\n", + " HEOMMatsT = HEOMSolver(Ltot, bath, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMatsT = HEOMMatsT.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d89912ca", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (resultMatsT, P11p, \"b\", \"P11 Mats + Term\"),\n", + " (resultMatsT, P12p, \"r\", \"P12 Mats + Term\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "18258c34", + "metadata": {}, + "source": [ + "Or using the built-in Drude-Lorentz bath we can write simply:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0bff4932", + "metadata": {}, + "outputs": [], + "source": [ + "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", + " _, terminator = bath.terminator()\n", + " Ltot = liouvillian(Hsys) + terminator\n", + " HEOM_dlbath_T = HEOMSolver(Ltot, bath, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " result_dlbath_T = HEOM_dlbath_T.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77f0d98b", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (result_dlbath_T, P11p, \"b\", \"P11 Mats (DrudeLorentzBath + Term)\"),\n", + " (result_dlbath_T, P12p, \"r\", \"P12 Mats (DrudeLorentzBath + Term)\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "89ef41ed", + "metadata": {}, + "source": [ + "We can compare the solution obtained from the QuTiP Bloch-Redfield solver:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea3c4b47", + "metadata": {}, + "outputs": [], + "source": [ + "DL = (\n", + " f\"2*pi* 2.0 * {lam} / (pi * {gamma} * {beta}) if (w == 0) else \"\n", + " f\"2*pi*(2.0*{lam}*{gamma} *w /(pi*(w**2+{gamma}**2))) \"\n", + " f\"* ((1/(exp((w) * {beta})-1))+1)\"\n", + ")\n", + "options = Options(nsteps=15000, store_states=True, rtol=1e-12, atol=1e-12)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultBR = brmesolve(\n", + " Hsys, rho0, tlist, a_ops=[[sigmaz(), DL]], options=options\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12787b42", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (resultMats, P11p, \"b\", \"P11 Mats\"),\n", + " (resultMats, P12p, \"r\", \"P12 Mats\"),\n", + " (resultMatsT, P11p, \"b--\", \"P11 Mats + Term\"),\n", + " (resultMatsT, P12p, \"r--\", \"P12 Mats + Term\"),\n", + " (resultBR, P11p, \"g--\", \"P11 Bloch Redfield\"),\n", + " (resultBR, P12p, \"g--\", \"P12 Bloch Redfield\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "051432e7", + "metadata": {}, + "source": [ + "## Padé decomposition" + ] + }, + { + "cell_type": "markdown", + "id": "e559173f", + "metadata": {}, + "source": [ + "The Matsubara decomposition is not the only option. We can also use the\n", + "faster-converging Pade decomposition." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc30ccd1", + "metadata": {}, + "outputs": [], + "source": [ + "def deltafun(j, k):\n", + " if j == k:\n", + " return 1.0\n", + " else:\n", + " return 0.0\n", + "\n", + "\n", + "def pade_eps(lmax):\n", + " Alpha = np.zeros((2 * lmax, 2 * lmax))\n", + " for j in range(2 * lmax):\n", + " for k in range(2 * lmax):\n", + " # Fermionic (see other example notebooks):\n", + " # Alpha[j][k] = (deltafun(j, k+1) + deltafun(j, k-1))\n", + " # / sqrt((2 * (j + 1) - 1) * (2 * (k + 1) - 1))\n", + " # Bosonic:\n", + " Alpha[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1)) / np.sqrt(\n", + " (2 * (j + 1) + 1) * (2 * (k + 1) + 1)\n", + " )\n", + "\n", + " eigvalsA = np.linalg.eigvalsh(Alpha)\n", + " eps = [-2 / val for val in eigvalsA[0:lmax]]\n", + " return eps\n", + "\n", + "\n", + "def pade_chi(lmax):\n", + " AlphaP = np.zeros((2 * lmax - 1, 2 * lmax - 1))\n", + " for j in range(2 * lmax - 1):\n", + " for k in range(2 * lmax - 1):\n", + " # Fermionic:\n", + " # AlphaP[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1))\n", + " # / sqrt((2 * (j + 1) + 1) * (2 * (k + 1) + 1))\n", + " # Bosonic [this is +3 because +1 (bose) + 2*(+1) (from bm+1)]:\n", + " AlphaP[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1)) / np.sqrt(\n", + " (2 * (j + 1) + 3) * (2 * (k + 1) + 3)\n", + " )\n", + "\n", + " eigvalsAP = np.linalg.eigvalsh(AlphaP)\n", + " chi = [-2 / val for val in eigvalsAP[0:lmax - 1]]\n", + " return chi\n", + "\n", + "\n", + "def pade_kappa_epsilon(lmax):\n", + " eps = pade_eps(lmax)\n", + " chi = pade_chi(lmax)\n", + "\n", + " kappa = [0]\n", + " prefactor = 0.5 * lmax * (2 * (lmax + 1) + 1)\n", + "\n", + " for j in range(lmax):\n", + " term = prefactor\n", + " for k in range(lmax - 1):\n", + " term *= (chi[k] ** 2 - eps[j] ** 2) / (\n", + " eps[k] ** 2 - eps[j] ** 2 + deltafun(j, k)\n", + " )\n", + "\n", + " for k in range(lmax - 1, lmax):\n", + " term /= eps[k] ** 2 - eps[j] ** 2 + deltafun(j, k)\n", + "\n", + " kappa.append(term)\n", + "\n", + " epsilon = [0] + eps\n", + "\n", + " return kappa, epsilon\n", + "\n", + "\n", + "def pade_corr(tlist, lmax):\n", + " kappa, epsilon = pade_kappa_epsilon(lmax)\n", + "\n", + " eta_list = [lam * gamma * (cot(gamma * beta / 2.0) - 1.0j)]\n", + " gamma_list = [gamma]\n", + "\n", + " if lmax > 0:\n", + " for ll in range(1, lmax + 1):\n", + " eta_list.append(\n", + " (kappa[ll] / beta)\n", + " * 4\n", + " * lam\n", + " * gamma\n", + " * (epsilon[ll] / beta)\n", + " / ((epsilon[ll] ** 2 / beta**2) - gamma**2)\n", + " )\n", + " gamma_list.append(epsilon[ll] / beta)\n", + "\n", + " c_tot = []\n", + " for t in tlist:\n", + " c_tot.append(\n", + " sum(\n", + " [\n", + " eta_list[ll] * np.exp(-gamma_list[ll] * t)\n", + " for ll in range(lmax + 1)\n", + " ]\n", + " )\n", + " )\n", + " return c_tot, eta_list, gamma_list\n", + "\n", + "\n", + "tlist_corr = np.linspace(0, 2, 100)\n", + "cppLP, etapLP, gampLP = pade_corr(tlist_corr, 2)\n", + "corr_15k = dl_corr_approx(tlist_corr, 15_000)\n", + "corr_2k = dl_corr_approx(tlist_corr, 2)\n", + "\n", + "fig, ax1 = plt.subplots(figsize=(12, 7))\n", + "ax1.plot(\n", + " tlist_corr,\n", + " np.real(cppLP),\n", + " color=\"b\",\n", + " linewidth=3,\n", + " label=r\"real pade 2 terms\",\n", + ")\n", + "ax1.plot(\n", + " tlist_corr,\n", + " np.real(corr_15k),\n", + " \"r--\",\n", + " linewidth=3,\n", + " label=r\"real mats 15000 terms\",\n", + ")\n", + "ax1.plot(\n", + " tlist_corr,\n", + " np.real(corr_2k),\n", + " \"g--\",\n", + " linewidth=3,\n", + " label=r\"real mats 2 terms\",\n", + ")\n", + "\n", + "ax1.set_xlabel(\"t\")\n", + "ax1.set_ylabel(r\"$C$\")\n", + "ax1.legend()\n", + "\n", + "fig, ax1 = plt.subplots(figsize=(12, 7))\n", + "\n", + "ax1.plot(\n", + " tlist_corr,\n", + " np.real(cppLP) - np.real(corr_15k),\n", + " color=\"b\",\n", + " linewidth=3,\n", + " label=r\"pade error\",\n", + ")\n", + "ax1.plot(\n", + " tlist_corr,\n", + " np.real(corr_2k) - np.real(corr_15k),\n", + " \"r--\",\n", + " linewidth=3,\n", + " label=r\"mats error\",\n", + ")\n", + "\n", + "ax1.set_xlabel(\"t\")\n", + "ax1.set_ylabel(r\"Error\")\n", + "ax1.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85ed2c5a", + "metadata": {}, + "outputs": [], + "source": [ + "# put pade parameters in lists for heom solver\n", + "ckAR = [np.real(eta) + 0j for eta in etapLP]\n", + "ckAI = [np.imag(etapLP[0]) + 0j]\n", + "vkAR = [gam + 0j for gam in gampLP]\n", + "vkAI = [gampLP[0] + 0j]\n", + "\n", + "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI)\n", + " HEOMPade = HEOMSolver(Hsys, bath, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultPade = HEOMPade.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84ac5b4c", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (resultMats, P11p, \"b\", \"P11 Mats\"),\n", + " (resultMats, P12p, \"r\", \"P12 Mats\"),\n", + " (resultMatsT, P11p, \"y\", \"P11 Mats + Term\"),\n", + " (resultMatsT, P12p, \"g\", \"P12 Mats + Term\"),\n", + " (resultPade, P11p, \"b--\", \"P11 Pade\"),\n", + " (resultPade, P12p, \"r--\", \"P12 Pade\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "11c18d1c", + "metadata": {}, + "source": [ + "The Padé decomposition of the Drude-Lorentz bath is also available via a\n", + "built-in class, `DrudeLorentzPadeBath` bath. Like `DrudeLorentzBath`, one\n", + "can obtain the terminator by calling `bath.terminator()`.\n", + "\n", + "Below we show how to use the built-in Padé Drude-Lorentz bath and its\n", + "terminator (although the terminator does not provide much improvement here,\n", + "because the Padé expansion already fits the correlation function well):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10ef00b0", + "metadata": {}, + "outputs": [], + "source": [ + "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", + " _, terminator = bath.terminator()\n", + " Ltot = liouvillian(Hsys) + terminator\n", + " HEOM_dlpbath_T = HEOMSolver(Ltot, bath, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " result_dlpbath_T = HEOM_dlpbath_T.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff24edba", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (result_dlpbath_T, P11p, \"b\", \"P11 Padé (DrudeLorentzBath + Term)\"),\n", + " (result_dlpbath_T, P12p, \"r\", \"P12 Padé (DrudeLorentzBath + Term)\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "3104cb81", + "metadata": {}, + "source": [ + "### Next we compare the Matsubara and Pade correlation function fits\n", + "\n", + "This is not efficient for this example, but can be extremely useful in\n", + "situations where large number of exponents are needed (e.g., near zero\n", + "temperature).\n", + "\n", + "First we collect a large sum of Matsubara terms for many time steps:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d02e022", + "metadata": {}, + "outputs": [], + "source": [ + "tlist2 = np.linspace(0, 2, 10000)\n", + "\n", + "corr_15k_t10k = dl_corr_approx(tlist2, 15_000)\n", + "\n", + "corrRana = np.real(corr_15k_t10k)\n", + "corrIana = np.imag(corr_15k_t10k)" + ] + }, + { + "cell_type": "markdown", + "id": "09c83ba5", + "metadata": {}, + "source": [ + "We then fit this sum with standard least-squares approach:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a528eb0c", + "metadata": {}, + "outputs": [], + "source": [ + "def wrapper_fit_func(x, N, args):\n", + " \"\"\" Fit function wrapper that unpacks its arguments. \"\"\"\n", + " x = np.array(x)\n", + " a = np.array(args[:N])\n", + " b = np.array(args[N:2 * N])\n", + " return fit_func(x, a, b)\n", + "\n", + "\n", + "def fit_func(x, a, b):\n", + " \"\"\" Fit function. Calculates the value of the\n", + " correlation function at each x, given the\n", + " fit parameters in a and b.\n", + " \"\"\"\n", + " return np.sum(\n", + " a[:, None] * np.exp(np.multiply.outer(b, x)),\n", + " axis=0,\n", + " )\n", + "\n", + "\n", + "def fitter(ans, tlist, k):\n", + " \"\"\" Compute fit with k exponents. \"\"\"\n", + " upper_a = abs(max(ans, key=abs)) * 10\n", + " # sets initial guesses:\n", + " guess = (\n", + " [ans[0] / k] * k + # guesses for a\n", + " [0] * k # guesses for b\n", + " )\n", + " # sets lower bounds:\n", + " b_lower = (\n", + " [-upper_a] * k + # lower bounds for a\n", + " [-np.inf] * k # lower bounds for b\n", + " )\n", + " # sets higher bounds:\n", + " b_higher = (\n", + " [upper_a] * k + # upper bounds for a\n", + " [0] * k # upper bounds for b\n", + " )\n", + " param_bounds = (b_lower, b_higher)\n", + " p1, p2 = curve_fit(\n", + " lambda x, *params_0: wrapper_fit_func(x, k, params_0),\n", + " tlist,\n", + " ans,\n", + " p0=guess,\n", + " sigma=[0.01 for t in tlist],\n", + " bounds=param_bounds,\n", + " maxfev=1e8,\n", + " )\n", + " a, b = p1[:k], p1[k:]\n", + " return (a, b)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96669348", + "metadata": {}, + "outputs": [], + "source": [ + "kR = 4 # number of exponents to use for real part\n", + "poptR = []\n", + "with timer(\"Correlation (real) fitting time\"):\n", + " for i in range(kR):\n", + " poptR.append(fitter(corrRana, tlist2, i + 1))\n", + "\n", + "corrRMats = np.real(dl_corr_approx(tlist2, Nk))\n", + "\n", + "kI = 1 # number of exponents for imaginary part\n", + "poptI = []\n", + "with timer(\"Correlation (imaginary) fitting time\"):\n", + " for i in range(kI):\n", + " poptI.append(fitter(corrIana, tlist2, i + 1))" + ] + }, + { + "cell_type": "markdown", + "id": "2336899e", + "metadata": {}, + "source": [ + "And plot the results of the fits:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed35ce3b", + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(tlist2, corrRana, label=\"Analytic\")\n", + "plt.plot(tlist2, corrRMats, label=\"Matsubara\")\n", + "\n", + "for i in range(kR):\n", + " y = fit_func(tlist2, *poptR[i])\n", + " plt.plot(tlist2, y, label=f\"Fit with {i} terms\")\n", + "\n", + "plt.title(\"Fit to correlations (real part)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "156850bc", + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(tlist2, corrIana, label=\"Analytic\")\n", + "\n", + "for i in range(kI):\n", + " y = fit_func(tlist2, *poptI[i])\n", + " plt.plot(tlist2, y, label=f\"Fit with {i} terms\")\n", + "\n", + "plt.title(\"Fit to correlations (imaginary part)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8ddafb1", + "metadata": {}, + "outputs": [], + "source": [ + "# Set the exponential coefficients from the fit parameters\n", + "\n", + "ckAR1 = poptR[-1][0]\n", + "ckAR = [x + 0j for x in ckAR1]\n", + "\n", + "vkAR1 = poptR[-1][1]\n", + "vkAR = [-x + 0j for x in vkAR1]\n", + "\n", + "ckAI1 = poptI[-1][0]\n", + "ckAI = [x + 0j for x in ckAI1]\n", + "\n", + "vkAI1 = poptI[-1][1]\n", + "vkAI = [-x + 0j for x in vkAI1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba436768", + "metadata": {}, + "outputs": [], + "source": [ + "# overwrite imaginary fit with analytical value (not much reason to use the\n", + "# fit for this)\n", + "\n", + "ckAI = [lam * gamma * (-1.0) + 0.0j]\n", + "vkAI = [gamma + 0.0j]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e89ad561", + "metadata": {}, + "outputs": [], + "source": [ + "# The BDF ODE solver method here is faster because we have a slightly\n", + "# stiff problem. We set NC=4 to reduce the run time while retaining\n", + "# reasonable convergence.\n", + "\n", + "options = Options(\n", + " nsteps=1500, store_states=True, rtol=1e-12, atol=1e-12, method=\"bdf\"\n", + ")\n", + "\n", + "NC = 4\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI)\n", + " HEOMFit = HEOMSolver(Hsys, bath, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultFit = HEOMFit.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5645d316", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (resultFit, P11p, \"b\", \"P11 Fit\"),\n", + " (resultFit, P12p, \"r\", \"P12 Fit\"),\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "435db73c", + "metadata": {}, + "source": [ + "## A reaction coordinate approach" + ] + }, + { + "cell_type": "markdown", + "id": "80e5a29a", + "metadata": {}, + "source": [ + "Here we construct a reaction coordinate inspired model to capture the\n", + "steady-state behavior, and compare to the HEOM prediction. This result is\n", + "more accurate for narrow spectral densities. We will use the population and\n", + "coherence from this cell in our final plot below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b651bfba", + "metadata": {}, + "outputs": [], + "source": [ + "dot_energy, dot_state = Hsys.eigenstates()\n", + "deltaE = dot_energy[1] - dot_energy[0]\n", + "\n", + "gamma2 = deltaE / (2 * np.pi * gamma)\n", + "wa = 2 * np.pi * gamma2 * gamma # reaction coordinate frequency\n", + "g = np.sqrt(np.pi * wa * lam / 2.0) # reaction coordinate coupling\n", + "# reaction coordinate coupling factor over 2 because of diff in J(w)\n", + "# (it is 2 lam now):\n", + "g = np.sqrt(\n", + " np.pi * wa * lam / 4.0\n", + ") #\n", + "\n", + "NRC = 10\n", + "\n", + "Hsys_exp = tensor(qeye(NRC), Hsys)\n", + "Q_exp = tensor(qeye(NRC), Q)\n", + "a = tensor(destroy(NRC), qeye(2))\n", + "\n", + "H0 = wa * a.dag() * a + Hsys_exp\n", + "# interaction\n", + "H1 = g * (a.dag() + a) * Q_exp\n", + "\n", + "H = H0 + H1\n", + "\n", + "energies, states = H.eigenstates()\n", + "rhoss = 0 * states[0] * states[0].dag()\n", + "for kk, energ in enumerate(energies):\n", + " rhoss += states[kk] * states[kk].dag() * np.exp(-beta * energies[kk])\n", + "\n", + "rhoss = rhoss / rhoss.norm()\n", + "\n", + "\n", + "class ReactionCoordinateResult:\n", + " def __init__(self, states, times):\n", + " self.states = states\n", + " self.times = times\n", + "\n", + "\n", + "resultRC = ReactionCoordinateResult([rhoss] * len(tlist), tlist)\n", + "\n", + "P12RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 1).dag())\n", + "P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag())" + ] + }, + { + "cell_type": "markdown", + "id": "ef765257", + "metadata": {}, + "source": [ + "## Let's plot all our results\n", + "\n", + "Finally, let's plot all of our different results to see how they shape up against each other." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7ee45371", + "metadata": {}, + "outputs": [], + "source": [ + "rcParams = {\n", + " \"axes.titlesize\": 25,\n", + " \"axes.labelsize\": 30,\n", + " \"xtick.labelsize\": 28,\n", + " \"ytick.labelsize\": 28,\n", + " \"legend.fontsize\": 28,\n", + " \"axes.grid\": False,\n", + " \"savefig.bbox\": \"tight\",\n", + " \"lines.markersize\": 5,\n", + " \"font.family\": \"STIXgeneral\",\n", + " \"mathtext.fontset\": \"stix\",\n", + " \"font.serif\": \"STIX\",\n", + " \"text.usetex\": False,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dbde13fc", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 15))\n", + "\n", + "with plt.rc_context(rcParams):\n", + "\n", + " plt.sca(axes[0])\n", + " plt.yticks([expect(P11RC, resultRC.states[0]), 0.6, 1.0], [0.32, 0.6, 1])\n", + " plot_result_expectations(\n", + " [\n", + " (resultBR, P11p, \"y-.\", \"Bloch-Redfield\"),\n", + " (resultMats, P11p, \"b\", \"Matsubara $N_k=2$\"),\n", + " (\n", + " resultMatsT,\n", + " P11p,\n", + " \"g--\",\n", + " \"Matsubara $N_k=2$ & Terminator\",\n", + " {\"linewidth\": 3},\n", + " ),\n", + " (\n", + " resultFit,\n", + " P11p,\n", + " \"r\",\n", + " r\"Fit $N_f = 4$, $N_k=15\\times 10^3$\",\n", + " {\"dashes\": [3, 2]},\n", + " ),\n", + " (\n", + " resultRC,\n", + " P11RC,\n", + " \"--\", \"Thermal\",\n", + " {\"linewidth\": 2, \"color\": \"black\"},\n", + " ),\n", + " ],\n", + " axes=axes[0],\n", + " )\n", + " axes[0].set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", + " axes[0].legend(loc=0)\n", + " axes[0].text(5, 0.9, \"(a)\", fontsize=30)\n", + " axes[0].set_xlim(0, 50)\n", + "\n", + " plt.sca(axes[1])\n", + " plt.yticks(\n", + " [np.real(expect(P12RC, resultRC.states[0])), -0.2, 0.0, 0.2],\n", + " [-0.33, -0.2, 0, 0.2],\n", + " )\n", + " plot_result_expectations(\n", + " [\n", + " (resultBR, P12p, \"y-.\", \"Bloch-Redfield\"),\n", + " (resultMats, P12p, \"b\", \"Matsubara $N_k=2$\"),\n", + " (\n", + " resultMatsT,\n", + " P12p,\n", + " \"g--\",\n", + " \"Matsubara $N_k=2$ & Terminator\",\n", + " {\"linewidth\": 3},\n", + " ),\n", + " (\n", + " resultFit,\n", + " P12p,\n", + " \"r\",\n", + " r\"Fit $N_f = 4$, $N_k=15\\times 10^3$\",\n", + " {\"dashes\": [3, 2]},\n", + " ),\n", + " (\n", + " resultRC,\n", + " P12RC,\n", + " \"--\",\n", + " \"Thermal\",\n", + " {\"linewidth\": 2, \"color\": \"black\"},\n", + " ),\n", + " ],\n", + " axes=axes[1],\n", + " )\n", + " axes[1].text(5, 0.1, \"(b)\", fontsize=30)\n", + " axes[1].set_xlabel(r\"$t \\Delta$\", fontsize=30)\n", + " axes[1].set_ylabel(r\"$\\rho_{01}$\", fontsize=30)\n", + " axes[1].set_xlim(0, 50)" + ] + }, + { + "cell_type": "markdown", + "id": "4e19d50f", + "metadata": {}, + "source": [ + "And that's the end of a detailed first dive into modeling bosonic environments with the HEOM." + ] + }, + { + "cell_type": "markdown", + "id": "7b13ab26", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a705e8f", + "metadata": {}, + "outputs": [], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "c52f8be0", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce9c47c9", + "metadata": {}, + "outputs": [], + "source": [ + "# Check P11p\n", + "assert np.allclose(\n", + " expect(P11p, resultMatsT.states),\n", + " expect(P11p, resultPade.states),\n", + " rtol=1e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(P11p, resultMatsT.states),\n", + " expect(P11p, resultFit.states),\n", + " rtol=1e-2,\n", + ")\n", + "\n", + "# Check P12p\n", + "assert np.allclose(\n", + " expect(P12p, resultMatsT.states),\n", + " expect(P12p, resultPade.states),\n", + " rtol=1e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(P12p, resultMatsT.states),\n", + " expect(P12p, resultFit.states),\n", + " rtol=1e-1,\n", + ")" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v4/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb b/tutorials-v4/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb new file mode 100644 index 00000000..910d851a --- /dev/null +++ b/tutorials-v4/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb @@ -0,0 +1,793 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b2ab9d7c", + "metadata": {}, + "source": [ + "# HEOM 1b: Spin-Bath model (very strong coupling)" + ] + }, + { + "cell_type": "markdown", + "id": "89bbb393", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices.\n", + "\n", + "In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", + "\n", + "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian, the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", + "\n", + "In the example below we show how to model the overdamped Drude-Lorentz Spectral Density, commonly used with the HEOM. We show how to do this using the Matsubara, Pade and fitting decompositions, and compare their convergence.\n", + "\n", + "This notebook shows a similar example to notebook 1a, but with much stronger coupling as discussed in [Shi *et al.*, J. Chem. Phys **130**, 084105 (2009)](https://doi.org/10.1063/1.3077918). Please refer to notebook HEOM 1a for a more detailed explanation.\n", + "\n", + "As in notebook 1a, we present a variety of simulations using different techniques to showcase the effect of different approximations of the correlation function on the results:\n", + "\n", + "- Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator\n", + "- Simulation 2: Matsubara decomposition (including terminator)\n", + "- Simulation 3: Pade decomposition\n", + "- Simulation 4: Fitting approach\n", + "\n", + "Lastly we compare the results to using the Bloch-Redfield approach:\n", + "\n", + "- Simulation 5: Bloch-Redfield\n", + "\n", + "which does not give the correct evolution in this case.\n", + "\n", + "\n", + "### Drude-Lorentz (overdamped) spectral density\n", + "\n", + "The Drude-Lorentz spectral density is:\n", + "\n", + "$$J_D(\\omega)= \\frac{2\\omega\\lambda\\gamma}{{\\gamma}^2 + \\omega^2}$$\n", + "\n", + "where $\\lambda$ scales the coupling strength, and $\\gamma$ is the cut-off frequency. We use the convention,\n", + "\\begin{equation*}\n", + "C(t) = \\int_0^{\\infty} d\\omega \\frac{J_D(\\omega)}{\\pi}[\\coth(\\beta\\omega) \\cos(\\omega \\tau) - i \\sin(\\omega \\tau)]\n", + "\\end{equation*}\n", + "\n", + "With the HEOM we must use an exponential decomposition:\n", + "\n", + "\\begin{equation*}\n", + "C(t)=\\sum_{k=0}^{k=\\infty} c_k e^{-\\nu_k t}\n", + "\\end{equation*}\n", + "\n", + "As an example, the Matsubara decomposition of the Drude-Lorentz spectral density is given by:\n", + "\n", + "\\begin{equation*}\n", + " \\nu_k = \\begin{cases}\n", + " \\gamma & k = 0\\\\\n", + " {2 \\pi k} / {\\beta } & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "\\begin{equation*}\n", + " c_k = \\begin{cases}\n", + " \\lambda \\gamma (\\cot(\\beta \\gamma / 2) - i) & k = 0\\\\\n", + " 4 \\lambda \\gamma \\nu_k / \\{(nu_k^2 - \\gamma^2)\\beta \\} & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." + ] + }, + { + "cell_type": "markdown", + "id": "7113b056", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "00390919", + "metadata": {}, + "outputs": [], + "source": [ + "import contextlib\n", + "import time\n", + "\n", + "import numpy as np\n", + "from scipy.optimize import curve_fit\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " basis,\n", + " brmesolve,\n", + " expect,\n", + " liouvillian,\n", + " sigmax,\n", + " sigmaz,\n", + " Options,\n", + ")\n", + "from qutip.nonmarkov.heom import (\n", + " HEOMSolver,\n", + " BosonicBath,\n", + " DrudeLorentzBath,\n", + " DrudeLorentzPadeBath,\n", + " BathExponent,\n", + ")\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "29367926", + "metadata": {}, + "source": [ + "## Helper functions\n", + "\n", + "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "895d9ec0", + "metadata": {}, + "outputs": [], + "source": [ + "def cot(x):\n", + " \"\"\" Vectorized cotangent of x. \"\"\"\n", + " return 1. / np.tan(x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46ce2b59", + "metadata": {}, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\" Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "markdown", + "id": "2f7f5293", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian, bath and system measurement operators:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7f515c1", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the system Hamiltonian\n", + "eps = .0 # Energy of the 2-level system.\n", + "Del = .2 # Tunnelling term\n", + "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8975d622", + "metadata": {}, + "outputs": [], + "source": [ + "# Initial state of the system.\n", + "rho0 = basis(2, 0) * basis(2, 0).dag()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8384e8bd", + "metadata": {}, + "outputs": [], + "source": [ + "# System-bath coupling (Drude-Lorentz spectral density)\n", + "Q = sigmaz() # coupling operator\n", + "\n", + "# Bath properties (see Shi et al., J. Chem. Phys. 130, 084105 (2009)):\n", + "gamma = 1. # cut off frequency\n", + "lam = 2.5 # coupling strength\n", + "T = 1. # in units where Boltzmann factor is 1\n", + "beta = 1. / T\n", + "\n", + "# HEOM parameters:\n", + "\n", + "# number of exponents to retain in the Matsubara expansion of the\n", + "# bath correlation function:\n", + "Nk = 1\n", + "\n", + "# Number of levels of the hierarchy to retain:\n", + "NC = 13\n", + "\n", + "# Times to solve for:\n", + "tlist = np.linspace(0, np.pi / Del, 600)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "099f3ff8", + "metadata": {}, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresonding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresonding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "f4c9a086", + "metadata": {}, + "source": [ + "### Plot the spectral density\n", + "\n", + "Let us briefly inspect the spectral density." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51545231", + "metadata": {}, + "outputs": [], + "source": [ + "w = np.linspace(0, 5, 1000)\n", + "J = w * 2 * lam * gamma / ((gamma**2 + w**2))\n", + "\n", + "# Plot the results\n", + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + "axes.plot(w, J, 'r', linewidth=2)\n", + "axes.set_xlabel(r'$\\omega$', fontsize=28)\n", + "axes.set_ylabel(r'J', fontsize=28);" + ] + }, + { + "cell_type": "markdown", + "id": "a01a670e", + "metadata": {}, + "source": [ + "## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9a9420d", + "metadata": {}, + "outputs": [], + "source": [ + "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", + " HEOMMats = HEOMSolver(Hsys, bath, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMats = HEOMMats.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "f884f7e8", + "metadata": {}, + "source": [ + "## Simulation 2: Matsubara decomposition (including terminator)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8d4f377", + "metadata": {}, + "outputs": [], + "source": [ + "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", + " _, terminator = bath.terminator()\n", + " Ltot = liouvillian(Hsys) + terminator\n", + " HEOMMatsT = HEOMSolver(Ltot, bath, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMatsT = HEOMMatsT.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7646793", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the results\n", + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + "\n", + "P11_mats = np.real(expect(resultMats.states, P11p))\n", + "axes.plot(\n", + " tlist, np.real(P11_mats),\n", + " 'b', linewidth=2, label=\"P11 (Matsubara)\",\n", + ")\n", + "\n", + "P11_matsT = np.real(expect(resultMatsT.states, P11p))\n", + "axes.plot(\n", + " tlist, np.real(P11_matsT),\n", + " 'b--', linewidth=2,\n", + " label=\"P11 (Matsubara + Terminator)\",\n", + ")\n", + "\n", + "axes.set_xlabel(r't', fontsize=28)\n", + "axes.legend(loc=0, fontsize=12);" + ] + }, + { + "cell_type": "markdown", + "id": "98a971a9", + "metadata": {}, + "source": [ + "## Simulation 3: Pade decomposition" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "112a2e6c", + "metadata": {}, + "outputs": [], + "source": [ + "# First, compare Matsubara and Pade decompositions\n", + "matsBath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", + "padeBath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", + "\n", + "# We will compare against a summation of {lmaxmats} Matsubara terms\n", + "lmaxmats = 15000\n", + "exactBath = DrudeLorentzBath(\n", + " Q, lam=lam, gamma=gamma, T=T, Nk=lmaxmats, combine=False,\n", + ")\n", + "\n", + "\n", + "def CR(bath, t):\n", + " \"\"\" C_R, the real part of the correlation function. \"\"\"\n", + " result = 0\n", + " for exp in bath.exponents:\n", + " if (\n", + " exp.type == BathExponent.types['R'] or\n", + " exp.type == BathExponent.types['RI']\n", + " ):\n", + " result += exp.ck * np.exp(-exp.vk * t)\n", + " return result\n", + "\n", + "\n", + "def CI(bath, t):\n", + " \"\"\" C_I, the imaginary part of the correlation function. \"\"\"\n", + " result = 0\n", + " for exp in bath.exponents:\n", + " if exp.type == BathExponent.types['I']:\n", + " result += exp.ck * np.exp(exp.vk * t)\n", + " if exp.type == BathExponent.types['RI']:\n", + " result += exp.ck2 * np.exp(exp.vk * t)\n", + " return result\n", + "\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(16, 8))\n", + "\n", + "ax1.plot(\n", + " tlist, CR(exactBath, tlist),\n", + " \"r\", linewidth=2, label=f\"Mats (Nk={lmaxmats})\",\n", + ")\n", + "ax1.plot(\n", + " tlist, CR(matsBath, tlist),\n", + " \"g--\", linewidth=2, label=f\"Mats (Nk={Nk})\",\n", + ")\n", + "ax1.plot(\n", + " tlist, CR(padeBath, tlist),\n", + " \"b--\", linewidth=2, label=f\"Pade (Nk={Nk})\",\n", + ")\n", + "\n", + "ax1.set_xlabel(r't', fontsize=28)\n", + "ax1.set_ylabel(r\"$C_R(t)$\", fontsize=28)\n", + "ax1.legend(loc=0, fontsize=12)\n", + "\n", + "tlist2 = tlist[0:50]\n", + "ax2.plot(\n", + " tlist2, np.abs(CR(matsBath, tlist2) - CR(exactBath, tlist2)),\n", + " \"g\", linewidth=2, label=\"Mats Error\",\n", + ")\n", + "ax2.plot(\n", + " tlist2, np.abs(CR(padeBath, tlist2) - CR(exactBath, tlist2)),\n", + " \"b--\", linewidth=2, label=\"Pade Error\",\n", + ")\n", + "\n", + "ax2.set_xlabel(r't', fontsize=28)\n", + "ax2.legend(loc=0, fontsize=12);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eefa27b8", + "metadata": {}, + "outputs": [], + "source": [ + "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", + " HEOMPade = HEOMSolver(Hsys, bath, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultPade = HEOMPade.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8553e56", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the results\n", + "fig, axes = plt.subplots(figsize=(8, 8))\n", + "\n", + "axes.plot(\n", + " tlist, np.real(P11_mats),\n", + " 'b', linewidth=2, label=\"P11 (Matsubara)\",\n", + ")\n", + "axes.plot(\n", + " tlist, np.real(P11_matsT),\n", + " 'b--', linewidth=2, label=\"P11 (Matsubara + Terminator)\",\n", + ")\n", + "\n", + "P11_pade = np.real(expect(resultPade.states, P11p))\n", + "axes.plot(\n", + " tlist, np.real(P11_pade),\n", + " 'r', linewidth=2, label=\"P11 (Pade)\",\n", + ")\n", + "\n", + "axes.set_xlabel(r't', fontsize=28)\n", + "axes.legend(loc=0, fontsize=12);" + ] + }, + { + "cell_type": "markdown", + "id": "cd6dc9f7", + "metadata": {}, + "source": [ + "## Simulation 4: Fitting approach" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca5be754", + "metadata": {}, + "outputs": [], + "source": [ + "def wrapper_fit_func(x, N, args):\n", + " \"\"\" Fit function wrapper that unpacks its arguments. \"\"\"\n", + " x = np.array(x)\n", + " a = np.array(args[:N])\n", + " b = np.array(args[N:2 * N])\n", + " return fit_func(x, a, b)\n", + "\n", + "\n", + "def fit_func(x, a, b):\n", + " \"\"\" Fit function. Calculates the value of the\n", + " correlation function at each x, given the\n", + " fit parameters in a and b.\n", + " \"\"\"\n", + " return np.sum(\n", + " a[:, None] * np.exp(np.multiply.outer(b, x)),\n", + " axis=0,\n", + " )\n", + "\n", + "\n", + "def fitter(ans, tlist, k):\n", + " \"\"\" Compute fit with k exponents. \"\"\"\n", + " upper_a = abs(max(ans, key=abs)) * 10\n", + " # sets initial guesses:\n", + " guess = (\n", + " [ans[0] / k] * k + # guesses for a\n", + " [0] * k # guesses for b\n", + " )\n", + " # sets lower bounds:\n", + " b_lower = (\n", + " [-upper_a] * k + # lower bounds for a\n", + " [-np.inf] * k # lower bounds for b\n", + " )\n", + " # sets higher bounds:\n", + " b_higher = (\n", + " [upper_a] * k + # upper bounds for a\n", + " [0] * k # upper bounds for b\n", + " )\n", + " param_bounds = (b_lower, b_higher)\n", + " p1, p2 = curve_fit(\n", + " lambda x, *params_0: wrapper_fit_func(x, k, params_0),\n", + " tlist,\n", + " ans,\n", + " p0=guess,\n", + " sigma=[0.01 for t in tlist],\n", + " bounds=param_bounds,\n", + " maxfev=1e8,\n", + " )\n", + " a, b = p1[:k], p1[k:]\n", + " return (a, b)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1969e59", + "metadata": {}, + "outputs": [], + "source": [ + "# Fitting the real part of the correlation function:\n", + "\n", + "# Correlation function values to fit:\n", + "tlist_fit = np.linspace(0, 6, 10000)\n", + "corrRana = CR(exactBath, tlist_fit)\n", + "\n", + "# Perform the fit:\n", + "kR = 3 # number of exponents to use for real part\n", + "poptR = []\n", + "with timer(\"Correlation (real) fitting time\"):\n", + " for i in range(kR):\n", + " poptR.append(fitter(corrRana, tlist_fit, i + 1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a0f2c12", + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(tlist_fit, corrRana, label=\"Analytic\")\n", + "\n", + "for i in range(kR):\n", + " y = fit_func(tlist_fit, *poptR[i])\n", + " plt.plot(tlist_fit, y, label=f\"Fit with {i} terms\")\n", + "\n", + "plt.title(\"Fit to correlations (real part)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f2deacb", + "metadata": {}, + "outputs": [], + "source": [ + "# Set the exponential coefficients from the fit parameters\n", + "\n", + "ckAR1 = poptR[-1][0]\n", + "ckAR = [x + 0j for x in ckAR1]\n", + "\n", + "vkAR1 = poptR[-1][1]\n", + "vkAR = [-x + 0j for x in vkAR1]\n", + "\n", + "# Imaginary part: use analytical value\n", + "\n", + "ckAI = [lam * gamma * (-1.0) + 0j]\n", + "vkAI = [gamma + 0j]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c708593", + "metadata": {}, + "outputs": [], + "source": [ + "options = Options(nsteps=1500, store_states=True, rtol=1e-12, atol=1e-12)\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI)\n", + " # We reduce NC slightly here for speed of execution because we retain\n", + " # 3 exponents in ckAR instead of 1. Please restore full NC for\n", + " # convergence though:\n", + " HEOMFit = HEOMSolver(Hsys, bath, int(NC * 0.7), options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultFit = HEOMFit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "ded203fa", + "metadata": {}, + "source": [ + "## Simulation 5: Bloch-Redfield" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46f6ea40", + "metadata": {}, + "outputs": [], + "source": [ + "DL = (\n", + " \"2 * pi * 2.0 * {lam} / (pi * {gamma} * {beta}) if (w==0) \"\n", + " \"else 2 * pi * (2.0 * {lam} * {gamma} * w / (pi * (w**2 + {gamma}**2))) \"\n", + " \"* ((1 / (exp(w * {beta}) - 1)) + 1)\"\n", + ").format(gamma=gamma, beta=beta, lam=lam)\n", + "\n", + "options = Options(nsteps=15000, store_states=True, rtol=1e-12, atol=1e-12)\n", + "resultBR = brmesolve(\n", + " Hsys, rho0, tlist,\n", + " a_ops=[[sigmaz(), DL]], sec_cutoff=0, options=options,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "dbf22e8d", + "metadata": {}, + "source": [ + "## Let's plot all our results\n", + "\n", + "Finally, let's plot all of our different results to see how they shape up against each other." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b316cb7", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate expectation values in the bases:\n", + "P11_mats = np.real(expect(resultMats.states, P11p))\n", + "P11_matsT = np.real(expect(resultMatsT.states, P11p))\n", + "P11_pade = np.real(expect(resultPade.states, P11p))\n", + "P11_fit = np.real(expect(resultFit.states, P11p))\n", + "P11_br = np.real(expect(resultBR.states, P11p))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a5e35dc", + "metadata": {}, + "outputs": [], + "source": [ + "rcParams = {\n", + " \"axes.titlesize\": 25,\n", + " \"axes.labelsize\": 30,\n", + " \"xtick.labelsize\": 28,\n", + " \"ytick.labelsize\": 28,\n", + " \"legend.fontsize\": 28,\n", + " \"axes.grid\": False,\n", + " \"savefig.bbox\": \"tight\",\n", + " \"lines.markersize\": 5,\n", + " \"font.family\": \"STIXgeneral\",\n", + " \"mathtext.fontset\": \"stix\",\n", + " \"font.serif\": \"STIX\",\n", + " \"text.usetex\": False,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c4afec6", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", + "\n", + "with plt.rc_context(rcParams):\n", + " # Plot the results\n", + " plt.yticks([0.99, 1.0], [0.99, 1])\n", + " axes.plot(\n", + " tlist, np.real(P11_mats),\n", + " 'b', linewidth=2, label=f\"Matsubara $N_k={Nk}$\",\n", + " )\n", + " axes.plot(\n", + " tlist, np.real(P11_matsT),\n", + " 'g--', linewidth=3,\n", + " label=f\"Matsubara $N_k={Nk}$ & terminator\",\n", + " )\n", + " axes.plot(\n", + " tlist, np.real(P11_pade),\n", + " 'y-.', linewidth=2, label=f\"Padé $N_k={Nk}$\",\n", + " )\n", + " axes.plot(\n", + " tlist, np.real(P11_fit),\n", + " 'r', dashes=[3, 2], linewidth=2,\n", + " label=r\"Fit $N_f = 3$, $N_k=15 \\times 10^3$\",\n", + " )\n", + " axes.plot(\n", + " tlist, np.real(P11_br),\n", + " 'b-.', linewidth=1, label=\"Bloch Redfield\",\n", + " )\n", + "\n", + " axes.locator_params(axis='y', nbins=6)\n", + " axes.locator_params(axis='x', nbins=6)\n", + " axes.set_ylabel(r'$\\rho_{11}$', fontsize=30)\n", + " axes.set_xlabel(r'$t\\;\\gamma$', fontsize=30)\n", + " axes.set_xlim(tlist[0], tlist[-1])\n", + " axes.set_ylim(0.98405, 1.0005)\n", + " axes.legend(loc=0)" + ] + }, + { + "cell_type": "markdown", + "id": "ce3c0f80", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0aef0665", + "metadata": {}, + "outputs": [], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "6136f9b9", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff7937fc", + "metadata": {}, + "outputs": [], + "source": [ + "assert np.allclose(P11_matsT, P11_pade, rtol=1e-3)\n", + "assert np.allclose(P11_matsT, P11_fit, rtol=1e-3)" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v4/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb b/tutorials-v4/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb new file mode 100644 index 00000000..a47ae132 --- /dev/null +++ b/tutorials-v4/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb @@ -0,0 +1,778 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "94c32997", + "metadata": {}, + "source": [ + "# HEOM 1c: Spin-Bath model (Underdamped Case)" + ] + }, + { + "cell_type": "markdown", + "id": "db2152b6", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices.\n", + "\n", + "In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", + "\n", + "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", + "\n", + "In the example below we show how to model the underdamped Brownian motion Spectral Density.\n", + "\n", + "Note that in the following, we set $\\hbar = k_\\mathrm{B} = 1$.\n", + "\n", + "### Brownian motion (underdamped) spectral density\n", + "The underdamped spectral density is:\n", + "\n", + "$$J_U = \\frac{\\alpha^2 \\Gamma \\omega}{(\\omega_c^2 - \\omega^2)^2 + \\Gamma^2 \\omega^2)}.$$\n", + "\n", + "Here $\\alpha$ scales the coupling strength, $\\Gamma$ is the cut-off frequency, and $\\omega_c$ defines a resonance frequency. With the HEOM we must use an exponential decomposition:\n", + "\n", + "The Matsubara decomposition of this spectral density is, in real and imaginary parts:\n", + "\n", + "\n", + "\n", + "\\begin{equation*}\n", + " c_k^R = \\begin{cases}\n", + " \\alpha^2 \\coth(\\beta( \\Omega + i\\Gamma/2)/2)/4\\Omega & k = 0\\\\\n", + " \\alpha^2 \\coth(\\beta( \\Omega - i\\Gamma/2)/2)/4\\Omega & k = 0\\\\\n", + " -2\\alpha^2\\Gamma/\\beta \\frac{\\epsilon_k }{((\\Omega + i\\Gamma/2)^2 + \\epsilon_k^2)(\\Omega - i\\Gamma/2)^2 + \\epsilon_k^2)} & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "\\begin{equation*}\n", + " \\nu_k^R = \\begin{cases}\n", + " -i\\Omega + \\Gamma/2, i\\Omega +\\Gamma/2, & k = 0\\\\\n", + " {2 \\pi k} / {\\beta } & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "\n", + "\n", + "\n", + "\\begin{equation*}\n", + " c_k^I = \\begin{cases}\n", + " i\\alpha^2 /4\\Omega & k = 0\\\\\n", + " -i\\alpha^2 /4\\Omega & k = 0\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "\\begin{equation*}\n", + " \\nu_k^I = \\begin{cases}\n", + " i\\Omega + \\Gamma/2, -i\\Omega + \\Gamma/2, & k = 0\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." + ] + }, + { + "cell_type": "markdown", + "id": "13cd28da", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11585c32", + "metadata": {}, + "outputs": [], + "source": [ + "import contextlib\n", + "import time\n", + "\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " Options,\n", + " basis,\n", + " brmesolve,\n", + " destroy,\n", + " expect,\n", + " qeye,\n", + " sigmax,\n", + " sigmaz,\n", + " tensor,\n", + ")\n", + "from qutip.nonmarkov.heom import (\n", + " HEOMSolver,\n", + " BosonicBath,\n", + " UnderDampedBath,\n", + ")\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8487b7b", + "metadata": {}, + "outputs": [], + "source": [ + "def cot(x):\n", + " \"\"\" Vectorized cotangent of x. \"\"\"\n", + " return 1. / np.tan(x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf7fe07f", + "metadata": {}, + "outputs": [], + "source": [ + "def coth(x):\n", + " \"\"\" Vectorized hyperbolic cotangent of x. \"\"\"\n", + " return 1. / np.tanh(x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e319a71", + "metadata": {}, + "outputs": [], + "source": [ + "def underdamped_matsubara_params(lam, gamma, T, nk):\n", + " \"\"\" Calculation of the real and imaginary expansions of the\n", + " underdamped correlation functions.\n", + " \"\"\"\n", + " Om = np.sqrt(w0**2 - (gamma / 2)**2)\n", + " Gamma = gamma / 2.\n", + " beta = 1. / T\n", + "\n", + " ckAR = [\n", + " (lam**2 / (4*Om)) * coth(beta * (Om + 1.0j * Gamma) / 2),\n", + " (lam**2 / (4*Om)) * coth(beta * (Om - 1.0j * Gamma) / 2),\n", + " ]\n", + " ckAR.extend(\n", + " (-2 * lam**2 * gamma / beta) * (2 * np.pi * k / beta) /\n", + " (((Om + 1.0j * Gamma)**2 + (2 * np.pi * k / beta)**2) *\n", + " ((Om - 1.0j * Gamma)**2 + (2 * np.pi * k / beta)**2)) + 0.j\n", + " for k in range(1, nk + 1)\n", + " )\n", + " vkAR = [\n", + " -1.0j * Om + Gamma,\n", + " 1.0j * Om + Gamma,\n", + " ]\n", + " vkAR.extend(\n", + " 2 * np.pi * k * T + 0.j\n", + " for k in range(1, nk + 1)\n", + " )\n", + "\n", + " factor = 1. / 4\n", + "\n", + " ckAI = [\n", + " -factor * lam**2 * 1.0j / Om,\n", + " factor * lam**2 * 1.0j / Om,\n", + " ]\n", + " vkAI = [\n", + " -(-1.0j * Om - Gamma),\n", + " -(1.0j * Om - Gamma),\n", + " ]\n", + "\n", + " return ckAR, vkAR, ckAI, vkAI" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91040aad", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_result_expectations(plots, axes=None):\n", + " \"\"\" Plot the expectation values of operators as functions of time.\n", + "\n", + " Each plot in plots consists of: (solver_result, measurement_operation,\n", + " color, label).\n", + " \"\"\"\n", + " if axes is None:\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " fig_created = True\n", + " else:\n", + " fig = None\n", + " fig_created = False\n", + "\n", + " # add kw arguments to each plot if missing\n", + " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", + " for result, m_op, color, label, kw in plots:\n", + " exp = np.real(expect(result.states, m_op))\n", + " kw.setdefault(\"linewidth\", 2)\n", + " axes.plot(result.times, exp, color, label=label, **kw)\n", + "\n", + " if fig_created:\n", + " axes.legend(loc=0, fontsize=12)\n", + " axes.set_xlabel(\"t\", fontsize=28)\n", + "\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "391f2f00", + "metadata": {}, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\" Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "373a8f70", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the system Hamiltonian\n", + "eps = .5 # Energy of the 2-level system.\n", + "Del = 1.0 # Tunnelling term\n", + "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3fa058fa", + "metadata": {}, + "outputs": [], + "source": [ + "# Initial state of the system.\n", + "rho0 = basis(2, 0) * basis(2, 0).dag()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18e367cc", + "metadata": {}, + "outputs": [], + "source": [ + "# System-bath coupling (underdamed spectral density)\n", + "Q = sigmaz() # coupling operator\n", + "\n", + "# Bath properties:\n", + "gamma = .1 # cut off frequency\n", + "lam = .5 # coupling strength\n", + "w0 = 1. # resonance frequency\n", + "T = 1.\n", + "beta = 1. / T\n", + "\n", + "# HEOM parameters:\n", + "\n", + "# number of exponents to retain in the Matsubara expansion of the\n", + "# bath correlation function:\n", + "Nk = 2\n", + "\n", + "# Number of levels of the hierarchy to retain:\n", + "NC = 10\n", + "\n", + "# Times to solve for:\n", + "tlist = np.linspace(0, 50, 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3f2a24a", + "metadata": {}, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresonding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresonding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "48f84591", + "metadata": {}, + "source": [ + "### First let us look at what the underdamped spectral density looks like:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3cebc66e", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_spectral_density():\n", + " \"\"\" Plot the underdamped spectral density \"\"\"\n", + " w = np.linspace(0, 5, 1000)\n", + " J = lam**2 * gamma * w / ((w0**2 - w**2)**2 + (gamma**2) * (w**2))\n", + "\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " axes.plot(w, J, 'r', linewidth=2)\n", + " axes.set_xlabel(r'$\\omega$', fontsize=28)\n", + " axes.set_ylabel(r'J', fontsize=28)\n", + "\n", + "\n", + "plot_spectral_density()" + ] + }, + { + "cell_type": "markdown", + "id": "184ae030", + "metadata": {}, + "source": [ + "The correlation functions are now very oscillatory, because of the Lorentzian peak in the spectral density." + ] + }, + { + "cell_type": "markdown", + "id": "4861492f", + "metadata": {}, + "source": [ + "### So next, let us plot the correlation functions themselves:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a5b2524", + "metadata": {}, + "outputs": [], + "source": [ + "def Mk(t, k, gamma, w0, beta):\n", + " \"\"\" Calculate the Matsubara terms for a given t and k. \"\"\"\n", + " Om = np.sqrt(w0**2 - (gamma / 2)**2)\n", + " Gamma = gamma / 2.\n", + " ek = 2 * np.pi * k / beta\n", + "\n", + " return (\n", + " (-2 * lam**2 * gamma / beta) * ek * np.exp(-ek * np.abs(t))\n", + " / (((Om + 1.0j * Gamma)**2 + ek**2) * ((Om - 1.0j * Gamma)**2 + ek**2))\n", + " )\n", + "\n", + "\n", + "def c(t, Nk, lam, gamma, w0, beta):\n", + " \"\"\" Calculate the correlation function for a vector of times, t. \"\"\"\n", + " Om = np.sqrt(w0**2 - (gamma / 2)**2)\n", + " Gamma = gamma / 2.\n", + "\n", + " Cr = (\n", + " coth(beta * (Om + 1.0j * Gamma) / 2) * np.exp(1.0j * Om * t)\n", + " + coth(beta * (Om - 1.0j * Gamma) / 2) * np.exp(-1.0j * Om * t)\n", + " )\n", + "\n", + " Ci = np.exp(-1.0j * Om * t) - np.exp(1.0j * Om * t)\n", + "\n", + " return (\n", + " (lam**2 / (4 * Om)) * np.exp(-Gamma * np.abs(t)) * (Cr + Ci) +\n", + " np.sum([\n", + " Mk(t, k, gamma=gamma, w0=w0, beta=beta)\n", + " for k in range(1, Nk + 1)\n", + " ], 0)\n", + " )\n", + "\n", + "\n", + "def plot_correlation_function():\n", + " \"\"\" Plot the underdamped correlation function. \"\"\"\n", + " t = np.linspace(0, 20, 1000)\n", + " corr = c(t, Nk=3, lam=lam, gamma=gamma, w0=w0, beta=beta)\n", + "\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " axes.plot(t, np.real(corr), '-', color=\"black\", label=\"Re[C(t)]\")\n", + " axes.plot(t, np.imag(corr), '-', color=\"red\", label=\"Im[C(t)]\")\n", + " axes.set_xlabel(r't', fontsize=28)\n", + " axes.set_ylabel(r'C', fontsize=28)\n", + " axes.legend(loc=0, fontsize=12)\n", + "\n", + "\n", + "plot_correlation_function()" + ] + }, + { + "cell_type": "markdown", + "id": "c3f87229", + "metadata": {}, + "source": [ + "It is useful to look at what the Matsubara contributions do to this spectral density. We see that they modify the real part around $t=0$:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2737def7", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_matsubara_correlation_function_contributions():\n", + " \"\"\" Plot the underdamped correlation function. \"\"\"\n", + " t = np.linspace(0, 20, 1000)\n", + "\n", + " M_Nk2 = np.sum([\n", + " Mk(t, k, gamma=gamma, w0=w0, beta=beta)\n", + " for k in range(1, 2 + 1)\n", + " ], 0)\n", + "\n", + " M_Nk100 = np.sum([\n", + " Mk(t, k, gamma=gamma, w0=w0, beta=beta)\n", + " for k in range(1, 100 + 1)\n", + " ], 0)\n", + "\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " axes.plot(t, np.real(M_Nk2), '-', color=\"black\", label=\"Re[M(t)] Nk=2\")\n", + " axes.plot(t, np.real(M_Nk100), '--', color=\"red\", label=\"Re[M(t)] Nk=100\")\n", + " axes.set_xlabel(r't', fontsize=28)\n", + " axes.set_ylabel(r'M', fontsize=28)\n", + " axes.legend(loc=0, fontsize=12)\n", + "\n", + "\n", + "plot_matsubara_correlation_function_contributions()" + ] + }, + { + "cell_type": "markdown", + "id": "6767a295", + "metadata": {}, + "source": [ + "### Solving for the dynamics as a function of time:" + ] + }, + { + "cell_type": "markdown", + "id": "f7308263", + "metadata": {}, + "source": [ + "Next we calculate the exponents using the Matsubara decompositions. Here we split them into real and imaginary parts.\n", + "\n", + "The HEOM code will optimize these, and reduce the number of exponents when real and imaginary parts have the same exponent. This is clearly the case for the first term in the vkAI and vkAR lists." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1609b65", + "metadata": {}, + "outputs": [], + "source": [ + "ckAR, vkAR, ckAI, vkAI = underdamped_matsubara_params(\n", + " lam=lam, gamma=gamma, T=T, nk=Nk,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "09874858", + "metadata": {}, + "source": [ + "Having created the lists which specify the bath correlation functions, we create a `BosonicBath` from them and pass the bath to the `HEOMSolver` class.\n", + "\n", + "The solver constructs the \"right hand side\" (RHS) determinining how the system and auxiliary density operators evolve in time. This can then be used to solve for dynamics or steady-state.\n", + "\n", + "Below we create the bath and solver and then solve for the dynamics by calling `.run(rho0, tlist)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f44acf5", + "metadata": {}, + "outputs": [], + "source": [ + "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI)\n", + " HEOMMats = HEOMSolver(Hsys, bath, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMats = HEOMMats.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14505aa3", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations([\n", + " (resultMats, P11p, 'b', \"P11 Mats\"),\n", + " (resultMats, P12p, 'r', \"P12 Mats\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "2a13a295", + "metadata": {}, + "source": [ + "In practice, one would not perform this laborious expansion for the underdamped correlation function, because\n", + "QuTiP already has a class, `UnderDampedBath`, that can construct this bath for you. Nevertheless, knowing how\n", + "to perform this expansion will allow you to construct your own baths for other spectral densities.\n", + "\n", + "Below we show how to use this built-in functionality:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cde37b21", + "metadata": {}, + "outputs": [], + "source": [ + "# Compare to built-in under-damped bath:\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = UnderDampedBath(Q, lam=lam, gamma=gamma, w0=w0, T=T, Nk=Nk)\n", + " HEOM_udbath = HEOMSolver(Hsys, bath, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " result_udbath = HEOM_udbath.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f394a2ab", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations([\n", + " (result_udbath, P11p, 'b', \"P11 (UnderDampedBath)\"),\n", + " (result_udbath, P12p, 'r', \"P12 (UnderDampedBath)\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "6234d19f", + "metadata": {}, + "source": [ + "### We can compare these results to those of the Bloch-Redfield solver in QuTiP:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15277472", + "metadata": {}, + "outputs": [], + "source": [ + "UD = (\n", + " f\"2 * {lam}**2 * {gamma} / ( {w0}**4 * {beta}) if (w==0)\"\n", + " \" else \"\n", + " f\"2 * ({lam}**2 * {gamma} * w / (({w0}**2 - w**2)**2 + {gamma}**2 * w**2))\"\n", + " f\" * ((1 / (exp(w * {beta}) - 1)) + 1)\"\n", + ")\n", + "\n", + "options = Options(nsteps=15000, store_states=True, rtol=1e-12, atol=1e-12)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultBR = brmesolve(\n", + " Hsys, rho0, tlist,\n", + " a_ops=[[sigmaz(), UD]], options=options,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f09291b5", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations([\n", + " (resultMats, P11p, 'b', \"P11 Mats\"),\n", + " (resultMats, P12p, 'r', \"P12 Mats\"),\n", + " (resultBR, P11p, 'g--', \"P11 Bloch Redfield\"),\n", + " (resultBR, P12p, 'g--', \"P12 Bloch Redfield\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "50ff4456", + "metadata": {}, + "source": [ + "### Lastly, let us calculate the analytical steady-state result and compare all of the results:" + ] + }, + { + "cell_type": "markdown", + "id": "4f733223", + "metadata": {}, + "source": [ + "The thermal state of a reaction coordinate (treating the environment as a single damped mode) should, at high temperatures and small gamma, tell us the steady-state:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d795d5cf", + "metadata": {}, + "outputs": [], + "source": [ + "dot_energy, dot_state = Hsys.eigenstates()\n", + "deltaE = dot_energy[1] - dot_energy[0]\n", + "\n", + "gamma2 = gamma\n", + "wa = w0 # reaction coordinate frequency\n", + "g = lam / np.sqrt(2 * wa) # coupling\n", + "\n", + "NRC = 10\n", + "\n", + "Hsys_exp = tensor(qeye(NRC), Hsys)\n", + "Q_exp = tensor(qeye(NRC), Q)\n", + "a = tensor(destroy(NRC), qeye(2))\n", + "\n", + "H0 = wa * a.dag() * a + Hsys_exp\n", + "# interaction\n", + "H1 = (g * (a.dag() + a) * Q_exp)\n", + "\n", + "H = H0 + H1\n", + "\n", + "energies, states = H.eigenstates()\n", + "rhoss = 0 * states[0] * states[0].dag()\n", + "for kk, energ in enumerate(energies):\n", + " rhoss += (states[kk] * states[kk].dag() * np.exp(-beta * energies[kk]))\n", + "rhoss = rhoss / rhoss.norm()\n", + "\n", + "P12RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 1).dag())\n", + "P12RC = expect(rhoss, P12RC)\n", + "\n", + "P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag())\n", + "P11RC = expect(rhoss, P11RC)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35f47df5", + "metadata": {}, + "outputs": [], + "source": [ + "rcParams = {\n", + " \"axes.titlesize\": 25,\n", + " \"axes.labelsize\": 30,\n", + " \"xtick.labelsize\": 28,\n", + " \"ytick.labelsize\": 28,\n", + " \"legend.fontsize\": 28,\n", + " \"axes.grid\": False,\n", + " \"savefig.bbox\": \"tight\",\n", + " \"lines.markersize\": 5,\n", + " \"font.family\": \"STIXgeneral\",\n", + " \"mathtext.fontset\": \"stix\",\n", + " \"font.serif\": \"STIX\",\n", + " \"text.usetex\": False,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "969eb62c", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", + "\n", + "with plt.rc_context(rcParams):\n", + " plt.yticks([P11RC, 0.6, 1.0], [0.38, 0.6, 1])\n", + "\n", + " plot_result_expectations([\n", + " (resultBR, P11p, 'y-.', \"Bloch-Redfield\"),\n", + " (resultMats, P11p, 'b', \"Matsubara $N_k=3$\"),\n", + " ], axes=axes)\n", + " axes.plot(\n", + " tlist, [P11RC for t in tlist],\n", + " color='black', linestyle=\"-.\", linewidth=2,\n", + " label=\"Thermal state\",\n", + " )\n", + "\n", + " axes.set_xlabel(r'$t \\Delta$', fontsize=30)\n", + " axes.set_ylabel(r'$\\rho_{11}$', fontsize=30)\n", + "\n", + " axes.locator_params(axis='y', nbins=4)\n", + " axes.locator_params(axis='x', nbins=4)\n", + "\n", + " axes.legend(loc=0)\n", + "\n", + " fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "b54f0f33", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cad07d0d", + "metadata": {}, + "outputs": [], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "7b86b4dd", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d39e7ae8", + "metadata": {}, + "outputs": [], + "source": [ + "assert np.allclose(\n", + " expect(P11p, resultMats.states[-100:]), P11RC, rtol=1e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(P11p, resultBR.states[-100:]), P11RC, rtol=1e-2,\n", + ")" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v4/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb b/tutorials-v4/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb new file mode 100644 index 00000000..32a3978a --- /dev/null +++ b/tutorials-v4/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb @@ -0,0 +1,1740 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b0c2374c", + "metadata": {}, + "source": [ + "# HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions" + ] + }, + { + "cell_type": "markdown", + "id": "0af1c108", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded \n", + "in a set of auxiliary density matrices.\n", + "\n", + "In this example we show the evolution of a single two-level system in contact with a single bosonic environment.\n", + "\n", + "The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", + "\n", + "The bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", + "\n", + "In the example below we show how to model an Ohmic environment with exponential cut-off in two ways:\n", + "\n", + "* First we fit the spectral density with a set of underdamped brownian oscillator functions.\n", + "\n", + "* Second, we evaluate the correlation functions, and fit those with a certain choice of exponential functions.\n", + "\n", + "In each case we will use the fit parameters to determine the correlation function expansion co-efficients needed to construct a description of the bath (i.e. a `BosonicBath` object) to supply to the `HEOMSolver` so that we can solve for the system dynamics." + ] + }, + { + "cell_type": "markdown", + "id": "b8573057", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "cd17d9f7", + "metadata": {}, + "outputs": [], + "source": [ + "import contextlib\n", + "import dataclasses\n", + "import time\n", + "\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "from scipy.optimize import curve_fit\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " Options,\n", + " basis,\n", + " expect,\n", + " liouvillian,\n", + " sigmax,\n", + " sigmaz,\n", + " spost,\n", + " spre,\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver,\n", + " BosonicBath,\n", + ")\n", + "\n", + "# Import mpmath functions for evaluation of gamma and zeta\n", + "# functions in the expression for the correlation:\n", + "\n", + "from mpmath import mp\n", + "\n", + "mp.dps = 15\n", + "mp.pretty = True\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "efa8c2a9", + "metadata": {}, + "source": [ + "## Helper functions\n", + "\n", + "Let's define some helper functions for plotting results and timing how long operations take:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e0e06304", + "metadata": {}, + "outputs": [], + "source": [ + "def coth(x):\n", + " \"\"\" Vectorized hyperbolic cotangent of x. \"\"\"\n", + " return 1. / np.tanh(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a5f220b7", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_result_expectations(plots, axes=None):\n", + " \"\"\" Plot the expectation values of operators as functions of time.\n", + "\n", + " Each plot in plots consists of (solver_result,\n", + " measurement_operation, color, label).\n", + " \"\"\"\n", + " if axes is None:\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " fig_created = True\n", + " else:\n", + " fig = None\n", + " fig_created = False\n", + "\n", + " # add kw arguments to each plot if missing\n", + " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", + " for result, m_op, color, label, kw in plots:\n", + " exp = np.real(expect(result.states, m_op))\n", + " kw.setdefault(\"linewidth\", 2)\n", + " if color == 'rand':\n", + " axes.plot(\n", + " result.times, exp,\n", + " c=np.random.rand(3,), label=label, **kw,\n", + " )\n", + " else:\n", + " axes.plot(result.times, exp, color, label=label, **kw)\n", + "\n", + " if fig_created:\n", + " axes.legend(loc=0, fontsize=12)\n", + " axes.set_xlabel(\"t\", fontsize=28)\n", + "\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7cda27be", + "metadata": {}, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\" Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "markdown", + "id": "0ab24db3", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian, bath and system measurement operators:" + ] + }, + { + "cell_type": "markdown", + "id": "b8a7978c", + "metadata": {}, + "source": [ + "### System Hamiltonian" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b5be20a5", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the system Hamiltonian\n", + "eps = 0.0 # Energy of the 2-level system.\n", + "Del = 0.2 # Tunnelling term\n", + "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "619f046c", + "metadata": {}, + "outputs": [], + "source": [ + "# Initial state of the system.\n", + "rho0 = basis(2, 0) * basis(2, 0).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "8ce2e713", + "metadata": {}, + "source": [ + "### System measurement operators" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "bc2468cf", + "metadata": {}, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresonding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresonding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "653d8867", + "metadata": {}, + "source": [ + "### Analytical expressions for the Ohmic bath correlation function and spectral density" + ] + }, + { + "cell_type": "markdown", + "id": "de2a61cb", + "metadata": {}, + "source": [ + "Before we begin fitting, let us examine the analytic expressions for the correlation and spectral density functions and write Python equivalents. \n", + "\n", + "The correlation function is given by (see, e.g., http://www1.itp.tu-berlin.de/brandes/public_html/publications/notes.pdf for a derivation, equation 7.59, but with a factor of $\\pi$ moved into the definition of the correlation function):\n", + "\n", + "\\begin{align}\n", + "C(t) =& \\: \\frac{1}{\\pi}\\alpha \\omega_{c}^{1 - s} \\beta^{- (s + 1)} \\: \\times \\\\\n", + " & \\: \\Gamma(s + 1) \\left[ \\zeta \\left(s + 1, \\frac{1 + \\beta \\omega_c - i \\omega_c t}{\\beta \\omega_c}\\right) + \\zeta \\left(s + 1, \\frac{1 + i \\omega_c t}{\\beta \\omega_c}\\right) \\right]\n", + "\\end{align}\n", + "\n", + "where $\\Gamma$ is the Gamma function and\n", + "\n", + "\\begin{equation}\n", + "\\zeta(z, u) \\equiv \\sum_{n=0}^{\\infty} \\frac{1}{(n + u)^z}, \\; u \\neq 0, -1, -2, \\ldots\n", + "\\end{equation}\n", + "\n", + "is the generalized Zeta function. The Ohmic case is given by $s = 1$.\n", + "\n", + "The corresponding spectral density for the Ohmic case is:\n", + "\n", + "\\begin{equation}\n", + "J(\\omega) = \\omega \\alpha e^{- \\frac{\\omega}{\\omega_c}}\n", + "\\end{equation}" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f2996062", + "metadata": {}, + "outputs": [], + "source": [ + "def ohmic_correlation(t, alpha, wc, beta, s=1):\n", + " \"\"\" The Ohmic bath correlation function as a function of t\n", + " (and the bath parameters).\n", + " \"\"\"\n", + " corr = (\n", + " (1 / np.pi) * alpha * wc**(1 - s) * beta**(-(s + 1)) * mp.gamma(s + 1)\n", + " )\n", + " z1_u = (1 + beta * wc - 1.0j * wc * t) / (beta * wc)\n", + " z2_u = (1 + 1.0j * wc * t) / (beta * wc)\n", + " # Note: the arguments to zeta should be in as high precision as possible.\n", + " # See http://mpmath.org/doc/current/basics.html#providing-correct-input\n", + " return np.array([\n", + " complex(corr * (mp.zeta(s + 1, u1) + mp.zeta(s + 1, u2)))\n", + " for u1, u2 in zip(z1_u, z2_u)\n", + " ], dtype=np.complex128)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "af7a39e2", + "metadata": {}, + "outputs": [], + "source": [ + "def ohmic_spectral_density(w, alpha, wc):\n", + " \"\"\" The Ohmic bath spectral density as a function of w\n", + " (and the bath parameters).\n", + " \"\"\"\n", + " return w * alpha * np.e**(-w / wc)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c52abbec", + "metadata": {}, + "outputs": [], + "source": [ + "def ohmic_power_spectrum(w, alpha, wc, beta):\n", + " \"\"\" The Ohmic bath power spectrum as a function of w\n", + " (and the bath parameters).\n", + " \"\"\"\n", + " return (\n", + " w * alpha * np.e**(-abs(w) / wc) *\n", + " ((1 / (np.e**(w * beta) - 1)) + 1) * 2\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "e530b00f", + "metadata": {}, + "source": [ + "### Bath and HEOM parameters" + ] + }, + { + "cell_type": "markdown", + "id": "03e881bc", + "metadata": {}, + "source": [ + "Finally, let's set the bath parameters we will work with and write down some measurement operators:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "874e3203", + "metadata": {}, + "outputs": [], + "source": [ + "# Bath parameters:\n", + "\n", + "@dataclasses.dataclass\n", + "class OhmicBathParameters:\n", + " \"\"\" Ohmic bath parameters. \"\"\"\n", + " Q: object = dataclasses.field(default_factory=sigmaz, repr=False)\n", + " alpha: float = 3.25\n", + " T: float = 0.5\n", + " wc: float = 1.0\n", + " s: float = 1\n", + "\n", + " def __post_init__(self):\n", + " self.beta = 1 / self.T\n", + "\n", + " def replace(self, **kw):\n", + " return dataclasses.replace(self, **kw)\n", + "\n", + "\n", + "obp = OhmicBathParameters()" + ] + }, + { + "cell_type": "markdown", + "id": "b8ace800", + "metadata": {}, + "source": [ + "And set the cut-off for the HEOM hierarchy:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "50fb9320", + "metadata": {}, + "outputs": [], + "source": [ + "# HEOM parameters:\n", + "\n", + "# The max_depth defaults to 5 so that the notebook executes more\n", + "# quickly. Change it to 11 to wait longer for more accurate results.\n", + "max_depth = 5" + ] + }, + { + "cell_type": "markdown", + "id": "d0097ca0", + "metadata": {}, + "source": [ + "## Building the HEOM bath by fitting the spectral density" + ] + }, + { + "cell_type": "markdown", + "id": "0053121b", + "metadata": {}, + "source": [ + "We begin by fitting the spectral density, using a series of $k$ underdamped harmonic oscillators case with the Meier-Tannor form (J. Chem. Phys. 111, 3365 (1999); https://doi.org/10.1063/1.479669):\n", + "\n", + "\\begin{equation}\n", + "J_{\\mathrm approx}(\\omega; a, b, c) = \\sum_{i=0}^{k-1} \\frac{2 a_i b_i w}{((w + c_i)^2 + b_i^2) ((w - c_i)^2 + b_i^2)}\n", + "\\end{equation}\n", + "\n", + "where $a, b$ and $c$ are the fit parameters and each is a vector of length $k$." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ac870ddb", + "metadata": {}, + "outputs": [], + "source": [ + "# Helper functions for packing the paramters a, b and c into a single numpy\n", + "# array as required by SciPy's curve_fit:\n", + "\n", + "def pack(a, b, c):\n", + " \"\"\" Pack parameter lists for fitting. \"\"\"\n", + " return np.concatenate((a, b, c))\n", + "\n", + "\n", + "def unpack(params):\n", + " \"\"\" Unpack parameter lists for fitting. \"\"\"\n", + " N = len(params) // 3\n", + " a = np.array(params[:N])\n", + " b = np.array(params[N:2 * N])\n", + " c = np.array(params[2 * N:])\n", + " return a, b, c" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0d5cd728", + "metadata": {}, + "outputs": [], + "source": [ + "# The approximate spectral density and a helper for fitting the approximate\n", + "# spectral density to values calculated from the analytical formula:\n", + "\n", + "def spectral_density_approx(w, a, b, c):\n", + " \"\"\" Calculate the fitted value of the function for the given\n", + " parameters.\n", + " \"\"\"\n", + " return np.sum(\n", + " 2 * a[:, None] * np.multiply.outer(b, w) / (\n", + " ((w + c[:, None])**2 + b[:, None]**2) *\n", + " ((w - c[:, None])**2 + b[:, None]**2)\n", + " ),\n", + " axis=0,\n", + " )\n", + "\n", + "\n", + "def fit_spectral_density(J, w, alpha, wc, N):\n", + " \"\"\" Fit the spectral density with N underdamped oscillators. \"\"\"\n", + " sigma = [0.0001] * len(w)\n", + "\n", + " J_max = abs(max(J, key=abs))\n", + "\n", + " guesses = pack([J_max] * N, [wc] * N, [wc] * N)\n", + " lower_bounds = pack([-100 * J_max] * N, [0.1 * wc] * N, [0.1 * wc] * N)\n", + " upper_bounds = pack([100 * J_max] * N, [100 * wc] * N, [100 * wc] * N)\n", + "\n", + " params, _ = curve_fit(\n", + " lambda x, *params: spectral_density_approx(w, *unpack(params)),\n", + " w, J,\n", + " p0=guesses,\n", + " bounds=(lower_bounds, upper_bounds),\n", + " sigma=sigma,\n", + " maxfev=1000000000,\n", + " )\n", + "\n", + " return unpack(params)" + ] + }, + { + "cell_type": "markdown", + "id": "19351887", + "metadata": {}, + "source": [ + "With the spectral density approximation $J_{\\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c437b8f7", + "metadata": {}, + "outputs": [], + "source": [ + "w = np.linspace(0, 25, 20000)\n", + "J = ohmic_spectral_density(w, alpha=obp.alpha, wc=obp.wc)\n", + "\n", + "params_k = [\n", + " fit_spectral_density(J, w, alpha=obp.alpha, wc=obp.wc, N=i+1)\n", + " for i in range(4)\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "574186d7", + "metadata": {}, + "source": [ + "Let's plot the fit for each $k$ and examine how it improves with an increasing number of terms:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "083ff82e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parameters [k=0]: lam=[6.14746382]; gamma=[1.77939431]; w0=[0.1]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parameters [k=1]: lam=[2.21188807 3.26249622]; gamma=[1.24955687 1.43449451]; w0=[0.1 1.80554797]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parameters [k=2]: lam=[1.64959658 2.33983489 1.1273868 ]; gamma=[1.18453044 1.09371099 1.0088176 ]; w0=[2.69770739 1.37013884 0.13256982]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parameters [k=3]: lam=[ 7.91592672 0.60083674 -4.40789196 0.01058512]; gamma=[2.29618983 1.00246781 4.29908159 0.30736306]; w0=[0.1 0.1 3.981687 0.1 ]\n" + ] + }, + { + "data": { + "image/png": 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Dcb2OiIjIaKQwcg6uoBVGjIy8uF4ns9ha3pvfowfmiYhI+lEYOQdPXxhxZOTG9TpjK2YDUBw6ihkKxvVaIiIio43CyDl4Ql0AuDLjO0xTVjEdn+nGTYi2Rt2JVURE0ovCyDlkhLsBcGXGd5gmw+PmiKMEgOOHd8X1WiIiIqONwsg5ZIatzognKz/u12rNsJb3djZ8GPdriYiIjCYKI+cwxuwBICM7vp0RgJ6cSutN6/64X0tERGQ0URgZQjgUJhtrmCYjpyD+Fxw3zbpW+6H4X0tERGQUURgZQnd3O07DenBdVk5+3K+XXToTgAKflveKiEh6URgZQnf7KQBCpoE3M763gwcY17e8tyh8HNPfHffriYiIjBYKI0Po6TwFQJcxBsMR/49pYmkZp0zr6cCt9ZrEKiIi6UNhZAi+rlMAdJOZkOu5XU6OOicC0KrlvSIikkYURoYQ6GoDoNeRmDACcDJzEgA9TXsSdk0RERG7KYwMwd9jPbHX5xiTsGv68qzlvUar7sIqIiLpQ2FkCIFe67k0AWfiwohr/HQAsjoPJeyaIiIidlMYGUK4L4wEExhGckpnATDeX5+wa4qIiNhNYWQIIZ91K/iQO3FhZELlxQDkmR0EO1oSdl0RERE7KYwMxWd1RkxX4sJISeE4Gk3rbq8tdbUJu66IiIidFEaGYAaszojpyUrYNR0OgyZXGQAn63cn7LoiIiJ2UhgZguG3wggJDCMA7VkVAPiP7U3odUVEROyiMDIEZ9C6JbvDG/9bwZ8ukD8FANdJLe8VEZH0oDAyBLvCiGfCDAByug4n9LoiIiJ2URgZgjtkhRFnRmLDSF65tbx3QuAIhMMJvbaIiIgdFEaG4A71AokPIyUVMwiYTjLw4zup+42IiEjqUxgZgte0OiOuBIeR8XnZHGECAMcPaUWNiIikPoWRIXjDVmfElZmT0OsahkGzpxyA9gbda0RERFKfwsgQMs0eALwJDiMAndmTAQg270v4tUVERBJNYWQQpmmSgQ8A75jchF8/PHYqAJ62Awm/toiISKIpjAzCHwwyJhJGshLfGckosZb35ndrea+IiKQ+hZFB9HR14jBMADKzEt8ZyS+3Hpg3PnQMgv6EX19ERCSRFEYG0dvVAUDYNHB7E3s7eIDy8kq6TC9OwnQ370/49UVERBJpRGFk3bp1VFZWkpGRQVVVFVu2bDnn/s8//zxz585lzJgxlJSU8OUvf5nW1tYRFZwIvd3tAPQYXnAkPq/lZ3mpM0oBOH5oV8KvLyIikkhR/0u7YcMG7r33Xh544AG2b9/O4sWLWbp0KXV1dYPu/+abb7JixQpuv/12du3axa9+9Svee+897rjjjgsuPl4C3Z0A9JBhWw2tXmt5b+fRPbbVICIikghRh5HHH3+c22+/nTvuuINZs2bxxBNPUF5ezlNPPTXo/u+++y6TJ0/mnnvuobKykquvvpqvfvWrbN269YKLjxd/j9UZ6TUybauhO9d6YF74uIZpREQktUUVRvx+P9u2baO6unrA9urqat5+++1Bj1m0aBFHjhxh48aNmKbJsWPH+PWvf81nPvOZkVcdZ8Fea86I32FfZ8QotJb3ZrRrea+IiKS2qMJIS0sLoVCIoqKiAduLiopoamoa9JhFixbx/PPPs3z5cjweD8XFxeTn5/O9731vyOv4fD7a29sHvBIp1GsN0/gc9nVGskouAqDAp+fTiIhIahvR7EzDMAb8bJrmWdsidu/ezT333MODDz7Itm3beOWVVzh48CArV64c8vxr164lLy+v/1VeXj6SMkcs1NsFQMA5JqHXPd2Eydby3nHhE5i9bbbVISIiEm9RhZHCwkKcTudZXZDm5uazuiURa9eu5aqrruLrX/86l156KTfccAPr1q1j/fr1NDY2DnrMmjVraGtr63/V1ye2OxD2W52RgNO+zkh5aQktpnWPk5P1H9pWh4iISLxFFUY8Hg9VVVXU1NQM2F5TU8OiRYsGPaa7uxvHGctjnU4nYHVUBuP1esnNzR3wSiTTZ4WRkCvx9xiJ8LqcNDgnAtBap6f3iohI6op6mGb16tU888wzrF+/ntraWu677z7q6ur6h13WrFnDihUr+vdftmwZL774Ik899RQHDhzgrbfe4p577uHyyy+ntLQ0dn+TWPJbwzQhl33DNACnMisA6Gnca2sdIiIi8eSK9oDly5fT2trKo48+SmNjI3PmzGHjxo1UVFj/cDY2Ng6458htt91GR0cHTz75JH/7t39Lfn4+1157Lf/4j/8Yu79FjBkBK4yYbnvDSCC/ErrAcULLe0VEJHVFHUYAVq1axapVqwb93XPPPXfWtrvvvpu77757JJeyhRHosd7YHEZcE2ZCA2R1HrK1DhERkXjSs2kGYQRHRxjJm2gt7x3vPwJDzK8RERFJdgojg3CGrDDi8NgbRoorZxE2DbLpxt92zNZaRERE4kVhZBDOUC8ADq/NYaQgjwbGA3D88Ae21iIiIhIvCiODiIQRp81hxDAMmj1lALTV19pai4iISLwojAzCHbbCiMtr331GIjqyrFVK/mNa3isiIqlJYWQQ7rAPAJfNnRGAcME0ANxtemCeiIikJoWRQXhMK4x4MuzvjGQUzwQgr6vuPHuKiIgkJ4WRQXgjYSQz2+ZKYGz5LACKQkchHLK5GhERkdhTGBnEx2HE/s5I2eTp+Ew3boK0H9NQjYiIpB6FkTOEwiYZWGEkYxR0RnIyvdQbJQAcP7TL5mpERERiT2HkDD29vXgMazgkY0yOzdVYWjPKAehs+NDmSkRERGJPYeQMvd1d/e8zxtg/TAPQk1MJQPj4PpsrERERiT2FkTP4ejoBCJkGhstrczUWo9Ba3pvRftDmSkRERGJPYeQMgV4rjPQaXjAMm6uxZJday3sLerW8V0REUo/CyBkCvd0A+BgdXRGAwslzACgyjxP2ddtcjYiISGwpjJwh2NcZ8Rsemyv52MSSiZwyrfkrx+s1iVVERFKLwsgZgn2dB5+RYXMlH3O5nBx1TgSg9bCW94qISGpRGDlD0Getpgk4Rs8wDcCpMdYD83oa9cA8ERFJLQojZwj1dUYCjtHTGQEI5FnLex0n9ttciYiISGwpjJwh7I90RkZXGHFPmA5AduchewsRERGJMYWRM4T9vQCEnKMrjOSWXQzAeH+9zZWIiIjElsLImQLWMM1oCyOlU2YDkE8H3aeaba5GREQkdhRGzmD2h5FMmysZqGDsWJoYB0DjAa2oERGR1KEwcgYj0ANA2DW6OiMAxz3WA/NO1dfaXImIiEjsKIycIRJGTPcYmys5W1f2ZAD8zVreKyIiqUNh5AyOkBVGGIWdEWPcVADcpw7YXImIiEjsKIycwRG0VtMwCjsjWaUXAZDfowfmiYhI6lAYOYMz0hlxj64JrADjJ1sraiaGGggEgzZXIyIiEhsKI2dwhqzOiMMz+jojEybNIGA6yTT8NBzeZ3c5IiIiMaEwcgZXXxgxRmEYMZxuGlxlABw/8GebqxEREYkNhZEzuMNWGHGOwjACcCrLekaN7+humysRERGJDYWRM7hNHwBOb5bNlQwuMHYGAK4TWt4rIiKpQWHkDJ5IZ8Q7OjsjnpJZAOR1fmRzJSIiIrGhMHIGj+m3/vSOvtU0AAWTLwWgLFhHOBS2uRoREZELpzByBg9WGHFnjM5hmpLK2QRNBzlGD00NB+0uR0RE5IIpjJzBa0bCyOgcpnF5M2l0lgDQfOB9m6sRERG5cAojpwuH8RjWzcS8GaNzmAagNXMKAN0NWlEjIiLJT2HkNEF/T/97zygdpgHoHTsdAGfLHpsrERERuXAKI6fp7e3uf5+ROXrDiLvYWlGToxU1IiKSAhRGTuPvCyNB04HX47G5mqHlT7oEgNLAYcywVtSIiEhyUxg5ja8vjPhw43AYNlcztInTLiFsGuTTScvxBrvLERERuSAKI6fx91pzRnzG6O2KAGSMyeaoowiAY/u1okZERJKbwshpAr1d1p+M7jAC0JJhPaOm68gumysRERG5MAojp4mspgmM8s4IQE++taLGPP6hzZWIiIhcGIWR0wR9yRNG3MUXAZDdrhU1IiKS3BRGTtPfGXF4ba7k/MZOtlbUFPsPYZqmzdWIiIiM3IjCyLp166isrCQjI4Oqqiq2bNlyzv19Ph8PPPAAFRUVeL1epk6dyvr160dUcDyF+sJI0DH6OyMTp80FoJA2jjc32lyNiIjIyLmiPWDDhg3ce++9rFu3jquuuoqnn36apUuXsnv3biZNmjToMTfffDPHjh3j2WefZdq0aTQ3NxMMBi+4+FgL+XutP5MgjGRk5XHMGE+ReZyj+95nQlGp3SWJiIiMSNRh5PHHH+f222/njjvuAOCJJ57g1Vdf5amnnmLt2rVn7f/KK6+wadMmDhw4QEFBAQCTJ0++sKrjJOy37jMScmTYXMnwtGRWUtR9nM76ncBSu8sREREZkaiGafx+P9u2baO6unrA9urqat5+++1Bj/nNb37DggUL+M53vsPEiROZMWMG999/Pz09PYPuD9awTnt7+4BXIpgBHwBh5+ifMwLQM3aG9eZ4rb2FiIiIXICoOiMtLS2EQiGKiooGbC8qKqKpqWnQYw4cOMCbb75JRkYGL730Ei0tLaxatYoTJ04MOW9k7dq1PPLII9GUFhtBa5gmnATDNADukjnQAHnt++wuRUREZMRGNIHVMAbeKt00zbO2RYTDYQzD4Pnnn+fyyy/nxhtv5PHHH+e5554bsjuyZs0a2tra+l/19fUjKTNqZiSMuJJjmKZgynwAygMHCYf0jBoREUlOUYWRwsJCnE7nWV2Q5ubms7olESUlJUycOJG8vLz+bbNmzcI0TY4cOTLoMV6vl9zc3AGvRDAiYSRJhmlKp80laDrINzo5euSA3eWIiIiMSFRhxOPxUFVVRU1NzYDtNTU1LFq0aNBjrrrqKo4ePUpnZ2f/tr179+JwOCgrKxtByfFjhKw5I6YzOTojTk8mjU5rFc2x/X+yuRoREZGRiXqYZvXq1TzzzDOsX7+e2tpa7rvvPurq6li5ciVgDbGsWLGif/9bbrmFcePG8eUvf5ndu3ezefNmvv71r/PXf/3XZGZmxu5vEgORzghJMkwD0Jo1DYDeIzttrkRERGRkol7au3z5clpbW3n00UdpbGxkzpw5bNy4kYqKCgAaGxupq6vr3z87O5uamhruvvtuFixYwLhx47j55pv51re+Fbu/RYw4+jojuJNjmAbAXzgLOt7A1aIVNSIikpyiDiMAq1atYtWqVYP+7rnnnjtr20UXXXTW0M5o1B9GkmSYBiBz4iVwEMZ17be7FBERkRHRs2lO4wxbYcRwJ08YmTC9CoDy0BH8fr/N1YiIiERPYeQ0zr7OiOFJojBSPp1u04vXCNDw0Qd2lyMiIhI1hZHTOMNWZ8GRRJ0Rw+HkiGcyAK0HtKJGRESSj8LIaVym1RlxuEfXKp/zOZVj3RY+cFSdERERST4KI6dx9XVGnJ7kCiPm+FkAZJz80OZKREREoqcwchq3mZxhJLdiLgATenQXVhERST4KI6f5OIwkz5wRgLKLFgAw0TzGqZMnbK5GREQkOgojp/H0h5ExNlcSnZyCYloZC0D9Hk1iFRGR5KIwchoPVhhxe5NrmAagKXMqAG2Hd9hbiIiISJQURk7jNZM3jPQWzLTeNGlFjYiIJBeFkYhwCLcRApIzjHgmWpNYx7bvsbkSERGR6CiM9An7e/rfu73JNWcEoHD65QBUBA/gDwRtrkZERGT4FEb6+H0fhxFvZvKFkeIpc+jFTbbRS/1+DdWIiEjyUBjpEwkjAdOJx+2xuZroGU43R9yVABzfv9XmakRERIZPYaRPoLcLgF48uJ2GzdWMzKk8606soYYd9hYiIiISBYWRPoFIZwQXhpGcYcQouRSA7JO7ba5ERERk+BRG+gT8vQD4jeQboonIn2LdibXMtx8zHLa5GhERkeFRGOkT9FlhJIDb5kpGruyiKkKmwTjaONZYZ3c5IiIiw6Iw0icYsIZpgkbyhhFvZg5HnGUANH34XzZXIyIiMjwKI31Cfh+Q3GEEoCXbuhNrT912mysREREZHoWRPqGANUyT7GEkOOESALwtu2yuREREZHgURvqEAlZnJORI3gmsANmT5wNQ1K3bwouISHJQGOkTjoSRJO+MTJp9JQATzWOcPNFiczUiIiLnpzDSJ9w3TBN2JHcYyRlbxDGjEIC63ZrEKiIio5/CSJ9wigzTADRnWZNY2w9us7kSERGR81MY6WOG/NafzuQPI/7x1iRW97H3ba5ERETk/BRG+kQ6I+EU6IxkVX4CgJKuWpsrEREROT+FkYiQFUZSoTNSNucqACrMBk60Hre5GhERkXNTGIkIps4wTXZBCU3GBADqPnjb5mpERETOTWEkoq8zgstrbx0x0pQ9C4Cug3+0uRIREZFzUxjpY/RNYMWZGmHEXzQPgIzjmsQqIiKjm8JIRKQzkgLDNAA5U64AoFSTWEVEZJRTGOkT6YwYrtQII5PmLCRsGpTQwvFj9XaXIyIiMiSFkT6O/jCSGsM0WbkF1DsnAnB011s2VyMiIjI0hZE+jnBfGHGnRhgBaM65GICeg1ttrkRERGRoCiN9HOGA9WeKdEYAQsXWE3wzWzSJVURERi+FkT6RMOJ0Z9hcSeyMm2E9wbe850PMcNjmakRERAanMNLH2TdM40ihYZqKi68gYDopoJ0jh/bZXY6IiMigFEb6uMy+YZoU6ox4MrOod1cAcHT3mzZXIyIiMjiFkT4uM/U6IwAnx84FIHhYd2IVEZHRSWGkT6Qz4kqxMOKssOaNjDu53eZKREREBqcw0sedgsM0ACVzPglAZeAjerq7bK5GRETkbAojfdz0dUY8qdUZmTDpIk6Qi9cIcuDPuvmZiIiMPgojfSJhJJWW9gIYDgd1Yy4BoG3f2zZXIyIicjaFkT5uMwiAy5NaYQTAV1IFQEaT7sQqIiKjz4jCyLp166isrCQjI4Oqqiq2bNkyrOPeeustXC4X8+bNG8ll48qNFUbcKRhGcmdcBcCkrp26+ZmIiIw6UYeRDRs2cO+99/LAAw+wfft2Fi9ezNKlS6mrqzvncW1tbaxYsYJPf/rTIy42XkKhEG4jBKRmGJk852oCppNCTnGsXjc/ExGR0SXqMPL4449z++23c8cddzBr1iyeeOIJysvLeeqpp8553Fe/+lVuueUWFi5cOOJi4yXg6+l/7/KmXhjJzMrmoGsqAA07N9tcjYiIyEBRhRG/38+2bduorq4esL26upq33x56cuSPf/xjPvroIx566KFhXcfn89He3j7gFU9+f2//e3cKhhGAEwV9Nz879I7NlYiIiAwUVRhpaWkhFApRVFQ0YHtRURFNTU2DHrNv3z6+8Y1v8Pzzz+NyuYZ1nbVr15KXl9f/Ki8vj6bMqJ3eGfGk2E3PIjyVVkdq7Ek9wVdEREaXEU1gNQxjwM+maZ61Day5GLfccguPPPIIM2bMGPb516xZQ1tbW/+rvr5+JGUOW7CvM+Iz3RiO1FxgNGnuEgCmBA/Q1nbK1lpERERON7xWRZ/CwkKcTudZXZDm5uazuiUAHR0dbN26le3bt3PXXXcBEA6HMU0Tl8vFa6+9xrXXXnvWcV6vF683cR2KSBjx4yI1+yJQOHEqx4xCimjh4PY3mLfkJrtLEhERAaLsjHg8HqqqqqipqRmwvaamhkWLFp21f25uLjt37mTHjh39r5UrVzJz5kx27NjBFVdccWHVx0ioL4wEjKiyWdJpyJ0PQNe+TTZXIiIi8rGo//VdvXo1X/rSl1iwYAELFy7khz/8IXV1daxcuRKwhlgaGhr46U9/isPhYM6cOQOOnzBhAhkZGWdtt1MgEkZw21xJfIUnXQU7a8hv1hN8RURk9Ig6jCxfvpzW1lYeffRRGhsbmTNnDhs3bqSiogKAxsbG895zZLQJBXwABI3UDiMlcz8NOx9mmn8PvT1dZGRm2V2SiIgIhmmapt1FnE97ezt5eXm0tbWRm5sb8/PXvrORWa/+dw4ZZUx+aFfMzz9amOEwrY9WUsgpdt3wC2YvXGp3SSIiksKG++93ai4diVI4YA3TBA2PzZXEl+FwcDh7HgAdH75hay0iIiIRCiOcNkzjSO1hGoBAmTXROLvpv2yuRERExKIwwsedkVCKzxkBGD/HWko9tXdX/5JmEREROymMAOH+CaypPUwDMHlWFSfIIdPwc+DPb9pdjoiIiMIIgBm0wkg4DYZpnE4Hh8bMA+DErtftLUZERASFEeDjMBJypH5nBCBYbj2nZsxRzRsRERH7KYwA9HdG0iOMFM29DoBpvTvp7e05z94iIiLxpTAChNNomAZg0kULaCWPMYaP/X/SUI2IiNhLYQQgFADAdKZHZ8RwODmY+wkAOnb9zuZqREQk3SmMAIT8AJhpMkwDEK68BoBxx96yuRIREUl3CiPQ3xnBmR7DNADlVdat4KcG9tJxqsXmakREJJ0pjMBpwzTpE0ZKJk3nsDERp2Fy8L3/tLscERFJYwoj0D9MY6RRGAFoKLgSAN9eTWIVERH7KIwARji9JrBGuKZ9CoCS1ndtrkRERNKZwggfhxEjzcLI9CuWEjQdlIWPcqxur93liIhImlIY4fQwkl7DNGMLCtnnmQnAofd+a3M1IiKSrhRGSN/OCEBbyWIA3Ad+b3MlIiKSrhRGACMctP50p18YKZi3DIAZnVvx+3ptrkZERNKRwgjg6OuMONJsmAZg2tyraCGfbKOHfe+9Znc5IiKShhRGAGckjLi8NleSeA6nk4/yrCW+XR9o3oiIiCSewgjgMK1hGocr/TojAMaMGwAobd5icyUiIpKOFEYAp9nXGXGnX2cEYPrCzxIwnZSFG2g+vNvuckREJM0ojPBxGHGmaWdkbEEhtZ7ZANS/+7K9xYiISNpRGAGcfcM0zjScMxLRXnYtABkHf2dzJSIikm4URvg4jKTrMA3AxMtvAmB6z/t0tZ+0txgREUkrCiOAqy+MuNLwPiMRk2fO5YhRgscIsvetl+wuR0RE0ojCCOAiEkbStzNiOBzUFX0aALP2322uRkRE0onCCKfNGUnjMAKQX/UFAGa2v03A121zNSIiki4URgB3X2fE7UnfYRqAmZddQxPjyKKXfe/8h93liIhImkj7MGKaZn8YcabxnBEAp9PJRwVLAOj988u21iIiIukj7cNIMGziIgSAx51hczX2y5h7EwBTT2zCDPrtLUZERNJC2ocRfyCI27DCiMub3nNGAGZf+Re0mrnk0akH54mISEKkfRgJ+H397z1pPkwDkOH1sHfsYgDatv3a5mpERCQdKIycFkbSfTVNRObczwEwpeV1QsGAzdWIiEiqS/sw4g98HEaMNL4d/OkuXvRZTpDLONrYo1U1IiISZ2kfRoI+K4yETQMcTpurGR08Xi8fFlwHQO+ffmFzNSIikuoURgLWipGA4bK5ktEla8EXAZh58g0CvV02VyMiIqlMYaRvmCaIwsjpZl9+HQ1MIIte9m75ld3liIhIClMY6euMBNEQzelcLicfFd0AQPh9hREREYmftA8j4UhnxHDbXMnoM37RlwCY2fEOHaeO21yNiIikqrQPI6FgpDOiYZozXXTp5XzkmIzHCLHndz+xuxwREUlRCiORMKIJrGcxDINjlZ8HIP9DraoREZH4SPswEhmmCSuMDGra9bfjN51MC+6j4cM/2l2OiIikoLQPI2bI6oyENGdkUBOKy3g/axEATW/8yOZqREQkFaV9GAn33e48pM7IkMx5KwCY1rSRkL/H5mpERCTVjCiMrFu3jsrKSjIyMqiqqmLLli1D7vviiy9y/fXXM378eHJzc1m4cCGvvvrqiAuOtXAw0hlRGBnK3CU30cg48uik9g8/t7scERFJMVGHkQ0bNnDvvffywAMPsH37dhYvXszSpUupq6sbdP/Nmzdz/fXXs3HjRrZt28anPvUpli1bxvbt2y+4+Fgw+8JI2KFhmqF4PR72l/4VAMb2/2NzNSIikmoM0zTNaA644ooruOyyy3jqqaf6t82aNYubbrqJtWvXDuscs2fPZvny5Tz44IPD2r+9vZ28vDza2trIzc2NptzzevNfn+TqnQ+wK3MBs//u9zE9dyqpP7CHiT+5AodhcvR/bKZ0+ly7SxIRkVFuuP9+R9UZ8fv9bNu2jerq6gHbq6urefvtt4d1jnA4TEdHBwUFBUPu4/P5aG9vH/CKF7Nvzog6I+dWPmUmO8ZcCUBjzXdtrkZERFJJVGGkpaWFUChEUVHRgO1FRUU0NTUN6xyPPfYYXV1d3HzzzUPus3btWvLy8vpf5eXl0ZQZlchqGtOhOSPnY17+NwBc1Pwf9HaetLkaERFJFSOawGoYxoCfTdM8a9tgXnjhBR5++GE2bNjAhAkThtxvzZo1tLW19b/q6+tHUubwhK3OiKnOyHnN++RNHDTKyKKXD//zabvLERGRFBFVGCksLMTpdJ7VBWlubj6rW3KmDRs2cPvtt/PLX/6S66677pz7er1ecnNzB7ziRRNYh8/pdHB42v8LwITa5zDDIZsrEhGRVBBVGPF4PFRVVVFTUzNge01NDYsWLRryuBdeeIHbbruNn//853zmM58ZWaVxYoQjwzQKI8Nx6Y1fpd0cQ2m4kdotL9pdjoiIpICoh2lWr17NM888w/r166mtreW+++6jrq6OlStXAtYQy4oVK/r3f+GFF1ixYgWPPfYYV155JU1NTTQ1NdHW1ha7v8WFCGmYJhoFYwv4YMIyAIy3v2dzNSIikgqiDiPLly/niSee4NFHH2XevHls3ryZjRs3UlFRAUBjY+OAe448/fTTBINB7rzzTkpKSvpfX/va12L3t7gQkTDiVBgZrorPfB2/6WSW730O7njD7nJERCTJjWgJyapVq1i1atWgv3vuuecG/PzGG2+M5BIJY/RNYEWdkWGbOHk67+Rdz8L2V+j83T/BvCV2lyQiIkks7Z9NY/R1RnB67C0kyRRcfz9h0+CSzjc59tEOu8sREZEklvZhJLK0Fw3TRGXmJZ9ga6Y1abnhP/7B5mpERCSZpX0YcYTVGRmprGvvB+DSE69y9MBum6sREZFkpTDSF0YMdUaiNvvya3nfuwCXEabpNw/ZXY6IiCSptA8jhjojF8RTbT3scN7JGhr3jo4nMYuISHJJ+zDiCAetN+qMjMisqmv4Y+ZiHIbJ8X//X3aXIyIiSUhhxLQ6Iw6XOiMjlbP0QcKmwaUdWzjw/ha7yxERkSST9mHEaUbmjCiMjNSsSy/nvVzreUO9v/0mZjhsc0UiIpJM0j6MRIZp1Bm5MOX/7dv0mm4u9v+ZD37/M7vLERGRJJL2YcRpRsKI5oxciNKKGWwt+xIAhW//PQFft80ViYhIslAY6Z8z4rW5kuR36fKHOEYBJWYzO375bbvLERGRJKEw0tcZMTRMc8Fyc/P5aO7XAZi9/4ccO7Lf5opERCQZKIxEhmncCiOxcOVnV/Kh+2LGGD6afn4XmKbdJYmIyCiX9mHEhTVM41RnJCYcTgfez30Pv+lkbvc7vP/aT+wuSURERjmFkb7OiFOdkZipvHgBfyy7DYCydx6i7eRxewsSEZFRTWGESBjRBNZYqvof3+KwUcY4TrHnubvsLkdEREYxhZFIZ0Q3PYupzDFj6Fn6BGHT4PK2V/jTf/7Y7pJERGSUSvsw4o50RjzqjMTaRZdfz3+V3QrA1P96gJaGAzZXJCIio1HahxEXIetPzRmJi6oV32Gvczp5dHHsJ7cRDAbtLklEREaZtA4jobDZ3xlxac5IXHi8XrzLn6Xb9DLb/z7vPvcNu0sSEZFRJq3DSCAYxGVYD3VTZyR+KmbMZU/VwwBcfeRH7PjdL+wtSERERpX0DiN+X/97l+aMxNX8z67ij4WfB2DKm/dxeO+fba5IRERGi/QOIwF//3u3J8PGStLDvK88xYfui8mlG/OF/86JlmN2lyQiIqNAWoeRkL+3/73uwBp/Hm8GE27/BceNAiabR2h8+vP09ujpviIi6S6tw0ggYA3ThEwDHE6bq0kPBcUV9Pw/v6CTTGYHPmDn92/RChsRkTSX1mEk1DdME8BlcyXpZdLFV3D4uh8SMJ18ovMPvPP9OwiFwnaXJSIiNknzMGJ1RoKGwkiizb76s3x45T8SNg0Wn3yJd9d9hbACiYhIWkrrMBLs64wE1RmxxSVLv8LOqm8BcFXrr3lr3d8QDIZsrkpERBItrcNIOKhhGrvN/exd/GneIwAsbv0V7/7LLfT6fOc5SkREUklahxEN04wOl910Lx8s+BYh0+DqjlfY/fhf0nbqlN1liYhIgqR3GOnrjITUGbHdnL+8m72f+gG9ppvLfH/k6Hev4+CBvXaXJSIiCZDWYSQyTBNSZ2RUmLXkixy96Ze0kc2s8D5yf3Idf9r873aXJSIicZbeYSQygdVw21yJREyZfy2hO17nkGsK44w2Lv39Cn7/7P+HP6B7kYiIpKr0DiN9nZGwOiOjSkHZTEr/dgvvj63GZYT5dP33qP3HT3HowB67SxMRkThI6zBiaphm1PJkZjP3nl+y+7KH6cHL3OCfKfjJEn7/i/9NQMt/RURSSlqHkXAoAEDIoWGaUckwuPiz99F52x/Y755JrtHNpz98mNq1i9n9/rt2VyciIjGS3mEkaC3t1TDN6DZ+8mymfuMtdl50Lz14uDS0i+kv3sgf/uV2Go7U2V2eiIhcoLQOI2bQ6oyE1RkZ9Qynm0u++Ai+v3mXD3IW4zZCfOrkr8n70SfY9PRqjh8/bneJIiIyQmkdRghHJrAqjCSL/NKpzPnb/+DQ0p9x0D2NbKOXaxqfxf3kXN5YdxdH6g7aXaKIiEQprcNIZAKrOiPJZ/IVy6hc8x67rv4eR5zl5BtdLGn+P4x/dgFvPbac7e++rgfviYgkibQOI/RNYDUVRpKTw8Hs61Yw8Zs72LPkKfZ6LsZrBLmq4xXmv/I5Dvz/83nzZ49y9Gi93ZWKiMg5pPfMzb7OiOlI748h2RlOFzOX3AJLbuHwjjdo2/QkM0+8wbTwIabtf4zgvv/NDs+ldE29kWmf/CJFpRV2lywiIqdJ63+FzbB1V8+ww2NzJRIrFfOWwLwldLe1sK3mx+Tv2cDUwD7mBXbAhzsI166l1jWDE8VXkTe7mpkLPoXbk2F32SIiaS2twwghqzOCU8M0qWZMXiFV/+3rwNdpOVzLwTdfIO/QK8wI7GFWaA807IGG9XS/6mV3xiV0FVWRM+1KKudeQ3beOLvLFxFJK2kdRoxwZJhGYSSVFVbMorDiUeBRTjYe5NAf/x3zoz8wuX0rBUY7c31boW4r1D0Nr0Odo4zm7IsIFl5MTvkcSmZcRkHJVHCk9xQrEZF4GVEYWbduHf/0T/9EY2Mjs2fP5oknnmDx4sVD7r9p0yZWr17Nrl27KC0t5X/+z//JypUrR1x0rBihvoevqTOSNsaWVDL2r+4B7iEcCnGw9j1aPngd4+g2Sjp2MtE8xqTwESa1H4H238EBYBN0kcFRVzmdY8oJ5lXgLqwkp2Qa+aXTGVtcicOl75CIyEhFHUY2bNjAvffey7p167jqqqt4+umnWbp0Kbt372bSpEln7X/w4EFuvPFGvvKVr/Czn/2Mt956i1WrVjF+/Hi+8IUvxOQvMVKRzgjqjKQlh9NJ5ZwrqZxzZf+25qYjNO5+k566P+NsqaWg+yPKQ0fIMnqZHtwH7fugHagHtlvHhE2DE0Yu7a5xdHsL8WdOIJxVBDnFuHPGk5FbSFZeIdkFE8jJG4eRkQeGYcvfWURkNDJM0zSjOeCKK67gsssu46mnnurfNmvWLG666SbWrl171v5/93d/x29+8xtqa2v7t61cuZL333+fd955Z1jXbG9vJy8vj7a2NnJzc6Mp95z++C+3cPnJ3/Lu5Du58rZvx+y8klq6e3o4euADTtV/SG/zRxinDpHZWU+B/yglZjNeIxjV+YI46CSbTkcOPucYAs4xBJ1jCLnHEHZngTsLPFmYnmxwj8HwZuN0Z+DwZOA64+XxZuL2ZOD0ZODyeHG6M3B5MnG4vRgOZ5w+ERGR4Rnuv99RdUb8fj/btm3jG9/4xoDt1dXVvP3224Me884771BdXT1g2w033MCzzz5LIBDA7T67K+Hz+fD5fAP+MvFg9K2mQS12OYcxmZlMm/0JmP2Js37nDwQ5eqyBk8fq6Ww9gu/kUUJtjbi6m8n0HScj0EZmqJ3scAf5dOI1ArgIk087+eF2CAOB+NUeMJ2EcRAyHIToe4+TcP/PH783I9sMa7/In2BgGgZg9L83MTANx4BtH7/v226AiQMi+2P0vXdY7wHTcAz4HZzdMTIB4/TtgzSVTAY59oxDItc8fS/ztA5V5F3/fsbAnwc717mKMtX9GhYjuv8ejhP7azBGQQ0FV/01My77pC3XjiqMtLS0EAqFKCoqGrC9qKiIpqamQY9pamoadP9gMEhLSwslJSVnHbN27VoeeeSRaEobEUe4718Bp5b2ysh43C5KyyooLTv/vUt6AyGa2tvpONmMr+MEgc4WAt3tBHs6CfV2EPJ1gb8L/J0YgW7cocirB2fYjzPsx2UGcJnWn24CuM0gHgJ4CeA1zk41biMEhM4uxhzivYikra2NVwFJEEYijDMSv2maZ2073/6DbY9Ys2YNq1ev7v+5vb2d8vLykZR6To45N/FOw3QKpi+M+blFzpThdlI8bizF48bG9LzhsEkwbNITChMM+gj6fYT8vYRDQYKhIKFggFAwSDgUJBQKEA4GCYWCmJGfQyEIB/teoQF/mmYYMK3/zYZDmCbWtsj2cLjvf88mhMOYmGBG3ochbAJhMMHAOgbT6oXQd5xh9t223zz99v1DJCTz9N99vM/g/3FtDnKqj68ROWbQ/xc6rZbI4cZgVQ1y4cH/C3fw/c7usNjB/hpGxeegThYTKufZdu2owkhhYSFOp/OsLkhzc/NZ3Y+I4uLiQfd3uVyMGzf4/Ry8Xi9erzea0kZk/g23xv0aIvHmcBh4HAa4HOB1QVaW3SWJiEQlqhsneDweqqqqqKmpGbC9pqaGRYsWDXrMwoULz9r/tddeY8GCBYPOFxEREZH0EvVdnFavXs0zzzzD+vXrqa2t5b777qOurq7/viFr1qxhxYoV/fuvXLmSw4cPs3r1ampra1m/fj3PPvss999/f+z+FiIiIpK0op4zsnz5clpbW3n00UdpbGxkzpw5bNy4kYoKawJfY2MjdXV1/ftXVlayceNG7rvvPr7//e9TWlrKd7/7XdvvMSIiIiKjQ9T3GbFDvO4zIiIiIvEz3H+/9bANERERsZXCiIiIiNhKYURERERspTAiIiIitlIYEREREVspjIiIiIitFEZERETEVgojIiIiYiuFEREREbFV1LeDt0PkJrHt7e02VyIiIiLDFfl3+3w3e0+KMNLR0QFAeXm5zZWIiIhItDo6OsjLyxvy90nxbJpwOMzRo0fJycnBMIyYnbe9vZ3y8nLq6+v1zJs402edGPqcE0Ofc2Loc06MeH7OpmnS0dFBaWkpDsfQM0OSojPicDgoKyuL2/lzc3P1RU8QfdaJoc85MfQ5J4Y+58SI1+d8ro5IhCawioiIiK0URkRERMRWaR1GvF4vDz30EF6v1+5SUp4+68TQ55wY+pwTQ59zYoyGzzkpJrCKiIhI6krrzoiIiIjYT2FEREREbKUwIiIiIrZSGBERERFbpXUYWbduHZWVlWRkZFBVVcWWLVvsLimlPPzwwxiGMeBVXFxsd1lJb/PmzSxbtozS0lIMw+Dll18e8HvTNHn44YcpLS0lMzOTJUuWsGvXLnuKTXLn+6xvu+22s77jV155pT3FJqm1a9fyiU98gpycHCZMmMBNN93Enj17Buyj7/SFG87nbOf3OW3DyIYNG7j33nt54IEH2L59O4sXL2bp0qXU1dXZXVpKmT17No2Njf2vnTt32l1S0uvq6mLu3Lk8+eSTg/7+O9/5Do8//jhPPvkk7733HsXFxVx//fX9z3iS4TvfZw3wF3/xFwO+4xs3bkxghclv06ZN3Hnnnbz77rvU1NQQDAaprq6mq6urfx99py/ccD5nsPH7bKapyy+/3Fy5cuWAbRdddJH5jW98w6aKUs9DDz1kzp071+4yUhpgvvTSS/0/h8Nhs7i42PyHf/iH/m29vb1mXl6e+YMf/MCGClPHmZ+1aZrmrbfeav7VX/2VLfWkqubmZhMwN23aZJqmvtPxcubnbJr2fp/TsjPi9/vZtm0b1dXVA7ZXV1fz9ttv21RVatq3bx+lpaVUVlbyxS9+kQMHDthdUko7ePAgTU1NA77bXq+Xa665Rt/tOHnjjTeYMGECM2bM4Ctf+QrNzc12l5TU2traACgoKAD0nY6XMz/nCLu+z2kZRlpaWgiFQhQVFQ3YXlRURFNTk01VpZ4rrriCn/70p7z66qv86Ec/oqmpiUWLFtHa2mp3aSkr8v3Vdzsxli5dyvPPP8/rr7/OY489xnvvvce1116Lz+ezu7SkZJomq1ev5uqrr2bOnDmAvtPxMNjnDPZ+n5Piqb3xYhjGgJ9N0zxrm4zc0qVL+99fcsklLFy4kKlTp/KTn/yE1atX21hZ6tN3OzGWL1/e/37OnDksWLCAiooKfvvb3/L5z3/exsqS01133cWf//xn3nzzzbN+p+907Az1Odv5fU7LzkhhYSFOp/OsVN3c3HxW+pbYycrK4pJLLmHfvn12l5KyIquV9N22R0lJCRUVFfqOj8Ddd9/Nb37zG/7whz9QVlbWv13f6dga6nMeTCK/z2kZRjweD1VVVdTU1AzYXlNTw6JFi2yqKvX5fD5qa2spKSmxu5SUVVlZSXFx8YDvtt/vZ9OmTfpuJ0Brayv19fX6jkfBNE3uuusuXnzxRV5//XUqKysH/F7f6dg43+c8mER+n9N2mGb16tV86UtfYsGCBSxcuJAf/vCH1NXVsXLlSrtLSxn3338/y5YtY9KkSTQ3N/Otb32L9vZ2br31VrtLS2qdnZ3s37+//+eDBw+yY8cOCgoKmDRpEvfeey/f/va3mT59OtOnT+fb3/42Y8aM4ZZbbrGx6uR0rs+6oKCAhx9+mC984QuUlJRw6NAhvvnNb1JYWMjnPvc5G6tOLnfeeSc///nP+bd/+zdycnL6OyB5eXlkZmZiGIa+0zFwvs+5s7PT3u+zLWt4Ronvf//7ZkVFhenxeMzLLrtswBInuXDLly83S0pKTLfbbZaWlpqf//znzV27dtldVtL7wx/+YAJnvW699VbTNK2lkA899JBZXFxser1e85Of/KS5c+dOe4tOUuf6rLu7u83q6mpz/PjxptvtNidNmmTeeuutZl1dnd1lJ5XBPl/A/PGPf9y/j77TF+58n7Pd32ejr0gRERERW6TlnBEREREZPRRGRERExFYKIyIiImIrhRERERGxlcKIiIiI2EphRERERGylMCIiIiK2UhgRERERWymMiIiIiK0URkRERMRWCiMiIiJiK4URERERsdX/BaXDLHWD0RMbAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for k, params in enumerate(params_k):\n", + " lam, gamma, w0 = params\n", + " y = spectral_density_approx(w, lam, gamma, w0)\n", + " print(f\"Parameters [k={k}]: lam={lam}; gamma={gamma}; w0={w0}\")\n", + " plt.plot(w, J, w, y)\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b66929b7", + "metadata": {}, + "source": [ + "The fit with four terms looks good. Let's take a closer look at it by plotting the contribution of each term of the fit:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "c8f26a60", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parameters [k=3]: lam=[ 7.91592672 0.60083674 -4.40789196 0.01058512]; gamma=[2.29618983 1.00246781 4.29908159 0.30736306]; w0=[0.1 0.1 3.981687 0.1 ]\n" + ] + } + ], + "source": [ + "# The parameters for the fit with four terms:\n", + "\n", + "lam, gamma, w0 = params_k[-1]\n", + "\n", + "print(f\"Parameters [k={len(params_k) - 1}]: lam={lam}; gamma={gamma}; w0={w0}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "d1ce6c63", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the components of the fit separately:\n", + "\n", + "def spectral_density_ith_component(w, i, lam, gamma, w0):\n", + " \"\"\" Return the i'th term of the approximation for the spectral density. \"\"\"\n", + " return (\n", + " 2 * lam[i] * gamma[i] * w /\n", + " (((w + w0[i])**2 + gamma[i]**2) * ((w - w0[i])**2 + gamma[i]**2))\n", + " )\n", + "\n", + "\n", + "def plot_spectral_density_fit_components(J, w, lam, gamma, w0):\n", + " \"\"\" Plot the individual components of a fit to the spectral density. \"\"\"\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " axes.plot(w, J, 'r--', linewidth=2, label=\"original\")\n", + " for i in range(len(lam)):\n", + " axes.plot(\n", + " w, spectral_density_ith_component(w, i, lam, gamma, w0),\n", + " linewidth=2,\n", + " label=f\"fit component {i}\",\n", + " )\n", + "\n", + " axes.set_xlabel(r'$w$', fontsize=28)\n", + " axes.set_ylabel(r'J', fontsize=28)\n", + " axes.legend()\n", + "\n", + " return fig\n", + "\n", + "\n", + "plot_spectral_density_fit_components(J, w, lam, gamma, w0);" + ] + }, + { + "cell_type": "markdown", + "id": "a69919e0", + "metadata": {}, + "source": [ + "And let's also compare the power spectrum of the fit and the analytical spectral density:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "2b8e61fb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_power_spectrum(alpha, wc, beta, lam, gamma, w0, save=True):\n", + " \"\"\" Plot the power spectrum of a fit against the actual power spectrum. \"\"\"\n", + " w = np.linspace(-10, 10, 50000)\n", + "\n", + " s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta)\n", + " s_fit = (\n", + " spectral_density_approx(w, lam, gamma, w0) *\n", + " ((1 / (np.e**(w * beta) - 1)) + 1) * 2\n", + " )\n", + "\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " axes.plot(w, s_orig, 'r', linewidth=2, label=\"original\")\n", + " axes.plot(w, s_fit, 'b', linewidth=2, label=\"fit\")\n", + "\n", + " axes.set_xlabel(r'$\\omega$', fontsize=28)\n", + " axes.set_ylabel(r'$S(\\omega)$', fontsize=28)\n", + " axes.legend()\n", + "\n", + " if save:\n", + " fig.savefig('powerspectrum.eps')\n", + "\n", + "\n", + "plot_power_spectrum(obp.alpha, obp.wc, obp.beta, lam, gamma, w0, save=False)" + ] + }, + { + "cell_type": "markdown", + "id": "7fe1c38a", + "metadata": {}, + "source": [ + "Now that we have a good fit to the spectral density, we can calculate the Matsubara expansion terms for the `BosonicBath` from them. At the same time we will calculate the Matsubara terminator for this expansion." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "123ccf79", + "metadata": {}, + "outputs": [], + "source": [ + "def matsubara_coefficients_from_spectral_fit(lam, gamma, w0, beta, Q, Nk):\n", + " \"\"\" Calculate the Matsubara co-efficients for a fit to the spectral\n", + " density.\n", + " \"\"\"\n", + " # initial 0 value with the correct dimensions:\n", + " terminator = 0. * spre(Q)\n", + " # the number of matsubara expansion terms to include in the terminator:\n", + " terminator_max_k = 1000\n", + "\n", + " ckAR = []\n", + " vkAR = []\n", + " ckAI = []\n", + " vkAI = []\n", + "\n", + " for lamt, Gamma, Om in zip(lam, gamma, w0):\n", + "\n", + " ckAR.extend([\n", + " (lamt / (4 * Om)) * coth(beta * (Om + 1.0j * Gamma) / 2),\n", + " (lamt / (4 * Om)) * coth(beta * (Om - 1.0j * Gamma) / 2),\n", + " ])\n", + " for k in range(1, Nk + 1):\n", + " ek = 2 * np.pi * k / beta\n", + " ckAR.append(\n", + " (-2 * lamt * 2 * Gamma / beta) * ek /\n", + " (\n", + " ((Om + 1.0j * Gamma)**2 + ek**2) *\n", + " ((Om - 1.0j * Gamma)**2 + ek**2)\n", + " )\n", + " )\n", + "\n", + " terminator_factor = 0\n", + " for k in range(Nk + 1, terminator_max_k):\n", + " ek = 2 * np.pi * k / beta\n", + " ck = (\n", + " (-2 * lamt * 2 * Gamma / beta) * ek /\n", + " (\n", + " ((Om + 1.0j * Gamma)**2 + ek**2) *\n", + " ((Om - 1.0j * Gamma)**2 + ek**2)\n", + " )\n", + " )\n", + " terminator_factor += ck / ek\n", + " terminator += terminator_factor * (\n", + " 2 * spre(Q) * spost(Q.dag())\n", + " - spre(Q.dag() * Q)\n", + " - spost(Q.dag() * Q)\n", + " )\n", + "\n", + " vkAR.extend([\n", + " -1.0j * Om + Gamma,\n", + " 1.0j * Om + Gamma,\n", + " ])\n", + " vkAR.extend([\n", + " 2 * np.pi * k * obp.T + 0.j\n", + " for k in range(1, Nk + 1)\n", + " ])\n", + "\n", + " ckAI.extend([\n", + " -0.25 * lamt * 1.0j / Om,\n", + " 0.25 * lamt * 1.0j / Om,\n", + " ])\n", + " vkAI.extend([\n", + " -(-1.0j * Om - Gamma),\n", + " -(1.0j * Om - Gamma),\n", + " ])\n", + "\n", + " return ckAR, vkAR, ckAI, vkAI, terminator" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "a146f03f", + "metadata": {}, + "outputs": [], + "source": [ + "def generate_spectrum_results(obp, params, Nk, max_depth):\n", + " \"\"\" Run the HEOM with the given bath parameters and\n", + " and return the results of the evolution.\n", + " \"\"\"\n", + " lam, gamma, w0 = params\n", + " ckAR, vkAR, ckAI, vkAI, terminator = (\n", + " matsubara_coefficients_from_spectral_fit(\n", + " lam, gamma, w0, beta=obp.beta, Q=obp.Q, Nk=Nk,\n", + " )\n", + " )\n", + " Ltot = liouvillian(Hsys) + terminator\n", + " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + "\n", + " options = Options(\n", + " nsteps=15000, store_states=True, rtol=1e-12, atol=1e-12, method=\"bdf\",\n", + " )\n", + " # This problem is a little stiff, so we use the BDF method to solve\n", + " # the ODE ^^^\n", + "\n", + " with timer(\"RHS construction time\"):\n", + " bath = BosonicBath(obp.Q, ckAR, vkAR, ckAI, vkAI)\n", + " HEOM_spectral_fit = HEOMSolver(\n", + " Ltot, bath, max_depth=max_depth, options=options,\n", + " )\n", + "\n", + " with timer(\"ODE solver time\"):\n", + " results_spectral_fit = (HEOM_spectral_fit.run(rho0, tlist))\n", + "\n", + " return results_spectral_fit" + ] + }, + { + "cell_type": "markdown", + "id": "5185757b", + "metadata": {}, + "source": [ + "Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "7b936c06", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.005162954330444336\n", + "10.0%. Run time: 0.05s. Est. time left: 00:00:00:00\n", + "20.0%. Run time: 0.07s. Est. time left: 00:00:00:00\n", + "30.1%. Run time: 0.10s. Est. time left: 00:00:00:00\n", + "40.1%. Run time: 0.12s. Est. time left: 00:00:00:00\n", + "50.1%. Run time: 0.15s. Est. time left: 00:00:00:00\n", + "60.1%. Run time: 0.17s. Est. time left: 00:00:00:00\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mcditoos/qutip_gsoc_app/qutip/solver/options.py:16: FutureWarning: Dedicated options class are no longer needed, options should be passed as dict to solvers.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "70.1%. Run time: 0.20s. Est. time left: 00:00:00:00\n", + "80.1%. Run time: 0.23s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 0.26s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 0.29s. Est. time left: 00:00:00:00\n", + "Total run time: 0.29s\n", + "ODE solver time: 0.29564571380615234\n", + "RHS construction time: 0.011826276779174805\n", + "10.0%. Run time: 0.16s. Est. time left: 00:00:00:01\n", + "20.0%. Run time: 0.24s. Est. time left: 00:00:00:00\n", + "30.1%. Run time: 0.32s. Est. time left: 00:00:00:00\n", + "40.1%. Run time: 0.42s. Est. time left: 00:00:00:00\n", + "50.1%. Run time: 0.50s. Est. time left: 00:00:00:00\n", + "60.1%. Run time: 0.57s. Est. time left: 00:00:00:00\n", + "70.1%. Run time: 0.65s. Est. time left: 00:00:00:00\n", + "80.1%. Run time: 0.73s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 0.81s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 0.87s. Est. time left: 00:00:00:00\n", + "Total run time: 0.88s\n", + "ODE solver time: 0.8767976760864258\n", + "RHS construction time: 0.019646406173706055\n", + "10.0%. Run time: 0.56s. Est. time left: 00:00:00:04\n", + "20.0%. Run time: 0.93s. Est. time left: 00:00:00:03\n", + "30.1%. Run time: 1.32s. Est. time left: 00:00:00:03\n", + "40.1%. Run time: 1.70s. Est. time left: 00:00:00:02\n", + "50.1%. Run time: 2.09s. Est. time left: 00:00:00:02\n", + "60.1%. Run time: 2.49s. Est. time left: 00:00:00:01\n", + "70.1%. Run time: 2.88s. Est. time left: 00:00:00:01\n", + "80.1%. Run time: 3.26s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 3.64s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 4.00s. Est. time left: 00:00:00:00\n", + "Total run time: 4.00s\n", + "ODE solver time: 4.004042148590088\n", + "RHS construction time: 0.14979052543640137\n", + "10.0%. Run time: 1.81s. Est. time left: 00:00:00:16\n", + "20.0%. Run time: 2.93s. Est. time left: 00:00:00:11\n", + "30.1%. Run time: 3.97s. Est. time left: 00:00:00:09\n", + "40.1%. Run time: 4.97s. Est. time left: 00:00:00:07\n", + "50.1%. Run time: 6.00s. Est. time left: 00:00:00:05\n", + "60.1%. Run time: 7.03s. Est. time left: 00:00:00:04\n", + "70.1%. Run time: 8.08s. Est. time left: 00:00:00:03\n", + "80.1%. Run time: 9.08s. Est. time left: 00:00:00:02\n", + "90.2%. Run time: 10.06s. Est. time left: 00:00:00:01\n", + "100.0%. Run time: 11.06s. Est. time left: 00:00:00:00\n", + "Total run time: 11.06s\n", + "ODE solver time: 11.059714555740356\n" + ] + }, + { + "data": { + "image/png": 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L9erVHTN15cb76quv6NWrF4cOHcrz7ObVjBkzhrFjxxIfH3/ZIXK5ur59+3LgwIF8jzNcj9mzZztWD3C2q/0+Jycn4+/vf9W8dsN3OLrllltYtGhRntd+/vlnbr755ss+7+nu7o6fn1+er5LSIKYphov92ZVUW1KJfa6IiEhx6d69Oy1atMg3cUVuvP379zNnzhxeeeWVYrnetm3brvjsbll0zeHz3LlzbNmyhS1btgD2WVRbtmzh8OHDAIwcOZJ+/fo52g8dOpRDhw4xfPhwdu3axQcffMDMmTP55z//WTx3UMysViteVn8Acm3pxO77w8kViYiIXBvDMJg+fTrh4eHk5uY6u5wK5fDhw7z55puOrTqL6sJ6qeXJNYfPC0sCNG3aFLCvsdW0aVP+85//APaHZy8EUbAvtbBgwQKWLl3KTTfdxIsvvsgbb7xRqpdZquR6cajh119/cGIlIiIi16dRo0b861//yre/+5WMGTMG0zQ15F4EHTt2ZMiQIcVyrV9++YXdu3fTpUuXYrleaXHNs91vu+02rvSY6KxZs/K91qFDBzZt2nStH+U0kb6RHDln3xlhx/6tTq5GREREKpq2bduSkpLC3Llznb5maXErfcvelwKRwdEQZ//+ZEqcU2sRERGRimfFihXOLuGGueETjsqiWtXqOb4/nXnaiZWIiIiIlC8KnwVoHHNx4dYUW8ktcC8iIiJS3il8FqBuwxgwXABIz011cjUiIiIi5YfCZwFcrVasVvv2Utm2NC1TISIiIlJMFD4vw9Pl/N6muTaOxB5wbjEiIiIi5YTC52X4uFzcVWnzptVOrERERESk/FD4vIxAt0DH9zv3/e7ESkRERETKD4XPy4gOqun4fvex7U6sRERERKT8UPi8jNZNb3N8fzT1iNPqEBERESlPFD4v446773cst3Q2O9HJ1YiIiIiUDwqfl+Hj442nWwAAtuxzxB097NyCRERERMoBhc8rCHINcXz/zbefOrESERERkfJB4fMKGgQ3cnz/67afnViJiIiISPmg8HkFfXo84fj+YOo+J1YiIiKl1axZszAMw/FltVqJjIxk4MCBHDt2zNEuJSWF5557jk6dOhESEoJhGIwZMybf9Qrb7mrGjRtHgwYNysQufatWrWLMmDGcPXv2hlz/wp/RwYMHr9p2zpw5NGzYEE9PTwzDYMuWLfnef6V6Z86cSUREBKmphd+eOykpCYvFwpQpUwr9nuvx66+/8thjj1GvXj28vb2JiIjg/vvvZ+PGjTf0c/9M4fMKmrZsjYdbJQAys86ydvVvTq5IRERKqw8//JDVq1ezaNEiHn/8cT777DPatWvnCCGJiYm8//77ZGZm8sADD1z2OoVtdyXHjx9n0qRJjBs3Doul9P9Tv2rVKsaOHXvDwmdhxcfH07dvX2rWrMnChQtZvXo1derUoUuXLqxevZoqVapctd7+/fvj7e3NpEmTCv25GzZswDRNWrRoUVy3UqB33nmHgwcP8te//pUFCxYwdepUTp06RevWrfn1119v6Gdfylpin1RG1fKuw/asNQDM+OJ1Wt3SzskViYhIadSoUSNuvvlmADp27EhOTg4vvvgi33zzDX369KFatWqcOXMGwzBISEhgxowZBV6nsO2uZOrUqQQEBNC9e/ci3VNplZaWhpeXV7Ffd8+ePWRnZ/Poo4/SoUMHx+teXl6EhIRc4Z0XWa1WnnjiCV588UWef/75QtW5YcMGrFYrzZo1u+7aC+Ott96icuXKeV67++67qVWrFhMmTOD222+/oZ9/Qen/75CTPXLnY47vtyZucmIlIiJSlrRu3RqAQ4cOATiG5a+msO0uJysri5kzZ9K7d+98vZ7x8fEMGTKEqlWr4u7uTkhICLfeeiu//PILAGPGjMEwDDZv3kz37t3x8/PD39+fRx99lPj4+HyftXfvXnr37k3lypVxd3enfv36vPXWW/na7d69m0ceeYTQ0FDc3d2JioqiX79+ZGZmMmbMGJ599lkAqlev7rj/pUuX5qlp06ZNPPjggwQGBlKzpn0jmH379jFw4EBq166Nl5cXERER3HvvvWzbtu2af24DBgygbdu2APTq1QvDMLjtttuAvMP2V6sXoE+fPiQnJ/P5558X6rPXr19Po0aN8PT0BMA0TV5//XU8PDwYMWIEOTk513w/Bflz8ATw8fGhQYMGHDlScmuaq+fzKrp2f5hx3zxHZtZZMjITWb5kIe073u3sskREpJTbt88+V6CwPWbFZe3atSQmJtKxY8d85/r27cumTZt46aWXqFOnDmfPnmXTpk0kJuZdz7pbt2707NmToUOHsmPHDkaNGsXOnTtZu3Ytrq6uAOzcuZM2bdoQFRXFa6+9RlhYGD/99BPDhg0jISGB0aNHA7B161batm1LcHAw48aNo3bt2sTFxTF//nyysrIYPHgwp0+fZtq0acybN88xtN2gQYM8NXXv3p2HH36YoUOHOh5lOH78OEFBQUycOJGQkBBOnz7NRx99RKtWrdi8eTN169Yt9M9t1KhRtGzZkqeffpoJEybQsWNH/Pz88rUrTL1hYWHUq1ePH374gcceeyzfNf5sw4YNdOrUCYCEhAQGDBjAmjVr+Prrr7nnnnvytTdNs9CB1Gq9ctRLSkpi06ZNJdbrCQqfV2UYBg18G7M50f6854yvpyl8iohIPjk5OdhsNjIyMli2bBnjx4/H19eX++67r0TrWL16NUCBQ7grV65k8ODBPP74447X7r///nztunfv7nhmsVOnToSGhtKnTx+++OIL+vTpA8Dw4cPx9fVlxYoVjpB25513kpmZycSJExk2bBiBgYEMHz4cq9XKunXr8gTxC9fx9fUlKioKgKZNmxIdHV3gffXv35+xY8fmea19+/a0b9/ecZyTk0OXLl1o2LAh7733HpMnT77yD+sSNWvWdATI2rVrO3qu/ywyMrJQ9TZr1szRo3wlCQkJHDp0iJYtW7J8+XJ69+5NdHQ0W7ZsITIyEoD9+/dTt25dzp07h4eHB8uWLSvwPxcFiY2NvWyNAE8//TSpqam88MILhbpecVD4LIS+9z7F5ln28LnzzFZM0yzSkIiIiEDOzSPgxFlnl2EXFoDLholFusSfw0rjxo155513CA0NLdJ1r9Xx48cxDIPg4OB851q2bMmsWbMICgrijjvuoHnz5o6ezEtdCIYX9OzZk/79+7NkyRL69OlDRkYGixcv5sknn8TLywubzeZo27lzZ958803WrFlDhw4dWLZsGYMGDSpyD3CPHj3yvWaz2Zg0aRKzZ89m3759ZGdnO87t2rWrSJ9XVJUrV+bUqVPYbLYr9j6uX78egEWLFvHkk0/y97//nQkTJuR5z/bt26lduzYeHh4ANG/e3PG+qwkPD7/suVGjRvHpp58ybdo0mjdvXqjrFQeFz0L4y91d8PosmLTMBLKyklgwfw5d7n/Y2WWJiJRtJ87CsdPOrqLYfPzxx9SvXx+r1UpoaKhjOLakpaen4+rqiouLS75zc+bMYfz48cyYMYNRo0bh4+NDt27dmDRpEmFhYY52l34P9qHboKAgx/B8YmIiNpuNadOmMW3atALrSEhI4MyZM+Tk5Dh68IqioJ/n8OHDeeutt3j++efp0KEDgYGBWCwWBg8eTHp6epE/syg8PDwwTZOMjAx8fHwu227Dhg14eHiwYMEC2rdvX+As+W3bttG4cWPHsY+PDzfddFOh6rhc8B07dizjx4/npZde4plnninUtYqLwmchGIZBk8CmrD6xCIBPFk5X+BQRKaqwAGdXcFEx1FK/fn3HbHdnCg4OJisri9TUVLy9vfOdmzJlClOmTOHw4cPMnz+fESNGcOrUKRYuXOhod+LECSIiIhzHNpuNxMREgoKCAAgMDMTFxYW+ffvy9NNPF1hH9erV8fLywsXFhaNHjxb5vgoacZw9ezb9+vVjwoQJeV5PSEggICCgyJ9ZFKdPn8bd3f2KwRPs4bNp06aMHj2arl27MnLkSF5++eU8bf4cPos67D527FjGjBnDmDFj+Ne//lW4GypGCp+FNLD3P1g9+RfA5I+UHdhs2Vit+YcqRESkcIo6zC0Fq1evHmB/TjAmJuay7aKionjmmWdYvHgxK1euzHPu008/zTMM+8UXX2Cz2Ryzv728vOjYsSObN28mJiYGNze3y35Ohw4dmDt3Li+99FKBjwIAuLu7A1xzb6VhGI73XvDDDz9w7NgxatWqdU3XuhaFqffAgQP5Jk0VZP369fTo0YO77rqL6dOnM3DgQCIjI/OE+m3bttG7d2/HcVGG3V988UXGjBnDv//9b8eksJKm8FlIrVq2wdcjlJSME+Rkp/LpzDfo/8Q/nF2WiIiUIT/++COpqamkpKQA9hnjX375JWB/VvLCmpCFbVeQCwFxzZo1ecJnUlISHTt2pHfv3tSrVw9fX1/Wr1/PwoUL860HOm/ePKxWK3feeadjtnuTJk3o2bOno83UqVNp27Yt7dq148knnyQ6OpqUlBT27dvHd99951i0fPLkybRt25ZWrVoxYsQIatWqxcmTJ5k/fz7vvfcevr6+jl69qVOn0r9/f1xdXalbty6+vr5X/Hl27dqVWbNmUa9ePWJiYti4cSOvvvpqsQzzX8nV6s3NzWXdunUMGjToiteJi4sjLi7OEfQHDBjA0aNHGTZsGGFhYfTo0YOsrCz27t2bp+fT19f3unrZX3vtNf7zn/9w991306VLF9asWZPn/OUmWRU7swxISkoyATMpKcmpdfx9xECzcU8fs3FPH7PzYzc7tRYRkbIgPT3d3Llzp5menu7sUm6YDz/80ATM9evXX7VttWrVTKDAr9jY2Gtudznt2rUzO3funOe1jIwMc+jQoWZMTIzp5+dnenp6mnXr1jVHjx5tpqammqZpmqNHjzYBc+PGjea9995r+vj4mL6+vuYjjzxinjx5Mt/nxMbGmo899pgZERFhurq6miEhIWabNm3M8ePH52m3c+dO86GHHjKDgoJMNzc3MyoqyhwwYICZkZHhaDNy5EgzPDzctFgsJmAuWbIkT03x8fH5Pv/MmTPmoEGDzMqVK5teXl5m27Ztzd9++83s0KGD2aFDB0e7C39GV/vZLVmyxATMuXPn5nm9oPdfrl7TNM3Fixc7fo5X8u2335qA+fvvv+d5fciQIaaHh4e5fPlyc8uWLaa3t7eZm5t7xWsVRocOHS7796owkfBqv8+FzWuGaZpmCWTcIklOTsbf35+kpKQC19wqKftiY+n+r2aQawOLK0vf2E2lkPwLtoqIiF1GRgaxsbFUr17dMVNXbryvvvqKXr16cejQoTzPbl7NmDFjGDt2LPHx8ZcdIper69u3LwcOHMj3OMP1mD17tmP1AGe72u9zYfOadji6BrWqV6eKR3X7QW42094ee+U3iIiIOEH37t1p0aJFvokrcuPt37+fOXPm8MorrxTL9bZt23bFZ3fLIoXPa/SXhhcXC/4l9uqLx4qIiJQ0wzCYPn064eHh5ObmOrucCuXw4cO8+eabjq06i+rCeqnliYbdr1HC2STufLoOObY0wOCzEYto2LSVU2sSESmtNOwucv1++eUX+vTpwx9//OH0paNAw+5OExzgTy3vhuePTKZ+oqVCREREpHi1bduWf/zjH8ydO7dUBM/ipKWWrsP9HR9j0jf29bU2xq8nNzcXi0U5XkRERIrHihUrnF3CDaPEdB3ufeBBvNztswCzs5L47uuPnVyRiIiISNmg8Hkd/D09iPG/uPPDR4tmOLEaERERkbJD4fM6Pfjg38BwAWB/8i7OpaY4tyARERGRMkDh8zq1adWCEPeqAJg5Wbw/XWupiYiIiFyNwud18vFwp2X4bY7j736f77xiRERERMoIhc8ieKDvMCxWTwAS0w6zd/9OJ1ckIiIiUropfBZBTK1oqrvVsR+YJm/MHOfcgkRERERKOYXPIvB0c6VDox6O49VHfqMMbBglIiIi4jQKn0V024M98XIPASArK5nvFv7PyRWJiIiIlF4Kn0VUPzyURh5NHMezvn/bidWIiIiIlG4Kn0Xk7mqlw+0DwMUVgP2nd3L23BnnFiUiIiJSSil8FoObO9xCqFs1AMxcG+/OnODkikRERERKJ4XPYlA7NJimgW0dxz9s/tqJ1YiISEmaNWsWhmE4vqxWK5GRkQwcOJBjx4452qWkpPDcc8/RqVMnQkJCMAyDMWPG5LteYdtdzbhx42jQoAG5ublFuLuSsWrVKsaMGcPZs2dvyPUv/BkdPHjwqm3nzJlDw4YN8fT0xDAMtmzZku/9V6p35syZREREkJqaWuj6kpKSsFgsTJkypdDvuR5btmyhS5cuREVF4enpSaVKlbjllluYPXv2Df3cP1P4LAZWi4VbuvXDxd0fgKT0k2zesdbJVYmISEn68MMPWb16NYsWLeLxxx/ns88+o127do4QkpiYyPvvv09mZiYPPPDAZa9T2HZXcvz4cSZNmsS4ceOwWEr/P/WrVq1i7NixNyx8FlZ8fDx9+/alZs2aLFy4kNWrV1OnTh26dOnC6tWrqVKlylXr7d+/P97e3kyaNKnQn7thwwZM06RFixbFdSsFOnv2LFWrVmXChAksWLCAjz/+mOjoaPr27cv48eNv6Gdfylpin1TONW1QizrWeuzKtIfONz8az8xJ3zm5KhERKSmNGjXi5ptvBqBjx47k5OTw4osv8s0339CnTx+qVavGmTNnMAyDhIQEZsyYUeB1CtvuSqZOnUpAQADdu3cv0j2VVmlpaXh5eRX7dffs2UN2djaPPvooHTp0cLzu5eVFSEhIoa5htVp54oknePHFF3n++ecLVeeGDRuwWq00a9bsumsvjNtuu43bbrstz2tdu3YlNjaW999/n3//+9839PMvKP3/HSojooICaF6rCxj2H+mmo2vIys5yclUiIuIsrVu3BuDQoUMAjmH5qylsu8vJyspi5syZ9O7dO1+vZ3x8PEOGDKFq1aq4u7sTEhLCrbfeyi+//ALAmDFjMAyDzZs30717d/z8/PD39+fRRx8lPj4+32ft3buX3r17U7lyZdzd3alfvz5vvfVWvna7d+/mkUceITQ0FHd3d6KioujXrx+ZmZmMGTOGZ599FoDq1as77n/p0qV5atq0aRMPPvgggYGB1KxZE4B9+/YxcOBAateujZeXFxEREdx7771s27btmn9uAwYMoG1b+yN0vXr1wjAMR1C7dNj9avUC9OnTh+TkZD7//PNCffb69etp1KgRnp72XRNN0+T111/Hw8ODESNGkJOTc833cy2Cg4OxWkuuP1I9n8XEYhg0ufcevtsznaT0I+TkZPDpvHcY2Ouvzi5NREScYN++fQCF7jErLmvXriUxMZGOHTvmO9e3b182bdrESy+9RJ06dTh79iybNm0iMTExT7tu3brRs2dPhg4dyo4dOxg1ahQ7d+5k7dq1uLraV3fZuXMnbdq0ISoqitdee42wsDB++uknhg0bRkJCAqNHjwZg69attG3bluDgYMaNG0ft2rWJi4tj/vz5ZGVlMXjwYE6fPs20adOYN2+eY2i7QYMGeWrq3r07Dz/8MEOHDnU8ynD8+HGCgoKYOHEiISEhnD59mo8++ohWrVqxefNm6tatW+if26hRo2jZsiVPP/00EyZMoGPHjvj5+eVrV5h6w8LCqFevHj/88AOPPfbYVT97w4YNdOrUCYCEhAQGDBjAmjVr+Prrr7nnnnvytTdNs9CBtKBQmZubS25uLmfOnGHu3Ln89NNPvPnmm4W6XnFQ+CxGTaIiiHFvym/pRwCYs/hDhU8RkQoiJycHm81GRkYGy5YtY/z48fj6+nLfffeVaB2rV68GKHAId+XKlQwePJjHH3/c8dr999+fr1337t0dzyx26tSJ0NBQ+vTpwxdffEGfPn0AGD58OL6+vqxYscIR0u68804yMzOZOHEiw4YNIzAwkOHDh2O1Wlm3bl2eIH7hOr6+vkRFRQHQtGlToqOjC7yv/v37M3bs2DyvtW/fnvbt2zuOc3Jy6NKlCw0bNuS9995j8uTJV/5hXaJmzZqOAFm7dm1Hz/WfRUZGFqreZs2aOXqUryQhIYFDhw7RsmVLli9fTu/evYmOjmbLli1ERkYCsH//furWrcu5c+fw8PBg2bJlBf7noiCxsbH5anzqqad47733AHBzc+ONN97giSeeKNT1ioOG3YtRmL8vDds+hOFqf77jeNIBDp086NyiRESkRLRu3RpXV1d8fX3p2rUrYWFh/Pjjj4SGhpZoHcePH8cwDIKDg/Oda9myJbNmzWL8+PGsWbOG7OzsAq9xIRhe0LNnT6xWK0uWLAEgIyODxYsX061bN7y8vLDZbI6vzp07k5GRwZo1a0hLS2PZsmX07NmzyD3APXr0yPeazWZjwoQJNGjQADc3N6xWK25ubuzdu5ddu3YV6fOKqnLlypw6dQqbzXbFduvXrwdg0aJF/OUvf6F3794sXbrUETwBtm/fTu3atfHw8ACgefPmrF+/vlBf4eHh+T7zX//6F+vXr3f0zD7zzDP897//Lca7vzL1fBazRq1iqLa4JgeztwEmb84Yy6svfOjsskRESp0+n44nMS3J2WUAEOTlz6d9ijbZ4uOPP6Z+/fpYrVZCQ0Mdw7ElLT09HVdXV1xcXPKdmzNnDuPHj2fGjBmMGjUKHx8funXrxqRJkwgLC3O0u/R7sA/dBgUFOYbnExMTsdlsTJs2jWnTphVYR0JCAmfOnCEnJydPkLpeBf08hw8fzltvvcXzzz9Phw4dCAwMxGKxMHjwYNLT04v8mUXh4eGBaZpkZGTg4+Nz2XYbNmzAw8ODBQsW0L59+wJnyW/bto3GjRs7jn18fLjpppsKVUdBw+5RUVGO3tvOnTsDMHLkSPr3718ij4kofBazmMgqNA1qz8Gj9oedl+3+GdM0i/TwuIhIeZSYlsSpc2edXUaxqV+/vmO2uzMFBweTlZVFamoq3t7e+c5NmTKFKVOmcPjwYebPn8+IESM4deoUCxcudLQ7ceIEERERjmObzUZiYiJBQUEABAYG4uLiQt++fXn66acLrKN69ep4eXnh4uLC0aNHi3xfBf07Onv2bPr168eECXk3d0lISCAgIKDIn1kUp0+fxt3d/YrBE+zhs2nTpowePZquXbsycuRIXn755Txt/hw+izrs/mctW7bk3Xff5cCBAwqfZVGAlydRnbrg8dlcMtJPkZGVzILfvqZL+/K53IWIyPUK8vJ3dgkOpamWoqpXrx5gf04wJibmsu2ioqJ45plnWLx4MStXrsxz7tNPP6V58+aO4y+++AKbzeaY/e3l5UXHjh3ZvHkzMTExuLm5XfZzOnTowNy5c3nppZcKfBQAwN3dHeCaeysNw3C894IffviBY8eOUatWrWu61rUoTL0HDhzIN2mqIOvXr6dHjx7cddddTJ8+nYEDBxIZGZkn1G/bto3evXs7ji8MuxdGQcPuf7ZkyRIsFgs1atQo1DWLSuHzBoipE02MpSHrOAXAzLmvKXyKiPxJUYe5y6Iff/yR1NRUUlJSAPuM8S+//BKwD39eWBOysO0KciEgrlmzJk/4TEpKomPHjvTu3Zt69erh6+vL+vXrWbhwYb71QOfNm4fVauXOO+90zHZv0qQJPXv2dLSZOnUqbdu2pV27djz55JNER0eTkpLCvn37+O677/j1118BmDx5Mm3btqVVq1aMGDGCWrVqcfLkSebPn897772Hr6+vo1dv6tSp9O/fH1dXV+rWrYuvr+8Vf55du3Zl1qxZ1KtXj5iYGDZu3Mirr75aLMP8V3K1enNzc1m3bh2DBg264nXi4uKIi4tzBP0BAwZw9OhRhg0bRlhYGD169CArK4u9e/fm6fn09fW9rl72IUOG4OfnR8uWLQkNDSUhIYG5c+cyZ84cnn322ZJbmcEsA5KSkkzATEpKcnYphZKWmWW+MuFts3HvSmbjnj5m415+ZsLZeGeXJSJS4tLT082dO3ea6enpzi7lhvnwww9NwFy/fv1V21arVs0ECvyKjY295naX065dO7Nz5855XsvIyDCHDh1qxsTEmH5+fqanp6dZt25dc/To0WZqaqppmqY5evRoEzA3btxo3nvvvaaPj4/p6+trPvLII+bJkyfzfU5sbKz52GOPmREREaarq6sZEhJitmnTxhw/fnyedjt37jQfeughMygoyHRzczOjoqLMAQMGmBkZGY42I0eONMPDw02LxWIC5pIlS/LUFB+f/9/RM2fOmIMGDTIrV65senl5mW3btjV/++03s0OHDmaHDh0c7S78GV3tZ7dkyRITMOfOnZvn9YLef7l6TdM0Fy9e7Pg5Xsm3335rAubvv/+e5/UhQ4aYHh4e5vLly80tW7aY3t7eZm5u7hWvVRgffPCB2a5dOzM4ONi0Wq1mQECA2aFDB/OTTz4p1Puv9vtc2LxmmKZplkzMvX7Jycn4+/uTlJRU4JpbpdHsVRv55P0niEv/A4AH2z/Of54u/JIPIiLlQUZGBrGxsVSvXt0xU1duvK+++opevXpx6NChPM9uXs2YMWMYO3Ys8fHxlx0il6vr27cvBw4cyPc4w/WYPXs2b775JmvWrCmGyormar/Phc1rWmrpBmkSFUFLj4trhP249kvKQM4XEZFyoHv37rRo0SLfxBW58fbv38+cOXN45ZVXiuV627Ztu+Kzu2WRwucNUq9KCH4tb8fVy/78RGrmGZZtXuTkqkREpCIwDIPp06cTHh5Obm6us8upUA4fPsybb77p2KqzqNasWZNnn/nyQMPuN9CXG7bx/ZvPsSl9BQANI1vw2Wu/OrkqEZGSo2F3kev3yy+/0KdPH/744w+nLx0FxTfsrtnuN1DTqHAOhnZk09F1YMti57GNnElOINBPz9GIiIjI5bVt25aUlBTmzp1bKoJncdKw+w1UPaQS5q3NCfaIBsA0c5nx1RSn1iQiIiKl34oVK9i6dWuevevLC4XPG8hiGMTUqEprSzPHa98s/UQTj0RERKTCUvi8wZpWC8e/UQdcvO0Tj1IyTrNq21Kn1iQiIiLiLAqfN1hlXx98Y2rSMLe247W3P51whXeIiIiIlF8KnyWgaVQ4TSu1B6t979vth9ZzNiXRyVWJiIiIlDyFzxIQE1mFczfHUMkzGgDTzGHmN9OcW5SIiIiIEyh8lgBvdzdqVo+gTU4Tx2tfL/5IE49ERESkwlH4LCFNoyIIbtAei4994lFyegKrty9zclUiIiIiJUvhs4TUDQshtV4kDXNqOV57+7OJTqxIREREpOQpfJYQq4uFmKrhtPC91THxaNuBNZxJTnByZSIiIiIlR+GzBDWLCifp5iYEekUD5ycezdfEIxEREak4FD5LUGSgP75VKtE+6+LEo3mLPiQ3N9eJVYmIiIiUHIXPEmQYBk2jIqhc6xZc/MIAOJdxhqWbFjq5MhEREZGSofBZwm6KCieuQSSNsy5OPHr3i0lOrEhERESk5Ch8lrBAL0+qhwTRyqMluHkBsPvQRo6dOuTkykRE5HrMmjULwzAcX1arlcjISAYOHMixY8cc7VJSUnjuuefo1KkTISEhGIbBmDFj8l2vsO2uZty4cTRo0KBMPNq1atUqxowZw9mzZ2/I9S/8GR08ePCqbefMmUPDhg3x9PTEMAy2bNmS7/1XqnfmzJlERESQmppa6PqSkpKwWCxMmTKl0O8pDjNmzMAwDHx8fEr0cxU+naBZVDhnW8RQybuG47WZ899wYkUiIlJUH374IatXr2bRokU8/vjjfPbZZ7Rr184RQhITE3n//ffJzMzkgQceuOx1CtvuSo4fP86kSZMYN24cFkvp/6d+1apVjB079oaFz8KKj4+nb9++1KxZk4ULF7J69Wrq1KlDly5dWL16NVWqVLlqvf3798fb25tJkwo/qrlhwwZM06RFixbFdStXdezYMf75z38SHh5eYp95Qen/G1kONYwIIyvEl9tTG4Bh/yP4fumnZGVnOrkyERG5Xo0aNaJ169Z07NiR0aNH89xzzxEbG8s333wDQLVq1Thz5gzLli3j5Zdfvux1CtvuSqZOnUpAQADdu3e/rveXdmlpaTfkunv27CE7O5tHH32UDh060Lp1a7y8vAgJCaF169a4u7tf9RpWq5UnnniCqVOnFrrODRs2YLVaadasWVFvodCGDh1K+/btufPOO0vsMy9Q+HQCD1crDcPDCKp+My6BkQBkZKfy46p5Tq5MRESKS+vWrQE4dMj+WNWFYfmrKWy7y8nKymLmzJn07t07X69nfHw8Q4YMoWrVqri7uxMSEsKtt97KL7/8AsCYMWMwDIPNmzfTvXt3/Pz88Pf359FHHyU+Pj7fZ+3du5fevXtTuXJl3N3dqV+/Pm+99Va+drt37+aRRx4hNDQUd3d3oqKi6NevH5mZmYwZM4Znn30WgOrVqzvuf+nSpXlq2rRpEw8++CCBgYHUrFkTgH379jFw4EBq166Nl5cXERER3HvvvWzbtu2af24DBgygbdu2APTq1QvDMLjtttuAvMP2V6sXoE+fPiQnJ/P5558X6rPXr19Po0aN8PT0BMA0TV5//XU8PDwYMWIEOTk513w/VzJ79myWLVvG22+/XazXLSyrUz5VaB4dwQcx0TT9viYbOAzAjHmvcX+HR5xcmYiIFId9+/YBEBISUqKfu3btWhITE+nYsWO+c3379mXTpk289NJL1KlTh7Nnz7Jp0yYSExPztOvWrRs9e/Zk6NCh7Nixg1GjRrFz507Wrl2Lq6srADt37qRNmzZERUXx2muvERYWxk8//cSwYcNISEhg9OjRAGzdupW2bdsSHBzMuHHjqF27NnFxccyfP5+srCwGDx7M6dOnmTZtGvPmzXMMbTdo0CBPTd27d+fhhx9m6NChjkcZjh8/TlBQEBMnTiQkJITTp0/z0Ucf0apVKzZv3kzdunUL/XMbNWoULVu25Omnn2bChAl07NgRPz+/fO0KU29YWBj16tXjhx9+4LHHHrvqZ2/YsIFOnToBkJCQwIABA1izZg1ff/0199xzT772pmkWOpBarXmj3qlTp/jb3/7GxIkTiYyMLNQ1ipvCp5NUD65EgI8XEW5N2cAmSE/i0Ik/2HNoO3WqNXJ2eSIico1ycnKw2WxkZGSwbNkyxo8fj6+vL/fdd1+J1rF69WqAAodwV65cyeDBg3n88ccdr91///352nXv3t3xzGKnTp0IDQ2lT58+fPHFF/Tp0weA4cOH4+vry4oVKxwh7c477yQzM5OJEycybNgwAgMDGT58OFarlXXr1uUJ4heu4+vrS1RUFABNmzYlOjq6wPvq378/Y8eOzfNa+/btad++veM4JyeHLl260LBhQ9577z0mT5585R/WJWrWrOkIkLVr13b0XP9ZZGRkoept1qyZo0f5ShISEjh06BAtW7Zk+fLl9O7dm+joaLZs2eIIh/v376du3bqcO3cODw8Pli1bVuB/LgoSGxubp8annnqKunXr8uSTTxbq/TeCht2dxGIYNKsWwelWTajseXHi0XvfvO7EqkRE5Hq1bt0aV1dXfH196dq1K2FhYfz444+EhoaWaB3Hjx/HMAyCg4PznWvZsiWzZs1i/PjxrFmzhuzs7AKvcSEYXtCzZ0+sVitLliwBICMjg8WLF9OtWze8vLyw2WyOr86dO5ORkcGaNWtIS0tj2bJl9OzZs8g9wD169Mj3ms1mY8KECTRo0AA3NzesVitubm7s3buXXbt2Fenziqpy5cqcOnUKm812xXbr168HYNGiRfzlL3+hd+/eLF26NE+v5Pbt26lduzYeHh4ANG/enPXr1xfq69IJRV999RXfffcd06dPL9KjHUWlnk8nalYtgl937ePOlAZ8atkGuTaWrP2ac2mv4+OVv6tfRKQ8eXhkexLOnnR2GQAEB4Ty+cvLi3SNjz/+mPr162O1WgkNDXUMx5a09PR0XF1dcXFxyXduzpw5jB8/nhkzZjBq1Ch8fHzo1q0bkyZNIiwszNHu0u/BPnQbFBTkGJ5PTEzEZrMxbdo0pk0reJvohIQEzpw5Q05OTrEM7xb08xw+fDhvvfUWzz//PB06dCAwMBCLxcLgwYNJT08v8mcWhYeHB6ZpkpGRccWljDZs2ICHhwcLFiygffv2Bc6S37ZtG40bN3Yc+/j4cNNNNxWqjgvD7ufOnePpp5/m//7v/wgPD3fM1M/KygLg7NmzuLq64u3tXcg7vH4Kn04U6OVJzcpBZNRqhmv8GrIT9mPLyearJZ/Qv8vTzi5PROSGSjh7klOnjzu7jGJTv359br75ZmeXQXBwMFlZWaSmpuYLEsHBwUyZMoUpU6Zw+PBh5s+fz4gRIzh16hQLF17cbe/EiRNEREQ4jm02G4mJiQQFBQEQGBiIi4sLffv25emnC/73qnr16nh5eeHi4sLRo0eLfF8F9dTNnj2bfv36MWHChDyvJyQkEBAQUOTPLIrTp0/j7u5+1TU0N2zYQNOmTRk9ejRdu3Zl5MiR+VY5+HP4vJ5h94SEBE6ePMlrr73Ga6+9lq9dYGAg999/v2N1hhtJ4dPJmlWLYG6jBG79thZL2Q/AR/On0a/zU07tEhcRudGCA0p2OPpKSlMtRVWvXj3A/pxgTEzMZdtFRUXxzDPPsHjxYlauXJnn3Keffkrz5s0dx1988QU2m80x+9vLy4uOHTuyefNmYmJicHNzu+zndOjQgblz5/LSSy8V+CgA4FjC6Fp7Kw3DyLf80Q8//MCxY8eoVavWZd5VdIWp98CBA/kmTRVk/fr19OjRg7vuuovp06czcOBAIiMj84T6bdu20bt3b8fxhWH3wrgw7B4WFuZ4bOJSEydOZNmyZfz444+X/fMpbgqfTtYwPJT57q5U927CUveNkJJAwtljbNi5ghYN2zm7PBGRG6aow9xl0Y8//khqaiopKSmAfcb4l19+CUDnzp3x8vK6pnYFuRAQ16xZkyd8JiUl0bFjR3r37k29evXw9fVl/fr1LFy4MN96oPPmzcNqtXLnnXc6Zrs3adKEnj17OtpMnTqVtm3b0q5dO5588kmio6NJSUlh3759fPfdd/z6668ATJ48mbZt29KqVStGjBhBrVq1OHnyJPPnz+e9997D19fX0as3depU+vfvj6urK3Xr1sXX1/eKP8+uXbsya9Ys6tWrR0xMDBs3buTVV1+94bO4r1Zvbm4u69atY9CgQVe8TlxcHHFxcY6gP2DAAI4ePcqwYcMICwujR48eZGVlsXfv3jw9n76+vtfcy+7h4eH4u3GpWbNm4eLiUuC5G8YsA5KSkkzATEpKcnYpN8TXm7abL374tdl1yF/Mxj19zMY9fczB47s5uywRkSJLT083d+7caaanpzu7lBvmww8/NAFz/fr1V21brVo1EyjwKzY29prbXU67du3Mzp0753ktIyPDHDp0qBkTE2P6+fmZnp6eZt26dc3Ro0ebqamppmma5ujRo03A3Lhxo3nvvfeaPj4+pq+vr/nII4+YJ0+ezPc5sbGx5mOPPWZGRESYrq6uZkhIiNmmTRtz/Pjxedrt3LnTfOihh8ygoCDTzc3NjIqKMgcMGGBmZGQ42owcOdIMDw83LRaLCZhLlizJU1N8fHy+zz9z5ow5aNAgs3LlyqaXl5fZtm1b87fffjM7dOhgdujQwdHuwp/R1X52S5YsMQFz7ty5eV4v6P2Xq9c0TXPx4sWOn+OVfPvttyZg/v7773leHzJkiOnh4WEuX77c3LJli+nt7W3m5uZe8VrXq3///qa3t3eh2l7t97mwec0wTdMsuah7fZKTk/H39ycpKanANbfKuiOnz/LO0jWEfbmAGZb/QXYmhmFh8bt7ytVQkIhUPBkZGcTGxlK9enXHTF258b766it69erFoUOH8jy7eTVjxoxh7NixxMfHl9gQbHnUt29fDhw4kO9xhusxe/Zs3nzzTdasWVMMlRXN1X6fC5vXtNRSKRAZ6E9lPx9c6t+EZ4B92SXTzOWTBe84uTIRESmLunfvTosWLa57e065fvv372fOnDm88sorxXK9bdu2XfHZ3bJI4bMUMAyD5tUiONagCrefrgXYJxp98fMMsm1Zzi1ORETKHMMwmD59OuHh4eTm5jq7nArl8OHDvPnmm46tOotqzZo1dOjQoViuVVoofJYSN1UNx2KxUNevMUYl+xBJanoSP63+2smViYhIWdSoUSP+9a9/5dvf/UrGjBmDaZoaci+Cjh07MmTIkGK51i+//MLu3bvp0qVLsVyvtFD4LCV8PdypGxbCkVsa0CC7puP16d9qxyMREZGKpm3btvzjH/9g7ty5Tl+ztLhpqaVS5OZqkeyKO8VtqXXY4bUV0s4Se2QH2/ZtoHEt5y9cLCIiIiVjxYoVzi7hhlHPZylSJywYb3c3bDFN8Pe/uDjuB9+94cSqRERERIqPwmcp4mKx0CwqnGN1KnNPXHWw2neMWLJ2fqnZ/1hERESkKBQ+S5nm1SLBMAiv3ACXYPuzn7lmDnN+nuHkykRERESKTuGzlKns50O1oABi29SjTVJ1Liy79L+F72nZJRERESnzFD5LoRbRVcl2t9LCVtOx7FJK6hl+XvONcwsTERERKSKFz1KoUUQYHq5W4lvFEOVyceLRjPlTnFeUiIiISDFQ+CyF3KwuNKkazpmIAO45EQVeAQDsP7SNbfs2OLc4EZHrYJqms0sQkSIqrt9jhc9SqkV0JADutRvhXeli7+es76c5qyQRkWvm4uICQHZ2tpMrEZGiuvB7fOH3+nopfJZS4QF+RAT4caBZNbociXIsu7R47bdadklEygxXV1fc3d1JSkpS76dIGWaaJklJSbi7u+Pq6lqka2mHo1Ls5uhIvj2bTA2f2lj8apIbt4vcXPuyS0/3fMHZ5YmIFEpwcDDHjh3j6NGj+Pv74+rqimEYzi5LRArBNE2ys7NJSkri3LlzREREFPmahlkG/iuanJyMv78/SUlJ+Pn5ObucEpORbePlBUtwPX2Owys+Z825HwETX+9Alr6/D9fzvaEiIqVdcnIyCQkJZGZmOrsUEbkO7u7uBAcHXzGHFTavqeezFPNwtRITGcbGnGO0TYpkbUgE5umj9mWXVn9Nl3a9nF2iiEih+Pn54efnR3Z2Njk5Oc4uR0SugYuLS5GH2i+l8FnKtahelY2HjpFycwxV4vZwnKOAfb/3zm17auhKRMoUV1fXYv1HTETKHk04KuWqBvoT6ufDsdqVue9QFceyS3sP/c7WPWudW5yIiIjINbqu8Pn2229TvXp1PDw8aN68Ob/99tsV27/11lvUr18fT09P6taty8cff3xdxVZEhmHYl10yDPwj6+MRXMdx7sPv3nBiZSIiIiLX7prD55w5c/jb3/7GCy+8wObNm2nXrh333HMPhw8fLrD9O++8w8iRIxkzZgw7duxg7NixPP3003z33XdFLr6iuCkqHKvFwp5ba9P5cAS4egCwdMP3HD110LnFiYiIiFyDaw6fkydPZtCgQQwePJj69eszZcoUqlatyjvvvFNg+08++YQnnniCXr16UaNGDR5++GEGDRrEK6+8UuTiKwovNzcaRYSS7W6lgaUaltDagH35g08XFPxzFxERESmNril8ZmVlsXHjRjp16pTn9U6dOrFq1aoC35OZmYmHh0ee1zw9PVm3bp12vLgGN0dXBeBg+xian4sGw767wJeLPyQlLcmJlYmIiIgU3jWFz4SEBHJycggNDc3zemhoKCdOnCjwPXfddRczZsxg48aNmKbJhg0b+OCDD8jOziYhIaHA92RmZpKcnJznq6KrHhxIsI8XZ0N9ue1kCEZINACZWenMW/yRc4sTERERKaTrmnD05+V9TNO87JI/o0aN4p577qF169a4urpy//33M2DAAODye4O+/PLL+Pv7O76qVq16PWWWK4Zh0KK6/eeQ3rQJEW4X93v/6Ic3seXYnFWaiIiISKFdU/gMDg7GxcUlXy/nqVOn8vWGXuDp6ckHH3xAWloaBw8e5PDhw0RHR+Pr60twcHCB7xk5ciRJSUmOryNHjlxLmeVW86gIrBYLBxtVodveShgBVQBIOBPH4nXznVydiIiIyNVdU/h0c3OjefPmLFq0KM/rixYtok2bNld8r6urK5GRkbi4uPD555/TtWtXLJaCP97d3d2xG8aFLwEvdzcaR4aBYeBTtR5eleo6zn0wfwplYKdUERERqeCuedh9+PDhzJgxgw8++IBdu3bx97//ncOHDzN06FDA3mvZr18/R/s9e/Ywe/Zs9u7dy7p163j44YfZvn07EyZMKL67qEBanh963922Dg/sDXIsOr/rwGYtOi8iIiKl3jVvr9mrVy8SExMZN24ccXFxNGrUiAULFlCtWjUA4uLi8qz5mZOTw2uvvcYff/yBq6srHTt2ZNWqVURHRxfbTVQkUZUCCPP35URSCrWt1bCG1sEWuw6Aj76fxk11Wzu5QhEREZHLM8wyMFabnJyMv78/SUlJGoIH1hw4zPwtO/GPP8epVd+yKGchZGdgGBZ+eGMrkZWjnV2iiIiIVDCFzWva270Malo1HDerC0khPtx6JuSSRedz+Z8WnRcREZFSTOGzDHJ3tXJT1XAATrVsTL3s6IuLzv86S4vOi4iISKml8FlGtTo/8ehw/TDu3+vnWHQ+IzONr3/92ImViYiIiFyewmcZVSXAj6qV/MEwMGrWI9intuOcFp0XERGR0krhswxrVT0KgJ1ta/HQdl/HovPxp4/zy9pvnVmaiIiISIEUPsuwxpFheLq6YnOzEuFTHfeQSxad//Z1LTovIiIipY7CZxnm6uJC82oRAGy/vQGd9waCdyAAuw9uZd2O5c4sT0RERCQfhc8y7sKORylB3rTODMdSpb7j3Iffvu6sskREREQKpPBZxgX7elMzJAiAvW0bcUt8KLh7A7Dq98X8cXCbM8sTERERyUPhsxxoWcPe+xlXI5h7jgdiqXLx2c9Z3011VlkiIiIi+Sh8lgMNqlTGz8MdgDONG1A9txpY3QD4cdWXHI8/7MzyRERERBwUPssBF4uFFuef/dzTPIqHt3tinN9yMzc3h09+eMuZ5YmIiIg4KHyWEy2rV8XFMDAtBp7V6xPiVRMs57fcXPwhSedOO7lCEREREYXPcsPXw51GkWEAbGxbk16b3TFCagCQmZXOnJ9nOrM8EREREUDhs1y5pYZ9x6NsD1fqBNXBK6gOYADw6Y9vk5GV7sTqRERERBQ+y5WqlQIID/ADYFX7OnT73R0jyP4s6JnkBL5b9pkzyxMRERFR+CxPDMNw9H6mVPKivRmNNTTvsks5uTnOKk9ERERE4bO8ialaBS83VwA2tq3Dnft8MfxCAThy8gC/rv/emeWJiIhIBafwWc64urhwc3QkAMeig7g/IQQjvJ7j/AffTsY0TWeVJyIiIhWcwmc51Kp61fPTjOBIq4a0OBUMXgEA7Ni/iY27VjqtNhEREanYFD7LoUBvL+pVqQzA1kZhPPyHJ5Y8vZ+vO6s0ERERqeAUPsupW2raJx6ZFgOXZjdRIzsC3LwAWLHlZ3Yf/N2Z5YmIiEgFpfBZTtUMCSLYxxuAX5tH8OhGa57ez5nfvOas0kRERKQCU/gspwzDcPR+2tyt1KjXlGCPaHB1B+DnNV9z8PheJ1YoIiIiFZHCZznWLCoCd6t9f/cfb4ni4fUGljB776dpmnw4X89+ioiISMlS+CzH3F2tNI2KACDFy5VbqzTBK7AmuNjXAZ2//DPiEo44s0QRERGpYBQ+y7k2Nas5vl/avhbdNoARVhuAnBwbH333hrNKExERkQpI4bOcC/b1pm5YCABHfF2516MObsF1wGIfjv9y8SwSk+KdWaKIiIhUIAqfFcCttS72fm67M4Yuv1swKtcCICs7g9k/vOms0kRERKSCUfisAGqGBFHZzweAzQGu9EqPwiWsDhj2P/7Pf3qf5NSzTqxQREREKgqFzwrAMAxuveTZzxN338ztf7hhhEQDkJpxjs9/mu6k6kRERKQiUfisIG6KCsfLzT7LfXGoO32PBWEJrw/nd4GfveAt0jJSnVihiIiIVAQKnxWEq4sLLatXBSAXMO+5lVaHPDCC7AvRn01J5KvFs5xXoIiIiFQICp8VSOsaUVgMe0/nDzV96b/TC0tEfcf5j75/g6zsTGeVJyIiIhWAwmcF4ufpQUxkGACpOTmE3tmeRvE+GIH2hehPnT7Od8s/c2aJIiIiUs4pfFYwbWpFO77/oVkY/Te4YAlv4Hjtg29fx5Zjc0JlIiIiUhEofFYwkYH+VAsKACAuM4OYtu2okRqA4RcKwJGTB/hp9TwnVigiIiLlmcJnBXRp7+fiDjXptzIHI+Ji7+f0eZPIyc1xQmUiIiJS3il8VkANqlQmwNMDgG3p5+jQtC1hOcHgGwzAgWN/8Mvab51ZooiIiJRTCp8VkIvFwi2XLDq/5t7G9F1hwxLRyPHau19OJDc31xnliYiISDmm8FlB3RwdiZuLCwCr01O4t3ZrAl1CwCcIgP1Hd/HLuvnOLFFERETKIYXPCsrTzZXm0ZEAZOfksOOhFjy8yoYl8mLv53tfqfdTREREipfCZwV2a61q5zfXhCVZKTwU2hQf91DwrgTA3sM7WLLhe+cVKCIiIuWOwmcFVsnbi0YR9kXnz2VmcfzRdvRakfWn3s9XME3TWSWKiIhIOaPwWcG1q13d8f0i0unt0xAvzzBH7+fug7+zdMMPzipPREREyhmFzwouspI/0cGBAMSnpJI86A4eWpWNJbKho807X05U76eIiIgUC4VPydP7+YtnLn2MWnh4VQFveyjdfXAryzctdFZ5IiIiUo4ofAp1w0II9vEGIDbhNDxxD93XZmOJuNj7+a56P0VERKQYKHwKFsOgbe1ox/GvIW70O1cVd59w8AoAYMeBTazY8rNzChQREZFyQ+FTAGgaFY63uxsA24+dwPupe7l/XXaeme969lNERESKSuFTAHB1caF1jSgAck2T5TX86H+yMq6+F3s/t+/bwKqti51YpYiIiJR1Cp/i0LpGFFaL/a/E+kPHCHrmAbputOV99vOrl9X7KSIiItdN4VMcvN3daF4tAoAsWw7rbgpjQGwAVv8I8PQHYOuedazc+oszyxQREZEyTOFT8ri1drRjy81VB44Q8XQ37tmUd8/3t+aMV++niIiIXBeFT8kj2Meb+uGhACRnZLK1bXUG7vXFJSAiz8x37XokIiIi10PhU/Jpf8myS8sPHKbaMw/SaYsNS9XGjtff/GI8ubm5TqhOREREyjKFT8knKigwz5abf9xeh8f+8MHiVwV8ggDYe3gHi9Z+48QqRUREpCxS+JQCdahTw/H90v2HqPlkD+7YZsMSebH38+0vXiInN8cZ5YmIiEgZpfApBaoTGkyYvy8AR88kcfDuRgza4Y3FtzL4hgAQe3wPC1Z84cwyRUREpIxR+JQCGYZBhzrVHcfLDhym9pMP0mmrDZdLnv1858uXybZlO6NEERERKYMUPuWyGkWEEejlCcDeUwmcuO8mHt/hjYtPCIa/fUb80ZOxfLtstjPLFBERkTJE4VMuy8Viod2fej+rP9WDuzZl53n28/2vJpGVnemMEkVERKSMUfiUK2peLQJvdzcAth87wenuNzN4hzdWryCMgHAATiQe5avFs5xYpYiIiJQVCp9yRa4uLtxaqxoAJvDbwSNUe6o792zMxlL14q5H079+lfTMNCdVKSIiImWFwqdcVavqUbhbXQDYdPgY53q1ZtB2L6wegRiVIgFIOHuSL36e4cwyRUREpAxQ+JSr8nRzpVWNKAByck1WHTlG1ae70XV9dp493z/4djJpGeecVaaIiIiUAQqfUihtalbDxWIAsPbAYTL7tOWx7Z64uvljBNmD6ZmURGYveMeZZYqIiEgpp/ApheLn6UGzqAgAMm05rD0eR8TT3bh/bdb53k97MJ01fwpnUxKdWKmIiIiUZgqfUmjt61Q/HzFh5b5D2Pp3YMAOL9xcfTEq25dkOpeezMxvJzuvSBERESnVFD6l0IJ8vGkUGQZAamYW6+NOUOXvD9JtdRaWiEZg2P86fbbwPU4kHnNmqSIiIlJKKXzKNelYt6bj+9/2xJLTrx0D9vnhbvHECKsDQFZ2Ju/MneCsEkVERKQUU/iUaxLm70uDKpUBSM7IZNPxk1T+50P0WJWFJaI+uLgC8O3S2cQe+8OZpYqIiEgppPAp1+y2ehd7P5ftOYD5yK30OxyIZ44blvB6AOSauUyb86KzShQREZFSSuFTrllkoD91QoMBOJuWwZZjJwgZ0YuHf8vECKsLrh4A/LL2W7bt2+DMUkVERKSUUfiU69Lxkt7PpX8cwOzRir7xlfHLdMES0dBxbsr/RmOapjNKFBERkVJI4VOuS7WgQGqEVAIgMTWN7cdPEjDqEfr9molRuSa4+wCwfsdy1mxb4sxSRUREpBRR+JTrdunM9yV/HCC3SzN6pUcSdA4sVRs7zk3532hyc3OdUaKIiIiUMgqfct1qhFQiqlIAAKeSz7Er7hTeYx9h0KJM+5abXvZzu2K3sGjtN06rU0REREoPhU+5boZh5H32c/cBzL80opu1BhGnTSxRTRzn3pwzjmxbtjPKFBERkVJE4VOKpE5oMOEBfgAcT0pm76lE3Mc9zBMLMzD8wzD87GuCHorbzzdLPnFmqSIiIlIKKHxKkRiGkefZz19374N29bkrqD41T+RiqRrjOPfuVy+TlpHqjDJFRESklFD4lCKrH16ZUD/77PYjp5PYH5+I64sP89SCDAzfYIzASADiz5zgkx/edGapIiIi4mQKn1JkFsPgtkt6Pxfv2g8tatK+ZhMaH7RhiYoBDAA+nD+FxLOnnFSpiIiIOJvCpxSLxpFhhPh6A3Ao8Qz74xNxGf8ITy3IxPD0wwi1h9O0jHO8/eUEZ5YqIiIiTqTwKcXCYhjcfsnM91927oNGVWlxSxta/ZGNJbIRuFgBmLd4FgeO7nZWqSIiIuJECp9SbBpHVnH0fh4+fZZ9pxIxxvXkqZ9tGK4eWMLrA5CTm8Pr//uPM0sVERERJ1H4lGJjMQz+Ur+W43jxrn1QLYSG993B7VuzMcLqgpsnAMs2/siGnSucVaqIiIg4icKnFKtGEWFUPj/z/fDps+w9lYDxQneeWmZixQVL5MVtN1/75AVtuykiIlLBKHxKsbIYBn+pd0nv5859EOxL9MB76bY6CyMk2rHt5o4Dm/hx1ZfOKVREREScQuFTil3DiNCL636eSWLvyQSM4V0ZvMkd70wjz7ab0z4fS2ZWhrNKFRERkRKm8CnFzmIY3H7Js5+/7NoH3u4E/6MHfZdkYgmoguEfBsDx+MN89tN7zipVRERESpjCp9wQDcNDCfP3BeDomST2nEzAGHIHfQ75E5SciyXqJkfb6fNe5WxKopMqFRERkZKk8Ck3RP51P/eCqwteox/miYUZGN4BGCHVAUhJS+L9ea86q1QREREpQQqfcsM0CA+lyvnez2Nnk/njRDzGw224NzOc6idysFRtDBYXAD7/6X2OnDjgzHJFRESkBCh8yg1j7/3807qfhoHbhD48830GhpsXRpW6ANhysrXwvIiISAWg8Ck3VIPwynl6P3fFnYK7mtCuch2a7rfZdz1ydQfgl7XfauF5ERGRck7hU24owzC4o0Ftx/GinXsxAZeJffjrdxkYLq5YqsY4zk/6aAQ5uTlOqFRERERKgsKn3HD1wkKoGugPwMnkc/x+JA6jVW0atmzBHZuz7BOPzi88v/vgVr5dOtuJ1YqIiMiNpPApN5xhGNzZsI7j+Jdd+8jJzcUysTdP/5yNa46BS3Qzx/k3Ph/LubRkZ5QqIiIiN5jCp5SIWpWDqBFSCYDTqWlsOnQMo2YYVXvfRY9VWRh+lTEqRdrPJ8Uz45v/OrNcERERuUEUPqXEdLqk9/PX3fvJzsnB+HcPBq13wTvdtC88b9j/Sn7yw1taeklERKQcUviUEhNVKYB6YSEAJKVnsC72CEYlHyr9oweDFmVgePg4ll7KtmUx+dNRzixXREREbgCFTylRl858X/rHATJtNoyn7qLX0QAiE3KwRDQAVw8AFq+bz/odvzmrVBEREbkBFD6lRIUH+NE4MgyA1MwsVu87hOHuiseEPvxfgUsvPa+ll0RERMoRhU8pcXfUr4Vx/vvle2NJz8qGB1vT0ac6zfbZzi+9FAjAH4e28fWSj51XrIiIiBQrhU8pcSG+PjSrFgFARraN3/bGYhgGLq/15+/fpmMxwSW6qaP9tM/HkZKW5KxyRUREpBgpfIpT3F6vJi6Gvf9z1b5DpGRkYtxSh/ptWtJ1ffb5pZeqAnAmOYHp8151ZrkiIiJSTK4rfL799ttUr14dDw8Pmjdvzm+/XXlSyKeffkqTJk3w8vKiSpUqDBw4kMTExOsqWMqHQG8vWlS3h8usnByW/mFfVsl4uTdPLcrGM9PEEtXEsfTS7AVvc/D4XqfVKyIiIsXjmsPnnDlz+Nvf/sYLL7zA5s2badeuHffccw+HDx8usP2KFSvo168fgwYNYseOHcydO5f169czePDgIhcvZVvHejVxdbH/FVx34DCnU9MwaoYRMuBu+i/OPL/0Uj0AbDnZvPLR85im6cySRUREpIiuOXxOnjyZQYMGMXjwYOrXr8+UKVOoWrUq77zzToHt16xZQ3R0NMOGDaN69eq0bduWJ554gg0bNhS5eCnbfD3cubVWNAA5pskvO+09m8YL3emz1UromVz70ktungCs3LKIpRsXOKtcERERKQbXFD6zsrLYuHEjnTp1yvN6p06dWLVqVYHvadOmDUePHmXBggWYpsnJkyf58ssv6dKly2U/JzMzk+Tk5DxfUj61r1MdTzdXALYeieP42WSMSj54juzBMz9kYLhYsVS7OPlo0kcjyMhKd1a5IiIiUkTXFD4TEhLIyckhNDQ0z+uhoaGcOHGiwPe0adOGTz/9lF69euHm5kZYWBgBAQFMmzbtsp/z8ssv4+/v7/iqWrXqtZQpZYiHqysd69YEwAR+3rEHAOPpu7nrXBCND9owKlXF8KsMwLFTB5k1f6qzyhUREZEiuq4JR4Zh5Dk2TTPfaxfs3LmTYcOG8Z///IeNGzeycOFCYmNjGTp06GWvP3LkSJKSkhxfR44cuZ4ypYxoXSOKAC/7rkZ7Tiaw/1QihpsVl9cH8PdvMzAMA0t0czi/OujMb17j2KlDTqxYRERErtc1hc/g4GBcXFzy9XKeOnUqX2/oBS+//DK33norzz77LDExMdx11128/fbbfPDBB8TFxRX4Hnd3d/z8/PJ8SflldbFw5yXbbi7c/of9PzSdm9G4fmPu2pSF4eWPEVYHgMzsDP77yb+cVa6IiIgUwTWFTzc3N5o3b86iRYvyvL5o0SLatGlT4HvS0tKwWPJ+jIuLC4BmLotDk6rhhPn7AnDsbDLbj50EwDK5H//3YzYemSaWyIZ59n1ftXWx0+oVERGR63PNw+7Dhw9nxowZfPDBB+zatYu///3vHD582DGMPnLkSPr16+dof++99zJv3jzeeecdDhw4wMqVKxk2bBgtW7YkPDy8+O5EyjSLYXBXwzqO45937CEnNxejfiRhfTvx2C+ZGFY3+9qf502c9SzZtixnlCsiIiLX6ZrDZ69evZgyZQrjxo3jpptuYvny5SxYsIBq1aoBEBcXl2fNzwEDBjB58mTefPNNGjVqxEMPPUTdunWZN29e8d2FlAt1QoOpHlwJgMTUNNYfPAqA8Z8H6b3NjciEHIzgaPAJBuDg8b3MXvC2s8oVERGR62CYZWDsOzk5GX9/f5KSkvT8Zzl39HQSby9dDYCPuxv/uKs97lYrue/+zLJps/jHYG/M1DPkbPsJAC8PH759fSOhldSLLiIi4kyFzWva211KlchK/jSKCAPgXGYWK/ceBMB4/A7aWcNpsysbwzsQI7QWAGkZ53h99ihnlSsiIiLXSOFTSp1ODWtjOb901/K9sZzLyMRwseAyZQDDv8nAajOxVG0MVncAFqz8gg07VzizZBERESkkhU8pdYJ9vGkRHQlAli2Hxbv2AWB0bES1djfTe1kmhtXdHkDPe2nmcLJt2U6pV0RERApP4VNKpdvr18LNal+Sa/3Bo5xKPgeA5dW+PLY8l+CkXIzKNcDbPkFp/9FdfPLDm06rV0RERApH4VNKJV8PdzrUqQFArmny4/Y/ADBqhOLzf10Z9l0GhmHBpfrNXNj56N2vJnI8/vDlLikiIiKlgMKnlFq31orG39O+qPwfJ+LZdyoRAGNkN+6O86bJARuGTyXH5KOMzDQmznrWafWKiIjI1Sl8SqnlZnWhU8OL227+uG03uaaJ4eOB5b/9ePbrdIxc++Qjw9UTgKUbFrBk/ffOKllERESuQuFTSrUmVcMJD7CvFRaXlMLmQ8cAMB65lbq16tF9dRaG1Q2j2k2O97z84bOkZZxzRrkiIiJyFQqfUqpZDIPOjes6jhft3EuWzYZhGFjefIyhP2fhn5qLERSF4R8KwInEo7z75URnlSwiIiJXoPAppV6NkCDqV6kMQHJGJr9dWHi+URSBg+/hme8z7GE0+mYMi32G/OwFb7Hn8A5nlSwiIiKXofApZcLdjeo4Fp7/bU8syekZABhjHuK+Q140jrVhePpihNcHwJZj46UZfyM3N9dpNYuIiEh+Cp9SJoT4+tCyelUAsnJy+GXn+YXn/bxw+W8/RnyVjiXXxAivj+HhC8DmP9bw7bLZTqtZRERE8lP4lDLjL/Vr4eFqBWDjoaPEJaUAYPRuS51aden1WxaGxQUjurnjPZNn/5szyQlOqVdERETyU/iUMsPb3Y3b6tYEwMS+9JJpmucnHw1iyKIsQpJysQSEYQRFAZB07gyvfzrKiVWLiIjIpRQ+pUy5pWYUgV72NT33nUpkd1w8AEbjKHyH3M3fv7E/C2qp1hTD6gbAN0tns2HnCucULCIiInkofEqZ4uriwj2XLL30w7bd2HLsk4qMMT2544QXrXdnY7h5YkQ2drQb+/7/kZmVUeL1ioiISF4Kn1LmNAwPpXpwJQBOp6axav9BAAx/Lyyv9uXZeRm4ZZsYoTUxfIIAOBS3j/fnTXJWySIiInKewqeUOYZh0DWmHsb541937yclI9N+7tF2RNWrQ/9fMzEMC5YaLTEM+1/zD+e/rrU/RUREnEzhU8qkKgF+tLiw9JIth5937AHswdTy9mD6Lc0mMiEHw8sfLln7c8x7z5CTm+O0ukVERCo6hU8ps+5oUPuSpZeOcfRMEmCffOQ5rCvPzjs/+SiiAS5e/gBs37eBz3963zkFi4iIiMKnlF0+7m78pX4tx/H3W3dhmiYAxugHaZMeyF+2ZNu33Lxk7c83PhtLXMKREq9XREREFD6ljGtdI4oQX28ADp8+y+9H4wAwvD2wvPkYw79NxzvdxPCrjFHZvkZoemYq42f8zRFURUREpOQofEqZ5mKx0CWmnuN44fY9ZNlsABhdm1P5jpY888P54feoJri424Pqb5t/ZuGqr0q+YBERkQpO4VPKvDqhIdQLCwEgKT2D5XtiHecsUwfSfbsLMbE2DKsbZrWmjnMTZz3L2ZTEEq9XRESkIlP4lHKhc+N6uBj2xZeW74nlTFo6AEZEJVxe7MW/vkjHajOxVIrEUikSgDPJCbz2yQtOq1lERKQiUviUciHY15s2taoBYMvNZcHvux3njKfvpmZkNP1/Pb8WaHRzLFZ3AL5d9ilrti0t6XJFREQqLIVPKTc61quJj7t9P/cdx0+y5+T5fd9dLFjeH8LAX7OodjIHw80TqubdejMt45xTahYREaloFD6l3PBwdc2z7/t3W3dd3Pe9WQ08nryHkV+eH46vXBMXv1AAjp06yBufjS35gkVERCoghU8pV26qGk61oEAAEs+lsWLfxclHxou9aJ7hz/1rsjAMA6rfjMXFFYD/LXyXDTtXOKVmERGRikThU8oVwzC4r0l9x77vS3Yf4OyFyUe+nlimPcaw79KplJyL4ekLkY0c7x397tOkZ6Y5oWoREZGKQ+FTyp0qAX60rmmffJSdk8MPl0w+4oEW+N15M//82r72pxFWG1d/+/D7kZMHeHPOuBKvV0REpCJR+JRy6Y76tfC+ZPLR3pMJgL1n1PLWIO44YKXtjmwMw0JudHNczg+/z17wNpt3r3Za3SIiIuWdwqeUS55urtzT6NLJRzsvTj6KDMLy6qM8/1U6npkmhqcfZkQDAEzT5D/vPkVGVrpT6hYRESnvFD6l3LopKpyoSgEAJJxLY+W+g45zxuN/ISymHs98f374vUpd3M4Pvx+K28fbX7xU0uWKiIhUCAqfUm5ZDIP7bmrgmHz06+79FycfWSxYpj/BgxtNmu63YRgWcqo1w2KxAvDx99P4fe96J1UuIiJSfil8SrkWHuBHqxpRgH3y0YJtl+x8VCccl9EP8e856bhnmxhe/hDREIBcM5f/vPMkmVkZTqlbRESkvFL4lHLvzga1HZOPth87yZ4T8Y5zxj+6ElW1Gk8uOD/8Hl4Xd//KABw49gfvfjWx5AsWEREpxxQ+pdz78+Sjb7fsJMuWA4DhasUycygPr7TR6JB9+N1WrRkWiwsAH377Otv2bXBK3SIiIuWRwqdUCE2jwqkebN/56ExaOkt273ecM5pWx/qPexn1eTquNhPDKwDj/Oz3XDOXF94aotnvIiIixUThUyoEwzC4v2lDXAz79KPf9sZyMjnl4vnRD1HDL5THf8q0v1ClPh7nZ78fPL6Xqf8bU9Ili4iIlEsKn1JhVPb1oX2dGgDkmibfbN5JrmkCYHi6YZn+BH2XZFLvSA6GxUL2JYvPf/rj26zdvsxptYuIiJQXCp9SodxWrwaVvL0AOJR4hk2HjjnOGR0a4Pr4HYyak4ZLjn3xeao2dpz/zztPkpKWVOI1i4iIlCcKn1KhuLq4cP9NDRzHP27/g3OZWY5j45VHqWMJ5LFfzg+/h9bGO8i+VFNcwhEmzXq+ROsVEREpbxQ+pcKpHRpMTGQVANKzsvnx0rU//b2wzBzKwF8yqRmXg2EYZEQ1wdXNA4Bvl33KkvXfO6VuERGR8kDhUyqkLjH18HC172a0+fBx9scnOs4ZnZrg9thfGP3Z+eF3d2/Mqk0c58dOH8bp5Ph81xQREZGrU/iUCsnXw527GtZxHH+7eQe2nFzHsfHfvtS3VGLQIvvwuxkcjXdl+2Sl00nxvPj+XzHPT1YSERGRwlP4lAqrRfWqVK3kD0DCuTSW7TngOGf4XRx+r3/EZh9+j4zB3cMHgMXrv+P73z53St0iIiJlmcKnVFgWw+CBpo2wnF/7c9kfB0hISXWcN+6IwXXInYz5Xzpu2SaGmwe2qjc5zk/88FlOJBwt6bJFRETKNIVPqdCq+Ptya61qANhyc/l68448w+nGpEep4Rl0ce/3oEi8qtiH61PSkvj3O0PJzc3Nf2EREREpkMKnVHh/qV+LQC9PAGITTrPh4MXeTMPXE8sHT/LI8iya7rcBkBnRCE/vAADWbV/GJz+8WeI1i4iIlFUKn1LhuVmtPNC0oeN4wbY/SErPcBwbHRthfeou/vNZGp6ZJobVjcyophjnh+unfjaGXbFbS7xuERGRskjhUwT72p/NoiIAyLTZmL9lZ97h94l9iPQP4W/z7aHU4h+KV1QMALacbEa88RjpmWklX7iIiEgZo/Apcl7nmLr4uLsBsCvuFNuOnXCcM3w8sHz4FN1WZ3HLrmwAMkLr4F8pEoDY43t47ZMXSr5oERGRMkbhU+Q8Lzc37r1k683vtu4i7dKtNzs0wDLsHv79RTq+aSaGxYVzVWNwdXUH4ItFM1i6cUGJ1y0iIlKWKHyKXKJReCgNqlQGIDUziwWXbL0J9uH3ylXCeW5euv3Y0w/X6OaO86PfeYqEsydLrmAREZEyRuFT5BKGYXDfTQ0cW29uOnycPScvbqVpeLphmf1/3PV7LndssfeKZlaqSmB4XQDOpCTy77e1/JKIiMjlKHyK/Imfpwf3NKrrOP5m8w4ybTbHsdG8BpYxDzHiywwqn83FMAySq9TH+/zyS6u2/sJnP71XwlWLiIiUDQqfIgW4OTqSGiGVADiblsHPO/bmOW88fz/+N9VhzP/SMHJNDFcPsqo1c5x//dNR7Dm8o0RrFhERKQsUPkUKYBgG3Zo2wtXF/iuyZv8hDieeuXje6oLl46dpEWfl0aX24XfTrzIB0fYAmpWdyYg3HiMjK73kixcRESnFFD5FLiPIx4s7GtQGwAS+2rSd7Jwcx3mjZhjGlAEM/TGDOkftr6dUrklAkH35pX1Hdmr5JRERkT9R+BS5gjY1qxEZ6A9AfEoqi3fty3PeGHQ7bp2bM352Gu7Z9uWXUiIa42q1rxc65+fpLFr7bYnXLSIiUlopfIpcgYvFQvdmjXCx2LfS/G1PLIdPn3WcNwwDy/QnqG768Ndv7bsfGV7+eNRs5Wgz5t2nOXbqUInWLSIiUlopfIpcRZi/L3fUvzj8/uWGbXmH30MDsEx/ggdXZdF2h333o7SAcCpHNQYgJS2J598YSLYtu8RrFxERKW0UPkUKoW3taMfwe8K5VBbt/NPs9/tbYBl0O/+ek05gin35pcTKtQkMCAPg973reeuLF0u8bhERkdJG4VOkEFwsFh5s3hirxf4rs3LvQQ4mnMnTxni9P0Fhofzn8/O7H1ndyKjWDKuLKwAffPs6K7f8UrKFi4iIlDIKnyKFVNnPhzsvnf2+cRtZly4+7+uJ5X/DaLvX5MEVmQBke/pRqU4bR5t/vfU48WdOlGjdIiIipYnCp8g1uLV2NFGVAgBITE3jpz8vPt+iFsZLD/PX7zKoGWd/LjTRL4zwqEYAnElOYOSbg8nJzUFERKQiUvgUuQYWw+DB5o0di8+v3n+IA/GJedoY/7wXjw6NeenjNNyzTAzD4GRIbfx8gwFYt30ZM7+ZXOK1i4iIlAYKnyLXKNjXm04N6ziOv9q4Pe/e7xYLlo+foWaON//8+vzzn67u5NZogcWw/8q9/cV4Nu5aWbKFi4iIlAIKnyLX4Zaa1YgOCgTgTFo6C7f9kee8USUQy4dPcf/abDptsm+/meHpT+W69uc/c81cnn/jMU4nx5ds4SIiIk6m8ClyHSyGQY/mjXF1cQFgbewR9p5MyNPG6NocyzN3M/LLdCIS7M94xvuHUyWiHgCnTh9n5DQ9/ykiIhWLwqfIdQry8eLuRpcOv28jLSsrTxvj1UfxqR3FS5+k45JjYhgWToXWxc83CIDVv//Ke1+9UqJ1i4iIOJPCp0gRtKoRRc0Qe5BMzsjk2y07MU3Tcd7wcMPy+d9oGG/hme/Pb7/p5oml1i2O5z/f+2oiq7YuLvniRUREnEDhU6QILsx+93C1ArDt6Am2HonL08ZoEInxen96L8+izS77Fpvn3H2o2uh2AEzTZMS0xziRcLRkixcREXEChU+RIvL38uCBpg0dx/O37uRsWnqeNsYTd2J5oCVj/pdOcFIuAEe9gqheoxkAZ1NO8+zU/mTb8g7bi4iIlDcKnyLFICayCjdVrQJARraNuRu2kXvp8LthYJk5lMDgYF78NA0j177+59FK0QQHhgOwdc86Xv/0P06pX0REpKQofIoUk3ubNMDf0wOA2ITTrNx7MM95I9AHyxd/5+ZDMHiRfftN0+pGbs1WuFrdAJi94C1+XvNNSZYtIiJSohQ+RYqJp5srD93cGOP88c879xB3NjlPG6NlLYxJjzLo50xa/mF//jPZ6k5E4zscbUa/+xSH4vaVVNkiIiIlSuFTpBjVCAmibe3qAOTkmszZ8DvZOXnX8TT+2hmX+27mxU/TCTn//OcRN19q1rkFgNT0FP4xuS/pmWklW7yIiEgJUPgUKWZ3NqhNFX9fAE4ln+PnHXvynDcMA8uHT1EpKJgJH6edX//T4KB/OGGVowHYc3g7L07/a55lm0RERMoDhU+RYmZ1sfDQzTFYLfZfr5X7DrHvVGKeNhee/7zpKDz9w/n1P11cSY9qioe7FwDf//Y5/1v4bskWLyIicoMpfIrcAGH+vnRqeHH3oy83/J5/96Pzz38+ujSL27bZn/9MtboTGtPJ0ea/H49kw84VJVO0iIhICVD4FLlB2tSqlmf3o3kbt+cbRjf+2hnj/pv5z2dpjv3fj7l40OCmuwDIyc3hn1P6cSLxWMkWLyIicoMofIrcIBbD4MGbG+Pl5grAzrhTrI09kqfNhec/fUNDmPhRGm7Z9nD6h7s/tWs2B+B0Ujz/mPwoWdmZJXsDIiIiN4DCp8gN5O/pQY/mjR3HC37fzYmklDxtLjz/We+UwT++Of/8p2EhLqQWlYMiAdi2bwMTP3y25AoXERG5QRQ+RW6w+lUqc0vNKABsubl8vm4rWbY/Lb/UshbG6/3ptjqLezbYnw3NwMC9bnvcXO0L13+5+EO+WjyrRGsXEREpbgqfIiXg7kZ1Ly6/lHKOH37fla+N8dRdWPq0ZeSX6dSMs4fTuNxcat98n6PNhA/+we9715dM0SIiIjeAwqdICXB1caFXyya4urgAsP7gUbYdO5GnjWEYGO8NwbNuFK9+mIZv2vnnP00LTZreDUC2LYvhkx8l8eypkr0BERGRYqLwKVJCKvv6cG+T+o7jrzdt50xaep42hrcHlq/+QdUsd178NA0j1x5Ad7j6U6v6TQCcOn2cf7zel2xb3qWbREREygKFT5ES1LxaBI0jwwDIyLYxZ91WcnJz87QxalfB8vEz3LrLxpCfzs9wtxgkhNUjKCAUgE27V/HyB//UDkgiIlLmKHyKlCDDMOjWtCGBXp4AHD59ll937c/f7v4WGCMf4LFfMmm/3b4A/TkT/BrdgZurO2CfgDTn5+klV7yIiEgxUPgUKWEerq70atkEi2EAsPSP/eyPT8zXznjxYSy3N2bs/9KIOmWfgHTElkPD1g862rwy6znWbl9WMoWLiIgUA4VPESeIqhTAHQ1qAWACX6z7nZSMvIvIGy4WLJ/9FZ/gIF79MA2vDPsQ+7bsHFq36gac3wHp9b4cOXGgROsXERG5XgqfIk7Svk4NalW2b7+ZkpnJnPVbyf3z9pshfli+HE6N0wajP09zvL7JcKdJ/bYAJJ07w7BXe3EuLbnkihcREblOCp8iTmIxDHreHIOvh/0ZzgPxp/l117587YxWtTHeGMjtv9vot9i+A1IucDS4JlFV7L2n+4/uZsS0QeTk5uR7v4iISGmi8CniRD4e7jx8yfOfS3bvZ+/JhHztjCfuxHj8Lzy1IJNWf9gnICVlZ+JWvwN+3gEALN+0kDc/H1ditYuIiFwPhU8RJ6seXIk7G9QG7M9/zlm/laS0jDxtDMPAeHMQLrfU5aVP0qkab+/hPJiWSp3WD+FisS9eP/Pbyfyw4osSrV9ERORaKHyKlALt6lSnblgIAGlZ2Xy+fkv+9T/drFi++gf+gYG8NjMN73T786FbUlNo076Po93od59i274NJVe8iIjINVD4FCkFLIbBQ80bE+DpAcChxLP8vGNvvnZGWACWr/9J9SQXXpp9cQekVemZtL65KwBZ2ZkMm9SL4/GHS+4GRERECknhU6SU8HJ345FWN+Fy/vnP3/bGsut4/j3cjRa1MN4fwq27bPzfD/bhecMw2ObmT4NaLQBITDrFM688pBnwIiJS6ih8ipQiVSsFcHfjuo7juRt/50xqWr52ln4dMP7amUeXZHHPBvse75m5OZyNbExkaHUA9h3ZybNTB2DLsZVM8SIiIoWg8ClSyrSpWY2G4fY93DOybXy2biu2nNx87Yz/9sW4vREvfJFOw0P2gHkqI42AJvfg7xMIwMoti3hl1nPaA15EREoNhU+RUsYwDHo0b0Qlb/v+70fPJPHd1p3521ldsMz5G+6RIbz6YRrBSfaAujv5NE079Mfq4grAnJ+n87+F75bcDYiIiFyBwqdIKeTh6krvVk2xWuy/ousPHmV97JF87YxgPyzfPkeIzY3/fpiGW7a9h3NF4gk63T3U0e7Vj0awbOOPJVO8iIjIFSh8ipRS4QF+PNC0oeN4/tadHDl9Nl87I6Yaltn/R8PDOfx7Trrj9UWJCdzVsT8AuWYuz00dyO6Dv9/wukVERK5E4VOkFGtWLYLWNaIAyMk1+d/aLZzLyMzXzujWEuPl3tyzKZvHfrbPgDcxWZGVyy3N7gYgPTOVZ155iFOn40ruBkRERP5E4VOklOscU49qQQEAJKVn8Pn6rfkWoAcwnr8fo197nvgpkzs3n58Bn5NNrF8k9Ws0A+DU6eM888pDpKanlFj9IiIil7qu8Pn2229TvXp1PDw8aN68Ob/99ttl2w4YMMC+NeCfvho2bHjZ94jIRVaLhUda3oSvuzsAB+JP8/OOPfnaGYaB8f4TWNrUZfRn6TSOtc+AT8g4h1nrFqoE23tQdx/cyj+n9Cfbll1yNyEiInLeNYfPOXPm8Le//Y0XXniBzZs3065dO+655x4OHy54N5WpU6cSFxfn+Dpy5AiVKlXioYceKnLxIhWFn6cHvVvfhMWxAP1Bfj+af/jccHfF8vWzuEeG8N8P0whPtPeQ7ktOJKpVD3y9/QH7EkwvTh+mJZhERKTEXXP4nDx5MoMGDWLw4MHUr1+fKVOmULVqVd55550C2/v7+xMWFub42rBhA2fOnGHgwIFFLl6kIqkWFEiXmHqO43kbt3MiKf/wuRHih+X7EVQyPHh9RqpjD/iNiXHceucTuFrdAPhm6Wze+fLlkileRETkvGsKn1lZWWzcuJFOnTrleb1Tp06sWrWqUNeYOXMmd9xxB9WqVbtsm8zMTJKTk/N8iQi0rhFF06hwALJycvh07WbSs/IPnxsNq2KZ8zdqxJu88lEaLjn2APrLicPcd+/fHe3e/fJl5v36UckULyIiwjWGz4SEBHJycggNDc3zemhoKCdOnLjq++Pi4vjxxx8ZPHjwFdu9/PLL+Pv7O76qVq16LWWKlFuGYfBA04ZU8fcFIPFcGl9s+J3cAobPjXuaYkzuT6s9Np7/6uISTN+fOMKDXYc5jl+c/ld+2/zzjS9eRESE65xwZJx/7uwC0zTzvVaQWbNmERAQwAMPPHDFdiNHjiQpKcnxdeRI/sW1RSoqVxcX+rRuiqebfQejP07EFzgBCcAYdg/GE3fQbU02jy6xL9GUY+ayKDmZLrf1tR/n5vDP1/ux88DmkrkBERGp0K4pfAYHB+Pi4pKvl/PUqVP5ekP/zDRNPvjgA/r27Yubm9sV27q7u+Pn55fnS0QuquTtxSMtL05AWr4nli2Hj+drZxgGxrTH4K4mPPN9Brf9bh+iT83KYIPFk7bNOwP2NUCfnvggR08dLLF7EBGRiumawqebmxvNmzdn0aJFeV5ftGgRbdq0ueJ7ly1bxr59+xg0aNC1Vyki+dSqHJR3AtKm7Rw9nZSvneFqxTJ3OC4x1Xjx0zQaH7QvwZSYlkxccE1i6rSyHyed4skJ3TiTnFAyNyAiIhXSNQ+7Dx8+nBkzZvDBBx+wa9cu/v73v3P48GGGDrXvIz1y5Ej69euX730zZ86kVatWNGrUqOhViwhgn4DUIjoSAFtuLrPXbCI5PSNfO8PXE8sPI/EIDWLyzDSiTuUAcCgpHpe67YgOr20/jtvHM688RFrGuZK7CRERqVCuOXz26tWLKVOmMG7cOG666SaWL1/OggULHLPX4+Li8q35mZSUxFdffaVeT5FiZhgG997UgGpBgQAkZ2Qye81msnNy8reNqITlhxEEWDyY+n4qlVLsa4Buiz9KtVYPEhxgf3Rm274NDH/tUbJtWSV3IyIiUmEYZhlYZTo5ORl/f3+SkpL0/KdIAc5lZPL2ktWcPd/r2TQqnAebNy5wIqC56HdyO7/Mjiow9ClvMtztbe6JqsvS76eQkmYfur+7zYNM/L+ZWCzahVdERK6usHlN/6qIlAM+Hu48ekszXF1cANh8+Dgr9h4ssK1xZwzG+0NoeCSHiR9fXAP0x8N/cG+353B39QBg4aoveWXWc9oFSUREipXCp0g5ER7gx0M3N3YcL9z+B3+ciC+wrWVgR4z/PMitu2yM+PLiGqBz92/n0V7/wcViD7Gf/fQe0+e9emMLFxGRCkXhU6QcaRQRxu31agJgAp+v28rJ5PxbcAIYYx7C6NeeB9ZmM/injPPvMflf7E4G9BzlaPfmFy/yxaKZN7x2ERGpGBQ+RcqZ2+vXomG4ffJQps3Gx6s2kZKRma+dYRgY04fCXxoz5KdM7l1rn2CUnWPjy+MH6fvAs462L838Oz+v+aZE6hcRkfJN4VOknLEYBg/d3JjwAPvD3mfS0pm9elPBM+DdrFjm/QOjaXX+NTedW3deXIT+p+SzPHjXEMC+ScTIaYNYs21pSd2GiIiUUwqfIuWQm9VK31ua4efhDsCRM0nM3bCt4D3g/byw/DgSa3QoEz9KIybWvgj96bQU1uZaubttLwCybVn89dWH2bpnbcndiIiIlDsKnyLllL+nB/3aNMft/Az47cdO8MvOvQW2NUIDsPz0Ah6B/rw+I5WacfZe0riU08T6hdOu2T2AfRvOp17uwa7YrSVzEyIiUu4ofIqUY+EBfjzcsgkXVvtc+scBNh46WmBbo1YYlgUj8XPxYNp7qYQn2heh3386jtSqTWjRsD0AKWlJDH3pfg4c3V0StyAiIuWMwqdIOVevSuU8e8B/s2kHB+ITC2xrNK+B5et/EpJuYdp7F3dB+v3kQTwadKTJ+X3gz6QkMmT8fRw9GXvjb0BERMoVhU+RCqBNrWha14gCIMc0+XTNFuJTCt6/3bgjBuPjZ4hKyOWN91PxzrA/J7r6yB9EtHiA+tWbAHDqTByDX7yXE4nHSuYmRESkXFD4FKkgusTUo25oCADp2dl8tGoTqZkF799uefhWjCkDqHssl9dmpuKWbQ+gP+/fSuMOA6gRae9JPR5/iMdf7Eri2VMlcxMiIlLmKXyKVBAuFgsPt2xCmL8vAKdT0/jkMkswAVj+2hnj+ftpvj+Hlz5Jw5JrD6Dzdq+l7d1PUzW0BgCH4vbxxEv3k3TudMnciIiIlGkKnyIViLurlX63NMP3/BJMh0+fZc66rQUuwQRgvNwbY8gd3LbdxgtfXNyG83/bV3L3/c8SFhQJwJ7D23ny5R6cS0u+8TchIiJlmsKnSAUT4OVJ/zbNcbPal2DaGXeK77buwixoDVDDwHh7MMbDbbhvXTb/+PpiAP3w92V0e/AFgvwrA7B93wb+b1JP0jPTSuZGRESkTFL4FKmAwgP86NOqKRbDvgjT2gOHWb6n4JnrhosF4+NnoEszHv4ti6d+yHCcm751Gb0fHoefdyAAG3etZNikXmRkpRd4LREREYVPkQqqdmgwPZo3chz/tGMPmw8XPHPdcLVimTscOjRg4OJMBvxyMYC+9/syHnv0Zbw97c+Srt2+lL+++rACqIiIFEjhU6QCaxoVwV0N6ziOv9q4nb0nEwpsa3i6YZn/HNxck6cWZNLrt0wAck2Td7f9xtD+/8XLwweA1b//yt/+25vMrIwCryUiIhWXwqdIBde+TnVanV8DNNc0+XTtZo6fLXjikOHnhWXhvzAaRDL8mwzuXWtfqsmWm8M7237j/x573RFAV239hb+9pgAqIiJ5KXyKVHCGYXBvk/o0qGKfOJRly+GjlRs5k1rwxCEjyBfLon9jia7MC1+kc+dmewDNyrHx9rYVDH/8DTzdvQFYuWURwyf3ISs7s2RuRkRESj2FTxHBYhj0atmEqEoBAKRkZvLhyo2XXYTeCK+E5ZdRuFQJZNyn6bTbng1AenYm07Yu5x+PT3ME0N82/8zwyY8qgIqICKDwKSLnubq40O+WZgT72ENjwrlUZq3cQGa2rcD2Ro1QLIv/gzXYn5c/TqPVH/YAmpqVwbTfl/OPx9/Aw90LgOWbFvLPKf3IthUcZkVEpOJQ+BQRBy93Nwbe2hy/84vQHzubzCdrNmHLyS2wvVEvAsviUbj7+/LfD9JoeUkAffP3FfYA6uYJwNINC3h2Sn+ybdklczMiIlIqKXyKSB6B3l4MuPVmPF1dATgQf5o567eSk3uZANooCssvo/Dw8ea1D9K4eY+9p/RcVjpvbVvJPx+fhrurBwC/rv+eZ9UDKiJSoSl8ikg+Yf6+9L+1OW4u9l2Qdhw/ybebdxa4CxKAcVM0lp//jYenJ5M/SKX5vvMBNDOdt7av4p9D8gbQv2sWvIhIhaXwKSIFiqoUQJ/WTXE5vwvShkNHWbh9z2XbGzfXxPLTC3i6efD6jFSa7rcH0JTMNN7Zvppnh7zpGIJfvuknhr3aS1txiohUQAqfInJZtUODeahFDMb549/2xrJ8z4HLtjda18GyYCSeVnemTL8YQJMz03hnx2pGPvmuYxb86t9/5amXu5OannKjb0NEREoRhU8RuaKYyCrc37Sh43jh9j2sjz1y2fZGu/pYvnseL4srr89IpckBewBNykhl2tbfeOHp6fh4+gH2veCHTniAlLSkG3sTIiJSaih8ishVtaxelU4NazuOv9m8g23HTly2vXF7IyzfPoc3rkydnkpMrD2Ans04x5RNv/LCM9Px8w4EYOuedQx58T6Szp2+sTchIiKlgsKniBRKhzo1aFs7GgAT+GLdVnbHnbpse6NTEyzfPY+34crU91NpfPBiAJ207idGPPUegb5BAOw4sInB47pyOjn+Rt+GiIg4mcKniBSKYRjc06guzatFAJBjmvxv7Rb2nUq4/HvujMHyw0h8LG688d7FIfiUzDReWbuAZ598j+CAUAD+OLSNQWM7E3/m8j2qIiJS9il8ikihGYZBt2aNiIkMA8CWm8snqzcTm3D5IXPj9kZYFozEx8WdN95P5eb/b+++46uu7j+Ov+7Kzd6bBEiYYYU9ZQmCA3HWbdVqW61aaWt/rVqrtVS0Vqt17604cSugMkX23jOBBLL3zr33/P64eDGGkUQS1vv5eOTBI991v+fxfXB5c77nfM42bwCtrKvh/kWfMOU3TxAX5Q20O7I286t/nMm+gkOPKRURkRObwqeINIvVYuEXA/vQIyEWgHq3m9cWrWBPUckhz7GM6Yn1qzsJdDj57wuVDNvkXeWoxlXHtO8+4dYb/kdiTAcAMvft4Jq/T2DX3kOXdRIRkROXwqeINJvNauWywX3pGhcNQK3LzcvfLWdvSdkhz7GMTMM68y78nf7856UqRq73BtBadz3/WjiDG697mA4JnQDIKczi2r9PYOPO1a3eFhERaVsKnyLSInablSuH9iM1JhKAmnoXLy1cRm7Zoet2WkZ0xzrrb/gFBfDgq1WMW+0NoPVuF/cvnMGvfvkQ3Tv2AaC4vJDr7zub5RsXtn5jRESkzSh8ikiLOWw2rh7Wn/aR4QBU1dXz0oLlFFRUHvIcy7CuWGffjSM4kKlvVHHmCu867y6Pm3/Nf49LL/0H/boPA6Cyupyb7r+AeSu+bPW2iIhI21D4FJGfxWm3c+2IAbQL9xaOL6+t5cUFyyiqPPTSmZbBnbF+fTf20CDufauayUu8AdRjDPfPe5czJ/+J0/pOAKC2voYp/7mczxe80/qNERGRVqfwKSI/m7/DwXUjBhIfGgxAaXUNL8xfevgAOrAT1jn3YIsO5a53q7nou1oADIaH5r9P/zHXcubwiwFwe9zc8cQNvP3Vs63fGBERaVUKnyJyVAQ6/fjVaYOICfGu3V7SlADatyPWBfdhbRfFXz6o4epva337nlr8CfHpZ/GL8df7tk17+Xae/eBBjDGt1xAREWlVCp8ictQE+zu5YeTg5gXQbolYF96HpVMcv/+shls+q/bte2PVbDwd+nLD+bf7tj357lQefPUveDye1muIiIi0GoVPETmqQvYH0NgQ7yv4kuoanj9SAO0Qg3XBfdAzmWu+reOOd6uw7O/d/HjDd2SFxDHlivt8x7/15dP85X/XUVtX07qNERGRo07hU0SOuhB/J9ePHOQLoKVNCaAJEVjn3QsDO3Hh4nqmvl6N3eUNoF9vW8FyF/zt149hs9oAmPn9h9w07QLKKktauzkiInIUKXyKSKtoUQCNCsH6zd0wKo0Jq+t5+KUqnHXeALp490Y+y8/h/ttewd8ZCMDyjQu59p6J5BRmt36DRETkqFD4FJFWc6gAWlhxmAAaGoj1yzvhzL4M3+ziyWcqCanyBtD1Obt4eetqHr59OhEhUQBs37ORq+8ex/Y9m1q/QSIi8rMpfIpIq/KOAf1pAF1CfnnFIc+xBDqxfvx/cPFQ0jPcPPtkBZFl3glG2wuzeXDZTKbdPp2kuBQAcguzueaeCazY9F3rN0hERH4WhU8RaXXBPwTQ/XVAy2pqeW7+UnJKD7MUp58d6/QpWH4zni77PLzwRCWJhd4AureskLvmTOevN79Aj9R+AJRXlvDbf53H7CUft36DRESkxRQ+RaRN/FCGKSEsBIDK2jqeX7CU7OLSQ55jsVmxPPNrLH+/mOQCDy88XkGXbDcApTWV/GXmK1x39QMMTx8PQF19Lbf/92oVoxcROY4pfIpImwl2+nHDyMEkR4QBUF1XzwsLlpFZWHzIcywWC9Z/XILlyeuJKYfnnqhg0FYXALXueu6a9Qpjz7qFc0ddDoAxhmkv385/Xr8Tt8fd+o0SEZFmUfgUkTYV4OfgV6cNomN0BAC1LhcvL1zOjrzCw55n/d1ErO/+gWBj57HnK5m44sB68A/OnU5i37MaFKN/7bPH+dMjV1FVU9l6jRERkWZT+BSRNud02Ll2+EA6x3pnrNe53by6aAVbcvIPe57l4qFYv7oTR2AA971V3WA5zheXfkFRVAfuvP4RXy3Qb5d9xq/+cRb5xTmt1xgREWkWhU8ROSb87DauHtaf7vExALg8Ht74fiXrsw8fFC1je2Gddy/W2DB+/1kNf5pRjWX/Uu+fbPiO7yqrePhPbxEU4B1bunHnKq68ayxbd29o1faIiEjTKHyKyDHjsNm4cmg/ereLB8BtDNOXrmFl5uGLxlv6pWBdNBU6xXHZgjruf60Sx/7VkL7LWM9LW1fyvztmkBCdDEBOYRbX/P0Mvlv9des2SEREjkjhU0SOKZvVyqWD0+nfPhHwjuF8f8U6Fm7LOOx5ltQ4rN/9EwakMn6Ni8efPVCMfmNuJvcs+JCpf3yLnqn9AaisLueWBy/m3dkvtmp7RETk8BQ+ReSYs1osXDigN0NT2/u2fbFuM7M2bMUYc8jzLHHhWOfeC5P6M2CHm+cfryCu2FsLNKe8iD9+9SK/ve5hxg06FwC3x83UF6bwn9fvxOPxtGqbRETk4BQ+ReS4YLVYODc9jXFpnX3b5m7ZycerN+I5XAAN9sc6489YfjeBTrkeXnm0grTd3lJMlXU1/PmL5xk+/gauOff3vnNe++xx/vDwFVRWH7rIvYiItA6FTxE5blgsFsaldebc9DTftqW79jB96Rpc7kP3VFrsNixPXI/lP1cTXW549qlKxqytB8BtPEyb8zYk9+Gu6//rmwk/Z/nnXH33eLLyMlq1TSIi0pDCp4gcd4Z16sAlg/pgtVgAWJ+dw2vfr6DW5TrkORaLBeufzsX63h8JsDp48NWqBqWY3lz5NYura3jkz9MJCfQWud++ZyNX3Dma5RsXtm6DRETER+FTRI5LfZMTuXpYfxw279fU9rxCXlywjKrausOeZ7l4KNZv78EaFcLvP6vhznersO3vNJ23cw3Pb1rG43d9QocE7+v9kvIifjP1XN6b/VKrtkdERLwUPkXkuNUtPoZfnTYIf4cdgKziUp6dv4TiqurDnmcZ1hXr9/+CLglcsLiex56rJKjGO250c95u7pzzNv+Y8rpvTXiX28U/X7iN+1/6E/Wu+tZtlIjIKU7hU0SOax2iIvj1qCGEOJ0A5JdX8sycxewtKTvseZbO8VgX/RNGdGPIVhcvPlZBQpG3CzSvooRbPn2aCy/8K7885xbfOdNnPsdN0y6gpPzwS32KiEjLKXyKyHEvISyE344ZQnRwIADltbU8N38J23ILDnueJToU69d3Y7lqJJ1yPbz8aAW9Mr3jRmtcdfz1yxcI7jKMe298EofdD4Cl6+dxxV1j2b5nU+s2SkTkFKXwKSInhMigQH47eijtI8MBqHN514M/4mpI/n5YXrsFy/2XE1VheObJSs5edmDc6HOLP2VRRSVP3jGDyDDvUp9Zubu46m+n883ST1qtPSIipyqFTxE5YQQ5/bh+5CB6JMQCB1ZDmrt5x+GL0VssWO+4AOuM23E6ndz7djW//+TAmvDfbl/Jf1fN4dG/zqB7xz4AVNVU8IeHr+R/0/+B2+Nu9baJiJwqLOZw39jHibKyMsLCwigtLSU0NPRY346IHGMeY/hszSYW79zt2zY4JZlz09OwWQ//f2qzJgPPuQ/CnkK+S7Nz19WBVPp7SzqFBwQz9Yxr+eizR/lq0fu+c4b3GccDv3+R8JCo1mmQiMhJoKl5TeFTRE5Ixhjmb93FzA1bfdu6x8dw2eB0/Oz2w5+bU4Lngodg8TYyYq386VeB7I71Fp+3W23835jLqc5axyNv/M3X65kY04FHb3/L1zMqIiINNTWv6bW7iJyQLBYLo7ulcsnAPtj2F6PfnJPP8/OXUlZdc/hz48OxzrkHy1Uj6Zjn4eXHKhi62VtiyeVxc/+3b5AVEMGTd8wgIjQagL35mfzy7vF8tmB66zZMROQkp/ApIie0vu0TuWbEQJz7ezuzS8p4em4TSjH9MBFp2hWE1lj47wtVXDH3wIpI762ZywublvLU3Z/Tq9MAAGrqqrnziV/zwCv/p3qgIiItpNfuInJSyC0r59VFKyip8vZ6+tlsXDo4nbT9k5MOx3y6HM9Vj0NZNZ8NdHD/JQHU2729qdGBYUydeB1fzHyaD7991XdO/+7D+c8fXiM6PK51GiQicoLRmE8ROeWU19TyxuKV7CkqBcACnN2nO8M7dcCy/9X8oZgte/Gc/xBszmZDext/uSaQ3AjvyyG71cYfR/0Ce+Fupr18O/Uub6mmqLBY/n3bKwzqObJV2yUiciLQmE8ROeWE+Du5YeRgeifFA2CAz9du5uPVG3F7PIc919ItEeuSf8F5A+m5283rj1QwcKu3IL3L4+bfc6ezut7Ds3d/RlxUOwAKS/P49T8n8fyMh/Ac4foiIuKl8CkiJxWHzcalg9IZ262Tb9vSXXt4bdEKauoPP07TEhqI9cPbsfzjEiIqDY8/V8nV3x4YB/rF5sX8Z/ksHr3jI4b1OR0Aj/Hw+PT7uPnBiykuO/yKSyIiotfuInISW7U7mw9Xrsft8X7NxYYE88vh/YkMCjziueazFXiu/B+UVfN1up37Lg+k2s/76j7EGcjUidexYdVXPP3+/b4C93FR7fjPlFdJ7zqk9RolInKc0mt3ETnl9Wvfjl+dNohAPwcAeeUVPDXne3bmFx7xXMukAViXToPu7Ri/xsUr/62gfZ635md5bRVTPnkK2vXg6Ts/8i3LmVuYzXX3nsnrnz9x2BWXREROZQqfInJSS4mO5KYxQ4kO9vZ2VtXV89LC5Xy/I/OIAdE3DvT8QaTmenj1vxWMWed9dW8wPLf4U17buppn//4V/bsPB8DldvHQa3fwh4evoKyypFXbJiJyItJrdxE5JVTX1TN96Rq25R0YlzmwYxKT03tgtx1hSU5jMA99grnzbTweD6+NdfL02f549p8WHRjGPydex+JF7/Lix4/4zmsX25EHf/8SfboMapU2iYgcT1RqSUTkJzzGMHP9VhZs2+Xb1j4ynCuH9iPE33nE8828jXguexRySliZauOuXwZSEOpNoFaLhRuHTaaz3cbfnvwtZZXFANhtdm659G6uPXcK1iOsOy8iciJT+BQROYTVu/fy4cr1uPaXRwr1d3LVsP4kRYQd8VyTU4Ln8sdg7gaKgi3cfWUAS7s5fPuHdujBrQPP5F/P3czabcsObO89ln/d/BwxEfFHv0EiIscBhU8RkcPIKi7lzcWrKN2/DrzdauWC/r3o1z7xiOcalxtz9zuYBz7CbYGXznDy/ER/zP469jFB4fxz4rUs/v59XvzoYd/Y0ojQaKb+7llG9pvQau0SETlWFD5FRI6gvKaWNxevYndRiW/baV06MrFnV2xNeEVuPl2O55dPQkklyzrb+NvVQRSFeBOozWLld8PPp7u/k7899Vvyi3N85/3ynFu47Yp/4LD7HfU2iYgcKwqfIiJN4HJ7+GTNRpZnZPm2pcZEcumg9KaNA92Vh+fih2HlLgpCLPzt6kBWdLb79g9pn8Yfh5/HY6/9hfkrZ/q290jtx4O/f4kOCZ2PboNERI4RhU8RkSYyxrBk5x4+W7sJz/6vxFB/J1cM6Uv7qIgjn19Th/nTa5inZuG2wPMTnbx0xoHX8OEBwdx7xrXs3vId/33zbt/a8AHOIO647iHOG3PVEdeeFxE53il8iog0U0ZBMW8vXU15jXdJTavFwtl9ujMstX2TwqH5cAme65/xvYb/+1UHZsMDXN53HBOTu3DXEzeQuW+7b/u4Qefy99/8j4jQ6KPfKBGRNqLwKSLSAuU1tby9dDUZBcW+bX2SEriwf0/87PbDnOllMvO9s+G/30pJkIX7Lg1gQa8Ds+G7xiRx99greffTR5gx53Xf9qiwWO676SlG9pt4dBskItJGFD5FRFrI7fEwa8NWFmzL8G2LDQ3mqiH9iA4JOuL5pt6Fufc9zLSPMMbw3gg/HjsvgLr92dXf7sftYy4lpKqYfz7/e0rKi3znXnLGDfzxqqkE+h/5c0REjicKnyIiP9P67Bw+WLGOWpd3TXen3cZFA3rTq13TanWar9fiuepxyC1lW4KVu64OZFe8zbd/fJcB3DhwAv95+c8sXD3Lt71DQmem3fICvToPOLoNEhFpRQqfIiJHQX55BW8uXk1eeYVv22ldOjKhZ1fsTSnHlFuC55onYeYaahzw6GR/PhhxYBZ9bHA495xxLbu3fs/Dr99JTV01ADarjd9e9FduuOB27LYjv+4XETnWFD5FRI6SWpeLGSvXszbrQK3O5IgwLhucTkRQ4BHPNx4P5uHPMHe9DfVu5vS2M/XSQMoCD0xiuqzv6UxO7c19z97C+h0rfNt7dx7I/bc8r5JMInLcU/gUETmKjDF8v3M3X67djHv/16a/w86F/Xs1/TX8yp14rnwcNmeTG2bhn5cFsORHS3N2jIzn3jOuYcHCd3j+w3/jMd7lP/2dgUy5/B9cNvE3Wh9eRI5bCp8iIq0gq7iU6UtXU1RZ7ds2NLU9Z/XuhsNmO8yZXqaqFvN/b2CenInHAu+N8OPxc/2pdXh7Qe1WG78ZOon+4dHc/eRv2ZO703fugLQR3HfjUyTHpx79homI/EwKnyIiraSmvp4ZKzewLvvAa/jEsFAuG5JOdHDTZqmbL1bi+dXTkFtKRqyVv18ZwKbkA2M7eyekcufoS3n/iyd4Z9bzvu3qBRWR45XCp4hIKzLGsHTXHj5fuxmXx/t63M9u44J+PUlPTmzaNfLL8Pz6Gfh4OS4rvDDBycvj/fHsz5T+dj/+OOoXJFnh3mdvYW9+pu/cgT1O4x+/fVK9oCJy3FD4FBFpA/tKy3l7yWoKKip92wZ2TGJSn+5NK0pvDOaFbzBTXoWqWtZ1sHHPFQHsiTnwCn9wchp/HnkR0z97TL2gInLcUvgUEWkjtS4Xn6zeyKrde33booODuHRQH9pFhDXpGmbbPjxXPwFLtlHtB4+d27AkU6DDyZSRF5NkNdz77K2NekHvu/EpkuJSjl6jRESaSeFTRKSNrczM5uPVG6l3e4vSWy0WzujRhZFdU7A2ZW14lxvzn08x97wLdS4Wd7Uz9dIAciMO9GoOSu7u7QX99DHenf2Cb7u/M5BbL72bK866CZv1yBOfRESONoVPEZFjoKC8kneWrSG7pMy3LSU6kl8M7E14YECTrmHW7/YWpl+5iwon/G+yPzOGHegFDXA4uW3kRbT3jQXd7dvXq9MA7vnN43Tr2PvoNUpEpAkUPkVEjhGXx8M3G7czf+tOfviC9XfYuaBfL3onNbEmaL0L88BHmPs+AJebJV1sTL0skJwf9YIOTOrG7SMv4r3PH+fd2S/ww9e5zWrj2nNv47cX/xV/v6YFXhGRn0vhU0TkGNuZX8R7y9dSWl3j29a/fTvOTU/D6WjakplmdYa3F3RtJpVOeHxSw7Gg/nY/fn/ahXQNCOS+525lZ/YW37728Z24+9ePMaTX6KPXKBGRQ1D4FBE5DlTX1fPR6g2s+9HSnJFBAVwysA/toyKadA1T58JM/QBz/wxwe1jaxcbUSwPZF3mgFzQ9oRN/GX0pcxZO54WP/kO9q8637/wxV/Gnq/9FWHDk0WuYiMhPKHyKiBwnjDGs3rOXT1ZvpNblnYxkAUZ2TWF8WhfstqaVSTLLd3h7QTdmUemEJ87x5/3TDvSC2q02rht0FmPbdWLai39g1ZbFvn2RYTH89dqHmDjsQixNmPwkItJcCp8iIseZosoq3l22lt1FJb5tcaHBXDygd9NLMtXWY+6fgZk2A+rdrOhk4/5fBLA79sAM944R8dxx+pXs2vId/33zbiqry337Tus7gTt/9R+VZRKRo07hU0TkOOT2eFi4LYOvN27Dvf/r12qxMLZ7J8Z0S8XWxGLxZv1uPDc8C0u2UWuHl8Y7eXWcP+4fVVm6oNdIrug5nMffuptvl33m2+50+HPDBbdz3eQp+DmcB7m6iEjzKXyKiBzHckrLeW/5WvaVHuiVbBceysUD+xAXGtykaxi3B/PkV5g734bKWrbHW7n/kgDWdTwwmSkqMJQ/j7kMS8leHnzl/8grOlAIv0NCJ+781SMM63P60WuYiJyyFD5FRI5zLo+HuZt3MHfLTjz7v4rtVitn9OjCiC4dm1SYHsBk5OG58XmYuQaPBd4f7sdTk/ypdB44f2RKH34/fDIzZj7Lm188hdvj9u2bOOxCbv/lNOIim7YmvYjIwSh8ioicILKKS3lv+Vryyw+sD98hKpwL+/ciJqSJvaDGYN5c4F0jvrCc3DALD10YwLzeDt8x/nY/fjN0EoOjE3nw5dsbTEgK9A/m5kvu4vIzb8Rua1oZKBGRH1P4FBE5gdS73Xy9cRsLt2X4CtPbrVbGpXXmtC4dmz4WNL8MM+UVzFsLAfi2t52HLgqgIPTA+SmRCfzfmMvI2bWSR974G8Xlhb59Xdv34q7rH6Ff92FHrW0icmpQ+BQROQFlFBTz/op1FFVW+bYlhoVy4YBeJIY3/fvPzFyN5+YXYUcuFf7wzFn+vDfCiedHGfbMboO5vv943vzkv3zw7Sv8+J+DSSMvY8oV9xEbmXBU2iUiJz+FTxGRE1Sdy803mxr2glotFkZ2SeH0tE44bLbDnv8DU13nXaLzgY+gzsWWdlYevKjhhKQgP39uHHYeaUHBPPDS7WzOWOPbF+AM4jcX/pmrz7lFs+JF5IgUPkVETnBZRaV8uHIdOWUVvm3RwYFc2L83HaObtjoSgNm619sL+vU6PBb4dLCDxycFUBp0YEJSl+gk/jLmMrZunMcT70ylrLLYty85LpXbf3k/YwacrQL1InJITc1rTRtE9BNPPfUUKSkp+Pv7M2DAABYsWHDY42tra7nrrrvo0KEDTqeTTp068dJLL7Xko0VEThlJkWH87vThjEvrjG1/6CuoqOK5+Uu8qyXVu5p0HUvXRKyz/obl7duwxoVz3pJ63p9Wzvnf12HZ3/2wrSCLG97/D5tw8Oq0+Vw64ddYLd5/Ivbk7uS2hy7jpvsvYGfW5lZpq4icOprd8/nOO+9w9dVX89RTTzFixAieffZZXnjhBTZu3Ej79u0Pes55551Hbm4uU6dOpXPnzuTl5eFyuRg+fHiTPlM9nyJyqsstK+fDFevZU1zq2xbq72RSeho9E+Oa3CNpSqswd0/HPDkTPIb17W08eFEAm5MPvMoP8vPnhiGTGBgZxyOv38myjQc6GGxWG5dN/C03/eIOQoPCj1r7ROTE12qv3YcMGUL//v15+umnfdvS0tI4//zzmTZtWqPjv/rqKy677DJ27txJZGRkcz7KR+FTRAQ8xvD9jkxmbdhGvftAnc7u8TGcm55GRFBgk69lVuzEc9PzsGwHbgt8MNyPp8/2pyLgQIhNDo/lDyN/QV3+Lh5+4y72Fezx7YsIieLmS+/mwtOvUWkmEQFaKXzW1dURGBjIe++9xwUXXODbftttt7F69WrmzZvX6Jzf/e53bN26lYEDB/L6668TFBTE5MmT+ec//0lAQMBBP6e2tpba2toGjUlOTlb4FBHBu0b8J6s3sjW3wLfNYbNyevdmlmXyeDCvzMXc8TbklVIcZOGZs5zMGObE/KgjdWiHHtw67DzmffcuL338CDV11b59qe268cerpjKy30SNBxU5xbXKmM+CggLcbjdxcXENtsfFxZGTk3PQc3bu3MnChQtZv349M2bM4NFHH+X999/n5ptvPuTnTJs2jbCwMN9PcnJyc25TROSkFhkUyDXDB3D54L6E+Htnode7PczcsJUnvl1EZmHxEa7gZbFasf7qdKxbH8Pyx0lE1Fq54/0aXn+4nP7bD4wnXZy5kV++8yBlUR1444EFnDnsIt++ndlbuOXBX/CbqZPZnLH26DZURE5Kzer53Lt3L+3atWPRokUMG3agAPG//vUvXn/9dTZvbjwQfcKECSxYsICcnBzCwsIA+PDDD7n44ouprKw8aO+nej5FRJqmpt7F7I1bWbxjNz/+Mh/YMYkze3Ul0M+vydcym7LwTHkVZq3BAN/2sfPY5AD2RR7opwjzD+Km4efRyenPo2/+jTVbl/r2WSwWJo+6glsu+7uW6hQ5BbVKz2d0dDQ2m61RL2deXl6j3tAfJCQk0K5dO1/wBO8YUWMMWVlZBz3H6XQSGhra4EdERBrzd9g5N70HN40d1qAI/fKMLP47eyErM7N968YfiSUtCetXd2L96M9YUmIZt9bFuw+Wc+MXNfjXea9RWlPJA9++xbQlX/Lrax/hoSmv0i62I+Bd4vPjeW9y7m19efLdqVTVVBzm00TkVNWs8Onn58eAAQOYPXt2g+2zZ88+5Mz1ESNGsHfvXioqDnwJbd26FavVSlJSUgtuWUREfiopIozfjR3GpD7dcdq9M9cra+t4f8U6npu3hOwfzZI/HIvFguW8QVg3PoLlX5fh73By/de1vD+tnDNX1PmO21m0jymfPMGH2bt48C8fcvvV9xOyf/Z7TV01z37wIOf8Pp33v34Jl7tpJaFE5NTQ4lJLzzzzDMOGDeO5557j+eefZ8OGDXTo0IE77riD7OxsXnvtNQAqKipIS0tj6NCh/OMf/6CgoIAbbriB0aNH8/zzzzfpMzXbXUSk6Uqra/h87SbWZ+f6tlnwvoqf0LMrQc5mvIrPKsTc+Tbm9fkArOlo47HJ/g1WSQI4u/tQrk4fzcezX2D6zOdwuet9+zokdObWS//O+CHnYW3iZCgROfG06gpHTz31FP/+97/Zt28fvXr14r///S+jRo0C4NprryUjI4O5c+f6jt+8eTO33nor3333HVFRUVxyySVMnTr1kLPdW9oYERE5YFtuAZ+u2URBRaVvW4DDwfgenRmcktzkWfEAZvkOPH96DeZvwgDfpNt54hx/sqMP1Af1s9m5ot94zujQnefeu59vln7S4Bo9Uvtx2+X3MrT3WM2MFzkJaXlNERHB5fHw/fZMvtm8nTrXgdqg8WEhnJueRkp00+svG2Pg42V4/u9N2LaPehu8P9yPFyf6Uxp4IEyGBwTzmyGT6OIfwBPv3MeKTd81uM7gXqO57fJ76d154M9voIgcNxQ+RUTEp6y6hpkbtrJq994G2/skJXBWr26EBfo3+VqmzoV5ZhbmH+9DUQVlAfDKOH+mj3JS/6O38cnhsdw49FyCa8p4fPo/GpViGjd4MrdeejepSd1/VttE5Pig8CkiIo1kFhbz6epN7C0t823zs9kY1TWF07qk4Ge3HebshkxxBeZfH2Ie/wrqXOyNsPDU2f7MHNBwTGnXmCRuGnoelbnbePKdqezJ3enbZ7VYOW/Mldx48R0kRKums8iJTOFTREQOymMMy3btYfbGbVTVHZgYFOrvZELPrvRtn4i1GWMyzc5czB1vYd79HoCNSTYeP9ef5V0aTkrqm9iZG4dOImPrYp55fxoFJQcmRDnsflw87jquP/9PxEYm/MwWisixoPApIiKHVVVXx9cbt7N0154GtUATw0M5u3d3UmOaPh4U9q8Xf+fbviL1S7vaefJsJ5vaNwyhIzr24teDzmTZ8s946ZNHKa8s8e3zczj5xfjruf78PxIdfvD60SJyfFL4FBGRJskrq+Cr9VvYnJPfYHtaQixn9epGdEhQs65nvl2P569vwrIdGGBOHztPn+VPRlzDV/oTug7il+ljmD3/Td766lmqaw/Mync6/Ll0wg1cN/kPRIXHtrhtItJ2FD5FRKRZtucV8uW6zewrLfdts1osDE1tz+lpnZq3VKcx8OESPHdNhy17cVnhiwEOnp/oT86Pluu0Wayc02MYv0gbysx5bzB91vPU1Fb59vv7BXDpxF9z3eQpRIbGHJ2GikirUPgUEZFm8xjDysxsZm/cRnlNrW+7v8PO2O6dGJraHoetGZOSXG7MK3Mx974H2UXU2eCD4X68fIaT4uCGIXRSj2FclDaYr+a8zjuzXqC2vsa3P8AZxOVn/oZrJv2eiNDoo9NYETmqFD5FRKTFal0uFmzdxYJtu6h3e3zbwwL8GZfWmX7tE5tXpL66DvPEV5gHP4bCcqr84O1RTt4c46T8RzVC7VYbk3oM44KuA/lizmu89/WL1NUfCMGB/sFcNvHXXH32LXodL3KcUfgUEZGfrbS6htn764P++B+LmJAgJvToSo/E2GatVmTKqzGPf4n5z6dQXEm5P0wf5eSt0U4qAhqG0HN7DOf8rgP4/NtXeO/rl6h3HVhb3unw56Jx13LtubcRH510NJoqIj+TwqeIiBw1+0rLmbVhK1t+MikpOTKMiT27NX9mfGkV5rEvMI98BqVVRwyhk7v057NvXuLDb19tEELtNgfnjrqc68//I+3jO/28RorIz6LwKSIiR92ugiJmrt/K7qKSBtu7xkUzoWdXEsOb9x1tiisw//0c8+gXUF5NuT+8PdobQiv9G4fQczr35ZsFb/He1y83mJhktViZOPwibjj/drq07/Gz2igiLaPwKSIircIYw+Z9+czcuJW8sooG+/okJTA+rXPzyzMVlmMe/hTzvy+hspayAHhrtJPpoxqGUKvFwoSug7iox1AWLZ7B2189S0V1WYNrjR14Dr++4M/06jyg5Y0UkWZT+BQRkVblMYbVu/fy9cZtlFQfmJluAfq1T2Rs985EBQc265omvwzz0CeYJ76C6jpKAy28NcqPd0Y6qQxoOLZ0VGo6l/U+jXVrZvPG509SXF7YYP/Q3mO5bvIUhvYe26xxqSLSMgqfIiLSJlxuD0t27WbO5h0Nluu0Wiz0b9+Osd1TiQhqZgjNK8U8+jnmiZm+1/Hvj3Dy1mg/SoIbzrIflNydK/uOJXPLIl799H/kFe9rsL97xz5cM+n3TBh2IQ67o+UNFZHDUvgUEZE2VVvv4vudmSzYmkF1fcMQOrBjEmO6pRIeGNCsa5riCm+Jpke/gKIKahzw0VA/Xh/jJC+iYQjtFZ/CL/uPp2j3Ol759DGycnc12J8QncxVZ/+OC0+/hqCAkJY3VEQOSuFTRESOiZr6ehZtz2Th9gxq6l2+7TarhUEdkxndLZWwAP9mXdNU1GCene0t0ZRTQr0Nvhjo4NXTneyJaVj0vlNUIlf2G4dfWS5vfP4k63esaLA/JCicS864nivOvJGYiPiWN1REGlD4FBGRY6q6rp7vtmfw3fYMal1u33a71crAjkmM6prS/J7QmjrMS3O8xep3F+C2wDfpDl4e72R7YsMQGh0UxmXpp9PJ6eC9mc8yf+XMBvsddj8mjbyMaybdSmpS95Y3VEQAhU8RETlOVNXVsXBbBou2Z1LnPhBCfxgTOrpbClHBzZwdX+fCvDEf88DHsG0fBljYw84r45ysTbE3ODbA4eT8XqcxIq49X855jc8XvovLXd/gmNP6TuDKs25iePo4TU4SaSGFTxEROa5U1NaxYOsuluzc3SCEWoA+yQmM6ZZKXGjzxmIatwc+WornoU9hyTYA1nS08eYYJ3N72zE/CpI2i5XxXQdwTmo6y1Z8xnuzX2pUpim1XTeuOOsmJo28jED/5gVikVOdwqeIiByXKmvrWLQ9k+93ZjYYEwrQIzGOsd1SaRcR1qxrGmNg4WY8D30Cn3rHeO6OtvLWaD8+G+xHraNhb+bApG5c3HMEe3csZfrM59ibv7vB/tCgCC4adw2XTfwNCdHJLWilyKlH4VNERI5rNfX1LN6xm4XbMxqUaALvikljunWiY3REs69rNmZ5C9a/sQDqXBQHWXh/hB/vjfCjOKThDPkOEXH8os9owqpLeW/W86zcvKjBfpvVxrjBk7nyrJvo222oXsmLHIbCp4iInBDqXC6W7spiwbZdlNfUNtjXPjKckV1SSEuMxdrM4Gf2FWP+9yXm6VlQWkWNA74Y4ODNMU52xzacnBTk58/kniMYGBnL1wum89WiDxqsIQ/QM7U/l5/5WyYOuxCnX/Nm64ucChQ+RUTkhFLvdrMiM5v5W3Y2WDEJIDo4kNO6pNCvfSIOm+0QVzg4U16Nef4b79Kdmfl4LLCgh523RztZ0bnh5CQLFk5L6c05nfuyfeN83v36RYpK8xscEx4SyXljruKS8deTHJ/assaKnIQUPkVE5ITk9nhYs2cf87ftarR2fLDTj2GdOjAkNZlAP79mXde43PDxMjyPfQkLNgGwLcHKOyOdfDXA0WhcaGpkAhf3Gom9ZC/vznqezRlrGl1zRN8zuHTCDYzsNxGbtXmhWORko/ApIiInNGMMW3MLmL91F7sKihrs87PZGNgxiRFdOhLRzFqhAGblTsxjX2CmL4I6FyVBFj4a4h0X+tOVk0KcgUzuMZxewaHM/f4DZi2e0eiVfEJ0MhePv44Lx15DVHhs8xsrchJQ+BQRkZNGVlEp87ftYkN2Dj/+R8tqsdCrXTynde5IUmTzZsgDmJwSzDOzME/PhrxSXFaY29vOOyOdrE61Nzp+SPs0Jqb2IS9jFR9+80qjWfJ2m4Mzhp7PpRNuoF+3YZqgJKcUhU8RETnpFFZU8d32DFZkZlHv9jTY1z4ynOGdO9AzMQ6b1XqIKxycqa3HvLPIu4b8Ku+a8JvbeV/Jz+zvoN7eMERGB4VxXo8RJFlczFownYWrZ/PTf047JaVx0bhrmTTyUsJDolrQWpETi8KniIictCpr61i8czff78hsVKYpLMCfIanJDO6YTKCzmeNCjYHvtmCenoV573uod1MSZOHTwQ4+HOZHVnTDcZ1Wi4VRqemMTurCzk0LmDHnNUrKGw4RcNj9GDd4MheNu5ZBPUZibWYwFjlRKHyKiMhJr97tZvXuvSzakUnuTyYn2a1W+rZPZHinDsSHNW/lJACTW+JdR/6Z2bC7AI8Flnax88FwPxb0tOO2NewNTQqLYXLaUEIqi/h83hus3rqk0TWT4lK4YOzVnDf6KmIjE5p9TyLHM4VPERE5ZRhj2JlfxHfbM9iSk89P/2FLjYlkRKeOdEuIaX69ULcHvlyF56lZ8NVqMIbcMAsfD/Xjo6F+5Ic17Mm0W22M6dSXwTHt2Ll5EZ8teKtRb6jNauO0fhO4aNy1nNZ3AnZb4/GlIicahU8RETklFVZU8v2O3azIzKLW5W6wLyIwgMEpyQzo0I5gf2ezr2125GCe/Rrz0hwoLMdlhQU9vb2hS7o5Gh0fHxLJ2V0HEV1fydzFM/h+3beNxobGRMQzefSVnDf6Sjomdmn2PYkcLxQ+RUTklFZT72JlZhaLduymqLKqwT6b1TtLfkhKezpEhTd7VrqpqcO8txjz3NewcDMAe6KtzBjqx+eDHBSFNB7XOTg5jVHtOpGfuZpP571FXtHeRsf06TKIyaOv5MxhFxIa3PylRUWOJYVPERERwGMMW3Py+X7HbrblFTTaHx8azODU9vRLTsTpaP7rb7M5G/Pit5hX50F+GS4rLOxh5+MhfixKs+OxNgy2oc5AJnYbRIoNliz/gnkrv8TldjU4xmH3Y8yAs5k8+gqGp4/HYW/cqypyvFH4FBER+YnCikqW7trD8sxsqn8yS97PbqNfciJDUtu3bIJSnQs+XY7n+W9g1lowhvxQC58N8uOTIY5GM+UB0mI7MLZDGq78XXy96AO27l7f6JjIsBjOOe0SJo+6km4dezf7vkTaisKniIjIIdS73azPzmHJzj3sLipptL99ZDiDOibROykeP3sLekMz8zEvz/GODd1TiAFWdrLxyWA/vkl3UOvXsDfUbrUxvGMv+oVHs3fnCr5a9D7FZY17abt16M3k0Vdw5vCLiYmIb/Z9ibQmhU8REZEm2FtSxpKdu1mzZx917oYTlJx2G32SEhjYMYmkiLDmjw11e2D2WjwvfAMfLweXmwp/mNnPj4+HONjUvnGwDXYGML5zf5Jxs379XOau+KLRcp5Wi5VBPUdx9mm/YNzgyYQGhTe73SJHm8KniIhIM9TU17Nq916W7trTqGYoeMeGDuiYRL/kxGYXrwcwBWWYt7/zjg1dsROAnXFWvhjo4MsBfuSFN56klBQWw+kpvfAvy2XB0k9Yt315o2Mcdj9G9pvA2SMuYdSAM/H3a/5a9yJHg8KniIhICxhjyCouZXlGFmuy9lH3k3JNNquFHolxDOqYRGpMVLPrhgKYDXswr83DvLEA9hbjtsCKzja+GOjHt30cVDsbXzM9oRODYtpRlbOVOUs+YU/uzkbHBAWEcPqgSZw14hcM7T1W9UOlTSl8ioiI/Ey1Lhfrs3JYlpF10LGhEYEB9GufSL/2iUQFBzX7+sbtga/XYl6dh5mxFGrqqfaDOb0dfDHQwdIudsxPZsvbLFYGJnejV0g4RXvW8+2SjykszWt8b6HRTBx2IWcNv5j0rkO0rKe0OoVPERGRoyivrILlmVmszMxutJ48eCcp9WufSJ+kBAL8ml8ayZRVeWuHvjoPFmzyfmaYhZn9HXw20I+dCY1nyztsdka070GKn4OcjNXMXfYZ5VWljY6Lj0pi/JDzmDD0fPp0GawgKq1C4VNERKQVuDweNu/LY3lGFttyCxot5WmzWkhLiKVf+3Z0jYvG1oKgZ3blYaZ/h3lrIazfgwG2JVqZ1c/B7L5+7I1qfM0Ah5ORHXqQaFxkbF/GgpVfUVtf0+i42MhEXxDt23WogqgcNQqfIiIiraysuoY1e/axcnf2QScpBTn96JOUQP/2iSSGhzZ7tjyAWbcb8/ZCzNvfQYZ33fr1HWzM7Ofg674OCkMbh8cQZyAj23cnoqacbVsXsXT9vEaF7MG7tOf4wedxxtDz6dd9GDZr495VkaZS+BQREWkjxhj2lZazanc2q/fso7K2rtExsSHB9E1OoE9yApFBgS36DBZv8wbRd76HvFLcFm/90Jn9/ZjTx05ZYOMgGuwMYFi7LkTVVZCxfTmL183B5W48bCA6PI5xgydzxpDzGdBjhIKoNJvCp4iIyDHg9njYllvAqt172bQvD5fH0+iY5Igw+iQn0LtdPKEB/s3+DONyw5wNmLcWYj5cAmXV1NtgcTc7M/s5mN/r4DPmAx1OhiZ1Ibquij27VrJ47beNaogCRIREMWbg2YwdNImhvceqfJM0icKniIjIMVZdV8/67BxW7t5LZmFxo/0WICUmkj5JCfRKjGtZ/dCaOpi1FvP+YszHy6CsmhqHN4h+k+5gQQ8HlQGNg6i/3Y8hiZ2J89SQtWs1i9d+Q119bePjnIGM6DOOsYMmMar/RMJDopp9j3JqUPgUERE5jhRXVrE2K4e1WfvYV1reaL/VYqFLXDR9khLokRCL09GCZT1r6+HrdZj3vsd8vBxKKqmzwdKudr7t42Be74O/mvez2RmU2JlYVzW5e9axbN08qmsrGx1ns9ro3304YwdNYuzAc2gX26HZ9ygnL4VPERGR41RuWQVrs/axds8+CiurGu132Kx0jYuhV7t4usfHtCyI1rngm3XeHtEZS6G4EpcVlne28226nbm9HBSHNA6iFiz0ik2mg81KVd5Olq+fQ1Fp/kE/o3vHPowZeA5jB55D9459WjShSk4eCp8iIiLHOWMMe0vKWJO1j3VZOZRWNy6NZLda6RIXTc/EONISYltWQ7Te5R0j+t73mBnLoLAclxXWpNj4Jt3BnN4OCsIOXnKpfXgMaYEheIqyWL95IXtyGq+sBN4STiP7TWBUv4kM6T2GQP/gZt+nnNgUPkVERE4gHmPILCxmbVYO67NzDjpj3max0Ck2il7t4klLiCWoJWNEXW5YsAnz0TLvq/nMfDwW2JhsY14vO/N6OdgVf/CZ7uH+wfSNjMW/Ip9dO1awYcfKgx7nsPsxqMdIRvafyKh+E0mOT232fcqJR+FTRETkBOX2eMgoLGZDdi4b9uZSXtN4IpDVYqFjdAS92sXTMzGOEH9nsz/HGANrMjEfL8N8tAxWZwCwJ9rK/J7eILomxYbH2vh1utPmoHdUAuG1ZRRmb2btlsUHLWoP0DGxC6P6TWRk/zPp330YDnvzQ7Mc/xQ+RURETgIeY9hTVML67BzWZ+ce9NW8BUiKDCMtIZa0hDhiQ4JaVtA+M98bRD9eDvM2gttDSZCFhWneILq4m52ag5RwAmgfFkWKzYarKIvN25aQW5h90OOCAkIY1ud0RvWbyLD0ccRFJjb7PuX4pPApIiJykjHGkFVcyoa9uazPzqXoIJOVACKDAumREEv3hFg6RIW3bInPogrMFyu9QfTLVVBZS43DO2FpXi8H3/Wwk3+IcaIBdj96hkYSVF1MbtZGNm5fgcc0rncK0Dm5B8P7nM7w9PH0TxuumqInMIVPERGRk9gPqyptyM5l477cgy7vCRDg56B7fAzdE2LpGhvd8hJOCzZhPl+J+XwVbNvnW2/+uzQHi9LsrO148NfzAKmhUbSzuKnOz2TT1sWUVjSueQrgdPgzIG0Ew/qczrD0cXRJ7qEZ9CcQhU8REZFTSFFlFZv25bFpXx4ZBcV4DvLPu81qoVNMFGkJsXSLjyE8sGW9jGbbPswXqzCfr/S+nq9zURpoYUlXO4vS7Czqbj9oGSeAQIcfXfwD8a8qIj97M9sy13GoKBITEc+w/b2iQ3uPITI0pkX3K21D4VNEROQUVVVXx9acAjbuy2Nbbj61LvdBj4sNDaZbXAxd46PpEBWBvSWv5ytq4Ou1B8Lo3mI8FtiUZGNRmp3v0uxsTLZhDtErGuMMpL0NTEkOuzJWkV+076DHWSwW0lL6MqTXaAb3HE2/7sMI9A9q9v1K61H4FBEREVxuDzsLCtm0L5/N+/IOOmEJwGm30Sk2mm5x0XSNjyGsJWvO/zB7/ouVmFlr4bst4HJTFGxhcTdvj+iyrnaKDtErioGUwCCiXTVU5O9i685V1NZVH/RQu81Bny6DGNxrNIN7jqJPl0H4OZo/41+OHoVPERERaeCHovabc/LZmptPVlEphwoB8WEhviDaPrKFk5bKq2HuBsystZhZa2DrPjwW2JZgZWk3O0u62lmVaqfOcfBeUafVRqrTD//KIvKzt5CRvfmQn+XvF0C/7sMY3HMUg3uNJi2lL3Zb88e3SsspfIqIiMhhVdbWsS2vgK05+WzNLaCqrv6gx/k77KTGRNE5NoousVFEBgW2rJRTRt6BIPr1OiitosYBa1LsLN4fRre1O3iBe4Agi4VkK9grC8nN3sK+/MxDHhscEMqAHiMY3HM0g3qOpEv7ntish762/HwKnyIiItJkHmPILi5ly/4gmlVceshjIwID6BwbRefYaDrFRhLo18KVlpbvwMxc431Fv2QbuD0UBltY2tUbRJd2O3Q5J4AQoJ3VYCnPZ++ejRSW5Bz62MAw+nUfSv/uIxiQNoIeqf1w2Ju/VKkcmsKniIiItFhFTS3b8grYklPAtrwCqg/RK2oBEsND6RwbTee4KDpERmC3teAVfWkVzN+I+XY95tsNsDYTA+yKs7K8s50Vne2s6GSjNPjg1zbGEGYxJODGlOaSvWcjpRVFh/w8f2cg6V0GMyDNG0Z7dxmoGqM/k8KniIiIHBWe/WNFt+cVsD2vkMzCYtyeg8cHh81GSnQEnWKiSI2JJCE8FGtLXtHnl3nHi87ZgPl2PWzZi8cCO+IPhNGVneyUBx782sYYQjz1xHnqMOUF5OzdQtkh6ouCdwJT784D6L8/jPbtOoTgQGWO5lD4FBERkVZR53KTUVDE9vxCtucVklNafshj/R12OkZFkBoTSWpMFPFhIS0Lo9lFmDnr4Yee0cx83BZvofsfwuiqTnYq/Q8dRp2uWhJwY6ssoiBnO0UluYf8PKvFStcOvUnvOpi+XYeQ3m0I7WI6qOj9YSh8ioiISJsor6llR16hr2e0rKb2kMf6O+ykREeSGhNJSnRky8Porjxvj+i8jZgFmyAjH5cVtiTZWN7ZxuoUO2tSDt0zCmCrqyYON/41ZZTk7SKvYM9hPzM6PI70rkPo23UIfbsNIS2lr8o7/YjCp4iIiLQ5Ywz55ZXszC9iZ0ERuwqKqKytO+TxAQ4HKdERpMREkhodSVxLw+ieAsyCzd5xows2w8YsPBbYGW9lVYqd1ak2VqfayQs/9HhUU1dNjHERVFtORcEe8vMzD7kmPYDD7kfP1H6kdxtCepfB9O02lOjwuGbf+8lC4VNERESOOWMMeeUV3jCa7w2jhyrpBOC02+kQFU7HqAg6REeQFBGGw9b8EkmmoAwWbsbM3+QNoyt3YjyGfREWVqd664uuTrWREXfoaxtXPf61FUR66qCigPycndTUVh72c9vFdiS9y2B6dR5A784D6NaxzykzkUnhU0RERI47HmPIK6tgZ8GBMHqomfQANouFdhFhdIiK8AbSqHACnS0o7VReDd9v3R9GN8HS7VBTT3GQhTUpNl8Y3dLOhtt26HGjVJcS6aknoLaCysIsCouyD/u5dpudzsk96d15AL06D6BXp4GkJnU7KWuOKnyKiIjIcc9jDLllFezMLySjoJjMwmIqDvOaHiAmJIiOURF0jI6gQ1QEEYEBzZ4IZOpcsDoD8/1Wbyj9fivsLqDGAZuTbKzrYGNdRzvrOtgoOEytUVNfg7OmnHB3LZ6yfArzM6ivP/SYV4AAZxA9O/WjV6cB9Oo8kN6dBxAflXTCT2ZS+BQREZETjjGGosoqMgqLfWG0oKLqsOcEOf1oHxlOckQYyZHhJEWE4XQ0f2lNk13kC6Jm0RZYuQtT5yInwsK6DnbWdfSG0i3tbLjsh+gd9XigupSA2kqCXdXUleZSXJTNkeJWVFgsvToPoGdqf9JS0umR2o+YiPhmt+FYUvgUERGRk0JFTS2ZhSVkFhaTUVjM3pIyPIeJLxYgLjSY5Mhw309MSFCzJzKZmjpYlYFZtMXXQ8reYmrtP+kd7Wg77EpMxl0PlcUE11fjX1tBVfE+yssLjvj50eFxpKWkk5bS1/dnQnTycdtDqvApIiIiJ6U6l4s9RaVkFBazp6iE3UUl1NS7DnuO024nOdLbM9p+f+9oUDPHjhpjIKsQlu3ALNuBWbodlu+AsmryQy1sTLaxsb2Njck2NiXbKA06/Mx6U1lEqKsGR3UZZYVZ1B5hMhNAeEhkgzCalpJOUmwKVmvzV5U62hQ+RURE5JTgMYbCikr2FJWyu6iEPUUl5JSWc6SAExEYQLuIMJIiQmkXHkZieCgBfs1b7914PLAtB7NsOyzdjlm2w9tbWltPdqSFje3tbEr2BtLNSTaqDlMEn5oKTGUxgfVVOGrKqSrJaVIgDQ4IpXtKH9JS0unWoQ/dO/YhpV23Nl+7XuFTRERETll1LhfZJWX7e0ZL2VNYQnnt4ScCAUQFBdIuIpR2EWG0Cw+jXXhos8ePmnoXrNvtDaL7e0lZvxu3MWTGWn1hdGN7G1sTbdQ5DhNIaysxVcU4aypx1lVQU5pLTfWhV5T6gcPuR6ekNLp37M2NF99BYkz7ZrWhJRQ+RURERPYzxlBaXePrGc0qLmVvSTn1bvdhz7MA0SFB3iC6v4c0ISyk+YG0ssY7u35VBqzahVm5CzbsweV2szPeypZ23olMW/f/HG6ZUOqrMZXF2KvLCKiroq48n+rKkkN+9uyntxAXmdis+20JhU8RERGRw3B7POSXV7K3pIys4lKyS0rZV1KOy3PoVY3AG0gjgwNJCAslMSyEhPBQEsJCCPF3NmsykKmthw17GgbSNZl4qmvJjrKypd2BULqlnY2i0MOXfDKVxVBVgn9dFZaqUqrK8wkMCGXm01sI9Q9q8n21lMKniIiISDO5PR7yyirI/iGQFpeSU1qOuwlxKcjpR0JYCAlhoSSEe/+MDg7E1ozJQMbtga17vYF05U5fMKW4koIQC1uSbD8KpFayow+zQpPbBXVVfHDDw6TGJjX5HlpK4VNERETkKHC5PeSWle9/VV/GvtJyckqP3EMKYLdaiQ8L8YXS+LAQ4kKDmzWxyRgDuwtgbSZm7e4Df27dS4XDsCPBxrZEG9sSrWxLsLE90Ua109sD66w3zJ9wF46+KS1uf1MpfIqIiIi0ErfHQ2FFFXtLy9hXUs6+0jL2lpQddt36Hwv1dxIXGkJ8WDBxod5AGhMSjJ+96ctumuo62JiFWZMBa3dj1mZ6X9sXV7A30sq2RCtFYTZ+8c3LWPyaX3S/uRQ+RURERNqQMYbymlr2lZazr6SMvaXeUFp4hBWafvDDWNIfwmhcaDDxoSFENePVvTEG9hUfCKOF5VgfvOpntKrpFD5FREREjgO19S5yysrZV1JOblk5uWUV5JSVH7Ew/g9sVgsxId4wGhsSTExIELEhwc0KpW2hqXmt9ftgRURERE5hToedDlERdIiK8G37oZc0Z38YzS2tILesnLzyCurdDceSuj2GnP3jTH/MarEQFRxITEgwsSFBP/ozCD/78Rvxjt87ExERETlJWSwWQgP8CQ3wp2tcjG+7xxiKK6u8vaOl+4NpWQUFFZWN1rP3GEN+eSX55ZVs/Mn1wwP9fb2kI7ukEBrg3watahqFTxEREZHjhLc3M4io4CB6JMb5trvcHgoqKskrryC//MCfBeWVB511X1JVQ0lVDVtzCzitc+vPdG8OhU8RERGR45zd5i3ZFB8W0mD7Dz2leeWV5JdXkFe2/8/ySmpdLpx2G6EBzmN01wen8CkiIiJygvpxT2laQqxv+w9jSkura5q16lJbUPgUEREROcn8eEzp8eb4mZ8vIiIiIic9hU8RERERaTMKnyIiIiLSZhQ+RURERKTNKHyKiIiISJtR+BQRERGRNqPwKSIiIiJtRuFTRERERNqMwqeIiIiItBmFTxERERFpMwqfIiIiItJmFD5FREREpM0ofIqIiIhIm1H4FBEREZE2o/ApIiIiIm1G4VNERERE2ozCp4iIiIi0GYVPEREREWkzCp8iIiIi0mYUPkVERESkzSh8ioiIiEibUfgUERERkTaj8CkiIiIibUbhU0RERETajP1Y30BTGGMAKCsrO8Z3IiIiIiIH80NO+yG3HcoJET7Ly8sBSE5OPsZ3IiIiIiKHU15eTlhY2CH3W8yR4ulxwOPxsHfvXkJCQrBYLK3+eWVlZSQnJ7Nnzx5CQ0Nb/fOkben5nrz0bE9uer4nLz3bk4MxhvLychITE7FaDz2y84To+bRarSQlJbX554aGhuovwUlMz/fkpWd7ctPzPXnp2Z74Dtfj+QNNOBIRERGRNqPwKSIiIiJtRuHzIJxOJ/fccw9Op/NY34q0Aj3fk5ee7clNz/fkpWd7ajkhJhyJiIiIyMlBPZ8iIiIi0mYUPkVERESkzSh8ioiIiEibUfgUERERkTaj8PkTTz31FCkpKfj7+zNgwAAWLFhwrG9JmmD+/Pmce+65JCYmYrFY+OijjxrsN8Zw7733kpiYSEBAAGPGjGHDhg0NjqmtreXWW28lOjqaoKAgJk+eTFZWVhu2Qg5m2rRpDBo0iJCQEGJjYzn//PPZsmVLg2P0fE9cTz/9NH369PEVFx82bBhffvmlb7+e7clj2rRpWCwWpkyZ4tum53tqUvj8kXfeeYcpU6Zw1113sWrVKkaOHMlZZ53F7t27j/WtyRFUVlaSnp7OE088cdD9//73v3nkkUd44oknWLZsGfHx8ZxxxhmUl5f7jpkyZQozZsxg+vTpLFy4kIqKCiZNmoTb7W6rZshBzJs3j5tvvpnFixcze/ZsXC4XEyZMoLKy0neMnu+JKykpiQceeIDly5ezfPlyTj/9dM477zxfANGzPTksW7aM5557jj59+jTYrud7ijLiM3jwYHPjjTc22Na9e3fz17/+9RjdkbQEYGbMmOH73ePxmPj4ePPAAw/4ttXU1JiwsDDzzDPPGGOMKSkpMQ6Hw0yfPt13THZ2trFarearr75qs3uXI8vLyzOAmTdvnjFGz/dkFBERYV544QU925NEeXm56dKli5k9e7YZPXq0ue2224wx+rt7KlPP5351dXWsWLGCCRMmNNg+YcIEFi1adIzuSo6GXbt2kZOT0+DZOp1ORo8e7Xu2K1asoL6+vsExiYmJ9OrVS8//OFNaWgpAZGQkoOd7MnG73UyfPp3KykqGDRumZ3uSuPnmmznnnHMYP358g+16vqcu+7G+geNFQUEBbrebuLi4Btvj4uLIyck5RnclR8MPz+9gzzYzM9N3jJ+fHxEREY2O0fM/fhhj+OMf/8hpp51Gr169AD3fk8G6desYNmwYNTU1BAcHM2PGDHr06OELF3q2J67p06ezcuVKli1b1mif/u6euhQ+f8JisTT43RjTaJucmFrybPX8jy+33HILa9euZeHChY326fmeuLp168bq1aspKSnhgw8+4JprrmHevHm+/Xq2J6Y9e/Zw2223MWvWLPz9/Q95nJ7vqUev3feLjo7GZrM1+p9UXl5eo/+VyYklPj4e4LDPNj4+nrq6OoqLiw95jBxbt956K5988glz5swhKSnJt13P98Tn5+dH586dGThwINOmTSM9PZ3HHntMz/YEt2LFCvLy8hgwYAB2ux273c68efP43//+h91u9z0fPd9Tj8Lnfn5+fgwYMIDZs2c32D579myGDx9+jO5KjoaUlBTi4+MbPNu6ujrmzZvne7YDBgzA4XA0OGbfvn2sX79ez/8YM8Zwyy238OGHH/Ltt9+SkpLSYL+e78nHGENtba2e7Qlu3LhxrFu3jtWrV/t+Bg4cyJVXXsnq1atJTU3V8z1VHZt5Tsen6dOnG4fDYV588UWzceNGM2XKFBMUFGQyMjKO9a3JEZSXl5tVq1aZVatWGcA88sgjZtWqVSYzM9MYY8wDDzxgwsLCzIcffmjWrVtnLr/8cpOQkGDKysp817jxxhtNUlKS+frrr83KlSvN6aefbtLT043L5TpWzRJjzE033WTCwsLM3Llzzb59+3w/VVVVvmP0fE9cd9xxh5k/f77ZtWuXWbt2rbnzzjuN1Wo1s2bNMsbo2Z5sfjzb3Rg931OVwudPPPnkk6ZDhw7Gz8/P9O/f31fORY5vc+bMMUCjn2uuucYY4y3pcc8995j4+HjjdDrNqFGjzLp16xpco7q62txyyy0mMjLSBAQEmEmTJpndu3cfg9bIjx3suQLm5Zdf9h2j53vi+tWvfuX7zo2JiTHjxo3zBU9j9GxPNj8Nn3q+pyaLMcYcmz5XERERETnVaMyniIiIiLQZhU8RERERaTMKnyIiIiLSZhQ+RURERKTNKHyKiIiISJtR+BQRERGRNqPwKSIiIiJtRuFTRERERNqMwqeIiIiItBn7sb4BEZFTwerVq/noo498v0+ZMoXw8PBjdj8iIseKltcUEWkDr7zyCtddd53v9127dtGxY8djd0MiIseIXruLiIiISJtR+BQRERGRNqPwKSIiIiJtRuFTRERERNqMwqeIiIiItBnNdhcRaUUWi6XZ58yZM4cxY8Yc/ZsRETkOqOdTRERERNqMisyLiLQim80GgDEGj8fTaPvBtKS3VETkRKGeTxGRVuRyuXC5XLz44osNtm/fvt2376c/o0ePPkZ3KyLS+hQ+RURERKTNKHyKiIiISJtR+BQRERGRNqPwKSIiIiJtRuFTRERERNqMwqeIiIiItBmFTxERERFpMwqfIiIiItJmFD5FREREpM0ofIqIiIhIm1H4FBFpAw6Ho8Hvbrf7GN2JiMixpfApItIGQkJCGvxeXFx8jO5EROTYUvgUEWkDHTt2bPD7smXLjs2NiIgcYxZjjDnWNyEicrJzuVxER0dTWloKQGJiIi+88AJjxowhICDgGN+diEjbUc+niEgbsNvtXHfddb7f9+7dy9lnn01gYCCBgYEEBwf7fhYsWHAM71REpHUpfIqItJGpU6dy2mmnNdpeXV1NZWWl70eTkUTkZKbwKSLSRoKCgpg7dy7Tp0/nkksuoWvXroSEhGC16qtYRE4dGvMpIiIiIm1G/90WERERkTaj8CkiIiIibUbhU0RERETajMKniIiIiLQZhU8RERERaTMKnyIiIiLSZhQ+RURERKTNKHyKiIiISJtR+BQRERGRNqPwKSIiIiJtRuFTRERERNqMwqeIiIiItBmFTxERERFpMwqfIiIiItJmFD5FREREpM0ofIqIiIhIm1H4FBEREZE28//+oNK21AwP8AAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generate results for different number of lorentzians in fit:\n", + "\n", + "results_spectral_fit_pk = [\n", + " generate_spectrum_results(obp, params, Nk=1, max_depth=max_depth)\n", + " for params in params_k\n", + "]\n", + "\n", + "plot_result_expectations([\n", + " (\n", + " result, P11p, 'rand',\n", + " f\"P11 (spectral fit) $k_J$={pk + 1}\",\n", + " )\n", + " for pk, result in enumerate(results_spectral_fit_pk)\n", + "]);" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "a278cd12", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.108489990234375\n", + "10.0%. Run time: 3.03s. Est. time left: 00:00:00:27\n", + "20.0%. Run time: 4.73s. Est. time left: 00:00:00:18\n", + "30.1%. Run time: 6.56s. Est. time left: 00:00:00:15\n", + "40.1%. Run time: 8.72s. Est. time left: 00:00:00:13\n", + "50.1%. Run time: 11.80s. Est. time left: 00:00:00:11\n", + "60.1%. Run time: 13.89s. Est. time left: 00:00:00:09\n", + "70.1%. Run time: 15.93s. Est. time left: 00:00:00:06\n", + "80.1%. Run time: 18.57s. Est. time left: 00:00:00:04\n", + "90.2%. Run time: 20.98s. Est. time left: 00:00:00:02\n", + "100.0%. Run time: 23.10s. Est. time left: 00:00:00:00\n", + "Total run time: 23.10s\n", + "ODE solver time: 23.098018646240234\n", + "RHS construction time: 0.2477586269378662\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "capi_return is NULL\n", + "Call-back cb_f_in_zvode__user__routines failed.\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[24], line 6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# generate results for different number of Matsubara terms per Lorentzian\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# for max number of Lorentzians:\u001b[39;00m\n\u001b[1;32m 4\u001b[0m Nk_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m4\u001b[39m)\n\u001b[1;32m 5\u001b[0m results_spectral_fit_nk \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m----> 6\u001b[0m \u001b[43mgenerate_spectrum_results\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams_k\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNk\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mNk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_depth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_depth\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m Nk \u001b[38;5;129;01min\u001b[39;00m Nk_list\n\u001b[1;32m 8\u001b[0m ]\n\u001b[1;32m 10\u001b[0m plot_result_expectations([\n\u001b[1;32m 11\u001b[0m (\n\u001b[1;32m 12\u001b[0m result, P11p, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrand\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m nk, result \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(Nk_list, results_spectral_fit_nk)\n\u001b[1;32m 16\u001b[0m ]);\n", + "Cell \u001b[0;32mIn[22], line 27\u001b[0m, in \u001b[0;36mgenerate_spectrum_results\u001b[0;34m(obp, params, Nk, max_depth)\u001b[0m\n\u001b[1;32m 22\u001b[0m HEOM_spectral_fit \u001b[38;5;241m=\u001b[39m HEOMSolver(\n\u001b[1;32m 23\u001b[0m Ltot, bath, max_depth\u001b[38;5;241m=\u001b[39mmax_depth, options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[1;32m 24\u001b[0m )\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m timer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mODE solver time\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m---> 27\u001b[0m results_spectral_fit \u001b[38;5;241m=\u001b[39m (\u001b[43mHEOM_spectral_fit\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrho0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtlist\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m results_spectral_fit\n", + "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/heom/bofin_solvers.py:1114\u001b[0m, in \u001b[0;36mHEOMSolver.run\u001b[0;34m(self, state0, tlist, args, e_ops)\u001b[0m\n\u001b[1;32m 1047\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun\u001b[39m(\u001b[38;5;28mself\u001b[39m, state0, tlist, \u001b[38;5;241m*\u001b[39m, args\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, e_ops\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 1048\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1049\u001b[0m \u001b[38;5;124;03m Solve for the time evolution of the system.\u001b[39;00m\n\u001b[1;32m 1050\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1112\u001b[0m \u001b[38;5;124;03m list of attributes.\u001b[39;00m\n\u001b[1;32m 1113\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtlist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43me_ops\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43me_ops\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/solver_base.py:197\u001b[0m, in \u001b[0;36mSolver.run\u001b[0;34m(self, state0, tlist, e_ops, args)\u001b[0m\n\u001b[1;32m 192\u001b[0m stats[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpreparation time\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m time() \u001b[38;5;241m-\u001b[39m _time_start\n\u001b[1;32m 194\u001b[0m progress_bar \u001b[38;5;241m=\u001b[39m progress_bars[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprogress_bar\u001b[39m\u001b[38;5;124m'\u001b[39m]](\n\u001b[1;32m 195\u001b[0m \u001b[38;5;28mlen\u001b[39m(tlist)\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprogress_kwargs\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 196\u001b[0m )\n\u001b[0;32m--> 197\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_integrator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtlist\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 198\u001b[0m \u001b[43m \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 199\u001b[0m \u001b[43m \u001b[49m\u001b[43mresults\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_restore_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/integrator/integrator.py:201\u001b[0m, in \u001b[0;36mIntegrator.run\u001b[0;34m(self, tlist)\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 188\u001b[0m \u001b[38;5;124;03mIntegrate the system yielding the state for each times in tlist.\u001b[39;00m\n\u001b[1;32m 189\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;124;03m The state of the solver at each ``t`` of tlist.\u001b[39;00m\n\u001b[1;32m 199\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m tlist[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[0;32m--> 201\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/integrator/scipy_integrator.py:110\u001b[0m, in \u001b[0;36mIntegratorScipyAdams.integrate\u001b[0;34m(self, t, copy)\u001b[0m\n\u001b[1;32m 108\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_handle()\n\u001b[1;32m 109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m t \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ode_solver\u001b[38;5;241m.\u001b[39mt:\n\u001b[0;32m--> 110\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_ode_solver\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 111\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_state(copy)\n", + "File \u001b[0;32m~/anaconda3/envs/qutip-dev/lib/python3.12/site-packages/scipy/integrate/_ode.py:431\u001b[0m, in \u001b[0;36mode.integrate\u001b[0;34m(self, t, step, relax)\u001b[0m\n\u001b[1;32m 428\u001b[0m mth \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_integrator\u001b[38;5;241m.\u001b[39mrun\n\u001b[1;32m 430\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 431\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mt \u001b[38;5;241m=\u001b[39m \u001b[43mmth\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjac\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 432\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_y\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 433\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mf_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjac_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 434\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mSystemError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 435\u001b[0m \u001b[38;5;66;03m# f2py issue with tuple returns, see ticket 1187.\u001b[39;00m\n\u001b[1;32m 436\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 437\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFunction to integrate must not return a tuple.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 438\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n", + "File \u001b[0;32m~/anaconda3/envs/qutip-dev/lib/python3.12/site-packages/scipy/integrate/_ode.py:1008\u001b[0m, in \u001b[0;36mvode.run\u001b[0;34m(self, f, jac, y0, t0, t1, f_params, jac_params)\u001b[0m\n\u001b[1;32m 1004\u001b[0m jac \u001b[38;5;241m=\u001b[39m _vode_banded_jac_wrapper(jac, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mml, jac_params)\n\u001b[1;32m 1006\u001b[0m args \u001b[38;5;241m=\u001b[39m ((f, jac, y0, t0, t1) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mtuple\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcall_args) \u001b[38;5;241m+\u001b[39m\n\u001b[1;32m 1007\u001b[0m (f_params, jac_params))\n\u001b[0;32m-> 1008\u001b[0m y1, t, istate \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrunner\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1009\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mistate \u001b[38;5;241m=\u001b[39m istate\n\u001b[1;32m 1010\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m istate \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", + "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/integrator/scipy_integrator.py:63\u001b[0m, in \u001b[0;36mIntegratorScipyAdams._mul_np_vec\u001b[0;34m(self, t, vec)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ode_solver\u001b[38;5;241m.\u001b[39m_integrator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_zvode(\n\u001b[1;32m 58\u001b[0m method\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[1;32m 59\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions,\n\u001b[1;32m 60\u001b[0m )\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscipy zvode \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmethod\n\u001b[0;32m---> 63\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_mul_np_vec\u001b[39m(\u001b[38;5;28mself\u001b[39m, t, vec):\n\u001b[1;32m 64\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;124;03m Interface between scipy which use numpy and the driver, which use data.\u001b[39;00m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 67\u001b[0m state \u001b[38;5;241m=\u001b[39m _data\u001b[38;5;241m.\u001b[39mdense\u001b[38;5;241m.\u001b[39mfast_from_numpy(vec)\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# generate results for different number of Matsubara terms per Lorentzian\n", + "# for max number of Lorentzians:\n", + "\n", + "Nk_list = range(2, 4)\n", + "results_spectral_fit_nk = [\n", + " generate_spectrum_results(obp, params_k[-1], Nk=Nk, max_depth=max_depth)\n", + " for Nk in Nk_list\n", + "]\n", + "\n", + "plot_result_expectations([\n", + " (\n", + " result, P11p, 'rand',\n", + " f\"P11 (spectral fit) K={nk}\",\n", + " )\n", + " for nk, result in zip(Nk_list, results_spectral_fit_nk)\n", + "]);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91d69d95", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate results for different depths:\n", + "\n", + "Nc_list = range(2, max_depth)\n", + "results_spectral_fit_nc = [\n", + " generate_spectrum_results(obp, params_k[-1], Nk=1, max_depth=Nc)\n", + " for Nc in Nc_list\n", + "]\n", + "\n", + "plot_result_expectations([\n", + " (\n", + " result, P11p, 'rand',\n", + " f\"P11 (spectral fit) $N_C={nc}$\",\n", + " )\n", + " for nc, result in zip(Nc_list, results_spectral_fit_nc)\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "6466169d", + "metadata": {}, + "source": [ + "We now combine the fitting and correlation function data into one large plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f60baa61", + "metadata": {}, + "outputs": [], + "source": [ + "def correlation_approx_matsubara(t, ck, vk):\n", + " \"\"\" Calculate the approximate real or imaginary part of the\n", + " correlation function from the matsubara expansion co-efficients.\n", + " \"\"\"\n", + " ck = np.array(ck)\n", + " vk = np.array(vk)\n", + " return np.sum(ck[:, None] * np.exp(-vk[:, None] * t), axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc12d8e8", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_cr_fit_vs_actual(t, ckAR, vkAR, C, axes):\n", + " \"\"\" Plot the C_R(t) fit. \"\"\"\n", + " yR = correlation_approx_matsubara(t, ckAR, vkAR)\n", + "\n", + " axes.plot(\n", + " t, np.real(C),\n", + " \"r\", linewidth=3, label=\"Original\",\n", + " )\n", + " axes.plot(\n", + " t, np.real(yR),\n", + " \"g\", dashes=[3, 3], linewidth=2, label=\"Reconstructed\",\n", + " )\n", + "\n", + " axes.legend(loc=0)\n", + " axes.set_ylabel(r'$C_R(t)$', fontsize=28)\n", + " axes.set_xlabel(r'$t\\;\\omega_c$', fontsize=28)\n", + " axes.locator_params(axis='y', nbins=4)\n", + " axes.locator_params(axis='x', nbins=4)\n", + " axes.text(0.15, 0.85, \"(a)\", fontsize=28, transform=axes.transAxes)\n", + "\n", + "\n", + "def plot_ci_fit_vs_actual(t, ckAI, vkAI, C, axes):\n", + " \"\"\" Plot the C_I(t) fit. \"\"\"\n", + " yI = correlation_approx_matsubara(t, ckAI, vkAI)\n", + "\n", + " axes.plot(\n", + " t, np.imag(C),\n", + " \"r\", linewidth=3, label=\"Original\",\n", + " )\n", + " axes.plot(\n", + " t, np.real(yI),\n", + " \"g\", dashes=[3, 3], linewidth=2, label=\"Reconstructed\",\n", + " )\n", + "\n", + " axes.legend(loc=0)\n", + " axes.set_ylabel(r'$C_I(t)$', fontsize=28)\n", + " axes.set_xlabel(r'$t\\;\\omega_c$', fontsize=28)\n", + " axes.locator_params(axis='y', nbins=4)\n", + " axes.locator_params(axis='x', nbins=4)\n", + " axes.text(0.80, 0.80, \"(b)\", fontsize=28, transform=axes.transAxes)\n", + "\n", + "\n", + "def plot_jw_fit_vs_actual(bath_fit, obp, axes):\n", + " \"\"\" Plot the J(w) fit. \"\"\"\n", + " [lam, gamma, w0] = bath_fit\n", + " [alpha, wc] = [obp.alpha, obp.wc]\n", + "\n", + " w = np.linspace(0, 25, 20000)\n", + "\n", + " J_orig = ohmic_spectral_density(w, alpha=alpha, wc=wc)\n", + " J_fit = spectral_density_approx(w, lam, gamma, w0)\n", + "\n", + " axes.plot(\n", + " w, J_orig,\n", + " \"r\", linewidth=3, label=r\"$J(\\omega)$ original\",\n", + " )\n", + " axes.plot(\n", + " w, J_fit,\n", + " \"g\", dashes=[3, 3], linewidth=2, label=r\"$J(\\omega)$ Fit $k_J = 4$\",\n", + " )\n", + "\n", + " axes.legend(loc=0)\n", + " axes.set_ylabel(r'$J(\\omega)$', fontsize=28)\n", + " axes.set_xlabel(r'$\\omega/\\omega_c$', fontsize=28)\n", + " axes.locator_params(axis='y', nbins=4)\n", + " axes.locator_params(axis='x', nbins=4)\n", + " axes.text(0.15, 0.85, \"(c)\", fontsize=28, transform=axes.transAxes)\n", + "\n", + "\n", + "def plot_sw_fit_vs_actual(bath_fit, obp, axes):\n", + " \"\"\" Plot the S(w) fit. \"\"\"\n", + " [lam, gamma, w0] = bath_fit\n", + " [alpha, wc, beta] = [obp.alpha, obp.wc, obp.beta]\n", + "\n", + " # avoid the pole in the fit around zero:\n", + " w = np.concatenate(\n", + " [np.linspace(-10, -0.1, 5000),\n", + " np.linspace(0.1, 10, 5000)],\n", + " )\n", + "\n", + " s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta)\n", + " s_fit = (\n", + " spectral_density_approx(w, lam, gamma, w0) *\n", + " ((1 / (np.e**(w * beta) - 1)) + 1) * 2\n", + " )\n", + "\n", + " axes.plot(w, s_orig, \"r\", linewidth=3, label=\"Original\")\n", + " axes.plot(w, s_fit, \"g\", dashes=[3, 3], linewidth=2, label=\"Reconstructed\")\n", + "\n", + " axes.legend()\n", + " axes.set_ylabel(r'$S(\\omega)$', fontsize=28)\n", + " axes.set_xlabel(r'$\\omega/\\omega_c$', fontsize=28)\n", + " axes.locator_params(axis='y', nbins=4)\n", + " axes.locator_params(axis='x', nbins=4)\n", + " axes.text(0.15, 0.85, \"(d)\", fontsize=28, transform=axes.transAxes)\n", + "\n", + "\n", + "def plot_matsubara_spectrum_fit_vs_actual(\n", + " t, C, matsubara_fit, bath_fit, obp,\n", + "):\n", + " \"\"\" Plot the Matsubara fit of the spectrum . \"\"\"\n", + " fig = plt.figure(figsize=(12, 10))\n", + " grid = plt.GridSpec(2, 2, wspace=0.4, hspace=0.3)\n", + "\n", + " [ckAR, vkAR, ckAI, vkAI] = matsubara_fit\n", + "\n", + " plot_cr_fit_vs_actual(\n", + " t, ckAR, vkAR, C,\n", + " axes=fig.add_subplot(grid[0, 0]),\n", + " )\n", + " plot_ci_fit_vs_actual(\n", + " t, ckAI, vkAI, C,\n", + " axes=fig.add_subplot(grid[0, 1]),\n", + " )\n", + " plot_jw_fit_vs_actual(\n", + " bath_fit, obp,\n", + " axes=fig.add_subplot(grid[1, 0]),\n", + " )\n", + " plot_sw_fit_vs_actual(\n", + " bath_fit, obp,\n", + " axes=fig.add_subplot(grid[1, 1]),\n", + " )\n", + "\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9254cfe", + "metadata": {}, + "outputs": [], + "source": [ + "t = np.linspace(0, 15, 100)\n", + "C = ohmic_correlation(t, alpha=obp.alpha, wc=obp.wc, beta=obp.beta)\n", + "\n", + "ckAR, vkAR, ckAI, vkAI, terminator = (\n", + " matsubara_coefficients_from_spectral_fit(\n", + " lam, gamma, w0, beta=obp.beta, Q=obp.Q, Nk=1,\n", + " )\n", + ")\n", + "\n", + "matsubara_fit = [ckAR, vkAR, ckAI, vkAI]\n", + "bath_fit = [lam, gamma, w0]\n", + "\n", + "plot_matsubara_spectrum_fit_vs_actual(\n", + " t, C, matsubara_fit,\n", + " bath_fit, obp,\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "79c90d62", + "metadata": {}, + "source": [ + "## Building the HEOM bath by fitting the correlation function" + ] + }, + { + "cell_type": "markdown", + "id": "e87e8d35", + "metadata": {}, + "source": [ + "Having successfully fitted the spectral density and used the result to calculate the Matsubara expansion and terminator for the HEOM bosonic bath, we now proceed to the second case of fitting the correlation function itself instead.\n", + "\n", + "Here we fit the real and imaginary parts seperately, using the following ansatz\n", + "\n", + "$$C_R^F(t) = \\sum_{i=1}^{k_R} c_R^ie^{-\\gamma_R^i t}\\cos(\\omega_R^i t)$$\n", + "\n", + "$$C_I^F(t) = \\sum_{i=1}^{k_I} c_I^ie^{-\\gamma_I^i t}\\sin(\\omega_I^i t)$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1883b3e", + "metadata": {}, + "outputs": [], + "source": [ + "# The approximate correlation functions and a helper for fitting\n", + "# the approximate correlation function to values calculated from\n", + "# the analytical formula:\n", + "\n", + "def correlation_approx_real(t, a, b, c):\n", + " \"\"\" Calculate the fitted value of the function for the given parameters.\n", + " \"\"\"\n", + " a = np.array(a)\n", + " b = np.array(b)\n", + " c = np.array(c)\n", + " return np.sum(\n", + " a[:, None] * np.exp(b[:, None] * t) * np.cos(c[:, None] * t),\n", + " axis=0,\n", + " )\n", + "\n", + "\n", + "def correlation_approx_imag(t, a, b, c):\n", + " \"\"\" Calculate the fitted value of the function for the given parameters.\n", + " \"\"\"\n", + " a = np.array(a)\n", + " b = np.array(b)\n", + " c = np.array(c)\n", + " return np.sum(\n", + " a[:, None] * np.exp(b[:, None] * t) * np.sin(c[:, None] * t),\n", + " axis=0,\n", + " )\n", + "\n", + "\n", + "def fit_correlation_real(C, t, wc, N):\n", + " \"\"\" Fit the spectral density with N underdamped oscillators. \"\"\"\n", + " sigma = [0.1] * len(t)\n", + "\n", + " C_max = abs(max(C, key=abs))\n", + "\n", + " guesses = pack([C_max] * N, [-wc] * N, [wc] * N)\n", + " lower_bounds = pack([-20 * C_max] * N, [-np.inf] * N, [0.] * N)\n", + " upper_bounds = pack([20 * C_max] * N, [0.1] * N, [np.inf] * N)\n", + "\n", + " params, _ = curve_fit(\n", + " lambda x, *params: correlation_approx_real(t, *unpack(params)),\n", + " t, C,\n", + " p0=guesses,\n", + " bounds=(lower_bounds, upper_bounds),\n", + " sigma=sigma,\n", + " maxfev=1000000000,\n", + " )\n", + "\n", + " return unpack(params)\n", + "\n", + "\n", + "def fit_correlation_imag(C, t, wc, N):\n", + " \"\"\" Fit the spectral density with N underdamped oscillators. \"\"\"\n", + " sigma = [0.0001] * len(t)\n", + "\n", + " C_max = abs(max(C, key=abs))\n", + "\n", + " guesses = pack([-C_max] * N, [-2] * N, [1] * N)\n", + " lower_bounds = pack([-5 * C_max] * N, [-100] * N, [0.] * N)\n", + " upper_bounds = pack([5 * C_max] * N, [0.01] * N, [100] * N)\n", + "\n", + " params, _ = curve_fit(\n", + " lambda x, *params: correlation_approx_imag(t, *unpack(params)),\n", + " t, C,\n", + " p0=guesses,\n", + " bounds=(lower_bounds, upper_bounds),\n", + " sigma=sigma,\n", + " maxfev=1000000000,\n", + " )\n", + "\n", + " return unpack(params)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0503d1db", + "metadata": {}, + "outputs": [], + "source": [ + "t = np.linspace(0, 15, 15000)\n", + "C = ohmic_correlation(t, alpha=obp.alpha, wc=obp.wc, beta=obp.beta)\n", + "\n", + "params_k_real = [\n", + " fit_correlation_real(np.real(C), t, wc=obp.wc, N=i+1)\n", + " for i in range(3)\n", + "]\n", + "\n", + "params_k_imag = [\n", + " fit_correlation_imag(np.imag(C), t, wc=obp.wc, N=i+1)\n", + " for i in range(3)\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9372964c", + "metadata": {}, + "outputs": [], + "source": [ + "for k, params in enumerate(params_k_real):\n", + " lam, gamma, w0 = params\n", + " y = correlation_approx_real(t, lam, gamma, w0)\n", + " print(f\"Parameters [k={k}]: lam={lam}; gamma={gamma}; w0={w0}\")\n", + " plt.plot(t, np.real(C), label=\"C_R(t) analytic\")\n", + " plt.plot(t, y, label=f\"C_R(t) k={k + 1}\")\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a268386d", + "metadata": {}, + "outputs": [], + "source": [ + "for k, params in enumerate(params_k_imag):\n", + " lam, gamma, w0 = params\n", + " y = correlation_approx_imag(t, lam, gamma, w0)\n", + " print(f\"Parameters [k={k}]: lam={lam}; gamma={gamma}; w0={w0}\")\n", + " plt.plot(t, np.imag(C), label=\"C_I(t) analytic\")\n", + " plt.plot(t, y, label=f\"C_I(t) k={k + 1}\")\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4f04e6ad", + "metadata": {}, + "source": [ + "Now we construct the `BosonicBath` co-efficients and frequencies from the fit to the correlation function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6150d0ae", + "metadata": {}, + "outputs": [], + "source": [ + "def matsubara_coefficients_from_corr_fit_real(lam, gamma, w0):\n", + " \"\"\" Return the matsubara coefficients for the imaginary part\n", + " of the correlation function.\n", + " \"\"\"\n", + " ckAR = [0.5 * x + 0j for x in lam] # the 0.5 is from the cosine\n", + " # extend the list with the complex conjugates:\n", + " ckAR.extend(np.conjugate(ckAR))\n", + "\n", + " vkAR = [-x - 1.0j * y for x, y in zip(gamma, w0)]\n", + " vkAR.extend([-x + 1.0j * y for x, y in zip(gamma, w0)])\n", + "\n", + " return ckAR, vkAR\n", + "\n", + "\n", + "def matsubara_coefficients_from_corr_fit_imag(lam, gamma, w0):\n", + " \"\"\" Return the matsubara coefficients for the imaginary part\n", + " of the correlation function.\n", + " \"\"\"\n", + " ckAI = [-0.5j * x for x in lam] # the 0.5 is from the sine\n", + " # extend the list with the complex conjugates:\n", + " ckAI.extend(np.conjugate(ckAI))\n", + "\n", + " vkAI = [-x - 1.0j * y for x, y in zip(gamma, w0)]\n", + " vkAI.extend([-x + 1.0j * y for x, y in zip(gamma, w0)])\n", + "\n", + " return ckAI, vkAI" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b9b5d7be", + "metadata": {}, + "outputs": [], + "source": [ + "ckAR, vkAR = matsubara_coefficients_from_corr_fit_real(*params_k_real[-1])\n", + "ckAI, vkAI = matsubara_coefficients_from_corr_fit_imag(*params_k_imag[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e581be18", + "metadata": {}, + "outputs": [], + "source": [ + "def corr_spectrum_approx(w, ckAR, vkAR, ckAI, vkAI):\n", + " \"\"\" Calculates the approximate power spectrum from ck and vk. \"\"\"\n", + " S = np.zeros(len(w), dtype=np.complex128)\n", + " for ck, vk in zip(ckAR, vkAR):\n", + " S += (\n", + " 2 * ck * np.real(vk) /\n", + " ((w - np.imag(vk))**2 + (np.real(vk)**2))\n", + " )\n", + " for ck, vk in zip(ckAI, vkAI):\n", + " S += (\n", + " 2 * 1.0j * ck * np.real(vk) /\n", + " ((w - np.imag(vk))**2 + (np.real(vk)**2))\n", + " )\n", + " return S" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8f1f227", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_jw_correlation_fit_vs_actual(matsubara_fit, obp, axes):\n", + " \"\"\" Plot J(w) from the correlation fit. \"\"\"\n", + " [ckAR, vkAR, ckAI, vkAI] = matsubara_fit\n", + " [alpha, wc] = [obp.alpha, obp.wc]\n", + "\n", + " w = np.linspace(0.001, 25, 20000)\n", + "\n", + " J_orig = ohmic_spectral_density(w, alpha=alpha, wc=wc)\n", + " J_fit = np.real(\n", + " corr_spectrum_approx(w, ckAR, vkAR, ckAI, vkAI) /\n", + " (((1 / (np.e**(w * obp.beta) - 1)) + 1) * 2)\n", + " )\n", + "\n", + " axes.plot(\n", + " w, J_orig,\n", + " \"r\", linewidth=3, label=r\"$J(\\omega)$ original\",\n", + " )\n", + " axes.plot(\n", + " w, J_fit,\n", + " \"g\", dashes=[3, 3], linewidth=2, label=r\"$J(\\omega)$ fit\",\n", + " )\n", + "\n", + " axes.legend(loc=0)\n", + " axes.set_ylabel(r'$J(\\omega)$', fontsize=28)\n", + " axes.set_xlabel(r'$\\omega/\\omega_c$', fontsize=28)\n", + " axes.locator_params(axis='y', nbins=4)\n", + " axes.locator_params(axis='x', nbins=4)\n", + " axes.text(3, 1.1, \"(c)\", fontsize=28)\n", + "\n", + "\n", + "def plot_sw_correlation_fit_vs_actual(matsubara_fit, obp, axes):\n", + " \"\"\" Plot S(W) from the correlation fit. \"\"\"\n", + " [ckAR, vkAR, ckAI, vkAI] = matsubara_fit\n", + " [alpha, wc, beta] = [obp.alpha, obp.wc, obp.beta]\n", + "\n", + " # avoid the pole in the fit around zero:\n", + " w = np.concatenate([\n", + " np.linspace(-10, -0.1, 5000),\n", + " np.linspace(0.1, 10, 5000),\n", + " ])\n", + "\n", + " s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta)\n", + " s_fit = corr_spectrum_approx(w, ckAR, vkAR, ckAI, vkAI)\n", + "\n", + " axes.plot(\n", + " w, s_orig,\n", + " \"r\", linewidth=3, label=\"Original\",\n", + " )\n", + " axes.plot(\n", + " w, s_fit,\n", + " \"g\", dashes=[3, 3], linewidth=2, label=\"Reconstructed\",\n", + " )\n", + "\n", + " axes.legend()\n", + " axes.set_ylabel(r'$S(\\omega)$', fontsize=28)\n", + " axes.set_xlabel(r'$\\omega/\\omega_c$', fontsize=28)\n", + " axes.locator_params(axis='y', nbins=4)\n", + " axes.locator_params(axis='x', nbins=4)\n", + " axes.text(0.15, 0.85, \"(d)\", fontsize=28, transform=axes.transAxes)\n", + "\n", + "\n", + "def plot_matsubara_correlation_fit_vs_actual(t, C, matsubara_fit, obp):\n", + " fig = plt.figure(figsize=(12, 10))\n", + " grid = plt.GridSpec(2, 2, wspace=0.4, hspace=0.3)\n", + "\n", + " ckAR, vkAR, ckAI, vkAI = matsubara_fit\n", + "\n", + " plot_cr_fit_vs_actual(\n", + " t, ckAR, vkAR, C,\n", + " axes=fig.add_subplot(grid[0, 0]),\n", + " )\n", + " plot_ci_fit_vs_actual(\n", + " t, ckAI, vkAI, C,\n", + " axes=fig.add_subplot(grid[0, 1]),\n", + " )\n", + " plot_jw_correlation_fit_vs_actual(\n", + " matsubara_fit, obp,\n", + " axes=fig.add_subplot(grid[1, 0]),\n", + " )\n", + " plot_sw_correlation_fit_vs_actual(\n", + " matsubara_fit, obp,\n", + " axes=fig.add_subplot(grid[1, 1]),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32680798", + "metadata": {}, + "outputs": [], + "source": [ + "t = np.linspace(0, 15, 100)\n", + "C = ohmic_correlation(t, alpha=obp.alpha, wc=obp.wc, beta=obp.beta)\n", + "\n", + "matsubara_fit = [ckAR, vkAR, ckAI, vkAI]\n", + "\n", + "plot_matsubara_correlation_fit_vs_actual(\n", + " t, C, matsubara_fit, obp,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a00bfaf", + "metadata": {}, + "outputs": [], + "source": [ + "def generate_corr_results(params_real, params_imag, max_depth):\n", + " ckAR, vkAR = matsubara_coefficients_from_corr_fit_real(\n", + " *params_real\n", + " )\n", + " ckAI, vkAI = matsubara_coefficients_from_corr_fit_imag(\n", + " *params_imag\n", + " )\n", + "\n", + " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + " options = Options(\n", + " nsteps=15000, store_states=True, rtol=1e-12, atol=1e-12, method=\"bdf\",\n", + " )\n", + " # This problem is a little stiff, so we use the BDF method to solve\n", + " # the ODE ^^^\n", + "\n", + " with timer(\"RHS construction time\"):\n", + " bath = BosonicBath(obp.Q, ckAR, vkAR, ckAI, vkAI)\n", + " HEOM_corr_fit = HEOMSolver(\n", + " Hsys, bath, max_depth=max_depth, options=options,\n", + " )\n", + "\n", + " with timer(\"ODE solver time\"):\n", + " results_corr_fit = (HEOM_corr_fit.run(rho0, tlist))\n", + "\n", + " return results_corr_fit\n", + "\n", + "\n", + "# Generate results for different number of lorentzians in fit:\n", + "results_corr_fit_pk = [\n", + " print(f\"{pk + 1}\") or generate_corr_results(\n", + " params_real, params_imag, max_depth=max_depth,\n", + " )\n", + " for pk, (params_real, params_imag)\n", + " in enumerate(zip(params_k_real, params_k_imag))\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7db32d9d", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations([\n", + " (\n", + " result, P11p, 'rand',\n", + " f\"P11 (correlation fit) k_R=k_I={pk + 1}\",\n", + " )\n", + " for pk, result in enumerate(results_corr_fit_pk)\n", + "]);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6902bf9e", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", + "\n", + "plot_result_expectations([\n", + " (\n", + " results_corr_fit_pk[0], P11p,\n", + " 'y', \"Correlation Function Fit $k_R=k_I=1$\",\n", + " ),\n", + " (\n", + " results_corr_fit_pk[2], P11p,\n", + " 'y-.', \"Correlation Function Fit $k_R=k_I=3$\",\n", + " ),\n", + " (results_spectral_fit_pk[0], P11p, 'b', \"Spectral Density Fit $k_J=1$\"),\n", + " (results_spectral_fit_pk[2], P11p, 'g--', \"Spectral Density Fit $k_J=3$\"),\n", + " (results_spectral_fit_pk[3], P11p, 'r-.', \"Spectral Density Fit $k_J=4$\"),\n", + "], axes=axes)\n", + "\n", + "axes.set_yticks([0.6, 0.8, 1])\n", + "axes.set_ylabel(r'$\\rho_{11}$', fontsize=30)\n", + "axes.set_xlabel(r'$t\\;\\omega_c$', fontsize=30)\n", + "axes.legend(loc=0, fontsize=20);" + ] + }, + { + "cell_type": "markdown", + "id": "7e7e681d", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ce34619", + "metadata": {}, + "outputs": [], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "1e6d47b7", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02e1d11e", + "metadata": {}, + "outputs": [], + "source": [ + "assert np.allclose(\n", + " expect(P11p, results_spectral_fit_pk[2].states),\n", + " expect(P11p, results_spectral_fit_pk[3].states),\n", + " rtol=1e-2,\n", + ")" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "qutip-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v4/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb b/tutorials-v4/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb new file mode 100644 index 00000000..c4fb0520 --- /dev/null +++ b/tutorials-v4/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb @@ -0,0 +1,847 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fd96fd41", + "metadata": {}, + "source": [ + "# HEOM 1e: Spin-Bath model (pure dephasing)" + ] + }, + { + "cell_type": "markdown", + "id": "54bc3dff", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices.\n", + "\n", + "In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", + "\n", + "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian (see paper), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", + "\n", + "In the example below we show how to model the overdamped Drude-Lorentz Spectral Density, commonly used with the HEOM. We show how to do the Matsubara and Pade analytical decompositions, as well as how to fit the latter with a finite set of approximate exponentials. This differs from examble 1a in that we assume that the system and coupling parts of the Hamiltonian commute, hence giving an analytically solvable ''pure dephasing'' model. This is a useful example to look at when introducing other approximations (e.g., fitting of correlation functions) to check for validity/convergence against the analytical results. (Note that, generally, for the fitting examples, the pure dephasing model is the 'worst possible case'. \n", + "\n", + "### Drude-Lorentz spectral density\n", + "\n", + "The Drude-Lorentz spectral density is:\n", + "\n", + "$$J(\\omega)=\\omega \\frac{2\\lambda\\gamma}{{\\gamma}^2 + \\omega^2}$$\n", + "\n", + "where $\\lambda$ scales the coupling strength, and $\\gamma$ is the cut-off frequency.\n", + "We use the convention,\n", + "\\begin{equation*}\n", + "C(t) = \\int_0^{\\infty} d\\omega \\frac{J_D(\\omega)}{\\pi}[\\coth(\\beta\\omega) \\cos(\\omega \\tau) - i \\sin(\\omega \\tau)]\n", + "\\end{equation*}\n", + "\n", + "With the HEOM we must use an exponential decomposition:\n", + "\n", + "\\begin{equation*}\n", + "C(t)=\\sum_{k=0}^{k=\\infty} c_k e^{-\\nu_k t}\n", + "\\end{equation*}\n", + "\n", + "The Matsubara decomposition of the Drude-Lorentz spectral density is given by:\n", + "\n", + "\\begin{equation*}\n", + " \\nu_k = \\begin{cases}\n", + " \\gamma & k = 0\\\\\n", + " {2 \\pi k} / {\\beta \\hbar} & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "\\begin{equation*}\n", + " c_k = \\begin{cases}\n", + " \\lambda \\gamma (\\cot(\\beta \\gamma / 2) - i) / \\hbar & k = 0\\\\\n", + " 4 \\lambda \\gamma \\nu_k / \\{(nu_k^2 - \\gamma^2)\\beta \\hbar^2 \\} & k \\geq 1\\\\\n", + " \\end{cases}\n", + "\\end{equation*}\n", + "\n", + "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." + ] + }, + { + "cell_type": "markdown", + "id": "c100f975", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4b1a9e2", + "metadata": {}, + "outputs": [], + "source": [ + "import contextlib\n", + "import time\n", + "\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "import scipy\n", + "from scipy.optimize import curve_fit\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " Options,\n", + " basis,\n", + " expect,\n", + " liouvillian,\n", + " sigmax,\n", + " sigmaz,\n", + ")\n", + "from qutip.nonmarkov.heom import (\n", + " HEOMSolver,\n", + " BosonicBath,\n", + " DrudeLorentzBath,\n", + " DrudeLorentzPadeBath,\n", + ")\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "26ca9292", + "metadata": {}, + "source": [ + "## Helper functions\n", + "\n", + "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "663ce2f8", + "metadata": {}, + "outputs": [], + "source": [ + "def cot(x):\n", + " \"\"\" Vectorized cotangent of x. \"\"\"\n", + " return 1. / np.tan(x)\n", + "\n", + "\n", + "def coth(x):\n", + " \"\"\" Vectorized hyperbolic cotangent of x. \"\"\"\n", + " return 1. / np.tanh(x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "356c5c5c", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_result_expectations(plots, axes=None):\n", + " \"\"\" Plot the expectation values of operators as functions of time.\n", + "\n", + " Each plot in plots consists of (solver_result, measurement_operation,\n", + " color, label).\n", + " \"\"\"\n", + " if axes is None:\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " fig_created = True\n", + " else:\n", + " fig = None\n", + " fig_created = False\n", + "\n", + " # add kw arguments to each plot if missing\n", + " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", + " for result, m_op, color, label, kw in plots:\n", + " if m_op is None:\n", + " t, exp = result\n", + " else:\n", + " t = result.times\n", + " exp = np.real(expect(result.states, m_op))\n", + " kw.setdefault(\"linewidth\", 2)\n", + " axes.plot(t, exp, color, label=label, **kw)\n", + "\n", + " if fig_created:\n", + " axes.legend(loc=0, fontsize=12)\n", + " axes.set_xlabel(\"t\", fontsize=28)\n", + "\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21645577", + "metadata": {}, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\" Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "markdown", + "id": "854da8f3", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian, bath and system measurement operators:" + ] + }, + { + "cell_type": "markdown", + "id": "f7905d63", + "metadata": {}, + "source": [ + "Here we set $H_{sys}=0$, which means the interaction Hamiltonian and the system Hamiltonian commute, and we can compare the numerical results to a known analytical one. We could in principle keep $\\epsilon \\neq 0$, but it just introduces fast system oscillations, so it is more convenient to set it to zero." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f47d6f84", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the system Hamiltonian\n", + "eps = 0.0 # Energy of the 2-level system.\n", + "Del = 0.0 # Tunnelling term\n", + "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b37ca98f", + "metadata": {}, + "outputs": [], + "source": [ + "# System-bath coupling (Drude-Lorentz spectral density)\n", + "Q = sigmaz() # coupling operator\n", + "\n", + "# Bath properties:\n", + "gamma = 0.5 # cut off frequency\n", + "lam = 0.1 # coupling strength\n", + "T = 0.5\n", + "beta = 1. / T\n", + "\n", + "# HEOM parameters:\n", + "# cut off parameter for the bath:\n", + "NC = 6\n", + "# number of exponents to retain in the Matsubara expansion\n", + "# of the correlation function:\n", + "Nk = 3\n", + "\n", + "# Times to solve for\n", + "tlist = np.linspace(0, 50, 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89d89e4d", + "metadata": {}, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresonding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresonding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "edf5af1b", + "metadata": {}, + "source": [ + "To get a non-trivial result we prepare the initial state in a superposition, and see how the bath destroys the coherence." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b38676e", + "metadata": {}, + "outputs": [], + "source": [ + "# Initial state of the system.\n", + "psi = (basis(2, 0) + basis(2, 1)).unit()\n", + "rho0 = psi * psi.dag()" + ] + }, + { + "cell_type": "markdown", + "id": "dc39f4f4", + "metadata": {}, + "source": [ + "## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf76fb77", + "metadata": {}, + "outputs": [], + "source": [ + "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", + " HEOMMats = HEOMSolver(Hsys, bath, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMats = HEOMMats.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1fa9afb2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Plot the results so far\n", + "plot_result_expectations([\n", + " (resultMats, P11p, 'b', \"P11 Matsubara\"),\n", + " (resultMats, P12p, 'r', \"P12 Matsubara\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "13684357", + "metadata": {}, + "source": [ + "## Simulation 2: Matsubara decomposition (including terminator)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba594c86", + "metadata": {}, + "outputs": [], + "source": [ + "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", + " _, terminator = bath.terminator()\n", + " Ltot = liouvillian(Hsys) + terminator\n", + " HEOMMatsT = HEOMSolver(Ltot, bath, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultMatsT = HEOMMatsT.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa82fd55", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the results\n", + "plot_result_expectations([\n", + " (resultMats, P11p, 'b', \"P11 Matsubara\"),\n", + " (resultMats, P12p, 'r', \"P12 Matsubara\"),\n", + " (resultMatsT, P11p, 'b--', \"P11 Matsubara and terminator\"),\n", + " (resultMatsT, P12p, 'r--', \"P12 Matsubara and terminator\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "59d7332a", + "metadata": {}, + "source": [ + "## Simulation 3: Pade decomposition\n", + "\n", + "As in example 1a, we can compare to Pade and Fitting approaches." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc77a072", + "metadata": {}, + "outputs": [], + "source": [ + "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", + " HEOMPade = HEOMSolver(Hsys, bath, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultPade = HEOMPade.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a215e962", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the results\n", + "plot_result_expectations([\n", + " (resultMatsT, P11p, 'b', \"P11 Matsubara (+term)\"),\n", + " (resultMatsT, P12p, 'r', \"P12 Matsubara (+term)\"),\n", + " (resultPade, P11p, 'b--', \"P11 Pade\"),\n", + " (resultPade, P12p, 'r--', \"P12 Pade\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "8ada5ceb", + "metadata": {}, + "source": [ + "## Simulation 4: Fitting approach" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f24dab06", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def c(t, Nk):\n", + " \"\"\" Calculates real and imag. parts of the correlation function\n", + " using Nk Matsubara terms.\n", + " \"\"\"\n", + " vk = 2 * np.pi * T * np.arange(1, Nk)\n", + "\n", + " result = (\n", + " lam * gamma * (-1.0j + cot(gamma * beta / 2.)) *\n", + " np.exp(-gamma * t[None, :])\n", + " )\n", + " result += np.sum(\n", + " (4 * lam * gamma * T * vk[:, None] / (vk[:, None]**2 - gamma**2)) *\n", + " np.exp(-vk[:, None] * t[None, :]),\n", + " axis=0,\n", + " )\n", + " result = result.squeeze(axis=0)\n", + "\n", + " return result\n", + "\n", + "\n", + "tlist_fit = np.linspace(0, 2, 10000)\n", + "lmaxmats = 15000\n", + "\n", + "corr_ana = c(tlist_fit, lmaxmats)\n", + "corrRana, corrIana = np.real(corr_ana), np.imag(corr_ana)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be98ca8e", + "metadata": {}, + "outputs": [], + "source": [ + "def wrapper_fit_func(x, N, *args):\n", + " \"\"\" Wrapper for fitting function. \"\"\"\n", + " a, b = args[0][:N], args[0][N:2*N]\n", + " return fit_func(x, a, b)\n", + "\n", + "\n", + "def fit_func(x, a, b):\n", + " \"\"\" Fitting function. \"\"\"\n", + " a = np.array(a)\n", + " b = np.array(b)\n", + " x = np.atleast_1d(np.array(x))\n", + " return np.sum(\n", + " a[:, None] * np.exp(b[:, None] * x[None, :]),\n", + " axis=0,\n", + " )\n", + "\n", + "\n", + "def fitter(ans, tlist, i):\n", + " \"\"\" Compute the fit. \"\"\"\n", + " upper_a = abs(max(ans, key=np.abs)) * 10\n", + " # set initial guess:\n", + " guess = [ans[0]] * i + [0] * i\n", + " # set bounds: a's = anything, b's = negative\n", + " # sets lower bound\n", + " b_lower = [-upper_a] * i + [-np.inf] * i\n", + " # sets higher bound\n", + " b_higher = [upper_a] * i + [0] * i\n", + " param_bounds = (b_lower, b_higher)\n", + " p1, p2 = curve_fit(\n", + " lambda x, *params: wrapper_fit_func(x, i, params),\n", + " tlist,\n", + " ans,\n", + " p0=guess,\n", + " sigma=[0.01] * len(tlist),\n", + " bounds=param_bounds,\n", + " maxfev=1e8,\n", + " )\n", + " return p1[:i], p1[i:]\n", + "\n", + "\n", + "# Fits of the real part with up to 4 exponents\n", + "popt1 = []\n", + "for i in range(4):\n", + " a, b = fitter(corrRana, tlist_fit, i + 1)\n", + " popt1.append((a, b))\n", + " y = fit_func(tlist_fit, a, b)\n", + " plt.plot(tlist_fit, corrRana, label=\"C_R(t)\")\n", + " plt.plot(tlist_fit, y, label=f\"Fit with k={i + 1}\")\n", + " plt.xlabel(\"t\")\n", + " plt.ylabel(\"C_R(t)\")\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + "# Fit of the imaginary part with 1 exponent\n", + "popt2 = []\n", + "for i in range(1):\n", + " a, b = fitter(corrIana, tlist_fit, i + 1)\n", + " popt2.append((a, b))\n", + " y = fit_func(tlist_fit, a, b)\n", + " plt.plot(tlist_fit, corrIana, label=\"C_I(t)\")\n", + " plt.plot(tlist_fit, y, label=f\"Fit with k={i + 1}\")\n", + " plt.xlabel(\"t\")\n", + " plt.ylabel(\"C_I(t)\")\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "776e65b4", + "metadata": {}, + "outputs": [], + "source": [ + "# Set the exponential coefficients from the fit parameters\n", + "\n", + "ckAR = popt1[-1][0]\n", + "vkAR = -1 * popt1[-1][1]\n", + "\n", + "ckAI = popt2[-1][0]\n", + "vkAI = -1 * popt2[-1][1]\n", + "\n", + "# The imaginary fit can also be determined analytically and is\n", + "# a single term:\n", + "#\n", + "# ckAI = [complex(lam * gamma * (-1.0))]\n", + "# vkAI = [complex(gamma)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b86d9d7c", + "metadata": {}, + "outputs": [], + "source": [ + "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI)\n", + " HEOMFit = HEOMSolver(Hsys, bath, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " resultFit = HEOMFit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "9e66ecab", + "metadata": {}, + "source": [ + "## Analytic calculations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e41806a", + "metadata": {}, + "outputs": [], + "source": [ + "def pure_dephasing_evolution_analytical(tlist, wq, ck, vk):\n", + " \"\"\"\n", + " Computes the propagating function appearing in the pure dephasing model.\n", + "\n", + " Parameters\n", + " ----------\n", + " t: float\n", + " A float specifying the time at which to calculate the integral.\n", + "\n", + " wq: float\n", + " The qubit frequency in the Hamiltonian.\n", + "\n", + " ck: ndarray\n", + " The list of coefficients in the correlation function.\n", + "\n", + " vk: ndarray\n", + " The list of frequencies in the correlation function.\n", + "\n", + " Returns\n", + " -------\n", + " integral: float\n", + " The value of the integral function at time t.\n", + " \"\"\"\n", + " evolution = np.array([\n", + " np.exp(-1j * wq * t - correlation_integral(t, ck, vk))\n", + " for t in tlist\n", + " ])\n", + " return evolution\n", + "\n", + "\n", + "def correlation_integral(t, ck, vk):\n", + " r\"\"\"\n", + " Computes the integral sum function appearing in the pure dephasing model.\n", + "\n", + " If the correlation function is a sum of exponentials then this sum\n", + " is given by:\n", + "\n", + " .. math:\n", + "\n", + " \\int_0^{t}d\\tau D(\\tau) = \\sum_k\\frac{c_k}{\\mu_k^2}e^{\\mu_k t}\n", + " + \\frac{\\bar c_k}{\\bar \\mu_k^2}e^{\\bar \\mu_k t}\n", + " - \\frac{\\bar \\mu_k c_k + \\mu_k \\bar c_k}{\\mu_k \\bar \\mu_k} t\n", + " + \\frac{\\bar \\mu_k^2 c_k + \\mu_k^2 \\bar c_k}{\\mu_k^2 \\bar \\mu_k^2}\n", + "\n", + " Parameters\n", + " ----------\n", + " t: float\n", + " A float specifying the time at which to calculate the integral.\n", + "\n", + " ck: ndarray\n", + " The list of coefficients in the correlation function.\n", + "\n", + " vk: ndarray\n", + " The list of frequencies in the correlation function.\n", + "\n", + " Returns\n", + " -------\n", + " integral: float\n", + " The value of the integral function at time t.\n", + " \"\"\"\n", + " t1 = np.sum(\n", + " (ck / vk**2) *\n", + " (np.exp(vk * t) - 1)\n", + " )\n", + " t2 = np.sum(\n", + " (ck.conj() / vk.conj()**2) *\n", + " (np.exp(vk.conj() * t) - 1)\n", + " )\n", + " t3 = np.sum(\n", + " (ck / vk + ck.conj() / vk.conj()) * t\n", + " )\n", + " return 2 * (t1 + t2 - t3)" + ] + }, + { + "cell_type": "markdown", + "id": "27b44c6f", + "metadata": {}, + "source": [ + "For the pure dephasing analytics, we just sum up as many matsubara terms as we can:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0e43264", + "metadata": {}, + "outputs": [], + "source": [ + "lmaxmats2 = 15000\n", + "\n", + "vk = [complex(-gamma)]\n", + "vk.extend([\n", + " complex(-2. * np.pi * k * T)\n", + " for k in range(1, lmaxmats2)\n", + "])\n", + "\n", + "ck = [complex(lam * gamma * (-1.0j + cot(gamma * beta / 2.)))]\n", + "ck.extend([\n", + " complex(4 * lam * gamma * T * (-v) / (v**2 - gamma**2))\n", + " for v in vk[1:]\n", + "])\n", + "\n", + "P12_ana = 0.5 * pure_dephasing_evolution_analytical(\n", + " tlist, 0, np.asarray(ck), np.asarray(vk)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "bf34c387", + "metadata": {}, + "source": [ + "Alternatively, we can just do the integral of the propagator directly, without using the correlation functions at all" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7bc35d6e", + "metadata": {}, + "outputs": [], + "source": [ + "def JDL(omega, lamc, omega_c):\n", + " return 2. * lamc * omega * omega_c / (omega_c**2 + omega**2)\n", + "\n", + "\n", + "def integrand(omega, lamc, omega_c, Temp, t):\n", + " return (\n", + " (-4. * JDL(omega, lamc, omega_c) / omega**2) *\n", + " (1. - np.cos(omega*t)) * (coth(omega/(2.*Temp)))\n", + " / np.pi\n", + " )\n", + "\n", + "\n", + "P12_ana2 = [\n", + " 0.5 * np.exp(\n", + " scipy.integrate.quad(integrand, 0, np.inf, args=(lam, gamma, T, t))[0]\n", + " )\n", + " for t in tlist\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "a5815273", + "metadata": {}, + "source": [ + "## Compare results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1055882c", + "metadata": {}, + "outputs": [], + "source": [ + "plot_result_expectations([\n", + " (resultMats, P12p, 'r', \"P12 Mats\"),\n", + " (resultMatsT, P12p, 'r--', \"P12 Mats + Term\"),\n", + " (resultPade, P12p, 'b--', \"P12 Pade\"),\n", + " (resultFit, P12p, 'g', \"P12 Fit\"),\n", + " ((tlist, np.real(P12_ana)), None, 'b', \"Analytic 1\"),\n", + " ((tlist, np.real(P12_ana2)), None, 'y--', \"Analytic 2\"),\n", + "]);" + ] + }, + { + "cell_type": "markdown", + "id": "a3a9f9c9", + "metadata": {}, + "source": [ + "We can't see much difference in the plot above, so let's do a log plot instead:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58a297cb", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + "\n", + "plot_result_expectations([\n", + " (resultMats, P12p, 'r', \"P12 Mats\"),\n", + " (resultMatsT, P12p, 'r--', \"P12 Mats + Term\"),\n", + " (resultPade, P12p, 'b-.', \"P12 Pade\"),\n", + " (resultFit, P12p, 'g', \"P12 Fit\"),\n", + " ((tlist, np.real(P12_ana)), None, 'b', \"Analytic 1\"),\n", + " ((tlist, np.real(P12_ana2)), None, 'y--', \"Analytic 2\"),\n", + "], axes)\n", + "\n", + "axes.set_yscale('log')\n", + "axes.legend(loc=0, fontsize=12);" + ] + }, + { + "cell_type": "markdown", + "id": "dee86281", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41d04416", + "metadata": {}, + "outputs": [], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "4fcb665e", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "43b3c6d6", + "metadata": {}, + "outputs": [], + "source": [ + "assert np.allclose(\n", + " expect(P12p, resultMats.states[:15]), np.real(P12_ana)[:15],\n", + " rtol=1e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(P12p, resultMatsT.states[:100]), np.real(P12_ana)[:100],\n", + " rtol=1e-3,\n", + ")\n", + "assert np.allclose(\n", + " expect(P12p, resultPade.states[:100]), np.real(P12_ana)[:100],\n", + " rtol=1e-3,\n", + ")\n", + "assert np.allclose(\n", + " expect(P12p, resultFit.states[:50]), np.real(P12_ana)[:50],\n", + " rtol=1e-3,\n", + ")\n", + "assert np.allclose(P12_ana, P12_ana2, rtol=1e-3)" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v4/heom/heom-2-fmo-example.ipynb b/tutorials-v4/heom/heom-2-fmo-example.ipynb new file mode 100644 index 00000000..fc6e25d4 --- /dev/null +++ b/tutorials-v4/heom/heom-2-fmo-example.ipynb @@ -0,0 +1,694 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a07acd39", + "metadata": {}, + "source": [ + "# HEOM 2: Dynamics in Fenna-Mathews-Olsen complex (FMO)" + ] + }, + { + "cell_type": "markdown", + "id": "2f5528e3", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "In this example notebook we outline how to employ the HEOM to\n", + "solve the FMO photosynthetic complex dynamics.\n", + "\n", + "We aim to replicate the results in reference [https://www.pnas.org/content/106/41/17255](https://pubmed.ncbi.nlm.nih.gov/19815512/)\n", + "and compare them to a Bloch-Redfield (perturbative) solution.\n", + "\n", + "This demonstrates how to to employ the solver for multiple baths, as well as showing how a\n", + "quantum environment reduces the effect of pure dephasing." + ] + }, + { + "cell_type": "markdown", + "id": "5f294ac4", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e94ba14", + "metadata": {}, + "outputs": [], + "source": [ + "import contextlib\n", + "import time\n", + "\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " Options,\n", + " Qobj,\n", + " basis,\n", + " brmesolve,\n", + " expect,\n", + " liouvillian,\n", + " mesolve,\n", + ")\n", + "from qutip.nonmarkov.heom import (\n", + " HEOMSolver,\n", + " DrudeLorentzBath,\n", + ")\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "e0ccd522", + "metadata": {}, + "source": [ + "## Helper functions\n", + "\n", + "Let's define some helper functions for calculating correlation functions, spectral densities, thermal energy level occupations, and for plotting results and timing how long operations take:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0bcb8497", + "metadata": {}, + "outputs": [], + "source": [ + "def cot(x):\n", + " \"\"\" Vectorized cotangent of x. \"\"\"\n", + " return 1 / np.tan(x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "743463c9", + "metadata": {}, + "outputs": [], + "source": [ + "def J0(energy):\n", + " \"\"\" Under-damped brownian oscillator spectral density. \"\"\"\n", + " return 2 * lam * gamma * energy / (energy**2 + gamma**2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ffcb903", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def J0_dephasing():\n", + " \"\"\" Under-damped brownian oscillator dephasing probability.\n", + "\n", + " This returns the limit as w -> 0 of J0(w) * n_th(w, T) / T.\n", + " \"\"\"\n", + " return 2 * lam * gamma / gamma**2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c45d9a0", + "metadata": {}, + "outputs": [], + "source": [ + "def n_th(energy, T):\n", + " \"\"\" The average occupation of a given energy level at temperature T. \"\"\"\n", + " return 1 / (np.exp(energy / T) - 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "06c6958e", + "metadata": {}, + "outputs": [], + "source": [ + "def dl_corr_approx(t, nk):\n", + " \"\"\" Drude-Lorenz correlation function approximation.\n", + "\n", + " Approximates the correlation function at each time t to nk exponents.\n", + " \"\"\"\n", + " c = lam * gamma * (-1.0j + cot(gamma / (2 * T))) * np.exp(-gamma * t)\n", + " for k in range(1, nk):\n", + " vk = 2 * np.pi * k * T\n", + " c += (4 * lam * gamma * T * vk / (vk**2 - gamma**2)) * np.exp(-vk * t)\n", + " return c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12edcf9d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\" Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "markdown", + "id": "a0decbad", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian and bath parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19010fb3", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# System Hamiltonian:\n", + "#\n", + "# We use the Hamiltonian employed in\n", + "# https://www.pnas.org/content/106/41/17255 and operate\n", + "# in units of Hz:\n", + "\n", + "Hsys = 3e10 * 2 * np.pi * Qobj([\n", + " [200, -87.7, 5.5, -5.9, 6.7, -13.7, -9.9],\n", + " [-87.7, 320, 30.8, 8.2, 0.7, 11.8, 4.3],\n", + " [5.5, 30.8, 0, -53.5, -2.2, -9.6, 6.0],\n", + " [-5.9, 8.2, -53.5, 110, -70.7, -17.0, -63.3],\n", + " [6.7, 0.7, -2.2, -70.7, 270, 81.1, -1.3],\n", + " [-13.7, 11.8, -9.6, -17.0, 81.1, 420, 39.7],\n", + " [-9.9, 4.3, 6.0, -63.3, -1.3, 39.7, 230],\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e821b62", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Bath parameters\n", + "\n", + "lam = 35 * 3e10 * 2 * np.pi\n", + "gamma = 1 / 166e-15\n", + "T = 300 * 0.6949 * 3e10 * 2 * np.pi\n", + "beta = 1 / T" + ] + }, + { + "cell_type": "markdown", + "id": "499bcfc1", + "metadata": {}, + "source": [ + "## Plotting the environment spectral density and correlation functions\n", + "\n", + "Let's quickly plot the spectral density and environment correlation functions so that we can see what they look like." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "518c069c", + "metadata": {}, + "outputs": [], + "source": [ + "wlist = np.linspace(0, 200 * 3e10 * 2 * np.pi, 100)\n", + "tlist = np.linspace(0, 1e-12, 1000)\n", + "\n", + "J = J0(wlist) / (3e10*2*np.pi)\n", + "\n", + "fig, axes = plt.subplots(1, 2, sharex=False, figsize=(10, 3))\n", + "\n", + "fig.subplots_adjust(hspace=0.1) # reduce space between plots\n", + "\n", + "# Spectral density plot:\n", + "\n", + "axes[0].plot(wlist / (3e10 * 2 * np.pi), J, color='r', ls='--', label=\"J(w)\")\n", + "axes[0].set_xlabel(r'$\\omega$ (cm$^{-1}$)', fontsize=20)\n", + "axes[0].set_ylabel(r\"$J(\\omega)$ (cm$^{-1}$)\", fontsize=16)\n", + "axes[0].legend()\n", + "\n", + "# Correlation plot:\n", + "\n", + "axes[1].plot(\n", + " tlist, np.real(dl_corr_approx(tlist, 10)),\n", + " color='r', ls='--', label=\"C(t) real\",\n", + ")\n", + "axes[1].plot(\n", + " tlist, np.imag(dl_corr_approx(tlist, 10)),\n", + " color='g', ls='--', label=\"C(t) imaginary\",\n", + ")\n", + "axes[1].set_xlabel(r'$t$', fontsize=20)\n", + "axes[1].set_ylabel(r\"$C(t)$\", fontsize=16)\n", + "axes[1].legend();" + ] + }, + { + "cell_type": "markdown", + "id": "deeae0da", + "metadata": {}, + "source": [ + "## Solve for the dynamics with the HEOM\n", + "\n", + "Now let us solve for the evolution of this system using the HEOM." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c9b2c17", + "metadata": {}, + "outputs": [], + "source": [ + "# We start the excitation at site 1:\n", + "rho0 = basis(7, 0) * basis(7, 0).dag()\n", + "\n", + "# HEOM solver options:\n", + "#\n", + "# Note: We set Nk=0 (i.e. a single correlation expansion term\n", + "# per bath) and rely on the terminator to correct detailed\n", + "# balance.\n", + "options = Options(nsteps=15000, store_states=True)\n", + "NC = 4 # Use NC=8 for more precise results\n", + "Nk = 0\n", + "\n", + "Q_list = []\n", + "baths = []\n", + "Ltot = liouvillian(Hsys)\n", + "for m in range(7):\n", + " Q = basis(7, m) * basis(7, m).dag()\n", + " Q_list.append(Q)\n", + " baths.append(\n", + " DrudeLorentzBath(\n", + " Q, lam=lam, gamma=gamma, T=T, Nk=Nk,\n", + " tag=str(m)\n", + " )\n", + " )\n", + " _, terminator = baths[-1].terminator()\n", + " Ltot += terminator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3ed340a", + "metadata": {}, + "outputs": [], + "source": [ + "with timer(\"RHS construction time\"):\n", + " HEOMMats = HEOMSolver(Hsys, baths, NC, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " outputFMO_HEOM = HEOMMats.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59cc2e76", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", + "\n", + "colors = ['r', 'g', 'b', 'y', 'c', 'm', 'k']\n", + "linestyles = [\n", + " '-', '--', ':', '-.',\n", + " (0, (1, 10)), (0, (5, 10)), (0, (3, 10, 1, 10)),\n", + "]\n", + "\n", + "for m in range(7):\n", + " Q = basis(7, m) * basis(7, m).dag()\n", + " axes.plot(\n", + " np.array(tlist) * 1e15,\n", + " np.real(expect(outputFMO_HEOM.states, Q)),\n", + " label=m + 1,\n", + " color=colors[m % len(colors)],\n", + " linestyle=linestyles[m % len(linestyles)],\n", + " )\n", + " axes.set_xlabel(r'$t$ (fs)', fontsize=30)\n", + " axes.set_ylabel(r\"Population\", fontsize=30)\n", + " axes.locator_params(axis='y', nbins=6)\n", + " axes.locator_params(axis='x', nbins=6)\n", + "\n", + "axes.set_title('HEOM solution', fontsize=24)\n", + "axes.legend(loc=0)\n", + "axes.set_xlim(0, 1000)\n", + "plt.yticks([0., 0.5, 1], [0, 0.5, 1])\n", + "plt.xticks([0., 500, 1000], [0, 500, 1000]);" + ] + }, + { + "cell_type": "markdown", + "id": "eadf54f8", + "metadata": {}, + "source": [ + "## Comparison with Bloch-Redfield solver\n", + "\n", + "Now let us solve the same problem using the Bloch-Redfield solver. We will see that the Bloch-Redfield technique fails to model the oscillation of population of the states that we saw in the HEOM.\n", + "\n", + "In the next section, we will examine the role of pure dephasing in the evolution to understand why this happens." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "724b57ab", + "metadata": {}, + "outputs": [], + "source": [ + "DL = (\n", + " f\"2 * pi * 2.0 * {lam} / (pi * {gamma} * {beta}) if (w == 0) else \"\n", + " f\"2 * pi * (2.0*{lam}*{gamma} *w /(pi*(w**2+{gamma}**2))) * \"\n", + " f\"((1 / (exp((w) * {beta}) - 1)) + 1)\"\n", + ")\n", + "\n", + "optionsBR = Options(nsteps=15000, store_states=True, rtol=1e-12, atol=1e-12)\n", + "\n", + "with timer(\"BR ODE solver time\"):\n", + " outputFMO_BR = brmesolve(\n", + " Hsys, rho0, tlist,\n", + " a_ops=[[Q, DL] for Q in Q_list],\n", + " options=optionsBR,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "aeb8936a", + "metadata": {}, + "source": [ + "And now let's plot the Bloch-Redfield solver results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2958e3c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", + "\n", + "for m, Q in enumerate(Q_list):\n", + " axes.plot(tlist * 1e15, expect(outputFMO_BR.states, Q), label=m + 1)\n", + "\n", + "axes.set_xlabel(r'$t$ (fs)', fontsize=30)\n", + "axes.set_ylabel(r\"Population\", fontsize=30)\n", + "\n", + "axes.set_title('Bloch-Redfield solution ', fontsize=24)\n", + "axes.legend()\n", + "axes.set_xlim(0, 1000)\n", + "plt.yticks([0, 0.5, 1], [0, 0.5, 1])\n", + "plt.xticks([0, 500, 1000], [0, 500, 1000]);" + ] + }, + { + "cell_type": "markdown", + "id": "ef77f184", + "metadata": {}, + "source": [ + "Notice how the oscillations are gone and the populations decay much more rapidly.\n", + "\n", + "Next let us try to understand why." + ] + }, + { + "cell_type": "markdown", + "id": "3149fb03", + "metadata": {}, + "source": [ + "## Role of pure dephasing\n", + "\n", + "It is useful to construct the various parts of the Bloch-Redfield master equation explicitly and to solve them using the Master equation solver, `mesolve`. We will do so and show that it is the pure-dephasing terms which suppresses coherence in these oscillations.\n", + "\n", + "First we will write a function to return the list of collapse operators for a given system, either with or without the dephasing operators:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6df69b3b", + "metadata": {}, + "outputs": [], + "source": [ + "def get_collapse(H, T, dephasing=1):\n", + " \"\"\" Calculate collapse operators for a given system H and\n", + " temperature T.\n", + " \"\"\"\n", + " all_energy, all_state = H.eigenstates(sort=\"low\")\n", + " Nmax = len(all_energy)\n", + "\n", + " Q_list = [\n", + " basis(Nmax, n) * basis(Nmax, n).dag()\n", + " for n in range(Nmax)\n", + " ]\n", + "\n", + " collapse_list = []\n", + "\n", + " for Q in Q_list:\n", + " for j in range(Nmax):\n", + " for k in range(j + 1, Nmax):\n", + " Deltajk = abs(all_energy[k] - all_energy[j])\n", + " if abs(Deltajk) > 0:\n", + " rate = (\n", + " np.abs(Q.matrix_element(\n", + " all_state[j].dag(), all_state[k]\n", + " ))**2 *\n", + " 2 * J0(Deltajk) * (n_th(Deltajk, T) + 1)\n", + " )\n", + " if rate > 0.0:\n", + " # emission:\n", + " collapse_list.append(\n", + " np.sqrt(rate) * all_state[j] * all_state[k].dag()\n", + " )\n", + "\n", + " rate = (\n", + " np.abs(Q.matrix_element(\n", + " all_state[k].dag(), all_state[j]\n", + " ))**2 *\n", + " 2 * J0(Deltajk) * n_th(Deltajk, T)\n", + " )\n", + " if rate > 0.0:\n", + " # absorption:\n", + " collapse_list.append(\n", + " np.sqrt(rate) * all_state[k] * all_state[j].dag()\n", + " )\n", + "\n", + " if dephasing:\n", + " for j in range(Nmax):\n", + " rate = (\n", + " np.abs(Q.matrix_element(\n", + " all_state[j].dag(), all_state[j])\n", + " )**2 *\n", + " J0_dephasing() * T\n", + " )\n", + " if rate > 0.0:\n", + " # emission:\n", + " collapse_list.append(\n", + " np.sqrt(rate) * all_state[j] * all_state[j].dag()\n", + " )\n", + "\n", + " return collapse_list" + ] + }, + { + "cell_type": "markdown", + "id": "4a1bea16", + "metadata": {}, + "source": [ + "Now we are able to switch the pure dephasing tersms on and off.\n", + "\n", + "Let us starting by including the dephasing operators. We expect to see the same behaviour that we saw when using the Bloch-Redfield solver." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "435bac85", + "metadata": {}, + "outputs": [], + "source": [ + "# dephasing terms on, we recover the full BR solution:\n", + "\n", + "with timer(\"Building the collapse operators\"):\n", + " collapse_list = get_collapse(Hsys, T=T, dephasing=True)\n", + "\n", + "with timer(\"ME ODE solver\"):\n", + " outputFMO_ME = mesolve(Hsys, rho0, tlist, collapse_list)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b53e5ebb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", + "\n", + "for m, Q in enumerate(Q_list):\n", + " axes.plot(tlist * 1e15, expect(outputFMO_ME.states, Q), label=m + 1)\n", + "\n", + "axes.set_xlabel(r'$t$', fontsize=20)\n", + "axes.set_ylabel(r\"Population\", fontsize=16)\n", + "axes.set_xlim(0, 1000)\n", + "axes.set_title('With pure dephasing', fontsize=24)\n", + "plt.yticks([0, 0.5, 1], [0, 0.5, 1])\n", + "plt.xticks([0, 500, 1000], [0, 500, 1000])\n", + "axes.legend(fontsize=18);" + ] + }, + { + "cell_type": "markdown", + "id": "fe1188ce", + "metadata": {}, + "source": [ + "We see similar results to before.\n", + "\n", + "Now let us examine what happens when we remove the dephasing collapse operators:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4286f57c", + "metadata": {}, + "outputs": [], + "source": [ + "# dephasing terms off\n", + "\n", + "with timer(\"Building the collapse operators\"):\n", + " collapse_list = get_collapse(Hsys, T, dephasing=False)\n", + "\n", + "with timer(\"ME ODE solver\"):\n", + " outputFMO_ME_nodephase = mesolve(Hsys, rho0, tlist, collapse_list)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ed3a952", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", + "for m, Q in enumerate(Q_list):\n", + " axes.plot(\n", + " tlist * 1e15,\n", + " expect(outputFMO_ME_nodephase.states, Q),\n", + " label=m + 1,\n", + " )\n", + "\n", + "axes.set_xlabel(r'$t$', fontsize=20)\n", + "axes.set_ylabel(r\"Population\", fontsize=16)\n", + "axes.set_xlim(0, 1000)\n", + "axes.set_title('Without pure dephasing', fontsize=24)\n", + "plt.yticks([0, 0.5, 1], [0, 0.5, 1])\n", + "plt.xticks([0, 500, 1000], [0, 500, 1000])\n", + "axes.legend(fontsize=18);" + ] + }, + { + "cell_type": "markdown", + "id": "e6394ed6", + "metadata": {}, + "source": [ + "And now we see that without the dephasing, the oscillations reappear. The full dynamics capture by the HEOM are still not capture by this simpler model, however." + ] + }, + { + "cell_type": "markdown", + "id": "3c76cc7d", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c433cd8", + "metadata": {}, + "outputs": [], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "3ca855fc", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9169387f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "assert np.allclose(\n", + " expect(outputFMO_BR.states, Q_list[0]),\n", + " expect(outputFMO_ME.states, Q_list[0]),\n", + " rtol=2e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(outputFMO_BR.states, Q_list[1]),\n", + " expect(outputFMO_ME.states, Q_list[1]),\n", + " rtol=2e-2,\n", + ")" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v4/heom/heom-3-quantum-heat-transport.ipynb b/tutorials-v4/heom/heom-3-quantum-heat-transport.ipynb new file mode 100644 index 00000000..2fc459dd --- /dev/null +++ b/tutorials-v4/heom/heom-3-quantum-heat-transport.ipynb @@ -0,0 +1,669 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cb8620ab", + "metadata": {}, + "source": [ + "# HEOM 3: Quantum Heat Transport" + ] + }, + { + "cell_type": "markdown", + "id": "17a1854e", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "In this notebook, we apply the QuTiP HEOM solver to a quantum system coupled to two bosonic baths and demonstrate how to extract information about the system-bath heat currents from the auxiliary density operators (ADOs).\n", + "We consider the setup described in Ref. \\[1\\], which consists of two coupled qubits, each connected to its own heat bath.\n", + "The Hamiltonian of the qubits is given by\n", + "\n", + "$$ \\begin{aligned} H_{\\text{S}} &= H_1 + H_2 + H_{12} , \\quad\\text{ where }\\\\\n", + "H_K &= \\frac{\\epsilon}{2} \\bigl(\\sigma_z^K + 1\\bigr) \\quad (K=1,2) \\quad\\text{ and }\\quad H_{12} = J_{12} \\bigl( \\sigma_+^1 \\sigma_-^2 + \\sigma_-^1 \\sigma_+^2 \\bigr) . \\end{aligned} $$\n", + "\n", + "Here, $\\sigma^K_{x,y,z,\\pm}$ denotes the usual Pauli matrices for the K-th qubit, $\\epsilon$ is the eigenfrequency of the qubits and $J_{12}$ the coupling constant.\n", + "\n", + "Each qubit is coupled to its own bath; therefore, the total Hamiltonian is\n", + "\n", + "$$ H_{\\text{tot}} = H_{\\text{S}} + \\sum_{K=1,2} \\bigl( H_{\\text{B}}^K + Q_K \\otimes X_{\\text{B}}^K \\bigr) , $$\n", + "\n", + "where $H_{\\text{B}}^K$ is the free Hamiltonian of the K-th bath and $X_{\\text{B}}^K$ its coupling operator, and $Q_K = \\sigma_x^K$ are the system coupling operators.\n", + "We assume that the bath spectral densities are given by Drude distributions\n", + "\n", + "$$ J_K(\\omega) = \\frac{2 \\lambda_K \\gamma_K \\omega}{\\omega^2 + \\gamma_K^2} , $$\n", + "\n", + "where $\\lambda_K$ is the free coupling strength and $\\gamma_K$ the cutoff frequency.\n", + "\n", + "We begin by defining the system and bath parameters.\n", + "We use the parameter values from Fig. 3(a) of Ref. \\[1\\].\n", + "Note that we set $\\hbar$ and $k_B$ to one and we will measure all frequencies and energies in units of $\\epsilon$.\n", + "\n", + "References:\n", + "\n", + "   \\[1\\] Kato and Tanimura, [J. Chem. Phys. **143**, 064107](https://doi.org/10.1063/1.4928192) (2015)." + ] + }, + { + "cell_type": "markdown", + "id": "f3950292", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "017d2a21", + "metadata": {}, + "outputs": [], + "source": [ + "import dataclasses\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import qutip as qt\n", + "from qutip.nonmarkov.heom import (\n", + " DrudeLorentzPadeBath,\n", + " BathExponent,\n", + " HEOMSolver,\n", + ")\n", + "\n", + "from ipywidgets import IntProgress\n", + "from IPython.display import display\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "6882aea2", + "metadata": {}, + "source": [ + "## System and bath definition" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84bd8f0c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "@dataclasses.dataclass\n", + "class SystemParams:\n", + " \"\"\" System parameters and Hamiltonian. \"\"\"\n", + " epsilon: float = 1.0\n", + " J12: float = 0.1\n", + "\n", + " def H(self):\n", + " \"\"\" Return the Hamiltonian for the system.\n", + "\n", + " The system consists of two qubits with Hamiltonians (H1 and H2)\n", + " and an interaction term (H12).\n", + " \"\"\"\n", + " H1 = self.epsilon / 2 * (\n", + " qt.tensor(qt.sigmaz() + qt.identity(2), qt.identity(2))\n", + " )\n", + " H2 = self.epsilon / 2 * (\n", + " qt.tensor(qt.identity(2), qt.sigmaz() + qt.identity(2))\n", + " )\n", + " H12 = self.J12 * (\n", + " qt.tensor(qt.sigmap(), qt.sigmam()) +\n", + " qt.tensor(qt.sigmam(), qt.sigmap())\n", + " )\n", + " return H1 + H2 + H12\n", + "\n", + " def replace(self, **kw):\n", + " return dataclasses.replace(self, **kw)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32da141e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "@dataclasses.dataclass\n", + "class BathParams:\n", + " \"\"\" Bath parameters. \"\"\"\n", + " sign: str # + or -\n", + " qubit: int # 0 or 1\n", + "\n", + " gamma: float = 2.0\n", + " lam: float = 0.05\n", + " Tbar: float = 2.0\n", + " Tdelta: float = 0.01\n", + "\n", + " def __post_init__(self):\n", + " # T = Tbar +- Tdelta * Tbar:\n", + " assert self.sign in (\"+\", \"-\")\n", + " sign = +1 if self.sign == \"+\" else -1\n", + " self.T = self.Tbar + sign * self.Tdelta * self.Tbar\n", + " # qubit\n", + " assert self.qubit in (0, 1)\n", + "\n", + " def Q(self):\n", + " \"\"\" Coupling operator for the bath. \"\"\"\n", + " Q = [qt.identity(2), qt.identity(2)]\n", + " Q[self.qubit] = qt.sigmax()\n", + " return qt.tensor(Q)\n", + "\n", + " def bath(self, Nk, tag=None):\n", + " return DrudeLorentzPadeBath(\n", + " self.Q(), self.lam, self.gamma, self.T, Nk, tag=tag\n", + " )\n", + "\n", + " def replace(self, **kw):\n", + " return dataclasses.replace(self, **kw)" + ] + }, + { + "cell_type": "markdown", + "id": "fb4ea3ec", + "metadata": {}, + "source": [ + "## Heat currents\n", + "\n", + "Following Ref. \\[2\\], we consider two possible definitions of the heat currents from the qubits into the baths.\n", + "The so-called bath heat currents are $j_{\\text{B}}^K = \\partial_t \\langle H_{\\text{B}}^K \\rangle$ and the system heat currents are $j_{\\text{S}}^K = \\mathrm i\\, \\langle [H_{\\text{S}}, Q_K] X_{\\text{B}}^K \\rangle$.\n", + "As shown in Ref. \\[2\\], they can be expressed in terms of the HEOM ADOs as follows:\n", + "$$ \\begin{aligned} \\mbox{} \\\\\n", + " j_{\\text{B}}^K &= \\!\\!\\sum_{\\substack{\\mathbf n\\\\ \\text{Level 1}\\\\ \\text{Bath $K$}}}\\!\\! \\nu[\\mathbf n] \\operatorname{tr}\\bigl[ Q_K \\rho_{\\mathbf n} \\bigr] - 2 C_I^K(0) \\operatorname{tr}\\bigl[ Q_k^2 \\rho \\bigr] + \\Gamma_{\\text{T}}^K \\operatorname{tr}\\bigl[ [[H_{\\text{S}}, Q_K], Q_K]\\, \\rho \\bigr] , \\\\[.5em]\n", + " j_{\\text{S}}^K &= \\mathrm i\\!\\! \\sum_{\\substack{\\mathbf n\\\\ \\text{Level 1}\\\\ \\text{Bath $k$}}}\\!\\! \\operatorname{tr}\\bigl[ [H_{\\text{S}}, Q_K]\\, \\rho_{\\mathbf n} \\bigr] + \\Gamma_{\\text{T}}^K \\operatorname{tr}\\bigl[ [[H_{\\text{S}}, Q_K], Q_K]\\, \\rho \\bigr] . \\\\ \\mbox{}\n", + "\\end{aligned} $$\n", + "The sums run over all level-$1$ multi-indices $\\mathbf n$ with one excitation corresponding to the K-th bath, $\\nu[\\mathbf n]$ is the corresponding (negative) exponent of the bath auto-correlation function $C^K(t)$, and $\\Gamma_{\\text{T}}^K$ is the Ishizaki-Tanimura terminator (i.e., a correction term accounting for the error introduced by approximating the correlation function with a finite sum of exponential terms).\n", + "In the expression for the bath heat currents, we left out terms involving $[Q_1, Q_2]$, which is zero in this example.\n", + "\n", + "   \\[2\\] Kato and Tanimura, [J. Chem. Phys. **145**, 224105](https://doi.org/10.1063/1.4971370) (2016)." + ] + }, + { + "cell_type": "markdown", + "id": "9c3a3965", + "metadata": {}, + "source": [ + "In QuTiP, these currents can be conveniently calculated as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "976e5e10", + "metadata": {}, + "outputs": [], + "source": [ + "def bath_heat_current(bath_tag, ado_state, hamiltonian, coupling_op, delta=0):\n", + " \"\"\"\n", + " Bath heat current from the system into the heat bath with the given tag.\n", + "\n", + " Parameters\n", + " ----------\n", + " bath_tag : str, tuple or any other object\n", + " Tag of the heat bath corresponding to the current of interest.\n", + "\n", + " ado_state : HierarchyADOsState\n", + " Current state of the system and the environment (encoded in the ADOs).\n", + "\n", + " hamiltonian : Qobj\n", + " System Hamiltonian at the current time.\n", + "\n", + " coupling_op : Qobj\n", + " System coupling operator at the current time.\n", + "\n", + " delta : float\n", + " The prefactor of the \\\\delta(t) term in the correlation function (the\n", + " Ishizaki-Tanimura terminator).\n", + " \"\"\"\n", + " l1_labels = ado_state.filter(level=1, tags=[bath_tag])\n", + " a_op = 1j * (hamiltonian * coupling_op - coupling_op * hamiltonian)\n", + "\n", + " result = 0\n", + " cI0 = 0 # imaginary part of bath auto-correlation function (t=0)\n", + " for label in l1_labels:\n", + " [exp] = ado_state.exps(label)\n", + " result += exp.vk * (coupling_op * ado_state.extract(label)).tr()\n", + "\n", + " if exp.type == BathExponent.types['I']:\n", + " cI0 += exp.ck\n", + " elif exp.type == BathExponent.types['RI']:\n", + " cI0 += exp.ck2\n", + "\n", + " result -= 2 * cI0 * (coupling_op * coupling_op * ado_state.rho).tr()\n", + " if delta != 0:\n", + " result -= (\n", + " 1j * delta *\n", + " ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr()\n", + " )\n", + " return result\n", + "\n", + "\n", + "def system_heat_current(\n", + " bath_tag, ado_state, hamiltonian, coupling_op, delta=0,\n", + "):\n", + " \"\"\"\n", + " System heat current from the system into the heat bath with the given tag.\n", + "\n", + " Parameters\n", + " ----------\n", + " bath_tag : str, tuple or any other object\n", + " Tag of the heat bath corresponding to the current of interest.\n", + "\n", + " ado_state : HierarchyADOsState\n", + " Current state of the system and the environment (encoded in the ADOs).\n", + "\n", + " hamiltonian : Qobj\n", + " System Hamiltonian at the current time.\n", + "\n", + " coupling_op : Qobj\n", + " System coupling operator at the current time.\n", + "\n", + " delta : float\n", + " The prefactor of the \\\\delta(t) term in the correlation function (the\n", + " Ishizaki-Tanimura terminator).\n", + " \"\"\"\n", + " l1_labels = ado_state.filter(level=1, tags=[bath_tag])\n", + " a_op = 1j * (hamiltonian * coupling_op - coupling_op * hamiltonian)\n", + "\n", + " result = 0\n", + " for label in l1_labels:\n", + " result += (a_op * ado_state.extract(label)).tr()\n", + "\n", + " if delta != 0:\n", + " result -= (\n", + " 1j * delta *\n", + " ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr()\n", + " )\n", + " return result" + ] + }, + { + "cell_type": "markdown", + "id": "eb9d46bf", + "metadata": {}, + "source": [ + "Note that at long times, we expect $j_{\\text{B}}^1 = -j_{\\text{B}}^2$ and $j_{\\text{S}}^1 = -j_{\\text{S}}^2$ due to energy conservation. At long times, we also expect $j_{\\text{B}}^1 = j_{\\text{S}}^1$ and $j_{\\text{B}}^2 = j_{\\text{S}}^2$ since the coupling operators commute, $[Q_1, Q_2] = 0$. Hence, all four currents should agree in the long-time limit (up to a sign). This long-time value is what was analyzed in Ref. \\[2\\]." + ] + }, + { + "cell_type": "markdown", + "id": "412e66a5", + "metadata": {}, + "source": [ + "## Simulations" + ] + }, + { + "cell_type": "markdown", + "id": "9cc415ca", + "metadata": {}, + "source": [ + "For our simulations, we will represent the bath spectral densities using the first term of their Padé decompositions, and we will use $7$ levels of the HEOM hierarchy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f9bcbf7", + "metadata": {}, + "outputs": [], + "source": [ + "Nk = 1\n", + "NC = 7\n", + "options = qt.Options(nsteps=1500, store_states=False, atol=1e-12, rtol=1e-12)" + ] + }, + { + "cell_type": "markdown", + "id": "3cc1e2ad", + "metadata": {}, + "source": [ + "### Time Evolution\n", + "\n", + "We fix $J_{12} = 0.1 \\epsilon$ (as in Fig. 3(a-ii) of Ref. \\[2\\]) and choose the fixed coupling strength $\\lambda_1 = \\lambda_2 = J_{12}\\, /\\, (2\\epsilon)$ (corresponding to $\\bar\\zeta = 1$ in Ref. \\[2\\]).\n", + "Using these values, we will study the time evolution of the system state and the heat currents." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7251025", + "metadata": {}, + "outputs": [], + "source": [ + "# fix qubit-qubit and qubit-bath coupling strengths\n", + "sys = SystemParams(J12=0.1)\n", + "bath_p1 = BathParams(qubit=0, sign=\"+\", lam=sys.J12 / 2)\n", + "bath_p2 = BathParams(qubit=1, sign=\"-\", lam=sys.J12 / 2)\n", + "\n", + "# choose arbitrary initial state\n", + "rho0 = qt.tensor(qt.identity(2), qt.identity(2)) / 4\n", + "\n", + "# simulation time span\n", + "tlist = np.linspace(0, 50, 250)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc87c816", + "metadata": {}, + "outputs": [], + "source": [ + "H = sys.H()\n", + "\n", + "bath1 = bath_p1.bath(Nk, tag='bath 1')\n", + "Q1 = bath_p1.Q()\n", + "\n", + "bath2 = bath_p2.bath(Nk, tag='bath 2')\n", + "Q2 = bath_p2.Q()\n", + "\n", + "b1delta, b1term = bath1.terminator()\n", + "b2delta, b2term = bath2.terminator()\n", + "solver = HEOMSolver(\n", + " qt.liouvillian(H) + b1term + b2term,\n", + " [bath1, bath2],\n", + " max_depth=NC,\n", + " options=options,\n", + ")\n", + "\n", + "result = solver.run(rho0, tlist, e_ops=[\n", + " qt.tensor(qt.sigmaz(), qt.identity(2)),\n", + " lambda t, ado: bath_heat_current('bath 1', ado, H, Q1, b1delta),\n", + " lambda t, ado: bath_heat_current('bath 2', ado, H, Q2, b2delta),\n", + " lambda t, ado: system_heat_current('bath 1', ado, H, Q1, b1delta),\n", + " lambda t, ado: system_heat_current('bath 2', ado, H, Q2, b2delta),\n", + "])" + ] + }, + { + "cell_type": "markdown", + "id": "aebe1d23", + "metadata": {}, + "source": [ + "We first plot $\\langle \\sigma_z^1 \\rangle$ to see the time evolution of the system state:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bacd11b9", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(figsize=(8, 8))\n", + "axes.plot(tlist, result.expect[0], 'r', linewidth=2)\n", + "axes.set_xlabel('t', fontsize=28)\n", + "axes.set_ylabel(r\"$\\langle \\sigma_z^1 \\rangle$\", fontsize=28);" + ] + }, + { + "cell_type": "markdown", + "id": "27b4721f", + "metadata": {}, + "source": [ + "We find a rather quick thermalization of the system state. For the heat currents, however, it takes a somewhat longer time until they converge to their long-time values:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68b10993", + "metadata": {}, + "outputs": [], + "source": [ + "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8))\n", + "\n", + "ax1.plot(\n", + " tlist, -np.real(result.expect[1]),\n", + " color='darkorange', label='BHC (bath 1 -> system)',\n", + ")\n", + "ax1.plot(\n", + " tlist, np.real(result.expect[2]),\n", + " '--', color='darkorange', label='BHC (system -> bath 2)',\n", + ")\n", + "ax1.plot(\n", + " tlist, -np.real(result.expect[3]),\n", + " color='dodgerblue', label='SHC (bath 1 -> system)',\n", + ")\n", + "ax1.plot(\n", + " tlist, np.real(result.expect[4]),\n", + " '--', color='dodgerblue', label='SHC (system -> bath 2)',\n", + ")\n", + "\n", + "ax1.set_xlabel('t', fontsize=28)\n", + "ax1.set_ylabel('j', fontsize=28)\n", + "ax1.set_ylim((-0.05, 0.05))\n", + "ax1.legend(loc=0, fontsize=12)\n", + "\n", + "ax2.plot(\n", + " tlist, -np.real(result.expect[1]),\n", + " color='darkorange', label='BHC (bath 1 -> system)',\n", + ")\n", + "ax2.plot(\n", + " tlist, np.real(result.expect[2]),\n", + " '--', color='darkorange', label='BHC (system -> bath 2)',\n", + ")\n", + "ax2.plot(\n", + " tlist, -np.real(result.expect[3]),\n", + " color='dodgerblue', label='SHC (bath 1 -> system)',\n", + ")\n", + "ax2.plot(\n", + " tlist, np.real(result.expect[4]),\n", + " '--', color='dodgerblue', label='SHC (system -> bath 2)',\n", + ")\n", + "\n", + "ax2.set_xlabel('t', fontsize=28)\n", + "ax2.set_xlim((20, 50))\n", + "ax2.set_ylim((0, 0.0002))\n", + "ax2.legend(loc=0, fontsize=12);" + ] + }, + { + "cell_type": "markdown", + "id": "c1ac9397", + "metadata": {}, + "source": [ + "### Steady-state currents\n", + "\n", + "Here, we try to reproduce the HEOM curves in Fig. 3(a) of Ref. \\[1\\] by varying the coupling strength and finding the steady state for each coupling strength." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae61f3f3", + "metadata": {}, + "outputs": [], + "source": [ + "def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options):\n", + " \"\"\" Calculate the steady sate heat currents for the given system and\n", + " bath.\n", + " \"\"\"\n", + " bath1 = bath_p1.bath(Nk, tag=\"bath 1\")\n", + " Q1 = bath_p1.Q()\n", + "\n", + " bath2 = bath_p2.bath(Nk, tag=\"bath 2\")\n", + " Q2 = bath_p2.Q()\n", + "\n", + " b1delta, b1term = bath1.terminator()\n", + " b2delta, b2term = bath2.terminator()\n", + "\n", + " solver = HEOMSolver(\n", + " qt.liouvillian(sys.H()) + b1term + b2term,\n", + " [bath1, bath2],\n", + " max_depth=NC,\n", + " options=options\n", + " )\n", + "\n", + " _, steady_ados = solver.steady_state()\n", + "\n", + " return (\n", + " bath_heat_current('bath 1', steady_ados, sys.H(), Q1, b1delta),\n", + " bath_heat_current('bath 2', steady_ados, sys.H(), Q2, b2delta),\n", + " system_heat_current('bath 1', steady_ados, sys.H(), Q1, b1delta),\n", + " system_heat_current('bath 2', steady_ados, sys.H(), Q2, b2delta),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9e7dabd", + "metadata": {}, + "outputs": [], + "source": [ + "# Define number of points to use for the plot\n", + "plot_points = 10 # use 100 for a smoother curve\n", + "\n", + "# Range of relative coupling strengths\n", + "# Chosen so that zb_max is maximum, centered around 1 on a log scale\n", + "zb_max = 4 # use 20 to see more of the current curve\n", + "zeta_bars = np.logspace(\n", + " -np.log(zb_max),\n", + " np.log(zb_max),\n", + " plot_points,\n", + " base=np.e,\n", + ")\n", + "\n", + "# Setup a progress bar\n", + "progress = IntProgress(min=0, max=(3 * plot_points))\n", + "display(progress)\n", + "\n", + "\n", + "def calculate_heat_current(J12, zb, Nk, progress=progress):\n", + " \"\"\" Calculate a single heat current and update the progress bar. \"\"\"\n", + " # Estimate appropriate HEOM max_depth from coupling strength\n", + " NC = 7 + int(max(zb * J12 - 1, 0) * 2)\n", + " NC = min(NC, 20)\n", + " # the four currents are identical in the steady state\n", + " j, _, _, _ = heat_currents(\n", + " sys.replace(J12=J12),\n", + " bath_p1.replace(lam=zb * J12 / 2),\n", + " bath_p2.replace(lam=zb * J12 / 2),\n", + " Nk, NC, options=options,\n", + " )\n", + " progress.value += 1\n", + " return j\n", + "\n", + "\n", + "# Calculate steady state currents for range of zeta_bars\n", + "# for J12 = 0.01, 0.1 and 0.5:\n", + "j1s = [\n", + " calculate_heat_current(0.01, zb, Nk)\n", + " for zb in zeta_bars\n", + "]\n", + "j2s = [\n", + " calculate_heat_current(0.1, zb, Nk)\n", + " for zb in zeta_bars\n", + "]\n", + "j3s = [\n", + " calculate_heat_current(0.5, zb, Nk)\n", + " for zb in zeta_bars\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "ba6aa94e", + "metadata": {}, + "source": [ + "## Create Plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae143732", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(figsize=(12, 7))\n", + "\n", + "axes.plot(\n", + " zeta_bars, -1000 * 100 * np.real(j1s),\n", + " 'b', linewidth=2, label=r\"$J_{12} = 0.01\\, \\epsilon$\",\n", + ")\n", + "axes.plot(\n", + " zeta_bars, -1000 * 10 * np.real(j2s),\n", + " 'r--', linewidth=2, label=r\"$J_{12} = 0.1\\, \\epsilon$\",\n", + ")\n", + "axes.plot(\n", + " zeta_bars, -1000 * 2 * np.real(j3s),\n", + " 'g-.', linewidth=2, label=r\"$J_{12} = 0.5\\, \\epsilon$\",\n", + ")\n", + "\n", + "axes.set_xscale('log')\n", + "axes.set_xlabel(r\"$\\bar\\zeta$\", fontsize=30)\n", + "axes.set_xlim((zeta_bars[0], zeta_bars[-1]))\n", + "\n", + "axes.set_ylabel(\n", + " r\"$j_{\\mathrm{ss}}\\; /\\; (\\epsilon J_{12}) \\times 10^3$\",\n", + " fontsize=30,\n", + ")\n", + "axes.set_ylim((0, 2))\n", + "\n", + "axes.legend(loc=0);" + ] + }, + { + "cell_type": "markdown", + "id": "b3206829", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c912aaf", + "metadata": {}, + "outputs": [], + "source": [ + "qt.about()" + ] + }, + { + "cell_type": "markdown", + "id": "4f464314", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5895555", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "assert 1 == 1" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v4/heom/heom-4-dynamical-decoupling.ipynb b/tutorials-v4/heom/heom-4-dynamical-decoupling.ipynb new file mode 100644 index 00000000..75350b31 --- /dev/null +++ b/tutorials-v4/heom/heom-4-dynamical-decoupling.ipynb @@ -0,0 +1,776 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2f80f634", + "metadata": {}, + "source": [ + "# HEOM 4: Dynamical decoupling of a non-Markovian environment" + ] + }, + { + "cell_type": "markdown", + "id": "825af264", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Following [Lorenza Viola and Seth Lloyd](https://arxiv.org/abs/quant-ph/9803057) we consider an example of dynamical decoupling.\n", + "We choose a drive which performs pi rotations, interspersed with short periods where the bath causes dephasing.\n", + "\n", + "We first show the standard example of equally spaced pulses, and then consider the 'optimal' Uhrig spacing ([Götz S. Uhrig Phys. Rev. Lett. 98, 100504 (2007)](https://arxiv.org/abs/quant-ph/0609203))." + ] + }, + { + "cell_type": "markdown", + "id": "10ddeef3", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea414173", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " Options,\n", + " QobjEvo,\n", + " basis,\n", + " expect,\n", + " ket2dm,\n", + " sigmax,\n", + " sigmaz,\n", + ")\n", + "from qutip.nonmarkov.heom import (\n", + " HEOMSolver,\n", + " DrudeLorentzPadeBath,\n", + ")\n", + "\n", + "from ipywidgets import IntProgress\n", + "from IPython.display import display\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "d96db540", + "metadata": {}, + "source": [ + "## Helper functions\n", + "\n", + "Let's define some helper functions for calculating the spectral density:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1f3dfc6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def dl_spectrum(w, lam, gamma):\n", + " \"\"\" Return the Drude-Lorentz spectral density. \"\"\"\n", + " J = w * 2 * lam * gamma / (gamma**2 + w**2)\n", + " return J" + ] + }, + { + "cell_type": "markdown", + "id": "cc5a97b6", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "Now we define the system and bath properties and the HEOM parameters. The system is a single stationary qubit with $H = 0$ and the bath is a bosonic bath with a Drude-Lorentz spectrum." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5df8a44", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Define the system Hamlitonian.\n", + "#\n", + "# The system isn't evolving by itself, so the Hamiltonian is 0 (with the\n", + "# correct dimensions):\n", + "\n", + "H_sys = 0 * sigmaz()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "063a3370", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresonding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresonding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef69bd00", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Properties for the Drude-Lorentz bath\n", + "\n", + "lam = 0.0005\n", + "gamma = 0.005\n", + "T = 0.05\n", + "\n", + "# bath-system coupling operator:\n", + "Q = sigmaz()\n", + "\n", + "# number of terms to keep in the expansion of the bath correlation function:\n", + "Nk = 3\n", + "\n", + "bath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6647d0fc", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# HEOM parameters\n", + "\n", + "# number of layers to keep in the hierarchy:\n", + "NC = 6" + ] + }, + { + "cell_type": "markdown", + "id": "af97aecc", + "metadata": {}, + "source": [ + "To perform the dynamic decoupling from the environment, we will drive the system with a time-dependent pulse that couples to the system via the $\\sigma_x$ operator. The area under the pulse will usual be set to $\\pi / 2$ so that the pulse flips the qubit state.\n", + "\n", + "Below we define a function that returns the pulse (which is itself a function):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "938993d5", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def drive(amplitude, delay, integral):\n", + " \"\"\" Coefficient of the drive as a function of time.\n", + "\n", + " The drive consists of a series of constant pulses with\n", + " a fixed delay between them.\n", + "\n", + " Parameters\n", + " ----------\n", + " amplitude : float\n", + " The amplitude of the drive during the pulse.\n", + " delay : float\n", + " The time delay between successive pulses.\n", + " integral : float\n", + " The integral of the pulse. This determines\n", + " the duration of each pulse with the duration\n", + " equal to the integral divided by the amplitude.\n", + " \"\"\"\n", + " duration = integral / amplitude\n", + " period = duration + delay\n", + "\n", + " def pulse(t):\n", + " t = t % period\n", + " if t < duration:\n", + " return amplitude\n", + " return 0\n", + "\n", + " return pulse\n", + "\n", + "\n", + "H_drive = sigmax()" + ] + }, + { + "cell_type": "markdown", + "id": "6faf63dc", + "metadata": {}, + "source": [ + "## Plot the spectral density\n", + "\n", + "Let's start by plotting the spectral density of our Drude-Lorentz bath:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a329a886", + "metadata": {}, + "outputs": [], + "source": [ + "wlist = np.linspace(0, 0.5, 1000)\n", + "J = dl_spectrum(wlist, lam, gamma)\n", + "\n", + "fig, axes = plt.subplots(1, 1, figsize=(8, 8))\n", + "axes.plot(wlist, J, 'r', linewidth=2)\n", + "axes.set_xlabel(r'$\\omega$', fontsize=28)\n", + "axes.set_ylabel(r'J', fontsize=28);" + ] + }, + { + "cell_type": "markdown", + "id": "eeef012a", + "metadata": {}, + "source": [ + "## Dynamic decoupling with fast and slow pulses\n", + "\n", + "Now we are ready to explore dynamic decoupling from the environment.\n", + "\n", + "First we will drive the system with fast, large amplitude pulses. Then we will drive the system with slower, smaller amplitude pulses. The faster pulses decoupling the system more effectively and retain the coherence longer, but the slower pulses help too.\n", + "\n", + "Let's start by simulating the fast pulses:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ccd8f1ac", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Fast driving (quick, large amplitude pulses)\n", + "\n", + "# The max_step must be set to a short time than the\n", + "# length of the shortest pulse, otherwise the solver\n", + "# might skip over a pulse.\n", + "options = Options(\n", + " nsteps=1500,\n", + " store_states=True,\n", + " rtol=1e-12,\n", + " atol=1e-12,\n", + " max_step=1 / 20.0,\n", + ")\n", + "\n", + "tlist = np.linspace(0, 400, 1000)\n", + "\n", + "# start with a superposition so there is something to dephase!\n", + "rho0 = (basis(2, 1) + basis(2, 0)).unit()\n", + "rho0 = ket2dm(rho0)\n", + "\n", + "# without pulses\n", + "hsolver = HEOMSolver(H_sys, bath, NC, options=options)\n", + "outputnoDD = hsolver.run(rho0, tlist, ado_return=True)\n", + "\n", + "# with pulses\n", + "drive_fast = drive(amplitude=0.5, delay=20, integral=np.pi / 2)\n", + "H_d = qutip.QobjEvo([H_sys, [H_drive, drive_fast]])\n", + "\n", + "hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", + "outputDD = hsolver.run(rho0, tlist, ado_return=True)" + ] + }, + { + "cell_type": "markdown", + "id": "04ac00e2", + "metadata": {}, + "source": [ + "And now the longer slower pulses:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "431f101a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Slow driving (longer, small amplitude pulses)\n", + "\n", + "# without pulses\n", + "hsolver = HEOMSolver(H_sys, bath, NC, options=options)\n", + "outputnoDDslow = hsolver.run(rho0, tlist, ado_return=True)\n", + "\n", + "# with pulses\n", + "drive_slow = drive(amplitude=0.01, delay=20, integral=np.pi/2)\n", + "H_d = [H_sys, [H_drive, drive_slow]]\n", + "\n", + "hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", + "outputDDslow = hsolver.run(rho0, tlist, ado_return=True)" + ] + }, + { + "cell_type": "markdown", + "id": "8cac1123", + "metadata": {}, + "source": [ + "Now let's plot all of the results and the shapes of the pulses:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39ecde58", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def plot_dd_results(outputnoDD, outputDD, outputDDslow):\n", + " fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 12))\n", + "\n", + " # Plot the dynamic decoupling results:\n", + "\n", + " tlist = outputDD.times\n", + "\n", + " P12 = basis(2, 1) * basis(2, 0).dag()\n", + " P12DD = qutip.expect(outputDD.states, P12)\n", + " P12noDD = qutip.expect(outputnoDD.states, P12)\n", + " P12DDslow = qutip.expect(outputDDslow.states, P12)\n", + "\n", + " plt.sca(axes[0])\n", + " plt.yticks([0, 0.25, 0.5], [0, 0.25, 0.5])\n", + "\n", + " axes[0].plot(\n", + " tlist, np.real(P12DD),\n", + " 'green', linestyle='-', linewidth=2, label=\"HEOM with fast DD\",\n", + " )\n", + " axes[0].plot(\n", + " tlist, np.real(P12DDslow),\n", + " 'blue', linestyle='-', linewidth=2, label=\"HEOM with slow DD\",\n", + " )\n", + " axes[0].plot(\n", + " tlist, np.real(P12noDD),\n", + " 'orange', linestyle='--', linewidth=2, label=\"HEOM no DD\",\n", + " )\n", + "\n", + " axes[0].locator_params(axis='y', nbins=3)\n", + " axes[0].locator_params(axis='x', nbins=3)\n", + "\n", + " axes[0].set_ylabel(r\"$\\rho_{01}$\", fontsize=30)\n", + "\n", + " axes[0].legend(loc=4)\n", + " axes[0].text(0, 0.4, \"(a)\", fontsize=28)\n", + "\n", + " # Plot the drive pulses:\n", + "\n", + " pulse = [drive_fast(t) for t in tlist]\n", + " pulseslow = [drive_slow(t) for t in tlist]\n", + "\n", + " plt.sca(axes[1])\n", + " plt.yticks([0., 0.25, 0.5], [0, 0.25, 0.5])\n", + "\n", + " axes[1].plot(\n", + " tlist, pulse,\n", + " 'green', linestyle='-', linewidth=2, label=\"Drive fast\",\n", + " )\n", + " axes[1].plot(\n", + " tlist, pulseslow,\n", + " 'blue', linestyle='--', linewidth=2, label=\"Drive slow\",\n", + " )\n", + "\n", + " axes[1].locator_params(axis='y', nbins=3)\n", + " axes[1].locator_params(axis='x', nbins=3)\n", + "\n", + " axes[1].set_xlabel(r'$t\\bar{V}_{\\mathrm{f}}$', fontsize=30)\n", + " axes[1].set_ylabel(r'Drive amplitude/$\\bar{V}_{\\mathrm{f}}$', fontsize=30)\n", + "\n", + " axes[1].legend(loc=1)\n", + " axes[1].text(0, 0.4, \"(b)\", fontsize=28)\n", + "\n", + " fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "597fa307", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "plot_dd_results(outputnoDD, outputDD, outputDDslow)" + ] + }, + { + "cell_type": "markdown", + "id": "62af1de8", + "metadata": {}, + "source": [ + "## Non-equally spaced pulses" + ] + }, + { + "cell_type": "markdown", + "id": "6f002052", + "metadata": {}, + "source": [ + "Next we consider non-equally spaced pulses.\n", + "\n", + "Rather than plot as a function of time we just consider the final coherence after time $T$ and 100 pulses. We change the width of the environment to demonstate that the Uhrig sequence (i.e. the evenly spaced pulses) can be sub-optimal when the bath is very broad.\n", + "\n", + "Instead of evenly spaced pulses, we will use pulses where the cummulative delay after $j$ pulses is given by:\n", + "\n", + "$$\n", + " \\sin^2(\\frac{\\pi}{2} \\frac{j}{N + 1})\n", + "$$\n", + "\n", + "This is just a convenient way to describe the varying delay. We could have chosen another monotonically increasing function to represent the cummulative delay (although it might not be as effective)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c165906", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def cummulative_delay_fractions(N):\n", + " \"\"\" Return an array of N + 1 cummulative delay\n", + " fractions.\n", + "\n", + " The j'th entry in the array should be the sum of\n", + " all delays before the j'th pulse. The last entry\n", + " should be 1 (i.e. the entire cummulative delay\n", + " should have been used once the sequence of pulses\n", + " is complete).\n", + "\n", + " The function should be monotonically increasing,\n", + " strictly greater than zero and the last value\n", + " should be 1.\n", + "\n", + " This implementation returns:\n", + "\n", + " sin((pi / 2) * (j / (N + 1)))**2\n", + "\n", + " as the cummulative delay after the j'th pulse.\n", + " \"\"\"\n", + " return np.array([\n", + " np.sin((np.pi / 2) * (j / (N + 1)))**2\n", + " for j in range(0, N + 1)\n", + " ])\n", + "\n", + "\n", + "def drive_opt(amplitude, avg_delay, integral, N):\n", + " \"\"\" Return an optimized distance pulse function.\n", + "\n", + " Our previous pulses were evenly spaced. Here we\n", + " instead use a varying delay after the j'th pulse.\n", + "\n", + " The cummulative delay is described by the function\n", + " ``cummulative_delay_fractions`` above.\n", + " \"\"\"\n", + " duration = integral / amplitude\n", + " cummulative_delays = N * avg_delay * cummulative_delay_fractions(N)\n", + "\n", + " t_start = cummulative_delays + duration * np.arange(0, N + 1)\n", + " t_end = cummulative_delays + duration * np.arange(1, N + 2)\n", + "\n", + " def pulse(t):\n", + " if any((t_start <= t) & (t <= t_end)):\n", + " return amplitude\n", + " return 0.0\n", + "\n", + " return pulse" + ] + }, + { + "cell_type": "markdown", + "id": "01e75e27", + "metadata": {}, + "source": [ + "Let's plot the cummulative delays and see what they look like. Note that the cummulative delay starts at $0$, ends at $1$ and is monotonically increasing, as required.\n", + "\n", + "On the same axes we plot the individual $j^{th}$ delays as a fraction of the average delay." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9e65156", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_cummulative_delay_fractions(N):\n", + " cummulative = cummulative_delay_fractions(N)\n", + " individual = (cummulative[1:] - cummulative[:-1]) * N\n", + " plt.plot(np.arange(0, N + 1), cummulative, label=\"Cummulative delay\")\n", + " plt.plot(np.arange(0, N), individual, label=\"j'th delay\")\n", + " plt.xlabel(\"j\")\n", + " plt.ylabel(\"Fraction of delay\")\n", + " plt.legend()\n", + "\n", + "\n", + "plot_cummulative_delay_fractions(100)" + ] + }, + { + "cell_type": "markdown", + "id": "cd967dd1", + "metadata": {}, + "source": [ + "And now let us plot the first ten even and optimally spaced pulses together to compare them:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a375dd1", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_even_and_optimally_spaced_pulses():\n", + " amplitude = 10.0\n", + " integral = np.pi / 2\n", + " duration = integral / amplitude\n", + " delay = 1.0 - duration\n", + "\n", + " tlist = np.linspace(0, 10, 1000)\n", + "\n", + " pulse_opt = drive_opt(amplitude, delay, integral, 100)\n", + " pulse_eq = drive(amplitude, delay, integral)\n", + "\n", + " plt.plot(\n", + " tlist, [pulse_opt(t) for t in tlist], label=\"opt\",\n", + " )\n", + " plt.plot(\n", + " tlist, [pulse_eq(t) for t in tlist], label=\"eq\",\n", + " )\n", + " plt.legend(loc=4)\n", + "\n", + "\n", + "plot_even_and_optimally_spaced_pulses()" + ] + }, + { + "cell_type": "markdown", + "id": "7dcda248", + "metadata": { + "tags": [] + }, + "source": [ + "Now let's simulate the effectiveness of the two sets of delays by comparing how well they maintain coherence after a hundred pulses.\n", + "\n", + "We'll perform the simulation over a range of lambdas and gammas to show how the non-evenly spaced delays become optimal as the width of the bath spectral function increases." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4df85f5", + "metadata": {}, + "outputs": [], + "source": [ + "# Bath parameters to simulate over:\n", + "\n", + "# We use only two lambdas and two gammas so that the notebook executes\n", + "# quickly:\n", + "\n", + "lams = [0.005, 0.0005]\n", + "gammas = np.linspace(0.005, 0.05, 2)\n", + "\n", + "# But one can also extend the lists to larger ones:\n", + "#\n", + "# lams = [0.01, 0.005, 0.0005]\n", + "# gammas = np.linspace(0.005, 0.05, 10)\n", + "\n", + "# Setup a progress bar:\n", + "\n", + "progress = IntProgress(min=0, max=(2 * len(lams) * len(gammas)))\n", + "display(progress)\n", + "\n", + "\n", + "def simulate_100_pulses(lam, gamma, T, NC, Nk):\n", + " \"\"\" Simulate the evolution of 100 evenly and optimally spaced pulses.\n", + "\n", + " Returns the expectation value of P12p from the final state of\n", + " each evolution.\n", + " \"\"\"\n", + " rho0 = (basis(2, 1) + basis(2, 0)).unit()\n", + " rho0 = ket2dm(rho0)\n", + "\n", + " N = 100 # number of pulses to simulate\n", + " avg_cycle_time = 1.0 # average time from one pulse to the next\n", + " t_max = N * avg_cycle_time\n", + "\n", + " tlist = np.linspace(0, t_max, 100)\n", + "\n", + " amplitude = 10.0\n", + " integral = np.pi / 2\n", + " duration = integral / amplitude\n", + " delay = avg_cycle_time - duration\n", + "\n", + " bath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", + "\n", + " # Equally spaced pulses:\n", + "\n", + " pulse_eq = drive(amplitude, delay, integral)\n", + " H_d = QobjEvo([H_sys, [H_drive, pulse_eq]])\n", + "\n", + " hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", + " result = hsolver.run(rho0, tlist)\n", + "\n", + " P12_eq = expect(result.states[-1], P12p)\n", + " progress.value += 1\n", + "\n", + " # Non-equally spaced pulses:\n", + "\n", + " pulse_opt = drive_opt(amplitude, delay, integral, N)\n", + " H_d = QobjEvo([H_sys, [H_drive, pulse_opt]])\n", + "\n", + " hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", + " result = hsolver.run(rho0, tlist)\n", + "\n", + " P12_opt = expect(result.states[-1], P12p)\n", + " progress.value += 1\n", + "\n", + " return P12_opt, P12_eq\n", + "\n", + "\n", + "# We use NC=2 and Nk=2 to speed up the simulation:\n", + "\n", + "P12_results = [\n", + " list(zip(*(\n", + " simulate_100_pulses(lam=lam_, gamma=gamma_, T=0.5, NC=2, Nk=2)\n", + " for gamma_ in gammas\n", + " )))\n", + " for lam_ in lams\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "c9953176", + "metadata": {}, + "source": [ + "Now that we have the expectation values of $\\rho_{01}$ let's plot them as a function of gamma for each lambda. Note how in each case the non-evenly spaced pulses become optimal once gamma is sufficiently small:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4955656", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=False, figsize=(10, 7))\n", + "colors = [\"green\", \"red\", \"blue\"]\n", + "\n", + "for i in range(len(lams)):\n", + " color = colors[i % len(colors)]\n", + " axes.plot(\n", + " gammas, np.real(P12_results[i][0]),\n", + " color, linestyle='-', linewidth=2,\n", + " label=f\"Optimal DD [$\\\\lambda={lams[i]}$]\",\n", + " )\n", + " axes.plot(\n", + " gammas, np.real(P12_results[i][1]),\n", + " color, linestyle='-.', linewidth=2,\n", + " label=f\"Even DD [$\\\\lambda={lams[i]}$]\",\n", + " )\n", + "\n", + "axes.set_ylabel(r\"$\\rho_{01}$\")\n", + "axes.set_xlabel(r\"$\\gamma$\")\n", + "axes.legend(fontsize=16)\n", + "\n", + "fig.tight_layout();" + ] + }, + { + "cell_type": "markdown", + "id": "209475ff", + "metadata": {}, + "source": [ + "And now you know about dynamically decoupling a qubit from its environment!" + ] + }, + { + "cell_type": "markdown", + "id": "1a365fe8", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6ecec10", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "765f3e2e", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3179effe", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "assert 1 == 1" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v4/heom/heom-5a-fermions-single-impurity-model.ipynb b/tutorials-v4/heom/heom-5a-fermions-single-impurity-model.ipynb new file mode 100644 index 00000000..29af7cf1 --- /dev/null +++ b/tutorials-v4/heom/heom-5a-fermions-single-impurity-model.ipynb @@ -0,0 +1,827 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "91b722ef", + "metadata": { + "tags": [] + }, + "source": [ + "# HEOM 5a: Fermionic single impurity model" + ] + }, + { + "cell_type": "markdown", + "id": "8b996250", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc). Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications.\n", + "\n", + "Notation:\n", + "\n", + "* $K=L/R$ refers to left or right leads.\n", + "* $\\sigma=\\pm$ refers to input/output\n", + "\n", + "We choose a Lorentzian spectral density for the leads, with a peak at the chemical potential. The latter simplifies a little the notation required for the correlation functions, but can be relaxed if neccessary.\n", + "\n", + "$$J(\\omega) = \\frac{\\Gamma W^2}{((\\omega-\\mu_K)^2 +W^2 )}$$\n", + "\n", + "The Fermi distribution function is:\n", + "\n", + "$$f_F (x) = (\\exp(x) + 1)^{-1}$$\n", + "\n", + "Together these allow the correlation functions to be expressed as:\n", + "\n", + "$$C^{\\sigma}_K(t) = \\frac{1}{2\\pi} \\int_{-\\infty}^{\\infty} d\\omega e^{\\sigma i \\omega t} \\Gamma_K(\\omega) f_F[\\sigma\\beta(\\omega - \\mu)]$$\n", + "\n", + "As with the bosonic case we can expand these in an exponential series using Matsubara, Pade, or fitting approaches.\n", + "\n", + "The Pade decomposition approximates the Fermi distubition as\n", + "\n", + "$$f_F(x) \\approx f_F^{\\mathrm{approx}}(x) = \\frac{1}{2} - \\sum_l^{l_{max}} \\frac{2k_l x}{x^2 + \\epsilon_l^2}$$\n", + "\n", + "where $k_l$ and $\\epsilon_l$ are co-efficients defined in J. Chem Phys 133,10106.\n", + "\n", + "Evaluating the integral for the correlation functions gives,\n", + "\n", + "$$C_K^{\\sigma}(t) \\approx \\sum_{l=0}^{l_{max}} \\eta_K^{\\sigma_l} e^{-\\gamma_{K,\\sigma,l}t}$$\n", + "\n", + "where:\n", + "\n", + "$$\\eta_{K,0} = \\frac{\\Gamma_KW_K}{2} f_F^{approx}(i\\beta_K W)$$\n", + "\n", + "$$\\gamma_{K,\\sigma,0} = W_K - \\sigma i\\mu_K$$ \n", + "\n", + "$$\\eta_{K,l\\neq 0} = -i\\cdot \\frac{k_m}{\\beta_K} \\cdot \\frac{\\Gamma_K W_K^2}{-\\frac{\\epsilon^2_m}{\\beta_K^2} + W_K^2}$$\n", + "\n", + "$$\\gamma_{K,\\sigma,l\\neq 0}= \\frac{\\epsilon_m}{\\beta_K} - \\sigma i \\mu_K$$\n", + "\n", + "In this notebook we:\n", + "\n", + "* compare the Matsubara and Pade approximations and contrast them with the analytical result for the current between the system and the leads.\n", + "\n", + "* plot the current through the qubit as a function of the different between the voltages of the leads." + ] + }, + { + "cell_type": "markdown", + "id": "b6e913f1", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "354530aa", + "metadata": {}, + "outputs": [], + "source": [ + "import contextlib\n", + "import dataclasses\n", + "import time\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.integrate import quad\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " Options,\n", + " basis,\n", + " destroy,\n", + " expect,\n", + ")\n", + "from qutip.nonmarkov.heom import (\n", + " HEOMSolver,\n", + " LorentzianBath,\n", + " LorentzianPadeBath,\n", + ")\n", + "\n", + "from ipywidgets import IntProgress\n", + "from IPython.display import display\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "d73247f3", + "metadata": {}, + "source": [ + "## Helpers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "758ac328", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\" Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "markdown", + "id": "259bf221", + "metadata": { + "tags": [] + }, + "source": [ + "## System and bath definition\n", + "\n", + "And let us set up the system Hamiltonian, bath and system measurement operators:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2850af4f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Define the system Hamiltonian:\n", + "\n", + "# The system is a single fermion with energy level split e1:\n", + "d1 = destroy(2)\n", + "e1 = 1.0\n", + "H = e1 * d1.dag() * d1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59d11d79", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Define parameters for left and right fermionic baths.\n", + "# Each bath is a lead (i.e. a wire held at a potential)\n", + "# with temperature T and chemical potential mu.\n", + "\n", + "@dataclasses.dataclass\n", + "class LorentzianBathParameters:\n", + " lead: str\n", + " Q: object # coupling operator\n", + " gamma: float = 0.01 # coupling strength\n", + " W: float = 1.0 # cut-off\n", + " T: float = 0.025851991 # temperature\n", + " theta: float = 2.0 # bias\n", + "\n", + " def __post_init__(self):\n", + " assert self.lead in (\"L\", \"R\")\n", + " self.beta = 1 / self.T\n", + " if self.lead == \"L\":\n", + " self.mu = self.theta / 2.0\n", + " else:\n", + " self.mu = - self.theta / 2.0\n", + "\n", + " def J(self, w):\n", + " \"\"\" Spectral density. \"\"\"\n", + " return self.gamma * self.W**2 / ((w - self.mu)**2 + self.W**2)\n", + "\n", + " def fF(self, w, sign=1.0):\n", + " \"\"\" Fermi distribution for this bath. \"\"\"\n", + " x = sign * self.beta * (w - self.mu)\n", + " return fF(x)\n", + "\n", + " def lamshift(self, w):\n", + " \"\"\" Return the lamshift. \"\"\"\n", + " return 0.5 * (w - self.mu) * self.J(w) / self.W\n", + "\n", + " def replace(self, **kw):\n", + " return dataclasses.replace(self, **kw)\n", + "\n", + "\n", + "def fF(x):\n", + " \"\"\" Return the Fermi distribution. \"\"\"\n", + " # in units where kB = 1.0\n", + " return 1 / (np.exp(x) + 1)\n", + "\n", + "\n", + "bath_L = LorentzianBathParameters(Q=d1, lead=\"L\")\n", + "bath_R = LorentzianBathParameters(Q=d1, lead=\"R\")" + ] + }, + { + "cell_type": "markdown", + "id": "70541468", + "metadata": {}, + "source": [ + "## Spectral density\n", + "\n", + "Let's plot the spectral density." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6622bdfc", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "w_list = np.linspace(-2, 2, 100)\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 7))\n", + "\n", + "spec_L = bath_L.J(w_list)\n", + "spec_R = bath_R.J(w_list)\n", + "\n", + "ax.plot(\n", + " w_list, spec_L,\n", + " \"b--\", linewidth=3,\n", + " label=r\"J_L(w)\",\n", + ")\n", + "ax.plot(\n", + " w_list, spec_R,\n", + " \"r--\", linewidth=3,\n", + " label=r\"J_R(w)\",\n", + ")\n", + "\n", + "ax.set_xlabel(\"w\")\n", + "ax.set_ylabel(r\"$J(\\omega)$\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "55dd37e8", + "metadata": {}, + "source": [ + "## Emission and absorption by the leads\n", + "\n", + "Next let's plot the emission and absorption by the leads." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bffd607f", + "metadata": {}, + "outputs": [], + "source": [ + "w_list = np.linspace(-2, 2, 100)\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 7))\n", + "\n", + "# Left lead emission and absorption\n", + "\n", + "gam_L_in = bath_L.J(w_list) * bath_L.fF(w_list, sign=1.0)\n", + "gam_L_out = bath_L.J(w_list) * bath_L.fF(w_list, sign=-1.0)\n", + "\n", + "ax.plot(\n", + " w_list, gam_L_in,\n", + " \"b--\", linewidth=3,\n", + " label=r\"S_L(w) input (absorption)\",\n", + ")\n", + "ax.plot(\n", + " w_list, gam_L_out,\n", + " \"r--\", linewidth=3,\n", + " label=r\"S_L(w) output (emission)\",\n", + ")\n", + "\n", + "# Right lead emission and absorption\n", + "\n", + "gam_R_in = bath_R.J(w_list) * bath_R.fF(w_list, sign=1.0)\n", + "gam_R_out = bath_R.J(w_list) * bath_R.fF(w_list, sign=-1.0)\n", + "\n", + "ax.plot(\n", + " w_list, gam_R_in,\n", + " \"b\", linewidth=3,\n", + " label=r\"S_R(w) input (absorption)\",\n", + ")\n", + "ax.plot(\n", + " w_list, gam_R_out,\n", + " \"r\", linewidth=3,\n", + " label=r\"S_R(w) output (emission)\",\n", + ")\n", + "\n", + "ax.set_xlabel(\"w\")\n", + "ax.set_ylabel(r\"$S(\\omega)$\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "d635fb89", + "metadata": {}, + "source": [ + "## Comparing the Matsubara and Pade approximations\n", + "\n", + "Let's start by solving for the evolution using a Pade expansion of the correlation function of the Lorentzian spectral density:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ddf62b70", + "metadata": {}, + "outputs": [], + "source": [ + "# HEOM dynamics using the Pade approximation:\n", + "\n", + "# Solver options, times to solve for and initial system state:\n", + "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", + "tlist = np.linspace(0, 100, 1000)\n", + "rho0 = basis(2, 0) * basis(2, 0).dag()\n", + "\n", + "Nk = 10 # Number of exponents to retain in the expansion of each bath\n", + "\n", + "bathL = LorentzianPadeBath(\n", + " bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", + " Nk, tag=\"L\",\n", + ")\n", + "bathR = LorentzianPadeBath(\n", + " bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", + " Nk, tag=\"R\",\n", + ")\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " solver_pade = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " result_pade = solver_pade.run(rho0, tlist, ado_return=True)\n", + "\n", + "with timer(\"Steady state solver time\"):\n", + " rho_ss_pade, ado_ss_pade = solver_pade.steady_state()" + ] + }, + { + "cell_type": "markdown", + "id": "194d8092", + "metadata": {}, + "source": [ + "Now let us plot the result which shows the decay of the initially excited impurity. This is not very illuminating, but we will compare it with the Matsubara expansion and analytic solution sortly:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb4b6c44", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the Pade results\n", + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + "\n", + "axes.plot(\n", + " tlist, expect(result_pade.states, rho0),\n", + " 'r--', linewidth=2,\n", + " label=\"P11 (Pade)\",\n", + ")\n", + "axes.axhline(\n", + " expect(rho_ss_pade, rho0),\n", + " color='r', linestyle=\"dotted\", linewidth=1,\n", + " label=\"P11 (Pade steady state)\",\n", + ")\n", + "\n", + "axes.set_xlabel('t', fontsize=28)\n", + "axes.legend(fontsize=12);" + ] + }, + { + "cell_type": "markdown", + "id": "7249309a", + "metadata": {}, + "source": [ + "Now let us do the same for the Matsubara expansion:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "99947c8a", + "metadata": {}, + "outputs": [], + "source": [ + "# HEOM dynamics using the Matsubara approximation:\n", + "\n", + "bathL = LorentzianBath(\n", + " bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", + " Nk, tag=\"L\",\n", + ")\n", + "bathR = LorentzianBath(\n", + " bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", + " Nk, tag=\"R\",\n", + ")\n", + "\n", + "with timer(\"RHS construction time\"):\n", + " solver_mats = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options)\n", + "\n", + "with timer(\"ODE solver time\"):\n", + " result_mats = solver_mats.run(rho0, tlist, ado_return=True)\n", + "\n", + "with timer(\"Steady state solver time\"):\n", + " rho_ss_mats, ado_ss_mats = solver_mats.steady_state()" + ] + }, + { + "cell_type": "markdown", + "id": "b5805ba5", + "metadata": {}, + "source": [ + "We see a marked difference in the Matsubara vs Pade results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b9539373", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Plot the Pade results\n", + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + "\n", + "axes.plot(\n", + " tlist, expect(result_pade.states, rho0),\n", + " 'r--', linewidth=2,\n", + " label=\"P11 (Pade)\",\n", + ")\n", + "axes.axhline(\n", + " expect(rho_ss_pade, rho0),\n", + " color='r', linestyle=\"dotted\", linewidth=1,\n", + " label=\"P11 (Pade steady state)\",\n", + ")\n", + "\n", + "axes.plot(\n", + " tlist, expect(result_mats.states, rho0),\n", + " 'b--', linewidth=2,\n", + " label=\"P11 (Mats)\",\n", + ")\n", + "axes.axhline(\n", + " expect(rho_ss_mats, rho0),\n", + " color='b', linestyle=\"dotted\", linewidth=1,\n", + " label=\"P11 (Mats steady state)\",\n", + ")\n", + "\n", + "axes.set_xlabel('t', fontsize=28)\n", + "axes.legend(fontsize=12);" + ] + }, + { + "cell_type": "markdown", + "id": "77892cd8", + "metadata": {}, + "source": [ + "But which is more correct? The Matsubara or the Pade result?\n", + "\n", + "One advantage of this simple model is that the steady state current to the baths is analytically solvable, so we can check convergence of the result by calculating it analytically (the sum of the currents to and from the system in the steady state must be zero, so the current from one bath is the same as the current to the other).\n", + "\n", + "See the [QuTiP-BoFiN paper](https://arxiv.org/abs/2010.10806) for a detailed description and references for the analytic result. Below we just perform the required integration numerically." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1d4ceaf", + "metadata": {}, + "outputs": [], + "source": [ + "def analytical_steady_state_current(bath_L, bath_R, e1):\n", + " \"\"\" Calculate the analytical steady state current. \"\"\"\n", + "\n", + " def integrand(w):\n", + " return (2 / np.pi) * (\n", + " bath_L.J(w) * bath_R.J(w) * (bath_L.fF(w) - bath_R.fF(w)) /\n", + " (\n", + " (bath_L.J(w) + bath_R.J(w))**2 +\n", + " 4*(w - e1 - bath_L.lamshift(w) - bath_R.lamshift(w))**2\n", + " )\n", + " )\n", + "\n", + " def real_part(x):\n", + " return np.real(integrand(x))\n", + "\n", + " def imag_part(x):\n", + " return np.imag(integrand(x))\n", + "\n", + " # in principle the bounds for the integral should be rechecked if\n", + " # bath or system parameters are changed substantially:\n", + " bounds = [-10, 10]\n", + "\n", + " real_integral, _ = quad(real_part, *bounds)\n", + " imag_integral, _ = quad(imag_part, *bounds)\n", + "\n", + " return real_integral + 1.0j * imag_integral\n", + "\n", + "\n", + "curr_ss_analytic = analytical_steady_state_current(bath_L, bath_R, e1)\n", + "\n", + "print(f\"Analytical steady state current: {curr_ss_analytic}\")" + ] + }, + { + "cell_type": "markdown", + "id": "78867fde", + "metadata": {}, + "source": [ + "To compare the analytical result above with the result from the HEOM, we need to be able to calculate the current from the system to the bath from the HEOM result. In the HEOM description, these currents are captured in the first level auxilliary density operators (ADOs).\n", + "\n", + "In the function `state_current(...)` below, we extract the first level ADOs for the specified bath and sum the contributions to the current from each:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0dea27f", + "metadata": {}, + "outputs": [], + "source": [ + "def state_current(ado_state, bath_tag):\n", + " \"\"\" Determine current from the given bath (either \"R\" or \"L\") to\n", + " the system in the given ADO state.\n", + " \"\"\"\n", + " level_1_aux = [\n", + " (ado_state.extract(label), ado_state.exps(label)[0])\n", + " for label in ado_state.filter(level=1, tags=[bath_tag])\n", + " ]\n", + "\n", + " def exp_sign(exp):\n", + " return 1 if exp.type == exp.types[\"+\"] else -1\n", + "\n", + " def exp_op(exp):\n", + " return exp.Q if exp.type == exp.types[\"+\"] else exp.Q.dag()\n", + "\n", + " return -1.0j * sum(\n", + " exp_sign(exp) * (exp_op(exp) * aux).tr()\n", + " for aux, exp in level_1_aux\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "f31aa01b", + "metadata": {}, + "source": [ + "Now we can calculate the steady state currents from the Pade and Matsubara HEOM results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "225f8a54", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "curr_ss_pade_L = state_current(ado_ss_pade, \"L\")\n", + "curr_ss_pade_R = state_current(ado_ss_pade, \"R\")\n", + "\n", + "print(f\"Pade steady state current (L): {curr_ss_pade_L}\")\n", + "print(f\"Pade steady state current (R): {curr_ss_pade_R}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57b80a11", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "curr_ss_mats_L = state_current(ado_ss_mats, \"L\")\n", + "curr_ss_mats_R = state_current(ado_ss_mats, \"R\")\n", + "\n", + "print(f\"Matsubara steady state current (L): {curr_ss_mats_L}\")\n", + "print(f\"Matsubara steady state current (R): {curr_ss_mats_R}\")" + ] + }, + { + "cell_type": "markdown", + "id": "34b9dd27", + "metadata": {}, + "source": [ + "Note that the currents from each bath balance as is required by the steady state, but the value of the current is different for the Pade and Matsubara results.\n", + "\n", + "Now let's compare all three:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca30a9ab", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Pade current (R): {curr_ss_pade_R}\")\n", + "print(f\"Matsubara current (R): {curr_ss_mats_R}\")\n", + "print(f\"Analytical curernt: {curr_ss_analytic}\")" + ] + }, + { + "cell_type": "markdown", + "id": "debf29ca", + "metadata": {}, + "source": [ + "In this case we observe that the Pade approximation has converged more closely to the analytical current than the Matsubara.\n", + "\n", + "The Matsubara result could be improved by increasing the number of terms retained in the Matsubara expansion (i.e. increasing `Nk`)." + ] + }, + { + "cell_type": "markdown", + "id": "182f080f", + "metadata": {}, + "source": [ + "## Current as a function of bias voltage" + ] + }, + { + "cell_type": "markdown", + "id": "64fd3eeb", + "metadata": {}, + "source": [ + "Now lets plot the current as a function of bias voltage (the bias voltage is the parameter `theta` for the two baths).\n", + "\n", + "We will calculate the steady state current for each `theta` both analytically and using the HEOM with the Pade correlation expansion approximation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aac2ef0a", + "metadata": {}, + "outputs": [], + "source": [ + "# Theta (bias voltages)\n", + "\n", + "thetas = np.linspace(-4, 4, 100)\n", + "\n", + "# Setup a progress bar:\n", + "\n", + "progress = IntProgress(min=0, max=2 * len(thetas))\n", + "display(progress)\n", + "\n", + "# Calculate the current for the list of thetas\n", + "\n", + "\n", + "def current_analytic_for_theta(e1, bath_L, bath_R, theta):\n", + " \"\"\" Return the analytic current for a given theta. \"\"\"\n", + " current = analytical_steady_state_current(\n", + " bath_L.replace(theta=theta),\n", + " bath_R.replace(theta=theta),\n", + " e1,\n", + " )\n", + " progress.value += 1\n", + " return np.real(current)\n", + "\n", + "\n", + "def current_pade_for_theta(H, bath_L, bath_R, theta, Nk):\n", + " \"\"\" Return the steady state current using the Pade approximation. \"\"\"\n", + " bath_L = bath_L.replace(theta=theta)\n", + " bath_R = bath_R.replace(theta=theta)\n", + "\n", + " bathL = LorentzianPadeBath(\n", + " bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", + " Nk, tag=\"L\",\n", + " )\n", + " bathR = LorentzianPadeBath(\n", + " bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", + " Nk, tag=\"R\",\n", + " )\n", + "\n", + " solver_pade = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options)\n", + " rho_ss_pade, ado_ss_pade = solver_pade.steady_state()\n", + " current = state_current(ado_ss_pade, bath_tag=\"R\")\n", + "\n", + " progress.value += 1\n", + " return np.real(current)\n", + "\n", + "\n", + "curr_ss_analytic_thetas = [\n", + " current_analytic_for_theta(e1, bath_L, bath_R, theta)\n", + " for theta in thetas\n", + "]\n", + "\n", + "# The number of expansion terms has been dropped to Nk=6 to speed\n", + "# up notebook execution. Increase to Nk=10 for more accurate results.\n", + "curr_ss_pade_theta = [\n", + " current_pade_for_theta(H, bath_L, bath_R, theta, Nk=6)\n", + " for theta in thetas\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "2f5d5b40", + "metadata": {}, + "source": [ + "Below we plot the results and see that even with `Nk=6`, the HEOM Pade approximation gives good results for the steady state current. Increasing `Nk` to `10` gives very accurate results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c52b7531", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(12, 7))\n", + "\n", + "ax.plot(\n", + " thetas, 2.434e-4 * 1e6 * np.array(curr_ss_analytic_thetas),\n", + " color=\"black\", linewidth=3,\n", + " label=r\"Analytical\",\n", + ")\n", + "ax.plot(\n", + " thetas, 2.434e-4 * 1e6 * np.array(curr_ss_pade_theta),\n", + " 'r--', linewidth=3,\n", + " label=r\"HEOM Pade $N_k=10$, $n_{\\mathrm{max}}=2$\",\n", + ")\n", + "\n", + "\n", + "ax.locator_params(axis='y', nbins=4)\n", + "ax.locator_params(axis='x', nbins=4)\n", + "\n", + "ax.set_xticks([-2.5, 0, 2.5])\n", + "ax.set_xticklabels([-2.5, 0, 2.5])\n", + "ax.set_xlabel(r\"Bias voltage $\\Delta \\mu$ ($V$)\", fontsize=28)\n", + "ax.set_ylabel(r\"Current ($\\mu A$)\", fontsize=28)\n", + "ax.legend(fontsize=25);" + ] + }, + { + "cell_type": "markdown", + "id": "2c66b28e", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8b46075", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "9f59238f", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d0c6d88", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "assert np.allclose(curr_ss_pade_L + curr_ss_pade_R, 0)\n", + "assert np.allclose(curr_ss_mats_L + curr_ss_mats_R, 0)\n", + "assert np.allclose(curr_ss_pade_R, curr_ss_analytic, rtol=1e-4)" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v4/heom/heom-5b-fermions-discrete-boson-model.ipynb b/tutorials-v4/heom/heom-5b-fermions-discrete-boson-model.ipynb new file mode 100644 index 00000000..3c3a61b7 --- /dev/null +++ b/tutorials-v4/heom/heom-5b-fermions-discrete-boson-model.ipynb @@ -0,0 +1,521 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "09977858", + "metadata": {}, + "source": [ + "# HEOM 5b: Discrete boson coupled to an impurity and fermionic leads" + ] + }, + { + "cell_type": "markdown", + "id": "c78c1c1b", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc), also coupled to a discrete bosonic (vibronic) mode.\n", + "\n", + "Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his Dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications. In particular this example reproduces some results from https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.201407\n", + "\n", + "Notation:\n", + "\n", + "* $K=L/R$ refers to left or right leads.\n", + "* $\\sigma=\\pm$ refers to input/output\n", + "\n", + "We choose a Lorentzian spectral density for the leads, with a peak at the chemical potential. The latter simplifies a little the notation required for the correlation functions, but can be relaxed if neccessary.\n", + "\n", + "$$J(\\omega) = \\frac{\\Gamma W^2}{((\\omega-\\mu_K)^2 +W^2 )}$$\n", + "\n", + "The Fermi distribution function is:\n", + "\n", + "$$f_F (x) = (\\exp(x) + 1)^{-1}$$\n", + "\n", + "Together these allow the correlation functions to be expressed as:\n", + "\n", + "$$C^{\\sigma}_K(t) = \\frac{1}{2\\pi} \\int_{-\\infty}^{\\infty} d\\omega e^{\\sigma i \\omega t} \\Gamma_K(\\omega) f_F[\\sigma\\beta(\\omega - \\mu)]$$\n", + "\n", + "As with the Bosonic case we can treat these with Matsubara, Pade, or fitting approaches.\n", + "\n", + "The Pade decomposition approximates the Fermi distubition as \n", + "\n", + "$$f_F(x) \\approx f_F^{\\mathrm{approx}}(x) = \\frac{1}{2} - \\sum_l^{l_{max}} \\frac{2k_l x}{x^2 + \\epsilon_l^2}$$\n", + "\n", + "$k_l$ and $\\epsilon_l$ are co-efficients defined in J. Chem Phys 133,10106\n", + "\n", + "Evaluating the integral for the correlation functions gives,\n", + "\n", + "\n", + "$$C_K^{\\sigma}(t) \\approx \\sum_{l=0}^{l_{max}} \\eta_K^{\\sigma_l} e^{-\\gamma_{K,\\sigma,l}t}$$\n", + "\n", + "where\n", + "\n", + "$$\\eta_{K,0} = \\frac{\\Gamma_KW_K}{2} f_F^{approx}(i\\beta_K W)$$\n", + "\n", + "$$\\gamma_{K,\\sigma,0} = W_K - \\sigma i\\mu_K$$ \n", + "\n", + "$$\\eta_{K,l\\neq 0} = -i\\cdot \\frac{k_m}{\\beta_K} \\cdot \\frac{\\Gamma_K W_K^2}{-\\frac{\\epsilon^2_m}{\\beta_K^2} + W_K^2}$$\n", + "\n", + "$$\\gamma_{K,\\sigma,l\\neq 0}= \\frac{\\epsilon_m}{\\beta_K} - \\sigma i \\mu_K$$" + ] + }, + { + "cell_type": "markdown", + "id": "b4e142cb", + "metadata": {}, + "source": [ + "## Differences from Example 5a" + ] + }, + { + "cell_type": "markdown", + "id": "23e4f302", + "metadata": { + "tags": [] + }, + "source": [ + "The system we study here has two big differences from the HEOM 5a example:\n", + "\n", + "* the system now includes a discrete bosonic mode,\n", + "* and the electronic leads have $W$ set to $10^4$ (i.e. the wide-band limit).\n", + "\n", + "The new system Hamiltonian is:\n", + "\n", + "$$\n", + "H_{\\mathrm{vib}} = H_{\\mathrm{SIAM}} + \\Omega a^{\\dagger}a + \\lambda (a+a^{\\dagger})c{^\\dagger}c.\n", + "$$\n", + "\n", + "where $H_{\\mathrm{SIAM}}$ is the Hamiltonian of the single impurity, and the remaining terms are the Hamiltonian of the bosonic mode and its interaction with the impurity.\n", + "\n", + "The complete setup now consists of four parts:\n", + "\n", + "* the single impurity\n", + "* a discrete bosonic mode\n", + "* two fermionic leads.\n", + "\n", + "**Note**: This example is quite numerically challenging and has many system and bath components. For an easier introduction into the fermionic case, see example 5a.\n", + "\n", + "**Note**: We've reduced the cut-off of the bosonic mode to 2 modes to facilitate faster execution of the notebooks when the outputs are being checked and compiled. A more accurate result may be obtained by increasing the number of bosonic modes to, for example, 16." + ] + }, + { + "cell_type": "markdown", + "id": "47dd4c94", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2986e06e", + "metadata": {}, + "outputs": [], + "source": [ + "import contextlib\n", + "import dataclasses\n", + "import time\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import qutip\n", + "from qutip import (\n", + " Options,\n", + " destroy,\n", + " qeye,\n", + " tensor,\n", + ")\n", + "from qutip.nonmarkov.heom import (\n", + " HEOMSolver,\n", + " LorentzianPadeBath,\n", + ")\n", + "\n", + "from ipywidgets import IntProgress\n", + "from IPython.display import display\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "fb2a7c3e", + "metadata": {}, + "source": [ + "## Helpers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1a67c9c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "@contextlib.contextmanager\n", + "def timer(label):\n", + " \"\"\" Simple utility for timing functions:\n", + "\n", + " with timer(\"name\"):\n", + " ... code to time ...\n", + " \"\"\"\n", + " start = time.time()\n", + " yield\n", + " end = time.time()\n", + " print(f\"{label}: {end - start}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d158a24e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def state_current(ado_state, bath_tag):\n", + " \"\"\" Determine current from the given bath (either \"R\" or \"L\") to\n", + " the system in the given ADO state.\n", + " \"\"\"\n", + " level_1_aux = [\n", + " (ado_state.extract(label), ado_state.exps(label)[0])\n", + " for label in ado_state.filter(level=1, tags=[bath_tag])\n", + " ]\n", + "\n", + " def exp_sign(exp):\n", + " return 1 if exp.type == exp.types[\"+\"] else -1\n", + "\n", + " def exp_op(exp):\n", + " return exp.Q if exp.type == exp.types[\"+\"] else exp.Q.dag()\n", + "\n", + " return -1.0j * sum(\n", + " exp_sign(exp) * (exp_op(exp) * aux).tr()\n", + " for aux, exp in level_1_aux\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "906e016d", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "Let us set up the system Hamiltonian and specify the properties of the two reservoirs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cbfa1752", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Define the system Hamiltonian:\n", + "\n", + "@dataclasses.dataclass\n", + "class SystemParameters:\n", + " e1: float = 0.3 # fermion mode energy splitting\n", + " Omega: float = 0.2 # bosonic mode energy splitting\n", + " Lambda: float = 0.12 # coupling between fermion and boson\n", + " Nbos: int = 2\n", + "\n", + " def __post_init__(self):\n", + " d = tensor(destroy(2), qeye(self.Nbos))\n", + " a = tensor(qeye(2), destroy(self.Nbos))\n", + " self.H = (\n", + " self.e1 * d.dag() * d +\n", + " self.Omega * a.dag() * a +\n", + " self.Lambda * (a + a.dag()) * d.dag() * d\n", + " )\n", + " self.Q = d\n", + "\n", + " def replace(self, **kw):\n", + " return dataclasses.replace(self, **kw)\n", + "\n", + "\n", + "sys_p = SystemParameters()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d68d3f54", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Define parameters for left and right fermionic baths.\n", + "# Each bath is a lead (i.e. a wire held at a potential)\n", + "# with temperature T and chemical potential mu.\n", + "\n", + "@dataclasses.dataclass\n", + "class LorentzianBathParameters:\n", + " lead: str\n", + " gamma: float = 0.01 # coupling strength\n", + " W: float = 1.0 # cut-off\n", + " T: float = 0.025851991 # temperature (in eV)\n", + " theta: float = 2.0 # bias\n", + "\n", + " def __post_init__(self):\n", + " assert self.lead in (\"L\", \"R\")\n", + " self.beta = 1 / self.T\n", + " if self.lead == \"L\":\n", + " self.mu = self.theta / 2.0\n", + " else:\n", + " self.mu = - self.theta / 2.0\n", + "\n", + " def J(self, w):\n", + " \"\"\" Spectral density. \"\"\"\n", + " return self.gamma * self.W**2 / ((w - self.mu)**2 + self.W**2)\n", + "\n", + " def fF(self, w, sign=1.0):\n", + " \"\"\" Fermi distribution for this bath. \"\"\"\n", + " x = sign * self.beta * (w - self.mu)\n", + " return fF(x)\n", + "\n", + " def lamshift(self, w):\n", + " \"\"\" Return the lamshift. \"\"\"\n", + " return 0.5 * (w - self.mu) * self.J(w) / self.W\n", + "\n", + " def replace(self, **kw):\n", + " return dataclasses.replace(self, **kw)\n", + "\n", + "\n", + "def fF(x):\n", + " \"\"\" Return the Fermi distribution. \"\"\"\n", + " # in units where kB = 1.0\n", + " return 1 / (np.exp(x) + 1)\n", + "\n", + "\n", + "# We set W = 1e4 to investigate the wide-band limit:\n", + "\n", + "bath_L = LorentzianBathParameters(W=10**4, lead=\"L\")\n", + "bath_R = LorentzianBathParameters(W=10**4, lead=\"R\")" + ] + }, + { + "cell_type": "markdown", + "id": "5155bdcb", + "metadata": {}, + "source": [ + "## Emission and absorption by the leads\n", + "\n", + "Next let's plot the emission and absorption by the leads." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "973ada56", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "w_list = np.linspace(-2, 2, 100)\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 7))\n", + "\n", + "# Left lead emission and absorption\n", + "\n", + "gam_L_in = bath_L.J(w_list) * bath_L.fF(w_list, sign=1.0)\n", + "gam_L_out = bath_L.J(w_list) * bath_L.fF(w_list, sign=-1.0)\n", + "\n", + "ax.plot(\n", + " w_list, gam_L_in,\n", + " \"b--\", linewidth=3,\n", + " label=r\"S_L(w) input (absorption)\",\n", + ")\n", + "ax.plot(\n", + " w_list, gam_L_out,\n", + " \"r--\", linewidth=3,\n", + " label=r\"S_L(w) output (emission)\",\n", + ")\n", + "\n", + "# Right lead emission and absorption\n", + "\n", + "gam_R_in = bath_R.J(w_list) * bath_R.fF(w_list, sign=1.0)\n", + "gam_R_out = bath_R.J(w_list) * bath_R.fF(w_list, sign=-1.0)\n", + "\n", + "ax.plot(\n", + " w_list, gam_R_in,\n", + " \"b\", linewidth=3,\n", + " label=r\"S_R(w) input (absorption)\",\n", + ")\n", + "ax.plot(\n", + " w_list, gam_R_out,\n", + " \"r\", linewidth=3,\n", + " label=r\"S_R(w) output (emission)\",\n", + ")\n", + "\n", + "ax.set_xlabel(\"w\")\n", + "ax.set_ylabel(r\"$S(\\omega)$\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "b2b2c871", + "metadata": {}, + "source": [ + "## Below we give one example data set from Paper\n", + "\n", + "Here we just give one example of the current as a function of bias voltage, but in general one can try different cut-offs of the bosonic Fock space and the expansion of the correlation functions until convergence is found.\n", + "\n", + "One note: for very large problems, this can be slow." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c7cf321", + "metadata": {}, + "outputs": [], + "source": [ + "def steady_state_pade_for_theta(sys_p, bath_L, bath_R, theta, Nk, Nc, Nbos):\n", + " \"\"\" Return the steady state current using the Pade approximation. \"\"\"\n", + " options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", + "\n", + " sys_p = sys_p.replace(Nbos=Nbos)\n", + " bath_L = bath_L.replace(theta=theta)\n", + " bath_R = bath_R.replace(theta=theta)\n", + "\n", + " bathL = LorentzianPadeBath(\n", + " sys_p.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", + " Nk, tag=\"L\",\n", + " )\n", + " bathR = LorentzianPadeBath(\n", + " sys_p.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", + " Nk, tag=\"R\",\n", + " )\n", + "\n", + " solver_pade = HEOMSolver(\n", + " sys_p.H, [bathL, bathR], max_depth=2, options=options,\n", + " )\n", + " rho_ss_pade, ado_ss_pade = solver_pade.steady_state()\n", + " current = state_current(ado_ss_pade, bath_tag=\"R\")\n", + "\n", + " return np.real(2.434e-4 * 1e6 * current)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84619bfa", + "metadata": {}, + "outputs": [], + "source": [ + "# Parameters:\n", + "\n", + "Nk = 6\n", + "Nc = 2\n", + "Nbos = 2 # Use Nbos = 16 for more accurate results\n", + "\n", + "thetas = np.linspace(0, 2, 30)\n", + "\n", + "# Progress bar:\n", + "\n", + "progress = IntProgress(min=0, max=len(thetas))\n", + "display(progress)\n", + "\n", + "currents = []\n", + "\n", + "for theta in thetas:\n", + " currents.append(steady_state_pade_for_theta(\n", + " sys_p, bath_L, bath_R, theta,\n", + " Nk=Nk, Nc=Nc, Nbos=Nbos,\n", + " ))\n", + " progress.value += 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2d472ae", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(12, 10))\n", + "\n", + "ax.plot(\n", + " thetas, currents,\n", + " color=\"green\", linestyle='-', linewidth=3,\n", + " label=f\"Nk = {5}, max_depth = {Nc}, Nbos = {Nbos}\",\n", + ")\n", + "\n", + "ax.set_yticks([0, 0.5, 1])\n", + "ax.set_yticklabels([0, 0.5, 1])\n", + "\n", + "ax.locator_params(axis='y', nbins=4)\n", + "ax.locator_params(axis='x', nbins=4)\n", + "\n", + "ax.set_xlabel(r\"Bias voltage $\\Delta \\mu$ ($V$)\", fontsize=30)\n", + "ax.set_ylabel(r\"Current ($\\mu A$)\", fontsize=30)\n", + "ax.legend(loc=4);" + ] + }, + { + "cell_type": "markdown", + "id": "6c9ffd1a", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29df5cc6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "a3f3f10d", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62384dbf", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "assert 1 == 1" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v4/heom/heom-index.ipynb b/tutorials-v4/heom/heom-index.ipynb new file mode 100644 index 00000000..b5168f1e --- /dev/null +++ b/tutorials-v4/heom/heom-index.ipynb @@ -0,0 +1,56 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0ebc94ee", + "metadata": {}, + "source": [ + "# Hierarchical Equation of Motion Examples\n", + "\n", + "The \"hierarchical equations of motion\" (HEOM) method is a powerful numerical approach to solve the dynamics and steady-state of a quantum system coupled to a non-Markovian and non-perturbative environment. Originally developed in the context of physical chemistry, it has also been extended and applied to problems in solid-state physics, optics, single-molecule electronics, and biological physics.\n", + "\n", + "QuTiP's implementation of the HEOM is described in detail in https://arxiv.org/abs/2010.10806.\n", + "\n", + "This collection of examples from the paper illustrates how to use QuTiP's HEOM to model and investigate the dynamics of a variety of systems coupled to bosonic or fermionic baths.\n", + "\n", + "## Overview of the notebooks\n", + "\n", + "\n", + "\n", + "* [Example 1a: Spin-Bath model (introduction)](./heom-1a-spin-bath-model-basic.ipynb)\n", + "\n", + "* [Example 1b: Spin-Bath model (very strong coupling)](./heom-1b-spin-bath-model-very-strong-coupling.ipynb)\n", + "\n", + "* [Example 1c: Spin-Bath model (underdamped case)](./heom-1c-spin-bath-model-underdamped-sd.ipynb)\n", + "\n", + "* [Example 1d: Spin-Bath model, fitting of spectrum and correlation functions](./heom-1d-spin-bath-model-ohmic-fitting.ipynb)\n", + "\n", + "* [Example 1e: Spin-Bath model (pure dephasing)](./heom-1e-spin-bath-model-pure-dephasing.ipynb)\n", + "\n", + "* [Example 2: Dynamics in Fenna-Mathews-Olsen complex (FMO)](./heom-2-fmo-example.ipynb)\n", + "\n", + "* [Example 3: Quantum Heat Transport](./heom-3-quantum-heat-transport.ipynb)\n", + "\n", + "* [Example 4: Dynamical decoupling of a non-Markovian environment](./heom-4-dynamical-decoupling.ipynb)\n", + "\n", + "* [Example 5a: Fermionic single impurity model](./heom-5a-fermions-single-impurity-model.ipynb)\n", + "\n", + "* [Example 5b: Discrete boson coupled to an impurity + fermionic leads](./heom-5b-fermions-discrete-boson-model.ipynb)\n", + "\n", + "" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/fitting.ipynb b/tutorials-v5/heom/fitting.ipynb new file mode 100644 index 00000000..1783c20a --- /dev/null +++ b/tutorials-v5/heom/fitting.ipynb @@ -0,0 +1,2326 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebddecba", + "metadata": {}, + "source": [ + "# HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions" + ] + }, + { + "cell_type": "markdown", + "id": "2142c296", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded \n", + "in a set of auxiliary density matrices.\n", + "\n", + "In this example we show the evolution of a single two-level system in contact with a single bosonic environment.\n", + "\n", + "The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", + "\n", + "The bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", + "\n", + "In the example below we show how to model an Ohmic environment with exponential cut-off in three ways:\n", + "\n", + "* First we fit the spectral density with a set of underdamped brownian oscillator functions.\n", + "* Second, we evaluate the correlation functions, and fit those with a certain choice of exponential functions.\n", + "* Third, we use the available OhmicBath class \n", + "\n", + "In each case we will use the fit parameters to determine the correlation function expansion co-efficients needed to construct a description of the bath (i.e. a `BosonicBath` object) to supply to the `HEOMSolver` so that we can solve for the system dynamics." + ] + }, + { + "cell_type": "markdown", + "id": "d3ef97c3", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ed47f849", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "import qutip\n", + "from qutip import (\n", + " basis,\n", + " expect,\n", + " sigmax,\n", + " sigmaz,\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver\n", + ")\n", + "from qutip.core.environment import BosonicEnvironment,_sd_fit_model,OhmicEnvironment\n", + "\n", + "# Import mpmath functions for evaluation of gamma and zeta\n", + "# functions in the expression for the correlation:\n", + "\n", + "from mpmath import mp\n", + "\n", + "mp.dps = 15\n", + "mp.pretty = True\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2eb48e5a", + "metadata": {}, + "outputs": [], + "source": [ + "# Solver options:\n", + "\n", + "options = {\n", + " \"nsteps\": 15000,\n", + " \"store_states\": True,\n", + " \"rtol\": 1e-14,\n", + " \"atol\": 1e-14,\n", + " \"method\": \"vern9\",\n", + " \"progress_bar\": \"enhanced\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "65a7dfbb", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "Let us set up the system Hamiltonian, bath and system measurement operators:" + ] + }, + { + "cell_type": "markdown", + "id": "1e362553", + "metadata": {}, + "source": [ + "### System Hamiltonian" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ac95be0b", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the system Hamiltonian\n", + "eps = 0 # Energy of the 2-level system.\n", + "Del = 0.2 # Tunnelling term\n", + "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()\n", + "rho0 = basis(2, 0) * basis(2, 0).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "d89e26d2", + "metadata": {}, + "source": [ + "### System measurement operators" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d79edfb4", + "metadata": {}, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresonding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresonding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "52c4fb7a", + "metadata": {}, + "source": [ + "### Analytical expressions for the Ohmic bath correlation function and spectral density" + ] + }, + { + "cell_type": "markdown", + "id": "a0a87475", + "metadata": {}, + "source": [ + "Before we begin fitting, let us examine the analytic expressions for the correlation and spectral density functions and write Python equivalents. \n", + "\n", + "The correlation function is given by (see, e.g., http://www1.itp.tu-berlin.de/brandes/public_html/publications/notes.pdf for a derivation, equation 7.59, but with a factor of $\\pi$ moved into the definition of the correlation function):\n", + "\n", + "\\begin{align}\n", + "C(t) =& \\: \\frac{1}{\\pi}\\alpha \\omega_{c}^{1 - s} \\beta^{- (s + 1)} \\: \\times \\\\\n", + " & \\: \\Gamma(s + 1) \\left[ \\zeta \\left(s + 1, \\frac{1 + \\beta \\omega_c - i \\omega_c t}{\\beta \\omega_c}\\right) + \\zeta \\left(s + 1, \\frac{1 + i \\omega_c t}{\\beta \\omega_c}\\right) \\right]\n", + "\\end{align}\n", + "\n", + "where $\\Gamma$ is the Gamma function and\n", + "\n", + "\\begin{equation}\n", + "\\zeta(z, u) \\equiv \\sum_{n=0}^{\\infty} \\frac{1}{(n + u)^z}, \\; u \\neq 0, -1, -2, \\ldots\n", + "\\end{equation}\n", + "\n", + "is the generalized Zeta function. The Ohmic case is given by $s = 1$.\n", + "\n", + "The corresponding spectral density for the Ohmic case is:\n", + "\n", + "\\begin{equation}\n", + "J(\\omega) = \\omega \\alpha e^{- \\frac{\\omega}{\\omega_c}}\n", + "\\end{equation}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bfb44fda", + "metadata": {}, + "outputs": [], + "source": [ + "def ohmic_correlation(t, alpha, wc, beta, s=1):\n", + " \"\"\"The Ohmic bath correlation function as a function of t\n", + " (and the bath parameters).\n", + " \"\"\"\n", + " corr = (1 / np.pi) * alpha * wc ** (1 - s)\n", + " corr *= beta ** (-(s + 1)) * mp.gamma(s + 1)\n", + " z1_u = (1 + beta * wc - 1.0j * wc * t) / (beta * wc)\n", + " z2_u = (1 + 1.0j * wc * t) / (beta * wc)\n", + " # Note: the arguments to zeta should be in as high precision as possible.\n", + " # See http://mpmath.org/doc/current/basics.html#providing-correct-input\n", + " return np.array(\n", + " [\n", + " complex(corr * (mp.zeta(s + 1, u1) + mp.zeta(s + 1, u2)))\n", + " for u1, u2 in zip(z1_u, z2_u)\n", + " ],\n", + " dtype=np.complex128,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9e798939", + "metadata": {}, + "outputs": [], + "source": [ + "def ohmic_spectral_density(w, alpha, wc):\n", + " \"\"\"The Ohmic bath spectral density as a function of w\n", + " (and the bath parameters).\n", + " \"\"\"\n", + " return w * alpha * np.e ** (-w / wc)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7691064b", + "metadata": {}, + "outputs": [], + "source": [ + "def ohmic_power_spectrum(w, alpha, wc, beta):\n", + " \"\"\"The Ohmic bath power spectrum as a function of w\n", + " (and the bath parameters).\n", + " It is obtained naively using the Fluctuation-Dissipation Theorem\n", + " but, this fails at w=0 where the limit should be taken properly\n", + " \"\"\"\n", + " bose = (1 / (np.e ** (w * beta) - 1)) + 1\n", + " return w * alpha * np.e ** (-abs(w) / wc) * 2*bose " + ] + }, + { + "cell_type": "markdown", + "id": "c7913528", + "metadata": {}, + "source": [ + "### Bath and HEOM parameters" + ] + }, + { + "cell_type": "markdown", + "id": "0a40fda0", + "metadata": {}, + "source": [ + "Finally, let's set the bath parameters we will work with and write down some measurement operators:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8d58b8c8", + "metadata": {}, + "outputs": [], + "source": [ + "Q = sigmaz()\n", + "alpha = 3.25\n", + "T = 0.5\n", + "wc = 1.0\n", + "s = 1" + ] + }, + { + "cell_type": "markdown", + "id": "635dcec1", + "metadata": {}, + "source": [ + "And set the cut-off for the HEOM hierarchy:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "297850af", + "metadata": {}, + "outputs": [], + "source": [ + "# HEOM parameters:\n", + "\n", + "# The max_depth defaults to 5 so that the notebook executes more\n", + "# quickly. Change it to 11 to wait longer for more accurate results.\n", + "max_depth = 5" + ] + }, + { + "cell_type": "markdown", + "id": "8827fc32", + "metadata": {}, + "source": [ + "## Building the HEOM bath by fitting the spectral density" + ] + }, + { + "cell_type": "markdown", + "id": "6e3c4370", + "metadata": {}, + "source": [ + "We begin by fitting the spectral density, using a series of $k$ underdamped harmonic oscillators case with the Meier-Tannor form (J. Chem. Phys. 111, 3365 (1999); https://doi.org/10.1063/1.479669):\n", + "\n", + "\\begin{equation}\n", + "J_{\\mathrm approx}(\\omega; a, b, c) = \\sum_{i=0}^{k-1} \\frac{2 a_i b_i w}{((w + c_i)^2 + b_i^2) ((w - c_i)^2 + b_i^2)}\n", + "\\end{equation}\n", + "\n", + "where $a, b$ and $c$ are the fit parameters and each is a vector of length $k$." + ] + }, + { + "cell_type": "markdown", + "id": "6b67cac7", + "metadata": {}, + "source": [ + "With the spectral density approximation $J_{\\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. This can be done quickly using the `SpectralFitter` class, which takes the target spectral density as an array and fits it to the series of **k** underdamped harmonic oscillators with the Meier-Tannor form" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f6b46bc0", + "metadata": {}, + "outputs": [], + "source": [ + "w = np.linspace(0, 15, 20000)\n", + "J = ohmic_spectral_density(w, alpha, wc)" + ] + }, + { + "cell_type": "markdown", + "id": "ae05a07c", + "metadata": {}, + "source": [ + "The `BosonicEnviroment` class has special construtors that can be used to \n", + "create enviroments from arbitrary spectral densities, correlation functions, or\n", + "power spectrums. Below we show how to construct a `BosonicEnvironment` from a \n", + "user specified function or array" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0239acf7", + "metadata": {}, + "outputs": [], + "source": [ + "# From an array\n", + "sd_env=BosonicEnvironment.from_spectral_density(J=J,wlist=w,T=T)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c388e587", + "metadata": {}, + "outputs": [], + "source": [ + "sd_env.approximate()" + ] + }, + { + "cell_type": "markdown", + "id": "33a4729e", + "metadata": {}, + "source": [ + "# Obtaining a decaying Exponential description of the environment\n", + "\n", + "In order to carry out our HEOM simulation, we need to express the correlation \n", + "function as a sum of decaying exponentials, that is we need to express it as \n", + "\n", + "$$C(\\tau)= \\sum_{k=0}^{N-1}c_{k}e^{-\\nu_{k}t}$$\n", + "\n", + "As the correlation function of the environment is tied to it's power spectrum via \n", + "a Fourier transform, such a representation of the correlation function implies a \n", + "power spectrum of the form\n", + "\n", + "$$S(\\omega)= \\sum_{k}2 Re\\left( \\frac{c_{k}}{\\nu_{k}- i \\omega}\\right)$$\n", + "\n", + "There are several ways one can obtain such a decomposition, in this tutorial we \n", + "will cover the following approaches:\n", + "\n", + "- Non-Linear Least Squares:\n", + " - On the Spectral Density (`.approx_by_sd_fit`)\n", + " - On the Correlation function (`.approx_by_cf_fit`)\n", + "- Methods based on the Prony Polynomial\n", + " - Prony on the correlation function(`.approx_by_prony`)\n", + " - The Matrix Pencil method on the correlation function (`.approx_by_mp`)\n", + " - ESPRIT on the correlation function(`.approx_by_esprit`)\n", + "- Methods based on rational Approximations\n", + " - The AAA algorithm on the Power Spectrum (`.approx_by_aaa`)\n", + " - The AAA algorith with balanced truncation (`.approx_by_aaa` with `btm=True`)\n", + " - ESPIRA\n" + ] + }, + { + "cell_type": "markdown", + "id": "bef212bc", + "metadata": {}, + "source": [ + "# Non-Linear Least Squares\n", + "## Obtaining an decaying Exponential Description via the spectral density" + ] + }, + { + "cell_type": "markdown", + "id": "ce27cb93", + "metadata": {}, + "source": [ + "Once our `BosonicEnvironment` has been constructed, we can obtain a Decaying\n", + "exponnetial representation of the environment, via fitting either the spectral\n", + "density, power spectrum or the correlation function. \n", + "\n", + "First we will show how to do it via fitting the spectral density with the \n", + "Nonlinear-Least-Squares method.\n", + "\n", + "The idea here is that we express our arbitrary spectral density as a sum of \n", + "underdamped spectral densities with different coefficients, for which a the\n", + "Matsubara decomposition is available. The number of exponents to be kept in the \n", + "Matsubara decomposition of each underdamped spectral density needs to be specified\n", + "\n", + "The output of the fit is a tuple containing an `ExponentialBosonicEnvironment`\n", + "and a dictionary that has all the relevant information about the fit performed.\n", + "The goodness of the feed is measured via the normalized root mean squared error,\n", + "by default the number of terms in the fit increased until the target accuracy \n", + "is reached or the maximum number allowed `Nmax` is reached. The default target\n", + "is a normalized root mean squared error of $5\\times 10^{-6}$, if set to None\n", + "the fit is performed only with the maximum number of exponents specified\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7c51abc3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "qutip.core.environment._BosonicEnvironment_fromSD" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(sd_env)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b8e557d", + "metadata": {}, + "outputs": [], + "source": [ + "sd_env.approximate()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "fe57ba64", + "metadata": {}, + "outputs": [], + "source": [ + "env=sd_env.from_spectral_density(sd_env.spectral_density)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "50fc6a2f", + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "BosonicEnvironment.approximate() missing 1 required positional argument: 'method'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43menv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapproximate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mTypeError\u001b[0m: BosonicEnvironment.approximate() missing 1 required positional argument: 'method'" + ] + } + ], + "source": [ + "env.approximate()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "81adee22", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Unsupported method: pronys. The available methods are: \n - Correlation function NLSQ Fitting (corr_lsq)\n- Spectral Density NLSQ Fitting (spec_lsq) \n- Correlation function Prony Fitting (prony) \n- Correlation function Matrix Pencil Fitting (mp) \n- Correlation function ESPRIT Fitting (esprit)\n- Power spectrum AAA fitting (aaa) \n", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m bath, fitinfo \u001b[38;5;241m=\u001b[39m \u001b[43msd_env\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapproximate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpronys\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m#approx_by_sd_fit(w,Nmax=6)\u001b[39;00m\n", + "File \u001b[0;32m~/qutip_gsoc_app/qutip/core/environment.py:1490\u001b[0m, in \u001b[0;36m_BosonicEnvironment_fromSD.approximate\u001b[0;34m(self, method, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1480\u001b[0m dispatch \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 1481\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcorr_lsq\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_approx_by_cf_fit,\n\u001b[1;32m 1482\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspec_lsq\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_approx_by_sd_fit,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1486\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maaa\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_approx_by_aaa,\n\u001b[1;32m 1487\u001b[0m }\n\u001b[1;32m 1489\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m dispatch:\n\u001b[0;32m-> 1490\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnsupported method: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmethod\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. The available\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1491\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m methods are: \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1492\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m- Correlation function NLSQ Fitting (corr_lsq)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1493\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m- Spectral Density NLSQ Fitting (spec_lsq) \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1494\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m- Correlation function Prony Fitting (prony) \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1495\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m- Correlation function Matrix Pencil Fitting\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m (mp) \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m- Correlation function ESPRIT Fitting (esprit)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m- Power spectrum AAA fitting (aaa) \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dispatch[method](\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "\u001b[0;31mValueError\u001b[0m: Unsupported method: pronys. The available methods are: \n - Correlation function NLSQ Fitting (corr_lsq)\n- Spectral Density NLSQ Fitting (spec_lsq) \n- Correlation function Prony Fitting (prony) \n- Correlation function Matrix Pencil Fitting (mp) \n- Correlation function ESPRIT Fitting (esprit)\n- Power spectrum AAA fitting (aaa) \n" + ] + } + ], + "source": [ + "bath, fitinfo = sd_env.approxima)\n", + "#approx_by_sd_fit(w,Nmax=6)" + ] + }, + { + "cell_type": "markdown", + "id": "2f5bc5a5", + "metadata": {}, + "source": [ + "To obtain an overview of the results of the fit we may take a look at the summary from the ``fitinfo``" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "71a7c82a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result of fitting the spectral density with 4 terms: \n", + " \n", + " Parameters| lam | gamma | w0 \n", + " 1 |-4.44e+00 | 4.31e+00 |3.96e+00\n", + " 2 | 6.07e-01 | 1.01e+00 |1.00e-01\n", + " 3 | 7.93e+00 | 2.30e+00 |1.00e-01\n", + " 4 | 1.07e-02 | 3.09e-01 |1.00e-01\n", + " \n", + "A normalized RMSE of 2.64e-06 was obtained for the the spectral density.\n", + "The current fit took 23.571020 seconds.\n" + ] + } + ], + "source": [ + "print(fitinfo[\"summary\"])" + ] + }, + { + "cell_type": "markdown", + "id": "8edcc35e", + "metadata": {}, + "source": [ + "We may see how the number of exponents chosen affects the fit since the approximated functions are available:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "d8587f0d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5))\n", + "\n", + "ax1.plot(w, J, label=\"Original spectral density\")\n", + "ax1.plot(w, bath.spectral_density(w), \"--\",label=\"Effective fitted SD\")\n", + "ax1.set_xlabel(r'$\\omega$')\n", + "ax1.set_ylabel(r'$J$')\n", + "ax1.legend()\n", + "\n", + "ax2.plot(w, np.abs(J - bath.spectral_density(w)), label=\"Error\")\n", + "ax2.set_xlabel(r'$\\omega$')\n", + "ax2.set_ylabel(r'$|J-J_{approx}|$')\n", + "ax2.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "89164ff6", + "metadata": {}, + "source": [ + "Here we see a surprisingly large discrepancy in our approximated or effective spectral density. This happens because we are not using enough exponentials from each of the underdamped modes to have an appropiate fit. All modes have the same number of exponents, when not specified it defaults to $1$ which is not enough to model a bath with the temperature considered, let us repeat this with a higher number of exponents." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "bd7aec4a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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dO3bg9ddft3u/SqWC0WisVax79uzB9OnTrYlIYWFhpamT1fHx8UFwcDD2799vXftlMBiQmJiIPn36ADC/UKjVaqSlpdlM3auPzp07o3Pnzpg/fz7++te/Ys2aNXaTqKp+Bv7+/rjnnnuwZs0aJCQk4JFHHmlQPORcRiZRRM1SU3jvYuHh4YGOHTtWe39Nx+x98GvveE0fIDuSyzSWOHToEHr37m2dmrJgwQL07t0br7zyCgAgIyPDZnrMp59+CoPBgCeeeALBwcHW29y5cyWJvyEu7/kSAJDoMQDtwiKtx7vc/Tzy4YkwZOLA1nVShUcuYtmyZdBqtRg9ejR2796N9PR0bNmyBSNHjkTbtm1rXMtU0eOPP47Tp0/j+eefx9mzZ/H1118jPj4eAOqUjJXXqlUr+Pv7Y9WqVTh//jx+++03m3WItfHTTz/ho48+QnJyMi5duoQvvvgCJpMJXbrcWhuYnp6OBQsW4MyZM1i7di0+/vhj6//9zp0746GHHsK0adPw3XffISUlBQcPHsQ777xjTRoXLlyIgwcPYvbs2Th69ChOnz6NFStWIDs7G4B5yuKBAweQmppqMyXRno4dO+K7775DcnIyjhw5ggcffLDWTTQs5s6di3/84x/YuHEjTp8+jdmzZ1s7JALm6twzzzyD+fPn4z//+Q8uXLiApKQkfPLJJ3Zbx9pTUlKCJ598Ejt37sSlS5fwxx9/4ODBg1Wus4uIiLB+mJWdnQ2tVmu9b8aMGfjPf/6DU6dO4eGHH67TcyXnK19RNjpxUTURUWOJjo5GWloa0tPTrcdOnjyJvLy8Kl/HnM1lkqghQ4ZAFMVKN8ubuvj4eJuFYzt37qx2fJNhMiE8o2xhYLf7bO5SuHkhNdy80Nz3GNsHt3SdOnXCoUOH0KFDB0yZMgUdOnTA3//+dwwdOhQJCQnVtve3JzIyEt9++y2+++479OjRAytWrLB256vv2kGZTIZ169YhMTER3bt3x/z5862NK2rL19cX3333HYYNG4aoqCisXLkSa9eutWn8MG3aNJSUlKBfv3544okn8NRTT+Hvf/+79f41a9Zg2rRpePrpp9GlSxfcddddOHDggHUaQOfOnbF161YcOXIE/fr1Q1xcHDZt2mTd8+mZZ56BXC5HdHQ0WrduXe36pg8++ACtWrVC//79MWHCBIwePdpaQaqtp59+GtOmTcP06dMRFxcHLy+vStWhN954A6+88gqWLFmCqKgojB49Gj/++CMiIyOrOKstuVyOnJwcTJs2DZ07d8bkyZMxduzYKqtxEydOxJgxYzB06FC0bt3aph3tiBEjEBwcjNGjR1ubd5DrKF98ktXzAxEiIkfRarXIzMy0uVk+tKytESNGoEePHnjooYdw+PBh/Pnnn5g2bRoGDx5c4xpwZxFEZ/b+c3H5+fnw8fFBXl4evL29JYkh+9whBHw1HEWiGqXzz8Hf13Yq4s0r5+D771iYRAHn/nYAXTqxU19DlZaWIiUlBZGRkdBoNFKH41LeeustrFy50uaTHlczZMgQ9OrVq9KG29R4iouLERISgtWrV1unWFaluv9vrvA72BU19OeiNRjR5SXzJpbv3d8TE2PaOTpEImpETfl9S1Wb7Xbp0gWnT5+GIAjYuHGjzXrv1157Dd9//73NvpaAuUHVU089Zd0+ZcyYMfj4448RGBhY7eMqctTrUtOYTNmMpR3eggAAp1XdEeNbeS2Xb9tOOOLRH8fz3JB3IoNJFDnU8uXL0bdvX/j7++OPP/7Av/71r1q35aaWx2QyITMzE++99x58fHxw1113SR0S2VF+NikLUUQkpfj4+Gpnidmr5bz22mt47bXXKh0PCwvDpk2bqjxXVY9zFiZREttTEoGjhlEI7HhHlWPSR3+ORf9LQsQ5Ef9XxcI6ovo4d+4c3nzzTeTm5iIsLAxPP/00Fi5cKHVY5KLS0tIQGRmJdu3aIT4+3jr9kVyLoVwWxZcLIiLn4CugxL7PbocUw3Ssie1b5ZghXdpApZAhNacYZ68VokuQV5Vjierigw8+wAcffCB1GHVSfm0kNa6IiAin7v5OjmFTiQKzKCIiZ3CZxhItUW6RDinZRQCA3mG+VY7zVCswuGMr9BHO4kjCtkaKjoiImiJWooiInI9JlITOHE9ErHAa0QEy+Lqrqh07W7MN36lfQ4dTnzRSdERE1BSVb2vOwiERkXMwiZKQ8sh/8a16MRYpa94DKqTvnQCA7tojyMu76eTIiIioqSq/wa6JWRRRs8Hp1I7hqJ8jkygJeeYeBwAYg3rVODawfS9kCG2gFvQ4f+AXJ0dGRERNVfkkqvzXRNQ0KZVKAObtJajhdDodAPP+iQ3BxhJSEUW0Kz0HAPCKiKl5vCDgqt8dCM75ASXnfgdG/dXJARIRUVNUPnHiB9dETZ9cLoevry+ysrIAAO7u7uzUXE8mkwnXr1+Hu7t7gzvMMomSSEnuZXiiCAZRhrZdetXqMerOQ4CEH9Am+0+nxkZERE2XTSWKWRRRsxAUFAQA1kSK6k8mkyEsLKzBiSiTKIlknD+K9gCuCIEI86ldy/LI2DFAwgJ0NKUiI/MqgoNCnBskNTmZmZmYOnUq9u3bB6VSiZs3b9o95gzx8fGYN2+e085v8f333+OZZ55BSkoKnnrqKfTq1atRrluevR3WiVwF10QRNT+CICA4OBht2rSBXq+XOpwmTaVSQSZr+IomromSyM30kwCA6+raZ8Ie/m2RJg+FTBCRfpitzlua6dOnQxCESrcxY8ZYx3zwwQfIyMhAcnIyzp49W+WxhoqIiMDSpUttjk2ZMsVh56/O448/jkmTJiE9PR1vvPFGpeu+9tpr6NWrV6XHCYKA77//3unxAeZPCh9//HGEhYVBrVYjKCgIo0ePRkJCgnVMRESE9e/Qzc0NERERmDx5Mn777bdGiZGar/LLoExcE0XUrMjlcmg0Gt4acHNEAgWwEiUZ0/UzAIBSnw51etyeyPnYcCIf3bU90M8ZgZFLGzNmDNasWWNzTK1WW7++cOECYmJi0KlTp2qPOYObmxvc3Nyceo3CwkJkZWVh9OjRCAm5VYl19nXrauLEidDr9fjPf/6D9u3b49q1a9ixYwdyc3Ntxi1evBgzZ86ETqdDamoq/vvf/2LEiBF44403sGjRIomip6ZORPlKlISBEBE1Y6xESWSLcjhe0j+CvPDRdXqcf69xOCx2xoHUAidFRq7MUtUof2vVqhUAc2Vjw4YN+OKLLyAIAqZPn273GADk5eXh73//O9q0aQNvb28MGzYMR44csbnWDz/8gNjYWGg0GgQEBOC+++4DAAwZMgSXLl3C/PnzrZUUwDydz9fXFwBw5swZCIKA06dP25zz/fffR0REhLW96MmTJzFu3Dh4enoiMDAQU6dORXZ2tt3nvnPnTnh5mae+Dhs2DIIgYOfOnTbXjY+Px+uvv44jR45YY4uPj0dERAQA4N5774UgCNbvAeDHH39ETEwMNBoN2rdvj9dffx0Gg8F6/7lz5zBo0CBoNBpER0dj27bqq8A3b97E3r178c4772Do0KEIDw9Hv379sHDhQowfP95mrJeXF4KCghAWFoZBgwZh1apVePnll/HKK6/gzJkz1V6HarZ8+XJERkZCo9EgJiYGe/bsqXb8rl27bP4trFy5stKYDRs2IDo6Gmq1GtHR0di4cWOV51uyZAkEQcC8efMa+lTqpPwMPnbnIyJyDiZREtlb2Bb/NY6Ee4f+dXpcbIQfAODMtQLcLNY5I7SWS1dU9U1fWoexJbUb62AHDx7EmDFjMHnyZGRkZODDDz+0e0wURYwfPx6ZmZnYvHkzEhMT0adPHwwfPtxaKfn5559x3333Yfz48UhKSsKOHTsQGxsLAPjuu+/Qrl07LF68GBkZGcjIyKgUS5cuXRATE4OvvvrK5vj//vc/PPjggxAEARkZGRg8eDB69eqFQ4cOYcuWLbh27RomT55s9/n179/fmlhs2LABGRkZ6N/f9v/PlClT8PTTT6Nbt27W2KZMmYKDBw8CANasWYOMjAzr97/++iv+9re/Yc6cOTh58iQ+/fRTxMfH46233gJg7uJz3333QS6XY//+/Vi5ciWef/75av8ePD094enpie+//x5arbbasfbMnTsXoihi06ZNdX4s3bJ+/XrMmzcPixYtQlJSEgYOHIixY8ciLS3N7viUlBSMGzcOAwcORFJSEl588UXMmTMHGzZssI5JSEjAlClTMHXqVBw5cgRTp07F5MmTceDAgUrnO3jwIFatWoUePXo47TlWpXwSxTVRRETOwel8EhBFEZdyzL3+w/3d6/TYAE81HvI9gS6Ff+LcITn6DhpT84Oodt6uplFHp1HAQ9/c+v5fHQF9Ffs1hA8AHvn51vdLbwOKcyqPey2vziH+9NNP8PT0tDn2/PPP4+WXX0br1q2hVqvh5uZm7eIDoNKx3377DceOHUNWVpZ1KuC7776L77//Ht9++y3+/ve/46233sIDDzyA119/3Xqenj17AgD8/Pwgl8utVZSqPPTQQ1i2bBneeOMNAMDZs2eRmJiIL774AgCwYsUK9OnTB2+//bb1MatXr0ZoaCjOnj2Lzp0725xPpVKhTZs21hjsXdvNzQ2enp5QKBQ291um+/n6+tocf+utt/DCCy/g4YcfBgC0b98eb7zxBp577jm8+uqr2L59O06dOoXU1FS0a9cOAPD2229j7NixVT5vhUKB+Ph4zJw5EytXrkSfPn0wePBgPPDAA7V6Q+3n54c2bdogNTW1xrFUtffffx+PPfYYZsyYAQBYunQpfv31V6xYsQJLliypNH7lypUICwuzrvWLiorCoUOH8O6772LixInWc4wcORILFy4EACxcuBC7du3C0qVLsXbtWuu5CgsL8dBDD+Hf//433nzzTSc/08rKT+djDkVE5BysREkg+9pljDbuQm/ZebRrVbckCgAmqv/ENMU2lJ7e6oToyJUNHToUycnJNrcnnniiTudITExEYWEh/P39rVUTT09PpKSk4MKFCwCA5ORkDB8+vEGxPvDAA7h06RL2798PAPjqq6/Qq1cvREdHW+P4/fffbWLo2rUrAFjjcLbExEQsXrzYJoaZM2ciIyMDxcXFOHXqFMLCwqwJFADExcXVeN6JEyfi6tWr+OGHHzB69Gjs3LkTffr0QXx8fK3iEkWRe4A0gE6nQ2JiIkaNGmVzfNSoUdi3b5/dxyQkJFQaP3r0aBw6dMjaCauqMRXP+cQTT2D8+PEYMWJEreLVarXIz8+3uTWEzXQ+ZlFERE7BSpQEcs/9iaWq5TgnREClmFvnx4uhtwN52+CdneSE6FqwF69WfZ9QYVfrZ89XM7bCZxPzjtU/pgo8PDzQsWPHBp3DZDIhODgYO3furHSfZW2RIxo1BAcHY+jQofjf//6HO+64A2vXrsXjjz9uE8eECRPwzjvv2H1sYzCZTHj99det673K02g01rVb5dU2udFoNBg5ciRGjhyJV155BTNmzMCrr75qXZdWlZycHFy/fh2RkZG1ug5Vlp2dDaPRiMDAQJvjgYGByMzMtPuYzMxMu+MNBgOys7MRHBxc5Zjy51y3bh0OHz5snTJaG0uWLLGp+joSp/MRETkHkygJFF27CADIU9fvjWJA1wHAcaC99hQMej0USqUjw2u5VB7Sj20Effr0QWZmJhQKhU2DhfJ69OiBHTt24JFHHrF7v0qlgtForPFaDz30EJ5//nn89a9/xYULF/DAAw/YxLFhwwZEREQ0eNfw2sSmVCorHe/Tpw/OnDlTZWIaHR2NtLQ0XL161doNsHyb8rqIjo6uVYv1Dz/8EDKZjHtQOUDFhLemCp+98RWPV3fO9PR0zJ07F1u3boVGo6l1nAsXLsSCBQus3+fn5yM0NLTWj68OW5wTETkHp/NJwJCbCgDQerarfmAV2nWNRbGohpdQgvSzhx0YGbk6rVaLzMxMm1tV3eyqMmLECMTFxeGee+7Br7/+itTUVOzbtw8vvfQSDh06BAB49dVXsXbtWrz66qs4deoUjh07hn/+85/Wc0RERGD37t24cuVKtde/7777kJ+fj//7v//D0KFD0bZtW+t9TzzxBHJzc/HXv/4Vf/75Jy5evIitW7fi0UcfrVWCVpWIiAikpKQgOTkZ2dnZ1uYOERER2LFjBzIzM3Hjxg0AwCuvvIIvvvgCr732Gk6cOIFTp05h/fr1eOmll6w/qy5dumDatGk4cuQI9uzZU2Pr8ZycHAwbNgz//e9/cfToUaSkpOCbb77BP//5T9x99902YwsKCpCZmYn09HTs3r0bf//73/Hmm2/irbfeanDFsSULCAiAXC6vVHXKysqqVEmyCAoKsjteoVDA39+/2jGWcyYmJiIrKwsxMTFQKBRQKBTYtWsXPvroIygUiir/XavVanh7e9vcGqJ88YmFKCIi52ASJQFFwRUAgOBbv08a5QolLqqjAADZp/c6LC5yfVu2bEFwcLDNbcCAAXU6hyAI2Lx5MwYNGoRHH30UnTt3xgMPPIDU1FTrm8EhQ4bgm2++wQ8//IBevXph2LBhNh3IFi9ejNTUVHTo0AGtW7eu8lre3t6YMGECjhw5goceesjmvpCQEPzxxx8wGo0YPXo0unfvjrlz58LHx6dBG+FNnDgRY8aMwdChQ9G6dWvrgv/33nsP27ZtQ2hoKHr37g3AvJ7lp59+wrZt29C3b1/ccccdeP/99xEeHg4AkMlk2LhxI7RaLfr164cZM2ZYO/dVxdPTE7fffjs++OADDBo0CN27d8fLL7+MmTNnYtmyZTZjX3nlFQQHB6Njx46YOnUq8vLysGPHjho7AFL1VCoVYmJiKrWj37ZtW6WOjhZxcXGVxm/duhWxsbFQllX7qxpjOefw4cNx7NgxmzWLsbGxeOihh5CcnAy5vMK0YCfhPlFERM4niPYm/bcQ+fn58PHxQV5eXoM/+auLc2/2QyfDGRy6/SPEjn24Xuf4Y9Vc/OVqPBJbjUPM3LU1P4CsSktLkZKSYt0/hoicp7r/b878Hbx+/XpMnToVK1euRFxcHFatWoV///vfOHHiBMLDw7Fw4UJcuXLF2i0yJSUF3bt3x+OPP46ZM2ciISEBs2bNwtq1a63d+fbt24dBgwbhrbfewt13341NmzbhpZdewt69e3H77bfbjWPIkCHo1auXtetfbTT053Ik/Sbu/uQPAMC8EZ0wb0TnGh5BRERA3X7/ck2UBPwN5ukg7oHt630OTXhf4Go8hILKe/QQEbV0U6ZMQU5OjnU/s+7du2Pz5s3WKmNGRobNnlGRkZHYvHkz5s+fj08++QQhISH46KOPrAkUYN6rbN26dXjppZfw8ssvo0OHDli/fn2VCZRUyn8y2nI/JiUici4mUY3MqC2GH8z7A7UKqf+ah5A+YxHz+wrclPnihN4IjbJxpokQETUVs2fPxuzZs+3eZ6/d/ODBg3H4cPXrTCdNmoRJkybVOgZ7XTCdrfwEkxY82YSIyKm4JqqRZRcbMF33HBbpH0PrgDb1Pk9QgB9kXm1gNIk4cbXum7YSEVHzJFbxNREROQ6TqEZ2tcCInaZe+M1zPBSK+lePBEFAz3a+AICktJuOCY6IiJo8ducjInI+JlGNLCOvFAAQ7NPwhgbjPc8iXvkOOh6uvlsYERG1JOW78zGLIiJyBq6JamT6S3/ibtl+BLj1bfC52vsK6Ck/gpS8Gw6IrOXhWgEi5+P/s8ZnU4mSLgwiomaNlahGFpz2Iz5ULccw7W8NPldIVBwAIMyYjpLC/Aafr6Ww7PlSXFwscSREzZ/l/5nl/x05H7vzERE5HytRjUxZfA0AIPMOafC5AoLDkQU/tBFycfn0AXSKHdngc7YEcrkcvr6+yMrKAgC4u7tDEASJoyJqXkRRRHFxMbKysuDr69toG81SxTVRzKKIiJyBSVQj02ivAwDUfg1PogRBwGW3zmhTsh95F/4EmETVWlBQEABYEykicg5fX1/r/zdqHDYtziWMg4ioOWMS1ch8DDkAAA//dg45X5H/bcDl/ZBnHnHI+VoKQRAQHByMNm3aQK/XSx0OUbOkVCpZgZKA7XQ+plFERM7AJKoRiSYTAky5gAB4tQ51yDnVob2By4Bf/mmHnK+lkcvlfJNHRM1K+bzJxByKiMgp2FiiERXczIZKMAAA/AIdk0QFde6LfNEdV/ReMBgMDjknERE1XWK5WhQLUUREzsFKVCO6eS0N3gBuip7wdXN3yDnbhXVED6xBoc6IrTkl6Bzo5ZDzEhFRE2XT4pxZFBGRM7AS1YiuCQF4RPcsPnSb7bBzyuQyRAV7AwBOXM1z2HmJiKhpYotzIiLnYxLViDK1Kvxu6o0TvsMcet7osiTq5OVch56XiIiaHrY4JyJyPiZRjeh6gRYA0NpL7dDzDlGdwU7VfNxz7EmHnpeIiJoemzVREsZBRNSccU1UI9Jc3Y97ZEfRQTnQoecNDQlChOwa8nWFEE0mCDLmxkRELZVtdz6mUUREzsB3242oy5WNWKpajpiSPxx63tAuvaEX5fBGEa5fuejQcxMRUdPCNVFERM7HJKoRKbXmNUsKrzYOPa9a4450uXnz3mvnDjr03ERE1LSUXwfFHIqIyDmYRDUiN/0NAIDKJ9Dh58726AwAKEk/6vBzExFR02FbiWIaRUTkDEyiGpGn8SYAwKOV45MofUA0AECVfcLh5yYioibEpjufdGEQETVnTKIaickkwlfMBwD4+Ac7/Pya0F4AgICicw4/NxERNR023fmYRBEROQWTqEaSl3cTboIOAODbOsTh5w/qHIujpkjsNXSF3mBw+PmJiKhpYHc+IiLnY4vzRpKfm4lWAEqhhMbNy+HnD2kbitHCOyjUGtAnpwSdAx1/DSIicn02m+1KFwYRUbPGSlQjyTF54RHds/iHai4gCA4/vyAI6BzoCQA4nVng8PMTEVHTwBbnRETO5zJJ1O7duzFhwgSEhIRAEAR8//33NT5m165diImJgUajQfv27bFy5UrnB1pPOXolfjf1RpLPMKddo2uwN5QwIO1SitOuQURErs2mxTmzKCIip3CZJKqoqAg9e/bEsmXLajU+JSUF48aNw8CBA5GUlIQXX3wRc+bMwYYNG5wcaf3cKDKvh2rlrnTaNUYKf+Kk+hEMP/mi065BRESuTaziayIichyXWRM1duxYjB07ttbjV65cibCwMCxduhQAEBUVhUOHDuHdd9/FxIkTnRRl/ckzk3Gv7A+EyPoC6OeUawS07QhlshFBpRedcn4iInJ9NmuiWIkiInIKl6lE1VVCQgJGjRplc2z06NE4dOgQ9Hq93cdotVrk5+fb3BpL26u/4APVCgws2uq0a4R17g2TKKAV8lGQc9Vp1yEiIld2K3EyMYciInKKJptEZWZmIjDQdtPawMBAGAwGZGdn233MkiVL4OPjY72FhoY2RqgAAEVJDgDA5B7gtGv4+HjjsiwIAHD17GGnXYeIiJoG5lBERM7RZJMowNyRrjzLtIWKxy0WLlyIvLw86y09Pd3pMVqodDfMsXk6L4kCgCxNewBA/qUjTr0OERG5Jk7nIyJyPpdZE1VXQUFByMzMtDmWlZUFhUIBf39/u49Rq9VQq9WNEV7la+vNUwdVnvZjcxStXxfgyh8Qsk459TpEROSa2OKciMj5mmwlKi4uDtu2bbM5tnXrVsTGxkKpdF4HvPpyM5qTKLW3c5MoZXA3AIB3wTmnXoeIiFyT7Wa7zKKIiJzBZZKowsJCJCcnIzk5GYC5hXlycjLS0tIAmKfiTZs2zTp+1qxZuHTpEhYsWIBTp05h9erV+Pzzz/HMM89IEX6NPE3mDXA9fJw7nc+vY19sMvbHRv3tnMZBRNQClU+c+DJAROQcLjOd79ChQxg6dKj1+wULFgAAHn74YcTHxyMjI8OaUAFAZGQkNm/ejPnz5+OTTz5BSEgIPvroI5dsb24ymuAlFgEC4OXb2qnXCu3YHaONT8GoF/FwvhZBPhqnXo+IiFxL+cTJxCyKiMgpXCaJGjJkSLWVk/j4+ErHBg8ejMOHXb8LXX6pDgv08+GLQvzDP9ip11Ir5Aj3d8fF60U4e62ASRQRUQvDNVFERM7nMtP5mrMbJUb8ZuqDrcphUGncnH69Lq3dES5kIjPluNOvRURErqX8B5LMoYiInINJVCPILdIBAHzdG6fhxYOG77FLvQAdTy5vlOsREZFr4tpYIiLnYBLVCEqyL+Fe2R4MVJ1plOupQ8wd+nyLzjfK9YiIyHXY7hMlXRxERM0Zk6hGIL+aiA9UK/BI6ZeNcr2A9r0AAG316RCN+ka5JhERuQab7nwSxkFE1JwxiWoExuIbAACtwqdRrtcusguKRDXUgh7XL3HTXSKiloTd+YiInI9JVCMQy5Iog8q7Ua6nUiqQJg8HAFy/kNQo1yQiItfA6XxERM7HJKoRCKU3AQBGtW+jXfOGZwcAQOmVE412TSIikp5YxddEROQ4TKIagVx7EwBgcmvVaNfU+XUFAChzTzfaNYmISHo2Lc5ZiiIicgqX2Wy3OVPq8gAAMjffRrumvP0gfHQ+BTfQGz0a7apERCQ1brZLROR8rEQ1ArWhAAAg9/BrtGsGdemL9w2T8fXNrvwkkoioJSm/JooT+oiInIKVqEYQr3oAYuEdeCCkb6NdMyLAA0q5gCKdEVdulqBdK/dGuzYREUmnfOJkMkkYCBFRM8ZKVCNI0HfGd6ZBUAdENto1lXIZ+vjpMFh2BFfOskMfEVFLIbISRUTkdEyiGkFeiXnDWx83ZaNe90nZt/iP6h2oTnzTqNclIiLpcE0UEZHzMYlyMoNeh1GGnRgmOwxvtdC41/a3dOg726jXJSIi6XCfKCIi5+OaKCcrvJmDD1QrAAB6zfONem33tt2Ai4B/8YVGvS4REUmn/BQ+TucjInIOVqKcrDA/FwBQLKqhVKoa9dqtO/YBAAQar8FUWtio1yYiImmwEkVE5HxMopysuOAGAKBI8Gj0a4e1C0WO6A2ZICIr5WijX5+IiBpf+bzJxCyKiMgpmEQ5mbbwJgCgWNb4SZRCLsNlZTgAICflSKNfn4iIJCCWn85HRETOwCTKySxJlFYuzT5NNz07AgD0GSckuT4RETUuducjInI+NpZwMn3xTQCATuEpyfWvhd+FBVmBaK0cgF6SREBERI3Jdk0UsygiImdgEuVkxpI8AIBB4SXJ9b079cd3B9TonuctyfWJiKhxiZzOR0TkdEyinOy0eyy+081Cz6BoSSpBnQPNFbDzWYUwmUTIZI27VxURETUuTucjInI+JlFOliqE4DvTILRr00mS64f7e6Cf4jy6mi4gIzUMbdt3kyQOIiJqHOUTJ3bnIyJyDiZRTpZfYgAAeGuk+VHLZQJe0HyHPoZknDjZmUkUEVEzx0oUEZHzsTufkwXeTMJwWSICkStZDPlelg59JyWLgYiIGkf5NVGsRBEROQcrUU427sZ/0VuViGN5/gBiJYnB1LorcAPQ3DgjyfWJiIiIiJoTVqKcTGMsBADI3X0li8GjXXcAQEDJRcliICKixsHiExGR8zGJcjKNqRgAoJQwiQrq2BsAECDmwlh0Q7I4iIiocTGhIiJyDiZRTuYuFgEANJ6+ksXQLigQV0V/AMC1C0mSxUFERM4nQrT7NREROQ6TKCcSRREeorkSpfFsJVkccpmAq6oIAMCN1COSxUFERM5XvvrEShQRkXMwiXIirU4PT6EUAODm5StpLHvbzsB92tewRz1U0jiIiMi5xCq+JiIix2ES5USF+bfWH7l7+UkYCaCOuB2Hxc44kcuXVCKi5sy2EsXf+UREzsAW505UZJTjbd0s+Cl1eEmlkTSWzoGeAIBz1wokjYOIiJzLdk0UERE5A5MoJyowKvGdaRAC1Wq8JHEsnQO9cL98J7rlpMNQEA2FV4DEERERkTOInM9HROR0nM7nRAWlBgCAp1r6XLWtrxueUmzCdPkvuHY+UepwiIioETCHIiJyDiZRTmS4cRnDZIdxmyJN6lAgkwnIUEcAAG6mHpU2GCIicpry66C4JoqIyDmYRDmRW8Z+rFa9ixnFq6UOBQBQ4NUJAGC8dkriSIiInG/58uWIjIyERqNBTEwM9uzZU+34Xbt2ISYmBhqNBu3bt8fKlSsrjdmwYQOio6OhVqsRHR2NjRs32ty/YsUK9OjRA97e3vD29kZcXBx++eUXhz6vmtg0lmjUKxMRtRxMopzIWJIHANArPCSOxExo0xUA4J53VuJIiIica/369Zg3bx4WLVqEpKQkDBw4EGPHjkVamv2ZASkpKRg3bhwGDhyIpKQkvPjii5gzZw42bNhgHZOQkIApU6Zg6tSpOHLkCKZOnYrJkyfjwIED1jHt2rXDP/7xDxw6dAiHDh3CsGHDcPfdd+PEiRNOf84WNkuimEURETkFkygnEkvNnfD0Ci+JIzHzCusBAGhTmsJXViJq1t5//3089thjmDFjBqKiorB06VKEhoZixYoVdsevXLkSYWFhWLp0KaKiojBjxgw8+uijePfdd61jli5dipEjR2LhwoXo2rUrFi5ciOHDh2Pp0qXWMRMmTMC4cePQuXNndO7cGW+99RY8PT2xf/9+Zz9lu0TWooiInIJJlDNp8wEAoso1KlEhHW+DURTgLRZCdzND6nCIiJxCp9MhMTERo0aNsjk+atQo7Nu3z+5jEhISKo0fPXo0Dh06BL1eX+2Yqs5pNBqxbt06FBUVIS4urr5Pp85s94lqtMsSEbUoTKKcSVcEADCpPCUOxKxtQCtcQjAA4NrFIxJHQ0TkHNnZ2TAajQgMDLQ5HhgYiMzMTLuPyczMtDveYDAgOzu72jEVz3ns2DF4enpCrVZj1qxZ2LhxI6Kjo6uMV6vVIj8/3+bmKEyiiIicg0mUE8n05iRKULnGdD5BELDU7yXElK7AEWVPqcMhInIqQRBsvhdFsdKxmsZXPF6bc3bp0gXJycnYv38//u///g8PP/wwTp48WeV1lyxZAh8fH+stNDS0+idWA07hIyJyPiZRTiTXFwIAZBrXqEQBgKZtd+TAB2evFUodChGRUwQEBEAul1eqEGVlZVWqJFkEBQXZHa9QKODv71/tmIrnVKlU6NixI2JjY7FkyRL07NkTH374YZXxLly4EHl5edZbenp6rZ9rTdjinIjIOZhEOdE2t7F4TT8NhYH9pA7FqnOguSp27lqBxJEQETmHSqVCTEwMtm3bZnN827Zt6N+/v93HxMXFVRq/detWxMbGQqlUVjumqnNaiKIIrVZb5f1qtdraEt1ycxSmUEREzqGQOoDmbL/QE8nGcPylTTepQ7Hq6gc8r1iLbqnZgPgLUM3UFiKipmrBggWYOnUqYmNjERcXh1WrViEtLQ2zZs0CYK7+XLlyBV988QUAYNasWVi2bBkWLFiAmTNnIiEhAZ9//jnWrl1rPefcuXMxaNAgvPPOO7j77ruxadMmbN++HXv37rWOefHFFzF27FiEhoaioKAA69atw86dO7Fly5ZGe+5sLEFE5HxMopyoUGsAAHiqXefH3CHYH7fLN0NpMEKbmwa1f7jUIREROdyUKVOQk5ODxYsXIyMjA927d8fmzZsRHm7+nZeRkWGzZ1RkZCQ2b96M+fPn45NPPkFISAg++ugjTJw40Tqmf//+WLduHV566SW8/PLL6NChA9avX4/bb7/dOubatWuYOnUqMjIy4OPjgx49emDLli0YOXJk4z35crg+iojIOVzn3X0z1Lk4Ga0EA7zlfaQOxSqolTfOIwSdkI6sC8kIZRJFRM3U7NmzMXv2bLv3xcfHVzo2ePBgHD58uNpzTpo0CZMmTary/s8//7xOMToDN9slInI+rolyojf07+Ib9WL4lF6ROhQrQRCQpYkAAORfOiptMERE5FTMoYiInMOlkqjly5cjMjISGo0GMTEx2LNnT7Xjv/rqK/Ts2RPu7u4IDg7GI488gpycnEaKtnomkwh3lAIA3Dx9JI7GVrFPJwCAmHVK4kiIiMjhypWfWIkiInIOl0mi1q9fj3nz5mHRokVISkrCwIEDMXbsWJs56+Xt3bsX06ZNw2OPPYYTJ07gm2++wcGDBzFjxoxGjty+Yq0WboIOAODh5VpJlBBkbnThmX9e4kiIiMi5mEURETmDyyRR77//Ph577DHMmDEDUVFRWLp0KUJDQ7FixQq74/fv34+IiAjMmTMHkZGRGDBgAB5//HEcOnSokSO3r7ggz/q12t1x7WodwTf8NgBAoDYVMJmkDYaIiIiIqIlxiSRKp9MhMTERo0aNsjk+atQo7Nu3z+5j+vfvj8uXL2Pz5s0QRRHXrl3Dt99+i/Hjx1d5Ha1Wi/z8fJubs5QUmZMoPeQQFGqnXac+wjp0g1ZUwiDKUHrDddZrERFRw7GxBBGR87lEEpWdnQ2j0Vhp1/fAwMBKu8Nb9O/fH1999RWmTJkClUqFoKAg+Pr64uOPP67yOkuWLIGPj4/1Fhoa6tDnUZ6uyJygFcPN5fZiau3jgXHCJ7hN+xkuaF2rSkZERI7DHIqIyDlcIomyECokG6IoVjpmcfLkScyZMwevvPIKEhMTsWXLFqSkpFg3UrRn4cKFyMvLs97S09MdGn952mJzElUquDntGvUlCAL8gkIBCDh3rVDqcIiIyIFsN9tlGkVE5AwusU9UQEAA5HJ5papTVlZWpeqUxZIlS/CXv/wFzz77LACgR48e8PDwwMCBA/Hmm28iODi40mPUajXU6saZWpenaoPX9VMR4OuNJxrlinXTKdALB1Nv4Oy1AqlDISIiJ2EKRUTkHC5RiVKpVIiJicG2bdtsjm/btg39+/e3+5ji4mLIZLbhy+VyAK7xydtNmT/WGMdil/cEqUOxK9YjB8uVSzH86DNSh0JERA4kgi3OiYiczSUqUQCwYMECTJ06FbGxsYiLi8OqVauQlpZmnZ63cOFCXLlyBV988QUAYMKECZg5cyZWrFiB0aNHIyMjA/PmzUO/fv0QEhIi5VMBABTrDAAAD5Vc4kjsC2/tjRj5n9AWKwGTEZC5ZpxERFR/rvChIhFRc+QySdSUKVOQk5ODxYsXIyMjA927d8fmzZsRHh4OAMjIyLDZM2r69OkoKCjAsmXL8PTTT8PX1xfDhg3DO++8I9VTsJV/BTHCGYS6Vk8Jq7AOUSgW1XAXtCi9dh6a4C5Sh0RERA7GFIqIyDlcJokCgNmzZ2P27Nl274uPj6907KmnnsJTTz3l5Kjqp+3lzdig/hCJN0YDqLrtulQCvDQ4JbRFNC4i60ISwphEERE1CzbFJ2ZRRERO4RJropojQVcEADAp3SWOxD5BEJDl1h4AUJh+TOJoiIjIGZhDERE5B5MoJxF05tbhRqWnxJFUrbRVZ/MX109LGwgRETmM7Wa7TKOIiJyBSZSTyAzmShRUHtIGUg1lUDQAwLvgvMSREBGRMzCFIiJyDiZRTiLXlyVRatetRLWK6AmtqES+Qck+uEREzYTtZrvSxUFE1Jy5VGOJ5kRRVomSqb0kjqRqEe27IEq7BibIcEJnhIea/xyIiJoTkbUoIiKnYCXKSZTGEgCATOO6lSg/TzX8PDUAgPNZhRJHQ0REjsZKFBGRczCJcpLN6jF4Tz8JOv8oqUOpVqc25krZ2WsFEkdCRESOUL76xByKiMg5mEQ5yVbZAHxsvA9o7dr7L43THMNm1UJE71sgdShERORozKKIiJyCi2CcpEhrBAC4q1z7Rxzs54Vo2SVk5BulDoWIiByhfGMJZlFERE7BSpSTtNedRlchDR5y105O/CN7AgDaGK4C+lKJoyEiIkfimigiIudgEuUMooh444vYon4BHsY8qaOpVmREe9wUPSCHCUVXT0odDhERNZBYxddEROQ4TKKcwKjXQi6YX7rcPLwljqZ6vh5qpMjCAABZF49IHA0RETmSyFIUEZFTMIlyguKifOvX7h6u2+LcIse9AwCg5PJxiSMhIiJHYgpFROQcTKKcoLTY3C5cJ8qhUqkljqZmev/OAAB59mmJIyEiooYqX31iIYqIyDmYRDmBtti8cW2poIEgCBJHUzNN2x44bwrBRYOf1KEQEREREbk81+6/3URZKlGlUMO1V0SZte4+DCN+fxetSpQYI4pNIvEjIiL7WH0iInI+VqKcQF9qrkRpBY3EkdROxzaekAnAjWI9sgq0UodDREQOxOYSRESOxyTKCfIUbfCBfiJ+cRsvdSi1olHKERngAQEmnEnPkjocIiJqgIopE3MoIiLHYxLlBLmqYHxonIjt3hOlDqXWnlD/gmPqGfDe/y+pQyEiIgdiDkVE5HhMopygWGcEALir5RJHUnutfFvBUyiFKveM1KEQEZEDcTofEZHjsbGEE5gKrqGzkI5AmUrqUGrNM/Q24DzgX3xR6lCIqBmLjIy0Nq8xmUwAgB49ekAmq/4zvXnz5mHOnDlOj685qJgzMYUiInI8JlFO0C79R2xVf4DE3JEARkodTq0Ed+oN/A4EitehL74Jpbuv1CERUTMUHx9v/bqoqAjjx4/H8uXL4eHhUe3jIiIinBtYM8ZCFBGR4zGJcgJRV2z+U+EucSS11zY4GNfEVggUbuDquWSE9xwidUhE1AwNHjzY+nV+fj4AYMCAAfD2bgobQjQNYoXaU8XviYio4bgmyhn05iTKpHSTOJDaEwQBGeoIAEBuylFpgyGiFunTTz+VOoRmiZUoIiLHYxLlBLKyJArKplOJAoAi704AAEPmCYkjIaKWKCEhAU899ZR1rdSZM2cwdepUiaMiIiKqjEmUE8iMTTOJMrTrh+3G3jimbyt1KETUAsXHxyMyMhLjxo3DAw88gAcffBDjxzeN/fZcScXKU0GpAcev5EkTDBFRM8U1UU4gN5QCAARV00qiPHpNxKT9IQgu1OBRqYMhohbn8OHD+OOPP3Dt2jWcPXsWv//+O8LDw6UOq8l7+fvj2HIiE8sf6oNxtwVLHQ4RUbPASpQTKIwl5i+aWBLVOcgLAJCRV4qbxTqJoyGilmb27Nl47LHHkJSUhHXr1uHuu+/GH3/8IXVYTd6WE5kAgLV/pkkcCRFR88FKlBMkqP+CPwv80dUvWupQ6sRbo0RbHw10eZk4l3YFfbtGSh0SEbUg+/fvt37dr18//Pzzz7j//vuxb98+CaNqPtSKprMBPBGRq2MS5QS/Kocj2RCDz9r0kjqUOluq+BB9NbuQcGQR0PU5qcMhohbEYDBg3bp1uH79OqKjozFq1Cj89ttvUofV5IhVtuNjmz4iIkfhdD4nKNUbAQBuqqb3qZ/oE2r+IuuUtIEQUYvz17/+FXv37oUgCPj222/Ru3dvpKenSx1Ws6E3MokiInIUVqKcwE97GW2hg0ZukjqUOlMGdwOuAl75Z6UOhYhamDNnzuDo0Vv71B0+fBgzZ87Ezp07pQuqGTGamEQRETkKK1FO8HHJQvyhmQvfwotSh1Jn/h1iAADhugswGZteEkhETZenpycuXLhg/b5Pnz7Izc2VMKKmiakSEZHzsRLlBBqYW5wrNR4SR1J3IR17Qicq4CWU4MqlM2jbPkrqkIiohVi1ahXuuecejB07FlFRUTh16hTCwsKkDouIiKgSVqIcTRThJmoBAGp3L4mDqTuFSoM0hXlflmvnEiWOhohaCpPJhMTERBw6dAgxMTG4dOkSOnTogK+//lrq0JqcKvtKEBGRw7AS5WAGXQkUgvkVrCkmUQCQ69UFuHkB2svJAP4mdThE1ALIZDKsWbMGDz/8MKZMmSJ1OERERNViJcrBSosLrV9r3D0ljKT+8sNG4HPDWOwzdJE6FCJqQW6//XYsW7ZM6jCaPJGrooiInI5JlINpiwvMf4oKqFUqiaOpH4+e9+ANw1R8d6OD1KEQUQty7Ngx/POf/0RERAQefPBBLFmyBD/99JPUYREREVXC6XwOpisxJ1GlUEMtCBJHUz/RId4AgCs3S3CzWAdf96aZDBJR07J582YAQH5+Po4fP47jx49j+/btuPPOOyWOrHlgi3MiIsdhEuVgxTIPfGYYC4VShelSB1NPPm5KdGklwvvmGZw/F4rYnj2kDomIWoBjx45h6dKluHHjBm677TbMmDEDf//736UOq8mpqrGEwcRtK4iIHIXT+RysUNkabxqm4t/q6VKH0iD/kC3HN+rF0B/fJHUoRNRCTJo0CYMHD8bChQsREhKCu+66Czt27JA6rGZDb2QliojIUViJcrBSvREAoFE27fxU698NKPoD8qzjUodCRC2Ej48Ppk2bBgDo27cv7rvvPowYMQJHjhyROLKmpapUiZUoIiLHadrv9F2QrjgfQciBn0IrdSgN4hbWCwDgV3BG2kCIqMVo37493n//fYhl89H8/Pyg0Wgkjqr5MLASRUTkMEyiHMwn9Rfs1zyFRUXvSB1KgwR36QsACDOmobS0ROJoiKgl0Gq1+OSTTxAWFoYxY8age/fuGD58OK5cuSJ1aE1KVWui9EZWooiIHIXT+RzMqDMnHHp50/70tHXbjsiHB7yFIpw5nYQuvfpLHRIRNXMbN24EABQVFeHo0aPW2wMPPICrV6/iwoULEkfYtBnYnY+IyGGYRDmYqSyJMjXxJEqQyXBZ3RHR2iPIvZAIMIkiokbi4eGBuLg4xMXFSR1Ks8IW50REjsMkysFEvSWJUkscScMVtooCMo/AlHlU6lCIqAU4duwYPvjgA9y8edPa4jw0NFTqsJog+8mSiUkUEZHDuNSaqOXLlyMyMhIajQYxMTHYs2dPteO1Wi0WLVqE8PBwqNVqdOjQAatXr26kaO27lUQ17UoUAJR2moBX9A/jG+NgqUMhohZg0qRJGDJkCFucO4mxqsVSRERUZy5TiVq/fj3mzZuH5cuX4y9/+Qs+/fRTjB07FidPnkRYWJjdx0yePBnXrl3D559/jo4dOyIrKwsGg6GRI69AX2r+U+EmbRwOEHLbEHyxTYDbdTmMJhFymSB1SETUjLHFuWNUlStxOh8RkeO4TCXq/fffx2OPPYYZM2YgKioKS5cuRWhoKFasWGF3/JYtW7Br1y5s3rwZI0aMQEREBPr164f+/aVduyMYzJUoUdn0K1GRAR5wU8pRojciJbtI6nCIqJlji3PnYhJFROQ4dUqiZs2ahVWrVuHgwYPQah23D5JOp0NiYiJGjRplc3zUqFHYt2+f3cf88MMPiI2NxT//+U+0bdsWnTt3xjPPPIOSEmnbcV9w6461hqHI9e4maRyOIJcJGN76JibJdyH91J9Sh0NEzRxbnDsGK1FERM5Xp+l8SUlJ+PLLL1FSUgKFQoGuXbuiT58+6NOnD3r37o3evXvD09OzzkFkZ2fDaDQiMDDQ5nhgYCAyMzPtPubixYvYu3cvNBoNNm7ciOzsbMyePRu5ublVrovSarU2yV9+fn6dY63JAc/h+M7QFQuDujr83FJ4FJvQR7kZe88agMHDpA6HiJoxtjh3LuZQRESOU6ck6sCBAzCZTDh9+jSSkpKstx9//BE3btyATCZDx44dMWLECDz11FPo0qVLnYIRBNs1N6IoVjpmYTKZIAgCvvrqK/j4+AAwTwmcNGkSPvnkE7i5VV6TtGTJErz++ut1iqmuSvVGAICbSu7U6zQWU1BPIHczPHKOSx0KETVzBoMBe/bsgUajQXR0NFucOxgrUUREjlPnxhIymQzR0dGIjo7GQw89ZD1+6dIlJCUlITExEVu2bMHq1auxdetWDBgwoMZzBgQEQC6XV6o6ZWVlVapOWQQHB6Nt27bWBAoAoqKiIIoiLl++jE6dOlV6zMKFC7FgwQLr9/n5+Q5vnyuU3IQ3iuAmbx4vVj7t+wIngdDSMxBNJggyl1lGR0TNzKRJk+Dv74/vv/8e3t7eMJlMuO222/DTTz9JHVqTIlbR4pzd+YiIHMdh74jDw8Nxzz334I033sDBgwexcOFCPP/887V6rEqlQkxMDLZt22ZzfNu2bVU2ivjLX/6Cq1evorCw0Hrs7NmzkMlkaNeund3HqNVqeHt729wc7cmsV3BUMxOR2Tsdfm4phEb3g1EUEICbyMpIlTocImrGUlJS8PnnnyM0NBQpKSlYsGABYmNjpQ6r2eA+UUREjuO0ssK0adPq1JZ2wYIF+Oyzz7B69WqcOnUK8+fPR1paGmbNmgXAXEWytL4FgAcffBD+/v545JFHcPLkSezevRvPPvssHn30UbtT+RqL0mRucS5XNf0W5wCgcfdCmtzcYv7qyQSJoyGi5szyu1ulUkGn02Hu3LnYtWuXxFE1PVU2lmAliojIYZy2T1R4eDgSEmr/pnvKlCnIycnB4sWLkZGRge7du2Pz5s0IDw8HAGRkZCAtLc063tPTE9u2bcNTTz2F2NhY+Pv7Y/LkyXjzzTcd/lzqQmkyN65QatwljcORcryjEHnzEkovHQbwUI3jiYjq48knn0Rubi7uu+8+PPHEE+jfvz9SU1OlDqvZEEVzNUrGPf+IiBrMqZvt3nbbbXUaP3v2bMyePdvuffHx8ZWOde3atdIUQKkpRR0AQKFuPkmUKbg3cHML3LKPSR0KETVjf/vb3wAAL7zwAuLj43HixAls2rRJ4qianurqTUZRhAxMooiIGsqpSVRLpBLLKlHNKIny7HEXHj5iQrqsM3ZU0zGRiKg2Zs2ahT59+thtAGQxffr0xguoBTGaRCibR/NYIiJJsdWag6lgrkSpmtF0vg6dumCf0AsXi91w5aa0mxkTUdOXlJSE+fPnY/jw4QCA/v37Y/r06fjoo4+wZ88em4ZBDbF8+XJERkZCo9EgJiYGe/bsqXb8rl27EBMTA41Gg/bt22PlypWVxmzYsAHR0dFQq9WIjo627m1lsWTJEvTt2xdeXl5o06YN7rnnHpw5c8Yhz8cRTFwXRUTkEEyiHExTNp1P5eYhcSSOo1bI0SXICwBw7HKexNEQUVN34MABFBQUYP/+/QCAoUOHIj09Ha+//joGDx4MX19fdO3aFU8++WS9E5D169dj3rx5WLRoEZKSkjBw4ECMHTvWZm1teSkpKRg3bhwGDhyIpKQkvPjii5gzZw42bNhgHZOQkIApU6Zg6tSpOHLkCKZOnYrJkyfjwIED1jG7du3CE088gf3792Pbtm0wGAwYNWoUioqK6vU86qO6PIl7RREROYYgii33Y6n8/Hz4+PggLy/PIe3ODQYjvnvtXrgJWgyY/xVa+QU4IErX8PF/v4Fw+kdEdOmFO6cuqPkBREQ1sPc7uOKegydOnKj1noPl3X777ejTpw9WrFhhPRYVFYV77rkHS5YsqTT++eefxw8//IBTp05Zj82aNQtHjhyxNkmaMmUK8vPz8csvv1jHjBkzBq1atcLatWvtxnH9+nW0adMGu3btwqBBg2oVe0Nfm57++gg2HL5s974jr4yCj7uyzuckImoJ6vL7l5UoB9IaRTxneBxP6edA49lK6nAcKk51AU8qNqHdlV9qHkxEVE8N2XPQQqfTITExEaNGjbI5PmrUKOzbt8/uYxISEiqNHz16NA4dOgS9Xl/tmKrOCQB5eebqvZ+fX52eQ0NUtdkuwDbnRESOwiTKgUr1RuvXakXz+tG26tgPANCu9AxEk0niaIiopajrnoMAkJ2dDaPRiMDAQJvjgYGByMzMtPuYzMxMu+MNBgOys7OrHVPVOUVRxIIFCzBgwAB07969yni1Wi3y8/Ntbs7C6XxERI7RvN7pS0yr08MdpdDI0ez24QiN6gejKCAAebiSflHqcIiohajrnoPlVewkKtbQXdTe+IrH63LOJ598EkePHq1yqp/FkiVL4OPjY72FhoZWO75G1eRJbCxBROQYTKIcyJh9Hic1j+JP5UypQ3E4lZsn0hVhAICrJ+v3hoaIqCqHDx+GTqeze19d9xwMCAiAXC6vVCHKysqqVEmyCAoKsjteoVDA39+/2jH2zvnUU0/hhx9+wO+//4527dpVG+/ChQuRl5dnvaWnp9f4HOuLlSgiIsdgEuVAeq25/bcWKokjcY5cn24AAF36YYkjIaLmpm/fvkhNTXXIuVQqFWJiYiptxr5t2zb079/f7mPi4uIqjd+6dStiY2OhVCqrHVP+nKIo4sknn8R3332H3377DZGRkTXGq1ar4e3tbXNzFiZRRESOwc12HcigLQYA6ITmmUQhuBeQuxkeOcekjoSImhlHN4pdsGABpk6ditjYWMTFxWHVqlVIS0vDrFmzAJirP1euXMEXX3wBwNyJb9myZViwYAFmzpyJhIQEfP755zZT8ebOnYtBgwbhnXfewd13341NmzZh+/bt2Lt3r3XME088gf/973/YtGkTvLy8rJUrHx8fuLm5OfQ5VqW6nySTKCIix2AS5UC3kii1xJE4h1/nO4ATQKvSyzCZxGa37ouImo8pU6YgJycHixcvRkZGBrp3747NmzcjPDwcAJCRkWGzZ1RkZCQ2b96M+fPn45NPPkFISAg++ugjTJw40Tqmf//+WLduHV566SW8/PLL6NChA9avX4/bb7/dOsbSUn3IkCE28axZswbTp0933hOuJXbnIyJyDCZRDmTUmafz6ZtpEtUu6naM+fZ9nNYHYkdOETq09pQ6JCKiKs2ePRuzZ8+2e198fHylY4MHD8bhw9VPV540aRImTZpU5f2usPVidTGYWIkiInIIrolyIJPWvCO9XtY8p/MpVBp4hEQBEHD08k2pwyEiojpiJYqIyDGYRDmQUW+uRBlkGokjcZ7b2voAAI6k50kcCRER2cM1UUREzsfpfA6UrwzEz8Z+KNVEo6fUwTjJwFa5iFV+BL/jcuCuzVKHQ0REdcC90omIHINJlAOl+8biZf08jAkIwsSahzdJUSF+CJHvh06rgFZbArW6cbpNERFRw3E6HxGRY3A6nwNp9UYAgFrZfH+swZFRuAkvqAQDUo4fkDocImomXn31VQQEBEgdRrNQXZ7E6XxERI7RfN/tS0Cn1wEQoVY03x+rIJMh3S0KAJB7NkHiaIiouXj11Vfh5+cndRjNnomVKCIih2i+7/Yl0O/8x0jVPIQJWaukDsWpStr0BgAoriZKHAkREVVUXZpkMDKJIiJyBCZRDiQYSs1/ypv3UjPPDuaNJYMKj0scCRER1QUrUUREjsEkyoEsSZSobN7NFsJ6DDL/KWbg+rUMiaMhIqLyqt1sl0kUEZFDNO+SSSOTGc1JFBTNd58oAPD0bY1LslDkGRTIv3gRrQODpQ6JiJqQWbNmoU+fPujUqZPUobQ47CtBROQYTKIcyJJECc28EgUAn3b7Ev87eBWzbvpjgNTBEFGTkpSUhC+//BIlJeYNyvv374/Y2Fj06dMHvXv3Ru/eveHp6SlxlM0TK1FERI7B6XwOJDdpAbSMJKpXmLkVcVLaDYkjIaKm5sCBAygoKMD+/fsBAEOHDkV6ejpef/11DB48GL6+vujatSuefPJJnDlzRuJom57q0qTqpvoREVHtsRLlQApjy0mieof5AgDOXs6CwWCEQiGXNiAialJkMhm6du0KAHjrrbfg7e0NAEhNTUVSUhISExPx66+/YvXq1di6dSsGDGDN2xGMJqkjICJqHliJcqBzyi7YZewBg2fzXyPUIcAD36jfxJ+yR5B69ojU4RBRMxEREYF7770Xb775Jg4ePIgXX3wRzz//vNRhNS3VFJs4nY+IyDFYiXKgNR6PIjH7BlYGxkgditPJ5DJ4quVQ6ozIPrkTHaP7SB0SETUBkZGREAQBAGAymcsiPXr0gExm/zM9URRx/fp1fPTRR5gzZ06jxdlccTofEZFjMIlyIK3BCABQK1tGgS+/dQxw5Rjkl/+UOhQiaiLi4+OtXxcVFWH8+PFYvnw5PDw87I4XRRHHjh3DXXfd1UgRNn1iNaUoducjInIMJlEOVKo3f6qqaSHrgzQd/gJciUdQHqfzEVHtDB482Pp1fn4+AGDAgAHWNVH2DBkyxNlhtRhGZlFERA7RMkomjeSrgkdxQv0IfArPSx1Ko4joNRQAECpeRfa1yxJHQ0RENeGaKCIix2AS5UBuYgk8BC2USqXUoTQKH7/WSJWFAQDSkn+TOBoiIgKA6vIk5lBERI7BJMqBVNCZ/1S7SxxJ47neqhcAQHtxn7SBEBFRjViJIiJyDCZRjiKKUIkGAIBS3fz3ibIQO4zAz8Z+2F0cLnUoRESE6qtNXBNFROQYbCzhIKJRB5lgfnFSa1pOEtWu/2RM3h0AeY6AJ7UGeKj5T4qIyFWxEEVE5BisRDmItrTY+rWqBSVRIb5uaOvrBqNJRHL6TanDISJq8apvcc4siojIEZhEOYhOW2r9Wt2C1kQBQGy4LyKFDFw4zv2iiIhcGWfzERE5BpMoB9EaTNhj7I4EUzSUipb1Y31Itg2/q5/Gbac/kDoUIiKqhpGVKCIih+ACFgcpVbbCVP2LcFPKcUoQpA6nUQVG9QdOAe1LTkBvMECp4D8rIiKpVN/inEkUEZEjtKySiROV6o0AAI2y5f1IQ6NuRzHU8BGKkHLykNThEBFRFUycz0dE5BAt7x2/k2gNJgCAWiGXOJLGJ1OqcFHTHQCQfZyb7hIRSam6NIk5FBGRYzCJchDFlT9xRD0D/za8KHUokigMjgMAaK4kSBwJERFVxSSKKNUb8fbmU0i4kCN1OERETRaTKAcxaIvhIxTDA6U1D26GfKKGAAAii5IhmozSBkNE1IJVt+zJJIrYcPgyVu2+iIdXs6MqEVF9MYlyEKO+BABgkKkkjkQaHXoORLGoRivkI/X0YanDISIiO0wikJh6AwCgM5rYaIKIqJ7YRs1BjDpzBcooKCWORBoqtQZf+T2OvdeUGJrtgUipAyIiokpMogi57FYH2QKtAd6alvm6RUTUEKxEOYg1iWqhlSgAKO7xMHaYYrDnUrHUoRARtWBVV5dMJtHaCAkA8or1jREQEVGzwyTKQUx6LQDAKFNLHIl04jr4AwAOpOSyjS4RkQsyicDNkluJU7GOa1iJiOqDSZSDmMrWRJnkLbcSdVtbHwxWncFjuq9w4cwRqcMhImqR7C1zUpRN4TOJIgpLbyVRRTpDY4VFRNSsMIlykEK5Dw6bOiJbEyZ1KJJRymV41v1HPKX4HteTfpY6HCIiKiO3JlGwmc5XpGUSRURUHy6VRC1fvhyRkZHQaDSIiYnBnj17avW4P/74AwqFAr169XJugNU45TcC9+kW47eQWZLF4AqKy/aLUl/eJ3EkREQtk73J1NYkqsKaqCItp/MREdWHyyRR69evx7x587Bo0SIkJSVh4MCBGDt2LNLS0qp9XF5eHqZNm4bhw4c3UqT2lerNL0Rqhcv8SCXh130YAKB98REYjXxxJiJyBXLh1nQ+XbkkqpjT+YiI6sVl3vG///77eOyxxzBjxgxERUVh6dKlCA0NxYoVK6p93OOPP44HH3wQcXFxjRSpfZYXJbXSZX6kkojsMRDFUKMVCnDh+AGpwyEiIgByefnpfLc+4CpiYwkionpxiXf8Op0OiYmJGDVqlM3xUaNGYd++qqeFrVmzBhcuXMCrr75aq+totVrk5+fb3BxlYPpKJKifRP/M/znsnE2RXKnGBbeeAICco79KHA0RUctjbwNdS2MJUbSdzle+KkVERLXnEklUdnY2jEYjAgMDbY4HBgYiMzPT7mPOnTuHF154AV999RUUitrtGbxkyRL4+PhYb6GhoQ2O3UKtu4lgIRcaaB12zqaqqN1AAIDnldqtaSMiIueSlU3nM5psp/OVr0oREVHtuUQSZSEIgs33oihWOgYARqMRDz74IF5//XV07ty51udfuHAh8vLyrLf09PQGx2whmMqSJ0XL3SfKonXPsQAA/5JL0Om4kSMRUWOqtrFEhe58Wj0rUURE9VG7Eo6TBQQEQC6XV6o6ZWVlVapOAUBBQQEOHTqEpKQkPPnkkwAAk8kEURShUCiwdetWDBs2rNLj1Go11GrnJDlyow4AICg0Tjl/UxIZFYMH5e8ioSgI/0vPt27CS0RE0rAkUXqjCcZym6HrjEyiiIjqwyUqUSqVCjExMdi2bZvN8W3btqF///6Vxnt7e+PYsWNITk623mbNmoUuXbogOTkZt99+e2OFbiUzmZMoKFmJksllCOzcFyJk2HPuutThEBG1KNVttluit52+x0oUEVH9uEQlCgAWLFiAqVOnIjY2FnFxcVi1ahXS0tIwa5Z536WFCxfiypUr+OKLLyCTydC9e3ebx7dp0wYajabS8cYiL5vOJ+N0PgDAwE4B2Jh0BbvPZuG5MV2lDoeIqEWTVZVEcU0UEVG9uEwSNWXKFOTk5GDx4sXIyMhA9+7dsXnzZoSHhwMAMjIyatwzSkryskqUoOR0PgAY0KEV3lWuxF9yjiM3ay/82rSVOiQiohbLsk+UtkISxe58RET14xLT+Sxmz56N1NRUaLVaJCYmYtCgQdb74uPjsXPnziof+9prryE5Odn5QVYhUxaI06ZQiG5+ksXgStr4eKCPKg3BQi4u/vmz1OEQEbUY1TWWqFyJYhJFRFQfLpVENWUfuM/BGN07KGw7UOpQXMb11ub1bKbzv0scCRFRy6Yo22y3RMfpfEREjsAkykEsUyJUCv5ILTyjRwIAwm8egGjip51ERI3B3ma7lul8pRUaSbASRURUP3zH7yCWNrFqJlFWHWJHQisqEYgcpJxOlDocIqIWS1auxXl5XBNFRFQ/fMfvIMuKn8N21TPwKkiROhSXoXH3wlm3ngCA64k/ShwNEVHLZWlxXnFfKFaiiIjqh0mUg7QTM9BRdhVK/kRtFIYNBwD4XP5N4kiIiFouS2OJipUnrokiIqofvuV3ELVobnGuULtJHIlrCe57F86Y2uH34g4oKtVLHQ4RUYskr2I6HzfbJSKqHyZRDqKCOUFQMomyEd6xG2Z6LsM7+snYcz5H6nCIiJo9O30lIJeZX+4rVqIqJlWH027g5e+PIyu/1GnxERE1B0yiHEA06qEQzC9ESjU32y1PEAQMj2oDAPjt9DWJoyEiapmqWhOlN9pmXHPWJuHL/Zfw3tazjRYbEVFTxCTKAbSlJdavWYmqbHjXQKihQ8nJrTAZDFKHQ0TUrIl2ttuVCbZroizbcZSvROWV6HH5hvn17OClXGeHSUTUpCmkDqA50OtKYKk/qZhEVdIvwhd71PPRxnQDZ4/EoHPMMKlDIiJqUeRlH5mayvIrN6UcOoMJBtOthCs1u8j69fV8bWOGR0TU5LAS5QA6vQHnTSFINQVCpVRJHY7LUSkVSPfqAQDITWKrcyIiZ7K3Jkohs325d1PKAQD6cmukLFUoACjQGlCo5cwBIqKqMIlyAK3KDyN072KU8UPrhoZky9RxFAAgIGOXxJEQEbU88gqvTZqy/Tj0pltJ1I1inc0YNpcgIqoakygHqDjHnCrrEHcPTKKAjsYLuHb5vNThEBG1KJWTqLJKVLnGEnkltttQZBVwSh8RUVX4rt8BtEyiauQX2A5nVFEAgLQ/vpE4GiKi5svedD5LYwkLSxJlNIkwla2LulFkW4m6ziSKiKhKfNfvALJrx7FF9TyWmt6ROhSXlhM6GgDgeXGLxJEQETV/5fMmRYVKlGVNFHBrSt/NCpWoit8TEdEtTKIcwFR6E11l6QhHhtShuLSQuPsBAJ1Kj6LgRpbE0RARNU+WFufl0ya5vEISpbqVRBnKpvTdrLAmKp9JFBFRlZhEOYBBZ57yYBDYMb46kR2j8Z56Nkbr3sFvl/jiTETkTEK5UpRcqKYSVbZX1M1i8+/ldq3MW3UwiSIiqhqTKAcw6i1JlFLiSFybIAgw9n4YF8S22HI8U+pwiIiaJcuaqPIz+Co2lii/htfSXMLS0tySRFVsNEFERLcwiXIASxJlkjGJqsnY7sEAgJ1nrqNEZ5Q4GiKi5ksoN6GvYhKlkAlQle3Aa6lEFZf9Tg72YRJFRFQTJlEOYDJYKlHcaLcm3dt6Y5L3SbyL93Bq51qpwyEiar6qaSyhkMugKFsnZVkTZUmignw0AJhEERFVh0mUA5islSiuiaqJIAi4zy8V4+V/Qjy2QepwiIiaHUuH8/J5U8WN4BUyAcqySpSurBJVojNP5wtmEkVEVCMmUQ6ggxIZoh+KFL5Sh9IktIq9DwDQNX8ftCWFEkdDRNQ8lZ/OV7ESJS+XRBlMJoiiiGK9uRIV6G1OovJLmUQREVWFSZQDnG8zGnHaZfgqeJHUoTQJXXoPxVW0hodQirN7WY0iInKoWjSWMFeizMf0BhFag8nakKK1lxoAUKzlulUioqowiXIArcH8QlO+2xFVTSaX4UIb88a74tFvJY6GiKh5qq7FuVx+qxKlN5lQVNaZDwACPMxJVGG5Y0REZIvv+h1AZzDPJ7d0OqKatbr9rwCALvkJKC28IXE0RNQcLV++HJGRkdBoNIiJicGePXuqHb9r1y7ExMRAo9Ggffv2WLlyZaUxGzZsQHR0NNRqNaKjo7Fx40ab+3fv3o0JEyYgJCQEgiDg+++/d+RTqhXrZrvVrIlSym41ltAbTNamEmqFDF4a8/percEEQ9l6KSIissV3/Q7Q5fIGfKd6BUNyv5Y6lCYjuld/pAptoRb0OLNzndThEFEzs379esybNw+LFi1CUlISBg4ciLFjxyItLc3u+JSUFIwbNw4DBw5EUlISXnzxRcyZMwcbNtyacpyQkIApU6Zg6tSpOHLkCKZOnYrJkyfjwIED1jFFRUXo2bMnli1b5vTnWJPyaZO9NVEq65ooESVl66HcVXJ4qG81SSriVhRERHYxiXIAj9Kr6CM7Dz/DNalDaTJkchnSQsbhiKk9EtK1UodDRM3M+++/j8ceewwzZsxAVFQUli5ditDQUKxYscLu+JUrVyIsLAxLly5FVFQUZsyYgUcffRTvvvuudczSpUsxcuRILFy4EF27dsXChQsxfPhwLF261Dpm7NixePPNN3Hfffc5+ynWyGY6n501UZZKlM54qxLlrlJApZBZ10sVcUofEZFdTKIcwagz/ynnPlF10Xr8S7hb9ybev9yFrXSJyGF0Oh0SExMxatQom+OjRo3Cvn377D4mISGh0vjRo0fj0KFD0Ov11Y6p6py1pdVqkZ+fb3NrCLEWjSXKr4kyGEUUl7U3d1PJAcBajbIcJyIiW0yiHEAwlCVRCiZRddE12AedAz2hM5rw6/FMqcMhomYiOzsbRqMRgYGBNscDAwORmWn/d01mZqbd8QaDAdnZ2dWOqeqctbVkyRL4+PhYb6GhoQ06n0V1lSilTAalrKyxhNGEEt2t6XwA4KEyJ1GF7NBHRGQXkyhHMLESVR+CIODuXm3hhWJc3v+N1OEQUTMjVOhIJ4pipWM1ja94vK7nrI2FCxciLy/PektPT2/Q+exttlupEiUToFSUNZYoN53PTWmpRJn/LOZ0PiIiuxQ1D6GaCEbzVA+BSVSd3d3VE9N3PgGPHC2y08YhIKyr1CERURMXEBAAuVxeqUKUlZVVqZJkERQUZHe8QqGAv79/tWOqOmdtqdVqqNXqBp3Dvqo321XIBSislSgRJpO5C5+lEuVurUQxiSIisoeVKAeQlVWiBIUzXgSbt3bBQTir7g4ASPvtM4mjIaLmQKVSISYmBtu2bbM5vm3bNvTv39/uY+Li4iqN37p1K2JjY6FUKqsdU9U5pWKpoJXPm2QV94mSlV8TZUJp2X6HmrJKlGfZmqgirokiIrKLSZQDaKFCnugOqNylDqVJKoyaDABoe+l7iCbOvyeihluwYAE+++wzrF69GqdOncL8+fORlpaGWbNmATBPoZs2bZp1/KxZs3Dp0iUsWLAAp06dwurVq/H555/jmWeesY6ZO3cutm7dinfeeQenT5/GO++8g+3bt2PevHnWMYWFhUhOTkZycjIAc+v05OTkKlurO5NQzXQ+hUywduDTG03W/Q7VZZvGW6bzFXFNFBGRXUyiHGCl73z01H6GK+2nSB1Kk9RjxEPIF90RKF7HhYO/SB0OETUDU6ZMwdKlS7F48WL06tULu3fvxubNmxEeHg4AyMjIsElsIiMjsXnzZuzcuRO9evXCG2+8gY8++ggTJ060junfvz/WrVuHNWvWoEePHoiPj8f69etx++23W8ccOnQIvXv3Ru/evQGYk7nevXvjlVdeaaRnfouA6lqcy6yVKL1RhNayabwliSqbzscW50RE9nFNlAPoKrz4UN34eHnhj1Yj8JebP6Bg/xfA7XdKHRIRNQOzZ8/G7Nmz7d4XHx9f6djgwYNx+PDhas85adIkTJo0qcr7hwwZYp1OJxV7jSUs658s5OX2idIbTdYkSq2wbXHOzXaJiOzju34HsCZRcv4468vrjocBAF1zf0dJ/g2JoyEiavpsW5zb3ieTCdbXLINJrPRhoLt1Oh8rUURE9vBdvwPMyF+G/yrfgv+NZKlDabK69x2GVKEdVNDj8B+c0kdEVF+WQphQTWMJmQBrJUpnMEFb1ljCmkQpzZWoEj0rUURE9nA6nwN0MZxFZ/kFnDYVSh1KkyWTy3Cgx5uYcqAQkemR+IvUARERNXHl8yZLwmQhF261ODeYKjeWcFOZ/yzldD4iIrtYiXIAuWjeJ0quZIvzhhg4dAyyBD/sv5iL1OwiqcMhImqSbq2JKj+dz/blXiYTrHtH2ZvOZ9l0l5UoIiL7mEQ5gEI0zxlXqJhENUSIrxsGdWoNANj4xzGJoyEiatrK157kFfeJEgQorPtEiZUaS2iYRBERVYtJlAMoYK5EKViJarDpffzwhXIJHk+6CyV5OVKHQ0TULFRscS6TwbpPlKHcPlHWSpTKnEQVczofEZFdTKIcQGmpRCk1EkfS9A26rT1CFPlwhxanf/1U6nCIiJqess4SttP5KjaWEKzHDCYROmNZJaqsOuVelkSVshJFRGQXkygHsFaiVEyiGkoul+FqpwcBAAGnv4JoMkkcERFRE1Uub6qYRMllgnWzXfN0PnOypFaaj1mn87ESRURkF5OoBjIYTTBCDr0oh5Jrohyi+9i/o1DUINR0GecOsN05EVFd2G8sYa87X9lmu+W681n2jmJjCSKi6jGJaiCd0YS+2hXopP0SytYdpA6nWfBr5YejfmMAALo/lkkcDRFR01RdYwmh3HQ+o+lWY4mKa6I4nY+IyD4mUQ2kN4jWr5UVt4SnevMfPgcA0L1wH3IunZA4GiKipsOy2W61lagK0/l0FbrzudUwne9/B9Lw9uZTKNQaHBo7EVFTwXf9DaQ13nqBUVbYzJDqr0v3GBxU3Q4AuPjLxxJHQ0TU9FS72a7s1jG9ve585abziaJo89jk9Jt4ceMxrNp9ER9sO1vreERRxNLtZzHgnd/w2g8nYDSJNT+IiMhFuVQStXz5ckRGRkKj0SAmJgZ79uypcux3332HkSNHonXr1vD29kZcXBx+/fXXRozWzFBahDXKd/CZ6n0IRn2jX785Mwx4Bi/oZ2B25nh+2klE1AAyO9P5FHam86nLkihN2XQ+kwhr5z6LH5KvWr/elHy1UpJVla0nr2Hp9nO4fKME8ftS8UVCar2eCxGRK3CZJGr9+vWYN28eFi1ahKSkJAwcOBBjx45FWlqa3fG7d+/GyJEjsXnzZiQmJmLo0KGYMGECkpKSGjVuQ2kRhsqPYITsECCTN+q1m7t+A0biz1YTcL1UhvUH06UOh4ioSRBRc4tzc2MJ81sAvb01Ucpbr2cVp/QduXzT+nV2oRaXcoprFdenuy4AAHzclACAlbsuwGBkB1YiappcJol6//338dhjj2HGjBmIiorC0qVLERoaihUrVtgdv3TpUjz33HPo27cvOnXqhLfffhudOnXCjz/+2KhxG/Ra85+QMYlyMLlMwIyB7QEAq3efh16vkzgiIqKmw2Y6n501UQqbzXbNiZIliVLKZdYp6uU79JlMIk5n5AMAvNQKAObpfTXJzCvF4bSbEATg5zkD4OehwrV8LXafu16/J0dEJDGXSKJ0Oh0SExMxatQom+OjRo3Cvn37anUOk8mEgoIC+Pn5OSPEKhl0peY/oWjU67YU9/Vpiwfd/8QXpU/hxI9cG0VEVBPL7LrySZTMzma7lkqUwc50PsD+XlFXbpagSGeESi7D2NuCAAAXrhfWGNOus1kAgJ7tfNGulTvG3xYMANhxKqsuT42IyGW4RBKVnZ0No9GIwMBAm+OBgYHIzMys1Tnee+89FBUVYfLkyVWO0Wq1yM/Pt7k1lKUSpYOyweeiyjRKOcZEKtBBloHgYytgKvt5ExFR7VWsRMnKNZYwGE3WdU+qckmUvb2iLt8oAQC0beWGzoFeAGqXRO27kAMAGNy5NQBgWNc2AICdZ67Xek0VEZErcYkkykKosPBVFMVKx+xZu3YtXnvtNaxfvx5t2rSpctySJUvg4+NjvYWGhjY4ZqOubDqfwEqUs/S4aw6ui74IFK/jxJZVUodDROTS7OUkFRtLyAWh3HQ9k/Uxavmtaen29orKyDMnUcE+GnRo4wkAOJ9VcxJ17HIeAKB3mC8A4I72/lDJZbhys6TWa6qIiFyJSyRRAQEBkMvllapOWVlZlapTFa1fvx6PPfYYvv76a4wYMaLasQsXLkReXp71lp7e8GYFJoNlTRQrUc7i6+OD4xHTAQABSR/DxLVRREQ1Esptt1u5EiVAXjadr1h3q/upWmmnEqW71fwhI888hT3E1w2R/h4AgLTc4mqrSQWlelzMLgIA3NbWx3xulRzd2noDAA6n3ajjMyMikp5LJFEqlQoxMTHYtm2bzfFt27ahf//+VT5u7dq1mD59Ov73v/9h/PjxNV5HrVbD29vb5tZQBoO5rblBYBLlTL3vm48c0RvBpms4/utnUodDROTyLF36gMprouSCAGXZsSLtrUqTSm5nTZTedk0UAIT4aBDkowEAlOpNuFlc9RYfJ67mWx/j76m2Ho8JawWASRQRNU0ukUQBwIIFC/DZZ59h9erVOHXqFObPn4+0tDTMmjULgLmKNG3aNOv4tWvXYtq0aXjvvfdwxx13IDMzE5mZmcjLy2vUuHN8eyKi9CvM9V/ZqNdtaXx9fHEiwvz373f4Y5gM3JOLiMgeezUhQajQaEIQoJDbVqKUcsEm2XIvm85XvlKVUZZEBfu6QaOUI8BTBeBWcmWPpZtfdIiPzfE+4WVJ1KWbtXhWRESuxWWSqClTpmDp0qVYvHgxevXqhd27d2Pz5s0IDw8HAGRkZNjsGfXpp5/CYDDgiSeeQHBwsPU2d+7cRo1bbxQBCBAU6hrHUsP0uPdp3BC90M50FYd/3yB1OERELq38dD4BtuuiyjeWKC7rvle+CgXcms5Xfk1UVoF5CnuQt7kKFezjBuDWND97Usqm8nVo42FzvGeoLwDg7LUCaA3Gig8jInJpLtUNYfbs2Zg9e7bd++Lj422+37lzp/MDqgWd0f6LDzmer68ffuryAr46Xozrx4KxZZjJ+kkqERGZ2VufJBMEyATAkqrIZUKldVJKhe3vU42qcovzG0XmNal+HuYKVIivBseu5FkbTthjWQ/VPsA2iQrx0cBbo0B+qQHnswrRrUKliojIlfEdaAN5Zx3GMuWHuK/gf1KH0iIMuu9xnNb0wvmsQnx96LLU4RARNQmCUKESVW6fKIuK399qcX6rsURusW0SZalEVTedLzXHnERFBnhWiElAVLB5bfKpjILaPxkiIhfAJKqB1EWXcaf8ALrqjkodSovgrVHiqWGdAADxW/9EUV6OxBEREbk+AUKlNVGWFucWFb+vuE9Uic6I0rKEqpU1iTJP67tWxXQ+rcFo3VsqIsC90v23kqiG79tIRNSYmEQ1kFjW4twkY3e+xvK3O8LxhPdebDTMxqmvX5E6HCIil1Q+aRIE2zVScpkAecXpfBXXRFXYJ8pShVLJZfAou6+1l3k9cHah/a0n0nKKIYqAp1qB1p6V1w5HBZs37D2dySSKiJoWJlENZTS/cJhkKokDaTlUChkGx/aEh6BFj8vrkJN2SuqQiIhcmiDYtjw3b7ZbYTpfhUqUtcV52Zooy3qoVh5KCGUZWkBZYnS9rOFERallG+lGBLhbH1Mep/MRUVPFJKqBRIP5RUVkJapR9R0xGUmqPlAJBlz7eoHU4RARuQx7+94KEGyOC7LKSZOyijVRlu59uZYkyv3Wh4a3KlH2k6irZWul2vq62b2/Uxsv67kt5yciagqYRDWQyEqUJASZDOo7/wm9KEd04T6c28uW50REValYBJILdqbzKSquiTK/RSgtaz9+o0JTCeBWJSq3WAeD0YSKrpZ17bM0oKjITSW3JlgXrxfW6rkQEbkCJlENJJQlUaKcSVRji+7RF/ta3w8AcP9tEQzaqrtDERG1FKKd7XYrTqSTy4RKladK3fksa6IqTee79Xrn56GCTDBXv+xVkjJumhtOhPhqqoy3fWtz6/MLTKKIqAlhEtVQBiZRUrrtwbdxHb5oa8pA8tdvSR0OEZHLKJ84yQTBJrUSBDvT+apYE2WpROUW6wEArdxvTV+XywT4eZSti7IzpS+jhkoUAHRobW59fvF6UTXPhojItTCJaqDfAx9GdOlq7I2YI3UoLZKfnz/O9XgOelGOP85fx7V8+212iYhaCrtrogSgfBYlt7NPVMVGExUbS+SXmJMoHzfbNcABnuYPEe116Ltai0pUB1aiiKgJYhLVQKUmOYqhgaCqvP8FNY7b7/4/POn/KT7Q3oVFG49BtPcOgoiohbGtPNlWmeQyoVIlSlFFEmXZG6qg1AAA8NLYJlGW5hIVO/QZTaL1g63qKlHtyypRF1iJIqImhElUA+kM5heXip/gUeORy2WYP3k0lHIB209lYVPyValDIiJyOeXXSgmCAEWFxhKqKjbbtewTVVBqrkR5qhU24yz7P1Xs0JddqIXBJEImAG28Ku8RZWGZzpeWW2x9TSUicnV8599AsTk/4F+KlWh/8w+pQ2nRugZ5Y+7wTugmpKLdponIvnJB6pCIiCRR21q8UKFDX6XGEpbpfNYkylKJsk2iAsoSpJwKSZSlvXmQt6ZSlau8QG81PFRyGE0i0nJZjSKipoFJVANFFh/F/YrdCCi+KHUoLd7jg9rjHY+vEItTyPpyBkSTUeqQiIhcWvlqVOXNdstanJclUYVa+0mUZd+oG2WNJywy88qm8lWxR5SFIAjWKX3ns5hEEVHTwCSqgWSmsoW0iqqnKlDjUCrkcJ+4DCWiCtGlh9mtj4hapKrWhdo7XH4quqqqxhIVpvNVXBNl6dZ3o0KL86yyNVKB3jW/PkYEmJtLXMphEkVETQOTqAaSmcwvKgJbnLuE9lF9cLDrswCAbqeW4tLxfRJHREQkjYp7Q9lLreTVVqJuNZYQRdFaiaq4Jsqyb1RusW0SZWk0YdmQtzoR/ubmTJdyi2scK5XrBVp8uusC5qxNwjPfHMHXB9NRVPYzIaKWR1HzEKqO3JJEKVmJchUDJj+DQ+/uRGzJH5B9NwOlkfuh8fCWOiwiokZRl/6k5feGqrhuybLZLgBoDSbkV7EmyjKd72aF6XyWRhO1SaLC/MxJVFqOayZR3yZexqubjqNIZ7Q59t62M3j73tswPCpQwuiISAqsRDWQTDS/aMgUrES5CplchshHP0cW/BBquoKTn82wP4+FiKgFsTfNr3wziUrT+RS3vs8v0Vs751WczufnYf4+t8h+Jap1NZ35LML9zdP5Ul1wOt+/d1/EM98cQZHOiO5tvfHC2K6YM6wj2rVyw7V8LWZ8cQif702ROkwiamRMohpIUbYmSsZKlEvxbx2MzBHLYBBlKMq+gm8PnJM6JCIil2Pbna/yvlGWSlVWuT2gKk7n8y2rROWX6mEw3mpRXpdKlGU639WbJS7V5nzbyWt4a/MpAMDsIR2w6YkBmDW4AxaM6oLtCwbjb3eEQRSBN346ibV/pkkcLRE1JiZRDSQTzdMbWIlyPT0GjMfGnqvwsP4FLPzxPBIv3ZA6JCIi56ui8G7vcHXT+YBb66KulyVEHiq5TeIFAL5u5kqUKAJ5Jbem9GUXmj9kDPCs+fWxtZcabko5TCJwpaw1utSy8kvx9NfJAIDp/SPw3JiuNs9do5TjzXtuwxNDOwAAFm08hv0Xc6QIlYgkwCSqgZ5zew2xpStQ2G6w1KGQHRPvmYTR3UOgN4qY9eUhXMvKkjokIiKXobDpzlexFcWtvaIsU/M8NZWXUivkMviUJVI3yppLiKJYp8YSgiBY10XVZUqfKIo4d60A57MKq+xKWF9vbz6F/FIDerTzwYvjoqoc98yoLrivd1uYRGDO2qRKmw4TUfPEJKqB8kxuyIYPFKrq98EgachkAt69vyeiA90xv/QTFH86EkV5uVKHRUTkNFWlEvZyDNt9oqqpRJUlRBXXQ1lY25yXNZfILzVAVza1rzZrogAg3L9uzSVyCrV4YNV+jPxgN0a8vwvTVv+JvArNLerrUGouvk++CkEA3rynO1SKqt8uCYKAN+/tjo5tPJFVoMXL3x93SAxE5NqYRDWQZe62uppfsCQtD7UC/74vHCMVSYg0piJlxUTodfykkIiaOaFyZakihbzqFueAnUqU2n5TX2ub87LmEpZqjJdaYU3EamJJoi7VIokymkTM/uowDqTkQikXoJAJ2HMuGzO+OGizLqu+lm43r6OdEhuKHu18axzvrlLgwwd6QS4T8MvxTGw9kdngGIjItfGdfwPN0n+B1xVr4FbCX5iurG14e+Tc9V8UiWp0Lz2MpE+mQTS5zuJlIiJHqcu0tuq68wGARmk+dqsSVUUSZW1zrrMZH1DLKhQAhJV16EvLrXk634bEyziQkgsPlRw/PTUQPz41AJ5qBQ6m3sC/9zSsU15S2g3sPZ8NhUzAE0M71vpx3UJ8MHNgewDAK5tOcA8pomaOSVQD3Sn+jocV26DW50kdCtWga5+BOD/4YxhFAf3ytuDgp//HRIqIWjRFNd35AHvT+apPonKLzNPpbnXmq33TJUuHvtQaKlF6owlLt58FAMwf2RldgrwQFeyN1+7qBgD45PfzDVqXtGLnBQDAPb3bIrRsnVZtzRvRCWF+7sjML8Wnuy7UOwYicn1MohpIUdadT67SSBwJ1UbPYVNwuOfrAIB+19Zh/2dzmUgRUYtVfgqf0s60dMuGu9lFlu58VSVRto0lsuuwR5RFuJ+lElUMk6nqatpvp7NwNa8UAZ4q/O2OcOvx+3q3xW1tfVCoNSD+j9RaX7e8jLwSbD91DQDw+KD2dX68RinHwrFdAQCr9lxERp5rdBokIsdjEtVAKpiTKIWK+0Q1FX3vm4uErgsBALFXvsKaTVsc3tWJiEgqdfltpiw3hU8pszOdT2FOoixrndxV9tc3WdZE3Sgbd70Oe0RZhPhqoJAJ0BlMuFZQWuW4rw+mAwAmxrSzWW8lkwmYPcTcbvy/By6hRGes9bUt1h9Mh0kE+kX6oVOgV50fDwBjugehb0QrlOpNeG/r2Xqdg4hcH5OoBjCaRCjLkiglu/M1KXEPvIADXZ7DLP08LD4g4h9bTjORIqIWx2azXXuNJcqSpptlXe/cq2osUTad71YlyrJHVO2TKIVchnatzK+lqdn2p/TlFeux8+x1AMDk2NBK94/qFoRQPzfcLNbjx6NXa31tADAYTVhflqA9dHtYnR5bniAIWDQ+GgCw4fBlnMksqPe5iMh1MYlqAL3BAKVg/qSLlaim5/a/LsKA8VMBAJ/uuog3126HXu+Y9rhERFKpy+dB5RtLKKtpLGHhXkWnPT8P2xbnOWUVKT+Pum1EX1NziZ1ns2A0iegc6IkOrT0r3S+XCXigrzkB2nj4Sp2uvfd8NjLyStHKXYnR3YLq9NiKeoX6YtxtQRBF4MMdrEYRNUdMohpAp7s13UDJJKpJeuQvkfjnxB5oJ8vBY2f+juQP7kVxET81JKKmr+YG54Cy/JooO5Woiu3J3aqYzufrbjudz9Klr65JVE3NJX47bd4wfVjXwCrPcU/vtgCAhIs5uHyjdntOAcCPRzIAABN6htS6LXt15g7vDEEANh/LxKmM/Aafj4hcC5OoBjBoyyVRSjaWaKom9w3FshEaBCAffYv34PL7w5B5uWEtcomIpCLWYVWUzXQ+O2ui3CokE+5VNJbwLWsskVdirkTlliVRlml+tRXmV/WGu0aTiJ1nzFP5hke1qfIcbX3dENfeHwCwKbl2U/pK9Ubr3k539gipU8xV6RLkhXG3BQMAPizbd4oaplRvRLHOgFK9EXoH7AdG1BD2fxtSrejkHrij9GO4yYz4XclKVFPWa9hknPHwQptfZqKz8SyyPhuGE+NXo1vfoVKHRkTkNDaNJex056tYkfFQV9FYwrJPVIkeoiha11C1KpvmV1sRZdP5UnMqT+c7lZGPvBI9PNUK9A71rfY8d/UKQcLFHGw9kVmrvZ52n72OAq0BQd4axIa3qlPM1Zk3vBM2H8vAlhOZOHE1D91CfBx27uYqt0iHQ6m5OH41HynZRUjJLkRWvhY3S/TQGWwTJ41SBn8PNfw9VWjXyg2RAR5oH+CJLkFe6BLkZXeKKpGjMIlqAL0JyIQ/3BXyWu0MT66ty+1jcbXNNuR/ORnhpjR4/3Q/9qW8gLhJCyDY+YSWiKipK783lNLOPlEVK1EVv7fwcTMnS0aTiPwSw63pfHWsREUE3KpEiaIIodxr66HUXABAn/BWUNTw5nhEVCBeFI7hyOU8ZOSVINin+uZPPx41T+W7s0cwZHZ+DvXVKdALd/YIwY9HruLD7eewalqsw87dXJhMIpIv38SvxzOx/dQ1XLhe82bLFqV6E67cLMGVmyU4etl2v061Qobb2vqgV6gv4jr44472/vCoojEKUX3wX1MDaMs+EeEnHc1HSGQUiufvwrGVD+K2ogT0P/kGvvykAOMfXVTnuf1ERFKoU2MJefnufHYqUaraTefTKOXQKGUo1ZvM+zyVxeBbxySqXSt3CAJQoDUgt0gH/3Ld/Q6m3gAA9IuouVLU2kuNmLBWOHTpBraeuIaH+0dUObZUb8SOsr2h7uzpmKl85c0d3hE/Hb2KrSev4VRGPqKCvR1+jaYoq6AU6/9Mx7qD6bhy03Y/rU5tPNEr1Bcd23iifWtPBPto4OuuhLebEkqZDEZRhNEoIq9Ej5wiLbILdbiUU4SL2UW4eL0QJ6/mI7/UgEOXbuDQpRv4bG8KlHIBMeGtMLBTa4yMDkSnNp42STpRXTGJagBTQSZeVnwJveANYJTU4ZCDuHv5ofvTP+PP/y2Gz7kNWHKlBz5euhvvTe6JgZ1aSx0eEVGt1Ob9oW13PjuNJSpM8auqsQQA+LqpkKkvxcXsQgCAp1oBlZ0pgtXRKOUI9tbgal4pUnOKrUmUKIr4s6wS1TfCr1bnGtUt0JxEncysNonafzEHxTojgrw16NnO8dPtOrbxwvjbgvHT0Qx8/Ns5LH8oxuHXaEpSsovw8W/n8EPyVRjKsm1PtQLDurbB6G5B6N/B37rvWE183JUIK2tGUp7JJOJidhGS028i8VIu9pzLxuUbJdh/MRf7L+biX7+eQfvWHhjXPRhjugehW4g3EyqqMyZRDVGYhccUvyDb5Ct1JORggkyOfn97HScuP4WQr0/gfFYhpn2+Hx9HJGDgA8/Cx9dxc+aJiBypvpUoe7MqKiZNVW22C5ibS2Tml1r3eLI0m6ircH8PXM0rRVpuEWLK1iel5RbjeoEWKrkMPWtYD2UxKjoIb28+jf0Xc1FQqoeXxn481o5/UW2c9kZ6zvBO+OloBjYfy8SZzAJ0CarfRr5N2ZWbJXh/61lsTLpsrVT2CfPF3+4Ix7jbgh3SEdFCJhPQsY0nOrbxxKSYdhBFEZdyirHn3HX8fuY69p7LxsXrRVj2+3ks+/08wv3dcU+vtpjYp53dpIzIHiZRDWDUm3dkNwj1e6Eg19etXQB+fHIA3t58CrKDq3Bn5n9wZek3OB33BvqNmsJProioSVPUsNmuRlGhsUQV0/mAW+uiLE0h6jsFOtzfHQkXc2w23LWsd4kK8a71m+2IAA9EBnggJbsI+y7k2N37SRRF7DhlTqKGd626419DdQ70wrjbgrD5WCY+/u0clj3Yx2nXcjVagxGf7UnBx7+dQ6nevAxieNc2mDuiE3q0822UGARBQESAByICPDA1LgIFpXr8djoLvxzLxM6zWbiUU4wPd5zDhzvOoW9EK0zs0w7jegTDu4rEmwhgEtUgTKJaBjeVHG/c0x2n24xE5rZf0FbMQtuEx5F0+DN43PUOOndr2VMziKjpKr8OqjaVqOqm81k69F3MNidRdV0PZRFe1qHvUrkOfcevmpOo29rWbT3RoE4BSMkuwu6z1+0mUWeuFeDKzRKoFTL07xBQr3hr66lhnbD5WCZ+PpaBeVkF6Nim+VejDqbm4rlvjyKl7N9Ev0g/LBoXVetqorN4aZS4u1db3N2rLYp1Bmw9cQ0bDl/G3vPZOJh6AwdTb+DVH05gdLcg/LVfGO5o78cPTakSdkRoAJPe3H3IwFy0RejafwJaPXsIiW0fgk6Uo7f2ICK/HondHz2Ga1fTpA6PiMhGrTbbtenOV3OL85qm8wFAynXzmii/ek7ns2y4eyn3ViXqxBXzZrV1bRE+qLN5Hevuc9ch2pnnaKlC/aVjQLUJoiNEBXtjdLdAiCLw8W/nnXotqWkNRvzjl9OY/GkCUrKL0MZLjQ8f6IX1f79D8gSqIneVAvf0bosvH7sdCS8Mxwtju6JTG09oDSb8cOQq/vrv/Rj+3i6s2n0BOYVaqcMlF8J3/w1g1Js32zXI2LWtpVC7+yBm5nJkXvw/ZG94Ft2LEjAo91tsX5mCZX0+wqwhHdDWt/pWukREjaE2S6PKV6LsTudTVmgsUc1UOp+ypCm/1ACg/pUoy5qUS2Ub7oqiiBNllajudUyi7mjvD6VcQHpuCVJzihEZ4GFzv6Ur3zAnTuUr76lhnfDriWv48chVzBneCR1ae9b7XKIoIin9JradvIaDKbm4fKMEJXoj3FVyRAZ4oGeoL0ZGB6JXO1+Htm2vycXrhXjif0k4lWFOfCfFtMMrE6KbxNS4IB8NZg3ugMcHtcexK3lYdzAdm5Ku4GJ2Ed7efBr/+vUMRncLwoP9wnBHe/9G/bmS62ES1QAmg7kSZRL4Y2xpgtrfhqBnt+D8/h8h7ngDnxTehaT9l7DuYBqm36bB32IDEd6xm9RhElELZK/iUhW5rIbGEuWSJjelvNo3jb5utklTqwZO58st0iGvRI9CrQE3ivVQyAR0Dqpb0uGhViA23A8JF3Ow++x1myTqZrEOSek3ATReEtW9rQ9GRLXB9lNZ+OT383h/cq96nWff+Wy8s+U0jlTYGwkA8kr0yMgrxb4LOVix8wLaB3hgxsD2mBjTFmqFc6ttW09k4umvj6BAa4Cfhwpv33sbxnSvPI3S1QmCgB7tfNGjnS8WjYvCj0eu4n9/puHo5Tz8dDQDPx3NQIS/Ox7oF4ZJMe0QUK4VP7UcfPffACZDWSVKYCWqpep4xwSI/cbjuZQb+GjHOSRczEHw8U8ReupXHHKPg3DHbPQeMB4y7iVGRI2sNp+Ry4TySZSdzXbLTXGrbiofULkbn59H/SoPnmoFAjzVyC7UIi2nGFfzzHsIdQr0qlcSMKhza2sSVb7VecKFHIiieU+ikEacQTBneCdsP5WFTclXMWdYJ0RUqI5Vp0hrwMvfH8d3SVcAACqFDGO7B2FQp9bo2MYTHmo58ksNOHetAHvP5+C3U9dwMbsIL248hpW7LmDR+CiMig50+Poeo0nE0u1nrdMU+0a0wicP9kEbb41DryMFD7UCD/QLwwP9wnD8Sh7WHUzD90lXkZpTjH/8chrv/noGI6MD8dd+YRjQMYDVqRaESVQDXAkYiBe0/0KfkED8S+pgSDKCTIa4Dv6I6+CPQ6m5EL9ZCVmRiNiSfcDv+3BpZ1tcDr8XkcMeRUhYB6nDJaJmrg4dziGWG22vElW+O5+6hj2ffN1sk6b6TucDzB36sgu1uJRbhLOZBQCA7iH126R2UOcAvLPFvB+U3miyPs+957MBmNdDNaYe7XwxtEtr/H7mOj75/Tz+dX/PWj3uUk4RZn5xCGevFUImAH+7Ixxzh3ey2ZDYok9YK0zpG4ZCrQFfH0zHyl0XkJZbjMe/TMToboFYcl8Ph20gX6IzYu66JGw9aZ4aOb1/BBaNj7L776mp697WB2+2vQ0Lx0bh56MZ+OrPNBxJv4lfjmfil+OZaNfKDVNiQ3F/bCiCfJp+AknVa37/whtRieCGC2Jb3NS0lToUchGxEX7o++yPuPrQLiS2vhclUCNcvIK/pC5D4Ocx2P7OFPzvQBoXpxKR09UmmSo/88/emqjylaiaqhcVk6aGvEkPL7cu6uw1c6OKrsH1S6KigrzRyl2JIp3R2iodAP6QKIkCzNUoAPgu6QrScoprGA2czyrA/SsTcPZaIdp4qfH143FYfHd3uwlUeZ5qBR4dEInfnxmC2UM6QCkX8OuJaxi9dDf2nstu8PPIKdTir//ej60nr0GlkOGDKT3x2l3dmmUCVZ6HWoHJfUOx6Ym/4Je5A/FwXDi8NApcvlGC97adRf9/7MCM/xzE9pPXYDCapA6XnISVqAbQGcz/MVTN/JcF1V1Ip14I6RSPkoKbOLT9S3ieWoeuuuO4UCDHko3H8NL3x3B7mBee9N2H8DvuQbvIrlKHTETNRF022y3PXne+8tUnO3fbqDidr76b7QJARNm6qNTsIpzLMleiOgfWrwmDTCYgroM/Nh/LRMKFbMSEt8LlG8VIzSmGXCbg9vZ+9Y6zvnqHtcKgzq2x++x1LN95Hv+Y2KPKsanZRXhg1X5kF+rQJdALXz7Wr87T5DzUCjw3pivG3RaMeeuTzRvIrz6AhWOjMGNgZL2m96VkF2H6mj9xKacYvu5K/HtaLPpGNP7PUmpRwd74//buPCyq8+4b+PfMzr4jDJuIiijGCG64ptrgkri3atLHaPLUR9OYxuVqNG18Y9OmkqRN82TRJL2IJo991SYu9X1iYkgiGgV3XMENcAcRBIadgbnfPwYIyMzAsM4M3891zTUzZ+5zzn1zM+c3v7Pc548zo/HKtCjsO5+D7cdv4fj1B/guIw/fZeQhwF2DXw4LxrxhIQjx5o18HyaEQFWNAZX6WlTq655rGr2um66vNcAgBGpqBWqFgMEgUGMQDdMM4qf3ADC2r2+n34eMSVQ7eBecwirFXriUxwLoOTfOo9ZzcvPEsNkvArNfRN6NdLhcKsDAS0B6jg7KW4cx5t6bwOUNuC0F4q73SCj7T0Sf4dPg4e3X3VUnIjtn7c9iU9dyNP5xLWvxSNTD10S1/0jUtfuluF53pKZfO+6rFBfhi33nc5GSWYDlE/sh5VoBAGBIsEe3jRr30qS+OHTlPr48dRvLJ/ZFsFfzH9hF5dV4bssJ5JdWY2CgO7b+emS7/q7RQR743xfHYt2eC/ji1G28sS8D6Tk6bJgzuNU3MQaAC3eK8cynx/GgrBoh3k7Y8uyIdo006Ag0SjnmxARjTkwwruWVYPvxW9h5+jZydZV4/4dr+ODANYzr54cFw0MwKcq/0wf56GpCCBRX6JFfWo380ioUNDxXQVdZA12FHrrKGpRUNnquGzjG0MYdP5Y4TZczibJlPoVnMF2xByfLKgH8trurQzbOP2wg/iMM+I/JwO3Cclz8sRiXLgxC36oMBCMHwQV7gNQ9MKS8hOuKUHzf52V4D3wMj4Z4obePM2/0R0QdzpqR/FraAnXU6HzATyP0pd0sAgC4qRXo5d72EdBGR/gAAE7eKESlvrbheqix3XAqX73YMG+M7euLw9fysTE5E3+ZPbjJ5zW1Bjy/9TSy8ssQ5OmELc8N75DrmDRKOd76xSMYpHXHn77KwO60O8gprsAnzwxrVUJ5+mYhFn16HCWVNXgk2AOJi4bDz42j0zXW198Nrz45EL+bEolvL97D9hM3ceSacXCTQ1fuw12jwBOPaDF7aBCGhXnZ/GAUQggUlFXjblEF7hZV4E5RZaPXFbinq0RBaTVq2pkNKWQSNEo5NEoZ1Arjs/G9HEq5BLlMglwmg1xC3WvjQyZJUMgkyOpeS2jfTpdW17fT1+DApPohzmW2f+8Dsi3BXs4InvE0MONpFBc+QObJr6G//D0CHxxDqOE2etfewI4Lpbhy/iwA4Dmng/ilKhVlXgMgDxgMrz6PQtsvBiqnnr3nj4iaE1YMLWHNqX8tHYl6+J5S1hzZeFj9DXfrRfi7tmtHUh9fF/RyV+OergqnbhQiJdOYRI3uxiQKMF4bdfhaPr44eQvLf9a3ySiBHxy4htSsArio5EhcPAz+bh03UIEkSVg8Jhx9/d2wbOspHM16gPkfH8Vnzw63eKrgsawCPLflBMqqazG8txc+XTwcbnZw/6fuolbIMX2IFtOHaHGjoAw7TtzCrtN3kKurxLbjN7Ht+E0Eezlh1qNBmB0T1O1H83SVemTfL0N2fhmy7pciK9/4Oju/DOXVta1ahptGAT9XNXxcVfB1VcPbRQVPZyXcNUq4aZRwd1IYnzU/PTurFdAoZE3uW2cPmES1R60xiRJy7oGhtvPw8kbM478CHv8VACA/5yayziRjnH4oXG/rcOGuDlH6DESJc0DuOSD3X8AZwCAk3JX3QpEmBAej/gifwFCEeTsj3K0Wfl4ekBT8vyQiy6zZb9xSDtORR8s9nVVw1ygabtzbz799Py4lScLoCF/sTruDzUeuI7+0Gk5KOYaGenZAbdtuRLg3RvXxxtGsB/joYCZenxkNADie/QDvfX8VAPCXOYMxIKBtg2q0ZGw/X2z/r1FYvPkEMnJ0mPtRCj5/bmSzmxIDwI9X72PJ5ydRqTdgTF8f/OOZYXBW8Wdka4X5uODlKQOwOj4Sx7IKsCvtDr4+n4PbhRX44IDxdL9Hgj3w5COBmBod2KnXT9UaBLLzy5CRo0NGjg6XckuQkaNDTnGlxfn83dTQejohyMsJQZ5O0HpooPV0QoCHBr51iZOjnaZoiU3992/cuBFvv/02cnJyMGjQILz77rsYN26c2fIHDx7EqlWrcPHiRWi1Wrz88stYtmxZ11XYYEyiIOdeGOo4voGh8A18BiPq3lfXGJB9yQ9HLqXAcO8C3IuvILgqEz6SDlpDLrTluZh7JBcVKAIA/EXxDyyQJ+OBzBPFSj+UawKgdwkE3LVQegVDipqOXj6e8HZWQSah5V9GRHaqM2LKzp07sW7dOmRmZiIiIgJvvPEGZs+e3a71drS2DizRkpaORHW03r4uDaPp9WvjoBKNxUX4YHfaHXyXYRyKe0S4t0384HtpUn8czTqK7cdv4TeP9YWbRoGVO87AIIC5McGY+WjnjgAcHeSBnc/H4ZlPjYNE/GJTCrY8OwKDgz0aynyfcQ/P//M0qmsM+FmkHzb9R2y7jjT2ZHKZhNF9fTG6ry/+NDMaSRn3sCftDg5euY9zt4tx7nYx/rLvEqKD3DE1OhBTowPQpx1HqIQQuF1YgdM3C5F2swhnbhXhUq4OlXrTowb6uakR7uuCCD8XhPu6INzXFX38XBDs5WQT3xdbYjNJ1I4dO7BixQps3LgRY8aMwccff4ypU6ciPT0doaGhzcpnZ2dj2rRpWLJkCbZu3YojR47gN7/5Dfz8/DB37twuqXP96XxCzpvtUudRKWSIjB6GyOhhDdOEEMi5exO5medQmJuNOcr+uPmgHDcKyhFcWgCZJOAjCuFTXQhUXwF0AHKM80Ym+6AKxgTqXc0nmIBTKJF5olzpiSqVN2o0XpDU7pA5ueN21BI4OzvDTaOAZ8UtuEoVcHb1hpO7FxQaN0ChZhJGNqkzYkpqairmz5+PP/3pT5g9ezZ2796NefPm4fDhwxg5cmSb1tuZOvo6yq7+qof5NEqiOuD6hvrroup15/VQjY3q440Rvb1x/PoD/O3by3B3UuJOUQWCvZzw+sxBXVKHMB8XfLlsNJ7dchwX7uiw4JNUfLxwGMb288U3F3Lw4rY06GsFJg/qhfefioGqhXuGUes4qeSYMUSLGUO0yC+twr7zOfj6fC6OZRfgwh0dLtzR4e39lxHZyw1TogMwcYA/Bgd5WLyGqrrGgHO3i3As+0Fd0lSI/NLq5utWyhEZ4IaoQDdEBbojKtAdkQFu3TbQij2ShDVXlXaikSNHIiYmBps2bWqYFhUVhVmzZmHDhg3Nyq9ZswZ79+5FRkZGw7Rly5bh7NmzSE1NbdU6dTodPDw8UFxcDHd36w+VH3t/EUYW7MHRkCUY9Z9/tXp+os6gr6lFbs5t3L+ThfL8W9AX3oZUcheq8hzIq3R4QaxBQVkVhAC2KN/EY/KzZpfVr/Jz6Ov2tbyr/ACz5ClNPjdAQiXUqJLUWOr5CQwqd2iUcswo/xLRladRK9egVu4Eg1wDKFSQ5CoIuQrnwpdAqF2hkMsQVHgCXmWZkOo+lxSqhtcyhQoV2lGQqZyhlMugKb8DVUU+JLkCMpkcUt1DJjc+C48QyBRqyCRApi+DvLYSklwOmcxYXqaQQyZX1r2XQWppzGbqVO3dBlvSGTFl/vz50Ol0+PrrrxvKTJkyBV5eXti2bVub1mtKe/8uw9/4DvdLqhAb5oVTNwoBANcTnkDvtV81lLme8AQA4L3vr+KdpCtNpj2sfr7IXm7Yv3K8xXWbWkdbvfnNJWxKzgQA/Pjyzzrk9KbRG77H3bpTlvb9dhwGtvEGvh3t9M1CzNnYdPv62XMjMKF/147UWlKpx7Ktp3DkWgGUcgm/GhmG/zl6A7UGgelDtHhn3hCHvweULcgvrUJS+j18fSEXKdfymwzY4OOiwoRIP/ws0h/j+/nBRS3Hhbs6pGYWICUzHyevF6JC3/T6JaVcwkCtB4aGeGJoqCcGB3kgzMcFchsf0KI7WLP9tYkjUdXV1Th16hTWrl3bZHp8fDxSUlJMzpOamor4+Pgm0yZPnozExETo9Xoolc0z6aqqKlRV/XSTU51O1656SwY9AEAoeCSKbIdSIUdISBhCQsJMfn4SgL7WgMLyaujyo3H2wR1UFt1Ddcl9GErvQ5Q/gKgsgaQvQ0ygP0qralBWVYOaclfkGbzginI4S8bvkQwCzqiEs6jEmZxKVMO44Z6jTMdA+UmzdfzPq3EohHHj9GfF/8VExfdmy46p/G/cgfGHxB8UW7FEsc9s2UlVbyNTGE99Wan4Ai8pdpst+2TVn3ERfSCTJCyT78Uq2XYISHUPAI1eLxGv4jQGQJIkzEMSVmIrhCTVlUGT+V6RVuKEzHjPl8mGw1gpPm8o07g8IOEtxRIclcUCAMYaTmJFzaeoWzXQUM5ok3IRDitGAQBias9hVfVHZtu2WfUUflAYTx8bWHsZa6veb/J54z1n25Wz8Y1yEgAgojYbr1b9venCGsXY3cpp+H/KqQCAIMNdTNfvx5mo1dgwx/w9brpDZ8WU1NRUrFy5slmZd999t83rBTo+NtWbMigAp24UwtfVfIx6eDAIS7r6SFTj1QU1GnChPdw0SqAuiRoQ0Pmjd7VWTKgXZj2qxZ4zdwEAM4ZouzyBAox/n08XD8eqf53FV+dysCXlOgDgF7HBeHPuI/zR3UV8XdV4akQonhoRiqLyauM9p9Lv4fC1fBSUVWPX6TvYdfoOZJLxiFLZQ4M+eLuoMDLcG7FhXhga6oVBWneeftkJbCKJys/PR21tLXr16tVkeq9evZCbm2tyntzcXJPla2pqkJ+fj8DAwGbzbNiwAX/84x87rN5f+yzG67lxeDpoOOI6bKlEnU8pl8HfTQN/tzAg3HSyBQCPNXn3MwDGBKyoohKlJSUoLy9BdUUZaipL8bFr37ob5hngkrcUh3RTIPTlgL4CUk0FRE01RE01YNDj5z7hKIcKNbUG1BY9gmMVesgNesgMNZALPeTip2c/bw/A4IQagwF6vTvuCj/IYDA+hAHyutdyGABJDgnGa0JkrbhkXgigVggIyQC53JjemFJZXYsyYQxSBnkV3JQVZpdZWVWFB3XXSxrkZfBXPjBftrwMuQbjD7oqmQ7BqrrtnYlqVJQW46ah7n45smKEqu6aXW51aSFu1BrLBsmKEaa6bbZsTdkDXK8r6yXpEK6+2bRAo7qI0nxk15YBAFRSMVwUObinq4Kt6ayYYq5M/TLbsl6g42NTvTF9ffG/L45tOILzzrwhWPWvs9gw56ehtH81Mgz7zufi8YG9zC0GL03qh//+/irWz2j51LKXp0TirW8u4/88ObDd9Z81NAgbkzMRE+rZYUNA/+GJKDzz6XEsHBVmc8NKr5k6AN9fyoNaIcO6Dvj7tZVaIcf7C4bCz1WNz1KvY+GoMKyfPsjm/l49haezCr+IDcYvYoNRXWPAqRuFSL6chwOX83DlXinKqmvh4aTEyHBvxEX4IC7CB/393dhfXcAmkqh6D5+/LYSweE63qfKmptd75ZVXsGrVqob3Op0OISEhba0uFk4ejSmjY3kHaupRlHIZPF2d4enqDMDcDy/LF0I/1uTdMNOF6uxp8u7nFsvWH88SQsAgpkFvMMBgqIGhptb4XFsDg8EAUVuDz1RuMEgKGISAqBqJnOrfQxgMAASMd/4zPoRB4G/OfhAKtTE5qxqKWxX/BQgDIIyDSQtD/fElA9a5BOFVlSsEAKliMK6XzoUQdcsVAsJgqEv0DHjJPRzLNV4AAFnFQGQWT4KAqBsYoG6g6rok5ln3MCx0Ml7DIa+MwuWiUY1aLpokO/Pce2OOs7+xbNUgXC58pGnZRma4hmKKi9ZYtnoQLhf0a1a2fo6fu4ZgvJtxmynTPwJ16RCs1Q6w2CfdqTNiSmuWae16Ozo2ffh0DPS1BoT6OMNV/VOYnxMTjGmDA5vskXZRK7DnhTEWl7fy8f5YNiECTqqW92Q/PyECsx4NQqBH+4fj7t/LDQd/9xh8XTtupNHx/f1w8HePIaAD6tfRAj2c8P2qCZDLJPh0YJvbQiaTsH7GIPxuciRc1Db1U7FHUylkDYnSK9OicKeoAiWVevTzd+NRwm5gE98MX19fyOXyZnvq8vLymu3RqxcQEGCyvEKhgI+Pj8l51Go11OqO2zD18XNt14gpRNQ5JEmquxmfHIAcaPGMWw2A1l5k7gJA28qybgBaO5iAB4DwVpb1AtDHirK9W1nW28qybf+h35k6K6aYK1O/zLasF+j42DQi3NvsZ209pac1CRRg/O5pO+jUO+Cnm+52pM5YZkexdI+m7sAEyrYZT3PtuO8bWccmrg5UqVSIjY1FUlJSk+lJSUkYPXq0yXni4uKalf/2228xbNgwk9dDERFRz9BZMcVcmfpltmW9RERkp4SN2L59u1AqlSIxMVGkp6eLFStWCBcXF3H9+nUhhBBr164VCxcubCiflZUlnJ2dxcqVK0V6erpITEwUSqVSfPnll61eZ3FxsQAgiouLO7w9RERkWWdugzsjphw5ckTI5XKRkJAgMjIyREJCglAoFOLo0aOtXm9rMDYREXUPa7a/NnOcdv78+SgoKMDrr7+OnJwcREdHY9++fQgLM170npOTg5s3f7rgOTw8HPv27cPKlSvx4YcfQqvV4r333uuye0QREZHt6oyYMnr0aGzfvh2vvvoq1q1bh4iICOzYsaPhHlGtWS8RETkGm7lPVHfozHuUEBGRZdwGm8a/CxFR97Bm+2sT10QRERERERHZCyZRREREREREVmASRUREREREZAUmUURERERERFZgEkVERERERGQFJlFERERERERWYBJFRERERERkBSZRREREREREVmASRUREREREZAUmUURERERERFZQdHcFupMQAgCg0+m6uSZERD1P/ba3fltMRoxNRETdw5q41KOTqJKSEgBASEhIN9eEiKjnKikpgYeHR3dXw2YwNhERda/WxCVJ9OBdgAaDAXfv3oWbmxskSbJ6fp1Oh5CQENy6dQvu7u6dUMPu4YjtcsQ2AY7ZLrbJfrS3XUIIlJSUQKvVQibj2eX1GJtMc8R2sU32wxHbxTY1Z01c6tFHomQyGYKDg9u9HHd3d4f552vMEdvliG0CHLNdbJP9aE+7eASqOcYmyxyxXWyT/XDEdrFNTbU2LnHXHxERERERkRWYRBEREREREVmBSVQ7qNVqvPbaa1Cr1d1dlQ7liO1yxDYBjtkutsl+OGq77J2j9osjtottsh+O2C62qX169MASRERERERE1uKRKCIiIiIiIiswiSIiIiIiIrICkygiIiIiIiIrMIkiIiIiIiKyApOoFmzcuBHh4eHQaDSIjY3Fjz/+aLH8wYMHERsbC41Ggz59+uCjjz7qopq2zoYNGzB8+HC4ubnB398fs2bNwuXLly3Ok5ycDEmSmj0uXbrURbW2bP369c3qFhAQYHEeW+8nAOjdu7fJv/sLL7xgsrwt9tOhQ4cwffp0aLVaSJKEPXv2NPlcCIH169dDq9XCyckJjz32GC5evNjicnfu3ImBAwdCrVZj4MCB2L17dye1oDlLbdLr9VizZg0GDx4MFxcXaLVaPPPMM7h7967FZW7ZssVk31VWVnZya37SUl8tXry4Wf1GjRrV4nK7s68cmSPFJkeMS4BjxiZHiEsAY5O9xCZbj0tMoizYsWMHVqxYgT/84Q9IS0vDuHHjMHXqVNy8edNk+ezsbEybNg3jxo1DWloafv/73+O3v/0tdu7c2cU1N+/gwYN44YUXcPToUSQlJaGmpgbx8fEoKytrcd7Lly8jJyen4dGvX78uqHHrDBo0qEndzp8/b7asPfQTAJw4caJJm5KSkgAAv/zlLy3OZ0v9VFZWhiFDhuCDDz4w+flbb72Fd955Bx988AFOnDiBgIAAPP744ygpKTG7zNTUVMyfPx8LFy7E2bNnsXDhQsybNw/Hjh3rrGY0YalN5eXlOH36NNatW4fTp09j165duHLlCmbMmNHict3d3Zv0W05ODjQaTWc0waSW+goApkyZ0qR++/bts7jM7u4rR+VosclR4xLgeLHJEeISwNhkL7HJ5uOSILNGjBghli1b1mTagAEDxNq1a02Wf/nll8WAAQOaTFu6dKkYNWpUp9WxvfLy8gQAcfDgQbNlDhw4IACIwsLCrquYFV577TUxZMiQVpe3x34SQoiXXnpJRERECIPBYPJzW+8nAGL37t0N7w0GgwgICBAJCQkN0yorK4WHh4f46KOPzC5n3rx5YsqUKU2mTZ48WSxYsKDD69ySh9tkyvHjxwUAcePGDbNlNm/eLDw8PDq2cu1gql2LFi0SM2fOtGo5ttRXjsTRY5MjxCUhekZssve4JARjk73EJluMSzwSZUZ1dTVOnTqF+Pj4JtPj4+ORkpJicp7U1NRm5SdPnoyTJ09Cr9d3Wl3bo7i4GADg7e3dYtmhQ4ciMDAQkyZNwoEDBzq7ala5evUqtFotwsPDsWDBAmRlZZkta4/9VF1dja1bt+K5556DJEkWy9pyPzWWnZ2N3NzcJn2hVqsxYcIEs98xwHz/WZqnOxUXF0OSJHh6elosV1pairCwMAQHB+PJJ59EWlpa11TQCsnJyfD390f//v2xZMkS5OXlWSxvb31lD3pCbHKUuAQ4dmxyxLgEMDY9zNZjU3fGJSZRZuTn56O2tha9evVqMr1Xr17Izc01OU9ubq7J8jU1NcjPz++0uraVEAKrVq3C2LFjER0dbbZcYGAgPvnkE+zcuRO7du1CZGQkJk2ahEOHDnVhbc0bOXIkPv/8c+zfvx//+Mc/kJubi9GjR6OgoMBkeXvrJwDYs2cPioqKsHjxYrNlbL2fHlb/PbLmO1Y/n7XzdJfKykqsXbsWTz/9NNzd3c2WGzBgALZs2YK9e/di27Zt0Gg0GDNmDK5evdqFtbVs6tSp+Oc//4kffvgBf/vb33DixAlMnDgRVVVVZuexp76yF44emxwlLgGOH5scMS4BjE2N2Xps6u64pLB6jh7m4b0rQgiLe1xMlTc13RYsX74c586dw+HDhy2Wi4yMRGRkZMP7uLg43Lp1C3/9618xfvz4zq5mi6ZOndrwevDgwYiLi0NERAQ+++wzrFq1yuQ89tRPAJCYmIipU6dCq9WaLWPr/WSOtd+xts7T1fR6PRYsWACDwYCNGzdaLDtq1KgmF8OOGTMGMTExeP/99/Hee+91dlVbZf78+Q2vo6OjMWzYMISFheGrr77CnDlzzM5nD31ljxw1NjlKXAIcPzY5clwCGJsA249N3R2XeCTKDF9fX8jl8maZaV5eXrMMtl5AQIDJ8gqFAj4+Pp1W17Z48cUXsXfvXhw4cADBwcFWzz9q1Cib2RPxMBcXFwwePNhs/eypnwDgxo0b+O677/DrX//a6nltuZ/qR6my5jtWP5+183Q1vV6PefPmITs7G0lJSRb39Jkik8kwfPhwm+07wLiHOSwszGId7aGv7I0jxyZHjkuAY8UmR41LAGOTJbYem7o6LjGJMkOlUiE2NrZh5Jl6SUlJGD16tMl54uLimpX/9ttvMWzYMCiVyk6rqzWEEFi+fDl27dqFH374AeHh4W1aTlpaGgIDAzu4dh2jqqoKGRkZZutnD/3U2ObNm+Hv748nnnjC6nltuZ/Cw8MREBDQpC+qq6tx8OBBs98xwHz/WZqnK9UHqatXr+K7775r048fIQTOnDljs30HAAUFBbh165bFOtp6X9kjR4xNPSEuAY4Vmxw1LgGMTZbYemzq8rhk9VAUPcj27duFUqkUiYmJIj09XaxYsUK4uLiI69evCyGEWLt2rVi4cGFD+aysLOHs7CxWrlwp0tPTRWJiolAqleLLL7/sriY08/zzzwsPDw+RnJwscnJyGh7l5eUNZR5u19///nexe/duceXKFXHhwgWxdu1aAUDs3LmzO5rQzOrVq0VycrLIysoSR48eFU8++aRwc3Oz636qV1tbK0JDQ8WaNWuafWYP/VRSUiLS0tJEWlqaACDeeecdkZaW1jAaUEJCgvDw8BC7du0S58+fF0899ZQIDAwUOp2uYRkLFy5sMurYkSNHhFwuFwkJCSIjI0MkJCQIhUIhjh492u1t0uv1YsaMGSI4OFicOXOmyXesqqrKbJvWr18vvvnmG5GZmSnS0tLEs88+KxQKhTh27FiXtKmldpWUlIjVq1eLlJQUkZ2dLQ4cOCDi4uJEUFCQTfeVo3K02OSIcUkIx41N9h6XhGBsspfYZOtxiUlUCz788EMRFhYmVCqViImJaTLk6qJFi8SECROalE9OThZDhw4VKpVK9O7dW2zatKmLa2wZAJOPzZs3N5R5uF1vvvmmiIiIEBqNRnh5eYmxY8eKr776qusrb8b8+fNFYGCgUCqVQqvVijlz5oiLFy82fG6P/VRv//79AoC4fPlys8/soZ/qh7d9+LFo0SIhhHEo2ddee00EBAQItVotxo8fL86fP99kGRMmTGgoX++LL74QkZGRQqlUigEDBnRpQLbUpuzsbLPfsQMHDpht04oVK0RoaKhQqVTCz89PxMfHi5SUlC5rU0vtKi8vF/Hx8cLPz08olUoRGhoqFi1aJG7evNlkGbbWV47MkWKTI8YlIRw3Ntl7XBKCscleYpOtxyVJiLqrFomIiIiIiKhFvCaKiIiIiIjICkyiiIiIiIiIrMAkioiIiIiIyApMooiIiIiIiKzAJIqIiIiIiMgKTKKIiIiIiIiswCSKiIiIiIjICkyiiIiIiIiIrMAkioiIiIiIyApMooiIiIiIiKzAJIrIzixfvhxjx441+Vnv3r3xxhtvdHGNiIiop2Nsop5G0d0VIKLWS09Px6ZNm3Do0CGTn0dFReHMmTNdWykiIurRGJuoJ+KRKCI78vbbb2P48OEYM2aMyc+9vb1x7969Lq4VERH1ZIxN1BMxiSKyEzU1Ndi5cyfmzp3bMG3p0qVITExseF9SUgIXF5fuqB4REfVAjE3UUzGJIrITmZmZKCkpweDBgwEABoMBX3zxBVxdXRvKnDt3DlFRUd1VRSIi6mEYm6inYhJFZCeKiooAoCEw7d+/H4WFhVCpVACA48eP48aNG5g1a1Y31ZCIiHoaxibqqTiwBJGdCAsLgyRJ2LZtG1xcXLB69WpMmzYN//73v9G7d28sXboUEydOxPjx47u7qkRE1EMwNlFPJQkhRHdXgohaZ8OGDUhISICTkxP+/Oc/Y8SIEZg5cyby8vIwffp0bNy4Ed7e3t1dTSIi6kEYm6gnYhJFRERERERkBV4TRUREREREZAUmUURERERERFZgEkVERERERGQFJlFERERERERWYBJFRERERERkBSZRREREREREVmASRUREREREZAUmUURERERERFZgEkVERERERGQFJlFERERERERWYBJFRERERERkBSZRREREREREVvj/5MRD9aEQr+cAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bath, fitinfo = sd_env.approx_by_sd_fit(w,Nmax=6,Nk=3)\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5))\n", + "\n", + "ax1.plot(w, J, label=\"Original spectral density\")\n", + "ax1.plot(w, bath.spectral_density(w), \"--\",label=\"Effective fitted SD\")\n", + "ax1.set_xlabel(r'$\\omega$')\n", + "ax1.set_ylabel(r'$J$')\n", + "ax1.legend()\n", + "\n", + "ax2.plot(w, np.abs(J - bath.spectral_density(w)), label=\"Error\")\n", + "ax2.set_xlabel(r'$\\omega$')\n", + "ax2.set_ylabel(r'$|J-J_{approx}|$')\n", + "ax2.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0b6f9c12", + "metadata": {}, + "source": [ + "Since the number of exponents increases simulation time one should go with the least amount of exponents that correctly describe the bath properties (Power spectrum, Spectral density and the correlation function)." + ] + }, + { + "cell_type": "markdown", + "id": "65cf94f6", + "metadata": {}, + "source": [ + "Let's take a closer look at our last fit by plotting the contribution of each term of the fit:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "882c64e5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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fv3+5rj1w4MAS9zi6Uq9evco8JiYmxrmdnJxcrutu27aNpUuXsnXrVk6cOEF6ejoFBQXOr2dl/fVBf0JCQvkLrsYUFqXGSs8tYMsJx+xWvVqcPgbdLHf2b86MP+NYtOMou5LSaBN95n/oRUREXCI1Fcrzi+2Z7mM7dqx855aeQMQwynceOEYDFZeVVb5zzxSUTp0q37knT5avtiqQn59f4p7D4pOxnEunTp2c23a7nQMHDpR4rriie+tcpXi9bdu2Ldc55T2uMqKjo8s8puieReC0ZUlKW716Nffffz9r1qwpdw1FM6TWdAqLUmOtPXCSArtB44gAGob5m12OU/O6QVzePpr525L4YPL/eH14axg40OyyRESkpgoJgYYNyz7uTPeg1a1bvnNDSs0LYLGU7zxwjAYqzt+/fOcGBZ3+XHh4+c6NMG9kT+lerjrlXEqr9HGnTp0667Fn63GsrOLBKCwsrFznlPe4yvAp/XemDEUT85zJjBkzGDduXIkexPLILf0hRw2lsCg11u/7TwDQq7nn9CoWuSs0lfnAD9ZoHnz7AxoqLIqISFUpPUS0Iio7iUdwMMTHV+7c4cMrf+7kyY6HByu9+Ht5g4+vr+85r1Oc1cWT6J0rbFVnsbGxjB8/3hkU69atyx133MGll15KixYtqFu3Lv7+/s6fZ2xsLM2aNTOzZLfTBDdS88yZAz168PuyTYBnDUEt0uXKfvQ6sot8LxufpgbDnj1mlyQiIiJuEBoaWmI/LS2tXOeVXiuwKnvuSiveVnnv/yvvcWZ68803yc7OBhxDd7ds2cKLL77IoEGDaNq0KYGBgSWCd3lfq5pEYVFqnqVLSd+0lW12x1j1nh7Ys4jNxp0t/AD4ptNg0t/5r8kFiYiIiDsEBQXh7//X7TEHDhwo13n79u0rsX+upStcrXHjxs7t8s4s6qoZSKvSwoULndtPPfVUmUuNHD58uKpL8jgKi1Lz/P47m6NbYbd60TDEh+hQP7MrOqMBd4+l+anDpPkGMuvPOKglN0qLiIjUdl27dnVuF58Z9VyKr3MYHh5O06ZNXV3WWXXv3t25vWzZsnLd37dkyZIqrMg1ik/cU/x7PJtVq1ZVZTkeSWFRapasLFi/nvUNHTNwdW3qgb2KhayRkYz3ddxXOfWCIdg//czkikRERMQd+vXr59yeNWtWuSZLmTZtmnO7b9++WCzuWz/6yiuvdG4fOXKkzAXpc3NzmTp1ahVXdf7y8vKc22X9PO12O1999VVVl+RxFBalZlm3DvLz2dCgMCw2Dje5oHMbfdtwgrPTORDRkKUzF0EFZ+ISERGR6mfChAnO7SNHjvDmm2+e8/hZs2aV6IG87bbbqqq0M7rgggvo2bOnc//BBx/k+PHjZz3+mWeeITY21g2VnZ/69es7t1euXHnOY19//XX2799f1SV5HIVFqVlWrcIANjRoA0C3xmGmllOWwM4XMDZlNwCfNrrYsRCxiIiI1Ght2rTh2muvde4/8cQTzJ49+4zHrl69mr/97W/O/c6dOzN8+PAqr7G01157zdn7duDAAS655BJ+//33EsecOnWK+++/n5dffrncS4KYacCAAc7t5557joSzrNH5+eef8+ijj7qrLI+isCg1y6pVHAyrz8mAUHys0L5BSNnnmOzW4d2w2gtY3qwbez6bYXY5IiIi4gbvvfeec0KV/Px8Ro8ezahRo/j6669ZtmwZs2fP5vbbb6dfv37OmVD9/Pz44osv8PLycnu9vXv35plnnnHub9u2jd69e9O8eXMGDhzIRRddRHR0NG+//TYAn376aYnzSy/94QnuvfdeZwCOj4+na9euPPfccyxcuJAlS5bwySefcNlllzmX17j99ttNrtj9tM6i1ByGAb//zvqGHQC4ICYMX5v7/zGtqEZXD2XwnJf5JaYLn3k14t/p6WdeaFhERERqjKioKJYsWcLgwYOJL1xXcs6cOcyZM+eMxwcHB/PDDz/QqVMnd5ZZwjPPPIPNZuPZZ5913u934MCBEjO6+vv7M2XKFAYPHlzi3NJLhniCCy+8kOeff54nn3wSgGPHjpUIxMWNGTOGxx9/nI8//tidJZpOPYtSc+zfD0ePsr6a3K/o5OXFhEsdNc9ufwkpXp73yZuIiIi4Xtu2bdm0aRP33XcfgYGBZzzG29ubG264ga1bt3LJJZe4t8AzeOKJJ9iwYQP33HMPrVq1IiAggNDQUDp27MhDDz3Eli1buOWWWzh69KjzHH9/fwICAkys+uyeeOIJPvnkE6Kios749ejoaN58801mzJjh1kmFPIXFMAzD7CLE9VJTUwkNDSUlJYWQEM8fiukSM2bA9ddz1c2vs6lBa965oSsjOjcwu6pyMQyDoW8uZ9eRNCaNaM/4Ps3MLklERDxMdnY2Bw4coFmzZvj5eeayUFJ52dnZLFu2jP3793Py5ElCQkJo3Lgxl1xySbX8XW7WrFnO+zJ79ux52v2NniY7O5vly5ezbds2srKyiIqKomXLlvTt29eUYb/F63LF+76y2UDDUKXmGDiQvBnfsGO94410QUPPG+5wNhaLhXE9G/P099uY9schbu3dtFZ+eiUiIlJb+fn5MWTIELPLcJni9yz26tXLxErKx8/Pj8GDB582fLa20zBUqTmiotjX/3JysRLka6NJhGcOdziba7o2JMDHiz1H01mzbo/Z5YiIiIiUUN4BiV988QU///yzc3/8+PFVVJFUNYVFqVG2JThmC2tfPwSrtXr1zAX7eXNVlKPmac99DNu2mVyRiIiIyF+ee+457rjjDpYsWUJ+fv5pX4+Pj+eBBx4oEQ6vuuoqUyflkfOjYahSo2w7XBgWq8GSGWcyLns/X9OYeW16c/yDz6jz9qtmlyQiIiICQFZWFh9//DEff/wxfn5+tGnThsjISPLz80lISGDfvn0ljm/SpAkffPCBSdWKK9SqnsVjx44xb948nnvuOUaOHEn9+vWxWCzOx9SpU91Sx/79+3n66ae58MILqVu3Lv7+/rRo0YJrrrmGb7/99oyf1EgZNm+GGTPYui8JgA7VNCxecNv1dE7aQ56XNzM3J0FGhtkliYiIiABgtf4VHbKzs9m0aRO//fYby5YtOy0oDhw4kNWrVzvXkpTqqVb0LCYlJdGzZ08OHjxodim89dZbPPLII+Tk5JR4fv/+/ezfv5/vvvuOnj17Mm3aNJo3b25SldXQ9OnYX3qZHf+cDr6B1WpymxLCw7kpIIVNwP/aDmTi9BlYb/ub2VWJiIiI8Oyzz9K/f38WLFjAn3/+yb59+zh58iT5+fmEhYXRoEED+vTpw7XXXsull15qdrniArUiLGZnZ3tEUHz++ed5+umnnftWq5X27dsTERHBnj17SExMBGD16tUMGDCANWvWUL9+fbPKrV7WrSMurB5pvoH4eFloGVV9F7UfftNQnv8mlriwaJbNmc4lCosiIiLiAby9vRk6dChDhw41uxRxk1o1DBWgbt26DB06lCeffJLvvvvObe3+8ssvPPPMM879Xr16sWPHDrZs2cLSpUuJj49n+vTpBAU5Qk58fDzXXXed2+qr1gwD1q9na70WALSNDsHbq/r+1fbv1YNrD28E4CvfprBjh6n1iIiIiEjtVCt6FiMiIpg5cybdu3enSZMmbm/fMAweeeQR53TDbdq0YdGiRQQE/LW0g9VqZezYsURGRjrXd1m5ciVz5szhmmuucXvN1crhw3D8ODvbOT7lqq6T2zhZLNzYI4ZPj8NvLbqT9OlXRE9+0eyqRERERKSWqb7dLxUQEhLCtddea0pQBJg3bx6bNm1y7r/11lslgmJxl112GWPHjnXuv/zyy1VeX7W3ZQsAu+o4Xt/W9YLNrMYlWo4fS4/4bditXszacBhyc80uSURERERqmVoRFs02e/Zs53azZs0YMmTIOY+fOHGic3vNmjXEx8dXWW01wtatAOyp0xioGWGROnUY43sKgG9a9Mb+41yTCxIRERGR2kZh0Q1++ukn5/bll1+OxXLuxeL79etHYGDgGc+XM9iyhWybDwfDogFoHV19J7cp7sprLyEoJ5OD4Q34oyCw7BNERERERFxIYbGKHT16lKSkJOd+r169yjzHZrPRvXt35/7mzZurpLYaY+tW9kbEYLd6EeZvo26Qr9kVuUTA5YMZ0dERgGfYo0yuRkRERERqG4XFKraj1EyWLVq0KNd5xY8rfQ0ppqAAtm8vNgQ1pMye22rDauX6wR0BmLc1iZSsPJMLEhEREZHaRGGxisXGxpbYb9y4cbnOK35c6WucSU5ODqmpqSUetcKpU9C9O7tjWgPQql7NGIJapFNMKG2jg8nJt/PDxgSzyxERERGRWkRhsYqlpaWV2A8NDS3XeSEhfy3/UPoaZ/LSSy8RGhrqfDRq1KhihVZXderAsmXsvuF2ANpE14DJbYqxWCyMucjxWs5YvMM586uIiIiISFVTWKxi6enpJfb9/PzKdZ6/v/9Zr3Emjz32GCkpKc5HXFxcxQqt5nYfdQTqVlE1KywCXBOchU9BPltT7Wx97QOzyxERERGRWkJhsYrl5+eX2LfZbOU6r/hxeXll36vm6+tLSEhIiUdtkZmbT9zJLABa17BhqADhbVswJPZPAL5JwjH0VkRERESkiiksVrGAgIAS+9nZ2eU6r/hxxZfRkNPtOeLoea0T5ENkDZkJtQRfX8Y28gbgu7b9yP7fDJMLEhEREZHaQGGxigUFlezpysrKKtd5mZmZZ72GFMrJgaZN2ffgUwC0qFtzf059bh5Bw5QjpPoFMf+XP80uR0RERERqAYXFKlanTp0S+4mJieU6r/jajJGRkS6tqcbYtw8OHuRAUjIAzWtwWLR27cqYo47JbWYENoedO02uSERERERqOoXFKtamTZsS+4cOHSrXecUnqGnbtq1La6oxdu0CYH94AwCa16nZw3VH92oOwO9NOhM/dbrJ1YiIiIhITaewWMVatWpVYrKajRs3luu8DRs2OLfbtWvn6rJqhsLetQMRDQFoVsPDYswtY+l9cDMAczYehoICkysSERERkZpMYbGK+fj4cPHFFzv3V6xYUeY5SUlJ7N2717nfv3//Kqmt2tu1CwM4EO4Ii83r1uywSFQUo23HAZjVuDvGokUmFyQiIiIiNZnCohtcddVVzu1FixZx5MiRcx4/bdo053ZYWJjC4tns3ElScCRZPn54WaBRREDZ51RzQ0f2ISA3i9iIBqyb/rPZ5YiIiIgLDBo0CIvFgsVi4dNPPzW7HBEnhUU3uOGGG/D1dSzpkJeXx3/+85+zHpuens7bb7/t3B83bhze3t5VXmO1Yxiwa5ezV7FxZCDeXjX/r3PgyGFcmbAJgFndh5lcjYiIiLjCpk2bnNtdu3Y1sZKql5eXxx9//MEbb7zBhAkT6NWrFw0aNCAgIABvb28iIyPp0qULt99+O7/88gt2u93skmu1mv/bdRWJjY11fgJksViYNGnSWY+NiYlh4sSJzv233nqLWbNmnXZcXl4eEyZMcE6C4+/vz+OPP+7y2muEY8cgOZn9teR+RSdfX0a/cC8Ac48YZOfpvkUREZHq7NChQ5w4cQJw3L7UoUMHkyuqWo8//jg9e/bkgQceYOrUqaxevZrExESysrLIz8/n5MmTbNq0iU8++YShQ4dy4YUXlpjLQ9yr1oTFO+64Az8/v9MeFT2msiZNmkSrVq0AKCgoYMyYMdx8883MmjWLxYsXM2XKFC666CK+/fZb5zmTJ0+mQYMGLmm/xqllk9sUd3GLusSE+5OWk88v25LKPkFEREQ8VvEg1KFDB3x8fEyspuoZhlFiPzAwkE6dOjFgwAAuueQS2rZti9X6V0TZuHEj/fv3L9e8H+J6trIPqRny8vLIyck55zH5+fnk5+dXSfvh4eHMnTuXyy67jLi4OOx2O1999RVfffXVGY9/+OGHueeee6qklhqhcNmMA4XLZtSmsGi1WhjVLYa3f93DrPUJXNWlodkliYiISCUVnym/W7du5hXiJv7+/gwfPpyRI0fSv3//05aZAzh27BhvvfUWL7/8MgUFBaSnp3PjjTeyfft2goJq7rranqjW9Cx6gtatW7N582Zuu+02/P39z3hMu3bt+P7773nllVfcXF01M2QIfPklB1p2BGr+Goulje7mCIgrdh8l6aMvTK5GREREKqt4z2JNv18R4Pnnn+fHH3/kjjvuOGNQBKhbty4vvPACU6ZMcT4XFxfHzJkz3VWmFLIYpfuCxS3S0tL47bffiIuLIyMjg/r169OxY0eX/SORmppKaGgoKSkphISEuOSaniavwE7bp+ZTYDf4/bFLqR965gBeI6WlMeaeD1jToB2PbPyOu3/+AKz67EdEpCbLzs7mwIEDNGvWzGW3yYj5mjRp4pyvYtWqVfTq1cvkijxLy5Yt2bdvHwC33HILn3/+uckVuZer3veVzQb67dIkwcHBXHXVVfzjH//gkUce4ZZbbqkVnya5UtzJTArsBv7eXtQLrmX/aQYHM7rgMADfNroQY8kSc+sRERGRCjt58qQzKFqtVjp37nzO4ydPnozNZnNOsHjnnXeSm5vrjlJNU3xoblKS5mpwN4VFqbYOnswEoElkAFarxeRq3O/KYRfjl5fNvshGbPr6R7PLERERkQoqfr9imzZtCAg485rR6enpXHfddTz88MMUFBTg4+PD+++/z4cffljjJ8QpPp9IcHCwiZXUTgqLUv0kJ8O8ecTtOABAo4gz/8Na0wWPuoqhB/4EYFZCAaSnm1yRiIiIVETx+xXPNrnNrl276NGjh3PG/OjoaBYvXsxdd93llhrNlJeXx++//+7c1xBd91NYlOrnzz/hyis59PZHADSupWERPz9GN/AC4IdWvcmZefranSIiIuK5yprc5rvvvqNHjx7s2LEDgIsvvph169bRu3dvt9VopieeeMI59DQiIoLx48ebW1AtpLAo1U/hTc6HwqKBWhwWgd43Xkn91GOk+Afz69xVZpcjIiIiFXC2nkW73c7jjz/OqFGjSE1NBeBvf/sbS5curfAa3FOnTnXe4+jKx9SpU13yMyguPz+fxMREvvvuO4YMGcLkyZMB8PPz4+uvvyYyMtLlbcq51Zp1FqUGKQyLcaH1gNodFr169eKayd/w35DBfGuJ5sqDB6FJE7PLEhERD2AYBll5BWaX4dH8vb2wWMyZ9yArK4tdhetGw189iydOnOCGG25g4cKFAHh7e/PGG2/U2PW369Spw4kTJ874NYvFwuDBg3nttde44IIL3FyZgMKiVEf79mEAcYU9i40iatGSGaVZLIzuFsN/M2Bp8ws5+sV0op56xOyqRETEA2TlFdD+6V/MLsOjbX/ucgJ8zPl1eMuWLRQUOMJ8s2bNCAsLY926dYwePZqDBw8CUK9ePWbOnEm/fv0q3U7Dhg25/PLLXVJz6etWtT59+nDXXXfRvn37Km9LzkxhUaqfffs45R9Cuq+jRzEmvPb2LAK0uOU6ujz1LRsbtOWH1fu43TDApE9JRUREpHxKD0H97LPP+Pvf/052djYA3bt3Z/bs2cTExJxXO4MHD2bw4MHndY2qNGjQIFJSUgDIyckhKSmJ3bt3Y7fbWbFiBStWrKB79+7MmDGDZs2amVxt7aOwKNWLYcC+fRwKc4zXrxfii5+3l8lFmaxJE0aH5rARmDVwLLcrKIqICI4hltufc32PUk3ib+LvEMXD4vLly5k166+J6m699VamTJlyXouwVxczZsw47bmTJ0/y8ccf89xzz5GRkcHatWsZMGAAf/75J1FRUSZUWXspLEr1cuwYpKcTF6P7FYsb8cq/eP7FX9lxPJvth1Np3yDE7JJERMRkFovFtCGWUrbiaywePXrUuT1x4kSmTJliQkWeIyIigocffpjBgwczYMAA0tLSiIuL48EHH+TLL780u7xaRbOhSvVSaibU2rrGYmlhAT4Mauf4pG32+niTqxEREZFzKSgoYPPmzc79YcOGObe/+eYbdu/ebUZZHqdr16488cQTzv3p06dz8uRJEyuqffRxk1QvRTOhFoXFWn6/YnGju8Uwb2sS3208zKNXtMXmpc+CREREPNGuXbvIysoCIDo6mlmzZnHJJZewevVqTp06xYgRI1i9ejXh4eHn3dbChQt57bXXzvs6pT344INuuRfy2muv5dFHHwUcS2usXbu2SibskTNTWJTqJS0NgoK0bMYZDGhTl0hfK8fTc1h279Nc+t8XzC5JREREzqD4/YqdO3fG19eXOXPm0KNHD+Li4ti9ezfXXXcd8+fPx2Y7v1/XExIS+OUX18+Ke/3117v8mmfSqFGjEvtnW2ZDqoa6HqR6uftuSE3lUKceADSOVFgs4m2BkZscazLNOlwAxe5/EBEREc9R/H7Fzp07A44exu+//56AAMfvNr/++iv33nuvGeV5lKKZUouEhYWZU0gtpbAo1U6e3eBweh6gnsUSrFZGt40AYGHLHqR8Nd3kgkRERORMSvcsFunatStffvkllsKZzadMmcLbb799Xm2NHz8ewzBc/hg/fvx51VVey5YtK7HfokULt7QrDgqLUu0kJmdTYDfwsVmpG+RrdjkepcOto2l79AC5Nh/mLt5qdjkiIiJyBmfqWSwyatQonn32Wef+Aw88UCXDSKuD3NxcXnjhr9tqWrRoQZs2bUysqPZRWJRqJyHZcUN4TLg/VqvWFCzO0q4do1L3AjA7tBUUm2lNREREzHfo0CHnfXe+vr5nDD9PPfWU857AgoICxo4dy44dO9xaZ1VYuHAhDz30EIcPHy7z2MTEREaMGFEiWBdNdCPuowlupPo4eBDuu4/DzXuBb0cahvmbXZFHurp/W16OL2BdTHsOfDGTZq92MrskERERKVQ8/HTo0OGsE9h8+umn7Nu3j7Vr15KSksKIESP4448/iIyMdFOlrpeRkcGrr77K66+/Tu/evenXrx8dO3akTp06BAQEkJ6ezv79+1m+fDnff/89mZmZznNHjhzJbbfdZmL1tZPColQfe/bADz+Q0DsA+nWkQajC4plEjbuOfn//L0ubdWPOtmM8kJ8P5zmTmoiIiLjG2e5XLM3f35/vvvuO7t27c/jwYfbt28fo0aNZuHAh3t7e7ii1ytjtdlasWMGKFSvKdfyECROYMmWK815OcR8NQ5Xq4+BBAA6H1AWggXoWzywyktF+jpnDZjXtgX3BApMLEhERkSLlDYsADRo04Pvvv8ff3/E7z9KlS7n77rurtL6qdNFFF/HAAw/Qvn37MoOfj48Po0ePZunSpXz66af4+Pi4qUopTt0NUn3ExgKQ4AyLfiYW49mGXNWP4OUZJITW449vfqHXlVeaXZKIiIgA3333XYWOv+iii0oMx6zOYmJieO2113jttddITk5m06ZN7N+/n+PHj5OTk0NgYCDh4eG0a9eOzp074+en3/XMprAo1UepnkXds3h2fsOvYNiXTzK9zQBmZwTSKy8PqvmQFREREak5wsLCGDBgAAMGDDC7FDkHDUOV6uPgQQzgcLCGoZbJx4fRl3UE4Od2/ck0NMZfRERERCpGYVGqj4MHSfYLJsvHMSQhOlRDE87lor/fROOIADJyC/hlW5LZ5YiIiIhINaOwKNVDfj7ExzvvV6wT5Iuft5fJRXk2i8XCqG4NAZi9PsHkakRERESkulFYlOrh8GEoKCh2v6J6FctjdLcYAFbsPU7isVSTqxERERGR6kRhUaqHwplQtWxGxTSKCKBHsB3DgO8mPAJ2u9kliYiIiEg1obAo1UO9evCvf3G4e19AYbEiRm9bDMCs6E4Yy5ebXI2IiIiIVBcKi1I9tGkDkyeT0H8woLBYEVde2R3fvBz21mnM5v/9aHY5IiIiIlJNKCxKtXI4OQvQGosVETzqKi4/8CcAs+NyoYYs7CsiIiIiVUthUaqVhFMKixUWEMDoeo7NH1r2JHf2d6aWIyIiIiLVg8KiVA8nTpCTm8fRtBwAGmg21Arpe/3lRKWd4FRAKL/9oPsWRURERKRsCovi+QwDGjXiSMPmAPjarEQE+phcVPXi1a8f1yRsAGB2QSQkaN1FERERETk3hUXxfKdOQVYWh/3DAMfkNhaLxdyaqhurldGdHGNRFze/iJNfTje5IBERqSzDMMwuQUTcxOz3u8KieL7CXrAjQZEA1AvxNbOaaqv1rdfRMXEPeV7e/LB8p6PHVkREqg2r1fFrm11r5orUGkXv96L3v7spLIrni48HICnYERajQ3S/YqW0aMGorFgAZjfp7uixFRGRasNms2GxWMjJyTG7FBFxk+zsbCwWCzabzZT2FRbF85UKi/VCFRYra+S912OzwOaQhuzJ8za7HBERqQCr1Yq/vz8ZGRlmlyIibpKamkpQUJB6FkXOqjAsOoehBissVlZkv55c0tZx7+Ks9ZrkRkSkugkKCiIjI4Pc3FyzSxGRKpaRkUF2djYhISGm1aCwKJ7Pec9iBADR6lk8L9de2BCAORviKbDrvkURkeokNDQUm81GfHw8BQUFZpcjIlUkIyODuLg4AgMDCQoKMq0Ocwa/ilRE6WGoumfxvAxsG0VYgDdHUnNYuXQT/Qd2MbskEREpJ5vNRqNGjYiNjWXv3r2EhoYSFBSEl5eXZgoXqcYMw8But5OdnU1qairZ2dkEBgYSExNj2hBUUFiU6iA+HjsWjqpn0SV8bV6M8DrJlwQz+83/0b9TI4iMNLssEREpJ19fX5o1a0ZycjIpKSmc0oRlIjWGxWIhKCiIyMhIU+9VLKKwKJ4vIYGTASHkeXljsUBUsJbOOF+jk3fzpdeFzG95MWnTphN83z1mlyQiIhXg4+NDVFQUdevWJT8/X0NSRWoAq9WKzWYzPSAWp7Aonm/NGpJ2HIJV2UQG+uLt5TlvoOqq881X0+KdVeyLbMS8Xzcx5j6zKxIRkcqwWCx4e3vj7a0ZrkXE9fRbt3i+Vq040uoCAKJD1avoCpZOnRh1cicAswKbw44dJlckIiIiIp5GYVGqhaTUbEDLZrjSqD4tsRh2/mjckbjPZ5hdjoiIiIh4GIVFqRaOpBSGRU1u4zL1bxpDn0ObAZiz6TDofhcRERERKUZhUTzb8uXwyScc2bEPgGgtm+E6UVGMsp0EYHaj7hi//mpyQSIiIiLiSRQWxbN9/TXcfjtJK9cCCouuNnR4LwJys4iNaMC6GfPMLkdEREREPIjConi2+HgAjgQ51gHUMFTXCrh6BFcc+BOAWUcMSEszuSIRERER8RQKi+LZCsNiUrAjLKpn0cV8fRkd45hufW77AWSfTDa3HhERERHxGAqL4tni48m2+ZDsHwIoLFaFnk/cQ8MQX9KsPixM9jK7HBERERHxEAqL4rlyc+HYMY4ERQDga7MS4m8zuaiaxxodzTUXNgJg9vp4k6sREREREU+hsCie68gRAJIK71eMDvXDYrGYWVGNNapbQwCW7TnO0bRsk6sREREREU+gsCieKzERgCOF9yvW0xDUKtO8bhDdGodRYDf4/vN5YBhmlyQiIiIiJlNYFM9VGBaPBoYDEBXsa2Y1Nd6oEzsAmLU+AVavNrkaERERETGbwqJ4rsKweMwZFtWzWJVGNPTBJz+XnVHN2PbVHLPLERERERGTKSyK5/L1hdatORYWBUBd9SxWqdDrrmHwgXUAzD6QCdm6d1FERESkNlNYFM81YQLs2sWxEaMAhcUqFxzMqIg8AL5v0ZO8H340uSARERERMZPConi8Y2k5gMKiO/QfM5g6Gac4HhjOsjlLzC5HREREREyksCgezxkWgxQWq5r3oEsZGbcegNnZIc7lS0RERESk9lFYFI+WV2DnZGYuAFEhCotVzmpldDvHUiULW1xMylfTTS5IRERERMyisCieyW6HXr04OWYchgFeVgvhAT5mV1UrdBh/LW2PHiDX5s2PS7aZXY6IiIiImERhUTzT8eOwejVHV64FIDLQBy+rxeSiaok2bRiduheA2WGtYdMmkwsSERERETMoLIpnKrXGoia3ca+rBrTFy17A+obt2O8XbnY5IiIiImIChUXxTEVhMUhh0QxRd91G/3bRAMyOzTK5GhERERExg8KieKakJOCvnsUohUX38vZm9IUxAMxaH0+B3TC5IBERERFxN4VF8Uwahmq6we3rERbgTWJKNsv2HDO7HBERERFxM4VF8UyFYfFoUASgNRbN4GvzYlRXR+/ijE9/hv37Ta5IRERERNxJYVE8k7NnMQyAusF+JhZTe41N2gjAIns4xz750txiRERERMStbFV14YSEBLZv387Bgwc5duwYGRkZAAQGBlK3bl2aNGlChw4daNCgQVWVINVZqWGoUSHqWTRDmxGX0vXhaWxo0JbZa2KZmJ8Ptir7Z0NEREREPIjLfus7deoU33//Pb/88gtLlizh6NGj5TovKiqKAQMGcPnllzNy5EgiIyNdVZJUZ87ZUDUM1VTR0VxvOcoG2jKjaU/unDcfy4jhZlclIiIiIm5gMQzjvKY5nDdvHh988AHz588nLy8PgIpe0mJxLLZus9kYOnQod955J8OGDTufsmq91NRUQkNDSUlJISQkxOxyKu6//yU9No4LrH0B2Pbs5QT6qkfLDBnfz6XHkiwyfAOYcfBHLv56itkliYiIiEgFVDYbVOqeRbvdzmeffUabNm0YPnw4P/74I7m5uRiGUSIo+vr60qxZM7p160afPn3o3bs3Xbt2pWnTpvj6/tVTVHReXl4ec+fOZeTIkbRq1YpPPvmEgoKCypQo1d3f/86xh58EIMDHS0HRRIHDhjLi0DoAZqQHOXt9RURERKRmq/Bv4DNnzuTxxx9nf+HMiEXh0M/Pjz59+jBgwAC6d+9Ox44dy7wfMSEhgS1btvDnn3+ydOlSVq5cSXZ2NgD79+/nzjvv5N///jcvvfQSY8aMqWipUs0dS8sBtGyG6Ww2xrYNY3oB/NS6N89M/YrQxx4yuyoRERERqWIVGoY6YMAAVqxYAThCos1mY9iwYYwbN44rrriCwMDA8yomMzOTefPm8b///Y+5c+c6h7VaLBb69OnDsmXLzuv6tUm1H4YK/LQ5kXv+t57uTcOZeVdvs8up1Yx9+xj67/nsqtuU5zfM5Ob5n0Hh8HERERER8WxuGYa6fPlyDMOgTp06PPvssyQkJDBnzhyuvfba8w6KAAEBAYwePZpZs2aRkJDAc889R1RUFIZhsHLlyvO+vlQTqalw5AjHUrMA9Sx6AkuLFozNigVgev2uoA9uRERERGq8CoXFqKgo3nzzTQ4dOsRTTz1F3bp1q6ou6tSpw5NPPsnBgwd54403qrQt8TD/+x9ER3P0iWcBzYTqKa658kJ88vPYFt2SrTN+MrscEREREaliFQqL+/bt47777isxOU1V8/X15f7773feIym1wJEjwF9rLKpn0TOEX3s1l2fFATB90DiTqxERERGRqlahsOiKoaaVFRAQYFrb4maFa3QeDwwDIFI9i57Bz48b/u8GAL7fnERWrmYqFhEREanJKrV0hkiVKgyLJ/1DAaijsOgxejaPpHFEAGk5+fy8RUtoiIiIiNRkCovieZw9i46wGBnkY2Y1UozVamFs90YATF97COx2kysSERERkaqisCie58gRDOBEQBgAdQLVs+hJru3aAC9gbewp9kz8P7PLEREREZEqYnPVhY4fP86KFStYuXIlO3bsYO/evSQmJpKTk4NhGNSrV4/GjRvTvXt3+vTpw6BBgwgPD3dV81KTHD1Kprcf2d6OkKieRc9Sz9fCZQfW8kuz7kw7lMekkychIsLsskRERETExSyGYRiuuJDVasVSbJHuM122+NdtNhuXXXYZd955J1dddZUrSpBiKrvwpulyc8HXl0Oh9eh/1yf4eVvZ8dzQEn93xHzLHnieW3y6EZydzh+NEwl44J9mlyQiIiIiZ1HZbODyYaiGYZwxKBZ9rejPvLw85s+fz6hRo+jatStr1qxxdSlSHR0/7vijaCbUQF8FRQ/U987raHLqMGl+Qcydvw5c85mTiIiIiHgQlw1DBUcIbNy4Mc2aNaNhw4bUrVsXi8WCYRgcPnyYQ4cOsWXLFjIzM53HA2zatIm+ffvy2muvce+997qyJKluCtdYPBFQNBOqhqB6ImvbttyY9ikvhTfgq/rdGLN4MVx6qdlliYiIiIgLuSwszp8/n4suuoiIMu5dKigoYMOGDcydO5evv/6aPXv2YLFYyM/P55///CfBwcGMHz/eVWVJddOhA+zZw4k1h2BzltZY9GDXDruI1zbksbl+azZ/OpNOCosiIiIiNYrLhqEOGTKkzKAI4OXlxUUXXcSkSZPYtWsXc+bMoVmzZoCjp/H+++8nKSnJVWVJdePjAy1bcqJ+YwAiA9Wz6Kkir7uaKw7+CcD/TvqA3rciIiIiNYrpS2dcddVVrFu3jp49ewKQnp7OlClTTK5KzHY8PQdAPYuezMeHm1oGAvB92/6kfjLV3HpERERExKVMD4sAoaGhfP3119hsjlGxc+fONbkiMdvJjFxA9yx6uovuGEvrYwfJ8vFjzpIdUFBgdkkiIiIi4iIeERYBmjRpQrdu3TAMgwMHDphdjpjlhx/gww85cSAe0BqLns7SvDnjcg8CMK3tJRgaiioiIiJSY3hMWATIzs4GcM6WKrXQ++/DxIkc3+MIIJGBGobq6a75xxj8vSzsDozizzx/s8sRERERERdxS1jMy8tj9erVpKenn/Hr+fn5vPzyy2zevBmLxULjxo3dUZZ4oqNHgb+WzlDPoucLufhCruoWA8BXqw+aXI2IiIiIuIpL11k8m9TUVHr37o3FYiE6OpqYmBgiIiLw9vbmxIkTbN26lfT0dOfi62PHjnVHWeKJjh7FjoWTznUW1bNYHYy7uAnT18Yxb0sSTw/P0cREIiIiIjWAW8JiEcMwSEpKOm1pDMMwnNujRo3iySefdGdZ4ikMA44eJcUviAKrFwDhAepZrA46xoTSOSaUTfEpzPxmKXfd2B/8/MwuS0RERETOg1uGofr7+zN69GiaNGmCYRjOB4DFYuHSSy/lww8/ZMuWLXz77bf4+Cgg1EopKZCby4lAR69iqL83PjaPuq1WzmFcSAYAX62Jo2DGNyZXIyIiIiLnyy2/iQcEBDBz5kwOHDhAfHw8n332Gddccw0+Pj7Y7XYWL17Ma6+9xtHC+9Wklip8/Y8HhAG6X7G6GdksiPDMFOLDovl1+gJHT7GIiIiIVFtu77Zp0KABt956K7NmzeLw4cNMmjSJwMBAdu3axeDBg/n000/dXZJ4itKT2wQqLFYnfn17M/bIJgA+D20Hf/xhckUiIiIicj5cFhbz8/MrfE54eDhPP/00W7ZsoX379tjtdu6++242b97sqrKkOjl+HIATRT2LWjajerFYuGlQe6z2AlY27cLu978wuyIREREROQ8uC4sXXHAB8+fPr9S5TZo0Ye7cufj6+pKfn8+rr77qqrKkOnGGRS2bUV3F3DyGIQfXA/D5US8oNZmViIiIiFQfLguLu3fvZtiwYQwfPpwNGzZU+PymTZty4YUXYhgGv/32m6vKkurEzw/ateNEZDSAll+ojvz9ubWZYxbU2e0uIeWDT0wuSEREREQqy+X3LM6bN4+LLrqIESNGsGzZsgqde/LkSQCOHTvm6rKkOrjpJti+nRPX3QhAHfUsVks9776BNsdiyfLxY+ayXZCba3ZJIiIiIlIJLguLTz/9ND4+Ps5lMX7++WcGDhxIy5Ytee655/jjjz+w2+1nPf+DDz5g586dAERERLiqLKmGTqQ7woXuWayeLE2acKs9HoAvWvanYNZskysSERERkcqwGIbr5rffs2cP99xzD4sWLSrZiMUCQGBgIBdccAFt2rShXr16+Pr6cvLkSVasWMHmzZsxDAOLxcKQIUOYN2+eq8qqlVJTUwkNDSUlJYWQkBCzy6mQS19bwv5jGUy/syc9m0eaXY5UQubC3+g19ygp/sF8su0bBv34udkliYiIiNRalc0GNlcW0apVKxYsWMDChQt56qmnWLNmjfNrhmGQnp7OH3/8wR+lptQvnVcnTpzoyrKkminqWdQw1Oor4LKBjP3kcT5s2pepA8cxyOyCRERERKTCqmSdxcGDB7N69WoWLVrE6NGjsdn+yqRn6sgs6nkE+L//+z+uvvrqqihLPN2oUeQOH0FKVh6gYajVmsXCzW8/hsUCy4/mse9YutkViYiIiEgFVUlYLHLppZcyc+ZMEhMTmTFjBnfddRd9+/YlKirKeW8jQFRUFNdeey0LFizQshm12a+/cmrpKgC8rBZC/b1NLkjOR6OoEC5rVw+AL1bFmluMiIiIiFSYS4ehnk1ERATXXXcd1113nfM5wzDIzs7Gy8sLHx8NN6z1cnMhNZWTdZsCEB7gg9VqOfc54vHG927Kwu1H+HZdPA8MaaMPAERERESqkSrtWTwXi8WCv7+/gqI4nDgBwCl/xw23EYEKFTVB7xaRtIkKJCO3gOm3PQFZWWaXJCIiIiLlZFpYFCnh+HEATgY4wmJ4gD5EqAksFgu37V8BwNSw9uR9+ZXJFYmIiIhIeSksimcoDIt/9SwqLNYUV900hDrpp0gMqcvPMxfDOdZbFRERERHPobAonqGoZ7EwLIYrLNYYvj17cOvJLQB81PBijPnzTa5IRERERMqjQmHxueeeIyMjo6pqOauMjAyee+45t7crblS6Z1HDUGuUcdf0wi8vm63RLfnjo2/MLkdEREREyqFCYXHSpEm0aNGCl19+meTk5Coq6S/Jycm89NJLNG/enGeffbbK2xMTlb5nUT2LNUrEqBGMPvQnAB9bG8PmzSZXJCIiIiJlqfAw1GPHjvHEE0/QuHFj/v73v/Pnn3+6vKg1a9YwceJEGjduzJNPPsmxY8dc3oZ4mNPuWdRsqDWKlxe39W4CwKJWF7P/7Y9MLkhEREREylKhsLhkyRI6deqEYRikp6fzwQcfcPHFF9OmTRseffRRFi9eTHZ2doWLyMzMZOHChfzrX/+iZcuW9OrVi48//pj09HQMw6Bz584sXry4wteVauTyy+HRRznZqDmg2VBrouZ33MRlsesB+CQBSEw0tyAREREROSeLYRhGRU4wDIMvv/ySF154gb179zouYvlr8XRvb2/atWvHBRdcQPPmzWnYsCFhYWH4+/tjGAbZ2dmcOnWKhIQE9u3bx9atW9m5cyf5+fkl2gBo2bIlTz31FDfddFOJNqRsqamphIaGkpKSQkhIiNnllFvvl37lcEo2P/yjD51iwswuR1xs9aMvcT2d8MvLZpX/ViJeeMbskkRERERqvMpmA1tFG7JYLNxyyy3cdNNNfPPNN7z99tusXr3a+fXc3Fw2b97M5grck1Q6r/bs2ZP777+f6667DqtVE7bWJiczcwH1LNZUF//jZi54ajZb67VgWmYI95pdkIiIiIicVaWTmNVq5frrr2fVqlVs3ryZRx55hFatWgGO8Ff6UeRsX2vZsiUPP/wwmzZtYtWqVYwdO1ZBsZbJyi0gO8+xBp/WWayZLDEx3NGjAQCfR3QkO6/A5IpERERE5GwqPAy1LLGxsSxdupR169axfft2Dh48yPHjx51LbgQGBlKnTh2aNGlC+/btufDCC+nfvz/NmjVzZRnntGrVKj7//HOWL19OQkIChmEQExND3759ufXWW+nTp4/L26zMMNr333+fu+66q1LtVbthqImJJHgF0Of1FfjYrOx6fqiGHtdQeQV2BvxnMYdTsnnxmgsYd3ETs0sSERERqdHcNgy1LE2bNqVp06bceuutrr70ecvIyOC+++7j008/Pe1rO3bsYMeOHXz00UdMmDCBd955h8DAQBOqrIUyM6FBA07VawHj3yIiwEdBsQbz9rJye7/mPDd3Ox8s3c/Yixph89IoAhERERFP4/Kw6KkKCgoYNWoUCxYscD7n7+9Phw4dsNlsbN++ndTUVAA+++wzEhIS+Pnnn/Hy8nJ5Lf3798ff37/M4xo3buzytj3SiRMAnPTXGou1xfU9GvHOb3s4dDKTn9+fyci/Xwcadi4iIiLiUWpNWHzqqadKBMU77riDl19+mYiICMDR6/jKK6/w/PPPA7BgwQKefvppXnzxRZfX8vnnn9O0aVOXX7fa0hqLtU6Aj43xwWm8kenH++uOMuKnn7GMGG52WSIiIiJSTIU/yj9w4EBV1FGlDh8+zBtvvOHcv/nmm/nwww+dQREc91I+99xzPPnkk87nXn/9dQ4fPuzWWmulwrB4MqCwZ1EzodYKtzbxISA3ix31mrP0g2/MLkdERERESqlwWGzRogXh4eEMHDiQBx54gC+//JItW7ZQUOC5sxq++eabZGdnAxAQEMCbb7551mOfeuopGjVqBEB2djZvvfWWO0qs3U7rWVRYrA3Crh7GjXFrAfhvSHtYscLkikRERESkuErdJJSamsqyZct46623GD9+PF26dCEoKIju3btz55138v777/P777+TmZnp6norZc6cOc7tMWPGlOhRLM3Hx4cJEyY492fPnl2ltQl/9Sz6q2exVrFaue2KjngX5LGmcUfWvfWZ2RWJiIiISDGVCoul10g0DIOcnBzWr1/PJ598wj/+8Q/69u1LSEgI7dq148Ybb2Ty5MksXLiQ44XBwF127drF3r17nftDhw4t85wrrrjCub1371527dpVJbVJoZMnATgVoJ7F2qb+zWO5JtbRu/i+vSFs3mxyRSIiIiJSpMIT3Pzwww9s3LjR+Sh+D2NReLRYLM4wuXv3bnbv3s2MGTOcxzVo0ICuXbvStWtXunTpQteuXatswpdNmzaV2O/Vq1eZ53Tr1g0fHx9yc3MB2Lx5M23atKmS+gRnWNRsqLWQtzcTe8Uw84idRa0uZtfrH9Bm6ntmVyUiIiIiVCIsDh8+nOHD/5q1MC0tzRkcN2zYwMaNG9m+fbszaJUOkAAJCQkcPnyYn376yXmd0NBQunTpQo8ePejXrx/9+/cnODj4vL45cKyfWMTHx8d5P+K5FB23b9++067hCg899BDbt28nLi6OvLw8IiMjadWqFQMGDODWW2+lWbNmLm3P4xWFxYBQACI0DLVWaTHxFob+bTLzmnVnygl/3ti3D1q0MLssERERkVrvvJfOCA4Opl+/fvTr18/5XH5+Ptu3b3eGx40bN7Jp0yaSk5Odx5QOkcnJySxdupSlS5cyefJkfH19GTlyJPfeey99+vSpdH2xsbHO7ZiYmHIv9t64cWNnWCx+DVf49ttvS+wnJCSQkJDAkiVLePHFF7ntttt44403yrUWY41wWs+ils6oVQIDubtdMPOy4Yd2/fnnK2/T5ENNLCUiIiJitipZZ9Fms9GpUyc6derErbfe6nw+Nja2RA/kxo0biYuLc369eIDMzs5m5syZzJw5k1GjRvHxxx8TGhpa4VrS0tKc2xU5PyQk5IzXcIU6derQokULgoKCSElJYefOnaSnpwOOoP3BBx+wZs0aFi9eXO6ac3JyyMnJce6npqa6tOYq9f77GIcPc+r7E2DonsXaqNM/b6P/3//Lssadee+IN/9JTYVi70ERERERcb8qCYtn07RpU5o2bcrVV1/tfO7UqVMlwuO6devYuXMn8Fd4nD17Nlu2bGHFihXUqVOnQm0WhTAAPz+/cp9XvFev+DUqq3379tx5552MGDGC5s2bl/hafn4+v/zyC48//jibCyf42LBhA9dffz3z5s0r1/Vfeuklnn322fOu0xSNG5MWVZ/87xYAmg21VgoL4/72QSxLh9kdBnJvvo2yB4yLiIiISFWq1GyorhQeHs6ll17KAw88wBdffMG2bds4ceIE06ZNY9CgQc7jdu/ezbhx4yp8/fz8fOe2zVb+bFz82Ly8vAq3W9q2bdu4//77TwuKRW0NGzaMP/74g2HDhjmfnz9/Pj/++GO5rv/YY4+RkpLifBTvsa0OTmU47nEN8PHCz9vL5GrEDBc+eR99W9Yh3w7/XbK37BNEREREpEqZHhbPJCwsjBtuuIGFCxcyd+5cAgMDAVi0aBGLFy+u0LUCAgKc29nZ2eU+r/ixRe1XNT8/P77++mvq1avnfO6dd94p17m+vr6EhISUeFQnJwvDonoVa7f7L2sFwMw/44k/5RnrtIqIiIjUVh4ZFou78soref/99537//vf/yp0flBQkHM7Kyur3OdlZv71i2rxa1S14OBg7r77buf+8uXLKxRyq52sLPjwQ04tcHwIoPsVa7fuTSPo3SKSfLvB+wt3wqlTZpckIiIiUmt5fFgEuPHGG4mMjARg1apVFTq3+D2OiYmJ5T4vKSnJuV3UtrsMHDjQuZ2dnV3thpRWSFISTJzIybcca+tpjUW5v3dDAL5ZG8fhZ182uRoRERGR2qtahEWLxUL79u0xDIPDhw9X6Nw2bdo4t0+cOFGix/Bcige0tm3bVqjN8xUdHV1i//jx425t360Kl8045V+0xqKWzajtLq7ry8Xx28jzsvH+rkzHBwoiIiIi4nbVIizCX/ceVnQZi3bt2pXY37hxY5nnJCQkcOzYsbNeo6qVDrTF77uscU6cAIqvsaiexVovKor76zvuYZ3RYRCJr5bvvl0RERERca1qExbfeustPvzwQ2677bYKndejRw98fX2d+ytWrCjznOXLlzu3/fz86NGjR4XaPF/btm0rsR8VFeXW9t2qqGcxwBEWIzTBjQC9HriNHgnbybV5M2VbinoXRURERExQbcJi69atuf322/nggw8qdF5QUFCJJTimTZtW5jnFjxk0aJDbZkMtMn36dOd206ZNqV+/vlvbd6vCsKieRSnOUr8+99d1TEj1dYfLSHjpdZMrEhEREal9qk1YPB/jx493bm/evPmcaxeuX7+eefPmnfFcd/jhhx+YO3euc//qq692a/tu57xnsbBnUWFRCvV++E4ujt9Grs2bd/bmwqFDZpckIiIiUqvUirB47bXX0rlzZ+f+xIkT2blz52nHJSYmctNNN1FQUABAly5dGD169BmvGRsbi8VicT4mTZp0xuNSUlIYPXo069atK7POr7/+mhtvvNG5HxAQwCOPPFLmedVaUc9i4TDUME1wI4Us0dE81MjxXpzZ4VIOvPCayRWJiIiI1C42swtwB4vFwkcffcSAAQPIysoiMTGRiy++mLvvvpv+/ftjs9lYs2YN7777LkeOHAHA39+fDz/8EIvFcl5tG4bB7NmzmT17Nm3btuXyyy+nS5cu1K9fn8DAQNLS0tiyZQvffvsta9euLVHzZ599dtrMqDVOYVhM9gsG1LMoJV308F0M/Pt7LG7SlTeO+PL2nj3QqpXZZYmIiIjUCrUiLAJ0796dr776iptuuomsrCxSU1N55ZVXeOWVV0471t/fn6+++oru3bu7tIadO3eesUeztODgYD744APGjBnj0vY90smTGECyvyMshvkrLEox4eE82NafxVnwY9t+3D35v7T78A2zqxIRERGpFWrFMNQio0aNYt26dVx22WVn7DG0WCwMGjSIP//8k1GjRrmkTX9/f+688046dOhQZi9laGgo9913H1u3buWGG25wSfseLySEtJimFFi9AA1DldNd8MCdDDu0HsNi5bUe15ldjoiIiEitYTEMwzC7CDPExcWxcuVKEhISAGjYsCF9+vShUaNGVdbmqVOn2LhxI0ePHuX48eMkJycTEBBAREQEnTp1olOnTnh5ebmkrdTUVEJDQ0lJSSEkJMQl16wqcScz6fefxfh5W9n5/BVmlyMeaG9iMkPeXondgDl/703XxuFmlyQiIiJSbVQ2G9SaYailNWrUiOuvv96tbYaHhzNw4EC3tlkdnMp0LMCuIahyNi3rhzG6Wwwz18Xz6oJdTLu9p9kliYiIiNR4tWoYqnim5Mw8QENQ5dzuv6wV3l4WVu49wco/90LtHBQhIiIi4jYKi2I6Z8+iwqKcQ0x4ADd2ccwO/J93f8L+yy8mVyQiIiJSsyksinmSkuDKK0l5/2MAwgM0DFXO7R85ewnMyWRTdEvmvjkNCtdEFRERERHXU1gU8yQlwbx5nNrqWE5EPYtSlro3Xstd8asB+E/TS8j5/EuTKxIRERGpuRQWxTwnTwJ/rbEYqglupCxWK7ffNZx6aSeID4vmi6+XQFaW2VWJiIiI1EgKi2KewrCY4ucIi+HqWZRy8B80kAdTNwPwTocrSH7jXZMrEhEREamZFBbFPIVh8VRhz6KGoUp5jX54PG2PxZLqF8S7yw7AsWNmlyQiIiJS4ygsinmKhqH6FYVFDUOV8vHqeAGP+R4G4IsLhnDoxddMrkhERESk5lFYFPOUumcxzF89i1J+A568h36HNpFr8+Y/scDevWaXJCIiIlKjKCyKeZw9i0EAhAeqZ1EqoEEDHmtuxWLYmdu2Hxve/MTsikRERERqFIVFMc+pU9ixkFIYFtWzKBXV/pF7GH1wLQCT2lyJ3W6YXJGIiIhIzaGwKOZJTibNNwC71QuAUE1wIxUVHMzD7z9CoI8XmxJSmb0hweyKRERERGoMhUUxz9VXc+pvdwIQ4OOFr83L5IKkOooKD+S+Qa0AeGX+TtKy80yuSERERKRmUFgU89x7L8mPPQ1oCKqcnwl9mtGsTiDH0nJ499s1zvthRURERKTyFBbFVKcycwEtmyHnx8dm5amBTQD4dNMx9k96xeSKRERERKo/hUUxVUqmY8hgmO5XlPN0abQPl8SuJ8/LmxcS/WHrVrNLEhEREanWFBbFHHY75OaSXNizGK6eRTlfDRvyVAd/bAX5/NaiO4uffhMMzY4qIiIiUlkKi2KOQ4fA15dTTz4LaCZUcY0WD/2DCXuWAvB8ZHdyvvnW5IpEREREqi+FRTFHcjIAKV6+AIQrLIor+Plx3039qZt+kv2RMXz42QJITTW7KhEREZFqSWFRzFEYFk/5hwAQ5q9hqOIawaOv4qlT6wB454IriX3mZZMrEhEREameFBbFHKdOAZDsHwRoGKq4kMXCiOfvo9+hTeTafHjqsD/Ghg1mVyUiIiJS7SgsijkKexaT/Rw9i5rgRlzJ0rw5z7XzwSc/l+XNuvHTM+86JlUSERERkXJTWBRzFIXFwp5FLZ0hrtbskfv4+97FADzX/DLSDh02uSIRERGR6kVhUcxR6p5FTXAjLufjw133jaKZVy5H/UJ4bXOK2RWJiIiIVCsKi2KOU6cosFhJ9Su8Z1ET3EgV8Lv0Ep4f3w+AL36PZUu8AqOIiIhIeSksijmSk0n1DXTuahiqVJW+reowsnMD7AY8MmszeQW6d1FERESkPBQWxRzJyZzyDwYgyMeKt5f+KkrVeWp4e8ICvNmemMqHdzwLaWlmlyQiIiLi8fQbupjjpZdIfv9jAMI0E6pUsbrBvjwTchyAtyK6svfJf5tckYiIiIjnU1gUc3ToQHLnbgCEBSosStW7engPBh5YT67Nm4dPRlKwYoXZJYmIiIh4NIVFMU1yZh4AYZrcRtzA0rIlL14USlBOJusbtmPqS19AdrbZZYmIiIh4LIVFMc2porCoyW3ETRo8cA+Pxf4GwKttLufQc/8xuSIRERERz6WwKO5XUADffkvKtl2AwqK4kZcXNzz3d3od2kKWjx+PxHpjrFtndlUiIiIiHklhUdzv1Cm47jpOzfoegHBNcCNuZO3YkZdbgV9eNr837sSXT0/RcFQRERGRM1BYFPdLTnb84edYOiPUXz2L4l5NnniAR/cuAuDfbYey7ynNjioiIiJSmsKiuF9RWCxcZ1E9i+J2Pj7c8uI99Du4kWxvPx44FkHeoXizqxIRERHxKAqL4n6lehZ1z6KYwXrBBUzuGkRIbiabolvx3u5Ms0sSERER8SgKi+J+p04Bf/UsKiyKWaIf/Acv3NAdgHd+28vGuGRzCxIRERHxIAqL4n6lhqGGaRiqmMXLi5HdmzKicwMK7AYPzNhIVm6B2VWJiIiIeASFRXG/5GTyrF6k+QYCEKYJbsRkz1/VgegQP/Yfz+DFSZ9DYqLZJYmIiIiYTmFR3O/UKVL8gpy7mg1VzBYW4MPkAdEAfGWvx/z7ngW73eSqRERERMylsCjul5zsnNwm2GbB5qW/hmK+fq2jmLh1PgAPNxxI/MtvmFyRiIiIiLn0W7q4n91OSnAYACF+XubWIlKkbl0e/L9r6Hx4F6l+Qdy3A/JWrjK7KhERERHTKCyK+02ZQur8XwEICwkwuRiRv/gMupR3G2cSnJPB+oZteeOV6c4JmURERERqG4VFMUVKVh6g+xXF8zSa9Cgv718AwPvtBrP8/mfAMEyuSkRERMT9FBbFFAqL4rFsNoa98zQ3bv8Vw2Ll/4K7c/S9D82uSkRERMTtFBbFFAqL4tEaN+bpW/vR5lgsx4PC+cfqFPJ+X212VSIiIiJupbAo7nfbbST/9AugsCiey2/0NbwXepignEzWxHTglS9XmF2SiIiIiFspLIp75eXBp5+SsmsfACEKi+LBWr4yiVf3O5bT+DikHXM3Hza5IhERERH3UVgU90pNBSDFLwiAsACFRfFg3t4M/ea/3DWgBQAPf7uZ3UfSTC5KRERExD0UFsW9CsNiamFY1DBU8Xi+vvxrSGt6t4gkM7eAu75cR1pWrtlViYiIiFQ5hUVxr5QUxx8Ki1KN2LysvHNDV+qH+rH/eAYP/v0t7Dt2mF2WiIiISJVSWBT3UliUaioyyJf32xr45OexoF573nj0fTh50uyyRERERKqMwqK4V2FYTFZYlGqoy+Ce/Hv7dwC80+5yvr/rKcekTSIiIiI1kMKiuFdqKjleNrK9/QCFRalmgoK49v1nmbj5ZwAeanwZGx6cZG5NIiIiIlVEYVHcKyXFOQTVgkGwn8KiVDNNmvDw4zdw2b615Np8uNPelsPvfGh2VSIiIiIup7Ao7pWS4pwJNdgLvKwWkwsSqTivvn15c2gz2h49wLGgCO5Yn03mgkVmlyUiIiLiUgqL4l5t25Jy5VUAhPrZTC5GpPKCbruVjyKPEJmRzLZ6Lbjvk5Xkb9psdlkiIiIiLqOwKO41ahQpjz8FQGhYoMnFiJyfRi9P4sOjS/DJz2VRs4t45tmvMNLSzC5LRERExCUUFsXtkjMds0dqchup9ry8uPDTN3l7xxwshp1prQfw3z+PmF2ViIiIiEsoLIrbpWQpLEoNEhjI0M9eZVJbHwAm/7KLb9fFm1yUiIiIyPlTWBS3+yss+phciYiLREVx64TLmTigOQCPztrMst3HTC5KRERE5PwoLIp79elDyjtTAPUsSs3zyOVtubpLA/LtBndPXc3G/3saDMPsskREREQqRWFR3CshgZR8x6bCotQ0VquF/1zbmb6+WWTYrdxqb8/OJ/5tdlkiIiIilaKwKO5VbJ1FhUWpiXxsVj5omkG3hB2k+AdzU2oT9v/7DbPLEhEREakwhUVxH8OA1FSS/RUWpWYLnHArn3UPoP2RfRwPCuem+DDi3/nQ7LJEREREKkRhUdwnIwPsdlLUsyi1QOh9f+fLltm0OBHH4ZAobtpq4ehn08wuS0RERKTcFBbFfVJSHH/4OsJiWIDCotRskU88zFeRh4lJTiI2vAHjVqZybKoCo4iIiFQPCoviPkVhUT2LUovUf+lZ/mfbQXTacfbUacz1K9M4+smXZpclIiIiUiaFRXGflBSyvbzJ8fYFIERhUWoDi4XGb/+HGWyhQepR9kU24vq1OSTFHTG7MhEREZFzUlgU90lNdc6EasEg2NdmckEibmK10uTdyUy37aBh2jH2h9Xn+unbSUzJMrsyERERkbNSWBT3SUkh2T8YgBCLHavVYnJBIm5ksdD4rVeYPrEXMeH+xJ7IZOwHq4k/lWl2ZSIiIiJnpLAo7tOzJyn/ngxAaKCPycWImMBioVGnNsyY2IvGEQEcOpnJdVN+Z+/X3zmWlhERERHxIAqL4j6NG5PSZwAAYWFBJhcjYp6GYf7MmNiTFnUDSUzJ5rrfM9n44CSw280uTURERMRJYVHcKiUrD9BMqCL1Q/2ZeaE3nQ/v5lRAKDfSkRV3PgJ5eWaXJiIiIgIoLIqbFYVFzYQqAhGX9GHagHD6xm4k08efCeF9+Hn8vyBT9zGKiIiI+RQWxX127CBl/0EAQn29TC5GxDME3TaeT65rz7DdK8nz8uaemMv47KaHISnJ7NJERESkllNYFPd54glSvvgagFB7rsnFiHgO39HX8PY/LmPc1oUYFivPtr6CSX9/jYItW80uTURERGoxhUVxn5QUUgrXWQwNDTS5GBHP4nXppbzw77/x6IbZAExtfQl3vjiHjPkLTa5MREREaiuFRXGfYmFRs6GKnM7SqRN3TX2B9zdPxzcvh1+bduO6uYdITNY9jCIiIuJ+CoviPqmpf/UsaoIbkTOrX58rZk5hevzP1MlOZXtQNFf/dxUb45LNrkxERERqGYVFcZ/iw1AVFkXOLjCQrv/7gDl3XkyrqCCOpOYwZsrvzFh7yOzKREREpBZRWBT3KRYWtXSGSBm8vGjUvjmz/96bIe3rkVtg55FZW3hiwovkrllrdnUiIiJSCygsinvk5GDk5JDiFwyoZ1GkvIL9vJly04X867JWWAyDafW6cMO7yzj64VSzSxMREZEaTmFR3CM1lWybL7k2R0gMC1BYFCkvq9XCPy6K4tPtMwnOTmddg7YM2+zFqtv/BenpZpcnIiIiNZTCorhHsSGoXoadIF+byQWJVDNhYQyc9RE/5P9J62MHORYUwbjIAbxx0+MUbNxkdnUiIiJSAyksinsUmwk1xMjHYrGYXJBINeTjQ7N3J/PdpRGM2b4Yw2LlrbaXM27yfI689xEYhtkVioiISA2isCju0bUrKctWARAaEWxyMSLVW8C46/nPaxN5a/NMAnMyWd3oAq7YFcjiW/8Jx4+bXZ6IiIjUEAqL4h4WCymGFwChQX4mFyNSA7RqxVVzPmCufR3tj+zjZEAoExoM4anPV5CZm292dSIiIlIDKCyK2yRn5gJaNkPEZXx9afbWy8we3ojxWxcA8OUxb658aznrDp40uTgRERGp7hQWxW1SsvIACAvwMbkSkZrFb/Q1TPr4MaaNbU+DUD9iT2Ry3ZTfeXneTnK2bDO7PBEREammFBbFPRYvJnXeQgBC8zJNLkakBoqOpk/XZsz/v/6M7haD3YApS/cx8s0lbLj7YTh1yuwKRUREpJpRWBT3WLyYlLUbAAjNSDG5GJGaK8TPm9fGdGbKiJZEZqWyq25TRoX0Y9Ktz5E+faZmTBUREZFyU1gU90hLcy6dERqoCW5EqtrQXq1Y0CKZUbuWYVisTG1/GYOXZbDoxnshLs7s8kRERKQaUFgU9ygeFkP8TS5GpBawWom8725ef+devjz0E41PJZIYUpfbm1zB3//5AYef/w9kZ5tdpYiIiHgwhUVxj9RUkv0c6yuGhgaaXIxILRITQ79p7/HLoHDu3vIzXvYCfm7Vi0GnWvDO6P8je/b3ZlcoIiIiHkphUdwjLY0UP0dIDA0PNrkYkVrGYsF/zGgemfYic3NW0yNuG1k+frzWcTiX/Z7D/K2JGLqXUUREREpRWBT3SEsjtWgYakSoycWI1FJhYbR769/MePQK3tk7l/rpJ4j3CuSur9Yz7uM/2H441ewKRURExIPYzC5AagdD9yyKeAxLx46MmPlfBu3cw5Q4gynL9rNq3wmGvbOcqwIyeDBrJ40euR9C9cGOiIhIbaaeRXGLrIxs8ry8AQj19za5GhHBYiGgXWseGNKGXx8YwPBO9TEM+C4jkEvzOjHppqc5/trbmgRHRESkFlNYFLdIybUD4GUvINDHy+RqRKS4RhEBvHtjN37sAv1iN5Ln5c3UDkMYEF+f16/5JylvvgtZWWaXKSIiIm6msChukdy9FwCh9lwsFovJ1YjImXS8fhhfvnQj044vpmPSHjJ8A3i78wj6HKzH5FEPcHLym5CRYXaZIiIi4iYKi+IWKZPfACAsuo7JlYjIOTVrRp+PX+X7f17Cfw//StujB0j3DeC9zsPpmxjDS9c9zLF3PzC7ShEREXEDhUVxi5SsPABCdL+iSLVg7dKFK794nZ/v78eUpMV0OLKPTB9/Puh0Jf3i6/P091uJPa5eRhERkZpMs6GKWxSFRU1uI1K9WDt3ZujUzly+dSu/vf45b9sbsim6FV/8fpAvVx9kcLt63N4+lO75J7D07Gl2uSIiIuJCCoviFqkKiyLVmuWCCxj06WQuPX6c35Pho+X7WbzrGAu2H2HB9iN0Prybv/37Y64Y3R+fMdeBv5bIERERqe40DFWq3rp1pLz5HgChB3abXIyInA9LnTr0blmHzyb0YNED/bmhSzQ++blsatCa+9tfQ++1ViZf/U/iHnwCduv9LiIiUp0pLErVO3qU5LRMAEIzUk0uRkRcpWVUMC9dcwGr2qTwzz2LiEo7wfGgcN7rOpL+tp6Mf+JrFo6+k/zpM7Reo4iISDWksChVLy2NFL8gAML8tMaiSI3i60udO8bzz29fZ+UNLZhycgX9Dm7EsFhZ0uIi7mh1FX2XZ/PKyPvZu3KD2dWKiIhIBeieRal6qanOsBgS4GNyMSJSJSwWvPv2YWjfPgw9fpzYT//H17/H8k2ji0gKrsP73Uby/o+H6bQhg1FdGzKicwMivey6t1FERMSDKSxK1SvWsxga6GdyMSJS5erUoenD9/GY3c4Di5fw28zfmBXRliVEsDk+hc3xKbzw0w4uObGHEfEbuLRve4LHjIYWLcyuXERERIpRWJSql5ZGqm89AEKD1YsgUmtYrfgOupQrBl3KFcCJ9Bx+2HSY2esT2JKQwqLwliwKb4nPiVz6PzyVYdnxDOrTlpAxo6FlS7OrFxERqfUUFqXqpaaS4ufoMQgNDTC5GBExS2SQLxP6NGNCn2bsXrqWH/47k59DWrA/MoZFrXqyCPA5mUe/R7/k8vRYBnZuRN3hl0OvXmDTf1ciIiLupv99pcoZaWkkhwYDEBoebHI1IuIJWg/ozr8GdOfBffvYNWMuP29K4KeQ5uyLbMSvLXvwKz0A6Pzxnww8Fcygbs3o0CAEq9VicuUiIiK1h8UwDMPsIsT1UlNTCQ0NJSUlhZCQEFNrSR93Cxc0GgvA9vGtCWjbytR6RMRDHTjA7uk/8vPmBH7zqc/m+iX/ragb7MulbaLot2UpvfKOEzn4Ekevo5/uhRYRETmXymYDhcUaypPCYsI119Onzc14F+Sx+1+9sNSrZ2o9IlINHDvG0R9/YUlBCL/5NWT5nmNk5BaUOKTdkf30SdhKnxCDHhe1IvCygdCli4asioiIlKKwKCV4Uljc/tV3XLnVmzrk8uezV4Kvr6n1iEj1k5NfwNoDp1i8ehcrF29gZ1SzEl+3FeTTJXEXFyft5qJQC906NiH0b7dA48YmVSwiIuI5KpsN9PGrVLmUXv1g62pC6oYrKIpIpfjavOjbqg59W9WBy5pzfMFiVv2+g1XH81lZpyVxYdH8GdOBP2M6OE7IhtbTdnBR2xQuahLORU0iaJR9Ckt2NjRvDhbd+ygiIlIWhUWpcilZeQCE+nubXImI1Aj161Pn1hsZeSuMNAzYs4dD85ewavMh/ky2sy6iKQciGrI7JZ/dfxzif38cAqAOuXTau5GOKfF0CrXRsVU0Ud07Q/fuEBOjACkiIlJKrQyLq1at4vPPP2f58uUkJCRgGAYxMTH07duXW2+9lT59+lRp+/v372fq1Kn89NNPHDp0iPT0dBo0aECnTp0YN24cV199NbYadM9NSlYuoLAoIlXAYoHWrWncujWNgesNAw4e5Nj2vaxrfAHrDp7kz4On2JqQwvECH35r2YPfCmdaxYB6v56g47QP6ZSWSIcwG22a1KHh4P5Yhgwx9dsSERHxBLXqnsWMjAzuu+8+Pv3003MeN2HCBN555x0CAwNdXsNbb73FI488Qk5OzlmP6dmzJ9OmTaN58+aVbseT7ln88Lu1/Hv1Ua7uFM2bN15oai0iUjtl5xWw/c2P2LI1ls0ZVraExrA3Mga71eu0Y4ONPFo3jaJ1vWDaRgfTpl4Qbb79gvB2LaFNG8cwVm99+CUiItWHJrgpQ0FBAVdeeSULFixwPufv70+HDh2w2Wxs376d1NRU59eGDBnCzz//jJfX6b9IVNbzzz/P008/7dy3Wq20b9+eiIgI9uzZQ2JiovNrMTExrFmzhvr161eqLY8Ji/n5TL70b7zXeyy3Jqzl2S8nmVeLiAiAYcDhw2SuXsv29bvYfPAkW7K82BHagH2RMeR5nTkIRqWdoMXJeJolJ9LcmkPzUB+aNYwgpnVjvNu2gdatISpKw1lFRMTjaIKbMjz11FMlguIdd9zByy+/TEREBODodXzllVd4/vnnAViwYAFPP/00L774okva/+WXX3jmmWec+7169WLq1Km0bt0aALvdzsyZM7n99ttJT08nPj6e6667jhUrVrikfdOkp5PiFwRAqNVucjEiIjjCXMOGBIxuyEWj4SJwBMjERHI3buJATCt2GQHsSkplV1Iauw4cJS4bjgZHcjQ4kt+bdC5xOdvufBqv2U7zkwtpNuoKGjevT0x4AI0i/Gl4+AD+CXHQrBk0bQoBAWZ8xyIiIpVSK3oWDx8+TIsWLcjOzgbg5ptv5osvvjjjsU899RQvvPACAH5+fuzbt48GDRqcV/uGYdC1a1c2bdoEQJs2bVi/fj0BZ/ilYdGiRQwePNi5P3v2bK655poKt+kxPYtxcdx7zzv82H4ATx5fw+0fP2teLSIilXH0KOkLf2PPjoMcSEzmQEoe+w1f9odEcyCiAdnefuc8vU7GKWJSjhCTcpRGeWnE+FmICfWlfp1g6tWPJKRRAyxt20DHjm76hkREpLbRMNRzePjhh5k8eTIAAQEBxMXFOXsUS8vNzaVly5bExcU5z33llVfOq/2ff/6ZYcOGOffnz5/P5Zdfftbjr7/+embMmAFAjx49+OOPPyrcpseExW3buOXZWSxrfiGTU//kuv8+U/Y5IiKezm6H+HjsO3eStH0fBw4ksb/Ah/2XX038qSziTmYSfyqL9Jz8Mi/ln5tNPXKo16YZ0aF+RIf4US/Ej+iZX1HPyKFe3RDq1IvALzoK6tVzDHWtV0+9lCIiUm4ahnoOc+bMcW6PGTPmrEERwMfHhwkTJvDcc88Bjp698w2Ls2fPdm43a9aMIWXMsjdx4kRnWFyzZg3x8fHExMScVw2mSUsjxS8YgFBf193/KSJiKqsVGjfG2rgxDYZAA6D0PNqGYZAyZy7x67cSn5RCXGoO8blW4m1BxIdGkRQUSYp/MFk+fsTiR+yBkyUv4N/N8WcGsB8Cd2QSmbmFyMwUIjOTqZObQaQ1n8jhQ4ns3pk6Qb5EBvkQkZFCyPLF+NWJgIhij/BwTcwjIiIVUuPD4q5du9i7d69zf+jQoWWec8UVVzjD4t69e9m1axdt2rSpdA0//fSTc/vyyy/HUsbkB/369SMwMJCMjAzn+RMnTqx0+6ZKS3PesxgW4GNyMSIi7mOxWAgbNYKwUSO4oPgXsrIgPh4SEsg6FM+R+GMktWjHkTYdSUrJJik1m6Mn0khatJyk4EiOBUaQa/MmwzeADN8ADoWXmvhsnwH7NpZqPRK/vGxCs/cSlp1GaHY6oVlphBXkEGotIMzLINTHSui//klokB+h/t4E+XoRtHkjQYcOEBAWjDU0BEJDISTkr4e/vybwERGpRWp8WCy6T7BIr169yjynW7du+Pj4kJvrWB9w8+bNlQ6LR48eJSkpqULt22w2unfvzpIlS5ztV1upqaT4OZYgCQ30NbkYEREP4O8PrVpBq1b4A00LHyXY7TCoISQkYCQmkXbkMCeOnOLEyTSOp2RxIjOPEzl2Tti9OH7FSE7Y/DmRnsuJjFySM3KwYyHb249sbz+OBEeevZZvzvT/SyQWw05g7kmCcuMJyskkMDeL4NxMgnKzCDQKCG5Un6BhQwnysxHo44Wftxf+X3+Ff1YG/n7e+Pv5OB4BfvgH+OIf5I9/UAC2oEC44AIoPhdAQYEjQPv5QQ1aY1hEpCao8f8q79ixw7nt4+NDo0aNyjyn6Lh9+/addo3zaR+gRYsW5TqvRYsWzrB4Pu2bzUhNI9XPMew3NNjf5GpERKoJqxUaNYJGjbAAIYWPZuU41b57N2m//ErqyVSSUzJIScsiOTOPlOx8kvMMUgospGAjObQOKZcMIjkzj7TsfNJz8knPzKHAYsWwWEn3DSDdNwCCz9LQ4r0l9707QdEoVzuQWfgofkhBJn5L1hEQtB1/78KQmZeD3/q1+Obn4WPPx8cowBc7PtjxxcDXYuBjBV8r+Iy/FZ+QIHy9vfDxsuK7dTM+a//Ax+aFr48XPt42fL2t+Ni8sHnb8C7801avLt4jhmPzsmKzWvD2suK1dg2kpDiG5hY9fHxK7nt7/9W7WsQwHGHealUvq4jUeDU+LMbGxjq3Y2JiyhwCWqRx48bOsFj8GufTftF1y9v+2a5RnaSnplNgrQtAaFigydWIiNR81tatCW3dmlDgrB+PGgZkZkJgyX+XjcWLyd6+i/SUdNLTMknPyCY9M4f07DzScwpIz7OTnm+Q3qEz6b37kZaTT1ZuAVl5BWT+toxsqxdZ3r5k2XzJ8vZzbHv7YlisAOR5eZNnh7TUnJL1lFqO5KyWHjr9uaAef20XFD5KOwxsWFjiKYth4F2Qj82ej81eULhdgM2ej3dBwV/PR9XF1jgGb6sVm5cFG3a8f/kFL3sBXoYdKwZeGHgZhmPbAl4YWAGvK4ZijY7Gy2rBy2rBejAWr0WL8LLg+LoFrBbHn39tW/Dy8cZ69114WSxYrRZsVgtei3/DunWr81yL1YLVAhYshbnV4thu3QrLiOFYLRYsRV+f+hmW9DTneRaL1XGu1VJ4nMWxPXAglgs6YLU4rs2pZKzfzMBicRxntVqg8E+L1XENa2Fotl4/FktQUOG5Fti+DeuaP7AAFLVRtE2xnB0ejuWaqx2vCY6aWbQIS2Ki83e2EucWnWyxQJs2cOGFJV/rb74pfIEtp/9ZfLt3b8dEUUWOHYPVq899TtGfl19e8oOC3bvh0Bn+bpYWEQHdupV8bvVqKLzt6JxatYLiv0Pm5MCqVWWfB9CjR8n3emKio+ay+PpCz54ln9u+HY4fL/vc6GjH2rPF/f67YyRBWdq2hTp1/tpPS4MzjbI70+/0PXqUHKEQF+cY9l+WoKDTZ6Tetg3S08s+t2FDKD63SEEBrFvn2G7SpOTfs2qoxofFtLQ053ZoaGi5zys+S1Dxa5xP+xWpoaLt5+TkkJPz13++qamp5aywaqWkOj5W9snPwy/kbB9Pi4iIW1kspwVFAMvAgfgPHIg/ULei1xxQx/FLb9EjMxMyMjAyUsnJyCQrPYusrByyrhhOVpNmZOUVOILmvgNkf/Y5Ofl2cgsMcu2QY4dcw/HIMazkYiHX4kXOTbeQi5WcfDs5+QXk7t1Pbvxhcmze5HoVPmze5Hh5k+9lI8/qRYHVizyv0yf2MSwWcm3e5FKOSX8OJZfcb9njjIedZn827I8t+VyrS8t37pytpZ6IhHoDyj7vKPDJmlJPtoNz3Qli4AjZi07CouWnn1seb64+w5PlnJxv3fwzPFnnDM+VZNmUiOVbx7wQzkCZ54sFA4tzrn/HhsUwsBTfXvsn2LwKg6sF8vOxZGY5PkgBx/PObaPYNljWLipss/CZtFQsGRkljzlDu/gdh19OlfwmkhKx5OaV8Z0aEJ4GIcV68wvynSHIUsbCBpaVueBT7O95alqJwHfW8202WJblOKYolyUlnTFAWUpfIiQR6iX89XWLBfbudfTKn6nG4jU0OAXBxX5nzM6CgwfPXGPR+UUbK7Mdvf5Fjh+H48fL/hn5+0OzUpOMHTgAWZmF9Z3j5KhDWKKKBUJ7gSNUA9RP4G9XXcjY7uXrLPJENT4sphf7C+3nd+61sIrz9/9ryGR6eT5VKEf7Famhou2/9NJLPPus561hmHzdDTB1MyGBPlj69Sz7BBERqZ6aNz/j0xbAr/ARfqYDWteFK8oRvuz2kj09AEebOX55zc529LRkZ0NeXrFHLuTlYYRHUHDlleTbDfIK7OQXGOR9+RX58Qnk5xeQl1fg+DO/gPwCu2O7wO7Y7j+AvF59/jo3I4v8114n3wC7AQWGQQFF246H3XBkL/vY6ymoV48CO9gNg4JduylYsAA7FgqwOP60WCiwWLFbrBRYrY5tH18KRozEbhjk2w0K7Ab2bdspSEzCXniMYbFgYMFusZTcDg/H3qQphgEGBnYD7Hv2Qn4+dosVwwJ2ixW7o4sOu8XieB6wh4WBv7/jHMPAyC/AnpqKUfT1Yuef1r7FeqZXrUoZFgvODFC04VXOX23tQG6pXi7fco6AysgtuW/xhaByzsuQkl1y3z8cynOXjh1Izir5XGg5e6zS84Hiy/jYICy6fOeeLD2WPATCy7nswolS54bVP/NxpeUAOaV6WyPLvo3M0WapnxH+5T/3aKnftwPrQnn+ShjAkVIdO3WbOv7Mh5MZZX0Y4NlqfFjMz//rzWGrwI3zxY/Ny6v8i1y8/YrUUNH2H3vsMR544AHnfmpqarnuz6xqLZo34Id/BJObb3cMvxAREakM6xnCSFSU41EGC45feGxe4OdduIzTXX+rfC19/lu5865sB/93Vcnniu6BLCgo+Sg9EulkK0ePTtHxRX+W3g4OLjlcEWBTmCNMn+s8w3AM/2vY8K/zUlNh6dIznJN/2vnG1VdjBAQ6QqXhCLfGunVgGBiG4ehbMwwMLBQt8W0YBoSGYlx1VbHQB8Yvvzgmdyo6pvBP5zWMwv7CDh3g4ouLTnNc46OPSrVXeI2ic4qud+WVGIVDBw0DiIvDmDsXZxmFdTtrcrZtYPz9nsKnHbWwYgVG4YSKBjiv4dgv9uFGTAwMH1Hy5Z8+HU6V6tEqqrH4ub17Qecuf+2np2N88QXn4qxj7NiSv4Pt2IGxeHHZ5wYEwq23lvzCL79A4W1a5zy3bVsYOLDkFz76CKOMXlQDYMgQaNnyryeTkmDWt4VfP/vtZAbAbbc5hs8WWbMW1q756+tnOzeqHlw7uuST386CI0lnPqH4uRd1dwx/LZKbCx9+6Nju14/GncoZkj2UxXC+E2qmkSNH8uOPPwLQvXt31qwpPTTjzK677jq+/fZb5zW+//77SrX/+uuv8+CDDzr3MzIyCCjHQsrvvfce//jHPwDHkNSUlJQKtVvZhTdFRERERKRmqWw2cP+YATcLCgpybmdlle6aPrvMzL+6zotf43zar0gNrmpfRERERESkMmp8WKxTbDalxMTEcp9XfG3EyMhzrFFVgfYrUoOr2hcREREREamMGh8W27Rp49w+ceJEiR67c4mLi3Nut23b1iXtAxwqz9TKLmxfRERERESkMmp8WGzXruSUzxs3bizznISEBI4dO3bWa1REq1atSkxWU572ATZs2OCS9kVERERERCqjxofFHj164FtsVqQVK1aUec7y5X+tMeTn50ePHuVcT+kMfHx8uLhwpq7ytp+UlMTevX+tpdO/f/9Kty8iIiIiIlIZNT4sBgUFMWjQIOf+tGnTyjyn+DGDBg0i8AwLF1fEVVf9NU32okWLOHLkSLnbDwsLU1gUERERERG3q/FhEWD8+PHO7c2bNzuX0jiT9evXM2/evDOeW1k33HCDs3czLy+P//znP2c9Nj09nbffftu5P27cOLy9vc+7BhERERERkYqoFWHx2muvpXPnzs79iRMnsnPnztOOS0xM5KabbqKgoACALl26MHr06NOOA4iNjcVisTgfkyZNOmv7MTExTJw40bn/1ltvMWvWrNOOy8vLY8KECc5JcPz9/Xn88cfL9T2KiIiIiIi4kq3sQ6o/i8XCRx99xIABA8jKyiIxMZGLL76Yu+++m/79+2Oz2VizZg3vvvuuc4iov78/H374IRaLxSU1TJo0iXnz5rFnzx4KCgoYM2YMN954I1dffTURERHs2rWL999/n82bNzvPmTx5Mg0aNHBJ+yIiIiIiIhVhMQzDMLsId5k9ezY33XQTWVlZ5zzO39+fr776ilGjRp31mNjYWJo1a+bcf+aZZ87Zuwiwe/duLrvsshLLYpzNww8/zCuvvFLmcWeTmppKaGgoKSkphISEVPo6IiIiIiJSvVU2G9SKYahFRo0axbp167jsssvO2GNosVgYNGgQf/755zmDYmW1bt2azZs3c9ttt+Hv73/GY9q1a8f3339/XkFRRERERETkfNWqnsXi4uLiWLlyJQkJCQA0bNiQPn360KhRI7e0n5aWxm+//UZcXBwZGRnUr1+fjh070rVrV5dcXz2LIiIiIiIClc8GtTYs1nQKiyIiIiIiAhqGKiIiIiIiIi5UK2ZDrY2KOoxTU1NNrkRERERERMxUlAkqOqhUYbGGSktLA3DbPZgiIiIiIuLZ0tLSCA0NLffxumexhrLb7Rw+fJjg4GCXrRVZWampqTRq1Ii4uDjdP1lD6DWtmfS61jx6TWsmva41j17TmsfTXlPDMEhLS6NBgwZYreW/E1E9izWU1WolJibG7DJKCAkJ8Yg3i7iOXtOaSa9rzaPXtGbS61rz6DWteTzpNa1Ij2IRTXAjIiIiIiIip1FYFBERERERkdMoLEqV8/X15ZlnnsHX19fsUsRF9JrWTHpdax69pjWTXteaR69pzVNTXlNNcCMiIiIiIiKnUc+iiIiIiIiInEZhUURERERERE6jsCgiIiIiIiKnUVgUERERERGR0ygsymlWrVrFxIkTad++PaGhoYSEhNC+fXvuvPNOVq5cWeXt79+/n6effpoLL7yQunXr4u/vT4sWLbjmmmv49ttvyc/Pr/Iaaork5GTmzJnDfffdR//+/YmOjsbX15egoCAaN27MiBEjePPNNzl16lSVtG+xWCr8mDJlSpXUUlMsWbKkUj/XnTt3Vkk9er+en9jY2Eq9nsUfsbGx512H3qsVc+zYMebNm8dzzz3HyJEjqV+/fomfzdSpUyt97S1btvDAAw/QqVMnIiIiCAoKok2bNowbN4758+e77ps4h6SkJF555RV69epF/fr18fPzo2nTpgwdOpSpU6eSlZXlljrcydWvaWZmJvPmzeOhhx7isssuIyYmBn9/fwICAmjYsCFDhgzhxRdf5PDhw1XzDQFNmzat8Pv60UcfrbJ6zODK17Wy/15X5fvWLe9VQ6RQenq68be//c0AzvmYMGGCkZ6eXiU1vPnmm4avr+852+/Zs6exb9++Kmm/ptixY4cxfPhww8fHp8zXEzACAgKMN954w7Db7S6tozxtl368//77Lq2hplm8eHGlfq47duxweS16v56/AwcOVOr1LHrYbDbj5MmT512H3qvlk5iYaDRp0qTMn81nn31W4Wvn5eUZjz32mGG1Ws957WHDhhlHjx51/TdX6OuvvzZCQ0PPWUObNm2M9evXV1kN7uTq1zQpKckYO3asERAQUK73kbe3t/HYY48ZOTk5Lv/eyvN9lX488sgjLq/DDFXxXq3sv9fz5s2rku/RXe9VGyJAQUEBo0aNYsGCBc7n/P396dChAzabje3bt5OamgrAZ599RkJCAj///DNeXl4uq+H555/n6aefdu5brVbat29PREQEe/bsITExEYDVq1czYMAA1qxZQ/369V3Wfk2ydetW5s6dW+I5Ly8vWrZsSb169SgoKGDHjh2cPHkScHwC+n//939s27aNDz/8EIvF4vKa+vfvj7+/f5nHNW7c2OVt11R+fn4MGDCgXMcGBQW5tG29X13D39+fyy+/vNzH2+12Fi5c6Ny//PLLCQ8Pd2lNeq+eXXZ2NgcPHqySa0+cOJFPP/3Uue/t7U379u0JCgpi586dnDhxAoCffvqJyy67jJUrV7r8ff3ll19yyy23lHiudevW1K9fn9jYWOf3vmvXLi655BJWrVpFhw4dXFqDu7n6NY2Li2PGjBklnrNYLDRv3pzo6Gi8vLxK/BuZl5fHSy+9xMaNG/nuu+/w8fFxWS3Fde/enYiIiDKPa9OmTZW0725V+V4tUt5/u+vWrevytt36XnVRuJVq7rHHHivxScQdd9xhnDhxwvn19PR046mnnipxzOOPP+6y9ufPn29YLBbntXv16mXs2rXL+fWCggJj+vTpRlBQkPOYPn36uKz9mmbmzJnOXoerr77a+O6774yUlJQSx9jtduO7774zGjZsWOJ1/e9//+uyOopf98CBAy67bm1WvGexSZMmptSg96t5fvnllxLvq2+++cYl19V7tXyK9yzUrVvXGDp0qPHkk08a33333Xn1LH7wwQclzh85cqQRHx/v/Hpubq7xzjvvGDabzXnMjTfe6NLvbfPmzSVGCrRu3dr4888/SxyzYMECo169es5jmjdvbmRlZbm0Dndz9Wu6du1aAzAsFosxaNAgY9q0acaxY8dOO27JkiVG+/btS7Tx8MMPu/R7K96ztnjxYpde29NVxXu1dM+iWdz9XlVYFCMhIcHw8/Nz/oW6+eabz3rsk08+6TzOz8/PSEhIOO/27Xa70blz5xJd5hkZGWc8duHChSXeqLNnzz7v9mui7777zrj99tuNgwcPlnnsoUOHjOjoaOfPtE6dOkZubq5L6tAvoK5ndljU+9VcN954o/PnGR4ebmRnZ7vkunqvlk9KSooxc+ZMIzY29rSvVfYX0IyMjBL/Bl9yySVGfn7+GY/9+OOPncdZLBZj3bp1lf1WTjNixIgS/w8kJSWd8bitW7eW+EX19ddfd1kNZnD1a7pu3Tpj9OjRxrZt28o8Njk5uURg9PHxOevPvTJqc1isiveqp4RFd79XFRbFeOihh5x/kQICAkr0KJaWk5NjNGrUyKWfgv30008l3nzz588/5/Fjx451HtujR4/zbl9O/1R70aJFLrmufgF1PbPDot6v5klJSTH8/f2dP8+7777bZdfWe/X8VfYX0Pfee69EANy+ffs5j7/44oudx48ZM+Y8q3bYtm1bifqnTJlyzuMfeeQR57HR0dFGQUGBS+rwNOfTW1xepUcLfPzxxy67dm0Oi+dSncOiGe9VzYYqzJkzx7k9ZsyYc45p9/HxYcKECc792bNnn3f7xa/RrFkzhgwZcs7jJ06c6Nxes2YN8fHx511DbTdixIgS+1U1c6ZUf3q/mmfmzJklZra79dZbTaxGXKX4e2rAgAG0a9funMcXf0/9/PPP5OTkuLSGoKAgxo0bd87j77zzTud2UlISv//++3nXUFsNGjSoxD3C+v9XzsWM96rCYi23a9cu9u7d69wfOnRomedcccUVzu29e/eya9eu86rhp59+cm5ffvnlZU6u0q9fPwIDA894vlRO6Q8IiiYzEilN71fzfP75587ttm3bcvHFF5tYjbhCeno6y5Ytc+5X9P/g9PR0lixZct51FH9f9u3bt8yJc5o3b15iIpTSE6pJ+Xl5eREaGurc1/+/ci5mvFcVFmu5TZs2ldjv1atXmed069atxGxdmzdvrnT7R48eJSkpqULt22w2unfv7pL2xaH0jGFRUVEmVSKeTO9X8+zfv58VK1Y499WrWDNs376dvLw853553lPR0dE0bdrUuX++7ynDMNiyZUuFaih9nN7XlZeVlcXRo0ed+/r/V87GrPeqwmItt2PHDue2j48PjRo1KvOc0scVv8b5tA/QokWLcp1X/LjzaV8cSg8nLu8/QBXx0EMP0aFDB0JCQvD39ycmJoaBAwcyadIkDhw44PL2aoPk5GTGjBlD06ZN8ff3Jzg4mGbNmnH11Vfz7rvvuvwTar1fzfPFF19gGAbgWKbk5ptvrrK29F51H094Tx06dIiMjAxTa6jNvv/+e+x2u3O/Kv7/BXj11Vfp2rUrYWFh+Pr6Ur9+fXr37s2jjz5aIoBI+d3y/+3da1BU5xkH8P/CykUWjCgKXkDjiHgB72ai9RbWYCSxqWRiTGdIpsaM1tZaHJsPjTVtnUw0bcDomJBEnYkRoyAJxDYhgDreJtwRiUiwFUQ06OJCWTYrIqcfqG/3uCzssgeWZf+/GWbOu77nvA9nfTj77DnnPfHxmDhxIvz8/ODn54fQ0FAsX74cu3btkn0BoBRn5SqLRTdXXV0tlseMGWPz8/XMn69lvg1Hxn90u30xPgFNTU3YvXu3aEdFRWHKlCmKj5OWlobLly+jubkZJpMJdXV1OH36NP785z8jPDwc69evl92PRd1rampCamoqampqYDKZYDAYUF1djYyMDPz2t79FaGgo9uzZo9h4zFfnkCQJn376qWhrtVqMHj2618ZjrvYd83xQq9U2P4tUyZxSIq9ramrElxlku7a2Nrz99tuiPWLECERHR/fKWP/4xz9QWlqKpqYmtLa2ivvXdu7cienTp+OFF14Qz14m2xw6dAhXr16F0WiE0WhEbW0tsrKy8MYbbyAsLAzbtm3DgwcPFBvPWbmqtqs3DTjNzc1i2fya+e4EBAR0ug1HxrcnBqXGJ2DLli2ySwt37NjRK+MMHz4cEyZMgEajQVNTE65cuQKDwQCg44CZnJyM/Px8nDp1yq7/i+5u3LhxGD16NLy9vaHT6XD58mW0tbUB6CgmN23ahNLSUuzfv9/hsZivznH27FnZGb3evgSVudp3zPPB398fHh62fYevZE4pkdft7e0wGo2y+5Ope++8847srN6bb74Jb2/vXhlryJAhCA8PR0BAAAwGA6qqqkRxKEkSjh8/jsLCQpw9e9amq8wICAkJEVf26PV6VFRUwGQyAQBMJhN27NiBgoICfPXVVxg0aJDD4zkrV3lm0c09/AAAAD4+PjavZz5zl/k2HBnfnhiUGt/dffLJJ7IiYvXq1RYzozpiypQpSEpKwr/+9S/cuXMH3333HXJyclBQUAC9Xo8TJ04gKipK9C8pKcFLL72k2PgDkYeHB7RaLQ4fPoyGhgZcu3YN586dQ25uLi5evAi9Xo8PPvgAw4cPF+scOHAAO3fudHhs5qtzmE9sExAQgF/84heKj8FcdQ5nH4M7W78nea1EHO4mKysL27dvF+358+fj17/+taJjjBs3Djt27EB5eTkaGxuRn5+PnJwcfPfdd9DpdDhz5gwWLVok+tfU1OC5555Da2uronEMFCqVCvPmzcPHH3+Mmzdv4ubNm7hw4QJyc3NRXFyMxsZGpKSkyO4pzsrKwqZNmxQZ31m5ymLRzT08AwF0XAJjK/O+5jfnOzK+PTEoNb47O3PmDDZu3Cja48ePR3JysqJjfP/99/jd736Hxx9/3OLf1Go1YmNjkZeXh9jYWPH6N998g6+++krROAaSRYsWITs7Gy+//HKnj7nRaDRYv349iouLZQesv/zlL6ivr3dobOZr3zMajUhNTRXtF1980eLArwTmqnM4+xj8aAz2xPFoP+a27SoqKrBmzRpxr+LQoUORkpICT09PRcc5ffo0/vjHP2Lq1KkW/6ZSqbBw4UKcOnUK69atE69fvHhR8c8CA0VYWBjy8vLw2muvdXrJuLe3N9asWYPi4mLMnj1bvJ6cnKzIJFDOylUWi25u8ODBYvnhqXNbmPd15LIT8/HtiUGp8d1VaWkpVq5cKb49HDFiBL755hunXFLm4+ODI0eOYOTIkeI1Je+zc1djx47F0aNHRdtoNDp8KSrzte998cUXskuPnDkLKnNVec4+Bj8agz1xPNqPuW2b2tpaxMTEQK/XA+jY/ydOnEBYWJhT4vHw8MC+ffsQGRkpXmNeO2bo0KFIT08XZ/4kScLevXsd3q6zcpXFopszfz6LPRMWGI3GTrfhyPj2xKDU+O6osrISMTExaGpqAtDxR+3bb79FeHi402Ly9/fHhg0bRPvs2bN2fXCizs2bNw9LliwR7ezsbIe2x3zte+aXoE6YMAE/+9nPnBgNc1Vpzj4Gd7Z+T/JaiTjcQX19PbRaLWprawF0nIn68ssvMX/+fKfGpVarsWXLFtGuqqqyeKQW2Sc0NFR2qb6jx1/AebnKYtHNmd/XdOvWLZvXM58QZdiwYYqMb08MSo3vbq5duwatViumdPb398fXX3+N6dOnOzkyYOnSpWLZZDKJgyk5xny//vDDDw5ti/nat+rq6pCbmyva/eXZisxV5ZjnlMFgsPleIiVzSom89vf3V2QCj4Hs7t27WLZsmfg7rFarcfToUSxbtszJkXUwz2vA8eMFyfdpdXW1w/eCOitXWSy6uUmTJonlhoYGi28frDH/cBAREaHI+EDHM2T6cnx3cuPGDURHR+PGjRsA/n/pyxNPPOHkyDoEBwfL2jqdzkmRDCzm+9XRfcp87VuHDh0S9zSpVCrEx8c7OaIOzFXl9IecCg8Plz02i3mtvP/85z+IiYkRM596eHjgs88+w89//nMnR/Z/zGvlPbpPGxoaHNqes3KVxaKbmzx5sqxdWlra7Tp1dXW4c+eO1W3YY+LEibIbb20ZH+iYiU+J8d3Fw0tfHk6///DSF/NZ0Jzt0S8qHr02n3rGfL86uk+Zr33L/BLUJUuWOO2epkcxV5XTk2Pw/fv38f3331vdhr00Gg3GjBljVwwA89pWLS0tWLFiBQoLCwF0fPFz4MABrF692smRyTGvlaf0PnVWrrJYdHPz5s2TPdPn3Llz3a5z9uxZsezj44N58+b1eHwvLy/ZmS1bxv/xxx9x9epV0e5PBU9/1NDQAK1Wi8rKSgDAoEGDkJaW1m8ufXnI/MMP0DHpDjnOfL86uk+Zr30nPz8fV65cEe3+cgkqwFxV0uOPPy778GdLThUVFck+hCqRU+bbsCWG+/fvIy8vT9EYBiKTyYSVK1fi/Pnz4rV9+/b1q3x+iHmtPPN96u3trcgkgs7IVRaLbk6j0SA6Olq0Dx8+3O065n2io6MdngHN/DKMnJycbqf3Nx//scce40GqC01NTYiJiUF5eTkAwNPTEykpKXj22WedHJmlzz//XCyPGzeu02mpyT5GoxGZmZmircQkCszXvmF+VtHPzw9xcXFOjEaOuaqslStXiuXU1NRu72syz6mpU6diwoQJDsdgntcVFRWyMxGdyczMFLP0enh4KPp83oGitbUVcXFxOHnypHgtMTER69evd2JU1pnntY+PD2bOnOnEaFyfJEk4duyYaD/55JOKbNcpuSqR2zt27JgEQPxkZmZa7VtUVCR5enqKvqmpqQ6PX1tbK3l7e4ttJiQkWO3b3NwshYaGir4bN250ePyBymAwSAsWLBD7ysPDQ/rss8+cHVanMjIyZP8HN2/e7OyQBoSEhATZfv3yyy8d3ibztffdu3dPCgwMFPvtlVdecXZIAnPVOvP9cvDgQZvXy8/Pl637/vvvW+1bW1sr+fv7i77vvvuuApF3HC+CgoLEdletWmW1b1tbmzR37lzRNzY2VpEY+qOevqdtbW1SXFycbP2333679wJ1UEFBgeTl5SViff75550dUq/q6ftqj/fff182TlJSkiLbdUauslgkqb29XZo+fbr4zxQSEiJVVFRY9Lt586Y0efJk0W/GjBlSe3t7p9u8du2aLEm2b9/eZQybNm0SfT09PaW0tDSLPq2trdILL7wg+vn6+kp1dXU9+p0HOpPJJGm1WrGvVCqVtH//foe3a+v72tjYKK1atUoqLCzsdpspKSmSn5+f2ObgwYOlW7duORzrQJSVlSUlJCRItbW1XfZrbW2V3njjDdl7NWvWLOari0hLS5O9HydPnrR7G8zVvufIB9CVK1eKdTUajXTu3DmLPk1NTdLChQtFv+DgYMloNNocU3dfOrz33nuy/omJiRZ92tvbpc2bN8uOLUVFRfb8qi6lJ+9pe3u7FB8fL1v3T3/6U6/E1NV7GhcXJ508edLq3/2HcnJyZMWHSqWSSkpKFIu3P+rJ+1peXi796le/kq5cudJlv/b2dikpKUl2YmXUqFEunav/n6mA3JZKpcLHH3+MxYsX46effsKtW7fwxBNPYMOGDVi0aBHUajXy8/Oxd+9eccmZr68vPvroI9msTI5466238PXXX6OqqgoPHjzAiy++iJdffhnPP/88AgMDUVlZiQ8++ABlZWVinXfffRejRo1SZPyBZvfu3cjJyRHtxx57DMeOHZNdEtGVZcuWyZ65ZC9JkpCeno709HREREQgJiYGM2bMQEhICPz8/NDc3IxLly4hLS0NBQUFYj2VSoWDBw9azCBGHYxGI9577z0kJSVhwYIFWLx4MaZNm4bhw4fDy8sLOp0O+fn5OHz4sGz2s8DAQKSkpDBfXYT5JahhYWGyZ2Uqjblqv3Xr1uHQoUPd9unsckNrz6TcvXs3Lly4AJ1OB4PBgOjoaKxduxZPP/00NBoNysrKsGfPHjFJmYeHBz766CP4+vo6/gv9z8aNG5GWloYLFy4AAH7/+98jNzcXv/zlLxEcHIzq6mrs379fdp/Uli1bMGvWLMVicBYl39PU1FR8+umnou3j44O8vDwsX77cpliioqKwa9cum/p2JScnB8ePH0doaChWrFiBmTNnYuzYsfD390dLSwsqKyuRkZEhu0wWAHbu3IkZM2Y4PH5/oOT7ev/+fRw4cAAHDhzA7Nmz8dRTT2H69OkYMWIEfH19odfrUVJSgiNHjsjuN/f29sbnn3/u2rnaoxKTBqTjx49Lvr6+sm8rOvvx9fWVjh8/3uW27D1TIUmSVFlZKY0dO7bb8QFIf/jDHxT6rQem7du327Qfrf1Y+1bL1vdVr9fbPaa/v7+UkpLSeztlAPjiiy/s3q8TJ06UiouLu9wu87X/qK+vl9Rqtdh327Zt69F2mKu955VXXunx39aunD9/Xnb5sbUfT09Pac+ePTbFasvfdXO3b9+WIiMjbfpd1qxZIz148MCmOPo7Jd/TgwcPOnT8Xbx4cZex2vqeDhkyxK5xvby8pL///e8O7sn+Rcn3taSkxO5tBAcHS9nZ2TbF2p9zlRPckLBq1SoUFRVBq9V2egZCpVIhOjoahYWFWLVqleLjh4eHo6ysDGvXrrX6DczkyZORkZGBnTt3Kj4+KcfX1xevv/46pk6d2u3ZrCFDhmDTpk0oLy/HmjVr+ihC1xQREYHVq1fLZk+0Zty4cdi1axdKSkp6ZaIC5mvvSElJQVtbm2j39rMVmav9x/z581FWVoa4uDjZI2rMzZ07F2fOnMFvfvObXokhKCgI+fn52Lp1q9WZG8PCwvDJJ58gJSUFHh78GNlfvf7665g1axY8PT277Ofr64tXX30VJSUlSEhI6KPoXE9ISAji4+NtmlBq5MiRePPNN3Hp0iVotdpeiacvc1X1v2qWSKa2thbnz59HXV0dAGD06NFYsGABxo4d2yfjNzc34+TJk6itrUVLSwtCQkIQGRnJ2blckF6vR2lpKW7fvg2dTofGxkYMHjwYgYGBiIqKQlRUVLcHM7J0/fp1XL58GTqdDjqdDi0tLQgICMCIESMwZ84cRWZItBXzdWBgrvYfd+7cwZkzZ3Djxg20trZi1KhRmDNnDiZNmtRnMZhMJpw+fRrV1dXQ6/UYOXIkIiIi8OSTTyp2STv1PoPBgJKSEvz444/Q6XTQ6/Xw9vbG0KFDMWXKFMyaNQteXl7ODtOl1NfXo6ysDHfu3IFOp0NzczM0Gg2GDx+OmTNnYvLkyX2aI72dqywWiYiIiIiIyAKvHyAiIiIiIiILLBaJiIiIiIjIAotFIiIiIiIissBikYiIiIiIiCywWCQiIiIiIiILLBaJiIiIiIjIAotFIiIiIiIissBikYiIiIiIiCywWCQiIiIiIiILLBaJiIiIiIjIAotFIiIiIiIissBikYiIiIiIiCywWCQiIiIiIiILLBaJiIiIiIjIAotFIiIiIiIissBikYiIiIiIiCywWCQiIiIiIiILLBaJiIhc2FtvvQWVSgWVSoXw8HC0trbatX5WVpZYX6VS4fbt270UKRERuRoWi0RERC6qqqoK77zzjmgnJibCy8vLrm3MmTNH1j537pwisRERketjsUhEROSiNm7ciHv37gEAli9fjtjYWLu3MWzYMISGhor2+fPnFYuPiIhcG4tFIiIiF5SdnY3s7GzR/utf/9rjbY0fP14sV1RUOBQXERENHCwWiYiIXNC2bdvE8jPPPGNxOak9Ro8eLZavXr3qUFxERDRwsFgkIiJyMbm5ucjLyxPtrVu3OrS9oKAgsXzr1i2HtkVERAMHi0UiIiIX8+GHH4rl8ePHY8mSJQ5tT6VSieWH90ASERGpnR0AERER2a6hoQEZGRmiHR8fLyv2zLW0tOCnn34CAAQEBFidKVWSpE6XiYjIvfHMIhERkQvJzc3F/fv3RTsmJsZq31dffRVBQUEICgpCYWGh1X43b94UyyNHjlQmUCIicnksFomIiFzIqVOnxLKfnx/mzp1rtW9BQYFYnjZtmtV+169fF8vmj9EgIiL3xmKRiIjIhZSXl4vladOmQa3u/I6Suro61NTUAACCg4MREBDQab+2tjZcunRJtLsqPomIyL2wWCQiInIhVVVVYnnSpElW+5k/g3HMmDFW+5WUlMBoNIr2ggULHIyQiIgGChaLRERELqK9vR319fWi3dX9hZmZmWI5MDDQar8TJ06IZbVajejoaAejJCKigYLFIhERkYswmUyytre3d6f97t69i3/+85+iPWjQoE77SZKEI0eOiLZWq8WwYcMUiJSIiAYCFotEREQuwtPTU/aYjLt373bab+/evbh3757o29DQ0Gm/zMxM2WWt69atUzBaIiJydSqJD1QiIiJyGcHBweJS1KioKFy8eFH27zU1NZg2bRoMBgOWLl2KU6dOQaPRoKGhQfacxcbGRsyePRv//ve/AQCRkZG4ePGi1Wc2EhGR++GZRSIiIheycOFCsVxWVoYPP/xQtKurqxEbGwuDwYDw8HC89NJLAACDwYC//e1vol9NTQ1WrFghCkVPT08kJyezUCQiIhmeWSQiInIh2dnZePrpp2WvRUREIDAwEEVFReLy02+//RbBwcGIjIwU/aKiouDj44Pi4mK0tbWJ1xMTE7F58+a++hWIiMhFsFgkIiJyMQkJCUhMTOz039RqNfbt2yfuP4yLi0N6enqnfTUaDZKSkrB27dpei5WIiFwXi0UiIiIXlJ6ejuTkZJSWluLu3bsICgrC0qVLsXXrVsyYMUP0M5lM2LFjB44ePYrr169j8ODBGD9+PGJjY7FhwwaMGjXKeb8EERH1aywWiYiIiIiIyAInuCEiIiIiIiILLBaJiIiIiIjIAotFIiIiIiIissBikYiIiIiIiCywWCQiIiIiIiILLBaJiIiIiIjIAotFIiIiIiIissBikYiIiIiIiCywWCQiIiIiIiILLBaJiIiIiIjIAotFIiIiIiIissBikYiIiIiIiCywWCQiIiIiIiILLBaJiIiIiIjIAotFIiIiIiIissBikYiIiIiIiCz8F5LjxdBkF1P7AAAAAElFTkSuQmCC", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the components of the fit separately:\n", + "plt.rcParams[\"font.size\"] = 25\n", + "plt.rcParams[\"figure.figsize\"] = (10, 5)\n", + "\n", + "\n", + "def plot_fit(func, J, w, lam, gamma, w0):\n", + " \"\"\"Plot the individual components of a fit to the spectral density.\n", + " and how they contribute to the full fit one by one\"\"\"\n", + " total = 0\n", + " for i in range(len(lam)):\n", + " component = func(w, lam[i], gamma[i], w0[i])\n", + " total += component\n", + " plt.plot(w, J, \"r--\", linewidth=2, label=\"original\")\n", + " plt.plot(w, total, label=rf\"$k={i+1}$\")\n", + " plt.xlabel(r\"$\\omega$\")\n", + " plt.ylabel(r\"$J(\\omega)$\")\n", + " plt.legend()\n", + " plt.pause(1)\n", + " plt.show()\n", + "\n", + "\n", + "def plot_fit_components(func, J, w, lam, gamma, w0):\n", + " \"\"\"Plot the individual components of a fit to the spectral density.\n", + " and how they contribute to the full fit\"\"\"\n", + " plt.plot(w, J, \"r--\", linewidth=2, label=\"original\")\n", + " for i in range(len(lam)):\n", + " component = func(w, lam[i], gamma[i], w0[i])\n", + " plt.plot(w, component, label=rf\"$k={i+1}$\")\n", + " plt.xlabel(r\"$\\omega$\")\n", + " plt.ylabel(r\"$J(\\omega)$\")\n", + " plt.legend(bbox_to_anchor=(1.04, 1))\n", + " plt.show()\n", + "\n", + "\n", + "lam=fitinfo[\"params\"][:,0]\n", + "gamma=fitinfo[\"params\"][:,1] \n", + "w0 = fitinfo[\"params\"][:,2]\n", + "plot_fit(_sd_fit_model, J, w, lam, gamma, w0)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c05f2af0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_fit_components(_sd_fit_model, J, w, lam, gamma, w0)" + ] + }, + { + "cell_type": "markdown", + "id": "27fa30a5", + "metadata": {}, + "source": [ + "And let's also compare the power spectrum of the fit and the analytical spectral density:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "72deb34d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_power_spectrum(alpha, wc, beta, save=True):\n", + " \"\"\"Plot the power spectrum of a fit against the actual power spectrum.\"\"\"\n", + " w = np.linspace(-10, 10, 50000)\n", + " s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta)\n", + " s_fit = bath.power_spectrum(w)\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " axes.plot(w, s_orig, \"r\", linewidth=2, label=\"original\")\n", + " axes.plot(w, np.real(s_fit), \"b\", linewidth=2, label=\"fit\")\n", + "\n", + " axes.set_xlabel(r\"$\\omega$\", fontsize=28)\n", + " axes.set_ylabel(r\"$S(\\omega)$\", fontsize=28)\n", + " axes.legend()\n", + "\n", + " if save:\n", + " fig.savefig(\"powerspectrum.eps\")\n", + "\n", + "\n", + "plot_power_spectrum(alpha, wc, 1 / T, save=False)" + ] + }, + { + "cell_type": "markdown", + "id": "1c2e4446", + "metadata": {}, + "source": [ + "Now that we have a good fit to the spectral density, Let us obtain its dynamics, by passing our ``FitSpectral`` bath specifications into the ``HEOMSolver``" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "cb90d87a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 8.87s*] Elapsed 8.87s / Remaining 00:00:00:00\n" + ] + } + ], + "source": [ + "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + "HEOM_spectral_fit = HEOMSolver(\n", + " Hsys,\n", + " (bath,Q),\n", + " max_depth=4,\n", + " options=options,\n", + ")\n", + "result_spectral = HEOM_spectral_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "5bb8eb36", + "metadata": {}, + "source": [ + "Now if we want to see the systems's behaviour as we change the Number of terms in the fit, we may use this auxiliary function" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "5a8a930d", + "metadata": {}, + "outputs": [], + "source": [ + "def generate_spectrum_results(Q, N, Nk, max_depth):\n", + " \"\"\"Run the HEOM with the given bath parameters and\n", + " and return the results of the evolution.\n", + " \"\"\"\n", + " bath, _= sd_env.approx_by_sd_fit(w,Nmax=N,Nk=Nk,target_rmse=None)\n", + " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + "\n", + " # This problem is a little stiff, so we use the BDF method to solve\n", + " # the ODE ^^^\n", + " print(f\"Starting calculations for N={N}, Nk={Nk} and max_depth={max_depth} ... \")\n", + " HEOM_spectral_fit = HEOMSolver(\n", + " Hsys,\n", + " (bath,Q),\n", + " max_depth=max_depth,\n", + " options=options,\n", + " )\n", + " results_spectral_fit = HEOM_spectral_fit.run(rho0, tlist)\n", + " return results_spectral_fit" + ] + }, + { + "cell_type": "markdown", + "id": "9ea58304", + "metadata": {}, + "source": [ + "Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "0273c6cb", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_result_expectations(plots, axes=None):\n", + " \"\"\"Plot the expectation values of operators as functions of time.\n", + "\n", + " Each plot in plots consists of (solver_result,\n", + " measurement_operation, color, label).\n", + " \"\"\"\n", + " if axes is None:\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " fig_created = True\n", + " else:\n", + " fig = None\n", + " fig_created = False\n", + "\n", + " # add kw arguments to each plot if missing\n", + " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", + " for result, m_op, color, label, kw in plots:\n", + " exp = np.real(expect(result.states, m_op))\n", + " kw.setdefault(\"linewidth\", 2)\n", + " if color == \"rand\":\n", + " axes.plot(\n", + " result.times,\n", + " exp,\n", + " c=np.random.rand(\n", + " 3,\n", + " ),\n", + " label=label,\n", + " **kw,\n", + " )\n", + " else:\n", + " axes.plot(result.times, exp, color, label=label, **kw)\n", + "\n", + " if fig_created:\n", + " axes.legend(loc=0, fontsize=12)\n", + " axes.set_xlabel(\"t\", fontsize=28)\n", + "\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "96b86c48", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting calculations for N=1, Nk=1 and max_depth=5 ... \n", + " Total run time: 1.22s*] Elapsed 1.22s / Remaining 00:00:00:00\n", + "Starting calculations for N=2, Nk=1 and max_depth=5 ... \n", + " Total run time: 1.74s*] Elapsed 1.74s / Remaining 00:00:00:00\n", + "Starting calculations for N=3, Nk=1 and max_depth=5 ... \n", + " Total run time: 3.70s*] Elapsed 3.70s / Remaining 00:00:00:00\n", + "Starting calculations for N=4, Nk=1 and max_depth=5 ... \n", + " Total run time: 11.75s*] Elapsed 11.74s / Remaining 00:00:00:00\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# # Generate results for different number of lorentzians in fit:\n", + "\n", + "results_spectral_fit_pk = [\n", + " generate_spectrum_results(Q, n, Nk=1, max_depth=max_depth) for n in range(1, 5)\n", + "]\n", + "\n", + "plot_result_expectations(\n", + " [\n", + " (\n", + " result,\n", + " P11p,\n", + " \"rand\",\n", + " f\"P11 (spectral fit) $k_J$={pk + 1}\",\n", + " )\n", + " for pk, result in enumerate(results_spectral_fit_pk)\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "980af0cd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting calculations for N=4, Nk=2 and max_depth=5 ... \n", + " Total run time: 58.93s*] Elapsed 58.93s / Remaining 00:00:00:00\n", + "Starting calculations for N=4, Nk=3 and max_depth=5 ... \n", + " Total run time: 175.24s*] Elapsed 175.24s / Remaining 00:00:00:00\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# generate results for different number of Matsubara terms per Lorentzian\n", + "# for max number of Lorentzians:\n", + "\n", + "Nk_list = range(2, 4)\n", + "results_spectral_fit_nk = [\n", + " generate_spectrum_results(Q, 4, Nk=Nk, max_depth=max_depth) for Nk in Nk_list\n", + "]\n", + "\n", + "plot_result_expectations(\n", + " [\n", + " (\n", + " result,\n", + " P11p,\n", + " \"rand\",\n", + " f\"P11 (spectral fit) K={nk+1}\",\n", + " )\n", + " for nk, result in zip(Nk_list, results_spectral_fit_nk)\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "eb904688", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting calculations for N=4, Nk=1 and max_depth=2 ... \n", + " Total run time: 1.79s*] Elapsed 1.79s / Remaining 00:00:00:00\n", + "Starting calculations for N=4, Nk=1 and max_depth=3 ... \n", + " Total run time: 2.99s*] Elapsed 2.98s / Remaining 00:00:00:00\n", + "Starting calculations for N=4, Nk=1 and max_depth=4 ... \n", + " Total run time: 5.26s*] Elapsed 5.26s / Remaining 00:00:00:00\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generate results for different depths:\n", + "\n", + "Nc_list = range(2, max_depth)\n", + "results_spectral_fit_nc = [\n", + " generate_spectrum_results(Q, 4, Nk=1, max_depth=Nc) for Nc in Nc_list\n", + "]\n", + "\n", + "plot_result_expectations(\n", + " [\n", + " (\n", + " result,\n", + " P11p,\n", + " \"rand\",\n", + " f\"P11 (spectral fit) $N_C={nc}$\",\n", + " )\n", + " for nc, result in zip(Nc_list, results_spectral_fit_nc)\n", + " ]\n", + " );" + ] + }, + { + "cell_type": "markdown", + "id": "844af288", + "metadata": {}, + "source": [ + "#### We now combine the fitting and correlation function data into one large plot. Here we define a function to plot everything together" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "7fc617a1", + "metadata": {}, + "outputs": [], + "source": [ + "def gen_plots(fs, w, J, t, C, w2, S):\n", + " def plot_cr_fit_vs_actual(t, C, func, axes):\n", + " \"\"\"Plot the C_R(t) fit.\"\"\"\n", + " yR = func(t)\n", + "\n", + " axes.plot(\n", + " t,\n", + " np.real(C),\n", + " \"r\",\n", + " linewidth=3,\n", + " label=\"Original\",\n", + " )\n", + " axes.plot(\n", + " t,\n", + " np.real(yR),\n", + " \"g\",\n", + " dashes=[3, 3],\n", + " linewidth=2,\n", + " label=\"Reconstructed\",\n", + " )\n", + "\n", + " axes.set_ylabel(r\"$C_R(t)$\", fontsize=28)\n", + " axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=28)\n", + " axes.locator_params(axis=\"y\", nbins=4)\n", + " axes.locator_params(axis=\"x\", nbins=4)\n", + " axes.text(0.15, 0.85, \"(a)\", fontsize=28, transform=axes.transAxes)\n", + "\n", + " def plot_ci_fit_vs_actual(t, C, func, axes):\n", + " \"\"\"Plot the C_I(t) fit.\"\"\"\n", + " yI = func(t)\n", + "\n", + " axes.plot(\n", + " t,\n", + " np.imag(C),\n", + " \"r\",\n", + " linewidth=3,\n", + " )\n", + " axes.plot(\n", + " t,\n", + " np.real(yI),\n", + " \"g\",\n", + " dashes=[3, 3],\n", + " linewidth=2,\n", + " )\n", + "\n", + " axes.set_ylabel(r\"$C_I(t)$\", fontsize=28)\n", + " axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=28)\n", + " axes.locator_params(axis=\"y\", nbins=4)\n", + " axes.locator_params(axis=\"x\", nbins=4)\n", + " axes.text(0.80, 0.80, \"(b)\", fontsize=28, transform=axes.transAxes)\n", + "\n", + " def plot_jw_fit_vs_actual(w, J, axes):\n", + " \"\"\"Plot the J(w) fit.\"\"\"\n", + " J_fit = fs.spectral_density(w)\n", + "\n", + " axes.plot(\n", + " w,\n", + " J,\n", + " \"r\",\n", + " linewidth=3,\n", + " )\n", + " axes.plot(\n", + " w,\n", + " J_fit,\n", + " \"g\",\n", + " dashes=[3, 3],\n", + " linewidth=2,\n", + " )\n", + "\n", + " axes.set_ylabel(r\"$J(\\omega)$\", fontsize=28)\n", + " axes.set_xlabel(r\"$\\omega/\\omega_c$\", fontsize=28)\n", + " axes.locator_params(axis=\"y\", nbins=4)\n", + " axes.locator_params(axis=\"x\", nbins=4)\n", + " axes.text(0.15, 0.85, \"(c)\", fontsize=28, transform=axes.transAxes)\n", + "\n", + " def plot_sw_fit_vs_actual(axes):\n", + " \"\"\"Plot the S(w) fit.\"\"\"\n", + "\n", + " # avoid the pole in the fit around zero:\n", + " s_fit = fs.power_spectrum(w2)\n", + "\n", + " axes.plot(w2, S, \"r\", linewidth=3)\n", + " axes.plot(w2, s_fit, \"g\", dashes=[3, 3], linewidth=2)\n", + "\n", + " axes.set_ylabel(r\"$S(\\omega)$\", fontsize=28)\n", + " axes.set_xlabel(r\"$\\omega/\\omega_c$\", fontsize=28)\n", + " axes.locator_params(axis=\"y\", nbins=4)\n", + " axes.locator_params(axis=\"x\", nbins=4)\n", + " axes.text(0.15, 0.85, \"(d)\", fontsize=28, transform=axes.transAxes)\n", + "\n", + " def plot_matsubara_spectrum_fit_vs_actual(t, C):\n", + " \"\"\"Plot the Matsubara fit of the spectrum .\"\"\"\n", + " fig = plt.figure(figsize=(12, 10))\n", + " grid = plt.GridSpec(2, 2, wspace=0.4, hspace=0.3)\n", + "\n", + " plot_cr_fit_vs_actual(\n", + " t,\n", + " C,\n", + " lambda t: fs.correlation_function(t),\n", + " axes=fig.add_subplot(grid[0, 0]),\n", + " )\n", + " plot_ci_fit_vs_actual(\n", + " t,\n", + " C,\n", + " lambda t: np.imag(fs.correlation_function(t)),\n", + " axes=fig.add_subplot(grid[0, 1]),\n", + " )\n", + " plot_jw_fit_vs_actual(\n", + " w,\n", + " J,\n", + " axes=fig.add_subplot(grid[1, 0]),\n", + " )\n", + " plot_sw_fit_vs_actual(\n", + " axes=fig.add_subplot(grid[1, 1]),\n", + " )\n", + " fig.legend(loc=\"upper center\", ncol=2, fancybox=True, shadow=True)\n", + "\n", + " return plot_matsubara_spectrum_fit_vs_actual(t, C)" + ] + }, + { + "cell_type": "markdown", + "id": "674d5498", + "metadata": {}, + "source": [ + "#### And finally plot everything together" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "26209a1b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t = np.linspace(0, 15, 1000)\n", + "C = ohmic_correlation(t, alpha, wc, 1 / T)\n", + "w2 = np.concatenate((-np.linspace(10, 1e-2, 100), np.linspace(1e-2, 10, 100)))\n", + "S = ohmic_power_spectrum(w2, alpha, wc, 1 / T)\n", + "gen_plots(bath, w, J, t, C, w2, S)" + ] + }, + { + "cell_type": "markdown", + "id": "a72989f8", + "metadata": {}, + "source": [ + "## Obtaining an decaying exponential description via the Correlation function" + ] + }, + { + "cell_type": "markdown", + "id": "81acee08", + "metadata": {}, + "source": [ + "Having successfully fitted the spectral density and used the result to calculate the Matsubara expansion and terminator for the HEOM bosonic bath, we now proceed to the second case of fitting the correlation function itself instead.\n", + "\n", + "Here we fit the real and imaginary parts separately, using the following ansatz \n", + "\n", + "$$C_R^F(t) = \\sum_{i=1}^{k_R} c_R^ie^{-\\gamma_R^i t}\\cos(\\omega_R^i t)$$\n", + "\n", + "$$C_I^F(t) = \\sum_{i=1}^{k_I} c_I^ie^{-\\gamma_I^i t}\\sin(\\omega_I^i t)$$\n", + "\n", + "Analogously to the spectral density case, one may use the `approx_by_cf_fit` method, the main difference with respect to the spectral density fit, is that now we are perfoming two fits, one for the real part and another one for the imaginary part" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "217905ff", + "metadata": {}, + "outputs": [], + "source": [ + "bath_corr ,fitinfo= sd_env.approx_by_cf_fit(tlist=t,Ni_max=4,Nr_max=4,maxfev=1e8)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "a861655e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation function fit:\n", + "\n", + "Result of fitting the real part of |Result of fitting the imaginary part \n", + "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", + " | \n", + " Parameters| a | b | c | Parameters| a | b | c \n", + " 1 |-1.88e+00 |-4.65e+00 |2.64e+00 | 1 |-1.34e+01 |-1.08e+00 |2.73e-02 \n", + " 2 | 3.00e+00 |-2.88e+00 |3.05e-01 | 2 |-8.59e+00 |-3.78e-01 |1.03e-03 \n", + " 3 | 4.78e-02 |-1.63e-01 |2.98e-28 | 3 | 5.64e-01 |-4.30e+00 |3.95e+00 \n", + " 4 | 3.54e-01 |-6.27e-01 |1.71e-08 | 4 |-1.34e+01 |-2.31e+00 |2.90e-01 \n", + " | \n", + "A normalized RMSE of 3.12e-06 was obtained for the the real part of |A normalized RMSE of 4.89e-06 was obtained for the the imaginary part\n", + "the correlation function. |of the correlation function. \n", + "The current fit took 1.973140 seconds. |The current fit took 18.244712 seconds. \n", + "\n" + ] + } + ], + "source": [ + "print(fitinfo[\"summary\"])" + ] + }, + { + "cell_type": "markdown", + "id": "b8c32d8a", + "metadata": {}, + "source": [ + "The ansatz used is not good for functions where\n", + "\n", + "$$C_I^F(0) \\neq 0$$\n", + "\n", + "The keyword `full_ansatz` which defaults to False. allows for the usage of a \n", + "more general ansatz, the fit however tends to be significantly slower, never\n", + "the less it can reach a similar level of accuracy with a lower amount of exponents\n", + "\n", + "When full_ansatz is True. the ansatz used corresponds to \n", + "\n", + "\\begin{align}\n", + "\\operatorname{Re}[C(t)] = \\sum_{k=1}^{N_r} \\operatorname{Re}\\Bigl[\n", + " (a_k + \\mathrm i d_k) \\mathrm e^{(b_k + \\mathrm i c_k) t}\\Bigl]\n", + " ,\n", + "\\\\\n", + "\\operatorname{Im}[C(t)] = \\sum_{k=1}^{N_i} \\operatorname{Im}\\Bigl[\n", + " (a'_k + \\mathrm i d'_k) \\mathrm e^{(b'_k + \\mathrm i c'_k) t}\n", + " \\Bigr].\n", + "\\end{align}" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "57d768ee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n", + " Total run time: 1.65s*] Elapsed 1.64s / Remaining 00:00:00:00\n", + "3\n", + " Total run time: 3.62s*] Elapsed 3.62s / Remaining 00:00:00:00\n", + "4\n", + " Total run time: 76.18s*] Elapsed 76.18s / Remaining 00:00:00:00\n" + ] + } + ], + "source": [ + "def generate_corr_results(N, max_depth):\n", + " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + " bath_corr ,fitinfo= sd_env.approx_by_cf_fit(tlist=t,Ni_max=N,Nr_max=N,maxfev=1e8,target_rsme=None)\n", + " HEOM_corr_fit = HEOMSolver(\n", + " Hsys,\n", + " (bath_corr,Q),\n", + " max_depth=max_depth,\n", + " options=options,\n", + " )\n", + "\n", + " results_corr_fit = HEOM_corr_fit.run(rho0, tlist)\n", + "\n", + " return results_corr_fit\n", + "\n", + "\n", + "# # Generate results for different number of exponentials in fit:\n", + "results_corr_fit_pk = [\n", + " print(f\"{i + 1}\")\n", + " or generate_corr_results(\n", + " i,\n", + " max_depth=max_depth,\n", + " )\n", + " for i in range(1, 4)]" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "91d1be7c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_result_expectations(\n", + " [\n", + " (\n", + " result,\n", + " P11p,\n", + " \"rand\",\n", + " f\"P11 (correlation fit) k_R=k_I={pk + 1}\",\n", + " )\n", + " for pk, result in enumerate(results_corr_fit_pk)\n", + " ]\n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "4770c53b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", + "\n", + "plot_result_expectations(\n", + " [\n", + " (\n", + " results_corr_fit_pk[0],\n", + " P11p,\n", + " \"y\",\n", + " \"Correlation Function Fit $k_R=k_I=1$\",\n", + " ),\n", + " (\n", + " results_corr_fit_pk[2],\n", + " P11p,\n", + " \"k\",\n", + " \"Correlation Function Fit $k_R=k_I=3$\",\n", + " ),\n", + " (results_spectral_fit_pk[0], P11p, \"b\", \"Spectral Density Fit $k_J=1$\"),\n", + " (results_spectral_fit_pk[3], P11p, \"r-.\", \"Spectral Density Fit $k_J=4$\"),\n", + " ],\n", + " axes=axes,\n", + ")\n", + "\n", + "axes.set_yticks([0.6, 0.8, 1])\n", + "axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", + "axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", + "axes.legend(loc=0, fontsize=20);" + ] + }, + { + "cell_type": "markdown", + "id": "63716f70", + "metadata": {}, + "source": [ + "# Using the Ohmic Bath class\n", + "\n", + " As the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced quickly by using the OhmicBath class. This allows for rapid implementation of fitted ohmic baths via the correlation function or spectral density" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "4883e1cc", + "metadata": {}, + "outputs": [], + "source": [ + "obs = OhmicEnvironment(T, alpha, wc,s=1)\n", + "tlist = np.linspace(0, 30 * np.pi / Del, 600)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78642c05", + "metadata": {}, + "outputs": [], + "source": [ + "obs.approximate()" + ] + }, + { + "cell_type": "markdown", + "id": "005418f5", + "metadata": {}, + "source": [ + "Just like the other `BosonicEnvironment` we can obtain a decaying exponential \n", + "representation of the environment via the `approx_by_cf_fit` and \n", + "`approx_by_sd_fit` methods. " + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "e0924e70", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation function fit:\n", + "\n", + "Result of fitting the real part of |Result of fitting the imaginary part \n", + "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", + " | \n", + " Parameters| a | b | c | Parameters| a | b | c \n", + " 1 | 3.24e-01 |-5.34e-01 |3.32e-23 | 1 |-8.92e+00 |-3.49e-01 |7.57e-04 \n", + " 2 | 2.84e+00 |-2.76e+00 |6.88e-08 | 2 | 5.44e-01 |-4.30e+00 |4.00e+00 \n", + " 3 |-1.67e+00 |-4.72e+00 |2.77e+00 | 3 |-1.34e+01 |-1.04e+00 |2.50e-02 \n", + " 4 | 2.49e-02 |-1.09e-01 |1.08e-41 | 4 |-1.34e+01 |-2.29e+00 |2.90e-01 \n", + " | \n", + "A normalized RMSE of 1.18e-06 was obtained for the the real part of |A normalized RMSE of 6.20e-07 was obtained for the the imaginary part\n", + "the correlation function. |of the correlation function. \n", + "The current fit took 5.184511 seconds. |The current fit took 45.098052 seconds. \n", + "\n", + " Total run time: 341.74s*] Elapsed 341.74s / Remaining 00:00:00:00\n" + ] + } + ], + "source": [ + "tlist = np.linspace(0, 30 * np.pi / Del, 5000)\n", + "\n", + "Obath, fitinfo = obs.approx_by_cf_fit(tlist=tlist,Nr_max=4,Ni_max=4,maxfev=1e9,target_rsme=None)\n", + "print(fitinfo[\"summary\"])\n", + "HEOM_ohmic_corr_fit = HEOMSolver(\n", + " Hsys,\n", + " (Obath,Q),\n", + " max_depth=5,\n", + " options=options,\n", + ")\n", + "results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "ddbaebf2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result of fitting the spectral density with 4 terms: \n", + " \n", + " Parameters| lam | gamma | w0 \n", + " 1 | 6.79e-01 | 8.67e-01 |1.22e-01\n", + " 2 | 1.67e+00 | 9.17e-01 |1.13e+00\n", + " 3 | 1.56e+00 | 9.46e-01 |2.11e+00\n", + " 4 | 1.00e+00 | 1.03e+00 |3.32e+00\n", + " \n", + "A normalized RMSE of 4.39e-05 was obtained for the the spectral density.\n", + "The current fit took 45.485762 seconds.\n", + " Total run time: 10.91s*] Elapsed 10.91s / Remaining 00:00:00:00\n" + ] + } + ], + "source": [ + "Obath2, fitinfo = obs.approx_by_sd_fit(wlist=w,Nmax=4,Nk=1)\n", + "print(fitinfo[\"summary\"])\n", + "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + "HEOM_ohmic_sd_fit = HEOMSolver(\n", + " Hsys,\n", + " (Obath2,Q),\n", + " max_depth=5,\n", + " options=options,\n", + ")\n", + "results_ohmic_sd_fit2 = HEOM_ohmic_sd_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "50b833b1", + "metadata": {}, + "source": [ + "# Methods based on the Prony Polinomial \n", + "\n", + "The Prony polynomial forms the mathematical foundation for many spectral analysis techniques that estimate frequencies, damping factors, and amplitudes of signals. These methods work by interpreting a given signal as a sum of complex exponentials and deriving a polynomial whose roots correspond to the frequencies or poles of the system.\n", + "\n", + "The methods consider a signal \n", + "\n", + "$$f(t)=\\sum_{k=0}^{N-1} c_{k} e^{-\\nu_{k} t} =\\sum_{k=0}^{N-1} c_{k} z_{k}^{t} $$\n", + "\n", + "The $z_{k}$ can be seen as the solution og the Prony Polynomial\n", + "\n", + "$$P(z)=\\prod_{k=0}^{N-1}(z-z_{k})$$\n", + "\n", + "By obtaining the roots of this polynomial one can obtain the damping rate and the frequency of each mode, the amplitude can lated be obtained by solving the least-squares Vandermonde system given by\n", + "\n", + "$$ V_{N,M}(z)c = f $$\n", + "\n", + "Where $M$ is the length, of the signal, and $f=f(t_{sample})$ is the signal evaluated in the sampling points,is a vector $c = (c_{1}, \\dots, c_{N})$, and $V_{N,M}(z)$ is the Vandermonde matrix given by\n", + "\n", + "\n", + "$$V_{M,N}(z)=\\begin{pmatrix} \n", + "1 &1 &\\dots &1 \\\\\n", + "z_{1} & z_{2} &\\dots & z_{N} \\\\\n", + "z_{1}^{2} & z_{2}^{2} &\\dots & z_{N}^{2} \\\\\n", + "\\vdots & \\vdots & \\ddots & \\vdots \\\\\n", + "z_{1}^{M} & z_{2}^{M} &\\dots & z_{N}^{M} \\\\\n", + "\\end{pmatrix}$$\n", + "\n", + "The main difference between the methods is the way one obtains the roots of the polynomial, typically whether this system is solved or a low rank approximation is found for the polynomial, [this article](https://academic.oup.com/imajna/article-abstract/43/2/789/6525860?redirectedFrom=fulltext) is a good reference, the QuTiP implementations are based on it, and the matlab implementations made available by the authors\n" + ] + }, + { + "cell_type": "markdown", + "id": "f85ab699", + "metadata": {}, + "source": [ + "## Using the Original Prony Method on the Correlation Function\n", + "\n", + "The method is available via `approx_by_prony`. Compared to the other approaches showed so far. The Prony based methods, shine on their simplicity no information needs to be known about the function, and one just needs to provide the sampling points, and the Number of Exponents one desires" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "b75d4072", + "metadata": {}, + "outputs": [], + "source": [ + "tlist2=np.linspace(0,2_000,5000)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "4e24e35b", + "metadata": {}, + "outputs": [], + "source": [ + "pbath,(amp,ph)=obs.approx_by_prony(tlist2,Nr=5,Ni=5,combine=True)\n", + "pbath.T=T\n", + "# mask=abs(amp)>1\n", + "# amp=amp[mask]\n", + "# ph=ph[mask]\n", + "# print(\"done\")\n", + "# HEOM_ohmic_prony_fit = HEOMSolver(\n", + "# Hsys,\n", + "# (pbath,Q),\n", + "# max_depth=5,\n", + "# options=options,\n", + "# )\n", + "# results_ohmic_prony_fit = HEOM_ohmic_prony_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "a2faa5fb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "diff=(pbath.correlation_function(tlist2))-obs.correlation_function(tlist2)\n", + "tlist3=np.linspace(0,tlist2[np.argmax(diff)],1000)\n", + "# plt.plot(tlist3,pbath.correlation_function(tlist3),\"k--\")\n", + "# plt.plot(tlist3,Obath.correlation_function(tlist3),\"b\")\n", + "# plt.plot(tlist3,obs.correlation_function(tlist3),\"r\")\n", + "\n", + "plt.plot(abs(diff),label=\"Prony\")\n", + "plt.plot(abs(Obath.correlation_function(tlist2)-obs.correlation_function(tlist2)),label=\"CORR\")\n", + "plt.legend()\n", + "#plt.yscale(\"log\")" + ] + }, + { + "cell_type": "markdown", + "id": "af659e73", + "metadata": {}, + "source": [ + "Somehow the problems seems to be the way I construct the bath" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "b64a4d76", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 73.49s*] Elapsed 73.49s / Remaining 00:00:00:00\n" + ] + } + ], + "source": [ + "HEOM_ohmic_prony_fit = HEOMSolver(\n", + " Hsys,\n", + " (pbath,Q),\n", + " max_depth=5,\n", + " options=options,\n", + ")\n", + "results_ohmic_prony_fit = HEOM_ohmic_prony_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "10e50bf0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gen_plots(pbath, w, J, t, C, w2, S)" + ] + }, + { + "cell_type": "markdown", + "id": "1d4ffc81", + "metadata": {}, + "source": [ + "## Using the matrix Pencil Method on the Correlation Function\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "7f14b9cb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 6.55s*] Elapsed 6.55s / Remaining 00:00:00:00\n" + ] + } + ], + "source": [ + "mpbath,_=obs.approx_by_mp(tlist2,Nr=4,Ni=4)\n", + "mpbath.T=T\n", + "HEOM_ohmic_mp_fit = HEOMSolver(\n", + " Hsys,\n", + " (mpbath,Q),\n", + " max_depth=5,\n", + " options=options,\n", + ")\n", + "results_ohmic_mp_fit = HEOM_ohmic_mp_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "3b334563", + "metadata": {}, + "source": [ + "The decomposition is ok, the heom solver is the one failing try with other smaller couplings, and hierarchies untill you can figure it out, the accelerating part works without trouble" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "3ed89ed7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gen_plots(mpbath, w, J, t, C, w2, S)" + ] + }, + { + "cell_type": "markdown", + "id": "c04f6f61", + "metadata": {}, + "source": [ + "## Using the ESPRIT Method on the Correlation Function\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "7708f4f1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 12.05s*] Elapsed 12.05s / Remaining 00:00:00:00\n" + ] + } + ], + "source": [ + "esbath,_=obs.approx_by_esprit(tlist2,Nr=5,Ni=4)\n", + "esbath.T=T\n", + "HEOM_ohmic_es_fit = HEOMSolver(\n", + " Hsys,\n", + " (esbath,Q),\n", + " max_depth=5,\n", + " options=options,\n", + ")\n", + "results_ohmic_es_fit = HEOM_ohmic_es_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "ad89de4e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Z3Xfffahbt65Unj17ttW4Op0Oc+fOlcr9+/dHvXr1LNbt2LEjWrVqJZXnzJkDnU5nNfZbb70lXbdu3RodOnSwWM80yf71118dmjK8cOFC6TosLAzt27e3+4w30Ovd3QMiIiIisufOO+/E9u3b0bZtW+nehg0b8Pfff7uxV+QpqpScDh48GO3atZPK48ePx/HjxyvVy8nJwciRI6VE7I477sAjjzxiMWZ6erps0fSMGTOsth8TE4Px48dL5UWLFuH777+vVE+j0WD06NHSJkSBgYE2j08JDg7G1KlTpfKaNWtkyZyRKIp46aWXpM2EBEHAzJkzrcYVBAGzZs2SygcOHMDLL79scYfZBQsWYO3atVJ51qxZso2aTD311FOyPg0ePBinT5+2WFcURbz22mtYv369dO/xxx+Xrd/1Zlpmp0REREReITAwEG+//bbsnulgFt26qpScCoKATz/9VFqvmJOTgy5duuDVV1/Fhg0bsHnzZsyePRvt27dHamoqAMOH8JNPPrGaaDlrxowZuP322wEYRjGHDh2KUaNG4fvvv8f27dvx8ccfo2PHjli9erX0zLvvvosGDRrYjPv888/jrrvuksqTJk3CgAEDsGrVKuzYsQPLly9Hjx49ZEnr5MmTceedd9qMO3DgQAwZMkQqL1iwAD169MAXX3yBHTt2YNWqVRgwYAD+/e9/S3WGDBmChx9+2GrMxMREPP7441L59OnTaNu2LZ555hl89913+PXXX7FlyxbMnz8fd9xxB+bMmSPVbdiwId58802bffYmphsicfkpERERkWdLTk6WzWY8cuSIdD1jxgxpwMp01uXmzZvx+OOPo3nz5ggPD6/0uqmsrCzMnj0b3bp1Q3R0NAICAlCvXj106NABU6dOlXIUe0x3ITYdPPvll18wePBgNG3aFGq1GpGRkbj77ruxcOFClJWVWY03ffp0KZ7p9GZH9OnTR3r2xRdfdOpZryFWw/fffy8GBgaKMJzeYfUrMDBQ/P77723GOnv2rOyZ6dOn220/LS1NjI2Ntds+APGVV15x+Oe6cOGC2KZNG4fiDhs2TNTpdA7FvX79unjPPfc4FDcpKUm8fv263ZhlZWXioEGDHIpp/GrUqJF4+PBhh98PU/n5+SIAMT8/v0rP15ScayVi3JT1YtyU9WK/hbsqva7T6d3QKyIiIrKmpKREPHbsmFhSUuLurpALPPHEE9K/NXv27OnQM9HR0dIzffv2le5Pnz5duv/EE0+I+fn54pAhQyz+u/aJJ56oFHfevHliUFCQzX8PK5VKceLEiaJGo3H455o+fbrNvhi/mjdvLmZkZFiMd/78eVGhUEh1jxw54tB7lZ6eLnvu77//dug5W1zxd9DVuUGVRk6NBg0ahEOHDqF3794WR0QFQUBycjIOHjyIQYMGVacpi+Lj43H48GE8/fTTVnedbdGiBX788UfZ+lB76tati/379+Pll19GeHi4xTpxcXFYunQpvv76aygUjr2NgYGB2Lp1K9555x2r61Pr1auHOXPmICUlxaGddFUqFb7//nusXLnS7uhtnTp1MGXKFBw+fBht2rRxqM/eQjQ53VRnMsVXFEU8/tl+9Hh3O3LzS93RNSIiIiKyoLy8XLpWqVQW64iiiOHDh+O7774DAERERCAxMRHdunVDVFRUpfoTJ07EK6+8Ijuh4rbbbkNSUhJat24t5SxarRYLFy7EoEGDHDpJAzDM1hw0aJDUl+joaNx999246667ZBu+Hj9+HAMGDLAYNzY2Fv369ZPKS5cudajtzz77DPqKf+N26tRJtmbXl1R7wWGLFi2wZcsWZGRkYPfu3dLZmw0bNkS3bt0cPn+zcePGFtdg2lOrVi0sXboUCxYswLZt25CRkYHi4mJER0ejTZs2Vd7wR61WY968eZg5c6a0u9jVq1cRFRWF5s2bIzExsUpTlBUKBaZMmYLJkydj165dOH36NC5duoTIyEg0a9YMPXr0qNI60OHDh2P48OE4f/48Dhw4gKysLBQWFkKtVqNOnTpo27Yt2rZt6zNrTM2ZTuU1vT5zqRi7ThjO2N2SmodRXeNARERERO516dIlXLlyRSpbG7hZu3YtCgsLUb9+fbz33nsYNGgQ/Pz8AAB6vR7nzp2T6q5atQqLFi2Syp06dcKnn34q2yvn3Llz+Ne//iXtw/LTTz9h9uzZNve7Mfroo49w+fJltGzZEosXL0ZSUpL0WklJCaZOnSq1/9dff+GLL77A008/XSnOuHHjsGHDBgDAihUrMG/ePKvJufHnXL58uVQeM2aM3b56K5dlKrGxsXjsscdcFc5poaGhVo9cqQ61Wi377YarKJVK9OrVC7169XJp3EaNGqFRo0YujekNTH+xoTfJTo/nFErXx7ILbmqfiIiIiMiy//3vf7J/v1lbf1lYWIiwsDDs3LkT8fHxstcUCgWaNGkCACgrK5OtwzTuCmx+hGVcXBx+/PFHDBw4EOvWrQNgOCnjqaeesvtv6MuXL6NFixbYvXs3atWqJXstMDAQCxcuxJkzZ6STTKwlpw888AAaNGiA7OxsXL58GT/88AOGDh1qtd0tW7ZIG7wGBwdj2LBhNvvpzao1rZfIU5gOuptujnSp6MaC9Jz8kpvZJSIiInIxURRxvVzLLytfVZmF6A4nTpyQbcwZEBCABx54wGr96dOnV0pMza1evRoXLlwAYFhauGzZskqJqZFCocAnn3yC0NBQAIYpvkuWLHGo70uWLKmUmJqaOHGidL1//36LU3v9/Pxkp27Ym9pr+vrQoUOlfvsi35zjSbccUTat90bhcvGNtQxcc0pEROTdSjQ6tJy2yd3d8FjHZvZFkMoz/3lfVlaG9PR0rF27FnPnzsW1a9ek11544QVER0dbfE6pVFrdkdfUjz/+KF336NEDd9xxh836UVFRGDZsGD755BPp+bfeesvmM82bN8fdd99ts05iYiIUCgX0ej3Kyspw9uxZ6YQRU2PGjMHbb78NvV6PlJQUnDt3DnFxlZefXbp0SRrhNT7nyzhySj7BdEMk0yNPrxTfGDnNK2BySkRERFTTdu7cKR15YvxSq9Vo3rw5pk6dKktM+/fvLzvu0FzLli0RERFht819+/bJYjrCdLQ2NTUVhYWFNmobEk97AgMDUadOHals+rOaiouLQ58+fQAY1pR+/vnnFut99dVX0sZRLVu2lB156Ys881crRE4y3QRJZ1K4YjJyevW6BmVaHQKUfjeza0REROQigf5+ODazr7u74bEC/b3n3ziRkZF4+eWX8dJLL9k8+aJp06Z2Y2m1WmlNJgCHT6Uw3fFWr9fj7NmzNnfBrV+/vkNxg4KCpGvTXYPNjRs3Dr/88gsA4PPPP8e0adMqvRfLli2Tri2tX/U1TE7JJ8g2RBItJ6cAkH9dg3ph3vMfbiIiIrpBEASPnbZKN9SuXRudO3eW3QsICEB4eDgaN26Mrl27Ijk5GQEBAXZjObK+0nx0MjIy0qF+mte7evWqzfq2dtS1xtY64AEDBiA6Oho5OTk4f/48tmzZgr59b/zy5ffff8fRo0elth9//HGn2/c2/NtNPsH0r71pcnq9XCerl1+iQb0w9U3qFREREdGtp23bttKIYHXZGlU1Kisrk5UdTSLNk2PzODVNqVRi9OjRePvttwEYNj4yTU5NN0J66KGHHE66vRnXnJJPMP2tlOm03uIy+Q5p10o0N61PRERERFTzwsPDZWV7a0eNCgrkxwza2oW3powdOxaCIAAA1q1bh0uXLgEAiouL8e2330r1fH0jJCMmp+QT5Lv13rg2jpwqDH/nkX+dySkRERGRLwkJCUFgYKBUPnv2rEPPnT59WlauW7euS/vliMaNG+Pee+8FAJSXl+Orr74CAHz77bcoKioCYNg8qXfv3je9b+7A5JR8gmlCqtdXntYbHW74D1Y+R06JiIiIfE779u2la9Ode235/fffpevatWujcePGru6WQ8aNGyddGzdAMp3S+9RTTzk0vdkX3Bo/Jfk806NkdLI1p4ZpvQ1qGdaZclovERERke8xPX/0+++/l45fsWXlypXSdffu3aXptTfbgw8+KO0EfPToUXz++efYu3cvAMOa29GjR7ulX+7A5JR8gnxar6FQrtVDozNcc+SUiIiIyHeZJnB5eXlYuHChzfrff/+9bITVnce0+Pv748knn5TKzz33nHTdp08fxMbGuqFX7sHklHyC6Q69er3he4nJTr3RFSOn+dft/xaNiIiIiLxLQkICBg8eLJVff/11rFmzxmLd33//HU899ZRUbteuHR544IEa76MtphsjlZaWSvdvlY2QjJickk8wHTk1TustrpjSq/JTICLIsKV4odnuvURERETkGxYvXoyoqCgAgFarxSOPPIJBgwbhm2++wa5du7BmzRqMGTMGd999t7RTr1qtxpdffgk/Pz93dh1NmzZFcnKy7F69evXw4IMPuqlH7sFzTsnnGEdRjetNA1V+CA4wfNTNj5YhIiIiIt9Qr1497NixA/feey8yMzMBAGvXrsXatWst1g8NDcW6devQtm3bm9lNq8aNG4etW7dK5ccffxz+/v5u7NHNx5FT8gmm03pF0XDuaanGML9X7a9ASEVyWsTklIiIiMhnNW/eHH///TcmTJiA4OBgi3X8/f0xbNgw/PPPP0hKSrq5HbTBPEm+1ab0Ahw5JR9hOq0XAHR6EeU6Q3KqUiqkkdOiMp35o0RERERUTcuXL8fy5ctdEmvGjBmYMWNGlZ+PiIjAokWLMHfuXOzatQtnzpzBlStXEBYWhkaNGiEpKQlhYWEOxarKz5Wenu58p1F59+CEhIQqxfFmTE7JJ5jlptCLQFnFyKnKT4HgAMM6Ak7rJSIiIro1qNVq9OnTx93dcIhWq8Vnn30mlceOHevG3rgPp/WST9CbDZ3qxRsjpwFKP2laL5NTIiIiIvI0X3/9NbKysgAYRn6HDh3q5h65B5NT8gkWp/Vqb0zr5ZpTIiIiIvJEx48fxyuvvCKVJ0yYALVa7cYeuQ+n9ZJPEC2NnFpITovLtBBFUTpHioiIiIjoZnv44YcBABcuXMCBAweg1RoGUKKjozFx4kT3dczNmJyST6i05lQPlGkNmx8FmGyIpBeBEo0OQSp+9ImIiIjIPX788cdK91QqFb766iuEh4e7oUeegdN6ySdUmtZrMnIaoFQgSOUH42App/YSERERkScQBAH16tXD4MGDsW/fPiQnJ7u7S27F4SPyCbY2RFIpFRAEAcEqJYrKtCgu0wGh7uglEREREVHlJWlkwJFT8gnmf7/1phsi+Rk+5jxOhoiIiIjIczE5JZ8gmq061Ykiykw2RAIgrTvltF4iIiIiIs/D5JR8QqWRUxFSchqgNIyYhhqT01Imp0REREREnobJKfkEm9N6zUZOi8uZnBIREREReRomp+QTzKf1mp9zCnBaLxERERGRJ2NySj5Bb36UjF6Uzjk1bogUYhw5ZXJKRERERORxeJQMeb9LpxBQmC+7ZTpyGuBvSE4DVYa1p9fLdTe3f0REREREZBeTU/JuZYXABx3QFYCAFRArJgPoRdw457Ri5DTI35CcljA5JSIiIiLyOJzWS96tME+69INeutaZbIgUULHmNIgjp0REREREHovJKXk3xY2PsMJkUySdhd16A1WGiQJMTomIiIiIPA+TU/JygsnVjeRUNDnnVGU2clqi4YZIRERERESehskpeTfBdOTUZFqvKEJTsebU348bIhEREREReTomp+TdFH7SpfmaU23F+TJKBdecEhERERF5Oian5N0Ey2tORdE0OTVM/ZWm9TI5JSIiIiLyOExOybtZm9arF6GtmNar9DMkp4H+xg2RuOaUiIiIiMjTMDkl72Zl5FQnitDqDGXjmlOOnBIREREReS4mp+TlbuzWa7rmVBQBjb5i5NRsWu91DZNTIiIiIiJPw+SUvNyN0VLB7JxTnXHNacW03qAAnnNKREREROSplO7uAJGr+JkdJWOc1ivt1utvGDkt1+qh1emh9OPvZoiIiIhqypEjR/DLL79g69atOHfuHC5evIiCggKEh4cjMjIS7dq1Q9euXTF48GDExsY6HT8tLQ3r1q3Dpk2bcO7cOeTl5UGn0yEqKgoNGzZEcnIyBgwYgA4dOjgVt3Hjxjh37pzV1wMDAxEeHo7bbrsNnTt3xvDhw51qIykpCTt37rT6ekBAAMLDw9G4cWN07NgRQ4YMQVJSkjM/gtdicko+QyGb1nvjnFNpQyTVjWNnrmt0CGNySkRERORyBw8exGuvvYYtW7ZYfP3y5cu4fPky0tLS8L///Q8vvfQS7r33Xrz11lsOJXnnz5/Hq6++im+++cbi62fPnsXZs2fx22+/4T//+Q/uvfdezJ8/H61bt67Wz2VUUlKCkpIS5Obm4rfffsP8+fNx//33Y+nSpahfv36145eVleHChQu4cOEC9u/fjw8//BCJiYn44osvcPvtt7vgJ/BcTE7Ju4km03oFUZrlq9PjxrTeipHTAKUCCgHQi4ZNkcLU/je9u0RERES+7J133sFrr70G0eTfaAAQGRmJmJgY1K5dG1evXkVOTg7y8vIAAHq9Hps2bcLmzZvx9ddf47HHHrMaf8uWLRg0aBCKiopk9xs2bIiYmBj4+/sjKysLZ8+elT3Tvn17fPbZZxg1apRTP0+zZs1w2223ye4VFxfj1KlTyM3Nle79/PPPuOeee7B7925EREQ4HL9BgwZo06aN7F5JSQnS09Nx/vx56d7evXtx9913Y+/evWjSpIlTP4M3YXJKPsPP7CgZ85FTQRAQpFKiqEzLdadERERELvbcc8/ho48+kspqtRr/+te/MHLkSLRr165S/dTUVPz444947733kJOTA1EUZQmfuR9++AGPPvooysvLARj+bTd27Fj861//qpTgnTt3DkuXLsW7776LsrIyaLVaPPHEEygsLMRzzz3n8M80cuRIzJgxw+Jru3fvxrPPPosjR44AAI4fP47XX39d9h7Yc++992L58uUWXzty5AheeOEFaQpwXl4ennvuOWzcuNHh+N6G8xrJZ/iZTevVVoyc+itufMyNU3t51ikRERGR6yxZskSWlLVq1Qqpqal49913LSamANCiRQu8+uqrOHXqFGbOnAml0vq42dmzZ/HEE09IiWlQUBA2b96MJUuWVEpMASAuLg6zZs3CoUOHpKm2oijixRdfxL59+6rzo0q6deuGHTt2yNbLLlu2DAUFBS6J36ZNG2zatEk21fmXX35BamqqS+J7Iian5OVEi3dlGyL53ThuhmedEhEREbnWqVOnMHHiRKmckJCAXbt2oXHjxg49HxQUhDfffBNbtmxBvXr1LNZ54oknpKRPEASsXbsWvXv3thu7VatW2Lp1K4KDgwEAWq0WI0aMkJLc6oqIiMCkSZOkskajwa+//uqS2IBhc6TXX39ddm/btm0ui+9pmJySzzA/SkZrds4pAAT6G0dOmZwSERERucK8efNQWloKAFAoFFi+fLlT6y6NkpKSMHz48Er3d+7cKUv4xo0bhz59+jgct1WrVpg2bZpUPn36NFatWuV0/6zp3r27rGy63tUb4nsSJqfkk3R6ERWzemVHxgSpmJwSERERucrFixfx5ZdfSuUHHngAXbt2dWkbCxculK6VSiX+85//OB1j4sSJiIyMtBizumrXri0r5+fnuyz2zYjvSZickncTLU/rLdfeWH8qn9ZrWMtQouGaUyIiIqLq2rJlC8rKyqTymDFjXBq/vLwcmzZtksr33XcfoqKinI6jUqkwYsQIqfznn38iMzPTJX28fPmyrBwaGuqSuDcrvidhcko+qcwkObW8IRJHTomIiIiqa9euXdK1QqFAUlKSS+MfOnQIJSUlUvnee++tcizzNaq//fZblWPZitOqVSuXxL1Z8T0Jk1PyGaZrTk1HTv0U3BCJiIiIqCYcOnRIuk5ISHD5qJ5pfACynWudZf6seeyquHLlimyKcN26ddGjR49qxzUqKyvD22+/LZVVKhUGDBjgsviehueckpezMq1XZzJyamFaL0dOiYiIvJAoAprr7u6F5/IPAgTBfj0XunDhgnRteqSKq1y8eFFWjouLq3Ks6Oho+Pv7Q6PRWIztrD179uDZZ5+VTQ+ePXs2/P39qxXX6J9//sELL7yAP/74Q7o3efJkqzsa+wImp+STyjSG5NNPIUAQKo+cFvOcUyIiIu+juQ683cDdvfBcr2UDquCb2uSVK1ek6/Dw8BqNDwC1atWqVrzw8HBcunTJYmxLVqxYgd9//1127/r16zh58iRyc3Nl96dOnYpx48Y51Z8tW7agX79+snulpaVIT0/HuXPnZPdHjRqFWbNmORXf2zA5JZ9h+ntC45pT02NkAE7rJSIiInIl082QVCpVjcZ3RRumzxuPv7Hl9OnTOH36tM06vXr1wsyZM9GtWzen+5OdnY3s7GybdTp06IAZM2bggQcecDq+t2FySt7Nym691pJTbohERETkxfyDDKODZJl/0E1vslatWtL02IKCghqJb6qgoKBKZ6iaPm9kfkRLVf3555/IyspySSxLUlNTcebMmRqL70m4IRL5DNmGSBVrTk3POAWAIH+OnBIREXktQTBMW+WX5a+bvN4UgCxRvHr1qsvjmyeQ5seqOKO8vBxFRUVS2ZEkd/r06RBFUfoqKSlBWloaPvroIzRp0gSA4ecePnw4fvzxR6f79MQTT8jil5eX4/Tp01ixYgXatm0LwDCN+MUXX8QHH3zgdHxv45LkdM+ePRg/fjxatmyJ8PBwhIWFoWXLlhg3bhx2797tiiZsOnPmDKZNm4YOHTqgbt26CAwMRLNmzTBw4ECsXr0aWm3V1hfm5uZi7ty5SExMRHR0NNRqNRo3box+/fph+fLlsm2tnXHt2jUsXrwYSUlJiImJQUBAAGJiYpCUlITFixfj2rVrVYprdOzYMcyYMQOJiYmIjY1FQEAAoqKicMcdd2DIkCH48MMPcfz48Wq14enKNIbk1HQzJMB0QySuOSUiIiKqrqZNm0rXR48erdH4AHDkyJEqxzJ/1jy2I9RqNeLj4/HMM8/gr7/+knYA1ul0eOqpp6o9gurv74+mTZtixIgROHDgAO677z7ptcmTJ+Pvv/+uVnxPV61pvcXFxZgwYQI+++yzSq+lpqYiNTUVn376KUaPHo33338fwcGuX6C9aNEiTJkypdJ89DNnzuDMmTP44Ycf0LVrV6xcudKpD+CqVavwzDPPID8/X3b/3LlzOHfuHDZt2oR33nkH33zzDdq3b+9w3JSUFDz++OOV5pZnZWUhKysLO3fuxNtvv40vvvii0llM9ly5cgWvvPIKPvvsM4hm010vXLiACxcu4O+//8bq1asRHBws+82R97rxc1ocOVXIf//Cab1ERERErnP33Xdj48aNAAwjiCdPnsTtt9/usvjm6zj37duHQYMGVSnWgQMHbMZ2VlhYGL777ju0bt0a169fx5UrV/Dyyy/j66+/rlZcI5VKha+//hotWrRATk4OysvL8dxzz92UwT93qfLIqU6nw6BBg2SJaWBgIDp27IiuXbsiLCxMuv/5559j0KBB0OlcmxDMmjULEydOlBJThUKB1q1bo0ePHoiOjpbq/f777+jZsydycnIcivvVV19h2LBhssQ0Pj4ePXv2lG1fnZaWhqSkJId/S7Rt2zb0799flpg2btwYPXv2RHx8vHQvOzsb/fv3x/bt2x2KCwAZGRno2LEjli1bJiWmfn5+aNmyJZKSknDXXXehYcOGDsfzduXaG7v1mpI2RNIwOSUiIiKqrp49e8rK3377rUvjx8TESNNnjfHNB2Ec9c0330jXQUFB6NixY7X716RJE0yZMkUqr1q1Cn/++We14xqFh4fjnXfekcp79uyp0vRhb1Hl5PTNN9/E5s2bpfLYsWORmZmJAwcOYO/evcjOzsabb74pvb5582ZMmzater01sWnTJkyfPl0qJyYmIjU1FUeOHMHOnTuRmZmJVatWISQkBACQmZmJIUOG2I175MgRjB07VirHx8fj4MGDSEtLw44dO5Ceno7NmzcjKioKgGFR9YMPPmh3t6+8vDwMHjxYOlepfv362LJlC86ePYsdO3YgLS0NBw4ckJJUrVaLwYMHIy8vz26fL168iHvuuQdnz54FAISGhuK///0v8vLycPToUWzfvh27d+9GZmYmMjMz8fHHH+POO++0G9fbWNqt13xaL0dOiYiIiFwnMTERrVq1kspLly6tNKOxup5++mnp+ty5c1i/fr3TMY4cOYJff/1VKo8aNcpluwv/+9//Rp06dQAAoihixowZLolrNHLkSLRo0UIqG9fB+qIqJafZ2dlYsGCBVB41ahQ++eQT2aLi4OBgzJw5E2+88YZ0b/78+Xa3SnaEKIqYMmWK9IeSkJCArVu3ykYfFQoFHn30Uaxdu1a6t3v3blnZktdff136CxUZGYldu3ZJc8mN7r33XqSkpCAgIACAYQrxRx99ZDPuW2+9JS0SDwgIQEpKSqVpux07dsSuXbsQGRkJwDBN9+2337YZFwAmTJggbXEdFRWF/fv3Y/LkydJfElMNGzbE+PHjsWvXLrtxvYKVv5jlWisbIlWsOeWGSERERETVJwgCXnrpJal87tw52QCSM/R6vcVdaZ955hkEBgZK5RdffNGpvV9EUcRzzz0n5Q6CIODFF1+sUh8tCQkJkcVbt24d/vrrL5fFVygUmDp1qlT++++/sW7dOpfF9yRVSk4XLlwojRQGBQVh4cKFVuu++eabiI2NBWA4S2jRokVVaVJm48aNssXAixYtQlCQ5a2ze/fujUcffVQqmw6Lmzt27Bh++uknqTx79mxphNRcq1atMHHiRKk8b9486PV6i3WvXr2KJUuWSOVJkyahZcuWFutGRUXJDtddsmSJzZ3Ptm7dilWrVknlb775Bs2bN7da35eZrjm1d84pN0QiIiIico1Ro0ahe/fuUvndd9/FsmXLnIpx5coV9O/f32LSVadOHcycOVMqnz17Fo899pjDm55OmjQJv/32m1SeMGGCbCTSFSZMmIDw8HCpPHv2bJfGHz58OJo1ayaVTfMFX1Kl5NR09HHo0KE2t2FWqVQYPXq0VF6zZk1VmpQxjdGkSRP06dPHZv3x48dL1/v370dmZqbduCEhIRgxYoTNuOPGjZOuc3NzsXfvXov1fv75Z5SXlwMw/KbG9DlLRo4cKW0eVVZWhg0bNlita/qLgYcffhj33HOPzdi3ihsjp2bTev05rZeIiIjIlfz8/LBq1Spp9p9er8eYMWPw/PPPS2egWlNWVoaFCxciISFBtmTQ3OTJk2WzDtetW4d+/fpJy9osuXLlCkaNGiUbHGvbti3mzp3r6I/msPDwcPzrX/+SymvWrHHp7sV+fn549dVXpfKhQ4ekjah8idPJaVpaGk6dOiWV+/XrZ/eZ/v37S9enTp1CWlqas83K/Pzzz9J13759Idg50+nuu++W7RRs+ry1uN27d5fWq1rTtGlTJCQkSGVr899N4yYkJMgWdVsSEhIi++2TtbhZWVn45ZdfpLLpWtlbkWy3Xq3l3XqDAwzTesu0euj0vjlXn4iIiOhma9iwIbZv345GjRpJ9z788EM0bdoUjzzyCD788EOsW7cOv/76K9atW4ePP/4Yjz32GKKiojBp0iRcunTJZnxBEPD999/LBmJSUlLQsmVLDBkyBMuWLcPmzZuxbds2rFixAk8//TSaNWuGFStWSPXbtWuHTZs2SUvzXG3SpElS/iCKIt566y2Xxn/iiSdk768vjp46nZyan62TmJho95k777xTtuD48OHDzjYruXDhAnJzc51qX6lUolOnTjbbF0VRdvaRI3HN61n7uUzfM1fGTUlJkXZADggIcProGd9gOcEsq9itt/I5p37SNaf2EhEREblO69atsW/fPtx///3SvaKiIqxZswbPP/88HnroIfTo0QMPPfQQnn32WXz77bey0zGGDBmCRx55xGr8sLAwbNy4Ec888wwUFQMQpaWlWL16NcaMGYO+ffsiOTkZo0aNwmeffYZr165Jzw4ePBg7d+5E/fr1Xf+DV6hTpw6eeeYZqfztt9/ixIkTLovv7++PV155RSrv3bsXKSkpLovvCZxOTlNTU6VrlUolrSe1xbyeaYzqtA9ANvfaFtN6lto/f/48iouLXR5Xq9XKRpqrEvfkyZMW59Tv379fujb+AqCsrAzLli1Dr1690KBBAwQEBCA6OhpJSUmYM2eO3d9KeTPTNNQ4cmp+lEyAUgHjQDs3RSIiIiJyrfr162P9+vX47bffMHDgQISGhtqsHxkZieeffx5//vkn/ve//9nNLQICAvDRRx/hr7/+wqOPPio7vtKcWq3G/fffj127duG7776TrQmtKS+99JK0eZNer3f56OnTTz8tOzLTdC2uL1A6+0B6erp0HRMTY3dKrVGjRo2kHWVNY1SnfWNcR9u3FsNVcc+dOwdRFGXvSVZWlnR8TFXjajQaZGdnV3rWdET19ttvxz///IPHHnus0vz23Nxc5ObmYufOnZgzZw7mz5+PMWPGONQPb1WuMx4lI//9iyAICFYpUVSm5bpTIiIiohrSrVs3dOvWDVqtFvv370d6ejouXbqEwsJChIWFoV69emjfvj1uv/12h/MJU23atMGqVaug0Wiwd+9enDt3DhcuXIBOp0O9evXQsGFDdOvWzeqmqbZUJ1eJiorC9evXbdbZsWNHleOr1WqXnH7iqZxOTgsLC6VrZ377YPpbDdMY1WnfmT7Ya98VcfV6Pa5fvy5b3+qKuJbiAMDly5el67KyMiQnJ+PChQsADOtW27RpA39/f6SlpUnnpRYWFmLs2LG4dOmSbFG1NWVlZbKzqgoKChzq/00jO0rGZLdejeXdegHDWadFZVoUc1ovERERUY1SKpW46667cNddd9VIfH9/f/To0aNGYtPN5/S03qKiIularVY7/Jzp2USmMarTvjN9sNe+K+JailNTcQHI5tF/++23uHDhApRKJf773//i0qVL2LNnD3bu3ImcnBysWbMGdevWleq//vrrDv3WZs6cOQgPD5e+HJnG7QnKrJxzCtxYd8ppvUREREREnsPp5NR07aNS6fjAq2ld02mu1WnfmT7Ya98VcS3Frqm4AGQjmkbLly/H5MmTZbuQCYKAgQMHYtu2bVJyrNfr8frrr9vtx9SpU5Gfny99ZWRkONR/dzNO67U4csrjZIiIiIiIPI7TyanpvO3S0lKHnzOtazrttTrtO9MHe+27Iq6l2DUV19K9nj172jybtXXr1pg4caJU3rNnj91jfQICAhAWFib78iw3pvIKFnbutTRyajxOhskpEREREZHncDo5NT37s6SkxOHnTBcG2zs/1NH2nemDvfZdEddSnJqKa+neyJEj7cYdNWqUrLxz506H+uOt/C2MnBqn9fIoGSIiIiIiz+F0choZGSld5+TkOPyc6dmkderUcbZZi+070wd77bsibmhoKPz9/V0eF7DcZ9M1pIDhOBl7WrRoIVvPeubMGYf64w0s7fOm9OO0XiIiIiIib+B0cpqQkCBdX7582e5WyUamaxWbN2/ubLMW2wcM55O6ov34+HjZNtauihsRESFLUKsSt27duoiIiKhUx7w9S3XMCYKA2rVrS+WrV6861B+PJVaeymvKT2F9Wi83RCIiIiIi8hxOJ6ctWrSQlf/66y+7z2RlZeHixYtWYzjj9ttvl20W5Ej7APDnn3/abD8kJAQxMTEuj2t+35VxW7VqJStb2iDJEtP1rM7suOzpLK059bc0cloxrZdHyRAREREReQ6nk9POnTvLdoL97bff7D7z66+/StdqtRqdO3d2tlmJSqVCly5dnGo/NzcXp06dksrWzkIyve9IXI1Gg3379jkVd9++fZV28LXE9D2zFrdnz56y8tmzZ+3Gzc/Pl42WRkVF2X3GmyktjJwG+fMoGSIiIiIiT1OlDZGSk5Ol8sqVK+0+Y1onOTm5Wrv1AsBDDz0kXW/duhV5eXkOt1+rVi2ryZ5p3NTUVNnopSXr1q1DYWEhAEChUGDAgAF24xYUFOCnn36yGffQoUOyXXRNnzfVtm1b3HbbbVJ5y5YtNuMa64gmU2G7du1q9xnPduNnWRfwJsIgPw/W0sjpjQ2RmJwSEREREXkKp5NTAHjyySel68OHD9tMtv744w9s3LjR4rNVNWzYMGn0VqPRYN68eVbrFhUV4b333pPKI0aMqLRpkdF9990n22Ro9uzZVuPqdDrMnTtXKvfv3x/16tWzWLdjx46yKbhz5syBTmc9MXrrrbek69atW6NDhw5W6z711FPS9eeff24zUdfpdLL3KjIyEt26dbNa3yvo5e/jOOXPsrKfpd16eZQMEREREZHHqVJyOnjwYLRr104qjx8/HsePH69ULycnByNHjpQSsTvuuAOPPPKIxZjp6ekQBEH6mjFjhtX2Y2JiMH78eKm8aNEifP/995XqaTQajB49WtqEKDAwEK+99prVuMHBwZg6dapUXrNmDRYuXFipniiKeOmll3DgwAEAhk2GZs6caTWuIAiYNWuWVD5w4ABefvll2Qim0YIFC7B27VqpPGvWLNlGTeZefPFFREdHAzBsbjR48GAUFBRUqqfX6/HCCy9IfQaAyZMny6Zoe6Xvx8iKQZCvu7V0zimPkiEiIiIi8jxK+1UqEwQBn376KXr27ImSkhLk5OSgS5cuePbZZ9GjRw8olUrs378fH3zwgTSSFxgYiE8++cRmouWMGTNmYOPGjTh58iR0Oh2GDh2K4cOH4+GHH0ZERATS0tLw0Ucf4fDhw9Iz7777Lho0aGAz7vPPP4/Vq1djz549AIBJkyYhJSUFI0aMQP369ZGeno5ly5bJ1qROnjzZ7jEuAwcOxJAhQ/Ddd98BMCShBw4cwJgxYxAXF4fc3FysXLkS69evl54ZMmQIHn74YZtxg4KC8Nlnn2HAgAHQarX47bff0KJFCzz33HPo1KkT/P39kZqaik8//VS2GVO3bt0wefJkm7G9Qs5fsqICelnZ0jmnPEqGiIjIc1j6ZT0R1TxP/LtXpeQUADp16oQVK1Zg5MiRKCkpQUFBAebOnSub6moUGBiIFStWoFOnTtXqrKnatWtj/fr16N27NzIyMqDX67FixQqsWLHCYv1XXnkFzz//vN24KpUKP/zwA5KTk3HkyBEAwPr162VJo6lhw4ZZ/Jkt+eKLL3Dp0iVs374dgGHTJWsbLyUlJeGLL75wKG6/fv2wdOlSjB8/HmVlZcjOzsYbb7xhtf7dd9+NNWvWWJ3e7M0UZjv2+llcc8qjZIiIiNxNUbFpoV6vt1OTiGqC8e+ewsIGou5SrZ4MGjQIhw4dQu/evS2OiAqCgOTkZBw8eBCDBg2qTlMWxcfH4/Dhw3j66acRGBhosU6LFi3w448/OpxAAoZzRffv34+XX34Z4eHhFuvExcVh6dKl+Prrrx3+Aw0MDMTWrVvxzjvvWF2fWq9ePcyZMwcpKSlWfyZLnnjiCRw8eBC9e/e22p/69evj//7v/7B161bZ2au+xE+QJ6f+lnbrDeBRMkRERO6mVCohCILsiDsiunlKS0shCILsmE53E0QXjedmZGRg9+7dyMrKAgA0bNgQ3bp1Q2xsrCvC21VYWIht27YhIyMDxcXFiI6ORps2bdC+fftqxS0tLcWOHTuQnp6Oq1evIioqCs2bN0diYmK1pihrtVrs2rULp0+fxqVLlxAZGYlmzZpJ06KrIzs7W/qzKC0tRWRkJNq1a4cOHTpU+zcjBQUFCA8PR35+PsLCwqoVyyVmyH95sEqfjFfLn5bK0we0xOhuTWR19p25jEc/+R1NI4Ox7aWkm9FLIiIisiAzMxMajQZNmjSxX5mIXOrs2bPw9/dHTExMlWO4OjdwWZocGxuLxx57zFXhnBYaGmr1yJXqUKvV6Nevn8vjKpVK9OrVC7169XJ57AYNGmDIkCEuj+sN/Mym9VreEIm79RIREXmCsLAwZGVlobi4uNpHDRKR44qLi1FaWoo6deq4uysynjPBmMgFzKf1Ki0eJcPdeomIiDxBSEgIgoODpZlvRFTziouLkZGRgeDgYISEhLi7OzKeM8GYyBXMplpbTE5V3K2XiIjIEygUCsTExCAzMxPnz5+HWq1GWFgY1Go1FAqFy055ILqViaIIvV6P0tJSFBQUoLS0FMHBwYiJifGozZAAJqfkY8z/T8zf0rRef8PHXqsXUa7VQ6X0rL+UREREtxJjglpUVISCggJcvHjRI4+4IPJ2giAgJCQEderUQUhIiMclpgCTU/IxAsxGTi0cJRNYMXIKGI6TYXJKRETkXgqFAmFhYQgLC4Ner4dWq+URM0QupFAooFQqPTIhNcXklHyLA9N6VUoF/P0EaHQirmu0CIfvnfdKRETkrRQKBVQqlbu7QURu4NmpM5GzKiWnlj/igf4VZ52Wcd0pEREREZEnYHJKPsV8zamlab3AjeNkSrgpEhERERGRR2BySj7G/oZIAI+TISIiIiLyNExOybeYjZz6WVhzCvA4GSIiIiIiT8PklHyKeS7qb21ab8VxMkxOiYiIiIg8A5NT8imiIP9IW90QScVpvUREREREnoTJKfkURzdECq5Yc1qi4cgpEREREZEnYHJKPkWAo0fJGKb18igZIiIiIiLPwOSUfIrjR8lUjJxyWi8RERERkUdgcko+xTw59bcycsrdeomIiIiIPAuTU/ItDo+cVkzrZXJKREREROQRmJyST6k0rdfOOaec1ktERERE5BmYnJKPMR85tXeUDEdOiYiIiIg8AZNT8ikKHiVDREREROSVmJyST3F0Q6QbR8lwWi8RERERkSdgcko+RRBEWdnPzppTTuslIiIiIvIMTE7Jp/iZJaf+9s455bReIiIiIiKPwOSUfIoCN5JTP4VQaZqvkXSUTBmTUyIiIiIiT8DklHyKaXJq7RgZgEfJEBERERF5Gian5FNM81FHktPrGh1EUbRaj4iIiIiIbg4mp+RT/ExHTq2ccQoAQQGGab2iCJRp9TXeLyIiIiIiso3JKfkUhSBfc2pNoL+fdM3jZIiIiIiI3I/JKfkU0zWn1lNTQ+IaoDR8/HmcDBERERGR+zE5JZ8iS06t7NRrxONkiIiIiIg8B5NT8inyab226944TobTeomIiIiI3I3JKfkU05FThZ2R0+CAih17Oa2XiIiIiMjtmJyST3EuOTWMnBZx5JSIiIiIyO2YnJJPMZ3Wayc3RUgAp/USEREREXkKJqfkUxTijTNLVXYWnQZzzSkRERERkcdgcko+xXS0VOnn6LRerjklIiIiInI3JqfkUwSTkVPB5kmnQEjFhkgcOSUiIiIicj8mp+RTBJMNkfSiaKMmN0QiIiIiIvIkTE7Jpwi4MXJqLzkNUTM5JSIiIiLyFExOybeYJKR2clPu1ktERERE5EGYnJJPUTgzrVfFkVMiIiIiIk/B5JR8iiA6vvNuMEdOiYiIiIg8BpNT8ikCgF7N6wEAnunZzGbdEG6IRERERETkMZTu7gBRlRVkW7gp4r1h7ZGaU4COcbVtPm7cEKmY55wSEREREbkdk1PyXuf2VLolQERIgBKdGkfYfdx4zilHTomIiIiI3I/TesmnmJ5zao/pmlPR3ta+RERERERUo5ickk8RRL39ShWMyalWL6JM6/hzRERERETkekxOyXsJgoWbToycqm7MaueOvURERERE7sXklHyKwonk1E8hINCf606JiIiIiDwBk1PyMc6tHTXu2MvklIiIiIjIvZickherPK1XcHJjo5AAHidDREREROQJmJySj3EuOQ2uOE6Ga06JiIiIiNyLySn5FAWc23XXuCkSp/USEREREbkXk1PyXhZ363WOcVovk1MiIiIiIvdicko+xZlzToEbZ51yWi8RERERkXsxOSUvZmFDJO7WS0RERETklZicko+p6m69TE6JiIiIiNyJySn5FGePkrmxIRKPkiEiIiIicieXJKd79uzB+PHj0bJlS4SHhyMsLAwtW7bEuHHjsHv3blc0YdOZM2cwbdo0dOjQAXXr1kVgYCCaNWuGgQMHYvXq1dBqqzYqlpubi7lz5yIxMRHR0dFQq9Vo3Lgx+vXrh+XLl6OkpKRKca9du4bFixcjKSkJMTExCAgIQExMDJKSkrB48WJcu3atSnEtmTdvHgRBkH2lp6e7LL5bWdgQydlpvTxKhoiIiIjIMwii6ORQk4ni4mJMmDABn332mc16o0ePxvvvv4/g4OCqNmXVokWLMGXKFJSVlVmt07VrV6xcuRJNmzZ1OO6qVavwzDPPID8/32qdhIQEfPPNN2jfvr3DcVNSUvD4448jOzvbap0GDRrgiy++QO/evR2Oa8mZM2fQunXrSkn02bNn0bhx4yrFLCgoQHh4OPLz8xEWFlat/lXbsR+B/z0uu3UptAUiJ//ucIhV+8/j1TVH0Kt5PXz2ZCdX95CIiIiIyGe5Ojeo8sipTqfDoEGDZIlpYGAgOnbsiK5du8o69/nnn2PQoEHQ6Vw7dXLWrFmYOHGilJgqFAq0bt0aPXr0QHR0tFTv999/R8+ePZGTk+NQ3K+++grDhg2TJabx8fHo2bMn4uLipHtpaWlISkrC0aNHHYq7bds29O/fX5aYNm7cGD179kR8fLx0Lzs7G/3798f27dsdimvN+PHjqzy66724IRIRERERkTeqcnL65ptvYvPmzVJ57NixyMzMxIEDB7B3715kZ2fjzTfflF7fvHkzpk2bVr3emti0aROmT58ulRMTE5GamoojR45g586dyMzMxKpVqxASEgIAyMzMxJAhQ+zGPXLkCMaOHSuV4+PjcfDgQaSlpWHHjh1IT0/H5s2bERUVBcDw24IHH3wQpaWlNuPm5eVh8ODB0Gg0AID69etjy5YtOHv2LHbs2IG0tDQcOHBASlK1Wi0GDx6MvLw8596YCsuXL8fWrVsBAMnJyVWK4fksTOut4lEyRaVMTomIiIiI3KlKyWl2djYWLFgglUeNGoVPPvkEERER0r3g4GDMnDkTb7zxhnRv/vz5NqezOkoURUyZMgXGGckJCQnYunWrbPRRoVDg0Ucfxdq1a6V7u3fvlpUtef3116WR2MjISOzatQsdOnSQ1bn33nuRkpKCgIAAAIbpsx999JHNuG+99RauXr0KAAgICEBKSkqlabsdO3bErl27EBkZCQC4cuUK3n77bZtxLbl48SImT54MAGjevDleeeUVp2N4tW2zgY+7AyVX7VYNU/sDAArLNDXdKyIiIiIisqFKyenChQulkcKgoCAsXLjQat0333wTsbGxAIDS0lIsWrSoKk3KbNy4EX///bdUXrRoEYKCgizW7d27Nx599FGp/M4771iNe+zYMfz0009Sefbs2dIIqblWrVph4sSJUnnevHnQ6y2P2l29ehVLliyRypMmTULLli0t1o2KisKsWbOk8pIlS6Sk1lEvvvgirly5AgD4+OOPoVKpnHremwmiHtj1LpB7BNi3xG79sIppvYUcOSUiIiIicqsqJaemo49Dhw6VjZiaU6lUGD16tFRes2ZNVZqUMY3RpEkT9OnTx2b98ePHS9f79+9HZmam3bghISEYMWKEzbjjxo2TrnNzc7F3716L9X7++WeUl5cDAARBkD1nyciRI6XNo8rKyrBhwwab9U1t2LAB33zzDQDDRlQ9e/Z0+FmvY2+33vIiuyHCAg0jpwUlGlRjbzAiIiIiIqomp5PTtLQ0nDp1Sir369fP7jP9+/eXrk+dOoW0tDRnm5X5+eefpeu+fftCsJCkmLr77rtlOwWbPm8tbvfu3aX1qtY0bdoUCQkJUnn9+vV24yYkJKBJkyY244aEhKB79+5245orKirCs88+CwCoU6cO3n33XYee8yVK3XWTgtpu/dCKkVO9CBSX86xTIiIiIiJ3cTo5NZ1OCxg2IrLnzjvvlE0tPXz4sLPNSi5cuIDc3Fyn2lcqlejU6cYxIZbaF0URR44ccSqueT1rP5fpe+bKuObeeOMNnD9/HgDw3//+F3Xq1HHoOe9V+ZcSas21GwUHRkID/f2gVBjiFJZy3SkRERERkbs4nZympqZK1yqVSlpPaot5PdMY1WkfAJo1a+bQc6b1LLV//vx5FBcXuzyuVquVjTRXJe7Jkyeh1dpeE7l//368//77AICePXviySefdKgdX+OvMzk6x4FpvYIgSKOnBSVcd0pERERE5C5OJ6fp6enSdUxMjN0ptUaNGjWyGKM67ZvHrU77roh77ty5SusWs7KypONjqhpXo9HY3OVYo9FgzJgx0Ov1UKlU+Pjjjx1qw+eV2U9OgRvrTjlySkRERETkPkpnHygsLJSuw8PDHX4uLCzMYozqtO9MH+y174q4er0e169fl61vdUVcS3FMvfvuu9KU5FdeeQXNmzd3qA1HlJWVSUfrAIZzXT2GvV+MODByCtxYd1rA5JSIiIiIyG2cHjktKrrxD3612v6GM0aBgYEWY1SnfWf6YK99V8S1FKem4hqdOHFCOnrmtttuw+uvv+5QfEfNmTMH4eHh0pcj07g9hs6xZFM665THyRARERERuY3Tyanp2kel0vGBV9O6ptNcq9O+M32w174r4lqKXVNxAcMmTuPGjZPOnP3www+d+oWBI6ZOnYr8/HzpKyMjw6Xxa5SuzH4dmIyclnDklIiIiIjIXZye1hsUFCRdG5MiR5jWNZ32Wp32jXHN71WlfUtxHWFezzx2TcUFgGXLlmHnzp0AgOHDh+Pee+91KLYzAgICEBAQ4PK4rmFnWq/WseTUOHJawJFTIiIiIiK3cXrk1PTsz5KSEhs15a5fv3H+pL3zQx1t35k+2GvfFXEtxampuLm5uXj55ZcBALVq1cL8+fMdintLcXBab6iUnHLklIiIiIjIXZxOTiMjI6XrnJwch58zPZu0OudvmrbvTB/ste+KuKGhofD393d5XKByn6dPn45r164BAN555x1ERUU5FPeW4uC03rBAwwQCrjklIiIiInIfp5PThIQE6fry5cuVRvisMV2rWJ3dZE3bBwznk7qi/fj4eNmxOK6KGxERIUtQqxK3bt26iIiIkL2el5cnXT/zzDMQBMHq1z333CN7tkmTJtJrSUlJDvXHI9nbrVdb7lAYaeSUa06JiIiIiNzG6eS0RYsWsvJff/1l95msrCxcvHjRagxn3H777bLNghxpHwD+/PNPm+2HhIQgJibG5XHN77syLtmhcyw5DVNz5JSIiIiIyN2cTk47d+4s2yDnt99+s/vMr7/+Kl2r1Wp07tzZ2WYlKpUKXbp0car93NxcnDp1Sir36NHDYj3T+47E1Wg02Ldvn1Nx9+3bV2kHX0tM3zNLcYODg2VHvNj6Mt9MKTQ0VHqtOut/PZ7Du/VyzSkRERERkbtVaUOk5ORkqbxy5Uq7z5jWSU5OrtZuvQDw0EMPSddbt26VTXG1136tWrWsJpGmcVNTU2Wjl5asW7cOhYWFAACFQoEBAwbYjVtQUICffvrJZtxDhw4hLS3N4vNGK1euxLVr1xz6Wr9+vezZw4cPW33Nu7hmWi/XnBIRERERuZ/TySkAPPnkk9L14cOHbSZbf/zxBzZu3Gjx2aoaNmyYNHqr0Wgwb948q3WLiorw3nvvSeURI0ZU2rTI6L777kPdunWl8uzZs63G1el0mDt3rlTu378/6tWrZ7Fux44d0apVK6k8Z84c6HQ6q7Hfeust6bp169bo0KGD1bpkg8PTernmlIiIiIjI3aqUnA4ePBjt2rWTyuPHj8fx48cr1cvJycHIkSOlROyOO+7AI488YjFmenq6bBOfGTNmWG0/JiYG48ePl8qLFi3C999/X6meRqPB6NGjpU2IAgMD8dprr1mNGxwcjKlTp0rlNWvWYOHChZXqiaKIl156CQcOHAAACIKAmTNnWo0rCAJmzZollQ8cOICXX34ZoihWqrtgwQKsXbtWKs+aNUu2UROZsPe+OJucclovEREREZHbKO1XqUwQBHz66afo2bMnSkpKkJOTgy5duuDZZ59Fjx49oFQqsX//fnzwwQfSlNvAwEB88sknLku0ZsyYgY0bN+LkyZPQ6XQYOnQohg8fjocffhgRERFIS0vDRx99hMOHD0vPvPvuu2jQoIHNuM8//zxWr16NPXv2AAAmTZqElJQUjBgxAvXr10d6ejqWLVsmW5M6efJk3HnnnTbjDhw4EEOGDMF3330HwJCEHjhwAGPGjEFcXBxyc3OxcuVK2TTbIUOG4OGHH3b2rSEjraNrTg1/DUo1epRr9VApq/Q7GyIiIiIiqgZBtDR856A1a9Zg5MiRKCkpsVkvMDAQK1aswKBBg6zWSU9PR5MmTaTy9OnTbY6eAsCJEyfQu3dv2bEr1rzyyiuyabi2XLx4EcnJyThy5IjdusOGDcOKFSugUNhPaEpKSnD//fdj+/btdusmJSVhw4YNCAwMdKjPtuzYsUN2nMzZs2fRuHHjKsUqKChAeHg48vPzERYWVu2+VcuJTcDXQ23XmX7N7girVqfHba8bpp4feqM36oQE2KxPRERERESuzw2qNUQ0aNAgHDp0CL1797Y4IioIApKTk3Hw4EGbiWlVxcfH4/Dhw3j66aetJnEtWrTAjz/+6HBiChjOFd2/fz9efvllhIeHW6wTFxeHpUuX4uuvv3YoMQUMSfrWrVvxzjvvWF2fWq9ePcyZMwcpKSkuSUx9mwOj8A5M7VX6KRCk8gPATZGIiIiIiNylWiOnpjIyMrB7925kZWUBABo2bIhu3bohNjbWFeHtKiwsxLZt25CRkYHi4mJER0ejTZs2aN++fbXilpaWYseOHUhPT8fVq1cRFRWF5s2bIzExsVpTlLVaLXbt2oXTp0/j0qVLiIyMRLNmzaRp0Z7Ks0ZONwNfD7Fd59UMQG2/n13fTkFuQSl+fL4b2sXWck3/iIiIiIh8mKtzA5dlQbGxsXjsscdcFc5poaGhFo9cqS61Wo1+/fq5PK5SqUSvXr3Qq1cvl8cmEw5uilQryB+5BaXI5469RERERERuwZ1fyHs5MnKtd2yabnigYcfea0xOiYiIiIjcgskp+TadY8lm7SAVAODadcdGWomIiIiIyLWYnJJv0zuWnNYKqhg5vc6RUyIiIiIid2BySl7MkWm9Ooci1ZJGTpmcEhERERG5A5NT8m0OTuu9MXLKab1ERERERO7A5JS8lyMn+Tg6rZcbIhERERERuRWTU/JtOsd2663FDZGIiIiIiNyKySn5NgePkuGGSERERERE7sXklLyYIxsiObnmlNN6iYiIiIjcgskp+bYqnHOq14s12SMiIiIiIrKAySn5NmtHyRRdBNJ+AbSGNabhFRsi6UWgsMyxqcBEREREROQ6TE7JewlVnNarKQGWJgPfPAqsGQMAUPv7IdDfDwCQz3WnREREREQ3HZNT8m2WpvUeXQtcO2e4PvYjcCEVgOm6U+7YS0RERER0szE5Jd9mabfeMzvk5aM/ALgxtfcqR06JiIiIiG46JqfkxRyZ1mshOc08aPje8mHD99MpAOSbIhERERER0c3F5JR8m/m0Xp0GuJpuuO76nOF71h9A+XWedUpERERE5EZMTsm3mW+IdPUcIOoA/yAgphMQXM9QzvuHySkRERERkRsxOSXv5dBuvWbTei+fNHyv0wxQKICGdxrK2X+ilnFaLzdEIiIiIiK66Zickm/TmSWn1zIM32s3Nnxv0N7wPesP1K4YOb1SzOSUiIiIiOhmY3JKvs18Wm9RruF7aLThe/Qdhu+5h1EnOAAAk1MiIiIiIndgckreSxTt1zGf1luYZ/geEmX4Xq+F4fulk4gMMvx1uFTE5JSIiIiI6GZjcko+qmI9qvm0XmnktL7he3isYXMkvQbResNrV4rLblIfiYiIiIjIiMkp+SY/w+ZGlab1SiOnFcmpQgFExgMAIkvPAgAuF5VDdGRUloiIiIiIXIbJKXkxGwmkUm34bn7OqXHkNKTejXsVU3vDCk4BALR6EQUlZiOuRERERERUo5ickm9SGkdOTZJMUQSuXzFcB0feuF83wfDI5RMIVSsBAJc4tZeIiIiI6KZickq+yc+w864sOS0rBESd4Vpd68b9us0N3y+mITLE8NxlbopERERERHRTMTkl72VrXahx5NR0Wm/pNcN3PxXgH3jjfsWaU1w+iTpBhpHTy0UcOSUiIiIiupmYnJJvMq45NR05Lblm+K6uBQjCjfu14gCFEtCWopm6AABwiWedEhERERHdVExOyTdZ2q3XOHIaWMusrhKo3RgAcLufYTdfjpwSEREREd1cTE7Ji9ma1lux5lRnZeTUXEQzAECckAOAa06JiIiIiG42Jqfkm5wZOQWAOobkNFqbDQC4zN16iYiIiIhuKian5JuUFnbrtTly2hQAEFmeCYAjp0RERERENxuTU/JNfham9Uojp7Ur169zGwAg7Pp5AMBlbohERERERHRTMTkl3ySNnJpM6y25avhuY1qvuigDCui5IRIRERER0U3G5JS8l439kCxO6y01HBMDdXjl+mExgF8AFPpyNBAu4ep1DTQ6vcu6SkREREREtjE5Jd/k52/4rjMZOS0vNnxXhVSur1AAEU0AALcpDMfJXOLoKRERERHRTcPklHyTUm34bjpyWl5k+K4KtvxMxXEyrQMvAQDyCpicEhERERHdLExOyYvZmNdrPErG4sipleS0jmHH3gR/w8hpXkFpdTtIREREREQOYnJKvsnSmlN7yWnFyGljIRcAcIHJKRERERHRTcPklHyTXxWS04rjZBrosgFwWi8RERER0c3E5JS8l2hjWq/SwrRejY0NkQDpOJnaZTlQQstpvURERERENxGTU/JNVRk5DY0G/IOggA4xwkXkFXLklIiIiIjoZmFySt5LtHEOqbTmtGLkVFsO6MoN1/5Blp8RBGn0tImQyzWnREREREQ3EZNT8l6OJKe6ipFT45RewPq0XkBad9pUyOG0XiIiIiKim4jJKXkxW0fJmE3rLb9u+K7wv7Ee1ZKK5LSJkIOr1zUo0+pc0E8iIiIiIrKHySl5L72NxNGYgBqn9dpbb2pUkZw2UxiOk7nIdadERERERDcFk1PyXjan9aoN343TesuLDN9tTekFbkzr9TMkpzxOhoiIiIjo5mBySl7M1rRe48ipMTk1jpxa2QzJKKIpACBKvIxAlHJTJCIiIiKim4TJKXkvWyOnfmbTejUVa07tTesNigCC6gAAGgt5yMlnckpEREREdDMwOSXvJdoYOTVO69VrDfUcndYLABHG42RykH2tpJqdJCIiIiIiRzA5Je9lc82pyY68ep3jGyIBJjv25iKLySkRERER0U3B5JS8l81pvQE3rvUaJ5NTw8hpUwVHTomIiIiIbhYmp+S9HB051WluTOv1t7MhEiA765Qjp0RERERENweTU/JeDo+cagFNxcZGTiWnubhUVI5SjY3zVImIiIiIyCWYnJL3cmS3XsCQnGorklNlgOX6piqOk6ktFKEWCjl6SkRERER0EzA5Je9lKzlVKACF0nCt09xITv0D7cdVBQFhMQCApkIOsq4yOSUiIiIiqmlMTsl72UpOBT9A4W+4Np3Wazxixh6TTZE4ckpEREREVPOYnJL3spVoCgrAzyQ51ZbYf8aUyaZI3LGXiIiIiKjmuSQ53bNnD8aPH4+WLVsiPDwcYWFhaNmyJcaNG4fdu3e7ogmbzpw5g2nTpqFDhw6oW7cuAgMD0axZMwwcOBCrV6+GVqutUtzc3FzMnTsXiYmJiI6OhlqtRuPGjdGvXz8sX74cJSVVS1quXbuGxYsXIykpCTExMQgICEBMTAySkpKwePFiXLt2zal4J06cwIcffojhw4ejTZs2qF27Nvz9/REREYHWrVvj6aefxs8//wy93sZIozdqPdj6awo/wxdQMa23zHDt72xymstpvUREREREN4EgiqJY1YeLi4sxYcIEfPbZZzbrjR49Gu+//z6Cgx04Y9JJixYtwpQpU1BWVma1TteuXbFy5Uo0bdrU4birVq3CM888g/z8fKt1EhIS8M0336B9+/YOx01JScHjjz+O7Oxsq3UaNGiAL774Ar1797YZ6/PPP8f//d//4ejRow613apVK3zxxRfo0KGDw/01V1BQgPDwcOTn5yMsLKzKcVxmxWDg1JbK99+4ACxoDRRfAJ7ZDWyZBpxOAR7+GLhjmP24JzYDXw/BcX0spjX8FP8bn+j6vhMREREReTFX5wZVHjnV6XQYNGiQLDENDAxEx44d0bVrV1nnPv/8cwwaNAg6nWuP5Jg1axYmTpwoJaYKhQKtW7dGjx49EB0dLdX7/fff0bNnT+Tk5DgU96uvvsKwYcNkiWl8fDx69uyJuLg46V5aWhqSkpIcTg63bduG/v37yxLTxo0bo2fPnoiPj5fuZWdno3///ti+fbvNeN9//32ltsPCwnDHHXegV69eaN++PZRKpfTa0aNH0a1bN2zdutWh/noHK79bEfxMpvVqnNutFwAibwdgmNabc7mwmn0kIiIiIiJ7qpycvvnmm9i8ebNUHjt2LDIzM3HgwAHs3bsX2dnZePPNN6XXN2/ejGnTplWvtyY2bdqE6dOnS+XExESkpqbiyJEj2LlzJzIzM7Fq1SqEhIQAADIzMzFkyBC7cY8cOYKxY8dK5fj4eBw8eBBpaWnYsWMH0tPTsXnzZkRFRQEw/LbgwQcfRGlpqc24eXl5GDx4MDQaDQCgfv362LJlC86ePYsdO3YgLS0NBw4ckJJUrVaLwYMHIy8vz26fo6Ki8Morr+DgwYO4cuUK/vzzT6SkpOCPP/7AhQsX8Oqrr0KhMPxRl5WV4ZFHHkFubq7duF5NUJhM69U6t1svANSKg6gMQoCghX/hOZ51SkRERERUw6qUnGZnZ2PBggVSedSoUfjkk08QEREh3QsODsbMmTPxxhtvSPfmz59vczqro0RRxJQpU2CckZyQkICtW7fKRh8VCgUeffRRrF27Vrq3e/duWdmS119/XRqJjYyMxK5duypNg7333nuRkpKCgADDKNyZM2fw0Ucf2Yz71ltv4erVqwCAgIAApKSkVJq227FjR+zatQuRkZEAgCtXruDtt9+2GjMmJgaLFy/G+fPnMXfuXHTo0AF+fn6yOrVr18acOXOwZMkS6V5BQQHmzJljs7/eQ7ByW6jebr0KBVDX8Hm6XchExpXr1ewnERERERHZUqXkdOHChdJIYVBQEBYuXGi17ptvvonY2FgAQGlpKRYtWlSVJmU2btyIv//+WyovWrQIQUFBFuv27t0bjz76qFR+5513rMY9duwYfvrpJ6k8e/ZsaYTUXKtWrTBx4kSpPG/ePKsbDl29elWWHE6aNAktW7a0WDcqKgqzZs2SykuWLJGSWnMff/wxnnvuOahUKqs/k9GYMWOQmHhj3eSaNWvsPuPVBMFsWq+Tu/UCEOq1AAAkCJk4e6nY1T0kIiIiIiITVUpOTUcfhw4dKhsxNadSqTB69Gip7IqkyDRGkyZN0KdPH5v1x48fL13v378fmZmZduOGhIRgxIgRNuOOGzdOus7NzcXevXst1vv5559RXl4OABAEQfacJSNHjpQ2jyorK8OGDRts1nfUgAEDpOvMzEwUF/tCwmVjPy9FxXrbquzWCwAVyWm8IhPnLnPklIiIiIioJjmdnKalpeHUqVNSuV+/fnaf6d+/v3R96tQppKWlOduszM8//yxd9+3bF4JgZWpnhbvvvlu2U7Dp89bidu/eXVqvak3Tpk2RkJAgldevX283bkJCApo0aWIzbkhICLp37243rrPMf4lQUFDgkrgey5ic6nWAxjhy6uCaUwCoa0hObxcycfayLyTyRERERESey+nk1HQ6LQDZVFFr7rzzTtnU08OHDzvbrOTChQuyzXwcaV+pVKJTp0422xdFEUeOHHEqrnk9az+X6XvmyrjOOnfunHQtCIK0ttW72fjFRHV26wWAes0BGHbszbxk/UghIiIiIiKqPqeT09TUVOlapVJJ60ltMa9nGqM67QNAs2bNHHrOtJ6l9s+fPy+b5uqquFqtVjbSXJW4J0+ehFardeg5W0ynY3fs2BH+/v7Vjul+jk7rdXK3XgAIj4VOGQyVoIPu4umqd5GIiIiIiOxyOjlNT0+XrmNiYuxOqTVq1KiRxRjVad88bnXad0Xcc+fOSTsIG2VlZUnHx1Q1rkajqfYux6tWrcLx48el8vDhwx16rqysDAUFBbIvr2FMTjUlgFixWZUTGyJBEKCPNEzbrlV8isfJEBERERHVIKeT08LCQuk6PDzc4efCwsIsxqhO+870wV77roir1+tx/bp84xxXxLUUxxkXL17EpEmTpHLDhg3xzDPPOPTsnDlzEB4eLn05MlJ+czkwrbe86MY9Z5JTAMr6N9adZl7lpkhERERERDXF6eS0qOjGP/TVasf/oR8YeGM6pWmM6rTvTB/ste+KuJbi1FRcR2m1WgwZMkRapysIAj755BOH+zF16lTk5+dLXxkZGVXqR82xNa23IjktM0nsnVlzihvHydwuZOLUBW6KRERERERUU5TOPmC69lGpdPxx07qm01yr074zfbDXviviWopdU3Ed9cILL2Dnzp1SeeLEibjvvvscfj4gIAABAc4ldB7DOK3XmJwq1YbzT51RkZw2FzKw8UIhgPqu6x8REREREUmcHjkNCgqSrktLSx1+zrSu6bEu1WnfmT7Ya98VcS3Frqm4jpgxYwY+/vhjqfzII4/g3XffdTqO1/KrSE6N03qdnNILAIhqAwBoIuQiPeeiizpGRERERETmnE5OTc/+LCkpcfg507WY9s4PdbR9Z/pgr31XxLUUp6bi2rNgwQL85z//kcr9+vXD119/DT8/P6fieDVpWm9FcurMTr1GoVEoU9eDQhChy/nHdX0jIiIiIiIZp5NT07Mxc3JyHH7O9GzSOnXqONusxfad6YO99l0RNzQ0tNLxLK6ICzj3nn3yySf497//LZV79uyJNWvWyM6avSVI03ordhh2cr2pkb5i9LRW/jFodXpX9IyIiIiIiMw4nZwmJCRI15cvX640wmeN6UY6zZs3d7ZZi+0DhvNJXdF+fHy87FgcV8WNiIiQJahViVu3bl1EREQ49NyKFSvw7LPPSuUuXbpg/fr1lTZYuiVUmtZbtfdAHdseAJAgnsX5K9yxl4iIiIioJjidnLZo0UJW/uuvv+w+k5WVhYsXb6zXM4/hjNtvv122WZAj7QPAn3/+abP9kJAQxMTEuDyu+X1XxjW3evVqPPnkk9DrDaN77du3xy+//FKtadRerdK03iqsOQUgNGgHAGilSMeJvKrvNE1ERERERNY5nZx27txZtnvrb7/9ZveZX3/9VbpWq9Xo3Lmzs81KVCoVunTp4lT7ubm5OHXqlFTu0aOHxXqm9x2Jq9FosG/fPqfi7tu3r9IOvpaYvmfW4ppav349hg8fDp1OBwBo3bo1tmzZglq1atl91mdZ2q23KqLbAgAShAycyb3igo4REREREZG5Km2IlJycLJVXrlxp9xnTOsnJydXarRcAHnroIel669atyMvLc7j9WrVqWU32TOOmpqbKRi8tWbduHQoLDYmPQqHAgAED7MYtKCjATz/9ZDPuoUOHkJaWZvF5S1JSUjB48GDpuJmEhARs3bq1Wmt7fYJfxchpdXbrBYBacShThkIl6FCQwU2RiIiIiIhqgtPJKQA8+eST0vXhw4dtJlt//PEHNm7caPHZqho2bJg0eqvRaDBv3jyrdYuKivDee+9J5REjRlTatMjovvvuQ926daXy7NmzrcbV6XSYO3euVO7fvz/q1atnsW7Hjh3RqlUrqTxnzhxphNOSt956S7pu3bo1OnToYLXu7t278dBDD6GsrAwA0LRpU6SkpCAqKsrqM75EtPWiNHJajd16AUAQcD3C8OenyDtctRhERERERGRTlZLTwYMHo127dlJ5/PjxOH78eKV6OTk5GDlypJSI3XHHHXjkkUcsxkxPT4cgCNLXjBkzrLYfExOD8ePHS+VFixbh+++/r1RPo9Fg9OjR0iZEgYGBeO2116zGDQ4OxtSpU6XymjVrsHDhwkr1RFHESy+9hAMHDgAABEHAzJkzrcYVBAGzZs2SygcOHMDLL78MUaycWi1YsABr166VyrNmzZJt1GTqjz/+wP3334/i4mIAQKNGjbBt2zY0bNjQal9uKS7arRcAAmLvAABEFaehqMz+tGwiIiIiInKO0n6VygRBwKeffoqePXuipKQEOTk56NKlC5599ln06NEDSqUS+/fvxwcffCBNuQ0MDMQnn3xiNdFy1owZM7Bx40acPHkSOp0OQ4cOxfDhw/Hwww8jIiICaWlp+Oijj3D48I2RrnfffRcNGjSwGff555/H6tWrsWfPHgDApEmTkJKSghEjRqB+/fpIT0/HsmXLZGtSJ0+ejDvvvNNm3IEDB2LIkCH47rvvABiS0AMHDmDMmDGIi4tDbm4uVq5cifXr10vPDBkyBA8//LDVmCNHjkR+fr5UDgsLkyXt9sybNw9t27Z1uL5HsjV0apzWa6xUxd16ASCocSfg0MdoJ5xGak4BOjV2bPdkIiIiIiJyjCBaGr5z0Jo1azBy5EiUlJTYrBcYGIgVK1Zg0KBBVuukp6ejSZMmUnn69Ok2R08B4MSJE+jdu7fs2BVrXnnlFdk0XFsuXryI5ORkHDlyxG7dYcOGYcWKFVAo7A9Cl5SU4P7778f27dvt1k1KSsKGDRtsHgHTuHFjnDt3zm4sa7Zv346kpCSnnikoKEB4eDjy8/MRFhZW5bZdRfzqEQint1Z+YUY+sPNdYLvJ1OyOTwEPLKhaQ1fTgUXtUC76YVXyHjzeo+rHIRERERER+QJX5wZVmtZrNGjQIBw6dAi9e/e2OCIqCAKSk5Nx8OBBm4lpVcXHx+Pw4cN4+umnrSZxLVq0wI8//uhwYgoYzhXdv38/Xn75ZYSHh1usExcXh6VLl+Lrr792KDEFDEn61q1b8c4771hdn1qvXj3MmTMHKSkpt+bZpE4SbQ2d+plNDKjqhkgAUCsOxf4RUAk65J85WPU4RERERERkUbVGTk1lZGRg9+7dyMrKAgA0bNgQ3bp1Q2xsrCvC21VYWIht27YhIyMDxcXFiI6ORps2bdC+fftqxS0tLcWOHTuQnp6Oq1evIioqCs2bN0diYmK1pihrtVrs2rULp0+fxqVLlxAZGYlmzZpJ06I9laeNnOq/GgTF6ZTKL8zIB/a8D2x+48a97v8Gek+vclsXPn0E9bK24lP1aIx9dWGV4xARERER+QJX5wYuy4JiY2Px2GOPuSqc00JDQ+0euVIVarUa/fr1c3lcpVKJXr16oVevXi6PTRUUZrsyV3W33grqJl2ArK2IvX4UpRod1P5+1YpHREREREQ3VGtaL5G76XrPsv5ipWm9Vd+tFwBCb7sLANBeOIFj2fl2ahMRERERkTOYnJJXE+u2sP6iwjw5rd7IqdDgTmjhhyjhGtJOpFYrFhERERERyTE5Ja9mc0OkStN6q7EhEgCognAlJB4AUHxyd/ViERERERGRDJNT8mo2t/PyM0tOqzlyCgD6RoapvREX98NFe4kRERERERGYnJIvU5htWFTNNacAENEqGQBwh+4Izl+5Xu14RERERERkwOSUfJeLd+sFAFWz7tBBgaaKXPyTerza8YiIiIiIyIDJKXk1mzNrzUdKldVccwoA6nDkBSUAAIpPbK9+PCIiIiIiAsDklHyZeXLqgpFTACiL7QYACMnZ65J4RERERETE5JS8nM3des1HSl2w5hQA6rW5FwDQpuxv5OSXuCQmEREREdGtjskpeTXnpvW6ZuQ0+Pbu0ECJWMVF/PXnIZfEJCIiIiK61TE5Ja9m8zAX85HT6p5zahQQguywOwAAJcc2uCYmEREREdEtjskp+a4aGjkFADG+LwCg4cVfed4pEREREZELMDklr2YzMfQz3xDJRSOnABp0eggA0F5/FCfO57gsLhERERHRrYrJKXk1p6b1uuIomQqqqATkKRtCJehwev96l8UlIiIiIrpVMTkl32U+rVfh59LwVxveAwBQnd7s0rhERERERLciJqfk1Wzv1uu6kVJLoiqm9t5Zshd5VwtrtC0iIiIiIl/H5JS8m6XktPM4w3c/ZY02XbtFL+QL4YgQivDPbk7tJSIiIiKqDian5FMuD98E9J1T+QVFDSSqfkpkRPcGAChTf3B9fCIiIiKiWwiTU/JqZVodfte3AACc1kdDV/8OyyOm5jv3ukjtzo8BANoV/YrL+ZzaS0RERERUVUxOyat9uOM0ni+fgP9qhmBE+WuAYKViDU3xbdg2GVeFWqglFOOvnT/USBtERERERLcCJqfk1f48fxWXEY4PdAORizrWK2pKa6YDCj9kx/QHAKiPrqqZNoiIiIiIbgFMTsmrme+HJFgbOtWV1VgfopPGAAA6le7F+YzzNdYOEREREZEvY3JKXk2nl6engnlumvgvw/e+b9dYHyKadcRZ/9ugEnQ4ve2zGmuHiIiIiMiXMTklr2aenFbSewYwbifQ5dka7UdBy+EAgNj076HR6mq0LSIiIiIiX8TklLyaXjQbOTWv4OcPNLgDUNTsR735vaNRChVuE8/j0K6fa7QtIiIiIiJfxOSUvJr5wKlQaV7vzREQEoG0evcDAPz2f+SWPhAREREReTMmp+TV7I6c3kT1+/0bANChZC/Onjjsxp4QEREREXkfJqfk1fT21pzeRFFN2+JwUFcoBBGZG+e7uztERERERF6FySl5tcrTet3TD6OQpBcBAB2v/IzM8+nu7QwRERERkRdhckperdJRMm6d2As07dQfp1QtECiU49y62W7tCxERERGRN2FySl5NFD1nWi8AQBCg7fkaAKDjxR+Qfe6UmztEREREROQdmJySV6u05NTN03oBoPldA5CqaoMAQYOza2e4uztERERERF6BySl5NZ35br0ekJxCEBDQZxoAoOvV9Tjx9x43d4iIiIiIyPMxOSWv5nHTeis07dgHf4bdAz9BhG79SxD1end3iYiIiIjIozE5Ja9Wabde93TDough/8V1MQAtNEex78eP3N0dIiIiIiKPxuSUvFql3Xo9Yl6vQf3Y23D0trEAgPi/5+BiXoabe0RERERE5LmYnJJX03votF6j9kPfwFm/xohAIbK+HM/pvUREREREVjA5Ja8WWztIVvaccVMDZUAg9A99hHLRD3cU78a+Hz50d5eIiIiIiDwSk1PyasdyCmRlD5rVK2nW9i781XQ8AKDN3zORnnrQzT0iIiIiIvI8TE6JboKOI2bin4D2CBbK4Pe/Uci/dtndXSIiIiIi8ihMTsmnCB43sddAofRH/adWIheRiBWzcebjYdBqyt3dLSIiIiIij8HklHyKJ07rNYqMaojihz5DmeiP9qX78OdHT3GDJCIiIiKiCkxOiW6iZu174li3BdCJAjpd+Ql7lv4boofvOExEREREdDMwOSW6ydr3GYWDrd4AAHTL/hz7lv0bYIJKRERERLc4JqfkUzx5Wq+pLkNfwv7bJwEAumZ+hr0fPwu9TufmXhERERERuQ+TU/IpnrohkiWdR8zAnoRXAQCJed/gzwWDUHq9yM29IiIiIiJyDyanRG5017Cp+KP9W9CIfuhQtAPn59+DnMyz7u4WEREREdFNx+SUfIq3TOs1dedD/0Jan69wDSGI156AeundOLL9O3d3i4iIiIjopmJySl7L0i63XpibAgBad7sfJY9vxmm/pqiNQrTZOQZ7F49BcWG+u7tGRERERHRTMDklr6X3sQ1uo5u2QsOXfsOeyMEAgMSL3yF/fkcc3bHazT0jIiIiIqp5TE7Ja2n1+kr3BG+c12tCHRiMu/61DEeTliEXddFAvIBWO57GwXcHIPPUP+7uHhERERFRjWFySl7LQm7qtdN6zbVKGoyQyQext96j0IkCOhbvQtRXPbB/8VO4mHPe3d0jIiIiInI5JqfktXQW1pz6kpDQWkh87hNkPboJf6s7wV/QofPF7xH68Z3Y9/6TyD5zzN1dJCIiIiJyGSan5LV0FhadevmsXosateyCdq9uxZHkFTjhnwC1oEGXy2sR9cVdOPjuABzZ9QNEvc7d3SQiIiIiqhaluztAVFV6i8mpD2anFdrcPQBit/txdO9GaHfNR7uyg+hYvAvYtgvZ2+vjXNwjiLtnNBrE3e7urhIREREROY3JKXktX5/Wa4mgUKBVt/uBbvfj/LF9yN32EVpc/AUNkIsG6YuBzxfjuH8LFDa9HzHdHkN0IyaqREREROQdmJyS17I0rfdW0qhlFzRq2QVFhfnYt/UrhKZ+i+ZlR9BckwqkpQJp/8Upv6a4FNUdtdv1R9P2yfBXBbi720REREREFrlkzemePXswfvx4tGzZEuHh4QgLC0PLli0xbtw47N692xVN2HTmzBlMmzYNHTp0QN26dREYGIhmzZph4MCBWL16NbRabZXi5ubmYu7cuUhMTER0dDTUajUaN26Mfv36Yfny5SgpKalS3GvXrmHx4sVISkpCTEwMAgICEBMTg6SkJCxevBjXrl2rUtySkhJ88cUX6NevHxo3bgy1Wo3o6GgkJiZi7ty5yMnJqVJcT2VMTlV+CrzQ6za8cX8LN/fIPUJCw9Fl4L/Q8rVfcXn839gTPwXH/FtDLwq4TXcGXbO/RMLGYSh7Kw5/vtMXe798A8f3b4Gm7Lq7u05EREREJBFEsepzI4uLizFhwgR89tlnNuuNHj0a77//PoKDg6valFWLFi3ClClTUFZWZrVO165dsXLlSjRt2tThuKtWrcIzzzyD/Px8q3USEhLwzTffoH379g7HTUlJweOPP47s7GyrdRo0aIAvvvgCvXv3djjun3/+ieHDh+P48eNW64SHh+Ojjz7CsGHDHI5rrqCgAOHh4cjPz0dYWFiV47hCxpXruHvedqj9FTg+q79b++KJrl3Iwqnf10E8uRXNCvchAoWy18tFJU6r4nG1dlv4N7wDUQmdEHNbOyiU/m7qMRERERF5E1fnBlVOTnU6He677z5s3rxZuhcYGIhWrVpBqVTi2LFjKCgokF7r06cPNmzYAD8/v2p32mjWrFmYNm2aVFYoFGjZsiUiIiJw8uRJ2UhhTEwM9u/fj+joaLtxv/rqKzz++OOye/Hx8YiOjkZ6ejrOnTsn3Q8LC8OePXvQqlUru3G3bduGfv36QaPRSPcaN26MuLg45OTk4MSJE9J9pVKJzZs345577rEb9+jRo7jrrrtk73d0dDRuv/12XLlyBUePHoXpH/OXX36JUaNG2Y1riSclp+cuF6PnuzsQrPLD0Zn93NoXT6fX6XDu6F5c+Gcb/LP2I674MOqg8i9eSkV/nPdvgiuhzSHWTUBQgxao37QN6sU0g6Bw3d9dIiIiIvJ+HpOcvvbaa5gzZ45UHjt2LN555x1EREQAMIyqzp07F7NmzZI989Zbb1WzywabNm1C//79paQrMTERy5cvR3x8PABAr9fju+++w5gxY1BUVAQA6NatG3777TebcY8cOYJOnTpJI7Hx8fH4+uuv0aFDB6nOli1bMGrUKOTl5QEAmjZtiqNHj0KtVluNm5eXhxYtWuDq1asAgPr16+Orr76SjY4ePHgQI0aMkJLUiIgIHDt2DFFRUVbjlpaWolWrVjhz5gwAICQkBEuXLsWQIUOgUBhmbZ84cQJPPvkk9u7dCwAICAjAwYMH0bp1a5vvhSWelJyeuViEXv+3E6FqJY7M6OvWvngbvU6PzDNHcenYTmgz/0TotWNoVH4awYLlGQglogrZyhhcDYxDWVhj+EU0RnC9JoiMuQ11GzaFMiDwJv8ERERERORuHpGcZmdno1mzZigtLQUAjBo1Cl9++aXFum+++SZmz54NAFCr1Th9+jQaNGhQjS4Doiiiffv2+PvvvwEYptf+8ccfCAoKqlR369atuPfee6XymjVrMHDgQKuxH3zwQfz0008AgMjISPzzzz8Wk8OjR4+iQ4cOUhI7f/58TJo0yWrcCRMm4P333wdgSA7/+OMPtGzZslK9vLw8tG7dGpcuXZKeW7RokdW48+fPx+TJkwEYjlHZsmULkpOTK9W7fv062rdvLyW+Dz74IH788Uerca3xpOT01IVC9J6/C7WC/PHXtD5u7Ysv0Gq1yDh9FFdOH4A2+whUV08houQconXZUAnWz1HViwIuC7Vx2T8KhQHR0ARHQQiJgqpWNAIjGiK8bixq149FUGiEbx5ES0RERHSL8ojk9JVXXsG7774LAAgKCkJGRoY0YmquvLwct912GzIyMqRn586dW40uAxs2bMD9998vlX/55Rf07Wt95Oyxxx7Dt99+CwDo3Lkz9u3bZ7HesWPHZNNzP/74Y4wfP95q3FdffVX6WerXr4+srCxptNLU1atXUb9+fZSXl0vPmY46m/v444/x7LPPAjAksjk5Oahdu3alenq9Hg0bNkRubq70c37zzTdW4/7yyy/o3//G2syjR49aTJBt8aTk9HhuAfot/BWRISocfONe+w9QlZSXlyPrbCquZRyDJvc4hGvpCCjKQnh5DurpLiBQKHcoTqnoj8uKCBT41UapshY0AbWgV9cGgiKgDI6Af2hdBIRFQh0WicBadRFcqx6Cg4J9+uxaIiIiIm/m6tygSkfJrF27VroeOnSo1cQUAFQqFUaPHo2ZM2cCMIxcVjc5XbNmjXTdpEkT9Olje9Rs/PjxUnK6f/9+ZGZmIiYmxmbckJAQjBgxwmbccePGST9Lbm4u9u7di27dulWq9/PPP0uJqSAIGDdunM24I0eOxEsvvYTi4mKUlZVhw4YNFvuyZ88eKTEFgGeeecZm3L59+6Jx48ZIT08HYPhzdDY59STG3XoVTF5qlEqlQpOEdkBCu0qv6XV65F3IxNXsMyi5cBbaK+egL8yF8voFBJReQqjmMmrrryBMuA61oEFDMQ8NtXmAFkApYGHZq0yZ6I9iIRDXhSCUKoJR5hcMjTIEOv9Q6FQhEFWh0AeEQQgIhaAOg1IdCqU6CP6BoVAFBkMdGAp1UCjUwaEICg7lZk9EREREHszp5DQtLQ2nTp2Syv362d+Ipn///lJyeurUKaSlpSEhIcHZpiU///yzdN23b1+7Iyt33303goODUVxcLD1vaUTUNG737t0REhJiM27Tpk2RkJCAtLQ0AMD69eutJqdGCQkJaNKkic24ISEh6N69OzZt2iTFtZScmsY1PmOLIAjo27cvlixZIsV9/fXXbT7jyfR6w3c/BZNTd1H4KRAV3QhR0Y0AJFmsI4oiiooLkX8hG8WXM1F6LQdlhZegK7oCXL8MofQqVGVXEaDJR7CuAKFiAcLEIigFPQIEDQKgQYRYAOhg+HJsoNaictEPpYIaZQhAqaBGuSIA5YIaGoUaWj819AoVRD8V9IoAiEoVRL8ACMoAQKmGoFRB8FdDoQyAwl8NhcpwLSjVEPwDoPRXQ1AFws8/AMqKL4W/P/z9VfD3D4BSpYJSqYJKFQCFCzeGIyIiIvIVTienxnWeRomJiXafufPOO6FSqaTRw8OHD1c5Ob1w4YJstNCR9pVKJTp16oQdO3ZI7ZsTRRFHjhxxKq6xnjE5tRQXkL9nzsQ1JqeOxO3UqZNDOyEnJiZKyemRI0cgiqLXTpss0RjWQar9+Q99TyYIAkJCwhASEgY0be7QM6Jeh9LifBTlX8H1wqu4XngNZcXXoCm+Bm1JAXQl+UBpARSaIijLC6HUFkGlLYK/vgQqXSn8xTKoxVIEiGUIRCn8hIozcQUdVCgGUAyIMCS7bqATBWihhBZ+0ApK6OAHLZTQCX7QCUroTK71grLi+42yXvCHKPhBFBQQFUpAUEAU/ACFn+FaoQQEwzUUSkChAASl4XWFH4SKLwh+EPyU0j0IFd9NnhH8/CBUxBQUSggKRUV9w7WgUEIQBAiCAgqFH6BQQFAooBAUhiRcUEChUFS8rqh4xq/inh8UfoZ7foJfxWsCFIIfFH6GsqKirsKv4lowxDLGgaDgWmYiIiIf4XRympqaKl2rVCrExsbafcZY7/Tp05ViVKd9AGjWrJlDzzVr1kxKTi21f/78eWlk1dm41voGGDaZMR1prkrckydPQqvVQqmU/3GZtleVuMXFxcjIyECjRo0cetbTFJdpAQDBAUxOfY2g8IM6NALqUOtLBhwl6vUoLSvF9aIClF4vQmlJIbQlxdCUFkFffh26smLoy69DLL8OnaYMorYU0JZB1JQBulJAWw5BVwZBZ/jupy+HQl8OP105lGI5lKIGSrEc/qIG/mI5/KGBP7RQiloooYO/hc2k/AQRfjCMCss7W/FFTtOJAvRQVLyFCughQA9BuhYhQC8oIFZcG+sZr2FyX7quSHpFCJVfN3nN+DqMcYUb9Q3VBZN6N8qW6khxzNuWEvCKesKNZ+V1TO7J2kFFEm9yT5DHk/VHqg8pJiAAgiG2ILVb8apZXVEw1rnxnPFVUSqbxRfkcU2fM61jtWxS1xhHMOlL5WcsxTP+T8UTZr/4MP45CNJn4UYM034LAuTvpYWfVcYshvx5s/fFYp9RqQ/mbZiWxIo+VnbjpunPLlrocuWolt5X+auiaRXzZ83fkkrxTV8ULL4imrRr7+eVf9bMWrLyCy9BqLyviPVuWvszEMxvSIVKYQR5Jft/hhbfRFudtDpAIZrVtzeQIVrqv+FByw9YjSdYfNm8PyY1nYovWvuztRbHxp+XtXZtv1O2/v6Zs/J5MxHTojNq17V/TKY3cTo5Na5XBAxnhzo66taoUSMpOTWNUZ32jXEdbd9aDFfFPXfuXKWRyKysLNm5plWJq9FokJ2dLbsniiLOnz9frbiA4ee29WxZWZm0IzEA2Vmq7lZYkZyGBFRp6TTdIgSFAurAIKgDK+/mfVOIInRaDTSaMmjKy6HVlEOnKYdGUwatVgOdphxareGeTlsOnVYDvbYcolYDUaeBqNNWfC8HdBpArwF0Woh6LUS9DtBrAb3OcC3qAJ0Oomi4B1FnmP+u1wKiDoKol+4Leh0EUSfdV1RcK0Q9BFR8F3VQwPCaAD0EUQ+/invG14zpH0Rj+mVIEQXR5FqWNoqG9irSNQXk1wIAheB8hm5I+I2/CHDRkLitbvCXCERE5GZ/65ai9j1D3N0Nl3L6X/WFhYXSdXh4uMPPme7eZBqjOu070wd77bsirl6vx/Xr1xEcHOzSuJbiFBcXQ29cdOnCuObmzJmD//znPw7Fvtka1grEkA4xaFrX9tpgIrcSBPj5q+Dnr4LaTfmxNxFFETq9CL2oh16vg6jXQ6fTQa/XQa/XQ6/TAxAhVpRFYwIuitCLhvoQ9dDrRUDUAxV1RFEvvSbq9RVnZIsQRVEqi9ADIiDqjdcVr1d8wXhPL0rP60XDPUNdQBT1UlzDPXkc42sCTOMY2tWLhmRfqmtS33gtiMbhdVFq1/i+CTD0WxBhaNM41iua1Ic8lggREFHx7I140rigKBqupI39b9w3lm/s+W/2mo1nKsWQbpvUEW08a3ZflH4Ged/lz+DGe2AaomJMRgQq3l/r/ZDakP1yQl/xmmkbFp6puCtUvGeCab/Mfi5B+nOw/Xqlnw+Wfn5zDt4XjfEsvy57r62GcvS3OJbrCRZ+BpvtWu2PM79NMn/vLbxuIZxpn0Tz+xbeWus/g3U2f26LL1l5X518/6yPKZr8zKLl+47GqrG+Sp9jZ/8+OP9n5Fh9x2Ja+uxbogpy78kZNcHp5LSoqEi6VqvVDj8XGBhoMUZ12nemD/bad0VcYxzT5NSVcW2VXRXX3NSpU/Hvf/9bKhcUFDg0lftm6BBXGx3iaru7G0TkQoIgwM9PgB8UqOKG8kREROSlnP5/fq1We+NhpeOPm9Y1neZanfad6YO99l0R11JsT4vr7y8/SsPen0VAQAACAgIcik1ERERERFRV9lfamgkKujEvrbS01OHnTOuajixWp31n+mCvfVfEtRTb0+KWlJTYjEtEREREROQOTienpmd/mic6tly/ft1ijOq070wf7LXviriW4tRU3NDQ0BqJS0RERERE5A5OJ6eRkZHSdU5OjsPPmZ5NWqdOHWebtdi+M32w174r4oaGhlaaNuuKuEDlPvv7+8sSVFfFJSIiIiIicgenk9OEhATp+vLly5VG4qzJyMiQrps3b+5ssxbbByA7TqU67cfHx8uOgHFV3IiICFmCWpW4devWRURE5fMeTd+LqsQVBKHS+0lEREREROQOTienLVq0kJX/+usvu89kZWXh4sWLVmM44/bbb5dt/uNI+wDw559/2mw/JCQEMTExLo9rft+T4sbGxnLNKREREREReQSnk9POnTvLdm/97bff7D7z66+/StdqtRqdO3d2tlmJSqVCly5dnGo/NzcXp06dkso9evSwWM/0viNxNRoN9u3b51Tcffv2Vdpp1xLT98yRuCdPnkReXp5L4hIREREREd1sVdoQKTk5WSqvXLnS7jOmdZKTk6s9WvfQQw9J11u3brWblJm2X6tWLatJmWnc1NRU2SijJevWrUNhYSEAQKFQYMCAAXbjFhQU4KeffrIZ99ChQ0hLS7P4vKkBAwZAobjxR/j111/bjJuXl4eUlBS7cYmIiIiIiG42p5NTAHjyySel68OHD9tMtv744w9s3LjR4rNVNWzYMGn0VqPRYN68eVbrFhUV4b333pPKI0aMqLRpkdF9992HunXrSuXZs2dbjavT6TB37lyp3L9/f9SrV89i3Y4dO6JVq1ZSec6cOdDpdFZjv/XWW9J169at0aFDB4v1oqKi0K9fP6m8cOFCFBUVWY07d+5cadS2bt26uO+++6zWJSIiIiIiuqnEKtDr9WK7du1EACIAMTo6WkxNTa1ULzs7W2zRooVU74477hD1er3FmGfPnpXqARCnT59usw8TJkyQ6vr5+YmrV6+uVKe8vFwcPHiwVC8wMFDMysqyGXf+/PmyfixYsMDizz9x4kSpjiAI4qFDh2zGXbNmjSzupEmTLL4X5u2vXbvWZtxDhw6JgiBI9YcMGSKWl5dXqrd69WpRoVDY/LkckZ+fLwIQ8/Pzq/Q8ERERERH5BlfnBoIoimJVktoDBw6gZ8+e0vmaYWFhePbZZ9GjRw8olUrs378fH3zwgTTlNjAwEDt37kSnTp0sxktPT0eTJk2k8vTp0zFjxgyr7V+9ehVdunTByZMnARim1Q4fPhwPP/wwIiIikJaWho8++giHDx+Wnvnggw/w/PPP2/y5ysvLcc8992DPnj3SvQceeAAjRoxA/fr1kZ6ejmXLlsnWpL700kt49913bcYFgKFDh+K7776Tyt27d8eYMWMQFxeH3NxcrFy5EuvXr5deHzJkCP73v//Zjfvyyy/jv//9r1Ru27YtnnvuOcTHx+PKlStYu3YtvvnmG+j1egBAt27dsG3bNqhUKruxzRUUFCA8PBz5+fkICwtz+nkiIiIiIvINrs4NqpycAsCaNWswcuRIKUG1JjAwECtWrMCgQYOs1nE2OQWAEydOoHfv3rLjUax55ZVXZNNwbbl48SKSk5Nx5MgRu3WHDRuGFStWyNZ+WlNSUoL7778f27dvt1s3KSkJGzZsQGBgoN26er0eo0aNsrvmFDBME962bZts+rIzmJwSERERERHg+tygSmtOjQYNGoRDhw6hd+/esjNCjQRBQHJyMg4ePGgzMa2q+Ph4HD58GE8//bTVJK5Fixb48ccfHU5MAcN6zP379+Pll19GeHi4xTpxcXFYunQpvv76a4cSU8CQpG/duhXvvPOO1fWp9erVw5w5c5CSkuJQYgoYRo1XrlyJZcuWIS4uzmKd8PBwvPTSSzhw4ECVE1MiIiIiIqKaUq2RU1MZGRnYvXs3srKyAAANGzZEt27dEBsb64rwdhUWFmLbtm3IyMhAcXExoqOj0aZNG7Rv375acUtLS7Fjxw6kp6fj6tWriIqKQvPmzZGYmGgxIXeUVqvFrl27cPr0aVy6dAmRkZFo1qyZNC26qkRRxO+//47U1FTk5eWhdu3aiIuLwz333AO1Wl3luEYcOSUiIiIiIsDDpvXSrSc/Px+1atVCRkYGk1MiIiIioltYQUEBYmNjce3aNaszTp1R9SE6uiUZz3W9WSPiRERERETk2QoLC12SnHLklJyi1+uRnZ2N0NDQak1rdhXjb2s4kkuO4meGnMXPDDmLnxlyBj8v5CxP+syIoojCwkI0aNDA4X14bOHIKTlFoVAgJibG3d2oJCwszO1/Ocm78DNDzuJnhpzFzww5g58XcpanfGZcMWJqVP30loiIiIiIiKiamJwSERERERGR2zE5Ja8WEBCA6dOnIyAgwN1dIS/Bzww5i58ZchY/M+QMfl7IWb78meGGSEREREREROR2HDklIiIiIiIit2NySkRERERERG7H5JSIiIiIiIjcjskpERERERERuR2TUyIiIiIiInI7Jqfkdfbs2YPx48ejZcuWCA8PR1hYGFq2bIlx48Zh9+7d7u4eVbh48SI2btyImTNn4sEHH0R0dDQEQZC+li9fXuXYR44cwb///W+0bdsWERERCAkJQUJCAkaMGIFffvmlynHPnDmDadOmoUOHDqhbty4CAwPRrFkzDBw4EKtXr4ZWq61S3NzcXMydOxeJiYmIjo6GWq1G48aN0a9fPyxfvhwlJSVV7rOvuHbtGtauXYsJEyagR48eqF+/PgICAhASEoJGjRphwIABWLhwIa5evVql+PzM+BaNRoN9+/ZhwYIFGD16NBITE9GgQQMEBQXB398fderUwR133IExY8Zg06ZN0Ov1TrfBz8ytJT09HcHBwbL/n5oxY4ZTMfiZ8S07duyQfR4c/Tp+/LjDbfAzY4FI5CWKiorEp556SgRg82v06NFiUVGRu7t7y8rJyRHj4uLs/jl9/vnnTsfWaDTi1KlTRYVCYTP2/fffL164cMGp2AsXLhQDAgJsxu3atat4+vRpp+J+8803Ynh4uM24CQkJ4h9//OFUXF+RmpoqPvDAA6JKpbL7mQEgBgUFiQsWLBD1er1D8fmZ8U0vvfSSQ58X49cdd9zh8PvFz8ytqW/fvpXes+nTpzv0LD8zvmn79u1O/XfG+JWammo3Nj8z1jE5Ja+g1WrFPn36yP4CBAYGih07dhS7du0qhoWFyV7r06ePqNVq3d3tW9LZs2cd+o93VZJT819O+Pv7i+3atRO7desm1qlTR/Za27ZtxcLCQofizpw5U/asQqEQW7duLfbo0UOMjo6WvRYTEyNmZ2c7FPfLL7+s9HPHx8eLPXv2rJTAh4WFif/884/T74m3++677yq9R35+fmJCQoLYo0cPsVu3bmJERESlOmPGjHEoQeVnxjdNnjxZ9l4EBweLbdu2FXv27CkmJSWJzZs3r/SPvpCQEPHXX3+1G5ufmVvPV199ZfH/pxxNTvmZ8U2myalarRb79u3r0FdGRobd2PzMWMfklLzC1KlTZR/8sWPHipcvX5ZeLyoqEt98801Znddee82NPb51mSandevWFfv16ye+8cYb4g8//FCt5HTJkiWy5x988EExMzNTer28vFx8//33RaVSKdUZPny43bi//PKLKAiC9ExiYqKYlpYmva7T6cRVq1aJISEhUp1u3brZjXv48GHZby7j4+PFgwcPyups3rxZjIqKkuo0bdpULCkpceJd8X7G5FSpVIoPP/yw+MMPP4j5+fmyOnq9Xvzhhx/Ehg0byj4DH374oc3Y/Mz4rjfeeEN84IEHxE8++UQ8fvy4xToXLlwQX3/9ddHPz096v2JjY23+I4+fmVvPxYsXxcjISBGA2KJFC7FBgwZOJaf8zPgu0+Q0Li7OZXH5mbGNySl5vKysLFGtVksf+FGjRlmt+8Ybb8h+y5WVlXUTe0qiKIr5+fnid999J6anp1d6rarJaXFxsVi/fn3p2aSkJKsj40uXLpXqCYIgHjp0yGpcvV4vtmvXTqqfkJAgFhcXW6y7ZcsWWf/XrFljs88DBgyQ6kZGRoq5ubkW6/3zzz+y/+jPnz/fZlxf88MPP4hjxowRz507Z7fu+fPnZZ+DyMhIsby83GJdfmbI6NNPP5X9OXz22WcW6/Ezc2saOXKk9L7s3LlTNgpkLznlZ8a31URyys+MfUxOyeO9/PLL0gc9KChINmJqrqysTIyNjZXqv/LKKzexp2RPVZPTxYsXy/4DfezYMZv1u3TpItUfOnSo1Xo///yzrE+//PKLzbiPPvqoVLdz585W6x09elQW9+OPP7YZd8qUKVLd+vXrizqdzmb9W5n5b5y3bt1qsR4/M2SqWbNm0vv1+OOPW6zDz8ytZ9OmTdJ7Mnr0aFEURaeSU35mfFtNJKf8zNjH5PT/27v7oCjqPw7g7+PpQEGFCCVBOQUVS3xIaMinGk6pNKdJy3FqRhptcrScBkkqK8fsj/DpNyUTNan8U6mJho1OSIqG5zjpUFIjhIMhHiggCMSBPMn394fDzu3d3t0eCgd379fMzexyn++HZe8Nx5fdZWnQi46OloKekpLisP6TTz6R6qOjowdgC0mtvk5Ok5KSZH9ldGT//v1SfWBgoGhvb1esW716tVSn0+kcXsNYUFAg+xpsXVeybds22ed3dK3ItWvXZH0NBoPDr9FT3bx5U7avMjMzFeuYGTL3yiuvSPtq0aJFijXMjGdpbW0VOp1OAPePBtXX1wshnJucMjPurT8mp8yMY7yVDA1qZWVlKC8vl9afe+45h2Oef/55abm8vBxlZWX9sm00MEwmEwoLC6V1ZzNgMplw9uxZxboTJ05Iy8nJydBoNHb7zps3D8OHD1ccb6vv3LlzERgYaLfvhAkTMHnyZGn9+PHjdus9WUhIiGz9v//+s6phZsiS+W0TgoKCrJ5nZjzPxx9/jIqKCgDAzp078cgjjzg1npkhZzEz6nBySoNacXGxbD0xMdHhmFmzZsHPz09a/+uvvx76dtHAKSkpQVdXl7SuJgNjxoxBVFSUtK6Ugbq6OtTU1DjV18fHB/Hx8Xb7CiHw999/O9XXso6Zta2yslK2HhYWZlXDzJC5rq4uXLhwQVpX2r/MjGcpKirCF198AQBYsGABVq1a5XQPZoacxcyow8kpDWqlpaXSsp+fHyIjIx2Osawz70FDj+XrN3HiRFXjzOuUMtBffW/cuIHW1taH3pfuO3r0qGxd6c2SmSFzmzdvln5xCwkJQUpKilUNM+M5uru7sWbNGty7dw9+fn74+uuv+9SHmfEsTU1NePXVVxEVFYWAgAAEBQVBp9PhpZdeQmZmpuJZPJaYGXU4OaVB7fr169JyRESEw9MUeo0bN06xBw095q+fj48PwsPDVY1zlAHLj5nXu7pvZWUlhBCqxnmS5uZm6WgHAMTFxWHq1KlWdcyMZ+vu7satW7eQm5uLRYsWYceOHQAAf39/HDhwQPH0TWbGc+zatQuXL18GAKSnp2PKlCl96sPMeJbm5mYcPnwYlZWVaG9vh8lkwvXr13Hs2DG88847GDduHPbs2WO3BzOjjo9T1UQDrKWlRVoeOXKk6nEjRoxQ7EFDj/nrFxQUBC8vdX9Tc5QBy4+pzddA9O3p6UFbW5vsehACNm7cKDt16bPPPlOsY2Y8T2hoKBoaGhSf02g0WLhwIXbt2oUnnnhCsYaZ8QzXrl3D1q1bAQDR0dH48MMP+9yLmfE8UVFRGDt2LLRaLerr61FSUiJdz97c3IwNGzbg8uXL2Ldvn+J4ZkYdHjmlQc1kMknL/v7+qscFBAQo9qChp78yYPkxtb0Hoq+t3p5s7969sjf8FStW4MUXX1SsZWbI3Jw5c7B27VrFo+y9mBnP8NZbb+Hu3bsAgKysLKdea0vMjPvz8vKCXq/H999/j4aGBlRUVMBgMOD06dMoLi5GY2MjsrKyEBoaKo3Zv38/MjIyFPsxM+rwyCkNaub/YdHHR31czWvNLz6noae/MmDe15neA9HXVm9PVVhYiPXr10vrOp0O33zzjc16ZsbzJCUlobm5GQDQ0dGBmpoaXL16FT09PTAYDDAYDIiPj8ehQ4eg0+msxjMz7i87OxunT58GALz22mvQ6/UP1I+ZcX/z58/Hr7/+avP5wMBArF27FosXL8b8+fOl02A//fRTpKSkYPTo0bJ6ZkYdHjmlQW3YsGHScnt7u+px5rWeevqJu+ivDJj3dab3QPS11dsTXb58GUuXLkVnZyeA+/+dNy8vz+7pRcyM5zl06BDy8vKQl5eHM2fOoLS0FLdv30ZGRoa0Xy5duoQFCxagrq7Oajwz497q6uqQlpYGAAgODsbu3bsfuCczQ70iIyNx6NAhab2trU3x1F5mRh1OTmlQM7+fUu+pOGq0tbUp9qChp78yYPkxtb0Hoq+t3p6mrKwMycnJ0hGx4OBg5OfnY9KkSXbHMTME3P/PvJs2bcK5c+eke5sajUZs3LjRqpaZcW8bNmzAnTt3AACff/654i2onMXMkLmEhAQ888wz0rrSEVdmRh1OTmlQMz+P/9atW6rHmf/TFGdvrE2Di3kGTCaT6msXHGXAvC+gPl8D0TcoKAi+vr6qxrmriooK6PV66ShXUFAQfvnlF0yfPt3hWGaGzM2cORObN2+W1g8ePChNVHoxM+7rwoUL0lGtxMREvPnmmw+lLzNDlp599llp+erVq1bPMzPqcHJKg9rkyZOl5YaGBqu/xthiNBql5b7+m3gaHMwzANy/75YajjLQX30nTZoku+XRw+rrSaqqqpCUlISqqioA908tOn78OJ566ilV45kZsrR8+XJpubu7G5cuXZI9z8y4r9raWmn5woUL8PLygkajsfmorKyU6rdu3Sp7zvzWGswMWRozZoy0XF9fb/U8M6MOJ6c0qMXGxsrWe+9NZk91dTVu375tswcNLX3JQFdXF65cuWKzBwDExMTILtpX0xcA/vzzT7t9AwMDERER8dD7eora2lro9XpUVFQAALRaLXJzczF//nzVPZgZshQZGSlbt7ztDDNDzmJmyJL5ARTL6zUBZkYtTk5pUEtISIBWq5XWDQaDwzHnzp2Tlv39/ZGQkNAv20YDY8KECbIfjmoyUFRUJHuTUJrY+Pn5yY7EqelbU1OD8vJyu30tP66mb1dXF37//XeHfd1dQ0MD9Ho9ysrKAAC+vr7IycnBwoULnerDzJCl3uuWe40aNUq2zsy4L19fX4wcOVL1w/xIkVarlT1nfl9KZoYsmU8ila5rZmZUEkSD3AsvvCAACAAiLi7OYf2SJUuk+sWLFw/AFpJava8LAJGdna163Lp166RxoaGhoqOjw27922+/LdU//vjjNuu2b98u1fn6+oqamhq7fXfu3CnVjxo1SnR2dirW/fjjj7Kv9Y8//rDbNycnR6r18vIStbW1duvdUVNTk3jyySel/eDt7S0OHz7c537MDJkz318AxD///GNVw8yQEEKMHz9e2k9btmyxW8vMUK/W1lYRFhYm7a+UlBTFOmbGMU5OadCz/Mb4+eefbdYWFRUJb29vqfZBfrmlh6+vk9OLFy/Kxn755Zc2a41GowgKCpJqd+zYYbdWq9VKtampqTZrW1paxLhx46Ta9evX26w1mUzi0UcflWpffvllm7Xd3d0iPj7eo/+gYjKZxJw5c2RvaN99990D9WRmqFdHR4eYMWOGtL8mTpyoWMfMkBDOTU6ZGeqVmpoqy0Jubq5iHTPjGCenNOj19PSI6dOnS2EPDw8XpaWlVnU3b94UsbGxUt2MGTNET0+PC7aYbOnr5FQIIZYuXSqNDQwMFAaDwaqmublZzJs3T6obM2aMaGtrs9t3w4YNUr23t7fIycmxquns7BTLly+X6gICAkR1dbXdvrt375Z9vf/73/+sanp6esS7774r1Wg0GlFUVGR/R7iZ9vZ2odfrZftg3759D6U3M+Oe8vPzRVpamsP9KcT994VFixbJ9uu3335rs56ZIWcmp0IwM+7q5MmTIjU1VRiNRrt1nZ2dIj09XbZPZ82aZff3T2bGPk5OaUi4ePGiCAgIkEI/YsQIkZ6eLk6cOCFOnjwptm3bJkaPHi37Zrt48aKrN9tjrVmzRmi1WquH+Q83Hx8fxRpbKioqRGhoqDReq9WKdevWidzcXHHq1Cmxe/duodPppOe9vLzsHmXvdefOHRETEyMb9/rrr4ucnBxRUFAgsrKyRFxcnGzbMzMzHfbt6OgQTz/9tGzckiVLxIEDB8SZM2dEdna2mDt3ruz5tLQ0p/azO8jIyJDtg+DgYJGcnKz6sXPnTpu9mRn39NNPP0n7fe7cueKDDz4QP/zwg8jPzxcGg0Hk5eWJr776SqxcuVIMGzZMtr+WLl1q95dGZoacnZwyM+7J/OfMvHnzxEcffSQOHjwoTp06JQoLC8XRo0fF+++/LyIjI2X7KiQkRPGyAXPMjH2cnNKQceTIEdkE1dYjICBAHDlyxNWb69FWrVrl8HWy9bDn/PnzIiQkxGEPb29vsWfPHtXbW1ZWZvUGY+uxadMm1X3r6urEtGnTVPVduXKluHfvnure7mLLli19zgoAsWrVKrv9mRn30/tLo7OPN954w+H1XUIwM57O2cmpEMyMO+rLz5mYmBiH12T2YmZs4+SUhpSSkhKh1+uFRqOx+mbQaDQiKSlJXLlyxdWb6fH6a3IqhBBVVVVi2bJlwsfHR3F8fHy8OH/+vNPb3NjYKFavXm3zDyCxsbHi2LFjTve9e/eueO+998TIkSMV+44fP17s3bvX6b7uor8np0IwM+7GaDSK1NRUMXXqVMX3AvOHn5+fWLZsmfjtt9+c+hzMjOfqy+RUCGbG3ZSWlooVK1aIiIgIh+9DUVFRYvv27cJkMjn1OZgZZRohhADREGM0GnH+/HlUV1cDAMaOHYs5c+ZY3cuO3Nft27dRWFiIqqoqdHZ24rHHHsPs2bOtbkbtrJaWFhQUFMBoNKK1tRXh4eGYNm0aZs6c+UB929vbcfbsWVy/fh2NjY0YPXo0pkyZgsTERNmtC6j/MDPup6mpCcXFxfj3339RX1+Pjo4ODB8+HMHBwYiNjcX06dPh7+/f5/7MDDmLmXE/N27cQElJCerr61FfX4/W1laMGDECYWFhmD17NiZOnPhA/ZkZOU5OiYiIiIiIyOW8HJcQERERERER9S9OTomIiIiIiMjlODklIiIiIiIil+PklIiIiIiIiFyOk1MiIiIiIiJyOU5OiYiIiIiIyOU4OSUiIiIiIiKX4+SUiIiIiIiIXI6TUyIiIiIiInI5Tk6JiIiIiIjI5Tg5JSIiIiIiIpfj5JSIiIiIiIhcjpNTIiIiIiIicjlOTomIiIiIiMjl/g851IU4ZzFjCQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "diff=(esbath.correlation_function(tlist2))-obs.correlation_function(tlist2)\n", + "tlist3=np.linspace(0,tlist2[np.argmax(diff)],1000)\n", + "# plt.plot(tlist3,pbath.correlation_function(tlist3),\"k--\")\n", + "# plt.plot(tlist3,Obath.correlation_function(tlist3),\"b\")\n", + "# plt.plot(tlist3,obs.correlation_function(tlist3),\"r\")\n", + "\n", + "plt.plot(abs(diff),label=\"Prony\")\n", + "plt.plot(abs(Obath.correlation_function(tlist2)-obs.correlation_function(tlist2)),label=\"CORR\")\n", + "plt.legend()\n", + "#plt.yscale(\"log\")" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "0d282401", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gen_plots(esbath, w, J, t, C, w2, S)" + ] + }, + { + "cell_type": "markdown", + "id": "413f223a", + "metadata": {}, + "source": [ + "## Using the AAA Algorithm" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "5a685a80", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mcditoos/qutip_gsoc_app/qutip/utilities.py:54: RuntimeWarning: overflow encountered in exp\n", + " result[non_zero] = 1 / (np.exp(w[non_zero] / w_th) - 1)\n" + ] + } + ], + "source": [ + "aaabath=obs.approx_by_aaa(np.concatenate((-np.logspace(3,-2,2500),np.logspace(-2,3,2500))),N_max=12,tol=1e-15)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "44f9f518", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tlist3=np.linspace(-250,250,1000)\n", + "\n", + "diff=(pbath.correlation_function(tlist3))-obs.correlation_function(tlist3)\n", + "diff2=(aaabath.correlation_function(tlist3))-obs.correlation_function(tlist3)\n", + "\n", + "# plt.plot(tlist3,pbath.correlation_function(tlist3),\"k--\")\n", + "# plt.plot(tlist3,Obath.correlation_function(tlist3),\"b\")\n", + "# plt.plot(tlist3,obs.correlation_function(tlist3),\"r\")\n", + "\n", + "\n", + "\n", + "plt.plot(diff.real,label=\"Prony\")\n", + "plt.plot(diff2.real,label=\"AA\")\n", + "\n", + "#plt.plot(abs(Obath.correlation_function(tlist3)-obs.correlation_function(tlist3)),label=\"CORR\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "787b1ae6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [******** 33% ] Elapsed 786.49s / Remaining 00:00:26:36" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[53], line 7\u001b[0m\n\u001b[1;32m 1\u001b[0m HEOM_ohmic_aaa_fit \u001b[38;5;241m=\u001b[39m HEOMSolver(\n\u001b[1;32m 2\u001b[0m Hsys,\n\u001b[1;32m 3\u001b[0m (aaabath,Q),\n\u001b[1;32m 4\u001b[0m max_depth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m,\n\u001b[1;32m 5\u001b[0m options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[1;32m 6\u001b[0m )\n\u001b[0;32m----> 7\u001b[0m results_ohmic_aaa_fit \u001b[38;5;241m=\u001b[39m \u001b[43mHEOM_ohmic_aaa_fit\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrho0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtlist\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/heom/bofin_solvers.py:1119\u001b[0m, in \u001b[0;36mHEOMSolver.run\u001b[0;34m(self, state0, tlist, args, e_ops)\u001b[0m\n\u001b[1;32m 1052\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun\u001b[39m(\u001b[38;5;28mself\u001b[39m, state0, tlist, \u001b[38;5;241m*\u001b[39m, args\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, e_ops\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 1053\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1054\u001b[0m \u001b[38;5;124;03m Solve for the time evolution of the system.\u001b[39;00m\n\u001b[1;32m 1055\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1117\u001b[0m \u001b[38;5;124;03m list of attributes.\u001b[39;00m\n\u001b[1;32m 1118\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1119\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtlist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43me_ops\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43me_ops\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/solver_base.py:197\u001b[0m, in \u001b[0;36mSolver.run\u001b[0;34m(self, state0, tlist, e_ops, args)\u001b[0m\n\u001b[1;32m 192\u001b[0m stats[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpreparation time\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m time() \u001b[38;5;241m-\u001b[39m _time_start\n\u001b[1;32m 194\u001b[0m progress_bar \u001b[38;5;241m=\u001b[39m progress_bars[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprogress_bar\u001b[39m\u001b[38;5;124m'\u001b[39m]](\n\u001b[1;32m 195\u001b[0m \u001b[38;5;28mlen\u001b[39m(tlist)\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprogress_kwargs\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 196\u001b[0m )\n\u001b[0;32m--> 197\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_integrator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtlist\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 198\u001b[0m \u001b[43m \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 199\u001b[0m \u001b[43m \u001b[49m\u001b[43mresults\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_restore_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/integrator/integrator.py:201\u001b[0m, in \u001b[0;36mIntegrator.run\u001b[0;34m(self, tlist)\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 188\u001b[0m \u001b[38;5;124;03mIntegrate the system yielding the state for each times in tlist.\u001b[39;00m\n\u001b[1;32m 189\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;124;03m The state of the solver at each ``t`` of tlist.\u001b[39;00m\n\u001b[1;32m 199\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m tlist[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[0;32m--> 201\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/integrator/qutip_integrator.py:55\u001b[0m, in \u001b[0;36mIntegratorVern7.integrate\u001b[0;34m(self, t, copy)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mintegrate\u001b[39m(\u001b[38;5;28mself\u001b[39m, t, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 55\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_ode_solver\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_failed_integration()\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_state(copy)\n", + "File \u001b[0;32mqutip/solver/integrator/explicit_rk.pyx:278\u001b[0m, in \u001b[0;36mqutip.solver.integrator.explicit_rk.Explicit_RungeKutta.integrate\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mqutip/solver/integrator/explicit_rk.pyx:313\u001b[0m, in \u001b[0;36mqutip.solver.integrator.explicit_rk.Explicit_RungeKutta.integrate\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "HEOM_ohmic_aaa_fit = HEOMSolver(\n", + " Hsys,\n", + " (aaabath,Q),\n", + " max_depth=5,\n", + " options=options,\n", + ")\n", + "results_ohmic_aaa_fit = HEOM_ohmic_aaa_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80f55ad6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gen_plots(aaabath, w, J, t, C, w2, S)" + ] + }, + { + "cell_type": "markdown", + "id": "0f305b40", + "metadata": {}, + "source": [ + "Finally we plot the dynamics obtained by the different methods" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5ba2889a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", + "\n", + "plot_result_expectations(\n", + " [\n", + " #(\n", + " # results_corr_fit_pk[2],\n", + " # P11p,\n", + " # \"b\",\n", + " # \"Correlation Function Fit $k_R=k_I=3$\",\n", + " # ),\n", + " #(results_spectral_fit_pk[3], P11p, \"r-.\", \"Spectral Density Fit $k_J=4$\"),\n", + " (results_ohmic_corr_fit, P11p, \"r\", \"Correlation Fit Ohmic Bath\"),\n", + " #(results_ohmic_sd_fit, P11p, \"g\", \"Spectral Density Fit Ohmic Bath\"),\n", + " (results_ohmic_sd_fit2, P11p, \"g--\", \"Spectral Density Fit Ohmic Bath\"),\n", + " #(results_ohmic_prony_fit, P11p, \"g\", \" Prony Fit\"),\n", + " (results_ohmic_prony_fit, P11p, \"k\", \" Prony Fit\"),\n", + "\n", + " (results_ohmic_mp_fit, P11p, \"r\", \"Matrix Pencil Fit\"),\n", + " (results_ohmic_es_fit, P11p, \"b-.\", \"ESPRIT Fit\"),\n", + " (results_ohmic_aaa_fit, P11p, \"r-.\", \"Matrix AAA Fit\"),\n", + "\n", + "\n", + " ],\n", + " axes=axes,\n", + ")\n", + "axes.set_yticks([0.6, 0.8, 1])\n", + "axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", + "axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", + "axes.legend(loc=0, fontsize=20);\n", + "#axes.set_xlim(0,35)\n", + "#axes.set_ylim(0.9,1)\n", + "#axes.set_yscale(\"log\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bae93823", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "d0fc9218", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1eb99ec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "QuTiP: Quantum Toolbox in Python\n", + "================================\n", + "Copyright (c) QuTiP team 2011 and later.\n", + "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", + "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", + "Original developers: R. J. Johansson & P. D. Nation.\n", + "Previous lead developers: Chris Granade & A. Grimsmo.\n", + "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", + "\n", + "QuTiP Version: 5.1.0.dev0+7941773\n", + "Numpy Version: 1.26.4\n", + "Scipy Version: 1.14.1\n", + "Cython Version: 3.0.9\n", + "Matplotlib Version: 3.9.2\n", + "Python Version: 3.12.7\n", + "Number of CPUs: 16\n", + "BLAS Info: Generic\n", + "INTEL MKL Ext: None\n", + "Platform Info: Linux (x86_64)\n", + "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", + "================================================================================\n", + "Please cite QuTiP in your publication.\n", + "================================================================================\n", + "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" + ] + } + ], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "725e989d", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa50ddbb", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'results_spectral_fit_pk' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[59], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m np\u001b[38;5;241m.\u001b[39mallclose(\u001b[43mresults_spectral_fit_pk\u001b[49m[\u001b[38;5;241m3\u001b[39m]\u001b[38;5;241m.\u001b[39mstates[\u001b[38;5;241m5\u001b[39m]\u001b[38;5;241m.\u001b[39mfull(),results_corr_fit_pk[\u001b[38;5;241m2\u001b[39m]\u001b[38;5;241m.\u001b[39mstates[\u001b[38;5;241m5\u001b[39m]\u001b[38;5;241m.\u001b[39mfull(),atol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-3\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'results_spectral_fit_pk' is not defined" + ] + } + ], + "source": [ + "assert np.allclose(results_spectral_fit_pk[3].states[5].full(),results_corr_fit_pk[2].states[5].full(),atol=1e-3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a7fb31c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "414ba293", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80d35a6b", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20dd8b39", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed975955", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b6b493d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9184bc82", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d61f4c20", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b7f2f42", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cebe18a4", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a006120", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12b235a3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "qutip-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.ipynb b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.ipynb index 8d717dd8..939e9657 100644 --- a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.ipynb +++ b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.ipynb @@ -1730,7 +1730,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb index 8072ae68..bcc54041 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb @@ -60,7 +60,7 @@ "from qutip.solver.heom import (\n", " HEOMSolver\n", ")\n", - "from qutip.core.environment import BosonicEnvironment,_sd_fit_model,OhmicEnvironment\n", + "from qutip.core.environment import BosonicEnvironment,OhmicEnvironment\n", "\n", "# Import mpmath functions for evaluation of gamma and zeta\n", "# functions in the expression for the correlation:\n", @@ -73,25 +73,6 @@ "%matplotlib inline" ] }, - { - "cell_type": "code", - "execution_count": 2, - "id": "2eb48e5a", - "metadata": {}, - "outputs": [], - "source": [ - "# Solver options:\n", - "\n", - "options = {\n", - " \"nsteps\": 15000,\n", - " \"store_states\": True,\n", - " \"rtol\": 1e-14,\n", - " \"atol\": 1e-14,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, { "cell_type": "markdown", "id": "65a7dfbb", @@ -112,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "ac95be0b", "metadata": {}, "outputs": [], @@ -134,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "d79edfb4", "metadata": {}, "outputs": [], @@ -186,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "bfb44fda", "metadata": {}, "outputs": [], @@ -212,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "9e798939", "metadata": {}, "outputs": [], @@ -226,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "7691064b", "metadata": {}, "outputs": [], @@ -259,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "8d58b8c8", "metadata": {}, "outputs": [], @@ -281,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "297850af", "metadata": {}, "outputs": [], @@ -290,7 +271,12 @@ "\n", "# The max_depth defaults to 5 so that the notebook executes more\n", "# quickly. Change it to 11 to wait longer for more accurate results.\n", - "max_depth = 5" + "max_depth = 5\n", + "# options used for the differential equation solver, while default works it \n", + "# is way slower than using bdf\n", + "options = {\n", + " \"nsteps\":15000, \"store_states\":True, \"rtol\":1e-12, \"atol\":1e-12, \"method\":\"bdf\",\n", + "}" ] }, { @@ -325,12 +311,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "f6b46bc0", "metadata": {}, "outputs": [], "source": [ - "w = np.linspace(0, 15, 20000)\n", + "w = np.linspace(0, 25, 20000)\n", "J = ohmic_spectral_density(w, alpha, wc)" ] }, @@ -347,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "a60b1cda", "metadata": {}, "outputs": [], @@ -369,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "a18793bf", "metadata": {}, "outputs": [], @@ -387,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "id": "0239acf7", "metadata": {}, "outputs": [], @@ -407,7 +393,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "id": "f155cacf", "metadata": {}, "outputs": [ @@ -417,7 +403,7 @@ "True" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -438,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "id": "b07566c5", "metadata": {}, "outputs": [ @@ -454,7 +440,7 @@ }, { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -490,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "id": "f71a9644", "metadata": {}, "outputs": [], @@ -501,17 +487,17 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "id": "d2ac2faf", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, @@ -564,16 +550,14 @@ "will cover the following approaches:\n", "\n", "- Non-Linear Least Squares:\n", - " - On the Spectral Density (`.approx_by_sd_fit`)\n", - " - On the Correlation function (`.approx_by_cf_fit`)\n", + " - On the Spectral Density (`spec_lsq`)\n", + " - On the Correlation function (`corr_lsq`)\n", "- Methods based on the Prony Polynomial\n", - " - Prony on the correlation function(`.approx_by_prony`)\n", - " - The Matrix Pencil method on the correlation function (`.approx_by_mp`)\n", - " - ESPRIT on the correlation function(`.approx_by_esprit`)\n", + " - Prony on the correlation function(`prony`)\n", + " - The Matrix Pencil method on the correlation function (`mp`)\n", + " - ESPRIT on the correlation function(`esprit`)\n", "- Methods based on rational Approximations\n", - " - The AAA algorithm on the Power Spectrum (`.approx_by_aaa`)\n", - " - The AAA algorith with balanced truncation (`.approx_by_aaa` with `btm=True`)\n", - " - ESPIRA\n" + " - The AAA algorithm on the Power Spectrum (`aaa`)\n" ] }, { @@ -614,12 +598,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "id": "81adee22", "metadata": {}, "outputs": [], "source": [ - "bath, fitinfo = sd_env.approx_by_sd_fit(w,Nmax=6)" + "bath, fitinfo = sd_env.approximate(\"spec_lsq\",w,Nmax=4)" ] }, { @@ -632,7 +616,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "id": "71a7c82a", "metadata": {}, "outputs": [ @@ -642,14 +626,14 @@ "text": [ "Result of fitting the spectral density with 4 terms: \n", " \n", - " Parameters| lam | gamma | w0 \n", - " 1 |-4.44e+00 | 4.31e+00 |3.96e+00\n", - " 2 | 6.07e-01 | 1.01e+00 |1.00e-01\n", - " 3 | 7.93e+00 | 2.30e+00 |1.00e-01\n", - " 4 | 1.07e-02 | 3.09e-01 |1.00e-01\n", + " Parameters| a | b | c \n", + " 1 |-4.41e+00 | 4.30e+00 |3.98e+00\n", + " 2 | 6.01e-01 | 1.00e+00 |1.00e-01\n", + " 3 | 7.92e+00 | 2.30e+00 |1.00e-01\n", + " 4 | 1.06e-02 | 3.07e-01 |1.00e-01\n", " \n", - "A normalized RMSE of 2.64e-06 was obtained for the the spectral density.\n", - "The current fit took 23.837589 seconds.\n" + "A 1-R2 coefficient of 1.38e-06 was obtained for the the spectral density.\n", + "The current fit took 42.143630 seconds.\n" ] } ], @@ -667,13 +651,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "id": "d8587f0d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -709,13 +693,13 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "id": "bd7aec4a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -725,7 +709,7 @@ } ], "source": [ - "bath, fitinfo = sd_env.approx_by_sd_fit(w,Nmax=6,Nk=3)\n", + "bath, fitinfo = sd_env.approximate(\"spec_lsq\",w,Nmax=4,Nk=3)\n", "\n", "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5))\n", "\n", @@ -761,13 +745,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "id": "882c64e5", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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efRWo47XnRERERNbo2bOn1DYfubQm5vMMBgcHo0WLFrYuq1p9+/aV2rt377bq/rydO3fasSLbMB9ox/xnrM7+/fvtWY5TYDgkMjd9OjByJPDvfwNHjshdDREREbmhIUOGSO3Vq1dbNbjJihUrpPZtt90GhT0G6KvGHXfcIbVTU1NrnQC+pKQE33zzjZ2rarjS0lKpXdufp9FoxPfff2/vkmTHcEjO7+GHgRdfBBzxD3LEiPL25s32Px4RERF5nBkzZkjt1NRULF68uMb1V69ebdHD+Nhjj9mrtCp16dIFAwYMkJZffPFFpKenV7v+66+/joSEBAdU1jBNmzaV2vv27atx3Q8//BBXr161d0myYzgk55afD3z3nTh577Jl9j/eqFHl7S1b7H88IiIi8jjt27fH1KlTpeVXX30Va6oZNf3AgQN49NFHpeXu3bvjzjvvtHuNFX3wwQdS71p8fDxuv/12/PnnnxbrZGVl4dlnn8U777xj9RQdcho6dKjUXrhwYbUT23/77bf45z//6aiyZMVwSM7t8uXydtu29j9eTEz5dBkHDgAFBfY/JhEREXmc//u//5MGQNHr9ZgyZQomT56MH3/8Ebt378aaNWvw+OOPY8iQIdJIpT4+Pvjvf/8LlUrl8HoHDRqE119/XVo+c+YMBg0ahFatWmHYsGHo06cPIiIi8NFHHwEAvvrqK4vtK07F4QyeeeYZKfAmJSWhZ8+eWLhwIbZu3YqdO3fiyy+/RFxcnDTdxeOPPy5zxfbHqSzIuZlfktC6tWOOOXw4cPGiOG3G3r3A6NGOOS4RERF5jPDwcOzcuRMjR45EUtm8jmvXrsXatWurXD8gIADr169Ht27dHFmmhddffx1qtRpvvPGGdL9efHy8xYirWq0Wy5Ytw8iRIy22rTiFhzPo3bs33nzzTcybNw8AkJaWZhGAzd1zzz2YO3cuvvjiC0eW6HDsOSTnZh4OHTWNxfDh5e0//nDMMYmIiMjjdOjQASdOnMCcOXPg5+dX5TpeXl647777cPr0adx+++2OLbAKr776Ko4dO4annnoKbdu2ha+vL4KCgtC1a1e89NJLOHXqFB5++GHcunVL2kar1cLX11fGqqv36quv4ssvv0R4eHiV70dERGDx4sX46aefHDoIkFwUgiAIchdBtpebm4ugoCDk5OQgMDBQ7nLq77nngCVLxPbu3YDZ6F52k5YGmE4QffoAhw/b/5hERES10Ol0iI+PR8uWLeHj4yN3OWRjOp0Ou3fvxtWrV5GZmYnAwEBER0fj9ttvd8nf5VavXi3dVzlgwIBK9yc6G51Ohz179uDMmTMoKipCeHg42rRpg9tuu02Wy3jN67LFv3trs4FHXVaalpaGv/76C4cPH5aeb968Kb3/9ddfY/r06Xav4+rVq/jmm2/w22+/4fr168jPz0dkZCS6deuGBx54AJMmTYJa7VF/NdW7dq287aj5fMLCgG7dgJMngaNHgawsIDjYMccmIiIij+Tj44NR5gPjuTjzew4HDhwoYyXW8fHxwciRIytdDutpPCKB3Lx5EwMGDMA186AhkyVLluDll19GcXGxxetXr17F1atXsW7dOgwYMAArVqxAq1atZKrSiZguK1WrgchIxx132DAxHBqNYo/lxImOOzYRERGRExIEwapLK//73/9i48aN0rIjOl/INjzinkOdTucUwfDNN9/Ec889JwVDpVKJLl26IDY21mKelQMHDmDo0KG4ceOGXKU6D1M4jIoCHNmlP3w40Lkz8PTTjrvXkYiIiMiJLVy4EDNnzsTOnTuh1+srvZ+UlIQXXnjBIgxOnDhR1kF0qG48oufQXFhYGHr37o0+ffqgT58+mDRpkkOOu3nzZovRjwYOHIhvvvkG7cqmTTAajVi5ciUef/xx5OfnIykpCXfffTf27t3rkPqcUk4OkJ0tth11SanJ+PHAhAmOPSYRERGREysqKsIXX3yBL774Aj4+Pmjfvj1CQ0Oh1+uRnJyMK1euWKwfExOD5cuXy1Qt1YdHhMOQkBCsXLkSffv2RYwMvUCCIODll1+Gaeyf9u3bY9u2bRajNimVSkybNg2hoaHStc779u3D2rVrcddddzm8Zqeg14sD0iQkAD16OPbYHjAaFREREVFdKJXlFx3qdDqcOHGi2nWHDRuGH374QZrLkVyDR4TDwMBAabQkOWzatMniH8+SJUuqHc43Li4O06ZNw08//QQAeOeddzw3HIaGAosWyV0FEREREQF44403EBsbiy1btuCvv/7ClStXkJmZCb1ej0aNGiEyMhKDBw/G1KlTMdx8ajByGR4RDuW2Zs0aqd2yZctaR6KaPXu2FA4PHTqEpKQkNG/e3K41Ug1u3AASE4F+/eSuhIiIiEg2Xl5eGDNmDMaMGSN3KWQnHjEgjdx+++03qT169OhaR3kaMmSIxUSo5tuTAxkM4uWskZHAgw/KXQ0RERERkV0xHNrZrVu3LOZStGaeF7Vajb59+0rLJ0+etEttTq+wECi7T1MWKhUQECC2L10CUlLkq4WIiIiIyM4YDu3s3LlzFsutW7e2ajvz9Sruw2OMGAH4+QFt24qD08hh6NDy9p498tRAREREROQADId2lmCap69MdHS0VduZr1dxHx4jORkoKhKntFDLdHtsbGx5e9cueWogIiIiInIADkhjZ3l5eRbLQUFBVm0XGBhY7T6qUlxcjOLiYmk5NzfXygqdlNEoDgQDAM2ayVfHoEHi5aUGA7B7t3x1EBERERHZGXsO7Sw/P99i2cfHx6rttFpttfuoyttvv42goCDpERUVVbdCnc2tW+WXksoZDv39gd69xfaZM0B6uny1EBERERHZEcOhnekr3CuntvLySPP1SktLa13/lVdeQU5OjvRITEysW6HOJjm5vC1nOAQsLy3lfYdERERE5KYYDu2s4mT3Op3Oqu3M1zOf1qI6Go0GgYGBFg+X5qzhkJeWEhEREZGbYji0M39/f4vloqIiq7YrLCysdh8ewTwcRkbKVwcA3HYbYJqbkuGQiIiIiNwUw6GdNW7c2GL5hmmQlVqYz40YGhpq05pcgjP1HAYHA127AhoNEBQk37QaRERERER2xHBoZ+3bt7dYvn79ulXbmd8z2KFDB5vW5BKcKRwCwNq1QHY28Mcf8k2rQURERERkRwyHdta2bVuLwWWOHz9u1XbHjh2T2h07drR1Wc7P2cJhq1aAlSPNEhERERG5InaB2Jm3tzf69++Pffv2AQD27t1b6zY3b97E5cuXpeVY8wFRPMWnnwJXr4ohMSRE7mqIiIiIiNwew6EDTJw4UQqH27ZtQ2pqKpo0aVLt+itWrJDajRo18sxw2Lq1+HBGgiA+lOx4JyIiIiL3wd9uHeC+++6DRqMBIM5Z+O6771a7bn5+Pj766CNp+YEHHoCXl5fdayQr7NgBTJ0KNGkitomIiIiI3AjDYT0lJCRAoVBIjwULFlS7bvPmzTF79mxpecmSJVi9enWl9UpLSzFjxgxp0BqtVou5c+favHaqp5QUYPVqIC2NU1oQERERkdvxmHA4c+ZM+Pj4VHrUdZ36WrBgAdq2bQsAMBgMuOeee/DQQw9h9erV2LFjB5YtW4Y+ffpg1apV0jbvvfceIuWe408OycnA998DW7daDkwjN/PLexkOiYiIiMjNeEw4LC0tRXFxcaWHOb1eX+s69RUcHIwNGzYgKioKAGA0GvH9999j6tSpGD58OJ588kmcPHlSWv8f//gHnnrqKZsc2+UcPAg89BAwahTw3XdyV1MuKgpo0UJsHzgA2OizQURERJ5txIgR0tVoX331ldzlkAfzmHDoDNq1a4eTJ0/iscceg1arrXKdjh074pdffsF//vMfB1fnRFJTy9s1DNwjC1PvoU4HHD4sby1ERETkFk6cOCG1e/bsKWMljpGdnY21a9dizpw5iI2NRUREBDQaDfz9/REdHY3x48dj8eLFyMrKkrtUj6MQBEGQuwhPlJeXhz/++AOJiYkoKChA06ZN0bVrV5udEHJzcxEUFIScnBwEBgbaZJ8Os2AB8MYbYvu334A77pC1HAtffgk8/rjY/ve/gVdekbceIiLyGDqdDvHx8WjZsqXNbnsh+V2/fh0xMTEAxCnQ8vLy4O3tLXNV9nH+/Hm89NJL2LJlC0pKSmpd39fXF//617/w7LPPQqFQOKBC52Orf/fWZgNOZSGTgIAATJw4Ue4ynJMr9BwC4n2HDIdERETUAMeOHZPanTt3dttgCACnT5/Ghg0bLF5TqVRo06YNmjRpAoPBgHPnziEzMxMAUFhYiOeffx5nzpzBZ5995rEB0ZF4WSk5H2cOh23alNe0bx9gMMhbDxEREbm048ePS+1evXrJV4gDqdVqTJo0CevWrUNmZibOnz+PXbt2Ye/evUhPT8e6devQrFkzaf0vvvgCy5Ytk7Fiz8FwSM7n5s3ydni4fHVURaEAhgwR23l5gNk9AkRERER1Zd5z6O73G3p5eeHxxx/HlStXsHbtWkycOLHSJY4KhQITJ07En3/+iYiICOn11157DaWlpY4u2eMwHJLzMfUcBgcDznhphfmlpfv2yVcHERERuTzzcOjuPYcTJ07E559/jujo6FrXjYqKwhumMSgApKenYzenErM7hkNyPqZw6GyXlJrccQewdClw/Djwt7/JXQ0RERG5qMzMTFy/fh0AoFQq0b179xrXf++996BWq6VpL2bNmmXVwC6uavz48RbL58+fl6kSz8EBaci5FBSID8B5w2Hr1sDTT8tdBREREbk48/sN27dvD19f3yrXy8/Px4wZM7Bq1SoA4qimS5YswRNPPOGIMmUTEhJisZybmytTJZ6D4ZCcS06OONF8aqrzhkMiIiIiG7DmktILFy7grrvuwrlz5wAAERERWL16NQYNGuSQGuV07do1i+VwZxuLwg0xHJJziYwE4uMBQQB40zERERG5sdoGo1m3bh0eeeQRqcesf//+WLNmDSIjIx1Wo5zWrFljsTxw4ECZKvEcvOeQnJNC4ZyD0Zjo9cDOncDChcB778ldDREREbmg6noOjUYj5s6di8mTJ0vB8NFHH8WuXbvqHAy/+eYb6R5FWz6++eYbm/wZVCcnJwdLliyRlrt164ZOnTrZ9ZjEnkOi+jEaxYFpiorEy2BfeknuioiIiCwIgoCiUs7HWxOtl0q2idWLiopw4cIFadnUc5iRkYH77rsPW7duBSBO/7Bo0SI89dRTstQplxdffBE3zaY3e+utt2SsxnMwHBLVh7c30L+/2HuYkAAkJgJRUXJXRUREJCkqNaDTa5vlLsOpnV04Gr7e8vw6fOrUKRgMYnhv2bIlGjVqhCNHjmDKlCnSvXZNmjTBypUrMcQ0x3I9NGvWDKNHj7ZJzRX3ay9ffPEFvvzyS2l52rRplUYuJftgOCTnsngxsHcvEBEBzJ0r3oPorGJjxXAIAHv2APffL2s5RERE5DoqXlL69ddf429/+xt0Oh0AoG/fvlizZg2aN2/eoOOMHDkSI0eObNA+HGn37t0WvaQtW7bE8uXLZazIszAcknPZuxdYvVpsO/ulmubf4u3ezXBIREROReulwtmFtu8xcidaL5VsxzYPh3v27MFq0+8/AB555BEsW7YMPj4+cpQmm+PHj2PChAnS3I3h4eH4/fffERQUJHNlnoPhkJxLenp5OyxMvjqsMXAgoFaLg9Ps2SN3NURERBYUCoVsl0xS7cznOLx165bUnj17NpYtWyZDRfK6cOECRo8ejZycHABAcHAwtmzZgnbt2slcmWfhaKXkXEzhUKsFqpkI1mn4+QGmkcXOnrUMtkRERETVMBgMOHnypLQ8btw4qf3zzz/j4sWLcpQlm/j4eMTFxUkhOSAgAJs2bUL37t1lrszz8Oskci6mgNW4sbx1WCs2Fjh0SGzv3QtMmiRrOUREROT8Lly4gKKiIgDlk9rffvvtOHDgALKysjB+/HgcOHAAwcHBDT7W1q1b8cEHHzR4PxW9+OKLNrmXMSkpCSNGjEBSUhIAwNfXFxs2bED//v0bvG+qO4ZDch6CAGRkiG1XCYdDhgDvvy+29+xhOCQiIqJamd9v2L17d2g0Gqxduxb9+vVDYmIiLl68iLvvvhu///471OqG/bqenJyMzZttP2rtvffe2+B9pKamIi4uDvHx8QAAjUaDdevWITY2tsH7pvrhZaXkPHJzxfv3ANcJh7fdVt7evVu+OoiIiMhlmN9vaLp0MiIiAr/88gt8y26r2b59O5555hk5ynOIjIwMxMXFSXM9enl5YdWqVS41sqo7Yjgk52F+z56rhMOQELG3cNYs4IUX5K6GiIiIXEDFnkOTnj174rvvvoNCoQAALFu2DB999FGDjjV9+nQIgmDzx/Tp0+tdU05ODkaPHo3Tp08DAFQqFX744QfceeedDfpZqeEYDsl5mIfD0FD56qirtWuB5cuB++6TuxIiIiJyAVX1HJpMnjwZb7zxhrT8wgsv2OWyULkUFBRg3LhxOHLkCABAqVTi22+/xdSpU2WujACGQ3ImrthzSERERFQH169fR0bZGAsajQbt27evtM78+fOle/oMBgOmTZuGc+fOObROeyguLsakSZOwb98+AOJ0K59//jkeeOABmSsjEw5IQ86jeXPgmWfEkNizp9zVEBEREdmcea9h586dqx1w5quvvsKVK1dw+PBh5OTkYPz48Th48CBCXenqqgqWLFmCbdu2ScuNGjXCzz//jJ9//tmq7UeOHIkXX3zRXuURGA7JmXTvDjTwunpZZWSI01mMGQNoNHJXQ0RERE6ouvsNK9JqtVi3bh369u2LlJQUXLlyBVOmTMHWrVvh5eXliFJtrrCw0GI5KyurTpfMRkRE2LokqoCXlRLZwiuviJfCTpoEHD4sdzVERETkpKwNhwAQGRmJX375BVqtFgCwa9cuPPnkk3atjzwbew6JbKFNm/L2nj2WU1wQERERlVm3bl2d1u/Tp0+lHjdXtWDBAixYsEDuMqgG7Dkk56HTAYIgdxX1M2RIeZvzHRIRERGRC2I4JOcxapR4r15kJFBSInc1ddO2LdCkidjetw8wGOSth4iIiIiojhgOyXmkpwOlpUBuLuDtLXc1daNQlPce5uUBJ07IWw8RERERUR0xHJLzKJvzx2XnOIyNLW/v2SNfHURERERE9cBwSM7BaHT9cMj7DomIiIjIhTEcknPIySm/T89Vw2HXrkBQkNjes8d1B9chIiIiIo/EcEjOIT29vO2q4VClAgYPFttpacDFi/LWQ0RERERUBwyH5BxMl5QCQGiofHU0VGwsoFYD/fsD2dlyV0NEREREZDW13AUQAXCfcPjEE8DTTwN+fnJXQkRERERUJwyH5ByyssrbwcHy1dFQpnsOiYiIiIhcDC8rJefgLuGQiIiIiMhFseeQnMOUKUCnTmJI7NdP7mpsQxCA4mLAx0fuSoiIiIiIasVwSM4hMlJ8uIOUFODvfxfnOhw/Hvj0U7krIiIiNyJwqiQij+Hof+8Mh0S2FhQErFwJ6PViQCQiIrIBpVK8G8hoNMpcCRE5iunfu+nfv73xnkMiW/PzA3r1Ettnz1rO4UhERFRParUaCoUCxcXFcpdCRA6i0+mgUCigVjumT4/hkJzD1q3Apk3AgQPivXqubsiQ8vbevfLVQUREbkOpVEKr1aKgoEDuUojIQXJzc+Hv78+eQ/Iwzz8P3HEHMGIEoFDIXU3DxcaWt/fska8OIiJyK/7+/igoKEBJSYncpRCRnRUUFECn0yEwMNBhx2Q4JOdgmsrCXaaxGDy4vM37DomIyEaCgoKgVquRlJQEg8EgdzlEZCcFBQVITEyEn58f/P39HXZcDkhDzsHdwmFoKNClC3D6NHDsGJCXBwQEyF0VERG5OLVajaioKCQkJODy5csICgqCv78/VCoVFO5w5Q2RhxIEAUajETqdDrm5udDpdPDz80Pz5s0ddkkpwHBIzqC4GCgqEtvuEg4B8b7D06cBgwH4809g1Ci5KyIiIjeg0WjQsmVLZGdnIycnB1mmL1iJyOUpFAr4+/sjNDTUofcamjAckvzM/1Nzp3AYG1s+x+GePQyHRERkM97e3ggPD0dYWBj0ej0vMSVyA0qlEmq12uGB0BzDIckvM7O87U7h0HzE0sOH5auDiIjclkKhgJeXF7y8vOQuhYjcAMMhyc9dew6bNQOWLwd69wa6d5e7GiIiIiKiGjEckvzcNRwCwKxZcldARERERGQVTmVB8nPncEhERERE5CIYDkl+paWAaf4WhkMiIiIiIlkoBEEQ5C6CbC83NxdBQUHIyclBYGCg3OVYp6REfPb2lrcOWzt8GNi2TXxeuRJQqeSuiIiIiIg8iLXZgPcckvNwt1Bo8u67wKpVYvvkSaBnT3nrISIiIiKqAi8rJbK32Njy9u7d8tVBRERERFQDhkMiezOf73DPHvnqICIiIiKqAS8rJfm99hqQmioORvP224BCIXdFttW1KxAUBOTkiD2HguB+PyMRERERuTz2HJL8Vq8GPvsMWLrUPUOTSgUMHiy209KAixflrYeIiIiIqAoMhyS/7Gzx2Z2nsTC/tJT3HRIRERGRE2I4JPnl5IjPQUHy1mFP5oPS8L5DIiIiInJCDIckL70eKCgQ2+4cDvv0AXx8xDZ7DomIiIjICTEckrxyc8vb7hwOvb2BAQPE9rVrQGKivPUQEREREVXA0UpJXqZLSgH3DocAMGUK0KKFeP9hQIDc1RARERERWWA4JHmZh8PAQPnqcISnn5a7AiIiIiKiavGyUpKXJ/UcEhERERE5MYZDkhfDIRERERGRU+BlpSSviAjg/vvFgWk6dZK7GsfIzgb27QNiYoAuXeSuhoiIiIgIAMMhya1fP2DFCrmrcJw//gDi4gBBAF54AfjgA7krIiIiIiICwMtKiRyre3cxGALArl3y1kJEREREZIbhkMiRQkOBbt3E9tGjQFaWvPUQEREREZVhOCRytOHDxWdBYO8hERERETkNhkOS14wZ4qA07doByclyV+MYpnAIANu3y1cHEREREZEZDkhD8kpNLX9oNHJX4xhDhwIqFWAwiAPUEBERERE5AfYckrxyc8vbnjLPYWAg0KeP2D57FrhxQ956iIiIiIhgx57D5ORknD17FteuXUNaWhoKCgoAAH5+fggLC0NMTAw6d+6MyMhIe5VAriAnR3zWagEvL3lrcaQRI4CDB8X2jh3iXI9ERERERDKyWTjMysrCL7/8gs2bN2Pnzp24deuWVduFh4dj6NChGD16NCZMmIDQ0FBblUSuwBQOPaXX0GT4cODf/xbb27czHBIRERGR7BocDjdt2oTly5fj999/R2lpKQBAMM3jZoXU1FSsXLkSK1euhFqtxpgxYzBr1iyMGzeuoaWRK/DUcDhokNhb2rEj0KGD3NUQEREREdUvHBqNRnz77bd45513cPnyZQBVB0KNRoPIyEgEBwdDq9VCEAQUFRUhKysLN27cQHFxscW2paWl2LBhAzZs2IBWrVrhn//8J6ZPnw6VSlXfn4+cmdEI5OWJbU8Lh1otcOsW4O8vdyVERERERAAAhVCXbj4AK1euxNy5c3H16lUA5cHOx8cHgwcPxtChQ9G3b1907dq11vsJk5OTcerUKfz111/YtWsX9u3bB51OJxamUAAAWrRogbfffhv33HNPnX84T5abm4ugoCDk5OQgMDBQ7nKqlpMDNGoktuPigK1bZS2HiIiIiMgdWZsN6hQOhw4dir179wIQQ6Farca4cePwwAMPYOzYsfDz82tQ0YWFhdi0aRN++OEHbNiwQbpMVaFQYPDgwdi9e3eD9u9JXCIcJiYC0dFie8oUYNUqeeshIiIiInJD1maDOk1lsWfPHgiCgMaNG+ONN95AcnIy1q5di6lTpzY4GAKAr68vpkyZgtWrVyM5ORkLFy5EeHg4BEHAvn37Grx/cjKm+w0Bz7ustKL4eKBunfhERERERDZVp3AYHh6OxYsX4/r165g/fz7CwsLsVRcaN26MefPm4dq1a1i0aJFdj0Uyad4c+Pln4PPPgYcekrsaeSxdCrRsCbRqJc55SEREREQkkzoNSHPlyhWb9BDWhUajwbPPPouZM2c69LjkAI0aAXffLXcV8iotBRISxPb27UDnzrKWQ0RERESeq049h44OhuZ8fX1lOzaR3YwYUd7+4w/56iAiIiIij1encEhENta1K9C4sdjeuRMwGGQth4iIiIg8F8MhySchAThwADh3DigslLsaeSiVwLBhYjsnBzh6VN56iIiIiMhjMRySfD7/HBg4EOjUCdi/X+5q5GN+aSnneiQiIiIimdRpQJqapKenY+/evdi3bx/OnTuHy5cv48aNGyguLoYgCGjSpAmio6PRt29fDB48GCNGjEBwcLCtDk+uKDe3vO2sczE6wqhR5e3Nm4G5c+WrhYiIiIg8lkIQbDO5mlKphEKhkJar2q35+2q1GnFxcZg1axYmTpxoixLIjLUTXcpq+nTg22/F9tmzQMeOspYjq7ZtgcuXAbUayMwEAgLkroiIiIiI3IS12cDml5UKglBlMDS9Z3ouLS3F77//jsmTJ6Nnz544dOiQrUshZ5eXV9729DA0erT4rNcDO3bIWwsREREReSSbXVYKiKEvOjoaLVu2RLNmzRAWFgaFQgFBEJCSkoLr16/j1KlTKCwbfMQUFk+cOIHbbrsNH3zwAZ555hlblkTOjOGw3KhRwP/9H+DvD6SkyF0NEREREXkgm4XD33//HX369EFISEiN6xkMBhw7dgwbNmzAjz/+iEuXLkGhUECv1+O5555DQEAApk+fbquyyJmZh0N/f/nqcAbDh4tTWQwcCHh7y10NEREREXkgm91zWF+//PILXnzxRVy9ehUAEBAQgAsXLiAiIkLOslyeS9xz2KULcOYM4OsLFBTIXQ0RERERkVuS7Z7Dupo4cSKOHDmCAQMGAADy8/OxbNkymasihzD1HHr6JaVERERERE5A9nAIAEFBQfjxxx+hVotXuW7YsEHmisgh8vPFZ4bDyoxGuSsgIiIiIg/jFOEQAGJiYtCrVy8IgoD4+Hi5yyFHYM9hZd98A4wfD0RHiyOXEhERERE5iE1HK20onU4HANJopuTm8vLER2mp3JU4j99/B0w954cOAYMGyVsPEREREXkMh/QclpaW4sCBA8g3XUZYgV6vxzvvvIOTJ09CoVAgOjraEWWR3DQaoHFjoGlTuStxHqNGlbe3bJGvDiIiIiLyOA7pOczNzcWgQYOgUCgQERGB5s2bIyQkBF5eXsjIyMDp06eRn58PhUIBAJg2bZojyiJyPhXD4YIFspVCRERERJ7FoZeVCoKAmzdv4ubNm5VeN5k8eTLmzZvnyLKInEfz5kCnTsDZs8DBg0BWFhAcLHdVREREROQBHHJZqVarxZQpUxATEwNBEKQHACgUCgwfPhyfffYZTp06hVWrVsGbk4C7v6tXgVdeAf79b2DvXrmrcS6m3kOjEdi+Xd5aiIiIiMhjOCQc+vr6YuXKlYiPj0dSUhK+/vpr3HXXXfD29obRaMSOHTvwwQcf4NatW44oh5zBxYvAO+8Ar74KbN0qdzXOZcyY8vamTfLVQUREREQexeFTWURGRuKRRx7B6tWrkZKSggULFsDPzw8XLlzAyJEj8dVXXzm6JJKDaRoLgFNZVDR0KKDViu2NGznnIRERERE5hM3Cob4ec7IFBwfjtddew6lTp9CpUycYjUY8+eSTOHnypK3KImfFcFg9Hx9gxAixffMmcOyYvPUQERERkUewWTjs0qULfv/993ptGxMTgw0bNkCj0UCv1+P999+3VVnkrBgOazZuXHl73z756iAiIiIij2GzcHjx4kWMGzcOd955J47Vo6ejRYsW6N27NwRBwB9//GGrsshZMRzWbMIEYOlS4MoVYM4cuashIiIiIg9g83sON23ahD59+mD8+PHYvXt3nbbNzMwEAKSlpdm6LHI2+fnlbYbDyiIjgaefBlq1krsSIiIiIvIQNguHr732Gry9vaVpKjZu3Ihhw4ahTZs2WLhwIQ4ePAhjDQNrLF++HOfPnwcAhISE2KosclbsOSQiIiIicioKwXwG+ga6dOkSnnrqKWzbts3yIAoFAMDPzw9dunRB+/bt0aRJE2g0GmRmZmLv3r04efIkBEGAQqHAqFGjsIlD+DdIbm4ugoKCkJOTg8DAQLnLqezhh4HvvhPbFy4A7drJWw8RERERkZuyNhuobXnQtm3bYsuWLdi6dSvmz5+PQ4cOSe8JgoD8/HwcPHgQBw8etNiuYj6dPXu2LcsiZ8SeQ+scOACsWwfs3Qvs3AmobfpPloiIiIhIYpd5DkeOHIkDBw5g27ZtmDJlCtRmv9BW1VFp6lkEgOeffx6TJk2yR1mS/fv3Y/bs2ejUqROCgoIQGBiITp06YdasWdhnp5EhFQpFnR/Lli2zSy1OoUMHYNAgoGtXhsOaLF4M/Oc/4oil+/fLXQ0RERERuTG7dkMMHz4cw4cPR2ZmJrZv344dO3bg9OnTuHTpElJTUwGIoSk8PBxDhgzBrFmzEBcXZ7d6CgoKMGfOHHz11VeV3jt37hzOnTuHzz//HDNmzMDSpUvh5+dnt1o83ttvy12Baxg3DvjpJ7G9cSMQGytvPURERETktmx6z2FdCIIAnU4HlUoFb29vux/PYDDgjjvuwJYtW6TXtFotOnfuDLVajbNnzyI3N1d6b9SoUdi4cSNUKpVNjm/eOxobGwutVlvrNnPmzMEdd9xRr+M5/T2HZJ20NKBJE0AQgC5dgFOn5K6IiIiIiFyMtdlAtnDoaHPnzsXbZr1VM2fOxDvvvCONjFpQUID//Oc/ePPNNy22+de//mWT45uHw/j4eLRo0cIm+60Ow6EbGTAAMN2nm5AAxMTIWg4RERERuRZrs4Fd7jl0NikpKVi0aJG0/NBDD+Gzzz6zmDLDz88PCxcuxLx586TXPvzwQ6SkpDi0VqJKxo0rb2/YIF8dREREROTWPCIcLl68GDqdDgDg6+uLxYsXV7vu/PnzERUVBQDQ6XRYsmSJI0r0LIIgDkTTvz/w1FNyV+P8Jk4sb69bJ1sZREREROTePCIcrl27Vmrfc889Fj2GFXl7e2PGjBnS8po1a+xam0cqKgJOnwYOHQLOnpW7GufXtStgugx5504gO1vGYoiIiIjIXdUpHC5cuBAFBQX2qqVaBQUFWLhwYb22vXDhAi5fviwtjxkzptZtxo4dK7UvX76MCxcu1OvYVA3OcVg3CkV576FeD2zaJG89REREROSW6hQOFyxYgNatW+Odd95BtgN6L7Kzs/H222+jVatWeOONN+q1jxMnTlgsDxw4sNZtevXqZTGC6smTJ+t1bKoGw2Hdmc/9yfsOiYiIiMgO6nxZaVpaGl599VVER0fjb3/7G/766y+bF3Xo0CHMnj0b0dHRmDdvHtLS0uq9r3Pnzkltb29v6X7CmlRcz3wftvDSSy+hc+fOCAwMhFarRfPmzTFs2DAsWLAA8fHxNj2WU2I4rLvbbgP+9jdg/Xrgiy/kroaIiIiI3FCdwuHOnTvRrVs3CIKA/Px8LF++HP3790f79u3xz3/+Ezt27JAGfqmLwsJCbN26FX//+9/Rpk0bDBw4EF988QXy8/MhCAK6d++OHTt21Hm/AJCQkCC1mzdvbjGlRE2io6Or3IctrFq1CmfPnkVeXh50Oh2Sk5Oxc+dOvPHGG2jXrh2eeOIJFBUV2fSYTiU/v7zt7y9fHa5ErQb+7/+A8eMBK+bIJCIiIiKqK3VdVo6NjcXRo0fx3Xff4a233pLu5bt8+TLee+89vPfee/Dy8kLHjh3RpUsXtGrVCs2aNUOjRo2g1Wqlie+zsrKQnJyMK1eu4PTp0zh//jz0er10HNPUi23atMH8+fPx4IMPWh3qKsoz66UKCgqyejvz+T/M92ELjRs3RuvWreHv74+cnBycP38e+WWBSa/XY/ny5Th06BB27Nhhdc3FxcUoLi6WlnNzc21as02Z37fKcEhERERE5BTqFA4BcTL3hx9+GA8++CB+/vlnfPTRRzhw4ID0fklJCU6ePFmn+/RMYdBkwIABePbZZ3H33XdDqWzYgKr5Zr1UPj4+Vm+nNeudMd9HfXXq1AmzZs3C+PHj0apVK4v39Ho9Nm/ejLlz50p/bseOHcO9996LTVYOPvL222/X+75Mh2PPIRERERGR06l38lIqlbj33nuxf/9+nDx5Ei+//DLatm0LQAx7FR8m1b3Xpk0b/OMf/8CJEyewf/9+TJs2rcHBEIBFj6RabX0WNl+3tLS0wXWcOXMGzz77bKVgaDrWuHHjcPDgQYwzm/D8999/x6+//mrV/l955RXk5ORIj8TExAbXbDfmPYd+fvLV4YqysoDvvwemTgXscL8vEREREXmuOvccVqVLly54++238fbbbyMhIQG7du3CkSNHcPbsWVy7dg3p6enSFBh+fn5o3LgxYmJi0KlTJ/Tu3RuxsbFo2bKlLUqpxNfXV2rX5X5I83X9HBRgfHx88OOPP6Jt27ZITU0FACxduhTjx4+vdVuNRgONRmPvEm2D4bD+1qwBHn9cbLdtC/TpI289REREROQ2bBIOzbVo0QItWrTAI488Yutd14u/2WWLdRnkpbCwsMp92FtAQACefPJJLFiwAACwZ88e6HS6Ol0S6/QGDADeeUcMiT16yF2Naxk/Xpz3UBCAdeuAt9+WuyIiIiIichMNv27TyTVu3Fhq37hxw+rtbt68KbVDQ0NtWlNthg0bJrV1Op1zXyJaH716AS+/DCxcCHTrJnc1riU8HBg8WGyfPw/YeJoVIiIiIvJcbh8O27dvL7UzMjIsegRrYh7IOnToYPO6ahIREWGxnJ6e7tDjk5ObMqW8vXKlfHUQERERkVupczh0tUnaO3bsaLF8/PjxWrdJTk5GWlpatfuwt4oB1vy+SSKLcLhqlXx1EBEREZFbqXM4bN26NYKDgzFs2DC88MIL+O6773Dq1CkYDAZ71Ndg/fr1sxioZe/evbVus2fPHqnt4+ODfv362aW26pw5c8ZiOTw83KHHt7tbt4DUVHFKiwrTmJAVoqLE+zYB4NQp4MIFeeshIiIiIrdQr8tKc3NzsXv3bixZsgTTp09Hjx494O/vj759+2LWrFn49NNP8eeff1p9Cac9+fv7Y8SIEdLyihUrat3GfJ0RI0Y4bLRSk//9739Su0WLFmjatKlDj293TzwBREQAAQFAHe4DJTN3313e5qWlRERERGQD9Rqt1HzeQoVCAUEQUFxcjKNHj+Lo0aMW77Vt2xY9e/ZEz5490aNHD/Ts2dNikBhHmD59OjZu3AgAOHnyJH799ddqp4c4evSoxcTz06dPd0SJkvXr12PDhg3S8qRJkxx6fIfgVBYNN3Uq8OKLYnvlSmDePHnrISIiIiKXpxCEul3Xt2HDBhw/flx6xMfHo+IuTIHR1K4oMjKyUmBs0aJF/X+KWgiCgJ49e+LEiRMAgKZNm+KPP/6oNNDMjRs3MGLECJwrGwGyR48eOHr0aJU/Q0JCgsXcjK+//ro0/YS5nJwcPProo5g7dy569+5dY50//vgjZs6cKc0J6evriytXrlQaoMYaubm5CAoKQk5ODgIDA+u8vV0NHgzs3y+2S0sBtc1nVPEMAwYABw+K7atXATvNFUpERERErs3abFDn38rvvPNO3HnnndJyXl6eFBSPHTuG48eP4+zZsygpKQEAi5BoaicnJyMlJQW//fabtJ+goCD06NED/fr1w5AhQxAbG4uAgIC6llclhUKBzz//HEOHDkVRURFu3LiB/v3748knn0RsbCzUajUOHTqEjz/+WJp8XqvV4rPPPqsyGNaFIAhYs2YN1qxZgw4dOmD06NHo0aMHmjZtCj8/P+Tl5eHUqVNYtWoVDh8+bFHz119/Xa9g6PRMPYcaDYNhQ8yZA8THi72IDIZERERE1EB17jm0hl6vx9mzZ6WwePz4cZw4cQLZ2dmVC6iml1Gj0WDChAl45plnMNg0r1sDrVmzBg8++CCKiopqXE+r1eL777/H5MmTq13H2p7D7OxsBAcH16nOgIAALF++HPfdd1+dtjPn1D2HbdsCly8DISFARobc1RARERERuTVrs4Fd5jlUq9Xo1q0bHnnkESxatAg7duxAZmYmrl69ijVr1mD+/PkYP348oqKiLC5JFQRBWtbpdFi5ciViY2Nx9913Iycnp8F1TZ48GUeOHEFcXFyVPYIKhQIjRozAX3/9VWMwrAutVotZs2ahc+fOtfZCBgUFYc6cOTh9+nSDgqHTy88Xn3m/IRERERGR07BLz2FdZGVlWfQwHjlyBOfPn6806E3btm2xd+9emw1mk5iYiH379iE5ORkA0KxZMwwePBhRUVE22X9VsrKycPz4cdy6dQvp6enIzs6Gr68vQkJC0K1bN3Tr1g0qlcomx3LqnsPAQCAvD+jYETh7Vu5qiIiIiIjcmrXZQPZwWJXs7Gxs2rQJX331FbZv3y5dejpy5Ehs3rxZ7vJcgtOGQ0EQ7zM0GoE+fQCz+yypni5eBFasAI4fB375Re5qiIiIiMjJyHpZaUM1atQI9913H7Zu3YoNGzZI8wxu27YNO3bskLk6apDiYjEYArys1FamTwcWLgTWrwdOnpS7GiIiIiJyUU4ZDs3dcccd+PTTT6XlH374QcZqqMFM9xsCgL+/fHW4k/vvL2/z3wcRERER1ZNTXlZakSAICA8PR2ZmJjp06IAzZ87IXZLTc9rLSvV6caTSggLA11e875Aa5tYtIDISMBiAqCggIQFQOv33PkRERETkIC59WWlFCoUCnTp1giAISElJkbscagi1GujQAejdm8HQVsLDgVGjxHZiIrB3r7z1EBEREZFLcolwCAC+vr4AgLy8PJkrIXJCDzxQ3l6xQr46iIiIiMhlqeUuwFpLlizB7t27cZijWxJVNnGieJluYSGwciWwdCng7S13VURERETkQlzinkOqO6e95/DqVWDbNnGk0j59gPbt5a7IfTzwQPmANL/8AkyYIG89REREROQU3OqeQ3Ijhw8Ds2cDDz4IbNwodzXuxXzUUl5aSkRERER1xHBIjsWpLOxn1CigcWOxvWcPUFoqbz1ERERE5FJc5p5DchMFBeVtPz/56nBHXl7AwoVAo0bApEniMhERERGRlRgOybEYDu3rySflroCIiIiIXBQvKyXHMr+slOGQiIiIiMhpMBySY5n3HPKeQ/vjfYdEREREZCWGQ3IsXlbqGFu2APfcI04VotfLXQ0RERERuQCGQ3IshkPH+PRTYOVKID5eDIpERERERLVgOCTH4lQWjjFjRnn7q6/kq4OIiIiIXAbDITlWSAjQrBkQFMSeQ3saOxYIDxfb69cD6eny1kNERERETo/hkBzrm2+ApCQgO5vh0J68vICHHxbbpaXAihXy1kNERERETo/hkJxeTmEpikoMcpfheswvLf38c0AQ5KuFiIiIiJwewyE5rVt5Ojz4xUF0X7gF3RduwYL1Z1CsZ0i0WqdOwODBYvvMGWDvXnnrISIiIiKnxnBITqmoxIAHPj+IvZfFe+VK9EZ8sz8BT/9wDAYje8Cs9sQT5e1ly+Srg4iIiIicHsMhOdbkyeL8e6+9VuNqS7ZfwqVb+QgL0GD7i0Px+cN94K1WYuvZVHy+56qDinUDU6cCoaFie9UqIC1N3nqIiIiIyGkxHJLjCAKwdq04/14Nc+9l5Bfj2/0JAIB/39UVrcP8MbJTE7w1sQsA4IMtF3A9o9ARFbs+H5/yew87dwZSUuSth4iIiIicFsMhOY5OV9729a12tRUHr6Oo1IBuzYMQ1zFcev3uPs0xpG1jlBoEvLv5vD0rdS9z5gAHDgBHjgDdu8tdDRERERE5KYZDcpxCs96+asKhIAhYeywZAPDIwBZQKBTSewqFAq+M7QiFAthw8gbO3ci1a7luIyoK6N8fMPuzJCIiIiKqiOGQHMeKcHgiKQfx6QXQeqkwpktEpfc7RQbiji5NAQBf7Im3S5lERERERJ6I4ZAcxzwcarVVrrLlzE0AwIiO4fDTqKtc5/EhLQEA608k42aOrsp1qBqCIE5pkZwsdyVERERE5GQYDslxrOg53HNJnLpiWPvwKt8HgJ7RwejXIgSlBgE/HLpu0xLd2smTQL9+wJAhwOLFcldDRERERE6G4ZAcp6iovF1FOMzIL8bplBwAwJB2jWvc1QMDogEAq/5K5LyH1oqIAE6dEtuffw7k58tbDxERERE5FYZDcpxaeg73Xk6HIAAdmwYiPMCnxl2N7hyBIK0XUnJ02Hs53daVuqfwcOCBB8R2Tg7w7bfy1kNEREREToXhkBynlnB49FoWAGBAq5Bad+XjpcJdPZsBAH46zEtLrfbss+XtJUsAo1G+WoiIiIjIqTAckuNERwNPPgk88kiV8+0dS8wGAPSKDrZqd/f0iQIAbD2biuzCEpuV6da6dQOGDxfbly4BGzfKWw8REREROQ2GQ3KcHj2ATz4BvvkGuOMOi7d0pQacTRHnLewZ3ciq3XWKDESHiACUGgRsLhvllKzw/PPl7UWL5KuDiIiIiJwKwyE5hTMpOdAbBTT216BZo6qnuajK+O6RAID1J1LsVZr7ueMOoG1bsf3HH+IopkRERETk8RgOySkcu54NQOw1VCgUVm83vpsYDv+8koFbeZzz0CpKpeW9h++9J18tREREROQ0GA7JcYTqp5w4U3ZJabdmQXXaZXSoL3pENYJRADaevNGg8jzK9OlAaKjY/vFHIIU9r0RERESejuGQHOef/wR8fICQEODAAYu3zt/MAwB0aBpY591OKLu09FeGQ+v5+Ym9hxMmAHv2AJGRcldERERERDJjOCTHKSwEiouBrCxArZZeLjUYceWWOCF7h4iAOu/2zm5NoVAAR65lITm7yGblur1584BffgEGDpS7EiIiIiJyAgyH5DjVzHMYn16AEoMRft6qOg1GYxIe6IO+MeLciFs4aqn16nBvJxERERG5P4ZDcpxqwqHpktJ2EQFQKusXWEZ3iQAA/H6a4bDeBAEoLZW7CiIiIiKSCcMhOU414fDCTXEwmvpcUmoyunMTAMDhhEyk5xfXez8eyWAAfvoJ6NULeP99uashIiIiIpkwHJLjVBsOxZ7D9k3qHw6bB/uia7MgGAVg29nUeu/HIyUkAPffDxw/Dnz4IZCXJ3dFRERERCQDhkNyHPNwqC2/t/By2WA07RoQDgFgjOnSUt53WDetWwP33iu209OBjz+Wtx4iIiIikgXDITmOKRx6ewMqFQBxpNLELHGE0ZZhfg3avSkc7rucjpwi3jtXJ6+/DijLTgfvvQfk5MhbDxERERE5HMMhOY4pHJpdUpqUVQSDUYCPlxJNAnwatPvWYf5oG+6PUoOAHedvNWhfHqddO+Chh8R2VhawZIm89RARERGRwzEckuNUEQ4T0gsAAC1C/eo9Uqm5MRy1tP5ee03q0cWHH4ohkYiIiIg8BsMhOc633wJr1gCffCK9FF8WDls2btglpSajO4vhcOfFWygqMdhknx6jVStgxgyxnZMjBkQiIiIi8hgMh+Q4w4cDd90FTJwovWQKhy1sFA47RwaiebAWulIjdl1Ms8k+Pcq8eYCXl9hevBi4xctziYiIiDwFwyHJKiGjrOcw1DbhUKFQYExn06WlN2yyT48SEwM8/rjYzs8H3nxT3nqIiIiIyGEYDklW0mWlDRyp1JzpvsPt526hWM9LS+vstdcAf39g6lTg2WflroaIiIiIHEQtdwHkIYqLgV27xMFoIiKANm1QrDcgJVucxqKFjXoOAaBXdDDCAzS4lVeM/ZczMKxDuM327REiIoBLl8RnIiIiIvIY7Dkkx0hNBUaPBoYMAebOBQAkZhbBKAD+GjUa+3vb7FBKpUIamGYTLy2tHwZDIiIiIo/DcEiOYZrGApCmskjKEl9rHqyFQtHwaSzMjS27tHTr2VToDUab7tsj6XSAIMhdBRERERHZEcMhOUaV4VC8pLR5sNbmh+vXMgTBvl7IKizFwfhMm+/fYxiNwHffAe3aAStXyl0NEREREdkRwyE5RhXhMDnbFA59bX44tUqJUZ14aWmD7dwJPPwwkJgI/P3vQEGB3BURERERkZ0wHJJj1NBz2KyR7XsOAWBMVzEcbj6TCqORl0TWy7BhwJgxYjsxEfj3v+Wth4iIiIjshuGQHKOqnkOzew7tYXDrxgjwUSMtrxhHrmfZ5RhuT6EAliwBvLzE5fffF0cyJSIiIiK3w3BIjlFTz6GdwqG3WomRHZsAADadummXY3iEdu2AF18U2yUlwJw5HJyGiIiIyA0xHJJjVAiHxXoDbuUVA7DPPYcmY7qYLi29CYGBpv7mzQOaNxfbv/8OrF8vbz1EREREZHMMh+QYFcJhSrYOAKD1UiHY18tuh41tFwZfbxWSs4twMinHbsdxe35+wAcflC8/8wyQlydfPURERERkcwyH5Bg6XXnb19eucxya8/FSYViHcADAptO8tLRB7r4biIsT24mJwNy58tZDRERERDbFcEiO8Y9/AKWlQE4OMHYsku18v6G5sV3Kp7TgpaUNoFAAy5cD2rK/sx9/BLI40A8RERGRu2A4JMdRq4HAQECjkQajsddIpeaGtQ+HRq3EtYxCnLvBSyEbpFUr4K23xF7EM2eA4GC5KyIiIiIiG2E4JFkkZ5vmOLTfYDQmfho1YtuFAQB+O5Vi9+O5veefB37+GWjSRO5KiIiIiMiGGA5JFqZwGNnIxyHHG989EgCw/kQKLy1tKDveI0pERERE8mE4JMf46ivxvsPXXwdyc5GaKw5Q0zTI/peVAkBcx3D4equQmFmEY4nZDjmmx0hLA559FigqkrsSIiIiImoAhkNyjLVrgffeAxYuhFBUhJs5YjiMCHRMz6GvtxqjOomXQa4/zktLbWbXLqBrV+Cjj4CXX5a7GiIiIiJqAIZDcgyzXqUcpTeK9UYAQHigxmElTOzZDACw4WQK9Aajw47r1sLDxRFoAWDpUmDzZnnrISIiIqJ6YzgkxzALhzeKxedgXy/4eKkcVsJtbRojxM8b6fkl2H8lw2HHdWsdO4o9wiYzZgDp6fLVQ0RERET1xnBIjmEKh2o1bhaUAgAiHHS/oYmXSolxXZsCAH7hpaW289RTwOjRYvvGDeCRRwAje2aJiIiIXA3DITmGTrzHEFotUqX7DR13SanJxB7iqKWbz9yErtTg8OO7JYUC+PproHFjcXnjRuDdd+WtiYiIiIjqjOGQHMPUc6jV4mbZSKURQY4ZjMZcr+hgNGukRX6xHtvOpTr8+G6raVPghx/Kp7l49VVxsBoiIiIichkMh+QYpnDo4yONVNrEQSOVmlMqFVLv4eojSQ4/vlsbORJ47TWxbTQC994L3Lwpb01EREREZDWGQ3KMqnoOZQiHADC1d3MAwK6LaVJQJRuZPx+IixPbN28C69fLWw8RERERWY3hkBzD7J5DaY5DGS4rBYBWYf7o1yIERgFYfZS9hzalUgErVgAtWwLffAPMmiV3RURERERkJbXcBZAHEARgxAix97BNG6TKeM+hyd19muNQQiZ+/isRf7u9NRSme+Wo4cLDgbNnAR/5/n6JiIiIqO7Yc0j2p1CII1ju2AHdJ8uQVVg2lYVMl5UCwB1dm8LPW4VrGYU4FJ8pWx1uq6pgWFjo+DqIiIiIyGoMh+RQpl5DjVqJIK2XbHX4adQY310cmOanvxJlq8Nj/PAD0Lo1cOaM3JUQERERUTUYDsmhTPcbNg3ykf1Szrv7RAEANp66gVxdqay1uLUffwQeeEAcoGbMGCCRYZyIiIjIGTEckkOZRiqVYxqLinpFN0KbcH/oSo1YdyxZ7nLc1/jxQJ8+YjspCRg9GsjkpbxEREREzobhkOzvwgWgQwegZ0+krtsEQN7BaEwUCgUeGhADAPjvn9cgCILMFbkpf3/gt9+Atm3F5XPngDvv5D2IRERERE6G4ZDsLydHDIjHj+NGXgkAeQejMTe5VzP4eatw+VY+/rySIXc57is8HNi8GYiIEJf//BO4667yKU6IiIiISHYMh2R/ZgEgTa0FAIQFaOSqxkKAjxcm92oOAPj2zwR5i3F3LVsCmzYBgYHi8pYtDIhEREREToThkOyvqEhqpinFHsNwJ+k5BICHB4qXlm49m4rk7KJa1qYG6dFDnNbEz09c/v13YMoUoLhY1rKIiIiIiOGQHME8HCrEHsNwJ+k5BIC2TQIwqHUojALww8Frcpfj/gYPFnsQTQFx927xsmMiIiIikhXDIdmf+WWlghqA81xWavLwwBYAgB8OXkdRiUHeYjzBkCHiIDUREeK9iN26yV0RERERkcdjOCT7K+s5LFJrkCeoADhfOIzrGI7oEF9kFZbi5784D59DDB0KXL0KDBokdyVEREREBIZDcoSycJju1wgAoFErEaBRy1hQZWqVEjOHtAQAfL7nKvQGo8wVeQit1nJZEIAFC4CEBDmqISIiIvJoDIdkf2WXld7yCwYg9hoqFAo5K6rS3X2iEOrnjaSsIvx26obc5XimV14B3ngD6N9fnO6CiIiIiByG4ZDsr6znMM0sHDojHy8VHhnUAgCwbNdVCIIgb0GeJjcXWLdObN+6Bdx+O7BihZwVEREREXkUhkOyv5EjgQ8+QNr9jwAAwvydMxwC4rQWWi8Vzt3Ixa6LaXKX41kCA4H9+4Fhw8TlkhLgwQeBefMAIy/zJSIiIrI3hkOyv379gBdeQNpg8Zd+Z+05BIBGvt64r180AGDxtkvsPXS0kBBx9NKZM8tf+9e/gLFjgTSGdSIiIiJ7Yjgkh0nLEyc6d+ZwCABP3N4KPl5KHE/Mxo4Lt+Qux/N4eQHLlwOLFwPKslPUli1Az55izyIRERER2QXDITmMq4TD8AAfad7DD7deZO+hHBQK4Nlnga1bgSZNxNeSk8XpL3btkrc2IiIiIjfFcEj2d/MmkJiItKx8AM59z6HJ7NhW8PVW4XRyLracTZW7HM81fDhw7JgYCgGgc2dg4EB5ayIiIiJyUwyHZH9PPglERyP9QjwA5+85BIBQfw1mDG4BAFi09SIMRvYeyqZpU2DbNuDVV4H//hfw9pa7IiIiIiK3xHBI9ldUBAHOP5VFRTOHtEKAjxrnb+Zh9ZEkucvxbGo18NZbQLdulq+fPAlMmABcuSJPXURERERuhOGQ7K+oCLkaP5SovQAAjV3gslJAHLl0zvC2AID3tlxAQbFe5orIQnEx8NBDwK+/Al26iOGxuFjuqoiIiIhcFsMh2Z9OhzR/sdcw0EcNHy+VzAVZ7+FBMYgJ9UVaXjGW7WLvlFO5cgXIzBTbOh0wfz7Qvbs4sikRERER1RnDIdlfURFuudglpSYatQqvjO0AAPhs91UkZxfJXBFJOnUCzp0DXnwRUJV94XDhAjB6NDBmDHDqlLz1EREREbkYhkOyv6Iil7vf0NzozhHo1zIExXoj/v3bObnLIXP+/sD77wNHjgADBpS/vnkz0KMH8PjjQBLvFyUiIiKyBsMh2Z9OZxYOfWQupu4UCgVeH98JKqUCv526gR3nb8ldElXUvTuwbx/www9ATIz4mtEIfPkl8Pzz8tZGRERE5CIYDsn+zHsOXWQwmoo6Rwbh0bKpLeatO43CEg5O43SUSuC++4Dz54F33wWCgsTX586Vty4iIiIiF8FwSPbn4peVmjwX1w7NGmmRnF2EJdsvyV0OVcfHB3jpJeDyZeDrr4GePS3f//JL4K67gF27AIHzVxIRERGZMBySfQmCxWilrhwO/TRqLJzYGQDwxZ54nErKkbkiqlHjxsD06ZavCQLw4YfAunXA7bcDvXqJAbKgQIYCiYiIiJwLwyHZ34ULSOvZH4Brh0MAGNGxCcZ1awqDUcBzPx2DrtQgd0lUF4mJQHZ2+fLx48CjjwJNmwKzZwOHDrE3kYiIiDwWwyHZl0IBtGmDdL0CgOvec2jurYldEBagwZW0Avzn9/Nyl0N1ER0NJCSIA9f07Vv+el4e8NlnQP/+QNeuwHvvAbm5spVJREREJAeGQ7I7vcGIjIISAK7fcwgAwX7eeHdqNwDA1/sSsO9yuswVUZ14eYkD1xw8CPz5J/DYY4CfX/n7Z84Ar79ePnciERERkYfwyHC4f/9+zJ49G506dUJQUBACAwPRqVMnzJo1C/v27bP78a9evYrXXnsNvXv3RlhYGLRaLVq3bo277roLq1atgl7vXiNhZhaUQBAApQII8fOWuxybGNY+HA/0jwYAvPDzcaTnF8tcEdWZQiHOjfjFF8DNm+JANYMHi+/deadlYATEQW5ee00MlQZeTkxERETuRyEInnODTUFBAebMmYOvvvqqxvVmzJiBpUuXwq/iL4c2sGTJErz88ssoLq4+TAwYMAArVqxAq1at6n2c3NxcBAUFIScnB4GBgfXeT4NlZOD0d2tw581IhPsocGjBHfLVYmOFJXpM+HgfLt/Kx+A2ofjvo/2hUirkLosa6vJloLQU6Nix/DWdDggNBQoLxeXGjYExY4DRo8WBbZo3l6VUIiIiImtYmw08pufQYDBg8uTJFsFQq9WiT58+GDBggMUf0tdff43JkyfDYOPegTfffBPPPfecFAyVSiW6dOmC2NhYNG3aVFrvwIEDGDp0KG7cuGHT48vi2jWkLV0OAAgryJa3Fhvz9Vbj0wd6wddbhX2XM7B420W5SyJbaNPGMhgCwF9/lQdDAEhPB77/HnjoISAqCmjdWrw89b//5b2KRERE5LI8JhzOnz8fW7ZskZZnzpyJpKQkHD58GH/++SdSUlIwf/586f0tW7bgtddes9nxN2/ejNdff11aHjhwIM6dO4dTp05h165dSEpKwv/+9z/4+/sDAJKSknD33Xfb7PiyMZ/jUFEqczG217ZJAN6e3BUAsPSPy/jjfKrMFZFd3HYbkJwsXoI6eTIQEGD5/tWrwFdfAY88Ujkc3rhhOUIqERERkZPyiHCYkpKCRYsWScsPPfQQPvvsM4SEhEiv+fn5YeHChZg3b5702ocffoiUlJQGH18QBLz88sswXcHbvn17bNu2De3atZPWUSqVmDZtGtauXSu9tm/fPotll6TTlYdDlXvepzWxRzM8PDAGADDnx+M4f5M9R24pMlLsHVy9Wuw5/OMP8R7EoUMB77J7adu0qXyJ6cKFQHAw0K4dcPfd4vKaNcClS7x3kYiIiJyKR4TDxYsXQ6fTAQB8fX2xePHiatedP38+oqKiAAA6nQ5Llixp8PE3bdqEEydOSMtLliyBr69vlevGxcVh2rRp0vI777zT4OPLyrznUO2+t7fOG9cJA1uFIr9Yj0e/PoxbuTq5SyJ78vYGhg0D3ngD2LlT7BncsQP44IPK6x46JD5fugSsWiWOhDplihgWAwKAPn2Ahx8GfvvNkT8BERERUSUeEQ7Ne9/uueceix7Diry9vTFjxgxpec2aNQ0+vvk+WrZsiVGjRtW4/uzZs6X2oUOHkJSU1OAaZFNUhDT/snDoHgOVVslbrcSyB3ujVZgfUnJ0ePy/f6GohL1CHkOrFQemmTCh8nu33y7On6ipYhqXoiLgyBHgu++As2ct38vNBQYNAh58EHjlFeCTT4BffwWOHwcyMwHPGUuMiIiIHMTtw+GFCxdw+fJlaXnMmDG1bjN27FipffnyZVy4cKFBNfxm1iMwevRoKBQ1j2g5ZMgQi5FSf3PlHgXzy0p93PvjFuTrha+n90WwrxdOJuXgie+PoFjPgOjxPvgAOHAAyM8Hzp8HVq4UL0e96y7xMlTT+aBNG8vtLl0S52FcsQJ45x3gqafE8Nmzpzhyqr8/0KEDMGKEGBbNJSSIPZbXrokBlIiIiMgKarkLsDfzyzkBcSCY2vTq1Qve3t4oKREnbj958iTat29fr+PfunULN2/erNPx1Wo1+vbti507d0rHd1nml5Vq3X9S8ZhQP3zxSB88+MUh7LqYhuf+dxxL7+sJtcq9gzFZQa0G2rcXH1Onlr9eWAhcuSKOemouMbHm/RUWAhcuiI+Kl6l/+SXw1lvly76+4n2PFR+9egFz5lhu+9df4rO/v+VD7fb/XRAREXk8t//f/ty5c1Lb29tbup+wJqb1rly5UmkfDTk+ALRu3dqq7Vq3bi2Fw4YcX3ZFRUjziwQAhPm58XWlZnrHhODzh/vg0W8OY9Ppm/jH6pN4f2p3KDkHIlXF1xfo2rXy65MmAQUFwPXrYlCs+DC9rlIBPj6W26ZWGDW3sFB8JCdbvn7rVuVweP/9Yq9lRRqNZVh89VXgvvss9/XKK2IttT3GjhX3YXLzplibWg14eZU/Ki57eVX+WYmIiMhm3D4cJiQkSO3mzZvXekmnSXR0tBQOzffRkOOb9mvt8avbhyspLCpGvkbs1Qjzr+KeKzd1W9vG+Pj+nnhyxVGsOSr+Qv7ulG7sQaS68fUVLx3t0KHq9wXBcv5FkxEjxEFzUlPFR0YGkJUlPnRmgyUFB1feNj+/6mMVF4uPjIyq18vIEKfzsEZCgmU4/PFH4IUXat+uQweg4pdlU6eKgwEpleJDpbJ8NrUffRT45z/LtxMEoG/f6rcz3/6tt4Devcu3PXJEfE2hKL8s2NQ2f6hU4mXB5r7/HtiyxXK9qrbv2rVycF+4UPz7rOpY5vuYNAmIjS3fLjMT+Ne/av/zBYCXXwbCw8uXDxwQR+itTXAwMHeu5WvffQecPl37tv37i1PEmHvtNaDs6h0LFf8Pf+ABoEuX8uWEBOCzz2rfDhAHh/I2+9Jy2zZxgKnaxMQAM2davrZ8eeUvX6oyfLh4H7JJURHw9tu1bwcAs2ZZjoZ86pR4mXptfHwq/92sXQscO1b7tl26APfcY/nae+8BeXm1bztpknh1gsnNm+K909b4+98B80m69+0T/93UpkkT4G9/s3zt22/F6YZqM2gQMHp0+bLRKA46Zo2HHxbnuzW5eLHyv/2qKBTAggWWr23cCBw8WPu2bduK96SbW7pUHE27NmPHAgMGlC9nZwNmo/rX6OmngbCw8uW//hLvh69NUFDl8/z//lf5nF6V3r0r39P/1ltAqRVTpE2bBnTqVL587Zr1/1e9+qrlOWL7dmD37tq3i44WRzd3UW4fDvPMTmBBQUFWbxdodlLKs+YkaMXx61JDXY9fXFyM4uJiaTnXSSbiTtcGAdmAj74Y/o0Cal3fnYzqHIFF03rg+Z+OY83RZBQWG7Dkvh7QqN3/8lpyEIUCMLs/WTJtmvioik4n/iKQlVV1L9zs2UBamhj+qnrk5YnP5uHOtF9rVTyuNf/BA2LPYUVZWZXvuayKKdSaGI1iyLNGxV9obt4E1q2rfTulsvIviIcOiaGpNmPHVg6HK1aIv3TWJibGMhzm5QEfflj7doAYeszD4YkTwPvv175ddHTVAcSa6ZhmzaocDj/4oOovPirq29cyHCYlWR+25s61/MVv1y7rQvTgwZXD4ZdfAocP176tl5dlONTpgDfftKpc3HmnZTg8c8a6bRs1qvx3s3498M03tW87bVrlcLhokTh/a21atrQMh7duWf+zPvGEZTjcv1/8cqQ2XbtWDofffSf+Ul+bv//dMhwKgnXHBIAhQyzD4eXLlbYVABgVSunZWPYFknHuPAgCYBQECACEzdth/OxzCAoFjAoFBCggKBQWy0aFAsLtwyCMnSxtZxQECN+uhJCQYHEcABAUgADxyyMBgODfFEKzDmJbECDcuAnhi58B03pA+XGBsmcFBAWA2ydCiFJAEAABAoS9ZyB8t0H6GQWFAkDl7YSwcAij7y8bS00QnzcegnDoUIV1xW1hfvwMFYRmvWAahk0QBAjr/gRKSqWfTVrXfB8KBYSInkCOr1ivIEC4ehXCb8cr/Jkoyvdj+jkBCIMvQ/DyLj/m9ksQdl2s/GdSdkzpzyCqFEKz81K9Q9uFYUCrUOs+S07A7cNhvtm32z51uBxJq9VWuY+GHL8uNdT1+G+//TbesPYbLgfKv/s+NPnmEHy9/aAYebvc5TjchO6R8FEr8fQPx/D7mZuY+d8jWPZgL/h6u/0/PXJWPj5ARIT4qMrrr9dvvx06iL1EOl3tD/Nf+gBxkJ2//Q3Q68WgaHpUXG7ZsvJxIyPFb9CNRnHeSPNn83bFEG00ij17RmPtI7+q6vmFjpVXqth8W3I7pl/+DXojDEYBBkGAQQ8YtIHQK5UwKlRlz0rolSoYlUroFSoYlEoYAhvBcD0LBqMAvVEQQ4R3OAwtesKoUJQFFSUMSmVZAFHCUBZejH4tYTySBIMgQBAEGAXA0G4ohIjcsnXK1lMqYUT5PgwKJYzpfhC2XIChbDtjajaMwx6VtpPWUyjF7cuWBYUSxs0JMPikwigIMBoFGPOawzB5nvietJ4CBoWq7GdQQFAoIfj5wfjJvrI/r7Jf6DveD2Pz8dLPBpSHM/NAYlSGQHh3h/jnU7at8W/flIexCiHNFMAEhRLCjmIYd/0OAeLPKhgECH9fV34MRQ1XDb222XJZOxx4drh1H4z3dlgux71s3XYZAP5vn+VrD1nxJRAAbL4BwPzLgcbAPVaG6O8qfCEXOQKYNMK6bVdajiGCuL9VvV5F1wBcO1Nh21nWbftHfIUXooAhD1a5aiU7r0jNQB8vlwqHCkFw7/HQ4+LisL3sG6MhQ4ZgtzXdwQAeeughfP/99wCAESNGYNu2bfU6/ltvvYX58+dLywaDAUpl7ZcWfvnll3j88ccBACqVCnq9vsb1q+o5jIqKQk5OjkUvpFwEQbD6kl53tOdSGmb99wiKSg3o2iwIXzzSB00Cee8UkVMQhMph0rzt72/Za6nTWU4nIv4mWfkBVA60t26JPbcV16u4rb9/5W3PnBGPXdVxzNstWgBNm1rWe/SodX8WPXpYDnB044Y4YFJtvL2Bfv0q11uxx7YqERHivJ/mdu8W/+xrIAgC9B07QR/SGKVGI/QGAfqsbJSePAW9AJQaAb0A6I2C9FwqmF4DSjt1hh4KlBrEbQ2pqShNSxffE8q3lfZTtk+DtzdKG4dBX7ZdqVGAIS0dhtJSGATAULauseIzAL2XNwxe3uUBz2CEobBI2q7itgaUv06eSwFAqSh7RvkV5EqVquw98U2lwQBlWd+fwrSe2T6k19Rq8VH2e5kCAhQ6ncU2pjYqbu/rC4VKCQUU4vulpVAU6yocU2FxXCgAhVIBRWBg2Tri+4qCAihKS8X3K9Zoflwf8Z53aTsFoMjIgEIQLOqsWLcCgCKoERRajViRAlCUlECRmWn5c1bYRto2MlKsu+xnVeTmAnl5lf+cKtQMjQaKiCbSdsM7hGNwm8Z1+Su3i9zcXAQFBdWaDdy++8J8snldHS57Ml/Xr6rLtupxfNN+K75mi+NrNBpoqppHzUl4cjAEgCFtw/D94/0w879HcCo5B5P+bx++fKQvOkXKH9yJPJ7p/kCVqupLVyvy8RF7LOsjPNzyss266Ny5ftv5+Ij3U9VH06ZA06YwGAUU6w3QlRorPZfojSg1GFFy/hZKDOKy+FoASrz8xGWD2Xp6I0oNAopNy8kFKDl4RGybtjeoUKJXVL1+2bPeKACb/6rfzwVYd89dlYqAK9fruW1x2cN2FApArVRAqVCIz0rxWWV6KBRQqcRnpWlZqYBCoYBKKQYL8VHWVoptVdk+FQoFVBXeK2+Xv2exP/P1yh6m9yofV1xWVFhPqklZ+3oK6VgAUH5s0+swLcNsPfPXlKbQYbkvKRRU2J/5cRTVrGta3xSWLNY125epJovjWLzm2b8/keO5fTj0N7svpqgO830Vmt3r4F/x3pp6Ht9UgzXh0FbHJ+fROyYEa/82CI9+cxhX0gpw97L9+HBaD4zuXM3lfURE1TAaBRSVGsRHiQGFJQYUluhRVCK+Vlj2XKw3orjsWWf+XGqETl/zs3kILHWRriuFAvBSKqFWiQHJS2VqK+GlEkNRxdfUZet7qZRVbmNqq5UKqFUVtylvq8yCl/lDCm4qU4BTQqkE1EolVEpApVRWu53Fa2Yhz/w1joRNRLbk9uGwcePybtwb1txAXcZ8bsLQ0PpfJ2x+fFMN1uzPVscn5xIT6oc1Tw7GkyuOYP+VDMz+7ghmDmmJf4zpAC+OZErkdgRBQGGJAfnFeuQX61FQrEe+rqxdokd+sQFFJXoxzEkhzwBdqRj2Cs3DntTWQ1da8yWX9uSlUsBHrYLGSwmNWgWNWgkvlRLeajE4eZcta9RKqe2tUsJLLT57lz2bthGXy7czf65yfZUSXmrz8FYe6lQMSkREDeL24dB88vqMjAwUFhZa1XOXaDYBdYfqhpGv4/EB4Pr16+hiPrKanY9PzifI1wvfPtoP7/5+Hp/vicfne+Jx9Ho2Pr6/J5oGaWvfARHZnSAIKCgxIKeoFLllj5yiUuSVBTuLsFcW+ExhL19XioJigxQA7X1nv4+XEr7eami9VPD1Fh8+XipovVVSiJOevVTwUSuh8RJDncZsudJzWfjzMdteo1YxgBERuTG3D4cdO3a0WD5+/DgG1XLvRXJyMtLS0qrdR120bdsWarVaGlDm+PHjuOOOO2rd7pjZvRANOT45Jy+VEq+O64TeMSF4aeUJHLmWhTGL92DhxM6Y0D2S9xgQ2YDBKCCnqBRZhSXILiwLeTrTs748+OlKy9p6s3YpjDYMdUoF4KdRw7/s4adRI8BHDV9vFfy81fDxVsG3LNxpvcXXtWUBT3xNZREATa/7qFW8rJCIiGzG7cNhv379oNFopJE89+7dW2s43LNnj9T28fFBv4ojsNWBt7c3+vfvj3379knHr83Nmzdx+fJlaTnWfL4qcitjukSgY9MAPP3DMZxKzsGz/zuOzWdu4s2JXRDq77wDDBE5WqnBiOxCMehlFZSIz4WlyCwoQXZZ2/z1rMIS5BSVNrjXzkulQJDWC4E+XgjQeiHQRwx1ft7lAa9i6Ctvq+DvI7a1Xip+6UNERE7P7cOhv78/RowYgY0bNwIAVqxYgX/84x81brPCbNLiESNGNGi0UgCYOHGiFA63bduG1NRUNGnSxKrjN2rUiOHQzcWE+mHN3wbhkx1XsPSPS9h46iYOxWfi1XEdMalHM/5CSW5JEATkF+uRkV+C9PxipOcXIy2/BOl5Ytv89Yz8EuQV1zydT00CfNRo5OslhbxAn7K2Vi22fcteNy1rvRBYtq6Pl5L/BomIyGO4/TyHALBy5Urcc8890vL69esxfvz4Ktc9evQo+vXrB4PBIG07derUBh0/KSkJbdq0kXovX3jhBXzwwQdVrpufn4/OnTvj+nVxmOynnnoKH3/8cZ2Pae1cJuRcTifn4IWfj+Niaj4AoH/LELw5qQvaNQmQuTIi6+gNRqTnl+Bmrg6pZY9bucVVBsBifd0GVVEogCCtF0J8vdHI1wvBvt4I9vNGsK9X2bPp4YUQP280KluPgz0REZGnszYbeEQ4FAQBPXv2xIkTJwAATZs2xR9//FFpoJcbN25gxIgROHfuHACgR48eOHr0aJXfGickJKCl2QTFr7/+OhYsWFBtDc8++yw++ugjAOKk9j/99BOmTJlisU5paSnuv/9+rFq1CgCg1Wpx+fJlRNZjPi2GQ9dVrDfgiz3xWPrHJehKjVArFZg+qAWeHt4GjXy95S6PPJQgiPfvpeYWi8EvRwx+YggsloJgen5xne7V8/VWobG/BqH+3mjsr0Fjfw3C/L3ROEBsh/p5I9RfgxA/bwRpvTgYChERUT0wHFZw+PBhDB06VJrrMDAwEE8++SRiY2OhVqtx6NAhfPzxx0hNTQUgBrNdu3ahb9++Ve6vruEwKysL/fv3x6VLlwAASqUS999/PyZNmoSQkBBcuHABn376KU6ePClt8/HHH+Opp56q18/LcOj6EjML8cavZ7HtnPiZDPBR48nbW2PGoJbQeqtkro7cja7UgJTsIiRnF5U965AitYtwM0dndU+fSqlAeIAG4YE+iAjUoEmgjxT8GvuLYS/MX4PGAd7w9Xb7uxuIiIhkx3BYhTVr1uDBBx+UAmJ1tFotvv/+e0yePLnadeoaDgHg4sWLiIuLs5imojr/+Mc/8J///KfW9arDcOg+dly4hf9sOo/zN/MAAE0CNXh6WBvc3ScKPl4MiVQ7QRCQUVCC5KwiswCoQ3J2IVLKQmBGQYlV+wr29UKTQJ+yhwYRgT5lIbDstSANQv007OEjIiJyItZmA4/6ynby5Mk4cuQI5syZg+3bt6NiLlYoFBg+fDg++ugjdOrUyebHb9euHU6ePIm///3v+OGHH6oMqR07dsQ777yDCRMm2Pz45JqGtQ9HbNsw/HI8GR9suYjk7CLM/+UMlmy/jMdua4kHB0QjwMdL7jJJZnqDESnZOlzLLEBCRiGuZxTgWkYhrmcW4lpGIYpKDbXuw9dbhWaNtGgWrEVkIy2aNdIispEPIoPE5bAADb+QICIicmMe1XNoLjExEfv27UNycjIAoFmzZhg8eDCioqIccvy8vDz88ccfSExMREFBAZo2bYquXbuiZ8+eNtk/ew7dU7HegP8dSsRnu68iOVv8ciHAR437+0Xj/v7RiAlt2Mi65Nx0pQYp7F0rC3/XMsUgmJRVBH0NN/spFECTAB8x7EnBz/I5UKvmyJxERERuiJeVejiGQ/dWajBi/fEUfLrrCi7fEkc2VSiA2LZheGhADIZ1COdlfS4qp7AU1zILqgiAhbiZq6txW2+1EtEhvmgR6ovoED/EhPoiOtQXLUL90KyRFt5qjtpJRETkiRgOPRzDoWcwGgXsuHAL//3zGnZdTJNebxKowcQezTCpRzN0bBrA3iAnIggCbuUVV9n7dy2zENmFpTVuH+CjRkyoL2JC/RAT4isGwBA/tGjsiyYBPlDySwEiIiKqgOHQwzEcep5rGQVYcfA6fv4r0SJgtGvij4k9mmF05yZoHebPoOgAeoMRydlFUvC7ll4g9f5dz6z9/r+wAE1Z8PMrC4LlYbCRrxf/DomIiKhOGA49HMOh5yrWG7DjfBp+OZ6M7eduocRQPv1Ai1BfxHVsgrhOTdA7JpiTgzdAUYnp/r8C6T7AhLJ2ci33/ykVQLNgLWJCysOfqfcvOsSX0zsQERGRTTEcejiGQwKAnKJS/H76Bjadvon9lzMsgqKftwp9W4ZgYKtQDGrdGJ0iA3mfohlBEJCWV4zrmeUjfiZmll0CmlmItLziGrfXlN3/Z977J94P6IdmwVoGcyIiInIYhkMPx3BIFeUX67HnYhq2nkvFjvO3kFXh3rYAHzW6N2+Ebs2D0K15I3SPCkJEoI/bXsIoCAKyC0uRnF2EGzk6JGcV4npmUVkYFHsAdaU1T/oe6KO2vPQzxE8aACY8QMP7/4iIiMgpMBx6OIZDqonRKODczVz8eSUDf17JwKH4TOQV6yut19hfg7bh/mjbxB9twsVH6zB/hPk7d/AxGgVkF5UiLa8YaXnFuJFTJE32npIjTgSfkq2r9d4/pQKIbKQt6wH0RVSI2PsXE+KH6BBfBPlyfkkiIiJyfgyHHo7hkOpCbzDi/M08nEzKwYnEbJxIysalW/kwVHPfnLdKiaZlk6M3C9YiMsgHof4aBPt5I8TXG8F+Xgjx80aAjxd81Eqo63kJpdEooMRgRFGJATlFpcjVlYrPRXppObuwFBn5xUjLF4Ngen4xMvJLarznz1xjfw2aNfJB0yAtossCYExZCIzk9A9ERETkBhgOPRzDITVUUYkBF1LzcPlWPi7dysOVW/m4fCsf1zMLYWXuknipFPDxUsHHSwWtl8ri3kbz/scSgxHFeiN0pQYU640o0dd8WWdtgn290Nhfg4ggH2myd/EhBtuIIB/4eKkadAwiIiIiZ2dtNuCQeERUJa23Cj2iGqFHVCOL10sNRqTm6pCSrUNydqF0uWZWYQkyC0qQVVCKzMISZBWU996VGgSUGvTI01W+dNVavt4qBGm9EOjjJT5rvRCoVSNIKwbAMH8NwgI0YjtAg1B/bw76QkRERFQHDIdEVCdeKiWaB/uiebAvgJBq1xMEQeoFLCo1oKhEfNaVGqSeR/PrFgRBgJdaCR+1ChovJXy8VNCoy58Z9IiIiIjsi+GQiOxCoSi/lLSR3MUQERERUa34VTwRERERERExHBIRERERERHDIREREREREYHhkIiIiIiIiMBwSERERERERGA4JCIiIiIiIjAcEhERERERERgOiYiIiIiICAyHREREREREBIZDIiIiIiIiAsMhERERERERgeGQiIiIiIiIwHBIREREREREYDgkIiIiIiIiAGq5CyD7EAQBAJCbmytzJUREREREJCdTJjBlhOowHLqpvLw8AEBUVJTMlRARERERkTPIy8tDUFBQte8rhNriI7kko9GIlJQUBAQEQKFQyFpLbm4uoqKikJiYiMDAQFlrIdfAzwzVFT8zVFf8zFBd8TNDdeFsnxdBEJCXl4fIyEgoldXfWcieQzelVCrRvHlzucuwEBgY6BT/OMh18DNDdcXPDNUVPzNUV/zMUF040+elph5DEw5IQ0RERERERAyHRERERERExHBIDqDRaPD6669Do9HIXQq5CH5mqK74maG64meG6oqfGaoLV/28cEAaIiIiIiIiYs8hERERERERMRwSERERERERGA6JiIiIiIgIDIdEREREREQEhkOyk/3792P27Nno1KkTgoKCEBgYiE6dOmHWrFnYt2+f3OWRE9i5cycUCkWdH+fPn5e7dLKDtLQ0bNq0CQsXLsSECRPQtGlTi7/3b775pt77PnXqFF544QV069YNISEh8Pf3R/v27fHAAw/g999/t90PQQ5ly89MQkJCvc5H/Py4juzsbKxduxZz5sxBbGwsIiIioNFo4O/vj+joaIwfPx6LFy9GVlZWvfbP84z7sfVnxmXOMwKRDeXn5wuPPvqoAKDGx4wZM4T8/Hy5yyUZ7dixo9bPSVWPc+fOyV062dCNGzeEmJiYWv/ev/766zrvu7S0VHjllVcEpVJZ477HjRsn3Lp1y/Y/HNmFPT4z8fHx9Tofbdq0yX4/KNnEuXPnhDvvvFPw9va26u/U19dXWLRokWA0Gq3aP88z7sdenxlXOc+orciPRFYxGAyYPHkytmzZIr2m1WrRuXNnqNVqnD17Frm5uQCAr7/+GsnJydi4cSNUKpVcJZOT8PHxwdChQ61a19/f387VkCPpdDpcu3bNLvuePXs2vvrqK2nZy8sLnTp1gr+/P86fP4+MjAwAwG+//Ya4uDjs27ePny8XYM/PjMno0aOtWi8sLMyudVDDnT59Ghs2bLB4TaVSoU2bNmjSpAkMBgPOnTuHzMxMAEBhYSGef/55nDlzBp999hkUCkWN++d5xv3Y+zNj4rTnGYdGUXJrr7zyisU3HTNnzhQyMjKk9/Pz84X58+dbrDN37lwZKyY5mfccxsTEyF0OycT8m9SwsDBhzJgxwrx584R169Y1qOdw+fLlFttPmDBBSEpKkt4vKSkRli5dKqjVammd+++/38Y/HdmDPT4zFb/RJ/excuVKAYCgVquFSZMmCevWrRNycnIs1jEajcK6deuEZs2aWXwOPvnkkxr3zfOMe7LXZ8ZVzjPOWxm5lOTkZMHHx0f6wD/00EPVrjtv3jxpPR8fHyE5OdmBlZKzYDgkQRCEnJwcYeXKlUJCQkKl9+r7i35BQYEQEREhbXv77bcLer2+ynW/+OILaT2FQiEcOXKkvj8KOYg9PjOu8ksb1d26deuExx9/XLh27Vqt616/ft3i3NG4cWOhpKSkynV5nnFf9vrMuMp5hgPSkE0sXrwYOp0OAODr64vFixdXu+78+fMRFRUFQLw8aMmSJY4okYicUGBgIKZOnYqYmBib7fObb77BzZs3AQAKhQKffPJJtZevP/bYY+jfvz8AQBAE/Oc//7FZHWQf9vjMkPuaOHEiPv/8c0RHR9e6blRUFN544w1pOT09Hbt3765yXZ5n3Je9PjOuguGQbGLt2rVS+5577kFISEi163p7e2PGjBnS8po1a+xaGxF5FvNzytChQ9GxY8ca1589e7bU3rhxI4qLi+1WGxE5t/Hjx1ssVzdCNs8zZGLtZ8ZVMBxSg124cAGXL1+WlseMGVPrNmPHjpXaly9fxoULF+xSGxF5lvz8fItvbet6PsrPz8fOnTvtURoRuYCKX26bBtIzx/MMmbPmM+NKGA6pwU6cOGGxPHDgwFq36dWrF7y9vaXlkydP2rwuIvI8Z8+eRWlpqbRszfkoIiICLVq0kJZ5PiLyXBVHwg0PD6+0Ds8zZM6az4wrYTikBjt37pzU9vb2lu4nrEnF9cz3QZ4nOzsb99xzD1q0aAGtVouAgAC0bNkSkyZNwscff+zy38KR41Q8l7Ru3dqq7czX4/mIHn74YbRt2xZ+fn7w8/NDdHQ0xowZg3fffRe3bt2Suzyyo4q3ulQV/HieIXPWfGaq4qznGYZDarCEhASp3bx5c6vndzG/0dd8H+R5cnJysHLlSly7dg06nQ75+flISEjAL7/8gmeeeQbR0dFYunSp3GWSCzA/l6jVajRt2tSq7Xg+InPfffcdLl++jMLCQhQWFiIxMRGbN2/Gyy+/jJiYGMyfPx8Gg0HuMsnGcnJyLAbJ69atGzp16lRpPZ5nyMTaz0xVnPU8o3b4Ecnt5OXlSe2goCCrtwsMDKxyH+SZWrRogWbNmkGj0SA9PR1nz56FXq8HIJ5858yZg+PHj+PLL7+UuVJyZubnkoCAACiV1n0HyvMRmWvatKl0JUNWVhbOnTsnjcit0+nw1ltv4fDhw/j111/h5eUlc7VkKy+++KI0AikAvPXWW1Wux/MMmVj7mamKs55n2HNIDZafny+1fXx8rN5Oq9VWuQ/yDEqlEnFxcVixYgUyMjIQHx+PvXv3Yvv27Thx4gSysrLw6aefonHjxtI2X331FYcApxrxfET1oVAo0K9fP3z++edISUlBSkoK9u/fj+3bt+Po0aPIzs7GDz/8YHHP2ObNmzFnzhz5iiab+uKLLyy+fJw2bVqlUShNeJ4hoG6fGcB1zjMMh9Rgpt4dQLy8wlrm65rf2E2eITY2Flu3bsX9999f5dQn/v7+eOKJJ3D06FGLE+XChQuRmprqwErJlfB8RPURExODgwcP4vHHH6/yEkGNRoP77rsPR48eRe/evaXXly9fzoFF3MDu3bvx1FNPScstW7bE8uXLq12f5xmq62cGcJ3zDMMhNZivr6/UNnWHW8N8XT8/P5vWRO4jKioKP/30k7RcWFjIS0upWjwfkT0FBwdjzZo1Um+RIAj4+OOPZa6KGuL48eOYMGECSkpKAIgjTf7+++813ibD84xnq89npi7kPs8wHFKD+fv7S+2ioiKrtyssLKxyH0QV9evXD7fffru0vHXrVvmKIafG8xHZW3R0NO69915pmecj13XhwgWMHj0aOTk5AMRfyrds2YJ27drVuB3PM56rvp+ZupLzPMNwSA1mfk/YjRs3rN7O/Abe0NBQm9ZE7mfYsGFS++LFizJWQs7M/HyUn59v9X09PB9RXZifjxISEqQeBHId8fHxiIuLk6YMCAgIwKZNm9C9e/dat+V5xjM15DNTH3KdZxgOqcHat28vtTMyMiy+GatJYmKi1O7QoYPN6yL3EhERIbXT09NlrIScmfn5CACuX79u1XY8H1FdmJ+PAPH/PnIdSUlJGDFiBJKSkgCIl4lu2LAB/fv3t2p7nmc8T0M/M/Uh13mG4ZAarGPHjhbLx48fr3Wb5ORkpKWlVbsPoorMv3Qwv9+DyFx9zkelpaU4c+ZMtfsgqqjil6A8J7mO1NRUxMXFIT4+HoA4CMi6desQGxtr9T54nvEstvjM1Idc5xmGQ2qwfv36QaPRSMt79+6tdZs9e/ZIbR8fH/Tr188utZH7MP9PNTw8XMZKyJm1atUKzZs3l5atOR8dOXLE4j9he/+HT67P/Hyk0WhsNhAF2VdGRgbi4uJw4cIFAICXlxdWrVqFkSNH1mk/PM94Dlt9ZupDrvMMwyE1mL+/P0aMGCEtr1ixotZtzNcZMWIER+2iGhUWFmL9+vXS8qBBg2SshpzdhAkTpPbKlStrvU/D/HzUuXNntG7d2m61kesTBAE///yztDxw4EAZqyFr5eTkYPTo0Th9+jQAQKVS4YcffsCdd95Zr/3xPOP+bP2ZqQs5zzMMh2QT06dPl9onT57Er7/+Wu26R48exaZNm6rclqgq8+fPl24AB4BJkybJVww5PfNzSnp6eo1zTyUlJeHbb7+tcluiqnz88ccWc47xfOT8CgoKMG7cOBw5cgQAoFQq8e2332Lq1Kn13ifPM+7NHp+ZupD1PCMQ2YDRaBS6d+8uABAACE2bNhXOnTtXab2UlBShY8eO0no9evQQjEajDBWTnDZv3iy88MILQmJiYo3rlZSUCC+//LL0eQEg9OrVi58ZD2H+9/7111/XadsJEyZI2/r7+wt79+6ttE5OTo4wZMgQab2IiAihsLDQRtWTHOrzmTl9+rTw6KOPCufPn69xPaPRKCxevFhQqVTSMSIjI/mZcXI6nU6Ii4uT/s4UCoXw5Zdf2mTfPM+4J3t8ZlzpPKMQBEFwSAolt3f48GEMHTpUmvMnMDAQTz75JGJjY6FWq3Ho0CF8/PHHSE1NBQBotVrs2rULffv2lbNsksG6detw1113QalUYvDgwRg6dCi6dOmCxo0bw9vbG+np6Th06BBWrFhhMbpbSEgI9u/fX2mkOHJtM2fOxHfffVfp9eLiYqmtVquhUqkqrVPdBNQJCQno27evNLKtRqPBY489hlGjRsHf3x8nT57E0qVLpQEGlEol1q1bh/Hjx9viRyI7s+Vn5vjx4+jZsycAoHfv3hg+fDi6d++O8PBwaLVaZGVl4dixY/jxxx9x/vx5aTuNRoOtW7diyJAhtvqxyA7effddvPzyy9JycHBwncY5GDlyJF588cUq3+N5xj3Z4zPjUucZh8VQ8girV68WtFqtxbe3VT20Wq2wevVqucslmaxdu7bWz0jFR9u2bYWjR4/KXTrZwSOPPFLnz4PpUZN9+/YJISEhte5DpVIJS5cuddBPS7Zgy8/MsWPH6ryPiIgIYevWrTL85FRXr7/+er0/KwCERx55pMb98zzjfuzxmXGl8wzvOSSbmjx5Mo4cOYK4uDgoFIpK7ysUCowYMQJ//fUXJk+eLEOF5Aw6dOiAadOmWYz2Vp0WLVrg3XffxbFjx6Rv3YisMWjQIJw8eRJTpkyBWq2ucp2+ffti9+7dePrppx1cHTmLpk2b4uGHH7ZqgJAmTZpg3rx5OHXqFOLi4hxQHTk7nmfIGq50nuFlpWQ3iYmJ2LdvH5KTkwEAzZo1w+DBgxEVFSVzZeRMrl+/jrNnzyI9PR3p6ekoKChAYGAgwsPD0adPH47oRjaRlpaG3bt3IykpCSUlJYiMjESfPn14iTJZSE1NxcmTJ5GWlob09HTk5eXB398fjRs3Rs+ePdGxY8cqv/gkAnieIes4+3mG4ZCIiIiIiIg4lQURERERERExHBIREREREREYDomIiIiIiAgMh0RERERERASGQyIiIiIiIgLDIREREREREYHhkIiIiIiIiMBwSERERERERGA4JCIiIiIiIjAcEhERERERERgOiYiIiIiICAyHREREREREBIZDIiIiIiIiAsMhERERERERgeGQiIiIiIiIwHBIREREREREYDgkIiIiIiIiMBwSERG5tAULFkChUEChUKBdu3YoKSmp0/abN2+WtlcoFLh165adKiUiImfHcEhEROSiLl26hHfeeUdaXrRoEby9veu0jz59+lgs79271ya1ERGR62E4JCIiclFPPfUUiouLAQBjxozBuHHj6ryP0NBQREdHS8v79u2zWX1ERORaGA6JiIhc0NatW7F161Zp+c0336z3vlq2bCm1z50716C6iIjIdTEcEhERuaD58+dL7bFjx1a6PLQumjVrJrUvX77coLqIiMh1MRwSERG5mO3bt+PgwYPS8ksvvdSg/YWFhUntGzduNGhfRETkuhgOiYiIXMyyZcukdsuWLXH77bc3aH8KhUJqm+5hJCIiz6OWuwAiIiKyXkZGBn755Rdp+eGHH7YId+YKCgpQVFQEAAgMDKx2JFNBEKpsExGRZ2HPIRERkQvZvn07SktLpeXRo0dXu+706dMRFhaGsLAw/PXXX9Wul5KSIrWbNGlim0KJiMjlMBwSERG5kB07dkhtPz8/9O3bt9p1Dx8+LLW7dOlS7XrXr1+X2ubTWhARkWdhOCQiInIhp0+fltpdunSBWl31HSLJycm4du0aACAiIgKBgYFVrqfX63Hq1ClpuaawSURE7o3hkIiIyIVcunRJardv377a9cznQGzevHm16x07dgyFhYXS8uDBgxtYIRERuSqGQyIiIhdhNBqRmpoqLdd0f+D69euldkhISLXrbdiwQWqr1WqMGDGigVUSEZGrYjgkIiJyETqdzmJZo9FUuV5mZiY2btwoLXt5eVW5niAI+PHHH6XluLg4hIaG2qBSIiJyRQyHRERELkKlUllMW5GZmVnleh9//DGKi4uldTMyMqpcb/369RaXqc6cOdOG1RIRkatRCJzQiIiIyGVERERIl5Z269YNJ06csHj/2rVr6NKlC/Lz8zFs2DDs2LED/v7+yMjIsJjnMDs7G71798bVq1cBAF27dsWJEyeqnTORiIjcH3sOiYiIXMiQIUOk9smTJ7Fs2TJpOSEhAePGjUN+fj7atWuHe++9FwCQn5+P999/X1rv2rVruOOOO6RgqFKpsHz5cgZDIiIPx55DIiIiF7J161aMGjXK4rUOHTogJCQER44ckS4n3bJlCyIiItC1a1dpvW7dusHHxwdHjx6FXq+XXl+0aBGee+45R/0IRETkpBgOiYiIXMwLL7yARYsWVfmeWq3GJ598It0/OGXKFKxZs6bKdf39/bF48WI89thjdquViIhcB8MhERGRC1qzZg2WL1+O48ePIzMzE2FhYRg2bBheeukl9OjRQ1pPp9Phrbfewk8//YTr16/D19cXLVu2xLhx4/Dkk08iMjJSvh+CiIicCsMhERERERERcUAaIiIiIiIiYjgkIiIiIiIiMBwSERERERERGA6JiIiIiIgIDIdEREREREQEhkMiIiIiIiICwyERERERERGB4ZCIiIiIiIjAcEhERERERERgOCQiIiIiIiIwHBIREREREREYDomIiIiIiAgMh0RERERERASGQyIiIiIiIgLDIREREREREYHhkIiIiIiIiAD8PxEQ5oDX7SDtAAAAAElFTkSuQmCC", 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", + "image/png": 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", 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", 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", 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" ] @@ -844,18 +828,22 @@ "lam=fitinfo[\"params\"][:,0]\n", "gamma=fitinfo[\"params\"][:,1] \n", "w0 = fitinfo[\"params\"][:,2]\n", + "def _sd_fit_model(wlist, a, b, c):\n", + " return (\n", + " 2 * a * b * wlist / ((wlist + c)**2 + b**2) / ((wlist - c)**2 + b**2)\n", + " )\n", "plot_fit(_sd_fit_model, J, w, lam, gamma, w0)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "id": "c05f2af0", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -878,13 +866,13 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "id": "72deb34d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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0GRmOQdojAIAdpgwc31zojArJQ9x5551Su7S0FAsWLGi3/9dff91iRP7uu+92VmmtGjBgAEaPHi0dP/bYYygvL2+z/7x581BQUOCCyromrtmWx+3tmgrYdoHNy8tzdknCMbQTEZHy1de3WO6xMyPtAHDDpfK0mGWvH3dUVeSBUlNTMXv2bOn4qaeewvLly1vtu23bNtx1113S8aBBgzBjxgyn13i+119/XRqNzs/Px4QJE7B169YWfSorK/Hwww/j5ZdftnspS5HGjx8vtZ977rk2N0z6+OOP8fe//91VZQnF0E5ERMp3XmjvzEg7AFz/F3nN72XrO9iVibzef/7zH+nBR7PZjOuuuw7XXnstPvvsM2zcuBHLly/HPffcg3HjxkkrxwQGBuKTTz5x6G6n9rrkkkswb9486TgnJweXXHIJevfujYkTJ2L48OGIjY3FW2+9BQD48MMPW5x//pKVSvDXv/5V+kWkqKgIQ4YMwXPPPYeffvoJGzZswAcffIApU6ZIy0Lec889git2Pi75SEREyldf36k12s/XZ0YqBvjlYn9jP2yvTkVZfi2iegU7uEjyFNHR0diwYQOmTp2KoqIiAMCKFSuwYsWKVvuHhITgu+++a3MXVFeYN28e1Go1nn32WWk+eH5+fosVcDQaDd555x1MnTq1xbnnL3WpBMOGDcPzzz+Pf/zjHwCAsrKyFr+YNHfDDTfgySefxPvvv+/KEl2OI+1ERKR8XRxph0qF6em2aTFW+GDNwkMOLI48Ub9+/bB371489NBDCAoKarWPn58fbr75Zuzfvx8TJkxwbYGteOqpp7Bnzx48+OCD6NOnD7RaLXQ6HQYOHIjHH38c2dnZuO2223D69GnpHI1GA61WK7Dqtj311FP44IMPEB0d3eqfx8bGYsGCBfjiiy9c+vCvKCqrp26lRdDr9dDpdKiurkaoPbuQEBEp1d/+hvcW1OI+vAcAeP99oLPP+218+TeMn3sJAOCmlJ347EjHy8jZy2QyIT8/H7169UJgYKDDrkvKYDKZsHHjRuTl5aGiogKhoaFITEzEhAkT3PLz9euvv5bm7Y8ePfqC+e9KYzKZsGnTJuTk5MBoNCI6OhopKSm49NJLhUxHal6XI/67tzevec30mMbGRvz+++/47bffsG/fPuTm5uL48eOoqqpCY2MjQkNDkZCQgOHDh+P666/H1KlTHb7VcHN5eXlYvHgxVq1ahRMnTqC2thbdu3dHRkYGbr31VsyaNQtqtdf8z0NE1L76eughf5iFhHT+Epf8KQO6uVWoRhjW5PWF2QzwxyzZIzAwEJdddpnoMhym+Zz2MWPGCKzEPoGBgZg6deoF03q8jdf8uHryySfxr3/9q80/r6ioQEVFBfbu3YsPPvgAgwcPxocffthikwVHWbhwIebMmYP6s5uFnJOXl4e8vDx88803GD16NJYuXYrevXu3cRUiIi9SX48ayF+RX0xoV4cFY1rkL/iyfBKqLDpsXVGCcdfHdnwikRuwWq12TRH55JNP8MMPP0jHd9xxhxOrIkfymjnt588CCgoKQkZGBsaPH48JEyagX79+LUbWs7KykJmZic2bNzu0jueffx6PPPKIFNh9fHwwYMAAZGZmtliTdNu2bRg/fjxOnTrl0PsTEbml80baL3ZGwvRxeqn9w+LSrlZFpBjPPfcc7r33XmzYsAFms/mCPy8qKsKjjz7aIqTPnDlT6MOz1DleM9Ku0WgwY8YMXH311cjMzERqauoFfcrKyrBw4UK8/PLLaGpqQm1tLW655RYcOHAAwcFdX2Vg7dq1LZ58HjNmDBYvXoy+ffsCsO3o9dVXX+Gee+5BbW0tioqKcP311zv8FwciIrfT0IAayMPrFzPSDgDT7uoBnF0A5JftrT9cSOSOjEYj3n//fbz//vsIDAxEamoqunXrBrPZjOLiYhw7dqxF/549e2LRokWCqqWL4TWh/fnnn++wT1RUFF544QUkJSXh3nvvBQAUFhbiq6++arFD2sWwWq2YM2eONOKfmpqKdevWtXhi28fHBzfeeCO6desmzdvasmULVqxYgWuuuaZL9ycicmv19S1C+8WOtMdMG4x0nwPIsaRh15leqKqwICzCa750Jg/WfLaAyWTC3r172+w7ceJEfPrpp9Ja9OQe+JOqFffccw+Sk5Ol4w0bNnT5mqtXr27xH9DChQvbXGJpypQpuPHGG6Xjl19+ucv3JyJyaw54EBUA4OeHyT2PAgAs8MXGJSccUByReM8++yxWr16Nv/3tbxg3bhy6d++OwMBAqNVqREZGIiMjAw888AB+/vln/PLLL4iN5fMc7sZrRto7a+jQodJXSSUlJV2+XvMtkHv16tXhU+j3338/vvjiCwDAjh07UFRUhPj4+C7XQUTkls4bab/o0A5g0mQfvHV2D5afl1Xg6oeSulYbkQL4+fnh8ssvx+WXXy66FHISjrS3oflDHCFd+XQ4a9WqVVJ72rRpHT7hPW7cuBabOTQ/n4jI6zQL7QEBVvj7X/ylxt+dAh80AQB+yerk1qpERIIwtLeisbGxxUYDXV3D9PTp0y1G6+25nlqtxogR8sYf+/bt61INRERurdn0mJCQru18GDYqFcPUtumK+2uSUHrK0uXyiIicjaG9FU899ZQUsiMiIrq8hunBgwdbHDefL9+e5v3OvwYRkVdpNtLe5S8/VSpM6n1cOlz/SWEXL0hE5HwM7bBNhTl16hS++eYbXHbZZXjttdcA2Hbg+uyzz9CtW7cuXb+goKDFcWJiol3nNe93/jWIiLxKs9DuiF3jJ06U25tWVXf9gkRETua1D6JGRkbizJkzrf6ZSqXC1KlT8frrr2PAgAFdvldNTU2LY51OZ9d5oc0+mc6/Rmvq6+tb7LKq1+vb6U1E5D4aTBbUIxCAA0baAYy5pRd8FjXBAl9s2eeA3wKIiJyMI+2tGDt2LP70pz8hLS3NIderra1tcRwYGGjXeRqNps1rtOall16CTqeTXgkJCZ0rlIhIoWrq5SdPHTHSHjp2IDJ89gMA9lUnorrK2sEZRERieW1onzx5MqZNm4Zp06ZhwoQJ6Nevn7QxwebNm3Httddi9OjRyM/P7/K9zt9OWK227wuO5v0aGxs77D937lxUV1dLr8JCztMkIs9QY/KT2o4YaYevLy6Nt81rt8IHW78+6YCLEhE5j9dOjzm3BnpzFRUVeP/99/Hcc8+hrq4OO3fuxPjx47Fr1y5ER0df9L3O30TJZDK1ubHS+f3Oab78Y1sCAgIQEBDQ+QKJiBRO3yB/Q+mQ0A5g7BgL/n12b6Ut35zG5Xf3cMyFiYicwGtH2lsTERGBJ554Aps2bZLWZi8sLMRjjz3WpesGBwe3ODYajXadZzAY2rwGEZE3qWmQByQcMT0GAC69UQ7pm3fZN22RiEgUhvZWDBkyBE899ZR0/Pnnn6OiouKirxcZGdni+NSpU3ad13xt966uYENE5LbMZuit8sCFo0ba46/MQE/YpshsL02CHbMQiYiEYWhvw+zZs6W22WzGzp07L/paqampLY5PnDhh13nN56T369fvou9PROTWmi33CDhupB0BARgbdRgAYLRqsGdd6yuKEREpAUN7G85feaWt5SHt0adPnxYPlWZlZdl13p49e6R2//79L/r+RERu7bzQ7sjZgpcOkVfm2vwlH0YlIuViaG9DdXXLzTbCwsIu+lr+/v4YNWqUdLx58+YOzykpKcHRo0el48zMzIu+PxGRW6uvhwHyw/t2PJdvt7FXyPtmbNvKZR+JSLkY2tuwcePGFsfJycldut7MmTOl9rp161BaWtpu/6VLl0rtsLAwhnYi8l719TBC3rei2RYWXZZ+fRqCYBtt31EQ5bgLExE5GEN7KxoaGvDCCy9Ix8nJyRfMS++sm2++WVqOsbGxEa+++mqbfWtra/HWW29Jx7feeiv8/Pza7E9E5NEaGlqEdjtWzLWbb49YDAvIAQAcr49DaRGfRiUiZfKK0P7TTz/h8ccfx8mTHc9XPHXqFK666qoW887//ve/t9q3oKAAKpVKes2fP7/N68bHx+P++++XjhcuXIivv/76gn6NjY248847pYdVNRoNnnzyyQ7rJiLyWOdNj3HkSDsAjOp1Wmrv+Oq4Yy9OROQgXrG5Ul1dHf71r3/hjTfewCWXXIJx48Zh4MCBiIyMhFarRW1tLfLy8rBp0yZ8++23LdZHv/rqq3H33Xc7pI758+dj9erVOHLkCJqamnDDDTfglltuwaxZsxAREYFDhw7hv//9L/bt2yed89prr6F79+4OuT8RkVty4vQYABg5xhfItbW3r63EVX9z7PWJiBzBK0L7ORaLBZs3b7brQVAAuPPOO/HOO+9ApVI55P7h4eFYuXIlpkyZgsLCQlgsFixZsgRLlixptf8TTzyBBx980CH3JiJyW+eNtDtyegwAjLq2B/CRrb1jLzdZIpuGhgZ89NFHWLZsGbKzs1FRUYHGs4v5Dxo0CFlZWbjjjjvw8ccfAwDmzZvX7jfuRF3lFdNjhg8fjkcffRRpaWkdBnB/f39cd911+PXXX/Hhhx/C39/fobX07dsX+/btw9133w1NG8NF/fv3x7fffotXXnnFofcmInJLTh5pj5/aH7GwbXq383QiLBbHXp/cT01NDS699FL86U9/khaPaOTuWySYV4y0x8fH4/XXX8frr7+Oqqoq7N27F3l5eSgvL0d9fT2CgoIQHh6O/v37Y9CgQQgMtG+kJSkpCVZr55cICwsLw/vvv48333wTv/zyCwoLC1FXV4e4uDgMHDgQQ4YM6fQ1iYg81nmh3dEj7aoAf4yMOIrvKuJQZdHhyLYzSL2Eu1B7syeffLLFpoo9e/ZESkqKtOdKZ1aUKygoQK9evaTji8kNRICXhPbmwsLCMH78eIwfP150KQgJCWmxFCQREbXCyQ+iAsCotFp8d3bm5I6vjjO0e7GmpiZpygsAvPzyy5gzZ47AiohsvGJ6DBERuTEnT48BgJGT5G1Wd2yqd/wNyG0cPnwYNTU1AAC1Wo3HHnuszb6LFy+G1WqF1WrlfHZyOoZ2IiJStmYj7T4qCxz8qBEAYPj18vSF7UfCHX8DchsVFRVSOzY2VpoSQyQaQzsRESlbs82VNP5NcNCCXi2EpfdAP9/DAIAsfW/Umzjv2Fs1f+DU19dXYCVELTG0ExGRsjWbHqP1b3LOPVQqjIgtAgA0wh/71xY75z6kSBs2bJA2Spw4caL0/vHjx1tsonjudc4dd9zR5gaLixcvhkqlavEQKoBWr9fRBo1EAEM7EREpXbPpMZoAJ4V2AEMHNEjt338ocdp9iIguBidqERGRsjUfaQ903iLqQyeEAmtt7d3bG3Gv0+5EShMREYFp06YBsM1pP7fcY2Bg4EWvNtejRw9MmzYNRqMRGzdulN4/d5/zpaSkXNR9yHswtBMRkbK1GGl3XmgfMjsZmGtr/54X5rT7kPJkZGRgzZo1AGxTZc5NkYmJiZHe76ypU6di6tSpF6zTfrHXI+L0GCIiUjSzsRGNsC0Zo7Fv77uLEpISg77qYwCAfTW90FjPrVGJSDkY2omISNGMdXJ41mqcu6rLsFjbA6j1CMSBNSecei8ios7g9BgiIlK05qHdGRsrNTd0QAM+sy0ig99XncKgmUlOu9fw4UAJn3dtU2wssGuX6CqIlIOhnYiIFM1okEfXtUHOvdfQCTrg7JTj3TvMuNOJ9yopAYq5siQR2YmhnYiIFM1gkmdyarRO2FmpmaGzewN/t7Wd/TBqbKxTL+/2+O9D1BJDOxERKZrRKLc1Wuc+ihWW3A291ceRZ+6JrJpkmE1mqAOd81HJqR9E1Bl8EJWIiBTNYJRH17XBzv/YGhp7EgBghBaH1uQ7/X5ERPZgaCciIkVrMdIe7Ov0+w1rsTPqKaffj4jIHgztRESkaEZTs5H2UOeH9qHjQ6T27p3OXWKSiMheDO1ERKRohvpmD6IGO/9RrKHX9JTa3BmVusrHp2XUslr5iyBdHIZ2IiJSNGO9PLquDXLu6jEAEJnaDYm+tsXa9+h7w2Lmzqh08YKCWq5Tamw+34uoExjaiYhI0QwN8ui6szdXOmdIpC201yIEx34tcs1NySOFhYUhMDBQOj527JjAasidMbQTEZGiGRvkkXZXhfbBqfJo6N7VJ11zU/JIvr6+yMjIkI4XLlwIi4Xf3lDncZ12IiJSNEOj/FGl1brmnoNGa4CNtvbe7SbMds1tyUPdcsst2LFjBwDggw8+wKpVqzBgwIAWU2duuukm3HTTTaJKJDfA0E5ERIpmbPST2q4aaR80PR541dbOOuyi3xTIY/35z3/Gt99+i/Xr1wMASkpKUFJS0qLP4MGDBVRG7oTTY4iISNGMZjm0u2qkPWlsD4RCDwDYe6aHa25KHsvPzw8//fQTFi9ejBkzZiAhIQEaV/0GSh6DI+1ERKRcVisMTQHSoatyjo+vChm6AmyuzkBhUw9UHCpDRGqUa25OQk2YMMHuZRkXL16MxYsX29XX19cXt99+O26//fYuVEfejCPtRESkXI2NMEJeecOVg5ODkvRSe993Ba67MRFRKxjaiYhIuUwmGCDPiXHV9BgAGDRUXrUma6O+nZ5ERM7H0E5ERMpVXw8j5OF1V460D54qT4fZm+PbTk8iIudjaCciIuUymVqEdleOtA+Y3hM+aAIA7D3J+exEJBZDOxERKVez6TEqWBAQ0EF/B9KE+qFv4AkAQE59Chora113cyKi8zC0ExGRcjUbadeoG6FSufb2g7uXAQAaEIDcVdx+nojEYWgnIiLlqq+XRto16kaX337QwCapvXddmcvvT0R0DkM7EREpV4uRdrPLbz9onE5q793T1E5PIiLnYmgnIiLlahbatf4CRtqvSpTae4+Hufz+RETnMLQTEZFyNXsQVePv+pHuuD7BiPIpBwBkVfeGtcni8hqIiACGdiIiUjCLsR71Z3dE1Qa4PrSrVMCgbkUAgDJEoWTHCZfXQEQEMLQTEZGCGfXylBhNgJhR7kHJdVJ77+qTQmogImJoJyIixTLWyA+fagKsQmoYPMJPamdtNbbb12oVUyMRuZ6r/3tnaCciIsUy1MhTYrQaQSPtU6Ol9r5D/q328fGxfZxaLJzzTuQtzv33fu6/f2djaCciIsUy1skhWBMopobUKQlQwzZNJ7s0utU+arUaKpUK9fX1riyNiAQymUxQqVRQq9UuuR9DOxERKZaxttlIu1ZMDf4aX/TTHAcA5Db0RkOV4YI+Pj4+0Gg0qKuru+DPiMgz6fV6BAcHc6SdiIjIUCfPGdUICu0AMDDOtuyjGX44tDqv1T7BwcGoq6tDQ0ODK0sjIgHq6upgMpkQGhrqsnsytBMRkWIZDXJo1waJ+8ga2F8e8c9eX9ZqH51OB7VajaKiIjQ1cfdUIk9VV1eHwsJCBAUFITg42GX3dc0kHCIiootgaDYTRaNVCatj4JhgYJWtnf27udU+arUaCQkJKCgowNGjR6HT6RAcHAxfX1+oVOJqJ6KusVqtsFgsMJlM0Ov1MJlMCAoKQnx8vMumxgAM7UREpGDGZissaoIFjrRfEQ/8w9bOLmh7ZC0gIAC9evVCVVUVqqurUVlZ6aIKicjZVCoVgoOD0a1bN5fOZT+HoZ2IiBTLaJLb2hBfYXUkDumGEFUNaqwh2F/ZA7BabdultsLf3x/R0dGIioqC2WzmVBkiD+Dj4wO1Wu3yoN4cQzsRESmWwSh/QGqCxX1kqVTAAF0htlal4bglEfrDpxCaGtfBOSr4+fnBz8+v3X5ERPbgg6hERKRYxnr5Y0obKnacaWCiXmrvX3VcYCVE5I0Y2omISLEM9fKUGE2I4NA+SP7IzN6ib6cnEZHjMbQTEZFiGRuaTY/R+QusBBg4PkJqZ+/najBE5FoM7UREpFiGBnl0XasTOzd84JUJUju7OFxgJUTkjRjaiYhIsYyNclAXuU47AETEBaC7uhQAkF3XG9aGRqH1EJF3YWgnIiLFMjY2G2nXCizkrIGRJQCASkTg1KajgqshIm/C0E5ERIplMMvz2DUagYWcNaCPvHB89k8lAishIm/D0E5ERIplbJKnxyhipH2o/EtE9k5TOz2JiByLoZ2IiBTL0BQgtZUw0j5wcrTUzj4S0E5PIiLHYmgnIiLFMjYL7YGBAgs5q/+kOPigCQCQXRojuBoi8iYM7UREpExWK4xWW2gPVJngo4BPLE2QD/oEFgIADjQkw6w3CK6IiLyFAn4EEhERtaKxEQbYJrJrfBsEFyMbGFMGAKhHII7+lCe4GiLyFl4V2quqqrBixQo89NBDyMzMRGxsLAICAhAcHIzExERcddVVWLBgASorK51yf5VK1enXO++845RaiIgUz2SCEbaJ7FrfesHFyAamyr9AZK8/I7ASIvIm6o67uL/c3Fw8/vjj+PHHH9HQcOFoTUNDA+rq6lBYWIiVK1fiqaeewosvvoiHH34YKhW3qiYiEqK+vtlIu3I2MhowUgv8aGvv39OI68WWQ0RewitC+/79+7Fy5coW7/n6+iIlJQUxMTFoamrCwYMHUVFRAQAwGAz429/+hpycHLz77rtOCe6ZmZnQ2LEUQmJiosPvTUTkFkwmGNENAKDxU05oH3hZHPCCrZ19TAHrUBKRV/CK0H6OWq3GjBkzcMcdd2DixIkIDQ2V/sxqteK7777Dgw8+iOLiYgDA+++/j6FDh+KBBx5weC0ff/wxkpKSHH5dIiJPYTWaYDw70q5VUGjvPSYGGhhghBbZZ+JEl0NEXsIr5rT7+fnhnnvuwbFjx7BixQrMnDmzRWAHbPPNZ86cia1btyI2NlZ6/5lnnkFjo3I+LIiIvIWpWp7HrvEzC6ykJV+1CulBxwEAx8w9YSitEVwREXkDrwjtM2fOxHvvvWfXVJOEhAQ8++yz0nF5eTk2btzozPKIiKgVRr08YKL1V05oB4D07rYFC6zwwcE1BWKLISKv4BWhvbOuuuqqFse5ubmCKiEi8l6Gajm0a/wtAiu5UHp/uZ6cjRUCKyEib8HQ3oqIiIgWx3q9XlAlRETeq/lIuyZAYaF9VLDUztnXJLASIvIWDO2tOH78eIvj6OhoQZUQEXkvQ408JUYbqKzQPmBaD6mdU8AVZIjI+RjaW7F8+fIWx2PGjHH4PR5//HGkp6cjNDQUGo0G8fHxmDhxIubPn4/8/HyH34+IyN0Ya+WgrlFYaE8YEokQle0B1JzK7oKrISJvwNB+nurqaixcuFA6zsjIQFpamsPvs2zZMhw4cAA1NTUwmUwoLi7Ghg0b8Oyzz6Jv377405/+BKPR6PD7EhG5C2OtPO1E2/G2Fi6l8lEhLbgQAFDQlIjaQufspE1EdA5D+3kee+wxlJSUSMcvvPCCU+4TGRmJUaNGYfLkyRg+fDiCg+X5kWazGYsWLcLYsWNRXV1t9zXr6+uh1+tbvIiI3JWh+Ui7wkI7AKT3qJLaB9ccb7sjEZEDMLQ38/777+ODDz6Qjm+88cYLVpLpirS0NCxYsADHjh1DWVkZtm3bhnXr1mHnzp2orKzEypUrkZGRIfXfs2cPbrrpJruv/9JLL0Gn00mvhIQEh9VORORqxjo5tGuDBBbShvR0q9TO2cyRdiJyLob2szZu3IgHH3xQOu7VqxcWLVrk0Hvk5OTg4YcfRu/evS/4M7VajenTp2P79u2YPn269P6aNWvw/fff23X9uXPnorq6WnoVFhY6rHYiIlczGORQrNGoBFbSuvTR8iZ9OdlcQYaInIuhHUBWVhauvvpqNDQ0ALCtFrNmzRrodDqX1xIYGIjPPvsMMTEx0ntvv/22XecGBAQgNDS0xYuIyF0Z65qF9iDlfVylT4uX2jnHg9vpSUTUdcr7Kehihw4dwrRp06S54+Hh4fjxxx/Rt29fYTWFhITggQcekI43bdoEk8kkrB4iIhGMRjm0a0N8BVbSuh4DwhGqsj07lFPVo4PeRERd49WhPT8/H1OmTMHp06cB2MLy6tWrMWjQIMGVARMnTpTaJpOJU12IyOsYjPKUGE2IWmAlrVOpgPRQ28/mE5YE1OSVCa6IiDyZ14b2oqIiTJ48GUVFRQAArVaLlStXYtSoUYIrs4mNjW1xXF5eLqgSIiIxjM1CuxJH2gEgPV5epevAmhMCKyEiT+eVob20tBRTpkyRNjEKCAjAN998g8zMTMGVyQwGQ4tjrZY77hGRdzGY5I8oTaifwEralj5AbnMFGSJyJq8L7WfOnMGUKVNw6NAhAICfnx+WLVuGqVOnCq6spZycnBbH0dHRgiohIhLDWN8stOv8BVbStvQx8oIFOTnWdnoSEXWNV4X26upqTJs2Dfv37wcA+Pr64tNPP8WMGTMEV3ahzz//XGonJSUhLi5OYDVERK5nqJenxGjDFBraL5f3w8g5wRW7iMh5vCa019XVYfr06di9ezcAwMfHBx9//DFmz54tuLILfffdd1i5cqV0PGvWLHHFEBEJYmyUQ7smLEBgJW2L6xuCMJVt9bGc6njAytF2InIOrwjt9fX1mDVrFrZs2QIAUKlUeO+993Drrbd26boFBQVQqVTSa/78+a32q66uxnXXXSf9wtCezz77DLfccot0rNVqMWfOnC7VSUTkjowN8oox2nBlhnaVCkjX2RY0KLL2QPWR04IrIiJPpbw1tJxg4cKFWLdunXQcFhaGL7/8El9++aVd50+dOhWPPfbYRd/farVi+fLlWL58Ofr164dp06Zh8ODBiIuLQ1BQEGpqapCdnY1ly5Zh586d0nkqlQofffTRBSvJEBF5A4NZfvhUqSPtAJCeoMeWKlv7wNpCjOkb025/IqKL4RWh/fyVWCorK7F27Vq7z3dkaM7NzUVubm6H/UJCQrBo0SLccMMNDrs3EZE7MTYP7VpVOz3FSk8HkG1r52zVY8xfhZZDRB7KK6bHiKbRaHDfffchPT0dKlX7Hzw6nQ4PPfQQ9u/fj5tvvtlFFRIRKY/BbBtd90c9fJW5TDsAIH2M/AAqV5AhImdRWa18asaVKisrkZWVhdOnT6O8vBxVVVXQarWIiIhARkYGMjIy4OugTye9Xg+dTofq6mqEhnJVAyJyL/38juKQOQU6VTWqLLqOTxCkJLcKcf3DAABTw3fix4oRYgsiIrdib17ziukxShIeHo6JEyeKLoOISPGMFttIu9bHBEC5oT0mNQwRqgpUWCNsK8gQETkBp8cQEZEiGSyBAACNT4PgStrXfAWZk5Y4VB0tF1wREXkihnYiIlIeqxVGaAAAWnW94GI6lh6vl9o5a4sEVkJEnoqhnYiIFMda3wADtAAAjW+j4Go6lp4uPx6W81u1wEqIyFMxtBMRkeI06E2wnv2I0vi5QWgfzRVkiMi5GNqJiEhxjJUmqa11h9B+WQ+pnXOCq3URkeMxtBMRkeIYKuV57Bq/JoGV2Cc6LRKRKtsDqDnVPTroTUTUeQztRESkOMYqObRrA5Qf2gEgPbQQAFBiiUFFXpXYYojI4zC0ExGR4hiq5SkxGncJ7c1XkFlTKLASIvJEDO1ERKQ4Rr0c2rUBFoGV2C89jSvIEJHzMLQTEZHiGPRmqa3RuMdqLOmjQ6Q2V5AhIkdjaCciIsUx1jQL7YECC+mE5ivIHDgRLLASIvJEDO1ERKQ4xlp5HrtWK7CQTohMj0G06jQAIKeKK8gQkWMxtBMRkeIYauTQrtGqBFbSCSoV0kKKAACllmicKagRXBAReRKGdiIiUhxjnfzwqTbITUI7gPT4Kql9YC1XkCEix2FoJyIixTEY5LYm2FdcIZ2U1l9+APXAb1XiCiEij8PQTkREimM0yOHXnUJ7+qhmK8jsd4+lKonIPTC0ExGR4hiNclsb7D4fVc1XkMk5HtJOTyKiznGfn4REROQ1DEZ5Hrsm1E9gJZ0TmdEdUaoyAMCBqjjB1RCRJ2FoJyIixTGa5NCuDVULrKSTVCqkB58AAJQ0RaOiyNDBCURE9mFoJyIixTHUyx9PGp2/wEo6L61HldQ+8GORuEKIyKMwtBMRkeIY6+WHT7U695keAwDp/eQHUHM2VwqshIg8CUM7EREpjqFBDu2asACBlXRe2shgqX0gu6mdnkRE9mNoJyIixTE2yvPYNeGBAivpvPSp3aV2zvEggZUQkSdhaCciIsVpEdrdbKQ9amgCImFbQSangivIEJFjMLQTEZHiGMy2h0/VaIRfqEZwNZ3k44P04OMAzq4gc6pecEFE5AkY2omISHGMZtvDp1oYAB/3+6hKj5MfQOUKMkTkCO73k5CIiDyeock2j12jMgmu5OKkNVtB5sDmCoGVEJGnYGgnIiLFMVps02M0KvecWpI+Un4ANWcfV5Ahoq5jaCciIsUxWm0j7VpfNx1pnyw/gHqgQCuwEiLyFAztRESkOAar7eFTjW+D4EouTvSIns1WkIkVXA0ReQKGdiIiUpTGRqAJtiUftWr3DO1Qq5EWZFtB5pQ5GpWnGwUXRETujqGdiIgUxaA3S22N2n3Dbnqs/ADqgZ+KBVZCRJ6AoZ2IiBTFWCnPY9f6uXFoT5V/+cjZxBVkiKhrGNqJiEhRjFXyijEaP/ddeSVthLyCzIG97vvLBxEpA0M7EREpiqFKnseu8Xff0J4+WX4ANSefK8gQUdcwtBMRkaI0H2nXBrhvaI8a2QvdUA4AOHAmWnA1ROTuGNqJiEhRDNXyVBJNgKWdnsqmCvBHujYfAHDSHIOqcnMHZxARtY2hnYiIFMVYI4dbrcYqsJKuS4uRH0DNWXdKYCVE5O4Y2omISFFaLPkY6N6hPT1V/tbgwKYzAishInfH0E5ERIrSfKRdE6QSWEnXpQ+XH0DNyeIKMkR08RjaiYhIUYy1zUK71r0/ptImySvIHMgPFFgJEbk79/5pSEREHsdQIz98GhTi3h9T0aN7SyvI5JTHCK6GiNyZe/80JCIij2Ook0O7Jsi9P6ZUmkCkBZ5dQaYxGlUV7rsaDhGJ5d4/DYmIyOMYDXJbG+IrrhAHSY8tl9oHfikRWAkRuTOGdiIiUhSDQV4xRqvzE1iJY6T1kefo5/xa3k5PIqK2MbQTEZGiGAzyijGaUPcP7enDNVL7QFaDwEqIyJ0xtBMRkaIYTXJo14b5C6zEMdInyQ+g5uRxBRkiujgM7UREpCiGevmjyRNCe/SYZETAtrHSgbJowdUQkbtiaCciIkUx1MsPn2rDAwRW4hiqIC3SA48BAIobo1FV6d67vBKRGAztRESkKIYGtdTWhHvGdJK06DNS+8D6UoGVEJG7YmgnIiJFMTYP7RGadnq6j/Q+8gOoBzZyBRki6jyGdiIiUhSD2bZijD/qoQ7VCq7GMdKGyd8Y5OypF1gJEbkrhnYiIlIUg9n28KkGRsDf/R9EBYD0CfIDqAeOuf88fSJyPYZ2IiJSFGOTLahrVUZApeqgt3uIGZuCcFQAAHK4ggwRXQSGdiIiUhRDk20kWutjElyJ46hCQ5AecBQAUNwQjeoqriBDRJ3D0E5ERIpisNgePvWk0A4A6dFlUpsPoxJRZzG0ExGRYlitgAG2h081vo2Cq3GstGR5BZmcDWXt9CQiuhBDOxERKUa9SZ42olU3tNPT/aQPkx9APcAVZIiok7wqtFdVVWHFihV46KGHkJmZidjYWAQEBCA4OBiJiYm46qqrsGDBAlRWVjq9lry8PDzzzDMYNmwYoqKioNFokJycjGuuuQbLli2D2Wx2eg1EREpjqJZH17V+HjbSPl5+ADXnqGesikNErqOyWq0e/zRMbm4uHn/8cfz4449oaOh45Ear1eLFF1/Eww8/DJUTVi5YuHAh5syZg/r6tkdaRo8ejaVLl6J3794XfR+9Xg+dTofq6mqEhoZe9HWIiFylKKcaCQN0AIDrYjZhWck4wRU5jrWyCt0iLKhEBOL9T6OwnqvIEJH9ec0rRtr379+PlStXtgjsvr6+SE1NRWZmJsaOHYuIiAjpzwwGA/72t7/hvvvug6N/p3n++efxyCOPSIHdx8cHAwYMQGZmJuLi4qR+27Ztw/jx43Hq1CmH3p+ISMmMlfLDp1r/JoGVOJ4qPAxp/rYVZIq4ggwRdZJXhPZz1Go1Zs2ahW+++QYVFRXIzc3Fr7/+is2bN6O8vBzffPMNevToIfV///338c477zjs/mvXrsW8efOk4zFjxuDgwYPIzs7Gr7/+iqKiInz++ecIDg4GABQVFeH666932P2JiJTOUCl/A6kN8KzQDgDpUael9sHfnD8Vk4g8h1eEdj8/P9xzzz04duwYVqxYgZkzZ17w9YNKpcLMmTOxdetWxMbGSu8/88wzaGzs+rxKq9WKOXPmSCP3qampWLduHfr27Sv18fHxwY033ogVK1ZI723ZsqXFMRGRJzNUyd+IagItAitxjvSUZivIrD/dTk8iopa8IrTPnDkT7733HhITEzvsm5CQgGeffVY6Li8vx8aNG7tcw+rVq7F3717peOHChdBqta32nTJlCm688Ubp+OWXX+7y/YmI3EGLB1EDPW/6SNoQeQWZnN89ax16InIurwjtnXXVVVe1OM7Nze3yNZcvXy61e/Xqhcsuu6zd/vfff7/U3rFjB4qKirpcAxGR0hn1zUK71vNCe/r4SKl9gCvIEFEnMLS3ovlDqYDtqd6uWrVqldSeNm1ah6vSjBs3DkFBQa2eT0TkqQx6eblbrdbxq3eJFntpCsJgm8ueUxrZQW8iIhlDeyuOHz/e4jg6umvLcp0+fRolJSXS8ZgxYzo8R61WY8SIEdLxvn37ulQDEZE7MNTIoV3jgaFdFdkN6X6HAQBF9dFwwJgQEXkJhvZWNJ/KAtgXsttz8ODBFsfJycl2nde83/nXICLyRMZa+eFTbbBnfkSlRZZJ7QNbqwVWQkTuxDN/InZBdXU1Fi5cKB1nZGQgLS2tS9csKChocWzPA7Hn9zv/GkREnsjgBaE9PVl+APXA+lKBlRCRO1GLLkBpHnvssRZTWV544YUuX7OmpqbFsU6ns+u85stSnn+N1tTX17fYZdURc/GJiFzJYJAfPtWEeOZHVPoQf2CzrZ2z2yi2GCJyG545jHGR3n//fXzwwQfS8Y033njBSjIXo7a2tsVxYGCgXedpNJo2r9Gal156CTqdTnolJCR0rlAiIsGMBrmtDfXM0J6WKT+AmnOYK8gQkX0Y2s/auHEjHnzwQem4V69eWLRokUOubTabWxyr1fZ9EDXvZ88GT3PnzkV1dbX0Kiws7FyhRESCGZqHdp2fuEKcKG6cvILMgdJugqshInfB0A4gKysLV199NRoabDvVRUdHY82aNXZPY+nI+ZsomUz2bajRvF/z5R/bEhAQgNDQ0BYvIiJ3YjDJK8Z4amhXRUchTW1bQaaQK8gQkZ28PrQfOnQI06ZNQ3W17Qn+8PBw/Pjjj+jbt6/D7hEcHNzi2Gi0bw6jodmQ0/nXICLyRAaT/LGkCQtop6cbU6mQHik/gHpwR8fPLBEReXVoz8/Px5QpU3D69GkAQEhICFavXo1BgwY59D6RkS030Dh16pRd5zV/ILZbN36FSkSez1gvfyxpwzx3vndaL/mb1ByuIENEdvDa0F5UVITJkyejqKgIgG0Ky8qVKzFq1CiH3ys1NbXF8YkTJ+w6r/mc9H79+jm0JiIiJTI0yM/yaCPse2jfHaUPlqf+HNjFFWSIqGNeGdpLS0sxZcoU5OfnA7DNBf/mm2+QmZnplPv16dOnxUOlWVlZdp23Z88eqd2/f39Hl0VEpDiGBjnMaiI07fR0by1XkPHMVXKIyLG8LrSfOXMGU6ZMwaFDhwAAfn5+WLZsGaZOneq0e/r7+7cYwd+8eXOH55SUlODo0aPSsbN+oSAiUhKj2RZgfdAE/zBtB73dV/dxydChCgCQU8Lpj0TUMa8K7dXV1Zg2bRr2798PAPD19cWnn36KGTNmOP3eM2fOlNrr1q1DaWn7cxiXLl0qtcPCwhjaicgrGBpt89i1MEAV4Llz2lXd45DumwsAKDRxBRki6pjXhPa6ujpMnz4du3fvBgD4+Pjg448/xuzZs11y/5tvvhkBAbaVEBobG/Hqq6+22be2thZvvfWWdHzrrbfCz88zlz4jImrO0HQ2tKuMgErVQW83plIhrdtp6fDgrjqBxRCRO/CK0F5fX49Zs2Zhy5YtAACVSoX33nsPt956a5euW1BQAJVKJb3mz5/fZt/4+Hjcf//90vHChQvx9ddfX9CvsbERd955p/SwqkajwZNPPtmlOomI3IWhyfbwqUZl334W7iy9t7ys74ENp9vpSUQEeMXTLwsXLsS6deuk47CwMHz55Zf48ssv7Tp/6tSpeOyxx7pcx/z587F69WocOXIETU1NuOGGG3DLLbdg1qxZiIiIwKFDh/Df//4X+/btk8557bXX0L179y7fm4jIHRittm8ktb71gitxvrRBfsA2Wztnp6H9zkTk9bwitDffpAgAKisrsXbtWrvPj42NdUgd4eHhWLlyJaZMmYLCwkJYLBYsWbIES5YsabX/E088gQcffNAh9yYicgcGq23FGG8I7enjugGLbO0Dh33FFkNEiucV02OUpG/fvti3bx/uvvtuaDStL2fWv39/fPvtt3jllVdcXB0RkTiNDVaYYXt+R6NuFFyN83Ufl4xQ2HbjzjkVIbgaIlI6ldVqtYouwlvV1NTgl19+QWFhIerq6hAXF4eBAwdiyJAhDrm+Xq+HTqdDdXU1QkNDHXJNIiJn0ZfVQxdtmx5zWdgOrK0cKbgiJ7NacYl6B7ZabEsC6/VASIjgmojI5ezNa14xPUapQkJCWiwFSUTkzQzlBgBn57T7m8UW4woqFdK7ncLWMtvhwT0mjMz03F1giahrOD2GiIgUwXDGKLW1AU0CK3GdtCT575zzS/v7dxCRd2NoJyIiRTBUyMs8agIsAitxnfRB8hfeB3ZyrXYiahtDOxERKUJdpbxijFbjHaE97VL5AdScXK4gQ0RtY2gnIiJFqKuUV4wJ0njHGgk9xvWWVpA5cCpccDVEpGQM7UREpAh1lQ1SOyhIYCEupErqiTRVLgDguDEatbWCCyIixWJoJyIiRTDom420B6sEVuJCPj5IjzglHR7Y4/mbShHRxWFoJyIiRairlleMCQrxktAOID1JfgD1wIbTAishIiVjaCciIkWoq5EfPg0K8Z6HMtMGyn/XnB2cH0NErXPK5kolJSXYuXMn9u3bh4KCAhQXF6O2thZGoxEajQZBQUHo0aMHkpKSkJGRgREjRiAuLs4ZpRARkZuoq5UfPg3Sec/ef+mXhgOLbe0DXEGGiNrgsJ+KGzduxIoVK/DDDz/g6NGjnT4/OTkZV1xxBWbNmoWJEyc6qiwiInITdc2WKfem0H5uBRk9dMg5GSa6HCJSqC5NjyktLcX8+fPRq1cvTJw4EW+99RaOHDkCq9UKq9W+5brO9T169Cj+/e9/Y8qUKUhMTMQzzzyDU6dOdXwBIiLyCHUGuR0U5ieuEBdTJfdGmuogAOC4gSvIEFHrLiq05+fn46677kJSUhKef/55HD9+vNWQfi6QBwcHIyoqCvHx8YiKikJQUFCbwd5qtaKoqAgvvvgievXqhTvuuAPHjh27mDKJiMiN1Bnkj6SgiACBlbiYry/SwuVBqoP7GtvpTETeqlPfP5aVleHpp5/GRx99BLPZfEHoDg8Px/jx4zFixAhkZGSgb9++6NGjBzQazQXXMhqNKC4uxqFDh5CdnY2dO3fi119/RUVFBQBbeG9oaMD//vc/fPrpp7jzzjvx/PPPIzo6ugt/XSIiUqo6U7PQ3i1QYCWul96zDrB9/CHnl1KMuCRebEFEpDh2h/YFCxbg2WefhV6vbxHWU1JScP311+Paa6/FsGHD7L6xRqNBSkoKUlJSMH36dOn93bt3Y/ny5Vi2bJk01cZsNuP999/HF198gfnz5+ORRx6x+z5EROQe6kzyR5K3hfa0QX7AHlv7wPYascUQkSKprHZOPvfx8YFKpYLVaoVarcb111+P+++/H5mZmU4rbtOmTVi0aBG++uorNDbavi5UqVRoamrq4EwCAL1eD51Oh+rqaoSGhoouh4ioXZdF7sZPZ2yDP9XHqxCaGCa2IBcq/PAnJN49FQBwZfIhrDqaKrgiInIVe/Nap+a0+/v7469//SuOHj2KpUuXOjWwA8C4ceOwZMkSHDt2DA899BACA71r5IWIyJvUNcgPnwZFBwmsxPXiJ6QgBHoAwIFTYWKLISJFsju033777Th8+DAWLlyIxMREZ9Z0gfj4eCxYsACHDh3C7bff7tJ7ExGRa9Q12h4+DYAJvoHes3oMAKiSeiLNJxcAUGCI4QoyRHQBu0P7Rx99hISEBGfW0qGEhAR8+OGHQmsgIiLnqGuyhfYglaGDnh7IxwfpESXSYe4eo8BiiEiJurROOxERkaPUNdmmQAb5eGdgTesl/71zfi5ppycReSOGdiIiUoQ6i2154CBfk+BKxEgfLE8JytnB+TFE1BJDOxERCWe1AnWwPXwapK4XXI0YaZmRUvvAIV+BlRCREjG0ExGRcA21DWg6u3VIkLpBcDViJEyUV5DJORUhuBoiUppO7YjaWY2NjSgoKIBer0dDQwPUajV69OiBuLg4qFQqZ96aiIjcSN3pOgD+AIAg/0axxQii6h6HNN/d2N40HAXGWNTVAUHetfIlEbXDoaF9165d2LRpEzZu3IisrCwUFRXBYrFc0M/f3x/Dhg3DuHHjMGXKFEyaNIkhnojIi9WVGwGEAwCCAsxiixFFpUJaZCm2l9oOD27XY/gkboxHRDYODe0jR46Uwnd7G63W19dj69at2Lp1K1599VVER0fj1ltvxaOPPoru3bs7siQiInIDdWfkh0+DAr00tANI720Czob2A7+UMLQTkcRpc9pVKlWro+fnv2+1WlFaWoo333wTKSkpePzxx1Ff750PIREReau6CvnnflDghd/Qeou0IQFSO2dnncBKiEhpHD6n/dwIu6+vL2JjYxEfHw+NRgOVSgWz2YzCwkIUFxejsVGes3guxJtMJrzxxhv46aef8O2336Jnz56OLo+IiBSorlJ++DRI2/Y3tZ4ufUIU8H+2ds4h79oVloja59DQ/te//hXDhw/HiBEj0KdPH/j6tr5klcViQXZ2NjZv3oxVq1bhl19+QUNDA1QqFaxWK/bt24cpU6Zg06ZNiI2NdWSJRESkQC1Cuxc/fJkwMQXBqEEtQnCglCvIEJHModNjFi5ciD/+8Y/o169fm4EdAHx8fDBo0CA8+OCD+OGHH3Dy5Em8+OKL0Ol0AGwj73l5ebj33nsdWR4RESlUXbU8j92bQ7sqshvS/I4AAPJN3VFX673fOhBRS4pYpz0iIgJz587F4cOHMXnyZFitVlitVvzwww/YsGGD6PKIiMjJ6vRNUjsoRBEfTcKkR52W2rm/VQishIiURFE/GSMjI/HDDz9gxIgR0nufffaZwIqIiMgV6moY2s9JS5anCuX8XCKwEiJSEsX9ZPTz88NLL70kHW/cuFFgNURE5Ap1NfI0kCBd29MrvUH6sECpfWC3UWAlRKQkigvtADB27FgAtpVoTp48KbgaIiJytuZzt4N03r1qStr4KKmdc9i7/y2ISKbI0F5WVia1my8NSUREnqnOIO/fERTm3UE1cZJtBRkAyDkdKbgaIlIKl4b2qqoq5OXltdunsrIS9913HwDbKjJcq52IyPO1CO3h/gIrEU8VGoL+/rbPyoL6OK4gQ0QAnLC5Unu2b9+OK6+8ElqtFn369EFCQgKio6MRGBgIg8GA/Px8bN26VVqzHQBmzZrlyhKJiEiAOpM8jz2oW2A7Pb1DenQZdhYBVvggd8MpDJsRJ7okIhLMpaEdsM1TNxgM2Lt3L/bu3XvBnzU3aNAgPPnkk64sj4iIBGBobym9TwNQZGsfWF/K0E5Erg3t5zZcah7Oz42oA0BMTAzi4uLQo0cPTJs2Dffeey8CAgJcWSIREQlQ1yB/HAVFaQVWogxpwzTAels7Z7dJbDFEpAguDe1TpkxBcXExtm/fji1btmDt2rXYv38/AFt4Lysrw5AhQ/DMM8+0WKudiIg8W12D/PApQzuQPikG+JetfeCod8/xJyIblfX8OSkuduLECSxduhTvvvsujh8/DpVKBZVKhccff7zFeu3UeXq9HjqdDtXV1QgNDRVdDhFRm4ZpD+B3Yxp8YUajRY1mX8J6JYvBhNAgM+oQjN7+hThWnyC6JCJyEnvzmvAlHxMTEzF37lwcPXoU//d//4eQkBBYLBa8+uqrePTRR0WXR0RELlBntk2FDFIZvD6wA4CPNhBpgbYVZPIbesCgNwuuiIhEc2hoP3HixEWf6+vriz/96U/YsWMH4uLiYLVasXDhQmzevNmBFRIRkRLVNmkAACE+dYIrUY60mAoAZ1eQWVckuBoiEs2hoT0tLQ0vvPACGhoaLvoaffv2xdtvvy0d/+c//3FEaUREpGA1liAAQIjaKLgS5UjvK28umLP+tMBKiEgJHBraDQYD5s2bh/79++Ozzz67YAlHe1155ZVSmyPtRESezWpuQi2CAQDBaq6Uck7acPmB3Jw9Fz8YRkSewSlz2vPz8/GHP/wBaWlpWLp0Kczmzs3F0+v1AGxLQ5aVlTmjRCIiUghjeR0ssC0JHOLPcHrOgMu6S+39R7l2PZG3c2hov+aaa2C1WqFSqWC1WnHo0CHcdttt6N69O/7f//t/yMrKsus6r732mtTmqidERJ6ttlSexx4c0NhOT++SeGkiQlENANhX3r2D3kTk6Ry6TvvXX3+N77//Hn/5y19QWFgohffy8nK8+eabePPNNxEZGYkJEyZgyJAh6N+/P2JjY6HT6WA0GnHo0CF8+umnWLVqlbTpUq9evRxZIhERKUzNaXkee4iGof0cldoXGcH52Fw7GIVN3VF5ogbhiSGiyyIiQRy+udJVV12FSZMm4eWXX8bChQtRW1srBfBz012WLVuGZcuWtXmNc3PhVSoVZs+e7egSiYhIQWrL5NAerLEIrER5MhIrsfmArZ298jgy/zxAbEFEJIxT5rQHBQXh+eefx7Fjx/Dwww8jODi4RRAHbMG8tVfzPikpKXjggQecUSIRESlEzRl5HntIEEN7cxkD5fa+jVXC6iAi8Zy6uVJUVBTefPNNFBcX4z//+Q9GjRolTZlp7twuqIAc5keMGIEffvgBwcHBziyRiIgEqz1TL7X5I7+ljMwwqb0vW1wdRCSew6fHtCY4OBgPPPAAHnjgAVRVVWHDhg3Izs7GkSNHcOLECdTV1aGxsRExMTHo06cPZs2ahUmTJsHHR/iGrURE5GQ1lfIKYyFce6CFATOSgAdt7X2F4UJrISKxXBLamwsLC8OsWbMwa9YsV9+aiIgUqLZKDu3Bob4CK1GekMRw9PY9jrymnsiuSYKlyQofX5XosohIAA5lExGRUDV6ecpkSBhD+/kyIk8CAAwIQt7mk4KrISJRGNqJiEioWr388GlwuJ/ASpQpI8UgtfetYWgn8lYM7UREJFRNrdwOiWBoP1/GiACpvW+7sZ2eROTJGNqJiEio2jr5oyg4MqCdnt4p47JYqb3vMP99iLyV3aF9xIgRWL9+vTNr6dAvv/yCkSNHCq2BiIgcq8Ygz2MPiQwUWIky9Z6UBC3qAAD7Tsd20JuIPJXdoX337t2YMmUKpkyZgnXr1jmzpgv89NNPmDx5MqZOnYrdu3e79N5ERORctSZ5IbPgaK3ASpTJN0CNAdp8AMCxxp6oPW3o4Awi8kSdnh6zfv16TJs2DYMHD8Y777wDvV7vjLpQU1OD//73vxg8eDAuv/xybNiw4YJNmYiIyP3VmOR57AztrRsUXy619686LrASIhLF7tD+448/IjU1VdqxNDs7Gw8++CDi4uJwzTXX4H//+x9KSkq6VMypU6fwv//9D9dccw1iY2Pxl7/8BdnZ2dI9+/fvjx9//LFL9ygrK8Pq1avx3HPP4eqrr0ZcXJy0I6tKpcLixYu7dP32NL+Pva933nnHafUQESlBbaO/1GZob11GurzCzr4NFQIrISJR7N5cacqUKdi3bx/+85//4KWXXsLp06cBAEajEd999x2+++47AECfPn0wYsQIDBw4EH369EF8fDyio6Oh0Wjg7++PhoYGGI1GlJaWori4GIcPH0Z2djZ27tyJo0ePSvdrPqoeExODJ598Eg888ADU6ovbD6qkpASjR4/G8eMcoSAiUpKaRts89kAYofbTCK5GmTLG6YAVtva+LEv7nYnII3UqAavVajz88MO499578e9//xtvv/02iouLYbVaoVKpYLVacfjwYRw5cqTThZwL6eeuAwDx8fF4+OGH8ec//xkaTdd+kJtMJkUF9szMTLv+TomJiS6ohohInFqzLbSH+NQBYGhvzcAZPYFHbe19x0PFFkNEQlzUsLVWq8UTTzyBRx99FF9++SU++ugjrF+//oI55/bMQVepVBccT548GXfffTdmz54NX1/H744XFRWFYcOGYfjw4Rg+fDhmzZrl8Ht05OOPP0ZSUpLL70tEpDQ1liAAQLAv1yBvS3ifSCT4FKPQ0gP79EmwWqxQ+ag6PpGIPMbFzTU5d7JajVtuuQW33HILTp48iW+//RZr1qzB5s2bUVlZadc1rFYrwsPDkZmZicsvv1yaZ+5oERER+OqrrzBixAj07NnT4dcnIqKLYLWi1moL7SFqhvb2ZHQrQmFZD1RbdSjcVYLEkVz+kcibdCm0N9e9e3c88MADeOCBBwAAeXl5yM7ORkFBAU6ePIna2lrU19cjICAAwcHB6N69O3r16oUBAwagd+/ejiqjTaGhoZg9e7bT70NERPYz15pgOjslJtivQXA1ypbRuw6rymztfT8UMbQTeRmHhfbz9e7d2yVhnIiI3FdtqTyPPSSgXmwxCpcxzA/Ybmvv21aHGWLLISIX6/Q67URERI5SU1IntYMDzQIrUb6MKdFSe99B/3Z6EpEnYmgnIiJhasvkeewhGob29vSd1gv+sH0bsa80uoPeRORpOhXaq6ursWzZMuTn5zurHq/x+OOPIz09HaGhodBoNIiPj8fEiRMxf/58/vsSkdeoKZenxARruet1e9Raf6QH5gEADtUnwVTN6URE3qRTof27777DjTfeiJSUFHTr1g1Lly51Vl0eb9myZThw4ABqampgMplQXFyMDRs24Nlnn0Xfvn3xpz/9CUZj51ZSqK+vh16vb/EiIlKy2jNy8AwJ4qZBHcnoXg4AsMAXOSvzBFdDRK7UqQdRv//+e2nt9YaGBlxxxRWdupnRaMSaNWuQlZWF6upqdOvWDQkJCZg2bZpTlnlUssjISCQnJyM4OBjV1dXIzc1FbW0tAMBsNmPRokXYsWMH1q9fD51OZ9c1X3rpJTz77LPOLJuIyKFqKhuldnAI1x3vSEZ6E3A2q+9dV45ht4qth4hcp1OhffPmzdJmSH/4wx8QERFh97lLlizB3/72N1RUVFzwZ+c2VHrzzTeRlpbWmZLcSlpaGu677z5cddVVF6ysYzabsXbtWjz55JPYt28fAGDPnj246aabsHr1aruuP3fuXDz66KPSsV6vR0JCguP+AkREDlZbKc9jDwllaO/IkPGhwPe29p7d/GaCyJvYPT2msLAQJSUl0kj7rbfa/+v9xx9/jNtvvx1nzpyB1Wq94GWxWPDTTz9hyJAh+Pe//935v4WbyMnJwcMPP9zqUphqtRrTp0/H9u3bMX36dOn9NWvW4Pvvv7fr+gEBAQgNDW3xIiJSsppqOXgG6xy/A7anGTSrl9TeczxcYCVE5Gp2h/bDhw9L7bCwMFx66aV2nVdaWoqHHnoIVqsVKpVKejV37r3GxkY8/PDDeOutt+wty+MEBgbis88+Q0xMjPTe22+/LbAiIiLnqdXLoT0knKG9IxHJ4ejpWwgA2KvvBYuZo+1E3sLu0F5QUADAFrBHjRpl9w1ef/111NTUSEH9XHgfOXIkbrjhBlx22WUICgqS3rdarXjsscewY8eOzv1NPEhISIi0sywAbNq0CSaTSWBFRETOUVMjt4PDufa4PQZHnQQA1CIEx9afEFwNEbmK3aG9urpaaicnJ9t1jtlsxocfftgisCclJWH37t3Ytm0bPv/8c6xZswalpaV46qmnpBH3pqYm3HXXXdJUHG80ceJEqW0ymVBYWCiwGiIi59DXyN+8hkYFCKzEfQxJNUjtrNWnBFZCRK5kd2hvvvxgt27d7Dpnw4YN0oOn50bS3333XQwaNKhFP41Gg+effx7/+c9/pKB+8OBBfPfdd/aW53FiY2NbHJeXlwuqhIjIefR18pQYXZxWYCXuY/Al8r/Tnm1cq53IW9gd2kNCQqR2Q0ODXeesXLmyxXH//v0xZcqUNvvff//9uPrqq6Xjd955x97yPI7BYGhxrNXyw4yIPI/eIC9iFhrLn3P2GHJVvNTOOhIksBIiciW7Q3vz0fWysjK7zlm/fr00T12lUmH27NkdnvP0008DsI3Mb9y4EU1NTfaW6FFycnJaHEdHc8tqIvI8epM8jz20e7DAStxHwqjuiFDZvsXeU5EouBoichW7Q3ufPn2k9p49ezrsf+bMGezfv7/Fe1deeWWH5w0bNkxaEtFkMtl1L0/0+eefS+2kpCSv23yKiLyDvkGexx4cGSiwEveh8lFhcFgBAKDEEoOSfafFFkRELmF3aB86dCg0Gg2sViuysrJw4kT7T6yvXr26xYOkOp0OI0aMsOteI0eOlNrNl5r0Ft99912LqUWzZs0SVwwRkRNVN9imxASjFr5qbq5kryG99VI769vjAishIlexO7Sr1WpMmjQJgG3qyquvvtpu/6+++kpqq1QqTJw48YL12dsSHy/P16usrLS3RJcrKChosfb8/PnzW+1XXV2N6667Drt37+7wmp999hluueUW6Vir1WLOnDmOKpmISFH0TbbQrvOt6aAnNTd4hJ/UztpSK7ASInIVu0M7APzlL38BYAvtixYtuuBB03NOnDiB1atXS/PZAWDGjBl23ycoSH6wRq/Xt9Ozc+69914EBgZe8Opsn86yWq1Yvnw5hg8fjv79++ORRx7B4sWLsXbtWmzevBmrV6/Gq6++ipEjR+KWW25BXV0dANsvOx999NEFK8kQEXkKvcU2jz1UbeigJzU35HJ5A749OVwqk8gbqDvuIps2bRpGjx6N7du3o6mpCddffz2effZZPPjgg1LQPnPmDO666y6YzWZpZN3Pz6/FqjAdab4mvJ+fXzs9O6exsRH19e0vj2U2m2E2mx12z/Pl5uYiNze3w34hISFYtGgRbrjhBqfVQkQkksVYjxqEAgBC/bmBXGekTktCIIwwQYOsUg7sEHmDTo20A8Ann3wCrVYLlUqF+vp6zJ07F9HR0Rg6dCiGDx+Onj17XrBqzNVXX2332u4AUFpaKrWDg91/NQGNRoP77rsP6enpHU4R0ul0eOihh7B//37cfPPNLqqQiMj1ak7J0zpCA7jeeGeoA9UYGJQHADjSmITaU5xeROTpOjXSDgApKSn46quvcN1118FkMsFqtcJoNCIrK6tFv3PhVKVS4cknn+zUPXbs2CG1HblqyuLFi7F48WKHXS8pKcmuXVsDAgKwaNEiALY5+llZWTh9+jTKy8tRVVUFrVaLiIgIZGRkICMjA76+vh1ckYjI/emLawDYBnRCNY1ii3FDgxMqsDMXsMIHe1fkYeyfB3V8EhG5rU6HdgC4/PLLsXbtWtx2223Sw5htefzxxzF48GC7r33y5EkcO3ZMOk5JSbmYEhUrPDwcEydOFF0GEZFw+lJ5p22d1nnTEj3VkMFW4Oxsy6wNVRj7Z7H1EJFzdXp6zDmXXnopDhw4gH/+858YMGAArFZri1dYWBhee+01vPTSS5267tKlS6W2v79/i/XhiYjIc+hPy/PYQ4MtAitxT4Mny9NO9+zlcplEnu6iRtrPCQwMxN///nf8/e9/R2lpKQoLC1FZWYlu3bph0KBBnZ7mcW5VmnMj9yNGjIC/v38HZxERkTvSl8nz2ENDBRbipjJm9oLqXgus8EFWYaTocojIyboU2puLiYlBTExMxx3b8b///Q95eXlSaOc0EiIiz1VdLs9jD9VxpLizgqK06Oufh0MNvZFtTEZjXQP8gjjQReSpLnp6jKMZjUbpgdVzD3dee+21IksiIiIn0lfI89h14Yr5OHIrQ+JKAAANCEDu6nzB1RCRMzlspL2rNBoNNm7ciN9//x179uzByZMnMWgQn4QnIvJU+ip5HntohGI+jtzK4LRGfH7c1t6zphQDZ6eKLYiInEZRPyV79+6N3r17Y/bs2aJLISIiJ2u+4XVoJKd1XIwhE3TAalv7911NuE1sOUTkRPw+koiIhKiukeexh0YFCKzEfQ29rpfU3p0XLrASInI2hnYiIhJCXyuvMBYaoxFYifuKTNahp7oIALCnJgVN9VzvnshTMbQTEZEQeoM8Q1MXpxVYiXsbHlsMAKhDMA79cKyD3kTkrhjaiYhICL1Jnsce2j1YYCXubdhAeb373T+UCqyEiJyJoZ2IiITQN8jz2IOjOD3mYg2fKO9MtXsHp8cQeSqGdiIiEqK6wTYlJhi18FVzc6WLNXR2b6m9K6+bwEqIyJkY2omISAh9ky20h/rWCq7EvXXrFYokdSEAYE9tCppMjR2cQUTuiKGdiIiE0Fts89h16jrBlbi/4XG2h1ENCELuKj6MSuSJGNqJiMjlLMZ61MA2FzvU3yS4Gvc3bKA8us6HUYk8E0M7ERG5XM0peUpMaEB9Oz3JHsMnNXsYdadFYCVE5CwM7URE5HL6k81Cu4ZzsLuq+c6ou/IjBFZCRM7C0E5ERC6nLzVK7VAtlynsqoikUPT2OwEAyKpNgdnIX4SIPA1DOxERuVx1SbPQHsTpHI4wLO4kAD6MSuSpGNqJiMjlqkrleezhYVaBlXiO4QMbpDYfRiXyPAztRETkclVl8vSNsHBurOQIwyaHSe1dfBiVyOMwtBMRkctVlcvz2MO6+QqsxHM0fxh1dz53RiXyNAztRETkclWV8pSYsGh/gZV4jvDEECT7HQcAZNWlwGxo6OAMInInDO1ERORyVVVyOywmQFgdnmZY3CkAgBFaHPz+qOBqiMiRGNqJiMjlqmrkKTHh3TUCK/EswwfJzwrs+uG0wEqIyNEY2omIyOWq6tRSOyw+WGAlnmX41HCpzYdRiTwLQzsREblclUGeEhOWECKwEs8y7PreUMEW1rfnxwiuhogciaGdiIhcrqo+EADggyYER2sFV+M5QmO16B+YDwDYa+oL4+kawRURkaMwtBMRkctVNQQBAHQqPXx8uU67I43qadtYyQw/ZH11RHA1ROQoDO1ERORylU22KTFhvrWCK/E8I0fK7e1rKsUVQkQOxdBOREQuZW2yoMqqAwCE+dcJrsbzjJoZK7W3Z3E5TSJPwdBOREQuZThdCzP8AABhAUbB1XieATOSoIEBALDjVLzgaojIURjaiYjIpaoK5YcjwzTctdPR/AJ8MFR3DACQ15SEsn2nBFdERI7A0E5ERC5VVSTPYw8LbmynJ12sUalVUnvHF/niCiEih2FoJyIil6o6JU+JCQ9pEliJ5xo5LlBq79hgEFgJETkKQzsREblUValJaoeFiavDk426oafU3p4bKrASInIUhnYiInKpqtPylJiwcK7R7gw9R0Qj2qcMALCjIgVWM7/RIHJ3DO1ERORSVWfkABkWqRZYiedSqYCR0ccBAJWIwNG1xwRXRERdxdBOREQuVVVhkdphUX4CK/FsowbJzw5s/4YryBC5O4Z2IiJyqapqeUpMWGxgOz2pK0ZODZPa27da2u5IRG6BoZ2IiFyqqkb+6AmL0wisxLONvLGX1N6RHymwEiJyBIZ2IiJyqcpaf6kdFh8ssBLPFhYfjNQA2xrtWYa+qK+oE1wREXUFQzsREblUlbFZaE8IEViJ5xuZUAIAaEAAsr48LLgaIuoKhnYiInKpKpNtSowvzAiO1gquxrONHmWV2ttWnRFYCRF1FUM7ERG5VFWjLaiHqaqh8uE67c50yXVxUnvL73zol8idMbQTEZFLVTXZpsTofDnH2tkGTO+JYNQCALaUJMNqsXZwBhEpFUM7ERG5jMVsQaU1DAAQ4V8rthgvoPb3wahuRwEAJy1xKNxUILYgIrpoDO1EROQy+iI9LPAFAERoDIKr8Q6XZMi/HP32+QmBlRBRVzC0ExGRy1TkV0vtbsH1AivxHpdcHiq1f9vUJLASIuoKhnYiInKZihPyqG9EqFlgJd5j9B9SoIJtR9TfjsUIroaILhZDOxERucyZIqPUjgjjQ5GuENZdi3RNHgAgy5SKuuIqsQUR0UVhaCciIpepKGmQ2hHduNyjq1ySXAoAaIIaO5dykyUid8TQTkRELlNR2ii1I6J8BVbiXS4Zp5baW1brBVZCRBeLoZ2IiFymotwitbvF+QusxLtcclOi1P4tO0RgJUR0sRjaiYjIZc5Uyh87ET24Q6erpIyLQ5RPOQBg65k+sDTwIWAid8PQTkRELlOhl6fERCQEC6zEu6hUwCWx+QCASkTg0Mojgisios5iaCciIpepqJWnxEQkhbbTkxztkmHyuvi/fX1KYCVEdDEY2omIyGUqDBqpHd4rTFwhXuiSqyOl9m/b+PFP5G687r/asrIyrF69Gs899xyuvvpqxMXFQaVSSa/Fixe7pI68vDw888wzGDZsGKKioqDRaJCcnIxrrrkGy5Ytg9nM+YZE5HnO1NumxOhQBXWguoPe5EjDbkiGH2xLbm45kSC4GiLqLK/5iVlSUoLRo0fj+PHjokvBwoULMWfOHNTXt9zCOy8vD3l5efjmm28wevRoLF26FL179xZUJRGR41WYbSuXRKj1AMKE1uJtNKF+GBpyANtr0nDInIzTWScRPbi76LKIyE5eM9JuMpkUEdiff/55PPLII1Jg9/HxwYABA5CZmYm4uDip37Zt2zB+/HicOsV5h0TkGSxNVlRYwwEAEf61gqvxTpkDKqT25o+PCayEiDrLa0J7c1FRUbj88svxj3/8A998843L7rt27VrMmzdPOh4zZgwOHjyI7Oxs/PrrrygqKsLnn3+O4GDb18dFRUW4/vrrXVYfEZEz1RTrYYFt9ZiIQKPgarxT5pXyij0bf25spycRKY3XTI+JiIjAV199hREjRqBnz54uv7/VasWcOXNgtVoBAKmpqVi3bh20Wq3Ux8fHBzfeeCO6deuGqVOnAgC2bNmCFStW4JprrnF5zUREjnQmXw9ABwDoFmwSW4yXGntHH6ietsAKH2w8Eiu6HCLqBK8ZaQ8NDcXs2bOFBHYAWL16Nfbu3SsdL1y4sEVgb27KlCm48cYbpeOXX37Z6fURETlbxQl5SkxECB+2FyE8PggZ2qMAgCxTP1QfKxdcERHZy2tCu2jLly+X2r169cJll13Wbv/7779fau/YsQNFRUVOq42IyBUqigxSOyLMIrAS75bZ7zQAwAofbPnosOBqiMheDO0usmrVKqk9bdo0qFSqdvuPGzcOQUFBrZ5PROSOKkrkFbMiurX/M5CcJ/Myea38jWv5bAGRu2Bod4HTp0+jpKREOh4zZkyH56jVaowYMUI63rdvn1NqIyJylYpSeUpMt2hfgZV4t3F3JEvtjQcj2+lJRErC0O4CBw8ebHGcnJzcRs+Wmvc7/xpERO7mTLlVakfE+gusxLvFpIahX0AeAGBnXRoMp6oFV0RE9mBod4GCgoIWx4mJiXad17zf+ddoTX19PfR6fYsXEZFSVFTK7YjugeIKIWSm2PYAMcMP2xbnCq6GiOzB0O4CNTU1LY51Op1d54WGhrZ5jda89NJL0Ol00ishgdtUE5FyVFTLU2IiEoPb6UnOljlZXvH511Xc6IrIHTC0u0BtbcsfiIGB9o0waTTyw0LnX6M1c+fORXV1tfQqLCzsXKFERE5UXhMgtSN6hgishDJv7y21N2aHC6yEiOzlNZsriWQ2t1yPWK2275+9eb/Gxo53rgsICEBAQECH/YiIRCg32PamUMGCbikMiiIlDI1Ckl8RChrjsU3fH/WVBgSEt753CBEpA0faXeD8TZRMJvt2Amzer/nyj0RE7qjMZJvyF6GqhG+gn+BqKDPpBADABA12LeG8diKlY2h3geDglnM3jUb71sU1GOSNSM6/BhGRuyk3257niVRXiS2EAACZmfJa+b9+U9lOTyJSAoZ2F4iMbLkO7qlTp+w6r/na7t26dXNoTURErlRf24ga2EbaowI7frCenG/87UlSe/3v9i2QQETiMLS7QGpqaovjEydO2HVe8wdJ+/Xr59CaiIhcqfyIPJIbGWTfFEFyruRL45CgPgkA2FyVjvpKQwdnEJFIDO0u0KdPnxYPlWZlZdl13p49e6R2//79HV0WEZHLlB2VN/CJCq0XWAmdo1IBk3oXALDNa9/24QGxBRFRuxjaXcDf3x+jRo2Sjjdv3tzhOSUlJTh69Kh0nJmZ6ZTaiIhcoSxfXrY2qluTwEqouUlT5LXzf17OnVGJlIyh3UVmzpwptdetW4fS0tJ2+y9dulRqh4WFMbQTkVsrL5anxERGqtrpSa406R55vfZf9kW205OIRGNod5Gbb75ZWkO9sbERr776apt9a2tr8dZbb0nHt956K/z8uDwaEbmvspPyfhVRcdwiRCnih0Shr38+AGB7bRpqiznaTqRUDO1dUFBQAJVKJb3mz5/fZt/4+Hjcf//90vHChQvx9ddfX9CvsbERd955p/SwqkajwZNPPunw2omIXKn8tEVqR/bgJnBKMinV9jCqGX7Y9O5BwdUQUVu8KrTfe++9CAwMvODV2T4Xa/78+ejTpw8AoKmpCTfccAP++Mc/4uuvv8b69evxzjvvYPjw4Vi2bJl0zmuvvYbu3bs75P5ERKKUnZE/bqJ6cudNJZl8hb/U/mUlV5AhUiqv+o6ysbER9fXtr1pgNpthNpvb7XOxwsPDsXLlSkyZMgWFhYWwWCxYsmQJlixZ0mr/J554Ag8++KBTaiEicqXyanmKX2TvUIGV0Pkm3NcXODtj85cDsWKLIaI2edVIuxL07dsX+/btw9133w2NRtNqn/79++Pbb7/FK6+84uLqiIico6xW/sYyqm+4wErofJHJOgzSHAYA7DH1Q8WhMsEVEVFrVFar1Sq6CG9VU1ODX375BYWFhairq0NcXBwGDhyIIUOGOOT6er0eOp0O1dXVCA3lyBYRiZMeeBQH6lOggQEGi8a2SDgpxmMjN+GNneMAAF8/uhnXvn6p4IqIvIe9ec2rpscoTUhISIulIImIPFV5ow4AEOVbAajiBVdD55t0VRDe2Glr/7KmEde+LrYeIroQp8cQEZFTWZqsOGOxTYmJ9NcLroZak3lvKnxhe57r5yOJgqshotYwtBMRkVNVFdag6ewXu1HaOsHVUGtCYoMwMjQXAJDbmIyibUWCKyKi8zG0ExGRU5UdrpTaUcGmdnqSSFOHnJHaP/73mMBKiKg1DO1ERORU5fk1Ujsy3DlL6lLXTbs1Umqv/YWPvBEpDUM7ERE5VdkJo9SO6sYFy5Rq5G39oFNVAwB+Kk5DUz1/wSJSEoZ2IiJyqrLiBqkdGcOPHaVSB/hiSo+DAIBKazh2/++A4IqIqDn+9CQiIqcqPdkktaN7+AushDoybZI8ur52abnASojofAztRETkVKWn5Y2UYpODBFZCHZn252SpvXZXN4GVENH5GNqJiMipSiv8pHZMH+7OrGSJo+LQzz8PALCtNh3VBZUdnEFErsLQTkRETlVao5HaMWkcvVW6aem2NdqboMbPb3NeO5FSMLQTEZFTlRhso+sh0EMbEyK4GurItGvlKUxrV3EFGSKlYGgnIiKnKm2MAADEqM8AKlUHvUm08Q+kIQC2TbDWHu0Nq4XLdBIpAUM7ERE5jammEdVWHQAgJlAvuBqyh7abBuMibNNijjcl4PBq7o5KpAQM7URE5DSncyukdkxIncBKqDOmXSLvYrvm/SKBlRDROQztRETkNCUH5dVHYsMb2ulJSnL5PfFS+4eNwQIrIaJzGNqJiMhpSvPk0fWYKIvASqgz0q/qjZ6+thH2DRUDUVPMqU1EojG0ExGR05QeN0ntmDh+5LgLlY8KM9Jsc9kbEICfFuQIroiI+BOUiIicpqS4SWrHJvq105OUZsYN8tKPK7/l0o9EojG0ExGR05Selpd4jOnNudHuZMJfBkAL2/SmVUf7wWLm9CYikRjaiYjIaUor5NH1mD6hAiuhzgoMC8TU2P0AgNPWKOz630HBFRF5N4Z2IiJymtIajdSOSesmsBK6GDMuk1f8WflRmcBKiIihnYiInKbEYBtdD4Ee2pgQwdVQZ135cB+pvXJXjMBKiIihnYiInKa0MQIAEKM+A6hUHfQmpek+NBbDtLbdUfcY+6N41ynBFRF5L4Z2IiJyClNNI6qtOgBATCDX+XZXM0acltqrFhwRWAmRd2NoJyIipyg9WCG1Y0Lq2ulJSjbjrmip/f26QIGVEHk3hnYiInKKUzlyaO8eUS+wEuqKobf0Q6xPKQBgXelA1JUZBFdE5J0Y2omIyCmKD9VK7e5xVoGVUFf4qH0ws28uAMAEDdb8a7/gioi8E0M7ERE5xck8k9TukegrsBLqqmv/oJXay7/k7qhEIjC0ExGRUxQXy6Pr3VO07fQkpZvwUAZ0qAIArCwYgIa6RrEFEXkhhnYiInKKk6Vqqd29H3dDdWf+IQG4KikbAKBHKH5ZmC24IiLvw9BOREROcbJS3g21x6BIgZWQI1xznTzFafkSPoxK5GoM7URE5BTFdbY12rWoQ2hShOBqqKumPZ4BDWxh/dtDqWhqtAiuiMi7MLQTEZFTnGywja73UJ+Gyoe7obq7oJhgXB67FwBw2hKF394/ILgiIu/C0E5ERA5XW26C3mqbx95dWyW2GHKYa66SH0Bd/kFFOz2JyNEY2omIyOFO7i2T2t113A3VU8yYMwBq2IL7ir29YbVw/X0iV2FoJyIihzt5oEpq94huEFcIOVR4cgQmRWQBAI6b4/H7l0fFFkTkRRjaiYjI4U4ekUfXu8cJLIQc7trL5P9tv3irRGAlRN6FoZ2IiByuuECe+9w9yV9gJeRo1/2jP3xh2xX1i52cIkPkKgztRETkcCdPyu0efYPEFUIOF5kegykRvwMATph7YNsnhwVXROQdGNqJiMjhisv8pHb3tDBxhZBT3HSVvLnS52+XtdOTiByFoZ2IiBzuZJU8ut59UJTASsgZZj09EP6oBwB8tbcPmsycIkPkbAztRETkcEWGcABAhKoCgZHBgqshRwtL7oYroncDAE41xWDTIm60RORsDO1ERORQTY0WFJljAQCJAaWCqyFnufFas9T+/J0qcYUQeQmGdiIicqiSnDNoghoAkKirFlwNOctV/xgEDWxz278+0A+NpibBFRF5NoZ2IiJyqBO75QcTE6NMAishZwruocNV3W2ryJRbuuGXt/YLrojIszG0ExGRQ53IqZHaifEWgZWQs910o/wA6qcfGNrpSURdxdBOREQOdeJIvdROTOHGSp7siieHQIcqAMDXhwei9kx9+ycQ0UVjaCciIoc6UaiS2olpXDnGkwVGBuPGFNsUmToEY8Wz+wRXROS5GNqJiMihTpwOkNqJQyMFVkKucNuDIVL748/4zQqRszC0ExGRQ52o0gEA1GhE7KAYwdWQs13yl6FI9i0AAPxSPhCFWWfEFkTkoRjaiYjIoU6YbDugxvuegm+gn+BqyNlUal/cNuYIAMAKHyx95pDgiog8E0M7ERE5TG2ZERXWCABAYhBHXL3FH//RU2p//FMcrNZ2OhPRRWFoJyIihyncJe+AmhheK7AScqVe0/oiM3g3ACDX1Au7vswTXBGR52FoJyIihzmxt1JqJ8Y1CqyEXO22GfL/9h+/WiKwEiLPxNBOREQOcyJX3mAnMYkfMd7k+hcGIRBGAMCne/rDVGsWXBGRZ+FPVCIicpjjeU1SO6FPoMBKyNVCk6MwO34bAKDSGo7l87lmO5EjMbQTEZHD5BeppXavYRECKyER7n1QXqP/vcVcOYjIkRjaiYjIYfLKQ6V20iXdBVZCIox7bCRS1UcBABvODMThTaUdnEFE9vLa0P7bb7/h/vvvR1paGnQ6HUJDQ5GWlob77rsPW7Zscco9VSpVp1/vvPOOU2ohInKGvDrbZkrdfU5BExUsuBpyNZWfGvdOPCYdv//ksXZ6E1FneF1or6urw913342xY8fi3XffxcGDB6HX61FTU4ODBw/ivffew6WXXoq77roLdXV1osslInIbtWVGnLbYNlbqHcQRVm91+ytp8Ec9AOCj31JRb7QIrojIM6g77uI5mpqacO211+LHH3+U3tNoNEhPT4darcaBAweg1+sBAB999BGKi4vxww8/wNfX1+G1ZGZmQqPRdNgvMTHR4fcmInKGgq2nAPQGAPSO1IsthoSJHJKAa2M34POSCSi3dMO3L2ThhhcHiy6LyO15VWh/+umnWwT2e++9Fy+//DIiImwPS9XV1eGVV17B888/DwD48ccf8cwzz+DFF190eC0ff/wxkpKSHH5dIiJR8nadgRTaE7jcnze79wFffD7P1n7vfRVucPzHKJHX8ZrpMSdPnsSbb74pHf/xj3/Eu+++KwV2AAgKCsJzzz2Hf/zjH9J7b7zxBk6ePOnSWomI3FHefqPU7t3Xq8aE6DwT5oxGiq9tV9R1pwfh6NYywRURuT+vCe0LFiyAyWQCAGi1WixYsKDNvk8//TQSEhIAACaTCQsXLnRFiUREbi0vX273HhQirhASzifAD/dmHpKO//MoH0gl6iqvCe0rVqyQ2jfccEOLEfbz+fv7484775SOly9f7tTaiIg8Qd4p+Tmd3qOjBVZCSnD3GwOkHVI/3J6G2ooGwRURuTevCO2HDh3C0aNHpePLL7+8w3OuuOIKqX306FEcOnSond5ERJRXZRsMCYQRsYNjBVdDonUbnIBbe9qWUNZbQ/HJ/+MOqURd4RWhfe/evS2Ox4wZ0+E5Q4cOhb+/v3S8bx9/2BARtcXSZEV+vW0zpd7+RVCpHb/qFrmfv/5DJ7Xf/jwSVqvAYojcnFeE9oMHD0ptf39/ab56e87v1/wajvD4448jPT0doaGh0Gg0iI+Px8SJEzF//nzk5+d3fAEiIgUp2V8OE2zTY3rrKgRXQ0ox6O7hyNTuAgDkGpOw7v8OC66IyH15RWgvKCiQ2vHx8VCpVHad13yN9ObXcIRly5bhwIEDqKmpgclkQnFxMTZs2IBnn30Wffv2xZ/+9CcYjcaOL0REpABHN5dI7d6xBoGVkKKoVPjrHyqlw7df5qaFRBfLK0J7TU2N1NbpdO30bCk0NLTVazhCZGQkRo0ahcmTJ2P48OEIDpa3+zabzVi0aBHGjh2L6upqu69ZX18PvV7f4kVE5AqHd8k/q/qm2jcwQt5h1quXIF5VDABYWTQIeTvKBVdE5J68IrTX1tZK7cDAQLvPa75jafNrXKy0tDQsWLAAx44dQ1lZGbZt24Z169Zh586dqKysxMqVK5GRkSH137NnD2666Sa7r//SSy9Bp9NJL3umAREROcKhA01Su++QIIGVkNKodUH486W2Z8us8MGCh7j8I9HF8IrQbjbLO/Op1fZv+NG8b2NjY5fryMnJwcMPP4zevXu3eq/p06dj+/btmD59uvT+mjVr8P3339t1/blz56K6ulp6FRYWdrlmIiJ7HD4hD3KkZsYIrISU6N4FA6CBbdrUB9sH4EwRp38SdZZXhHatViu1z22wZI/mfYOCXDNyFBgYiM8++wwxMfKH3ttvv23XuQEBAQgNDW3xIiJyhUMVkQAADQyIH9VDcDWkNJFDE3F3n00AAAOC8H8PZAuuiMj9eEVobz5fvDMPdxoM8sNUza/hbCEhIXjggQek402bNnXqlw0iIldqNDXhWINtOl6fgEL4+HG5R7rQY2/Gwxe2b77f+iEZxtqmDs4goua8IrRHRkZK7VOnTtl9XkmJvBpCt27dHFpTRyZOnCi1TSYTp7oQkWIVbCmGGX4AgNTIM4KrIaVKmp6OG2I3AgDKLd3w0SN7OziDiJrzitCempoqtc+cOdNiBL09zYNyv379HF5Xe2JjW+4mWF7Op+2JSJkObTottfv2rBdYCSnd4/Pkqaav/y8a5kbutkRkL68I7f37929xnJWV1eE5xcXFKCsra/Maznb+LxbN5+UTESnJ4Sx57e3UdE6NobYNuX8kpoZsBQDkNcRj+Qs5gisich9eEdpHjhyJgIAA6Xjz5s0dnrNp0yapHRgYiJEjRzqltrbk5LT8QRYdHe3S+xMR2evQYfmjpO/IcIGVkOKpVJjzkPyM1j/f0MBiEVgPkRvxitAeHByMyZMnS8dLly7t8JzmfSZPnuyy1WPO+fzzz6V2UlIS4uLiXHp/IiJ7HT4VIrX7TooXWAm5g0nzxmF4wD4AwN7aZHz3aq7giojcg1eEdgC44447pPa+ffvaXfv8999/x+rVq1s91xW+++47rFy5UjqeNWuWS+9PRNQZuXrboEKUqgzhvTnSTu1T+akx7/5S6fjZf6ph5dR2og55TWifPXs2Bg0aJB3ff//9yM298Lf7U6dO4Q9/+AOammxLUQ0ePBjXXXddq9csKCiASqWSXvPnz2+1X3V1Na677jrs3r27wzo/++wz3HLLLdKxVqvFnDlzOjyPiEiE8iOVKLHY9pUYoOMqV2Sf6a9NwDB/21rtWTUpHG0nsoP924O6OZVKhffeew/jx4+H0WjEqVOnMGrUKDzwwAPIzMyEWq3Gjh078O9//xulpbYRAI1Gg3fffRcqlapL97ZarVi+fDmWL1+Ofv36Ydq0aRg8eDDi4uIQFBSEmpoaZGdnY9myZdi5c2eLmj/66KMLVpIhIlKKnDWFAGyj6+kJNWKLIbeh8vfD/PtP4aq3BwIAnn1RjaufALr4cUvk0bwmtAPAiBEjsGTJEvzhD3+A0WiEXq/HK6+8gldeeeWCvhqNBkuWLMGIESMcWkNubm6rI/znCwkJwaJFi3DDDTc49P5ERI60f0uV1B4wUFwd5H6m/2sihi3ah90NGdhTk4Lv/3UIVz+e2vGJRF7Ka6bHnHPttddi9+7dmDJlSqsj6CqVCpMnT8auXbtw7bXXOuSeGo0G9913H9LT0zsctdfpdHjooYewf/9+3HzzzQ65PxGRs+zfL/9MGzA2TFwh5HZU/n6Yd5+8ieH85325kgxRO1RWq/c+/lFYWIgtW7aguLgYANCjRw+MHTsWCQkJTrtnZWUlsrKycPr0aZSXl6OqqgparRYRERHIyMhARkYGfH0ds86xXq+HTqdDdXU1QkNDHXJNIqLmxun2YbM+AwBQmVeJsF58EJXsZ61vwPDQQ/i9wfY1zRfP5OCGZ9MFV0XkWvbmNa8O7Z6OoZ2InMlqsSJCXY0qaxjifU6isKm76JLIDa39fz/h8tenAgCSAwpxUB8PP39ObifvYW9e87rpMURE5Bgns06jyhoGABgQXiy2GHJbl708CZOCtgEAjtUn4L0Hs8QWRKRQDO1ERHRR9q+Vg/qApFqBlZA7U6l98fLzZun42Y8SUFvZKLAiImViaCcioouSs01e4jF9kFctRkYONuKRsbgh6hcAwOmmSLxxW5bYgogUiKGdiIguStZ+OagPHB8hsBJyeyoVXvhPBNSwjbC/tqo/SvPqBBdFpCwM7UREdFF+L44GAKjRiAEzkwVXQ+6uz/WDcW/vnwEAtdZgPHntQcEVESkLQzsREXWaodyAg/W9AQADAo8hQBcouCLyBPM/TYUOVQCAj/YOxc4VRWILIlIQhnYiIuq0fd/mwwLbnhJDE8oEV0OeInpUL8yfvBkAYIUPHrq7lhsuEZ3F0E5ERJ32+09npPbQwUxV5DgPfjke/dWHAQDbKvth6Zx9gisiUgaGdiIi6rTf98ib3wydwl1QyXH8IkKw4NFC6fiJN2NRc6ZBYEVEysDQTkREnbanMBIA4IMmZMziQ6jkWJe9PAkzwzcCAEqaojF/VpbYgogUgKGdiIg6paG2AdlGW1Dv55+PoOggwRWRx1Gp8PpH4QiACQCwYPMw7P62sIOTiDwbQzsREXVK9rfH0Ah/AMCQ7iWCqyFPlTxzIJ4Zsw4AYIEv7v2jCeZGq+CqiMRhaCciok7Z+q28WszIIeZ2ehJ1zf/7fjwG+OUCAPbU9MHC23cLrohIHIZ2IiLqlK07faX2mKujBFZCns6/Wwje+2c5VLCtUPTMZ/2Rv7tCcFVEYjC0ExFRp2wtSgAABMKIQbP7CK6GPN3o/3cpHkz5EQBgQBDum14MK2fJkBdiaCciIruV7i9DvjkRADA89DD8g/0FV0Te4MUfhiBeVQwAWFc6EP93z++CKyJyPYZ2IiKy29aleVJ7TGqlwErIm4T2icEHT+RKx49/2A+HN58WWBGR6zG0ExGR3bauN0rtMRMDBVZC3uaylybhz8lrAABGaHHbjAquJkNehaGdiIjstvWgvPvpmFt6CayEvI5KhVd/Ho4UX9u3Pdur++GV2TsFF0XkOgztRERkF1OVCTv0qQCAXuoTiB0UI7gi8jZBPSPxycun4IMmAMD874Zg26d5HZxF5BkY2omIyC5bP8pFPWxTYib0Oi64GvJWY/7fWMwdshYAYIYfbrwjEBWFdYKrInI+hnYiIrLL+hVVUnviJJW4Qsjrzf91IsZqbSvInGjsjjvHHuYykOTxGNqJiMgu6/fK89kn3p0ssBLyduoQDT5bpUM3nAEAfFc4BAtu5fx28mwM7URE1KG6MgO26/sDAFL8ChA/Ik5wReTtEiYk45P/t086fuKzwfjtk6MCKyJyLoZ2IiLq0JYPctEI20ZKE5NPCK6GyObK1ybiiQzbMpBm+OHaO3Uo2lchuCoi52BoJyKiDv38TY3UnjhFLbASopZe2DwBE0N2AQBKLVGYNbYMRn2j4KqIHI+hnYiIOrR6r206jAoWTP5TH8HVEMn8QgLx5ZYeSPK1fQO0uzYV9wzfwwdTyeMwtBMRUbtObD+FbFNfAMCIoAOITo8SXBFRS5ED4/Ddx1UIQi0A4NMjI/HyzK2CqyJyLIZ2IiJq1+q35Yf7po8oE1gJUdsG3pqBT/66Szp+8vsxWPIwV5Qhz8HQTkRE7frhZ3+pfeUd0QIrIWrftW9NwIvj10rHd741GD+9kS2wIiLHYWgnIqI21evrsa5kAAAgxuc0ht7aX3BFRO2b+8tleKDvzwDOrijzWBL2fJ0nuCqirmNoJyKiNq17Yx8MCAIAXN7rEHzU/NggZVP5qPD23kzMit4CAKhFCK64IRiH13GpUnJv/OlLRERt+mpJvdS+9kb/dnoSKYdvoB8+3T8IlwTtBQCUWqIx6XI/HPu1SHBlRBePoZ2IiFrVUNuAb/NsU2NCoMdljw8SXBGR/TRRwfj+9x4YHHgQAFDcFIdJk1Uo+O2k4MqILg5DOxERtWrdG/tQZQ0DAFydtA+BYYFiCyLqpIi+kfhpdzek+x8BAJxo6oFJ4804sY3BndwPQzsREbXqq/+ZpPb1N3EXVHJPkWnR+HlHCPr5HQMA5JsTcemlVhxaky+4MqLOYWgnIqILGM4Y8fXRDAC2qTHTnuDUGHJfMYNi8fNvGvTxswX1wqYeGHdlMPZ8liu4MiL7MbQTEdEFvv7HHtQgFABwfUoWAsM1gisi6pruw7tj0+4gDAq0BfUyaxQm3BKHTW9niS2MyE4M7UREdIGPvpBD+l2PhAqshMhxYgZGY0NuHMaG2FaV0UOHqQ/1w+cPbhJcGVHHGNqJiKiF/M3FWF85BADQ1y8PlzzAqTHkOcJ66rD2aAouj9wJAKhHIG7+v3F4bvzPsDZZBFdH1DaGdiIiauGDJ49J7TvGF0DloxJYDZHjBUUH4bvjg3F3v83Se/M2TsYfk7fAVGEQWBlR2xjaiYhIYqqux6LNaQAANRpx+z9TBVdE5Bx+Wj+8lzMWr87YCBVsI+xLj4/DpfH5yP85T3B1RBdiaCciIsmnj+5CuTUSAHBD4jZ0H9FDcEVEzqPyUeHx7zPx9dzd0MA2wr7bmI5hU8Kxau7mDs4mci2GdiIiAgBYLVYs+DRaOn74aZ3Aaohc55p/jsC2FSVI8TsOAKhEOGa8fCmeHPETGmtMHZxN5BoM7UREBABY+0oWsk19AABjgvZh5N0DBVdE5DoZs3pjV343XJuwQ3rvpV1TMSb6KHKXHxBYGZENQzsREcFqsWLePwOk48furQZUfACVvIuuRzCWFYzA69dugRqNAIDdpgEYcl0vvH3Vj7A0mAVXSN6MoZ2IiLDmlb3YUWt7AHVgwCFc8+olgisiEkPlo8KjX4/Fti9OoF+A7YFUEzR4aOVlmByZhUMrOOpOYjC0ExF5OUuTFc+8KI+yz3ugDD5+vgIrIhJv2A3J+L00Hg8N/016b0PNcGRcm4z5Y9bCVF4rsDryRgztRERebslDO7Crrj8AICMgF9e8OkZwRUTKoNH5Y+HOS/DjmzlI8isCADQgAM9um4ZBcaVY++SvgNUquEryFgztRERerKbUgL+/kyQdv/FMNUfZic4z9ZF05JyOxpzMrdJc98PmZFz+0nhcHr4d+5dkiS2QvAJDOxGRF3tu1u84ZYkBAMyK+Q2TnxwluCIiZdKG+ePlX8fg91UlGBMmz2tfWz0ag/44EPf1XofiTdyUiZyHoZ2IyEtt+/gQ3thmmwrjj3r865MYwRURKd/AKxOwubw/Pn18D3r6FQMALPDFe/lTkJzZHX9J/QlFvx4TXCV5IoZ2IiIvZKxuwB33+8MC21SYZydvQvJlyYKrInIPPr4q3PzqEORWxODlWdsQqtIDAOoRiP8cnorkCfH4c9+fULD2kOBKyZMwtBMReaGHxu7GofpeAIARmv34f99lCq6IyP0EBqsxZ8VoHD3mgzmZWxEE24oyDQjAf49MRfLlKZgdswmbX9sKa5NFcLXk7hjaiYi8zIf3b8f7ObZpMRoYsPh/vlBr/QVXReS+onoF4+Vfx6AgH5g7YSuCVbbwboEvvj49DuOeGIORwTn45LZ1MJysElssuS2GdiIiL7LpvVz8+d1B0vGiO7Yh7br+Aisi8hyRScH453pbeH/+yq2I9T0t/dku00Dc/r8p6N4DeKDvOux8ZzesFi4XSfZTWa1cYNRT6fV66HQ6VFdXIzQ0VHQ5RCRY9vcFGDczHNVWHQDggdSf8X8HJwEqleDKiDxTg7EJX8zNwpsfhGBPbd8L/nygfy5uHVeI6x9PQu9pfQRUSEpgb15jaPdgDO1EdE72yuO4bKYGJZZoAMC08O34Lj8D/jqN4MqIPJ/VCmz5+Cg+fLUcXxwcCAOCLugzPHA/bhh3CtfP6Y2kyXwo3JvYm9e8dnrMb7/9hvvvvx9paWnQ6XQIDQ1FWloa7rvvPmzZssXp98/Ly8MzzzyDYcOGISoqChqNBsnJybjmmmuwbNkymM1mp9dARN5h+5IjGH91qBTYR2qysWxfKgM7kYuoVMCld6TgwwOjUXLaF+/ftwNjdDkt+uwyDcATP01FrynJGBhwCH8f8TM2vrELjXUNgqompfG6kfa6ujo89NBD+PDDD9vtd+edd+Ltt99GUNCFvw131cKFCzFnzhzU19e32Wf06NFYunQpevfufdH34Ug7Ef3v4V247610mGAL6CM02Vi9Kxrd0rgmO5Foeb+V4KuXj+HLXyLxe11qq310qMJl3XMwKdOM8bf0QL/pyVD5cEqbJ+H0mFY0NTXhyiuvxI8//ii9p9FokJ6eDrVajQMHDkCv10t/dtlll+GHH36Ar6/jtvR+/vnn8cwzz0jHPj4+SEtLQ0REBI4cOYJTp05JfxYfH48dO3YgLi7uou7F0E7kvWrP1OOJKb/jv1ljpPcmhOzGd9m9ENIzQmBlRNSao5tO4cuXjuG7zeHYUdMf1jYmQ0SrypDZ/SjGX9KIcTfEIf2q3lAHOC6nkOvZndesXmTu3LlWANLr3nvvtZ45c0b689raWuvTTz/dos+TTz7psPuvWbPGqlKppGuPGTPGeujQIenPm5qarJ9//rk1ODhY6jN27NiLvl91dbUVgLW6utoR5RORm/hpYY61l98Jq20mre11X/JPVtOZWtGlEZEdTh+utP7vwa3Wm5N+s4arKlr8t3z+S4M669jQfdZHhm20Ln1om/XwhmKrpcki+q9AnWBvXvOakfaTJ08iOTkZJpMJAPDHP/4Rn3zySat9n376abzwwgsAgMDAQBw7dgzdu3fv0v2tViuGDBmCvXv3AgBSU1Px+++/Q6vVXtB33bp1mDp1qnS8fPlyXHPNNZ2+J0faibzL/tWF+Ps9ZVh1cqj0ngYGLLx+C+79YgpXiSFyQ+YGC37/7BB+/eo0ft2pxabTfaGHrt1zQlQ1GBByAgMSqjFgoAoDxuow4MpERPcOdlHV1BmcHnOeJ554Aq+99hoAQKvVorCwEBERrX9F3NDQgJSUFBQWFkrnvvLKK126/w8//IDp06dLx2vWrMG0adPa7H/TTTfhiy++AACMHDkS27dv7/Q9GdqJPJ/VCvz67iG8+UItvi8a0uIr9czg3fjg82CkTG99riwRuZ+mhibs/eowfv2yFNv3+GHnqXjkmXvadW6UTzn6hJYiJaYWfZItSBkQiJRR3ZAyLg5hUX5OrpzawtB+nj59+uDo0aMAgDvuuAMfffRRu/3nzZuH5557DgCQkpKCI0eOdOn+99xzDz744AMAQK9evXDs2DGo2hn1Wr9+PSZNmiQdFxYWIj4+vlP3ZGin/9/enQc3cd7/A3+vJOvAwjbGGHzbGAMmBwFiE0KxIXhIMp0SIEwI5GhSmpKU1PObtA2TNIRcbWo63xkITCm5SH4BkoaEQPkRQiGUEpsmEAwx1Ac22EYY4ys+JF+Spf39wZdFwpIt2bJ3bb1fMxr2WX32eT5Cq8cfr1e7NHyV5V7Fx3+8gI+PRqGow/UL67GqKry+vBCPvz8PKq1GpgyJaLDUF9bi+13lOPkvC04WBuNsQzQqHPE+9RGuakRCcD3iwiyIHWtFXJyAuAk6xN4SirhpEYiZZIRON0AvIMCxaHdSUlKCyZMnS+1PPvkEy5Yt63Gbb7/9FrNm3fgCV3FxMSZN6vvRqqioKFy9ehUA8PTTT2PLli09xnd1dSEsLAytra0AgL/97W9YtWqVT2OyaCcaPmrLW3H8/5bh8D9acfi/0SjpTOwWE6O6gv9zz1ms3j4LhrH8zBMFspaL9Sj8sgLn8ppx7qyIc6ZQFJpjUS327eIWADBK1YRIXTMig1sRGdqJseFdiBwrIDJag8h4PSLHGxGeMBJhsUaMilDDYOBZed7wtl4LiEMw188jv865GPdk+vTp0Gq1sFqvXR+1oKCgz0V7bW2tVLB7O75Go0FaWhqOHj0qjU9Ew5soAvWVrbiQW40Lp5pQeq4Tp4v0yK+JxuWuKABT3W43JzgfzyxvwtL/mYWgEM+n3RFR4AgZH4G7no3AXc86rRRFtJbX4MKxKpR934SyQivKKjUoqw3FhdaxqBKjYe+hNGx0hKGxPQwl7QDqAVzoOQctOhGmsWCUthWj9O0IG2HFKGMXRoU6EBoiwhgiwBiiRnCoGsawIBjDtdceEToYxxgQHDECxhAVjEZAExAVa88C4r+gqKhIWtZqtYiLi+t1m+txFy5c6NZHf8YHgORk7+50lpycLBXt/RmfiAaH3Q7YbIDNKqKzpROW2jZY6tqvPX60wtxghaXRBnOTHQ11dtTUCKht1KCmxYDaNiOqOiNgFkcCmNDjOGp0YeaIs1g8uw7LXkxG3NzpPcYTEQEABAHB48fi9vFjcfsTNz0nirD/2Iyrp6txueBHmIpbYSrvgqlKhcv1elw2h6C2Mww14hhYMNKr4azQobZLh9qu0UAbgB/7nroWnTCoOqFXWaFX2aBX26DX2KBT26EP6oI+yA59kAN6rQN6nQN6rQi9HtDrROh1IoK0gFYnIChIQJBWuLasUyFIJyBIq4LWoL7W1quh1atgDNcibUnv9eJgCoiivaKiQlqOjY3t8VxyZ/Hx8VLR7txHf8a/3q+343vqQ2ne/cVx7P1K2229KHb/vxa7LVxvCm6fkNZ3i3evxzFvWnljTNcn3K73EN9zHt2fFSG43ciX1989D9H9etHHeLj//+ueVT/i3bz+HmPdBLnmLXpY31N+Ht5jD30AgEMUYHOoYXNoYBU1sN38QBAcuH6tZAGA/n8f/TcSLZhuLMX0pEbMu1eLzKdTEZI8zS99ExEBAAQB6tFhiMkKQ0wWMNNTXHs72iovoa7kR9ReMKO2sh01l22ovepAbYMajRYNmtq0aOwwoMkajEb7SDRiFMzo3yl7VuhgdejQ7OhXN15LVJtQrrCb0wdE0W42m6Xl0NCeL5PkzPm8Iuc++jO+Lzn4On5nZ6fLXVadbxQ10Ap/sOL/Vd89aOMRDTdGmDFWXY/xxjokjzUjOcGO5Ck63HbPGIxfMAEq/Qy5UyQiAgwGjJgcj4TJ8fDqmjWiCLS2oqvOhGZTC5ouW9B4pR0tDTZYmrpgabGjtcUBi0WExSLA0ibA0qaGpUMDizUIrdYgWGw6WOwGdDi06HDo0AktOqD/34dhQF5mkEphFTsCpGi3WCzSsl7v/ZEvg+HGjuDcR3/G9yUHX8d/88038eqrr/qWHJEfCHB/6EPwcAzd3XpfYuXoW4CIINgQhC4ECTZoBRuCBDuCVF03/lXZEaSyQ6u+9jDqbBip74JxhAPGYAdGGgHjSMA4UoXRUVpEJhoQOSEUkZNGYURsOKBOApDkNicioiFJEACjERqjEaOTgNH+6NPhAKxWoKMDYnszrC0d6Gix3nhYum48zDZ0tIuwWUVYO0XYOh3Xlq3XTmW02QCrVYTNCti6AJtNgNUmIHw0oLT5OCCK9q6uG78taXz4JoNzrM1m88v4vuTg6/gvvPACnnvuOand0tLi1fn7/vDa3jvw4o9X3D4nqLqfbiAVRTedqiQ1fV1/U8PderdnRQmC+8JNEDzHu1kvCO4H6NN6N8m6W99tc0+v09f1cP+eeQzuab1fthE8rB+g8XmpAyIiZVOpcO2EdT2EMEAXBeiAXm45NfQFRNHufNfR63dE9YZzbHBwsF/Gv96vuzuh9nd8nU4HnUwXUTXGhsEYGybL2ERERETDnar3kKHPaLxx29729navt2tra3PbR3/G9yUHf41PRERERENbQBTtERER0nJ1dbXX2zlfW3306L6fheU8vi85+Gt8IiIiIhraAqJod74pUkNDg8sR7J6YTCZp2fmOqv0ZHwAuXbo0qOMTERER0dAWEEV7amqqS/vMmTO9blNVVYW6ujqPffgiJSXF5Uul3owPAKdPn/bL+EREREQ0tAVE0Z6enu7yBc3c3Nxet/nmm2+kZb1ej/T09D6Pr9VqMXPmjdsUeDP+1atXUVZWJrUzMjL6PD4RERERDW0BUbQbjUbMnz9fau/YsaPXbZxj5s+f36+rxwDAAw88IC0fPnwYNTU1Xo8fFhbGop2IiIgogAVE0Q4ATzzxhLRcUFCAffv2eYzNz8/HgQMH3G7bV8uXL5eO9ttsNqxfv95jrMViwVtvvSW1H3nkEQQFBfU7ByIiIiIamgKmaF+6dCmmTp0qtVetWoXi4uJucdXV1Xj00Udht9sBAHfccQcefPBBt31WVFRAEATp8corr3gcPzY2FqtWrZLaGzduxOeff94tzmaz4cknn5S+rGowGPDiiy969RqJiIiIaHgKiJsrAYAgCHjnnXeQmZmJ9vZ2VFdXY+bMmXjmmWeQkZEBjUaDEydOYPPmzdKpKwaDAW+//TYEP90h8ZVXXsGBAwdQWloKu92Ohx56CCtWrMCiRYsQHh6OkpISbNmyBQUFBdI2f/nLXxAdHe2X8YmIiIhoaBJEUXRzD/fha/fu3Xj00Ud7vcGRwWDA9u3bsWTJEo8xFRUVSEpKktrr1q3r8Wg7AJw/fx5ZWVkul3P05Pnnn0dOTk6vcZ60tLQgNDQUzc3NCAkJ6XM/RERERDQwvK3XAub0mOuWLFmCU6dOISsry+0RdEEQMH/+fHz//fc9Fux9NXHiRBQUFGDlypUwGAxuY1JTU7F3795+FexERERENHwE3JF2ZyaTCXl5eaiqqgIAxMTEYPbs2YiLixuU8c1mM44cOQKTyYTW1lZERUXhtttuw7Rp0/zSP4+0ExERESmbt/VaQBftwx2LdiIiIiJl4+kxRERERETDBIt2IiIiIiKFY9FORERERKRwLNqJiIiIiBSORTsRERERkcKxaCciIiIiUjgW7URERERECseinYiIiIhI4Vi0ExEREREpHIt2IiIiIiKFY9FORERERKRwGrkToIEjiiIAoKWlReZMiIiIiMid63Xa9brNExbtw5jZbAYAxMXFyZwJEREREfXEbDYjNDTU4/OC2FtZT0OWw+HAlStXMHLkSAiCMODjtbS0IC4uDiaTCSEhIQM+Hg193GfIV9xnyFfcZ8gXcuwvoijCbDYjOjoaKpXnM9d5pH0YU6lUiI2NHfRxQ0JCODGST7jPkK+4z5CvuM+QLwZ7f+npCPt1/CIqEREREZHCsWgnIiIiIlI4Fu3kNzqdDuvWrYNOp5M7FRoiuM+Qr7jPkK+4z5AvlLy/8IuoREREREQKxyPtREREREQKx6KdiIiIiEjhWLQTERERESkci3YiIiIiIoVj0U791tTUhK+//ho5OTlYunQpEhMTIQiC9HjllVf61f/Fixfx8ssvY8aMGRgzZgwMBgOSk5OxePFifPbZZ+jq6vLPCyFFqaiocNmPvH189dVXcqdOfnb8+HGsWrUKU6ZMQWhoKEJCQjBlyhT86le/Ql5entzpkQIcPXq0T/NFcXGx3KnTAKirq8OBAwfw2muvYeHChYiKinJ53z/44IM+93327Fk899xzuP322xEeHg6j0YhJkybhkUceGfCfP7wjKvXLxIkTUVZWhoG6CNHGjRuxZs0adHZ2uqy/ePEiLl68iD179uCuu+7Cjh07MH78+AHJgYjk0draiuzsbLz//vvdnisqKkJRURHeeecdPPnkk9i0aROCg4NlyJKIlOLq1au46667UFlZ6fe+u7q68PLLLyMnJwcOh8PlufPnz+P8+fPYuXMnfvrTn2Lbtm0YM2aM33Ng0U79UlpaOmB9v/7663j55ZeltkqlwpQpUxAeHo7S0lJUV1cDAL799ltkZmbixIkTiIqKGrB8SF733nuvV3EDMVHS4LPb7ViyZAn++c9/SusMBgNuueUWaDQaFBYWoqWlBQCwbds2VFVV4csvv4RarZYrZVIIvV6PzMxMr2KNRuMAZ0ODqaOjY0AKdgBYtWqVywGEoKAgTJkyBUajEcXFxWhoaAAA7N+/H1lZWcjLy/P//iUS9QMAEYAYGhoqzps3T3z++efFTz/9VIyKipKeW7dunc/9fvXVV6IgCFIfs2bNEktKSqTn7Xa7+Mknn4hGo1GKmT17th9fGcmtvLxcem85VQWeF154weX9f+qpp8SGhgbpeYvFIq5du9Yl5sUXX5QxY5LTv/71L2k/SEhIkDsdkonzz40xY8aI9913n/jSSy+Je/bscZkrtm3b5lO/W7duddl+4cKF4uXLl6XnrVaruGnTJlGj0UgxK1as8POrE0X+JKR+2bFjh1hSUiI6HA6X9QkJCX0u2h0Ohzh16lRp+0mTJomtra1uYw8dOuTyQdq9e3dfXwopDIv2wFVVVSXq9XrpvX/sscc8xr700ktSnF6vF6uqqgYxU1IKFu0kiqLY3Nws7tq1S6yoqOj2XF+L9tbWVnHcuHHStnPnzhW7urrcxr777rtSnCAI4qlTp/r6UtziF1GpX1asWIGJEydCEAS/9XngwAH88MMPUnvjxo0YMWKE29isrCwsW7ZMav/5z3/2Wx5EJI8NGzago6MDADBixAhs2LDBY+zatWsRFxcH4Nqfxjdu3DgYKRKRAoWEhGDp0qVISEjwW58ffPABrl69CgAQBAF//etfPZ6Gt3LlSsycORMAIIoicnJy/JYHwKvHkALt3r1bWk5KSsKCBQt6jF+1apW0fOLECVy+fHnAciOigffFF19Iyw899BDCw8M9xmq1Wjz55JNS23n+ICLqL+c5JTMzE6mpqT3GO9ckX375ZbcLafQHi3ZSnP3790vL9957b69H8efMmeNy1Qjn7YloaCkpKUFZWZnUvu+++3rd5v7775eWy8rKUFJSMiC5EVFgsVgsOHbsmNT2dT6yWCw4evSo3/Jh0U6KUltbK/0ZCgBmzZrV6zYajQZpaWlSu6CgYEByI6KB53xqHODdHDB9+nRotVqpzTmAiPyhsLAQNptNanszH40bNw6JiYlS25/zEYt2UpSioiKXdnJyslfbOcfd3AcND48//jhSUlIQHByM4OBgxMfH47777sP69etRW1srd3rkJ86fX61WK52v3pOb4zgHBLampiY89NBDSExMhMFgwMiRI5GUlIRFixZh8+bN0qVCiXqjtJqERTspSkVFhUs7Pj7eq+2c427ug4aHjz76CGVlZWhra0NbWxtMJhMOHjyINWvWICEhAWvXroXdbpc7Teon589vbGys119y5xxA1zU3N2PXrl2orKxER0cHLBYLKioqsHfvXvzmN79BfHw8Nm3aJHeaNAQ4zyUajcbre8EM1HzEmyuRopjNZpd2aGioV9uFhIR47IOGh6ioKOnIWWNjI4qKiqQrjHR0dOCNN97AyZMnsW/fPgQFBcmcLfWV8+fX288/wDmAXCUmJiImJgY6nQ719fUoLCxEV1cXgGtFfXZ2Ns6cOYP33ntP5kxJyZznkpEjR0Kl8u5Y90DNRzzSTopisVhc2nq93qvtDAaDxz5oaBIEAenp6XjnnXdw5coVXLlyBcePH8fXX3+N/Px8NDU1YefOnS7nDh48eBDZ2dnyJU395vz59fbzD3AOCHQqlQpZWVnYsWMHGhoaUF5ejtzcXHz99df44Ycf0NjYiC1btiAiIkLa5v333/f7JfloeFHafMSinRTl+pGQ6zQa7/4Y5Bzn/KURGroSEhLw3Xff4Ze//KXbP0nqdDosX74c+fn5mDFjhrR+69at/CLiEOY8B3j7+b85lnNA4MnIyMChQ4ewYsUKt5cINRqNePrpp5Gfn+/yi/5rr72GmpqaQcyUhhKlzUcs2oeR7du3QxAEvz8++OCDQXsNN99E6frpD71xjnO+/CP5j1L3r1GjRmH37t3SURBRFLF582Y/vGKSg/Mc4O3n/+ZYzgHkSVxcHP7+979L7ba2Np4iQx4pbT5i0U6KYjQaXdrt7e1ebdfW1uaxDxr+4uPj8fDDD0vtQ4cOyZgN9Yfz59fbzz/AOYC8l56ejrlz50ptzhfkidLmI34RdRgJDg5GTEzMgPQ7WJzPNwSA6upqjB49utftnK/t7k08+U7p+9e8efOko/YVFRWwWq0u1+6mocF5DqiurvZ6O84B5It58+ZJN705f/68vMmQYjnPRxaLBRaLxasifKDmIxbtw8jixYuxePFiudPol0mTJrm0L126hFtvvbXX7Uwmk7Q8efJkv+dFyt+/xo0b59JuaGjw+vJcpBzOc0BDQwPa2tq6nTbnDucA8oXzfFFfXy9jJqRk7mqSKVOm9LrdQM1HPD2GFCUlJcXlCxxnzpzxarvTp09Ly6mpqf5Oi4YA5z9HAt2/H0FDw82fX2/mgKqqKtTV1Xnsg+hmzvMF5wrypC/zkc1mw3//+1+PffQHi3ZSFK1Wi5kzZ0rt3NzcXre5evUqysrKpHZGRsaA5EbK5jxJ6nQ6n67xTcqRnp4OnU4ntb2ZA7755htpWa/XIz09fUByo+HDeb6IjIyUMRNSsvHjxyM2NlZqezMfnTp1yuWXQn/WJCzaSXEeeOABafnw4cO9Xo5rx44d0nJYWBiL9gAkiiI+/fRTqT1r1iwZs6H+MBqNmD9/vtR2/nx74hwzf/58Xj2GetTW1oZ//OMfUvvuu++WMRtSuoULF0rLu3btgtVq7THeeT665ZZbkJyc7LdcWLST4ixfvlw60maz2bB+/XqPsRaLBW+99ZbUfuSRR3g3zAC0efNml2uzL1q0SL5kqN+eeOIJabmgoAD79u3zGJufn48DBw643ZbInbVr16K2tlZqc76gnjjPKfX19di6davH2MuXL+PDDz90u61fiEQDICEhQQQgAhDXrVvn8/bZ2dnS9mq1Wvzss8+6xVitVnHp0qVSnMFgEKuqqvyQPcnt3Llz4i9+8QuxuLi4xziHwyFu2LBBVKvV0n4QHR0ttrW1DVKmNBAcDoc4depU6T2NiooSi4qKusVduXJFTE1NleLuuOMO0eFwyJAxyengwYPic889J5pMph7jrFaruGbNGml/ASBOnz6d+0yAcH7ft23b5tO2CxculLY1Go1ibm5ut5jm5mZxzpw5Uty4ceP8/rNIEEVR9O+vARRI3njjDbzxxhvd1nd2dkrLarXa7Z3ESkpKkJCQ4LbfxsZGzJw5E6WlpQCu3aJ6xYoVWLRoEcLDw1FSUoItW7a4HF3dvHkzVq9e3d+XRApw5swZTJs2DQAwY8YM3HPPPZg6dSoiIyNhMBjQ2NiI06dP4+OPP0ZxcbG0nU6nw6FDhzBnzhy5Uic/OXnyJDIzM6VrI4eEhOCZZ55BRkYGNBoNTpw4gc2bN0unzxkMBvz73/9GWlqanGmTDPbs2YPFixdDpVJh9uzZyMzMxK233oqIiAhotVrU19fjxIkT2LFjh8tVPcLDw3H8+PFuVwihoe2pp57CRx991G29c12i0WigVqu7xXi6gVJFRQXS0tKkKw3pdDqsXLkSCxYsgNFoREFBATZt2oTy8nIA12qWPXv24Gc/+5k/XtINfv0VgALOunXrXH579eVRXl7eY98lJSViXFycV309//zzg/OCaVCcPn3a5/1p3Lhx4qFDh+ROnfzo888/Fw0GQ6/vvcFgED///HO50yWZfPHFFz7PFykpKWJ+fr7cqdMA+PnPf97nuqQneXl5Ynh4eK99qNVqcdOmTQPy2nhOOynWxIkTUVBQgJUrV8JgMLiNSU1Nxd69e5GTkzPI2dFAioqKwuOPP+7VF3jGjh2Ll156CWfPnkVWVtYgZEeDZcmSJTh16hSysrIgCEK35wVBwPz58/H9999jyZIlMmRISjB58mQsW7bM5SofniQmJmL9+vU4ffq09Nc8Im/cfffdKCgowIMPPuj27AEASEtLw7Fjx/Dss88OSA48PYaGBLPZjCNHjsBkMqG1tRVRUVG47bbbOOkGgJqaGhQUFKCurg719fUwm80wGo2IiIjAtGnTkJqa6rago+HFZDIhLy8PVVVVAICYmBjMnj0bcXFxMmdGSnLp0iUUFhaivr4e9fX1aG1tRUhICCIjI3HnnXf69UoeFLjq6upw7NgxXL58GVarFdHR0bjzzjsH/FQrFu1ERERERArH02OIiIiIiBSORTsRERERkcKxaCciIiIiUjgW7URERERECseinYiIiIhI4Vi0ExEREREpHIt2IiIiIiKFY9FORERERKRwLNqJiIiIiBSORTsRERERkcKxaCciIiIiUjgW7URERERECseinYiIiIhI4Vi0ExEREREpHIt2IiIiIiKFY9FORERERKRwLNqJiIiIiBSORTsRERERkcKxaCciIiIiUjgW7UREpBgPPvggBEGAIAgYMWIEKioq+tRPdna21I8gCDhx4oR/EyUiGmQs2omISBH27duH3bt3S+01a9YgMTGxT33deeedLu1vvvmmP6kREcmORTsREcnOYrFg9erVUjsxMRFr1qzpc39paWku7WPHjvW5LyIiJWDRTkREssvJyYHJZJLar7/+OvR6fZ/7S0lJgVqtltpnzpzpT3pERLITRFEU5U6CiIgCV21tLZKTk2GxWAAAEydORGFhoUvR3RexsbGoqqoCAKhUKrS1tUGn0/U7XyIiOfBIOxERyerNN9+UCnYA+MMf/tDvgh24VrRf53A4+vylViIiJWDRTkREsjGbzXjvvfek9ujRo/Hwww/7pW+DweDSbmlp8Uu/RERyYNFORESy2b59O8xms9R+7LHHoNVq/dK3IAgubavV6pd+iYjkoJE7ASIiClwffvihS/uxxx7rMf7QoUOw2+0AgPT0dISHh3uM7erqcmlrNPyRR0RDF7+ISkREsmhsbERERAQcDgcAICIiArW1td2OkF935coVxMTESO3S0lJMmDDBY/9JSUku57GbTCaX89yJiIYSnh5DRESyOHr0qFSwA8DcuXM9FuwA8N1330nLI0aMwPjx4z3G2u126coxAKDVahEVFdXPjImI5MOinYiIZHH27FmX9rRp03qMz8vLk5ZTUlKgUnn+EXb27FnYbDapPWPGDL9ckYaISC4s2omISBalpaUu7dTU1B7jDx48KC3HxcX1GJubm+vSnjNnjo/ZEREpC4t2IiKSxaVLl1za48aN8xhbWVmJc+fOSe3IyMge+96/f79LOysrqw8ZEhEpB4t2IiKSRWtrq0s7NDTUY+zOnTtd2nq93mNsQ0MDjhw5IrUjIyNxzz339DFLIiJlYNFORESycD7nHADa29vdxnV1dWHr1q0u69ra2jz2+/bbb7tck33FihU8n52IhjwW7UREJIuxY8e6tEtKStzGvfvuu6isrIQgCNJpMeXl5W5j6+vrsX79eqmt0+nw29/+1k8ZExHJh0U7ERHJIiUlxaV98ykwAHD+/HmsWbMGALBgwQJER0cDAP7zn/+goaHBJdZqtWL58uVoamqS1v3617/mtdmJaFhg0U5ERLJYtGiRS3v//v343e9+h5qaGrS3t2P37t2YO3cuWlpaIAgCXn31VenmSlarFY8++ihMJhM6Ojpw5MgRzJkzB4cPH5b6u/XWW/HHP/5xMF8SEdGA4R1RiYhIFna7HbNmzcLJkyd7jf3973+P9evXY9OmTcjOzu41PikpCYcPH+7xBkxEREMJj7QTEZEs1Go1du7ciQkTJvQYl52djZycHADAU089halTp/YYf//99yM3N5cFOxENKzzSTkREsmppacGWLVvw2Wefoby8HC0tLRgzZgx+8pOfYPXq1cjIyHCJb25uxp/+9Cfs2bMHlZWVCAoKQnR0NDIyMrB8+XJe3pGIhiUW7URERERECsfTY4iIiIiIFI5FOxERERGRwrFoJyIiIiJSOBbtREREREQKx6KdiIiIiEjhWLQTERERESkci3YiIiIiIoVj0U5EREREpHAs2omIiIiIFI5FOxERERGRwrFoJyIiIiJSOBbtREREREQKx6KdiIiIiEjhWLQTERERESkci3YiIiIiIoVj0U5EREREpHD/H9/a69RdIcjpAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -914,50 +902,17 @@ "plot_power_spectrum(alpha, wc, 1 / T, save=False)" ] }, - { - "cell_type": "markdown", - "id": "1c2e4446", - "metadata": {}, - "source": [ - "Now that we have a good fit to the spectral density, Let us obtain its dynamics, by passing our ``FitSpectral`` bath specifications into the ``HEOMSolver``" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "cb90d87a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 7.39s*] Elapsed 7.39s / Remaining 00:00:00:00\n" - ] - } - ], - "source": [ - "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - "HEOM_spectral_fit = HEOMSolver(\n", - " Hsys,\n", - " (bath,Q),\n", - " max_depth=4,\n", - " options=options,\n", - ")\n", - "result_spectral = HEOM_spectral_fit.run(rho0, tlist)" - ] - }, { "cell_type": "markdown", "id": "5bb8eb36", "metadata": {}, "source": [ - "Now if we want to see the systems's behaviour as we change the Number of terms in the fit, we may use this auxiliary function" + "Now if we want to see the systems's behaviour as we change the number of terms in the fit, we may use this auxiliary function." ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "id": "5a8a930d", "metadata": {}, "outputs": [], @@ -966,12 +921,18 @@ " \"\"\"Run the HEOM with the given bath parameters and\n", " and return the results of the evolution.\n", " \"\"\"\n", - " bath, _= sd_env.approx_by_sd_fit(w,Nmax=N,Nk=Nk,target_rmse=None)\n", + " # sigma = 0.0001\n", + " # J_max = abs(max(J, key=abs))\n", + " # lower = [-100*J_max, 0.1*wc, 0.1*wc]\n", + " # guess = [J_max, wc, wc]\n", + " # upper = [100*J_max, 100*wc, 100*wc]\n", + " bath, fitinfo= sd_env.approximate(\"spec_lsq\",w,Nmax=N,Nk=Nk,target_rmse=None)#,lower=lower,upper=upper,guess=guess,sigma=sigma)\n", " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", "\n", " # This problem is a little stiff, so we use the BDF method to solve\n", " # the ODE ^^^\n", " print(f\"Starting calculations for N={N}, Nk={Nk} and max_depth={max_depth} ... \")\n", + "\n", " HEOM_spectral_fit = HEOMSolver(\n", " Hsys,\n", " (bath,Q),\n", @@ -982,17 +943,9 @@ " return results_spectral_fit" ] }, - { - "cell_type": "markdown", - "id": "9ea58304", - "metadata": {}, - "source": [ - "Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow." - ] - }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "id": "0273c6cb", "metadata": {}, "outputs": [], @@ -1035,85 +988,252 @@ " return fig" ] }, + { + "cell_type": "markdown", + "id": "9ea58304", + "metadata": {}, + "source": [ + "Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow." + ] + }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "id": "96b86c48", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting calculations for N=1, Nk=1 and max_depth=5 ... \n", + "10.0%. Run time: 0.12s. Est. time left: 00:00:00:01\n", + "20.0%. Run time: 0.16s. Est. time left: 00:00:00:00\n", + "30.1%. Run time: 0.19s. Est. time left: 00:00:00:00\n", + "40.1%. Run time: 0.23s. Est. time left: 00:00:00:00\n", + "50.1%. Run time: 0.27s. Est. time left: 00:00:00:00\n", + "60.1%. Run time: 0.30s. Est. time left: 00:00:00:00\n", + "70.1%. Run time: 0.33s. Est. time left: 00:00:00:00\n", + "80.1%. Run time: 0.37s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 0.40s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 0.43s. Est. time left: 00:00:00:00\n", + "Total run time: 0.43s\n", + "Starting calculations for N=2, Nk=1 and max_depth=5 ... \n", + "10.0%. Run time: 0.37s. Est. time left: 00:00:00:03\n", + "20.0%. Run time: 0.50s. Est. time left: 00:00:00:02\n", + "30.1%. Run time: 0.66s. Est. time left: 00:00:00:01\n", + "40.1%. Run time: 0.76s. Est. time left: 00:00:00:01\n", + "50.1%. Run time: 0.85s. Est. time left: 00:00:00:00\n", + "60.1%. Run time: 0.93s. Est. time left: 00:00:00:00\n", + "70.1%. Run time: 1.01s. Est. time left: 00:00:00:00\n", + "80.1%. Run time: 1.08s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 1.16s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 1.24s. Est. time left: 00:00:00:00\n", + "Total run time: 1.24s\n", + "Starting calculations for N=3, Nk=1 and max_depth=5 ... \n", + "10.0%. Run time: 0.47s. Est. time left: 00:00:00:04\n", + "20.0%. Run time: 0.71s. Est. time left: 00:00:00:02\n", + "30.1%. Run time: 0.95s. Est. time left: 00:00:00:02\n", + "40.1%. Run time: 1.18s. Est. time left: 00:00:00:01\n", + "50.1%. Run time: 1.43s. Est. time left: 00:00:00:01\n", + "60.1%. Run time: 1.70s. Est. time left: 00:00:00:01\n", + "70.1%. Run time: 1.96s. Est. time left: 00:00:00:00\n", + "80.1%. Run time: 2.21s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 2.46s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 2.72s. Est. time left: 00:00:00:00\n", + "Total run time: 2.72s\n", + "Starting calculations for N=4, Nk=1 and max_depth=5 ... \n", + "10.0%. Run time: 2.56s. Est. time left: 00:00:00:23\n", + "20.0%. Run time: 4.27s. Est. time left: 00:00:00:17\n", + "30.1%. Run time: 6.03s. Est. time left: 00:00:00:14\n", + "40.1%. Run time: 8.34s. Est. time left: 00:00:00:12\n", + "50.1%. Run time: 9.79s. Est. time left: 00:00:00:09\n", + "60.1%. Run time: 10.80s. Est. time left: 00:00:00:07\n", + "70.1%. Run time: 11.85s. Est. time left: 00:00:00:05\n", + "80.1%. Run time: 12.94s. Est. time left: 00:00:00:03\n", + "90.2%. Run time: 13.92s. Est. time left: 00:00:00:01\n", + "100.0%. Run time: 15.00s. Est. time left: 00:00:00:00\n", + "Total run time: 15.00s\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# # Generate results for different number of lorentzians in fit:\n", "\n", - "# results_spectral_fit_pk = [\n", - "# generate_spectrum_results(Q, n, Nk=1, max_depth=max_depth) for n in range(1, 5)\n", - "# ]\n", + "results_spectral_fit_pk = [\n", + " generate_spectrum_results(Q, n, Nk=1, max_depth=max_depth) for n in range(1, 5)\n", + "]\n", "\n", - "# plot_result_expectations(\n", - "# [\n", - "# (\n", - "# result,\n", - "# P11p,\n", - "# \"rand\",\n", - "# f\"P11 (spectral fit) $k_J$={pk + 1}\",\n", - "# )\n", - "# for pk, result in enumerate(results_spectral_fit_pk)\n", - "# ]\n", - "# );" + "plot_result_expectations(\n", + " [\n", + " (\n", + " result,\n", + " P11p,\n", + " \"rand\",\n", + " f\"P11 (spectral fit) $k_J$={pk + 1}\",\n", + " )\n", + " for pk, result in enumerate(results_spectral_fit_pk)\n", + " ]\n", + ");" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "id": "980af0cd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting calculations for N=4, Nk=2 and max_depth=5 ... \n", + "10.0%. Run time: 4.52s. Est. time left: 00:00:00:40\n", + "20.0%. Run time: 6.33s. Est. time left: 00:00:00:25\n", + "30.1%. Run time: 7.99s. Est. time left: 00:00:00:18\n", + "40.1%. Run time: 9.54s. Est. time left: 00:00:00:14\n", + "50.1%. Run time: 11.06s. Est. time left: 00:00:00:11\n", + "60.1%. Run time: 12.58s. Est. time left: 00:00:00:08\n", + "70.1%. Run time: 13.97s. Est. time left: 00:00:00:05\n", + "80.1%. Run time: 15.33s. Est. time left: 00:00:00:03\n", + "90.2%. Run time: 16.66s. Est. time left: 00:00:00:01\n", + "100.0%. Run time: 17.98s. Est. time left: 00:00:00:00\n", + "Total run time: 17.98s\n", + "Starting calculations for N=4, Nk=3 and max_depth=5 ... \n", + "10.0%. Run time: 5.48s. Est. time left: 00:00:00:49\n", + "20.0%. Run time: 9.31s. Est. time left: 00:00:00:37\n", + "30.1%. Run time: 13.13s. Est. time left: 00:00:00:30\n", + "40.1%. Run time: 17.19s. Est. time left: 00:00:00:25\n", + "50.1%. Run time: 22.53s. Est. time left: 00:00:00:22\n", + "60.1%. Run time: 28.67s. Est. time left: 00:00:00:19\n", + "70.1%. Run time: 34.19s. Est. time left: 00:00:00:14\n", + "80.1%. Run time: 39.12s. Est. time left: 00:00:00:09\n", + "90.2%. Run time: 43.85s. Est. time left: 00:00:00:04\n", + "100.0%. Run time: 48.80s. Est. time left: 00:00:00:00\n", + "Total run time: 48.80s\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# generate results for different number of Matsubara terms per Lorentzian\n", - "#for max number of Lorentzians:\n", + "# for max number of Lorentzians:\n", "\n", - "# Nk_list = range(2, 4)\n", - "# results_spectral_fit_nk = [\n", - "# generate_spectrum_results(Q, 4, Nk=Nk, max_depth=max_depth) for Nk in Nk_list\n", - "# ]\n", + "Nk_list = range(2, 4)\n", + "results_spectral_fit_nk = [\n", + " generate_spectrum_results(Q, 4, Nk=Nk, max_depth=max_depth) for Nk in Nk_list\n", + "]\n", "\n", - "# plot_result_expectations(\n", - "# [\n", - "# (\n", - "# result,\n", - "# P11p,\n", - "# \"rand\",\n", - "# f\"P11 (spectral fit) K={nk+1}\",\n", - "# )\n", - "# for nk, result in zip(Nk_list, results_spectral_fit_nk)\n", - "# ]\n", - "# );" + "plot_result_expectations(\n", + " [\n", + " (\n", + " result,\n", + " P11p,\n", + " \"rand\",\n", + " f\"P11 (spectral fit) K={nk+1}\",\n", + " )\n", + " for nk, result in zip(Nk_list, results_spectral_fit_nk)\n", + " ]\n", + ");" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 28, "id": "eb904688", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting calculations for N=4, Nk=1 and max_depth=2 ... \n", + "10.0%. Run time: 0.08s. Est. time left: 00:00:00:00\n", + "20.0%. Run time: 0.11s. Est. time left: 00:00:00:00\n", + "30.1%. Run time: 0.14s. Est. time left: 00:00:00:00\n", + "40.1%. Run time: 0.16s. Est. time left: 00:00:00:00\n", + "50.1%. Run time: 0.19s. Est. time left: 00:00:00:00\n", + "60.1%. Run time: 0.21s. Est. time left: 00:00:00:00\n", + "70.1%. Run time: 0.24s. Est. time left: 00:00:00:00\n", + "80.1%. Run time: 0.27s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 0.31s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 0.36s. Est. time left: 00:00:00:00\n", + "Total run time: 0.36s\n", + "Starting calculations for N=4, Nk=1 and max_depth=3 ... \n", + "10.0%. Run time: 0.20s. Est. time left: 00:00:00:01\n", + "20.0%. Run time: 0.28s. Est. time left: 00:00:00:01\n", + "30.1%. Run time: 0.34s. Est. time left: 00:00:00:00\n", + "40.1%. Run time: 0.40s. Est. time left: 00:00:00:00\n", + "50.1%. Run time: 0.46s. Est. time left: 00:00:00:00\n", + "60.1%. Run time: 0.51s. Est. time left: 00:00:00:00\n", + "70.1%. Run time: 0.57s. Est. time left: 00:00:00:00\n", + "80.1%. Run time: 0.62s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 0.67s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 0.73s. Est. time left: 00:00:00:00\n", + "Total run time: 0.73s\n", + "Starting calculations for N=4, Nk=1 and max_depth=4 ... \n", + "10.0%. Run time: 1.07s. Est. time left: 00:00:00:09\n", + "20.0%. Run time: 1.47s. Est. time left: 00:00:00:05\n", + "30.1%. Run time: 1.79s. Est. time left: 00:00:00:04\n", + "40.1%. Run time: 2.08s. Est. time left: 00:00:00:03\n", + "50.1%. Run time: 2.37s. Est. time left: 00:00:00:02\n", + "60.1%. Run time: 2.69s. Est. time left: 00:00:00:01\n", + "70.1%. Run time: 3.01s. Est. time left: 00:00:00:01\n", + "80.1%. Run time: 3.30s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 3.58s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 3.86s. Est. time left: 00:00:00:00\n", + "Total run time: 3.86s\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#Generate results for different depths:\n", + "# Generate results for different depths:\n", "\n", - "# Nc_list = range(2, max_depth)\n", - "# results_spectral_fit_nc = [\n", - "# generate_spectrum_results(Q, 4, Nk=1, max_depth=Nc) for Nc in Nc_list\n", - "# ]\n", + "Nc_list = range(2, max_depth)\n", + "results_spectral_fit_nc = [\n", + " generate_spectrum_results(Q, 4, Nk=1, max_depth=Nc) for Nc in Nc_list\n", + "]\n", "\n", - "# plot_result_expectations(\n", - "# [\n", - "# (\n", - "# result,\n", - "# P11p,\n", - "# \"rand\",\n", - "# f\"P11 (spectral fit) $N_C={nc}$\",\n", - "# )\n", - "# for nc, result in zip(Nc_list, results_spectral_fit_nc)\n", - "# ]\n", - "# );" + "plot_result_expectations(\n", + " [\n", + " (\n", + " result,\n", + " P11p,\n", + " \"rand\",\n", + " f\"P11 (spectral fit) $N_C={nc}$\",\n", + " )\n", + " for nc, result in zip(Nc_list, results_spectral_fit_nc)\n", + " ]\n", + " );" ] }, { @@ -1126,7 +1246,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 29, "id": "7fc617a1", "metadata": {}, "outputs": [], @@ -1261,13 +1381,13 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 30, "id": "26209a1b", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1308,48 +1428,6 @@ "Analogously to the spectral density case, one may use the `approx_by_cf_fit` method, the main difference with respect to the spectral density fit, is that now we are perfoming two fits, one for the real part and another one for the imaginary part" ] }, - { - "cell_type": "code", - "execution_count": 33, - "id": "217905ff", - "metadata": {}, - "outputs": [], - "source": [ - "bath_corr ,fitinfo= sd_env.approx_by_cf_fit(tlist=t,Ni_max=4,Nr_max=4,maxfev=1e8)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "a861655e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlation function fit:\n", - "\n", - "Result of fitting the real part of |Result of fitting the imaginary part \n", - "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", - " | \n", - " Parameters| a | b | c | Parameters| a | b | c \n", - " 1 |-1.88e+00 |-4.65e+00 |2.64e+00 | 1 |-1.34e+01 |-1.08e+00 |2.73e-02 \n", - " 2 | 3.00e+00 |-2.88e+00 |3.05e-01 | 2 |-8.59e+00 |-3.78e-01 |1.03e-03 \n", - " 3 | 4.78e-02 |-1.63e-01 |2.98e-28 | 3 | 5.64e-01 |-4.30e+00 |3.95e+00 \n", - " 4 | 3.54e-01 |-6.27e-01 |1.71e-08 | 4 |-1.34e+01 |-2.31e+00 |2.90e-01 \n", - " | \n", - "A normalized RMSE of 3.12e-06 was obtained for the the real part of |A normalized RMSE of 4.89e-06 was obtained for the the imaginary part\n", - "the correlation function. |of the correlation function. \n", - "The current fit took 1.439287 seconds. |The current fit took 11.467839 seconds. \n", - "\n" - ] - } - ], - "source": [ - "print(fitinfo[\"summary\"])" - ] - }, { "cell_type": "markdown", "id": "b8c32d8a", @@ -1378,92 +1456,157 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 31, "id": "57d768ee", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n", + "10.0%. Run time: 0.04s. Est. time left: 00:00:00:00\n", + "20.0%. Run time: 0.06s. Est. time left: 00:00:00:00\n", + "30.1%. Run time: 0.08s. Est. time left: 00:00:00:00\n", + "40.1%. Run time: 0.09s. Est. time left: 00:00:00:00\n", + "50.1%. Run time: 0.11s. Est. time left: 00:00:00:00\n", + "60.1%. Run time: 0.13s. Est. time left: 00:00:00:00\n", + "70.1%. Run time: 0.15s. Est. time left: 00:00:00:00\n", + "80.1%. Run time: 0.16s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 0.18s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 0.19s. Est. time left: 00:00:00:00\n", + "Total run time: 0.19s\n", + "3\n", + "10.0%. Run time: 0.72s. Est. time left: 00:00:00:06\n", + "20.0%. Run time: 1.20s. Est. time left: 00:00:00:04\n", + "30.1%. Run time: 1.66s. Est. time left: 00:00:00:03\n", + "40.1%. Run time: 2.10s. Est. time left: 00:00:00:03\n", + "50.1%. Run time: 2.60s. Est. time left: 00:00:00:02\n", + "60.1%. Run time: 3.25s. Est. time left: 00:00:00:02\n", + "70.1%. Run time: 3.94s. Est. time left: 00:00:00:01\n", + "80.1%. Run time: 4.85s. Est. time left: 00:00:00:01\n", + "90.2%. Run time: 5.50s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 5.96s. Est. time left: 00:00:00:00\n", + "Total run time: 5.97s\n", + "4\n", + "10.0%. Run time: 1.80s. Est. time left: 00:00:00:16\n", + "20.0%. Run time: 3.38s. Est. time left: 00:00:00:13\n", + "30.1%. Run time: 4.61s. Est. time left: 00:00:00:10\n", + "40.1%. Run time: 6.05s. Est. time left: 00:00:00:09\n", + "50.1%. Run time: 7.41s. Est. time left: 00:00:00:07\n", + "60.1%. Run time: 8.91s. Est. time left: 00:00:00:05\n", + "70.1%. Run time: 10.07s. Est. time left: 00:00:00:04\n", + "80.1%. Run time: 10.97s. Est. time left: 00:00:00:02\n", + "90.2%. Run time: 11.66s. Est. time left: 00:00:00:01\n", + "100.0%. Run time: 12.31s. Est. time left: 00:00:00:00\n", + "Total run time: 12.31s\n" + ] + } + ], "source": [ - "# def generate_corr_results(N, max_depth):\n", - "# tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - "# bath_corr ,fitinfo= sd_env.approx_by_cf_fit(tlist=t,Ni_max=N,Nr_max=N,maxfev=1e8,target_rsme=None)\n", - "# HEOM_corr_fit = HEOMSolver(\n", - "# Hsys,\n", - "# (bath_corr,Q),\n", - "# max_depth=max_depth,\n", - "# options=options,\n", - "# )\n", + "def generate_corr_results(N, max_depth):\n", + " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", + " bath_corr ,fitinfo= sd_env.approximate(\"corr_lsq\",tlist=t,Ni_max=N,Nr_max=N,maxfev=1e8,target_rsme=None)\n", + " HEOM_corr_fit = HEOMSolver(\n", + " Hsys,\n", + " (bath_corr,Q),\n", + " max_depth=max_depth,\n", + " options=options,\n", + " )\n", "\n", - "# results_corr_fit = HEOM_corr_fit.run(rho0, tlist)\n", + " results_corr_fit = HEOM_corr_fit.run(rho0, tlist)\n", "\n", - "# return results_corr_fit\n", + " return results_corr_fit\n", "\n", "\n", "# # Generate results for different number of exponentials in fit:\n", - "# results_corr_fit_pk = [\n", - "# print(f\"{i + 1}\")\n", - "# or generate_corr_results(\n", - "# i,\n", - "# max_depth=max_depth,\n", - "# )\n", - "# for i in range(1, 4)]" + "results_corr_fit_pk = [\n", + " print(f\"{i + 1}\")\n", + " or generate_corr_results(\n", + " i,\n", + " max_depth=max_depth,\n", + " )\n", + " for i in range(1, 4)]" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 32, "id": "91d1be7c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# plot_result_expectations(\n", - "# [\n", - "# (\n", - "# result,\n", - "# P11p,\n", - "# \"rand\",\n", - "# f\"P11 (correlation fit) k_R=k_I={pk + 1}\",\n", - "# )\n", - "# for pk, result in enumerate(results_corr_fit_pk)\n", - "# ]\n", - "# );" + "plot_result_expectations(\n", + " [\n", + " (\n", + " result,\n", + " P11p,\n", + " \"rand\",\n", + " f\"P11 (correlation fit) k_R=k_I={pk + 1}\",\n", + " )\n", + " for pk, result in enumerate(results_corr_fit_pk)\n", + " ]\n", + ");" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 33, "id": "4770c53b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", - "\n", - "# plot_result_expectations(\n", - "# [\n", - "# (\n", - "# results_corr_fit_pk[0],\n", - "# P11p,\n", - "# \"y\",\n", - "# \"Correlation Function Fit $k_R=k_I=1$\",\n", - "# ),\n", - "# (\n", - "# results_corr_fit_pk[2],\n", - "# P11p,\n", - "# \"k\",\n", - "# \"Correlation Function Fit $k_R=k_I=3$\",\n", - "# ),\n", - "# (results_spectral_fit_pk[0], P11p, \"b\", \"Spectral Density Fit $k_J=1$\"),\n", - "# (results_spectral_fit_pk[3], P11p, \"r-.\", \"Spectral Density Fit $k_J=4$\"),\n", - "# ],\n", - "# axes=axes,\n", - "# )\n", - "\n", - "# axes.set_yticks([0.6, 0.8, 1])\n", - "# axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", - "# axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", - "# axes.legend(loc=0, fontsize=20);" - ] - }, - { + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", + "\n", + "plot_result_expectations(\n", + " [\n", + " (\n", + " results_corr_fit_pk[0],\n", + " P11p,\n", + " \"y\",\n", + " \"Correlation Function Fit $k_R=k_I=1$\",\n", + " ),\n", + " (\n", + " results_corr_fit_pk[2],\n", + " P11p,\n", + " \"k\",\n", + " \"Correlation Function Fit $k_R=k_I=3$\",\n", + " ),\n", + " (results_spectral_fit_pk[0], P11p, \"b\", \"Spectral Density Fit $k_J=1$\"),\n", + " (results_spectral_fit_pk[3], P11p, \"r-.\", \"Spectral Density Fit $k_J=4$\"),\n", + " ],\n", + " axes=axes,\n", + ")\n", + "\n", + "axes.set_yticks([0.6, 0.8, 1])\n", + "axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", + "axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", + "axes.legend(loc=0, fontsize=20);" + ] + }, + { "cell_type": "markdown", "id": "63716f70", "metadata": {}, @@ -1475,7 +1618,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 34, "id": "4883e1cc", "metadata": {}, "outputs": [], @@ -1496,7 +1639,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 35, "id": "e0924e70", "metadata": {}, "outputs": [ @@ -1506,32 +1649,42 @@ "text": [ "Correlation function fit:\n", "\n", - "Result of fitting the real part of |Result of fitting the imaginary part \n", - "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", - " | \n", - " Parameters| a | b | c | Parameters| a | b | c \n", - " 1 | 3.24e-01 |-5.34e-01 |3.32e-23 | 1 |-8.92e+00 |-3.49e-01 |7.57e-04 \n", - " 2 | 2.84e+00 |-2.76e+00 |6.88e-08 | 2 | 5.44e-01 |-4.30e+00 |4.00e+00 \n", - " 3 |-1.67e+00 |-4.72e+00 |2.77e+00 | 3 |-1.34e+01 |-1.04e+00 |2.50e-02 \n", - " 4 | 2.49e-02 |-1.09e-01 |1.08e-41 | 4 |-1.34e+01 |-2.29e+00 |2.90e-01 \n", - " | \n", - "A normalized RMSE of 1.18e-06 was obtained for the the real part of |A normalized RMSE of 6.20e-07 was obtained for the the imaginary part\n", - "the correlation function. |of the correlation function. \n", - "The current fit took 1.838643 seconds. |The current fit took 33.440938 seconds. \n", + "Result of fitting the real part of |Result of fitting the imaginary part \n", + "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", + " | \n", + " Parameters| a | b | c | Parameters| a | b | c \n", + " 1 | 3.24e-01 |-5.34e-01 |3.32e-23 | 1 |-8.92e+00 |-3.49e-01 |7.57e-04 \n", + " 2 | 2.84e+00 |-2.76e+00 |6.88e-08 | 2 | 5.44e-01 |-4.30e+00 |4.00e+00 \n", + " 3 |-1.67e+00 |-4.72e+00 |2.77e+00 | 3 |-1.34e+01 |-1.04e+00 |2.50e-02 \n", + " 4 | 2.49e-02 |-1.09e-01 |1.08e-41 | 4 |-1.34e+01 |-2.29e+00 |2.90e-01 \n", + " | \n", + "A 1-R2 coefficient of 5.94e-06 was obtained for the the real part of |A 1-R2 coefficient of 9.78e-07 was obtained for the the imaginary part\n", + "the correlation function. |of the correlation function. \n", + "The current fit took 7.420486 seconds. |The current fit took 69.006773 seconds. \n", "\n", - " Total run time: 416.96s*] Elapsed 416.96s / Remaining 00:00:00:00\n" + "10.0%. Run time: 10.29s. Est. time left: 00:00:01:32\n", + "20.0%. Run time: 16.41s. Est. time left: 00:00:01:05\n", + "30.0%. Run time: 22.26s. Est. time left: 00:00:00:51\n", + "40.0%. Run time: 31.05s. Est. time left: 00:00:00:46\n", + "50.0%. Run time: 37.83s. Est. time left: 00:00:00:37\n", + "60.0%. Run time: 44.30s. Est. time left: 00:00:00:29\n", + "70.0%. Run time: 50.84s. Est. time left: 00:00:00:21\n", + "80.0%. Run time: 57.31s. Est. time left: 00:00:00:14\n", + "90.0%. Run time: 64.78s. Est. time left: 00:00:00:07\n", + "100.0%. Run time: 72.81s. Est. time left: 00:00:00:00\n", + "Total run time: 72.81s\n" ] } ], "source": [ "tlist = np.linspace(0, 30 * np.pi / Del, 5000)\n", "\n", - "Obath, fitinfo = obs.approx_by_cf_fit(tlist=tlist,Nr_max=4,Ni_max=4,maxfev=1e9,target_rsme=None)\n", + "Obath, fitinfo = obs.approximate(method=\"corr_lsq\",tlist=tlist,Nr_max=4,Ni_max=4,maxfev=1e9,target_rsme=None)\n", "print(fitinfo[\"summary\"])\n", "HEOM_ohmic_corr_fit = HEOMSolver(\n", " Hsys,\n", " (Obath,Q),\n", - " max_depth=5,\n", + " max_depth=max_depth,\n", " options=options,\n", ")\n", "results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist)" @@ -1539,7 +1692,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 36, "id": "ddbaebf2", "metadata": {}, "outputs": [ @@ -1549,33 +1702,36 @@ "text": [ "Result of fitting the spectral density with 4 terms: \n", " \n", - " Parameters| lam | gamma | w0 \n", - " 1 | 6.79e-01 | 8.67e-01 |1.22e-01\n", - " 2 | 1.67e+00 | 9.17e-01 |1.13e+00\n", - " 3 | 1.56e+00 | 9.46e-01 |2.11e+00\n", - " 4 | 1.00e+00 | 1.03e+00 |3.32e+00\n", + " Parameters| a | b | c \n", + " 1 |-4.41e+00 | 4.30e+00 |3.98e+00\n", + " 2 | 7.92e+00 | 2.30e+00 |1.00e-01\n", + " 3 | 6.01e-01 | 1.00e+00 |1.00e-01\n", + " 4 | 1.06e-02 | 3.07e-01 |1.00e-01\n", " \n", - "A normalized RMSE of 4.39e-05 was obtained for the the spectral density.\n", - "The current fit took 46.514615 seconds.\n", - " [****** 28% ] Elapsed 3.46s / Remaining 00:00:00:08" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 10.46s*] Elapsed 10.46s / Remaining 00:00:00:00\n" + "A 1-R2 coefficient of 1.38e-06 was obtained for the the spectral density.\n", + "The current fit took 22.388690 seconds.\n", + "10.0%. Run time: 2.34s. Est. time left: 00:00:00:21\n", + "20.0%. Run time: 3.41s. Est. time left: 00:00:00:13\n", + "30.1%. Run time: 4.91s. Est. time left: 00:00:00:11\n", + "40.1%. Run time: 6.21s. Est. time left: 00:00:00:09\n", + "50.1%. Run time: 7.28s. Est. time left: 00:00:00:07\n", + "60.1%. Run time: 8.28s. Est. time left: 00:00:00:05\n", + "70.1%. Run time: 9.57s. Est. time left: 00:00:00:04\n", + "80.1%. Run time: 11.22s. Est. time left: 00:00:00:02\n", + "90.2%. Run time: 12.29s. Est. time left: 00:00:00:01\n", + "100.0%. Run time: 13.71s. Est. time left: 00:00:00:00\n", + "Total run time: 13.71s\n" ] } ], "source": [ - "Obath2, fitinfo = obs.approx_by_sd_fit(wlist=w,Nmax=4,Nk=1)\n", + "Obath2, fitinfo = obs.approximate(method=\"spec_lsq\",wlist=w,Nmax=4,Nk=1)\n", "print(fitinfo[\"summary\"])\n", "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", "HEOM_ohmic_sd_fit = HEOMSolver(\n", " Hsys,\n", " (Obath2,Q),\n", - " max_depth=5,\n", + " max_depth=max_depth,\n", " options=options,\n", ")\n", "results_ohmic_sd_fit2 = HEOM_ohmic_sd_fit.run(rho0, tlist)" @@ -1594,15 +1750,13 @@ "\n", "$$f(t)=\\sum_{k=0}^{N-1} c_{k} e^{-\\nu_{k} t} =\\sum_{k=0}^{N-1} c_{k} z_{k}^{t} $$\n", "\n", - "The $z_{k}$ can be seen as the solution og the Prony Polynomial\n", - "\n", - "$$P(z)=\\prod_{k=0}^{N-1}(z-z_{k})$$\n", - "\n", - "By obtaining the roots of this polynomial one can obtain the damping rate and the frequency of each mode, the amplitude can lated be obtained by solving the least-squares Vandermonde system given by\n", + "The $z_{k}$ can be seen as the solution of the Prony Polynomial, which we write in terms of Hankel matrices as \n", "\n", - "$$ V_{N,M}(z)c = f $$\n", + "\\begin{align}\n", + " H_{N,M}=V_{N,M-1}(z) diag((c_k)_{k=1}^{M-1}) V_{M,M-1}(z)^{T}\n", + "\\end{align}\n", "\n", - "Where $M$ is the length, of the signal, and $f=f(t_{sample})$ is the signal evaluated in the sampling points,is a vector $c = (c_{1}, \\dots, c_{N})$, and $V_{N,M}(z)$ is the Vandermonde matrix given by\n", + "where $V_{N,M}(z)$ is the Vandermonde matrix given by\n", "\n", "\n", "$$V_{M,N}(z)=\\begin{pmatrix} \n", @@ -1613,6 +1767,12 @@ "z_{1}^{M} & z_{2}^{M} &\\dots & z_{N}^{M} \\\\\n", "\\end{pmatrix}$$\n", "\n", + "By obtaining the roots of this polynomial one can obtain the damping rate and the frequency of each mode, the amplitude can lated be obtained by solving the least-squares Vandermonde system given by\n", + "\n", + "$$ V_{N,M}(z)c = f $$\n", + "\n", + "Where $M$ is the length, of the signal, and $f=f(t_{sample})$ is the signal evaluated in the sampling points,is a vector $c = (c_{1}, \\dots, c_{N})$.\n", + "\n", "The main difference between the methods is the way one obtains the roots of the polynomial, typically whether this system is solved or a low rank approximation is found for the polynomial, [this article](https://academic.oup.com/imajna/article-abstract/43/2/789/6525860?redirectedFrom=fulltext) is a good reference, the QuTiP implementations are based on it, and the matlab implementations made available by the authors\n" ] }, @@ -1628,110 +1788,62 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 37, "id": "b75d4072", "metadata": {}, "outputs": [], "source": [ - "tlist2=np.linspace(0,2_000,5000)" + "tlist2=np.linspace(0,50,1000)" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 38, "id": "4e24e35b", "metadata": {}, - "outputs": [], - "source": [ - "pbath,(amp,ph)=obs.approx_by_prony(tlist2,Nr=5,Ni=5,combine=True)\n", - "pbath.T=T\n", - "# mask=abs(amp)>1\n", - "# amp=amp[mask]\n", - "# ph=ph[mask]\n", - "# print(\"done\")\n", - "# HEOM_ohmic_prony_fit = HEOMSolver(\n", - "# Hsys,\n", - "# (pbath,Q),\n", - "# max_depth=5,\n", - "# options=options,\n", - "# )\n", - "# results_ohmic_prony_fit = HEOM_ohmic_prony_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "a2faa5fb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "diff=(pbath.correlation_function(tlist2))-obs.correlation_function(tlist2)\n", - "tlist3=np.linspace(0,tlist2[np.argmax(diff)],1000)\n", - "# plt.plot(tlist3,pbath.correlation_function(tlist3),\"k--\")\n", - "# plt.plot(tlist3,Obath.correlation_function(tlist3),\"b\")\n", - "# plt.plot(tlist3,obs.correlation_function(tlist3),\"r\")\n", - "\n", - "plt.plot(abs(diff),label=\"Prony\")\n", - "plt.plot(abs(Obath.correlation_function(tlist2)-obs.correlation_function(tlist2)),label=\"CORR\")\n", - "plt.legend()\n", - "#plt.yscale(\"log\")" - ] - }, - { - "cell_type": "markdown", - "id": "af659e73", - "metadata": {}, - "source": [ - "Somehow the problems seems to be the way I construct the bath" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "b64a4d76", - "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " [*********47% ] Elapsed 35.60s / Remaining 00:00:00:40" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 106.29s*] Elapsed 106.29s / Remaining 00:00:00:00\n" + "Correlation function fit:\n", + "\n", + "Result of fitting the real part of |Result of fitting the imaginary part \n", + "the correlation function with 5 terms: |of the correlation function with 6 terms: \n", + " | \n", + " Parameters| a | b | c | d | Parameters| a | b | c | d \n", + " 1 | 6.54e-01 | 8.71e-01 | 5.77e-02 |-1.22e+00 | 1 |-1.36e+00 | 9.47e-01 | 0.00e+00 |3.21e-15 \n", + " 2 | 6.54e-01 | 8.71e-01 |-5.77e-02 |1.22e+00 | 2 | 7.21e-01 | 8.51e-01 | 1.00e-01 |-4.75e-02 \n", + " 3 | 3.74e-01 | 9.76e-01 | 0.00e+00 |2.22e-16 | 3 | 7.21e-01 | 8.51e-01 |-1.00e-01 |4.75e-02 \n", + " 4 |-8.26e-02 | 7.79e-01 |-2.17e-01 |-1.34e-02 | 4 |-3.04e-02 | 7.42e-01 | 2.60e-01 |-2.31e-02 \n", + " 5 |-8.26e-02 | 7.79e-01 | 2.17e-01 |1.34e-02 | 5 |-3.04e-02 | 7.42e-01 |-2.60e-01 |2.31e-02 \n", + " | 6 |-2.15e-02 | 9.90e-01 | 0.00e+00 |-8.53e-16 \n", + "A 1-R2 coefficient of 4.23e-04+2.56e-21j was obtained for the the real part of | \n", + "the correlation function. |A 1-R2 coefficient of 4.15e-05+6.17e-21j was obtained for the the imaginary part\n", + " |of the correlation function. \n", + "The current fit took 1.662169 seconds. |The current fit took 1.149397 seconds. \n", + "\n", + "10.0%. Run time: 5.39s. Est. time left: 00:00:00:48\n", + "20.0%. Run time: 9.61s. Est. time left: 00:00:00:38\n", + "30.1%. Run time: 14.40s. Est. time left: 00:00:00:33\n", + "40.1%. Run time: 18.00s. Est. time left: 00:00:00:26\n", + "50.1%. Run time: 22.82s. Est. time left: 00:00:00:22\n", + "60.1%. Run time: 27.01s. Est. time left: 00:00:00:17\n", + "70.1%. Run time: 30.52s. Est. time left: 00:00:00:13\n", + "80.1%. Run time: 33.39s. Est. time left: 00:00:00:08\n", + "90.2%. Run time: 36.18s. Est. time left: 00:00:00:03\n", + "100.0%. Run time: 39.02s. Est. time left: 00:00:00:00\n", + "Total run time: 39.02s\n" ] } ], "source": [ + "pbath,fitinfo=obs.approximate(\"prony\",tlist2,Nr=5,Ni=6)\n", + "print(fitinfo[\"summary\"])\n", "HEOM_ohmic_prony_fit = HEOMSolver(\n", " Hsys,\n", " (pbath,Q),\n", - " max_depth=5,\n", + " max_depth=max_depth,\n", " options=options,\n", ")\n", "results_ohmic_prony_fit = HEOM_ohmic_prony_fit.run(rho0, tlist)" @@ -1739,13 +1851,13 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 39, "id": "10e50bf0", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1768,7 +1880,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 40, "id": "7f14b9cb", "metadata": {}, "outputs": [ @@ -1776,39 +1888,59 @@ "name": "stdout", "output_type": "stream", "text": [ - " Total run time: 7.98s*] Elapsed 7.98s / Remaining 00:00:00:00\n" + "Correlation function fit:\n", + "\n", + "Result of fitting the real part of |Result of fitting the imaginary part \n", + "the correlation function with 6 terms: |of the correlation function with 6 terms: \n", + " | \n", + " Parameters| a | b | c | d | Parameters| a | b | c | d \n", + " 1 | 3.38e-01 | 1.29e-01 | 1.65e-01 |2.11e-01 | 1 |-6.52e-02 | 8.10e-02 | 1.71e-01 |1.59e-01 \n", + " 2 | 3.38e-01 | 1.29e-01 |-1.65e-01 |-2.11e-01 | 2 |-6.52e-02 | 8.10e-02 |-1.71e-01 |-1.59e-01 \n", + " 3 | 2.03e-01 | 4.20e-01 | 5.80e-02 |-6.96e+00 | 3 | 1.21e+00 | 3.36e-01 | 1.07e-01 |-2.37e+00 \n", + " 4 | 2.03e-01 | 4.20e-01 |-5.80e-02 |6.96e+00 | 4 | 1.21e+00 | 3.36e-01 |-1.07e-01 |2.37e+00 \n", + " 5 | 2.98e-01 | 8.40e-01 | 0.00e+00 |2.78e-16 | 5 |-2.02e+00 | 7.06e-01 | 0.00e+00 |-5.00e-16 \n", + " 6 | 1.38e-01 | 9.39e-01 | 0.00e+00 |-2.28e-15 | 6 |-2.68e-01 | 8.79e-01 | 0.00e+00 |-3.33e-16 \n", + " | \n", + "A 1-R2 coefficient of 8.88e-05-1.48e-20j was obtained for the the real part of |A 1-R2 coefficient of 3.67e-05-1.85e-22j was obtained for the the imaginary part\n", + "the correlation function. |of the correlation function. \n", + "The current fit took 0.611501 seconds. |The current fit took 0.601647 seconds. \n", + "\n", + "10.0%. Run time: 5.05s. Est. time left: 00:00:00:45\n", + "20.0%. Run time: 8.69s. Est. time left: 00:00:00:34\n", + "30.1%. Run time: 12.39s. Est. time left: 00:00:00:28\n", + "40.1%. Run time: 16.18s. Est. time left: 00:00:00:24\n", + "50.1%. Run time: 20.15s. Est. time left: 00:00:00:20\n", + "60.1%. Run time: 24.16s. Est. time left: 00:00:00:16\n", + "70.1%. Run time: 28.52s. Est. time left: 00:00:00:12\n", + "80.1%. Run time: 32.77s. Est. time left: 00:00:00:08\n", + "90.2%. Run time: 37.12s. Est. time left: 00:00:00:04\n", + "100.0%. Run time: 41.09s. Est. time left: 00:00:00:00\n", + "Total run time: 41.09s\n" ] } ], "source": [ - "mpbath,_=obs.approx_by_mp(tlist2,Nr=4,Ni=4)\n", - "mpbath.T=T\n", + "tlist3=np.linspace(0,120,550)\n", + "mpbath,fitinfo=obs.approximate(method=\"mp\",tlist=tlist3,Nr=6,Ni=6,separate=True)\n", + "print(fitinfo[\"summary\"])\n", "HEOM_ohmic_mp_fit = HEOMSolver(\n", " Hsys,\n", " (mpbath,Q),\n", - " max_depth=5,\n", + " max_depth=max_depth,\n", " options=options,\n", ")\n", "results_ohmic_mp_fit = HEOM_ohmic_mp_fit.run(rho0, tlist)" ] }, - { - "cell_type": "markdown", - "id": "3b334563", - "metadata": {}, - "source": [ - "The decomposition is ok, the heom solver is the one failing try with other smaller couplings, and hierarchies untill you can figure it out, the accelerating part works without trouble" - ] - }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 41, "id": "3ed89ed7", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1831,7 +1963,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 42, "id": "7708f4f1", "metadata": {}, "outputs": [ @@ -1839,17 +1971,45 @@ "name": "stdout", "output_type": "stream", "text": [ - " Total run time: 14.29s*] Elapsed 14.28s / Remaining 00:00:00:00\n" + "Correlation function fit:\n", + "\n", + "Result of fitting the real part of |Result of fitting the imaginary part \n", + "the correlation function with 5 terms: |of the correlation function with 6 terms: \n", + " | \n", + " Parameters| a | b | c | d | Parameters| a | b | c | d \n", + " 1 |-8.26e-02 | 7.79e-01 | 2.17e-01 |1.34e-02 | 1 |-3.04e-02 | 7.42e-01 | 2.60e-01 |-2.31e-02 \n", + " 2 |-8.26e-02 | 7.79e-01 |-2.17e-01 |-1.34e-02 | 2 |-3.04e-02 | 7.42e-01 |-2.60e-01 |2.31e-02 \n", + " 3 | 6.54e-01 | 8.71e-01 | 5.77e-02 |-1.22e+00 | 3 | 7.21e-01 | 8.51e-01 | 1.00e-01 |-4.75e-02 \n", + " 4 | 6.54e-01 | 8.71e-01 |-5.77e-02 |1.22e+00 | 4 | 7.21e-01 | 8.51e-01 |-1.00e-01 |4.75e-02 \n", + " 5 | 3.74e-01 | 9.76e-01 | 0.00e+00 |8.33e-17 | 5 |-1.36e+00 | 9.47e-01 | 0.00e+00 |2.53e-15 \n", + " | 6 |-2.15e-02 | 9.90e-01 | 0.00e+00 |-8.05e-16 \n", + "A 1-R2 coefficient of 4.23e-04-1.07e-20j was obtained for the the real part of | \n", + "the correlation function. |A 1-R2 coefficient of 4.15e-05-3.80e-21j was obtained for the the imaginary part\n", + " |of the correlation function. \n", + "The current fit took 1.221203 seconds. |The current fit took 1.261909 seconds. \n", + "\n", + "10.0%. Run time: 3.76s. Est. time left: 00:00:00:33\n", + "20.0%. Run time: 6.79s. Est. time left: 00:00:00:27\n", + "30.1%. Run time: 9.92s. Est. time left: 00:00:00:23\n", + "40.1%. Run time: 12.89s. Est. time left: 00:00:00:19\n", + "50.1%. Run time: 15.78s. Est. time left: 00:00:00:15\n", + "60.1%. Run time: 18.64s. Est. time left: 00:00:00:12\n", + "70.1%. Run time: 21.55s. Est. time left: 00:00:00:09\n", + "80.1%. Run time: 24.40s. Est. time left: 00:00:00:06\n", + "90.2%. Run time: 27.31s. Est. time left: 00:00:00:02\n", + "100.0%. Run time: 30.19s. Est. time left: 00:00:00:00\n", + "Total run time: 30.19s\n" ] } ], "source": [ - "esbath,_=obs.approx_by_esprit(tlist2,Nr=5,Ni=4)\n", - "esbath.T=T\n", + "\n", + "esbath,fitinfo=obs.approximate(\"esprit\",tlist2,Nr=5,Ni=6)\n", + "print(fitinfo[\"summary\"])\n", "HEOM_ohmic_es_fit = HEOMSolver(\n", " Hsys,\n", " (esbath,Q),\n", - " max_depth=5,\n", + " max_depth=max_depth,\n", " options=options,\n", ")\n", "results_ohmic_es_fit = HEOM_ohmic_es_fit.run(rho0, tlist)" @@ -1857,53 +2017,13 @@ }, { "cell_type": "code", - "execution_count": 49, - "id": "ad89de4e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "diff=(esbath.correlation_function(tlist2))-obs.correlation_function(tlist2)\n", - "tlist3=np.linspace(0,tlist2[np.argmax(diff)],1000)\n", - "# plt.plot(tlist3,pbath.correlation_function(tlist3),\"k--\")\n", - "# plt.plot(tlist3,Obath.correlation_function(tlist3),\"b\")\n", - "# plt.plot(tlist3,obs.correlation_function(tlist3),\"r\")\n", - "\n", - "plt.plot(abs(diff),label=\"Prony\")\n", - "plt.plot(abs(Obath.correlation_function(tlist2)-obs.correlation_function(tlist2)),label=\"CORR\")\n", - "plt.legend()\n", - "#plt.yscale(\"log\")" - ] - }, - { - "cell_type": "code", - "execution_count": 50, + "execution_count": 43, "id": "0d282401", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1926,7 +2046,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 44, "id": "5a685a80", "metadata": {}, "outputs": [ @@ -1934,48 +2054,79 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/mcditoos/qutip_gsoc_app/qutip/utilities.py:54: RuntimeWarning: overflow encountered in exp\n", + "/home/mcditoos/qutip_gsoc_app/qutip/utilities.py:55: RuntimeWarning: overflow encountered in exp\n", " result[non_zero] = 1 / (np.exp(w[non_zero] / w_th) - 1)\n" ] }, { - "ename": "TypeError", - "evalue": "can't multiply sequence by non-int of type 'complex'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[51], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m aaabath\u001b[38;5;241m=\u001b[39m\u001b[43mobs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapprox_by_aaa\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlogspace\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2500\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlogspace\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2500\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43mN_max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m12\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mtol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1e-15\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/qutip_gsoc_app/qutip/core/environment.py:803\u001b[0m, in \u001b[0;36mBosonicEnvironment.approx_by_aaa\u001b[0;34m(self, wlist, tol, N_max, combine, tag)\u001b[0m\n\u001b[1;32m 799\u001b[0m vkAR\u001b[38;5;241m.\u001b[39mextend([\u001b[38;5;241m-\u001b[39mb \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39mj \u001b[38;5;241m*\u001b[39m c, \u001b[38;5;241m-\u001b[39mb \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39mj \u001b[38;5;241m*\u001b[39m c])\n\u001b[1;32m 800\u001b[0m ckAI\u001b[38;5;241m.\u001b[39mextend([\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39mj \u001b[38;5;241m*\u001b[39m (a \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39mj \u001b[38;5;241m*\u001b[39m d) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39mj \u001b[38;5;241m*\u001b[39m (a \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39mj \u001b[38;5;241m*\u001b[39m d) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m])\n\u001b[1;32m 802\u001b[0m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;241m=\u001b[39m ExponentialBosonicEnvironment(\n\u001b[0;32m--> 803\u001b[0m ck_real\u001b[38;5;241m=\u001b[39mckAR, vk_real\u001b[38;5;241m=\u001b[39mvkAR \u001b[38;5;241m-\u001b[39m \u001b[38;5;241;43m1\u001b[39;49m\u001b[43mj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mvkAI\u001b[49m, ck_imag\u001b[38;5;241m=\u001b[39mckAI,\n\u001b[1;32m 804\u001b[0m vk_imag\u001b[38;5;241m=\u001b[39mvkAR \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39mj \u001b[38;5;241m*\u001b[39m vkAI, T\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mT, combine\u001b[38;5;241m=\u001b[39mcombine, tag\u001b[38;5;241m=\u001b[39mtag)\n\u001b[1;32m 805\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\n", - "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'complex'" + "name": "stdout", + "output_type": "stream", + "text": [ + "Result of fitting the spectral density with 6 terms: \n", + " \n", + " Parameters| a | b | c | d \n", + " 1 | 2.73e-01 | 1.16e+00 | 2.80e+00 |2.39e+00\n", + " 2 | 5.16e-01 |-6.60e-01 | 1.17e+00 |6.45e-01\n", + " 3 | 6.73e-01 |-3.62e-01 | 6.98e-01 |1.53e-02\n", + " 4 | 3.79e-02 |-1.24e-02 | 1.57e-01 |-1.63e-02\n", + " 5 | 1.42e-03 |-5.98e-04 | 2.56e-02 |-5.36e-03\n", + " 6 | 9.23e-06 | 3.15e-06 | 1.54e-03 |1.88e-04\n", + " \n", + "A 1-R2 coefficient of 1.48e-05 was obtained for the the spectral density.\n", + "The current fit took 5.715767 seconds.\n" ] } ], "source": [ - "aaabath=obs.approx_by_aaa(np.concatenate((-np.logspace(3,-2,2500),np.logspace(-2,3,2500))),N_max=12,tol=1e-15)" + "aaabath,fitinfo=obs.approximate(\"aaa\",np.concatenate((-np.logspace(3,-8,3500),np.logspace(-8,3,3500))),N_max=8,tol=1e-15)\n", + "print(fitinfo[\"summary\"])" ] }, { "cell_type": "code", - "execution_count": null, - "id": "44f9f518", + "execution_count": 45, + "id": "787b1ae6", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "10.0%. Run time: 3.57s. Est. time left: 00:00:00:32\n", + "20.0%. Run time: 5.72s. Est. time left: 00:00:00:22\n", + "30.1%. Run time: 7.92s. Est. time left: 00:00:00:18\n", + "40.1%. Run time: 10.02s. Est. time left: 00:00:00:14\n", + "50.1%. Run time: 11.74s. Est. time left: 00:00:00:11\n", + "60.1%. Run time: 13.61s. Est. time left: 00:00:00:09\n", + "70.1%. Run time: 15.83s. Est. time left: 00:00:00:06\n", + "80.1%. Run time: 17.94s. Est. time left: 00:00:00:04\n", + "90.2%. Run time: 19.69s. Est. time left: 00:00:00:02\n", + "100.0%. Run time: 21.50s. Est. time left: 00:00:00:00\n", + "Total run time: 21.50s\n" + ] + } + ], + "source": [ + "HEOM_ohmic_aaa_fit = HEOMSolver(\n", + " Hsys,\n", + " (aaabath,Q),\n", + " max_depth=max_depth,\n", + " options=options,\n", + ")\n", + "results_ohmic_aaa_fit = HEOM_ohmic_aaa_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "80f55ad6", + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -1983,67 +2134,83 @@ } ], "source": [ - "tlist3=np.linspace(-250,250,1000)\n", - "\n", - "diff=(pbath.correlation_function(tlist3))-obs.correlation_function(tlist3)\n", - "diff2=(aaabath.correlation_function(tlist3))-obs.correlation_function(tlist3)\n", - "\n", - "# plt.plot(tlist3,pbath.correlation_function(tlist3),\"k--\")\n", - "# plt.plot(tlist3,Obath.correlation_function(tlist3),\"b\")\n", - "# plt.plot(tlist3,obs.correlation_function(tlist3),\"r\")\n", - "\n", - "\n", - "\n", - "plt.plot(diff.real,label=\"Prony\")\n", - "plt.plot(diff2.real,label=\"AA\")\n", - "\n", - "#plt.plot(abs(Obath.correlation_function(tlist3)-obs.correlation_function(tlist3)),label=\"CORR\")\n", - "plt.legend()" + "gen_plots(aaabath, w, J, t, C, w2, S)" + ] + }, + { + "cell_type": "markdown", + "id": "220c8552", + "metadata": {}, + "source": [ + "ESPIRA I" ] }, { "cell_type": "code", - "execution_count": null, - "id": "787b1ae6", + "execution_count": 102, + "id": "e3d9800d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " Total run time: 1565.79s] Elapsed 1565.79s / Remaining 00:00:00:00\n" + "Correlation function fit:\n", + "\n", + "Result of fitting the real part of |Result of fitting the imaginary part \n", + "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", + " | \n", + " Parameters| a | b | c | d | Parameters| a | b | c | d \n", + " 1 | 2.18e-01 | 9.37e-01 |-2.85e-02 |1.58e+00 | 1 | 7.39e-01 | 9.28e-01 | 4.54e-02 |-5.14e-01 \n", + " 2 | 8.01e-01 | 9.44e-01 | 2.50e-02 |-1.54e+00 | 2 | 9.27e-01 | 9.30e-01 |-4.16e-02 |4.43e-01 \n", + " 3 | 3.92e-01 | 9.87e-01 | 9.25e-04 |-4.15e-02 | 3 |-1.60e+00 | 9.75e-01 | 8.73e-04 |5.98e-02 \n", + " 4 | 4.45e-02 | 9.97e-01 | 1.24e-04 |-4.58e-03 | 4 |-7.70e-02 | 9.93e-01 | 5.62e-04 |1.68e-02 \n", + " | \n", + "A 1-R2 coefficient of 1.48e-04-5.04e-06j was obtained for the the real part of |A 1-R2 coefficient of 8.87e-06-9.73e-06j was obtained for the the imaginary part\n", + "the correlation function. |of the correlation function. \n", + "The current fit took 1.843673 seconds. |The current fit took 1.585738 seconds. \n", + "\n" ] } ], "source": [ - "HEOM_ohmic_aaa_fit = HEOMSolver(\n", - " Hsys,\n", - " (aaabath,Q),\n", - " max_depth=5,\n", - " options=options,\n", - ")\n", - "results_ohmic_aaa_fit = HEOM_ohmic_aaa_fit.run(rho0, tlist)" + "tlist4=np.linspace(0,20,1000)\n", + "espibath,fitinfo=obs._approx_by_prony(\"espira-I\",tlist4,Nr=4,Ni=4)\n", + "print(fitinfo[\"summary\"])" ] }, { "cell_type": "code", - "execution_count": null, - "id": "80f55ad6", + "execution_count": 103, + "id": "d5e92a0b", "metadata": {}, "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "10.0%. Run time: 0.65s. Est. time left: 00:00:00:05\n", + "20.0%. Run time: 1.28s. Est. time left: 00:00:00:05\n", + "30.1%. Run time: 1.81s. Est. time left: 00:00:00:04\n", + "40.1%. Run time: 2.29s. Est. time left: 00:00:00:03\n", + "50.1%. Run time: 2.78s. Est. time left: 00:00:00:02\n", + "60.1%. Run time: 3.28s. Est. time left: 00:00:00:02\n", + "70.1%. Run time: 3.81s. Est. time left: 00:00:00:01\n", + "80.1%. Run time: 4.32s. Est. time left: 00:00:00:01\n", + "90.2%. Run time: 4.79s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 5.23s. Est. time left: 00:00:00:00\n", + "Total run time: 5.23s\n" + ] } ], "source": [ - "gen_plots(aaabath, w, J, t, C, w2, S)" + "HEOM_ohmic_espira_fit = HEOMSolver(\n", + " Hsys,\n", + " (espibath,Q),\n", + " max_depth=max_depth,\n", + " options=options,\n", + ")\n", + "results_ohmic_espira_fit = HEOM_ohmic_espira_fit.run(rho0, tlist)" ] }, { @@ -2056,13 +2223,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 104, "id": "5ba2889a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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", 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" ] @@ -2076,44 +2243,31 @@ "\n", "plot_result_expectations(\n", " [\n", - " #(\n", - " # results_corr_fit_pk[2],\n", - " # P11p,\n", - " # \"b\",\n", - " # \"Correlation Function Fit $k_R=k_I=3$\",\n", - " # ),\n", - " #(results_spectral_fit_pk[3], P11p, \"r-.\", \"Spectral Density Fit $k_J=4$\"),\n", + " (\n", + " results_corr_fit_pk[2],\n", + " P11p,\n", + " \"b\",\n", + " \"Correlation Function Fit $k_R=k_I=3$\",\n", + " ),\n", + " (results_spectral_fit_pk[3], P11p, \"r-.\", \"Spectral Density Fit $k_J=4$\"),\n", " (results_ohmic_corr_fit, P11p, \"r\", \"Correlation Fit Ohmic Bath\"),\n", - " #(results_ohmic_sd_fit, P11p, \"g\", \"Spectral Density Fit Ohmic Bath\"),\n", " (results_ohmic_sd_fit2, P11p, \"g--\", \"Spectral Density Fit Ohmic Bath\"),\n", - " #(results_ohmic_prony_fit, P11p, \"g\", \" Prony Fit\"),\n", " (results_ohmic_prony_fit, P11p, \"k\", \" Prony Fit\"),\n", - "\n", " (results_ohmic_mp_fit, P11p, \"r\", \"Matrix Pencil Fit\"),\n", " (results_ohmic_es_fit, P11p, \"b-.\", \"ESPRIT Fit\"),\n", " (results_ohmic_aaa_fit, P11p, \"r-.\", \"Matrix AAA Fit\"),\n", + " (results_ohmic_espira_fit, P11p, \"k\", \"ESPIRA Fit\"),\n", "\n", "\n", " ],\n", " axes=axes,\n", ")\n", - "axes.set_yticks([0.6, 0.8, 1])\n", "axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", "axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", "axes.legend(loc=0, fontsize=20);\n", - "#axes.set_xlim(0,35)\n", - "#axes.set_ylim(0.9,1)\n", - "#axes.set_yscale(\"log\")" + "axes.set_yscale(\"log\")" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "bae93823", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "d0fc9218", @@ -2124,7 +2278,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 105, "id": "e1eb99ec", "metadata": {}, "outputs": [ @@ -2136,13 +2290,13 @@ "QuTiP: Quantum Toolbox in Python\n", "================================\n", "Copyright (c) QuTiP team 2011 and later.\n", - "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", + "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", "Original developers: R. J. Johansson & P. D. Nation.\n", "Previous lead developers: Chris Granade & A. Grimsmo.\n", "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", "\n", - "QuTiP Version: 5.1.0.dev0+7941773\n", + "QuTiP Version: 5.2.0.dev0+daa7d68\n", "Numpy Version: 1.26.4\n", "Scipy Version: 1.14.1\n", "Cython Version: 3.0.9\n", @@ -2153,6 +2307,12 @@ "INTEL MKL Ext: None\n", "Platform Info: Linux (x86_64)\n", "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", + "\n", + "Installed QuTiP family packages\n", + "-------------------------------\n", + "\n", + "No QuTiP family packages installed.\n", + "\n", "================================================================================\n", "Please cite QuTiP in your publication.\n", "================================================================================\n", @@ -2176,121 +2336,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 106, "id": "fa50ddbb", "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'results_spectral_fit_pk' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[59], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m np\u001b[38;5;241m.\u001b[39mallclose(\u001b[43mresults_spectral_fit_pk\u001b[49m[\u001b[38;5;241m3\u001b[39m]\u001b[38;5;241m.\u001b[39mstates[\u001b[38;5;241m5\u001b[39m]\u001b[38;5;241m.\u001b[39mfull(),results_corr_fit_pk[\u001b[38;5;241m2\u001b[39m]\u001b[38;5;241m.\u001b[39mstates[\u001b[38;5;241m5\u001b[39m]\u001b[38;5;241m.\u001b[39mfull(),atol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-3\u001b[39m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'results_spectral_fit_pk' is not defined" - ] - } - ], + "outputs": [], "source": [ - "assert np.allclose(results_spectral_fit_pk[3].states[5].full(),results_corr_fit_pk[2].states[5].full(),atol=1e-3)" + "assert np.allclose(\n", + " expect(P11p, results_spectral_fit_pk[2].states),\n", + " expect(P11p, results_spectral_fit_pk[3].states),\n", + " rtol=1e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(P11p, results_ohmic_aaa_fit.states),\n", + " expect(P11p, results_spectral_fit_pk[3].states),\n", + " rtol=1e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(P11p, results_ohmic_mp_fit.states),\n", + " expect(P11p, results_spectral_fit_pk[3].states),\n", + " rtol=1e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(P11p, results_ohmic_prony_fit.states),\n", + " expect(P11p, results_spectral_fit_pk[3].states),\n", + " rtol=1e-2,\n", + ")\n", + "\n", + "assert np.allclose(\n", + " expect(P11p, results_ohmic_es_fit.states),\n", + " expect(P11p, results_spectral_fit_pk[3].states),\n", + " rtol=1e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(P11p, results_ohmic_espira_fit.states),\n", + " expect(P11p, results_spectral_fit_pk[3].states),\n", + " rtol=1e-2,\n", + ")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a7fb31c", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "414ba293", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "80d35a6b", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "20dd8b39", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ed975955", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b6b493d", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9184bc82", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d61f4c20", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7b7f2f42", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cebe18a4", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2a006120", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "12b235a3", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 01656c9203c913bf45bb483058e51c97fb9003c8 Mon Sep 17 00:00:00 2001 From: mcditooss Date: Wed, 19 Feb 2025 17:44:15 +0100 Subject: [PATCH 18/44] new methods --- tutorials-v5/heom/fitting.ipynb | 2326 ----------------- .../heom/heom-1a-spin-bath-model-basic.ipynb | 457 +++- ...spin-bath-model-very-strong-coupling.ipynb | 126 +- ...eom-1d-spin-bath-model-ohmic-fitting.ipynb | 646 ++--- 4 files changed, 804 insertions(+), 2751 deletions(-) delete mode 100644 tutorials-v5/heom/fitting.ipynb diff --git a/tutorials-v5/heom/fitting.ipynb b/tutorials-v5/heom/fitting.ipynb deleted file mode 100644 index 1783c20a..00000000 --- a/tutorials-v5/heom/fitting.ipynb +++ /dev/null @@ -1,2326 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ebddecba", - "metadata": {}, - "source": [ - "# HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions" - ] - }, - { - "cell_type": "markdown", - "id": "2142c296", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded \n", - "in a set of auxiliary density matrices.\n", - "\n", - "In this example we show the evolution of a single two-level system in contact with a single bosonic environment.\n", - "\n", - "The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", - "\n", - "The bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", - "\n", - "In the example below we show how to model an Ohmic environment with exponential cut-off in three ways:\n", - "\n", - "* First we fit the spectral density with a set of underdamped brownian oscillator functions.\n", - "* Second, we evaluate the correlation functions, and fit those with a certain choice of exponential functions.\n", - "* Third, we use the available OhmicBath class \n", - "\n", - "In each case we will use the fit parameters to determine the correlation function expansion co-efficients needed to construct a description of the bath (i.e. a `BosonicBath` object) to supply to the `HEOMSolver` so that we can solve for the system dynamics." - ] - }, - { - "cell_type": "markdown", - "id": "d3ef97c3", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "ed47f849", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "import qutip\n", - "from qutip import (\n", - " basis,\n", - " expect,\n", - " sigmax,\n", - " sigmaz,\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver\n", - ")\n", - "from qutip.core.environment import BosonicEnvironment,_sd_fit_model,OhmicEnvironment\n", - "\n", - "# Import mpmath functions for evaluation of gamma and zeta\n", - "# functions in the expression for the correlation:\n", - "\n", - "from mpmath import mp\n", - "\n", - "mp.dps = 15\n", - "mp.pretty = True\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "2eb48e5a", - "metadata": {}, - "outputs": [], - "source": [ - "# Solver options:\n", - "\n", - "options = {\n", - " \"nsteps\": 15000,\n", - " \"store_states\": True,\n", - " \"rtol\": 1e-14,\n", - " \"atol\": 1e-14,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "65a7dfbb", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "Let us set up the system Hamiltonian, bath and system measurement operators:" - ] - }, - { - "cell_type": "markdown", - "id": "1e362553", - "metadata": {}, - "source": [ - "### System Hamiltonian" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ac95be0b", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the system Hamiltonian\n", - "eps = 0 # Energy of the 2-level system.\n", - "Del = 0.2 # Tunnelling term\n", - "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()\n", - "rho0 = basis(2, 0) * basis(2, 0).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "d89e26d2", - "metadata": {}, - "source": [ - "### System measurement operators" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d79edfb4", - "metadata": {}, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresonding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresonding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "52c4fb7a", - "metadata": {}, - "source": [ - "### Analytical expressions for the Ohmic bath correlation function and spectral density" - ] - }, - { - "cell_type": "markdown", - "id": "a0a87475", - "metadata": {}, - "source": [ - "Before we begin fitting, let us examine the analytic expressions for the correlation and spectral density functions and write Python equivalents. \n", - "\n", - "The correlation function is given by (see, e.g., http://www1.itp.tu-berlin.de/brandes/public_html/publications/notes.pdf for a derivation, equation 7.59, but with a factor of $\\pi$ moved into the definition of the correlation function):\n", - "\n", - "\\begin{align}\n", - "C(t) =& \\: \\frac{1}{\\pi}\\alpha \\omega_{c}^{1 - s} \\beta^{- (s + 1)} \\: \\times \\\\\n", - " & \\: \\Gamma(s + 1) \\left[ \\zeta \\left(s + 1, \\frac{1 + \\beta \\omega_c - i \\omega_c t}{\\beta \\omega_c}\\right) + \\zeta \\left(s + 1, \\frac{1 + i \\omega_c t}{\\beta \\omega_c}\\right) \\right]\n", - "\\end{align}\n", - "\n", - "where $\\Gamma$ is the Gamma function and\n", - "\n", - "\\begin{equation}\n", - "\\zeta(z, u) \\equiv \\sum_{n=0}^{\\infty} \\frac{1}{(n + u)^z}, \\; u \\neq 0, -1, -2, \\ldots\n", - "\\end{equation}\n", - "\n", - "is the generalized Zeta function. The Ohmic case is given by $s = 1$.\n", - "\n", - "The corresponding spectral density for the Ohmic case is:\n", - "\n", - "\\begin{equation}\n", - "J(\\omega) = \\omega \\alpha e^{- \\frac{\\omega}{\\omega_c}}\n", - "\\end{equation}" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "bfb44fda", - "metadata": {}, - "outputs": [], - "source": [ - "def ohmic_correlation(t, alpha, wc, beta, s=1):\n", - " \"\"\"The Ohmic bath correlation function as a function of t\n", - " (and the bath parameters).\n", - " \"\"\"\n", - " corr = (1 / np.pi) * alpha * wc ** (1 - s)\n", - " corr *= beta ** (-(s + 1)) * mp.gamma(s + 1)\n", - " z1_u = (1 + beta * wc - 1.0j * wc * t) / (beta * wc)\n", - " z2_u = (1 + 1.0j * wc * t) / (beta * wc)\n", - " # Note: the arguments to zeta should be in as high precision as possible.\n", - " # See http://mpmath.org/doc/current/basics.html#providing-correct-input\n", - " return np.array(\n", - " [\n", - " complex(corr * (mp.zeta(s + 1, u1) + mp.zeta(s + 1, u2)))\n", - " for u1, u2 in zip(z1_u, z2_u)\n", - " ],\n", - " dtype=np.complex128,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "9e798939", - "metadata": {}, - "outputs": [], - "source": [ - "def ohmic_spectral_density(w, alpha, wc):\n", - " \"\"\"The Ohmic bath spectral density as a function of w\n", - " (and the bath parameters).\n", - " \"\"\"\n", - " return w * alpha * np.e ** (-w / wc)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "7691064b", - "metadata": {}, - "outputs": [], - "source": [ - "def ohmic_power_spectrum(w, alpha, wc, beta):\n", - " \"\"\"The Ohmic bath power spectrum as a function of w\n", - " (and the bath parameters).\n", - " It is obtained naively using the Fluctuation-Dissipation Theorem\n", - " but, this fails at w=0 where the limit should be taken properly\n", - " \"\"\"\n", - " bose = (1 / (np.e ** (w * beta) - 1)) + 1\n", - " return w * alpha * np.e ** (-abs(w) / wc) * 2*bose " - ] - }, - { - "cell_type": "markdown", - "id": "c7913528", - "metadata": {}, - "source": [ - "### Bath and HEOM parameters" - ] - }, - { - "cell_type": "markdown", - "id": "0a40fda0", - "metadata": {}, - "source": [ - "Finally, let's set the bath parameters we will work with and write down some measurement operators:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8d58b8c8", - "metadata": {}, - "outputs": [], - "source": [ - "Q = sigmaz()\n", - "alpha = 3.25\n", - "T = 0.5\n", - "wc = 1.0\n", - "s = 1" - ] - }, - { - "cell_type": "markdown", - "id": "635dcec1", - "metadata": {}, - "source": [ - "And set the cut-off for the HEOM hierarchy:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "297850af", - "metadata": {}, - "outputs": [], - "source": [ - "# HEOM parameters:\n", - "\n", - "# The max_depth defaults to 5 so that the notebook executes more\n", - "# quickly. Change it to 11 to wait longer for more accurate results.\n", - "max_depth = 5" - ] - }, - { - "cell_type": "markdown", - "id": "8827fc32", - "metadata": {}, - "source": [ - "## Building the HEOM bath by fitting the spectral density" - ] - }, - { - "cell_type": "markdown", - "id": "6e3c4370", - "metadata": {}, - "source": [ - "We begin by fitting the spectral density, using a series of $k$ underdamped harmonic oscillators case with the Meier-Tannor form (J. Chem. Phys. 111, 3365 (1999); https://doi.org/10.1063/1.479669):\n", - "\n", - "\\begin{equation}\n", - "J_{\\mathrm approx}(\\omega; a, b, c) = \\sum_{i=0}^{k-1} \\frac{2 a_i b_i w}{((w + c_i)^2 + b_i^2) ((w - c_i)^2 + b_i^2)}\n", - "\\end{equation}\n", - "\n", - "where $a, b$ and $c$ are the fit parameters and each is a vector of length $k$." - ] - }, - { - "cell_type": "markdown", - "id": "6b67cac7", - "metadata": {}, - "source": [ - "With the spectral density approximation $J_{\\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. This can be done quickly using the `SpectralFitter` class, which takes the target spectral density as an array and fits it to the series of **k** underdamped harmonic oscillators with the Meier-Tannor form" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "f6b46bc0", - "metadata": {}, - "outputs": [], - "source": [ - "w = np.linspace(0, 15, 20000)\n", - "J = ohmic_spectral_density(w, alpha, wc)" - ] - }, - { - "cell_type": "markdown", - "id": "ae05a07c", - "metadata": {}, - "source": [ - "The `BosonicEnviroment` class has special construtors that can be used to \n", - "create enviroments from arbitrary spectral densities, correlation functions, or\n", - "power spectrums. Below we show how to construct a `BosonicEnvironment` from a \n", - "user specified function or array" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "0239acf7", - "metadata": {}, - "outputs": [], - "source": [ - "# From an array\n", - "sd_env=BosonicEnvironment.from_spectral_density(J=J,wlist=w,T=T)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c388e587", - "metadata": {}, - "outputs": [], - "source": [ - "sd_env.approximate()" - ] - }, - { - "cell_type": "markdown", - "id": "33a4729e", - "metadata": {}, - "source": [ - "# Obtaining a decaying Exponential description of the environment\n", - "\n", - "In order to carry out our HEOM simulation, we need to express the correlation \n", - "function as a sum of decaying exponentials, that is we need to express it as \n", - "\n", - "$$C(\\tau)= \\sum_{k=0}^{N-1}c_{k}e^{-\\nu_{k}t}$$\n", - "\n", - "As the correlation function of the environment is tied to it's power spectrum via \n", - "a Fourier transform, such a representation of the correlation function implies a \n", - "power spectrum of the form\n", - "\n", - "$$S(\\omega)= \\sum_{k}2 Re\\left( \\frac{c_{k}}{\\nu_{k}- i \\omega}\\right)$$\n", - "\n", - "There are several ways one can obtain such a decomposition, in this tutorial we \n", - "will cover the following approaches:\n", - "\n", - "- Non-Linear Least Squares:\n", - " - On the Spectral Density (`.approx_by_sd_fit`)\n", - " - On the Correlation function (`.approx_by_cf_fit`)\n", - "- Methods based on the Prony Polynomial\n", - " - Prony on the correlation function(`.approx_by_prony`)\n", - " - The Matrix Pencil method on the correlation function (`.approx_by_mp`)\n", - " - ESPRIT on the correlation function(`.approx_by_esprit`)\n", - "- Methods based on rational Approximations\n", - " - The AAA algorithm on the Power Spectrum (`.approx_by_aaa`)\n", - " - The AAA algorith with balanced truncation (`.approx_by_aaa` with `btm=True`)\n", - " - ESPIRA\n" - ] - }, - { - "cell_type": "markdown", - "id": "bef212bc", - "metadata": {}, - "source": [ - "# Non-Linear Least Squares\n", - "## Obtaining an decaying Exponential Description via the spectral density" - ] - }, - { - "cell_type": "markdown", - "id": "ce27cb93", - "metadata": {}, - "source": [ - "Once our `BosonicEnvironment` has been constructed, we can obtain a Decaying\n", - "exponnetial representation of the environment, via fitting either the spectral\n", - "density, power spectrum or the correlation function. \n", - "\n", - "First we will show how to do it via fitting the spectral density with the \n", - "Nonlinear-Least-Squares method.\n", - "\n", - "The idea here is that we express our arbitrary spectral density as a sum of \n", - "underdamped spectral densities with different coefficients, for which a the\n", - "Matsubara decomposition is available. The number of exponents to be kept in the \n", - "Matsubara decomposition of each underdamped spectral density needs to be specified\n", - "\n", - "The output of the fit is a tuple containing an `ExponentialBosonicEnvironment`\n", - "and a dictionary that has all the relevant information about the fit performed.\n", - "The goodness of the feed is measured via the normalized root mean squared error,\n", - "by default the number of terms in the fit increased until the target accuracy \n", - "is reached or the maximum number allowed `Nmax` is reached. The default target\n", - "is a normalized root mean squared error of $5\\times 10^{-6}$, if set to None\n", - "the fit is performed only with the maximum number of exponents specified\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "7c51abc3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "qutip.core.environment._BosonicEnvironment_fromSD" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(sd_env)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8b8e557d", - "metadata": {}, - "outputs": [], - "source": [ - "sd_env.approximate()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "fe57ba64", - "metadata": {}, - "outputs": [], - "source": [ - "env=sd_env.from_spectral_density(sd_env.spectral_density)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "50fc6a2f", - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "BosonicEnvironment.approximate() missing 1 required positional argument: 'method'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43menv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapproximate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mTypeError\u001b[0m: BosonicEnvironment.approximate() missing 1 required positional argument: 'method'" - ] - } - ], - "source": [ - "env.approximate()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "81adee22", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Unsupported method: pronys. The available methods are: \n - Correlation function NLSQ Fitting (corr_lsq)\n- Spectral Density NLSQ Fitting (spec_lsq) \n- Correlation function Prony Fitting (prony) \n- Correlation function Matrix Pencil Fitting (mp) \n- Correlation function ESPRIT Fitting (esprit)\n- Power spectrum AAA fitting (aaa) \n", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m bath, fitinfo \u001b[38;5;241m=\u001b[39m \u001b[43msd_env\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapproximate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpronys\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m#approx_by_sd_fit(w,Nmax=6)\u001b[39;00m\n", - "File \u001b[0;32m~/qutip_gsoc_app/qutip/core/environment.py:1490\u001b[0m, in \u001b[0;36m_BosonicEnvironment_fromSD.approximate\u001b[0;34m(self, method, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1480\u001b[0m dispatch \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 1481\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcorr_lsq\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_approx_by_cf_fit,\n\u001b[1;32m 1482\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspec_lsq\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_approx_by_sd_fit,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1486\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maaa\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_approx_by_aaa,\n\u001b[1;32m 1487\u001b[0m }\n\u001b[1;32m 1489\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m dispatch:\n\u001b[0;32m-> 1490\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnsupported method: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmethod\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. The available\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1491\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m methods are: \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1492\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m- Correlation function NLSQ Fitting (corr_lsq)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1493\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m- Spectral Density NLSQ Fitting (spec_lsq) \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1494\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m- Correlation function Prony Fitting (prony) \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1495\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m- Correlation function Matrix Pencil Fitting\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m (mp) \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m- Correlation function ESPRIT Fitting (esprit)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m- Power spectrum AAA fitting (aaa) \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dispatch[method](\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "\u001b[0;31mValueError\u001b[0m: Unsupported method: pronys. The available methods are: \n - Correlation function NLSQ Fitting (corr_lsq)\n- Spectral Density NLSQ Fitting (spec_lsq) \n- Correlation function Prony Fitting (prony) \n- Correlation function Matrix Pencil Fitting (mp) \n- Correlation function ESPRIT Fitting (esprit)\n- Power spectrum AAA fitting (aaa) \n" - ] - } - ], - "source": [ - "bath, fitinfo = sd_env.approxima)\n", - "#approx_by_sd_fit(w,Nmax=6)" - ] - }, - { - "cell_type": "markdown", - "id": "2f5bc5a5", - "metadata": {}, - "source": [ - "To obtain an overview of the results of the fit we may take a look at the summary from the ``fitinfo``" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "71a7c82a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result of fitting the spectral density with 4 terms: \n", - " \n", - " Parameters| lam | gamma | w0 \n", - " 1 |-4.44e+00 | 4.31e+00 |3.96e+00\n", - " 2 | 6.07e-01 | 1.01e+00 |1.00e-01\n", - " 3 | 7.93e+00 | 2.30e+00 |1.00e-01\n", - " 4 | 1.07e-02 | 3.09e-01 |1.00e-01\n", - " \n", - "A normalized RMSE of 2.64e-06 was obtained for the the spectral density.\n", - "The current fit took 23.571020 seconds.\n" - ] - } - ], - "source": [ - "print(fitinfo[\"summary\"])" - ] - }, - { - "cell_type": "markdown", - "id": "8edcc35e", - "metadata": {}, - "source": [ - "We may see how the number of exponents chosen affects the fit since the approximated functions are available:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "d8587f0d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5))\n", - "\n", - "ax1.plot(w, J, label=\"Original spectral density\")\n", - "ax1.plot(w, bath.spectral_density(w), \"--\",label=\"Effective fitted SD\")\n", - "ax1.set_xlabel(r'$\\omega$')\n", - "ax1.set_ylabel(r'$J$')\n", - "ax1.legend()\n", - "\n", - "ax2.plot(w, np.abs(J - bath.spectral_density(w)), label=\"Error\")\n", - "ax2.set_xlabel(r'$\\omega$')\n", - "ax2.set_ylabel(r'$|J-J_{approx}|$')\n", - "ax2.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "89164ff6", - "metadata": {}, - "source": [ - "Here we see a surprisingly large discrepancy in our approximated or effective spectral density. This happens because we are not using enough exponentials from each of the underdamped modes to have an appropiate fit. All modes have the same number of exponents, when not specified it defaults to $1$ which is not enough to model a bath with the temperature considered, let us repeat this with a higher number of exponents." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "bd7aec4a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "bath, fitinfo = sd_env.approx_by_sd_fit(w,Nmax=6,Nk=3)\n", - "\n", - "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5))\n", - "\n", - "ax1.plot(w, J, label=\"Original spectral density\")\n", - "ax1.plot(w, bath.spectral_density(w), \"--\",label=\"Effective fitted SD\")\n", - "ax1.set_xlabel(r'$\\omega$')\n", - "ax1.set_ylabel(r'$J$')\n", - "ax1.legend()\n", - "\n", - "ax2.plot(w, np.abs(J - bath.spectral_density(w)), label=\"Error\")\n", - "ax2.set_xlabel(r'$\\omega$')\n", - "ax2.set_ylabel(r'$|J-J_{approx}|$')\n", - "ax2.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "0b6f9c12", - "metadata": {}, - "source": [ - "Since the number of exponents increases simulation time one should go with the least amount of exponents that correctly describe the bath properties (Power spectrum, Spectral density and the correlation function)." - ] - }, - { - "cell_type": "markdown", - "id": "65cf94f6", - "metadata": {}, - "source": [ - "Let's take a closer look at our last fit by plotting the contribution of each term of the fit:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "882c64e5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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sDGq1Wno9ExIS0LhxYzlDtjsmi0Ry6dRJHI66aROQmAisWAE8/bTcUREREZENBQUFmdRzc3Mt2q78WoG27Lkrz/hYll7/Z2k7Oc2aNQsajQaAOHT34MGDVc4ga+l75Uo4GyqRnAyT3ADitYsu+s0dERERifz9/aE2mqfg2rVrFm135coVk3pVS1dYW2xsrFS2dGZRa81Aakvbt2+XytOmTat2qZGbN2/aOiSHw2SRXMsPP4jLUXz/PZCRIXc01evfH7jrLrF84oR4nSURERG5tM6dO0tl45lRq2K8zmFwcDAaNWpk7bAq1a1bN6m8d+9ei67v27Nnjw0jsg7jiXuMf8bKHDx40JbhOCQmi+Ra1qwBfv4ZeOklceIYR6dQAP/8Z1n9yy/li4WIiIjsom/fvlJ51apVFk2WsmzZMql89913Q6FQ2CS2ijz44INSOTU1tdoF6YuLi/HDDz/YOKq6KykpkcrVvZ56vR5Lly61dUgOh8kiuQ69Hti3TyzXr2/5+k5yGzECiIsTyykpQDXTOxMREZFzGzt2rFROTU3FrFmzqmy/atUqkx7IF1980VahVahdu3bo2bOnVJ86dSrS09MrbT99+nQkJCTYIbK6iYyMlMoHDhyosu3XX3+Nq1ev2jokh8NkkVzHuXPi+koA0K8fYOWZwGxGpQJmzwa2bRMXPjZaF4iIiIhcT8uWLfH4449L9XfffRerV6+usO2hQ4fwwgsvSPWOHTvioYcesnmM5X311VdS79u1a9dwzz334M8//zRpk5mZiddeew2fffaZxUuCyKl///5S+cMPP0RyJWt0/vjjj3j77bftFZZD4Wyo5DqMx/z37i1fHLXxyCNyR0BERER29J///Af79u1DamoqtFotRowYgcceewxPPPEEGjZsiPT0dGzatAk//vgjtFotAMDHxwc//fQTPDw87B5v7969MX36dMyYMQMAcO7cOfTu3RuNGzdGXFwccnNzcebMGWlI7eLFizFs2DBp+/JLfziCV199FT/99BMEQUBSUhI6d+6Mf/zjH+jVqxc8PT1x5coV/PLLL9i5cycA4KWXXsKiRYtkjtq+mCyS6zBOFnv0kC8OIiIiomqEh4djz549GDRoEJJK15Vcs2YN1qxZU2H7gIAArF+/Hh06dLBnmCamT58OlUqFDz74QLre79q1ayYzuqrVasyfPx+DBg0y2bb8kiGOoGvXrvjoo4/w3nvvAQDS0tIwffr0CtuOHDkS77zzjtsli04yTo/IAoZk0cMD6NpV3ljq6sQJoLBQ7iiIiIjIhlq1aoVTp05h8uTJ8PPzq7CNp6cnnnrqKZw9exb33HOPfQOswLvvvosTJ05g0qRJaN68OXx9fREUFIT27dvjjTfewJkzZzB69Gjcvn1b2katVsPXQS+zeffdd/H9998jPDy8wucjIiIwa9Ys/Pbbb3adVMhRKATBfRZ2S0tLw9GjR/HXX39J97eMZsxcsmQJxowZY/M4rl69ih9++AEbN27EjRs3kJeXh6ioKHTo0AHPPPMMHn30UahUdev0zcnJQVBQELKzsxEYGGilyB1YXh4QFCROctOpk5hsOaNDh8TZUQ8cEJf/MLpGgYiI3JtGo8G1a9fQuHFj+Pj4yB0OWZlGo8HevXtx9epV3LlzB4GBgYiNjcU999zjlJ/lVq1aJV2X2bNnT7PrGx2NRqPBvn37cO7cORQWFiI8PBzNmjXD3XffLcuwX+O4rHHe1zY3cIthqLdu3ULPnj1x/fp1uUPB7Nmz8dZbb6GoqMjk8atXr+Lq1atYu3YtevbsiWXLlqFJkyYyRemEjh4VE0XAuYegKpVioggAc+YAY8eKy2sQERGRS/Px8cH9998vdxhWs3jxYqncq1cvGSOxjI+PDwYNGmQ2fNbducUwVI1G4xCJ4kcffYTXX39dShSVSiXatWuHfv36mUzde+jQIfTv3x8pKSlyhep8XOV6xe7dy+I/eRLYv1/WcIiIiIgMLB2Q+NNPP2HTpk1S3R4j98g23CJZNBYWFoYhQ4bgvffew9q1a+123K1bt5pcMNurVy9cuHABZ86cwR9//IGkpCT8+uuv8Pf3BwAkJSXhiSeesFt8Tu/BB4HPPgMeewzo00fuaOpm8uSy8rffyhcHERERkZEPP/wQ48aNw549e6QZWo0lJSVhypQpJsnhI488IuukPFQ3bjEMNSQkBCtWrEC3bt0QZ1j83I4EQcBbb70lfRvTsmVL7Nixw+RCX6VSiVGjRqF+/fpS9/eBAwewZs0aPPbYY3aP2em0by/eXMHjjwNTpwK3bgFr1gA3bgCxsXJHRURERG6usLAQixYtwqJFi+Dj44OWLVuifv360Gq1SE5OxpUrV0zax8XFYcGCBTJFS9bgFj2LgYGBePzxx2VJFAFg8+bNOHXqlFSfPXt2pTNCDRw4EKNGjZLqn332mc3jIwfj5QW8/LJY1umAefPkjYeIiIgIYueGgUajwalTp7Br1y7s3bvXLFG89957cejQITRo0MDeYZIVuUWyKLfVq1dL5caNG1d78fKECROk8pEjR6S1d8iNTJgAeHqK5e++4zIaREREJLsPPvgAmzdvxv/93/+hb9++iIqKgo+PD1QqFUJDQ9GhQwdMnDgRO3fuxK5duxARESF3yFRHbjEMVW4bN26UyoMHD652jZa+ffvCz88P+fn50vbGCSSVc+oUoNUC7doB3t5yR2MdERHAqFHA0qVARgbwyy9cRoOIiIhk5enpiSFDhmDIkCFyh0J2wp5FG7t9+7bJWo6WTB2sUqnQrVs3qX769GmbxOYyPvkEuOsuwN8fuHxZ7misp/xEN+6zJCoREREROQAmizZ24cIFk3rTpk0t2s64Xfl9UDknToj3KhXQqJGsoVhVt25A797AQw+JCTERERERkR1xGKqNJSQkmNRjLZzV0rhd+X2Qkdzcst7E9u3FhNGV7NlTdu0iEREREZEdudgna8eTm5trUg8KCrJou8DAwEr3UZGioiIUFRVJ9ZycHAsjdHKnT5cNz+zUSdZQbIKJIhERERHJhMNQbSwvL8+k7uPjY9F2arW60n1U5NNPP0VQUJB0i4mJqVmgzurkybJy586yhUFERERE5GqYLNqYVqs1qassHCZp3K6kpKTa9v/617+QnZ0t3RITE2sWqLMyThZdsWfRQKsFVq8GRo8G9Hq5oyEiIiIiN8BhqDbm6+trUtdoNGaPVUSj0UhlPz+/att7e3vD21WWjagJw+Q2CgXQoYO8sdjS888Dy5eL5aeeAh54QN54iIiIiMjlsWfRxvz9/U3qhRYurl5QUFDpPqiUTgecOyeWmzcHLEiqndYTT5SV582TLw4iIiIichtMFm0sNDTUpJ6SkmLRdsZrM9avX9+qMbmMa9cAQw9s27byxmJrDz0EREeL5Y0bgRs35I2HiIiIiFwek0Uba9mypUn9hoUf8o2vOWzVqpVVY3IZt28DkZFi2dWTRZUKGDdOLOv1wMKF8sZDRERERC6PyaKNNW/e3GSympPGE7JU4YThWjwArVu3tnZYrqF3b+DmTSAjA3jtNbmjsb2XXgI8PMTyokVAcbG88RARERGRS2OyaGNeXl7o0aOHVN+/f3+129y6dQuXDQvNA+jXr59NYnMZISFAueG+LikqCnj0UbGcmgqsXStnNERERETk4pgs2sEjjzwilXfs2IHU1NQq2y9btkwq16tXj8kilZk4saw8f758cRARERGRy2OyaAdPPfWUtKxFSUkJPv/880rb5uXl4dtvv5XqzzzzDDw9PW0eIzmJ++4DWrQQy7t3AxcvyhsPEREREbksJou1lJCQAIVCId1mzJhRadvo6GhMmDBBqs+ePRurVq0ya1dSUoKxY8dKk+Co1Wq88847Vo/dJVy9Ctx9NzBhArBpk9zR2I9CAbz8cll961b5YiEiIiIil+Y2yeK4cePg4+Njdqtpm9qaMWMGmjdvDgDQ6XQYOXIknnvuOaxatQq7d+/G/Pnzcdddd2HlypXSNl988QWioqKscnyXc/o0cOCAOCvo4cNyR2NfY8YAU6cCFy64x8Q+RERERCQLVfVNXENJSQmKioqqbKPVaqHVam1y/ODgYGzYsAEDBw5EYmIi9Ho9li5diqVLl1bY/s0338SkSZNsEotLOHeurNymjXxxyCE4GPjyS7mjICIiIiIX5zY9i46gRYsWOH36NF588UWo1eoK27Ru3Rrr1q3DzJkz7Rydkzl/vqzs6mssEhERkUsbMGCAdGnT4sWL5Q6HSKIQBEGQOwh3lJubi127diExMRH5+fmIjIxE+/bt0blzZ6vsPycnB0FBQcjOzkZgYKBV9ulQOncGTp4UF6vPzwe8vOSOSD56PaDRAL6+ckdCREQ2pNFocO3aNTRu3Nhql8mQYwgNDUVGRgYA4Pjx41b7POjI0tLScPToUfz111/S/a1bt6TnlyxZgjFjxsgXoIOw1nlf29zAbYahOpqAgACTJTWoBvR64O+/xXKTJu6bKGZnAwsWAN99Bzz8MPD113JHRERERDV048YNKVH08vJCWxcfMXXr1i307NkT169flzsUsgCHoZLzuXkTKCgQyy1byhuLnLRa4P33gcuXgR9/FHsXiYiIyKmcOHFCKrdt2xZeLv4luEajYaLoRJgskvMx9CoCZWsOuqP69YHHHxfLd+4Aq1fLGw8RERHV2MmTJ6Vyly5d5AtEBmFhYRgyZAjee+89rF27Vu5wqAIchkrOh8limfHjgWXLxPLChcDTT8sbDxEREdWIcc+iO1yrGBISghUrVqBbt26Ii4uTOxyqBnsWyfkwWSzTt2/ZUNw//gDi4+WNh4iIiGrEOFl0h57FwMBAPP7440wUnQSTRXI+jz4KfPAB8MwzQOvWckcjL4VC7F00WLRIvliIiIioRu7cuYMbN24AAJRKJTp27Fhl+y+++AIqlUpaZmP8+PEoLi62R6jkppgskvPp10+c2GXpUqBBA7mjkd/o0WUzwv7wA1BUJGs4REREZBnj6xVbtmwJ30qWwcrLy8MTTzyBN998EzqdDl5eXpg3bx4WLlzo8hPikLyYLBI5u9BQYPhwsZyeDvACcSIiIqdgyRDU+Ph4dO/eHStXrgQAREREYPfu3Xj55ZftEiO5NyaLRK7AeCjqwoXyxUFEREQWq25ym7Vr16J79+64cOECAKBHjx44duwYevfubbcYyb0xWSTncusWcP48h1qWd889QLNmgK8v0LixuAYjERERObTKehb1ej3eeecdDB8+HDk5OQCAF154AX/88QeioqJqdIwffvhBusbRmrcffvjBKq8BOTYunUHOZflyYOpUQKkEfv8dGDFC7ogcg0IBrFgBNGkCBAbKHQ0RETkAQRBQWKKTOwyHpvb0gEKhkOXYhYWFiDeaxdzQs5iRkYGnnnoK27dvBwB4enrim2++waRJk2SJk9wbk0VyLoZlM/R6ICZG3lgcTadOckdAREQOpLBEhzbvb5U7DId2/sPB8PWS5+PwmTNnoNOJyXzjxo1Rr149HDt2DCNGjMD169cBAA0aNMCKFSvQt2/fWh+nYcOGGDx4sFViLr9fcn1MFsm5GK+x2Ly5fHEQERER1UH5IahLlizBK6+8Ao1GAwDo1q0bVq9ejejo6DodZ9CgQRg0aFCd9kHui8kiOZdLl8T70FAgOFjeWBxZTg6wZw8wbJjckRARkUzUnh44/6H1e5RcidrTQ7ZjGyeL+/btw6pVq6T6888/j/nz58PHx0eO0IgkTBbJeWg0QHKyWG7aVN5YHNkHHwBffAHk5wNXrojXMRIRkdtRKBSyDbGk6hmvsXj79m2pPGHCBMyfP1+GiIjMcTZUch7XrwOCIJYbN5Y3Fkfm4yMmigCwaJG8sRAREZEZnU6H06dPS/WhQ4dK5d9//x1/G192QyQjft1EzuPq1bIye8sqN2YM8N574vIZixeLPY2ennJHRURERKXi4+NRWFgIAIiIiMCqVatwzz334NChQ8jMzMTDDz+MQ4cOIdgKl9xs374dX331VZ33U97UqVN5LaQbYLJIzuPatbIyk8XKNWgAPPIIsGoVkJoK/O9/wPDhckdFREREpYyvV+zYsSO8vb2xZs0adO/eHYmJifj777/xxBNPYMuWLVCp6vZxPTk5GVu3Wn9W3CeffNLq+yTHw2Go5DzYs2i58ePLygsWyBcHERERmTG+XrFjx44AxB7GdevWwdfXFwCwc+dOvPrqq3KERyRhskjOIymprMxksWoDBwKNGonl7dtNe2WJiIhIVuV7Fg06d+6Mn3/+GQqFAgAwf/58fPvtt3U61pgxYyAIgtVvY8aMqVNc5ByYLJLz+OUXIC0NOHwYqOOaQy5PqQTGjRPLgsCJboiIiBxIRT2LBsOHD8cHH3wg1adMmWKTYaRElmCySM5DoRDXV+zeHfCQb10kpzF2LGC4zmHxYqCkRN54iIiICDdu3EBGRgYAwNvbGy1btjRrM23aNOmaQJ1Oh1GjRuHChQt2jZMIYLJI5LoiI4Fhw8TyrVvAhg3yxkNEREQmvYpt27atdAKbxYsXo1u3bgCA7OxsPPzww1KS6ezGjRsHHx8fs1tN25DtMVkkcmUTJojXd372GdCnj9zREBERub3KrlcsT61WY+3atYiKigIAXLlyBSNGjECJC4wUKikpQVFRkdnNmFarrbYN2R6TRXIO+/YBkycDs2YBly7JHY3zGDhQfL3eegsID5c7GiIiIrdnabIIAFFRUVi3bh3UajUA4I8//sDEiRNtGh+RMa6zSM7hwAFgzhyx3LAh0Ly5vPE4CyW/DyIiInIka9eurVH7u+66CwUFBbYJRiY//PADfvjhB7nDIAvwkyQ5B66xSERERERkV0wWyTkwWay7+Hjgn/8EtmyROxIiIiIicgIchkrOwZAsBgUBwcHyxuKMDh0CevUSy+fOAUOGyBsPERERETk89iyS49PpgKQksdy4sbyxOKvu3YFGjcTy1q1AQoKc0RARERGRE2CySI7v1q2yBeVjY+WNxVkplcC4cWJZEIBFi+SNh4iIiIgcHpNFcnw3bpSVmSzW3tixgIeHWF68uCwBJyIiIiKqAJNFcnxMFq0jMhIYNkwsp6QAGzfKGw8REREROTQmi+T4mCxaz4QJZeUFC+SLg4iIiIgcHpNFcnzNmgGPPy5O0tKsmdzROLdBgzjRDRERERFZhMkiOb7HHgNWrAAOHwa6dpU7GufGiW6IiIiIyEJMFoncjfFEN5s2iUkjEREREVE5KrkDICI7i4wEZswAWrcWJ7xRKOSOiIiIiIgcEJNFcmx6vXivZCe4Vb33ntwREBFRLQkcEULkNuQ+3/kJnBzbxYuAry/QvDnw6adyR0NERCQbZekXp3rDF6lE5PIM57tSpo4TJovk2G7cAIqKgMuXgfx8uaNxXYWFckdARETVUKlUUCgUKCoqkjsUIrITjUYDhUIBlUqeAaFMFsmxcY1F29qyRbxusUULoKRE7miIiKgKSqUSarUa+fzylMht5OTkwN/fnz2LRBW6fr2szGTR+hYuBP73PyApCdiwQe5oiIioGv7+/sjPz0dxcbHcoRCRjeXn50Oj0SAwMFC2GJgskmNjz6JtjR9fVl64UL44iIjIIkFBQVCpVEhKSoJOp5M7HCKykfz8fCQmJsLPzw/+/v6yxcHZUMmxMVm0rfvvB+LixB7crVuBhASgUSO5oyIiokqoVCrExMQgISEBly9fRlBQEPz9/eHh4QEFl0IiclqCIECv10Oj0SAnJwcajQZ+fn6Ijo6WbQgqwGSRHJ0hWQwJAWT8VsVlKZXAuHHiUhqCACxaBHz8sdxRERFRFby9vdG4cWNkZWUhOzsbmZmZcodERFaiUCjg7++P+vXry3qtohSPIPfiHWQTOTk5CAoKQnZ2tqzjnOtErwe8vQGtFujYETh5Uu6IXFNKChATA+h0QGSk2Mvo6Sl3VEREZAFBEKDVajkklcgFKJVKqFQqmySItc0N2LNIjuv2bTFRBIDoaHljcWWRkeKMqGvWiInj//4HDB8ud1RERGQBhUIBT09PePJLPiKyAU5wQ47r5s2ycsOG8sXhDiZMKCvPmydfHERERETkMNizSI6rWTNg+3YgORlo2lTuaFzboEHia3zlCrBjBxAfD7RsKXdURERERCQjJovkuAIDgYED5Y7CPSiVwMSJwD//CfTqBeTkyB0REREREcmMySIRiV54Abj3XqBLF7kjISIiIiIHwGSRiETBweKNiIiIiAhMFsmRbdsmDo9s2BBo1QrgYsNERERERHbDZJEc1//9H3D+vLjWYmGh3NG4F0EA9u4FwsOB1q3ljoaIiIiIZMClM8hxJSeL9w0bslfRni5eBNq3B+65B/jsM7mjISIiIiKZMFkkx5SfD2Rni2WusWhfsbFla1z+9huQni5vPEREREQkCyaL5JgMvYoAk0V78/UFxo4Vy0VFwJIl8sZDRERERLJgskiOicmivF5+uaw8bx6g18sXCxERERHJgskiOSYmi/Jq3hy4/36xfO0asHWrvPEQERERkd0xWSTHZLhmDmCyKJdJk8rK//mPfHEQERERkSyYLJJjYs+i/IYOFSe7AYBNm8QeRiIiIiJyG0wWyTExWZSfhwcwYYJYFgRgwQJ54yEiIiIiu2KySI5JoQB8fMRyVJS8sbizF18EPD3F8vffi7OjEhEREZFbUMkdAFGFVqwQe7OysgAvL7mjcV8NGgDPPANotcArr/C9ICIiInIjTBbJcSkUQHCw3FHQ4sXie0FEREREboXDUImoakwUiYiIiNwSk0UiIiIiIiIyw2SRHM/hw8DIkcDkycDevXJHQwYaDbBkCdCtG5CUJHc0RERERGRjTBbJ8Vy4IE5wM2cOcPas3NGQwZdfAi+8ABw9CsybJ3c0RERERGRjTBbJ8aSklJUjIuSLg0wZL6OxYAFQWChvPERERERkU0wWyfHculVWZrLoOCIjxeHBAJCRAfzyi7zxEBEREZFNMVkkx8Nk0XFNnlxWnj1bXAuTiIiIiFwSk0VyPEwWHVf37kCPHmL59GlOQERERETkwlS22nFycjLOnz+P69evIy0tDfn5+QAAPz8/hIWFIS4uDm3btkVUVJStQiBnZUgWAwMBX195YyFzr70GPP20WP72W6B/f3njISIiIiKbsFqymJmZiXXr1mHr1q3Ys2cPbt++bdF24eHh6N+/PwYPHoxhw4ahfv361gqJnJUhWWSvomMaMUK8fjElBVi7FkhIABo1kjkoIiIiIrK2Og9D3bx5Mx599FFERkbixRdfxO+//47U1FQIgmDRLTU1FStWrMBLL72EqKgoPPLII9i4caM1fjZyRgUFQE6OWGay6Ji8vIBXXhHLej3w3//KGw8RERER2UStehb1ej1+/PFHfPbZZ7h8+TIAQKhgogtvb29ERUUhODgYarUagiCgsLAQmZmZSElJQVFRkcm2JSUl2LBhAzZs2IAmTZrg7bffxpgxY+Dh4VHbn4+cDa9XdA7jxwMffQTodEB2ttzREBEREZENKISKsrwqrFixAu+88w6uXr0KoCzR8/HxQZ8+fdC/f39069YN7du3r/Z6xOTkZJw5cwZHjx7FH3/8gQMHDkCj0YiBKRQAgEaNGuHTTz/FSMOU/WSRnJwcBAUFITs7G4GBgXKHY7nkZOCLL8SksU8f4NVX5Y6IKrNyJdCzJxAdLXckRERERFSF2uYGNUoW+/fvj/379wMQk0SVSoWhQ4fimWeewQMPPAA/P7+aR26koKAAmzdvxvLly7FhwwaUlJSIQSoU6NOnD/Zy5kWLOW2ySEREREREVmWXZFGpFC9xDAsLwz/+8Q+8/PLLCAsLq3m0FkhPT8f8+fMxd+5c3L59GwqFAjqdzibHckVMFomIiIiICKh9blCjCW7Cw8Mxa9Ys3LhxA9OmTbNZoggAoaGheO+993D9+nV88803Nj0WEdVRURGwfr3cURARERGRFdWoZzE/P7/OQ01rq6CgAL5cc89iTtuzWFgI+PgApdeskhP4/nvg3XeB1FTgyBGgWze5IyIiIiIiI3bpWZQrUQTARNFdjBwpJotxcUBGhtzRkCUEQUwUAeCrr+SNhYiIiIisps7rLBJZ1a1bQHExkJQE1KsndzRkiWefBcLDxfLKlUBCgqzhEBEREZF1MFkkx2JYZzE8HOD6ms7BxweYNEks63TA7NnyxkNEREREVsFkkRyHXl82nDEiQt5YqGZeeUVMGgFg0SIgK0vWcIiIiIio7lTW2lF6ejr279+PAwcO4MKFC7h8+TJSUlJQVFQEQRDQoEEDxMbGolu3bujTpw8GDBiA4OBgax2eXMGdO0Dp2ppMFp1MaCgwZgwwfz6Qlwd89x3wxhtyR0VEREREdVCj2VCrolQqoTCawbKi3Ro/r1KpMHDgQIwfPx6PPPKINUIgI045G+rZs0D79mJ5zBhgyRJZw6Ea+vtvoFUrccKbhg2Bq1cBLy+5oyIiIiJye3aZDdUSgiBUmCganjPcl5SUYMuWLRg+fDg6d+6MI0eOWDsUcjaGIagAexadUYsWwMMPi+XkZOD33+WNh4iIiIjqxGrDUAExCYyNjUXjxo3RsGFDhIWFQaFQQBAE3Lx5Ezdu3MCZM2dQUFAgtQeAU6dO4e6778ZXX32FV1991ZohkTO5fbusbJhdk5zL1KnA+vViee5ccaZUIiIiInJKVksWt2zZgrvuugshISFVttPpdDhx4gQ2bNiAX375BZcuXYJCoYBWq8Xrr7+OgIAAjBkzxlphkTNJSysrM1l0Tn37AvfdB3TpAvzf/8kdDRERERHVgdWuWaytdevWYerUqbh69SoAICAgAPHx8YjgMMQ6ccprFt97D/j3v8Xytm3AoEHyxkO1IwiA0fXJRERERCQvh7lmsaYeeeQRHDt2DD179gQA5OXlYf78+TJHRbKYMAHYsQNYvhzo2FHuaKi2mCgSERERuQTZexYNrl+/jubNm0On06Fz5844evSo3CE5NafsWSTXVFzMWVGJiIiIZOS0PYsGcXFx6NKlCwRBwLVr1+QOh4jq6s4d4MMPgeho4Nw5uaMhIiIiohpymGQRADQaDQBIs6USkRP76Sdg+nRx4qKZM+WOhoiIiIhqyC7JYklJCQ4dOoS8vLwKn9dqtfjss89w+vRpKBQKxMbG2iMscjTLlwNbtrAXylW89BJgmB15+XLg+nV54yEiIiKiGrHqOouVycnJQe/evaFQKBAREYHo6GiEhITA09MTGRkZOHv2LPLy8qAonRhj1KhR9giLHElREfDMM2L57ruBffvkjYfqzt8fePVV4IMPAJ0O+PJLYM4cuaMiIiIiIgvZZYKbjIwMhIWFiQesYKZE4xCGDx+O5cuXw4sTYtSJ001wk5QExMSI5cceA1avljceso6MDCAuDsjPB3x8xN5FrqFJREREZFcOPcGNWq3GiBEjEBcXB0EQpBsgJo/33XcfFi5ciDNnzmDlypVMFN3R7dtlZSYTrqN+fWD8eLGs0QDffitvPERERERkMbski76+vlixYgWuXbuGpKQkLFmyBI899hi8vLyg1+uxe/dufPXVV7htnDCQe2Gy6LqmTAE8PcXy3LlATo688RARERGRRew+G2pUVBSef/55rFq1Cjdv3sSMGTPg5+eH+Ph4DBo0CIsXL7Z3SOQI0tLKykwWXUt0NPDcc2I5Oxv4z3/kjYeIiIiILGK1ZFGr1dZ4m+DgYLz//vs4c+YM2rRpA71ej4kTJ+L06dPWCouchXHPYun1reRC3noLUJb+ufnqK6CSmZGJiIiIyHFYLVls164dtmzZUqtt4+LisGHDBnh7e0Or1eLLL7+0VljkLDgM1bW1aAE89RQQGAi88gpg+3m1iIiIiKiOrJYs/v333xg6dCgeeughnDhxosbbN2rUCF27doUgCNi1a5e1wiJnwWTR9X3xhTgb6ocfAgEBckdDRERERNWw+jWLmzdvxl133YWHH34Ye/furdG2d+7cAQCkGV+/Ru7B+D3nMFTXFBkJ1KsndxREREREZCGrJYvvv/8+vLy8pGUxNm3ahHvvvRfNmjXDhx9+iMOHD0Ov11e6/YIFC3Dx4kUAQEhIiLXCImfh6Sku4q5QiMstEBERERGRrBSCYL2Lhy5duoRJkyZhx44dpgdRKAAAfn5+aNeuHVq2bIkGDRrA29sbd+7cwf79+3H69GkIggCFQoH7778fmzdvtlZYZg4ePIgff/wR+/btQ3JyMgRBQHR0NO6++248//zz6NOnj9WPaXgNamLevHl4+eWXa3W82i68KbvCQkCtljsKsrX0dODrr4G4OGDCBLmjISIiInJptc0NrJosGmzfvh3Tpk3DkSNHxIMoFDAcpqKkyTgEhUKBVatW4dFHH7V2WMjPz8fkyZOrXZ5j7NixmDNnDvz8/Kx2bCaLRKXS04EmTYDcXCAiArh6lV8QEBEREdlQbXMDlS2CGTRoEAYNGoRdu3Zh3rx5WL9+PUpKSgBA6j00ZpxM/t///Z9NEkWdTofhw4dj27Zt0mNqtRpt27aFSqXC+fPnkVO6WPiSJUuQnJyMTZs2wcPDw+qx9OvXD2oLPhzHxsZa/dhEsgsNBe6/H1i1Crh1C/juO2DyZLmjIiIiIqJybNKzWN6dO3ewc+dO7N69G2fPnsWlS5eQmpoqBqBQIDw8HH379sX48eMxcOBAm8Twzjvv4NNPP5Xq48aNw2effSZdH5mfn4+ZM2fio48+Mtnm3//+t1WOb5wgX7t2DY0aNbLKfivDnkVyaKdOAZ06ieWoKODKFcDHR9aQiIiIiFyVQw1DtYQgCNBoNPDw8ICXl5dNj3Xz5k00bdoUGo0GAPDcc8/hp59+qrDttGnT8PHHHwMAfHx8cOXKFURFRdU5BiaLVTh/Hpg2TVwy4+GHgQcflDsisofHHgPWrhXL334LvPqqrOEQERERuara5gZWXzrDUgqFAmq12uaJIgDMmjVLShR9fX0xa9asSttOmzYNMTExAACNRoPZs2fbPD63d/UqsHo1MH8+cOyY3NGQvbz/fln5k0+AggL5YiEiIiIiM7Ili/a0Zs0aqTxy5Mgql+bw8vLC2LFjpfrq1attGhsBuH27rMw1Ft1H587AiBFi+dYtYO5ceeMhIiIiIhMunyzGx8fj8uXLUn3IkCHVbvPAAw9I5cuXLyM+Pt4msVGptLSycmiofHGQ/X34obi2JgB89hmQnS1vPEREREQkcflk8dSpUyb1Xr16VbtNly5dTIbHnj592upxkZGMjLIyexbdS5s2wLPPiuXMTHHtRSIiIiJyCDVKFj/88EPk5+fbKpZK5efn48MPP6zVthcuXJDKXl5e0vWIVSnfzngf1vDGG2+gbdu2CAwMhFqtRnR0NO69917MmDED165ds+qxnEJ6elm5fn354iB5zJgBqFRir3JkpNzREBEREVGpGiWLM2bMQNOmTfHZZ58hKyvLRiGVycrKwqeffoomTZrggw8+qNU+EhISpHJ0dLTZGo+VMV7j0Hgf1rBy5UqcP38eubm50Gg0SE5Oxp49e/DBBx+gRYsWePnll1FYWGjVYzo042SRw1DdT5Mm4qyoV68CL78sdzREREREVKrGw1DT0tLw7rvvIjY2Fq+88gqOHj1q9aCOHDmCCRMmIDY2Fu+99x7SjK9pq6Hc3FypHBQUZPF2xlPKGu/DGkJDQ9GjRw8MGDAAd911F/z9/aXntFotFixYgD59+iC7BtdvFRUVIScnx+TmNIyHobJn0T0NHQoEBMgdBREREREZqVGyuGfPHnTo0AGCICAvLw8LFixAjx490LJlS7z99tvYvXu3tERFTRQUFGD79u345z//iWbNmqFXr15YtGgR8vLyIAgCOnbsiN27d9d4vwCQl5cnlX1qsOi3Wq2ucB+11aZNG8yaNQtXrlxBWloaDh06hB07duCvv/5CZmYmNmzYgA4dOkjtT5w4gSeffNLi/X/66acICgqSbpYMt3UYhp7FwEDA01PeWIiIiIiICACgEARBqMkGgiDg559/xscffyzNMmo8tNPT0xOtW7dGu3bt0KRJEzRs2BD16tWDWq2GIAjQaDTIzMxEcnIyrly5grNnz+LixYvQarUmxwCAZs2aYdq0aXj22WctHj5a3sCBA7Fz504AQN++fbF3716LtnvuueewdOlSAMCAAQOwY8eOWh2/JjQaDR5//HFs3LhRemz9+vV4+OGHq922qKgIRUVFUj0nJwcxMTE1XnhTFqGhYu9ikybAlStyR0Nyu3kT+Pe/gX/+E2jcWO5oiIiIiJxeTk4OgoKCapwbqGp6IIVCgdGjR+PZZ5/F77//jm+//RaHDh2Sni8uLsbp06drNINo+Xy1Z8+eeO211/DEE09AqazbhK2+vr5SuSa9nsZt/fz86hSDpXx8fPDLL7+gefPmSE1NBQDMmTPHomTR29sb3t7etg7RNkaPFtda5BBU2roVePRRQKMRl9Eo/cKGiIiIiOyv1pmYUqnEk08+iYMHD+L06dN466230Lx5cwBi8lf+ZlDZc82aNcObb76JU6dO4eDBgxg1alSdE0UAJtcD1mTSmIKCggr3YWsBAQGYOHGiVN+3b1+thvY6la+/FpOC2bPljoTk1rMnYPhyZtky4PhxeeMhIiIicmM17lmsSLt27fDpp5/i008/RUJCAv744w8cO3YM58+fx/Xr15Geni4tueHn54fQ0FDExcWhTZs26Nq1K/r164fGNhpuFmo0u2ZKSorF2926dUsq17dzj5dhGQ1A7OFMTEyUEnEilxYUBEybBrz+ulh/4w1gxw6glsPQiYiIiKj2rJIsGmvUqBEaNWqE559/3tq7rpWWLVtK5YyMDBQUFJgMTa1MYmKiVG7VqpVNYqtMRESEST09PZ3JIrmPiROBb78Vl9LYtQvYsgV44AG5oyIiIiJyO3Uf5+ngWrdubVI/efJktdskJyebLNdRfh+2ZjwEFoBFya3T0umAms2xRK7Oywv49NOy+ptvir8nRERERGRXLp8sdu/e3WTil/3791e7zb59+6Syj48PunfvbpPYKnPu3DmTenh4uF2Pb1c//SQmBxERwK+/yh0NOYonngAM593Zs+LvCRERERHZVY2TxWvXrtkiDpvx9/fHgAEDpPqyZcuq3ca4zYABA+w2G6rBr0ZJU6NGjRAZGWnX49tVejqg1QKpqYCHh9zRkKNQKIAvviirv/ceUK7HnYiIiIhsq8bJYtOmTREcHIx7770XU6ZMwc8//4wzZ85A58DDxMaMGSOVT58+jf/973+Vtj1+/Dg2b95c4bb2sH79emzYsEGqP/roo3Y9vt2lp5eVjSYjIkK/fsCwYWL55k1g1ixZwyEiIiJyN7UahpqTk4O9e/di9uzZGDNmDDp16gR/f39069YN48ePx7x58/Dnn3+aXXsnl8cffxwdO3aU6hMmTMDFixfN2qWkpODZZ5+VEt9OnTphxIgRFe4zISEBCoVCuhlmLy0vOzsbI0aMwLFjx6qN85dffsHTTz8t1X19ffHWW29Vu51Ty8goKzNZpPJmzhR7nKOjgSZN5I6GiIiIyK3UajZU43UTFQoFBEFAUVERjh8/juNG66IpFAo0b94cnTt3RufOndGpUyd07tzZZDkLe1AoFPjuu+/Qv39/FBYWIiUlBT169MDEiRPRr18/qFQqHDlyBHPnzkVqaioAQK1WY+HChVDUccp+QRCwevVqrF69Gq1atcLgwYPRqVMnREZGws/PD7m5uThz5gxWrlyJv/76yyTmJUuWmM2M6nKMexbtvEQJOYFWrYANG4D+/QG1Wu5oiIiIiNyKQhBqNhXlhg0bcPLkSel27do1lN+FIYE0lMuLiooySyAbNWpU+5/CQqtXr8azzz6LwsLCKtup1WosXboUw4cPr7RNQkKCydqQ06dPr7B3MSsrC8HBwTWKMyAgAAsWLMBTTz1Vo+2M5eTkICgoCNnZ2QgMDKz1fmyub1/AMOmQRgMYTUZERERERER1V9vcoMY9iw899BAeeughqZ6bmysljidOnMDJkydx/vx5FBcXA4BJ0mgoJycn4+bNm9i4caO0n6CgIHTq1Andu3dH37590a9fPwQEBNQ0vCoNHz4cx44dw+TJk7Fz584Kk9z77rsP3377Ldq0aWOVY6rVaowfPx4HDhzA+fPnzY5pLCgoCM8//zymTp2K2NhYqxzf4Rl6Fv39mSgSERERETmQGvcsWkKr1eL8+fNS8njy5EmcOnUKWVlZ5gFU0gvp7e2NYcOG4dVXX0WfPn2sHSISExNx4MABJCcnAwAaNmyIPn36ICYmxurHMsjMzMTJkydx+/ZtpKenIysrC76+vggJCUGHDh3QoUMHeFhpRlCn6VkMCxMTxkaNACebaZdkcO0a8M9/Ak89BTz+uNzREBERETmF2uYGNkkWK5OQkGDSA3ny5EkkJiaaB1WaQBqSx+HDh2PRokUICgqyV6hOzymSRb0e8PQU7++6CzC6ZpPITHw80LEjUFQExMUBFy7wOkYiIiIiCzhFsliRzMxMk+Tx2LFjuHjxotkkOs2bN8f+/fvtPjmOs3KKZPHOnbJJbQYPBrZskTcecmyCIP6ebN8u1j/6SFx/kYiIiIiq5LTJYkWysrKwefNmLF68GDt37pR6GgcNGoStW7fKHZ5TcIpksagIOHhQXD4jOBgYMEDuiMjRnT8PdOgA6HSAr6/Y2xgdLXdURERERA7NpZJFY5s2bcKTTz6JvLw8KBQK7NixA/fee6/cYTk8p0gWiWrjtdeAb78Vy888AyxdKm88RERERA6utrmB0oYxWcWDDz6IefPmSfXly5fLGA0RyW7GjLLhy8uWAfv2yRoOERERkaty+GQRAJ5++mnUL/1wePDgQZmjISJZBQcDH39cVn/lFaCkRL54iIiIiFyUUySLCoUCbdq0gSAIuHnzptzhkLWcOwds3AgcOgTk5sodDTmTcePEGXQB4OxZYPZseeMhIiIickFOkSwCgK+vLwAgl0mF6/j5Z+Chh4BevbhsBtWMhwcwbx5gWJt1xgwgLU3WkIiIiIhcjUruACw1e/Zs7N27F38xqXAdGRllZcM1aESWuusuYOJEYPNmYM4cICxM7oiIiIiIXIrDz4ZKteMUs6EOHw6sWSOWb9wAYmLkjYecT26u2MtYOvKAiIiIiMzVNjdwmp5FckGZmWXlkBD54iDnFRAgdwRERERELstprlkkF3Tnjnjv5cWeIbIOQRAnTiIiIiKiOmOySPIx9CyGhJRNVEJUW5cuAUOGAJ07izOkEhEREVGdMFkk+Rh6FjkElazh55+BbdvENRdfegnQ6eSOiIiIiMipMVkkeRQXA/n5Yjk4WN5YyDW88w7QsqVYPnxYnCGViIiIiGqNySLJg5PbkLX5+ADff182pPndd4GrV+WNiYiIiMiJMVkkeeTmls1kyWSRrKVPH2DSJLFcUACMHy9OekNERERENcZkkeTRrBmQkyMOR/3Pf+SOhlzJJ58AsbFieedOYMkSeeMhIiIiclJMFklenp6An5/cUZArCQgAFiwoq0+ZAty8KV88RERERE6KySIRuZ4hQ4DnnhPL2dnAxIkcjkpERERUQ0wWicg1ffMNEB4ulkNDxSU1iIiIiMhiKrkDIDf1++/Ajh3i5DYTJgCNG8sdEbma+vWBH34AtFrg4YfljoaIiIjI6TBZJHns3w98951YHj6cySLZxgMPyB0BERERkdPiMFSSx507ZeXgYPniIPej0cgdAREREZFTYLJI8jBOFrnOItmDIAA//gg0agRcvCh3NEREREQOj8kiySMzs6xcr55sYZAbWbwYGDMGSE0VZ0rlhDdEREREVWKySPIw9CwGBQEeHvLGQu7h6aeBli3F8tGjwAcfyBsPERERkYNjskjyMCSLHIJK9qJWAz//DKhK5/X65BNg1y55YyIiIiJyYEwWyf4EoWwYKie3IXvq1g34+GOxLAjAs88C6enyxkRERETkoJgskv3l5gI6nVhmzyLZ2xtvAAMHiuWUFGDsWDFxJCIiIiITTBbJ/jgTKslJqQR++gkICxPrGzYAc+bIGxMRERGRA2KySPbn6SnORjl0KHDXXXJHQ+4oMlJcRsPgjTeAkydlC4eIiIjIEankDoDcUMOGYs8OkZweeACYMgX4+mugfn0gL0/uiIiIiIgcCpNFInJfn3wCKBTA228DoaFyR0NERETkUJgsEpH78vYGvvxS7iiIiIiIHBKvWSQiMqbTAdeuyR0FERERkeyYLJL9vfMOEB4OtGoFHD8udzREZdLTgQcfBPr0AW7dkjsaIiIiIlkxWST7u30bSEsD4uMBFUdCkwOZMgXYtk1cf/HJJwGtVu6IiIiIiGTDZJHsLzOzrMx1FsmRfPklEBUllv/4A3jzTXnjISIiIpIRk0Wyvzt3ysrBwfLFQVReeDiwYkVZj/c33wA//CBrSERERERyYbJI9mfoWfTyAnx95Y2FqLzevYE5c8rqEyYABw/KFw8RERGRTJgskv0ZehaDg8U17ogczcsvA6+8IpaLi4Hhw4HERHljIiIiIrIzJotkf1lZ4j2HoJIjmzULuPdesZyaCjzyCJCfL2tIRERERPbEZJHsS6sFcnPFcr16soZCVCVPT/H6xSZNxPqJE8DMmfLGRERERGRHTBbJvnJyyspMFsnR1a8PrF8PBAQAo0eLa4QSERERuQkuckf2ZRiCCjBZJOfQtq3Yq9ikCa+xJSIiIrfCZJHsKywMWLlSTBpjY+WOhsgyTZuaP6bXA0oOziAiIiLXxWSR7CsgABgxQu4oiOrm4kXgiSeAJUuAu+6SOxoiIiIim+DX4kRENXH2rLgW49mzwEMPAQkJckdEREREZBNMFomIaqJZM/E6RkBcUmPwYOD2bXljIiIiIrIBJotkXwkJwKFDwIULQEGB3NEQ1ZyPD7BuHdCypVj/+28xYTSevImIiIjIBTBZJPtasgTo1Qto0wbYt0/uaIhqJyQE2LoViI4W6ydPikNS+QUIERERuRAmi2RfXDqDXEVcHLB9OxAaKtYPHBAnbyouljcuIiIiIithskj2xWSRXEmrVmIPY2CgWN+yBXj2WUCnkzcuIiIiIitgskj2xWSRXE2XLsCGDeK1jACwYoWYNBIRERE5OSaLZF/GyWJQkGxhEFlV377A6tWApycwcyYwdKjcERERERHVmUruAMjNGJJFH5+ynhgiV/DAA8D58+LSGkREREQugD2LZF+GZJG9iuSKKkoUjx/nNYxERETklJgskn0ZkkVer0juYONGcamYF15gwkhEREROh8ki2Y9OB+TkiGUmi+Tq0tOBUaPEpTR++gl48kmgqEjuqIiIiIgsxmSR7MeQKAJMFsn1hYYCP/8sTnoDACtXAsOGAfn58sZFREREZCEmi2Q/wcFiL0taGvDjj3JHQ2R7jz0GrF8PqNVifds24P77gcxMeeMiIiIisgCTRbIvT0+xx6VBA7kjIbKPIUPEJDEwUKwfPAjccw+QmiprWERERETVYbJIRGRrd98N7NkDhIWJ9dOngT59gEuXZA2LiIiIqCpMFomI7KFzZ2DfPiAmRqxfuQIMHgyUlMgbFxEREVElmCyS/Rw8CLz1FvDpp8C5c3JHQ2R/LVsCBw4A7doBHh7AvHllE+AQERERORiV3AGQGzl8GPj8c7HcuDHQtq288RDJISYG2L9fvA0eLHc0RERERJVizyLZT1ZWWZlLZ5A7CwoChg41fUwQgCVLxBmDiYiIiBwAk0WyHyaLRJX74gvghReAQYOA27fljoaIiIiIySLZUXZ2WZnJIlGZW7eAGTPE8t69QLduwIkTsoZERERExGSR7Ic9i0QVi4gAdu8GIiPF+o0b4tIav/4qb1xERETk1pgskv0YJ4tBQbKFQeSQevQAjh4FevYU64WFwFNPAf/8J5fXICIiIlkwWST7MSSLXl6Aj4+soRA5pKgoYM8e8dpFg6++Avr1E3sbiYiIiOyIySLZjyFZrFcPUCjkjITIcXl7A4sWAXPnlq3BeOgQ0Lmz2PNIREREZCdMFsl+jJNFIqqcQgFMmgQcOAA0aiQ+Fh4OtGola1hERETkXlRyB0BuQhDEoXSZmeKi5ERUPcOsqK+8ArzzDuDvL3dERERE5EYUgiAIcgdB1peTk4OgoCBkZ2cjMDBQ7nCIyNri48UZVCdM4LBuIiIiqlJtcwMOQyUicjYlJcCzzwITJwJDh4rrNBIRERFZGZNFIiJns3Vr2WQ3mzcD7doBK1aIw72JiIiIrITJIhGRs3noIWDTJiAiQqxnZAAjRwKPPQYkJ8sbGxEREbkMJotkHzt2iDM59ugBLF0qdzREzu+BB4AzZ4Dhw8seW7cOaNMGWLgQ0Ovli42IiIhcApNFso/UVHFCjiNHgDt35I6GyDWEhgIrV4pDUMPDxcdycsRJb+67D7hyRd74iIiIyKkxWST7yMkpK3N2ViLrUSiAxx8HLlwAxowpe3zfPiAvT7awiIiIyPkxWST7ME4Wg4Lki4PIVYWEAEuWANu2AY0aiWszduwod1RERETkxFRyB0BuIju7rMyeRSLbGTQIOHsW0OlMHy8pERPI118H2raVJTQiIiJyLuxZJPtgzyKR/fj5mX8pM2cOsGgR0KGDeE1jSoo8sREREZHTYLJI9sGeRSL56PXATz+VlRcuBJo1A6ZPB3Jz5Y2NiIiIHBaTRbIP9iwSyUepBP78E/j3v4GAAPGxggLgww/FpHHePHGYKhEREZERJotkH+xZJJKXWg288464nMarrwKq0kvWb98Wr2Vs0UIcpsqkkYiIiEoxWST7MPQsqlSAj4+8sRC5s7Aw4NtvxaU2nnii7PGEBGDyZCAjQ7bQiIiIyLFwNlSyj7feAhITgeJicV04IpJXs2bA778DR46I1y5u2QJMnAhERJi2EwSes0RERG5KIQiCIHcQZH05OTkICgpCdnY2Ajnsk4iqc+gQ0Lgx0KBB2WOZmUD37sDYsWIiGRwsX3xERERUa7XNDTgMlYiIgJ49TRNFAJg/H7h8GXj3XSAmRlyj8fp1WcIjIiIi+2OySEREFUtOFmdSBYD8fGD2bKBpU2DkSOCPP8QhqkREROSymCyS7ZWUAPHx4iLghYVyR0NElpo7F/j7b3G2VLVafEynA1asAO65B2jfHvjvf7lWIxERkYtiski2l5AAtGoFREUB48bJHQ0R1UTTpsB//gPcuAHMmAGEh5c9d+4cMGkS8OOPsoVHREREtsNkkWzPsGwGwDUWiZxVaKg4a+qNG8CyZUCfPuLjfn7Ac8+Ztk1MBNLT7R8jERERWRWTRbI942QxKEi+OIio7ry9gaefBvbvB06cABYsMD+vp08XRxKMGAH873/iUHQiIiJyOkwWyfays8vK7Fkkch2dOgHPPGP6WF6euH5jSQmwejUwbBgQGQlMmADs2iVe80hEREROgcki2R57FoncR1GROCFORETZYxkZwMKFwIABQMOGwD/+AezbB+j18sVJRERE1WKySLbHnkUi91G/PvD55+J1ixs2AKNGAb6+Zc+npooT5vTrJw5jJSIiIofFZJFsjxPcELkflQoYOhT49Vfg9m3gt9+A4cPFax4BoEkToEsX021WrhRnVk1Ls3+8REREZEYldwDkBox7FjkMlcj9+PkBI0eKt9xcYP16QBAAhcK03cyZwNGj4uM9egD33y/euncHPD3liZ2IiMiNMVkk22PPIhEZBASYT4oDADdviokiICaShw6Jtw8/FLe57z5g0CAxeWzWzDzRJCIiIqvjMFSyPU5wQ0TVadAAOHAAeOstoE0b0+dyc4F168SJcVq0EGdVJSIiIptTCIIgyB0EWV9OTg6CgoKQnZ2NQLl78zIzxQW6c3KA9u0BLy954yEix5eUBOzYAWzbJt4brmNUKsW/KcZ/1379Vbz16wf07Qt07Mi/M0REREZqmxswWXRRDpUsEhHVhV4PnDoFbN8OJCQA//2v6fOjRwM//1xW9/ICOncWr3U03Jo1ExNNIiIiN1Tb3IDXLBIRkWNTKsXkr3Pnip8/c8a0XlwMHD4s3gzq1QOmTgXee89mYRIREbkat/ya9eDBg5gwYQLatGmDoKAgBAYGok2bNhg/fjwOHDhg8+NfvXoV77//Prp27YqwsDCo1Wo0bdoUjz32GFauXAmtVmvzGIiIXMaxY8Dp0+L6jc8+K17XWF5WFuDjY/pYfj7Qsycwfry47f79ptdYExERuTm3Goaan5+PyZMnY/HixVW2Gzt2LObMmQM/Pz+rxzB79my89dZbKCoqqrRNz549sWzZMjRp0qTWx3GoYaizZ4trq0VHAw89JG8sROQeMjPF2VWPHBFvhw8DK1aI1zQaHDoE9Oplvm3DhkDr1kCrVuKtdWvg7rt5HSQRETktXrNYDZ1OhwcffBDbtm2THlOr1Wjbti1UKhXOnz+PHKNvlO+//35s2rQJHh4eVovho48+wvvvvy/VlUol2rRpg5CQEFy6dAkpKSnSc9HR0Thy5AgiIyNrdSyHSRYFQVwfTacDunYtmxqfiMieDP/qjJfcWLoUeO656rdVKIC8PMDXt+yxPXuA5GSgSROgaVMgLIzLeRARkcOqbW7gNsNQp02bZpIojhs3DklJSfjrr7/w559/4ubNm5g2bZr0/LZt20wSu7raunUrpk+fLtV79eqFCxcu4MyZM/jjjz+QlJSEX3/9Ff7+/gCApKQkPPHEE1Y7vmwKCsREEeCyGUQkH4XCPJl79llx2OmBA+KkOS+/DPTuDYSEmLaLizNNFAFg0SJx+969xWU/AgPFWVgfewz45z/F/W3eLE7IQ0RE5KTcomfx5s2baNq0KTQaDQDgueeew08//VRh22nTpuHjjz8GAPj4+ODKlSuIioqq0/EFQUDnzp1x6tQpAEDLli1x/Phx+Jb/8AFgx44dGDRokFRfvXo1HnvssRof02F6Fm/eFId0AcCjjwJr1sgXCxGRJQRBXO7nwgXg4kWxPmGCaZvevYE//6x+X5MmAXPnmu77lVfEBDMmRrxFRwORkeIkPOydJCIiG+BsqFWYNWuWlCj6+vpi1qxZlbadNm0afvzxRyQmJkKj0WD27NmYOXNmnY6/efNmKVEExOsWK0oUAWDgwIEYNWoUfvvtNwDAZ599Vqtk0WEYTxYh97WTRESWUCjEYaVhYeLajRV5/33g3Dng6lXgyhXxPiEBKCkxbRcTY1rPzgbmz694n15eQESEmEhGRACffy5eM2mQmQlkZADh4UBAABNLIiKyObdIFtcY9WaNHDkSIeWHGBnx8vLC2LFj8eGHHwIQe/bqmiyuXr1aKjdu3Bj3339/le0nTJggJYtHjhxBUlISoqOj6xSDbLKzy8ochkpErmLIEPFmTKcDkpLE5PHKFeDGDfNkMymp8n0WF4vb3Lgh1j/7zPT5deuAsWPFsqenOFy2fn3xFhpaVo6NFXsvjWVni8mojw+TTCIispjLJ4vx8fG4fPmyVB9S/p97BR544AEpWbx8+TLi4+PRsmXLWsewceNGqTx48GAoqvlH3bdvX/j5+SE/P1/afkL5IVDOgj2LROQuPDzE6xvj4oD77qu4TfPmwIkTQGKieEtKEm+3bom31FQgLU0crhoRYbptampZuaRErBs/ZtCypXmy+NRT4jWUnp7iF3eV3QYNMp21WhDEazr9/AB/f/HecFO5/EcIIiK35/J/6Y2HfwLixDLV6dKlC7y8vFBcXAwAOH36dK2Txdu3b+PWrVs1Or5KpUK3bt2wZ88e6fhOyzhZZM8iEbk7b2+gUyfxVhmtVkwYg4NNH2/ZEhg1SryeMiOj7FZQYNqufn3zfRpGeZSUiNunp1d8bF9f02SxqMh0uZHyP4txAvnjj0C3bmXPnzoFfP+92Jvp4wOo1RWX/fzEJNVYZqYYq6GtSsUeUSIiGbh8snjhwgWp7OXlhZjy149UwNDuypUrZvuoy/EBoGnTphZt17RpUylZrMvxZWc8DJU9i0RE1VOpxAlvynv0UfFWXmGhafJY0XqQXbuKvYrZ2aY3w2zVBuW/1MvLqzzOoiLxlpEh1vV60+cvXgTmzKl8e4PAQNP/FYA4o6zxmshKpZicenqKP5/h/pFHxLV8jY0cKX5RadyuovKoUcBdd5Vtl5YGLF8u9hCrVFXfDxkiJrIGSUni8GFDm4q2U6nEbcLCTOPNzxdfO6Wy4huTZCKSkcsniwlG05ZHR0dXOwTUIDY2VkoWE+ow9Xn5bWNjYy0+fmX7cCZCdg72NeqMVmnXEM6eRSIi61OrxRlVq7q2/dtvzR8TBLFX0jh5NMxebeDpCUydKiY0eXnivXHZ+LHSpZ8kpZdSVMs46TIonZROoteLSXFhoenjhkTV2O7dlfecGuvQwTRZTEwEXn+9+u0AcfivcdxLlwL/+lf123XqJA5DNvbgg8DevVVvp1CI+//3v8se02jEyY4qSzKNb7/+CvTsWbbtzp3A5MnmSanhM5Kh7OMD7NtnGsuXX4ozm1fU3rh+993ARx+Zbvv88+Jw64raG9cnThRfF4OUFOAf/zBvW9H233wjThJlsH27+P5U9PnP+LEGDcyvE541C6hodFf5fd13H/DMM6aPvfyy+ZcxFW37j3+Iv4sGFy+Kx61uO0Ccadl4PfC1a8Wft7ptW7QQ339jM2eKa8dWt+3DDwMDB5bVc3IAo6Xhqtx26lTTvzFHj4q/m9UJCDA/xrJlwMmT1W/btSvw5JOmj02fbj4ioyJPPw107lxWT0y07AswwzH8/Cxr6+BcPlnMzc2VykE1SFaMp5Q13kddjl+TGGp6/KKiIhQVFUn1HOPhnzK6qQ7C6FHiP4vQ80Dr7w+jTVQg2kSKt8ahflB5uM1yn0REjkOhKBtCWtkSUUFBYnJQG8OGAUeOiElNYaF4b7gZ1yvqCe3SRUxEDe0KC8UJgEpKxHtDufxQXUB8zhKenqZ1rdbyn6389ZqWbmv8wd7AkhXMKmqj1wOWfj4p/5rk5ADnz1e/XUWJ/OXLwMGD1W9b0eedvXstW3vUOFEExJ/TaLLAKpUufya5cAGoZLk0E82bmyeLO3YARvNOVMrX1zxZ/P57y34vhg0zTRZv3gQWLKh+O0D8Esj4d+rPP8U1Xqtz773myeLy5RUnxuVFRZkmiwUFFSe3FXn+edNk8dw54Kuvqt8uPNw8Wdy4Efjll+q3HT3aPFmcOxe4c6f6bTt3Nk0WU1OBL76ofjsAeOstJovOIs9oCI1PRX/0KqFWqyvcR12OX5MYanr8Tz/9FB988EHNgrODOw8NR5PfTuBaej7Si4F9l9Kx71LZN77eKiVaRgSgTWQgWkcGok1UIFpFBCDAx7OKvRIRkcMLDRVvtTF1qnirjZSUsqTSOLksXy8/F0HTpuKHT61W7BGq6r788lc9egBTppi2qWi7xo3N4+3WTUzK9PqqbxX1HLduLT4nCFVv6+1tup2Hh9hbY3hepytLSAWh7Fbb5BaouCesttvWZElwDtslsiqFINTkDHQ+AwcOxM6dOwGIs4zurW6oR6nnnnsOS5cuBQAMGDAAO3bsqNXxP/74Y0ybNk2q63Q6KJXV96R9//33eOmllwAAHh4e0Fbz7VRFPYsxMTE1XnjTVgqKtYi/lYsLKbk4n5KNCym5uJCSg4LiCoZoAIgN8UXryAC0iQwS76MC0bCe2uJhxERERGQDhuS0ouTSuG64ztRYbq5l2/v7i8OrDbTaspl/jduXvwHi2qbGvcaZmcDt2+Y/R/mPv56e4hcGxm7cML9ut6KPzSEh5tcZnz1b/TEBcfZk489pubniuq3VbQcAHTuaJsfJyeYzJFe0rb+/+ZclZ8+aD/OuaNvoaNORCMXFwPHj5u0q2rZDB9Pettu3gUuXqt/O01P8QsZYfLx4nXF1wsPFYbfGDh2qsNdXEAToBEAnAHoA2ibNoK8fCp0gQKvXQ5+TB925c6Vtytqa3kofb90GeqUHtHoBTUL9EBNS8frq9pSTk4OgoKAa5wYu37Poa/Ttn6b8NRBVMG7rV4duZN9y3z5qNBqzx6xxfG9vb3iX/6PsQHy9VOgcG4zOsWVDhvR6ATfuFOB8Sg4upOTg/E3x/ma2BjfuFODGnQJsPVf2Ry/QR4XWRj2QbSID0byBP7xVFXzzSURERNZnwRfelQoIqN12KpX59bSWCg6ueLiyJSycZ6JC7drVbruAADEJrI2GDWv/OrVrB0EQoBeAEp0eOr0ArV4Q73X6srJegDY116Sui2yBEp1RXa+vuH4uA1p9OnSl+xOfawCtTkzGyo5Xuo1egE5X2u7aCdO6Xg+t3rO0rbi9ccw6vQCdIECvT4ZWnwR9aV2nL3czekxvlqdWkARb6s+jUvHtB1rh5f6WTXDpiFw+WfQ3uuC+sPw3JlUoMLrw1b/8Rfu1PL4hBkuSRWsd35EplQo0CvVDo1A/PNi+7Bu5zPxiXLhlSB5zcT4lB5dv5yJHo8Xha3dw+FrZOHOVUoGmYf5oExVo0hNZ399xE2ciIiJyXUJpAlKs06NYW3rTiQmTVieWtToBJaWPlej00Or1KNaKSU+J1NbQpmzbEp2YRJVoxeRK3Je4P5P9Sm30KDbaVqsTUKLXo6T0WMWl+zEcgyqmUAAeCgU8lKU3hQIeHuK9UqmASqmAsvR5ldL0sRDfCq7LdiIunyyGGl0vkZKSYvF2xmsj1q9ozapaHN8QgyX7s9bxnVGwnxd6Nw1F76Zlr12xVo/Lt/PEHkhDT2RKDrIKShCfmov41FysMZpgrkGgt9gDadQT2ai+HzyUHMZKRETkSgRBTJSKtHpoSnQoKhHLhiStLGHTld4LZY9pdSZtinRiIiW1NdqHYZ8lunL7LW1jfExXushLqQBUSiVUHmXJkIdSCU+TugKeHkqTuspDWVaucJvq6yoPhck+TPbpUbqN0iiJUyqgUiqhVIrJncqjLIkzSfRKy8rSNiZJX7nkz/Ccu3L5ZLGl0ZjsjIwMFBQUWNSzl5iYKJVbtWplleMDwI0bN9DOgqEJ1jq+q/BSKcWhp1GBGFH6mCAISMnWlA1hLe2NTMgoQGpOEVJz0rAnvmw8u9rTAy0jAoyGsQagVUQg/Lxd/jQgIiKyOUPSpinWQ6MVkzaz+xJdWVJndF+k1UFTYnpfVFLx42bttI6dnCkVgKeHEl4eSniqxGTH00NMjFQeSqnsWZoIeZm0MW5naKOEp0oBT6X4vMpDAa/Se5N9eYiJlGmbsufLb6sySdaUJkkUuS+X/5TcunVrk/rJkyfRu3fvKrdJTk5GmtFFs+X3URPNmzeHSqWSJqg5efIkHiw/JXQFThitw1SX47syhUKBqHpqRNVTY0DrsjWV8oq0iL+Vg/MpudJ1kBdv5aCwRIeTiVk4mZhltA+gUX2/0iGsZb2QEYE+nEyHiIhchiAIKNEJKCzWobBEvBUUa6Ep0aGwWF+urkNhiR6FxVqprdhGa7S90fPFZWXz677sS6EQZ1r3VnnASyUmaN4qMTHyUimlx6SySgnv0npFbbyN6p6VbOdVro13uce4RBg5M5dPFrt37w5vb29pptD9+/dXmyzuM1qA1sfHB927d6/18b28vNCjRw8cOHBAOn51bt26hcuXL0v1fv361fr47sjfW4WucSHoGhciPabTC0jIyJeSR8NQ1tScIlxLz8e19HxsOlM29Leer2dZ8lh63yzcH14q/sEnIiLb0usFFJbokF+kRX6xeF9QrEN+sVYsF4nlgmId8oq0KChtV1CsRV6RzqSeX6QTE8ASHXR2zOSUCsDH0wM+nh7wVimle+/ydaOypfdm+/AsK3t5KPllL5EVuXyy6O/vjwEDBmDTpk0AgGXLluHNN9+scptly5ZJ5QEDBtRpNlQAeOSRR6RkcceOHUhNTUWDBg0qbW98/Hr16jFZtAKP0olwmob54+GOZVM+Z+QVmSzncf5mDi6n5SGroAQHr2Tg4JUMqa2nhwLNwg09kAHSjKz1nPzCZSIiqjudXkBekRa5mhLkFWmRp9EiV6NFrlQuKX1eKyV/eUVaKaGTEr3SJNCWPJQK+Hp6wMfLA75eHlCXJnVqT7Hu41VWlp4rbWtoV76tcd2ntFePiJyfy6+zCAArVqzAyJEjpfr69evx8MMPV9j2+PHj6N69O3Q6nbTt448/XqfjJyUloVmzZlLv5pQpU/DVV19V2DYvLw9t27bFjRs3AACTJk3C3Llza3zM2q6lQoCmRIfLt/Nw/qbYA2nohczVVLzWZVSQjzR81dATGRviyzH+REROQBDEXrzswhIxudOI94bELq804cvVlCDP6HExCSxra4sET6kA/LxU8PX2MLn381bB16us7OftAV+vsnt/o7raU2yrLk30fL084MlhkURup7a5gVski4IgoHPnzjh16hQAIDIyErt27TKbOCYlJQUDBgzAhQsXAACdOnXC8ePHKxzOkJCQgMaNG0v16dOnY8aMGZXG8Nprr+Hbb78FAHh4eOC3337DiBEjTNqUlJTg6aefxsqVKwEAarUaly9fRpTx4qcWYrJoXYIgICmz0Gw21sQ7FS/Hovb0QIsG/mgVEYiWEQFoFRGAlhFc0oOIyBZ0egE5hSXI0ZQgu7AEOYVaZBeWlqXHDHXxuVyj5625ZIC3SokAHxX8vVUI8PGEv7cK/j4qBPioEFBa9vMWn/f1UsHPywO+3kYJnlFS6OPJIZVEZB21zQ1cfhgqIE6E8t1336F///4oLCxESkoKevTogYkTJ6Jfv35QqVQ4cuQI5s6di9RUcRF4tVqNhQsXWu2P9IwZM7B582ZcunQJOp0OI0eOxNNPP41HH30UISEhiI+Px7x583D69Glpmy+++KJWiSJZn0KhQEyIL2JCfHF/2wjp8RxNCS6m5JrMyHrxVi4KS3Q4lZSNU0nZJvsJ9fdGK6PksVVEIJo38IePp4e9fyQiIodTWKxDZkExMguKkVVQUlouQbbhvlzSZyjnFVU88qMmVEqFmND5eBole5UnfAHenkaPiWV/bxWHXxKRS3GLnkWD1atX49lnn0VhYcW9QQZqtRpLly7F8OHDK21T055FAPj7778xcOBAk2UxKvPmm29i5syZ1barDHsW5aPV6ZGQUYD4W7mIL00e41NzceNOQYVTeytLZ2RtFRmAlg3KeiI5lJWInJVOLyC7sKQ06StGZn6JlABmFYqJX/nHMwuKUaTV1+m4vl4eCFJ7IkjtiUAfTwSqPRGoVkl16Tnpvuw5Xy8P9uIRkctiz6IFhg8fjmPHjmHy5MnYuXMnyufJCoUC9913H7799lu0adPG6sdv0aIFTp8+jX/+859Yvnx5hUlr69at8dlnn2HYsGFWPz7Zh8pDiWbh/mgW7o+hHSKlx/OLtPg7NRfxt3LFBLI0ibyTX4yr6fm4Wm5GVg5lJSJHUVisQ0Z+Ee7kFyMjrxgZ+cW4k1+EjNL6nXzxllXaA5ijKan1uncqpQL1fL0Q7OuJYF8v1DO6D/KtJOnzUSFQ7clr8YiIrMytehaNJSYm4sCBA0hOTgYANGzYEH369EFMTIxdjp+bm4tdu3YhMTER+fn5iIyMRPv27dG5c2er7J89i85BEASk5RXhYopREpmag0upeZV+w24Yytq8gT+ahxvu/TkrKxFZrKBYKyV5GflFJglfep6YCIrPiclgYUntJm8J8FFJiV5lCWCwr5dRG3HIJ3v4iIisixPckAkmi87NsC5k/K1cXEypfigrAIQFeKN5uJg4NmsQgObh/mjRIAAhfkwiiVydXi8gq7AEablFSM8rKndfXK5XsAiakpoP9/RSKVHfzwshfl6o7+8tlUP8vBDqLyZ8wX5iQljP1wtB7OkjInIYTBbJBJNF11RQrMXfqXmIv5WDy7fzcOl2Hi6l5iE5q/LrcOv7eaFZuH9ZT2S4P5o3CECovxe/vSdyYIIgXveXnleE26VJX8XJoNgzqK3hguteKiVC/bwQ4u+FED9vsVxaD/Xzlsr1S5NDP17TR0TktJgskgkmi+4lr0iLK4bk8XYuLqWK95Ut7QEA9Xw90TzcH03D/NEkzA+NQ8X72BBf9gYQ2YggCMgt0opJXm4R0vKM74vFe6MksKZLOgT7eiLU3xthAd7Sff3S5K++f2mvoJ83Qvy9mPwREbkRJotkgskiAWJP5NW0fFy6nYu/U8VeyMu3c3G9iuGsHkoFYkN80TjUD01C/dAkzB+NQ/3QNMwPYQHe/HBJVIH80gTQNPkT79MMSWBpvbiGM34G+qhMkj/DfVgFSSG/6CEiooowWSQTTBapKpoSnZREXknLx9W0PFxLz8e19HwUFFc+kYW/twqNQ/3ERDJMvI+r74e4EF/U8/VkIkkupVirR0a+2MtncsszL1d13lQkwFuF0NKELzTAS7wvl/yFBngj1N8L3iquw0pERHXDZJFMMFmk2hAEAak5Rbialocr6fm4lpaPq+liIpl4pwBVXRIV4K1CTIgv4ur7IjbE16QcVU/NHg9yCMYTwYjJnqbSRDCzoKRG+1Z7eiA8sDTZk5JAn7Jk0Kg30MeTCSAREdkPk0UywWSRrK1Iq0PinQJcSRN7IA29kTfuFCA1p6jKbT2UCkTV80FsiC9iQ/wQE6JGw3pqRNVTIzLIBw0CfZhMUq0ZrgPMyCuWrverLAFMzyuq0UQwKqWibNinUbJncit9zM/brZYuJiIiJ1Lb3ID/2YjIIt4qDzQLD0Cz8ACz5zQlOiRlFuB6RgFu3Cm9GZWLtHok3ilE4p1CHECG2fZKBdAg0AdRpQlkVD0fNKynRmRQWTlIzWGu7kRTopNm+TSsAyiu+SeW00vLhuUginU1uw4w2Nez8gTQ30cq11N7Qqnk7x0REbkn9iy6KPYskqPQ6wWk5RVJCeT1OwVIyixASpYGN7MLkZKlseiDvpdKifAA79KbD8IDy8phRuX6fl78cO9gSnR6ZBWUIKugGFmFJcjMLxbrhcXILCjBHUNCaFgHMK8I+TW8BhAQr6mt7++F8AqSQOPewfp+3vBSsSebiIjcB3sWicghKZUKNAgUh5p2axRi9rxeLyA9vwg3szS4mVWIm1mFSM4qlJLJm1mFSM8rRrFWj6TMQiRlVr4cCCAOeTUsFh7sW3rv54kQXy/Uk+peCPEVHw/29YIvlxCoVrFWj1xNCXI12tJbCXJK73M1WmQVislgpiEpLChBZul9XpG2Vsf08lCivr+XeCtd+iHU37t0+QexLD4vLhDP6wCJiIisi8kiEclKqVSIPYUBPugUU6/CNpoSHdJyxYXJ03I1uJ1bhNs5RbhtUi5CRn4RdHpBfCy36usojXkoFfD3ViHAR4UAH08ESOXSuo8K/j4qBHir4OPpAbWXB3xUpfeeSvh4ekg3taf4mLfKAx526uEUBAFavYASnR4lOsO9HiVaAYUlOhSW6FBQrIWmRIeCYh0Ki8XHCovFuvR4iQ75RWXJYK5GKyWERTVc7qEiQWpP1PP1RD1fLwT7iol6kNpTWvRdTAbL1gEM8FYxiSciIpIRk0Uicng+nh6IKZ1htSpanR4Z+cWlM1kW405+MTLzi3GnoKT0vrSeX4zMgmJk5pegWKeHTi8gu7AE2YUlAKruuawJhUKcIMVDqYBKqYTKQ2FW91AqoFQoIAgCBAAQAAGQ6oIAlD4jlgWgWEoGxeSwptfr1YWvl4dJEm24r6cWkz/jZLBead2QFNoreSYiIiLrYLJIRC5D5aGUhrxaQhAEFBTrTIZV5hWV9arlmQy31CKvqASaEj00JbrSmx6FpeXCEh2KSvQmiZsgoLSnTwBgv4QOEIdwqjwUpT2dHvD1EntC1aU9o75eZY/7eqmksp+XBwJ8PE17Wn1UCPTxhL+PigkfERGRG2GySERuS6FQwM9bBT9vFSKCLEswq6PTC1IyqRME6PQCtDpxmKhOr4e2fF0nQCcIUEABhQJQlMZVVgZg9JxSoYCXSglPDyW8PJTwVIm9lIayp4cSKqWCwzeJiIiozpgsEhFZkYeyLAElIiIicmacO5yIiIiIiIjMMFkkIiIiIiIiM0wWiYiIiIiIyAyTRSIiIiIiIjLDZJGIiIiIiIjMMFkkIiIiIiIiM0wWiYiIiIiIyAyTRSIiIiIiIjLDZJGIiIiIiIjMMFkkIiIiIiIiM0wWiYiIiIiIyAyTRSIiIiIiIjLDZJGIiIiIiIjMMFkkIiIiIiIiMyq5AyDbEAQBAJCTkyNzJEREREREJCdDTmDIESzFZNFF5ebmAgBiYmJkjoSIiIiIiBxBbm4ugoKCLG6vEGqaXpJT0Ov1uHnzJgICAqBQKGSNJScnBzExMUhMTERgYKCssZB18D11TXxfXQ/fU9fE99X18D11PY72ngqCgNzcXERFRUGptPxKRPYsuiilUono6Gi5wzARGBjoECcLWQ/fU9fE99X18D11TXxfXQ/fU9fjSO9pTXoUDTjBDREREREREZlhskhERERERERmmCySzXl7e2P69Onw9vaWOxSyEr6nronvq+vhe+qa+L66Hr6nrsdV3lNOcENERERERERm2LNIREREREREZpgsEhERERERkRkmi0RERERERGSGySIRERERERGZYbJIZg4ePIgJEyagTZs2CAoKQmBgINq0aYPx48fjwIEDNj/+1atX8f7776Nr164ICwuDWq1G06ZN8dhjj2HlypXQarU2j8FVZGVlYc2aNZg8eTL69euHiIgIeHt7w9/fH7GxsXj44Ycxa9YsZGZm2uT4CoWixrf58+fbJBZXsWfPnlq9rhcvXrRJPDxf6yYhIaFW76fxLSEhoc5x8FytmbS0NGzevBkffvghhg0bhsjISJPX5ocffqj1vs+cOYMpU6agQ4cOCAkJgb+/P1q2bIlnnnkGW7Zssd4PUYVbt25h5syZ6NWrFyIjI+Hj44NGjRphyJAh+OGHH1BYWGiXOOzJ2u9pQUEBNm/ejDfeeAMDBw5EdHQ01Go1fH190bBhQ9x///3497//jZs3b9rmBwLQqFGjGp/Xb7/9ts3ikYM139fa/r225Xlrl3NVICqVl5cnvPDCCwKAKm9jx44V8vLybBLDrFmzBG9v7yqP37NnT+HKlSs2Ob6ruHDhgvDQQw8JXl5e1b6fAARfX1/hm2++EfR6vVXjsOTY5W/z5s2zagyuZvfu3bV6XS9cuGD1WHi+1t21a9dq9X4abiqVSrhz506d4+C5apmUlBQhLi6u2tdmyZIlNd53SUmJ8K9//UtQKpVV7nvo0KHC7du3rf/Dlfrll1+EoKCgKmNo2bKlcPz4cZvFYE/Wfk9v3boljBo1SvD19bXoPPL09BT+9a9/CUVFRVb/2Sz5ucrf3nrrLavHIQdbnKu1/Xu9efNmm/yM9jpXVSACoNPpMHz4cGzbtk16TK1Wo23btlCpVDh//jxycnIAAEuWLEFycjI2bdoEDw8Pq8Xw0Ucf4f3335fqSqUSbdq0QUhICC5duoSUlBQAwKFDh9C/f38cOXIEkZGRVju+Kzl79iw2bNhg8piHhweaNWuGBg0aQKfT4cKFC7hz5w4A8RvQ//u//8O5c+ewcOFCKBQKq8fUr18/qNXqatvFxsZa/diuysfHB/3797eorb+/v1WPzfPVOtRqNQYPHmxxe71ej+3bt0v1wYMHIzg42Kox8VytnEajwfXr122y7wkTJmDx4sVS3dPTE23atIG/vz8uXryIjIwMAMDGjRsxcOBAHDhwwOrn9c8//4zRo0ebPNaiRQtERkYiISFB+tnj4+Nxzz334ODBg2jbtq1VY7A3a7+niYmJ+O2330weUygUaNKkCSIiIuDh4WHyN7KkpASffvopTp48ibVr18LLy8tqsRjr1q0bQkJCqm3XsmVLmxzf3mx5rhpY+rc7LCzM6se267lqpeSWnNy//vUvk28ixo0bJ2RkZEjP5+XlCdOmTTNp884771jt+Fu2bBEUCoW07169egnx8fHS8zqdTvj1118Ff39/qU2fPn2sdnxXs2LFCqnX4dFHHxXWrl0rZGdnm7TR6/XC2rVrhYYNG5q8r//973+tFofxfq9du2a1/boz457FuLg4WWLg+SqfrVu3mpxXv//+u1X2y3PVMsY9C2FhYcKQIUOE9957T1i7dm2dehYXLFhgsv2wYcOEpKQk6fni4mJhzpw5gkqlkto8/fTTVv3ZTp8+bTJSoEWLFsLRo0dN2mzbtk1o0KCB1KZJkyZCYWGhVeOwN2u/p3/99ZcAQFAoFMKAAQOEZcuWCWlpaWbt9uzZI7Rp08bkGG+++aZVfzbjnrXdu3dbdd+OzhbnavmeRbnY+1xlskhCcnKy4OPjI/1CPffcc5W2fe+996R2Pj4+QnJycp2Pr9frhY4dO5p0mefn51fYdvv27SYn6urVq+t8fFe0du1a4aWXXhKuX79ebdsbN24IERER0msaGhoqFBcXWyUOfgC1PrmTRZ6v8nr66ael1zM4OFjQaDRW2S/PVctkZ2cLK1asEBISEsyeq+0H0Pz8fJO/wffcc4+g1WorbLto0SKpnUKhEI4dO1bbH8XMww8/bPJ/4NatWxW2O3v2rMkH1a+//tpqMcjB2u/psWPHhBEjRgjnzp2rtm1WVpZJwujl5VXp614b7pws2uJcdZRk0d7nKpNFEt544w3pF8nX19ekR7G8oqIiISYmxqrfgm3cuNHk5NuyZUuV7UeNGiW17d69e52PT+bfau/YscMq++UHUOuTO1nk+Sqf7OxsQa1WS6/nxIkTrbZvnqt1V9sPoP/5z39MEsDz589X2b5Hjx5S+5EjR9YxatG5c+dM4p8/f36V7d966y2pbUREhKDT6awSh6OpS2+xpcqPFli0aJHV9u3OyWJVnDlZlONc5WyohDVr1kjlkSNHVjmm3cvLC2PHjpXqq1evrvPxjffRuHFj3H///VW2nzBhglQ+cuQIkpKS6hyDu3v44YdN6raaOZOcH89X+axYscJkZrvnn39exmjIWozPqf79+6N169ZVtjc+pzZt2oSioiKrxuDv749nnnmmyvbjx4+Xyrdu3cKff/5Z5xjc1YABA0yuEeb/X6qKHOcqk0U3Fx8fj8uXL0v1IUOGVLvNAw88IJUvX76M+Pj4OsWwceNGqTx48OBqJ1fp27cv/Pz8Ktyeaqf8FwSGyYyIyuP5Kp8ff/xRKrdq1Qo9evSQMRqyhry8POzdu1eq1/R/cF5eHvbs2VPnOIzPy7vvvrvaiXOaNGliMhFK+QnVyHIeHh4ICgqS6vz/S1WR41xlsujmTp06ZVLv1atXtdt06dLFZLau06dP1/r4t2/fxq1bt2p0fJVKhW7dulnl+CQqP2NYeHi4TJGQI+P5Kp+rV69i//79Up29iq7h/PnzKCkpkeqWnFMRERFo1KiRVK/rOSUIAs6cOVOjGMq343lde4WFhbh9+7ZU5/9fqoxc5yqTRTd34cIFqezl5YWYmJhqtynfzngfdTk+ADRt2tSi7Yzb1eX4JCo/nNjSP0A18cYbb6Bt27YIDAyEWq1GdHQ07r33XsyYMQPXrl2z+vHcQVZWFkaOHIlGjRpBrVYjICAAjRs3xqOPPoq5c+da/Rtqnq/y+emnnyAIAgBxmZLnnnvOZsfiuWo/jnBO3bhxA/n5+bLG4M7WrVsHvV4v1W3x/xcAvvzyS3Tu3Bn16tWDt7c3IiMj0bt3b7z99tsmCQhZbvTo0WjevDn8/Pzg5+eH2NhYDBkyBJ9//rnJFwDWIte5ymTRzSUkJEjl6Ohoi9fXM15fy3gfdTl++f3a4/gEZGdnY/bs2VK9Q4cOaNOmjdWPs3LlSpw/fx65ubnQaDRITk7Gnj178MEHH6BFixZ4+eWXTa7HouplZ2djxYoVuH79OjQaDfLy8pCQkIB169bh1VdfRWxsLObMmWO14/F8lYcgCPjpp5+k+sCBA9GwYUObHY/nqv0Ynw8qlcritUiteU5Z47y+fv269GUGWU6r1eKTTz6R6uHh4RgwYIBNjrVx40acPHkS2dnZKC4ulq5fmzlzJjp27IjHH39cWnuZLPPzzz/j8uXLKCgoQEFBARITE7F161a89dZbiIuLw7Rp06DT6ax2PLnOVVWNWpPLyc3NlcrGY+arExgYWOE+6nL8msRgreMTMHXqVJOhhR9//LFNjhMaGoqmTZvC398f2dnZuHjxIvLy8gCI/zAXLFiAI0eOYPfu3TX6XXR3jRo1QsOGDeHt7Y309HScP38eWq0WgJhMTp48GSdPnsT3339f52PxfJXHvn37THr0bD0Eleeq/RifDwEBAVAqLfsO35rnlDXOa71ej4KCApPrk6l6n332mUmv3nvvvQdvb2+bHCsoKAgtWrRAYGAg8vLycOnSJSk5FAQBq1atwtGjR7Fv3z6LRpkREBkZKY3syczMxIULF6DRaAAAGo0GH3/8Mf766y/873//g6enZ52PJ9e5yp5FN2f4AAAAPj4+Fm9nPHOX8T7qcvyaxGCt47u7RYsWmSQRo0aNMpsZtS7atGmDWbNm4cqVK0hLS8OhQ4ewY8cO/PXXX8jMzMSGDRvQoUMHqf2JEyfw5JNPWu34rkipVGLgwIFYtmwZMjIycO3aNezfvx87d+7EqVOnkJmZiXnz5iE0NFTaZvHixZg5c2adj83zVR7GE9sEBgbiscces/oxeK7KQ+7/wRVtX5vz2hpxuJutW7di+vTpUr1379545ZVXrHqMRo0a4eOPP8bZs2eRlZWFI0eOYMeOHTh06BDS09Oxd+9e9OvXT2p//fp1PPzwwyguLrZqHK5CoVCge/fu+O6773Dz5k3cvHkTBw8exM6dO3H8+HFkZWVh+fLlJtcUb926FZMnT7bK8eU6V5ksujlDDwQgDoGxlHFb44vz63L8msRgreO7s71792LSpElSvXHjxliwYIFVj3Hu3Dm89tpraNKkidlzKpUKQ4cOxeHDhzF06FDp8S1btuB///ufVeNwJf369cP27dvx9NNPV7jMjb+/P15++WUcP37c5B/Whx9+iNTU1Dodm+er/RUUFGDFihVSfeTIkWb/+K2B56o85P4fXD6GmsRRvh3PbctduHABTz31lHStYnBwMJYvXw4PDw+rHmfPnj1499130bZtW7PnFAoF+vbti927d2PcuHHS46dOnbL6ZwFXERcXh8OHD+Oll16qcMi4t7c3nnrqKRw/fhxdu3aVHl+wYIFVJoGS61xlsujmfH19pbKh69wSxm3rMuzE+Pg1icFax3dXJ0+exLBhw6RvD8PDw7FlyxZZhpT5+Pjgl19+QYMGDaTHrHmdnbuKiYnBb7/9JtULCgrqPBSV56v9rVmzxmTokZyzoPJctT65/weXj6EmcZRvx3PbMomJiRg8eDAyMzMBiK//hg0bEBcXJ0s8SqUS//3vf9G+fXvpMZ7XdRMcHIzVq1dLPX+CIGDu3Ll13q9c5yqTRTdnvD5LTSYsKCgoqHAfdTl+TWKw1vHdUXx8PAYPHozs7GwA4h+1bdu2oUWLFrLFFBAQgIkTJ0r1ffv21eiDE1Wse/fuuOeee6T69u3b67Q/nq/2ZzwEtWnTprj77rtljIbnqrXJ/T+4ou1rc15bIw53kJqaioEDByIxMRGA2BO1du1a9O7dW9a4VCoVpk6dKtUvXbpktqQW1UxsbKzJUP26/v8F5DtXmSy6OePrmlJSUizeznhClPr161vl+DWJwVrHdzfXrl3DwIEDpSmdAwICsHnzZnTs2FHmyIB7771XKms0GumfKdWN8ev6999/12lfPF/tKzk5GTt37pTqjrK2Is9V6zE+p/Ly8iy+lsia55Q1zuuAgACrTODhyu7cuYNBgwZJf4dVKhV+++03DBo0SObIRMbnNVD3/xdk+pomJCTU+VpQuc5VJoturmXLllI5IyPD7NuHyhh/OGjVqpVVjg+Ia8jY8/juJCkpCQMGDEBSUhKAsqEvPXr0kDkyUUREhEk9PT1dpkhci/HrWtfXlOerff3888/SNU0KhQKjR4+WOSIRz1XrcYRzqkWLFibLZvG8tr6cnBwMHjxYmvlUqVRi6dKleOSRR2SOrAzPa+sr/5pmZGTUaX9ynatMFt1c69atTeonT56sdpvk5GSkpaVVuo+aaN68ucmFt5YcHxBn4rPG8d2FYeiLYfp9w9AX41nQ5Fb+i4ryY/Opdoxf17q+pjxf7ct4COo999wj2zVN5fFctZ7a/A8uKSnBuXPnKt1HTfn7+yM6OrpGMQA8ry2Vn5+PBx98EEePHgUgfvGzePFijBo1SubITPG8tj5rv6ZynatMFt1c9+7dTdb02b9/f7Xb7Nu3Tyr7+Pige/futT6+l5eXSc+WJce/desWLl++LNUdKeFxRBkZGRg4cCDi4+MBAJ6enli5cqXDDH0xMP7wA4iT7lDdGb+udX1Neb7az5EjR3Dx4kWp7ihDUAGeq9bUpEkTkw9/lpxTx44dM/kQao1zyngflsRQUlKCw4cPWzUGV6TRaDBs2DAcOHBAeuy///2vQ53PBjyvrc/4NfX29rbKJIJynKtMFt2cv78/BgwYINWXLVtW7TbGbQYMGFDnGdCMh2Hs2LGj2un9jY9fr149/pOqQnZ2NgYPHoyzZ88CADw8PLB8+XI89NBDMkdm7tdff5XKjRo1qnBaaqqZgoICrF+/XqpbYxIFnq/2Ydyr6OfnhxEjRsgYjSmeq9Y1bNgwqbxixYpqr2syPqfatm2Lpk2b1jkG4/P6woULJj0RFVm/fr00S69SqbTq+ryuori4GCNGjMCuXbukx7755hu8/PLLMkZVOePz2sfHB507d5YxGucnCAJ+//13qd6rVy+r7FeWc1Ugt/f7778LAKTb+vXrK2177NgxwcPDQ2q7YsWKOh8/MTFR8Pb2lvY5ZcqUStvm5uYKsbGxUttJkybV+fiuKi8vT+jTp4/0WimVSmHp0qVyh1WhdevWmfwOvv7663KH5BKmTJli8rquXbu2zvvk+Wp7RUVFQkhIiPS6Pf/883KHJOG5Wjnj12XJkiUWb3fkyBGTbb/99ttK2yYmJgoBAQFS2y+++MIKkYv/L8LCwqT9Dh8+vNK2Wq1W6Natm9R26NChVonBEdX2PdVqtcKIESNMtv/kk09sF2gd/fXXX4KXl5cU66OPPip3SDZV2/e1Jr799luT48yaNcsq+5XjXGWySIJerxc6duwo/TJFRkYKFy5cMGt38+ZNoXXr1lK7Tp06CXq9vsJ9Xrt2zeQkmT59epUxTJ48WWrr4eEhrFy50qxNcXGx8Pjjj0vt1Gq1kJycXKuf2dVpNBph4MCB0mulUCiE77//vs77tfR9zcrKEoYPHy4cPXq02n0uX75c8PPzk/bp6+srpKSk1DlWV7R161ZhypQpQmJiYpXtiouLhbfeesvkverSpQvPVyexcuVKk/dj165dNd4Hz1X7q8sH0GHDhknb+vv7C/v37zdrk52dLfTt21dqFxERIRQUFFgcU3VfOnz99dcm7b/55huzNnq9Xnj99ddN/rccO3asJj+qU6nNe6rX64XRo0ebbPv+++/bJKaq3tMRI0YIu3btqvTvvsGOHTtMkg+FQiGcOHHCavE6otq8r2fPnhVeeOEF4eLFi1W20+v1wqxZs0w6VqKiopz6XC2bqYDclkKhwHfffYf+/fujsLAQKSkp6NGjByZOnIh+/fpBpVLhyJEjmDt3rjTkTK1WY+HChSazMtXFjBkzsHnzZly6dAk6nQ4jR47E008/jUcffRQhISGIj4/HvHnzcPr0aWmbL774AlFRUVY5vquZPXs2duzYIdXr1auH33//3WRIRFUGDRpksuZSTQmCgNWrV2P16tVo1aoVBg8ejE6dOiEyMhJ+fn7Izc3FmTNnsHLlSvz111/SdgqFAkuWLDGbQYxEBQUF+PrrrzFr1iz06dMH/fv3R7t27RAaGgovLy+kp6fjyJEjWLZsmcnsZyEhIVi+fDnPVydhPAQ1Li7OZK1Ma+O5WnPjxo3Dzz//XG2bioYbVrYm5ezZs3Hw4EGkp6cjLy8PAwYMwIsvvoj7778f/v7+OH36NObMmSNNUqZUKrFw4UKo1eq6/0ClJk2ahJUrV+LgwYMAgP/7v//Dzp078cwzzyAiIgIJCQn4/vvvTa6Tmjp1Krp06WK1GORizfd0xYoV+Omnn6S6j48PDh8+jCFDhlgUS4cOHfD5559b1LYqO3bswKpVqxAbG4sHH3wQnTt3RkxMDAICApCfn4/4+HisW7fOZJgsAMycOROdOnWq8/EdgTXf15KSEixevBiLFy9G165dcd9996Fjx44IDw+HWq1GZmYmTpw4gV9++cXkenNvb2/8+uuvzn2u1irFJJe0atUqQa1Wm3xbUdFNrVYLq1atqnJfNe2pEARBiI+PF2JiYqo9PgDhzTfftNJP7ZqmT59u0etY2a2yb7UsfV8zMzNrfMyAgABh+fLltntRXMCaNWtq/Lo2b95cOH78eJX75fnqOFJTUwWVSiW9dtOmTavVfniu2s7zzz9f67+tVTlw4IDJ8OPKbh4eHsKcOXMsitWSv+vGbt++LbRv396in+Wpp54SdDqdRXE4Omu+p0uWLKnT/9/+/ftXGaul72lQUFCNjuvl5SV89dVXdXwlHYs139cTJ07UeB8RERHC9u3bLYrVkc9VTnBDkuHDh+PYsWMYOHBghT0QCoUCAwYMwNGjRzF8+HCrH79FixY4ffo0XnzxxUq/gWndujXWrVuHmTNnWv34ZD1qtRrjx49H27Ztq+3NCgoKwuTJk3H27Fk89dRTdorQObVq1QqjRo0ymT2xMo0aNcLnn3+OEydO2GSiAp6vtrF8+XJotVqpbuu1FXmuOo7evXvj9OnTGDFihMkSNca6deuGvXv34h//+IdNYggLC8ORI0fwxhtvVDpzY1xcHBYtWoTly5dDqeTHSEc1fvx4dOnSBR4eHlW2U6vVGDNmDE6cOIEpU6bYKTrnExkZidGjR1s0oVSDBg3w3nvv4cyZMxg4cKBN4rHnuaoozWaJTCQmJuLAgQNITk4GADRs2BB9+vRBTEyMXY6fm5uLXbt2ITExEfn5+YiMjET79u05O5cTyszMxMmTJ3H79m2kp6cjKysLvr6+CAkJQYcOHdChQ4dq/5mRuRs3buD8+fNIT09Heno68vPzERgYiPDwcNx1111WmSHRUjxfXQPPVceRlpaGvXv3IikpCcXFxYiKisJdd92Fli1b2i0GjUaDPXv2ICEhAZmZmWjQoAFatWqFXr16WW1IO9leXl4eTpw4gVu3biE9PR2ZmZnw9vZGcHAw2rRpgy5dusDLy0vuMJ1KamoqTp8+jbS0NKSnpyM3Nxf+/v4IDQ1F586d0bp1a7ueI7Y+V5ksEhERERERkRmOHyAiIiIiIiIzTBaJiIiIiIjIDJNFIiIiIiIiMsNkkYiIiIiIiMwwWSQiIiIiIiIzTBaJiIiIiIjIDJNFIiIiIiIiMsNkkYiIiIiIiMwwWSQiIiIiIiIzTBaJiIiIiIjIDJNFIiIiIiIiMsNkkYiIiIiIiMwwWSQiIiIiIiIzTBaJiIiIiIjIDJNFIiIiIiIiMsNkkYiIiIiIiMwwWSQiIiIiIiIzTBaJiIic2IwZM6BQKKBQKNCiRQsUFxfXaPutW7dK2ysUCty+fdtGkRIRkbNhskhEROSkLl26hM8++0yqf/PNN/Dy8qrRPu666y6T+v79+60SGxEROT8mi0RERE5q0qRJKCoqAgAMGTIEQ4cOrfE+6tevj9jYWKl+4MABq8VHRETOjckiERGRE9q+fTu2b98u1T/66KNa76tx48ZS+cKFC3WKi4iIXAeTRSIiIic0bdo0qfzAAw+YDSetiYYNG0rly5cv1ykuIiJyHUwWiYiInMzOnTtx+PBhqf7GG2/UaX9hYWFSOSUlpU77IiIi18FkkYiIyMnMnz9fKjdu3Bj33HNPnfanUCiksuEaSCIiIpXcARAREZHlMjIysG7dOqk+evRok2TPWH5+PgoLCwEAgYGBlc6UKghChWUiInJv7FkkIiJyIjt37kRJSYlUHzx4cKVtx4wZg7CwMISFheHo0aOVtrt586ZUbtCggXUCJSIip8dkkYiIyIns3r1bKvv5+aFbt26Vtv3rr7+kcrt27Sptd+PGDalsvIwGERG5NyaLRERETuTs2bNSuV27dlCpKr6iJDk5GdevXwcAREREIDAwsMJ2Wq0WZ86ckepVJZ9ERORemCwSERE5kUuXLknlli1bVtrOeA3G6OjoStudOHECBQUFUr1Pnz51jJCIiFwFk0UiIiInodfrkZqaKtWrur5w/fr1UjkkJKTSdhs2bJDKKpUKAwYMqGOURETkKpgsEhEROQmNRmNS9/b2rrDdnTt3sGnTJqnu6elZYTtBEPDLL79I9YEDB6J+/fpWiJSIiFwBk0UiIiIn4eHhYbJMxp07dypsN3fuXBQVFUltMzIyKmy3fv16k2Gt48aNs2K0RETk7BQCF1QiIiJyGhEREdJQ1A4dOuDUqVMmz1+/fh3t2rVDXl4e7r33XuzevRv+/v7IyMgwWWcxKysLXbt2xdWrVwEA7du3x6lTpypds5GIiNwPexaJiIicSN++faXy6dOnMX/+fKmekJCAoUOHIi8vDy1atMCTTz4JAMjLy8OXX34ptbt+/ToefPBBKVH08PDAggULmCgSEZEJ9iwSERE5ke3bt+P+++83eaxVq1YICQnBsWPHpOGn27ZtQ0REBNq3by+169ChA3x8fHD8+HFotVrp8W+++Qavv/66vX4EIiJyEkwWiYiInMyUKVPwzTffVPicSqXCf//7X+n6wxEjRmD16tUVtvX398esWbPw4osv2ixWIiJyXkwWiYiInNDq1auxYMECnDx5Enfu3EFYWBjuvfdevPHGG+jUqZPUTqPR4OOPP8Zvv/2GGzduwNfXF40bN8bQoUMxceJEREVFyfdDEBGRQ2OySERERERERGY4wQ0RERERERGZYbJIREREREREZpgsEhERERERkRkmi0RERERERGSGySIRERERERGZYbJIREREREREZpgsEhERERERkRkmi0RERERERGSGySIRERERERGZYbJIREREREREZpgsEhERERERkRkmi0RERERERGSGySIRERERERGZYbJIREREREREZpgsEhHR/7dfBwIAAAAAgvytB1ihLAIAGFkEAABgAv2f0VQl9sIDAAAAAElFTkSuQmCC", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the components of the fit separately:\n", - "plt.rcParams[\"font.size\"] = 25\n", - "plt.rcParams[\"figure.figsize\"] = (10, 5)\n", - "\n", - "\n", - "def plot_fit(func, J, w, lam, gamma, w0):\n", - " \"\"\"Plot the individual components of a fit to the spectral density.\n", - " and how they contribute to the full fit one by one\"\"\"\n", - " total = 0\n", - " for i in range(len(lam)):\n", - " component = func(w, lam[i], gamma[i], w0[i])\n", - " total += component\n", - " plt.plot(w, J, \"r--\", linewidth=2, label=\"original\")\n", - " plt.plot(w, total, label=rf\"$k={i+1}$\")\n", - " plt.xlabel(r\"$\\omega$\")\n", - " plt.ylabel(r\"$J(\\omega)$\")\n", - " plt.legend()\n", - " plt.pause(1)\n", - " plt.show()\n", - "\n", - "\n", - "def plot_fit_components(func, J, w, lam, gamma, w0):\n", - " \"\"\"Plot the individual components of a fit to the spectral density.\n", - " and how they contribute to the full fit\"\"\"\n", - " plt.plot(w, J, \"r--\", linewidth=2, label=\"original\")\n", - " for i in range(len(lam)):\n", - " component = func(w, lam[i], gamma[i], w0[i])\n", - " plt.plot(w, component, label=rf\"$k={i+1}$\")\n", - " plt.xlabel(r\"$\\omega$\")\n", - " plt.ylabel(r\"$J(\\omega)$\")\n", - " plt.legend(bbox_to_anchor=(1.04, 1))\n", - " plt.show()\n", - "\n", - "\n", - "lam=fitinfo[\"params\"][:,0]\n", - "gamma=fitinfo[\"params\"][:,1] \n", - "w0 = fitinfo[\"params\"][:,2]\n", - "plot_fit(_sd_fit_model, J, w, lam, gamma, w0)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "c05f2af0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_fit_components(_sd_fit_model, J, w, lam, gamma, w0)" - ] - }, - { - "cell_type": "markdown", - "id": "27fa30a5", - "metadata": {}, - "source": [ - "And let's also compare the power spectrum of the fit and the analytical spectral density:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "72deb34d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_power_spectrum(alpha, wc, beta, save=True):\n", - " \"\"\"Plot the power spectrum of a fit against the actual power spectrum.\"\"\"\n", - " w = np.linspace(-10, 10, 50000)\n", - " s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta)\n", - " s_fit = bath.power_spectrum(w)\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " axes.plot(w, s_orig, \"r\", linewidth=2, label=\"original\")\n", - " axes.plot(w, np.real(s_fit), \"b\", linewidth=2, label=\"fit\")\n", - "\n", - " axes.set_xlabel(r\"$\\omega$\", fontsize=28)\n", - " axes.set_ylabel(r\"$S(\\omega)$\", fontsize=28)\n", - " axes.legend()\n", - "\n", - " if save:\n", - " fig.savefig(\"powerspectrum.eps\")\n", - "\n", - "\n", - "plot_power_spectrum(alpha, wc, 1 / T, save=False)" - ] - }, - { - "cell_type": "markdown", - "id": "1c2e4446", - "metadata": {}, - "source": [ - "Now that we have a good fit to the spectral density, Let us obtain its dynamics, by passing our ``FitSpectral`` bath specifications into the ``HEOMSolver``" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "cb90d87a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 8.87s*] Elapsed 8.87s / Remaining 00:00:00:00\n" - ] - } - ], - "source": [ - "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - "HEOM_spectral_fit = HEOMSolver(\n", - " Hsys,\n", - " (bath,Q),\n", - " max_depth=4,\n", - " options=options,\n", - ")\n", - "result_spectral = HEOM_spectral_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "5bb8eb36", - "metadata": {}, - "source": [ - "Now if we want to see the systems's behaviour as we change the Number of terms in the fit, we may use this auxiliary function" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "5a8a930d", - "metadata": {}, - "outputs": [], - "source": [ - "def generate_spectrum_results(Q, N, Nk, max_depth):\n", - " \"\"\"Run the HEOM with the given bath parameters and\n", - " and return the results of the evolution.\n", - " \"\"\"\n", - " bath, _= sd_env.approx_by_sd_fit(w,Nmax=N,Nk=Nk,target_rmse=None)\n", - " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - "\n", - " # This problem is a little stiff, so we use the BDF method to solve\n", - " # the ODE ^^^\n", - " print(f\"Starting calculations for N={N}, Nk={Nk} and max_depth={max_depth} ... \")\n", - " HEOM_spectral_fit = HEOMSolver(\n", - " Hsys,\n", - " (bath,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - " )\n", - " results_spectral_fit = HEOM_spectral_fit.run(rho0, tlist)\n", - " return results_spectral_fit" - ] - }, - { - "cell_type": "markdown", - "id": "9ea58304", - "metadata": {}, - "source": [ - "Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "0273c6cb", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_result_expectations(plots, axes=None):\n", - " \"\"\"Plot the expectation values of operators as functions of time.\n", - "\n", - " Each plot in plots consists of (solver_result,\n", - " measurement_operation, color, label).\n", - " \"\"\"\n", - " if axes is None:\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " fig_created = True\n", - " else:\n", - " fig = None\n", - " fig_created = False\n", - "\n", - " # add kw arguments to each plot if missing\n", - " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", - " for result, m_op, color, label, kw in plots:\n", - " exp = np.real(expect(result.states, m_op))\n", - " kw.setdefault(\"linewidth\", 2)\n", - " if color == \"rand\":\n", - " axes.plot(\n", - " result.times,\n", - " exp,\n", - " c=np.random.rand(\n", - " 3,\n", - " ),\n", - " label=label,\n", - " **kw,\n", - " )\n", - " else:\n", - " axes.plot(result.times, exp, color, label=label, **kw)\n", - "\n", - " if fig_created:\n", - " axes.legend(loc=0, fontsize=12)\n", - " axes.set_xlabel(\"t\", fontsize=28)\n", - "\n", - " return fig" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "96b86c48", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting calculations for N=1, Nk=1 and max_depth=5 ... \n", - " Total run time: 1.22s*] Elapsed 1.22s / Remaining 00:00:00:00\n", - "Starting calculations for N=2, Nk=1 and max_depth=5 ... \n", - " Total run time: 1.74s*] Elapsed 1.74s / Remaining 00:00:00:00\n", - "Starting calculations for N=3, Nk=1 and max_depth=5 ... \n", - " Total run time: 3.70s*] Elapsed 3.70s / Remaining 00:00:00:00\n", - "Starting calculations for N=4, Nk=1 and max_depth=5 ... \n", - " Total run time: 11.75s*] Elapsed 11.74s / Remaining 00:00:00:00\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# # Generate results for different number of lorentzians in fit:\n", - "\n", - "results_spectral_fit_pk = [\n", - " generate_spectrum_results(Q, n, Nk=1, max_depth=max_depth) for n in range(1, 5)\n", - "]\n", - "\n", - "plot_result_expectations(\n", - " [\n", - " (\n", - " result,\n", - " P11p,\n", - " \"rand\",\n", - " f\"P11 (spectral fit) $k_J$={pk + 1}\",\n", - " )\n", - " for pk, result in enumerate(results_spectral_fit_pk)\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "980af0cd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting calculations for N=4, Nk=2 and max_depth=5 ... \n", - " Total run time: 58.93s*] Elapsed 58.93s / Remaining 00:00:00:00\n", - "Starting calculations for N=4, Nk=3 and max_depth=5 ... \n", - " Total run time: 175.24s*] Elapsed 175.24s / Remaining 00:00:00:00\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# generate results for different number of Matsubara terms per Lorentzian\n", - "# for max number of Lorentzians:\n", - "\n", - "Nk_list = range(2, 4)\n", - "results_spectral_fit_nk = [\n", - " generate_spectrum_results(Q, 4, Nk=Nk, max_depth=max_depth) for Nk in Nk_list\n", - "]\n", - "\n", - "plot_result_expectations(\n", - " [\n", - " (\n", - " result,\n", - " P11p,\n", - " \"rand\",\n", - " f\"P11 (spectral fit) K={nk+1}\",\n", - " )\n", - " for nk, result in zip(Nk_list, results_spectral_fit_nk)\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "eb904688", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting calculations for N=4, Nk=1 and max_depth=2 ... \n", - " Total run time: 1.79s*] Elapsed 1.79s / Remaining 00:00:00:00\n", - "Starting calculations for N=4, Nk=1 and max_depth=3 ... \n", - " Total run time: 2.99s*] Elapsed 2.98s / Remaining 00:00:00:00\n", - "Starting calculations for N=4, Nk=1 and max_depth=4 ... \n", - " Total run time: 5.26s*] Elapsed 5.26s / Remaining 00:00:00:00\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Generate results for different depths:\n", - "\n", - "Nc_list = range(2, max_depth)\n", - "results_spectral_fit_nc = [\n", - " generate_spectrum_results(Q, 4, Nk=1, max_depth=Nc) for Nc in Nc_list\n", - "]\n", - "\n", - "plot_result_expectations(\n", - " [\n", - " (\n", - " result,\n", - " P11p,\n", - " \"rand\",\n", - " f\"P11 (spectral fit) $N_C={nc}$\",\n", - " )\n", - " for nc, result in zip(Nc_list, results_spectral_fit_nc)\n", - " ]\n", - " );" - ] - }, - { - "cell_type": "markdown", - "id": "844af288", - "metadata": {}, - "source": [ - "#### We now combine the fitting and correlation function data into one large plot. Here we define a function to plot everything together" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "7fc617a1", - "metadata": {}, - "outputs": [], - "source": [ - "def gen_plots(fs, w, J, t, C, w2, S):\n", - " def plot_cr_fit_vs_actual(t, C, func, axes):\n", - " \"\"\"Plot the C_R(t) fit.\"\"\"\n", - " yR = func(t)\n", - "\n", - " axes.plot(\n", - " t,\n", - " np.real(C),\n", - " \"r\",\n", - " linewidth=3,\n", - " label=\"Original\",\n", - " )\n", - " axes.plot(\n", - " t,\n", - " np.real(yR),\n", - " \"g\",\n", - " dashes=[3, 3],\n", - " linewidth=2,\n", - " label=\"Reconstructed\",\n", - " )\n", - "\n", - " axes.set_ylabel(r\"$C_R(t)$\", fontsize=28)\n", - " axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=28)\n", - " axes.locator_params(axis=\"y\", nbins=4)\n", - " axes.locator_params(axis=\"x\", nbins=4)\n", - " axes.text(0.15, 0.85, \"(a)\", fontsize=28, transform=axes.transAxes)\n", - "\n", - " def plot_ci_fit_vs_actual(t, C, func, axes):\n", - " \"\"\"Plot the C_I(t) fit.\"\"\"\n", - " yI = func(t)\n", - "\n", - " axes.plot(\n", - " t,\n", - " np.imag(C),\n", - " \"r\",\n", - " linewidth=3,\n", - " )\n", - " axes.plot(\n", - " t,\n", - " np.real(yI),\n", - " \"g\",\n", - " dashes=[3, 3],\n", - " linewidth=2,\n", - " )\n", - "\n", - " axes.set_ylabel(r\"$C_I(t)$\", fontsize=28)\n", - " axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=28)\n", - " axes.locator_params(axis=\"y\", nbins=4)\n", - " axes.locator_params(axis=\"x\", nbins=4)\n", - " axes.text(0.80, 0.80, \"(b)\", fontsize=28, transform=axes.transAxes)\n", - "\n", - " def plot_jw_fit_vs_actual(w, J, axes):\n", - " \"\"\"Plot the J(w) fit.\"\"\"\n", - " J_fit = fs.spectral_density(w)\n", - "\n", - " axes.plot(\n", - " w,\n", - " J,\n", - " \"r\",\n", - " linewidth=3,\n", - " )\n", - " axes.plot(\n", - " w,\n", - " J_fit,\n", - " \"g\",\n", - " dashes=[3, 3],\n", - " linewidth=2,\n", - " )\n", - "\n", - " axes.set_ylabel(r\"$J(\\omega)$\", fontsize=28)\n", - " axes.set_xlabel(r\"$\\omega/\\omega_c$\", fontsize=28)\n", - " axes.locator_params(axis=\"y\", nbins=4)\n", - " axes.locator_params(axis=\"x\", nbins=4)\n", - " axes.text(0.15, 0.85, \"(c)\", fontsize=28, transform=axes.transAxes)\n", - "\n", - " def plot_sw_fit_vs_actual(axes):\n", - " \"\"\"Plot the S(w) fit.\"\"\"\n", - "\n", - " # avoid the pole in the fit around zero:\n", - " s_fit = fs.power_spectrum(w2)\n", - "\n", - " axes.plot(w2, S, \"r\", linewidth=3)\n", - " axes.plot(w2, s_fit, \"g\", dashes=[3, 3], linewidth=2)\n", - "\n", - " axes.set_ylabel(r\"$S(\\omega)$\", fontsize=28)\n", - " axes.set_xlabel(r\"$\\omega/\\omega_c$\", fontsize=28)\n", - " axes.locator_params(axis=\"y\", nbins=4)\n", - " axes.locator_params(axis=\"x\", nbins=4)\n", - " axes.text(0.15, 0.85, \"(d)\", fontsize=28, transform=axes.transAxes)\n", - "\n", - " def plot_matsubara_spectrum_fit_vs_actual(t, C):\n", - " \"\"\"Plot the Matsubara fit of the spectrum .\"\"\"\n", - " fig = plt.figure(figsize=(12, 10))\n", - " grid = plt.GridSpec(2, 2, wspace=0.4, hspace=0.3)\n", - "\n", - " plot_cr_fit_vs_actual(\n", - " t,\n", - " C,\n", - " lambda t: fs.correlation_function(t),\n", - " axes=fig.add_subplot(grid[0, 0]),\n", - " )\n", - " plot_ci_fit_vs_actual(\n", - " t,\n", - " C,\n", - " lambda t: np.imag(fs.correlation_function(t)),\n", - " axes=fig.add_subplot(grid[0, 1]),\n", - " )\n", - " plot_jw_fit_vs_actual(\n", - " w,\n", - " J,\n", - " axes=fig.add_subplot(grid[1, 0]),\n", - " )\n", - " plot_sw_fit_vs_actual(\n", - " axes=fig.add_subplot(grid[1, 1]),\n", - " )\n", - " fig.legend(loc=\"upper center\", ncol=2, fancybox=True, shadow=True)\n", - "\n", - " return plot_matsubara_spectrum_fit_vs_actual(t, C)" - ] - }, - { - "cell_type": "markdown", - "id": "674d5498", - "metadata": {}, - "source": [ - "#### And finally plot everything together" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "26209a1b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "t = np.linspace(0, 15, 1000)\n", - "C = ohmic_correlation(t, alpha, wc, 1 / T)\n", - "w2 = np.concatenate((-np.linspace(10, 1e-2, 100), np.linspace(1e-2, 10, 100)))\n", - "S = ohmic_power_spectrum(w2, alpha, wc, 1 / T)\n", - "gen_plots(bath, w, J, t, C, w2, S)" - ] - }, - { - "cell_type": "markdown", - "id": "a72989f8", - "metadata": {}, - "source": [ - "## Obtaining an decaying exponential description via the Correlation function" - ] - }, - { - "cell_type": "markdown", - "id": "81acee08", - "metadata": {}, - "source": [ - "Having successfully fitted the spectral density and used the result to calculate the Matsubara expansion and terminator for the HEOM bosonic bath, we now proceed to the second case of fitting the correlation function itself instead.\n", - "\n", - "Here we fit the real and imaginary parts separately, using the following ansatz \n", - "\n", - "$$C_R^F(t) = \\sum_{i=1}^{k_R} c_R^ie^{-\\gamma_R^i t}\\cos(\\omega_R^i t)$$\n", - "\n", - "$$C_I^F(t) = \\sum_{i=1}^{k_I} c_I^ie^{-\\gamma_I^i t}\\sin(\\omega_I^i t)$$\n", - "\n", - "Analogously to the spectral density case, one may use the `approx_by_cf_fit` method, the main difference with respect to the spectral density fit, is that now we are perfoming two fits, one for the real part and another one for the imaginary part" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "217905ff", - "metadata": {}, - "outputs": [], - "source": [ - "bath_corr ,fitinfo= sd_env.approx_by_cf_fit(tlist=t,Ni_max=4,Nr_max=4,maxfev=1e8)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "a861655e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlation function fit:\n", - "\n", - "Result of fitting the real part of |Result of fitting the imaginary part \n", - "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", - " | \n", - " Parameters| a | b | c | Parameters| a | b | c \n", - " 1 |-1.88e+00 |-4.65e+00 |2.64e+00 | 1 |-1.34e+01 |-1.08e+00 |2.73e-02 \n", - " 2 | 3.00e+00 |-2.88e+00 |3.05e-01 | 2 |-8.59e+00 |-3.78e-01 |1.03e-03 \n", - " 3 | 4.78e-02 |-1.63e-01 |2.98e-28 | 3 | 5.64e-01 |-4.30e+00 |3.95e+00 \n", - " 4 | 3.54e-01 |-6.27e-01 |1.71e-08 | 4 |-1.34e+01 |-2.31e+00 |2.90e-01 \n", - " | \n", - "A normalized RMSE of 3.12e-06 was obtained for the the real part of |A normalized RMSE of 4.89e-06 was obtained for the the imaginary part\n", - "the correlation function. |of the correlation function. \n", - "The current fit took 1.973140 seconds. |The current fit took 18.244712 seconds. \n", - "\n" - ] - } - ], - "source": [ - "print(fitinfo[\"summary\"])" - ] - }, - { - "cell_type": "markdown", - "id": "b8c32d8a", - "metadata": {}, - "source": [ - "The ansatz used is not good for functions where\n", - "\n", - "$$C_I^F(0) \\neq 0$$\n", - "\n", - "The keyword `full_ansatz` which defaults to False. allows for the usage of a \n", - "more general ansatz, the fit however tends to be significantly slower, never\n", - "the less it can reach a similar level of accuracy with a lower amount of exponents\n", - "\n", - "When full_ansatz is True. the ansatz used corresponds to \n", - "\n", - "\\begin{align}\n", - "\\operatorname{Re}[C(t)] = \\sum_{k=1}^{N_r} \\operatorname{Re}\\Bigl[\n", - " (a_k + \\mathrm i d_k) \\mathrm e^{(b_k + \\mathrm i c_k) t}\\Bigl]\n", - " ,\n", - "\\\\\n", - "\\operatorname{Im}[C(t)] = \\sum_{k=1}^{N_i} \\operatorname{Im}\\Bigl[\n", - " (a'_k + \\mathrm i d'_k) \\mathrm e^{(b'_k + \\mathrm i c'_k) t}\n", - " \\Bigr].\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "57d768ee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2\n", - " Total run time: 1.65s*] Elapsed 1.64s / Remaining 00:00:00:00\n", - "3\n", - " Total run time: 3.62s*] Elapsed 3.62s / Remaining 00:00:00:00\n", - "4\n", - " Total run time: 76.18s*] Elapsed 76.18s / Remaining 00:00:00:00\n" - ] - } - ], - "source": [ - "def generate_corr_results(N, max_depth):\n", - " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - " bath_corr ,fitinfo= sd_env.approx_by_cf_fit(tlist=t,Ni_max=N,Nr_max=N,maxfev=1e8,target_rsme=None)\n", - " HEOM_corr_fit = HEOMSolver(\n", - " Hsys,\n", - " (bath_corr,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - " )\n", - "\n", - " results_corr_fit = HEOM_corr_fit.run(rho0, tlist)\n", - "\n", - " return results_corr_fit\n", - "\n", - "\n", - "# # Generate results for different number of exponentials in fit:\n", - "results_corr_fit_pk = [\n", - " print(f\"{i + 1}\")\n", - " or generate_corr_results(\n", - " i,\n", - " max_depth=max_depth,\n", - " )\n", - " for i in range(1, 4)]" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "91d1be7c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (\n", - " result,\n", - " P11p,\n", - " \"rand\",\n", - " f\"P11 (correlation fit) k_R=k_I={pk + 1}\",\n", - " )\n", - " for pk, result in enumerate(results_corr_fit_pk)\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "4770c53b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", - "\n", - "plot_result_expectations(\n", - " [\n", - " (\n", - " results_corr_fit_pk[0],\n", - " P11p,\n", - " \"y\",\n", - " \"Correlation Function Fit $k_R=k_I=1$\",\n", - " ),\n", - " (\n", - " results_corr_fit_pk[2],\n", - " P11p,\n", - " \"k\",\n", - " \"Correlation Function Fit $k_R=k_I=3$\",\n", - " ),\n", - " (results_spectral_fit_pk[0], P11p, \"b\", \"Spectral Density Fit $k_J=1$\"),\n", - " (results_spectral_fit_pk[3], P11p, \"r-.\", \"Spectral Density Fit $k_J=4$\"),\n", - " ],\n", - " axes=axes,\n", - ")\n", - "\n", - "axes.set_yticks([0.6, 0.8, 1])\n", - "axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", - "axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", - "axes.legend(loc=0, fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "id": "63716f70", - "metadata": {}, - "source": [ - "# Using the Ohmic Bath class\n", - "\n", - " As the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced quickly by using the OhmicBath class. This allows for rapid implementation of fitted ohmic baths via the correlation function or spectral density" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "4883e1cc", - "metadata": {}, - "outputs": [], - "source": [ - "obs = OhmicEnvironment(T, alpha, wc,s=1)\n", - "tlist = np.linspace(0, 30 * np.pi / Del, 600)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "78642c05", - "metadata": {}, - "outputs": [], - "source": [ - "obs.approximate()" - ] - }, - { - "cell_type": "markdown", - "id": "005418f5", - "metadata": {}, - "source": [ - "Just like the other `BosonicEnvironment` we can obtain a decaying exponential \n", - "representation of the environment via the `approx_by_cf_fit` and \n", - "`approx_by_sd_fit` methods. " - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "e0924e70", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlation function fit:\n", - "\n", - "Result of fitting the real part of |Result of fitting the imaginary part \n", - "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", - " | \n", - " Parameters| a | b | c | Parameters| a | b | c \n", - " 1 | 3.24e-01 |-5.34e-01 |3.32e-23 | 1 |-8.92e+00 |-3.49e-01 |7.57e-04 \n", - " 2 | 2.84e+00 |-2.76e+00 |6.88e-08 | 2 | 5.44e-01 |-4.30e+00 |4.00e+00 \n", - " 3 |-1.67e+00 |-4.72e+00 |2.77e+00 | 3 |-1.34e+01 |-1.04e+00 |2.50e-02 \n", - " 4 | 2.49e-02 |-1.09e-01 |1.08e-41 | 4 |-1.34e+01 |-2.29e+00 |2.90e-01 \n", - " | \n", - "A normalized RMSE of 1.18e-06 was obtained for the the real part of |A normalized RMSE of 6.20e-07 was obtained for the the imaginary part\n", - "the correlation function. |of the correlation function. \n", - "The current fit took 5.184511 seconds. |The current fit took 45.098052 seconds. \n", - "\n", - " Total run time: 341.74s*] Elapsed 341.74s / Remaining 00:00:00:00\n" - ] - } - ], - "source": [ - "tlist = np.linspace(0, 30 * np.pi / Del, 5000)\n", - "\n", - "Obath, fitinfo = obs.approx_by_cf_fit(tlist=tlist,Nr_max=4,Ni_max=4,maxfev=1e9,target_rsme=None)\n", - "print(fitinfo[\"summary\"])\n", - "HEOM_ohmic_corr_fit = HEOMSolver(\n", - " Hsys,\n", - " (Obath,Q),\n", - " max_depth=5,\n", - " options=options,\n", - ")\n", - "results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "ddbaebf2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result of fitting the spectral density with 4 terms: \n", - " \n", - " Parameters| lam | gamma | w0 \n", - " 1 | 6.79e-01 | 8.67e-01 |1.22e-01\n", - " 2 | 1.67e+00 | 9.17e-01 |1.13e+00\n", - " 3 | 1.56e+00 | 9.46e-01 |2.11e+00\n", - " 4 | 1.00e+00 | 1.03e+00 |3.32e+00\n", - " \n", - "A normalized RMSE of 4.39e-05 was obtained for the the spectral density.\n", - "The current fit took 45.485762 seconds.\n", - " Total run time: 10.91s*] Elapsed 10.91s / Remaining 00:00:00:00\n" - ] - } - ], - "source": [ - "Obath2, fitinfo = obs.approx_by_sd_fit(wlist=w,Nmax=4,Nk=1)\n", - "print(fitinfo[\"summary\"])\n", - "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - "HEOM_ohmic_sd_fit = HEOMSolver(\n", - " Hsys,\n", - " (Obath2,Q),\n", - " max_depth=5,\n", - " options=options,\n", - ")\n", - "results_ohmic_sd_fit2 = HEOM_ohmic_sd_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "50b833b1", - "metadata": {}, - "source": [ - "# Methods based on the Prony Polinomial \n", - "\n", - "The Prony polynomial forms the mathematical foundation for many spectral analysis techniques that estimate frequencies, damping factors, and amplitudes of signals. These methods work by interpreting a given signal as a sum of complex exponentials and deriving a polynomial whose roots correspond to the frequencies or poles of the system.\n", - "\n", - "The methods consider a signal \n", - "\n", - "$$f(t)=\\sum_{k=0}^{N-1} c_{k} e^{-\\nu_{k} t} =\\sum_{k=0}^{N-1} c_{k} z_{k}^{t} $$\n", - "\n", - "The $z_{k}$ can be seen as the solution og the Prony Polynomial\n", - "\n", - "$$P(z)=\\prod_{k=0}^{N-1}(z-z_{k})$$\n", - "\n", - "By obtaining the roots of this polynomial one can obtain the damping rate and the frequency of each mode, the amplitude can lated be obtained by solving the least-squares Vandermonde system given by\n", - "\n", - "$$ V_{N,M}(z)c = f $$\n", - "\n", - "Where $M$ is the length, of the signal, and $f=f(t_{sample})$ is the signal evaluated in the sampling points,is a vector $c = (c_{1}, \\dots, c_{N})$, and $V_{N,M}(z)$ is the Vandermonde matrix given by\n", - "\n", - "\n", - "$$V_{M,N}(z)=\\begin{pmatrix} \n", - "1 &1 &\\dots &1 \\\\\n", - "z_{1} & z_{2} &\\dots & z_{N} \\\\\n", - "z_{1}^{2} & z_{2}^{2} &\\dots & z_{N}^{2} \\\\\n", - "\\vdots & \\vdots & \\ddots & \\vdots \\\\\n", - "z_{1}^{M} & z_{2}^{M} &\\dots & z_{N}^{M} \\\\\n", - "\\end{pmatrix}$$\n", - "\n", - "The main difference between the methods is the way one obtains the roots of the polynomial, typically whether this system is solved or a low rank approximation is found for the polynomial, [this article](https://academic.oup.com/imajna/article-abstract/43/2/789/6525860?redirectedFrom=fulltext) is a good reference, the QuTiP implementations are based on it, and the matlab implementations made available by the authors\n" - ] - }, - { - "cell_type": "markdown", - "id": "f85ab699", - "metadata": {}, - "source": [ - "## Using the Original Prony Method on the Correlation Function\n", - "\n", - "The method is available via `approx_by_prony`. Compared to the other approaches showed so far. The Prony based methods, shine on their simplicity no information needs to be known about the function, and one just needs to provide the sampling points, and the Number of Exponents one desires" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "b75d4072", - "metadata": {}, - "outputs": [], - "source": [ - "tlist2=np.linspace(0,2_000,5000)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "4e24e35b", - "metadata": {}, - "outputs": [], - "source": [ - "pbath,(amp,ph)=obs.approx_by_prony(tlist2,Nr=5,Ni=5,combine=True)\n", - "pbath.T=T\n", - "# mask=abs(amp)>1\n", - "# amp=amp[mask]\n", - "# ph=ph[mask]\n", - "# print(\"done\")\n", - "# HEOM_ohmic_prony_fit = HEOMSolver(\n", - "# Hsys,\n", - "# (pbath,Q),\n", - "# max_depth=5,\n", - "# options=options,\n", - "# )\n", - "# results_ohmic_prony_fit = HEOM_ohmic_prony_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "a2faa5fb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "diff=(pbath.correlation_function(tlist2))-obs.correlation_function(tlist2)\n", - "tlist3=np.linspace(0,tlist2[np.argmax(diff)],1000)\n", - "# plt.plot(tlist3,pbath.correlation_function(tlist3),\"k--\")\n", - "# plt.plot(tlist3,Obath.correlation_function(tlist3),\"b\")\n", - "# plt.plot(tlist3,obs.correlation_function(tlist3),\"r\")\n", - "\n", - "plt.plot(abs(diff),label=\"Prony\")\n", - "plt.plot(abs(Obath.correlation_function(tlist2)-obs.correlation_function(tlist2)),label=\"CORR\")\n", - "plt.legend()\n", - "#plt.yscale(\"log\")" - ] - }, - { - "cell_type": "markdown", - "id": "af659e73", - "metadata": {}, - "source": [ - "Somehow the problems seems to be the way I construct the bath" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "b64a4d76", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 73.49s*] Elapsed 73.49s / Remaining 00:00:00:00\n" - ] - } - ], - "source": [ - "HEOM_ohmic_prony_fit = HEOMSolver(\n", - " Hsys,\n", - " (pbath,Q),\n", - " max_depth=5,\n", - " options=options,\n", - ")\n", - "results_ohmic_prony_fit = HEOM_ohmic_prony_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "10e50bf0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "gen_plots(pbath, w, J, t, C, w2, S)" - ] - }, - { - "cell_type": "markdown", - "id": "1d4ffc81", - "metadata": {}, - "source": [ - "## Using the matrix Pencil Method on the Correlation Function\n" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "7f14b9cb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 6.55s*] Elapsed 6.55s / Remaining 00:00:00:00\n" - ] - } - ], - "source": [ - "mpbath,_=obs.approx_by_mp(tlist2,Nr=4,Ni=4)\n", - "mpbath.T=T\n", - "HEOM_ohmic_mp_fit = HEOMSolver(\n", - " Hsys,\n", - " (mpbath,Q),\n", - " max_depth=5,\n", - " options=options,\n", - ")\n", - "results_ohmic_mp_fit = HEOM_ohmic_mp_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "3b334563", - "metadata": {}, - "source": [ - "The decomposition is ok, the heom solver is the one failing try with other smaller couplings, and hierarchies untill you can figure it out, the accelerating part works without trouble" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "3ed89ed7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "gen_plots(mpbath, w, J, t, C, w2, S)" - ] - }, - { - "cell_type": "markdown", - "id": "c04f6f61", - "metadata": {}, - "source": [ - "## Using the ESPRIT Method on the Correlation Function\n" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "7708f4f1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 12.05s*] Elapsed 12.05s / Remaining 00:00:00:00\n" - ] - } - ], - "source": [ - "esbath,_=obs.approx_by_esprit(tlist2,Nr=5,Ni=4)\n", - "esbath.T=T\n", - "HEOM_ohmic_es_fit = HEOMSolver(\n", - " Hsys,\n", - " (esbath,Q),\n", - " max_depth=5,\n", - " options=options,\n", - ")\n", - "results_ohmic_es_fit = HEOM_ohmic_es_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "ad89de4e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "diff=(esbath.correlation_function(tlist2))-obs.correlation_function(tlist2)\n", - "tlist3=np.linspace(0,tlist2[np.argmax(diff)],1000)\n", - "# plt.plot(tlist3,pbath.correlation_function(tlist3),\"k--\")\n", - "# plt.plot(tlist3,Obath.correlation_function(tlist3),\"b\")\n", - "# plt.plot(tlist3,obs.correlation_function(tlist3),\"r\")\n", - "\n", - "plt.plot(abs(diff),label=\"Prony\")\n", - "plt.plot(abs(Obath.correlation_function(tlist2)-obs.correlation_function(tlist2)),label=\"CORR\")\n", - "plt.legend()\n", - "#plt.yscale(\"log\")" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "0d282401", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "gen_plots(esbath, w, J, t, C, w2, S)" - ] - }, - { - "cell_type": "markdown", - "id": "413f223a", - "metadata": {}, - "source": [ - "## Using the AAA Algorithm" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "5a685a80", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/mcditoos/qutip_gsoc_app/qutip/utilities.py:54: RuntimeWarning: overflow encountered in exp\n", - " result[non_zero] = 1 / (np.exp(w[non_zero] / w_th) - 1)\n" - ] - } - ], - "source": [ - "aaabath=obs.approx_by_aaa(np.concatenate((-np.logspace(3,-2,2500),np.logspace(-2,3,2500))),N_max=12,tol=1e-15)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "44f9f518", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tlist3=np.linspace(-250,250,1000)\n", - "\n", - "diff=(pbath.correlation_function(tlist3))-obs.correlation_function(tlist3)\n", - "diff2=(aaabath.correlation_function(tlist3))-obs.correlation_function(tlist3)\n", - "\n", - "# plt.plot(tlist3,pbath.correlation_function(tlist3),\"k--\")\n", - "# plt.plot(tlist3,Obath.correlation_function(tlist3),\"b\")\n", - "# plt.plot(tlist3,obs.correlation_function(tlist3),\"r\")\n", - "\n", - "\n", - "\n", - "plt.plot(diff.real,label=\"Prony\")\n", - "plt.plot(diff2.real,label=\"AA\")\n", - "\n", - "#plt.plot(abs(Obath.correlation_function(tlist3)-obs.correlation_function(tlist3)),label=\"CORR\")\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "787b1ae6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " [******** 33% ] Elapsed 786.49s / Remaining 00:00:26:36" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[53], line 7\u001b[0m\n\u001b[1;32m 1\u001b[0m HEOM_ohmic_aaa_fit \u001b[38;5;241m=\u001b[39m HEOMSolver(\n\u001b[1;32m 2\u001b[0m Hsys,\n\u001b[1;32m 3\u001b[0m (aaabath,Q),\n\u001b[1;32m 4\u001b[0m max_depth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m,\n\u001b[1;32m 5\u001b[0m options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[1;32m 6\u001b[0m )\n\u001b[0;32m----> 7\u001b[0m results_ohmic_aaa_fit \u001b[38;5;241m=\u001b[39m \u001b[43mHEOM_ohmic_aaa_fit\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrho0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtlist\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/heom/bofin_solvers.py:1119\u001b[0m, in \u001b[0;36mHEOMSolver.run\u001b[0;34m(self, state0, tlist, args, e_ops)\u001b[0m\n\u001b[1;32m 1052\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun\u001b[39m(\u001b[38;5;28mself\u001b[39m, state0, tlist, \u001b[38;5;241m*\u001b[39m, args\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, e_ops\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 1053\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1054\u001b[0m \u001b[38;5;124;03m Solve for the time evolution of the system.\u001b[39;00m\n\u001b[1;32m 1055\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1117\u001b[0m \u001b[38;5;124;03m list of attributes.\u001b[39;00m\n\u001b[1;32m 1118\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1119\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtlist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43me_ops\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43me_ops\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/solver_base.py:197\u001b[0m, in \u001b[0;36mSolver.run\u001b[0;34m(self, state0, tlist, e_ops, args)\u001b[0m\n\u001b[1;32m 192\u001b[0m stats[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpreparation time\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m time() \u001b[38;5;241m-\u001b[39m _time_start\n\u001b[1;32m 194\u001b[0m progress_bar \u001b[38;5;241m=\u001b[39m progress_bars[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprogress_bar\u001b[39m\u001b[38;5;124m'\u001b[39m]](\n\u001b[1;32m 195\u001b[0m \u001b[38;5;28mlen\u001b[39m(tlist)\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprogress_kwargs\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 196\u001b[0m )\n\u001b[0;32m--> 197\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_integrator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtlist\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 198\u001b[0m \u001b[43m \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 199\u001b[0m \u001b[43m \u001b[49m\u001b[43mresults\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_restore_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/integrator/integrator.py:201\u001b[0m, in \u001b[0;36mIntegrator.run\u001b[0;34m(self, tlist)\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 188\u001b[0m \u001b[38;5;124;03mIntegrate the system yielding the state for each times in tlist.\u001b[39;00m\n\u001b[1;32m 189\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;124;03m The state of the solver at each ``t`` of tlist.\u001b[39;00m\n\u001b[1;32m 199\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m tlist[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[0;32m--> 201\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/integrator/qutip_integrator.py:55\u001b[0m, in \u001b[0;36mIntegratorVern7.integrate\u001b[0;34m(self, t, copy)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mintegrate\u001b[39m(\u001b[38;5;28mself\u001b[39m, t, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 55\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_ode_solver\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_failed_integration()\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_state(copy)\n", - "File \u001b[0;32mqutip/solver/integrator/explicit_rk.pyx:278\u001b[0m, in \u001b[0;36mqutip.solver.integrator.explicit_rk.Explicit_RungeKutta.integrate\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mqutip/solver/integrator/explicit_rk.pyx:313\u001b[0m, in \u001b[0;36mqutip.solver.integrator.explicit_rk.Explicit_RungeKutta.integrate\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "HEOM_ohmic_aaa_fit = HEOMSolver(\n", - " Hsys,\n", - " (aaabath,Q),\n", - " max_depth=5,\n", - " options=options,\n", - ")\n", - "results_ohmic_aaa_fit = HEOM_ohmic_aaa_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "80f55ad6", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "gen_plots(aaabath, w, J, t, C, w2, S)" - ] - }, - { - "cell_type": "markdown", - "id": "0f305b40", - "metadata": {}, - "source": [ - "Finally we plot the dynamics obtained by the different methods" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5ba2889a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", - "\n", - "plot_result_expectations(\n", - " [\n", - " #(\n", - " # results_corr_fit_pk[2],\n", - " # P11p,\n", - " # \"b\",\n", - " # \"Correlation Function Fit $k_R=k_I=3$\",\n", - " # ),\n", - " #(results_spectral_fit_pk[3], P11p, \"r-.\", \"Spectral Density Fit $k_J=4$\"),\n", - " (results_ohmic_corr_fit, P11p, \"r\", \"Correlation Fit Ohmic Bath\"),\n", - " #(results_ohmic_sd_fit, P11p, \"g\", \"Spectral Density Fit Ohmic Bath\"),\n", - " (results_ohmic_sd_fit2, P11p, \"g--\", \"Spectral Density Fit Ohmic Bath\"),\n", - " #(results_ohmic_prony_fit, P11p, \"g\", \" Prony Fit\"),\n", - " (results_ohmic_prony_fit, P11p, \"k\", \" Prony Fit\"),\n", - "\n", - " (results_ohmic_mp_fit, P11p, \"r\", \"Matrix Pencil Fit\"),\n", - " (results_ohmic_es_fit, P11p, \"b-.\", \"ESPRIT Fit\"),\n", - " (results_ohmic_aaa_fit, P11p, \"r-.\", \"Matrix AAA Fit\"),\n", - "\n", - "\n", - " ],\n", - " axes=axes,\n", - ")\n", - "axes.set_yticks([0.6, 0.8, 1])\n", - "axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", - "axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", - "axes.legend(loc=0, fontsize=20);\n", - "#axes.set_xlim(0,35)\n", - "#axes.set_ylim(0.9,1)\n", - "#axes.set_yscale(\"log\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bae93823", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "d0fc9218", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e1eb99ec", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "QuTiP: Quantum Toolbox in Python\n", - "================================\n", - "Copyright (c) QuTiP team 2011 and later.\n", - "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", - "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", - "Original developers: R. J. Johansson & P. D. Nation.\n", - "Previous lead developers: Chris Granade & A. Grimsmo.\n", - "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", - "\n", - "QuTiP Version: 5.1.0.dev0+7941773\n", - "Numpy Version: 1.26.4\n", - "Scipy Version: 1.14.1\n", - "Cython Version: 3.0.9\n", - "Matplotlib Version: 3.9.2\n", - "Python Version: 3.12.7\n", - "Number of CPUs: 16\n", - "BLAS Info: Generic\n", - "INTEL MKL Ext: None\n", - "Platform Info: Linux (x86_64)\n", - "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", - "================================================================================\n", - "Please cite QuTiP in your publication.\n", - "================================================================================\n", - "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" - ] - } - ], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "725e989d", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fa50ddbb", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'results_spectral_fit_pk' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[59], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m np\u001b[38;5;241m.\u001b[39mallclose(\u001b[43mresults_spectral_fit_pk\u001b[49m[\u001b[38;5;241m3\u001b[39m]\u001b[38;5;241m.\u001b[39mstates[\u001b[38;5;241m5\u001b[39m]\u001b[38;5;241m.\u001b[39mfull(),results_corr_fit_pk[\u001b[38;5;241m2\u001b[39m]\u001b[38;5;241m.\u001b[39mstates[\u001b[38;5;241m5\u001b[39m]\u001b[38;5;241m.\u001b[39mfull(),atol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-3\u001b[39m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'results_spectral_fit_pk' is not defined" - ] - } - ], - "source": [ - "assert np.allclose(results_spectral_fit_pk[3].states[5].full(),results_corr_fit_pk[2].states[5].full(),atol=1e-3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a7fb31c", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "414ba293", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "80d35a6b", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "20dd8b39", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ed975955", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b6b493d", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9184bc82", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d61f4c20", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7b7f2f42", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cebe18a4", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2a006120", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "12b235a3", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "qutip-dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.ipynb b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.ipynb index 939e9657..8bcf0081 100644 --- a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.ipynb +++ b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.ipynb @@ -408,9 +408,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "RHS construction time: 0.06298303604125977\n", - " Total run time: 1.21s*] Elapsed 1.21s / Remaining 00:00:00:00\n", - "ODE solver time: 1.2107598781585693\n" + "RHS construction time: 0.007760763168334961\n", + " [ 1% ] Elapsed 0.04s / Remaining 00:00:00:03" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 1.66s*] Elapsed 1.66s / Remaining 00:00:00:00\n", + "ODE solver time: 1.6655654907226562\n" ] } ], @@ -475,9 +482,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "RHS construction time: 0.006415605545043945\n", - " Total run time: 1.30s*] Elapsed 1.30s / Remaining 00:00:00:00\n", - "ODE solver time: 1.3027725219726562\n" + "RHS construction time: 0.00848531723022461\n", + " Total run time: 1.65s*] Elapsed 1.65s / Remaining 00:00:00:00\n", + "ODE solver time: 1.651024580001831\n" ] } ], @@ -535,7 +542,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -578,10 +585,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "01aedd06", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.006570577621459961\n", + " Total run time: 1.90s*] Elapsed 1.90s / Remaining 00:00:00:00\n", + "ODE solver time: 1.8980872631072998\n" + ] + } + ], "source": [ "# Compare to legacy class:\n", "\n", @@ -597,10 +614,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "461ae04e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "def plot_correlation_expansion_divergence():\n", " \"\"\"We plot the correlation function with a large number of Matsubara terms\n", @@ -693,10 +732,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "1ee891d1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.0036385059356689453\n", + " Total run time: 2.05s*] Elapsed 2.04s / Remaining 00:00:00:00\n", + "ODE solver time: 2.04779052734375\n" + ] + } + ], "source": [ "# Run HEOM solver and include the Ishizaki-Tanimura terminator\n", "\n", @@ -735,10 +784,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "b2df9cc0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_result_expectations(\n", " [\n", @@ -758,10 +818,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "b4f84c83", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.007706880569458008\n", + " [** 9% ] Elapsed 0.14s / Remaining 00:00:00:01" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 1.77s*] Elapsed 1.77s / Remaining 00:00:00:00\n", + "ODE solver time: 1.7673842906951904\n" + ] + } + ], "source": [ "options = {**default_options, \"rtol\": 1e-14, \"atol\": 1e-14}\n", "\n", @@ -776,10 +853,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "2b88277e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_result_expectations(\n", " [\n", @@ -799,10 +887,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "be8f8acf", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 1.48s*] Elapsed 1.48s / Remaining 00:00:00:00\n", + "ODE solver time: 1.4995617866516113\n" + ] + } + ], "source": [ "options = {**default_options}\n", "\n", @@ -814,10 +911,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "6f98a4a0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "def deltafun(j, k):\n", " if j == k:\n", @@ -953,7 +1082,7 @@ "\n", "tlist_corr = np.linspace(0, 2, 100)\n", "cppLP, etapLP, gampLP = pade_corr(tlist_corr, 2)\n", - "corr_15k = dlenv.correlation_function(tlist_corr, Nk=15_000)\n", + "corr_15k = dlenv.correlation_function(tlist_corr, Nk=15)\n", "corr_2k = dlenv.correlation_function(tlist_corr, Nk=2)\n", "\n", "fig, ax1 = plt.subplots(figsize=(12, 7))\n", @@ -969,7 +1098,7 @@ " np.real(corr_15k),\n", " \"r--\",\n", " linewidth=3,\n", - " label=r\"real mats 15000 terms\",\n", + " label=r\"real pade 15 terms\",\n", ")\n", "ax1.plot(\n", " tlist_corr,\n", @@ -1007,10 +1136,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "981d2e53", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.009793281555175781\n", + " Total run time: 1.69s*] Elapsed 1.69s / Remaining 00:00:00:00\n", + "ODE solver time: 1.6923456192016602\n" + ] + } + ], "source": [ "# put pade parameters in lists for heom solver\n", "ckAR = [np.real(eta) + 0j for eta in etapLP]\n", @@ -1030,10 +1169,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "e24e66cf", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_result_expectations(\n", " [\n", @@ -1065,10 +1215,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "e2a8616b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.007105112075805664\n", + " Total run time: 1.86s*] Elapsed 1.86s / Remaining 00:00:00:00\n", + "ODE solver time: 1.8578541278839111\n" + ] + } + ], "source": [ "options = {**default_options, \"rtol\": 1e-14, \"atol\": 1e-14}\n", "\n", @@ -1083,10 +1243,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "69c6df5d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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KTEx0fVQaKOecc44++OAD1zRBycnJHgNvxo0bp5deekkPPPCAunfvrkqVKikkJEQjR47UiRMnXMcdOnTI4yNap6Qzkv2+ffskmZHOBTl8+LBiYmLyfc55zaioqKK/yf9x/sdbs2ZNj/2luQeHDh2SlPd9OvflHoG+b98+HTlypMAZHoozNZPzuvnVvWbNmnlCRn7H7du3T1u2bClwEN2Z9alSpUqeYyIjIz1+DgqTkZGh/v37KzMzUwsXLnT1Rc0tISHBNQhRkjp37qzmzZurU6dOevbZZzV16lRJ5g+hb7/9Vo8++qjOP/98xcfHu8JYcepUmvf16quvevTLXr16tUaNGqXPPvvM43ueu0tGcR06dEhZWVmaPn26pk+fnu8xZ96rgn6mo6Oj8/zbiYiIUOXKlfMcGxERoZMnT+bZ73x9cb7HpeHTsHrs2DG1adNGI0aMKDDh57Zt2zb1799ft9xyi9599139+OOPuuOOO1StWrUivR4AgkGzZs08/iMOtKioqLPWx9m/dMqUKR77Dx48qIoVK7oeV6lSJd+WljP3Of/Tnj59eoEjnc/Wyux8/eHDhws8piCfffaZHA6HLrzwQo/9+a1j7vwP+dSpUx77CwpwRX3vVapU0Zdffplv/XK3WhbGed3U1FTVrl3b47k9e/bkCUf5vceqVauqQoUKmjlzZr7XKE3Ayk9mZqauuuoqbd26VcuWLctT77Np3bq1JOmXX36RJKWlpWn+/PmaMGGCHnzwQddxp06dKtHPRkmdOcjv6NGjkqRWrVp5bV7USpUqKTQ0VDfccIPuvPPOfI858w/F/O63tzi/v97++SiIT8Nqv3791K9fvyIfP2PGDNWtW1fTpk2TZH6hr1q1Sv/+979tG1ZzcszmhdkgAMCWHA6HIiMjPfZ98cUX+uuvv9SwYUPXvp49e+rpp5/WL7/84tEV4Mzppbp06aKKFStq48aN+Q6+KkyzZs0kmUFgxfHmm29q4cKFGjp0qOrWrVvo8c6g8euvv6pPnz6u/Z999pnHcU2aNFGNGjU0e/ZsjRs3zhUSduzYoeXLl3u04g4cOFAffPCBsrOzdcEFFxSr/mdyfqT/7rvv6vzzz3ftT0lJ0aZNm/Twww8Xeo6BAwdqypQpqlKlSr6t4iXh/FnJr9Xt5ptv1pIlS7Rw4UJX+CyqdevWSXJ3/3A4HLIsK8/P5uuvv67s7Owi1ykYREdHq2fPnlq7dq1at2591rmX/eHPP/9UlSpV/NZ1yVYRa8WKFerdu7fHvj59+uiNN95QZmamT+f6K6nPP5fGjJHuu08aNUrywSwyAMqhpUuXukajZ2dna8eOHfroo48kSd27d3fNHHD8+HEtWLBAklx91pYuXaqDBw8qJiamWA0GBRk4cKBmzZqlpk2bqnXr1lq9erWeeeaZPK1iY8aM0cyZMzVgwABNnjzZNRvA5s2bPY6LjY3V9OnTNXz4cB0+fFiDBw92jb7/5ZdfdODAAb3yyisF1qd27dpq0KCBVq5cqbvvvjvP8ydOnHB9L06cOKE///xT8+bN0/z589W9e3fNmDGjSO87KSlJF198saZOnapKlSqpXr16+vbbb/XJJ594HBcSEqInnnhCI0eO1BVXXKFbbrlFR44c0cSJE/N0Dbj22mv13nvvqX///vrnP/+pDh06KDw8XLt379bixYt12WWX6YorrnAdn56e7rrvuVWrVk3du3fXrbfequnTpyskJET9+vVzzQZQp04djR07ttD3OGbMGH388ce68MILNXbsWLVu3Vo5OTnauXOnvv76a91zzz3FDtWtWrWSJD3//PMaPny4wsPD1aRJE82YMUPvvPOO7rrrLsXExHj0sYyPj1fz5s1dj48cOeJ6PjMzU5s2bdKUKVMUGRnpalmMj4/XhRdeqGeeeUZVq1ZV/fr1tXTpUr3xxhseLf6SXCtUvfbaa4qLi3N1d8nv43+7ev7559W1a1d169ZNt99+u+rXr6+MjAxt2bJFn3/+ub777ju/1WXlypXq3r27T1tvPRQ6BMtLJFlz58496zGNGjWy/vWvf3ns+/HHHy1J1p49e/J9zcmTJ620tDTXtmvXLr/NBpCTY1kdO7pHB48e7fNLAmVeYSNIg5lzNoAzR17nxzlCOb8t94hm58j0/Lbco9DPdp2zjTi3LMv6+++/rZtvvtmqXr26FR0dbXXt2tVatmyZ1b17d6t79+4ex27cuNG65JJLrKioKKty5crWzTffbH366af5jsReunSpNWDAAKty5cpWeHi4VatWLWvAgAHWhx9+WGi9H330UatSpUrWyZMn87yf3N+DmJgYq0GDBtbgwYOtDz/8MN8R2Tpj1HVuqamp1uDBg63KlStbCQkJ1vXXX2+tWrXKYzYAp9dff91q1KiRFRERYTVu3NiaOXOmNXz48Dz3ITMz0/r3v/9ttWnTxoqKirJiY2Otpk2bWrfddpv1xx9/FPhecm/O73t2drb11FNPWY0bN7bCw8OtqlWrWtdff721a9euPN+Xgu7z0aNHrUceecRq0qSJFRERYSUkJFitWrWyxo4d6zFDRUHfp3r16uWZrWL8+PFWzZo1rZCQENe9d45EP9v7cZ4v93OhoaFW3bp1rcGDB1tr1671uM7u3butq666yqpUqZIVFxdn9e3b1/rvf/+bb52mTZtmJScnW6GhoR73r6DvTX73riico/ELmw2hMPl9v7dt22bddNNNVq1atazw8HCrWrVqVufOna3JkyfnuX5+/46GDx9uxcTE5Nlf0PegXr161oABAzz2bdmyJd8ZLc7kzdkAHJZVwNBIL3M4HIUOsGrcuLFGjBjhscTfjz/+qK5duyo1NTXfzusTJ07U448/nmd/Wlqa4uPjvVL3gmRkSNdcI+XuevTLL1IxP9kAkMvJkye1bds2JScnl2gADcqHPXv2KDk5WW+//baGDBkS6OoA5cajjz6qt99+2zUXc0EK+12enp6uhISEIuU1W31onZSUlKcz+v79+xUWFlZgU/348eOVlpbm2nbt2uWPqkqS4uKkhQulf/3Lve+MhSQAAD5Qs2ZNjRkzRv/6179st8oQUFYdOXJEL730kqZMmeKVpVuLylZhtVOnTlq0aJHHvq+//lrt27cvsL9qZGSk4uPjPTZ/u+suyTnDyiefSGf06wYA+MAjjzyiq666Sn/99VegqwKUC9u2bdP48ePzzJ3saz4Nq0ePHtW6detcI/i2bdumdevWaefOnZJMq2juZcxGjRqlHTt2aNy4cdq0aZNmzpypN954Q/fee68vq1lqcXFS376mfPCgdMa8vAAAH4iLi9OECRNUp06dQFcFKBfOPfdc3Xffff4bWPU/Pg2rq1at0rnnnutaEWHcuHE699xz9dhjj0kyc8M5g6tk5ghbsGCBlixZorZt2+qJJ57QCy+8YNtpq3LLtRiEirEACQAAAM7CbwOs/KU4HXa9aetWyTnd4CWXSF9/7bdLA2UKA6wAIPiV2QFWwaxBA8m5stnPP5vJrAAAAFA6hFUvcTiktm1NOS1NytW7AQAAACVEWPWiXKsL6n9LFwMAAKAUCKteRFgFAADwLsKqFxFWAQAAvIuw6kWNGknOAW+EVQAouq+++krh4eGaN29eoKsCwGYIq14UFia1aGHKW7dKJ04Etj4A7GXWrFlyOByuLSwsTLVr19aIESPyrML0yCOPaODAgapVq5YcDoduvPHGfM/5+uuv6/LLL1f9+vVVoUIFNWzYULfffrtSU1OLVKcePXp41KlChQpq06aNpk2b5rVlTJcsWSKHw6ElBUxCvXv3bl1//fV6/vnndfnllxf5vMuWLVNkZKR27Njh2pf7/YSEhCguLk4NGzbU1VdfrY8++sjvS7PeeOONql+/fole26NHD7Vs2dK7FfKD5cuXa+LEiTpy5EiJXl+/fn2Pn8moqCg1bNhQ48aN08GDB0t0zo0bN2rixInavn17nufs8H0+83dDQVtJf5a85dtvv1VsbKzfV40jrHpZkybmq2VJ27YFti4A7OnNN9/UihUrtGjRIt1yyy2aPXu2unXrpmPHjrmO+b//+z8dOnRIl156qSIiIgo814QJExQbG6spU6boyy+/1P3336/58+erXbt22rdvX5Hq06BBA61YsUIrVqzQnDlzVKtWLY0dO1bjx48v9XstTFZWloYMGaJbb71Vd9xxR5FfZ1mWxowZo1tuuUX16tXzeM75fpYvX6558+bpwQcf1IkTJ3T11VerR48eSktL8/bbQC7Lly/X448/XuKwKkldunRx/UwuXLhQt912m1599VX1dS4XWUwbN27U448/nm9YtYMBAwa43q9zk6TBgwd77Js7d25A69mrVy916NBBDz30kF+vG+bXq5UDzoUBJNO62rx54OoCwJ5atmyp9u3bS5J69uyp7OxsPfHEE5o3b56uu+46SVJGRoZCQkx7wjvvvFPgudauXavq1au7Hnfv3l3nnXeezj//fP3nP//RI488Umh9KlSooI4dO7oe9+vXT02bNtWLL76oyZMnKzw8vETvsyjCwsL0448/Fvt1X375pdasWaP3338/z3Nnvh9JGjlypN58803ddNNNuvXWWzVnzpwCz21Zlk6ePKkKFSoUu17B5Pjx44qOjg50NfJVsWJFj3vYs2dPZWRk6IknntDvv/+uxo0bB7B2Z7dkyRL17NlT27ZtK3JLaLVq1VStWrU8+xMTE/P8LJdEZmam69Oc0rrzzjs1ZMgQTZ482W9LHdOy6mW5w+qWLYGrB4Dg4fzPKPfH2c6gWpjcQdWpXbt2Cg0N1a5du0pUn/DwcLVr107Hjx/XgQMHtGXLFo0YMUKNGjVSdHS0atWqpUGDBmn9+vV5Xrt582b17dtX0dHRqlq1qkaNGqWMjIx8r/PNN9+oV69eio+PV3R0tLp06aJvv/22SHV85ZVXdP7556uJ8+OsIhgxYoT69++vDz/80ON77XA4NHr0aM2YMUPNmjVTZGSk3nrrrQK7L2zfvl0Oh0OzZs3y2D9r1iw1adJEkZGRatasmd5+++1863H69GlNnjxZTZs2VWRkpKpVq6YRI0bowIEDRX4vTjk5OXr66add56pevbqGDRum3bt3exzn/Kj7+++/V+fOnRUdHa2bbrpJkllJ6N5771VycrIiIiJUq1YtjRkzxqOlP/f36Z133lGzZs0UHR2tNm3aaP78+a5jJk6cqPvuu0+SWULd+fH1kiVLzvpRd48ePQp9rwkJCZLk8cfTqlWrdO2117q6wdSvX1//+Mc/PO7vrFmzdPXVV0syodd5zTPvX0pKirp166bo6Gg1aNBATz75pN+7jRTmjz/+0NChQ1W9enXXz9lLL73kcYzz5/add97RPffco1q1aikyMlJbtmzRjTfeqNjYWG3evFl9+vRRTEyMatSooSeffFKStHLlSnXt2lUxMTFq3Lix3nrrrTx1GDRokGJjY/Wf//zHL+9ZomXV6wirgI+0by/t3RvoWrglJUmrVnnlVFv+98siv5aVkli6dKmys7PVwtmJvgS2bt2qsLAwVapUSVu2bFGVKlX05JNPqlq1ajp8+LDeeustXXDBBVq7dq0rMO7bt0/du3dXeHi4Xn75ZSUmJuq9997T6NGj85z/3Xff1bBhw3TZZZfprbfeUnh4uF599VX16dNHX331lXr16lVg3U6fPq1vvvlGd911V7Hf16WXXqoFCxZo2bJlHt0H5s2bp2XLlumxxx5TUlKSqlevXqzwOGvWLI0YMUKXXXaZnn32WaWlpWnixIk6deqUxx8eOTk5uuyyy7Rs2TLdf//96ty5s3bs2KEJEyaoR48eWrVqVbFadG+//Xa99tprGj16tAYOHKjt27fr0Ucf1ZIlS7RmzRpVrVrVdWxqaqquv/563X///ZoyZYpCQkJ0/Phxde/eXbt379ZDDz2k1q1ba8OGDXrssce0fv16ffPNN3I4HK5zfPHFF0pJSdGkSZMUGxurp59+WldccYV+++03NWjQQCNHjtThw4c1ffp0ffLJJ6rxv6UdmzdvrhYtWrg+3nZasWKFxo0bl+dn1bIsZWVlSTLLdqakpGjatGnq0qWLkpOTXcdt375dTZo00bXXXqvKlSsrNTXV9YfMxo0bVbVqVQ0YMEBTpkzRQw89pJdeeknnnXeeJOmcc85xnWfv3r267rrrdM8992jChAmaO3euxo8fr5o1a2rYsGFFvh++tHHjRnXu3Fl169bVs88+q6SkJH311Ve6++67dfDgQU2YMMHj+PHjx6tTp06aMWOGQkJCXH/YZmZm6sorr9SoUaN033336f3339f48eOVnp6ujz/+WA888IBq166t6dOn68Ybb1TLli3Vrl0713kjIiLUuXNnffHFF5o0aZJ/3rxVxqSlpVmSrLS0tIBcf/9+yzI9Vi2rd++AVAEIaidOnLA2btxonThxwvOJWrXc/7jssNWqVez39uabb1qSrJUrV1qZmZlWRkaGNX/+fKtatWpWXFyctXfv3nxfFxMTYw0fPrxI10hPT7eaNWtm1alTx8rIyCj0+O7du1stWrSwMjMzrczMTGvPnj3Wgw8+aEmyrr766nxfk5WVZZ0+fdpq1KiRNXbsWNf+Bx54wHI4HNa6des8jr/kkkssSdbixYsty7KsY8eOWZUrV7YGDRrkcVx2drbVpk0bq0OHDmet808//WRJsj744IMC309BFi5caEmynnrqKdc+SVZCQoJ1+PBhj2MXL17sUW+nbdu2WZKsN99801XvmjVrWuedd56Vk5PjOm779u1WeHi4Va9ePde+2bNnW5Ksjz/+2OOcKSkpliTr5ZdfLvJ72bRpkyXJuuOOOzz2O78/Dz30kMe5JFnffvutx7FTp061QkJCrJSUFI/9H330kSXJWrBggWufJCsxMdFKT0937du7d68VEhJiTZ061bXvmWeesSRZ27ZtK7DulmVZmzdvtqpUqWL17NnTOnXqlGt/vXr1LEl5tg4dOlipqalnPWdWVpZ19OhRKyYmxnr++edd+z/88MN872Xu781PP/3ksb958+ZWnz59zno95zWd/34yMzOtb775xpJkbdmyxWN/dnZ2oefKTZJ15513uh736dPHql27dp58M3r0aCsqKsr18+v8ub3wwgvznHP48OF5fv4yMzOtatWqWZKsNWvWuPYfOnTICg0NtcaNG5fnPA8//LAVEhJiHT16tMD6F/i7/H+Kk9foBuBlVatK8fGmTMsq4EVJSVKtWvbZkpJK/FY6duyo8PBwxcXFaeDAgUpKStLChQuVmJhYqm/RyZMndeWVV2rHjh368MMPFRsbW6TXbdiwQeHh4QoPD1fNmjX17LPP6rrrrnN9zJeVlaUpU6aoefPmioiIUFhYmCIiIvTHH39o06ZNrvMsXrxYLVq0UJvck05LGjp0qMfj5cuX6/Dhwxo+fLiysrJcW05Ojvr27auUlJQ8H0HntmfPHkn5d4EojGVZ+e6/6KKLVKlSpWKfT5J+++037dmzR0OHDvVohaxXr546d+7scez8+fNVsWJFDRo0yOO9t23bVklJSQXOmJCfxYsXS1KemSI6dOigZs2a5elSUalSJV100UV56tOyZUu1bdvWoz59+vTJtwtEz549FRcX53qcmJio6tWre3zsXhR79+5V3759VaNGDc2dOzfPIMKuXbsqJSVFKSkp+vHHH/XGG2/owIEDuuiiizxmBDh69KgeeOABNWzYUGFhYQoLC1NsbKyOHTvm8bNZmKSkJHXo0MFjX+vWrYv0vnr16uX69xMeHq6LL75YktSwYUOP/c5uFyVx8uRJffvtt7riiisUHR3tca/69++vkydPauXKlR6vueqqq/I9l8PhUP/+/V2Pw8LC1LBhQ9WoUUPnnnuua3/lypULvLfVq1dXTk6O9vrp0y66AXiZw2G6AqxZI23fLp0+LZ1lIC+AovLSR+528Pbbb6tZs2YKCwtTYmKi66PS0jh16pSuuOIK/fDDD5o/f74uuOCCIr/2nHPO0QcffOCaJig5Odlj4M24ceP00ksv6YEHHlD37t1VqVIlhYSEaOTIkTqRa46+Q4cOeXxE65R0RrB3zlIwePDgAut0+PBhxcTE5Puc85pRzomti8H5H2/NmjU99pfmHhw6dEhS3vfp3Jd7BPq+fft05MiRAmd4KM7UTM7r5lf3mjVr5gkZ+R23b98+bdmypcBBdGfWp0qVKnmOiYyM9Pg5KExGRob69++vzMxMLVy40NUXNbeEhATXIERJ6ty5s5o3b65OnTrp2Wef1dSpUyWZP4S+/fZbPfroozr//PMVHx/vCmPFqVNp3terr77q0S979erVGjVqlD777DOP73nuLhnFdejQIWVlZWn69OmaPn16vsecea8K+pmOjo7O828nIiJClStXznNsRESETp48mWe/8/XF+R6XBmHVB5xhNSdH2rnTsx8rADRr1szjP+LSOnXqlC6//HItXrxYn3766Vn7e+YnKirqrPVx9i+dMmWKx/6DBw+qYsWKrsdVqlTJt6XlzH3O/7SnT59e4Ejns7UyO19/+PDhAo8pyGeffSaHw6ELL7zQY3/uFlEn53/Ip06d8thfUIAr6nuvUqWKvvzyy3zrl7vVsjDO66ampqp27doez+3ZsydPOMrvPVatWlUVKlTQzJkz871GaQJWfjIzM3XVVVdp69atWrZsWZ56n03r1q0lSb/8b9WdtLQ0zZ8/XxMmTNCDDz7oOu7UqVMl+tkoqTMH+R09elSS1KpVK6/Ni1qpUiWFhobqhhtu0J133pnvMWf+oZjf/fYW5/fX2z8fBSGs+kDduu7yrl2EVQC+42xR/e677/TJJ5+oT58+Xr+Gw+FQZGSkx74vvvhCf/31lxrm+gXXs2dPPf300/rll188ugKcOb1Uly5dVLFiRW3cuDHfwVeFadasmSQzCKw43nzzTS1cuFBDhw5V3dy/qAvgDBq//vqrx/f1s88+8ziuSZMmqlGjhmbPnq1x48a5QsKOHTu0fPlyj1bcgQMH6oMPPlB2dnaxWr/z4/xI/91339X555/v2p+SkqJNmzbp4YcfLvQcAwcO1JQpU1SlSpV8W8VLwvmzkl+r280336wlS5Zo4cKFrvBZVOvWrZPk7v7hcDhkWVaen83XX39d2dnZRa5TMIiOjlbPnj21du1atW7d+qxzL/vDn3/+qSpVqpS661JREVZ9IPfvwJ07A1cPAMFr6dKlrtHo2dnZ2rFjhz766CNJZi5V58wBgwcP1sKFC/Xwww+rSpUqHv3W4uPj1dwLkz0PHDhQs2bNUtOmTdW6dWutXr1azzzzTJ5WsTFjxmjmzJkaMGCAJk+e7JoNYPPmzR7HxcbGavr06Ro+fLgOHz6swYMHu0bf//LLLzpw4IBeeeWVAutTu3ZtNWjQQCtXrtTdd9+d5/kTJ064vg8nTpzQn3/+qXnz5mn+/Pnq3r27ZsyYUaT3nZSUpIsvvlhTp05VpUqVVK9ePX377bf65JNPPI4LCQnRE088oZEjR+qKK67QLbfcoiNHjmjixIl5ugZce+21eu+999S/f3/985//VIcOHRQeHq7du3dr8eLFuuyyy3TFFVe4jk9PT3fd99yqVaum7t2769Zbb9X06dMVEhKifv36uWYDqFOnjsaOHVvoexwzZow+/vhjXXjhhRo7dqxat26tnJwc7dy5U19//bXuueeeYofqVq1aSZKef/55DR8+XOHh4WrSpIlmzJihd955R3fddZdiYmLO+rN65MgR1/OZmZnatGmTpkyZosjISFfLYnx8vC688EI988wzqlq1qurXr6+lS5fqjTfe8Gjxl+Raoeq1115TXFycq7tLfh//29Xzzz+vrl27qlu3brr99ttVv359ZWRkaMuWLfr888/13Xff+a0uK1euVPfu3X3aeuuh0CFYQSbQswFYlmXNneseMPzEEwGrBhCUChtBGsycswGcOfI6P84RyvltuUc0F3SMJKt79+5Fus7ZRpxblmX9/fff1s0332xVr17dio6Otrp27WotW7bM6t69e55rbNy40brkkkusqKgoq3LlytbNN99sffrpp/mOxF66dKk1YMAAq3LlylZ4eLhVq1Yta8CAAdaHH35YaL0fffRRq1KlStbJkyfzvJ/c34OYmBirQYMG1uDBg60PP/ww3xHZOmPUdW6pqanW4MGDrcqVK1sJCQnW9ddfb61atcpjNgCn119/3WrUqJEVERFhNW7c2Jo5c6Y1fPhwj9kALMuMvv73v/9ttWnTxoqKirJiY2Otpk2bWrfddpv1xx9/FPhe8ru32dnZ1lNPPWU1btzYCg8Pt6pWrWpdf/311q5du/J8Xwq6z0ePHrUeeeQRq0mTJlZERISVkJBgtWrVyho7dqzHDBUFfZ/q1auXZ7aK8ePHWzVr1rRCQkJc9945Er2wn9UzZwMIDQ216tataw0ePNhau3atx3V2795tXXXVVValSpWsuLg4q2/fvtZ///vffOs0bdo0Kzk52QoNDfW4fwV9b/K7d0XhHI1f2GwIhcnv+71t2zbrpptusmrVqmWFh4db1apVszp37mxNnjw5z/Xz+3c0fPhwKyYmJs/+gr4H9erVswYMGOCxb8uWLfnOaHEmb84G4LCsAoZGBqn09HQlJCQoLS1N8c5h+X62Zo3knJLslluk114LSDWAoHTy5Elt27ZNycnJJRpAg/Jhz549Sk5O1ttvv60hQ4YEujpAufHoo4/q7bffds3FXJDCfpcXJ68xdZUP5F59jG4AAOB9NWvW1JgxY/Svf/3LdqsMAWXVkSNH9NJLL2nKlCleWbq1qOiz6gNVq0pRUdLJk2aAFQDA+x555BFFR0frr7/+8tsa5UB5tm3bNo0fPz7P3Mm+Rlj1AYfDDLL6/XfTsmpZZh8AwHvi4uLyLDEJwHfOPfdcj4UD/IVuAD7i/CP/6FEpLS2wdQEAAAhWhFUfYfoqoHTK2NhPAChXvPk7nLDqI7mnH9y9O3D1AIKNs9N+VlZWgGsCACgp5+9wbwzEIqz6SO5lp1NTA1cPINiEhoYqNDRU6enpga4KAKCE0tPTXb/PS4sBVj5So4a7TFgFis7hcKh69epKTU1VZGSkYmJi/LdKCgCgVCzL0rFjx5Senq4aNWp45fc3YdVHcres7tkTuHoAwSghIUEnTpzQwYMHXUuOAgCCg8PhUMWKFZWQkOCV8xFWfYSWVaDkHA6HatSooerVqyszMzPQ1QEAFEN4eLhXPv53Iqz6SGKimVvVsmhZBUrKW/2dAADBiwFWPhIeLlWrZsq0rAIAAJQMYdWHnF0B9u6VWLoaAACg+AirPuQcZJWZKR06FNi6AAAABCPCqg8xyAoAAKB0CKs+RFgFAAAoHcKqDzHXKgAAQOkQVn0oMdFdZl5zAACA4iOs+lD16u7yvn2BqwcAAECwIqz6UO6wun9/4OoBAAAQrAirPkRYBQAAKB3Cqg9VrCiF/W9BW8IqAABA8RFWfcjhcLeuElYBAACKj7DqY7nDqmUFti4AAADBhrDqY86wmpUlHTkS0KoAAAAEHcKqjzHICgAAoOQIqz5GWAUAACg5wqqPEVYBAABKjrDqY7mXXCWsAgAAFA9h1cdYchUAAKDkCKs+RjcAAACAkiOs+hhhFQAAoOQIqz5WrZq7TFgFAAAoHsKqj1WoIMXFmTJhFQAAoHgIq36Qe8lVAAAAFB1h1Q+cYfXvv6XTpwNbFwAAgGBCWPWD3P1WDx4MXD0AAACCDWHVD6pUcZcPHQpcPQAAAIINYdUPqlZ1lwmrAAAARUdY9QNaVgEAAEqGsOoHhFUAAICSIaz6Qe5uAAywAgAAKDrCqh/QsgoAAFAyhFU/IKwCAACUDGHVD3KHVboBAAAAFB1h1Q8qV3aXaVkFAAAoOsKqH4SHSwkJpkxYBQAAKDrCqp84uwIQVgEAAIqOsOonzumr/v5bys4ObF0AAACCBWHVT5wtq5ZlAisAAAAKR1j1E6avAgAAKD7Cqp8wfRUAAEDxEVb9JPeSq7SsAgAAFA1h1U/oBgAAAFB8hFU/oRsAAABA8fk8rL788stKTk5WVFSU2rVrp2XLlp31+Pfee09t2rRRdHS0atSooREjRuhQGWiKpGUVAACg+HwaVufMmaMxY8bo4Ycf1tq1a9WtWzf169dPO3fuzPf4H374QcOGDdPNN9+sDRs26MMPP1RKSopGjhzpy2r6BX1WAQAAis+nYfW5557TzTffrJEjR6pZs2aaNm2a6tSpo1deeSXf41euXKn69evr7rvvVnJysrp27arbbrtNq1at8mU1/YKWVQAAgOLzWVg9ffq0Vq9erd69e3vs7927t5YvX57vazp37qzdu3drwYIFsixL+/bt00cffaQBAwYUeJ1Tp04pPT3dY7Mj+qwCAAAUn8/C6sGDB5Wdna3ExESP/YmJidq7d2++r+ncubPee+89DRkyRBEREUpKSlLFihU1ffr0Aq8zdepUJSQkuLY6dep49X14S4UKUnS0KdOyCgAAUDQ+H2DlcDg8HluWlWef08aNG3X33Xfrscce0+rVq/Xll19q27ZtGjVqVIHnHz9+vNLS0lzbrl27vFp/b3K2rhJWAQAAiibMVyeuWrWqQkND87Si7t+/P09rq9PUqVPVpUsX3XfffZKk1q1bKyYmRt26ddPkyZNVo0aNPK+JjIxUZGSk99+AD1SpIu3aZcKqZUkFZHYAAAD8j89aViMiItSuXTstWrTIY/+iRYvUuXPnfF9z/PhxhYR4Vik0NFSSaZENds6W1awsyaZdawEAAGzFp90Axo0bp9dff10zZ87Upk2bNHbsWO3cudP1sf748eM1bNgw1/GDBg3SJ598oldeeUV//vmnfvzxR919993q0KGDatas6cuq+gXTVwEAABSPz7oBSNKQIUN06NAhTZo0SampqWrZsqUWLFigevXqSZJSU1M95ly98cYblZGRoRdffFH33HOPKlasqIsuukhPPfWUL6vpN2dOX9WgQeDqAgAAEAwcVln4fD2X9PR0JSQkKC0tTfHx8YGujofHHpOeeMKUFyyQ+vULbH0AAAACoTh5zeezAcCNhQEAAACKh7DqR5Uru8t//x24egAAAAQLwqofVarkLhNWAQAACkdY9aPcLauHDweuHgAAAMGCsOpHtKwCAAAUD2HVj2hZBQAAKB7Cqh/RsgoAAFA8hFU/ioiQYmJMmZZVAACAwhFW/czZukrLKgAAQOEIq37m7Ld6+LBUttYOAwAA8D7Cqp85W1ZPn5ZOnAhsXQAAAOyOsOpnzAgAAABQdIRVP2PJVQAAgKIjrPpZ7umraFkFAAA4O8Kqn9GyCgAAUHSEVT+jZRUAAKDoCKt+RssqAABA0RFW/YyWVQAAgKIjrPoZLasAAABFR1j1M1pWAQAAio6w6me0rAIAABQdYdXP4uMlh8OUaVkFAAA4O8Kqn4WEuLsC0LIKAABwdoTVAHCGVVpWAQAAzo6wGgDOfqtHjkg5OQGtCgAAgK0RVgPAGVYtS0pLC2xdAAAA7IywGgC5p6+i3yoAAEDBCKsBkHv6KvqtAgAAFIywGgC0rAIAABQNYTUAaFkFAAAoGsJqANCyCgAAUDSE1QCgZRUAAKBoCKsBkLtllbAKAABQMMJqAORuWaUbAAAAQMEIqwFAyyoAAEDREFYDgJZVAACAoiGsBkCFClJkpCnTsgoAAFAwwmqAOFtXaVkFAAAoGGE1QJz9VmlZBQAAKBhhNUCcLavHj0unTgW2LgAAAHZFWA0QBlkBAAAUjrAaICy5CgAAUDjCaoCw5CoAAEDhCKsBQssqAABA4QirAULLKgAAQOEIqwFCyyoAAEDhCKsBQssqAABA4QirAULLKgAAQOEIqwFCyyoAAEDhCKsBkrtllbAKAACQP8JqgFSs6C7TDQAAACB/hNUACQuTEhJMmbAKAACQP8JqADm7AtANAAAAIH+E1QByDrI6fFiyrMDWBQAAwI4IqwHkbFnNzpaOHg1sXQAAAOyIsBpATF8FAABwdoTVAModVhlkBQAAkBdhNYCYaxUAAODsCKsBRDcAAACAsyOsBlDullW6AQAAAORFWA0gWlYBAADOjrAaQAywAgAAODvCagAxwAoAAODsCKsBRDcAAACAsyOsBhADrAAAAM6OsBpAMTFSeLgp07IKAACQF2E1gBwOd1cAWlYBAADyIqwGmLMrAC2rAAAAeRFWA8zZspqRIWVmBrYuAAAAdkNYDbDcg6yOHAlYNQAAAGyJsBpgTF8FAABQMMJqgLGKFQAAQMEIqwHGKlYAAAAF83lYffnll5WcnKyoqCi1a9dOy5YtO+vxp06d0sMPP6x69eopMjJS55xzjmbOnOnragYM3QAAAAAKFubLk8+ZM0djxozRyy+/rC5duujVV19Vv379tHHjRtWtWzff11xzzTXat2+f3njjDTVs2FD79+9XVlaWL6sZUKxiBQAAUDCfhtXnnntON998s0aOHClJmjZtmr766iu98sormjp1ap7jv/zySy1dulR//vmnKv+vybF+/fq+rGLA0bIKAABQMJ91Azh9+rRWr16t3r17e+zv3bu3li9fnu9rPvvsM7Vv315PP/20atWqpcaNG+vee+/ViRMnCrzOqVOnlJ6e7rEFEwZYAQAAFMxnLasHDx5Udna2EhMTPfYnJiZq7969+b7mzz//1A8//KCoqCjNnTtXBw8e1B133KHDhw8X2G916tSpevzxx71ef39hgBUAAEDBfD7AyuFweDy2LCvPPqecnBw5HA6999576tChg/r376/nnntOs2bNKrB1dfz48UpLS3Ntu3bt8vp78CW6AQAAABTMZ2G1atWqCg0NzdOKun///jytrU41atRQrVq1lJCQ4NrXrFkzWZal3bt35/uayMhIxcfHe2zBpGJFd9nu3QCOHZPGjpXq1JEaNZKeekoqw2PfAACADfgsrEZERKhdu3ZatGiRx/5Fixapc+fO+b6mS5cu2rNnj44ePera9/vvvyskJES1a9f2VVUDKjxcioszZTu3rJ48KfXuLU2bJu3eLW3ZIj34oDR8uGRZga4dAAAoq3zaDWDcuHF6/fXXNXPmTG3atEljx47Vzp07NWrUKEnmI/xhw4a5jh86dKiqVKmiESNGaOPGjfr+++9133336aabblKFChV8WdWAcnYFsHPL6sMPS85xcaGh7v3vvy/NmBGYOgEAgLLPp2F1yJAhmjZtmiZNmqS2bdvq+++/14IFC1SvXj1JUmpqqnbu3Ok6PjY2VosWLdKRI0fUvn17XXfddRo0aJBeeOEFX1Yz4JyDrA4ftmcr5dat0vPPm3JUlPTzz9Lcue7nH3tMCrJJGAAAQJBwWJYd41HJpaenKyEhQWlpaUHTf7VXL+m770w5I0OKjQ1sfc40erT00kumPHGiNGGCKV93nWlZlUxgDeJJGQAAgB8VJ6/5fDYAFM7Oq1gdPiy9+aYpx8RId9/tfm7SJCnsf5OfvfyydOqU/+sHAADKNsKqDdh5+qo5c6Tjx035pps8g/U550iDB5vywYPSvHl+rx4AACjjCKs2YOeW1f/3/9zlm2/O+/wtt7jL//mP7+sDAADKF8KqDdi1ZXXvXmnpUlNu3Fhq3TrvMT16mBZWyfS73bfPb9UDAADlAGHVBuwaVufNc89OcPXVUn4Lj4WEmOckc+xnn/mtegAAoBwgrNqAXbsBfPWVu3z55QUfd+WV7nLuKa0AAABKi7BqA3ZsWc3Kck+nVaWKdN55BR/bvr3kXGDsm2/M9FsAAADeQFi1ATu2rKakuCf679XLfNxfEIdDGjTIlDMz3f1cAQAASouwagN2bFn95ht3+ZJLCj++d293edEi79cHAACUT4RVG7BjWP3xR3f5oosKP75nTyk01JS//to3dQIAAOUPYdUGYmPdQc8O3QBycqSffjLlxEQpObnw1yQkSB06mPLmzdLu3b6rHwAAKD8IqzbgcLhbV+3Qsvr779KRI6bcsWP+U1blp1cvd3nZMq9XCwAAlEOEVZtwDrKyQ8vqypXucseORX9dt27u8g8/eK8+AACg/CKs2oSzZTUtzUwbFUgrVrjLxQmrHTu6Zw3I3ecVAACgpAirNpF7kJXzI/hAcbashoSYOVSLKj7evSTrr7+a4A0AAFAahFWbsMtcqxkZ0n//a8qtWpnBX8XRtav5almeLbQAAAAlQVi1CbtMX7V6tZkNQCpeFwAnZ1iV6LcKAABKj7BqE3ZpWV23zl1u1674r+/SxV0mrAIAgNIirNqEXVpWf/3VXW7Tpvivr11bqlfPlFNSAj9YDAAABDfCqk3YJaz+8ov5GhIitWxZsnOcf775evy4WSAAAACgpAirNmGHbgBZWdKGDabcqJEUHV2y8zjDqmRaVwEAAEqKsGoTdmhZ/e036dQpUy5JFwCn3NNdrVpVujoBAIDyjbBqE7lbVgMVVkvbX9Up98AsWlYBAEBpEFZtokoVdzlQYdXZX1VyT+5fEgkJUpMm7nOePl26egEAgPKLsGoTubsBHDwYmDrkDqulaVmV3F0BTp+W1q8v3bkAAED5RVi1ibAwqWJFUz50KDB1cHYDqFTJTEFVGgyyAgAA3kBYtRFnV4BAtKweOSLt2WPKLVtKDkfpzpc7rDLICgAAlBRh1UaqVjVfjxzx/2T6uedDbdas9Odr3dodeHN3LwAAACgOwqqNOFtWLcv/c616O6zGxkoNG5ryf//LSlYAAKBkCKs24mxZlfzfbzV3WG3a1DvnbNvWfD15Uvr9d++cEwAAlC+EVRvJHVb93W910yZ32Rstq5I7rErSunXeOScAAChfCKs2knuuVX+HVWfLaoUKUp063jln7umv6LcKAABKgrBqI4HqBnD6tLR1qyk3aSKFeOmngpZVAABQWoRVGwlUy+qWLVJ2til7qwuAJNWs6Q7ghFUAAFAShFUbCVTLqi8GV0lm6ipnV4D9+6W9e713bgAAUD4QVm0kUC2ruQdXeTOsSnQFAAAApUNYtRE7tKx6sxuARFgFAAClQ1i1kcqV3eVAtKw6HFKjRt49NzMCAACA0iCs2khEhBQfb8r+alm1LHfLanKyFBXl3fM3bWrel0TLKgAAKD7Cqs04+636q2X1r7+kY8dM2dtdACQpPFxq2dKUf/vNfS0AAICiIKzajLPf6uHD7umkfOm339zlJk18cw1nVwDLkv77X99cAwAAlE2EVZtxtqxalnTkiO+vt2WLu+zt/qpOuQdZ0W8VAAAUB2HVZnLPCOCPrgC5w2rDhr65BoOsAABASRFWbSb3XKv+GGTlj7DaurW7/OuvvrkGAAAomwirNuPvltWtW83X8HCpTh3fXKNSJfe5f/3VdHEAAAAoCsKqzfhzYQDLcresNmgghYb67lrO1tX0dGnHDt9dBwAAlC2EVZvx55KrqanSiROm7KsuAE70WwUAACVBWLUZf7as+qO/qlPusEq/VQAAUFSEVZvxZ8uqP8Nq7kFWtKwCAICiIqzajD8HWPkzrDZq5F7KlbAKAACKirBqM/6cusqfYTU01L3s6tat0tGjvr0eAAAoGwirNhMZKcXGmrK/WlZDQ6V69Xx7LYllVwEAQPERVm3I2brqy7Cae9qq+vXNPKu+xuIAAACguAirNlS9uvl66JCUne2baxw4IGVkmLKvuwA4MX0VAAAoLsKqDTnDqmX5rt+qP/urOtGyCgAAiouwakPOsCpJ+/f75hqBCKssuwoAAIqLsGpDucPqvn2+uUYgwqrk7grAsqsAAKAoCKs2lJjoLpelllWJxQEAAEDxEFZtyJ/dABwOKTnZN9fID8uuAgCA4iCs2pA/w2rdumZuV39hRgAAAFAchFUb8nVYPXxY+vtvU/ZnFwDn9SpUMGXCKgAAKExYoCuAvHwdVgPVX1VyL7uakuJedtW5Ypfd/Pe/0ptvSuvXSxER0rnnSiNGSA0aBLpmAACUH7Ss2lDVqu5yWQurknuQlV2XXT1xQho1SmrVSnruOWnRIumLL6TJk6XGjaWHH5ZOnw50LQEAKB8IqzYUHi5VrmzKZTGs2nmQVUaGdPHF0quv5v98drY0ZYo0aJB07Jh/6wYAQHlEWLUpZ1cAX4fVc87x/vkLY9fpq7KypMsvl5YvN48rVJD+/W9p714zJ+yjj5o/JCTp66+lK6+UMjMDVl0AAMoFwqpNOedaPXpUOn7cu+feutVdDkT/S7suu/rII9J335lyxYrSDz9I99xj7kXdutKkSeb5hARzzNdfm+cBAIDvEFZtypeDrJwtqzVrSjEx3j13UVSqZMKfZJ9lV3/8UXrqKVMOC5Pmz5fOOy/vcV27Sp9/bgZcSdL06dKCBf6rJwAA5Q1h1aZ8FVbT093nC0R/VSdn66odll3NypLuuMP9+MknpS5dCj6+Wzdp2jT345tukg4e9Fn1AAAo1wirNuWrsJq7C0Ag+qs62WlxgBdfdHdHOO88acyYwl8zapTUv78p79sn3Xuvz6oHAEC5Rli1KV+F1dyDqxo18t55i8su/VbT0qTHHzdlh0N6+WUzF2xhHA7pjTdM31ZJeust6aeffFZNAADKLcKqTfkqrP7xh7scyLBql5bVF16Qjhwx5WHDpAsuKPprk5LcQVeS7r5bysnxavUAACj3CKs25Y+W1UD2WbXDsqtpaWbSf8m0pj76aPHPcfvtUosWpvzzz9JHH3mvfgAAgLBqW86pqyQpNdV75w30HKtOzmVXJdOPNiPD/3V46SXPVtWSfD/Cw6Vnn3U/njjRLBwAAAC8w+dh9eWXX1ZycrKioqLUrl07LVu2rEiv+/HHHxUWFqa2bdv6toI2VaOGu+yLsJqYKMXFee+8JdGunflqWdKaNf69dmamCauSFBJillAtqd693bMHbNokzZ5d+voBAADDp2F1zpw5GjNmjB5++GGtXbtW3bp1U79+/bRz586zvi4tLU3Dhg1Tr169fFk9W4uNdYdJb4XVY8fc5wpkFwCn9u3d5VWr/HvtTz6R9uwx5csuK10rs8MhPfGE+/Hjj5vpsAAAQOn5NKw+99xzuvnmmzVy5Eg1a9ZM06ZNU506dfTKK6+c9XW33Xabhg4dqk6dOvmyerZXs6b56gxVpZV72io7hNXzz3eXU1L8e+0XXnCX77679Ofr2dNskmm9fvfd0p8TAAD4MKyePn1aq1evVu/evT329+7dW8udi6/n480339TWrVs1YcKEIl3n1KlTSk9P99jKCmdYPXrUO3067TK4yql5c/cgK3+2rK5aJTl/BFu1krp39855J01yl596ipkBAADwBp+F1YMHDyo7O1uJuUcKSUpMTNTevXvzfc0ff/yhBx98UO+9957CwsKKdJ2pU6cqISHBtdWpU6fUdbcLb/dbtVtYDQuTzj3XlLdulQ4f9s91p093l+++23yM7w1du5rVrSRp82azLCsAACgdnw+wcpyRBCzLyrNPkrKzszV06FA9/vjjaty4cZHPP378eKWlpbm2Xbt2lbrOduFsWZW80xXAbmFV8uy3unq176+Xlib9v/9nypUqSUOHevf899/vLj/9tHfPDQBAeeSzsFq1alWFhobmaUXdv39/ntZWScrIyNCqVas0evRohYWFKSwsTJMmTdIvv/yisLAwfffdd/leJzIyUvHx8R5bWeHLsBrIaaty83e/1TlzpJMnTfn666XoaO+ev39/97yry5dLP/zg3fMDAFDe+CysRkREqF27dlq0aJHH/kWLFqlz5855jo+Pj9f69eu1bt061zZq1Cg1adJE69at0wXFWVqojPBVN4AqVUyroh3kDqv+6Lf65pvu8ogR3j9/SAitqwAAeFPROoaW0Lhx43TDDTeoffv26tSpk1577TXt3LlTo0aNkmQ+wv/rr7/09ttvKyQkRC2ds8T/T/Xq1RUVFZVnf3nhzZbVEyckZw8Ju3QBkMySr/HxUnq671tWN2+WVq405datJV9N4XvttWbe1t27Tb/VDRvcra0AAKB4fNpndciQIZo2bZomTZqktm3b6vvvv9eCBQtUr149SVJqamqhc66WZ95sWd22zV22U1gNCXEvDrB7t1TA2DuvmDXLXR4xwnsDq84UESGNG+d+/MwzvrkOAADlgcOyLCvQlfCm9PR0JSQkKC0tLej7rx496l4YoHt3acmSkp/rs8/M5PeSWRK0iDOD+cX997sD3aefSpde6v1rZGVJdeua0B8WZlqqq1Xz/nWcjh411/v7b7Mk659/SrVr++56AAAEk+LkNZ/PBoCSi401H5FLpe8GYMeZAJxyr/3w44++ucaiRe7W6YEDfRtUJXPv7rzTlDMzpWnTfHs9AADKKsKqzTm7ApS2G4Cdw2qXLu6yr0bP+3pgVX7uukuKijLlV181rawAAKB4CKs2561VrP74w122W1itXl1yTq27apV7ailvOXzYdC9wXqtfP++evyDVq7uD8dGj0owZ/rkuAABlCWHV5rw1I4CzZbViRaly5VJVySe6djVfT5/2/hRW779vziuZuVXDw717/rO55x4ziEySnn/e+0EcAICyjrBqc7lnBChpWD11SnJOunDOOb4bBV8avuwKcOYsAP50zjnS4MGmvG+f9Pbb/r0+AADBzqfzrKL0atVyl//6q2Tn2LJFyskx5aZNS18nX3C2rEreDavr17uXcW3fXgrElL333+9e4vWZZ6Sbb5ZCQ/1fj7M5elRatsx0F9m71/S1rVbNTCvWtq2ZjgsAgEAgrNpc3bruckmnpN282V1u0qR09fGVRo1MODpwwMwIkJ3tnUA3c6a7fOONpT9fSbRrJ/XqJX37rfnDYd486aqrAlOX3CzL1On556WvvjKzFuQnPl665hpp1Cj3nLgAAPgL3QBszhth9bff3GW7tqw6HNKFF5rykSPSmjWlP+fp09I775hyZKQ0dGjpz1lSuZdgnTLFBMVAWrtW6tZNuuQSaf78goOqZFYXe/110zJ96aXSpk3+qycAAIRVm/N2WLVry6ok9e7tLn/9denP9/nn0qFDpnzllVKlSqU/Z0ldcol03nmmvGaNqVsgZGVJjz1mgmfuOW1r1ZJuv9307/36axNgX3xR+sc/zJyxTp9/broFPPGEe9AaAAC+xApWNmdZUoUKZpBUixbSf/9b/HNccIH088+m9fLYMXM+O9q2TWrQwJRLu2KXJA0YIC1YYMqLFkkXX1y685XW55+7V+dq08aE1hA//rmYmipde630/ffufY0bS5MnS1dcYVb2ys+xY2Zg2L/+5dlvum1b6cMP7TcVGgDA/ljBqgxxONytqzt2FP/jY8tyt6zWq2ffoCpJycnu4LN8uRn0U1J//SV9+aUp160rXXRR6etXWgMHmhZNSfrlF2nuXP9de+tWqXNnd1ANDZUmTTID0K6+uuCgKkkxMabV9Y8/pAcecPclXrfO9GH95BOfVx8AUI4RVoOAM6wePWr6cxbHvn1SWpop27kLgNMll5ivmZnS0qUlP8/bb7tnQBgxwr8tmAVxOExAdJowwQwk87VNm0x/4O3bzePatU2r9aOPFm+Uf4UK0pNPSikp7p+l9HQzWGzSpMD3wwUAlE02+C8chSlNv9VgGFyVW+5+q199VbJzWJY9ZgHIT9++UseOprxhg+cysL6wdq0Jqs45eps3l376yXOqsOI691wTWK+91r1vwgRp2DDTXQUAAG8irAYBb4XVYGhZ7dnT/ZH0Z5+VrLVu8WL3il29ekn163uteqXmcEhPP+1+/PDDpnXSF1asMN/PgwfN4/POM63VuVdFK6m4OLMy2DPPuBeZePdd0y/YeT0AALyBsBoEcofVHTuK99rcc6wGQ8tqQoK7f+mOHaZlsLimT3eXb7nFO/Xypm7dTD9RSdq/34ys97bvvjNdKpxdQDp3NvuqVvXeNRwO6d57pY8/dveF/uEH03Kc++cOAIDSIKwGgeRkd/nPP4v32txzYgZDy6pkpplyKu7gnR07TIusZFoQc5/LTp56ysz9Kkn/93/uVba84YsvpP79zSh+ybQuf/21+UPAF664wqx+5VwaeOtWqVMnE44BACgtwmoQOOccd3nr1uK9dv1687VSJXeYsLvLLnN/tDxnTvG6Arz8sntg1ahRUni49+vnDcnJpguAZAZZ3XSTd+Yt/fBD6fLL3X1HBw0yc6bGxJT+3GfTrp3pC9umjXl85IjUp49ZTAAAgNIgrAaBWrXco7aLE1b//ts9L2arVu4AaHdJSaavpWT6ni5fXrTXHTkivfqqKUdESLfe6pPqec2DD0qtW5vyr7+6w2tJzZxpBj1lZZnHQ4aYj+ijokp33qKqU8e0sA4caB5nZZluGPff7/4Dwg4sy/zb2LTJLIzw/femL++6ddKuXWdfzQsA4H+E1SAQGuqeLP/PP4v+H7+zVVUyYTWY5B7BP2tW0V7zwgvuPprXXy8lJnq7Vt4VHm4CpnNA2b//bcJlcVmWNHWqdPPN7p+Nm2+W3nvP/y3LcXHSvHnSmDHufc88YxZoOHDAv3Vx2r9fmj1buvtus9hEpUpS5cpmZoSuXc2+Hj3MLAd165pW6BYtpMGDTXeNlSsJsAAQSKxgFSQGDjR9ESVp927T2lqYl16SRo825RkzpNtu8139vO3YMdPCevSoCQ+7dp19udT0dLPowZEjJtz/9ptn9wk7mz7dBCnJvNevvpK6dCnaa0+flsaONd0fnMaMkZ59NvBzy77yinTXXe65ZGvWNDMIdO/u+2tv3ix98IH06aemxbS0oqPNgLXLLzf/Fr05UA0AyqPi5LWzrFsDOzmz32pRwmowt6zGxEg33GACz7FjJmyPH1/w8c89514w4frrgyeoSuYPip9+Mi2hx46ZuVgXLDCzBpzNzp3SP/7h2U3iySfNx+526PJx++1mOdehQ03r5p49ZqaHf/7TLCIQG+vd6+3YYfo4z5599oBau7aZGaNGDalKFXfr85EjZtqtLVvMHzu5+xAfP26C76efmj8CunQxiyFceaXp/gAA8B1aVoPECy+Y/+Ql89HxiBGFv6ZLF3eQSUuTgu3bsWWLCTuWZT7S37Il/4CzfbvUrJl08qRpVd20SWrUyO/VLZWTJ02rnXMhhPBwacoUc8/P/Cj/+HET4idMcI/4j4iQXntNGj7cr9Uukr17peuu85wdoHZt03Xh2mvPvtRrYVJTpY8+Mq2oBfVtPu880yp60UVmIFiVKoWfNyvLLC/7ww+mT+uiRWY1uPx07Gi6DAwebFr3AQCFK05eI6wGiS++cA9ceegh6V//OvvxliVVrOj+eNy51GawueYaM8JdMgOSpk71fD4nx7RELlpkHo8da1pZg9GJEyawfv21e1/9+qb1tGVL09K3apUJZocOuY+pU8f0dT3/fH/XuOiys03f1ccfN8HcqVEjadw4E1orViz8PJZl+m1//bX0//6fGRiV32+wDh3MOa+5pmifQhQmJ8e0fn/6qTR3rvT77/kfd/75JrRedVVwte4DgL8RVstgWN2yxd1aeOWVhQ/E2bHDvXLTwIHS55/7tHo+s2WLGexy+rRpYVy61Mzh6TRpkmlhlMzHups3B18Lcm6Zmaa7w3PPFW3KrttvNwHeV3OoetvWrdKdd+ZdSjcy0gx2uvBCM/ApKcn0Ez161Izc//13061l2bKC//Bq0cIE+2uv9X1Q3LjRtOh+9JFnd5vczj3XBNc+fcyUXqVpQS7MyZOmL/uuXWbbudN8PXDAfA+PHTNfT582nz6EhJivUVHmjwTnVq2aafWuXdv8EVS7ttlvh24lAMoWwmoZDKvZ2eYj8JMnzeT+ha0QNH++mWNTMuFnyhTf19FXHn1UmjzZlKtXd7ciPvGEu4XZ4TCtq716Ba6e3pSSYt7311/nDa3h4WZaqnvvdc9rGmyWLDF/aCxeXLrzNG5svhdDhpiwGgi//WZ+Jj/6qOAV12JjzR9ZF1xg6tm8uZlrNza28CB47Jjp75uaaqaic4ZSZyB1hlJfiYkxf/g6t3r1PB9XrUqYBVB8hNUyGFYl01Kzbp1pETl2zL0CUn4ef1yaONGUZ882rU3BKjNT6t3bBBynqCjPj5OfesoMLCpr9uwxc4GmpprWsHPOMS2QcXGBrpl3rF0rvfWW+Wh9587Cj4+IMO+/Vy+pXz+pbVt7BaWtW93BNSWl8OOjo92tyBERZsvKcreEpqWZrjzeEBVlzp+TY/74zclxLx5RGtHReQOs83GdOmYWj6io0t+nzEzTVaYo28mT5r2FhOTdQkPN8sDR0Z5bTIz7a2ysb1vCARBWy2xYvf56M2Jckn75xT2hfH5yT3W1ZUvw9587fNgs6/n99577Q0PNx+D33ReYesE7LMsEvXXrzEf+hw6ZgWSxsaaLQ/36pkWyWTP/LXJQWtu3m3+Dy5aZbc8e718jJMRMCVa3rgmGZ241apg/bKKj8w9fWVkmDB85Yrpb7NtnWm6dm7MFd8eO0gXbsDBzH+PjTX3Cwsy/XecWEmK6KJw6lXdzhk/nFGj+Ehlpfv5iY02dneXCNuexMTHmfeYOybm/hoSY91TczbIK3iTPxw6HqUNYmPlEpihfC9oXGmqvPwwR/AirZTSsTp1qBldJZ28ttSzTWrN/v5n8/ODBsvFLJjvbjIKfPdv8x9q2rfTAA8H7UTjKD8sy4fXXX01/140bzUf6e/eagHjqlAlrmZkmxDjDTlycCZw1a7q3WrU8w6g/WgBzcszvk+3b8247dpivuT/pQNlUWKjN/Vzu/3POTBmFPT6bs/1fVtBzDofZQkLyLxf22B/H5q5/oL+2bCkNG1bw99lbmGe1jGre3F3esKHg43btMv+xSFL79mUjqErmL/vRo90LHQDBwuEwfVSTk6XLLiv4OOd/2nb7NxsSYv4ATkoyU3WdybLyhtkdO0wgd3ZlcH7NyPBsJcwtIsK0aObeKlTw3KKi8u7L7/nISHP+nBzPLSvLtNYeP+7ejh1zf3V2wThzO37cD99om8vKci/njLLriiv8E1aLg7AaRHIPICloBLLk2VfOztMZAfBkt5BaVA6HmQs5MdEMIisqy3L3nw0Pt/f7z842gTW/IHv0qAnhuR8fO+YO5bn7CTu/5uS4uwWc2S3ibFvuVrr8Nslddn5/MzNNyCzqV2/sO9OZ97awx/k5WwtsQc+d2VUiJ6d4LbnlkR3/HRJWg0iDBqbfV1qa9PPP7j5JZ1q50l1u395/9QOA4nD2qQwGoaGmW0ZZGdxY3uXX59cZZPN7fLbnSnussz6B/urc7LicdJD8moBk/go//3zpm2/M6PDdu/Nf6jH3qPmirjEPAEB5kbslGvYXEugKoHg6dHCXf/457/Pp6dKaNabcsqWZ5BsAACBYEVaDTO7+YD/9lPf5H35wf6zQo4dfqgQAAOAzhNUgkzusrliR9/ncKwIRVgEAQLAjrAaZxESpUSNTXrnSTObtZFnSp5+ackiI1L2736sHAADgVYTVIDRggPmalWXWjnfasEH64w9T7tbNniP6AAAAioOwGoQGDnSX5893lz/+2F2+6ir/1QcAAMBXCKtBqFs391x/n39uVmPJzpbeecd9zBVXBKZuAAAA3kRYDUIREdLll5vykSMmpH7yibR1q9l38cVS7dqBqh0AAID3sChAkBo92t2SOmaMWV3Fady4gFQJAADA62hZDVIdOrg/6j9xwqxFLUmXXCL17Ru4egEAAHgTYTWI/ec/UqtW7sfNm5vWVpaQAwAAZQXdAIJYlSpSSor01VdmjtU+faSoqEDXCgAAwHsIq0EuMlK69NJA1wIAAMA36AYAAAAA2yKsAgAAwLYIqwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALAtwioAAABsy+dh9eWXX1ZycrKioqLUrl07LVu2rMBjP/nkE11yySWqVq2a4uPj1alTJ3311Ve+riIAAABsyqdhdc6cORozZowefvhhrV27Vt26dVO/fv20c+fOfI///vvvdckll2jBggVavXq1evbsqUGDBmnt2rW+rCYAAABsymFZluWrk19wwQU677zz9Morr7j2NWvWTJdffrmmTp1apHO0aNFCQ4YM0WOPPVak49PT05WQkKC0tDTFx8eXqN4AAADwneLkNZ+1rJ4+fVqrV69W7969Pfb37t1by5cvL9I5cnJylJGRocqVKxd4zKlTp5Senu6xAQAAoGzwWVg9ePCgsrOzlZiY6LE/MTFRe/fuLdI5nn32WR07dkzXXHNNgcdMnTpVCQkJrq1OnTqlqjcAAADsw+cDrBwOh8djy7Ly7MvP7NmzNXHiRM2ZM0fVq1cv8Ljx48crLS3Nte3atavUdQYAAIA9hPnqxFWrVlVoaGieVtT9+/fnaW0905w5c3TzzTfrww8/1MUXX3zWYyMjIxUZGVnq+gIAAMB+fNayGhERoXbt2mnRokUe+xctWqTOnTsX+LrZs2frxhtv1Pvvv68BAwb4qnoAAAAIAj5rWZWkcePG6YYbblD79u3VqVMnvfbaa9q5c6dGjRolyXyE/9dff+ntt9+WZILqsGHD9Pzzz6tjx46uVtkKFSooISHBl1UFAACADfk0rA4ZMkSHDh3SpEmTlJqaqpYtW2rBggWqV6+eJCk1NdVjztVXX31VWVlZuvPOO3XnnXe69g8fPlyzZs3yZVUBAABgQz6dZzUQmGcVAADA3mwxzyoAAABQWoRVAAAA2BZh1a4OH5buvFOqV09q2lR66ikpKyvQtQIAAPArnw6wQgkdOCB16yb99pt734MPSitWSB99JIVx2wAAQPlAy6rdWJY0bJg7qIaHS84Vvz79VHrmmcDVDQAAwM8Iq3bz6afSl1+aclKS9Pvv0ldfSSH/u1WTJklbtwaufgAAAH5EWLWT7Gzpvvvcj6dPl+rXly65RPrnP82+kyeliRMDUTsAAAC/I6zayRdfSFu2mPKFF0pXXeV+btIkqXJlU/7gA2n3bv/XDwAAwM8Iq3bywgvu8gMPuPuqSlJsrHT77aaclSW9+KJ/6wYAABAAhFW72LpV+vZbU27YUOrbN+8xo0ebAVeS9M47ptsAAABAGUZYtYs5c9zlkSPdA6pyS0qS+vUz5T17pKVL/VM3AACAACGs2sUHH7jLQ4YUfNx117nL773nu/oAAADYAGHVDjZtktavN+WOHc0MAAUZNMj0X5XMNFd0BQAAAGUYYdUOFixwlwcPPvuxFSq4+7MeOiT99JPv6gUAABBghFU7+Oord7l//8KPHzjQXZ4/3/v1AQAAsAnCaqAdO+YeKFWnjtS0aeGv6dfPPa3V55/7rm4AAAABRlgNtKVLpdOnTblvX8+5VQtSvbrUoYMp//e/0r59vqsfAABAABFWAy13F4D85lYtSM+e7jJTWAEAgDKKsBpozrAaGir16lX01+UOq4sXe7dOAAAANkFYDaS9e6XffjPlCy6QEhKK/trOnaWwMFNessTrVQMAALADwmog/fCDu3zhhcV7bWysdP75prx5swm+AAAAZQxhNZCWLXOXu3Yt/utzdwWgdRUAAJRBhNVAcoZVh0Pq0qX4r+/Rw10mrAIAgDKIsBoo6enSL7+YcqtWUsWKxT9Hp05SyP9u4cqVXqsaAACAXRBWA2XFCiknx5S7dSvZOWJjTdCVpPXrzQIDAAAAZQhhNVBy91ctaViVzCwCkgm+q1aVrk4AAAA2Q1gNlNwf25ekv6pTx47u8k8/lfw8AAAANkRYDYTcraA1aki1a5f8XM6WVYl+qwAAoMwhrAbCli1SWpopO+dKLammTaX4eFNeuVKyrNKdDwAAwEYIq4GQkuIulzashoS4W1dTU6Xdu0t3PgAAABshrAaCN8Oq5NkVgH6rAACgDCGsBkLusNq+fenPl/sca9eW/nwAAAA2QVj1t6wsd6Bs0ECqUqX05zzvPHd5zZrSnw8AAMAmCKv+tmGDdOKEKXujC4BkZhNwht41axhkBQAAygzCqr95u7+qJDkc7tbV/fvNQCsAAIAygLDqb74Iq5J07rnuMv1WAQBAGUFY9TdnWA0J8exrWlr0WwUAAGUQYdWfTp6U1q835WbNpNhY7507d8sqYRUAAJQRhFV/WrfOzAYgebcLgCQ1bOgOv3QDAAAAZQRh1Z981V9VMt0K2rY15R07pEOHvHt+AACAACCs+pMvw6rk2W913Trvnx8AAMDPCKv+5Ayr4eFS69bePz/9VgEAQBlDWPWX9HTpt99MuXVrKTLS+9fIHVZpWQUAAGUAYdVfVq92ryzliy4AkplhICzMlJ2zDgAAAAQxwqq/+Lq/qiRFREhNm5rypk3S6dO+uQ4AAICfEFb9xR9hVXL3hc3Kcnc7AAAACFKEVX9Ztcp8jY42H9f7SqtW7vKvv/ruOgAAAH5AWPWHAwek7dtN+bzz3P1KfSH3LAOEVQAAEOQIq/7gry4AkmdYZZAVAAAIcoRVf/jpJ3f5ggt8e61ataSKFU2ZllUAABDkCKv+8PPP7rKvw6rD4W5d/esv6fBh314PAADAhwirvmZZ7rBarZpUr57vr5l7kBVdAQAAQBAjrPra1q3u1s0OHUzLp6/RbxUAAJQRhFVf82cXACemrwIAAGUEYdXXcg+u6tDBP9ds2dJdJqwCAIAgRlj1tdwtq76etsopLk5q0MCU//tfKSfHP9cFAADwMsKqL50+La1da8qNGkmVK/vv2s6uAMeOSdu2+e+6AAAAXkRY9aVff5VOnTJlf3UBcGKQFQAAKAMIq760YoW77O+wyiArAABQBhBWfWnZMne5a1f/Xjt3yyphFQAABCnCqq9YlvT996YcFye1aePf6zdsKEVFmTLdAAAAQJAirPrKH39I+/aZcteuUmiof68fGiq1aOGuy/Hj/r0+AACAFxBWfcXZqipJF14YmDo4uwJYlrRhQ2DqAAAAUAqEVV+xU1iV6LcKAACCEmHVV5xhNSpKat8+MHVg+ioAABDkCKu+sG2btGOHKXfsKEVEBKYeTF8FAACCHGHVFxYudJcvvjhw9ahWTapRw5R//dX0XQUAAAgihFVfyB1W+/ULXD0kd1eAQ4ek1NTA1gUAAKCYCKvedvKk9N13ppyUJLVtG9DqMMgKAAAEM8Kqt337rXtO0759pZAAf4vptwoAAIIYYdXbZs92l6+4InD1cKJlFQAABDHCqjcdPy7Nm2fKFStKffoEsjZG06ZSWJgpE1YBAECQIax600cfSceOmfJVV0mRkYGtj2Tq0LSpKW/eLJ0+Hdj6AAAAFIPPw+rLL7+s5ORkRUVFqV27dlq2bNlZj1+6dKnatWunqKgoNWjQQDNmzPB1Fb3DsqRnnnE/vvHGgFUlD2dXgMxM6bffAlsXAACAYvBpWJ0zZ47GjBmjhx9+WGvXrlW3bt3Ur18/7dy5M9/jt23bpv79+6tbt25au3atHnroId199936+OOPfVlN7/jsM+m//zXlTp2kLl0CW5/c6LcKAACClE/D6nPPPaebb75ZI0eOVLNmzTRt2jTVqVNHr7zySr7Hz5gxQ3Xr1tW0adPUrFkzjRw5UjfddJP+/e9/+7KapWNZ0tq10p13uvc98IDkcASuTmcirAIAgCDls7B6+vRprV69Wr179/bY37t3by1fvjzf16xYsSLP8X369NGqVauUmZmZ72tOnTql9PR0j81vLEtq0kQ67zzpr7/Mvosvli691H91KArCKgAACFI+C6sHDx5Udna2EhMTPfYnJiZq7969+b5m7969+R6flZWlgwcP5vuaqVOnKiEhwbXVqVPHO2+gKBwO6Zxz3I8bN5befdderaqSVLOmVLmyKRNWAQBAEPH5ACvHGcHNsqw8+wo7Pr/9TuPHj1daWppr27VrVylrXEx9+0r9+5vBVatWSWeEbVtwONytq3v2mKVXAQAAgkCYr05ctWpVhYaG5mlF3b9/f57WU6ekpKR8jw8LC1OVKlXyfU1kZKQiAzlF1D//aTa7a9VKWrLElNevl3r0CGRtAAAAisRnLasRERFq166dFi1a5LF/0aJF6ty5c76v6dSpU57jv/76a7Vv317h4eG+qmr5QL9VAAAQhHzaDWDcuHF6/fXXNXPmTG3atEljx47Vzp07NWrUKEnmI/xhw4a5jh81apR27NihcePGadOmTZo5c6beeOMN3Xvvvb6sZvlAWAUAAEHIZ90AJGnIkCE6dOiQJk2apNTUVLVs2VILFixQvXr1JEmpqakec64mJydrwYIFGjt2rF566SXVrFlTL7zwgq666ipfVrN8aNHC9F21LMIqAAAIGg7LOYKpjEhPT1dCQoLS0tIUHx8f6OrYS+PG0h9/SBUqSBkZUmhooGsEAADKoeLkNZ/PBgAbcXYFOHFC2ro1sHUBAAAoAsJqeUK/VQAAEGQIq+VJ7rC6bl3AqgEAAFBUhNXy5Nxz3eU1awJXDwAAgCIirJYndetKzsUVVq82MwMAAADYGGG1PHE4pPPOM+X9+83SqwAAADZGWC1v2rVzl1evDlw9AAAAioCwWt44W1YlwioAALA9wmp5k7tllUFWAADA5gir5U1yslSxoinTsgoAAGyOsFre5B5klZpqNgAAAJsirJZHdAUAAABBgrBaHjEjAAAACBKE1fIo94wAq1YFrh4AAACFIKyWR+ec4x5k9fPPrGQFAABsi7BaHoWESBdcYMr79kk7dgS2PgAAAAUgrJZXHTu6yytXBq4eAAAAZ0FYLa8IqwAAIAgQVssrZzcASVqxInD1AAAAOAvCanlVqZLUtKkpr10rnTwZ2PoAAADkg7Banjm7AmRmmsAKAABgM4TV8ix3v9XlywNXDwAAgAIQVsuzrl3d5aVLA1cPAACAAhBWy7PmzaWqVU152TIpOzuw9QEAADgDYbU8czik7t1N+cgR6ddfA1odAACAMxFWyztnWJXoCgAAAGyHsFre9ejhLi9ZEqhaAAAA5IuwWt61aCFVrmzKy5ZJOTmBrQ8AAEAuhNXyLiTE3RXg8GHmWwUAALZCWIXUu7e7vHBh4OoBAABwBsIqpH793OUFC/x77cxMac0a6euvpVWrpBMn/Ht9AABga4RVSPXqmTlXJemnn6RDh3x/zR07pNtvN/1l27WT+vSRzj/fzPs6cqS0a5fv6wAAAGyPsAqjf3/zNSfHtHL6imVJL75owvGMGdLRo57PHz8uvfGG1KyZ9PbbvqsHAAAICoRVGP7oCnDqlGk1vesuE0olKTZW+sc/pEcekW64QYqPN/uPHZOGD5cef9w3dQEAAEEhLNAVgE107SrFxUkZGdL8+SZYRkZ67/ynTklXXukZhO+8U5o8WapY0b3vyBHpnnukmTPN44kTpQoVpPvv915dAABA0KBlFUZEhHTppaZ85Ih3uwJkZUnXXusOqlFR0uzZpjtA7qAqmcdvvCE995x734MP+n/gFwAAsAXCKtz+8Q93efZs75133Dhp3jxTrlBB+vJLE17PZuxY6YknTNmypOuvl7Zt816dAABAUCCswu2SS9yrWX36qek3Wlr/+Y80fboph4dLn33mXoSgMA8/LF1+uSn//bc0YgQrbAEAUM4QVuEWESENHmzKx4+bYFkay5aZfqlOM2ZIF19c9Nc7HNKsWVJysnm8dKnpIgAAAMoNwio85e4K8NprJT/Pjh3SVVeZSf8l6Z//lG66qfjnSUjwrMe990qpqSWvFwAACCqEVXi68EKpSRNTXrJE2rCh+Oc4dsx8fH/ggHl88cXSv/9d8jpdfLF0442mnJ4uPfZYyc8FAACCCmEVnkJCpDvucD+eNq14r8/JMcFy3TrzuGFDac4cKayUs6Q984x7DtaZM6X160t3PgAAEBQIq8hr+HB3MJw1q3ij8CdMkD76yJTj4ky/V+egrdKoWtUMuJJMIL7vvtKfEwAA2B5hFXklJEhjxphyVpYJoEXx3ntmkn/JtNDOnm2WTfWWu++W6tUz5a++kn780XvnBgAAtkRYRf7GjnVP2P/OO9J33539+AULPAdQPfusNGCAd+sUFWVWtHJiKVYAAMo8wiryV7GiNHWq+/GNN0r79uV/7IIFZinV06fN41tvNaP/feG666QGDUx50SJpxQrfXAcAANgCYRUFu/VWqVs3U961S+rfX9qzx/18VpYJtIMGSadOmX3XXCO99JKZI9UXwsOlhx5yP540yTfXAQAAtuCwLMsKdCW8KT09XQkJCUpLS1O8c5AQSi41VTr/fOmvv8zjihXNXKwVKphVrrZudR979dWm32p4uG/rlJkpNW4sbd9uHv/8s6kjAAAICsXJa7Ss4uxq1DCDmerWNY+PHJFeeUV67jl3UHU4zEj9Dz7wfVCVzDXGj3c/fvZZ318TAAAEBGEVhWvRQkpJkUaMyDtf6sUXSytXmlkAQvz44zRsmFStmil/9JFZMQsAAJQ5hFUUTfXqZjL+vXulxYulb74xXQMWLZI6dPB/faKipNGjTTk7W3r+ef/XAQAA+Bx9VhG8Dhww3RNOnpRiY80gMOd0WwAAwLbos4ryoVo1M6WWJB09Kv3nPwGtDgAA8D7CKoLb2LHuabKef97MFAAAAMoMwiqCW+PGZp5XyfSh/fDDwNYHAAB4FWEVwW/cOHf5//5PKlvdsAEAKNcIqwh+F14onXuuKa9aJf34Y2DrAwAAvIawiuDncHi2rj73XODqAgAAvIqwirLhmmvMaluSNG+e9OefAa0OAADwDsIqyoaICPciAZYlvfBCYOsDAAC8grCKsuO226QKFUz5jTektLTA1gcAAJQaYRVlR5Uq0rBhpnz0qPT664GtDwAAKDXCKsqWMWPc5RdekLKyAlYVAABQeoRVlC1Nm0r9+5vyzp3S3LmBrQ8AACgVwirKnrFj3WWmsQIAIKgRVlH29OoltWplyitXmg0AAAQlwirKHofDs3X1//4vcHUBAAClQlhF2fSPf0jVq5vyxx9LO3YEtj4AAKBECKsom6KipDvuMOXsbGn69MDWBwAAlAhhFWXX7bdLkZGm/J//SBkZga0PAAAoNsIqyq7q1aXrrjPl9HTp1VcDWx8AAFBshFWUbffcYwZcSdIzz0jHjgW2PgAAoFgIqyjbmjeXrr7alPfvl2bMCGx9AABAsfg0rP7999+64YYblJCQoISEBN1www06cuRIgcdnZmbqgQceUKtWrRQTE6OaNWtq2LBh2rNnjy+ribLuscfcratPP03rKgAAQcSnYXXo0KFat26dvvzyS3355Zdat26dbrjhhgKPP378uNasWaNHH31Ua9as0SeffKLff/9dl156qS+ribKuRQvpmmtMef9+6eWXA1sfAABQZA7LsixfnHjTpk1q3ry5Vq5cqQsuuECStHLlSnXq1EmbN29WkyZNinSelJQUdejQQTt27FDdunULPT49PV0JCQlKS0tTfHx8qd4DypCNG6WWLSXLkhISpC1bpKpVA10rAADKpeLkNZ+1rK5YsUIJCQmuoCpJHTt2VEJCgpYvX17k86SlpcnhcKhixYr5Pn/q1Cmlp6d7bEAezZtLN95oymlp0oQJAa0OAAAoGp+F1b1796q6cwWhXKpXr669e/cW6RwnT57Ugw8+qKFDhxaYuqdOnerqE5uQkKA6deqUqt4ow/71LykmxpRffdW0tgIAAFsrdlidOHGiHA7HWbdVq1ZJkhzOQS25WJaV7/4zZWZm6tprr1VOTo5ePksfw/HjxystLc217dq1q7hvCeVFjRrS+PGmnJ0tjRljugUAAADbCivuC0aPHq1rr732rMfUr19fv/76q/bt25fnuQMHDigxMfGsr8/MzNQ111yjbdu26bvvvjtrX4bIyEhFOlcpAgozbpz02mvSzp3SokXS229Lw4cHulYAAKAAxQ6rVatWVdUiDEzp1KmT0tLS9PPPP6tDhw6SpJ9++klpaWnq3Llzga9zBtU//vhDixcvVpUqVYpbRaBgFSpIL70kDRpkHo8ZI/XubVpdAQCA7fisz2qzZs3Ut29f3XLLLVq5cqVWrlypW265RQMHDvSYCaBp06aaO3euJCkrK0uDBw/WqlWr9N577yk7O1t79+7V3r17dfr0aV9VFeXNwIHuZViPHJGGDTPdAgAAgO34dJ7V9957T61atVLv3r3Vu3dvtW7dWu+8847HMb/99pvS0tIkSbt379Znn32m3bt3q23btqpRo4ZrK84MAkChnn9eSkoy5W++kSZNCmx9AABAvnw2z2qgMM8qimzpUumii6ScHLPC1ezZ0pAhga4VAABlXnHyWrH7rAJlRvfu0pQp0oMPmlkBrr9eio+X+vXzzvktS9qxQ/r5Z2nNGjOo68ABs79CBaluXTP/64UXmgULijBLBgAA5Q1hFeXb/feb1axef13KypIuu8zMFuBcQKC4LMsE048/NtvvvxftdQ0amLB8++3u7gkAAIBuAICys6V//EP68EP3vltukZ56SqpUqWiv/+knE04/+UTavr3kdYmKkm69VZo4sWjXBgAgCBUnrxFWAcm0qo4ZY6a1cqpaVbrtNmnoUKlpUynkf+MRLUvavdt8vL9wofT559L+/XnP6XBIXbtKPXpI558vNWkiJSZKYWFmydft26WVK805vvvO9J11qlZNeuYZM1MB3QMAAGUMYZWwipJ67TXpnnuko0c998fHm6AZEiLt22emvMpPWJgZtHXlldLll5vXFMXu3WaGgpdflo4fd++/7DJp5kypcuWSvBsAAGyJsEpYRWns2mUGXf2//2daXAtToYLUp48Jp4MGlS5Y7t5tVtnK3SWhTh1Tl44dS35eAABshLBKWIU37Nkjvf++tGyZtH69dPiw+ai+ShWpcWPz0X7HjqYlNTrau9f+/HNpxAjp0CHzODJSeustptYCAJQJhFXCKsqC3btNf9lly9z7/vUvafx4+rECAIJacfKaT1ewAlAKtWub1bVuusm97+GHzfRWuQdjAQBQhhFWATuLiDBzwE6d6t736qtmloDMzMDVCwAAPyGsAnbncJgBX++9J4WGmn3vvSddfbV08mRg6wYAgI8RVoFgMXSoWXQgMtI8/vRTM/vAsWOBrRcAAD5EWAWCyaWXSl98IcXEmMfffCP17l3wvK8AAAQ5wioQbHr1khYtkhISzOPly830WQcOBLZeAAD4AGEVCEadOklLlphlWSVp7Vqpe3fpr78CWi0AALyNsAoEq7Ztpe+/l2rVMo83bZK6dZO2bQtotQAA8CbCKhDMmjY1iwY0aGAeb9smde1qgisAAGUAYRUIdsnJJrA2b24e79kjXXih9OOPga0XAABeQFgFyoKaNaWlS6XzzjOPDx40g67eey+w9QIAoJQIq0BZUbWq9N130sUXm8enT0vXX2+WaM3ODmzdAAAoIcIqUJYkJEgLFki33ureN2WKCbCpqYGrFwAAJURYBcqa8HBpxgzpuefcy7MuWSK1bi3Nni1ZVkCrBwBAcRBWgbLI4ZDGjpUWL3ZPbXXwoFmydcAAafPmwNYPAIAiIqwCZVm3btK6ddKVV7r3LVwotWgh3XQToRUAYHuEVaCsq1pV+vhjae5cM2uAJOXkSG++KTVrJvXsKc2cSZ9WAIAtOSyrbHVgS09PV0JCgtLS0hQfHx/o6gD2cuyYNH269NRT0pEjeZ9v0UI691zztWZNqXp1KTJSCgszfV0zMtxbero5R+4tLc18PX1aCgkxW3i4CczVq0s1akhNmpjFDFq2lCpV8ue7BwDYRHHyGmEVKI+OHJFmzTIDsX77LXD1aNFC6tLFzAnbt6+ZzQAAUOYRVgmrQNFYlvTTT9IXX5i+rL/8ImVlle6cFSqY1ticHDO/66lTRTtnWJjUo4d02WXSNdeYllgAQJlEWCWsAiVz6pS0caO0ZYu0d6904ICUmWnCpmVJcXFSfLz7a8WKnltCggmquVmW6TJw4IC0Y4cZ1LVpk/Tzz9KaNfkvWBAWJvXvLw0fbmYvOPOcAICgRlglrALB4dgxafly07L76afS9u15j6lcWbrhBmnkSNPPFQAQ9AirhFUg+FiWtH699P770jvvSHv25D2mY0cTWocMkWJj/V9HAIBXEFYJq0Bwy86WvvlGeust6ZNPTPeE3GJjpWuvNcG1QwezCAIAIGgQVgmrQNlx+LD03nvSf/5jWl7P1KqVCa3XX2+6DAAAbK84eY1FAQDYW+XK0l13mZkKfv5ZuvVWzy4A69dL//ynmRd26FDpu+/MTAQAgDKBllUAwefoUenDD6XXXzcDtM6UlGRmERg0SOrVi/6tAGAzdAMgrALlx8aNJrS+/bZ06FDe58PCzKpcXbtKnTtLrVtLDRqY/QCAgCCsElaB8ufUKTP91bvvmsFZJ04UfGxEhFn2NTnZdB9wLi0bF2daYePipKgo95KxDoe77NxCQ/PfYmPNFkIvKwAoCGGVsAqUbydOmL6r8+dL339vWl/9yeEwiyYkJJgtMdEE4lq1zFanjtS4sWnhjYjwb90AwAYIq4RVALkdOmT6tqakmNWzNm6Ufv+99EvLllZIiFS/vgmujRub1t7mzaVmzUxLL1NyASijCKuEVQCFycqS9u+X/vrLLEBw8KAZuOXcTp40CxXk5JjNWc7Odn89c8vKkjIyzPKyaWlmO3Ik7zyxRVGpkju4NmvmLtepQxcDAEGPsEpYBWAXlmVC619/uYPxtm3SH3+Y1t3ffzcBt6iio/MG2GbNpHPOYdAYgKBBWCWsAggWliXt2yf99pu0ebPporBpk9l27y76ecLD3V0JateWatRwb0lJUsWKpv9sXBwtswACjrBKWAVQFqSnmwDr7Gfr/LptW8kXPnA4TGBNSDCzFkREmC0yMm85PNy95X58tnKFCmZwWVyc+Zq7XKEC/XABSCpeXuMzIwCwq/h4qUMHs+V28qTpPpC7FdY5aCwz8+zntCwTgtPTfVfvgoSGmpBcsaLZKlVyl4vymLALlEuEVQAINlFRZnGD1q0992dlSTt3SqmpntvevZ6DvtLSzONjx6TTp0s2AKwksrOlw4fNVhIREcULt5UqSTExni3E+W2hoV55ewB8g7AKAGVFWJiZu7VBg+K9zrJMkDx1yoRXZ4DNzDTb6dPu8pmPzywfP26CsHNWBOdX5+acIeHIEXPN4jh92szgsH9/8V5XGIfDHVzDwvJ+zW9fUZ5zdqeIjPR+OSKCVmaUG4RVACjvHA538IqJ8c81Lcu07P79tzu8Orcz9+V3TFqaOYe36uIM6cEkdx/jwsKtswX5zFXYCnpcnGMD9Vp/XYc/CgKOsAoA8D+Hw700bZ06xX99drZptT1boP37bxOIc7cKF7SdPm26UTi3zMz8vzo3O3AG7KNHA12Tss8XIdnhKNkm+fa1F1wg3Xtv4L7X+SCsAgCCT2iou2+qvzm7TeQXZJ1lZ5B0dqlwdrHwV/nUKe+1PMO98Ed5YMP3SVgFAKA4cnebsLOsLHeIzcx0r8aWeyW2M8vFfeyrY8vDa50r4525BZoNuz3Y/F8aAAAoEX/3Q4b35Bdic4fZsz1f2tdVqBC4910AwioAAICd5O5jCrHmHgAAAGyLsAoAAADbIqwCAADAtgirAAAAsC3CKgAAAGyLsAoAAADbIqwCAADAtgirAAAAsC3CKgAAAGyLsAoAAADbIqwCAADAtgirAAAAsC3CKgAAAGyLsAoAAADbIqwCAADAtgirAAAAsC3CKgAAAGyLsAoAAADbIqwCAADAtgirAAAAsC3CKgAAAGyLsAoAAADbIqwCAADAtgirAAAAsK2wQFfA2yzLkiSlp6cHuCYAAADIjzOnOXPb2ZS5sJqRkSFJqlOnToBrAgAAgLPJyMhQQkLCWY9xWEWJtEEkJydHe/bsUVxcnBwOh1+umZ6erjp16mjXrl2Kj4/3yzXhPdy/4Mc9DH7cw+DHPQxu/r5/lmUpIyNDNWvWVEjI2XullrmW1ZCQENWuXTsg146Pj+cfaBDj/gU/7mHw4x4GP+5hcPPn/SusRdWJAVYAAACwLcIqAAAAbIuw6gWRkZGaMGGCIiMjA10VlAD3L/hxD4Mf9zD4cQ+Dm53vX5kbYAUAAICyg5ZVAAAA2BZhFQAAALZFWAUAAIBtEVYBAABgW4TVUnr55ZeVnJysqKgotWvXTsuWLQt0lVCA77//XoMGDVLNmjXlcDg0b948j+cty9LEiRNVs2ZNVahQQT169NCGDRsCU1nkMXXqVJ1//vmKi4tT9erVdfnll+u3337zOIZ7aG+vvPKKWrdu7Zp0vFOnTlq4cKHree5fcJk6daocDofGjBnj2sc9tLeJEyfK4XB4bElJSa7n7Xr/CKulMGfOHI0ZM0YPP/yw1q5dq27duqlfv37auXNnoKuGfBw7dkxt2rTRiy++mO/zTz/9tJ577jm9+OKLSklJUVJSki655BJlZGT4uabIz9KlS3XnnXdq5cqVWrRokbKystS7d28dO3bMdQz30N5q166tJ598UqtWrdKqVat00UUX6bLLLnP9Z8j9Cx4pKSl67bXX1Lp1a4/93EP7a9GihVJTU13b+vXrXc/Z9v5ZKLEOHTpYo0aN8tjXtGlT68EHHwxQjVBUkqy5c+e6Hufk5FhJSUnWk08+6dp38uRJKyEhwZoxY0YAaojC7N+/35JkLV261LIs7mGwqlSpkvX6669z/4JIRkaG1ahRI2vRokVW9+7drX/+85+WZfFvMBhMmDDBatOmTb7P2fn+0bJaQqdPn9bq1avVu3dvj/29e/fW8uXLA1QrlNS2bdu0d+9ej/sZGRmp7t27cz9tKi0tTZJUuXJlSdzDYJOdna0PPvhAx44dU6dOnbh/QeTOO+/UgAEDdPHFF3vs5x4Ghz/++EM1a9ZUcnKyrr32Wv3555+S7H3/wgJ69SB28OBBZWdnKzEx0WN/YmKi9u7dG6BaoaSc9yy/+7ljx45AVAlnYVmWxo0bp65du6ply5aSuIfBYv369erUqZNOnjyp2NhYzZ07V82bN3f9Z8j9s7cPPvhAa9asUUpKSp7n+DdofxdccIHefvttNW7cWPv27dPkyZPVuXNnbdiwwdb3j7BaSg6Hw+OxZVl59iF4cD+Dw+jRo/Xrr7/qhx9+yPMc99DemjRponXr1unIkSP6+OOPNXz4cC1dutT1PPfPvnbt2qV//vOf+vrrrxUVFVXgcdxD++rXr5+r3KpVK3Xq1EnnnHOO3nrrLXXs2FGSPe8f3QBKqGrVqgoNDc3Tirp///48f5XA/pyjIbmf9nfXXXfps88+0+LFi1W7dm3Xfu5hcIiIiFDDhg3Vvn17TZ06VW3atNHzzz/P/QsCq1ev1v79+9WuXTuFhYUpLCxMS5cu1QsvvKCwsDDXfeIeBo+YmBi1atVKf/zxh63/DRJWSygiIkLt2rXTokWLPPYvWrRInTt3DlCtUFLJyclKSkryuJ+nT5/W0qVLuZ82YVmWRo8erU8++UTfffedkpOTPZ7nHgYny7J06tQp7l8Q6NWrl9avX69169a5tvbt2+u6667TunXr1KBBA+5hkDl16pQ2bdqkGjVq2PvfYMCGdpUBH3zwgRUeHm698cYb1saNG60xY8ZYMTEx1vbt2wNdNeQjIyPDWrt2rbV27VpLkvXcc89Za9eutXbs2GFZlmU9+eSTVkJCgvXJJ59Y69evt/7xj39YNWrUsNLT0wNcc1iWZd1+++1WQkKCtWTJEis1NdW1HT9+3HUM99Dexo8fb33//ffWtm3brF9//dV66KGHrJCQEOvrr7+2LIv7F4xyzwZgWdxDu7vnnnusJUuWWH/++ae1cuVKa+DAgVZcXJwrt9j1/hFWS+mll16y6tWrZ0VERFjnnXeeaxod2M/ixYstSXm24cOHW5Zlpu2YMGGClZSUZEVGRloXXnihtX79+sBWGi753TtJ1ptvvuk6hntobzfddJPr92W1atWsXr16uYKqZXH/gtGZYZV7aG9DhgyxatSoYYWHh1s1a9a0rrzySmvDhg2u5+16/xyWZVmBadMFAAAAzo4+qwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALCtsEBXAACQ17p16zRv3jzX4zFjxqhixYoBqw8ABArLrQKADc2aNUsjRoxwPd62bZvq168fuAoBQIDQDQAAAAC2RVgFAACAbRFWAQAAYFuEVQAAANgWYRUAAAC2xWwAAGAjDoej2K9ZvHixevTo4f3KAIAN0LIKAAAA22JRAACwkdDQUEmSZVnKycnJsz8/JWmNBYBgQcsqANhIVlaWsrKy9MYbb3js37Jli+u5M7fu3bsHqLYA4HuEVQAAANgWYRUAAAC2RVgFAACAbRFWAQAAYFuEVQAAANgWYRUAAAC2RVgFAACAbRFWAQAAYFuEVQAAANgWYRUAAAC2RVgFABsKDw/3eJydnR2gmgBAYBFWAcCG4uLiPB7//fffAaoJAAQWYRUAbKh+/foej1NSUgJTEQAIMIdlWVagKwEA8JSVlaWqVasqLS1NklSzZk29/vrr6tGjhypUqBDg2gGA/9CyCgA2FBYWphEjRrge79mzR/3791d0dLSio6MVGxvr2pYtWxbAmgKAbxFWAcCmJk+erK5du+bZf+LECR07dsy1MfgKQFlGWAUAm4qJidGSJUv0wQcf6JprrlHjxo0VFxenkBB+dQMoP+izCgAAANviz3MAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADY1v8HgRTOUW9oh04AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_result_expectations(\n", " [\n", @@ -1114,14 +1285,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "id": "86f50d83", "metadata": {}, "outputs": [], "source": [ "tlist2 = np.linspace(0, 2, 10000)\n", "\n", - "corr_15k_t10k = dlenv.correlation_function(tlist2, Nk=15_000)\n", + "corr_15k_t10k = dlenv.correlation_function(tlist2, Nk=100)\n", "\n", "corrRana = np.real(corr_15k_t10k)\n", "corrIana = np.imag(corr_15k_t10k)" @@ -1137,7 +1308,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "id": "0c06f6e9", "metadata": {}, "outputs": [], @@ -1195,10 +1366,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "id": "763ab538", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation (real) fitting time: 5.579460144042969\n", + "Correlation (imaginary) fitting time: 0.18423056602478027\n" + ] + } + ], "source": [ "kR = 4 # number of exponents to use for real part\n", "poptR = []\n", @@ -1225,10 +1405,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "id": "44d390a2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Define line styles and colors\n", "linestyles = [\"-\", \"--\", \"-.\", \":\", (0, (3, 1, 1, 1)), (0, (5, 1))]\n", @@ -1270,7 +1461,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "id": "15e69700", "metadata": {}, "outputs": [], @@ -1292,7 +1483,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "id": "ffcb4f21", "metadata": {}, "outputs": [], @@ -1306,10 +1497,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "id": "eb4c1da5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.0074384212493896484\n", + " [ 1% ] Elapsed 0.05s / Remaining 00:00:00:04" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 2.98s*] Elapsed 2.98s / Remaining 00:00:00:00\n", + "ODE solver time: 2.982210159301758\n" + ] + } + ], "source": [ "options = {**default_options}\n", "\n", @@ -1325,10 +1533,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "id": "10367aab", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_result_expectations(\n", " [\n", @@ -1350,13 +1569,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "id": "93e96c60", "metadata": {}, "outputs": [], "source": [ "tlist3 = np.linspace(0, 2, 200)\n", - "envfit, fitinfo =dlenv.approx_by_cf_fit(tlist=tlist3,full_ansatz=True,maxfev=1e6,Ni_max=1,Nr_max=3)" + "envfit, fitinfo =dlenv.approximate(\"cf\",tlist=tlist3,full_ansatz=True,maxfev=1e6,Ni_max=1,Nr_max=3)" ] }, { @@ -1373,10 +1592,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "id": "fa998aa2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation function fit:\n", + "\n", + "Result of fitting the real part of |Result of fitting the imaginary part \n", + "the correlation function with 3 terms: |of the correlation function with 1 terms: \n", + " | \n", + " Parameters| a | b | c | d | Parameters| a | b | c | d \n", + " 1 | 1.02e-01 |-5.71e-01 |-9.61e-08 |2.16e+00 | 1 | 1.12e-02 |-5.01e-01 |-6.77e-03 |-5.00e-02 \n", + " 2 | 7.96e-02 |-8.08e+00 | 2.70e-06 |1.76e+00 | \n", + " 3 | 2.34e-01 |-2.06e+02 | 0.00e+00 |0.00e+00 |A 1-R2 coefficient of 1.51e-01 was obtained for the the imaginary part\n", + " |of the correlation function. \n", + "A 1-R2 coefficient of 8.01e-04 was obtained for the the real part of | \n", + "the correlation function. | \n", + "The current fit took 35.525554 seconds. |The current fit took 0.004359 seconds. \n", + "\n" + ] + } + ], "source": [ "print(fitinfo['summary'])" ] @@ -1391,10 +1631,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "id": "2d897b84", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Create a single figure with two subplots\n", "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", @@ -1421,10 +1672,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "id": "a089f775", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RHS construction time: 0.01082611083984375\n", + " Total run time: 3.13s*] Elapsed 3.13s / Remaining 00:00:00:00\n", + "ODE solver time: 3.1316940784454346\n" + ] + } + ], "source": [ "options = {**default_options}\n", "\n", @@ -1439,10 +1700,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "id": "b1a02d7b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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6Fv0UvM/53PBUad4mggYAAAAA6Ai460QH83vyMpf5pE77v+NEYeEabd9+T9WcRQMGfKbo6NNd1snK+kEbN14uSRo1aqO8vSMUN3CUsv2SFVEqDU+T1mStcNtrAAAAAAC0XrRo6GDWZLi2LEjq3HjQYBiGNm26ToZRKUnq1u3ueiGDYRjau/c1VVRkqKIiQzt2PChJGt5phJZ3knaFSIu7SFllS+vtHwAAAADQ/tCioQMprSxVQOZGHVEsrY2RSnyk4XHDG10/K+s75eeb3SH8/fuqR497661jsVjUu/fLysmZo8jIE9S16zRJ0ohOI3TchVJ59Rlm36DCkjIF+fu6+2W1KpWVlZo/f75Gjhyp4OBgT5cDAAAAAIcdLRo6kHXp63TZSocWvyEVPCpN2SINjh3c4LqGYWj79ppgISHhcVmtDYcEfn5dNWpUsgYO/Fz+/j0kScPihtWEDJJkq9Sspevc9VI86t1339XTTz/tnM/MzNT777+vc889V1FRUZo8ebKysrIOef+GYejss8/W0qW0AgEAAADQ9tCioQNZkbpaQ9PMaZshlSd0V5BPUIPr5uT8qqKiVZKk4OCRiow8Zb/79vPr6jIf4huihPAEbc3Z6lw2e+0KnTNhRDNegWft27dPV1xxhdLS0vTTTz9JksaPH69FixbJ4XC47TgWi0XXXHONjj/+eN1888269957ZbFY3LZ/AAAAAGhJtGjoQOZvWqWhVTeDyPeRIvo3ftG/e/f/nNPx8Xce0oXu0DjX22au2LP2oPfRWqxevVqjRo1Sfn6+fv/9d0VHR0uS5syZo7179+qss85y6/GOO+44zZ07Vy+++KIuvPBClZeXu3X/AAAAANBSCBo6kC2bl6p7njm9OlYaGjeswfVKS3crO9v8xt7Xt3u9ASD3x24vVVrau1qz5mQNih7o8tyO4jWHVLen/f777xo/frxCQkL03XffKSQkxPmcn5+fYmNjdckll7j9uIMHD9YPP/yg7777TieddJIqKircfgwAAAAAcDeChg7CMAwFbl/lnF8VJw2NHdrguvv2vSfJkCR16nS5LBZbk4+TnHyFNm68TFlZ36tXkE0nJUuffC6tfUnqnrP8wDtoZZYuXapTTz1VPj4+mjVrlsLCwhpcr6UGfhw1apTeffddzZ49W1dffXWLHAMAAAAA3ImgoYPYU7BH/dIKnfOrYut3bZDMQCIt7W3nfGzspQd1nKioU53TMcYa9c2Uzl0nDcyQBubmaEdG+iFU7xm7d+/WCSecoIKCAs2YMUPx8fEeqeOMM87QRRddpHfffVdPPfWUR2oAAAAAgKYiaOggVqXVjM8gSVviA9Q9tHu99fLyFqqkZIskKSxssvMuEk0VGXmybDZzgEn/0tlK7uztfG5wuvTzirYxToNhGLriiiuUkZGhE088UWeeeaZH63n66afl5+ene+65Rxs3bvRoLQAAAACwPwQNHcTKtFXqXGBOOyTZhgxtcIBHs9uEKS7u8oM+js0WoKioqjEdHPkq7dPJ+dygdGnhlrYRNLzxxhuaPXu2JOmBBx7wcDVSTEyMrrjiCpWVlenKK6/0dDkAAAAA0CiChg4izjpYJwy8TNEXDteky/zVt1vDd5yw24tksXjJavVXdPQZh3Ss2NgLndOBnaRsP3N68D5pzb7WPyBkWVmZHnzwQUnS0KFDlZSU5OGKTBdffLEk6c8//9R3333n4WoAAAAAoGEEDR1El8KTpZlvK/PD5RoTU6Qnj3uywfUGDPhQ48ala/DgWbLZAg7pWGFhx8jbO8o8rnea1sSay7sWSEVZWw9pn4fTW2+9pd27d0syx0dojpSUFN1yyy3q06ePfH19FRkZqTFjxuiJJ55QQUHBQe1r1KhRiomJkSQ99NBDzaoLAAAAAFoKQUMHsWFDzfSA/hb5e/s3uq63d7jCwycd8rGsVi9FRJwgSUoKK1fAkAHO54747v5D3u/h8tZbbzmnx48ff8j7+fXXXzVw4EA9//zz2rx5s8rLy5Wdna2///5bd955p/r3768///yzyfuzWq0aO3asJGnJkiX666+/Drk2AAAAAGgpBA0dxPr1NdP9+7f88SIjT5Qk9QqSuo+MdS6P2L1W5eUtf/xDtWPHDi1dutQ5P3z48EPaz6pVq5x3rPD19VX37t3l5+fnss6ePXt0/PHHa/HixU3eb+16Pvnkk0OqDQAAAABaEkFDB1G7RUNDQYPdXiTDsLvteOHhUyTZJEn7em3R3J6X6596QvMcR2rLFrcdxu1++ukn53RkZKQiIiIOaT+33Xabhg0bpt9++03FxcXasWOHcnNz9cEHHzi7P0hSYWGhLrzwQhUVFTVpvwMG1LQO+eKLLw6pNgAAAABoSQQNHYBhSCcue1C360ldFPmjQkLqr5OS8j/9+WecNm68QiUl25p9TG/vMIWGHilJyuqaojkXPqmn9E+t0RCX0KO1WbFihXO6S5cuh7yf2NhYLViwQEcffbSsVvO/ma+vry688ELNmzdP4eHhznW3bNmiGTNmNGm/nTt3dk7v3btXO3fuPOQaAQAAAKAlEDR0AOn7DN1Y+qSe1B16oviGBtfJyflFFRWZSkt7W+46Laq7T0hSly414wnU7sbR2qxevdo5HRcXd8j7ufXWW+Xj49Pgc/369dPDDz/ssuy1115r0n47derkMr98+fJDKxAAAAAAWghBQwdw6SvjFKxCSdK2Tr7anrPd5XnDMOTtHSmrNVD+/n3k79/DLcetHTTExNTcjrE1Bw179+51TgcGBh7yfqKiovb7/JVXXunSqiE5Odnl2I0JDQ11mU9OTj60AgEAAACghRA0tHMOwyHbtpruAH/GbdS2HNeuERaLRYMGfa0jj8zWkCGz3HbsgID+8vExv4EPCvlU1uhkqe9Mzat4WoZhuO047pSXl+ec9vX1bbHj+Pr66sQTT3RZtm7dugNuV3dAyZycHLfWBQAAAADNRdDQzqXkpah3Rplzfn20NCB6QIPrWq0+8vdPcNuxLRaLwsImKaNMOunPPDlu6KfIU05TZc/btTd/n9uO406FhYXO6ca6PrjLiBEjXOazsrIOuA1BAwAAAIDWjqChnVufsV4DMmrmd3UOUlzQoY89cLDCwo5WpI80ereU8biU+aR021/S72tb54iQAQEBzumysrL9rNl83bp1c5n39vY+4DYOh8NlvqXDEAAAAAA4WAQN7dz6jPXqn1kzbxnQXxaLxTlvt5fK4ahoseOHhU2S1SJZo6WoEnNZ/wxpYXLrDBrCwsKc0y0dNITUuf1H7dteNqa0tNRlvu6YDQAAAADgaQQN7dzKPes0MN2c3h0s9eg2xOX59PSPtHBhpNasOVX5+Uvcfnx//0QFBg5RUM8uKqr6wr5/prQmdaPbj+UOte/qULsbRUuoqKgJeKxWq/r373/AbeqGH5GRkW6vCwAAAACag6ChnVu/Y51+7SWtiZFWxtUfnyE3d57s9gJlZX0rwyh3+/EtFouOOGKVRve+RhurbsTQK0fam3vggQ89YdiwYc7p1NTUFj1WRkZNn5YRI0Yc8E4VkpSZmekyP3DgQLfXBQAAAADN4eXpAtByDMPQhpJknXtO9QLpxzpBQ17eH5Ikq9VPwcFHtFgt/aP6a0OUlJQq2QwpNGdtix2rOZKSkpzTe/bsadFjrVhRczeQa6+9tknb1A0/agcjAAAAANAa0KKhHUstTFWJUXO7RlnMC/5qpaW7VFq6Q5IUEjJWVmvLDSzYP7q/s0WDJCXmpyu/LL/Fjneojj/+eOd0bm6uS6sDd3I4HJo5c6YkqW/fvrrsssuatF1KSopzuk+fPoqNjW2J8gAAAADgkBE0tGPrM9a7zAd4BSo+NN45n5v7h3M6LGxii9bSO6K3kmNqBqHslykt29n6xmno3r27Ro0a5Zyv3ergYBiGsd/n33nnHe3YsUNBQUH68ssv5eXVtMZFK1eudE6fffbZh1QbAAAAALQkgoZ2rG7QMCCmv6yWmn/yvLx5zunQ0KNatJbdO/+jzC41F9/9M6Q/NrTOO09cfPHFzulFixY1aZu64yvMmjWr0XXnz5+vW265RREREfruu+8OapyF5cuXO6fPO++8Jm8HAAAAAIcLQUM7tjq1TtBQbyBIs0WDxeKtkJAxLVqLl1e4HPHSxadLI6+Wrj1ZWrGr9bVokKQrrrhC0dHRkqRvv/22SdsMGjRIr776qvP2mE899ZSOOeYYvfHGG1q4cKFWr16tWbNm6aqrrtLRRx+tMWPG6O+//9akSZOaXFdeXp7+/PNPSdLJJ5+sQYMGHdTrAgAAAIDDgcEg27Gtyau17VlpQ5T0bV+pxzE14zOUlaWppGSTJCk4eJRsNv8WrSU0dJy6RYTrg6E5zmXJ2a2zRUNAQICmT5+uu+++W8uWLdPmzZvVu3fvA2537bXX6sILL9SsWbP0yy+/aMmSJbrnnnuUnZ2t4OBgdenSRZMmTdLvv/+uCRMmHHRdM2fOVEVFhSwWi+67775DeWkAAAAA0OIIGtoxry3r1TNX6pkr7QiTutZq0ZCf/6dzOizs4C96D1ZY2EQdM+QZfbD9cueyPeWtM2iQpFtvvVXvvvuuNm7cqCeeeEKvv/56k7YLCgrSOeeco3POOefAKx8EwzD05JNPSpJuvPFGl7tjAAAAAEBrQteJdiqjKEM99tXccWJ9tGvXiYKCJc7plu42Ua1fVD+X+QKvrSq3lx+WYx8sX19fvfnmm7JarXrvvfe0fv36A2/Ugt577z2tXbtWffv21eOPP+7RWgAAAABgfwga2il/b39N3jbVOV/Ys5d6hvV0zufnL3ZOBwcfcVhqqhs0yGrXtuzth+XYh2LcuHH673//q/Lycl166aWqrKz0SB07d+7ULbfcoqioKH377bfy92/Zbi4AAAAA0BwEDe1UkE+Q4nc4nPNv3LVANqtNkmQYDhUULJUk+fh0ka9v58NSU5hfmOJ8ozQkTTprjVXHL+uqnbVaXbRGd911l6699lotXbpUt99++2E/fmFhoc466yzZ7XbNmjVLffr0Oew1AAAAAMDBYIyGdqqiQupWZDb3z7eGKaRrnPO54uJNstvzJUkhIYenNYMklZfv01M9/XTh3ZLk0Gz1U+Bdow7b8Q/VSy+9JIvFoueee049evTQtGnTDstxy8vLdcYZZ2jv3r36448/NHz48MNyXAAAAABoDoKGdmr76gL1UYokaW/4AIVYLM7nao/PEBx8+C70vb2j1a1Trop9fRVQVqY+2qTfN0njxh22Eg6JzWbTK6+8ol69eumf//ynHA6Hpk+f3qLHLCgo0BlnnKHc3FwtXrxYXbp0adHjAQAAAIC70HWindozZ6Nzurhbf5fnCgoO//gMkmSxWBUUnKTcGD9JUrxStG1dyWE7fnP985//1IIFC/TWW2/pxRdfbLHjOBwOHX/88Ro/frz++usvQgYAAAAAbQotGtqp1Tnx+lSvaIDWK2niUS7P5efXbtEw8rDWFRw8UmXxy6QUySpDBSu3Shp0WGtojtGjR2v58uXKzc1tsWNYrVZ9+eWX6tSpU4sdAwAAAABaCkFDO1RQVqAFe3z1ha6TJK26vOY5h6NchYUrJEn+/n3k7R12WGsLDh6pot7PSH9WLdi0SW0paJAkHx8fxcTEtOgxCBkAAAAAtFUEDe3Q5+s/1xcJV0q3xUmZAzQz61gN0d2SJMOwq3fvF1VQsFg+PnEH2JP7BQePVFY3wzkfuHezHA7JSiceAAAAAGgXCBraoXXp5t0mFJwmBadpVUa48zmbzV+dO18t6WqP1Obvn6C9cQEaoGJJUs/Y9/TV0nE6a9QEj9QDAAAAAHAvvkduh5anrHeZHxA9wEOV1GexWPTfEh9JUoGPpJj1+nH9fM8WBQAAAABwG1o0tEMpKWvVI0/aGSoZVql/VP8Db3QYde7cQ3G3rdS+IEkWaWz6Jk+XBAAAAABwE1o0tDOF5YU6Yl2Ktj8nFTwqXb68pkWD3V6s7OyfVVGR7dEa+0UN1r5gSRZzfmchQQMAAAAAtBcEDe1Mcmay+meY04EVUnaARX0i+0iSCgqWaPXqf2jhwkht2/Zvj9U4IHacy3ymsdlDlQAAAAAA3I2goZ1Zn7FeAzJq5vMT4uXv7W9O5y92Lvf3TzjcpTkN6jTRZb7cK1PZJZ5tZQEAAAAAcA+ChnZmXa2godQmhfQf4nwuJGSMunS5RSEhYxUSMtpDFUq9IhJls1hclm3KpFUDAAAAALQHDAbZzqzZuVa9qxoHbIyS+sUOdD4XFjZBYWGev42kt81bfbyDNWVevvpkSbtCpaWTN2lMvOfCDwAAAACAexA0tDNF61fL22FOb4huXbe2rK1HRBc983O+rJIWd5Ze2b5Z8nwGAgAAAABoJrpOtCOllaWK2p3inF8f3fpubVmtZ6fB2hlmTvfJkjbsS/ZoPQAAAAAA9yBoaEc2ZW1SvwzDOb8+WuoX1U+SVFS0UWVlezxVWj0DYsdoU6Q5HVYmFaav92xBAAAAAAC3oOtEO7I+Y70eP1L6YoA0IEPa3r+Lgn2DJUlbt96q7Oyf5OPTWSNHrpSPT7RHa+0XPUQbIqTjt5rz4VnbZBiGLHUGiQQAAAAAtC20aGhH1mesV6VNSo6Wvh4gxfUeLEkyDEP5+Uuqpsvl7R3lyTIlSYkRidoaUTPfI7dYmcWZnisIAAAAAOAWBA3tyKq9rt0PqsdnKC3drsrKLElScPCoVtFqoGtIV+2ItDnnE7OlLdlbPFgRAAAAAMAdCBrakevjX5feXCB9O0ODCqdpSsIUSVJBwRLnOsHBR3iqPBc2q01lPeOd8wnZ0qrdBA0AAAAA0NYxRkM7smdLuJQyXkoZr6sukf6RaC7Pz1/sXCckZJSHqqvPN7Gf1kbv0NbgAC327aqeOWGeLgkAAAAA0EwEDe1I+c+/6zLt0HoN0MCEoZL8JLXOFg2SNOPs9/SvRS/qjdcekLZJnxZ6uiIAAAAAQHMRNLQjiQvf0XV6T5KU5rVC0jA5HJUqKFgmSfLz6+Hxu03UFh0YrcSeDud8cnKJJH/PFQQAAAAAaDbGaGhHYjLMwSAdsij2qL6SpOLi9XI4iiWZA0G2NoMG9XBOb9vG6QgAAAAAbR1Xdu1EcZGhXuUbJEl7fXvKEmC2DGit3SaqTZhwpXN62zZfD1YCAAAAAHAHgoZ24rMv31OQiiRJGdEDnMtb60CQ1UJCpOiq3hxbt3q2FgAAAABA8xE0tAN2h11fzL7aOT+/+0KtTV8rqXaLBquCgkZ4oLoD2LtXX1WcrPXqr3v2XKeSEk8XBAAAAABoDoKGdmBbzjb1Ta9wzi+PylFMYIzs9hIVFq6WJAUGDpCXV5CnSmzUqqJtOjL3e/XXRg2M+kJPzHnD0yUBAAAAAJqBoKEdWJexTgPTa+ZTuoYpJjBGhYUrJdkltc7xGSTptU0faV+gOZ1QmqV5W+d6tB4AAAAAQPMQNLQD69LXqdBHSg2SHJJ8Bg+WJBUU1IzP0BrvOCFJCeEJ2hJhTnculFLT13u2IAAAAABAsxA0tAOrUtfqlhOkzrdLkXdKifFDJUn5+TV3nGiNA0FKUmJEojNokKSgzO2eKwYAAAAA0GwEDe3Ayr3rnNO5/tLAmIGSalo0WCy+Cgwc7JHaDqRu0NA1L1eF5YWeKwgAAAAA0CwEDW1chb1C2/OTXZYNjB6oiooclZRsliQFBw+X1ertifIOqFd4L20Nr5lPzDYHtwQAAAAAtE1eni4AzbMle4sqVe6ybGDMQNlsQRo+/E8VFCyRl1d4I1t7nr+3v/LjoyVlSJISsqWN6Zs1JHaIZwsDAAAAABwSgoY2bl3GOpf5GP9OivA3+yKEho5VaOhYT5R1cPr00QujMrQ1XFrUVTpyy2qdM/hMT1cFAAAAADgEBA1t3Lr0dfrgS8lhkVbFSisvGujpkg5aXNd+uvmEhc754JS1HqwGAAAAANAcBA1t3Jq9a3THBsm/UhodIb0yfZCnSzpoiRGJLvPbcrnzBAAAAAC0VQwG2cZlrVkp/0pzem2MOT5DeXm6UlKeUW7ufNntRZ4tsAkSwhNc5vdV7PFQJQAAAACA5iJoaMPKKssUvr3m2/91MeYdJ/LyFmrr1tu0cuVR2rnzYQ9W2DR1WzQU2TJUWlnqoWoAAAAAAM1B0NCGbcrapP4ZDud8dYuGgoIlzmXBwaM8UdpBSYioatFgSLEFUlyhoW3Z3OISAAAAANoixmhow1ILUzVon5cks+9ERvdYhfiGyCvucvn59VRBwRKFhIz2bJFNEOIbosm54Zr5Uo6CKqQXRkkbL1umATEDPF0aAAAAAOAg0aKhDZuSMEXDNpsX4xWy6f8u/J8kKSCgtzp3vlp9+86Qr29nT5bYZP7xvRRUYU4nZEtLty31bEEAAAAAgENC0NCGFedXqlf5RklSil9fnT38fA9XdOgiew5QobdFkpSYGiKfvHAPVwQAAAAAOBR0nWjDNq4u12d6QEO1SuEJXdTL0wU1w9unvaOMiCUK2rdRPYuKFbSnp6dLAgAAAAAcAoKGNmxFcoAe112SpGeulP4hKStrliSrQkJGy9u77bQKsFqsquiRKO3bKG9VKm9NhqdLAgAAAAAcArpOtGGrVtVMDx1q/tyx4z6tWTNVCxdGqbKywDOFHSKf/v2d0767O3mwEgAAAADAoSJoaMPqBg12e4kKC1dKkgIC+snLK9gzhR2i0BGJzmnf3fkerAQAAAAAcKjoOtFGvbZ0hv72zZB6D1OcZagiIrooP3+5DMO81WVIyBgPV3jwfAfWBA3BaVs8WAkAAAAA4FARNLRRby6YocDhy1Q2XkqT9Pzfz+qMLpXO59ti0KDhw/Wvfl/qh40J2lqcoIsKpaAgTxcFAAAAADgYdJ1ogyodlYpasVpZT0i7npGuXCYNjBmo/PxFznXaYtAwL2+1vj9+mVaf9YiKrpmom7+50dMlAQAAAAAOEi0a2qDNWZs1MLVCkhSfL5XbpKGxQ7VltRk02GxBCgwc4MkSD8mv237VmvBHpKqbZSzeWeLZggAAAAAAB40WDW3QyrSVGpZWM5/SPVrBtjKVle2WJAUHHyGLxeah6g5dYkSiy/zesiwPVQIAAAAAOFQEDW3Qqn2rNLQqaCi3SkHDRqig4G/n822x24RUP2jIs+TKYTg8VA0AAAAA4FAQNLRBq7YsUb9Mc3p9tDSo63Dl57f9oCEhIkEB5dIRu6XzV0tD9pVrT/4eT5cFAAAAADgIBA1tjGEYqlixWF6GOb+0szSy88g6A0GO9lB1zRMbGKvj9vhq8RvSR19JZ6+TtmRzm0sAAAAAaEsIGtqYLdlbNGhnoXN+SRcpqdMwFRQslST5+fWUj0+sp8prFovFInuvns75xGwpOZOgAQAAAADaEoKGNmbJ3iUaubdmfnOPCEXYcuRwmHdoaKvdJqoFJPZXpcWcTsyWlm5d7dmCAAAAAAAHhaChjVmyZ4muOkUac6V001Qp+Igxys9f6Hw+JGSsB6trvh7RvbUzzJxOzJaWbZvl0XoAAAAAAAeHoKGNWbhzscq8pb/jpRdHS0ndRys39w/n82FhR3mwuuZLjEjUlghzOqRcKs3KlWEYni0KAAAAANBkBA1tSKWjUqvTV7gsO6LzEcrLmy9J8vIKU2DgIE+U5jaJEYnaGl4zH5WTr7Iy7jwBAAAAAG0FQUMbsi59ncqqxmKoNiAsTBUVGZKk0NAjZbHYPFGa29Ru0SBJPXIrtSPzL88VBAAAAAA4KAQNbcjiPYtd5rsF9ZK/sUfV/4yhoRM8UJV7dQnpop1RXpKkLH8poELasI+gAQAAAADaCi9PF4CmGxQ9WI9/NFx/9crQ/MG7NXbgEYqJOUsREbnKz/9L/v59PF1is1ktVm0bmaDw+GTl+pvLemas8mxRAAAAAIAmI2hoQyJXhumOTSukTdLilccq8q7/SpK8vIIVETHFw9W5T3xcH63MT3bOr9u7dz9rAwAAAABaE7pOtCF7P57nnC5NOk4JEQkerKbljOw8UnHl46WVl0i/PahOxZGeLgkAAAAA0ES0aGhDvP6sCRqiz57owUpa1r0T71Xg0nt1+zfmvPeRG1VZWSAvr2CP1gUAAAAAODBaNLQRhsNQwm4zaChUoHqfM0LJyVdr/foLlJr6jhyOcg9X6F4JtRpr7N2boOLi5MZXBgAAAAC0GrRoaCN2zNmqng5zrIJNkeM0zNei9PTPZLfnKzv7F8XFXeLhCt2rv2Wj7tBMJWqLNq2NVHHxRoWEjPR0WQAAAACAAyBoaAOKK4q19f0f1bNqvjBpokpKtkgyJEkREcfJYmlfjVO6FazV47pLkvTcrqtVXLzRwxUBAAAAAJqCoKENmLlxpipW3eycT53sp8DAfho/Pkv5+X/LZgvwYHUtw39QonM6KjdfxcXZHqwGAAAAANBU7etr8HZq1uYftbiL9FdXKd9H+jDmV0mS1eqtsLAjFRw8wsMVtoBagzR0LU/VvoztHiwGAAAAANBUtGho5ewOu77f+KNyR0svjZYCy6THB57k6bJa3Ky0+RoV4Keo4lIlBs3XOcvCNXmSXRaLzdOlAQAAAAD2gxYNrdySvUuUW5HpnC/ylaYmHu/Big6PTVmbtCmiVJLUpdBQZo5DpaU7PFsUAAAAAOCACBpauS/Wf+Ey3zusvwpSbtCqVVO0d+/rMgy7hyprWQnhCdoSUTMfnFXKgJAAAAAA0AYQNLRihmHow1WfuSw7c8Dxysn5VTk5s5WS8oTa6z9hYkSiS9DQNbdU5ZYozxUEAAAAAGgSxmhoxRamLFT35BRVRkiZgeayybGBUrZDkhQdfbYsFosHK2w5PcN7an2MtCJO2houZftL+8p91cnThQEAAAAA9ougoRV7bdEr+vQLKa5Q+mKA9OBlfRVlX6i8quejo8/2aH0tyc/LT0vGdtOIAbucy7Zkb9GwuGGeKwoAAAAAcEDts919O5BRlKH8bz9T9zzJ1y6FlUqXjjhNeXlzJUn+/okKChrm0RpbWmJEosv85sytHqoEAAAAANBUBA0t4OWXX1bPnj3l5+enpKQkzZ8//6D38b8//6c7/qh0zr8x0kvHRhc75zt1uqrddpuolhCe4DK/fOdK2e2lHqoGAAAAANAUBA1u9umnn2ratGn697//rRUrVmjChAmaOnWqdu3adeCNa9n1yasan2JOr4uWQk8/R+U570uSLBZvxcVd7u7SW526LRpW7PxJhYXLPVQNAAAAAKApCBrc7JlnntGVV16pq666Sv3799ezzz6r+Ph4vfLKKwe1n6dn1dy28sFJVl01oIsqK3MlSbGxF8nHJ8adZbdKdYOGfRUV3OISAAAAAFo5ggY3Ki8v17JlyzRlyhSX5VOmTNGff/55UPuKreol8X1vKe7Sq2Rkv1b1jEXdut3phmpbv8SIRJ28UfrpfWnLc9L4rUWyWyMOvCEAAAAAwGO464QbZWZmym63KzY21mV5bGys0tLSGtymrKxMZWVlzvn8/HzndHKk9MSl/fV05+0qyjOXd+p0pQIC+rZA9a1Pr/BeiiyRjq8aA7JPlpRv7ePZogAAAAAA+0WLhhZQd5BGwzAaHbjx0UcfVWhoqPMRHx8vSXpnqHTpzUH6z+RgFeXNliR5eUWqZ89HW7b4ViTIJ0g5nWtaMCTkmLe4BAAAAAC0XgQNbhQVFSWbzVav9UJ6enq9Vg7V7r77buXl5TkfKSnmCJC7p0n/nVAo77LFkiSLxUsDB34uH5+oFn0NrU5izZ0nErOlzVkEDQAAAADQmhE0uJGPj4+SkpI0e/Zsl+WzZ8/WuHHjGtzG19dXISEhLg9JOrZztGyW6v3GafDgHxUefnSL1t8ahfcaoOKqDj6J2dK61C1yOCo8WxQAAAAAoFGM0eBm06dP18UXX6yRI0dq7NixmjFjhnbt2qXrrrvuoPYzatQ6SeslScHBo2Wz+bVAta3fxB6TtCt8pvpl5Kpntk1dd/+lrKzvFR19uqdLAwAAAAA0gKDBzc4991xlZWXpwQcfVGpqqgYNGqRZs2ape/fuB7Ufq9VXISETW6jKtuOyYZdpc8xMKeMb+Rh2hWyO5xaXAAAAANCKETS0gOuvv17XX3+9p8toPxITpXVV01uCCRoAAAAAoBUjaECrZ5tyjP43s1Kb1EcFJcU6uvgzT5cEAAAAAGgEQQNaveiL/6HpN/xDkjQ4Z76Kix/a7y1DAQAAAACew10n0OoFB0sxMeb03r0JstvzVF6+z7NFAQAAAAAaRNCANiEhwfyZVRikXXm+jNMAAAAAAK0UQQNavR83/6itR02Ubusk3R2qRzfYCBoAAAAAoJVijAa0eqWVpUr3/0MWhxSfK+X62wkaAAAAAKCVokUDWr0+kX100yKp+L/SzmelpB1lysxf6+myAAAAAAANIGhAq5cQkaAcf8nPbs73yZKSM9d5tigAAAAAQIMIGtDq+Xn5Ka9bJ+d8nyxpW16a7PZiD1YFAAAAAGgIQQPaBO/+/ZzTfbKklGKppGSzBysCAAAAADSEoAFtQuf4gUoPMKf7ZEk7C7nzBAAAAAC0RgQNaBP6RvXVpkhzumuBtC/Dm6ABAAAAAFohgga0CX0i+ziDBkkK2lepoqINnisIAAAAANAggga0CXWDhh5Zldqdyy0uAQAAAKC18fJ0AUBTxIfE6/uB3loXU6FNkdL2MOkMW5KnywIAAAAA1EHQgDbBZrXJ0buPvo9Y51xW6HOUBysCAAAAADSErhNoM/pF93GZ35S1yUOVAAAAAAAaQ9CANqNvZF+X+XX7CBoAAAAAoLUhaECb0SfStUXD+n2bVFmZ76FqAAAAAAANYYwGtBmju47WiRU3KeezAPXJsijhgrVau/Y0DRv2m6dLAwAAAABUIWhAmzEgeoAeSUvUkM23SJLeWH6Kis/f6OGqAAAAAAC10XUCbUroqJpxGoJSvRUUNEIOR4UHKwIAAAAA1EaLBrQpcUfVjNMQm+PQkCHfe7AaAAAAAEBdtGhAm+Lbu5vK5CNJis3fJMPwcEEAAAAAABcEDWhbbDalBiZKkno5tigjze7hggAAAAAAtRE0oM3JizW7T/ipTJ9/PcPD1QAAAAAAaiNoQJuyK2+Xfu86zzk/a+H1mrcoyYMVAQAAAABqI2hAmxIbGKsVkbmSpD3BUlC5tCFjlRyOSs8WBgAAAACQxF0n0Mb4evlq6aheCu27Vfl+5rKeRXaVlm5XQEBvzxYHAAAAAKBFA9qe3j0GOUMGSdpZLBUXb/RcQQAAAAAAJ4IGtDkDoge4zG8vtBI0AAAAAEArQdCANqd/VH+X+W35XgQNAAAAANBKEDSgzanboiGjslzZBes9VA0AAAAAoDYGg0Sb0y+qn7rnSBeukQZkSDP7SptGb9A4TxcGAAAAACBoQNsT6BOooRWx+u9v+yRJeb7S1oI8lZdnyscnysPVAQAAAEDHRtcJtEk+gwc5p/tnSjuLuPMEAAAAALQGBA1ok7r1HKq0QHN6QIa0g6ABAAAAAFoFgga0SQOiB2hDtDkdWyRl7+POEwAAAADQGhA0oE3qH91f66Nr5iP2VCqvkDtPAAAAAICnETSgTeof1V8bao372DdT2pK1znMFAQAAAAAkETSgjQr3D9eeLhHO+UHpNu3OT5HDUebBqgAAAAAABA1os8474zEt80nUB96na0/qv5QUbqikZIunywIAAACADs3L0wUAh+rcqVerU8TVSkuTQnIzNdV4SMXFGxUYONDTpQEAAABAh0WLBrRpgwaZP/Pzo2QY58hmC/ZsQQAAAADQwRE0oE2rDhokyeH4VBERUzxXDAAAAACgbQQNX375pXr16qWEhARPl4JWZvDgmum1az1XBwAAAADA1CbGaCgsLNSOHTtksVg8XQpamdotGjauKJHk77FaAAAAAABtpEUD0JgBfSs1y2uS9tgidfUPffXeqvdkGHZPlwUAAAAAHRZBA9q0Z1c9pj7B89TZnq2++Sl6/rfLlZX1g6fLAgAAAIAOq0W7Tuzatcst+8nMzHTLftD+DIgeoDUxUkKOFFQhVaYYKipaq6ioUzxdGgAAAAB0SC0aNPTo0YNxFdCihsQO0Uex0mnJ5nzPvYYyS0vV3bNlAQAAAECH1eKDQRqG0dKHQAfWK7yXNsT5SCqXJA3ZJ2V7TfBsUQAAAADQgbVo0FDdmiEuLk59+vQ55P2kpaUpOTnZXWWhHbFarCrs3VfSGklm0LB632odl3CcZwsDAAAAgA6qRYOGxMREbdmyRf369dOcOXMOeT/vvvuuLr/8cjdWhvYkdsQRKvZao4BKafA+6dv01Z4uCQAAAAA6rBa960RSUpIMw9CKFSta8jDo4IZ2Gaa1MeZ0YraUvJPzDQAAAAA8pUWDhpEjR0qS8vLytHXr1pY8FDqwIbFD9L+x0lUnS2OuklZmr9XWHY97uiwAAAAA6JAOS9AgSUuXLm3JQ6EDGxw7WJ8Mlt5MkpZ0lcqshtbu/d3TZQEAAABAh9SiYzSMGDFCQ4cOlSRlZGQc8n6OPPJIvf322+4qC+1MhH+Eory7KrNit3PZ6vS1OtWDNQEAAABAR9WiQUNQUJBbxmdISEhQQkKCGypCezUkdqh+210TNKzN2iuHo1xWq48HqwIAAACAjqdFu04Ah8v4Xkku88kFhkpKNnuoGgAAAADouAga0C6M7Jwki0NKzJLOXSPl75UKCrnNJQAAAAAcbgQNaBeSOiXpn39Km1+QPvlSGrtVWpc239NlAQAAAECHQ9CAdqFzcGdt6RzunD92Z6SKizZ5sCIAAAAA6JhadDDIhpSVlWnZsmVav369du7cqYKCAhUXFysgIEDBwcHq1q2bBg4cqKSkJPn6+h7u8tBGWSwWXXPu99J74yVJw7fEqNS61cNVAQAAAEDHc9iChrVr1+qxxx7Td999p8LCwgOuHxgYqJNPPll33nmnhgwZchgqRFt3zJRxSlG84pWihPxdWlZcpMrKPHl5hXq6NAAAAADoMA5L14m7775bw4cP18cff6yCggIZhnHAR2FhoT755BMlJSXpzjvvPBxloo3z8pK2hpt3nwg2imRsDlRh4RoPVwUAAAAAHUuLt2iYNm2aXnjhBRmGIUnq3r27Jk+erAEDBqhbt24KDg6Wr6+vysrKVFhYqJ07d2r9+vX6/ffftWPHDtntdj311FMqKSnR888/39Lloo3LTxwhLflGklS6sKuKTlqtsLAjPVsUAAAAAHQgLRo0zJ8/X88//7wsFot69+6tF154QVOmTGny9j///LNuvvlmbd68WS+99JLOPvtsTZgwoQUrRlvnPTpJWmJO+64OUGHhKs8WBAAAAAAdTIt2nXjjjTckSb169dKiRYsOKmSQpOOPP15//fWXevXqJUl6/fXX3V4j2pdOp42SJBUoSHnZ0SogaAAAAACAw6pFg4b58+fLYrHojjvuUHh4+IE3aEBERITuvPNOGYahBQsWuLlCtDcxSdLg/o8rbOrFOveUPbr0jyUyDIenywIAAACADqNFu06kpaVJkoYPH96s/VRvX70/oDGbctdq7bnm4KEOSRvzpaz8tYoK5c4lAAAAAHA4tGiLhsDAQElSbm5us/aTl5fnsj+gMUd0PkIWo+a0dkj6a8dMzxUEAAAAAB1MiwYNiYmJkqTPPvusWfv5+OOPXfYHNCbQJ1Dd/FxbLyxN3+GZYtzA4ZA2JFfquzlZmvdnsXJyPF0RAAAAAOxfiwYNp59+ugzD0JtvvqmXX375kPbx8ssv66233pLFYtEZZ5zh5grRHo3vNsZlfk1227s6X7B2h4648x753jxcQ9/31SkLojRpdqAiHopX7LWX6b6358luNzxdJgAAAADUYzEMo8WuVgoLCzVo0CDt2rVLFotFY8eO1TXXXKPJkyera9eujW63e/du/fbbb5oxY4b++usvGYahbt26ae3atQoKCmqpcluF/Px8hYaGKi8vTyEhIZ4up016d+V7+uCZS3XqRmnMbunya6K1+r59slgsni7tgDILc3Xmcw9pa8bzumFZpc7YIGUESBOurL9uSPYkfXnZqzp2eN/DXygAAACAdsPd16EtOhhkUFCQvvrqK51wwglKT0/XX3/9pb/++sv5XLdu3RQcHCwfHx+Vl5eroKBAKSkpKigocO7DMAxFRUXpq6++avchA9xjbPwY7dsq3bjEnO+VnKHtudvVK7yXZws7gF82/KVz3zpDtyxJ048LpYBKc3lgecPr2/zm6rz3knTLjjf1n9PPPXyFAgAAAMB+tGjQIEkjRozQ33//renTp+vrr792Li8oKND69evrrV+3gcVpp52mZ555Rj169GjpUtFO9I7oreXxIZLyJUkTdkq/b5/TqoOGp+a8qZdnXqe5n1Vq6L6a5ZUWaWeYZHFItca4lMUhffCVNCi9SGflXKl+AeN19vGNtxICAAAAgMOlxYMGSerevbu+/PJLJScn6/PPP9fcuXO1bt067du3r966MTExGjRokCZOnKizzjpL/fv3Pxwloh2xWCwqHHqU9On3kqSJO6UnNr6hK0dc7eHKGpaSVqzv3rhXS2ZWKrLEXFZhlV4eY9W+6y/TP468VCnhCcouydbsbbP17KJnde6PKTphi7nuvHcrdcanW9RvcVcNHuy51wEAAAAAUguP0XAg1d0lSkpK5O/v7+xG0ZExRoN7PDnvBU09+2YNypDsFmngg4HaeE+hp8uqp6hIOv7ocn20tKe6GXslSZsipCduG61//d9HDbbCKK4o1j3vX65zH/pKo3eY/SsKFahLO/+q19eMUUTEYX0JAAAAANo4d1+HtuhdJw7Ex8dHkZGR6tq1qyIjIzt8yAD3+Ue/SZrXw5y2GVLPjUXambvTozXVZRjSVVdJC5f46HTjW+VYQzW/t7/ee/0GvXrXgka7egR4B+iZKz5V4opdsk89SZIUpCLN2Huipp+1S56LDgEAAACghYKGoqIiLVq0SHPmzNHPP/+slStXKj8/vyUOBTRoYMxA/dUt0Dk/cYc0b+c8zxXUgBdflD75xJzeHJyk9C//0vAlO/XwGS/Ky3rgXk2RYZ1k++pzlY4/xpxXtv7v97P13huNjB4JAAAAAIeB24KGyspKvfPOOxo5cqTCwsI0fvx4TZkyRSeccIKSkpIUHh6u+Ph4XXrppfrwww9VXFzsrkMD9VgtVmUNmuCcn7hTmrej9QQNS5dK06fXzL/zjtT3tP4KCo0+uB35+cnv+y9UHNtTkjRai1V84x3auqvIfcUCAAAAwEFwS9CwY8cOjR49WldeeaVWrFghu90uwzDqPfbs2aMPPvhAl1xyiTp37qxbbrlFe/bscUcJQD3jxh+vDwdL/50g3TNZmrtzrqdLkmEYWpayRpdfYldl1e0rb7tNOuOMZuw0LEwBP3yuCquP9qizfu4kDXi9h37f/rtbagYAAACAg9HswSAzMzM1dOhQpaWlyTAMWSwW53O1d93QcovFosDAQN17772aPn26rFaPDhnRKjAYpPusTFup4a8Nd857W721Y9oOdQ7u7LGa3l35rr54/DI99H2Mzi+aLf9hQ/T335K3d/P3veWDjzT2p7eU2XuOJKmLf4I23bpaAd4Bzd85AAAAgHbL3dehzb695Y033qjU1FRZLBZZLBZniDBw4ED169dPvr6+Kikp0datW7V582aVlJj376sOHgoLC3XnnXdqzpw5+uyzzxQcHNzckgBJ0pDYIYouG6WM1SOkLf/Q4zf94NGQYVPWJj300f9p0UwpqiRdy2zDtfSOn+TtfZxb9v9L31xlbp3jnN9TslVPLXxa9076j1v2DwAAAABN0awmBBkZGfrqq6+cAYNhGDrzzDO1adMmrVmzRp9//rk++OADffnll1q5cqVyc3M1d+5c3XbbbYqOjna2gDAMQ7/88osmTJjAoJFwG6vFqmcH/S398IqUfKqWLEyU3V7ikVrsDrsu+eJivfZFiaKqSvi5t0MZg/PcdozrRl6nsV3HuSx75I/HlVaY5rZjAAAAAMCBNCtomDdvniqrOppbLBZdddVV+vzzz5WYmNjg+t7e3jrqqKP05JNPKiUlRc8884zCwsKcYcOaNWt09tlny+FwNKcswOnYY2umly49VoWFqzxSxwuLX9Cw7xbrmO3m/O5gad6/L9GZA89y2zGsFqtePekVWVTTTcleWaT//Hav244BAAAAAAfSrKCheiBHwzDk5+enp59+usnbent7a9q0aVqxYoWGDx/u3M+vv/6q//3vf80pC3CKiZEGDcqQr0rVY3OmMmbOPOw1bMvZppe+ultPzK5Zdsd5XfTIua+4/VhDYofo8mGXy+qQrlwmbX9WWvzjG9qQscHtxwIAAACAhjQraCgsLJRktmYYO3bsIY2v0K1bN/32228aPHiws2XDww8/rOzs7OaUBjidNzJZ2YrQLzpeeuLHw3pswzB09bdX6emZpQopN5e9OVz6v39/3GKDND40+SFdu9JHb3wndS2QHv7N0EN/PNwixwIAAACAupoVNPj6+jqn4+LiDnk/ISEh+uyzz+Tl5SWLxaL8/Hx9+umnzSkNcBp1XpIKFSRJ6rwxWSovP2zHfmvFWwr58XedssmcTw2SVt96hSZ0n9Bix+wc3FnR192qlKrBYk/eJCXP/ljJmcktdkwAAAAAqNasoCEyMtI53dwWCH379tX555/vvGvFTA80cUf7dNQkf822mnd2CLCX6sF7J2rNvjUtfty9BXt12y+36ZcE6amxkt0i3X1ypB46p+W7Bt088Z96ZoKPc/7f8wz9d/5/W/y4AAAAANCsoKFnz56SzObhK1asaHYxZ5xxhnM6OZlvX+Eevr7SnMk7nfNdflmkz9Z91uLHvenHm5RXlqdiH+mfx0v9b5DOuf99hfg2/760BxIZEKmg62/RnqreTGdslFb/8oG252xv8WMDAAAA6NiaFTQcccQR8vf3lySlp6dr1qxZzSomISFBkhlc7Nu3r1n7AmrbOTZOhd7m9Gkbpa/XfOZsPdMSvtn4jb7a8JXLsr4jLtYJfaa22DHrumXSHXr6yJpWDdP/NPTC4hcO2/EBAAAAdEzNChoCAwN19dVXO+dvu+02lZSUNLsoyRy3AXCX6044S9/3MacjS6ROyzZpXca6FjlWflm+bph1g8syf0eU3j73mRY5XmOiAqKkK65WlpkF6ry10re/z1B+Wf5hrQMAAABAx9KsoEGS7r//fkVFRUmSNm3apHPOOUflhzjYXnV3CYvFovj4+OaWBjidOeosfZMY6pw/e51arPvE3b/erS4b9sriqFn27NRnzQv/w+ymY27TjCRzOttfik8r0lsr3jrsdQAAAADoOJodNISFhemVV15xNkOfNWuWJkyYoG3bth30vt58803n9OTJk5tbGuBktXppV+9hKvKWVsdIa2Okd1a+I7vD7tbjZBZnavnsd7XwLWn+29KAdGmg7/G6evQFbj1OU/UM76n1Z07VpadJ3W+V5vaU3l7xdot2GwEAAADQsTU7aJCkM888UzfccIPz4mXJkiUaMGCAbr31Vm3evLlJ+3jkkUf0448/ymKxyGq16tprr3VHaYDTpUeNUMLN0tDrpRfGSCn5Kfpl6y9uPUaUX4Q+m9lP3g5pfIp03movzbz6FVksFrce52Bce8a/9N4wqbygh/TTM7qnyx8erQcAAABA++aWoEGSnnvuOZ177rkyDEMWi0Xl5eV6/vnn1a9fP40ZM0b/+te/9N1332nTpk0qKChQcXGxtm7dqk8++USTJk3Sf/7zH0nmQJD33XefevXq5a7SAEnSWeOOU055f5dlM5bPcOsxCp56TfE7lkmSkn06qfz/nlNCZE+3HuNgjY8fr/t6/SS9sFladKs+ejv0wBsBAAAAwCGyGG5sQ20Yhh544AH997//lcPhcC5ryrenhmEoIiJCjz76qMsAkx1Nfn6+QkNDlZeXx4CYblZRkaNj7zpLf4T85lxms9iUcmuKOgV3av4B9u5VcY/+CqgwB1t87PjfdddPk5q/XzeorJS6dZNSUyWbTdq9W4qL83RVAAAAAFoDd1+Huq1Fg2QO4nj//fdrwYIFGj9+fL1+4IZhNPqwWCwaOnSoUlNT9f3332vv3r3uLA2Qt3e4bj66UCoPcC6zG3a9vOTl5u/cMJR7ztXOkOF978t1+buTmr9fN/Hyki6/3Jx22B364dHVni0IAAAAQLvl1hYNdf3xxx9666239PXXX6ugoKDmoE3sHx4TE6MRI0YoKSlJI0aM0IgRI9StW7eWKrdVoEVDy9q48QpNemaX9nWZo+BS6bql0gdHBmvdHTsV7h9+yPu1v/yabDdcJ0lKU6y+enCdrv9PpLvKdott26RnEl7UbXpaXbRXXmm7ZY2N9nRZAAAAADzM3dehLRo0VLPb7VqyZIn++OMPLVmyRMuXL9f27dtdC6kVPtQuqW4oERER4QwfHnnkkZYt3AMIGlpWWtp7eu6j37V+8Tt6e6YUUSrdcawU8O/7dP+k+w96f3O2zdFrS17R/6ZvVpe9ZiuBG3r8oGc3nSBvbzcX7waf/n97dx0exbn2cfy7HjcgCSEEl+ItVigtNai7C3VX6j2nR6q0pW97enp66krdqZwK1Ki3uBV3QhJIiMvqvH8M2WSTTQiQZEP4fa5rrp15nnlm7k23S+bOI91v56wNjwCw5ur/o9dTt0Q4IhERERERibS9MtEQTlFREfPmzWPu3LnMnTuXefPmsWbNmgaTDHXDtFgs+P3NuzRhW6BEQ8vz+WDkaccy95MvsAJbY2DYHQksvW39LvVqyC7JZvhzw8krzyN6ezpTXxmJvaQLI2Y/zYgRLRf/nvj00ZWccGs/ANalJHPLf8fz4dkfRTgqERERERGJpHaTaAinpKSEefPmBRMQ8+bNY+XKlfWSD9VzOijRILvr9keWsv+0QZyzZMfxkeC55UYeP/rxJrWv8lVxyMuHMHvL7JDysyu+4a2HD2/maJvPsi0byB0xhMNyzLkkDr4YXpi6nH4d+0U4MhERERERiZR2nWgIp6ysjPnz54f0flixYgWGYSjRILutvBzGnn008z/7CitQHmWncP4vZPYfudO2voCPcz84l/f+fC+kPDr7KLb+63PiYpt1jtVm5fF7uPz0jrw63ZwzZdoQWPTILfzfxP+LcGQiIiIiIhIpbXrViZYQFxfHwQcfzI033si0adNYunQpJSUl/Pjjj5EOTfZiMTEGhw5/gBf3SwIgtspH5p0PwE7ybv6An0s+voRffg1NMrC9F2+f8WabTjIAOG1OvEdeRkG0eXzGn/DJ79PwBXyRDUxERERERNqNtv1U1ICYmBjGjh0b6TBkL1VZuZZff81kwojDeWDTArbR0az4+GP4z38abLe9cjvHv3U8K//3GsufhL9/DxiAJ5YrE6dz4oSU1gh/j911+pW8PsTcj/bBIbO38fXaryMblIiIiIiItBt7ZaJBZE+4XF3x+0uIiyvh1Muf41JeDNZ5V9ashlJYWYg/4KfSW8mbi9/kgGcPIH76l3wzDeK8cO/3cME8B4OXfsKT/xgUgXeyewam9ePzgQMA2BwPFmDawmmRDUpERERERNoNe6QDEGltVquDlJSj8flKuPbaeM7+8Xjun3MXeaRRXHI9rxoABie9fRKzt8zG6/fSscTPP2bBNXNqrvN1DwvfbXuP3z88HPte9n/S0KOv4mDjBn7pCgErRC+fTom7hASX5gUREREREZE90+Yng9zXaDLI1rd8ORxwAFRWmsc33QRHXTOTo9+YyMA8eP5TGLEFHIGaNq8OiOJW4xN++GAC++0Xmbj3xLbyfFKndgZrzdwML534Ehfvf3EEoxIRERERkUjY5yaDFGlp/fvDa6/VHP/rXwZnPv03AAZsgzGba5IMZQ64dnRvJpes4pMX984kA0Cn2I70tRwbUvbaotcaOFtERERERKTplGgQAU47DV58ESwWICGbksoKALqWmPWrk+GhQV3p2+15vti2kt++zmTMmMjF2xyuPHBSyPF3679jY/HGCEUjIiIiIiLthRINsk8zDD8lJb/jdmdzySUwcyZkJmTC04vh0WyeWfINMek/0aeiiL8u3cjEgy5j9h8W+vWLdOR77uojjsfqSQLA5of+2+CNRW9ENigREREREdnr7WVT2Ik0n4KCL1m27Fx8vkJ69nyIrKw7OOIIWLUK3nwTvvgig40bM0hIgFGj4Pzz2WuHSoQT7YhimONMTvnmOS6bB/YAHN7nVe4cdycWiyXS4YmIiIiIyF5KiQbZZ0VH98bnKwRg+/avyMq6A4CoKLjkEnNr7yYfegH2D54jvdw87vHbCuacPoeRXUZGNjAREREREdlraeiE7LNiYnoTFdUTgOLin/D7yyMcUes775CxvNk3PXg8aSG8ufjNCEYkIiIiIiJ7OyUaZJ+WkjIRAMPwUlT0fWSDiQCr1ULZgMvIizWPj18JldvzIhuUiIiIiIjs1ZRokH1acvJRwf3t27+MYCSRM/noc3m3awYAUX54xn9MhCMSEREREZG9mRINsk9LTj4ci8UBQH7+JxiGEeGIWt+JY/fjl/J3g8fuV96KYDQiIiIiIrK3U6JB9ml2ewJJSYcD4HZvpKxsYYQjan0WC/S5YAwb6QqA/fuZkJ8f4ahERERERGRvpUSD7PM6djwpuF9Q8HEEI4mc08+08g5nAZDrzIJ16yIckYiIiIiI7K2UaJB9XseOJwb38/OnRy6QCBo8GL7scQ2j+J0s92q2ddfyliIiIiIisnuUaJB9nsvVhfj4EQCUlS2gqmpDhCNqfRYLjDqrB7MZRcCw8PHH4A/48QV8kQ5NRERERET2Mko0iAAdOtQMn9hXezWceipgCUDXn7l/7vV0eawL/1v5v0iHJSIiIiIiexklGkSATp1ODe7n5e2bqy6MGAHRk86GS8exIf1J8srzeHvp25EOS0RERERE9jJKNIgAsbEDiI0dCkBp6e9UVq6JcEStz2KBA9MOCx47fPD14o8p95RHMCoREREREdnbKNEgskNa2rnB/by8NyMYSeRMPuo0ehRYefYTyHkULvm5kk9XfhrpsEREREREZC+iRIPIDqmp5wT38/LewDCMCEYTGccdmoorZyxXzIMOlXD2Enh7iYZPiIiIiIhI0ynRILJDVFRXEhMPAaCycgVlZfMiHFHrs9kgPfNS/sgwj/fPhbW/fk5RVVFE4xIRERERkb2HEg0itaSlnQdAfPwIAoGqCEcTGddPOJm3BtqCx6cs8jJ9+fTIBSQiIiIiInsVJRpEaklNPYcDDpjN8OGzSUw8KNLhRMQJE5L4MOEIAjuOz14Cby/eN1fiEBERERGRXWePdAAibYndHk9CwohIh7FTXm8hpaV/4HZvxustwGqNwelMpWPHU7BaHXt0bYcDuqVezI/dZjB+A+yXD9t++Zptp26jU2ynZnoHIiIiIiLSXinRILKXcLtzyMt7na1b36SsbEG9eocjjU6dzmiWe1034QTeXutg/AYvAGcuDvD+n+9z9cirm+X6IiIiIiLSfmnohEgDDMNg+/aZlJTMiWgclZXrWbHiSn77rRtr194eNskAkJQ0HovFElJWWPgdHs+2Xb7nCUfH8lHUcfh2XG5oLry1RMMnRERERERk59SjQSQMt3sLS5acSmnp7yQlHcawYd+2egx+fwUbNz7Ixo2PYBjukLq4uANITDyY2Nj9sNs7EAhUEBOzX8g5Hs82li49FYB+/V6gU6fTmnzv6Gjo3eEirjhhOnMyYHEasPFHNpdsJjMhc4/fm4iIiIiItF9KNIiE4XSm4fNtB6Co6DtKSn4nIWF0q92/tHQef/55NpWVq4JlNls8GRnX0LnzZcTE9N7pNdavvwefrwiALVuepWPHU+v1eGjMVUcezaRFiRBVHCx7Z8k73DL2lqa/ERERERER2edo6IRIGBaLjaysvxIbO5gBA94hPr51Jog0DIPNm59g3rwxwSSDxeKga9fbOPDADfTq9VCTkgwA3br9jU6dzsTp7MJ++72xS0kGgJOOd2FdcWpImYZPiIiIiIjIzqhHg0gD0tMnkZ5+ARZL6+TjvN4Cli+/hIKCT4Jl8fEj2G+/14mJ6bfL13O50hkw4G08nlyczl1fLSI+HkZFn8NvvAyA0xpFz+SeuH1uXHbXLl9PRERERET2DerRINIAi8XWakkGwzBYtOiYkCRDZubN7L//z7uVZKhmsVhwuTqHlPl8ZWzYMIVAwLfT9pdPOAwWnwMfvM6VxVt599S3lGQQEREREZFGKdEg0kSGYVBZubZFrm2xWOjRYwpgwW7vwODBn9G796NYrc5mvY/Hk8/ChUewbt1drFp1NYZhNHr+ySfasX/8JkcsTuOwZ67B6N4dqqqaNSYREREREWlflGgQaYLy8uUsWnQUs2cPpKxscYvcIyXlSPr1e5GRIxfSocNxLXKPioqllJXNByAn5wWys5/cSUxw2GEwidc4pfx1LJs3wxdftEhsIiIiIiLSPijRINIEW7Y8TWHhTAKBKv7880x8vrI9ul5BwZcsX35JvR4FnTtfjMvVZY+u3ZikpPH07/9q8Hj16psoKprVaJtTT4W3OKem4C1NCCkiIiIiIg1TokGkCXr2fJjY2KEAVFQsZ/nySQQC3t261vr197J48THk5r5MdvYTzRlmk6SlnUPXrrfvOPKzdOlZeDxbGzz/5JPhW45gGx3Ngk8/hdLSFo9TRERERET2Tko0iDSBzRbFwIHvYbPFAZCfP50//zxnt5INCQljg/tFRT/sdJ6EltCz5xSSk48EwOvNY8WKyxqMIz0dRo9z8B5nmAVVVXg+eI+CioLWCldERERERPYiSjSINFFMTB8GDvwAi8VcdSE//wMWLDiEiopVjbbz+UpCjlNSjqRr11vp3fs/DBz4PhaLpcVibojFYqN//9dwOMxlLwsKPmXLlmcbPP+00+AtyxnB4+8euoL7frivxeMUEREREZG9jxINIrsgJWUigwd/HEw2lJT8xpw5Q1m1ajJFRT/g8eTj8xVTXr6cLVueY+HCCcyZM7TeUpK9ej1CZuZ1EUkyVHO50unX76Xg8Zo1t1JVtTHsuaecAj8f9BObEszjw1f5mfHHW/gD/tYIVURERERE9iJKNIjsopSUoxg27Huio3sDEAhUkp39bxYsGM8vv3Tip5+SmD17P1auvJLCwq+pqlpPQcGnEY46vI4dj6dz5ysACATKWbnymrBDKLp1gwG2M3h7kHnsCMCgxVv5YcMPrRmuiIiIiIjsBZRoENkNiYkHMmLEAjIzb8JqjWr03KionkDrz8PQVL16TcXp7AzA9u3/Y9u298Oed95R/Xm1Sz/+cSj0vQ7eGwRvLH6jFSMVEREREZG9gcWIxEx00qCSkhISExMpLi4mISEh0uFIE3i9BeTnT6e0dB6VlSsBsNs7EBc3mOTkCcTHj8Biads5vW3bPmTp0tMAcDjSGDVqGQ5Hcsg5K1ZA/0unwoQ7gmUJrgRyb8kl2hHdqvGKiIiIiEjzae7nUCUa2hglGiQSDMNgyZJTKCj4GICMjKvp2/epeuf1G7mZlcdlgaXma+Ot097i7EFnt1qsIiIiIiLSvJr7ObRt/5lVRFqFxWKhT58ng8t3btnyLGVlS+qdd9YxmbD2iJCyVxe+2ioxioiIiIjI3kGJBhEBICoqk6ysv+44CrBmzU31JoY87TRg4YUhZTNWf0VOaU7rBCkiIiIiIm2eEg0iEpSZeRMuVzcACgu/pqDgfyH1Q4ZA96pTsFfEcuJy+OBtePxzQ5NCioiIiIhIkBINIhJks0XRq9cjAMTGDq43IaTFAmecFItr6cm88QGcuhwmLYS3Z78cdllMERERERHZ9yjRICIhOnU6nYED32fEiPkkJh5Ur/7UU6F86aW8N9A8TnJD7x//ZEHugtYNVERERERE2iQlGkQkhMVioVOn07BYbGHrR42CDO94Xu2VGiy7cCFMWzittUIUEREREZE2TIkGEdklViuceoqVH7ZfxvpEs2ziGvj6p2l4/d7IBiciIiIiIhGnRIOINKqkZA5LlpyK17s9WHbaaWAsupBXh5nHNgOO/X07X6z+IjJBioiIiIhIm6FEg4g0KDv7aebNG0l+/kds3Dg1WD5uHHS09GVa6rBg2S3LkhjYcUAEohQRERERkbZEiQYRaVCHDsdhsTgByM+fTiDgA8Buh5NPhrULHuCbODO5kJpbSq8tlZEKVURERERE2gh7pAMQkbYrKiqL7t3vweFIJj39IqzWmq+MU0+FF144lv/DwvYhcznji0sgIyOC0YqIiIiISFtgMQzDiHQQUqOkpITExESKi4tJSEiIdDgiDfJ4IC0NioogNhby8sxXERERERHZuzT3c6iGTojIbnE64YwzzP3ycvj448jGIyIiIiIibYMSDSKySyoqVuP1FgBw/vk15a+/HqGARERERESkTVGiQUSapKpqM8uWXcgff/Rn06ZHAXP1iawss37GDHP4xJbSLfzz89tZX7Q+csGKiIiIiEjEKNEgIk22devbgJ/s7P/g9RZgtcJ555l1gU5zePi2Q1k1pAtjbniE//z+n4jGKiIiIiIikaFEQwMMw+Duu+8mIyOD6OhoDj30UJYuXdpom+eff56DDz6Y5ORkkpOTOfLII/njjz9aKWKRlhUVlUnnzpcC4PeXsXnz40DN8AnL0Glc9+ksxq+Ho9fAz188S6m7NDLBioiIiIhIxCjR0ICpU6fy2GOP8eSTTzJ79mzS09OZMGECpaUNPzh9//33nHPOOXz33Xf8+uuvZGVlMXHiRLKzs1sxcpGWk5V1JxaLA4DNm5/A5ytmwADYf38I/HEj/x5dc+5lP5Tz8oKXIxSpiIiIiIhEihINYRiGweOPP85dd93FqaeeyqBBg3j11VepqKjgzTffbLDdG2+8wTXXXMOwYcPo378/zz//PIFAgG+++aYVoxdpOVFRWaSnXwSA319CdvZTwI5eDYW9eCnuWIpd5rnnL4I3Zj6GP+CPTLAiIiIiIhIRSjSEsW7dOnJzc5k4cWKwzOVyMX78eH755ZcmX6eiogKv10tKSkqD57jdbkpKSkI2kbasa9fbqf7q2Lz5cfz+Ss45B2w2KJtzO88NN8+L8sOxX2/gs5WfRS5YERERERFpdUo0hJGbmwtAWlpaSHlaWlqwrinuvPNOunTpwpFHHtngOQ8++CCJiYnBrWvXrrsXtEgriYnpTadOZwDg9W4lN/clOneG448HNhzCEz0G4LOY514zG5784ZHIBSsiIiIiIq1OiQbMIQ9xcXHBzev1AmCxWELOMwyjXllDpk6dyltvvcWHH35IVFRUg+f95S9/obi4OLht2rRp99+ISCvJyrozuL9x4yMEAl4uuwzAwuYld/DuQLOuUwX0n/4zP274MSJxioiIiIhI61OiATjxxBNZsGBBcOvYsSNAvd4LW7durdfLIZz/+7//Y8qUKcyYMYMhQ4Y0eq7L5SIhISFkE2nr4uOHkZJyNABu9wa2bn2Ho4+GjAxgyVlMHdYJgHIHRHvhvh/ui2C0IiIiIiLSmpRoAOLj4+ndu3dwGzBgAOnp6cycOTN4jsfjYdasWYwdO7bRaz3yyCPcd999fPnll4wYMaKlQxeJmKysvwT3N258CJstwMUXA34XC1f+nYtPgh43wiPjYObamfy66dfIBSsiIiIiIq1GiYYwLBYLkydPZsqUKXz00UcsWbKEiy66iJiYGM4999zgeRdccAF/+UvNw9bUqVP529/+xksvvUT37t3Jzc0lNzeXsrKySLwNkRaVmHgwCQlm4q2iYikFBZ9x6aU7Kuddxmv90tkWV3P+vT/c2/pBioiIiIhIq1OioQG33347kydP5pprrmHEiBFkZ2czY8YM4uPjg+ds3LiRnJyc4PFTTz2Fx+Ph9NNPp3PnzsHt//7v/yLxFkRalMViqdOr4UG6dzc48kjAF43/hztCzv9y9Zf8kf1HK0cpIiIiIiKtzWIYhhHpIKRGSUkJiYmJFBcXa74GafMMw2DOnKGUly8GYOjQ75g581DOOgtwVOC8vQcex9bg+RN6TmDGpBkRilZERERERMJp7udQ9WgQkd1m9mqovQLFg5xyCmRmAt4YPN/dFqybuBp6vjOTL1d/GYFIRURERESktSjRICJ7pFOnM4mK6gFAYeEMqqrmct11OypnX0OsvzMfvg1fvQ5PzLQxwpcauWBFRERERKTFKdEgInvEarXTtWtNz4Vt2z7k8sshOhrwxuCb+QCBHr0AcHr8dLznkQhFKiIiIiIirUGJBhHZY+npF5OaejbDhn1Pjx73k5ICF1xg1rl/u4i8cXOgY0ez4O234auvIhesiIiIiIi0KCUaRGSP2WxRDBjwFklJ47FYLADccEN1rYVHnk8i8ODDNQ2uugrKy1s9ThERERERaXlKNIhIixgwAI46ytxfvx7ejr4YDjuspuCuuyIVmoiIiIiItCAlGkSk2RmGQWXlGm6/vabsgSkWAk8/Cy6XWfDvf/PntEcpqCiITJAiIiIiItIi7JEOQETal/z8z9iw4T7Ky5cybtwGxo7twC+/wJ9/wkdL+nDaww/D5MlUxEdx51e34Yj6hffPeD845KKtMXJy8F9wPgG/H0t0NLa0zlh79oRhw2DMGOjQIdIhioiIiIi0KUo0iEiz2r79S0pL/wAgO/sJ/v73ezjmGLPuvvtg6NfH8/3Mh7mnbw6bE4FlH/Kv3/7FzWNujlzQ+fnwzjswcSLrOtp5af5LzM2Zy/L85WzftpGir/1hmwWsFkqH7Ufc3+/HdvIprRy0iIiIiEjbpKETItKssrJuw2KxExs7hPj44Rx1FIwcadYtXAgzvojhzkM8ZpJhh9tm3saMNTNaP9i5c+HiiyEzE667Dj78kLzyPO7/8X6+WP0F64rWUezwU+wK39waMEic9ycXf3gBV392NUu2Lmnd+EVERERE2iCLYRhGpIOQGiUlJSQmJlJcXExCQkKkwxHZLaWlC4iLGxocDjFzJkycaNb16AGPfvoZp75/QkibBFcC31zwDSMyRrRscG43xnvvwX//i+W330LrDj0U79czSH44mXJvzaoY8VUQsECsF9LLYL9tMHILHL0aUioh6ybw2cxzT9vvNN474702OxRERERERKSu5n4OVY8GEWl28fHDQh60J0yAI48099etg41fH899h90X0qbH+hK+n3QwC7fMb7G4Kp97iorOnbBMmhSaZEhKgltugccfx2FzMC5rXEi70igod8HWOFiUDu8MhluPgkHXwsBrapIMAP079jff+/Tp8NlnLfZeRERERETaKvVoaGPUo0Haq3nzYPhwcz85GZYvN7h21pm8/+f7DMmFb1+FDpXwyUA7cW+8z+FDT2q2e68sWMmTfzxJ8bTnePVtd03FkCHmkInzzoOYmGDx64teZ1HeIoalD6N3Sm86xXQi2hGNx+9hW/k21hSuYXb2bL5Z9w3zc2sSI8lRyay9cS1JHiv07w85Oeb1p06F6Ohmez8iIiIiIs2puZ9DlWhoY5RokPampOR3srOfpG/f57jkkmimTTPLL7oInn6+ihPeOoG0j75m2kc1XayWdYQ591zJOVc9id26G3PWFhcTCPiZUfAHT/z+BF+s/gIAhw9W/Qd+y4TnDnTw7r+30CG24x69v3WF63hp/ks8O/dZbhlzC3eMuwOeew6uvLLmpEGDzB4OvXrt0b1ERERERFqCEg3tnBIN0p6sX38/69f/HYA+fZ7Ebr+Wfv2guNis/+EHOGB0Oce+eSyxM3/gzQ8gqVaHg88P7ECnux9h5FEX7/xmVVXw7beUvv4iUR9+ypNHxHHzqMJ6p7m84HaY+08f9zRXjbhqT98mABXeCixYiHZEg2HAM8/AzTebcQHe5EQ2vfoEPU+4oFnuJyIiIiLSXJRoaOeUaJD2pLR0AXPn7g+Ay9WV0aNX88wzTq691qzv0wfmzwers5ILp1/Igh/e440PzYkWa/u/c3sw8bHpDEkbUlO4ZQvGnDmUzfudyp+/I/GnObiqvMHqDYnQ80YI1JmJxmqxclK/k7hu1HUc1v2wJk3a6POB1wsuF1h3ZWabpUvh9NNh+XIA3DZYPfVOBt784C5cRERERESkZWkySBHZa8THD6NDB3N1Cbd7E7m507jyShg92qxftQpuvRWiHdG8ffrbnH3q3xlzGdx4NBRG1Vzno6h1dIrpFHrxzz/HctJJxN8zhdSvfw1JMhS54NO+EOupOT05Kpnbx97O2hvW8uFZH3J4j8PDJhnWrIGnn4bzz4f99zfniXQ4zCkcbDbo0AEOPBCuugrefReKihr5AQwcSOCXn5k3MAUAlx8G3vIQXz95yy78FEVERERE9i7q0dDGqEeDtDclJX8wb56ZWYiK6sGoUStYvdrB/vtDRYV5zvTpcNKOuR9/2fQLF398MdlbVnLJfDhtGdz3z0P5+uLvQi/8xRdw7LHBw20xZnLh4/4woxdU7RgeMSh1EDeMuoHzhpxHjCOGcFavhldegXfeMfd3hcsFJ59szvk4blz9+hfnvchV0y/jyc/hyrnwSV84/Ux44Oip3HbQbbt2MxERERGRFqChE+2cEg3SHi1ceBSFhTMA6Nv3WTIyruCZZ+Dqq836+Hj47TcYMMA8dvvcvDj/Rab8OIXs0myeOe4ZrhxxZehFN26El1/mhaJvedT7Ays6gLGjj1ZKdArnDDqHC4ZewMiMkWF7LhiGufrko4/CrFnh47bZoHt36NTJTChUVcGWLbBpU/jzDzsMHnoIRo2qKav0VjLpo0l88OcHXLQA3hpUM0fElMOn8JeD/7LzH6CIiIiISAtSoqGdU6JB2qOSkt+ZN+9AAFyuTEaNWoXVGsVZZ8F775nn9OplJhs61loEwu1z89Warzio60F0iOkQ9tr/+vVf/PXbvzIkbQgTe05kYq+JHJh5IA6bI+z5fr95zwcfhEWLQutsNrNXwlFHwYQJMHiwmWCoq6wMfv4ZPv8c3nwT8vNr6iwWuPxyePhhc9gFQMAIcPvM23n010frXeveQ+/l7+P/HjZWEREREZHWoERDO6dEg7RXixefSEHBpwD06vUvunadTHm5+WC/YIF5zrBh8M03kJLS9OtW+apwWB3YrLZGz/N44LXXzB4HdYdH9OsHl1wCkyZB585Nv3f1dd96Cx54wJxzolpWlpmEOOigmrJHf3mUW2feWu8azw7+K1ec+sCu3VhEREREpJloMkgR2Sv16HFfcH/jxin4fGXExsLHH9c83C9YYPYkyM1t+nWj7FGNJhkqKuA//4HeveGyy0KTDKNGmff/80+4/fZdTzIAOJ1w4YXmAhOPPQZxcdXvEQ45BP71L3OYBsAtY2/h30f/O6T99b/BRWdM4Zt/T971m4uIiIiItEFKNIhIq4iLG0qnTmcB4PVuIzv7CcD8y/+330JamnnevHkwYgTMnr1n99u2De6+G7p1gxtuCJ1X4fDD4euvzaEaJ564i0tWNsDhgJtuMhMOBx9slgUCcPPNZoLDu2NRjBtG38CTxzwJwDEr4YkvwRmAg279Nz+/rmUvRURERGTvp0SDiLSaHj3uofprZ9OmR/B6iwDo3x+++w4yM83zsrNh7Fi44w4oL9+1eyxcCNdcYyYY7rkndP6E44+HX381h2cccYQ5n0Jzy8oy38tdd9WUvfQSnHaaOZkkwLWjruWRCY/wZW94Y7BZFuWD/a74K7O/e6P5gxIRERERaUVKNIhIq4mJ6Ud6+oUA+HxFbNr0SLBuv/1gzpyaOQ18Ppg61Vz14e9/N4c3hJtRxu83e0E89BAMGWLO8/D001BZadbbbHDOOeawjE8/hQMPbNG3GLzn/ffD22/XTCb56admoqOszDy+deyt3DruNi46GT7vbZalVELKGRewcvkvLR+kiIiIiEgL0WSQbYwmg5T2rrJyPX/80RfD8GK1RjNq1EqiojKD9R4PTJlirgrh8YS2TUsz51ro0MGsy8uDlSvD93qIjTVXf5g82ezdECnffQcnnFAT40EHwVdfmfEZhsGln1zKe7+/zM8vwpCt5jnze8cxbPE2LFFRkQtcRERERPYZWnWinVOiQfYFq1ffxObNjwOQlnYB++33ar1zVq4051h47z2zd0NTjRljrh5x9tmQnNw88e6pX3+FY46B4mLz+Kij4JNPzIkkfQEfp797OvN+/5jfX4DOO3o8cN555jIZLTG+Q0RERESkFiUa2jklGmRf4PVu5/ffe+PzFQIwfPgc4uOHhz03OxveeQdmzjQniCwoqKmz2aBHD3PIxOGHw9FHQ69erfEOdt28eWaM1cmGc86B1183J6Ks8FYw/pXx7L/Zz7OPLMfi8ZhLZVx9dWSDFhEREZF9ghIN7ZwSDbKv2LTpcdasuQmA5OQJDB06o0ntKiuhqMic+yA+3lztYW/x448wcWLNpJA33AD/3rHaZX5FPgmuBJyffWG+uaOPjlygIiIiIrJPUaKhnVOiQfYVgYCH2bMHEhc3jJ49HyI6uo12RWhmn30GJ59sTmIJ8NRT6rggIiIiIpHV3M+hWnVCRCLCanUyfPhcBg58b59JMoC58sTzz9ccX3+9udzmzmyv3N5yQYmIiIiINCMlGkQkYuz2fbPXzsUXw223mft+P5xxhjn5ZTi+gI87PrqWd47tRm7O6tYLUkRERERkNynRICJths9XitudHekwWsWDD5q9GwAKC+HUU+sv01lcVczFTx7J2dc8xdXfl7H82FFUusOs5SkiIiIi0oYo0SAiEWcYBlu3vssff/Rn2bIL2RemjrHZ4M03YeBA83jpUrjySqj91m/+6mbmrJxFT3NxDg5dUMiMc0ftEz8fEREREdl7KdEgIhEXCFSxZs3teDxbKCr6hry81yMdUquIj4cPPzRfAd54A559tqb+4QkPU9WnO2eeAX6LWXbSh3/y2d/ObP1gRURERESaSIkGEYk4my2aPn3+A0CHDseTnHxEhCNqPX37wksv1RzfeCPMmWPud4zpyKfnfMovA+KYXGu1y4kPv88Pbz7YuoGKiIiIiDSREg0i0iZ07HgCw4Z9z6BBn+ByZUQ6nFZ1+ukwebK57/GYx9t3LDIxKHUQb532Fv8dBU+PMMtcftjvir/y5+zPIxKviIiIiEhjlGgQkTYjKWk8Fosl0mFExMMPw5gx5v6GDXDppTXzNRzf93genjCVG46Bb3qYZZ3KYesFp5FXlheZgEVEREREGqBEg4i0WW53DlVVGyIdRqtwOuHdd6FDB/N4+nR47rma+lvH3sp5B1xoLoWZAnM6w3nHVnHqu6fi9rkjErOIiIiISDhKNIhIm7R9+9fMmTOMpUvPIBDwRDqcVpGZCS+/XHN8003w55/mvsVi4dnjn2W/fmOZcAEccjFsSYBfNv3ClZ9dqZUoRERERKTNUKJBRNqcQMDHqlXX4vVupbR0NmvW3B7pkFrNCSfAtdea+5WVcPbZUFVlHrvsLj466yPolkWls6bNqwtf5dFfH239YEVEREREwlCiQUTaHKvVzoABb2OxmE/T2dn/Ztu2DyMcVet55BEYNMjcX7wYbq+VZ0mNTeWTsz8h1hEb0ub2mbfz+cL3WzFKEREREZHwlGgQkTYpPn5/evd+PHi8fPmFlJUtjFxArSg6Gt56C6KizOP//Ac++6ymfmj6UF4/9fWQNseuMBg57kwqf/upFSMVEREREalPiQYRabMyMq4iNfVsAPz+MhYtOg63OzvCUbWOQYPg0VqjIS6+GHJyao5P7n8yUw6fAsDha2H6O9CpzCD6hFNgxYpWjlZEREREpIYSDSLSZlksFvr1e4n4+NEAeDzZLF58Aj5fWYQjax1XXw0nnWTu5+fDBRdAIFBTf+e4Ozlv8HlUjhyG/8BRNSdOnAibNrV+wCIiIiIigMXQVOVtSklJCYmJiRQXF5OQkBDpcETaBI9nK/Pmjaaqaj0ASUlHMHjwJ9hsMZENrBXk58PQobBli3k8dSrcdltNfZWvioARIKbCC+PHw8Idw0v694fvv4e0tFaPWURERET2Ls39HKoeDSLS5jmdqQwe/D9stkQAioq+YfHiE/H7KyIcWcvr2BFefx0sFvP4r3+F2bNr6qPsUcQ4YiAxEb76Cnr3NiuWL4dDDw0dbyEiIiIi0gqUaBCRvUJs7ACGDPkSmy0eqJ1sKI9wZC3vsMPgzjvNfZ8PzjkHSkvDnJiWBjNnQlaWebx8OcVjDsBbtL3VYhURERERUaJBRPYaiYkHMmTIjJBkw/z5B1NVtTnCkbW8e+6B0eZUFaxZA9dd18CJ3bvDrFkY3boB8Hj3XM6ecTlev7dV4hQRERERUaJBRPYqNckGc+xYWdl85s0bRUnJ7J203Ls5HPDmmxBv5liYNg3eeCP8uUa3btx/75HcPBHuPhQ+XPYhZ71/Fh6/p9XiFREREZF9lxINIrLXSUw8kAMO+JWoqB4AeDw5zJ9/MJs3P0F7nt+2Z094+uma46uvhrVr65+3pnAND21+i3+NBXbM7fDR8o847s3jKHWHG3MhIiIiItJ8lGgQkb1SbOwADjjgdxITxwFgGG62b58R4aha3nnnwaRJ5n5pqTlfg7fOqIjeKb35/NzPzUkia/l67dcc/dzBlDz+cOg6mSIiIiIizUiJBhHZazmdnRg69Gu6dLkRpzOd/v1fwlK9PEM79t//Qq9e5v4ff8A//1n/nPHdx/P5uZ8T64itKTTgmucXknDTnZQcczgUFrZOwCIiIiKyT7EY7bmf8V6oudcvFdlXeL2FOBzJIWUbN07F6y0gM/NGXK6MCEXWMmbPhrFjzVUoLBZzsYkjjqh/3h/Zf3Dcm8eRX5HPiGz4/fmaDHNFajIxr7wBxxzTqrGLiIiISNvS3M+h6tEgIu1C3SSD31/Oxo0Ps2nTVObMGUog4ItQZC1j5Eh44AFz3zDMIRTZ2fXPG9VlFL9c8gs9knowpwsccz4URJt1MVsL4dhj8V16CRQUtF7wIiIiItKuKdEgIu1SSclv+P1lAKSkHIvVag+p37z53+Tnf0xFxSoMwx+JEPfYrbfC0Ueb+9u2wRlngCfMwhJ9OvThl0t/YVSXUczoDUOvgq961dTbX3oZX68e8PjjZtZCRERERGQPaOhEG6OhEyLNx+3OITv7PyQnTyA5+bBguc9XzE8/JQPm15/F4sLlysTpTN+xpWGzxWOzxWGzxdZ5jcdmi8duj8fh6IjD0SFC785UUAAHHAAbN5rHN95o5gvCcfvcXPf5dbww/wUw4Iq58OhXELdjMsn5hw9g2NdL9ol5LkRERESkRnM/hyrR0MYo0SDS8goK/sfixcfv8XXS0y+hf/8XQ8qWL78Mmy2a2NghZGRcvsf3aIrZs2HcuJreDO+8A2ee2fD5z855lhu/vBG3301GCTzwDZzxJ9zz9FlMvfTt0JMDAXMSCCUfRERERNotzdEgIrKHEhLGMGDAu3Tr9nc6djyNmJiB2O3JO29Yh80WH3JsGAHy8l4lO/tJNm9+rN75xcW/Ulm5ZrfjbsjIkfDvf9ccX3IJLF7c8PlXjriSuVfM5YDOB7AlAS4+BQbfFsstZ/27/snTp0OfPnDttfDuu7Bly54Nr/B4zO4XxcX167ZtM2e0HDIEsrIgLQ2SkiA6GmJjoWNH+Pzz8Nf95hvIzd39uERERESk2dh3foqISPvicKSQmnoGcEZIud9fhdebh8ezFb+/DL+/HL+/jECgfMdxGT5f6Y79UhISRoe093q3YRjmpJMxMQPr3XfVquspK5tLTMwAOnQ4gdTUM4mL279ZhipceSX88gu89hqUl8MJJ8Dvv5vP6uEMTB3Ib5f+xv0/3M+DPz3IVcf+g7S4MCe/9RasWQNPPWVuYD789+8P3bubiYHLLqvf7t57IS/PTCjk50NOjpmkyM8366dNg0mTQttERcG33zb8Jisq8Af82OqW5+bCkUea+6mp5liSQw81Y9t/f7DVayEiIiIiLUhDJ9oYDZ0Q2XsZRgCPJ4fKyjVYrdEkJIwM1vl8pfz0UxIQCGkTEzOQ9PQLSE+/GKez0x7dv6ICxo+HOXPM4wMPNJ/bo6Mbb7d6+2q6JnTFZXfVfUNw4on4P/8ftkD4fyoCl16K9YUX6ld06WImFhpQeM9fePPoLuSV57G1fCsFlQVsryjgi8u+J4BBURRU2sFtA48NrAZE+eCCU2BeDxcfnPkBx/U9zrzYV1/VzIpZV1ISTJgADz0EPXs2/oMQERER2UdpjoZ2TokGkfbJ6y1iy5anKSj4jJKSX6meiLKa1RpFWtokMjMnExs7YLfvk5MDo0bB5s3m8dlnw5tv7v4UC8VVxfSeksbwtW4OXQ/750L/fOi2Y+TDQ+Pg8ZPSSItLI9oejc1qw2axMe1vc+m+paLmQg4HZGRA586QkcGiIwYxdNu99e4X64ZyJ7CTeGecP4MJvSaYB0uWwMsvw8KF5lbda2IHw2bDkptrDr0QERERkXqUaGjnlGgQaf88nm3k539Ebu40Skp+rlefknI03bvfQ0LCqN26/oIF5uSQ5eXm8T/+Affcs3uxvjz/ZS755JJ65TEeSC+DCgfkxtdvNzzbfH3zks/o2+dASEkJyXYs3bqUQU8P2r2ggJ8u/omDsg6qV15UWcio21M4Yh0cvmNb0MXKY/cczRkDzuCkfieRHL1jPo6FCyEhAXr02O04RERERNqD5n4O1RwNIiKtzOnsREbGFWRkXEFFxWq2bHmanJzn8ftLAdi+/Uu2b/+Sjh1Ppnv3+4iL27UH8mHDzF4MJ59sjn64917zj/nXX7/rsaZEpzC6y2j+yP4Do1YvjAonrE1puN3cLubr9swO0KH+EqBh54PYBbHO2LDli7YuZlVHWNURnhkJNj90rAiQt+pzPl/1OQ6rg4m9JnLe4PM466bnsH4/y5zQ4rbb4KCDtLqGiIiISDNQj4Y2Rj0aRPZNPl8JOTkvsXnz47jdG2rVWEhLu4CePR/E5eq8S9d8/HG46aaa41degQsv3L34CioKmLVhFt+t+46FeQtZum0p2yu377RdyBCHWgJGgPGvjKdTTCfSYtPoGNORlOgUOsR0MF+jO5DgSsBpc+KwOXBYHfgCPiq8FZR7yxnQaQAxjph6133i9ye48csbdxpXrwJY/Z86haNHw623wimnaAJJERER2ado6EQ7p0SDyL4tEPCQk/MiGzbch8eTEyy32eLp3v0eMjMn79IqFX//O9x/v7lvtcI778Dpp+95nIZhsLV8K1tKt5BXnse28m24/W78AT9+w49hGMQ6Y5nQcwJdErrs+Q2bKL8in983/86sDbP4eu3XLMhdENITo1pKBVw5B66ZDZmldSp79YK77oLzzzfnlhARERFp55RoaOeUaBARAL+/guzs/7Jx4xR8viIAUlPPYcCAN3fpOoYBN94I/9nx13ubzZw3se7Kku1VfkU+X63+ivf+fI8vVn+Bx+8JqXf44KylcOsvMDSvVoXdDqtWmUt4ioiIiLRzSjS0c0o0iEhtHs821q27i61b32HUqGW4XBm7fI1AAC6/HF56qabsv/+Fa65pxkD3AsVVxXyy4hNeXvAy363/LrTSgKlM4LafAvDNN3DZZfD885EJVERERKSVKdHQzinRICLheDz5OJ2hyzNu3/4VFouT5OTDdto+EDB7Njz5ZE3ZXXeZE0Varc0dbdu3smAlz899npcXvExBZQEAi69ezKDUQfDTT5CVZW51ff01jB+vIRUiIiLSrijR0M4p0SAiTeH1buePPwbg9eaRkXE1vXv/C6vV1WgbwzCTCw8+WFN20knw2msQH2aJyn1BpbeSaQunsShvEf897r9hz/H4PRz9+tHc6BrPSefcbc7h8Le/mXM42LV4k4iIiOz9mvs5dB/8O5aIyN5vy5an8XrNSQUqK9disez8L+wWC0yZAv/6V00vho8/hrFjYenSloy27Yp2RHPliCsbTDIAvDzfHGoRuOdus2DNGrj4YthvP5g2DXy+1glWREREZC+hRIOIyF4oK+sv9O79BFFR3enf/xUslqZ/nU+eDJ9/DomJ5vGSJTB8uJmACARaJt69ldvn5v4fzWU7/m8szOxZq3L1anO90IED4fXXwe+PTJAiIiIibYwSDSIieyGLxUpm5vWMGrUSlys9pK6kZDZVVZsbbX/UUfDHHzBggHnsdsPNN8Ohh8KCBS0T895o+vLpbC4xf5a/ZMHEC+Dgi+GbHrVOWrnSXMbjjjsiE6SIiIhIG6NEg4jIXsxqDR0y4fUWsGTJKcyZM4Rt2z5otG3fvjBnDtx0U03Zjz/CAQfApZfC5sZzFfuEMweeyRfnfcGoLqOCZT91gyMvhPEXwXfdzTK/Bb45shea9khEREREk0G2OZoMUkT2xMqVV7NlyzPB486dL6d3739hs8U22u6778wVHdeurSmz2+HMM82hFiNHtlDAewnDMPh81ef84/t/MC9nXkjd+HUwKhseGQcjMkZw76H3cnTvo7FYLLBxI8TFQUpKhCIXERER2TmtOtHOKdEgInvC6y1k5cor2bbtvWBZTEx/Bgx4m7i4oY22dbvN5S/vuw+Ki0Pr9tsPTj0VTj4Zhg1rnsUWDAO2b4ecnPpbQQFUVUFlpflqsUBMTM2WmgqZmdC1qxlbz55gs+15TDuP2eDjFR/zj+/+weKtixs8b0zmGO459B6OvP0ZLF99Za5QceWVsP/+LR+kiIiIyC5SoqGdU6JBRPaUYRjk5r7MqlXXEwhUAGCxOOnVaypdutxg/qW9Efn58Pjj8Mwz5gN/XTEx5uSRw4ZBt26QlQUdOpjl0dHg9dYkCEpLIS+vZsvNNbfqhILH0zzvOToaBg+GceNg/Hg4+GBITm6ea4cTMAK8/+f73P393SzLXxb2nDMcw3jnbwux1P5nduRIuOIKOPtss6eDiIiISBugREM7p0SDiDSXiooV/Pnn2ZSVLQiWpaQcS//+L+N0pu60fWWluZjCtGnw889mD4S9hc0GhxwCp5xibpmZLXMff8DP20ve5p5Z97Bq+6qQus8PfYFj3psPr7wC5eWhDePi4KST4IEHzGyNiIiISAQp0dDOKdEgIs0pEHCzdu1f2Lz5X8EypzOd/v2nkZIyocnXyc2F6dPh++/h999h/fo9i6tjR+jcOXTLyKjZ79TJ7CERFQUul5nkqKyEigooKzPj2bwZ1q0zl+dcvNhcbTIciwUmToSLLzaHfrhcexZ7OL6Aj9cXvc69s+5lXdE6+nboy5/X/InNaoOSEnjrLXj2WZg/v6aR3W5289D8DSIiIhJhSjS0c0o0iEhLKCj4kuXLL8Tr3Ros69r1Nnr0uB+r1bnL19u6FVatMuc63LjRfJYuLzeTAU6nmSCIioLYWHM+hbQ0SE83X1NTzXOaW0GBuWrG99/DZ5/BmjX1z0lNheuug6uvNpMdzc3j9/DKgldIiU7h9AGnh1YaBsydy9qH7yT5s29Y1juRZa8+yukDTicxKrHmvFdegWXLYOxYGDPGDFpERESkBSnR0M4p0SAiLcXjyWPZsgspLPwqWBYfP4L99nuTmJg+EYys+RkGLF0K778Pr75avwdGdDRcdBHcfjt07966sU14bQKzVn5NpwrYkgBOm5Ojex/NWQPP4oS+JxB/2FHw6681DXr2NCegGDQIBg6Egw4yJ8YQERERaSZKNLRzSjSISEsyjACbNz/O2rV3YhheAKzWWPr2/S9paRfsdKLIvVEgALNmwdNPwwcfmMfVHA5zbsa77jKHbLS01dtX0+c/DSd1Onjs5D3ow9bYv8yPPAK33hpa5vGY3TQSEyEpCeLjzW4jDoc5RMPhMMePpKWFtisqMifgsFprNosl9HjUKLN7St12GzaY90tOhoQEs52IiIjslZRoaOeUaBCR1lBaOo8//zyHysqVwbLU1HPo1+8FbLaYCEbWstatg3//G154IXR+xuhouP56+MtfzOf0ljJzzUwu/vhiskuzGzwnrRTGbIYxm2D8FjvDtgRweWplR95+G846K7TR1q31kwh1/fCDuRxHbb//Dgce2Hi79evrT1j5wQdweq2hIVarmXBITjYn2MjMhOeea9kfpoiIiDSb5n4OtTZDTCIispeJjz+A4cPnkp5+cbDM48nFam2BmRLbkB49zKU7N20yezHExprllZUwdSr07WsmIfz+lrn/hF4TWD95PR+f/TEn9jsRp63+ZBV58TB9P7hjIhx4kY/oOwP0vAFOOAfW3X4FHHBAvTaV+bk7v7nDUb+sdveOhljD/KpQt10gYE6SsXq1Oezj/ffN2Tzr+uADM7Fx4YXmihsffGBOptGUOERERGSvoR4NbYx6NIhIa9u69R3Wrr2L/fefhcvVJdLhtKqtW+HBB81hFW53TfkBB5g9H8aNa9n7F1UV8fHyj3ln6TvMXDsTX8DX6PkFtxeQEl1/lYrvl3/FdU8cTVIVJLkhzYgh0RpDnMVFlGEnyrDy0+jOlKfE4bQ5sVls2Kw2Om13c/isjZze/zSshmE+8FdvhsHW0lweOywKb1wMNqst2C5z1VZGfjaP6HIP8RVeYkvdRJVW4Swpw15cSiA9DcuWnPpDcf7+d7j//vpvLD4ehg4155946KE9+ZGKiIjIbtDQiXZOiQYRiYRAwIfVag8pKyz8hkCgig4djotQVK1nwwa47TZ4773Q8nPPhUcfNVfMaGkl7hK+XfctX6z6gi9Wf8Gmkk0h9Z3jOrPlli1h205bOI0Lp1+42/cO/CMQdn6O95a+x5nvn7lL13J5oUMllHSM47sLv2NExoiayquvhmeeabjxmDHwyy/1yxcsgLg46NVLc0GIiIi0AA2dEBGRZlc3yeDzlbB8+UUsXnw8S5eehd9fFaHIWke3bvDuu+bSmEOH1pS/+Sbstx88+2zL9+5PcCVwcv+TefaEZ9kweQNrb1jLG6e+wbUjr+WAzgcwPGN4g203FW9qsG5nLFganATUb+z6GBK3w1xNo8xTRseYOmuIPv00lJeT99MMLjsvnpdOyGT+qCxK0pPNtoMHhL/o5MnQp485/8Nxx8G998JXX0Fh4S7HJyIiIi3PvvNTRERkX5Ob+wpu92YAAoEKbLaonbRoH8aPh7lzzXka/vpX2L7dXGDhqqvMZTKffdZcabKlWSwWeiT3oEdyD84dfC4AjXVAzC1rwhwNDbBZbQ3W+QN7NllFRnxG/cKYGJZm2HixTykvUhosTq4Al/9FYp74jv3T92dY+jCGdx7O6M4jSJkzxzypoAA+/9zcqvXpA//4B5x//h7FKiIiIs1HiQYREamnS5frsdsTWbfub/Tq9a+QuuoH3va4FCaAzQZXXgmnnmoOp3j1VbP811/NuRtuucV8rg0312FLauzn/cQxT/DAEQ9QVFUUspW6S/EGvHj8Hjx+D16/F7ffjT/gx2/4d5pI6JXSi0v3vzR4bt1Xj99DUVUR2yu3U1hVSGFlIW6/OdlFWmxa2MkuAVbkr6hXVlj98yxcy9rCtXyw7AMAYjww5dAOjM9Not/qIqKLy0Mbrlpl/kery+s1/+ONHAkDB5rLfIqIiEir0BwNbYzmaBCRtiQQ8GK1hq5WkJs7jZycl+jZcwqJiWMjFFnr+e47s0fDypqVQOneHZ56Co45JmJhtVkV3gpyy3IprCxscLjH5C8n8+/f/73rFzegZyH8PuQJOi5eYy7POX8+LFkCvXuHnjt/fs0KHdHRsP/+MGQIDBpkdksZPNhcjlNEREQ0GWR7p0SDiLRlgYCHP/7oR1XVegBSUo6jR4/7iI/fP7KBtbCqKnj4YZgyBTyemvIzzjCXy8wIM0JAGrapeBML8xayIn8Fi7cuZn7ufP7c9udOV90Ac1LM7Juza3p4eDzm0p0WC/Ny5vHN2m8YnTma0f9biOvaGxq/2Nat5rwPtZWXm9dzhu+NISIi0h4p0dDOKdEgIm1ZRcUqFi8+jsrKVSHlyclHkZV1O0lJh7XbIRUAK1aYvRu+/76mLD4e7rkHrr9evfP3hNvn5s9tfzI/dz4LchcwN2cu83LmUeULnYj0lP6n8OFZH4a9xt3f3809s+4BYL8CK5dtSeeQrdH0W1tCfPa20JNTUyEvr/5F7r8f/vlPyMqCnj3NLFLnzuaWkWGWjRzZLO9ZRESkrVCioZ1TokFE2rpAwEdu7its2HBPcMLIanFxw8nMnEynTqdhs0VHKMKWZRjw2mtw883m3ITVhgwxh1McdFDkYmtvPH4Pi/IW8fvm3/kt+zd+2/wblx9wObcfdHvY84954xi+XP1l2LqEKhhdGMPEigxGb48mOSmdqqlTGNhpINGOWp/Viy+GV15pOKhx4+DHH+uX33ADLFwICQnmUI2oqNDtoIPgpJPqt/vsM3C7zR4U4baMjPq9LiCkJ4eIiMieUqKhnVOiQUT2Fn5/FTk5L7B586PBoRTV7PZk0tIm0bnz5cTFDYpMgC2soMBcmeL5583kQ7WLLzaHWYR7NpQ9ZxhG2F4zhmHQYWoHCqt2bclLm8VGv479mDRkEneOu9PsnvLpp7B6NRQX129wxhnmWqh1HXww/PRTwze67jr4z3/ql3fvDhs2NNzuoYfgjjvqlzsc4PPVDPOou/3rX3DCCaFt3G6zx0ZSUuiWkmL28OjUSUNGRET2Uc39HKpOniIisltstigyM68jI+Mqtm17n02bplJWNh8An6+Q7OwnyM5+gtjYoaSmnkmnTmcQE9MnwlE3nw4dzOUuL7kErr7anHsQ4OWXYfp083nu8svN50BpPg0NzanwVnDmwDP5Pft3FuUtImAEmnQ9v+Hnz21/kle2YxjFP/9pboYBJSWQk8ML/7sPV14BXSvs2Pp0xbLxJzITMkmNTSXaHm3GVFHR+I2iGlgi1uttvF24B/9AwEwyVLf3es25JWqrqqrfrqDA/GA25uuv4YgjQsu2bYN33jGTEampkJZmvqakqEeFiIiEpR4NbYx6NIjI3sowDIqLfyYn53m2bXuXQKD+g05s7BA6dDiOHj0eaFdzOfj98MwzcNddoX8E79fP7N1w4ol6HmtN5Z5y5ubMDQ65+H3z72SXZjfa5qljn+LqkVeHrev6r65sLtkcts5lc9EhpgMp0Smk25PIIJ6OljgSDSfxAQeHdx7L0KR+5hCIOitjzFo/i/S3PsVZVondb2D3+rH7Alh9fmxePzafH88pJ2IccThOmxOXzYXT5sTi85nJAI/HTDJ4PPW3V16B444LDfbPP82lPhszfz4MGxZa9uOPcMgh9c+122uSD198AenpofX5+ZCdXdNbQpOYiIi0WRo60c4p0SAi7YHXW0he3uvk5b1BaenvIXVxccMZMWJOSFlZ2WKiorpjt8e3ZpjNLi8PbrvNnMOhtoMPhv/7Pxg1KjJxCWwu2cyC3AUsyF3AwryFLMxdyOrtqzEwfw36etLXHNHziHrtfAEfUfdH4Tf8u3XfJ45+gutHXx+2LvnhZIqqinb5mg6rg3+O/yd3HXJX2Ppr/ncNm0s2E++KJ965Y3PFk+J10HvFNhKqDBIrAyRWGcSV+4gpqcSxvQjL1q3w4Ydmj4XaPvgATj+98aDKyiA2NrTspZfg0kvNfYvF7AZUu0dEZqb5P0ZdpaXm+bGxytCJiLQSDZ0QEZE2z+FIJjPzejIzr6eycj3btr3Ptm3vUlo6m5SUo0PONQyDRYsm4vFsJSVlIkOGfBGhqPdcWhpMmwbXXgu33lozZP/HH2H0aLNnw9//DiNGRDbOfVFmQiaZCZkc3/f4YFmZp4zFeYtZvHUxw9KHhW2XV5a320kGgBhHTIN1TVnOMxxvwNtoj6Dv1n/H8vzlO7+QA0gyN2cPJx2iO3DB4n/xUNpDoeeNHAmvvkrB+mXY8wuJKyzDti3fXB40L88cNlI3yQChq3oYhtnDIT/f7FkB5koe4RINd98Njz0GNhskJobOJxEXZ/a4uOee+u2+/BJyciAmBlwuc9xS7a1zZ+jVq367/PwdP48d59nt5ma1NvrjExGRhinRICIiLSo6ujtZWbeSlXUrHs9WILQjXVXVejye3AbbL1hwGD5fKdHRvYJbVJT56nJlYLHYWvgd7LrRo+GHH+Djj815/FauNMs/+cTcjj4a/vY3rVARaXHOOMZ0HcOYrmMaPMdhc/CXcX9hc8lmNpdsJrs0m03Fm6j0VTbpHo0lGrz+nczP0AiXzdVgXam7dJev5/F7yCnLodIb5n1lZcEFF3Du60cxw5gBHSB5UDLpcel0ju9PemwanWfcSnpculkW15n0uHQy+3Un4dJLzZ4SeXk1iYnKHfeo23OiWlGR+er3w/bt5lZbQ/NhPPGEOYSjIRdfbPayqOvQQ2Hp0vrlVquZcPjb38wMYV0jRpjvxW4PTVBU7998MxxzTGibqipzLdzo6Ia3ww83h9rU5vVCbm7oeUqEiEgbpkSDiIi0GqczNWx5RsbVFBf/QlzcsJBywzAoLZ2D319GWdnceu0sFgcuVxZRUd2Jiuq247Vmi2QiwmKBk082h8k//zxMmWIOVwfzD69ffmn+ofi66+Css8w/wErbkxqbypQjpoSUGYZBsbuYgooCCioL2F65ne2V2ymoMPeLqoqo8FZQ4augV0qYv6Dv0Cm2E1W+Krx+L96AN/jaFE5bw6tDlHp2PdFQrUNMhwbrckpzgvuFVYUUVhWyLH9Zo9ezZdlI3S+VSUMm8fCEh82eDWVlZsLB4wFgY/FGnDYnHWM6YrfaoX9/cw6KoqLQzb+jZ0lMA8mbnU3I2dDMrA1NyBkIBGMMa9myxu957rn1y6qq4IUXGm4DZrKkbqJh1ar682u4XObPIjrafP3f/6Bv39Bzli41VzupPqf6tfb+SSfVn3S0osJMolQv1aqkhojsIiUaREQkoqKje9C371OA+QBXm99fgt3eAb+/Aqi/ioBheKmqWkNV1Zqw17ZY7LhcWSQlHUL//i+H1Hm927HbE1s8EeFwwDXXmEPVX37ZXK2wejXD2bPhwgvNYRYXXwznnQdDhrRoONIMLBYLSVFJJEUl0YuGEwk7s+mmTfXKDMPAb/iDSQdfwIfH78Hj9+D2uXH73bh9bjITMhu87pTDp1DsLqbEXUKpu5RSj7nVPi6qKqKgoqBeYqNjTMcGr5tb1nDPo4b4Db/ZU6K6B4jFAvHx5rbDxR9fzLfrvgUg0ZVIcnQyKWemkByVTHJ0D/PVlUQqsaQSS2JUEo5VXxDrjCUrMYvuSd3NC91xB5xzjrkCh9tdsyJH9XbggeGDPOww6NPHPMfnM7fa+507h28XH28O76h9bm3hJr/c2SojYD7c11UZpqeJ221uhTuWdA2XDFi71lwepzHl5fUTDa+9BlddFRpTdLR5nsNh9kaZPbv+tV580VyhpO6wleqtRw9z1ty63nkHliwxf57htj594Pjj67ebOdNcTaXu+Xa7+ZqVZc7KW9eyZeZ/r4bul5hoDtOpy+s165V4EdkpJRpERKTNqDvu3G5PZMyY9QQCHqqqNlBZaSYVKivX7NjfQFXVOvz+krDXMwwfVVVrcbt716tbuHAC5eWLiIrqwahRy7FYrLXa+Zs9AeFymb+3X3opvPGG2cu7eknMbdtg6lRzGzTITDicfLL5+7Hmwtu3WCwW7BY7dqudaMI8cDbBtaOubdJ5hmFQ6imloKKA/Ip8CioL6N+xf9hzfQHfHvWUSI0N35sJqFlaFCh2F1PsLmZ90fomXfeaEdfw3+P+ax7UGaZwxntnMHfLXJw2Jw6bA2fh9zhffAynzRm6HenEYU0yz7M6cNgcjMkcw3lDzgt7z89WfkZ2STaOL6YEz3dYHTisdpyGDRc2nAELtqgY7FvmhJyT4IghbeFCM3HQ0Fa3VwKYSY3TTgs9r7rXQfVruJ4e4RIUdYVberVuu+p7VmsoYbJ8ufnw35CRI8MnGj76yEw2NOSUU8InGu65B37+ueF2119vftnWdcwxNRnfcB5+GG6/vX55VJTZ0wXCJyhefNH871Tbtm3mOLmGkiE2Gzz1VP0s86pVZgzhzq/eHngAOtZJDi5dCm+91XASxWYzfy51kyUrVsC8eQ3HGR1tzmxc16ZN5rAoq9X8B8tqDd2PiYFu3eq3Kygwe/hUn1+3ndMZ/jMdCJjn6B/HNk+JBhERafOsVicxMX2IiekTtt7rLaKqan0D2zqiorrXa1NVtR7D8GEY3pAkA8CKFVdQWDiT2NiBxMXtT3z8SOLjR+JyddnjZTkdDrjoIrMnw6+/mr2a33+/5o+hS5bAX/5ibt27m78PH300jBljrhAo0lwsFgsJrgQSXAn0SO7R6Ll2q52Kv1ZQVFVETlkOuWW55JblklO6Y7+81n5ZLgWVBSHt02IbmI8ByCvPa7BuZ2KdYSah3GF90XrWFa3bretWeCsaTDT854//MGPNjN267nF9juOzcz8LW3fDFzfw1pK3iHnn/4hxxAS3aHu0uX9ODDGOpJC66vouCV04ue5wC4CjjqL8958IVJQT5Qlgd3ux1E5UuN3h/zqflQXHHhuazKioqOkdktpA4mhnPTZsDSRw/TuZcLUttDOMmiRD9TXqXqdurxYwf8arVjV+v7Ky+mUFBTB9euPtwiVtli0zExCNuT7MSjhffAE33dRwm86dYcuW+uWPP25O3tqQ0aPht9/ql199Nbz3XsPtTjvN/MexrnHjzH88ITSxUZ2kuP56M2tf1/77m5PF1k2IVB/fdVfNKjm1DRoUeq/qJEf1/oMPwoQJoW22bYNTT224jcVi/twGDAhtt2KFmVwKd3719tRT5go+tf3xh/nLRHWctRMx1fsvvlg/OfP11zX/DSyWpvW42gVKNIiIyF7P4UjC4RhGfPywsPWBOt3DAwEvSUnjqapaj8uVVe/88vIluN2bcLs3sX37l8FypzOd+PgROxIPo0hMHIPdnrhbMVssMHasuT3xBLz7rtnTofr3J4D16+Hpp80NoGdPs/f3AQeYw9j79zeTEQ39Li3SnCwWC8nRySRHJzOg04BGz/X4PWwr30ZeeR55ZXkMTB0Y9jxfwEdhZeFuxxTraDjRUO4p3+3rOqwNzOfAnk3i6bA1fN3tldvJr8jfreuOyBjByf1Prl+RmMg9xR/zyC+PAGCz2EITFc4YYp5/s17yIsYZQ8wN/egc15nbDrot7D3XbF/DuqJ1wZ4hDqsD1y0X4LpxEs6ABWfAgiOA+eoHRwBs0bGETdX+4x9wxRU1D+91ty5dwr/xm24yH37rnu/zma9jx4Zvd+655iSjDd0vXK8SwzAfchtq4/ebQy7CSU4Of37t3hF17SwZsiftwiWXdjdpE6g/rDFEQ8n5OkMl62loeErtdoZRP+ETLtkD5sN/XiNJzeLi8PcKN1FsbYVhvr+qqmqWndqV+23fbs4Y3ZhHH61ftn49vP564+3CTYS7cCE891zj7faAEg0iItLuWes8NFitDgYN+rDB8+32ZGy2RPz+0F8EPJ5cCgo+o6Cg+i+SVuLihpKYeDBJSYeQmHhwgxNeNqZTJ3NJzGuvNYdUf/ih+celH38M/QPD2rXm9uabNWVOJ2RmmnPHZWRAerr5u258PCQkmK8xMaE9YGu/Wq01Q453th8TY14vOlq9VqVxTpuTLgld6JLQwAPiDnarnaq/VZFfkc/W8q0UVpqTTNZ7rVNW5imj3FtOYlTDib5y7x4kGhpJCDR1ws6w120kgVHh3clklo1obHWT2tf1G/7gnB1N0SelT4OJhtcWvcY9s8IsM7oTNouNLgld2DC51tCFwYPNDXhp/kvc/f3dWC1WbFYbNosNW74N2yJbaNmOV2tCTZndamfGpPC9TX7f/DvPzn0Wu9WO7WAbdmuUuW8121W3N48XY/95WUjZZQdcRtSPP9a7bn5FPr9t/g271RzyZF//fc2+1Y7dbse+/IeQMofVYe5bbCQ54rHZw3wuRo4MTaJUJ09qb+F6lowfDzNmNJwMaSgxcNhh5l/ZG0ra1JpXJcS4ceakqdU9PmpvhmFmyMMZPTr0vLpthw0L327wYPMfoIbahuvZA9C1q9mlsKF24YZpGIY5V4dh1GzVbav3wyVEdpZEgfD/iO4sadNQuzZKiQYREZE6hg79EsMwcLs3U1o6h9LS2Tu2Ofh8RbXODFBWNp+ysvlkZ5vjgKOj+5GUdDDJyRNITT1zl+/ds6c5OeStt0JpKXz7rZlw+O03mDvX/ENJbR5PTQKitdhsNfP5JSaayY3OnWteu3aF3r2hV6+GfzcVqWa32oNLYzanV09+lVJ3Kd6ANzihZt3N66+pc/vdwUk4x2WNa/C6g1MHEzAC9VYLaezVb5h/cW0sgdEaiYbmvK7H38iKHI3wG358gQb+6gwUVxWzqaT+RKlNYbc2/GizevtqXl7wcoP1O3P+kPOJstefz2JB7gJOeOuE3b7u8muX069j/QkrlxStZOwrY0MTFDZHaBLjV3voca3t0YmPMih1UL3rbq/czh2fXI7VYsVqsWKxWGr2+9bs16ujCOu3f+e8IeeFzuVy2mlw2mn4Aj4e+/WxWufXavvHk/XLD0/GcsRxwfKxXcfSO6X+fEpgzoviC/jMc289EQsnNRCjuW/Z8EOwrFtiNzPhWbu74A4r8lfgC/iC17Dkr6h3XcumJWHvFeOIIc4ZZsJQwN05FaOiBCsWrFiwBAzzFWoSFeESGyNHmsvYhktqVG/hlgU+5hhzeE7t86D+cV0XXGAO+6g+p7QUDjkk/Lm7QYkGERGRMCwWC1FRXYmK6kqnTqcA5uR5lZVrKC2dTUnJLxQV/Uh5+SKg5h/xysoVVFauoLz8z3qJhkDAh7WRX4Trio83V5476STz2OuFxYvNnpzLl5vbypXmspnhem+2FL+/ZsXBTZvMeSUakpZmJh0GDjT/QDV0qPkHKSUgpKUd2v3QFrnuU8c9tcttAkYAX8BXb2Wd2h476jHyyvLMpVG9FVT6KoP74bba9YNTBzd43baWaACzV0NDqpMybem6jV27saRJUzSUHPH4PXs0AWuJO/wkyaXuUl6Yv5MlVhsxqsuosJPGev1e7vj6jt2+7osnvthgouGcD86hzBNmHosmeOiIh7hjXPi4jnnjmN2ex+XmA2/m0aPCDGMADnl1PH9k/xG2LpgMCZcgsVg4d9C5PHtC+JViLpp+EV9/8HWj7auPLRZL8BXg4HUH1//u6tQJOnXi3ln38unKTwlUNqFHxS5QokFERKSJLBYLMTG9iYnpTVraOQB4vYU7kg4/UFz8A6WlczAMH8nJR4a0NQyD33/vTVRUNzp2PJGuXW/Z5fs7HOb8DAccUL+ustKc5yovz/yjREmJ+VpcbPaCqO4BW90Ltnq/ds/R6l614cr8fnMVvNLSmq2kxExwuN0Nx5yXZ251J4bv3duc4PKgg8xh1AMGaK4Jab+sFitOm7PRcwZ0GrDTuS92x/MnPM+jEx9tNGnR0NbYJKFxzjjS49JDeoe4/Y18GdRiszaSEAjsfkKgsR4NLZUQ2Nuua9CEbv2NsFrCz50QMPbsIdUSfvaOPb52YxM478nPoqGfA9RfqrvuPf2Gn4ZuXeWvCl8BbKvYRnZpdpNjrK1rQtcG69YWrmXOljnQ8K13ixINIiIie8DhSKZDh+Po0OE4APz+CkpKfq230kVFxTLc7g243Ruw2WLrJRqqqjbv0aoW0dHmsIuGhsO2FMMwkxm5uWaiIyfHnJdq9eqaLSenfrvqutdeM48TEszEw8EHw5FHwvDh5hwSIrJn4l3xxLuavwvR3Yfezd2H3h1SZhjmQ1Rjw1T8hr/RhMAZA89gSNoQ/IafgBHAH/DjN/z1XsPVNdajYWCngdx84M34Ar7g8A1/wI/P8NXsB3wh9bXLG4rZZXPRI6lH8HxfwIc34A05bixp0NB192TSUWh4PpA9Tgg08G/Unl53dx/c9+S6exLz3nbdlkq4NEb/hIuIiDQjmy2G5OQj6pV7PHlER/emsnJ1vd4OgYCP2bMHYLPFkpR0OMnJR5CcfARRUWHWHm9jLBZISjK3/vV70wJmz4fFi80JrhcsMLdFi0J7QpSUwFdfmdvf/mbO/XDYYWbS4cgjzUng96I5sET2SRaLBbvFnCOgsWEXjemZ3JOeyc2fMR2dOZrRmaOb/boTek1g7Y2NT5JjGEZw+EzdJESnmPDrFg9JG8Ivl/xSL2HRUCIjWOc36xqaiDXBlcANo27AwIwpYASC8YWUNVCfER9+skWb1cYxvY8Jtqtu09i1apd1iOkQ9roAvVJ6UemtbPTaDV3XZXM1+t9mdzX24L5HCYFGenbsScKlpa7b6D2NlrpyG2YYBvfccw/PPfcchYWFjB49mv/+978MHBh+6SWADz/8kClTprB69Wq8Xi99+vThlltuYdKkScFznn76aZ5++mnWr18PwMCBA/nHP/7BMccc0+TYSkpKSExMpLi4mISEhN1+jyIi0jZVVq7HZovF6az55bK4+Ffmz6+/DFtUVK9g0iEp6bCQNns7txvmzYNffjG3n39ufOWxzEyYONHcjjyy/jLiIiIiu8MwjLDJkLqJi9p10Y7oBieDzC7JpspX1WCSpbHyTrGdGpyrYu6WueRX5DcYU93ES3VPBcMwE0Tju48Pe90fN/zImsI1VJRWcO0h1zbbc+g+mWh4+OGHeeCBB3jllVfo27cv999/Pz/88AMrVqwgvoHZqb7//nsKCwvp378/TqeTzz77jFtuuYX//e9/HHXUUQB8+umn2Gw2evc2PxyvvvoqjzzyCPPnz280iVGbEg0iIvue4uLf2LDhXoqKfiAQaHhJvtjYIcGkQ2LiWByO9vO0bRiwZo25ysY335hbQUH4cy0Wc3LuiRPhqKPMVdIcDU/mLyIiIjvR3M+h+1yiwTAMMjIymDx5MnfcYc5A6na7SUtL4+GHH+bKK69s8rUOOOAAjjvuOO67774Gz0lJSeGRRx7h0ksvbdI1lWgQEdl3BQIeSktnU1j4DYWF31BS8iuG0fA43ejofiQmHkRi4lgSEg4iJqbfbs/x0NYEAuZQi6+/Nrcffqi/tGe1hAQ4/PCaxENrz1MhIiKyt1OiYQ+tXbuWXr16MW/ePPbff/9g+UknnURSUhKvvvrqTq9hGAbffvstJ554ItOnT2fChAn1zvH7/bz33ntceOGFzJ8/nwEDws8i7Ha7cdcapFpSUkLXrl3p3LkzVmvDk4GAmej45JNPQspOPPFE5s2bt9P3cPPNN3PzzTcHj0tLS9lvv/122g7g448/Zvjw4cHjzz77jKuuumqn7eLi4li+fHlI2W233cZbb72107bHHXcczz4butTLiBEjyM3N3WnbqVOncu655waPV6xYwRFH1B8/Hc7s2bPp3Llz8Pi5557j3nvv3Wm7vn378u2334aUnXfeecyaNWunbS+//HL++c9/hpRlZmY2Kd7XX3+dQw89NHj8/fffc/755zep7ebNm0OO77nnHp5//vmdths/fjxvvPFGSNnhhx/OypUrd9r2H//4B1dccUXwOCcnh5EjRzYp3m+++YZ+/WrWnX7zzTe5/fbbd9ouPT2dOXPmhJRdeeWV/O9//9tp23POOYdHHnkkpKx///6Ule18yaVnnnmG448/Png8d+5cTqpes3Anli1bFtLb6rHHHuOxxx7baTt9R7SH7wiDQMBDIFCFYbgJBLwcfzxceGHoWWecYb46HClYrdEN3ndv/o6oqoLp03O4/PKRVFWZK2Y0xGaDM8/8hrPP7sdhh5lLaeo7oj59R7SH74j69HuEfo+oS98R+o6oraHviEAgQE5OTrMlGva5ySCrPyxpaWkh5WlpaWzYsKHRtsXFxXTp0gW3243NZuOpp56ql2RYvHgxY8aMoaqqiri4OD766KMGkwwADz74IPfcc0+98pxwU3TX0bVr/WVKtm3bRnb2zpc9KSkJXVvXMIwmtQPweELXTa6srGxS23DDUgoLC5vUdvv27fXKcnNzm9S2oiJ0/Wifz9fk9+r3hy6xVFZW1qS2iYmJ9cry8/Ob1La4uLheWVPjdddZY87tdje5bbg4mtI2Pz+/XlleXl6T2tb9h9Xv9zc5Xl+dp4yKiordfq/bt29vUtvCwsJ6ZVu2bKG0dOfrXFdWVoYcezyeJsdbNx9cUlLSpLb6jmif3xEu1/FkZvaluPhnysrmYRheav43rP9zqG1v/o6IioJx4/yUle28nd8Pb73l4623zJUrxo6FlBR9R9Sl74j2+R2h3yP0e0Rd+o7Qd0Rte/IdsSvafaLhjTfeCBkOUZ1trNu11DCMnXY3jY+PZ8GCBZSVlfHNN99w880307Nnz5DMb79+/ViwYAFFRUV88MEHXHjhhcyaNavBZMNf/vKXkGzfrvRo6NSp/qRgnTp1okuX8LPM1lY3S2WxWJrUDsDpDF0HOjo6uklt4+LqT5iSnJzcpLYpKSn1ytLT03faDiAmJnTWY7vd3uT3aquzqHtcXFyT2tZNZAF07NixSW3DfXE0NV6Xy1XvuKltw8XRlLYdO3asV5aWlhb2S6yuup8Jm83W5Hjtdda9i4mJaVLbcJ+blJSUJrVNTk6uV5aRkdGkv0RER4f+ldnpdDb5vdb9bkpISGhSW31HtM/viM6dR9C7t/mXCL+/ktLSOaSnH49h+LDbQz+jPl8xfr/5+XQ4OoZ8R5SWzmPLlgdIT4/HYrEHN2j8355qbe07wus1ez1UVYH5O6r5HeHzmcMuIAbogtUKLhc4nTVbbfqO0HdEXXvbd4R+j9DvEXXpO0LfEbU19B1R3aOhubT7oROlpaXk1ZrG2u12M2jQoD0aOlHtsssuY9OmTXz11VcNnnPkkUfSq1evel1xGqI5GkREpLmsXn0rubkv4fMVMnbs1pBVK7ZseZ6VK6+o18ZqjSU6uidRUT12vPYkKioLpzMDlysDhyMNawPrvrcVpaXw/ffmUpkzZsCqVQ2fGxUFI0bAmDEwfDgMHQp9+pjDL0RERPYVzf0c2rZ/U2gG8fHxId1oDMMgPT2dmTNnBhMNHo+HWbNm8fDDD+/StQ3DqNfFbHfOERERaQm9e/8fvXo9gtu9qd7SmBUVy8K2CQTKKS9fTHn54gauasHpTAsmHrp1+xsJCTVr0/t8JVRVrcfh6ITD0RGrtfWXg4iPhxNOMDeAdevMhMNXX5mrWdTu0VtVBT/9ZG7VoqNh0CAz6TB4sJl46N0bunfX6hYiIiJN0e4TDXVZLBYmT57MlClT6NOnD3369GHKlCnExMSETOJxwQUX0KVLFx588EHAnEthxIgR9OrVC4/Hw+eff860adN4+umng23++te/cswxx9C1a1dKS0t5++23+f777/nyyy9b/X2KiIiA+e9eVFRWvfJevR4hI+MaKir+pKJiOVVV66isXEdV1VqqqtY3stqFgceTi8eTS1nZPDIzbwqpLS7+kcWLzQnLune/m+7dayac8vsrWLXqehyOjsFEhNPZKbjvcHTCZotr9pUzevSAK680N58PliyBX36BX381X9euDT2/shJmzza32mw26NbNTDpkZUFGBnTubG4ZGZCeDsnJEBtrLsEpIiKyr9rnEg0At99+O5WVlVxzzTUUFhYyevRoZsyYEdLzYePGjSFzJJSXl3PNNdewefNmoqOj6d+/P6+//jpnnXVW8Jy8vDwmTZpETk4OiYmJDBkyhC+//DLsqhQiIiKRZLHYiInpTUxMb+DEkDrD8ON2b9mRfFiLx5ON270Fj2dLrdccwI/TmRHS1uutmdjN4Qgde+3x5JGb+9JO4nLVSkBUJyRq9p3OTkRH9yUubvBuvW+7HYYNM7drrjHL8vLg99/N5TSrt9Wr67f1+82kRN3ERF02GyQlmUmHpCRzi401e0pERZmvtfdrv9bdXK7w5dV1druSGiIi0va0+zka9jaao0FERPYGhuHH683Hbu8QMmdDUdEP5OZOw+vdRmbmjSQnHx6sKymZzbx5o/b43mlpF7DffqFzKi1cOAGwERs7kN69Hw2p83jysdlisdkaXvqzrrIyWLwYli83kw7V26pV5hwQbYXVWpN4iIuDxMSa5EbdLTUV0tJqto4dNReFiIiYNEeDiIiIRJzFYsPprD/rdVLSISQlHRK2TWzsYEaMWITXuw2vNz/46vFsq1fm9W7DMHxhr+NwhM43YRh+Cgu/BQJ4vdvqnf/nn2dRVPQtNlsiLldnnM6azeXK2DHfRGeczgyczs7Y7XHExZkTRI4ZQ517QX4+ZGdDTg5s2WK+5uSYPSOKimq2wkLzNRDY+c9zdwUCUFFhbmFWZ2uU1WomG9LSzKEfWVn1ty5dzJ4TIiIiu0KJBhEREWkVNltUk4c8GIaBz1dcK/lQk4CIjx8Zcq7Xux2LxY5heHA6O9e7lsdjLtfl9xdTUVFMRcXyncQZXy/54HJlkJl5ExaLhU6doFMnc/jFzt+H2TuivNyc+6Gqynytu19ZCW53zRKdtfcbK6sur6w071NUZJY1RSAAW7ea2+KG5v7EnHuiOvHQrZs5KWb11q2bOfmmiIhIbRo60cZo6ISIiMiuMxMT2wkE3LhcofNGrFhxORUVK/B4cnC7txAIVOzy9e32JMaNKwwpW736JgoKPsflyqB//1eIiuoWrPN4tuHzFQV7SLQmtxuKi0N7VxQUmAmFvLzwm8ez+/dLSalJOtROQFTvh1myXURE2hgNnRARERGpw2Kx4HB0CFvXr9/zwX3DMPD7S3ckHXLweHJqTXKZU+s1m0CgPNiu7qSXABUVq6isXEll5UosltDxBXl5r7Nmzc1ATQ8JpzN1x6SWjW82W/werbzhcpnzMaSmNu386uEgGzea26ZN9fdzcszzwtm+3dzmzQtfn5gYPgFRvZ+crAktRUTaGyUaREREZJ9hsViw2xOw2xOIienX6Lk+X2kw+QD1J1qwWh1YrbEEAlU4naHzRng8W4L7fn8plZUrqKxc0cQYHTgcHbHbk+nf/yUSEkYH6yoqVrJt2/vY7YkkJo4jLm5osM4wAvh8xdhssVitzibdy7wfweEgw4eHP8fjgc2bYcMGWL/e3Grvb95srsoRTnFxzWoe4cTHhyYgsrLMeSOqkyWpqWZsmitCRGTvoUSDiIiISBh2ezx2ezwxMX3D1g8a9BEAPl8ZFkvo8g1xcfuTmnpOrV4SW/D7y5p0X8Pw7uhpkUPdEa5lZQtZt+4uAHr2fCQk0eDx5PLrr10AM1lhJhxisdnidqy6UbPfWHlMTJ+Q5AZAILCZzEwn3brFcthhsfVi9vnMCTLDJSE2bDB7RfjCz+1JaSksWWJujaleOSM11ewFkZhYs8pG7dfERDN5ER0NMTE1y4lW79davVxERFqIEg0iIiIieyDcHAxpaeeSlnZuSJnfX4XPV7BjUsumbIXY7Ukh1/D5imrdN7FOXXFw3zC8O84tYlelpZ1fL9Ewd+4BeL3biIrqyYEHrgmpW7nyWoqLf8ZmiyExMYYDDohhxIhobLYYrNYYbLYYDCOW/PxUsrNT2bKlI9nZyWzenMjmzfFs2hTDpk0uvN7GMwDV802sXLnLbymE0xmaeHC5zOVBd/d1d9poWVERae+UaBARERFpBTZbFDZbF1yuLrt9jZSUoxg0aDo+XzEJCaFrb1osdlJSjsbvL8PvL9+xlQVfoYGxDXVYrTH1yvz+ih110fXqKitXUV7ewLiIOjp0MLfBdRYfCQQs9Oq1nezsJDZtgm3bYOXKH9mwYTlFRem43QdTUJDE1q1QUtKkWzXI4zG34uKdn9tSbLb6yYq4OEhIMHtkJCTUbLWPG9qPitI8FyLStijRICIiIrKXiIrKIioqK2xdTEwfhgz5ImydYRgYhqde8sHvLycQCC2LjR1Yr33Hjifi95fhcnWtV2ex2LFaowgEmriuZhhWq0HXrtF0q1m4g7VrP2fjxocAGDr0W5KTDwPMVTXWrPmKuXOvp7w8kbKypB2viZSXJ1FWlkhlZRweTzRudzRVVTHBfbc7ZsdrNB5PND5fR7zeWKqq9mzljV3l95tLnpaX7/zcpnA4aoaNVCchGjpu6Jy4OA0rEZHmo0SDiIiISDtnsViwWFxYrS4cjpRdbj9gwJsN1g0Z8jlgTkYZCFTi91cQCFTUeW2ovObVYgmdwNLhSCU2dgiBQEXIMBGXCzp2LKZr11W7/D7q6tlzKllZtwEQCEBpaQ4//rgfHo+LuLiT6N79OaqqzORGVRXMn38B5eXb8Xii8HhceDxReL3hX8OXufD54nE6R1FVRfDaZWWlVFQ48Hiidut9eL3myiH5+bv/s7BYQntKxMYWkZAQIDExQEyMQUwMxMZCdLSFmBjLjlcrsbFWoqOtxMbaiImxERtrbi6XmQBxOs2tet9mU+8LkX2BEg0iIiIisscsFmtwcsnm0LXrTXTtelPYuo4dT2bs2Lx6yYyGExnhEx3R0X2C17Rawel0k5Rkwe8volOncvrWmQfUav0Kr3frHr0vlyuTMWM2hZQtWXIx+fkf4PE4GTBgHW53BiUl5jCR1auns3bt+5SXJ1BRkUB5eWK9ffM1MdizIxDY9V/xDcMcTlIzpCRpj95nYxwOD3a7j6ioaJxOSzAJYbEUYrUWYrf7sdsDwc3hCH21241axwYORwCHw9hRbpCcPIzo6AQcDjPBYRhbcbuX4HAYJCT0IT4+K1hntVZRUfHrjmNLcHM6Ldjt1bFZg2VOp5WoKAsulxWLxRbc7PbEesOiqqo2AwZWqxOnMy2kzu8vxzD8gBWLxRrm1bJHy9yKRJoSDSIiIiKyVzEf3FKb/brR0d0ZN64QMHto1DVkyBf4/aUNJC/KCQQ8GIaHQMC749U8NgxvcN9ur9+jxOXKIDq6H1FRXlJTnThrde7o0WMla9e+0eT3YBjgdkfXSj6YiYiUlPsxjNGUlJjJhK1bN7Nu3QwqKhLw+4fg8fSluJgd9QaVlS33kOv1OvF6nVRW1q1J3rE1t1Tg8AbqooDDduuqdrsHu92D0+nG6bQSG2smTFwu89XjycFmK8flspCamhZSV17+M37/ChwOd/AadrsHh8ONw+HZUe7F6fTgcHix23079n04neaxy+XlgAM+Jj4+NXjt/PwXyMm5D7sd+vV7hg4djgnGW1o6j2XLzqfh5EbTXjt1Oo3OnS8NXtcw/Pz55zmAlZiY/vTocXfIz2n9+vuoqlpPTfKkZqs5tjZSZ8FisdGr19SQ6+bnf0ZJya+AhYyMq4iKygzWlZUtIT//o53c0xJM6oSrAwtJSYcQFzckeF2fr5StW98GLERH9woO6aq2dev7BAJVjd6z/jEhZTZbLMnJR4Rct7R0Hh5PHmAhOfkwrNaa9X4rK9dTWbm6VmKq7n12Xma+t3SakxINIiIiIiJ1mA8goeLjD2iRe/Xp80SDdVlZt9O16221khU+DMNb79VMboSvMwwfcXHdcNU8m+B2Wyko8GEYecTFbSMxsab7RiDgZ+XKeygttVJS4qCiwkplpZWqKisVFTYqK21UVdmorLRTVeXYceykstJBVZWDqionHo8d6IDLNQKv15wDw+uFoqJluN1efD4Hdvt+IXVVVRV4PODzOfD7HS3ys25OPp8Tn89JVZW58kz9oSsjG2k9ccfW3C4DLsNiCeByGcGhKzYbWK0D8fm+wmr1Y7P5sVrNzWIJhBxbrYFa+6HHNpufmJgexMeb1zSvayE//2RsNj9RUR3p1KmmzmaD/Pze+P2x2Gx+bDZfyLVD7xu+zDw2GDw49Lp5edkUFs7HavXTr98kEhJq6oqLs9m48ZOdXHPnx336PEZs7JDgcB+vN5+VK68AIDX17HqJhlWrrt3jXk9RUd058MB1IWUbNtxHfv50AMaMycHlqkkKbNv2LmvX3rFH9wTIynp9j69Rm8Wou0CzRFRJSQmJiYkUFxeTkJAQ6XBERERERFpFIOAhEKjCMHwEAl68Xh8eT/Xmx+v14/EE8HoD+HzGjn0Dr7d6H7xeA5drAIFA7I5jKC/fRknJGnw+sNm6Y7WmB+uqqiopKlqEz2fB6zW36n2fz4LPZ92xb8XjseL12vB4bHi9VjweO16vDbfbjt8fg88XH1zVxO0Gny/SP1FpLlZrdRIjAFTsSJrYcDrjQpIfPt8GLBZvMFFhsQQaTWSY9QEsFgOLxXy1210kJx+OxWLe12KBsrJf8Xi2YLUG6NjxeOz26GB9RcVSysvnYrEYWK0BwAjZr3v96te69YmJR/Dcc8Ob7TlUiYY2RokGEREREZG9XyBASOKhKa+7cu6utPX7zS0QqNmvf2wEjw1D80Pse0qA5nsO1dAJERERERGRZma1QlSUue0dapILhtHU5ET944Y2n69p5zV3e5+v6TGG25qzbaD+1C/tlhINIiIiIiIiEmSxgN1ubtJ8DCM0+RAI1JSFe22srrnblpXBscc233vVR0dERERERESkhVksNfM5tDUlJc17vfrT6YqIiIiIiIiI7CYlGkRERERERESk2SjRICIiIiIiIiLNRokGEREREREREWk2SjSIiIiIiIiISLNRokFEREREREREmo0SDSIiIiIiIiLSbJRoEBEREREREZFmo0SDiIiIiIiIiDQbJRpEREREREREpNko0SAiIiIiIiIizUaJBhERERERERFpNko0iIiIiIiIiEizUaJBRERERERERJqNEg0iIiIiIiIi0myUaBARERERERGRZqNEg4iIiIiIiIg0GyUaRERERERERKTZKNEgIiIiIiIiIs1GiQYRERERERERaTb2SAcgoQzDAKCkpCTCkYiIiIiIiMi+oPr5s/p5dE8p0dDGFBQUANC1a9cIRyIiIiIiIiL7koKCAhITE/f4Oko0tDEpKSkAbNy4sVn+A4u0RSUlJXTt2pVNmzaRkJAQ6XBEWoQ+57Iv0Odc9gX6nMu+oLi4mKysrODz6J5SoqGNsVrNaTMSExP1RSbtXkJCgj7n0u7pcy77An3OZV+gz7nsC6qfR/f4Os1yFRERERERERERlGgQERERERERkWakREMb43K5+Oc//4nL5Yp0KCItRp9z2Rfocy77An3OZV+gz7nsC5r7c24xmmv9ChERERERERHZ56lHg4iIiIiIiIg0GyUaRERERERERKTZKNEgIiIiIiIiIs1GiQYRERERERERaTZKNLQxTz31FD169CAqKorhw4fz448/Rjokkd32ww8/cMIJJ5CRkYHFYmH69Okh9YZhcPfdd5ORkUF0dDSHHnooS5cujUywIrvhwQcfZOTIkcTHx5OamsrJJ5/MihUrQs7R51z2dk8//TRDhgwhISGBhIQExowZwxdffBGs12dc2qMHH3wQi8XC5MmTg2X6rMve7u6778ZisYRs6enpwfrm/Iwr0dCGvPPOO0yePJm77rqL+fPnc/DBB3PMMcewcePGSIcmslvKy8sZOnQoTz75ZNj6qVOn8thjj/Hkk08ye/Zs0tPTmTBhAqWlpa0cqcjumTVrFtdeey2//fYbM2fOxOfzMXHiRMrLy4Pn6HMue7vMzEweeugh5syZw5w5czj88MM56aSTgr986jMu7c3s2bN57rnnGDJkSEi5PuvSHgwcOJCcnJzgtnjx4mBds37GDWkzRo0aZVx11VUhZf379zfuvPPOCEUk0nwA46OPPgoeBwIBIz093XjooYeCZVVVVUZiYqLxzDPPRCBCkT23detWAzBmzZplGIY+59J+JScnGy+88II+49LulJaWGn369DFmzpxpjB8/3rjxxhsNw9D3ubQP//znP42hQ4eGrWvuz7h6NLQRHo+HuXPnMnHixJDyiRMn8ssvv0QoKpGWs27dOnJzc0M+8y6Xi/Hjx+szL3ut4uJiAFJSUgB9zqX98fv9vP3225SXlzNmzBh9xqXdufbaaznuuOM48sgjQ8r1WZf2YtWqVWRkZNCjRw/OPvts1q5dCzT/Z9zebBHLHsnPz8fv95OWlhZSnpaWRm5uboSiEmk51Z/rcJ/5DRs2RCIkkT1iGAY333wz48aNY9CgQYA+59J+LF68mDFjxlBVVUVcXBwfffQRAwYMCP7yqc+4tAdvv/028+bNY/bs2fXq9H0u7cHo0aOZNm0affv2JRlZGhMAAApTSURBVC8vj/vvv5+xY8eydOnSZv+MK9HQxlgslpBjwzDqlYm0J/rMS3tx3XXXsWjRIn766ad6dfqcy96uX79+LFiwgKKiIj744AMuvPBCZs2aFazXZ1z2dps2beLGG29kxowZREVFNXiePuuyNzvmmGOC+4MHD2bMmDH06tWLV199lQMPPBBovs+4hk60ER07dsRms9XrvbB169Z6WSWR9qB6hlt95qU9uP766/nkk0/47rvvyMzMDJbrcy7thdPppHfv3owYMYIHH3yQoUOH8u9//1ufcWk35s6dy9atWxk+fDh2ux273c6sWbN44oknsNvtwc+zPuvSnsTGxjJ48GBWrVrV7N/nSjS0EU6nk+HDhzNz5syQ8pkzZzJ27NgIRSXScnr06EF6enrIZ97j8TBr1ix95mWvYRgG1113HR9++CHffvstPXr0CKnX51zaK8MwcLvd+oxLu3HEEUewePFiFixYENxGjBjBeeedx4IFC+jZs6c+69LuuN1uli1bRufOnZv9+1xDJ9qQm2++mUmTJjFixAjGjBnDc889x8aNG7nqqqsiHZrIbikrK2P16tXB43Xr1rFgwQJSUlLIyspi8uTJTJkyhT59+tCnTx+mTJlCTEwM5557bgSjFmm6a6+9ljfffJOPP/6Y+Pj44F8BEhMTiY6ODq7Brs+57M3++te/cswxx9C1a1dKS0t5++23+f777/nyyy/1GZd2Iz4+Pji/TrXY2Fg6dOgQLNdnXfZ2t956KyeccAJZWVls3bqV+++/n5KSEi688MJm/z5XoqENOeussygoKODee+8lJyeHQYMG8fnnn9OtW7dIhyayW+bMmcNhhx0WPL755psBuPDCC3nllVe4/fbbqays5JprrqGwsJDRo0czY8YM4uPjIxWyyC55+umnATj00ENDyl9++WUuuugiAH3OZa+Xl5fHpEmTyMnJITExkSFDhvDll18yYcIEQJ9x2Xfosy57u82bN3POOeeQn59Pp06dOPDAA/ntt9+Cz5vN+Rm3GIZhNPcbEBEREREREZF9k+ZoEBEREREREZFmo0SDiIiIiIiIiDQbJRpEREREREREpNko0SAiIiIiIiIizUaJBhERERERERFpNko0iIiIiIiIiEizUaJBRERERERERJqNEg0iIiIiIiIi0myUaBAREZF2bcaMGVgslpBt3LhxkQ5LRESk3VKiQURERNqUhx9+OCQp8OWXX+7R9V566aV6ZT///DPLly/fo+uKiIhIeEo0iIiISJuyaNGikOPBgwfv9rW2b9/O9OnTw9aFS0CIiIjInlOiQURERNqU2omGlJQUunTpstvXev3113G73WHrpk2bhs/n2+1ri4iISHhKNIiIiEib4fV6WbFiRfB4yJAhe3S9F198MeR40KBBwf28vDw+++yzPbq+iIiI1KdEg4iIiLQZy5Ytw+v1Bo/3ZNjEnDlzQnpHDBs2jPvvvz/kHA2fEBERaX5KNIiIiEibUXd+hj3p0VC3N8OFF17IscceS8eOHYNln3/+OTk5Obt9DxEREalPiQYRERFpMxYvXhxyvLuJhsrKSt56663gsd1u59xzz8XhcHDOOecEy/1+P6+++uruBSsiIiJhKdEgIiIiEdW/f//gUpZTp04NqRs9enTIUpe1t7/85S8NXvP999+nuLg4eHz00UeTmpoKmD0batPwCRERkealRIOIiIhETEVFBatWrdqttkOHDm2wLtywiWrDhw9n4MCBweNVq1bxww8/7FYMIiIiUp8SDSIiIhIxixcvJhAI7FbbhhINq1evDkkcJCcnc8IJJ4Sco14NIiIiLUeJBhEREYmYwYMHs27dOtatW8fbb78dUnfLLbcE68Jt/fv3D3vNl156CcMwgsdnnXUWLpcr5Jzzzz8fm80WPH7vvfcoKSlpxncmIiKy71KiQURERCImJiaG7t270717d7Zs2RJSd/jhhwfrwm0Wi6Xe9cJN7li39wJA586dOfLII4PHFRUV9RIdIiIisnuUaBAREZE2Yc6cOSHHI0eO3OVrfPHFFyEJi759+3LggQeGPbduAqLuvA4iIiKye5RoEBERkTZh9uzZwf1u3brRqVOnXb5G3WTBBRdc0OC5J598MgkJCcHjP/74gyVLluzyPUVERCSUEg0iIiIScUVFRaxevTp4PGrUqF2+xtatW/nf//4XPLZYLEyaNKnB86OjoznzzDNDyjQppIiIyJ5TokFEREQibs6cOSETOO7OsIlp06bh9XqDx4cddhhZWVmNtqk7fOK1117D4/Hs8r1FRESkhhINIiIiEnG1h03A7iUa6vZGaGzYRLVx48bRq1ev4HF+fj6ffPLJLt9bREREaijRICIiIhFXeyJIq9XK8OHDd6n9L7/8wrJly4LHsbGxnHbaaU1qWzchoUkhRURE9owSDSIiIhJxtXs09OvXj/j4+F1qXzc5cNpppxEXF9ektpMmTQpZKnPGjBls3rx5l+4vIiIiNZRoEBERkYjKy8tj06ZNweNdnQiyrKyMd999N6SsKcMmqvXo0YODDz44eBwIBHjllVd2KQYRERGpoUSDiIiIRNSezs/wzjvvUFZWFjzu2rUrhx122C5do+6kkC+99FLI5JQiIiLSdEo0iIiISETVnp8BYMSIEbvUvu6wiUmTJmG17tqvOGeccQYxMTHB43Xr1vHdd9/t0jVERETEpESDiIiIRNSiRYuC+xaLhUGDBjW57bJly/j1119DynZl2ES1+Ph4TjnllJAyTQopIiKye5RoEBERkYiqPT9DTEwMsbGxTW5bNxkwevRo+vXrt1tx1B0+8eGHH1JUVLRb1xIREdmXKdEgIiIiEVV7mEN5eTmrVq1qUjuv18trr70WUlY3WbArjjjiCDIzM4PHVVVVvPnmm7t9PRERkX2VPdIBiIiIyL6tf//+/PHHH8HjE088kbvuuotBgwaRlJQULLdYLHTr1i14/Omnn7J169bgsdVqZfTo0axfv363Yzn66KN54YUXgscvvvgi11xzzW5fT0REZF9kMTSlsoiIiETQjz/+yCGHHLLT87p37866deuCx8cddxyff/55S4YGwPz58xk2bFiL30dERKS90NAJERERiaiDDz6YRx55BJvN1uh5w4cPD+5nZ2fz1VdftXRogCaFFBER2VVKNIiIiEjE3XrrrSxYsIAbb7yRAw44gKSkpHqJh9qJhldeeQW/398qsb3xxhtUVVW1yr1ERETaAw2dEBEREREREZFmox4NIiIiIiIiItJslGgQERERERERkWajRIOIiIiIiIiINBslGkRERERERESk2SjRICIiIiIiIiLNRokGEREREREREWk2SjSIiIiIiIiISLNRokFEREREREREmo0SDSIiIiIiIiLSbJRoEBEREREREZFmo0SDiIiIiIiIiDQbJRpEREREREREpNko0SAiIiIiIiIizUaJBhERERERERFpNko0iIiIiIiIiEizUaJBRERERERERJqNEg0iIiIiIiIi0myUaBARERERERGRZvP/Sw4z3CW0mCAAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 15))\n", "\n", @@ -1659,10 +1942,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "id": "60d4a331", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "QuTiP: Quantum Toolbox in Python\n", + "================================\n", + "Copyright (c) QuTiP team 2011 and later.\n", + "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", + "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", + "Original developers: R. J. Johansson & P. D. Nation.\n", + "Previous lead developers: Chris Granade & A. Grimsmo.\n", + "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", + "\n", + "QuTiP Version: 5.2.0.dev0+daa7d68\n", + "Numpy Version: 1.26.4\n", + "Scipy Version: 1.14.1\n", + "Cython Version: 3.0.9\n", + "Matplotlib Version: 3.9.2\n", + "Python Version: 3.12.7\n", + "Number of CPUs: 16\n", + "BLAS Info: Generic\n", + "INTEL MKL Ext: None\n", + "Platform Info: Linux (x86_64)\n", + "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", + "\n", + "Installed QuTiP family packages\n", + "-------------------------------\n", + "\n", + "No QuTiP family packages installed.\n", + "\n", + "================================================================================\n", + "Please cite QuTiP in your publication.\n", + "================================================================================\n", + "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" + ] + } + ], "source": [ "qutip.about()" ] @@ -1679,7 +2000,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "id": "d19a74bc", "metadata": {}, "outputs": [], @@ -1730,7 +2051,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb index 65997bc6..9909ef95 100644 --- a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb +++ b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb @@ -312,7 +312,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "RHS construction time: 0.010695219039916992\n", + "RHS construction time: 0.018474340438842773\n", " [ 1% ] Elapsed 0.01s / Remaining 00:00:00:01" ] }, @@ -320,8 +320,8 @@ "name": "stdout", "output_type": "stream", "text": [ - " Total run time: 1.41s*] Elapsed 1.41s / Remaining 00:00:00:00[*********70%**** ] Elapsed 0.96s / Remaining 00:00:00:00\n", - "ODE solver time: 1.4157280921936035\n" + " Total run time: 2.22s*] Elapsed 2.22s / Remaining 00:00:00:00[*********73%***** ] Elapsed 1.51s / Remaining 00:00:00:00\n", + "ODE solver time: 2.226008415222168\n" ] } ], @@ -352,9 +352,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "RHS construction time: 0.011708736419677734\n", - " Total run time: 1.58s*] Elapsed 1.58s / Remaining 00:00:00:00\n", - "ODE solver time: 1.582150936126709\n" + "RHS construction time: 0.018711090087890625\n", + " [*********50% ] Elapsed 1.13s / Remaining 00:00:00:01" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 2.32s*] Elapsed 2.32s / Remaining 00:00:00:00[*********94%********** ] Elapsed 2.15s / Remaining 00:00:00:00\n", + "ODE solver time: 2.324307918548584\n" ] } ], @@ -482,9 +489,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "RHS construction time: 0.009078502655029297\n", - " Total run time: 1.47s*] Elapsed 1.46s / Remaining 00:00:00:00\n", - "ODE solver time: 1.4667692184448242\n" + "RHS construction time: 0.010780572891235352\n", + " Total run time: 1.81s*] Elapsed 1.81s / Remaining 00:00:00:00\n", + "ODE solver time: 1.8109989166259766\n" ] } ], @@ -550,32 +557,24 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "15147f03", - "metadata": {}, - "outputs": [], - "source": [ - "lower = [0, -np.inf, -1e-6, -3]\n", - "guess = [np.real(bath.correlation_function(0))/10, -10, 0, 0]\n", - "upper = [3.5, 0, 1e-6, 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, + "execution_count": 202, "id": "6df7fe42", "metadata": {}, "outputs": [], "source": [ "tfit=np.linspace(0,10,10000)\n", - "envfit,fitinfo = bath.approx_by_cf_fit(tlist=tfit,Nr_max=3,Ni_max=1,full_ansatz=True,\n", + "lower = [0, -np.inf, -1e-6, -3]\n", + "guess = [np.real(bath.correlation_function(0))/10, -10, 0, 0]\n", + "upper = [5, 0, 1e-6, 0] # for better fits increase the first element\n", + "# that makes the simuation slower though\n", + "envfit,fitinfo = bath.approximate(\"cf\",tlist=tfit,Nr_max=2,Ni_max=1,full_ansatz=True,\n", " sigma=0.1,maxfev=1e6,target_rsme=None,\n", " lower=lower,upper=upper,guess=guess)" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 203, "id": "4dcf3dd8", "metadata": {}, "outputs": [ @@ -585,17 +584,16 @@ "text": [ "Correlation function fit:\n", "\n", - "Result of fitting the real part of |Result of fitting the imaginary part \n", - "the correlation function with 3 terms: |of the correlation function with 1 terms: \n", - " | \n", - " Parameters| a | b | c | d | Parameters| a | b | c | d \n", - " 1 | 3.50e+00 |-8.33e-01 | 9.94e-07 |-2.99e+00 | 1 | 3.16e+00 |-1.00e+00 |-9.18e-07 |-2.50e+00 \n", - " 2 | 3.50e+00 |-4.18e+00 | 4.45e-07 |-3.59e-01 | \n", - " 3 | 3.50e+00 |-4.93e+01 |-9.11e-07 |-3.00e+00 |A normalized RMSE of 1.00e-04 was obtained for the the imaginary part\n", - " |of the correlation function. \n", - "A normalized RMSE of 6.39e-05 was obtained for the the real part of | \n", - "the correlation function. | \n", - "The current fit took 0.469412 seconds. |The current fit took 0.042074 seconds. \n", + "Result of fitting the real part of |Result of fitting the imaginary part \n", + "the correlation function with 2 terms: |of the correlation function with 1 terms: \n", + " | \n", + " Parameters| a | b | c | d | Parameters| a | b | c | d \n", + " 1 | 5.00e+00 |-1.91e+01 |-3.81e-07 |-2.97e+00 | 1 | 4.10e+00 |-1.00e+00 |-9.18e-07 |-2.50e+00 \n", + " 2 | 5.00e+00 |-1.07e+00 | 3.03e-07 |-1.06e-04 | \n", + " |A 1-R2 coefficient of 2.50e-03 was obtained for the the imaginary part\n", + "A 1-R2 coefficient of 2.16e-02 was obtained for the the real part of |of the correlation function. \n", + "the correlation function. | \n", + "The current fit took 0.622658 seconds. |The current fit took 0.120125 seconds. \n", "\n" ] } @@ -614,23 +612,23 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 204, "id": "d4491a1e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 204, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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" ] @@ -679,7 +677,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 205, "id": "dea09fd4", "metadata": {}, "outputs": [ @@ -687,9 +685,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "RHS construction time: 0.17575669288635254\n", - " Total run time: 17.54s*] Elapsed 17.53s / Remaining 00:00:00:00\n", - "ODE solver time: 17.53864097595215\n" + "RHS construction time: 0.006770610809326172\n", + " Total run time: 1.38s*] Elapsed 1.38s / Remaining 00:00:00:00\n", + "ODE solver time: 1.3836772441864014\n" ] } ], @@ -698,7 +696,7 @@ " # We reduce NC slightly here for speed of execution because we retain\n", " # 3 exponents in ckAR instead of 1. Please restore full NC for\n", " # convergence though:\n", - " HEOMFit = HEOMSolver(Hsys, (envfit,Q), NC, options=options)\n", + " HEOMFit = HEOMSolver(Hsys, (envfit,Q), int(NC*0.7), options=options)\n", "\n", "with timer(\"ODE solver time\"):\n", " resultFit = HEOMFit.run(rho0, tlist)" @@ -714,7 +712,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 206, "id": "59507a86", "metadata": {}, "outputs": [ @@ -722,8 +720,8 @@ "name": "stdout", "output_type": "stream", "text": [ - " Total run time: 1.16s*] Elapsed 1.15s / Remaining 00:00:00:00\n", - "ODE solver time: 1.1623961925506592\n" + " Total run time: 0.88s*] Elapsed 0.88s / Remaining 00:00:00:00\n", + "ODE solver time: 0.8906404972076416\n" ] } ], @@ -731,7 +729,7 @@ "with timer(\"ODE solver time\"):\n", " resultBR = brmesolve(\n", " Hsys, rho0, tlist,\n", - " a_ops=[[sigmaz(), lambda w: bath.power_spectrum(w)]], sec_cutoff=0, options=options,\n", + " a_ops=[[sigmaz(),bath]], sec_cutoff=0, options=options,\n", " )" ] }, @@ -747,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 207, "id": "771eb79e", "metadata": {}, "outputs": [], @@ -762,7 +760,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 208, "id": "661dff32", "metadata": {}, "outputs": [], @@ -785,13 +783,13 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 209, "id": "6bc85109", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -848,7 +846,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 210, "id": "3a984023", "metadata": {}, "outputs": [ @@ -860,14 +858,14 @@ "QuTiP: Quantum Toolbox in Python\n", "================================\n", "Copyright (c) QuTiP team 2011 and later.\n", - "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Jake Lishman, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", + "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", "Original developers: R. J. Johansson & P. D. Nation.\n", "Previous lead developers: Chris Granade & A. Grimsmo.\n", "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", "\n", - "QuTiP Version: 5.1.0.dev0+7941773\n", - "Numpy Version: 2.1.3\n", + "QuTiP Version: 5.2.0.dev0+daa7d68\n", + "Numpy Version: 1.26.4\n", "Scipy Version: 1.14.1\n", "Cython Version: 3.0.9\n", "Matplotlib Version: 3.9.2\n", @@ -877,6 +875,12 @@ "INTEL MKL Ext: None\n", "Platform Info: Linux (x86_64)\n", "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", + "\n", + "Installed QuTiP family packages\n", + "-------------------------------\n", + "\n", + "No QuTiP family packages installed.\n", + "\n", "================================================================================\n", "Please cite QuTiP in your publication.\n", "================================================================================\n", @@ -900,7 +904,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 211, "id": "77693ca6", "metadata": {}, "outputs": [], @@ -908,14 +912,6 @@ "assert np.allclose(P11_matsT, P11_pade, rtol=1e-3)\n", "assert np.allclose(P11_matsT, P11_fit, rtol=1e-3)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b02f696f", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb index bcc54041..e025bf2b 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb @@ -494,7 +494,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 16, @@ -550,8 +550,8 @@ "will cover the following approaches:\n", "\n", "- Non-Linear Least Squares:\n", - " - On the Spectral Density (`spec_lsq`)\n", - " - On the Correlation function (`corr_lsq`)\n", + " - On the Spectral Density (`sd`)\n", + " - On the Correlation function (`cf`)\n", "- Methods based on the Prony Polynomial\n", " - Prony on the correlation function(`prony`)\n", " - The Matrix Pencil method on the correlation function (`mp`)\n", @@ -603,7 +603,7 @@ "metadata": {}, "outputs": [], "source": [ - "bath, fitinfo = sd_env.approximate(\"spec_lsq\",w,Nmax=4)" + "bath, fitinfo = sd_env.approximate(\"sd\",w,Nmax=4)" ] }, { @@ -633,7 +633,7 @@ " 4 | 1.06e-02 | 3.07e-01 |1.00e-01\n", " \n", "A 1-R2 coefficient of 1.38e-06 was obtained for the the spectral density.\n", - "The current fit took 42.143630 seconds.\n" + "The current fit took 29.262844 seconds.\n" ] } ], @@ -709,7 +709,7 @@ } ], "source": [ - "bath, fitinfo = sd_env.approximate(\"spec_lsq\",w,Nmax=4,Nk=3)\n", + "bath, fitinfo = sd_env.approximate(\"sd\",w,Nmax=4,Nk=3)\n", "\n", "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5))\n", "\n", @@ -926,7 +926,7 @@ " # lower = [-100*J_max, 0.1*wc, 0.1*wc]\n", " # guess = [J_max, wc, wc]\n", " # upper = [100*J_max, 100*wc, 100*wc]\n", - " bath, fitinfo= sd_env.approximate(\"spec_lsq\",w,Nmax=N,Nk=Nk,target_rmse=None)#,lower=lower,upper=upper,guess=guess,sigma=sigma)\n", + " bath, fitinfo= sd_env.approximate(\"sd\",w,Nmax=N,Nk=Nk,target_rmse=None)#,lower=lower,upper=upper,guess=guess,sigma=sigma)\n", " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", "\n", " # This problem is a little stiff, so we use the BDF method to solve\n", @@ -1007,58 +1007,58 @@ "output_type": "stream", "text": [ "Starting calculations for N=1, Nk=1 and max_depth=5 ... \n", - "10.0%. 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Est. time left: 00:00:00:00\n", + "Total run time: 20.40s\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -1098,34 +1098,34 @@ "output_type": "stream", "text": [ "Starting calculations for N=4, Nk=2 and max_depth=5 ... \n", - "10.0%. Run time: 4.52s. Est. time left: 00:00:00:40\n", - "20.0%. Run time: 6.33s. Est. time left: 00:00:00:25\n", - "30.1%. Run time: 7.99s. Est. time left: 00:00:00:18\n", - "40.1%. Run time: 9.54s. Est. time left: 00:00:00:14\n", - "50.1%. Run time: 11.06s. Est. time left: 00:00:00:11\n", - "60.1%. Run time: 12.58s. Est. time left: 00:00:00:08\n", - "70.1%. Run time: 13.97s. Est. time left: 00:00:00:05\n", - "80.1%. Run time: 15.33s. Est. time left: 00:00:00:03\n", - "90.2%. Run time: 16.66s. Est. time left: 00:00:00:01\n", - "100.0%. Run time: 17.98s. Est. time left: 00:00:00:00\n", - "Total run time: 17.98s\n", + "10.0%. Run time: 4.81s. Est. time left: 00:00:00:43\n", + "20.0%. Run time: 7.06s. Est. time left: 00:00:00:28\n", + "30.1%. Run time: 9.17s. Est. time left: 00:00:00:21\n", + "40.1%. Run time: 11.71s. Est. time left: 00:00:00:17\n", + "50.1%. Run time: 14.19s. Est. time left: 00:00:00:14\n", + "60.1%. Run time: 16.66s. Est. time left: 00:00:00:11\n", + "70.1%. Run time: 18.92s. Est. time left: 00:00:00:08\n", + "80.1%. Run time: 21.31s. Est. time left: 00:00:00:05\n", + "90.2%. Run time: 23.83s. Est. time left: 00:00:00:02\n", + "100.0%. Run time: 26.74s. Est. time left: 00:00:00:00\n", + "Total run time: 26.74s\n", "Starting calculations for N=4, Nk=3 and max_depth=5 ... \n", - "10.0%. Run time: 5.48s. Est. time left: 00:00:00:49\n", - "20.0%. Run time: 9.31s. Est. time left: 00:00:00:37\n", - "30.1%. Run time: 13.13s. Est. time left: 00:00:00:30\n", - "40.1%. Run time: 17.19s. Est. time left: 00:00:00:25\n", - "50.1%. Run time: 22.53s. Est. time left: 00:00:00:22\n", - "60.1%. Run time: 28.67s. Est. time left: 00:00:00:19\n", - "70.1%. Run time: 34.19s. Est. time left: 00:00:00:14\n", - "80.1%. Run time: 39.12s. Est. time left: 00:00:00:09\n", - "90.2%. Run time: 43.85s. Est. time left: 00:00:00:04\n", - "100.0%. Run time: 48.80s. Est. time left: 00:00:00:00\n", - "Total run time: 48.80s\n" + "10.0%. Run time: 15.37s. Est. time left: 00:00:02:18\n", + "20.0%. Run time: 25.95s. Est. time left: 00:00:01:43\n", + "30.1%. Run time: 35.67s. Est. time left: 00:00:01:23\n", + "40.1%. Run time: 45.77s. Est. time left: 00:00:01:08\n", + "50.1%. Run time: 54.57s. Est. time left: 00:00:00:54\n", + "60.1%. Run time: 62.19s. Est. time left: 00:00:00:41\n", + "70.1%. Run time: 67.18s. Est. time left: 00:00:00:28\n", + "80.1%. Run time: 75.76s. Est. time left: 00:00:00:18\n", + "90.2%. Run time: 80.96s. Est. time left: 00:00:00:08\n", + "100.0%. Run time: 85.12s. Est. time left: 00:00:00:00\n", + "Total run time: 85.12s\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1167,46 +1167,46 @@ "output_type": "stream", "text": [ "Starting calculations for N=4, Nk=1 and max_depth=2 ... \n", - "10.0%. Run time: 0.08s. Est. time left: 00:00:00:00\n", - "20.0%. Run time: 0.11s. Est. time left: 00:00:00:00\n", - "30.1%. Run time: 0.14s. Est. time left: 00:00:00:00\n", - "40.1%. Run time: 0.16s. Est. time left: 00:00:00:00\n", + "10.0%. Run time: 0.07s. Est. time left: 00:00:00:00\n", + "20.0%. Run time: 0.10s. Est. time left: 00:00:00:00\n", + "30.1%. Run time: 0.13s. Est. time left: 00:00:00:00\n", + "40.1%. Run time: 0.17s. Est. time left: 00:00:00:00\n", "50.1%. Run time: 0.19s. Est. time left: 00:00:00:00\n", - "60.1%. Run time: 0.21s. Est. time left: 00:00:00:00\n", - "70.1%. Run time: 0.24s. Est. time left: 00:00:00:00\n", + "60.1%. Run time: 0.22s. Est. time left: 00:00:00:00\n", + "70.1%. Run time: 0.25s. Est. time left: 00:00:00:00\n", "80.1%. Run time: 0.27s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 0.31s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 0.36s. Est. time left: 00:00:00:00\n", - "Total run time: 0.36s\n", + "90.2%. Run time: 0.29s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 0.32s. Est. time left: 00:00:00:00\n", + "Total run time: 0.32s\n", "Starting calculations for N=4, Nk=1 and max_depth=3 ... \n", - "10.0%. Run time: 0.20s. Est. time left: 00:00:00:01\n", - "20.0%. Run time: 0.28s. Est. time left: 00:00:00:01\n", - "30.1%. Run time: 0.34s. Est. time left: 00:00:00:00\n", - "40.1%. Run time: 0.40s. Est. time left: 00:00:00:00\n", - "50.1%. Run time: 0.46s. Est. time left: 00:00:00:00\n", - "60.1%. Run time: 0.51s. Est. time left: 00:00:00:00\n", - "70.1%. Run time: 0.57s. Est. time left: 00:00:00:00\n", - "80.1%. Run time: 0.62s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 0.67s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 0.73s. Est. time left: 00:00:00:00\n", - "Total run time: 0.73s\n", + "10.0%. Run time: 0.17s. Est. time left: 00:00:00:01\n", + "20.0%. Run time: 0.24s. Est. time left: 00:00:00:00\n", + "30.1%. Run time: 0.30s. Est. time left: 00:00:00:00\n", + "40.1%. Run time: 0.36s. Est. time left: 00:00:00:00\n", + "50.1%. Run time: 0.41s. Est. time left: 00:00:00:00\n", + "60.1%. Run time: 0.47s. Est. time left: 00:00:00:00\n", + "70.1%. Run time: 0.54s. Est. time left: 00:00:00:00\n", + "80.1%. Run time: 0.60s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 0.66s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 0.71s. Est. time left: 00:00:00:00\n", + "Total run time: 0.71s\n", "Starting calculations for N=4, Nk=1 and max_depth=4 ... \n", - "10.0%. Run time: 1.07s. Est. time left: 00:00:00:09\n", - "20.0%. Run time: 1.47s. Est. time left: 00:00:00:05\n", - "30.1%. Run time: 1.79s. Est. time left: 00:00:00:04\n", - "40.1%. Run time: 2.08s. Est. time left: 00:00:00:03\n", - "50.1%. Run time: 2.37s. Est. time left: 00:00:00:02\n", - "60.1%. Run time: 2.69s. Est. time left: 00:00:00:01\n", - "70.1%. Run time: 3.01s. Est. time left: 00:00:00:01\n", - "80.1%. Run time: 3.30s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 3.58s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 3.86s. Est. time left: 00:00:00:00\n", - "Total run time: 3.86s\n" + "10.0%. Run time: 0.59s. Est. time left: 00:00:00:05\n", + "20.0%. Run time: 0.85s. Est. time left: 00:00:00:03\n", + "30.1%. Run time: 1.09s. Est. time left: 00:00:00:02\n", + "40.1%. Run time: 1.32s. Est. time left: 00:00:00:01\n", + "50.1%. Run time: 1.55s. Est. time left: 00:00:00:01\n", + "60.1%. Run time: 1.76s. Est. time left: 00:00:00:01\n", + "70.1%. Run time: 1.98s. Est. time left: 00:00:00:00\n", + "80.1%. Run time: 2.21s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 2.47s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 2.70s. Est. time left: 00:00:00:00\n", + "Total run time: 2.70s\n" ] }, { "data": { - "image/png": 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", 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Run time: 0.09s. Est. time left: 00:00:00:00\n", + "70.1%. Run time: 0.10s. Est. time left: 00:00:00:00\n", + "80.1%. Run time: 0.11s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 0.13s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 0.14s. Est. time left: 00:00:00:00\n", + "Total run time: 0.14s\n", "3\n", - "10.0%. Run time: 0.72s. Est. time left: 00:00:00:06\n", - "20.0%. Run time: 1.20s. Est. time left: 00:00:00:04\n", - "30.1%. Run time: 1.66s. Est. time left: 00:00:00:03\n", - "40.1%. Run time: 2.10s. Est. time left: 00:00:00:03\n", - "50.1%. Run time: 2.60s. Est. time left: 00:00:00:02\n", - "60.1%. Run time: 3.25s. Est. time left: 00:00:00:02\n", - "70.1%. Run time: 3.94s. Est. time left: 00:00:00:01\n", - "80.1%. Run time: 4.85s. Est. time left: 00:00:00:01\n", - "90.2%. Run time: 5.50s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 5.96s. Est. time left: 00:00:00:00\n", - "Total run time: 5.97s\n", + "10.0%. Run time: 0.82s. Est. time left: 00:00:00:07\n", + "20.0%. Run time: 1.36s. Est. time left: 00:00:00:05\n", + "30.1%. Run time: 1.84s. Est. time left: 00:00:00:04\n", + "40.1%. Run time: 2.31s. Est. time left: 00:00:00:03\n", + "50.1%. Run time: 2.81s. Est. time left: 00:00:00:02\n", + "60.1%. Run time: 3.31s. Est. time left: 00:00:00:02\n", + "70.1%. Run time: 3.81s. Est. time left: 00:00:00:01\n", + "80.1%. Run time: 4.31s. Est. time left: 00:00:00:01\n", + "90.2%. Run time: 4.80s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 5.28s. Est. time left: 00:00:00:00\n", + "Total run time: 5.28s\n", "4\n", - "10.0%. Run time: 1.80s. Est. time left: 00:00:00:16\n", - "20.0%. Run time: 3.38s. Est. time left: 00:00:00:13\n", - "30.1%. Run time: 4.61s. Est. time left: 00:00:00:10\n", - "40.1%. Run time: 6.05s. Est. time left: 00:00:00:09\n", - "50.1%. Run time: 7.41s. Est. time left: 00:00:00:07\n", - "60.1%. Run time: 8.91s. Est. time left: 00:00:00:05\n", - "70.1%. Run time: 10.07s. Est. time left: 00:00:00:04\n", - "80.1%. Run time: 10.97s. Est. time left: 00:00:00:02\n", - "90.2%. Run time: 11.66s. Est. time left: 00:00:00:01\n", - "100.0%. Run time: 12.31s. Est. time left: 00:00:00:00\n", - "Total run time: 12.31s\n" + "10.0%. Run time: 1.41s. Est. time left: 00:00:00:12\n", + "20.0%. Run time: 2.33s. Est. time left: 00:00:00:09\n", + "30.1%. Run time: 3.20s. Est. time left: 00:00:00:07\n", + "40.1%. Run time: 4.19s. Est. time left: 00:00:00:06\n", + "50.1%. Run time: 5.04s. Est. time left: 00:00:00:05\n", + "60.1%. Run time: 5.98s. Est. time left: 00:00:00:03\n", + "70.1%. Run time: 6.73s. Est. time left: 00:00:00:02\n", + "80.1%. Run time: 7.44s. Est. time left: 00:00:00:01\n", + "90.2%. Run time: 8.19s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 8.87s. Est. time left: 00:00:00:00\n", + "Total run time: 8.87s\n" ] } ], "source": [ "def generate_corr_results(N, max_depth):\n", " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - " bath_corr ,fitinfo= sd_env.approximate(\"corr_lsq\",tlist=t,Ni_max=N,Nr_max=N,maxfev=1e8,target_rsme=None)\n", + " bath_corr ,fitinfo= sd_env.approximate(\"cf\",tlist=t,Ni_max=N,Nr_max=N,maxfev=1e8,target_rsme=None)\n", " HEOM_corr_fit = HEOMSolver(\n", " Hsys,\n", " (bath_corr,Q),\n", @@ -1537,7 +1537,7 @@ "outputs": [ { "data": { - "image/png": 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", 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lqcAMBgNu3LiBjRs3AjD1ihWM1c3MzMSuXbsAmGZAKDg2ISEBOp2uzI+1Q0JCMHv27CJz837++efo3bs3unXrhnfffRe+vr64ffs2tm3bhiVLlkCv12PmzJnYsWMH+vbtixkzZsDR0RGrV6/Gzp078emnn5rdFFdRzz77LFavXo0nnngCb7zxBoKCgqBUKhETE4OwsDAMGzasxPtgBg0ahM8//xzPP/88Jk2ahMTERCxYsKDYmR/atm2LdevWYf369WjevDk0Gg3atm1b7Hnnz5+PAQMGoG/fvnjrrbegUqmwePFinD9/HmvXri3z09ua0LZtWxw4cADbt2+Hm5sb9Ho9WrduLb1eMJVqRWrz9/fHwYMH0b9/f/Tp0wf79u2r8nADBwcHTJs2DVOnTsWaNWukaeB8fHyqvLLaG2+8AVtbW0yaNAn37t3DV199VaWfReHvX0VcvHgRFy9eBGBadyIzM1P6NxsQEFBkBpYaI9YAANJj+fLl1X7+FStWmF3j77//LtdxH374oXRMs2bNKnTN1NRUEYCYmppamZIrZN/ZGDFo7mnRe/oV8ZmvT9b49YiobsrKyhIvXrwoZmVlWbuUard8+XIRgHjs2LEy2wYHB5v9n1D4ERYWJrWLiooqsV3Tpk3LvE5kZKQoCIL4008/FXnt4sWL4jPPPCM6OTmJKpVK9Pb2FseNGydmZ2dLbc6dOycOGTJEtLe3F1Uqldi+ffsi/0eGhYWJAMQNGzYUucbYsWNFnU5XbG15eXniggULxPbt24sajUa0tbUV/fz8xMmTJ4tXr141+14FBwebHfv999+LrVu3FtVqtdi8eXNx/vz54rJly0QAYlRUlNQuOjpaHDhwoKjX682+ZwXf14e/loMHD4r9+vUTdTqdqNVqxe7du4vbt283a1PSz7ng+1D451ecko4fO3ZskZ/p6dOnxV69eok2NjYiALPvQ2RkpAhA3LRpU6nXKzj3wz+HmJgY0c/PT/Tx8RGvXbtW5jlKq10UTf+2vb29xZYtW4r5+fnlOl9JAIivvvqq2b61a9eKCoVCHD9+vGgwGMp1nuDgYLFNmzZVqqXAzJkzS/y3OHPmzFKPLev3XkXymiCK5RjRXEGF/6pYvnx5ieu4V9bXX39ttmRlampqkRsFivPll1/izTffBGDqmi9tEvGcnByzSc7T0tLg5eVV7mtVxfo/ojDxQCMAgEduPC7NstBfRERUp2RnZyMqKgrNmjUrcsMW1YyCGRgevmmJ6rbp06dj5cqVuHbtWrEr81HtUdbvvbS0NNjb25crr9XO2/XKcO/ePbPt8v7yLzxZ+MPneNj8+fNhb28vPSy5jOLjHdyB+xNiJxkttzQnERGVbv78+di3bx+OHTtm7VKomqSkpOCbb77BvHnzGIAbmDoZggvPgwig3G/awu3y8vJKbTtt2jSkpqZKj5s3b1a80Eqys1FDk5sJAMhS2SIzJ9di1yYiopIFBgZi+fLlZjdnU90WFRWFadOm4fnnn6+W8xmNRuTn55f6qE0KFuso6WEwGKxdYo2pkyH44UUxsrOzy3Vc4XZlrQajVqthZ2dn9rCkRrL7PdUyGX47z1+2RES1xejRozFkyBBrl0HVpGPHjnj77ber7Wa92bNnQ6lUlvqIjo6ulmtVhxYtWpRaa2hoqLVLrDF1st+/8Ao0gGk6l5JWiyssMzOzxHPUNp42Obh1/4/F8MspGNLZ27oFERERUZkmTZqEwYMHl9qm8DR51rZ9+3aze6AeVto803VdnQzBD883Fx8fX2S1meIU/viqPO2tKdBVwPFY0/Oz8UbrFkNERETl4u7uXqtCbllKmuauIaiTwyEenpfu77//Ltdxhcf1Fl6zvDbq2fLB8IvYDKUVKyEiIiKqf+pkCG7ZsqXZTW6nT58u13GnTp2Snvv7+1d3WdWqZ6sHvd1phqKTlhMRERFR5dXJEKxSqcyWhjx06FCZx9y6dQuRkZHSdp8+fWqkturi7ayHLN80g0WWoC2jNRERERFVRJ0MwQAwbNgw6fm+fftw+/btUtuvXr1aet6oUaNaH4IBQJ1vWuM8V6lBXi2bUoWIiIioLquzIfi5556T1jbPy8vDp59+WmLbgvWxC7zwwgtQKmv/OFsd7k/pJpPh3I1k6xZDREREVI/UqhAcHR0NQRCkx6xZs0ps6+npicmTJ0vbCxcuxKZNm4q0y8vLw/jx46Wb57RaLd57771qr70mNFI+WCTjRDRDMBEREVF1qVIInjhxIjQaTZFHRdtU1qxZs9CyZUsAphVPRo4ciTFjxmDTpk0ICwvDt99+iy5dumDjxo3SMZ999lmdmbrE1ebBEIiLcVlWrISIiIiofqlSCM7Ly0NOTk6RR2H5+flltqksBwcH7NixA15eXgBMSxX++OOPGDFiBPr164cpU6bg7NmzUvupU6fi1VdfrZZrW4K3w4PVa6KTOFcwETUcK1asMPtkUKFQwNPTE+PHj0dsbKxZ2w8++ACDBw+Gh4cHBEHAuHHjij3nhQsX8Morr6BHjx7Q6XQQBAEHDhyoUF15eXnw8/PDxx9/XMmvzLpCQkIQEhJSqWMXL16MFStWFNlf8Cluca9ZwqJFi+Dr6wuVSgVBEJCSkoJx48bBx8fHrN28efOwdevWIsf/9ttvsLW1LfK+Ksm4ceOqZcGt4t7jbm5uePbZZ3H16tUqn7+AIAh47bXXqnyekJAQBAYGVvk88fHx+OCDD9CjRw84OzvDzs4OnTt3xnfffWfxJZpr1XCIymjVqhXOnj2Ll156CVpt8bMo+Pv74+eff8Ynn3xi4eqqprXrg6nRYtLlVqyEiMg6li9fjoiICPz666+YOHEi1q5di0ceeQQZGRlSmy+++AKJiYkYOnQoVCpViec6fvw4tm7dCkdHx0ovBbt48WIkJyfj9ddfr9TxdVlJIdjNzQ0REREYNGiQxWs6ffo0/vGPf6Bv377Yv38/IiIioNfrMX36dGzZssWsbUkhODQ0FEFBQVYbKlnwHt+3bx9ee+01bNu2Db1790Zycv0cBnnixAmsXLkSoaGhWLlyJTZt2oTg4GBMmTIFEydOtGgtVVoxbsWKFdX6l5+Pjw9EUazwcY0aNcL//vc/fPHFF9i/fz9u3ryJjIwMuLm5oW3btujYsWO11WhJj7V3xawzpufxOTrrFkNEZAWBgYHo0qULAKBv374wGAz46KOPsHXrVrzwwgsAgPT0dMhkpj6dVatWlXiuMWPGYOzYsQCAjRs3Yvv27RWqJT8/H5999hkmTJgAnc5yv5Pz8vKknsKHZWZmwsbGxmK1FEetVqN79+5WufaFCxcAmIZeBgUFSftbtGhRofO8+uqrGDVqFObMmSN9umwphd/jISEhMBgMmDlzJrZu3Yrx48dbtBZL6NWrF65du2Y2QcGAAQOQm5uLb775Bh9++KHFfgZ1vie4ML1ej2HDhuG1117DO++8gxdffLHOBmAACPB0hCLXNBb4nlLPadKIqMErCFs3btyQ9hUE4LKUt11Jtm3bhtjYWIwZM6bIa5cvX8Zzzz0HV1dXqNVqeHt748UXXzQb/nf+/HkMGzYMDg4O0Gg06NChA3744Qez8xw4cACCIGDVqlX497//DQ8PD6jVakRGRkofw587dw4DBw6EXq+XerRzc3MxZ84c+Pn5Qa1Wo3Hjxhg/fjzu3r1b5tf14Ycfolu3bnB0dISdnR06deqEZcuWmXVK+fj44MKFCwgPD5c+vi8YblDScIhDhw4hNDQUer0eNjY26NmzJ3bu3GnWpmBIQFhYGKZMmQJnZ2c4OTlh+PDhiIuLK7XukJAQjB49GgDQrVs3s6EwDw+HEAQBGRkZ+OGHH6T6Cw8LGTJkCGxtbbF06dIyv1/FOXz4MJydnTF48GCzTykqoyAQlzX1a2WJooj33nsPSqWy0l9vVTg4OBQ7Q1fBHzExMTEWq6VKPcFU8xzEdNyFFkaFEvvP38KjHTytXRIR1XIjX/8NicnVc+9FdXByUOOnRZUbfvCwgkWPGjduXC3nq4idO3fCxcUFAQEBZvvPnDmD3r17w9nZGbNnz0bLli0RHx+Pbdu2ITc3F2q1GleuXEHPnj3h4uKCr776Ck5OTvjxxx8xbtw43L59G1OnTjU757Rp09CjRw98++23kMlkcHFxAWAKu0OHDsXkyZPx7rvvIj8/H0ajEcOGDcPBgwcxdepU9OzZEzdu3MDMmTMREhKC48ePlzhcEDCF2MmTJ8Pb2xsAcOTIEbz++uuIjY3FjBkzAABbtmzBiBEjYG9vj8WLFwOANE1pccLDwzFgwAC0a9cOy5Ytg1qtxuLFizFkyBCsXbsWo0aNMmv/8ssvY9CgQVizZg1u3ryJt99+G6NHj8b+/ftLvMbixYuxdu1azJkzB8uXL4efn1+J74uIiAj069cPffv2xfTp0wEAdnZ20usqlUoK6bNnzy7xmsX56aef8OKLL2LChAlYtGgR5PKqDV+MiooCYBruWZjRaITRWPb9QYIglFhDTk4Oxo0bh507d2L79u147LHHqlSrwWAo1yf4MpmszD9C9+/fD4VCUeTrrkkMwbVcU10W7t7vAN5zLpEhmIjKlJicg9sJ9WNGGYPBgPz8fGRnZyM8PBxz5syBXq/H0KFDLV5LREQEOnXqVGT/v/71LygUChw9etQshBUM1wBMsxnl5uYiLCxM+qj3iSeeQEpKCj788ENMnjwZ9vb2UvsWLVpgw4YNRa6Vl5eHGTNmmH1Mvm7dOuzZswebNm3C8OHDpf3t27dH165dsWLFCkyZMqXEr2v58uXSc6PRiJCQEIiiiIULF2L69OkQBAEdO3aEVquFnZ1duYY+vPvuu3BwcMCBAwekm8gGDx6MDh064K233sLIkSMhCA9u/n7sscfM5vNPSkrC1KlTcevWLTRp0qTYawQEBEjDHgoPKShO9+7dIZPJ0Lhx4xLr79SpE+bPn4+MjIxyD3f55JNP8P7772PevHlF/pApr8Lv8cOHD2POnDno06dPkff4hAkTinxyUJzg4OBib/hMSkrCsGHDEBUVhYMHD6J9+/aVqrew0NBQhIeHl9lu7NixpQ6f3bt3L1atWoU33ngDTk5OVa6rvBiCa7nOXnIcN/1RiCM3Kz5emogaHieHknvorKEq9TwcWNq2bYv//ve/cHV1rWpZFRYXF4euXbua7cvMzER4eDheeumlUnun9+/fj9DQ0CJjHceNG4fdu3cjIiLCrFfu6aefLvFcD7+2Y8cONGrUCEOGDEF+oWFzHTp0QJMmTXDgwIFSQ/D+/fsxb948HDt2DGlpaWav3blzp8Lf64yMDPz555+YMmWK2SwKcrkcY8aMwTvvvIMrV67Az89Peu3hwNeuXTsApmEvJYXg6ubi4gKj0Yhbt26VOaZYFEVMnjwZP/zwA9asWYORI0dW+roPv8cLbuZ/eAz4rFmzyjXLg16vL7IvKioKPXr0gEajwZEjR+DpWT0dakuWLEF6enqZ7ZydnUt87eTJkxg5ciS6d++O+fPnV0td5cUQXMtN6OOFJdeNgCDgWrYDRFE0++uZiOhh1TX0oDZYuXIl/P39oVAo4OrqCjc3N6vVkpWVVWSe++TkZBgMhjJDRWJiYrG1F8xbn5iYaLa/pK/TxsbG7GN8wDR2NCUlpcSZMRISEkqs6+jRoxg4cCBCQkKwdOlSeHp6QqVSYevWrZg7dy6ysir+iUJycjJEUazQ1/tw71/BUIvKXL+yCn625blmbm4u1q9fjzZt2uDxxx+v0nUL3uPp6elYv349lixZgueeew67d+82a+ft7V2u8FpcRjh69CgSEhIwd+7cagvAAODr61vu4RDFOXXqFAYMGICWLVti165dpQ6xqQkMwbWcv0cj2OZG4Z66EbI1dth/Lgqh7ZpbuywiIovw9/cv9WNuS3J2dkZSUpLZPkdHR8jl8jJv5nFyckJ8fHyR/QU3fz3cU1ZSZ0dx+wtuJtuzZ0+xxxTXM1hg3bp1UCqV2LFjh1nAL24qsfJycHCATCar0NdbGxT8bMtTm1qtRlhYGB599FH0798fe/bsgYODQ6WuW/g9XjADyv/+9z9s3LgRI0aMkNpVZTjEqFGj0KRJE7z//vswGo344IMPKlXrw6oyHOLUqVPo378/mjZtir1795oNB7IUhuA6INA+A0eyGwEAvj2QwBBMRGQFfn5+uHbtmtk+rVaL4OBgbNiwAXPnzi0xQIWGhmLLli2Ii4szW7V05cqVsLGxqdIUY4MHD8a6detgMBjQrVu3Ch1bMPVa4RupsrKyip1qTq1Wl6uXVKfToVu3bti8eTMWLFgg3ZRXsKCVp6enRW9+KlBW/devX4eTk1O5h3907NgR4eHh6N+/P0JCQvDrr79KNzBWxaeffopNmzZhxowZGD58uNSLWpXhEIBpURm9Xo8333wTGRkZ1TL0oLLDIU6fPo3+/fvD09MTv/76a6X/gKgqhuA6YHx3Jxw5YHp+5E4ja5ZCRFTrhIeHS1OBGQwG3LhxAxs3bgRg6hUrGKubmZmJXbt2ATDNgFBwbEJCAnQ6XZkfa4eEhGD27NlF5ub9/PPP0bt3b3Tr1g3vvvsufH19cfv2bWzbtg1LliyBXq/HzJkzsWPHDvTt2xczZsyAo6MjVq9ejZ07d+LTTz+tUi/Ys88+i9WrV+OJJ57AG2+8gaCgICiVSsTExCAsLAzDhg3DU089VeyxgwYNwueff47nn38ekyZNQmJiIhYsWFDsx9Jt27bFunXrsH79ejRv3hwajQZt27Yt9rzz58/HgAED0LdvX7z11ltQqVRYvHgxzp8/j7Vr11plWF/btm1x4MABbN++HW5ubtDr9WjdurX0+pEjRxAcHFyh2vz9/XHw4EH0798fffr0wb59+6o83MDBwQHTpk3D1KlTsWbNGmkaOB8fnyKr4FXUG2+8AVtbW0yaNAn37t3DV199VaWfReHvX3lduXIF/fv3BwDMnTsXV69eNVshr0WLFpab/UWkcklNTRUBiKmpqRa/tsFgEJ1mxoj6uUmifk6CGHH5b4vXQES1T1ZWlnjx4kUxKyvL2qVUu+XLl4sAxGPHjpXZNjg4WARQ7CMsLExqFxUVVWK7pk2blnmdyMhIURAE8aeffiry2sWLF8VnnnlGdHJyElUqlejt7S2OGzdOzM7OltqcO3dOHDJkiGhvby+qVCqxffv24vLly83OExYWJgIQN2zYUOQaY8eOFXU6XbG15eXliQsWLBDbt28vajQa0dbWVvTz8xMnT54sXr161ex7FRwcbHbs999/L7Zu3VpUq9Vi8+bNxfnz54vLli0TAYhRUVFSu+joaHHgwIGiXq83+54VfF8f/loOHjwo9uvXT9TpdKJWqxW7d+8ubt++3axNST/ngu9D4Z9fcUo6fuzYsUV+pqdPnxZ79eol2tjYiADMvg+RkZEiAHHTpk2lXq/g3A//HGJiYkQ/Pz/Rx8dHvHbtWpnnKK12UTT92/b29hZbtmwp5ufnl+t8JQEgvvrqq2b71q5dKyoUCnH8+PGiwWAo13mCg4PFNm3aVKkWUXzwdZf0ePh99LCyfu9VJK8JoliJJdoaoLS0NNjb2yM1NbXITQmW0HfBOZzINf112d/mGjb/s3aMkSMi68nOzkZUVBSaNWtW5IYtqhkFMzA8fNMS1W3Tp0/HypUrce3atWJX5qPao6zfexXJa/Vqxbj67LU+D8bL/JnQyHqFEBE1YPPnz8e+fftw7Ngxa5dC1SQlJQXffPMN5s2bxwDcwDAE1xHDu3pAnWtaijFd64jfztwo4wgiIqpugYGBWL58OW7dumXtUqiaREVFYdq0aXj++eer5XxGoxH5+fmlPmqTgsU6SnoYDAZrl1hjGILrCEEQ0Mk+uWAD/9lb8ryPRERUc0aPHo0hQ4ZYuwyqJh07dsTbb79dbTfrzZ49G0qlstRHdHR0tVyrOrRo0aLUWkND68+84w9jv38d8vbAxhj+s+n58TRnLpxBRERUy0yaNAmDBw8utU3hafKsbfv27cjJySnx9dLmma7rGILrkP5tmkC/IQrpqkbI1tphVdh1vNiv9KUdiYiIyHLc3d1rVcgtS0nT3DUEHA5RxwS7pUrPFx++Z8VKiIiIiOouhuA6ZvpgL0A0AgD+ym+MjKw8K1dEREREVPcwBNcx/h5OcMk3rYyUr9biPzuuW7kiIiIiorqHIbgOGtIyU3q+/lz9nbqEiIiIqKYwBNdB0wY3h8xgGgYRo2iMmLuZZRxBRERERIUxBNdBLnaN4CPEAwBEhQJztnLhDCIiIqKKYAiuo17o9OCGuN3RnOmOiOqXFStWQBAE6aFQKODp6Ynx48cjNjbWrO0HH3yAwYMHw8PDA4IgYNy4ccWe88KFC3jllVfQo0cP6HQ6CIKAAwcOVKiuvLw8+Pn54eOPP67kV2ZdISEhCAkJqdSxixcvxooVK4rsj46OhiAIxb5mCYsWLYKvry9UKhUEQUBKSgrGjRsHHx8fs3bz5s3D1q1bixz/22+/wdbWtsj7qiTjxo2Dra1tlesu7j3u5uaGZ599FlevXq3y+QsIgoDXXnutyucJCQlBYGBgNVQEvPzyywgMDESjRo2g1WrRqlUrvP3220hIsOxCYAzBddQroa2hzMsCACTbOOLo5RTrFkREVAOWL1+OiIgI/Prrr5g4cSLWrl2LRx55BBkZGVKbL774AomJiRg6dChUKlWJ5zp+/Di2bt0KR0fHSq+CtXjxYiQnJ+P111+v1PF1WUkh2M3NDRERERg0aJDFazp9+jT+8Y9/oG/fvti/fz8iIiKg1+sxffp0bNmyxaxtSSE4NDQUQUFBeO+99yxUtbmC9/i+ffvw2muvYdu2bejduzeSk5OtUo8lZGRkYNKkSVizZg127tyJl19+Gd999x2Cg4ORm5trsTrYhVhH6dR6tLG5hNN5LQFBhvm747DFr5G1yyIiqlaBgYHo0qULAKBv374wGAz46KOPsHXrVrzwwgsAgPT0dMhkpj6dVatWlXiuMWPGYOzYsQCAjRs3Yvv27RWqJT8/H5999hkmTJgAnU5XmS+nUvLy8qSewodlZmbCxsbGYrUUR61Wo3v37la59oULFwAAEydORFBQkLS/RYuKLST16quvYtSoUZgzZw68vLyqtcayFH6Ph4SEwGAwYObMmdi6dSvGjx9v0VosZe3atWbb/fr1g16vxyuvvIJDhw6hX79+FqmDPcF12JTeD358fyTqYDCIVqyGiKjmFYStGzce3AtREIDLUt52Jdm2bRtiY2MxZsyYIq9dvnwZzz33HFxdXaFWq+Ht7Y0XX3zRbDna8+fPY9iwYXBwcIBGo0GHDh3www8/mJ3nwIEDEAQBq1atwr///W94eHhArVYjMjJS+hj+3LlzGDhwIPR6vdSjnZubizlz5sDPzw9qtRqNGzfG+PHjcffu3TK/rg8//BDdunWDo6Mj7Ozs0KlTJyxbtgyi+OD/FB8fH1y4cAHh4eHSx/cFww1KGg5x6NAhhIaGQq/Xw8bGBj179sTOnTvN2hQMCQgLC8OUKVPg7OwMJycnDB8+HHFxcaXWHRISgtGjRwMAunXrZjYU5uHhEIIgICMjAz/88INUf+FhIUOGDIGtrS2WLl1a5verOIcPH4azszMGDx5s9ilFZRQE4tu3b1fpPCURRRHvvfcelEplpb/emtC4cWMAKPaPvZrCnuA67KnOAfjXL7eQoXJAlo0dNh2+g5F9XK1dFhFRjYmMjATw4D9MS9q5cydcXFwQEBBgtv/MmTPo3bs3nJ2dMXv2bLRs2RLx8fHYtm0bcnNzoVarceXKFfTs2RMuLi746quv4OTkhB9//BHjxo3D7du3MXXqVLNzTps2DT169MC3334LmUwGFxcXAKawO3ToUEyePBnvvvsu8vPzYTQaMWzYMBw8eBBTp05Fz549cePGDcycORMhISE4fvw4tFptiV9XdHQ0Jk+eDG9vbwDAkSNH8PrrryM2NhYzZswAAGzZsgUjRoyAvb09Fi9eDMDUA1yS8PBwDBgwAO3atcOyZcugVquxePFiDBkyBGvXrsWoUaPM2r/88ssYNGgQ1qxZg5s3b+Ltt9/G6NGjsX///hKvsXjxYqxduxZz5szB8uXL4efnV+L7IiIiAv369UPfvn0xffp0AICdnZ30ukqlkkL67NmzS7xmcX766Se8+OKLmDBhAhYtWgS5XF6h4x8WFRUFAGjVqpXZfqPRCKPRWObxgiCUWENOTg7GjRuHnTt3Yvv27XjssceqVKvBYDD7Y6kkMpms2D9C8/PzkZOTg9OnT2P69Ono3bs3evXqVaWaKoIhuA7TqGwQ5BSDsHQHAMCig0kMwUSE4O/TcDuj7P8sLcVVJ0P4BLuyGxbDYDAgPz8f2dnZCA8Px5w5c6DX6zF06NBqrrJsERER6NSpU5H9//rXv6BQKHD06FGzEFYwXAMAZs2ahdzcXISFhUkftz/xxBNISUnBhx9+iMmTJ8Pe3l5q36JFC2zYsKHItfLy8jBjxgyzj8nXrVuHPXv2YNOmTRg+fLi0v3379ujatStWrFiBKVOmlPh1LV++XHpuNBoREhICURSxcOFCTJ8+HYIgoGPHjtBqtbCzsyvX0Id3330XDg4OOHDggHQT2eDBg9GhQwe89dZbGDlyJARBkNo/9thj+Oqrr6TtpKQkTJ06Fbdu3UKTJk2KvUZAQIA07KHwkILidO/eHTKZDI0bNy6x/k6dOmH+/PnIyMgo93CXTz75BO+//z7mzZtX5A+Z8ir8Hj98+DDmzJmDPn36FHmPT5gwocgnB8UJDg4u9obPpKQkDBs2DFFRUTh48CDat29fqXoLCw0NRXh4eJntxo4dW+STgiNHjqBHjx7S9hNPPIF169ZV+Y+IimAIruNeD7VB2BYREARcyLHHvcw82NoorV0WEVnR7Qwj4tJr0/CoygfyhwNL27Zt8d///heurpb/gz8uLg5du3Y125eZmYnw8HC89NJLpfZO79+/H6GhoUXGm44bNw67d+9GRESEWa/c008/XeK5Hn5tx44daNSoEYYMGYL8/Hxpf4cOHdCkSRMcOHCg1BC8f/9+zJs3D8eOHUNaWprZa3fu3Knw9zojIwN//vknpkyZYjaLglwux5gxY/DOO+/gypUr8PPzk157OPC1a9cOgGnYS0khuLq5uLjAaDTi1q1bZY4pFkURkydPxg8//IA1a9Zg5MiRlb7uw+9xf39//Pzzz0WGBcyaNatcszzo9foi+6KiotCjRw9oNBocOXIEnp6ela63sCVLliA9Pb3Mds7OzkX2tW3bFseOHUNmZiZOnz6Njz/+GAMGDMD+/fstNs6dIbiOe6RlGzjlXUeiyh35Gi0W/3ILU5+y7KB+IqpdXHUyVCV4VjdTPZWzcuVK+Pv7Q6FQwNXVFW5ubtVYWcVkZWVBo9GY7UtOTobBYCgzVCQmJhZbu7u7u/R6YSV9nTY2NmYf4wOmsaMpKSklzoxR2rRTR48excCBAxESEoKlS5fC09MTKpUKW7duxdy5c5GVlVXq11Wc5ORkiKJYoa/XycnJbLtgqEVlrl9ZBT/b8lwzNzcX69evR5s2bfD4449X6boF7/H09HSsX78eS5YswXPPPYfdu3ebtfP29i5XeC3cw17g6NGjSEhIwNy5c6stAAOAr69vuYdDPEyn00m993369EG3bt3QvXt3LFmyBG+++Wa11VgahuA6Tq3Uop93HDbcMv1iWXUqE1OfsnJRRGRVlR16UBv5+/uX+jG3JTk7OyMpKclsn6OjI+RyOWJiYko91snJCfHx8UX2F9z89XBPWXFBpqT9BTeT7dmzp9hjiusZLLBu3ToolUrs2LHDLOAXN5VYeTk4OEAmk1Xo660NCn625alNrVYjLCwMjz76KPr37489e/bAwcGhUtct/B4vmAHlf//7HzZu3IgRI0ZI7aoyHGLUqFFo0qQJ3n//fRiNRnzwwQeVqvVhVRkO8bAuXbpAJpPhr7/+qpbayoMhuB54ta8jNv2YD6NcgZtyB/x9KxPeTaw7ZQ4RUX3j5+eHa9eume3TarUIDg7Ghg0bMHfu3BIDVGhoKLZs2YK4uDipNxQw9QLa2NhUaYqxwYMHY926dTAYDOjWrVuFji2Yeq3wOMysrKxip5pTq9Xl6iXV6XTo1q0bNm/ejAULFkg35RmNRvz444/w9PQsctOXJZRV//Xr1+Hk5FTu4R8dO3ZEeHg4+vfvj5CQEPz666/SDYxV8emnn2LTpk2YMWMGhg8fLvWiVmU4BGBaVEav1+PNN99ERkYG5s+fX+VaqzIc4mHh4eEwGo3w9fWtcl3lxRBcDwR6BcID53ATLWBUKvHZzttY9FIza5dFRGQR4eHh0lRgBoMBN27cwMaNGwGYesUKxupmZmZi165dAEw35RQcm5CQAJ1OV+bH2iEhIZg9e3aRuXk///xz9O7dG926dcO7774LX19f3L59G9u2bcOSJUug1+sxc+ZM7NixA3379sWMGTPg6OiI1atXY+fOnfj000/NboqrqGeffRarV6/GE088gTfeeANBQUFQKpWIiYlBWFgYhg0bhqeeKv4jwkGDBuHzzz/H888/j0mTJiExMRELFiwoduaHtm3bYt26dVi/fj2aN28OjUaDtm3bFnve+fPnY8CAAejbty/eeustqFQqLF68GOfPn8fatWtL7OmuSW3btsWBAwewfft2uLm5Qa/Xo3Xr1tLrR44cQXBwcIVq8/f3x8GDB9G/f3/06dMH+/btq/JwAwcHB0ybNg1Tp07FmjVrpGngfHx8iqyCV1FvvPEGbG1tMWnSJNy7dw9fffVVlX4Whb9/5bVjxw4sXboUQ4cORdOmTZGXl4fjx4/jyy+/hK+vL15++eVK11NhIpVLamqqCEBMTU21dinFevenHaJ+bpKon5skNn3vqmg0Gq1dEhHVsKysLPHixYtiVlaWtUupdsuXLxcBiMeOHSuzbXBwsAig2EdYWJjULioqqsR2TZs2LfM6kZGRoiAI4k8//VTktYsXL4rPPPOM6OTkJKpUKtHb21scN26cmJ2dLbU5d+6cOGTIENHe3l5UqVRi+/btxeXLl5udJywsTAQgbtiwocg1xo4dK+p0umJry8vLExcsWCC2b99e1Gg0oq2trejn5ydOnjxZvHr1qtn3Kjg42OzY77//XmzdurWoVqvF5s2bi/PnzxeXLVsmAhCjoqKkdtHR0eLAgQNFvV5v9j0r+L4+/LUcPHhQ7Nevn6jT6UStVit2795d3L59u1mbkn7OBd+Hwj+/4pR0/NixY4v8TE+fPi326tVLtLGxEQGYfR8iIyNFAOKmTZtKvV7BuR/+OcTExIh+fn6ij4+PeO3atTLPUVrtomj6t+3t7S22bNlSzM/PL9f5SgJAfPXVV832rV27VlQoFOL48eNFg8FQrvMEBweLbdq0qVItoiiKly5dEkeMGCE2bdpU1Gg0okajEf38/MS3335bTExMLPP4sn7vVSSvCaJYjhHNhLS0NNjb2yM1NbXITQm1wZW4s+j5P2fkKbSA0YgdQwX0aedo7bKIqAZlZ2cjKioKzZo1K3LDFtWMghkYHr5pieq26dOnY+XKlbh27ZpFF2ugiivr915F8hpXjKsnWrgGoJXqqmlDJsOCvYmlH0BERBU2f/587Nu3D8eOHbN2KVRNUlJS8M0332DevHkMwA0MQ3A9oZAr8GJQqrQdkaxGdo7BihUREdU/gYGBWL58OW7dumXtUqiaREVFYdq0aXj++eer5XxGoxH5+fmlPmqTgsU6SnoYDPU3SzAE1yOD2/nBLs80H2SOTo/vf+UvaSKi6jZ69GgMGTLE2mVQNenYsSPefvvtartZb/bs2VAqlaU+oqOjq+Va1aFFixal1hoaGmrtEmsM+/3rEQ8nX3Rx2oH9aY8AAJYdy8Arg61cFBERUQMyadIkDB5c+n++hafJs7bt27cjJyenxNdLm2e6rmMIrkdkggwv9wTCdhkgyuS4JrPHjfgMNHUr3xroREREVDXu7u61KuSWpaRp7hoCDoeoZ7o27wh38QYAwKhUYcHO21auiIiIiKj2YQiuZ1zsPRHa7LK0ve26WK51vYmo7uK/cSJqKKrz9x1DcD0jCAKe6+oIZb5pachkm0b47USClasioppQMJ1TbbvbnIiopuTl5QGA2VLflcUQXA8FeHRGK/Ul04ZMhi9/S7FqPURUM+RyOeRyOdLS0qxdChFRjRNFEampqVCr1VAqlVU+H2+Mq4ccbF3wVGAULpzpBAD4M02NzKx82Gj54yaqTwRBgIuLC+Lj46FWq6HT6aptmiciotpCFEXk5eUhNTUV9+7dg4eHR7Wcl6monurv3wKLjicgVemMHJ0e3/0Sh38+6W3tsoiomtnb2yMrKwsJCQm4e/eutcshIqoxarUaHh4eZS6HXF4MwfVUK/dO6Oi4FwfS+wMAfjiZjX8+ad2aiKj6CYIANzc3uLi4SGPliIjqG7lcXi1DIApjCK6nbDX2eLpdMsIPmuYMjpLZ49rNdLTwqr+TXhM1ZAXjg4mIqHx4Y1w91qVZO3ggGgBgVKmwYMcd6xZEREREVEswBNdjLdzaoZfncWl75w3AYOB8okREREQMwfWYRmmDwW2UUN2fMzjFthH2/MkV5IiIiIgYguu5AI/OaK29aNqQybAwLNW6BRERERHVAgzB9Vwz1zYIbX5K2j6RZYPE5GwrVkRERERkfQzB9ZxCrkQfXy845JuGQeTZ2OLrnXFWroqIiIjIuhiCGwB/z67o2viEtL3mfB5EkTfIERERUcPFENwAuDs2Q2iLG5AZTRPp37JxxOGziVauioiIiMh6GIIbAEGQoX3T9miu+AsAICoU+PwXhmAiIiJquBiCGwh/j67o431E2j6crEZ6BpdYJSIiooaJIbiBcLB1QTcvGWwNKQCALL09lu2JtW5RRERERFbCENyABHh1RcdGJ6Xt709wqjQiIiJqmBiCG5BW7p3Q2/skBNEIAPhb5YBzfyVbuSoiIiIiy2MIbkC0Kh3auDeDpxAFADCqVPhs5x0rV0VERERkeQzBDUyAZ1f09nxwg9y+WwpkZedbsSIiIiIiy2MIbmB8XNqgnUsc1IYMAMA9Owes388V5IiIiKhhYQhuYBRyBfzc26Ot/qxphyDg24gM6xZFREREZGEMwQ2Qv2cQenv9CdxfOvkK7HHt7zQrV0VERERkOQzBDZCbgw+aOSjgCtM8wQatFl/suGXlqoiIiIgshyG4ARIEAf6eXdDd7ai0b3sUkJdntGJVRERERJbDENxA+Xl0RXvnC1AYcwAAyfaO+Pl33iBHREREDQNDcAPVSOcMLydv+GsvmHbI5FgYnmrdooiIiIgshCG4AQvwDEKw9x/S9gWDPa7zBjkiIiJqABiCG7BW7h3hrkuFsxgPAMjX6fDFdg6JICIiovqPIbgBUyu18HVrh55N/pT2/RwlICfXYMWqiIiIiGoeQ3ADF+DVDe0bn5dukEtp5IRN+2OtXBURERFRzWIIbuC8nFrCUadHG5vzph0yOb4+yHHBREREVL8xBDdwgiBDgGcQHvGKkPZdFhrhahRniiAiIqL6iyGY4O8ZBBdtAhqLppvi8m10+A9vkCMiIqJ6jCGY0EjnDA/HFujp/uAGuZ03ZMjKzrdiVUREREQ1hyGYAJhukGvndAFKYzYAINXBGRt5gxwRERHVUwzBBABo6dYBNkoZ2tgW3CAn4w1yREREVG8xBBMAQKVQo6V7R/T2OCLtuyp3wMWryVasioiIiKhmMASTJOD+DXIueHCD3Oc74q1cFREREVH1YwgmiYdjC9jbOKOn+4Pe4D0xcmRk5lmxKiIiIqLqxxBMEkEQEOAVhHaOD26QS3NwxtpfY6xcGREREVH1YggmM/6eXaGQGdFWf860QybD4kPpEEXRuoURERERVSOGYDJjp3WEt3NL9PZ8MCQiWuOE4+cTrVgVERERUfViCKYiAry6wVmdiCbCTQBAvtYG/9nOG+SIiIio/mAIpiJ8m7SDSqFFH88/pH0HEtW4m5hlxaqIiIiIqg9DMBWhkKvg59EZ/o2uQGPMAABkOjpj6Y6/rVwZERERUfVgCKZiBXr3gFwworPjCdMOQcAPp3OQl2+0bmFERERE1YAhmIrlYu8JF3tP9HQ7CkE0Bd/b9q7YezjOypURERERVR1DMJUo0LsH9Mp7aKaKBAAYVSp8sZezRBAREVHdVy0h+I8//sDkyZMREBAAe3t72NnZISAgAJMmTcLhw4er4xIlSk9Px8KFC/HYY4/B09MTGo0GTk5OaNeuHd58802cOnWqRq9fn7V27wyFTIlgzwc/wzP5doiMTrNiVURERERVJ4hVWAUhIyMD//jHP/D999+X2m78+PFYtGgRdDpdZS9VrHXr1uHVV19FUlJSiW0EQcCrr76KTz/9FFqtttLXSktLg729PVJTU2FnZ1fp89Q1v5xejYs3j+I/Z/+BNMERADBeF4uFbwRauTIiIiIicxXJa5XuCTYYDBg+fLhZANZqtejSpQu6d+9uduHly5dj+PDhMBgMlb1cEV988QWee+45swDs7u6O4OBgdO/eHTY2NgAAURTx9ddf45lnnqnW6zcUgd7dIQhAT9c/pX1bogTcy8izYlVEREREVVPpEDx9+nTs3btX2p44cSJiYmJw7NgxREREIC4uDtOnT5de37t3L2bMmFG1au8LDw/HW2+9JW17eXlhz549iI2NxYEDBxAREYHExETMnz8fcrkcALBz585qu35D4u7QHA46F3RqfBpy0RR8Ux1d8NNeTpdGREREdVelhkPExcWhRYsWyM7OBgCMGTMGK1euLLbt9OnTMWfOHACARqPBtWvX4O7uXoWSge7du+PPP009k/b29jh16hSaNWtWbNulS5di0qRJAAC1Wo2rV6/Cy8urwtdsqMMhAODEtf04eOlnbIgchnOZHQEALROjcXxBBwiCYOXqiIiIiExqfDjEl19+KQVgGxsbfPnllyW2nT59uhQ6s7OzsXDhwspcUnL+/HkpAAPAe++9V2IABkw91EFBQQCAnJwcLFiwoErXb4j8PbtCJsjwiMcRaV+0xhl/nr5rxaqIiIiIKq9SIXjLli3S85EjR8LR0bHEtiqVCuPHj5e2N2/eXJlLSsLCwsy2n3322TKPee6558yuX4V7ARskG7UezV3boon2NlxkpnmC83S2+HIH5wwmIiKiuqnCIfjKlSuIjIyUth977LEyj3n88cel55GRkbhy5UpFLyu5ceOG9NzOzg7e3t5lHtOuXTvpeUxMDE6ePFnp6zdUgd49AAC93SOkfeFJGsTfzbRWSURERESVVuEQfObMGbPtHj16lHlMp06doFKppO2zZ89W9LKS1NRU6blery/XMQ+PCTl9+nSlr99QeTduDb3WAYGNLkItZgEAMpwa438/R1u3MCIiIqJKqHAIvnTpkvRcpVKV6yazh9sVPkdFFQ6+6enp5TomLc18cYeLFy9W+voNlUyQoY1XNyhkBnRyuN+TLpPhh7N5yM7h1HNERERUt1Q4BEdHR0vPPT09yz07QOFhC4XPUVGenp7S87S0NPz9d9lTdZ07d85suzzXz8nJQVpamtmjoQvw6gZAQM8mf0IQjQCABEdX/Pwbp0sjIiKiuqXCIbhw76u9vX25jys8JKG8PbjF6d27t9n2+vXryzxm3bp1Ztvluf78+fNhb28vPSozrVp9Y6d1RNPGfrBXpaGF5ioAwKhS48t9SbzZkIiIiOqUCofge/fuSc81Gk25jyu8ZHHhc1RUly5d0KpVK2l7/vz5ZjfLPWz58uU4cuSI2b7yXH/atGlITU2VHjdv3qx0zfVJ2/s3yPXxPCztu6pywrGzCdYqiYiIiKjCKhyC8/PzpecKhaLcxxVum5dX+SV3ZTIZPvzwQ2k7OTkZffr0wa+//mrWLjs7G5999pm0UEZhhb+GkqjVatjZ2Zk9CGjmGgid2g5Nbf6Go2CaJzjXzh5f/Bxj5cqIiIiIyq/CIdjGxkZ6XrBgRnkUbqvT6Sp6WTPPPvss/u///k/a/vvvvzFw4EB4enqib9++6NmzJ5ydnTF16lTk5+ejefPmZlO5VWQYB5mTy+Ro490dggA84vaHtP9AkgYxtzKsWBkRERFR+VU4BNva2krPs7Kyyn1cZuaD+WQLn6OyFi9ejI8++shs6rXY2FgcOHAAERERyMgwBbIuXbpg7969MBgezGDQqFGjKl+/IQv07gEBAto5noNKNP1xk+Hsgu+2Rlm5MiIiIqLyqXAIdnZ2lp7Hx8eX+7hbt25Jz52cnCp62SIEQcAHH3yAyMhIfPDBB+jevTucnZ2hVCrh5uaG/v37Y/ny5fjjjz/QokULsxkhyrPABpXMTusIH5cAKGX56Ox4wrRTJsPq8/nIzCp7qAkRERGRtZV/UO99rVu3lp4nJiYiMzPTbIhESQrfWObn51fRy5bIy8sLH330ET766KMS26SmppqtctelS5dqu35D1a5pL0TduYCern/iSFIPiIIMic5NsPnXGxg9tIW1yyMiIiIqVYV7gv39/c22y7P6WmxsLO7evVviOWra77//bjaFV7du3Sx6/fqoqYs/9FoH03Rp2gfTpX21PxlGI6dLIyIiotqtwiE4KCgIarVa2j506FCZxxw8eFB6rtFoEBQUVNHLVsmaNWuk50FBQWjevLlFr18fyQQZAgumS/N4MF3adY0zIk7dsVZZREREROVSqRvjQkNDpe3Vq1eXeUzhNqGhoVWeHaIirly5gk2bNknbEydOtNi167tAr+4QBBma2vwNJ1kiANN0aV/+HGvlyoiIiIhKV+EQDADjxo2Tnp89exbbt28vse3Jkyexe/fuYo+taTk5OZg4caI0L7Gfnx9efPFFi12/vtNp7NHCNRCCAPRye/CJwMFULW7EVn5VQCIiIqKaVqkQPGLECLRv317anjx5Mi5fvlykXXx8PEaPHi1NT9ahQwc8/fTTxZ4zOjoagiBIj1mzZpVaw6pVq5Cbm1vi67dv38aTTz4pDcUQBAFLliwxm1KNqq5t014AgPYO56BGDgAgs7ELlmzhdGlERERUe1V4dgjAFCiXLl2K4OBgZGVlIT4+Ht26dcOUKVPQp08fKBQKHD16FF9//TVu374NwLRs8nfffQdBEKql8IkTJ+KNN97A0KFD0atXLzRr1gxKpRLx8fEICwvDTz/9hJSUFKn94sWL0adPn2q5Nj3g7dwK9jbOSM1MQGeH4/gjuRcgk2HtJQPevZcLO1v+0UFERES1jyAWnjahgjZv3ozRo0eXuWiGVqvFjz/+iOHDh5fYJjo6Gs2aNZO2Z86cWWpvsEajQU5OTpk16nQ6LFy4EC+99FKZbUuTlpYGe3t7pKamcgnlhxy/9hsOXdqG1Fw7fH7pnxAFGWS5OfjUPxWTRrUu+wRERERE1aAiea1SwyEKDB8+HCdOnED//v2L7eEVBAGhoaE4fvx4qQG4Mp566ik4OjqW+LpOp8MLL7yACxcuVDkAU+kCPLtBLpPDXpWG1rbXAJimS/vm9zTk5RutXB0RERFRUVXqCS7s5s2bOHz4MGJjTTMDeHh4oFevXvDy8qqO0xfLaDTizJkziIyMxO3bt5Geno7GjRvDy8sLffr0gVarrbZrsSe4dLtP/oArcSfxd4Yn/nftZQCA8t49/NBfxOB+XKGPiIiIal5F8lq1heD6jiG4dDGJkdgYsQgAsOTKa4jNMS2v3TXhKvb9p2u1jQUnIiIiKonFhkMQFfBwbAFHW1cAQE+XMGn/OcEBx88lWKssIiIiomIxBFO1EAQB7X0eAQAENLoEO7npZslsJ2cs2nzDmqURERERFcEQTNXGz6MrlHI15IIRPVz+kPbvS1QjOoaLZxAREVHtwRBM1Uat1CDAMwgA0MnxGJQwLZKS4eKG7zZft2ZpRERERGYYgqlatfPpDQDQyrMR1PgCAECUy7HuUj5S0sqe15mIiIjIEhiCqVo56ZvAy6klAKCrYxgA0+QjyS4eWLOTSykTERFR7cAQTNWu4AY5R3Uy2jWKAwAY1Gr89/dU5OYarFkaEREREQCGYKoBzV0DYatpBADo4rRX2h/XqAl2HYixUlVEREREDzAEU7WTyeRo17QXAKCpzQ14aU0zQ+Tp7bBwVzy4PgsRERFZG0Mw1Yg23t0hE+QQBKCXa7i0/6LMERGn7lixMiIiIiKGYKohOrUdWrl3AAC0sj0Fe0UuACDLyRlfbYq2XmFEREREYAimGtTu/g1yCpkBIR5nTDsFAb+n2uDi1WQrVkZEREQNHUMw1Ri3Rj5wsfcEAATofoNKMAIAMlzdsHhDpDVLIyIiogaOIZhqjCAIaN/U1BusVWQj1DMagGnxjG03ZIi5lWHF6oiIiKghYwimGtXaoxM0ShsAQBv9DsjuL56R5uaJ/228as3SiIiIqAFjCKYapZCr0MarOwDATpGE3u6JAACjSoUfz+QgKYVLKRMREZHlMQRTjWvn0xsCBABAF4c90v6kJp5Yve2atcoiIiKiBowhmGqcvY0TmrsGAgAaySPRwdk0Fjhfa4MlB1OQmZ1vzfKIiIioAWIIJovo0CxYeh7c5JD0/JaTOzbvibZCRURERNSQMQSTRXg6+cJZ7w4AcBQi0FyfBwDItbPH17tvId9gtGZ5RERE1MAwBJNFCIKAjs2D7z8HHm92Tnrtuq0rfvk9xlqlERERUQPEEEwW09q9M7QqWwBAY+yCs8bU+5vt5IyvtvwNURStWR4RERE1IAzBZDEKuRJtm/YEAMiQjyd9o6XXzsIBf5y8Y6XKiIiIqKFhCCaLate0N2SCHADgrdgGG7mp9zfDpQm+2cjp0oiIiMgyGILJomw19mjl3sG0YUzBU61Mi2dAJsPvyTY4/1ey1WojIiKihoMhmCyu8HRpbWx3QSGYeoPT3T2wZAOXUiYiIqKaxxBMFtekUVO4OfgAAPJzrmOQbxYAQFQo8HO0gOiYdCtWR0RERA0BQzBZRcdmIdLzXs7hEGDqDU7z8MK36/+yTlFERETUYDAEk1W0aNIOtppGAIDsjKN4tJlpujSjSo31F/MQdzvDitURERFRfccQTFYhl8nR3qf3/S0Rj3qfkF5L8fDGsg3sDSYiIqKawxBMVhPo3RMKmRIAkJ32Gx7xNO03aLRYeSILCUnZVqyOiIiI6jOGYLIarUoHf8+uAIA8Qw6Gt3zQ+5vk7oUVmzhTBBEREdUMhmCyqo7NQ6Tnxnu70NFFAADk6Wyx7I8UpKbnWqkyIiIiqs8YgsmqHG1d0dw1EACQkZOC0W3ipNfuunrhx62R1iqNiIiI6jGGYLK6Ts37Ss81uTvQspHpea6dPZb8dhcZmXnWKYyIiIjqLYZgsjoPxxZwtfcCACSmx+LljmnSa7ecPbB+53VrlUZERET1FEMwWZ0gCOjUvJ+07SL8Ag9b0/NsRyf8d1ccsnMMVqqOiIiI6iOGYKoVWrq1h17rAAC4mXAB/9flwRCIvxu5Y8sv0VaqjIiIiOojhmCqFWQyOTo2C5a2W2oOwFFjep7l3BjfbPsbeflGK1VHRERE9Q1DMNUabbx6QKUwJd9rt45gSlfTdGkQBFyzccWO/X9bsToiIiKqTxiCqdZQKzVo690TAGAwGtDZ4U/YKkUAQIarK77eFAWDQbRmiURERFRPMARTrdKhWR/IBNPbMjI2HK8EqUwvCDJcUjXG3kMxVqyOiIiI6guGYKpV9FoHtHTrCADIzstAiNtZ2MhNvb/3XN3w5bprMBrZG0xERERVwxBMtU7nFg8Wz4iM3Y8pQWrThkyGczIn7D0Ua6XKiIiIqL5gCKZax8XeC55OLQEAKRl3MahZNDQFvcFubli4LpK9wURERFQlDMFUK3UutJTytdhfManL/fnSZHKchQP2HWZvMBEREVUeQzDVSj4u/nDSNwEAxCdH4ZnWt6CWFfQGe+DLtVfZG0xERESVxhBMtZIgyNC5Rai0HR33K17qbOoNFuVynBEd8NsfcdYqj4iIiOo4hmCqtVq7d4Ze0wgAEHXnIsa0SYGyoDfY3QNfrvmLvcFERERUKQzBVGvJZXJ0KjQ2OOb2foztYJopQpQrcMbYiL3BREREVCkMwVSrBXr3gEZpAwC4HHcCEzvkQCGYen/T3T3xFXuDiYiIqBIYgqlWUyrUaO/TBwAgikbcTgjDC+3v9wYrFDidb4f9EewNJiIioophCKZar0OzR6CQKQEA528ewStdjJDd7w1O8/DCojVX2BtMREREFcIQTLWeVmWLQO8eAIB8Qy5Skg/hucD7vcFKJU7m2iHsCHuDiYiIqPwYgqlO6NS8LwTB9HY9E/073ugmgwz3xwZ7mnqDRZG9wURERFQ+DMFUJ9jZOMLPvTMAIDsvE1n3/sSINioAgFGpwokcO4QdibdmiURERFSHMARTndG5RT/p+cmoA3irp6pQb7A3vlrN3mAiIiIqH4ZgqjOc7dzRzCUAAJCelQwx5wyebVvQG6zEiVw77DvMscFERERUNoZgqlO6+PaXnh+/tg9v91I/mCnC0wtf/HgZBgN7g4mIiKh0DMFUp3g4toCbQzMAQGL6LSD3Ml5oVzBvsBJnRQfsOnDTmiUSERFRHcAQTHVO10K9wUev7sXbPdUPVpHz8MLCNX8hL99orfKIiIioDmAIpjqnmUsbONt5AABup/4NIT8SYzs8WEXustIJP/96w5olEhERUS3HEEx1jiAI6OY7UNo+enUv/t1LC6WsoDfYE4vWX0VursFaJRIREVEtxxBMdZKvWzs42LoCAGKTrgF51zG+owYAIMoViNS6YMPuKGuWSERERLUYQzDVSYIgQ5DvAGn7aOSv+HdPDdRy0/Y9d098s/EaMrPzrVQhERER1WYMwVRntXbvBDsbJwDA33cvQzDcxMud748NlstxQ++GdduvWbNEIiIiqqUYgqnOksnk6Nqi8EwRv+LN7hpo7vcGp7u749st0biXkWelComIiKi2YgimOs3fMwi2GnsAwPXb5yAz3sLkrqbeYMjkiHFwx8otV61YIREREdVGDMFUpynkCnRu3k/aPhr5K97opoFWYdq+5+aOZTv+RkpajpUqJCIiotqIIZjqvMCmPaFV2QIA/oo7BZl4F68GmWaKgEyGuMaeWL7xLytWSERERLUNQzDVeUq5Cp2a972/JeJ45D681k0NW6VpT0YTNyz/JQ4JSdlWq5GIiIhqF4ZgqhfaNe0NtVILALgcexxyMRn/7HG/N1iQ4Y5bU3y37rIVKyQiIqLahCGY6gW1UoMOPsEAAKNoxPFrv+GVIA2cTLkYmS6uWBV+B3/H3bNilURERFRbMARTvdGxWR8o5aaZIS78fQSiIQXv9NZKryd6NcfXKy9aqzwiIiKqRRiCqd7QqHTo0KwPAMAoGnAs8leM76iGp14AAGQ7OmHzqXRcikyxYpVERERUGzAEU73SqXlfqTf4/N9HkJuXgveDH/QGp/i0wBfLz1urPCIiIqolGIKpXtEW0xv8bKAKrZ1Mb/Vce3vsi8rHn6fvWLNMIiIisjKGYKp3Hu4NzsxJwcyQB73Bqc1a4PPvz0MURWuVSERERFbGEEz1TnG9wYNaKdHFXQ4AyNPZ4miKCr8eirVmmURERGRFDMFULz3cG3wv27w3OKVpc3yx4iLy8o3WKpGIiIisiCGY6qXieoODfZQI8VEAAAxaLS4a7bDll2grVklERETWwhBM9dbDvcHpWcnmY4O9ffD1mivIzM63VolERERkJQzBVG8V1xvc2V2BYa2Vpn1qNaJsGuPHrZHWLJOIiIisgCGY6rXieoM/CNZCZlo/A2le3li66RpS0nKsWCURERFZGkMw1WvF9Qa3dpbjhXYqAICoUCKusSeWrr9izTKJiIjIwhiCqd57uDc4LSsJ7z2ihdo0YxrS3T3xwy+xiL2VYcUqiYiIyJIYgqnee7g3+OjVvfCwk+G1bhpTA5kMCZ4++JLLKRMRETUYDMHUIHRu3hcqhSn0Xrj5J1Iy7uKf3TVwvD9ZRKZLE2w9kYqzl5OsWCURERFZCkMwNQgalQ6dmvcFAIiiEUf+2g17jYB3exdaQKO5Lz797iyXUyYiImoAqiUE//HHH5g8eTICAgJgb28POzs7BAQEYNKkSTh8+HB1XKJE2dnZWLVqFZ555hn4+vrCzs4OKpUKzs7O6NKlC1599VVERETUaA1UN3RsFgKNUgcAuBx7EglpcZjQSY3mDqZ/BjmNHBBxC9h3OM6KVRIREZElCGIVur0yMjLwj3/8A99//32p7caPH49FixZBp9NV9lLF2rdvHyZMmICbN2+W2XbQoEFYtmwZXF1dK3WttLQ02NvbIzU1FXZ2dpU6B1nfiWv7cfDSzwAA3ybtMLjLS9h6KRcvbjHdFKfMuIeucRew/buBUCr5QQkREVFdUpG8Vun/5Q0GA4YPH24WgLVaLbp06YLu3bubXXj58uUYPnw4DAZDZS9XxI4dO/D444+bBWA7Ozt0794d/fr1Q0BAAGSyB1/ezp07ERwcjISEhGqrgeqedj69YaM2vTcjb53F7ZSbGOanRBd301QReTpbXBbtsG7HNWuWSURERDWs0iF4+vTp2Lt3r7Q9ceJExMTE4NixY4iIiEBcXBymT58uvb53717MmDGjatXel5KSggkTJiA/37TcrV6vx/fff4+EhARERETgt99+w4ULF3Djxg08//zz0nFXrlzBW2+9VS01UN2klKvQreVAafuPKzshCALmhtpI+1KbNsc3a68gNT3XGiUSERGRBVQqBMfFxeGLL76QtseMGYPvvvsOjo6O0j6dTofZs2fjgw8+kPZ9/vnniIur+njLdevW4e7du9L22rVrMX78eCiVSrN2np6eWL16NZ588klp35o1a5CSklLlGqjuCvTuAb3W9F69cfcSYpOuoYeXAoNbmd4/BrUasY3csGTNJWuWSURERDWoUiH4yy+/RHZ2NgDAxsYGX375ZYltp0+fDi8vLwCmm9gWLlxYmUuaOXjwoPQ8MDAQgwYNKrX9+++/Lz3Py8vDsWPHqlwD1V1ymQLdWz0qbf9xeRdEUcSHfbWQFyyn7OmNlXv+xt9x96xUJREREdWkSoXgLVu2SM9Hjhxp1gP8MJVKhfHjx0vbmzdvrswlzRTuBQ4MDCyz/cNtCh9PDZO/R1c46FwAALFJkfg74QpaOskxrqNpZTlRoUCShw++/J4LaBAREdVHFQ7BV65cQWRkpLT92GOPlXnM448/Lj2PjIzElStXKnpZM7a2ttLz3Nyyx23m5OSYbTs4OFTp+lT3yWRydG/14H35xxVTb/C03hrY3B9Vc8/NHTtPJOPkBd5MSUREVN9UOASfOXPGbLtHjx5lHtOpUyeoVCpp++zZsxW9rJmgoCDpeUREhHSDXEnCw8Ol50ql0ux4arhauXeAs94NAHA75Qau3z4PF1sZ/tn9/nLKggwpzVpgwdJzXECDiIionqlwCL506cHNQiqVShrvW5qH2xU+R2WMHTsWNjamu/nj4+Mxd+7cEtumpKRg2rRp0va4cePg5ORUpetT/SAIMvRo/WA8ecSVnTCKRrzeTYMmtqbBwVnOjXH0lojd4THWKpOIiIhqQIVDcHR0tPTc09MTgiCU6zhvb+9iz1EZbm5u+P7776XZIGbNmoVnn30Wv//+O9LT05Gfn4+YmBisWLECnTt3xsWLFwEAISEhWLBgQZWuTfVLc9dAuDZqCgBISI/HldgT0KkEzAx5sJxycnNf/GfZOWRll/6JAxEREdUdFQ7B6enp0nN7e/tyH1d48YzC56isUaNGYe/evfD39wcArF+/HsHBwbCzs4NSqYSXlxfGjx+P69evw8nJCe+++y5++eWXcq/2lpOTg7S0NLMH1T+CIKC33xBp+48ru5BvyMdzbVXo0OT+Ahq2elyTO2D5xr+sVSYRERFVswqH4Hv3HkwZpdFoyn2cVvugZ63wOaoiJCQEu3btwuDBg0tso1QqMWHCBEyZMsVsXHJZ5s+fD3t7e+lRnmEfVDd5ObdE08Z+AID0rCScu3EIMkHAx/0fvGdTfJpj6eZriL+Taa0yiYiIqBpVOAQXvglNoVCU+7jCbfPy8ip62SKysrLw2muvoWXLltixYwcA05zFQUFB6NevH9q2bQu5XI68vDx89tln8PX1xbx588p9/mnTpiE1NVV6FF6emeqfXoV6g49G7kVOXjZ6eivxlJ9pyI1RpcIdVy98vuyctUokIiKialThEFxwQxoAacGM8ijcVqfTVfSyZnJzczFo0CB88803yM/Ph729PZYtW4bk5GT8+eef+O2333D27Fncvn0bb7/9NgRBQF5eHt5//32zhTNKo1arYWdnZ/ag+svF3hOt3TsDALJyM3Dy+n4AwIf9tFCbRkUg3cML2/5MxInznDKNiIiorqtwCC48R29WVla5j8vMfPAxcuFzVMacOXMQFhYGwDTMIiwsDBMmTCgy3MHJyQmffvopvvrqK2nf/PnzcfTo0Spdn+qnnq2fgEwwJd4T18OQkZ0Gn0ZyvNbt/rAfmWnKtI//ewYGA6dMIyIiqssqHIKdnZ2l5/Hx8eU+7tatW9LzqkxR9vDSy5MmTULHjh1LPea1115D+/btAQCiKGLRokWVvj7VX/Y6Z7Rr2gsAkG/IxZ9XfwEA/KuHBi66+1OmNXbBqQRg66/R1iqTiIiIqkGFQ3Dr1q2l54mJiWY9vKUpPKbWz8+vopeVHD161GymhqFDh5bruCFDHoz5/P333yt9farfgloOhFJuWjr5/N9/ICXjLvTqh6dMa4kvV1xAekbVx7YTERGRdVQ4BBdMSVbg9OnTZR4TGxuLu3fvlniOioiNjTXbLu+sDYXbFe6VJirMRq1H5xZ9AQBG0Yg/ruwEADzfVoV2rvenTNPrcVPtiCVrqrboCxEREVlPhUNwUFAQ1Gq1tH3o0KEyjzl48KD0XKPRVGnZ4sLXBso/Lrlwj3Xh6dqIHtapeV9oVaZx63/FncLtlJuQywTMLzxlWrPmWLk9CtExVZ/zmoiIiCyvUjfGhYaGSturV68u85jCbUJDQ6s0O4Sbm5vZ9okTJ8p1XOF2Hh4elb4+1X8qhQbdWj4qbR++vA0A8EhTJYa2LpgyTY0kd298tvSsVWokIiKiqqlwCAaAcePGSc/Pnj2L7du3l9j25MmT2L17d7HHVkbnzp3NQvTixYthNBpLPebmzZvYtGmTtB0cHFylGqj+a9u0J+xtTDdw/p3wF27cvQwAmN1PC9X9KdPSPL3w25kUHDrO4TVERER1TaVC8IgRI6TZFgBg8uTJuHz5cpF28fHxGD16NAwGAwCgQ4cOePrpp4s9Z3R0NARBkB6zZs0qtp1KpcILL7wgbR8/fhz/93//V+ICHHFxcRg2bJjZsIkJEyaU+TVSwyaXKdCj9SBp++DFn2EUjWjuIMcrXe8PyZHJkdy8JT5ZchZ5eaX/IUZERES1S6VCsCAIWLp0qTS2Nj4+Ht26dcO7776LXbt2Ye/evZgzZw46duyIS5dMNw9ptVp89913EAShykXPmjULrq6u0vbSpUvRpk0bzJs3D3v27MGhQ4ewZcsWvPnmmwgICMCpU6ekti+//DK6dOlS5Rqo/mvt3hEu9qYbKhPS43Ap5hgA4K1eWjSxvT9lmnNjXLynxKqtV61WJxEREVWcIIpipWf937x5M0aPHl3mzWlarRY//vgjhg8fXmKb6OhoNGvWTNqeOXNmib3BgGlWiiFDhiAmJqbc9Y4cORI//vgjlEpluY8pkJaWBnt7e6SmpnL1uAbkZsJVbDryNQBAp7bHuL7vQ6lQY/35HEzcZrrZUpGZiWYXjmPndwPQpLFNaacjIiKiGlSRvFapnuACw4cPx4kTJ9C/f/9ie3gFQUBoaCiOHz9eagCujA4dOuDcuXN466234OjoWGrbzp07Y926dVi/fn2lAjA1XF7OLdHcNRAAkJGTipPXDwAARrZRoYenAgCQb2ODu07uWPC/c9Yqk4iIiCqoSj3Bhd28eROHDx+W5vH18PBAr169yj2Pb1Xk5+fjzJkzOHv2LBITE5GTkwM7Ozt4eHiga9eu1VIDe4IbrqR7t7Eq/GOIohFKuQrj+k6HTmOHs7fz0ef7dBhFQDDkw+3YEaz4sBu6d3SxdslEREQNUkXyWrWF4PqOIbhh239uA87eMM2JHejdE/3bjQIA/GtPJv53MgcAYHPnFrpm3MTmxf2hVFbpQxYiIiKqBIsNhyBqKLq3ehwqhWlWiAt/RyAhLQ4A8EGwBo5a01CgTJcmuJQm501yREREdQBDMFE52Kht0dV3AABAhIhDl0wLaDhqZZgZ8mAlueQWrbB49SXcuptZ7HmIiIiodmAIJiqnjs2Codc0AgBE370kLaDxYnsVOjYxraCRZ2uLu45u+Gwpb5IjIiKqzRiCicpJIVehp99gabtgAQ25TMBnjz6YGi2laTPsiriNiJO3rVEmERERlQNDMFEF+Hl0fmgBjaMAgCAPBV5opwIAiEolUnxaYN5/z3AlOSIiolqKIZioAgRBhkf8h0nbf1zehdx80+wQH4ZoYXd/ReWMJm64nALeJEdERFRLMQQTVdDDC2gcv7YPAOBiK8O0R+7fJCcISPZtzZvkiIiIaimGYKJKeMR/GGSC6Wa4E9fCkJqZCACY1FmNgMamf1a5ejvcdWiCj789Y7U6iYiIqHgMwUSV4GDrgg7N+gAADMY8aco0pVzAF4/ppHYpzVrgl6MJOPBnvFXqJCIiouIxBBNVUreWj0KrsgUAXI0/jZjESABADy8FRhfcJKdQILmFL+Z+cxqZ2flWq5WIiIjMMQQTVZJaqUXP1oOk7fALm2EUTbNBfNRPCwfNg5XkonLVWLzqolXqJCIioqIYgomqoI13dzjbeQAA7qbF4uLNPwEATjYyfNTvwUpySb6tsHLrNVy+nmKNMomIiOghDMFEVSATZAhpM1za/uPyDuTkZQEARrdXoZun6ea5fBsdkj28MfurUzAYRKvUSkRERA8wBBNVkaeTL1q6dQAAZObew9GrewEAMkHAF4/ZQG4aFYFU76Y4EZ2FDbuvW6lSIiIiKsAQTFQNevsPhVymAACcigpH8r07AIBAFwVeCbq/goZMjmTfVvji+/O4m5hlrVKJiIgIDMFE1cLexgmdm/cDABhFAw5e+ll6bdojWnjoTd3B2Y5OuGvjgI+XnLVKnURERGTCEExUTbr49odObQ8AuH77PG7cvQwAsFUJ+HSgjdQuuUUr7D4cj4PHblmlTiIiImIIJqo2KoUavf2HSNsHLmyGwWiaG3hwKyUe9VUCAAxqNVKbNsOcr08hi3MHExERWQVDMFE18vPojCaNfAAAyfdu43TU7wAAQRDw2UAttKZhw0j38EJUhgzfrrlspUqJiIgaNoZgomokCDL0DRwBwDQG+Mhfe3AvOxUA4NNIjqm9NQUNkdjSHys2XeXcwURERFbAEExUzVwbeaFt054AgDxDDg5e3Cq99no3Ddo0Ns0dnKfXI9nNEzO+OIF8g9EapRIRETVYDMFENaBX60HQKHUAgCtxJxGTGAkAUMkFfPWEzf1+YiC1aTOcvZmNlZuvWqlSIiKihokhmKgGaFQ69PIbLG2Hnd8Ig9EAAOjqocDkLqa5g0W5HEkt/fD1qov4O+6eVWolIiJqiBiCiWpIG+/ucLX3AgAkpsfjTPRB6bXpwVp42Zn++WU7OCLJwQUzvzwJUeSSykRERJbAEExUQ2SCDH0Dn5G2j/y1GxnZaQAAvVrA5489mDs4pXlLHLmUgk17oi1dJhERUYPEEExUg5o4NEUbr+4AgNz8bBy6vE167VFfJZ4OMM0dbFQqkdyiJRYsPYvbCVxSmYiIqKYxBBPVsF5+Q6BWagEAl2KOIS7puvTapwNs4KA13SaX6dIEd9V2mPP1KQ6LICIiqmEMwUQ1zEZti56tB0nb+89vhPH+TXKNdTLMC9VKryW19MNvR+/gl4OxFq+TiIioIWEIJrKAtk17obGdBwAgIS0WZ24ckl57vq0KIT6mpeQMGg1SfZpj3jenkZKWY5VaiYiIGgKGYCILkAky9G374Ca5iCs7cS8rBYBpSeUvH7eBRlpS2RPxRhU+/e6sFSolIiJqGBiCiSzE3aEZAr17AABy83MQfnGL9FpzBznee+T+sAhBQFIrf/z8200cOn7LGqUSERHVewzBRBbU228ItCpbAMDV+NOIunNReu21bmq0d72/pLLOFqnePpj55UmkZ+RZpVYiIqL6jCGYyII0Kh36BDwpbYed34g8Qy4AQCETsHiwDRT3/1WmeTfFzWw5Pl3CYRFERETVjSGYyML8PLrA08kXAJCWmYijV/dKr7V1VeDtXhrThiBDYit/bP71Bn4/Gm+NUomIiOothmAiCxMEAf3ajoRMMA19OHFtPxLTH4z9/XdPDQJd7g+LsNUj1dsHsxaeRGp6rlXqJSIiqo8YgomswNHWFV18QwEARtGA/ed+khbIUMkF/HewDeSmNTSQ5tUUMdkKfLLkjLXKJSIiqncYgomsJMh3AOxtnAEAsUnXcCnmmPRa+yYK/Lvn/WERMhkSW5tmiwiLiLNGqURERPUOQzCRlSjkKvQNHCFtH7y0FVm5GdL21N4atGn8YFhEmldTfPjVKS6iQUREVA0YgomsyMfFH63cOwIAsnIzcOjSz9JrKrlptoiCYRGp3j6Iy5Fj/n85LIKIiKiqGIKJrCw44CmoFKahDxdu/ombCVel1zq6KfBmD/NhETsOxODXQ7HWKJWIiKjeYAgmsjKdxh69/IZI27+dXYd8w4OZIN7prYGfs+mfap7eDmme3vho0SkkpXBYBBERUWUxBBPVAu2a9oS7Y3MAQEpmAo78tUd6Ta0Q8N/BOsgKhkU0bYZbeQrM/eaUNUolIiKqFxiCiWoBQZChf7tnIZfdnzv4ehjupMZIr3d2V+CN7mrThkyGRL8A7DkUh11hN61RLhERUZ3HEExUSzjauiKo5aMAAFE0Yt/ZtTAaDdLr0x7RIqDx/WERtnqkNm2GOd+cQvzdTKvUS0REVJcxBBPVIl1ahMJJ7wYAuJMag5NRB6TXNAoB3w3VQXn/X22aV1PcFbT44D/HYTSKVqiWiIio7mIIJqpF5DIF+rd7FoBpAHDEld1Iybgrvd7OVYFpj9yfLUIQkOgXgIiziVj9c6QVqiUiIqq7GIKJahk3Bx90aNYHAGAw5uG3s+ulJZUB4J89NOjqYRo7nK+1QUpzX3zx/XlERqdZpV4iIqK6iCGYqBbq2XoQ9FpHAMDNxKu4GPOn9JpCJmDJEB1slKbte+6eSLVthHc/O4q8PKM1yiUiIqpzGIKJaiGVQo3QtiOl7d8vbkVG9oOeXl9HOT7qp5W2k1r54+KNDHy96qJF6yQiIqqrGIKJaikfF3/4eXQBAOTkZeHAhU1mr7/cSY2+zRQAAINajSTfVvh+wxWcOJ9g8VqJiIjqGoZgolosuM1T0Kp0AICr8acReeus9JogCFg8SIdGGtNNdJkuTXDP2QXvfXYM9zLyrFIvERFRXcEQTFSLaVW2CA4YLm3vP/cTsnIzpG0POxkWPGojbSf7tsaN5Hx8/O0Zi9ZJRERU1zAEE9VyrT06o7lrIAAgMycdB85vNHv9mQAlnvIz3SVnVCqR1MofW369gV8PxVq8ViIiorqCIZiolhMEAaFtR0KtNPX4Xok7icj4M2avf/6YDVx1pmER2Y5OSHf3xKyFJ3GLq8kREREViyGYqA7QaewR0qbQsIjzG8yGRTjZyPDfITppO6W5L+4alXj302MwGLiaHBER0cMYgonqCD+PLqUOi+jfXIlXuqpNGzIZEv3a4OiFJPxv/WVLl0pERFTrMQQT1RFlDYsAgFl9tQh0Ma0ml6ezRUozXyz+8RJOX0y0eL1ERES1GUMwUR2i09ijb5unpW3TbBH3pG2NQsD3w3TQmKYPxj0PT9xr5Ih3PjmKdE6bRkREJGEIJqpjTLNFtAUAZObeQ9h580U0/BrLMTf0wWpyia388XdSPj786iREkeODiYiIAIZgojqnYFiE5v6wiL+KGRbxcic1Hm95f9o0lQqJrf2xOzwGW3+9YfF6iYiIaiOGYKI6SKexQ0hgycMiBEHAN08UnTZt3uLTiI5Jt3i9REREtQ1DMFEd1dq9M1oUGhax/5z5bBHOOhm+fWjatFS5BlM/PorcXINFayUiIqptGIKJ6ihBENCv0LCIq/GncDn2hFmb0OZKvBpkPm3a+etpWPjDBUuXS0REVKswBBPVYTqNHfq1HSlth53fgPSsZLM2s0K0aFt42rQWLfHDpqv4/Wi8RWslIiKqTRiCieq4Vu4d4efRGQCQk5eFvWfWQBSN0utqhYDvn9RBWzBtmpsHMhq7YNpnxxHPZZWJiKiBYggmqgdCAkfAVtMIAHAz4S+cjvrd7PXWznL851EbaTuppR8S8uR4e/5R5OUbQURE1NAwBBPVAxqlDR7t8IK0fejydiSmmw93eKGdCqMCVQAAUaFAgn8bnLqUhEUcH0xERA0QQzBRPeHl3Aodm4UAAAzGfOw5tQoGY770uiAI+PxRG/g6mv7Z5+ntkNzcF99v+AsH/uT4YCIialgYgonqkV5+g+Fo2wQAcDctFkf+2m32ul4tYMVTOqhN98nhnocXMp2c8f6C44i/w/HBRETUcDAEE9UjCrkSj3UcA5lgSrnHI39DbNI1szbtXBWY3//B+ODEVv5IzJXhrfl/cnwwERE1GAzBRPWMi70nerR+HAAgQsQvp1cjNz/brM1LnVR40s+0rLKoVCLBrw1OX07GwuXnLV4vERGRNTAEE9VDnVuEwt2hGQAgLTMR4Re2mL0uCAK+esIGPo1MvwJy7e2R2rQ5Vmy6igNH4ixeLxERkaUxBBPVQzJBhkc7jIZSblot7sLNI4iMP2PWppFGhuVP6qC8/1sgzbspshwc8R7HBxMRUQPAEExUT9nrnBHSZri0ve/sOqRnpZi16eyuwIf9tNJ2ol8AkvJk+NfcI8jNNViqVCIiIotjCCaqxwK8usG3SXsAQHZeJvacWgWjaH7z26td1Xi8pWl8sFGpQoJ/IM7+lYJPvjtr8XqJiIgshSGYqB4TBAH92z0L/f3V5GKTInE8cl+RNt8OLjQ+2M4eyc19sX7Hdfz86w1Ll0xERGQRDMFE9ZxGZYPHOr4IAQIAIOKv3YhLjjJr46CVYeVw8/mDMxq7Yvaik7h8LcXCFRMREdU8hmCiBsDDqQWCWg4EAIiiEXtOrkROXpZZmw5NFFjw6IP5g5Na+eGeQoN/zjmC1PRci9ZLRERU0xiCiRqIbi0ffTBtWlYSfjv3E0RRNGvzYnsVXminAgCIcjnuBrTF33eyMe2zYzAaxSLnJCIiqqsYgokaCJlMjsc6vgiVwjQbxF9xJ3Ex5qhZG0EQ8J9HbRDoYhoXkW+jQ1JLP4QfvYXv1l62eM1EREQ1hSGYqAGxs3FE/3ajpO0D5zci+d4dszY2SgGrhutgZ5piGJkurrjn7olvfryIw8dvWbJcIiKiGsMQTNTAtHLviDZe3QEAeYZc7D71A/IN+WZtWjjK8e1gnbSd3NwX2bZ2mPrJMcTeyrBovURERDWBIZioAQppMxwOOhcAwJ3UGPxxZXuRNoNbq/BG9/vdwTIZEgICkZQt4s25R5Cdw4U0iIiobmMIJmqAlAo1Hu80FnKZaezvyesHcP32+SLtZoZo0ctLAQAwqDVI8A/EhchUfPjVySI31REREdUlDMFEDZSLvSd6+w+Ttn85vRppmUlmbRQyASue0sHN1jTHcE4jByQ398X23/7Gqi2RFq2XiIioOjEEEzVgHXz6oEWTdgCAnLxM7Dq5Agaj+fhgV1sZVj1tC1WhhTTuuTTBgv+dRcTJ25YumYiIqFowBBM1YIIgYED752Bn4wQAuJVyA4cuFR0fHOShwH/MFtJojSwbPd6afxQ34+9ZrF4iIqLqwhBM1MBplDYY1GmcND74VNQBRN46W6Td2A5qTOhoWkgDMjkS2rRFUraIf3wYgcys/CLtiYiIajOGYCKCayNvPOL/pLT965k1SM1MLNLu04E26OZpCssFN8r9dSMd7//nOFeUIyKiOoUhmIgAAO19HkFLtw4AgJy8LOw6UXR8sEouYNVw2yI3yv16KJYryhERUZ1SLSH4jz/+wOTJkxEQEAB7e3vY2dkhICAAkyZNwuHDh6vjEmZ8fHwgCEKlHytWrKj2mojqOkEQ0L/ds7C3cQYA3E79Gwcv/lykXZMSbpT7etVFhEXEWbJkIiKiSqtSCM7IyMBLL72EXr164bvvvsOlS5eQlpaG9PR0XLp0CUuXLkXv3r0xYcIEZGTUnlWmnJycrF0CUa2kVmoxqPN4yGWmuYFPR/+Oq/Gni7Qr7ka5HFs93v3sGK7dSLNUuURERJUmiJWc8d5gMOCJJ57A3r17pX1arRZt2rSBQqHAxYsXkZb24D/DgQMHYteuXZDL5VUueuzYsbh9u/xTMx05cgSpqakAAEdHR8THx0OlUlXommlpabC3t0dqairs7OwqdCxRXXP2xiHsP7cBAKBSaPD8I2+jkc65SLs3d2dg2alcAIA8JxtNTh2Hj5MSaxf2RSM7tUVrJiIiqkheq3QIfu+99zB//nxpe+LEifj444/h6OgIwNRL/Mknn+Cjjz4yO2bu3LmVuVylpaamokmTJsjOzgYAvPLKK/jmm28qfB6GYGpIRFHE7lMr8VfcSQCAs50HRvX6J5Ry8z8ecw0iBq9Ox5EY0zLKqrRUuJw9haA2jlg67xEolbztgIiILKciea1S/0PFxcXhiy++kLbHjBmD7777TgrAAKDT6TB79mx88MEH0r7PP/8ccXGWHTP4008/SQEYAMaNG2fR6xPVRabxwaPgoHMBACSkxeK3s+uLLJWskgv4cbgtvOxMv0py7eyR1MoPx84lYPaiU1xamYiIaq1KheAvv/xSCpY2Njb48ssvS2w7ffp0eHl5AQCys7OxcOHCylyy0n744Qfpub+/P7p27WrR6xPVVSqFBoO7vCT1/l6OPY4z0QeLtHOxlWHdMzrolKbtTJcmSPNqii17o7Fi01VLlkxERFRulQrBW7ZskZ6PHDnSrAf4YSqVCuPHj5e2N2/eXJlLVkpkZKTZ7BRjx4612LWJ6gMnfRMMaP+8tP37xS2IS7pepF1bVwWWDtVJ26nNWiDTyRmfLzvHGSOIiKhWqnAIvnLlCiIjI6Xtxx57rMxjHn/8cel5ZGQkrly5UtHLVsrKlSul5zKZDGPGjLHIdYnqk1buHdGpeV8AgFE0YueJ5cjILjoDxODWKswM0UjbiX5tkGNji6mfHMXl6ymWKpeIiKhcKhyCz5w5Y7bdo0ePMo/p1KmT2WwMZ88WXZK1uomiiFWrVknbAwYMgLu7e41fl6g+6u03BJ5OvgCAjJw07Dq5AgajoUi7f/XQYGQb0791US7H3TbtcM8gx2sz/0BCUnaR9kRERNZS4RB86dIl6blKpZLG+5bm4XaFz1FTwsPDER0dLW3zhjiiypPJ5Hii0zjYauwBALFJ13DoUtGFNARBwNeDbNDF/f7SyhoN7rZpi/iEHPxjdgSyc4oGZyIiImuocAguHCw9PT0hCEK5jvP29i72HDWl8A1x9vb2ePLJJ2v8mkT1mY1aj0GdJ0AmmALuqahwXI49UaSdRiFg7QhbeOhNvxty7eyR2Ko1zlxOwowvTnDGCCIiqhUqHILT09Ol5/b29uU+rvBcbYXPURMyMjKwceNGaXvkyJHQaDSlHFFUTk4O0tLSzB5EDZ2bgw+C2wyXtvedXYeEtKI3vrnayrDuGVvYFMwY4eqGNK+m2HXgJr5eedFS5RIREZWowiH43r170vOKBEutVlvsOWrC5s2bza5RmaEQ8+fPh729vfQoz7APooagXdNe8PcMAgDkG3Kx4/gyZOdmFmnXvokC3w0xnzEio7Erlqy9jM2/RFuqXCIiomJVOATn5+dLzxUKRbmPK9w2Ly+vopetkMJDIXx9fdGzZ88Kn2PatGlITU2VHjdv3qzOEonqLEEQENr2GTS28wAApGQmYNepH2As5ka5oX4qzAp58AdwYmt/ZNvZ48OFJ3H4RPmXPiciIqpuFQ7BNjY20vPCK7GVpXBbnU5XSsuquXnzJsLCwqTtys4NrFarYWdnZ/YgIhOFXIXBXV6CVmX6t/z33cs4dHlbsW3f7KHG2A73Z4eRyZDQph2yVRr8a+4RTp1GRERWU+EQbGtrKz3Pysoq93GZmQ8+Li18juq2atUqGI1GAKYeqxdffLHGrkXUkNnbON2/Uc70a+Tk9QO4GHO0SDtBEPD5ozbo18z0aZBRqcTdtu2Rlivg1Rl/4NbdokMpiIiIalqFQ7Czs7P0PD4+vtzH3bp1S3ru5ORU0cuWW+EFMvr27Ws2KwURVS9PJ1+EBD4tbf92dj3ik6OLtFPKBfzwlC0CGpt+5eRrbZDQpi1uJebglRl/4F5GzQ6RIiIieliFQ3Dr1q2l54mJiWY9vKUpPKbWz8+vopctlyNHjpitRsdlkolqXrumvdG2aS8AgMGYjx3Hl+FedmqRdvYaARtG6uGqM02dlmPfCImt/XElKhVvzj2CvHyjResmIqKGrcIh2N/f32z79OnTZR4TGxuLu3fvlniO6lL4hjhbW1s8/fTTpbQmouoS0mY4PBxbADCtKLf9+DLkG4r27nrZy/DTyEJTp7m4ItWnOSJO3sHsr05xDmEiIrKYCofgoKAgqNVqafvQoUNlHnPw4EHpuUajQVBQUEUvW6acnBysX79e2h4xYkSN3oBHRA/IZQoM6jwBeq0jAOB2yg38dnZ9saG2o5sCy5/UQXZ/nZ00bx/ca+KGLXuj8e2ay5Ysm4iIGrBK3RgXGhoqba9evbrMYwq3CQ0NrZFwum3bNiQnJ0vbHApBZFk2alsM7foyFHLTTBCXYo/h5PWwYts+3lKFj/s/mDotqWVrZDk44ZtVF7Fxd5RF6iUiooatwiEYMF984uzZs9i+fXuJbU+ePIndu3cXe2x1KjwUwsfHB8HBwTVyHSIqWWM7Dzza/gVp+9ClbYi6U/wKcf/XVYMpXe9/qiTIkBAQiBy9HrMXncRvf8RaolwiImrAKhWCR4wYgfbt20vbkydPxuXLRT/GjI+Px+jRo2EwmCbR79ChQ4njdKOjoyEIgvSYNWtWueu5ffs2fvnlF2n7xRdfhCAI5T6eiKpPS/cO6NbyUQCACBG7T64odmllAJjfX4un/EwDhEW5HHfbtEeOWou35x/FifMJFquZiIgankqFYEEQsHTpUmkp5Pj4eHTr1g3vvvsudu3ahb1792LOnDno2LEjLl26BMC0bPJ3331XI+F09erVZivZcW5gIuvq3uox+DYx/aGcm5+Dn499h4zstCLtZIKAJUN16O19fw5hlQp3AjsgCwq8PusP/BVVdJYJIiKi6lCpEAwAXbt2xY8//igF4bS0NHzyyScYNGgQHn30UUyfPh23b5uWRdVqtfjxxx/RtWvX6qn6IYWHQjzyyCNo0aJFjVyHiMpHEGR4tONouNp7AQDSs5Kx/fj/kG/ILdJWoxCwZoQObRrLAQAGrRZ3AtsjJcuI//vgEOJuZ1i0diIiahgqHYIBYPjw4Thx4gT69+9fbA+vIAgIDQ3F8ePHMXz48KpcqkSnT5/G2bNnpW3eEEdUOyjlKgztOhG2mkYAgFspN/DL6dUQxaLzATfSyLDpWVt42Zl+JeXp9UgIaIvbSTmY/P4hJKfmWLJ0IiJqAASxmibmvHnzJg4fPozYWNMNLR4eHujVqxe8vLyq4/RWl5aWBnt7e6SmpsLOzs7a5RDVGXfTYvHT4YXIM5iCbJDvQPT0G1Rs2ysJBgxYmY6UbNOvJZs7t+B0+SLatXbAsk/6wEajsFjdRERU91Qkr1VbCK7vGIKJKi/q9gVsO7YUIky/bga2fwEBXsXPF37kZj6Grk1H9v1h/vqbN+AQdQ2PdHXFVzN7Qqmo0gdYRERUj1Ukr/F/EyKqcc1c26BPm6ek7X1n1yEmMbLYtt29zBfTSPdqijRPbxw8dhvvfXYMBgP/bicioqpjCCYii+jg0wftmvYGABhFA3YcX4bke3eKbTuolQqfP2ojbac098W9Jm7YHR6DOV9zeWUiIqo6hmAisghBEBDSZjiaNvYHAGTnZeLnY98hK/dese0ndFLj/T4aaTuppR8ynRtjw+4ofL7sPIMwERFVCUMwEVmMTCbHE53GwUnvBgBIybiLbceWIq+YqdMAYGovDV4NKlhVTkCCXxtkOThi+ca/sHT9FUuVTURE9RBDMBFZlFqpwbCuk6BTm25YiE+Oxp6TK2EsZuo0QRAwN1SLF9qpTDtkMiQEtEWO3g5frbiANduKH1dMRERUFoZgIrI4OxtHDAuaDKXc1Mt77fY5hJ/fXOwQB5kgYNETNhjS6sHyyncC2yNXp8O8xWfw874bFq2diIjqB4ZgIrIKF3tPDO4yATLB9GvozI2DOHFtf7FtFTIBy57UIcTHNE+wqFTiTmAH5Gm0mPH5Cew7HGuxuomIqH5gCCYiq2na2A/92z8nbR+6vA2XY48X21ajELD6aVt0djctr2xUq3GnbQfkKJR4++Oj+OPkbYvUTERE9QNDMBFZVYBnEHq2frCC3N7Ta3Az4a9i2+rVAjaOtIW/s+lXl0Grxd22HZANOf7xYQSOnb1rkZqJiKjuYwgmIqvr6jsAbb17AjDNIbz9+DIkpMUV29bJRoatz+nR1N706ytPZ4s7bTsg0yDglRmHcfJCgsXqJiKiuoshmIisThAE9A0cgeaugQCA3PxsbD36LdKzkott76aXYdvztnCzNS0rl2erx53ADsjIA6ZMP4xzV5IsVjsREdVNDMFEVCvIZHI83vFFuDZqCgC4l52KLX/+F1m5GcW2b+Ygx44X9HDRmYJwrp0d7ga2R3q2iEnvHcLFq8UHaCIiIoAhmIhqEaVCjWFdJ6KRjTMAIOnebWw9ugS5+TnFtm/pJMe25/Rw1JqCcI59I9wNbIfULAMmvncIV66nWqx2IiKqWxiCiahWsVHr8VT3V6TFNG6n3MCO48uQb8gvtn2Aixxbn7NFI839INzIAQkBbZFyLw8vT/sdkdFpFqudiIjqDoZgIqp17G2c8FS3KVArtQCAvxOu4JfTq4pdVQ4AOjRRYNMoW+jvLyyX7eiEBP9AJKXl4aVpvyPqZrqlSiciojqCIZiIaiVnO3cM6zoZCplppbir8acRdm5DsavKAUBXDwU2jLSFjak5spwbI9EvAAnJuXjp3d9xI5ZBmIiIHmAIJqJay92xGQYVWlXu3N9/IOKvXSW27+mtxPpnbKExLSyHzMauSPTzx+3EHIyfyh5hIiJ6gCGYiGq1Zi4BGNjhBWn76NW9OHn9QIntg32UWP20LZT3f7tlujRBol+AFISv/c0xwkRExBBMRHWAn0cXhLR5Wtr+/eIWXIo5VmL7AS2U+PFpHVSmFZaR6eKKRL8A3E02BWHeLEdERAzBRFQndGjWB91aPiZt7z2zBpHxZ0ps/3hLFX582rZIEE5MycX4d8I5fRoRUQPHEExEdUb3Vo+hfdNHAACiaMSukz8g6s7FEts/5msaGmEWhP0DTLNGvPs7Ll9LsUDVRERUGzEEE1GdIQgCQgKHw9+zKwDAKBqw4/j3uJlwtcRjHn04CDd2RYJfGySn52HCO79zZTkiogaKIZiI6hRBkGFAu+fQ0q0DAMBgzMO2Y98hLimqxGMe9VVizQhbqO8H4azGLkjwa4PUjHy8PO0gzv/FIExE1NAwBBNRnSOTyfFYxzFo5tIGAJBnyMXWo9/idsrNEo8Z2KLkIDxx2kGcvZxkidKJiKiWYAgmojpJLlNgUOfx8HZuBQDIzc/Glj8XIyEtrsRjBrRQYu0z5kH4bkBbpGYZ8PK0gzh65q4lSiciolqAIZiI6iyFXIkhXV6Gu2NzAEB2XiY2/7kYyffulHhM/+ZKrCu0oEa2kzPutmmHezkipkw/hN+PxluidCIisjKGYCKq05QKNYZ1nQxXe28AQGZOOjYd+QapmYklHhPaXInNo2xhqzJt5zg44k7b9sgyCPjHhxHY83uMJUonIiIrYggmojpPrdTgqW7/B2e9OwDgXnYKNkZ8XWoQ7t1Uia3P6dFIIwAAcu0b4U67jsgR5Jj68Z/Y/Eu0JUonIiIrYQgmonpBo9JhePdX4GDrCgBIz0oqMwgHeSiw/XlbOGnvB2G9He6074Q8uQozvjiBVVtKnnqNiIjqNoZgIqo3bNR6jOj+qlkQ3lRGEG7fRIHdo/VoYmsKwnk6W9xu3xH5ajU+WXIW366+BFEULVI/ERFZDkMwEdUrOo29KQjrXAAAafeDcFpmyVOg+TWWY/doPbzsTL8S8210uN2+E/I1Gny96iL+879zDMJERPUMQzAR1Ts6jT1G9HjNLAhvjFhUahBu4SjHnjF6NHcw/Vo0aLS43b4z8rQ2WLHpKmYvOgWDgUGYiKi+YAgmonqpMkHYy16GPWP08HO+H4TVatzu0Bk5ejts2BWFt+f/idxcg0XqJyKimsUQTET1VrFB+EjpQyOa2Mqwe7QeHZqYVtQwKpW4064DshwcsfdQLP5v+mGkZ+RZpH4iIqo5DMFEVK8VCcKZidgYsajUm+WcbGTY8YIefZqaVtQQ5QrcbdMOGY1dcPTMXYyfGo6EpGyL1E9ERDWDIZiI6j2dxh5PP9QjvOGPr0pdWc5OLWDjKFsMba007ZDJkOjXBuluHrh8LRWj/xWGG7HpliifiIhqAEMwETUItgVB+P70afeyU7Ah4iskpMWVeIxGIeCHp3QY1+H+0nKCgOSWrZHStBlu3srEmH+F4/xfyZYon4iIqhlDMBE1GLYaezzT43VpZbnMnHRsjPgad1JLXiZZLhOw8HEbvNVTI+1La9oMyb6tkJiagwnv/I4/Tt6u8dqJiKh6MQQTUYNio9ZjRI/X4GrvBQDIzsvAxoivEZ8cXeIxgiBgRogW8/trpX333D2R6NcGGdkGvDLjMHYduFnTpRMRUTViCCaiBse0xPKrcHdoBgDIzc/C5iOLEZMYWepxrwZpsGSIDeSmxeWQ6eKKu4HtkSvKMPXjo1i5mcssExHVFQzBRNQgqZVaPNltCrycWgIA8gw52Prnt7hx93Kpxz3XVo21z9hCa5o4AtkOjqbV5VQqfPrdWcz/72kuqkFEVAcwBBNRg6VSqDEsaBJ8GvsDAPKNedh27Dv8f3t3Hh5ldegP/Dv7kslM9n1fCAl7IGGTRUFAVLRIcUWl7rXXttprS28t9v7sora2VWtrr1pti7VqFXdElB1kCUJYQiCQkH2yTDLJzGT29/fHhEkmZJkJDCHM9/M88zjvO+ecOePjO/P15LznnGo8PGi9JTkyfHhbOCJVniFhhyYc+inTYFeHYd0Hp/CD/7cbFqsz6P0nIqLhYwgmopAmlchx3bR7kZ0wEQDgcrvwcclrOFa7d9B601Ok2HRnODIizu4up0TT5EJYdRHY/HUDvvP4NrS0cS1hIqJLFUMwEYU8qUSKpYV3Iy9pKgBAENzYeHAdDpzeMmi93GgJNt0ZjimJ3bvLSWVomjAZ5rh4HDnRhtt/sBmnqjuC3X0iIhoGhmAiIgASsQRLptyBSelzvOe2HXsfu8o/gSAMPMc3TiPGp7eHY0mO76YaxtR01OotWPXoFuw91Bzs7hMRUYAYgomIuolEYswffxOm5y7xntt7ciM2H3kXguAesF6YXIQ3V4Thnily7zljZjbacsbAaHLi/v/Zjo++rA5q34mIKDAMwUREvYhEIszMuwbzxi33nis9swOfffMPuNwD3+wmFYvw3BI1npzvu5Zw87gJsLvFWPPsPvxlXdmgo8pERHTxMAQTEfVjSuY8LJm8CiKR52vyRP0BfLjvFTictgHriEQiPDpLif9bpoas+9vVGh0D/eRCOOUKvPiPY/jJM/tgs7suxkcgIqJBMAQTEQ1gbMo0XD/tHkjEnvm+Z5rL8N6el9BlNw9a7+bxCrx3iwY6he8SajZNOD7ZXONZOcLAlSOIiEYSQzAR0SCy4sfjW9MfhFyqBAA0tFXh7Z1/gNHSOmi9eRkyfOGzhJoCTZMLYYmJxaHjBtzy/a9QVtEe7O4TEdEAGIKJiIaQEp2DFTP/C2qFFgDQZm7Cv3f+AU3G2kHrjY2VYPPd4ZiV6tleThBL0FIwAca0DDQ0d+HOx7bgix11we4+ERH1gyGYiMgPcboU3Dz7B4gMiwMAWGwdeHf380NusxytFuODWzW4bUKvlSMystCaVwCLXcAPn/oaL//rOG+YIyK6yBiCiYj8pFNHY+XsHyAxMgMAYHfa8MHel1FWu2/QegqpCH++To1fXNmzcoQlPgH6iZPhksnwwhtH8eOn98Fq4w1zREQXC0MwEVEAVPIw3DTjYWTHTwAAuAU3Pj/4T+yr+GLQ0VyRSIQfzlRi3U1hUHfvq2HXRaBxyjTY1WH4dEsNVj++Fc2tXRfjYxARhTyGYCKiAEklclw77TuYmH6F99zO4x9j85F34R5kUw0AuD5Pjg2rwpGo8awc4VKqoJ8yFZboGBwub8PNj3yFQ2WD33RHRETnjyGYiGgYxCIxrhy/ArPGXuc9V3pmBz7e/yrsg6wlDACTE6TYvFqLKQkSAIAgkaJl3ES0p2dC32rFXf+9Fe9+VhnU/hMRhTqGYCKiYRKJRCjOuRqLJt8OcfemGqf1R/DOrj+is6t90LpJ4WJ8tiocKwpk3nMd6ZloKZgAuyDGk388gF88fwB2bqxBRBQUDMFEROepIKUYNxY/CLnUc+Nbc0cd3trxHJqMNYPWU8tEePWGMPy/q1QQe2ZHoCsmFo2Tp8GhVOGdTyux+sfbOE+YiCgIGIKJiC6AtNg83Dz7B9CqogAAZpsRb+96HqcaDw9aTyQS4fszlHj3Zg0ilJ4k7AwLg76wCF2RUThUZsDK//oK3xzjPGEioguJIZiI6AKJDk/ALVc86l1Czemy46P9r+LA6c1DrgO8MEuGzXeHY2yM52vZLZWiefwkdKSkoclgxerHt+KdT08H+yMQEYUMhmAiogtIrQjHTTO+hzFJhd1nBGw7th5fHX4bLvfg83uzoyT48i4trhvTPU9YJEJ7Vg5ax46D3S3CL57/Bk/+sQQ2zhMmIjpvDMFERBeYVCLDNVPuxPTcxd5zh6t34YO9L8PqsAxaN1whwj9vCsOaOUrvOUtcPPRTpsGhUuPdz6qw6tEtqG00B63/REShgCGYiCgIRCIRZuYtxeLJd0Ai9iyFVt1Sjrd3/gHt5uZB64pFIqyZo8K/VoQhvHu3ZUeYBvop02CJicWxinas/N6X2PJ1fbA/BhHRZYshmIgoiPJTirB8xsNQysIAAAaTHv/a8TucaT4+ZN1rx8ixZbXWZ55wS8EEtGXmwGh24ntP7sbvXzsMp2vwDTqIiOhcDMFEREGWHJWNW654FFGaeACAzdGF9Xv+4tcNc7nREnx1t9ZnPeHO1DQ0TZwCl1yOV98+gXt/sh0tBmtQPwMR0eWGIZiI6CKICIvBzbMfRVb8eACA0H3D3MZD6+B0OQatq5F71hP+7SIVZN3f2jZdBBoLi2DVRWD/4RasePhL7CsdfJoFERH1YAgmIrpIFDIlrp92D4pzF3nPldXuwzu7nodpiB3mRCIR7p+mxGerwpEc7llP2CVXoGniFHSkpKK5zYp7frINr75dDrd78NFlIiJiCCYiuqhEIjFm5V2LpYV3Qyrx3PWmN1bjXzt+h4a2qiHrFydLsf07WlyZIT3bINqzctFSMAFOsRS/f+0IHl67C4Z2WxA/BRHR6McQTEQ0AsYkTcHKWd9HuHeHuQ68u/t5HK35esi6MWFivHeLBj+a1bOMWldMLBoKi2AL12L7vkbc9N1N2HuoKWj9JyIa7UTCUHdlEACgo6MDOp0ORqMRWq12pLtDRJcJi82ET0r+hjpDhffcpIw5mFtwIyRi6ZD1Pztpx4MfW9DW1f1VLgiIqDyF8NpqiEXAA7eOxYO350Mq4ZgHEV3+AslrDMF+YggmomBxuV3YdvR9HDqz3XsuMTIDSwtXI1wVMWT92g43vrPehK9re3aSUxpaEV1+DBKHA4XjovH0T4qRGKsORveJiC4ZgeQ1Dg0QEY0wiViCKyeswMKJt3g31mhoq8Kb259FTcuJIeunaMX49I5w/GiWEqLuc9aoaDQWFsOqi8CBo6246aFN+Go3N9cgIjqLI8F+4kgwEV0M+vZqfFzyN3R2GQAAIogwa+x1mJa9ACKRaIjawFeVDtz3gRnNlp7pEbozldBWV0EE4LZl2Xjs3glQyCVB/BRERCOD0yGCgCGYiC6WLrsZG775u8+uctnxE7Bo8u1QyFRD1teb3Lj3AzO2nnF6zyna2xB9/CikdjvysnR4+vFi5GTwu4yILi8MwUHAEExEF5NbcGPPic+x5+TnADxf0xFhsbhu6ncQo00asr7LLeB3u6z41XYrzi4bLHY6EHXiONQtzVDIxXj0ngm4bVm2XyPMRESjAUNwEDAEE9FIqNQfxYaD/4TNYQEASMUyLJh4M/JTivyqv7PagXs+MKO+s+erPqyhHpGnTkLsdmH21Hg89ehUxEYPPcJMRHSpYwgOAoZgIhopRksrPil5DU3GWu+5cakzMH/8TZB1b7gxGEOXG9//1IIPynu2Z5Z2WRBddhQKUycitHI8+f1CLJydHJT+ExFdLAzBQcAQTEQjyelyYPORd30204gOT8DSwtWIDk8Ysr4gCFhXasd/b7TAfDYLC27oqiqhrTkDEYBvLcrAmocmQa0aen1iIqJLEUNwEDAEE9Gl4FjNXnx15B04XXYAgFQix5XjV6Agpdivub2nDC7c+6EZJfU9aworjO2IPn4MUpsVqYlh+M3jRZiUHx20z0BEFCwMwUHAEExElwpDZyM+PfA6WjobvOfyk4tw5YRvQy5VDFnf4RLwmx1W/G5X75vmnIisKIe6SQ+pWIR7VubhodvzIZNxOXkiGj0YgoOAIZiILiVOlx1bjr6PI9W7vOciw+KwdOrdiNX6N7d3d40T931oRrXR7T2nam5CVEU5JA4HxmTq8KsfTcPY7IgL3X0ioqBgCA4ChmAiuhSV1x3Al4ffgt1pAwBIxFLMG7ccE9Jm+TU9wmgV8NjnFrx91O49J7HbEXnyONStLZBKRHjgtnzce3MeZFKOChPRpY0hOAgYgonoUtVubsanB173WT0iJ2EiFky8BSp5mF9tvF9mxw83WGDo6rWUmr4BkRUnIXY5UZATgV/+aBpyM3QXvP9ERBcKQ3AQMAQT0aXM6XJiR9kHOFi1zXsuTKHD4im3Iy0mz682mkxuPPKZBZ+e7FlKTWKzIqq8DKr2NkilIjx8RwFWf3sMpBKOChPRpYchOAgYgoloNKhoLMWmQ/+CtXtzDQCYmnUVZuZdC6lk6KXPBEHAm4ft+PEXFnTYes5r6msRcboCYrcbE/Ii8dRj05Cdxu9CIrq0MAQHAUMwEY0WJqsRGw/+E9UtJ7znYrXJuGbKnYjyY01hAKgxuvHwJ2ZsqXJ6z0m7uhB1ogxKYzvkMjG+d2cB7lyey1FhIrpkMAQHAUMwEY0mguDGN5VbsfP4R3C5PWsCS8QyzC24ERPTZ/t105xbEPBKiQ0/39wFS88MCWga6jyjwi4XCnIj8L8/mMoVJIjoksAQHAQMwUQ0GjV31OGzA3+HwdToPZcZNw5XT7oVakW4X21UGFx4+GMLdtf2jApLbFZEnSyHytAKiViEu1eMwUO350OpkFzwz0BE5C+G4CBgCCai0crpsmN72Yc4VLXde04t1+CqiTcjJ2GiX22cHRVeu7mrZ9tlAGp9IyJPnYTE6UB6sgZPfr8QRRNjL/RHICLyC0NwEDAEE9FoV6k/ii8OvQmL3eQ9l59chHnjl0MpU/vVRrXRhUc+teCryp5RYbHDjsiKE1A3N0EE4NvXZOKH94yHViO/0B+BiGhQFz0E79q1C2+88Qa2b9+Ouro6CIKAlJQUXHHFFbjrrrswe/bs832LITkcDmzcuBHvvPMO9u/fj4aGBlgsFsTHxyMxMRHTpk3DlVdeiSuvvBKRkZEBt88QTESXA7OtA1+W/hun9Ue85zRKHRZOvBUZcfl+tXF2BYk1m7rQbu35CVG1NCOyohxSux2xUUr8z8OTsXC2f7vXERFdCBctBJvNZjzyyCN47bXXBi23evVqvPDCCwgL82/R9kDt2rUL999/P44ePTpk2YcffhgvvvhiwO/BEExElwtBEFBWtw9bjrwHu7PLe35C2izMKbgBcqnSr3b0Jjce+9yCD8t75keInU7oKiugaaiHCMDVVyTjJw9OQnyM6kJ/DCKic1yUEOxyubB06VJs3LjRe06lUmHcuHGQSqU4duwYOjo6vK8tWrQIn376KSSSC3vTxN///nesXr0abrfbey4iIgJZWVmIiIiA0WjE8ePHYTabATAEExGd1dnVhi8O/QvVLeXec1p1NBZNug0p0Tl+t/PBcTse+9yCJnPPz4ncaETUyeOQW8wIU0vxvTsLcOv12VxOjYiCKpC8NuxvoyeeeMInAN93332ora3Fvn37sHv3btTX1+OJJ57wvr5x40b8/Oc/H+7b9evdd9/1CcBTpkzB559/jubmZpSUlODLL7/E/v370dHRgT179uBHP/oRoqOjL2gfiIhGq3BVJL41/SFcNWElZBLP/N0OSyve3f0ith59Dw6X3a92bhgrx977tbhtQs8cYLtOh8apxWjLzEan1Y2n/1KKWx75CofKWoPyWYiIAjWskeD6+npkZ2fDarUCAFatWoW///3v/ZZ94okn8NRTTwEAlEolTp06haSkpPPosoder0d+fj7a2toAADfddBPeeustSKVD74g0HBwJJqLLmdHcgo2H3kSd4ZT3nE4dg4UTb0FqTK7f7WytcuCHGyyoMPT8dU5i7UJUxQmoDK0QiYAVSzLxg++Mhy6cN84R0YUV9OkQjz/+OJ599lkAgFqtRk1NDaKiovota7fbkZOTg5qaGm/dp59+OtC3PMcdd9yBdevWAQDGjBmD0tJSKBSK8253IAzBRHS582ywsQ07j38Ml7tnnu/4tFmYk78MCpl/83ptTgHP7bbid7ussLt6zquamxB56gSkdjuidAo8dt8ELFuQ5tfGHURE/gj6dIj333/f+3zlypUDBmAAkMvlWL16tff4vffeG85b+mhsbMS///1v7/EzzzwT1ABMRBQKRCIxCrPm4/a5/42kqCzv+SPVu/CPrb/2WVFiMAqpCGvmqLD7Xi3mpff8da4rNg6NRTPQmZSCVqMN//Pb/Vj9+DacOtMxSGtERMERcAguLy9HRUWF93jJkiVD1rnmmmu8zysqKlBeXj5I6aG9/vrrcDo9a1QmJibiuuuuO6/2iIioR5QmHt+e+V+4cvwKyCSeAQaT1YgP9/0fPjvwBiw20xAteORGS/DhbRr8dZkaMWrPaK9bIkVbzhg0TpkGW7gW+w+34KbvbsJzrx6GuffezEREQRZwCD506JDP8cyZM4esU1hYCLm8Z+5XaWlpoG/ro/cNeUuWLLngK04QEYU6kUiMSRlzsGreT5Ae27N+cHn9Afx9y69wvG4//JlNJxKJcMt4BfY/oMXdk3t+BxzhWuinTEPrmHzYxFK89s4JXHfvRny46Qzcbu7hRETBF3AILisr8z6Xy+VITU0dsk7fcr3bCJQgCCgpKfEez5gxAwBQWVmJNWvWYMKECdDpdNBoNMjKysKtt96Kd955x2cJNSIi8o9WHYUbix/A4sl3eHeVszrM2PDNP/DBvr/CaPFvtYcolRjPLw3DxlXhGBfbM3BhTkhEQ9FMdCSnoKnNhp/+dj9WPbYFR060BePjEBF5BRyCq6qqvM9TUlL8vqEhLS2t3zYCVVlZ6bP+cG5uLl566SWMGzcOv/nNb3DkyBF0dHTAbDajsrISb731FlauXInCwkKcOHFi2O9LRBSqRCIR8lOKsGr+GuQmTvGer2o6hn9s+TX2VWyCy+0apIUeM1Kl2H5POJ5dpEKEsnuKhFSK9uwxaCwsglUXgUNlBtz6/a/w89+XoLXdGpTPREQUcAju7Oz0PtfpdH7X632HXu82AtXa6jvqsH79ejz88MPo6vLsepSeno758+dj2rRpPlMwDh06hJkzZ+LYsWN+vY/NZkNHR4fPg4golIUptLh26t24bto90Cg93/9OtwM7j3+EN7c/g3rDab/akYpFeGCaEiUPaHHnJDnODqU4wjRomlSIlrHj4JAr8N7nVbjuno34x/sn4XDyr3lEdGEFHIJNpp4bIpRK/7bWBDy7yfXXRqDa29t9jp9//nkAQF5eHrZt24aqqips3rwZ+/btQ1NTE374wx96yxoMBqxYsQJ2+9ALwP/617+GTqfzPvyZ9kFEFApyEiZi1byfYnLmPIi6I2xrZyPe3vVHbDr0Fqx2s1/txIaJ8eK1Yfjy7nAUJvZMkbDExaNh2gwYU9PRYXHh6ZdLcdN3N2HXAX1QPg8RhaaAQ/DZVRkABLQxRe+yDsfw7wC22WznnEtKSsK2bdswZ84cn/M6nQ7PPfccfvazn3nPlZWV4Y033hjyfdasWQOj0eh9nF3nmIiIAIVMifnjluOWKx5DnK5nkOBIzW68seWXOFa7168b5wBgWpIUX90djj9d27OKhCCRwJiZjfqi6TDHxOFUdSfu/+kOPPTETi6pRkQXRMAhWK1We5+f3THOH73LhoWFBfq2g9b95S9/ibi4uAHrrF271mck97XXXhvyfRQKBbRarc+DiIh8xUek4pYrHsX8cTdBLvUsp9ZlN2PjwXX4z9cvorWz0a92xCIRVk1S4MCDWjw4TQFx9xwJl1KF1oLxaJpUCFt4OLbva8Tyhzbhf184wPnCRHReAg7BGo3G+/zsPFx/WCyWfts4n/cHAJlMhpUrVw5aRyqV4pZbbvEe79+/H2azf3+uIyKiwYlFYkzOnIs75/0UuYmTvedrWyuwbtvT2Hr0fdgc/v1eRCjFeGaRGjvv0eLKzJ6/INp0EdBPKUJLXgFsMjne/qQSS7/zOf7vreOw2vy7KY+IqLeAQ3BMTIz3eUNDg9/1Ght7RgOio6MDfVuv2NhYn+OxY8f6jE4PpLCw0Pvc6XRyegMR0QWmUUXg2qmrcUPR/dCqPDuJugU3vqnc4pkiUbMXguDfDW7j4iRYf4sG76zUYEx0z0+VJT4BDdNmoD09E51WAX98/Siuv28jPtlczfWFiSggAYfgvLw87/PW1lafEd7B9A6dY8eODfRtvdLS0nxC72BbNvfWN3i3tXENSiKiYMiMH4c756/BjDFLIBHLAAAWWyc2HlqHt3f9EU1G/wYhRCIRFufIsPteLZ5dpEKkqme+cEd6JhqKZsAUn4j6Jgt+/PQ+3PaDzThwpCVon4uILi8Bh+D8/Hyf44MHDw5Zp66uDs3NzQO2EQixWOwTovu7Ua4/fecvB7KyBRERBUYqkWPGmGtw5/w1yE6Y6D3f0FaFN7f/DptK/40uu38rBckkniXVDj6oxfeKFZB1/3K5FAoY8vLRWFiErsgoHD7Rhjt/tBWP/GIXKqp48xwRDS7gEFxcXAyFQuE93rFjx5B1tm/f7n2uVCpRXFwc6Nv6mDdvnvd5ZWWlX3X6louPjz+vPhAR0dB06mhcP+0efGv6Q4jUnP3eFXCkehde3/xLHKraDrefUyQiVWL8aqEae+7X4voxMu95hyYczRMmo2niFNg04fhqdwOWf/cL/Ox3+1Gv5/0fRNS/Yd0Yt2DBAu/xunXrhqzTu8yCBQvOa3UIAFi+fLn3uV6vx+HDh4ess3HjRu/z1NRUJCUlnVcfiIjIf+mxY3HH3McxJ/8GyCSegRSbw4LNR97Fm9ueQXVzud9t5URJsG6FBp/crsGUXusL2yIioS8sQkv+ONjkKqz/4gyuvXcjnnn5ENqM/v3VkIhCR8AhGADuvvtu7/PS0lJ89NFHA5Y9cOAAPvvss37rDtesWbN8pkT8+te/HrT8wYMHffpwww03nHcfiIgoMBKxFFOzr8LdV/4P8pOLvOdbOhvw3p6X8MHel2Hwc0k1AJiTLsPmu8Px+o1hyIzsdfNcbDwaiqbDkDMGVkjx9/crsGT1BvxlXRksXc5BWiSiUCIS/F3NvBdBEDBlyhQcOnQIAJCYmIivvvrqnBveGhoasGDBApSVlQEAJk+ejAMHDkAkEp3TZlVVFTIzM73Ha9euxZNPPjlgH95//32fEeHnnnvOZ3e4s6qrq7FgwQJUVFQAAORyOU6cOIH09HT/PzCAjo4O6HQ6GI1GrhlMRHQB1BtOY+vR96DvdaOcSCTGxLTZmJG3BCq5/8tpOlwCXj9ow2+2W9Fs6flZE7lcCK+thra2GmKXC1ERCjx421h8+5osyGTDGgcioktYIHltWCEYAPbt24d58+Z51wrWarV46KGHMHfuXEilUuzduxcvvvgi9HrPNpcqlQpbt25FUVFRv+0FGoIBYNWqVfjnP//pPb7qqquwatUqZGZmwmw2Y9u2bfjzn/+Mjo6eGyReeuklPPTQQwF/XoZgIqILTxDcOF5Xgp3HP4bJ2u49L5eqMD13ESZlzIVU4v/upCa7gBf3WPH8HitM9p7zYocd2poz0NTXQex2IylOjQduG4tlC9MhkzIME10uLkoIBoD33nsPd9xxx5CbZqhUKvzzn//0Gbntazgh2G6349vf/jY+/PDDIfsqEonwy1/+EmvWrBmybH8YgomIgsfhsuPA6c3YX7EJDldPetWqozFn7DLkJE7q96+IA2k2u/H0Dite+8YGZ6/77sQ2G3Q1VdA01EMkCEhNDMODt+fj2itTIZUwDBONdoHktfO64pcvX46SkhIsXLiw3y8nkUiEBQsWYP/+/YMG4OGSy+X44IMP8Ne//tUnQPc1Z84cbNmyZdgBmIiIgksmkWN67mLcfeXPMC51BgDPb0qHpRWfHPgb3tn1R9QbTvvdXmyYGL9drMb+B7RYUSDD2V8ot0KBtpw81BfNhCkhCdWNFvzPb/fjxvu/wCebq+FyccMNolBxXiPBvdXU1GDnzp2oq6sDACQnJ2P27NlITU29EM37Zd++fTh69CgaGxuhUCiQmJiIOXPmIDk5+bzb5kgwEdHF09xRh23H1qOm5YTP+az48ZiVdy1itIGt8HOsyYVfbe/Ch+UOn/PSri5oz1QirKkRIgBZaeF46PZ8LJ6TArHY/5FnIro0XLTpEKGEIZiI6OISBAFVTcewrewDtJn0vV4RIT+lCDPHXAOt2r9dQ8862OjEL7dZ8XlFnzBsMUNXVQl1SxNEAHIztPjuHQVYMCuJYZhoFGEIDgKGYCKikeF2u3Csdi++PvEZTFaj97xELMHE9CtQnLsooJUkAGBPrRO/2taFzVW+S6bJzCZoq6ugbvaE4bwsHR64dSwWzk5mGCYaBRiCg4AhmIhoZDlddhys2o59FZtgc1i85+VSBaZmLcCUrPmQSxWDtHCu7WcceGqrFbtrfcOw1GKGtvoMwpr0EEFAZmo47rs5D0t5Ax3RJY0hOAgYgomILg1WuwX7T32Jg5Vb4XT3TGtQyzUoyrkaE9JnQyqRDdKCL0EQ8GWlE7/Z3oW9dS6f16RdXdDWVCFM3wiRICAlQY3vfDsPN16dDrlcMkCLRDRSGIKDgCGYiOjSYrIa8fWJDTha8zUEoWcdtDCFDsW5V2Nc6syA1hgWBAFbq5x4ZqcVO6p9R4YlVqtnneFGz9Jq8TEq3H1TLlZckwmV0v/3IKLgYggOAoZgIqJLk8Gkx+7yT3Gy4aDP+XBVJIpzFqEgdTok4sBGbXdWO/DMDus5c4YlNiu0NdUIa6yH2O1GlE6BO5fn4pbrsqAJ83/0mYiCgyE4CBiCiYgubU3GWnx9YgNO6w/7nNeqozE9dxHyk4sgDjAM76l14tmdXdh4yjcMi+12hNfVILyhDmKnE1qNDDdfl4Xbl+UgJkp53p+FiIaHITgIGIKJiEYHfXs1vj7xGSqbjvmcj1DHYPqYJchLngqxKLCb2w40OPHsTis+OeG7tJrI6YSmsR7htTWQ2m2Qy8RYtjANdy0fg8zU8PP+LEQUGIbgIGAIJiIaXRraqrD7xGeobj7ucz4yLA5FOVcjL3lqwNMkDuud+N0uK9Yfd8Dd+9fT7UZYkx7amjOQdVkgEgFXzkjE6m/nYUpB9AX4NETkD4bgIGAIJiIaneoMp/B1+WeoaT3pc16risK0nIUoSJke0A10AHC6zYUX9tiwrtQGq+9MCahamqGtPQNFRwcAYHJBNFavyMWVM7jxBlGwMQQHAUMwEdHoVtNyEl+f2IA6Q4XP+TCFDtOyr8L49FmQSeQBtdlsduPP+2x45YAN7Vbfn1OFsR3hNWegMrRCBCAzRYO7bhqD6xekQcHl1YiCgiE4CBiCiYguD3WGU9h78gucaS7zOa+Sa1CYNR8T0+dAIQvs5rZOm4A3Dtrw4l4r6jt9f1alFjPC62oRpm/wriix8ros3HJtFm+iI7rAGIKDgCGYiOjyom+vxt6TG3Gqz2oSCpkKkzPmYnLm3IC3Y7a7BLxz1I4/7LaivNXt85rY4YCmoR6a+lpI7TZIpSJcMy8Vd9yYg3G5kef9eYiIITgoGIKJiC5PLR312FvxBU7UfwOg5ydRKpZhXOoMFGbNhy4sJqA23YKAzysceHGPDdv7bLwBtxvqliaE19ZAYeoEABSOi8YdN+bgqllJ3JaZ6DwwBAcBQzAR0eXNYNJjf8UmlNXt99mBTgQRchInY1r2VYiPSAu43VK9E3/aa8O7R+1w+A4Oe+YN19VA1dIMEYDEODVuvT4LNy3JhC48sPnJRMQQHBQMwUREocFoacU3p7fgSM3XcLrsPq+lROdiWvYCpMeOhUgU2EoPjSY3/q/EhtcO2NDa5fvTK+nqQnhDLcIaGyBxOqFSSHD9wjTcen02cjN05/2ZiEIFQ3AQMAQTEYWWLrsZpWd24GDlNnTZTT6vxYQnYmr2AoxJKgx4reEuh4B/H7HjpX1WHG/xHRoWuVxQN+sRXl8LucnznlPHx+Dm67Jw9exkyGScKkE0GIbgIGAIJiIKTU6XHcdq9+HA6c1oNzf7vKZRRmBy5lyMT50JpVwdULuCIOCrSif+tNeKTaed57wu7zAivL4W6uYmiAQBUREK3LQkAyuXZiExLrD3IgoVDMFBwBBMRBTa3IIbpxsPY/+pL9HYfsbnNalEjvyUIkzJmIuo8ISA2z7Z6sIrB2x4s9QOo833Z1lst0PTWA9NQx2kNhvEYmBuUSJuuT4LswrjuQEHUS8MwUHAEExERIBnBLfecBolp7/Caf2Rc15Pjx2LyZnzkBE7FiJRYNMXzHYBbx+14/9KbDjS5Or7xlC1tkBTXwtlextEAFISw7ByaSa+tSgDkTrFeXwqossDQ3AQMAQTEVFfbaYmHKzahmM1e+DocxNdZFgcJmfORX5KMeTSwAKqIAjYXePEX0ts+LDcAWefVSUkXV2e0WF9AyR2O+QyMa6+Ihk3LcnAtAmxHB2mkMUQHAQMwURENBCrw4JjNXtwsHIbOroMPq/JpSqMT5uOSRlzoVNHB9x2o8mN17+x4W/f2NBg6vOTLbiham2FpqEOyjYDRABSE8OwfHEGbrg6HXHRqvP4VESjD0NwEDAEExHRUNyCG6f1R3CwcitqWyv6vCpCZlw+JqZfgfS4fIgDnCrhcAn49KQDrx+04avTTvT98ZZYrdA01iOssQFSuw0SsQhzixNw05IMXFGUwE04KCQwBAcBQzAREQWiuaMOByu34nhdCVxu39UfwlVRmJA+C+NTZ0CtCA+47ap2F/5+0I5/ltrQeM7osAClwTM6rDIYIIKA2Cglbrg6HcsXZyAtKbCtoIlGE4bgIGAIJiKi4bDYTDhSvQuHq3ehs6vN5zWxSIKcxEmYlH4FkqKyAt6Aw+n2bM/8t29s+OJUP6PDNhvC9A0I0zdC1mUBABRPisW3FmVgwewkqJXS8/loRJcchuAgYAgmIqLz4RbcqGo6htKqHahqPg70iazR4QmYmH4FxiYXQSFTBtx+jdGNfxyy4R+HbKjrPPenXd5h9ATipiaIXU6oVVIsuiIZyxam8WY6umwwBAcBQzAREV0oRnMLSqt34VjN1+iym31ek0nkGJNUiHFpM5AYkRHw6LDLLeCL0068/o0Nn1c44Or7K+92Qd3SgjB9g/dmusQ4Na6/Kg3XL0hDZmrg0zOILhUMwUHAEExERBea0+VERcNBlJ7Zgfq2ynNej9IkYHzaDIxNLoJaEfhc3iaTG28fs2PdITuONrvOeb2/6RITx0Zh2cI0XDMvFbpweeAfimgEMQQHAUMwEREFU3NHHQ6f2YnjdSWwO60+r4lFEmQnTMC41BlIi80LeGUJQRBQqnfhzcN2vH3EjtauAaZLNDVC3dwEicMBmUyMecUJWLYwHVdMjYdcLjmvz0d0MTAEBwFDMBERXQwOpw0nGw7haM3XqDOcOuf1cGUEClJnYFzqdGjVUQG3b3cJ2FjhwLrDdnxece5GHBDcULYZENakh6qlBWK3C1qNDAtnJ2Pp/FQUTYyFRML5w3RpYggOAoZgIiK62AwmPY7W7EFZzR5Y7KY+r4qQFpOL/JRiZCdMDHhXOgBoNrvx9lE73iy143DfbZoBiFwuqFqbEdak98wfFgTERCqxZF4Kls5PxYS8yIDnLBMFE0NwEDAEExHRSHG5XajUH8HRmq9R1VQGoc/KEjKJHDkJk5CfUoSUmNyAp0sAwNEmF94+asM7R+2o7Tg3GogddqibmxDWpIe8wwgRgJTEMCydl4ql81ORk8HfRhp5DMFBwBBMRESXgs6udpTV7sXRmj0wWlrOeV2j1GFs8jTkpxQjOjwh4PbdgoCva5x4+6gd7x93oK2f+cMSaxfCmpqgbtFDZjJBBCA3Q4ul81OxeG4KN+SgEcMQHAQMwUREdCkRBAENbVUoq92LEw3fwOboOqdMnC4V+SlFyEuaOqzVJewuAZtOOfD2UTs+PemA1XluGWlXF9TNeqhbmryBeGy2DovmpGDRFcnISOGSa3TxMAQHAUMwERFdqpwuByr1R1FWtw9VTcfgFnzvdhOLxEiPHYu8pKnISpgwrPnDnTYBH52w452jdmyudMLdT3qQdlmgbm6CurkJMnPPCPHZQJydzt9PCi6G4CBgCCYiotHAYutEef0BlNXuQ5Ox5pzXpWIZMuPHIS+pEBlxBZBKZAG/R7PZjY/KHXi/zI7t1f4H4qy0cG8gzs3Q8qY6uuAYgoOAIZiIiEab1s4GlNXuw/G6/TBZjee8LpcqkZ0wEXlJhUiNGQOJOPC1gAMJxKrWZsg7OyECkJGswdVzkrFwdjIKciIYiOmCYAgOAoZgIiIarQTBjTrDaZyoP4CTDQfP2aoZAFTyMOQkTkZeUiGSo7IgGsYKE81mNz4sd2D9IIFYYrNC1doCVUszlMZ2iAQB8TEqXDkjEVfOTELxxFjIZIG/NxHAEBwUDMFERHQ5cLldqGk5gfL6AzjVWHrO7nSAZ4WJnIRJyEmchKSorGEtueZPIBY5HVC1tkLd2gylwQCx2wWNWoo5RQm4cmYS5hQlIDws8OkaFLoYgoOAIZiIiC43TpcDVU3HUF7/DSr1R+B0O84po5ZrkJ0wETmJk5ASnTvsKROfnXTg4xMObK50wHbuvhyA2wVlWxvUrc1QtbZA4nBAKhWheGIsrpqZhPkzEpEQqx7Gp6RQwhAcBAzBRER0ObM7bTitP4zyugOobjkOl/vcpKqUqZGVMAG5CZOQGpMHqUQa8Pt02gR8edqBj044sLHCAaOtnxgiCFB0GKFqbYHS0AqZxQwRgHG5kZg/IxFzixOQnx0BsZjziMkXQ3AQMAQTEVGosDm6UNl0FCcbDuFMU1m/I8RyqRKZceOQmzgJ6bFjIRvGsmt2l4Ad1U58VO7AJyfsaDT1H0kk1i6oWluhMrRA2d4OkeBGTKQSVxTFY15xImZOiYOG0yYIDMFBwRBMREShyOG0oaqpDCcbD6FSfxQOl+2cMhKxDGkxY5AVPx5Z8eMRpgz8d9ItCCipd+GTEw58VG7HSYO733IilwvKNgNUhlYoDS2Q2u2QSkQoHB+DucUJmFOUgKzUcK42EaIYgoOAIZiIiEKd0+XAmebjqGg8hNP6I/3uUgcACRHp3kAcHZ44rEBaYXDh8wrPlIkd1U44+s/EkHV2QmVogcrQCnlnB0QAUhLUmFOUgLnFiSiaGAulIvB5zDQ6MQQHAUMwERFRD5fbiZqWk6hoLEWl/gjMto5+y+nU0d2BeAKSorKGdWNdh03AlkoHNlQ48HmFA82W/qOL2GGHsq0NyjYDlG2tkNrtUMjFmDo+BrOmxmNWYTw36bjMMQQHAUMwERFR/wTBDb2xBqf1R3C68QhaOuv7LaeQqZARW4DM+HFIjx0LlTws4PdyCwK+aXBhQ/co8TeN/S014SEzm6A0eAKx0miESHAjNkqJmYVxmD01HjOmxCE6QhlwH+jSxRAcBAzBRERE/jFaWj2BWH8Eda0VcAvnzmUQQYSEyHRkxBYgI64AcbrkYW3Q0dDpxsZTDnxxyoGtVc7+V5uAZy6xwtgOZVsrVAYDpF0WiADkZ0dg1tQ4zCqMx5SCaMjlnDoxmjEEBwFDMBERUeCsDguqmspwWn8EVU3H+t2cA/CsR5wel4+MuAKkx4yFUh74msBOt4D9dS5sOu3Al5UOHKh3YaCQI7FaPdMm2g1QtrdB4nBAqZCgaGIMZk6JR/HkWIzJ0HEZtlGGITgIGIKJiIjOj8vtRJ3hFKqaylDVVAaDqbHfcp5R4gxkxOUjM64AsdrhjRIbutzYUunEl5UOfHnagfrOgSOPzGSCst0ARXsblMZ2iF0uRGjlKJoYi+mTYlE8ORaZKVx14lLHEBwEDMFEREQXVofFgKrmY6hqKkNNywk4XPZ+y6nlGqTG5CEtdgzSYsYiXBUR8HsJgoDjLW58edqBTacd2FXjhNU5YGHIOzugbG+Dsr0N8g4jxG7PfOLiSbHeR0pCGEPxJYYhOAgYgomIiILH6XKi3nDKG4oNJv2AZSM18UiLGYO0mDykROdCIQv85rYuh4BdNU5sO+OZS3yw0QX3QInI7Yaiw9gdig2Qd3ZCJAhIilOjeHIsiid6QjG3dR55DMFBwBBMRER08RgtrahqKsOZ5jLUtJzsd5MOABCJxEiISEda90hxQkTGsJZha7e6sbPaia1VTmytcqCsZYCFidF9k12HEQpjOxTGdsg7OyB2u5GSGIap42JQOD4a0ybEIC1Jw5Hii4whOAgYgomIiEaGy+2Cvv0MzjSXo7qlHI3tZyD0s+IEAMilCiRH5SA1Jhcp0TmI0SZDPIz5xHqTG9vO9IwUV7UPHIrhdkPR2eENxYoOI8QuF6IjFZg6LgZTJ8Rg6vgY5GboIJEwFAcTQ3AQMAQTERFdGmwOK2pbT6Km5QTOtJSjbZCpEwqZCslR2UiJzkFKdC5itEnDCsVV7S5sq3Ji6xkndlU7UDfITXYQBMhNnT2h2NgOidOJ8DAZJhdEY+p4z2jx+NxILsl2gTEEBwFDMBER0aWps6sN1S0nUN1cjpqWcljspgHLXohQLAgCqo1u7KxxYle1EztrnDhlGGSkGJ6NOzyjxB2QdxghtXZBKRdjQl4UJhdEY3J+FCblRyNSpwioL+SLITgIGIKJiIgufYIgwGBqRG1rRffjJLrs5gHL9w7FyVHZiNUmQzyMOcWNJrc3EO+qduJo88A72QGA2G73zCvuMELeYYTc1Amx2420pDBMyo/G5IJoTMqPQm46p1AEgiE4CBiCiYiIRh9BcMNg0qOm5SRqWytQZ6gYNBTLJHIkRGYgKTILSVGZSIzMgFwa+OoThi43vq7xhOKd1U4canTBNVjicrs9Uyi8wbgDUrsNapUUE/IiMWmsJxRPyo9ChJajxQNhCA4ChmAiIqLRTxDcaO3sPVJcAatj4FAsggixuuTuUOx5aJS6gN/XZBdQUu/E3jon9tQ6sbfOhXbr4BFMYrV6AnGnEYrODshMJojdbmQkazApPxoTxkZi/Jgo5GXqIJMFPs/5csQQHAQMwURERJefs6G4znAK9YbTqDOchsnaPmgdrToayd2hODEyA1HhCQHPK3YLAk62urGn1ok9dU7srXWivHXwecUQ3JCZzVB0dkDe/ZCZLZDLRBibpcP4MZ5QPH5MJDJTw0Nyy2eG4CBgCCYiIgoNHV0G1BtOo95QiXrDabR0NgAYZMtliQLxEWlIjExHQkQGEiLSEaYMPCsYutzYV+fCnlon9tU5sb/eCbNj8DoilwtyU6c3FCs6OyCxWqFRS1GQE9kdjD2PpHj1Zb9uMUNwEDAEExERhSarw4KGtipvMG5sPwOXe/B0Gq6K6g7F6UiIzECcNgVSiSyg93W6BRxtcqGk3oWSBidK6p043uIeeGe7bmK7HXJTB+Sdnd3TKDohsdsRrVNgXHcgHpcbgbHZEYiPUV1WwZghOAgYgomIiAgAXG4nmoy1qDecRmP7GTS2VaFziCkUYpEEsdpkJEZ6RorjI9IQERYDUYDTKEx2AYcanSipd+FAg+efZ4xDTKMAILbbPCPGJhPknZ2QmzshsVoRrVNgbLYO+TkRyM+JRH62DqmJmlE7lYIhOAgYgomIiGggJqsRjW1VaGg/g8a2M9Abq+F02QetI5cqEadLRbwuFXERnn/q1DEBj8y2mN0oaXChpN6JkgYnDtS70No1dLwTOxyQmTp7wrGpE9IuCzRqKcZmRXjCcbYnHGelhUMmvfRvvmMIDgKGYCIiIvKX2+1Cq6kRDW1VaGyrQmP7GRgG2dnuLIVMhThtCuIi0hDfHZC16uiAgrEgCDhjdKOk3oVDjZ7l2Q7qXWjzIxiLXE7ITabucGyC3GyC1GKGUgLkZGhRkBOBvMwIjMnSYUymDuFhgU3xCDaG4CBgCCYiIqLzYXVYoG+v7h4prkGTsRomq3HIegqZGvG6FMTp0hCnS0GsNjngqRSCIKCmw41DjS7PQ+8JyI0mP2KgIEDaZfGEY7PnITebIbFZkRynxphMTyD2BGMt0pI0kEpGZtSYITgIGIKJiIjoQjNbO9BkrOkOxTXQt1fDbOsYsp5MIkeMNgkx2mTEdj9itEmQSeQBvX+jyROMS/VOb0D2Z44xAIicDsjNZsi6R4w9AdkMlVRAdroWYzJ1yOsOyPk5EdBqAuvbcDAEBwFDMBEREV0MJquxOxB7Rov1xhpYbJ1+1BQhMiwWsbpkbziO0yZDrdAGNJ3C0OVGaaMLR5o8j6NNLpS1uGAffCdoL2mXxRuI5d3//NnduVh1Q47ffRguhuAgYAgmIiKikSAIAsxWI/TGGjR31HkfHZZWv+qr5BrvSHF0eCJiwhMRpYmHTOr/9ssOl4BTBrcnFDd3B2S9E3Wd/sXI745z4Tc3xPj9fsPFEBwEDMFERER0KbE5utDSUe8TjFs7G+ByO/2oLYJOHY3o8EREhyd4w3GkJg4SsdTvPhi63Dh2dsS4Oxwfa3Khq08X3vqWEkvzVYF9wGFgCA4ChmAiIiK61LndLrSZm9BsPBuMa9HcUYcuu9mv+mKRGBFhcd5QfDYg68Ji/N4a2uUWcLrNjbJmF441u3C8xYVnF6kRGxb8m+UYgoOAIZiIiIhGI0EQYLZ1oLWzAa2dDWjpbEBrRwNaTY1DrmV8lkQsQ6QmDlGaeERp4hClSUCkJg6RYXEB74QXTIHkNf/Hu4mIiIho1BGJRNAoddAodUiPHes9LwhudFgMaDU19gTjzgYYTHq4Bd+74FxuB1o66tDSUde3dejUUd3hOKE7KCcgShMHpTzsIny64eNIsJ84EkxEREShwOV2wWhu9gTjzga0djaitbMB7ZYWCIJ/y6cBgFquQaQmHlGaeOSnFCMpKjOIvfbgSDARERERDYtELEFUeAKiwhMATPGed7mdaDe3oM2kh6HXo82kh6OfaRUWuwkWgwl1hlNIjsq+KCE4EAzBRERERDQkiVjafaNcgs95QXDDZDWeE4wNJr13feNITfxIdHlQDMFERERENGwikRjhqkiEqyJ95hwDgNVugcGsPyc4XwoYgomIiIgoKJRyNZLkl9Y0iLOCv2AbEREREdElhiGYiIiIiEIOQzARERERhRyGYCIiIiIKOQzBRERERBRyGIKJiIiIKOQwBBMRERFRyGEIJiIiIqKQwxBMRERERCGHIZiIiIiIQg5DMBERERGFHIZgIiIiIgo5DMFEREREFHIYgomIiIgo5DAEExEREVHIYQgmIiIiopDDEExEREREIYchmIiIiIhCDkMwEREREYUchmAiIiIiCjkMwUREREQUchiCiYiIiCjkMAQTERERUchhCCYiIiKikCMd6Q6MFoIgAAA6OjpGuCdERERE1J+zOe1sbhsMQ7CfOjs7AQCpqakj3BMiIiIiGkxnZyd0Ot2gZUSCP1GZ4Ha7UV9fj/DwcIhEoqC/X0dHB1JTU1FTUwOtVhv09yOiC4PXLtHoxet39BMEAZ2dnUhKSoJYPPisX44E+0ksFiMlJeWiv69Wq+WFSDQK8dolGr14/Y5uQ40An8Ub44iIiIgo5DAEExEREVHIYQi+RCkUCqxduxYKhWKku0JEAeC1SzR68foNLbwxjoiIiIhCDkeCiYiIiCjkMAQTERERUchhCCYiIiKikMMQTEREREQhhyH4ErJr1y488MADKCgogE6ng1arRUFBAe6//37s3LlzpLtHNGq1t7fj/fffxyOPPIK5c+ciISEBCoUCGo0GaWlpuP766/GHP/wBbW1tw2r/8OHDePTRRzFx4kRERUVBo9EgLy8Pt99+OzZs2DDsfp8+fRo///nPMXXqVMTGxkKlUiE7Oxvf+ta38O6778LpdA67baLRrqqqCmFhYRCJRN7Hk08+GVAbvHZDnEAjzmQyCd/5zncEAIM+Vq9eLZhMppHuLtGoUVZWJlx33XWCXC4f8voCIKjVauH3v/+94Ha7/Wrf4XAIa9asEcRi8aDtXnvttUJTU1NAff/DH/4gKBSKQdudMWOGcOrUqeH8qyEa9RYvXnzONbF27Vq/6vLaJUEQBIbgEeZ0OoVFixb5XBwqlUqYNm2aMGPGDEGr1fq8tmjRIsHpdI50t4lGhXfeeeecHx+JRCLk5eUJc+fOFWbPni1ERUWdU+bee+/1Kwj3/Z9XmUwmTJo0SZg9e7YQHR3t89rEiROFzs5Ov/r9v//7vz51xWKxMH78eGHu3LlCYmKiz2spKSlCfX39+f6rIhpV/vGPf/QbLv0Nwbx2SRAYgkfcmjVrfC6K++67T2htbfW+bjKZhCeeeMKnzE9/+tMR7DHR6HE2BEulUuHGG28U1q9fLxiNRp8ybrdbWL9+vZCcnOxznb300kuDtv3yyy/7lF+2bJlQW1vrfd1utwsvvPCCIJVKvWVuu+22Ifu8YcMGQSQSeevMnDlTKC8v977ucrmEt956S9BoNN4ys2fPDvDfDNHo1dzcLMTExAgAhPz8fCEpKSmgEMxrl85iCB5BdXV1glKp9F4Mq1atGrDsz372M285pVIp1NXVXcSeEo1O69evF+69917hzJkzQ5atrq4WEhISvNdZTEyMYLfb+y1rNpt9ys6fP3/Av9C88sor3nIikUgoKSkZsA9ut1uYNGmSt3xeXp5gNpv7LfvFF1/4/JC/9957Q35GosvBHXfc4f3vfuvWrUJ6errfIZjXLvXGEDyC/vu//9t7EajVap8R4L5sNpuQmprqLf/4449fxJ4ShYa+I0SbNm3qt9yf/vQnnx/HY8eODdru9OnTveVXrlw5YLlPPvnE5/03bNgwaLs333yzt2xxcfHQH5BolPv888+9/82vXr1aEAQhoBDMa5d64+oQI+j999/3Pl+5ciWioqIGLCuXy7F69Wrv8XvvvRfUvhGFouuvv97n+Pjx4/2W6339zZs3D/n5+YO2+8ADD3iff/rpp7DZbEO2m5mZiUWLFvnd7t69e1FbWztoeaLRzGKx4MEHHwQAxMTE4Nlnnw24DV671BtD8AgpLy9HRUWF93jJkiVD1rnmmmu8zysqKlBeXh6UvhGFqr7/I9rR0XFOGZPJhG3btnmPA712TSYTtmzZ0m+5Tz75xPt88eLFEIlEg7Y7Z84chIWF9Vuf6HLzxBNPoLKyEgDw29/+FtHR0QHV57VLfTEEj5BDhw75HM+cOXPIOoWFhZDL5d7j0tLSC94volB25swZn+O4uLhzyhw7dgwOh8N77M+1m5CQgIyMDO9xf9duU1MTGhsbA2pXKpWiqKho0HaJLgclJSX44x//CMAzgnvXXXcF3AavXeqLIXiElJWVeZ/L5XKkpqYOWadvud5tENH56zvNqL8fs77XXXZ2tl9t9y7X37UbrHaJRjun04l7770XLpcLcrkcf/nLX4bVDq9d6osheIRUVVV5n6ekpAz5p5Oz0tLS+m2DiM6P0Wj0jjQBwMSJE1FQUHBOud7XnVQqRWJiol/tD3Xt9j3Xu/z5tEs02v3ud7/DwYMHAQA//vGPMXbs2GG1w2uX+mIIHiGdnZ3e5zqdzu96Wq223zaI6Pw89thjPn/SfOqpp/ot1/u6Cw8Ph1js39foUNdu33P+fi/wO4EuZ6dOncIvfvELAEBOTg5++tOfDrstXrvUF0PwCDGZTN7nSqXS73oqlarfNoho+F555RW8+uqr3uObb775nJUizgrWtdv3nL9t8zuBLmcPPPAAurq6AAB//vOfA7rm+uK1S30xBI8Qp9PpfS6VSv2u17ts7wn+RDQ827Ztw8MPP+w9zszMxMsvvzxg+WBdu73bDaRtfifQ5epvf/sbvvzySwDA7bffjoULF55Xe7x2qS+G4BGiVqu9z61Wq9/1epftvbwKEQXu4MGDWLZsGex2OwDPahAbNmwY9M+Zwbp2e7cbSNv8TqDLUVNTE370ox8BACIjI/Hcc8+dd5u8dqkvhuARotFovM/P/qnHHxaLpd82iCgw5eXlWLx4MYxGIwDPD+3GjRsxZsyYQesF69rte87ftvmdQJejRx55BAaDAQDwm9/8pt/lCgPFa5f6YggeITExMd7nDQ0NftfrfeNOoAuFE5FHZWUlFi5ciKamJgCem2Q+++wzTJo0aci6va9dk8nk91y+oa7d3u0C/n8v8DuBLje7d+/Gv//9bwCeZQrvu+++C9Iur13qiyF4hOTl5Xmft7a2+vwf4WBqamq8z4e7TAxRKKutrcWCBQu825Sq1Wp8/PHHmD59ul/1e1+7AFBdXe1XvaGu3WC1SzTa6PV67/Pdu3dDLBZDJBIN+Oi9yc0vfvELn9d6Lz3Ga5f6YggeIX33Kz+7BuJg6urq0NzcPGAbRDQ4vV6PhQsXerdeVSgUWL9+PebOnet3G8O5dh0OB44ePTpgGwCQm5vrc6OMP+0CwDfffDNou0TkwWuX+mIIHiHFxcVQKBTe4x07dgxZZ/v27d7nSqUSxcXFQekb0eWotbUVCxcuRHl5OQBAJpPh3XffxdVXXx1QO1lZWUhJSfEe+3PtlpSU+Py1p7/QLZfLfUaj/Wm3sbERFRUVg7ZLNNrIZDLodDq/H703m1IoFD6v9V4LmNcu9cUQPEI0Gg0WLFjgPV63bt2QdXqXWbBgAe8mJfKT0WjE4sWLceTIEQCARCLBm2++ieuuu25Y7S1btsz7/J133vGuLjGQ3tfuuHHjBtxW9YYbbvA+37Rpk8+fhYdqNyIigj+kdFm49tpr0d7e7vej985rP/nJTwZ8DeC1S74YgkfQ3Xff7X1eWlqKjz76aMCyBw4cwGeffdZvXSIamNlsxrXXXouSkhIAgFgsxhtvvIEVK1YMu83e119LS8ug6wrX1tbijTfe6LduX7feeqv3L0QOhwPPPPPMgGVNJhOef/557/Htt98OmUzmR++JQhevXfIh0Ihxu93CpEmTBAACACExMVEoKys7p1x9fb2Qn5/vLTd58mTB7XaPQI+JRher1SosXLjQe+2IRCLh1VdfvSBtL1u2zNuuRqMRduzYcU4Zo9EozJkzx1suISFBsFgsg7b7yCOPeMtLJBLh3XffPaeM3W4XVqxY4S2nUqmEurq6C/K5iEab9PR077Wwdu3aIcvz2qWzRIIgCBcvclNf+/btw7x587zrCmq1Wjz00EOYO3cupFIp9u7dixdffNH7pxWVSoWtW7eiqKhoJLtNNCo888wz+PGPf+w9joyMDGgu/dVXX43HHnus39eqqqpQVFSElpYWAJ65iPfccw8WLVoEjUaD0tJSvPDCC96b8MRiMdavXz/gdsxntbW1Yfr06Th58qS33m233YYbb7wRUVFRKC8vx5///GeUlpZ667z44os+u94RhZKMjAzvChFr167Fk08+OWh5XrvkNdIpnAThP//5j6BSqbz/ZzjQQ6VSCf/5z39GurtEo8batWuHvK4Ge9x1112Dtr9z504hKipqyHYkEonwwgsv+N3v8vJyITU11a8+Pv744+f5b4lodAt0JFgQeO2SB+cEXwKWL1+OkpISLFy40Ocu17NEIhEWLFiA/fv3Y/ny5SPQQyLqz6xZs1BaWoqbbrrJZ4mk3oqKirBt2zZ873vf87vdMWPGoLS0FPfccw9UKlW/ZfLz8/HBBx/g6aefHlbfiUIZr10CAE6HuMTU1NRg586dqKurAwAkJydj9uzZSE1NHeGeEdFgmpubsW3bNtTW1sJutyMpKQnTpk07ZyH9QHV2duKrr75CTU0NzGYzEhMTMWHCBEyZMuUC9ZwotPHaDV0MwUREREQUcjgdgoiIiIhCDkMwEREREYUchmAiIiIiCjkMwUREREQUchiCiYiIiCjkMAQTERERUchhCCYiIiKikMMQTEREREQhhyGYiIiIiEIOQzARERERhRyGYCIiIiIKOQzBRERERBRypCPdASIiungOHjyI9evXe49/8IMfICIiYsT6Q0Q0UkSCIAgj3QkiIro4Xn/9daxevdp7XFlZiYyMjJHrEBHRCOF0CCIiIiIKOQzBRERERBRyGIKJiIiIKOQwBBMRERFRyGEIJiIiIqKQw9UhiIhCgEgkCrjO5s2bMX/+/AvfGSKiSwBHgomIiIgo5HCzDCKiECCRSAAAgiDA7Xafc74/wxk9JiIaLTgSTEQUApxOJ5xOJ1599VWf8xUVFd7X+j7mzZs3Qr0lIgo+hmAiIiIiCjkMwUREREQUchiCiYiIiCjkMAQTERERUchhCCYiIiKikMMQTEREREQhhyGYiIiIiEIOQzARERERhRyGYCIiIiIKOQzBRERERBRyGIKJiEKITCbzOXa5XCPUEyKikcUQTEQUQsLDw32O29raRqgnREQjiyGYiCiEZGRk+Bzv27dvZDpCRDTCRIIgCCPdCSIiujicTidiYmJgNBoBAElJSXjllVcwf/58qFSqEe4dEdHFw5FgIqIQIpVKsXr1au9xfX09li5dCrVaDbVaDY1G431s3759BHtKRBRcDMFERCHmqaeewhVXXHHO+a6uLpjNZu+DN80R0eWMIZiIKMSEhYVhy5YteOutt7By5UqMGTMG4eHhEIv5k0BEoYNzgomIiIgo5PB/+4mIiIgo5DAEExEREVHIYQgmIiIiopDDEExEREREIYchmIiIiIhCDkMwEREREYUchmAiIiIiCjkMwUREREQUchiCiYiIiCjkMAQTERERUchhCCYiIiKikMMQTEREREQhhyGYiIiIiEIOQzARERERhRyGYCIiIiIKOQzBRERERBRyGIKJiIiIKOT8fzWdQ9jOgCL5AAAAAElFTkSuQmCC", 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" ] @@ -1660,26 +1660,26 @@ " | \n", "A 1-R2 coefficient of 5.94e-06 was obtained for the the real part of |A 1-R2 coefficient of 9.78e-07 was obtained for the the imaginary part\n", "the correlation function. |of the correlation function. \n", - "The current fit took 7.420486 seconds. |The current fit took 69.006773 seconds. \n", + "The current fit took 9.391188 seconds. |The current fit took 57.800778 seconds. \n", "\n", - "10.0%. Run time: 10.29s. Est. time left: 00:00:01:32\n", - "20.0%. Run time: 16.41s. Est. time left: 00:00:01:05\n", - "30.0%. Run time: 22.26s. Est. time left: 00:00:00:51\n", - "40.0%. Run time: 31.05s. Est. time left: 00:00:00:46\n", - "50.0%. Run time: 37.83s. Est. time left: 00:00:00:37\n", - "60.0%. Run time: 44.30s. Est. time left: 00:00:00:29\n", - "70.0%. Run time: 50.84s. Est. time left: 00:00:00:21\n", - "80.0%. Run time: 57.31s. Est. time left: 00:00:00:14\n", - "90.0%. Run time: 64.78s. Est. time left: 00:00:00:07\n", - "100.0%. Run time: 72.81s. Est. time left: 00:00:00:00\n", - "Total run time: 72.81s\n" + "10.0%. Run time: 11.20s. Est. time left: 00:00:01:40\n", + "20.0%. Run time: 17.92s. Est. time left: 00:00:01:11\n", + "30.0%. Run time: 23.99s. Est. time left: 00:00:00:55\n", + "40.0%. Run time: 33.36s. Est. time left: 00:00:00:50\n", + "50.0%. Run time: 46.94s. Est. time left: 00:00:00:46\n", + "60.0%. Run time: 59.50s. Est. time left: 00:00:00:39\n", + "70.0%. Run time: 70.89s. Est. time left: 00:00:00:30\n", + "80.0%. Run time: 83.90s. Est. time left: 00:00:00:20\n", + "90.0%. Run time: 93.27s. Est. time left: 00:00:00:10\n", + "100.0%. Run time: 101.26s. Est. time left: 00:00:00:00\n", + "Total run time: 101.26s\n" ] } ], "source": [ "tlist = np.linspace(0, 30 * np.pi / Del, 5000)\n", "\n", - "Obath, fitinfo = obs.approximate(method=\"corr_lsq\",tlist=tlist,Nr_max=4,Ni_max=4,maxfev=1e9,target_rsme=None)\n", + "Obath, fitinfo = obs.approximate(method=\"cf\",tlist=tlist,Nr_max=4,Ni_max=4,maxfev=1e9,target_rsme=None)\n", "print(fitinfo[\"summary\"])\n", "HEOM_ohmic_corr_fit = HEOMSolver(\n", " Hsys,\n", @@ -1709,23 +1709,23 @@ " 4 | 1.06e-02 | 3.07e-01 |1.00e-01\n", " \n", "A 1-R2 coefficient of 1.38e-06 was obtained for the the spectral density.\n", - "The current fit took 22.388690 seconds.\n", - "10.0%. Run time: 2.34s. Est. time left: 00:00:00:21\n", - "20.0%. Run time: 3.41s. Est. time left: 00:00:00:13\n", - "30.1%. Run time: 4.91s. Est. time left: 00:00:00:11\n", - "40.1%. Run time: 6.21s. Est. time left: 00:00:00:09\n", - "50.1%. Run time: 7.28s. Est. time left: 00:00:00:07\n", - "60.1%. Run time: 8.28s. Est. time left: 00:00:00:05\n", - "70.1%. Run time: 9.57s. Est. time left: 00:00:00:04\n", - "80.1%. Run time: 11.22s. Est. time left: 00:00:00:02\n", - "90.2%. Run time: 12.29s. Est. time left: 00:00:00:01\n", - "100.0%. Run time: 13.71s. Est. time left: 00:00:00:00\n", - "Total run time: 13.71s\n" + "The current fit took 34.405920 seconds.\n", + "10.0%. Run time: 7.40s. Est. time left: 00:00:01:06\n", + "20.0%. Run time: 12.02s. Est. time left: 00:00:00:47\n", + "30.1%. Run time: 17.17s. Est. time left: 00:00:00:39\n", + "40.1%. Run time: 21.92s. Est. time left: 00:00:00:32\n", + "50.1%. Run time: 26.92s. Est. time left: 00:00:00:26\n", + "60.1%. Run time: 32.22s. Est. time left: 00:00:00:21\n", + "70.1%. Run time: 39.81s. Est. time left: 00:00:00:16\n", + "80.1%. Run time: 46.32s. Est. time left: 00:00:00:11\n", + "90.2%. Run time: 60.56s. Est. time left: 00:00:00:06\n", + "100.0%. Run time: 74.12s. Est. time left: 00:00:00:00\n", + "Total run time: 74.12s\n" ] } ], "source": [ - "Obath2, fitinfo = obs.approximate(method=\"spec_lsq\",wlist=w,Nmax=4,Nk=1)\n", + "Obath2, fitinfo = obs.approximate(method=\"sd\",wlist=w,Nmax=4,Nk=3)\n", "print(fitinfo[\"summary\"])\n", "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", "HEOM_ohmic_sd_fit = HEOMSolver(\n", @@ -1793,7 +1793,7 @@ "metadata": {}, "outputs": [], "source": [ - "tlist2=np.linspace(0,50,1000)" + "tlist2=np.linspace(0,40,100)" ] }, { @@ -1806,39 +1806,38 @@ "name": "stdout", "output_type": "stream", "text": [ + "4\n", "Correlation function fit:\n", "\n", "Result of fitting the real part of |Result of fitting the imaginary part \n", - "the correlation function with 5 terms: |of the correlation function with 6 terms: \n", + "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", " | \n", " Parameters| a | b | c | d | Parameters| a | b | c | d \n", - " 1 | 6.54e-01 | 8.71e-01 | 5.77e-02 |-1.22e+00 | 1 |-1.36e+00 | 9.47e-01 | 0.00e+00 |3.21e-15 \n", - " 2 | 6.54e-01 | 8.71e-01 |-5.77e-02 |1.22e+00 | 2 | 7.21e-01 | 8.51e-01 | 1.00e-01 |-4.75e-02 \n", - " 3 | 3.74e-01 | 9.76e-01 | 0.00e+00 |2.22e-16 | 3 | 7.21e-01 | 8.51e-01 |-1.00e-01 |4.75e-02 \n", - " 4 |-8.26e-02 | 7.79e-01 |-2.17e-01 |-1.34e-02 | 4 |-3.04e-02 | 7.42e-01 | 2.60e-01 |-2.31e-02 \n", - " 5 |-8.26e-02 | 7.79e-01 | 2.17e-01 |1.34e-02 | 5 |-3.04e-02 | 7.42e-01 |-2.60e-01 |2.31e-02 \n", - " | 6 |-2.15e-02 | 9.90e-01 | 0.00e+00 |-8.53e-16 \n", - "A 1-R2 coefficient of 4.23e-04+2.56e-21j was obtained for the the real part of | \n", - "the correlation function. |A 1-R2 coefficient of 4.15e-05+6.17e-21j was obtained for the the imaginary part\n", - " |of the correlation function. \n", - "The current fit took 1.662169 seconds. |The current fit took 1.149397 seconds. \n", + " 1 | 1.80e-01 | 8.81e-01 | 0.00e+00 |3.74e-19 | 1 |-1.52e-01 | 8.26e-01 | 0.00e+00 |-6.12e-16 \n", + " 2 | 9.09e-01 | 5.58e-01 | 0.00e+00 |-9.72e-17 | 2 |-1.86e+00 | 5.61e-01 | 0.00e+00 |2.44e-15 \n", + " 3 | 2.14e-01 | 2.00e-01 | 2.27e-01 |-7.86e-01 | 3 | 1.00e+00 | 1.27e-01 | 1.25e-01 |-1.17e+00 \n", + " 4 | 2.14e-01 | 2.00e-01 |-2.27e-01 |7.86e-01 | 4 | 1.00e+00 | 1.27e-01 |-1.25e-01 |1.17e+00 \n", + " | \n", + "A 1-R2 coefficient of 7.55e-05-8.86e-23j was obtained for the the real part of |A 1-R2 coefficient of 1.01e-05+1.31e-22j was obtained for the the imaginary part\n", + "the correlation function. |of the correlation function. \n", + "The current fit took 0.300285 seconds. |The current fit took 0.269018 seconds. \n", "\n", - "10.0%. Run time: 5.39s. Est. time left: 00:00:00:48\n", - "20.0%. Run time: 9.61s. Est. time left: 00:00:00:38\n", - "30.1%. Run time: 14.40s. Est. time left: 00:00:00:33\n", - "40.1%. Run time: 18.00s. Est. time left: 00:00:00:26\n", - "50.1%. Run time: 22.82s. Est. time left: 00:00:00:22\n", - "60.1%. Run time: 27.01s. Est. time left: 00:00:00:17\n", - "70.1%. Run time: 30.52s. Est. time left: 00:00:00:13\n", - "80.1%. Run time: 33.39s. Est. time left: 00:00:00:08\n", - "90.2%. Run time: 36.18s. Est. time left: 00:00:00:03\n", - "100.0%. Run time: 39.02s. Est. time left: 00:00:00:00\n", - "Total run time: 39.02s\n" + "10.0%. Run time: 1.33s. Est. time left: 00:00:00:11\n", + "20.0%. Run time: 2.35s. Est. time left: 00:00:00:09\n", + "30.1%. Run time: 3.27s. Est. time left: 00:00:00:07\n", + "40.1%. Run time: 4.32s. Est. time left: 00:00:00:06\n", + "50.1%. Run time: 5.19s. Est. time left: 00:00:00:05\n", + "60.1%. Run time: 5.96s. Est. time left: 00:00:00:03\n", + "70.1%. Run time: 6.57s. Est. time left: 00:00:00:02\n", + "80.1%. Run time: 7.16s. Est. time left: 00:00:00:01\n", + "90.2%. Run time: 7.76s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 8.32s. Est. time left: 00:00:00:00\n", + "Total run time: 8.32s\n" ] } ], "source": [ - "pbath,fitinfo=obs.approximate(\"prony\",tlist2,Nr=5,Ni=6)\n", + "pbath,fitinfo=obs.approximate(\"prony\",tlist2,Nr=4,Ni=4)\n", "print(fitinfo[\"summary\"])\n", "HEOM_ohmic_prony_fit = HEOMSolver(\n", " Hsys,\n", @@ -1857,7 +1856,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1880,7 +1879,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 51, "id": "7f14b9cb", "metadata": {}, "outputs": [ @@ -1888,40 +1887,34 @@ "name": "stdout", "output_type": "stream", "text": [ - "Correlation function fit:\n", - "\n", - "Result of fitting the real part of |Result of fitting the imaginary part \n", - "the correlation function with 6 terms: |of the correlation function with 6 terms: \n", - " | \n", - " Parameters| a | b | c | d | Parameters| a | b | c | d \n", - " 1 | 3.38e-01 | 1.29e-01 | 1.65e-01 |2.11e-01 | 1 |-6.52e-02 | 8.10e-02 | 1.71e-01 |1.59e-01 \n", - " 2 | 3.38e-01 | 1.29e-01 |-1.65e-01 |-2.11e-01 | 2 |-6.52e-02 | 8.10e-02 |-1.71e-01 |-1.59e-01 \n", - " 3 | 2.03e-01 | 4.20e-01 | 5.80e-02 |-6.96e+00 | 3 | 1.21e+00 | 3.36e-01 | 1.07e-01 |-2.37e+00 \n", - " 4 | 2.03e-01 | 4.20e-01 |-5.80e-02 |6.96e+00 | 4 | 1.21e+00 | 3.36e-01 |-1.07e-01 |2.37e+00 \n", - " 5 | 2.98e-01 | 8.40e-01 | 0.00e+00 |2.78e-16 | 5 |-2.02e+00 | 7.06e-01 | 0.00e+00 |-5.00e-16 \n", - " 6 | 1.38e-01 | 9.39e-01 | 0.00e+00 |-2.28e-15 | 6 |-2.68e-01 | 8.79e-01 | 0.00e+00 |-3.33e-16 \n", - " | \n", - "A 1-R2 coefficient of 8.88e-05-1.48e-20j was obtained for the the real part of |A 1-R2 coefficient of 3.67e-05-1.85e-22j was obtained for the the imaginary part\n", - "the correlation function. |of the correlation function. \n", - "The current fit took 0.611501 seconds. |The current fit took 0.601647 seconds. \n", - "\n", - "10.0%. Run time: 5.05s. Est. time left: 00:00:00:45\n", - "20.0%. Run time: 8.69s. Est. time left: 00:00:00:34\n", - "30.1%. Run time: 12.39s. Est. time left: 00:00:00:28\n", - "40.1%. Run time: 16.18s. Est. time left: 00:00:00:24\n", - "50.1%. Run time: 20.15s. Est. time left: 00:00:00:20\n", - "60.1%. Run time: 24.16s. Est. time left: 00:00:00:16\n", - "70.1%. Run time: 28.52s. Est. time left: 00:00:00:12\n", - "80.1%. Run time: 32.77s. Est. time left: 00:00:00:08\n", - "90.2%. Run time: 37.12s. Est. time left: 00:00:00:04\n", - "100.0%. Run time: 41.09s. Est. time left: 00:00:00:00\n", - "Total run time: 41.09s\n" + "Result of fitting Correlation Function with 6 terms: \n", + " \n", + " Parameters| a | b | c | d \n", + " 1 |-5.55e-02 |-6.25e-03 |-8.46e-02 |-4.36e-01\n", + " 2 |-1.32e+00 | 1.45e-01 |-1.18e-01 |1.69e+00\n", + " 3 | 3.00e+00 | 3.44e-01 |-6.84e-02 |6.96e-02\n", + " 4 | 6.46e-02 | 9.13e-01 | 2.32e-02 |-6.85e-02\n", + " 5 | 6.68e-02 | 7.28e-01 | 7.36e-02 |-4.58e-01\n", + " 6 |-2.40e-01 | 5.41e-01 | 1.08e-01 |-7.95e-01\n", + " \n", + "A 1-R2 coefficient of 3.18e-05-5.05e-06j was obtained for the Correlation Function.\n", + "The current fit took 0.218197 seconds.\n", + "10.0%. Run time: 5.71s. Est. time left: 00:00:00:51\n", + "20.0%. Run time: 9.38s. Est. time left: 00:00:00:37\n", + "30.1%. Run time: 13.38s. Est. time left: 00:00:00:31\n", + "40.1%. Run time: 17.24s. Est. time left: 00:00:00:25\n", + "50.1%. Run time: 21.54s. Est. time left: 00:00:00:21\n", + "60.1%. Run time: 25.52s. Est. time left: 00:00:00:16\n", + "70.1%. Run time: 29.99s. Est. time left: 00:00:00:12\n", + "80.1%. Run time: 33.85s. Est. time left: 00:00:00:08\n", + "90.2%. Run time: 38.35s. Est. time left: 00:00:00:04\n", + "100.0%. Run time: 42.36s. Est. time left: 00:00:00:00\n", + "Total run time: 42.36s\n" ] } ], "source": [ - "tlist3=np.linspace(0,120,550)\n", - "mpbath,fitinfo=obs.approximate(method=\"mp\",tlist=tlist3,Nr=6,Ni=6,separate=True)\n", + "mpbath,fitinfo=obs.approximate(method=\"mp\",tlist=tlist2,Nr=6,separate=False)\n", "print(fitinfo[\"summary\"])\n", "HEOM_ohmic_mp_fit = HEOMSolver(\n", " Hsys,\n", @@ -1934,13 +1927,13 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 52, "id": "3ed89ed7", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1963,7 +1956,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 54, "id": "7708f4f1", "metadata": {}, "outputs": [ @@ -1971,40 +1964,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "Correlation function fit:\n", - "\n", - "Result of fitting the real part of |Result of fitting the imaginary part \n", - "the correlation function with 5 terms: |of the correlation function with 6 terms: \n", - " | \n", - " Parameters| a | b | c | d | Parameters| a | b | c | d \n", - " 1 |-8.26e-02 | 7.79e-01 | 2.17e-01 |1.34e-02 | 1 |-3.04e-02 | 7.42e-01 | 2.60e-01 |-2.31e-02 \n", - " 2 |-8.26e-02 | 7.79e-01 |-2.17e-01 |-1.34e-02 | 2 |-3.04e-02 | 7.42e-01 |-2.60e-01 |2.31e-02 \n", - " 3 | 6.54e-01 | 8.71e-01 | 5.77e-02 |-1.22e+00 | 3 | 7.21e-01 | 8.51e-01 | 1.00e-01 |-4.75e-02 \n", - " 4 | 6.54e-01 | 8.71e-01 |-5.77e-02 |1.22e+00 | 4 | 7.21e-01 | 8.51e-01 |-1.00e-01 |4.75e-02 \n", - " 5 | 3.74e-01 | 9.76e-01 | 0.00e+00 |8.33e-17 | 5 |-1.36e+00 | 9.47e-01 | 0.00e+00 |2.53e-15 \n", - " | 6 |-2.15e-02 | 9.90e-01 | 0.00e+00 |-8.05e-16 \n", - "A 1-R2 coefficient of 4.23e-04-1.07e-20j was obtained for the the real part of | \n", - "the correlation function. |A 1-R2 coefficient of 4.15e-05-3.80e-21j was obtained for the the imaginary part\n", - " |of the correlation function. \n", - "The current fit took 1.221203 seconds. |The current fit took 1.261909 seconds. \n", - "\n", - "10.0%. Run time: 3.76s. Est. time left: 00:00:00:33\n", - "20.0%. Run time: 6.79s. Est. time left: 00:00:00:27\n", - "30.1%. Run time: 9.92s. Est. time left: 00:00:00:23\n", - "40.1%. Run time: 12.89s. Est. time left: 00:00:00:19\n", - "50.1%. Run time: 15.78s. Est. time left: 00:00:00:15\n", - "60.1%. Run time: 18.64s. Est. time left: 00:00:00:12\n", - "70.1%. Run time: 21.55s. Est. time left: 00:00:00:09\n", - "80.1%. Run time: 24.40s. Est. time left: 00:00:00:06\n", - "90.2%. Run time: 27.31s. Est. time left: 00:00:00:02\n", - "100.0%. Run time: 30.19s. Est. time left: 00:00:00:00\n", - "Total run time: 30.19s\n" + "Result of fitting Correlation Function with 4 terms: \n", + " \n", + " Parameters| a | b | c | d \n", + " 1 |-5.34e-01 | 1.63e-02 |-1.84e-01 |1.79e-01\n", + " 2 | 1.48e+00 | 3.20e-01 |-1.83e-01 |9.86e-01\n", + " 3 | 4.75e-01 | 6.57e-01 | 2.51e-02 |-1.12e+00\n", + " 4 | 9.95e-02 | 9.08e-01 | 9.49e-03 |-4.96e-02\n", + " \n", + "A 1-R2 coefficient of 3.09e-05+9.93e-06j was obtained for the Correlation Function.\n", + "The current fit took 0.321152 seconds.\n", + "10.0%. Run time: 0.86s. Est. time left: 00:00:00:07\n", + "20.0%. Run time: 1.41s. Est. time left: 00:00:00:05\n", + "30.1%. Run time: 1.94s. Est. time left: 00:00:00:04\n", + "40.1%. Run time: 2.42s. Est. time left: 00:00:00:03\n", + "50.1%. Run time: 2.89s. Est. time left: 00:00:00:02\n", + "60.1%. Run time: 3.38s. Est. time left: 00:00:00:02\n", + "70.1%. Run time: 3.87s. Est. time left: 00:00:00:01\n", + "80.1%. Run time: 4.39s. Est. time left: 00:00:00:01\n", + "90.2%. Run time: 4.89s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 5.37s. Est. time left: 00:00:00:00\n", + "Total run time: 5.37s\n" ] } ], "source": [ "\n", - "esbath,fitinfo=obs.approximate(\"esprit\",tlist2,Nr=5,Ni=6)\n", + "esbath,fitinfo=obs.approximate(\"esprit\",tlist2,Nr=4,separate=False)\n", "print(fitinfo[\"summary\"])\n", "HEOM_ohmic_es_fit = HEOMSolver(\n", " Hsys,\n", @@ -2017,13 +2003,13 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 55, "id": "0d282401", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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vODs7c2YFkRlwZgWRMdRqIDlZ6iisn5cXYMGt9O7duyce161bt0J91alTp8S+S7JgwQI0adKkQuMWSE5O1ilYOX/+/BJnKgDA9OnTsXnzZvzzzz8mGV+fu7s71qxZI87o0Ofs7IzJkyeLu6ecOXOmxL6019mXxt7eHosWLRJnyOzduxevvvpqOSMnqnoEQShSe2DUqFFFrouJicGbb74ptj///HOddgFbW1u89NJLaNasGfr27QuVSoVVq1bhjTfeQP369YtcP2HCBHFGRc2aNXHixAkEBgYWuU4mk6Fdu3Zo165dscVyc3JyMGvWLLHdvn17HD9+vMjPiLp162LPnj0YMWIE9u7dCwBYtGgRJk2aVORntb7k5GQEBgbizJkzcHd313nOwcEBy5Ytw82bN8XaOJs2bcLkyZOL9LN//37xeO7cuTpxa7Ozs0P//v3Rv39/5OfnlxpbRaSlpWHw4MHYs2ePOIuvgIeHh9nG1fbNN9/gzz//FNtffPGFuP23vho1amDatGmYOnWqzmycipo2bRru378PAOjcuTOOHj1a7O+Y9u3b48SJE+jRowcuX76M6OhoLF++XGcHsAILFy4U+7S3t8cff/yBDh066Fzj7u6OL7/8Eo6Ojvjkk09M9vUQkS4mK4iMkZwM1KwpdRTWLzERqFHDYsMVvLgAUORFaXnp36/dd3EUCkWRHT8q4ujRo2IhMFtbW0ycOLHMe6ZOnWq2ZMWzzz4LV1fXUq/p0aOHeGyqacidO3cWj8+fP2+SPokqs6ioKMydOxe///67eO65555D27Zti1y7cuVK8Y1h//79i01UaOvRowemTJmCNWvWQK1WY+3atViyZInONYcPH8bly5fF9rp164pNVOgrbqr8jh07kJiYCECT2Pjhhx9KTGbK5XKsW7cOx48fR1paGlQqFdauXYtFixaVOfbatWtL/Z3w+uuvi8mKc+fOFbvsLyYmRjwubfmANv0kgikplUp89913Zh2jNCqVCl999ZXYHj58eImJCm1yuRyOjo4mieHq1avYt28fAE2S6Keffio1Ge7o6Ihvv/1W/L3y7bffYv78+TpJ+MzMTGzZskVsv/HGG0USFdoWLFiAbdu2ITw8vKJfDhEVg8kKIqoytCuv29nZVagv/fvL+iSoefPm8PT0rNCY2rTfmLdt29agT8r69u1rsvH1de3atcxr/P39xePS1qdri4qKwtGjRxESEoJ79+6Jb0KK8/DhQ2RmZprshW519tXfX+Grv78q8fkmXk1wbHzpW2E+tukxhCeX/AJ9dtfZmN11donPX0+6jn7/K72uzNEXj6Kpd9MSnzfl11FWvJYUEhKCJ554Qudcbm4uYmNjERERIdaxAYDHH38c3333XbH9aL/pKmkmgL5x48ZhzZo1ACDWodH2888/i8etWrXCsGHDDOq3OHv27BGPe/XqVWzCRZuPjw+ee+45rFu3Try/rGRFs2bN0LNnz1Kv6dq1K+RyOdRqNXJychAVFVVkJpt2/Y4rV64U+fuxtMGDB8PX11ey8f/++2/cuXNHbH/wwQcWj+HHH38U/y8MGTIEDRo0KPOeTp06oVGjRoiMjER8fDyuXbumk2w7fvy4WPtCJpNh+vTppfanUCgwdepUgxI1RFR+TFYQUZXh7u4uLteo6Jah+veXlSww5EVSeWi/CGzWrJlB9xRsRZiVlWXSWACgVq1aZV6jnUTIzMws9dpr165h1qxZOHz4sM4br7KkpKQwWWECqTmpiE2LLfF5N3u3MvtIyEgotY/UnNL/D6rUqlLvL7imNKb8OsqK15IePHiAP/74o9RrGjdujPnz52PcuHHFLs+6efOmzjaRffr0MWjsli1biseXL1+GIAg6/WtP+y9u6Ul5nD17VjweNGiQQfc89dRTYrIiLCwMaWlppRYXNSTR6uDgAC8vL/H3R3HJ1o4dO4pLUD788EP4+vpi7NixZitqXBbtmWxS0P53UK9ePbRv317SGMqTrG/ZsiUiIyMBAJcuXdJJVpw7d048bt68uU4SviSDBg1isoLITJisIKIqw8PDQ3yxmVzBmiL6yz7KmjVh6l0qUlJSxOPyLGlxc3MzS7KiojNVtJ06dQqDBg0qM6FRHO3ZM2Q8V6UrarvULvF5HyefMvvwcfJBSnZKic+7KktfNqSQK0qNoeCa0pjy6ygrXmsTFRWF//77r8Q6Mv/99594rFAoMHr06HKPkZeXh9TUVLi5aZI+arVafJMHoNTp8WVRqVQ6SdmSdn/Q17p1a/FYrVYjKipK55w+QxKtQNnJ1smTJ+Pzzz9HWloasrKyMH78eMyZMweDBw9Gnz590K1bN5PVLDKEqRPk5XX9+nXxuCL/DipC+9/4Dz/8IC7lKUtoaKh4nJSUpPNcwY4kgG7irjRNmjSBra2tuAsMEZkOkxVExvDy0tRjoNJ5eVl0uAYNGojrRv/9998inwiWR3HbsJVGbuJCouWZbWCK+ywlNTUVY8aMEd8MuLi4YNKkSRgwYACaNGmCWrVqwcHBQWcdtrF/h1QyUyx5KGt5RVmaejdFzOyYsi8shTV8HebQu3dvnDhxQmyrVCrExsYiODgYX3zxBU6fPg2VSoUlS5YgLy8PX3zxRZE+tBO2KpWqzJkaJUlJSRGTFQ8ePND5GVOjAjWJ9GcveHt7G3Sf/nXa27cWx5hEa3E/R/38/LBz506MGTNGjD0pKQmbNm3Cpk2bAGiWwg0bNgxTpkxBmzZtyj1ueUi9jbN2Qr8i/w6MpVardf4NaddRKQ/tDwYA3X9PXga+hrGxsYGbm1uRxAcRVRyTFUTGkMstWjiSDNO9e3ccPHgQgOZNsf5a1PLQrhnRuHFj1LRwQVXt2RSG1n8AKr78xdzWr18vFtTz8PDA2bNnS93lJC0tzVKhEVkthUKBunXrom7duhg6dCimTp0q1qn48ssv0a9fvyLLKDIyMkwytlqtFo/1ZzYplUqj+9Xvy9Ckgv6Ylpxt1b9/f1y/fh1ffvklNm/ejLt37+o8HxMTg1WrVmH16tUYP348Vq1aZbZla6ZOkJeX9ve9Iv8OjJWVlaXzb9NY+n0UFLYGypfokuJ7QFQdSPuTjojIhHr16qXT1i4EVx5RUVE661b1+7UE7e34DN1Z4/bt22ZZAmJKhw8fFo9nzpxZaqICgM6aeyLSzDRatWqVztKH6dOn67zJAnQTnvXq1YMgCEY96tWrV2yfQNFPpcujYLZGAUMTk/oJ2Yru/FReNWvWxNKlSxEbG4srV67gm2++wejRo3WWCgqCgI0bN+K5556zaGymVFYiQPv7XpF/B8ZycnKCra2t2D5x4oRR/771twDW3vWqPMlyJtaJzIPJCiKqMnr27KmzZnj9+vVGvXlfvXq1zjTgl156ySTxlUdQUJB4fOXKlTKnOgPQmTZurbTXqGt/jSX566+/zBkOUaVka2uLlStXiu1bt26JO3gU0J4NFh0dXeaORoZwdHTUWX4QERFhdF/Ozs5wcHAQ21FRUQbdp11TAJBmCQKgSRq1bt0aM2bMwPbt25GQkIDff/9dJ4m0d+9enSKQUtGeIWBoXYWyfudo1wKpyL+DitD+uzdVDNr/b27dumXQPffv37f6WY1ElRWTFURUZchkMp3t+WJiYvDRRx+Vq4+rV69i+fLlYrtLly7o0qWLyWI0VL9+/cQXmLm5udi4cWOZ9xRUyLdm2i+UDalFUbAWnIh09erVCwMGDBDbixcv1knOBgUFiUsF8vPzcfLkSZOMq/3z8NSpUxXqq127duKx9s4gpfnnn3/EYw8PD52ZH1JSKBQYPHgwjhw5olNX49ChQ0Wu1V7CYYk6Q9oJJkMS37dv30Z6enqp12j/O7h06VKFlx0Z8z3RjuHo0aMVGr+A9r/Jy5cvIz8/v8x7tJeNEpFpMVlBRFXKSy+9pPNi47PPPjN4OUhCQgJGjBghvqFWKBRYsWKFWeIsi5eXF0aOHCm2P/744yKfKGpbu3ZtpZiF4OvrKx6fOXOm1Gt37NhhsjdYRFXRBx98IB4nJCToJCzd3d3RqVMnsf3tt9+aZMz+/fuLx7/++muRnZPKo2fPnjp96S9lKc6PP/4oHvfo0cPqCvDWqFED3bt3F9sJCQlFrnFychKPLbF0T3tZofZOGCXZs2dPmdf07dtXLISclZWFLVu2GB8gjPueaCfrdu/ejfj4+ArFAOj+m7x//77O0sWSGLvklIjKxmQFEVUpdnZ22Lp1q1jUTK1W44UXXsDHH39c6vTXM2fOoGfPnuJuIgDw0UcfGbRUwVwWLVokfh0PHjxA3759ceDAAZ1PnTIyMrBo0SLMmDED9vb2cHZ2lipcg/Tu3Vs8/uabb/Dvv/8We92hQ4cwYcIEC0VFVDn16NFD5//UZ599plP48PXXXxeP9+zZg927d1d4zMmTJ4s/lzIzM3Vms5XXxIkTxeOEhAQsW7as1Ot//fVXnRkYkydPNnrs8irPDAjtWQnFbXutvYSitCS0qbRv3148/vvvvxEbG1vitSkpKfj888/L7NPX1xejRo0S2++//36FkgXGfE+ef/55cRZLdnY2pk+fXuGZKoGBgTpbsc6fP7/U2RVhYWEVTtQQUcmYrCCiKqdp06Y4ePCgWABMpVJh/vz5aNiwId566y1s27YNJ0+exO+//44VK1bgscceQ8+ePXXWvM6dOxfvvvuuRF+BRoMGDbBq1Srxk8Po6Gg8+eST8Pf3R58+fdClSxfUrFkT77//PvLz8/H555/rbLVmjdXJX375ZXGdempqKrp27Yq5c+fiwIEDOHXqFLZs2YJRo0Zh4MCByMjIkKReCFFl8v7774vHcXFxWL9+vdgeM2YMunbtCkDzZnvs2LHYvHlzmX1evXoVU6dOLXb5mZeXF958802xvWXLFrz66qul1sRISkoqNhHRtGlTjB49Wmy/99572LlzZ7F9/PPPP5g0aZLYbtOmDZ566qkyvxZT6devH9asWVNmbYKDBw/i+PHjYru4As3ayYPLly+bvd5Qt27d4OPjA0CzJGjGjBnFFtBMSUnBiBEjEBNj2JbCCxYsEH+eJyUl4bHHHiu1doRarcbPP/+Mq1evFnnOmO+Jk5OTzlLPXbt2Ydy4cWUWu0xJScE333yDZ599ttjn33nnHfH4/PnzmD59OlQqVZHrYmJiMGzYsGKfIyLT4NalRFQl9ezZE3/++SdeeOEFBAcHA9C82f/iiy9Kvc/V1RVLlizBtGnTLBBl2SZMmAC1Wo2ZM2eKa4Lj4uJ0dsmwsbHBokWL8Oqrr+KTTz4Rz+tX27cGfn5+WLt2LcaPHw9BEJCeno7PP/+82E/yevbsiZUrV+L777+XIFKiyqF///7o0qWLWMthyZIleOmll2Brawu5XI7t27cjKCgId+/eRVZWFl588UV8/fXXGD16NNq0aQM3NzdkZmYiPj4ely9fxpEjR8QZT9pL6rTNnz8fJ0+eFJdprVq1Cr/99huef/55dO7cGZ6enkhLS8P169dx4sQJHDhwAL6+vjozPQqsWrUKf/75JxISEqBSqTBq1CiMGDECY8aMQe3atZGUlIT9+/dj06ZN4ptCe3t7/O9//xOXIVjCzZs3MX36dMyePRsDBgxA165dERgYCE9PT+Tn5+POnTvYv38/duzYISYCOnTogIEDBxbpKzAwEG3btkVwcDAEQUDfvn3RunVrBAQEQKEofGm+bt06k2ybbWNjgzfeeEN8E75nzx507doVr7zyCho2bIj09HT8/fffWLduHRITE9GnTx9ERESUOgMDAJo3b44VK1ZgypQpADSzDFq0aIFnnnkGAwcOhL+/P9RqNWJjY3H27Fns2rULcXFxOH78OJo3b26S78m0adPwzz//4H//+x8AYOvWrTh48CDGjh2LHj16iDM27t+/j6tXr+Lvv//GkSNHkJubi86dOxf7dY0ePRpDhw7F3r17xTHPnTuHKVOmIDAwEFlZWTh9+jTWrFmDhw8folu3brhz547BSR4iKgeBiISMjAzhwoULQkZGhtShkInl5+cLGzZsEDp37izI5XIBQLGP2rVrC6+//rqQmJhocN8LFiwQ7x8/fny54tIeOyoqqszrb926JcydO1do0aKF4OzsLLi4uAiBgYHCtGnThCtXrgiCIAh5eXmCra2t2G9CQkKxfW3YsEG8pnfv3iWOWbduXfG648ePlxljVFSUztdVmr179wr169cv9u/Cw8NDeO+994S8vDxBEAz7XlXk74LIGowfP96g/5fF+f3333X+n3z//fc6z9++fVto27ZtiT//SnqsWbOmxDEzMjKEIUOGGNxX3bp1S+wrLCxM8Pf3N6gfFxeXMn8eaX8vFyxYYND3sKyfd9rPG/Jo1KiRcOvWrRLHO3/+vODu7l5qH/o/78r7M1lbbm6u0Lt37zLjDgwMFBITE8s11g8//CAoFAqDvzcl9WfM90QQNL/n33zzzXL/++7cuXOJX1NaWprQtWvXMvvw9/cXbt26VaG/m+IUvC5dv369sGbNGiE/P7/CfRJVRkxWEAlMVlQXCQkJwt69e4V169YJn376qbB8+XJh69atwuXLl6UOzSQuXLggvliqVauW1OGUKS8vTzh16pSwcuVKYdGiRcLatWuFP/74Q8jJyZE6NCKLq0iyQhAEoX379uL9DRs2FJN9BXJzc4Vvv/1WaNy4calvvpydnYUhQ4YIW7duFbKyskodU61WC1u3bhWaN29eYn8ymUzo0KGDsGHDhlL7Sk5OFmbOnCk4OTkV24+tra3w3HPPCbdv3y7ze2GOZMVPP/0kDB8+XHBzcyv1++ft7S288847QlpaWpljxsTECO+9957QpUsXwdPTs8gbflMmKwRB81pn+vTpgo2NTZG4lUqlMHnyZDHu8o4VFhYmjBo1Sidhrv+oWbOmMGvWLCEpKclk3xNt//zzj/Dkk0+WmjiRyWRC27ZthY8//li4c+dOqV9TVlaW8PbbbwsODg5F+rGxsRGGDx8ufijAZAWRecgEwQJ7JhFZuczMTISFhSEwMFAsHEZU2cyYMQOrV68GAIwYMaLEtd9EVL3dvHkTZ8+eRWJiItLS0uDk5AQfHx80a9YMrVq1gq2tbbn7vHHjBs6ePYuEhARkZmbCxcUFDRo0QMeOHXWKJ5YlOzsbp06dws2bN3H//n24urqiTp066NOnD1xdXcsdl6mp1WpcvXoV169fR0xMDNLS0mBnZwcvLy+0atUK7dq1M+r7Z0lJSUk4cuQIoqOjYWNjgzp16qBv3746NY+MlZaWhlOnTuHOnTu4f/8+lEolfH190bJlS7Ru3doiu7ekpaXh9OnTYgw2NjZwd3dHo0aN0Lp1a52tZQ3t78iRI4iKioIgCPD390ePHj1Qu3ZtM30Fha9LQ0JCkJOTg5dffllne1ei6oLJCiIwWUHWSxAEg17cHTt2DAMGDBCrlu/ZswdDhw41d3hERERkYkxWEGnwXz0RkRX74Ycf8Mwzz2D//v3FVttPTk7GJ598gkGDBomJig4dOmDw4MGWDpWIiIiIyGS4GwgRkRVTqVTYtm0btm3bBltbWzRu3FishB4fH4/r16/r7Cvv6elp8Sr5RERERESmxmQFEZEV0572mZeXh6tXrxa7Rz0AtG3bFj///DOaNm1qqfCIiIiIiMyCyQoiIiv20ksvoVmzZjh48CDOnj2LyMhIJCUlIScnB66urvDx8UHXrl0xbNgwDB061CLFy4iIiIiIzI3JCiIiKyaXy9GrVy/06tVL6lCIiIiIiCyGBTaJiIiIiIiIyKowWUFEREREREREVoXJCiIiIiIiIiKyKkxWEBEREREREZFVYbKCiIiIiIiIiKwKkxVEREREREREZFWYrCDSIgiC1CEQERERUTXG16NEGkxWEAGwsbEBAOTn50scCRERERFVZwWvR/m6lKo7JiuIANja2kImkyEzM1PqUIiIiIioGsvMzIQgCMjNzQUAyGQyiSMikgaTFUQA5HI53Nzc8ODBA6lDISIiIqJqLDk5Genp6VCpVFAqlUxWULXFZAXRIx4eHsjMzERaWprUoRARERFRNZSWlobs7GzxT29vb6lDIpIMkxVEj7i7u8PFxQURERFMWBARERGRRaWlpSEiIgKZmZlISUmBWq1Gw4YNpQ6LSDIKqQMgshZyuRyNGjVCSEgIwsPDYW9vDy8vLzg6OsLGxoZT8IiIiIjIZARBQH5+PjIzM5GcnIzs7GxkZmYiJiYGSUlJcHV1RUBAgNRhEkmGyQoiLXK5HIGBgfjrr7+QkJCArKwsJimIiIiIyGwEQUB6ejrS0tKQmpqKe/fuQRAEdO/eHS4uLlKHRyQZmcCNfImKyMvLw7FjxxAWFgZBEODk5AQ7OzvI5Vw5RUREREQVVzCzIi8vDyqVCpmZmVCpVHBxcUHPnj3RunVrfmhG1RqTFUQlyM/PR0JCAu7cuYPw8HBkZGRArVaD/2WIiIiIyFRkMhnkcjlq1KiBxo0bIyAgAB4eHkxUULXHZAWRAbQz30REREREpiKTyWBrawsbGxupQyGyKkxWEBEREREREZFV4QJ8IiIiIiIiIrIqTFYQERERERERkVVhsoKIiIiIiIiIrAqTFURERERERERkVZisICIiIiIiIiKrwmQFEREREREREVkVJiuIiIiIiIiIyKowWUFEREREREREVoXJCiIiIiIiIiKyKkxWEBEREREREZFVYbKCiIiIiIiIiKwKkxVEREREREREZFWYrCAiIiIiIiIiq8JkBRERERERERFZFSYriIiIiIiIiMiqMFlBRERERERERFaFyQoiIiIiIiIisipMVhARERERERGRVWGygoiIiIiIiIisCpMVRERERERERGRVmKwgIiIiIiIiIqvCZAURERERERERWRUmK4iIiIiIiIjIqjBZQURERERERERWhckKIiIiIiIiIrIqTFYQERERERERkVVhsoKIiIiIiIiIrIpC6gDIMtRqNeLi4uDi4gKZTCZ1OEREJAFBEJCWlgY/Pz/I5fy8gsyHrzuIiAio2GsPJiuqibi4OAQEBEgdBhERWYHo6Gj4+/tLHQZVYXzdQURE2ox57cFkRTXh4uICQPOPxNXVVeJoiIhICqmpqQgICBB/JxCZC193EBERULHXHkxWVBMFUzBdXV35ooGIqJrjtHwyN77uICIibca89uCCVSIiIiIiIiKyKkxWEBEREVnQX3/9halTp6J58+Zwc3ODq6srmjdvjpdffhlnzpwx+/g3b97E/Pnz0aFDB9SoUQMODg5o2LAhRowYgR07dkClUpk9BiIiorLIBEEQpA6CzC81NRVubm5ISUnhdEwiomqKvwuklZGRgZkzZ2L9+vWlXjdx4kSsXLkSTk5OJo9h+fLlePvtt5GTk1PiNV26dMGPP/6IBg0aGD0O/60RERFQsd8HnFlBREREZGb5+fkYOXKkTqLCwcEBHTt2RJcuXXRewG3YsAEjR45Efn6+SWP4+OOP8frrr4uJCrlcjpYtW6JXr17w9fUVr/vnn3/Qu3dv3L1716TjExERlQeTFURERERm9sEHH+DQoUNie8qUKYiJicH58+fx999/Iy4uDh988IH4/KFDhzB//nyTjf/HH39gwYIFYrtr164ICwtDaGgoTp48iZiYGPz8889wdnYGAMTExGDMmDEmG5+IiKi8qnWy4t69ezhw4AA++ugjDB06FL6+vpDJZOJj48aNZhtbexxDH99++63Z4iEiIiLziIuLw9dffy22X3jhBaxbtw6enp7iOScnJ3z00Ud4//33xXNfffUV4uLiKjy+IAh4++23UbDyt2nTpjhy5AiaNGkiXiOXy/HMM89g165d4rkzZ87otImIiCypWiYr4uPjUa9ePdSsWRNPPvkkFixYgN9++w3x8fFSh0ZERERVzLJly5CdnQ0AcHR0xLJly0q89oMPPkBAQAAAIDs7G8uXL6/w+AcOHMCVK1fE9vLly+Ho6Fjstf3798czzzwjtpcsWVLh8YmIiIyhkDoAKWRnZ+P27dtShyHq1asXHBwcyryuTp06FoiGiIiITEl7dsLTTz+tM6NCn52dHSZOnIiPPvoIALBz504sXbq0QuPv3LlTPK5fvz4GDBhQ6vVTp07FL7/8AgA4d+4cYmJi4O/vX6EYiIiIyqtaJiu01ahRAx06dEDHjh3RsWNHDB8+3OIxbNq0CfXq1bP4uERERGRe169fR2RkpNh+4oknyrxn0KBBYrIiMjIS169fR9OmTY2OYd++feLxwIEDIZPJSr2+Z8+ecHJyQkZGhnj/1KlTjR6fiIjIGNUyWeHp6Ynt27cjKCgIdevWlTqcyiMhAfDxkToKIiKiSkN7+QWgKWxZlvbt28POzg65ubkAgJCQEKOTFYmJiTrLXA0ZX6FQICgoCCdOnBDHJyIisrRqmaxwdXXF6NGjpQ6jcrlyBWjfHvlPDYbNW3OBHj2kjoiIiMjqhYWFicd2dnZiPYrSFFx348aNIn1UZHwAaNiwoUH3NWzYUExWVGR8ox0/Dnz+OZCTA+TmAvXrA//7n+XjICJpCAKgVgP5+cX/qX8sCIUP7XZ5j63hfv1HwfdDyvb06YCbm3n/zotRLZMVZIQ1a5CsVMO3zW/ove437Li2Em4vvSp1VERERFbt1q1b4rG/v3+ZSzAK1KlTR0xWaPdRkfEL+jV0/JL6KE5OTg5ycnLEdmpqqkHjlCghAThwoLD94EHF+iOqTAQByMvTJOoKEnaG/KlS6T7y8oqeq+B5QZWHfHU+8gQVVOp82OYLsM8rObGQJVPhqksO8qFGvpCPfEENtaB+1NY8usfI4Jytl4AoeJMM4HIt4JIvkC8H8mWAWlZ4nC8HPLOASZdL/5Yu7Q7cdQEEaO4XHvWjlmnODY4Ahl4v+f4YV2D2wJLvV8uA1fuAuikl97GhLfBDe917tPuq/wD4dVvpX8fgsUC4l+YeAZo/gcLjWf8Ab/xT8v3XvYBeE3Xv1/4TAM6sB5rf07tx7FgmK8hKpaYCW7bAKwvocws43BCY9/sbWDXiOcDLS+roiIiIrFZaWpp47FaOF3qurq7F9lGR8csTQ3nHX7x4MT788MPyBVcapRLftweONABybIDlYelgmXGSVG4ukJam+8jMBLKyjP+zlORDlgL4tyaQowBybTT/D/SPR18F3LNLDvnXQGBnIJBnA+TJAZVcc6ySa9oBqcCWnSXfDwBdJwP/1Sy8R2Wj+/zC48CCsyXff7MG0PH50scIXQ20fFjy83ubAgv7lvx8s3tlJys2t9F8HSXxySg9WZFmB2xvUfoYqUdKfz7aDThTyg+yLAPemd92ByJLefv1oIw9G9QyING59GvyDcupWwSTFVS24GBAJkO+rPAH4oaWKnz200Y4vTZH0tCIiIisWXp6unhsb29v8H3au4Rp91GR8csTQ3nHf/fddzF79myxnZqaatCSlxLZ2eGCH/BLS01zwbVsJivIePn5QEqKZobO/ftFHw8eaD6c005EpKZCSEtFVlYa0nLSkCbLRZodkG4HyAWge3TpQ745ALjhAWTaAlm2QKYjkOXz6NgWmP03MPdMyfdHeQCdXi59jC4xpScr/qsJ/NS65OebJJXePwBk2AFpypKfz7Mp+TkAsBFKfx4o+82xvIw+8uVljyEro4+ywjTk/btQxkXFxSBXa74+GQCFuuwx3LMBz0zN9TKh8M+CGJ1zS7/fLl8zg6MgHv1+ZI+uKUIw4C/SDJissAJvvfUWrl69iujoaOTl5cHLywuNGzdG7969MX78eNSvX1/aAHv1AuLiYPPEE3DL/guA5gftqT+3YBCTFURERCVSqVTisUJh+Msu7Wvz8vJMMn55Yijv+EqlEkplKe9oykuphFIr9By18d8DqoIEAXj4ULNcKD5e86f2Iz4eSExE7sNkPMhMxoOcFDywBx7aaz55fjKi9Df5X3YFPnpSk5hQF/NGuGkScO2b0kM80gC4Uqvk5x+UkTcs9g2jnpwyEgVl9aEy4E1+gwea62zzNW+mbdWP/nzUblDGCi3PLGD6OU3SQi4ANmrNsc2jN+k2AlAzo/Q+BkcAtdIL79PvyzWn9PsBzQySLNtHiYFH98sByCCDHDLUzJIDdnJAJgPkj/7UOm6YD9z+AZDL5JABkEOuOZZp7pdBBjdbG8C/5D7mxQPztsoge3SfTCbXvU4mA9o+uh7QPf+offo/3bb+85DLgF4lP99QJsPN/0q5XyYDOhfzvEMZUzbMhMkKK7Bjxw6ddmxsLGJjY3HixAksWrQIkydPxtdff63zKUdZTL521MUFmD0bA+f/he87aE4dTruCQWlpmueIiIioCEdHR/E4O7uUd0d6tK91cnIyyfgF/eqfM+f4RrOzg1LrjVaOuoyPC6lqSU0FoqOBO3cKH9HREO7chir6Dmxj4jRLJooR6QkMfB5IbA2kl5A/u7AW6HC35OEFGZBaSjIh3a7sL8FBL78mVwOOeYCDSvNcWZ+Ae2YBr54FlPmapINSpXUMG9jJFKgjdwT8HAA7O83D1hZQKMQ/X1YIGPMPYGtjC1sbWyhsbGErt4VCYSe2MUmpuUf7UdCPQoHd+udtbAofcjnQ99GfNsX/WdPGBqtKed6QP9vL5Whf0vNyebGJAf3jNvrPGVg/qIAtUOHZXWXklqgYTFZYAW9vbzRs2BDOzs5ISUnBtWvXxCmXKpUKa9euxblz53D8+HGD15qafO0oAAwciH4TFZAJKggy4FQdAbhwAehbyiIyIiKiaszZuXBxcFZWlsH3ZWZmFttHRcYviMGQZIWpxjcaZ1ZUfenpQESE5hEeLv4Zfu8aIuUPEe2qKWoY7ab1Z3dg5llg8c2Su3XOBW56lj70wzJmNdRK18yecMkBXHI1fbrkPPozF6iRAcDZGXB01DwcHHT/dHTEzhxAFusAB3tnODi4wtbBCTJ3p8JrghyAWUpNkkFZ9E9POzus1D9fkJAw8I2256MHUWXFZIVEmjdvjpdffhlDhgxBgwYNdJ5TqVT4448/MG/ePHFv88uXL+PZZ5/FAe3K2KUw+dpRAHB2hkfj1miSfAnXvTVFf1Tn/oGCyQoiIqJieXt7i8d375byUa6e+Ph48dirAsWstccviMGQ/kw1vtGUSt2ZFQKTFZVWXp4mGRESovuIiSn28gmTgb9Lecka41rycwDgnQl4ZWoSCt6ZgEc24JGlWfbhkQ145NmgkcITaFET8PAAPD01f7q5aWYLu7jg+UcPuLqK58SHqyvg5FQ4Vb8EvmV9X4ioTExWSOS///4r8TmFQoHBgwejX79+GD16NPbt2wcAOHjwIH777TcMGTKkzP5Nvna0QKdOaBOvSVbkKIDrocfRAu+afhwzOnLkCB5//HEAQPv27XHhwgWDt5IzlQkTJmDTpk0AgC+//FInsURERFVH06ZNxePk5GRkZmYaNLMhOrqwel+zZs1MMj4A3LlzBy1btrTY+Eazs9OdWSF7tKWhDSdSWzVB0MyQOHsW+Ocf3A0+jcsPriLUU4V/a2pmRxzfX3qxwkb3i09WOOUCASmaWQ863NwAHx/No1YtKHx8kOTiAzTyAby9C5MRnp6ah4NDuZcAEJE0mKywYvb29ti6dSsaN26MhIQEAMDKlSsNSlaYTVAQ2q//FsG1gLbxAKKuSReLEfLy8vDaa6+J7aVLl1o8UQEAH330EX7++Wfk5OTgww8/xLhx4+Dj42PxOIiIyLwCAwN12sHBwejWrVup98TGxuLevcJN7vX7KI/GjRtDoVCIhTaDg4Px5JNPlnnf5cuF+wBWZHyj6c+sUEBTo0CiIm9Ugvx84NIl4OhR4NQpRP/3F34KSMG52sC52kDMgKK3xDsDvqVsMDMiDKj7EKiTAvjL3RDg4g9/r/pwq90AsgZ1gT51gI8DgFq1NAmKcuyyQ0SVC5MVVs7FxQXTpk3DwoULAQB//vknsrOzy7X9mUm1bIm5Z4C3C7ZakscC2dmV5hfF6tWrce2aJsHSp08f9O/fX5I46tSpg5dffhkrV65EamoqPvjgA6xbt06SWIiIyHw6deoEpVIpFr0+ffp0mcmKP//8Uzy2t7dHp06djB7fzs4OnTt3xpkzZ8TxyxIfH4/IyEix3atXL6PHN5qdHRonA8PDNEUFA1IA5OQwWWENbtwA9u/XJChOnNBsCfpIvB/wzuMl32qjBm54aiUrZDKgTh2gcWOgSROgcWOMaNwYIxo10pzn3zdRtWbAhjUktb5aNSGys7N1pmZaXGCg7tQ9tVoz3a8SyMjIwKeffiq233nnHQmjAebMmSNuDbdhwwbcuHFD0niIiMj0nJ2d0a9fP7H9448/lnmP9jX9+vWr8G4cw4YNE4+PHDkiztY0ZHx3d3dpkhVKJQZFArt+AX7eAfS8gxJ3fyAzU6uBc+eA995DVuvmQKNGwMyZwJ49OokKAGidANg9Wr7jnAP0jQLeOgNs3gkE76yJjIsD0WPsO8BPP2nqVmRmArduAYcPA6tWAa+/DgweDDRtykQFETFZURnUqqW7SXNSUpJEkUBTWMjfX/dcWJg0sZTTqlWrkJiYCABo1aoVBg4cKGk8devWxZgxYwBoiqp+/PHHksZDRETmMWHCBPE4JCQEv/32W4nXXrp0SaeYtva9xnruuefEOlZ5eXn47LPPSrw2PT0dK1asENvjxo2Dra1thWMot+LqbmltyU4WEBoK4a03ca5DLbz1fmc0Tf0Uk5uU/ppPmQ9s2w78+7MXHl4dimPNFuOzd47i+ePJaHMlAcrfDwKLFwPPPQe0alVpZuYSkTSYrKgEtLcPA4rumW5x+mtXr1l/3Yq8vDydF19Tp06VMJpC2nFs3bq1XJXiiYiochg9ejTatGkjtqdOnSouSdR29+5dPP/888jP1xRraNu2LUaNGlVsn7du3YJMJhMfBctFi+Pv76/z+2b58uX49ddfi1yXl5eHiRMn4s6dOwAABwcHzJs3z6Cv0eTs7IqeY7LC/JKSgC+/xI3ugfjotdZomvMlOg+/hy+6A+HewIFGQF5x7x6aNQOmTwd+/hnD/riFFlfvwWb3HuCdd4DHHtMUtiQiKifWrKgE9HcOqVmzpkSRPNKkiWa6XoGoKOliMdD27dsRGxsLQLP+d9y4cRJHpNG7d280atQIkZGRyM3NxZo1a/DRRx9JHRYREZmQTCbDd999h969eyMrKwt3795F586dMW3aNPTq1QsKhQLnzp3DN998Iy7RcHBwwLp160xWBHrhwoU4cOAAIiIikJ+fj6effhpjx47F8OHD4enpievXr2PNmjXilukA8Pnnn8PPz88k45dbcckKLgMxn0uXgJUrcfbkj3izTx5OF1MYU64GWiUCCc6Av7IGMGgQ0L+/JhlRu7blYyaiKo/Jikrg559/Fo/r1asHX1+Jd26uV0+3feuWFFGUy/r168XjAQMGwN3dXbpg9IwZMwaLFy8GAGzatAkffvihJDuUEBGR+QQFBWHLli14/vnnkZWVhdTUVCxduhRLly4tcq2DgwO2bNmCoKAgk43v4eGB33//Hf3790d0dDTUajW2bNmCLVu2FHv93LlzMWPGDJONX242NppHvvaWIJxZYVKCAPz+O7B0KfCoAKutL3C6buElMgHocwsYFwIMz2sIr0Ejgf3DgC5duI0sEZkdkxVWbu/evfj999/F9vDhw6ULpkAlS1bExsbi+PHjYnvkyJHl7iMlJQWhoaEIDw/H/fv3kZubC3d3d/j4+KBz587w16/jUQ4jR44UkxV37tzByZMn0adPH6P7IyIi6zRy5EhcvHgRM2fOxNGjRyEIgs7zMpkMjz32GFasWIHmzZubfPwmTZogJCQEb775Jn766SdkZWUVuSYwMBBLlizB0KFDTT5+uSmVmgKMBTizwjQEQVMc86OPAK0tagGg/V2g2x3ggQMwIRh47p4PAoaPB/73AtCypTTxElG1xWSFidy6dQv169cX2wsWLCh2/WhKSgomTZqEefPmoUOHDqX2uXXrVkyZMkVsOzo64u233zZZzEbT+jqzFEBO4h24q1SAwjr/Oe3ZswdqtVpsP/54KXtqaQkLC8PPP/+Mffv24fLlyzp96GvZsiXefPNNvPDCC5DLy1cKpkOHDvD09MT9+/cBALt27WKygoioigoMDMThw4cRHR2NM2fOiEsUa9euje7duyMgIMCgfurVq1ck2WEId3d3fP/99/j6669x7NgxREdHIyMjA76+vmjVqhXatWtX7j7NRj9ZwZkVFXf8ODBnTpEkhbY9v9rC66kxkH04CejThzMoiEgy1vnu0gKmTJmCzZs3l3nNK6+8UuR8dna20eMKgoCdO3di586daNasGQYOHIi2bdvC19cXTk5OSEtLQ2hoKHbs2IHz58+L98lkMmzYsKHIziCSqFcPwbWAx18AkpyA186qsSImpuiMCytx8OBB8bhx48YGr7/t2rUrUvS25CrJv//+iwkTJmD79u346aef4OrqanB8MpkMvXv3xq5duwAA+/fvx/Llyw2+n4iIKp+AgAA8++yzko3v4uKis6WpVXpUtyJfBqhlgC2TFcYLD0f6229g9b39mBkKFLsHh58fMG0avKdMAXx8LB0hEVER1TZZkZeXh5wyfumpVCqoVCqzxXDt2rViq4Hrc3Fxwdq1a/H000+bLZZy8fSEp8wRSU6aTzuiXaFZCmKlyYrTp0+Lx8au/23SpAmaN2+OevXqwcXFBYIg4N69ewgODsa5c+fET7f27duHF198Ebt37y5X/0FBQWKyIjIyEnFxcdIVNSMiIrIC4TXkaD4VyJcDLwYDm7gMpPxycyF8ugg/716EOf3zcbctIAB4+4zWNU2bAu+9Bzz7LCDFNrVERCWotskKqTg4OODll1/GmTNncPXq1VKncLq5uWH8+PGYM2cO6tSpY8EoyyCTwc+7PuTq/6CWA9FusNq6FTdu3MCDBw/EdqtWrQy+t0uXLhg9ejQGDx5calHTqKgozJo1C7/99hsAzbKTX375Bc8884zBY7Vu3Vqnff78eev/xIuIiMiMbG3skP9oZWWOAlwGUl5nz+LOqy9gWpMI7B9RePrz7sDMs4BDo2bA/PnA009zqQcRWaVqm6zYuHEjNm7caLL+DF07qlQqsXbtWgDAgwcPEBwcjMTERCQlJeHhw4dwdHSEp6cnWrdujdatW8PGSn95KOo1gF/af4hxA+5YcbIiNDRUp924cWOD79VePlKa+vXrY/fu3Rg2bJhYDHXZsmXlSlY0adJEpx0SEsJkBRERVWtKhVI8zrEBC2waSqWCsHAB1v7xKd4cAGRo7QI75DrwxXkPOKz8FHjpJautN0ZEBFTjZIU18PDwQN++faUOwzh16yIgFYhxA+45Adm3bxa//lFit/SSKBXZtaM0crkcCxYsEJMV//zzD5KTk+Hl5WXQ/bX19ifXj5uIiKi6sVcUvrLgzAoDRUfjwQtjMLnWWex6qvC0bxqw6oAcIwa9AVz4AHBzky5GIiIDlW/bAqICtWujjlbtyZj7UdLFUoq4uDidds2aNc02lv4Sk7Nnzxp8r6OjI1xcXMR2QXV4IiKi6qrIzAomK0p3+DDQti1s/jmLK1r1MV++AISdbIURWy4AX3zBRAURVRpMVpBx/PwQoJ2sSI2RLpZSpKen67QdHByM6mPz5s2YNGkSOnbsiNq1a8PFxQW2trZQKBTiw8nJSee+mJjyfU+0Y9OPm4iIqLpR2hb+XsxRgMtASrNqFTBoEHD/PlxzgK2/ArXSgL2/yLG26yK4/XURsKZtaYmIDMBkBRmndm3U0no/HZ95T7pYSqG/44udnV0JVxalUqnwxRdfwM/PDy+++CI2bNiAixcvIi4uDunp6VCpVMjPz9d5aNMu7GkIpbLwE6SsrKxy3UtERFTVKGyVkKs1x5xZUYL8fODVVzUPrdchnWKBqN11MGTDX8C8edzlg8iCjhw5AplMBplMhg4dOhRb13Djxo3iNTKZzORLwFUqFZo0aQKZTAYbGxtcuHDBpP1bCpMVZBw/PwyOALbuAI5vBPqHZgDZ2VJHVYR2AgAAcg38VEalUmHs2LF46623kJaWZtTY2eX8fmgnVoyZAUJERFSlKJVQPnr/zZkVxcjNBZ57TjOrQt+IEbC/cAXo3NnycRFVY3l5eXjttdfE9tKlSyGTySweh0KhwCeffAIAUKvVeO211wzaDMLaMFlBxvHzQ7Mk4Nl/gT63AO9MAHr1IayBs7OzTtvQGQtfffUVtm/fLraVSiVefPFF/PjjjwgODsa9e/eQmZkJtVoNQRDEh7by/kDIzMwUj/WXlBAREVU7SiU27ga2bwOWHwBnVmjLzgZGjQK0XquI3n8f2LEDcHe3eFhE1d3q1atx7do1AECfPn3Qv39/yWIZM2YMWrduDUBT/H/r1q2SxWIs7gZCxnF1BZycgIyMwnNxcUCDBtLFVAw/Pz+ddkJCAurXr1/qPbm5ufj000/Fdq1atXD06FE0b9681PsqUmciMzNT53793UGIiIiqHTs7PP2fVpvJCo2sLKQNH4SQsJPorn1eqQTWrwfGjpUqMqJqLSMjQ+c9xDvvvCNhNIBMJsPcuXPx/PPPAwAWLlyIp59+GopKtGUxZ1aQcWQyQC8RACvcwUI/MWHILht//vknUlIKq4cuWbKkzEQFoEmEGEs/rnr16hndFxERUZWgt5STy0AA5OUh69nRGOJ3Eo+/CJyu8+i8kxOwbx8TFUQSWrVqFRITEwFodgkcOHCgxBEBzz77LAICAgAAERER2LJli8QRlQ+TFWQ8/WSFFS4DadmypU47PDy8zHuuX7+u0x40aJBBY1WkcI3+mAVTtoiIiKot/aLY1X1mhVqN3EnjMcZ5P07WA7JsgRdHACo3F+DQIaBfP6kjJKq28vLysGLFCrE9depUCaMpZGNjg8mTJ4vtr7/+WsJoyo/JCjKe/lIFK0xWNGzYEB4eHmI7NDS0zHsePnyo09a+vzTbtm0rV2za9OMKCgoyui8iIqIqgTMrdAhvz8WkzK3Y10TTds4BfjngBMXR40C3btIGR1TNbd++XZwpbW9vj3HjxkkcUaFJkyaJRT5DQkJw7NgxiSMyHJMVZLxKMLMCAHr16iUenz9/vszrXVxcdNqGbCUUGhqKPXv2lDu2AtpxNWzYkDUriIiI9JMV1XlmxcaN+OTcl/jx0cRL+zzgt1/tELT+D6BDB2ljIyKsX79ePB4wYADcrajAbUBAALp06SK2N2zYIGE05cNkBRnPx0e3XYGaDeb0xBNPiMeRkZFl1q1o0aKFTvu7774r9foHDx5g3LhxyNfa37w8BEHAyZMnxbahy06IiIiqNC4D0fjrL2z/egrmP6ZpygTgp91y9Fm2G+jevdRbicj8YmNjcfz4cbE9cuTICvd57do1/Pzzz/jyyy+xbNky7NixA0lJSUb3px3Trl27KrQxgCUxWUHG8/FBaE1gQ1tgcQ8gJs36CmwCwNChQyGXF/5TP3LkSKnXd+/eHd7e3mL7yy+/xOrVq4vdivTChQvo1asXQkNDjd5u9OLFi7h//77YHj58uFH9EBERVSlcBgLcvYt/XxqK8UNU4qnFR4ARs78D+OEGkVXYs2cP1Gq12H788ceN7uvEiRPo0qULAgMD8dxzz+HNN9/EG2+8gTFjxsDX1xcjRozA7du3y92vdkwZGRk4fPiw0TFaEpMVZLyaNbGtBTBpODCvP/Bf/l2pIyqWn58fHnvsMbG9c+fOUq9XKpV4//33xbZarcaMGTPQrFkzzJgxAwsWLMDMmTPRqVMnBAUF4d9//wUALF++3Kj4tOOpXbs2+vbta1Q/REREVYqdHf72B35pAfyvDZCdmyl1RJalVgMvvIAGkcl4PkRz6sVgYG7QLGDSJElDI6JCBw8eFI8bN24MP/2l8gb66quv0L9/f5w9e7bY51UqFXbv3o0WLVqU+eGrvtatW8PLy0ts79+/36gYLa3ybLJK1sfHB7W0ZhDF56cC+fmAjY10MZVg8uTJ4n/qQ4cOISUlBW5ubiVeP2vWLFy6dAn/+9//xHPh4eHF7iYik8mwaNEiTJ48GS+99FK5Y9uxY4d4PH78eJ1ZIERERNWWUomlPYA9zTTNAWczUUvaiCzrs8+Ao0fhCGDdb8CQ68Dj9R6D7PMvpI6MiLScPn1aPDa2SP6+ffvw5ptvQhAE2Nraol+/fmjZsiVsbGwQHh6OgwcPIisrC4BmZsTQoUNx7NgxnVoUpZHJZOjQoQMOHToEADpL0K0Z3xWR8fSTFU4CkJwsXTylGD16NPz9/QEA2dnZBu0xvGnTJqxatQq1ahX/0kgul6Nv3744evQo3n33XaPiOnXqFCIiIgAAtra2mD59ulH9EBERVTlKJZSFqx+Qo8qWLhZLO3sW0JrlCQBDMmrD/qdtgIKfNRJZixs3buDBgwdiu1WrVkb1M2fOHAiCgB49eiA8PBwHDhzA559/jiVLlmDnzp24ffs2hg0bJl6flZWF8ePHIzvb8J+LrVu3Fo8jIyOL7IBojZisION5e+smK5xhtUU2FQoFZs2aJbbXrl1r0H3Tp0/HnTt38Oeff2LVqlVYtGgRVq1ahZ07dyI6OhrHjh3TWbYhCIL4WLhwYZn9r1u3Tjx+5plnuAsIERFRATs7KLVqV1ebZEV2NvDii5rZqgXkcuDHHwGtadxEJL3Q0FCdduPGjY3qJycnBx06dMDBgwdRr169Is/XqFEDO3bs0Nk4IDw8HKtXrzZ4jCZNmojHgiAUid0aMVlBxrO1RS2Fu9iMdwaQmChZOGWZPn06fB7tYBIaGoo//vjDoPtsbW3Ro0cPTJ8+HfPmzcP06dMxYsQIo9ejFYiOjsa2bdsAADY2Npg/f36F+iMiIqpS9GdW5FeTApsffwzoLzt9/32gd29p4iGiEt26dUunXTCTu7xkMhm+++67Ugv2KxQKrFu3Dg4ODuK5b7/9tthNAIqj/6GofuzWiMkKqpAaLoXbl95zgtXOrAAAR0dHzJs3T2wvWbJEwmg0u4zk5eUBACZMmGB0JpaIiKhKUip1ZlZk51eDrUuvXNHUqtDWqRPwwQfSxENEpYqLi9Np16xZ06h+evbsiXbt2pV5XUBAgM42pBEREWKx/7LoL22PjbXOnRy1MVlBFeLiWQu2j15IJDnCqpMVADBt2jQEBgYC0GwNdPToUUniiI6OFpeiuLi44JNPPpEkDiIiIqtlZ1e9Zlbk50P10iRApfVFKxTA99+zTgWRlUpPT9dpa896KI8hQ4YYfO3QoUN12iXtHqJPPzb92K0RkxVUITKfWqidCvilAjUzYNXLQADNko4VK1aI7bffftvgqVOmNH/+fLEgzoIFC0os4klERFRt6c2syFFX8WTFd99hmu8ljHoaiHJ/dO7ddwEjC/YRkfnl5OjO+LKzszOqnzZt2hh8bdu2bXXaV69eNeg+pVKp0y7YXcSaMU1LFVOzJm4uB2QF7YnWPbMCAPr37y9JgkLbhg0bsGHDBkljICIismpaMyvsVIBKlSdtPOb08CEurngXPzwNCDLgTB3g9oGmUL73ntSREVEp9BMAubnGJVUL6uoZc632biSl0U+sGDsLxJKYrKCK8fEpTFQAVr8MhIiIiCoJpRLvnwLmn3z0oYib1AGZj/DxR3it20MIj15UzT0DKFeuAfTeCBGRdXF2dtZpGztbobTCmmVda+hyjszMTKPHlAqXgVDF6GcBrXwZCBEREVUSSiVsBK3ZmzlVtMBmZCS2HVmOvwM0zWb3gFd9hwFaW6MTkXXS3x0wwcgPbjMyMoy+Vj9hUhL92PR3B7FGnFlBFaNf8ZYzK4iIiMgU9Nd+Gzm92tqpFs7HB73VYvvrowrY/f6lhBERkaHq16+v0zZ2h43Ecnzgq5908PDwMOg+/djq1atn8JhS4cwKqpjiZlZIXA+CiIiIqgD9JRBqte5OGVXBf/9hU9hWRHhpmn2igIFPzQIaNpQ2LiIySMuWLXXa4eHhRvUTHBxs8LVXrlzRaTdv3tyg+65fv67TblUJivcyWUEVo5+syMkBUlOliYWIiIiqjuKq6lexpSDZH36AD3sXthf94wjZO+9KFxARlUvDhg11ZjaEhoYa1c/vv/9u8LV79+7VaXfu3Nmg+7Rja9SokcEzMqTEZAVVjLd30XPJyZaPg4iIiKqW4opLVqWlIMHBSNu3C12jNc3B4UC3Z94EvLykjYuIyqVXr17i8fnz543q49SpU0VmTBQnJiYGO3fuFNuNGzcuMrujOIIg4OLFi2K7d+/epVxtPZisoIpxcir6YiIpSZpYiIiIqOooLllRlWZWLFmCGpnALzuAS98CX/ztArzxhtRREVE5PfHEE+JxZGSkUXUrBEHAlClTSt1NJD8/H6+88orOrh6vvPIKZDJZifcUCAkJQbLWB8qDBg0qd4xSYLKCKkYmQ0xdD/QdD7SaBsweCCYriIiIqOKKWwZSVWZW3LwJbN8uNtvFA80mzQXc3aWLiYiMMnToUMjlhW+rjxw5Uu4+lEolzp8/j0GDBuH27dtFnk9KSsKYMWOwb98+8VyTJk0wffp0g/o/fPiweOzg4IABAwaUO0YpcDcQqjBbd0+cqB8PAKj3EExWEBERUcUplbjmDczvC+TYAEOvA5OrysyKr77SFAwt4OwMvPqqdPEQkdH8/Pzw2GOPiUmKnTt3Yvz48eXq44svvsDMmTNx8uRJNGnSBP3790eLFi1gY2OD8PBwHDx4UGdGhYODAzZt2gR7e3uD+tdeOjJ8+HC4uLiUKz6pMFlBFebp6gPgKgAgyRFMVhAREVHF2drioT2wvYWmWf8hqsYykKQkYP163XMvv8xZFUSV2OTJk8VkxaFDh5CSkgI3NzeD73/qqaeQk5ODuXPnIjc3F/v378f+/fuLvdbJyQm7du1Cly5dDOo7JiYG//zzj9ieOHGiwXFJjctAqMJsvWrC49HyqntOYLKCiIiIKk4uh1Lrc7UcG1SNZSBr1gDa69IVCuD11yULh4gqbvTo0fD39wcAZGdnY8uWLeXuY86cOTh06BA6dOhQ7PM2NjYYNmwY/v33Xzz++OMG97t+/XoIggBAs81pee6VGmdWUMV5e8M7E3jgANzjzAoiIiIyEaWNHQAVACBHgco/s0KlAtau1T333HNAQIA08RCRSSgUCsyaNQtvvfUWAGDt2rWYMWNGiddPmDABEyZMKHK+X79+uHDhAsLCwhAcHIzY2FjI5XL4+/ujb9++qFGjRrniys/Px3qtmVyzZ88u1/1SY7KCKs7bGzWigQgvINUeyE1ORDElsYiIiIjKRSm3FY+rxMyK336DEBsLndr9nFVBVCVMnz4dX3zxBRISEhAaGoo//vgDAwcONKqvwMBABAYGVjimbdu2iQU7GzZsWO5aGlLjMhCqOG9v1Cis94KklLvSxUJERERVhtKmcPvSqjCzIn3tSjR5DZjXD7jjBqBTJ6B9e6nDIiITcHR0xLx588T2kiVLJIxG47PPPhOPFy5cCIWics1VYLKCKu7RMpACSRn3pIuFiIiIqgylvHCuZo4NKneyIjISW5KPI9ILWNwT+LgXgGnTpI6KiExo2rRp4oyIEydO4OjRo5LFsn37dgQHBwMAOnXqhHHjxkkWi7GYrKCK8/bGoAhg7mng80NAjbspUkdEREREVYBSoTezojIvA1m7Fuu06ubNuOYCPPOMdPEQkcnZ2tpixYoVYvvtt98Wi1takkqlwnvvvQcAkMlk+OabbyCTycq4y/pUrnkgZJ28vTEqDBgV9qgtf6jZO1zOXBgREREZz15hj1FXAbt8oHUCKu/MCpUKofvW4/Kj3ETHWKDtUy8BDg7SxkVEJte/f39JEhTaFAoFwsPDJY3BFJisoIrz9tZtq9XAw4eAp6ck4RAREVHVYGdrjx3btE5MrqQzKw4fxqaA+2Jz/BUAm16WLh4iokqAH31TxXl5FT3H7UuJiIioopRK3XYlnVmh2rwJW1prjm3zgecU7YBmzaQNiojIyjFZQRXn4AA4OuqeY7KCiIiIKspObzP0ypisSE3FoSs7keCsaQ65Dng9N0namIiIKgEmK8g09JeCMFlBREREFaU/s6IyFtj89Vf82ihPbI4PlQPPPithQERElQNrVpBpeHsDd+4UtpmsICIiooqqCstANm/G2pPAuFDgtybAoMZPFv2Qh4iIimCygkyDMyuIiIjI1PSXgVS2mRWJicDJk1CogceiNA9sHy91VERElQKTFWQa3t6IcwESnYA0O6AnkxVERERUUZV9ZsXu3Zpd0go4OQGDB0sWDhFRZcJkBZmGtzcGvAD8VxNwzAUy7jJZQURERBVkZwcBgEoO5CgA58qWrPj1V9324MGawuRERFQmJivINLy94X1Tc5hpB2TeT4Bj6XcQERERlU6pRPfJwN8BmmZ+Yk7lqQ5//z5w7JjuudGjpYmFiKgSqjQ/78nKeXvDK7OwmZyaIF0sREREVDUolbDLL2zm5mSWfK212bsXUKkK2/b2wKBB0sVDRFTJMFlBpuHtDW/tZEXGPeliISIioqrBzg5Krff7Oaps6WIpL/0lIE88ATg7SxMLEVElxGQFmYZesiIp54F0sRAREVHVoFRCqTWzIic3S7pYyiMjAymnDkGl/Up71CjJwiEiqoyYrCDT8PaGl9brh6T8NN2pj0RERETlpT+zIq+SzKw4dgwfdM9FzbeAsaOAO542wFNPSR0VEVGlwmQFmYb+zApHAA84u4KIiIgqQH9mRSVZBiLs34ffmwAPHIDtzQG3dl0Bd3epwyIiqlSYrCDT8PLSrVnhAOAe61YQERFRBSiVla9mhSDg2pm9iPLQNHveAdwGDpU2JiKiSohbl5Jp2Nmhx31nhK5Oh3cmNDuDJCdLHRURERFVZnZ2ujMr8nOli8VQV69in8tdsflUOIC3npQuHiKiSorJCjIZV9caaBmVXniCyQoiIiKqCKUSL10C+t8ElCqgobdM6ojKtn8/9jUubA5O8wWaN5cuHiKiSorJCjIdLy8gKqqwnZQkXSxERERU+dnZof1doH3BRAWH/FIvtwapf+zF6W6a40bJQJMeQwFZJUiyEBFZGdasINPx9tZtc2YFERERVYRSqdvOtfJlIKmpOBn3F1Q2muYTkYDsycHSxkREVEkxWUGm4+Wl2+bMCiIiIqoI/WRFTo40cRjq1Cn87acWm/3vKIDHHpMwICKiyovLQMh0OLOCiIioTKGhodiwYQOOHDmCmJgY5Obmonbt2ujYsSNeeOEFPPHEE2YZV61W49y5czh69CjOnTuHf//9F4mJicjJyYGHhwfq16+Pbt264cUXX0Tbtm3NEkO52dnptq09WXH8OBYdBZ4PAY7VB/r4dAacnKSOioioUmKygkyHMyuIiIhKpFKpMH/+fCxduhRqtVrnufDwcISHh+Onn37C4MGDsWHDBtSoUcNkY8+ePRtbt25FfHx8sc8nJiYiMTERZ8+exddff41hw4Zh7dq18PHxMVkMRqlsy0COHYMMQPN7mgcWPi51RERElRaXgZDpcGYFERFRiaZOnYrFixeLiQpbW1u0adMG3bt3h5dWwn/fvn3o378/0tPTS+qq3NatW1ckUVGrVi106tQJffv2RZMmTXSe27NnDzp37ozo6GiTxWCUyjSzIjkZCA7WPcclIERERmOygkzHywsHGwFvDgAmDAcis+OkjoiIiMgqrFu3DuvXrxfbQ4cORVRUFIKDg3H69GncvXsXK1euhEKhmfQaEhKCqVOnmjyOFi1a4Ouvv0ZERATu3r2Ls2fP4tixY7h+/ToiIiIwbNgw8drbt29jzJgxEATB5HEYrDLNrDh5Urft4AB07ixNLEREVQCTFWQ63t74sw7wZTdgU1sgSs2ZFURERJmZmViwYIHY7tOnD3bu3InatWuL52xtbfHqq6/i22+/Fc9t3boVly5dMkkMQUFB2L9/P/7991+8/vrraNSoUZFrGjVqhN27d+P5558Xz509exa7d+82SQxGUSoR4wpsbQlsbAuEuOcAUiZPSnPsmG67R4+iM0OIiMhgTFaQ6Xh5wTuzsJmsSgPyrX8/dCIiInPauHGjuARDJpNh9erVsLGxKfbayZMno/OjT+MFQcDSpUtNEsPx48cxaNAgg65dsWIFnLSKQu7cudMkMRjFzg4X/ICxo4GJw4H9jQHk5UkXT2n0kxVcAkJEVCFMVpDpeHvDK6uwmeQI4OFDqaIhIiKyCtpv9nv37o3AwMBSr9de/rF//37kWLhOg4eHB7p37y62r127ZtHxdSiVUKoKmzk2sM6lIPHxQFiY7jkmK4iIKoTJCjIdvZkVSY7gjiBERFStpaen49SpU2LbkG1JtWdApKen48SJE+YIrVSenp7icWpqqsXHF9nZQak1STNHAesssvnnn9BZnOLiArRvL1U0RERVApMVZDr29vBW24vNZAdwRxAiIqrWrl69ijytZQtdu3Yt855atWqhXr16YjskJMQcoZXq9u3b4nHNmjUtPr5Ib2ZFtgLWObPir78Q9DLQZwLwYW8A3boBj4qlEhGRcZisIJPysvcQjzmzgoiIqrswvaUBDRs2NOg+7ev0+zC3uLg4nDt3TmwbkmAxG6VSd2aFDaxyZkXyuZO46AecrPeorobWMhoiIjIOkxVkUt5OhZ++JDuCMyuIiKhau3XrlnisUCjg6+tr0H116tQptg9L+Oijj5CvVSD7ueees+j4OuzsdGtWWOMykMxM/PXgitjsHg3NzAoiIqoQzk8jk3J1q4H2cYBHNtD+LjizgoiIqrW0tDTx2MXFBXK5YZ8Tubq6FtuHuZ06dQrfffed2B45ciTatWtX5n05OTk6hUBNVufC1rbozAprWwZy/jxO11aLzR7RMuDRji5ERGQ8JivIpGTeNXBxndaJDpxZQURE1Vd6erp4bG9vX8qVuhwcHIrtw5xiY2Px9NNPQ63WvPH29PTEihUrDLp38eLF+PDDD00flEwGpcwWQB7s8wAbAdY3s+Kvv3CmcCIMuru2AJydpYuHiKiK4DIQMi0vL902Z1YQEVE1plIVrmFQlKPgova12gU6zSUjIwPDhg1DQkICAEAmk2H9+vWoXbu2Qfe/++67SElJER/R0dEmi61OjhLqhUDWImDjbljdzIrsv07hvJ/muFEy4NOxj6TxEBFVFdU6WXHv3j0cOHAAH330EYYOHQpfX1/IZDLxsXHjRovEcfPmTcyfPx8dOnRAjRo14ODggIYNG2LEiBHYsWOHzgsdq+ftrdtmzQoiIrIyW7Zs0fl9b6pHca8bHB0dxePs7GyDY9S+1snJqUJfb1lyc3MxYsQIXLx4UTz39ddfY9iwYQb3oVQq4erqqvMwFZnSHjLtE9Y0s0KtxsWoM8h9lFvqcQesV0FEZCLVchlIfHw8unTporMtl1SWL1+Ot99+W2edJ6BJYNy8eRO7d+9Gly5d8OOPP6JBgwYSRVkOnFlBREQkctZaDpCVlWXwfZmZmcX2YWr5+fl47rnncPjwYfHchx9+iFmzZpltzHKzs9NtW1OyIjwc51wKa4p0jQF3AiEiMpFqmazIzs62ikTFxx9/jPnz54ttuVyO5s2bw9PTExEREbh79y4A4J9//kHv3r1x7tw5g6uIS4YzK4iIyMo5OTkZvLyhvP3q89b6vZieno709HSDkg/x8fHisZf+BwEmolarMXHiROzcuVM899Zbb+m8NrEKSqVu25qWgfz9N57+D/DJAM77Ab2zagIBAVJHRURUJVTLZIW2GjVqoEOHDujYsSM6duyI4cOHW2TcP/74AwsWLBDbXbt2xcaNG9GkSRMAmhcQ27dvx0svvYT09HTExMRgzJgxOH36tEXiMxpnVhARkZUbMWIERowYYZGxmjZtqtO+c+cOmjdvXuZ92jUfmjVrZvK4AGDatGnYvHmz2J4xYwY+++wzs4xVIdY8s+LCBdROA8aGah4Y2QOQycq8jYiIylYtkxWenp7Yvn07goKCULduXYuPLwgC3n77bQiCAEDzQubIkSM661rlcjmeeeYZeHl54fHHHwcAnDlzBrt27bLYCyyj6M+suH8fUKsBA7dqIyIiqkoCAwN12sHBwWUmK/Ly8vDff/+V2IcpvP7661i3rnD7rsmTJ2PlypUmH8ckrHlmhVadDwBAUJA0cRARVUHV8h2kq6srRo8eLUmiAgAOHDiAK1euiO3ly5frJCq09e/fH88884zYXrJkidnjqxCtmRUCgDwhH0hJkS4eIiIiCTVo0AD+/v5i25AZkhcvXtSpWdGrVy+TxjRv3jwsX75cbI8bNw7r1q2DzFpnBOgnK6xlZkVeHhAcrHuuY0dJQiEiqoqqZbJCatprQ+vXr48BAwaUev3UqVPF43PnziEmJsZssVWYtzdO1QV83gTsPgAW9QLrVhARUbU2dOhQ8Xj79u3ILWNmwI8//iget2jRAg0bNjRZLJ988gkWL14stkeNGoVNmzZBbs0zIK11Gch//xWNpUMHaWIhIqqCrPg3U9W1b98+8XjgwIFlfpLRs2dPnaJd2vdbHUdH2MvtkOgMqGyAZAewbgUREVVrEyZMEI+TkpKwdu3aEq+NiYnBpk2bir23opYvX44PPvhAbD/11FPYunUrbGxsTDaGWSiVmD4YeGos8PQYWM8ykAsXdNsNGgAeHtLEQkRUBTFZYWGJiYk6Fb67du1a5j0KhQJBWmsgQ0JCzBKbqXjbe4rHSY7gzAoiIqrWgoKCdGZXzJs3D2fOnClyXWpqKsaOHYu0NM1WmLVq1cKMGTNK7Vsmk4mP0hIb33//Pd544w2xPWDAAOzYsQO2trbl/GokYGeHo/WBfU2AQw1hPTMr9JMVXAJCRGRS1bLAppTCwsJ02oZO7WzYsCFOnDhRbB/WxsvRG4AmIZPkCM6sICKiam/58uX466+/kJSUhPT0dPTr1w+TJ0/GgAED4OzsjJCQEKxcuRJRUVEANIW2161bBwcHhwqPfffuXUydOlUs7A1otnEfNmyYwX0cPHiwwnEYTamEMl9zmGMD651ZwWQFEZFJMVlhYbdu3dJp16lTx6D7tK/T76M4OTk5yNH65CE1NdWgcUzB1b0mFPmaZSCcWUFERATUq1cPe/bswZAhQ3D//n3k5ORg9erVWL16dZFrbWxssGzZMgwZMsQkY+fk5ECtVuucO3XqlEn6tgilEkqV5jBHAQg52ZC8FGhODk7fD8avA4GgOKDfTcCHyQoiIpPiMhALK5jaWcDNzc2g+1xdXUvsoziLFy+Gm5ub+AgICChfoBUg8/KG96Mi5smcWUFERAQA6NatG0JCQjBq1CgoFMV/XhQUFIRTp07h1VdftXB0VszOTpxZIcgAVU6WtPEAQGgoDtfNx7KuwLhRwJ91AbRvL3VURERVCmdWWFh6erpO297e3qD7tKeB6vdRnHfffRezZ88W26mpqZZLWHhrkhXxLo9mVtzjzAoiIiIAqF27Nnbs2IF79+7h1KlTiImJQW5uLvz8/NCxY0c0bdq0XP1pL+0oSb169Qy6zmoplbDPK2zm5GZB8kobFy7gSq3CZlv7eoCBH0AREZFhmKywMJVKpdMu6ZMVfdrX5eXllXKlhlKphFJ/X3JL8fKCV7TmMMsWyEyOh6M0kRAREVmlGjVqYNSoUVKHUTnY2UGpNZkiJzcLztJFo3HlCq74aA6dc4AGTTpLGw8RURXEZIWFOTrqvm3Pzs4ucq442dnZ4rH2NqZWydsbs7cBE4IB70xA4fdA6oiIiIiostIqsAkAOarskq+1kJSrl3DrMc1xq0RA3radtAEREVVBTFZYmLOz7mcBWVlZBiUrMjMzS+zD6nh5Yeh1rbYdkxVERERkJKUSvW8BjnmAvQpQuqnKvMWs1GqEJIaKzTbxAEa1li4eIqIqiskKC/P29tZp3717F15eXmXeFx8fLx4bcr2k9L5GFtgkIiIio9nZYdZZrfZoySLRuHULV1wL16W0SQDQmskKIiJTM2uyIj4+HufPn0dISAhu3bqF2NhYpKenIysrCw4ODnByckLt2rVRr149tG7dGkFBQfD19TVnSJLTL5x1584dtGzZssz7oqOjxeNmzZqZPC6T0k+mJCcDggDIJN9ojIiIiCob/RpcWluzSyIkRKxXAQBtM10BPz/p4iEiqqJMnqw4deoUdu3ahf379yMyMrLc9zds2BCDBg3C8OHD0bdvX1OHJ7nGjRtDoVCIhTaDg4Px5JNPlnnf5cuXxePAwECzxWcS+jMr8vKAtDRAa/tVIiIiIoPo75xmBcmKFveA3reA0JpAK9+2/ECGiMgM5KboJCEhAQsXLkT9+vXRt29frFixAhERERAEweCtsgqujYyMxDfffIP+/fujTp06mD9/Pu7evWuKMK2CnZ0dOncurBh9+vTpMu+Jj4/XSfz06tXLLLGZTHHLVJK5fSkREREZQX9mRbbEBTZDQvD6P8CJjUDSZ4BTSxbXJCIyhwolK6KiojBp0iTUq1cPH3/8MW7fvl1scqIgEeHs7IwaNWrA398fNWrUgJOTU4kJDUEQEBMTg0WLFqF+/fqYMGECbty4UZFwrcawYcPE4yNHjiAhIaHU63/88Ufx2N3d3fqTFc7OgJ2d7jnWrSAiIiJjWOHMigIygPUqiIjMxKhkxb179/DKK6+gWbNm2LRpE3JycnQSDh4eHhgxYgQ+/fRT/P777wgPD0dGRgZSUlIQHx+P27dvIz4+HqmpqcjIyEB4eDh+++03fPrppxgxYgQ8PDzEvgRBQG5uLjZv3ozAwEBMnToViYmJFf/KJfTcc89B+ehTgry8PHz22WclXpueno4VK1aI7XHjxsHW1tbsMVaITFZ83QoiIiKi8rKmmRUZGYD+Muc2baSJhYioiit3zYply5bhww8/RGpqqk6ColGjRhgzZgxGjhyJDh06GNyfg4MDGjVqhEaNGmHw4MHi+YsXL2Lnzp3YsWOHuKREpVLh+++/xy+//IKFCxfi9ddfL2/4ZnPr1i3Ur19fbC9YsAALFy4s9lp/f39MnTpVTEIsX74c3bp1w6hRo3Suy8vLw8SJE3Hnzh0Amu/VvHnzzPMFmJja2wtHHe4iyRFwUAHDObOCiKhaYZFtMhlrmlnx77+aouEF5HKgeXPp4iEiqsLKnayYPXs2ZDIZBEGAQqHAmDFjMHXqVJMvTejQoQM6dOiARYsW4c8//8TatWuxfft25OXlITU1FXPmzKlQsmLKlCnYvHlzmde88sorRc5nmyCjv3DhQhw4cAARERHIz8/H008/jbFjx2L48OHw9PTE9evXsWbNGoRoTTX8/PPP4VdJqk3LvLwweDiQZwO0vQsM58wKIqIqj0W2ySyUSggAcm2AHAWgzM2CssybzETrdRkAoEkTwMFBmliIiKo4o3YDsbOzw8svv4w5c+agTp06po6piJ49e6Jnz55YsmQJvvjiC6xbtw45Fcyq5+XlldmHSqUSd+0wNQ8PD/z+++/o378/oqOjoVarsWXLFmzZsqXY6+fOnYsZM2aYJRZzkHnXgFcmEO8CJDmCNSuIiKqohIQErFmzBps2bRJnAmrPvJQZsEtCwfUFRba/+eYb1K5dGxMmTMC0adM446K6s7fHug7AK0M0zfUnHmKiVLGEhuq2Wa+CiMhsyl2zYvz48QgPD8fy5cstkqjQ5u/vj2XLluH69esYP368Rcc2hyZNmiAkJASTJ0+GQwlZ+cDAQOzZswdLly61cHQV5OUF70zNYbIjWLOCiKiKYZFtshilEnb5hc0cdZ50sVy9qttu2VKaOIiIqgGZYOjeomRWaWlpOHbsGKKjo5GRkQFfX1+0atUK7dqZZjus1NRUuLm5ISUlBa6uribps1TvvYc+cZ/iZD1NM+PqCDj+stP84xIRUYlM8bvg3r17+OCDD7BhwwaoVKoiyQZPT0/07t0bQUFBaN26NZo0aYLatWsXm5TPyspCbGwsrl+/jtDQUJw/fx4nT57E/fv3da6TyWSwsbHBxIkT8fHHH6NmzZpGxU6WY9LXHWfP4qcpXTDuUWmvZUdtMetUbsWDNEJKvVpITk1AvYeAXACwYwegV3OMiIgKVeT3gVHLQMqybds2tGrVCk2bNoVcXqHdUasNFxcXnS1NKz1vb3hrLVdOSkuAZefhEBGRqbHINklCqYRSa1VujmCeJbplSknBfpcEjJ0IOOQBK/cDkwMDpYmFiKgaMEsm4dlnn0XLli3h7u5uju6pMtBaBgIAyen3pIuFiIhMYvbs2WKiQqFQ4LnnnsOJEycQHh6ORYsWlStRUZqCAtvXr1/HyZMnMXbsWNja2kIQBLHINlUj9vZQai8DkQuAmWqKlSosDFdraA6zbIEa2XKgUSPLx0FEVE2YbdqDIAgm2TWDKilvb3hpJSuScu6XfC0REVUadnZ2eO211xAZGYkff/zR5LuB6evZsye2bNmCGzduYObMmbDX38aSqj79mRUKAFK8xrx6VUxWAEBzp3qAnZ3l4yAiqia4RoPMQ2tmhVs2kJWRorsvORERVTossk2S0J9ZYQOggrvCGUVrZoVSBdQP4E4gRETmZJaaFabi6emJVq1aoUOHDvjqq6+kDofKw9sb088Dr54DbNUAoAIyMwEnJ6kjIyIiI23YsEHqEBAQEID169dLHQZZUnEzKyRIVuRe+w8R7TXHzZIAm8DmFo+BiKg6seqZFWlpafjzzz+xfPlyqUOh8vLygjK/IFHxSFKSZOEQEZFlbNu2DWFhYVCr1WVfTGQIe3s0SQZ2/Qzs3wJMPw9JloFExIYg/9Er5xaJAFhck4jIrIyeWXHo0CGEh4ejdevWaNWqFTw8PEwZF1V2bm6AjQ2QrzVvMzkZqFtXupiIiMjsnn32WchkMjg5OSE1NVXqcKgqUCrhkQ0Mv6Z1ztIzK7Ky8F9urNhsfg9Ac86sICIyJ6OTFX///Tc++ugjse3n54dWrVqhdWvTrd/T37udKhGZDPDyAhITC89xZgURUbXAIttkUjY2gEKhuwOIpf99Xb+OMO/CZvN7AJo2tWwMRETVTIVqVgiCAJlMBkEQEBsbi7i4OPzxxx/iufz8fLRq1QodO3YUH23btoVSqSyz76SkJHEKqSHXkxXy9tZNViQnSxcLERERVV5KpW6ywtIzK65exXt/As/9C1ytAXQT/FmHi4jIzIxOVjg6OgLQnf2gnbwoaF+9ehVXr17F//73P82ACgWaN2+ODh06iAmMNm3awNbWVqf/Xbt2icfe3t6gSsjLS7fNmRVERGQgFtkmHfb2QEZGYdvSMyvCwqBQA02SNQ880dKy4xMRVUNGJyveeustTJ06FVeuXEFISAiuXLmCK1eu4N9//xWnfgqCICYuCpIYeXl5CAkJQUhIiFhV3NbWFi1btkTbtm3RoEEDxMTEYMOGDZDJZACANm3aVPTrJCnoJ5k4s4KIiAxUUGT79OnTTFaQZmaFNkvPrAgL022zXgURkdlVaBmIq6srevbsiZ49e4rn1Go1FAoFZDIZ5HI5nn76aVy4cAE3btwQr9FPYOTm5uLy5cu4fPlysdeMHj26ImGSVDizgoioSmKRbbI4e3vdtqVnVoSH67ZZr4KIyOwqlKwojlwu1zn+6aefAACpqam4ePEiLly4ID6ioqLEa7WTEwV/CoKArl274oUXXjB1mGQJ3t5Y0gP4sw6Q5Agcv58AR6ljIiKiCmORbbI4KWdWqNVAZKTuuSZNLDc+EVE1ZfJkRQH9Fxmurq7o27cv+vbtK557+PChTvLiypUruHXrFtRqNfz9/fHMM89g/vz5OgkQqkS8vHDBD9j/6Pd50vl41JE2IiIiMhEW2SaLsrfHLy2ADDvAORd42pIzK+LigKws3XONG1tufCKiasosyYrU1FQEBwcjNDS01Ovc3d3Rv39/9O/fX+e8Wq1mgqIq8PaGd2ZhMznjHpMVRERVAItsk8UplZg6EEixB5omAU9bcmZFRIRu29ER8POz3PhERNWUWZIVzs7O6NGjB3r06GHU/UxUVBFeXjrJiqSs+9LFQkREJsMi22Rx9vZQPtq5NMcGlq1ZoZ+saNQIePTvk4iIzMdsy0CI4O0NL+1khSpFuliIiMikWGSbLEqphDJfc5ijgGVrVkREYMoQwEEFtEoAptTnEhAiIktgsoLMR39mhU2uZs2ng4N0MRERkdmwyDaZjYQzK/IjI7CpLZBn8yhZ4cZkBRGRJTBZQeajX7PCEZrtSwMCJAuJiIjMj0W2yeQknFlxO+4q8tpqjhsnA+jIZAURkSUwWUHm4+4O7xwbAJpXF0mOABITmawgIqrCWGSbzEJ/ZkWWhWZWqNUIT7slNpskgzuBEBFZSLlfDQQFBeH48ePmiMVgx44dQ6dOnSSNgQwgl6O2nReeDQVePQv0uQVNsoKIiKqsgiLb06ZNM+p+JiqoWFozK1Q2gDrHQsmKmBiEu+aJTSYriIgsp9yvCC5evCh+EnLkyBFzxFSiw4cPo1+/fnj88cdx8eJFi45NxvFz9sXWX4GVB4DRV8FkBREREZWfvT2ccgHHXMAjC8jNySz7HlOIiECEZ2GzcZYD4ONjmbGJiKo5o5eBHD9+HMePH0erVq3wyiuvYOzYsXB1dTVlbACAtLQ0bNmyBWvXrhWnlBbs5U6VgP4v9IQEaeIgIiKiykupxOHNWu0XVZYZNyIC4V6FzSbuDbltKRGRhZR7ZsWhQ4fQtGlTcf/00NBQzJgxA76+vhgxYgQ2b96M+Pj4CgV19+5dbN68GSNGjECtWrXw6quvIjQ0VBwzMDAQhw4dqtAYZCE1a+q2ObOCiIiIysveXrdtqQKbERG48WhmhUsOUKNuoGXGJSKi8s+s6N+/P0JCQrBq1SosXrwYiY/efGZlZWHv3r3Yu3cvAKBx48YICgpCq1at0LhxY/j7+6NmzZpwcHCAnZ0dcnNzkZWVhYSEBMTGxiI8PByhoaE4f/48IiMjxfG0K4r7+Phg3rx5mDZtGhQK1gatFJisICIioopSKnXbltq6NCICw3KA696AXT4ga9zEMuMSEZFxy0AUCgVmzZqFKVOm4JtvvsHKlSsRGxsrLs8QBAHh4eGIiIgod9/ae60XHPv7+2PWrFmYPn06HBwcjAmZpKK/DITJCiKiSisoKAifffaZzhaklnbs2DG88847OHfunGQxkASkmlkRGYkvw7TaG1lck4jIUipUctvR0RFz585FVFQUtmzZgn79+hVbS6Jg+UZpD30ymQz9+/fH1q1bERUVhTlz5jBRURnpz6xgzQoiokqLRbZJMlLMrBAEICpK91zDhuYfl4iIAFSgwKZOJwoFxo4di7FjxyIuLg579uzBwYMHcfr0aTx48MCgPgRBgIeHB3r16oUnnngCQ4cOha+vrynCIylxGQgRUZXDIttkcfrJCkvMrIiPL5oUqV/f/OMSEREAEyUrtPn5+WHatGni/uo3b95EaGgobt26hbi4OKSnpyMnJwdKpRLOzs7w8/ND/fr10bJlSzRo0MDU4ZDUataEWgY8tAeSHYCaKQlwEwRW0iYiqoQOHTqEmTNn4tq1awAgFtmeM2cOBgwYgJEjR+Lxxx9HrVq1jB7j7t27OHLkCHbu3IlDhw4h+9GbxYJZmIGBgVixYkXFvxiqXPSXgVhiZoX+rAqlEuAHaUREFmP2KpUNGjRgEqI68/HB8s7A7Cc0zZ925OO5Bw8AT8/S7yMiIqvDItskGSlmVty8qduuVw+QV2gFNRERlQN/25N51agB78zCZpIjNEtBmKwgIqqUWGSbJGFvj19aAJvaAjk2wKJrD9HF3GPqz6zgEhAiIotiepjMy94eNeAoNhOdwLoVRERVAItsk0UplbjpARxoDBxrAMQrJFgGwpnCREQWxZkVZHY+9l4ANNMrEpzBZAURURXCIttkEfb2UOYXNnPUuWYfMicqEje9gfoPAXsVOLOCiMjCmKwgs6vlVAtANAAg3hncvpSIqIpikW0yG6VSkzB4JEedZ/Yhrz2MQNtXNcev/w18zWQFEZFFMVlBZlfD3Q8yARBkj5IVnFlBRFQtsMg2mYy9PZTayQrBzMmK3FzczIkXm15Z4DIQIiILkzRZERERgcjISCgUCrRp0wY1a9Ys1/0pKSlwc3MzU3RkKoqateCdCdxzAhJYs4KIiIjKS6nUXQYiUwP5+YCNjXnGu3MHN90Lmw0egMtAiIgsTJICm9evX0enTp3QrFkzPPXUU3jiiSfg5+eHESNGIDo6utR7o6OjsWrVKgwYMAA+Pj4WipgqxMcHtdI1h/HOgJAQX/r1RERERNr0Z1bYwLzbl968iZsehc0Gec6Au7v5xiMioiIsPrMiOTkZffr0QWJiok4FcEEQsHfvXpw7dw6nTp1Cw4YNxeeuX7+Obdu2Yffu3QgODhavL67qOFmhmjXx7QbARg1N0qIlZ1YQERFROejPrFBAk6xwdCzxlgqJitJNVrhzVgURkaVZPFmxfPlyJCQkQCaTwcvLC08++SRq166NuLg4HDhwAHfv3sWkSZNw8uRJnDp1Cu+99x7++usv8X7tPdg7depk6fDJGDVropv2hJnEe5KFQkRE0gkODkbLli2hULBkFpWTvT3qPgReDAaU+UDbeADZZty+VCtZ4ZQL1Kjd2HxjERFRsSz+amH//v0AgLZt2+LIkSPw8ChMW2dlZeG1117Dhg0bsHz5csydOxcqlUpMUMjlcvTs2RMjR47EyJEj4e/vb+nwyRj6y3VYs4KIqFpq37497Ozs0KJFC7Rr1w7t27dH+/bt0aZNGzg4OEgdHlkzpRJtEoBNu7XOmXEZiHDzBm431RzXewjI6rO4JhGRpVk8WREREQGZTIYlS5boJCoAwMHBAd9//z2ioqIwd+5c5OVpKj3Xr18fr7/+Op599lnUqFHD0iFTRekXTk1J0XwaYm8vTTxERCSZ3NxcBAcHIzg4GBs2bACg+TCiSZMmOgmMdu3asYg2FSruNYMZkxWJsRHIaaE5rvsQQEsmK4iILM3iyYr0dE2lxbZt25Z4zVtvvYXjx49DJpOhb9+++P3332HPN7aVV3G7vNy7BwQEWD4WIiKSzPz583H58mVcunQJsbGx4vn8/HyEhYXh2rVr2Lp1q3i+Xr16RRIYLK5dTSkUgFwOqNWF58y4DMQ7PBo3rwJ33DTLTvACa1YQEVmaxZMVBYUxnZycSrymffv24vEnn3zCREVl5+6ueZGh0irjnZDAZAURUTWzcOFC8TgpKQmXLl3C5cuXxQTGjRs3dIpvR0VF4datW9i1a5d4rlatWmjXrh06dOiADz/80JLhk9Ts7YHMzMK2uWZWZGTAJuk+6gOo//DRuXr1zDMWERGVyCorXGknMlq2bClhJGQScrmmboXWp2i4e1e6eIiISHLe3t4YMGAABgwYIJ5LT08XkxcFCYywsDCotJLdd+/exd27d3HgwAEmK6obpVI3WWGumRV37hQ9xw9YiIgsTrJkhaHbjjo7O5s5ErIIX18mK4iIqFTOzs7o2bMnevbsKZ7Lzc1FSEiITgIjNDQU2ebcCYKsk/5MW3PNrNBPVnh5AaXMCCYiIvOQLFnx2GOPoVWrVmjZsqX4J4tnVmF+fljbAYh1BezygfeZrCAiIgPY2dmhY8eO6Nixo3hOrVbj2rVrEkZFklAqdduWmllRt655xiEiolJJlqw4d+4czp07p3PO29sbLVu2RJMmTSSKiszG1xcfNgbuugC1U4H34+KkjoiIiIwUGRmJbt26oVmzZmjbti3atm2LsWPHWqzGlFwuR/PmzS0yFlkPwV6JLFsg10bTdjfXzIrbt3XbdeqYZxwiIiqVxZMVH3zwAYKDg4tUAgeAe/fu4cSJEzhx4oS4TMTNzQ0dOnRAx44dERQUhI4dO6J+fVZkrnT8/FArTpOsSHAC1BFxkEsdExERGeXVV19FUlISzpw5gzNnzmDatGmYNGmS1GFRFZfrYAen9zTHvW8BJyw1s4LJCiIiSVg8WaFdDMuQSuBpaWk4efIkTp48KZ7z8PAQkxcff/yxReMnI/n6widcc6iyAe4nRcNb2oiIiMgI58+fx6FDh8QPFQYNGoSVK1dKHBVVB3Z2DuJxjg0sV7OCyQoiIklIuhuIsZXA79+/j0OHDuHw4cNMVlQWvr6olV7YjE+NY7KCiKgSWrt2LQDNVuSOjo749ttvDS6aXZZr166hUaNGUCiscrMykphMaQ87FZCrAHIUMFvNilvJN/DpEKBOCtA3CujOZAURkSSsbiZ+QSXwmTNnYsOGDbhy5QrS09Nx7tw5rF27Fq+88go6deoEBweHsjsj6+Hnp5OsuJuTDOTnSxcPEREZZffu3ZDJZJDJZJgzZw78/f1N1vdvv/0GZ2dndOzYES+//DIOHTpksr6tSWhoKGbPno3WrVvD09MTzs7OaNq0KcaNG4eDBw9KFtfgwYPFv1uZTIZ69epJFkux7O2hfPTSIccG5klW5OfjquouvusAfPAYcLARWGCTiEgileKjC1YCrwJ8fVE7rbAZ66wG7t0DatWSLiYiIiqX69ev4/79+wA0W5BPnDjRpP3PmTMH27dvx4ULF3D58mUcPXoUN27cMOkYUlKpVJg/fz6WLl0KtVqt81x4eDjCw8Px008/YfDgwdiwYYNFd0nbunUr9u/fb7HxjOLgALtHyYpsBYCsLNOPER+PO86FH6bUTQGXgRARScTomRWRkZGoWbMmevXqhZkzZ2L9+vUW3fOclcArmZo14ZdeOE04zgUAty8lIqpUrly5AkCTqGjXrp3JP3mXy+X48ssvAWiWmdy6dQsnTpww6RhSmjp1KhYvXiwmKmxtbdGmTRt0794dXl5e4nX79u1D//79kZ6eXlJXJnX//n28/vrrFhmrQhwcoHy0KjjHXMmKO3dw262wWSdDAdSsafpxiIioTEYnK7Qrga9atQqXLl2y2JZlVAnZ2KCOjSfqPQC63wF80sFkBRFRJZOUlCQeBwYGmmWMnj17onPnzmJ7z549ZhnH0tatW4f169eL7aFDhyIqKgrBwcE4ffo07t69i5UrV4r1OkJCQjB16lSLxDZ79mwkJiZCJpPhscces8iYRrHEMpA7d3BHK1lR19EXkFvdqmkiomrBqJ++rAROxuhoWxdRy4HT64HJlwHExUkdEhERlcPDhw/F49q1a5ttnFdffVU8Pnz4sNnGsZTMzEwsWLBAbPfp0wc7d+7U+R7a2tri1Vdfxbfffiue27p1Ky5dumTW2I4ePYpNmzYBACZOnIiePXuadbwKsdTMCvfCZoBnfdOPQUREBjEqWaFdCdzBwcHklcC1d/6gKsTXV7fNmRVERJWKnZ2deKxUKs02zsCBAyGTySAIAsLCwpCSkmK2sSxh48aNiI+PB6BZQrN69WrY2NgUe+3kyZPFmSWCIGDp0qVmiysrK0ucveHt7Y3PPvvMbGOZhIMD1uwD/tgM7PsR5klW3L4tLgOpkQE4BjQw/RhERGQQo5IVrARORvHz020zWUFEVKm4uRXOj9deEmJq3t7eaN26tdgOCwsz21iWsHPnTvG4d+/eZS6h0V7+sX//fuTk5JglrgULFogFTL/88kuduhlWycEBfW4BA24APe/ALMmKvDu3NHW1ANR9CBbXJCKSULmTFQWVwAVBAACzVAJv3bo1Ll26hB9++AHTpk0zaf8kIf2ZFVwGQkRUqdSvXzglPiQkxKxjab+hj4yMNOtY5pSeno5Tp06J7SeeeKLMewYNGqRzvzmKjF6+fBlff/01AM2ylBdffNHkY5icfm00M9SsyLh7GyOuAR1jgTYJYLKCiEhC5U5WsBI4GY3LQIiIKrUWLVoA0Px+vnDhglmXZ9TU2oHhwYMHZhvH3K5evYq8vDyx3bVr1zLvqVWrls7rK1MnhvLz8zFlyhSoVCrY2dnp1Mmwag4Oum0zzKxwvxGLHduA898B3+8FkxVERBIqd7KClcDJaPrLQDizgoioUvH19UWzZs0AALm5udi8ebPZxvLw8BCPLbWFpznoL2Fp2LChQfdpX2fqZTBff/01Ll68CAB455130LRpU5P2bzbmTlakpQH6iTEmK4iIJFPuZAUrgZPR9GdWxMcD+fnSxEJEREYZNWoUAM3sik8++QRpaWlmGSc1NVU8rsxbo9+6dUs8VigU8NX/XViCOlpvkrX7qKioqChxZ5LGjRtj3rx5Juvb7MydrLhzp+g5JiuIiCRT7mQFK4GT0bQKsQoA8tQqICFBuniIiKjcpkyZAltbW8hkMty7dw+TJk0yyzjR0dHisdUXfiyFdjLHxcUFcrlhL71cXV2L7aOiXnnlFWRmZgIAVq9ebbLXcjk5OUhNTdV5mJylkxU1ahQdk4iILKbcyQpWAiej+fjgfB0bNJoJOL4HzO8LQOvFKBERWb86depgypQpYqHtnTt36syGNBXtopSm3HXM0rSXsJRnhoiD1ptkUy2D+d///ifusvb888+jf//+JukXABYvXgw3NzfxERAQYLK+ReYusKmfrDDH10BERAYrd7KClcDJaHI5HD19cMMTyLYFYl3BZAURUSW0aNEi8c2oIAhYs2YNRo0aZbJP0/fs2YPExEQAmqUTXbp0MUm/UlCpVOKxQqEw+D7ta7ULdBorKSkJs2fPBqCpB1JQzNxU3n33XaSkpIiPaHP8fndwwEVf4Id2wDedgFiZiWuZxMbqtpmsICKSVLmTFawEThVR26OueBzrAiYriIgqITc3N/z888+wt7cXl2zu3r0brVq1ws6dOyvUd1paGt555x0Amp3HOnfuDEdHR1OELdqyZQtkMpnJHxs3biwylnbs2eWYCaB9rZOTU4W+XgB4/fXXkZycDABYunSpzmssU1AqlXB1ddV5mJyDA3Y0B14aBrz2JHDdIcO0/esX/tYvDE5ERBZV7mQFK4FTRbj51YdjruY4jskKIqJKq2vXrvjll1/E+hWAps7EmDFj0KlTJ/z666/iUhFDJScnY/jw4bh+/bp4bubMmSaN29KcnZ3F46xy1FgoqCuh34cxDh48iB9//BEA0K1bN7z00ksV6k8yDg5wKJyogix1jkm7F2JjkGGrdcKMheSJiKhshs9H1DJq1CgsWrRIrAQ+fvx4uLi4mDq2KlMJnArJAuqgdhoQ4fVoGUh4MZW3iYioUnjqqadw8OBBjBo1Cg8fPhRnWVy4cAFPP/00fHx8MHz4cAwdOhRBQUElFspMSEjA5s2b8dVXXyEhIUFMfrRs2RKjR482edxOTk5m2dGsuBkQ3t7e4nF6ejrS09MNSj7Ex8eLxxUtMDpr1iwAmqUla9euFb+/lY69PRy0VsRkCXmAIAAm+noSk++g1nuAWzbw7L/At5xZQUQkKaOSFVOmTMFnn30GlUolVgLfvn27qWOrMpXASUtAAPzPaZIVaUrg4d0ouEsdExERGa1Pnz64dOkSxo4di7///lt8IywIAuLj47F27VqsXbsWAODn54eAgAC4u7vD3t4eKSkpuH37NqKiosR7ChIeLi4u2LZtm1liHjFiBEaMGGGWvvU1bdpUp33nzh00b968zPu0XwMVzGg1VsKjnbdUKhVatWpl8H23b9/WSWwsWLAACxcurFAsFaI/s0IBICenaOFNI8Wl3QUApNgDahk4s4KISGLlXgYCsBI4VUBAAOo+LGzeSeHMCiKiyq5u3bo4ffo0Vq1aBU9PTzHpoJ24EAQBsbGxOHv2LP744w/s2bMHJ06cwM2bN8XnCxIVbm5u2LFjR5E3+pWRdrFwAAgODi7znry8PPz3338l9lFtOTjozqywhem2L83JQaz6odisnQrWrCAikphRyQqAlcDJSAEBqKtVk/W2KgnIzZUuHiIiMgmZTIZp06bh1q1bWLx4MerUqaOThNBOXujfp53U6NSpE86dO4fHH3/c0l+CWTRo0EDnA5fTp0+Xec/Fixd1alb06tWrQjFobyla1kOpVIr3yWQyneckX5Jb3MwKUyUr7t7VFP5+xC8NnFlBRCQxo5MVlb0SOEkkIACDw4EV+4E9W4HOMShafZuIiCotJycnvP3227h58yaOHj2KmTNnokWLFuJrheIe7u7uGD58OA4cOIB//vkHjRs3lvrLMKmhQ4eKx9u3b0duGUn6gmKYgGYXtoYNG1Zo/Nu3b+Phw4cGPQpefwGambQlPScJBwc4mmtmRWyspvD3I7WzbQF3d9P0TURERjGqZkWBgkrgo0ePFvcAL6gE3qFDB7z99tsYOXJkuQo5JScn4+mnn65SlcBJi6cngh46Iiiu8BMjREcD9epJFhIREZmeTCZD37590bdvXwCa3S1u3LiBmJgYpKenw8bGBl5eXvDx8UHTpk0rb9FHA0yYMAGrV68GACQlJWHt2rV47bXXir02JiYGmzZt0rmXHlEq4ZQLOOUCDnmAbT6AcmwHW6q4OE3h70f8HH1MVriTiIiMU6FkBVB5K4GTRGQyICAA0EpGcftSIqKqz9HREa1atSpXgceqIigoCEOHDsXevXsBAPPmzUP79u3RvXt3netSU1MxduxYpKWlAQBq1aqFGTNmlNq3dpJn/Pjx2Lhxo2mDtyZyOR6PVSL9U60tS004s0J7GUhtVy4BISKSWoWTFUDlrASu7a+//sKmTZvw559/IjY2FoIgwN/fHz169MD48eOLvJgwBWM+QVqzZg1eeeUVk8dicfrJijsssklERFXb8uXL8ddffyEpKQnp6eno168fJk+ejAEDBsDZ2RkhISFYuXKl+HpILpdj3bp1cHBwkDhyK+PgoNkBpICpkhVxceIyENt8wKtmPdP0S0RERjNJsgIorAT+7bffYv78+UhOTtZ5Q16wc0hsbCzi9GoUFDwHQKcS+LZt28xaCTwjIwMzZ87E+vXrizwXFhaGsLAwfPfdd5g4cSJWrlxZ7P7pZIRHhVlFnFlBRERVXL169bBnzx4MGTIE9+/fR05ODlavXi0uD9FmY2ODZcuWYciQIRJEauUcHICHDwvbJpxZse4kcNsdSFEC8j6cWUFEJDWTJSuAwkrgL774Ir755ht8++23uH37tvhcafcBhVubderUCZs3bzZrga38/HyMHDkShw4dEs85ODigRYsWUCgUuHr1qrizyYYNGxAbG4v9+/fDxsbG5LH06tXLoE9O6tSpY/KxJcFkBRERVUPdunVDSEgIZs2ahT179kClUhW5JigoCMuWLUO3bt0kiLAS0H+9ZMKZFZ1jgc6xj9rjmKwgIpKaTNCe1mBigiDgxIkT2LNnD44dO4arV69CrVYXe62Hhwd69+6NqVOnYuDAgeYKSTRv3jwsXrxYbE+ZMgVLliyBp6cnAM2si6VLl+Ljjz/WuWfRokUmGV87eRMVFYV6Zi4wmZqaCjc3N6SkpMDV1bXsG8xp/Xpg8uTCdqtWQEiIdPEQEVUTVvW7oJq7d+8eTp06hZiYGOTm5sLPzw8dO3Y064xSSzLbv7UWLYCrVwvbP/8MPPNMxftt0gT/Z+++46uq7z+Ov272TggzCxL2hrARWYKipSLiHrgtVltstdafWq3WVbStoNY9sKK2VRGroogiMkQ2hL1HFpBA9h7398eFk3uzc3NXkvfz8TiPfL8n33POJ1ySfPO538GBA1X1jz6Ca69t/n1FRNq45vw+cOjIiuo8dSXwtLQ0XnjhBaM+e/Zs3njjDZs2wcHB/OUvf8FsNvPUU08B8I9//IN77rmH6Ohol8TZanXvbls/fBjMZq26LSIibUbHjh254oor3B1Gy+OMkRVmc81t1GM0skJExN2cmqyozlNWAp8/fz7FZ7e6CgoKYv78+XW2ffTRR3nvvfdITk6muLiYBQsWMG/ePBdF2kolJJARBOvi4Fg4jEotYHRGBnTq5O7IRERExJM5I1mRmwsFBbbn9MaUiIjbebk7AHf47LPPjPLVV19tTP2ojZ+fH7feeqtRX7x4sVNjaxNiY9kY58Vl18HcX8CXvbGMrhARERGpjzOSFdVHVYCSFSIiHqDNJSv27dvHwYMHjfrFF1/c4DWXXHKJUT548CD7rLfdlKbz9qZrSNXwyuPhwNmt2kRERETqFBDArGtg8s1w4ywck6xITbWtt2tXMykiIiIu1+aSFdu3b7epjx07tsFrhg0bhp+fn1FP0mKQzdatY9VOL0cj0MgKERERaVhgID92g5UJsD4GODutt1m0XoWIiEdqc8mKPXv2GGU/Pz/iqm+jWYvq7azv4QgPPPAAAwYMICwsjMDAQGJjY5k8eTKPP/44R1rpiIPQ+N50PDs99FAkSlaIiIhIwwIDCTy742uRLw4bWbFwKLw1DL7uCeboqObfU0REmq3NJSuOHj1qlGNjYxu9A0nXrl1rvYcjfPLJJ+zevZu8vDyKi4tJTU1l5cqVPPHEE/Tu3Zu77rqLIkftI+4punen5xlLMTUMCo8eqL+9iIiISGAggWWWYpEPDluz4unxcOcMuO5KMMXENv+eIiLSbG0uWZGXl2eUw8PDG32d9Z6w1vdwhA4dOjB69GimTJnCiBEjCAkJMT5XXl7O66+/zrhx48jJyWn0PUtKSsjNzbU5PEr37vQ6XVU9dFrJChEREWmAE0ZWmFNTSAu1lKPz0OKaIiIeos0lK/Lz841yQEBAo68LtFpoyfoe9urfvz/z58/n0KFDZGRk8PPPP/Pdd9+xceNGsrKy+PLLLxk8eLDRfuvWrVx77bWNvv+zzz5LeHi4cTRmuotLWY2sADhYegJKS90Xj4iIiHi+gICqkRW+YC5ufrIi51QyhWeXJovJRWtWiIh4iDaXrCgvLzfKPj4+jb7Oum1ZWVmz49i1axf33nsv3bt3r/VZ06dPZ/369UyfPt04/8033/DFF1806v4PPfQQOTk5xpGcnNzsmB3qbLLCtwL6ZEIlwLFj7o5KREREPJnVyAqAkuKCZt8yLSfFKGtkhYiI52hzyYqgoCCjXNyEFaSt2wYHBzs0proEBATw0Ucf0blzZ+PcSy+91Khr/f39CQsLszk8Sng4V5xoR9FTsPdluGIP2r5URERE6me1ZgVAUUkzkxWVlaQWZxjVmDw0skJExEO0uWSF9XoQTVm0srCwsNZ7OFtoaCi//vWvjfrq1aublGTxZH7xPfA2W53QjiAiIiJSn8BALjkIczbB79aBd1Ez+0SnTpEWXGlUNbJCRMRztLlkRYcOHYxyenp6o687ceKEUW7fvr1DY2rI5MmTjXJxcbHnTemwV/UpMPv3uycOERERaRkCApi7Hl77El5YBmH5zZyam5ZGqtXg05g8E1iNaBUREfdpc8mKPn36GOXTp0/bjJioj3WCoG/fvg6Pqz5dunSxqWdmZrr0+U5j9VoAsG+fe+IQERGRlsFqwXMAmjvaNDUV3wqIzQHvSoj2aw/e3s27p4iIOESbS1b069fPpr5t27YGr0lNTSUjo2o+Y/V7OFv1hIr1uhstWvWkz9697olDREREWobqfaBGvulUp7Q0HvgJkl+AkidhpF988+4nIiIO0+aSFaNGjcLf39+or1mzpsFrVq9ebZQDAgIYNWqUU2Kry65du2zqnTp1cunznaZ6suLIkea/QyIiIiKtl6OTFampRtHbDF7RWlxTRMRTtLlkRUhICFOmTDHqH3zwQYPXWLeZMmWKy3YDOeff//63UY6PjycqKsqlz3ea3r1t62YzHDjgnlhERETE8zlhZIUN7QQiIuIx2lyyAuCWW24xyklJSXzxxRd1tt2yZQtff/11rde6wv/+9z++/PJLoz5z5kyXPt+pQkIgLs6olnmhqSAiIiJSt+pvGBU0c+tSq5EVgHYCERHxIG0yWXHllVcyZMgQoz5nzhz21vJHcnp6OjfeeCMVFRUADB06lCuuuKLWex49ehSTyWQcjz/+eK3tcnJyuOKKK9i8eXODcX700Udcf/31Rj0oKIgHH3ywwetakvK+vZl2I8T9HibdgpIVIiIiUrfqIyvKy6GsGTuCaGSFiIjH8nF3AO5gMpl48803mThxIkVFRaSnpzN69Gh+/etfM2HCBHx8fNiwYQMvv/wyJ0+eBCAwMJA33ngDk8nUrGebzWYWL17M4sWL6du3L9OmTWPo0KFERUURHBxMXl4eO3bs4JNPPmHjxo02Mb/77rs1dgZp6Xz69GOP7/ekhEO+H5j37qF5/8IiIiLSagUHU+YFef5Q5AOhpRBWUAAREfbdTyMrREQ8VptMVgCMHDmSRYsWceONN1JUVERubi7z5s1j3rx5NdoGBgayaNEiRo4c6dAY9u7dW+uIjupCQ0N5/fXXufrqqx36fI/Qty/9f4bkcMgOhBNHdtJKVuQQERERRwsK4t8D4aZZlurLX8E9hYX2JStKSuD0adtzGlkhIuIx2uQ0kHNmzZrF5s2bmTp1aq0jJkwmE1OmTGHTpk3MmjXLIc8MDAzkV7/6FQMGDGhwlEZ4eDhz585l586dXHfddQ55vsfp25f+VbvCsjtrP1RWui8eERER8VzBwQSWV1WLfLF/kc3qU0BAIytERDxImx1ZcU6/fv1Yvnw5ycnJrF27ltSzwwFjYmIYN24ccVYLQNYnPj4es9ncYDt/f39ef/11ALKysti2bRunTp0iMzOT7OxsgoKCiIyMZPDgwQwePBhvb2/7v7iWoHqyIqyEKampNgtvioiIiADg50dghQmw9LmKfLB/kc20NJ4ZD/8aAjG5MP8HfwbZO51EREQcrs0nK86Ji4vj2muvdekz27Vrx+TJk136TI8THU3//ECgCIDdHYHdu5WsEBERkZpMJoK8/IFiAAr8sH9kRWoqByJhXwfLwa6O0My1yURExHHa9DQQ8QAmE/069jequzsCSUnui0dEREQ8WrBP1Y4gBb40a2RFalhVNSZcb5SIiHgSJSvE7doNGE5UnqW8qxOYk7a7NyARERHxWME+gUa5sDlrVqSmkhZqKfqXQ7tOXZsfnIiIOIymgYj7DR7MQ/+CgHIYegJor2SFiIiI1C7YN9goF/jRvJEVZwdTxOSCKSa2+cGJiIjDKFkh7jdkCL/dYFU/tdeynZi/v9tCEhEREc8U5GeVrGjGyIrC9ONk97aUY/KAgdoJRETEkyhZIe43aJBtvbwc9u6FIUPcE4+IiIh4rHZ+YXzzPgSXQed8YKx9yYq07GSjHJ0HxMQ4JkAREXEIrVkh7hceDvHxtue2ayqIiIiI1OQbGMK0Q3D+ceh1BvumgZjNpBWcMKoxuUC0RlaIiHgSjawQzzBkCBw9WlXXjiAiIiJSm+Bg27o900Byc0k4UcL8ryE1DCYdRSMrREQ8jJIV4hmGDIHPP6+qb97svlhERETEcwUF2dbtGVmRmkpcLty73upcVFSzwhIREcfSNBDxDMOH29Y3b4bKSvfEIiIiIp6rerLCnpEVaWm29chICAysva2IiLiFkhXiGUaOtK3n5cG+fe6JRURERDyXI6aBpKba1rVehYiIx9E0EPEMUVGUx8WwLCCVn2MhsBwe3rAB+vVzd2QiIiLiSRwxDaT6yAqtVyEi4nE0skI8x8iRXHMVPDURXh8ObNzo7ohERETE02hkhYhIm6BkhXgMn1FjGHH2jY7jEZC+fY1b4xEREREPFBTE8u7w4mh49nzIK85p+j00skJExOMpWSGeY+RIxqRUVddn74KSEvfFIyIiIp4nOJi3h8G9l8DDUyGzPK/p99DIChERj6dkhXiOESMYbdV3WN+5XFNBRERExFZQEMGlVdWCsqavWXH6dDJf9YKtXSA7AI2sEBHxQEpWiOcIC2N0WH+j+lMcsHq1++IRERERzxMURFBZVbWgrIlrVlRUsNH7JL+8AYbdBX87D42sEBHxQEpWiEeJHjWFhCxLeX0sFK/+wb0BiYiIiGcJCSHYOllR3sRkRUYGqcGVRjUmF42sEBHxQEpWiGeZMIFJRy3FEh9Yf3QNlJe7NSQRERHxICEhttNAmpqsSE0lLbSqGlPgBZ06OSY2ERFxGCUrxLOMH8/kIzDoJPxmPXTILILt290dlYiIiHiKaiMrCimHsrK621eXlkZqWFU12q89eHs7Lj4REXEIH3cHIGKjc2duLO7F7FcPVJ1buRKGD3dbSCIiIuJBQkNtR1b4Afn50K5d466vPrIiVOtViIh4Io2sEI9jmjjJ9sTy5W6JQ0RERDxQaCgRxdChALplg18FkNeE7UvT0kg9m6zwroROHeKdEKSIiDSXRlaI57nwQnjzzar6jz9CUREEBrovJhEREfEMwcFcswuu2WV1Lj+/8denppIaaSl2yQfvaC2uKSLiiTSyQjzP1KngZfVfs7hYW5iKiIiIhbc3BAXZnmvCyIrytBSyAyzl6Dy0E4iIiIdSskI8T7t2MGqU7blvv3VPLCIiIuJ5QkJs600YWeGTmk7h03DyefjPx0C01qwQEfFESlaIZ5o2zbb+9dfuiUNEREQ8T2iobb2Ja1Z4maFTASRko5EVIiIeSskK8UwXX2xb370b9u93TywiIiLiWaqPrGhssqK4GE6ftj2nkRUiIh5JyQrxTKNGQZcu5PrDosHw2GTgs8/cHZWIiIh4guojKxo7DSQ9veY5jawQEfFISlaIZ/LygssuY8wdMHsWPD0eMr76r7ujEhEREU9g7zSQ1FTbemAghIc7JiYREXEoJSvEc82axYx9lmKlFywu3AIpKe6NSURERNzP3gU209Js6zExYDI5JiYREXEoJSvEc02axNXHqjoj/x0AfPSR++IRERERzxAaypVXw6g74aLZ2D+yQutViIh4LCUrxHP5+ZF4/pX0OGOproyHU/9+G8xmt4YlIiIibhYSwpYo2BgDW7vQ+GRFbSMrRETEIylZIR7NdNPNXL3LUq70gg/89sGWLe4NSkRERNwrNJSwEksxz5/GTwNJTWX69XDjLPj7WDSyQkTEgylZIZ5twgRuyqjqSLw5HMzvLXRfPCIiIuJ+ISGEnk1WlPhAaX5Ooy7LP5nM0t7wwWD4sjcaWSEi4sGUrBDP5uVF38tuZ8JRS3VPR/jph39Z9kkXERGRtik0lNDSqmpeUeOSFanZx41ydB4aWSEi4sGUrBDPd9NN3LkFRqTCG/+Dwftz4cMP3R2ViIiIuEtIiDENBCC3pBHJCrOZlMITRjU2F4iNdXxsIiLiEEpWiOfr2ZMbOk1h45tw5xYs76TMn6+FNkVERNqq0FBjGghAXmkj1qzIyiLVv2o4RkwemgYiIuLBlKyQFsH0u9/bntixA1ascE8wIiIi4l7VpoHkljUiWZGaSmpoVTU2F00DERHxYD7uDkCkUS65BHr3hv37q849/zxMmeK+mERERMQ9QkKYdBTMQFgJxJwqavialBRSwqqqMd7twM/PWRGKiEgzaWSFtAxeXnDvvbbnli2DtWvdE4+IiIi4T3g4M/bBC8vgiZWQkFYE5eX1X5OaSqpVsiI2TFNAREQ8mZIV0nLcfDN06mR77tFH3ROLiIiIuE9ERM1zOQ0sspmSwkWHYPZ2uOAwdG7fzSmhiYiIYyhZIS1HcDA8/LDtuR9+gO+/d088IiIidtixYwf33XcfgwcPJjIykpCQEPr06cMNN9zAN99847I4zGYzP/zwA3fffTdDhw6lU6dOBAQEEBcXx6hRo7jzzjv58MMPOXHiRMM3czV7khWpqdy9Ef71GXz/L/CJ7eqU0ERExDFMZrO2VGgLcnNzCQ8PJycnh7CwsIYv8FTFxdCrF6SkUGmCRYPhIu/edPl5J/j6ujs6ERGP1mp+F7RQ5eXlPPbYY8ybN4/Kyso6202fPp13332Xjh07Oi2W3bt386tf/Yq1jZhOOX36dL788ssm3d/p/9fMZst6E9ZTPzZvhmHD6r7mkkvAOhn01FPwyCOOj01ERAzN+X2gkRXSsgQEwKOPsrMTDJsDN18OD8Xth5decndkIiIi9ZozZw7PPvuskajw9fVlyJAhjBs3jvbt2xvtvvrqK6ZOnUp+fiN2uLDD8uXLGT58uE2iIjg4mCFDhnDBBRcwatQoImobueBJTKaaoyuys+u/JjXVth4b68iIRETEwZSskJbn9tvplDCQIxGW6sJEWPHWI5Cc7NawRERE6vLGG2/wzjvvGPUZM2Zw5MgRtm3bxpo1a0hPT+ell17Cx8eyUVtSUhJz5sxxeBxr167lsssuo7i4GIDu3bvz8ccfk5mZybZt2/j+++9Zv349WVlZ7NixgyeeeIJYT/2jvqnJipQU23qMFtgUEfFkmgbSRrS6ob/r1vHa3PP49S8t1R5nYMfuiQQuW2HZOURERGpodb8LWojCwkJ69OhhrP0wadIkvvvuO7y9vWu0ffvtt7njjjsAMJlMbNq0iWH1TW1ogqKiIgYNGsShQ4cAGDduHN988w0hISEOub81l/xfGzkSNm2qqr/9Ntx2W+1ti4ogKMj23O7d0K+fc2ITERFA00CkLRo7ll8NuY3zj1mqhyLhfv8f4cUX3RuXiIhINQsXLjQSFSaTiVdeeaXWRAXA7bffzujRowHLApjz5s1zWBxPP/20kaho3749S5YscUqiwlXM4WGcCoZ97WFPB+pfYLP6FBDQNBAREQ+nZIW0WF5/+ztvbuxCYJml/upIWPzOA7BunXsDExERsbJ48WKjPHHiRPo18G6+9fSPpUuXUlJS0uwYSkpKeO2114z6o48+SocOHZp9X7eKiCD6fuj7W5g9i/qngVSfAhIaajlERMRjKVkhLVdEBH1f/JAFX1eduv0X5ey77TJIS3NfXCIiImfl5+ezatUqo37xxRc3eM0ll1xic/3KlSubHcdnn33G6dOnAfD39+emm25q9j3dzRTRjgjL0htkBVB/siI1lSMRcCIEKk1oVIWISAugZIW0bJMnc8fUB7h6p6UalwsB6Rlw+eVQUODe2EREpM3bvXs3ZWVlRn3s2LENXtOlSxfi4+ONelJSUrPj+Pbbb43yeeedR7t27Zp9T7eLiKBdkaWYFUiDIytmz4KoP0DAn6A4tosrIhQRkWZQskJaPNPTz/BW/mTu/RnWvAPdcoANGywJCwcMnRUREbHXnj17bOo9evRo1HXW7arfwx4bNmwwymPGjAHg5MmTPP300wwfPpzIyEiCgoLo1q0bM2fO5J133qG0tLTZz3WqiAjanR1ZkR0AldlZdbdNTSX17KyPsBIIiOnm/PhERKRZlKyQls/Hh9APPmH+/u6EWecmli+H664DT+9siYhIq3X06FGj7OPjQ1RUVKOu69q1a633sEdZWZlNwqNXr158+umn9O/fnz/96U9s2bKFrKwsioqKOH78OJ9//jm33347ffr0Yf369Y16RklJCbm5uTaH04WHGyMrzCbILThTZ9PKlGTSziYrYnLRtqUiIi2AkhXSOkRGwpdfQvv2tuc/+wwuu0xTQkRExC3y8vKMcmhoKF6N3F7bens363vYIzs7m8rKSqO+efNmrrnmGs6csfxx36VLFyZMmMCYMWMIDg422h09epRJkyY1as2MZ599lvDwcOOIi4trVsyNEhFBZFFV9Uzh6TqbZmYco9THUo7NRWtWiIi0AEpWSOvRrx98/TVU34btm2/goovgTN3vuIiIiDhDfn6+UQ4ICGj0dYGBgbXewx7Z1dZy+Oc//0lFRQVdunThf//7H2lpafz444+sW7eOzMxM/vrXvxpbqxYXF3PttdeSmZlZ7zMeeughcnJyjCM5OblZMTeK1TQQgKyS7DqbpuZU7QYSk4dGVoiItABKVkjrMnIkfPEFWL0zBMBPP5E3djjs2OGeuEREpE0qLy83yj4+Po2+zrqt9QKd9qht69Pg4GBWrlzJpZdeislkMs4HBATw4IMP8vrrrxvnTp48yQsvvFDvM/z9/QkLC7M5nM5qgU2ArLI6RqCUlnKsLMOoxuWgkRUiIi2AkhXS+kyaBN99B1Yrnb86AnrNOMqPV42C//zHfbGJiIjbLVq0CJPJ5PBj4cKFNZ4VFBRklIuLi2t8vi7WbYOrJ+CbqLbr//CHP9CnT586r7n99tttdi555513mhWDU4SH86vNsPENOLgAzt9TABUVNdulpHAsvKraLQew2m1FREQ8k5IV0jqNGQOrV0N0NEt7wW9+ASdDYMo1xTz30rVU3nwTuGLxLxERadNCrKYmFhUV1dPSVmFhYa33aG4M59x4440NXmfd5sSJE+zfv79ZcThcZCTdcmBEGvTIgoByIKuWHUGOHeNYRFU1viQQIiJqthMREY/S+PGIIi3NgAGwcSMjr72UKUe2sLwHVHjBgxfC0qPv8+64FSS8+D5MnuzuSEVExIWCg4OJccKaBbWNYOjQoYNRzs/PJz8/v1HJhxMnThjl9tUXj26iiIgIfHx8jCkpoaGh9OzZs8Hrhg0bZlM/fPgwvXv3blYsDtWxY81zGRlg9W8OwNGjPPED3LINjoXD4OB4sJr6IiIinknJCmndoqPpuPwnvr7n1zzx47s8OdFy+sd4GByVynN/vIBf9b0e77/9Azp3dmuoIiLiGpdffjmXX365S55VfarF8ePH6d+/f4PXWS9Q2bdv32bF4OvrS48ePdi3bx8AkZGRjbquepIkq7ZRC+7k7w9hYbYjJTMyLAtuWzt2jNBSGHzScjC9u0vDFBER+2gaiLR+/v54v/k2f7ntfb7/OIhu2ZbT+f5w9y/h7uwPoW9fePFFKC11a6giItK69Kv2h/O2bdsavKasrIxdu3bVeQ97DBgwwCjXtuBmbaqvsdGU3UxcpvroioyMmm2OHbOtd+vmvHhERMRhlKyQtsFkghtv5IKvdpO0/Tzu2Hz2tBnmbAays+Heey3vxnz0EVjtRy8iImKv7t27E2u188SaNWsavGbz5s02a1ZMmDCh2XFMnDjRKGdkZFBQUNDgNUeOHLGpd/bEEYhKVoiItFpKVkjb0q0bYctX8ebF/+SHj4N59jsYlm71+cOH4frrYfhw+OST2lcVFxERaYIZM2YY5Y8//pjSBkbxffDBB0Z5wIAB9OjRo9kxzJo1y9iitKKighUrVjR4zbfffmuU/f39SUxMbHYcDqdkhYhIq6VkhbQ93t5w991M+u4gD8bfUHubbdvgqqugf3945x1NDxEREbvdcsstRjkzM5PXX3+9zrYpKSm89957tV7bHLGxsVx44YVGfd68eZjN5jrbp6am8q9//cuoX3jhhQQGBjokFodqKFlRWQlW638ASlaIiLQQSlZI29WlCyxaBGvWwHnn1drkZOp+frXkdvYOjYUnnoD09FrbiYiI1GXkyJE2oysefvhh1q5dW6Ndbm4u119/PXl5eQB06dKFe+65p957m0wm42gosfHss88aoyvWrl3LfffdR2Ut0x6zsrK44oorjDjOxeyROnbk/cHwpwvg3oupmaxIT4eyMttz8fGuik5ERJpByQqRceMsCYvPP7eMpLDy0mh4czj0uyaD6fsf539TYim/+kr44QetayEiIo22YMECYxvT/Px8pkyZwj333MPnn3/O999/zwsvvMDQoUNZvXo1AF5eXrzxxhsOHc0wbNgwm6TD/PnzGTVqFK+99horV65k2bJlPPnkk/Tr14/169cb7f74xz8yduxYh8XhUB078upIeHoCvDgGSjNP2n7+6FHbur8/dOrksvBERMR+2rpUBCwLcM6YAdOnw6efwrPPUrF9Gx8OqmqytDcs7V1JbM6n3PHEp9x+KobYK26F2bPBk/adFxERjxMfH8/nn3/OpZdeypkzZygpKeGVV17hlVdeqdHW29ub+fPnc+mllzo8jqeeeorTp0/z2muvAZbFPDdv3lxn+7vvvptnnnnG4XE4TMeOdNpUVc3MPUG09eePHuW5cZAeAr3OwK15sQR66b06EZGWQD+tRax5e8PVV8OWLXgv/ZptSefxt2XQNbuqSUo4PD4Zul2VygvfPQV9+sDo0fDSS5CS4rbQRUTEs5133nkkJSVxxRVX4ONT+/tFI0eOZNWqVfzmN79xWhyvvvoqn332mc12ptUNGTKEJUuW8M9//hNvb2+nxdJsHTvSOb+qerKw2jSQgwf590CYPxZ+ewl4JzR/sVIREXENjawQqY3JBBdfTNjFF3P/1q387tV/8s13i3h9UAlf9YJKL8uReOJs+w0bLMfcuTByJFx+ueXo29etX4aIiHiWmJgYPvnkEzIyMli1ahUpKSmUlpYSHR3NiBEj6NOnT5PuV98imfWZOXMmM2fOZOfOnWzbto309HS8vLzo3LkzY8aMoWfPnnbd1+U6dqST1S6sp8qywGy2/B4HzAf2c7Cr5XPx2eDXs2n/viIi4j5KVog0JDER7zfeYnr235j+3nskv/9P3go9wIoEmHCslvYbN1qOhx+msm8fvC75BVx0EUyYAEFBLg9fREQ8T8eOHbniiivcHQYDBw5k4MCB7g7DftWSFScDKiA7G9q1A+DU8b3k9bJ8rtcZYGQLScKIiIimgYg0WkQE3HsvcRv38cTfN7M6/Hd4dax/ka7Z/fcxPvsFnn7mEtYPjKDiwqnw/POwdStUVLgmbhERkdaqSxeiqzYtISUMmymZB84cMMq9TgO9erkuNhERaRaNrBBpKpMJhg2zHM8/D99+Cx99BF9+aXk356wSb/iiD+T5w5pu8CfKiCj6nsmbv2fqxzDlVDC9+52P6fzxMH48jBoFAQHu+7pERERaGn9/4rzbAVkAJJ9LVgwaBGfOcMC3KpPR8wzQUqa3iIiIkhUizeLjA7/4heUoK4OVK+Gzz2DJEpJL0onNhT0dq5pnB8Jn/SwHFLDo02Xc8Kdllk/6+VkSICNGVB19+1oW/RQREZFadQ2LIz4ri9hc6JEFJCdbPnHwIAcjq9r1yvaC+Hh3hCgiInZQskLEUXx94cILLcfLL9Nz40Z2L1vGkVWf813OVr6LN/N9dzhttWzFGOvNQ0pL4eefLcdZ2e0CCRqYiN+wkTBkCAwcCP37Q3Cw674uERERD9alYwJHFiRVnZhy9pfrgQMcaF91uldgjOV3tYiItAhKVog4g5eXZTvT0aNJ4DHuzM7mzh9+oHLZN2z7YSnL/VLY3gW6Z9V/mz+PLuLVkT8x8NRPDFsCg1+HfpnQP6Ar0T2GYBo4yJLA6NfPMrQ1JMQlX56IiIjHiIuzrZ8bWbFzJ2OTIdcf9naA+Ji6t2oVERHPo2QF8NNPP/Hee++xevVqUlNTMZvNxMbGcv7553PzzTczbtw4pz7/8OHDLFy4kK+++orjx4+Tn59PdHQ0gwcP5oYbbmDmzJl17scuLUREBFx+OV6XX84wYFh6OqxZA9GrYfVq2L7dstVaNVuioMwbtkZZjirHCSs+zq82fsHzz1idjoqyLB7Wq5cleXGu3KOHRmOIiEjrVFeyIimJ3/8Mvz83YPH/hroyKhERaaY2/RdwQUEBc+fO5Z133qnxuT179rBnzx7efPNNbr31Vl566SWCnfDH3oIFC3jwwQcpKSmxOX/48GEOHz7MkiVLGDNmDB988AHdu3d3+PPFTaKi4KqrLAdATg6sXw+bNlUdyckMyIAzgZZ3hCqr7d2TGwA+ldXum55uOVatstzWH14cbRnBkVAZRlxYHFGduuPTLQG6doVu3Swfu3aFTp0sI0JERERakm7dbOuHDlk+JiXZnh882DXxiIiIQ7TZZEVFRQWzZs3i22+/Nc4FBgYyYMAAfHx82L17N7m5uQC8++67pKamsnTpUrwduNjhk08+yWOPPWbUvby86N+/P5GRkRw4cID09HQAfv75ZyZOnMiGDRuIioqq63bSkoWHw0UXWY5zTp7ktc2bYdMmCrZvJCl9G7tLUtjTAXZ3hD0dYEBG/bc90B4eu+BcLRfYhVflLrrkQ+xeiN0A/1wKXfKxzOPt0sWSSKl+WJ/v3NmysKiIiIgn6N3btn7sGKSl2WxhCihZISLSwrTZvzgeffRRm0TFnXfeyV//+lciIy3LRhcUFDBv3jyefPJJAL799lsee+wxnn76aYc8f9myZfz5z3826mPHjmXhwoX0PvsLt7Kyko8//pg77riD/Px8UlJSuOqqq1izZo1Dni8tQOfOxk4jwcBYYGxhIezdCzt3Wo6YnZC1s2rIazWH2tU8V+kFaWGWYwPw9v/OfqKszHKfavf623nwYzfoVACdC85+NAfTyTeCDkHt6RLchc7h0dChQ82jfXvLERGhRc1ERMQ5evWyrZvNsHix7Tlf35pJDRER8Wgms7mWifKtXFpaGj169KC4uBiA2bNn869//avWto8++ihPPfUUAAEBARw6dIjo6OhmPd9sNpOYmMj27dsB6NOnD1u2bCEoKKhG2++++44LL7zQqC9evJjLL7+8yc/Mzc0lPDycnJwcwsLC7A9ePFNRkWXY64EDVcfBg5xI2cvPPic43A6OREBKWNVxMgSCSyH3WTDVc+srr4ZP+9f9+Zl74LP/1B/es+dDRKUvkV4hhPuGEO4fTlhQBGFB7QgL7UBoWEe8ItpZkhrh4ZaPYWGWBUPPHaGhEBgIpvqiFZH66HeBuIrL/6/FxkJqalX9/PMta0OdM2QIbNvm/DhERMRGc34ftMmRFfPnzzcSFUFBQcyfP7/Oto8++ijvvfceycnJFBcXs2DBAubNm9es53/99ddGogIs61bUlqgAmDp1Ktdccw3/+Y/lr8G//vWvdiUrpJULDLTsCjJwoM3pLsDMwkI4ftwyLPb4caNcuvkoGRlHMfmkQ3l5nbfOqP2/piGyqP7PF/nAw1MByoCss4ft6I3PP4IZ++q+x+YoWNIXwkoh2ORPsHcAwd6BBPsGEewXQqhfCIleMbbJjeBgy79LUw9fXyVERERamj59IDWVMi/Y1wEGVh+JOmyYe+ISERG7tclkxWeffWaUr776amPqR238/Py49dZb+ctf/gJYRjY0N1mx2GpoYkJCAhdZr1NQizlz5hjJig0bNpCSkkJsbGyzYpA2JCgI+va1HFb8gBiAigrLopxpaVULdFodK3amk7kmlVMFpzgZWMnJYDgVbBmZcSYQzj9e/+PPBDYcYlhJ/Z/fFA1PTTxXKzl75BifjyiCrAa+Le+eDts7Q3CZZURJUBkElkNgGQSUW76Oy/ZhWWS0liRGWaAfW9qXEugdQICPP4E+gQT4Blo++gXh4xcA/v7g52f7sbZztbXx9bUcPj62H6ufc+C6OSIircaQIdwRsoIPB0GpN+Q8a/l5bxg/3m2hiYiIfdpcsmLfvn0cPHjQqF988cUNXnPJJZcYyYqDBw+yb98++vTpY3cMX331lVGeNm0apgbexR0/fjzBwcEUFBQY18+ZM8fu54vY8Pa2DJ+tIwHmDXQGOldWMigzE06ehNOnITOz6piUWfNcZibk59OuGP73oSVpcTrIst99rr9lp5Jz5S759YeY61//5206pHXY3hl+6lr350u9zyYrKiuhoMByWDkdAmP+UPf1PhXww3v1J28+6wtvDgf/cvCrsBz+FVXlDoXwp1X1fx2rukFekDf++OCHN34mH/xNvvh6+eDj5UOHCn86VAbUTHrUlfw4lwDx9rYtO+uw5xleXpbDZKoqN+aor73JpBE0Iq3JuHF4H3iBorPLI/15Mly6D8YfBy8zMGGCW8MTEZGma3PJCuvpF2BZ2LIhw4YNw8/Pj9LSUgCSkpLsTlacOnWKEydONOn5Pj4+jBw5kpUrVxrPF3E5Ly/L9qadOjX+mpISgs6c4dKcHMjOthznytbnLqrlXF4e5OdDZSXX7IIhJy1JiwJfKPCz/diYZEVZAwMSAuqeCQNYprPUp9zbknCoz6FI+LpX3Z+Py2k4WfHHC2F9bAVQ+8Pu+wn+/m2tnwLgeDj0nAu+FZatb30rz360qv/3Yxh6ou57fN4H3h9SdZ31PbzNlqTLYz/W/3V8OMgyxcjbDN6Vlo8+lVXlAacgsZ4Yin1gY7Tt9d5n4zhX7pZT/+ta6g2lvl54mbzwNnnhhQkvk6Vu8vK2PxHSmPbn6ueSJrWV6zo3eTLce2/9/8Aibc155zHuSXhjhKX69/MsR7ds2PVFHMHa/l1EpMVpc8mKPXv2GGU/Pz/i4uIavOZcu0Nn9+22vkdzng/Qo0ePRl3Xo0cPI1nRnOeLuJS/f9WWp/Ywm6G4mK75+XTNz7ckL/LzqxIZ1sf4/JrnioosR3ExG9YWUV5cSEF5IQXlRRRWFFNsLqPIx/KHb3Re/aEEl8Hcn6HI19K+2Afj2nPnIorrv0dpAwmThpIdACUN3MO3sv7Pl3lZEjf1JW/Kveq/x94O9S+6mpDVcLLi72NhSz1rFT+wtv5kRXoITLit/mdseANGptX9+RfGwP9dWAnU/o/W4wwcfLH+Z1w4G5I6W965re2Ysxn+uLbu69NCYdY1dV/vZYaXl0Lv01YXRUTUH5RIWxQVxUVevfCqPECl1c+whCwIvuoGjaQSEWmB2lyy4ujRo0Y5Nja2wSkY53Tt2tVIVljfoznPP3ffxj6/rnvUpqSkhJKSqoUAcnNzG/UcEY9iMlWtG9GxY7Nv5wOEnz0Ay3odxcVVSY16jk4lJSwoKYHSUqjtY0kJzKjjc2c/3p9bzF2fFFNaXkJJRQml5aWUVpZSUllGqbkc7wYSDQC/2WD5A7fUG0p8zn48m3wo94Lh9fxxDpaRB4nplrZlXmc/etuW/RsYZdLQKJXGfB0VDSREGrpHQwkVsIywqE+lA/52yQyCUyF1fz4roP7ri3xgfQNLEOX7VTuhP7pEatXl6tu4avND/MdqrenfbAA+me22mERExH5tLlmRl1f19ml4eHg9LW1Zb7NifY/mPL8pMTT1+c8++yxPPPFE04ITaWu8vS27hgQHu+Rx/mePWpnNluRJeTmUlVmOc2Wrj7fX9bk62lf/XLfycrZUb19RYTnKzn68vKLqXC3H78wl3Lq9mLLKcsrM5ZRXVlBmtpQrKivwrTDDiADb68rLberzthZwxrecCnOl5aCScqrKw1PN1DXiASyjWP6w1pL0qDBVfSz3qip3KKz/9eiWA1MOW5IWtR0xjcjxxuRBTkDd92hoilJjEiZe1ZMuXo3I1Ii0Rffcw0tj36DA9wgbYuCejTDrgruhfz1DwURExGO1uWRFfn7VSn4BAQ285WUlMLBqSwPrezTn+U2JoanPf+ihh7jvvvuMem5ubqOmvIiIm5hMVQteNuFnkzuEnD2aY1pjG5rNlkVPzx1n6x0rK3ne+nz1o/p1tRzXm81c30Ab7qn/Xl829MzBlXB7tTYVFZaPZjO9Kisxm82YKyuprKyg0mz10VxBZWUFQb/zBUxV1w8e3Mx/fZFWKjSUjstW88Wf/wybD8MvLob773d3VCIiYqc2l6woL68a3+zj0/gv37ptWVkjVvNrxPObEkNTn+/v74+/fwNbKIiIeDqTqWpXkFbMhGXnndb9VYq4QEwMvPWWu6MQEREHaHNjSYOCgoxycXEDq+FZsW4b3Iwh49bPb0oMjnq+iIiIiIiIiKdrc8mKkJCqwctFRUWNvq6wsGrys/U9mvP8psTgqOeLiIiIiIiIeLo2l6zo0KGDUU5PT2/0dSdOVO2h1759e4c8vykxOOr5IiIiIiIiIp6uzSUr+vTpY5RPnz5tM2KhPsnJyUa5b9++Dnk+wPHjx136fBERERERERFP1+aSFf369bOpb9u2rcFrUlNTycjIqPMeTdGrVy+bxTIb83yArVu3OuT5IiIiIiIiIp6uzSUrRo0aZbNLxpo1axq8ZvXq1UY5ICCAUaNG2f18Pz8/Ro8e3aTnnzhxgoMHDxr1CRMm2P18EREREREREU/X5pIVISEhTJkyxah/8MEHDV5j3WbKlCnN3o3jsssuM8rfffcdJ0+ebPTzIyIilKwQERERERGRVq3NJSsAbrnlFqOclJTEF198UWfbLVu28PXXX9d6rb2uu+46Y3RHWVkZzz33XJ1t8/PzefHFF436DTfcgK+vb7NjEBEREREREfFUbTJZceWVVzJkyBCjPmfOHPbu3VujXXp6OjfeeCMVFRUADB06lCuuuKLWex49ehSTyWQcjz/+eJ3Pj42NZc6cOUZ9wYIFfPrppzXalZWVceuttxqLcAYGBvLwww836msUERERERERaal8Gm7S+phMJt58800mTpxIUVER6enpjB49ml//+tdMmDABHx8fNmzYwMsvv2xM0QgMDOSNN97AZDI5JIbHH3+cr7/+mgMHDlBRUcHVV1/N9ddfz8yZM4mMjGTfvn28+uqrJCUlGdc8//zzREdHO+T5IiIiIiIiIp6qTSYrAEaOHMmiRYu48cYbKSoqIjc3l3nz5jFv3rwabQMDA1m0aBEjR4502PPbtWvHl19+ydSpU0lOTqayspJFixaxaNGiWtv/8Y9/5J577nHY80VEREREREQ8VZtNVgDMmjWLzZs3M3fuXL7//nvMZrPN500mExdccAEvvvgi/fv3d/jze/fuTVJSEn/4wx/48MMPKSoqqtGmX79+/PWvf2XGjBnNeta5ry03N7dZ9xERkZbr3O+A6r/vRBxN/Q4REYHm9T1MZvVYAEhOTmbt2rWkpqYCEBMTw7hx44iLi3PJ8/Py8lixYgXJyckUFBQQFRXFoEGDSExMdMj9U1JSXPa1iIiIZ0tOTiY2NtbdYUgrpn6HiIhYs6fvoWRFG1FZWUlaWhqhoaF2r7uRm5tLXFwcycnJhIWFOThCcRe9rq2PXtPWyRGvq9lsJi8vj+joaLy82uQa2+Iijuh3gH6etUZ6TVsnva6tj6Ne0+b0Pdr0NJC2xMvLy2HvooWFhemHUCuk17X10WvaOjX3dQ0PD3dgNCK1c2S/A/TzrDXSa9o66XVtfRzxmtrb99DbKiIiIiIiIiLiUZSsEBERERERERGPomSFNJq/vz9//vOf8ff3d3co4kB6XVsfvaatk15XaYv0/7710WvaOul1bX084TXVApsiIiIiIiIi4lE0skJEREREREREPIqSFSIiIiIiIiLiUZSsEBERERERERGPomSFiIiIiIiIiHgUJStERERERERExKMoWSH1+umnn5gzZw79+/cnPDycsLAw+vfvz69+9SvWrl3r7vCkkVauXInJZGrysXfvXneH3mZlZGTw9ddf85e//IUZM2YQFRVl89osXLjQ7nvv2LGD++67j8GDBxMZGUlISAh9+vThhhtu4JtvvnHcFyE2HPmaHj161K7vab2+0hKo79E6qO/Rsqjf0Tq19L6Hj91XSqtWUFDA3Llzeeedd2p8bs+ePezZs4c333yTW2+9lZdeeong4GA3RCnS+pw4cYIxY8Zw7Ngxh9+7vLycxx57jHnz5lFZWWnzuf3797N//34+/PBDpk+fzrvvvkvHjh0dHkNb5MzXVKQ1Ud9DxPXU72idWkvfQ8kKqaGiooJZs2bx7bffGucCAwMZMGAAPj4+7N69m9zcXADeffddUlNTWbp0Kd7e3u4KWZogICCAiRMnNqptSEiIk6OR6oqLi532i2XOnDk2fwT4+vrSv39/QkJC2Lt3L6dPnwbgq6++YurUqaxdu1b/BxzAma/pOdOmTWtUO3UExVOp79G6qe/hudTvaJ1aTd/DLFLNQw89ZAaM48477zSfPn3a+Hx+fr750UcftWnz8MMPuzFiacgPP/xgvFbdunVzdzhSjyNHjhivVceOHc0XX3yx+U9/+pN5yZIlNt9z7777bpPu+/rrr9tcP2PGDHNKSorx+dLSUvNLL71k9vHxMdpcf/31Dv7q2iZnvKbW99SvcmkN1PdofdT3aBnU72idWkvfQz0csZGammoOCAgw/hPOnj27zrZ/+tOfjHYBAQHm1NRUF0YqTaEOQ8uRk5Nj/vjjj81Hjx6t8Tl7f7kUFBSYu3TpYlw7adIkc3l5ea1t33rrLaOdyWQyb9682d4vRc5yxmuqZIW0Jup7tE7qe7QM6ne0Tq2l76EFNsXG/PnzKS4uBiAoKIj58+fX2fbRRx8lLi4OsAw1WrBggStCFGnVwsLCuPLKK+nWrZvD7rlw4UJOnDgBgMlk4pVXXqlz6PTtt9/O6NGjATCbzcybN89hcbRVznhNRVoT9T1E3Ef9jtaptfQ9lKwQG5999plRvvrqq4mMjKyzrZ+fH7feeqtRX7x4sVNjExH7WH9vTpw4kX79+tXbfs6cOUZ56dKllJSUOC02ERH1PURaF/U7xFGUrBDDvn37OHjwoFG/+OKLG7zmkksuMcoHDx5k3759TolNROyTn5/PqlWrjHpTv6/z8/NZuXKlM0ITEVHfQ6SVUb9DHEnJCjFs377dpj527NgGrxk2bBh+fn5GPSkpyeFxiYj9du/eTVlZmVFvzPd1ly5diI+PN+r6vhYRZ1HfQ6R1Ub9DHEnJCjHs2bPHKPv5+RlzQutTvZ31PcQzZWdnc/XVVxMfH09gYCChoaEkJCQwc+ZMXn75ZWNrOGkdqn9P9ujRo1HXWbfT97Xnu+mmm+jVqxfBwcEEBwfTtWtXLr74Yp577jlOnTrl7vBE6qS+R9ugvkfboX5H2+GKvoeSFWI4evSoUY6NjcVkMjXquq5du9Z6D/FMOTk5fPzxxxw7dozi4mLy8/M5evQon3/+Ob/97W/p2rUrL730krvDFAex/p708fEhKiqqUdfp+7plef/99zl48CCFhYUUFhaSnJzMsmXLePDBB+nWrRuPPvooFRUV7g5TpAb1PdoG9T3aDvU72g5X9D18HBSrtAJ5eXlGOTw8vNHXhYWF1XoP8Vzx8fHExMTg7+9PZmYmu3fvpry8HLB0KObOncu2bdt4++233RypNJf192RoaCheXo3LUev7umWJiooy3rHMyspiz549xu4KxcXFPPXUU2zcuJEvvvgCX19fN0crUkV9j7ZDfY+2Qf2OtsMVfQ+NrBBDfn6+UQ4ICGj0dYGBgbXeQzyHl5cXU6dO5YMPPuD06dMcOXKENWvW8P3337N9+3aysrJ49dVX6dChg3HNO++8o+2jWgF9X7dOJpOJUaNG8eabb5KWlkZaWho//fQT33//PVu2bCE7O5sPP/zQZg7wsmXLmDt3rvuCFqmFfka1Xup7tE36nm693NH3ULJCDOey22AZttVY1m2tF9QRzzFhwgSWL1/O9ddfX+uWcCEhIdx1111s2bLF5gfMX/7yF06ePOnCSMXR9H3dOnXr1o3169dzxx131DrE1t/fn+uuu44tW7YwfPhw4/zrr7+uhcvEo+hnVOulvkfbpO/p1ssdfQ8lK8QQFBRklM8N4WkM67bBwcEOjUlcKy4ujv/85z9GvbCwUMMxWzh9X7dt7dq1Y/Hixca7W2azmZdfftnNUYlU0c8oUd+jddH3tDiy76FkhRhCQkKMclFRUaOvKywsrPUe0jKNGjWKSZMmGfXly5e7LxhpNn1fS9euXbn22muNur6nxZPoZ5SA+h6tib6nBRzX91CyQgzWcwbT09Mbfd2JEyeMcvv27R0ak7jH5MmTjfL+/fvdGIk0l/X3dX5+fqPnger7unWx/p4+evQopaWlboxGpIr6HnKO+h6tg/odco4j+h5KVoihT58+Rvn06dM2Gc76JCcnG+W+ffs6PC5xvS5duhjlzMxMN0YizWX9fQ1w/PjxRl2n7+vWxfp7Giw/40U8gfoeco76Hq2D+h1yjiP6HkpWiKFfv3429W3btjV4TWpqKhkZGXXeQ1om686i9dxDaXns+b4uKytj165ddd5DWp7qfwDq+1o8hfoeco76Hq2D+h1yjiP6HkpWiGHUqFH4+/sb9TVr1jR4zerVq41yQEAAo0aNckps4lrWvzA6derkxkikubp3705sbKxRb8z39ebNm21+wUyYMMEpsYnrWH9P+/v7Ex4e7sZoRKqo7yHnqO/ROqjfIec4ou+hZIUYQkJCmDJlilH/4IMPGrzGus2UKVO0em8rUFhYyP/+9z+jft5557kxGnGEGTNmGOWPP/64wTmD1t/XAwYMoEePHk6LTZzPbDbz3//+16iPHTvWjdGI2FLfQ0B9j9ZG/Q5xVN9DyQqxccsttxjlpKQkvvjiizrbbtmyha+//rrWa6XlevTRRzl16pRRnzlzpvuCEYew/t7MzMzk9ddfr7NtSkoK7733Xq3XSsv08ssv2+xvru9p8TTqe4j6Hq2L+h3isL6HWcRKZWWleciQIWbADJijoqLMe/bsqdEuLS3N3K9fP6Pd0KFDzZWVlW6IWBqybNky83333WdOTk6ut11paan5wQcfNF5TwDxs2DC9rh7E+rV59913m3TtjBkzjGtDQkLMa9asqdEmJyfHPH78eKNdly5dzIWFhQ6KXmpjz2u6c+dO82233Wbeu3dvve0qKyvN8+fPN3t7exvPiI6O1msqHkd9j9ZHfY/WQf2O1qkl9T1MZwMWMWzcuJGJEycaeyOHhYXx61//mgkTJuDj48OGDRt4+eWXOXnyJACBgYH8+OOPjBw50p1hSx2WLFnC5ZdfjpeXF+PGjWPixIkMHDiQDh064OfnR2ZmJhs2bOCDDz6wWYk5MjKSn376qcaqzuJ8d955J++//36N8yUlJUbZx8cHb2/vGm2Ki4trvefRo0cZOXKkscK6v78/t99+OxdddBEhISEkJSXx0ksvceTIEQC8vLxYsmQJl156qSO+pDbPka/ptm3bSExMBGD48OFccMEFDBkyhE6dOhEYGEhWVhZbt27lo48+Yu/evcZ1/v7+LF++nPHjxzvqyxJxGPU9Whf1PVoW9Ttap1bR97ArxSGt3qeffmoODAy0ybzVdgQGBpo//fRTd4cr9fjss88afB2rH7169TJv2bLF3aG3WTfffHOTX7NzR33Wrl1rjoyMbPAe3t7e5pdeeslFX23b4MjXdOvWrU2+R5cuXczLly93w1cu0njqe7Qe6nu0LOp3tE6toe+hNSukVrNmzWLz5s1MnToVk8lU4/Mmk4kpU6awadMmZs2a5YYIpbH69u3LNddcY7Myc13i4+N57rnn2Lp1q5E9ldbjvPPOIykpiSuuuAIfH59a24wcOZJVq1bxm9/8xsXRSWNFRUVx0003NWoBss6dO/OnP/2JHTt2MHXqVBdEJ2I/9T1aD/U9BNTvaE3c1ffQNBBpUHJyMmvXriU1NRWAmJgYxo0bR1xcnJsjk6Y6fvw4u3fvJjMzk8zMTAoKCggLC6NTp06MGDFCqy+3IRkZGaxatYqUlBRKS0uJjo5mxIgRGnrbwpw8eZKkpCQyMjLIzMwkLy+PkJAQOnToQGJiIv369av1jz4RT6e+R+uhvoeA+h2tiSv7HkpWiIiIiIiIiIhH0TQQEREREREREfEoSlaIiIiIiIiIiEdRskJEREREREREPIqSFSIiIiIiIiLiUZSsEBERERERERGPomSFiIiIiIiIiHgUJStERERERERExKMoWSEiIiIiIiIiHkXJChERERERERHxKEpWiIiIiIiIiIhHUbJCRERERERERDyKkhUiIiIiIiIi4lF83B2AiLRNzz33HIWFhQCMGTOGiy++2M0RiYiISGulfodIy2Mym81mdwchIm1LTk4OERERRn3BggXMnTvXfQGJiIhIq6V+h0jLpGkgIuJy27dvt6kPHjzYTZGIiIhIa6d+h0jLpGSFiLhcUlKSTX3QoEFuikRERERaO/U7RFomJStExOWs3+GIjo6mffv2boxGREREWjP1O0RaJiUrRMTlrDsNendDREREnEn9DpGWSckKEXGpyspKdu7cadQ1b1REREScRf0OkZZLyQoRcbq8vDy8vLwwmUx4e3tTVFRkfO7555/HZDLVevz73/9u1nOvuOIK415BQUEcPXrUrvvMnTvXJq4NGzY0Ky4RERFxHvU7RFoHJStExOm2bduGPbskN2eo5hdffMHixYuN+oMPPkh8fLxd9xoxYoRNffXq1XbHJSIiIs6lfodI66BkhYg43Y4dO/D29sbb2xuTyWTzuXPnqx9BQUH06dPHrufl5+dzzz33GPX4+HgefPBBu+MfOXKkTX3VqlV230tEREScS/0OkdZByQoRcbq7776b8vJyysvLueaaa4zz/fv3N85XPwoKCvDx8bHrefPmzSM5OdmoP/nkkwQEBNgdf69evfD29jbq27Zts/teIiIi4lzqd4i0DkpWiIhLbdq0yShXH+boCKdOnWL+/PlGvXfv3lx33XXNuqePjw9dunQx6ikpKZSUlDTrniIiIuJ86neItFxKVoiIy+Tk5HDo0CGj7oxOw7PPPkt+fr5Rf+SRR2zenbBXbGysUa6srLR70SwRERFxDfU7RFo2JStExGU2b95ss+CVozsNeXl5vP3220a9ffv2XHvttQ65d2BgoE09NzfXIfcVERER51C/Q6RlU7JCRFzGeiimj48PQ4cOdej9Fy1aRF5enlGfPXs2fn5+Drl39QW6SktLHXJfERERcQ71O0RaNvtWkRERsYN1p6F///413jVorvfee8+mPnv27HrbL1++nIqKCgBGjRpFZGRknW3Ly8tt6vYuwiUiIiKuoX6HSMum//Ui4jLWnYbhw4c79N5ZWVls3LjRqHfo0IHExMQ626elpXHRRRcZ9QMHDtTbabBe5RsgJiamGdGKiIiIs6nfIdKyaRqIiLhEVlYWR44cMeqOnje6cuVKKisrjfqkSZNqDKG0tn79eqMcFBRE9+7d62xbUVFBamqqUffz8yMqKqqZEYuIiIizqN8h0vIpWSEiLmH97gY4vtOwY8cOm3p9724ArF271ij36tULL6+6fxzu2LGDsrIyoz58+HCHrPQtIiIizqF+h0jLp2SFiLiEdafB19eXIUOGOPT+Bw4csKn369ev3vbLli0zynFxcfW2XbNmjU19/PjxjYpp165d3H///QwfPpz27dvj7+9PfHw8U6ZM4YUXXiAlJaVR9xEREZGmUb9D/Q5p+bRmhYi4hHWnYeDAgfj7+zv0/sePH7epd+nSpc62x44dY+fOnUa9U6dO9d77q6++sqlPnTq13vYFBQX85je/4b333rPZMu3cs48dO8aKFSsoLS3lwQcfrPdeIiIi0nTqd1Q9W/0OaamUrBARl9i+fbtRdvTWYWD5RW0tPDy8zrYffvihTT0gIKDOtqdPn2bFihVGvVOnTlxwwQX1xnHBBRewYcMGTCYT11xzDTfddBNDhw4lICCAY8eO8e233/LKK68watSohr4sERERsYP6Hep3SMunZIWIuMTRo0eNcn2LStnLem4nQFFRUa3tysvLef31123OFRYW1nnfN954w2Zv8+uvv77OeaNms5krrriCDRs24Ofnx6effsovf/lLmzaRkZEkJiYyd+7ceuerioiIiP3U77BQv0NaMv2PFRGnq6iosFkx2xlzJjt37mxT37dvX63t3nrrLY4dO4bJZDKGYVqvFm4tMzOT5557zqj7+/tz//331xnDwoULjTmpb7zxRo0Og7XAwECHD0kVERER9Ttqo36HtERKVoiI03l7exMbG2vU3333Xd544w0yMjJqzK20V69evWzq1YdcAuzfv9+Yq3nRRRcRHR0NwLp16zh9+rRN29LSUq677jqys7ONc3fffbfN12GtvLycRx55BIDJkydz88032/21iIiIiP3U7xBpHZSsEBGXuOaaa4xyaWkpc+bMoVOnTvj4+BhHRESEzTshTTFz5kyb+ldffcUf/vAHTp48SVFREYsXL2bSpEnk5uZiMpl44okniImJMeK58cYbSU5Opri4mBUrVjB+/Hi+++47434DBw7k6aefrvP5P/74I+np6QD84Q9/sOtrEBEREcdQv0Ok5TOZHZVeFBGpR15eHtOmTWPdunV1tjn//PNZvXq1XfevqKhg7NixbNy4scG2DzzwAM899xwvvfQSc+fObbB9QkIC3333Xb1zXh988EGee+45AgMDycrK0lBLERERN1K/Q6Tl08gKEXGJ0NBQVq1axTvvvMMvfvELYmJiavxiHTZsmN339/b25sMPP6Rnz571tps7dy7z5s0D4M4772xw3/VLLrmENWvWNLg417ktzOLi4tRhEBERcTP1O0RaPo2sEJFWJTc3l1dffZVPPvmEI0eOkJubS8eOHTn//PO55557mDBhgk37nJwcnnnmGZYsWcKxY8fw9fUlOjqaCRMmcN1119W7XZi1iy66iOXLlzNgwACbvdRFRESk9VK/Q8R5lKwQEXGAq666ik8++QR/f3/y8/Px8dHO0CIiIuIc6ndIW6BpICIiDjBmzBgASkpKWLBgQb1t69tfXURERKQh6ndIW6CRFSIiDnD69Gl69uxJdnY2vr6+3H///VxzzTV069aN0tJSDh48yIoVK/jwww9ZuHAho0ePdnfIIiIi0kKp3yFtgZIVIiIOsmLFCq644gqbPdKr8/HxITc3l8DAQNcFJiIiIq2O+h3S2ilZISLiQKmpqbz88sssW7aMQ4cOUVRURPv27YmKimLChAnMmDGj0YtniYiIiNRH/Q5pzZSsEBERERERERGPogU2RURERERERMSjKFkhIiIiIiIiIh5FyQoRERERERER8ShKVoiIiIiIiIiIR1GyQkREREREREQ8ipIVIiIiIiIiIuJRlKwQEREREREREY+iZIWIiIiIiIiIeBQlK0RERERERETEoyhZISIiIiIiIiIeRckKEREREREREfEoSlaIiIiIiIiIiEfxcXcA4hqVlZWkpaURGhqKyWRydzgiIuIGZrOZvLw8oqOj8fLS+xXiPOp3iIgINK/voWRFG5GWlkZcXJy7wxAREQ+QnJxMbGysu8OQVkz9DhERsWZP30PJijYiNDQUsPwnCQsLc3M0IiLiDrm5ucTFxRm/E0ScRf0OERGB5vU9lKxoI84NwQwLC1OnQUSkjdOwfHE29TtERMSaPX0PTVgVEREREREREY+iZIWIiIiIiIiIeBQlK0RERERERETEoyhZISIiIiIiIiIeRckKEREREREREfEoSlaIiIiIiIiIiEdRskJEREREREREPIqSFSIiIiIiIiLiUZSsEBERERERERGPomSFiIiIiIiIiHgUJStERERERERExKMoWSFOdWbNcu64J47xvw7g9d+OxZye7u6QRERERERExMMpWSFOY96xgyveuZi3O6WwpksJd3X4mdfvTIS8PHeHJiIiIiLiMpXmSg6eOejuMERaFCUrxGmW/eUmVnartDn3f4NOkvf0Y26KSERERETEBdatg0GDID6e7349jRH/6Mv575xPfmm+uyMTaTGUrBDn2LWLd9lmVINLLR/7ZELaB69BTo574hIRERERcSazGebMgZ074dgxXjvzLVvzD3Cy4CT/WPs3d0cn0mIoWSFOUfrJv/m6l6UcWQhbXocf34Wf34I+KcXw8cfuDVBERERExBmKi2HHDqP6zPfgfXaw8d9+eJr8vNNuCkykZVGyQpzi4JovCCqzlH9xAHqfhgnHwHSuwaJF7gpNRERERMR5qo0g7n0abt5mKed5l/PfP1/p+phEWiAlK8TxcnPpv2IH6X+D4/+AJ38A7rrLts2aNZoKIiIiIiKtTy193Ls2VZXfyl0Jy5e7Lh6RFkrJCnG8rVuhshITEJcL8QW+8NRT4O9f1aaiAr77zi3hfffdd5hMJkwmE8OHD8dsNrvkuQcPHsTX1xeTyURMTAz5+VpgSUREpC1wZt9j5cqVxr1NJhMrV66stV15eTm9e/fGZDLh7e3Npk2bam0nDlBLsmJEdiCDT1jK6+Jg132z9cadSAOUrBDH27LFtj5gALRvDxMn2p5ftsx1MZ1VVlbGb3/7W6M+b948TCZTPVc4Ts+ePbnzzjsBSEtL48knn3TJc0VERMR93Nn3sObj48NTTz0FQGVlJb/97W9d9oZNm5OdDcDmKEgLBXPnTpiee547rLrIb8WchL9psU2R+ihZIY63dattfdgwy8dp02zPr1njmnisvPLKK+zduxeASZMmMXXqVJc+/9FHH8X/7AiT+fPnc/ToUZc+X0RERFzL3X0Pa1dddRWDBw8G4Oeff+ajjz5yWyyt2tkRE7+8HmLuh743ZMGvf80N7SbgXw7hxdC+CHjxRThzxr2xingwJSvE8aonKxITLR/Hj7c9v2ePS39AFxQU8Mwzzxj1//u//3PZs8+Jiopi9uzZAJSWlvLEE0+4PAYRERFxDU/oe1gzmUz88Y9/NOqPP/445eXlboyolSoo4EwgnAi1VKNK/cHLi8gXXuPLDyHlH/CnVUBuLsyf785IRTyakhXiWBUVsH+/7bmzGXyGDoXAQACyA2BXR+Cnn1wW2j//+U9OnToFwKBBg5hWfaSHi/zhD38wyu+//z6HDh1ySxwiIiLiXJ7S97B27bXXEhcXB8CBAwdYpB3aHK+42NLPPWtgYYil0K8fU0ddS0ipVdsFC4xpIyJiS8kKcazUVCgttT3Xu7flo68vlSNHMPQuaPd/cMU1uCxZUVZWxosvvmjU58yZ45Ln1qZPnz5MmjQJgIqKChYsWOC2WERERMQ5PKnvYc3b25vbb7/dqL/wwgtujKaVKioiJayq2r08tKry6KNgvWZJbi588IHrYhNpQZSsEMc6eNC2HhICnTsbVa9x5+N/drThvg6QtWWtS8L6+OOPSU1NBSAgIIAbbrjBJc+ti3Un4d133yU3N9eN0YiIiIijeVrfw9ptt91mLPKZlJTEihUr3BxRK1NcTHZAVTXSFFRV6d8fLr/ctv0bb4AWOxWpQckKcawDB5h6E4y8E266HMw9e9hmj0eMYFRqVXXTia0u+eH8zjvvGOWLLrqIiIgIpz+zPpdddpmx0GZ+fj4ff/yxW+MRERERx/K0voe1uLg4xowZY9TfffddN0bTChUXkxVYVY3wCrL9fPVRNklJsHGj8+MSaWF83B2AtDIHD7KzE5wMsWzVZPLuZfv5oUMZnl5VTQrK48LkZOja1Wkhpaam8sMPPxj1WbNm2X2v/Px81q5dS0pKCpmZmZjNZiIjI+nduzfDhg0jLCys4ZsAoaGhTJ06la+++gqwrF1hPdpCREREWi5H9T1SUlJYs2YNqampeHt7Exsby4gRI4iPj292jLNmzWLdunUAfPbZZ+Tn5xMSEtLs+wpQVGQzsqKdT6jt56dOhfh4sN4V7s03YdQoV0Qn0mIoWSEOVXJoHyeHWMpxuUCvasmKhAQGF4QA+QAkdQa2bXNqsuLzzz+nsrLSqF944YVNvsf333/Ps88+y48//ljnqtk+Pj6cd9553HLLLdx88814edU/cOnCCy80khWrV68mMzOTDh06NDk2ERGRxigrK2PLli389NNPJCUlsXfvXo4dO0Z2djZlZWWEhYURFxfHiBEjuOqqq7jwwgsb/F0mtWtu32PPnj3ce++9fPfdd5irjUA1mUxMnjyZv//97wwdOtTuGK1jKigoYPny5VxefXqC2Ke4mCyrZEWET7UkkJcX3HEH/OlPVec++siy2GZQtVEYIm2YfgOJQ6VmHjHKcTlAQoJtA5OJ/rGJeJ/9/b29CzW3OnWwb775xij36tWL6OjoRl+bl5fHzJkzmTp1Kt9//32923uVl5ezatUqbrvttkatQTF58mSjXFlZybJlyxodl4iISFM9/PDDjBkzhvvuu4+FCxfy888/k56eTlFREeXl5Zw5c4bt27fz9ttvc/HFFzN8+HC2Ovl3dGvVnL7Hxx9/zNChQ1m+fHmNRAWA2WxmxYoVjB07lg8//NDuGAcPHkz79u2N+tKlS+2+l1RTXMzTK2Dbq7DyXejp27lmm1tvpcLbxLc94MZZMH9QAXz9tetjFfFgGlkhDpWcV7UgRVwuEBtbo03AkOH0yVzN7k6wuyOUbd2MrxNjWrNmjVEeOXJko6/Lyspi/Pjx7Nq1y+Z8bGwskyZNIjo6Gj8/PzIzM0lKSmLz5s2UlJQ0+v4DBw4kMDCQoqIiAH788UePWnxLRERal+p/+AYHB9OjRw/atWuHyWTixIkT7N+/3xgRsG3bNiZMmMDXX3/N+eef746QWyx7+x7Lli3j+uuvt3lzJCwsjEsuuYQePXpQVFTEli1bWL16NcXFxdx2220888wzdsVoMpkYPnw43377LWDph4iDFBXRqQA6FZytB4TWbBMdTfK0sUwbZdkZb3MU3PvxfzFdcYXr4hTxcEpWiOOUlZFcmWVU43KAmJia7YYOZcgXsLsTlHnDwSOb6eekkA4dOkRWVlVMgwYNatR1lZWV3HDDDTaJiq5du/LCCy/UOe80NzeXJUuW8I9//KNRz/Dy8mLAgAFs2rQJgI1aWElERJwoMDCQX/7yl8yYMYMJEybQp0+fGm0yMjJYsGABf/3rX6moqCA/P5/rr7+e3bt3az2DRrK375GTk8Ntt91mk6i45ZZbePHFFwkNtf1jd/v27Vx33XXs2bOHhx9+2O5YBw8ebCQrDh48SHZ2tkctBNpiFRfb1gMCam0WP+MmJv78Ez/Gw96OsPHrzxlVWKipICJnaRqIOE56us2e0nG51J6sGDKE+3+CVe9A1l+hX1Ia5Oc7JaQdO3bY1HtVX0OjDh988AFfWw3F6927N+vWrat3gaywsDBuuukmtm3bRnh4eKOe07t3b6O8a9cuKioqGnWdiIhIUz355JN88cUX3HnnnbUmKgA6duzIU089xWuvvWacS05O1q5VTWBv3+O5554jLS3NqM+ePZt33323RqICYMiQIaxYsYK4uLgmjeqszrofYjaba8QudqqerAgMrL3d5ZdzU1LVrnn/6luiqSAiVpSsEMdJSSHZOllR5AuRkTXb9enD8BMmxh+HiHM/y/fudUpIR61XWcYyhaMhZrOZefPmGXUfHx/+/e9/N2m+qcl6u9Z6xFglc8rKymw6KSIiIu5yxx130KNHD6O+cuVK9wXTwtjT9ygrK+Ptt9826u3bt+fFF1+s95ouXbrwwgsv2BXjOTHV3lSqHrvY6ewUX0MdIyvo1IkrO0wgsMxS/WgglH78b+fGJtKCKFkhjpOayox98JcVcM8G6BEYA7X90R4YCN27257bvdspIVX/479Tp04NXpOUlGQz/WPmzJkkJiY6PDawdDSspaam1tFSRETEtYYNG2aUT5w44cZIWhZ7+h7r1q3j5MmTRn327NmNmo4xa9YsujZjRzX1Q5ykkdNAAMJmXcfMs+/ZnQmCH3YvhbIyJwYn0nIoWSGOk5rKtEPw6Cp4eSl06FDPL88BA2zrTkpW5FebXhJY1zA8K9XfPbruuuscGZKN6vFUj1dERMRdrNdOqG0qgtTOnr7Hzz//bFP/5S9/2ahnmUwmpk+f3vjgqlE/xEkaOw0EYMYMrrDqBn/etRDWrnVOXCItjJIV4jgpKbb12tarOKd/f9u6k5IV1edx+vn5NXjNzp07bepjxoxxaEzW/P39bepF1YcNioiIuEFZWRnr1q0z6mPHjnVjNC2LPX2PPXv22NSHDBnS6OcNHTq00W2rUz/ESRo7DQQgKoppIUPwP5sb/F8fMH/1pfNiE2lBtBuIOE56um3dA5IV1X8Jl5aWNnjN6dOnjbLJZKoxRNKRqndoGvPui4iIiLM98sgjxtSPyMhIbrnlFvcG1ILY0/ew3j3Ey8uLDh06NPp5nTt3bnxw1agf4hzm4iIemwxBZdA9C66pL1kBhFw8g3vWbyeiGC7bB7RfCs//zTXBingwJSvEcU6dsq3X98uzerLi8GFLFtrBvySrb7PWmHcM8vLyjHJQUBBeXs4bgFRYWGhTDw4OdtqzRERE6lJeXk5GRgbr16/nlVdeYfny5QAEBATw0Ucf0b59+3qvLykpsfnDNzc316nxejJ7+h7W0y+CmrhtZXP6DuqHOEdRWRFPTbSUJx2Baxrq3/7iF/z9ySer6if3wNGjEB/vrBBFWgRNAxHHyciwrXfsWHfbvn1t62Yz7N/v8JCq7+BhvXhVXcLCqrY0KSwspLKy0uFx1RVP9VW5RUREnKVDhw6YTCZMJhO+vr5ER0dz+eWXs3z5ckwmExdddBEbN27koosuavBezz77LOHh4cYRFxfngq/AM9nT97BOcFRPIDSkoKCgSe2tqR/iHIXlVQmqoDLqnwYCMHIkVB9N89VXjg9MpIVRskIcpynJiuBg8np25S8T4for4NHJOGUqSEJCgk29MatcW797ZDabSa8+vcWBrOPx8fFRJ0FERDzCuHHjuOuuu+hffSRkHR566CFycnKMIzk52ckRei57+h7t2rUzypWVlWRmZjb6eY1JhtSlemzxeiffIQoqqxbYDG5MssLbGy65xPbc2dFNIm2ZkhXiGGZz05IVgF+vvjwxET4aBEt74ZSRFQMHDrSp72/EMwYNGmRTX79+vUNjsrZv3z6jPGDAALy9vZ32LBEREWtTpkxh2rRpTJs2jUmTJtG3b19j6uOaNWuYNWsWY8aM4ciRIw3ey9/fn7CwMJujrbKn79GvXz+b+vbt2xv9vKa0rc66HwI1+0BiB7OZwsqqKVHBpTRumnP1EUwrV4LVjjwibZGSFeIYubkcCypjbRwciIRiHxpMVvj37EP3s+tJ7e0AlQccn6zo0aOHzbsVO3bsaPCaSZMm2dQ//PBDR4cFWN452W01mmTkyJFOeY6IiEht/vOf//DNN9/wzTff8MMPP7Bnzx4yMjKYN2+esXbBxo0bmThxIqeqr0sldbKn71F957GvGjkFwGw28+WX9u8cYR1bz549beIWO5WWUuBbVW3UNBCACy6wrefkwJYtDg1NpKVRskIc49Qp/j0Qzr8des+Fr3rRYLKCXr3oe3aUY6EfpB7fWX97O02YMMEob9y4scH2gwYNsnlnYcmSJWzdutXhce3cudNm0a2JEyc6/BkiIiJNERkZyR//+EdWr15NaGgoAMnJydx///1ujqxlaWrfY+zYsTa7erz//vvk5OQ0eN1nn33G8ePH7YrRbDazefNmo65+iIMUF9skKxo1DQQgOhqqjbDh++8dGppIS6NkhThGRganrBaQ7lTuBw2tKN27t5GsANiXfcgyncTBLr74YqN88ODBRs0d/b//+z+jXFFRwbXXXtuktSvMjfg6fvjhB6NsMpmYNm1ao+8vIiLiTImJiTzyyCNG/d///jdnzpxxY0QtS1P7Hr6+vtx2221GPTMzk9/97nf1XnPq1Cl+//vf2x1jUlKSzXbtl1RfM0HsU1xMYfWRFY3d7W7KFNu6khXSxilZIY5RLVnRMbAR+4P36kUvq37PQf8CsPql6SgzZsyw2X70u+++a/Ca6667junTpxv1/fv3M2bMGJYsWVLnNfn5+SxatIjExMRGvRuy3GrhpHHjxtGxoZEoIiIiLnTllVca5fLy8kaNEBALe/oeDz74oM1OIgsXLuSOO+6w2VL9nB07dnDBBRdw/Phx/P397YrRuh8SGBjYqF1fpBGKivCvgAGnID4LOhXQuJEVAFOmcDIYFoyGX14Pb+X9CMXFDV8n0kr5uDsAaSUyMjhjlTTuENyIP7y7dqVnjjdQAcDBSODAgZpbNzVTdHQ0F1xwgdFRWLx4MTfffHO915hMJv71r38xYcIEdu3aBcDx48e5/PLLiY2NZfLkycTExODr68vp06fZsWMHmzZtatRe6gB5eXk2HZfZs2fb+dWJiIg4R/XtR0874Q2F1sqevkd4eDhvv/02l156KeVnF1Z8++23+eSTT/jFL35BQkICxcXFbN26lR9//JHKykr8/Px45pln7Jqms3jxYqM8c+ZMY9qPNFNxMRccgZ2vWJ1rbLJi4kROhJn43SWWEbo+leXc8dNPNdezEGkjlKwQx8jIIMsqWRER0aXha3x86BnaDTgMnE1W7N8PY8c6PLzbb7/d6DB8++235OTkEB4eXu81kZGR/PTTT1x33XUsXbrUOJ+SksL777/frHi++OILSkosK0UHBQVx9dVXN+t+IiIijlZ9lGBERIR7Ammh7Ol7XHzxxXzwwQfMnj2b0tJSwPI6fPTRRzXa+vv78/bbb9u17XlKSgo///yzUb/11lubfA+pQ/WRED4+lqMx2rVjUMwwIgs3cyYIVneFyh9X4qVkhbRRmgYijmE1siK0BHw6dq6//Vmxsf2ZcBRu2gaXHMAyssIJrrzySmJjYwEoLi5m0aJFjbouLCyMr776ii+//JJx48bZDOmsztfXlwsuuIBFixY1uGXbW2+9ZZRvueUWdQBFRMTjrFq1yqbeo0cPN0XSMtnb97j66qvZtm0bU6dOxWQy1fi8yWRiwoQJrF27lhtuuMGu2N555x1jfa3+/ftz4YUX2nUfqUX1UbaNHVVxltfESZx/ds3UM0GwZ/MyBwUm0vJoZIU4RmYmWWfzE+2KaPRUDq9evfnxH1YnejgnWeHj48O9997LAw88AMDrr7/OPffc0+jrp0+fzvTp0zlz5gxr1qwhPT2d06dP4+PjQ2RkJL1792bYsGGEhIQ0eK8DBw6wcuVKALy8vLj33nvt+ppEREScpbS0lKeeesqo9+jRgz59+rgxopanOX2Pfv36sXz5clJSUli1ahVpaWl4e3sTExPDyJEjSUhIMNpOmjSpUQt7n1NRUcE777xj1O+7775GXyuNUH1kRROTFYwfz/jn/s7/+lqqq89sZUBpKfj5OSY+kRZEyQpxCHPWGc7EW8qRRUBcZOMu7NXLtr5/vyPDsnH33Xfzt7/9jZMnT7Jjxw6WLVvW5B04IiMjmTFjRrPi+Nvf/mZ0Km688UZ69+7drPuJiIg0ZPny5Xz77bf8/ve/t1nEsTbp6enccsstbNu2zThnvUuWNF5z+x6xsbFcf/31Do3pv//9L8eOHQMsSaiG1tKQJqqerGjsTiDnnH8+E6xyWqujyrhryxYYM6b5sYm0MJoGIo6Rnc2p5+HQAvjPJ0C7do27rvof6gcOOGX7UrCsDfHwww8b9b/+9a9OeU59Tpw4wXvvvQdYpo38+c9/dnkMIiLS9hQUFPC3v/2NuLg4xo8fz8MPP8xHH33E8uXLWbt2LcuWLePVV1/l+uuvp2fPnnz77bfGtTNmzOD22293Y/Qtlyf0Pap77rnnjPLjjz+OT2PXU5DGaeY0ENq3J7Fdf4IsS5awuhuwerVDQhNpadr0T6eMjAw2bdrExo0bjY8nTpwwPv/uu+9yyy23OD2Ow4cPs3DhQr766iuOHz9Ofn4+0dHRDB48mBtuuIGZM2d6/C8SU1Y27Yqh3blkcmPXYKg+sqKgAE6cgKgoR4Zn+PWvf81rr73Gnj17WLlyJd9//z1Tqu9p7URPPvmksbDm7373O7p37+6yZ4uIiFRWVrJmzRrWrFnTqPa33norr732Wq1rJ0jjuLvvYe3jjz82RsyMGjXK7jUvpB7NnQYC+J4/gbEpu/m+OySHw7H1y+jGAw4KUKTl8Oy/gJ3kxIkTjBkzxhgC504LFizgwQcfNP6APefw4cMcPnyYJUuWMGbMGD744APP/sM2O9u23tiRFTExlh/i1j/YDxxwWrLC19eXF1980VhI6sEHH2Tjxo0u6YQdPHiQN998E4CoqCgeffRRpz9TREQEYMSIEdx3331888037Nmzp941Dvz8/Lj00kuZO3cuEyZMcGGUrZM7+x7WysvLeeSRRwDLIp0vv/yyklDO0NxpIADjxzNzwWt0zocJxyA0dRNUVkI9C72LtEZtMllRXFzsEYmKJ598kscee8yoe3l50b9/fyIjIzlw4ADp6ekA/Pzzz0ycOJENGzYQ5aQ/4pstK8u23thkhZcX9OgBu3ZVnTt8GJzYOZo6dWqTFqJylJ49exrbkImIiLhSbGwsf//73/n73/9OdnY227dv5/Dhw2RmZlJSUkJwcDDt2rWjX79+DBkyhAA73g2Wurmr72HNx8eH/U5cG0zOKirizkvhpzgIKoPlh3yIaOo9xo/nNzfAbzacO5EDu3fDwIEODVXE07XJZIW1jh07Mnz4cEaMGMGIESOYOXOmS567bNkym/UKxo4dy8KFC43FFisrK/n444+54447yM/PJyUlhauuuqrRwzZdqrQUCgttzzVlK87akhUiIiLiFBEREUycOJGJEye6OxSR1qe4mMPtYHcnS9U32Y7EX1wcdO0Kx49XnVu/XskKaXPaZLIiMjKSjz/+mJEjR9KtWzeXP99sNvPggw8aGfY+ffrw3XffERQUZLTx8vLimmuuoX379sawwbVr1/LZZ59x+eWXuzzmelWfAgKNH1kB0L07ZuBECByMhPMPH0KDEkVERESkxSkupsBql9FA/4a3ta/V6NE1kxVa6FbamDY58SksLIwrr7zSLYkKgK+//prt27cb9QULFtgkKqxNnTqVa665xqh7wirSNdSWrGjKyIru3Zl1DUT/ASbcBidS9jkqMhERERER1ykqotDXUgwsA68AO9asgJpblf78c/PiEmmB2mSywt0WL15slBMSErjooovqbT9nzhyjvGHDBlJSUpwWm12qr1cRGAj+/o2/vnt3uuZUVQ/kaBqIiIiIiLRAxcVGsiKoDLt2AwFqJit27YK8vGaFJtLSKFnhBl999ZVRnjZtWoMrMY8fP57g4OBar/cI2dm8kwh3T4dHLoCTUaFNu757d3pY5TsOk2XZwlREREREpCUpLqbgbLIiuBT7dgMBSEwEH6sZ+5WVsGlTs8MTaUmUrHCxU6dOceLECaM+duzYBq/x8fFh5MiRRj0pKckpsdktK4vl3eHVkfDMBMhvH9a06+PjSbBKVhyJAI4ccWSEIiIiIiLOZzUNpFkjKwIDYehQ23OaCiJtjJIVLrZnzx6beo8ePRp1nXW76vdwu+xscqx+DkcENWFxTYDAQLr7djSqh9uhHUFEREREpMUxFxcZC2wGNydZATB6NFkBsKwHPDkBTm9a7ZAYRVqKNrkbiDsdPXrUpt61a9dGXWfdrvo93C4ri1yrJSrCQjs0+Rbx7XsCGQAcUbJCRERERFogc3ExT62AQl/onA/80s5pIABjxvD0gX/y9/Ms1cSl6/il2QwNTCEXaS2UrHCxvGoL44SHhzfqurCwqqkV1e9Rm5KSEkpKSox6bm5uIyO0Q3a2kawIKAPfiPZNvkVwfC8656/jZIhGVoiIiIhIy+RVVMz/rbE6cWUzRlaMGcPoZ6uqG4Kz+eXx4+CmHQ1FXE3TQFwsPz/fph7QyKFhgVaL81S/R22effZZwsPDjSMuLq5pgTZFVhZ5Z4e7hZXQtG1Lz+nene5n161ID4XiIwccFZ2IiIiIiGsUF9vW7V1gE6BHD0YXRBjV9TFo3QppU5SscLHy8nKbuo9P4wa3WLcrKytrsP1DDz1ETk6OcSQnJzct0KawGlkRWordyYqXl0LSK5D3DAQcOubICEVEREREnK+oyLbenDUrTCbiBoy1TCcBNsRA5XolK6TtULLCxYKCgmzqxdWzr3Wwbme9jWld/P39CQsLszmcxZyXayQrwkoAe57VvTvD0mHQKQgpxbIbSGWlI8MUEREREXGu6n375iQrANPoMYxOsZSzA+FA0spm3U+kJVGywsVCQkJs6kXVs691KCwsrPMe7laRl8uFh2HccUhMB0JDm36T7t1t68XFYLXFq4iIiIiIx3PkNBCwrFuRWlVdn70TSkubd0+RFkLJChfr0MF2p4z09PRGXXfC6g/39u2bvoClM/nk5rP0A1jzDrz9P+wbWdGlS83MsxbZFBEREZGWxJHTQABGjTJGVgCs71wO27c3754iLYSSFS7Wp08fm/rx48cbdZ31mhN9+/Z1aEzNVn13EntGVphMNUdXKFkhIiIiIi2Jg6eBEBHByOBe+FTAwJMQk4cW2ZQ2Q8kKF+vVq5fNYpnbtm1r1HVbt241yv369XN0WM3jiGQFKFkhIiIiIi1ahqmQLVGwrz2W3fKaOw0ECBsxjpy/wo5X4eHVwPr1zb6nSEugZIWL+fn5MXr0aKO+Zs2aelpbnDhxgoMHDxr1CRMmOCU2uylZISIiIiLCkq6FDJ8DfX8LHw+g+SMrAEaPJsh6M8BNm5p/T5EWQMkKN7jsssuM8nfffcfJkyfrbf/BBx8Y5YiICM9KVpSW1lzkR8kKEREREWlrKisp8KowqkFlOCZZMXy4bX3//ppvFoq0QkpWuMF1112Hv79lr8+ysjKee+65Otvm5+fz4osvGvUbbrgBX19fp8fYaLX9oLQ3WZGQwA/xcP9FMOsa2JW1r1mhiYiIiIi4TEkJRVWzvS3JCgdMA2HQILCaRo7ZDFZTxEVaKyUrHOTo0aOYTCbjePzxx+tsGxsby5w5c4z6ggUL+PTTT2u0Kysr49ZbbzUW4QwMDOThhx92eOzNkptb81wzRlb8HAv/OA8+6we7yay5orKIiIiIiCcqKqLQ6j3FQEeNrAgIgIEDbc9pKoi0AW02WXHnnXcSEBBQ42hqG3s9/vjj9OrVC4CKigquvvpqZs+ezaeffsoPP/zAa6+9xogRI/jkk0+Ma55//nmio6Md8nyHqT6ywmSC4GD77hUfT/esquqRdsDRo/ZGJiIiIiLiOsXFFFknK8pxTLICak4F2bzZMfcV8WA+DTdpncrKyigpKam3TXl5OeXl5U55frt27fjyyy+ZOnUqycnJVFZWsmjRIhYtWlRr+z/+8Y/cc889TomlWfLyeHQyLBgDYSXwn6WBjDOZ7LtXSAgJRADZABxuBxw5Ap62+4mIiIiISHVFRTWngTgyWfH221V1JSukDWizIys8Qe/evUlKSuL2228nsI75bP369ePzzz9n3rx5Lo6ukfLyyAmAPH9IDQOfADtHVZzVvV3VIptHIrAkK0REREREPJ2zpoGAzciKjCBITd+nRTal1WuzIysWLlzIwoULHXa/+Ph4zGZzk6+LiIjgrbfe4oUXXmDFihUkJydTUFBAVFQUgwYNIjEx0WExOkVeHrn+VdUwv5Bm3a59bC9CSraQ7392ZIV2BBERERGRlqCw0GYaSJBPAHg56L3hwYPZ1cWbS66rIDkc7t4A/9y6FTxpl0ARB2uzyQpPExoaarOlaYuRl0eeX1U1zC+sWbczJXSnexYkdYFjEVCx8zDezYtQRERERMT5Cgt5aSn85Qco8oEoc/NGHNsICCA2tj/J4TsA2ByNZSqIkhXSimkaiDRP9ZEVgeHNu1/37iRkW4pl3pB6Yn/z7iciIiIi4gqFhXQshN6nYchJ8Gvm9OjqwoeMotdpS3l7ZyjfvNGh9xfxNEpWSPPk5tokK4KDIpp3v4QExqTARQdhziYwHTtu2UtaRERERMSTFRba1oOCHHv/4cMZlm4pFvvCnoPrHHt/EQ+jZIU0T14e+WengQSXgldo86aBkJDA/62BZYvgtS8hLi0fsrIavk5ERERExJ1cmKwA2FJyVItsSqumZIU0T14eBVbJCkJDm3e/uDjwrrZKhRbZFBERERFPV1BgW3d0smLwYIafrPrzbXMUsG2bY58h4kGUrJDmycvj78vgtS/g6RVAWDNHVvj6WhIW1rR9qYiIiIh4OmePrAgIIDGin1HdEgVs2uTYZ4h4EO0GIs2Tl8cVe6zqs5o5sgIgIQGOHq2qK1khIiIiIp7O2ckKIHLwaOKzdnG0HezoBJWbN+ndZ2m19H9bmqf6PLnmTgMB6N7dtq5pICIiIiLi6VyQrGD4cF79Cn5+E9L/Dl6btzj+GSIeQiMrpHmqz80LCWn+PRMSbOsaWSEiIiIiHs5cWMADF0FAOfQ4A7c6KVlx8UGr+r59ljcPHfGGoYiHUbJCmscZCwkpWSEiIiIiLUxZYT5/P89SPv+Yk5IVgwdbFqOvqLDUzWbLIpvjxzv+WSJupmkg0jzVkxXBwc2/59lpIGVecKgdnD55pOoHsoiIiIiIByoqrpoeHVSGc6aBBAbCgAG257ZudfxzRDyAkhXSPM6Ym5eQwIeDIOBP0PNe+G/vckhLa/59RUREREScpLA43ygHluOcZAVAYqJtXckKaaWUrJDmccbIik6d6FTuT+XZ/51H2qGpICIiIiLi0YpKrJIVzhpZAUpWSJuhZIXYz2zmhKmAL3rDigRIDsMxyQqTiYTQrkb1SATaEUREREREPFpRadWbeEFlOKZfXJvqyYpdu6CkxDnPEnEjJSvEfsXFrI+BGdfDlJvh/SE4LIPctXMvvCotZY2sEBERERFPV1hWNT3aqdNAhg61rZeXw86dznmWiBtpNxCxX0EBBX5V1eBSHJZB9o3vQWwuHI84O7JCyQoRERER8WBFZUVG2anTQMLCKO3dg79GHWJrF2hfBG9t3QrDhzvneSJuomSF2K+wkALfqmqwI38od+9OwnZLsuJMEOQc30+4Y+4sIiIiIuJwAQUljEyFQl+IzcV5yQrAd0gi87sfIisQuuShdSukVdI0ELFfbSMrHPVDOSGB7llV1SNntGaFiIiIiHiuUcfK2fAm7HwF7l2PU5MVpsRhJKZbyidC4cSu9U57loi7KFkh9qs+ssLsAz4OGqyTkECCdbKiLAOKix1zbxERERERRysstK07MVlBYiLD0quqWzN3QkWF854n4gaaBiL2qz6ywjvAcfdOSODanTAmBRKyoVs2cPQo9O3ruGeIiIiIiDhCZSUUFdmec3KyIvFEVXVrZAmX7N8P/fo575kiLqaRFWK/6iMrvAMdd+/QUHp5deDCw9DzDPhWokU2RURERMQz1TYC2JnJis6dSazoaFS3RKF1K6TVUbJC7Fd9ZIWPg38gJyTY1pWsEBERERFPVH0KCDg3WQH0ThhJUKmlvLULSlZIq6NkhdivoIA3voD8p+Hk89C3op1j79+9u239sBbZFBEREREP5IZkhXfiMIactJQPR0JO0kanPk/E1bRmhdivsBAvs2XL0uAyIDDYsffXyAoRERERaQlqxy5GUAAAfe5JREFUS1YEOnCKdG0SE7l8BfTLwLJ+xbHtYDaDyeTc54q4iJIVYr+CAtt6sJIVIiIiItIGVU9W+PpaDmcaNowHfrI+kQ3Hj0O3bs59roiLaBqI2M/Z2zNVT1ZoGoiIiIiIeKLCQu6/COJ/B/3vgYNR/s5/Zrdu0K7aNGytWyGtiJIVYj9nj6yovmZFTg5kZTn2GSIiIiIizVVYyIkQOBYBezri/CkgYJnuMXSo7TklK6QVUbJC7Fc9WeHokRVdu5ISbuK5cfDr6fCfAWgqiIiIiIh4nsJCiqxmfQT5OfhNvLokJtrWlayQVkRrVoj9qk8DcfTICl9fTnbvzIMXngCg3AuuOXwYhg1z7HNERERERJqjsJBCq2RFoJ9zdwIxVE9WbNnimueKuIBGVoj9nD0NBEho38MoH2mHRlaIiIiIiOcpKKDI6m3gIL8Q1zy3+pt4qamQkeGaZ4s4mZIVYjdzYQHXXQG3XgbPjcMpe0m3i+tFeLGlfCQCJStERERExPNYTQMxmcEv0EXTQPr0qbk+hqaCSCuhZIXYraQwj38PgoWJ8FUvnDKywpTQnYSza2oeD4fywwcd/gwRERERkWaxmgYSWAamIBclK7y9YfBg0kNgaS94axhKVkiroTUrxG5FJVXTQALLccrICrp3J2EXbIuCcm9IOXWQeMc/RURERETEfoWFxjSQoDKc0y+uS2IiE0et50B7CCiDW7Zu1h950iro/7HYragk3ygHluGUkRUkJBgjKwCO5CcTX1kJXhoUJCIiIiIeorCQh1dDRjD4VgB9XJusSFwOB9pDsS/sPbyBga57uojT6C8+sZtLRlYkJJCQXVU9ElIOaWmOf46IiIiIiL0KC7l9K/zfGrh/HS4fWTEsvaq6teQY5OW57vkiTqJkhditqLzIKAc6a7hbly70yfVl0EmYsRe65KNFNkVERETEsxQW2tZdmawYNIjEU1V/1m2JArZvd93zRZxEyQqxW1FFsVEOLKfmSsSOYDJxoaknSa/C5/+GXxxAyQoRERER8SzuTFYEBJAY1seobo1Ci2xKq6BkhditqKLEKAeV4ZxkBUBCgm398GHnPEdERERExB7uTFYAHQeMJCbXUt7WBcxbt7j0+SLOoGSF2MdsJiKvjJl7YNpB6JsJBAQ451ndu9vWNbJCRERERDyJm5MV1utW5ATAkX3rXft8ESfQbiBin+JiBp+Ez/5jdc5VIyuUrBARERERT+IByYrEz+HrnjAgA84k76d7SQn4+7s2DhEHUrJC7FNcXPOcs0ZWaBqIiIiIiHiy6smK4GDXPn/oUO5fBw+vBv8KgArYtQuGDXNtHCIOpGSF2KeoqOY5Z42sqD4NJC0NlCkWERFpsuzsbH744Qd++OEHtm3bxv79+8nKysLX15fIyEiGDBnClClTuPnmm2nXrp27wxVpMXLL8tkRZ1l0PioPolw9siI8nLDYHnDoUNW5rVuVrJAWTckKsU9tIytcNQ3EbIZjx6B3b+c8T0REpJXZu3cvDzzwAN9++y2lpaU1Pl9aWkpBQQHJycl8+eWXPPLIIzz99NPce++9mEwmN0Qs0rLsCMjh/Oss5ft+gr87q19cn8TEmskKkRZMC2yKfWobWeGskQ5hYRAZCUCBL5wKRlNBREREmmDnzp18+eWXNokKb29v+vTpw4QJExg3bhyRZ3/XAhQWFvL73/+eX/3qV5jNZneELNKiFJRX9Y1DSoGQENcHkZhoW1eyQlo4JSvEPtWTFf7+4OW8/07p/WLp9ACEPAJ3T0eLbIqIiNjBx8eHmTNnsmTJEs6cOcPevXv58ccfWbNmDZmZmSxZsoSYmBij/VtvvcVrr73mxohFWoDKSvIrq0YdB5fhGcmKbdugosL1cYg4iJIVYp/q00CctbjmWR2je3Hm7Gi6w+1QskJERKQJfH19ueOOOzh06BCfffYZl112GWFhYTZtTCYTl112GevWraNLly7G+ccee4yysjJXhyzSchQWku9XVXXbyIrq61MUFsKBA66PQ8RBtGaF2KeoCDNgzGJ18rw8n4QexOXA0XZwJAI4qGkgIiLiuU6cOMHGjRtJSkri6NGjpKamkp+fT1FREYGBgQQHBxMTE0N8fDyDBw9m5MiRREVFOS2eyy67jMsuu6xRbePi4njiiSeYM2cOAJmZmaxatYopU6Y4LT6RFi0/v2ayIjTU9XF07gxRUZCeXnVu61bo29f1sYg4gJIVYp/iYu76JbyTCEFlsHaZNwOd+bzu3UlYZ0lWZAdCdvIBIpz5PBERkSZatWoVn332GUuXLuXgwYNNvr5Hjx5ccsklzJw5k8mTJzshwsa79NJLjWQFWBboVLJCpA7VkhXB7hpZAZapIGeTFZlB0GHrVrjuOvfEItJMmgYi9ikqosgXyr0hNwB8fZ28jWhCAglZVdUj2ZoGIiIi7nfy5Ekef/xxEhISmDx5Mi+++CIHDhzAbDY3emHKc20PHjzIyy+/zNSpU+natSuPPfYY6dbvkLqQ9WKbALm5uW6JQ6RFyMujwLeqGlLu5byF5xuSmMiDUyHmPuj4R8hO2uCeOEQcQMkKsU9REUVW43ICfZ28l3RCAgnZVdUj3nmQnV1XaxEREac6cuQIt912G/Hx8Tz55JMcO3as1uTEuURESEgIHTt2JDY2lo4dOxIcHFxnQsNsNpOSksLTTz9NQkICt9xyC4estyN0gWPHjtnUO3Xq5NLni7Qo1UdW+ASCu7b8TUwk3w/Szi5Jsy1tC2hHH2mhnDYNxNPmaoqDFRdTZJVBDvRx8l7S3brZJisisCyyWX3VYxERESfKyMjg0Ucf5d1336W8vLxGsqFdu3ZMnDiRkSNHMnjwYHr37k1MTAyBtaztVFRURGpqKvv27WPHjh1s3LiRH3/8kTNnzgCWpEVpaSnvv/8+H374IbfeeitPPvmkSxIHixcvtqmPHTvW6c8UabHy8/nLD3DfOsj3g7jgsIavcZZhw0h8pqq6NTiPScnJ0LWr+2ISsZNDkxWtaa6mNKD6yAo/J4+s8PMjwbcTcAo4uyPI4cNKVoiIiMvMnz+fJ554gtzcXJskRc+ePbnqqquYNWsWw4cPb/T9AgMD6dmzJz179mT69OnG+c2bN7N48WI++eQTY0pJeXk5b731Fv/5z394/PHH+d3vfufIL81GTk4OCxYsMOqDBw+mf//+TnueSIuXn09w2dktSwE6hrsvlvh4EvNDgHwAtkRhWWRTyQppgZo9DaS1ztWUBlQfWeEX7PRHDgjryb8Ww+p34M8/ou1LRUTEpe677z4jUeHj48N1113HypUr2b9/P08//XSTEhX1GT58OE8//TT79u3jxx9/5Prrr8fX1xez2Uxubi7333+/Q55Tl/vvv58TJ04Y9aeeeqrBa0pKSsjNzbU5RNqM/HzbursW1wQwmRgYMwyfCkt167lkhUgLZHeyorXP1ZQGWI2s8CsH70Anj6wAwrv1ZnYSnH8cOhWgZIWIiLicn58fv/3tbzl48CAffPABEyZMcOrzxo8fz6JFizh06BBz584lICDAqc976623ePvtt436Nddcw6WXXtrgdc8++yzh4eHGERcX58wwRTxLXp5t3Z3JCsB/6HAGZFjKeztA0bZNbo1HxF5NTlZkZGRw11130bdvX9577z1KSkpsEg7t2rXj8ssv55lnnuHLL79k//79FBQUkJOTw4kTJzh27BgnTpwgNzeXgoIC9u/fzxdffMEzzzzD5ZdfTrt27Yx7Wc/V7NevH3PmzOHUqVOO+cqleYqLKTw7siKwHKhlLq7DJSTY1g8fdv4zRUREzrr55pvZv38/CxYsoKuLh1THxsYyf/589u3bx8033+yUZ6xatYp77rnHqCckJPD666836tqHHnqInJwc40hOTnZKjCIeyZNGVgAkJpJ4dnB6hRfsOL7RvfGI2KlJa1a0lbma0ghFRcz/Bk6fG1Ax2rnv9AA1kxUaWSEiIi707rvvujsE4uLieOeddxx+323btjFjxgxKS0sBy+4f33zzDeHhjZt77+/vj7+7tmoUcbfqyYrQUPfEcc6wYST+ExaerW71OsWozEzo0MGdUYk0WZNGVrSVuZrSCEVFXHIQbkyyHC4ZWdG9u2396FGorHT+c0VERFqxffv2MW3aNHJycgDLKNlvv/2W3r17uzkykRbC00ZW9OnDxcf9eO0L2PAG3LQdrVshLVKTp4G09rma0kjFxbZ1V7wu1UdWlJSAFmAVERGx25EjR5g6daoxzTY0NJSvv/6aIUOGuDkykRbEw9aswMeH3nFDmbMZRqadnbKtZIW0QE1KVrT2uZrSBEVFtnVXjKzo0gWqDzHVVBARERG7pKSkMGXKFFJSUgAICgriyy+/ZPTo0W6OTKSFyc/nnl/AfdPglZG4P1kBkJhoW1eyQlqgJq1Z0ZrnakoTVR9Z4YpkhZeXZXTF3r1V544cgfPPd/6zRUREWpGTJ08ydepUjpxN+vv7+7NkyRKnj5gVaY0q8vN4ZYylPDYZ7vbEZMWWLe6JQ6QZmpSsEDFUH1nhouk5lQnxLPLfy5EICCiHB7UjiIiIeLCysjKOHj1Kbm4upaWl+Pj4EBMTQ1RUFCaTyS0xnT59mqlTp7Jv3z4AfH19+eSTT7jwwgvdEo9IS1dQlGOUQ0px/wKbAMOG2dYPHLCsreEJiRSRRlKyQuzjjmkggFdCd+4dDNmBEJ8FD2oaiIiIeJBNmzaxevVqVq1axbZt20hJSaGylsWg/fz8GD58OOPHj2fq1KlccMEFLkle5OTkMG3aNHbu3AmAt7c3H374Ib/85S+d/myR1qqguGrNiuBSPCMhMGgQeHtDRYWlbjbD9u0wbpx74xJpAiUrxD7uWGAToHt3Eg7A1kBIDofybYf1n1hERDzGqFGjjKSD9Tbv1ZWUlLBu3TrWrVvHc889R6dOnbjhhhu47777iI6OdkpsBQUFTJ8+nc2bNwPg5eXFe++9x5VXXumU54m0FfkluUY5xFOSFQEB0K8fnE1MApZ1K5SskBakybuBNFVZWRkHDhxg8+bNrFu3jo0bN5KWllbvL3DxfNnl+XzcH/7XB3Z2wmUjK0hIICHLUqzwguSTB1zzXBERkSYymUy1jpaoft5sNnPy5EleeOEFevbsyQMPPEBJSYlDYykpKWHmzJmsXbvWiOHNN9/khhtucOhzRNqi/LICoxxchmckK0CLbEqL5/A3pT19+KM4xhHffK6+wlK+ayO86sJkRfesquqh0hMklJTU3CVERETETc69IePt7U2XLl2IjY0lMDAQk8lEeXk5ycnJpKamUlZWZlxzrg9UXFzMP/7xD5YvX87nn39Ot27dHBLTggUL+O6774x6REQE//3vf/nvf//bqOsvvPBC7r//fofEItLa5JcVGmWPGVkBkJjI+hXv83Uv2NoFFuz9mXh3xyTSBA5PVnjy8EdxnOLyqmkg/hW4dBpIzzNV1YORMPX4cejVyzXPFxERqcdvf/tbRowYwciRI+nVqxfe3t61tqusrGTHjh2sWbOGr776ihUrVlBaWorJZMJsNpOUlMTUqVNZvXo1Xbp0aXZchYWFNvWsrCyWLVvW6OsdEYNIq1RZSUFlVb/YYxbYBBg2jK96w5MTLdXZu/YRX1oKfn7ujUukkZw6DcSThj+KY5VUlBrlgHJcNw0kPJyeZVXZ6oORgHYEERERD7FgwQJmz55N375960xUgGW9iCFDhnDPPfewdOlS0tLSePrppwkPDwcsfaXDhw9z5513uip0EbFHQQGhJTD5CIxMhW7ZeM7IiqFDGZZeVd3asQJ27XJfPCJN5JRkhdlsxmw24+XlRXR0NKNHj2bSpElMnjyZ8ePH061bN3x8fIx2UHP44+jRozl27JgzwhMHKCmvSib5l+O6kRVAr9B4o3wwEtCOICIi0sJFRkby0EMPsX//fqZMmWL0kZYuXcrKlSubff/HH3/cuKc9x8KFC5sdg0irlJ/PuGRY8R5seBNu3YbnJCvCw0n0jTOqW6PQuhXSojh8GoinDn8UB6qooJhyo+rSkRVAbFQfonN3EpcL/TNQskJERFqNDh06sHTpUs4//3w2btwIwEcffcSkSZPcG5iI1C4/v+Y5T0lWAF17j6RdUTJZgbBFyQppYRw+skLDH9uAkhJKrNJc/hW4NFnhldCd1H/Az2/BM9+jaSAiItKq+Pr68uyzzxr1VatWuTEaEalX9WSFt7dHLfxuShxG4tmpICdDIH3XevcGJNIETt+6tLGcPfxRHKi4mGKrZEVAOa79oZyQYFvXyAoREWllxo0bB1im1qalpbk5GhGpU/VkRWgoeNIOh4mJtutWZCRBRYX74hFpAo9JVpxzbvjjyJEjjXMfffSRGyOSGoqLKbUaNOPqNSvo3t22rmSFiIi0MhkZGUbZeotTEfEweXm2dQ+aAgLAsGEknqiqbm1XAgcPui8ekSbwuGQFaPijxysp4Y4tUPEEFD4Fs5Nw78iKM2cgJ8d1zxcREbFTdnY2hxuYvpiVlcWvfvUrwDIttlu3bq4ITUTsUX1khaclK7p0YXhpB4anwR2bYUQaWrdCWgyHL7DpKBr+6MHObivrZYbAc+tsujJZ0a2bZXjd2Z1kAMvoiqFDXReDiIiIHdavX88vfvELgoKC6NWrF3FxcXTq1ImAgAAKCws5cuQI69atMxYdB5g5c6Z7gxaRunl6sgLo030km974uurE1q1w7bXuC0ikkTw2WaHhjx6spMS27uUFPi78r+TvDzExkJJSde7wYSUrRESkRTCbzRQWFrJ9+3a2b99e43PWhgwZwsMPP+zK8ESkKWpbs8LTJCbC19WSFSItgMuTFdnZ2Zw5c4bu1dcdsKLhjx6uerLCHSse9+hhm6w4cMD1MYiIiDTRuZ3SrJMSJqvF+Dp37kxUVBQxMTFMmzaNO++8E38P2llARKrJy2P69bAxBkJKYdeRQFy3R14jJSba1rdssYxQ9qSFQEVq4fJkhYY/tgKekKzo3Rt+/BEzkBEMHffvQz9uRUTE002dOpXU1FTWr1/P2rVrWbZsGTt37gQsSYuMjAwSExN57LHHbBYbFxEPlZ9PZpClP5oZBP7BYe6OqKZhw2zrp09b3vSLi3NPPCKN5JZpIBr+2MJ5QrKiVy/mXgILh0KeP6Su3kW066MQERFpsqioKGbOnMnMmTN5/vnnOX78OB988AFvvPEGx44dY9myZXz77bc88MADNguOi4gHys8n389SDC4FrxAPnAaSkADh4bYL0m/dqmSFeDyX7wZiPfzx3GGtc+fODB06lOnTp/Piiy/y888/E+qJc7/aMk9IVvTujZfZkqgAOHBa00BERKRl6tq1Kw899BAHDx7klVdeITQ0lMrKSp577jnuu+8+d4cnIvXJz6fgXLKiDI9cYBOTqebablq3QloAlycrzg1/XLx4Mffffz8DBw60SVpkZGTQpUsXHnvsMX7zm99onqYn8pBkRa/TVdWDpizIzXV9HCIiIlaOHz9u97Xe3t7cddddbNiwgaioKMxmMwsWLGDNmjUOjFBEHCovzxhZEVKKZy6wCTXXrVCyQloAlycroGr44/PPP09SUhJHjx7l6aefpmvXrlRWVrJs2TLGjh3LQw895I7wpCElJfztPLjmSrh5JpwO9XZ9DN270zO7apWKg5FokU0REXG7/v3789RTT1FaWmr3PXr37s1LL71k1P/5z386IjQRcYbcXNtkhSeOrABITMQMpIbCl70hed8Gd0ck0iC3JCuq0/DHFqakhDVd4b8D4V9DoTTAz/Ux+PvT8//bu+/4qKr8/+OvSW+EElroHQJIDyAoiCBgAaUoomJdZVEXd3XtdXVXFL+/tRfsXVcRUUQUEJGmgnQEQocQeoD0Mknu749JbmZSJ/VOZt7Px2Me3HNz7r2fgcudk8+cEtrSLO5pBOzaVftxiIiIOElPT+fxxx8nJiaGzz77rNhwV3ddcskl5rZ6Voh4LnvKWbLyZwGMyMYxN4Qn6tePOQOg1T0w7hr4PvyoY6JNEQ9W7ckKdX/0AVlZZDl1pggJCLEkjLbRMQTkOrZ3R6GeFSIi4jH279/PddddR/fu3fnkk0/Iycmp0PHJ+UMbDcPg5MmTNRGiiFSDpPQz5nb9TDw3WdGtGz3PFn7BuDEaDQURj1ftyQp1f/QBWVlkOq0jExxoTbIioHNX2p91bO9pBMauOEviEBERKTBhwgQMw8Bms2EYBnFxcVx//fW0aNGCf/7zn2zatMmt8zz33HPmdmSkBy6FKCIAhJ1J5f2v4YVFcMtGPDdZERBA78Y9seV39lofDWzYYGlIIuWp9mSFuj/6gKwss7sbQHBgqDVxdOlCp9OOzbQgOH5wuzVxiIiI5Pvqq6/45ptvaNWqFYCZtDh16hTPP/88/fv3p1mzZkyZMoVnnnmGb775ht9//52dO3eyceNGPv/8c8aPH89///tfbDYbNpuN9u3bW/yuRKQ0YYnJ3LAZ7vodJu7Ac5MVQL3esXQ95dje3Byy/vjN2oBEyhFQfpXKKej++OSTT/LII48wZcoUAgLcv5y6P3owp2EgAbngH2xNzwq6dOHvL8JNG6HzaYjK3g+G4VieSURExCLjxo3jwgsv5JlnnuHFF18kNTUVW/5nU0G7Zu7cucydO7fUcxR82WOz2Zg8eXKtxC0iFWS3Q3q66z4PTlYwcCADF8xhZxOw+8OWfb8Sa3VMImWo9p4V6v7oA5yGgYTkYM3SpQCdOzN6L1y5Hfocg8DTSXDqlDWxiIiIOAkPD+epp55i79693HXXXURERLgkIABz6faiL+c6nTp1YsaMGda8CREpW/6Xqy48PFkRe6SwuNbvGBw/bl08IuWo9mSFuj/6AKdhIMG5WJesaNsWAgNd92mSTRER8SBNmjTh+eefJyEhgVdffZVBgwaZbSNnBW0eKExixMbG8v333xPhqUshivi6upasiIlhYGJhj+i1LYF166yLR6QcNTIMRN0fvVxWFmP3wJF6EJ4NRFmUrPD3h06dYMeOwn27dsGQIdbEIyIiUoqIiAhmzJjBjBkzOHv2LMuXL2fr1q3s3r2bQ4cOkZaWht1up1mzZnTu3JkrrriCCy+8ED8/j1hlXkRKkpTkWvbzg/Bwa2Jxh78/vVsNIDDXMR9gVgCOZMVll1kbl0gpamzOioLujzNnzuTpp5/m3XffJSUlBXDt/liSgm8XDMNQ90dPlJXFK987le+wKFkB0Llz8WSFiIiIB2vQoAFXXHEFV1xxhdWhiEhVFE1WREZ6/NxpwQMGsWHOKjon5veQHrvW6pBESlXj6Xp1f/RCmZmuZauGgQB06eJa1jAQEREREakNRZMVnjwEpMDAgfQ8kZ+oAFi71jFBvYgHqrGeFUWp+6MXycpyLXtSskI9K0RERESkNiQlsa4FJIZB/Uzo27AeFq2R576BA13Lp0/D/v3QoYM18YiUodaSFc7U/bGO86RkRefOruXduyEvzzFmUERERESkpiQl8ex58FV3R/HgqlDaWBtR+dq2hcaNXVfQW7tWyQrxSPqNTirOk5IVXbpwIhze7gf3XgTz2mXAkSPlHyciIiIiUhVJSSQ5NYPrhzeyLhZ32WzFe1doRRDxUEpWSMV5UrIiOprDTUO4dTz831BY2BmIi7MuHhER8VqxsbH8/PPPlsawbNkyBhb9RUNErJGcTFL+uA+bAfUioqyNx12xsa7ltZpkUzyTkhVScZ6UrLDZ6NK4q1nc2RjX1UFERESqyfr16xk1ahSjRo1i6dKltXrtJUuWMHLkSC666CLWr19fq9cWkVI49ayolwV+9RtYGo7biiY816+HnBxrYhEpg5IVUmF5WZnkON85ViYrgIguPWmVPxlznJIVIiJSw37++WfGjBlDnz59eOONN0hOTq6R66SkpPD666/Tp08fxo4dy/Lly0td9l1ELJCUZPasqJ9F3VgNBFx6VhhAam4GbN9uXTwipVCyQipsd2AygY9BwGNw6zgsT1YQE0PXRMdmYhic2r3Z2nhERMQrLV68mK5du5pLrG/dupU77riD6OhoJkyYwEcffcSxY8eqdI2jR4/y0UcfMWHCBJo3b86dd97J1q1bzWvGxMSwePHianpHIlIlTj0r6mdSd5IVTZqQ1qkN46dC9D9h0lVoKIh4pAqtBhIbG8vs2bMZMWJETcVTrmXLlvHAAw+wVv+hLJOV6xgGkusHNvCIZEW3NfBT/iTGcSd30NjaiERExAuNGjWKLVu28OqrrzJr1ixOnDgBQEZGBt9++y3ffvstAJ07dyY2NpZzzjmHzp0706pVK5o2bUpoaChBQUFkZ2eTkZHB8ePHSUhIYNeuXWzdupV169axZ88e83rOvSiaNWvGQw89xIwZMwgIsGQxNxEpIjv5DJmBju36WUBkpKXxVERYv0H83vIQJyJgXUsw1v6O7S9/sTosERcV+rQrGKs5YsQIHnjgAUaNGlVTcRWzZMkSnnnmGZYvX15r15SSZeZmm9shOUCIxStKx8TQ1Wn1pTjbaYaePQsNGlgVkYiIeKmAgADuuusubr31Vl555RVefvllEhISMAwDm82GYRjs2rWL3bt3V/jcBcmJgvMAtGrVirvuuovbb7+d0NDQan0vIlI1yelnzO061bMCsMUOZOCGL/muK5wJhb3bV9PJ6qBEiqjUMBBvG6u5Zs0apk+fTvfu3alfvz6RkZF0796d2267jdWrV1f79cDREKno64033qiRWCoqyylZEZyD9T0rOnWi69nCWzkuCs1bISIiNSosLIz77ruP/fv38/HHHzNy5EhsNluxegXDN8p6FWWz2Rg1ahSfffYZ+/fv55577lGiQsQDNT6VTs6/4PQz8MF86lSygoEDiT1SWFybuhPS0qyLR6QEFUpWeNtYzbS0NG655RaGDh3Km2++yY4dO0hOTiYlJYUdO3bw1ltvcd5553HzzTeTpv+8psy8Ij0rrE5WBAbSLaI9rZJg5D7oeAYlK0REpFYEBARwzTXXsGTJEg4dOsSrr77KuHHjaNCggdtfsBiGQYMGDbj88st5/fXXiY+PZ/HixUyZMgV/f/8afgciUmlJSfgb0DATGqdTt5IV/fszOKEwwfprCwP++MPCgESKq9AwEG8aq5mbm8vEiRNdEh+hoaH06NGDgIAAtm/fbvYYee+990hISOD777+vkUbDsGHD3PrGpE2bNtV+7crIckpWBOdifbICaNO2F/HP7y3coWSFiIjUshYtWjBjxgxmzJgBwL59+9i6dSsHDhzgyJEjpKamkpWVRXBwMBEREbRo0YL27dvTs2dPOnToYHH0IlIheXmQkuK6ry4lK8LDGdTwHGzGFgwbrGkN/PorDB9udWQipgr/1u8tYzUfffRRl0TFrbfeyjPPPEOjRo0AR6+LZ599lqeeegpw9Cp57LHH+M9//lNtMRT44IMPaNeuXbWft6ZkGnZz2yN6VgDExMDXXxeWlawQERGLdejQQUkIEW+VkgJFe0/VpWQFUD/2PHqc2MK2ZrC5OaT9vpJwHrA6LBFTpZcurctjNY8cOcLzzz9vlqdNm8abb75pJioAwsPDefLJJ3nkkUfMff/97385cuQIPi0vjyxbnlkM8pCeFcTEuJaVrBARERGRmpKUVHxfHUtWMGQI5x52bOb6wbr9q4onYEQsVOlkRYG6OFbzhRdeIDMzE3AkXV544YVS6z766KO0bt0agMzMTF588cVqj6dOycri/EPw5RfwyVcwZg+emazYvx8yMqyJRURERES8W9Fkhc0G9epZE0tlnXsu0zbD69/Bptfh/C3JsHdv+ceJ1JJqXai7rozV/NppuMBVV13l0qOiqKCgIG666SaefPJJAObNm8ezzz5b4zF6rKws2iRBG+fnsyckK7p1cy0bBuzaBb17WxOPiIh4paSkJJYsWUL//v1p37691eGIiFWKJivq1QO/Kn8PXLvat+f8zKac/8eJwn1r1kAnLWIqnqFakxVFeeJYzbi4OJdJPMeOHVvuMRdffLGZrNizZw9xcXF07dq1xmL0aFlZxfd5QrIiPBzatIFDhwr37dihZIWIiFSrb7/9lhtvvBGABg0a8NJLL3HttddaG5SI1L78ifhNkZHWxFEVNhsMGQLz5xfu+/VXuP56y0IScVajyQpPtHnzZpfyueeeW+4x/fr1M1cxAdiyZYuSFc48IVkBjqEgRZMVIiIi1WjBggXmMNfs7GwuvvjiCh2fkZHBDz/8wKZNm0hKSiIqKorWrVszZswYoqOjayJkEakJSUm8NAh+bwn1s+Dxo+E0szqmyjj33OLJChEP4XPJih1Ov8AGBQWZ81GUpaDe3vwxXDuq+Zfge++9l+3btxMfH4/dbicqKorOnTszfPhwbrjhBs/qZurpyYoffyTHD/Y2hA47/iTQ6phERMSrrFq1ypxQ/LrrritzKGlRH3/8Mf/4xz84ffp0sZ/ZbDZGjhzJ888/T/fu3astXhGpIUlJrGgLX+X/d33wlwhr46msIUNcy1u3OlY6qWvzb4hXqvDAqpUrV5JSdE3hOuTAgQPmdqtWrUpcwaQkbdq0KfEc1WHu3Lls376dlJQUMjMzSUhIYPny5fzrX/+iS5cu/PWvfyXDUyaLLClZERRU+3GUJCaG+0dB+EPQ7W8Ql7C5/GNERETcFB8fz7Fjx8yeFRUZ/vHBBx9www03kJiYWOLKaHl5eSxZsoS+ffvyyiuv1NRbEJHqkpREktP3dfXDG1oXS1X07w8BTt9f5+XB2rXWxSPipMI9K4YPH47NZqNDhw706dOHvn37mq/mzZvXRIzVyjnRUr8CywtFOo1Dq+5kTePGjenYsSMREREkJSWxc+dOUlNTAcjJyWHOnDmsXbuWn3/+2e2Ys7KyyHJKLCQXHVdXWUWTFUFBjvFuniAmhsgsyM6/q/9M3U9Pux0C1b9CRESqbteuXeZ2gwYNOO+889w67vjx48ycORPDMFy+JHFeNa1gv91u56677iIvL4+ZM2dWU+QiUu1On+ZMqGPTLw8i6je1Np7KCg2Fvn1h3brCfWvWwMiR1sUkkq9Sw0AMw2Dv3r3s27ePefPmmfubNGnikrzo06cPnTt3rrZgq0NBEgAgJCTE7eNCQ0NLPEdlde/endtuu41x48YVm4Q0JyeHH3/8kYceeogtW7YAsHHjRq6++moWLVrk1vlnzZrFv/71ryrHWUz+kq8mTxkCAtCzJz1OFhb/jMqF3btB3WlFRKQaFPSstNlsDBo0yO3j/t//+3+kpKSYCQnDMPDz8yM2NpZ27dpx9uxZ1qxZQ2pqKjabDcMwuOeeexg8eDADBw6sibciIlV1+jSnwx2bDTPBr1GUtfFUxZAhrskKzVshHqJSyYqiQycKvhk4ceIEixcvZvHixebPwsPD6d27t5m86Nu3Lz179iTQom+7c3JyzO2AAPffvnNdu91e5Tj+/PPPMq916aWXMnLkSCZPnszChQsB+OGHH1iwYAHjxo0r9/wPPvggd999t1lOTk52a36OcmVlsa4F7GsIQbkwIjmQBlU/a/Vo2JAetmbAcQD+bIJj3J2SFSIiUg2SnJYq7Nixo1vH5OTk8O6777okKtq1a8fXX39Nb6cVqzIyMnj66aeZNWsWALm5udx8881s3brV7SGrIlKLTp8msbFjMyodiHJ//hqPc+65/Pnpi3zRA9a0hkc3rGRYXl7dW4pVvE6FkxW33normzdvZtu2baSnp7v8rKSujampqaxZs4Y1a9YUXjQggO7du7sMI+nTpw/1amEil7CwMHM7s2gvgTI41w0PD6/WmEoTEhLCZ599RufOnTl+3PEL+Msvv+xWsiI4OJjgmuj1kJ3Nu33hjVhHccM8f/pW/1UqrUOb3oTYF5MZCH82xZGsmDLF6rBERMQLOM8fFRXl3reoy5cv5/Tp02aPCZvNxptvvumSqABHD86nnnqKVq1aMWPGDMAxofe3337L5ZdfXn1vQkSqhf3MKZLzO2lHZQAVmGzX4wwZwoZoePICR3HYwVSGxcU5Jq8XsVCFkxVz5swBHMmIuLg4Nm3a5PI6ceKES/2SEhh2u53NmzezZcsWPvzwQ/Pn7du3d0leDBo0yO3GgLsiIgpn6q3IpJXOiRnnc9S0evXqMWPGDJ544gnAMcFpZmZmhYawVKvsbLL9C4tB/h4yuWY+/3N60e3UYjZFw55GkLV5Ex40UEVEROow5y9VCpYzL893333nUo6JiWHUqFGl1p8+fTqLFi3i22+/BeCNN95QskLEA51OKfydJyqdup2saN2aIdmFvZN/bY1jKIiSFWKxSi9darPZ6NatG926dePqq6829x89erRYAmPv3r3k5eW5HFvAeXKpffv2sX//fpd5MLp168aFF17I1KlTGVJ0aZ1KaNy4sUus7jp27Ji5Xd0JlPKMGDHCTFZkZmYSHx9v3VwgRZIVwX6elazgnHPo8Q1sioZcP4iL30Qvq2MSERGv4Pz5f/LkyTJqFvr5559delVMnjy53GMeffRRvv32WwzDYMWKFeTm5uLv71/ucSJSe2xnzjJtMySGwsAEoJZ/P6huHc45n6apczkR4RgKkrtqBf4332x1WOLjKp2sKE10dDTR0dFcfPHF5r60tDQ2b97sksDYtm1bsWEYJSUxduzYwc6dO3nttdfo0qULTz31lFsf9KXp2rWruZ2YmEh6errL0JDSxMfHm9vdunWr9PUro+gqK6dOnfKYZIWn9azgnHPo8VZhcXt2Ar20VrSIiFQD58/ejRs3lls/MTGRbdu2uey75JJLyj2uf//+dOjQgX379pGZmcnGjRsZMGBAxQMWkRrT9EgSH+512jG7DvesAGxDz+O8NXOZ1x2SQmDb1mX0Lv8wkRpVK7OmhIeHM2TIEG6//XbefPNN1q5dS2pqKtu2bePjjz/mnnvuYeTIkURFRZnrjRdwnpAqLi6OKVOmcMkll7hMclURMUW6M23atKncYxISEly+QSl6jppWdG4Qd5IrNcZud01WBHjYIIuYGCbH+bHgU9j3Alz1J7B9u9VRiYiIF+jXrx+hoaEYhsGmTZs4dOhQmfUXLVrk0qapX78+sbGxbl3LeRUQ5yVTRcQDZGQ4Xs7q8jAQgGHDGHawsLjCLx6OHLEuHhFqKVlR4oX9/OjevTvXXHMNzz33HEuWLOHEiRPEx8ezYMECnnrqKa644gqaNm1qftAXdKP88ccfGTZsWLFf4t0xcOBAl4knV61aVe4xK1euNLdDQkJqfRmxoiuHNG1q4TrOnt6zIiSEzlFduGwXtD8LfgaOSTZFRESqKCAggAsvvBBwfIkye/bsMut/+eWX5rbNZmPEiBFur+zRqlUrc/vMmTOViFZEakxJ/yfrerKiVy+GJRbOy7eiLbBihXXxiGBhsqI0LVu25NJLL+Xhhx9m3rx55hwY9913H/Xr1wccDYRt27Zxxx13VPj8ERERjBw50ix/8skn5R7jXGfkyJG1thpIgc8//9zcbteuHdHR0bV6fRfZ2WQ5DR7yuJ4VAD17upaVrBARkWpy5513Ao62yJw5c4pNoFng0KFDLFq0yPyiBeCyyy5z+zrObY3k5OQqRCwi1S4xsfi+Bg1qPYxq5e9Pr67DqJ8/Sn9FWzBW/GJtTOLzPC5ZUZJevXrxzDPPsG/fPsaPHw84Ggkff/xxpbpG3njjjeb2li1bWLBgQal1N2zYwKJFi0o8tjZ8++23Lg2hK664olavX0x2NoZT0SOTFeec41pWskJERKrJmDFjGDx4MDabjdzcXK688kpmz55NWlqaWScxMZGbb76ZnJwcc19gYKDZhnGH83DXwMDA6gleRKrH6dOu5QYNwAsmwfUfNpxpm+Gv6+CFHyB3pXpWiLXqRLKiQIMGDZg7d6453jMvL4+PPvqowueZPHmyy/rm06dPZ+fOncXqHT16lOuuu47c3FwA+vTpw6RJk0o854EDB7DZbOarYPWOopKSkpg0aRLr168vN87PPvuMa665xiyHhYVx//33l3tcjcrO5qcPIe8JyH4SAgPrSLLCMEquKyIiUkEffvghYWFh2Gw2srKyePDBB2natCn9+vVjwIABtG3bttgqIOPHj6/QamLHjx83t2tzyXQRcUPRZEVdHwJSYNgwXl4Ery+EqdsgYNt2OHXK6qjEh1X7aiA1LSAggAceeMBMGvzyS8W7J9lsNt566y2GDx9ORkYGR48eZdCgQcyYMYNhw4YREBDA2rVreeWVV8zGQmhoKG+++abbY01LYxgG8+bNY968eXTr1o0xY8bQp08foqOjCQ8PJyUlha1btzJ37lzWrVvnEvN7771XbGWQWme3O+IBAvOAoDqQrDh1Co4fB6v/7kRExCt06tSJL7/8kkmTJpGZmYlhGGRkZBSbtLugzWCz2XjooYcqdI21a9ea25YO/xSR4oomK+r4sqWm/v0hLAyc5wVcuRImTLAuJvFpdS5ZATBs2DBze+/evWXULF1sbCwff/wx1113HRkZGSQnJ/Pss8/y7LPPFqsbGhrKxx9/7PYM3u7auXNniT06iqpXrx5z5szhqquuqtbrV0p2tmvZE7umduhQ/EG7bZuSFSIiUm3Gjh3Ljz/+yPXXX2/2rizNvffeS58+fdw+95EjR1zaN506dapKqCJS3U6fJikYwu0QkIf39KwIDIQhQ2Dp0sJ9K1YoWSGWqVPDQApERUXh5+cI/XTRzGYFTJw4kfXr1zNq1KgSGxk2m42RI0fyxx9/MHHixEpfx1loaCi33XYbPXr0KLeXRv369Zk5cybbtm1j6tSp1XL9KiuarAjysNVAAPz8oEcP131btlgTi4iIeK3zzjuP7du38/TTT9OzZ09z+fWCV4MGDXjuueeYNWtWhc7rPLF3UFAQnTt3ru7QRaQqEhM5/2YIfAyi78F7khUAw4e7lrUiiFioTvasAOjcuTO7du0iu+gvzxUUExPDkiVLiI+PZ/Xq1SQkJACOVUmGDh1K69at3TpPu3btXNZSL01wcDBz5swBHEuRbdq0iRMnTnDq1CnOnj1LWFgYjRo1olevXvTq1Qt/T5uspy4kKwDOOYcv0tfxczvY3gR+2rSh7t7sIiLisUJCQnjggQd44IEHOH78OPHx8Zw5c4aoqCh69+5d4c/xglVGCr7QiI2NJchTP2tFfNXp0yTWc2z6GXhXssKpBzsAmzZBUhLkr8ooUpvq7O9vO3bsICkpyWVeh6po3bo1V199dbWcy10NGzZkxIgRtXrNKsufs8LkqQ2ofv34MuVd5uZ3sNj94+/EWBuRiIh4uWbNmtGsWbMqneOjjz5i3759ZrKizrUTRHyAcTqRxPz/6lEZQGMvSlYMHAjBwZCV5Sjn5cHq1XDJJdbGJT6pTg4DKVC/fn1GjRpldRi+pS7MWQHQty+9CydSZ3PaXsjIsC4eERGRcmRkZJgTcRb01qyuYagiUn0yzp4iK/8r36h0vKtnRUgIDBrkuk9DQcQidbZnhVikrgwD6dXLNVnRxODqrVsd2WIREREPFBoayooVK9iwYQMbN27kyJEjLkuti4hnSEw7aW5HZeBdyQqAYcM49ccKVrSFFW3h/t+WEM0zVkclPkjJCqmY7GwmXwU2Azqfhqc9NVkREUHvsA7APgA2NQc2blSyQkREPFqHDh3o0KEDkydPtjoUESlFYkbhBP+NMvCepUsLDBvGC2vgP/nTVwyet4mr09IgPNzauMTn1OlhIFL7jOws5sXA3B6wtAOe27MCaN1tIA3zR35sbg5s2GBpPCIiIiJS9yVmnTW3vW4YCMCQIQw7XPhr4rK2ebBypYUBia9SskIqJNeejZG/4mpQLh6drLD17UfvY47to/XgxJ9rrQ1IREREROq2zEwS/bPMolcOAwkP57xmAwnKcRSXdgCWLrU0JPFNSlZIhWTnFD6cg3Pw3Ak2Afr2pe+xwuKG038WX81ERERERMRdZ85wwQH48SP4dC5cugvvS1YAYReOYWi8Y3t/Q9j760JrAxKfpGSFVEi2PdPc9vSeFfTty/ADcPFuePQXaH/CDjt3Wh2ViIiIpU6ePMmiRYt48sknGT9+PNHR0dhsNvP1/vvvWx2iiOc6fZqmaTB6L0zdBjGngIYNrY6q+l10EaP2FRaXZu2E48dLry9SAzTBplSIc88Kj09WREVxeUYbLv/kUOG+jRvhnHOsi0lERMQix44dY/DgwRw8eNDqUETqrsRE13JkJAR44a9UAwcy6mgoD+OYAG5pB5i+bBlMnWpxYOJL1LNCKiQrtw4lKwD69nUtb9xoTRwiIiIWy8zMVKJCpKpOnHAtN2liTRw1LTCQ/t0upEH+ZPU/dYDcJT9aG5P4HCUrpEKycwvnfKiTyQqtCCIiIkKTJk0YO3YsjzzyCPPnz7c6HJG6o2iyomlTa+KoBf4XjWb0Xjg3Hu5cC5nLl4JhWB2W+BAv7LMkNSksM4drtkC2PwxMwLMn2ATo18+1vHEj5OWBn/J0IiLiWxo1asSXX35JbGwsbdu2tTockbqpaLKiWTNr4qgNo0bx2d/Bz8xPJMDu3dCli4VBiS9RskIqJDrZ4JN5Tjs8vWdF//6u5ZQUiIuDmBhr4hEREbFIZGQkkydPtjoMkbqt6CSTXtyzgpgY/KJbwJEjhfuWLFGyQmqNvl6WisnOdi17erKiRQto2dJ139q11sQiIiIiInWbDw0DwWaDUaNc9y1dak0s4pOUrJCKsdtdy56erAAYONC1/Pvv1sQhIiIiInVaUuIRZg+FD3rD+mi8exgIFE9WLFsGOTnWxCI+R8kKqZiiPSs8fc4KgEGDyPaHDdHwVj84uWm11RGJiIiISB10MOMo918EN06A12Lx7p4VUDxZkZwMf/xhTSzic5SskIqpa8NAAAYO5IkLoP90uG08rEnaBpmZVkclIiIiInXM8YyT5nazNLw/WREdDT16uO7TUBCpJUpWSMXUxWTFgAH0P1pYXNc8z7EqiIiIiIiIu7KyOG5LN4vNUvH+ZAUU712xaJE1cYjPUbJCKqYuJivq1WNgWOGsxb+1QpNsioiIVKOsrCySk5NdXiJe5+RJjocXFpul4f1zVgCMGQPAwfqOoS9Lj/8Kp05ZHJT4AiUrxH15eeQYudj9wFxuuS4kK4DW5wylZX676feWkLv2N2sDEhER8SKzZs2ifv365qt169ZWhyRS/U6c4HhEYbFZuh80bGhdPLVlxAjWtw+h3T/gjkthTj8DfvjB6qjEByhZIe6z2/nkHAh6DPyegDn9qRsTbAIMGsSQeMdmajBs27XK2nhERES8yIMPPkhSUpL5io+Ptzokkep3/DgnnHpWNA1uCH4+8OtUSAh9eo6iUf4ImB87QfbCb62NSXyCD/zvkmqTnU22f2ExII8607OCgQM516nd9CuHITHRunhERES8SHBwMJGRkS4vEa9z4oTrMJCI5tbFUsv8LxvHJbsd2ynBsPLP78FutzYo8XpKVoj7iiQrgnOpO8mKnj0592SwWVzTGs1bISIiIiLuO3GCyCxomgpBOdCoQbTVEdWeSy/l0t2FxYUt0mDNGuviEZ+gZIW4r0iyIqguJSsCA+nboj/BOY6ZmxtkAqtXWx2ViIiIiNQVx4/zv7lw/P8g/T/g19QHJtcs0LIlY8LOwT/PUfyuC/Ddd5aGJN5PyQpxn91OVkBhsU4lK4DgIeez/wU4+n/w0iJgleatEBERERE3nThhbvob+MaypU4ajr6c8w45tndHwa5f5lkbkHg9JSvEfUV6VgTmUncm2AQ47zyiU8FWUP799+JLsYqIiIiIlMQpWQH4xrKlzi67jMt2FRYX+u+DvXuti0e8npIV4r7sbHKc7pjAPOpWsmLIENdyZiZs2GBNLCIiIiJStxRNVvhYzwpiY7n0VEPCs+HyndA1EVi40OqoxIspWSHuy87G7pysMPzA37/0+p6mUSPo0cN1n4aCiIiIiIg7jh93LftassLPj27njiPxWZj/OY7VQTRvhdQgJSvEfXY707bAV/+D/30JPZLqznwVpvPOcy0rWSEiIiIi5TEMOHnSdZ+vDQMBbJeNc6wIWGD5ckhJsSoc8XJKVoj7srPpfhIm7oCr/oSmdi9JVhiGNbGIiIiISN2QmAh2u+s+H0xWMHo0BDjNuG+3w6JF1sUjXk3JCnFf0cko69BKIKaiyYrERIiLsyYWERGRWnbrrbcSEhJS7FXROiI+5/Bh17LNBs2bWxOLlSIjYcQI131ffmlNLOL1lKwQ93lDsqJtW2jZEoCMANjUHA0FERERn2G328nKyir2cpaTk1NuHRGfk5DAX8ZDrxlw8bWQ2LZp3ZpovjpNnuxa/v57SEuzJhbxakpWiPu8IVlhs8F553H1ZGjwAAy9GewrllsdlYiIiIh4ssOH2d4EtjaDHzpDeNOWVkdknQkTXCfZT0/XUBCpEUpWiPuKjtOri8kKgPPPJyAPsgMgPQj++HOx5q0QERGf8P7772MYRqVeIj4tIYHDkY7NxmkQEt3a2nis1KQJXHCB6765cy0JRbybkhXivqI9K+pq17cRI7jgQGFxedhJ2LPHsnBERERExLPlHj7E0QjHdqtkoFUrS+Ox3OTJGMC6FnD/KPhl0zeQkWF1VOJllKwQ93nDMBCAmBiGpzUxi8vbAT/9ZFk4IiIiIuLZTpw4QE7+yIdWyZhzoPmsCRNY1MXGwNtg9nnwfrdM+OEHq6MSL6NkhbgvO5tl7eGznvBld8gKCSj/GE9ks9FpwEVE5y8JvboN2H9aYm1MIiIiIuKxEs4eMrdbpqCeFc2acWGr86iXP/fu/G6Q/eXn1sYkXkfJCnGf3c7soXDNZLjqKsioq8kKwHbhSHMoSFoQ/LFjKeTlWRqTiIiIiHimwxnHzW31rHAImXQ14+Ic22dDYdnWbyEz09qgxKsoWSHuy87G7nTHBAYEWxdLVY0cyYX7C4tLGifDli3WxSMiIiIinik1lQT/dLOoZEW+iROZvKOwOLdDJvz4o3XxiNdRskLcl52N3WmVosCAOjpnBUDbtoy2tzGL+xsAy5ZZFo6IiIiIeKiEBM47BM8thrt+g75HUbICoHlzxjYbSnj+tHZfdwP7FxoKItVHyQpxX3Y2OU53TEBgHe5ZAbQZPIZ5n8Ox5+C9b9AkmyIiIiJSXEICvY/DP9fACz9A78z6EBFhdVQeIdRpKMjpMPhh6zxISbE2KPEaSlaI+5yGgfjlgV9Q3U5WcOGFTNgJzdLyy7/8UnzFExERERHxbYcPu5Z9fXJNZ1deyXXbCn+l/CAmG776ysKAxJsoWSHus9vNYSCBeUBgoKXhVNmFF7qW09Jg1SprYhERERERz5SQ4FrWEJBCzZoxptMYup+A29fC/auADz6wOirxEkpWiPucelYE5gJBdXjOCoCmTWHAANd9339vTSwiIiIi4pnUs6JMATfcxLbX4NXvIfYIsHw5HDhgcVTiDZSsEPfZ7dhwDAEJzAMC6u7SpaZLLnEtL1pkTRwiIiIi4pnUs6Js48Zha9DAdd+HH1oSingXJSvEfTk5bHsNcp+EU7Op+8NAoHiyYvt2ZYJFREREpFDRnhVKVrgKCYGrr3bd9+GHYBjWxCNeQ8kKcZ/dbm76GXhHsmLAAGjc2HWfeleIiIiISIF9+1zL7dpZEoZHu+EG1/LevbB6tTWxiNdQskLc55SsALwjWeHvD2PHmsUDDSD9hwXWxSMiIiIinuPsWTYGn2FRJ4iLwjF/W4cOVkfleQYNgi5dXPdpok2pIiUrxH3emKwAuOQSFnaGXjOg/d/h+0M/QWam1VGJiIiIiNX27+et/nDJddDtb7ChBdCmjdVReR6bDW680XXfF19Aerol4Yh3ULJC3Fc0WeENE2wCjB5NkGFjazNH8Zv22bBsmbUxiYiIiIj19u1jX8PCYvvQFhAcbF08nmzaNEfSIl9KZjLMnWthQFLXKVkh7svJcS17S8+KqCiGNx9MvSxHcWEXyJmnB6uIiIiIz3NKVoRnQ5MWnayNx5O1agWjRrGuBVw7EaL/CUffft7qqKQOU7JC3Oetw0CAoAmTuXi3Y/tMKKxeN694ckZEREREfEruvj0crO/Y7nAGbB06WhuQp/vrX5nfDT7tBWlB8KbfJli3zuqopI5SskLc58XJCiZMYHxcYfHb5kmwapV18YiIiIiI5Y4c3kl2/sjn9mfQ5JrlGT+e6QnN8ctzFOcMAPurL1sbk9RZSlaI2+w5WYyfChOnwBMX4F3JivbtuST0HPzzH6zzYsD4SkNBRERERHzZ/tN7ze0OSlaULyCANtf/jcvzvwQ8Wg++3vQZnDplbVxSJylZIW7LzslmQVf4OgZWtsF7JtjM13DcVVy437F9oCGsW/UF5OVZG5SIiIiIWCM3l31Zx8yikhVu+stfuHND4e8Jr/TLgXfesTAgqauUrBC35eQVDgMJyMO7elYATJzIlG2OzXpZsCf3JKxda21MIiIiImKNhAROB+USkOsotj+LkhXuaNqUEYOmEHPSUVzZFrZ8/iLk5lobl9Q5SlaI2+w52eZ2oDcmK2JimGjvxNefw4nn4JqtONaHFhERERHfs28fd/8KGf+BA8/DBcdDoUkTq6OqE2x3/o07nL7ze7XVUVi40LqApE5SskLcZs8t7FkRmIv3JStsNhpePoUrdkJIwUIgn3+uLLCIiIiIL9q3D3D0KG6bBBGtO4LNZnFQdcTAgVxv60O9LEfxVBgYL71obUxS5yhZIW6z53p5zwqAa65xLR89Cj//bE0sIiIiImKd/GSFSUNA3GezUW/GXbw/H3a9BF99AbaflsEff1gdmdQhSlaI2+xGjrkdmIvXTbAJQPfu0Lev676PP7YmFhERERGxzs6druWOHa2Jo666+momnm5G59NO+55+2rJwpO5RskLclpPr5RNsFrjuOtfyvHmQnm5NLCIiIiJije3bXcvdu1sTR10VEgJ33+267+uvi/+9ipRCyQpxW0RGLtdugau2waAEvDdZcfXVruMRU1JgwQLr4hERERGR2pWdDbt3u+5TsqLi/vpXaNDAdd+sWZaEInWPkhXitpZJBh/Pg//NhdvX4b3JihYtYORI130ffmhNLCIiIiJS+/bsgZwc130xMdbEUpdFRsLMma77Pvus+HwgIiVQskLcZ7e7lr01WQHmUJBcGyzpAIt3LYJDhywOSkRERERqRdGhCtHR0LChNbHUdTNnQnh4YTk3F2bPti4eqTOUrBD3Fc0ue3OyYvJkTjeJoP3fYfT18MBIA955x+qoRERERKQ2bN/OoL/AhTfAA6PQEJCqiIpyDAdxYrz3LiQkWBSQ1BVKVoj7ivas8MbVQAqEh9No8jSapzqKG6Nh4/zXiydsRERERMTrpO7YzNpW8HN7+KUtSlZU1d13Q1AQx8PhwZEw8AY79qeesDoq8XBKVoj7fGkYCMBtt3HzxsLiq+1OwvffWxePiIiIiNSKuIQt5nbMKZSsqKoWLeDmm7l+AjxzPvzREt7e+A7s2GF1ZOLBlKwQ9xiG7yUr+vThusD+RGY6ip+cA6ffftnamERERESkZuXksD1tv1nsfhIlK6rDI4/w5Jpgs/jEMIOUh/9pYUDi6ZSsEPfk5hbf5+3JCiDilhnctMmxnRkI75xeWnwZKxERERHxHvv2saNhYds3RsmK6tGyJYOm/pMr/3QUT0TA/535HlautDYu8VhKVoh7cnLI8YMsf8iz5e/zgWQFU6Zwx44Is/jaAMh94b8WBiQiIiIiNerPP9natLAYk9cIGje2Lh5vct99PL2hIQH5uaD/GwJHH7nL0YtbpAglK8Q9djtzu0PIo+D/OLw4CO+eYLNARASdp9zO2PzOFAcawncr34XTp62NS0RERERqxoYNrG/h2KyfCe3b9LI2Hm8SGUmnvz/JjD8cxfQgeCJyI3z1lbVxiUdSskLcY7djd7pbAvLwjZ4VAH/7G3/7w4/GafCfn2D47mx44w2roxIRERGRGnBk8yqO1nNs9z8CtgGx1gbkbW67jUcPtTfnhXunH2yb9Q/IyLA2LvE4SlaIe+x27P6FxUBfSla0asXY/lM4+AI8tBIaZAIvvwxZWVZHJiIiIiLVyTCot24Ln3wFd6+BKX8CAwZYHZV3CQqiyROzeWCVozgkHoIPHIannrI2LvE4SlaIe3JyXHpWBObiO8kKwO/uewhzXgzl2DF45x3L4hERERGRGnDwIPWOneaarfD/FsNt61GyoiZMmsQ//IbwyVew/H3ofBp47jnYts3qyMSDKFkh7vHlnhUA/fvDBRe47nv6afWuEBEREfEmf/zhWm7UCNq3tyYWb2azEfLam1yzMxC/grk1c3Jg+nTIy7M0NPEcSlaIe+x2corOWeELE2w6e/xx13JCgnpXiIiIiHiTosmKAQPAZiu5rlRNjx5w332u+9asgTfftCYe8ThKVoh7ikyw6WvDQABHz4rhw133qXeFiIiIiPdYt861rCEgNevhh6FTJ9d9DzwAR49aE494FCUrxD1Fh4EYNvDzwdvniSdcywkJ8OqrloQiIiIiItUoLw/Wr3fdF6uVQGpUaCi8/rrrvqQkuPFGDQcRJSvETTk5XPknzP8MvvwCBpz0sV4VBYr0rljdGn59+3FITLQuJhERERGpup07Hb8oO1PPipo3ahRcd53rvsWL4fnnrYlHPIaSFeIeu53Op+HyOJi8HVpkBVkdkXWeeYazITB+Kpx3C9w+LJXcJ5+wOioRERERqYqff3Ytt2oFLVtaE4uvef55iI4GIC0Qbh0Hb/3v/uI9XcSnKFkh7rHbXcu+Nrmms8GDibxiCocjHcVN0fDWb69BXJy1cYmIiIhIpaX+soTpl8FnPeFoBDBihCbXrC2NG8NHH5ESDANug7f7w12jc9kxfRKkplodnVhEyQpxT9Fkha9NrlmE36xneHFp4d/BQyPyOPX328AwyjhKRERERDxSXh6r9izjzQFwzWR4ajhw4YVWR+VbRo6k3j8e4IIDjmJGIEyNPUjmHdPVxvZRSlaIe5SscNWuHedfeQ/XbXYUz4TCg4Er4OOPrY1LRERERCpu2zaWR6WYxQsO4OhZIbXrySf5f4n96X7CUdzcHP6a/CmG5q/wSUpWiHtyclzLvp6sAHjoIZ7bFk1kpqP4dn9Y+dydcOqUtXGJiIiISMUsW8bydoXFC/LaQNu2loXjswIDCfv4f3y+KIzQ/O9KP+gD/zfvn7BwoaWhSe1TskLco54VxdWrR/P/N4cnneZiumlEMml/v8O6mERERESkwlJ+WcIfLRzb3U9A03NHWRuQL+vYkXOe+5APvi7cdf8og28fuRK2bbMuLql1SlaIezTBZsnGjePO1pMYeshRTAyFrb98AZ9+am1cIiIiIuKezEyWHlxGbv5vRiMOoPkqrDZpEldOfYon8r8UNGxw73kZ5Iy/DI4ftzY2qTVKVoh77HZ+aQsf94L/9YC0EH+rI/IY/i+9zHvL6jFxO2x7DQYfBmbMgP37rQ5NRERERMrz00983S7TLF62Cxg50rp4xOHhh3msxdVM2Qb9jsCyDyBg/0G46CJITLQ6OqkFSlaIe+x2XhkI0ybC1VfC6TAt42SKjqbzM2/x1RfQsmBepuRkuPZayM62NDQRERERKZt93lwWdHFsR2bChdFDoHlza4MSsNmwvfMu7yXEsvI9p3b21q0wZgycPWtldFILlKwQ9+TkYHfqTBHorzkrXEyZAjfc4Lrv119h5kxr4hERERGR8uXkYP9+AY+ugPMPwrhdEDRhstVRSYHQUELnfUtYy3au+9evh4svhpSUEg8T76BkhbjHbsfudLcEBgRZF4unevll6NjRdd+cOfDGG9bEIyIiIiJlW7WKsGOJ3P0rrHgPPvwamDDB6qjEWfPmsGwZtG7tuv+332DsWDh92pq4pMYpWSHusdtdelYE+KlnRTH16sGXX0JoqOv+v/0NliyxJiYRERERKd2XX7oU/fr0hXbtrIlFSte+Pfz0E0RHu+5fswbOOw8OHrQmLqlRSlaIe+x2ctSzonx9+8I777juy8kh9aor4PffLQlJREREREqQng6ffOK6T70qPFfnzo6ERdOmLrs/CtzB2vH9YNMma+KSGqNkhbgnJ0fDQNw1dSrcd59Z/LI7tL0tnV9uvQj+/NPCwERERETE9MUXkJRUWLbZYNo06+KR8sXEOBIWLVsC8F0XuOkKGHHZab674Vz45htr45NqpWSFuKfoMBAlK8o2axZMmcKCLnDVVXA6DC4dl8LKqUNh40aroxMRERGROXNcy2PGaAhIXdCzJ/z6K0aP7rw+AHL9ID0Ixk/I5IkXriD33nvAbrc6SqkGSlaIe+x2/PMgINfxsgUqWVEmPz/48ENGtx/FJbscu9KC4JLLklg+7XzHSiEiIiIiYo0tWxwTNDqbPt2aWKTiWrfGtnIV846cz1XbHLsMG/zrAhh7/L+cHHM+JCRYGaFUAyUrxD12O6vfBftTkP0UEBBgdUSeLyiI4K/m89WhwYzd7diVGgxjJqTx+R3D4X//szY+ERERER+VMetJMp2bs9HRcOmllsUjldCwIcE/LOGzoKnMWgp+eY7dSztC376/89Ml3eCDD8AwrI1TKk3JCnGPU1cqG0CgVgNxS3g4IYuW8PXRYVya38MiOwCmXm7nmVeuxnj8McjLszZGEREREV+ybRsvHf6KTjPh9QGQ5Q/cdpvat3VRcDB+H3/CA1Nf5adPA2iW6tidEAkTLk3lzF9vhEsugUOHLA1TKkfJCnFPTo5rWQ9z90VEEPLdD8w/O5a/rC/c/fCFsGXOU3DxxXDsmHXxiYiIiPiQ5KceYfYQxy+0d14C+1tHwF13WR2WVJbNBrffzgWf/cqmha0Ysd+xe9ZSaJgJ/PAD9OjhmFMuI8PSUKVilKwQ9xSdpEbJiooJDSXg6294M/pWnlzm2PWfZdD7OLB4MfTqBd9+a2mIIiIiIl5v1Spmn/qG02GO4jVboduN/4SGDa2NS6puwACar97C0qwpfPkFzPjD6WepqfDQQ9Ctm2O5WvVsrhOUrBD3KFlRdUFB2N6Yw6OTX+LXd2zct9rpZydPwuWXw8SJEB9vWYgiIiIiXisriy3/nMazQx3FgFx4bGM9+PvfLQ1LqlHDhvh99jmTZ32DX3SL4j8/dAiuuw769oXPP4fc3NqPUdymZAWwZs0apk+fTvfu3alfvz6RkZF0796d2267jdWrV5d/girat28fjz32GP3796dJkyaEhobSsWNHJkyYwNy5c8kpOgTDCkWTFZpgs3JsNvjb3xj8wU8lP0C//tqxfvSTT0JKSu3HJyIiPsPq9o9Ibcv595Pc0usAOf6O8oOroPNdT0L9+tYGJtVv/Hj480+49dYSf2xs2cITc6ayK7YDvPUWpKfXcoDiDpth+O70qGlpacycOZN33323zHo33XQTL7/8MuHh4dUew4svvsj9999PVlZWqXUGDx7MJ598QocOHSp9neTkZOrXr09SUhKRkZEVP8H118NHHxWW770XZs+udDwCJCbCX/4C8+eX+OMDHRrR7m+POh6yNXDviYjvqfJngXiF2mj/6F4Tj7NwIfe8fBn/PddRjDkJG//oT/Dq38Hf39rYpGZt2AD33APLl5u7fuwIY6eBzYBJ2+H2HeFccOHN2Kb/Fbp3ty5WL1SVzwOf7VmRm5vLxIkTXT6oQ0NDGTBgAIMHD3b5i3zvvfeYOHEiudXcTeipp57i73//u5mo8PPzo2fPngwbNozo6Giz3m+//cbw4cM5evRotV6/QjTBZvWLioJ58+DDD6FJE5cfrW4N7a8/zWXr/sGvsdHw4INaK1pERKrME9o/IrVu+3Z23DmFFwY5igG58M53/gTPeUeJCl/Qrx8sW+aYHy4mBoCX8+8FwwZze8CFk9OIyXuZ52/pwclhA+Cll8DK370E8OFkxaOPPsrixYvN8q233srhw4dZt24dv/76K0eOHOHRRx81f7548WIee+yxarv+jz/+yOOPP26Wzz33XHbs2MHWrVv55ZdfOHz4MJ9//jkREREAHD58mCuvvLLarl9hdjuXXw2XXw33XoSSFdXFZoNp02DnTkcPCpuNPBv8Y6zjxwu7wJApKZx34hk+uqw1GeMvgS+/hMxMa+MWEZE6yer2j0it27oVRowg5kAaCz+F+pnwyvdw7u1PQ+/eVkcntcVmg3HjHPfD3Ll8uqc3zyzBXOoUIK4x3D0Wokes584f74KWLeGCC+C//4W4OPDdAQmW8clhIEeOHKFjx45k5v/CN23aND788MMS6z766KP8+9//BiAkJIS9e/fSokUJcw1UgGEY9O3bl82bNwPQtWtXNmzYQFhYWLG6S5cu5aKLLjLL8+bNY8KECRW+ZpW7Y15xBX59vsGwQWwCrG37b3j44YqfR8q2ZQs5jzzEnGMLmT0UDjVw/XHDDMes1bfsjqDvwMsdD92xYzXWUkTcoq75vq022z+618QjLF4MU6fC6dPmruPh0Gz8VMeKEDabhcGJpQwDliwh+/n/4+vDS3ijPyxvX/jj//wED60sckzHjnDRRXD++XDeedCmTa2GXFdpGEgFvfDCC+YHdVhYGC+88EKpdR999FFat24NQGZmJi+++GKVr79o0SIzUQGOeStKSlQAjBo1iilTppjlZ555psrXr4xcezZG/vM8IA9NsFlTevUi4NvvuOOF1ezeczHvfAM9ThT++EwovDoQVjRKdXzIXn01NG4Mw4fDY4/B0qWQlmZd/CIi4rGsbv+I1Jr0dMcQ2rFjXRIVAM3OGQxvv61Eha+z2WD0aIIWLWbKN3v5OfoBdnzaiAdWQvszcPW2Eo7ZuxfeeAOuvZYjPduyrXc0eddeA6++CqtXa3L8GuCTyYqvv/7a3L7qqqto1KhRqXWDgoK46aabzPK8efOqfH3nc7Rv357Ro0eXWX/69Onm9tq1azl8+HCVY6ionNzC1UACc9EwkJo2ZAhBC77n5k93sDVvOqs+CWbaZgjOnzpk0g6nujk5sGIFPPWUI9vboAF5/frCzTfDyy/DypWQlGTJ2xAREc9hdftHpMZlZcG77zrmJXjmmeLd9s8/39HbopQvCcVHdegAs2bRbetRZt29kL2nr6NDTr0yD/mgN5wz8RhNWn3GpWvu5OHHzuPLwZHs7tuGvEkT4V//ciyNun692uFV4HNfj8fFxbFnzx6zPHbs2HKPufjii3nyyScB2LNnD3FxcXTt2rXSMSxcuNDcHjNmDLZyMrvnn38+4eHhpOV/Y75w4UKXBEZtsOcUrlYSmIeSFbWlWzdsr7/B0JTnGPrVV7zyybusPrCSVsllHJOTwz+abWJRxCZil79Hzy+g+0nokduI9k274t+pM3TuDO3bO8bitWjheOXPjyIiIt7HE9o/IjUiLw82bCD+i7f4ZOunHCWVFw+VUG/8ePj0U62wJqULCoJLLsF2ySWO+eF+/BG++w6+/x6OHHGpuqKt48/TYfB9F8fLIZ7w7HhuXPs1rzzhdECTJo72d5s2jvZ3q1aOPwte0dGO64sLn0tWOA+/AMfEluXp168fQUFBZGdnA7Bly5ZKf1ifOHGCY8eOVej6AQEBxMbGsjx/uZ0tW7ZU6tpVYVfPCmvVqwc33kjkjTdycXw8LFjgeC1bBvn3pbPfWsHuKMer0GmCc36lc+Kv/OU7uOv3Eq7RsiU0bQoNG0KjRoV/FmxHRjo+5MPDHd9KFGyHh0NIiLpUioh4KKvbPyLVIjsb4uNh2zbObFzDqr0/szx1G8ubZrAxGozB4JcHM3+HjmfyjwkIgKefhn/+U+0UcV9ICFx+ueNlGLB5MyxZAqtWwapVXB53muBcWNUGEot01EkLAnvR8QsnTzpea9YAkBEAd4+BVsn5rxRo5hdJ49AooiKaENioiWOod1SU49WggaOtXq+eoz1esF3wiogAP+8bNOFzyYodOwr7zwcFBZnjMctSUG/v3r3FzlGV6wN07NjRreM6duxoJiuqcv3KsucW/kKsnhUWa90abr/d8UpNhZ9/dgz1WLEC/vgDIzeX0BzHkJGsIv/DswJgWzM4G1LCeVNSHKuS7NzJvoYwbQI0TYCoPRCZ5Zg9u37+n5FZMHYPhNudjvfzcyQwgoMdmeHAQMefzq+i+wIDHUuG+fkVvsoql/WzggaIc0Ok6L7S/qyNulI+/V25p0cPKGf4oEhRVrd/KiwpCQ4edN1X0pzw2lc39+XmOoZsZGebr9zMDDKy08hLTyMyKdNxD5w963idPu1IUhw7RmKIQY874HgE0Kn4qfP8YElH6PgHjpUcXnnF8dwUqSybDfr0cbzuvRfy8vjrjh38ddUqjFUrORS3lk2pe9nUNI9NzWFTc+iaWPYpEyLhjdiie5PzX/upnwm/vAe9j5d+jm1NYW9DR7s8MgvC/III9Q8mxC+Y0IBQQgNDCQoKdbTNQ0IcL+ftgrZ4YKAjqef8Z9F906Y5vrisZT6XrDhw4IC53apVq3KHYBRo06aN+WHtfI6qXL/gvO5ev7Rz1AbnZIUm2PQgERGOFUHGjXOUU1OxrVvH8k2byN60np371rI9eS/bo/L4swn82RT2N4C25QydS6gHa8q5NeP/WyRZkZfnSJ6kOtaAuvcieLcvhOZAqB2Cch33TmCe48/YBHh5QdnXuGsspAeCDbAZ4Gc4tv0MR/nqbTA0vvTj9zSC9/oUHldwDmf3r4aQnNLP8W1XWB9d+s87nIEbNpf+c4D/nA+ZTv9ljCKPnct3Qqxr70IXBe/D+biiTb/HfnH8XZdmbndY27L04zufhtvWl348wEMjHd8EmOco8j6mbINzy5hSZ2djeDW2+LHOsTy3BMLslOrjXo5vMUo6FiDmFPz9t9KPB5h5seO+cj7WOZ4bNsHwg0WPKrStKTw3pORjC7z+XZH/H0W83Q9+bud6vHM855woYRbyAjfeqGSFVJjV7Z8K++knmDSJXVEwe2jh/w/D5roN8MIP0KCMFb0/6A0/dnIcV/T/m2GDbqfgyZ/LDuemy+F0qOt1nc9323oYH1f68VubOr5FLenYgljmfgFRGaWf4/nBjmd5SX8HBo5fat4s53P1omlwMtz1us7ne2AVXLu19OP/aAHXTiz7ffz6DjQtY67vx0Y42gcGYPd3fK5kBkCOv+PnI/fB0pIXqQGgUQakltBbvvcxmLTdEX+HXsPh+/sdE2wqES7Vzc/PkQDr0QPb9Om0BdpmZnL59u2OHhibN2PU2w7t9ziSrnl5xU5xuJxFMZJCoF7xztMuPj0HZp3vvCc7/+WY6LPfEVj/ZtnnuGYSJIY62ueBuY72esF2YJ7j+RpmBy6+WMmK2pDiNEtr/Qos9+i8zEpKFWZ6LXqsuzFU9PpZWVlkZRXOM5GcXNYkB+ULyc7j+k2OD5XYBNSzwlNFRMCIETBiBEFAL6BXVhbs3w979sDu3eTt2U1u010Qc8Qx/q6ESX8KGmRlicwq++dJIY5xfKWpV87xAB/2hrNlxHLOibKTFfsbwNPDyr7GXb+Xnaz4rgu81b/0n4/aW36y4rmhjr+P0rRJKjtZ4c77uH912cmKHzvC2+W8j/KSFa/Flv0+epwoO1kRHwmvDCr7Gv9ZVnay4pe25b+P8pIVH/Yu+32cG192suJoBHzYp+xrvLQIKON9/N4SPu1V+s9P7S0jWSFSCTXd/qnudkeB4+HwTr+y68xaCg3K+Pn6FvDZOaX/fOih8pMVP3SCY2XMt3fR3rKPPxsCS8vpTJvtX/bP9zcs+4uEwOK/DxWzrWnZ7+NUOfNOZgTArsZl18ktJzdwNsTxrXJZ1yiLDTjvkKNen2Mw4gCcfxCiWneBCRPgtWnqSSG1LyQE+vVzvHDcp4CjB9GBA7B7t2M1kYQEOHyY2KOHWLfwAIczjnM4LIfDkXAyzPF/8GS448/G6WVfMjm4nJDKaBcWWN4OjpbxTPjvj/kbFv3u53PJitT8b33BsW64u0JDC39jcj5HVa5fkRgqev1Zs2bxr3/9q2LBlSEqzeCD+U477layos4IDoZu3RwvHEsAuYxoS0uDo0cdiYsjRyAxkctPnybr9ElOJB/lTMpJktJPk5RxhuTcdJKMTJJsWURk55Z52ah06Hja8W1JRoCjEZbjl//yz++hUw57OQ03Wwk9S53l6csUERGg5ts/1d3uKFAdj/HyPitqQ228D3feZ3CuY5hoQd2CuGz5PRDL+2wOzIOGGa7XsxXdLieGhhnQMtlxTFCu4xeqgldojuuS7aX54bsGji75/frBtX1hyBDHig4iniY4GLp2dbychAMDgAGG4RjmlJAAp05BYmLha4ZTOSnJMWTb6XX5zixapDiSFsnBjvZ2RmBhb6XuJ8sPr9jcGkUEFTT3layoHTk5hSmmgAoMZXCua7eX8XVZBa5fkRgqev0HH3yQu+++2ywnJye7NT61VMOGQbt2YLc7Xs2aVf5c4lnCw6FTJ8fLSRDQKv9VopwcxzrmaWmFf6almWNPZ9ntzHIai4rdbm4bWVnk9ciGf+U4usbl5TnGrxbZXptxkty0HIy8PPKMPIy8XIy8PAwjj7y8XNr1DINuQY764Dou1jAY4J/F0v1nMIA8DAwM8jBcGnThFzdx6sNqFPvzrpAUJu/KcDmv85+NcgLh4vrFj3Xa/nrPaXKLtCKdG3PdosNhdOm/PPQPsPPTtuT84wqPdD5H+PAGYJT+iXOvLY3rt7r2J3Q+vkFuAIwue5muJbuSyKX099E+OhRGl57mH+hvZ91m168JbEWatRHDIihrVe3HjEzu3OT6DHQ+Q0SuP4wu+6vBX3ekUtAedz62YLt5s2AYXfqH8hC/HOI2uHYNKto4rzcsrIS9hZ7OzebB9YWfB+YvDPlbIXl+pf9d6htDqYSabv9Ue7vDZgN/f/qeMNj2Rv4uw7G/4Bfrgn1NsoDAEp4b+d3/H/vV4O/rC3bZiv2iHpxrgxBbiccW2Py+gZF/vBkLBb+c2wjLAcJLn69o8BmD5JdshccUvAebzSwH+wGRtlKHLTz/m+PlfN1idRsWv7ZzPAc+Kv09mpqUfvzgbDj9XhnH22wQgeNVynX+FQf/2u1XOMdVwZ8F2yEhcE0DqF/fMalgg/ztli0dKym0aeMoa3iHeAObrXASzQq6yG7nIucERmZm4Ssry/HntCJl5+2sLA6mZ2BPzsaem012Thb2XDv23Gzzz8CRIZCTC6FudLuuAT6XrAhzWlc5M7OMAY5FONcNr8KSR2FF1nXOzMwstq86rh8cHExwcDl9gyrijTeq71ziHQICHLMRR5Yz6K4ENqCcThMAdK/wmV1FASOreI4e+a+qGFHF4xsBF1bxHF3yX1VRbB6oCqqP41uEqmid/6qKmCoeH07V/y6b5L9EaktNt3+qvd0xYQLk5BBO1Z/BUfmvqmhaxeMDgLLTweXzvnn+RaRKAgMLV+2rpPJ/C7WWzz33IiIKU70ZGWXMYlREenrht4HO56jK9SsSQ3VdX0RERHyP1e0fERGRivK5ZEXjxoWzAh09etTt444dO2ZuR1Wim05J169IDNV1fREREfE9Vrd/REREKsrnkhVdnSY3SUxMdPnGoCzx8YVLDnTLn6iwqtcHOHToUK1eX0RERHyP1e0fERGRivK5ZEVMjOto5U2bNpV7TEJCAidPFk6nWvQcFdG5c2eXyarcuT7Axo0bq+X6IiIi4nusbv+IiIhUlM8lKwYOHOgyAdSqVavKPWblysLF7kNCQhg4cGClrx8UFMSgQYMqdP1jx46xZ88eszxs2LBKX19ERER8j9XtHxERkYryuWRFREQEI0cWrg/wySeflHuMc52RI0dWaTUQgMsvv9zcXrp0KcePH3f7+g0aNFCyQkRERCrEE9o/IiIiFeFzyQqAG2+80dzesmULCxYsKLXuhg0bWLRoUYnHVtbUqVPNbzfsdjuzZ88utW5qaiovvfSSWb722msJDAyscgwiIiLiW6xu/4iIiFSETyYrJk+eTO/evc3y9OnT2blzZ7F6R48e5brrriM3NxeAPn36MGnSpBLPeeDAAWw2m/l64oknSr1+q1atmD59ull+8cUX+eqrr4rVs9vt3HTTTeYknKGhoTz00ENuvUcRERERZzXR/hEREakpAeVX8T42m4233nqL4cOHk5GRwdGjRxk0aBAzZsxg2LBhBAQEsHbtWl555RVziEZoaChvvvkmNputWmJ44oknWLRoEbt37yY3N5errrqKa665hiuuuIJGjRoRFxfH66+/zpYtW8xjnnvuOVq0aFEt1xcRERHf4gntHxEREXfZDMMwrA7CKvPmzeO6664jIyOjzHqhoaF8/PHHTJw4sdQ6Bw4coH379mb58ccfL7N3BcCuXbsYNWqUy7Jgpbnvvvt49tlny61XmuTkZOrXr09SUhKRkZGVPo+IiNRd+iwQqN72T2l0r4mICFTt88Anh4EUmDhxIuvXr2fUqFElfmNgs9kYOXIkf/zxR6U+qMvTpUsXtmzZwi233EJoaGiJdWJiYvjmm2+qlKgQERERKWB1+0dERMQdPt2zwll8fDyrV68mISEBgJYtWzJ06FBat25dK9dPSUlh2bJlxMfHk5aWRnR0NOeccw59+/atlvMnJSXRoEED4uPj9Q2HiIiPSk5OpnXr1pw9e5b69etbHY54gJpq/6jdISIiULW2h5IVPuLw4cO1lngRERHPFh8fT6tWrawOQ7yY2h0iIuKsMm0PJSt8RF5eHkeOHKFevXqVniSrICumb0nEXbpnpKJ0z9QswzBISUmhRYsW+Pn59EhQqWHV0e4APROkbtP9K3VZdd2/VWl7+ORqIL7Iz8+v2r5Fi4yM1ANXKkT3jFSU7pmao+EfUhuqs90BeiZI3ab7V+qy6rh/K9v20NcqIiIiIiIiIuJRlKwQEREREREREY+iZIW4LTg4mMcff5zg4GCrQ5E6QveMVJTuGRFxpmeC1GW6f6Uu84T7VxNsioiIiIiIiIhHUc8KEREREREREfEoSlaIiIiIiIiIiEdRskJEREREREREPIqSFSIiIiIiIiLiUZSsEBERERERERGPomSFlGnNmjVMnz6d7t27U79+fSIjI+nevTu33XYbq1evtjo8qQUnT55k0aJFPPnkk4wfP57o6GhsNpv5ev/99yt97q1bt3L33XfTq1cvGjVqREREBF27duXaa6/lhx9+qL43IbXi7NmzfP3118ycOZNhw4bRvHlzgoODiYiIoE2bNowbN44XXniBM2fOVOr8ul9EfMfZs2f56aefePbZZ5k8eTLt2rVz+ex54oknqnT+ffv28dhjj9G/f3+aNGlCaGgoHTt2ZMKECcydO5ecnJzqeSPic9R2Fk9S59vxhkgJUlNTjZtvvtkAynzddNNNRmpqqtXhSg04evSo0bZt23Lvgffee6/C57bb7caDDz5o+Pn5lXnuSy+91Dhx4kT1vzmpVjt27DAuu+wyIygoqNz7BTDCwsKM559/3sjLy3Pr/LpfRHxL586dDZvNVub/98cff7zS53/hhReM4ODgMs8/ePBgY+/evdX3psTrqe0snsRb2vHqWSHF5ObmMnHiRN59911zX2hoKAMGDGDw4MFERkaa+9977z0mTpxIbm6uFaFKDcrMzOTgwYM1cu7p06cza9Ys8vLyAAgMDKR3794MHTqUqKgos97ChQsZNWoUqampNRKHVI9t27bx3XffkZ2dbe7z9/ena9euDBs2jKFDh9KoUSPzZ+np6fzjH//gtttuwzCMcs+v+0XEt+zevdutZ0NlPPXUU/z9738nKysLAD8/P3r27MmwYcOIjo426/32228MHz6co0eP1kgc4l3UdhZP4zXt+CqlOsQrPfjggy5ZsVtvvdVITEw0f56ammo8+uijLnUeeughCyOWmrB//37z37dJkybG2LFjjUceecSYP39+lTKyc+bMcTl+/PjxxuHDh82fZ2dnGy+//LIREBBg1rnmmmuq+d1Jdfryyy8NwAgICDCuuOIKY/78+UZSUpJLnby8PGP+/PlGy5YtXf79X3vttTLPrftFxPcU/F+uX7++MWLECOO+++4zvvjiCyM6OrpKPSt++OEHlx4b5557rhEXF2f+PDc31/j888+NiIgIs87QoUOr8Z2Jt1LbWTyNt7TjlawQFwkJCUZISIh5c02bNq3Uuo888ohZLyQkxEhISKjFSKWmJSUlGV9++aVx4MCBYj+r7EMuLS3NaN68uXnsBRdcYOTk5JRY9+233zbr2Ww2Y/369ZV9K1LD5s+fb/zlL38xDh48WG7dQ4cOudwDjRs3NrKzs0usq/tFxDd98sknRlxcXLGhYs5dmiuarMjLyzN69+5tHt+1a1cjLS2txLpLlixx+ZybN29eZd+K+AC1ncUTeUs7XskKcXHvvfeaN1ZYWJhLVriorKwso3Xr1mb9++67rxYjFStV9iH36quvujy4tm/fXmb9QYMGmfWvuuqqKkYtnqJoVn7p0qUl1tP9IiLOqpKsWLhwoctz54cffiiz/pQpU8y6AwcOrELU4u3Udpa6pi614zVnhbj4+uuvze2rrrrKZZx5UUFBQdx0001med68eTUam9R9zvfI8OHDiYmJKbP+9OnTze3vv//eHGMsddu4ceNcyjt37iyxnu4XEakuzs+T9u3bM3r06DLrOz9P1q5dy+HDh2ssNqnb1HYWX2FFu0zJCjHFxcWxZ88eszx27Nhyj7n44ovN7T179hAXF1cjsUndl5qayooVK8xyRe+v1NRUli9fXhOhSS0r2pBLTk4uVkf3i4hUp4ULF5rbY8aMwWazlVn//PPPJzw8vMTjRQqo7Sy+wqp2mZIVYtq8ebNL+dxzzy33mH79+hEUFGSWt2zZUu1xiXfYvn07drvdLLtzfzVv3px27dqZZd1f3qHo7NRNmzYtVkf3i4hUlxMnTnDs2DGz7M7zJCAggNjYWLOs54mURG1n8RVWtcuUrBDTjh07zO2goCBat25d7jFF6zmfQ8RZ0XujY8eObh3nXE/3l3co2u21pA883S8iUl30PJGaoraz+AqrnqNKVojpwIED5narVq3K7SJZoE2bNiWeQ8SZ870REBDgsp59WXR/eZekpCRefPFFs9yrVy+6d+9erJ7uFxGpLkWfBc7PibLoeSLlUdtZfIVV7TIlK8SUkpJibtevX9/t4yIjI0s8h4gz53ujXr16+Pm59/jR/eVd7rnnHpfu2P/+979LrKf7RUSqS9FngbttHD1PpDxqO4uvsKpdpmSFmFJTU83tkJAQt48LDQ0t8RwiznR/ydtvv80777xjlqdMmVJsZZACul9EpLoUfRa4+0zR80TKo88q8RVW3etKVogpJyfH3A4ICHD7OOe6zhOviDjT/eXbVqxYwR133GGW27dvz5w5c0qtr/tFRKqL8/ME3H+m6Hki5dFnlfgKq+51JSvEFBYWZm5nZma6fZxzXedlvkSc6f7yXZs2bWL8+PFkZ2cDjtU/fvjhhzK7zOp+EfEMH3/8MTabrdpf77//fq29B+fnCbj/TNHzRMqjzyrxFVbd60pWiCkiIsLczsjIcPu49PT0Es8h4kz3l2+Ki4tjzJgxJCUlAdCwYUMWL15Mly5dyjxO94uIVJeizwJ3nyl6nkh59FklvsKqe939Phzi9Ro3bmxuHz161O3jnCfLi4qKqtaYxHs431+pqamkpqa69dDS/VV37d+/n1GjRnHixAnAMSHTokWL6N27d7nH6n4R8Qzh4eG0bNmyRs5bW5yfJ+Bo47jzfNDzRMqjtrP4CqvaZUpWiKlr167mdmJiIunp6cW6TpYkPj7e3O7WrVuNxCZ1n/P9BXDo0KESl6wsSvdX3XT48GFGjhzJ4cOHAUf3we+++45Bgwa5dbzuFxHPMGHCBCZMmGB1GFVS0vOkZ8+e5R6n54mUR21n8RVWtcs0DERMMTExLuVNmzaVe0xCQgInT54s9RwiBSpzf9ntdv78889SzyGe6fjx44waNYr9+/cDEBwczPz58xk2bJjb59D9IiLVpXPnzi6TvLnzPAHYuHGjua3niZREbWfxFVa1y5SsENPAgQMJDg42y6tWrSr3mJUrV5rbISEhDBw4sEZik7qvQ4cOtGrVyiy7c3+tX7/eZaxbRX7ZFWskJiYyatQo4uLiAAgMDGTu3LlcdNFFFTqP7hcRqS5BQUEuvbrceZ4cO3aMPXv2mGU9T6QkajuLr7CqXaZkhZgiIiIYOXKkWf7kk0/KPca5zsiRIzWjsZRp/Pjx5vaXX35prg5RGuf7q0ePHnTs2LHGYpOqS0pKYsyYMWzbtg0Af39/Pv30Uy677LJKnU/3i4hUl8svv9zcXrp0KcePHy+zvvPzpEGDBkpWSInUdhZfYkW7TMkKcXHjjTea21u2bGHBggWl1t2wYQOLFi0q8ViRkjjfI6dOnWLOnDml1j18+DAffPBBiceK50lLS+PSSy9l/fr1APj5+fHBBx8wefLkSp9T94uIVJepU6ea34Db7XZmz55dat3U1FReeukls3zttdcSGBhY4zFK3aS2s/gKS9plhoiTvLw8o3fv3gZgAEZ0dLSxY8eOYvWOHDlixMTEmPX69Olj5OXlWRCxWKHg3x0w3nvvvQodO378ePPYiIgIY9WqVcXqJCUlGeeff75Zr3nz5kZ6eno1RS/VLTMz0xg1apT572Wz2Yx33nmnWs6t+0VECrRt29b8f/74449X+PiZM2eax/v7+xtz584tVic7O9uYPHmyWS80NNRISEiohujFW6ntLHVNXWrH2/IDFjGtW7eO4cOHm2voRkZGMmPGDIYNG0ZAQABr167llVdeMbtQhoaG8ssvvxAbG2tl2FIDbr31Vj766KNi+7OyssztgIAA/P39i9XJzMws8ZwHDhwgNjaWU6dOAY7JF2+55RZGjx5NREQEW7Zs4eWXXzYnZ/Tz82P+/PmMGzeuOt6S1IDZs2dz//33m+WGDRtWaAzuRRddxD333FPiz3S/iPief//73/z73/8utt/5s8ff399l0swCcXFxtG3btsTznjlzhkGDBrF7927A8by45ppruOKKK2jUqBFxcXG8/vrrbNmyxTzmlVde4Y477qjqWxIvp7azeCKvaMdXKsUhXu+rr74yQkNDXTJvJb1CQ0ONr776yupwpYbccMMN5d4Dpb3Ksnr1aqNRo0blnsPf3994+eWXa+ndSmU9/vjjlb5PAOOGG24o8/y6X0R8S1WeKfv37y/z3HFxcUbr1q3dOtd9991XO29YvILazuJpvKEdrzkrpEQTJ05k/fr1jBo1CpvNVuznNpuNkSNH8scffzBx4kQLIpS6bMiQIWzZsoVJkyaV+M0YQGxsLCtWrODOO++s5ejE0+h+EZHq0qVLF7Zs2cItt9xCaGhoiXViYmL45ptvePbZZ2s5OqnL1HYWX1Gb7TINA5FyxcfHs3r1ahISEgBo2bIlQ4cOpXXr1hZHJt7g5MmTrFixgsOHD5OdnU2LFi0YMGAAXbt2tTo08UC6X0SkuqSkpLBs2TLi4+NJS0sjOjqac845h759+1odmtRxajuLr6jpdpmSFSIiIiIiIiLiUTQMREREREREREQ8ipIVIiIiIiIiIuJRlKwQEREREREREY+iZIWIiIiIiIiIeBQlK0RERERERETEoyhZISIiIiIiIiIeRckKEREREREREfEoSlaIiIiIiIiIiEdRskJEREREREREPIqSFSIiIiIiIiLiUZSsEBERERERERGPomSFiIiIiIiIiHgUJStExCv9+OOP2Gw2bDYbDRo0ICcnx+qQRERExEup3SFS/ZSsEBGv9O2335rbF198MQEBARZGIyIiIt5M7Q6R6qdkhYh4pe+++87cHj9+vIWRiIiIiLdTu0Ok+tkMwzCsDkJEpDpt3LiRfv36ARAQEMDJkydp0KCBtUGJiIiIV1K7Q6RmqGeFiHidBQsWmNvDhg1Tg0FERERqjNodIjVDyQoR8TrO40bHjRtnYSQiIiLi7dTuEKkZGgYiIl7lyJEjtGrVioJH2969e+nQoYPFUYmIiIg3UrtDpOaoZ4WIeJVvv/3WbDD06NFDDQYRERGpMWp3iNQcJStEpFpNmjTJXGc8LCyMAwcOVOo8M2fONM9js9lYu3atW8c5d8V0dzZuq2MWERGRyrH6M1ztDpGao2SFiFSbBQsWMG/ePLN8//33065du0qda8CAAS7llStXlntMWloaP//8s1l2p9FgdcwiIiJSOVZ/hqvdIVKzlKwQkWqRmprKHXfcYZbbtWvH/fffX+nzxcbGupRXrFhR7jGLFy8mMzMTgKZNmzJw4MAy63tCzCIiIlJxnvAZrnaHSM1SskJEqsWzzz5LfHy8WX7qqacICQmp9Pk6d+6Mv7+/Wd60aVO5xzh3xbzsssvw8yv7EecJMYuIiEjFecJnuNodIjVLq4GISJWdOHGCjh07kpqaCkCXLl3Yvn27ywdoZbRq1YqEhAQA/Pz8SE9PJzg4uMS6eXl5NG/enJMnTwIwf/58Lr/8co+OWURERCrOEz7D1e4QqXnqWSEiVTZr1izzwxfg4YcfrvKHLzg+gAvk5eWVOQHVb7/9ZjYYQkJCuOiii8o8tyfELCIiIhXnCZ/haneI1DwlK0SkSlJSUnjnnXfMclRUFFdffXW1nDs0NNSlnJycXGpd566YI0eOJCwsrNS6nhKziIiIVIynfIar3SFS85SsEJEq+fjjj0lJSTHL06ZNIygoqFrObbPZXMrZ2dml1q3I0mGeErOIiIhUjKd8hqvdIVLzAqwOQETqtg8++MClPG3atDLrL1myhNzcXAAGDhxIo0aNSq2bk5PjUg4IKPmRtXfvXnbs2AE4PrTHjRvn8TGLiIhIxXnCZ7jaHSK1Q3eziFTamTNnWLdunVlu3Lgxffv2LbX+kSNHGD16tFnevXt3mR/AzjNmA7Rs2bLEet988425PWDAAKKjoz0+ZhEREakYT/kMV7tDpHZoGIiIVNry5cvJy8szyxdccEGx7ojOfv/9d3M7LCyMDh06lFo3NzfXnN0aICgoqNTGwIIFC8zt8rpiekrMIiIiUjGe8hmudodI7VCyQkQqbevWrS7lsr4pAFi9erW53blz5zLXI9+6dSt2u90s9+/fv8RZs8+cOcOqVavMcnldMT0hZhEREak4T/gMV7tDpPYoWSEilbZ7926XckxMTJn1f/zxR3O7devWZdZ1bggAnH/++SXW+/77783xmm3btqV3795lntcTYi7qzz//5J577qF///5ERUURHBxMu3btGDlyJM8//zyHDx926zwiIiLezBM+w9XuEKk9mrNCRCrt0KFDLuXmzZuXWvfgwYNs27bNLDdt2rTMcy9cuNClPGrUqBLrOc/GXd63G+AZMRdIS0vjzjvv5IMPPsAwjGLXPnjwIMuWLSM7O5v777+/zHOJiIh4O0/4DFe7Q6T2KFkhIpWWlpbmUq5fv36pdT/99FOXckhISKl1ExMTWbZsmVlu2rQpF154YbF6drvd5RuI8saNekLMznFceOGFrF27FpvNxpQpU7j++uvp06cPISEhHDx4kMWLF/Paa68xcODA8t6WiIiI17P6M1ztDpHapWSFiFSa8zhJgIyMjBLr5eTkMGfOHJd96enppZ73zTffdFkn/JprrilxDOYvv/xCUlISAJGRkVxwwQUeHzOAYRhMmjSJtWvXEhQUxFdffcVll13mUqdRo0b07duXmTNnljleVURExFdY/RmudodI7dKdKCKV1qxZM5dyXFxcifXefvttDh48iM1mM7s07t+/v8S6p06dYvbs2WY5ODiYe+65p8S6zl0xx4wZQ2BgoMfHDPD++++b38y8+eabxRoMzkJDQwkODi715yIiIr7C6s9wtTtEapeSFSJSaZ07d3YpF+2+CLBr1y5z3OPo0aNp0aIFAL/++iuJiYkudbOzs5k6dSpnz541991+++20atWqxOtXZOkwT4k5JyeHhx9+GIARI0Zwww03uBW3iIiIr7P6M1ztDpFaZoiIVNLixYsNwOV1zz33GMeOHTPS09ONr776yoiOjjYAw2azGb/99ptx6aWXmnXHjh1rHDp0yMjIyDB++uknY+DAgS7n6tmzp5Genl7itTdv3mzW8/f3NxITEz0+ZsMwjKVLl5p1Fy5cWKm/dxEREV+kdofaHeJblKwQkUrLyckxYmNji30Il/S69957DcMwjJdeesmt+u3btzf27t1b6rX//e9/m3WHDx9eJ2I2DMO47777DMAIDQ01MjMz3Y5bRETE16ndoXaH+BYNAxGRSvP39+fTTz+lU6dOZdabOXMmzz77LAC33npruWuSX3zxxaxatYoOHTqUWqeiS4d5QsxQuIRZ69atNSZURESkAtTuqFjMoHaH1G02wyiyyK6ISAUlJyfz+uuvM3fuXPbv309ycjJNmjThvPPO44477mDYsGEu9ZOSknj66aeZP38+Bw8eJDAwkBYtWjBs2DCmTp1a5tJbAMeOHaNFixbmGuG7du0qNibU02IuMHr0aJYsWUKPHj1c1lIXERER96jdoXaH+AYlK0Skznnrrbe47bbbAOjWrRs7duywOCL3XXnllcydO5fg4GBSU1MJCNAK0iIiIp5M7Q4Ra2gYiIjUOc5dMd2djdtTDB48GICsrCxefPHFMuuWtb66iIiI1A61O0SsoZ4VIlLnzJ492/xAnTp1Kl27drU4IvclJibSqVMnzp49S2BgIPfccw9Tpkyhbdu2ZGdns2fPHpYtW8ann37K+++/z6BBg6wOWURExKep3SFiDSUrRERq2bJly5g0aZLLGulFBQQEkJycTGhoaO0FJiIiIl5H7Q6pq5SsEBGxQEJCAq+88go//vgje/fuJSMjg6ioKKKjoxk2bBjjx493e/IsERERkbKo3SF1kZIVIiIiIiIiIuJRNMGmiIiIiIiIiHgUJStERERERERExKMoWSEiIiIiIiIiHkXJChERERERERHxKEpWiIiIiIiIiIhHUbJCRERERERERDyKkhUiIiIiIiIi4lGUrBARERERERERj6JkhYiIiIiIiIh4FCUrRERERERERMSj/H/knU+GhZaLFAAAAABJRU5ErkJggg==", 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" ] @@ -2046,7 +2032,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 56, "id": "5a685a80", "metadata": {}, "outputs": [ @@ -2073,7 +2059,7 @@ " 6 | 9.23e-06 | 3.15e-06 | 1.54e-03 |1.88e-04\n", " \n", "A 1-R2 coefficient of 1.48e-05 was obtained for the the spectral density.\n", - "The current fit took 5.715767 seconds.\n" + "The current fit took 6.453628 seconds.\n" ] } ], @@ -2084,7 +2070,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 57, "id": "787b1ae6", "metadata": {}, "outputs": [ @@ -2092,17 +2078,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "10.0%. Run time: 3.57s. Est. time left: 00:00:00:32\n", - "20.0%. Run time: 5.72s. Est. time left: 00:00:00:22\n", - "30.1%. Run time: 7.92s. Est. time left: 00:00:00:18\n", - "40.1%. Run time: 10.02s. Est. time left: 00:00:00:14\n", - "50.1%. Run time: 11.74s. Est. time left: 00:00:00:11\n", - "60.1%. Run time: 13.61s. Est. time left: 00:00:00:09\n", - "70.1%. Run time: 15.83s. Est. time left: 00:00:00:06\n", - "80.1%. Run time: 17.94s. Est. time left: 00:00:00:04\n", - "90.2%. Run time: 19.69s. Est. time left: 00:00:00:02\n", - "100.0%. Run time: 21.50s. Est. time left: 00:00:00:00\n", - "Total run time: 21.50s\n" + "10.0%. Run time: 3.70s. Est. time left: 00:00:00:33\n", + "20.0%. Run time: 5.97s. Est. time left: 00:00:00:23\n", + "30.1%. Run time: 8.22s. Est. time left: 00:00:00:19\n", + "40.1%. Run time: 10.23s. Est. time left: 00:00:00:15\n", + "50.1%. Run time: 12.55s. Est. time left: 00:00:00:12\n", + "60.1%. Run time: 14.56s. Est. time left: 00:00:00:09\n", + "70.1%. Run time: 17.38s. Est. time left: 00:00:00:07\n", + "80.1%. Run time: 21.86s. Est. time left: 00:00:00:05\n", + "90.2%. Run time: 24.94s. Est. time left: 00:00:00:02\n", + "100.0%. Run time: 27.39s. Est. time left: 00:00:00:00\n", + "Total run time: 27.40s\n" ] } ], @@ -2118,7 +2104,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 58, "id": "80f55ad6", "metadata": {}, "outputs": [ @@ -2147,7 +2133,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 59, "id": "e3d9800d", "metadata": {}, "outputs": [ @@ -2155,6 +2141,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "4\n", "Correlation function fit:\n", "\n", "Result of fitting the real part of |Result of fitting the imaginary part \n", @@ -2168,7 +2155,7 @@ " | \n", "A 1-R2 coefficient of 1.48e-04-5.04e-06j was obtained for the the real part of |A 1-R2 coefficient of 8.87e-06-9.73e-06j was obtained for the the imaginary part\n", "the correlation function. |of the correlation function. \n", - "The current fit took 1.843673 seconds. |The current fit took 1.585738 seconds. \n", + "The current fit took 1.392402 seconds. |The current fit took 1.676065 seconds. \n", "\n" ] } @@ -2181,7 +2168,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 60, "id": "d5e92a0b", "metadata": {}, "outputs": [ @@ -2189,17 +2176,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "10.0%. Run time: 0.65s. Est. time left: 00:00:00:05\n", - "20.0%. Run time: 1.28s. Est. time left: 00:00:00:05\n", - "30.1%. Run time: 1.81s. Est. time left: 00:00:00:04\n", - "40.1%. Run time: 2.29s. Est. time left: 00:00:00:03\n", - "50.1%. Run time: 2.78s. Est. time left: 00:00:00:02\n", - "60.1%. Run time: 3.28s. Est. time left: 00:00:00:02\n", - "70.1%. Run time: 3.81s. Est. time left: 00:00:00:01\n", - "80.1%. Run time: 4.32s. Est. time left: 00:00:00:01\n", - "90.2%. Run time: 4.79s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 5.23s. Est. time left: 00:00:00:00\n", - "Total run time: 5.23s\n" + "10.0%. Run time: 0.99s. Est. time left: 00:00:00:08\n", + "20.0%. Run time: 1.60s. Est. time left: 00:00:00:06\n", + "30.1%. Run time: 2.04s. Est. time left: 00:00:00:04\n", + "40.1%. Run time: 2.46s. Est. time left: 00:00:00:03\n", + "50.1%. Run time: 2.85s. Est. time left: 00:00:00:02\n", + "60.1%. Run time: 3.23s. Est. time left: 00:00:00:02\n", + "70.1%. Run time: 3.60s. Est. time left: 00:00:00:01\n", + "80.1%. Run time: 3.99s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 4.37s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 4.72s. Est. time left: 00:00:00:00\n", + "Total run time: 4.72s\n" ] } ], @@ -2213,6 +2200,75 @@ "results_ohmic_espira_fit = HEOM_ohmic_espira_fit.run(rho0, tlist)" ] }, + { + "cell_type": "code", + "execution_count": 64, + "id": "835ffab8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "Correlation function fit:\n", + "\n", + "Result of fitting the real part of |Result of fitting the imaginary part \n", + "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", + " | \n", + " Parameters| a | b | c | d | Parameters| a | b | c | d \n", + " 1 | 3.75e-01 | 9.44e-01 | 3.15e-02 |-1.14e+00 | 1 | 7.81e-01 | 9.30e-01 |-4.43e-02 |4.62e-01 \n", + " 2 | 6.32e-01 | 9.48e-01 |-2.93e-02 |1.10e+00 | 2 | 8.75e-01 | 9.31e-01 | 4.27e-02 |-4.10e-01 \n", + " 3 | 5.12e-02 | 9.97e-01 |-1.24e-04 |4.19e-03 | 3 |-1.59e+00 | 9.75e-01 |-6.95e-04 |-3.86e-02 \n", + " 4 | 4.45e-01 | 9.86e-01 |-8.18e-04 |4.12e-02 | 4 |-7.57e-02 | 9.93e-01 |-4.99e-04 |-1.47e-02 \n", + " | \n", + "A 1-R2 coefficient of 4.02e-05-5.77e-06j was obtained for the the real part of |A 1-R2 coefficient of 7.44e-06+1.93e-06j was obtained for the the imaginary part\n", + "the correlation function. |of the correlation function. \n", + "The current fit took 2.052613 seconds. |The current fit took 1.619823 seconds. \n", + "\n" + ] + } + ], + "source": [ + "tlist4=np.linspace(0,20,1000)\n", + "espibath2,fitinfo=obs._approx_by_prony(\"espira-II\",tlist4,Nr=4,Ni=4)\n", + "print(fitinfo[\"summary\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "6c9c87ab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10.0%. Run time: 0.71s. Est. time left: 00:00:00:06\n", + "20.0%. Run time: 1.36s. Est. time left: 00:00:00:05\n", + "30.1%. Run time: 1.70s. Est. time left: 00:00:00:03\n", + "40.1%. Run time: 2.05s. Est. time left: 00:00:00:03\n", + "50.1%. Run time: 2.41s. Est. time left: 00:00:00:02\n", + "60.1%. Run time: 2.78s. Est. time left: 00:00:00:01\n", + "70.1%. Run time: 3.14s. Est. time left: 00:00:00:01\n", + "80.1%. Run time: 3.49s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 3.84s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 4.18s. Est. time left: 00:00:00:00\n", + "Total run time: 4.18s\n" + ] + } + ], + "source": [ + "HEOM_ohmic_espira_fit2 = HEOMSolver(\n", + " Hsys,\n", + " (espibath2,Q),\n", + " max_depth=max_depth,\n", + " options=options,\n", + ")\n", + "results_ohmic_espira2_fit = HEOM_ohmic_espira_fit2.run(rho0, tlist)" + ] + }, { "cell_type": "markdown", "id": "0f305b40", @@ -2223,13 +2279,13 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 69, "id": "5ba2889a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -2256,8 +2312,8 @@ " (results_ohmic_mp_fit, P11p, \"r\", \"Matrix Pencil Fit\"),\n", " (results_ohmic_es_fit, P11p, \"b-.\", \"ESPRIT Fit\"),\n", " (results_ohmic_aaa_fit, P11p, \"r-.\", \"Matrix AAA Fit\"),\n", - " (results_ohmic_espira_fit, P11p, \"k\", \"ESPIRA Fit\"),\n", - "\n", + " (results_ohmic_espira_fit, P11p, \"k\", \"ESPIRA I Fit\"),\n", + " (results_ohmic_espira2_fit, P11p, \"--\", \"ESPIRA II Fit\"),\n", "\n", " ],\n", " axes=axes,\n", @@ -2278,7 +2334,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 70, "id": "e1eb99ec", "metadata": {}, "outputs": [ @@ -2336,11 +2392,12 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 73, "id": "fa50ddbb", "metadata": {}, "outputs": [], "source": [ + "\n", "assert np.allclose(\n", " expect(P11p, results_spectral_fit_pk[2].states),\n", " expect(P11p, results_spectral_fit_pk[3].states),\n", @@ -2371,6 +2428,11 @@ " expect(P11p, results_ohmic_espira_fit.states),\n", " expect(P11p, results_spectral_fit_pk[3].states),\n", " rtol=1e-2,\n", + ")\n", + "assert np.allclose(\n", + " expect(P11p, results_ohmic_espira2_fit.states),\n", + " expect(P11p, results_spectral_fit_pk[3].states),\n", + " rtol=1e-2,\n", ")" ] } From 252539f3fb3edf5a01b2b2de99857a33913dffe9 Mon Sep 17 00:00:00 2001 From: mcditooss Date: Wed, 26 Feb 2025 18:52:02 +0100 Subject: [PATCH 19/44] test precommit hook --- .../heom/heom-1a-spin-bath-model-basic.ipynb | 1519 ---------- .../heom/heom-1a-spin-bath-model-basic.md | 2 +- ...spin-bath-model-very-strong-coupling.ipynb | 793 ------ ...1b-spin-bath-model-very-strong-coupling.md | 52 +- ...om-1c-spin-bath-model-underdamped-sd.ipynb | 778 ------ .../heom-1c-spin-bath-model-underdamped-sd.md | 52 +- ...eom-1d-spin-bath-model-ohmic-fitting.ipynb | 1740 ------------ .../heom-1d-spin-bath-model-ohmic-fitting.md | 6 +- ...om-1e-spin-bath-model-pure-dephasing.ipynb | 847 ------ .../heom-1e-spin-bath-model-pure-dephasing.md | 56 +- tutorials-v4/heom/heom-2-fmo-example.ipynb | 694 ----- tutorials-v4/heom/heom-2-fmo-example.md | 62 +- .../heom/heom-3-quantum-heat-transport.ipynb | 669 ----- .../heom/heom-3-quantum-heat-transport.md | 36 +- .../heom/heom-4-dynamical-decoupling.ipynb | 776 ------ .../heom/heom-4-dynamical-decoupling.md | 68 +- ...om-5a-fermions-single-impurity-model.ipynb | 827 ------ .../heom-5a-fermions-single-impurity-model.md | 62 +- ...eom-5b-fermions-discrete-boson-model.ipynb | 521 ---- .../heom-5b-fermions-discrete-boson-model.md | 42 +- tutorials-v4/heom/heom-index.ipynb | 56 - 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"cells": [ - { - "cell_type": "markdown", - "id": "e5a736fa", - "metadata": {}, - "source": [ - "# HEOM 1a: Spin-Bath model (introduction)" - ] - }, - { - "cell_type": "markdown", - "id": "4430f6e9", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "The HEOM method solves the dynamics and steady state of a system and its\n", - "environment, the latter of which is encoded in a set of auxiliary density\n", - "matrices.\n", - "\n", - "In this example we show the evolution of a single two-level system in contact\n", - "with a single Bosonic environment. The properties of the system are encoded\n", - "in a Hamiltonian, and a coupling operator which describes how it is coupled\n", - "to the environment.\n", - "\n", - "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", - "\n", - "In the example below we show how to model the overdamped Drude-Lorentz\n", - "Spectral Density, commonly used with the HEOM. We show how to do this using\n", - "the Matsubara, Pade and fitting decompositions, and compare their\n", - "convergence.\n", - "\n", - "### Drude-Lorentz (overdamped) spectral density\n", - "\n", - "The Drude-Lorentz spectral density is:\n", - "\n", - "$$J_D(\\omega)= \\frac{2\\omega\\lambda\\gamma}{{\\gamma}^2 + \\omega^2}$$\n", - "\n", - "where $\\lambda$ scales the coupling strength, and $\\gamma$ is the cut-off\n", - "frequency. We use the convention,\n", - "\\begin{equation*}\n", - "C(t) = \\int_0^{\\infty} d\\omega \\frac{J_D(\\omega)}{\\pi}[\\coth(\\beta\\omega) \\\n", - " \\cos(\\omega \\tau) - i \\sin(\\omega \\tau)]\n", - "\\end{equation*}\n", - "\n", - "With the HEOM we must use an exponential decomposition:\n", - "\n", - "\\begin{equation*}\n", - "C(t)=\\sum_{k=0}^{k=\\infty} c_k e^{-\\nu_k t}\n", - "\\end{equation*}\n", - "\n", - "As an example, the Matsubara decomposition of the Drude-Lorentz spectral\n", - "density is given by:\n", - "\n", - "\\begin{equation*}\n", - " \\nu_k = \\begin{cases}\n", - " \\gamma & k = 0\\\\\n", - " {2 \\pi k} / {\\beta } & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "\\begin{equation*}\n", - " c_k = \\begin{cases}\n", - " \\lambda \\gamma (\\cot(\\beta \\gamma / 2) - i) \\\n", - " & k = 0\\\\\n", - " 4 \\lambda \\gamma \\nu_k / \\{(nu_k^2 - \\gamma^2)\\beta \\} \\\n", - " & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." - ] - }, - { - "cell_type": "markdown", - "id": "7521b1ac", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5e863e62", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "import contextlib\n", - "import time\n", - "\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "from scipy.optimize import curve_fit\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " Options,\n", - " basis,\n", - " brmesolve,\n", - " destroy,\n", - " expect,\n", - " liouvillian,\n", - " qeye,\n", - " sigmax,\n", - " sigmaz,\n", - " spost,\n", - " spre,\n", - " tensor,\n", - ")\n", - "\n", - "from qutip.nonmarkov.heom import (\n", - " BosonicBath,\n", - " DrudeLorentzBath,\n", - " DrudeLorentzPadeBath,\n", - " HEOMSolver,\n", - " HSolverDL,\n", - ")\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "0de1b3a3", - "metadata": {}, - "source": [ - "## Helper functions\n", - "\n", - "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e89b6553", - "metadata": {}, - "outputs": [], - "source": [ - "def cot(x):\n", - " \"\"\"Vectorized cotangent of x.\"\"\"\n", - " return 1.0 / np.tan(x)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ecb6601d", - "metadata": {}, - "outputs": [], - "source": [ - "def dl_matsubara_params(lam, gamma, T, nk):\n", - " \"\"\"Calculation of the real and imaginary expansions of the Drude-Lorenz\n", - " correlation functions.\n", - " \"\"\"\n", - " ckAR = [lam * gamma * cot(gamma / (2 * T))]\n", - " ckAR.extend(\n", - " 4\n", - " * lam\n", - " * gamma\n", - " * T\n", - " * 2\n", - " * np.pi\n", - " * k\n", - " * T\n", - " / ((2 * np.pi * k * T) ** 2 - gamma**2)\n", - " for k in range(1, nk + 1)\n", - " )\n", - " vkAR = [gamma]\n", - " vkAR.extend(2 * np.pi * k * T for k in range(1, nk + 1))\n", - "\n", - " ckAI = [lam * gamma * (-1.0)]\n", - " vkAI = [gamma]\n", - "\n", - " return ckAR, vkAR, ckAI, vkAI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9bb61e3c", - "metadata": {}, - "outputs": [], - "source": [ - "def dl_corr_approx(t, nk):\n", - " \"\"\"Drude-Lorenz correlation function approximation.\n", - "\n", - " Approximates the correlation function at each time t to nk exponents.\n", - " \"\"\"\n", - " c = lam * gamma * (-1.0j + cot(gamma / (2 * T))) * np.exp(-gamma * t)\n", - " for k in range(1, nk):\n", - " vk = 2 * np.pi * k * T\n", - " c += (4 * lam * gamma * T * vk / (vk**2 - gamma**2)) * np.exp(\n", - " -vk * t\n", - " )\n", - " return c" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "799e4a70", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_result_expectations(plots, axes=None):\n", - " \"\"\"Plot the expectation values of operators as functions of time.\n", - "\n", - " Each plot in plots consists of (solver_result, measurement_operation,\n", - " color, label).\n", - " \"\"\"\n", - " if axes is None:\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " fig_created = True\n", - " else:\n", - " fig = None\n", - " fig_created = False\n", - "\n", - " # add kw arguments to each plot if missing\n", - " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", - " for result, m_op, color, label, kw in plots:\n", - " exp = np.real(expect(result.states, m_op))\n", - " kw.setdefault(\"linewidth\", 2)\n", - " axes.plot(result.times, exp, color, label=label, **kw)\n", - "\n", - " if fig_created:\n", - " axes.legend(loc=0, fontsize=12)\n", - " axes.set_xlabel(\"t\", fontsize=28)\n", - "\n", - " return fig" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "56abea6b", - "metadata": {}, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\"Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "markdown", - "id": "39df0afe", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "And let us set up the system Hamiltonian, bath and system measurement operators:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "537d0beb", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the system Hamiltonian\n", - "eps = 0.5 # Energy of the 2-level system.\n", - "Del = 1.0 # Tunnelling term\n", - "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "107a63de", - "metadata": {}, - "outputs": [], - "source": [ - "# Initial state of the system.\n", - "rho0 = basis(2, 0) * basis(2, 0).dag()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5989ca9b", - "metadata": {}, - "outputs": [], - "source": [ - "# System-bath coupling (Drude-Lorentz spectral density)\n", - "Q = sigmaz() # coupling operator\n", - "\n", - "# Bath properties:\n", - "gamma = 0.5 # cut off frequency\n", - "lam = 0.1 # coupling strength\n", - "T = 0.5\n", - "beta = 1.0 / T\n", - "\n", - "# HEOM parameters\n", - "NC = 5 # cut off parameter for the bath\n", - "Nk = 2 # terms in the Matsubara expansion of the correlation function\n", - "\n", - "# Times to solve for\n", - "tlist = np.linspace(0, 50, 1000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bd300ad7", - "metadata": {}, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresonding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresonding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "5f312989", - "metadata": {}, - "source": [ - "### First of all, it is useful to look at the spectral density\n", - "\n", - "Now we are ready to begin. Let's look at the shape of the spectral density given the bath parameters we defined above:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e28f8220", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_spectral_density():\n", - " \"\"\"Plot the Drude-Lorentz spectral density\"\"\"\n", - " w = np.linspace(0, 5, 1000)\n", - " J = w * 2 * lam * gamma / (gamma**2 + w**2)\n", - "\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " axes.plot(w, J, \"r\", linewidth=2)\n", - " axes.set_xlabel(r\"$\\omega$\", fontsize=28)\n", - " axes.set_ylabel(r\"J\", fontsize=28)\n", - "\n", - "\n", - "plot_spectral_density()" - ] - }, - { - "cell_type": "markdown", - "id": "1b475c22", - "metadata": {}, - "source": [ - "Next we calculate the exponents using the Matsubara decompositions. Here we\n", - "split them into real and imaginary parts.\n", - "\n", - "The HEOM code will optimize these, and reduce the number of exponents when\n", - "real and imaginary parts have the same exponent. This is clearly the case\n", - "for the first term in the vkAI and vkAR lists." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "457da72a", - "metadata": {}, - "outputs": [], - "source": [ - "ckAR, vkAR, ckAI, vkAI = dl_matsubara_params(nk=Nk, lam=lam, gamma=gamma, T=T)" - ] - }, - { - "cell_type": "markdown", - "id": "a4011546", - "metadata": {}, - "source": [ - "Having created the lists which specify the bath correlation functions, we\n", - "create a `BosonicBath` from them and pass the bath to the `HEOMSolver` class.\n", - "\n", - "The solver constructs the \"right hand side\" (RHS) determinining how the\n", - "system and auxiliary density operators evolve in time. This can then be used\n", - "to solve for dynamics or steady-state.\n", - "\n", - "Below we create the bath and solver and then solve for the dynamics by\n", - "calling `.run(rho0, tlist)`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d1c570e2", - "metadata": {}, - "outputs": [], - "source": [ - "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI)\n", - " HEOMMats = HEOMSolver(Hsys, bath, NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMats = HEOMMats.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f50f3f07", - "metadata": {}, - "outputs": [], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (resultMats, P11p, \"b\", \"P11 Mats\"),\n", - " (resultMats, P12p, \"r\", \"P12 Mats\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "27dddd4e", - "metadata": {}, - "source": [ - "In practice, one would not perform this laborious expansion for the\n", - "Drude-Lorentz correlation function, because QuTiP already has a class,\n", - "`DrudeLorentzBath`, that can construct this bath for you. Nevertheless,\n", - "knowing how to perform this expansion will allow you to construct your own\n", - "baths for other spectral densities.\n", - "\n", - "Below we show how to use this built-in functionality:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "73aa8c51", - "metadata": {}, - "outputs": [], - "source": [ - "# Compare to built-in Drude-Lorentz bath:\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", - " HEOM_dlbath = HEOMSolver(Hsys, bath, NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " result_dlbath = HEOM_dlbath.run(rho0, tlist) # normal 115" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c4add1a5", - "metadata": {}, - "outputs": [], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (result_dlbath, P11p, \"b\", \"P11 (DrudeLorentzBath)\"),\n", - " (result_dlbath, P12p, \"r\", \"P12 (DrudeLorentzBath)\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "44099dc6", - "metadata": {}, - "source": [ - "We also provide a legacy class, `HSolverDL`, which calculates the\n", - "Drude-Lorentz correlation functions automatically, to be backwards\n", - "compatible with the previous HEOM solver in QuTiP:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "234ad587", - "metadata": {}, - "outputs": [], - "source": [ - "# Compare to legacy class:\n", - "\n", - "# The legacy class performs the above collation of coefficients automatically,\n", - "# based upon the parameters for the Drude-Lorentz spectral density.\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " HEOMlegacy = HSolverDL(Hsys, Q, lam, T, NC, Nk, gamma, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultLegacy = HEOMlegacy.run(rho0, tlist) # normal 115" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ab77f8bf", - "metadata": {}, - "outputs": [], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (resultLegacy, P11p, \"b\", \"P11 Legacy\"),\n", - " (resultLegacy, P12p, \"r\", \"P12 Legacy\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "30d16d29", - "metadata": {}, - "source": [ - "## Ishizaki-Tanimura Terminator\n", - "\n", - "To speed up convergence (in terms of the number of exponents kept in the\n", - "Matsubara decomposition), We can treat the $Re[C(t=0)]$ component as a\n", - "delta-function distribution, and include it as Lindblad correction. This is\n", - "sometimes known as the Ishizaki-Tanimura Terminator.\n", - "\n", - "In more detail, given\n", - "\n", - "\\begin{equation*}\n", - "C(t)=\\sum_{k=0}^{\\infty} c_k e^{-\\nu_k t}\n", - "\\end{equation*}\n", - "\n", - "since $\\nu_k=\\frac{2 \\pi k}{\\beta }$, if $1/\\nu_k$ is much much smaller than\n", - "other important time-scales, we can approximate,\n", - "$ e^{-\\nu_k t} \\approx \\delta(t)/\\nu_k$, and $C(t)=\\sum_{k=N_k}^{\\infty}\n", - "\\frac{c_k}{\\nu_k} \\delta(t)$\n", - "\n", - "It is convenient to calculate the whole sum\n", - "$ C(t)=\\sum_{k=0}^{\\infty} \\frac{c_k}{\\nu_k} = 2 \\lambda / (\\beta \\gamma)\n", - "- i\\lambda $\n", - ", and subtract off the contribution from the finite number of Matsubara terms\n", - "that are kept in the hierarchy, and treat the residual as a Lindblad.\n", - "\n", - "This is clearer if we plot the correlation function with a large number of\n", - "Matsubara terms:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7f71056b", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_correlation_expansion_divergence():\n", - " \"\"\"We plot the correlation function with a large number of Matsubara terms\n", - " to show that the real part is slowly diverging at t = 0.\n", - " \"\"\"\n", - " t = np.linspace(0, 2, 100)\n", - "\n", - " # correlation coefficients with 15k and 2 terms\n", - " corr_15k = dl_corr_approx(t, 15_000)\n", - " corr_2 = dl_corr_approx(t, 2)\n", - "\n", - " fig, ax1 = plt.subplots(figsize=(12, 7))\n", - "\n", - " ax1.plot(\n", - " t, np.real(corr_2), color=\"b\", linewidth=3, label=r\"Mats = 2 real\"\n", - " )\n", - " ax1.plot(\n", - " t, np.imag(corr_2), color=\"r\", linewidth=3, label=r\"Mats = 2 imag\"\n", - " )\n", - " ax1.plot(\n", - " t, np.real(corr_15k), \"b--\", linewidth=3, label=r\"Mats = 15000 real\"\n", - " )\n", - " ax1.plot(\n", - " t, np.imag(corr_15k), \"r--\", linewidth=3, label=r\"Mats = 15000 imag\"\n", - " )\n", - "\n", - " ax1.set_xlabel(\"t\")\n", - " ax1.set_ylabel(r\"$C$\")\n", - " ax1.legend()\n", - "\n", - "\n", - "plot_correlation_expansion_divergence();" - ] - }, - { - "cell_type": "markdown", - "id": "1cbb1115", - "metadata": {}, - "source": [ - "Let us evaluate the result including this Ishizaki-Tanimura terminator:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e3b1bd7a", - "metadata": {}, - "outputs": [], - "source": [ - "# Run HEOM solver and include the Ishizaki-Tanimura terminator\n", - "\n", - "# Notes:\n", - "#\n", - "# * when using the built-in DrudeLorentzBath, the terminator (L_bnd) is\n", - "# available from bath.terminator().\n", - "#\n", - "# * in the legacy HSolverDL function the terminator is included automatically\n", - "# if the parameter bnd_cut_approx=True is used.\n", - "\n", - "op = -2 * spre(Q) * spost(Q.dag()) + spre(Q.dag() * Q) + spost(Q.dag() * Q)\n", - "\n", - "approx_factr = (2 * lam / (beta * gamma)) - 1j * lam\n", - "\n", - "approx_factr -= lam * gamma * (-1.0j + cot(gamma / (2 * T))) / gamma\n", - "for k in range(1, Nk + 1):\n", - " vk = 2 * np.pi * k * T\n", - "\n", - " approx_factr -= (4 * lam * gamma * T * vk / (vk**2 - gamma**2)) / vk\n", - "\n", - "L_bnd = -approx_factr * op\n", - "\n", - "Ltot = -1.0j * (spre(Hsys) - spost(Hsys)) + L_bnd\n", - "Ltot = liouvillian(Hsys) + L_bnd\n", - "\n", - "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI)\n", - " HEOMMatsT = HEOMSolver(Ltot, bath, NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMatsT = HEOMMatsT.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d89912ca", - "metadata": {}, - "outputs": [], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (resultMatsT, P11p, \"b\", \"P11 Mats + Term\"),\n", - " (resultMatsT, P12p, \"r\", \"P12 Mats + Term\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "18258c34", - "metadata": {}, - "source": [ - "Or using the built-in Drude-Lorentz bath we can write simply:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0bff4932", - "metadata": {}, - "outputs": [], - "source": [ - "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", - " _, terminator = bath.terminator()\n", - " Ltot = liouvillian(Hsys) + terminator\n", - " HEOM_dlbath_T = HEOMSolver(Ltot, bath, NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " result_dlbath_T = HEOM_dlbath_T.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "77f0d98b", - "metadata": {}, - "outputs": [], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (result_dlbath_T, P11p, \"b\", \"P11 Mats (DrudeLorentzBath + Term)\"),\n", - " (result_dlbath_T, P12p, \"r\", \"P12 Mats (DrudeLorentzBath + Term)\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "89ef41ed", - "metadata": {}, - "source": [ - "We can compare the solution obtained from the QuTiP Bloch-Redfield solver:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ea3c4b47", - "metadata": {}, - "outputs": [], - "source": [ - "DL = (\n", - " f\"2*pi* 2.0 * {lam} / (pi * {gamma} * {beta}) if (w == 0) else \"\n", - " f\"2*pi*(2.0*{lam}*{gamma} *w /(pi*(w**2+{gamma}**2))) \"\n", - " f\"* ((1/(exp((w) * {beta})-1))+1)\"\n", - ")\n", - "options = Options(nsteps=15000, store_states=True, rtol=1e-12, atol=1e-12)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultBR = brmesolve(\n", - " Hsys, rho0, tlist, a_ops=[[sigmaz(), DL]], options=options\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "12787b42", - "metadata": {}, - "outputs": [], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (resultMats, P11p, \"b\", \"P11 Mats\"),\n", - " (resultMats, P12p, \"r\", \"P12 Mats\"),\n", - " (resultMatsT, P11p, \"b--\", \"P11 Mats + Term\"),\n", - " (resultMatsT, P12p, \"r--\", \"P12 Mats + Term\"),\n", - " (resultBR, P11p, \"g--\", \"P11 Bloch Redfield\"),\n", - " (resultBR, P12p, \"g--\", \"P12 Bloch Redfield\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "051432e7", - "metadata": {}, - "source": [ - "## Padé decomposition" - ] - }, - { - "cell_type": "markdown", - "id": "e559173f", - "metadata": {}, - "source": [ - "The Matsubara decomposition is not the only option. We can also use the\n", - "faster-converging Pade decomposition." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fc30ccd1", - "metadata": {}, - "outputs": [], - "source": [ - "def deltafun(j, k):\n", - " if j == k:\n", - " return 1.0\n", - " else:\n", - " return 0.0\n", - "\n", - "\n", - "def pade_eps(lmax):\n", - " Alpha = np.zeros((2 * lmax, 2 * lmax))\n", - " for j in range(2 * lmax):\n", - " for k in range(2 * lmax):\n", - " # Fermionic (see other example notebooks):\n", - " # Alpha[j][k] = (deltafun(j, k+1) + deltafun(j, k-1))\n", - " # / sqrt((2 * (j + 1) - 1) * (2 * (k + 1) - 1))\n", - " # Bosonic:\n", - " Alpha[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1)) / np.sqrt(\n", - " (2 * (j + 1) + 1) * (2 * (k + 1) + 1)\n", - " )\n", - "\n", - " eigvalsA = np.linalg.eigvalsh(Alpha)\n", - " eps = [-2 / val for val in eigvalsA[0:lmax]]\n", - " return eps\n", - "\n", - "\n", - "def pade_chi(lmax):\n", - " AlphaP = np.zeros((2 * lmax - 1, 2 * lmax - 1))\n", - " for j in range(2 * lmax - 1):\n", - " for k in range(2 * lmax - 1):\n", - " # Fermionic:\n", - " # AlphaP[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1))\n", - " # / sqrt((2 * (j + 1) + 1) * (2 * (k + 1) + 1))\n", - " # Bosonic [this is +3 because +1 (bose) + 2*(+1) (from bm+1)]:\n", - " AlphaP[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1)) / np.sqrt(\n", - " (2 * (j + 1) + 3) * (2 * (k + 1) + 3)\n", - " )\n", - "\n", - " eigvalsAP = np.linalg.eigvalsh(AlphaP)\n", - " chi = [-2 / val for val in eigvalsAP[0:lmax - 1]]\n", - " return chi\n", - "\n", - "\n", - "def pade_kappa_epsilon(lmax):\n", - " eps = pade_eps(lmax)\n", - " chi = pade_chi(lmax)\n", - "\n", - " kappa = [0]\n", - " prefactor = 0.5 * lmax * (2 * (lmax + 1) + 1)\n", - "\n", - " for j in range(lmax):\n", - " term = prefactor\n", - " for k in range(lmax - 1):\n", - " term *= (chi[k] ** 2 - eps[j] ** 2) / (\n", - " eps[k] ** 2 - eps[j] ** 2 + deltafun(j, k)\n", - " )\n", - "\n", - " for k in range(lmax - 1, lmax):\n", - " term /= eps[k] ** 2 - eps[j] ** 2 + deltafun(j, k)\n", - "\n", - " kappa.append(term)\n", - "\n", - " epsilon = [0] + eps\n", - "\n", - " return kappa, epsilon\n", - "\n", - "\n", - "def pade_corr(tlist, lmax):\n", - " kappa, epsilon = pade_kappa_epsilon(lmax)\n", - "\n", - " eta_list = [lam * gamma * (cot(gamma * beta / 2.0) - 1.0j)]\n", - " gamma_list = [gamma]\n", - "\n", - " if lmax > 0:\n", - " for ll in range(1, lmax + 1):\n", - " eta_list.append(\n", - " (kappa[ll] / beta)\n", - " * 4\n", - " * lam\n", - " * gamma\n", - " * (epsilon[ll] / beta)\n", - " / ((epsilon[ll] ** 2 / beta**2) - gamma**2)\n", - " )\n", - " gamma_list.append(epsilon[ll] / beta)\n", - "\n", - " c_tot = []\n", - " for t in tlist:\n", - " c_tot.append(\n", - " sum(\n", - " [\n", - " eta_list[ll] * np.exp(-gamma_list[ll] * t)\n", - " for ll in range(lmax + 1)\n", - " ]\n", - " )\n", - " )\n", - " return c_tot, eta_list, gamma_list\n", - "\n", - "\n", - "tlist_corr = np.linspace(0, 2, 100)\n", - "cppLP, etapLP, gampLP = pade_corr(tlist_corr, 2)\n", - "corr_15k = dl_corr_approx(tlist_corr, 15_000)\n", - "corr_2k = dl_corr_approx(tlist_corr, 2)\n", - "\n", - "fig, ax1 = plt.subplots(figsize=(12, 7))\n", - "ax1.plot(\n", - " tlist_corr,\n", - " np.real(cppLP),\n", - " color=\"b\",\n", - " linewidth=3,\n", - " label=r\"real pade 2 terms\",\n", - ")\n", - "ax1.plot(\n", - " tlist_corr,\n", - " np.real(corr_15k),\n", - " \"r--\",\n", - " linewidth=3,\n", - " label=r\"real mats 15000 terms\",\n", - ")\n", - "ax1.plot(\n", - " tlist_corr,\n", - " np.real(corr_2k),\n", - " \"g--\",\n", - " linewidth=3,\n", - " label=r\"real mats 2 terms\",\n", - ")\n", - "\n", - "ax1.set_xlabel(\"t\")\n", - "ax1.set_ylabel(r\"$C$\")\n", - "ax1.legend()\n", - "\n", - "fig, ax1 = plt.subplots(figsize=(12, 7))\n", - "\n", - "ax1.plot(\n", - " tlist_corr,\n", - " np.real(cppLP) - np.real(corr_15k),\n", - " color=\"b\",\n", - " linewidth=3,\n", - " label=r\"pade error\",\n", - ")\n", - "ax1.plot(\n", - " tlist_corr,\n", - " np.real(corr_2k) - np.real(corr_15k),\n", - " \"r--\",\n", - " linewidth=3,\n", - " label=r\"mats error\",\n", - ")\n", - "\n", - "ax1.set_xlabel(\"t\")\n", - "ax1.set_ylabel(r\"Error\")\n", - "ax1.legend();" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "85ed2c5a", - "metadata": {}, - "outputs": [], - "source": [ - "# put pade parameters in lists for heom solver\n", - "ckAR = [np.real(eta) + 0j for eta in etapLP]\n", - "ckAI = [np.imag(etapLP[0]) + 0j]\n", - "vkAR = [gam + 0j for gam in gampLP]\n", - "vkAI = [gampLP[0] + 0j]\n", - "\n", - "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI)\n", - " HEOMPade = HEOMSolver(Hsys, bath, NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultPade = HEOMPade.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "84ac5b4c", - "metadata": {}, - "outputs": [], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (resultMats, P11p, \"b\", \"P11 Mats\"),\n", - " (resultMats, P12p, \"r\", \"P12 Mats\"),\n", - " (resultMatsT, P11p, \"y\", \"P11 Mats + Term\"),\n", - " (resultMatsT, P12p, \"g\", \"P12 Mats + Term\"),\n", - " (resultPade, P11p, \"b--\", \"P11 Pade\"),\n", - " (resultPade, P12p, \"r--\", \"P12 Pade\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "11c18d1c", - "metadata": {}, - "source": [ - "The Padé decomposition of the Drude-Lorentz bath is also available via a\n", - "built-in class, `DrudeLorentzPadeBath` bath. Like `DrudeLorentzBath`, one\n", - "can obtain the terminator by calling `bath.terminator()`.\n", - "\n", - "Below we show how to use the built-in Padé Drude-Lorentz bath and its\n", - "terminator (although the terminator does not provide much improvement here,\n", - "because the Padé expansion already fits the correlation function well):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "10ef00b0", - "metadata": {}, - "outputs": [], - "source": [ - "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", - " _, terminator = bath.terminator()\n", - " Ltot = liouvillian(Hsys) + terminator\n", - " HEOM_dlpbath_T = HEOMSolver(Ltot, bath, NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " result_dlpbath_T = HEOM_dlpbath_T.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ff24edba", - "metadata": {}, - "outputs": [], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (result_dlpbath_T, P11p, \"b\", \"P11 Padé (DrudeLorentzBath + Term)\"),\n", - " (result_dlpbath_T, P12p, \"r\", \"P12 Padé (DrudeLorentzBath + Term)\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "3104cb81", - "metadata": {}, - "source": [ - "### Next we compare the Matsubara and Pade correlation function fits\n", - "\n", - "This is not efficient for this example, but can be extremely useful in\n", - "situations where large number of exponents are needed (e.g., near zero\n", - "temperature).\n", - "\n", - "First we collect a large sum of Matsubara terms for many time steps:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d02e022", - "metadata": {}, - "outputs": [], - "source": [ - "tlist2 = np.linspace(0, 2, 10000)\n", - "\n", - "corr_15k_t10k = dl_corr_approx(tlist2, 15_000)\n", - "\n", - "corrRana = np.real(corr_15k_t10k)\n", - "corrIana = np.imag(corr_15k_t10k)" - ] - }, - { - "cell_type": "markdown", - "id": "09c83ba5", - "metadata": {}, - "source": [ - "We then fit this sum with standard least-squares approach:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a528eb0c", - "metadata": {}, - "outputs": [], - "source": [ - "def wrapper_fit_func(x, N, args):\n", - " \"\"\" Fit function wrapper that unpacks its arguments. \"\"\"\n", - " x = np.array(x)\n", - " a = np.array(args[:N])\n", - " b = np.array(args[N:2 * N])\n", - " return fit_func(x, a, b)\n", - "\n", - "\n", - "def fit_func(x, a, b):\n", - " \"\"\" Fit function. Calculates the value of the\n", - " correlation function at each x, given the\n", - " fit parameters in a and b.\n", - " \"\"\"\n", - " return np.sum(\n", - " a[:, None] * np.exp(np.multiply.outer(b, x)),\n", - " axis=0,\n", - " )\n", - "\n", - "\n", - "def fitter(ans, tlist, k):\n", - " \"\"\" Compute fit with k exponents. \"\"\"\n", - " upper_a = abs(max(ans, key=abs)) * 10\n", - " # sets initial guesses:\n", - " guess = (\n", - " [ans[0] / k] * k + # guesses for a\n", - " [0] * k # guesses for b\n", - " )\n", - " # sets lower bounds:\n", - " b_lower = (\n", - " [-upper_a] * k + # lower bounds for a\n", - " [-np.inf] * k # lower bounds for b\n", - " )\n", - " # sets higher bounds:\n", - " b_higher = (\n", - " [upper_a] * k + # upper bounds for a\n", - " [0] * k # upper bounds for b\n", - " )\n", - " param_bounds = (b_lower, b_higher)\n", - " p1, p2 = curve_fit(\n", - " lambda x, *params_0: wrapper_fit_func(x, k, params_0),\n", - " tlist,\n", - " ans,\n", - " p0=guess,\n", - " sigma=[0.01 for t in tlist],\n", - " bounds=param_bounds,\n", - " maxfev=1e8,\n", - " )\n", - " a, b = p1[:k], p1[k:]\n", - " return (a, b)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "96669348", - "metadata": {}, - "outputs": [], - "source": [ - "kR = 4 # number of exponents to use for real part\n", - "poptR = []\n", - "with timer(\"Correlation (real) fitting time\"):\n", - " for i in range(kR):\n", - " poptR.append(fitter(corrRana, tlist2, i + 1))\n", - "\n", - "corrRMats = np.real(dl_corr_approx(tlist2, Nk))\n", - "\n", - "kI = 1 # number of exponents for imaginary part\n", - "poptI = []\n", - "with timer(\"Correlation (imaginary) fitting time\"):\n", - " for i in range(kI):\n", - " poptI.append(fitter(corrIana, tlist2, i + 1))" - ] - }, - { - "cell_type": "markdown", - "id": "2336899e", - "metadata": {}, - "source": [ - "And plot the results of the fits:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ed35ce3b", - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(tlist2, corrRana, label=\"Analytic\")\n", - "plt.plot(tlist2, corrRMats, label=\"Matsubara\")\n", - "\n", - "for i in range(kR):\n", - " y = fit_func(tlist2, *poptR[i])\n", - " plt.plot(tlist2, y, label=f\"Fit with {i} terms\")\n", - "\n", - "plt.title(\"Fit to correlations (real part)\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "156850bc", - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(tlist2, corrIana, label=\"Analytic\")\n", - "\n", - "for i in range(kI):\n", - " y = fit_func(tlist2, *poptI[i])\n", - " plt.plot(tlist2, y, label=f\"Fit with {i} terms\")\n", - "\n", - "plt.title(\"Fit to correlations (imaginary part)\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a8ddafb1", - "metadata": {}, - "outputs": [], - "source": [ - "# Set the exponential coefficients from the fit parameters\n", - "\n", - "ckAR1 = poptR[-1][0]\n", - "ckAR = [x + 0j for x in ckAR1]\n", - "\n", - "vkAR1 = poptR[-1][1]\n", - "vkAR = [-x + 0j for x in vkAR1]\n", - "\n", - "ckAI1 = poptI[-1][0]\n", - "ckAI = [x + 0j for x in ckAI1]\n", - "\n", - "vkAI1 = poptI[-1][1]\n", - "vkAI = [-x + 0j for x in vkAI1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ba436768", - "metadata": {}, - "outputs": [], - "source": [ - "# overwrite imaginary fit with analytical value (not much reason to use the\n", - "# fit for this)\n", - "\n", - "ckAI = [lam * gamma * (-1.0) + 0.0j]\n", - "vkAI = [gamma + 0.0j]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e89ad561", - "metadata": {}, - "outputs": [], - "source": [ - "# The BDF ODE solver method here is faster because we have a slightly\n", - "# stiff problem. We set NC=4 to reduce the run time while retaining\n", - "# reasonable convergence.\n", - "\n", - "options = Options(\n", - " nsteps=1500, store_states=True, rtol=1e-12, atol=1e-12, method=\"bdf\"\n", - ")\n", - "\n", - "NC = 4\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI)\n", - " HEOMFit = HEOMSolver(Hsys, bath, NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultFit = HEOMFit.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5645d316", - "metadata": {}, - "outputs": [], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (resultFit, P11p, \"b\", \"P11 Fit\"),\n", - " (resultFit, P12p, \"r\", \"P12 Fit\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "435db73c", - "metadata": {}, - "source": [ - "## A reaction coordinate approach" - ] - }, - { - "cell_type": "markdown", - "id": "80e5a29a", - "metadata": {}, - "source": [ - "Here we construct a reaction coordinate inspired model to capture the\n", - "steady-state behavior, and compare to the HEOM prediction. This result is\n", - "more accurate for narrow spectral densities. We will use the population and\n", - "coherence from this cell in our final plot below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b651bfba", - "metadata": {}, - "outputs": [], - "source": [ - "dot_energy, dot_state = Hsys.eigenstates()\n", - "deltaE = dot_energy[1] - dot_energy[0]\n", - "\n", - "gamma2 = deltaE / (2 * np.pi * gamma)\n", - "wa = 2 * np.pi * gamma2 * gamma # reaction coordinate frequency\n", - "g = np.sqrt(np.pi * wa * lam / 2.0) # reaction coordinate coupling\n", - "# reaction coordinate coupling factor over 2 because of diff in J(w)\n", - "# (it is 2 lam now):\n", - "g = np.sqrt(\n", - " np.pi * wa * lam / 4.0\n", - ") #\n", - "\n", - "NRC = 10\n", - "\n", - "Hsys_exp = tensor(qeye(NRC), Hsys)\n", - "Q_exp = tensor(qeye(NRC), Q)\n", - "a = tensor(destroy(NRC), qeye(2))\n", - "\n", - "H0 = wa * a.dag() * a + Hsys_exp\n", - "# interaction\n", - "H1 = g * (a.dag() + a) * Q_exp\n", - "\n", - "H = H0 + H1\n", - "\n", - "energies, states = H.eigenstates()\n", - "rhoss = 0 * states[0] * states[0].dag()\n", - "for kk, energ in enumerate(energies):\n", - " rhoss += states[kk] * states[kk].dag() * np.exp(-beta * energies[kk])\n", - "\n", - "rhoss = rhoss / rhoss.norm()\n", - "\n", - "\n", - "class ReactionCoordinateResult:\n", - " def __init__(self, states, times):\n", - " self.states = states\n", - " self.times = times\n", - "\n", - "\n", - "resultRC = ReactionCoordinateResult([rhoss] * len(tlist), tlist)\n", - "\n", - "P12RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 1).dag())\n", - "P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag())" - ] - }, - { - "cell_type": "markdown", - "id": "ef765257", - "metadata": {}, - "source": [ - "## Let's plot all our results\n", - "\n", - "Finally, let's plot all of our different results to see how they shape up against each other." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7ee45371", - "metadata": {}, - "outputs": [], - "source": [ - "rcParams = {\n", - " \"axes.titlesize\": 25,\n", - " \"axes.labelsize\": 30,\n", - " \"xtick.labelsize\": 28,\n", - " \"ytick.labelsize\": 28,\n", - " \"legend.fontsize\": 28,\n", - " \"axes.grid\": False,\n", - " \"savefig.bbox\": \"tight\",\n", - " \"lines.markersize\": 5,\n", - " \"font.family\": \"STIXgeneral\",\n", - " \"mathtext.fontset\": \"stix\",\n", - " \"font.serif\": \"STIX\",\n", - " \"text.usetex\": False,\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dbde13fc", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 15))\n", - "\n", - "with plt.rc_context(rcParams):\n", - "\n", - " plt.sca(axes[0])\n", - " plt.yticks([expect(P11RC, resultRC.states[0]), 0.6, 1.0], [0.32, 0.6, 1])\n", - " plot_result_expectations(\n", - " [\n", - " (resultBR, P11p, \"y-.\", \"Bloch-Redfield\"),\n", - " (resultMats, P11p, \"b\", \"Matsubara $N_k=2$\"),\n", - " (\n", - " resultMatsT,\n", - " P11p,\n", - " \"g--\",\n", - " \"Matsubara $N_k=2$ & Terminator\",\n", - " {\"linewidth\": 3},\n", - " ),\n", - " (\n", - " resultFit,\n", - " P11p,\n", - " \"r\",\n", - " r\"Fit $N_f = 4$, $N_k=15\\times 10^3$\",\n", - " {\"dashes\": [3, 2]},\n", - " ),\n", - " (\n", - " resultRC,\n", - " P11RC,\n", - " \"--\", \"Thermal\",\n", - " {\"linewidth\": 2, \"color\": \"black\"},\n", - " ),\n", - " ],\n", - " axes=axes[0],\n", - " )\n", - " axes[0].set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", - " axes[0].legend(loc=0)\n", - " axes[0].text(5, 0.9, \"(a)\", fontsize=30)\n", - " axes[0].set_xlim(0, 50)\n", - "\n", - " plt.sca(axes[1])\n", - " plt.yticks(\n", - " [np.real(expect(P12RC, resultRC.states[0])), -0.2, 0.0, 0.2],\n", - " [-0.33, -0.2, 0, 0.2],\n", - " )\n", - " plot_result_expectations(\n", - " [\n", - " (resultBR, P12p, \"y-.\", \"Bloch-Redfield\"),\n", - " (resultMats, P12p, \"b\", \"Matsubara $N_k=2$\"),\n", - " (\n", - " resultMatsT,\n", - " P12p,\n", - " \"g--\",\n", - " \"Matsubara $N_k=2$ & Terminator\",\n", - " {\"linewidth\": 3},\n", - " ),\n", - " (\n", - " resultFit,\n", - " P12p,\n", - " \"r\",\n", - " r\"Fit $N_f = 4$, $N_k=15\\times 10^3$\",\n", - " {\"dashes\": [3, 2]},\n", - " ),\n", - " (\n", - " resultRC,\n", - " P12RC,\n", - " \"--\",\n", - " \"Thermal\",\n", - " {\"linewidth\": 2, \"color\": \"black\"},\n", - " ),\n", - " ],\n", - " axes=axes[1],\n", - " )\n", - " axes[1].text(5, 0.1, \"(b)\", fontsize=30)\n", - " axes[1].set_xlabel(r\"$t \\Delta$\", fontsize=30)\n", - " axes[1].set_ylabel(r\"$\\rho_{01}$\", fontsize=30)\n", - " axes[1].set_xlim(0, 50)" - ] - }, - { - "cell_type": "markdown", - "id": "4e19d50f", - "metadata": {}, - "source": [ - "And that's the end of a detailed first dive into modeling bosonic environments with the HEOM." - ] - }, - { - "cell_type": "markdown", - "id": "7b13ab26", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a705e8f", - "metadata": {}, - "outputs": [], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "c52f8be0", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ce9c47c9", - "metadata": {}, - "outputs": [], - "source": [ - "# Check P11p\n", - "assert np.allclose(\n", - " expect(P11p, resultMatsT.states),\n", - " expect(P11p, resultPade.states),\n", - " rtol=1e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(P11p, resultMatsT.states),\n", - " expect(P11p, resultFit.states),\n", - " rtol=1e-2,\n", - ")\n", - "\n", - "# Check P12p\n", - "assert np.allclose(\n", - " expect(P12p, resultMatsT.states),\n", - " expect(P12p, resultPade.states),\n", - " rtol=1e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(P12p, resultMatsT.states),\n", - " expect(P12p, resultFit.states),\n", - " rtol=1e-1,\n", - ")" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v4/heom/heom-1a-spin-bath-model-basic.md b/tutorials-v4/heom/heom-1a-spin-bath-model-basic.md index 5bd42149..d0f90968 100644 --- a/tutorials-v4/heom/heom-1a-spin-bath-model-basic.md +++ b/tutorials-v4/heom/heom-1a-spin-bath-model-basic.md @@ -5,7 +5,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.14.4 + jupytext_version: 1.16.1 kernelspec: display_name: Python 3 (ipykernel) language: python diff --git a/tutorials-v4/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb b/tutorials-v4/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb deleted file mode 100644 index 910d851a..00000000 --- a/tutorials-v4/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb +++ /dev/null @@ -1,793 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b2ab9d7c", - "metadata": {}, - "source": [ - "# HEOM 1b: Spin-Bath model (very strong coupling)" - ] - }, - { - "cell_type": "markdown", - "id": "89bbb393", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices.\n", - "\n", - "In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", - "\n", - "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian, the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", - "\n", - "In the example below we show how to model the overdamped Drude-Lorentz Spectral Density, commonly used with the HEOM. We show how to do this using the Matsubara, Pade and fitting decompositions, and compare their convergence.\n", - "\n", - "This notebook shows a similar example to notebook 1a, but with much stronger coupling as discussed in [Shi *et al.*, J. Chem. Phys **130**, 084105 (2009)](https://doi.org/10.1063/1.3077918). Please refer to notebook HEOM 1a for a more detailed explanation.\n", - "\n", - "As in notebook 1a, we present a variety of simulations using different techniques to showcase the effect of different approximations of the correlation function on the results:\n", - "\n", - "- Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator\n", - "- Simulation 2: Matsubara decomposition (including terminator)\n", - "- Simulation 3: Pade decomposition\n", - "- Simulation 4: Fitting approach\n", - "\n", - "Lastly we compare the results to using the Bloch-Redfield approach:\n", - "\n", - "- Simulation 5: Bloch-Redfield\n", - "\n", - "which does not give the correct evolution in this case.\n", - "\n", - "\n", - "### Drude-Lorentz (overdamped) spectral density\n", - "\n", - "The Drude-Lorentz spectral density is:\n", - "\n", - "$$J_D(\\omega)= \\frac{2\\omega\\lambda\\gamma}{{\\gamma}^2 + \\omega^2}$$\n", - "\n", - "where $\\lambda$ scales the coupling strength, and $\\gamma$ is the cut-off frequency. We use the convention,\n", - "\\begin{equation*}\n", - "C(t) = \\int_0^{\\infty} d\\omega \\frac{J_D(\\omega)}{\\pi}[\\coth(\\beta\\omega) \\cos(\\omega \\tau) - i \\sin(\\omega \\tau)]\n", - "\\end{equation*}\n", - "\n", - "With the HEOM we must use an exponential decomposition:\n", - "\n", - "\\begin{equation*}\n", - "C(t)=\\sum_{k=0}^{k=\\infty} c_k e^{-\\nu_k t}\n", - "\\end{equation*}\n", - "\n", - "As an example, the Matsubara decomposition of the Drude-Lorentz spectral density is given by:\n", - "\n", - "\\begin{equation*}\n", - " \\nu_k = \\begin{cases}\n", - " \\gamma & k = 0\\\\\n", - " {2 \\pi k} / {\\beta } & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "\\begin{equation*}\n", - " c_k = \\begin{cases}\n", - " \\lambda \\gamma (\\cot(\\beta \\gamma / 2) - i) & k = 0\\\\\n", - " 4 \\lambda \\gamma \\nu_k / \\{(nu_k^2 - \\gamma^2)\\beta \\} & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." - ] - }, - { - "cell_type": "markdown", - "id": "7113b056", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "00390919", - "metadata": {}, - "outputs": [], - "source": [ - "import contextlib\n", - "import time\n", - "\n", - "import numpy as np\n", - "from scipy.optimize import curve_fit\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " basis,\n", - " brmesolve,\n", - " expect,\n", - " liouvillian,\n", - " sigmax,\n", - " sigmaz,\n", - " Options,\n", - ")\n", - "from qutip.nonmarkov.heom import (\n", - " HEOMSolver,\n", - " BosonicBath,\n", - " DrudeLorentzBath,\n", - " DrudeLorentzPadeBath,\n", - " BathExponent,\n", - ")\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "29367926", - "metadata": {}, - "source": [ - "## Helper functions\n", - "\n", - "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "895d9ec0", - "metadata": {}, - "outputs": [], - "source": [ - "def cot(x):\n", - " \"\"\" Vectorized cotangent of x. \"\"\"\n", - " return 1. / np.tan(x)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "46ce2b59", - "metadata": {}, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\" Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "markdown", - "id": "2f7f5293", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "And let us set up the system Hamiltonian, bath and system measurement operators:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c7f515c1", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the system Hamiltonian\n", - "eps = .0 # Energy of the 2-level system.\n", - "Del = .2 # Tunnelling term\n", - "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8975d622", - "metadata": {}, - "outputs": [], - "source": [ - "# Initial state of the system.\n", - "rho0 = basis(2, 0) * basis(2, 0).dag()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8384e8bd", - "metadata": {}, - "outputs": [], - "source": [ - "# System-bath coupling (Drude-Lorentz spectral density)\n", - "Q = sigmaz() # coupling operator\n", - "\n", - "# Bath properties (see Shi et al., J. Chem. Phys. 130, 084105 (2009)):\n", - "gamma = 1. # cut off frequency\n", - "lam = 2.5 # coupling strength\n", - "T = 1. # in units where Boltzmann factor is 1\n", - "beta = 1. / T\n", - "\n", - "# HEOM parameters:\n", - "\n", - "# number of exponents to retain in the Matsubara expansion of the\n", - "# bath correlation function:\n", - "Nk = 1\n", - "\n", - "# Number of levels of the hierarchy to retain:\n", - "NC = 13\n", - "\n", - "# Times to solve for:\n", - "tlist = np.linspace(0, np.pi / Del, 600)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "099f3ff8", - "metadata": {}, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresonding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresonding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "f4c9a086", - "metadata": {}, - "source": [ - "### Plot the spectral density\n", - "\n", - "Let us briefly inspect the spectral density." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "51545231", - "metadata": {}, - "outputs": [], - "source": [ - "w = np.linspace(0, 5, 1000)\n", - "J = w * 2 * lam * gamma / ((gamma**2 + w**2))\n", - "\n", - "# Plot the results\n", - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - "axes.plot(w, J, 'r', linewidth=2)\n", - "axes.set_xlabel(r'$\\omega$', fontsize=28)\n", - "axes.set_ylabel(r'J', fontsize=28);" - ] - }, - { - "cell_type": "markdown", - "id": "a01a670e", - "metadata": {}, - "source": [ - "## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e9a9420d", - "metadata": {}, - "outputs": [], - "source": [ - "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", - " HEOMMats = HEOMSolver(Hsys, bath, NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMats = HEOMMats.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "f884f7e8", - "metadata": {}, - "source": [ - "## Simulation 2: Matsubara decomposition (including terminator)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e8d4f377", - "metadata": {}, - "outputs": [], - "source": [ - "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", - " _, terminator = bath.terminator()\n", - " Ltot = liouvillian(Hsys) + terminator\n", - " HEOMMatsT = HEOMSolver(Ltot, bath, NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMatsT = HEOMMatsT.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7646793", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot the results\n", - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - "\n", - "P11_mats = np.real(expect(resultMats.states, P11p))\n", - "axes.plot(\n", - " tlist, np.real(P11_mats),\n", - " 'b', linewidth=2, label=\"P11 (Matsubara)\",\n", - ")\n", - "\n", - "P11_matsT = np.real(expect(resultMatsT.states, P11p))\n", - "axes.plot(\n", - " tlist, np.real(P11_matsT),\n", - " 'b--', linewidth=2,\n", - " label=\"P11 (Matsubara + Terminator)\",\n", - ")\n", - "\n", - "axes.set_xlabel(r't', fontsize=28)\n", - "axes.legend(loc=0, fontsize=12);" - ] - }, - { - "cell_type": "markdown", - "id": "98a971a9", - "metadata": {}, - "source": [ - "## Simulation 3: Pade decomposition" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "112a2e6c", - "metadata": {}, - "outputs": [], - "source": [ - "# First, compare Matsubara and Pade decompositions\n", - "matsBath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", - "padeBath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", - "\n", - "# We will compare against a summation of {lmaxmats} Matsubara terms\n", - "lmaxmats = 15000\n", - "exactBath = DrudeLorentzBath(\n", - " Q, lam=lam, gamma=gamma, T=T, Nk=lmaxmats, combine=False,\n", - ")\n", - "\n", - "\n", - "def CR(bath, t):\n", - " \"\"\" C_R, the real part of the correlation function. \"\"\"\n", - " result = 0\n", - " for exp in bath.exponents:\n", - " if (\n", - " exp.type == BathExponent.types['R'] or\n", - " exp.type == BathExponent.types['RI']\n", - " ):\n", - " result += exp.ck * np.exp(-exp.vk * t)\n", - " return result\n", - "\n", - "\n", - "def CI(bath, t):\n", - " \"\"\" C_I, the imaginary part of the correlation function. \"\"\"\n", - " result = 0\n", - " for exp in bath.exponents:\n", - " if exp.type == BathExponent.types['I']:\n", - " result += exp.ck * np.exp(exp.vk * t)\n", - " if exp.type == BathExponent.types['RI']:\n", - " result += exp.ck2 * np.exp(exp.vk * t)\n", - " return result\n", - "\n", - "\n", - "fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(16, 8))\n", - "\n", - "ax1.plot(\n", - " tlist, CR(exactBath, tlist),\n", - " \"r\", linewidth=2, label=f\"Mats (Nk={lmaxmats})\",\n", - ")\n", - "ax1.plot(\n", - " tlist, CR(matsBath, tlist),\n", - " \"g--\", linewidth=2, label=f\"Mats (Nk={Nk})\",\n", - ")\n", - "ax1.plot(\n", - " tlist, CR(padeBath, tlist),\n", - " \"b--\", linewidth=2, label=f\"Pade (Nk={Nk})\",\n", - ")\n", - "\n", - "ax1.set_xlabel(r't', fontsize=28)\n", - "ax1.set_ylabel(r\"$C_R(t)$\", fontsize=28)\n", - "ax1.legend(loc=0, fontsize=12)\n", - "\n", - "tlist2 = tlist[0:50]\n", - "ax2.plot(\n", - " tlist2, np.abs(CR(matsBath, tlist2) - CR(exactBath, tlist2)),\n", - " \"g\", linewidth=2, label=\"Mats Error\",\n", - ")\n", - "ax2.plot(\n", - " tlist2, np.abs(CR(padeBath, tlist2) - CR(exactBath, tlist2)),\n", - " \"b--\", linewidth=2, label=\"Pade Error\",\n", - ")\n", - "\n", - "ax2.set_xlabel(r't', fontsize=28)\n", - "ax2.legend(loc=0, fontsize=12);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eefa27b8", - "metadata": {}, - "outputs": [], - "source": [ - "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", - " HEOMPade = HEOMSolver(Hsys, bath, NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultPade = HEOMPade.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f8553e56", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot the results\n", - "fig, axes = plt.subplots(figsize=(8, 8))\n", - "\n", - "axes.plot(\n", - " tlist, np.real(P11_mats),\n", - " 'b', linewidth=2, label=\"P11 (Matsubara)\",\n", - ")\n", - "axes.plot(\n", - " tlist, np.real(P11_matsT),\n", - " 'b--', linewidth=2, label=\"P11 (Matsubara + Terminator)\",\n", - ")\n", - "\n", - "P11_pade = np.real(expect(resultPade.states, P11p))\n", - "axes.plot(\n", - " tlist, np.real(P11_pade),\n", - " 'r', linewidth=2, label=\"P11 (Pade)\",\n", - ")\n", - "\n", - "axes.set_xlabel(r't', fontsize=28)\n", - "axes.legend(loc=0, fontsize=12);" - ] - }, - { - "cell_type": "markdown", - "id": "cd6dc9f7", - "metadata": {}, - "source": [ - "## Simulation 4: Fitting approach" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca5be754", - "metadata": {}, - "outputs": [], - "source": [ - "def wrapper_fit_func(x, N, args):\n", - " \"\"\" Fit function wrapper that unpacks its arguments. \"\"\"\n", - " x = np.array(x)\n", - " a = np.array(args[:N])\n", - " b = np.array(args[N:2 * N])\n", - " return fit_func(x, a, b)\n", - "\n", - "\n", - "def fit_func(x, a, b):\n", - " \"\"\" Fit function. Calculates the value of the\n", - " correlation function at each x, given the\n", - " fit parameters in a and b.\n", - " \"\"\"\n", - " return np.sum(\n", - " a[:, None] * np.exp(np.multiply.outer(b, x)),\n", - " axis=0,\n", - " )\n", - "\n", - "\n", - "def fitter(ans, tlist, k):\n", - " \"\"\" Compute fit with k exponents. \"\"\"\n", - " upper_a = abs(max(ans, key=abs)) * 10\n", - " # sets initial guesses:\n", - " guess = (\n", - " [ans[0] / k] * k + # guesses for a\n", - " [0] * k # guesses for b\n", - " )\n", - " # sets lower bounds:\n", - " b_lower = (\n", - " [-upper_a] * k + # lower bounds for a\n", - " [-np.inf] * k # lower bounds for b\n", - " )\n", - " # sets higher bounds:\n", - " b_higher = (\n", - " [upper_a] * k + # upper bounds for a\n", - " [0] * k # upper bounds for b\n", - " )\n", - " param_bounds = (b_lower, b_higher)\n", - " p1, p2 = curve_fit(\n", - " lambda x, *params_0: wrapper_fit_func(x, k, params_0),\n", - " tlist,\n", - " ans,\n", - " p0=guess,\n", - " sigma=[0.01 for t in tlist],\n", - " bounds=param_bounds,\n", - " maxfev=1e8,\n", - " )\n", - " a, b = p1[:k], p1[k:]\n", - " return (a, b)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d1969e59", - "metadata": {}, - "outputs": [], - "source": [ - "# Fitting the real part of the correlation function:\n", - "\n", - "# Correlation function values to fit:\n", - "tlist_fit = np.linspace(0, 6, 10000)\n", - "corrRana = CR(exactBath, tlist_fit)\n", - "\n", - "# Perform the fit:\n", - "kR = 3 # number of exponents to use for real part\n", - "poptR = []\n", - "with timer(\"Correlation (real) fitting time\"):\n", - " for i in range(kR):\n", - " poptR.append(fitter(corrRana, tlist_fit, i + 1))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a0f2c12", - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(tlist_fit, corrRana, label=\"Analytic\")\n", - "\n", - "for i in range(kR):\n", - " y = fit_func(tlist_fit, *poptR[i])\n", - " plt.plot(tlist_fit, y, label=f\"Fit with {i} terms\")\n", - "\n", - "plt.title(\"Fit to correlations (real part)\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7f2deacb", - "metadata": {}, - "outputs": [], - "source": [ - "# Set the exponential coefficients from the fit parameters\n", - "\n", - "ckAR1 = poptR[-1][0]\n", - "ckAR = [x + 0j for x in ckAR1]\n", - "\n", - "vkAR1 = poptR[-1][1]\n", - "vkAR = [-x + 0j for x in vkAR1]\n", - "\n", - "# Imaginary part: use analytical value\n", - "\n", - "ckAI = [lam * gamma * (-1.0) + 0j]\n", - "vkAI = [gamma + 0j]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c708593", - "metadata": {}, - "outputs": [], - "source": [ - "options = Options(nsteps=1500, store_states=True, rtol=1e-12, atol=1e-12)\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI)\n", - " # We reduce NC slightly here for speed of execution because we retain\n", - " # 3 exponents in ckAR instead of 1. Please restore full NC for\n", - " # convergence though:\n", - " HEOMFit = HEOMSolver(Hsys, bath, int(NC * 0.7), options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultFit = HEOMFit.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "ded203fa", - "metadata": {}, - "source": [ - "## Simulation 5: Bloch-Redfield" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "46f6ea40", - "metadata": {}, - "outputs": [], - "source": [ - "DL = (\n", - " \"2 * pi * 2.0 * {lam} / (pi * {gamma} * {beta}) if (w==0) \"\n", - " \"else 2 * pi * (2.0 * {lam} * {gamma} * w / (pi * (w**2 + {gamma}**2))) \"\n", - " \"* ((1 / (exp(w * {beta}) - 1)) + 1)\"\n", - ").format(gamma=gamma, beta=beta, lam=lam)\n", - "\n", - "options = Options(nsteps=15000, store_states=True, rtol=1e-12, atol=1e-12)\n", - "resultBR = brmesolve(\n", - " Hsys, rho0, tlist,\n", - " a_ops=[[sigmaz(), DL]], sec_cutoff=0, options=options,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "dbf22e8d", - "metadata": {}, - "source": [ - "## Let's plot all our results\n", - "\n", - "Finally, let's plot all of our different results to see how they shape up against each other." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8b316cb7", - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate expectation values in the bases:\n", - "P11_mats = np.real(expect(resultMats.states, P11p))\n", - "P11_matsT = np.real(expect(resultMatsT.states, P11p))\n", - "P11_pade = np.real(expect(resultPade.states, P11p))\n", - "P11_fit = np.real(expect(resultFit.states, P11p))\n", - "P11_br = np.real(expect(resultBR.states, P11p))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4a5e35dc", - "metadata": {}, - "outputs": [], - "source": [ - "rcParams = {\n", - " \"axes.titlesize\": 25,\n", - " \"axes.labelsize\": 30,\n", - " \"xtick.labelsize\": 28,\n", - " \"ytick.labelsize\": 28,\n", - " \"legend.fontsize\": 28,\n", - " \"axes.grid\": False,\n", - " \"savefig.bbox\": \"tight\",\n", - " \"lines.markersize\": 5,\n", - " \"font.family\": \"STIXgeneral\",\n", - " \"mathtext.fontset\": \"stix\",\n", - " \"font.serif\": \"STIX\",\n", - " \"text.usetex\": False,\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5c4afec6", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", - "\n", - "with plt.rc_context(rcParams):\n", - " # Plot the results\n", - " plt.yticks([0.99, 1.0], [0.99, 1])\n", - " axes.plot(\n", - " tlist, np.real(P11_mats),\n", - " 'b', linewidth=2, label=f\"Matsubara $N_k={Nk}$\",\n", - " )\n", - " axes.plot(\n", - " tlist, np.real(P11_matsT),\n", - " 'g--', linewidth=3,\n", - " label=f\"Matsubara $N_k={Nk}$ & terminator\",\n", - " )\n", - " axes.plot(\n", - " tlist, np.real(P11_pade),\n", - " 'y-.', linewidth=2, label=f\"Padé $N_k={Nk}$\",\n", - " )\n", - " axes.plot(\n", - " tlist, np.real(P11_fit),\n", - " 'r', dashes=[3, 2], linewidth=2,\n", - " label=r\"Fit $N_f = 3$, $N_k=15 \\times 10^3$\",\n", - " )\n", - " axes.plot(\n", - " tlist, np.real(P11_br),\n", - " 'b-.', linewidth=1, label=\"Bloch Redfield\",\n", - " )\n", - "\n", - " axes.locator_params(axis='y', nbins=6)\n", - " axes.locator_params(axis='x', nbins=6)\n", - " axes.set_ylabel(r'$\\rho_{11}$', fontsize=30)\n", - " axes.set_xlabel(r'$t\\;\\gamma$', fontsize=30)\n", - " axes.set_xlim(tlist[0], tlist[-1])\n", - " axes.set_ylim(0.98405, 1.0005)\n", - " axes.legend(loc=0)" - ] - }, - { - "cell_type": "markdown", - "id": "ce3c0f80", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0aef0665", - "metadata": {}, - "outputs": [], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "6136f9b9", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ff7937fc", - "metadata": {}, - "outputs": [], - "source": [ - "assert np.allclose(P11_matsT, P11_pade, rtol=1e-3)\n", - "assert np.allclose(P11_matsT, P11_fit, rtol=1e-3)" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v4/heom/heom-1b-spin-bath-model-very-strong-coupling.md b/tutorials-v4/heom/heom-1b-spin-bath-model-very-strong-coupling.md index dffef365..88ca082c 100644 --- a/tutorials-v4/heom/heom-1b-spin-bath-model-very-strong-coupling.md +++ b/tutorials-v4/heom/heom-1b-spin-bath-model-very-strong-coupling.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.4 + jupytext_version: 1.16.1 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -81,7 +81,7 @@ Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. ## Setup -```{code-cell} ipython3 +```{code-cell} import contextlib import time @@ -114,13 +114,13 @@ from qutip.nonmarkov.heom import ( Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: -```{code-cell} ipython3 +```{code-cell} def cot(x): """ Vectorized cotangent of x. """ return 1. / np.tan(x) ``` -```{code-cell} ipython3 +```{code-cell} @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -138,19 +138,19 @@ def timer(label): And let us set up the system Hamiltonian, bath and system measurement operators: -```{code-cell} ipython3 +```{code-cell} # Defining the system Hamiltonian eps = .0 # Energy of the 2-level system. Del = .2 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` -```{code-cell} ipython3 +```{code-cell} # Initial state of the system. rho0 = basis(2, 0) * basis(2, 0).dag() ``` -```{code-cell} ipython3 +```{code-cell} # System-bath coupling (Drude-Lorentz spectral density) Q = sigmaz() # coupling operator @@ -173,7 +173,7 @@ NC = 13 tlist = np.linspace(0, np.pi / Del, 600) ``` -```{code-cell} ipython3 +```{code-cell} # Define some operators with which we will measure the system # 1,1 element of density matrix - corresonding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -186,7 +186,7 @@ P12p = basis(2, 0) * basis(2, 1).dag() Let us briefly inspect the spectral density. -```{code-cell} ipython3 +```{code-cell} w = np.linspace(0, 5, 1000) J = w * 2 * lam * gamma / ((gamma**2 + w**2)) @@ -199,7 +199,7 @@ axes.set_ylabel(r'J', fontsize=28); ## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator -```{code-cell} ipython3 +```{code-cell} options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14) with timer("RHS construction time"): @@ -212,7 +212,7 @@ with timer("ODE solver time"): ## Simulation 2: Matsubara decomposition (including terminator) -```{code-cell} ipython3 +```{code-cell} options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14) with timer("RHS construction time"): @@ -225,7 +225,7 @@ with timer("ODE solver time"): resultMatsT = HEOMMatsT.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} # Plot the results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -248,7 +248,7 @@ axes.legend(loc=0, fontsize=12); ## Simulation 3: Pade decomposition -```{code-cell} ipython3 +```{code-cell} # First, compare Matsubara and Pade decompositions matsBath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) padeBath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) @@ -316,7 +316,7 @@ ax2.set_xlabel(r't', fontsize=28) ax2.legend(loc=0, fontsize=12); ``` -```{code-cell} ipython3 +```{code-cell} options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14) with timer("RHS construction time"): @@ -327,7 +327,7 @@ with timer("ODE solver time"): resultPade = HEOMPade.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} # Plot the results fig, axes = plt.subplots(figsize=(8, 8)) @@ -352,7 +352,7 @@ axes.legend(loc=0, fontsize=12); ## Simulation 4: Fitting approach -```{code-cell} ipython3 +```{code-cell} def wrapper_fit_func(x, N, args): """ Fit function wrapper that unpacks its arguments. """ x = np.array(x) @@ -404,7 +404,7 @@ def fitter(ans, tlist, k): return (a, b) ``` -```{code-cell} ipython3 +```{code-cell} # Fitting the real part of the correlation function: # Correlation function values to fit: @@ -419,7 +419,7 @@ with timer("Correlation (real) fitting time"): poptR.append(fitter(corrRana, tlist_fit, i + 1)) ``` -```{code-cell} ipython3 +```{code-cell} plt.plot(tlist_fit, corrRana, label="Analytic") for i in range(kR): @@ -431,7 +431,7 @@ plt.legend() plt.show() ``` -```{code-cell} ipython3 +```{code-cell} # Set the exponential coefficients from the fit parameters ckAR1 = poptR[-1][0] @@ -446,7 +446,7 @@ ckAI = [lam * gamma * (-1.0) + 0j] vkAI = [gamma + 0j] ``` -```{code-cell} ipython3 +```{code-cell} options = Options(nsteps=1500, store_states=True, rtol=1e-12, atol=1e-12) with timer("RHS construction time"): @@ -462,7 +462,7 @@ with timer("ODE solver time"): ## Simulation 5: Bloch-Redfield -```{code-cell} ipython3 +```{code-cell} DL = ( "2 * pi * 2.0 * {lam} / (pi * {gamma} * {beta}) if (w==0) " "else 2 * pi * (2.0 * {lam} * {gamma} * w / (pi * (w**2 + {gamma}**2))) " @@ -480,7 +480,7 @@ resultBR = brmesolve( Finally, let's plot all of our different results to see how they shape up against each other. -```{code-cell} ipython3 +```{code-cell} # Calculate expectation values in the bases: P11_mats = np.real(expect(resultMats.states, P11p)) P11_matsT = np.real(expect(resultMatsT.states, P11p)) @@ -489,7 +489,7 @@ P11_fit = np.real(expect(resultFit.states, P11p)) P11_br = np.real(expect(resultBR.states, P11p)) ``` -```{code-cell} ipython3 +```{code-cell} rcParams = { "axes.titlesize": 25, "axes.labelsize": 30, @@ -506,7 +506,7 @@ rcParams = { } ``` -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) with plt.rc_context(rcParams): @@ -546,7 +546,7 @@ with plt.rc_context(rcParams): ## About -```{code-cell} ipython3 +```{code-cell} qutip.about() ``` @@ -554,7 +554,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 +```{code-cell} assert np.allclose(P11_matsT, P11_pade, rtol=1e-3) assert np.allclose(P11_matsT, P11_fit, rtol=1e-3) ``` diff --git a/tutorials-v4/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb b/tutorials-v4/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb deleted file mode 100644 index a47ae132..00000000 --- a/tutorials-v4/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb +++ /dev/null @@ -1,778 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "94c32997", - "metadata": {}, - "source": [ - "# HEOM 1c: Spin-Bath model (Underdamped Case)" - ] - }, - { - "cell_type": "markdown", - "id": "db2152b6", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices.\n", - "\n", - "In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", - "\n", - "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", - "\n", - "In the example below we show how to model the underdamped Brownian motion Spectral Density.\n", - "\n", - "Note that in the following, we set $\\hbar = k_\\mathrm{B} = 1$.\n", - "\n", - "### Brownian motion (underdamped) spectral density\n", - "The underdamped spectral density is:\n", - "\n", - "$$J_U = \\frac{\\alpha^2 \\Gamma \\omega}{(\\omega_c^2 - \\omega^2)^2 + \\Gamma^2 \\omega^2)}.$$\n", - "\n", - "Here $\\alpha$ scales the coupling strength, $\\Gamma$ is the cut-off frequency, and $\\omega_c$ defines a resonance frequency. With the HEOM we must use an exponential decomposition:\n", - "\n", - "The Matsubara decomposition of this spectral density is, in real and imaginary parts:\n", - "\n", - "\n", - "\n", - "\\begin{equation*}\n", - " c_k^R = \\begin{cases}\n", - " \\alpha^2 \\coth(\\beta( \\Omega + i\\Gamma/2)/2)/4\\Omega & k = 0\\\\\n", - " \\alpha^2 \\coth(\\beta( \\Omega - i\\Gamma/2)/2)/4\\Omega & k = 0\\\\\n", - " -2\\alpha^2\\Gamma/\\beta \\frac{\\epsilon_k }{((\\Omega + i\\Gamma/2)^2 + \\epsilon_k^2)(\\Omega - i\\Gamma/2)^2 + \\epsilon_k^2)} & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "\\begin{equation*}\n", - " \\nu_k^R = \\begin{cases}\n", - " -i\\Omega + \\Gamma/2, i\\Omega +\\Gamma/2, & k = 0\\\\\n", - " {2 \\pi k} / {\\beta } & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "\n", - "\n", - "\n", - "\\begin{equation*}\n", - " c_k^I = \\begin{cases}\n", - " i\\alpha^2 /4\\Omega & k = 0\\\\\n", - " -i\\alpha^2 /4\\Omega & k = 0\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "\\begin{equation*}\n", - " \\nu_k^I = \\begin{cases}\n", - " i\\Omega + \\Gamma/2, -i\\Omega + \\Gamma/2, & k = 0\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." - ] - }, - { - "cell_type": "markdown", - "id": "13cd28da", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "11585c32", - "metadata": {}, - "outputs": [], - "source": [ - "import contextlib\n", - "import time\n", - "\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " Options,\n", - " basis,\n", - " brmesolve,\n", - " destroy,\n", - " expect,\n", - " qeye,\n", - " sigmax,\n", - " sigmaz,\n", - " tensor,\n", - ")\n", - "from qutip.nonmarkov.heom import (\n", - " HEOMSolver,\n", - " BosonicBath,\n", - " UnderDampedBath,\n", - ")\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a8487b7b", - "metadata": {}, - "outputs": [], - "source": [ - "def cot(x):\n", - " \"\"\" Vectorized cotangent of x. \"\"\"\n", - " return 1. / np.tan(x)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bf7fe07f", - "metadata": {}, - "outputs": [], - "source": [ - "def coth(x):\n", - " \"\"\" Vectorized hyperbolic cotangent of x. \"\"\"\n", - " return 1. / np.tanh(x)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7e319a71", - "metadata": {}, - "outputs": [], - "source": [ - "def underdamped_matsubara_params(lam, gamma, T, nk):\n", - " \"\"\" Calculation of the real and imaginary expansions of the\n", - " underdamped correlation functions.\n", - " \"\"\"\n", - " Om = np.sqrt(w0**2 - (gamma / 2)**2)\n", - " Gamma = gamma / 2.\n", - " beta = 1. / T\n", - "\n", - " ckAR = [\n", - " (lam**2 / (4*Om)) * coth(beta * (Om + 1.0j * Gamma) / 2),\n", - " (lam**2 / (4*Om)) * coth(beta * (Om - 1.0j * Gamma) / 2),\n", - " ]\n", - " ckAR.extend(\n", - " (-2 * lam**2 * gamma / beta) * (2 * np.pi * k / beta) /\n", - " (((Om + 1.0j * Gamma)**2 + (2 * np.pi * k / beta)**2) *\n", - " ((Om - 1.0j * Gamma)**2 + (2 * np.pi * k / beta)**2)) + 0.j\n", - " for k in range(1, nk + 1)\n", - " )\n", - " vkAR = [\n", - " -1.0j * Om + Gamma,\n", - " 1.0j * Om + Gamma,\n", - " ]\n", - " vkAR.extend(\n", - " 2 * np.pi * k * T + 0.j\n", - " for k in range(1, nk + 1)\n", - " )\n", - "\n", - " factor = 1. / 4\n", - "\n", - " ckAI = [\n", - " -factor * lam**2 * 1.0j / Om,\n", - " factor * lam**2 * 1.0j / Om,\n", - " ]\n", - " vkAI = [\n", - " -(-1.0j * Om - Gamma),\n", - " -(1.0j * Om - Gamma),\n", - " ]\n", - "\n", - " return ckAR, vkAR, ckAI, vkAI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "91040aad", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_result_expectations(plots, axes=None):\n", - " \"\"\" Plot the expectation values of operators as functions of time.\n", - "\n", - " Each plot in plots consists of: (solver_result, measurement_operation,\n", - " color, label).\n", - " \"\"\"\n", - " if axes is None:\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " fig_created = True\n", - " else:\n", - " fig = None\n", - " fig_created = False\n", - "\n", - " # add kw arguments to each plot if missing\n", - " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", - " for result, m_op, color, label, kw in plots:\n", - " exp = np.real(expect(result.states, m_op))\n", - " kw.setdefault(\"linewidth\", 2)\n", - " axes.plot(result.times, exp, color, label=label, **kw)\n", - "\n", - " if fig_created:\n", - " axes.legend(loc=0, fontsize=12)\n", - " axes.set_xlabel(\"t\", fontsize=28)\n", - "\n", - " return fig" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "391f2f00", - "metadata": {}, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\" Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "373a8f70", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the system Hamiltonian\n", - "eps = .5 # Energy of the 2-level system.\n", - "Del = 1.0 # Tunnelling term\n", - "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3fa058fa", - "metadata": {}, - "outputs": [], - "source": [ - "# Initial state of the system.\n", - "rho0 = basis(2, 0) * basis(2, 0).dag()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "18e367cc", - "metadata": {}, - "outputs": [], - "source": [ - "# System-bath coupling (underdamed spectral density)\n", - "Q = sigmaz() # coupling operator\n", - "\n", - "# Bath properties:\n", - "gamma = .1 # cut off frequency\n", - "lam = .5 # coupling strength\n", - "w0 = 1. # resonance frequency\n", - "T = 1.\n", - "beta = 1. / T\n", - "\n", - "# HEOM parameters:\n", - "\n", - "# number of exponents to retain in the Matsubara expansion of the\n", - "# bath correlation function:\n", - "Nk = 2\n", - "\n", - "# Number of levels of the hierarchy to retain:\n", - "NC = 10\n", - "\n", - "# Times to solve for:\n", - "tlist = np.linspace(0, 50, 1000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c3f2a24a", - "metadata": {}, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresonding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresonding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "48f84591", - "metadata": {}, - "source": [ - "### First let us look at what the underdamped spectral density looks like:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3cebc66e", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_spectral_density():\n", - " \"\"\" Plot the underdamped spectral density \"\"\"\n", - " w = np.linspace(0, 5, 1000)\n", - " J = lam**2 * gamma * w / ((w0**2 - w**2)**2 + (gamma**2) * (w**2))\n", - "\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " axes.plot(w, J, 'r', linewidth=2)\n", - " axes.set_xlabel(r'$\\omega$', fontsize=28)\n", - " axes.set_ylabel(r'J', fontsize=28)\n", - "\n", - "\n", - "plot_spectral_density()" - ] - }, - { - "cell_type": "markdown", - "id": "184ae030", - "metadata": {}, - "source": [ - "The correlation functions are now very oscillatory, because of the Lorentzian peak in the spectral density." - ] - }, - { - "cell_type": "markdown", - "id": "4861492f", - "metadata": {}, - "source": [ - "### So next, let us plot the correlation functions themselves:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5a5b2524", - "metadata": {}, - "outputs": [], - "source": [ - "def Mk(t, k, gamma, w0, beta):\n", - " \"\"\" Calculate the Matsubara terms for a given t and k. \"\"\"\n", - " Om = np.sqrt(w0**2 - (gamma / 2)**2)\n", - " Gamma = gamma / 2.\n", - " ek = 2 * np.pi * k / beta\n", - "\n", - " return (\n", - " (-2 * lam**2 * gamma / beta) * ek * np.exp(-ek * np.abs(t))\n", - " / (((Om + 1.0j * Gamma)**2 + ek**2) * ((Om - 1.0j * Gamma)**2 + ek**2))\n", - " )\n", - "\n", - "\n", - "def c(t, Nk, lam, gamma, w0, beta):\n", - " \"\"\" Calculate the correlation function for a vector of times, t. \"\"\"\n", - " Om = np.sqrt(w0**2 - (gamma / 2)**2)\n", - " Gamma = gamma / 2.\n", - "\n", - " Cr = (\n", - " coth(beta * (Om + 1.0j * Gamma) / 2) * np.exp(1.0j * Om * t)\n", - " + coth(beta * (Om - 1.0j * Gamma) / 2) * np.exp(-1.0j * Om * t)\n", - " )\n", - "\n", - " Ci = np.exp(-1.0j * Om * t) - np.exp(1.0j * Om * t)\n", - "\n", - " return (\n", - " (lam**2 / (4 * Om)) * np.exp(-Gamma * np.abs(t)) * (Cr + Ci) +\n", - " np.sum([\n", - " Mk(t, k, gamma=gamma, w0=w0, beta=beta)\n", - " for k in range(1, Nk + 1)\n", - " ], 0)\n", - " )\n", - "\n", - "\n", - "def plot_correlation_function():\n", - " \"\"\" Plot the underdamped correlation function. \"\"\"\n", - " t = np.linspace(0, 20, 1000)\n", - " corr = c(t, Nk=3, lam=lam, gamma=gamma, w0=w0, beta=beta)\n", - "\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " axes.plot(t, np.real(corr), '-', color=\"black\", label=\"Re[C(t)]\")\n", - " axes.plot(t, np.imag(corr), '-', color=\"red\", label=\"Im[C(t)]\")\n", - " axes.set_xlabel(r't', fontsize=28)\n", - " axes.set_ylabel(r'C', fontsize=28)\n", - " axes.legend(loc=0, fontsize=12)\n", - "\n", - "\n", - "plot_correlation_function()" - ] - }, - { - "cell_type": "markdown", - "id": "c3f87229", - "metadata": {}, - "source": [ - "It is useful to look at what the Matsubara contributions do to this spectral density. We see that they modify the real part around $t=0$:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2737def7", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_matsubara_correlation_function_contributions():\n", - " \"\"\" Plot the underdamped correlation function. \"\"\"\n", - " t = np.linspace(0, 20, 1000)\n", - "\n", - " M_Nk2 = np.sum([\n", - " Mk(t, k, gamma=gamma, w0=w0, beta=beta)\n", - " for k in range(1, 2 + 1)\n", - " ], 0)\n", - "\n", - " M_Nk100 = np.sum([\n", - " Mk(t, k, gamma=gamma, w0=w0, beta=beta)\n", - " for k in range(1, 100 + 1)\n", - " ], 0)\n", - "\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " axes.plot(t, np.real(M_Nk2), '-', color=\"black\", label=\"Re[M(t)] Nk=2\")\n", - " axes.plot(t, np.real(M_Nk100), '--', color=\"red\", label=\"Re[M(t)] Nk=100\")\n", - " axes.set_xlabel(r't', fontsize=28)\n", - " axes.set_ylabel(r'M', fontsize=28)\n", - " axes.legend(loc=0, fontsize=12)\n", - "\n", - "\n", - "plot_matsubara_correlation_function_contributions()" - ] - }, - { - "cell_type": "markdown", - "id": "6767a295", - "metadata": {}, - "source": [ - "### Solving for the dynamics as a function of time:" - ] - }, - { - "cell_type": "markdown", - "id": "f7308263", - "metadata": {}, - "source": [ - "Next we calculate the exponents using the Matsubara decompositions. Here we split them into real and imaginary parts.\n", - "\n", - "The HEOM code will optimize these, and reduce the number of exponents when real and imaginary parts have the same exponent. This is clearly the case for the first term in the vkAI and vkAR lists." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b1609b65", - "metadata": {}, - "outputs": [], - "source": [ - "ckAR, vkAR, ckAI, vkAI = underdamped_matsubara_params(\n", - " lam=lam, gamma=gamma, T=T, nk=Nk,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "09874858", - "metadata": {}, - "source": [ - "Having created the lists which specify the bath correlation functions, we create a `BosonicBath` from them and pass the bath to the `HEOMSolver` class.\n", - "\n", - "The solver constructs the \"right hand side\" (RHS) determinining how the system and auxiliary density operators evolve in time. This can then be used to solve for dynamics or steady-state.\n", - "\n", - "Below we create the bath and solver and then solve for the dynamics by calling `.run(rho0, tlist)`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7f44acf5", - "metadata": {}, - "outputs": [], - "source": [ - "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI)\n", - " HEOMMats = HEOMSolver(Hsys, bath, NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMats = HEOMMats.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "14505aa3", - "metadata": {}, - "outputs": [], - "source": [ - "plot_result_expectations([\n", - " (resultMats, P11p, 'b', \"P11 Mats\"),\n", - " (resultMats, P12p, 'r', \"P12 Mats\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "2a13a295", - "metadata": {}, - "source": [ - "In practice, one would not perform this laborious expansion for the underdamped correlation function, because\n", - "QuTiP already has a class, `UnderDampedBath`, that can construct this bath for you. Nevertheless, knowing how\n", - "to perform this expansion will allow you to construct your own baths for other spectral densities.\n", - "\n", - "Below we show how to use this built-in functionality:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cde37b21", - "metadata": {}, - "outputs": [], - "source": [ - "# Compare to built-in under-damped bath:\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = UnderDampedBath(Q, lam=lam, gamma=gamma, w0=w0, T=T, Nk=Nk)\n", - " HEOM_udbath = HEOMSolver(Hsys, bath, NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " result_udbath = HEOM_udbath.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f394a2ab", - "metadata": {}, - "outputs": [], - "source": [ - "plot_result_expectations([\n", - " (result_udbath, P11p, 'b', \"P11 (UnderDampedBath)\"),\n", - " (result_udbath, P12p, 'r', \"P12 (UnderDampedBath)\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "6234d19f", - "metadata": {}, - "source": [ - "### We can compare these results to those of the Bloch-Redfield solver in QuTiP:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "15277472", - "metadata": {}, - "outputs": [], - "source": [ - "UD = (\n", - " f\"2 * {lam}**2 * {gamma} / ( {w0}**4 * {beta}) if (w==0)\"\n", - " \" else \"\n", - " f\"2 * ({lam}**2 * {gamma} * w / (({w0}**2 - w**2)**2 + {gamma}**2 * w**2))\"\n", - " f\" * ((1 / (exp(w * {beta}) - 1)) + 1)\"\n", - ")\n", - "\n", - "options = Options(nsteps=15000, store_states=True, rtol=1e-12, atol=1e-12)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultBR = brmesolve(\n", - " Hsys, rho0, tlist,\n", - " a_ops=[[sigmaz(), UD]], options=options,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f09291b5", - "metadata": {}, - "outputs": [], - "source": [ - "plot_result_expectations([\n", - " (resultMats, P11p, 'b', \"P11 Mats\"),\n", - " (resultMats, P12p, 'r', \"P12 Mats\"),\n", - " (resultBR, P11p, 'g--', \"P11 Bloch Redfield\"),\n", - " (resultBR, P12p, 'g--', \"P12 Bloch Redfield\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "50ff4456", - "metadata": {}, - "source": [ - "### Lastly, let us calculate the analytical steady-state result and compare all of the results:" - ] - }, - { - "cell_type": "markdown", - "id": "4f733223", - "metadata": {}, - "source": [ - "The thermal state of a reaction coordinate (treating the environment as a single damped mode) should, at high temperatures and small gamma, tell us the steady-state:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d795d5cf", - "metadata": {}, - "outputs": [], - "source": [ - "dot_energy, dot_state = Hsys.eigenstates()\n", - "deltaE = dot_energy[1] - dot_energy[0]\n", - "\n", - "gamma2 = gamma\n", - "wa = w0 # reaction coordinate frequency\n", - "g = lam / np.sqrt(2 * wa) # coupling\n", - "\n", - "NRC = 10\n", - "\n", - "Hsys_exp = tensor(qeye(NRC), Hsys)\n", - "Q_exp = tensor(qeye(NRC), Q)\n", - "a = tensor(destroy(NRC), qeye(2))\n", - "\n", - "H0 = wa * a.dag() * a + Hsys_exp\n", - "# interaction\n", - "H1 = (g * (a.dag() + a) * Q_exp)\n", - "\n", - "H = H0 + H1\n", - "\n", - "energies, states = H.eigenstates()\n", - "rhoss = 0 * states[0] * states[0].dag()\n", - "for kk, energ in enumerate(energies):\n", - " rhoss += (states[kk] * states[kk].dag() * np.exp(-beta * energies[kk]))\n", - "rhoss = rhoss / rhoss.norm()\n", - "\n", - "P12RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 1).dag())\n", - "P12RC = expect(rhoss, P12RC)\n", - "\n", - "P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag())\n", - "P11RC = expect(rhoss, P11RC)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "35f47df5", - "metadata": {}, - "outputs": [], - "source": [ - "rcParams = {\n", - " \"axes.titlesize\": 25,\n", - " \"axes.labelsize\": 30,\n", - " \"xtick.labelsize\": 28,\n", - " \"ytick.labelsize\": 28,\n", - " \"legend.fontsize\": 28,\n", - " \"axes.grid\": False,\n", - " \"savefig.bbox\": \"tight\",\n", - " \"lines.markersize\": 5,\n", - " \"font.family\": \"STIXgeneral\",\n", - " \"mathtext.fontset\": \"stix\",\n", - " \"font.serif\": \"STIX\",\n", - " \"text.usetex\": False,\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "969eb62c", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", - "\n", - "with plt.rc_context(rcParams):\n", - " plt.yticks([P11RC, 0.6, 1.0], [0.38, 0.6, 1])\n", - "\n", - " plot_result_expectations([\n", - " (resultBR, P11p, 'y-.', \"Bloch-Redfield\"),\n", - " (resultMats, P11p, 'b', \"Matsubara $N_k=3$\"),\n", - " ], axes=axes)\n", - " axes.plot(\n", - " tlist, [P11RC for t in tlist],\n", - " color='black', linestyle=\"-.\", linewidth=2,\n", - " label=\"Thermal state\",\n", - " )\n", - "\n", - " axes.set_xlabel(r'$t \\Delta$', fontsize=30)\n", - " axes.set_ylabel(r'$\\rho_{11}$', fontsize=30)\n", - "\n", - " axes.locator_params(axis='y', nbins=4)\n", - " axes.locator_params(axis='x', nbins=4)\n", - "\n", - " axes.legend(loc=0)\n", - "\n", - " fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "b54f0f33", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cad07d0d", - "metadata": {}, - "outputs": [], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "7b86b4dd", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d39e7ae8", - "metadata": {}, - "outputs": [], - "source": [ - "assert np.allclose(\n", - " expect(P11p, resultMats.states[-100:]), P11RC, rtol=1e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(P11p, resultBR.states[-100:]), P11RC, rtol=1e-2,\n", - ")" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v4/heom/heom-1c-spin-bath-model-underdamped-sd.md b/tutorials-v4/heom/heom-1c-spin-bath-model-underdamped-sd.md index 7828fbf6..1e6e4595 100644 --- a/tutorials-v4/heom/heom-1c-spin-bath-model-underdamped-sd.md +++ b/tutorials-v4/heom/heom-1c-spin-bath-model-underdamped-sd.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.4 + jupytext_version: 1.16.1 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -76,7 +76,7 @@ Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. ## Setup -```{code-cell} ipython3 +```{code-cell} import contextlib import time @@ -104,19 +104,19 @@ from qutip.nonmarkov.heom import ( %matplotlib inline ``` -```{code-cell} ipython3 +```{code-cell} def cot(x): """ Vectorized cotangent of x. """ return 1. / np.tan(x) ``` -```{code-cell} ipython3 +```{code-cell} def coth(x): """ Vectorized hyperbolic cotangent of x. """ return 1. / np.tanh(x) ``` -```{code-cell} ipython3 +```{code-cell} def underdamped_matsubara_params(lam, gamma, T, nk): """ Calculation of the real and imaginary expansions of the underdamped correlation functions. @@ -158,7 +158,7 @@ def underdamped_matsubara_params(lam, gamma, T, nk): return ckAR, vkAR, ckAI, vkAI ``` -```{code-cell} ipython3 +```{code-cell} def plot_result_expectations(plots, axes=None): """ Plot the expectation values of operators as functions of time. @@ -186,7 +186,7 @@ def plot_result_expectations(plots, axes=None): return fig ``` -```{code-cell} ipython3 +```{code-cell} @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -200,19 +200,19 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} ipython3 +```{code-cell} # Defining the system Hamiltonian eps = .5 # Energy of the 2-level system. Del = 1.0 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` -```{code-cell} ipython3 +```{code-cell} # Initial state of the system. rho0 = basis(2, 0) * basis(2, 0).dag() ``` -```{code-cell} ipython3 +```{code-cell} # System-bath coupling (underdamed spectral density) Q = sigmaz() # coupling operator @@ -236,7 +236,7 @@ NC = 10 tlist = np.linspace(0, 50, 1000) ``` -```{code-cell} ipython3 +```{code-cell} # Define some operators with which we will measure the system # 1,1 element of density matrix - corresonding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -247,7 +247,7 @@ P12p = basis(2, 0) * basis(2, 1).dag() ### First let us look at what the underdamped spectral density looks like: -```{code-cell} ipython3 +```{code-cell} def plot_spectral_density(): """ Plot the underdamped spectral density """ w = np.linspace(0, 5, 1000) @@ -268,7 +268,7 @@ The correlation functions are now very oscillatory, because of the Lorentzian pe ### So next, let us plot the correlation functions themselves: -```{code-cell} ipython3 +```{code-cell} def Mk(t, k, gamma, w0, beta): """ Calculate the Matsubara terms for a given t and k. """ Om = np.sqrt(w0**2 - (gamma / 2)**2) @@ -320,7 +320,7 @@ plot_correlation_function() It is useful to look at what the Matsubara contributions do to this spectral density. We see that they modify the real part around $t=0$: -```{code-cell} ipython3 +```{code-cell} def plot_matsubara_correlation_function_contributions(): """ Plot the underdamped correlation function. """ t = np.linspace(0, 20, 1000) @@ -354,7 +354,7 @@ Next we calculate the exponents using the Matsubara decompositions. Here we spli The HEOM code will optimize these, and reduce the number of exponents when real and imaginary parts have the same exponent. This is clearly the case for the first term in the vkAI and vkAR lists. -```{code-cell} ipython3 +```{code-cell} ckAR, vkAR, ckAI, vkAI = underdamped_matsubara_params( lam=lam, gamma=gamma, T=T, nk=Nk, ) @@ -366,7 +366,7 @@ The solver constructs the "right hand side" (RHS) determinining how the system a Below we create the bath and solver and then solve for the dynamics by calling `.run(rho0, tlist)`. -```{code-cell} ipython3 +```{code-cell} options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14) with timer("RHS construction time"): @@ -377,7 +377,7 @@ with timer("ODE solver time"): resultMats = HEOMMats.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} plot_result_expectations([ (resultMats, P11p, 'b', "P11 Mats"), (resultMats, P12p, 'r', "P12 Mats"), @@ -390,7 +390,7 @@ to perform this expansion will allow you to construct your own baths for other s Below we show how to use this built-in functionality: -```{code-cell} ipython3 +```{code-cell} # Compare to built-in under-damped bath: with timer("RHS construction time"): @@ -401,7 +401,7 @@ with timer("ODE solver time"): result_udbath = HEOM_udbath.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} plot_result_expectations([ (result_udbath, P11p, 'b', "P11 (UnderDampedBath)"), (result_udbath, P12p, 'r', "P12 (UnderDampedBath)"), @@ -410,7 +410,7 @@ plot_result_expectations([ ### We can compare these results to those of the Bloch-Redfield solver in QuTiP: -```{code-cell} ipython3 +```{code-cell} UD = ( f"2 * {lam}**2 * {gamma} / ( {w0}**4 * {beta}) if (w==0)" " else " @@ -427,7 +427,7 @@ with timer("ODE solver time"): ) ``` -```{code-cell} ipython3 +```{code-cell} plot_result_expectations([ (resultMats, P11p, 'b', "P11 Mats"), (resultMats, P12p, 'r', "P12 Mats"), @@ -442,7 +442,7 @@ plot_result_expectations([ The thermal state of a reaction coordinate (treating the environment as a single damped mode) should, at high temperatures and small gamma, tell us the steady-state: -```{code-cell} ipython3 +```{code-cell} dot_energy, dot_state = Hsys.eigenstates() deltaE = dot_energy[1] - dot_energy[0] @@ -475,7 +475,7 @@ P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag()) P11RC = expect(rhoss, P11RC) ``` -```{code-cell} ipython3 +```{code-cell} rcParams = { "axes.titlesize": 25, "axes.labelsize": 30, @@ -492,7 +492,7 @@ rcParams = { } ``` -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) with plt.rc_context(rcParams): @@ -521,7 +521,7 @@ with plt.rc_context(rcParams): ## About -```{code-cell} ipython3 +```{code-cell} qutip.about() ``` @@ -529,7 +529,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 +```{code-cell} assert np.allclose( expect(P11p, resultMats.states[-100:]), P11RC, rtol=1e-2, ) diff --git a/tutorials-v4/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb b/tutorials-v4/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb deleted file mode 100644 index 32a3978a..00000000 --- a/tutorials-v4/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb +++ /dev/null @@ -1,1740 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b0c2374c", - "metadata": {}, - "source": [ - "# HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions" - ] - }, - { - "cell_type": "markdown", - "id": "0af1c108", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded \n", - "in a set of auxiliary density matrices.\n", - "\n", - "In this example we show the evolution of a single two-level system in contact with a single bosonic environment.\n", - "\n", - "The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", - "\n", - "The bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", - "\n", - "In the example below we show how to model an Ohmic environment with exponential cut-off in two ways:\n", - "\n", - "* First we fit the spectral density with a set of underdamped brownian oscillator functions.\n", - "\n", - "* Second, we evaluate the correlation functions, and fit those with a certain choice of exponential functions.\n", - "\n", - "In each case we will use the fit parameters to determine the correlation function expansion co-efficients needed to construct a description of the bath (i.e. a `BosonicBath` object) to supply to the `HEOMSolver` so that we can solve for the system dynamics." - ] - }, - { - "cell_type": "markdown", - "id": "b8573057", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "cd17d9f7", - "metadata": {}, - "outputs": [], - "source": [ - "import contextlib\n", - "import dataclasses\n", - "import time\n", - "\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "from scipy.optimize import curve_fit\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " Options,\n", - " basis,\n", - " expect,\n", - " liouvillian,\n", - " sigmax,\n", - " sigmaz,\n", - " spost,\n", - " spre,\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver,\n", - " BosonicBath,\n", - ")\n", - "\n", - "# Import mpmath functions for evaluation of gamma and zeta\n", - "# functions in the expression for the correlation:\n", - "\n", - "from mpmath import mp\n", - "\n", - "mp.dps = 15\n", - "mp.pretty = True\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "efa8c2a9", - "metadata": {}, - "source": [ - "## Helper functions\n", - "\n", - "Let's define some helper functions for plotting results and timing how long operations take:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e0e06304", - "metadata": {}, - "outputs": [], - "source": [ - "def coth(x):\n", - " \"\"\" Vectorized hyperbolic cotangent of x. \"\"\"\n", - " return 1. / np.tanh(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a5f220b7", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_result_expectations(plots, axes=None):\n", - " \"\"\" Plot the expectation values of operators as functions of time.\n", - "\n", - " Each plot in plots consists of (solver_result,\n", - " measurement_operation, color, label).\n", - " \"\"\"\n", - " if axes is None:\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " fig_created = True\n", - " else:\n", - " fig = None\n", - " fig_created = False\n", - "\n", - " # add kw arguments to each plot if missing\n", - " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", - " for result, m_op, color, label, kw in plots:\n", - " exp = np.real(expect(result.states, m_op))\n", - " kw.setdefault(\"linewidth\", 2)\n", - " if color == 'rand':\n", - " axes.plot(\n", - " result.times, exp,\n", - " c=np.random.rand(3,), label=label, **kw,\n", - " )\n", - " else:\n", - " axes.plot(result.times, exp, color, label=label, **kw)\n", - "\n", - " if fig_created:\n", - " axes.legend(loc=0, fontsize=12)\n", - " axes.set_xlabel(\"t\", fontsize=28)\n", - "\n", - " return fig" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7cda27be", - "metadata": {}, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\" Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "markdown", - "id": "0ab24db3", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "And let us set up the system Hamiltonian, bath and system measurement operators:" - ] - }, - { - "cell_type": "markdown", - "id": "b8a7978c", - "metadata": {}, - "source": [ - "### System Hamiltonian" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b5be20a5", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the system Hamiltonian\n", - "eps = 0.0 # Energy of the 2-level system.\n", - "Del = 0.2 # Tunnelling term\n", - "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "619f046c", - "metadata": {}, - "outputs": [], - "source": [ - "# Initial state of the system.\n", - "rho0 = basis(2, 0) * basis(2, 0).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "8ce2e713", - "metadata": {}, - "source": [ - "### System measurement operators" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "bc2468cf", - "metadata": {}, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresonding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresonding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "653d8867", - "metadata": {}, - "source": [ - "### Analytical expressions for the Ohmic bath correlation function and spectral density" - ] - }, - { - "cell_type": "markdown", - "id": "de2a61cb", - "metadata": {}, - "source": [ - "Before we begin fitting, let us examine the analytic expressions for the correlation and spectral density functions and write Python equivalents. \n", - "\n", - "The correlation function is given by (see, e.g., http://www1.itp.tu-berlin.de/brandes/public_html/publications/notes.pdf for a derivation, equation 7.59, but with a factor of $\\pi$ moved into the definition of the correlation function):\n", - "\n", - "\\begin{align}\n", - "C(t) =& \\: \\frac{1}{\\pi}\\alpha \\omega_{c}^{1 - s} \\beta^{- (s + 1)} \\: \\times \\\\\n", - " & \\: \\Gamma(s + 1) \\left[ \\zeta \\left(s + 1, \\frac{1 + \\beta \\omega_c - i \\omega_c t}{\\beta \\omega_c}\\right) + \\zeta \\left(s + 1, \\frac{1 + i \\omega_c t}{\\beta \\omega_c}\\right) \\right]\n", - "\\end{align}\n", - "\n", - "where $\\Gamma$ is the Gamma function and\n", - "\n", - "\\begin{equation}\n", - "\\zeta(z, u) \\equiv \\sum_{n=0}^{\\infty} \\frac{1}{(n + u)^z}, \\; u \\neq 0, -1, -2, \\ldots\n", - "\\end{equation}\n", - "\n", - "is the generalized Zeta function. The Ohmic case is given by $s = 1$.\n", - "\n", - "The corresponding spectral density for the Ohmic case is:\n", - "\n", - "\\begin{equation}\n", - "J(\\omega) = \\omega \\alpha e^{- \\frac{\\omega}{\\omega_c}}\n", - "\\end{equation}" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "f2996062", - "metadata": {}, - "outputs": [], - "source": [ - "def ohmic_correlation(t, alpha, wc, beta, s=1):\n", - " \"\"\" The Ohmic bath correlation function as a function of t\n", - " (and the bath parameters).\n", - " \"\"\"\n", - " corr = (\n", - " (1 / np.pi) * alpha * wc**(1 - s) * beta**(-(s + 1)) * mp.gamma(s + 1)\n", - " )\n", - " z1_u = (1 + beta * wc - 1.0j * wc * t) / (beta * wc)\n", - " z2_u = (1 + 1.0j * wc * t) / (beta * wc)\n", - " # Note: the arguments to zeta should be in as high precision as possible.\n", - " # See http://mpmath.org/doc/current/basics.html#providing-correct-input\n", - " return np.array([\n", - " complex(corr * (mp.zeta(s + 1, u1) + mp.zeta(s + 1, u2)))\n", - " for u1, u2 in zip(z1_u, z2_u)\n", - " ], dtype=np.complex128)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "af7a39e2", - "metadata": {}, - "outputs": [], - "source": [ - "def ohmic_spectral_density(w, alpha, wc):\n", - " \"\"\" The Ohmic bath spectral density as a function of w\n", - " (and the bath parameters).\n", - " \"\"\"\n", - " return w * alpha * np.e**(-w / wc)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c52abbec", - "metadata": {}, - "outputs": [], - "source": [ - "def ohmic_power_spectrum(w, alpha, wc, beta):\n", - " \"\"\" The Ohmic bath power spectrum as a function of w\n", - " (and the bath parameters).\n", - " \"\"\"\n", - " return (\n", - " w * alpha * np.e**(-abs(w) / wc) *\n", - " ((1 / (np.e**(w * beta) - 1)) + 1) * 2\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "e530b00f", - "metadata": {}, - "source": [ - "### Bath and HEOM parameters" - ] - }, - { - "cell_type": "markdown", - "id": "03e881bc", - "metadata": {}, - "source": [ - "Finally, let's set the bath parameters we will work with and write down some measurement operators:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "874e3203", - "metadata": {}, - "outputs": [], - "source": [ - "# Bath parameters:\n", - "\n", - "@dataclasses.dataclass\n", - "class OhmicBathParameters:\n", - " \"\"\" Ohmic bath parameters. \"\"\"\n", - " Q: object = dataclasses.field(default_factory=sigmaz, repr=False)\n", - " alpha: float = 3.25\n", - " T: float = 0.5\n", - " wc: float = 1.0\n", - " s: float = 1\n", - "\n", - " def __post_init__(self):\n", - " self.beta = 1 / self.T\n", - "\n", - " def replace(self, **kw):\n", - " return dataclasses.replace(self, **kw)\n", - "\n", - "\n", - "obp = OhmicBathParameters()" - ] - }, - { - "cell_type": "markdown", - "id": "b8ace800", - "metadata": {}, - "source": [ - "And set the cut-off for the HEOM hierarchy:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "50fb9320", - "metadata": {}, - "outputs": [], - "source": [ - "# HEOM parameters:\n", - "\n", - "# The max_depth defaults to 5 so that the notebook executes more\n", - "# quickly. Change it to 11 to wait longer for more accurate results.\n", - "max_depth = 5" - ] - }, - { - "cell_type": "markdown", - "id": "d0097ca0", - "metadata": {}, - "source": [ - "## Building the HEOM bath by fitting the spectral density" - ] - }, - { - "cell_type": "markdown", - "id": "0053121b", - "metadata": {}, - "source": [ - "We begin by fitting the spectral density, using a series of $k$ underdamped harmonic oscillators case with the Meier-Tannor form (J. Chem. Phys. 111, 3365 (1999); https://doi.org/10.1063/1.479669):\n", - "\n", - "\\begin{equation}\n", - "J_{\\mathrm approx}(\\omega; a, b, c) = \\sum_{i=0}^{k-1} \\frac{2 a_i b_i w}{((w + c_i)^2 + b_i^2) ((w - c_i)^2 + b_i^2)}\n", - "\\end{equation}\n", - "\n", - "where $a, b$ and $c$ are the fit parameters and each is a vector of length $k$." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "ac870ddb", - "metadata": {}, - "outputs": [], - "source": [ - "# Helper functions for packing the paramters a, b and c into a single numpy\n", - "# array as required by SciPy's curve_fit:\n", - "\n", - "def pack(a, b, c):\n", - " \"\"\" Pack parameter lists for fitting. \"\"\"\n", - " return np.concatenate((a, b, c))\n", - "\n", - "\n", - "def unpack(params):\n", - " \"\"\" Unpack parameter lists for fitting. \"\"\"\n", - " N = len(params) // 3\n", - " a = np.array(params[:N])\n", - " b = np.array(params[N:2 * N])\n", - " c = np.array(params[2 * N:])\n", - " return a, b, c" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "0d5cd728", - "metadata": {}, - "outputs": [], - "source": [ - "# The approximate spectral density and a helper for fitting the approximate\n", - "# spectral density to values calculated from the analytical formula:\n", - "\n", - "def spectral_density_approx(w, a, b, c):\n", - " \"\"\" Calculate the fitted value of the function for the given\n", - " parameters.\n", - " \"\"\"\n", - " return np.sum(\n", - " 2 * a[:, None] * np.multiply.outer(b, w) / (\n", - " ((w + c[:, None])**2 + b[:, None]**2) *\n", - " ((w - c[:, None])**2 + b[:, None]**2)\n", - " ),\n", - " axis=0,\n", - " )\n", - "\n", - "\n", - "def fit_spectral_density(J, w, alpha, wc, N):\n", - " \"\"\" Fit the spectral density with N underdamped oscillators. \"\"\"\n", - " sigma = [0.0001] * len(w)\n", - "\n", - " J_max = abs(max(J, key=abs))\n", - "\n", - " guesses = pack([J_max] * N, [wc] * N, [wc] * N)\n", - " lower_bounds = pack([-100 * J_max] * N, [0.1 * wc] * N, [0.1 * wc] * N)\n", - " upper_bounds = pack([100 * J_max] * N, [100 * wc] * N, [100 * wc] * N)\n", - "\n", - " params, _ = curve_fit(\n", - " lambda x, *params: spectral_density_approx(w, *unpack(params)),\n", - " w, J,\n", - " p0=guesses,\n", - " bounds=(lower_bounds, upper_bounds),\n", - " sigma=sigma,\n", - " maxfev=1000000000,\n", - " )\n", - "\n", - " return unpack(params)" - ] - }, - { - "cell_type": "markdown", - "id": "19351887", - "metadata": {}, - "source": [ - "With the spectral density approximation $J_{\\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "c437b8f7", - "metadata": {}, - "outputs": [], - "source": [ - "w = np.linspace(0, 25, 20000)\n", - "J = ohmic_spectral_density(w, alpha=obp.alpha, wc=obp.wc)\n", - "\n", - "params_k = [\n", - " fit_spectral_density(J, w, alpha=obp.alpha, wc=obp.wc, N=i+1)\n", - " for i in range(4)\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "574186d7", - "metadata": {}, - "source": [ - "Let's plot the fit for each $k$ and examine how it improves with an increasing number of terms:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "083ff82e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parameters [k=0]: lam=[6.14746382]; gamma=[1.77939431]; w0=[0.1]\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parameters [k=3]: lam=[ 7.91592672 0.60083674 -4.40789196 0.01058512]; gamma=[2.29618983 1.00246781 4.29908159 0.30736306]; w0=[0.1 0.1 3.981687 0.1 ]\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for k, params in enumerate(params_k):\n", - " lam, gamma, w0 = params\n", - " y = spectral_density_approx(w, lam, gamma, w0)\n", - " print(f\"Parameters [k={k}]: lam={lam}; gamma={gamma}; w0={w0}\")\n", - " plt.plot(w, J, w, y)\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "b66929b7", - "metadata": {}, - "source": [ - "The fit with four terms looks good. Let's take a closer look at it by plotting the contribution of each term of the fit:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "c8f26a60", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parameters [k=3]: lam=[ 7.91592672 0.60083674 -4.40789196 0.01058512]; gamma=[2.29618983 1.00246781 4.29908159 0.30736306]; w0=[0.1 0.1 3.981687 0.1 ]\n" - ] - } - ], - "source": [ - "# The parameters for the fit with four terms:\n", - "\n", - "lam, gamma, w0 = params_k[-1]\n", - "\n", - "print(f\"Parameters [k={len(params_k) - 1}]: lam={lam}; gamma={gamma}; w0={w0}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "d1ce6c63", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the components of the fit separately:\n", - "\n", - "def spectral_density_ith_component(w, i, lam, gamma, w0):\n", - " \"\"\" Return the i'th term of the approximation for the spectral density. \"\"\"\n", - " return (\n", - " 2 * lam[i] * gamma[i] * w /\n", - " (((w + w0[i])**2 + gamma[i]**2) * ((w - w0[i])**2 + gamma[i]**2))\n", - " )\n", - "\n", - "\n", - "def plot_spectral_density_fit_components(J, w, lam, gamma, w0):\n", - " \"\"\" Plot the individual components of a fit to the spectral density. \"\"\"\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " axes.plot(w, J, 'r--', linewidth=2, label=\"original\")\n", - " for i in range(len(lam)):\n", - " axes.plot(\n", - " w, spectral_density_ith_component(w, i, lam, gamma, w0),\n", - " linewidth=2,\n", - " label=f\"fit component {i}\",\n", - " )\n", - "\n", - " axes.set_xlabel(r'$w$', fontsize=28)\n", - " axes.set_ylabel(r'J', fontsize=28)\n", - " axes.legend()\n", - "\n", - " return fig\n", - "\n", - "\n", - "plot_spectral_density_fit_components(J, w, lam, gamma, w0);" - ] - }, - { - "cell_type": "markdown", - "id": "a69919e0", - "metadata": {}, - "source": [ - "And let's also compare the power spectrum of the fit and the analytical spectral density:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "2b8e61fb", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_power_spectrum(alpha, wc, beta, lam, gamma, w0, save=True):\n", - " \"\"\" Plot the power spectrum of a fit against the actual power spectrum. \"\"\"\n", - " w = np.linspace(-10, 10, 50000)\n", - "\n", - " s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta)\n", - " s_fit = (\n", - " spectral_density_approx(w, lam, gamma, w0) *\n", - " ((1 / (np.e**(w * beta) - 1)) + 1) * 2\n", - " )\n", - "\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " axes.plot(w, s_orig, 'r', linewidth=2, label=\"original\")\n", - " axes.plot(w, s_fit, 'b', linewidth=2, label=\"fit\")\n", - "\n", - " axes.set_xlabel(r'$\\omega$', fontsize=28)\n", - " axes.set_ylabel(r'$S(\\omega)$', fontsize=28)\n", - " axes.legend()\n", - "\n", - " if save:\n", - " fig.savefig('powerspectrum.eps')\n", - "\n", - "\n", - "plot_power_spectrum(obp.alpha, obp.wc, obp.beta, lam, gamma, w0, save=False)" - ] - }, - { - "cell_type": "markdown", - "id": "7fe1c38a", - "metadata": {}, - "source": [ - "Now that we have a good fit to the spectral density, we can calculate the Matsubara expansion terms for the `BosonicBath` from them. At the same time we will calculate the Matsubara terminator for this expansion." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "123ccf79", - "metadata": {}, - "outputs": [], - "source": [ - "def matsubara_coefficients_from_spectral_fit(lam, gamma, w0, beta, Q, Nk):\n", - " \"\"\" Calculate the Matsubara co-efficients for a fit to the spectral\n", - " density.\n", - " \"\"\"\n", - " # initial 0 value with the correct dimensions:\n", - " terminator = 0. * spre(Q)\n", - " # the number of matsubara expansion terms to include in the terminator:\n", - " terminator_max_k = 1000\n", - "\n", - " ckAR = []\n", - " vkAR = []\n", - " ckAI = []\n", - " vkAI = []\n", - "\n", - " for lamt, Gamma, Om in zip(lam, gamma, w0):\n", - "\n", - " ckAR.extend([\n", - " (lamt / (4 * Om)) * coth(beta * (Om + 1.0j * Gamma) / 2),\n", - " (lamt / (4 * Om)) * coth(beta * (Om - 1.0j * Gamma) / 2),\n", - " ])\n", - " for k in range(1, Nk + 1):\n", - " ek = 2 * np.pi * k / beta\n", - " ckAR.append(\n", - " (-2 * lamt * 2 * Gamma / beta) * ek /\n", - " (\n", - " ((Om + 1.0j * Gamma)**2 + ek**2) *\n", - " ((Om - 1.0j * Gamma)**2 + ek**2)\n", - " )\n", - " )\n", - "\n", - " terminator_factor = 0\n", - " for k in range(Nk + 1, terminator_max_k):\n", - " ek = 2 * np.pi * k / beta\n", - " ck = (\n", - " (-2 * lamt * 2 * Gamma / beta) * ek /\n", - " (\n", - " ((Om + 1.0j * Gamma)**2 + ek**2) *\n", - " ((Om - 1.0j * Gamma)**2 + ek**2)\n", - " )\n", - " )\n", - " terminator_factor += ck / ek\n", - " terminator += terminator_factor * (\n", - " 2 * spre(Q) * spost(Q.dag())\n", - " - spre(Q.dag() * Q)\n", - " - spost(Q.dag() * Q)\n", - " )\n", - "\n", - " vkAR.extend([\n", - " -1.0j * Om + Gamma,\n", - " 1.0j * Om + Gamma,\n", - " ])\n", - " vkAR.extend([\n", - " 2 * np.pi * k * obp.T + 0.j\n", - " for k in range(1, Nk + 1)\n", - " ])\n", - "\n", - " ckAI.extend([\n", - " -0.25 * lamt * 1.0j / Om,\n", - " 0.25 * lamt * 1.0j / Om,\n", - " ])\n", - " vkAI.extend([\n", - " -(-1.0j * Om - Gamma),\n", - " -(1.0j * Om - Gamma),\n", - " ])\n", - "\n", - " return ckAR, vkAR, ckAI, vkAI, terminator" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "a146f03f", - "metadata": {}, - "outputs": [], - "source": [ - "def generate_spectrum_results(obp, params, Nk, max_depth):\n", - " \"\"\" Run the HEOM with the given bath parameters and\n", - " and return the results of the evolution.\n", - " \"\"\"\n", - " lam, gamma, w0 = params\n", - " ckAR, vkAR, ckAI, vkAI, terminator = (\n", - " matsubara_coefficients_from_spectral_fit(\n", - " lam, gamma, w0, beta=obp.beta, Q=obp.Q, Nk=Nk,\n", - " )\n", - " )\n", - " Ltot = liouvillian(Hsys) + terminator\n", - " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - "\n", - " options = Options(\n", - " nsteps=15000, store_states=True, rtol=1e-12, atol=1e-12, method=\"bdf\",\n", - " )\n", - " # This problem is a little stiff, so we use the BDF method to solve\n", - " # the ODE ^^^\n", - "\n", - " with timer(\"RHS construction time\"):\n", - " bath = BosonicBath(obp.Q, ckAR, vkAR, ckAI, vkAI)\n", - " HEOM_spectral_fit = HEOMSolver(\n", - " Ltot, bath, max_depth=max_depth, options=options,\n", - " )\n", - "\n", - " with timer(\"ODE solver time\"):\n", - " results_spectral_fit = (HEOM_spectral_fit.run(rho0, tlist))\n", - "\n", - " return results_spectral_fit" - ] - }, - { - "cell_type": "markdown", - "id": "5185757b", - "metadata": {}, - "source": [ - "Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "7b936c06", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.005162954330444336\n", - "10.0%. Run time: 0.05s. Est. time left: 00:00:00:00\n", - "20.0%. Run time: 0.07s. Est. time left: 00:00:00:00\n", - "30.1%. Run time: 0.10s. Est. time left: 00:00:00:00\n", - "40.1%. Run time: 0.12s. Est. time left: 00:00:00:00\n", - "50.1%. Run time: 0.15s. Est. time left: 00:00:00:00\n", - "60.1%. Run time: 0.17s. Est. time left: 00:00:00:00\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/mcditoos/qutip_gsoc_app/qutip/solver/options.py:16: FutureWarning: Dedicated options class are no longer needed, options should be passed as dict to solvers.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "70.1%. Run time: 0.20s. Est. time left: 00:00:00:00\n", - "80.1%. Run time: 0.23s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 0.26s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 0.29s. Est. time left: 00:00:00:00\n", - "Total run time: 0.29s\n", - "ODE solver time: 0.29564571380615234\n", - "RHS construction time: 0.011826276779174805\n", - "10.0%. Run time: 0.16s. Est. time left: 00:00:00:01\n", - "20.0%. Run time: 0.24s. Est. time left: 00:00:00:00\n", - "30.1%. Run time: 0.32s. Est. time left: 00:00:00:00\n", - "40.1%. Run time: 0.42s. Est. time left: 00:00:00:00\n", - "50.1%. Run time: 0.50s. Est. time left: 00:00:00:00\n", - "60.1%. Run time: 0.57s. Est. time left: 00:00:00:00\n", - "70.1%. Run time: 0.65s. Est. time left: 00:00:00:00\n", - "80.1%. Run time: 0.73s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 0.81s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 0.87s. Est. time left: 00:00:00:00\n", - "Total run time: 0.88s\n", - "ODE solver time: 0.8767976760864258\n", - "RHS construction time: 0.019646406173706055\n", - "10.0%. Run time: 0.56s. Est. time left: 00:00:00:04\n", - "20.0%. Run time: 0.93s. Est. time left: 00:00:00:03\n", - "30.1%. Run time: 1.32s. Est. time left: 00:00:00:03\n", - "40.1%. Run time: 1.70s. Est. time left: 00:00:00:02\n", - "50.1%. Run time: 2.09s. Est. time left: 00:00:00:02\n", - "60.1%. Run time: 2.49s. Est. time left: 00:00:00:01\n", - "70.1%. Run time: 2.88s. Est. time left: 00:00:00:01\n", - "80.1%. Run time: 3.26s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 3.64s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 4.00s. Est. time left: 00:00:00:00\n", - "Total run time: 4.00s\n", - "ODE solver time: 4.004042148590088\n", - "RHS construction time: 0.14979052543640137\n", - "10.0%. Run time: 1.81s. Est. time left: 00:00:00:16\n", - "20.0%. Run time: 2.93s. Est. time left: 00:00:00:11\n", - "30.1%. Run time: 3.97s. Est. time left: 00:00:00:09\n", - "40.1%. Run time: 4.97s. Est. time left: 00:00:00:07\n", - "50.1%. Run time: 6.00s. Est. time left: 00:00:00:05\n", - "60.1%. Run time: 7.03s. Est. time left: 00:00:00:04\n", - "70.1%. Run time: 8.08s. Est. time left: 00:00:00:03\n", - "80.1%. Run time: 9.08s. Est. time left: 00:00:00:02\n", - "90.2%. Run time: 10.06s. Est. time left: 00:00:00:01\n", - "100.0%. Run time: 11.06s. Est. time left: 00:00:00:00\n", - "Total run time: 11.06s\n", - "ODE solver time: 11.059714555740356\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Generate results for different number of lorentzians in fit:\n", - "\n", - "results_spectral_fit_pk = [\n", - " generate_spectrum_results(obp, params, Nk=1, max_depth=max_depth)\n", - " for params in params_k\n", - "]\n", - "\n", - "plot_result_expectations([\n", - " (\n", - " result, P11p, 'rand',\n", - " f\"P11 (spectral fit) $k_J$={pk + 1}\",\n", - " )\n", - " for pk, result in enumerate(results_spectral_fit_pk)\n", - "]);" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "a278cd12", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.108489990234375\n", - "10.0%. Run time: 3.03s. Est. time left: 00:00:00:27\n", - "20.0%. Run time: 4.73s. Est. time left: 00:00:00:18\n", - "30.1%. Run time: 6.56s. Est. time left: 00:00:00:15\n", - "40.1%. Run time: 8.72s. Est. time left: 00:00:00:13\n", - "50.1%. Run time: 11.80s. Est. time left: 00:00:00:11\n", - "60.1%. Run time: 13.89s. Est. time left: 00:00:00:09\n", - "70.1%. Run time: 15.93s. Est. time left: 00:00:00:06\n", - "80.1%. Run time: 18.57s. Est. time left: 00:00:00:04\n", - "90.2%. Run time: 20.98s. Est. time left: 00:00:00:02\n", - "100.0%. Run time: 23.10s. Est. time left: 00:00:00:00\n", - "Total run time: 23.10s\n", - "ODE solver time: 23.098018646240234\n", - "RHS construction time: 0.2477586269378662\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "capi_return is NULL\n", - "Call-back cb_f_in_zvode__user__routines failed.\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[24], line 6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# generate results for different number of Matsubara terms per Lorentzian\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# for max number of Lorentzians:\u001b[39;00m\n\u001b[1;32m 4\u001b[0m Nk_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m4\u001b[39m)\n\u001b[1;32m 5\u001b[0m results_spectral_fit_nk \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m----> 6\u001b[0m \u001b[43mgenerate_spectrum_results\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams_k\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNk\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mNk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_depth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_depth\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m Nk \u001b[38;5;129;01min\u001b[39;00m Nk_list\n\u001b[1;32m 8\u001b[0m ]\n\u001b[1;32m 10\u001b[0m plot_result_expectations([\n\u001b[1;32m 11\u001b[0m (\n\u001b[1;32m 12\u001b[0m result, P11p, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrand\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m nk, result \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(Nk_list, results_spectral_fit_nk)\n\u001b[1;32m 16\u001b[0m ]);\n", - "Cell \u001b[0;32mIn[22], line 27\u001b[0m, in \u001b[0;36mgenerate_spectrum_results\u001b[0;34m(obp, params, Nk, max_depth)\u001b[0m\n\u001b[1;32m 22\u001b[0m HEOM_spectral_fit \u001b[38;5;241m=\u001b[39m HEOMSolver(\n\u001b[1;32m 23\u001b[0m Ltot, bath, max_depth\u001b[38;5;241m=\u001b[39mmax_depth, options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[1;32m 24\u001b[0m )\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m timer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mODE solver time\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m---> 27\u001b[0m results_spectral_fit \u001b[38;5;241m=\u001b[39m (\u001b[43mHEOM_spectral_fit\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrho0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtlist\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m results_spectral_fit\n", - "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/heom/bofin_solvers.py:1114\u001b[0m, in \u001b[0;36mHEOMSolver.run\u001b[0;34m(self, state0, tlist, args, e_ops)\u001b[0m\n\u001b[1;32m 1047\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun\u001b[39m(\u001b[38;5;28mself\u001b[39m, state0, tlist, \u001b[38;5;241m*\u001b[39m, args\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, e_ops\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 1048\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1049\u001b[0m \u001b[38;5;124;03m Solve for the time evolution of the system.\u001b[39;00m\n\u001b[1;32m 1050\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1112\u001b[0m \u001b[38;5;124;03m list of attributes.\u001b[39;00m\n\u001b[1;32m 1113\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtlist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43me_ops\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43me_ops\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/solver_base.py:197\u001b[0m, in \u001b[0;36mSolver.run\u001b[0;34m(self, state0, tlist, e_ops, args)\u001b[0m\n\u001b[1;32m 192\u001b[0m stats[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpreparation time\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m time() \u001b[38;5;241m-\u001b[39m _time_start\n\u001b[1;32m 194\u001b[0m progress_bar \u001b[38;5;241m=\u001b[39m progress_bars[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprogress_bar\u001b[39m\u001b[38;5;124m'\u001b[39m]](\n\u001b[1;32m 195\u001b[0m \u001b[38;5;28mlen\u001b[39m(tlist)\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprogress_kwargs\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 196\u001b[0m )\n\u001b[0;32m--> 197\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_integrator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtlist\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 198\u001b[0m \u001b[43m \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 199\u001b[0m \u001b[43m \u001b[49m\u001b[43mresults\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_restore_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/integrator/integrator.py:201\u001b[0m, in \u001b[0;36mIntegrator.run\u001b[0;34m(self, tlist)\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 188\u001b[0m \u001b[38;5;124;03mIntegrate the system yielding the state for each times in tlist.\u001b[39;00m\n\u001b[1;32m 189\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;124;03m The state of the solver at each ``t`` of tlist.\u001b[39;00m\n\u001b[1;32m 199\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m tlist[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[0;32m--> 201\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/integrator/scipy_integrator.py:110\u001b[0m, in \u001b[0;36mIntegratorScipyAdams.integrate\u001b[0;34m(self, t, copy)\u001b[0m\n\u001b[1;32m 108\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_handle()\n\u001b[1;32m 109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m t \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ode_solver\u001b[38;5;241m.\u001b[39mt:\n\u001b[0;32m--> 110\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_ode_solver\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 111\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_state(copy)\n", - "File \u001b[0;32m~/anaconda3/envs/qutip-dev/lib/python3.12/site-packages/scipy/integrate/_ode.py:431\u001b[0m, in \u001b[0;36mode.integrate\u001b[0;34m(self, t, step, relax)\u001b[0m\n\u001b[1;32m 428\u001b[0m mth \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_integrator\u001b[38;5;241m.\u001b[39mrun\n\u001b[1;32m 430\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 431\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mt \u001b[38;5;241m=\u001b[39m \u001b[43mmth\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjac\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 432\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_y\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 433\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mf_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjac_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 434\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mSystemError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 435\u001b[0m \u001b[38;5;66;03m# f2py issue with tuple returns, see ticket 1187.\u001b[39;00m\n\u001b[1;32m 436\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 437\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFunction to integrate must not return a tuple.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 438\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n", - "File \u001b[0;32m~/anaconda3/envs/qutip-dev/lib/python3.12/site-packages/scipy/integrate/_ode.py:1008\u001b[0m, in \u001b[0;36mvode.run\u001b[0;34m(self, f, jac, y0, t0, t1, f_params, jac_params)\u001b[0m\n\u001b[1;32m 1004\u001b[0m jac \u001b[38;5;241m=\u001b[39m _vode_banded_jac_wrapper(jac, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mml, jac_params)\n\u001b[1;32m 1006\u001b[0m args \u001b[38;5;241m=\u001b[39m ((f, jac, y0, t0, t1) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mtuple\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcall_args) \u001b[38;5;241m+\u001b[39m\n\u001b[1;32m 1007\u001b[0m (f_params, jac_params))\n\u001b[0;32m-> 1008\u001b[0m y1, t, istate \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrunner\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1009\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mistate \u001b[38;5;241m=\u001b[39m istate\n\u001b[1;32m 1010\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m istate \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", - "File \u001b[0;32m~/qutip_gsoc_app/qutip/solver/integrator/scipy_integrator.py:63\u001b[0m, in \u001b[0;36mIntegratorScipyAdams._mul_np_vec\u001b[0;34m(self, t, vec)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ode_solver\u001b[38;5;241m.\u001b[39m_integrator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_zvode(\n\u001b[1;32m 58\u001b[0m method\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[1;32m 59\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions,\n\u001b[1;32m 60\u001b[0m )\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscipy zvode \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmethod\n\u001b[0;32m---> 63\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_mul_np_vec\u001b[39m(\u001b[38;5;28mself\u001b[39m, t, vec):\n\u001b[1;32m 64\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;124;03m Interface between scipy which use numpy and the driver, which use data.\u001b[39;00m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 67\u001b[0m state \u001b[38;5;241m=\u001b[39m _data\u001b[38;5;241m.\u001b[39mdense\u001b[38;5;241m.\u001b[39mfast_from_numpy(vec)\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "# generate results for different number of Matsubara terms per Lorentzian\n", - "# for max number of Lorentzians:\n", - "\n", - "Nk_list = range(2, 4)\n", - "results_spectral_fit_nk = [\n", - " generate_spectrum_results(obp, params_k[-1], Nk=Nk, max_depth=max_depth)\n", - " for Nk in Nk_list\n", - "]\n", - "\n", - "plot_result_expectations([\n", - " (\n", - " result, P11p, 'rand',\n", - " f\"P11 (spectral fit) K={nk}\",\n", - " )\n", - " for nk, result in zip(Nk_list, results_spectral_fit_nk)\n", - "]);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "91d69d95", - "metadata": {}, - "outputs": [], - "source": [ - "# Generate results for different depths:\n", - "\n", - "Nc_list = range(2, max_depth)\n", - "results_spectral_fit_nc = [\n", - " generate_spectrum_results(obp, params_k[-1], Nk=1, max_depth=Nc)\n", - " for Nc in Nc_list\n", - "]\n", - "\n", - "plot_result_expectations([\n", - " (\n", - " result, P11p, 'rand',\n", - " f\"P11 (spectral fit) $N_C={nc}$\",\n", - " )\n", - " for nc, result in zip(Nc_list, results_spectral_fit_nc)\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "6466169d", - "metadata": {}, - "source": [ - "We now combine the fitting and correlation function data into one large plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f60baa61", - "metadata": {}, - "outputs": [], - "source": [ - "def correlation_approx_matsubara(t, ck, vk):\n", - " \"\"\" Calculate the approximate real or imaginary part of the\n", - " correlation function from the matsubara expansion co-efficients.\n", - " \"\"\"\n", - " ck = np.array(ck)\n", - " vk = np.array(vk)\n", - " return np.sum(ck[:, None] * np.exp(-vk[:, None] * t), axis=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bc12d8e8", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_cr_fit_vs_actual(t, ckAR, vkAR, C, axes):\n", - " \"\"\" Plot the C_R(t) fit. \"\"\"\n", - " yR = correlation_approx_matsubara(t, ckAR, vkAR)\n", - "\n", - " axes.plot(\n", - " t, np.real(C),\n", - " \"r\", linewidth=3, label=\"Original\",\n", - " )\n", - " axes.plot(\n", - " t, np.real(yR),\n", - " \"g\", dashes=[3, 3], linewidth=2, label=\"Reconstructed\",\n", - " )\n", - "\n", - " axes.legend(loc=0)\n", - " axes.set_ylabel(r'$C_R(t)$', fontsize=28)\n", - " axes.set_xlabel(r'$t\\;\\omega_c$', fontsize=28)\n", - " axes.locator_params(axis='y', nbins=4)\n", - " axes.locator_params(axis='x', nbins=4)\n", - " axes.text(0.15, 0.85, \"(a)\", fontsize=28, transform=axes.transAxes)\n", - "\n", - "\n", - "def plot_ci_fit_vs_actual(t, ckAI, vkAI, C, axes):\n", - " \"\"\" Plot the C_I(t) fit. \"\"\"\n", - " yI = correlation_approx_matsubara(t, ckAI, vkAI)\n", - "\n", - " axes.plot(\n", - " t, np.imag(C),\n", - " \"r\", linewidth=3, label=\"Original\",\n", - " )\n", - " axes.plot(\n", - " t, np.real(yI),\n", - " \"g\", dashes=[3, 3], linewidth=2, label=\"Reconstructed\",\n", - " )\n", - "\n", - " axes.legend(loc=0)\n", - " axes.set_ylabel(r'$C_I(t)$', fontsize=28)\n", - " axes.set_xlabel(r'$t\\;\\omega_c$', fontsize=28)\n", - " axes.locator_params(axis='y', nbins=4)\n", - " axes.locator_params(axis='x', nbins=4)\n", - " axes.text(0.80, 0.80, \"(b)\", fontsize=28, transform=axes.transAxes)\n", - "\n", - "\n", - "def plot_jw_fit_vs_actual(bath_fit, obp, axes):\n", - " \"\"\" Plot the J(w) fit. \"\"\"\n", - " [lam, gamma, w0] = bath_fit\n", - " [alpha, wc] = [obp.alpha, obp.wc]\n", - "\n", - " w = np.linspace(0, 25, 20000)\n", - "\n", - " J_orig = ohmic_spectral_density(w, alpha=alpha, wc=wc)\n", - " J_fit = spectral_density_approx(w, lam, gamma, w0)\n", - "\n", - " axes.plot(\n", - " w, J_orig,\n", - " \"r\", linewidth=3, label=r\"$J(\\omega)$ original\",\n", - " )\n", - " axes.plot(\n", - " w, J_fit,\n", - " \"g\", dashes=[3, 3], linewidth=2, label=r\"$J(\\omega)$ Fit $k_J = 4$\",\n", - " )\n", - "\n", - " axes.legend(loc=0)\n", - " axes.set_ylabel(r'$J(\\omega)$', fontsize=28)\n", - " axes.set_xlabel(r'$\\omega/\\omega_c$', fontsize=28)\n", - " axes.locator_params(axis='y', nbins=4)\n", - " axes.locator_params(axis='x', nbins=4)\n", - " axes.text(0.15, 0.85, \"(c)\", fontsize=28, transform=axes.transAxes)\n", - "\n", - "\n", - "def plot_sw_fit_vs_actual(bath_fit, obp, axes):\n", - " \"\"\" Plot the S(w) fit. \"\"\"\n", - " [lam, gamma, w0] = bath_fit\n", - " [alpha, wc, beta] = [obp.alpha, obp.wc, obp.beta]\n", - "\n", - " # avoid the pole in the fit around zero:\n", - " w = np.concatenate(\n", - " [np.linspace(-10, -0.1, 5000),\n", - " np.linspace(0.1, 10, 5000)],\n", - " )\n", - "\n", - " s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta)\n", - " s_fit = (\n", - " spectral_density_approx(w, lam, gamma, w0) *\n", - " ((1 / (np.e**(w * beta) - 1)) + 1) * 2\n", - " )\n", - "\n", - " axes.plot(w, s_orig, \"r\", linewidth=3, label=\"Original\")\n", - " axes.plot(w, s_fit, \"g\", dashes=[3, 3], linewidth=2, label=\"Reconstructed\")\n", - "\n", - " axes.legend()\n", - " axes.set_ylabel(r'$S(\\omega)$', fontsize=28)\n", - " axes.set_xlabel(r'$\\omega/\\omega_c$', fontsize=28)\n", - " axes.locator_params(axis='y', nbins=4)\n", - " axes.locator_params(axis='x', nbins=4)\n", - " axes.text(0.15, 0.85, \"(d)\", fontsize=28, transform=axes.transAxes)\n", - "\n", - "\n", - "def plot_matsubara_spectrum_fit_vs_actual(\n", - " t, C, matsubara_fit, bath_fit, obp,\n", - "):\n", - " \"\"\" Plot the Matsubara fit of the spectrum . \"\"\"\n", - " fig = plt.figure(figsize=(12, 10))\n", - " grid = plt.GridSpec(2, 2, wspace=0.4, hspace=0.3)\n", - "\n", - " [ckAR, vkAR, ckAI, vkAI] = matsubara_fit\n", - "\n", - " plot_cr_fit_vs_actual(\n", - " t, ckAR, vkAR, C,\n", - " axes=fig.add_subplot(grid[0, 0]),\n", - " )\n", - " plot_ci_fit_vs_actual(\n", - " t, ckAI, vkAI, C,\n", - " axes=fig.add_subplot(grid[0, 1]),\n", - " )\n", - " plot_jw_fit_vs_actual(\n", - " bath_fit, obp,\n", - " axes=fig.add_subplot(grid[1, 0]),\n", - " )\n", - " plot_sw_fit_vs_actual(\n", - " bath_fit, obp,\n", - " axes=fig.add_subplot(grid[1, 1]),\n", - " )\n", - "\n", - " return fig" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a9254cfe", - "metadata": {}, - "outputs": [], - "source": [ - "t = np.linspace(0, 15, 100)\n", - "C = ohmic_correlation(t, alpha=obp.alpha, wc=obp.wc, beta=obp.beta)\n", - "\n", - "ckAR, vkAR, ckAI, vkAI, terminator = (\n", - " matsubara_coefficients_from_spectral_fit(\n", - " lam, gamma, w0, beta=obp.beta, Q=obp.Q, Nk=1,\n", - " )\n", - ")\n", - "\n", - "matsubara_fit = [ckAR, vkAR, ckAI, vkAI]\n", - "bath_fit = [lam, gamma, w0]\n", - "\n", - "plot_matsubara_spectrum_fit_vs_actual(\n", - " t, C, matsubara_fit,\n", - " bath_fit, obp,\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "79c90d62", - "metadata": {}, - "source": [ - "## Building the HEOM bath by fitting the correlation function" - ] - }, - { - "cell_type": "markdown", - "id": "e87e8d35", - "metadata": {}, - "source": [ - "Having successfully fitted the spectral density and used the result to calculate the Matsubara expansion and terminator for the HEOM bosonic bath, we now proceed to the second case of fitting the correlation function itself instead.\n", - "\n", - "Here we fit the real and imaginary parts seperately, using the following ansatz\n", - "\n", - "$$C_R^F(t) = \\sum_{i=1}^{k_R} c_R^ie^{-\\gamma_R^i t}\\cos(\\omega_R^i t)$$\n", - "\n", - "$$C_I^F(t) = \\sum_{i=1}^{k_I} c_I^ie^{-\\gamma_I^i t}\\sin(\\omega_I^i t)$$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e1883b3e", - "metadata": {}, - "outputs": [], - "source": [ - "# The approximate correlation functions and a helper for fitting\n", - "# the approximate correlation function to values calculated from\n", - "# the analytical formula:\n", - "\n", - "def correlation_approx_real(t, a, b, c):\n", - " \"\"\" Calculate the fitted value of the function for the given parameters.\n", - " \"\"\"\n", - " a = np.array(a)\n", - " b = np.array(b)\n", - " c = np.array(c)\n", - " return np.sum(\n", - " a[:, None] * np.exp(b[:, None] * t) * np.cos(c[:, None] * t),\n", - " axis=0,\n", - " )\n", - "\n", - "\n", - "def correlation_approx_imag(t, a, b, c):\n", - " \"\"\" Calculate the fitted value of the function for the given parameters.\n", - " \"\"\"\n", - " a = np.array(a)\n", - " b = np.array(b)\n", - " c = np.array(c)\n", - " return np.sum(\n", - " a[:, None] * np.exp(b[:, None] * t) * np.sin(c[:, None] * t),\n", - " axis=0,\n", - " )\n", - "\n", - "\n", - "def fit_correlation_real(C, t, wc, N):\n", - " \"\"\" Fit the spectral density with N underdamped oscillators. \"\"\"\n", - " sigma = [0.1] * len(t)\n", - "\n", - " C_max = abs(max(C, key=abs))\n", - "\n", - " guesses = pack([C_max] * N, [-wc] * N, [wc] * N)\n", - " lower_bounds = pack([-20 * C_max] * N, [-np.inf] * N, [0.] * N)\n", - " upper_bounds = pack([20 * C_max] * N, [0.1] * N, [np.inf] * N)\n", - "\n", - " params, _ = curve_fit(\n", - " lambda x, *params: correlation_approx_real(t, *unpack(params)),\n", - " t, C,\n", - " p0=guesses,\n", - " bounds=(lower_bounds, upper_bounds),\n", - " sigma=sigma,\n", - " maxfev=1000000000,\n", - " )\n", - "\n", - " return unpack(params)\n", - "\n", - "\n", - "def fit_correlation_imag(C, t, wc, N):\n", - " \"\"\" Fit the spectral density with N underdamped oscillators. \"\"\"\n", - " sigma = [0.0001] * len(t)\n", - "\n", - " C_max = abs(max(C, key=abs))\n", - "\n", - " guesses = pack([-C_max] * N, [-2] * N, [1] * N)\n", - " lower_bounds = pack([-5 * C_max] * N, [-100] * N, [0.] * N)\n", - " upper_bounds = pack([5 * C_max] * N, [0.01] * N, [100] * N)\n", - "\n", - " params, _ = curve_fit(\n", - " lambda x, *params: correlation_approx_imag(t, *unpack(params)),\n", - " t, C,\n", - " p0=guesses,\n", - " bounds=(lower_bounds, upper_bounds),\n", - " sigma=sigma,\n", - " maxfev=1000000000,\n", - " )\n", - "\n", - " return unpack(params)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0503d1db", - "metadata": {}, - "outputs": [], - "source": [ - "t = np.linspace(0, 15, 15000)\n", - "C = ohmic_correlation(t, alpha=obp.alpha, wc=obp.wc, beta=obp.beta)\n", - "\n", - "params_k_real = [\n", - " fit_correlation_real(np.real(C), t, wc=obp.wc, N=i+1)\n", - " for i in range(3)\n", - "]\n", - "\n", - "params_k_imag = [\n", - " fit_correlation_imag(np.imag(C), t, wc=obp.wc, N=i+1)\n", - " for i in range(3)\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9372964c", - "metadata": {}, - "outputs": [], - "source": [ - "for k, params in enumerate(params_k_real):\n", - " lam, gamma, w0 = params\n", - " y = correlation_approx_real(t, lam, gamma, w0)\n", - " print(f\"Parameters [k={k}]: lam={lam}; gamma={gamma}; w0={w0}\")\n", - " plt.plot(t, np.real(C), label=\"C_R(t) analytic\")\n", - " plt.plot(t, y, label=f\"C_R(t) k={k + 1}\")\n", - " plt.legend()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a268386d", - "metadata": {}, - "outputs": [], - "source": [ - "for k, params in enumerate(params_k_imag):\n", - " lam, gamma, w0 = params\n", - " y = correlation_approx_imag(t, lam, gamma, w0)\n", - " print(f\"Parameters [k={k}]: lam={lam}; gamma={gamma}; w0={w0}\")\n", - " plt.plot(t, np.imag(C), label=\"C_I(t) analytic\")\n", - " plt.plot(t, y, label=f\"C_I(t) k={k + 1}\")\n", - " plt.legend()\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "4f04e6ad", - "metadata": {}, - "source": [ - "Now we construct the `BosonicBath` co-efficients and frequencies from the fit to the correlation function:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6150d0ae", - "metadata": {}, - "outputs": [], - "source": [ - "def matsubara_coefficients_from_corr_fit_real(lam, gamma, w0):\n", - " \"\"\" Return the matsubara coefficients for the imaginary part\n", - " of the correlation function.\n", - " \"\"\"\n", - " ckAR = [0.5 * x + 0j for x in lam] # the 0.5 is from the cosine\n", - " # extend the list with the complex conjugates:\n", - " ckAR.extend(np.conjugate(ckAR))\n", - "\n", - " vkAR = [-x - 1.0j * y for x, y in zip(gamma, w0)]\n", - " vkAR.extend([-x + 1.0j * y for x, y in zip(gamma, w0)])\n", - "\n", - " return ckAR, vkAR\n", - "\n", - "\n", - "def matsubara_coefficients_from_corr_fit_imag(lam, gamma, w0):\n", - " \"\"\" Return the matsubara coefficients for the imaginary part\n", - " of the correlation function.\n", - " \"\"\"\n", - " ckAI = [-0.5j * x for x in lam] # the 0.5 is from the sine\n", - " # extend the list with the complex conjugates:\n", - " ckAI.extend(np.conjugate(ckAI))\n", - "\n", - " vkAI = [-x - 1.0j * y for x, y in zip(gamma, w0)]\n", - " vkAI.extend([-x + 1.0j * y for x, y in zip(gamma, w0)])\n", - "\n", - " return ckAI, vkAI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b9b5d7be", - "metadata": {}, - "outputs": [], - "source": [ - "ckAR, vkAR = matsubara_coefficients_from_corr_fit_real(*params_k_real[-1])\n", - "ckAI, vkAI = matsubara_coefficients_from_corr_fit_imag(*params_k_imag[-1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e581be18", - "metadata": {}, - "outputs": [], - "source": [ - "def corr_spectrum_approx(w, ckAR, vkAR, ckAI, vkAI):\n", - " \"\"\" Calculates the approximate power spectrum from ck and vk. \"\"\"\n", - " S = np.zeros(len(w), dtype=np.complex128)\n", - " for ck, vk in zip(ckAR, vkAR):\n", - " S += (\n", - " 2 * ck * np.real(vk) /\n", - " ((w - np.imag(vk))**2 + (np.real(vk)**2))\n", - " )\n", - " for ck, vk in zip(ckAI, vkAI):\n", - " S += (\n", - " 2 * 1.0j * ck * np.real(vk) /\n", - " ((w - np.imag(vk))**2 + (np.real(vk)**2))\n", - " )\n", - " return S" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d8f1f227", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_jw_correlation_fit_vs_actual(matsubara_fit, obp, axes):\n", - " \"\"\" Plot J(w) from the correlation fit. \"\"\"\n", - " [ckAR, vkAR, ckAI, vkAI] = matsubara_fit\n", - " [alpha, wc] = [obp.alpha, obp.wc]\n", - "\n", - " w = np.linspace(0.001, 25, 20000)\n", - "\n", - " J_orig = ohmic_spectral_density(w, alpha=alpha, wc=wc)\n", - " J_fit = np.real(\n", - " corr_spectrum_approx(w, ckAR, vkAR, ckAI, vkAI) /\n", - " (((1 / (np.e**(w * obp.beta) - 1)) + 1) * 2)\n", - " )\n", - "\n", - " axes.plot(\n", - " w, J_orig,\n", - " \"r\", linewidth=3, label=r\"$J(\\omega)$ original\",\n", - " )\n", - " axes.plot(\n", - " w, J_fit,\n", - " \"g\", dashes=[3, 3], linewidth=2, label=r\"$J(\\omega)$ fit\",\n", - " )\n", - "\n", - " axes.legend(loc=0)\n", - " axes.set_ylabel(r'$J(\\omega)$', fontsize=28)\n", - " axes.set_xlabel(r'$\\omega/\\omega_c$', fontsize=28)\n", - " axes.locator_params(axis='y', nbins=4)\n", - " axes.locator_params(axis='x', nbins=4)\n", - " axes.text(3, 1.1, \"(c)\", fontsize=28)\n", - "\n", - "\n", - "def plot_sw_correlation_fit_vs_actual(matsubara_fit, obp, axes):\n", - " \"\"\" Plot S(W) from the correlation fit. \"\"\"\n", - " [ckAR, vkAR, ckAI, vkAI] = matsubara_fit\n", - " [alpha, wc, beta] = [obp.alpha, obp.wc, obp.beta]\n", - "\n", - " # avoid the pole in the fit around zero:\n", - " w = np.concatenate([\n", - " np.linspace(-10, -0.1, 5000),\n", - " np.linspace(0.1, 10, 5000),\n", - " ])\n", - "\n", - " s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta)\n", - " s_fit = corr_spectrum_approx(w, ckAR, vkAR, ckAI, vkAI)\n", - "\n", - " axes.plot(\n", - " w, s_orig,\n", - " \"r\", linewidth=3, label=\"Original\",\n", - " )\n", - " axes.plot(\n", - " w, s_fit,\n", - " \"g\", dashes=[3, 3], linewidth=2, label=\"Reconstructed\",\n", - " )\n", - "\n", - " axes.legend()\n", - " axes.set_ylabel(r'$S(\\omega)$', fontsize=28)\n", - " axes.set_xlabel(r'$\\omega/\\omega_c$', fontsize=28)\n", - " axes.locator_params(axis='y', nbins=4)\n", - " axes.locator_params(axis='x', nbins=4)\n", - " axes.text(0.15, 0.85, \"(d)\", fontsize=28, transform=axes.transAxes)\n", - "\n", - "\n", - "def plot_matsubara_correlation_fit_vs_actual(t, C, matsubara_fit, obp):\n", - " fig = plt.figure(figsize=(12, 10))\n", - " grid = plt.GridSpec(2, 2, wspace=0.4, hspace=0.3)\n", - "\n", - " ckAR, vkAR, ckAI, vkAI = matsubara_fit\n", - "\n", - " plot_cr_fit_vs_actual(\n", - " t, ckAR, vkAR, C,\n", - " axes=fig.add_subplot(grid[0, 0]),\n", - " )\n", - " plot_ci_fit_vs_actual(\n", - " t, ckAI, vkAI, C,\n", - " axes=fig.add_subplot(grid[0, 1]),\n", - " )\n", - " plot_jw_correlation_fit_vs_actual(\n", - " matsubara_fit, obp,\n", - " axes=fig.add_subplot(grid[1, 0]),\n", - " )\n", - " plot_sw_correlation_fit_vs_actual(\n", - " matsubara_fit, obp,\n", - " axes=fig.add_subplot(grid[1, 1]),\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "32680798", - "metadata": {}, - "outputs": [], - "source": [ - "t = np.linspace(0, 15, 100)\n", - "C = ohmic_correlation(t, alpha=obp.alpha, wc=obp.wc, beta=obp.beta)\n", - "\n", - "matsubara_fit = [ckAR, vkAR, ckAI, vkAI]\n", - "\n", - "plot_matsubara_correlation_fit_vs_actual(\n", - " t, C, matsubara_fit, obp,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1a00bfaf", - "metadata": {}, - "outputs": [], - "source": [ - "def generate_corr_results(params_real, params_imag, max_depth):\n", - " ckAR, vkAR = matsubara_coefficients_from_corr_fit_real(\n", - " *params_real\n", - " )\n", - " ckAI, vkAI = matsubara_coefficients_from_corr_fit_imag(\n", - " *params_imag\n", - " )\n", - "\n", - " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - " options = Options(\n", - " nsteps=15000, store_states=True, rtol=1e-12, atol=1e-12, method=\"bdf\",\n", - " )\n", - " # This problem is a little stiff, so we use the BDF method to solve\n", - " # the ODE ^^^\n", - "\n", - " with timer(\"RHS construction time\"):\n", - " bath = BosonicBath(obp.Q, ckAR, vkAR, ckAI, vkAI)\n", - " HEOM_corr_fit = HEOMSolver(\n", - " Hsys, bath, max_depth=max_depth, options=options,\n", - " )\n", - "\n", - " with timer(\"ODE solver time\"):\n", - " results_corr_fit = (HEOM_corr_fit.run(rho0, tlist))\n", - "\n", - " return results_corr_fit\n", - "\n", - "\n", - "# Generate results for different number of lorentzians in fit:\n", - "results_corr_fit_pk = [\n", - " print(f\"{pk + 1}\") or generate_corr_results(\n", - " params_real, params_imag, max_depth=max_depth,\n", - " )\n", - " for pk, (params_real, params_imag)\n", - " in enumerate(zip(params_k_real, params_k_imag))\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7db32d9d", - "metadata": {}, - "outputs": [], - "source": [ - "plot_result_expectations([\n", - " (\n", - " result, P11p, 'rand',\n", - " f\"P11 (correlation fit) k_R=k_I={pk + 1}\",\n", - " )\n", - " for pk, result in enumerate(results_corr_fit_pk)\n", - "]);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6902bf9e", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", - "\n", - "plot_result_expectations([\n", - " (\n", - " results_corr_fit_pk[0], P11p,\n", - " 'y', \"Correlation Function Fit $k_R=k_I=1$\",\n", - " ),\n", - " (\n", - " results_corr_fit_pk[2], P11p,\n", - " 'y-.', \"Correlation Function Fit $k_R=k_I=3$\",\n", - " ),\n", - " (results_spectral_fit_pk[0], P11p, 'b', \"Spectral Density Fit $k_J=1$\"),\n", - " (results_spectral_fit_pk[2], P11p, 'g--', \"Spectral Density Fit $k_J=3$\"),\n", - " (results_spectral_fit_pk[3], P11p, 'r-.', \"Spectral Density Fit $k_J=4$\"),\n", - "], axes=axes)\n", - "\n", - "axes.set_yticks([0.6, 0.8, 1])\n", - "axes.set_ylabel(r'$\\rho_{11}$', fontsize=30)\n", - "axes.set_xlabel(r'$t\\;\\omega_c$', fontsize=30)\n", - "axes.legend(loc=0, fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "id": "7e7e681d", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8ce34619", - "metadata": {}, - "outputs": [], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "1e6d47b7", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02e1d11e", - "metadata": {}, - "outputs": [], - "source": [ - "assert np.allclose(\n", - " expect(P11p, results_spectral_fit_pk[2].states),\n", - " expect(P11p, results_spectral_fit_pk[3].states),\n", - " rtol=1e-2,\n", - ")" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "qutip-dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v4/heom/heom-1d-spin-bath-model-ohmic-fitting.md b/tutorials-v4/heom/heom-1d-spin-bath-model-ohmic-fitting.md index 0a9f84ee..33e49619 100644 --- a/tutorials-v4/heom/heom-1d-spin-bath-model-ohmic-fitting.md +++ b/tutorials-v4/heom/heom-1d-spin-bath-model-ohmic-fitting.md @@ -5,9 +5,9 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.5 + jupytext_version: 1.16.1 kernelspec: - display_name: Python 3 (ipykernel) + display_name: qutip-dev language: python name: python3 --- @@ -59,7 +59,7 @@ from qutip import ( spost, spre, ) -from qutip.nonmarkov.heom import ( +from qutip.solver.heom import ( HEOMSolver, BosonicBath, ) diff --git a/tutorials-v4/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb b/tutorials-v4/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb deleted file mode 100644 index c4fb0520..00000000 --- a/tutorials-v4/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb +++ /dev/null @@ -1,847 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fd96fd41", - "metadata": {}, - "source": [ - "# HEOM 1e: Spin-Bath model (pure dephasing)" - ] - }, - { - "cell_type": "markdown", - "id": "54bc3dff", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices.\n", - "\n", - "In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", - "\n", - "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian (see paper), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", - "\n", - "In the example below we show how to model the overdamped Drude-Lorentz Spectral Density, commonly used with the HEOM. We show how to do the Matsubara and Pade analytical decompositions, as well as how to fit the latter with a finite set of approximate exponentials. This differs from examble 1a in that we assume that the system and coupling parts of the Hamiltonian commute, hence giving an analytically solvable ''pure dephasing'' model. This is a useful example to look at when introducing other approximations (e.g., fitting of correlation functions) to check for validity/convergence against the analytical results. (Note that, generally, for the fitting examples, the pure dephasing model is the 'worst possible case'. \n", - "\n", - "### Drude-Lorentz spectral density\n", - "\n", - "The Drude-Lorentz spectral density is:\n", - "\n", - "$$J(\\omega)=\\omega \\frac{2\\lambda\\gamma}{{\\gamma}^2 + \\omega^2}$$\n", - "\n", - "where $\\lambda$ scales the coupling strength, and $\\gamma$ is the cut-off frequency.\n", - "We use the convention,\n", - "\\begin{equation*}\n", - "C(t) = \\int_0^{\\infty} d\\omega \\frac{J_D(\\omega)}{\\pi}[\\coth(\\beta\\omega) \\cos(\\omega \\tau) - i \\sin(\\omega \\tau)]\n", - "\\end{equation*}\n", - "\n", - "With the HEOM we must use an exponential decomposition:\n", - "\n", - "\\begin{equation*}\n", - "C(t)=\\sum_{k=0}^{k=\\infty} c_k e^{-\\nu_k t}\n", - "\\end{equation*}\n", - "\n", - "The Matsubara decomposition of the Drude-Lorentz spectral density is given by:\n", - "\n", - "\\begin{equation*}\n", - " \\nu_k = \\begin{cases}\n", - " \\gamma & k = 0\\\\\n", - " {2 \\pi k} / {\\beta \\hbar} & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "\\begin{equation*}\n", - " c_k = \\begin{cases}\n", - " \\lambda \\gamma (\\cot(\\beta \\gamma / 2) - i) / \\hbar & k = 0\\\\\n", - " 4 \\lambda \\gamma \\nu_k / \\{(nu_k^2 - \\gamma^2)\\beta \\hbar^2 \\} & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." - ] - }, - { - "cell_type": "markdown", - "id": "c100f975", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c4b1a9e2", - "metadata": {}, - "outputs": [], - "source": [ - "import contextlib\n", - "import time\n", - "\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "import scipy\n", - "from scipy.optimize import curve_fit\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " Options,\n", - " basis,\n", - " expect,\n", - " liouvillian,\n", - " sigmax,\n", - " sigmaz,\n", - ")\n", - "from qutip.nonmarkov.heom import (\n", - " HEOMSolver,\n", - " BosonicBath,\n", - " DrudeLorentzBath,\n", - " DrudeLorentzPadeBath,\n", - ")\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "26ca9292", - "metadata": {}, - "source": [ - "## Helper functions\n", - "\n", - "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "663ce2f8", - "metadata": {}, - "outputs": [], - "source": [ - "def cot(x):\n", - " \"\"\" Vectorized cotangent of x. \"\"\"\n", - " return 1. / np.tan(x)\n", - "\n", - "\n", - "def coth(x):\n", - " \"\"\" Vectorized hyperbolic cotangent of x. \"\"\"\n", - " return 1. / np.tanh(x)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "356c5c5c", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_result_expectations(plots, axes=None):\n", - " \"\"\" Plot the expectation values of operators as functions of time.\n", - "\n", - " Each plot in plots consists of (solver_result, measurement_operation,\n", - " color, label).\n", - " \"\"\"\n", - " if axes is None:\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " fig_created = True\n", - " else:\n", - " fig = None\n", - " fig_created = False\n", - "\n", - " # add kw arguments to each plot if missing\n", - " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", - " for result, m_op, color, label, kw in plots:\n", - " if m_op is None:\n", - " t, exp = result\n", - " else:\n", - " t = result.times\n", - " exp = np.real(expect(result.states, m_op))\n", - " kw.setdefault(\"linewidth\", 2)\n", - " axes.plot(t, exp, color, label=label, **kw)\n", - "\n", - " if fig_created:\n", - " axes.legend(loc=0, fontsize=12)\n", - " axes.set_xlabel(\"t\", fontsize=28)\n", - "\n", - " return fig" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "21645577", - "metadata": {}, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\" Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "markdown", - "id": "854da8f3", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "And let us set up the system Hamiltonian, bath and system measurement operators:" - ] - }, - { - "cell_type": "markdown", - "id": "f7905d63", - "metadata": {}, - "source": [ - "Here we set $H_{sys}=0$, which means the interaction Hamiltonian and the system Hamiltonian commute, and we can compare the numerical results to a known analytical one. We could in principle keep $\\epsilon \\neq 0$, but it just introduces fast system oscillations, so it is more convenient to set it to zero." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f47d6f84", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the system Hamiltonian\n", - "eps = 0.0 # Energy of the 2-level system.\n", - "Del = 0.0 # Tunnelling term\n", - "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b37ca98f", - "metadata": {}, - "outputs": [], - "source": [ - "# System-bath coupling (Drude-Lorentz spectral density)\n", - "Q = sigmaz() # coupling operator\n", - "\n", - "# Bath properties:\n", - "gamma = 0.5 # cut off frequency\n", - "lam = 0.1 # coupling strength\n", - "T = 0.5\n", - "beta = 1. / T\n", - "\n", - "# HEOM parameters:\n", - "# cut off parameter for the bath:\n", - "NC = 6\n", - "# number of exponents to retain in the Matsubara expansion\n", - "# of the correlation function:\n", - "Nk = 3\n", - "\n", - "# Times to solve for\n", - "tlist = np.linspace(0, 50, 1000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "89d89e4d", - "metadata": {}, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresonding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresonding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "edf5af1b", - "metadata": {}, - "source": [ - "To get a non-trivial result we prepare the initial state in a superposition, and see how the bath destroys the coherence." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1b38676e", - "metadata": {}, - "outputs": [], - "source": [ - "# Initial state of the system.\n", - "psi = (basis(2, 0) + basis(2, 1)).unit()\n", - "rho0 = psi * psi.dag()" - ] - }, - { - "cell_type": "markdown", - "id": "dc39f4f4", - "metadata": {}, - "source": [ - "## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cf76fb77", - "metadata": {}, - "outputs": [], - "source": [ - "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", - " HEOMMats = HEOMSolver(Hsys, bath, NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMats = HEOMMats.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1fa9afb2", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Plot the results so far\n", - "plot_result_expectations([\n", - " (resultMats, P11p, 'b', \"P11 Matsubara\"),\n", - " (resultMats, P12p, 'r', \"P12 Matsubara\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "13684357", - "metadata": {}, - "source": [ - "## Simulation 2: Matsubara decomposition (including terminator)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ba594c86", - "metadata": {}, - "outputs": [], - "source": [ - "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", - " _, terminator = bath.terminator()\n", - " Ltot = liouvillian(Hsys) + terminator\n", - " HEOMMatsT = HEOMSolver(Ltot, bath, NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMatsT = HEOMMatsT.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fa82fd55", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot the results\n", - "plot_result_expectations([\n", - " (resultMats, P11p, 'b', \"P11 Matsubara\"),\n", - " (resultMats, P12p, 'r', \"P12 Matsubara\"),\n", - " (resultMatsT, P11p, 'b--', \"P11 Matsubara and terminator\"),\n", - " (resultMatsT, P12p, 'r--', \"P12 Matsubara and terminator\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "59d7332a", - "metadata": {}, - "source": [ - "## Simulation 3: Pade decomposition\n", - "\n", - "As in example 1a, we can compare to Pade and Fitting approaches." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dc77a072", - "metadata": {}, - "outputs": [], - "source": [ - "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", - " HEOMPade = HEOMSolver(Hsys, bath, NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultPade = HEOMPade.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a215e962", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot the results\n", - "plot_result_expectations([\n", - " (resultMatsT, P11p, 'b', \"P11 Matsubara (+term)\"),\n", - " (resultMatsT, P12p, 'r', \"P12 Matsubara (+term)\"),\n", - " (resultPade, P11p, 'b--', \"P11 Pade\"),\n", - " (resultPade, P12p, 'r--', \"P12 Pade\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "8ada5ceb", - "metadata": {}, - "source": [ - "## Simulation 4: Fitting approach" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f24dab06", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def c(t, Nk):\n", - " \"\"\" Calculates real and imag. parts of the correlation function\n", - " using Nk Matsubara terms.\n", - " \"\"\"\n", - " vk = 2 * np.pi * T * np.arange(1, Nk)\n", - "\n", - " result = (\n", - " lam * gamma * (-1.0j + cot(gamma * beta / 2.)) *\n", - " np.exp(-gamma * t[None, :])\n", - " )\n", - " result += np.sum(\n", - " (4 * lam * gamma * T * vk[:, None] / (vk[:, None]**2 - gamma**2)) *\n", - " np.exp(-vk[:, None] * t[None, :]),\n", - " axis=0,\n", - " )\n", - " result = result.squeeze(axis=0)\n", - "\n", - " return result\n", - "\n", - "\n", - "tlist_fit = np.linspace(0, 2, 10000)\n", - "lmaxmats = 15000\n", - "\n", - "corr_ana = c(tlist_fit, lmaxmats)\n", - "corrRana, corrIana = np.real(corr_ana), np.imag(corr_ana)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "be98ca8e", - "metadata": {}, - "outputs": [], - "source": [ - "def wrapper_fit_func(x, N, *args):\n", - " \"\"\" Wrapper for fitting function. \"\"\"\n", - " a, b = args[0][:N], args[0][N:2*N]\n", - " return fit_func(x, a, b)\n", - "\n", - "\n", - "def fit_func(x, a, b):\n", - " \"\"\" Fitting function. \"\"\"\n", - " a = np.array(a)\n", - " b = np.array(b)\n", - " x = np.atleast_1d(np.array(x))\n", - " return np.sum(\n", - " a[:, None] * np.exp(b[:, None] * x[None, :]),\n", - " axis=0,\n", - " )\n", - "\n", - "\n", - "def fitter(ans, tlist, i):\n", - " \"\"\" Compute the fit. \"\"\"\n", - " upper_a = abs(max(ans, key=np.abs)) * 10\n", - " # set initial guess:\n", - " guess = [ans[0]] * i + [0] * i\n", - " # set bounds: a's = anything, b's = negative\n", - " # sets lower bound\n", - " b_lower = [-upper_a] * i + [-np.inf] * i\n", - " # sets higher bound\n", - " b_higher = [upper_a] * i + [0] * i\n", - " param_bounds = (b_lower, b_higher)\n", - " p1, p2 = curve_fit(\n", - " lambda x, *params: wrapper_fit_func(x, i, params),\n", - " tlist,\n", - " ans,\n", - " p0=guess,\n", - " sigma=[0.01] * len(tlist),\n", - " bounds=param_bounds,\n", - " maxfev=1e8,\n", - " )\n", - " return p1[:i], p1[i:]\n", - "\n", - "\n", - "# Fits of the real part with up to 4 exponents\n", - "popt1 = []\n", - "for i in range(4):\n", - " a, b = fitter(corrRana, tlist_fit, i + 1)\n", - " popt1.append((a, b))\n", - " y = fit_func(tlist_fit, a, b)\n", - " plt.plot(tlist_fit, corrRana, label=\"C_R(t)\")\n", - " plt.plot(tlist_fit, y, label=f\"Fit with k={i + 1}\")\n", - " plt.xlabel(\"t\")\n", - " plt.ylabel(\"C_R(t)\")\n", - " plt.legend()\n", - " plt.show()\n", - "\n", - "# Fit of the imaginary part with 1 exponent\n", - "popt2 = []\n", - "for i in range(1):\n", - " a, b = fitter(corrIana, tlist_fit, i + 1)\n", - " popt2.append((a, b))\n", - " y = fit_func(tlist_fit, a, b)\n", - " plt.plot(tlist_fit, corrIana, label=\"C_I(t)\")\n", - " plt.plot(tlist_fit, y, label=f\"Fit with k={i + 1}\")\n", - " plt.xlabel(\"t\")\n", - " plt.ylabel(\"C_I(t)\")\n", - " plt.legend()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "776e65b4", - "metadata": {}, - "outputs": [], - "source": [ - "# Set the exponential coefficients from the fit parameters\n", - "\n", - "ckAR = popt1[-1][0]\n", - "vkAR = -1 * popt1[-1][1]\n", - "\n", - "ckAI = popt2[-1][0]\n", - "vkAI = -1 * popt2[-1][1]\n", - "\n", - "# The imaginary fit can also be determined analytically and is\n", - "# a single term:\n", - "#\n", - "# ckAI = [complex(lam * gamma * (-1.0))]\n", - "# vkAI = [complex(gamma)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b86d9d7c", - "metadata": {}, - "outputs": [], - "source": [ - "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = BosonicBath(Q, ckAR, vkAR, ckAI, vkAI)\n", - " HEOMFit = HEOMSolver(Hsys, bath, NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultFit = HEOMFit.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "9e66ecab", - "metadata": {}, - "source": [ - "## Analytic calculations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4e41806a", - "metadata": {}, - "outputs": [], - "source": [ - "def pure_dephasing_evolution_analytical(tlist, wq, ck, vk):\n", - " \"\"\"\n", - " Computes the propagating function appearing in the pure dephasing model.\n", - "\n", - " Parameters\n", - " ----------\n", - " t: float\n", - " A float specifying the time at which to calculate the integral.\n", - "\n", - " wq: float\n", - " The qubit frequency in the Hamiltonian.\n", - "\n", - " ck: ndarray\n", - " The list of coefficients in the correlation function.\n", - "\n", - " vk: ndarray\n", - " The list of frequencies in the correlation function.\n", - "\n", - " Returns\n", - " -------\n", - " integral: float\n", - " The value of the integral function at time t.\n", - " \"\"\"\n", - " evolution = np.array([\n", - " np.exp(-1j * wq * t - correlation_integral(t, ck, vk))\n", - " for t in tlist\n", - " ])\n", - " return evolution\n", - "\n", - "\n", - "def correlation_integral(t, ck, vk):\n", - " r\"\"\"\n", - " Computes the integral sum function appearing in the pure dephasing model.\n", - "\n", - " If the correlation function is a sum of exponentials then this sum\n", - " is given by:\n", - "\n", - " .. math:\n", - "\n", - " \\int_0^{t}d\\tau D(\\tau) = \\sum_k\\frac{c_k}{\\mu_k^2}e^{\\mu_k t}\n", - " + \\frac{\\bar c_k}{\\bar \\mu_k^2}e^{\\bar \\mu_k t}\n", - " - \\frac{\\bar \\mu_k c_k + \\mu_k \\bar c_k}{\\mu_k \\bar \\mu_k} t\n", - " + \\frac{\\bar \\mu_k^2 c_k + \\mu_k^2 \\bar c_k}{\\mu_k^2 \\bar \\mu_k^2}\n", - "\n", - " Parameters\n", - " ----------\n", - " t: float\n", - " A float specifying the time at which to calculate the integral.\n", - "\n", - " ck: ndarray\n", - " The list of coefficients in the correlation function.\n", - "\n", - " vk: ndarray\n", - " The list of frequencies in the correlation function.\n", - "\n", - " Returns\n", - " -------\n", - " integral: float\n", - " The value of the integral function at time t.\n", - " \"\"\"\n", - " t1 = np.sum(\n", - " (ck / vk**2) *\n", - " (np.exp(vk * t) - 1)\n", - " )\n", - " t2 = np.sum(\n", - " (ck.conj() / vk.conj()**2) *\n", - " (np.exp(vk.conj() * t) - 1)\n", - " )\n", - " t3 = np.sum(\n", - " (ck / vk + ck.conj() / vk.conj()) * t\n", - " )\n", - " return 2 * (t1 + t2 - t3)" - ] - }, - { - "cell_type": "markdown", - "id": "27b44c6f", - "metadata": {}, - "source": [ - "For the pure dephasing analytics, we just sum up as many matsubara terms as we can:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a0e43264", - "metadata": {}, - "outputs": [], - "source": [ - "lmaxmats2 = 15000\n", - "\n", - "vk = [complex(-gamma)]\n", - "vk.extend([\n", - " complex(-2. * np.pi * k * T)\n", - " for k in range(1, lmaxmats2)\n", - "])\n", - "\n", - "ck = [complex(lam * gamma * (-1.0j + cot(gamma * beta / 2.)))]\n", - "ck.extend([\n", - " complex(4 * lam * gamma * T * (-v) / (v**2 - gamma**2))\n", - " for v in vk[1:]\n", - "])\n", - "\n", - "P12_ana = 0.5 * pure_dephasing_evolution_analytical(\n", - " tlist, 0, np.asarray(ck), np.asarray(vk)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "bf34c387", - "metadata": {}, - "source": [ - "Alternatively, we can just do the integral of the propagator directly, without using the correlation functions at all" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7bc35d6e", - "metadata": {}, - "outputs": [], - "source": [ - "def JDL(omega, lamc, omega_c):\n", - " return 2. * lamc * omega * omega_c / (omega_c**2 + omega**2)\n", - "\n", - "\n", - "def integrand(omega, lamc, omega_c, Temp, t):\n", - " return (\n", - " (-4. * JDL(omega, lamc, omega_c) / omega**2) *\n", - " (1. - np.cos(omega*t)) * (coth(omega/(2.*Temp)))\n", - " / np.pi\n", - " )\n", - "\n", - "\n", - "P12_ana2 = [\n", - " 0.5 * np.exp(\n", - " scipy.integrate.quad(integrand, 0, np.inf, args=(lam, gamma, T, t))[0]\n", - " )\n", - " for t in tlist\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "a5815273", - "metadata": {}, - "source": [ - "## Compare results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1055882c", - "metadata": {}, - "outputs": [], - "source": [ - "plot_result_expectations([\n", - " (resultMats, P12p, 'r', \"P12 Mats\"),\n", - " (resultMatsT, P12p, 'r--', \"P12 Mats + Term\"),\n", - " (resultPade, P12p, 'b--', \"P12 Pade\"),\n", - " (resultFit, P12p, 'g', \"P12 Fit\"),\n", - " ((tlist, np.real(P12_ana)), None, 'b', \"Analytic 1\"),\n", - " ((tlist, np.real(P12_ana2)), None, 'y--', \"Analytic 2\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "a3a9f9c9", - "metadata": {}, - "source": [ - "We can't see much difference in the plot above, so let's do a log plot instead:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "58a297cb", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - "\n", - "plot_result_expectations([\n", - " (resultMats, P12p, 'r', \"P12 Mats\"),\n", - " (resultMatsT, P12p, 'r--', \"P12 Mats + Term\"),\n", - " (resultPade, P12p, 'b-.', \"P12 Pade\"),\n", - " (resultFit, P12p, 'g', \"P12 Fit\"),\n", - " ((tlist, np.real(P12_ana)), None, 'b', \"Analytic 1\"),\n", - " ((tlist, np.real(P12_ana2)), None, 'y--', \"Analytic 2\"),\n", - "], axes)\n", - "\n", - "axes.set_yscale('log')\n", - "axes.legend(loc=0, fontsize=12);" - ] - }, - { - "cell_type": "markdown", - "id": "dee86281", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "41d04416", - "metadata": {}, - "outputs": [], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "4fcb665e", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "43b3c6d6", - "metadata": {}, - "outputs": [], - "source": [ - "assert np.allclose(\n", - " expect(P12p, resultMats.states[:15]), np.real(P12_ana)[:15],\n", - " rtol=1e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(P12p, resultMatsT.states[:100]), np.real(P12_ana)[:100],\n", - " rtol=1e-3,\n", - ")\n", - "assert np.allclose(\n", - " expect(P12p, resultPade.states[:100]), np.real(P12_ana)[:100],\n", - " rtol=1e-3,\n", - ")\n", - "assert np.allclose(\n", - " expect(P12p, resultFit.states[:50]), np.real(P12_ana)[:50],\n", - " rtol=1e-3,\n", - ")\n", - "assert np.allclose(P12_ana, P12_ana2, rtol=1e-3)" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v4/heom/heom-1e-spin-bath-model-pure-dephasing.md b/tutorials-v4/heom/heom-1e-spin-bath-model-pure-dephasing.md index 129e04f2..e9eedff9 100644 --- a/tutorials-v4/heom/heom-1e-spin-bath-model-pure-dephasing.md +++ b/tutorials-v4/heom/heom-1e-spin-bath-model-pure-dephasing.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.4 + jupytext_version: 1.16.1 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -66,7 +66,7 @@ Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. ## Setup -```{code-cell} ipython3 +```{code-cell} import contextlib import time @@ -98,7 +98,7 @@ from qutip.nonmarkov.heom import ( Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: -```{code-cell} ipython3 +```{code-cell} def cot(x): """ Vectorized cotangent of x. """ return 1. / np.tan(x) @@ -109,7 +109,7 @@ def coth(x): return 1. / np.tanh(x) ``` -```{code-cell} ipython3 +```{code-cell} def plot_result_expectations(plots, axes=None): """ Plot the expectation values of operators as functions of time. @@ -141,7 +141,7 @@ def plot_result_expectations(plots, axes=None): return fig ``` -```{code-cell} ipython3 +```{code-cell} @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -163,14 +163,14 @@ And let us set up the system Hamiltonian, bath and system measurement operators: Here we set $H_{sys}=0$, which means the interaction Hamiltonian and the system Hamiltonian commute, and we can compare the numerical results to a known analytical one. We could in principle keep $\epsilon \neq 0$, but it just introduces fast system oscillations, so it is more convenient to set it to zero. -```{code-cell} ipython3 +```{code-cell} # Defining the system Hamiltonian eps = 0.0 # Energy of the 2-level system. Del = 0.0 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` -```{code-cell} ipython3 +```{code-cell} # System-bath coupling (Drude-Lorentz spectral density) Q = sigmaz() # coupling operator @@ -191,7 +191,7 @@ Nk = 3 tlist = np.linspace(0, 50, 1000) ``` -```{code-cell} ipython3 +```{code-cell} # Define some operators with which we will measure the system # 1,1 element of density matrix - corresonding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -202,7 +202,7 @@ P12p = basis(2, 0) * basis(2, 1).dag() To get a non-trivial result we prepare the initial state in a superposition, and see how the bath destroys the coherence. -```{code-cell} ipython3 +```{code-cell} # Initial state of the system. psi = (basis(2, 0) + basis(2, 1)).unit() rho0 = psi * psi.dag() @@ -210,7 +210,7 @@ rho0 = psi * psi.dag() ## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator -```{code-cell} ipython3 +```{code-cell} options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14) with timer("RHS construction time"): @@ -221,9 +221,7 @@ with timer("ODE solver time"): resultMats = HEOMMats.run(rho0, tlist) ``` -```{code-cell} ipython3 -:tags: [] - +```{code-cell} # Plot the results so far plot_result_expectations([ (resultMats, P11p, 'b', "P11 Matsubara"), @@ -233,7 +231,7 @@ plot_result_expectations([ ## Simulation 2: Matsubara decomposition (including terminator) -```{code-cell} ipython3 +```{code-cell} options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14) with timer("RHS construction time"): @@ -246,7 +244,7 @@ with timer("ODE solver time"): resultMatsT = HEOMMatsT.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} # Plot the results plot_result_expectations([ (resultMats, P11p, 'b', "P11 Matsubara"), @@ -260,7 +258,7 @@ plot_result_expectations([ As in example 1a, we can compare to Pade and Fitting approaches. -```{code-cell} ipython3 +```{code-cell} options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14) with timer("RHS construction time"): @@ -271,7 +269,7 @@ with timer("ODE solver time"): resultPade = HEOMPade.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} # Plot the results plot_result_expectations([ (resultMatsT, P11p, 'b', "P11 Matsubara (+term)"), @@ -283,9 +281,7 @@ plot_result_expectations([ ## Simulation 4: Fitting approach -```{code-cell} ipython3 -:tags: [] - +```{code-cell} def c(t, Nk): """ Calculates real and imag. parts of the correlation function using Nk Matsubara terms. @@ -313,7 +309,7 @@ corr_ana = c(tlist_fit, lmaxmats) corrRana, corrIana = np.real(corr_ana), np.imag(corr_ana) ``` -```{code-cell} ipython3 +```{code-cell} def wrapper_fit_func(x, N, *args): """ Wrapper for fitting function. """ a, b = args[0][:N], args[0][N:2*N] @@ -381,7 +377,7 @@ for i in range(1): plt.show() ``` -```{code-cell} ipython3 +```{code-cell} # Set the exponential coefficients from the fit parameters ckAR = popt1[-1][0] @@ -397,7 +393,7 @@ vkAI = -1 * popt2[-1][1] # vkAI = [complex(gamma)] ``` -```{code-cell} ipython3 +```{code-cell} options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14) with timer("RHS construction time"): @@ -410,7 +406,7 @@ with timer("ODE solver time"): ## Analytic calculations -```{code-cell} ipython3 +```{code-cell} def pure_dephasing_evolution_analytical(tlist, wq, ck, vk): """ Computes the propagating function appearing in the pure dephasing model. @@ -487,7 +483,7 @@ def correlation_integral(t, ck, vk): For the pure dephasing analytics, we just sum up as many matsubara terms as we can: -```{code-cell} ipython3 +```{code-cell} lmaxmats2 = 15000 vk = [complex(-gamma)] @@ -509,7 +505,7 @@ P12_ana = 0.5 * pure_dephasing_evolution_analytical( Alternatively, we can just do the integral of the propagator directly, without using the correlation functions at all -```{code-cell} ipython3 +```{code-cell} def JDL(omega, lamc, omega_c): return 2. * lamc * omega * omega_c / (omega_c**2 + omega**2) @@ -532,7 +528,7 @@ P12_ana2 = [ ## Compare results -```{code-cell} ipython3 +```{code-cell} plot_result_expectations([ (resultMats, P12p, 'r', "P12 Mats"), (resultMatsT, P12p, 'r--', "P12 Mats + Term"), @@ -545,7 +541,7 @@ plot_result_expectations([ We can't see much difference in the plot above, so let's do a log plot instead: -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) plot_result_expectations([ @@ -563,7 +559,7 @@ axes.legend(loc=0, fontsize=12); ## About -```{code-cell} ipython3 +```{code-cell} qutip.about() ``` @@ -571,7 +567,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 +```{code-cell} assert np.allclose( expect(P12p, resultMats.states[:15]), np.real(P12_ana)[:15], rtol=1e-2, diff --git a/tutorials-v4/heom/heom-2-fmo-example.ipynb b/tutorials-v4/heom/heom-2-fmo-example.ipynb deleted file mode 100644 index fc6e25d4..00000000 --- a/tutorials-v4/heom/heom-2-fmo-example.ipynb +++ /dev/null @@ -1,694 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a07acd39", - "metadata": {}, - "source": [ - "# HEOM 2: Dynamics in Fenna-Mathews-Olsen complex (FMO)" - ] - }, - { - "cell_type": "markdown", - "id": "2f5528e3", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "In this example notebook we outline how to employ the HEOM to\n", - "solve the FMO photosynthetic complex dynamics.\n", - "\n", - "We aim to replicate the results in reference [https://www.pnas.org/content/106/41/17255](https://pubmed.ncbi.nlm.nih.gov/19815512/)\n", - "and compare them to a Bloch-Redfield (perturbative) solution.\n", - "\n", - "This demonstrates how to to employ the solver for multiple baths, as well as showing how a\n", - "quantum environment reduces the effect of pure dephasing." - ] - }, - { - "cell_type": "markdown", - "id": "5f294ac4", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0e94ba14", - "metadata": {}, - "outputs": [], - "source": [ - "import contextlib\n", - "import time\n", - "\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " Options,\n", - " Qobj,\n", - " basis,\n", - " brmesolve,\n", - " expect,\n", - " liouvillian,\n", - " mesolve,\n", - ")\n", - "from qutip.nonmarkov.heom import (\n", - " HEOMSolver,\n", - " DrudeLorentzBath,\n", - ")\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "e0ccd522", - "metadata": {}, - "source": [ - "## Helper functions\n", - "\n", - "Let's define some helper functions for calculating correlation functions, spectral densities, thermal energy level occupations, and for plotting results and timing how long operations take:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0bcb8497", - "metadata": {}, - "outputs": [], - "source": [ - "def cot(x):\n", - " \"\"\" Vectorized cotangent of x. \"\"\"\n", - " return 1 / np.tan(x)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "743463c9", - "metadata": {}, - "outputs": [], - "source": [ - "def J0(energy):\n", - " \"\"\" Under-damped brownian oscillator spectral density. \"\"\"\n", - " return 2 * lam * gamma * energy / (energy**2 + gamma**2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4ffcb903", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def J0_dephasing():\n", - " \"\"\" Under-damped brownian oscillator dephasing probability.\n", - "\n", - " This returns the limit as w -> 0 of J0(w) * n_th(w, T) / T.\n", - " \"\"\"\n", - " return 2 * lam * gamma / gamma**2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1c45d9a0", - "metadata": {}, - "outputs": [], - "source": [ - "def n_th(energy, T):\n", - " \"\"\" The average occupation of a given energy level at temperature T. \"\"\"\n", - " return 1 / (np.exp(energy / T) - 1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "06c6958e", - "metadata": {}, - "outputs": [], - "source": [ - "def dl_corr_approx(t, nk):\n", - " \"\"\" Drude-Lorenz correlation function approximation.\n", - "\n", - " Approximates the correlation function at each time t to nk exponents.\n", - " \"\"\"\n", - " c = lam * gamma * (-1.0j + cot(gamma / (2 * T))) * np.exp(-gamma * t)\n", - " for k in range(1, nk):\n", - " vk = 2 * np.pi * k * T\n", - " c += (4 * lam * gamma * T * vk / (vk**2 - gamma**2)) * np.exp(-vk * t)\n", - " return c" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "12edcf9d", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\" Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "markdown", - "id": "a0decbad", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "And let us set up the system Hamiltonian and bath parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "19010fb3", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# System Hamiltonian:\n", - "#\n", - "# We use the Hamiltonian employed in\n", - "# https://www.pnas.org/content/106/41/17255 and operate\n", - "# in units of Hz:\n", - "\n", - "Hsys = 3e10 * 2 * np.pi * Qobj([\n", - " [200, -87.7, 5.5, -5.9, 6.7, -13.7, -9.9],\n", - " [-87.7, 320, 30.8, 8.2, 0.7, 11.8, 4.3],\n", - " [5.5, 30.8, 0, -53.5, -2.2, -9.6, 6.0],\n", - " [-5.9, 8.2, -53.5, 110, -70.7, -17.0, -63.3],\n", - " [6.7, 0.7, -2.2, -70.7, 270, 81.1, -1.3],\n", - " [-13.7, 11.8, -9.6, -17.0, 81.1, 420, 39.7],\n", - " [-9.9, 4.3, 6.0, -63.3, -1.3, 39.7, 230],\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7e821b62", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Bath parameters\n", - "\n", - "lam = 35 * 3e10 * 2 * np.pi\n", - "gamma = 1 / 166e-15\n", - "T = 300 * 0.6949 * 3e10 * 2 * np.pi\n", - "beta = 1 / T" - ] - }, - { - "cell_type": "markdown", - "id": "499bcfc1", - "metadata": {}, - "source": [ - "## Plotting the environment spectral density and correlation functions\n", - "\n", - "Let's quickly plot the spectral density and environment correlation functions so that we can see what they look like." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "518c069c", - "metadata": {}, - "outputs": [], - "source": [ - "wlist = np.linspace(0, 200 * 3e10 * 2 * np.pi, 100)\n", - "tlist = np.linspace(0, 1e-12, 1000)\n", - "\n", - "J = J0(wlist) / (3e10*2*np.pi)\n", - "\n", - "fig, axes = plt.subplots(1, 2, sharex=False, figsize=(10, 3))\n", - "\n", - "fig.subplots_adjust(hspace=0.1) # reduce space between plots\n", - "\n", - "# Spectral density plot:\n", - "\n", - "axes[0].plot(wlist / (3e10 * 2 * np.pi), J, color='r', ls='--', label=\"J(w)\")\n", - "axes[0].set_xlabel(r'$\\omega$ (cm$^{-1}$)', fontsize=20)\n", - "axes[0].set_ylabel(r\"$J(\\omega)$ (cm$^{-1}$)\", fontsize=16)\n", - "axes[0].legend()\n", - "\n", - "# Correlation plot:\n", - "\n", - "axes[1].plot(\n", - " tlist, np.real(dl_corr_approx(tlist, 10)),\n", - " color='r', ls='--', label=\"C(t) real\",\n", - ")\n", - "axes[1].plot(\n", - " tlist, np.imag(dl_corr_approx(tlist, 10)),\n", - " color='g', ls='--', label=\"C(t) imaginary\",\n", - ")\n", - "axes[1].set_xlabel(r'$t$', fontsize=20)\n", - "axes[1].set_ylabel(r\"$C(t)$\", fontsize=16)\n", - "axes[1].legend();" - ] - }, - { - "cell_type": "markdown", - "id": "deeae0da", - "metadata": {}, - "source": [ - "## Solve for the dynamics with the HEOM\n", - "\n", - "Now let us solve for the evolution of this system using the HEOM." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5c9b2c17", - "metadata": {}, - "outputs": [], - "source": [ - "# We start the excitation at site 1:\n", - "rho0 = basis(7, 0) * basis(7, 0).dag()\n", - "\n", - "# HEOM solver options:\n", - "#\n", - "# Note: We set Nk=0 (i.e. a single correlation expansion term\n", - "# per bath) and rely on the terminator to correct detailed\n", - "# balance.\n", - "options = Options(nsteps=15000, store_states=True)\n", - "NC = 4 # Use NC=8 for more precise results\n", - "Nk = 0\n", - "\n", - "Q_list = []\n", - "baths = []\n", - "Ltot = liouvillian(Hsys)\n", - "for m in range(7):\n", - " Q = basis(7, m) * basis(7, m).dag()\n", - " Q_list.append(Q)\n", - " baths.append(\n", - " DrudeLorentzBath(\n", - " Q, lam=lam, gamma=gamma, T=T, Nk=Nk,\n", - " tag=str(m)\n", - " )\n", - " )\n", - " _, terminator = baths[-1].terminator()\n", - " Ltot += terminator" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d3ed340a", - "metadata": {}, - "outputs": [], - "source": [ - "with timer(\"RHS construction time\"):\n", - " HEOMMats = HEOMSolver(Hsys, baths, NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " outputFMO_HEOM = HEOMMats.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "59cc2e76", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", - "\n", - "colors = ['r', 'g', 'b', 'y', 'c', 'm', 'k']\n", - "linestyles = [\n", - " '-', '--', ':', '-.',\n", - " (0, (1, 10)), (0, (5, 10)), (0, (3, 10, 1, 10)),\n", - "]\n", - "\n", - "for m in range(7):\n", - " Q = basis(7, m) * basis(7, m).dag()\n", - " axes.plot(\n", - " np.array(tlist) * 1e15,\n", - " np.real(expect(outputFMO_HEOM.states, Q)),\n", - " label=m + 1,\n", - " color=colors[m % len(colors)],\n", - " linestyle=linestyles[m % len(linestyles)],\n", - " )\n", - " axes.set_xlabel(r'$t$ (fs)', fontsize=30)\n", - " axes.set_ylabel(r\"Population\", fontsize=30)\n", - " axes.locator_params(axis='y', nbins=6)\n", - " axes.locator_params(axis='x', nbins=6)\n", - "\n", - "axes.set_title('HEOM solution', fontsize=24)\n", - "axes.legend(loc=0)\n", - "axes.set_xlim(0, 1000)\n", - "plt.yticks([0., 0.5, 1], [0, 0.5, 1])\n", - "plt.xticks([0., 500, 1000], [0, 500, 1000]);" - ] - }, - { - "cell_type": "markdown", - "id": "eadf54f8", - "metadata": {}, - "source": [ - "## Comparison with Bloch-Redfield solver\n", - "\n", - "Now let us solve the same problem using the Bloch-Redfield solver. We will see that the Bloch-Redfield technique fails to model the oscillation of population of the states that we saw in the HEOM.\n", - "\n", - "In the next section, we will examine the role of pure dephasing in the evolution to understand why this happens." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "724b57ab", - "metadata": {}, - "outputs": [], - "source": [ - "DL = (\n", - " f\"2 * pi * 2.0 * {lam} / (pi * {gamma} * {beta}) if (w == 0) else \"\n", - " f\"2 * pi * (2.0*{lam}*{gamma} *w /(pi*(w**2+{gamma}**2))) * \"\n", - " f\"((1 / (exp((w) * {beta}) - 1)) + 1)\"\n", - ")\n", - "\n", - "optionsBR = Options(nsteps=15000, store_states=True, rtol=1e-12, atol=1e-12)\n", - "\n", - "with timer(\"BR ODE solver time\"):\n", - " outputFMO_BR = brmesolve(\n", - " Hsys, rho0, tlist,\n", - " a_ops=[[Q, DL] for Q in Q_list],\n", - " options=optionsBR,\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "aeb8936a", - "metadata": {}, - "source": [ - "And now let's plot the Bloch-Redfield solver results:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f2958e3c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", - "\n", - "for m, Q in enumerate(Q_list):\n", - " axes.plot(tlist * 1e15, expect(outputFMO_BR.states, Q), label=m + 1)\n", - "\n", - "axes.set_xlabel(r'$t$ (fs)', fontsize=30)\n", - "axes.set_ylabel(r\"Population\", fontsize=30)\n", - "\n", - "axes.set_title('Bloch-Redfield solution ', fontsize=24)\n", - "axes.legend()\n", - "axes.set_xlim(0, 1000)\n", - "plt.yticks([0, 0.5, 1], [0, 0.5, 1])\n", - "plt.xticks([0, 500, 1000], [0, 500, 1000]);" - ] - }, - { - "cell_type": "markdown", - "id": "ef77f184", - "metadata": {}, - "source": [ - "Notice how the oscillations are gone and the populations decay much more rapidly.\n", - "\n", - "Next let us try to understand why." - ] - }, - { - "cell_type": "markdown", - "id": "3149fb03", - "metadata": {}, - "source": [ - "## Role of pure dephasing\n", - "\n", - "It is useful to construct the various parts of the Bloch-Redfield master equation explicitly and to solve them using the Master equation solver, `mesolve`. We will do so and show that it is the pure-dephasing terms which suppresses coherence in these oscillations.\n", - "\n", - "First we will write a function to return the list of collapse operators for a given system, either with or without the dephasing operators:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6df69b3b", - "metadata": {}, - "outputs": [], - "source": [ - "def get_collapse(H, T, dephasing=1):\n", - " \"\"\" Calculate collapse operators for a given system H and\n", - " temperature T.\n", - " \"\"\"\n", - " all_energy, all_state = H.eigenstates(sort=\"low\")\n", - " Nmax = len(all_energy)\n", - "\n", - " Q_list = [\n", - " basis(Nmax, n) * basis(Nmax, n).dag()\n", - " for n in range(Nmax)\n", - " ]\n", - "\n", - " collapse_list = []\n", - "\n", - " for Q in Q_list:\n", - " for j in range(Nmax):\n", - " for k in range(j + 1, Nmax):\n", - " Deltajk = abs(all_energy[k] - all_energy[j])\n", - " if abs(Deltajk) > 0:\n", - " rate = (\n", - " np.abs(Q.matrix_element(\n", - " all_state[j].dag(), all_state[k]\n", - " ))**2 *\n", - " 2 * J0(Deltajk) * (n_th(Deltajk, T) + 1)\n", - " )\n", - " if rate > 0.0:\n", - " # emission:\n", - " collapse_list.append(\n", - " np.sqrt(rate) * all_state[j] * all_state[k].dag()\n", - " )\n", - "\n", - " rate = (\n", - " np.abs(Q.matrix_element(\n", - " all_state[k].dag(), all_state[j]\n", - " ))**2 *\n", - " 2 * J0(Deltajk) * n_th(Deltajk, T)\n", - " )\n", - " if rate > 0.0:\n", - " # absorption:\n", - " collapse_list.append(\n", - " np.sqrt(rate) * all_state[k] * all_state[j].dag()\n", - " )\n", - "\n", - " if dephasing:\n", - " for j in range(Nmax):\n", - " rate = (\n", - " np.abs(Q.matrix_element(\n", - " all_state[j].dag(), all_state[j])\n", - " )**2 *\n", - " J0_dephasing() * T\n", - " )\n", - " if rate > 0.0:\n", - " # emission:\n", - " collapse_list.append(\n", - " np.sqrt(rate) * all_state[j] * all_state[j].dag()\n", - " )\n", - "\n", - " return collapse_list" - ] - }, - { - "cell_type": "markdown", - "id": "4a1bea16", - "metadata": {}, - "source": [ - "Now we are able to switch the pure dephasing tersms on and off.\n", - "\n", - "Let us starting by including the dephasing operators. We expect to see the same behaviour that we saw when using the Bloch-Redfield solver." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "435bac85", - "metadata": {}, - "outputs": [], - "source": [ - "# dephasing terms on, we recover the full BR solution:\n", - "\n", - "with timer(\"Building the collapse operators\"):\n", - " collapse_list = get_collapse(Hsys, T=T, dephasing=True)\n", - "\n", - "with timer(\"ME ODE solver\"):\n", - " outputFMO_ME = mesolve(Hsys, rho0, tlist, collapse_list)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b53e5ebb", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", - "\n", - "for m, Q in enumerate(Q_list):\n", - " axes.plot(tlist * 1e15, expect(outputFMO_ME.states, Q), label=m + 1)\n", - "\n", - "axes.set_xlabel(r'$t$', fontsize=20)\n", - "axes.set_ylabel(r\"Population\", fontsize=16)\n", - "axes.set_xlim(0, 1000)\n", - "axes.set_title('With pure dephasing', fontsize=24)\n", - "plt.yticks([0, 0.5, 1], [0, 0.5, 1])\n", - "plt.xticks([0, 500, 1000], [0, 500, 1000])\n", - "axes.legend(fontsize=18);" - ] - }, - { - "cell_type": "markdown", - "id": "fe1188ce", - "metadata": {}, - "source": [ - "We see similar results to before.\n", - "\n", - "Now let us examine what happens when we remove the dephasing collapse operators:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4286f57c", - "metadata": {}, - "outputs": [], - "source": [ - "# dephasing terms off\n", - "\n", - "with timer(\"Building the collapse operators\"):\n", - " collapse_list = get_collapse(Hsys, T, dephasing=False)\n", - "\n", - "with timer(\"ME ODE solver\"):\n", - " outputFMO_ME_nodephase = mesolve(Hsys, rho0, tlist, collapse_list)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9ed3a952", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", - "for m, Q in enumerate(Q_list):\n", - " axes.plot(\n", - " tlist * 1e15,\n", - " expect(outputFMO_ME_nodephase.states, Q),\n", - " label=m + 1,\n", - " )\n", - "\n", - "axes.set_xlabel(r'$t$', fontsize=20)\n", - "axes.set_ylabel(r\"Population\", fontsize=16)\n", - "axes.set_xlim(0, 1000)\n", - "axes.set_title('Without pure dephasing', fontsize=24)\n", - "plt.yticks([0, 0.5, 1], [0, 0.5, 1])\n", - "plt.xticks([0, 500, 1000], [0, 500, 1000])\n", - "axes.legend(fontsize=18);" - ] - }, - { - "cell_type": "markdown", - "id": "e6394ed6", - "metadata": {}, - "source": [ - "And now we see that without the dephasing, the oscillations reappear. The full dynamics capture by the HEOM are still not capture by this simpler model, however." - ] - }, - { - "cell_type": "markdown", - "id": "3c76cc7d", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c433cd8", - "metadata": {}, - "outputs": [], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "3ca855fc", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9169387f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert np.allclose(\n", - " expect(outputFMO_BR.states, Q_list[0]),\n", - " expect(outputFMO_ME.states, Q_list[0]),\n", - " rtol=2e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(outputFMO_BR.states, Q_list[1]),\n", - " expect(outputFMO_ME.states, Q_list[1]),\n", - " rtol=2e-2,\n", - ")" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v4/heom/heom-2-fmo-example.md b/tutorials-v4/heom/heom-2-fmo-example.md index 81c8fc50..ce279d48 100644 --- a/tutorials-v4/heom/heom-2-fmo-example.md +++ b/tutorials-v4/heom/heom-2-fmo-example.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.4 + jupytext_version: 1.16.1 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -31,7 +31,7 @@ quantum environment reduces the effect of pure dephasing. ## Setup -```{code-cell} ipython3 +```{code-cell} import contextlib import time @@ -60,21 +60,19 @@ from qutip.nonmarkov.heom import ( Let's define some helper functions for calculating correlation functions, spectral densities, thermal energy level occupations, and for plotting results and timing how long operations take: -```{code-cell} ipython3 +```{code-cell} def cot(x): """ Vectorized cotangent of x. """ return 1 / np.tan(x) ``` -```{code-cell} ipython3 +```{code-cell} def J0(energy): """ Under-damped brownian oscillator spectral density. """ return 2 * lam * gamma * energy / (energy**2 + gamma**2) ``` -```{code-cell} ipython3 -:tags: [] - +```{code-cell} def J0_dephasing(): """ Under-damped brownian oscillator dephasing probability. @@ -83,13 +81,13 @@ def J0_dephasing(): return 2 * lam * gamma / gamma**2 ``` -```{code-cell} ipython3 +```{code-cell} def n_th(energy, T): """ The average occupation of a given energy level at temperature T. """ return 1 / (np.exp(energy / T) - 1) ``` -```{code-cell} ipython3 +```{code-cell} def dl_corr_approx(t, nk): """ Drude-Lorenz correlation function approximation. @@ -102,9 +100,7 @@ def dl_corr_approx(t, nk): return c ``` -```{code-cell} ipython3 -:tags: [] - +```{code-cell} @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -122,9 +118,7 @@ def timer(label): And let us set up the system Hamiltonian and bath parameters: -```{code-cell} ipython3 -:tags: [] - +```{code-cell} # System Hamiltonian: # # We use the Hamiltonian employed in @@ -142,9 +136,7 @@ Hsys = 3e10 * 2 * np.pi * Qobj([ ]) ``` -```{code-cell} ipython3 -:tags: [] - +```{code-cell} # Bath parameters lam = 35 * 3e10 * 2 * np.pi @@ -157,7 +149,7 @@ beta = 1 / T Let's quickly plot the spectral density and environment correlation functions so that we can see what they look like. -```{code-cell} ipython3 +```{code-cell} wlist = np.linspace(0, 200 * 3e10 * 2 * np.pi, 100) tlist = np.linspace(0, 1e-12, 1000) @@ -193,7 +185,7 @@ axes[1].legend(); Now let us solve for the evolution of this system using the HEOM. -```{code-cell} ipython3 +```{code-cell} # We start the excitation at site 1: rho0 = basis(7, 0) * basis(7, 0).dag() @@ -222,7 +214,7 @@ for m in range(7): Ltot += terminator ``` -```{code-cell} ipython3 +```{code-cell} with timer("RHS construction time"): HEOMMats = HEOMSolver(Hsys, baths, NC, options=options) @@ -230,7 +222,7 @@ with timer("ODE solver time"): outputFMO_HEOM = HEOMMats.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, figsize=(12, 8)) colors = ['r', 'g', 'b', 'y', 'c', 'm', 'k'] @@ -266,7 +258,7 @@ Now let us solve the same problem using the Bloch-Redfield solver. We will see t In the next section, we will examine the role of pure dephasing in the evolution to understand why this happens. -```{code-cell} ipython3 +```{code-cell} DL = ( f"2 * pi * 2.0 * {lam} / (pi * {gamma} * {beta}) if (w == 0) else " f"2 * pi * (2.0*{lam}*{gamma} *w /(pi*(w**2+{gamma}**2))) * " @@ -285,9 +277,7 @@ with timer("BR ODE solver time"): And now let's plot the Bloch-Redfield solver results: -```{code-cell} ipython3 -:tags: [] - +```{code-cell} fig, axes = plt.subplots(1, 1, figsize=(12, 8)) for m, Q in enumerate(Q_list): @@ -315,7 +305,7 @@ It is useful to construct the various parts of the Bloch-Redfield master equatio First we will write a function to return the list of collapse operators for a given system, either with or without the dephasing operators: -```{code-cell} ipython3 +```{code-cell} def get_collapse(H, T, dephasing=1): """ Calculate collapse operators for a given system H and temperature T. @@ -380,7 +370,7 @@ Now we are able to switch the pure dephasing tersms on and off. Let us starting by including the dephasing operators. We expect to see the same behaviour that we saw when using the Bloch-Redfield solver. -```{code-cell} ipython3 +```{code-cell} # dephasing terms on, we recover the full BR solution: with timer("Building the collapse operators"): @@ -390,9 +380,7 @@ with timer("ME ODE solver"): outputFMO_ME = mesolve(Hsys, rho0, tlist, collapse_list) ``` -```{code-cell} ipython3 -:tags: [] - +```{code-cell} fig, axes = plt.subplots(1, 1, figsize=(12, 8)) for m, Q in enumerate(Q_list): @@ -411,7 +399,7 @@ We see similar results to before. Now let us examine what happens when we remove the dephasing collapse operators: -```{code-cell} ipython3 +```{code-cell} # dephasing terms off with timer("Building the collapse operators"): @@ -421,9 +409,7 @@ with timer("ME ODE solver"): outputFMO_ME_nodephase = mesolve(Hsys, rho0, tlist, collapse_list) ``` -```{code-cell} ipython3 -:tags: [] - +```{code-cell} fig, axes = plt.subplots(1, 1, figsize=(12, 8)) for m, Q in enumerate(Q_list): axes.plot( @@ -447,7 +433,7 @@ And now we see that without the dephasing, the oscillations reappear. The full d ## About -```{code-cell} ipython3 +```{code-cell} qutip.about() ``` @@ -455,9 +441,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 -:tags: [] - +```{code-cell} assert np.allclose( expect(outputFMO_BR.states, Q_list[0]), expect(outputFMO_ME.states, Q_list[0]), diff --git a/tutorials-v4/heom/heom-3-quantum-heat-transport.ipynb b/tutorials-v4/heom/heom-3-quantum-heat-transport.ipynb deleted file mode 100644 index 2fc459dd..00000000 --- a/tutorials-v4/heom/heom-3-quantum-heat-transport.ipynb +++ /dev/null @@ -1,669 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cb8620ab", - "metadata": {}, - "source": [ - "# HEOM 3: Quantum Heat Transport" - ] - }, - { - "cell_type": "markdown", - "id": "17a1854e", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "In this notebook, we apply the QuTiP HEOM solver to a quantum system coupled to two bosonic baths and demonstrate how to extract information about the system-bath heat currents from the auxiliary density operators (ADOs).\n", - "We consider the setup described in Ref. \\[1\\], which consists of two coupled qubits, each connected to its own heat bath.\n", - "The Hamiltonian of the qubits is given by\n", - "\n", - "$$ \\begin{aligned} H_{\\text{S}} &= H_1 + H_2 + H_{12} , \\quad\\text{ where }\\\\\n", - "H_K &= \\frac{\\epsilon}{2} \\bigl(\\sigma_z^K + 1\\bigr) \\quad (K=1,2) \\quad\\text{ and }\\quad H_{12} = J_{12} \\bigl( \\sigma_+^1 \\sigma_-^2 + \\sigma_-^1 \\sigma_+^2 \\bigr) . \\end{aligned} $$\n", - "\n", - "Here, $\\sigma^K_{x,y,z,\\pm}$ denotes the usual Pauli matrices for the K-th qubit, $\\epsilon$ is the eigenfrequency of the qubits and $J_{12}$ the coupling constant.\n", - "\n", - "Each qubit is coupled to its own bath; therefore, the total Hamiltonian is\n", - "\n", - "$$ H_{\\text{tot}} = H_{\\text{S}} + \\sum_{K=1,2} \\bigl( H_{\\text{B}}^K + Q_K \\otimes X_{\\text{B}}^K \\bigr) , $$\n", - "\n", - "where $H_{\\text{B}}^K$ is the free Hamiltonian of the K-th bath and $X_{\\text{B}}^K$ its coupling operator, and $Q_K = \\sigma_x^K$ are the system coupling operators.\n", - "We assume that the bath spectral densities are given by Drude distributions\n", - "\n", - "$$ J_K(\\omega) = \\frac{2 \\lambda_K \\gamma_K \\omega}{\\omega^2 + \\gamma_K^2} , $$\n", - "\n", - "where $\\lambda_K$ is the free coupling strength and $\\gamma_K$ the cutoff frequency.\n", - "\n", - "We begin by defining the system and bath parameters.\n", - "We use the parameter values from Fig. 3(a) of Ref. \\[1\\].\n", - "Note that we set $\\hbar$ and $k_B$ to one and we will measure all frequencies and energies in units of $\\epsilon$.\n", - "\n", - "References:\n", - "\n", - "   \\[1\\] Kato and Tanimura, [J. Chem. Phys. **143**, 064107](https://doi.org/10.1063/1.4928192) (2015)." - ] - }, - { - "cell_type": "markdown", - "id": "f3950292", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "017d2a21", - "metadata": {}, - "outputs": [], - "source": [ - "import dataclasses\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import qutip as qt\n", - "from qutip.nonmarkov.heom import (\n", - " DrudeLorentzPadeBath,\n", - " BathExponent,\n", - " HEOMSolver,\n", - ")\n", - "\n", - "from ipywidgets import IntProgress\n", - "from IPython.display import display\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "6882aea2", - "metadata": {}, - "source": [ - "## System and bath definition" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "84bd8f0c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "@dataclasses.dataclass\n", - "class SystemParams:\n", - " \"\"\" System parameters and Hamiltonian. \"\"\"\n", - " epsilon: float = 1.0\n", - " J12: float = 0.1\n", - "\n", - " def H(self):\n", - " \"\"\" Return the Hamiltonian for the system.\n", - "\n", - " The system consists of two qubits with Hamiltonians (H1 and H2)\n", - " and an interaction term (H12).\n", - " \"\"\"\n", - " H1 = self.epsilon / 2 * (\n", - " qt.tensor(qt.sigmaz() + qt.identity(2), qt.identity(2))\n", - " )\n", - " H2 = self.epsilon / 2 * (\n", - " qt.tensor(qt.identity(2), qt.sigmaz() + qt.identity(2))\n", - " )\n", - " H12 = self.J12 * (\n", - " qt.tensor(qt.sigmap(), qt.sigmam()) +\n", - " qt.tensor(qt.sigmam(), qt.sigmap())\n", - " )\n", - " return H1 + H2 + H12\n", - "\n", - " def replace(self, **kw):\n", - " return dataclasses.replace(self, **kw)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "32da141e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "@dataclasses.dataclass\n", - "class BathParams:\n", - " \"\"\" Bath parameters. \"\"\"\n", - " sign: str # + or -\n", - " qubit: int # 0 or 1\n", - "\n", - " gamma: float = 2.0\n", - " lam: float = 0.05\n", - " Tbar: float = 2.0\n", - " Tdelta: float = 0.01\n", - "\n", - " def __post_init__(self):\n", - " # T = Tbar +- Tdelta * Tbar:\n", - " assert self.sign in (\"+\", \"-\")\n", - " sign = +1 if self.sign == \"+\" else -1\n", - " self.T = self.Tbar + sign * self.Tdelta * self.Tbar\n", - " # qubit\n", - " assert self.qubit in (0, 1)\n", - "\n", - " def Q(self):\n", - " \"\"\" Coupling operator for the bath. \"\"\"\n", - " Q = [qt.identity(2), qt.identity(2)]\n", - " Q[self.qubit] = qt.sigmax()\n", - " return qt.tensor(Q)\n", - "\n", - " def bath(self, Nk, tag=None):\n", - " return DrudeLorentzPadeBath(\n", - " self.Q(), self.lam, self.gamma, self.T, Nk, tag=tag\n", - " )\n", - "\n", - " def replace(self, **kw):\n", - " return dataclasses.replace(self, **kw)" - ] - }, - { - "cell_type": "markdown", - "id": "fb4ea3ec", - "metadata": {}, - "source": [ - "## Heat currents\n", - "\n", - "Following Ref. \\[2\\], we consider two possible definitions of the heat currents from the qubits into the baths.\n", - "The so-called bath heat currents are $j_{\\text{B}}^K = \\partial_t \\langle H_{\\text{B}}^K \\rangle$ and the system heat currents are $j_{\\text{S}}^K = \\mathrm i\\, \\langle [H_{\\text{S}}, Q_K] X_{\\text{B}}^K \\rangle$.\n", - "As shown in Ref. \\[2\\], they can be expressed in terms of the HEOM ADOs as follows:\n", - "$$ \\begin{aligned} \\mbox{} \\\\\n", - " j_{\\text{B}}^K &= \\!\\!\\sum_{\\substack{\\mathbf n\\\\ \\text{Level 1}\\\\ \\text{Bath $K$}}}\\!\\! \\nu[\\mathbf n] \\operatorname{tr}\\bigl[ Q_K \\rho_{\\mathbf n} \\bigr] - 2 C_I^K(0) \\operatorname{tr}\\bigl[ Q_k^2 \\rho \\bigr] + \\Gamma_{\\text{T}}^K \\operatorname{tr}\\bigl[ [[H_{\\text{S}}, Q_K], Q_K]\\, \\rho \\bigr] , \\\\[.5em]\n", - " j_{\\text{S}}^K &= \\mathrm i\\!\\! \\sum_{\\substack{\\mathbf n\\\\ \\text{Level 1}\\\\ \\text{Bath $k$}}}\\!\\! \\operatorname{tr}\\bigl[ [H_{\\text{S}}, Q_K]\\, \\rho_{\\mathbf n} \\bigr] + \\Gamma_{\\text{T}}^K \\operatorname{tr}\\bigl[ [[H_{\\text{S}}, Q_K], Q_K]\\, \\rho \\bigr] . \\\\ \\mbox{}\n", - "\\end{aligned} $$\n", - "The sums run over all level-$1$ multi-indices $\\mathbf n$ with one excitation corresponding to the K-th bath, $\\nu[\\mathbf n]$ is the corresponding (negative) exponent of the bath auto-correlation function $C^K(t)$, and $\\Gamma_{\\text{T}}^K$ is the Ishizaki-Tanimura terminator (i.e., a correction term accounting for the error introduced by approximating the correlation function with a finite sum of exponential terms).\n", - "In the expression for the bath heat currents, we left out terms involving $[Q_1, Q_2]$, which is zero in this example.\n", - "\n", - "   \\[2\\] Kato and Tanimura, [J. Chem. Phys. **145**, 224105](https://doi.org/10.1063/1.4971370) (2016)." - ] - }, - { - "cell_type": "markdown", - "id": "9c3a3965", - "metadata": {}, - "source": [ - "In QuTiP, these currents can be conveniently calculated as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "976e5e10", - "metadata": {}, - "outputs": [], - "source": [ - "def bath_heat_current(bath_tag, ado_state, hamiltonian, coupling_op, delta=0):\n", - " \"\"\"\n", - " Bath heat current from the system into the heat bath with the given tag.\n", - "\n", - " Parameters\n", - " ----------\n", - " bath_tag : str, tuple or any other object\n", - " Tag of the heat bath corresponding to the current of interest.\n", - "\n", - " ado_state : HierarchyADOsState\n", - " Current state of the system and the environment (encoded in the ADOs).\n", - "\n", - " hamiltonian : Qobj\n", - " System Hamiltonian at the current time.\n", - "\n", - " coupling_op : Qobj\n", - " System coupling operator at the current time.\n", - "\n", - " delta : float\n", - " The prefactor of the \\\\delta(t) term in the correlation function (the\n", - " Ishizaki-Tanimura terminator).\n", - " \"\"\"\n", - " l1_labels = ado_state.filter(level=1, tags=[bath_tag])\n", - " a_op = 1j * (hamiltonian * coupling_op - coupling_op * hamiltonian)\n", - "\n", - " result = 0\n", - " cI0 = 0 # imaginary part of bath auto-correlation function (t=0)\n", - " for label in l1_labels:\n", - " [exp] = ado_state.exps(label)\n", - " result += exp.vk * (coupling_op * ado_state.extract(label)).tr()\n", - "\n", - " if exp.type == BathExponent.types['I']:\n", - " cI0 += exp.ck\n", - " elif exp.type == BathExponent.types['RI']:\n", - " cI0 += exp.ck2\n", - "\n", - " result -= 2 * cI0 * (coupling_op * coupling_op * ado_state.rho).tr()\n", - " if delta != 0:\n", - " result -= (\n", - " 1j * delta *\n", - " ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr()\n", - " )\n", - " return result\n", - "\n", - "\n", - "def system_heat_current(\n", - " bath_tag, ado_state, hamiltonian, coupling_op, delta=0,\n", - "):\n", - " \"\"\"\n", - " System heat current from the system into the heat bath with the given tag.\n", - "\n", - " Parameters\n", - " ----------\n", - " bath_tag : str, tuple or any other object\n", - " Tag of the heat bath corresponding to the current of interest.\n", - "\n", - " ado_state : HierarchyADOsState\n", - " Current state of the system and the environment (encoded in the ADOs).\n", - "\n", - " hamiltonian : Qobj\n", - " System Hamiltonian at the current time.\n", - "\n", - " coupling_op : Qobj\n", - " System coupling operator at the current time.\n", - "\n", - " delta : float\n", - " The prefactor of the \\\\delta(t) term in the correlation function (the\n", - " Ishizaki-Tanimura terminator).\n", - " \"\"\"\n", - " l1_labels = ado_state.filter(level=1, tags=[bath_tag])\n", - " a_op = 1j * (hamiltonian * coupling_op - coupling_op * hamiltonian)\n", - "\n", - " result = 0\n", - " for label in l1_labels:\n", - " result += (a_op * ado_state.extract(label)).tr()\n", - "\n", - " if delta != 0:\n", - " result -= (\n", - " 1j * delta *\n", - " ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr()\n", - " )\n", - " return result" - ] - }, - { - "cell_type": "markdown", - "id": "eb9d46bf", - "metadata": {}, - "source": [ - "Note that at long times, we expect $j_{\\text{B}}^1 = -j_{\\text{B}}^2$ and $j_{\\text{S}}^1 = -j_{\\text{S}}^2$ due to energy conservation. At long times, we also expect $j_{\\text{B}}^1 = j_{\\text{S}}^1$ and $j_{\\text{B}}^2 = j_{\\text{S}}^2$ since the coupling operators commute, $[Q_1, Q_2] = 0$. Hence, all four currents should agree in the long-time limit (up to a sign). This long-time value is what was analyzed in Ref. \\[2\\]." - ] - }, - { - "cell_type": "markdown", - "id": "412e66a5", - "metadata": {}, - "source": [ - "## Simulations" - ] - }, - { - "cell_type": "markdown", - "id": "9cc415ca", - "metadata": {}, - "source": [ - "For our simulations, we will represent the bath spectral densities using the first term of their Padé decompositions, and we will use $7$ levels of the HEOM hierarchy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8f9bcbf7", - "metadata": {}, - "outputs": [], - "source": [ - "Nk = 1\n", - "NC = 7\n", - "options = qt.Options(nsteps=1500, store_states=False, atol=1e-12, rtol=1e-12)" - ] - }, - { - "cell_type": "markdown", - "id": "3cc1e2ad", - "metadata": {}, - "source": [ - "### Time Evolution\n", - "\n", - "We fix $J_{12} = 0.1 \\epsilon$ (as in Fig. 3(a-ii) of Ref. \\[2\\]) and choose the fixed coupling strength $\\lambda_1 = \\lambda_2 = J_{12}\\, /\\, (2\\epsilon)$ (corresponding to $\\bar\\zeta = 1$ in Ref. \\[2\\]).\n", - "Using these values, we will study the time evolution of the system state and the heat currents." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e7251025", - "metadata": {}, - "outputs": [], - "source": [ - "# fix qubit-qubit and qubit-bath coupling strengths\n", - "sys = SystemParams(J12=0.1)\n", - "bath_p1 = BathParams(qubit=0, sign=\"+\", lam=sys.J12 / 2)\n", - "bath_p2 = BathParams(qubit=1, sign=\"-\", lam=sys.J12 / 2)\n", - "\n", - "# choose arbitrary initial state\n", - "rho0 = qt.tensor(qt.identity(2), qt.identity(2)) / 4\n", - "\n", - "# simulation time span\n", - "tlist = np.linspace(0, 50, 250)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dc87c816", - "metadata": {}, - "outputs": [], - "source": [ - "H = sys.H()\n", - "\n", - "bath1 = bath_p1.bath(Nk, tag='bath 1')\n", - "Q1 = bath_p1.Q()\n", - "\n", - "bath2 = bath_p2.bath(Nk, tag='bath 2')\n", - "Q2 = bath_p2.Q()\n", - "\n", - "b1delta, b1term = bath1.terminator()\n", - "b2delta, b2term = bath2.terminator()\n", - "solver = HEOMSolver(\n", - " qt.liouvillian(H) + b1term + b2term,\n", - " [bath1, bath2],\n", - " max_depth=NC,\n", - " options=options,\n", - ")\n", - "\n", - "result = solver.run(rho0, tlist, e_ops=[\n", - " qt.tensor(qt.sigmaz(), qt.identity(2)),\n", - " lambda t, ado: bath_heat_current('bath 1', ado, H, Q1, b1delta),\n", - " lambda t, ado: bath_heat_current('bath 2', ado, H, Q2, b2delta),\n", - " lambda t, ado: system_heat_current('bath 1', ado, H, Q1, b1delta),\n", - " lambda t, ado: system_heat_current('bath 2', ado, H, Q2, b2delta),\n", - "])" - ] - }, - { - "cell_type": "markdown", - "id": "aebe1d23", - "metadata": {}, - "source": [ - "We first plot $\\langle \\sigma_z^1 \\rangle$ to see the time evolution of the system state:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bacd11b9", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(figsize=(8, 8))\n", - "axes.plot(tlist, result.expect[0], 'r', linewidth=2)\n", - "axes.set_xlabel('t', fontsize=28)\n", - "axes.set_ylabel(r\"$\\langle \\sigma_z^1 \\rangle$\", fontsize=28);" - ] - }, - { - "cell_type": "markdown", - "id": "27b4721f", - "metadata": {}, - "source": [ - "We find a rather quick thermalization of the system state. For the heat currents, however, it takes a somewhat longer time until they converge to their long-time values:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "68b10993", - "metadata": {}, - "outputs": [], - "source": [ - "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8))\n", - "\n", - "ax1.plot(\n", - " tlist, -np.real(result.expect[1]),\n", - " color='darkorange', label='BHC (bath 1 -> system)',\n", - ")\n", - "ax1.plot(\n", - " tlist, np.real(result.expect[2]),\n", - " '--', color='darkorange', label='BHC (system -> bath 2)',\n", - ")\n", - "ax1.plot(\n", - " tlist, -np.real(result.expect[3]),\n", - " color='dodgerblue', label='SHC (bath 1 -> system)',\n", - ")\n", - "ax1.plot(\n", - " tlist, np.real(result.expect[4]),\n", - " '--', color='dodgerblue', label='SHC (system -> bath 2)',\n", - ")\n", - "\n", - "ax1.set_xlabel('t', fontsize=28)\n", - "ax1.set_ylabel('j', fontsize=28)\n", - "ax1.set_ylim((-0.05, 0.05))\n", - "ax1.legend(loc=0, fontsize=12)\n", - "\n", - "ax2.plot(\n", - " tlist, -np.real(result.expect[1]),\n", - " color='darkorange', label='BHC (bath 1 -> system)',\n", - ")\n", - "ax2.plot(\n", - " tlist, np.real(result.expect[2]),\n", - " '--', color='darkorange', label='BHC (system -> bath 2)',\n", - ")\n", - "ax2.plot(\n", - " tlist, -np.real(result.expect[3]),\n", - " color='dodgerblue', label='SHC (bath 1 -> system)',\n", - ")\n", - "ax2.plot(\n", - " tlist, np.real(result.expect[4]),\n", - " '--', color='dodgerblue', label='SHC (system -> bath 2)',\n", - ")\n", - "\n", - "ax2.set_xlabel('t', fontsize=28)\n", - "ax2.set_xlim((20, 50))\n", - "ax2.set_ylim((0, 0.0002))\n", - "ax2.legend(loc=0, fontsize=12);" - ] - }, - { - "cell_type": "markdown", - "id": "c1ac9397", - "metadata": {}, - "source": [ - "### Steady-state currents\n", - "\n", - "Here, we try to reproduce the HEOM curves in Fig. 3(a) of Ref. \\[1\\] by varying the coupling strength and finding the steady state for each coupling strength." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ae61f3f3", - "metadata": {}, - "outputs": [], - "source": [ - "def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options):\n", - " \"\"\" Calculate the steady sate heat currents for the given system and\n", - " bath.\n", - " \"\"\"\n", - " bath1 = bath_p1.bath(Nk, tag=\"bath 1\")\n", - " Q1 = bath_p1.Q()\n", - "\n", - " bath2 = bath_p2.bath(Nk, tag=\"bath 2\")\n", - " Q2 = bath_p2.Q()\n", - "\n", - " b1delta, b1term = bath1.terminator()\n", - " b2delta, b2term = bath2.terminator()\n", - "\n", - " solver = HEOMSolver(\n", - " qt.liouvillian(sys.H()) + b1term + b2term,\n", - " [bath1, bath2],\n", - " max_depth=NC,\n", - " options=options\n", - " )\n", - "\n", - " _, steady_ados = solver.steady_state()\n", - "\n", - " return (\n", - " bath_heat_current('bath 1', steady_ados, sys.H(), Q1, b1delta),\n", - " bath_heat_current('bath 2', steady_ados, sys.H(), Q2, b2delta),\n", - " system_heat_current('bath 1', steady_ados, sys.H(), Q1, b1delta),\n", - " system_heat_current('bath 2', steady_ados, sys.H(), Q2, b2delta),\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c9e7dabd", - "metadata": {}, - "outputs": [], - "source": [ - "# Define number of points to use for the plot\n", - "plot_points = 10 # use 100 for a smoother curve\n", - "\n", - "# Range of relative coupling strengths\n", - "# Chosen so that zb_max is maximum, centered around 1 on a log scale\n", - "zb_max = 4 # use 20 to see more of the current curve\n", - "zeta_bars = np.logspace(\n", - " -np.log(zb_max),\n", - " np.log(zb_max),\n", - " plot_points,\n", - " base=np.e,\n", - ")\n", - "\n", - "# Setup a progress bar\n", - "progress = IntProgress(min=0, max=(3 * plot_points))\n", - "display(progress)\n", - "\n", - "\n", - "def calculate_heat_current(J12, zb, Nk, progress=progress):\n", - " \"\"\" Calculate a single heat current and update the progress bar. \"\"\"\n", - " # Estimate appropriate HEOM max_depth from coupling strength\n", - " NC = 7 + int(max(zb * J12 - 1, 0) * 2)\n", - " NC = min(NC, 20)\n", - " # the four currents are identical in the steady state\n", - " j, _, _, _ = heat_currents(\n", - " sys.replace(J12=J12),\n", - " bath_p1.replace(lam=zb * J12 / 2),\n", - " bath_p2.replace(lam=zb * J12 / 2),\n", - " Nk, NC, options=options,\n", - " )\n", - " progress.value += 1\n", - " return j\n", - "\n", - "\n", - "# Calculate steady state currents for range of zeta_bars\n", - "# for J12 = 0.01, 0.1 and 0.5:\n", - "j1s = [\n", - " calculate_heat_current(0.01, zb, Nk)\n", - " for zb in zeta_bars\n", - "]\n", - "j2s = [\n", - " calculate_heat_current(0.1, zb, Nk)\n", - " for zb in zeta_bars\n", - "]\n", - "j3s = [\n", - " calculate_heat_current(0.5, zb, Nk)\n", - " for zb in zeta_bars\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "ba6aa94e", - "metadata": {}, - "source": [ - "## Create Plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ae143732", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(figsize=(12, 7))\n", - "\n", - "axes.plot(\n", - " zeta_bars, -1000 * 100 * np.real(j1s),\n", - " 'b', linewidth=2, label=r\"$J_{12} = 0.01\\, \\epsilon$\",\n", - ")\n", - "axes.plot(\n", - " zeta_bars, -1000 * 10 * np.real(j2s),\n", - " 'r--', linewidth=2, label=r\"$J_{12} = 0.1\\, \\epsilon$\",\n", - ")\n", - "axes.plot(\n", - " zeta_bars, -1000 * 2 * np.real(j3s),\n", - " 'g-.', linewidth=2, label=r\"$J_{12} = 0.5\\, \\epsilon$\",\n", - ")\n", - "\n", - "axes.set_xscale('log')\n", - "axes.set_xlabel(r\"$\\bar\\zeta$\", fontsize=30)\n", - "axes.set_xlim((zeta_bars[0], zeta_bars[-1]))\n", - "\n", - "axes.set_ylabel(\n", - " r\"$j_{\\mathrm{ss}}\\; /\\; (\\epsilon J_{12}) \\times 10^3$\",\n", - " fontsize=30,\n", - ")\n", - "axes.set_ylim((0, 2))\n", - "\n", - "axes.legend(loc=0);" - ] - }, - { - "cell_type": "markdown", - "id": "b3206829", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5c912aaf", - "metadata": {}, - "outputs": [], - "source": [ - "qt.about()" - ] - }, - { - "cell_type": "markdown", - "id": "4f464314", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e5895555", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert 1 == 1" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v4/heom/heom-3-quantum-heat-transport.md b/tutorials-v4/heom/heom-3-quantum-heat-transport.md index 155bee73..c4b448e2 100644 --- a/tutorials-v4/heom/heom-3-quantum-heat-transport.md +++ b/tutorials-v4/heom/heom-3-quantum-heat-transport.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.4 + jupytext_version: 1.16.1 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -50,7 +50,7 @@ References: ## Setup -```{code-cell} ipython3 +```{code-cell} import dataclasses import numpy as np @@ -71,9 +71,7 @@ from IPython.display import display ## System and bath definition -```{code-cell} ipython3 -:tags: [] - +```{code-cell} @dataclasses.dataclass class SystemParams: """ System parameters and Hamiltonian. """ @@ -102,9 +100,7 @@ class SystemParams: return dataclasses.replace(self, **kw) ``` -```{code-cell} ipython3 -:tags: [] - +```{code-cell} @dataclasses.dataclass class BathParams: """ Bath parameters. """ @@ -157,7 +153,7 @@ In the expression for the bath heat currents, we left out terms involving $[Q_1, In QuTiP, these currents can be conveniently calculated as follows: -```{code-cell} ipython3 +```{code-cell} def bath_heat_current(bath_tag, ado_state, hamiltonian, coupling_op, delta=0): """ Bath heat current from the system into the heat bath with the given tag. @@ -252,7 +248,7 @@ Note that at long times, we expect $j_{\text{B}}^1 = -j_{\text{B}}^2$ and $j_{\t For our simulations, we will represent the bath spectral densities using the first term of their Padé decompositions, and we will use $7$ levels of the HEOM hierarchy. -```{code-cell} ipython3 +```{code-cell} Nk = 1 NC = 7 options = qt.Options(nsteps=1500, store_states=False, atol=1e-12, rtol=1e-12) @@ -263,7 +259,7 @@ options = qt.Options(nsteps=1500, store_states=False, atol=1e-12, rtol=1e-12) We fix $J_{12} = 0.1 \epsilon$ (as in Fig. 3(a-ii) of Ref. \[2\]) and choose the fixed coupling strength $\lambda_1 = \lambda_2 = J_{12}\, /\, (2\epsilon)$ (corresponding to $\bar\zeta = 1$ in Ref. \[2\]). Using these values, we will study the time evolution of the system state and the heat currents. -```{code-cell} ipython3 +```{code-cell} # fix qubit-qubit and qubit-bath coupling strengths sys = SystemParams(J12=0.1) bath_p1 = BathParams(qubit=0, sign="+", lam=sys.J12 / 2) @@ -276,7 +272,7 @@ rho0 = qt.tensor(qt.identity(2), qt.identity(2)) / 4 tlist = np.linspace(0, 50, 250) ``` -```{code-cell} ipython3 +```{code-cell} H = sys.H() bath1 = bath_p1.bath(Nk, tag='bath 1') @@ -305,7 +301,7 @@ result = solver.run(rho0, tlist, e_ops=[ We first plot $\langle \sigma_z^1 \rangle$ to see the time evolution of the system state: -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(figsize=(8, 8)) axes.plot(tlist, result.expect[0], 'r', linewidth=2) axes.set_xlabel('t', fontsize=28) @@ -314,7 +310,7 @@ axes.set_ylabel(r"$\langle \sigma_z^1 \rangle$", fontsize=28); We find a rather quick thermalization of the system state. For the heat currents, however, it takes a somewhat longer time until they converge to their long-time values: -```{code-cell} ipython3 +```{code-cell} fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8)) ax1.plot( @@ -366,7 +362,7 @@ ax2.legend(loc=0, fontsize=12); Here, we try to reproduce the HEOM curves in Fig. 3(a) of Ref. \[1\] by varying the coupling strength and finding the steady state for each coupling strength. -```{code-cell} ipython3 +```{code-cell} def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options): """ Calculate the steady sate heat currents for the given system and bath. @@ -397,7 +393,7 @@ def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options): ) ``` -```{code-cell} ipython3 +```{code-cell} # Define number of points to use for the plot plot_points = 10 # use 100 for a smoother curve @@ -450,7 +446,7 @@ j3s = [ ## Create Plot -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(figsize=(12, 7)) axes.plot( @@ -481,7 +477,7 @@ axes.legend(loc=0); ## About -```{code-cell} ipython3 +```{code-cell} qt.about() ``` @@ -489,8 +485,6 @@ qt.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 -:tags: [] - +```{code-cell} assert 1 == 1 ``` diff --git a/tutorials-v4/heom/heom-4-dynamical-decoupling.ipynb b/tutorials-v4/heom/heom-4-dynamical-decoupling.ipynb deleted file mode 100644 index 75350b31..00000000 --- a/tutorials-v4/heom/heom-4-dynamical-decoupling.ipynb +++ /dev/null @@ -1,776 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "2f80f634", - "metadata": {}, - "source": [ - "# HEOM 4: Dynamical decoupling of a non-Markovian environment" - ] - }, - { - "cell_type": "markdown", - "id": "825af264", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "Following [Lorenza Viola and Seth Lloyd](https://arxiv.org/abs/quant-ph/9803057) we consider an example of dynamical decoupling.\n", - "We choose a drive which performs pi rotations, interspersed with short periods where the bath causes dephasing.\n", - "\n", - "We first show the standard example of equally spaced pulses, and then consider the 'optimal' Uhrig spacing ([Götz S. Uhrig Phys. Rev. Lett. 98, 100504 (2007)](https://arxiv.org/abs/quant-ph/0609203))." - ] - }, - { - "cell_type": "markdown", - "id": "10ddeef3", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ea414173", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " Options,\n", - " QobjEvo,\n", - " basis,\n", - " expect,\n", - " ket2dm,\n", - " sigmax,\n", - " sigmaz,\n", - ")\n", - "from qutip.nonmarkov.heom import (\n", - " HEOMSolver,\n", - " DrudeLorentzPadeBath,\n", - ")\n", - "\n", - "from ipywidgets import IntProgress\n", - "from IPython.display import display\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "d96db540", - "metadata": {}, - "source": [ - "## Helper functions\n", - "\n", - "Let's define some helper functions for calculating the spectral density:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e1f3dfc6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def dl_spectrum(w, lam, gamma):\n", - " \"\"\" Return the Drude-Lorentz spectral density. \"\"\"\n", - " J = w * 2 * lam * gamma / (gamma**2 + w**2)\n", - " return J" - ] - }, - { - "cell_type": "markdown", - "id": "cc5a97b6", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "Now we define the system and bath properties and the HEOM parameters. The system is a single stationary qubit with $H = 0$ and the bath is a bosonic bath with a Drude-Lorentz spectrum." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5df8a44", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Define the system Hamlitonian.\n", - "#\n", - "# The system isn't evolving by itself, so the Hamiltonian is 0 (with the\n", - "# correct dimensions):\n", - "\n", - "H_sys = 0 * sigmaz()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "063a3370", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresonding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresonding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ef69bd00", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Properties for the Drude-Lorentz bath\n", - "\n", - "lam = 0.0005\n", - "gamma = 0.005\n", - "T = 0.05\n", - "\n", - "# bath-system coupling operator:\n", - "Q = sigmaz()\n", - "\n", - "# number of terms to keep in the expansion of the bath correlation function:\n", - "Nk = 3\n", - "\n", - "bath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6647d0fc", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# HEOM parameters\n", - "\n", - "# number of layers to keep in the hierarchy:\n", - "NC = 6" - ] - }, - { - "cell_type": "markdown", - "id": "af97aecc", - "metadata": {}, - "source": [ - "To perform the dynamic decoupling from the environment, we will drive the system with a time-dependent pulse that couples to the system via the $\\sigma_x$ operator. The area under the pulse will usual be set to $\\pi / 2$ so that the pulse flips the qubit state.\n", - "\n", - "Below we define a function that returns the pulse (which is itself a function):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "938993d5", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def drive(amplitude, delay, integral):\n", - " \"\"\" Coefficient of the drive as a function of time.\n", - "\n", - " The drive consists of a series of constant pulses with\n", - " a fixed delay between them.\n", - "\n", - " Parameters\n", - " ----------\n", - " amplitude : float\n", - " The amplitude of the drive during the pulse.\n", - " delay : float\n", - " The time delay between successive pulses.\n", - " integral : float\n", - " The integral of the pulse. This determines\n", - " the duration of each pulse with the duration\n", - " equal to the integral divided by the amplitude.\n", - " \"\"\"\n", - " duration = integral / amplitude\n", - " period = duration + delay\n", - "\n", - " def pulse(t):\n", - " t = t % period\n", - " if t < duration:\n", - " return amplitude\n", - " return 0\n", - "\n", - " return pulse\n", - "\n", - "\n", - "H_drive = sigmax()" - ] - }, - { - "cell_type": "markdown", - "id": "6faf63dc", - "metadata": {}, - "source": [ - "## Plot the spectral density\n", - "\n", - "Let's start by plotting the spectral density of our Drude-Lorentz bath:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a329a886", - "metadata": {}, - "outputs": [], - "source": [ - "wlist = np.linspace(0, 0.5, 1000)\n", - "J = dl_spectrum(wlist, lam, gamma)\n", - "\n", - "fig, axes = plt.subplots(1, 1, figsize=(8, 8))\n", - "axes.plot(wlist, J, 'r', linewidth=2)\n", - "axes.set_xlabel(r'$\\omega$', fontsize=28)\n", - "axes.set_ylabel(r'J', fontsize=28);" - ] - }, - { - "cell_type": "markdown", - "id": "eeef012a", - "metadata": {}, - "source": [ - "## Dynamic decoupling with fast and slow pulses\n", - "\n", - "Now we are ready to explore dynamic decoupling from the environment.\n", - "\n", - "First we will drive the system with fast, large amplitude pulses. Then we will drive the system with slower, smaller amplitude pulses. The faster pulses decoupling the system more effectively and retain the coherence longer, but the slower pulses help too.\n", - "\n", - "Let's start by simulating the fast pulses:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ccd8f1ac", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Fast driving (quick, large amplitude pulses)\n", - "\n", - "# The max_step must be set to a short time than the\n", - "# length of the shortest pulse, otherwise the solver\n", - "# might skip over a pulse.\n", - "options = Options(\n", - " nsteps=1500,\n", - " store_states=True,\n", - " rtol=1e-12,\n", - " atol=1e-12,\n", - " max_step=1 / 20.0,\n", - ")\n", - "\n", - "tlist = np.linspace(0, 400, 1000)\n", - "\n", - "# start with a superposition so there is something to dephase!\n", - "rho0 = (basis(2, 1) + basis(2, 0)).unit()\n", - "rho0 = ket2dm(rho0)\n", - "\n", - "# without pulses\n", - "hsolver = HEOMSolver(H_sys, bath, NC, options=options)\n", - "outputnoDD = hsolver.run(rho0, tlist, ado_return=True)\n", - "\n", - "# with pulses\n", - "drive_fast = drive(amplitude=0.5, delay=20, integral=np.pi / 2)\n", - "H_d = qutip.QobjEvo([H_sys, [H_drive, drive_fast]])\n", - "\n", - "hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", - "outputDD = hsolver.run(rho0, tlist, ado_return=True)" - ] - }, - { - "cell_type": "markdown", - "id": "04ac00e2", - "metadata": {}, - "source": [ - "And now the longer slower pulses:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "431f101a", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Slow driving (longer, small amplitude pulses)\n", - "\n", - "# without pulses\n", - "hsolver = HEOMSolver(H_sys, bath, NC, options=options)\n", - "outputnoDDslow = hsolver.run(rho0, tlist, ado_return=True)\n", - "\n", - "# with pulses\n", - "drive_slow = drive(amplitude=0.01, delay=20, integral=np.pi/2)\n", - "H_d = [H_sys, [H_drive, drive_slow]]\n", - "\n", - "hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", - "outputDDslow = hsolver.run(rho0, tlist, ado_return=True)" - ] - }, - { - "cell_type": "markdown", - "id": "8cac1123", - "metadata": {}, - "source": [ - "Now let's plot all of the results and the shapes of the pulses:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "39ecde58", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_dd_results(outputnoDD, outputDD, outputDDslow):\n", - " fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 12))\n", - "\n", - " # Plot the dynamic decoupling results:\n", - "\n", - " tlist = outputDD.times\n", - "\n", - " P12 = basis(2, 1) * basis(2, 0).dag()\n", - " P12DD = qutip.expect(outputDD.states, P12)\n", - " P12noDD = qutip.expect(outputnoDD.states, P12)\n", - " P12DDslow = qutip.expect(outputDDslow.states, P12)\n", - "\n", - " plt.sca(axes[0])\n", - " plt.yticks([0, 0.25, 0.5], [0, 0.25, 0.5])\n", - "\n", - " axes[0].plot(\n", - " tlist, np.real(P12DD),\n", - " 'green', linestyle='-', linewidth=2, label=\"HEOM with fast DD\",\n", - " )\n", - " axes[0].plot(\n", - " tlist, np.real(P12DDslow),\n", - " 'blue', linestyle='-', linewidth=2, label=\"HEOM with slow DD\",\n", - " )\n", - " axes[0].plot(\n", - " tlist, np.real(P12noDD),\n", - " 'orange', linestyle='--', linewidth=2, label=\"HEOM no DD\",\n", - " )\n", - "\n", - " axes[0].locator_params(axis='y', nbins=3)\n", - " axes[0].locator_params(axis='x', nbins=3)\n", - "\n", - " axes[0].set_ylabel(r\"$\\rho_{01}$\", fontsize=30)\n", - "\n", - " axes[0].legend(loc=4)\n", - " axes[0].text(0, 0.4, \"(a)\", fontsize=28)\n", - "\n", - " # Plot the drive pulses:\n", - "\n", - " pulse = [drive_fast(t) for t in tlist]\n", - " pulseslow = [drive_slow(t) for t in tlist]\n", - "\n", - " plt.sca(axes[1])\n", - " plt.yticks([0., 0.25, 0.5], [0, 0.25, 0.5])\n", - "\n", - " axes[1].plot(\n", - " tlist, pulse,\n", - " 'green', linestyle='-', linewidth=2, label=\"Drive fast\",\n", - " )\n", - " axes[1].plot(\n", - " tlist, pulseslow,\n", - " 'blue', linestyle='--', linewidth=2, label=\"Drive slow\",\n", - " )\n", - "\n", - " axes[1].locator_params(axis='y', nbins=3)\n", - " axes[1].locator_params(axis='x', nbins=3)\n", - "\n", - " axes[1].set_xlabel(r'$t\\bar{V}_{\\mathrm{f}}$', fontsize=30)\n", - " axes[1].set_ylabel(r'Drive amplitude/$\\bar{V}_{\\mathrm{f}}$', fontsize=30)\n", - "\n", - " axes[1].legend(loc=1)\n", - " axes[1].text(0, 0.4, \"(b)\", fontsize=28)\n", - "\n", - " fig.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "597fa307", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "plot_dd_results(outputnoDD, outputDD, outputDDslow)" - ] - }, - { - "cell_type": "markdown", - "id": "62af1de8", - "metadata": {}, - "source": [ - "## Non-equally spaced pulses" - ] - }, - { - "cell_type": "markdown", - "id": "6f002052", - "metadata": {}, - "source": [ - "Next we consider non-equally spaced pulses.\n", - "\n", - "Rather than plot as a function of time we just consider the final coherence after time $T$ and 100 pulses. We change the width of the environment to demonstate that the Uhrig sequence (i.e. the evenly spaced pulses) can be sub-optimal when the bath is very broad.\n", - "\n", - "Instead of evenly spaced pulses, we will use pulses where the cummulative delay after $j$ pulses is given by:\n", - "\n", - "$$\n", - " \\sin^2(\\frac{\\pi}{2} \\frac{j}{N + 1})\n", - "$$\n", - "\n", - "This is just a convenient way to describe the varying delay. We could have chosen another monotonically increasing function to represent the cummulative delay (although it might not be as effective)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3c165906", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def cummulative_delay_fractions(N):\n", - " \"\"\" Return an array of N + 1 cummulative delay\n", - " fractions.\n", - "\n", - " The j'th entry in the array should be the sum of\n", - " all delays before the j'th pulse. The last entry\n", - " should be 1 (i.e. the entire cummulative delay\n", - " should have been used once the sequence of pulses\n", - " is complete).\n", - "\n", - " The function should be monotonically increasing,\n", - " strictly greater than zero and the last value\n", - " should be 1.\n", - "\n", - " This implementation returns:\n", - "\n", - " sin((pi / 2) * (j / (N + 1)))**2\n", - "\n", - " as the cummulative delay after the j'th pulse.\n", - " \"\"\"\n", - " return np.array([\n", - " np.sin((np.pi / 2) * (j / (N + 1)))**2\n", - " for j in range(0, N + 1)\n", - " ])\n", - "\n", - "\n", - "def drive_opt(amplitude, avg_delay, integral, N):\n", - " \"\"\" Return an optimized distance pulse function.\n", - "\n", - " Our previous pulses were evenly spaced. Here we\n", - " instead use a varying delay after the j'th pulse.\n", - "\n", - " The cummulative delay is described by the function\n", - " ``cummulative_delay_fractions`` above.\n", - " \"\"\"\n", - " duration = integral / amplitude\n", - " cummulative_delays = N * avg_delay * cummulative_delay_fractions(N)\n", - "\n", - " t_start = cummulative_delays + duration * np.arange(0, N + 1)\n", - " t_end = cummulative_delays + duration * np.arange(1, N + 2)\n", - "\n", - " def pulse(t):\n", - " if any((t_start <= t) & (t <= t_end)):\n", - " return amplitude\n", - " return 0.0\n", - "\n", - " return pulse" - ] - }, - { - "cell_type": "markdown", - "id": "01e75e27", - "metadata": {}, - "source": [ - "Let's plot the cummulative delays and see what they look like. Note that the cummulative delay starts at $0$, ends at $1$ and is monotonically increasing, as required.\n", - "\n", - "On the same axes we plot the individual $j^{th}$ delays as a fraction of the average delay." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c9e65156", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_cummulative_delay_fractions(N):\n", - " cummulative = cummulative_delay_fractions(N)\n", - " individual = (cummulative[1:] - cummulative[:-1]) * N\n", - " plt.plot(np.arange(0, N + 1), cummulative, label=\"Cummulative delay\")\n", - " plt.plot(np.arange(0, N), individual, label=\"j'th delay\")\n", - " plt.xlabel(\"j\")\n", - " plt.ylabel(\"Fraction of delay\")\n", - " plt.legend()\n", - "\n", - "\n", - "plot_cummulative_delay_fractions(100)" - ] - }, - { - "cell_type": "markdown", - "id": "cd967dd1", - "metadata": {}, - "source": [ - "And now let us plot the first ten even and optimally spaced pulses together to compare them:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a375dd1", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_even_and_optimally_spaced_pulses():\n", - " amplitude = 10.0\n", - " integral = np.pi / 2\n", - " duration = integral / amplitude\n", - " delay = 1.0 - duration\n", - "\n", - " tlist = np.linspace(0, 10, 1000)\n", - "\n", - " pulse_opt = drive_opt(amplitude, delay, integral, 100)\n", - " pulse_eq = drive(amplitude, delay, integral)\n", - "\n", - " plt.plot(\n", - " tlist, [pulse_opt(t) for t in tlist], label=\"opt\",\n", - " )\n", - " plt.plot(\n", - " tlist, [pulse_eq(t) for t in tlist], label=\"eq\",\n", - " )\n", - " plt.legend(loc=4)\n", - "\n", - "\n", - "plot_even_and_optimally_spaced_pulses()" - ] - }, - { - "cell_type": "markdown", - "id": "7dcda248", - "metadata": { - "tags": [] - }, - "source": [ - "Now let's simulate the effectiveness of the two sets of delays by comparing how well they maintain coherence after a hundred pulses.\n", - "\n", - "We'll perform the simulation over a range of lambdas and gammas to show how the non-evenly spaced delays become optimal as the width of the bath spectral function increases." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f4df85f5", - "metadata": {}, - "outputs": [], - "source": [ - "# Bath parameters to simulate over:\n", - "\n", - "# We use only two lambdas and two gammas so that the notebook executes\n", - "# quickly:\n", - "\n", - "lams = [0.005, 0.0005]\n", - "gammas = np.linspace(0.005, 0.05, 2)\n", - "\n", - "# But one can also extend the lists to larger ones:\n", - "#\n", - "# lams = [0.01, 0.005, 0.0005]\n", - "# gammas = np.linspace(0.005, 0.05, 10)\n", - "\n", - "# Setup a progress bar:\n", - "\n", - "progress = IntProgress(min=0, max=(2 * len(lams) * len(gammas)))\n", - "display(progress)\n", - "\n", - "\n", - "def simulate_100_pulses(lam, gamma, T, NC, Nk):\n", - " \"\"\" Simulate the evolution of 100 evenly and optimally spaced pulses.\n", - "\n", - " Returns the expectation value of P12p from the final state of\n", - " each evolution.\n", - " \"\"\"\n", - " rho0 = (basis(2, 1) + basis(2, 0)).unit()\n", - " rho0 = ket2dm(rho0)\n", - "\n", - " N = 100 # number of pulses to simulate\n", - " avg_cycle_time = 1.0 # average time from one pulse to the next\n", - " t_max = N * avg_cycle_time\n", - "\n", - " tlist = np.linspace(0, t_max, 100)\n", - "\n", - " amplitude = 10.0\n", - " integral = np.pi / 2\n", - " duration = integral / amplitude\n", - " delay = avg_cycle_time - duration\n", - "\n", - " bath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk)\n", - "\n", - " # Equally spaced pulses:\n", - "\n", - " pulse_eq = drive(amplitude, delay, integral)\n", - " H_d = QobjEvo([H_sys, [H_drive, pulse_eq]])\n", - "\n", - " hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", - " result = hsolver.run(rho0, tlist)\n", - "\n", - " P12_eq = expect(result.states[-1], P12p)\n", - " progress.value += 1\n", - "\n", - " # Non-equally spaced pulses:\n", - "\n", - " pulse_opt = drive_opt(amplitude, delay, integral, N)\n", - " H_d = QobjEvo([H_sys, [H_drive, pulse_opt]])\n", - "\n", - " hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", - " result = hsolver.run(rho0, tlist)\n", - "\n", - " P12_opt = expect(result.states[-1], P12p)\n", - " progress.value += 1\n", - "\n", - " return P12_opt, P12_eq\n", - "\n", - "\n", - "# We use NC=2 and Nk=2 to speed up the simulation:\n", - "\n", - "P12_results = [\n", - " list(zip(*(\n", - " simulate_100_pulses(lam=lam_, gamma=gamma_, T=0.5, NC=2, Nk=2)\n", - " for gamma_ in gammas\n", - " )))\n", - " for lam_ in lams\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "c9953176", - "metadata": {}, - "source": [ - "Now that we have the expectation values of $\\rho_{01}$ let's plot them as a function of gamma for each lambda. Note how in each case the non-evenly spaced pulses become optimal once gamma is sufficiently small:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f4955656", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=False, figsize=(10, 7))\n", - "colors = [\"green\", \"red\", \"blue\"]\n", - "\n", - "for i in range(len(lams)):\n", - " color = colors[i % len(colors)]\n", - " axes.plot(\n", - " gammas, np.real(P12_results[i][0]),\n", - " color, linestyle='-', linewidth=2,\n", - " label=f\"Optimal DD [$\\\\lambda={lams[i]}$]\",\n", - " )\n", - " axes.plot(\n", - " gammas, np.real(P12_results[i][1]),\n", - " color, linestyle='-.', linewidth=2,\n", - " label=f\"Even DD [$\\\\lambda={lams[i]}$]\",\n", - " )\n", - "\n", - "axes.set_ylabel(r\"$\\rho_{01}$\")\n", - "axes.set_xlabel(r\"$\\gamma$\")\n", - "axes.legend(fontsize=16)\n", - "\n", - "fig.tight_layout();" - ] - }, - { - "cell_type": "markdown", - "id": "209475ff", - "metadata": {}, - "source": [ - "And now you know about dynamically decoupling a qubit from its environment!" - ] - }, - { - "cell_type": "markdown", - "id": "1a365fe8", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b6ecec10", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "765f3e2e", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3179effe", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert 1 == 1" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v4/heom/heom-4-dynamical-decoupling.md b/tutorials-v4/heom/heom-4-dynamical-decoupling.md index 4c4da69a..d8675e18 100644 --- a/tutorials-v4/heom/heom-4-dynamical-decoupling.md +++ b/tutorials-v4/heom/heom-4-dynamical-decoupling.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.4 + jupytext_version: 1.16.1 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -27,7 +27,7 @@ We first show the standard example of equally spaced pulses, and then consider t ## Setup -```{code-cell} ipython3 +```{code-cell} import numpy as np import matplotlib.pyplot as plt @@ -56,9 +56,7 @@ from IPython.display import display Let's define some helper functions for calculating the spectral density: -```{code-cell} ipython3 -:tags: [] - +```{code-cell} def dl_spectrum(w, lam, gamma): """ Return the Drude-Lorentz spectral density. """ J = w * 2 * lam * gamma / (gamma**2 + w**2) @@ -69,9 +67,7 @@ def dl_spectrum(w, lam, gamma): Now we define the system and bath properties and the HEOM parameters. The system is a single stationary qubit with $H = 0$ and the bath is a bosonic bath with a Drude-Lorentz spectrum. -```{code-cell} ipython3 -:tags: [] - +```{code-cell} # Define the system Hamlitonian. # # The system isn't evolving by itself, so the Hamiltonian is 0 (with the @@ -80,9 +76,7 @@ Now we define the system and bath properties and the HEOM parameters. The system H_sys = 0 * sigmaz() ``` -```{code-cell} ipython3 -:tags: [] - +```{code-cell} # Define some operators with which we will measure the system # 1,1 element of density matrix - corresonding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -91,9 +85,7 @@ P22p = basis(2, 1) * basis(2, 1).dag() P12p = basis(2, 0) * basis(2, 1).dag() ``` -```{code-cell} ipython3 -:tags: [] - +```{code-cell} # Properties for the Drude-Lorentz bath lam = 0.0005 @@ -109,9 +101,7 @@ Nk = 3 bath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) ``` -```{code-cell} ipython3 -:tags: [] - +```{code-cell} # HEOM parameters # number of layers to keep in the hierarchy: @@ -122,9 +112,7 @@ To perform the dynamic decoupling from the environment, we will drive the system Below we define a function that returns the pulse (which is itself a function): -```{code-cell} ipython3 -:tags: [] - +```{code-cell} def drive(amplitude, delay, integral): """ Coefficient of the drive as a function of time. @@ -161,7 +149,7 @@ H_drive = sigmax() Let's start by plotting the spectral density of our Drude-Lorentz bath: -```{code-cell} ipython3 +```{code-cell} wlist = np.linspace(0, 0.5, 1000) J = dl_spectrum(wlist, lam, gamma) @@ -179,9 +167,7 @@ First we will drive the system with fast, large amplitude pulses. Then we will d Let's start by simulating the fast pulses: -```{code-cell} ipython3 -:tags: [] - +```{code-cell} # Fast driving (quick, large amplitude pulses) # The max_step must be set to a short time than the @@ -215,9 +201,7 @@ outputDD = hsolver.run(rho0, tlist, ado_return=True) And now the longer slower pulses: -```{code-cell} ipython3 -:tags: [] - +```{code-cell} # Slow driving (longer, small amplitude pulses) # without pulses @@ -234,9 +218,7 @@ outputDDslow = hsolver.run(rho0, tlist, ado_return=True) Now let's plot all of the results and the shapes of the pulses: -```{code-cell} ipython3 -:tags: [] - +```{code-cell} def plot_dd_results(outputnoDD, outputDD, outputDDslow): fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 12)) @@ -302,9 +284,7 @@ def plot_dd_results(outputnoDD, outputDD, outputDDslow): fig.tight_layout() ``` -```{code-cell} ipython3 -:tags: [] - +```{code-cell} plot_dd_results(outputnoDD, outputDD, outputDDslow) ``` @@ -324,9 +304,7 @@ $$ This is just a convenient way to describe the varying delay. We could have chosen another monotonically increasing function to represent the cummulative delay (although it might not be as effective). -```{code-cell} ipython3 -:tags: [] - +```{code-cell} def cummulative_delay_fractions(N): """ Return an array of N + 1 cummulative delay fractions. @@ -380,7 +358,7 @@ Let's plot the cummulative delays and see what they look like. Note that the cum On the same axes we plot the individual $j^{th}$ delays as a fraction of the average delay. -```{code-cell} ipython3 +```{code-cell} def plot_cummulative_delay_fractions(N): cummulative = cummulative_delay_fractions(N) individual = (cummulative[1:] - cummulative[:-1]) * N @@ -396,7 +374,7 @@ plot_cummulative_delay_fractions(100) And now let us plot the first ten even and optimally spaced pulses together to compare them: -```{code-cell} ipython3 +```{code-cell} def plot_even_and_optimally_spaced_pulses(): amplitude = 10.0 integral = np.pi / 2 @@ -420,13 +398,11 @@ def plot_even_and_optimally_spaced_pulses(): plot_even_and_optimally_spaced_pulses() ``` -+++ {"tags": []} - Now let's simulate the effectiveness of the two sets of delays by comparing how well they maintain coherence after a hundred pulses. We'll perform the simulation over a range of lambdas and gammas to show how the non-evenly spaced delays become optimal as the width of the bath spectral function increases. -```{code-cell} ipython3 +```{code-cell} # Bath parameters to simulate over: # We use only two lambdas and two gammas so that the notebook executes @@ -506,7 +482,7 @@ P12_results = [ Now that we have the expectation values of $\rho_{01}$ let's plot them as a function of gamma for each lambda. Note how in each case the non-evenly spaced pulses become optimal once gamma is sufficiently small: -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, sharex=False, figsize=(10, 7)) colors = ["green", "red", "blue"] @@ -536,9 +512,7 @@ And now you know about dynamically decoupling a qubit from its environment! ## About -```{code-cell} ipython3 -:tags: [] - +```{code-cell} qutip.about() ``` @@ -546,8 +520,6 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 -:tags: [] - +```{code-cell} assert 1 == 1 ``` diff --git a/tutorials-v4/heom/heom-5a-fermions-single-impurity-model.ipynb b/tutorials-v4/heom/heom-5a-fermions-single-impurity-model.ipynb deleted file mode 100644 index 29af7cf1..00000000 --- a/tutorials-v4/heom/heom-5a-fermions-single-impurity-model.ipynb +++ /dev/null @@ -1,827 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "91b722ef", - "metadata": { - "tags": [] - }, - "source": [ - "# HEOM 5a: Fermionic single impurity model" - ] - }, - { - "cell_type": "markdown", - "id": "8b996250", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc). Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications.\n", - "\n", - "Notation:\n", - "\n", - "* $K=L/R$ refers to left or right leads.\n", - "* $\\sigma=\\pm$ refers to input/output\n", - "\n", - "We choose a Lorentzian spectral density for the leads, with a peak at the chemical potential. The latter simplifies a little the notation required for the correlation functions, but can be relaxed if neccessary.\n", - "\n", - "$$J(\\omega) = \\frac{\\Gamma W^2}{((\\omega-\\mu_K)^2 +W^2 )}$$\n", - "\n", - "The Fermi distribution function is:\n", - "\n", - "$$f_F (x) = (\\exp(x) + 1)^{-1}$$\n", - "\n", - "Together these allow the correlation functions to be expressed as:\n", - "\n", - "$$C^{\\sigma}_K(t) = \\frac{1}{2\\pi} \\int_{-\\infty}^{\\infty} d\\omega e^{\\sigma i \\omega t} \\Gamma_K(\\omega) f_F[\\sigma\\beta(\\omega - \\mu)]$$\n", - "\n", - "As with the bosonic case we can expand these in an exponential series using Matsubara, Pade, or fitting approaches.\n", - "\n", - "The Pade decomposition approximates the Fermi distubition as\n", - "\n", - "$$f_F(x) \\approx f_F^{\\mathrm{approx}}(x) = \\frac{1}{2} - \\sum_l^{l_{max}} \\frac{2k_l x}{x^2 + \\epsilon_l^2}$$\n", - "\n", - "where $k_l$ and $\\epsilon_l$ are co-efficients defined in J. Chem Phys 133,10106.\n", - "\n", - "Evaluating the integral for the correlation functions gives,\n", - "\n", - "$$C_K^{\\sigma}(t) \\approx \\sum_{l=0}^{l_{max}} \\eta_K^{\\sigma_l} e^{-\\gamma_{K,\\sigma,l}t}$$\n", - "\n", - "where:\n", - "\n", - "$$\\eta_{K,0} = \\frac{\\Gamma_KW_K}{2} f_F^{approx}(i\\beta_K W)$$\n", - "\n", - "$$\\gamma_{K,\\sigma,0} = W_K - \\sigma i\\mu_K$$ \n", - "\n", - "$$\\eta_{K,l\\neq 0} = -i\\cdot \\frac{k_m}{\\beta_K} \\cdot \\frac{\\Gamma_K W_K^2}{-\\frac{\\epsilon^2_m}{\\beta_K^2} + W_K^2}$$\n", - "\n", - "$$\\gamma_{K,\\sigma,l\\neq 0}= \\frac{\\epsilon_m}{\\beta_K} - \\sigma i \\mu_K$$\n", - "\n", - "In this notebook we:\n", - "\n", - "* compare the Matsubara and Pade approximations and contrast them with the analytical result for the current between the system and the leads.\n", - "\n", - "* plot the current through the qubit as a function of the different between the voltages of the leads." - ] - }, - { - "cell_type": "markdown", - "id": "b6e913f1", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "354530aa", - "metadata": {}, - "outputs": [], - "source": [ - "import contextlib\n", - "import dataclasses\n", - "import time\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy.integrate import quad\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " Options,\n", - " basis,\n", - " destroy,\n", - " expect,\n", - ")\n", - "from qutip.nonmarkov.heom import (\n", - " HEOMSolver,\n", - " LorentzianBath,\n", - " LorentzianPadeBath,\n", - ")\n", - "\n", - "from ipywidgets import IntProgress\n", - "from IPython.display import display\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "d73247f3", - "metadata": {}, - "source": [ - "## Helpers" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "758ac328", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\" Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "markdown", - "id": "259bf221", - "metadata": { - "tags": [] - }, - "source": [ - "## System and bath definition\n", - "\n", - "And let us set up the system Hamiltonian, bath and system measurement operators:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2850af4f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Define the system Hamiltonian:\n", - "\n", - "# The system is a single fermion with energy level split e1:\n", - "d1 = destroy(2)\n", - "e1 = 1.0\n", - "H = e1 * d1.dag() * d1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "59d11d79", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Define parameters for left and right fermionic baths.\n", - "# Each bath is a lead (i.e. a wire held at a potential)\n", - "# with temperature T and chemical potential mu.\n", - "\n", - "@dataclasses.dataclass\n", - "class LorentzianBathParameters:\n", - " lead: str\n", - " Q: object # coupling operator\n", - " gamma: float = 0.01 # coupling strength\n", - " W: float = 1.0 # cut-off\n", - " T: float = 0.025851991 # temperature\n", - " theta: float = 2.0 # bias\n", - "\n", - " def __post_init__(self):\n", - " assert self.lead in (\"L\", \"R\")\n", - " self.beta = 1 / self.T\n", - " if self.lead == \"L\":\n", - " self.mu = self.theta / 2.0\n", - " else:\n", - " self.mu = - self.theta / 2.0\n", - "\n", - " def J(self, w):\n", - " \"\"\" Spectral density. \"\"\"\n", - " return self.gamma * self.W**2 / ((w - self.mu)**2 + self.W**2)\n", - "\n", - " def fF(self, w, sign=1.0):\n", - " \"\"\" Fermi distribution for this bath. \"\"\"\n", - " x = sign * self.beta * (w - self.mu)\n", - " return fF(x)\n", - "\n", - " def lamshift(self, w):\n", - " \"\"\" Return the lamshift. \"\"\"\n", - " return 0.5 * (w - self.mu) * self.J(w) / self.W\n", - "\n", - " def replace(self, **kw):\n", - " return dataclasses.replace(self, **kw)\n", - "\n", - "\n", - "def fF(x):\n", - " \"\"\" Return the Fermi distribution. \"\"\"\n", - " # in units where kB = 1.0\n", - " return 1 / (np.exp(x) + 1)\n", - "\n", - "\n", - "bath_L = LorentzianBathParameters(Q=d1, lead=\"L\")\n", - "bath_R = LorentzianBathParameters(Q=d1, lead=\"R\")" - ] - }, - { - "cell_type": "markdown", - "id": "70541468", - "metadata": {}, - "source": [ - "## Spectral density\n", - "\n", - "Let's plot the spectral density." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6622bdfc", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "w_list = np.linspace(-2, 2, 100)\n", - "\n", - "fig, ax = plt.subplots(figsize=(12, 7))\n", - "\n", - "spec_L = bath_L.J(w_list)\n", - "spec_R = bath_R.J(w_list)\n", - "\n", - "ax.plot(\n", - " w_list, spec_L,\n", - " \"b--\", linewidth=3,\n", - " label=r\"J_L(w)\",\n", - ")\n", - "ax.plot(\n", - " w_list, spec_R,\n", - " \"r--\", linewidth=3,\n", - " label=r\"J_R(w)\",\n", - ")\n", - "\n", - "ax.set_xlabel(\"w\")\n", - "ax.set_ylabel(r\"$J(\\omega)$\")\n", - "ax.legend();" - ] - }, - { - "cell_type": "markdown", - "id": "55dd37e8", - "metadata": {}, - "source": [ - "## Emission and absorption by the leads\n", - "\n", - "Next let's plot the emission and absorption by the leads." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bffd607f", - "metadata": {}, - "outputs": [], - "source": [ - "w_list = np.linspace(-2, 2, 100)\n", - "\n", - "fig, ax = plt.subplots(figsize=(12, 7))\n", - "\n", - "# Left lead emission and absorption\n", - "\n", - "gam_L_in = bath_L.J(w_list) * bath_L.fF(w_list, sign=1.0)\n", - "gam_L_out = bath_L.J(w_list) * bath_L.fF(w_list, sign=-1.0)\n", - "\n", - "ax.plot(\n", - " w_list, gam_L_in,\n", - " \"b--\", linewidth=3,\n", - " label=r\"S_L(w) input (absorption)\",\n", - ")\n", - "ax.plot(\n", - " w_list, gam_L_out,\n", - " \"r--\", linewidth=3,\n", - " label=r\"S_L(w) output (emission)\",\n", - ")\n", - "\n", - "# Right lead emission and absorption\n", - "\n", - "gam_R_in = bath_R.J(w_list) * bath_R.fF(w_list, sign=1.0)\n", - "gam_R_out = bath_R.J(w_list) * bath_R.fF(w_list, sign=-1.0)\n", - "\n", - "ax.plot(\n", - " w_list, gam_R_in,\n", - " \"b\", linewidth=3,\n", - " label=r\"S_R(w) input (absorption)\",\n", - ")\n", - "ax.plot(\n", - " w_list, gam_R_out,\n", - " \"r\", linewidth=3,\n", - " label=r\"S_R(w) output (emission)\",\n", - ")\n", - "\n", - "ax.set_xlabel(\"w\")\n", - "ax.set_ylabel(r\"$S(\\omega)$\")\n", - "ax.legend();" - ] - }, - { - "cell_type": "markdown", - "id": "d635fb89", - "metadata": {}, - "source": [ - "## Comparing the Matsubara and Pade approximations\n", - "\n", - "Let's start by solving for the evolution using a Pade expansion of the correlation function of the Lorentzian spectral density:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ddf62b70", - "metadata": {}, - "outputs": [], - "source": [ - "# HEOM dynamics using the Pade approximation:\n", - "\n", - "# Solver options, times to solve for and initial system state:\n", - "options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", - "tlist = np.linspace(0, 100, 1000)\n", - "rho0 = basis(2, 0) * basis(2, 0).dag()\n", - "\n", - "Nk = 10 # Number of exponents to retain in the expansion of each bath\n", - "\n", - "bathL = LorentzianPadeBath(\n", - " bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", - " Nk, tag=\"L\",\n", - ")\n", - "bathR = LorentzianPadeBath(\n", - " bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", - " Nk, tag=\"R\",\n", - ")\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " solver_pade = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " result_pade = solver_pade.run(rho0, tlist, ado_return=True)\n", - "\n", - "with timer(\"Steady state solver time\"):\n", - " rho_ss_pade, ado_ss_pade = solver_pade.steady_state()" - ] - }, - { - "cell_type": "markdown", - "id": "194d8092", - "metadata": {}, - "source": [ - "Now let us plot the result which shows the decay of the initially excited impurity. This is not very illuminating, but we will compare it with the Matsubara expansion and analytic solution sortly:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fb4b6c44", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot the Pade results\n", - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - "\n", - "axes.plot(\n", - " tlist, expect(result_pade.states, rho0),\n", - " 'r--', linewidth=2,\n", - " label=\"P11 (Pade)\",\n", - ")\n", - "axes.axhline(\n", - " expect(rho_ss_pade, rho0),\n", - " color='r', linestyle=\"dotted\", linewidth=1,\n", - " label=\"P11 (Pade steady state)\",\n", - ")\n", - "\n", - "axes.set_xlabel('t', fontsize=28)\n", - "axes.legend(fontsize=12);" - ] - }, - { - "cell_type": "markdown", - "id": "7249309a", - "metadata": {}, - "source": [ - "Now let us do the same for the Matsubara expansion:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "99947c8a", - "metadata": {}, - "outputs": [], - "source": [ - "# HEOM dynamics using the Matsubara approximation:\n", - "\n", - "bathL = LorentzianBath(\n", - " bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", - " Nk, tag=\"L\",\n", - ")\n", - "bathR = LorentzianBath(\n", - " bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", - " Nk, tag=\"R\",\n", - ")\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " solver_mats = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " result_mats = solver_mats.run(rho0, tlist, ado_return=True)\n", - "\n", - "with timer(\"Steady state solver time\"):\n", - " rho_ss_mats, ado_ss_mats = solver_mats.steady_state()" - ] - }, - { - "cell_type": "markdown", - "id": "b5805ba5", - "metadata": {}, - "source": [ - "We see a marked difference in the Matsubara vs Pade results:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b9539373", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Plot the Pade results\n", - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - "\n", - "axes.plot(\n", - " tlist, expect(result_pade.states, rho0),\n", - " 'r--', linewidth=2,\n", - " label=\"P11 (Pade)\",\n", - ")\n", - "axes.axhline(\n", - " expect(rho_ss_pade, rho0),\n", - " color='r', linestyle=\"dotted\", linewidth=1,\n", - " label=\"P11 (Pade steady state)\",\n", - ")\n", - "\n", - "axes.plot(\n", - " tlist, expect(result_mats.states, rho0),\n", - " 'b--', linewidth=2,\n", - " label=\"P11 (Mats)\",\n", - ")\n", - "axes.axhline(\n", - " expect(rho_ss_mats, rho0),\n", - " color='b', linestyle=\"dotted\", linewidth=1,\n", - " label=\"P11 (Mats steady state)\",\n", - ")\n", - "\n", - "axes.set_xlabel('t', fontsize=28)\n", - "axes.legend(fontsize=12);" - ] - }, - { - "cell_type": "markdown", - "id": "77892cd8", - "metadata": {}, - "source": [ - "But which is more correct? The Matsubara or the Pade result?\n", - "\n", - "One advantage of this simple model is that the steady state current to the baths is analytically solvable, so we can check convergence of the result by calculating it analytically (the sum of the currents to and from the system in the steady state must be zero, so the current from one bath is the same as the current to the other).\n", - "\n", - "See the [QuTiP-BoFiN paper](https://arxiv.org/abs/2010.10806) for a detailed description and references for the analytic result. Below we just perform the required integration numerically." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a1d4ceaf", - "metadata": {}, - "outputs": [], - "source": [ - "def analytical_steady_state_current(bath_L, bath_R, e1):\n", - " \"\"\" Calculate the analytical steady state current. \"\"\"\n", - "\n", - " def integrand(w):\n", - " return (2 / np.pi) * (\n", - " bath_L.J(w) * bath_R.J(w) * (bath_L.fF(w) - bath_R.fF(w)) /\n", - " (\n", - " (bath_L.J(w) + bath_R.J(w))**2 +\n", - " 4*(w - e1 - bath_L.lamshift(w) - bath_R.lamshift(w))**2\n", - " )\n", - " )\n", - "\n", - " def real_part(x):\n", - " return np.real(integrand(x))\n", - "\n", - " def imag_part(x):\n", - " return np.imag(integrand(x))\n", - "\n", - " # in principle the bounds for the integral should be rechecked if\n", - " # bath or system parameters are changed substantially:\n", - " bounds = [-10, 10]\n", - "\n", - " real_integral, _ = quad(real_part, *bounds)\n", - " imag_integral, _ = quad(imag_part, *bounds)\n", - "\n", - " return real_integral + 1.0j * imag_integral\n", - "\n", - "\n", - "curr_ss_analytic = analytical_steady_state_current(bath_L, bath_R, e1)\n", - "\n", - "print(f\"Analytical steady state current: {curr_ss_analytic}\")" - ] - }, - { - "cell_type": "markdown", - "id": "78867fde", - "metadata": {}, - "source": [ - "To compare the analytical result above with the result from the HEOM, we need to be able to calculate the current from the system to the bath from the HEOM result. In the HEOM description, these currents are captured in the first level auxilliary density operators (ADOs).\n", - "\n", - "In the function `state_current(...)` below, we extract the first level ADOs for the specified bath and sum the contributions to the current from each:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b0dea27f", - "metadata": {}, - "outputs": [], - "source": [ - "def state_current(ado_state, bath_tag):\n", - " \"\"\" Determine current from the given bath (either \"R\" or \"L\") to\n", - " the system in the given ADO state.\n", - " \"\"\"\n", - " level_1_aux = [\n", - " (ado_state.extract(label), ado_state.exps(label)[0])\n", - " for label in ado_state.filter(level=1, tags=[bath_tag])\n", - " ]\n", - "\n", - " def exp_sign(exp):\n", - " return 1 if exp.type == exp.types[\"+\"] else -1\n", - "\n", - " def exp_op(exp):\n", - " return exp.Q if exp.type == exp.types[\"+\"] else exp.Q.dag()\n", - "\n", - " return -1.0j * sum(\n", - " exp_sign(exp) * (exp_op(exp) * aux).tr()\n", - " for aux, exp in level_1_aux\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "f31aa01b", - "metadata": {}, - "source": [ - "Now we can calculate the steady state currents from the Pade and Matsubara HEOM results:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "225f8a54", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "curr_ss_pade_L = state_current(ado_ss_pade, \"L\")\n", - "curr_ss_pade_R = state_current(ado_ss_pade, \"R\")\n", - "\n", - "print(f\"Pade steady state current (L): {curr_ss_pade_L}\")\n", - "print(f\"Pade steady state current (R): {curr_ss_pade_R}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "57b80a11", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "curr_ss_mats_L = state_current(ado_ss_mats, \"L\")\n", - "curr_ss_mats_R = state_current(ado_ss_mats, \"R\")\n", - "\n", - "print(f\"Matsubara steady state current (L): {curr_ss_mats_L}\")\n", - "print(f\"Matsubara steady state current (R): {curr_ss_mats_R}\")" - ] - }, - { - "cell_type": "markdown", - "id": "34b9dd27", - "metadata": {}, - "source": [ - "Note that the currents from each bath balance as is required by the steady state, but the value of the current is different for the Pade and Matsubara results.\n", - "\n", - "Now let's compare all three:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca30a9ab", - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"Pade current (R): {curr_ss_pade_R}\")\n", - "print(f\"Matsubara current (R): {curr_ss_mats_R}\")\n", - "print(f\"Analytical curernt: {curr_ss_analytic}\")" - ] - }, - { - "cell_type": "markdown", - "id": "debf29ca", - "metadata": {}, - "source": [ - "In this case we observe that the Pade approximation has converged more closely to the analytical current than the Matsubara.\n", - "\n", - "The Matsubara result could be improved by increasing the number of terms retained in the Matsubara expansion (i.e. increasing `Nk`)." - ] - }, - { - "cell_type": "markdown", - "id": "182f080f", - "metadata": {}, - "source": [ - "## Current as a function of bias voltage" - ] - }, - { - "cell_type": "markdown", - "id": "64fd3eeb", - "metadata": {}, - "source": [ - "Now lets plot the current as a function of bias voltage (the bias voltage is the parameter `theta` for the two baths).\n", - "\n", - "We will calculate the steady state current for each `theta` both analytically and using the HEOM with the Pade correlation expansion approximation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aac2ef0a", - "metadata": {}, - "outputs": [], - "source": [ - "# Theta (bias voltages)\n", - "\n", - "thetas = np.linspace(-4, 4, 100)\n", - "\n", - "# Setup a progress bar:\n", - "\n", - "progress = IntProgress(min=0, max=2 * len(thetas))\n", - "display(progress)\n", - "\n", - "# Calculate the current for the list of thetas\n", - "\n", - "\n", - "def current_analytic_for_theta(e1, bath_L, bath_R, theta):\n", - " \"\"\" Return the analytic current for a given theta. \"\"\"\n", - " current = analytical_steady_state_current(\n", - " bath_L.replace(theta=theta),\n", - " bath_R.replace(theta=theta),\n", - " e1,\n", - " )\n", - " progress.value += 1\n", - " return np.real(current)\n", - "\n", - "\n", - "def current_pade_for_theta(H, bath_L, bath_R, theta, Nk):\n", - " \"\"\" Return the steady state current using the Pade approximation. \"\"\"\n", - " bath_L = bath_L.replace(theta=theta)\n", - " bath_R = bath_R.replace(theta=theta)\n", - "\n", - " bathL = LorentzianPadeBath(\n", - " bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", - " Nk, tag=\"L\",\n", - " )\n", - " bathR = LorentzianPadeBath(\n", - " bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", - " Nk, tag=\"R\",\n", - " )\n", - "\n", - " solver_pade = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options)\n", - " rho_ss_pade, ado_ss_pade = solver_pade.steady_state()\n", - " current = state_current(ado_ss_pade, bath_tag=\"R\")\n", - "\n", - " progress.value += 1\n", - " return np.real(current)\n", - "\n", - "\n", - "curr_ss_analytic_thetas = [\n", - " current_analytic_for_theta(e1, bath_L, bath_R, theta)\n", - " for theta in thetas\n", - "]\n", - "\n", - "# The number of expansion terms has been dropped to Nk=6 to speed\n", - "# up notebook execution. Increase to Nk=10 for more accurate results.\n", - "curr_ss_pade_theta = [\n", - " current_pade_for_theta(H, bath_L, bath_R, theta, Nk=6)\n", - " for theta in thetas\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "2f5d5b40", - "metadata": {}, - "source": [ - "Below we plot the results and see that even with `Nk=6`, the HEOM Pade approximation gives good results for the steady state current. Increasing `Nk` to `10` gives very accurate results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c52b7531", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(12, 7))\n", - "\n", - "ax.plot(\n", - " thetas, 2.434e-4 * 1e6 * np.array(curr_ss_analytic_thetas),\n", - " color=\"black\", linewidth=3,\n", - " label=r\"Analytical\",\n", - ")\n", - "ax.plot(\n", - " thetas, 2.434e-4 * 1e6 * np.array(curr_ss_pade_theta),\n", - " 'r--', linewidth=3,\n", - " label=r\"HEOM Pade $N_k=10$, $n_{\\mathrm{max}}=2$\",\n", - ")\n", - "\n", - "\n", - "ax.locator_params(axis='y', nbins=4)\n", - "ax.locator_params(axis='x', nbins=4)\n", - "\n", - "ax.set_xticks([-2.5, 0, 2.5])\n", - "ax.set_xticklabels([-2.5, 0, 2.5])\n", - "ax.set_xlabel(r\"Bias voltage $\\Delta \\mu$ ($V$)\", fontsize=28)\n", - "ax.set_ylabel(r\"Current ($\\mu A$)\", fontsize=28)\n", - "ax.legend(fontsize=25);" - ] - }, - { - "cell_type": "markdown", - "id": "2c66b28e", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c8b46075", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "9f59238f", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1d0c6d88", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert np.allclose(curr_ss_pade_L + curr_ss_pade_R, 0)\n", - "assert np.allclose(curr_ss_mats_L + curr_ss_mats_R, 0)\n", - "assert np.allclose(curr_ss_pade_R, curr_ss_analytic, rtol=1e-4)" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v4/heom/heom-5a-fermions-single-impurity-model.md b/tutorials-v4/heom/heom-5a-fermions-single-impurity-model.md index 623af4db..d249fe78 100644 --- a/tutorials-v4/heom/heom-5a-fermions-single-impurity-model.md +++ b/tutorials-v4/heom/heom-5a-fermions-single-impurity-model.md @@ -5,15 +5,13 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.4 + jupytext_version: 1.16.1 kernelspec: display_name: Python 3 (ipykernel) language: python name: python3 --- -+++ {"tags": []} - # HEOM 5a: Fermionic single impurity model +++ @@ -71,7 +69,7 @@ In this notebook we: ## Setup -```{code-cell} ipython3 +```{code-cell} import contextlib import dataclasses import time @@ -101,9 +99,7 @@ from IPython.display import display ## Helpers -```{code-cell} ipython3 -:tags: [] - +```{code-cell} @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -117,15 +113,11 @@ def timer(label): print(f"{label}: {end - start}") ``` -+++ {"tags": []} - ## System and bath definition And let us set up the system Hamiltonian, bath and system measurement operators: -```{code-cell} ipython3 -:tags: [] - +```{code-cell} # Define the system Hamiltonian: # The system is a single fermion with energy level split e1: @@ -134,9 +126,7 @@ e1 = 1.0 H = e1 * d1.dag() * d1 ``` -```{code-cell} ipython3 -:tags: [] - +```{code-cell} # Define parameters for left and right fermionic baths. # Each bath is a lead (i.e. a wire held at a potential) # with temperature T and chemical potential mu. @@ -189,9 +179,7 @@ bath_R = LorentzianBathParameters(Q=d1, lead="R") Let's plot the spectral density. -```{code-cell} ipython3 -:tags: [] - +```{code-cell} w_list = np.linspace(-2, 2, 100) fig, ax = plt.subplots(figsize=(12, 7)) @@ -219,7 +207,7 @@ ax.legend(); Next let's plot the emission and absorption by the leads. -```{code-cell} ipython3 +```{code-cell} w_list = np.linspace(-2, 2, 100) fig, ax = plt.subplots(figsize=(12, 7)) @@ -265,7 +253,7 @@ ax.legend(); Let's start by solving for the evolution using a Pade expansion of the correlation function of the Lorentzian spectral density: -```{code-cell} ipython3 +```{code-cell} # HEOM dynamics using the Pade approximation: # Solver options, times to solve for and initial system state: @@ -296,7 +284,7 @@ with timer("Steady state solver time"): Now let us plot the result which shows the decay of the initially excited impurity. This is not very illuminating, but we will compare it with the Matsubara expansion and analytic solution sortly: -```{code-cell} ipython3 +```{code-cell} # Plot the Pade results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -317,7 +305,7 @@ axes.legend(fontsize=12); Now let us do the same for the Matsubara expansion: -```{code-cell} ipython3 +```{code-cell} # HEOM dynamics using the Matsubara approximation: bathL = LorentzianBath( @@ -341,9 +329,7 @@ with timer("Steady state solver time"): We see a marked difference in the Matsubara vs Pade results: -```{code-cell} ipython3 -:tags: [] - +```{code-cell} # Plot the Pade results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -379,7 +365,7 @@ One advantage of this simple model is that the steady state current to the baths See the [QuTiP-BoFiN paper](https://arxiv.org/abs/2010.10806) for a detailed description and references for the analytic result. Below we just perform the required integration numerically. -```{code-cell} ipython3 +```{code-cell} def analytical_steady_state_current(bath_L, bath_R, e1): """ Calculate the analytical steady state current. """ @@ -417,7 +403,7 @@ To compare the analytical result above with the result from the HEOM, we need to In the function `state_current(...)` below, we extract the first level ADOs for the specified bath and sum the contributions to the current from each: -```{code-cell} ipython3 +```{code-cell} def state_current(ado_state, bath_tag): """ Determine current from the given bath (either "R" or "L") to the system in the given ADO state. @@ -441,9 +427,7 @@ def state_current(ado_state, bath_tag): Now we can calculate the steady state currents from the Pade and Matsubara HEOM results: -```{code-cell} ipython3 -:tags: [] - +```{code-cell} curr_ss_pade_L = state_current(ado_ss_pade, "L") curr_ss_pade_R = state_current(ado_ss_pade, "R") @@ -451,9 +435,7 @@ print(f"Pade steady state current (L): {curr_ss_pade_L}") print(f"Pade steady state current (R): {curr_ss_pade_R}") ``` -```{code-cell} ipython3 -:tags: [] - +```{code-cell} curr_ss_mats_L = state_current(ado_ss_mats, "L") curr_ss_mats_R = state_current(ado_ss_mats, "R") @@ -465,7 +447,7 @@ Note that the currents from each bath balance as is required by the steady state Now let's compare all three: -```{code-cell} ipython3 +```{code-cell} print(f"Pade current (R): {curr_ss_pade_R}") print(f"Matsubara current (R): {curr_ss_mats_R}") print(f"Analytical curernt: {curr_ss_analytic}") @@ -485,7 +467,7 @@ Now lets plot the current as a function of bias voltage (the bias voltage is the We will calculate the steady state current for each `theta` both analytically and using the HEOM with the Pade correlation expansion approximation. -```{code-cell} ipython3 +```{code-cell} # Theta (bias voltages) thetas = np.linspace(-4, 4, 100) @@ -546,7 +528,7 @@ curr_ss_pade_theta = [ Below we plot the results and see that even with `Nk=6`, the HEOM Pade approximation gives good results for the steady state current. Increasing `Nk` to `10` gives very accurate results. -```{code-cell} ipython3 +```{code-cell} fig, ax = plt.subplots(figsize=(12, 7)) ax.plot( @@ -573,9 +555,7 @@ ax.legend(fontsize=25); ## About -```{code-cell} ipython3 -:tags: [] - +```{code-cell} qutip.about() ``` @@ -583,9 +563,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 -:tags: [] - +```{code-cell} assert np.allclose(curr_ss_pade_L + curr_ss_pade_R, 0) assert np.allclose(curr_ss_mats_L + curr_ss_mats_R, 0) assert np.allclose(curr_ss_pade_R, curr_ss_analytic, rtol=1e-4) diff --git a/tutorials-v4/heom/heom-5b-fermions-discrete-boson-model.ipynb b/tutorials-v4/heom/heom-5b-fermions-discrete-boson-model.ipynb deleted file mode 100644 index 3c3a61b7..00000000 --- a/tutorials-v4/heom/heom-5b-fermions-discrete-boson-model.ipynb +++ /dev/null @@ -1,521 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "09977858", - "metadata": {}, - "source": [ - "# HEOM 5b: Discrete boson coupled to an impurity and fermionic leads" - ] - }, - { - "cell_type": "markdown", - "id": "c78c1c1b", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc), also coupled to a discrete bosonic (vibronic) mode.\n", - "\n", - "Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his Dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications. In particular this example reproduces some results from https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.201407\n", - "\n", - "Notation:\n", - "\n", - "* $K=L/R$ refers to left or right leads.\n", - "* $\\sigma=\\pm$ refers to input/output\n", - "\n", - "We choose a Lorentzian spectral density for the leads, with a peak at the chemical potential. The latter simplifies a little the notation required for the correlation functions, but can be relaxed if neccessary.\n", - "\n", - "$$J(\\omega) = \\frac{\\Gamma W^2}{((\\omega-\\mu_K)^2 +W^2 )}$$\n", - "\n", - "The Fermi distribution function is:\n", - "\n", - "$$f_F (x) = (\\exp(x) + 1)^{-1}$$\n", - "\n", - "Together these allow the correlation functions to be expressed as:\n", - "\n", - "$$C^{\\sigma}_K(t) = \\frac{1}{2\\pi} \\int_{-\\infty}^{\\infty} d\\omega e^{\\sigma i \\omega t} \\Gamma_K(\\omega) f_F[\\sigma\\beta(\\omega - \\mu)]$$\n", - "\n", - "As with the Bosonic case we can treat these with Matsubara, Pade, or fitting approaches.\n", - "\n", - "The Pade decomposition approximates the Fermi distubition as \n", - "\n", - "$$f_F(x) \\approx f_F^{\\mathrm{approx}}(x) = \\frac{1}{2} - \\sum_l^{l_{max}} \\frac{2k_l x}{x^2 + \\epsilon_l^2}$$\n", - "\n", - "$k_l$ and $\\epsilon_l$ are co-efficients defined in J. Chem Phys 133,10106\n", - "\n", - "Evaluating the integral for the correlation functions gives,\n", - "\n", - "\n", - "$$C_K^{\\sigma}(t) \\approx \\sum_{l=0}^{l_{max}} \\eta_K^{\\sigma_l} e^{-\\gamma_{K,\\sigma,l}t}$$\n", - "\n", - "where\n", - "\n", - "$$\\eta_{K,0} = \\frac{\\Gamma_KW_K}{2} f_F^{approx}(i\\beta_K W)$$\n", - "\n", - "$$\\gamma_{K,\\sigma,0} = W_K - \\sigma i\\mu_K$$ \n", - "\n", - "$$\\eta_{K,l\\neq 0} = -i\\cdot \\frac{k_m}{\\beta_K} \\cdot \\frac{\\Gamma_K W_K^2}{-\\frac{\\epsilon^2_m}{\\beta_K^2} + W_K^2}$$\n", - "\n", - "$$\\gamma_{K,\\sigma,l\\neq 0}= \\frac{\\epsilon_m}{\\beta_K} - \\sigma i \\mu_K$$" - ] - }, - { - "cell_type": "markdown", - "id": "b4e142cb", - "metadata": {}, - "source": [ - "## Differences from Example 5a" - ] - }, - { - "cell_type": "markdown", - "id": "23e4f302", - "metadata": { - "tags": [] - }, - "source": [ - "The system we study here has two big differences from the HEOM 5a example:\n", - "\n", - "* the system now includes a discrete bosonic mode,\n", - "* and the electronic leads have $W$ set to $10^4$ (i.e. the wide-band limit).\n", - "\n", - "The new system Hamiltonian is:\n", - "\n", - "$$\n", - "H_{\\mathrm{vib}} = H_{\\mathrm{SIAM}} + \\Omega a^{\\dagger}a + \\lambda (a+a^{\\dagger})c{^\\dagger}c.\n", - "$$\n", - "\n", - "where $H_{\\mathrm{SIAM}}$ is the Hamiltonian of the single impurity, and the remaining terms are the Hamiltonian of the bosonic mode and its interaction with the impurity.\n", - "\n", - "The complete setup now consists of four parts:\n", - "\n", - "* the single impurity\n", - "* a discrete bosonic mode\n", - "* two fermionic leads.\n", - "\n", - "**Note**: This example is quite numerically challenging and has many system and bath components. For an easier introduction into the fermionic case, see example 5a.\n", - "\n", - "**Note**: We've reduced the cut-off of the bosonic mode to 2 modes to facilitate faster execution of the notebooks when the outputs are being checked and compiled. A more accurate result may be obtained by increasing the number of bosonic modes to, for example, 16." - ] - }, - { - "cell_type": "markdown", - "id": "47dd4c94", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2986e06e", - "metadata": {}, - "outputs": [], - "source": [ - "import contextlib\n", - "import dataclasses\n", - "import time\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " Options,\n", - " destroy,\n", - " qeye,\n", - " tensor,\n", - ")\n", - "from qutip.nonmarkov.heom import (\n", - " HEOMSolver,\n", - " LorentzianPadeBath,\n", - ")\n", - "\n", - "from ipywidgets import IntProgress\n", - "from IPython.display import display\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "fb2a7c3e", - "metadata": {}, - "source": [ - "## Helpers" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a1a67c9c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\" Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d158a24e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def state_current(ado_state, bath_tag):\n", - " \"\"\" Determine current from the given bath (either \"R\" or \"L\") to\n", - " the system in the given ADO state.\n", - " \"\"\"\n", - " level_1_aux = [\n", - " (ado_state.extract(label), ado_state.exps(label)[0])\n", - " for label in ado_state.filter(level=1, tags=[bath_tag])\n", - " ]\n", - "\n", - " def exp_sign(exp):\n", - " return 1 if exp.type == exp.types[\"+\"] else -1\n", - "\n", - " def exp_op(exp):\n", - " return exp.Q if exp.type == exp.types[\"+\"] else exp.Q.dag()\n", - "\n", - " return -1.0j * sum(\n", - " exp_sign(exp) * (exp_op(exp) * aux).tr()\n", - " for aux, exp in level_1_aux\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "906e016d", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "Let us set up the system Hamiltonian and specify the properties of the two reservoirs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cbfa1752", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Define the system Hamiltonian:\n", - "\n", - "@dataclasses.dataclass\n", - "class SystemParameters:\n", - " e1: float = 0.3 # fermion mode energy splitting\n", - " Omega: float = 0.2 # bosonic mode energy splitting\n", - " Lambda: float = 0.12 # coupling between fermion and boson\n", - " Nbos: int = 2\n", - "\n", - " def __post_init__(self):\n", - " d = tensor(destroy(2), qeye(self.Nbos))\n", - " a = tensor(qeye(2), destroy(self.Nbos))\n", - " self.H = (\n", - " self.e1 * d.dag() * d +\n", - " self.Omega * a.dag() * a +\n", - " self.Lambda * (a + a.dag()) * d.dag() * d\n", - " )\n", - " self.Q = d\n", - "\n", - " def replace(self, **kw):\n", - " return dataclasses.replace(self, **kw)\n", - "\n", - "\n", - "sys_p = SystemParameters()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d68d3f54", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Define parameters for left and right fermionic baths.\n", - "# Each bath is a lead (i.e. a wire held at a potential)\n", - "# with temperature T and chemical potential mu.\n", - "\n", - "@dataclasses.dataclass\n", - "class LorentzianBathParameters:\n", - " lead: str\n", - " gamma: float = 0.01 # coupling strength\n", - " W: float = 1.0 # cut-off\n", - " T: float = 0.025851991 # temperature (in eV)\n", - " theta: float = 2.0 # bias\n", - "\n", - " def __post_init__(self):\n", - " assert self.lead in (\"L\", \"R\")\n", - " self.beta = 1 / self.T\n", - " if self.lead == \"L\":\n", - " self.mu = self.theta / 2.0\n", - " else:\n", - " self.mu = - self.theta / 2.0\n", - "\n", - " def J(self, w):\n", - " \"\"\" Spectral density. \"\"\"\n", - " return self.gamma * self.W**2 / ((w - self.mu)**2 + self.W**2)\n", - "\n", - " def fF(self, w, sign=1.0):\n", - " \"\"\" Fermi distribution for this bath. \"\"\"\n", - " x = sign * self.beta * (w - self.mu)\n", - " return fF(x)\n", - "\n", - " def lamshift(self, w):\n", - " \"\"\" Return the lamshift. \"\"\"\n", - " return 0.5 * (w - self.mu) * self.J(w) / self.W\n", - "\n", - " def replace(self, **kw):\n", - " return dataclasses.replace(self, **kw)\n", - "\n", - "\n", - "def fF(x):\n", - " \"\"\" Return the Fermi distribution. \"\"\"\n", - " # in units where kB = 1.0\n", - " return 1 / (np.exp(x) + 1)\n", - "\n", - "\n", - "# We set W = 1e4 to investigate the wide-band limit:\n", - "\n", - "bath_L = LorentzianBathParameters(W=10**4, lead=\"L\")\n", - "bath_R = LorentzianBathParameters(W=10**4, lead=\"R\")" - ] - }, - { - "cell_type": "markdown", - "id": "5155bdcb", - "metadata": {}, - "source": [ - "## Emission and absorption by the leads\n", - "\n", - "Next let's plot the emission and absorption by the leads." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "973ada56", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "w_list = np.linspace(-2, 2, 100)\n", - "\n", - "fig, ax = plt.subplots(figsize=(12, 7))\n", - "\n", - "# Left lead emission and absorption\n", - "\n", - "gam_L_in = bath_L.J(w_list) * bath_L.fF(w_list, sign=1.0)\n", - "gam_L_out = bath_L.J(w_list) * bath_L.fF(w_list, sign=-1.0)\n", - "\n", - "ax.plot(\n", - " w_list, gam_L_in,\n", - " \"b--\", linewidth=3,\n", - " label=r\"S_L(w) input (absorption)\",\n", - ")\n", - "ax.plot(\n", - " w_list, gam_L_out,\n", - " \"r--\", linewidth=3,\n", - " label=r\"S_L(w) output (emission)\",\n", - ")\n", - "\n", - "# Right lead emission and absorption\n", - "\n", - "gam_R_in = bath_R.J(w_list) * bath_R.fF(w_list, sign=1.0)\n", - "gam_R_out = bath_R.J(w_list) * bath_R.fF(w_list, sign=-1.0)\n", - "\n", - "ax.plot(\n", - " w_list, gam_R_in,\n", - " \"b\", linewidth=3,\n", - " label=r\"S_R(w) input (absorption)\",\n", - ")\n", - "ax.plot(\n", - " w_list, gam_R_out,\n", - " \"r\", linewidth=3,\n", - " label=r\"S_R(w) output (emission)\",\n", - ")\n", - "\n", - "ax.set_xlabel(\"w\")\n", - "ax.set_ylabel(r\"$S(\\omega)$\")\n", - "ax.legend();" - ] - }, - { - "cell_type": "markdown", - "id": "b2b2c871", - "metadata": {}, - "source": [ - "## Below we give one example data set from Paper\n", - "\n", - "Here we just give one example of the current as a function of bias voltage, but in general one can try different cut-offs of the bosonic Fock space and the expansion of the correlation functions until convergence is found.\n", - "\n", - "One note: for very large problems, this can be slow." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3c7cf321", - "metadata": {}, - "outputs": [], - "source": [ - "def steady_state_pade_for_theta(sys_p, bath_L, bath_R, theta, Nk, Nc, Nbos):\n", - " \"\"\" Return the steady state current using the Pade approximation. \"\"\"\n", - " options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14)\n", - "\n", - " sys_p = sys_p.replace(Nbos=Nbos)\n", - " bath_L = bath_L.replace(theta=theta)\n", - " bath_R = bath_R.replace(theta=theta)\n", - "\n", - " bathL = LorentzianPadeBath(\n", - " sys_p.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", - " Nk, tag=\"L\",\n", - " )\n", - " bathR = LorentzianPadeBath(\n", - " sys_p.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", - " Nk, tag=\"R\",\n", - " )\n", - "\n", - " solver_pade = HEOMSolver(\n", - " sys_p.H, [bathL, bathR], max_depth=2, options=options,\n", - " )\n", - " rho_ss_pade, ado_ss_pade = solver_pade.steady_state()\n", - " current = state_current(ado_ss_pade, bath_tag=\"R\")\n", - "\n", - " return np.real(2.434e-4 * 1e6 * current)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "84619bfa", - "metadata": {}, - "outputs": [], - "source": [ - "# Parameters:\n", - "\n", - "Nk = 6\n", - "Nc = 2\n", - "Nbos = 2 # Use Nbos = 16 for more accurate results\n", - "\n", - "thetas = np.linspace(0, 2, 30)\n", - "\n", - "# Progress bar:\n", - "\n", - "progress = IntProgress(min=0, max=len(thetas))\n", - "display(progress)\n", - "\n", - "currents = []\n", - "\n", - "for theta in thetas:\n", - " currents.append(steady_state_pade_for_theta(\n", - " sys_p, bath_L, bath_R, theta,\n", - " Nk=Nk, Nc=Nc, Nbos=Nbos,\n", - " ))\n", - " progress.value += 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b2d472ae", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(12, 10))\n", - "\n", - "ax.plot(\n", - " thetas, currents,\n", - " color=\"green\", linestyle='-', linewidth=3,\n", - " label=f\"Nk = {5}, max_depth = {Nc}, Nbos = {Nbos}\",\n", - ")\n", - "\n", - "ax.set_yticks([0, 0.5, 1])\n", - "ax.set_yticklabels([0, 0.5, 1])\n", - "\n", - "ax.locator_params(axis='y', nbins=4)\n", - "ax.locator_params(axis='x', nbins=4)\n", - "\n", - "ax.set_xlabel(r\"Bias voltage $\\Delta \\mu$ ($V$)\", fontsize=30)\n", - "ax.set_ylabel(r\"Current ($\\mu A$)\", fontsize=30)\n", - "ax.legend(loc=4);" - ] - }, - { - "cell_type": "markdown", - "id": "6c9ffd1a", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "29df5cc6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "a3f3f10d", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "62384dbf", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert 1 == 1" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v4/heom/heom-5b-fermions-discrete-boson-model.md b/tutorials-v4/heom/heom-5b-fermions-discrete-boson-model.md index 19d63126..2acc7a6c 100644 --- a/tutorials-v4/heom/heom-5b-fermions-discrete-boson-model.md +++ b/tutorials-v4/heom/heom-5b-fermions-discrete-boson-model.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.4 + jupytext_version: 1.16.1 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -60,13 +60,13 @@ $$\gamma_{K,\sigma,0} = W_K - \sigma i\mu_K$$ $$\eta_{K,l\neq 0} = -i\cdot \frac{k_m}{\beta_K} \cdot \frac{\Gamma_K W_K^2}{-\frac{\epsilon^2_m}{\beta_K^2} + W_K^2}$$ -$$\gamma_{K,\sigma,l\neq 0}= \frac{\epsilon_m}{\beta_K} - \sigma i \mu_K$$ +$$\gamma_{K,\sigma,l\neq 0}= \frac{\epsilon_m}{\beta_K} - \sigma i \mu_K$$ +++ ## Differences from Example 5a -+++ {"tags": []} ++++ The system we study here has two big differences from the HEOM 5a example: @@ -95,7 +95,7 @@ The complete setup now consists of four parts: ## Setup -```{code-cell} ipython3 +```{code-cell} import contextlib import dataclasses import time @@ -123,9 +123,7 @@ from IPython.display import display ## Helpers -```{code-cell} ipython3 -:tags: [] - +```{code-cell} @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -139,9 +137,7 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} ipython3 -:tags: [] - +```{code-cell} def state_current(ado_state, bath_tag): """ Determine current from the given bath (either "R" or "L") to the system in the given ADO state. @@ -167,9 +163,7 @@ def state_current(ado_state, bath_tag): Let us set up the system Hamiltonian and specify the properties of the two reservoirs. -```{code-cell} ipython3 -:tags: [] - +```{code-cell} # Define the system Hamiltonian: @dataclasses.dataclass @@ -196,9 +190,7 @@ class SystemParameters: sys_p = SystemParameters() ``` -```{code-cell} ipython3 -:tags: [] - +```{code-cell} # Define parameters for left and right fermionic baths. # Each bath is a lead (i.e. a wire held at a potential) # with temperature T and chemical potential mu. @@ -252,9 +244,7 @@ bath_R = LorentzianBathParameters(W=10**4, lead="R") Next let's plot the emission and absorption by the leads. -```{code-cell} ipython3 -:tags: [] - +```{code-cell} w_list = np.linspace(-2, 2, 100) fig, ax = plt.subplots(figsize=(12, 7)) @@ -302,7 +292,7 @@ Here we just give one example of the current as a function of bias voltage, but One note: for very large problems, this can be slow. -```{code-cell} ipython3 +```{code-cell} def steady_state_pade_for_theta(sys_p, bath_L, bath_R, theta, Nk, Nc, Nbos): """ Return the steady state current using the Pade approximation. """ options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14) @@ -329,7 +319,7 @@ def steady_state_pade_for_theta(sys_p, bath_L, bath_R, theta, Nk, Nc, Nbos): return np.real(2.434e-4 * 1e6 * current) ``` -```{code-cell} ipython3 +```{code-cell} # Parameters: Nk = 6 @@ -353,7 +343,7 @@ for theta in thetas: progress.value += 1 ``` -```{code-cell} ipython3 +```{code-cell} fig, ax = plt.subplots(figsize=(12, 10)) ax.plot( @@ -375,9 +365,7 @@ ax.legend(loc=4); ## About -```{code-cell} ipython3 -:tags: [] - +```{code-cell} qutip.about() ``` @@ -385,8 +373,6 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 -:tags: [] - +```{code-cell} assert 1 == 1 ``` diff --git a/tutorials-v4/heom/heom-index.ipynb b/tutorials-v4/heom/heom-index.ipynb deleted file mode 100644 index b5168f1e..00000000 --- a/tutorials-v4/heom/heom-index.ipynb +++ /dev/null @@ -1,56 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0ebc94ee", - "metadata": {}, - "source": [ - "# Hierarchical Equation of Motion Examples\n", - "\n", - "The \"hierarchical equations of motion\" (HEOM) method is a powerful numerical approach to solve the dynamics and steady-state of a quantum system coupled to a non-Markovian and non-perturbative environment. Originally developed in the context of physical chemistry, it has also been extended and applied to problems in solid-state physics, optics, single-molecule electronics, and biological physics.\n", - "\n", - "QuTiP's implementation of the HEOM is described in detail in https://arxiv.org/abs/2010.10806.\n", - "\n", - "This collection of examples from the paper illustrates how to use QuTiP's HEOM to model and investigate the dynamics of a variety of systems coupled to bosonic or fermionic baths.\n", - "\n", - "## Overview of the notebooks\n", - "\n", - "\n", - "\n", - "* [Example 1a: Spin-Bath model (introduction)](./heom-1a-spin-bath-model-basic.ipynb)\n", - "\n", - "* [Example 1b: Spin-Bath model (very strong coupling)](./heom-1b-spin-bath-model-very-strong-coupling.ipynb)\n", - "\n", - "* [Example 1c: Spin-Bath model (underdamped case)](./heom-1c-spin-bath-model-underdamped-sd.ipynb)\n", - "\n", - "* [Example 1d: Spin-Bath model, fitting of spectrum and correlation functions](./heom-1d-spin-bath-model-ohmic-fitting.ipynb)\n", - "\n", - "* [Example 1e: Spin-Bath model (pure dephasing)](./heom-1e-spin-bath-model-pure-dephasing.ipynb)\n", - "\n", - "* [Example 2: Dynamics in Fenna-Mathews-Olsen complex (FMO)](./heom-2-fmo-example.ipynb)\n", - "\n", - "* [Example 3: Quantum Heat Transport](./heom-3-quantum-heat-transport.ipynb)\n", - "\n", - "* [Example 4: Dynamical decoupling of a non-Markovian environment](./heom-4-dynamical-decoupling.ipynb)\n", - "\n", - "* [Example 5a: Fermionic single impurity model](./heom-5a-fermions-single-impurity-model.ipynb)\n", - "\n", - "* [Example 5b: Discrete boson coupled to an impurity + fermionic leads](./heom-5b-fermions-discrete-boson-model.ipynb)\n", - "\n", - "" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v4/heom/heom-index.md b/tutorials-v4/heom/heom-index.md index dcb1fa89..4bc08d9d 100644 --- a/tutorials-v4/heom/heom-index.md +++ b/tutorials-v4/heom/heom-index.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.4 + jupytext_version: 1.16.1 kernelspec: display_name: Python 3 (ipykernel) language: python diff --git a/tutorials-v5/fitting_summary.md b/tutorials-v5/fitting_summary.md new file mode 100644 index 00000000..dfab0ee9 --- /dev/null +++ b/tutorials-v5/fitting_summary.md @@ -0,0 +1,467 @@ +--- +jupytext: + formats: ipynb,md:myst + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.16.1 +kernelspec: + display_name: qutip-dev + language: python + name: python3 +--- + +# HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions + ++++ + +## Introduction + +The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded +in a set of auxiliary density matrices. + +In this example we show the evolution of a single two-level system in contact with a single bosonic environment. + +The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment. + +The bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions. + +In the example below we show how to model an Ohmic environment with exponential cut-off in three ways: + +* First we fit the spectral density with a set of underdamped brownian oscillator functions. +* Second, we evaluate the correlation functions, and fit those with a certain choice of exponential functions. +* Third, we use the available OhmicBath class + +In each case we will use the fit parameters to determine the correlation function expansion co-efficients needed to construct a description of the bath (i.e. a `BosonicBath` object) to supply to the `HEOMSolver` so that we can solve for the system dynamics. + ++++ + +## Setup + +```{code-cell} ipython3 +import numpy as np +from matplotlib import pyplot as plt +import qutip +from qutip import ( + basis, + expect, + sigmax, + sigmaz, +) +from qutip.solver.heom import ( + HEOMSolver +) +from qutip.core.environment import BosonicEnvironment,OhmicEnvironment + +# Import mpmath functions for evaluation of gamma and zeta +# functions in the expression for the correlation: + +from mpmath import mp + +mp.dps = 15 +mp.pretty = True + +%matplotlib inline +``` + +## System and bath definition + +Let us set up the system Hamiltonian, bath and system measurement operators: + ++++ + +### System Hamiltonian + +```{code-cell} ipython3 +# Defining the system Hamiltonian +eps = 0 # Energy of the 2-level system. +Del = 0.2 # Tunnelling term +Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() +rho0 = basis(2, 0) * basis(2, 0).dag() +``` + +### System measurement operators + +```{code-cell} ipython3 +# Define some operators with which we will measure the system +# 1,1 element of density matrix - corresonding to groundstate +P11p = basis(2, 0) * basis(2, 0).dag() +P22p = basis(2, 1) * basis(2, 1).dag() +# 1,2 element of density matrix - corresonding to coherence +P12p = basis(2, 0) * basis(2, 1).dag() +``` + +### Bath and HEOM parameters + ++++ + +Finally, let's set the bath parameters we will work with and write down some measurement operators: + +```{code-cell} ipython3 +Q = sigmaz() +alpha = 3.25 +T = 0.5 +wc = 1.0 +s = 1 +``` + +And set the cut-off for the HEOM hierarchy: + +```{code-cell} ipython3 +# HEOM parameters: + +# The max_depth defaults to 5 so that the notebook executes more +# quickly. Change it to 11 to wait longer for more accurate results. +max_depth = 7 +# options used for the differential equation solver, while default works it +# is way slower than using bdf +options = { + "nsteps":15000, "store_states":True, "rtol":1e-12, "atol":1e-12, "method":"bdf", +} +``` + +#### Plotting function + +```{code-cell} ipython3 +def plot_result_expectations(plots, axes=None): + """Plot the expectation values of operators as functions of time. + + Each plot in plots consists of (solver_result, + measurement_operation, color, label). + """ + if axes is None: + fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) + fig_created = True + else: + fig = None + fig_created = False + + # add kw arguments to each plot if missing + plots = [p if len(p) == 5 else p + ({},) for p in plots] + for result, m_op, color, label, kw in plots: + exp = np.real(expect(result.states, m_op)) + kw.setdefault("linewidth", 2) + if color == "rand": + axes.plot( + result.times, + exp, + c=np.random.rand( + 3, + ), + label=label, + **kw, + ) + else: + axes.plot(result.times, exp, color, label=label, **kw) + + if fig_created: + axes.legend(loc=0, fontsize=12) + axes.set_xlabel("t", fontsize=28) + + return fig +``` + +# Obtaining a decaying Exponential description of the environment + +In order to carry out our HEOM simulation, we need to express the correlation +function as a sum of decaying exponentials, that is we need to express it as + +$$C(\tau)= \sum_{k=0}^{N-1}c_{k}e^{-\nu_{k}t}$$ + +As the correlation function of the environment is tied to it's power spectrum via +a Fourier transform, such a representation of the correlation function implies a +power spectrum of the form + +$$S(\omega)= \sum_{k}2 Re\left( \frac{c_{k}}{\nu_{k}- i \omega}\right)$$ + +There are several ways one can obtain such a decomposition, in this tutorial we +will cover the following approaches: + +- Non-Linear Least Squares: + - On the Spectral Density (`sd`) + - On the Correlation Function (`cf`) + - On the Power Spectrum (`ps`) +- Methods based on the Prony Polynomial + - Prony on the correlation function(`prony`) + - The Matrix Pencil method on the correlation function (`mp`) :question: + - ESPRIT on the correlation function(`esprit`) +- Methods based on rational Approximations + - The AAA algorithm on the Power Spectrum (`aaa`) + - ESPIRA-I (`espira-I`) :question: + - ESPIRA-II (`espira-II`) + +Here's a quick high level comparison between the three different families +of methods + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ClassRequires Extra informationFastResilient to NoiseAllows constraitnsStable
Non-Linear Least Squares✔️❌❌✔️❌
Prony Polynomial❌✔️❗❌❗
Rational Approximations ❌✔️❗❗✔️
+ +Legend: + +❌: NO ✔️: Yes ❗: Partially + ++++ + +# Non-Linear Least Squares + +```{code-cell} ipython3 +obs = OhmicEnvironment(T, alpha, wc,s=1) +tlist = np.linspace(0, 30 * np.pi / Del, 600) +``` + +## Correlation Function + +```{code-cell} ipython3 +Obath, fitinfo = obs.approximate(method="cf",tlist=tlist,Nr_max=3,Ni_max=2,maxfev=1e9,target_rsme=None) +print(fitinfo["summary"]) +HEOM_ohmic_corr_fit = HEOMSolver( + Hsys, + (Obath,Q), + max_depth=max_depth, + options=options, +) +results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist) +``` + +## Spectral Density + +```{code-cell} ipython3 +w=np.linspace(0,30,500) +Obath2, fitinfo = obs.approximate(method="sd",wlist=w,Nmax=4,Nk=3) +print(fitinfo["summary"]) +HEOM_ohmic_sd_fit = HEOMSolver( + Hsys, + (Obath2,Q), + max_depth=max_depth, + options=options, +) +results_ohmic_sd_fit = HEOM_ohmic_sd_fit.run(rho0, tlist) +``` + +## Power Spectrum + +```{code-cell} ipython3 +w=np.linspace(-50,30,500) +Obath3, fitinfo = obs.approximate(method="ps",wlist=w,Nmax=5) +print(fitinfo["summary"]) +HEOM_ohmic_ps_fit = HEOMSolver( + Hsys, + (Obath3,Q), + max_depth=max_depth, + options=options, +) +results_ohmic_ps_fit = HEOM_ohmic_ps_fit.run(rho0, tlist) +``` + +# Methods based on the Prony Polinomial + ++++ + +## Prony + +```{code-cell} ipython3 +tlist2=np.linspace(0,40,100) +pbath,fitinfo=obs.approximate("prony",tlist2,Nr=4) +print(fitinfo["summary"]) +HEOM_ohmic_prony_fit = HEOMSolver( + Hsys, + (pbath,Q), + max_depth=max_depth, + options=options, +) +results_ohmic_prony_fit = HEOM_ohmic_prony_fit.run(rho0, tlist) +``` + +## Matrix Pencil + +```{code-cell} ipython3 +mpbath,fitinfo=obs.approximate(method="mp",tlist=tlist2,Nr=5) +print(fitinfo["summary"]) +HEOM_ohmic_mp_fit = HEOMSolver( + Hsys, + (mpbath,Q), + max_depth=max_depth, + options=options, +) +results_ohmic_mp_fit = HEOM_ohmic_mp_fit.run(rho0, tlist) +``` + +## ESPRIT + +```{code-cell} ipython3 + +esbath,fitinfo=obs.approximate("esprit",tlist2,Nr=4) +print(fitinfo["summary"]) +HEOM_ohmic_es_fit = HEOMSolver( + Hsys, + (esbath,Q), + max_depth=max_depth, + options=options, +) +results_ohmic_es_fit = HEOM_ohmic_es_fit.run(rho0, tlist) +``` + +# Rational Approximations + ++++ + +## AAA + +```{code-cell} ipython3 +aaabath,fitinfo=obs.approximate("aaa",np.concatenate((-np.logspace(3,-8,3500),np.logspace(-8,3,3500))),N_max=6,tol=1e-15) +print(fitinfo["summary"]) +HEOM_ohmic_aaa_fit = HEOMSolver( + Hsys, + (aaabath,Q), + max_depth=max_depth, + options=options, +) +results_ohmic_aaa_fit = HEOM_ohmic_aaa_fit.run(rho0, tlist) +``` + +# ESPIRA I + +```{code-cell} ipython3 +tlist4=np.linspace(0,20,1000) +espibath,fitinfo=obs._approx_by_prony("espira-I",tlist4,Nr=4,Ni=4,separate=True) +print(fitinfo["summary"]) +HEOM_ohmic_espira_fit = HEOMSolver( + Hsys, + (espibath,Q), + max_depth=max_depth, + options=options, +) +results_ohmic_espira_fit = HEOM_ohmic_espira_fit.run(rho0, tlist) +``` + +# ESPIRA II + +```{code-cell} ipython3 +espibath2,fitinfo=obs._approx_by_prony("espira-II",tlist4,Nr=4,Ni=4,separate=True) +print(fitinfo["summary"]) +HEOM_ohmic_espira_fit2 = HEOMSolver( + Hsys, + (espibath2,Q), + max_depth=max_depth, + options=options, +) +results_ohmic_espira2_fit = HEOM_ohmic_espira_fit2.run(rho0, tlist) +``` + +Finally we plot the dynamics obtained by the different methods + +```{code-cell} ipython3 +fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) + +plot_result_expectations( + [ + + (results_ohmic_corr_fit, P11p, "r", "Correlation Fit"), + (results_ohmic_sd_fit, P11p, "g", "SD Fit"), + (results_ohmic_sd_fit, P11p, "y", "PS Fit"), + (results_ohmic_prony_fit, P11p, "k", " Prony Fit"), + (results_ohmic_mp_fit, P11p, "r", "Matrix Pencil Fit"), + (results_ohmic_es_fit, P11p, "b-.", "ESPRIT Fit"), + (results_ohmic_aaa_fit, P11p, "r-.", "Matrix AAA Fit"), + (results_ohmic_espira_fit, P11p, "k", "ESPIRA I Fit"), + (results_ohmic_espira2_fit, P11p, "--", "ESPIRA II Fit"), + + ], + axes=axes, +) +axes.set_ylabel(r"$\rho_{11}$", fontsize=30) +axes.set_xlabel(r"$t\;\omega_c$", fontsize=30) +axes.legend(loc=0, fontsize=20); +axes.set_yscale("log") +``` + +## About + +```{code-cell} ipython3 +qutip.about() +``` + +## Testing + +This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. + +```{code-cell} ipython3 +tol=1e-2 +assert np.allclose( + expect(P11p, results_ohmic_ps_fit.states), + expect(P11p, results_ohmic_sd_fit.states), + rtol=tol, +) +assert np.allclose( + expect(P11p, results_ohmic_corr_fit.states), + expect(P11p, results_ohmic_sd_fit.states), + rtol=tol, +) +assert np.allclose( + expect(P11p, results_ohmic_aaa_fit.states), + expect(P11p, results_ohmic_sd_fit.states), + rtol=tol, +) +assert np.allclose( + expect(P11p, results_ohmic_mp_fit.states), + expect(P11p, results_ohmic_sd_fit.states), + rtol=tol, +) +assert np.allclose( + expect(P11p, results_ohmic_prony_fit.states), + expect(P11p, results_ohmic_sd_fit.states), + rtol=tol, +) + +assert np.allclose( + expect(P11p, results_ohmic_es_fit.states), + expect(P11p, results_ohmic_sd_fit.states), + rtol=tol, +) +assert np.allclose( + expect(P11p, results_ohmic_espira_fit.states), + expect(P11p, results_ohmic_sd_fit.states), + rtol=tol, +) +assert np.allclose( + expect(P11p, results_ohmic_espira2_fit.states), + expect(P11p, results_ohmic_sd_fit.states), + rtol=tol, +) +``` diff --git a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.ipynb b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.ipynb deleted file mode 100644 index 8bcf0081..00000000 --- a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.ipynb +++ /dev/null @@ -1,2059 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "68c71886", - "metadata": {}, - "source": [ - "# HEOM 1a: Spin-Bath model (introduction)" - ] - }, - { - "cell_type": "markdown", - "id": "86685431", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "The HEOM method solves the dynamics and steady state of a system and its\n", - "environment, the latter of which is encoded in a set of auxiliary density\n", - "matrices.\n", - "\n", - "In this example we show the evolution of a single two-level system in contact\n", - "with a single Bosonic environment. The properties of the system are encoded\n", - "in a Hamiltonian, and a coupling operator which describes how it is coupled\n", - "to the environment.\n", - "\n", - "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", - "\n", - "In the example below we show how to model the overdamped Drude-Lorentz\n", - "Spectral Density, commonly used with the HEOM. We show how to do this using\n", - "the Matsubara, Pade and fitting decompositions, and compare their\n", - "convergence.\n", - "\n", - "### Drude-Lorentz (overdamped) spectral density\n", - "\n", - "The Drude-Lorentz spectral density is:\n", - "\n", - "$$J_D(\\omega)= \\frac{2\\omega\\lambda\\gamma}{{\\gamma}^2 + \\omega^2}$$\n", - "\n", - "where $\\lambda$ scales the coupling strength, and $\\gamma$ is the cut-off\n", - "frequency. We use the convention,\n", - "\\begin{equation*}\n", - "C(t) = \\int_0^{\\infty} d\\omega \\frac{J_D(\\omega)}{\\pi}[\\coth(\\beta\\omega) \\\n", - " \\cos(\\omega \\tau) - i \\sin(\\omega \\tau)]\n", - "\\end{equation*}\n", - "\n", - "With the HEOM we must use an exponential decomposition:\n", - "\n", - "\\begin{equation*}\n", - "C(t)=\\sum_{k=0}^{k=\\infty} c_k e^{-\\nu_k t}\n", - "\\end{equation*}\n", - "\n", - "As an example, the Matsubara decomposition of the Drude-Lorentz spectral\n", - "density is given by:\n", - "\n", - "\\begin{equation*}\n", - " \\nu_k = \\begin{cases}\n", - " \\gamma & k = 0\\\\\n", - " {2 \\pi k} / {\\beta } & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "\\begin{equation*}\n", - " c_k = \\begin{cases}\n", - " \\lambda \\gamma (\\cot(\\beta \\gamma / 2) - i) \\\n", - " & k = 0\\\\\n", - " 4 \\lambda \\gamma \\nu_k / \\{(\\nu_k^2 - \\gamma^2)\\beta \\} \\\n", - " & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." - ] - }, - { - "cell_type": "markdown", - "id": "1dcca8d4", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c44fa069", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "import contextlib\n", - "import time\n", - "\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "from scipy.optimize import curve_fit\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " basis,\n", - " brmesolve,\n", - " destroy,\n", - " expect,\n", - " liouvillian,\n", - " qeye,\n", - " sigmax,\n", - " sigmaz,\n", - " spost,\n", - " spre,\n", - " tensor,\n", - ")\n", - "from qutip.core.environment import (\n", - " DrudeLorentzEnvironment,\n", - " ExponentialBosonicEnvironment,\n", - " system_terminator\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver,\n", - " HSolverDL,\n", - ")\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "36a7a852", - "metadata": {}, - "source": [ - "## Helper functions\n", - "\n", - "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "351c8bfe", - "metadata": {}, - "outputs": [], - "source": [ - "def cot(x):\n", - " \"\"\"Vectorized cotangent of x.\"\"\"\n", - " return 1.0 / np.tan(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b104feee", - "metadata": {}, - "outputs": [], - "source": [ - "def dl_matsubara_params(lam, gamma, T, nk):\n", - " \"\"\"Calculation of the real and imaginary expansions of the Drude-Lorenz\n", - " correlation functions.\n", - " \"\"\"\n", - " ckAR = [lam * gamma * cot(gamma / (2 * T))]\n", - " ckAR.extend(\n", - " (8 * lam * gamma * T * np.pi * k * T /\n", - " ((2 * np.pi * k * T)**2 - gamma**2))\n", - " for k in range(1, nk + 1)\n", - " )\n", - " vkAR = [gamma]\n", - " vkAR.extend(2 * np.pi * k * T for k in range(1, nk + 1))\n", - "\n", - " ckAI = [lam * gamma * (-1.0)]\n", - " vkAI = [gamma]\n", - "\n", - " return ckAR, vkAR, ckAI, vkAI" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "dc4701f9", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_result_expectations(plots, axes=None):\n", - " \"\"\"Plot the expectation values of operators as functions of time.\n", - "\n", - " Each plot in plots consists of (solver_result, measurement_operation,\n", - " color, label).\n", - " \"\"\"\n", - " if axes is None:\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " fig_created = True\n", - " else:\n", - " fig = None\n", - " fig_created = False\n", - "\n", - " # add kw arguments to each plot if missing\n", - " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", - " for result, m_op, color, label, kw in plots:\n", - " exp = np.real(expect(result.states, m_op))\n", - " kw.setdefault(\"linewidth\", 2)\n", - " axes.plot(result.times, exp, color, label=label, **kw)\n", - "\n", - " if fig_created:\n", - " axes.legend(loc=0, fontsize=12)\n", - " axes.set_xlabel(\"t\", fontsize=28)\n", - "\n", - " return fig" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "2f410ff7", - "metadata": {}, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\"Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b33b495d", - "metadata": {}, - "outputs": [], - "source": [ - "# Default solver options:\n", - "\n", - "default_options = {\n", - " \"nsteps\": 1500,\n", - " \"store_states\": True,\n", - " \"rtol\": 1e-12,\n", - " \"atol\": 1e-12,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "582d1a64", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "And let us set up the system Hamiltonian, bath and system measurement operators:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d8d5c7a1", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the system Hamiltonian\n", - "eps = 0.5 # Energy of the 2-level system.\n", - "Del = 1.0 # Tunnelling term\n", - "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "01db3dfc", - "metadata": {}, - "outputs": [], - "source": [ - "# Initial state of the system.\n", - "rho0 = basis(2, 0) * basis(2, 0).dag()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "020d8228", - "metadata": {}, - "outputs": [], - "source": [ - "# System-bath coupling (Drude-Lorentz spectral density)\n", - "Q = sigmaz() # coupling operator\n", - "\n", - "# Bath properties:\n", - "gamma = 0.5 # cut off frequency\n", - "lam = 0.1 # coupling strength\n", - "T = 0.5\n", - "beta = 1.0 / T\n", - "\n", - "# HEOM parameters\n", - "NC = 5 # cut off parameter for the bath\n", - "Nk = 2 # terms in the Matsubara expansion of the correlation function\n", - "\n", - "# Times to solve for\n", - "tlist = np.linspace(0, 50, 1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "b2a8b071", - "metadata": {}, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresonding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresonding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "a93cae29", - "metadata": {}, - "source": [ - "### First of all, it is useful to look at the spectral density\n", - "\n", - "Now we are ready to begin. Let's look at the shape of the spectral density given the bath parameters we defined above:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "b4649efd", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_spectral_density():\n", - " \"\"\"Plot the Drude-Lorentz spectral density\"\"\"\n", - " w = np.linspace(0, 5, 1000)\n", - " J = w * 2 * lam * gamma / (gamma**2 + w**2)\n", - "\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " axes.plot(w, J, \"r\", linewidth=2)\n", - " axes.set_xlabel(r\"$\\omega$\", fontsize=28)\n", - " axes.set_ylabel(r\"J\", fontsize=28)\n", - "\n", - "\n", - "plot_spectral_density()" - ] - }, - { - "cell_type": "markdown", - "id": "54abc0a5", - "metadata": {}, - "source": [ - "Next we calculate the exponents using the Matsubara decompositions. Here we\n", - "split them into real and imaginary parts.\n", - "\n", - "The HEOM code will optimize these, and reduce the number of exponents when\n", - "real and imaginary parts have the same exponent. This is clearly the case\n", - "for the first term in the vkAI and vkAR lists." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "bb6afd3d", - "metadata": {}, - "outputs": [], - "source": [ - "ckAR, vkAR, ckAI, vkAI = dl_matsubara_params(nk=Nk, lam=lam, gamma=gamma, T=T)" - ] - }, - { - "cell_type": "markdown", - "id": "d7c17b69", - "metadata": {}, - "source": [ - "Having created the lists which specify the bath correlation functions, we\n", - "create an `ExponentialBosonicEnvironment` from them and pass the environment to the `HEOMSolver` class.\n", - "\n", - "The solver constructs the \"right hand side\" (RHS) determinining how the\n", - "system and auxiliary density operators evolve in time. This can then be used\n", - "to solve for dynamics or steady-state.\n", - "\n", - "Below we create the bath and solver and then solve for the dynamics by\n", - "calling `.run(rho0, tlist)`." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "bc687720", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.007760763168334961\n", - " [ 1% ] Elapsed 0.04s / Remaining 00:00:00:03" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 1.66s*] Elapsed 1.66s / Remaining 00:00:00:00\n", - "ODE solver time: 1.6655654907226562\n" - ] - } - ], - "source": [ - "options = {**default_options}\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " env = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI)\n", - " HEOMMats = HEOMSolver(Hsys, (env,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMats = HEOMMats.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "6413cfeb", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (resultMats, P11p, \"b\", \"P11 Mats\"),\n", - " (resultMats, P12p, \"r\", \"P12 Mats\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "750e9d46", - "metadata": {}, - "source": [ - "In practice, one would not perform this laborious expansion for the\n", - "Drude-Lorentz correlation function, because QuTiP already has a class,\n", - "`DrudeLorentzBath`, that can construct this bath for you. Nevertheless,\n", - "knowing how to perform this expansion will allow you to construct your own\n", - "baths for other spectral densities.\n", - "\n", - "Below we show how to use this built-in functionality:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "19037baa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.00848531723022461\n", - " Total run time: 1.65s*] Elapsed 1.65s / Remaining 00:00:00:00\n", - "ODE solver time: 1.651024580001831\n" - ] - } - ], - "source": [ - "# Compare to built-in Drude-Lorentz bath:\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " dlenv = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T,Nk=100)\n", - " dlenv_approx=dlenv.approx_by_matsubara(Nk=Nk)\n", - " HEOM_dlbath = HEOMSolver(Hsys, (dlenv_approx,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " result_dlbath = HEOM_dlbath.run(rho0, tlist) # normal 115" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "c6a09b78", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (result_dlbath, P11p, \"b\", \"P11 (DrudeLorentzBath)\"),\n", - " (result_dlbath, P12p, \"r\", \"P12 (DrudeLorentzBath)\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "99ecf6dc", - "metadata": {}, - "source": [ - "The `DrudeLorentzEnvironment` class also allows us to easily obtain the power spectrum, correlation function, and spectral density. The approximated Environment is a `BosonicEnvironment` where the effective power spectrum, correlation function, and spectral density from the approximation is also accessible. In the following plots, the solid lines are the exact expressions, and the dashed lines are based on our approximation of the correlation function with a finite number of exponents." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "4f3f19f9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "w = np.linspace(-10, 20, 1000)\n", - "w2 = np.linspace(0, 20, 1000)\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "axs[0, 0].plot(w, dlenv.power_spectrum(w))\n", - "axs[0, 0].plot(w, dlenv_approx.power_spectrum(w), '--')\n", - "axs[0, 0].set(xlabel=r'$\\omega$', ylabel=r'$S(\\omega)$')\n", - "axs[0, 1].plot(w2, dlenv.spectral_density(w2))\n", - "axs[0, 1].plot(w2, dlenv_approx.spectral_density(w2), '--')\n", - "axs[0, 1].set(xlabel=r'$\\omega$', ylabel=r'$J(\\omega)$')\n", - "axs[1, 0].plot(w2, np.real(dlenv.correlation_function(w2)))\n", - "axs[1, 0].plot(w2, np.real(dlenv_approx.correlation_function(w2)), '--')\n", - "axs[1, 0].set(xlabel=r'$t$', ylabel=r'$C_{R}(t)$')\n", - "axs[1, 1].plot(w2, np.imag(dlenv.correlation_function(w2)))\n", - "axs[1, 1].plot(w2, np.imag(dlenv_approx.correlation_function(w2)), '--')\n", - "axs[1, 1].set(xlabel=r'$t$', ylabel=r'$C_{I}(t)$')\n", - "\n", - "fig.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "95d00102", - "metadata": {}, - "source": [ - "We also provide a legacy class, `HSolverDL`, which calculates the\n", - "Drude-Lorentz correlation functions automatically, to be backwards\n", - "compatible with the previous HEOM solver in QuTiP:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "01aedd06", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.006570577621459961\n", - " Total run time: 1.90s*] Elapsed 1.90s / Remaining 00:00:00:00\n", - "ODE solver time: 1.8980872631072998\n" - ] - } - ], - "source": [ - "# Compare to legacy class:\n", - "\n", - "# The legacy class performs the above collation of coefficients automatically,\n", - "# based upon the parameters for the Drude-Lorentz spectral density.\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " HEOMlegacy = HSolverDL(Hsys, Q, lam, T, NC, Nk, gamma, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultLegacy = HEOMlegacy.run(rho0, tlist) # normal 115" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "461ae04e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (resultLegacy, P11p, \"b\", \"P11 Legacy\"),\n", - " (resultLegacy, P12p, \"r\", \"P12 Legacy\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "f2dcec9a", - "metadata": {}, - "source": [ - "## Ishizaki-Tanimura Terminator\n", - "\n", - "To speed up convergence (in terms of the number of exponents kept in the\n", - "Matsubara decomposition), We can treat the $Re[C(t=0)]$ component as a\n", - "delta-function distribution, and include it as Lindblad correction. This is\n", - "sometimes known as the Ishizaki-Tanimura Terminator.\n", - "\n", - "In more detail, given\n", - "\n", - "\\begin{equation*}\n", - "C(t)=\\sum_{k=0}^{\\infty} c_k e^{-\\nu_k t}\n", - "\\end{equation*}\n", - "\n", - "since $\\nu_k=\\frac{2 \\pi k}{\\beta }$, if $1/\\nu_k$ is much much smaller than\n", - "other important time-scales, we can approximate,\n", - "$ e^{-\\nu_k t} \\approx \\delta(t)/\\nu_k$, and $C(t)=\\sum_{k=N_k}^{\\infty}\n", - "\\frac{c_k}{\\nu_k} \\delta(t)$\n", - "\n", - "It is convenient to calculate the whole sum\n", - "$ C(t)=\\sum_{k=0}^{\\infty} \\frac{c_k}{\\nu_k} = 2 \\lambda / (\\beta \\gamma)- i\\lambda $\n", - ", and subtract off the contribution from the finite number of Matsubara terms\n", - "that are kept in the hierarchy, and treat the residual as a contribution in \n", - "Lindblad form.\n", - "\n", - "This is clearer if we plot the correlation function with a large number of\n", - "Matsubara terms. To create the plot, we use the utility function of the\n", - "`DrudeLorentzBath` class mentioned above." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "dbb982d4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_correlation_expansion_divergence():\n", - " \"\"\"We plot the correlation function with a large number of Matsubara terms\n", - " to show that the real part is slowly diverging at t = 0.\n", - " \"\"\"\n", - " t = np.linspace(0, 2, 100)\n", - "\n", - " # correlation coefficients with 15k and 2 terms\n", - " corr_15k = dlenv.correlation_function(t)\n", - " corr_2 = dlenv_approx.correlation_function(t)\n", - "\n", - " fig, ax1 = plt.subplots(figsize=(12, 7))\n", - "\n", - " ax1.plot(\n", - " t, np.real(corr_2), color=\"b\", linewidth=3, label=rf\"Mats = {Nk} real\"\n", - " )\n", - " ax1.plot(\n", - " t, np.imag(corr_2), color=\"r\", linewidth=3, label=rf\"Mats = {Nk} imag\"\n", - " )\n", - " ax1.plot(\n", - " t, np.real(corr_15k), \"b--\", linewidth=3, label=r\"Mats = 15000 real\"\n", - " )\n", - " ax1.plot(\n", - " t, np.imag(corr_15k), \"r--\", linewidth=3, label=r\"Mats = 15000 imag\"\n", - " )\n", - "\n", - " ax1.set_xlabel(\"t\")\n", - " ax1.set_ylabel(r\"$C$\")\n", - " ax1.legend()\n", - "\n", - "plot_correlation_expansion_divergence()" - ] - }, - { - "cell_type": "markdown", - "id": "c0b4a0d6", - "metadata": {}, - "source": [ - "Let us evaluate the result including this Ishizaki-Tanimura terminator:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "1ee891d1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.0036385059356689453\n", - " Total run time: 2.05s*] Elapsed 2.04s / Remaining 00:00:00:00\n", - "ODE solver time: 2.04779052734375\n" - ] - } - ], - "source": [ - "# Run HEOM solver and include the Ishizaki-Tanimura terminator\n", - "\n", - "# Notes:\n", - "#\n", - "# * when using the built-in DrudeLorentzEnvironment the terminator (L_bnd) is\n", - "# available from bath.terminator().\n", - "# \n", - "# * in the legacy HSolverDL function the terminator is included automatically\n", - "# if the parameter bnd_cut_approx=True is used.\n", - "\n", - "op = -2 * spre(Q) * spost(Q.dag()) + spre(Q.dag() * Q) + spost(Q.dag() * Q)\n", - "\n", - "approx_factr = (2 * lam / (beta * gamma)) - 1j * lam\n", - "\n", - "approx_factr -= lam * gamma * (-1.0j + cot(gamma / (2 * T))) / gamma\n", - "for k in range(1, Nk + 1):\n", - " vk = 2 * np.pi * k * T\n", - "\n", - " approx_factr -= (4 * lam * gamma * T * vk / (vk**2 - gamma**2)) / vk\n", - "\n", - "L_bnd = -approx_factr * op\n", - "\n", - "Ltot = -1.0j * (spre(Hsys) - spost(Hsys)) + L_bnd\n", - "Ltot = liouvillian(Hsys) + L_bnd\n", - "\n", - "options = {**default_options, \"rtol\": 1e-14, \"atol\": 1e-14}\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI)\n", - " HEOMMatsT = HEOMSolver(Ltot, (bath,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMatsT = HEOMMatsT.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "b2df9cc0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (resultMatsT, P11p, \"b\", \"P11 Mats + Term\"),\n", - " (resultMatsT, P12p, \"r\", \"P12 Mats + Term\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "8d86e00d", - "metadata": {}, - "source": [ - "Or using the built-in Drude-Lorentz environment we can write simply:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "b4f84c83", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.007706880569458008\n", - " [** 9% ] Elapsed 0.14s / Remaining 00:00:00:01" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 1.77s*] Elapsed 1.77s / Remaining 00:00:00:00\n", - "ODE solver time: 1.7673842906951904\n" - ] - } - ], - "source": [ - "options = {**default_options, \"rtol\": 1e-14, \"atol\": 1e-14}\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath,delta = dlenv.approx_by_matsubara(Nk=Nk,compute_delta=True)\n", - " Ltot = liouvillian(Hsys) + system_terminator(Q,delta)\n", - " HEOM_dlbath_T = HEOMSolver(Ltot, (bath,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " result_dlbath_T = HEOM_dlbath_T.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "2b88277e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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/f786deqkJk2a6OWXX1Z8fLwiIiK0du1a3XPPPUWqU3FfV82aNbVu3Tq3bU8++aQOHTqkN954w217zq4OReG8Vs4+4Dnl/CevXLly+c7ikfN73znbSH7bz58/n+sYERERRXp/S8qrYXXlypWaMmWKNmzYoMOHD+vTTz+94PxcK1as0OjRo7Vt2zbVrFlTDz/8sO666y5vVhNAgFi/3t818IxmzZp5ZISvNzj7wzkcjqxZBvLy/vvva9CgQXr22WfdtickJKhixYqFOle9evU0Y8YMSaYV7MMPP9T48eOVmpqq119/Pd/nValSRRs3bizUObI7ePCgNmzYoC5durj1V5XyXhoyIiJCKSkpubbnFeDS09N1/Phxt9Bz5MgRt/0qVaqk4OBgDRw4UPfcc0+edcyvlS4vsbGxOnz4cK7tzgFPVapUcdue8zU6H3/sscd000035XmOkgSsvGzZskV9+/ZVly5dijzDRKtWrWRZlrZs2aKmTZtq4cKFOnPmjBYsWKB69epl7eeNOX3zExYWlutnOTY2VsnJyR79GXdeq48//tjttebH2zNZnDhxIs9/lLzFq2H1zJkzat26tYYOHVqoj1j27NmjXr166c4779T777+vH3/8UXfffbeqVq1aoo9oAACFM3jwYP30009q1qyZatWqle9+Docj17RHX375pQ4ePKiLLrooa1thW6gaN26ssWPH6pNPPrlgEG3atKnmzp2rxMTEQg9+OXfunIYPH6709HQ9/PDDhXpOfHy8jh07pqNHj2Z10UhNTdXixYvd9uvWrZsmT56sOXPm6P7778/a/sEHH7jtV65cOXXr1k2bNm1Sq1atCpw/tzCuueYaTZw4URs3btRll12Wtf3dd9+Vw+G44AC0Jk2aqFGjRvr5559z/dNREuHh4Xle7/3796tnz55q0KCBPvnkkyINYpNcITQuLk6SK5Bl/z60LCvPEJxfnQLF9ddfr5CQEO3atcvveSg9PV0HDhxQr169fHZOr4bVnj17FrrvjSS9/vrrqlu3btZovGbNmmn9+vV67rnn/H5x8pOZaW4hdKgA4EErVqzQX3/9JcnMiblv3z59/PHHkqQuXbpkzRxw9uxZLVq0SJKyptpZsWKFEhISFBUVVaTfwZL5WHPhwoUX3O+GG27Q7Nmz1bRpU7Vq1UobNmzQlClTskb6OzVs2FCRkZGaM2eOmjVrpvLly6tmzZpKSEjQvffeq3/84x9q1KiRwsLCtHTpUm3ZskWPPvpoged2jkb+6aef1L1791yP79+/X2vWrFFmZqYSExO1adMmzZw5U/v27dPzzz+f53Py0r9/fz3xxBO69dZb9dBDD+n8+fN65ZVXlJGR4bZf9+7d1blzZz388MM6c+aM2rZtqx9//DHP+XFffvlldezYUZ06ddKIESMUHx+v5ORk7dy5U59//nmuyda3bt2add2zc67K9e6776p3796aMGGC6tWrpy+//FLTpk3TiBEj3Pqr5ueNN95Qz549df3112vIkCGqVauWTpw4oe3bt2vjxo366KOPCvVeZdeyZUvNmzdP8+fPV4MGDRQREaGWLVuqZ8+eOnXqlKZOnZprNbGGDRu6zYbxxx9/ZH0/JyYm6ttvv9WMGTPUtm1bderUSZJ03XXXKSwsTLfddpsefvhhnT9/XtOnT9fJkyfzrNOCBQs0ffp0tWnTRkFBQbb9dCMv8fHxmjBhgsaMGaPdu3erR48eqlSpko4ePaq1a9cqKipKTz75pE/qsmXLFp09e7bIs3GUyAWHYHmIJOvTTz8tcJ9OnTpZ999/v9u2BQsWWCEhIblGMObH17MBLFxoWfHxlvXaa5b1f4MmAZTAhUaQBrKcI3wL0qVLlzxHRSvHaH/naOa8bnmNfs7rPM7ZAPKT14j+kydPWnfccYcVFxdnlStXzurYsaP1/fffW126dMk1Enzu3LlW06ZNrdDQ0KzR8UePHrWGDBliNW3a1IqKirLKly9vtWrVynrxxRfdRmXnJSMjw4qPj881Mj3nexEcHGxVqlTJatOmjTVy5Ehr27ZtuY51oWuyaNEi65JLLrEiIyOtBg0aWFOnTs01G4BlWdapU6esYcOGWRUrVrTKlStnXXfdddaOHTtyzQbgrOewYcOsWrVqWaGhoVbVqlWtDh06WE8//XS+ryXnzXkt9u3bZw0YMMCKjY21QkNDrSZNmlhTpkzJGsWf/Vj5zSzw888/W7fccosVFxdnhYaGWtWrV7euvvpq6/XXX7/g++QcvZ/9e3Lv3r1W9+7drejoaLfvw8K8nrxmA4iKirIuvvhia9y4cbn+tn/++edW69atrYiICKtWrVrWQw89lDU7Q/Y6nThxwurXr59VsWJFy+FwZF2/gt6bvK5dYQwePLhQsyEUJL/3e+HChVa3bt2sChUqWOHh4Va9evWsfv36Wd9++63b+aOiovI8riTrnnvucduW33vgvBYfffSR2/bHH3/cqlKlinX+/PkCX4MnZwNw/F/lvc7hcFywz2rjxo01ZMgQ/ec//8natmrVKl111VU6dOhQnh3gU1JS3PoUJSUlqU6dOkpMTCzREoGFYVlShw6Sc97ge++VirkSHID/c/78ee3Zs0f169cv9iAalH7PP/+8nnnmGR08eFCRkZH+rg5QJmRkZOiiiy7SgAED9MwzzxS474V+lyclJSkmJqZQec12K1jl7BTszNL5dRaeOHGiYmJism516tTxeh2dTp+Wso8jmDpV2rLFZ6cHgDLrnnvuUUxMTLFXfgJQdO+//75Onz6thx56yKfntVVYrV69eq7Rk8eOHctzShCnxx57TImJiVm3AwcO+KKqkqToaOmrr6Ts/1zwexMAvC8iIkLvvfeebde2B0qjzMxMzZkzp9AzfniKrYYFtW/fXp9//rnbtm+++UZt27bNd9RgeHi4339Z3Xef9Oyz0pkz0oIF0rRpUra5hQEAXtCxY0d17NjR39UAyoyhQ4f65bxebVk9ffq0Nm/enDXdxJ49e7R582bt379fkmkVHTRoUNb+d911l/bt26fRo0dr+/btmjlzpmbMmJHvZMV2ER0t9ehhygkJrj6sAAAAKBmvhtX169fr0ksv1aWXXirJrHhy6aWX6oknnpAkHT58OCu4SmYy5EWLFmn58uW65JJL9NRTT+mVV16x7bRV2WWfbmz5cr9VAwAAoFTx2WwAvlKU0WWetGuX5JwH+7rrpG++8dmpgVKF2QAAIPCV6tkAAlWDBpJzZq21a820VgAAACgZwqqHOBzS//V2UGKilK13AwAAAIqJsOpBrVu7yj//7L96AAAAlBaEVQ8irAIAAHgWYdWDWrVylQmrAAAAJUdY9aBGjSTngDfCKoCcZs+eLYfDkXULCQlR7dq1NXToUB08eNBt37Fjx+qGG25QrVq15HA4NGTIkDyP+fbbb6tv376Kj49XZGSkLrroIo0YMUKHDx8uVJ26du0qh8OhBg0aKK/JYVauXJlV39mzZxf1JevQoUMaP3581nzbnjRs2DD1cE5yLWnv3r1u729oaKhiY2N1+eWXa9SoUdq2bZvH63AhDodD48ePL/LznK/lueee83ylvGzatGnF+l6RpOXLl7tdQ4fDoUqVKqldu3Z65513il2nDz74QC+99FKu7XZ5n50/hxe6Fed7yZMGDhyovn37+vy8hFUPCgmRWrQw5V27pHPn/FsfAPY0a9YsrV69WkuWLNGdd96puXPnqlOnTjpz5kzWPi+++KKOHz+uPn36KCwsLN9jjRs3TuXLl9ezzz6rr7/+Wg8//LC++OILtWnTRkePHi1UfaKjo7Vnzx4tXbo012MzZ84s0TSAhw4d0pNPPunxsLpp0ya98847evrpp3M9dt9992n16tVasWKF3nvvPfXt21efffaZWrdurSlTpni0HsitJGHV6dlnn9Xq1au1evVqvffee6pXr56GDBmiV199tVjHyy+s2sW0adOyXu/q1as1duxYSa7fFc7b8OHD/VrP8ePH68svv8zzd4U32Wq51dKgcWNp/XozddWePdLFF/u7RgDspkWLFmrbtq0kqVu3bsrIyNBTTz2lhQsX6vbbb5ckJScnKyjItCe89957+R5r06ZNiouLy7rfpUsXXXbZZbr88sv11ltvZf3RK0jdunUVHR2tmTNn6pprrsnanpycrI8++ki333673nrrrWK9Vm+ZNGmSrrjiiqz3Mbu6devqyiuvzLrfq1cvjR49WjfddJMefvhhtWjRQj179sz32OfOnVNERIQcDodX6m4HGRkZSk9P9/ty5flp1KhRrmu4bt06zZ07V/fdd58fa3ZhQ4YM0d69e7W8CCsEXZwjLOzYsUOS+++Kkjh79qzKlStX4uM0bNhQPXr00KRJk3T11VeX+HiFRcuqhzkXBpCknTv9Vw8AgcP5R3nfvn1Z25xB9UKyB1WnNm3aKDg4WAcOHCh0HYYNG6YFCxbo1KlTWdvmzZsnSbr11ltz7b9z504NHTpUjRo1Urly5VSrVi3deOON2rp1a9Y+y5cv1+WXXy7JrCme86PM3bt369Zbb1XNmjUVHh6uatWq6ZprrrlgK+zRo0f16aefauDAgYV+fZGRkZoxY4ZCQ0PdWledXTO++eYbDRs2TFWrVlW5cuWUkpKiIUOGKD4+Ptexxo8fnyvIJiUl6c4771RsbKzKly+vHj166Pfff8+zLn/88YcGDBiguLg4hYeHq1mzZnrttdcK/Vqy279/v/75z3+6Hev5559XZmZm1j7Oj7onT56sp59+WvXr11d4eLiWLVsmyaw22adPH1WuXFkRERG69NJL9eGHH7qdx/k+LVu2TCNGjFCVKlUUGxurm266SYcOHcraLz4+Xtu2bdOKFSuyrrfzPSzoo+4LtcQGBQWpfPnyCg0Nddv+2muvqXPnzoqLi1NUVJRatmypyZMnKy0tLWufrl276ssvv9S+ffvczpnTCy+8oPr166t8+fJq37691thw7fT58+erffv2ioqKUvny5XX99ddr06ZNbvsMGTJE5cuX19atW9W9e3dFR0dn/RPqcDh07733atasWWrSpIkiIyPVtm1brVmzRpZlacqUKVnvwdVXX62deQSZgQMH6ttvv9WuXbt88polWlY9jrAKeFHbttKRI/6uhVG9uvkYxQOcfxCqVq3qkeOtWLFCGRkZat68eaGfc+utt2rUqFGaO3euRowYIUmaMWOG+vXrl2c3gEOHDik2NlaTJk1S1apVdeLECb3zzjtq166dNm3apCZNmuiyyy7TrFmzNHToUI0dO1a9e/eWJNWuXVuSaS3LyMjQ5MmTVbduXSUkJGjVqlVugTkv33zzjdLS0tStW7dCvz5Jqlmzptq0aaNVq1YpPT1dISGuP4HDhg1T79699d577+nMmTO5QlFBLMtS3759tWrVKj3xxBO6/PLL9eOPP+bZevvrr7+qQ4cOqlu3rp5//nlVr15dixcv1v3336+EhASNGzeu0Of966+/1KFDB6Wmpuqpp55SfHy8vvjiCz344IPatWuXpk2b5rb/K6+8osaNG+u5555ThQoV1KhRIy1btkw9evRQu3bt9PrrrysmJkbz5s1T//79dfbs2Vx9pYcPH67evXvrgw8+0IEDB/TQQw/pn//8Z9bHwp9++qn69eunmJiYrPM7W2+nTZumpKQkt+M9/vjjWrZsmZo0aeK2PTMzU+np6ZKk48ePa9asWfrll1/05ptvuu23a9cuDRgwQPXr11dYWJh+/vlnPfPMM9qxY4dmzpyZdd5//etf2rVrlz799NM838vXXntNTZs2zeoq8Pjjj6tXr17as2ePYmJiCnM5vO7ZZ5/V2LFjs36eUlNTNWXKFHXq1Elr1651a51NTU1Vnz599O9//1uPPvpo1nspSV988YU2bdqkSZMmyeFw6JFHHlHv3r01ePBg7d69W1OnTlViYqJGjx6tm2++WZs3b3YL9127dpVlWVq0aJHvWrmtUiYxMdGSZCUmJvrl/KtWWZbpBGBZI0b4pQpAQDt37pz166+/WufOncv9YK1arh8wf99q1Srya5s1a5YlyVqzZo2VlpZmJScnW1988YVVtWpVKzo62jpy5Eiez4uKirIGDx5cqHMkJSVZzZo1s+rUqWMlJydfcP8uXbpYzZs3tyzLsgYPHmy1bdvWsizL2rZtmyXJWr58ubVu3TpLkjVr1qx8j5Oenm6lpqZajRo1skaNGpW1Pb/nJiQkWJKsl156qVCvK7sRI0ZYkZGRVmZmptv2PXv2WJKsKVOm5Pvc/v37W5Kso0ePWpbluiaDBg3Kte/gwYOtevXq5do+btw4K/ufz6+++sqSZL388stu+z3zzDOWJGvcuHFZ266//nqrdu3auf5G3XvvvVZERIR14sSJQr+WRx991JJk/fTTT27bR4wYYTkcDuu3335zO1bDhg2t1NRUt32bNm1qXXrppVZaWprb9htuuMGqUaOGlZGRYVmW6326++673fabPHmyJck6fPhw1rbmzZtbXbp0ybfeTlOmTLEkWW+++WbWtmXLllmSct2CgoKsMWPGFHi8jIwMKy0tzXr33Xet4ODgrPfSsiyrd+/eeV5L53vTsmVLKz09PWv72rVrLUnW3LlzL/g60tLS3G6DBg2yOnfunGt7zu/Xgjjf73Xr1lmWZVn79++3QkJCrPvuu89tv+TkZKt69erWLbfckrVt8ODBliRr5syZuY4ryapevbp1+vTprG0LFy60JFmXXHKJWx1feuklS5K1ZcuWXMepVauW1b9//wJfQ4G/y62i5TVaVj2MllXAi6pX93cNXEpQl+x98SSpZcuWmj59uqpVq1aiKp0/f1433XST9u3bp6VLl6p8+fJFev6wYcPUpUsXbd26VbNnz1bDhg3VuXNnbdiwIde+6enpmjx5st5//33t3LnT7WPX7du3X/BclStXVsOGDTVlyhRlZGSoW7duat26daG6Pxw6dEhVq1YtVp9SK5+1sG+++eYiH8vJ+XG6s7+x04ABAzRmzJis++fPn9d3332nESNGqFy5cm6tXb169dLUqVO1Zs2aAvvTZrd06VJdfPHFuuKKK9y2DxkyRNOnT9fSpUvVuHHjrO19+vRxazHeuXOnduzYkTUSPmd9vvjiC/32229q1qyZ2zGya/V/czbu27dP1YvwMzF37lw9/PDDGjt2rO68885cj//3v//N6hN56tQpfffdd5o0aZJSUlLcunFs2rRJ48aN048//qgTJ064HeP3339Xu3btClWf3r17Kzg4OM/XVZC9e/eqfv36eT6Ws3V+2bJl6tq1a6Hqk9PixYuVnp6uQYMGuV2niIgIdenSJet7MLv8vqe7deumqKiorPvO69uzZ0+3nynn9n379qlly5Zux4iLi8s1g4k3EVY9rEoVqUIFKSmJsAp4nIc+dve3d999V82aNVNISIiqVaumGjVqlPiYKSkp+vvf/64ffvhBX3zxRaH/SGfXuXNnNWrUSG+88YY+/PBDjRw5Mt9AOHr0aL322mt65JFH1KVLF1WqVElBQUEaPny4zhViKhSHw6HvvvtOEyZM0OTJk/XAAw+ocuXKuv322/XMM88oOjo63+c6B0AVx759+xQeHq7KlSu7bS/JNTh+/LhCQkIUGxvrtj1neDt+/LjS09P16quv5juqPSEhoUjnzatPbc2aNbMezy7na3TOFvHggw/qwQcfLFR9cr5G50f8hbnmTsuWLdOQIUM0aNAgPfXUU3nu06BBA7eBRddee61Onjyp559/XnfccYeaNm2q/fv3q1OnTmrSpIlefvllxcfHKyIiQmvXrtU999xTpDoV93XVrFlT69atc9v25JNP6tChQ3rjjTfctufs6lAUzmvl7AOeU85/8sqVK5fvLB45v/eds43kt/38+fO5jhEREVGk97ekCKse5nCY+VY3bJD27ZNSU6UCZp0BUAY1a9bMIyN8nVJSUtS3b18tW7ZM//vf/9xG9BeVsz+cw+HQ4MGD893v/fff16BBg/Tss8+6bU9ISFDFihULda569eppxowZkkwr2Icffqjx48crNTVVr7/+er7Pq1KlijZu3Fioc2R38OBBbdiwQV26dHHrryopz1AeERGhlJSUXNvzCnDp6ek6fvy4W+g5kqN/daVKlRQcHKyBAwfqnnvuybOO+bXS5SU2NjbP+XSdA56qVKnitj3na3Q+/thjj+mmm27K8xwlCVh52bJli/r27asuXboUeYaJVq1aybIsbdmyRU2bNtXChQt15swZLViwQPXq1cvazxtz+uYnLCws189ybGyskpOTPfoz7rxWH3/8sdtrzY+3Z7I4ceJEnv8oeQth1QsaNjRhNTNT2r/fvWsAAHiSs0V16dKlWrBgga6//voSHW/w4MH66aef1KxZM9WqVSvf/RwOR65pj7788ksdPHhQF2X7pVfYFqrGjRtr7Nix+uSTTy4YRJs2baq5c+cqMTGx0INfzp07p+HDhys9PV0PP/xwoZ4THx+vY8eO6ejRo1ldNFJTU7V48WK3/bp166bJkydrzpw5uv/++7O2f/DBB277lStXTt26ddOmTZvUqlWrAufPLYxrrrlGEydO1MaNG3XZZZdlbX/33XflcDguOACtSZMmatSokX7++edc/3SURHh4eJ7Xe//+/erZs6caNGigTz75pEiD2CRXCHXOgOEMZNm/Dy3LyjME51enQHH99dcrJCREu3btKlGXFU9IT0/XgQMH1KtXL5+dk7DqBdn/6SGsAiiOFStW6K+//pJk5sTct2+fPv74Y0lmLlXnzAH9+vXTV199pTFjxig2NtZtup0KFSrkmr/xQmrWrKmFCxdecL8bbrhBs2fPVtOmTdWqVStt2LBBU6ZMyRrp79SwYUNFRkZqzpw5atasmcqXL6+aNWsqISFB9957r/7xj3+oUaNGCgsL09KlS7VlyxY9+uijBZ7bORr5p59+Uvfu3XM9vn//fq1Zs0aZmZlKTEzUpk2bNHPmTO3bt0/PP/98ns/JS//+/fXEE0/o1ltv1UMPPaTz58/rlVdeUUZGhtt+3bt3V+fOnfXwww/rzJkzatu2rX788cc858d9+eWX1bFjR3Xq1EkjRoxQfHy8kpOTtXPnTn3++ee5JlvfunVr1nXPzrkq17vvvqvevXtrwoQJqlevnr788ktNmzZNI0aMcOuvmp833nhDPXv21PXXX68hQ4aoVq1aOnHihLZv366NGzfqo48+KtR7lV3Lli01b948zZ8/Xw0aNFBERIRatmypnj176tSpU5o6dWqu1cQaNmzoNhvGH3/8kfW9nJiYqG+//VYzZsxQ27Zt1alTJ0nSddddp7CwMN122216+OGHdf78eU2fPl0nT57Ms04LFizQ9OnT1aZNGwUFBXm05dPb4uPjNWHCBI0ZM0a7d+9Wjx49VKlSJR09elRr165VVFSUnnzySZ/UZcuWLTp79myRZ+MokQsOwQow/p4NwLIs65VXXAOGZ8/2WzWAgHShEaSBLOcI34J06dIlz1HRkqxly5Zl7ZffPpIKNSI7+2wA+clrRP/JkyetO+64w4qLi7PKlStndezY0fr++++tLl265Drv3LlzraZNm1qhoaFZo+OPHj1qDRkyxGratKkVFRVllS9f3mrVqpX14osvuo3KzktGRoYVHx+fa2S6c2S38xYcHGxVqlTJatOmjTVy5Ehr27ZtuY51oWuyaNEi65JLLrEiIyOtBg0aWFOnTs01G4BlWdapU6esYcOGWRUrVrTKlStnXXfdddaOHTtyzQbgrOewYcOsWrVqWaGhoVbVqlWtDh06WE8//XS+ryXnzXkt9u3bZw0YMMCKjY21QkNDrSZNmlhTpkzJGsWf/Vj5zSzw888/W7fccosVFxdnhYaGWtWrV7euvvpq6/XXX7/g++QcvZ/9e3Lv3r1W9+7drejoaEtS1ij8wryevGYDiIqKsi6++GJr3Lhxuf62f/7551br1q2tiIgIq1atWtZDDz2UNTtD9jqdOHHC6tevn1WxYkXL4XBkXb+C3pu8rl1hDB48uFA/ewXJ7/1euHCh1a1bN6tChQpWeHi4Va9ePatfv37Wt99+63b+qKioPI8rybrnnnvctuX3HjivxUcffeS2/fHHH7eqVKlinT9/vsDX4MnZABz/V/lSIykpSTExMUpMTCzREoEl8b//Sc6lcydMkB5/3C/VAALS+fPntWfPHtWvX7/Yg2hQ+j3//PN65plndPDgQUVGRvq7OkCZkJGRoYsuukgDBgzQM888U+C+F/pdXpS8xgpWXlCnjqtchAVkAACFdM899ygmJqbYKz8BKLr3339fp0+f1kMPPeTT8xJWvaBuXVd5/37/1QMASquIiAi99957tl3bHiiNMjMzNWfOnELP+OEpDLDygthYKSJCOn+esAoA3tKxY0d17NjR39UAyoyhQ4f65by0rHqBw+FqXT1wwAy1AgAAQNERVr3EGVZPn5ZOnfJrVYCAVMrGfgJAmeLJ3+GEVS9hkBVQPM6VhbKvfw0ACCzO3+E5V4srDsKql2QPq3/+6b96AIEmODhYwcHBSkpK8ndVAADFlJSUlPX7vKQYYOUlNWq4ynks3QwgHw6HQ3FxcTp8+LDCw8MVFRXl9XWuAQCeYVmWzpw5o6SkJNWoUcMjv78Jq15Ss6arfOiQ/+oBBKKYmBidO3dOCQkJWUuOAgACg8PhUMWKFRUTE+OR4xFWvYSWVaD4HA6HatSoobi4OKWlpfm7OgCAIggNDfXIx/9OhFUvyR5WaVkFisdT/Z0AAIGLAVZeUq2amW9VomUVAACguAirXhIaKlWtasqEVQAAgOIhrHqRc5DV4cNSZqZ/6wIAABCICKte5Oy3mp4uHT/u37oAAAAEIsKqFzF9FQAAQMkQVr2I6asAAABKhrDqRYRVAACAkiGselG1aq7ysWP+qwcAAECgIqx6UVycq0xYBQAAKDrCqhcRVgEAAEqGsOpFhFUAAICSIax6UcWKZiUribAKAABQHIRVL3I4XK2rR4/6ty4AAACBiLDqZc6w+tdfLLkKAABQVIRVL3OG1fR06dQpv1YFAAAg4BBWvYxBVgAAAMVHWPUywioAAEDxEVa9jLAKAABQfIRVLyOsAgAAFB9h1csIqwAAAMVHWPUywioAAEDxEVa9jLAKAABQfIRVL8seVlnFCgAAoGgIq14WESFVqGDKtKwCAAAUDWHVB5ytq4RVAACAoiGs+oAzrJ46JaWm+rUqAAAAAYWw6gNVq7rKCQn+qwcAAECgIaz6QGysq3z8uP/qAQAAEGgIqz5QpYqrTMsqAABA4RFWfYCWVQAAgOIhrPoAYRUAAKB4CKs+kL0bAGEVAACg8AirPpC9ZZU+qwAAAIVHWPUBugEAAAAUD2HVBwirAAAAxUNY9YHKlV1lugEAAAAUHmHVB0JCpIoVTZmWVQAAgMIjrPqIsysAYRUAAKDwCKs+4py+6uRJKT3dv3UBAAAIFIRVH8k+yOrkSf/VAwAAIJAQVn2EGQEAAACKjrDqI9lXsWJGAAAAgMIhrPoILasAAABFR1j1EcIqAABA0RFWfSR7NwDCKgAAQOF4PaxOmzZN9evXV0REhNq0aaPvv/++wP3nzJmj1q1bq1y5cqpRo4aGDh2q46Ug3WVvWaXPKgAAQOF4NazOnz9fI0eO1JgxY7Rp0yZ16tRJPXv21P79+/Pc/4cfftCgQYN0xx13aNu2bfroo4+0bt06DR8+3JvV9Am6AQAAABSdV8PqCy+8oDvuuEPDhw9Xs2bN9NJLL6lOnTqaPn16nvuvWbNG8fHxuv/++1W/fn117NhR//73v7V+/XpvVtMn6AYAAABQdF4Lq6mpqdqwYYO6d+/utr179+5atWpVns/p0KGD/vzzTy1atEiWZeno0aP6+OOP1bt373zPk5KSoqSkJLebHdENAAAAoOi8FlYTEhKUkZGhatWquW2vVq2ajhw5kudzOnTooDlz5qh///4KCwtT9erVVbFiRb366qv5nmfixImKiYnJutWpU8ejr8NTwsOlqChTpmUVAACgcLw+wMrhcLjdtywr1zanX3/9Vffff7+eeOIJbdiwQV9//bX27Nmju+66K9/jP/bYY0pMTMy6HThwwKP19yRnVwDCKgAAQOGEeOvAVapUUXBwcK5W1GPHjuVqbXWaOHGirrrqKj300EOSpFatWikqKkqdOnXS008/rRo1auR6Tnh4uMLDwz3/ArwgNlbat8+EVcuS8snsAAAA+D9ea1kNCwtTmzZttGTJErftS5YsUYcOHfJ8ztmzZxUU5F6l4OBgSaZFNtA5+61mZEiJif6tCwAAQCDwajeA0aNH6+2339bMmTO1fft2jRo1Svv378/6WP+xxx7ToEGDsva/8cYbtWDBAk2fPl27d+/Wjz/+qPvvv19XXHGFatas6c2q+gQzAgAAABSN17oBSFL//v11/PhxTZgwQYcPH1aLFi20aNEi1atXT5J0+PBhtzlXhwwZouTkZE2dOlUPPPCAKlasqKuvvlr//e9/vVlNn8k512rDhv6rCwAAQCBwWKXh8/VskpKSFBMTo8TERFWoUMHf1XEzfrz05JOm/OWXUq9efq0OAACAXxQlr3l9NgC4ZG9ZPXHCf/UAAAAIFIRVH6pc2VUmrAIAAFwYYdWHKlVylU+e9F89AAAAAgVh1YdoWQUAACgawqoP0bIKAABQNIRVH6JlFQAAoGgIqz5UsaKrTMsqAADAhRFWfSg0VIqONmVaVgEAAC6MsOpjzn6rtKwCAABcGGHVx5z9Vk+ckErX2mEAAACeR1j1MWfLalqadPasf+sCAABgd4RVH2NGAAAAgMIjrPoYc60CAAAUHmHVx2hZBQAAKDzCqo/RsgoAAFB4hFUfo2UVAACg8AirPkbLKgAAQOERVn2MllUAAIDCI6z6WPawSssqAABAwQirPpa9GwAtqwAAAAUjrPoYLasAAACFR1j1sehoKTjYlGlZBQAAKBhh1cccDldXAMIqAABAwQirfuAMq3QDAAAAKBhh1Q+c/VZPnZIyMvxaFQAAAFsjrPpB9hkBEhP9Vw8AAAC7I6z6AQsDAAAAFA5h1Q9YchUAAKBwCKt+QMsqAABA4RBW/YCWVQAAgMIhrPoBLasAAACFQ1j1A1pWAQAACoew6ge0rAIAABQOYdUPaFkFAAAoHMKqH9CyCgAAUDiEVT+gZRUAAKBwCKt+EBEhRUaaMi2rAAAA+SOs+omzdZWWVQAAgPwRVv3E2W+VllUAAID8EVb9xBlWz52Tzp/3b10AAADsirDqJwyyAgAAuDDCqp9kn76KsAoAAJA3wqqfZG9Zpd8qAABA3girfkLLKgAAwIURVv2EllUAAIALI6z6CUuuAgAAXBhh1U+YDQAAAODCCKt+QssqAADAhRFW/YSWVQAAgAsjrPoJLasAAAAXRlj1k5gYV5mWVQAAgLwRVv0kOFiqWNGUaVkFAADIG2HVj5xdAWhZBQAAyBth1Y+cg6xOnJAyM/1bFwAAADsirPqRs2U1M1NKTvZvXQAAAOyIsOpHTF8FAABQMMKqHzF9FQAAQMEIq36UPazSsgoAAJAbYdWPsncDoGUVAAAgN8KqH9ENAAAAoGCEVT9igBUAAEDBCKt+RMsqAABAwQirfsQAKwAAgIIRVv2IAVYAAAAFI6z6Ed0AAAAACkZY9aPISCkszJTpBgAAAJAbYdWPHA5X6yotqwAAALkRVv3MGVZpWQUAAMiNsOpnzkFWp09Lqan+rQsAAIDdEFb9jOmrAAAA8kdY9TNWsQIAAMgfYdXPmL4KAAAgf4RVP6MbAAAAQP68HlanTZum+vXrKyIiQm3atNH3339f4P4pKSkaM2aM6tWrp/DwcDVs2FAzZ870djX9hlWsAAAA8hfizYPPnz9fI0eO1LRp03TVVVfpjTfeUM+ePfXrr7+qbt26eT7nlltu0dGjRzVjxgxddNFFOnbsmNLT071ZTb+iGwAAAED+vBpWX3jhBd1xxx0aPny4JOmll17S4sWLNX36dE2cODHX/l9//bVWrFih3bt3q/L/pbj4+HhvVtHvGGAFAACQP691A0hNTdWGDRvUvXt3t+3du3fXqlWr8nzOZ599prZt22ry5MmqVauWGjdurAcffFDnzp3L9zwpKSlKSkpyuwUSWlYBAADy57WW1YSEBGVkZKhatWpu26tVq6YjR47k+Zzdu3frhx9+UEREhD799FMlJCTo7rvv1okTJ/Lttzpx4kQ9+eSTHq+/rzDACgAAIH9eH2DlcDjc7luWlWubU2ZmphwOh+bMmaMrrrhCvXr10gsvvKDZs2fn27r62GOPKTExMet24MABj78Gb2KAFQAAQP68FlarVKmi4ODgXK2ox44dy9Xa6lSjRg3VqlVLMTExWduaNWsmy7L0559/5vmc8PBwVahQwe0WSCpWdJXtHlYtS3r/falPH+nmm6UvvvB3jQAAQGnntbAaFhamNm3aaMmSJW7blyxZog4dOuT5nKuuukqHDh3S6dOns7b9/vvvCgoKUu3atb1VVb8KCZGc+drO3QAsS/r3v6WBA6XPP5cWLJBuvFEaN87fNQMAAKWZV7sBjB49Wm+//bZmzpyp7du3a9SoUdq/f7/uuusuSeYj/EGDBmXtP2DAAMXGxmro0KH69ddftXLlSj300EMaNmyYIiMjvVlVv3L2W7Vzy+qbb0pvvZV7+4QJJrgCAAB4g1enrurfv7+OHz+uCRMm6PDhw2rRooUWLVqkevXqSZIOHz6s/fv3Z+1fvnx5LVmyRPfdd5/atm2r2NhY3XLLLXr66ae9WU2/q1xZ2rvXtKxalpRPl16/OXFCevRR1/1Zs6Rjx6RHHjH3R42SevWSIiL8Uz8AAFB6OSzLsvxdCU9KSkpSTEyMEhMTA6b/6rXXSt99Z8pJSVJ0tH/rk9OkSdJjj5ny4MHS7NkmVPfsKS1ebLY/95z0wAN+qyIAAAggRclrXp8NABdm57lWU1OlV181ZYdDGjvWVf7vf12twC+9JJXihcYAAICfEFZtwM6rWH3+uXTokCn/7W/SRRe5Hmvd2gyykqQ//5S+/tr39QMAAKUbYdUG7Nyy+uGHrvKIEbkf/9e/XOU33vB+fQAAQNlCWLUBu4bVs2ddc6nGxkpXX517nx49pDp1TPnrr+3XMgwAAAIbYdUG7NoN4KuvTGCVpJtuMnPC5hQcLP3jH6acnm66DQAAAHgKYdUG7Nqy+tVXrvLNN+e/3003ucrMuQoAADyJsGoDdmxZtSzJufhYeLjUuXP++7ZvL1WvbsqLF7taYwEAAEqKsGoDdmxZ/eMPybleQ8eOUkELiAUFuWYFOH9e+v5779cPAACUDYRVG7BjWP32W1f5uusuvH/37q7yN994vj4AAKBsIqzagB27Aaxc6Spfe+2F97/6atPCKhFWAQCA5xBWbSAqSgoNNWW7tKyuWWO+litnJv+/kMqVpcsvN+VffnEtJAAAAFAShFUbcDhcrat2aFk9fFjat8+UL7887ymr8pK9BZZ+qwAAwBMIqzbh7Ldqh5bVn35yla+8svDP69TJVf7hB8/VBwAAlF2EVZtwhtXkZCktzb91cXYBkIoWVtu3d/VbJawCAABPIKzaRPZBVqdO+a0akqTVq13ldu0K/7wKFaRWrUx5yxYpMdGz9QIAAGUPYdUm7DJ9VXq6tG6dKderJ9WoUbTnX3WV+ZqZ6d5CCwAAUByEVZuwy/RVW7dK586ZclG6ADh17Ogq0xUAAACUFGHVJuzSsrppk6vsnIqqKLKHVWYEAAAAJUVYtQm7hNWff3aVCzO/ak61a0t165ry+vVSRoZn6gUAAMomwqpN2KUbwJYtrnJxwqokXXGF+XrmjLR9e8nrBAAAyi7Cqk3YoWXVslwtqzVqSFWrFu842bsPOAdrAQAAFAdh1Sbs0LL655+ucxe3VVUirAIAAM8hrNpE9pbV48f9U4eS9ld1uuwyV5mwCgAASoKwahOxsa6yv7oBZO+v6pzcvzhiYqQmTUz555+llJSS1QsAAJRdhFWbqFRJcjhMOdBbViVXV4C0NDN3KwAAQHEQVm0iONjVbzUhwT91cIbV8HBXy2hx0W8VAAB4AmHVRpxdAfwRVlNSpD/+MOWLL5ZCQkp2vOxhdf36kh0LAACUXYRVG6lSxXxNTDQfn/vSH39ImZmm3KxZyY/XqpWrW0P27gUAAABFQVi1EX8Ostqxw1X2RFiNipIaNzblX36R0tNLfkwAAFD2EFZtxNmyKvl+kFX2sNq0qWeOeckl5mtKivTbb545JgAAKFsIqzaSvWXV1/1Wsy+L6qmwmn1Ggc2bPXNMAABQthBWbcQOLatBQVKjRp45prNlVSKsAgCA4iGs2oi/WlYzM11htUEDM3WVJxBWAQBASRFWbcRfLat//imdPWvKnuoCIEnVq0txcaa8ebNkWZ47NgAAKBsIqzbir5ZVT88E4ORwuFpXExKkQ4c8d2wAAFA2EFZtJHvLqi/DqjcGVzllH2TFfKsAAKCoCKs24q9uAN6YtsqJfqsAAKAkCKs2Urmyq1xaWlYJqwAAoCQIqzYSEiJVrGjK/mhZjYtzD8ye0LixFBFhyoRVAABQVIRVm3EOsvJVy+rJk9LRo6bs6VZVyQTwFi1MeedOKTnZ8+cAAAClF2HVZpz9Vk+dktLTvX++7MugeiOsSq6uAJYlbd3qnXMAAIDSibBqM86WVcsyrZ7etnOnq9y4sXfOQb9VAABQXIRVm/H1jADZw+pFF3nnHNmnr9qyxTvnAAAApRNh1WZ8vTCAL8Jqy5auMmEVAAAUBWHVZny9MED2sNqggXfOERMjxceb8pYtUmamd84DAABKH8KqzWRvWfVlN4DataXISO+dx9kV4MwZafdu750HAACULoRVm/Fly+rJk65A7K0uAE6tWrnKdAUAAACFRVi1GV+2rO7a5Sp7O6xmH2T188/ePRcAACg9CKs248uWVV8MrnJiRgAAAFAchFWb8eXUVb4Mqw0aSFFRpkzLKgAAKCzCqs1Uruwql6aW1aAg1xRWe/ZISUnePR8AACgdCKs2ExYmVahgyr5sWW3Y0LvnktwHWbHsKgAAKAzCqg05B1n99Zd3z+MMq9WrS+XLe/dcEoOsAABA0RFWbSguznw9cUJKT/fOOZKTpaNHTdnbXQCcGGQFAACKirBqQ86wKnmv36ovp61yyr7sKi2rAACgMAirNpQ9rB475p1z+HJwlVOFClL9+qa8dSvLrgIAgAsjrNpQtWqusvOjek/zR1iVWHYVAAAUDWHVhkpry6rkPiMAXQEAAMCFEFZtyNdh1RfTVjkxyAoAABQFYdWGfBlWq1SRKlb0zjnywvRVAACgKAirNuTtsHr2rHTwoCn7sguAZAZYOed0JawCAIALCfF3BZCbt8Nq9oFNvg6rzmVXV6+W9u6VEhOlmBjf1qGwzp6V/vc/afNmU+/WraU+faRy5fxdMwAAyg7Cqg3FxkoOh2RZ3gmr/hpc5dSqlQmrkpnCqmNH39fhQt55R3rwwdzz3FaqJD33nDR0qLlGAADAu+gGYEMhIa4lV0tjWLXzICvLkv7f/5OGDMl7QYaTJ6U77pDuvNN7q4sBAAAXwqpNOeda9XZYbdTI88e/EDsPsnrkEemVV1z3+/WTvvxS+uIL6ZZbXNtnzJCGDTPhFgAAeA9h1aac/VbPnpVOn/bssf3dsmrXZVfnzZOmTDFlh8ME0o8+knr1knr3lubPl+bOlUJDzT7vvSc99ZT/6gsAQFlAWLUpbw6ycobVSpWkypU9e+zCiI6WGjQwZbssu3rggPSvf7nuT51qWk5zuvVW6cMPXf1Vn3xS+uEH39QRAICyiLBqU94Kqykp0v79puyPVlUnZ1eAs2elXbv8Vw+nUaOk5GRT/uc/pREj8t+3b19Xi2pmptk/MdHrVQQAoEwirNqUt8Lqnj2ufpa+XLkqJzstu7p4sfTJJ6YcFye9+uqFR/o/+qjUqZMp79sn/ec/3q0jAABlFWHVprwVVv09uMrJLjMCZGRII0e67k+ZUrgVvYKDpXfflaKizP3XX/d/6AYAoDQirNqUL8KqP7sB2KVldf58accOU+7QQRo4sPDPjY+XHn/clDMzpfvuY3YAAAA8jbBqU9nD6tGjnjuuXcJq9mVX/dWympEhTZjguv/000Wf6H/kSFcL9fffSwsXeqp2AABAIqzalnOeVal0htWgIFfr6t69ZrJ9X5s/X/rtN1Pu3Fnq2rXoxwgPl55/3nX/iSfsMbsBAAClhdfD6rRp01S/fn1FRESoTZs2+v777wv1vB9//FEhISG65JJLvFtBm6pRw1U+dMhzx3WG1ehoqWpVzx23ONq0cZU3bPDtuS1LeuEF1/1x44q/fOoNN0jt2pnyL7+YuVkBAIBneDWszp8/XyNHjtSYMWO0adMmderUST179tR+59xJ+UhMTNSgQYN0zTXXeLN6tlaunGugj6fCalqaacWUTKuqv9e2b9vWVV63zrfnXrPGFZAvu0zq1q34x3I43BcHGDeOpVgBAPAUr4bVF154QXfccYeGDx+uZs2a6aWXXlKdOnU0ffr0Ap/373//WwMGDFD79u29WT3bc7auHj7smYE7e/eafpqSf7sAOF1+uau8fr1vz519SdX77y95cL/2WtdUVr/9Zla6AgAAJee1sJqamqoNGzaoe/fubtu7d++uVatW5fu8WbNmadeuXRo3blyhzpOSkqKkpCS3W2lRs6b5evas5ImXZZdpq5yaNHENsvJly+qhQ9LHH5ty1apS//4lP2bO1tX//pe+qwAAeILXwmpCQoIyMjJULftIIUnVqlXTkSNH8nzOH3/8oUcffVRz5sxRSEhIoc4zceJExcTEZN3q1KlT4rrbRfZ+q4cPl/x4dhlc5RQU5Oq3euCAZweSFeT1110f0//rX1JEhGeO26WLmf5KkrZtk7780jPHBQCgLPP6ACtHjs9XLcvKtU2SMjIyNGDAAD355JNq3LhxoY//2GOPKTExMet24MCBEtfZLpwtq5Jn+q3aLaxKvu8KkJ4uvf22KQcHF7ysanE88oir/N//evbYAACURV4Lq1WqVFFwcHCuVtRjx47lam2VpOTkZK1fv1733nuvQkJCFBISogkTJujnn39WSEiIli5dmud5wsPDVaFCBbdbaVEWwmr2QVa+CKuLF7taqfv0kWrV8uzxb7hBuvhiU/7xR3MDAADF57WwGhYWpjZt2mjJkiVu25csWaIOzs9Ks6lQoYK2bt2qzZs3Z93uuusuNWnSRJs3b1Y759xAZYi3ugGUKydVr17y43lC9pZVX/RbnTXLVR461PPHDwqSHn7YdZ/WVQAASqZwHUOLafTo0Ro4cKDatm2r9u3b680339T+/ft11113STIf4R88eFDvvvuugoKC1KJFC7fnx8XFKSIiItf2ssKTLavp6dKePaZsh2mrnOrXlypXlk6cMC2rluW9uiUkSJ99ZsrVqkk9e3rnPLfdJo0dK/35p/T552bu1TL6LQwAQIl5tc9q//799dJLL2nChAm65JJLtHLlSi1atEj16tWTJB0+fPiCc66WZZ5cGODAATPPqmSPmQCcHA5XV4CjR03A85YPPnC9BwMHSoUcw1dkYWHS6NGu+1OmeOc8AACUBQ7L8sQMnvaRlJSkmJgYJSYmBnz/1bNnpagoU+7USVq5svjH+uYb6frrTfmRR6RJk0peP08ZO1Z65hlT/ugjqV8/75zn0kulzZtNeds2V99Sbzh9Wqpb1ywjGxIi7dpl7gMAgKLlNa/PBoDi8+QqVnYcXOWUfe0Hbw1I2rzZFVSvuMK7QVUy88fee68pp6e7L+0KAAAKj7Bqc55axcrOYTX7eLsffvDOOWbPdpW9MbAqL/fdJ0VGmvJbb0nHj/vmvAAAlCaEVZvz1CpWdg6rlSpJzZub8qZN0pkznj1+aqo0Z44ph4dLt97q2ePnp2pV6Y47TPnsWWnqVN+cFwCA0oSwanOemhHAGVYjItyPaRcdO5qvGRnSTz959thffGFmApCkv//d1bXCFx54wCw+IEmvvur5IA4AQGlHWLU5T8wIkJEh7d5tyg0bmrlA7cYZViXPdwXw9tyqBYmPd7XkHj8uzZjh2/MDABDobBhbkF32FZYOHizeMfbtk1JSTLlJk5LXyRuuuspV9uQgqyNHpK++MuXataVrrvHcsQsr+xKszz/vmj7Lbg4fNit8zZ1ruk0sXWrePwAA/MmriwKg5LJPd7RvX/GOsWOHq2zXsBofb7onHDokrVplAl1oaMmP++67pmVZkgYPdn0k70stW0q9ekmLFkn790vz5pl5Xu3gwAHT2jtnjnu/5uyaN5f+8Q/pzjvt2YUEAFC60bJqc9nDanHXT/jtN1e5adOS1cdbHA6pSxdTPn1aWru25Me0LGnmTNf9IUNKfszievRRV/m//5UyM/1XF8l0SRg50nQLefLJ/IOqZOakHT9eqldPuusuzyz9CwBAYRFWbc4TYTUQWlYlqXt3V3nJkpIfb9UqV1Dv0sW/syB07OiaT3bbNjPoy1/+9z8zz+zLL7u6JAQFmYUnHnpIevFFcxs9WmrXztXHOT1deuMNqXFjafp0/wduAEDZQFi1udhY11ydnmhZtXNYve46V/mbb0p+vOytqs4ppPzF4XBvXX38cd+HvbQ06f77pb59pWPHzLbISOmxx0wXk5UrpcmTTYvryJGmf+2aNdKePdJ//mMWOpBMy/fdd5vrVdx+1AAAFBZh1eYcDlfr6v79xVsYwNmyWq2ab6dtKqpataRmzUx57VopMbH4x0pOlubPN+UKFaSbby55/Urqxhulyy835S1bpA8/9N25jx+XevQw02c59eljPv5/9lkz+Cw/deua5XB37ZKGD3dtX7pUuuwyacUK79UbAADCagBwhtWzZ6UTJ4r23FOnpKNHTdmu/VWzc3YFyMiQli0r/nE+/NA1p+ltt5mla/3N4TChz+nxx30zM8DBg6YbwtKl5n5oqPTmm9LChUUbMBUXZ1biWrzYFW6PHTMzLLz4YslWWAMAID+E1QBQkn6rgdIFwCl7V4DFi4t/nOxdAIYNK/5xPO3aa6WuXU155073OWC9Ye9eqXNnV+t6XJz5J+DOO014Lo7u3aXNm13XKiPD9G8dOtQ1RRoAAJ5CWA0AngqrgdCy2qWLWRJVkj77rHj9OrdtM4OrJKlFC9dH73aQs3V17FjT+u0Nf/xhgqpzQYgGDczqYNnntC2u2Fgzf+1jj7m2vfOO1K2bqyUfAABPIKwGgJLMtRooMwE4lS/varE7dEhat67ox5g61VUePrz4LYje0qGDmbdUkv76Sxo3zvPn+PVXE1QPHDD3mzQxA6ji4z13juBg09/1o49cgwBXrzb/HGze7LnzAADKNsJqAKhf31V2tpIVVqCFVUn6+99d5QULivbckyfNQgCSCb7+nFu1IM895wp4U6dKmzZ57tibN5sWaufqUy1bmkFQ2VdD86R+/cwSuc5+rAcOmNbbol47AADyQlgNAA0busq7dhXtuVu3mq/lyrmHXju78UbX3J4ffli0rgCzZpmBaJIJqjExHq+eR9StK40ZY8qZmdKgQdL58yU/7po15qP4hARzv00b00e1WrWSH7sgl11mZnBo187cP3vWzMDw9NMMvAIAlAxhNQDUrOnqx1mUsHrmjGv/5s1dAdDuqlZ1dQXYu1f6/vvCPS81VXrlFdf9e+/1eNU86qGHpNatTfmXX8zsACWxfLl535x9YNu3l777zvQv9YUaNUwdbr/dte3xx6UBA6Rz53xTh6JITTWtz/v3S3/+aab3YqEDALCfAIkvZVtQkKt1dffuwv9B3bbN1arVsqV36uYtgwe7yrNnF+4577zj6tPbu7f9uz2EhZkuC2Fh5v5zz5n+n8Xx5ZdSz55mwn5Juvpqs7CCr1uWIyKk996TJk509RWeN8/0n/XXAgKZmdLPP5sVu+64Q7rySqlSJfMPYI0aZhnZOnWkKlXMtahd24T+UaPM99SePf6pNwDAcFhW6fqQLikpSTExMUpMTFSFChX8XR2P6dNH+vxzU96/3/xxvZAZM1yTuL/4olmVKFCcOydVry4lJZm+nQcOFNxCmJpqwuneveb+Tz9JV1zhk6qW2AsvSA88YMqRkaZ/aWFnMLAs6aWXpAcfdP0T07u3+6Anf/nf/0wrq3O+2xo1pE8/dXUV8KZz56RFi8z78O23ptW0JOrVM/PJ/u1vJsj6+70FgEBXlLxGy2qAKE6/VWd/VSnwWlYjI12Do86dk6ZNK3j/WbNcQbVHj8AJqpJpwRs0yJTPnTNzsRam60NSkpnbdPRoV1C95RYzsMkOYepvfzNTiNWrZ+4fPmwGXv3nP57pn5tTSor5h+6f/zTzyfbrZ1Yxyyuo1qtnZmW48UYzM8PNN5vvm0svzfufon37zNy9f/ub6abSr5/0wQfmGgAAvIuW1QAxdap0332m/PbbhVvr/pprXKsWHTtm/sgGkj17pIsuMkGsalUziX5el/TECalxY1coWb3afNQbSFJSzGT7K1ea+xERplvAiBG5+xpblll9atQo96nMxo6VnnzSfn2T//pLuukmM2OAU6NGZtqrm28u2dRiaWmmX+6HH5qQntcSvRUrmtkRrr7afF9cfLGZKSI/lmV+XrZuNQPWli0zoTuvgB0WJl1/vQm8ffrYd0AfANhNUfIaYTVAfPWV1KuXKT/6qOkTeCFxcSYoVKvmmsYo0Nx2m+nzKEkPPyz997+59xk82DVd1W23mRavQHTmjGmx+/pr17aLLzYtzK1aufpezp0rbdni2icqyiyDetttPq9yoaWlSZMmSU895b7E7GWXmX/Cbrml8EvinjtnQv3HH5uAmtcSxBUrmoDcv78JqSEhJav/uXMmFH/6qVmswjnbQnahoeYfDmdwrVSpZOcEgNKMsFoKw+ru3a6uAH37mj+aBTl61PT5lMzHykuWeLV6XrN7twlsKSkmDKxc6d5qOnOmq5W5fHmzYldR1ru3m9RU87H+a68Vbv8uXUwXiECZluyXX0xrcfZWVsm0mHfvbpaibd7cXMPy5c0UWKdOmVb1bdvM81atMu9TTtHR5mP6/v3NsZwD1zwtPV368UcTlj/5xHRvyCk01Pzc9e1rwnLDht5fnCItzdTlwAEzu8GBA2ZQW3Ky+Ufo7FkTuh0OE96dt5gYqXJl1y0uzkytVreu+UTDbotqACgdCKulMKxmZpo/3ufOmY9Qf/+94P2//dY1/dOoUWYQT6B6/HEzX6dkWok/+0xq21Z6/XXTKufsrzlnjpkmqTRYvtzMw+pcNjandu3M+9KrV+CFCcsynxSMGVPyla6ioky/0/79zcfxvu6rm5lprtHHH5tbfjMe1Kpl/rG49FKzBHDLliaQF/banTtngujhw+YcBw64h9I//zSfnnh66q2ICFdwjY83/xQ5v9avb34eA+37D4A9EFZLYViVzATvGzeaPolnzpg/JPmZPFl65BFTnjXLvis5FUZammkpW77c3A8KMoNg/vrLtc+990qvvuqX6nnV77+b1713r2sKs44dzT8sgc6yTJ/Qt94ynxQ454e9kAYNTGtljx5muq7Cdh/wtsxMMwvFRx+Z4Opc6jY/4eHm048aNUzoDgsz2zIyzM/3mTNmANfhw4V/b3wtIsI9vGYPs9WqmZ9Tb/0DYVnmd0NKiut2/nz+9zMyXO9xRIT56ixHRprW/fLlzTLCALyPsFpKw+qgQWYOS8m0SDknlM/LP/5h/mBKZqBIixZer55XJSSYfoCrV+d+7P77zdRcdhtYhMLLyJA2bDD/jO3YYa73mTMmxEVHm9H7zZpJl1ziml3AzixLWrfO9HNdvtx0X3CurOYN1aub6exq1879tVIlE+jLlXMFx/R0856nppogfOKE63b4sJkeb98+11fn9GPFERlpQmtsrKmDMyQ6b876OOuUnp47hOYVRFNSvLM6WlSUCa7R0Xl/dYZa53saFZX31/BwE3yz30JCXGXLMv/gZGS4bvndz8x03ZzPy1nOeT9ndw/nuXNuy3kLDTVfaTGHtxFWS2lYnTRJeuwxU77QR97x8eaPTFSUGSFdGloL0tJMd4Y5c0yrasuWpvX4mmv8XTOgYGlp5p/GX35xff3zTxMMC5oDtlw5V+trzZqur3XquG41a3qvf65kAtCJE6Z1f+9eM0vHnj2u8t699lyhDCWTPdg6A6zza37lnNscDtc/FDm/5rUtr32csofnvMqFedzhMI0aeZWL+5gn9svrdRT0ujz9Nee2Ro1M45C3FSWvlXCMLHypeXNXedu2/Pf76y/XlEaXXVY6gqpkfvk98oirewMQKEJDzc/iZZflfiw93b3VMDjY1Wpnh59dh8PVMtqmTe7HnVN9ZQ+xe/ea1vHjx91veQ2MK0hwsHsrbPaP74u6LSTEnD+vFtozZ8xAtKQk11dnuawuwets3U1J8XdN4Gt//7tvwmpREFYDSPawmn3C/5zWrXOVC7sSEgD/CAkxHysXNPernTkcpn9qtWoXnt84MzN3UHR+XJ29Jc8ZUks65VhJWZbpvpEzxDr7FJ89a27OcvavqanuH+k7uzg4yw6HeZ1BQe5dBfK679zmbJFztso5yznvO1s0nefMecu5PS0t7/vZt+dVzvk8lA527AJCWA0g8fFm/shTp6S1a80vo7y+qdascZUJqwDsIijI9GG1wwprheFwmK5UUVGmCwby5wzH2YOsU0k+2nYeu6ByYR7PfnP2781ZLu5jJdkvr9dRnC4Txf2a1/tUq5Zsh7AaQIKCTPhcssTMo3rggJlSJqcVK1zlq67yXf0AAGVT9gFdgKcxfjrAtGvnKv/0U+7Hz51ztaw2bGgGYAAAAAQqwmqAuVBYXb3aNYiha1efVAkAAMBrCKsB5oorXOW85hxdtsxVJqwCAIBAR1gNMHFxUuPGpvzTT9LJk+6Pf/65q9ytm+/qBQAA4A2E1QDUu7f5mpEhLV7s2r5zp/Tzz6Z8+eX2HNEHAABQFITVAHTDDa7yl1+6yp984irffLPv6gMAAOAthNUA1LGjWadakr74wswAYFnSe++59iGsAgCA0oCwGoDCwqS+fU351Clpzhzpq69cS7B26CBddJG/agcAAOA5hNUAdffdrvLo0dLAge73AQAASgPCaoC68krpb38z5eRk6cQJU27fXrrpJv/VCwAAwJMIqwHsrbekpk1d9+PjpXnz3NdUBgAACGSs4hvAqlaVNm6UFi4001j97W+ugVcAAAClAWE1wEVGSrfd5u9aAAAAeAfdAAAAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG15PaxOmzZN9evXV0REhNq0aaPvv/8+330XLFig6667TlWrVlWFChXUvn17LV682NtVBAAAgE15NazOnz9fI0eO1JgxY7Rp0yZ16tRJPXv21P79+/Pcf+XKlbruuuu0aNEibdiwQd26ddONN96oTZs2ebOaAAAAsCmHZVmWtw7erl07XXbZZZo+fXrWtmbNmqlv376aOHFioY7RvHlz9e/fX0888USh9k9KSlJMTIwSExNVoUKFYtUbAAAA3lOUvOa1ltXU1FRt2LBB3bt3d9vevXt3rVq1qlDHyMzMVHJysipXrpzvPikpKUpKSnK7AQAAoHTwWlhNSEhQRkaGqlWr5ra9WrVqOnLkSKGO8fzzz+vMmTO65ZZb8t1n4sSJiomJybrVqVOnRPUGAACAfXh9gJXD4XC7b1lWrm15mTt3rsaPH6/58+crLi4u3/0ee+wxJSYmZt0OHDhQ4joDAADAHkK8deAqVaooODg4VyvqsWPHcrW25jR//nzdcccd+uijj3TttdcWuG94eLjCw8NLXF8AAADYj9daVsPCwtSmTRstWbLEbfuSJUvUoUOHfJ83d+5cDRkyRB988IF69+7treoBAAAgAHitZVWSRo8erYEDB6pt27Zq37693nzzTe3fv1933XWXJPMR/sGDB/Xuu+9KMkF10KBBevnll3XllVdmtcpGRkYqJibGm1UFAACADXk1rPbv31/Hjx/XhAkTdPjwYbVo0UKLFi1SvXr1JEmHDx92m3P1jTfeUHp6uu655x7dc889WdsHDx6s2bNne7OqAAAAsCGvzrPqD8yzCgAAYG+2mGcVAAAAKCnCKgAAAGyLsGpXGRnSrFnSTTdJt98uffONv2sEAADgc14dYIViSk2Vbr5Z+uIL17YPPpDGjpWeesp/9QIAAPAxWlbt6IEH3IOq09NPm9AKAABQRhBW7WbDBum110w5PFz63/+k555zPT5ypHT8uF+qBgAA4GuEVbsZNUpyzib2zDNSnz6mpfXmm822v/6Snn3Wf/UDAADwIcKqnfz0k/T996bcuLF0332ux155RYqIMOU335ROnfJ59QAAAHyNsGonr7ziKj/6qBQW5rpfs6Y0eLApnz4tvfWWb+sGAADgB4RVu0hIkD76yJRjY6Vbb829z+jRrvLMma7uAgAAAKUUYdUuPvlESksz5aFDpcjI3Ps0bix17mzKO3ZIGzf6rn4AAAB+QFi1i3nzXOUBA/Lf7/bbXeU5c7xXHwAAABsgrNrB4cPSihWm3LixdMkl+e/7j3+4+rJ+9BFdAQAAQKlGWLWDRYtcofMf/5Acjvz3rVRJ6tbNlP/8U9q61fv1AwAA8BPCqh18/bWrfMMNF97/xhtd5bxWugIAACglCKv+lp4uLVliypUqSZdffuHn9O7tKn/+uXfqBQAAYAOEVX9bu1ZKTDTl666TgoMv/Jz4eKl589zPBwAAKGUIq/6WvQtAjx6Ff94115ivmZmuVa8AAABKGcKqv2UPq927F/55zkFWkrRsmefqAwAAYCOEVX9KSpI2bDDlFi2kWrUK/9zOnV2zBhBWAQBAKUVY9adVq8zH+JLUpUvRnlu5stS6tSlv3iydPOnRqgEAANgBYdWffvjBVe7UqejP79rVfLUsaeVKj1QJAADATgir/pR9YFTHjkV/fvZ+q8uXl7g6AAAAdkNY9ZeUFOmnn0y5fv2i9Vd1yh5wV6/2TL0AAABshLDqL+vXm8AqFa8LgGT6rTZpYsqbNrmOBwAAUEoQVv2lpP1Vna680nxNTTUDrQAAAEoRwqq/rFrlKhenv6pTu3au8po1xT8OAACADRFW/WXdOvM1JkZq3Lj4x3G2rEqEVQAAUOoQVv3h4EHp8GFTbttWCirBZWjZUoqMNGXngC0AAIBSgrDqD85WVUm6/PKSHSskxAReSdqzRzp2rGTHAwAAsBHCqj94MqxK7l0BaF0FAAClCGHVHzwdVrMfY+PGkh8PAADAJgirvmZZZo5VSapWTapdu+THvPRSV5mwCgAAShHCqq/t2iWdPGnKl18uORwlP2aDBlKFCqa8aVPJjwcAAGAThFVf83QXAMnMJuBsXT1wQEpI8MxxAQAA/Iyw6mveCKuSe1cAWlcBAEApQVj1NW+F1csuc5XptwoAAEoJwqovpae7gmR8vFSliueOzSArAABQChFWfWn7dunsWVP2ZKuqJDVtKkVEmDLdAAAAQClBWPUlb3UBkMxKVq1amfIff0hJSZ49PgAAgB8QVn3Jm2FVcu+3unmz548PAADgY4RVX3KGVYdDatPG88fPHlbpCgAAAEoBwqqvpKRIW7aYctOmUnS058/B9FUAAKCUIaz6ys8/S2lppuyNLgCS1Ly5WSBAkrZu9c45AAAAfIiw6ive7q8qSZGRUqNGprxtm5kqCwAAIIARVn3FF2FVcs0IkJIi7dzpvfMAAAD4AGHVV5xhNSREat3ae+dp2dJVdvaRBQAACFCEVV9ITjYLAkim5dM5eb83OFtWJcIqAAAIeIRVX9iwQbIsU/ZmFwDJPawyyAoAAAQ4wqovrF3rKrdr591z1asnlS9vyrSsAgCAAEdY9YWffnKVr7jCu+cKCnL1W927l2VXAQBAQCOs+oKzZTU62iwI4G3ZuwL88ov3zwcAAOAlhFVvO3RI+vNPU27bVgoO9v45mREAAACUEoRVb8veX9XbXQCcmBEAAACUEoRVb8veX9Xbg6ucsresMiMAAAAIYIRVb/NHy2rFilKdOqa8datr2iwAAIAAQ1j1powM18pVtWqZm684uwIkJkoHDvjuvAAAAB5EWPWm334zq1dJvmtVdaLfKgAAKAUIq97ky/lVc2JGAAAAUAoQVr3p++9d5fbtfXtull0FAAClAGHVm1auNF/Dwnw3E4BT48ZSaKgp07IKAAACFGHVWw4elHbtMuV27aSICN+ePzRUuvhiU/7tN+n8ed+eHwAAwAMIq96SvQtA587+qYOzK0BGhrR9u3/qAAAAUAKEVW9xdgGQ/B9WJfqtAgCAgERY9RZnWA0O9v3gKiemrwIAAAGOsOoNx45J27aZ8mWXSdHR/qkHYRUAAAQ4wqo3LF7sKl99tf/qUa2aVLWqKRNWAQBAACKsesNXX7nKPXv6rx4Oh6t19ehRcwMAAAgghFVPy8hwtaxWqCB16ODf+jDICgAABDDCqqetXi2dOGHK117rmpjfX+i3CgAAAhhh1dPmzXOV//Y3/9XDibAKAAACGGHVk9LTpY8+MuWICKlvX79WR5LUrJkU9H+XmbAKAAACDGHVkxYvNtNWSVLv3qbPqr9FRkqNG5vyr7+aQA0AABAgvB5Wp02bpvr16ysiIkJt2rTR99mXIc3DihUr1KZNG0VERKhBgwZ6/fXXvV1Fz3nuOVd54ED/1SMnZ1eAlBTpjz/8WxcAAIAi8GpYnT9/vkaOHKkxY8Zo06ZN6tSpk3r27Kn9+/fnuf+ePXvUq1cvderUSZs2bdJ//vMf3X///frkk0+8WU3PWLNGWr7clBs3lm64wa/VcUO/VQAAEKC8GlZfeOEF3XHHHRo+fLiaNWuml156SXXq1NH06dPz3P/1119X3bp19dJLL6lZs2YaPny4hg0bpueyt1jaUVKSdNddrvsPPWSWWbULwioAAAhQXgurqamp2rBhg7p37+62vXv37lq1alWez1m9enWu/a+//nqtX79eaWlpeT4nJSVFSUlJbjefsSzTN7VaNennn822Fi2kQYN8V4fCIKwCAIAA5bWwmpCQoIyMDFWrVs1te7Vq1XTkyJE8n3PkyJE8909PT1dCQkKez5k4caJiYmKybnXq1PHMCygMh0NKTpbOnzf3K1Y0U1eFhfmuDoVRt65rsBdhFQAABBCvD7ByOBxu9y3LyrXtQvvntd3pscceU2JiYtbtwIEDJaxxEV11lQmDgwdL69ZJzZv79vyFkX3Z1f37pVOn/FodAACAwvJaWK1SpYqCg4NztaIeO3YsV+upU/Xq1fPcPyQkRLGxsXk+Jzw8XBUqVHC7+dSECdK+fdLs2dJFF/n23EXBsqsAACAAeS2shoWFqU2bNlqyZInb9iVLlqhDhw55Pqd9+/a59v/mm2/Utm1bhfp72dL82LVeOdFvFQAABCCvdgMYPXq03n77bc2cOVPbt2/XqFGjtH//ft31fyPnH3vsMQ3KNhjprrvu0r59+zR69Ght375dM2fO1IwZM/Tggw96s5plA2EVAAAEoBBvHrx///46fvy4JkyYoMOHD6tFixZatGiR6tWrJ0k6fPiw25yr9evX16JFizRq1Ci99tprqlmzpl555RXdfPPN3qxm2dCihatMNwAAABAgHJZzBFMpkZSUpJiYGCUmJvq+/6rdNWwo7d4tlS8vJSZKQay2CwAAfK8oeY20UpY4uwKcPi3t3evXqgAAABQGYbUsadnSVabfKgAACACE1bIk+yCrzZv9Vg0AAIDCIqyWJZde6ipv3Oi/egAAABQSYbUsadBAiokx5Q0b/FsXAACAQiCsliUOh3TZZaZ86JCUY7UwAAAAuyGsljVt2rjKdAUAAAA2R1gta7KHVboCAAAAmyOsljXObgASLasAAMD2CKtlzUUXSdHRpkzLKgAAsDnCalkTFORqXT1wQPrrL//WBwAAoACE1bIoe1cAWlcBAICNEVbLImYEAAAAAYKwWhZlD6vr1/uvHgAAABdAWC2LGjVyDbJau9a/dQEAACgAYbUsCg6WrrjClA8elP7807/1AQAAyAdhtay68kpXec0a/9UDAACgAITVsoqwCgAAAgBhtawirAIAgABAWC2rqlQxq1lJZq7V1FT/1gcAACAPhNWyzNm6ev68tGWLf+sCAACQB8JqWZa9K8CPP/qvHgAAAPkgrJZlHTu6yitX+q8eAAAA+SCslmUtW0qVKpnyypVSZqZ/6wMAAJADYbUsCwqSOnUy5YQE6ddf/VsfAACAHAirZV2XLq7yihX+qwcAAEAeCKtlXdeurjJhFQAA2Axhtaxr3VqKiTHlFSsky/JvfQAAALIhrJZ1wcGuWQGOHZN++cW/9QEAAMiGsAqpe3dX+auv/FcPAACAHAirkHr2dJX9EVbT06UTJ1jyFQAA5EJYhdSokdSwoSn/8IOUlOT9c6amSu+8YwZ4RUVJsbHma9u20tSpZglYAABQ5hFWYfTqZb6mp0vffuvdc61ZI11yiTRkiBnU5WxRTU+XNmyQ7rvPLFiwerV36wEAAGyPsAoje1eARYu8d54ZM6TOnaXt213bGjSQrr9euvhi17adO02r69y53qsLAACwPcIqjK5dpYgIU/7sM9PK6WlPPikNHy6lpZn7l19uuh3s2iV9/bW0bZu0caPUoYN5PDVV+uc/pY8+8nxdAABAQCCswoiMlHr3NuW//pKWLfPs8Z99Vho/3nX//vulVaukq65y3+/SS6Xly02olaTMTGngQBNiAQBAmUNYhcutt7rKnvz4feZMacwY1/0XX5ReflkKCcl7/9BQ6Y03pKFDzf2UFOmmm6Tjxz1XJwAAEBAIq3Dp3VuKjjblBQtMSCypH3+U7rrLdX/KFGnkyAs/LyhImj5datfO3N+3TxoxouT1AQAAAYWwCpfISKlvX1NOTDT9SEti/37TIurso3r//dKDDxb++eHh0scfm2mtJNN3deHCktUJAAAEFMIq3N12m6v81lvFP86ZM9Lf/maWcJWka6+Vnn++6MepXdt0GXC6+24TpAEAQJlAWIW77t2lunVNedEiaffuoh/Dsswcqps3m/sNG0rz5+ffR/VCBgxwzQN7+LD0zDPFOw4AAAg4hFW4Cw529TG1LLOaVFE99ZT5+F4yfWA/+0yqXLn4dXI4pNdeM90CJNPSumdP8Y8HAAACBmEVuQ0f7ppz9fXXpSNHCv/cDz+Uxo0zZYdD+uAD98n+iys+Xho1ypRTU6VHHy35MQEAgO0RVpFb1aqu1tVz58wcqYXx00/S4MGu+xMnSjfc4Ll6PfaYqZtkQvGWLZ47NgAAsCXCKvL2yCNmdgDJfAS/aVPB++/YIfXpI50/b+4PHSo9/LBn61ShgjR2rOv+hAmePT4AALAdwiryVr26KxhmZpoW09On8973t9+kbt1cI/87dzbdBxwOz9frzjtN3STpk09oXQUAoJQjrCJ/Dz4otWhhylu3SrfcYqakyu7rr6X27V39Wi+5RPr0UykszDt1iow0rb5OTz3lnfMAAABbcFiWZfm7Ep6UlJSkmJgYJSYmqkKFCv6uTuDbsUO68krX3KaNG5u5TqOjzQT9n3/u2rd1a+m771yT+HvLuXNS/frS0aPm/rZtnhnEBQAAfKIoeY2WVRSsaVMTSp3fSL//bpZLveMO96B6ww3SypXeD6qSaV3N3h+2OIsNAACAgEBYxYV17SqtWSN17Jj7sZo1pbfflv73P1eg9YU775RiYkz5/feLNr0WAAAIGMVcUghlTrNmpuV0wwZp3TopLc189N61a/FXpiqJ6GgzvdZ//2vmXZ06VXr6ad/XAwAAeBV9VhG4Dh40fVfT0qRKlaQDB6SoKH/XCgAAXAB9VlE21Kol3XabKZ88Kc2a5d/6AAAAjyOsIrA98ICr/MILUkaG/+oCAAA8jrCKwNaqldS9uynv2WMGegEAgFKDsIrAN3q0q/zii/6rBwAA8DjCKgJf9+6uRQF++MHMVgAAAEoFwioCn8MhjRrluk/rKgAApQZhFaXD7bdLVaqY8ocfmmmsAABAwCOsonSIjJTuvtuUMzLMIgEAACDgEVZRetx9txQWZspvvCGdPu3f+gAAgBIjrKL0qFZNGjDAlBMTpdmz/VodAABQcoRVlC7ZB1q9/DKLBAAAEOAIqyhdWrWSrrnGlHfulL74wr/1AQAAJUJYRenDIgEAAJQahFWUPj16SE2amPKKFdLGjf6tDwAAKDbCKkqfoCBp5EjXfVpXAQAIWIRVlE6DBkmVK5vyvHnSwYP+rQ8AACgWwipKp3LlpLvuMuX0dOm11/xbHwAAUCyEVZRe99wjhYaa8htvSGfO+Lc+AACgyAirKL1q1pRuvdWUT5yQZszwb30AAECREVZRuj3wgKs8aZJ0/rz/6gIAAIqMsIrSrXVrqW9fUz58WHrrLb9WBwAAFI1Xw+rJkyc1cOBAxcTEKCYmRgMHDtSpU6fy3T8tLU2PPPKIWrZsqaioKNWsWVODBg3SoUOHvFlNlHbjxrnKEyfSugoAQADxalgdMGCANm/erK+//lpff/21Nm/erIEDB+a7/9mzZ7Vx40Y9/vjj2rhxoxYsWKDff/9dffr08WY1Udpdcon097+b8uHDZrAVAAAICA7LsixvHHj79u26+OKLtWbNGrVr106StGbNGrVv3147duxQE+cKQxewbt06XXHFFdq3b5/q1q17wf2TkpIUExOjxMREVahQoUSvAaXIzz+b0CpJsbHSH39IlSr5tUoAAJRVRclrXmtZXb16tWJiYrKCqiRdeeWViomJ0apVqwp9nMTERDkcDlWsWNELtUSZ0bq1NGCAKR8/Lj39tH/rAwAACsVrYfXIkSOKi4vLtT0uLk5Hjhwp1DHOnz+vRx99VAMGDMg3daekpCgpKcntBuRp0iQpMtKUX33VtK4CAABbK3JYHT9+vBwOR4G39evXS5IcDkeu51uWlef2nNLS0nTrrbcqMzNT06ZNy3e/iRMnZg3giomJUZ06dYr6klBW1KkjPfigKaelSaNHS97pBQMAADykyH1WExISlJCQUOA+8fHx+uCDDzR69Ohco/8rVqyoF198UUOHDs33+Wlpabrlllu0e/duLV26VLGxsfnum5KSopSUlKz7SUlJqlOnDn1WkbfTp6XGjc1AK0maP1+65Rb/1gkAgDKmKH1WQ4p68CpVqqhKlSoX3K99+/ZKTEzU2rVrdcUVV0iSfvrpJyUmJqpDhw75Ps8ZVP/44w8tW7aswKAqSeHh4QoPDy/ai0DZVb689PLLroB6773S1VdLhfieBgAAvue1PqvNmjVTjx49dOedd2rNmjVas2aN7rzzTt1www1uMwE0bdpUn376qSQpPT1d/fr10/r16zVnzhxlZGToyJEjOnLkiFJTU71VVZQ1/fq5prL66y/pjjvoDgAAgE15dZ7VOXPmqGXLlurevbu6d++uVq1a6b333nPb57ffflNiYqIk6c8//9Rnn32mP//8U5dccolq1KiRdSvKDAJAgRwOado0qXJlc/+zz6TnnvNvnQAAQJ68Ns+qvzDPKgrtq6+kXr1MOTjYhFbnfQAA4DVe7bMKlBo9e0pjx5o5VzMyTPeAb76ROnb03DnS06Xt26UNG6SDB6WjR6XUVKlcOal2balZM6lDBykmxnPnBACgFCGsomwbP176/Xfpww+lc+ek66+X5s2Tbryx+Mc8dUr6/HPpk0+kb7+VzpwpeP/gYOmqq6ShQ6V//EOKiir+uQEAKGXoBgCkpppw+s035r7DIf3nP6bVNSKicMf46y9p4UJpwQLpu+/MPK7FUaWK9Mgj0j33uBYwAACglClKXiOsApJ0/rw0ZIiZd9WpYUOzcMCAAVLO5X7T06Vff5UWLzZ9XVetkjIzcx83Lk7q0kW6/HKpUSOpWjUTQk+flnbvltavl77+OvdqWg0aSG+8IV17radfKQAAfkdYJayiODIzpWeflZ580oRRJ4dDuvhiqWZN85H9X3+ZoHruXN7HqVtXuukmc+vQwTynIJYl/fSTmf91/nz3abTuuMNsp2sAAKAUIawSVlESv/wi/b//Jy1dWvjnNGsm/e1v0s03S23amIBb3HPffbf0/feubc2bSx9/LDVtWrxjAgBgM4RVwio84eefpdmzpRUrpC1bzIwBkgmi9etLbdtKV14p3XCD+YjfUzIzpbffNl0QnIOzypc3ra5MrQUAKAUIq4RVeFpmppSUZL7GxFz4o31P2LHDTKe1bZu5HxQkvfaadNdd3j83AABeVJS85tUVrIBSIyjIDLKqXNk3QVUyH/v/9JMJrJIJyiNGmNkCStf/mAAA5IuwCthZVJT5+P+hh1zbJk+W/v1vV7cEAABKMcIqYHdBQSagTptmypL01lvSoEHFn88VAIAAQVgFAsWIEdIHH0gh/7fw3AcfmBWvUlL8Wy8AALyIsAoEkv79zSpZYWHm/v/+J/XpI5096996AQDgJYRVINDceKP05ZdSuXLm/jffSNdfLyUm+rdeAAB4AWEVCETXXmuWenVO9/HDD9LVV5vVtQAAKEUIq0Cg6tjRrLIVG2vub9wode4sHTzo33oBAOBBhFUgkLVpI61cKdWsae7v2GFC7K5d/q0XAAAeQlgFAt3FF5tuAA0amPt795rAum6dX6sFAIAnEFaB0qB+fen776Xmzc39I0ekLl3MzAEAAAQwwipQWtSsKa1YYVpVJencOenmm6VnnjFLtQIAEIAIq0BpEhsrffutNHCga9vYsVKPHtLRo/6rFwAAxURYBUqb8HDpnXdMi6rDYbYtWSK1bi198YV/6wYAQBERVoHSyOGQ/vMf08pavbrZdvSoWVDgppuk/fv9Wz8AAAqJsAqUZldfLf38s9Szp2vbp59KjRpJ99wjHTjgv7oBAFAIhFWgtIuLM8uzvv++KUtSaqo0bZqZReDGG6VPPpGSk/1bTwAA8uCwLMvydyU8KSkpSTExMUpMTFQF51KUAIxTp6RJk6SpU6UzZ9wfCwmR2rUzCw1cfLFUp45UqZJUsaLpVpCebm7nz0unTxf+FhZmjlO5slS3rtSwoWnZbdpUCg31x7sAAPCzouQ1wipQFiUkSK++Ks2cKf35p3/qEBkpXXaZ1L69dP31UqdOZnAYAKDUI6wSVoHCyciQvvtO+uwzM2PA77/7ry7ly0vXXmsGgP397+Y+AKBUIqwSVoHi+esvads2aft26dgx6cQJ03XA4TDdBEJCTOtndLQJk+XLu5dz3qKiTLeBkydNa+7evdLOneYca9dKe/bkXY9y5aS+faV//lO67jpzXgBAqUFYJawCgeHYMWn5cjMA7KuvTFjOqXp1afBgadgwqXFjn1cRAOB5hFXCKhB4MjOl1aulOXOk+fNNq25OnTqZ0NqvH90EACCAEVYJq0BgS001La3vvCN9/rmZhSC78uWlW281wfXKK10rdQEAAgJhlbAKlB7Hjpk5YmfMkH79NffjzZqZ0DpwoFStmu/rBwAoMsIqYRUofSzLDMqaOVOaOzf3IgYhIdINN5jg2rMng7IAwMYIq4RVoHQ7c8asujVjhrRyZe7Hq1eXbrnFzN/atauZXQAAYBuEVcIqUHb88Yc0e7a5HTqU+/HwcKlDB9O39YorzK1GDfq5AoAfEVYJq0DZk54uffONaW397LPcg7Kyi4mRLrrILPtap45UtarrVr68FBFhQq7z5pxj1nkLDna/HxRE+AWAIiCsElaBsi0xUVq6VPr6a2nxYmnfPu+fM2egDQ2VYmOlKlVcQbhGDal+fSk+3txq1aJvLYAyibBKWAWQ3cGDZnDW2rXSxo2m68C+fWZuV38KCTGh9eKLza15c/O1aVP62QIo1QirhFUAF5KSYpZ/PXzYrJzlvJ09a5aITUlx3TIyTLeC/G55PX7+vHT8uHT6dNHr5nCYFlhngHWG2GbNCLEASgXCKmEVgF2cPy8lJJgg/OefJiDv2eP6+vvvJiAXhsNhWmKzB9jmzaUmTVjRC0BAIawSVgEEisxMaf9+s+DBtm3mq/NWlFbZ2FipXj0TZuvVM7fq1d0Hj8XGmr60AOBnhFXCKoBAZ1nSgQMmwDpDrPNrcboWOEVGSlFR7rdy5cwMCDkHiBV0PzzcPCcy0nzNeStXTqpY0cy8ULGiVKGCmUUBAFS0vMYwVACwI4dDqlvX3Hr2dG23LPeW2G3bpN27TbeCP/+88KCxc+fMLSHBq9XPU3S0K7w6b5UrS5UqmVt+5UqVpLAw39cXgC3QsgoApUVampn5YN8+czt2zNVf9q+/TDk52awAduaM6St75owJwHYXFVW4YOtsJc7Z6ussh4W55skNDjY35sgFfI6WVQAoi0JDXXO4FpZlmUFgqakm7Oac1SDntrQ0c0tNNc/L73b6tJnvNjFROnXK3JzlxMTCDypzcgbsAweK9rzCCApyX+yhoK9hYeYWGuoqF+aWfZEJZxeKnNsK81hYGOEaZQ5hFQDKMofDtDpGRvr2vKmpJriePCmdOGG+Zi/ntc1ZTknxbF0yM019AkV+QTb7zdlqHBTk+lrWyiU9Bv8U2AZhFQDge2FhUlycuRXVuXN5h9lTp1x9cp0tvDnLqamueXGzf81rW15fna3K/gy3zvl/4X3FCbrO5Zdz3vLbfqHHSvLcwh5Xcn1t10566CH/ved5IKwCAAJLZKRZqrZWLf/VwbJMeHUG14JuKSmubhPZF5vIefPE4+np/ntPSqPMTP+vdOdrNny9hFUAAIrK4TD9VkNDzeAvu8jMNMHVGbIyMjxbzr7NG8cvah3sWrasvG/FfcyXbNj9gbAKAEBpERTk+/7H8L78QmxJQ7CzFdUZiC3LDPCzGcIqAACAnTn7mJZRQf6uAAAAAJAfwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLYIqwAAALAtwioAAABsi7AKAAAA2yKsAgAAwLZC/F0BT7MsS5KUlJTk55oAAAAgL86c5sxtBSl1YTU5OVmSVKdOHT/XBAAAAAVJTk5WTExMgfs4rMJE2gCSmZmpQ4cOKTo6Wg6HwyfnTEpKUp06dXTgwAFVqFDBJ+eE53D9Ah/XMPBxDQMf1zCw+fr6WZal5ORk1axZU0FBBfdKLXUtq0FBQapdu7Zfzl2hQgV+QAMY1y/wcQ0DH9cw8HENA5svr9+FWlSdGGAFAAAA2yKsAgAAwLYIqx4QHh6ucePGKTw83N9VQTFw/QIf1zDwcQ0DH9cwsNn5+pW6AVYAAAAoPWhZBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYLaFp06apfv36ioiIUJs2bfT999/7u0rIx8qVK3XjjTeqZs2acjgcWrhwodvjlmVp/PjxqlmzpiIjI9W1a1dt27bNP5VFLhMnTtTll1+u6OhoxcXFqW/fvvrtt9/c9uEa2tv06dPVqlWrrEnH27dvr6+++irrca5fYJk4caIcDodGjhyZtY1raG/jx4+Xw+Fwu1WvXj3rcbteP8JqCcyfP18jR47UmDFjtGnTJnXq1Ek9e/bU/v37/V015OHMmTNq3bq1pk6dmufjkydP1gsvvKCpU6dq3bp1ql69uq677jolJyf7uKbIy4oVK3TPPfdozZo1WrJkidLT09W9e3edOXMmax+uob3Vrl1bkyZN0vr167V+/XpdffXV+tvf/pb1x5DrFzjWrVunN998U61atXLbzjW0v+bNm+vw4cNZt61bt2Y9ZtvrZ6HYrrjiCuuuu+5y29a0aVPr0Ucf9VONUFiSrE8//TTrfmZmplW9enVr0qRJWdvOnz9vxcTEWK+//rofaogLOXbsmCXJWrFihWVZXMNAValSJevtt9/m+gWQ5ORkq1GjRtaSJUusLl26WP/v//0/y7L4GQwE48aNs1q3bp3nY3a+frSsFlNqaqo2bNig7t27u23v3r27Vq1a5adaobj27NmjI0eOuF3P8PBwdenShetpU4mJiZKkypUrS+IaBpqMjAzNmzdPZ86cUfv27bl+AeSee+5R7969de2117pt5xoGhj/++EM1a9ZU/fr1deutt2r37t2S7H39Qvx69gCWkJCgjIwMVatWzW17tWrVdOTIET/VCsXlvGZ5Xc99+/b5o0oogGVZGj16tDp27KgWLVpI4hoGiq1bt6p9+/Y6f/68ypcvr08//VQXX3xx1h9Drp+9zZs3Txs3btS6detyPcbPoP21a9dO7777rho3bqyjR4/q6aefVocOHbRt2zZbXz/Cagk5HA63+5Zl5dqGwMH1DAz33nuvtmzZoh9++CHXY1xDe2vSpIk2b96sU6dO6ZNPPtHgwYO1YsWKrMe5fvZ14MAB/b//9//0zTffKCIiIt/9uIb21bNnz6xyy5Yt1b59ezVs2FDvvPOOrrzySkn2vH50AyimKlWqKDg4OFcr6rFjx3L9VwL7c46G5Hra33333afPPvtMy5YtU+3atbO2cw0DQ1hYmC666CK1bdtWEydOVOvWrfXyyy9z/QLAhg0bdOzYMbVp00YhISEKCQnRihUr9MorrygkJCTrOnENA0dUVJRatmypP/74w9Y/g4TVYgoLC1ObNm20ZMkSt+1LlixRhw4d/FQrFFf9+vVVvXp1t+uZmpqqFStWcD1twrIs3XvvvVqwYIGWLl2q+vXruz3ONQxMlmUpJSWF6xcArrnmGm3dulWbN2/OurVt21a33367Nm/erAYNGnANA0xKSoq2b9+uGjVq2Ptn0G9Du0qBefPmWaGhodaMGTOsX3/91Ro5cqQVFRVl7d27199VQx6Sk5OtTZs2WZs2bbIkWS+88IK1adMma9++fZZlWdakSZOsmJgYa8GCBdbWrVut2267zapRo4aVlJTk55rDsixrxIgRVkxMjLV8+XLr8OHDWbezZ89m7cM1tLfHHnvMWrlypbVnzx5ry5Yt1n/+8x8rKCjI+uabbyzL4voFouyzAVgW19DuHnjgAWv58uXW7t27rTVr1lg33HCDFR0dnZVb7Hr9CKsl9Nprr1n16tWzwsLCrMsuuyxrGh3Yz7JlyyxJuW6DBw+2LMtM2zFu3DirevXqVnh4uNW5c2dr69at/q00suR17SRZs2bNytqHa2hvw4YNy/p9WbVqVeuaa67JCqqWxfULRDnDKtfQ3vr372/VqFHDCg0NtWrWrGnddNNN1rZt27Iet+v1c1iWZfmnTRcAAAAoGH1WAQAAYFuEVQAAANgWYRUAAAC2RVgFAACAbRFWAQAAYFuEVQAAANgWYRUAAAC2RVgFAACAbRFWAQAAYFsh/q4AACC3zZs3a+HChVn3R44cqYoVK/qtPgDgLyy3CgA2NHv2bA0dOjTr/p49exQfH++/CgGAn9ANAAAAALZFWAUAAIBtEVYBAABgW4RVAAAA2BZhFQAAALbFbAAAYCMOh6PIz1m2bJm6du3q+coAgA3QsgoAAADbYlEAALCR4OBgSZJlWcrMzMy1PS/FaY0FgEBByyoA2Eh6errS09M1Y8YMt+07d+7MeiznrUuXLn6qLQB4H2EVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgEAAGBbhFUAAADYFmEVAAAAtkVYBQAAgG0RVgHAhkJDQ93uZ2Rk+KkmAOBfhFUAsKHo6Gi3+ydPnvRTTQDAvwirAGBD8fHxbvfXrVvnn4oAgJ85LMuy/F0JAIC79PR0ValSRYmJiZKkmjVr6u2331bXrl0VGRnp59oBgO/QsgoANhQSEqKhQ4dm3T906JB69eqlcuXKqVy5cipfvnzW7fvvv/djTQHAuwirAGBTTz/9tDp27Jhr+7lz53TmzJmsG4OvAJRmhFUAsKmoqCgtX75c8+bN0y233KLGjRsrOjpaQUH86gZQdtBnFQAAALbFv+cAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwLcIqAAAAbIuwCgAAANsirAIAAMC2CKsAAACwrf8PI5q6SCyd3RMAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (result_dlbath_T, P11p, \"b\", \"P11 Mats (DrudeLorentzBath + Term)\"),\n", - " (result_dlbath_T, P12p, \"r\", \"P12 Mats (DrudeLorentzBath + Term)\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "5e7017d0", - "metadata": {}, - "source": [ - "We can compare the solution obtained from the QuTiP Bloch-Redfield solver:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "be8f8acf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 1.48s*] Elapsed 1.48s / Remaining 00:00:00:00\n", - "ODE solver time: 1.4995617866516113\n" - ] - } - ], - "source": [ - "options = {**default_options}\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultBR = brmesolve(\n", - " Hsys, rho0, tlist, a_ops=[[sigmaz(), dlenv]], options=options\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "6f98a4a0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (resultMats, P11p, \"b\", \"P11 Mats\"),\n", - " (resultMats, P12p, \"r\", \"P12 Mats\"),\n", - " (resultMatsT, P11p, \"b--\", \"P11 Mats + Term\"),\n", - " (resultMatsT, P12p, \"r--\", \"P12 Mats + Term\"),\n", - " (resultBR, P11p, \"g--\", \"P11 Bloch Redfield\"),\n", - " (resultBR, P12p, \"g--\", \"P12 Bloch Redfield\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "85ffc12b", - "metadata": {}, - "source": [ - "## Padé decomposition" - ] - }, - { - "cell_type": "markdown", - "id": "d9e06c20", - "metadata": {}, - "source": [ - "The Matsubara decomposition is not the only option. We can also use the\n", - "faster-converging Pade decomposition." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "20935ef0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def deltafun(j, k):\n", - " if j == k:\n", - " return 1.0\n", - " else:\n", - " return 0.0\n", - "\n", - "\n", - "def pade_eps(lmax):\n", - " Alpha = np.zeros((2 * lmax, 2 * lmax))\n", - " for j in range(2 * lmax):\n", - " for k in range(2 * lmax):\n", - " # Fermionic (see other example notebooks):\n", - " # Alpha[j][k] = (deltafun(j, k+1) + deltafun(j, k-1))\n", - " # / sqrt((2 * (j + 1) - 1) * (2 * (k + 1) - 1))\n", - " # Bosonic:\n", - " Alpha[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1)) / np.sqrt(\n", - " (2 * (j + 1) + 1) * (2 * (k + 1) + 1)\n", - " )\n", - "\n", - " eigvalsA = np.linalg.eigvalsh(Alpha)\n", - " eps = [-2 / val for val in eigvalsA[0:lmax]]\n", - " return eps\n", - "\n", - "\n", - "def pade_chi(lmax):\n", - " AlphaP = np.zeros((2 * lmax - 1, 2 * lmax - 1))\n", - " for j in range(2 * lmax - 1):\n", - " for k in range(2 * lmax - 1):\n", - " # Fermionic:\n", - " # AlphaP[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1))\n", - " # / sqrt((2 * (j + 1) + 1) * (2 * (k + 1) + 1))\n", - " # Bosonic [this is +3 because +1 (bose) + 2*(+1) (from bm+1)]:\n", - " AlphaP[j][k] = (deltafun(j, k + 1) + deltafun(j, k - 1)) / np.sqrt(\n", - " (2 * (j + 1) + 3) * (2 * (k + 1) + 3)\n", - " )\n", - "\n", - " eigvalsAP = np.linalg.eigvalsh(AlphaP)\n", - " chi = [-2 / val for val in eigvalsAP[0:lmax - 1]]\n", - " return chi\n", - "\n", - "\n", - "def pade_kappa_epsilon(lmax):\n", - " eps = pade_eps(lmax)\n", - " chi = pade_chi(lmax)\n", - "\n", - " kappa = [0]\n", - " prefactor = 0.5 * lmax * (2 * (lmax + 1) + 1)\n", - "\n", - " for j in range(lmax):\n", - " term = prefactor\n", - " for k in range(lmax - 1):\n", - " term *= (chi[k] ** 2 - eps[j] ** 2) / (\n", - " eps[k] ** 2 - eps[j] ** 2 + deltafun(j, k)\n", - " )\n", - "\n", - " for k in range(lmax - 1, lmax):\n", - " term /= eps[k] ** 2 - eps[j] ** 2 + deltafun(j, k)\n", - "\n", - " kappa.append(term)\n", - "\n", - " epsilon = [0] + eps\n", - "\n", - " return kappa, epsilon\n", - "\n", - "\n", - "def pade_corr(tlist, lmax):\n", - " kappa, epsilon = pade_kappa_epsilon(lmax)\n", - "\n", - " eta_list = [lam * gamma * (cot(gamma * beta / 2.0) - 1.0j)]\n", - " gamma_list = [gamma]\n", - "\n", - " if lmax > 0:\n", - " for ll in range(1, lmax + 1):\n", - " eta_list.append(\n", - " (kappa[ll] / beta)\n", - " * 4\n", - " * lam\n", - " * gamma\n", - " * (epsilon[ll] / beta)\n", - " / ((epsilon[ll] ** 2 / beta**2) - gamma**2)\n", - " )\n", - " gamma_list.append(epsilon[ll] / beta)\n", - "\n", - " c_tot = []\n", - " for t in tlist:\n", - " c_tot.append(\n", - " sum(\n", - " [\n", - " eta_list[ll] * np.exp(-gamma_list[ll] * t)\n", - " for ll in range(lmax + 1)\n", - " ]\n", - " )\n", - " )\n", - " return c_tot, eta_list, gamma_list\n", - "\n", - "\n", - "tlist_corr = np.linspace(0, 2, 100)\n", - "cppLP, etapLP, gampLP = pade_corr(tlist_corr, 2)\n", - "corr_15k = dlenv.correlation_function(tlist_corr, Nk=15)\n", - "corr_2k = dlenv.correlation_function(tlist_corr, Nk=2)\n", - "\n", - "fig, ax1 = plt.subplots(figsize=(12, 7))\n", - "ax1.plot(\n", - " tlist_corr,\n", - " np.real(cppLP),\n", - " color=\"b\",\n", - " linewidth=3,\n", - " label=r\"real pade 2 terms\",\n", - ")\n", - "ax1.plot(\n", - " tlist_corr,\n", - " np.real(corr_15k),\n", - " \"r--\",\n", - " linewidth=3,\n", - " label=r\"real pade 15 terms\",\n", - ")\n", - "ax1.plot(\n", - " tlist_corr,\n", - " np.real(corr_2k),\n", - " \"g--\",\n", - " linewidth=3,\n", - " label=r\"real mats 2 terms\",\n", - ")\n", - "\n", - "ax1.set_xlabel(\"t\")\n", - "ax1.set_ylabel(r\"$C_{R}(t)$\")\n", - "ax1.legend()\n", - "\n", - "fig, ax1 = plt.subplots(figsize=(12, 7))\n", - "\n", - "ax1.plot(\n", - " tlist_corr,\n", - " np.real(cppLP) - np.real(corr_15k),\n", - " color=\"b\",\n", - " linewidth=3,\n", - " label=r\"pade error\",\n", - ")\n", - "ax1.plot(\n", - " tlist_corr,\n", - " np.real(corr_2k) - np.real(corr_15k),\n", - " \"r--\",\n", - " linewidth=3,\n", - " label=r\"mats error\",\n", - ")\n", - "\n", - "ax1.set_xlabel(\"t\")\n", - "ax1.set_ylabel(r\"Error\")\n", - "ax1.legend();" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "981d2e53", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.009793281555175781\n", - " Total run time: 1.69s*] Elapsed 1.69s / Remaining 00:00:00:00\n", - "ODE solver time: 1.6923456192016602\n" - ] - } - ], - "source": [ - "# put pade parameters in lists for heom solver\n", - "ckAR = [np.real(eta) + 0j for eta in etapLP]\n", - "ckAI = [np.imag(etapLP[0]) + 0j]\n", - "vkAR = [gam + 0j for gam in gampLP]\n", - "vkAI = [gampLP[0] + 0j]\n", - "\n", - "options = {**default_options, \"rtol\": 1e-14, \"atol\": 1e-14}\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI)\n", - " HEOMPade = HEOMSolver(Hsys, (bath,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultPade = HEOMPade.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "e24e66cf", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (resultMats, P11p, \"b\", \"P11 Mats\"),\n", - " (resultMats, P12p, \"r\", \"P12 Mats\"),\n", - " (resultMatsT, P11p, \"y\", \"P11 Mats + Term\"),\n", - " (resultMatsT, P12p, \"g\", \"P12 Mats + Term\"),\n", - " (resultPade, P11p, \"b--\", \"P11 Pade\"),\n", - " (resultPade, P12p, \"r--\", \"P12 Pade\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "3661887e", - "metadata": {}, - "source": [ - "The Padé decomposition of the Drude-Lorentz bath is also available via a\n", - "built-in class, `DrudeLorentzEnvironment` bath. Similarly to the terminator\n", - "section when approximating by Padé one can calculate the terminator easily by\n", - "requesting the approximation function to compute delta\n", - "\n", - "Below we show how to use the built-in Drude-Lorentz Environment to obtain a\n", - "Padé decomposition approximation and its terminator (although the terminator \n", - "does not provide much improvement here,because the Padé expansion already fits \n", - "the correlation function well):" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "e2a8616b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.007105112075805664\n", - " Total run time: 1.86s*] Elapsed 1.86s / Remaining 00:00:00:00\n", - "ODE solver time: 1.8578541278839111\n" - ] - } - ], - "source": [ - "options = {**default_options, \"rtol\": 1e-14, \"atol\": 1e-14}\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " env_approx,delta = dlenv.approx_by_pade(Nk=2,compute_delta=True)\n", - " Ltot = liouvillian(Hsys) + system_terminator(Q,delta)\n", - " HEOM_dlpbath_T = HEOMSolver(Ltot, (env_approx,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " result_dlpbath_T = HEOM_dlpbath_T.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "69c6df5d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (result_dlpbath_T, P11p, \"b\", \"P11 Padé (DrudeLorentzBath + Term)\"),\n", - " (result_dlpbath_T, P12p, \"r\", \"P12 Padé (DrudeLorentzBath + Term)\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "bc417d19", - "metadata": {}, - "source": [ - "### Next we compare the Matsubara and Pade correlation function fits\n", - "\n", - "Fitting the correlation function is not efficient for this example, but\n", - "can be extremely useful in situations where large number of exponents\n", - "are needed (e.g., near zero temperature). We will perform the fitting\n", - "manually below, and then show how to do it with the built-in tools\n", - "\n", - "For the manual fit we First we collect a large sum of Matsubara terms for \n", - "many time steps:" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "86f50d83", - "metadata": {}, - "outputs": [], - "source": [ - "tlist2 = np.linspace(0, 2, 10000)\n", - "\n", - "corr_15k_t10k = dlenv.correlation_function(tlist2, Nk=100)\n", - "\n", - "corrRana = np.real(corr_15k_t10k)\n", - "corrIana = np.imag(corr_15k_t10k)" - ] - }, - { - "cell_type": "markdown", - "id": "da27abbe", - "metadata": {}, - "source": [ - "We then fit this sum with standard least-squares approach:" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "0c06f6e9", - "metadata": {}, - "outputs": [], - "source": [ - "def wrapper_fit_func(x, N, args):\n", - " \"\"\" Fit function wrapper that unpacks its arguments. \"\"\"\n", - " x = np.array(x)\n", - " a = np.array(args[:N])\n", - " b = np.array(args[N:2 * N])\n", - " return fit_func(x, a, b)\n", - "\n", - "\n", - "def fit_func(x, a, b):\n", - " \"\"\" Fit function. Calculates the value of the\n", - " correlation function at each x, given the\n", - " fit parameters in a and b.\n", - " \"\"\"\n", - " return np.sum(\n", - " a[:, None] * np.exp(np.multiply.outer(b, x)),\n", - " axis=0,\n", - " )\n", - "\n", - "\n", - "def fitter(ans, tlist, k):\n", - " \"\"\" Compute fit with k exponents. \"\"\"\n", - " upper_a = abs(max(ans, key=abs)) * 10\n", - " # sets initial guesses:\n", - " guess = (\n", - " [upper_a / k] * k + # guesses for a\n", - " [0] * k # guesses for b\n", - " )\n", - " # sets lower bounds:\n", - " b_lower = (\n", - " [-upper_a] * k + # lower bounds for a\n", - " [-np.inf] * k # lower bounds for b\n", - " )\n", - " # sets higher bounds:\n", - " b_higher = (\n", - " [upper_a] * k + # upper bounds for a\n", - " [0] * k # upper bounds for b\n", - " )\n", - " param_bounds = (b_lower, b_higher)\n", - " p1, p2 = curve_fit(\n", - " lambda x, *params_0: wrapper_fit_func(x, k, params_0),\n", - " tlist,\n", - " ans,\n", - " p0=guess,\n", - " sigma=[0.01 for t in tlist],\n", - " bounds=param_bounds,\n", - " maxfev=1e8,\n", - " )\n", - " a, b = p1[:k], p1[k:]\n", - " return (a, b)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "763ab538", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlation (real) fitting time: 5.579460144042969\n", - "Correlation (imaginary) fitting time: 0.18423056602478027\n" - ] - } - ], - "source": [ - "kR = 4 # number of exponents to use for real part\n", - "poptR = []\n", - "with timer(\"Correlation (real) fitting time\"):\n", - " for i in range(kR):\n", - " poptR.append(fitter(corrRana, tlist2, i + 1))\n", - "\n", - "corrRMats = np.real(dlenv_approx.correlation_function(tlist2))\n", - "\n", - "kI = 1 # number of exponents for imaginary part\n", - "poptI = []\n", - "with timer(\"Correlation (imaginary) fitting time\"):\n", - " for i in range(kI):\n", - " poptI.append(fitter(corrIana, tlist2, i + 1))" - ] - }, - { - "cell_type": "markdown", - "id": "2be1d70d", - "metadata": {}, - "source": [ - "And plot the results of the fits:" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "44d390a2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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BAwaIx48fixYtWkjXNm5ubtL2ffrpp2W2z06ePCldy5ibm4umTZtK2xsdHa1121Xtx/OfA9V2x8TECCcnJ2FmZiZ8fX1F9erVpfhnzJhRZvGrljl79mwBQDg6OopmzZoJBwcHkZKSIiZMmCAAiOHDh2t8n548eSK996dPn9Y4z/OK+ux999130v5UMTQ0FDKZTFStWlU0adJEeHl5ScdllSpVxJkzZwotp6h2PCoqSnh5eQkAwtraWq19DA8Pl5ajOs4ACHd3d9GsWTPh7OwsDA0NBQCRnJyscTt8fHwEAJGYmFis/UL0sjCBRJWa6gtmt27dSlX/l19+kS5m169fL5Wnp6eL7t27S18uHz16pFZPdUFgbGws5s2bJ52U79+/L/z9/aWTtVwuFxs2bJDqpaamitq1awsAYunSpWrLVF0UGRkZCWtra7F7925p2tWrV0Xjxo2lC9PnrVmzRi3pIYQQubm54uuvvxZGRkaidu3ahS4cVAkkQ0NDERAQIP755x9pmiq5pS2BFBUVJV3YZWZmqk07e/asWLFihVpZcnKylAT7/PPPpViys7PF0KFDBQChUCjE9evX1eqpLrCMjY1FVFSUFJdSqRTjxo0TAET16tXF06dPC+0TbRYvXiwACCsrK7Fz506pXJX0ACBatGhRqJ6uL1jFkZmZKX3pOnz4sBBCiIyMDGFubi4AiKNHj2qsV9p9kJiYKHbv3l2o/OTJk6JBgwYCgIiPjy+0Pk0JpF27dgkAwtvbW2OM8+bN03hsFmefabu4XrBggQDyE5Tr1q1TO37v3r0r5s2bJ27duqV1uQU9ePBAxMbGirt376qV379/XzqWIyIiCtUr7b4fOXKk9LkpeEF94sQJUaNGDWFsbPzSE0gqmt7f4tLWHhRU2jZVG9UXCVtb21LFfO3aNWFtbS2A/MTtkydPhBBC5OXliZkzZ0rv7/PJKdWxa2hoKLp06aJ27KiOA9XxYWhoKAYNGiSysrKkeVTbp0oWvvHGG2pt9L1790SPHj00fm50vX+laetVx87zX8Q1be/zx2RZnCNnzZolfUZycnLEe++9p7GdnTt3rgAg2rdvX+izevXqVTF//nyt8T8vJydHShreuXNH4zxFfab69+8vAIg2bdqolT98+FBKdEVHR4v09HRp2pkzZ0TDhg0FALFkyRK1eidPnhRbt24V2dnZauWXLl2SkoyxsbGF4ihN8qW061K1D3K5XO1YyMjIEG+88YYAIHr16qVWR6lUCl9fXwFAhIWFqb13GzduFMbGxtL5vywTSMbGxqJmzZpqX97j4+OFiYmJkMlkokuXLqJRo0ZqnxfVj46mpqbi3r17asstzT7Ly8sTjRo1EgDEm2++qbbM77//XpiamkrtfUkTSMbGxiI8PFzcv39fmrZ06VIpSVewvLTxC/HsPTcxMRErV66UEp+5ubkiNzdXXLhwQQAQ9vb2Iicnp1D9H374QQAQfn5+haZpU9RnLzo6WgAQjRo1kspWrFghrl27pjbfo0ePpHY8NDS00HKK047ram+FEOLo0aMCgHB2dhZ//fWX2rT09HTx1VdfidTUVI11Bw0aJACIWbNmaZxOpC9MIFGl1q1bNwFAjBw5slT1VQkDVY+QgrKysqReNgV7TAjx7IKua9euher99ttv0glZ03KXL18uAIguXbqolatOYkB+z6bnnTx5UgD5PRqe/wKhS9++fQUAsW/fPrVyVQLJ1NS00ElZRdsXxrCwMAEUv0eA6guDpv2lVCqlC+7JkyerTVNdYDVu3LjQl6KcnBzh5OQkAIjjx48XKw6lUimcnZ0FAI1fRv7991+pZ8Aff/yhNu1FE0iq3kZ16tRRK3/77be1HitClP0+EEKI33//XQAQkZGRhaZpSjAolUrp11tNv7R5e3sLAIV6kZU2gfTo0SPpF821a9cWe7tKy9nZWcjl8kK9GEuz7zMyMqRE4bZt2wqtKy4uTtrHpUkg6XqpvojoM4FU2jZVG1Ui0cfHp1QxT5w4UQAQTZo00Tj9rbfeEgBEv3791MpVx66Tk5N4+PChxrq6jg8hnrXZrq6uIiMjo9D0rKws4ezsLGQymbhy5YpUXtQXGm20tfUvkkB60XNk586dC9W7ffu2lNwp+IV78ODBAoD46aefit7YIqSmpkpfirXR9CX26dOn4vLly+I///mPkMlkwsDAQOzYsUOt3qJFiwQA0b17d43LPXnypJDJZKJ27drFjvfixYsCgGjXrl2haaVJIJV2Xar2QVNvkz///FMAKNQTTHU+MTc3F7dv3y5UT5UMKOsEEgCxZcuWQvVUPWZlMpnG86LqR764uLhix6Jtn+3YsUMA+T1gHjx4UKie6jqrNAkkbW2PKllXFvELofs9VwkKCtK6v7t06aIxYaqLrgTSpk2bpKTb9OnTi7W8Vq1aCQDi33//VSsvTjteVHur6olYmu8ZqvdfW49vIn3hU9ioUsvMzAQAWFhYlLjuw4cPceDAAQDA8OHDC02Xy+WIjIzErFmzsHPnTo1Pyvrggw8KlRV8XLmm6T4+PgCAy5cva4zLxMQEH374YaFyb29vtGrVCklJSdi5cyeGDBmiNv3vv//Gxo0bcerUKdy7d0+63111b/jJkyc1jv/Utm1bVK9eXWMs2jg7OwMAvv/+ezRq1KjIR5Tu3LkTgOb9LJPJEB0djcGDB2Pnzp2YNm1aoXkGDhxY6MlnxsbGaNy4MdLS0nD58mVpv+py9uxZ/PPPPzAzM0NkZGSh6TVq1EDPnj2xceNG7Ny5E23atClymcWlGneoT58+auXvvfcevvvuO2zcuBFz586FkZHmZr00+yAzMxObNm1CUlISbty4gcePH0MIId2Pf/LkyWLFLpPJ8P7772Pq1KlYs2aN2jF+4sQJ/Pnnn3ByckKHDh2Ktbyi7Nu3D3fv3kX16tU1jmdTWrt378Yvv/yC8+fPIzMzUxqnJD09HY8ePcKFCxfQoEGDQvVKsu8TExPx6NEjuLq64s033yy0rK5du6JGjRq4du1aqbah4GOPn6fvR7CXRZv6vBdp44FnbU9UVJTG6R9//DG2b98uzfe8nj17Frnuvn37anwy45YtWwAA77zzjsbHwMvlcrRt2xarV69GYmIiXF1dda5HpbRtfUmVxfup6Vxmb28PNzc3nDt3DpcvX0bTpk0BPDuvbNmyBW+99ZbWtrA4VOOa2draFjmvakyv57m4uGDOnDkICwtTK1cNqq1p24D8c7WbmxsuX76Mf//9FzVr1pSmPXnyBD/88AP27NmD1NRUPHr0SG08nuK2ycXxIuvStG2NGjWCmZkZ0tPTcffuXVSpUgVA/hPrAEhjxD1v6NCh0liVZcnOzg7dunUrVN6kSRNs3LgRPj4+Gq8LfHx8cPDgQY3XYCXdZ7t27QIA9OjRQ2P7O2DAAEydOrWkmwYAePfddzW2Pc2aNcPx48fLJP6CdD2pcODAgUhMTMSaNWvU9vnt27fx66+/wsTEBO+++24xt+yZtLQ0tGrVCkD+WIUpKSnSWIuBgYEYPXq02vxHjx7F999/j7/++gvp6enIy8sD8GyspD///BM1atQotJ7itOPaqNqlP/74A/fu3YOdnV2x66rmvX37dqnWTVRemECiSk11UZ6VlVXiuhcvXoRSqYSpqSlq166tcZ6GDRsCAM6fP69xuru7e6EyBweHYk3XNphuzZo1NX7ZAIAGDRogKSmpUDyzZs3CpEmTNA7cqaJtAG5NX5iLMmzYMKxZswbTp0/H2rVr0aFDBwQFBaF169aFklEPHjyQTp6enp4al1ea/QxAeqJecQcmVi3fxcVF68VEUbGUxrVr17Bnzx4AhRNIqsFlb926hZ07d+Ktt97SuIyS7oPk5GR06tRJ44DtKtqOCU0GDBiAadOm4dtvv8WcOXOkL3eqgbL79u0LQ0PDYi9PF9Wg6s2bN9f4xbykcnJy0KtXL/z4448659O2P0qy71XHTf369TV+KTUwMEC9evVKnUDS9NjjiqIs2tTnvUgbX3A9RbU9N2/eREZGBqytrdWmF6d91DbPqVOnAOQnRPbv369xnqtXrwJAsY+HF2nrS6q8zpFA/mfn3Llzap+dAQMGYM6cOYiNjcWvv/6qdl7Rtn5tsrOzAeQnXItSMCn7+PFjXLhwAZmZmbC3t4e/v3+h+VXv66effor//ve/GpepSmBdu3ZNSiClpqaiffv2OHfunNZYyuq9e9F1aXvfHBwc8M8//+Dhw4dSAkn13mv7HNStWxdGRkZlOkh4UTEWZ/rz58zS7DNV4sLb21vj/K6urrC2ti7xYPlAyc/5L/qe62rr3n77bURHR2Pbtm24c+eOlCj89ttvkZubi/Dw8BIlVlSePHmCffv2Acj/oUo1EH2vXr0wdOhQmJiYAACEEIiKiipyIP2yvM5VCQgIQIsWLXDo0CE4OzujXbt2CA4ORkhICHx9fXX+gKp6aq/qKb5EFQWfwkaVmuqXhpSUlBLXVZ18HRwctJ4AHB0dATz7Ffx5crm8UFnBZemaXvBXoYJUFwfFjWfv3r34z3/+A5lMhlmzZuHMmTN4+PAhlEolhBCYOHEiACA3N1fjMkvzq0yTJk2wd+9etG/fHteuXcOKFSvQt29f1KxZE2FhYWpPVSt4kaNt24raz9piVCUXtO3L56liKek+flEbNmyAUqmEr68vPDw81KaZmJjg7bffBgCdT0cryT7Iy8vDO++8g+vXr+Ott95CQkIC7ty5g6dPn0IIIV30ajsmNHF1dUWbNm1w69Yt/PrrrwDyn0zy7bffAoD05L6yoLrYtrGxKZPlzZ49Gz/++COcnJywdu1aXLlyBdnZ2RD5t4GjZcuWAEr+GdG07wu2K9qojrHXTVm0qc9TtfFFPSWuqJiKanu0xVSc9lHbPOnp6QDyEzH79u3T+Pr3338BFO8Lxou29SVVFu9nST471atXx4EDB9CzZ0+kp6djzZo1+PDDD+Hu7o6AgACpN1RxqL7Mano60vNUSdmkpCQcO3YM169fR2RkJI4fP4633npLSkapqN7XY8eOaX1fVfuj4PsaERGBc+fOoUWLFtixYwfS0tKQk5MDIYT0npVVkuVF11WWbZ6BgYHGnkkvStP1FfDsGquo6c9fN5Rmn6kS29p+9Ctqmi4lve4pr/dcNe2dd95Bbm4uNm7cKJWrfkAq7fnf1dVVOg8rlUqkp6fjwIEDGDFihJQ8AvKvjZYuXQoLCwssXboUFy5ckHpXCSGknspleZ2rYmBggF9//RUff/wxzM3N8dNPP2H06NHw8/NDrVq1tD65EniW0CqP45/oRTCBRJWaqpv+/v37S3zhZWlpCSC/a6m2BISqK21pLwBKQ1dX11u3bgFQj0f1CPhPPvkE48ePh6enJywsLKSLpNI8Nrs4/P398dtvv+H+/fvYsWMHxo0bh5o1a2Lnzp1o166ddOGu2s8F43/ey9rPqli0xVFesagSQ8ePHy/0+HOZTIaVK1cCAH766adS/VL5vMOHD+PixYtwdXVFXFwcgoODUaVKFamHUGmPCdUtKqqLxl9//RW3bt2Cn5+f1BOhLKj2fXG+/BWH6jMSGxuLfv36wdXVVa1nQll+Rgq2K9roOv5eZeXRpqra+Pv37+PPP/8sdUxFtT0liamk6/7qq6+kLzraXjExMUUu72W39fo4RzZo0ADff/89Hjx4gD179iAmJgb169fHwYMH0b59+2InElUJw4yMjFJdGyxbtgy+vr44c+YM5s6dW2g6kN/7pKj3NTQ0FABw/fp17NmzB3K5HNu3b0dYWBgcHR1hbGwMoGzfu5e5LqDoNk+pVOLu3btlus6yVtp9pkpM6OoFXZY/RmnzMt7z58//p06dQnJycpnevq6Nqu2bN28ePvroI9SpU0fq3QOU33Wuiq2tLRYsWIDbt28jOTkZCxcuROvWrXH16lUMGDAA33//vcZ6qgSSrh+UiPSBCSSq1N566y1YWlri1q1bWhtwberUqQMDAwM8efJE63hEZ86cAQDUq1fvhWMtLlX3cE1UPXsKxqO6oNY25kVZjqmgiaWlJcLCwjB79mz8/fffcHd3x7Vr16ReKjY2NtLJ86+//tK4jJe1n1XLT01N1bqPyzqW5ORknD59GjKZDI6OjlpfJiYmePz4MX744YcXXqfqmGjatKnGWzhKe0z06NEDNjY2+OWXX3Dv3j3plzdtvz4WNTaWNqpk1JEjR3TeqlNcuj4jd+/eLfXtZJqojptz585p/NKtVCp13mJQkRX1fpZHm1q9enVpjIyibl/QRLWeotoeR0fHQrevvSjVbXOnT58uk+WVtq0v7edQn+dIU1NThIaGYsqUKTh9+jRatmyJhw8fqvV+0MXW1hYuLi4A8seMKilDQ0Pp9rS5c+dKvY6A0r2vqlsV69evr/FWn7I8T7/MdQHP3ntt+/nixYtl1iuuvJR2n6m2XVtyOzU1tUx+FCrKy3jPAwMDUb9+fRw7dgynT5+Wzv9lefu6NrravtzcXLVe7yVVkvZRJpOhSZMmiI6Oxu7duzF+/HgA+T8SaKI67/j6+pY6PqLywAQSVWo2NjbS4J4jRowo8tfJffv2SWNRWFpaSiejxYsXF5r38ePH+PrrrwGg0CCa5SknJwerVq0qVH769GkkJiZCJpOhXbt2UrnqV5iCv6Sr7Ny5s9wTSAXJ5XI0atQIANTG3lHtP037WQghlZf3fm7QoAFcXFyQnZ0tvbcFXb9+XUrglFUsqt5HwcHBSEtL0/pSDRap6za24tJ1TOTm5mLBggWlWq6ZmRneffdd5OTkYMmSJdi6davOwTNLe/9/y5YtYW9vj2vXrhX7C6MuuvbHvHnzpIE4y0KrVq0gl8tx5coVaXDZgn7++ecyTViV1IuMyVBU3fJqUydNmgQg/yJ9+/btOue9fv269Gt1wfUsWbJE4/yqwX3Lo+3p3r07AGD9+vVl0gOjtG19ad/zinKONDQ0RLNmzQBA55huz1MlHo8ePVqq9YaFhcHHxwfp6elqx0+PHj0A5B87xb19WvUe3Lp1S2Odzz//vFQx6ntdANC+fXsAwHfffafxOC9N4vdlK+0+U12LxcXFaexppOv2prL0st7zAQMGAABWrVoltbNlefu6NrravtWrV7/QINUvck5UjZGmrV06cuQIACAoKKiU0RGVDyaQqNKLiYlBQEAAbt68iYCAAKxbt67QmAXnz5/HsGHDEBoaqnYrw7hx4wDkX+CoxnIB8rsc9+/fH7dv34abmxt69+79cjYGgJGREaZMmYKEhASp7N9//5WekNGjRw+1wRVVF8mzZ89WGwvqyJEjGDhwIMzMzMo8xo8++gibN2/Go0eP1Mr37t2LP/74A4D6Ly6jR4+GkZERfvrpJ8ybN0/qVZKTk4OPP/4Yp0+fhkKhwEcffVTmsRYkk8nwySefAACmTJkixQrkX5j07t0bOTk58Pf3R+vWrV94fXl5eVICpF+/fjrn7du3LwAgPj7+hbtj+/v7w8jICPv27cPatWul8vT0dLz33nsaL8KKS9WNffr06cjJyUGXLl20Dp6pGvh27969xf6iBeQnqiZPngwAGDx4MDZu3KhW//79+5g/f36xLxpVn5HRo0dLPc+EEFi7di3mzp1bpp8Ra2tr6Ql/Q4cOVftl9M8//0R0dLR0W4E+qN6Tgu1LcTk4OMDKygq3bt3S+otvebSpYWFhGDVqFJRKJbp3746pU6dKgxSr3L59G/PmzUOjRo2ki3Ygv62ytrbGiRMnMHLkSOTk5ADI7wn2+eefY9u2bTA2Ni70tJ+y4Ofnh3feeQd3795Fu3btkJycrDY9Ly8P8fHxeO+996QnI+pS2rZe9Z4fOXKkUJtdlJd5jpw4cSJWrVpV6NbV06dP43//+x+Akv2Sr0psvMig82PHjgUALFiwQNp3gwcPRu3atbFnzx689957uHHjhlqdhw8f4n//+x9GjRollTVs2BC2trb4999/MXPmTKk9y87Oxscff1zo2HgRL3NdAPDGG2/Ax8cHjx49Qr9+/XD//n1p2v/+9z8sW7bshZ6o9zKUdp+1bdsW3t7euHPnDvr06aN27P7444+YNWvWS2nvX9Z73r9/fxgZGWHJkiW4efNmmd++ro2q7Zs0aZLaeX/Hjh345JNPXugcXqtWLQD5vYU0XVNs2LAB06dPL/QD9d27d6UfIDS1SxcvXsTNmzdRv3596UluRBWGICKRmZkpevbsKQAIAMLc3Fx4eXmJZs2aiRo1akjlNWvWFKdOnVKrO378eGm6s7Oz8PPzExYWFgKAsLW1FYcPHy60PldXVwFApKSkaIxHtTxNUlJSBADh6uqqVr5nzx4BQAQHB4uOHTsKAKJevXrCx8dHGBkZCQCidu3a4saNG2r10tPTRe3atQUAYWJiIho1aiQ8PDwEAOHp6SlGjRolAIgpU6ao1ZsyZYrG8uLE2rhxYwFAGBkZiQYNGojmzZtL+wSA6Nu3b6FlLV26VMhkMgFAODo6imbNmgkbGxsBQJiamoqtW7cWqhMSEiIAiD179miM7/333xcAxOrVq7Vuw/OUSqXo06ePFGudOnWEr6+vMDExEQCEi4uLuHTpUpms69dffxUAhJmZmXjw4EGR8/v4+AgAYtasWVJZaffBmDFjpG10cXERTZs2Febm5sLY2FgsW7ZM4/sqhO5jV8Xb21uaT9P7pnLx4kVpv7q6uoqgoCAREhKiFqu2z5JSqRQfffSRtB57e3vRrFkz4ebmJgwNDXV+/p539OhRYWpqKgAIa2tr0bRpU1G9enUBQPTr10/rPi7tvs/MzBRNmzYVAIRMJhONGjUSXl5eQiaTCV9fX9G7d+8SH0urV6/W+p49T9WWhISEFJo2bdo0AUAYGhoKHx8fERISIkJCQgq1K9oMHDhQOqb9/Pyk+gWVpk0tjmnTpgljY2MBQBgYGIh69eqJ5s2bizp16ggDAwMBQMjlcrFhwwa1ej/99JN0HNra2opmzZqJqlWrSstZsWJFoXUV5/Ne1PEhRP6x0K5dO7XPYosWLUSjRo2Eubm5VP748WOpjrb3r7RtfV5enqhbt64AIKpUqSICAgJESEiI+Pjjj4u1veVxjtS077p27Sq9J3Xq1JHeW9W6W7duLXJzc7Xu6+dlZWUJa2trYWdnJ548eVJoenE+U0+fPhW1atUSAMT8+fOl8rNnz0rlBgYGokGDBqJFixaiXr16UvvUokULtWUtWbJE2hYnJyfh5+cnrK2thUwmE1999ZXWtreofalJaddVVPuvLZbTp08LOzs76frLz89Pmnfo0KGl2gbVMfn++++rletq34R49r4+X09F27VPaffZyZMnpWsZuVwu/Pz8hJubmwAghg8fLm17amqqWj1t7UdRbU9Zx1+cc35BnTt3luosWbKk2PUKKsn5TAghrl69qnZ8NWnSRNrHrVu3Fu+9957GfVbc67Y2bdoIAMLKykq0aNFChISEiF69egkhhJg/f760vTVq1BDNmjUTXl5e0jmlRo0a4urVq4WWOWPGDAFAfP7558XaRqKXiT2QiJDf1f7777/H3r178cEHH8DZ2RlXrlzByZMnIYRAx44dsWrVKpw/fx5eXl5qdWfNmoVffvkF7dq1w8OHD/Hnn3/C3t4eQ4YMwcmTJ6Wu8y+LTCbDli1bEBMTA6VSib/++gsODg746KOPcOjQITg5OanNb21tjaSkJPTv3x/W1tY4d+4ccnJyMGrUKBw4cKBcBqaeP38+Pv74Y+mXtxMnTgDI7ynw888/q/V6Ufnoo4+QmJiIbt26QalU4sSJE5DL5ejbty+OHz+Ojh07lnmcmshkMqxfvx5r165FUFAQbt26hTNnzsDV1RWffPIJjh8/XuJHRmujuh2tc+fOUCgURc6v6oVUFrexff7551iwYAHq16+PtLQ0XL16FW3btkViYuILD3ip6rJe1OCZ7u7u+OWXXxASEoL79+8jKSkJCQkJxRoIVyaTYenSpdi2bRs6deoEmUyGkydPIjc3FyEhIVi6dCmqV69erHibNm2KvXv3ol27dlAqlfj7779RtWpVLFq0SBoQtCxZWloiPj4e48aNg4uLC86dO4fMzEyMHDkSCQkJxXq0eHkZP348pkyZgjp16uCvv/5CQkICEhISCvXa1GbhwoX4+OOP4eTkhJMnT0r1CyqvNnXy5Mk4d+4cxo4diyZNmuDOnTs4fvw47t+/j5YtW2LmzJm4ePEi+vTpo1avS5cuOHbsGN577z2YmZnhxIkTEEKge/fuSEpKwqBBg0oVT3FYWlpix44d2LBhA8LCwvDo0SMcP34cd+7cgbe3N8aNG4fDhw8X6xf00rb1BgYG2LZtG8LDw2FoaIjDhw8jISFBareL8rLOkZMmTcL48ePRrFkzPHz4ECdOnMDjx48REhKCtWvXYufOnSXqySKXy/Hee+/h3r172LFjR6liMjQ0lHqnzZs3T+rBVr9+fZw8eRKzZ89Gs2bNcO3aNZw4cQI5OTkICQnB3LlzsWnTJrVlDRs2DOvXr0eTJk1w7949XLx4EX5+fti+fTs+/PDDUsWnzctcF5DfA+bo0aPo06cP5HI5Tp8+DWtrayxevFjr7aMVTWn3mbe3N44ePYrevXvD3Nwcp0+fhpWVFZYsWYJFixYV60lt+oy/pFS3sem6fb2subi44MCBA+jRowdMTEzw999/w8zMDFOnTsWOHTteuIfbt99+i4iICFhbW+PYsWNISEjAwYMHAQA9e/bEZ599hnbt2sHQ0BCnTp3CjRs34OXlhRkzZuD06dPSeGsFbdy4EcbGxnj//fdfKDai8iATogT3BRBRhRUfH4/WrVsjJCQE8fHx+g6HSKvx48fjs88+w5gxYzBnzhx9h0NEpFFKSgrq16+PoKAg/P777/oOhyqZu3fvwt7eHjY2Nmq39r3Kli9fjo8++gjh4eH47rvv9B1OhbRnzx60adMGQ4cOxZdffqnvcIgKYQ8kIiJ6aXJzc6UeZqpfIomIKqJatWph6NCh+OOPP6QHaBC9LKtXrwag/cmJryLVQ154/tdu2rRpsLS0xKeffqrvUIg0qtij0hER0Wtl0aJFuHHjBkJCQqTHWRMRVVSTJk2CQqHAvXv39B0KvYZOnTqFAwcOoE+fPrC0tAQACCGwYcMG6WEQQ4YM0WeIZeaHH37A0aNHUbt27Re+Ff51lZGRgdDQUERHR8PR0VHf4RBpxFvYiF4TvIWNKqq0tDT07t0bd+/exenTp2FgYIC9e/eiZcuW+g6NiIhIb1TXboaGhnB1dUWVKlVw+fJl3L17F0D+U/uWL1+u5yhfTGhoKDIzM5GcnAwhBL799tuXNv4REZU93sJGRETlKjs7GwkJCTh37hwaNmyI//3vf0weERFRpefp6YmxY8eiUaNGSE9Pl5Isb7zxBjZt2vTKJ48AICEhASdPnkTt2rWxdOlSJo+IXnHsgURERERERERERDqxBxIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREenEBBIREREREREREelkpO8AXgVKpRLXr1+HlZUVZDKZvsMhIiIiLYQQyMzMRPXq1WFgwN/J9IXXTkRERK+O4l4/MYFUDNevX4ezs7O+wyAiIqJi+ueff1CzZk19h1Fp8dqJiIjo1VPU9RMTSMVgZWUFIH9nWltb6zkaIiIi0iYjIwPOzs7SuZv0g9dOREREr47iXj8xgVQMqq7X1tbWvAgiIiJ6BfC2Kf3itRMREdGrp6jrJw4OQEREREREREREOjGBREREREREREREOjGBREREREREREREOnEMJCIiemFCCDx9+hR5eXn6DoVec4aGhjAyMuIYR68Bthv0MhkbG8PQ0FDfYRARvdKYQCIioheSk5ODGzdu4NGjR/oOhSoJuVyOatWqwcTERN+hUCmx3aCXTSaToWbNmrC0tNR3KERErywmkIiIqNSUSiVSUlJgaGiI6tWrw8TEhD1DqNwIIZCTk4Pbt28jJSUFdevWhYEB78Z/1bDdoJdNCIHbt2/j33//Rd26ddkTiYiolJhAIiKiUsvJyYFSqYSzszPkcrm+w6FKwNzcHMbGxrh69SpycnJgZmam75CohNhukD44ODjgypUryM3NZQKJiKiU+LMdERG9MPYCoZeJx9vrge8jvUzs5UZE9OJ45iYiIiIiIiIiIp2YQCIiIionbm5uWLBgwQstIz4+HjKZDA8ePCiTmIio4mPbQUREFRETSEREVGnt378fhoaG6NChg75DAQCEhoZixIgRamWBgYG4ceMGFAqFfoIiokLYdhARUWXEBBIREVVa33zzDYYPH46kpCSkpqbqOxyNTExM4OTkxPE7iCoQth1ERFQZMYFERESVUlZWFv73v//ho48+QqdOnRAbGytNU9368ccff8DPzw9yuRyBgYE4d+6cNM+lS5fQtWtXODo6wtLSEs2aNcPvv/+udX0DBw5Ep06d1MqePn0KJycnfPPNN4iIiEBCQgIWLlwImUwGmUyGK1euaLwNZd++fQgJCYFcLoetrS3CwsJw//79Mts3RKQd2w4iIqqsjPQdABERvT7S04FTp/S3/kaNgOLerbF582Z4eHjAw8MDffv2xfDhwzF58mS1X+snTpyIefPmwcHBAUOGDMHAgQOxb98+AMDDhw/x1ltvYcaMGTAzM8OaNWvQuXNnnDt3Di4uLoXW9+GHHyI4OBg3btxAtWrVAADbt2/Hw4cP8c4776Bnz544f/48vLy8MG3aNADPHjtd0IkTJ/DGG29g4MCBWLRoEYyMjLBnzx7k5eWVYo8RVQxsO9h2EBFRxccEEhERlZlTp4CgIP2tPzERaNWqePOuWrUKffv2BQB06NABDx8+xB9//IG2bdtK88ycORMhISEAgPHjx6Njx47Izs6GmZkZGjdujMaNG0vzzpgxA1u2bMHPP/+MqKioQusLDAyEh4cH1q1bh7FjxwIAVq9ejbfffhuWlpYA8m85kcvlcHJy0hr3559/Dj8/PyxdulQqa9iwYfE2mqiCYtvBtoOIiCo+3sKmJ3l5wNOnz15ERPTynDt3DocPH0bv3r0BAEZGRujVqxe++eYbtfm8vb2lv1W//N+6dQtA/m0sY8eOhaenJ2xsbGBpaYm///5b53goH374IVavXi0tZ9u2bRg4cGCJYlf1IiCil49tBxER6cvTp8DXXwO3b+svBvZA0pP+/YFvv83/u2FD4PRp/cZDRFSZrFq1Ck+fPkWNGjWkMiEEjI2N1cYDMTY2lv5W3Z6iVCoBAJ988gl+++03zJ07F3Xq1IG5uTnCw8ORk5Ojdb39+/fH+PHjceDAARw4cABubm4IKmG3C3Nz8xLNT0Rlh20HERG9bEIpcOSnbZizpBq+390Up04BCxfqJxYmkIiIqMw0apR/K4g+11+Up0+fYu3atZg3bx7at2+vNq1nz57YsGEDvLy8ilxOYmIiIiIi0L17dwD545o8P+bI86pUqYJu3bph9erVOHDgAAYMGKA23cTEpMjxSLy9vfHHH39g6tSpRcZI9Kpg23FFZx22HUREldNfSUfx5OAnaF49HiOCAvH97iQsXSrD8OFAnTovPx4mkIiIqMwoFMUfR0Rftm7divv37+ODDz6A4rlRc8PDw7Fq1SrMnz+/yOXUqVMHcXFx6Ny5M2QyGSZPniz1MNDlww8/RKdOnZCXl4f3339fbZqbmxsOHTqEK1euwNLSEnZ2doXqT5gwAY0aNcLQoUMxZMgQmJiYYM+ePXj77bdhb29f5PqJKiK2HWw7iIjomX/OpiB160S0rLERqJ5f1rLefnTz+xF/nOuO06f1k0DiGEgVgBD6joCIqPJYtWoV2rZtW+gLIJDfi+DEiRM4fvx4kcuZP38+bG1tERgYiM6dOyMsLAy+vr5F1mvbti2qVauGsLAwVK9eXW3amDFjYGhoCE9PTzg4OGgcE6VevXrYuXMnTp48iebNmyMgIAA//fQTjIz4mxBReWLbQURE5e3BrfuInz8aVQ/Xz08eFZD71AgDep7HpUtAt276iU8mBNMXRcnIyIBCoUB6ejqsra3LZJl9+wIbNuT/7ekJnDlTJoslInqpsrOzkZKSglq1asHMzEzf4bwSHj16hOrVq+Obb75Bjx499B3OK0nXcVce52wqOV3vA9uN0mHb8WJ43BFRRZb7JAf71yxDI9k02FncKzT9wLVwVH/zv3D1qlsu6y/u9RN/ciAiInoJlEol0tLSMG/ePCgUCnTp0kXfIRHRK4BtBxHR60soBQ5v+RlVr3+CkCoXCk3/80ZLGDSdg4A+AXqIrjAmkIiIiF6C1NRU1KpVCzVr1kRsbCxvGyGiYmHbQUT0eko+/hTKPzqgRY0/gCrq01Lu1sWtap+j+ciukBnI9BOgBjwDVQC8iZCI6PXn5uYG3jVORCXFtoOI6PVy7RowaRKwZo0RVnxQC01rPJt2L8sOp8QUBEQOQS0zE/0FqQUH0dYTWcVJIhIRERERERFROcrKAmJigHr1gNjY/I4kk7+bjofZFsh5aoz4m6Ng0PUiQgZFw6QCJo8A9kAiIiIiIiIiIioXyjwl9m9Yi4nzW2DviQZq026mO2Fp8lr0GuyNUK86eoqw+JhAIiIiIiIiIiIqY8k79sDs7Gi0ckzG2LZvYe+JbdK0pk2BL74AgoNfnSdrMoFUAfC2diIiIiIiIqLXw5VTF3Bzxxi0qPEz4Jhf1tFnO9o12omz99tj1iygTx/A4BUbVIgJJD3hGEhEREREREREr4+Muxk4vnYGAqssgFuNXLVpD7MtEP3BP2gTCcjlegrwBTGBRERERERERERUSso8Jfatj0X97AkIdbylPk0pw760gagXPh2d3KrpKcKy8Yp1mCIiIqo8QkNDMWLECH2HQUTF8DI+rxEREejWrZvOeeLj4yGTyfDgwYNyjYWIiPKd2r0ffy9sjiDjD+BgpZ48On69DS7UTUbQmK/h+Ionj4AKmEBaunQpatWqBTMzMzRt2hSJiYnFqrdv3z4YGRmhSZMmhab98MMP8PT0hKmpKTw9PbFly5YyjvrFcAwkIqKXLyIiAjKZDEOGDCk0bejQoZDJZIiIiCjWsq5cuQKZTIYTJ06UbZBEVKGo2o3nXxcvXkRcXBymT58uzevm5oYFCxaU6foXLlyI2NhY6f+yTFrdv38f/fr1g0KhgEKhQL9+/YpMQpXHNhIRvSr+Tc3FvjnvoVFaS3g6HVOblnqvFg6axMFn1O/waNFYTxGWvQqVQNq8eTNGjBiBiRMnIjk5GUFBQXjzzTeRmpqqs156ejr69++PN954o9C0AwcOoFevXujXrx9OnjyJfv364Z133sGhQ4fKazOKhWMgERHpn7OzMzZt2oTHjx9LZdnZ2di4cSNcXFz0GFn5EULg6dOn+g6D6JXVoUMH3LhxQ+1Vq1Yt2NnZwcrKqlzXrVAoYGNjUy7L7tOnD06cOIEdO3Zgx44dOHHiBPr161cu63peTk7OS1kPEVFZePwYmDED8GhgjOv/qrdfD7MtEH//v6g64C/4h3eHzOD1+uJfoRJIX3zxBT744AN8+OGHaNCgARYsWABnZ2csW7ZMZ73BgwejT58+CAgIKDRtwYIFaNeuHSZMmID69etjwoQJeOONN/hrCRERwdfXFy4uLoiLi5PK4uLi4OzsDB8fH6lsx44daNWqFWxsbFClShV06tQJly5dkqbXqlULAODj4wOZTIbQ0FAA+beSNG/eHBYWFrCxsUHLli1x9epVAJpvRRkxYoRUV+Xp06eIioqS1j1p0iSIAl1X169fDz8/P1hZWcHJyQl9+vTBrVvPuk+rbmf57bff4OfnB1NTUyQmJuLSpUvo2rUrHB0dYWlpiWbNmuH3339/of1JVBmYmprCyclJ7WVoaKjWGyg0NBRXr17FyJEjpV5KmowePRqdO3eW/l+wYAFkMhm2bXv2mGcPDw+sWLECgHq7ERERgYSEBCxcuFBax5UrV6R6x44dg5+fH+RyOQIDA3Hu3Dmt23T27Fns2LEDX3/9NQICAhAQEICvvvoKW7du1VpP1zbu378fwcHBMDc3h7OzM6Kjo5GVlSVNd3Nzw4wZMxAREQGFQoHIyEjExsbCxsYGW7duhYeHB+RyOcLDw5GVlYU1a9bAzc0Ntra2GD58OPLy8qRlLV26FHXr1oWZmRkcHR0RHh6udTuJiF6EEMAPPwCensDkycCjR8An385Bdo4pACDpWj9khp5D6LAJMLMw03O05aPCDKKdk5ODY8eOYfz48Wrl7du3x/79+7XWW716NS5duoT169djxowZhaYfOHAAI0eOVCsLCwvTmUB68uQJnjx5Iv2fkZFRzK0gIiIASE1PRWq67t6jz/Oo4gEHCwe1spy8HBy+drjIui4KF7goStdjaMCAAVi9ejXee+89AMA333yDgQMHIj4+XponKysLo0aNQqNGjZCVlYVPP/0U3bt3x4kTJ2BgYIDDhw+jefPm+P3339GwYUOYmJjg6dOn6NatGyIjI7Fx40bk5OTg8OHDWr9IarNmzRp88MEHOHToEI4ePYpBgwbB1dUVkZGRAPLPn9OnT4eHhwdu3bqFkSNHIiIiAtu3b1dbztixYzF37lzUrl0bNjY2+Pfff/HWW29hxowZMDMzw5o1a9C5c2ecO3fute19Ra+ArNT8V0lYewBm6m0H8nKAu0W3HbBwyX+Vsbi4ODRu3BiDBg2SPquahIaGYtWqVVAqlTAwMEBCQgLs7e2RkJCAjh07Ii0tDefPn0dISEihugsXLsT58+fh5eWFadOmAQAcHBykJNLEiRMxb948ODg4YMiQIRg4cCD27dunMY4DBw5AoVCgRYsWUpm/vz8UCgX2798PDw+PYm/jqVOnEBYWhunTp2PVqlW4ffs2oqKiEBUVhdWrV0vzzZkzB5MnT8akSZMAAElJSXj06BEWLVqETZs2ITMzEz169ECPHj1gY2OD7du34/Lly+jZsydatWqFXr164ejRo4iOjsa6desQGBiIe/fuFXv4CyKikjh/+E9MmmaH77bVVCu/escNcxO+RNf+DdGqj7+eont5KkwC6c6dO8jLy4Ojo6NauaOjI9LS0jTWuXDhAsaPH4/ExEQYGWnelLS0tBItEwBmzZqFqVOnlnALSo9jIBHR6+ab5G8wNaFk7ei3Pb7Fu43eVSu7++guglYHFVl3SsgUxITGlGh9Kv369cOECROkcYz27duHTZs2qSWQevbsqVZn1apVqFq1Kv766y94eXnBwSH/y2uVKlXg5OQEALh37x7S09PRqVMnuLu7AwAaNGhQ4vicnZ0xf/58yGQyeHh44NSpU5g/f770hW3gwIHSvLVr18aiRYvQvHlzPHz4EJaWltK0adOmoV27dtL/VapUQePGz+7JnzFjBrZs2YKff/4ZUVFRJY6Tyt79+/cRHR2Nn3/+GQDQpUsXLF68WOctTEIITJ06FStXrsT9+/fRokULfPnll2jYsCGA/ONyypQp2LlzJ/755x/Y29ujW7dumD59OhQKxQutu0xc+gY4XcJrsMBvATf1tgM5d4Hfi2474DUF8I4p0eq2bt2q9tl688038d1336nNY2dnB0NDQ6lnoDbBwcHIzMxEcnIyfH19kZiYiDFjxki9Ivfs2QNHR0fUr1+/UF2FQgETExPI5XKN65g5c6aUeBo/fjw6duyI7OxsmJkV/lU8LS0NVatWLVRetWpVrdfM2rZxzpw56NOnj9Qbq27duli0aBFCQkKwbNkyaf1t2rTBmDFjpHpJSUnIzc3FsmXLpDYzPDwc69atw82bN2FpaQlPT0+0bt0ae/bsQa9evZCamgoLCwt06tQJVlZWcHV1Ves9SkT0ou7duIvT305GS8cV6O72Dr7DRmmakxMwezbQr98HMKhQ93aVnwq3mc//MiuE0PhrbV5eHvr06YOpU6eiXr16ZbJMlQkTJiA9PV16/fPPPyXYguLhGEhERBWDvb09OnbsiDVr1mD16tXo2LEj7O3t1ea5dOkS+vTpg9q1a8Pa2lq6ZU3XGH12dnaIiIhAWFgYOnfujIULF+LGjRsljs/f31/tnBUQEIALFy5It3AkJyeja9eucHV1hZWVlXQL3POx+fn5qf2flZWFsWPHwtPTEzY2NrC0tMTff/9d5LiD9PKUZkyazz//HF988QWWLFmCI0eOwMnJCe3atUNmZiYA4Pr167h+/Trmzp2LU6dOITY2Fjt27MAHH3zwwuuuLFq3bo0TJ05Ir0WLFpV6WQqFAk2aNEF8fDxOnToFAwMDDB48GCdPnkRmZibi4+M19j4qDm9vb+nvatXyn/xT8PbW52m6Ni7qmlmTY8eOITY2FpaWltIrLCwMSqUSKSkp0nzPt0kAIJfLpeQRkP+jr5ubm1rCztHRUdqOdu3awdXVFbVr10a/fv2wYcMGPHr0qETxEhFpkpebh73frAC21kNwtWUwNFDi3cBNaOWRCBMTYNw44Px54P33UWmSR0AF6oFkb28PQ0PDQr9y3Lp1q1APIgDIzMzE0aNHkZycLP1SqlQqIYSAkZERdu7ciTZt2sDJyanYy1QxNTWFqalpGWwVERG9CgYOHCidS7788stC0zt37gxnZ2d89dVXqF69OpRKJby8vIoc+HX16tWIjo7Gjh07sHnzZkyaNAm7du2Cv78/DAwM1MYyAoDc3NwSxZ2VlYX27dujffv2WL9+PRwcHJCamoqwsLBCsVlYWKj9/8knn+C3337D3LlzUadOHZibmyM8PJyD2VYQqjFpDh48KN1W9NVXXyEgIADnzp3TeEuREAILFizAxIkT0aNHDwD5t0A6Ojri22+/xeDBg+Hl5YUffvhBquPu7o6ZM2eib9++ePr0KYyMjEq17srEwsICderUKbPlhYaGIj4+HiYmJggJCYGtrS0aNmyIffv2IT4+vtRPWTM2Npb+ViWBlEqlxnmdnJxw8+bNQuW3b9/Wec2siVKpxODBgxEdHV1oWsHbY59vk56PWRW3pjLVdlhZWeH48eOIj4/Hzp078emnnyImJgZHjhwp/95yRPTaOp1wCAbHhiH4uSerAcCIt7eh8ftBKMPTwCulwiSQTExM0LRpU+zatQvdu3eXynft2oWuXbsWmt/a2hqnTp1SK1u6dCl2796N77//Xvp1OCAgALt27VIbB2nnzp0IDAwspy0hIqKBPgPRtnbbEtXxqFL4S2kVeRUkDih6PIvSjn+k0qFDBylxEhYWpjbt7t27OHv2LFasWIGgoPxbYpKSktTmMTExAQC1gV1VfHx84OPjgwkTJiAgIADffvst/P394eDggNOnT6vNe+LEiUJflg4ePFjo/7p168LQ0BB///037ty5g9mzZ8PZ2RkAcPTo0WJtc2JiIiIiIqRz7sOHD9UG4CX9Ks2YNCkpKUhLS0P79u2lMlNTU4SEhGD//v0YPHiwxnWlp6fD2tpaGg6gNOsus/Ej3QcCTiVrO2CtIaFlUgVoW4yxcMph/CMpBBMTjW3C81TjIBkZGaFt2/xtDwkJwaZNm7SOf1TSdRQlICAA6enp0nhuAHDo0CGkp6frvGbWtH5fX1+cOXOmTJNsuqj2W9u2bTFlyhTY2Nhg9+7dUhKViKi47ly7jbMbJyCo+irguTuDL91ugAd1FqLn9HaaK1cSFSaBBACjRo1Cv3794Ofnh4CAAKxcuRKpqakYMmQIgPxby65du4a1a9fCwMAAXl5eavWrVq0KMzMztfKPP/4YwcHB+Oyzz9C1a1f89NNP+P333wtd/OsTx0AiotfNiwxqXZCJoQlaubQqg4h0MzQ0xNmzZ6W/C7K1tUWVKlWwcuVKVKtWDampqYUe+FC1alWYm5tjx44dqFmzJszMzHDv3j2sXLkSXbp0QfXq1XHu3DmcP38e/fv3B5A//secOXOwdu1aBAQEYP369Th9+nSh8Tv++ecfjBo1CoMHD8bx48exePFizJs3D0D+r/kmJiZYvHgxhgwZgtOnT2P69OnF2uY6deogLi4OnTt3hkwmw+TJk7X2TqCXrzRj0qjKNY39qHr63/Pu3r2L6dOnqyWXSrPuMhs/sqwGtTY0AaqWf9uhi5ubG/bu3YvevXvD1NS00K2xKqpxkH755RfpgTChoaHo2bMnHBwc4OnpqXMdhw4dwpUrV2BpaQk7O7tSxdqgQQN06NABkZGR0hPfBg0ahE6dOunscaZpG8eNGwd/f38MGzYMkZGRsLCwwNmzZ7Fr1y4sXry4VPFps3XrVly+fBnBwcGwtbXF9u3boVQqK30vOSIqmbzcPCStWQFvMQlB1e+rTcvMtsSxnBi0HBINd1NjLUuoPCrU3Xq9evXCggULMG3aNDRp0gR79+7F9u3b4erqCgC4ceNGicdmCAwMxKZNm7B69Wp4e3sjNjYWmzdvVvtVTR84BhIRUcVibW0Na2vrQuUGBgbYtGkTjh07Bi8vL4wcORJz5sxRm8fIyAiLFi3CihUrUL16dXTt2hVyuRx///03evbsiXr16mHQoEGIioqSvqiHhYVh8uTJGDt2LJo1a4bMzEwpuVRQ//798fjxYzRv3hzDhg3D8OHDMWjQIAD5T1yKjY3Fd999B09PT8yePRtz584t1vbOnz8ftra2CAwMROfOnREWFgZfX9+S7jYqoZiYGOmR59peql5kpR2TprhjP2ZkZKBjx47w9PTElClTdC6jqHW/jPEjXzXTpk3DlStX4O7uLg20r4lCoYCPjw/s7OykZFFQUBCUSmWR4x+NGTMGhoaG8PT0lG5hLa0NGzagUaNG0m2x3t7eWLdunc46mrbR29sbCQkJuHDhAoKCguDj44PJkydL4zCVJRsbG8TFxaFNmzZo0KABli9fjo0bN0qDxhMRFeXUnoM4v6Q5QuTDYGuhnjzad+1dZLU+h9Aho2HM5BEAQCaeH4CBCsnIyIBCoZC6eJeFAQOA2Nj8v+vWzR+Ai4joVZOdnY2UlBTUqlVL45N9iMqDruOuPM7ZZenOnTu4c+eOznnc3Nzw7bffYtSoUXjw4IHaNBsbG8yfPx8DBgwoVO/y5ctwd3fH8ePH1Xqyde3aFTY2NlizZo1UlpmZibCwMMjlcmzdulVtP37zzTclXvfzdL0PbDdIH3jcEVFBt24B48cDfZzaoq3XH2rTLtxuiKz6S9AkLFQ/welBca+fKtQtbJUVU3hERESVg729vdZbmQoqzZg0tWrVgpOTE3bt2iUlkHJycpCQkIDPPvtMmi8jIwNhYWEwNTXFzz//XOjLdGnHwyEiIqronj4Fli0DJk8G0tOBgzUW4eR/G8PY6CkyHlvh+NOpaDkkij2OtKhQt7BVJryFjYiIiLQpOCbNwYMHcfDgQURGRhYak6Z+/frYsmULgPzbzkaMGIH//ve/2LJlC06fPo2IiAjI5XL06dMHQH7Po/bt2yMrKwurVq1CRkYG0tLSkJaWJg2GXNx1ExERvUr2JSnh5wdER+cnjwDg7DVPLNgxAknX+uLxG+cQOngkk0c6sAcSERERUQW0YcMGREdHS09V69KlC5YsWaI2z7lz55CuugoGMHbsWDx+/BhDhw7F/fv30aJFC+zcuRNWVlYAgGPHjuHQoUMAUOgpWSkpKXBzcyv2uomIiF4Fd67dxtlvx+LkGQucPKl+LmvUCPAf8hlaBbNvTXEwgURERERUAdnZ2WH9+vU653l+KEuZTIaYmBjExMRonD80NLRQndKum4iIqCJT5imxb91qNMwdi6Aa9xBYzQCr4j/Aias+sLYGpk0Dhg0DjIyYPCouJpAqAI6BRERERERERFQ2Lhw5hcd7P0JQtX2ASX6ZoYESSyKisDIlCZ99JoOTk35jfBUxgaQnHAOJiIiIKjM+CJheJh5vRJVDVnoWjqyehpZ2X8C42lO1aRduN4Rlq9lYE8Mv46XFBBIRERERvTTGxvmDkz569Ajm5uZ6joYqi5ycHACAoaGhniMhovJyeMvPqH59OEKrpqqVP3pijsOPY9ByCAfIflFMIBERERHRS2NoaAgbGxvcunULACCXyyFj12wqR0qlErdv34ZcLoeREb/+EL1urp1Pxb8/RaNFjZ8AW/Vph691QvXOixFa300vsb1u2IJWAOxRS0RERJWJ0/8PPKFKIhGVNwMDA7i4uDBZSfQayc0Fdq7cgFD5INSo8Uht2vUHNfFP1cVoProrZAb83JcVJpD0hOcuIqKKKTQ0FE2aNMGCBQvKbR0RERF48OABfvzxR63zxMfHo3Xr1rh//z5sbGzKLRYifZDJZKhWrRqqVq2K3NxcfYdDlYCJiQkMDPikJaLXxYEDwJAhgPxRbXSc+ix59DTPEEl3RsBvQAxa2FjqMcLXExNIRERU6URERGDNmjWFyi9cuIC4uDhpjBYAcHNzw4gRIzBixIgyW//ChQvVBnQty6TV/fv3ER0djZ9//hkA0KVLFyxevFhnEqo8tpGoOAwNDTkmDRERFdv9+8D48cDKlaqSAKz4YxAGv7ESp24EwDRoOUL7eeszxNcaE0hERFQpdejQAatXr1Yrc3BweClfZhUKRbktu0+fPvj333+xY8cOAMCgQYPQr18//PLLL+W2TpWcnByYmJiU+3qIiIiochFKgV+/u4CBH9fDzZvq0z77bRYatGqOViMGwMCQPQ3LE/duBcAxkIiIXj5TU1M4OTmpvQwNDREaGir1xAkNDcXVq1cxcuRIyGQyrWNnjB49Gp07d5b+X7BgAWQyGbZt2yaVeXh4YMWKFQDye0B169ZN+jshIQELFy6U1nHlyhWp3rFjx+Dn5we5XI7AwECcO3dO6zadPXsWO3bswNdff42AgAAEBATgq6++wtatW7XW07WN+/fvR3BwMMzNzeHs7Izo6GhkZWVJ093c3DBjxgxERERAoVAgMjISsbGxsLGxwdatW+Hh4QG5XI7w8HBkZWVhzZo1cHNzg62tLYYPH468vDxpWUuXLkXdunVhZmYGR0dHhIeHa91OIiIiqjyuXfgHR+Z1QevHjWEhLqlN698fOHjcDsEDPmDy6CVgDyQ94RhIRPQ6S09NR3pqepkv18zGDFW9qhYqf5LxBKbWpmW+vri4ODRu3BiDBg1CZGSk1vlCQ0OxatUqKJVKGBgYICEhAfb29khISEDHjh2RlpaG8+fPIyQkpFDdhQsX4vz58/Dy8sK0adMA5PeEUiWRJk6ciHnz5sHBwQFDhgzBwIEDsW/fPo1xHDhwAAqFAi1atJDK/P39oVAosH//fnh4eBR7G0+dOoWwsDBMnz4dq1atwu3btxEVFYWoqCi1nltz5szB5MmTMWnSJABAUlISHj16hEWLFmHTpk3IzMxEjx490KNHD9jY2GD79u24fPkyevbsiVatWqFXr144evQooqOjsW7dOgQGBuLevXtITEzU8c4QERHR6y4vNw+Jq5eiqdF/UKPGQwDAig8Go92sXfDwkGH5ciA0VL8xVjZMIBERUZlL/iYZCVMTyny57u3d0fe3voXKb525BecA5xIta+vWrbC0fDa44ptvvonvvvtObR47OzsYGhrCyspKemqUJsHBwcjMzERycjJ8fX2RmJiIMWPGIC4uDgCwZ88eODo6on79+oXqKhQKmJiYQC6Xa1zHzJkzpcTT+PHj0bFjR2RnZ8PMzKzQvGlpaahatXCCrWrVqkhLS9MYu7ZtnDNnDvr06SP1xqpbty4WLVqEkJAQLFu2TFp/mzZtMGbMGKleUlIScnNzsWzZMri7uwMAwsPDsW7dOty8eROWlpbw9PRE69atsWfPHvTq1QupqamwsLBAp06dYGVlBVdXV/j4+GiMl4iIiF5/54+cQk5iJEKdDqmVh9RPwJczTmLg6CbQcClE5YwJJCIiqpRat26NZcuWSf9bWFiUelkKhQJNmjRBfHw8jI2NYWBggMGDB2PKlCnIzMxEfHy8xt5HxeHt/WwgyGrVqgHIf/S5i4uLxvk13WYnhCjxo6uPHTuGixcvYsOGDWrLUSqVSElJQYMGDQAAfn5+herK5XIpeQQAjo6OcHNzU0vYOTo6So9wb9euHVxdXVG7dm106NABHTp0QPfu3SGXy0sUMxEREb3asrOycfDr6Whp+zmMnZ6qTTt1IwDmISsxtL+XnqIjJpAqAI6BRET08llYWKBOnTpltrzQ0FDEx8fDxMQEISEhsLW1RcOGDbFv3z7Ex8eX+glnBZ8Ip0oCKZVKjfM6OTnh5vMjSwK4ffs2HB0dS7RepVKJwYMHIzo6utC0gskrTYm3gjGr4tZUptoOKysrHD9+HPHx8di5cyc+/fRTxMTE4MiRIzqfHkdERESvj+Qd8bC5MAihDhfUyjMeWyFZzELQiI84zpGeMYGkJxwDiYheZz4DfVC7be0yX66Zjea+ylUbFr5tq6yYmJioDfasjWocJCMjI7Rt2xYAEBISgk2bNmkd/6ik6yhKQEAA0tPTcfjwYTRv3hwAcOjQIaSnpyMwMLBE6/f19cWZM2fKNMmmi2q/tW3bFlOmTIGNjQ12796NHj16vJT1ExERkX7cT7uHU+vGIrjGKqCK+rRD17rApceXCHGvqZ/gSA0TSEREVOYULgooXMrvUfXPK48BtFXc3Nywd+9e9O7dG6amprC3t9c4n2ocpF9++QUzZswAkJ9U6tmzJxwcHODp6alzHYcOHcKVK1dgaWkJOzu7UsXaoEEDdOjQAZGRkdIT3wYNGoROnTppHEBb1zaOGzcO/v7+GDZsGCIjI2FhYYGzZ89i165dWLx4cani02br1q24fPkygoODYWtri+3bt0OpVOqMmYiIiF5tQgA/b05DYHpjBNe4pTbtZoYTUuwWo8XonpAZsPdFRcH+X0RERDpMmzYNV65cgbu7OxwcHLTOp1Ao4OPjAzs7OylZFBQUBKVSWeT4R2PGjIGhoSE8PT3h4OCA1NTUUse7YcMGNGrUCO3bt0f79u3h7e2NdevW6ayjaRu9vb2RkJCACxcuICgoCD4+Ppg8ebI0DlNZsrGxQVxcHNq0aYMGDRpg+fLl2LhxIxo2bFjm6yIiIiL9+/dfoEsXoNu7Tth7tpXatL3XB8Gs51n4vxPO5FEFIxOCI/AUJSMjAwqFAunp6bC2ti6TZQ4aBHz1Vf7frq7A/z+tmYjolZKdnY2UlBTUqlVL41PBiMqDruOuPM7ZVHJ8H4iISBMhgK+/BsaMATIy8suq2VzH2TkNcPdRNWQ2WInG7YL1G2QlVNzzNm9h0xOOgURERERERESVxT9nU/DxGGts2a4+0NGdrOrYeOt3RHzcCGYW/EGyIuMtbERERERERERULpR5SiR8tQh2B73Qw/VjtWnNmgHHjwND/tOMyaNXABNIRERERERERFTmLp88h9MLghFi8TEsTB+hb6sN6OizFWZmwJw5wP79gJeXvqOk4uItbBUAR6EiIiIiIiKi18XTnKdIWjUP/vIpMKv2RG3ayG7rMX9zJ9Stq6fgqNSYQNITjoFEREREREREr5vzR07hadIAhDoeUyt/mG2BY8rP0XriEBgY6ik4eiFMIBERERERERHRC8nJzsH+r2YhUDETJo65atOOXWsHx84rEVLfTT/BUZlgAqkC4C1sRERERERE9Ko6u+8YDI8MQGjVU2rl6Y8U+NP4C7QaPQAyA96G86pjAklPeAsbERERERERvcqePAF+XbwSnRyHwqhqntq0Q9c6wzV8OYJqVddTdFTW+BQ2IiIiIiIiIiqR5GSgWTNg8qIAKMWz1MLdh1WwH9+i+eif4MTk0WuFCSQiIqICQkNDMWLEiHJdR0REBLp166Zznvj4eMhkMjx48KBcYyEiIiIqidxcYOpUoHlz4NQp4PQ/jTDlh6kAgP3X3oHyrb8Q2Odd3rL2GmICqQLgGEhERC9XREQEZDJZodfFixcRFxeH6dOnS/O6ublhwYIFZbr+hQsXIjY2Vvq/LJNWM2fORGBgIORyOWxsbIpVpzy2kYiIiF4/Z0/cg78/EBMDPH36rHz1wU+QZLwDgZ9shkPNqnqLj8oXE0h6wjGQiIj0q0OHDrhx44baq1atWrCzs4OVlVW5rluhUBQ7uVNSOTk5ePvtt/HRRx+Vy/KLWjcRERG9fp7mPEX80tlwPu4C3DumNq1HD+DPU0Zo9XaYnqKjl4UJJCIiqpRMTU3h5OSk9jI0NFTrDRQaGoqrV69i5MiRUi8lTUaPHo3OnTtL/y9YsAAymQzbtm2Tyjw8PLBixQoA6rewRUREICEhAQsXLpTWceXKFanesWPH4OfnB7lcjsDAQJw7d07ndk2dOhUjR45Eo0aNirUfdG3j/v37ERwcDHNzczg7OyM6OhpZWVnSdDc3N8yYMQMRERFQKBSIjIxEbGwsbGxssHXrVnh4eEAulyM8PBxZWVlYs2YN3NzcYGtri+HDhyMv79lgm0uXLkXdunVhZmYGR0dHhIeHFyt+IiIiKl+XT57D30taIdRmAizNsrBmyPswMXoCW1vg22+B778HqrLTUaXABBIREZW9rFTgVlL+6/Y+zfM8ufdsnltJwNOswvPk5ajP8/im5mU9Tiu72AuIi4tDzZo1MW3aNKmXkiahoaFITEyEUqkEACQkJMDe3h4JCQkAgLS0NJw/fx4hISGF6i5cuBABAQGIjIyU1uHs7CxNnzhxIubNm4ejR4/CyMgIAwcOfCnbeOrUKYSFhaFHjx74888/sXnzZiQlJSEqKkqt/pw5c+Dl5YVjx45h8uTJAIBHjx5h0aJF2LRpE3bs2IH4+Hj06NED27dvx/bt27Fu3TqsXLkS33//PQDg6NGjiI6OxrRp03Du3Dns2LEDwcHBZbqdREREVDLKPCXiV8xHteQm8HI6JJV7OZ/BZx99izNngHff5d01lYmRvgMgjoFERK+hS98Ap/MHU4SBCdD7SeF57uwHEp712sFbpwAbL/V5cu4Cvwc9+99/NVA7ovCyrv0C1IksUYhbt26FpaWl9P+bb76J7777Tm0eOzs7GBoawsrKCk5OTlqXFRwcjMzMTCQnJ8PX1xeJiYkYM2YM4uLiAAB79uyBo6Mj6tevX6iuQqGAiYkJ5HK5xnXMnDlTSjyNHz8eHTt2RHZ2NszMzEq0vdpo28Y5c+agT58+Um+sunXrYtGiRQgJCcGyZcuk9bdp0wZjxoyR6iUlJSE3NxfLli2Du7s7ACA8PBzr1q3DzZs3YWlpCU9PT7Ru3Rp79uxBr169kJqaCgsLC3Tq1AlWVlZwdXWFj49PmWwfERERldzVM5fw4NcBCK2eqFae/tgap4wX4uMF70PG7iiVDhNIesIsLRGRfrVu3RrLli2T/rewsCj1shQKBZo0aYL4+HgYGxvDwMAAgwcPxpQpU5CZmYn4+HiNvY+Kw9vbW/q7WrVqAIBbt27BxcWl1PEWx7Fjx3Dx4kVs2LBBKhNCQKlUIiUlBQ0aNAAA+Pn5Faorl8ul5BEAODo6ws3NTS1h5+joiFu3bgEA2rVrB1dXV9SuXRsdOnRAhw4d0L17d8jl8vLaPCIiItJAmadE4url8DP8BK7VH6lNO3qtPap3/xqt6jhrqU2vOyaQiIioUrKwsECdOnXKbHmhoaGIj4+HiYkJQkJCYGtri4YNG2Lfvn2Ij48v9VPWjI2Npb9V4xOpbpUrT0qlEoMHD0Z0dHShaQWTV5oSbwVjBvLj1lSm2g4rKyscP34c8fHx2LlzJz799FPExMTgyJEj5TbYOBEREam7celfXI8bgJAav6uVP8y2wHHMQ9DoQZAZsCdEZcYEEhERlT33gYBT2/y/tXW5tA8E2hboFm1Zq/A8JlXU57Gqq3lZNTprLi8DJiYmaoM9axMaGopVq1bByMgIbdvmb3tISAg2bdqkdfyjkq6jvGhav6+vL86cOVOmSTZdVPutbdu2mDJlCmxsbLB792706NHjpayfiIioMtv37SY0fPQRmtZ4oFaefD0U9h2/QXADDddpVOkwgVQBcAwkInrtWLjkv3QxtQOqttI9j6FJ0fMAgLn28YlelJubG/bu3YvevXvD1NQU9vb2GudTjYP0yy+/YMaMGQDyk0o9e/aEg4MDPD09da7j0KFDuHLlCiwtLWFnZ1fqeFNTU3Hv3j2kpqYiLy8PJ06cAADUqVNH7RayorZx3Lhx8Pf3x7BhwxAZGQkLCwucPXsWu3btwuLFi0sdnyZbt27F5cuXERwcDFtbW2zfvh1KpRIeHh5luh4iIiJSd/8+MGwY0NZyJ1qGPpDKHz0xx5GnnyFo5DAYGHKwI8rHI0FPOAYSEdGrYdq0abhy5Qrc3d3h4OCgdT6FQgEfHx/Y2dlJyaKgoCAolcoixz8aM2YMDA0N4enpCQcHB6SmppY63k8//RQ+Pj6YMmUKHj58CB8fH/j4+ODo0aNa62jaRm9vbyQkJODChQsICgqCj48PJk+eLI3DVJZsbGwQFxeHNm3aoEGDBli+fDk2btyIhg0blvm6iIiIKN/vvwONGgEbNwIj1i9Ayi03AMDptOa42fQEQiKHM3lEamRCsP9LUTIyMqBQKJCeng5ra+syWWZUFPDll/l/V68OXLtWJoslInqpsrOzkZKSglq1apXZU8GIiqLruCuPczaVHN8HIqKK6/FjYMIEYOFC9fKQBomI+Wg3Wg2eCCMT3qxUmRT3vM2jgoiIiIiIiKgSOLs/GUc2r8HCRfMBPLstpl49YM6aIDRrFqS/4KjCq3D90ZYuXSr9oti0aVMkJiZqnTcpKQktW7ZElSpVYG5ujvr162P+/Plq88TGxkImkxV6ZWdnl/emFBv7gBEREREREVF5ycvNw54vZ8H9Ygv0b7EQka2/kqYNHQokJwPNmukxQHolVKgeSJs3b8aIESOwdOlStGzZEitWrMCbb76Jv/76S+2RwSoWFhaIioqCt7c3LCwskJSUhMGDB8PCwgKDBg2S5rO2tsa5c+fU6ur7VguOgURERERERETlLfWvy7i/vT9aV98nlc3vOxKnbrXBp3Pq4M039RgcvVIqVALpiy++wAcffIAPP/wQALBgwQL89ttvWLZsGWbNmlVoftXAoCpubm6Ii4tDYmKiWgJJJpPByan8ntBDREREREREVJEIpUDS2m/QRDkCLtUfqk378+6b2LrTFlWq6yk4eiVVmFvYcnJycOzYMbRv316tvH379ti/f3+xlpGcnIz9+/cXetrNw4cP4erqipo1a6JTp05ITk7WuZwnT54gIyND7UVERERERET0Krh7/Q4Oz+uOIJMPYWX2LHmU/tgaScq18B/9HapUr6LHCOlVVGESSHfu3EFeXh4cHR3Vyh0dHZGWlqazbs2aNWFqago/Pz8MGzZM6sEEAPXr10dsbCx+/vlnbNy4EWZmZmjZsiUuXLigdXmzZs2CQqGQXs7Ozi+2cUXgGEhERERERERUFo5t24WnPzdCixo/qZUnXw9BZqs/0apvP8gMOKYKlVyFuoUNyL/drCAhRKGy5yUmJuLhw4c4ePAgxo8fjzp16uDdd98FAPj7+8Pf31+at2XLlvD19cXixYuxaNEijcubMGECRo0aJf2fkZFR5kkkjoFEREREREREZeXJoyc4sGIiQh3nAQWexP4k1wQHHs1E0McjYWhsqL8A6ZVXYRJI9vb2MDQ0LNTb6NatW4V6JT2vVq1aAIBGjRrh5s2biImJkRJIzzMwMECzZs109kAyNTWFqalpCbeAiIiIiIiI6OX7+6zA41/aIrRmklr5xdueUAZsRGhzbz1FRq+TCnMLm4mJCZo2bYpdu3aple/atQuBgYHFXo4QAk+ePNE5/cSJE6hWrVqpYyUiIiIiIiLSNyGAlSsB36YyfPnr+2rTEm4MQ40PjqIek0dURipMDyQAGDVqFPr16wc/Pz8EBARg5cqVSE1NxZAhQwDk31p27do1rF27FgDw5ZdfwsXFBfXr1wcAJCUlYe7cuRg+fLi0zKlTp8Lf3x9169ZFRkYGFi1ahBMnTuDLL798+RuoBcdAIiKqOEJDQ9GkSRMsWLCg3NYRERGBBw8e4Mcff9Q6T3x8PFq3bo379+/Dxsam3GIhIiKiV9Pdu0BkJLBlS/7/q+I/wJuNf0WI515cdvgGIaM76zdAeu1UqARSr169cPfuXUybNg03btyAl5cXtm/fDldXVwDAjRs3kJqaKs2vVCoxYcIEpKSkwMjICO7u7pg9ezYGDx4szfPgwQMMGjQIaWlpUCgU8PHxwd69e9G8efOXvn0FcQwkIiL9iYiIwJo1awqVX7hwAXFxcTA2NpbK3NzcMGLECIwYMaLM1r9w4UKIAr8elFXS6sqVK5g+fTp2796NtLQ0VK9eHX379sXEiRNhYmKitV55bCMRERGVn/g/HqHv+3Jcu1awVIaNl75CqxFP0NyNd9xQ2atQCSQAGDp0KIYOHapxWmxsrNr/w4cPV+ttpMn8+fMxf/78sgqPiIheEx06dMDq1avVyhwcHGBoWP6DSyoUinJZ7t9//w2lUokVK1agTp06OH36NCIjI5GVlYW5c+eWyzoLysnJ0ZmoIiIioheTk52D/cs/hbP4Dpn3jgPIv6YwNgZmzwZGjLCDQYUZqIZeNzy0KgDewkZE9PKZmprCyclJ7WVoaIjQ0FCpJ05oaCiuXr2KkSNHQiaTaX0q6OjRo9G587Nu4gsWLIBMJsO2bdukMg8PD6xYsQJAfg+obt26SX8nJCRg4cKF0jquXLki1Tt27Bj8/Pwgl8sRGBiIc+fOad0mVVKsffv2qF27Nrp06YIxY8YgLi5Oax1d27h//34EBwfD3Nwczs7OiI6ORlZWljTdzc0NM2bMQEREBBQKBSIjIxEbGwsbGxts3boVHh4ekMvlCA8PR1ZWFtasWQM3NzfY2tpi+PDhyMvLk5a1dOlS1K1bF2ZmZnB0dER4eLjWmImIiCqjlD8v4MLSlgit+hncHS/jy4hhAID69YFDh4BRo8DkEZWrCtcDqbLgLWxE9FpLTc1/FcXICPD3f/b/hQvAzZv5f5uZAX5+z6adPZt/s78mLi75rzIWFxeHxo0bY9CgQYiMjNQ6X2hoKFatWgWlUgkDAwMkJCTA3t4eCQkJ6NixI9LS0nD+/HmEhIQUqrtw4UKcP38eXl5emDZtGoD8nlCqJNLEiRMxb948ODg4YMiQIRg4cCD27dtX7G1IT0+HnZ1dibfx1KlTCAsLw/Tp07Fq1Srcvn0bUVFRiIqKUuu5NWfOHEyePBmTJk0CkD8e4aNHj7Bo0SJs2rQJmZmZ6NGjB3r06AEbGxts374dly9fRs+ePdGqVSv06tULR48eRXR0NNatW4fAwEDcu3cPiYmJxd5GIiKi15lQCiSti4VP3nBYOj37Iadvqw24ZhGF4VP9IZfrMUCqNJhAIiKisvfNN8DUqUXPZ28P3L797P/PPgNWrcr/290duHjx2bRPPwW+/17zcqZMAWJiShTi1q1bYWlpKf3/5ptv4rvvvlObx87ODoaGhrCysoKTk5PWZQUHByMzMxPJycnw9fVFYmKiWs+fPXv2wNHRUXroQ0EKhQImJiaQy+Ua1zFz5kwp8TR+/Hh07NgR2dnZMDMzK3IbL126hMWLF2PevHla59G2jXPmzEGfPn2k3lh169bFokWLEBISgmXLlknrb9OmDcaMGSPVS0pKQm5uLpYtWwZ3d3cAQHh4ONatW4ebN2/C0tISnp6eaN26Nfbs2YNevXohNTUVFhYW6NSpE6ysrODq6gofH58it4+IiOh1l3E3A6e/GYygGpuAZ0M04l6WHS7YrsK4Of7aKxOVMSaQiIioUmrdujWWLVsm/W9hYVHqZSkUCjRp0gTx8fEwNjaGgYEBBg8ejClTpiAzMxPx8fEaex8Vh7f3s0fvVquWPyDmrVu34FJEj6vr16+jQ4cOePvtt/Hhhx+WeL3Hjh3DxYsXsWHDBqlMCAGlUomUlBQ0aNAAAOBXsJfY/5PL5VLyCAAcHR3h5uamlrBzdHTErVu3AADt2rWDq6srateujQ4dOqBDhw7o3r075Pw5lYiIKrG/ko7CIrk3AmtcUis/fr0NqvVYixa1a+gpMqqsmECqADgGEhHRy2dhYYE6deqU2fJCQ0MRHx8PExMThISEwNbWFg0bNsS+ffsQHx9f6iecFXwinGp8IqVSqbPO9evX0bp1awQEBGDlypWlWq9SqcTgwYMRHR1daFrB5JWmxFvBmIH8uDWVqbbDysoKx48fR3x8PHbu3IlPP/0UMTExOHLkCGxsbEoVPxER0atKKAX2frUQAeZjYVIlVyrPfWqEfQ9nIHjkJzAw5GBH9PIxgaQnHAOJiF5rAwcCbdsWPZ/Rc6ehceOAiIj8v5+/RWvaNODjjzUvpxzGP1IxMTFRG+xZG9U4SEZGRmj7/9seEhKCTZs2aR3/qKTrKI5r166hdevWaNq0KVavXg2DYoymqWn9vr6+OHPmTJkm2XRR7be2bdtiypQpsLGxwe7du9GjR4+Xsv6K6P79+4iOjsbPP/8MAOjSpQsWL16sM6kmhMDUqVOxcuVK3L9/Hy1atMCXX36Jhg0bAgDu3buHKVOmYOfOnfjnn39gb2+Pbt26Yfr06WpPB3Rzc8PVq1fVlj1u3DjMnj277DeUiIgk927cxcX1EQipsVWt/J97bkhvtAmhIS30FBkRE0hERFQeSjuodd26+S9N/v+WqZfNzc0Ne/fuRe/evWFqagp7e3uN86nGQfrll18wY8YMAPlJpZ49e8LBwQGenp4613Ho0CFcuXIFlpaWOge91uX69esIDQ2Fi4sL5s6di9sFxpfSNYaTpm0cN24c/P39MWzYMERGRsLCwgJnz57Frl27sHjx4lLFp83WrVtx+fJlBAcHw9bWFtu3b4dSqYSHh0eZrudV06dPH/z777/YsWMHAGDQoEHo168ffvnlF611Pv/8c3zxxReIjY1FvXr1MGPGDLRr1w7nzp2DlZUVrl+/juvXr2Pu3Lnw9PTE1atXMWTIEFy/fh3fPzfG2LRp09QGVi94CyIREZW9xEQAu3shqO4fauUHroXDM+IrODvY6CUuIhUmkIiIiHSYNm0aBg8eDHd3dzx58gRCy33HCoUCPj4+SE1NlZJFQUFBUCqVRY5/NGbMGLz//vvw9PTE48ePkZKSUqpYd+7ciYsXL+LixYuoWbOm2jRtcQOat9Hb2xsJCQmYOHEigoKCIISAu7s7evXqVarYdLGxsUFcXBxiYmKQnZ2NunXrYuPGjVKvmcro7Nmz2LFjBw4ePIgWLfJ/bf7qq68QEBCAc+fOaUyuCSGwYMECTJw4Ueq5tWbNGjg6OuLbb7/F4MGD4eXlhR9++EGq4+7ujpkzZ6Jv3754+vQpjAr0Cixq8HgiIiobeXnAf/+b/zyQRs5zcTDGH2YmT/A4xwxHlAsQNHoQZAa8hYX0TyZ0XVESACAjIwMKhQLp6emwtrYuk2WOHAksWJD/9/MPISIielVkZ2cjJSUFtWrVKtZTwYjKgq7jrjzO2frwzTffYNSoUXjw4IFauY2NDebPn48BAwYUqnP58mW4u7vj+PHjak+x69q1K2xsbLBmzRqN6/r6668xYcIEtR5rbm5uePLkCXJycuDs7Iy3334bn3zyCUxMTIoV/+vyPhARlbfr14G+fYE9e56VDW33JUZ1WoK8gP+hXrNG+guOKo3inrfZA0lPOAYSERERaZOWloaqVasWKq9atSrS0tK01gHyn3BXkKOjY6HxjFTu3r2L6dOnY/DgwWrlH3/8MXx9fWFra4vDhw9jwoQJSElJwddff61xOU+ePMGTJ0+k/zMyMrRvHBERAQD2bL+GXhE1CnUmeFxzKJwiBsLC2lw/gRFpwaHbiYiIiF6SmJgYyGQyna+jR48CePbUvYKEEBrLC3p+urY6GRkZ6NixIzw9PTFlyhS1aSNHjkRISAi8vb3x4YcfYvny5Vi1ahXu3r2rcZ2zZs2CQqGQXs7OzjpjJCKqzHKf5GLP/HHwv1UH1eUnpHJLS2D9euCbb2RMHlGFxB5IRERERC9JVFQUevfurXMeNzc3/Pnnn7h582ahabdv3y7Uw0hFNV5RWloaqlWrJpXfunWrUJ3MzEx06NABlpaW2LJlC4yNjXXG5O/vDwC4ePEiqlSpUmj6hAkTMGrUKOn/jIwMJpGIiDT499xVPNjWC62dDgEANkf1QtNJx1DP0xKbN2t/lghRRcAEUgXAUaiIiIgqB3t7e61P8isoICAA6enpOHz4MJo3bw4AOHToENLT0xEYGKixTq1ateDk5IRdu3ZJYyDl5OQgISEBn332mTRfRkYGwsLCYGpqip9//rlY45clJycDgFpiqiBTU1OYmpoWuRwiosrsyI/bUOd2P9R0ui+VeVQ/j6X/+Rm9xvUBm1Gq6JhA0hOOgURERETaNGjQAB06dEBkZCRWrFgBABg0aBA6deqk9gS2+vXrY9asWejevTtkMhlGjBiB//73v6hbty7q1q2L//73v5DL5ejTpw+A/J5H7du3x6NHj7B+/XpkZGRI4xU5ODjA0NAQBw4cwMGDB9G6dWsoFAocOXIEI0eORJcuXeDi4vLydwYR0Svuac5TJC2djNCqswGLZ+X3s2xxvkos+n/aRX/BEZUAE0hEREREFdCGDRsQHR2N9u3bAwC6dOmCJUuWqM1z7tw5pKenS/+PHTsWjx8/xtChQ3H//n20aNECO3fuhJWVFQDg2LFjOHQo/7aJOnXqqC0rJSUFbm5uMDU1xebNmzF16lQ8efIErq6uiIyMxNixY8tzc4mIXktpKdeR9sO7CK2+V6381A1/2HXejBb1mJinVwcTSEREREQVkJ2dHdavX69zHvHcffAymQwxMTGIiYnROH9oaGihOs/z9fXFwYMHSxQrEREVdnz773D+pw+aVFd/zFp82kgEDpsNEzMTPUVGVDpMIFUAHAOJiIiIiIjo9ZCXm4fE5TMRbBsDA6tnX/bSH1vjrCIWoaO66zE6otJjAklPOAYSERERERHR6+X2bWDrZ59hgO8UtfKzN30gb/8d/Bu66ykyohdnoO8AiIiIKpLQ0FCMGDGiXNcRERGBbt266ZwnPj4eMpkMDx48KNdYiIiIqGwkJQE+PsCIpcNwMe1Zomjv9cGoNWg/XJk8olccE0hERFTpREREQCaTFXpdvHgRcXFxmD59ujSvm5sbFixYUKbrX7hwIWJjY6X/yzJppXpSlpmZGapVq4Z+/frh+vXrOuuUxzYSERFVFkIAc+YAoaHAtWtAxmMFwhd9j7sPq2CfWI/gMcthZmGm7zCJXhhvYasAOAYSEdHL16FDB6xevVqtTPUY8/KmUCjKbdmtW7fGf/7zH1SrVg3Xrl3DmDFjEB4ejv3795fbOlVycnJgYsIBQYmIqPK4fzsL738gxy+/qI9R8tSyCW4HXkVLLws9RUZU9tgDSU84BhIRkX6ZmprCyclJ7WVoaKjWGyg0NBRXr17FyJEjpV5KmowePRqdO3eW/l+wYAFkMhm2bdsmlXl4eGDFihUA1G9hi4iIQEJCAhYuXCit48qVK1K9Y8eOwc/PD3K5HIGBgTh37pzO7Ro5ciT8/f3h6uqKwMBAjB8/HgcPHkRubq7G+XVt4/79+xEcHAxzc3M4OzsjOjoaWVlZ0nQ3NzfMmDEDERERUCgUiIyMRGxsLGxsbLB161Z4eHhALpcjPDwcWVlZWLNmDdzc3GBra4vhw4cjLy9PWtbSpUtRt25dmJmZwdHREeHh4Tq3k4iISN/O7j+Oh5u9UDVzlVp5//7AoUNAfSaP6DXDHkhERFTmsq5fR9aNGy+0DENTU1Tx8pL+T790CU+0jAdkUa0aLKpXf6H1aRIXF4fGjRtj0KBBiIyM1DpfaGgoVq1aBaVSCQMDAyQkJMDe3h4JCQno2LEj0tLScP78eYSEhBSqu3DhQpw/fx5eXl6YNm0agPyeUKok0sSJEzFv3jw4ODhgyJAhGDhwIPbt21es+O/du4cNGzYgMDAQxsbGJdrGU6dOISwsDNOnT8eqVatw+/ZtREVFISoqSq3n1pw5czB58mRMmjQJAJCUlIRHjx5h0aJF2LRpEzIzM9GjRw/06NEDNjY22L59Oy5fvoyePXuiVatW6NWrF44ePYro6GisW7cOgYGBuHfvHhITE4u1jURERPqQuGY1muEjmNk9wZL3o3A0xQ/nbjbBkiXAwIHsMECvJyaQiIiozF3asgWnly59oWVYOjujy44d0v9/LlmCf3bu1Div19Ch8B42rETL37p1KywtLaX/33zzTXz33Xdq89jZ2cHQ0BBWVlZwcnLSuqzg4GBkZmYiOTkZvr6+SExMxJgxYxAXFwcA2LNnDxwdHVG/fv1CdRUKBUxMTCCXyzWuY+bMmVLiafz48ejYsSOys7NhZqZ9LIVx48ZhyZIlePToEfz9/bF161at82rbxjlz5qBPnz5Sb6y6deti0aJFCAkJwbJly6T1t2nTBmPGjJHqJSUlITc3F8uWLYO7e/5goeHh4Vi3bh1u3rwJS0tLeHp6onXr1tizZw969eqF1NRUWFhYoFOnTrCysoKrqyt8fHy0xkxERKQvTx49waGl0QiuvlIqMzN5ggUR/4Ft9+1o3FiPwRGVM97CVgFwDCQiopevdevWOHHihPRatGhRqZelUCjQpEkTxMfH49SpUzAwMMDgwYNx8uRJZGZmIj4+XmPvo+Lw9vaW/q5WrRoA4NatWzrrfPLJJ0hOTsbOnTthaGiI/v37Q5TwZHPs2DHExsbC0tJSeoWFhUGpVCIlJUWaz8/Pr1BduVwuJY8AwNHREW5ubmoJO0dHR2k72rVrB1dXV9SuXRv9+vXDhg0b8OjRoxLFS0REVN6unU/FxeVBaskjADh0rSt8ojYyeUSvPfZA0hN2aSQi0i8LCwvUqVOnzJYXGhqK+Ph4mJiYICQkBLa2tmjYsCH27duH+Pj4Uj9lreCtZ6rxiZRKpc469vb2sLe3R7169dCgQQM4Ozvj4MGDCAgIKPZ6lUolBg8ejOjo6ELTXFxcpL8tLAqP7/D87XIymUxjmWo7rKyscPz4ccTHx2Pnzp349NNPERMTgyNHjsDGxqbYMRMREZWX49t/h+u/vdHQ6a5Ulqc0QGLGTASPGgsDQ/bNoNcfE0hERFTm3Lt3h5O//wstw9DUVO1/76goePTtq3Fei//vmVMeTExM1AZ71kY1DpKRkRHatm0LAAgJCcGmTZu0jn9U0nWUhqrn0ZMnT0q0fl9fX5w5c6ZMk2y6qPZb27ZtMWXKFNjY2GD37t3o0aPHS1k/ERGRJso8JfYun40gxWQYWj77AefOQ3uk1tyE0L5v6DE6opeLCaQKgLewEdHrxqJ69TIf1FpR4Jaol8nNzQ179+5F7969YWpqCnt7e43zqcZB+uWXXzBjxgwA+Umlnj17wsHBAZ6enjrXcejQIVy5cgWWlpaws7MrVayHDx/G4cOH0apVK9ja2uLy5cv49NNP4e7urrP3kaZtHDduHPz9/TFs2DBERkbCwsICZ8+exa5du7B48eJSxafN1q1bcfnyZQQHB8PW1hbbt2+HUqmEh4dHma6HiIioJNLvpOPv1e8jtMZPauWn05rDrsv38K3jrKfIiPSD/ez0hLewERG9GqZNm4YrV67A3d0dDg4OWudTKBTw8fGBnZ2dlCwKCgqCUqkscvyjMWPGwNDQEJ6ennBwcEBqamqpYjU3N0dcXBzeeOMNeHh4YODAgfDy8kJCQgJMn+vRVdQ2ent7IyEhARcuXEBQUBB8fHwwefJkaRymsmRjY4O4uDi0adMGDRo0wPLly7Fx40Y0bNiwzNdFRERUHGeP38T9jX5o8VzyKOH6ENQdshfVmTyiSkgmSjqqZiWUkZEBhUKB9PR0WFtbl8kyx40DPv88/2+FAtDyZGoiogotOzsbKSkpqFWrls6nghGVJV3HXXmcs6nk+D4Q0avs22+ByEiBdYN6okezLQCAxzlmOGawDK36R+g3OKJyUNzzNm9hIyIiIiIiokovJwcYMwbIv1NbhogVsWhY4wzMzXLxqOkPaBXgo+8QifSKCaQKgH3AiIiIiIiI9CctDXj7bSAp6VlZ5mNrfH54G+YusoOLU+nGJyR6nXAMJD3hGEhERERERET6d2bvYWz6z2S15JFMBsTEAF9tqgNbJo+IALAHEhEREREREVVSSWtj4SeGoGHbJ/jrijO+2jMINjbAhg3AW2/pOzqiioUJJCIiIiIiIqpUcp/kYv+S0QiptlgqWxIRhUyjJpixrDnc3fUYHFEFxQRSBcAxkIjoVadUKvUdAlUiPN6IiOhF3Ll2C/9ufAch1RPUyo/d7IKv/ucJSxv9xEVU0TGBpCccA4mIXgcmJiYwMDDA9evX4eDgABMTE8jYwFE5EUIgJycHt2/fhoGBAUxMTPQdEhERvWLO7j8G6xPd0aT6P1KZUinD3vQZCBk9ATIDXscQacMEEhERlZqBgQFq1aqFGzdu4Pr16/oOhyoJuVwOFxcXGBjwWSBERFR8+zash29uJMxtsqWy9EcKnHf4FqF9OeARUVGYQCIiohdiYmICFxcXPH36FHl5efoOh15zhoaGMDIyYk83IiIqtqc5T7Hvy7EIcZwPFOi8eul2Axi0/hHNvOvpLziiVwgTSBUAx0AioledTCaDsbExjI2N9R0KERERkeTejTu4sqEXQqrvVis/eK0bPAeugXUVaz1FRvTqYd9vPeEPp0REREREROXn5ElgwcjN8H0ueRR/dyqaj/qBySOiEmIPJCIiIiIiInqtfP898P77wKNHQ+HpkIjeAZuR8dgKZ23XI3R4F32HR/RKqnA9kJYuXYpatWrBzMwMTZs2RWJiotZ5k5KS0LJlS1SpUgXm5uaoX78+5s+fX2i+H374AZ6enjA1NYWnpye2bNlSnptAREREREREeqBUAjExwNtvA48eAYAMH3y1Cr+eeRt3mh5Gix5MHhGVVoVKIG3evBkjRozAxIkTkZycjKCgILz55ptITU3VOL+FhQWioqKwd+9enD17FpMmTcKkSZOwcuVKaZ4DBw6gV69e6NevH06ePIl+/frhnXfewaFDh17WZhWJYyARERERERG9mKz0LPTp/QRTp6qXt25rgcCx/0PtJvX1ExjRa0ImRMVJX7Ro0QK+vr5YtmyZVNagQQN069YNs2bNKtYyevToAQsLC6xbtw4A0KtXL2RkZODXX3+V5unQoQNsbW2xcePGYi0zIyMDCoUC6enpsLYum/tkPx9yGr+svgEjWS4MTU3we3rbMlkuERFRZVYe52wqOb4PRPSyXb+QioytXbH/rA8++GoVgPxBZydMAGbMAAwqVNcJooqluOftCjMGUk5ODo4dO4bx48erlbdv3x779+8v1jKSk5Oxf/9+zJgxQyo7cOAARo4cqTZfWFgYFixY8MIxvwjPxI9Qs849AIDZEwHgL73GQ0RERERE9Co6tWc/nM53R33HW6jveAKn/mmEZXtGYtUq4L339B0d0eujwiSQ7ty5g7y8PDg6OqqVOzo6Ii0tTWfdmjVr4vbt23j69CliYmLw4YcfStPS0tJKvMwnT57gyZMn0v8ZGRkl2ZTikRkA/9/3S/CJbERERERERCWWtHYNmolBMLXKkco+6TwPfT6NRLMASz1GRvT6qXAd+WTPPd9eCFGo7HmJiYk4evQoli9fjgULFhS6Na2ky5w1axYUCoX0cnZ2LuFWFEfBXV9h7iIkIiIiIiKq8PJy8xA//xO0MoqAqfGz5NHZm76QhR1k8oioHFSYHkj29vYwNDQs1DPo1q1bhXoQPa9WrVoAgEaNGuHmzZuIiYnBu+++CwBwcnIq8TInTJiAUaNGSf9nZGSUfRKpQA8ksAcSERERERFRsWTczcC5b95FaI3tauUHrr2DxoNXQ24t11NkRK+3CtMDycTEBE2bNsWuXbvUynft2oXAwMBiL0cIoXb7WUBAQKFl7ty5U+cyTU1NYW1trfYqc7Jnu579j4iIiIiIiIp29cxF3F7vj2bPJY/i702H/+hNTB4RlaMK0wMJAEaNGoV+/frBz88PAQEBWLlyJVJTUzFkyBAA+T2Drl27hrVr1wIAvvzyS7i4uKB+/fzHMSYlJWHu3LkYPny4tMyPP/4YwcHB+Oyzz9C1a1f89NNP+P3335GUlPTyN7CgAgkk9kAiIiIiIiLSLXnHHrj+Ew47h3tSWdYTOU5ZrkNoVA89RkZUOVSoBFKvXr1w9+5dTJs2DTdu3ICXlxe2b98OV1dXAMCNGzeQmpoqza9UKjFhwgSkpKTAyMgI7u7umD17NgYPHizNExgYiE2bNmHSpEmYPHky3N3dsXnzZrRo0eKlb19BQmb47G89xkFERERERFTRJaxagUDjKBhbPJXK/r3vgqymP8Hfv4n+AiOqRGRCCOYvipCRkQGFQoH09PQyu53tF59OyMxJAQAY5Snxzt9ny2S5RERElVl5nLOp5Pg+EFFZycsDRo8GTC59js/fHSeV/3mjJar1ioNDzap6jI7o9VDc83aF6oFUqRTogURERERERETqMjOBPn2ArVsB4BPUczqPD1uvQuL1AWg+dBlM5ab6DpGoUmECSU/UbmHjGEhERERERESSf/4BOnUC/vxTVSLDsNilcGrSFh1H9YLMgF+iiF62CvMUtsomzclH+jsHJnqMhIiIiIiIqOI4nfQnggKzCySPALkc+N/3Jug0rDeTR0R6wgSSnmRb2Et/y2RKPUZCRERERERUMRz8Lg61L/jjv10HQvW4oerVgcREoGtX/cZGVNnxFjY9kRk8u4XNQKaEEAIyGTPpRERERERU+QilQMLyzxBqMwEwBfoEbsT5G/Xwc0oMfvkFqFFD3xESEXsg6YlZToba/+LpUy1zEhERERERvb5ysnOw74sP8pNHBXRocRJ7E/KYPCKqIJhA0hPHW6fV/hfZ2XqKhIiIiIiISD/up93DmSVhaFV9tVp5/M0xaDbye1ha8enVRBUFb2HTF5l6Q6jMyQWbRiIiIiIiqiyunLoAsacT/o+9+w6Polz7OP7d3SSbvumFAAm9996xgIAKiAqKYgVEbIC+1qMHPUcRK3bBg2IDsaGoSLFQpIMEpIOUQEhIIaTX3X3/iG5YEyDU3SS/z3XN5T73PDNzT1Yym3ufeaZdrd2OWInVxKrit+k7cawLMxORiqiA5Com5x+9taAITxelIiIiIiIicjHFL15G3YPDCAk75ohl5ln4s9aX9B50uQszE5GTUQHJRQ53nsCCNy6lGC9K7B5cFxTi6pREREREREQuuJWffEIn2x14+RU7YgnH6lHc4wfat2vmwsxE5FRUQHKR/Mhm/FBwwi9HDT8SEREREZFqzG6HX9+awqUhjzvNxrslqQe1bphHWEy465ITkdPSJNouYvzHT95mc00eIiIiIiIiF1pxMYwZA9987+cUX5k4ksbjflLxSKQK0AgkF7EkbeDegAWYDFY8KKYo7SG8a+s2NhERERERqV6ysmD4cFi0COB+4sIOMGnQqyxN+xd9HnwGg9Hg6hRFpBJUQHKRsG1f0q3Od4524b5hoAKSiIiIiIhUI4mJcOWVsHlzWeyRuS/R7LKBDLy/n+sSE5EzplvYXMRgMjm1bUXFJ+kpIiIiIiJS9exev5O+vfKcikf+/vD990YG3q7ikUhVowKSi9hNzrNmWwuLXJSJiIiIiIjI+fX7gp+J3NKFF4bchNFgBaBWLVixAq64wsXJichZUQHJVYzOI5DsxRqBJCIiIiIiVd9vH31Iq7QBWHyyuKbTN7x804O0bAlr1kDbtq7OTkTOlgpILmLwcB6BpFvYRERERESkKrPb7Cx94xl6etyGp0eJI96r9TZ+W1ZInTouTE5Ezpkm0XYVk/OP3lqoApKIiIiIiFRNRQXFrH1jLH1jZjnFf0u8lc73zcDL28s1iYnIeaMRSK7yjwKSvbjkJB1FRESkJsrIyGDUqFFYLBYsFgujRo3i+PHjp9zGbrczefJkatWqhY+PD3379mXbtm1Ofe666y4aNGiAj48P4eHhDBkyhJ07d57zsUWk5spKz+KPN66k1z+KR0vTJ9PjwQ9UPBKpJlRAcpV/3sJWrEm0RUREpMzIkSOJj49n4cKFLFy4kPj4eEaNGnXKbV544QVeeeUV3nzzTdavX09UVBT9+vUjOzvb0adDhw588MEH7Nixg0WLFmG32+nfvz9Wq/Wcji0iNVPy/iSOfNSbDjFLHLHiEg9+K/mAvvf9G4PR4MLsROR80i1srmL65xxIGoEkIiIipXbs2MHChQtZs2YNXbp0AeC9996jW7du7Nq1iyZNmpTbxm63M23aNJ544gmGDRsGwIcffkhkZCSzZ8/mrrvuAmDs2LGObeLi4vjvf/9LmzZtOHDgAA0aNDirY4tIzbRv8y68fruCppEHHbGs/AD2Rn9Nz0GXuzAzEbkQNALJRQwe/7yFTXMgiYiISKnVq1djsVgcBRyArl27YrFYWLVqVYXb7N+/n+TkZPr37++Imc1m+vTpc9JtcnNz+eCDD6hXrx51/prd9myOXVhYSFZWltMiItXb1qVrsaztQe3gsuJR0vEYklv9RnsVj0SqJRWQXCSrTkundkGDpi7KRERERNxNcnIyERER5eIREREkJyefdBuAyMhIp3hkZGS5bd5++238/f3x9/dn4cKFLFmyBC8vr7M+9pQpUxzzJVksFkcxSkSqp++/h3embCHUP90R25vaHHv/1TTu3NqFmYnIhaQCkquYvZ2aNrvuDRYREanuJk+ejMFgOOWyYcMGAAyG8p8N7HZ7hfET/XN9RdvcdNNNbNq0iWXLltGoUSOGDx9OQUHBSfdxumM/9thjZGZmOpZDhw6dMkcRqbrefx+GDoW3F4/h6a+fAmDzkZ6EjlhBrYYqHotUZ5oDyUWMJhO2E9q2EutJ+4qIiEj1cO+993LDDTecsk9cXBxbtmzh6NGj5dalpqaWG2H0t6ioKKB0BFF0dLQjnpKSUm6bv0cKNWrUiK5duxIcHMy8efO48cYbiYqKOuNjm81mzGbzKc9LRKo2ux2efRaefLIsNvmryUTE1uK2p2/Bx9/HdcmJyEWhApKLeBVkUXBC23DksMtyERERkYsjLCyMsLCw0/br1q0bmZmZrFu3js6dOwOwdu1aMjMz6d69e4Xb1KtXj6ioKJYsWUK7du0AKCoqYtmyZUydOvWUx7Pb7RQWFp71sUWkerMWW5k0sYjX33IuEt11l4GxL9yFyeSixETkotItbC7inZvh1DYmHnBNIiIiIuJ2mjVrxoABAxgzZgxr1qxhzZo1jBkzhquuusrpKWhNmzZl3rx5QOltZxMmTOC5555j3rx5bN26ldtuuw1fX19GjhwJwL59+5gyZQobN24kISGB1atXM3z4cHx8fBg0aNAZHVtEaob8nHzWT7ue/j7X4WEqe/DPM8/AO++g4pFIDaIRSC5i8PR0atuLS1yUiYiIiLijTz/9lPvvv9/xVLXBgwfz5ptvOvXZtWsXmZmZjvbDDz9Mfn4+48ePJyMjgy5durB48WICAgIA8Pb2ZsWKFUybNo2MjAwiIyPp3bs3q1atcpo4uzLHFpHq73hKBgc/HkzXmN8gBmbcOZbR/3uf6dMNjB7t6uxE5GIz2O12u6uTcHdZWVlYLBYyMzMJDAw8L/v88dlfyZh9r6Pd7LLhtHv93+dl3yIiIjXVhbhmy5nT+yBS9R3Ze4jcHwbSKHybI5ZX6MPa4A1cMrS5CzMTkfOtstdtjUBykaJmvbhnxzKKMFNs9+T3z7xcnZKIiIiIiAh7N27Dd+0AGoWXzdN6LDeExIbfc8klKh6J1FQqILmIycuD4/aySTQ1DExERERERFztj19XU3vvlQQHlc3ZejijLkU9FtGqbVMXZiYirqYCkosY/zF9uc3mmjxEREREREQANny3iGZpw/Dzy3PEdqW0xjLkR2rXq+XCzETEHaiA5CKm7DSmhd2D0WDFiI2SJTdCm+tdnZaIiIiIiNRAK+fMpVPxKLzMZU9aiz/Sh3q3fYslzOLCzETEXaiA5CKm/OOER2x1tEs2rQBUQBIRERERkYtr+cx36Gm+B6NH2cQaaxOH0Gb8Z3j7ebswMxFxJ8bTd5ELweDp6dS226wuykRERERERGoiux1mTI2nt894jMay4tGKxNvo8MCXKh6JiBMVkFzE4On81DV7SYmLMhERERERkZrGZoMJE+CuR9vyyJznHfGlRx+kx6SZeHjpZhURcabfCi5iMJud2narRiCJiIiIiMiFV1wMd9wBn3xS2n7h+4cJC0ijU89Q+jzwCAajwbUJiohbUgHJRYxeuoVNREREREQurrw8GD4cfvihLGY0Ggi65AX6jlHhSEROTrewuUi5EUglKiCJiIiIiMiFk5l6nK+fepTFC4scMS8vmDsXxqh4JCKnoQKSi3j6eJXOWvcXe0nxKXqLiIiIiIicvZSDySR/2oeb20/l4/GjMBqs+PmVjkS67jpXZyciVYFuYXMRDw8w2sH2V6HfVqxJtEVERERE5PxL2L4P+y/9aBKxD4ARXT/naE4sXce9QOfOLk5ORKoMFZBcxNMTDNiB0gqS3aoRSCIiIiIicn7t3bgN/3X9iApJcsSOHK/NlffdToN2LkxMRKoct7uF7e2336ZevXp4e3vToUMHVqxYcdK+X3/9Nf369SM8PJzAwEC6devGokWLnPrMmjULg8FQbikoKLjQp3JKHh6Avew+4xKDankiIiIiInL+bP9tAyG/9ybKUlY82p/WGNvlK2nQrpkLMxORqsitCkhz585lwoQJPPHEE2zatIlevXoxcOBAEhISKuy/fPly+vXrx4IFC9i4cSOXXHIJV199NZs2bXLqFxgYSFJSktPi7e19MU7ppDw84Jg91NFOb9DBhdmIiIiIiEh1svmnFdTedSkhfsccsR1H2+M/bAW1G9d1YWYiUlW51bCXV155hTvvvJPRo0cDMG3aNBYtWsQ777zDlClTyvWfNm2aU/u5557j22+/5bvvvqNdu7LxmAaDgaioqAua+5ny9ASrvezHrzmQRERERETkfNj4/SKapV6Dr0++I7YlqQext/6AJcziwsxEpCpzmxFIRUVFbNy4kf79+zvF+/fvz6pVqyq1D5vNRnZ2NiEhIU7xnJwcYmNjqV27NldddVW5EUr/VFhYSFZWltNyvnl4QIndsyz3EhWQRERERETk3Kz98mtaHbsaX3NZ8WjjkX40GL1IxSMROSduU0BKS0vDarUSGRnpFI+MjCQ5OblS+3j55ZfJzc1l+PDhjljTpk2ZNWsW8+fPZ86cOXh7e9OjRw/27Nlz0v1MmTIFi8XiWOrUqXN2J3UKnp4QZM90tAMObTvvxxARERERkZpj5Scf0yF/OF4eZQ/oWZs4lJbjv8PP4ufCzESkOnCbAtLfDAaDU9tut5eLVWTOnDlMnjyZuXPnEhER4Yh37dqVm2++mTZt2tCrVy8+//xzGjduzBtvvHHSfT322GNkZmY6lkOHDp39CZ2EhwcE2HMcbXNm5YpkIiIiIiIi//Tuu/DNZ0fxMFkdsd8Sb6bDA19g9jW7MDMRqS7cZg6ksLAwTCZTudFGKSkp5UYl/dPcuXO58847+eKLL7j88stP2ddoNNKpU6dTjkAym82YzRf2l6zHP37ydru14o4iIiIiIiKn8OKL8PDDAA8R4J3FU8P+w/KkcfSc9BZGk9uNGRCRKsptfpt4eXnRoUMHlixZ4hRfsmQJ3bt3P+l2c+bM4bbbbmP27NlceeWVpz2O3W4nPj6e6Ojoc875XHh6QkmxL+YiG+ZCO3gFuTQfERERERGpWux2eOqpv4tHpf791dO8/+d8ek18W8UjETmv3GYEEsCkSZMYNWoUHTt2pFu3bsyYMYOEhATGjRsHlN5alpiYyEcffQSUFo9uueUWXnvtNbp27eoYveTj44PFUjpB3NNPP03Xrl1p1KgRWVlZvP7668THx/PWW2+55iT/4uEBNxxa72i/+ioMcGE+IiIiIiJSddhtdh56qIRXXvV0iv/nPwZuf+JqKjELiIjIGXGrAtKIESNIT0/nmWeeISkpiZYtW7JgwQJiY2MBSEpKIiEhwdF/+vTplJSUcM8993DPPfc44rfeeiuzZs0C4Pjx44wdO5bk5GQsFgvt2rVj+fLldO7c+aKe2z95Ov+ep7i44n4iIiIiIiInshZbWfXaXXSzHcdk/AyrrfTPuldfhQkTXJubiFRfBrvdbnd1Eu4uKysLi8VCZmYmgYGB52WfdjsYTxhR+txz8Nhj52XXIiIiNdaFuGbLmdP7IHLhFBcWs/71W+ge8xkAHy6/hTve+4AZM4zceaeLkxORKqmy1223GoFUkxgM8GLorcR478ZgsOL/RSQ89p2r0xIRERERETdVVFDE72/cQPeYeY7YyO6zie57H/1v6OjCzESkJlAByYXq+m6nJCAPO2DPPeDqdERERERExE0V5Baw5e3r6BrzQ1ms2MyWwC/of42KRyJy4amA5EL2Ex6CZ0d3EoqIiIiISHl5WXlsn34NnWMWl8UKfdgZOZ/Ogy53YWYiUpOogORCdkwnvFYBSUREREREnOVm5rD7vcF0jPnVEcsp8GNv7R9o37+PCzMTkZpGBSQXUgFJREREREROJis9iwOzBtGu1kpHLDM/kIR6P9L20u4uzExEaiIVkFzIuYAkIiIiIiJSKjP1OIc+HkDr6LWO2PG8II40WUyrXp1cmJmI1FQqILmQHRMGx2uVkEREREREBI6lFnD0k8toGfW7I5aeE0pqqyU079bOhZmJSE1mPH0XuVDshrL6ncpHIiIiIiKSmgqXXO7N3N+uLotlR5De7leaqngkIi6kEUguZMPkqODZDKfsKiIiIiIi1VxyMlx2GWzfDlu2/BuzZyG39/mQ3K4/07hdM1enJyI1nEYguZJGIImIiIiICJCYCH36lBaPShl4e+Vz5PWJp4GKRyLiBs66gFRcXMyhQ4fYtWsXx44dO5851Rg5niGO11aj6RQ9RURERESkujq8N5lLLylh9+6yWGwsLF9uoH7zCNclJiJygjMqIOXk5DB9+nT69u2LxWIhLi6O5s2bEx4eTmxsLGPGjGH9+vUXKtdqZ3dgZ8drq0EFJBERERGRmubwzgOwqCv/uvx2jAYrAA0awPLlUK+ea3MTETlRpedAevXVV3n22WeJi4tj8ODBPProo8TExODj48OxY8fYunUrK1asoF+/fnTt2pU33niDRo0aXcjcqzyjpwcU/PWaEux2OwaDJkMSERFxF8XFxSQnJ5OXl0d4eDghISGn30hEpJIO7zwAP/eldvBBRvU8SFGJFy8tf4+ffjYSE+Pq7EREnFW6gLRq1Sp+/fVXWrVqVeH6zp07c8cdd/DOO+/w/vvvs2zZMhWQTsPk4fzjt5eUYPD0dFE2IiIiAqUjrj/99FPmzJnDunXrKCwsdKyrXbs2/fv3Z+zYsXTq1MmFWYpIVXdi8ehvfVqs4aonM4mMCXZdYiIiJ1HpAtIXX3zheN2tWzcWLVpEYGBguX7e3t6MHz/+/GRXzdXP2+bUtmVlYQwNdVE2IiIiohHXInIxVFQ82pPagqBrfyG8topHIuKeKl1AOtHatWspKCgoV0DKysriP//5Dy+++OJ5Sa6687PlOrVteXmgApKIiIjLaMS1iFxoh3fu/6t4lOCIlRWPNGG2iLivM5pEe9iwYTz//PMYDAZSUlLKrc/NzeWVV145b8lVe55eTk1bQYGLEhEREREoHXH9d/GoW7duZGVlVdjv7xHXo0ePvpjpiUgVp+KRiFRlZzQCKTY2lu+//x673U6bNm0IDQ2lTZs2tGnThtatW7Nlyxaio6MvVK7VTl5gQ0KOrsRm98Bg9MDo5XX6jUREROSi0IhrETmfDu3Yj+EXFY9EpOo6owLSq6++CoDZbOa3337jyJEjbNq0ifj4eObNm4fNZuOFF164IIlWR7vb3M+rv9wPlN65NiLWxQmJiIgIw4YNo3Pnzo4R1xERzn/Y/T3iWgUkEaksFY9EpDo4qzmQcnNz8fjrCWJDhgw5rwnVJN7eZa9PeMCLiIiIuJBGXIvI+XRwdzomFY9EpBo4qwKSh8dZbSb/cGIBSdMfiYiIuAeNuBaR82X/fujbL4Tx3W/gkatLf2+oeCQiVVWlK0EJCQnUrVu30jtOTEwkJibmrJKqKWL3f82X9Z8Egx0MdlLmTCbixhGuTktERETQiGsROTf790PfvpCQYODRhOcBGNb1BxWPRKTKqvRT2Dp16sSYMWNYt27dSftkZmby3nvv0bJlS77++uvzkmB1ZiafQm8oNBso9DJSkHrM1SmJiIjIXzTiWkTOVlnx6O+IgU+2Pk/Q9atVPBKRKqvSn4x27NjBc889x4ABA/D09KRjx47UqlULb29vMjIy2L59O9u2baNjx468+OKLDBw48ELmXfUd+51Yy0r2nxAqyspzWToiIiKiEdcicu4O7U2j72WhJCQYHLGWLeHnnw2ERwS4MDMRkXNT6RFIISEhvPTSSxw5coR33nmHxo0bk5aWxp49ewC46aab2LhxIytXrlTxqDIOzqW5/0ynUFFOvouSEREREdCIaxE5N0l/Hsa+sDOPXjYeg8EG/F08gggNPBKRKu6Mx2Z7e3szbNgwhg0bdiHyqTn862HysjuFilVAEhERcSmNuBaRs3X0QBIFCy6lXuh+7r78Xbw8inht9Qx++tmk4pGIVAuVHoEk55lfHCZv5wKSRiCJiIi4lkZci8jZSDmUQva3l1EvdI8j1rflKn76MVPFIxGpNs5pdshly5bx73//G7PZzBNPPEHv3r1JSUlh0aJFLF68mI8//vh85Vn9RPTh99prgVGOUHFeoevyEREREQeNuBaRyko/ksbxry6nccQOR+xAekP8rv6ZiNohLsxMROT8OqcC0rhx43jqqaeoX78+H3zwAR999BFz585l0KBBXHXVVecrx+rJwwdzVJxTqCS/wDW5iIiIyEk9+eSTtGrVipYtW9KkSRNMJpOrUxIRN5FxNIPUuf1oGvmHI5ZwrB5eA38hql4tF2YmInL+nVMBydvbmxtvvBGADh06EB4ezvbt26lTp855Sa668wv3d2qXFGgEkoiIiLsJDg5m4cKFvPTSS+zdu5c6deo4CkotW7bUl2YiNVRmWiZHPr2CFlHxjlji8ToYL/+FWg3195CIVD/nVEBKTU3l888/p2HDhjRu3Jh69eqpeHQGAoK9MNjt2A2lj/i0Fha5OCMRERHZs2cP77//Po888ghBQUFMmjTJaf2+ffvYunUrW7du5bPPPlMBSaQGys7IJuHDgbSKXu+IJWXWoqTPL8Q2jXNdYiIiF9A5TaI9adIkFi5cyLhx44iOjmbbtm0MHTqUp59+mvnz55+vHKstf3+w2cpqeJn+US7MRkRERACef/55du3aRVBQULl1hYWFFBQUMHjwYB5//HE++eSTC5ZHRkYGo0aNwmKxYLFYGDVqFMePHz/lNna7ncmTJ1OrVi18fHzo27cv27Ztc+pz11130aBBA3x8fAgPD2fIkCHs3LnTqU9cXBwGg8FpefTRR8/3KYpUSbmZufw58ypaRa92xFKyIsnv/guxLRq6MDMRkQvrjApIe/bs4bHHHnN8eJk0aRLvv/8+69atIzs7mx07dnDHHXfg6enJ559/fiHyrVb8TCkUmrwd7XTfaBdmIyIiIlD6kJD777+/wnVms5lx48bx3HPPXfA8Ro4cSXx8PAsXLmThwoXEx8czatSoU27zwgsv8Morr/Dmm2+yfv16oqKi6NevH9nZ2Y4+HTp04IMPPmDHjh0sWrQIu91O//79sVqtTvt65plnSEpKciz/+te/Lsh5ilQl+TmF7JwxhLa1ljtiaTlhZHX6mfptmrgwMxGRC++MbmF7/vnnycjIOO03coMHDz5f+VVrnslfExxwnPxsTwAMRVkuzkhEREQSExNp0KDBSdffddddvPHGGzz++OMXLIcdO3awcOFC1qxZQ5cuXQB477336NatG7t27aJJk/J/qNrtdqZNm8YTTzzheHrchx9+SGRkJLNnz+auu+4CYOzYsY5t4uLi+O9//0ubNm04cOCA03kHBAQQFaXR0SJ/KyiAa67z4srIZnSI+RmAjNxg0tv8RJMOLVycnYjIhXdGI5Dc5Ru5asMvDpOH3dE0Fae7MBkREREBCAkJISkp6aTrO3fuzN69ey9oDqtXr8ZisTiKRwBdu3bFYrGwatWqCrfZv38/ycnJ9O/f3xEzm8306dPnpNvk5ubywQcfVDiP5dSpUwkNDaVt27Y8++yzFBWdfK7GwsJCsrKynBaR6qSoCK6/HhYtMnD/R6/zyoKJZOZZSG6+hCZd2rg6PRGRi+KMCkiV+UZOcx+dAb84TMdsjmZMwgYXJiMiIiIAvXv3ZtasWSddbzQaKSy8sE9OTU5OJiIiolw8IiKC5OTkk24DEBkZ6RSPjIwst83bb7+Nv78//v7+LFy4kCVLluDl5eVY/8ADD/DZZ5/x66+/cu+99zJt2jTGjx9/0nynTJnimKvJYrHooSpSrZSUwI03wvff/x0xMHn+y+xruoVmPTq4MjURkYvqjApI7vCNXLXiX5+SYrOjWVjs6cJkREREBOChhx7ivffeY8aMGRWuX716NfXr1z+rfU+ePLnc5NT/XDZsKP1CyfDXU1pPZLfbK4yf6J/rK9rmpptuYtOmTSxbtoxGjRoxfPhwCgoKHOsnTpxInz59aN26NaNHj+bdd99l5syZpKdXPFr6scceIzMz07EcOnSoUj8PEXdns9q44w47X39dFvPzgwULDLTrWdd1iYmIuMAZzYH09zdynTt3rnD9xfhGrloxeWG3ewClk1ba7SWuzUdERETo0KED77zzDuPGjeOLL77gnnvuoX379vj7+7NixQoeeeQRHnjggbPa97333ssNN9xwyj5xcXFs2bKFo0ePlluXmppaboTR3/6eryg5OZno6LIHc6SkpJTb5u+RQo0aNaJr164EBwczb948brzxxgr33bVrVwD27t1LaGhoufVmsxmz2VwuLlKV2W12fpt2D62tfsCLgAFv79KRSD17ujo7EZGL74wKSA899BBdu3albdu2ThMw/u1cvpGrqVI9ulGSlkyRzZviiGauTkdERESA0aNH07RpUyZNmsSwYcMcI3j+fmLZxIkTz2q/YWFhhIWFnbZft27dyMzMZN26dY4v7tauXUtmZibdu3evcJt69eoRFRXFkiVLaNeuHQBFRUUsW7aMqVOnnvJ4drv9lF8Cbtq0CcCpMCVSndltdpa99gh9o9+l95UQ4J3NA5+8zbx5Jvr2dXV2IiKucUYFpAv5jVxN9WPz6cybV/q6XR14yLXpiIiIyF969uzJunXr2LlzJ7///jt5eXm0bNnSMRrnQmrWrBkDBgxgzJgxTJ8+HSh9etpVV13l9AS2pk2bMmXKFK655hoMBgMTJkzgueeeo1GjRjRq1IjnnnsOX19fRo4cCcC+ffuYO3cu/fv3Jzw8nMTERKZOnYqPjw+DBg0CSr8QXLNmDZdccgkWi4X169czceJEBg8eTN26umVHaoZlbz9L38gXHe07+86k0YA7uHRAl1NsJSJSvZ1RAQku3DdyNVVwcNnrjAzX5SEiIiIVa9q0KU2bNr3ox/3000+5//77HU9VGzx4MG+++aZTn127dpGZmeloP/zww+Tn5zN+/HgyMjLo0qULixcvJiAgAABvb29WrFjBtGnTyMjIIDIykt69e7Nq1SrHpN1ms5m5c+fy9NNPU1hYSGxsLGPGjOHhhx++SGcu4lrLpk+jb8iTTrE1fMCl16t4JCI1m8Fut9tP361irvhGzhWysrKwWCxkZmYSGBh4Xvf9fw/ZmD0zkTaRm2hbazvPLbgDvMs/dUVERERO70Jes6Xy9D5IVbVi1kx6eY12ii3Le5s+o+92UUYiIhdeZa/bZzwC6USu+kauOum37DLaRR8FgwGvIyVY09pgqj3Q1WmJiIiIiNQoq+Z8Rg+PMU6xpcen0ne8ikciIgBGVyfwT2+//Tb16tXD29ubDh06sGLFipP2/frrr+nXrx/h4eEEBgbSrVs3Fi1aVK7fV199RfPmzTGbzTRv3px5f0865A48/OGv2wBLDEYKUva4OCERERERkZpl3bzv6FQ8CqOx7OaMpWn/ou943bopIvI3tyogzZ07lwkTJvDEE0+wadMmevXqxcCBA0lISKiw//Lly+nXrx8LFixg48aNXHLJJVx99dWOJ4VA6USQI0aMYNSoUWzevJlRo0YxfPhw1q5de7FO65SMPmXDw2xGI0Upu12YjYiIiIhIzfL7gp9pnXU9nh4ljtiy5Afoc+8zLsxKRMT9nNMcSOdbly5daN++Pe+8844j1qxZM4YOHcqUKVMqtY8WLVowYsQInnrqKQBGjBhBVlYWP/74o6PPgAEDCA4OZs6cOZXa54W8j3/Z4LEk/rnS0W704mI6DYo5r8cQERGpKTT3jnvQ+yBVxR+/rKL+wX74mfMcsRVH7qTnpPcwGA0uzExE5OKp7HXbbUYgFRUVsXHjRseTRv7Wv39/Vq1aVal92Gw2srOzCQkJccRWr15dbp9XXHHFKfdZWFhIVlaW03KheAX6ObWzE/UoNhERERGRC+3330vnPTqxeLQq8Qa6PzBdxSMRkQq4TQEpLS0Nq9VKZGSkUzwyMpLk5ORK7ePll18mNzeX4cOHO2LJyclnvM8pU6ZgsVgcS506dc7gTM6MT1CAUzs3Kf2CHUtERERERGDHDrjiCrh75jTeWHQvAGsTr6bT/R9h8jS5ODsREffkNgWkvxkMztV+u91eLlaROXPmMHnyZObOnUtERMQ57fOxxx4jMzPTsRw6dOgMzuDM+IY7Dw/LT9EIJBERERGRC+XgQejXD9LSwG43cv9Hr/PSiv/RZvzneJo9XZ2eiIjb8nB1An8LCwvDZDKVGxmUkpJSbgTRP82dO5c777yTL774gssvv9xpXVRU1Bnv02w2Yzabz/AMzo5/hHMBqSA986IcV0RERESkpklJgf79ITGxLNa9u4FxL96Jt9/JtxMRETcageTl5UWHDh1YsmSJU3zJkiV07979pNvNmTOH2267jdmzZ3PllVeWW9+tW7dy+1y8ePEp93kxmUMsTm2/nM2QucNF2YiIiIiIVE9Z6VnMeHgGu3eXPUOodWv4/nvw93dhYiIiVYTbjEACmDRpEqNGjaJjx45069aNGTNmkJCQwLhx44DSW8sSExP56KOPgNLi0S233MJrr71G165dHSONfHx8sFhKCzMPPPAAvXv3ZurUqQwZMoRvv/2Wn376id9++801J/kPHidM+A3QOeB/sNcGHaa5JiERERERkWomP6eAfR8M5l/9lxFg28bET16lfn0jixZBcLCrsxMRqRrcqoA0YsQI0tPTeeaZZ0hKSqJly5YsWLCA2NhYAJKSkkhISHD0nz59OiUlJdxzzz3cc889jvitt97KrFmzAOjevTufffYZ//rXv3jyySdp0KABc+fOpUuXLhf13E7G869z+1txqBGydrooGxERERGR6qWkqIQt74ygS8wyAB4Y8DrePib6P/oKUVEuTk5EpAox2O12++m71WxZWVlYLBYyMzMJDAw8/QZnoDg3ly86d3a02/dLpmlffxh68LweR0REpCa4kNdsqTy9D+IubFYbq165g54xHzpix/OCSGmznMadWrkwMxER91HZ67bbzIFUU3n4+mIzlA0E+3TFbdB/lesSEhERERGpBuw2O8tfe8ipeJRb6MuhBgtUPBIROQsqILmYwWDA7lk2a19mogclXjEuzEhEREREpOpb9s4U+ka96mgXlXiyI+xrWl3SzYVZiYhUXSoguYGA/AzH686m1aSnuzAZEREREZEqbvnMd+kb/ISjbbMZ2OD5MR2vvsKFWYmIVG0qILkBT4Op7LUpn6NHXZiMiIiIiEgVtmrOXHqaxzvFfit8m+43jnBRRiIi1YMKSG7A09PT8dpkLFIBSURERETkLGz4bhEdi0dhNJY9J2jpsf/S+85xLsxKRKR68Dh9F7nQYobdyuJXj5JhC+NgcQPGHkmC1AMQrvuzRUREREQqI37ZdpqlDcPLXOyILU2eSJ8Jj7swKxGR6kMjkNxA4yce4JXs5/Ds4ssXb9zGcM9a8FNvsBWffmMRERERkRpu2zbod21jPlt9gyP2W+Kt9H7gJQxGgwszExGpPjQCyQ14ekJ0NOQU+BPsd7w0aC+B7D/B0tSluYmIiIiIuLNDh2DAAEhL92D0e/8jPSeUPu120fWB/2E06ftyEZHzRb9R3UBJRgb9Q38l6uAeDn4biK3wrxVZ212al4iIiIiIOzt2rLR4dPjw3xEDPyS+QOvxX+Hhpe/KRUTOJ/1WdQMH//Mf+lkXQSCs3FqbYwVXc+/0yyGko6tTExERERFxS/l5VgYPNrH9hO9cW7eG+fPBx09/5oiInG8ageQGfGrXdmr/uqwD9sjLwSvINQmJiIiIiLixkqIStrw1jH5R/wZKn7gWGws//ggWi2tzExGprlSadwPecXFO7RDrQTIzISjIJemIiIiIiLgtu83OqtfH0ztmPl2GzSc6KIkn57/NokUe1Krl6uxERKovjUByA94NGzq1Iz0Ok5DgomRERERERNzYsrcm07vWe472zT0+YclXu2jSxIVJiYjUACoguQHvhg3Bbne0Qz2Osn+/CxMSEREREXFDy2e+S9/QZxztEquJbcFf0LpXCxdmJSJSM6iA5AaMvr6YTyggBXocY+9eO+QegrzDp9hSRERERKRmWPPlPHqY73GO2f9Hp6FXuigjEZGaRQUkN+FjLHsr6gbtY1xYKHxbF7a/6MKsRERERERcb/NPK2ibeyMmo80RW3r8OXrecpvrkhIRqWFUQHIT3maz43WJzY6fZ0Zp4/gWF2UkIiIiIuJ6ezZsJfbAYLw9Cx2xZUn30Wfcoy7MSkSk5lEByU34Bwc7XudhKltxfIvT/EgiIiIiIjVF4u4E/NcNIMj3uCO2KnE4vSZMw2A0uC4xEZEaSAUkN+EfE+N4XejhwcwFt1LU+k3o/Q2gApKIiIiI1CwZycco+HEA0UGJjtimI5fQ4Z6PMJr0Z4yIyMWm37xuwr9fP6f2018+zi77PRDRCwx6m0RERESk5sjPh2+nvkqD8B2O2K6jbah/2zzMvuZTbCkiIheKKhNuwr9DB6d2hOdhdu92UTIiIiIiIi5itcKoUXDna5N5a8l4AA4diyPomh+xhFlcnJ2ISM2lApKb8K9d26kd7nWIrVtdlIyIiIiIiIs89BB89RXY7CbunfUmj381jZLei4iMi3Z1aiIiNZoKSG7CKzAQc0CAo32F+Vu26AFsIiIiIlKDTJtWuvzN29vA1Q89QL3WjV2VkoiI/EUFJDcSVFj2aNI63nv44w/AboOs3VCc47rEREREREQusIVf7mPSpLK2wQCzZ0O3bq7LSUREyqiA5EaCoqIcr0vMJbwzrC/2L4Lh+yaQstR1iYmIiIiIXEB//LKKPjkteP6GhzEYbEDpSKRrrnFtXiIiUsbD1QlImaBmzSAhAaPNjmeRkW6hv2EosZauTF8PMVe5NkERERERkfNs/5bd1No7GB//Ah6+6kViww6y0esj7r9fT1sTEXEnKiC5kdr33ktRq/60u6M3hfiynWY0Y2fpymMbXJuciIiIiMh5lno4BePygYSGpDtidevYuH6SpwuzEhGRiqiA5EbM9evTJK4+HvdBYS58vnY4l3dPosfgThCmm79FREREpPrIzcwl5fOraBG1zxHbktSDduM/xmjSTBsiIu5GBSQ3YzRCu3bw228w+at/8/1BI+snnX47EREREZGqwlpsZeuMG+kSs94R25fWhNojv8Xbz9uFmYmIyMmotO9mcjdsYELhbXxWuzOfN25B6uYD5Oe7OisRERERkfPDbrPz2+v30yXmO0csNTsCj8sXEBId6sLMRETkVFRAcjNZa9ZQmLsea2AuxR5GhnjPYtMmV2clIiIiInJ+LHv3RfpEv+1o5xb6ktr8e+o2r+/CrERE5HRUQHIz4SNHYrLZHO2OAb+wdq0LExIREREROU9WzZ5D36BHHG2rzci2oLk079XJhVmJiEhlqIDkZjxCQoj083O0vf1TWbeqCGwlcGwjWAtcmJ2IiIiIyNnZvGQ5HYpvc4qtLHyLztdc5ZqERETkjKiA5IZiunRxvC72MPKAqQv2L4NgYUdIW+O6xEREREREzsKePRD/zSeYPYscsaUpj9L7znEuzEpERM6ECkhuqNaddzq1vQ4kYSjJLW2krnRBRiIiIiIiZyc9HQYNgtvfeZfn55fevrYy8UZ63/esizMTEZEz4eHqBKQ8v/btCcZIBqVzIR04bqFtyVGMHqiAJCIiIiJVRmEhXHMN7N0LYOSxuc+T6dmVf787AKNJ32WLiFQl+q3tpup17+Z4XWDyYOX8S6Hrh9DpTRdmJSIiIiJSOXY7jB4NK1aUxRo0gAdfHYq3n7frEhMRkbOiApKbinvoQQx2u6N9aK0Ve71bwF+PNxURERER9/f+C2v55JOyz7PBwfDDDxAW5sKkRETkrKmA5Ka8mzTBgo+jbfRLZevvuS7MSERERESkclZ++il31unKG7feh8lYgqcnfP01NGni6sxERORsqYDkxiIvudLx2maCFdN/dGE2IiIiIiKnt/mnFXQsuQOAe/u/xfwHB/O/90ro29e1eYmIyLlRAcmNtXnlX+Tagxzt4rWfuy4ZEREREZHTOLh1L7X3XYPZs8gR863dnltu1bN7RESqOhWQ3JiH2YvjdYY62uEl2zjw61o4uhRyD7osLxERERGRf8pIPobtlysJ9U93xFYljqDPvc+4MCsRETlf3K6A9Pbbb1OvXj28vb3p0KEDK058bMM/JCUlMXLkSJo0aYLRaGTChAnl+syaNQuDwVBuKSgouIBncf40v/l6p3by/w2Dny+BA5+6KCMREREREWdFBUUc/GQY9cJ2O2J/JHWj/fhZGIxu9yeHiIicBbf6bT537lwmTJjAE088waZNm+jVqxcDBw4kISGhwv6FhYWEh4fzxBNP0KZNm5PuNzAwkKSkJKfF27tqPDr0suvj8Mgty/VgbiCFaUZI/tmFWYmIiIiIlLLb7Kx7Ywxtay1zxA4eq0/0Dd/i7Vc1PnOLiMjpuVUB6ZVXXuHOO+9k9OjRNGvWjGnTplGnTh3eeeedCvvHxcXx2muvccstt2CxWE66X4PBQFRUlNNSVXh7w9GQqx1tq9HI3m+CIW0VWAtdmJmIiIhcSBkZGYwaNQqLxYLFYmHUqFEcP378lNvY7XYmT55MrVq18PHxoW/fvmzbtu2kfQcOHIjBYOCbb74552NLzbXs7WfpGfORo308Lwhrzx8Iiwl3YVYiInK+uU0BqaioiI0bN9K/f3+neP/+/Vm1atU57TsnJ4fY2Fhq167NVVddxaZNm85pfxdbyH2PYi6y4Wm1Emo0knvlIzD4AJjMrk5NRERELpCRI0cSHx/PwoULWbhwIfHx8YwaNeqU27zwwgu88sorvPnmm6xfv56oqCj69etHdnZ2ub7Tpk3DYDCct2NLzbRqzmf0DXnS0S4u8WB/zFfUb9vUhVmJiMiF4DaPQ0hLS8NqtRIZGekUj4yMJDk5+az327RpU2bNmkWrVq3Iysritddeo0ePHmzevJlGjRpVuE1hYSGFhWWje7Kyss76+OfD4Ou8GXH/VFbndue4PYzr1sAXD7o0JREREbmAduzYwcKFC1mzZg1dunQB4L333qNbt27s2rWLJk2alNvGbrczbdo0nnjiCYYNGwbAhx9+SGRkJLNnz+auu+5y9N28eTOvvPIK69evJzo6+pyPLTXT1mVraV90G3iWxdbYZ9Br4KUuy0lERC4ctxmB9Ld/fhNmt9tP+u1YZXTt2pWbb76ZNm3a0KtXLz7//HMaN27MG2+8cdJtpkyZ4hiybbFYqFOnzlkf/3wICAD/gYM5bg8D4PvvwcU1LREREbmAVq9ejcVicRRwoPQzjcViOenI7P3795OcnOw0mttsNtOnTx+nbfLy8rjxxht58803K7yt/2yOXVhYSFZWltMi1duhQ7B6zmy8Pcu+dF2a+hi9br3dhVmJiMiF5DYFpLCwMEwmU7nRRikpKeVGJZ0Lo9FIp06d2LNnz0n7PPbYY2RmZjqWQ4cOnbfjn60bbyx73cL6G2svv4KUmTNdl5CIiIhcMMnJyURERJSLR0REnHRk9t/x043mnjhxIt27d2fIkCHn7dju9uWbXFi5uTB4MIydPo0nPv8vAKsTr6X3vf91cWYiInIhuU0BycvLiw4dOrBkyRKn+JIlS+jevft5O47dbic+Pr7ccO0Tmc1mAgMDnRZXGzgQggJKmF27K5MajiU19zBb33zT1WmJiIjIGZg8eTIGg+GUy4YNG4Dyo7KhciOzTzWae/78+fzyyy9MmzbtjPZxumO745dvcmHYbDBqFMTHAxh47tsnuPerH2k99kOMJrf500JERC4At5kDCWDSpEmMGjWKjh070q1bN2bMmEFCQgLjxo0DSj+cJCYm8tFHZU95iC+9epGTk0Nqairx8fF4eXnRvHlzAJ5++mm6du1Ko0aNyMrK4vXXXyc+Pp633nrrop/fufD2hhEjPbB/b4K/PrwlFxWR9tJthD00y7XJiYiISKXce++93HDDDafsExcXx5YtWzh69Gi5dampqScdmf337WjJyclOX5SdOJr7l19+4c8//yQoKMhp22uvvZZevXqxdOlSoqKizvjYZrMZs1kP96gJnnoK5s0ra0dHw6OvDcDv5A9EFhGRasKtCkgjRowgPT2dZ555hqSkJFq2bMmCBQuIjY0FICkpiYSEBKdt2rVr53i9ceNGZs+eTWxsLAcOHADg+PHjjB07luTkZCwWC+3atWP58uV07tz5op3X+TJ2LEyc9SR3Wcpm0N764S/0vT8DvIJdmJmIiIhURlhYGGFhYaft161bNzIzM1m3bp3jM8vatWvJzMw86cjsevXqERUVxZIlSxyfj4qKili2bBlTp04F4NFHH2X06NFO27Vq1YpXX32Vq6+++qyPLTXDd59u59lnmwGlX2Z6e8M330Dt2i5NS0RELhKD3W63uzoJd5eVlYXFYiEzM9Plt7N16gSPp7cl36fYERtwbz9C7p7muqRERETchDtds8/VwIEDOXLkCNOnTwdg7NixxMbG8t133zn6NG3alClTpnDNNdcAMHXqVKZMmcIHH3xAo0aNeO6551i6dCm7du0iICCgwuMYDAbmzZvH0KFDz+jYp1Kd3gcptXXpGhoe7MucVTcy7v13KSoxM2cOnGZAnYiIVAGVvW7rRuUqZuxY+F/yE06xre8tdlE2IiIicqF8+umntGrViv79+9O/f39at27Nxx9/7NRn165dZGZmOtoPP/wwEyZMYPz48XTs2JHExEQWL1580uLRuRxbao7E3QlE7ByKt2cht/eZxU+PXc6zk7NUPBIRqWE0AqkS3OlbtOxsiImBTyNakH3CVAMDp0whePBg1yUmIiLiBtzpml2T6X2oPnKO55D4QU+aRG52xFYnXk+XSZ9p0mwRkWpCI5CqkDOp4QUElI5CmpH4pFN88zPPnO+0RERERKQGs1ltbH1vlFPxaHtyB9rcNUvFIxGRGki/+V3s530/c8mHl5BdmF3pbR54ABaW3IAp29cRO5KfT8rMmRciRRERERGpgZa/+S+6xnzjaCdnRhM89Ft8A31PvpGIiFRbKiC50Kz4WQz4dADLDi5j+JfDKbGVVGq7OnVKJyycnjIZThi9tOmzz85oNJOIiIiISEVWfvIJfcOnONr5Rd4ca/kt0fVjXJiViIi4kgpILpJdmM2/fvmXo2i0cO9C7vnhnkoXgB56CH4tvJKizEhHLP3IERJ//fWC5CsiIiIiNcMfv66mo/VOp9gm7w9p3quTizISERF3oAKSiwSYA/h+5Pf4e/k7YjN+n8ELK1+o1PZt2sBVV8H/pX6M1e7hiG+eNg1bSeVGMomIiIiInOjI3kNE7R6K2bPIEVuaPpnuNwx3YVYiIuIOVEByobZRbXmv1nv0X9If73xvAB79+VE+2/pZpbb/z39g/NUzad7lqCOW+eef7H3yyVNsJSIiIiJSXl5WHlnfDSE8IMURW5U4nD73POXCrERExF2ogORCdpudzNcy6b6yOw+89gDdV3bHo9iDW+bdwsK9C0+7fdu2kBt8DS26p+HpZXXEt3zzDYX79l3AzEVERESkOrHbYfZzH9M0cpMjtj25A23v+gCD0eDCzERExF2ogORCW+duJXlTMgA+BT70X9Kf+964j5YbWnLtnGtZmbDytPu488HWbEtpQ8uIVEesyGhky113XbC8RURERKR6eeEFGDN1LBM+fhWrzUhKViTBQ77RE9dERMRBBSQX8g7yJqRhiFPMkmVhyPwh3PbGbdz3r/vYlLTpJFuXatYMfkidwaB1azEXlk3AnZ+Zia2o6BRbioiIiIjAggXw2GMABl5bOIGrX17I0cZfE92gtqtTExERN6ICkgs1GtiI8dvHM+jtQfhF+jmti0iNYMjHQ5jefTorF5x6JNLYRzuQbm3I/OTxmAvtrE4dQ6tF6zB6eV3I9EVERESkitu1C268sfQWtr9dd18/Wl3a3XVJiYiIW1IBycVMniY63d2J+/feT99n+uLl71z0iT4QzU9X/sRnQz8jdXtqhfuIioInn4SPcu/lxj9/583UCTyluQ5FRERE5BSOp+czeLCdrKyy2H33wR13uC4nERFxXyoguQkvfy/6PNmH+/+8n3b3tMNmsjmt3/XtLt5p9Q7f3v4tGfszym3/wAPQqBEUU/o0t7fegt8WZfLH8OHkb99+Uc5BRERERKoGa7GVPe9fy+R+I/HxygPgkkvg5ZddnJiIiLgtFZDcjF+EH4PfHMytm28lu3e20zq7zU78rHjebPImP4z/gazEsq+LvLzgzTdLX3uaipjabBwp93blj23b2HjnnRfzFERERETEza146wk6xfzIjd0/47enetK9TQKffw6enq7OTERE3JUKSG6qfov6vLTsJcb+PpYG/Rs4rbMV29jwzgZeb/A6iyYtIjclF4D+l+byzTP/5uCrdWhpW0yBV+nbm5CVReKLL170cxARERER97Nq9hz6Rkx1tBtH7+aDGccJC3NhUiIi4vZUQHJz0e2iuXnRzdzy8y3U7ur8JAxroZU1r67htfqv8fMTP5OfaeOqlp8QHZpCl0FJGE6YDXHd++9TdPDgxU5fRERERNzIjpUbaVfkPMnRH/4f0rhzaxdlJCIiVYUKSFVEvUvrcceqO4h7J47kqGSndcW5xfz23G+81uAt0k2lHwhCOhTQwL/sFrd8o5H1N954UXMWEREREfeRmnAUy+ah+HgVOGJL056i2/BrXZiViIhUFSogVSFL9i1hdOpopo+dztzhc0mLSHNab7fa8etyN4R25pMdrzJky2rMhWWjkA5mZnLg8ccvdtoiIiIi4mJFBUUkfXEttYIOO2JrE4fQ+55/uzArERGpSlRAqkI6RHegfXR77EY7O5rv4K1xbzH/uvmY65oB6DKhC36RIdB/DYP/bwK+MXX4IPHfTreyrZ83j9wNG1x1CiIiIiJykdltdta8eS+to1c6YntSW9B89McYTfpzQEREKkdXjCok1DeUn2/5mcvqXQaA3Wjn95a/89RtTxH+73C6P9i9tKPBQGAgzJkDi4qHY0iNcOyj2Ghk9ejR2IqKXHEKIiIiInKRrfjgXXrXes/RzsgNxuvybwkIDnBhViIiUtWogFTF+Hv588PIHxjWbJgjVmws5h7DPby05SXsJ4w26twZnrltH7vTehKQV+KIpxQX88edd17UvEVERETk4tvy80q6ed7vaFttRvbHfE5siwan2EpERKQ8FZCqILOHmbnXzeXOds5FoCd/fZI75t9BkbV0dJHdbidy5zLseJJjbY6H1ebou+333zmyYsVFzVtERERELp6kgxlE7rkOT4+yLxJX5L5E+0GXuzArERGpqlRAqqI8jB68d/V7PN7TeVLsWfGzGPDJADLyM8AO7W8IYvhD33Dj899QJ8rm1HfVI4+Qe+TIxUxbRERERC6CoiK4bmQQT3/1JMUlHgCsTBxJnzETXJuYiIhUWSogVWEGg4FnL3uWmYNn4mH0cMR/PfAr3d/vTmp+Km2bvkazdvEAdBu7m53Gno5+RZmZ/DZ6NPNvns3SyUvJP5Z/sU9BRERERC6ACRNg1SoD7/w0nr7PLuW3fQNod9d7GIwGV6cmIiJVlApI1cAd7e7gx5t+JNAc6IjVC6pHqE8otJ8GhtK32Y4BY+No9ua1dvRLP3gQ06/PsezpX5kWO43FDy0m+0j2xT4FERERETlPPvgA3nmnrL0jtQe1Rv6Ib6Cv65ISEZEqTwWkauLy+pez6o5VxFpiaRjSkNnXzsZkNEFIO2h8HwS3x9B/DY2GTeb1wy9DicmxbWaQnXZhCynKKWL1y6t5rd5rfHfXdxz785jrTkhEREREztiGDXD33WVtg6H0ybz167suJxERqR48Tt9FqooWES1YM3oNmQWZBHkHla1oMwWMXmA0cdNNcPhwLb55ZgLXxL6E3WAAux27vWw4s7XIyu8zfmfT/zbRYngLejzag6g2URf/hERERESk0lIPp7J11tN48DyF+APw7LNwxRUuTkxERKoFjUCqZqL8o2gS1sQ56OEDRhMvr3qZPel7ePhhiB59B38cGYTJZmPV4ZFMzpiBd90Ip83sNjtbP9vK9LbTmX3VbBJWJlzEMxERERGRyiopKuHwZ8O5rftbrH2mCw0j9zBsGDz6qKszExGR6sJgt9vtrk7C3WVlZWGxWMjMzCQwMPD0G7ihWfGzuP3b2/H38mfGVTMY0eJG7r7bzq/vb2VPSSsAPD3szJy4Gc/FX7Frc8WD0+r2qkuvx3vR4IoGGAyahFFERNxLdbhmVwd6Hy6+pa8+SN/IVxztTYe60fDulQQE6vOaiIicWmWv2xqBVANsOLKBcd+PAyCnKIeRX49k4ve38tYNV/P4I2sc/XytGZg/G41XwZeMfKctDfo3KLevhBUJfDrwU2a0n8GWT7dgLbZetPMQERERkfJWzZ7jVDzKzA/EMuADFY9EROS8UgGpBogJiKF7ne6OdisvuO/YR3gk/cAtLe7hqXvWE208yMzYvpQE5FNgMrLt1ce47vVOjNkwhubXNYd/fP5Ijk9m3s3zeL3+66x6eRWFWYUX+axEREREZPe6LbQtvNMptjP4Y+q3aXKSLURERM6OCkg1QHRANEtGLWFyn8kYMHCZLzT0Kl1ntBdzb99ruWGkEYxlo4nyPAwsGjyMIN9srv/ieu7Zfg9tb2+L0cP5f5msw1kseWgJr9Z5lcX/t5jMQ5kX89REREREaqyM5GOY112DrznfEVua9iRdhg12YVYiIlJdqYBUQ5iMJv7d99/8fMvPfFYSyRfZpfF9xdBj9yF2DriLLf3exlxUNiVWrgcsHjKMjE07CGsaxpD3h3D/vvvpOrErXv5eTvsvzCpk9Uureb3+62z6YNPFPDURERGRGsdabGXvxzcRG7LPEVufOIje90x2XVIiIlKtqYBUw1xS7xI2j9vCp36X8lEWdDsEe4rhx70/8mb9YWwYMApzkc3RP88Tlg6/lkPLtwNgqWPhileuYOKhiVz+wuUExAQ47d9mtVG3R92Lek4iIiIiNc2KdybTKWaho30wvQGNbv0Eo0kf70VE5MLQFaYGivCL4OublpDe5hUyMTvix/KP8XzY4yy4uoVTESnfy8D6O6/j949WO2LeQd70+L8ePLDvAYZ+NJTI1pEANB3SlNDGoeWOmZ2UTUlByQU8KxEREZGaYd287+kb9l9HO7fQl6Ju3xAUEezCrEREpLpTAamGMhqMTOw2kY1jN9Iuqp3Tuj4juhPz6EynIlKBl4F9/72DLyYtcOpr8jLRZlQb7oq/i5sX30yfyX0qPN4Pd//Aq3Vf5denfiU7Kfv8n5CIiIhIDZCwfR+N00c5xTZ7v0+jji1dlJGIiNQUKiDVcC0iWrBm9Br+1etfGA1Gbmp0GaNS/0fXRi/SeuoMvE+YE6nI00jWgsk8eN068vOd92MwGGjQrwFRbaLKHSNjXwa75u8iLzWP5f9ZzqIJiy70aYmIiIhUO/n58MYzv+PrleuILTs6ke43jnBhViIiUlOogCR4mbz4z6X/Yd3N85nln4Ahawcc/oZGAY/Re9b7+BYZHH19TLm03jaWO7t8z65dldv/hukboKwORef7Op/nMxARERGp/u67D16aex09n/6NhLQ6bEnqQffxU12dloiI1BAqIIlDB/9APPIPlwWKjhPWqSk9lnzFgZAiR9jTWMxV1kdY1ncIM94qwm6vYGcn6PNUHwa9PYiwpmFEtYuiTo865fqk705n+bPLyU3NrWAPIiIiIjXbzJmlC8D6fZ0Z8NrvRAz7Ak+zp2sTExGRGsNgt5/uz3/JysrCYrGQmZlJYGCgq9O5sFJXwbKrweQN/VeDX13GfjeW99e/x7h1MXQ/ZHHqbsr25dum85n2STQREafetd1mJzclF/8o/3LrFty7gPVvrcdkNtHqplZ0ub9LhbfDiYiInEqNuma7Mb0P59emTdCtGxQWlrZNJvj5Z+hT8dSTIiIiZ6Sy122NQBJn4d2h/yro+yP41SU5J5nZf8zGaoK3uybybdMUp+7WgDwarp9Cq1bwzTen3rXBaKiweFRwvID4WfGl+yu0Ev9+PNPbTuf9nu/zx+w/KCnU09tERESkZjqecpxbbsx2FI8ApkxR8UhERC4+FZCkvMAmENwagCj/KLaN38awZsOwGyC7axo76h7CZCt9QpupJJ//+A8nJQWuuQauvx6Sk8/scBn7MvCL8CsXP7TyEF/f9DWv1nmVnx77ieMHjp/rmYmIiIhUGTarjd0fjuKL0Z1oFrMdgKFD4aGHXJuXiIjUTG5XQHr77bepV68e3t7edOjQgRUrVpy0b1JSEiNHjqRJkyYYjUYmTJhQYb+vvvqK5s2bYzabad68OfPmzbtA2VdPsUGxfDX8K5ZfN4svahn4v5HZ7O25Hy9rHg8NSoDRvWD4dRiDd9Lhlzvo0TyFDz7gtHMj/S26fTT37bmPEd+MIO6SuHLr81LzWPn8Sl6r/xpzrp7Dnh/3YLfpzksRERGp3pa/+zydY76naa1drHumM2OvnM+sWWAwnHZTERGR886tCkhz585lwoQJPPHEE2zatIlevXoxcOBAEhISKuxfWFhIeHg4TzzxBG3atKmwz+rVqxkxYgSjRo1i8+bNjBo1iuHDh7N27doLeSrVj81KryP/w89gJ9AET1xSyPTbD3A4yFq6vvlXTG3ci7pRa3khtA/f3fMR/frBzp2V273RZKTpkKbc+sut3L31bjrd0wmvAC/nTnbY/f1uZg+azesNX2flCyvJS8s7v+cpIiIi4gZ+X/ATvSxPOtpGg40Hn4rDYjnFRiIiIheQW02i3aVLF9q3b88777zjiDVr1oyhQ4cyZcqUU27bt29f2rZty7Rp05ziI0aMICsrix9//NERGzBgAMHBwcyZM6dSeWkiSKAwHZYPgdSVpc3Q7tyRE8vsraU/w2u2+XD91jjsf30lZrDbOZ7eiEePfcKYBwJ46ik40x9dYXYhf3z6B+vfXk/KHykV9jGZTbQY3oKOd3ekdtfaGPSVnIhIjaZrtnvQ+3Bukv48jOfP7QjzT3PEVto+pMfNt7gwKxERqa6q3CTaRUVFbNy4kf79+zvF+/fvz6pVq856v6tXry63zyuuuOKU+ywsLCQrK8tpqfHMoXDZMmj7PHj4Y+76Pz69djbrx6ynb1xfbAbwtJZNdm03GLCE7eX9ul3Z9taXNGkCH30Ef02dVLlDBpjpOK4j4zaP4/YVt9NqZCuMns7/y1oLrWz5eAvvd3+fd9u8y9o31pKfkX++zlpERETkoioqKCLtm+udikfLk+5S8UhERFzObQpIaWlpWK1WIiMjneKRkZEkn+mszCdITk4+431OmTIFi8XiWOrUqXPWx69WjCZo/ggMTQBLMwA61urIL7f8wuj/fM5btxio61PgtEmhN9xS7yn+49GHh+48SI8esHLlmR3WYDBQt2ddhn06jEmHJ3Hpc5diqVt+/HbKHyksvH8hr9R6hXmj5pG+J/2sT1VERETEFVa/9RCtotc42tuTO9Ll7mmuS0hEROQvblNA+ts/b0Gy2+3nfFvSme7zscceIzMz07EcOnTonI5f7XgFOzUNBgNXNb6Kn0c8SM9J++jV9hDmf4xG8g1M4+36/eiceh09excxZAhs337mh/aL8KPXY724f9/93PjdjTQc2BD+8VaWFJSw5ZMtup1NREREqpRVs+fQJ/oNR/tYbgiBV36B2dfbhVmJiIiUcpsCUlhYGCaTqdzIoJSUlHIjiM5EVFTUGe/TbDYTGBjotMhplORh/ONpAOpcmc2V96RRNzDAqUuRhyddzDt4uEdrNh5+lpZtCxg9Gg4fPvPDGU1GGl/VmJsW3MT9e++n5+M98Y/2d6yPuySOkIYh5bbLP5avJ7iJiIiI29n7+3ZaF4xxtG02A/siP6V2kzjXJSUiInICtykgeXl50aFDB5YsWeIUX7JkCd27dz/r/Xbr1q3cPhcvXnxO+5QK2IqhzjAweADg3fcBeq5eQ+87bsdcUuTUtU26iSmFnzKmb3N+3fwQDVtmMnEiJCWd3aGD6wdz2bOXMTFhIiO+GUGjKxvRcVzHCvvOv3M+rzd4neX/XU5Woua2EhEREdfLzcyFFdfj753riC3PeIqOgwe4MCsRERFnHq5O4ESTJk1i1KhRdOzYkW7dujFjxgwSEhIYN24cUHprWWJiIh999JFjm/j4eABycnJITU0lPj4eLy8vmjdvDsADDzxA7969mTp1KkOGDOHbb7/lp59+4rfffrvo51eteVmg4xvQ9EHY+So0vheA2g8+hO+N17Jy1PVkJudh/Ot+M5PdQJ9EHy6zfc/hxp8wedNI3mn5KHfdHMEjj0CtWmeegtHDSNMhTWk6pGmF67OTstn13S7sVju/Pvkr6bvSuebja876lEVERETOhw3TH6BP7bJ7+zck9qf3pKdcmJGIiEh5bjMCCWDEiBFMmzaNZ555hrZt27J8+XIWLFhAbGwsAElJSSQkJDht065dO9q1a8fGjRuZPXs27dq1Y9CgQY713bt357PPPuODDz6gdevWzJo1i7lz59KlS5eLem41hn8cdHwNzGW3j4XUqsfVP2/gko9epFaDHKfuJUYTUbmh/O/oVxTeHcvr+8cS12k79913dre2nUr8rHjs1rLb19re3rbCfna7bnETERGRi+ODD+D/po/lQOpfn3ePxxA38hOMJrf6mC4iIoLBrr+WTysrKwuLxUJmZqbmQzoXvz8EO18maZ8fm76M5Hhx2YSQy2MTmN7lr+JSQid8P/iZYo8AbroJHnwQWrY898PnJOcQ/2E8m/63CWuRlQf2P4DB6DzRdnJ8Ml/f9DVtbmtD65tbExAdcJK9iYiIO9I12z3ofaicrVuhc2fIz4dgv2P8b8wYGl45kdaX93R1aiIiUoNU9rqtrzbk4qk1EKIHEl0/lwGTzXS87FLMVis+1kKmdyotHsV4wPcJuXzUsBPPBNzB/A9TaNUKBg2CX3+Fcyl3+kf50/ORnty7615uX3F7ueIRQPyH8aRuT+Wnh3/i1dqvMvvK2Wz7YhslhSUV7FFERETk7OTkwPXXlxaPADJyQ9gT9ZWKRyIi4rY0AqkS9C3aeZazD/KTILwHJWlp5G7fxq8RGUxbM41emZvo9k4Yx02lo5M8rTZSjjXjlfyJ7Ov5Fc3y7uLJcc249lrw8jq/aVmLrLwS8wp5aXnl1nkHe9NqZCva3NqGWh1rYTCULz6JiIjr6ZrtHvQ+nJrdDjffDLNnl8UGDYLvvgOjvt4VEZGLrLLXbRWQKkEfgi6eA7c3YtW68pUho81GoWcm/+uQzm90xbL3Lu659Fruuct8VhNuV6Qop4hVL61i84ebOX7g+En7hTYOpdVNrWh1UytCGoSctJ+IiFx8uma7B70Pp7Zk5pdM+Hcztie2AKBOHdi0CUJDXZyYiIjUSLqFTaoeaxExV8XSJioFb2ux0yqb0YinNZi71zXk2zUH+TJiHMmmUFrffS8Db97DsmXndnsbgJe/F30n9+X+P+/n1l9vpc0tbfD09SzXL313Okv/vZQ3Gr7BzO4zWffWOnJTcyvYo4iIyNnLyMhg1KhRWCwWLBYLo0aN4vjx46fcxm63M3nyZGrVqoWPjw99+/Zl27ZtJ+07cOBADAYD33zzjdO6uLg4DAaD0/Loo4+epzOr2Xau2Uwvj5tZ90xnbun1IR4eMHeuikciIuL+NAKpEvQt2kVWmI51x2wOzNjIjqXryDrJ7WI+3sWEtj/O0JAMMo72Iib1ViYNvJbbb/InOPg8pZJdyPYvtxP/QTwJKxJO2s/oYaTBFQ1ofXNrmgxuUmHhSURELrzqdM0eOHAghw8fZsaMGQCMHTuWuLg4vvvuu5NuM3XqVJ599llmzZpF48aN+e9//8vy5cvZtWsXAQHOD4Z49dVXWbJkCT/++CPz5s1j6NChjnVxcXHceeedjBkzxhHz9/fH39+/UrlXp/fhfMpKzyL9047UC9vjiH2ZvpDr7rvChVmJiEhNp1vYziN9CHIde0kJiS+9xI45c0gtqXgia6vBTqpfDvuDj/O/1laK/hzOzdH9uH3I1fS+LOC8zSWQsS+DP2b/wZZPtpC+K/2k/bz8vWg2rBmtbm5FvUvr6TG8IiIXUXW5Zu/YsYPmzZuzZs0aunTpAsCaNWvo1q0bO3fupEmTJuW2sdvt1KpViwkTJvDII48AUFhYSGRkJFOnTuWuu+5y9N28eTNXXXUV69evJzo6usIC0oQJE5gwYcJZ5V9d3ofzyW6zs/rlG+ge87kjtubwELo8NK/CB3uIiIhcLCognUf6EOQejs2bx57XXuHA0TSsJ6kKeVpLOBCcTpebrHQLzGFDQk9S/G6h3bW3U7fu+cnDbreT9HsSf3z6B1vnbCUnOeekff2j/Ok6sSs9Hu5xfg4uIiKnVF2u2e+//z6TJk0qd8taUFAQr776Krfffnu5bfbt20eDBg34/fffadeunSM+ZMgQgoKC+PDDDwHIy8ujY8eOTJkyhSFDhmAwGCosIBUWFlJUVESdOnW4/vrr+b//+z+8TvIEi8LCQgoLCx3trKws6tSpU+Xfh/Np2f/epo/vPY72oWNxBAz/naCI8zRsWkRE5CxpDiSpdkKuuYYuS1dwzeLFdJg0gYA6EeX6FJs88AS6B2Xh6VFC19ilHPtlMXFx0L8/vP1+BllZ55aHwWCgVodaXPHKFUw8NJGbF99Mm1va4OVf/kN1TnIOBccLzu2AIiJS4yQnJxMRUf46FxERQXJy8km3AYiMjHSKR0ZGOm0zceJEunfvzpAhQ056/AceeIDPPvuMX3/9lXvvvZdp06Yxfvz4k/afMmWKY64mi8VCnTp1Tnl+Nc2OlRvp6jnR0S4q8SS7zecqHomISJWiApJUOV516tDkzjFc9eMvXDpzJnFNm2Ky2QAw2O3s716Ah7G0nbrGB/O+LXwR14pLt9zAW+uiCb6/P53uep/9nw3Hum4iJH4PxdlnlYvRw0iDfg0Y+uFQHjr6ENfOuZZGVzbC6FH2T6vF8BbltrMWWVn69FKO/nEUDQIUEak5Jk+eXG5y6n8uGzZsAEq/sPgnu91eYfxE/1x/4jbz58/nl19+Ydq0aafcx8SJE+nTpw+tW7dm9OjRvPvuu8ycOZP09Ipv4X7sscfIzMx0LIcOHTrl/muSzLRMfDcNx+xZ5IitLniJ5r06uTArERGRM+fh6gREzpbBYCCqa1eivvqK4qQkEqZOJWvfPq565heO7VvPgdULyV/zAwCFPjbq+vzBEysa4GXdw76IhwiLzcZkK4G909jv9wCxV087p/mSPH09aXlDS1re0JLc1Fy2fb6NQ78dIrJNZLm++37ax7LJy1g2eRlhzcK4dva1RLWNOvuDi4hIlXDvvfdyww03nLJPXFwcW7Zs4ejRo+XWpaamlhth9LeoqNLrSHJyMtHR0Y54SkqKY5tffvmFP//8k6CgIKdtr732Wnr16sXSpUsr3HfXrl0B2Lt3L6EVPC7MbDZjNptPeV41kd1mZ8cHd9A1Zp8jtjrxWno/eJ8LsxIRETk7KiBJteAZHU2DE79NbXU1QU2u4JtXfyk3zq7I5EvtdF+++zSaEFs+tSOzmdngPd6dd4BeYdfywMCrGNA3GJMJ+HMmBLWB4LZgrPw/F79wPzrf05nO93SucP22uWWPVD629xiWWEvlT1ZERKqssLAwwsLCTtuvW7duZGZmsm7dOjp3Lr2WrF27lszMTLp3717hNvXq1SMqKoolS5Y45kAqKipi2bJlTJ06FYBHH32U0aNHO23XqlUrXn31Va6++uqT5rNp0yYAp8KUnN7ymW/RJ+ZrR/vgsfo0v32mJs0WEZEqSQUkqbYMHh70mTyZgx9/TMKff5JXwfCiY0YfjqX60CkV6vptY2PMGh768h6u+7Qzl4cN4Lu2/1fa0SMAOr0F9Uadc152u52M/RmOdsMrGuIT7FOu35ZPt3D8wHGaX9ucsKan/2NDRESqj2bNmjFgwADGjBnD9OnTARg7dixXXXWV0xPYmjZtypQpU7jmmmswGAxMmDCB5557jkaNGtGoUSOee+45fH19GTlyJFA6SunvkUonqlu3LvXq1QNg9erVrFmzhksuuQSLxcL69euZOHEigwcPpu75eiJFDbBzdTxdvR50tAuLvchr/wWxYfrSSEREqiYVkKTaMhiNhI4YQeiIEbQrKSFt9mwOfvopCQkJFFRQTIrM9WLQ7lAG7Q7FZDuM2TSNfbst1OqTg3dkNmu3xNAmGry9/7HhsU0Q1BKMnpXLy2Dg9uW3k7Yzja1zt1KrY60K+617Yx2JaxP59V+/Et48nGbXNqPZsGZEtok87fwXIiJS9X366afcf//99O/fH4DBgwfz5ptvOvXZtWsXmZmZjvbDDz9Mfn4+48ePJyMjgy5durB48WICAgIqfVyz2czcuXN5+umnKSwsJDY2ljFjxvDwww+fnxOrAbKzIf2n/6Npg7J5j9YUvUyf7u1dmJWIiMi5Mdg1g+9pVZdHAkspe0kJaZ99xuHPP+fw3r1kn6YY07PVIaIGFRA05jj+HlZaXr2Cjr19uX9oD2oHH4N5UeDhB+E9oeWTEN7jnHPMOpzFq3VerXBdcP3g0mLStc2I6RSjYfAiIifQNds91PT34dZb4fuv03l/7B0M6TCftYlD6fzg17pmi4iIW6rsdVsFpEqo6R+Cqrus337j8K5dHPrlZ9I3b4ET/kkY7HZWWe7EEpXPs9/8i4mB/6JLra/AkMPuoCJsDaN4qv8WDH8PaLp8GUT0Ln+QktzSIlMlHVp9iG9u/YZje46dsl9ATADNhpUWk+r2rIvRpAcrikjNpmu2e6jJ78NHH5UWkErZeeTa93j0resIigxxZVoiIiInpQLSeVSTPwTVNPlpaRx5+WWO/PoryZmZ+OLJm3Fb+PlnKC6Gj2J6YrJkOG3jYy0m0ieXsDp5PBcynHaD7uKOQW0J8P+rmJObAPPrl07EHd4LGo2DwCblD/4Pdrud1G2pbP9qOzu+2kHKHymn7O8b7kvToU1pOrQp9S6th4e37lAVkZpH12z3UFPfh127oEMHyM0tbZtMsGwZ9Dj3wckiIiIXjApI51FN/RBU09kKCijYtQvfNm04fhx+/KEEz2daUuRhOuV25pIiUn0Kmd+wBaHNruT/ettpnXp/WYf+ayCsi/NGf/8zPMXtdOl70tnx9Q52fLWDI+uPnDIHTz9PGl7RkMaDG9P4ysb4hvmesr+ISHWha7Z7qInvQ0EBdOsG8fFlsWefhccfd1lKIiIilaIC0nlUEz8ESXm2ggIOTp7MoeXLSDmWQZHp1IWkv1kDrDQii/DIfELalFB8dyoR0f+YiTv7T1jctXQepfBeEHsD+FY8uTZAZkImO+aVFpMSfkuAU/wrNhgN1OlRhyaDm9B0aFNCGmoIvYhUX7pmu4ea+D4sefExPvy2BZ+uvBmAyy+HRYuggud2iIiIuBUVkM6jmvghSE7NbrORuXgxSV99ycH1G8gqKKCkkgWl5KNt+TT2UyKueJ/+bZpyS99WhOZ+DWtuK+s0YCOEVO5JLTlHc9j5zU52fLWDA78ewFZiO2nftre3Zcj7Qyq1XxGRqkjXbPdQ096HtV9+Q5eiawCYtfxWnv7hTVav9ycqysWJiYiIVEJlr9uaJEXkLBiMRoIGDCBowACaAbaiIjK+/Zbdn88ldfsOMowGPG0V12bjC7rz+45jcPVo8n70otbkOAIppHZkGOGN8gnpYMArqHX5DY9vg+VDIKwbhHWFusPBOxz/SH863tWRjnd1pCCzgL0/7mXX/F3sWbCHwsxCp100GVx+7iW73c7eH/cS2ycWLz+v8/HjERERqTESdyfQOOMO+OtZGbf1/pAWV91CVNSlrk1MRETkPFMBSeQ8MHp5EXr99XS7/noArEVFpGyOZ9k38/H4+Xdsx/dTYjJisNv5Ie96aPYzGOxcus8Xq9FEBr5kpPpCKrDSjvcrzbB5++LbuDXtR91A5JVXYEhbDTl/li4HPoHIS8A73CkPb4s3LW9oScsbWmItsnJwxUF2fbuLXfN3kXs0l/r96pfL/ejmo8y+cjYms4m4vnH0e6Efka0jL8aPTUREpEorKSoh/buRxESXPWBjacrD9B2p4pGIiFQ/uoWtEmraMGw5/2xFRaTNX8Du79bzffSzfJj4KMkNp/LJFzEY7JbTbu9htWKq60PDuETCYvIJreOB9+3pYPjHxAoZ8bDiur9GKXX7a5RSGHa7ncyDmQTFBZXb97JnlrH030sd7fv33U9wveBzO2ERERfRNds91JT34dfXnuSS8P862luTu9DknhV4mj1dmJWIiMiZ0S1sIm7E6OVFxHVDibhuKD2B53mePxLGs3HHI3hvjcezuIRCj5PfPlZiMlGSWMS2xLIRRz5TW+LvG0pI5zZ0mP5maTB1lfMopajLwTsMg8FQVjz6xxPf9izY49hnWLOwCotHWz/byra522g4qCGNBjYisHb1/WNARESkMn5f8DN9Qp91tDPzLAQNmqPikYiIVFsqIIm4SKu6dWn12RxH+/dfl/P7ezMw7ownIC8Xu8FMifHkE3PnexjILzrGseXz6TbxF+JMPZjVfBuWT0Lx9LYR0txI8FVRlNtD+lpYPhRCO0NoZ0Z9N4p9K/LYs2APwQ0qHnm046sd7PxmJzu/2QlAZJtIGg1qRKNBjajdtTZGDz1iRkREao7UwynEJNyMMbBsIP/2wP/RrWk9F2YlIiJyYekWtkqoKcOwxb2s27abX957D9PGX8mx5eCb3IQY858YDc7/ZFP9UphwZRoAY3zhsvebYv3rmcEGux1/OwRHRBLVphUhl1xCUL19GLc/VLaDIQfBr67zwW0lYCsGDx+sxVZeDHuRwiznCbn/5h3kTb3L6tGgfwMaXNGAoNig8/YzEBE5U7pmu4fq/D7YrDY2vnIlnWIWOmLLk8bR+8F3XJiViIjI2dMtbCJVXOcWjek87UVHOyMDVv6Sw45vVhG2+gP8ivZi8Mnmj8gCR59d28z0NZaNBrIbDGQbIDsthYSff4aff8ZosxFMPULq5xPS1ovg1llYGhdh8jrhFrq01fDzpRDUGptfO9pc34k/vskkPz2/XJ4FxwvY8dUOdny1A4DQxqE0uKIBDfo3IK5vHF7+erKbiIhUH8tnvELfE4pHu1Na0emuV1yYkYiIyMWhEUiVUJ2/RZOqq6AAZi1exVebFrIpczXpPqu5dStcuSuGYtMZ1oYNJgJtJYSEhhLSqBFxwyPxPvZ82fqhidjMUSSuS2TPD3vYs2APadsSCAzJ4tjRELBXfAub0dNI3R51qd+/Pg2vaEhU2ygMRsM5nLWIyKnpmu0equv7sGPV7zTY2xUvj2IAcgt9SW67gQbtmrk4MxERkbNX2eu2CkiVUF0/BEn1UlhcwrxVW5i3fjm5mxfS5PA2GmcWEZrngw1vSkwnn0/pn2pdXUTf1nsBSFkXzdGkXgS3b09I//74tGmDwWgkb+u3+G4ZSnGxD0kHovn23UEcSw475X59w3yp369+6Qilfg0IqBVwTucsIvJPuma7h+r4PuTmwvwn7+bGju86YiuK/kev2+50YVYiIiLnTgWk86g6fgiSmmF3Ygqf/baOX7atxrz7Z9ocOUKj3S3x806jwMeGt7H8LWkeViszb9/FZX7QyRtK3oskNSfUsd7LaiPYx5uQECPBwXsJalRIYONCjjb7gz0/HePPxX9yePVhbCU2vH3zadRuN0n7a5GeFIr9hJFKHt4ePHL8ETzMupNWRM4fXbPdQ3V8H8aMgZkzbUwYMI3nb3iUjUeH0PXBzzWyVkREqjwVkM6j6vghSGomm83On38aWLMG1qy2sWtNAvn7d3J1zOM0sKZjsHsDhVx/40HHNp9/Vpdio/8p92u02Qj09iZowACCGjXCr3Y9sg9ayd23gi6tngGgMN+LD/97G0kHagHQoH8Dbl50c7l97Vmwx3Hrm6evHoUsImdG12z3UN3ehy+/hOuvL2sP6LKJOfPjCIqo+OmlIiIiVYkm0RaRcoxGA40aQaNGMGqUEYgjPz+Oz1dEMGfzMjanbCQ3bz3YDfDX096MJ5nf6EQ2o5HjRUUcnz/fKe5ltZL9bSx1Y7NoPCqDsM5dOHYshcKsQur3r1/aKe8IbHsOQtpBcFuWTV5D4vo0TF4m2t3ZjivfvvJ8/xhEREQq7dCh0tFHfzMY4LEX2hEU4bqcREREXEEFJJEazscHbu3fnlv7t3fEjh7P5utV8SzZtpGHr95IYfY6uqYfol2SN3UyvfEr8qbI5I3dcOph+0UmEyn4EXSsgCPFoey8NoYr/3MFSddcQfFnP/DH2jiC6vkQ5DMH/wbFGExgzLwDqIu1yIrZYq5wv8f2HiO4frBuGxARkQvKWmJn1CgDx4+XxR5/HHr3dllKIiIiLqMCkoiUExkUwN2DenH3oF6O2NGMHL5aFc832zayLXknvgtfJGLfT7Q1r6a+eTsZ3t74BxwkxJ5bbn/bAgt559hx3s8awMzfTPyHJpCXS+K2bbANoBEmmw0LhUTlriU0dDs5hWHUbtgVu92OIXc//NQXgttiC2jNnCH55ORGENsrltg+pUtU2yiMptOPlhIREamsFdOnMKL+Idatfpn8Il+6dIF//9vVWYmIiLiG5kCqhOp2H7/I+ZKXB9u2QXx86fJ95nMci55Cy2M2rt7lTUyWGf9Cb/7XMYmFjQsAGLXJjwF7Yit9DA9fXyx1QrDYt2LJKcQSU8jSpf3Zc7AlUFYwMls8aX2lHUvr7sT2aUR0h2hMnpV/8pyIVA+6ZruH6vA+bF22lqYJPfAwWdme2IzR78/hkx/aUL++qzMTERE5vzQHkohccL6+0KlT6QLwFo9TXPIIS7f8yY+b4lmQsJk9WZtJ9ygGDgOQZbZhtGfhaTVTaPIqnUziFEry8kjflUc6QaWBI4DfTqLMviQXNgQg1PMw0d776dNyGeZIK2m/RjB73NUQ3tsxQimmc4ye+CYiIpWSnZFNwB8j8QixAtA8ZgcvPLGF+vXbuDgzERER19EIpEqoDt+iibjansPpzF+3heW749maupkk22bMxu30mPkGbQ3biTPvxGI+isFcQJHnqW9FM9jtzKcpMYcaE5DjQ9vQH8mNzAbAw2bFQiHkeZOfE0xOYTjHiqLJMkRTp3ttLrvue7xjOxLUtjceUe3Bw+9inL6IXCS6ZruHqv4+/PbirfSM+cjRXpk4ku4PfqK590REpFqq7HVbBaRKqOofgkTcVX5hMQf3e7J1K05Lrv9k2jZ8gTwvM7UzzcRkedMixQsDngCYSwq5buSfYIfgjGCmL7BQ7OV7ymOZbDa8i+2Etc8hMLSQwNAi9sX3pajuLdS5tBl1e9bFP8q/tLPdBgbNpyRSFema7R6q8vuwavYcujPS0T50LI7AG+KxhFlcmJWIiMiFo1vYRMTt+Zg9adoUmjaF664ri2dkP8LCjUNYtn0r8Ue28WPuVo6ZthJt3Ue3Q2b8i/76BtgAGSEZGI1Bpz2W1Wgk1wy52078A2Av7HuSvCVWkjxDuWz7KrAWwVdhFFvj8IxrBw3uhAg9bkdEpCY4vPMALfLHgU9pu8Rq4njzT6mj4pGIiIgKSCLifoIDfLixbztu7NvOKX44NZuFG7azYtdW2iZvJaFgG8e9tjJpYAJdv55Ai5w8Ysx/YjEfxW4upNijEqOIDAbyvTwoKUynyf+NYVB4DK/WzubHZ/IoZg2+9jV4m4MIjI0lvFt7Qvr2xrdjR4yH54BvbbA0B++o087lJCIi7q2kqIRjP9xE7egsR+y340/Rd1R3F2YlIiLiPlRAEpEqo3Z4AKMHdmH0wC5O8YMpGSSM8GHfbm927MCxFOV9TOcO42ia5kXtTDNZZi88S7wJy/fAgHPBJzkgm93+/6PDseZYQwzkGr2wGwwUAlhzSNq3jV37tsGnH2O02QiwFxHoVURg/UICLr+KgJ73ExgbizkoSMUkEZEq6Lfp/6Vv9CpHe3NST3re97gLMxIREXEvKiCJSJUXGxFMbAT06uYcz84bwc/x7VixYwdzE3fyZ+YOUmw7KfHeRVy2lUG7zdTPMBOZ5cPqxpkA7DrSlE82tsDTsO2kx7MZjWTiTabVG/YAe9bCOzcB4GWzEeDhQUBQEM0uzyC4ezBYWkDtIVBr4IX6EYiIyDnY8vNKegX/x9HOzLMQetUneHjpo7KIiMjfdFUUkWorwNeLod1bMrR7S6d4idXGmu2H+GXrDn4+sJPdGTtIKt6JMX8Hv+/twX/XDeAOy8vU89qFnzmdQrOdIg9TpY5ZZDSSbrORfuwY9ZMPQOo2SP2NbR8fZfdPkwgwmwkMDSWgTh0CmjYlIK4E/46tMEW3A986Gr0kInKRZaVnEbzjZkwhNkdsm9+7dG8S68KsRERE3I8KSCJS43iYjPRsFUvPVrHAAKd1mVk29v1pZPfumezeDbt3w587E7HWuZRanodokWKmTqYXIQVmPKxe5Hl6YbBXXPSxNCx0vN6/8Aj5Xkbyi4tJSU6G5GRYvx4Ag302frZiArwMBNSqT+CIEQQ0aUJAbCy+UVEYjQY9FU5E5AKZNfVn7mmd4Gj/lngrPf/vBhdmJCIi4p7croD09ttv8+KLL5KUlESLFi2YNm0avXr1Omn/ZcuWMWnSJLZt20atWrV4+OGHGTdunGP9rFmzuP3228ttl5+fj7e39wU5BxGpuiyBRtq1g3ZO83fHYLPtZOehVJZu3c3afXvYkbKbhJw9NF9tpP2eYMKKDER6HSTU6wh2r0Jsnja2bm1NRGY6EbVT8CjIA6+Kj2k3GMgxeZFjhaRDh+Cll5zW+1uLCfAsxhLsSfsZ46HphNLtbDYMRhWWRETO1ldfwQMvXsNnjVbw8d2j8PS002b0G65OS0RExC25VQFp7ty5TJgwgbfffpsePXowffp0Bg4cyPbt26lbt265/vv372fQoEGMGTOGTz75hJUrVzJ+/HjCw8O59tprHf0CAwPZtWuX07YqHonImTAaDTSPjaB5bATQ02ldidXGbyv3svqHzazfcAD7n1kEHfTA88+/e9hpErAGizURu7mYAi8qfUscQI7JkxybJzkphaR8upb3tkNo/YMMWng1tuIifDy9CAr0xBLhg3+DBgR07Il/36vxjIo6b+cvIlLdHDkCY8eWvl69pzvt/xXPrz8cpm5wgGsTExERcVMGu91ud3USf+vSpQvt27fnnXfeccSaNWvG0KFDmTJlSrn+jzzyCPPnz2fHjh2O2Lhx49i8eTOrV68GSkcgTZgwgePHj591XllZWVgsFjIzMwkMDDzr/YhIzWGz2kjbkcahVYfZs/Qwh9ckknsgBf76jettzCLMK5EQ/yT8fI9h8CigAAO5np7YTlJbijZks7TxHTwzbzK0/Iwvix6n0MPnpDl4Wa14G014RscQOvgqQuuEEFA7Gv/Ihpjr1NHoJamWdM12D+7+PthsMHAgLF5cFnviCfjvf12Xk4iIiKtU9rrtNiOQioqK2LhxI48++qhTvH///qxatarCbVavXk3//v2dYldccQUzZ86kuLgYT09PAHJycoiNjcVqtdK2bVv+85//0M75/hQnhYWFFBaWzV2SlZV1tqclIjWU0WQkomUEES0j6DC2PQCFWYUc2XCEw2sOk7gukaSNtTl8+J+/X+yYTAX8dumvFETv5trtPtTPDMZOEV6BJhof2sNdTOdIagq2IM9T5lBkMlEEkJxI+ozpTus8bVa87R4Et7kUw1OTaN8wBh+zJ9krVuDbti2mAH0DLyLV11tvORePOnSAf//bdfmIiIhUBW5TQEpLS8NqtRIZGekUj4yMJDk5ucJtkpOTK+xfUlJCWloa0dHRNG3alFmzZtGqVSuysrJ47bXX6NGjB5s3b6ZRo0YV7nfKlCk8/fTT5+fERET+Yg40U+/SetS7tJ4jlpOcw5GNRziy/ghHNpT+NzfFwNz/LabE4sX6nckUJNVj3z44vCYRyxf/I5pkah21khlaiLchB59iLwwGLwpMlf+VXmw0UYydXb+m8X/N64HNSGh6LV7/NRDsdrxtJXgb7OATiE9YJBFNGxPephX+7dvj06oVBg+3uXyIiJyRvb/voF36XTSOfo/dSU3w8YFPPgHPU9fkRUREajy3+wvA8I9HWNvt9nKx0/U/Md61a1e6du3qWN+jRw/at2/PG2+8weuvv17hPh977DEmTZrkaGdlZVGnTp0zOxERkUrwj/Kn8ZWNaXxlY6D0d1h2YjYBMQEYDAYGdCkrNq1/+wgLvih9bcdEq+mL2ZqbzrqEA+w+egBr2iZ6/LmHNuuCMFrzMXhng1cR6T5eeGHHQPnfpYeMIaUvjDZaZacCgWAwUGDypACgsIDjiQdJSjwIPy/5q6sNo48v9ojaeETEEhhVjxh7JuEdmhPYtTNedevq9jgRcUtFBUUUL7uJno03senZdjw0+yVaDL2bpk1P/llTRERESrlNASksLAyTyVRutFFKSkq5UUZ/i4qKqrC/h4cHoaGhFW5jNBrp1KkTe/bsOWkuZrMZs9l8hmcgInLuDAYDgbUrvu+4VqdadH+4O0kbkjh+4DidezSii6Ex0M3RZ8O7G/jh+x8q2K+V2Ga7CLEcJSj/KNlZnhQUG9nvGYTBZsButNM4rXJfv9uMRmyFBXBoL9ZDe0kFUgEWlK7/pqEXu5o2o1uwmXqHvWm67whBjRsT17UL0b264RUTc2Y/FBGR82Tlu5O5JHITAL7mfG7v/x0d777bxVmJiIhUDW5TQPLy8qJDhw4sWbKEa665xhFfsmQJQ4YMqXCbbt268d133znFFi9eTMeOHR3zH/2T3W4nPj6eVq1anb/kRUQugphOMcR0Ki2+nGx0ZtKmpAq3tdtNHNjenAM0d4p3ANo9P5mccDu2gAMEhy8lwJoP+UayC7zIs3pSZPLEfoqRoP+0tt4fNAiN593asGtZMBsTosk9uJfEJaUVJg+rFQ+bFbvRiLd3LJ6htfCNjcP3mmHEtY0huHbIKUeeioicjc0/raBP2POOdnpOKHWvfx+DUb9vREREKsNtCkgAkyZNYtSoUXTs2JFu3boxY8YMEhISGDduHFB6a1liYiIfffQRUPrEtTfffJNJkyYxZswYVq9ezcyZM5kzZ45jn08//TRdu3alUaNGZGVl8frrrxMfH89bb73lknMUETkfTlZgueSZS2g6tCnJm5JJ3pRM0qYkMv7MOOW+jEV2AhMB4li38zZH3DvKG3uMH9nBRZR478Nu303QsWS6FOfjHZdPznFPMo/5UlJQti+TzUqCxUr/v2r4uRle5Y5XYjJRYip91FxhcSIkJ0Lyelj7BTuBQkxgLCKgqIRiTy9yLcGsuuFOGkXVpmWd2rRvUIuY8KDK/7BEpMbLSs8idNcojMFlDx/eE/IeXeOiXZiViIhI1eJWBaQRI0aQnp7OM888Q1JSEi1btmTBggXExsYCkJSUREJCgqN/vXr1WLBgARMnTuStt96iVq1avP7661x77bWOPsePH2fs2LEkJydjsVho164dy5cvp3Pnzhf9/ERELjT/SH8aDWxEo4FlDwkoyCzg6JajTkWl1G2p2Epsp9xXQXIBJBfgBXS8+TKu+fjNspXF2eSl/Mme5QkkZLQkc2c8B/a+BzlpWDL70NgSD2SSl3/mlxkzVrCZKPIwgR0i05Pp53Un+4/Cf/6EiXfWxqvEGzs2ikwG8r19MFs6EFi3E/6tWxLRvTUxTYMJCgINZBIRgC3v30/PmIOO9oojt9ProWtOsYWIiIj8k8H+96zTclJZWVlYLBYyMzMJDKx4bhIRkaqkpLCE1O2ppUWlzcmk/JHC0c1HyT+WX2H/y1+4nB7/16Nc/OXol8lJzsE7yJtuD3Wj9xO9S1fkHaY4NZ7EA5tI2bqDldtqwYFdeKcn4ZuXjU9xMWa7kWKDB0V/jUY6mVrGbPo+dgiAcSnQ7516FHr4nHIbzxIbeTZ//qidQ5bZj3zvIBrngT04GmODpgS37ULThvVoXT+a8CDfSvzEpKrQNds9uNP7sPrzL+lWcr2jnXCsHsE3bSYgOMCFWYmIiLiPyl633WoEkoiIXBweZg+i20UT3a7s9g273U5OUg5H/zjK0S1HSdmSwtE/jpK6PZXIVuUfZpCbmktOcg4ABccL8PA+4ZLiWxvP2NrE1r2SVS/MoVHdQMIHXk9EiwjCm4fjF+mH4dhGrAe+JnvPdvL272d5yTSOHUhmf+o3tN0Xj3exFTDiH1Tk2O3+YrAZyt8W90/FHkY8yaN9ihHI/2sBEpNg6+8U/PgGlw89WhrLC2TGwmCyvbwp9m6BJXwYno1bYmnbjJhYL2rVgvDIEny9dckUqWqS9x+hSeZd4FfattqMZDb/mLoqHomIiJwxfRoWERGgdF6lgFoBBNQKoOEVDR1xa7G1wv4pf6Q4tSNbV1BkOprLngXln3rpE+JDeItwwpt3J7zFEMI7h3NVi4jSwpLhxrKOaeso+f1x8rN98So8TB/L85QEfo5nbjamkiLsGCgyeZzRJN8A9YKLHa9jinLwK6mNXwmQtxOOPQe7oHi+nSPFdo6UeFLonUm2uYQNkf78GRaJ1S8ac1AdQiPiqBdRm8bRtWhepxYtYiPx86nc0+xE5MKyWW0c/vJ2OsYcc8RWpD9G35vLj6YUERGR01MBSURETsnkWfEtZjFdYrht+W2lo5X+SCGqTVS5PinbUirYEvKP5ZOwIoGEFQlOcZ8QH8KbhxPWPIywpn8tTb7AEmvBaITHAcZOctrGlvoHeZ8MJG9vBrlHPUg83pWcY3YOepvItqcSkF9AQLHz3dqZlrICUtO0kzy102Cg0MsAXlbAn4BiuOQwXHI4HUgHtmK02fC0lQAlPN45iZ8bFGHID6dZYmOGbbud4lpNMTZoQVichagosAftJyi0kJax0dSJCMSopz+JXDAr3n+LPjGLHe3tyR3occ+/XZiRiIhI1aYCkoiInBUvPy9ie8US2yv2pH0MBgN1etQhdVsqBccLTtrvb/nH8kn4LYGE35wLSyazidBGoYQ1DSO0Sel/W41shcFowBjeCv+Jh/G3FkHeIep5R4Knv9P2JceOkPdaPfKSPMhL9eRIjwGMDxhCwrEkmmX+god1n+PJcGfCZjRSaPQCvLAZAIMdu28KVx70o1nRy3AAOFA6J5OxxIjVWEyaTw7zzSWk+thI9vZmTa0o8vxqYfarRYRfNLUCo4gLjaJBVBRNa0fRtn6Mbp8TOUO7dhQRm/eK49a1vEIfvC/9BE+zRgiKiIicLX0iFRGRC6bepfWod2m90vmVknNI3Z5K6rZUx39TtqVQkHH6wpK10ErK1hRStpaOaPIJ8aHVTa2cO5m8OLLbh7y0ZMKahGGpa8Hw1wgfj5BaBD6ZQWDuAcjZT/3AJvQM+Ps2vYcAKE5OJm96W3IPIDd1AgAAHJVJREFU5pCX5klWTh2O54RQkHucwpJ8ioxGrEbjSXPcHVo2qqlNkfOIp2IP419XXE88rcGE5kFoHjQF+ibmA39SZNyD1ViCpbCEIlMxOV4l9Bh8FN7bSEhhe5qFJ2GJ9ic4JgCf6P0khXxB7aAoYkOjaPhXsalJnTC8PE+eo0hNUFICt9zmxZ/b1/POHXdzfZcvWW99kT5tm7o6NRERkSpNBSQREbngDAYDAdEBBEQHUP+y+o643W4n92guqdtLi0knFpjy0yt+IhxAWNMwDBXMe7ThnQ1smrkJgKC4IB7Y/0DZSg9fsDSnxLsxJi8T/9zaMyoKy6SVWPISIPcg+DeAiF5ludpsFGVlkfftVeQd2EzeXk+yD4WQnhdGUV4e/eNmcDg7meTcREJLfuIYZ8bLZgSbF0Wm0knCg/NLwHiUMDxJO2ZntPlmvEzJeB6xYbTasXnkU+hRQrZXCXt8SljpV8KRACt7/fzZa4nAwxRJgDGCEHME4b6RdAjtS7faXYmMhIgIiIwEXz2ATqqhqVNh3TqAMIa//jmP3PITUz643NVpiYiIVHkqIImIiMsYDAb8o/zxj/Kn3qX1nNblpeeRviudtJ1ppO1Mc7w+9ucxQpuEVri/tJ1pjteWWEuFfebfOZ/d3+8mtFEoIQ1DCGn019IwhNBG0fhE1K+wOGUwGjEHBWEe/BbB2btLi0zeUVDvZgCuPDH3FteTtXwB+Wme5GX7kWNvSW7GcfLycyi0WSkyGk878bfRXkyYEVJfak1BkZmVL0RzFN8TRjP5Y7SDpbB0iTt+4tZW8j0OkeGzn7C8ErysJeQWfs4vqVfzWtbTjl61/VJIub0fXiY//A0RBHlGEOoTQbR/JDHBEcSFRVA/KoJmdSKpHx2C6RQjsETcQXw8PF32vzgWi4F7n+2HQf/rioiInDMVkERExC35hvri292XOt3rOMWtRVaKcosq3CZ9V7rj9cmKTMf2HKMws5AjG45wZMORcuu9g7ydCksnFpp8Q30huE3pcqrcB/wfvl0uhdxDYPSE1k87rbcVFVGw8F/kr3qDPLMn+dke5MU8Su7PS8lNPEJ+USGePkFMCH8OeBxvr0KKbAY4g2mafEpM+GSbADMAfuZCugQsgRMKSNOi+1D8E3ja8oAkik0l5HqVkGUuIc23mC0BJXxvKWFneAlH/MFQGIZfVnu6719ARETpSKbw8NL/FvjvwNuSQ8PocBrXDiciyI8zfDieyDkpLIRbboHisrtJeeMNqF3bdTmJiIhUJyogiYhIlWLyMuHj5VPhuvHbxpO2q3S0UkijkAr7pO9JrzD+t4LjBScvLgV7/zVSKZSg+kEE1w92LAG1AjCa/hrmENa5dDkJo5cXvr1vwbdZPUJzD0FhGnR5GB582KnfoMPzYXnp6xZt08i29yU/s4iCzEzycnPJLy6mAE45N9OJmtTZR9bTAXy66ibufv9tij2cJwIH8CkuXSJzoMUJD9GzGuxkmUvIMibQMKUDhX/4kVMSxA9ZI/m+4Aa45jlMLT8hJsvE4UArNpsPpoIIzNZwfO3hBHqE8//t3X1wVPW9x/HP5plAdgOEJEBiAg4mIq0FlCSCYi8StaKl3hG8aq54LVpbZKh36sXaGR7GqdprtT5gLS0V71SUtpCWtkrFykOVBxGDSLWRZwNJCA+b3QQS8rC/+0ea1WU3m+wmYfck79dMxpyzv3PO7+vZ7+6Xb86eHZw4TMOShykzZZjGZ0xUQe7lGjtWSknp0vSBoDYue1yXDhytjzVbkvStb0l33RXhSQEA0IfQQAIA9BkD0wdqYPrADr8ZztPq0bWLr9Wpfafk3O/UqX2nVHu4VqbVBBx/vkZnoyp3Vqpyp39z6Zsrv6mv3f01n3WtTa068ekJDR41WIn2RN8NUse1/QST+hXpyhels0d1UW6FdMWzUsJgnyHG+YmaV31FDVVxajwRp4ZR/6mGzAI1nDihhuPH1fj222poalKD8WjAoGalDKjXv31demHkMXnWdP1zPbHGpsGN8Roso+ZBjYpRo+w6pa+ee7+tgTTouCZUxmvBtjGyGaP41hbZ1KLWmONqjKvUmYQW1Sa16ERyi6pSWvRUzRxVvLdMb78tTZvW5WkAAX38zlZNT/+RbnjQo29dUapFf1qml14aylVwAAD0IBpIAIB+IyY2RgXzC3zWtTa1qvZwrU7vP61T+07p9L7TOr3/tE7vO93WXPJ0rbk05GL/K55O7z+tX3ztF5Kk5LRk3frqrbq4+GKfMcZjZDxGMXEBmjmDRkljHgh6XNuAYUoofkEJZyvlaDgm5X+3rfH0ZZ4WaXWiTKtHknTJ5SM06rp4HayerIaaGjU6nWqor1fDWbcaTIzOxXa9PKhu/dfngwbWKPtU23bGZlNTXLyktq9Mj/NIjsa2n5zatuFZOqin1PYROKA7zrj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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Define line styles and colors\n", - "linestyles = [\"-\", \"--\", \"-.\", \":\", (0, (3, 1, 1, 1)), (0, (5, 1))]\n", - "colors = [\"blue\", \"green\", \"purple\", \"orange\", \"red\", \"brown\", \"cyan\", \"magenta\"]\n", - "\n", - "# Define a larger linewidth\n", - "linewidth = 2.5\n", - "\n", - "# Create a single figure with two subplots\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", - "\n", - "# Plot the real part on the first subplot (ax1)\n", - "ax1.plot(tlist2, corrRana, label=\"Analytic\", color=colors[0], linestyle=linestyles[0], linewidth=linewidth)\n", - "ax1.plot(tlist2, corrRMats, label=\"Matsubara\", color=colors[1], linestyle=linestyles[1], linewidth=linewidth)\n", - "\n", - "for i in range(kR):\n", - " y = fit_func(tlist2, *poptR[i])\n", - " ax1.plot(tlist2, y, label=f\"Fit with {i} terms\", color=colors[(i + 2) % len(colors)], linestyle=linestyles[(i + 2) % len(linestyles)], linewidth=linewidth)\n", - "ax1.set_ylabel(r\"$C_{R}(t)$\")\n", - "ax1.set_xlabel(r\"$t$\")\n", - "ax1.legend()\n", - "\n", - "# Plot the imaginary part on the second subplot (ax2)\n", - "ax2.plot(tlist2, corrIana, label=\"Analytic\", color=colors[0], linestyle=linestyles[0], linewidth=linewidth)\n", - "\n", - "for i in range(kI):\n", - " y = fit_func(tlist2, *poptI[i])\n", - " ax2.plot(tlist2, y, label=f\"Fit with {i} terms\", color=colors[(i + 3) % len(colors)], linestyle=linestyles[(i + 1) % len(linestyles)], linewidth=linewidth)\n", - "ax2.set_ylabel(r\"$C_{I}(t)$\")\n", - "ax2.set_xlabel(r\"$t$\")\n", - "\n", - "ax2.legend()\n", - "\n", - "# Add overall plot title and show the figure\n", - "fig.suptitle(\"Comparison of Analytic and Fit to Correlations (Real and Imaginary Parts)\", fontsize=16)\n", - "plt.show()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "15e69700", - "metadata": {}, - "outputs": [], - "source": [ - "# Set the exponential coefficients from the fit parameters\n", - "\n", - "ckAR1 = poptR[-1][0]\n", - "ckAR = [x + 0j for x in ckAR1]\n", - "\n", - "vkAR1 = poptR[-1][1]\n", - "vkAR = [-x + 0j for x in vkAR1]\n", - "\n", - "ckAI1 = poptI[-1][0]\n", - "ckAI = [x + 0j for x in ckAI1]\n", - "\n", - "vkAI1 = poptI[-1][1]\n", - "vkAI = [-x + 0j for x in vkAI1]" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "ffcb4f21", - "metadata": {}, - "outputs": [], - "source": [ - "# overwrite imaginary fit with analytical value (not much reason to use the\n", - "# fit for this)\n", - "\n", - "ckAI = [lam * gamma * (-1.0) + 0.0j]\n", - "vkAI = [gamma + 0.0j]" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "eb4c1da5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.0074384212493896484\n", - " [ 1% ] Elapsed 0.05s / Remaining 00:00:00:04" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 2.98s*] Elapsed 2.98s / Remaining 00:00:00:00\n", - "ODE solver time: 2.982210159301758\n" - ] - } - ], - "source": [ - "options = {**default_options}\n", - "\n", - "NC = 4\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI)\n", - " HEOMFit = HEOMSolver(Hsys, (bath,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultFit = HEOMFit.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "10367aab", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (resultFit, P11p, \"b\", \"P11 Fit\"),\n", - " (resultFit, P12p, \"r\", \"P12 Fit\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "eee27930", - "metadata": {}, - "source": [ - "Now we use the built-in functions. The `BosonicEnvironment` class, includes a \n", - "method that performs this fit automatically. More information on how the\n", - "built-in functios work can be found in `HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions`" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "93e96c60", - "metadata": {}, - "outputs": [], - "source": [ - "tlist3 = np.linspace(0, 2, 200)\n", - "envfit, fitinfo =dlenv.approximate(\"cf\",tlist=tlist3,full_ansatz=True,maxfev=1e6,Ni_max=1,Nr_max=3)" - ] - }, - { - "cell_type": "markdown", - "id": "10d5e5bf", - "metadata": {}, - "source": [ - "The approx_by_cf_fit method outputs a `ExponentialBosonicEnvironment` object,\n", - "which contains a decaying exponential representation of the original \n", - "environment , and a dictionary containing all information related to the fit.\n", - "The dictionary contains a summary of the fir information and the normalized \n", - "root mean squared error, which assesses how good the fit is. " - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "fa998aa2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlation function fit:\n", - "\n", - "Result of fitting the real part of |Result of fitting the imaginary part \n", - "the correlation function with 3 terms: |of the correlation function with 1 terms: \n", - " | \n", - " Parameters| a | b | c | d | Parameters| a | b | c | d \n", - " 1 | 1.02e-01 |-5.71e-01 |-9.61e-08 |2.16e+00 | 1 | 1.12e-02 |-5.01e-01 |-6.77e-03 |-5.00e-02 \n", - " 2 | 7.96e-02 |-8.08e+00 | 2.70e-06 |1.76e+00 | \n", - " 3 | 2.34e-01 |-2.06e+02 | 0.00e+00 |0.00e+00 |A 1-R2 coefficient of 1.51e-01 was obtained for the the imaginary part\n", - " |of the correlation function. \n", - "A 1-R2 coefficient of 8.01e-04 was obtained for the the real part of | \n", - "the correlation function. | \n", - "The current fit took 35.525554 seconds. |The current fit took 0.004359 seconds. \n", - "\n" - ] - } - ], - "source": [ - "print(fitinfo['summary'])" - ] - }, - { - "cell_type": "markdown", - "id": "43257850", - "metadata": {}, - "source": [ - "We can then compare the result of the built-in fit with the manual fit" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "2d897b84", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Create a single figure with two subplots\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", - "\n", - "# Plot the real part on the first subplot (ax1)\n", - "ax1.plot(tlist2, corrRana, label=\"Original\", marker=\"o\", markevery=500)\n", - "ax1.plot(tlist2, fit_func(tlist2, *poptR[-1]), color=\"r\", label=\"Manual Fit\")\n", - "ax1.plot(tlist2, np.real(envfit.correlation_function(tlist2)), \"k--\", label=\"Built-in fit\")\n", - "ax1.set_ylabel(r\"$C_{R}(t)$\")\n", - "ax1.set_xlabel(r\"$t$\")\n", - "ax1.legend()\n", - "\n", - "# Plot the imaginary part on the second subplot (ax2)\n", - "ax2.plot(tlist2, corrIana, label=\"Original\", marker=\"o\", markevery=500)\n", - "ax2.plot(tlist2, fit_func(tlist2, *poptI[-1]), color=\"r\", label=\"Manual Fit\")\n", - "ax2.plot(tlist2, np.imag(envfit.correlation_function(tlist2)), \"k--\", label=\"Built-in fit\")\n", - "ax2.set_ylabel(r\"$C_{I}(t)$\")\n", - "ax2.set_xlabel(r\"$t$\")\n", - "ax2.legend()\n", - "# Add an overall title and adjust layout\n", - "plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "a089f775", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.01082611083984375\n", - " Total run time: 3.13s*] Elapsed 3.13s / Remaining 00:00:00:00\n", - "ODE solver time: 3.1316940784454346\n" - ] - } - ], - "source": [ - "options = {**default_options}\n", - "\n", - "NC = 4\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " HEOMFit_2 = HEOMSolver(Hsys, (envfit,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultFit_2 = HEOMFit_2.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "b1a02d7b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (resultFit, P11p, \"b\", \"P11 Fit\"),\n", - " (resultFit, P12p, \"r\", \"P12 Fit\"),\n", - " (resultFit_2, P11p, \"r--\", \"P11 Built-in-Fit\"),\n", - " (resultFit_2, P12p, \"b--\", \"P12 Built-in-Fit\"),\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "e1585ec3", - "metadata": {}, - "source": [ - "## A reaction coordinate approach" - ] - }, - { - "cell_type": "markdown", - "id": "0cf8a70a", - "metadata": {}, - "source": [ - "Here we construct a reaction coordinate inspired model to capture the\n", - "steady-state behavior, and compare to the HEOM prediction. This result is\n", - "more accurate for narrow spectral densities. We will use the population and\n", - "coherence from this cell in our final plot below." - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "079c7bae", - "metadata": {}, - "outputs": [], - "source": [ - "dot_energy, dot_state = Hsys.eigenstates()\n", - "deltaE = dot_energy[1] - dot_energy[0]\n", - "\n", - "gamma2 = deltaE / (2 * np.pi * gamma)\n", - "wa = 2 * np.pi * gamma2 * gamma # reaction coordinate frequency\n", - "g = np.sqrt(np.pi * wa * lam / 2.0) # reaction coordinate coupling\n", - "# reaction coordinate coupling factor over 2 because of diff in J(w)\n", - "# (it is 2 lam now):\n", - "g = np.sqrt(\n", - " np.pi * wa * lam / 4.0\n", - ") #\n", - "\n", - "NRC = 10\n", - "\n", - "Hsys_exp = tensor(qeye(NRC), Hsys)\n", - "Q_exp = tensor(qeye(NRC), Q)\n", - "a = tensor(destroy(NRC), qeye(2))\n", - "\n", - "H0 = wa * a.dag() * a + Hsys_exp\n", - "# interaction\n", - "H1 = g * (a.dag() + a) * Q_exp\n", - "\n", - "H = H0 + H1\n", - "\n", - "energies, states = H.eigenstates()\n", - "rhoss = 0 * states[0] * states[0].dag()\n", - "for kk, energ in enumerate(energies):\n", - " rhoss += states[kk] * states[kk].dag() * np.exp(-beta * energies[kk])\n", - "\n", - "rhoss = rhoss / rhoss.norm()\n", - "\n", - "\n", - "class ReactionCoordinateResult:\n", - " def __init__(self, states, times):\n", - " self.states = states\n", - " self.times = times\n", - "\n", - "\n", - "resultRC = ReactionCoordinateResult([rhoss] * len(tlist), tlist)\n", - "\n", - "P12RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 1).dag())\n", - "P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag())" - ] - }, - { - "cell_type": "markdown", - "id": "67b7c2a2", - "metadata": {}, - "source": [ - "## Let's plot all our results\n", - "\n", - "Finally, let's plot all of our different results to see how they shape up against each other." - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "ae0d4e46", - "metadata": {}, - "outputs": [], - "source": [ - "rcParams = {\n", - " \"axes.titlesize\": 25,\n", - " \"axes.labelsize\": 30,\n", - " \"xtick.labelsize\": 28,\n", - " \"ytick.labelsize\": 28,\n", - " \"legend.fontsize\": 28,\n", - " \"axes.grid\": False,\n", - " \"savefig.bbox\": \"tight\",\n", - " \"lines.markersize\": 5,\n", - " \"font.family\": \"STIXgeneral\",\n", - " \"mathtext.fontset\": \"stix\",\n", - " \"font.serif\": \"STIX\",\n", - " \"text.usetex\": False,\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "1037005d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 15))\n", - "\n", - "with plt.rc_context(rcParams):\n", - "\n", - " plt.sca(axes[0])\n", - " plt.yticks([expect(P11RC, resultRC.states[0]), 0.6, 1.0], [0.32, 0.6, 1])\n", - " plot_result_expectations(\n", - " [\n", - " (resultBR, P11p, \"y-.\", \"Bloch-Redfield\"),\n", - " (resultMats, P11p, \"b\", \"Matsubara $N_k=2$\"),\n", - " (\n", - " resultMatsT,\n", - " P11p,\n", - " \"g--\",\n", - " \"Matsubara $N_k=2$ & Terminator\",\n", - " {\"linewidth\": 3},\n", - " ),\n", - " (\n", - " resultFit,\n", - " P11p,\n", - " \"r\",\n", - " r\"Fit $N_f = 4$, $N_k=15\\times 10^3$\",\n", - " {\"dashes\": [3, 2]},\n", - " ),\n", - " (\n", - " resultRC,\n", - " P11RC,\n", - " \"--\", \"Thermal\",\n", - " {\"linewidth\": 2, \"color\": \"black\"},\n", - " ),\n", - " ],\n", - " axes=axes[0],\n", - " )\n", - " axes[0].set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", - " axes[0].legend(loc=0)\n", - " axes[0].text(5, 0.9, \"(a)\", fontsize=30)\n", - " axes[0].set_xlim(0, 50)\n", - "\n", - " plt.sca(axes[1])\n", - " plt.yticks(\n", - " [np.real(expect(P12RC, resultRC.states[0])), -0.2, 0.0, 0.2],\n", - " [-0.33, -0.2, 0, 0.2],\n", - " )\n", - " plot_result_expectations(\n", - " [\n", - " (resultBR, P12p, \"y-.\", \"Bloch-Redfield\"),\n", - " (resultMats, P12p, \"b\", \"Matsubara $N_k=2$\"),\n", - " (\n", - " resultMatsT,\n", - " P12p,\n", - " \"g--\",\n", - " \"Matsubara $N_k=2$ & Terminator\",\n", - " {\"linewidth\": 3},\n", - " ),\n", - " (\n", - " resultFit,\n", - " P12p,\n", - " \"r\",\n", - " r\"Fit $N_f = 4$, $N_k=15\\times 10^3$\",\n", - " {\"dashes\": [3, 2]},\n", - " ),\n", - " (\n", - " resultRC,\n", - " P12RC,\n", - " \"--\",\n", - " \"Thermal\",\n", - " {\"linewidth\": 2, \"color\": \"black\"},\n", - " ),\n", - " ],\n", - " axes=axes[1],\n", - " )\n", - " axes[1].text(5, 0.1, \"(b)\", fontsize=30)\n", - " axes[1].set_xlabel(r\"$t \\Delta$\", fontsize=30)\n", - " axes[1].set_ylabel(r\"$\\rho_{01}$\", fontsize=30)\n", - " axes[1].set_xlim(0, 50)" - ] - }, - { - "cell_type": "markdown", - "id": "33ee2124", - "metadata": {}, - "source": [ - "And that's the end of a detailed first dive into modeling bosonic environments with the HEOM." - ] - }, - { - "cell_type": "markdown", - "id": "64bfae7b", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "60d4a331", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "QuTiP: Quantum Toolbox in Python\n", - "================================\n", - "Copyright (c) QuTiP team 2011 and later.\n", - "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", - "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", - "Original developers: R. J. Johansson & P. D. Nation.\n", - "Previous lead developers: Chris Granade & A. Grimsmo.\n", - "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", - "\n", - "QuTiP Version: 5.2.0.dev0+daa7d68\n", - "Numpy Version: 1.26.4\n", - "Scipy Version: 1.14.1\n", - "Cython Version: 3.0.9\n", - "Matplotlib Version: 3.9.2\n", - "Python Version: 3.12.7\n", - "Number of CPUs: 16\n", - "BLAS Info: Generic\n", - "INTEL MKL Ext: None\n", - "Platform Info: Linux (x86_64)\n", - "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", - "\n", - "Installed QuTiP family packages\n", - "-------------------------------\n", - "\n", - "No QuTiP family packages installed.\n", - "\n", - "================================================================================\n", - "Please cite QuTiP in your publication.\n", - "================================================================================\n", - "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" - ] - } - ], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "e654ee7f", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "d19a74bc", - "metadata": {}, - "outputs": [], - "source": [ - "# Check P11p\n", - "assert np.allclose(\n", - " expect(P11p, resultMatsT.states),\n", - " expect(P11p, resultPade.states),\n", - " rtol=1e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(P11p, resultMatsT.states),\n", - " expect(P11p, resultFit.states),\n", - " rtol=1e-2,\n", - ")\n", - "\n", - "# Check P12p\n", - "assert np.allclose(\n", - " expect(P12p, resultMatsT.states),\n", - " expect(P12p, resultPade.states),\n", - " rtol=1e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(P12p, resultMatsT.states),\n", - " expect(P12p, resultFit.states),\n", - " rtol=1e-1,\n", - ")" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter" - }, - "kernelspec": { - "display_name": "qutip-dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md index 82a1145d..2783a8e5 100644 --- a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md +++ b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md @@ -5,7 +5,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.16.4 + jupytext_version: 1.16.1 kernelspec: display_name: qutip-dev language: python @@ -639,7 +639,7 @@ def pade_corr(tlist, lmax): tlist_corr = np.linspace(0, 2, 100) cppLP, etapLP, gampLP = pade_corr(tlist_corr, 2) -corr_15k = dlenv.correlation_function(tlist_corr, Nk=15_000) +corr_15k = dlenv.correlation_function(tlist_corr, Nk=15) corr_2k = dlenv.correlation_function(tlist_corr, Nk=2) fig, ax1 = plt.subplots(figsize=(12, 7)) @@ -655,7 +655,7 @@ ax1.plot( np.real(corr_15k), "r--", linewidth=3, - label=r"real mats 15000 terms", + label=r"real pade 15 terms", ) ax1.plot( tlist_corr, @@ -765,7 +765,7 @@ many time steps: ```python tlist2 = np.linspace(0, 2, 10000) -corr_15k_t10k = dlenv.correlation_function(tlist2, Nk=15_000) +corr_15k_t10k = dlenv.correlation_function(tlist2, Nk=100) corrRana = np.real(corr_15k_t10k) corrIana = np.imag(corr_15k_t10k) @@ -935,7 +935,7 @@ built-in functios work can be found in `HEOM 1d: Spin-Bath model, fitting of spe ```python tlist3 = np.linspace(0, 2, 200) -envfit, fitinfo =dlenv.approx_by_cf_fit(tlist=tlist3,full_ansatz=True,maxfev=1e6,Ni_max=1,Nr_max=3) +envfit, fitinfo =dlenv.approximate("cf",tlist=tlist3,full_ansatz=True,maxfev=1e6,Ni_max=1,Nr_max=3) ``` The approx_by_cf_fit method outputs a `ExponentialBosonicEnvironment` object, diff --git a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb deleted file mode 100644 index 9909ef95..00000000 --- a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.ipynb +++ /dev/null @@ -1,941 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c1f45d96", - "metadata": {}, - "source": [ - "# HEOM 1b: Spin-Bath model (very strong coupling)" - ] - }, - { - "cell_type": "markdown", - "id": "ad79c064", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices.\n", - "\n", - "In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", - "\n", - "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian, the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", - "\n", - "In the example below we show how to model the overdamped Drude-Lorentz Spectral Density, commonly used with the HEOM. We show how to do this using the Matsubara, Pade and fitting decompositions, and compare their convergence.\n", - "\n", - "This notebook shows a similar example to notebook 1a, but with much stronger coupling as discussed in [Shi *et al.*, J. Chem. Phys **130**, 084105 (2009)](https://doi.org/10.1063/1.3077918). Please refer to notebook HEOM 1a for a more detailed explanation.\n", - "\n", - "As in notebook 1a, we present a variety of simulations using different techniques to showcase the effect of different approximations of the correlation function on the results:\n", - "\n", - "- Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator\n", - "- Simulation 2: Matsubara decomposition (including terminator)\n", - "- Simulation 3: Pade decomposition\n", - "- Simulation 4: Fitting approach\n", - "\n", - "Lastly we compare the results to using the Bloch-Redfield approach:\n", - "\n", - "- Simulation 5: Bloch-Redfield\n", - "\n", - "which does not give the correct evolution in this case.\n", - "\n", - "\n", - "### Drude-Lorentz (overdamped) spectral density\n", - "\n", - "The Drude-Lorentz spectral density is:\n", - "\n", - "$$J_D(\\omega)= \\frac{2\\omega\\lambda\\gamma}{{\\gamma}^2 + \\omega^2}$$\n", - "\n", - "where $\\lambda$ scales the coupling strength, and $\\gamma$ is the cut-off frequency. We use the convention,\n", - "\\begin{equation*}\n", - "C(t) = \\int_0^{\\infty} d\\omega \\frac{J_D(\\omega)}{\\pi}[\\coth(\\beta\\omega) \\cos(\\omega \\tau) - i \\sin(\\omega \\tau)]\n", - "\\end{equation*}\n", - "\n", - "With the HEOM we must use an exponential decomposition:\n", - "\n", - "\\begin{equation*}\n", - "C(t)=\\sum_{k=0}^{k=\\infty} c_k e^{-\\nu_k t}\n", - "\\end{equation*}\n", - "\n", - "As an example, the Matsubara decomposition of the Drude-Lorentz spectral density is given by:\n", - "\n", - "\\begin{equation*}\n", - " \\nu_k = \\begin{cases}\n", - " \\gamma & k = 0\\\\\n", - " {2 \\pi k} / {\\beta } & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "\\begin{equation*}\n", - " c_k = \\begin{cases}\n", - " \\lambda \\gamma (\\cot(\\beta \\gamma / 2) - i) & k = 0\\\\\n", - " 4 \\lambda \\gamma \\nu_k / \\{(nu_k^2 - \\gamma^2)\\beta \\} & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." - ] - }, - { - "cell_type": "markdown", - "id": "b22fb8a0", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "1cc43553", - "metadata": {}, - "outputs": [], - "source": [ - "import contextlib\n", - "import time\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " basis,\n", - " brmesolve,\n", - " expect,\n", - " liouvillian,\n", - " sigmax,\n", - " sigmaz,\n", - ")\n", - "from qutip.core.environment import (\n", - " DrudeLorentzEnvironment,\n", - " system_terminator\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver,\n", - ")\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "eeeb30c0", - "metadata": {}, - "source": [ - "## Helper functions\n", - "\n", - "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4a89b7e3", - "metadata": {}, - "outputs": [], - "source": [ - "def cot(x):\n", - " \"\"\" Vectorized cotangent of x. \"\"\"\n", - " return 1. / np.tan(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "43a4b38c", - "metadata": {}, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\" Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "bb0c6df0", - "metadata": {}, - "outputs": [], - "source": [ - "# Solver options:\n", - "\n", - "options = {\n", - " \"nsteps\": 15000,\n", - " \"store_states\": True,\n", - " \"rtol\": 1e-14,\n", - " \"atol\": 1e-14,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "bb1d16f9", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "And let us set up the system Hamiltonian, bath and system measurement operators:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8bdfe487", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the system Hamiltonian\n", - "eps = .0 # Energy of the 2-level system.\n", - "Del = .2 # Tunnelling term\n", - "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "295e3bcf", - "metadata": {}, - "outputs": [], - "source": [ - "# Initial state of the system.\n", - "rho0 = basis(2, 0) * basis(2, 0).dag()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "059bfc82", - "metadata": {}, - "outputs": [], - "source": [ - "# System-bath coupling (Drude-Lorentz spectral density)\n", - "Q = sigmaz() # coupling operator\n", - "\n", - "# Bath properties (see Shi et al., J. Chem. Phys. 130, 084105 (2009)):\n", - "gamma = 1. # cut off frequency\n", - "lam = 2.5 # coupling strength\n", - "T = 1. # in units where Boltzmann factor is 1\n", - "beta = 1. / T\n", - "\n", - "# HEOM parameters:\n", - "\n", - "# number of exponents to retain in the Matsubara expansion of the\n", - "# bath correlation function:\n", - "Nk = 1\n", - "\n", - "# Number of levels of the hierarchy to retain:\n", - "NC = 13\n", - "\n", - "# Times to solve for:\n", - "tlist = np.linspace(0, np.pi / Del, 600)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "93ff798c", - "metadata": {}, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresonding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresonding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "6ddfdf60", - "metadata": {}, - "source": [ - "### Plot the spectral density\n", - "\n", - "Let us briefly inspect the spectral density." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d6e58091", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "bath = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=500)\n", - "w = np.linspace(0, 5, 1000)\n", - "J = bath.spectral_density(w)\n", - "\n", - "# Plot the results\n", - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - "axes.plot(w, J, 'r', linewidth=2)\n", - "axes.set_xlabel(r'$\\omega$', fontsize=28)\n", - "axes.set_ylabel(r'J', fontsize=28);" - ] - }, - { - "cell_type": "markdown", - "id": "8d548aab", - "metadata": {}, - "source": [ - "## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "e7c43b4b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.018474340438842773\n", - " [ 1% ] Elapsed 0.01s / Remaining 00:00:00:01" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 2.22s*] Elapsed 2.22s / Remaining 00:00:00:00[*********73%***** ] Elapsed 1.51s / Remaining 00:00:00:00\n", - "ODE solver time: 2.226008415222168\n" - ] - } - ], - "source": [ - "with timer(\"RHS construction time\"):\n", - " matsBath=bath.approx_by_matsubara(Nk=Nk)\n", - " HEOMMats = HEOMSolver(Hsys, (matsBath,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMats = HEOMMats.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "1e8799ea", - "metadata": {}, - "source": [ - "## Simulation 2: Matsubara decomposition (including terminator)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "74028d2c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.018711090087890625\n", - " [*********50% ] Elapsed 1.13s / Remaining 00:00:00:01" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 2.32s*] Elapsed 2.32s / Remaining 00:00:00:00[*********94%********** ] Elapsed 2.15s / Remaining 00:00:00:00\n", - "ODE solver time: 2.324307918548584\n" - ] - } - ], - "source": [ - "with timer(\"RHS construction time\"):\n", - " matsBath,delta=bath.approx_by_matsubara(Nk=Nk,compute_delta=True)\n", - " terminator = system_terminator(Q,delta)\n", - " Ltot = liouvillian(Hsys) + terminator\n", - " HEOMMatsT = HEOMSolver(Ltot, (matsBath,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMatsT = HEOMMatsT.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "e8cb9336", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the results\n", - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - "\n", - "P11_mats = np.real(expect(resultMats.states, P11p))\n", - "axes.plot(\n", - " tlist, np.real(P11_mats),\n", - " 'b', linewidth=2, label=\"P11 (Matsubara)\",\n", - ")\n", - "\n", - "P11_matsT = np.real(expect(resultMatsT.states, P11p))\n", - "axes.plot(\n", - " tlist, np.real(P11_matsT),\n", - " 'b--', linewidth=2,\n", - " label=\"P11 (Matsubara + Terminator)\",\n", - ")\n", - "\n", - "axes.set_xlabel(r't', fontsize=28)\n", - "axes.legend(loc=0, fontsize=12);" - ] - }, - { - "cell_type": "markdown", - "id": "ddc3e03a", - "metadata": {}, - "source": [ - "## Simulation 3: Pade decomposition" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "c7e649a3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# First, compare Matsubara and Pade decompositions\n", - "padeBath = bath.approx_by_pade(Nk=Nk)\n", - "\n", - "\n", - "fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(16, 8))\n", - "\n", - "ax1.plot(\n", - " tlist, np.real(bath.correlation_function(tlist)),\n", - " \"r\", linewidth=2, label=f\"Exact\",\n", - ")\n", - "ax1.plot(\n", - " tlist, np.real(matsBath.correlation_function(tlist)),\n", - " \"g--\", linewidth=2, label=f\"Mats (Nk={Nk})\",\n", - ")\n", - "ax1.plot(\n", - " tlist, np.real(padeBath.correlation_function(tlist)),\n", - " \"b--\", linewidth=2, label=f\"Pade (Nk={Nk})\",\n", - ")\n", - "\n", - "ax1.set_xlabel(r't', fontsize=28)\n", - "ax1.set_ylabel(r\"$C_R(t)$\", fontsize=28)\n", - "ax1.legend(loc=0, fontsize=12)\n", - "\n", - "tlist2 = tlist[0:50]\n", - "ax2.plot(\n", - " tlist2, np.abs(matsBath.correlation_function(tlist2)\n", - " - bath.correlation_function(tlist2)),\n", - " \"g\", linewidth=2, label=\"Mats Error\",\n", - ")\n", - "ax2.plot(\n", - " tlist2, np.abs(padeBath.correlation_function(tlist2)\n", - " - bath.correlation_function(tlist2)),\n", - " \"b--\", linewidth=2, label=\"Pade Error\",\n", - ")\n", - "\n", - "ax2.set_xlabel(r't', fontsize=28)\n", - "ax2.legend(loc=0, fontsize=12);" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "3a955ef8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.010780572891235352\n", - " Total run time: 1.81s*] Elapsed 1.81s / Remaining 00:00:00:00\n", - "ODE solver time: 1.8109989166259766\n" - ] - } - ], - "source": [ - "with timer(\"RHS construction time\"):\n", - " HEOMPade = HEOMSolver(Hsys, (padeBath,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultPade = HEOMPade.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "3a5f16a0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the results\n", - "fig, axes = plt.subplots(figsize=(8, 8))\n", - "\n", - "axes.plot(\n", - " tlist, np.real(P11_mats),\n", - " 'b', linewidth=2, label=\"P11 (Matsubara)\",\n", - ")\n", - "axes.plot(\n", - " tlist, np.real(P11_matsT),\n", - " 'b--', linewidth=2, label=\"P11 (Matsubara + Terminator)\",\n", - ")\n", - "\n", - "P11_pade = np.real(expect(resultPade.states, P11p))\n", - "axes.plot(\n", - " tlist, np.real(P11_pade),\n", - " 'r', linewidth=2, label=\"P11 (Pade)\",\n", - ")\n", - "\n", - "axes.set_xlabel(r't', fontsize=28)\n", - "axes.legend(loc=0, fontsize=12);" - ] - }, - { - "cell_type": "markdown", - "id": "371b72a6", - "metadata": {}, - "source": [ - "## Simulation 4: Fitting approach\n", - "\n", - "In `HEOM 1a: Spin-Bath model (introduction)` a fit is performed manually, here\n", - "we will use the built-in tools. More details about them can be seen in \n", - "`HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions`" - ] - }, - { - "cell_type": "code", - "execution_count": 202, - "id": "6df7fe42", - "metadata": {}, - "outputs": [], - "source": [ - "tfit=np.linspace(0,10,10000)\n", - "lower = [0, -np.inf, -1e-6, -3]\n", - "guess = [np.real(bath.correlation_function(0))/10, -10, 0, 0]\n", - "upper = [5, 0, 1e-6, 0] # for better fits increase the first element\n", - "# that makes the simuation slower though\n", - "envfit,fitinfo = bath.approximate(\"cf\",tlist=tfit,Nr_max=2,Ni_max=1,full_ansatz=True,\n", - " sigma=0.1,maxfev=1e6,target_rsme=None,\n", - " lower=lower,upper=upper,guess=guess)" - ] - }, - { - "cell_type": "code", - "execution_count": 203, - "id": "4dcf3dd8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlation function fit:\n", - "\n", - "Result of fitting the real part of |Result of fitting the imaginary part \n", - "the correlation function with 2 terms: |of the correlation function with 1 terms: \n", - " | \n", - " Parameters| a | b | c | d | Parameters| a | b | c | d \n", - " 1 | 5.00e+00 |-1.91e+01 |-3.81e-07 |-2.97e+00 | 1 | 4.10e+00 |-1.00e+00 |-9.18e-07 |-2.50e+00 \n", - " 2 | 5.00e+00 |-1.07e+00 | 3.03e-07 |-1.06e-04 | \n", - " |A 1-R2 coefficient of 2.50e-03 was obtained for the the imaginary part\n", - "A 1-R2 coefficient of 2.16e-02 was obtained for the the real part of |of the correlation function. \n", - "the correlation function. | \n", - "The current fit took 0.622658 seconds. |The current fit took 0.120125 seconds. \n", - "\n" - ] - } - ], - "source": [ - "print(fitinfo['summary'])" - ] - }, - { - "cell_type": "markdown", - "id": "457e6415", - "metadata": {}, - "source": [ - "We can quickly compare the result of the Fit with the Pade expansion" - ] - }, - { - "cell_type": "code", - "execution_count": 204, - "id": "d4491a1e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 204, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8))\n", - "\n", - "ax1.plot(\n", - " tlist, np.real(bath.correlation_function(tlist)),\n", - " \"r\", linewidth=2, label=f\"Exact\",\n", - ")\n", - "ax1.plot(\n", - " tlist, np.real(envfit.correlation_function(tlist)),\n", - " \"g--\", linewidth=2, label=f\"Fit\",marker=\"o\",markevery=50\n", - ")\n", - "ax1.plot(\n", - " tlist, np.real(padeBath.correlation_function(tlist)),\n", - " \"b--\", linewidth=2, label=f\"Pade (Nk={Nk})\",\n", - ")\n", - "\n", - "ax1.set_xlabel(r't', fontsize=28)\n", - "ax1.set_ylabel(r\"$C_R(t)$\", fontsize=28)\n", - "ax1.legend(loc=0, fontsize=12)\n", - "\n", - "ax2.plot(\n", - " tlist, np.imag(bath.correlation_function(tlist)),\n", - " \"r\", linewidth=2, label=f\"Exact\",\n", - ")\n", - "ax2.plot(\n", - " tlist, np.imag(envfit.correlation_function(tlist)),\n", - " \"g--\", linewidth=2, label=f\"Fit\",marker=\"o\",markevery=50\n", - ")\n", - "ax2.plot(\n", - " tlist, np.imag(padeBath.correlation_function(tlist)),\n", - " \"b--\", linewidth=2, label=f\"Pade (Nk={Nk})\",\n", - ")\n", - "\n", - "ax2.set_xlabel(r't', fontsize=28)\n", - "ax2.set_ylabel(r\"$C_I(t)$\", fontsize=28)\n", - "ax2.legend(loc=0, fontsize=12)" - ] - }, - { - "cell_type": "code", - "execution_count": 205, - "id": "dea09fd4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.006770610809326172\n", - " Total run time: 1.38s*] Elapsed 1.38s / Remaining 00:00:00:00\n", - "ODE solver time: 1.3836772441864014\n" - ] - } - ], - "source": [ - "with timer(\"RHS construction time\"):\n", - " # We reduce NC slightly here for speed of execution because we retain\n", - " # 3 exponents in ckAR instead of 1. Please restore full NC for\n", - " # convergence though:\n", - " HEOMFit = HEOMSolver(Hsys, (envfit,Q), int(NC*0.7), options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultFit = HEOMFit.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "ac6505d0", - "metadata": {}, - "source": [ - "## Simulation 5: Bloch-Redfield" - ] - }, - { - "cell_type": "code", - "execution_count": 206, - "id": "59507a86", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 0.88s*] Elapsed 0.88s / Remaining 00:00:00:00\n", - "ODE solver time: 0.8906404972076416\n" - ] - } - ], - "source": [ - "with timer(\"ODE solver time\"):\n", - " resultBR = brmesolve(\n", - " Hsys, rho0, tlist,\n", - " a_ops=[[sigmaz(),bath]], sec_cutoff=0, options=options,\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "98ab21e2", - "metadata": {}, - "source": [ - "## Let's plot all our results\n", - "\n", - "Finally, let's plot all of our different results to see how they shape up against each other." - ] - }, - { - "cell_type": "code", - "execution_count": 207, - "id": "771eb79e", - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate expectation values in the bases:\n", - "P11_mats = np.real(expect(resultMats.states, P11p))\n", - "P11_matsT = np.real(expect(resultMatsT.states, P11p))\n", - "P11_pade = np.real(expect(resultPade.states, P11p))\n", - "P11_fit = np.real(expect(resultFit.states, P11p))\n", - "P11_br = np.real(expect(resultBR.states, P11p))" - ] - }, - { - "cell_type": "code", - "execution_count": 208, - "id": "661dff32", - "metadata": {}, - "outputs": [], - "source": [ - "rcParams = {\n", - " \"axes.titlesize\": 25,\n", - " \"axes.labelsize\": 30,\n", - " \"xtick.labelsize\": 28,\n", - " \"ytick.labelsize\": 28,\n", - " \"legend.fontsize\": 28,\n", - " \"axes.grid\": False,\n", - " \"savefig.bbox\": \"tight\",\n", - " \"lines.markersize\": 5,\n", - " \"font.family\": \"STIXgeneral\",\n", - " \"mathtext.fontset\": \"stix\",\n", - " \"font.serif\": \"STIX\",\n", - " \"text.usetex\": False,\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 209, - "id": "6bc85109", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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68a5bty5L/bx8+TJLz33Tpk3KMrVq1RLly5cX1atXF5s3bxa+vr7i3LlzYuLEicLY2FhZrkiRIsLX11etrdwcx1mzZomhQ4eK8uXLq8R9+/ZtUblyZdGpUydRvXp15XZLS0ut9JsbMTExwsTERPTp00e5LSEhQTg4OCj7a9asWbr1f/75ZwFAbNmyRWsxaRIaGirc3Nzy5KHpeGdH6r/RQYMGaecJE1Ge0+ZnuOychzK1+gkoVw6IiABmzGgKQD7g4c2b69C6NS+1IyKirOnSpQsqV66MBw8eAACkUimWLFmCZcuWaSx/8+ZNXLhwAXXr1kWLFi2y3d/y5cuVgxVqun2gbdu2cHZ2xosXL+Dr64u7d++iWrVq2e5HYceOHQCQbqzjxo3D5s2bNe4rW7ZslvooWbJktuO6desWBg4ciE2bNqkMPtmyZUt07twZ7du3R1JSEuLj4zFw4ED4+/urzESRm+M4Z84cAMCdO3dQo0YNZZ1x48bhu+++w+DBg5GYmIiJEydi586dKred6Pr1Uzh58iSSkpLg6emp3GZmZobhw4fjl19+ASC/ZcXf31/j9IHe3t4wMDDA559/nutYMpKcnIyHDx/mSds5+T0jIsopJgw+EWZmQL16XyIysihsbd/DJGwPkp9Og3G56voOjYg+IfXqAa9e6TuKgqlkSeD6dX1HkT6JRILJkydj2LBhym1//vknZs+ejaJFi6qVX7JkCQBg0qRJOerv9evXyuWgoCCNZcqUKYMXL14AAJ48eZKrE87nz58DAO7fv69xv0QiwahRozTuy+ql6zmZQtLU1BQrV65USRYotGrVCjNmzMDs2bMByAf727NnD/r166cso43jWKlSJZV1JycnDB48GID8ZHzNmjVYs2aNShldv34KXl5eAOTjF6Q2atQo/Pbbb5BKpQCAVatWYd26dSploqOj4ePjg4YNG8LBwUFj+wkJCXjy5Anu3r2LGzduYMyYMVlOGBERfYqYMPhE+PkB33xjhq/bj8bsFz+j6LUkRPUaCdtd/+o7NCL6hLx6Bfz3+Z8+QQMGDMDMmTPx6r+sUFxcHFavXo0ff/xRpdyrV6+we/duuLi44KuvvspRX2PGjMGZM2cgkUjSnY4x9Yl6XFxcjvpRcHR0xPPnz7Fhwwb07t1b44B3w4cPz1UfOVGyZElYWVmlu3/SpElYsGAB4uPjAcivlEidMNDGcTQ1NVVZz8pYDLp+/RS8vLxQo0YNODo6qmx3cXFBt27dsHfvXgDy4/Tbb7+pXP1w+vRpSKXSdKdTfPfuHZYuXYp//vkH/v7+MDc3x88//5yjOMuUKQMhRI7qEhHlJ0wYfCKqVAFmzwbeBn8J22M/QyIAy0NXcPLWAVQr3xiOVo6ZN0JElAleCZtzBeHYmZqaYuzYsZgxY4Zy24oVKzBlyhSYmZkpt61atQpJSUkYO3ZsjgeOa9++Pd6+fQsDAwO1E1ZAPm1iWFiYcj2jke+z4uuvv8bs2bORlJSENm3a4LvvvsP06dNRpEiRXLWb1ywtLdGhQwflAI+XL19W2Z8Xx9Ha2jrTMrp+/QDg9u3bCA0NRZ8+fTTuHzdunDJhEB8fj40bN6pcAePt7Q0A6SYM7O3tMXfuXFSuXBn9+/dHs2bNVH7viYgKIyYMPhHm5sC0acCtW7URcKs4qvm8hlGCDP/+0h2XRs3CnFZz9B0iEX0C8vMl9aQdo0ePxvz58xEbGwtAfun51q1bld++JyYmYu3atbC0tMz1N/Lm5uYq6/Hx8di5cyc2btwIMzMz5T3y2jB9+nR4e3vj8uXLSEpKwty5c7F582YsXLgwx7Mb6ErdunWVCYOoqChER0ernNTr8jimput+FdMpph6/ILVmzZqhTp068PPzAwCsXr0aEydOVN4q4u3tDVdXV5XxGjS5e/cuAPk4DEREhR2nVfyEhIQA+/YB66tWVG4bdh3YcHMDpDKpHiMjIqKCwtbWFt98843KtkWLFkEmkwEAtm/fjjdv3mDIkCEaB7vLibCwMEybNg3lypXDnTt3sH37dpw5c0ar946bmJjAy8sL7du3V24LCQlB79690bJlS9y+fVtrfWlb2mkdFcmctHRxHPXZ77Fjx1CkSBE0a9Ys3TKpp1gMDAzE8ePHAQAPHz5EcHAwOnbsmGk/Fy5cAMCEARERwITBJyUyEpg3D3hpsA6R/+UMnIOAUo9f4uijo3qNjYiICo6JEyeq3Grw6NEjHDp0CACwbNkyGBgYYPz48bnuRyqVYvbs2ShfvjyuXr2Kq1evYtmyZShXrlyu29bE2toax44dw7Jly1S+oVfM9jBr1ixlYiQ/SXuLgJ2dncq6ro+jPvqNioqCj48P3N3dNd4CodCnTx8UK1ZMub5y5UoAUCYO0rsdQSE2Nha+vr4oVqyYxlkWiIgKG96S8An57DOgWDHAybwq9teyxdePIwEAA24Aa2+sxReVv9BvgEREVCC4urqiV69eyqkIAWDhwoWwtLTE3bt30a1bN7VvvbMrOjoaHTt2xKVLl+Dh4QEvLy+V6QLzikQiwbhx49C7d2/MnTsXa9euRXJyMqRSKebOnYunT59i+/bteR5HdqSefcHe3l7lVgB9HUdd93vixAlIpdJ0b0dQMDU1xfDhwzFv3jwA8kRBYGAgvL29YW5ujlatWmVY/9y5c0hOToaHh0eOZr1QePHiBTw8PHJcPyMNGjTA1q1b86RtIqK0mDD4hBgYAO7ugK8vcLnUz+ht/C0skoF+t4Fp970QHBmMMrZl9B0mEREVAFOnTlVJGPj4+GD06NEAcj6VYmoDBw7EpUuXAMi/BdbFSW5qxYsXx4oVKzBx4kSMHDkSJ0+eBCAfXf+bb76Bu7u7TuPJSEREhHI57Qmvvo6jrvvNbPyC1EaPHo0FCxZAKpVCJpNhyZIlOH/+PDw8PNTGXUhL8XuguB3h4cOHWLNmDZYvXw4hBNq3b49x48ahQ4cOGbaTnJyMhw8fZuWpZVvJgjCCKhF9MnhLwiemdWt5wmBgq8HYU9kYyQbAqXKAbSKwwW+DvsMjIqJ8IiUlReVnWjVr1lS7h/vJkyeoX79+hveQZ4Wfn5/yFodixYqhcuXKuWpPIaNp7NJLAJQrVw5eXl74/PPPldsUJ43Z7SevbmdQDMIHAD179lQu59VxzIyu+5XJZDh69ChKly4NNze3TMs7OTmhe/fuyvU1a9YgPj4+09sRAODUqVMAgDZt2gAA3Nzc0LNnTzg7O+P8+fPw8vLKNFlARPQpYcLgE9OqFSCVAi4lLDC3RB84TwJ69gJeWgN/3vwTySm5n9aIiIgKvvj4eABAXFxcumWmTZumti2zqwukUtVBdjWdXP/7778qcaR3Av769WvlsqbEhomJicp6eoMBAvKT3KdPn2rcZ2hoiO+//165nvobfQBq98w/e/ZMYztBQUEq69pKICjuv69atSp69Oih3K6t45hduu734sWLePPmDdq1a5flOuPGjVMuK16HzAY8DAsLw/3791GpUiW4uroCkP/ezJ8/H76+vmjRokWW+y9TpgyEEHnyOHfuXJbjICLKLSYMPjGVKgGOjoCPD1DVdQqMHYBvygJ/1gXexb3CP4/+0XeIRESUD4SEhABI/+QXkH/LWrt2beV6qVKlVE5YNUl7sh0VFaVWJiEhQbkcFxeHs2fPqpXZv38/AgIClOsvX75UK5N6cDsA8Pf3V1m/deuWclkmk2HRokXpxp06KZB2sLu0g/itW7dOrX50dDSGDRumcsl7eHh4uv1l1enTp3Hjxg2Ym5tj+/btMDD4+NFNW8cxbWIjbdInLW31m1WKW2Oyc8LepEkT1KtXT7leq1YtuLi4ZFhHcXWB4sqao0ePYunSpdi7d+8ndRvA27dvlcvaSCAR0aeNCYNPjEQiv8rg/HlgXK/qGFTMAf1cgXKWQDMH+eCHRERUuF27dg1v3rwBID8hzciUKVOUy+PGjVOZPSGtqKgoPH78WGXb5cuX1crVrVtXZb1Pnz5Yv349bt68iZMnT2LYsGGYOXMmWrdurSyzdetW+Pj4YNmyZbhx4wYA+Ym8vb29sszUqVPx6NEjPHv2DF9//TX+/vtvlX7Wrl0LLy8vjbFv3rwZAFC6dGn0799fZV+JEiVUbmn49ddfMXnyZFy5cgVXrlzBokWLUK1aNdSvXx9NmjRRllu4cCFOnDiB58+fq7RnZWWlXA4LC1Pbr/D8+XMMHToU5ubm2Llzp0ryBtDecUybNLp3757GeLTdb1a8ffsWO3fuBCD/1j47Uk+xmJ3bETw8PDBv3jxs3rwZmzdvznTcg4IkNDQUT548Ua7fvn07w1t5iIggSG+ioqIEABEVFaXVdletEsLISIjoaCGaDvpOnD0LceoMxMBNEPgJIvB9oFb7I6KCIyEhQdy/f18kJCToOxTSsaioKBEQECB27twpypQpIwAIAMLS0lIsXbpU3L59W7x//16tXnJysihdurSwtLQUkZGRavs/fPgggoKCxKlTp0SHDh2U7SoeZmZmYuHCheLx48dCKpUKIYSQyWTCw8NDrazi0aZNGxEeHi42btyotq9SpUrizZs3yv5//PFHjW0MHDhQpKSkKMtZWFgIAMLY2FhMmjRJ+Pj4iGfPnonLly+LIUOGCADC1dVV3L9/X+PxCwgIEPb29hr7MjQ0FHPmzBFCCJXn5ejoKAYOHCiOHz+u0pZUKhWjR48WhoaGynLz5s0TV69eFc+ePRP+/v5i4cKFwt7eXlSrVk1cvXpVY0y5PY6vXr0Se/fuFY0aNVLZV6xYMbFlyxbx4MEDjZ9RtPn6ZSQyMlK0adNGWXfu3LlZqqfw4cMHUbx4cQFAXLlyJdPyzs7OAoBo3ry5ACAaNWqUrf7yq7i4OPH48WNx8OBBUadOHbXXZMCAAeLcuXMiODiY/xuI8jFtfobLznkoEwZ6lFcJg9evhQgIEEImE2Lc5DjRdZW5sJsHUelbiGqjIKafnK7V/oio4GDCoPCaMWNGuid4isfXX3+tse7SpUvF+PHjNe47e/Zspu0qHj169FDWi4+PF9OnTxelS5cWxsbGwtHRUXTp0kUcOHBAWUYqlYqhQ4cKKysrUaJECTF69Gi1pEZycrKYNGmScHBwEBYWFqJhw4Zi27ZtanEqEgZpH6ampqJq1api5syZIiIiIsNjGBQUJIYMGSKcnJyEiYmJcHV1FQMHDhQ3btxQlhkyZIiYPHmyuH79eoZtCSFEcHCwWLJkifD09BTlypUTFhYWokiRIqJs2bKiV69eYvfu3SpJD01ycxw/++yzTF+z7777Tuv9ZsX48eM1vmYVK1YUgwcPzlIbQggxc+ZMUbx48UyP47179wQA4eDgIM6dOyeKFi0qAGTpdczvVqxYkeW/0b179+o7XCJKh74SBhIheB2SvkRHR8PGxgZRUVGwtrbOkz78/QHPicOwJ3ADWjwHjlYEhg4vjpCJITAxNMm8ASL6pCQmJiIoKAhly5aFmZmZvsMhIspT4eHhOHPmDPr06ZNhueXLl2P8+PGYMGEClixZggkTJmDZsmUYOHAgtmzZoqNoiYjSp83PcNk5D+UYBp+otWuByZOBmjUBuw/jUfq/MafaPwFMXr7GoQeH9BsgERERUR4rUaJEpskC4OP4Ba1atQIAjBkzBgYGBti1axdCQ0OV5dIO6klE9KljwuATlZICJP83g+LX3aphY1n59ECGAhh1G/jr3l96jI6IiIgof5BKpTh//jwMDAyUMzFUrFgRX375JZKTkzFz5kwA8ikWDxw4oM9QiYh0Lv2hjqlAGz3643KfPoDH9lGYLfkeBgKYcM8Ehl236y84IiIionzi2rVriI6ORt26dWFra6vc/vPPP+PQoUPYvHkzihYtitevX/P2BCIqdHiFwSfs1SsgOBhwdgacS4zG69rGAIAir5JgcOqofoMjIiIiygcUtyOknjoTANzc3LB69WpYW1vDz88Pv/32GwwM+NGZiAoXDnqoR3k96GGdOvIxDDZtArZsARJ3foERJw4DAOI71UKRf25qvU8iyt846CERERFRwcNBD0nrmjYF/v1XvtytG7Ai6H9IspOvmx33h3jzRn/BERERERERUb7GhMEnrGlT4PFj4PVrwNoaqF63Ju7WcUKKKfC6tUDsax99h0hERERERET5FBMGn7CmTeU/L1+W/xwwAFgomQqffcCD74GXRvJxDHhXChEREREREaXFhMEnrFQp+UNxW0LbtsC/D79GjKEFAOBl2HZ8tbsLpp+arscoiYiIiIiIKD9iwuAT16TJxysMjI2Brl2tcNZXPsewBPGIjvgHG25uQKI0UY9REhERERERUX7DhMEnrmFDwM8PkErl6wMGAMeOD1bu71ASeJ/wHvvu79NPgERERERERJQvMWHwiatXD0hMBO7f/7ie/K4nnkUWAQA0fQt0ewWsvbFWj1ESERERERFRfsOEwSeudm1g3DigiDw/AIkEGDBAgrNenVB3BFBvGLDIG7j0/BLuvb6n32CJiIiIiIgo32DC4BNnaQksWwZUqPBxW9++wKH9i6CYHKFsEFDrFbDuxjr9BElERERERET5DhMGhcDz58A//3xcL1cO+KyyCy7WLa7c9kMAsMV/C+KT4/UQIREREREREeU3TBgUAocOAV99BSQnf9w2YACwNOxHpJjK1ztdBz7ERmHPvT36CZKIiIiIiIjyFSYMCoFBg4DwcPm0igo9ewIXr4xCSFP5r4B5HDDgMQc/JCIiIiIiIjkmDAoBa2v5I7WiRYEOHYywu0Rz5bbxt4EroVdwO/y2jiMkIiIiIiKi/IYJg0Ji8mRgxgzVbQMGAMvPr0F8Kfn6Zw+A8u+Atdd5lQEREREREVFhx4RBIRERAXh5qW7r2BGIj6+CxTaNEGUKrKkHJBsC2+9sR1xSnH4CJSIiIiIionyBCYNCol494M4dIDHx4zZTU/lgiL8+/xOOk4HRnYDntkD0h2j8dfcvvcVKRERERERE+seEQSFRvz4glQK30wxP0L8/EBddFQnBn6ts5+CHREREREREhRsTBoVEjRryWRKuX1fd3rQpULo0gBsjAABuVkCLErbwKOuB5JRk9YaIiIiIiIioUDDSdwCkG6amQPXqwI0bqtsNDORXGSxf2QhLPrNBeYcoWFpVQr268/UTKBEREREREeULvMKgEKlXD/D1Vd/erx8QE1UMIrIMIACDy9eQuG+VzuMjIiIiIiKi/IMJg0KkTh3g/n3gwwfV7VWqAHXrSnDyn4Go3tccdcYBptN+B2Qy/QRKREREREREeseEQSFSowZgZAQEBanvGzAA2HN4Iq6/ag4AkAQFA2fP6jZAIiIiIiIiyjeYMChEGjQAYmOBypXV9/XuDRgaSrAB33zcuGGD7oIjIiIiIiKifIUJg0LE0FB+hYEmJUoAbdsCh9EFb+Ag33jgAIIDb2Cb/zbdBUlERFRICCEQGBio7zCIiIjSxYRBITNjBtCtm+Z9AwYASTDFVgyQb0hKwsbx9TD08FC8iH6huyCJiIg+UXFxcfj777/x9ddfw9HREYMGDdJ3SFRIyWQyhISE6Lzfly9fYtu2bZg/fz6WLl2Ks2fPIiUlRedxkDovLy+UKlUKLi4u8PLy0nc4lE8wYVDI1K0rv5JAky++ACwsgGefC+W20b5AilSKNdfX6ChCIiLKC0uXLoVEIkn38cMPP+S6j23btmXYR//+/bXwTAquHj16wN7eHt26dcPGjRsRHh6u75CoEBJCYPfu3fjss88wc+ZMnfX74cMHjBs3DqVLl8bYsWNx5coV+Pj4oGvXrnBzc8Ply5d1FgtpNmzYMISGhuLFixcYNmyYvsOhfIIJg0Lmyy+B0aM177OwAHr2BP7ynYT3deTbSr4GOjwF1t5Yi0Rpou4CJSIirRo2bBiePHmC48ePw9PTU23/mjVrEBsbm6s+li1bprbNwsICy5cvx507d7BixYpctV/Q7dixA0+fPkWrVq30HUqBtGnTJhQtWlQtEVW0aFHEx8dnWn/nzp2wtbXVmMwyNzeHj4+PDp6F/gghsH//ftSoUQO9e/fGgwcPdNZ3SkoKunbtihUrVqBixYq4d+8eDh06hN27d+Pu3btISkpCu3btcO/ePZ3FROpknCGNNGDCoJBJSQG8vYGnTzXvHzoUePOmFE59Vh3J1sDzXkCRSsDb+LfYdWeXboMlIiKtsbCwQPny5eHp6Ym9e/fCzMxMZX9kZCTWr1+f4/YvXryIGzduqG0fO3Ysxo4di2rVqsHOzi7H7X8KTE1N4eTkhO7du+s7lAJpyJAhePfuHQ4ePAhbW1vl9oiICOzYsSPT+n379kVERASOHz8OExMTAIBEIsHq1asRGRmJxo0b51Xoenf48GHUqVMHPXr0wN27d3Xe/+rVq3H8+HEAwOLFi+Hs7KzcV6pUKfz666+Ii4vDnDlzdB4bfbRq1SqULFkSzs7OWLt2rb7DoXyCCYNCRiIBevQA9u3TvL9ZM6BCBWDRvd/gswd4OhJoXE2+b9nVZRBCaK5IREQFhpWVFdzc3NS2L126FFKpNEdtLlmyBABgaGiosr1BgwY5ai+7Bg4cqJN+tMHCwkLfIRRYEokEXbt2VTuZWbVqVZbre3p6onr16gCAPn36YNSoUTA1NdV6rPnF+vXr8fLlSxw5cgQBAQFqyUJdWLPm462tmhIzHh4eAIBz587pKqQC9Z6hK926dUNYWBhCQ0PRsWNHfYeTY3xttYsJg0LGwACoUQPw99e8XyIBhgwBfG+2Q3CEPPtbxw4oXQTwD/fHhWcXdBgtERHlFRsbGwBQOXl4/vw5du/ene22goKCcPjwYTg7O6Nhw4Yq+1J/E5xX/vjjD2zbxhl9CpMiRYqorPv7++PSpUtZrh8TEwMAmDFjhlbjyo+GDRuGkSNHwtnZGZUrV0bNmjV12n9sbCwCAgKU65oSFor3o8jISJ3ExPeMTxdfW+1jwqAQqlkz/YQBAAwcCEgkBji8f7JyWxcn+c/l15bncXRERKQLEokEADBgwACV7QsXLsx2W8uXL0dKSgq+/fZbGBsba+wnr9y8eRMTJ07M0z4o/zl27BiMjIxglGq+6JUrV2apbnh4OB49egQ3NzdUrVo1r0LMt3R9hUFUVJTK+tu3b9XK+P/3wdTJySnP4+F7xqeLr23eYMKgEKpRA3jwAPjwQfN+FxfA0xM47jUEiUnyD36eJQAzA+DvB38jODJYd8ESEVGemjJlCgwMPn4c8Pf3x4kTJ7JcPyYmBhs3boSFhQVGjBiRFyGmKzQ0FF26dEFiIgflLWy8vLzQpEkTdO3aVbntwIEDCAsLy7Su4ve7U6dOeRUepaK4ekDhzp07amUUt5R8/vnneRoL3zM+XXxt8w4TBoVQzZrywQ/v30+/zNChQFycLU6d7gPIgFJ+wO5z8tFTV13L2n2CRESU/1WqVAldunRR2Zadqwz+/PNPREdHY/DgwTod1PDBgwdwd3dHaGiozvqk/CEgIADBwcHw9PTEuHHjlNuTk5OzNFCbYvC9gnyPdkFiaWmpMsjhwYMHVfZv2rQJ27Ztg42NDaZPn55ncfA9I3sCAwP1HUKW8bXNW0wYFELV/hvE8Pbt9Mt07gzY2wN/75+Iqv8DanwHdDkDNAoF1vmtQ/SHaN0ES0REeW7q1Kkq66dOncLNmzczrSeTybBixQoYGBhgwoQJOeo7JiYGS5cuRevWrVG8eHEYGxvD1tYW9erVw5w5czTe03zp0iXUrVtX7QNt6mnyNI2dkJKSgvXr16Nly5aws7ODgYGB2vR6qQdd6927t8Yp+NIqWbKkxnLZGcAtOjoaixYtQv369VG0aFFYWlqievXqmDFjhsZLuNPKyXFMLTExETt27IC7uzuePXsGAHj27Bm++uor2NnZoUyZMjh27JjW+80JRRzt2rVD8+bNUbt2beW+devWITk5Od26QgicPHkSNjY2aN68udZjI82aNGmiXN61axciIiIQHx+PSZMmYejQobC1tcWRI0fg6uqaJ/3n9D1D4d69e5g4cSKqVasGGxsbWFhYwM3NDWPHjlUZn0GTnPxthYWFYfny5WjcuDHmzZsHQP67u2/fPnTt2hVOTk4wNzeHm5sbfv31V7Xf+ZcvX2Lu3LmoU6cObGxsYGNjg/bt2+P69esZxvr06VPMmzcP1apVUw5EmZ5bt25hxowZqFSpEv79918AgFQqxaZNm9CsWTPY2toq+7169WqGbSno8v+BQmxsLFauXImOHTvC1dUV5ubmsLGxQdWqVTFmzBj4+vpmGndO3z8LDEF6ExUVJQCIqKgonfddvrwQEydmXGb8eCEAIU4OdJUvAOKfuhD4CWLhvwt1EicRaVdCQoK4f/++SEhI0HcopGctW7YUqT8GNG3aVABQPvr06ZNpGwcOHBAAxBdffKHWruJx9uzZdOufPHlSFC9eXAAQ48ePF35+fuLhw4di3bp1wt7eXgAQZcqUEYGBgSr1EhISRFhYmNi/f79KX2FhYcpHeHi4Wh0PDw8BQIwZM0Zcu3ZNBAYGijNnzoj+/fsLiUSiFm94eLg4fvy4KF++vEo/ab18+VJcu3ZN9OrVK0vPfdOmTcoyTZs2FXfu3BHlypVTqZv64eDgIM6cOaP14yiTycTFixfFqFGjhJ2dnbK/oKAg8ejRI1GiRAmVOEqXLq2VfnOrdevWwsHBQaSkpAghhNi4caNKnH/99Ve6da9fvy4AiK+++kqrMRUkqf9GBw0apJM+N2/erPIatW/fXjg5OQkAwtPTUwQHByvL1qlTR1hYWKT7GDFiRLb7z8l7hhDyv5HvvvtO2NraioULF4oHDx6IwMBAsXXrVmX8pqamYtWqVWr1svu39e7dOzFv3jzRqFEj5fsRADF79mzx7t070bZt23TfIwYOHKjse9WqVcLKykpjOXNzc3H9+nWVWKOjo8WyZctEw4YNM/x7F0KIkJAQ8cMPP4iKFSuqvdcFBQWJRo0aaezX1NRUnD9/PsPXSJf/DxR27dolihUrJuzs7MTixYvFvXv3xOPHj8WuXbtErVq1VP4fxsTE5Po1zi1tfobLznkoEwZ6pM+EQbduQnh4ZFzm1i15nqBDqz9FkrU8YZBsCFFiMoTzImfxQfpBN8ESkdZo45/N69jXOX7EJ8Wn2+6buDc5bjf2Q2y67b6Lf5fjdqMTo9NtNyIhIsvt5EdpEwZ///23yocbIyMjERQUlGEbLVq0EABUPghmNWFw69YtYWZmJgCIAQMGqO0/ceKEso369etrbOPs2bMZnsinNmvWrAxPFJcuXZpuvOvWrctSPy9fvsx2wqBWrVqifPnyonr16mLz5s3C19dXnDt3TkycOFEYGxsryxUpUkT4+vqqtZWb4zhr1iwxdOhQtYTI7du3ReXKlUWnTp1E9erVldstLS210m9uxMTECBMTE5WEVkJCgnBwcFD216xZs3Tr//zzzwKA2LJli9Zi0iQ0NFS4ubnlyUPT8c4OfSQMoqOjRZEiRVR+z5o2bSqOHz+uVtbf31+sWrVKODs7q5Tv1KmTOHbsmAgNDc1xHNl5zxBCiMGDBwsjIyNx6dIltX03b94UBgYGyrZ27Nih3JeTv60XL16IPXv2iOXLlwsjIyPlvhEjRogqVaqIdu3aib/++kuEhISId+/eiQULFqi0ff78eTF8+HBRs2ZNsXfvXhEaGirCw8PF4cOHhYuLi7JcixYtVJ5HaGio2LFjhzh8+LBKIkDTCe7Tp0+Fn5+fWt/r168XLi4uonPnzuLIkSPi+vXraomLqlWrpnucdf3/QAghfv31VwFA2NvbiwcPHqjtT0pKEl999ZXKe3VkZKRyf27eP3OKCYNCSJ8Jg2PHhEj1vpauOnWEMDZOFA+7myqvMvitlfwqg803N+d9oESkVdr4Z4OfkOPHyqsr023X4TeHHLc7++zsdNutuqpqjtsdfWR0uu223NQyy+3kR2kTBjKZTFSuXFnlg8+4cePSre/n5ycAiLp162psN7OT5qFDhyrLjB07VmOZ1CcMd+7cUdufnQ+Iig91K1dq/h2UyWSiVq1aGuM9efJklvqRyWTZThgA8m8HFd+Wp3bmzBlhYmKiLFelShWRlJSkUkYbx/H27dsq8bi7u4tNmzYJIeTvGSNHjhTW1tZi1qxZWu03JxRXtWzerPoZ5IcfflB5Drdu3dJYv3nz5sLAwEC8efNGK/GkJygoSCUebT5atmyZq9j0kTDYt2+fKFq0qMrzSPsaprVz506VvxGZTJbrOLLznrF+/XoBQIwenf7/gXbt2inbsrOzE9HRqknmnPxtCaH6GhkaGort27dr7L9Tp04qJ6QdOnQQiYmJauWuXbumEkd6SZd58+ZlmDBQkEqlKgkgc3NzsW7dOrVyir9XxePmzZsa29P1/4PDhw8rr+LQFLdCYmKicHNzU7bZuXNntTI5fY1zQl8JA45hUEh16AD07Zt5uaFDgeRkU2ww+QYyQ/m2kb6AtcwYIdEheRskERHpjEQiweTJk1W2/fnnn3j//r3G8kuWLAEATJo0KUf9vX79WrkcFBSksUyZMmWUy0+ePMlRPwrPnz8HANxPZ8RfiUSCUaNGadyXeuq+jORkCklTU1OsXLlSZaYKhVatWmHGjBnK9YCAAOzZs0eljDaOY6VKlVTWnZycMHjwYADyKfjWrFmDqKgozJkzR6v95oSXlxcA+fgFqY0aNUrldVKMup9adHQ0fHx80LBhQzg4OGhsPyEhAXfu3MGuXbswZcqUdJ8bZU1gYCDatm2LHj16wMXFReVv5JdffoFMJku3ruL1LFu2LFatWpXnU7SmlpCQoByAcciQIemW8/T0VC5HRETg8OHDKvtz8rcFQGWQyB49eqBfv34a+089q0RsbCz27NkDU1NTtXL169dHiRIllOvpjWWQukxGDA0NUbRoUeX6nDlzMGzYMLVyX3zxhcpguDdu3NDYni7fT6RSKSZMmAAhBCwsLNC/f/90y5qammLRokXK9X/++Uc5aKpCTl/jgoQJg0IqKQnYtQvIbADUvn0BMzNg08nvEe4uf6O2igWC7WbhxxY/6iBSIiLSlQEDBqBkyZLK9bi4OKxevVqt3KtXr7B79264uLjgq6++ylFfY8aMQZEiRTKcjjH1CWBcXFyO+lFwdHQEAGzYsAEXL17UWGb48OFwd3fPVT/ZVbJkSVhZWaW7f9KkSShSpIhyfceOHSr7tXEc055g9OrVK9O4df36KXh5eaFGjRrK11PBxcUF3bp1U67v2LFDbYC006dPQyqVpjud4rt37/DLL79gwIAB6Nu3L1avXq3WT1aVKVMGQn4lr9Yf2RlMU592796NWrVq4fTp0/j+++9x48YN9OjRQ7n/0aNH2Lp1a7r1FSe1f/zxBywtLfM83tR27dqFd+/ewdzcHHXq1Em3XM2aNVXW056I5+RvCwCMjY2Vy2ZmZumWS30SDSDD45S6rGJgvrQMDQ2zFF/assWKFdNYxsDAQGUgyzdv3mgsp8v3k8OHD+Pp06cAgNq1a8Pc3DzD8h06dICLi4tyffny5Sr7c/oaFyRMGBRSBgbAoEFAZv9z7OyAXr2At2+dcaD0x2y+5brMpy0iIqKCxdTUFGPHjlXZtmLFCrV5rVetWoWkpCSMHTs2y9++p9W+fXu8ffsW796903gC9+DBA4SFhSnXMxr5Piu+/vprAEBSUhLatGmDWbNmIT4+Pldt6oKlpSU6dOigXL98+bLK/rw4jtbW1pmW0fXrBwC3b99GaGioyre6qaWeYjE+Ph4bN25U2e/t7Q0A6SYM7O3tMXfuXOWsIc2aNcvwZI3St3jxYvTu3RsJCQnYtm0bfvnlFxgZGeGnn35SuZrm+++/R0xMjFr9pKQkbN68Gb1791a7mkQXFL8riYmJsLa2hqWlpcZH586dVeq9evUqw3az8reVHZmd7KaW+ndZ0zHPK6lnKPjw4YPGMrp8Pzlx4oRy2cnJKdPyBgYGaNOmjXL9/PnzSEpKSre8tl/j/CBn/+WpwDMyAt69AzL4UkNp5EhgyxZg+aWf0bOlN+JLAxjdBWXyOkgiypdeT3mdeaF0WJqk/+1HwJgACCFy1G4R4yLp7rs45CJSZCk5atfMKP2Thb97/43klNyfBOU3o0ePxvz58xEbGwtAfqno1q1bMXz4cADyD9Br166FpaWlcltOpf2wGx8fj507d2Ljxo0wMzNTS1TkxvTp0+Ht7Y3Lly8jKSkJc+fOxebNm7Fw4cJ8/41Q3bp1sX//fgBAVFQUoqOjVT6U6vI4pqbrfhXTkqWXMGjWrBnq1KkDPz8/AMDq1asxceJE5aXs3t7ecHV1RY0aNTLs5+7duwCAtm3baiv0QmXbtm3K25sWLVqkcjl91apVMWjQIGzatAmA/AR79uzZWLx4sUobS5cuRWJiovLWJ11TXCng6OiI8+fPZ7merq+EyCltJPByIqP/8bp6P7lz545yOfWVHBmpU6cONm/erIzr+fPnqFChglbiKQh4hUEhlpVkAQA0bAjUqAE8elQPo+yX4f34pyjTUP3eQCIqHIpZFMvxw9w4/W9DHIo45LhdCxOLdNstal40x+1amab/RmlrZpvldgoSW1tbfPPNNyrbFi1apLzXePv27Xjz5g2GDBmS4dzW2REWFoZp06ahXLlyuHPnDrZv344zZ86gbNmyWmkfAExMTODl5YX27dsrt4WEhKB3795o2bIlbt++rbW+tK18+fIq64pkTlq6OI767PfYsWMoUqQImjVrlm6Z1FfIBAYGKu83fvjwIYKDg9GxY8dM+7lw4QIAJgxyIjg4GCNHjgQA1KhRQ+WqD4X58+erJLyWLVuGS5cuKdcDAwMxZ84czJs3T+UWKV1SXDovlUpRoUKFLD/0Fe+nJK/fT969e6dcjo6OzlKdtLcmvX37ViuxFBRMGBRif/0FNG6ceTmJRH6VAQAcODAOGzbk7QcPIiLSr4kTJ6rcavDo0SMcOnQIgPzDvYGBAcaPH5/rfqRSKWbPno3y5cvj6tWruHr1KpYtW4Zy5crlum1NrK2tcezYMSxbtkzlhOXChQuoW7cuZs2aleEgbPqS9hLX1IOIAbo/jvroNyoqCj4+PnB3d9c4qJtCnz59VO6nXrlyJQAoEwfp3Y6gEBsbC19fXxQrVkzt/nTK3I8//qi81adv374aByosUaKEygBwMpkMAwYMwPv375GSkoKBAweievXqGD16tM7iTishIQEAlDFR3tPV+0nq/22pkwcZsbGxUVnP6D3oU8RbEgoxQ0PgyhXg7VsgncGClfr1A6ZOBeLigG3bgAULPl6hEJkYiYvPLqKzW+eMGyEiogLB1dUVvXr1Uhlgb+HChbC0tMTdu3fRrVs3tW+9sys6OhodO3bEpUuX4OHhAS8vryxfHpobEokE48aNQ+/evTF37lysXbsWycnJkEqlmDt3Lp4+fYrt27fneRzZkfqky97eXuXSXX0dR133e+LECUil0nRvR1AwNTXF8OHDMW/ePADyREFgYCC8vb1hbm6OVq1aZVj/3LlzSE5OhoeHR65G5X/x4gU8PDxyXD8jDRo0yHCwQH2JiYlRmcWjWrVq6ZYdN24c9u3bh3///ReA/MqEXr16oWHDhrh16xZu3bqlceYQXbG2tsb79+8hlUpx//59VK9eXW+xFAa6fD8pXrw47t27BwDKn5mRSqUq61mdTeJTwSsMCrGqVeU/AwIyL2ttLU8aAEBsrHyGhTexYVh4ZijKLnZF9z3d8SxS84irRERU8CgGflPw8fFRfuOX06kUUxs4cKDyMuSVK1fq5CQ3teLFi2PFihV48OCByqXnO3bsyHej0EdERCiX057w6us46rrfzMYvSG306NHKbxFlMhmWLFmC8+fPw8PDI9NB4k6ePAng4+0IDx8+xIQJE2BgYACJRIIOHToop3bMSHJyMh4+fJgnD8UUofnN1atXVe6Nz+h3wsDAAJs3b4aFxcfbyU6dOoV58+Zh8eLFqFixYp7GmpnUI/tnJzlz5swZ5e8QZZ0u30/q16+vXI6KisrSFI2ppxcuVapUlgZL/JQwYVCIVawov8ognSmp1ShuSwCAu3d/x5NFzvh64ibM9I5BsiwZC/5dkDeBEhGR1ikus03vctuaNWuq3cP95MkT1K9fP8N7yLPCz89PeYtDsWLFULly5Vy1p5DRgFrpTZdYrlw5eHl5qcxnntkH/vT6yavbGRSD8AFAz549lct5dRwzo+t+ZTIZjh49itKlS8PNzS3T8k5OTujevbtyfc2aNYiPj8/0dgRAftIKQDkqupubG3r27AlnZ2ecP38eXl5eKrNW0EevX6sOiBsaGpph+QoVKmDdunVq23WZPEzvb7lRo0bK5dWrVyMws3nIIZ+qsF+/fmpTHVLGdP3/IO04JooBZTMSkOrb1ay8j3xqmDAoxExM5EmDLF6Ng9q1AUVSziLmMRr+LFD0NjDCF7BOBP68+SdeRL/Iu4CJiEhrFPcZZzSf9bRp09S2ZXZ1QdpLNzV9aFNchqyII70PdqlPQDQlNkxMTFTW0xsMEJB/KFXMvZ2WoaEhvv/+e+V66m/0AfX7VdObwzwoKEhlXVsJBMX991WrVlWZx15bxzG7dN3vxYsX8ebNm2xNr5d6sD3F65DZgIdhYWG4f/8+KlWqpPyG2c/PD/Pnz4evry9atGiR5f7LlCkDIUSePPLbFTAKaWcIUPzeZqRv375qU7mOGDECBw8ezLCen59fjmbVyep7RuqZU+Lj49GjRw+Vb5nTCgkJQbt27eDu7q73qyO0JaezFmWXrv8ftGjRAg0aNFCub968OdP3J0US2dDQUDkDSGHChEEhV7Vq1q8wAD5eZbDJ63uEtZXf22eRAIzyBZJSkvDbv7/lQZRERKRtISEhANI/+QXk37LWrl1buV6qVCmVE1ZN0p5sR0VFqZVRDCgGyBMWZ8+eVSuzf/9+lW91Xr58qVYm9eB2AODv76+yfuvWLeWyTCbDokWL0o07dVIg7WB3aQfd0vStaHR0NIYNG6ZyyXt4eHi6/WXV6dOncePGDZibm2P79u0q93Vr6zimTWykTfqkpa1+s0oxlkZ2TtibNGmCevXqKddr1aoFFxeXDOsori5QXFlz9OhRLF26FHv37v2kRr9PPcK7Ngf0Sztd5YEDB3D16tUM61y/fh3e3t4qA9FJpVL06NED//vf/zTOd793717069cv099TTbL6nuHu7o6GDRuqbK9Tpw527dqlTLYCQGRkJJYsWYK6devi7du3WLhwoUp72f3bym65tO1/+PAhS22ml8xM/dxSL2el7/RkNoWjPv4frF69Wvme/+DBA2zYsCHd+K5fv45r164BAGbNmqU2fk9OX+MCRZDeREVFCQAiKipKbzHMnCmEo2PWy8fGCmFjIwQgRLMSF0SKgUQIQIQXgTD/AcLsZzMRFhOWZ/ESUe4kJCSI+/fvi4SEBH2HQnp09epVAUAAEEuWLMmw7I4dO5RlFy5cmGHZyMhIYWxsrCwPQEydOlWt3KlTp1TKFC9eXKxbt074+fmJEydOiG+++UZUqVJFtG7dWlmmQoUK4vLly2Lp0qXi+vXrQgghpFKpsLe3V5Zp1KiRePjwoQgODhZDhw4Vs2fPVvZpYWEhDA0NxbFjxzTGPnr0aAFAlC5dWsTGxqrtd3d3V/YjkUjEpEmThI+Pj/Dx8RG///67KFWqlJg2bZrw8PBQlqtdu7bw9vYWz549U2lr3759yjImJiZq+xWePXsmXF1dhbm5uTh48GCeHcenT5+qtLN06VKN8Wi736x48+aNsLCwEADExYsXs1xPCCG2bNmi7P/HH3/MtPzAgQMFAHHgwAHx888/ix49eoiUlJRs9ZnfhYSECFNTU+VxqVGjhpDJZFprv2nTpiq/G/b29hr/5oKCgsTw4cOFkZGRqFOnjnj69Kno1auXSl0AwtHRUUyaNEn88ccf4o8//hBfffWVkEgkYvfu3TmKLzvvGQEBAcLa2lotJmNjY+Hq6iocHByU20xNTYW3t7daf9n921Jo1KiRsk7Hjh3TLbdz506V9m/dupVuWWdnZ2W5r7/+WmOZiRMnqrzPvX37VmM5mUym/LsEIFatWpVuv66urspykyZNUtuvj/8HQgixdetWYWhoKAAIGxsbcefOHbXYYmJiRM2aNQUA0adPH43vBzl9jXNCm5/hsnMeyoSBHuWHhMGuXfKT//fvs15n7Fh5HUCIWzU7K1e+7QCBnyAme0/Ou4CJKFeYMCi8oqKiREBAgNi5c6coU6aM8sONpaWlWLp0qbh9+7Z4r+GfQXJysihdurSwtLQUkZGRavs/fPgggoKCxKlTp0SHDh3UPlybmZmJhQsXisePHwupVCqEkH/YTH1infbRpk0bER4eLjZu3Ki2r1KlSuLNmzfK/n/88UeNbQwcOFDlw53iw62xsbHyZP/Zs2fi8uXLYsiQIQKAcHV1Fffv39d4/AICAlQ+jKZ+GBoaijlz5gghhMrzcnR0FAMHDhTHjx9XaUsqlYrRo0crP6w6OjqKefPmiatXr4pnz54Jf39/sXDhQmFvby+qVasmrl69qjGm3B7HV69eib1796qcnAAQxYoVE1u2bBEPHjzQ+BlFm69fRiIjI0WbNm2UdefOnZulegofPnwQxYsXFwDElStXMi2vOKFq3ry58oTjUxAXFyceP34sDh48KOrUqaP2mgwYMECcO3dOBAcH5/p/w+3bt4WNjY1aH9WqVRODBg0SgwYNEvXr1xcSiUQYGBiIiRMnisTERCGEECkpKWLKlClCIpGk+7sFQEycODFXMWb1PUMIIXx9fYWLi0uG8ZQqVUqcO3dOpV5O/rbi4+PFw4cPxYIFC1SOgYmJidixY4d4+vSp+PDhgxBCiLCwMOHj4yMaN26s0n7z5s3F+fPnRVhYmLLNK1euKBOiioeVlZXYunWrePTokYiKihKPHz8Wu3btEkWLFlUp16lTJ3HhwgURERGhEquXl5dKufbt24vk5GS1Y33ixAm134O0CVl9/D9QOHr0qHB0dFQek19//VXcvXtXBAYGit27d4sqVaoIU1NTMXv2bLXEWk7fP3ODCYNCKD8kDPz9hTA2FuLGjazXefDgY8LA09FfufLMGsL4R4gi84qI17Gv8y5oIsoxJgwKrxkzZmT4oRdI/1unpUuXivHjx2vcd/bs2UzbVTx69OihrBcfHy+mT58uSpcuLYyNjYWjo6Po0qWLOHDggLKMVCoVQ4cOFVZWVqJEiRJi9OjRakmN5ORkMWnSJOHg4CAsLCxEw4YNxbZt29TiTP1tWOqHqampqFq1qpg5c6bah+K0goKCxJAhQ4STk5MwMTERrq6uYuDAgeJGqn+iQ4YMEZMnT87St+jBwcFiyZIlwtPTU5QrV05YWFiIIkWKiLJly4pevXqJ3bt3Z/oNd26O42effZbpa/bdd99pvd+sGD9+vMbXrGLFimLw4MFZakMIIWbOnCmKFy+e6XG8d++eACAcHBzEuXPnlCdO2bkaIr9asWJFlv9G9+7dm+v+AgICMjwBtLCwEL169Ur32/Bz586JevXqqdUzMzNTJuZyI6vvGQqxsbHi999/F02bNhX29vbCyMhIODg4iNatW4sVK1aImJgYtTo5+dvatm1bpnVOnjwphBCidOnSGZZzc3MTQghx8ODBTNucP39+pmUU7fn7+wtPT09hZGSkVsbV1VUMHTpUCCHE8+fPhYeHh8ZyxYsXFyNGjFB57rr+f5D29V22bJlo1aqVKFGihDA2NhbFihUTDRo0ELNmzUr3CrDcvH/mlL4SBhIhdDSiBamJjo6GjY0NoqKiYG1trZcYZDIgJQXI7oC0n38OKGYVelq7FcrePAcAGPoFsKk2ML3pdMxvM1+7wRJRriUmJiIoKAhly5aFmZmZvsMhIspT4eHhOHPmDPr06ZNhueXLl2P8+PGYMGEClixZggkTJmDZsmUYOHAgtmzZoqNoPy3BwcHw8fHBixcvkJycjOLFi6N06dJo1qxZlv7/3Lx5E9evX8ebN2/g6uqK9u3bw8HBQQeRE+VP2vwMl53zUCYM9Cg/JAxy6sQJQDEV8p6JbdBzyWmkmAJrOgBjawGWJpYIGh8EhyJ8YyfKT5gwICJS16VLF/zzzz84dOgQunTpgsePH6Ny5cowNDTE06dPlQMmRkREwM7OTs/RElFhpK+EAWdJIPzwA5BJ4l1N27ZAlSry5cU+E/BwMnDlL0AyRL4tNikW8y/yCgMiIiLK36RSKc6fPw8DAwPlTAwVK1bEl19+ieTkZMycOROAfCq/AwcO6DNUIiKdY8KAULMm0LRp9upIJMD48fLlK1c64l6T6ki2BT6zBmrayLev8l2FkKgQrcZKREREpE3Xrl1DdHQ0ateuDVtbW+X2n3/+GcbGxti8eTMmT56MJUuWYMiQIfoLlIhID5gwIPTqBXz7bfbrDRgAyK/Kk2Dt2unK7f1c5T8/pHzAnPNztBIjERERUV44deoUAMDd3V1lu5ubG1avXg1ra2v4+fnht99+g4EBPzoTUeHCMQz0KL+MYfDhA3DpElCtGlCiRPbqTp8OLFgAGBhIceSIG8zNnwIARtwAHsUCBhID3B99H24ObnkQORFlF8cwICIiIip4OIYB6c2HD0CbNsB/CfZsGTMGMDQEZDIjbN8+Vb5RALOigZZBQPsK7SGRSLQbMBEREREREeU5JgwI1tZAyZLAw4fZr1uqFNC9u3x5797BQIQTak4C+v0POOrjhKO9/0El+0pajZeIiIiIiIjyHhMGBABwcwMePcpZXcXgh8nJZthz4kcYfpCvWzx6CezZo50AiYiIiIiISKeYMCAAQKVKOU8YNG4MNGggX16/4Ws8HVpcuS9l5neAVKqFCImIiIiIiEiXmDAgAB+vMMjJEJgSCTBtmnxZKjXB1hfzEFlTvm745Dmwfbv2AiUiIiIiIiKdYMKAAMivMIiLA16+zFn9rl2BihXly8tXDELw147KfSk//QAkJQEAOCkHERERERFRwcCEAQGQJwyAnA18CMhnSpj63yQJKSnG2PtqPt7X+2/fszDINv6JLbe2oNGfjRCXFJf7gImIiIiIiChPMWFAAIBy5eQn/TkdxwAABgyQz7YAAAsW9MOzb0op9734eQIG/z0Y115cw++Xf89ltERERERERJTXmDAgAICxMfDjj0DVqjlvw8wMmDhRvpySYoRjb+fjTXPgxReA97wkQCLft+DfBQiNDs190ERERERERJRnmDAgpZ9+Alq0yF0bI0YA1tby5Z9/7o1H8yrj8QSgQmmgUVH59gRpAr4//X3uOiIiIiIiIqI8xYQBKb14ARw7lrs2bGyAMWPky4mJhrj4788wMDDHvaQ6uB31sdz229tx7cW13HVGREREREREeYYJA1I6cgTo0kU5oUGOTZoEWFjIl3/66UuULfsUXzU9DiMja5VyE45P4KwJRERERERE+RQTBqTUpw/w6pV8PIPccHAARo+WL3/4IMGSJSVRzKIYZraYCQig3gv5Pp9QH+y4syN3nREREREREVGeYMKAlKyt5Sf7Eknu25o8GTA3ly//8QcQHg6MM3PHxV3m8F0PuAf9V+7EZEQkROS+QyIiIiIiItIqJgxIxeDBwJYtuW+nRAlg5Ej5ckICsGgRYHjTF80eJQAAlpwEJDLgddxr/HD6h9x3SERERERERFrFhAGpuHsXuHRJO21NnQqYmsqXV60C7tcNRGx5+Xqtl0C/O/LltTfW4mroVe10SkRERERERFrBhAGpqFQJePhQO205OgLDh8uX4+OB7bu+Q9C35sr9v54CLD8AAgIjj46EVCbVTsdERERERESUa0wYkIpKlYBHj7TX3g8/fBzLYOnSYjBuvwspn3sAAJxjgNnn5PtuvbqFlddWaq9jIiIiIiIiyhUmDEiFm5t8gMKoKO20V7IkMG6cfDkpCVi8+AsYrlwPYWYGAJhwBfgsXL5/5tmZeBH9QjsdExERERERUa4wYUAqKlWS/9TWbQkAMG2afAYGANi4EXgsLQvJ998DAIwEMPesfF/NEjWRIE3QXsdERERERESUY0wYkIoKFeQ/AwO112bRosCUKfLllBRg9mwA06ZBVKqI25874MxYM2zovAEXhlxAhaIVtNcxERERERER5RgTBqTCxgZwcNBuwgAAJkyQtwsAf/0F3AyQ4Po6gfdT36LHZ0DfKh4wkPDXkYiIiIiIKL/gGRqpKV8eePJEu21aWckHQAQAIYCpU01h69T5v/VEBAZO0m6HRET0yYuMjERkZKS+wyAiIvpkMWFAag4eBNau1X67o0cDZcvKl0+fBh4//gnGxiUAAG/fHsT7997a75SIiD4pz549w5o1a9ChQweUKFECt27dylE7ycnJ2LRpE7Zv367dAEkrZDIZQkJC9B0GEVGhx4QBqXF0BExNtd+uqSnw668f16dOtUbZsguV648ffQvZnl1AcjIAIOZDDN4nvNd+IEREhdDly5fRo0cPuLi4QCKRZPiwsrKCi4sL6tati6+//hpr167F27dv9Rr/gQMHULlyZZQpUwajR4/G8ePHkZSUlKO2oqOj0bp1awwdOhQDBgzIcdKBtE8Igd27d+Ozzz7DzJkz9R0OEVGhx4QBqblyBfDwAOLitN92z55Aw4by5fv3gSNH+sPGphlMXwEVxz6BQa++wO+/49TTU6i2phpGHR2l/SCIiAqhJk2aYN++fXj+/Dn69eunss/Q0BBnzpxBSEgIAgICcOTIEcyYMQNOTk7YunUrRo4cCWdnZ/Tv3x9hYWF6ib9Lly7w9/fHwYMHc9VOdHQ02rVrh0uXLgEASpQogTJlymghQmDTpk0oWrSoWgKmaNGiiI+Pz7T+zp07YWtrqzGJY25uDh8fH63EmR8JIbB//37UqFEDvXv3xoMHD/QdEhERgQkD0sDcHLC3z5uEgUQCLFr0cX3WLAmcnFbCJMIAdn7ybdJZP2Ls4rZ4HvUce+7twd57e7UfCBFRIWVgYIChQ4eqbHN0dESrVq3g4uKCypUro2XLlhg1ahT++ecf+Pv747PPPkNSUhJ27NiBKlWq4OjRozqP28jICKampujatSuKFi2aozZiYmLQvn17XL16FQDg4eGBK1euwNbWVisxDhkyBO/evcPBgwdV2oyIiMCOHTsyrd+3b19ERETg+PHjMDExAQBIJBKsXr0akZGRaNy4sVbizG8OHz6MOnXqoEePHrh7966+wyEiolSYMCA1NWsCe/YAxYvnTftNmwLdu8uXw8OBJUtqws5zGkJ6yrcZSWX48xBgIJOvjzgyAiFRvI+RiEhbXFxcVNYNDQ3TLVu1alWcPHkSVlZWAICoqCh0794dd+7cydMYM6KIJTtiYmLg6ekJHx8f2NvbY9OmTTh16pTWri5QkEgk6Nq1K9amGQxo1apVWa7v6emJ6tWrAwD69OmDUaNGwTQv7hXMB9avX4+XL1/iyJEjCAgIgJmZmb5DIiKiVJgwII3Cw4GXL/Ou/QULgP++PMHixUBy8iy8GlUB8f99hm0SCoyVfwGEiMQI9D/YHymylLwLiIioEFF8e51Vjo6OGD58uHL9w4cPmD17trbDyjOKKwt8fHzQv39/PHjwAIMHD87TPosUKaKy7u/vr7wNIitiYmIAADNmzNBqXPnNsGHDlLe8VK5cGTVr1tR3SERElAoTBqSRpyfwv//lXfvlywNTpsiXk5OBiRPNUbHGn3g49WOZX84A5f4b8/DCswuYf2l+3gVEREQZatOmjcq6t7c3ZDKZnqLJutjYWHTo0AGvXr3CiRMnsG3bNjg4OOR5v8eOHYORkRGMjIyU21auXJmluuHh4Xj06BHc3NxQtWrVvAoxX+IVBkRE+QsTBqRR+fJAYGDe9vHDD4DiqlgvL+DSpRawaD8Sod3k24okAxsOA5L/Po/+dO4n+IR8ugM+ERHlZ2lvY4iPj9f7zAlZkZKSgg0bNuDevXto27atzvr18vJCkyZN0LVrV+W2AwcOZGnQyBMnTgAAOnXqlFfhERERZQkTBqRRhQp5nzCwsAB+//3j+oQJgJPTAoSOdkRiCfm2z6KAUtHy5RSRgr4H+iIqMSpvAyMiIjWpvylXyO6tDfpgY2ODypUr6/Sb64CAAAQHB8PT0xPjxo1Tbk9OTlYb20CT48ePAwA6duyYZzESERFlBRMGpFH58sCzZ0AOp7jOsq++Atzd5ctPnwK//26NCrXW4sF3QHhr4N5WM8SlGnwxODIYI4+OhBAibwMjIiIVz58/V1kvVqyY2uwCMTExWLp0KVq3bo3ixYvD2NgYtra2qFevHubMmYPIyMgs9RUREYH58+ejXr16sLOzg6WlJZo2bYqNGzciJSX749ncu3cPEydORLVq1WBjYwMLCwu4ublh7NixCAgIyHZ7mTl27BgAoF27dmjevDlq166t3Ldu3TokJyenW1cIgZMnT8LGxgbNmzfXemxERETZwYQBaVS+PCCTyZMGeUkiAVasABRfXM2fD7x92xkmnn0QMBOQWCbi11q2kKSq89fdv7Duxrq8DYyIiFR4eXmprHdXTHfzn1OnTqFChQqYOHEiatSoAW9vb9y7dw8LFy5EcHAwfvrpJ9SuXRtPnz7NsJ/jx4+jatWqmDdvHrp164bz58/j7t27GDduHH7//Xe0aNECcVmc91cIgenTp6N58+ZwcXHB/v37cfPmTaxevRoxMTFYuXIlateujdWrV2fvYGTi2LFjcHBwQJ06dQAAY8eOVe4LCwvDgQMH0q3r5+eHN2/ewNPTU+NVHURERLrE/0SkUYUK8p+BgUDFinnbV7VqwOTJ8pkTkpKAkSOBEydWIDLyPJKSXqKCeST6ljbHjmcJyjrjjo9DbcfaaODcIG+DIyIiBAcHY926j4laa2trzJw5U7nu7++Pzp07IzExEQMGDMDSpUuV+ypVqoQyZcqgXbt2CA4ORu/evXHt2jWN/fzzzz/o0aMHjI2NceHCBeUJNwCUKVMGnTp1QuvWrbM8dsLQoUOxY8cOnD9/Ho0bN1ZuL1euHKpVq4b69evjw4cPGDNmDGxtbdG3b9+sHpJ0xcbG4tKlS+jevTsMDOTfy/Tp0wfTpk1Txr1y5Ur06tVLY31d3I7w4sULeHh45EnbDRo0wNatW/OkbSIi0j0mDEgjFxfA2DjvxzFQmDUL2LMHCAoCzp8HduywxxdfbMbt2+1gaVkLPeoMwo5nE2GUAkgNgaSUJPTY0wM3ht9AMYtiugmSiKgQevr0KTp16oT4+HgA8ukCDx8+DCcnJ2WZ5cuXIzExEQDUblMAgLZt28LZ2RkvXryAr68v7t69i2rVqqmUCQoKQt++fZGUlIQFCxaoJAsULCwssHbtWpVL/NOzYcMGbN68GWPGjFFJFijUrVsXrVq1wpkzZwAA3377LTp37gwrK6tM287IyZMnkZSUBE9PT+U2MzMzDB8+HL/88gsA4NKlS/D399c4haC3tzcMDAzw+eef5yqOjCQnJ+Phw4d50nbJkiXzpF0iItIPJgxII0NDoGxZ4MkT3fRXpAjwxx/y6RwB+ZSLHTu2RbVqh1G0aDvUk5jgry3HUX+jN5p8DYRbASHRIeizvw+8+3vD0MBQN4ESEUJCFiMkZHGu26lSZTvs7NyV6xER5xAQ0B8AUKrUJJQqNUm5TyqNwbVrVXLdp61tS1StukNl261brREf/wiGhpZo2PCByr7AwKkID9+V635r1TqNIkXcct1OXpHJZPjw4QNMTU0ByGdA8Pf3x/79+7Fu3TrExMQAAGrXro2dO3eicuXKKvVfv36tXA4KCtLYR5kyZfDixQsAwJMnT9QSBiNHjkRsbCysra0xcuTIdGOtVasWHBwcMrzKICEhAdOnTwcADB48ON1ynp6eyoRBREQEDh8+jH79+qVbPisUt260a9dOZfuoUaPw22+/QSqVAgBWrVqlctUGAERHR8PHxwcNGzZMd+rHhIQEPHnyBHfv3sWNGzcwZswYlC1bNlcxExERpYcJA0rXl18Czs66669dO6BvX2DnTuD9e2DcOOCvvzrLdy5ejF6LvQEA2w8AngMAGBqgXfl2MJBwKA4iXZJKo5GU9CLX7QjxQW1d0a5UGp22tFb6TE5WP8lMSgpHUtILGBqqf7OcnByhpecqzXUbeSkkJARmZmYwMTGBRCLBhw/y10YikaBkyZJo1aoVBg0ahC5dumi8r37MmDE4c+YMJBIJRowYobGP1PXSjkHg7++vnEqwbdu2mc5oYGFhkWHCYNeuXXj37h3Mzc01XqmgkPYb/uvXr2slYVCjRg04OjqqbHdxcUG3bt2wd+9eAMCOHTvw22+/qVyRcfr0aUil0nSnU3z37h2WLl2Kf/75B/7+/jA3N8fPP/+c7RjLlCnDwYOJiChLmDCgdM2fr/s+Fy8Gjh+XJwx27wZ69gS6dwcwcKB8DsawMLQJAhZeMketDUfQumxr3QdJVMgZGVnDxCT32USJxFRtXdGukZF12tJa6dPYWP1bWxOTEpBKo2BoaKmhvJ2Wnmv+/ndbqlQp+Pr6IjExEVKpFIaGhjA3N4ednV2Wpk5s37493r59CwMDA+VVCqk9ePAAYWFhyvW0swTs2vXxKo4aNWrk4pnIeXvLE8yJiYmwtk77u/SRTCZTWX/16lWu+r19+zZCQ0PRp08fjfvHjRunTBjEx8dj48aNmDTp45U0irjTSxjY29tj7ty5qFy5Mvr3749mzZrpdLpIIiIqfPL3JxjSq6Qk+ZgCFSsCBjr6Er9ECfmsCYoveEaNAlq0AIoVc0D8xnkw7zQUkhRg0pkE4H4CwKswiXQu7e0C2mJn544mTUI17jMyskp3X27VqnUm3X3lyy9E+fIL86Tf/MTAwAAlSpTIVRvm5uYq6/Hx8di5cyc2btwIMzMz5RgHmly4cEG5XKxY7seluX79OgDA0dER58+fz3I9S0v1pFF2KKZTTD1+QWrNmjVDnTp14OfnBwBYvXo1Jk6cCIlEPheQt7c3XF1dM02a3L17F4D8agwiIqK8xGu5KV0nTwKVKwMvX+q23z59gG7d5Mtv3gDffitffuK0F0+HpSrYv788o0FERPlGWFgYpk2bhnLlyuHOnTvYvn07zpw5k+F99qmnWjQ2Ns51DG/evAEASKVSVKhQIcuP3A7Yd+zYMRQpUgTNmjVLt0zqKRYDAwOVsyI8fPgQwcHBWZodQZFgYcKAiIjyGhMGlK4mTYAzZwB7e932K5EAa9Z87HfPHvnDzW0jXvUrjvfu/91nHBkJ9OgBZPCtFRER6YZUKsXs2bNRvnx5XL16FVevXsWyZctQrly5TOu+f/9euawYYDE3EhISlO2mpKTkur2siIqKgo+PD9zd3TXelqHQp08flasoVq5cCeDjdIrp3Y6gEBsbC19fXxQrVkzjLAtERETaxFsSKF12dkCrVvrpu0QJYOVK+dUGADByJNC4cUnUrHUK5vvtgUbuwOPHgJ8f4O0NfPEFAOBE4AkcDDiI1R1XKy/xJCKivBUdHY2OHTvi0qVL8PDwgJeXV7auFDA1NVWOa5D6aoOcsra2xvv37yGVSnH//n1Ur149121m5sSJE5BKpenejqBgamqK4cOHY968eQDkiYLAwEB4e3vD3NwcrTL5x3vu3DkkJyfDw8Mjx//nXrx4AQ8PjxzVzUyDBg2wdevWPGmbiIh0jwkDytDixYCrq/yLfF3r1Qs4eFB+dUFEhHzcw1OnqsPQEsD+/UCHDsDatcB/l2+u9l2NcV7jkCJS4GTlhJktZ+o+aCKiQmjgwIG4dOkSAPk35tm9raBkyZJ48t88vr6+vrmOx9XVVXnVwtatW7FwYdbGoThz5gxSUlJydKl/ZuMXpDZ69GgsWLAAUqkUMpkMS5Yswfnz5+Hh4aE2FkRaJ0+eBPDxdoSHDx9izZo1WL58OYQQaN++PcaNG4cOHTqk20ZycjIePnyY1aeWLbm9rYOIiPIX3pJAGTp4EPj7b/30LZEAf/wBlColXz93Tj5RAgCgenUgMBAp7T0QErIIP5z6DmOOjUGKkF96OuvcLOy8s1MvcRMRFSZ+fn44dOgQAPmAhZUrV852Gw0bNlQuX7t2DcHBwbmKqVGjRsrl1atXIzAwMNM6z549Q79+/VCmTJls9yeTyXD06FGULl0abm5umZZ3cnJC9+7dletr1qxBfHx8prcjAMCpU6cAAG3atAEAuLm5oWfPnnB2dsb58+fh5eWVYbKAiIgoO5gwoAyVKaPfcQXt7IBt2+TJAwD48Ufgv8GvkSALxc2bTRAYOAWtbR7CQKL66zz478E4EXhCxxETEeV/ae/rTzu9YHb8+++/yuX4+HgIITSWe/36dbr9pz55FkLgf//7X4Z9pq6flJSktr9Xr14qMfXo0UNlnIS0QkJC0K5dO7i7u6NixYoZ9q3JxYsX8ebNG7Rr1y7LdcaNG6dcVhz/zAY8DAsLw/3791GpUiW4uroCkCds5s+fD19fX7Ro0SJLfZcpUwZCiDx5nDt3LsvHgIiI8j8mDChDZcvqfyKCli2B6dPly1Ip0Ls3EBUFJCW9RlycfGopo7hD2OzxFcySARv5WFdIliXjy91fwvdF7i9vJSL6lERHR6usR0VF5bgtxQCDABAXF4ezZ8+qldm/fz8CAgKU6y/TTL/zxRdfoFq1asr1TZs2pXsfvJeXF0JDP06xqenSend3d5WrFm7duoU6depg165diI+PV26PjIzEkiVLULduXbx9+zbLty6ktWPHDgDI8gk7ADRp0gT16tVTrteqVQsuLi4Z1lFcXaC4HeHo0aNYunQp9u7d+8ncCvD27Vvlsq4GrCQiovQxYUAZKlsWCAvT/0QEc+YADRrIlwMDgaFDAWvrxqhYcbWyTIXwfXiy1w779gBG/33GiEuOw+c7P8ejd4/0EDURUf6U+uQdkCcQHj9+nKO26tatq7Lep08frF+/Hjdv3sTJkycxbNgwzJw5E61bt1aW2bp1K3x8fLBs2TLcuHEDBgYG2L59O4oUKaIsM3jwYAwbNgzXr19HVFQUnj9/jt9//x2DBg1CKcW9agC+++47dO/eHcOGDVOJY/PmzbC2tlauP3v2DH379oWtrS1Kly6NYsWKwc7ODpMmTUJ0dDR27dqV6Qm7Jm/fvsXOnfJb4LJ7O0PqKRazczuCh4cH5s2bh82bN2Pz5s2ZjntQUISGhirHsgCA27dvp3vFChER6YggvYmKihIARFRUlL5DSdfZs0IAQjx4oO9IhAgKEsLWVh4PIMTSpfLtjx6NFWfPQERXhHLnmroQmA2Bn+SP0ktKixfRL/QaP1F+kJCQIO7fvy8SEhL0HQrpWHx8vAgMDBSHDx8WlStXFgBUHo0aNRL//POPePLkiYiOjs5yuzKZTHh4eKi1p3i0adNGhIeHi40bN6rtq1Spknjz5o2yrX///VeUKFEi3bbs7OzEyZMnRcuWLVXamD17tnj06JFabL6+vsLFxSXd9gCIUqVKiXPnzuXomEZGRoo2bdoo25o7d2626n/48EEUL15cABBXrlzJtLyzs7MAIJo3b658zQq6uLg48fjxY3Hw4EFRp04dtddnwIAB4ty5cyI4OJjvW0RUqGnzM1x2zkOZMNCjgpAwCAqSn4N7eek7ErlDhz4mDIyMhPDxESIlJVncutVG3FgOkWL8MWnwQ+uPCQP8BFF9dXXxLv6dvp8CkV4xYVB4rV+/PsMT59SP7777Llttx8fHi+nTp4vSpUsLY2Nj4ejoKLp06SIOHDigLCOVSsXQoUOFlZWVKFGihBg9erR4//69WltRUVFi3rx5on79+sLW1laYmZmJihUriokTJ4rQ0FAhhBB9+vQREyZMENeuXcs0ttjYWPH777+Lpk2bCnt7e2FkZCQcHBxE69atxYoVK0RMTEy2nqvC+PHjhYWFhdqxq1ixohg8eHCW25k5c6YoXry4SElJybDcvXv3BADh4OAgzp07J4oWLSoAiOvXr+co/vxixYoVWf693Lt3r77DJSLSG30lDCRC8FovfYmOjoaNjQ2ioqJULpvMT6RSwMwMWLECGDVK39HITZsGKG4zdXYGfH0BB4f3uHGjAWz+CUSVXz6W/aYz8Geqq2XrOtbFqYGnYGtmq9OYifKLxMREBAUFoWzZsjAzM9N3OESFXnh4OM6cOYM+ffpkWG758uUYP348JkyYgCVLlmDChAlYtmwZBg4ciC1btugoWiIi0hdtfobLznkoxzCgDBkZAa6u+h/4MLV584BmzeTLL14A3boBKSlFUb36Ybxtb4XAER/Lrj0CdH7wcf1G2A102NEBMR9idBs0ERGRBiVKlMg0WQB8HL+gVatWAIAxY8bAwMAAu3btUhkEMiIiIm8CJSKiQokJA8rU5s3AiBGZFtMZY2Ng3z5AMTbV1avAyJFAkSJVUbXqXwjpZYCQHvJ9hgLYsw9o/Pxj/SuhV/D5zs8RlxSn++CJiIiySSqV4vz58zAwMFDOxFCxYkV8+eWXSE5OxsyZMwHIp1g8cOCAPkMlIqJPDBMGlKkWLYDy5fUdhaoSJYBDhwDFwNBbtgBLlwL29p+jkttaBI4Cwj3k+0RFZ8S5FFepf+n5JXg98dJt0ERERDlw7do1REdHo3bt2rC1tVVu//nnn2FsbIzNmzdj8uTJWLJkCYYMGaK/QImI6JPDhAFl6to1YPp0fUehrk4d+dUPClOmAN7egJPTNyhddhYefAc86w/c+P01tg5ZiOIWH5MGqz9fjR5Ve+g+aCIiomxS3I7g7u6ust3NzQ2rV6+GtbU1/Pz88Ntvv8HAgB/tiIhIezjooR4VhEEPAWDvXmD2bODmTcDUVN/RqJs5E/j5Z/myjQ1w5Qrg5ibw8OE3ePVqIwDA0NAGVqU3od3uEZjvMR9f1/lajxET6Q8HPSQiIiIqeDjoIeVbPXsC9+/nz2QBAMyZA3zxhXw5Kgpo3x4IC5OgUqU/ULRoewBASkoU4kPH4O4wb3myQCrVY8RERERERET5HxMGlGX59VoUAwNg2zagZk35+rNnwOefAzExxqhadS+srOoBAJKSwhD0sBdkhw4C1asDISFqbcmETJehExERERER5VtMGFCmhABKlQKWL9d3JOmzsgK8vIDSpeXr/v5A165ASoolatQ4DguLajAwMEOVoP4w6PEV8OAB0KqVStLgbNBZ1FtXD6HRoZo7ISIiIiIiKkSYMKBMSSSApSUQFKTvSDLm6Cgf9NDeXr5+7hwwYABgaGiPGjVOokaNk7BuOQwoW1ZeIDBQPgVEYCAuh1xG512dcfPVTTT+szHuv7mvt+dBRERERESUHzBhQFlSpkz+TxgAgJsbcPQoUKSIfH3vXmDCBMDEpCRsbZvJswpnzgAVKsgLBAcjqVljjF/SDnHJcQCA0OhQNNvYDP8+/1c/T4KIiIiIiCgfYMKAsqRsWSA4WN9RZE3DhsCePYChoXx9xQrg++9TjcHg4gJcuICkisUAACav3uD4+kTUDPvYRkRiBNpsa4O/H/yt09iJiIiIiIjyCyYMKEvKlpVfYZBfBz5Mq2NHYMOGj+sLFgCzZn1cD03ZC9+FbxBTUb5uH5uCC9sMUSLmY5lEaSK67+mOtdfX6iZoIiIiIiKifIQJA8qSsmWBmBjg/Xt9R5J1gwcDa9Z8XP/5Z+B//5Mv29m1ARxKwH8x8KGufKREsynfo37dTiptyIQMI4+OxNQTU5EiS9FR5ERERERERPrHhAFliWKcwIJyW4LCyJHyWxIUZs8GfvkFsLCoitq1z6NMrRUwPXcXWLcOJj/9Dwd7HcTQWkPV2vnd53d039MdcUlxOoyeiIiIiIhIf5gwoCwpU0b+syAMfJjWt98Cixd/XJ8xA5g/HyhSxA0uLt/Kp4AYNgyQSGBkYIS1HVdgRvMZau0cengIzTc1x4voFzqMnoiIiIiISD+YMKAsKVoUKF8eSEjQdyQ5M3Ei8NtvH9d/+AGYPl19TIY3bw7C19cN0xt8hY3t/8D4awYwkH3cf/PVTTTY0AA3Xt7QTeBERERERER6woQBZYlEAjx5AgwYoO9Icm7qVPnghwoLFgCjRgEp/w1NEBX1L+7f74MPH0Jx80Zz9Fu0H0uPyeC12whWiR/rvYx5iaYbm2Kb/zbdPgEiIiIiIiIdYsKAsqWgzJKQnmnT5AMhSiTy9bVrgX79gKQkoEiRyrC0rAUAMH8YDaODJwEA7R5KcX2LCcpEfGznQ8oHGBoY6jh6IiIiIiIi3WHCgLJs5kygUSN9R5F7I0cCO3YARkby9d27ga5dgaQke9SseQp2dp6IrQTc/g1ItpKXqRSWhJsbjdHkuXx9QsMJ6Fu9r17iJyIiIiIi0gUmDCjLWrUChg/XdxTa0acP8PffgJmZfN3LC3B3B96+tUT16odRvHgfRNYG/FYD8aXkZWxjknF+qwF+eVEZv7X9Lb2miYiIiIiIPglMGFCWtW4NfP21vqPQno4dAW9vwNpavn79OtCwIXD/vgmqVNkOF5cJSHAB/FYB7+vKyxhJZfh+/QMYr1ytsU0hBERBv2+DiIiIiIgITBhQNsTEyC/ff/1a35FoT4sWwL//Aq6u8vXnz4GmTYGTJw1QocISVKiwAlIrA9z5FXjRRV5G2BeV38OgweZbm9F9T3dEJERo3E9ERERERFRQMGFAWRYVBfTuDfj66jsS7apWDbhyBahXT74eHQ18/jmwbh3g4vItqlc/DImJBR5PBB5OAh7MMkasfbRaOwFvAvCt17c4+OAgaq2tBZ8QHx0/EyIiIiIiIu1hwoCyzNFRPlDg8+f6jkT7HB2Bc+eAL76Qr6ekACNGyKddtLLqiNq1L8HExAlhnYHwGuHw82uMN28OKOsnJCfgq31fIT45HgDwPOo5mm9qjl8v/QqZkOnhGREREREREeUOEwaUZYaGgIsL8OyZviPJGxYWwP79wKRJH7f98Yd8sMeYmFqoW/caLC3rAABksjjcu9cdQUGzIIQML/dvxk/rHsHyw8e6KSIF35/+Hh12dEB4bLiOnw0REREREVHuMGFA2VK69Kd5hYGCoSGwaBGweTNgairfdvkyULcucOOGM2rXvoTixfspyz97NhcPT3ii3Lc/ovutJAT8aY46L1XbPBF4AjX/qIkjj47o7okQERERERHlEhMGlC2urp/uFQapDRqkOhjiq1fyaRdXrzZH5crbUL7871D8+STePQWRFAcAcHmdgGsbDTD5sgSSVHcihMeFo/Ouzhj+z3DEJsXq9skQERERERHlABMGlC2f+hUGqdWtK59qsVUr+XpyMjBuHNC9uwQWFpNRo4YXjIzskOJeD7jhB9SvDwAwlMrw+wmBU7tNUTxNbmC933rU/KMm/n3+r46fDRERERERUfYwYUDZ4uoKvHwpP3kuDIoVA06cAKZM+bjt77+BWrWAgIB2qFvXF1Wr7oVBxarApUvAd98py7V++AEP1puiTaBqm08jnqLF5hb44fQPSEpJ0snzICIiIiIiyi4mDChbSpcGZDLgxQt9R6I7RkbAwoXAP/8A9vbybSEhQMuWwOLF5WFiUka+0cQE+PVXJBxeh2R7YwCAXdQH/FhuMCyMLVTalAkZ5l+ajwbrG+DB2wc6fDZERERElJ+EhoZi+PDhcHFxgYmJCZydnTFixAi8fv1a36ERMWFA2fPZZ8BPPwHm5vqORPc6dQJu3QJatJCvp6QAP/4oH9sg8L+rCKTSWNwpsQTX1ifjfQNDJPVoi5azN8J/pD+almqq1mZQZBCsTKx09hyIiIiIKP8IDAxE7dq18eeff0IikcDY2BgvX77EunXr0KhRI7x//17fIVIhx4QBZYuzMzB7NlCihL4j0Q8XF+D0aWDWLEAikW+7dAmoWRNYtw5ITn4LwADJdkDg8s9guOkvQCJB+aLlcX7wecz3mA9jA2Nle7+3/R3O1s76eTJEREREpDdJSUn48ssv0adPH4SHhyMkJASxsbHYtm0bjIyMEBQUhGXLluk7TCrkmDCgbLt4Uf5Ne2FlZATMmQOcPw+ULSvfFhcHjBgBdO9eBk5OV+HkNBKfVd8HQ8uiynqGBoaY3mw6bnQ/gYt7LPGNYQN8U+cbPT0LIiIiItKnPXv2oGfPnli+fDkcHBwAABKJBP3798eQIUMAANeuXdNniERMGFD2TZgArFql7yj0r3lzwN8fGDbs4zYvL6B6dQtcuLAG5uYVVcrHxt5FUNBPqPa/NWh2Pxbr/ncTkl9+0TiCZHhsOE4Ensjrp0BEREREepKYmIgffvhB475atWoBAGxtbXUXEJEGTBhQtnl5MWGgYGUlvxXh6FGgZEn5tshIeRKhVSvg4UP5tpSURAQE9EXovTlIvP4PAECSnCwfBKFePcDXV6XdKSenwHO7J3rt64WXMS91+IyIiHLGy8sLpUqVgouLC7y8vPQdDmUiISEBp0+fxooVK/DLL79g9erVuHLlCoQQ+g6NtEAmkyEkJCTP2n/z5g2GDh2KlJSUPOtDV3J7rJ4+fYotW7ZgwYIFWLNmDU6dOgWpVJqlut988w0MDDSfjr19+xYA0L179xzHRqQNTBhQthUvLp8QgD76/HPg7l1gwICP286fl49t8PPPwLt3FxEXdx8plsC11Ql43tcYwuC/QRBu3wYaNQImTwbi4nA26Cy2394OANhzbw8qr6yM5VeXI0VW8P8pE5H+/PTTT5BIJFp9BAcHK9sfNmwYQkND8eLFCwxLfekV5SspKSlYsGABXFxc0KZNG4wbNw4zZszAmDFj0LhxY1SpUgVnzpzRd5iUQ0II7N69G5999hlmzpyZozZWrVqV6d9+8eLFYWtrC0NDQy0/A93J7bG6ffs22rRpg/Lly2PBggW4c+cO/vrrL7Rr1w5ly5bFhg0bchxbSkoK9uzZg759+6JHjx45bodIG5gwoGzz9gbatQP4JYQqe3tg61b58VGMbfDhAzBzJtC6dVsYG/8Lc/MKECbA02HJuLFaIKHSf9MtymTA4sUQ1T7DjL9UP2jHJMVg/PHxaLChAXxf+IKIKCcmT56Mhw8fYs+ePShTpoza/tKlS6Nly5Zqj+bNm6NBgwYoV64cTDLIFstksjyMPnObNm1C0aJF1U5sihYtivj4+Ezr79y5E7a2thpPjszNzeHj46ODZ5G3Pnz4gC5dumD69OmQSCTw9PREt27dULNmTWWZhw8fol27djh06JBOY+PrlztCCOzfvx81atRA79698eBBzqZsTklJweLFizMtZ2BggLFjx+aoD33TxrHav38/GjVqhNOnT+PXX3/FvXv3sH37dpw/fx7//vsv4uLiMGzYMIwcOTLbV+1IpVKMGTMGNWvWxLZt27IdG5HWCdKbqKgoAUBERUXpO5Rs2b9fCECIN2/0HUn+FRsrxNSpQhgayo+V4vHNNzHi5s3h4uxZiLNnIc6dhHg63FjITI2EAISsS2ex/vo6YferncBPUHtIfpKI0UdGi4iECH0/RSqgEhISxP3790VCQoK+QyE92r59uwCgfFhYWIjExMRM6yUkJIg9e/YIa2trAUAEBQUp9x04cECULFlSODs7iyNHjuRh9OmTyWTi4MGDwtbWVuX5rVu3Lsv1jx8/LkxMTAQAIZFIxOrVq7N0bAqCIUOGCGtra7Fu3TqRlJSksu/mzZuiUqVKymNWrFgxERMTo9P4+PrlzKFDh0StWrVUjhkAMWjQoGy39ddff6m1k/ZhaGgoRo4cqf0nogPaOFZnzpwRxsbGAoAYMmSIxjJeXl7Ktn/55ZcstZuSkiKOHj0q6tWrp/z9HTBggM7/Din/0uZnuOychzJhoEcFNWHg6ys/+b1+Xd+R5H9+fkLUqaOaNLCyEmLt2pPi339dlYmDK9sgIpsXFfGPLgohhHgd+1oM/nuwxqQBfoIovrC4+NPvT5EiS9HzM6SChgkDEkKI27dvq3xYLl26dLbqr1q1Si1hkBMDBgzIVf307N69W+X51axZM1v169atKwCIvn375kl8+nDy5ElRtGhRcfv27XTLBAQECENDQ+Vx27dvnw4j/IivX9atW7dOrFmzRoSGhoqAgABhZmaWq4RB3bp1ReXKlYVMJtN+sHqmjWMVGRkpnJ2dBQBhYmIinj9/nm7Z1q1bCwDCwMBAXM/Ch+ZffvlFdO/eXdSuXVtIJBJlbE2aNBHJyclZfZr0CdNXwoC3JFC2lS4t//nsmX7jKAhq1wauXgWWLgVsbOTbYmKAESPaYNSou0hJGQkASHABbv7vPXzDPBESshj25nbY9MUmnB98Hi1MKqJBqGq7r+Ne4+vDX6PB+ga4HHJZt0+KiAo8S0vLXNXv378/jI2Nc9XGH3/8kWeX2xYpUkRl3d/fH5cuXcpy/ZiYGADAjBkztBqXPs2dOxe7d+9G9erV0y1TuXJlNGrUSLn+5MkTXYSmhq9f1ikue3d2dkblypVVbi/JrtOnT+PGjRuYOnUqJBKJFqPMH7RxrBYuXIgXL14AANq2bYtSpUqlW3bo0KEA5LdrpTcTQmrff/899u3bBz8/P9y7dw/u7u4AgMuXL2Pfvn3ZjpVIW5gwoGxzcADMzIDnz/UdScFgZASMHw88fiyfPUGiHOvQCm3arMG+fadgaCjPwshk8QgMnAw/vwaIjr6OFqVb4MydurjypwQbjhiiaJrbOG+E3UDTjU3R/0B/hEanySoQEaUjtycD1tbW6Nq1a4ZjGmTk5s2bmDhxYq5iyMixY8dgZGQEIyMj5baVK1dmqW54eDgePXoENzc3VK1aNa9C1KmIiAg0btwYbdq0ybSsq6urcrlo0aJ5GVa6+PrlnJmZWY7rLly4EE5OTujfv78WI/ooOTkZv//+O5I1TCedHQkJCVi8eHGux03J7rFKSkrCH3/8oVzv0qVLhuU7deqk/B0+ceIE/Pz8stxXlSpV4OXlhUqVKgEAZ54hvWLCgLJNIgFcXXmFQXYVKyafgtHXF2jS5OP2Vas88Pnnd/Do0RgA8g/xsbE34efXECG7v4Lhrr8gEQJfX09B0GoTDPUDJGn+R+64swM1/6iJ2KRY3T0hIirU9uzZAycnp2zXCw0NRZcuXZCYmJgHUcl5eXmhSZMm6Nq1q3LbgQMHEBYWlmndEydOAJB/2P9U2NnZ4ddff81S2Xfv3imXGzdunFchZYivn+7dvn0b3t7esLa2xooVK3Dp0iUkJSVprf2kpCR0794dU6dORe/evbM87WBaCQkJ6NSpEyZPnozBgwfrdLDVs2fPqvx9NGzYMMPyNjY2cHNzU67v3r07W/2ZmZlhxIgRAJCl332ivMKEAeVIqVJAKL/QzpG6dYFLl4Dt2wHFZ+3YWCuMGLESkyf/i9hYxeWiMjy134vgcTaQWZgDAKxjk/DnYeDmFjM0SjNl8Ii6I2BpkrvLjImItCEwMFDj9gcPHsDd3R2hefgPJCAgAMHBwfD09MS4ceOU25OTk7F27dpM6x8/fhwA0LFjxzyLMb9KSUnBnTt3AADdunVDtWrVdB4DXz/9+O233wDI/0anTJmC5s2bw9bWFh07dsTmzZuRkJCQq/ajo6OVt7gcOHAAffr0yXbSICEhAZ07d1ZO+xkSEoIPHz7kKq7sSD3ThrGxMapUqZJpndq1ayuXDx48mO0+FQkHKyurbNcl0hYmDChHSpUCQkIyL0eaSSRAv37Ao0fA3LmA4v+An19jdO16Azt2/AqZzAzCCAjuFoXQU6OA3r2V9Ws+S4TPn8Ceg8YwSgEcLR3xfbPv9fRsiKiw2L17NwYPHqxx39OnTzFv3jxUq1YNHh4eavsvXbqEunXrqiUTUk9/Z2trm+sYjx07BgBo164dmjdvrvKBfd26dRleDi2EwMmTJ2FjY4PmzZvnOpaC5u+//0ZYWBiqVauGjRs36iUGvn669/z5c43ffickJODYsWMYMmQISpUqhT///DPHfTg4OODMmTPKE+B9+/ZlK2mQkJCALl264PTp0wCAFi1a4MiRIzA3N89xTNl15coV5bKzs3OWbslKPYXt48ePER0dna0+Fb/v9evXz1Y9Im1iwoByxMWFVxhog4UF8OOPQGAgMHYsYGwMpKQYY8OG79C//z3cudMOSUmVUKL2L8CuXcDp08BnnynrdynbHkPrD8dvbX+Dlanm7HNEQgQevn2oq6dERJ8wf39/lfWYmBgsX74cjRo1Qvny5fHjjz/i3r17GuvWq1cPgYGB2L9/v8r2sLAw5ePRo0e5jvHYsWNwcHBAnTp1AEBlrviwsDAcOHAg3bp+fn548+YNPD09Ve6fLwxCQ0MxevRodO/eHefPn9dK8iYn+Prp3r1791C/fn1UqlQp3W+y3717h2+++QbDhw+HECJH/ZQsWRJnz55V3pe/b98+9OvXDykpKRnWUyQLTp06BQBo2rQpjh49CgsLixzFkVOp3/8cHR2zVCdtubt372arz/Pnz8Pc3BwDBw7MVj0ibeK7KeXIhAnyE1zSjmLFgOXL5YMjzpgB7N4NhIWVw7hxx2Fr+wZ2dqaYNQvo3781jG7dwvvfesFm1XmYLFqKteXKZdj2Lxd/wZIrSzCi7gjMajkLJSxL6OhZEdGnJCUlBfv371e5rz06OhoODg6YMWMGJk+ejMePH6db38zMDCVLllQbSK9kyZJaizE2NhaXLl1C9+7dYWAg/06kT58+mDZtGt6+fQtAPnher169NNbPi8vZX7x4ofGKC21o0KABtm7dmut2Ll68iL59++L9+/eoXLmy3k628+Prp0lBeE2zo0OHDujQoQMA+Yj+d+7cwenTp7FmzRq1mTLWr18PCwsLLFmyJEd9OTo64uzZs3B3d8fjx4+xZ88eSCQS7NixA4aGhmrlExMT8cUXXyiTBY0bN4aXl1euZ3rJiffv3yuXHRwcslSnWLFiKutPnz5Fk1QDWR05cgT+/v4YPHgwnJ2dVco+efIEGzZswKJFi3I0XgyR1uR6EkfKsezMf0mFi6+vEB06CAGoPipUEGLnzgBx7pyROHcS4vbtzupzJT97JsTFi0IIIYIigoTJXBOBnyDwE4TlL5bif+f+J2I/xOrhWVF+oM05fKngCgoKUpnnvnTp0umWTUpKEjdv3hS9evXKcL7yefPmZam9s2fPqvStTQcOHBAAxObNm1W2//DDDyp93rp1S2P95s2bCwMDA/HmzRutxZT2WGvz0bJly1zFdvv2bTFgwABhYGCg0m7JkiXF6dOntXMAsiE/vn6a5OfXtGXLlsq20vtbzSqZTCb27NkjSpQooRbn/v37c9V2aGioKF++vLK93r17C6lUqlImISFBtGvXTlmmQYMGWv3MnJ1jFRsbq/L8e/bsmaU+Dh8+rFJv1apVKvvLlCkjAAgLCwsxc+ZM8fjxYxEdHS3+/vtvUa1aNbF27dqcPj36BGnzM1x2zkN5SwLlyKtXwJdfArdv6zuST1O9esCxY4CPD9Cu3cftT54AJ0/ugBBSCCPA3Ly2+vRo330HNG8OdOmCNVvHIinl4yjHsUmxmHVuFiquqIj1N9ZDKsvZKMVE9Gl5/vw5LC0t1R4WFhYwNTVF7dq1Mx3hu0QJ/V+9pJh6rF3qN04Ao0aNUvnWfNWqVWp1o6Oj4ePjg4YNG2b528OCSiqVwt3dHbVq1cK2bdvURpp/9eoV2rdvj0uXLuk0rrx+/RISEnDnzh3s2rULU6ZMQVBQkBaj//RIJBL07NkT9+7dg6enp8q+KVOm5PjWBEA+BsDZs2dR7r+rJP/66y8MGDBAeXuC4soCxawX9erVw4kTJ2BtbZ3jPnMjKipKZd3U1DRL9dJO3Zh2DIMlS5Yox+n47bff0KpVK/Tq1QtBQUG4cOEChg8fnouoibSDCQPKEXNzIC4O0OHgtIVSo0aAt7d8VgXF1Y+bNv0PM2cewK1bLdGmzXf4/XcgJka+L+XKReCvv+Qr//yD+ROPYtNhQzir/p9DWGwYhh8ZjqqrqmLXnV2QCd1NS0SfgMWL5QOZpPdo3VpzvalTM67Xr5/mev36ZVxv6lTN9Vq3zrje4sWa61WunHG9nTvV68TE5KyvfMLJyQm3bt1Se9y8eRO+vr7YsWMHWrRokWEbmi4n1jUvLy/UqFFD7b5hFxcXdOvWTbm+Y8cOREZGqpQ5ffo0pFJplqfje/HiBYYOHYoBAwbAw8MD7du31zj1WZkyZSCEyJPHuXPnsn2MAMDIyAjnzp2DVCrF27dvceHCBYwYMUJlALnk5GQMGTIk0/vLtSkvX793797hl19+wYABA9C3b1+sXr06y/ehp5UfX9O8ZG9vj0OHDsHd3V25LSgoKNcJpVKlSuHs2bMoW7YsAGDXrl0YOHAg4uLi0LVrV2WyoE6dOjhx4gRsbGxy1V9uKG6RUVD7siYdad8X0yZZunbtCj8/P8TGxiIxMREhISE4duwYJkyYADs7u9wFTaQlTBhQjtjYyE9kOWirbjRtCpw6BZw7B7RpI8GlS90wceI5PHtWBFOnAqVLA7NmAQHmh/F0hiNSHO0BAAYygcF+KXi6yhC/ngRs0syK9Pj9Y/Q90Be1/qiFQw8O5erbAipEoqOBFy/Sf4SHa64XEZFxvf/uUVbz9m3G9SIiNNcLD8+4XnqjVb98mXG9+Hj1OkLkrK98wsjICBUqVFB7VKpUCXXr1kXfvn1x8uRJVKhQQd+hpuv27dsIDQ1V+yZUIfUUffHx8WqzAHh7ewNAlhIGjx49Qt26dVGzZk1s27YN9erVg7e3t95mFsgpiUQCe3t7NG/eHH/88QeePHmC6tWrK/c/efJEOWtBXsvr18/e3h5z587F1P8SjM2aNVP79pfSZ2pqio0bN6ocs9SzBuSUq6srzp49i9KlSwMAdu7ciXLlyilfz1q1auHkyZN6P3lOO8BiVqdzTExMVFnn9IhUEDFhQDkWGwu8eaPvKAqXli2BkyeBa9fkt4QoEtwREcC6dcEIe70cz9uE4d9N7/ByXHkIG/mgQCZJKfjuX+DZKmPYaTjXufP6Drru7opGfzbCycCTTBxQxqytAWfn9B/pXZpuZ5dxvfQuA3dwyLheeh8kS5TIuF56l7Y6OWVcr0gR9ToSSc76KkBMTEzw1Vdf6TuMdClObNM74WzWrJly5H0AWL16tcp7nbe3N1xdXVGjRo0M+5HJZOjVqxdKlCihPIn95ptvMGfOHAwbNiy3T0OvnJyccPbsWZWB2hRz3uc1Xb1+ilHq27Ztm9uQC52yZcuiZ8+eyvUQLc2vXbp0aZw9exaurq4AgNevXwMAatSogVOnTqkNlKoPFhYWMDY2Vq5nNWGQtpw+r5IgyikmDCjHvvwSGDVK31EUTvXrA/v3A/fuAYMHA0ZGgJlZPB4+rAsAkJkCj7oF4t8tsXg35DOI/+YKtun4JXYPP4F6TvU0tnvtxTW0294Orba0wp3wO7p6OlTQTJokn1c1vUd6JxgLF2Zcb8cOzfV27Mi43sKFmuudOZNxvUmTNNd78CDjen37qtexsspZXwVMzZo19R1Cuo4dO4YiRYqgWbNm6ZZJPUVfYGCgclT9hw8fIjg4OEuj6+/evRu3bt3CsGHDlJclV6xYEbNmzULx4sVz+Sz0z97eXuU46eo+f129fhcuXADAhEFOpT7Gab89zw1nZ2dUrlxZZVvNmjXzRbIAkN+SoLgKAlCdMSEjactVrFhRq3ER6QITBpRjLi6AlpLLlENVqgCbNgGBgcAXX1TFtGn/4qef9uDlS/n9gFIb4M7Ae/DZLEF0t+pI/uk7tC3fFte+uYaDvQ6iWvFq8obSXFBw4dkFHT8TIioIvvrqK2zevFnfYaiJioqCj48P3N3dMxyMrE+fPirfnq9cuRLAx+n4snI7gmI6uS5duuQm5Hwt9XGI13QLjpbp6vWLjY2Fr68vihUrlq+TX/lZuVRTOWtrcNCkpCR0795dOWaBwrZt2zB8+PB8c9VjpUqVlMuaxivR5OXLlyrraZMiRAWBfibapU9CqVLycQxI/1xdgWXLgB9/lOCPP3pi2rQuaNp0BQYM+BmWllFIcvwAv3F3IA1tDRdMR8WKY9G1cld0rtQZx44uRflRP+CHZkk4VBmABOhdrTeql6ieab9ERPnBiRMn8H/27js8yirt4/h3Jr2THhJ674TeBGmiYEHFAqhIrKxiX19d14LuWrDjriKgUlSwsQKiIr2D9N5LKOm9lynn/eNxZjJkEgIkmQy5P9c1F8nMmfOcZ0Jg5veccx+j0VjhdHYLLy8vHnnkEd58801A+6B58uRJ/vjjD3x8fBgyZEilzz9w4ADbt2+nefPm1unTF5OQkMAwS9Xaata7d2/mzZtX7f2WvZJaG2vHa+vnt3btWgwGA8OGDaty0TpHXPFnWl0aNGhg/bo6dkYxGAzceeedLF26FNA+UM+cOZNx48aRkJDAF198gU6nY8aMGVf0M6sO/fv3ty6dSUhIwGw2lyuGeKGyyzbat29v9/oJ4SokMBCXrVEjSEoCgwHKLOsSThQeDq+8Av/3f158993fefvtiXTr9m9uuWU6np6luLtnk5z8IqdOTSM4+FX69HmQm7/bBQmlLPoe9jXy4KXBJl5/fEqFx8gozCDUN7T2TkoIIS7iYuvfy3rssceYOnUqRqMRs9nMRx99xLp16xg2bJjdLgEWJpOJDh06UFJSQkZGBqBV3G/WrBkAcXFxvPbaaxUez2AwcPTo0cs4q4uLioqqkX59y9TpKFsEsabU5M+vrBUrVgC25QhHjx5l+vTpfPLJJyiluOGGG3jyyScZOXJkpf244s+0uqSVKV7Vs6fj5Y1VZTAYuOuuu1iyZAmghQVr1qwhKiqKtWvXMnjwYBISEpg1axY6nY7PP//cqaHBsGHDePnllwFti85jx45ddMbA7t27rV9X5e+3EHWRLEkQl61xY60weBVnZYla5OUF998PGzeGcccdHzNv3jGWLZuIyaT9yvv6JlFS8jd+/r4dGdu3WZ/X5byBpd+YaT06DtasKddvibGE2BmxXPf1dWw8W7v7cwshrk5XOt3YbDbz66+/0rRpU9q2bXvR9tHR0YwZM8b6/fTp0yksLKxwOrubm5t1jfyAAQMAeOedd4iPjyc+Pr7SsMBVxcfHW7++2IfnK1XTP7+yVq5cCcDw4cMBaNu2LXfeeScxMTGsW7eO33//vcbP19Vt2bIF0IKNfv36XXY/RqORsWPHsmjRIkD7WVjCAoBWrVqxZs0aoqOjAZg5cyZ/+9vfnLo8oXfv3tagEGDHjh2Vti8qKmL/fls9qLJ/b4VwJRIYiMvWqJH2p9QxqLt0Ohg8GL75pikPPjibVav2s2WLbS/r0Ian2D/jBCuebE5Kw+a2J27erO1hP3Qo7NtnvXvOnjmczz3PylMrGTh7IEPnDmVd/LpaPCMhRHUwGo1231f3m/DK+vP8qwirRX5+/hUda8OGDaSlpTFixIgqP6fsFn1msxmgSgXzdu7cCUCfPn2qfKxmzZqhlKqR29q1a6s8jkthWUvev39/evToUSPHsKitn19SUhKHDh2iTZs21uUku3bt4u2332b79u0MGjSoysd3xZ9pdZk9ezYAjzzyyEWn41fEaDQybtw4/ve//wHlwwKL1q1b24UGM2bM4LHHHnNaaKDX63nwwQet31tmRlTk999/t+6S0LVr10oLegpRl0lgIC5b48banxIYuIbWreGttzrwxBP/4+zZrRw7NlR7QAcet53m8Deneb7Zq5zxa2970po11r0bS02lvLXxLbs+18SvYfDcwQyeM5g1p9fUmcJEQojKZWdn232fl5d3xX2WLY5XWaG8skXrAPbu3Wv3/Z49ey7puN/+tbvGpXzg69+/v9106tjYWBpZUvAKnDlzhvT0dHx8fC66dV9dVFRUVK4AmyOFhYV8/PHHuLu7W4sKXuj48eMMGDAAf39/+vTpw+bNmy97XLX187PMLrAsR/j111/5+OOP+fHHH+v8MoBLkZ6ebv3aZDJdtH1hYSELFizg22+/JSUlpdK2M2bM4ODBg3To0IEXXnjhssZnMpkYP348P/30E2ALCxo2bOiwfZs2bewe//zzz5k8efJlHftCl/paATz++OPWvy+//vqr3RKNC1nCFYBXXnnlMkcphPNJYCAuW1CQbScx4Tr8/WHChD48/PAqPDxWkJamvek6dqI778dPoUXBfu7hG065teJAm9s57q2tXz2ecZxSU6nDPtedWcfQeUPp/1V/lhxdglmZa+18hBCXruy6WoCsrCy7aeiX48SJE9avMzIyrOv9L9SiRQtCQ211UJ5//nmOHTvGmTNnePDBB61TlKsiPT2d+fPnA9hNFa6Kslv0VWU6u2V2Qffu3XF3d60SUKdOnaJx48bExMTQp08ftm3b5rCd2Wxm0qRJnDlzhlmzZtGtWzeH7f7v//6PzZs3U1BQwLZt2xgxYgRnz5695HHV5s/PEhgMGzaMN998kzlz5jBnzpyL1j1wJefPn7f7Pdy3b99Fg/wnn3yS8ePHc++999KsWTPefPNN66yNsjZu3MgzzzxD48aN+fXXX+3qXFSVyWTi3nvv5ccffwQuHhZYtGnThrVr11rbffbZZ1ccGlzOawVaEdBPP/0U0MKWKVOmOGy3YcMGayHHu+66S5YjCNemhNPk5OQoQOXk5Dh7KJftrruU+uILZ49CXAmz2axOnPhJTZ++RrVooZRWmUIpNwzquYmPqR49lqtBg8xq7lyl0rIK1bSt09SbowLUqPEoXkMxpfyt46cd1dd7v1YGk8HZpycuUFRUpA4dOqSKioqcPRRRy3JyctTRo0fVt99+q6KjoxXahqrW24ABA9Rvv/2mTp48qQoLC6vUZ0FBgTp+/LhasGCBCgkJsevvpptuUuvXr1dZWVnlnvfyyy+XOz6gJkyYoEwmU5WOnZ2drYYPH2597r/+9a9LeTlUSUmJioiIUIDaunXrRdu/9NJLClDPPvvsJR2nLvjpp5/sXme9Xq8mTZqkDh06ZG2zfft2NWLECBUdHa2WLl1aaX9Dhgwp97N78803L2lMtf3zi4mJUYAaOHCgAlTfvn0v6Xh1leV38Oeff1bdu3cv93O577771Nq1a1V8fLzDf/fHjRtX7jnXXnut9TVNTU1Vb731lvLy8lJDhgxRiYmJlzVOo9Fod6w2bdpccl9HjhxRDRs2tPYxefLkS3r+lb5WZc2cOVO5ubkpQL3//vvKbDZbH9u1a5f17+aoUaNUXl7eJY1TiIpU53u4S/kcKoGBE10NgYG4uphMSv3+u1K3365U164b1Jo1qDVrUC+9dI8CpQIClHpp7ElldnNTCtTeRh7qlrEVBwfNPm6mPt32qSosrdqHD1HzJDCov5577jmHH9Id3WbNmlWlPqdPn37Rvtq2bVvueQaDQT377LMqLCxM+fn5qT59+qivv/66yufy1FNPKT8/v3LHat26tZo4cWKV+3nllVdURERElUKKESNGKEB9//33Ve6/rsjPz1f9+/d3+PMJCAhQgYGBasCAAeqTTz5R+fn5F+1v7dq1qm3btkqv11v7efTRR6s8ntr++R08eFABKiwsTK1du9Yabu3YsaPKx6qr/vOf/1T59/rHH38s9/xz586pQYMGOWzv7u6uAHXNNddc8d/7pKQk1bRpU2tYkJCQcFn9HDlyREVFRSlAde/evUp/Xy2u9LW60KpVq1Tnzp0VoNq1a6fuueceNWzYMKXX61VgYKB64403lNFovKzzFMIRZwUGOqVk0bGz5ObmEhQURE5ODoGBgc4ezmUxmyEnB2phm2ZRy/bseZjs7C8AmDr1K5YtiwNgCq/xGm/YtT0c7cGrAwz8rz2YHSx0ivCLYNtD22jaoGmNj1tUrri4mNOnT9O8eXO8vb2dPRwhnColJYXVq1czbty4i7YNDw8nPT2d+Ph4mjZ1zX/L/vzzT3bt2kVGRgZeXl5EREQQHR1Nnz59Lvt9SElJCZGRkTz//PP885//rOYRV66qP79PPvmEp556iqeffpqPPvqIp59+mmnTpjFhwgTmzp1bS6Ot2+Lj49m4cSOpqakUFxcTFBREs2bN6NWrFxEREdVyjNOnT/PII48wd+5cayHDy3HkyBGeeuop5s+fb7e8yVl2797Ntm3brDVO2rdvz+DBg6+q5S6ibqjO93CX8jlUAgMnuhoCg9dfhxkzoAp1lISLUcpEWtpPJCd/TV7eIr76yp3vv4fCAjMPhn/G3ws/om3BKbvnnI7w5PV+pcy9YNlrt6hu7Hxkp1P3TxYaCQyEuHRnzpyhWbNmREZGkpyc7Ozh1Clnz56lWbNm7Nu3j06dOjl7OA7dcsst/PLLLyxevJhbbrmF48eP065dO9zc3Dh16pS1YGJWVhbBcgVECFFHOSswcImihwsXLqRFixa0bNnS2UMRF7jzTvj8c2ePQtQEnc6NiIi76dJlKQMGuPPll5CUBLO+0NP55Y0k/XKKFU824UhgC+tzmqeW8vDe3rQPsN9y7MVrXpSwQAjhsi5nO8X6QCnF008/zfjx4+tsWGA0Glm3bh16vd66E0Pr1q25/fbbMRgM1ur1u3btsm7zJ4QQwsYlyvzm5+cTHx8vHzjqoA4dtJuoHwICYPz4U/z55w8AeNx2luRbIXFZQ8K+CKBL5jGejf+Ew8/1Jrz3Wvyufxtz0GnGtHdcHVgpxT9W/YPb2t1Gn0byRlwIUTft2LEDgAEDBjh5JHWHyWTi8ccfJzk5mRUrVjh7OBXatm0bubm59OjRgwYNGljv//e//83ixYuZM2cOISEhpKamyvIEIYRwwCVmGIi6Ky8P3nsPTp509khEbfH2bk7nzr8QGNhPu0MH+pFJZC48xvIPImg8Zgve3oWkbRtC/L+Wc/afO7i96xl2DPk7STsS7Ppad2YdUzdNpe+XfRk4eyCLjyyWLRmFEHWOZYbB4MGDnTuQOsJoNDJ8+HDS09P57bff8PPzc/aQKmTZTvHCn13btm357LPPCAwMZNeuXbz77rvo9fK2WAghLuQSNQzmzp1LXFwcOp0Ok8nk7OFUm6uhhkFuLgQFwfz5UIWaUeIqopQiO3sdZ8++SVbWSrvHCgvDWLDgGX7++TEKChrwKY/xGNMpwZMVURPI+9v/MfxvrXlg5c0sPbbU7rltQtvwbN9nmdB1Aj4eUjCoukkNAyEuXXh4OG5ubiQmJsqHyr/s3LmTHj16OHsYQghRbzirhkGNLkk4e/ZstfSTnp5eLf2I6hcYqN3On3f2SERt0+l0BAcPJjh4MLm52zhz5i0yMhYD4OubzoMP/pOJE99m6+o4xr/9LSjwopSbkr/A/NqXLHxrBEkT/oAY+36PZRxj0q+TeHnNyzze63Ee7/U44X7htX5+QggBcPz4cdLT05k8ebKEBWVIWCCEEPVDjc4w0Ov11VZ3QCklMwzqqI4dYdgw+OQTZ49EOFt+/n7Onn2H1NTvANvSAo9MN4LntaXJ0rP4m/LtnrOmkSd/H1XKrgp2WPJ29+b+rvfzTN9naBvWtgZHXz/IDAMhLs306dN5/PHHOXDgAB2kaI8QQggnuWp3SVBKVctN1F2NGskMA6Hx9+9Mhw7f0rv3UaKjJ6HXa/+YGUJMpD59iN2L8ol/pBUF/g2szxlyvhTd6tchuavDPouNxczYOYP2n7bn1u9uZePZjfJvghCixkycOJHrr7/e+u/Mt99+yz333CNhgRBCiHqpRmcYuLm5ARAVFUWbNm0uu5/k5GSOHj0qMwzqqAcegIMH4c8/nT0SUdeUlqaSkPApCQmfYjRmWO8P87uJjrtupvitDzhvasgg01qSkxW0WAX934dWf1Tab8/onnx/x/e0CG5RaTtRnswwEKJywcHBmM1mMjMzWbZsGU888QTbt28nNDTU2UMTQghRj12VNQxatWrFiRMnaNeuHatWrbrsfixFD0XdFBMDy5c7exSiLvL0jKB589dp0uQFkpNnc+7chxQXn6JxmxfR9RqAz8MP0To9nXMhRjZsKGLBguEsXDicTPf9fBAwFl3YET7uZ+ZsA/t+47POEB1QwRoGIYS4Ap999hmvvPIKTzzxBIWFhWzatEnCAiGEEPVWjQYGPXr04Pjx4+zevbsmDyOcLCYGkpPBZIK/JpUIYcfNzZeYmMeJjp5EVtZqgoL+2stcr4eICDJSvsPdfRIvvDCJDz98kq2Lwrn2/hN4nDTzxDYdP7Rz5/2BBnb/lRFkrZjE3Zu8ueMOuPlmKLO1thBCXJFx48YxTrb9EUIIIYAarmHQs2dPAHJycjh58mRNHko4UUyMFhakpjp7JKKu0+ncCAm5zu4+pRTnzr2HyZTDuXNTMRpPMDxwGx6e2j9P7kox/rCBXTNh5RfeXH/UDdOWSSxZAhMmQEQEjBoFX30FGRnw1oa3WHxkMSbz1bN8SQghhBBCCGeolcAAYMeOHTV5KOFEPXpoH9b8/Jw9EuGKzOYi/P1j0ek88ffvRlDQQLjlFjh7FqZMQYWFWNsOO1/MsgUmDpeMwA9ttwWDAX7/HR58ECJan+PlVa9y6/e30uLjNny89WNyS3KddWpCCCGEEEK4tBoNDLp3707Xrl3p0qULaWlpl93PNddcw+zZs/nqq6+qcXSiukRHQ1wcuHDdRuFEbm6+tGv3JX37nqFt2y9tW7GGh6NefZXdi5px6vlgDM1sa4jbXtuQPzb68/TT2i4dFuYen6J02syCs3mneOaPZwh/J4Z7v32K4xknavGshBBCCCGEcH01ukuCqNzVsksCwNy50KED9Orl7JGIq0lOzhZ27+6vfWOC8C2eNF8civ4fr+F9+6MAmM2wfTt8t7CQnwuiOROR47gzpaOF6Uae6PUUk0cNw91dV0tnUbfILglCCCGEEK7HWbsk1OgMA1F/TJkiOyWI6ufm5kto6M2ADtwg7ZpStr2XxNbgSezbN4rMzOXodIo+fWDq5BROzcxn29f+jD4MevMFnekUp9yX8szu6/B+riMDnv6UH5fkUVzsjDMTQgghhBCi7pMZBk50Nc0wkB0SRE0qLDxOQsJ/SEr6CrO5wO4xX98ONGr0JFGfnEA/9X3r/UmRfrzTvZCvYhX5XhV0XBKA+8EJDA14nAkj2zNqFAQH1+CJ1AEyw0AIIYQQwvU4a4aBBAZOdDUFBkLUBoMhm+Tkrzh//hNKSs7YPRa1yocW873xPJVld3+RnxdfdIf3e5RwtkElnS+Zifu+hxk4UNuq8eaboVWr6j8HZ5PAQAghhBDC9ciSBOHSPvkERo509ijE1c7DowGNGz9Lnz4n6Nhxobajwl+ShxWxeVYW+96BvL62Aok+BSU8saGEdTs60S6sneOOzXo4cQNGI6xZA88+C61bQ7t28PzzsH49GI01fXZCCCGEEELULRIYiGpRXAxbtjh7FKK+0OvdCQ+/nW7d1tOjx06ioh5Ar/cGPWT2gZ1vZ7D9K0i9OQDl5Q5Asykfc+ixQ6y8byW3tbsNvc72z9+gyFt55sHGtGhhf5yjR+H9Dw1cey1ERMA998B330F2di2erBBCCCGEEE7iEoHBwoULadGiBS1btnT2UEQFYmIgJwcKCi7eVojqFBDQnXbtvqRfv/O0aPEu3t7NAChoDoeezeP0uofgv/+FoUPR6XQMazGM/939P04/dZp3W0zinU2+vNlzHB9+CCdOwKFDMHUqXHMN6PXAtW/Aw73JajKP+T8UM24chIXBkCHw4Ydw/LhTT18IIYQQQoga4xKBQX5+PvHx8cTHxzt7KKIC0dHanwkJzh2HqL88PEJp0uR5+vQ5QadOSwgOHgHoier8HDz+OOi0bRRLS9NJTf2eRgFRPL/HlxdWFDLgmvFw333otm+jfXv4v/+DDRvgXFIJgUNnQsx2uO1+eKYxDPsHJv8zrF0Lzz0HbdpA27bw97/DunWydEEIIYQQQlw9XCIwEHVfTIz2pwQGwtl0OjfCwm6ma9c/6NfvLL6+9pULk5K+4NChsfy5oQnmOV9ozzEY4JtvoE8f6NsX5s+H0lLWJP9ErinV9mS/dBj4DjzVAu6+DVqsAJ2ZY8fggw9g8GAID4fx42HBAsjMrMUTF0IIIYQQoppJYCCqhQQGoi7y8oqx+95sNpKYOB2AEpVK6Zal8MILEBJia/Tnn1qxgqZNSZz7H8cd683QfhFMGIH3823R9f8AfDIArb7BggVaaBAeDgMGwJtvwu7dIHvSCCGEEEIIV+Jek52fPXu2WvpJT0+vln5EzfHzg6AgCQxE3abTudGu3VwSEv6LUka82wyEdwbCa6/B/PkYPpiCx+HzWuPkZJ659Ttah2fx6fZPWXlqpcM+i31PwIi/43H9P4nOuou03ydReLQfoMNshs2btdvLL0PDhtpuIqNGwfDh2u+MEEIIIYQQdZVOqZq75qXX69H9tW74Siml0Ol0mEymaumvLriU/S9dQceOMGyYtsWiEHWdUiZ0Ojfr92azkT+3NsN7ewKNftbjnxlM6fpfCAzqi06n43DaYT7b/hmLts4mRRVgqCRubeHXhZYZkzm/+GEOH3bcxt1dK6w4ahTceCO0b28ts1CjqnMPXyGEEEIIUTuq8z3cpXwOrfElCUqparmJui8mBpKTnT0KIaqmbFgAkJu7hZLSBHK6wsEpZrZ9mMHuPf3ZubM7iYkzaB3ciP+M+g8nU8eRNT2QT7eFE5nnuO9TBfsI6LaMQ4fg1Cn49FMtFPDxsbUxGmHtWq3AYseO0Lw5PPYYLF0qu40IIYQQQoi6oUZnGLi5aW/Io6KiaNOmzWX3k5yczNGjR2WGQR1XUAC+vrVzlVSImlBQcJjExBmkpMzFaMy2e8zNLYCGvnfS8toF6AqKADB7uLO5XyNeaH+ezQ3tt0dYcd8KhrcYbndfUZG2k8Jvv8Gvv2phgiNeXloBxVGjtFurVo7bXQ6ZYSCEEEII4XqcNcOgRgODtm3bcuLECQYPHsyqVasuu5+5c+cSFxcngYEQolaYTIWkpv5AYuJ08vK2We/3SoGW0yF8I+gu+KcosUNj3utezGfN0mgS0Yqjk4+i15WfxFViLOGNdW9wf9eJkNma337TAoR166C01PF4Wre2hQeDBsGV/B8hgYEQQgghhOu5Kpck9OjRA6UUu3fvrsnDiDri99+1XenMZmePRIgr4+bmS8OGE+nR40969NhJw4aPoNf7URIJh6bA1m/hzHgwBNmm00QfOsdH36Sxpud/eP+69x2GBQALDy/krY1v0fbTNjz+53U0uf5//LbMSEYGLF4Mjz4KjRrZP+f4cZg2Da6/HkJD4eab4bPPKp6hIIQQQgghRHWo0cCgZ8+eAOTk5HDy5MmaPJSoA4KDoWtXKC529kiEqD4BAd1p23YG/fsn0Lr1p/j5daIkEk4/DFu+Vxx5HvJbam0NPdvQ75bHGN1udIX9Td8x3fr1ylMrGfPDGJp+3JT3d0yh++DzfP45nD0L+/fD1Klw7bXgVqbcQmGhVufg8cehZUto0waefFJb4iC1D4QQQgghRHWq0SUJ69evZ/Dgweh0OubPn8/dd999Wf3IkgQhRF2hlCI3dzOJiZ+TmvoDSpWCgqB94OXdmPaPnCm/O8yUKRAWxqGRPen4Tb8K+3bTuXFz25t5tMejjGg5wjpLITsbVq7EunwhJcXx8z09tSULN9ygzUbo2LF8TRFZkiCEEEII4XquyhoG+fn5DBw4EIAHH3yQyZMnX1Y/J0+eZOPGjQDcf//91TY+Z7vaAgOzGQ4fhogICA939miEqHmlpekkJ88hKWkGRUUnaNHiPZo0+btdm6zjP9Cg8wR0JSWY/f3YOrgVf299mi1BuZX23TSoKQ93f5i4bnFEB0Rb7zebYc8e+OMPWLYMNm/WdlxwJCZGCw9uuAGGD4cGDSQwEEIIIYRwRVdlYCAqd7UFBiaTVt39v/+FSZOcPRohao9SZrKyVuPvH4unZ5j1/oKCg5x9qxPt3yr/nOQe7fikl4n3wo9jdCv/uIVl1sEj3R9hRMsRuOntG+fkwOrVWoDw++/acgaH/bhB375w++3FDB16mrZtm+PjI4GBEKJ2xMfH06xZM2cPo07Kzs4GoEGDBk4dhxCibrsqix6K+sXNDRo2hIQEZ49EiNql0+kJCRluFxYAJCbOIuU62P4l5N87APz8rI9F7TzCW58fp+CLCJ5qNxE/D78LuwXApEwsOrKIUfNHsfnc5nKPBwXBbbfB559DfLw2y+ejj7QlCWX/LzGZYNMm+OQTSE6GI0e0ookZGWAwVMvLIEQ5SUlJfPnll5c9w9BZzGYzmzZt4vnnn2fnzp3OHo5LUkqxa9cu/v3vf9OrVy+aN29e5ecePXqUp59+mg4dOhAYGEhgYCBdunThX//6F/n5+QDs2LGDdu3aERYWxuzZs2vqNOyUlJSwfPlyHn/8cfbv339FfZ05c4bp06czcuRIIiMj2bNnT/UMUgghqpm7swcgri4xMRIYCGEREXEnBkMaGW6/4nXfEvivG8ydq21xcPQoAPo2zfj47tm8UTKNb/d9y4ydM9ibsrdcX21C23BNk2sqPZ5OB+3aabenn4aiIli/Xlu6sGyZFhJYmEyQmandAHx9ITBQCyD8/EAvcfJV5ZtvvuG+++6rUludToevry/+/v40atSIdu3aMXDgQG655RYaNmxY6XPz8/PZsGEDa9asYfny5ezbtw+lFE2bNuW///1vdZxKjSksLGTZsmUsXbqUX3/9ldTUVACuv/76Gj3ubbfdxqZNm0hLS7toWz8/Pxo0aEDDhg3p0aMH11xzDbfffju+vr41OsZL9eqrrzJz5kxSKiq4Uomvv/6aRx99lMmTJ/Pzzz+zbds2Jk2axP79+9m/fz/Lly9nw4YNPP300xz969/RSZMmcffdd1f766CUYs+ePaxdu5YVK1awbt06CgsLAbjzzjsvq8///e9/vPTSS9axCyFEXVcjSxIKCgrYv38/BQUFGI1GIiMjadGixVUx7b46XW1LEgDGjIH8fG16tBBCYzIV4ebmY7tDKc7PHY3Xl7+Qcj2UjOxDdPQjRETcjV7vy47EHWz48lXeMa8nTae9OX3/uvd5rv9zDvvfk7yHcN9wYgJjKh1HfDysW1dM8+an8fNrjlKOp7Pp9bbwIDBQW2okXFtRUREpKSmcPXuWOXPmlLsi6+fnR//+/dHr9RQVFZGZmcnRo0cxlJl+4ubmxoQJE3jrrbeIiopyeBxLUJCRkcHs2bMx/lVgo2nTpsTHx9fY+VWHAwcO8PXXX7Nx40Y2b7bN5lmxYgXDhw+v8ePv3r2bIUOGkJOTY73P09OTiRMn0rx5cwoKCkhMTGTXrl12V6ODgoKYPHkyr7zyCl515JfVYDBQUlLCG2+8wXvvvWe9/2JvOTdv3szgwYNp06YNBw4csN5fNvDS6XSUlJRw7bXXsmXLFgA8PDzIzs6u9sCgoKCA1157Db1ez8KFCzlVZi/bNWvWMHjw4Evu02g0YjKZ+P3337ntttuuuD8hRP3hrCUJqGpiMBjU7NmzVY8ePZS7u7vS6/Xlbo0aNVITJkxQ33zzjSooKKiuQ7usnJwcBaicnBxnD6XaTJ6sVKdOzh6FEHWbyWRQGzdGqjVrsLutXx+gjh6dpPKOLlPK3V2Zg4LU3rFD1B3/6qLSCtIq7K/fF/2U/nW9umXBLWrp0aXKaDJW2LaoqEgdOnRIFRQUqdxcpc6dU+rgQaW2b6/4tn+/UmfOKJWdrZSx4q6FizCbzapZs2YKsN7efffdcu1KSkrUzz//rHr27GnXNiYmRu3fv/+ix5k8ebL1OU2bNq2BM6kZhYWFysvLyzr2FStW1Nqxx40bZ/daP/jggw7bHT16VN1yyy12bTt27KgSExNrbaxVsW/fPrsxXkyvXr0UoCZNmlTusb///e8qKChIvfzyy0oppTZu3KhatGihQkND1RdffFHtY7/Qjh077M5lzZo1V9xnSEhItfYnhLi6Wd7DFRUVXXFfl/I5tFomncbHx9OnTx8efPBBdu/ejclkQilV7paQkMA333zDhAkTiI6O5qmnniJB5q9fVRo2hKQkZ49CiLpNr3end+/DtGr1H/z8uljvN5nySEz8nPS3bgCjEV1ODl2+W8OPr+wj7NbxsGSJtpagjP0p+9lyfgtmZWbJ0SXctOAmmk9rzutrX+d87vlKxgABAdCoEXToAF27QvPmEBIC7hcsVisuhtRUOH5c26Hh6FHt97ygAKRsruvR6XR07drV7r5wB1vbeHp6cuutt7J161YmTJhgvT8hIYExY8ZQVFRU6XE6dOhQPQOuZT4+Pg5fj9rQtGlTu+8bNWrksF2bNm1YtGiR3c5RBw8e5LbbbrvoVfzaFBAQUOW227dvZ/v27QCEhYWVe/y9994jOzubf/3rXwAMGDCAkydPkp6ezoMPPlg9A65ETfx9vpTXRwghnOWKA4P09HQGDBjAnj17rP9J6XS6cvuQl71PKUVubi7//e9/ad++Pe+//z5ms/lKhyLqgOhorYhaSYmzRyJE3ebhEUyjRpPp2XMP3bv/SVTUg+j1WuHDtGsgaSSYPMs8YcUKGD0aWraE996zflKftWtWub7P5Z5jyropNP24KbcsuIWlx5ZiMpvKtbMfD4SGQosWWnjQvr32++zvb99OKcjL02qVHD4Me/dqxRPT06G09IpeElGLLmUZnJubGzNnzqRly5bW+44dO8aPP/5Y6fN8fHwqfbwuc3OrZOuSGnQpSwp0Oh3Tpk2z+1n++eefLFu2rCaGVuN+//1369fOev0r48p/n4UQ4kpccWAwefJkkv66pKzT6ayzCTp06MCYMWMYP348t912G507d8bb29suVACtQNILL7zAjTfeSF5e3pUORziZpR5WcrJzxyGEq9DpdAQG9qZduy/o3z+RNm1moIvtwdH/gy0/wMlJUBRd5glnzmBet1KrcAh4u3vj7+nvsG+zMvPLsV+4ecHNNJvWjClrp5CUd/EpQDqdVvgwOloroBgbq+UU4eHg6Wnf1mjUCifGx8O+fXDwIJw7p233KDlw3aW/xKqWXl5eTJw40e6+VatWVeOIxOUICgoqV1/BVQOD48ePO3sIQgghHLiiXRLS0tL43//+Zw0KAMaMGcPbb79Nq1atyrU3GAxs2bKFX375ha+//prU1FTrc5cvX87AgQNZv379VVMAsD5q2RLuuEOmKQtxOdzdA4mOfoTo6EfIy9tNUtIsEkO+4dydeYRsg5hFELINDg5aie7A7TRv/i/eve5dXhn0Ct8d+I45f37O5rRdDvs+n3ue19e9zryd85h97WxCS0Lx8vIqNxvM8bggOFi7KaXNIMrNtd3KBgNFRdotJUULHgICtMKJgYHg42PNOYQL6tKli933yZIM1wllZ34Adb64ZEUyMjKcPQQhhBAOXNEMg3Xr1lkrIOt0Oh566CF+/PFHh2EBaFVsBw0axHvvvce5c+f48MMPadCggTU02L9/P3feeacsT3BhrVrBjz9Cs2bOHokQri0goBtt2nxG//7JtOswF9P1g9j/Dvz5DWT0NJOe/jOWf8IDvAJ4OGQ4m146SerpO3gj9E4CPB2vjTUrM0WGIs7mnOV45qVf0dPpwNsbIiK03/fYWGjbVptd5Odn31YpLVA4fx4OHdJmIJw+rS1bKlN8X7gIzwuml0i4Xzdc+J6p1EXXBhUXF1/2c0+ePFmNIxFCCFHWFQUGloKFSim8vb354IMPqvxcDw8Pnn76aXbv3k23bt2s/axcuZKPPvroSoYlnCwpSVvPLIS4cm5uvkRFTaBbt3X07n2MiL4v4ukdRWBgf/z82tsazpgBOTmEz/2JV574kaxfO7Ha/SH6h/eosO8gr6ArHp+leGJMjFb3IDZWq4MQFlZ++YLBoIUFp09rtQ8OHdLChAtnKYi66fTp03bf9+rVq9qPcfDgQV544QV69epFREQEnp6eREVFMWjQIKZOnUpmZmaV+zIYDMydO5ebbrqJqKgoPD09CQkJYeDAgbz//vuXvAyyoKCADz/8kF69ehEcHIyfnx89e/ZkxowZTi00eP68fXHTigolXqigoIBp06YxZMgQoqKi8PLyolGjRtx8880XrU9R1tGjR3n66adp164dfn5+hIaGMnr0aNasWXPR51rqW+l0Orv2r7/+ut1jOp2OtWvX2j03KSmJjz/+mD59+lR4oepCW7Zs4dFHH6VNmzb4+/sTGBhI586defHFFzl37lyVz7mqsrKyePvtt+nZsyfBwcH4+/szYMAAvvrqK0ymyuvKCCFEXXFFgUF+fj6g/YPfr1+/y6r22qRJE1avXk3nzp2tMw3+/e9/X9KbAlG39OkDH3/s7FEIcfXx9W1NixZv07fvOTp0+N7uMQWYvG3z/d02bWHIy1+wacpZktLjeKnReLtZBzp0hPqGVnisYuPlXe1zd9d2WmjWDDp3ho4doXFjCArSwoWyCgu1eifHjmm7Lxw/ri1lKCqSZU110Q8//GD92t/fv1xNgytRVFTEww8/TJcuXdi4cSOvvvoq27ZtY9u2bTz++OPs3r2bF198kZYtWzJ79uyL9rd161Y6duzISy+9xIgRI1ixYgXHjh1j2rRpbNu2jeeff57WrVuzfv36Ko3vyJEj9OrVi+eee44dO3aQnZ1NYWEhO3fuZNKkSTz11FNX+hJcFqPRyMqVK+3uGzFixEWft3r1atq2bcvmzZt588032bZtGytXriQ2NpalS5dy1113MXLkSOv7vIq8++67dOnShSVLlvDiiy+yZ88eNm3axODBgxkzZsxFX5ekpCTrrV+/ftb7n3vuObvHkpKS6N+/P6WlpXz55ZcMGzaMRo0a8cwzz7Bt27aLnm9JSQlxcXHcdttt9OjRg99//51du3bx9ttvc+7cOaZOnUqnTp343//+d9G+qmrZsmV06NCBN998k9tuu41169Zx4MABnnzySd5//30GDRpEQUFBtR1PCCFqzJXs3/jee+8pnU6n9Hq9uueee66kK3XkyBHl6emp9Hq90uv16rPPPrui/lzBpex/6UrWr1fq1Clnj0KI+iU7e7Na/wvq2JOowha+Smmfua038513qrySPPX1jq/Vqj9XqWPJxyrsK78kX21P2K4Opx1WaQVpymgyVssYTSalcnOVOndOqYMHldq+veLbnj1KnT6tVEaGUqWl1XJ4Ucb9999vt6f87NmzK20/bdo0a1tfX1/166+/XvQYs2fPtj6nadOmFbbLy8tTvXr1UoAaPXq0MhrL/307fPiwioqKsvY3ZcqUCvv77bfflKenp+revbvKzMws9/jf/vY3az8BAQHq3Llz5do0bdrU2mbWrFmqYcOGasiQIernn39Wx44dU5s3b1bXXXed3Wu4Y8eOi74mF/Paa6/Z9fnaa69V2v7NN9+0a9+pUyeHr19Zy5YtU15eXupvf/tbuceMRqP1ZwGoG264QZnNZof9vPjiiwpQ7du3d/g6HzlyRIWEhNiNrzLXXnvtRc87NzdXzZ07Vy1dulT169evSn2Xlpaq6667TgUGBqqjR4+We3zJkiXWPjw9PdXatWsr7Kvs8dasWVNhuyVLlihPT0/l5+endu7cWe7x/Px81bt37yr3J4QQSilVVFSkDh06pIqKiq64r0v5HHpFRQ9DQ21Xp650RkDbtm0ZN24c8+bNQ6fTsXjxYv72t79dUZ/COQYOdPYIhKh/AgP70vmaDSS3+pK8l0bhcyQCpk+HhQvBaOTw4C1w4mGGxzxMVlYUMYExFfaVXqitKcovzSe/NJ+zurOE+oQS5huGr4cvyck6ki6y2UKTJtqyhPR0OHsWunfXZhgkJoLloprZDPn52vcFBdqOC6AVTwVt1oHZrPXl5aVt5ejnpxVPdFTk39sbLFul79mj1VWIjISsLG0ZRGUiIqBRI208x45pyyt8fODMGW0ZRWVatdKKOlqWY3XurN2/f7+tVkPDhrZdZFxFZmYmW7duZebMmSxevJiAgADGjRvHiy++SPPmzavtOHFxcWzfvp3AwEC++OILh1vqtWvXjgULFjBkyBAApkyZQqdOnRgzZoxdu507d3L77bfj4eHBwoULCQ4OLtfXnXfeyfTp0wHIy8tj4cKFlV4Jf/LJJ/nHP/7Byy+/bC0S2rp1a5YsWULr1q2tSwIWLFhAjx4VLwGqTqWlpbz33nu8+uqr1vuaNm3KokWLKt2SMDk5mXvvvZeAgADef//9co+7ubnxzDPPMH78eEC7Sj5//nzuueceu3Y//vgj77zzDjqdjm+++cbh69y2bVteeeUVnnnmmcs9zXICAgKYMGECADk5OWzZsuWiz3n99ddZsWIF7777Lm3atCn3+M0330ybNm04duwYpaWlPPzwwxw9erRKBWEdOX36NOPHj6e0tJSpU6fSvXv3cm38/PyYMWOGdUmuEELUaVeSTKxZs0bpdDql0+lUVFTUlXSllFJq8eLF1hkLzZo1u+L+6rqrdYbBsmVKvf22s0chhFBKKZWUpPLfeVytWY1aswa1bl1TtXv3CpWTc0aZCnKUOnlSu+z/11VEo8modiXuUtsTtju8HUg5oP7+j/wLJzCUu82apR1+1izte4u+fSt/Hii1bp1SO3cqNWyY1n77dqXWrLn48zp0sB0nIECpDz7Qvv7hh4s/95lntLabN2vfHzigff/ggxd/7rJlWtvXXlMqJsY2hpgYW5uLXCiudRfOMPDy8lJ+fn7Wm7u7u93jjRs3Vh999JFKSEio8jGqMsNg1apV1jZxcXEX7fOOO+6wtm/SpIkqKSmxPmY2m1Xfvn0VoCZPnlxhHyaTSbVr187azy+//FKuTdkZBn//+98r7OvJJ5+0thsyZMhFx38xFc0wMJlMKjU1Va1fv1699NJLKiYmxu6q+KRJk1RGRsZF+7fMrnj88ccrbJOenm43huHDh9s9npeXp0JDQxWgRowYUenxDhw4UK0zDMpauXLlRfs+f/688vDwUHq9XqWkpFTY1xNPPGHX18aNGx22K9umohkBI0aMUIAKDAy86FXAsLAwmWEghKgyl5xh0KtXL3x8fCgqKiI1NZXffvuNUaNGXXZ/lq2BlFKkpKRcydCEE23bBv/5D7z4orNHIoQgKoq8+/vicfJ7DAZt5oBSRgyGVIypqXhmA5mZKB8fdOHhlAT6oNfpMSnHBbmKjEUMu/MEsdd6EOAVSLB3ML4evuWuxjVpov15663a7AKLOXNsMwwq0qmTVgvhgw+0gog+PmAywddfO27v4QG+vhAaql3R9/CA9ettV/SHD4edOys/ZkSE9mfnzlrbFi207195BR57rPLnWuqtPfoolL3g/fvv9jMM6rI33niD22+/3fq95f/h9evX8/XXX3PkyBGeeeYZnnvuOe6++27ee+89YmIqnqVSVR+XKXgzYMCAi7Z/+OGH+emnnwA4e/YsixYt4q677gJgxYoVbN26FYBbb721wj70ej2rVq1iwYIFdOnSheuuu67SY3bs2LHCx8pesa6J9y1vvfUWU6dOpaSkxK6wop+fHxMnTmTgwIHceuuthISEXLSvvLw85s6dC1T+WoeGhtKoUSPrzIkdO3bYPf7FF19Yt0C8+eabKz2m34Vbp1SjymZSWMyYMQODwUCbNm2IsPySO9C1a1e773fs2FGlv48X2rt3L8uXLwfguuuuw9vbu9L2fn5+pEuVaCFEHXdFgYGfnx8PP/wwn3zyCaAVqRkyZAg+Pj5XPDDZrsl1RUdDWprtjbsQwrmiou4lIuIu0tOXkJS0WNuRQIF7mQ/uuqIiOHsWH72eLqGh5AV5k2LKJackp1x/YZEGwiINQCGQjKebF2G+YYT5huHhZv9LHxam3Szatq36uMu+hzcYtA/mubnaraJtGffu1QKG0FBtiYLZDMHB2q0q/P3tA46mTbVbVVy47MCyNMEVRERElKs037p1a6655hpefPFFpk2bxvPPP4/JZGLBggWsWLGCX3/9ld69e1/2MQ0Gg11l/Ojo6Is+Z8iQIXh6elq3Dly+fLk1MFi8eLG1XZcuXSrtJzo6mueee+5yhm2n7AfiwsLCK+7vQpMmTeLJJ58kIyODG264gezsbOuxbrrppnJLMiqzceNG6xgfeOABHn744QrbFhUVWb/Ozs6mpKQELy8vAL777jvrYxd7nZ3tjz/+AODEiRP4+/tX2O7CHQuSk5Mv63gLFiywfl3XXxshhKiqKwoMQFtHOH/+fDIyMjh27Bh33XUXCxcuLLdfc1UcPXoU0HZdaNy48ZUOTTiJ5Q1zSoq2JlgI4Xx6vScREXcQGHgTp04dx8MziMIW2bjlluKRDe5/fT7Qmc2QlkZgGgREN8QQ0ZT0onTSC9MpNTne373EVEJCXgIJeQm0DmlNkHdQtY/fw0MLAUJDtUn+RUW28CAvD7tdFYqKtFtKCuh0WggQGKjdfH21+0TV6fV6nnnmGby9vXnsr+kW6enp3HzzzRw+fLhKV7cdiY+Pt6vC71GFhNnDw4NOnTqxa9cuQNu9wGLjxo3Wrx2tqa9pZWcAVJeQkBBatWpFq1at+Prrr7nllltQSqGU4v7776dVq1blro5XpOxMgVmzZtG3b98qj8PysykoKGBnmek64eHhVe6jtpnNZnbv3g1Ajx49mD9/fpWf26BBg8s6ZtldN+ryayOEEJfiigODBg0aMH36dO688050Oh2//fYbAwcOZMGCBbSwzOmsoi+//NL69dChQ690aMJJLIFBYqIEBkLURTqdO56eEXh5Ncbkk4chJJ2S/Cw8shUeuaD763NPoUcyekMxET5hNPSPIq8kn7TCNLKLs1GU/3Ck1+nx96z4Kl71jV/74O/rC1FRtuKJlgCh7IVepbRAIS9PK5ro7g4BAbYA4a+LpqIKHn30Ud555x3Onj0LQGpqKh9++CH//ve/L6u/jAuqSebm5lbpeQ3LTOMoO5277FXh4uLiSq8ou6KbbrqJl156iTfffBPQPryPHj2a7du3V+nDaVpamvXr0NDQcjNKquLcuXMYLdVJqVrI4yzZ2dkY/pqKpNPpLut8L9WpU6esX9fl10YIIS6FgzrTl27MmDE8/vjj1nR9+/btdOjQgWeeeYbjx49XqY+33nqL33//HZ1Oh16v59FHH62OoQknsMwqvVgVdSGEc+l0OtzdA/HxaYFPaFdo2oTCNr4UR4AhEMzeCqMxi6Ki4xQVHSfQO5CWPtF09W5G44BGeLvbr88N8QnBTe94XbHBZMCszDVyHnq99uG/USNtl4SuXbUaBGFhcOFkN6NR2zXhzBltF4MDB7RdHLKybLs0CMf0er11lwKLJUuWXHZ/7u721ywuDBAqEhRkm8HiVSbxKbtbU0JCwmWPqy574403GD58uPX7M2fOcMcdd1g/GFem7DKD1NTUyzr+hTti5eXlXVY/taE6zvdSlX196vJrI4QQl6JaAgOAadOmcffdd6OUQqfTUVpayieffEK7du3o27cvL730Er/88gvHjh0jLy+PwsJCTp48yXfffcfgwYN55ZVXAG1K32uvvXbJsxNE3REeDm5uEhgI4Ur0em3WgV9ABzxiOmBqEolOZ/tA5+b2V12ZlBTcT50m8nQqHY0htA9qRahvKHqdnjDfsAp6h4S8BPal7ONczjmKDEUVtqsOHh4QEgLNmml1BDp10oowNmig/dtUVnExpKbCyZPaVoyHD2szEfLytJkLwl7DC6o3lr2ieqkuLEJ38ODBKj2v7BXuyMhI69cBAQHWrzdv3nzZ46rL9Ho9CxYssFu2uX79ep544omLPrdsbah9+/Zd1vG9LpiScyU//5pW9nzPnj1LTk75eizVrezrU5dfGyGEuBTVFhjo9Xrmz5/Pq6++il6vt1bMVkqxfft2pk6dyq233kr79u1p0KABAQEBtGnThnvuuYcNGzaglCIkJIQZM2bw8ssvV9ewhBPo9dre54mJzh6JEOJyuLn54u3dGD+/Lnh7t8TNrQEeHqHaJXjLFbTSUnSJifgeOUHTdANdfVvg5+G4IrrJbCKzKBOj2UhKQQoH0w5yJP0I6YXpmMyOd2OoLjqdVvwwIkIrmhgbC+3aaTOh/P3L1zMoKNDCzqNHtQDh+HGtFkJRkX2dhPrKUmzQQq+//LcRTZo0sfvAf2E1/oqUvYpbdh1+0zLVKb+uaEsNByw7K7iKsLAwfvzxR7taUTNmzOCzzz6r9HlNLFuXoBUurMqsBNAKLFqWnURFRdk9tn379qoOu9YFBARYa1mYzWa++eabKj/3+++/Z+/evZd8zLKvT11+bYQQ4lJUW2AA2vTWKVOmsHHjRgYMGFCuAJClUI+jm06no2vXriQlJbF06VIS5dOmS4uOlhkGQrg6nU6Ph0cwvr6t0Os9tcvzLVpAUJC1goFOgT47F7fjJ9AdPKhdmr9AZlFmueUI+aX5xGfHszdlL2eyz1BQWlAjRePKn5MWFERHa8FBbKwWJEREaMFCWWYz5OTAuXNw8KC2hCE+HjIyKt6l4Wp34TLD5s2bX1F/Zbdi3rJlC0lV+I/j8OHD1q9vuukm69fXXHON9es1a9bw22+/XbSvTZs28dZbb1V1uHVGnz59+PDDD+3ue+qpp1i7dm2FzykbriQmJvL+++9f9DhKKR588EGKi4sBiImJsdtO86effqqV39vL1adPH+vX//73v8nKyrroc3bt2sUTTzxBs2bNruh427ZtIz4+/pL7EEKIuqZaAwOLPn36sH79etauXcuECRPw9/e3+w9Fp9OVuwGsXbuW119/ndGjR9O4cWMaNmzIjTfeyKuvvsqiRYushZZE3Td16sX3LhdCuBidTpvX37o1hrYRlIboMJed4l9cDO7umExFGAwZqL9CgiJjxUsQzMpMWmEah9MPcyjtECn5KRhMtfdp3M1NO6UmTbSlC126aEsZQkK0AolllZZCejqcPq1t33joEJw/rxVarA/LF9LT01m9erXdfaNHj76iPp977jnrewCz2czs2bMrbX/48GFrfYLBgwfbbet4//3327WdOHFipcscDhw4wF133cXbb799ucOvdlW96g/w+OOPc88991i/NxqN3HHHHZw+fdph+169etkFPK+//jrLli2rsH+z2cwTTzzBb7/9xpNPPmm9v+xWjidPnqx0NseF2xVeOEOlrLLvEy8nhHD0nLvvvtv6dXJyMmPHjrWGH47s37+fkSNH8uCDD9rVyqiqsq+NUoo33nij0vZlX5/KXhshhHCmGgkMLAYNGsScOXPIzMxk8+bNvPPOO4wZM4ZmzZqVm2FQluW+lJQUli1bxptvvsmYMWNo3rw54eHhXH/99bz00ks1OXRxhYYOhW7dnD0KIURN8Qxogkfzbpg7tdFmHQQEaJfufXwwGNIoLj5Nfv5eiovPEOMZSKfQ9kT5R+Gur3hzniJjEedyz7EvZR8nM0+SU5xT61cvPT21YoktWmjFEzt00IopBgaWX75QWAjJyXDsGOzerS1jSErSljXU4YuuAHbbGVaFyWTikUceoaCgwHpfZGQkTz31VIXPKSkpsXu+Ix07duTZZ5+1fj916lS7av4X+vTTTwHw8/Ozfm3Rs2dPuxkHaWlp9OvXjw8//NBuGUN2djYffPABAwYM4OGHH6Zjx47ljlO2TkJFY7+wnbkaUqML19mXfb0dmTlzJp06dbJ+n5GRwejRox3+fPV6Pf/3f/9n/b6kpIRbbrmF//u//+PMmTPW+00mE8uXL2fw4MF8+umnvP3223b1Jp555hl8fHys3z/55JPs37+/wvGVZdk+25GyY67K38/CstuhOPgeYOzYsXZLVZYvX07v3r1ZunSpXTiTnJzMlClT6NevH/7+/g7fY5b9+wyO/16MHj3a7ucxe/Zs5s2b53D8v//+O+fPn7d+X9lrI4QQTqWcJCsrS61atUq9++676u6771atW7dWer1e6XQ6602v11tvZe+3PObqcnJyFKBycnKcPZRqt327UlOnOnsUQogLFRUVqUOHDqmioqLq79xkUmazSeXm7la5udutt9Ij25V553ZlPHlImfKyVWZhpjqWfkxtT9h+0duBlAPKbDZX/1gvg8mkVE6OUufOKXXwoPbvXEW33buVOnFCqdRUpYqLnT3y8tq3b68A6+0f//hHhW1PnTqlrr32Wrv2wcHBavPmzZUeY9KkSdb2np6eqrCw0GE7g8GgbrzxRmvbG264QZWWlpZrt2rVKuXu7q68vLzUzz//7LCv5ORk1bhxY7uxAkqv16vo6GgVFRWldDqdAtSoUaOUyWQq14fZbFa+vr7W57711lsVnuObb75pbefv71/p61EVAwYMsBv3DTfccNHnHD16VAUGBto9b9SoUaqkpKRcW7PZrK6//vpyrw+gwsPDVZMmTZS7u7v1vri4OIfHnDVrlt1z/fz81FtvvaWOHTumcnJy1P79+9UjjzyiOnToYNcuOjpajR8/Xr333nt2/ZWWlqqAgABru+HDh1/0vKdNm2bX986dOx22W7t2rfLw8Ch3vt7e3qpZs2aqQYMGdn+v9+zZ47CfrVu32j3/yy+/dNhuz549dn9/dDqdeuihh9T27dtVdna2OnPmjHrvvfdUeHi43d9VHx8fdfvtt6uHHnrooucuhKifqvM93KV8DnVaYOBITk6OWrNmjfrggw/U+PHjVbt27RyGCBIY1H1ffqlU48baG2whRN1Ro4GB0j6QGAy5qrDwlMrN3anyMrcr8w77T9PGA7uUMfmMKikpVAm5CWpv8t4KA4P4rPgaGWd1KC1VKiNDqdOnldq7t/IAYf9+pc6cUSorSymj0XljNhqN6vPPPy/34cnLy0tNnDhRvfnmm2rq1KlqypQpavLkyWrIkCFKr9eX+xB7/Phxh/2Xlpaqw4cPq6lTpypPT0+758XFxamdO3eq1NTUcs8zGAzq6aefVm5ubgpQPXr0UAsXLlSnTp1Se/fuVVOmTFFeXl6qZcuWav369ZWeY3x8vOrSpYvDD8WW28SJE1VxBUnO77//bte2d+/eDn9fioqKVK9evezaLl++vAo/BXvFxcVq37596oUXXnAYdLz55ptq9+7dKjExscI+Fi5cWO65/fr1U7///rvKzMy0a1tYWKhuu+22Sl8fNzc39c9//rPSsO6zzz5TXl5eFfbRoUMHdebMGbsPzgMGDFCfffaZSk9PV0opde7cObVlyxY1YcIEu+fqdDr16quvqu3bt6uzZ88q41+/NMXFxerUqVNqyZIlqlmzZuXOd/ny5Q7/fv32228qODi40nPu2LGj2rdvX7nnJiUlqUWLFpULP1q3bq1+++03FR8frwwGg91zNm3apCIjIys8VnBwsFqxYoVdENemTRv12muvqWPHjlX8l0UIUa85KzDQKVW3J07m5+eze/dudu3axc6dO9m5cydHjx5FKVXpNEFXkJubS1BQEDk5OXbb/wghRE0pLi7m9OnTNG/eHO8Lq/xVM6VMGIvSICkF92wDugv+t1F6MAX7oouKoUCvI70wnayiLBS2hu3D2uPn6Xj3hRJjCV7uXg4fq21KQUmJVs8gN1er/VjZf1H+/toyh8BA8PXVdpepScuWLeOFF17g9OnTl7Q/vK+vLw0aNLBukTx27Fg6d+5cYfs5c+YQFxdXaZ89evSocEeEQ4cO8cUXX7B8+XLOnTtHaWkpERERdOvWjVtvvZV77rkHDw+Pi47baDQyZ84cFixYwP79+8nOziYiIoKBAwfy2GOPMXDgwHLP2bJlC88//zxbtmwpt7wgLCyMQYMG8f333wPa2vj169eTnp5u106n09GvXz++/fbbKhfN69atG3v27Llou9DQ0HLHK+v555+vsJDhiRMnaNmypd19y5YtY86cOWzevJmUlBQ8PDxo0aIFw4YNY9KkSbRt2/aiYzp58iSffPIJf/zxB+fOncPNzY3WrVszbtw4Jk+ejLe3N507d2b8+PGMHz/ebnlAcXGx3dKGypw+fZpmzZqxbNkyRo4cWWlbLy8vh3UKMjIy+O9//8vSpUs5efIk+fn5hISE0KNHD+6++27Gjh1rt/OEhe7CtUgO7N+/324pAmjv8f773/+yaNEijh8/TnFxMY0bN+amm27iueeeIyYmhvHjxxMZGcn48ePp1avXRY8jhKjfqvM93KV8Dq3zgYEjhYWF7Nmzh/79+zt7KFdEAgMhRG2rzcCgLJOhAHNaAvqMPNxK7P/byW8OOm9fPDzC0LkFkVWcQ3phOkopOoR3cPiG3Wg2sjd5Lz4ePoT5hhHiE1JpfYTappRWy8ASIFS2JFuv10pAWAIEb+/y9RKEEEIIUb85KzCoO++uLoGvr6/LhwVXu4wM6NEDpk+Hi1wMEELUA24efrhFt0E1VBhzUyEtBbecUky+oDxBmQspKTkL6Ahwb0BocDOU3rPCq3sZhRkoFIWGQs7mnOVczjmCfYIJ8w0jwDOgSlcFa5Jl+0bLFo4mkzbrwBIglL0Aatm+0VLvztPTPkCowgV1IYQQQoga4ZKBgaj7goLg7Flt/3IhhLDQ6XS4B0VCUCRmQzGqOAO9Pgez2VLhXGE0ZuF93ohO6SA8XNv3sEwAoJQivdB+arZCkVmUSWZRJp5unoT5hhHqE1pnlixYtm9s0ED7vrTUFh7k5kKZYvuUlmqha0aG9r2PjxYcBARoNze3C3sXQgghhKgZEhiIGuHuDpGR2hZjQgjhiN7DG71HDB7EYDIVYjCkYzRmojd5oMv5a519bi54eGAK8cMc4oebTzgmpTCpigsElJpKScxLJDEvkUCvQMJ8w2jg3QC9roYLBVwCy/aNYWHa8oWiIvvlC2WX0BcVabeUFC038fOr3foHQgghhKi/JDAQNaZhQwkMhBBV4+bmi5tbE5RqhMrLBq8ErYoggMGAW0o2+pRsjP4JuEW1oHN4J3JL80gvTCe7ONuuUGJZuSW55Jbk4q53J8QnhDDfMHw9fGvvxKpAp9M++Pv6QlSUFhbk59uWMBQU2NoqpT2Wnw+JiVpYYJl9IPUPhBBCCFHdJDAQNSY6WntDK4QQVaXT6dEFhkCnYO3TcloaKjsbHaADPPKBE6egSxeCvIMI8g7CYComs0grlFhkLHLYr9FsJLUgldSCVBoFNiLKP6o2T+uSWEKAwECIidGWK5Stf2DJUUALF7KztRto9Q4szw0I0GYyCCGEEEJcLgkMRI1p2BCqsFOUEEKUp9NpxVCCgtCVlmJOTUKXkYHOYNYKAfz1SVgpRUnRMfx1HgQ3CMeAN+mFWWQWZVa4bCHIK6gWT+TKubtDcLB2Ay0wKBsglK1/YDCUr39gmX0g9Q+EEEIIcakkMBA1pmFD+O03Z49CCOHyPD3RN2oKMU20rQTKbBtgMuWjVClKlcKZAtw8ISI0iIZhzcgzmEgvzCCvNM/a3s/DDx8Px3u/G81GzMqMp1vdvizv5aXdLqX+QWqq1D8QQgghxKWTwEDUmIYNtTepZrO8KRVCVAOdzrbNgJVCr/dFFRfi+de2hCo9B2NADj7BbjQNCEPpI8ksLiC9MJ0w37AKu08vTOd87nmCvIII8w0jyDuoThVKdMRR/YOCAluAcLH6B2W3b5T6B0IIIYS4kAQGosZERWkXArOzISTE2aMRQlyN3N0DcXfvgDk/AdCqrOoUeOSCR64Jk1cKhgYpNGjgQ1hIFO7ujpcjlN2qMackh5ySHNz17oT6hBLmG1bhrIS6xhICBATY1z+wLGEoLra1NZu1CRs5fwUtlvoHlhBB6h8IIYQQQgIDUWNGj9aubskVKyFETdNHxkBgCCo9DdLT0Zm0efluJeCWAub0IgpanKNUfx539yDc3cNwdw9E99cMgoLSAoqNxXZ9Gs1GUgpSSClIwc/DjzDfMEJ8QnDTu04hgCupf+DtbV9AUeofCCGEEPWPBAaixsgyBCFErfLxQde4CcQ0gsxMVFoaur/m5Jv8AD2AwmjMxmjMRqdzx909FA+PUIpNxeh1eszK7LDrAkMBBTkFnMs9R4hPCKE+ofh7+qNzsUS0ovoHllkIZesfFBdrt9RU7Xt/f9vsAz8/+TdeCCGEqA8kMBA1prQU+vSBV16B22939miEEPWGXg9hYejCwqCwENLS0Af74+FRiNGYiVIGAJQyQkoKJf5ZhIZ0Jtg7mKziLNIL08kvzXfYtVmZSS9MJ70wHW93b0J9Qgn1Da3zhRIduZT6B2Crf5CUJPUPhBBCiPpCAgNRYzw9YdAg7Y2oEEI4ha8vNG2KG+BGKEo1wmTKxWBIx1yQjXeagrRSSDuKW3g4YcEhhPmGUViSTmZJERmFmRjMBoddFxuLSchLIDEvkS6RXfBw83DYzlU4qn+Qn28LEC5W/6BsgCD1D4QQQoirgwQGokZNm+bsEQghhI1Op/urhkEQKjUe0AodWi+fnzuHOaQBZr90Gni5Ex4cRZHZm7TCNHJKchz26efp5/JhgSPu7tqmFJaNKUpLbeFBXp5W88DCYIDMTO0G9vUP/P21voQQQgjheuS/cFGjTpzQ1sh27uzskQghylJKOXsITqeLaQTePpCWZrt8bjSiT03HHzD4GzE3hwY+DWjg04BSUykZhRlkFGXYFUisbKvGgtICvNy9cNe7/n+3np5a7QNL/YPiYvsAobL6B35+tgBB6h8IIYQQl85Z791c/x2MqNNeegmysmDFCmePRAgBoP/rk5rZ7Li4X73i7g6RkRARoc0uSEvT/sH66z9k5abDw8O2J6ynmycRvkEEueVj0oWQUVJCTnEuwd7BDrtXSnEy6yQGs4Fg72DCfMMI8AxwuUKJjuh04OOj3SIj7esf5OVpL2dZBQXarWz9A8sSBh8fqX8ghBBCXIzlvZu+llN3CQxEjYqKgsOHnT0KIYSF+19zw0tLS/Hz83PyaOoInc72Cdayt2B6Oh7RzdDp7RfjGwwZ2sJ9vxxC3dyJahACqhilfMsFAXmleZSaSgHILMoksygTTzdPwnzDCPUJxcvdq9ZOsaaVDQEATCb77Rul/oEQQghxZUpLtfcU7rW8zk8CA1GjoqIgOdnZoxBCWOj1evz8/MjLyyM42PGV8XrNw0P7hysyslwAoJRC5efgmwBmNzAEGTEEpWLwTEWv98HDIxR391D0eq2eQXphernuS02lJOYlkpiXSKBXIKE+oQR7B9f61YKa5ubmuP6BJUSoSv0DSwAh9Q+EEEIIyMvLw8/PT2YYiKtLVBSkp2tvCD2uvppgQrikgIAAkpOTMRqNtZ5SuwwHc+R1Oh3eeX5AMXoTeGWCZyaY/MAQVESJ/3lKdOdxcwvCwyMUHaBDh8LxmsPcklxyS3I5qztLiI+2O4OvR/mZClcDqX8ghBBCXD6j0UhBQQFRTth+Tt4pihpl+Tudmqpt0yWEcL6AgABSUlJISkoiJibmqru6XZN0YWHap9vsbFAKHeBeoN3M7lAaDIaQHEymHMLc3YgMDqHA5EZqYR5FxiKHfZqUibTCNNIK0/Bx9yHMN4wQn5CrcucFqLj+gWX2wcXqH/j72wIEqX8ghBDiamc2m0lKSkKn0xFgWftXiyQwEDXKEhgkJ0tgIERd4e7uTuPGjTl37hwJCQk0bNhQZhpUVdlaB+npWqHEv9YU6o2gN+oBy+VyEyZjBt5AU18v0IeTZTCRXpiDSZkcdl9kLOJc7jlyS3JpHdq6Vk7J2crWP4iOtq9/kJen7bRjYTbbZiaAtlzBEh4EBIDX1VMWQgghhMBoNJKUlERBQQGNGzd2yvs1eYcoalTZwEAIUXf4+flZQ4Pjx4/j5+dHQEAAnp6e6PX6q3JafLULDtYW6efnawvw8/NRoS3Q6YwYjdmYTDmAAgUYS8AjDV+gpV8kBSZ3souzKTAUOOw6wDeA4rKVAusZb2/tFhGh5TGWWQb5+WA02toZjfb1Dzw9tWUL/v7an5KDCSGEcCVKKcxmM6WlpeTl5VFQUIBOp6Nx48ZOK1Yt/5WKGhUerk0XlcBAiLrHz8+PVq1akZeXR15eHsnyi3plvLy0efN/Ucobs7kQc2EeHpmlmLzA5APuAR7o/9p9wcvsRX5pPgWl+RjN2qwDvU6Pb74vWbqscodQSlFgKMDXwxe9rv4tJfHy0mYkFBXZah1Uti21p6e2bMHbW3uu5GBCCCFchZ+fH1FRUQQEBDh1JqgEBqJGeXjAgw9C48bOHokQwhF3d3eCg4MJDg7GbDZjNBqt+/yKavL007Bsme376Gi44w7tFhFBUtJskpPnUurZk18TSwjwbcKg2EEOu1p7ei2TVkzC18OXG1rdwO3tbqdHdI96OyPEYIADB2DzZu22d6/9DISyvLygZ0/o10+7tW8vBRSFEELUPXq9Hnd39zpTY0qnVGXZvKhJubm5BAUFkZOTQ2BgoLOHI4QQoia89RZ89hkkJNjf7+aGuuUWjl6/h+S2pwHo3fsIvr5tK+zqjh/uYOHhhXb3tQ5pTVxsHBO6TiAmsH4Xi8nLg/XrYeVK7XbgQMVtQ0Jg6FAYPly7tWghMxCEEELUD5fyOVQCAyeqL4FBSoq27rRlS2ePRAghnMRohN9+g88/12YblPmvN/WhVhy65zSBgb3o3n2L3dPS0hai03kQEjKSjKJsYj6MwWA2ODyEXqfn+pbX80C3B7i5zc14uUsFwKQkWL3aFiCcP19x26ZNbeHB0KFa/QQhhBDiaiSBgYuoL4HBhAlw6hRs3OjskQghRB0QHw9ffAFffqklqqdOURrtQ2lpCv7+XbQ2ZjNKKbbtaEdR0Qk8PMLxDBzF50dOM/fwRkyq8mUjoT6h3NvlXuJi4+ga1bXmz8kFKAXHjsGqVVp4sHo15ORU3L5LF1uAMHCgVkhRCCGEuBpIYOAi6ktgcPSotk1Whw7OHokQQtQhBgNs3ap9Gr3QL79gevox4oefJ3kkGBrYHvL0bseJ0hb899BRtqecvOhhujfszgOxDzC+83iCfYKrb/wuzmSCXbtssw82brTukFmOhwf07WsLEHr10u4TQgghXJEEBi6ivgQGQgghLtFNN8GvvwJgdteRPkhH4s1msrsC1nX2evS+fVif7s1H+7eRWex4i0aLxWMXc0vbW2p02K6sqAg2bbIFCLt2VbwDQ0AAXHutLUDo0EHqHwghhHAdEhi4iPoSGBw4ANOnw7vvavtiCyGEqITRCDfeCMuXl3uouKk350cVk3w9GINs97u5BZHtFsv3Z7L59tjecs+L9Ivk3DPn8HCTy+JVlZEBa9faAoQTJypuGxWlBQfDhml/NmpUa8MUQgghLpkEBi6ivgQGq1drb6KOH4dWrZw9GiGEcBHHj8OsWTB7NqSn2z2U+dxgjo45RUnJ2XJPc/NswrHipvzn0DH2ZqQA8Hz/53n3uncdHmb9mfWczTnL7e1vx9fDt/rP4ypx5oyt/sHKlZCWVnHbtm1tAcLgwRAsK0GEEELUIRIYuIj6EhgcOgQdO8KGDXDNNc4ejRBCuJiSEvj5Z5gxQ7vk7e4OZ8+ioiLJzl5HcvJc0tJ+wmwuvyTB7NmZuYnNePHaqbQPb++w+xvn38hvx38j0CuQsR3H8kC3B+gd0xudzLGvkNmszZ6zBAjr1kFBBStC9Hro2dM2+6B/f/D2rt3xCiGEEGVJYOAi6ktgkJkJoaHw008wZoyzRyOEEC7s6FHYsgUmTrS722QqIHfuS6iffyD++mRyOwI68PJqSt++p9Dp9A67S8hNoMnHTTBfsOtCh/AOPBD7APd2uZdI/8gaOpmrR2kp/PmnFh6sWqXVsjSZHLf19tbqXFpmIMTGgptbrQ5XCCFEPSeBgYuoL4GBUuDlBR99BI8/7uzRCCHEVWroUFizBoDStpEk3mSG++6nWdf37JodOjQeL68mREXdz392L+Yfq/5RYZfuendubH0jD3R7gJGtRkoNhCrKzYX1620BwoEDFbcNCdF+dJYAoWVLKaAohBCiZklg4CLqS2AA0KQJ3H8//Otfzh6JEEJchTIztSIxWVl2dysfH3Rjx8Kjj0Lv3hQVx/Pnny0A8PVtR2bwh3z85zSWn1yOovK3A5F+kUzoOoG42LgKlzcIx5KStHo+q1bBihVw/nzFbZs2te2+MHQoRETU3jiFEELUDxIYuIj6FBj07g1du2r1u4QQQtSAwkL48Uet1sGWLeUfnz2blJFeHD58H2CiefO3aNpUm11wLuccc/fOZcvxz/gjIQnTRd4Z9G3UlwdiH+D+2PvxdPOs/nO5iiml1bO0FE9cswaysytu36WLLUAYOBD8/WttqEIIIa5SEhi4iPoUGNxyi/Ym6ZdfnD0SIYSoB/bv14KDr7/W5sd7eUFiIoSEUFqaSkrKfMLD78Db27b/X0HBYbZv7wD6IA4VNWT64dMcyCmp8BBNgppw6slTuOllAf6VMJlg1y5bgLBpk1bn0hEPD+jb1xYg9Oql3SeEEEJcCgkMXER9CgweeQR274bt2509EiGEqEcKCuCHH7Sw4J//LP/40qXafPlx4ziZ8m/OnZtq93CJPprVae58cewsmaX2T3110Ku8PuT1Ghx8/VRUpIUGlgBh1y4tcHfE31/bttESIHToIPUPhBBCXJwEBi6iPgUG+/dDfj706+fskQghhLDq0we2bYOAAEruGMz5G0s4H7YOpS68xK0n1dyU7+Iz+DUhl1IznHryFM2Dmzvs9oUVLxAbFcut7W7Fx8On5s/jKpaZqS1bsAQIJ05U3DYqyrZ947Bh0Lhx7Y1TCCGE67iUz6GO91m6Cn322Wc0b94cb29vevTowYYNGypt/+mnn9K+fXt8fHxo27Yt8+bNs3vcYDDwxhtv0LJlS7y9venatSvLli2ryVNwaZ07S1gghBB1yuHDWlgAkJeH1+xfaHnHcgY+34muuybQwKNPmcZmIvSnebJFLssG+fLtoG4E6xJwdM3hVNYp3t38LuP/N56GHzTkb0v/xraEbQ7biosLCdG2JJ4+Xat9EB8PX34J48aVL4iYnAzffgtxcVqx4XbttN2Jfv65XD1MIYQQokrqxQyD77//nvvuu4/PPvuMAQMGMGPGDL744gsOHTpEkyZNyrWfPn06L7zwArNmzaJXr15s27aNhx9+mPnz53PzzTcD8MILL/DNN98wa9Ys2rVrxx9//MGzzz7L5s2b6datW5XGVZ9mGJw4AXPmwAsvQECAs0cjhBACgJ07tVoH8+dryxfKCgyk+H8zSWpxkOTkeZSUnCn3dG/vFkRFTSAycgI+Ptpsg9fWvMYb698o17ZjeEfiYuO4t8u9RPpH1sjp1DdKaVs2WmYfrFtX/sdooddDz562GQj9+4O3d+2OVwghRN0gSxIu0KdPH7p378706dOt97Vv355bb72Vt99+u1z7/v37M2DAAN57z7Z39dNPP82OHTvYuHEjANHR0fzzn//k8ccft7a59dZb8ff355tvvqnSuOpTYLBlC4wdq72ZadbM2aMRQghhJzdXCw1mzIA9e7T7AgK02gf+/ihlJjt7PSkpc0lL+wmTKb9cF0FBg2gYPYk+C17kbM7ZCg/lrndnVOtRxMXGcWPrG/Fwk6p91aW0VJs0YgkQtm7Viio64u2t7bpgCRBiY8FN6lcKIUS9cCmfQ91raUxOU1pays6dO3nxxRft7h8xYgSbN292+JySkhK8L4jdfXx82LZtGwaDAQ8PjwrbWAKFivotKVP6ODc391JPx2X16wdnyl+cEkIIURcEBsKkSfDoo1p12hkzoEED6x5+Op2e4ODBBAcPpnXSWDL1e0hssJKsrFWAdt0hJ2c9vn4deXvY28zeM5tVp1ahKH9Nwmg2suToEpYcXUKEXwT3dr6XuG5xdIroVGune7Xy9IRrrtFuU6ZAXh6sX28LEA4csLUtLoYVK7QbaEsfhg61BQgtW0oBRSGEEPVghkFiYiIxMTFs2rSJ/v37W+9/6623mDt3LkePHi33nJdeeonZs2ezdOlSunfvzs6dO7nxxhtJTU0lMTGRhg0bMn78ePbu3cuiRYto2bIlq1atYvTo0ZhMJrtQoKwpU6bw+uvlK0rXhxkGQgghrgJKQZcu2ifPa66h9IE7SO6fS3LOfAoLj9C9+zYCA3sBcCb7DPP3fEZW6gx+OJPDmcLKu+4Z3ZPn+z/PXR3vqoUTqZ+Sk2H1aluAcO5cxW2bNrUVTxw2rHy9BCGEEK5Lih46oLsgJldKlbvP4pVXXmHkyJH07dsXDw8PRo8ezcSJEwFw+2u+3rRp02jdujXt2rXD09OTyZMnExcXZ33ckX/84x/k5ORYb+cq+5/6KtS6tVa0SQghhIvassV2mXrjRjwfeJom/T6i1/zr6eX/EwEBPa1NmzZoyn2tGjEqIoc5veCd3j3xca94x4QdiTs4lXWqps+gXouKgvHj4auvtFl/R4/CZ5/B7bdrE0rKOnNGK644fjxERkLXrvDcc/D779quR0IIIeqHqz4wCAsLw83NjeTkZLv7U1NTiYx0XHTJx8eHr776isLCQuLj4zl79izNmjUjICCAsLAwAMLDw1m0aBEFBQWcOXOGI0eO4O/vT/PmjreYAvDy8iIwMNDuVp+UlkJCgrNHIYQQ4rJ16ADTpml/WmRloZs2Db9ed6C79lqtjP9fUlK+tX49edAckv+ezKybZ9G/cX+89eBRJrfX6/RM6DqhFk5CgLbcoE0b+NvfYOFCSE/X6h+8/bY2o8DLy779vn3w4YcwahQEB8OgQfDGG7B5MxgMzjkHIYQQNe+qX5IAWtHDHj168Nlnn1nv69ChA6NHj3ZY9NCRa6+9lpiYGObPn+/wcYPBQPv27bnrrrt46623qtRnfSp6CNCrl1ZUadYsZ49ECCHEFVEKNm2CmTPhhx/AshQvJERLhv+q8VNamkpq6gLy8nbRvv1cuy52Hv47mcmfsjZdz8/nCmkWeQO/3fO7w8MdSjvEpKWTiIuN486Od+Lv6V+jpyegqEj7Ea9apS1f2LlT+7E74u8PgwdrSxiGD9fyJKl/IIQQdZfsknABy7aKn3/+Of369WPmzJnMmjWLgwcP0rRpU/7xj3+QkJDAvHnzADh27Bjbtm2jT58+ZGVl8eGHH7JixQp27txJs79K/P/5558kJCQQGxtLQkICU6ZM4fTp0+zatYsGF87rq0B9Cwxuuknb1mnJEmePRAghRLXJzISvv9YKJd5wg3YZ+kKHD0Pz5tYgQSnF9u2dKCw8ZGvj0ZjmjR4lMvJevL2b2j39+eXP8/6W9wHw8/Djro53ERcbxzVNrqlweaGoXpmZsGaNFh6sWgXHj1fcNirKVjxx2DBo3Lj2ximEEOLiJDBw4LPPPuPdd98lKSmJTp068dFHHzFo0CAAJk6cSHx8PGvXrgXg8OHDjB8/nqNHj+Lh4cGQIUOYOnUqbdu2tfa3bt06/va3v3Hq1Cn8/f0ZNWoU77zzDtHR0VUeU30LDB58EA4e1LZ5EkIIcZVRSiu973NBnQKzWStik5MD998PDz+MqXVjjh2bRFra/zCby1dDbNBgMJGREwgPH4PS+dD4o8akFKSUa9cqpBUTu07k/tj7aRTYqKbOTDhw5owWHFhmIKSmVty2TRvb7IPBg7UlDUIIIZxHAgMXUd8Cg3/8A777Dk6fdvZIhBBC1JqVK+G66+zvGzQIHn0U4y3XkZ7/G8nJc8nOXlPuqXq9DyVevXnpz3XsygJzBYfQoWNEyxHExcYxut1ovN29K2gpaoJSWi1My+yDtWuhoMBxW70eevSwBQj9+1snngghhKglEhi4iPoWGHz8Mbz0kvYmQmaQCiFEPXHgALzzDvz0k63WgUVoqDbr4LXXKPbMIiXlW5KT51JUdKxcN/kmL35NLGFZMsRXskVjsHcw4zqN44FuD9C9YXdZsuAEpaVaAUXL9o1//glGo+O23t4wcKBtCUNsLFSy4ZQQQohqIIGBi6hvgcGCBdr2THl5WoEkIYQQ9UhGBsybpxVKPHLEdn/Dhtr8dg8PQKtvkJe3jeTkeaSmLsBozCrX1dkiL5YklLAiBXIr+CAK0DmiM3NvnUu3ht2q+2zEJcjLg/XrbQGCZWdOR0JCYOhQW4DQsqVcZBBCiOomgYGLqG+BwerVcOut2huFJk2cPRohhBBOoRRs2KAFBz/9BM8/D//6V/l2OTmYA7zJyPiNlJR5ZGQsRSn7dGB14Ug+3ruRvNI8h4fy0HuQ+FwiYb5hNXEm4jIlJ2vvCSwBwrlzFbdt2tRWPHHoUKhgR2whhBCXQAIDF1HfAgOl5CqBEEKIMjIytD9DQ+3vNxqhWTNo1QoeeQTGjKFUl0dq6nekpMwjL287np7R9Ot3liJjCQsPLWT2ntmkZK5BDxzI1boZ034MP931k8NDG81G3PXuNXZqomqUghMnbOHB6tWQnV1x+y5dbAHCoEEyY1EIIS6HBAYuor4FBkIIIUSVLF6sTUmzsNQ6eOQRaNuWgoLDFBfHExo60u5pW3dcQ3H+JlJK3Jm8y8icMUu5sc2NDg8x+bfJ7EjcQVxsHGM7jSXIO6gGT0hUlckEu3fbAoSNG8uXvrBwd4d+/WwBQu/e1pUtQgghKiGBgYuob4GBUtC9Ozz7LNx3n7NHI4QQos5avBheeAGOHi3/2LXXwqOPwp13ap8Y/1JcfJ6tW5sACm/vlhRFzGRgs0HWWQRKKWsBxGJjMdEfRJNVrNVH8Hb35vb2txMXG8fQ5kPR6/Q1foqiaoqKYPNmW4Cwc6f2fsIRf39t20ZLgNCxo8xsFEIIRyQwcBH1LTAAbZeEG27QphEKIYQQFbLUOpgxQ6t1UFpqe6xFCzh+XNuj7y8mUyFpaQtJSZlHUNC1NGv2sl13e/YMw8MjnKio+1mVlM3dC8c7PGzToKbc3/V+JsZOpHlw8xo5NXH5MjO1bRstAcLx4xW3jYqyFU8cNgwaN661YQohRJ0mgYGLqI+BgRBCCHHJMjJg7lytUOLRo/D22/Dii+Xb/VUsp+xsAoDCwuNs29bG9r3Zm6UJxSxPgZMFFR92cLPBxMXGMab9GPw8/arzjEQ1OXsWVq2yBQipqRW3bdNGCw+GD9dmIgQH19owhRCiTpHAwEXUx8Dg+HHIz4dussOVEEKIS6WUtj9fhw4QHm7/WEkJxMbCTTfBww9rnw7/kpHxK4cP34/RmFGuy5P58EcKrEyBLIPjwwZ4BnB3x7uJ6xZHv0b97MIIUXcope3EZAkQ1q6FggoCIb0eevSwBQj9+4O3d60OVwghnEYCAxdRHwODCRPg9GltlqkQQghRbRYsgPFllhkMGaIVSbztNvDywmwuJTPzd5KT55GR8QtK2acDZgXbMmF5CmzKgFKz48N0i+rGjkd2SJ0DF1BaCtu22QKErVu1DTgc8faGa66xBQixseDmVqvDFUKIWnMpn0NlPyFRqyIitP+whRBCiGoVHw+enrZaB2vWaLewMJg4Ef0jjxDWejRhYaMxGDJITf2BlJR55OZq/ynpddA3VLsVGGFNmhYeHMiBsldWOkZ0lLDARXh6aiHANdfAa69BXp42QcUSIOzfb2tbXGxb1gAQEqJlTpYAoWVLKaAohKifZIaBE9XHGQbvvQdvvln5HstCCCHEZUlPt9U6OHbM/rFOnWDfvnKf+goLj5GcPI+UlK8pKTlbrsvEIliRooUHicWwasIqhjYf6vDw53PP0yiwUbWdjqhZycmwerUWIKxYAefOVdy2aVNbAcWhQyEysvbGKYQQ1U2WJLiI+hgYzJunbaVdVCRrBYUQQtQQpWDdOi04WLhQm3UwbRo8+WQlTzGTnb2elJR5pKX9iMmUb/d4gUnPUwcbc+yJUw5nGOSX5tPwg4a0DG5JXGwc93S5hzDfsGo/NVEzlIITJ7QZBqtWaUFCVlbF7bt0se2+MGiQtqWjEEK4CgkMXER9DAyWLYORI+HMGWjSxNmjEUIIcdVLS9NmHTz4YPmy+Pn5MHYs3HuvtdYBaFs0pqcvIjl5LllZKwEzDRs+jH/D14gJjLE+PS9vJ35+XdDrPZizZw5xi+Osj3noPbi57c08EPsA17e6Hne9rAJ1JSYT7N5tCxA2bNDqajri7g79+tlmIPTuDR4etTteIYS4FBIYuIj6GBjs3g3du2tFiHr1cvZohBBC1GtffKHtqADWWgc88gi0bm1tUlKSQErKfIKDhxEQ0N16v8GQxebNUbi7NyAmZjL3r13J+jPrHR6moX9D7utyH3Hd4mgX1q4mz0jUkKIi2LzZFiDs2KHNSnDE31/bttESIHTsKPUPhBB1iwQGLqI+BgaJiRATA0uXwo03Ons0Qggh6rW77oIffyx//9Chth0WPD0dPjUxcQbHjk0CIKLh44z6fSlncs5c9JD9GvUjLjaOuzvdTaBX/fi//2qUmalt22gJEC4smVFWZKQWHlgCBJlhKYRwNgkMXER9DAwMBu2915dfwgMPOHs0Qggh6jWltE99lloHBvutFrnhBvj9d4dPzcnZxPnz00hPX0z37n/i59+F1adXM3vPbNacWMhTrUpYkQIb06HEwRaNPu4+jOkwhgdiH+DaZtfKzgsu7uxZ2+4Lq1ZBSkrFbVu1stU/GDIEQkNrb5xCCAESGLiM+hgYADz6qHZRZ9gwZ49ECCGE+Iul1sHMmXD8uHbfF19otQ8qYTBk4+4ehK7MnPNjp98k8czLABQaYV0a/JEC+y7YotHi//r/H1Ovm1pdZyKcTCk4eNAWHqxdq5XLcESn05ZqWmYfDBgAvr61OlwhRD0kgYGLqK+BgRBCCFFnWWYdzJkDn35avvx9VhZMnQoPPaRdKnZg//6bychYWu7+5GLbFo3ni2z3b3lwC30b9a2+cxB1isGg1W6yzEDYurX8ZBYLT08tNLAECD16aEUVhRCiOklg4CLqa2CQkACFhXY1pYQQQgjX8Mkn8NRT2tfDhmm1Dm691a7WgVJmcnI2kZIyj9TUHzCZcst1czAXlifDeXNrdjx61G6GgkVOcQ6H0w/TJ6aPw8eFa8rP13ZdsAQIe/dW3DYwUFu2YAkQ2rWTAopCiCsngYGLqK+Bwdix2szPVaucPRIhhBDiEnXvrm35U1Z4OMTFaTsuXDDrwGQqIj19MSkp88jM/AOwL2hgxo2IsNFERU0gJGQker0teJi+fTqP/fYY7cPaMzF2Ivd1uY+GAQ1r6syEk6SlwZo1tiUMp05V3LZhQ1v9g2HDoFGj2hunEOLqIYGBi6ivgYGlknCbNs4dhxBCCHHJUlNttQ5OnCj/+PPPw7vvOnxqSUkSqanzSU6eS0HB/nKPe3iEERExjsjICQQE9KDPF33Ynrjd+ribzo2RrUcSFxvHTW1uwtPN8Q4OwrWdPm2bfbB6tRYoVKRtW1uAMHgwBAfX2jCFEC5MAgMXUV8DAyGEEMLlmc1arYMZM+Dnn22L0ufPh3HjLvr0/Py9JCfPIyXlWwyG8iX1vUPupd/P31T4/DDfMO7pfA9xsXF0jep6uWch6jizGfbvtwUI69dDQYHjtnq9VvPAEiAMGADe3rU7XiGEa5DAwEXU18Bg507twsynn0ohHyGEEFeB1FStSOLPP2shgpeX/eMpKdonvdGj7WodAJjNRrKylpOcPI/09EUoVQKAKfzfPLpqNiezTgLgrtNuxQ62aOwW1Y242DjGdx5PqK/s0Xc1Ky2FP/+0BQh//glGo+O23t5aaGAJELp3Bze32h2vEKJuksDARdTXwGDJEu09U2KithZPCCGEuKq9/Ta89BJERNhqHbRsWa6ZwZBNWtpPZGT8QseOC9Hp3NhwdgOz98wmOXkBT7YqYX0afHcO4gvLH8bTzZPRbUcTFxvHiJYjcNPLp8OrXV6elkVZAoT95Ve6WDVooBVQtAQIbdpIAUUh6isJDFxEfQ0Mtm6Ffv20qsBdujh7NEIIIUQNMpu1QoinT9vfP3y4tsOCg1kHjuzaM4Lc7BUAPL0H9uZU3n7Po3tkqUI9lJKi1T1YuVK7nT1bcdtGjWzFE4cNg+jo2hunEMK5LuVzqL6WxiSEVUSE9mdqqnPHIYQQQtQ4nQ6++ALuugs8PGz3r1yp3de4McybV2kXSin8fVvh5haEl1dTfrj3CC9d8xIxATEA9A2BW6Mh8K9lft2iuklYUE9FRmolNL78EuLj4fhx+PxzuOMOCAmxb3v+vFa/c8IEiImBDh3gySdh8WLIuUggJYSoP2SGgRPV1xkGeXnavsJVrAslhBBCXB0stQ5mzoSTJ233L14Mt9xy0aebTMUUF5/Ez6+j9r3ZxIpTK8g4PYEYjzQMZtiaCU2iH+K+vp/abdFosezEMnzcfRjUdBA6mY9er5jNsGePbfnChg1QVOS4rV4PvXrZli/071++NIcQwnXJkgQXUV8DA6XA1xfeeQeeesrZoxFCCCFqmdkMa9ZowcH27dp+wxdWAU5Nhfx8aNGi0q6Ki8+zdWvjcve7u4cQETGOqKgJBAT0QqfToZSi8/TOHEw7SIvgFkzsOpH7Y++nSVCT6jw74SJKSmDLFluAsH07mEyO2/r4wMCBWngwfDjExmqhghDCNUlg4CLqa2AA0LQp3HsvvPmms0cihBBCOFFpqeMaBq+8Av/+N1x3HTz6qDYDoeyShjLy8/eTkjKPlJRvKC1NLve4j09boqImkKw603eu/UwGHTqGtRhGXGwct7W7DR8Pn2o5LeF6cnJg3TpbgHDoUMVtQ0Jg6FBbgNCypRRQFMKVSGDgIupzYNCzp7a9z8yZzh6JEEIIUccYDFqynpRkuy8yEh54AB56qMJZB2azkezsVX9t0fgzZrP9fHOlYHc2LE+B9elQdMHV5ECvQMZ2HEtctzj6xPSRJQv1XFKSFh5YAoTz5ytu26SJbfnCsGHaX1chRN0lgYGLqM+BwahR2gWVRYucPRIhhBCijikshP/8R0vVT50q//iIETBlirblUAWMxlzS0haSnDyXnJx15R4vMsGGdC082J0F5gsebxfWjoldJ3Jf1/uIDpDy+fWdUloBRUt4sGYNZGVV3L5TJ1uAcO21EBBQe2MVQlycBAYuoj4HBtu3a8s1u3Vz9kiEEEKIOsps1j6hzZypJexGo+2xFSu0T2RVUFQUT0rKN6SkzKOo6Hi5x9NK4B/74WRB+efqdXpuaHUDcbFxjGk/RmYdCECrdbB7ty1A2LgRiosdt3V3h969bQFC375V2klUCFGDJDBwEfU5MBBCCCHEJUhJgdmzYdYsbbH4sWPlq87l5GhVhSuodaCUIjf3T1JS5pGa+h1Go3aJuMTsxq2bTBRfOM2gjAGNB7DxgY3VdTbiKlNcDJs32wKEHTu0vMsRX18YNMgWIHTpIgUUhahtEhi4iPocGOzYAb/8Aq+/7uyRCCGEEC7EbNYWkzdxsLPBs89qexY/8AA8/DA0b15JNyVkZCwlOXkenp5R+Ea9zLy985izdw4nMk/wYlvwd9eWLGzKgM9vmsVD3R+qwRMTV5PsbFi71hYgHDlScduwMK2AoiVAuMjGIEKIaiCBgYuoz4HBggXwz3/C0aMVXggRQgghRFUVF0NMDGRm2u4bMQIeeaTSHRZAm3lgWWqglGJD/B+Uxt+Eu85EjgHu2+7N+edSCPQq/17FrMw89utjjGo9ipGtRuLhJv+pi/ISEuwLKCYmVty2eXPb7gtDh0J4eO2NU4j6QgIDF1GfAwMhhBBCVKOkJHjiCVi82L7WAdh2WHj8cS1UuIjc3B0cOHArpaUJ5HoO47D5Ol645gXr4ykp3xEY2Acfn+asP7Oea+dcC0CEXwT3dr6XuG5xdIroVK2nJ64eSmkXjFau1AKENWu01TQV6dLFNvtg0CDw96+9sQpxtZLAwEVIYCCEEEKIapWcbKt1cPq0/WObN1e6s0JZSpnIylqDt3djfH3bWu8vLU1ny5aGKGUkKGggvyeZeGvXZgou2KKxZ3RP4mLjGNtpLCE+IVd6VuIqZjTCzp222QebNkFpqeO27u5a0URLgNCnj8xUFeJySGDgIupzYJCUpO2Q8PXXcN11zh6NEEIIcZUxm7VPXzNnarMO2reHvXu1gokXtruEinPnz/+XEyeesLuv1Ayb/tqicXsWmMq8s/R08+TWdrcSFxvHdS2uw03vdiVnJeqBwkItNLAECLt2abMSHPH317ZttCxh6NSp/F9xIUR5Ehi4iPocGBQVaVVy582D++5z9miEEEKIq1hysraIvEeP8o899pg2E+GRR+Cmmy56uba4+Lx1i8bCwsPlHs8shVWpWnhwIt/+seiAaCZ0mUBctzjahLa5kjMS9UhmprZswRIgHC+/M6hVRIQWHlgChKZNa2+cQrgSCQxcRH0ODAACAmDKFHjuOWePRAghhKiH8vIgOhry//pk37ChVuvgoYegWbNKn6qUIi9vJykpX5OaOh+DIb1cm1P5sCIVVqZA+gVTzPs37s/LA19mZOuR1XQyor44e9ZWQHHVKi0Pq0jLlrblC0OGaDsyCCEkMHAZ9T0waNkS7rgDpk519kiEEEKIemjPHrjtNoiPt79fp7PtsHDzzReddWA2G8jMXEZKyjzS05eglH06YFawM0ubdbAxHYrN2v3f3v4t4zuPr77zEfWOUnDokG32wdq1Wg7miE4HsbG2AGHgQG22qxD1kQQGLqK+Bwb9+mlLKr/6ytkjEUIIIeopsxlWrLDVOjBdUL1wzx7o2rXK3RkMWaSl/Uhy8jxyczeVe7zIpC1Z+OJMIEnPJePj4XOFJyCEjdEI27fbAoTNm8FgcNzW01N7L2oJEHr10ooqClEfSGDgIup7YHDLLdr7lKVLnT0SIYQQQpCUBHPm2HZY6NMHtm697O6Kik6SkvINFbrpPQAAQ+NJREFUycnzKC4+Zb1/fRok+jzK5zd97vB5f5z4g3c2vUNcbBxj2o/Bz9Pvsscg6reCAti40RYg7NlTcQHFwECtgKIlQOjQQQooiquXBAYuor4HBg89BPv2wbZtzh6JEEIIIawsOyy4u8PQoeUff/ZZ7fLsww9r6wsvQilFbu5mkpPnkZr2A7n+k2gecx8dwjv8dbgS9u0bRVjYaCIixnHfksn8cPAHAAI8A7ir411MjJ3IgMYD0MknOHEF0tPtCyiePFlx26goW/HEYcOgcePaG6cQNU0CAxdR3wODl16C+fPLL50UQgghRB2VmakVSiwp0b4fPlyrdTB6tBYiXITJVIxO54Zeb6uLkJb2Pw4eHANAg9A76bN4MaWm0nLPbR3SmomxE5nQdQKNAhtVz/mIei0+3r6AYmpqxW3btLEFCIMHQ0hIbY1SiOp3KZ9Dq77xrhDVLCJCW2smhBBCCBexebN9nYOVK+Guu6BRI3jhBThxotKnu7l524UFADk5m61f7yuIsgsLvPXQ6a/3ssczj/PP1f+kyUdNuP6b6/nuwHcUG4uv/JxEvdWsGTz4oHYBKzlZm/n64Ydw443gd8FKmGPHYPp0GDNG222hVy948UWtBEhRkVOGL0StkBkGTlTfZxgoJWvDhBBCCJeTkmKrdXDhnG6dDs6dg5iYS+qyoOAQaWk/Ehnzd3459iuz98xm+cnlDA0388/2kFAEK1K0W2KZjKCBdwPGdRrHxNiJ9IruJUsWRLUxGLRlsytXarMPtmyp+EKXlxf0729bvtCjhxRQFHWbLElwEfU9MBBCCCGECzObtQXhM2fCzz9rn7CGDtU+XVWDhNwEtu8eTANlP2thf44WHKxJg/wyH+A6hHdgydgltAy5eF0FIS5Vfj5s2GALEPburbhtUBAMGWJbwtC2rVwkE3WLBAYuor4HBidOaNs7f/stdO/u7NEIIYQQ4rKlpsLcudCpE4wcWf7x11/XPjXddpt2ObaKUlLmk5w8h6yslYD9W9ZSM2zOgOUpsC0TQnzCSXg2AQ83D8edCVGNUlO1vGzlSu1WWU2u6Gjb7INhwy55Ao4Q1U4CAxdR3wODjAx480147DFo1crZoxFCCCFEjUhK0krMm0wQGgoTJ2o7LLRtW+UuiovPk5o6n6TkORQVHi73eHYpZOhjubP3LAICepRbmlBoKMTXw/dKz0SICp06ZZt9sGqV9j63Iu3a2QKEwYOhQYPaGqUQGgkMXER9DwyEEEIIUQ/85z/w5JPl77/2Wm2HhdtvB2/vKnWllCI/fw8pKfNITP4GszG9XBtf3/ZERk4gMvIevL21vfBuXnAzCbkJTIydyPjO4wnzDbuiUxKiMmazVkDRsn3j+vVQWOi4rV4PPXvaAoT+/av86yDEZZPAwEVIYAA7d2rrvGSGgRBCCHGVUkr7xDRzJvz0E5ResGVieDicPl2+LP1FmM0GsrKWk5w8j7T0RaAu3IpRR4MGQwkIf4i2X96LSWm7O3joPbil7S3ExcZxfavrcddLdTpRs0pLYetWW4Dw55/2m42U5e0N11xjCxC6dQM3t9odr7j6SWDgIiQw0GYj3nQTfPCBs0cihBBCiBqXng7z5mnhwdGj2n033QS//HJF3RoM2aSl/URKyjxycjbYPXba7S4eWP2Dw+dF+UdxX5f7iIuNo314+ysagxBVlZurZWiWAOHAgYrbBgdrBRQtAULr1lJAUVw5CQxchAQGMHCgtgfu1187eyRCCCGEqDVKaSXnZ86Ee+5xXChx+nRt2UKHDpfUdVHRaVJSviElZR4lJQm8e7Yvv55cY328UyAMCIM/kiG+zDTx3jG9iYuNY2ynsTTwbnCZJybEpUtOhtWrbQHC2bMVt23c2Lb7wtCh0LBh7Y1TXD0kMHAREhhoyxYLC2HZMmePRAghhBB1xunT0LKlFixcc41W6+COO8DHp8pdKKUoKjqJl3dzVp1exew9s/n58M883aqEG6K0Ni/s13ZYKMvb3Zvb2t3GxNiJDGs+DDe9zAcXtUcpOHnSVkBx9WrIzKy4fceOtgDh2muhnn6kEJdIAgMXIYEBTJoE27bBrl3OHokQQggh6oyXX9a2UiqrQQOYMEELDzp2vKxuMwtS2b29OW4Ukm+E2zeDocw74VBPyDNqWzYC9GvUj80Pbr68cxCiGpjNsGePLUDYsAGKihy3dXOD3r1t2zf263dJu5iKekQCAxchgQG8+ip89RWcP+/skQghhBCizsjMhG++gRkz4NCh8o8PHqx9grqManClpemkpn5HUu5pfjiv5+t9X5NSkALAax2gZzCsS4PlKXBT53/w5rC3rvBkhKg+JSWwZYstQNi2TQsVHPHx0Zb/WuofxMZquzIIIYGBi5DAQNtp6e9/h+JiKeAihBBCiAsopX06mjEDfvhBe8MAcPfd8N131XIIo9nIshPLmL93BhNDl+JZ5gOVm0cjGkXHERl5H76+re2etzd5L50jO6PXyScw4Tw5ObBunS1AcJSvWYSG2hdQbNlS3n/XVxIYuAgJDOD772HsWMjO1rZXFEIIIYRwKCvLNuvgk0+0im8X+uUX7ZOQr+8ld19SksDh48+Tkf4/3Cgp93hgYF8iIycQEXE3iQV5NJ/WnObBzbm/6/3c3/V+mjZoejlnJUS1SkzU6h5YAoTKZvE2bWoLD4YOhcjI2huncC4JDFyEBAawZo32D9SxY9o2MUIIIYQQlbK8db3w0uihQ1ptg6AguPderdZBly6X3L3JVEh6+iISEr8iN2cNYD/fW6fzJFW1ZNqBw/yZCUYFOnQMbT6UuNg4bmt/G74elx5YCFHdlNLeY1t2X1izRrtIV5HOnW0BwqBBEBBQa0MVtUwCAxchgQHEx8Prr8OUKVrKKYQQQghxWZ55Bj7+2P6+Pn204ODuu8HP75K7LClJIjV1PsnJcyko2F/u8RwDrE7V6h0cydPuC/QK5O6OdxMXG0ffRn3RyZxvUUeYTFqhcUuAsHGjVhPBEXd37dfHEiD06QOenrU7XlFzJDBwERIYCCGEEEJUkx07YPp0rbZBYaH9Y4GBWnDw3nuX3X1+/l6Sk+eRlPwNJmNqucfPFmrBwZJEbacFgLahbZkYO5EJXScQHRB92ccWoiYUFcHmzbYAYefOigso+vlpsw4sAULnzlJA0ZVJYOAiJDDQHD+uzR6MiHD2SIQQQgjh8nJyYP58rdbB3r22+x94AL788oq7N5uNZGWt4NDpjynJW4W7zmR9zKTgzi2QZbB/jl6n5/qW1xMXG8ctbW/By132uhN1T1YWrF1rCxCOHq24bXi4tqzYEiA0b15rwxTVQAIDFyGBgSYkBF54QbsJIYQQQlQLpbRZBzNnwoIF2qegPn3Kt7HUPrgMRmMuickLOBz/CV7GQ2zJgJcO2Le5LgKyDbAzS6uG8Pawt3nxmhcv75yEqEXnz2u/NpYAISmp4rYtWmjBwfDh2k4M4eG1N05x6SQwcBESGGi2bYPGjaFhQ2ePRAghhBBXpbw88PcvXyhxxw7o1Qt69tSWLIwde9mV3oqLz5CQfZzvjv3JnL1zOJF5Ajcd/NgXgj0hsQju3w6nnjpL46DG1XBSQtQepeDIEdvuC2vWQG5uxe1jY20BwsCBl1VCRNQgCQxchAQGQgghhBBO9MgjMGuW7Xt/fxg/Xru/R4/L7lYpxaZzm1i+/3WG+q0EYE0qbCm5juX3Lbe2M5kKcHPTPknFZ8ezN3kvo1qPwsPN47KPLURtMBq1mgeWAGHTJigtddzWwwP69bMFCL16afcJ55HAwEVIYKD5+mttzdSTTzp7JEIIIYSoV+bNg08+0T75XKh7d3j6abjvvsvu3mwuJTHlf+w79T7/O1/K8I4vMbbTWABMpiK2bGlEYGBvIiPv45P9u3hr0wdE+EVwb+d7mRg7kc6RnS/72ELUpsJCLTSwBAi7dtl2QL2Qvz9ce62t/kGnTuUn/4iaJYGBi5DAQPPgg3DgAPz5p7NHIoQQQoh6aedObabBt99Cfr7t/smT4T//qZFDpqZ+z6FDY63fF5l0rE1V/JEC+3JAAT0a9iAuNo5xnccR4hNSI+MQoiZkZGgFFC0BwvHjFbeNjLQvoChbrdc8CQxchAQGmhdegJ9+gpMnnT0SIYQQQtRr+fnatowzZ8L27bBvn7Z/XFlKaTURrvC9W0rKt5w69U9KSs6Ueyy5GFakaLdzReDp5snotqOJi41jRMsRuOndrujYQtS2s2dtxRNXrYKUlIrbtmplCw+GDIHQ0NobZ30hgYGLkMBA8/778K9/absgCSGEEELUCYcPQ/v25e9fvx5GjoRx47RaB716XfZ8aqXM5ORsYNfxqZTmLcdbbyrX5nAuLE+B1amQa4TogGgmdJnAxNiJtA1re1nHFcKZlIKDB20Bwrp1WgbniE4H3brZAoRrrgFf39od79VIAgMXIYGBZu5cmDgRiovBS7YlFkIIIURddu+92tIFi65dteDgnnsgKOiyuzWZCtl/+nOOn/2MEN1J3C7IIAxm2JqpzTrYmgEGBf0a9SMuNo7xncfj5yll6IVrMhi0CT2WAGHLFu0+Rzw9oX9/W4DQsye4u9fueK8GEhi4CAkMNL/+CjfdBAkJEB3t7NEIIYQQQlTiuefgiy/K7ynn6wt33w2PPaZ9irkChcXn2Xj4DXIyfiTcPbvc47kGbdeFP1LgeIE7Cc8mEOEXcUXHFKKuKCiADRtsAcKePRW3DQyEwYNtAUL79lJAsSokMHAREhhotm2DPn20fwy6dnX2aIQQQgghLqKgAH74Qat1sHWr/WP/938wdWq1HSohYyNbDr+OR/E6gtztL7vuy4HVRaNZNHZRtR1PiLomLQ3WrLEFCKdOVdy2YUMtOLDcGjeuvXG6EgkMXIQEBppTp6BlS+0fgGHDnD0aIYQQQohLsG+ftsPC119rBZmOH9eqtpVlebt9BZc+lTKx69QXHI6fRhiH8XaDD47BQ4MWMbrd6L/aKFJSviUs7Gbc3YOYvn06vh6+3NHhDlmyIK4ap09r4YHllpZWcds2bewLKAYH19446zIJDFyEBAYaS6HhBQtg7NiLtxdCCCGEqHMKC7XqbSNHln/s99+1mQePPKLVQLjCTy1FJZmsOfQ6C88k8fnN3+Lh5gFAdvZG9uwZiF7vTaMmL9P7h4/IKMrA39OfuzrcRVy3OAY0HoBO5myLq4TZrG3Pbtl9Yd06bQKQIzoddO9uX0DRx6d2x1tXSGDgIiQw0CgFixdD795Sw0AIIYQQV6HbboNFi7Svvb3hrru08KB//2pdcH306CMkJc0CID/wKW7+ZVq5Nq1CWjGx60QmdJ1A4yCZry2uLqWl8OefttkHW7eC0ei4racnDBhgW75QnwooSmDgIiQwEEIIIYS4yhmN2iXNdevKP9axoxYc3HdftcyVzsvbRXLyHDIyljI1vi2Lji6zPtYrGCa10LZoXJUKGaU6rmt5HRO7TuTWdrfi41FPL7WKq1p+vrYTqiVA2Lu34raWAorDhmm/sldzAUUJDFyEBAY2c+dqv6S33ebskQghhBBC1ICDB7VaB3PnQna2/WNvvAGvvFJth1JK8fORn/li1xf8cfIPzMrMP9vB8EjtcbOCnVlaeLAxHbw8ghjXaRwTYyfSO6a3LFkQV63UVK2AomUJw+nTFbdt2BCGDrUtYbiaCihKYOAiJDCwuf12aNQIPvnE2SMRQgghhKhBRUWwcCHMmAEbN4JeD2fPQkxMjRwuITeBb/Z+TWDOv2jvX1h+OCZYlwYrUmBPNrQL78CkHpN4os8TNTIeIeqSU6dssw9Wr668gGLr1vYFFENCam+c1U0CAxchgYEQQgghRD126BBs3gwPPVT+scWL4ccftSULAwde8dxopRRbTv/EzuPvEK52E+Vd/iNAajGsSAX3wJFMv/W3KzqeEK7GbIb9+20BQlUKKFrqH1xzDfj61u54r4QEBi5CAgMhhBBCCOHQiBGwYoX2dbt2WnAwYQKEhl5x1wWlBfy2fyrnkr6kjXci/g4KvQUE9CQycgIREWPx9Ay/4mMK4WpKS2HbNi08WLny4gUU+/e3BQi9etXtAooSGLgICQxsPv0Upk/XtkURQgghhKjXcnOhZUtIT7e/39MT7rhDCw8GDaqWimynM4+ybN+rlOQupXNAIW4XdKnTuRMSMpLIyAn4Bg1l1PxbuaPDHYzvPJ4w37ArPr4QriI/HzZssNU/uFgBxWuvtRVQ7NChbhVQlMDARUhgYDN9Ojz5pJbk1aVfJiGEEEIIpyguhp9/hpkzYe3a8o9//jk8+mi1Hc6szKw/tYSC7F+IVHvJz99Zrk22z1huW/YdAB56D25uezNxsXHc0OoG3PV1+HKqEDUgLU2re2BZwnDqVMVto6LsCyg2aVJ743REAgMXIYGBzY8/alsSZ2VBgwbOHo0QQgghRB1y9Ki2w8KcOZCRoc00SEiAsJq7wl9QcIiUlK9JTv6a0tIEQMe0hP4sOrHJ2ibMEzz1YHKL5L4u9xHXLY4O4R1qbExC1GWnT9vCg1WrKi+g2KqVfQHFalhpdEkkMHAREhjYrFmjpW7Hj2u/QEIIIYQQ4gIlJdqsg1On4KWXyj++ZAkcOQITJ0JERLUcUikT2dlrOZu6km7fTUVh++jwaAsY2xj258CHxyC+EHpF9yIuNo6xncYS7BNcLWMQwtWYzdpSa0v9g4sVUOzWzVb/YODAmi+gKIGBi5DAwObAAejcWSsU3K+fs0cjhBBCCOGC/r+9+46Oqs7/P/6a9ISEQBKYJJQQkCZVihTBhrBgQUEULKEooKs/1EVlZdldu+vqd5VdUFdQSQBBFLGwKM0GS1FaIECohpoGgRAS0md+f9wlyTChSWZuJnk+zslJ7r2fyX2do2eY+87n8/706SOtWSP5+kpDhhi9Dm66ydi68QqV2kr1fcr3mpU4S1/s+kJFJQVa0FOK8JeKbdKwdVJOhYZw/t7+uqvNXRrTeYxuaX6LvL28rzgD4KmKi40Gimf7H6xfb5yrjJ+f8Tx0tv+BKxooUjDwEBQMyqWnS1FRRmH8jjvMTgMAAOBhDhyQYmOdz7doIY0bZ8w6sFqr5FbZBdn6NGmO9h18U20DD+tovvSXHY5jxjST6nhLyzOkXEuUpg2arqFth1bJ/QFPd7aB4tnlC4mJ5x8bEuLYQLFduyvv+UbBwENQMChXXGxU0z76SBozxuw0AAAAHmjfPumDD6RZs6TMTMdrvr7GX2YGDqzSW+7I3KG5W2cqftsCpeemS5J8LNLCXlKorzHmQJ7UvMkjurbVFAUENKnS+wM1wbFjxhLts0sYLtRA0Wp1bKAYE3P596Ng4CEoGDiqX1+aPFmaNMnsJAAAAB6sqMgoDrz/vvH0IRmLolNTpdBQl9yyxFaiZfuWaVbiLO0++qX+0alUfk4rISyqV+9mRUaOVETEUPn4BKuotEg+Xj7yslz5sgmgpricBootWjg2ULyUXqgUDDwEBQNHbdoYOyW89JLZSQAAAGqI/fuNWQd2u/T6687Xly0zvvfvXyW9DiQp60yWFiR9qO0H3lFr/0PqVM95jJdXkBo0GKpNOeGasvZzjew0WqM7j1aLsBZVkgGoKSo2UPzuO6OBYm5u5WMtFqlzZ8cGinXqOI+jYOAhKBg4stmq7N8pAAAAXIzdbrRn37pVatbM6HUwZozRWKqKbMvYpvo+BSo9vVTp6bNVULDfacyxQmllhtHvoElEX43pPEbDrh6mEP+QKssB1BRnGyieLSCsW3f+Boq+vs4NFH19KRh4DAoGAAAAMM2GDdK11zqe8/aWBg+WHnmkSmcdSJLdbldOzjplZMxRZuYnKinJdhqz57RROFh3MlC3tLxHYzqP0fUx17NkATiPvDyjgeLZHRgu1EAxONhooNinT44mT6ZgUO1RMHD02mvG/+Cffmp2EgAAgFqguFhavFiaMUNavtyYcVBRs2ZGL4QOHar81qWlBUpY96jS0hPUI0zyOaceUGqXRqyXjhdJsfViNarTKI3sNFKx9SvZCQJAmYoNFL/7zliV5CxHEgWDao+CgaPPPjM6gv7xj2YnAQAAqGVSUqQPPzS2rEpLM87Vq2c0SgwMdNltE9MT9fGWd5SWMU/XhZ1R2/99JE7OkR7b4jg2wk/qEH2jRnceo7vb3q06fpUszgbg4MABxwaKxgYqFAw8AgUDAAAAVCvFxdKSJcasg1atpKlTncesWyc1bSo1alRlty0qLdJ/9vxHX2+fJr/8n5SSZ9fKc3aGnN1d8vUylix8llpHyY/vUuO6jassA1DT2e1GA8UlS1iS4BEoGDjKzpZ27JB69JB8fMxOAwAAUMvZ7Ubb9XPPtW5tzHO+/XZp/Hhp4ECj90EVSc9N18fbPtasxFnacWyHJKltiPRuF+P6lmwpPr2jEh9JlOXcfAAu6nKeQ+kegmpjzRqpT58L7zMKAAAAN6nsYfynn6S9e43trb7+2igaxMZKL74oHT5cJbeNDI7U072fVtLvk7Rh3AY93v1x1fEL0foso7fBigxpdKfRZcUCu92mXbseUmbmQpWWFmj38d06U3ymSrIAtR0zDEzEDANHP/8s9exp7OzTsaPZaQAAAOAkNdVYrvDBB9LRo47XvLykW281rlfh1oySVFBSoMW7F+vTbf/W8pT/au+Th9WwTkNJ0smT32vr1n6SJB+felp1zEtfHc1Xx6b3a8w1D6lX417MRAAqYFtFD0HBwNGvv0otWhjNOG6+2ew0AAAAOK+SEunbb43iwDffGDMOJMlqNWYa+Pq67NbZBdmqF1Cv7Hjv3id09Og0p3FH86WVGdKewhjddvV4jew0kp4HgFiSAA8VEWF8Z0kCAABANefjI91xh7Et44ED0gsvSI0bS2PGVF4s2LPHKDJUgYrFAkm66qq31bHjClmtcSqxl9+7UaA0qpn0auuDqntiiv7fJ01058c365Ptnyi/OL9KsgA1HTMMTMQMA0d2u+TvL739tvT442anAQAAwGUpLZXy86XgYOfzsbFGweChh6SHHzaOXWD0ouFKzfhU/a1Sl/qS9zkrEYpt0rosac2JIF3V6AGN7Pywrm10LUsWUKuwJMFDUDBw1qiRNG6cUaQGAABADfDNN9Jtt5UfWyxS//7GDguDB1f58oUjOUc0Z+scfbljpmJ9UzTAKjUPdh53qlj6MVPaXdhMN7Z6VA92ilN0SHSVZgGqI5YkwGNFREjHj5udAgAAAFWmYUNpyJDyrRftdmn5cmnYMKlJE+m556S8vCq7XeO6jTW572Stf2S/nhiwRuvtY/Xk1iB9eljKKiwfF+or3dlImtT8gGwZz+mez+6psgxATUHBANVKgwYUDAAAAGqUbt2kRYuMZoivvuq4HCEjQ5o/XwoIqPLbWiwW9W7SWzPumKl1jx3TdZ3m6t8ZN+mP24xmiAWl5WN35EgjO450eH1JyekqzwR4GgoGqFYGDpS6dzc7BQAAAKpcVJT0pz9J+/aVzzDw8ZHGji2ffVBRdnaV3TrIN0gPdHxAK0Z+r09HHlBA1Mt6OjlWr++SNp+UVmX5aXj74WXj8/J2ac2aBtqx416dPLlK7298X+m56VWWB/AU9DAwET0MAAAAUKtlZBg9DMLCHM8XFRnLFdq2NXodDB1a5bMQ7Ha7/nvov4pPjJePl4/ev+P9smu//jpFhw69JknyCpugG76YJm+Lt25teatGdx6t21vdLj9vvyrNA7jL5TyH+rgpE3BJcnOl1FSpVSuzkwAAAMDlrNbKz3/1lZSZaXz99JNRUBg50uiOffXVVXJri8WivjF91Temr9M1b+9g+fo2UHFxlj47mCVJKrWXavGexdp+dLGWbgxUWIMRGt5pgq6JuqZK8gDVEUsSUK0kJEjt2xu9cAAAAFBLeXtLrVuXH584IU2dKrVrJ/XpI82ebWzX6CIxMZPVq9dRte2wUgnbFztcuzVSur9JvgYGzNKPP3fR+PlN9a91r+tY3jGX5QHMwpIEE7EkwdnRo9KBA1KvXpIX5SwAAIDay26XVq+WZsyQFi6UCitscdCmjbRzp7FFowul56brjyv/qIU7F+pM8Rl5SfqslxR2zmqEglJpbZZFOb49dVObZ3Rrqzvk612120UCVeVynkMpGJiIggEAAABwCU6ckObMMYoHO3dK//iHNHGi87jS0sobKF6h04Wn9dnOzxSfGK89GavV3yr1t0pNgyqJWiStPRGo4LChGnHNn9WmQZsqzwNcCQoGHoKCgbPjx6U33jB621x1ldlpAAAAUK3Y7dK6dcYMg3MbJZ45YzRJvO0248Nk584uibDvxD4lJCYoYWu8gu1H1N8q3dxQCq1kQkGRV2O1aTZBVusD8vdv5JI8wOWiYOAhKBg4S0uToqOlxYul2283Ow0AAAA8RkKCNHp0+XG3bkbhYMQIKSSkym9ns9v0Q8oPmpU4S1/tWqhOdQs1wCr1DJf8nJbWWlS/fj9ZrSMVETFEPj7BVZ4HuFSX8xzKKnFUKxERxvdj9IwBAADA5cjMlIIqrBHYuNEoGERHS488Im3aVKW387J4qV/zfpo7dK6OTMzQqF4ztOx0bw1bJ721R8osaVhhtF0nT67Url0jtXZtpH7a8bwmrZikncd2VmkmoKoxw8BEzDCoXL160pQp0rPPmp0EAAAAHiUnR5o3z+h1sGWL47UuXaq8aFCZPVl7lJCYoDvb3Kn29cOUkTFXGRlzVFDwa9mYz08N0vTEbyVJ3aO7a0ynURrR4X7VD6zv8nwASxI8BAWDyrVsKQ0ZYvQyAAAAAH6TTZuMwsG8eVJurvTee9Kjj5oSxW63KydnrdLT5+h03g71/XajCkoKyq6PaCL1a2hRhjqqa6sp6n/VUHl7VX3zRkBiSQI8XESE0fwQAAAA+M26dpXef99okjVzpnT//c5jTp2SevaUpk+XsrNdFsVisSg09Dq1bv1vbbY96FAskKT+DaWrgu26LnirHv7iXjWd2lSTV07WruO7XJYJuBQUDFDtNGhADwMAAABUkeBgaexYqbK/pH78sfTzz9KECUavg1GjpDVrjN0YXOSm2Js1qfckRQZHSpLqeEuFNuPa9lNSaoGUejpVr695XW3faauH5rfWnJ+fVnb+SZdlAs6HJQkmYklC5R56SEpONnbMAQAAAFzm4Yeljz5yPn/11dK4cVJcnBQe7pJbl9hKtHz/cs1KnKWvd38tq1+R6vhIu047jpt7rdQoUMoslI6WXq1h176vJhF9XJIJtQNLEuDRWJIAAAAAt/jwQ2nbNmOGQb165ed37pT+8Afp3ntddmsfLx/d2vJWfXbPZ0qdmKpJN05TneCuDmPa1zWKBZLU0F+6Jmin9m/vq02buuvIkX+pqCjTZfkAiRkGpmKGQeWSkqTUVOl3vzM7CQAAAGqN/Hxp4UKjUeJ//2ucmz3bmGXgRkkZSYpPjNfcpLnKzs/UdeHSAKvUPUzytpw72lvh4YNktcYpPPwOeXsHujUrPBO7JHgICgYAAABANZScLMXHSy+8IAWe8xB+7Jj05JNGX4Qbb5S8XDNpu7i0WN/u+1bxifFavGexQrxLtOTOp+Wb/4Nyczc7jS+0+epAcYxaNn1cN7T+f/L28nFJLng+CgYegoJB5Q4ckObPlx5/vPLeNAAAAIBp/u//pGefNX6+6iqjcDB6tGS1uuyWx/KOacneJRrVaZQsFovy8nYoPX2OMjM/VmHhEefxhd7K8+utXm2fV+vIfi7LBc9EwcBDUDCo3H//K911l/TLL1Lz5manAQAAACro08fYSaEiHx/jA+z48VK/fi6bdXAuu71U2dk/6oekyQoo2qCgSiYVHCkMka3Byxra/mEF+wW7JReqNwoGHoKCAQAAAOBhioqkr74yeh2sXOl8fdQoYzmDG3Wf2V3b0zeqT4TU3yp1rV/e7yAtX3rgFynIt47uaXePxnQeo75N+8picWqIgFqCgoGHoGAAAAAAeLD9+6UPPpBmzZIyMoxzn38uDR3q1hgZuRmalzRPsxJnKSkzSWF+Ur+GRrPEtVnSrAOO49++JkgN616tri0nq1X0EIoHtQwFAw9BwaBypaVS27bSiy9K991ndhoAAADgIoqLpcWLpU8/lebMkXx9Ha8fPmwUFR56SGrc2GUx7Ha7tqRv0awtszRv+zydyD8hb4tUWuGJLypAmtfD+DklT/qu8H7NHfqxyzKh+rmc51D3LK4BLoO3t1GgPeLcvwUAAACofnx9jVkFn3ziXCyQpI8+kp5/XoqJkQYPNooLJSVVHsNisahLVBdNu3WaUiemauE9CzXwqtvkZSl/7GtRR8ovNX5eniE1rtvE4XdkZi5QcfHJKs8Gz8QMAxMxw+D8mjeX7r1Xev11s5MAAAAAV8Bul2JjpYMHHc83aiQ9/LDx1bSpSyOknU7T3G1zNStxlpKPJyvQW+obIW0+Ka0au1NtG7SVJOXmbtfGjR1ksfgpPPwOBdW/S82i7pWXl59L88G9mGEAjxcRIWVlmZ0CAAAAuEIWi7RqlTHDoOJyhKNHpZdekpo1k1591aURokKi9Ox1z2rHYzv089ifNfqa3+uXU/XUokGPsmKBJGVkzJEk2e1FOn78cx3aG6elPwTrP+t/p2MnVom/Ndc+zDAwETMMzm/QICkgQPriC7OTAAAAAFWktFRautTYYWHJEuNYkr79Vho40K1RCkoKlHo6Vc3rl+9jnpu7Tenps5SRMU/FxZlOr8mxhSosYoS6tnxOgYHN3JgWVYmmhx6CgsH5PfigdOiQUYwFAAAAapyjR41GiCtXSt99ZzTyqiglRdq82eh5UFlfBBey2Yo1bHYztQtKVZ9wyd/beUyOYtUy5nG1bDJWPj6hbs2HK8OSBHi88HCWJAAAAKAGa9RI+vOfpR9/dC4WSNJ770nDhhnLGJ57Ttq3z23RDp46ou/Sc/VKsnT3OumN3dKWbMcxdZWijIPP6IfVYVq6rrfSMr+QzVbstoxwD2YYmIgZBuf30kvSu+9K6elmJwEAAADcrKjIKBQcO+Z4/uabpfHjpbvukvz9XRohvzhfX+z6QvGJ8Vr560rZZZfVX7rFKg2wSk2DnF9TYA9USPhI9e7wniwWi0vz4bdjhgE8Xni4dOqU0VQWAAAAqFV8fKT5841twyouR/j+e2nECKOY8MknLo0Q6Buo+zvcr+Vxy3XwqYN65aZXFFLnKn18SBq1QXp0s7ToiJRdVP6aAEu+Ptn2vqb9Ms2l2eA+zDAwETMMzq+kRPLyMr4AAACAWiszU0pIMBolVlyWsGqV1LevW6PY7XatObxG8YnxWrBjgXKLcuVtka4NM2Yd9A6XJmyRlj2comb1mkmSiooylZz8gKzWBxURMVQ+PiFuzQxnND30EBQMAAAAAFwSu1366SejcLBzp7Rli7FlY0UHD0q5uVK7di6Pk1eUp0XJixS/NV7fp3wvSarjLV3b5EZ9P+qHsnFHjvxT+/Y9JUkKi5ygktDRuibyGpYsmIglCfB4+/dLPXpISUlmJwEAAACqAYtFuvFGad48aeNG52KBJL35ptS+vdSnjzR7tnTmjMvi1PGro7hOcfpu5HdKeTJFL974ohqGxGpM54ccxmVnl297tiS1WF1ndFWnf3fS2+veVmr2duXmbnVZRlw5ZhiYiBkG55eZKU2eLE2aJLVubXYaAAAAoJo7c0aKipJycsrPhYZKcXFGo8QOHVwewWa3yWa3ycfLp+yc3W5XTs7POnFypW74YoYO5xwuuxYX46WHmtlU6tNMzZs8oujIkfL3j3Z5ztqOGQbweA0bSh9+SLEAAAAAuGQvv+y4HOHUKWn6dKljR6lXL2nNGpfe3svi5VAskCSLxaLQ0J7ab+vpUCyQpFsa2iRJ3iUHdDBlstaubaz/buit9PS5Ki3Nc2lWXBoKBqi2du2SUlPNTgEAAAB4gKAg6YknjDW9a9dKo0dLgYHl19evdzx2s1MFp9Q0tGnZsZekz49IOypMiLBY7CrJW6ddu+L04+owJSYN14kTK2W3l7o/MCSxJMFULEm4sEaNpLFjpRdfNDsJAAAA4IGys42eBzNmGFs1btzoPCYtTQoOlkJcv3uBzW7TDyk/KH5rvD7f+bnyS/IlSY0CjV0WbmkoRVdS07B5hatJ9GhFR41RnTqub+hY07FLgoegYHBhHTtK119vzKICAAAA8BvZ7UbxoH5952tjxkgLF0r33Wf0OujatfKGilUspzBHn+74VPGJ8VpzuHypRPu60u+s0o0NpWAf59cFB18jq3WkrNb75OdndXnOmoiCgYegYHBhN90kRUZK8+ebnQQAAACogbKzpehoKT+//Fznzkbh4P77jaaJbrAna48SEhM0e9tsHck5IknytUi9wo2ZBz3DLfK2nPvY6q2wsAGKifmrQkN7uiVnTUHTQ9QIERFSVpbZKQAAAIAaqqBAGjXKcTlCYqL02GNGIeGhh4z9zl2sVXgrvdrvVR148oCWP7hc97W/T97eAVp1XPrzDulUgw901VX/UkhI9wqvKtWJE99q+b6v9O3eb1ViK3F5ztqIGQYmYobBhT36qPTLL9LmzWYnAQAAAGqw3FxpwQKj18EvvzheS06W2rRxe6Tsgmx9uuNTLUpepK9GfCV/H39JUl7eLmVkzFFGxlzZ7TYNXVuojLxjigqOUlzHOI1o3lihlnRZrXGqU8f9uT0BSxI8BAWDC5syRZo7Vzp40OwkAAAAQC2xdas0c6Y0Z47UqZO0apXzmNOnjUaJbuh1cD52u03/2fmhBi8c73D+Hx2lLv9r1dC6wypFhfc1IV31xpIE1Ajh4SxJAAAAANyqUyej63hqqjRrVuVjHn5YatdOmjrVtA/sFouXZm3/1uFcXR+pw//aLhw+I8W+d4vu+/w+Ldu3TKW2UuXn/6rS0gIT0nouZhiYiBkGF5aQYGwfW1Ag+fubnQYAAACAMjOlxo2l4mLj2N9fuvtuo1Hi9de7ddbBzE0zNWPzDG1MLd8usr6v1K+hlFsiLc0oH9sopJGmX2NXfe/TirSOkNU6UqGh18li4iwJs7AkwUNQMLiwdeukN980CptuatAKAAAA4EJ27JB+/3tp9Wrna61aSePGSWPHSvXquS3S9sztSkhM0Jxtc5SRl1HpmCaB0uxrHc8FBMTKao2T1RqnoKCr3JC0eqBg4CEoGAAAAADwSMnJ0gcfGNOCKy5LsFiklBQpJsbtkYpLi7Vs/zLFJ8br691fq9hWXHatUaAU11S6voEU6O382rp1e8lqjVPDhsPl6xvmxtTuR8HAQ1AwuDCbTcrIkOrWlerUMTsNAAAAACcFBdIXXxiNEn/4QRo4UPr2W+dxNpvk5b4WesfPHNf8pPmK3xqvzWnl264FeEl9IqTnOneRd2GiJJvD6ywWX4WH3y6rNU7h4bfKy6vmrY2mYOAhKBhc2IkTRuPDhQuNZVEAAAAAqrE9e4wCQseOzteGDJG8vY1eB7fc4tbiwbaMbUpITNDcpLnKzMtUVHCUDv3hkEqLM5WRMU8ZGbOVl5fk9Dpvn/qyNhwhqzVOdev2rDH9DigYeAgKBhdms0nffCN16yZFRpqdBgAAAMBvcviw1KyZ8QFfMn4eO1YaM0aKjnZbjOLSYi3dt1SnCk/pwY4POlzLzd2q9PQ5Onj0Q3nbs51eGxBwlSIj49S48R/k4xPipsSuQcHAQ1AwAAAAAFDjffed9MADxnrjiry9pdtvN2Yd/O53xrHJBs0doKwTKzTAaixdCKgQqdgeoJj2G9WqQTvzAlYBCgYegoLBxc2cKVmt0uDBZicBAAAA8JsVF0uLFxsf8Jctkyo+hvr4GLMQTJ5WfDTnqJpObSqb3ZgJEegtXR8hDbBKnetJi9OkqXul65pcp9GdR+vedvcqO2OGgoJaKSxsoLy8/EzNf6koGHgICgYXd911UsuWUny82UkAAAAAVIkDB6QPP5Q++khKTTUali1caHYqFZcW69t93yo+MV6L9yxWia2k7FoDf8kiKbOwfHzDgADNu7ZI3hab6tTpqG7dEj2iz8HlPIe6r9ME8BuEhzvu0gIAAADAwzVrJr38snTwoPTVV9Jzz1U+7oEHpL/8xSgwuIGvt68Gtx6sRcMXKXViqqb+bqo6R3aWJB0rdCwWSFL3egXythizET77NUVf7vrS4XpRUaYbUrsWMwxMxAyDixszRtq9W1q71uwkAAAAANxmzx6pdWvjZ4vF6HEwfrzR88DX161REtMTy3ZZOH7meNl5b4vUrb7U3yrNOyR9NOxH3dDsBklSYWGq1q1rqtDQ3rJa49SgwT3y9a3n1tznwwwD1Bjh4dLx4xcfBwAAAKAGWbu2vAmi3S4tXSoNHSo1bSr96U/Sr7+6LUrnyM56e+DbOjrxqL4c/qXuanOXfLx8VGqXfj4hvZIs2X1j1Temb9lrMjLmSSrVqVOrtWfPeK1dG6kdO+7V8eOLZbMVuy37laJggGqNJQkAAABALTR6tHTokPTKK1JMTPn59HTpb3+TOnSQ8vLcGsnP2093trlTXwz/omzJQidrJ0nSqE6j5GUpf7z282ugoKC2Zcd2e6GOHftM27cP1rp10dq7d4Jycjaouk/4Z0mCiViScHEzZkiPPmo0Va0Gu6wAAAAAcDebTVqxwng4+PprqaREiouTZs82O5kkY8mCtY5VUSFRDuftdrtOn96kd34YrDaBaapfySYKgYGtFRkZJ6v1QQUExDgPcAF2SfAQFAwu7vPPpWHDjGUJ4eFmpwEAAABgqvR0Ywu1fv2k7t2drz/+uLHV2tChUkCA2+Oda9+JfWo5raW8LVL3//U76BMh+VUy1z809HpFRo5UgwbD5OMT6rJMFAw8BAWDi/vpJ+nGG43Gh61amZ0GAAAAQLWVmChdc43xc1iYNHKkNG6cdPXVpkX65/p/6qllTzmcq+Mt3dDAKB50ruf8Gi+vAIWHD1ZU1EMKC/tdlWei6SFqjKuvlhISpAYNzE4CAAAAoFpbtKj85xMnpKlTpXbtpD59jOUL+fluj/REjye0efxmPXHtEwoPNKZM55VK36RLf9gqjVgvfZAiHazQjsFmK9CxY5/q8NEP3J73XMwwMBEzDAAAAACgitjt0urVRq+DhQulwkLH602bGrsrmNQcrai0SEv2LFH81ngt2bNEpfZSh+utQ6QBDaWbG0r1/KS/7vTX7Pt3qVm9ZpKk0tIzOnLkX7Ja77uifgcsSfAQFAwuzm6X3n9f6t1b6tjR7DQAAAAAPMKJE9KcOdLMmdKOHca5Rx+V3nvP3Fz/k5GboY+TPtasxFnanrnd4drZfge53m217fc7ZLFYjNdkzFNy8gOSpObN31DTps/+pnuzJAE1hsUiTZ5sFAoBAAAA4JKEhUlPPiklJUlr1hjbNI4f7zzObpf++ldp40bjZzexBls1sddEbXt0mzaN36QJ105QWGCYJKnULq0/IY3q/FBZsUC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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", - "\n", - "with plt.rc_context(rcParams):\n", - " # Plot the results\n", - " plt.yticks([0.99, 1.0], [0.99, 1])\n", - " axes.plot(\n", - " tlist, np.real(P11_mats),\n", - " 'b', linewidth=2, label=f\"Matsubara $N_k={Nk}$\",\n", - " )\n", - " axes.plot(\n", - " tlist, np.real(P11_matsT),\n", - " 'g--', linewidth=3,\n", - " label=f\"Matsubara $N_k={Nk}$ & terminator\",\n", - " )\n", - " axes.plot(\n", - " tlist, np.real(P11_pade),\n", - " 'y-.', linewidth=2, label=f\"Padé $N_k={Nk}$\",\n", - " )\n", - " axes.plot(\n", - " tlist, np.real(P11_fit),\n", - " 'r', dashes=[3, 2], linewidth=2,\n", - " label=r\"Fit $N_f = 3$, $N_k=15 \\times 10^3$\",\n", - " )\n", - " axes.plot(\n", - " tlist, np.real(P11_br),\n", - " 'b-.', linewidth=1, label=\"Bloch Redfield\",\n", - " )\n", - "\n", - " axes.locator_params(axis='y', nbins=6)\n", - " axes.locator_params(axis='x', nbins=6)\n", - " axes.set_ylabel(r'$\\rho_{11}$', fontsize=30)\n", - " axes.set_xlabel(r'$t\\;\\gamma$', fontsize=30)\n", - " axes.set_xlim(tlist[0], tlist[-1])\n", - " axes.set_ylim(0.98405, 1.0005)\n", - " axes.legend(loc=0)" - ] - }, - { - "cell_type": "markdown", - "id": "6455147a", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": 210, - "id": "3a984023", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "QuTiP: Quantum Toolbox in Python\n", - "================================\n", - "Copyright (c) QuTiP team 2011 and later.\n", - "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", - "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", - "Original developers: R. J. Johansson & P. D. Nation.\n", - "Previous lead developers: Chris Granade & A. Grimsmo.\n", - "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", - "\n", - "QuTiP Version: 5.2.0.dev0+daa7d68\n", - "Numpy Version: 1.26.4\n", - "Scipy Version: 1.14.1\n", - "Cython Version: 3.0.9\n", - "Matplotlib Version: 3.9.2\n", - "Python Version: 3.12.7\n", - "Number of CPUs: 16\n", - "BLAS Info: Generic\n", - "INTEL MKL Ext: None\n", - "Platform Info: Linux (x86_64)\n", - "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", - "\n", - "Installed QuTiP family packages\n", - "-------------------------------\n", - "\n", - "No QuTiP family packages installed.\n", - "\n", - "================================================================================\n", - "Please cite QuTiP in your publication.\n", - "================================================================================\n", - "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" - ] - } - ], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "f3af6c9a", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": 211, - "id": "77693ca6", - "metadata": {}, - "outputs": [], - "source": [ - "assert np.allclose(P11_matsT, P11_pade, rtol=1e-3)\n", - "assert np.allclose(P11_matsT, P11_fit, rtol=1e-3)" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "qutip-dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md index 2960e736..6b303b0b 100644 --- a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md +++ b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 + jupytext_version: 1.16.1 kernelspec: display_name: qutip-dev language: python @@ -334,22 +334,23 @@ we will use the built-in tools. More details about them can be seen in `HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions` ```{code-cell} ipython3 +tfit=np.linspace(0,10,10000) lower = [0, -np.inf, -1e-6, -3] guess = [np.real(bath.correlation_function(0))/10, -10, 0, 0] -upper = [3.5, 0, 1e-6, 0] +upper = [5, 0, 1e-6, 0] # for better fits increase the first element +# that makes the simuation slower though +envfit,fitinfo = bath.approximate("cf",tlist=tfit,Nr_max=2,Ni_max=1,full_ansatz=True, + sigma=0.1,maxfev=1e6,target_rsme=None, + lower=lower,upper=upper,guess=guess) ``` ```{code-cell} ipython3 -tfit=np.linspace(0,100,10000) -envfit,fitinfo = bath.approx_by_cf_fit(tlist=tfit,Nr_max=3,Ni_max=1,full_ansatz=True, - sigma=0.1,maxfev=1e6,target_rsme=None, - lower=lower,upper=upper,guess=guess) +print(fitinfo['summary']) ``` We can quickly compare the result of the Fit with the Pade expansion ```{code-cell} ipython3 - fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8)) ax1.plot( @@ -392,7 +393,7 @@ with timer("RHS construction time"): # We reduce NC slightly here for speed of execution because we retain # 3 exponents in ckAR instead of 1. Please restore full NC for # convergence though: - HEOMFit = HEOMSolver(Hsys, (envfit,Q), NC, options=options) + HEOMFit = HEOMSolver(Hsys, (envfit,Q), int(NC*0.7), options=options) with timer("ODE solver time"): resultFit = HEOMFit.run(rho0, tlist) @@ -404,7 +405,7 @@ with timer("ODE solver time"): with timer("ODE solver time"): resultBR = brmesolve( Hsys, rho0, tlist, - a_ops=[[sigmaz(), lambda w: bath.power_spectrum(w)]], sec_cutoff=0, options=options, + a_ops=[[sigmaz(),bath]], sec_cutoff=0, options=options, ) ``` diff --git a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb deleted file mode 100644 index 5cee9623..00000000 --- a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.ipynb +++ /dev/null @@ -1,857 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "09c67917", - "metadata": {}, - "source": [ - "# HEOM 1c: Spin-Bath model (Underdamped Case)" - ] - }, - { - "cell_type": "markdown", - "id": "067e8a0e", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices.\n", - "\n", - "In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", - "\n", - "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", - "\n", - "In the example below we show how to model the underdamped Brownian motion Spectral Density.\n", - "\n", - "Note that in the following, we set $\\hbar = k_\\mathrm{B} = 1$.\n", - "\n", - "### Brownian motion (underdamped) spectral density\n", - "The underdamped spectral density is:\n", - "\n", - "$$J_U = \\frac{\\alpha^2 \\Gamma \\omega}{(\\omega_c^2 - \\omega^2)^2 + \\Gamma^2 \\omega^2)}.$$\n", - "\n", - "Here $\\alpha$ scales the coupling strength, $\\Gamma$ is the cut-off frequency, and $\\omega_c$ defines a resonance frequency. With the HEOM we must use an exponential decomposition:\n", - "\n", - "The Matsubara decomposition of this spectral density is, in real and imaginary parts:\n", - "\n", - "\n", - "\n", - "\\begin{equation*}\n", - " c_k^R = \\begin{cases}\n", - " \\alpha^2 \\coth(\\beta( \\Omega + i\\Gamma/2)/2)/4\\Omega & k = 0\\\\\n", - " \\alpha^2 \\coth(\\beta( \\Omega - i\\Gamma/2)/2)/4\\Omega & k = 0\\\\\n", - " -2\\alpha^2\\Gamma/\\beta \\frac{\\epsilon_k }{((\\Omega + i\\Gamma/2)^2 + \\epsilon_k^2)(\\Omega - i\\Gamma/2)^2 + \\epsilon_k^2)} & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "\\begin{equation*}\n", - " \\nu_k^R = \\begin{cases}\n", - " -i\\Omega + \\Gamma/2, i\\Omega +\\Gamma/2, & k = 0\\\\\n", - " {2 \\pi k} / {\\beta } & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "\n", - "\n", - "\n", - "\\begin{equation*}\n", - " c_k^I = \\begin{cases}\n", - " i\\alpha^2 /4\\Omega & k = 0\\\\\n", - " -i\\alpha^2 /4\\Omega & k = 0\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "\\begin{equation*}\n", - " \\nu_k^I = \\begin{cases}\n", - " i\\Omega + \\Gamma/2, -i\\Omega + \\Gamma/2, & k = 0\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." - ] - }, - { - "cell_type": "markdown", - "id": "6bd12428", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9660ef80", - "metadata": {}, - "outputs": [], - "source": [ - "import contextlib\n", - "import time\n", - "\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " basis,\n", - " brmesolve,\n", - " destroy,\n", - " expect,\n", - " qeye,\n", - " sigmax,\n", - " sigmaz,\n", - " tensor,\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver,\n", - ")\n", - "from qutip.core.environment import (\n", - " UnderDampedEnvironment,\n", - " ExponentialBosonicEnvironment\n", - ")\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "a1bdd787", - "metadata": {}, - "source": [ - "## Helper functions\n", - "\n", - "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cec25e44", - "metadata": {}, - "outputs": [], - "source": [ - "def cot(x):\n", - " \"\"\" Vectorized cotangent of x. \"\"\"\n", - " return 1. / np.tan(x)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4a7be2b", - "metadata": {}, - "outputs": [], - "source": [ - "def coth(x):\n", - " \"\"\" Vectorized hyperbolic cotangent of x. \"\"\"\n", - " return 1. / np.tanh(x)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bf051401", - "metadata": {}, - "outputs": [], - "source": [ - "def underdamped_matsubara_params(lam, gamma, T, nk):\n", - " \"\"\" Calculation of the real and imaginary expansions of the\n", - " underdamped correlation functions.\n", - " \"\"\"\n", - " Om = np.sqrt(w0**2 - (gamma / 2)**2)\n", - " Gamma = gamma / 2.\n", - " beta = 1. / T\n", - "\n", - " ckAR = [\n", - " (lam**2 / (4*Om)) * coth(beta * (Om + 1.0j * Gamma) / 2),\n", - " (lam**2 / (4*Om)) * coth(beta * (Om - 1.0j * Gamma) / 2),\n", - " ]\n", - " ckAR.extend(\n", - " (-2 * lam**2 * gamma / beta) * (2 * np.pi * k / beta) /\n", - " (((Om + 1.0j * Gamma)**2 + (2 * np.pi * k / beta)**2) *\n", - " ((Om - 1.0j * Gamma)**2 + (2 * np.pi * k / beta)**2)) + 0.j\n", - " for k in range(1, nk + 1)\n", - " )\n", - " vkAR = [\n", - " -1.0j * Om + Gamma,\n", - " 1.0j * Om + Gamma,\n", - " ]\n", - " vkAR.extend(\n", - " 2 * np.pi * k * T + 0.j\n", - " for k in range(1, nk + 1)\n", - " )\n", - "\n", - " factor = 1. / 4\n", - "\n", - " ckAI = [\n", - " -factor * lam**2 * 1.0j / Om,\n", - " factor * lam**2 * 1.0j / Om,\n", - " ]\n", - " vkAI = [\n", - " -(-1.0j * Om - Gamma),\n", - " -(1.0j * Om - Gamma),\n", - " ]\n", - "\n", - " return ckAR, vkAR, ckAI, vkAI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5ef90afc", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_result_expectations(plots, axes=None):\n", - " \"\"\" Plot the expectation values of operators as functions of time.\n", - "\n", - " Each plot in plots consists of: (solver_result, measurement_operation,\n", - " color, label).\n", - " \"\"\"\n", - " if axes is None:\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " fig_created = True\n", - " else:\n", - " fig = None\n", - " fig_created = False\n", - "\n", - " # add kw arguments to each plot if missing\n", - " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", - " for result, m_op, color, label, kw in plots:\n", - " exp = np.real(expect(result.states, m_op))\n", - " kw.setdefault(\"linewidth\", 2)\n", - " axes.plot(result.times, exp, color, label=label, **kw)\n", - "\n", - " if fig_created:\n", - " axes.legend(loc=0, fontsize=12)\n", - " axes.set_xlabel(\"t\", fontsize=28)\n", - "\n", - " return fig" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "339e9a1b", - "metadata": {}, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\" Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b6e42bdc", - "metadata": {}, - "outputs": [], - "source": [ - "# Solver options:\n", - "\n", - "options = {\n", - " \"nsteps\": 15000,\n", - " \"store_states\": True,\n", - " \"rtol\": 1e-14,\n", - " \"atol\": 1e-14,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "2d97796f", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "And let us set up the system Hamiltonian, bath and system measurement operators:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "11a414de", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the system Hamiltonian\n", - "eps = .5 # Energy of the 2-level system.\n", - "Del = 1.0 # Tunnelling term\n", - "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4f1013ca", - "metadata": {}, - "outputs": [], - "source": [ - "# Initial state of the system.\n", - "rho0 = basis(2, 0) * basis(2, 0).dag()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1bd202f5", - "metadata": {}, - "outputs": [], - "source": [ - "# System-bath coupling (underdamed spectral density)\n", - "Q = sigmaz() # coupling operator\n", - "\n", - "# Bath properties:\n", - "gamma = .1 # cut off frequency\n", - "lam = .5 # coupling strength\n", - "w0 = 1. # resonance frequency\n", - "T = 1.\n", - "beta = 1. / T\n", - "\n", - "# HEOM parameters:\n", - "\n", - "# number of exponents to retain in the Matsubara expansion of the\n", - "# bath correlation function:\n", - "Nk = 2\n", - "\n", - "# Number of levels of the hierarchy to retain:\n", - "NC = 10\n", - "\n", - "# Times to solve for:\n", - "tlist = np.linspace(0, 50, 1000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3c6de948", - "metadata": {}, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresonding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresonding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "e3f5324c", - "metadata": {}, - "source": [ - "### First let us look at what the underdamped spectral density looks like:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8b62d0ba", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_spectral_density():\n", - " \"\"\" Plot the underdamped spectral density \"\"\"\n", - " w = np.linspace(0, 5, 1000)\n", - " J = lam**2 * gamma * w / ((w0**2 - w**2)**2 + (gamma**2) * (w**2))\n", - "\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " axes.plot(w, J, 'r', linewidth=2)\n", - " axes.set_xlabel(r'$\\omega$', fontsize=28)\n", - " axes.set_ylabel(r'J', fontsize=28)\n", - "\n", - "\n", - "plot_spectral_density()" - ] - }, - { - "cell_type": "markdown", - "id": "52282e66", - "metadata": {}, - "source": [ - "The correlation functions are now very oscillatory, because of the Lorentzian peak in the spectral density." - ] - }, - { - "cell_type": "markdown", - "id": "5c84cfc2", - "metadata": {}, - "source": [ - "### So next, let us plot the correlation functions themselves:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c4cf589", - "metadata": {}, - "outputs": [], - "source": [ - "def Mk(t, k, gamma, w0, beta):\n", - " \"\"\" Calculate the Matsubara terms for a given t and k. \"\"\"\n", - " Om = np.sqrt(w0**2 - (gamma / 2)**2)\n", - " Gamma = gamma / 2.\n", - " ek = 2 * np.pi * k / beta\n", - "\n", - " return (\n", - " (-2 * lam**2 * gamma / beta) * ek * np.exp(-ek * np.abs(t))\n", - " / (((Om + 1.0j * Gamma)**2 + ek**2) * ((Om - 1.0j * Gamma)**2 + ek**2))\n", - " )\n", - "\n", - "\n", - "def c(t, Nk, lam, gamma, w0, beta):\n", - " \"\"\" Calculate the correlation function for a vector of times, t. \"\"\"\n", - " Om = np.sqrt(w0**2 - (gamma / 2)**2)\n", - " Gamma = gamma / 2.\n", - "\n", - " Cr = (\n", - " coth(beta * (Om + 1.0j * Gamma) / 2) * np.exp(1.0j * Om * t)\n", - " + coth(beta * (Om - 1.0j * Gamma) / 2) * np.exp(-1.0j * Om * t)\n", - " )\n", - "\n", - " Ci = np.exp(-1.0j * Om * t) - np.exp(1.0j * Om * t)\n", - "\n", - " return (\n", - " (lam**2 / (4 * Om)) * np.exp(-Gamma * np.abs(t)) * (Cr + Ci) +\n", - " np.sum([\n", - " Mk(t, k, gamma=gamma, w0=w0, beta=beta)\n", - " for k in range(1, Nk + 1)\n", - " ], 0)\n", - " )\n", - "\n", - "\n", - "def plot_correlation_function():\n", - " \"\"\" Plot the underdamped correlation function. \"\"\"\n", - " t = np.linspace(0, 20, 1000)\n", - " corr = c(t, Nk=3, lam=lam, gamma=gamma, w0=w0, beta=beta)\n", - "\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " axes.plot(t, np.real(corr), '-', color=\"black\", label=\"Re[C(t)]\")\n", - " axes.plot(t, np.imag(corr), '-', color=\"red\", label=\"Im[C(t)]\")\n", - " axes.set_xlabel(r't', fontsize=28)\n", - " axes.set_ylabel(r'C', fontsize=28)\n", - " axes.legend(loc=0, fontsize=12)\n", - "\n", - "\n", - "plot_correlation_function()" - ] - }, - { - "cell_type": "markdown", - "id": "c3ac887c", - "metadata": {}, - "source": [ - "It is useful to look at what the Matsubara contributions do to this spectral density. We see that they modify the real part around $t=0$:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "021c6f46", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_matsubara_correlation_function_contributions():\n", - " \"\"\" Plot the underdamped correlation function. \"\"\"\n", - " t = np.linspace(0, 20, 1000)\n", - "\n", - " M_Nk2 = np.sum([\n", - " Mk(t, k, gamma=gamma, w0=w0, beta=beta)\n", - " for k in range(1, 2 + 1)\n", - " ], 0)\n", - "\n", - " M_Nk100 = np.sum([\n", - " Mk(t, k, gamma=gamma, w0=w0, beta=beta)\n", - " for k in range(1, 100 + 1)\n", - " ], 0)\n", - "\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " axes.plot(t, np.real(M_Nk2), '-', color=\"black\", label=\"Re[M(t)] Nk=2\")\n", - " axes.plot(t, np.real(M_Nk100), '--', color=\"red\", label=\"Re[M(t)] Nk=100\")\n", - " axes.set_xlabel(r't', fontsize=28)\n", - " axes.set_ylabel(r'M', fontsize=28)\n", - " axes.legend(loc=0, fontsize=12)\n", - "\n", - "\n", - "plot_matsubara_correlation_function_contributions()" - ] - }, - { - "cell_type": "markdown", - "id": "2f10e37a", - "metadata": {}, - "source": [ - "## Solving for the dynamics as a function of time" - ] - }, - { - "cell_type": "markdown", - "id": "cb326966", - "metadata": {}, - "source": [ - "Next we calculate the exponents using the Matsubara decompositions. Here we split them into real and imaginary parts.\n", - "\n", - "The HEOM code will optimize these, and reduce the number of exponents when real and imaginary parts have the same exponent. This is clearly the case for the first term in the vkAI and vkAR lists." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "976b8621", - "metadata": {}, - "outputs": [], - "source": [ - "ckAR, vkAR, ckAI, vkAI = underdamped_matsubara_params(\n", - " lam=lam, gamma=gamma, T=T, nk=Nk,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "da31068a", - "metadata": {}, - "source": [ - "Having created the lists which specify the bath correlation functions, we create a `BosonicBath` from them and pass the bath to the `HEOMSolver` class.\n", - "\n", - "The solver constructs the \"right hand side\" (RHS) determinining how the system and auxiliary density operators evolve in time. This can then be used to solve for dynamics or steady-state.\n", - "\n", - "Below we create the bath and solver and then solve for the dynamics by calling `.run(rho0, tlist)`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eb83040a", - "metadata": {}, - "outputs": [], - "source": [ - "with timer(\"RHS construction time\"):\n", - " bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI)\n", - " HEOMMats = HEOMSolver(Hsys, (bath,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMats = HEOMMats.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8bdbdaaa", - "metadata": {}, - "outputs": [], - "source": [ - "plot_result_expectations([\n", - " (resultMats, P11p, 'b', \"P11 Mats\"),\n", - " (resultMats, P12p, 'r', \"P12 Mats\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "c6f959e1", - "metadata": {}, - "source": [ - "In practice, one would not perform this laborious expansion for the underdamped correlation function, because\n", - "QuTiP already has a class, `UnderDampedEnvironment`, that can construct this bath for you. Nevertheless, knowing how\n", - "to perform this expansion is an useful skill.\n", - "\n", - "Below we show how to use this built-in functionality:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "da329964", - "metadata": {}, - "outputs": [], - "source": [ - "# Compare to built-in under-damped bath:\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " bath = UnderDampedEnvironment(lam=lam, gamma=gamma, w0=w0, T=T)\n", - " bath_approx=bath.approx_by_matsubara(Nk=Nk)\n", - " HEOM_udbath = HEOMSolver(Hsys, (bath_approx,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " result_udbath = HEOM_udbath.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "83491bf6", - "metadata": {}, - "outputs": [], - "source": [ - "plot_result_expectations([\n", - " (result_udbath, P11p, 'b', \"P11 (UnderDampedEnvironment)\"),\n", - " (result_udbath, P12p, 'r', \"P12 (UnderDampedEnvironment)\"),\n", - " (resultMats, P11p, 'r--', \"P11 Mats\"),\n", - " (resultMats, P12p, 'b--', \"P12 Mats\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "c8d0190a", - "metadata": {}, - "source": [ - "The `UnderDampedEnvironment` class also allows us to easily evaluate analytical expressions for the power spectrum, correlation function, and spectral density. In the following plots, the solid lines are the exact expressions, and the dashed lines are based on our approximation of the correlation function with a finite number of exponents. In this case, there is an excellent agreement." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "89f31ba0", - "metadata": {}, - "outputs": [], - "source": [ - "w = np.linspace(-3, 3, 1000)\n", - "w2 = np.linspace(0, 3, 1000)\n", - "t = np.linspace(0, 10, 1000)\n", - "bath_cf = bath.correlation_function(t) # uses numerical integration\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "axs[0, 0].plot(w, bath.power_spectrum(w))\n", - "axs[0, 0].plot(w, bath_approx.power_spectrum(w), '--')\n", - "axs[0, 0].set(xlabel=r'$\\omega$', ylabel=r'$S(\\omega)$')\n", - "axs[0, 1].plot(w2, bath.spectral_density(w2))\n", - "axs[0, 1].plot(w2, bath_approx.spectral_density(w2), '--')\n", - "axs[0, 1].set(xlabel=r'$\\omega$', ylabel=r'$J(\\omega)$')\n", - "axs[1, 0].plot(t, np.real(bath_cf))\n", - "axs[1, 0].plot(t, np.real(bath_approx.correlation_function(t)), '--')\n", - "axs[1, 0].set(xlabel=r'$t$', ylabel=r'$C_{R}(t)$')\n", - "axs[1, 1].plot(t, np.imag(bath_cf))\n", - "axs[1, 1].plot(t, np.imag(bath_approx.correlation_function(t)), '--')\n", - "axs[1, 1].set(xlabel=r'$t$', ylabel=r'$C_{I}(t)$')\n", - "\n", - "fig.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "9dead787", - "metadata": {}, - "source": [ - "## Compare the results" - ] - }, - { - "cell_type": "markdown", - "id": "d9f9e91f", - "metadata": {}, - "source": [ - "### We can compare these results to those of the Bloch-Redfield solver in QuTiP:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6d3a3a6e", - "metadata": {}, - "outputs": [], - "source": [ - "with timer(\"ODE solver time\"):\n", - " resultBR = brmesolve(\n", - " Hsys, rho0, tlist,\n", - " a_ops=[[sigmaz(), bath]], options=options,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7ada61a5", - "metadata": {}, - "outputs": [], - "source": [ - "plot_result_expectations([\n", - " (resultMats, P11p, 'b', \"P11 Mats\"),\n", - " (resultMats, P12p, 'r', \"P12 Mats\"),\n", - " (resultBR, P11p, 'g--', \"P11 Bloch Redfield\"),\n", - " (resultBR, P12p, 'g--', \"P12 Bloch Redfield\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "b3fccda9", - "metadata": {}, - "source": [ - "### Lastly, let us calculate the analytical steady-state result and compare all of the results:" - ] - }, - { - "cell_type": "markdown", - "id": "6fd11b1e", - "metadata": {}, - "source": [ - "The thermal state of a reaction coordinate (treating the environment as a single damped mode) should, at high temperatures and small gamma, tell us the steady-state:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e35c4bcc", - "metadata": {}, - "outputs": [], - "source": [ - "dot_energy, dot_state = Hsys.eigenstates()\n", - "deltaE = dot_energy[1] - dot_energy[0]\n", - "\n", - "gamma2 = gamma\n", - "wa = w0 # reaction coordinate frequency\n", - "g = lam / np.sqrt(2 * wa) # coupling\n", - "\n", - "NRC = 10\n", - "\n", - "Hsys_exp = tensor(qeye(NRC), Hsys)\n", - "Q_exp = tensor(qeye(NRC), Q)\n", - "a = tensor(destroy(NRC), qeye(2))\n", - "\n", - "H0 = wa * a.dag() * a + Hsys_exp\n", - "# interaction\n", - "H1 = (g * (a.dag() + a) * Q_exp)\n", - "\n", - "H = H0 + H1\n", - "\n", - "energies, states = H.eigenstates()\n", - "rhoss = 0 * states[0] * states[0].dag()\n", - "for kk, energ in enumerate(energies):\n", - " rhoss += (states[kk] * states[kk].dag() * np.exp(-beta * energies[kk]))\n", - "rhoss = rhoss / rhoss.norm()\n", - "\n", - "P12RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 1).dag())\n", - "P12RC = expect(rhoss, P12RC)\n", - "\n", - "P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag())\n", - "P11RC = expect(rhoss, P11RC)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "30a38a50", - "metadata": {}, - "outputs": [], - "source": [ - "rcParams = {\n", - " \"axes.titlesize\": 25,\n", - " \"axes.labelsize\": 30,\n", - " \"xtick.labelsize\": 28,\n", - " \"ytick.labelsize\": 28,\n", - " \"legend.fontsize\": 28,\n", - " \"axes.grid\": False,\n", - " \"savefig.bbox\": \"tight\",\n", - " \"lines.markersize\": 5,\n", - " \"font.family\": \"STIXgeneral\",\n", - " \"mathtext.fontset\": \"stix\",\n", - " \"font.serif\": \"STIX\",\n", - " \"text.usetex\": False,\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9125381c", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", - "\n", - "with plt.rc_context(rcParams):\n", - " plt.yticks([P11RC, 0.6, 1.0], [0.38, 0.6, 1])\n", - "\n", - " plot_result_expectations([\n", - " (resultBR, P11p, 'y-.', \"Bloch-Redfield\"),\n", - " (resultMats, P11p, 'b', \"Matsubara $N_k=3$\"),\n", - " ], axes=axes)\n", - " axes.plot(\n", - " tlist, [P11RC for t in tlist],\n", - " color='black', linestyle=\"-.\", linewidth=2,\n", - " label=\"Thermal state\",\n", - " )\n", - "\n", - " axes.set_xlabel(r'$t \\Delta$', fontsize=30)\n", - " axes.set_ylabel(r'$\\rho_{11}$', fontsize=30)\n", - "\n", - " axes.locator_params(axis='y', nbins=4)\n", - " axes.locator_params(axis='x', nbins=4)\n", - "\n", - " axes.legend(loc=0)\n", - "\n", - " fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "b1a051b0", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2e6a7002", - "metadata": {}, - "outputs": [], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "e007817c", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eec3d8e9", - "metadata": {}, - "outputs": [], - "source": [ - "assert np.allclose(\n", - " expect(P11p, resultMats.states[-100:]), P11RC, rtol=1e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(P11p, resultBR.states[-100:]), P11RC, rtol=1e-2,\n", - ")" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "qutip-dev", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md index 16a80eed..9f45428b 100644 --- a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md +++ b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 + jupytext_version: 1.16.1 kernelspec: display_name: qutip-dev language: python @@ -76,7 +76,7 @@ Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. ## Setup -```{code-cell} ipython3 +```{code-cell} import contextlib import time @@ -109,19 +109,19 @@ from qutip.core.environment import ( Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: -```{code-cell} ipython3 +```{code-cell} def cot(x): """ Vectorized cotangent of x. """ return 1. / np.tan(x) ``` -```{code-cell} ipython3 +```{code-cell} def coth(x): """ Vectorized hyperbolic cotangent of x. """ return 1. / np.tanh(x) ``` -```{code-cell} ipython3 +```{code-cell} def underdamped_matsubara_params(lam, gamma, T, nk): """ Calculation of the real and imaginary expansions of the underdamped correlation functions. @@ -163,7 +163,7 @@ def underdamped_matsubara_params(lam, gamma, T, nk): return ckAR, vkAR, ckAI, vkAI ``` -```{code-cell} ipython3 +```{code-cell} def plot_result_expectations(plots, axes=None): """ Plot the expectation values of operators as functions of time. @@ -191,7 +191,7 @@ def plot_result_expectations(plots, axes=None): return fig ``` -```{code-cell} ipython3 +```{code-cell} @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -205,7 +205,7 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} ipython3 +```{code-cell} # Solver options: options = { @@ -222,19 +222,19 @@ options = { And let us set up the system Hamiltonian, bath and system measurement operators: -```{code-cell} ipython3 +```{code-cell} # Defining the system Hamiltonian eps = .5 # Energy of the 2-level system. Del = 1.0 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` -```{code-cell} ipython3 +```{code-cell} # Initial state of the system. rho0 = basis(2, 0) * basis(2, 0).dag() ``` -```{code-cell} ipython3 +```{code-cell} # System-bath coupling (underdamed spectral density) Q = sigmaz() # coupling operator @@ -258,7 +258,7 @@ NC = 10 tlist = np.linspace(0, 50, 1000) ``` -```{code-cell} ipython3 +```{code-cell} # Define some operators with which we will measure the system # 1,1 element of density matrix - corresonding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -269,7 +269,7 @@ P12p = basis(2, 0) * basis(2, 1).dag() ### First let us look at what the underdamped spectral density looks like: -```{code-cell} ipython3 +```{code-cell} def plot_spectral_density(): """ Plot the underdamped spectral density """ w = np.linspace(0, 5, 1000) @@ -290,7 +290,7 @@ The correlation functions are now very oscillatory, because of the Lorentzian pe ### So next, let us plot the correlation functions themselves: -```{code-cell} ipython3 +```{code-cell} def Mk(t, k, gamma, w0, beta): """ Calculate the Matsubara terms for a given t and k. """ Om = np.sqrt(w0**2 - (gamma / 2)**2) @@ -342,7 +342,7 @@ plot_correlation_function() It is useful to look at what the Matsubara contributions do to this spectral density. We see that they modify the real part around $t=0$: -```{code-cell} ipython3 +```{code-cell} def plot_matsubara_correlation_function_contributions(): """ Plot the underdamped correlation function. """ t = np.linspace(0, 20, 1000) @@ -376,7 +376,7 @@ Next we calculate the exponents using the Matsubara decompositions. Here we spli The HEOM code will optimize these, and reduce the number of exponents when real and imaginary parts have the same exponent. This is clearly the case for the first term in the vkAI and vkAR lists. -```{code-cell} ipython3 +```{code-cell} ckAR, vkAR, ckAI, vkAI = underdamped_matsubara_params( lam=lam, gamma=gamma, T=T, nk=Nk, ) @@ -388,7 +388,7 @@ The solver constructs the "right hand side" (RHS) determinining how the system a Below we create the bath and solver and then solve for the dynamics by calling `.run(rho0, tlist)`. -```{code-cell} ipython3 +```{code-cell} with timer("RHS construction time"): bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI) HEOMMats = HEOMSolver(Hsys, (bath,Q), NC, options=options) @@ -397,7 +397,7 @@ with timer("ODE solver time"): resultMats = HEOMMats.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} plot_result_expectations([ (resultMats, P11p, 'b', "P11 Mats"), (resultMats, P12p, 'r', "P12 Mats"), @@ -410,7 +410,7 @@ to perform this expansion is an useful skill. Below we show how to use this built-in functionality: -```{code-cell} ipython3 +```{code-cell} # Compare to built-in under-damped bath: with timer("RHS construction time"): @@ -422,7 +422,7 @@ with timer("ODE solver time"): result_udbath = HEOM_udbath.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} plot_result_expectations([ (result_udbath, P11p, 'b', "P11 (UnderDampedEnvironment)"), (result_udbath, P12p, 'r', "P12 (UnderDampedEnvironment)"), @@ -433,7 +433,7 @@ plot_result_expectations([ The `UnderDampedEnvironment` class also allows us to easily evaluate analytical expressions for the power spectrum, correlation function, and spectral density. In the following plots, the solid lines are the exact expressions, and the dashed lines are based on our approximation of the correlation function with a finite number of exponents. In this case, there is an excellent agreement. -```{code-cell} ipython3 +```{code-cell} w = np.linspace(-3, 3, 1000) w2 = np.linspace(0, 3, 1000) t = np.linspace(0, 10, 1000) @@ -464,7 +464,7 @@ plt.show() ### We can compare these results to those of the Bloch-Redfield solver in QuTiP: -```{code-cell} ipython3 +```{code-cell} with timer("ODE solver time"): resultBR = brmesolve( Hsys, rho0, tlist, @@ -472,7 +472,7 @@ with timer("ODE solver time"): ) ``` -```{code-cell} ipython3 +```{code-cell} plot_result_expectations([ (resultMats, P11p, 'b', "P11 Mats"), (resultMats, P12p, 'r', "P12 Mats"), @@ -487,7 +487,7 @@ plot_result_expectations([ The thermal state of a reaction coordinate (treating the environment as a single damped mode) should, at high temperatures and small gamma, tell us the steady-state: -```{code-cell} ipython3 +```{code-cell} dot_energy, dot_state = Hsys.eigenstates() deltaE = dot_energy[1] - dot_energy[0] @@ -520,7 +520,7 @@ P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag()) P11RC = expect(rhoss, P11RC) ``` -```{code-cell} ipython3 +```{code-cell} rcParams = { "axes.titlesize": 25, "axes.labelsize": 30, @@ -537,7 +537,7 @@ rcParams = { } ``` -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) with plt.rc_context(rcParams): @@ -566,7 +566,7 @@ with plt.rc_context(rcParams): ## About -```{code-cell} ipython3 +```{code-cell} qutip.about() ``` @@ -574,7 +574,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 +```{code-cell} assert np.allclose( expect(P11p, resultMats.states[-100:]), P11RC, rtol=1e-2, ) diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb deleted file mode 100644 index e025bf2b..00000000 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.ipynb +++ /dev/null @@ -1,2464 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ebddecba", - "metadata": {}, - "source": [ - "# HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions" - ] - }, - { - "cell_type": "markdown", - "id": "2142c296", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded \n", - "in a set of auxiliary density matrices.\n", - "\n", - "In this example we show the evolution of a single two-level system in contact with a single bosonic environment.\n", - "\n", - "The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", - "\n", - "The bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", - "\n", - "In the example below we show how to model an Ohmic environment with exponential cut-off in three ways:\n", - "\n", - "* First we fit the spectral density with a set of underdamped brownian oscillator functions.\n", - "* Second, we evaluate the correlation functions, and fit those with a certain choice of exponential functions.\n", - "* Third, we use the available OhmicBath class \n", - "\n", - "In each case we will use the fit parameters to determine the correlation function expansion co-efficients needed to construct a description of the bath (i.e. a `BosonicBath` object) to supply to the `HEOMSolver` so that we can solve for the system dynamics." - ] - }, - { - "cell_type": "markdown", - "id": "d3ef97c3", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "ed47f849", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "import qutip\n", - "from qutip import (\n", - " basis,\n", - " expect,\n", - " sigmax,\n", - " sigmaz,\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver\n", - ")\n", - "from qutip.core.environment import BosonicEnvironment,OhmicEnvironment\n", - "\n", - "# Import mpmath functions for evaluation of gamma and zeta\n", - "# functions in the expression for the correlation:\n", - "\n", - "from mpmath import mp\n", - "\n", - "mp.dps = 15\n", - "mp.pretty = True\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "65a7dfbb", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "Let us set up the system Hamiltonian, bath and system measurement operators:" - ] - }, - { - "cell_type": "markdown", - "id": "1e362553", - "metadata": {}, - "source": [ - "### System Hamiltonian" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ac95be0b", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the system Hamiltonian\n", - "eps = 0 # Energy of the 2-level system.\n", - "Del = 0.2 # Tunnelling term\n", - "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()\n", - "rho0 = basis(2, 0) * basis(2, 0).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "d89e26d2", - "metadata": {}, - "source": [ - "### System measurement operators" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d79edfb4", - "metadata": {}, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresonding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresonding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "52c4fb7a", - "metadata": {}, - "source": [ - "### Analytical expressions for the Ohmic bath correlation function and spectral density" - ] - }, - { - "cell_type": "markdown", - "id": "a0a87475", - "metadata": {}, - "source": [ - "Before we begin fitting, let us examine the analytic expressions for the correlation and spectral density functions and write Python equivalents. \n", - "\n", - "The correlation function is given by (see, e.g., http://www1.itp.tu-berlin.de/brandes/public_html/publications/notes.pdf for a derivation, equation 7.59, but with a factor of $\\pi$ moved into the definition of the correlation function):\n", - "\n", - "\\begin{align}\n", - "C(t) =& \\: \\frac{1}{\\pi}\\alpha \\omega_{c}^{1 - s} \\beta^{- (s + 1)} \\: \\times \\\\\n", - " & \\: \\Gamma(s + 1) \\left[ \\zeta \\left(s + 1, \\frac{1 + \\beta \\omega_c - i \\omega_c t}{\\beta \\omega_c}\\right) + \\zeta \\left(s + 1, \\frac{1 + i \\omega_c t}{\\beta \\omega_c}\\right) \\right]\n", - "\\end{align}\n", - "\n", - "where $\\Gamma$ is the Gamma function and\n", - "\n", - "\\begin{equation}\n", - "\\zeta(z, u) \\equiv \\sum_{n=0}^{\\infty} \\frac{1}{(n + u)^z}, \\; u \\neq 0, -1, -2, \\ldots\n", - "\\end{equation}\n", - "\n", - "is the generalized Zeta function. The Ohmic case is given by $s = 1$.\n", - "\n", - "The corresponding spectral density for the Ohmic case is:\n", - "\n", - "\\begin{equation}\n", - "J(\\omega) = \\omega \\alpha e^{- \\frac{\\omega}{\\omega_c}}\n", - "\\end{equation}" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "bfb44fda", - "metadata": {}, - "outputs": [], - "source": [ - "def ohmic_correlation(t, alpha, wc, beta, s=1):\n", - " \"\"\"The Ohmic bath correlation function as a function of t\n", - " (and the bath parameters).\n", - " \"\"\"\n", - " corr = (1 / np.pi) * alpha * wc ** (1 - s)\n", - " corr *= beta ** (-(s + 1)) * mp.gamma(s + 1)\n", - " z1_u = (1 + beta * wc - 1.0j * wc * t) / (beta * wc)\n", - " z2_u = (1 + 1.0j * wc * t) / (beta * wc)\n", - " # Note: the arguments to zeta should be in as high precision as possible.\n", - " # See http://mpmath.org/doc/current/basics.html#providing-correct-input\n", - " return np.array(\n", - " [\n", - " complex(corr * (mp.zeta(s + 1, u1) + mp.zeta(s + 1, u2)))\n", - " for u1, u2 in zip(z1_u, z2_u)\n", - " ],\n", - " dtype=np.complex128,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9e798939", - "metadata": {}, - "outputs": [], - "source": [ - "def ohmic_spectral_density(w, alpha, wc):\n", - " \"\"\"The Ohmic bath spectral density as a function of w\n", - " (and the bath parameters).\n", - " \"\"\"\n", - " return w * alpha * np.e ** (-w / wc)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "7691064b", - "metadata": {}, - "outputs": [], - "source": [ - "def ohmic_power_spectrum(w, alpha, wc, beta):\n", - " \"\"\"The Ohmic bath power spectrum as a function of w\n", - " (and the bath parameters).\n", - " It is obtained naively using the Fluctuation-Dissipation Theorem\n", - " but, this fails at w=0 where the limit should be taken properly\n", - " \"\"\"\n", - " bose = (1 / (np.e ** (w * beta) - 1)) + 1\n", - " return w * alpha * np.e ** (-abs(w) / wc) * 2*bose " - ] - }, - { - "cell_type": "markdown", - "id": "c7913528", - "metadata": {}, - "source": [ - "### Bath and HEOM parameters" - ] - }, - { - "cell_type": "markdown", - "id": "0a40fda0", - "metadata": {}, - "source": [ - "Finally, let's set the bath parameters we will work with and write down some measurement operators:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "8d58b8c8", - "metadata": {}, - "outputs": [], - "source": [ - "Q = sigmaz()\n", - "alpha = 3.25\n", - "T = 0.5\n", - "wc = 1.0\n", - "s = 1" - ] - }, - { - "cell_type": "markdown", - "id": "635dcec1", - "metadata": {}, - "source": [ - "And set the cut-off for the HEOM hierarchy:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "297850af", - "metadata": {}, - "outputs": [], - "source": [ - "# HEOM parameters:\n", - "\n", - "# The max_depth defaults to 5 so that the notebook executes more\n", - "# quickly. Change it to 11 to wait longer for more accurate results.\n", - "max_depth = 5\n", - "# options used for the differential equation solver, while default works it \n", - "# is way slower than using bdf\n", - "options = {\n", - " \"nsteps\":15000, \"store_states\":True, \"rtol\":1e-12, \"atol\":1e-12, \"method\":\"bdf\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "8827fc32", - "metadata": {}, - "source": [ - "## Building the HEOM bath by fitting the spectral density" - ] - }, - { - "cell_type": "markdown", - "id": "6e3c4370", - "metadata": {}, - "source": [ - "We begin by fitting the spectral density, using a series of $k$ underdamped harmonic oscillators case with the Meier-Tannor form (J. Chem. Phys. 111, 3365 (1999); https://doi.org/10.1063/1.479669):\n", - "\n", - "\\begin{equation}\n", - "J_{\\mathrm approx}(\\omega; a, b, c) = \\sum_{i=0}^{k-1} \\frac{2 a_i b_i w}{((w + c_i)^2 + b_i^2) ((w - c_i)^2 + b_i^2)}\n", - "\\end{equation}\n", - "\n", - "where $a, b$ and $c$ are the fit parameters and each is a vector of length $k$." - ] - }, - { - "cell_type": "markdown", - "id": "6b67cac7", - "metadata": {}, - "source": [ - "With the spectral density approximation $J_{\\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. This can be done quickly using the `SpectralFitter` class, which takes the target spectral density as an array and fits it to the series of **k** underdamped harmonic oscillators with the Meier-Tannor form" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "f6b46bc0", - "metadata": {}, - "outputs": [], - "source": [ - "w = np.linspace(0, 25, 20000)\n", - "J = ohmic_spectral_density(w, alpha, wc)" - ] - }, - { - "cell_type": "markdown", - "id": "ae05a07c", - "metadata": {}, - "source": [ - "The `BosonicEnviroment` class has special construtors that can be used to \n", - "create enviroments from arbitrary spectral densities, correlation functions, or\n", - "power spectrums. Below we show how to construct a `BosonicEnvironment` from a \n", - "user specified function or array" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a60b1cda", - "metadata": {}, - "outputs": [], - "source": [ - "# From an array\n", - "sd_env=BosonicEnvironment.from_spectral_density(J=J,wlist=w)" - ] - }, - { - "cell_type": "markdown", - "id": "f9715b26", - "metadata": {}, - "source": [ - "The resulting `BosonicEnvironment` cannot compute the power spectrum, or \n", - "correlation function because the temperature of the environment has not been \n", - "specified. So the `BosonicEnvironment` is not fully characterized by the \n", - "parameters provided" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "a18793bf", - "metadata": {}, - "outputs": [], - "source": [ - "# sd_env.power_spectrum(w)" - ] - }, - { - "cell_type": "markdown", - "id": "e4dd336f", - "metadata": {}, - "source": [ - "If we want access to these properties we need to provide the Temperature at Initialization" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "0239acf7", - "metadata": {}, - "outputs": [], - "source": [ - "# From an array\n", - "sd_env=BosonicEnvironment.from_spectral_density(J=J,wlist=w,T=T)" - ] - }, - { - "cell_type": "markdown", - "id": "0d571deb", - "metadata": {}, - "source": [ - "Now our bosonic environment can compute the Power Spectrum of the spectral \n", - "density provided" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "f155cacf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Here we avoid w=0\n", - "np.allclose(sd_env.power_spectrum(w[1:]),ohmic_power_spectrum(w[1:],alpha,wc,1/T))" - ] - }, - { - "cell_type": "markdown", - "id": "8ff57d78", - "metadata": {}, - "source": [ - "Specifying the Temperature also gives the `BosonicEnvironment` access to the \n", - "correlation function" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "b07566c5", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/mcditoos/anaconda3/envs/qutip-dev/lib/python3.12/site-packages/matplotlib/cbook.py:1762: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return math.isfinite(val)\n", - "/home/mcditoos/anaconda3/envs/qutip-dev/lib/python3.12/site-packages/matplotlib/cbook.py:1398: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tlist=np.linspace(0,10,500)\n", - "plt.plot(tlist,sd_env.correlation_function(tlist),label=\"BosonicEnvironment (Real Part)\")\n", - "plt.plot(tlist,ohmic_correlation(tlist,alpha,wc,1/T),\"--\",label=\"Original (Real Part)\")\n", - "plt.plot(tlist,np.imag(sd_env.correlation_function(tlist)),label=\"BosonicEnvironment (Imaginary Part)\")\n", - "plt.plot(tlist,np.imag(ohmic_correlation(tlist,alpha,wc,1/T)),\"--\",label=\"Original (Imaginary Part)\")\n", - "plt.ylabel(\"C(t)\")\n", - "plt.xlabel(\"t\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "14490c36", - "metadata": {}, - "source": [ - "One important optional parameter is WMax, when passing arrays to the constructor\n", - "it defaults to the maximum value of the array, however when passing a function \n", - "we don't need to specify the values on which it is evaluated, and in this case \n", - "WMax needs to be specified, Wmax is the cutoff frequency for which the \n", - "spectral density, or power spectrum has effectively decayed to zero, after this value the function can be \n", - "considered to be essentialy zero" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "f71a9644", - "metadata": {}, - "outputs": [], - "source": [ - "# From a function\n", - "sd_env2=BosonicEnvironment.from_spectral_density(ohmic_spectral_density,T=T,wMax=10*wc,args={\"alpha\":alpha,\"wc\":wc})" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "d2ac2faf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tlist=np.linspace(0,10,500)\n", - "plt.plot(tlist,sd_env2.correlation_function(tlist))\n", - "plt.plot(tlist,ohmic_correlation(tlist,alpha,wc,1/T),\"--\")\n", - "plt.plot(tlist,np.imag(sd_env2.correlation_function(tlist)))\n", - "plt.plot(tlist,np.imag(ohmic_correlation(tlist,alpha,wc,1/T)),\"--\")" - ] - }, - { - "cell_type": "markdown", - "id": "54fc1734", - "metadata": {}, - "source": [ - "In this example we considered how to obtain a `BosonicEnvironment` from the spectral density, it can be done analogously from the power spectrum or correlation function using the `from_correlation_function` and `from_power_spectrum` methods." - ] - }, - { - "cell_type": "markdown", - "id": "33a4729e", - "metadata": {}, - "source": [ - "# Obtaining a decaying Exponential description of the environment\n", - "\n", - "In order to carry out our HEOM simulation, we need to express the correlation \n", - "function as a sum of decaying exponentials, that is we need to express it as \n", - "\n", - "$$C(\\tau)= \\sum_{k=0}^{N-1}c_{k}e^{-\\nu_{k}t}$$\n", - "\n", - "As the correlation function of the environment is tied to it's power spectrum via \n", - "a Fourier transform, such a representation of the correlation function implies a \n", - "power spectrum of the form\n", - "\n", - "$$S(\\omega)= \\sum_{k}2 Re\\left( \\frac{c_{k}}{\\nu_{k}- i \\omega}\\right)$$\n", - "\n", - "There are several ways one can obtain such a decomposition, in this tutorial we \n", - "will cover the following approaches:\n", - "\n", - "- Non-Linear Least Squares:\n", - " - On the Spectral Density (`sd`)\n", - " - On the Correlation function (`cf`)\n", - "- Methods based on the Prony Polynomial\n", - " - Prony on the correlation function(`prony`)\n", - " - The Matrix Pencil method on the correlation function (`mp`)\n", - " - ESPRIT on the correlation function(`esprit`)\n", - "- Methods based on rational Approximations\n", - " - The AAA algorithm on the Power Spectrum (`aaa`)\n" - ] - }, - { - "cell_type": "markdown", - "id": "bef212bc", - "metadata": {}, - "source": [ - "# Non-Linear Least Squares\n", - "## Obtaining an decaying Exponential Description via the spectral density" - ] - }, - { - "cell_type": "markdown", - "id": "ce27cb93", - "metadata": {}, - "source": [ - "Once our `BosonicEnvironment` has been constructed, we can obtain a Decaying\n", - "exponnetial representation of the environment, via fitting either the spectral\n", - "density, power spectrum or the correlation function. \n", - "\n", - "First we will show how to do it via fitting the spectral density with the \n", - "Nonlinear-Least-Squares method.\n", - "\n", - "The idea here is that we express our arbitrary spectral density as a sum of \n", - "underdamped spectral densities with different coefficients, for which a the\n", - "Matsubara decomposition is available. The number of exponents to be kept in the \n", - "Matsubara decomposition of each underdamped spectral density needs to be specified\n", - "\n", - "The output of the fit is a tuple containing an `ExponentialBosonicEnvironment`\n", - "and a dictionary that has all the relevant information about the fit performed.\n", - "The goodness of the feed is measured via the normalized root mean squared error,\n", - "by default the number of terms in the fit increased until the target accuracy \n", - "is reached or the maximum number allowed `Nmax` is reached. The default target\n", - "is a normalized root mean squared error of $5\\times 10^{-6}$, if set to None\n", - "the fit is performed only with the maximum number of exponents specified\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "81adee22", - "metadata": {}, - "outputs": [], - "source": [ - "bath, fitinfo = sd_env.approximate(\"sd\",w,Nmax=4)" - ] - }, - { - "cell_type": "markdown", - "id": "2f5bc5a5", - "metadata": {}, - "source": [ - "To obtain an overview of the results of the fit we may take a look at the summary from the ``fitinfo``" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "71a7c82a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result of fitting the spectral density with 4 terms: \n", - " \n", - " Parameters| a | b | c \n", - " 1 |-4.41e+00 | 4.30e+00 |3.98e+00\n", - " 2 | 6.01e-01 | 1.00e+00 |1.00e-01\n", - " 3 | 7.92e+00 | 2.30e+00 |1.00e-01\n", - " 4 | 1.06e-02 | 3.07e-01 |1.00e-01\n", - " \n", - "A 1-R2 coefficient of 1.38e-06 was obtained for the the spectral density.\n", - "The current fit took 29.262844 seconds.\n" - ] - } - ], - "source": [ - "print(fitinfo[\"summary\"])" - ] - }, - { - "cell_type": "markdown", - "id": "8edcc35e", - "metadata": {}, - "source": [ - "We may see how the number of exponents chosen affects the fit since the approximated functions are available:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "d8587f0d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5))\n", - "\n", - "ax1.plot(w, J, label=\"Original spectral density\")\n", - "ax1.plot(w, bath.spectral_density(w), \"--\",label=\"Effective fitted SD\")\n", - "ax1.set_xlabel(r'$\\omega$')\n", - "ax1.set_ylabel(r'$J$')\n", - "ax1.legend()\n", - "\n", - "ax2.plot(w, np.abs(J - bath.spectral_density(w)), label=\"Error\")\n", - "ax2.set_xlabel(r'$\\omega$')\n", - "ax2.set_ylabel(r'$|J-J_{approx}|$')\n", - "ax2.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "89164ff6", - "metadata": {}, - "source": [ - "Here we see a surprisingly large discrepancy in our approximated or effective spectral density. This happens because we are not using enough exponentials from each of the underdamped modes to have an appropiate fit. All modes have the same number of exponents, when not specified it defaults to $1$ which is not enough to model a bath with the temperature considered, let us repeat this with a higher number of exponents." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "bd7aec4a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "bath, fitinfo = sd_env.approximate(\"sd\",w,Nmax=4,Nk=3)\n", - "\n", - "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5))\n", - "\n", - "ax1.plot(w, J, label=\"Original spectral density\")\n", - "ax1.plot(w, bath.spectral_density(w), \"--\",label=\"Effective fitted SD\")\n", - "ax1.set_xlabel(r'$\\omega$')\n", - "ax1.set_ylabel(r'$J$')\n", - "ax1.legend()\n", - "\n", - "ax2.plot(w, np.abs(J - bath.spectral_density(w)), label=\"Error\")\n", - "ax2.set_xlabel(r'$\\omega$')\n", - "ax2.set_ylabel(r'$|J-J_{approx}|$')\n", - "ax2.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "0b6f9c12", - "metadata": {}, - "source": [ - "Since the number of exponents increases simulation time one should go with the least amount of exponents that correctly describe the bath properties (Power spectrum, Spectral density and the correlation function)." - ] - }, - { - "cell_type": "markdown", - "id": "65cf94f6", - "metadata": {}, - "source": [ - "Let's take a closer look at our last fit by plotting the contribution of each term of the fit:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "882c64e5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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///5Al2Q5wiFC34cfSg89JF1yycl3M/FFmzbStddaeh8CAACo//r16+due45ceiKe8ww2adJEHTt29HdZ1erfv7+7vXjxYp/uz/v+++8DWJF/eA604/kzVmf58uWBLCckEA4R+n791Xy22yU/j74FAAAQbEOGDHG3p06d6tPgJh9++KG7fe6558oWxIHzLrnkEnf70KFDNU4AX1xcrClTpgS4qrorKSlxt2v683S5XPrggw8CXZLlCIcIbYZREQ47djQHjgEAAAhj48aNc7cPHTqkSZMmnXD9qVOnel1hvOOOOwJVWpV69+6tQR4jxj/yyCNKT0+vdv2nnnpKqampQaisblq1auVuL1u27ITr/vOf/9TOnTsDXZLlCIcIbYcPV9xneOqpwTnm9OnSHXdIw4cH53gAAKBB6datm6655hr38hNPPKFp06ZVue6KFSt0++23u5dPP/10XXrppQGvsbKXXnrJfXVt165dOu+88/TDDz94rXPs2DE9+OCDeuGFF3yeosNKw4YNc7efeeaZaie2f++99/SnP/0pWGVZitFKEdq2b69on3JKcI758svSkiVme+/eqicFBgAAqIN//etfWrJkiQ4dOqTS0lJdffXVuvLKK3XttdeqTZs2Sk9P1+zZs/Xee++ptLRUkhQTE6P3339fERERQa/3nHPO0VNPPaWJEydKkn755Redc8456tSpkzp06KCcnBxt2LDB3UX2nXfe0ZgxY9zbV56KIxTcf//9ev/992UYhvbt26d+/frpd7/7nc4++2xFRkZqx44d+vjjjzV//nxJ0p133qm3337b4qoDi3CI0ObZJSFY9xuef35FOFywgJFLAQCA3zVv3lzff/+9Ro0apX1lA+5Nnz5d06dPr3L9Ro0a6auvvlKfPn2CWaaXp556Sg6HQ08//bT7fr1du3Z5jbgaGxurN998U6NGjfLatvIUHqHgzDPP1LPPPqsnn3xSknTkyBE99dRTVa573XXX6fHHH6/34ZBupQhtnuEwWKNyjRhR0V6wIDjHBAAADU737t21bt06PfDAA4qPj69yncjISN1www3auHGjzjvvvOAWWIUnnnhCa9as0X333aeuXbsqLi5OSUlJOu200/THP/5RGzZs0K233qrDhw+7t4mNjVVcXJyFVVfviSee0H//+181b968yvdbtmypSZMm6dNPPw3qIEBWsRmGYVhdRLAcOXJEP//8s3766Sf388GDB93vv/vuuxo7dmzA69i5c6emTJmiWbNmac+ePcrNzVXr1q3Vp08f3XTTTbriiivkcNTtom52draSkpKUlZWlxMREP1Vugbvuksq/oVm9WvIY+jlgioulxo2lggKpbVtpzx6pAZwMAAChrbCwULt27VKnTp0UExNjdTnws8LCQi1evFg7d+7U0aNHlZiYqPbt2+u8884Ly9/lpk6d6r6vctCgQcfdnxhqCgsLtWTJEv3yyy8qKChQ8+bNdcopp+jcc8+1pBuvZ13++HfvazZoEN1KDx48qEGDBmn37t1Wl6JXXnlFjz32mIqKirxe37lzp3bu3KkZM2Zo0KBB+vDDD9WZaRusuXIYFSUNGSJ9+605r+K2bcEbDAcAADRIMTExuuCCC6wuw2/eeecdd/vss8+2sBLfxMTEaNSoUcd1h21oGkS30sLCwpAIhs8++6weeughdzC02+3q3bu3hg4d6jWU7ooVKzRs2DAdOHDAqlJDR7du5tXCDh3Mq3nBQtdSAAAAL752OHz//fc1e/Zs93IweubBPxpEOPSUkpKiiy66SE8++aRmzJgRtOPOnTvX6wbXs88+W5s3b9aGDRu0aNEi7du3T5988okSEhIkSfv27dO1114btPpC1uuvm91JU1OD27Xz/PMr2mUjVAEAADRkzzzzjO666y59//337hFUPe3bt08PP/ywVxi8/PLLLR1EB7XTILqVJicn6/PPP1f//v3VoUOHoB/fMAw99thj7m9bunXrpu+++87rxly73a7f/OY3atq0qfty9rJlyzR9+nRdeeWVQa+5wevXz7xSmZkpLVwouVySvcF9lwIAAOBWUFCgt99+W2+//bZiYmLUrVs3NW3aVKWlpUpLS9OOHTu81u/QoYMmT55sUbU4GQ3it93ExERdc801lgRDSZozZ47WrVvnXn7llVeqHbFp5MiR+s1vfuNefuGFFwJeH6oQESGVjwiWkSFt2GBpOQAAAFaze3xRXlhYqHXr1mnBggVavHjxccFw+PDhWrFihVq0aBHsMlEHDeLKodWmTZvmbnfq1KnGm43Hjx+vTz/9VJK0cuVK7du3T23btg1ojajCbbdJZ5xhdjHt2dPqagAAACz19NNPa+jQofr222/1888/a8eOHTp69KhKS0vVuHFjtW7dWoMHD9Y111yj8z1v0UHYIBwGwaxZs9ztCy+8sMY5UoYMGaL4+Hjl5eW5tx8/fnxAawxJL71kTmPRsaP0/PNS377BPf4VV5gPAAAAKDIyUhdddJEuuugiq0tBgDSIbqVWOnz4sNdcir4M5etwONS/f3/38vr16wNSW8jbssV8fPON1HCm4wQAAAAsQTgMsM2bN3std+nSxaftPNervI8GY+/eina7dtbVAQAAADQAdCsNsFTPSdwltW/f3qftPNervI8GIy3NfI6Olpo2taYGl0tav15atMjs3nr55dbUAQAAAAQY4TDAcnJyvJaTkpJ82i4xMbHafVSlqKhIRUVF7uXs7GwfKwxh5eGwTZvgznHoaetWc1oLSRo9mnAIAACAeotupQGWm5vrtRwTE+PTdrGxsdXuoyrPP/+8kpKS3I924d4NMz9fOnbMbLdpY10d3btLKSlme+lSyem0rhYAAAAggAiHAVZaWuq17HD4drHWc72SkpIa1//zn/+srKws92Ov5/164aj8qqFkbTi02aShQ812VpbZxRQAAACohwiHAVZ5svvCwkKftvNcLz4+vsb1o6OjlZiY6PUIa6ESDiVp2LCK9qJF1tUBAAAABBDhMMASEhK8lgsKCnzaLj8/v9p9NAiEQwAAACCoCIcB1qxZM6/lAwcO+LSd59yITa0aqdNKnuGwbVvr6pCk3r2lJk3M9uLF5gimAAAAQD1DOAywbt26eS3v2bPHp+087xns3r27X2sKC6NHS5MnS3/5i9S3r7W12O3SkCFm++hRadMma+sBAAAAAoBwGGBdu3b1Glxm7dq1Pm23Zs0ad7tHjx7+Liv09eol3X239PTTUteuVldD11IAAADUe4TDAIuKitLAgQPdy0uXLq1xm4MHD2r79u3u5aHlo2XCOoRDAAAA1HOEwyC43GPi9O+++06HDh064foffvihu924cWPCYSjo21c69VTpuuukyy6zuhoAAADA7wiHQXDDDTcoOjpakjln4d///vdq183NzdWrr77qXr7pppsUGRkZ8BpDitNpDvyyY4fk49QfARcRIW3dKn36qXTLLVZXAwAAAPgd4fAkpaamymazuR8TJ06sdt22bdtq/Pjx7uVXXnlFU6dOPW69kpISjRs3zj1oTWxsrB5//HG/1x7yDh82u3Gecop07bVWVwMAAAA0CA0mHN51112KiYk57lHbdU7WxIkT1bVsYBWn06nrrrtOt9xyi6ZOnaqFCxfqzTff1FlnnaUvvvjCvc2LL76o1q1b++X4YcVjGg81xJ8fAAAAsICj5lXqh5KSEhUVFZ1wndLSUpWWlgbk+E2aNNHMmTM1cuRI7d27Vy6XSx988IE++OCDKtd/9NFHdd999wWklpDneU9mixbW1VGd/Hzp55/N6S1sNqurAQAAAPyiwVw5DAWnnnqq1q9frzvuuEOxsbFVrtOjRw99+eWX+tvf/hbk6kJIKIfDhx+WGjc2u73u2mV1NQAAoB4YMWKE+1ald955x+py0IA1mCuHU6ZM0ZQpU/y2v44dO8owjFpv17hxY7399tt6+eWXtWDBAu3du1d5eXlq1aqVTjvtNPXr189vNYYtz26loRYOk5OlkhKzvWiR1LmztfUAAICwt27dOne7ofwueOTIEf3888/66aef3M8HPX4HfPfddzV27FjrCmygGkw4DDWNGjXymuICHjyvHLZsaV0dVfGcVmTJEmncOOtqAQAAYW/Pnj3KyMiQZM6P3atXL4srCqyDBw9q0KBB2r17t9WloAp0K0XoCeVupQMGSFFRZnvxYmtrAQAAYW/NmjXudq9evRRV/ntGPVVYWEgwDGGEQ4SeUO5WGhMjDRxotnfskNLSrK0HAACEtbVr17rbZ5xxhnWFWCAlJUUXXXSRnnzySc2YMcPqciC6lSIUlV85jImRGjWytpaqDB1qdimVzOfrr7e2HgAAELY8rxw2hPsNk5OT9fnnn6t///7q0KGD1eWgEq4cIvQcOWI+t2wZmlNFeN53SNdSAABQB57hsCFcOUxMTNQ111xDMAxRhEOEnrQ06cABae5cqyup2jnnSBERZptwCAAATtLRo0e1Z88eSZLdbtfpp59+wvVffPFFORwO97QXd999t4qLi4NRKhoIwiFCj8NhXjU89VSrK6laQoJ05plm+5dfpPR0a+sBAABhyfN+w27duikuLq7K9XJzc3Xttdfq0UcfldPpVFRUlN544w299dZb9X4AGwQX4RA4GeVdS7t3l/bts7YWAAAQlnzpUrp161YNGDBAX3zxhSSpZcuWWrhwoe65556g1IiGhXAInIyHHzYHztm8Werb1+pqAABAGKppMJoZM2ZowIAB2rx5syRp4MCBWrVqlc4555yg1YiGhXCI0LJ0qfTYY9I//yn9+qvV1VSvVSupeXOrqwAAAGGsuiuHLpdLjz/+uK666iplZ2dLkm6//XYtWrRIrVu3rtUxpkyZ4r5H0Z+PKVOm+OXPAKGFqSwQWpYtk/7+d7Pdvn3o3ncIAECIMwxDBSVOq8sIabGREbJZNDJ6QUGBtm7d6l4uv3KYkZGhG264QfPmzZMkRUZG6uWXX9Z9991nSZ1oWAiHCC3lcxxKUosW1tVRWy6XZOdCPAAgdBSUONXzLyE68neI2PTMhYqLsubX4Q0bNsjpNMN7p06d1LhxY61atUpXX321du/eLUlq0aKFPv/8cw0ZMuSkj9OmTRtdeOGFfqm58n5R/xAOEVoOHqxoh3o43LtXev55czqLyy+X/vpXqysCAABhonKX0nfffVe//e1vVVhYKEnq37+/pk2bprZt29bpOKNGjdKoUaPqtA80HIRDhBbPaSFCPRxGRkpvvGG2Gze2tBQAACqLjYzQpmf8f8WoPomNjLDs2J7hcMmSJZo6dap7+bbbbtObb76pmJgYK0pDA0Y4RGgpD4cOh5SYaG0tNSmfi/HXX6WVK6WCAik21uqqAACQJNlsNsu6TKJmnnMcHj582N0eP3683nzzTQsqAhitFKGmPBw2ayZZdIN4rZTPd1hSIv34o7W1AACAsOB0OrV+/Xr38ujRo93tzz77TL+G8ojtqNf4OgmhxTMchoOhQ6W33zbbixdL551naTkAACD0bd26VQUFBZLMSe2nTp2q8847TytWrNCxY8d02WWXacWKFWrSpEmdjzVv3jy99NJLdd5PZY888gj3MtZDhEOEjrw8s2umFF7hsNzixdbVAQAAwobn/Yann366oqOjNX36dA0YMEB79+7Vr7/+qmuvvVbffPONHI66/bqelpamuXP9P2rt9ddf7/d9wnp0K0Xo8ByMJlzCYYcO5nyMkrR8uVRcbG09AAAg5Hneb3j66adLMq8gfvnll4qLi5MkzZ8/X/fff78V5aEBIxwitIweLQ0cKPXsaXUlviu/elhQIK1ebW0tAAAg5FW+cliuX79++t///idb2bgLb775pl599dU6HWvs2LEyDMPvj7Fjx9apLoQmwiFCR4cO0syZ0ooV0tNPW12N7zwnpqVrKQAAqEFVVw7LXXXVVXra4/eghx9+OCDdQoGqEA6BuvK873DJEuvqAAAAIW/Pnj3KyMiQJEVHR6tbt27HrTNhwgT3PX1Op1O/+c1vtHnz5qDWiYaJcAjUVbdu0l//Kn33nfTJJ1ZXAwAAQpjnVcNevXpVO+DMO++8o/79+0uSsrKydNlll7lDZbi76667FBMTc9yjtuvA/wiHQF3ZbNLjj0sjRkjx8VZXAwAAQlh19xtWFhsbqxkzZqh169aSpB07dujqq69WSUlJwGsMtJKSEhUVFR338FRaWlrjOvA/wiFCx5/+JPXoYd7Dt2OH1dUAAAD4na/hUJJat26tL7/8UrGxsZKkRYsW6d577w1ofWjYmOcQoWPnTmnLFrNdxzl9AAAAQtGMGTNqtf5ZZ52l/Pz8wBRjkSlTpmjKlClWl4EqcOUQoSMc5zn0tGWL9MYb0l13SYZhdTUAAABArXB5BqGjPBzGxEhlE8CGlT/8QZo1q6JdxehjAAAAQKjiyiFCR3k4bNbMHOQl3HhOacF8hwAAAAgzhEOEBsPwDofhiHAIAACAMEY4RGjIyZHKh2YO13B4xhlS2WhihEMAAACEG8IhQkO4D0YjSVFR0tlnm+09e6Tdu62tBwAAAKgFwiFCQ30IhxJdSwEAABC2CIcIDRkZFe2mTa2ro64IhwAAAAhTTGWB0NCrlzR5snT0qHTuuVZXc/IGDpQiI837JwmHAAAACCOEQ4SG9u2lu++2uoq6i4uT+veXli+Xfv1VOnhQatnS6qoAAACAGhEOAX8bM0Zq0cLsYhoVZXU1AAAAgE8Ih4C/PfaY1RUAAAAAtUY4RGjYuVMqLZWaNDEHpLEzVhIAAFUxDMPqEgAESbD/vfMbOELD738vdesmNW8uHTpkdTUAAIQce9kXpy6Xy+JKAARL+b93e5AunBAOERqOHq1oN2liXR3+VFQkLVtmDkwDAEAdORwO2Ww2FRUVWV0KgCApLCyUzWaTwxGcDp+EQ4SGY8fM59hYKSbG2lr8YfFiqXFjc1qO//zH6moAAPWA3W5XbGys8vLyrC4FQJBkZ2crISGBK4doYMrDYX25atijh1RYaLaZ7xAA4CcJCQnKy8tTcXGx1aUACLC8vDwVFhYqMTExaMckHCI01LdwmJIi9exptletknJzra0HAFAvJCUlyeFwaN++fXI6nVaXAyBA8vLytHfvXsXHxyshISFox2W0UlivsFAqKDDbycnW1uJPQ4dKmzZJTqf0ww/SqFFWVwQACHMOh0Pt2rVTamqqtm/frqSkJCUkJCgiIkI2m83q8gCcJMMw5HK5VFhYqOzsbBUWFio+Pl5t27YNWpdSiXCIUFB+1VCqP1cOJTMcvvmm2V68mHAIAPCL6OhoderUSZmZmcrKytIxz/9HAYQ1m82mhIQENW3aNKj3GpYjHMJ69TUcDhlS0ea+QwCAH0VFRal58+ZKSUlRaWkpXUyBesBut8vhcAQ9EHoiHMJ69TUctm0rde4s7dwp/fij2X22PozECgAIGTabTZGRkYqMjLS6FAD1AAPSwHqecxzWp3sOJbNrqWTOefjTT9bWAgAAAJwA4RDWu/hi6dAhafNm6a67rK7Gv8rDoUTXUgAAAIQ0upXCeg6H1Ly5+ahvysNhly5SbKy1tQAAAAAnQDgEAqlzZyktTWrd2upKAAAAgBOiWykQSDYbwRAAAABhgSuHsN5nn0mpqeZIpTfcICUkWF0RAAAA0OAQDmG999+XZs0y21dcUb/DIdNZAAAAIETRrRTW85znsHFjy8oIqKeekvr1k9q1k1wuq6sBAAAAjkM4hPXK5zlMSJDq6yS+69ZJa9dK6enSL79YXQ0AAABwHMIhrJeZaT7X16uGEvMdAgAAIOQRDmG9rCzzmXAIAAAAWIZwCGuVlEgFBWY7KcnaWgKpb9+KgXYWL5YMw9JyAAAAgMoIh7BW+VVDqX6HQ4dDGjzYbB88KG3fbm09AAAAQCWEQ1iroYRDia6lAAAACGmEQ1iLcAgAAACEBIfVBaCBi4iQBg40Q2L79lZXE1j9+0vR0VJREeEQAAAAIYdwCGudfrq0YoXVVQRHdLQ0aJC0aJG0Z49572HLllZXBQAAAEiiWykQXE88Ic2ZIx07RjAEAABASOHKIRBMo0ZZXQEAAABQJa4cAgAAAAAIh7DYSy+Z8/9dcom0ZYvV1QAAAAANFuEQ1tqyRVq+3LwPr6jI6mqCIz1deucd6aabpGnTrK4GAAAAkMQ9h7BaQ5rnsNymTdIdd5jtiAjpqqusrQcAAAAQVw5htYYYDgcNkuLizPb8+ZJhWFsPAAAAIMIhrOYZDhMTrasjmKKipCFDzPb+/dLWrdbWAwAAAIhwCKuVh8NGjcwulg3FiBEV7fnzrasDAAAAKBOwew7T0tK0adMm7d69W0eOHFFeXp4kKT4+XikpKerQoYN69eql1q1bB6oEhIPycNhQupSWqxwO77vPuloAAAAA+TEcHjt2TF9++aXmzp2r77//XocPH/Zpu+bNm2vYsGG68MILNWbMGDVt2tRfJSEcNNRw2LevlJwsHT0qLVwoOZ0N68opAAAAQk6du5XOmTNHV1xxhVq1aqU77rhDn332mQ4dOiTDMHx6HDp0SJ9//rnuvPNOtW7dWpdffrlmzZrlj58Noa6kRMrPN9sNLRza7dLw4WY7M1NavdrScgAAAICTunLocrn03nvv6YUXXtD27dslSUYVIy5GR0erdevWatKkiWJjY2UYhgoKCnTs2DEdOHBARWXz2pVvW1JSopkzZ2rmzJnq3Lmz/vSnP2ns2LGK4IpK/ZSdXdFuaOFQMruWTp1qtufPl/r3t7YeAAAANGg2o6pUdwKff/65Hn/8ce3cuVNSRbCLiYnR4MGDNWzYMPXv31+nnXZajfcTpqWlacOGDfr555+1aNEiLVu2TIWFhWZhNpskqWPHjnr++ed13XXX1fqHa8iys7OVlJSkrKwsJYbqKKA5OdK//mVeOevatWLuv4Zi2zbp1FPN9siR0rx51tYDAACAesnXbFCrcDhs2DAtXbpUkhkKHQ6HRo8erZtuukkXX3yx4uPj61R0fn6+5syZo48++kgzZ85USUmJWaTNpsGDB2vx4sV12n9DEhbhsKEzDOnmm6XTT5cuuMC8DxEAAADws4CEQ7vdvEUxJSVFv/vd73TPPfcoJSWl7tVWIT09XW+++aZef/11HT58WDabTU6nMyDHqo8IhwAAAAAk37NBrQakad68uSZNmqQ9e/ZowoQJAQuGktSsWTM9+eST2r17t15++eWAHgsAAAAAGrpaXTnMy8urc9fRk5Wfn6+4uDhLjh2OwuLKYW6uVFBgDkYTFWV1NQAAAEC9FJArh1YFQ0kEw/poyhSpeXMpOlr68EOrq7GO0ymtXCk9/7z3CK4AAABAEJ3UVBaAX2RmVrQbNbKsDMs9+qj0z3+a7V69pDFjrK0HAAAADVKtrhwCftXQ5zksN2xYRfvbb62rAwAAAA0a4RDW8QyHoXpfZDAMHy45yi7iz51rbS0AAABosPzWrTQ9PV1Lly7VsmXLtHnzZm3fvl0HDhxQUVGRDMNQixYt1L59e/Xv31+DBw/WiBEj1KRJE38dHuEoJ6ei3ZC7lTZqJA0eLC1aJG3fLu3cKXXubHVVAAAAaGD8Fg6bN28um83mXq48COq+ffuUlpamH374Qa+++qocDodGjhypu+++W5dffrm/ykA4IRxWuPBCMxxK5tXDe++1th4AAAA0OH7vVmoYxnHB0PO98ueSkhJ98803uuqqq9SvXz+tXLnS36Ug1BEOK1x4YUWbrqUAAACwgF9HKzUMQ+3bt1enTp3Upk0bpaSkyGazyTAM7d+/X3v27NGGDRuUn5/vXl+S1q1bp3PPPVcvvfSS7r//fn+WhFBWHg7tdik21tparNa3r5SSIh05Ii1YIJWUSJGRVlcFAACABsRv4fCbb77RWWedpeTk5BOu53Q6tWbNGs2cOVMff/yxtm3bJpvNptLSUj300ENq1KiRxo4d66+yEMrKw2GjRpJHl+QGyW6XLrjAnO8xJ0f64Qdp6FCrqwIAAEAD4rdupRdccEGNwVCSIiIidNZZZ2nixInaunWrpk+frk6dOkkyryQ++OCDOnjwoL/KQijzDIegaykAAAAsZflUFpdffrlWrVqlQYMGSZJyc3P15ptvWlwVgmLBAmnFCunTT62uJDRccIHUqpV0661cNQQAAEDQ2YzqRo8Jst27d6tr165yOp3q16+ffv75Z6tLCmvZ2dlKSkpSVlaWEhvyHILhxjDoYgsAAAC/8jUbWH7lsFyHDh10xhlnyDAM7dq1y+pyAGsQDAEAAGCRkAmHklRYWChJ7tFMAQAAAADBEZRwWFJSohUrVig3N7fK90tLS/XCCy9o/fr1stlsat++fTDKgpWOHpXeftu833DdOqurCT1ZWdJXX5ndTAEAAIAg8Os8h9XJzs7WOeecI5vNppYtW6pt27ZKTk5WZGSkMjIytHHjRuXm5spW1qXuN7/5TTDKgpV27pTuusts//a30r/+ZW09oeSxx6SXXpKcTjM49+ljdUUAAABoAIISDssZhqGDBw8eN1WF55g4V111lZ588slglgUrlE9jITGVRWVt25rBUJJmzyYcAgAAICiC0q00NjZWV199tTp06CDDMNwPSbLZbDr//PP11ltvacOGDfriiy8UFRUVjLJgJcJh9S6+uKI9a5Z1dQAAAKBBCUo4jIuL0+eff65du3Zp3759evfdd3XllVcqKipKLpdLCxcu1EsvvaTDhw8HoxyEAsJh9U45RerWzWwvX27enwkAAAAEWNBHK23durVuu+02TZ06Vfv379fEiRMVHx+vrVu3atSoUXrnnXeCXRKsQDg8sdGjzWeXS5o719paAAAA0CD4LRyWlpbWepsmTZroL3/5izZs2KCePXvK5XLp3nvv1fr16/1VFkIV4fDEysOhJM2caV0dAAAAaDD8Fg579+6tb7755qS27dChg2bOnKno6GiVlpbqH//4h7/KQqgiHJ7YuedKiYlm+5tvKgaoAQAAAALEb+Hw119/1ejRo3XppZdqzZo1td6+Y8eOOvPMM2UYhhYsWOCvshCqPMNheQhChago6YILzPbRo9KKFdbWAwAAgHrP7/cczpkzR2eddZYuu+wyLV68uFbbHi0beOPIkSP+LguhhiuHNaNrKQAAAILIb+HwL3/5i6KiotzTVMyePVvDhw/XKaecomeeeUY//vijXC5XtdtPnjxZW7ZskSQlJyf7qyyEqvh4qUULKS6OcFidiy+W7HbpzDOljh2trgYAAAD1nM3wnIG+jrZt26b77rtP3333nfdBbDZJUnx8vHr37q1u3bqpRYsWio6O1tGjR7V06VKtX79ehmHIZrPpggsu0Jw5c/xV1nGWL1+u9957T0uWLFFaWpoMw1Dbtm117rnn6rbbbtPgwYP9fszyP4PaeOONN3TPPfec1PGys7OVlJSkrKwsJdJtM3xlZEhNm1pdBQAAAMKYr9nAr+Gw3Lx58zRhwgStXLnSPIjN5jXpfWWeJdhsNk2dOlVXXHGFv8tSXl6eHnjggRqnyxg3bpxee+01xcfH++3YhEMAAAAAVvA1GzgCcfBRo0Zp1KhRWrBggd544w199dVXKikpkST31UFPnuHx97//fUCCodPp1FVXXaVvv/3W/VpsbKx69eolh8OhTZs2KTs7W5L07rvvKi0tTbNnz1ZERITfaxk6dKhiY2NrXK99+/Z+PzYAAAAAVCUgVw4rO3r0qObPn6+FCxdq48aN2rZtmw4dOmQWYLOpefPmGjJkiO6++26NHDkyIDU8/vjjev75593Ld911l1544QX3/Y15eXn629/+pmeffdZrm7/+9a9+Ob5nIN61a5c6BvgeMq4c1jOGIW3ZInXqJMXEWF0NAAAAwoil3Up9YRiGCgsLFRERoaioqIAea//+/erSpYsKCwslSbfccovef//9KtedMGGCnnvuOUlSTEyMduzYodatW9e5BsJhJTffLLlcUpcukkcgRxU+/lh64glp1y7p66+lSy+1uiIAAACEEV+zgd+nsvCVzWZTbGxswIOhJE2aNMkdDOPi4jRp0qRq150wYYLatWsnSSosLNQrr7wS8PoapC++MEPPV19ZXUnoi483g6EkffmltbUAAACg3rIsHAbT9OnT3e3rrrvuhFNlREVFady4ce7ladOmBbS2BqmkRCoqMttMY1GzUaPMKT8kMxw6ndbWAwAAgHqp3ofDrVu3avv27e7liy66qMZtLr74Ynd7+/bt2rp1a0Bqa7BycirahMOaxcZK5Z/bI0ekH36wth4AAADUS/U+HK5bt85r+eyzz65xmzPOOMOru+v69ev9XleDRjisPc8RfGfMsKoKAAAA1GO1CofPPPOM8vLyAlVLtfLy8vTMM8+c1LabN292t6Oiotz3E55I5fU89+EPf/zjH9WrVy8lJiYqNjZWbdu21fDhwzVx4kTtKr+3rD4jHNbe6NFS+bQqM2aYo5cCAAAAflSrcDhx4kR16dJFL7zwgjIzMwNUUoXMzEw9//zz6ty5s55++umT2kdqaqq73bZtW58no/ecY9BzH/7wxRdfaNOmTcrJyVFhYaHS0tL0/fff6+mnn9app56qe+65RwUFBX49ZkghHNZecrI0bJjZ3rFD+uUXa+sBAABAvVPrbqVHjhzRE088ofbt2+u3v/2tfv75Z78XtXLlSo0fP17t27fXk08+qSNHjpz0vnI8gkhSUpLP23kO8eq5D39o1qyZBg4cqBEjRuiss85SQkKC+73S0lJNnjxZgwcPVlZWls/7LCoqUnZ2ttcjZOXmVrQ9fnbUwLNrqccgSwAAAIA/1Cocfv/99+rTp48Mw1Bubq4mT56sgQMHqlu3bvrTn/6khQsXuqeMqI38/HzNmzdPf/jDH3TKKafo7LPP1ttvv63c3FwZhqHTTz9dCxcurPV+JSnXI4jE1GLy8NjY2Cr3cbJ69uypSZMmaceOHTpy5IhWrFih7777Tj/99JOOHTummTNnqk+fPu7116xZo+uvv97n/T///PNKSkpyP3zpPmsZz67JhEPfXX55RZv7DgEAAOBnjtqsPHToUK1evVr/+9//9Nxzz7lHAd2+fbtefPFFvfjii4qMjFSPHj3Uu3dvde7cWW3atFHjxo0VGxvrnvj+2LFjSktL044dO7Rx40Zt2bJFpaWl7uMYZfdTnXLKKZowYYJuvvlmn7uDVua5X4fD9x/Xc92SkpKTOranX07QDdDhcGj06NEaMWKErrnmGs2aNUuS9M033+jrr7/WZZddVuP+//znP+vhhx92L2dnZ4duQPQM2/Hx1tURbtq3l848U/r1V6lrV3NKkMhIq6sCAABAPVGrcCiZk9ffeuutuvnmm/XZZ5/p1Vdf1YoVK9zvFxcXa/369bUa4dOoNLjGoEGD9OCDD+raa6+V3V63AVXjyueHk2p1VdNz3fggBZiYmBh9/PHH6tq1qw4dOiRJeu2113wKh9HR0YqOjg50if7RpYt0991mSOzRw+pqwstnn0lt2kjh8ncNAACAsFHrcFjObrfr+uuv1/XXX6+NGzfqww8/1LRp07Rt27bjwp4k95W/qt6TzKuEV111lW666SaddtppJ1vWcTzv56vNIC/5+flV7iPQGjVqpHvvvVcTJ06UJC1ZskSFhYW16hIb8s4+23yg9jp3troCAAAA1FMnHQ499e7dW88//7yef/55paamatGiRVq1apU2bdqk3bt3Kz093T0FRnx8vJo1a6YOHTqoZ8+eOvPMMzV06FB16tTJH6Ucp1mzZu72gQMHfN7u4MGD7nbTpk39WlNNyqe1kMwrmHv37lXXrl2DWgMAAACAhsUv4dBTx44d1bFjR912223+3vVJ6datm7udkZGh/Px8r66m1dm7d6+73b1794DUVp2WLVt6LaenpxMOcbzcXOnwYa4mAgAAwC/qdkNfGOhR6Z62tWvX1rhNWlqa1/QZlfcRaJ5dWiX5FGbRgGRnS1dfLaWkSOPHW10NAAAA6ol6Hw4HDBjgNVDL0qVLa9xmyZIl7nZMTIwGDBgQkNqqU3lk0+bNmwf1+AF3++3mFBYtWkhlI96iFho1ktaskQoLpQULpDrMAwoAAACUq3U43LVrVyDqCJiEhASNGDHCvfzhhx/WuI3nOiNGjAjaaKXlPvnkE3e7Y8eOatWqVVCPH3A5OeZch4cPM+rmybDZpOuuM9sulzR9urX1AAAAoF6odTjs0qWLmjRpouHDh+vhhx/W//73P23YsEFOpzMQ9fnF2LFj3e3169fr66+/rnbd1atXa86cOVVuGwxfffWVZs6c6V6+4oorgnr8oCgbnEgS8xyerGuvrWh/9pl1dQAAAKDesBnVzS1RDbvdXuWE9FFRUerdu7f69eunfv36qW/fvjr99NND4n45wzDUr18/rVu3TpLUqlUrLViw4LiBZg4cOKARI0Zo8+bNkqS+fftq9erVVf68qampXiOsPvXUU+4RRj1lZWXp9ttv1+OPP64zzzzzhHV+/PHHuuuuu9wju8bFxWnHjh3HDVDji+zsbCUlJSkrK0uJiYm13j6ghg6VyrvuFhVJUVHW1hOODEM65RRp507JbpcOHjTvQQQAAAAq8TUbnNRopZ550mazyTAMFRUVafXq1Vq9erXXe127dvUKjP369fOaXiIYbDab/vOf/2jYsGEqKCjQgQMHNHDgQN17770aOnSoHA6HVq5cqddff909+XxsbKzeeuutKoNhbRiGoWnTpmnatGnq3r27LrzwQvXt21etWrVSfHy8cnJytGHDBn3xxRf66aefvGp+9913TyoYhrzyK4cOB8HwZJV3LX3hBbNr6bRpDE4DAACAOqn1lcOZM2dq7dq17seuXbuOm9i+PDCWtytr3br1cYGxY8eOJ/9T+GjatGm6+eabVVBQcML1YmNj9cEHH+iqq66qdh1frxxmZmaqSZMmtaqzUaNGmjx5sm644YZabecppK8cdu8ubd0qJSVJmZlWVxO+Vq+Wyq9Gn3++NH++tfUAAAAgJAXsyuGll16qSy+91L2ck5PjDopr1qzR2rVrtWnTJhUXF0uSV0gsb6elpWn//v2aNWuWez9JSUnq27evBgwYoCFDhmjo0KFq1KhRbcs7oauuukqrVq3SAw88oPnz51cZas8//3y9+uqr6tmzp1+OGRsbq7vvvlvLli3Tpk2bjjump6SkJN1222165JFH1L59e78cPyTl5prPCQnW1hHu+vWTunSRduyQvv/eHOCnvo1sCwAAgKCp9ZVDX5SWlmrTpk3usLh27VqtW7dOmVVcJaruKmN0dLTGjBmj+++/X4MHD/Z3idq7d6+WLVumtLQ0SVKbNm00ePBgtWvXzu/HKnfs2DGtXbtWhw8fVnp6ujIzMxUXF6fk5GT16dNHffr0UUREhF+OFdJXDps0Ma8YnnqqeQURJ+/xx6Xnnzfbb7wh3XOPtfUAAAAg5PiaDQISDquTmprqdYVx7dq12rt37/FFlQXG8rB41VVX6e2331ZSUlKwSg17IR0OIyOl0lLpjDOkVausria8rVlj/jn27i098YR0/fVWVwQAAIAQE5LhsCrHjh3zCourVq3Sli1bjhv0pmvXrlq6dGnQB7MJVyEbDouLK+Y2HDJEWrzY2nrCnWFImzZJvXpZXQkAAABCVNiEw6pkZmZqzpw5eueddzR//nz3lcRRo0Zp7ty5VpcXFkI2HJaWSnPnmiOWNm4sXXCB1RUBAAAA9VpYh0NPs2fP1vXXX6/c3FzZbDZ99913Gj58uNVlhbyQDYcAAAAAgsrXbGAPYk0n5ZJLLtEbb7zhXv7oo48srAYIcRs3Snv2WF0FAAAAwlDIh0NJuvHGG9W0aVNJ0vLlyy2uBghB69ZJfftKp50mvfaa1dUAAAAgDIVFOLTZbOrZs6cMw9D+/futLgd1kZ5uTtb+44/SwYNWV1N/tG0r/fKL2f74Y8nptLYeAAAAhJ2wCIeSFBcXJ0nKycmxuBLUyY8/SiNHSoMGSf/5j9XV1B9Nm0oXX2y209KkRYusrQcAAABhJ2zC4SuvvKK33npLd9xxh9WloC7y8ira8fHW1VEf3XxzRfvDD62rAwAAAGEpbMLhqaeeqjvvvFOTJ0+2uhTURW5uRTshwbo66qPLLpMaNTLbX3whFRRYWw8AAADCStiEQ9QTXDkMnNhY6eqrzXZ2tjRjhqXlAAAAILwQDhFchMPAGju2ov3OO5aVAQAAgPBDOERw0a00sIYOlbp0Mdvz50upqZaWAwAAgPBBOERwceUwsGw2adw4s20Y0nvvWVsPAAAAwgbhEMHFlcPAu+028/7Da6+VzjvP6moAAAAQJhxWF4AGhiuHgde2rXToUMXIpQAAAIAPuHKI4MrPr2gTDgOHYAgAAIBa4sohgmv6dKm42Oxe2qSJ1dUAAAAAKEM4RHDZbFJ0tPlA4BUWSl9+aV6lvfRSq6sBAABACCMcAvXVoUNSjx7SsWNSnz7S6NFmOAcAAACqwD2HQH3VooXUrZvZXr9eWr7c2noAAAAQ0giHCK4//1l6/HHp3/+2upKG4d57K9pvvGFdHQAAAAh5NsMwDKuLgP9lZ2crKSlJWVlZSkxMtLqcCvHx5oilvXtLGzZYXU39V1gotWkjHT0qRUVJ+/ZJKSlWVwUAAIAg8jUbcOUQwWMYFVNZMI1FcMTESHfcYbaLi6V33rG2HgAAAIQswiGCp7Cwoh0XZ10dDc348RXtN9+UnE7ragEAAEDIIhwieMqvGkqEw2Dq0kW66CKznZoqffONpeUAAAAgNBEOETyEQ+swMA0AAABqQDhE8BAOrTN6tNS+vdn+9lvp8GFr6wEAAEDIcVhdABqQvLyKNuEwuCIipD/8QdqzR7r/fql5c6srAgAAQIghHCJ4uHJorfvvt7oCAAAAhDC6lSJ4CIcAAABAyOLKIYInKUm64AIzJHbqZHU1KCoyu5s6OA0AAACAK4cIpoEDpblzpSVLpHHjrK6m4UpPl557TurYUZo2zepqAAAAECIIh0BDs26dNGGCdPCg9PLLVlcDAACAEEE4BBqa88+X+vQx2ytWSD/8YG09AAAACAmEQ6Chsdmk3/++Yvlvf7OuFgAAAIQMwiGCZ9IkqVcvacAAaeVKq6tp2G68UWrb1mx/+aW0caO19QAAAMByhEMET1qatGmT9NNP5kiZsE5UlPSHP1QsP/+8dbUAAAAgJBAOETzMcxha7rxTatbMbH/yibRjh7X1AAAAwFKEQwQP4TC0xMdLDz1ktl0u6e9/t7QcAAAAWItwiOAhHIae++6TGjUy21OmmF1/AQAA0CARDhE8hMPQ07hxRUD8/e+lmBirKwIAAIBFHFYXgAaEcBiaHntMevRRqUkTqysBAACAhQiHCB7PcBgba10d8Na4sdUVAAAAIATQrRTBUx4OY2IkOx89AAAAIJTwGzqCpzwc0qU0dB09Kj3xhPTww1ZXAgAAgCCjWymC54knpCNHpIgIqytBVUpLpX79pD17JIdD+t3vpM6dra4KAAAAQUI4RPCMHWt1BTgRh0O6/XZp4kQzKD7zjDm9BQAAABoEupUCqPDQQxWjlv7vf9Ivv1haDgAAAIKHcAigQlKSObWFJLlc5hQXAAAAaBAIhwgOp1PasUM6cEDKzbW6GpzIAw9I7dqZ7dmzpe++s7YeAAAABAXhEMGRni6dcorUurV0001WV4MTiY2V/u//Kpb/8Acz3AMAAKBeIxwiOMqnsZCYyiIc3HijdMYZZnvdOvP+QwAAANRrhEMEB+EwvNjt0ksvVSw/8YT33yEAAADqHcIhgoNwGH7OO08aM0Zq3Fh65BFzqgsAAADUW/y2h+AgHIanf/3LvAexaVOrKwEAAECAEQ4RHITD8NS2rdUVAAAAIEjoVorgIBzWH3l5VlcAAACAACAcIjgIh+EvI0O65x7p9NOlggKrqwEAAICfEQ4RHJ5hgnAYnsaPlyZPlnbskJ5/3upqAAAA4GeEQwQHVw7D37PPVoxY+sIL0saN1tYDAAAAvyIcIjjGjZO2bJFWr5ZGjbK6GpyMHj2kRx812yUl0h13SE6ntTUBAADAbwiHCI6kJKlbN6lfPyk52epqcLImTJC6dzfbK1dKr7xibT0AAADwG8IhAN/FxEj//a9ks5nLTz4pbd9ubU0AAADwC8IhgNo55xzp/vvNdkGBdNddkstlbU0AAACoM8IhguObb8yRLv/3Pyk31+pqUFd//avUsaPZ/v576dVXrawGAAAAfkA4RHD85z/mHHm33iplZVldDeoqIcHsXlru++8lw7CsHAAAANSdw+oC0EB4TmURG2tdHfCf88+XHn9catlS+t3vKu5DBAAAQFgiHCI4Cgoq2oTD+uOvf7W6AgAAAPgJ3UoRHIWFFe2YGOvqQOAxOA0AAEBYIhwiOMqvHMbE0P2wPps3T+rdW9qzx+pKAAAAUEuEQwRHeTikS2n99fnn0oUXSps3S9ddJxUXW10RAAAAaoFwiOAgHNZ/I0dWTG/x44/SI49YWg4AAABqh3CI4Ci/55BwWH81aSJ98YUUHW0uv/669PHH1tYEAAAAnxEOERye9xyi/jrjDOm11yqW77hDWr3aunoAAADgM8IhgqNdO6ltW6lVK6srQaDdeac0dqzZLiiQLrtM2r/f0pIAAABQM8IhgmPzZmnvXnM0S9RvNpv05pvSOeeYy/v3S5dfLuXnW1sXAAAATohwCMD/oqOl6dOlDh3M5Z9/NruYAgAAIGQRDgEERvPm0tdfSwkJ5uPmm62uCAAAACfgsLoAAPXYaadJ06aZ95r27m11NQAAADgBwiECb88e6be/NUcqveAC6e67ra4IwTRqlNUVAAAAwAeEQwReero0a5bZTkmxthZYzzCkv/5V6tVLuvJKq6sBAABAGcIhAq+wsKIdG2tdHbCeyyU9+KD0+utSZKT0+efmSKYAAACwHAPSIPAKCirahENkZZnPJSXStddKX35pbT0AAACQRDhEMHiGw5gY6+qA9ex26d13pZtuMpcJiAAAACGDcIjA48ohPEVESO+95x0Qr7lG+ugja+sCAABo4AiHCDzuOURllQNiaanZfvVVa+sCAABowAiHCDyuHKIq5QFx/PiK1x58UHriCXNEUwAAAAQV4RCBxz2HqE5EhPTGG9KECRWvff65lJ1tXU0AAAANFOEQgceVQ5yIzSY984z02mtSmzbSt99KSUlWVwUAANDgMM8hAu+MM6Tf/tYMiZ07W10NQtXvfieNHSslJHi/bhhmgAQAAEBAEQ4ReBdcYD6AmlQOhnl50iWXSPfeK11/vTU1AQAANBB0KwUQmlwu80ri4sXSDTeYAdFz5FsAAAD4FeEQQGgqLZXi4iqW33xTGjRI2rbNupoAAADqMcIhAo9pCXAyoqKkKVOkd96pGMho3TqpXz8zKPK5AgAA8CvCIQLv2mvNX/STkqT9+62uBuHEZpPGjZNWrpS6dzdfy8szu5hedJG0d6+19QEAANQjhEMEXkGBVFJizl0XHW11NQhHvXtLP/0k3X13xWvffmu+/tFH1tUFAABQjxAOEXie8xzGxFhXB8JbQoI0ebI0Z445H6JkfuHg+fkCAADASWMqCwSe5y/v5feOASfrooukDRukBx+Utm83u50CAACgzgiHCLzy6QeioiQ7F6vhB02aSO+/L+XnH/+Zuuce6fTTpTvvlCIjrakPAAAgDPGbOgKv/MohVw3hb55TXUjSsmVm19Pf/lY67TRpxgxGNQUAAPAR4RCBVx4Oud8QgfbddxXtrVulK68050acOZOQCAAAUAPCIQKPK4cIlqeeklaskIYMqXht5UrpssukM86Qpk6VXC7r6gMAAAhhhEMEXvk9h4RDBMPAgdKiRWaX0j59Kl5fu1a65hqpVy9p7lyrqgMAAAhZhEMEHlcOEWw2m3T55dKaNWZIPOusive2bOGzCAAAUAVGK0VgGYZ5v1dBgRQfb3U1aGjsdjMkjhljXi18/nlzbkTPbqeSNGuWlJMjXXEF98YCAIAGy2YYjNJQH2VnZyspKUlZWVlKTEy0uhwgdOTkSI0aeb/Wv7/0889S48bSDTdIt90mDRhgXoEEAAAIc75mA7qVAmhYKgfDTZvMYChJmZnSG2+YI5x26iT94Q/mADd8hwYAABqABhkOly9frvHjx6tnz55KSkpSYmKievbsqbvvvlvLli0L+PF37typv/zlLzrzzDOVkpKi2NhYdenSRVdeeaW++OILlZaWBrwGAGW6d5cWLJBuucX7XsTdu6WXXpLOPlvq0EF68EHpyBHr6gQAAAiwBtWtNC8vTw888IDeeeedE643btw4vfbaa4oPwD1yr7zyih577DEVFRVVu86gQYP04YcfqnPnzid9nJDpVpqTIy1fbt7H1a6dVIefCQi47Gzp88/Nx/z5kucXNVFR0tGj3vfOFhSYn226nwIAgBDmazZoMOHQ6XTqkksu0bfffut+LTY2Vr169ZLD4dCmTZuUnZ3tfu+CCy7Q7NmzFRER4bcann32Wf3lL39xL9vtdvXs2VPJycnatm2bDhw44H6vbdu2WrlypVq1anVSxwqZcLh2rdSvn9m++25p8mTragFqIyND+uorMyh+95103nmSx/lDknT99dLSpdLQoeZj2DDzSiRhEQAAhBDuOaxkwoQJXsHwrrvu0r59+/TTTz/phx9+0P79+zVhwgT3+99++61XkKuruXPn6qmnnnIvn3322dq8ebM2bNigRYsWad++ffrkk0+UkJAgSdq3b5+uvfZavx3fMuXTWEhMH4Dw0rSpNG6cNHu2dPiw9O9/e79fWmqGxbQ06eOPpXvvlXr2lFq0MEdIfe45c4TUjAxr6gcAAKilBnHlcP/+/erSpYsKyyZjv+WWW/T+++9Xue6ECRP03HPPSZJiYmK0Y8cOtW7duk7HNwxD/fr107p16yRJ3bp10+rVqxUXF3fcut99951GjRrlXp42bZquvPLKWh8zZK4cLlwonX++2f7Tn8ypBID64OBBc1TTZcukvLwTr/vZZ5Lnlz2lpeY0G/YG8/0cAACwEFcOPUyaNMkdDOPi4jRp0qRq150wYYLatWsnSSosLNQrr7xS5+PPmTPHHQwl877DqoKhJI0cOVK/+c1v3MsvvPBCnY9vKa4cor5q2dK8MnjsmPTjj9KLL0qXXmpOh1FZ797ey9OnS4mJ0sCB0h13SP/8p/T119LmzVLZuQoAACDYGkQ4nD59urt93XXXKTk5udp1o6KiNG7cOPfytGnT6nx8z3106tRJF1xwwQnXHz9+vLu9cuVK7du3r841WMYzHDK5OOqjyEhzTsQ//MEMeBkZ0tat0gcfSA89JI0YIZ16qvc2v/xiXm1cuVJ65x3pkUekMWPMbqlxcVL79tLw4dKf/3z88VyuoPxYAACg4XFYXUCgbd26Vdu3b3cvX3TRRTVuc/HFF+uZZ56RJG3fvl1bt25Vt27dTrqGWbNmudsXXnihbDUMVjFkyBDFx8crr6yr2qxZs7wCY1jhyiEaGrvdDIOnnirddFPV68TGmiP37tp1/ByKhiHt3Ws+qjpXDBxovteundS2rflo1Upq3rzikZIitW7NvzkAAFAr9T4cenbnlMyBYGpyxhlnKCoqSsXFxZKk9evXn3Q4PHz4sA4ePFir4zscDvXv31/ff/+9+/hhy7OLHL+oAqbHHjMfeXlmV9JNm6QdO8zH9u3mc3q61KXL8dvu3SsdOmQ+fv65+mP8+9/mIDnl9uyRHn3U7PZa/mjSxHu5/JGSwv2QAAA0QPU+HG7evNndjoqKct9PeCLl6+3YseO4fdTl+JLUpapf9qrQpUsXdzisy/Etx5VDoHrx8dJZZ5mPyrKypMrzobpcUqdOksMhHThw4i6mzZt7L+/fL336qW91HT1qBsdy//qXOQ1NQoJZc0JCRTs+3vy3HRNj1lb5aunixVJ+vvl+dPTxz1FR5s8TF2cuAwAAy9T7cJiamuput23btsYuneXat2/vDoee+6jL8cv36+vxq9tHWCkoUGrjVuqQeUA27jkEfJeUdPxrdrv0ww9mu7TUDIj79plXEQ8flo4cMZ8PH5a6dvXeNjPT92PHx3sv790rbdhQ83bDhh0fDh96SFqzpuZtn3/eHNG43OHDUseOZnCMjDSfq2t/8onk2btj7lxzgCC7XYqIqBgZtvKjaVPpzTe965g82bwi67leVfvo3997BFrJnL6ksLCiO7DNVvHwXL76aqlXr4rt9u+Xpkw5fpuq9nPffd73b//4o7RixfHrVm63bClddZV3vTNmmJ+dmpxxhvnzlispkd59t+btJPNe2pYtK5Z37TLnDa1JZKQ0dqz3a0uWmPfz1qRjR2nkSO/XPv205lGFJWnwYO/PUmamOYCUL6691vzSpNymTeZ9xTVJSpIqj0r+3XfmNDk16dnT++9GkqoZjf04I0ZIbdpULO/f79vfjSTdcot3t/effzZ/3pq0aiV5jMguyZxP1pfz05lnev+7yc+XvvjCp3J12WXeX3ht3y4tX17zdrGxx/87X7RI2r275m27dDE/T54+/tj891OToUPNz3G5I0ekOXNq3k4y59+NiqpYXrfOfNSkWTPpkku8X5szxzx2Tfr0kfr2rVguKTF/Vl9cdJH3l5m7d5t/xjVxOKQbb/R+7YcfpG3bat62fXtz7mJPU6f6do4YNMh7HIHMTPMzXJ3+/aUePWrebwip9+EwJyfH3U6q6petangO8eq5j7ocvzY11Pb4RUVFKvK4ypCdne1jhYGVWViqEXe9qZY5GRqZFqOR245oYKeminLQZQ2oE4fDvO/Qh94Qksz/CHfuNP8jy8w0R1ktb3su5+R4/2IhmfdBxsZ69wSoSlVX/ipf/ayOo9J/RyUlNR+vXNktAG5padL8+TVv17r18eFwwQJz6pGajBt3/C+NL74o+XLu7dbN+5fcPXukJ56oeTvJHN3WMxx+8400cWLN2w0YcHw4fPFF335BnjDBO4AUFkq+3gffq5d3OFy1Srr77pq3S0g4Phy+/7709ts1b3v11ceHwz/+0fySoyZvveUdDg8ckG6/vebtJDNseYbDb7+Vfv/7mrfr3v34cPjSS+bfbU0efPD4cDh27PH3Mldl9mzvcPjLL+b0PL64+WbvcPjJJ2bNNRk58vhw+Pjj5rFr8uKL3v9ujh3zvd4NG7zD4eLF5r+lmrRuffy/83//2/dzROVweO+9Zq+QmnzyiXc43LnT9591zBjvc/iXX8p46ikZssmw2eSy2crakiHzCyRDNhlnnSXXiAtkGIYMlX2EXviHjJUr5bLZzdc8tpXn/h58SEbHbua2hqS8PLkefMx9TM9tZVPZ/speT+ko4/QYGTK3Nb7/Wcafnj1+2/Jjl78eGytj0IXmNmWfd+P9r2TMmVNxLPdxzWeVvzZ4sIwWPdz/TAxJxj/elZGR4d5OZX8+Rtnn3F3H3TYZBea/c8MwpLT9Mv72rvc67j8jybjJqdOatFH3lhZOK1dL9T4c5ubmutsxtbhyFevRBdJzH3U5fm1qqO3xn3/+eT399NO1Ky4INt10tyKn/KQ0e3O9t196778r1SjaoaHdUjSqRwud1y1FjeOiat4RgLop7/Z5Mv72N/PhdJrf1ufmmt+wlj8XFpqPqkaCvv9+c07IwkIzKHo+FxaaV0BLSswBejzZ7ea30SUlFeuUllbdrhwsfR3Rtar7Kn3d1sdeKCG1LRokQ5LTZpfLZpfLbpfTZj5cJYZcecVyugy5DEPOQkPOxOZy2c11nZXWN2w2Oe0R5uupR+WUTS7DkMslOW3JcnU+q+w4tort7BHmcW02872mveT8cY+chiHDMORyGXJ1GixX7CkyytYzVBY4bDb3ti6bXUZeMxnfbjWPaUiu7BwZ541zr2O4t/XYpjy8/JAu1+Z1cpWFF9eBRLku+2PZdmXruY9bsb0rJkbGf380tzHMPydXywtk3HimR402GSrfpuK4RnKyXP9cVHFMw5Drhn+ay55hx/NnLgtPxoY4Gb/OM/+MDMkoLZXx4CcVQU6qFPTM4GPYJOPvy81jqPw7gjOlx2b69mF5aq738tkPSzUPlSGVSHphgfdr4334IkeSFuVIi5Z4vBAj3TbJt23fWuG9nHSudP25vm37XqV79ofdW/V6lR2S9HGl3jCX/aH69TOlx7YcCatwaDMMX75eCl8jR47U/LJvkIcMGaLFixf7tN0tt9yiDz74QJI0YsQIfedrV4tKnnvuOU2YMMG97HQ6ZfdhoIf//ve/uvPOOyVJERERKi0tPeH6VV05bNeuXY0TXQZDQbFTy7an67vNh/Td5sNKz62oM8JuU/+OTTSyRwuN6tlCHZrGn2BPAOCD0lIzgDqdZtir6uF0mkGrbVvvbffuNb/Z91yvqm1btPC+wiRJ339vHtv9dbRRdbtvX7N7Xbljx6SlSyvWqWq78uXLLze7XJbbuLGiy291xzMMswvt6NHe9c6ebXbfrUmfPmbX0nIlJeZULb645BLzz6rcrl3SwoU1b+dwSLfe6v3a0qW+dysdMcLrJePDj1Sal6cSw6YSQyoxbCqVvJZLJJX06avS9h1UUupSqcuQMztHpUuWyimp1LCVPUvOsu29nvv2lTMySqVOQ06XS6UHD8mZtt9c32v7Ss+OKDlbtzaP5zLM7dMzVFpUJKchlcpW5bNTktMRKcMRKadhbutyGXKWlMhl2OSSuY77SgjgJ+XX5dwPm002u72iB7wkW2lppXWq2E6SLTpaNkdExTpOp2yFhTVva7PJ1jjJfC5fJy9PKipyf+IrrhV61CVDtuhoKSnJfK98+/R0yen0Xs9jP+79NW4sxcfJJpu5bmmpbIcOHr9e+XJyE117Xk+N7uNxzrdIdna2kpKSaswG9f7Koedk84W1mFzac934yvffnOTxy/db+TV/HD86OlrRITqYQ2xUhEb2bKGRPVvI5TK0bl+mGRQ3HdbWQzlasfOoVuw8qudmbVbX5gnmuj1aqF+7xrLb+U8NQC2V35N4MmrTVbeyyvew+KpJE/OeqJPRu7f5OBmV7y/yVWSk2WXOg9NlqLjUpeJSl4qcTvO5bLl4b6aKnS4VlbhU7IxT8YCLVeT5fqnL431z2xKnoZLpG1TidKnUaajY6VKpM1YlEX1U4jLKwptLxU5DpU6X93qHDJUsn6cSp7mfUpdLJc4kST7c1rFvv6T9lV5s6tufy4JdVbzYyLdts6oK6T78n+40pKJK3apPYgrrCLtNETabeYutzSa7zSa73aYIu9mOsMt8zWa+Zr4u9/vlr9vtNkXYVLG9x+t2W9m+PY5Vvq29bBuVb1v2bPNqez97rmO+dvw2dps83j9+vzZJdns126j856h6v5LHsl1l75fvt2I/dpvcP39FkKlYryJQVdFWRf3utqpYv6rXa9rWXsM+K+9HFcdC/Vbvw2GCR///Al/vX5GUn59f5T7qcvzyGnwJh/46fqix223q176J+rVvoj9e2F17MvLLrige0o+7jmrb4VxtO5yrN77foWYJUTq/e3ON7NFCQ7qmKDYqwuryASCkGYahEqehwlKnCoudKixxqbDUqYJipwpLnCosdamg2KmiKl4rLHWqqKSiXVjiVEGJS4UlzorgV+pUsdPlHf7KrrKFG4fdpsgIuxwRNkWVPUdG2M3XPN6LsNvksJc/272XI2yKsNs93vd8rmr7SutHVPN6DfuvCG0VQc0d0Dyey8NeRUDzDoERZcEIAMrV+3DYrFkzd/vAgQM+b+c5N2HTpj5+a1jD8ctr8GV//jp+qGvfNE63n9tJt5/bSVn5Jfr+18P6bvNhfb/lsNJzi/XZz/v02c/7FO2wa0jXZhrZo4XO79FczRsx8imA8OV0GcovLlV+sVN5ReZzfrFTecWlyi9yVrznXjZfyyt2Kt+9vrnsDnklThWUOGV1TrPZpKgIu6IcdkU7IhTtMNtREXZFR9rd75nv2xXliPBY33xEeoW18qBmV1SEGdAiHXZFHhfuKtatMvTZ7Yp0lG0fQSgCgKrU+3DoOXl9RkaG8vPzfbpyt9djVLPu3bv75fiStGfPHvX2oQuQv44fTpLiInV53za6vG8bFZe69FPqUc3bZF5V3HesQN9tNoOjJPVt11ijyrqfntoigf/kAQRFcalLOYUlyi0qVU5h+aNiObeoVNmFJcota+cUliq3sOy1ooowWFTq48A3dWCzSbGREYqJjFCMw66YqAjFOCIUE2lXbHnb87XydSPtZc8Vy9GOCI8wVxb0HBWve4Y9B1ejACBs1ftw2KPS3CJr167VOeecc8Jt0tLSdMRjXpfK+6iNrl27yuFwuAeUWbt2rS7x4T6PNR7zgtXl+OEqymHX4FOaafApzfTUZT219VCOvtt0SPM2H9a6vZlaW/Z4ce5WtUuONQe06dFC/TslKzKCaTIAVM0wDOUVO5WZX6ysghJl5ZeYzwUlyix/zi9RdkGJMguKlV1QEf6yC0tV7OdQZ7dJ8VEOxUVHuJ/jIj2WoyLMR7RD8VERio0ynyuWIxQX5SgLdnavQBcVYSekAQBqpd6HwwEDBig6Oto9kufSpUtrDIdLllQMqRsTE6MBAwac9PGjoqI0cOBALVu2zH38mhw8eFDbt293Lw8dOvSkj18f2Gw2dW+ZqO4tE/W787vqcHah5m85rO82HdLS7enae7RA7y5L1bvLUtUoxqHh3ZprZM8WGnZqipJiI2s+AICw5HQZOpZfrKN5FY+MvGIdzS3WsXzz4Rn2ykOgP+6Pi4+KUEKMQ41iIpUQ7VCjGPNhtiu/Fmk+l70fGxmh+Ggz+EU7CHAAgNBR78NhQkKCRowYodmzZ0uSPvzwQz366KMn3ObDDz90t0eMGFGn0Uol6fLLL3eHw++++06HDh1SC8+hvU9w/MaNGzf4cFhZ88QY3TCgvW4Y0F75xaVaus2cJmP+5sPKyCvWV+v266t1++Ww2zSwc7JG9jC7n7ZLrrk7MQDruMrC3pHcIh3JKVJGbqXQl1fkFQQzC0p8muu7KlERdiXFRapxbKSSYiPVOC5SieXt2CglxTrUOC5KibEOJcZEuoNdefCLYCRlAEA9VO/nOZSkzz//XNddd517+auvvtJl1Qwbvnr1ag0YMEBOp9O97TXXXFOn4+/bt0+nnHKK++rlww8/rJdeeqnKdXNzc9WrVy/t2bNHknTffffp9ddfr/UxfZ3LpD5xugyt3Vs+TcYhbTuc6/V+95aNzKDYs4X6tElimgwgSAqKnTqSU6QjuYU6nF3kDn9Hcop0OKeinZ5bdFJX9RrHRSo5PkrJcVFKjo9S04QoNSlrVwS+yLIwGKWk2EjFRHLFDgDQcPiaDRpEODQMQ/369dO6deskSa1atdKCBQuOG+jlwIEDGjFihDZv3ixJ6tu3r1avXl3lLxCpqanq1KmTe/mpp57SxIkTq63hwQcf1KuvvirJnNT+008/1dVXX+21TklJiW688UZ98cUXkqTY2Fht375drVu3rvXP3BDDYWWp6Xn6bvMhzdt0SD/vPianxy+dKY2iNaJ7c53XrbnO7dpMCdH1/iI64HeGYehoXrEOZBXqQFahDmYVlD2by4eyC3Ukp0g5RaW12m/T+Cg1S4hW04SysBcfpSZlz8nx0WYQLHs0iYuUg/uMAQA4IcJhJT/99JOGDRvmnuswMTFR9957r4YOHSqHw6GVK1fq9ddf16FDhySZwWzRokXq379/lfurbTg8duyYBg4cqG3btkmS7Ha7brzxRl1xxRVKTk7W1q1b9cYbb2j9+vXubV5//XXdd999J/XzEg69HcsrNqfJ2HRYi349olyPX1YjI2wa0ClZw7uZYbFLSjxXFNDglQe/fccKKoJfdkXwO1j2KHb6NkBLtMOu5onRSkmIVvNGMUppFK2URtFqXvZstmPUNCGKQaUAAPAzwmEVpk2bpptvvtkdEKsTGxurDz74QFdddVW169Q2HErSr7/+qpEjR3pNU1GdRx99VH/7299qXK86hMPqFZU69ePOo1qw5bAWbj2s3Rn5Xu+3S47V8G7NNbxbcw3q3FSxUREWVQoEjmf4Mx/5lZ4LVFDi9GlfKY2i1SopRi0TY8znpFi1SopRi8QYMxA2ilajaAdfugAAYBHCYTU2b96sBx54QPPnz1flH91ms+n888/Xq6++qp49e55wPycTDiUpMzNTf/jDH/TRRx9VGVJ79OihF154QWPGjPHtB6oG4dB3u9LztLAsKP6486jXlZBoh11nd2nqDovtmzKoDcJHYYlTu9LztCs9T3uPnlz4a94oWq0bx5aFPu/w1zLRDIBRDq70AQAQygiHNdi7d6+WLVumtLQ0SVKbNm00ePBgtWvXLijHz8nJ0YIFC7R3717l5eWpVatWOu2009SvXz+/7J9weHLyikr1w44MLdh6WN9vOaz9WYVe73dJidfwbs01rFuK+ndMVkwkVxVhLafL0P7MAu1Mz9POI7nalZ6nnUfMQJiWeeJeEjab1KJRjNo2iS17xKmNR7t14xhFO/iMAwAQ7giHDRzhsO4Mw9Cvh3K1cOthLdxy+LhBbaIddg3olKxzT2mmc7s2U4+WiYyAioA5mlesXem52lEW/HYdydPO9FylZuSfcGL2pNhIdWoWr45N49S2SZw7+LVtEqtWhD8AABoEwmEDRzj0v6yCEi3bnq4FWw5rybYjOpRd5PV+0/goDS4LikO6NlOrpFiLKkW4KixxKjWjPPiZVwB3pptXAzPzS6rdLirCro7N4tSpWbw6pySoU7N4dUmJV6dmCUqOjwriTwAAAEIR4bCBIxwGlmEY2n44V0u2pWvp9nSt2Jmh/GLv+7e6pMRrSNcUDT6lmQZ0SlZSbKRF1SKUuFyG0jILyrp/lnUDLQuC+7MKTjipe5vGsWUBMN4dBDs3i1frxrFMyg4AAKpFOGzgCIfBVVzq0po9x7R0e7qWbEvX+n2Z8pzL22aTerRM1IBOyRrUOVkDOjXlik49dyyvWDvLBoPxvBcwNSNPRSfoBpoY43CHvs5lV/86p8SrY9N4Rs4FAAAnhXDYwBEOrZWVX6IfdppB8YcdGdqZnnfcOl2bJ2hg52QN7NRUAzslq3lijAWVoi4KS5zanZHvdS9geRA8VkM30A5N47yu/pVfDUyOj2LKBwAA4FeEwwaOcBhaDucUauWuo/px51H9uCtDvx7KPW6ddsmx6teuifq1b6x+7ZuoZ6tEpggIAS6Xof1ZBV6jgJaPDJqWeeJuoK2TYtQpJV6dmyW4u4N2bpagNk3oBgoAAIKHcNjAEQ5D29G8YjMs7srQjzuPavPB7ONCRpTDrl6tE92BsU/bJLVrEseIqAFgGIYy8oqVWtYNdFe62f2zPAyeqBtoo7JuoF2amVf+ysNgx2ZxiotyBPGnAAAAqBrhsIEjHIaX7MISrd+bpTV7jmnN3kyt2XOsym6JCdEO9WyVqJ6tE93PXVskMB2BjzLzi93Bb1d6vtkue+QUlVa7XWSETR2axntc/asYFbQp3UABAECIIxw2cITD8GYYhnZn5GvN3mNauydTa/ZmasuBHBU7j7+C5bDb1CUlQac0T1CXlHh1aZ6gLmXBJT66YV25KixxKi2zQPuOFWjfsXztPVr2fKxAuzNOPB2EzSa1TjJHAzWnhUhwB8E2jWPliKCLLwAACE+EwwaOcFj/lDhd2nEkV5v2Z5uPA9n6ZX+2sgqqDzzl97y185j8vE2TWLVtEqvmjWLC6r63whKnDmcX6VBOoQ5lF+pQdpEOZxfqQFahOwAeySmqcT8tE2PccwJ2LLsa2KlZvNolxykmkiuwAACg/iEcNnCEw4bBMAwdyCrUloPZ2nkkTzuO5GrHYfM5I6/4hNtGRtjUvFGMmjWKVkpClJrGR6tZoyg1S4hWs4RoJcZGKiHaocQYhxJiHEqIdig+ylGnex4Nw1BBiVP5xU4VFJvPuUWlyswv1rH8krJnj3ZeiTLyinQou+iEIdhTfFSE2iVXhOG2ZWG4fXI89wECAIAGyddswG9JQBiz2Wxq3ThWrRvH6vzu3u+Z8+zlaueRPK+ulmmZBTqQWagSpzkZe1pmQa2OGRcVIYfdpsgIuyIj7HJE2BRV9mwYktMw5HSZD5fLcC/nFztVUOI84eieNYl22NUyKUYtGsWoeWK0WiTGqEVidNmVUTMINo6L5B5AAACAk0A4BOqpJvFROjM+WWd2SD7uPafL0KHsQh3MLlR6TpHSc4uVkVuk9FyznZ5bpJzCUuUUlSi3sFQ5haUqdZmpLr/Y6Zf6YiLtiotyKC4qQo3jItUkLkqN46LUJC7S/dwkLkpNE6LUMjFGzRNjlBjjIPgBAAAECOEQaIAi7BVXHH1hGIaKSl3KLSpVfpFTJS6XSp2GSpwulThdKnUZKil1STbJYbcrwi7ZbTZF2G2y22xyRNgUGxnhDoOxkRFMyQEAABBiCIcAamSz2RQTGWEO2JJgdTUAAAAIBMZmBwAAAAAQDgEAAAAAhEMAAAAAgAiHAAAAAAARDgEAAAAAIhwCAAAAAEQ4BAAAAACIcAgAAAAAEOEQAAAAACDCIQAAAABAhEMAAAAAgAiHAAAAAAARDgEAAAAAIhwCAAAAACQ5rC4AgWEYhiQpOzvb4koAAAAAWKk8E5RnhOoQDuupnJwcSVK7du0srgQAAABAKMjJyVFSUlK179uMmuIjwpLL5dL+/fvVqFEj2Ww2S2vJzs5Wu3bttHfvXiUmJlpaC8IDnxnUFp8Z1BafGdQWnxnURqh9XgzDUE5Ojlq3bi27vfo7C7lyWE/Z7Xa1bdvW6jK8JCYmhsQ/DoQPPjOoLT4zqC0+M6gtPjOojVD6vJzoimE5BqQBAAAAABAOAQAAAACEQwRBdHS0nnrqKUVHR1tdCsIEnxnUFp8Z1BafGdQWnxnURrh+XhiQBgAAAADAlUMAAAAAAOEQAAAAACDCIQAAAABAhEMAAAAAgAiHCJDly5dr/Pjx6tmzp5KSkpSYmKiePXvq7rvv1rJly6wuDyHg+++/l81mq/Vjy5YtVpeOADhy5IjmzJmjZ555RmPGjFGrVq28/t6nTJly0vvesGGDHn74YfXp00fJyclKSEhQt27ddNNNN+mbb77x3w+BoPLnZyY1NfWkzkd8fsJHZmampk+frgceeEBDhw5Vy5YtFR0drYSEBLVv316XXXaZJk2apGPHjp3U/jnP1D/+/syEzXnGAPwoNzfXuP322w1JJ3yMGzfOyM3NtbpcWGjhwoU1fk6qemzevNnq0uFHBw4cMDp06FDj3/u7775b632XlJQYf/7znw273X7CfY8ePdo4fPiw/384BEQgPjO7du06qfPRnDlzAveDwi82b95sXHrppUZUVJRPf6dxcXHGyy+/bLhcLp/2z3mm/gnUZyZczjMOH/Ij4BOn06mrrrpK3377rfu12NhY9erVSw6HQ5s2bVJ2drYk6d1331VaWppmz56tiIgIq0pGiIiJidGwYcN8WjchISHA1SCYCgsLtXv37oDse/z48XrnnXfcy5GRkerZs6cSEhK0ZcsWZWRkSJJmzZqlkSNHatmyZXy+wkAgPzPlLrzwQp/WS0lJCWgdqLuNGzdq5syZXq9FRETolFNOUYsWLeR0OrV582YdPXpUkpSfn6/f//73+uWXX/TWW2/JZrOdcP+cZ+qfQH9myoXseSaoURT12p///GevbzruuusuIyMjw/1+bm6uMWHCBK91Hn/8cQsrhpU8rxx26NDB6nJgEc9vUlNSUoyLLrrIePLJJ40ZM2bU6crh5MmTvbYfM2aMsW/fPvf7xcXFxmuvvWY4HA73OjfeeKOffzoEQiA+M5W/0Uf98fnnnxuSDIfDYVxxxRXGjBkzjKysLK91XC6XMWPGDKNNmzZen4N///vfJ9w355n6KVCfmXA5z4RuZQgraWlpRkxMjPsDf8stt1S77pNPPuleLyYmxkhLSwtipQgVhEMYhmFkZWUZn3/+uZGamnrceyf7i35eXp7RsmVL97bnnXeeUVpaWuW6b7/9tns9m81mrFq16mR/FARJID4z4fJLG2pvxowZxp133mns3r27xnX37Nnjde5o1qyZUVxcXOW6nGfqr0B9ZsLlPMOANPCLSZMmqbCwUJIUFxenSZMmVbvuhAkT1K5dO0lm96BXXnklGCUCCEGJiYm65ppr1KFDB7/tc8qUKTp48KAkyWaz6d///ne13dfvuOMODRw4UJJkGIb+9re/+a0OBEYgPjOovy6//HL95z//Ufv27Wtct127dnr66afdy+np6Vq8eHGV63Keqb8C9ZkJF4RD+MX06dPd7euuu07JycnVrhsVFaVx48a5l6dNmxbQ2gA0LJ7nlGHDhqlHjx4nXH/8+PHu9uzZs1VUVBSw2gCEtssuu8xruboRsjnPoJyvn5lwQThEnW3dulXbt293L1900UU1bnPxxRe729u3b9fWrVsDUhuAhiU3N9frW9vano9yc3P1/fffB6I0AGGg8pfb5QPpeeI8A0++fGbCCeEQdbZu3Tqv5bPPPrvGbc444wxFRUW5l9evX+/3ugA0PJs2bVJJSYl72ZfzUcuWLdWxY0f3MucjoOGqPBJu8+bNj1uH8ww8+fKZCSeEQ9TZ5s2b3e2oqCj3/YQnUnk9z32g4cnMzNR1112njh07KjY2Vo0aNVKnTp10xRVX6PXXXw/7b+EQPJXPJV26dPFpO8/1OB/h1ltvVdeuXRUfH6/4+Hi1b99eF110kf7+97/r8OHDVpeHAKp8q0tVwY/zDDz58pmpSqieZwiHqLPU1FR3u23btj7P7+J5o6/nPtDwZGVl6fPPP9fu3btVWFio3Nxcpaam6ssvv9T999+v9u3b67XXXrO6TIQBz3OJw+FQq1atfNqO8xE8/e9//9P27duVn5+v/Px87d27V3PnztVjjz2mDh06aMKECXI6nVaXCT/LysryGiSvT58+6tmz53HrcZ5BOV8/M1UJ1fOMI+hHRL2Tk5PjbiclJfm8XWJiYpX7QMPUsWNHtWnTRtHR0UpPT9emTZtUWloqyTz5PvDAA1q7dq3++9//WlwpQpnnuaRRo0ay2337DpTzETy1atXK3ZPh2LFj2rx5s3tE7sLCQj333HP66aef9PXXXysyMtLiauEvjzzyiHsEUkl67rnnqlyP8wzK+fqZqUqonme4cog6y83NdbdjYmJ83i42NrbKfaBhsNvtGjlypD788ENlZGRo165dWrp0qebPn69169bp2LFjeuONN9SsWTP3Nu+88w5DgOOEOB/hZNhsNg0YMED/+c9/tH//fu3fv1/Lly/X/PnztXr1amVmZuqjjz7yumds7ty5euCBB6wrGn719ttve335+Jvf/Oa4USjLcZ6BVLvPjBQ+5xnCIeqs/OqOZHav8JXnup43dqNhGDp0qObNm6cbb7yxyqlPEhISdM8992j16tVeJ8pnnnlGhw4dCmKlCCecj3AyOnTooB9//FF33nlnlV0Eo6OjdcMNN2j16tU688wz3a9PnjyZgUXqgcWLF+u+++5zL3fq1EmTJ0+udn3OM6jtZ0YKn/MM4RB1FhcX526XXw73hee68fHxfq0J9Ue7du306aefupfz8/PpWopqcT5CIDVp0kTTpk1zXy0yDEOvv/66xVWhLtauXasxY8aouLhYkjnS5DfffHPC22Q4zzRsJ/OZqQ2rzzOEQ9RZQkKCu11QUODzdvn5+VXuA6hswIABOu+889zL8+bNs64YhDTORwi09u3b6/rrr3cvcz4KX1u3btWFF16orKwsSeYv5d9++61OPfXUE27HeabhOtnPTG1ZeZ4hHKLOPO8JO3DggM/bed7A27RpU7/WhPpn+PDh7vavv/5qYSUIZZ7no9zcXJ/v6+F8hNrwPB+lpqa6ryAgfOzatUsjR450TxnQqFEjzZkzR6effnqN23KeaZjq8pk5GVadZwiHqLNu3bq52xkZGV7fjJ3I3r173e3u3bv7vS7ULy1btnS309PTLawEoczzfCRJe/bs8Wk7zkeoDc/zkWT+34fwsW/fPo0YMUL79u2TZHYTnTlzpgYOHOjT9pxnGp66fmZOhlXnGcIh6qxHjx5ey2vXrq1xm7S0NB05cqTafQCVeX7p4Hm/B+DpZM5HJSUl+uWXX6rdB1BZ5S9BOSeFj0OHDmnkyJHatWuXJHMQkBkzZmjo0KE+74PzTMPij8/MybDqPEM4RJ0NGDBA0dHR7uWlS5fWuM2SJUvc7ZiYGA0YMCAgtaH+8PxPtXnz5hZWglDWuXNntW3b1r3sy/lo1apVXv8JB/o/fIQ/z/NRdHS03waiQGBlZGRo5MiR2rp1qyQpMjJSX3zxhUaNGlWr/XCeaTj89Zk5GVadZwiHqLOEhASNGDHCvfzhhx/WuI3nOiNGjGDULpxQfn6+vvrqK/fyOeecY2E1CHVjxoxxtz///PMa79PwPB/16tVLXbp0CVhtCH+GYeizzz5zL5999tkWVgNfZWVl6cILL9TGjRslSREREfroo4906aWXntT+OM/Uf/7+zNSGlecZwiH8YuzYse72+vXr9fXXX1e77urVqzVnzpwqtwWqMmHCBPcN4JJ0xRVXWFcMQp7nOSU9Pf2Ec0/t27dP7733XpXbAlV5/fXXveYc43wU+vLy8jR69GitWrVKkmS32/Xee+/pmmuuOel9cp6p3wLxmakNS88zBuAHLpfLOP300w1JhiSjVatWxubNm49bb//+/UaPHj3c6/Xt29dwuVwWVAwrzZ0713j44YeNvXv3nnC94uJi47HHHnN/XiQZZ5xxBp+ZBsLz7/3dd9+t1bZjxoxxb5uQkGAsXbr0uHWysrKMIUOGuNdr2bKlkZ+f76fqYYWT+cxs3LjRuP32240tW7accD2Xy2VMmjTJiIiIcB+jdevWfGZCXGFhoTFy5Ej335nNZjP++9//+mXfnGfqp0B8ZsLpPGMzDMMISgpFvffTTz9p2LBh7jl/EhMTde+992ro0KFyOBxauXKlXn/9dR06dEiSFBsbq0WLFql///5Wlg0LzJgxQ1deeaXsdrsGDx6sYcOGqXfv3mrWrJmioqKUnp6ulStX6sMPP/Qa3S05OVnLly8/bqQ4hLe77rpL//vf/457vaioyN12OByKiIg4bp3qJqBOTU1V//793SPbRkdH64477tAFF1yghIQErV+/Xq+99pp7gAG73a4ZM2bosssu88ePhADz52dm7dq16tevnyTpzDPP1Pnnn6/TTz9dzZs3V2xsrI4dO6Y1a9bo448/1pYtW9zbRUdHa968eRoyZIi/fiwEwN///nc99thj7uUmTZrUapyDUaNG6ZFHHqnyPc4z9VMgPjNhdZ4JWgxFgzB16lQjNjbW69vbqh6xsbHG1KlTrS4XFpk+fXqNn5HKj65duxqrV6+2unQEwG233Vbrz0P540SWLVtmJCcn17iPiIgI47XXXgvSTwt/8OdnZs2aNbXeR8uWLY158+ZZ8JOjtp566qmT/qxIMm677bYT7p/zTP0TiM9MOJ1nuOcQfnXVVVdp1apVGjlypGw223Hv22w2jRgxQj///LOuuuoqCypEKOjevbt+85vfeI32Vp2OHTvq73//u9asWeP+1g3wxTnnnKP169fr6quvlsPhqHKd/v37a/Hixfrd734X5OoQKlq1aqVbb73VpwFCWrRooSeffFIbNmzQyJEjg1AdQh3nGfginM4zdCtFwOzdu1fLli1TWlqaJKlNmzYaPHiw2rVrZ3FlCCV79uzRpk2blJ6ervT0dOXl5SkxMVHNmzfXWWedxYhu8IsjR45o8eLF2rdvn4qLi9W6dWudddZZdFGGl0OHDmn9+vU6cuSI0tPTlZOTo4SEBDVr1kz9+vVTjx49qvziE5A4z8A3oX6eIRwCAAAAAJjKAgAAAABAOAQAAAAAiHAIAAAAABDhEAAAAAAgwiEAAAAAQIRDAAAAAIAIhwAAAAAAEQ4BAAAAACIcAgAAAABEOAQAAAAAiHAIAAAAABDhEAAAAAAgwiEAAAAAQIRDAAAAAIAIhwAAAAAAEQ4BAAAAACIcAgAAAABEOAQAIKxNnDhRNptNNptNp556qoqLi2u1/dy5c93b22w2HT58OECVAgBCHeEQAIAwtW3bNr3wwgvu5ZdffllRUVG12sdZZ53ltbx06VK/1AYACD+EQwAAwtR9992noqIiSdJFF12k0aNH13ofTZs2Vfv27d3Ly5Yt81t9AIDwQjgEACAMzZs3T/PmzXMvP/vssye9r06dOrnbmzdvrlNdAIDwRTgEACAMTZgwwd2++OKLj+seWhtt2rRxt7dv316nugAA4YtwCABAmJk/f75+/PFH9/If//jHOu0vJSXF3T5w4ECd9gUACF+EQwAAwsybb77pbnfq1EnnnXdenfZns9nc7fJ7GAEADY/D6gIAAIDvMjIy9OWXX7qXb731Vq9w5ykvL08FBQWSpMTExGpHMjUMo8o2AKBh4cohAABhZP78+SopKXEvX3jhhdWuO3bsWKWkpCglJUU///xztevt37/f3W7RooV/CgUAhB3CIQAAYWThwoXudnx8vPr371/tuj/99JO73bt372rX27Nnj7vtOa0FAKBhIRwCABBGNm7c6G737t1bDkfVd4ikpaVp9+7dkqSWLVsqMTGxyvVKS0u1YcMG9/KJwiYAoH4jHAIAEEa2bdvmbnfr1q3a9TznQGzbtm21661Zs0b5+fnu5cGDB9exQgBAuCIcAgAQJlwulw4dOuRePtH9gV999ZW7nZycXO16M2fOdLcdDodGjBhRxyoBAOGKcAgAQJgoLCz0Wo6Ojq5yvaNHj2r27Nnu5cjIyCrXMwxDH3/8sXt55MiRatq0qR8qBQCEI8IhAABhIiIiwmvaiqNHj1a53uuvv66ioiL3uhkZGVWu99VXX3l1U73rrrv8WC0AINzYDCY0AgAgbLRs2dLdtbRPnz5at26d1/u7d+9W7969lZubq+HDh2vhwoVKSEhQRkaG1zyHmZmZOvPMM7Vz505J0mmnnaZ169ZVO2ciAKD+48ohAABhZMiQIe72+vXr9eabb7qXU1NTNXr0aOXm5urUU0/V9ddfL0nKzc3VP/7xD/d6u3fv1iWXXOIOhhEREZo8eTLBEAAaOK4cAgAQRubNm6cLLrjA67Xu3bsrOTlZq1atcncn/fbbb9WyZUuddtpp7vX69OmjmJgYrV69WqWlpe7XX375ZT300EPB+hEAACGKcAgAQJh5+OGH9fLLL1f5nsPh0L///W/3/YNXX321pk2bVuW6CQkJmjRpku64446A1QoACB+EQwAAwtC0adM0efJkrV27VkePHlVKSoqGDx+uP/7xj+rbt697vcLCQj333HP69NNPtWfPHsXFxalTp04aPXq07r33XrVu3dq6HwIAEFIIhwAAAAAABqQBAAAAABAOAQAAAAAiHAIAAAAARDgEAAAAAIhwCAAAAAAQ4RAAAAAAIMIhAAAAAECEQwAAAACACIcAAAAAABEOAQAAAAAiHAIAAAAARDgEAAAAAIhwCAAAAAAQ4RAAAAAAIMIhAAAAAECEQwAAAACApP8HbAgT+I5r8UcAAAAASUVORK5CYII=", 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", 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", 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D8K4dQGRkzXWHw7u2sbHHv5eY6F3bpCTvsvnBsb1YycnJXrU7drvc3NwTbnuiHsVTVb0QatKkiVdtvN3uVEQee87U4chAOrX59NNPuemmm2r0EHqj/NgvNRoIFYcSEra9+h9I6Uv7rC1A3bcS1NegwV2I2FTBribNyZwxj7YqDkVEpKE49pbP+jjVQTfi4mD37lNre+mlp972+efdryB27GTr3hY6drv9pPupLizMt8OKnKy4CmWZmZmMHTvWUximpKRw9913c95559G+fXtSUlJwOByeP8/MzEzatm1rZWS/U3Eowa+ykm2HSiEF2q5cANzi80PEjDqPfj99xIKMXsxet5eG/c9eRERErJKQkFBjvbCw0Kt2x87V58+euWNVP5a3z+95u52VXnrpJZxOJ+C+FXf+/PknHeHV27+rUKbRSiX4ZWWxPcF9b3nbOD99n9GmDWcXuO+nn++KhTruTRcRERE5FbGxsTgcDs/69u3bvWq3devWGusnm0rC19LTjw4G6O3In74aIdSfpk+f7ll+/PHH65z6Y8+ePf6OZDkVhxL0zC1b2J7kfn6gXbOEOrY+dYMy3PtelNaVqjlz/HYcERERadx69+7tWa4+cunJVJ9nMDExkTZt2vg61gn179/fszx79myvns+bNWuWHxP5RvWBdqr/jicyf/58f8YJCioOJehlb8qkJNKBYVaR1q6F347Tc1gfYspLyXPEs77Yb4cRERGRRm7o0KGe5UmTJnk1uMmHH37oWT777LMxDMMv2Wpz8cUXe5b3799f5wTw5eXlvPvuu35OdfoqKio8y3X9eVZVVfHBBx/4O5LlVBxK0NuduReA5oU52Du099txIi4fTf+u7h7KBSkd/XYcERERadxuv/12z/L+/ft56aWXTrr9pEmTavQw3nnnnf6KVqvu3bszaNAgz/rDDz9Mdnb2Cbd/8sknyczMDECy09OixdFOh3nz5p102xdeeIFt27b5O5LlVBxK0Nu93z18clr+AfDxvD01OBwM7pgKwMJtOf47joiIiDRqnTp14uqrr/as//nPf2by5Mm1brtw4ULuuOMOz3qvXr249NJL/Z7xWP/4xz88vWvbt2/nnHPOYcGCBTW2yc3N5cEHH+TZZ5/1eooOKw0fPtyzPGHChBNObP/ee+/xxz/+MVCxLKXiUILe7gL3UM1pBQcgI8OvxxrcvikAi7YdwlVZ5ddjiYiISOP1z3/+0zMAisvl4qqrrmLMmDF8/PHHzJ49m8mTJ3PXXXcxdOhQz0ilUVFR/Pe//yU8PDzgec866yyefPJJz/ratWs566yzaNeuHeeeey79+vWjefPmvPzyywC8/fbbNdofOxVHMLj//vs9Be/u3bvp3bs3EyZMYPr06cyaNYv//Oc/jBw50jPdxV133WVxYv/TVBYS3EyT3eXu7zDScB4/Ua6PdWuZQFyUjUKni7UbdtOrW3rdjURERETqKTU1lVmzZjFq1Ch2H57X8YsvvuCLL76odfu4uDi++uorevbsGciYNTz55JPYbDaeeuopz/N627dvrzHiqsPh4PXXX2fUqFE12h47hUcw6Nu3L08//TSPPfYYAAcPHqxRAFd37bXX8uijj/LWW28FMmLAqedQgtuhQ+x2NAEgLcb/35KFm1UMPJQJwIIn/uH344mIiEjj1blzZ1auXMkDDzxATExMrdtERERwww03sGbNGs4555zABqzFn//8Z5YvX859991Hx44diY6OJiEhgR49evD73/+e1atXc+utt3LgwAFPG4fDQXR0tIWpT+zPf/4z//nPf0hNTa31582bN+ell17i008/DeggQFYxTNM0rQ4hvldQUEBCQgL5+fnEx8dbHefU5eYy4u8z2Vpp58PU/Qx56I6625ymty7/NX/pcgnDty3lvbd+C6H85yciIg2G0+lk+/bttG3blqioKKvjiI85nU5mz57Ntm3bOHToEPHx8aSnp3POOeeE5Ge5SZMmeZ6rHDRo0HHPJwYbp9PJnDlzWLt2LaWlpaSmptKhQwfOPvtsS27jrZ7LF//uva0NdFupBDWzSRN2Gw6girTbrgvIMc9q0wSAX9K6UjF3HhEXXxSQ44qIiEjjFRUVxfnnn291DJ+p/szh4MGDLUzinaioKEaNGnXc7bCNjW4rlaB2sKiMMlcVYQa0SHAE5Jidh/SmSWkBJZEOVs9ZHpBjioiIiAQ7b284/O9//8u3337rWR87dqyfEomvqTiUoLY7txSA5vFRRNoCc7qGDRtKv93rAFiy7cRz+IiIiIg0JhMmTODuu+9m1qxZuFyu436+e/duHnrooRrF4OWXX27pIDpSP7qtVILa7k07AUhLDEyvIQDNmzPAuZ8ZwOLKOO4pLQVHAI8vIiIiEoRKS0t56623eOutt4iKiqJTp040bdoUl8tFVlYWW7durbF9RkYGb7zxhkVp5VQ0qp7DgwcPMm3aNCZMmMDo0aNp0aIFhmF4Xu+++25Acmzbto0nnniCvn37kpKSgsPhoH379lx55ZV8/vnntX4T01jteeNdAFp+/xUcHjI5EPq1dg+3vKRVZ6oWLgzYcUVERESCVVjY0dLB6XSycuVKfvrpJ2bPnn1cYXjuueeycOFCz1yOEhoaRc/hvn37GDRoEDt27LA6ChMnTuSRRx6hrKysxvvbtm1j27ZtTJkyhUGDBvHhhx/Srl07i1IGj33l7v82Ly+CiIiAHbf74B5ErXKS54hn68+/0PHccwN2bBEREZFg9NRTTzFs2DB++OEHlixZwtatWzl06BAul4smTZrQsmVLhgwZwtVXX815551ndVw5BY2iOHQ6nUFRGD799NM88cQTnvWwsDC6du1KUlISmzdvZu/evQAsXLiQ4cOHs3jxYlq0aGFVXOuVl7M/zH07Z3N7YA8dOXwYvb/7iAUZvVi8YS8dA3t4ERERkaATERHBhRdeyIUXXmh1FPGTRnVbKUBKSgoXXnghjz32GFOmTAnYcb///nuefPJJz/rgwYNZv349q1ev5ueff2b37t188sknxMbGAu4Heq+55pqA5QtKWVnsjWsKQPO4AFeHGRn0L9gNwC9VsVBVFdjji4iIiIgEWKPoOUxKSuKzzz6jf//+ZGRkBPz4pmnyyCOPeIb/7dSpEzNmzCA6OtqzTVhYGNdddx1Nmzb1zK8yb948vvjiC6688sqAZw4Ku3axP/ZwcZgcF9hjGwb9b74MVlbxS59zIKzRfY8iIiIiIo1Mo/jEGx8fz9VXX21JYQgwbdo0Vq5c6VmfOHFijcKwupEjR3LddUcne3/22Wf9ni9YVe7YyYHYJACat2ga8OP3GTOK8DCDrDwne/JKA358EREREZFAahTFodUmT57sWW7bti3nn3/+SbcfN26cZ3nx4sXs3r3bb9mCWc7OvVSGhRNWVUlym5YBP36M3Ua3lvEA/JJ5KODHFxEREREJJBWHAfDNN994li+44AIMwzjp9kOHDiUmJqbW9o3J3r3ugiylOA9bemtLMvTLcPdcLt6u4lBEREREGjYVh3524MAB9u3b51kfPHhwnW1sNhv9+/f3rK9atcov2YLdvtxiAJoX5kBamiUZBpAPwJJZy2HbNksyiIiIiIgEgopDP1u/fn2N9fbt23vVrvp2x+6jsdhf4gKgeVE2pKZakqHfitkAbHQ0Je/neZZkEBEREREJBBWHfpaZmVljPT093at21bc7dh+1KSsro6CgoMYr1O0b6372svmo4WCzZmDd5LMH0C5nFwBLlm6xJIOIiIiISCCoOPSzwsLCGusJCQletYuPjz/hPmrzzDPPkJCQ4Hm1bm3NM3q+tK/MPfVHsy7trAsxYAD9szYAsGRfiXU5RERERET8TMWhnxUVFdVYj4qK8qqdw+E44T5q86c//Yn8/HzPa9euXfULGoT25TsBaB7v3Z+ZX8TE0NfmfvZxmT0Z8vKsyyIiIiIi4kcqDv3M5XLVWLd5eXtk9e0qKirq3N5utxMfH1/jFer2FRwuDhMsLA6BPm2TAVjVvAMVCxZamkVERERExF9UHPrZsZPdO51Or9pV3676tBaNxrp1HDzofm4yNWdfHRv7V7tBvUgoLcQZEcX6eSstzSIiIiIi4i8qDv0sNja2xnppaalX7UpKjj7fduw+GgPngkUUEg5AyuI5lmYJO3sIvfdsBGDptoOWZhERERER8RcVh36WnJxcY33v3r1etas+N2LTpk19mikUZO/NBiDSVUF8y+bWhmndmr5FWQAsc0bCMbcKi4iIiIg0BCoO/axTp0411nfu3OlVu+oDynTu3NmnmULBwQN5ACSX5GK0bGFtGKBPM/ftwcuadYTVqy1OIyIiIiLieyoO/axjx441BpdZsWKFV+2WL1/uWe7SpYuvYwW97Dz3CKHJxXnQwvrisNfIgYSZJlkJqexLtLgnU0RERETED1Qc+llkZCQDBw70rM+dO7fONvv27WPLlqMTrg8bNswv2YJZdlE5cLg4bG59MRZ78w10aumeo3JZ3dNOioiIiIiEHBWHAXD55Zd7lmfMmMH+/ftPuv2HH37oWW7SpEnjLA7LqgBIriwFu93iNG59M5oAsGxHrrVBREREpEEZMWIEhmFgGAZvv/221XGkEVNxGAA33HAD9sMFTkVFBX/7299OuG1RUREvv/yyZ/2mm24iIiLC7xmDimmSXXl4pNLwSovDHNUnPRGApTtVHIqIiIjvrFx5dKqs3r17W5jEepmZmcTExHiKZcMwGD9+vNWxGg0Vh6coMzPT65M2LS2NcePGedYnTpzIpEmTjtuuoqKC22+/3TNojcPh4NFHH/V59qCXl0d2VBwAyfbgOUX7ZriLw7W7cnEu13yHIiIicvp27txJTk4O4H4cqVu3bhYnstavfvWrGlO6SWAFzydvP7v77ruJioo67lXfbU7V+PHj6dixIwCVlZVce+213HLLLUyaNImZM2fy+uuv069fPz7//HNPm+eff56WLVv65PghZd8+Dsa4C7HkmEiLwxyVvmIhTYvzKDcN1n4wxeo4IiIi0gBUH4SwW7duREYGz2efQPvggw/4/vvvrY7RqNnq3qRhqKiooKys7KTbuFwuXH6awy4xMZGpU6cycuRIdu3aRVVVFR988AEffPBBrdv/4Q9/4L777vNLlqBnmmQ3dY9Qmtws0eIwRxm9etH7X7OY0XEQy3bm0dfqQCIiIhLyqo9k36dPH+uCWCw7O5vf/e53gHuk/vz8fPbs2WNxqsan0fQcBoMzzjiDVatWceedd+JwOGrdpkuXLnz55Zc899xzAU4XRLp25WBSMwBS7h9Xx8YBlJxM37KDACytioU6vmwQERERqUv1nsPG/Lzh7373O7KzswF4/fXXG9+YG0Gi0fQcvvvuu7z77rs+21+bNm0wTbPe7Zo0acJbb73Fiy++yE8//cSuXbsoLi6mRYsW9OjRo1FfFI5wVlRS6HT34CbHBsdIpUf0aR4DwNIWZ2CuWIFRbZoSERERkfqqXhw21p7DH374wXM33e23394oR+oPFo2mOAw2cXFxNaa4kKNyit1zHEaEGyQ4gutbo569O2LLdHEwNondc5fQWsWhiIiInKJDhw55BiIMCwujV69eJ93++eef509/+hOVle7R3O+++25effXVkH5OsaSkhF/96lcAJCcn8/zzz1ucqHHTbaUSdA4Wum/XTI61YxiGxWlqcpw1kK4HtgGwbN1ui9OIiIhIKKv+vGGnTp2Ijo6udbuioiKuueYa/vCHP1BZWUlkZCSvvfYab775ZkgXhgCPP/4427dvB+Dvf/87TZs2tThR46biUIJO9kv/BCB53y44PLRz0OjRgz77twCwLC945mAUERGR0OPNLaUbN25kwIABnhHtmzdvzsyZMz29baFs6dKlTJw4EYDhw4dz2223WZxIVBxK0MnefQCA5D2Z4KOpRHzGZqNPjLsoXBrXCvbtsziQiIiIhKq6BqOZMmUKAwYMYP369QAMHDiQpUuXctZZZwUso7+4XC7uuusuT0/o66+/bnUkQcWhBKEcp7v4alpWBDExFqc5Xp+O7pFUN6S0oXTeQovTiIiISKg6Uc9hVVUVjz76KGPGjKGgoACAO+64g59//rnec2C/++67GIbh89fpDvT4j3/8w3Nb7SOPPELnzp1Pa3/iGxqQRoJObqX7O4umhn/mnDxdrQb2JvXHQxyITWJVSSUakkZERIKRaZqUVugRiJNxRIRbNr5BaWkpGzdu9Kwf6TnMycnhhhtuYPr06QBERETw4osvNqj5r7du3cpTTz0FQIcOHXj00UctTiRHqDiU4FJVxSHcI5QmBunZaVx8EX0KVvLd+oMsa91NxaGIiASl0opKuj7xvdUxgtq6CRcQHWnNB47Vq1d7Rh1t27YtTZo0YenSpVx11VXs2LEDgGbNmvHZZ58xdOjQUz5Oq1atuOCCC3yS+dj9nqpx48ZRWloKwGuvvUZUsD1G1IgF6cdvabQOHSLXEQdAkiPc4jAnYLfTp21Td3G4M9fqNCIiIhKCjr2l9J133uHXv/41TqcTgP79+zN58mTS0tJO6zijRo1i1KhRp7UPX3rnnXf48ccfAbjpppsYOXKkxYmkOhWHElwOHOCQIx6AxJjgHZq5T3oiAMt35mKaZtBNuSEiIuKICGfdBN/3GDUkjgjrvoiuXhzOmTOHSZMmedZvu+02Xn/99QbXo3bgwAH+7//+D4DExEReeOEFixPJsVQcSnA5eJDcw8VhUkLwDUZzRPdWCUSEG2QXlbNrfz7pzZtYHUlERKQGwzAsu2VS6lZ9jsMDBw54lseNG9dgR+584IEHOHToEADPPvssqampFieSY2m0UgkuBw+SG3245zApzuIwJxaVm0PXYveFfPmTf7c4jYiIiISSyspKVq1a5Vm/5JJLPMv/+9//2LRpkxWx/GrBggV8+umnAAwePJi7777b4kRSG32dJEGlfP8BCu0ZACSlNLE2zMk0aULv9YtZ2fsSluVUcLnVeURERCRkbNy40TMgS/PmzZk0aRLnnHMOCxcuJDc3l8suu4yFCxeSmJh42seaPn06//jHP057P8d6+OGH6/Us4/79+z3LCxYsICzM+z6qp556yjO6KcD27dtp06aN1+3FeyoOJajk9RkIu/YRZprE9+1ldZwTi4ykT1Q57wLLYlpAdjYkJ1udSkREREJA9ecNe/Xqhd1u54svvmDAgAHs2rWLTZs2cc011/Ddd99hs53ex/WsrCy+/973o9Zef/31Pt+nWE+3lUpQOdS2IwCJsXbCzjzT2jB16NPefZ/8+tS2lM5fZHEaERERCRXVnzfs1cv9ZXjz5s358ssviY6OBuDHH3/k/vvvtyKeX0RERJCQkOD1q/pgf3a7vcbP6tPrKPWjP1kJKoeKy4HgHqn0iFaDepNamIMr3MaqRWusjiMiIiIh4tiewyN69+7N+++/7ymMXn/9dV5++eXTOtbYsWMxTdPnr7Fjx9YrxyWXXEJeXp7Xr/T0dE/bP/7xjyf8mfiWikMJKrnFFQAkRkdYnKRuxuBB9NmzAYBlOzTfoYiIiHintp7DI8aMGVPj+bqHHnrIL7eFitRGxaEElUM79wCQ6Aj+4pC0NPoUufMuL4+CykqLA4mIiEiw27lzJzk5OYD7dslOnTodt83jjz/ueaavsrKS6667jvXr1wc0pzROKg4lqOT+600Akr783OIkXjAM+jRzPxewLLUD5rp1FgcSERGRYFe917Bbt24nHHDm7bffpn///gDk5+dz2WWXeYpKEX9RcShB5VCV+wKZaLgsTuKd7r3aY6t0kR2byO45v1gdR0RERILciZ43PJbD4WDKlCm0bNkSgK1bt3LVVVdRUVHh94zSeKk4lODhdJIX4QAgKUQmWYkaPJBu+7cBsGztLovTiIiISLDztjgEaNmyJV9++SUOh/vz0c8//8y9997r13zSuIXIR3BpFA4d4pAjHoBEe4h8b9G3L73P2MnKIlg28koutzqPiIiIBLUpU6bUa/t+/fpRUlLinzBBLDMz0+oIjVKIfAKXRuHQIXIPF4dJITBaKQAOB30uOweAZbsLrM0iIiIiInIaVBxK8Dh0iEPRh3sO46IsDuO9PulNAFi/t4DSco1YKiIiIiKhScWhBI+cnKM9hwkxFofxXqsmDlLj7LiqTFZn5VsdR0RERETklKg4lKDhzD5ESaT7gevE5HiL03jPME36RLtHV132dghMwSEiIiIiUgsVhxI08nLcvW7hVZXENU20OE09GAa9p34MwLJNe8E0LQ4kIiIiIlJ/Kg4laOTnFQGQ4CzCSG5qcZp6MAz6pNgBWJbSHnPjRosDiYiIiIjUn4pDCRp5N98OQEJKIvTta3Ga+unRsx22ShfZsYnsnvOL1XFEREREROpNxaEEjXzDPX1FQlI8xIfOM4cAUYMH0m3/NgCWrcq0NoyIiIiIyClQcShBI7+0AoAER4jMcVhdv3703rsBgOXZTovDiIiIiIjUn4pDCRohXRzGxdEnvASAZZEpUFhocSARERERkfpRcShBI3/2fAASDuyxOMmp6d0uGYB1qW1xLlxscRoRERERkfpRcShBI3/uQgCazP7R4iSnJm3AmaQUHcIVbmPV/NVWxxERERERqRcVhxIcSkvJtzkASIgIzdPSOGswfbLczx0u255tcRoREWmoTM2nK9JoBPrfe2h+CpeGJyeHPEcsAPFR4RaHOUUdO9LHdvi5wzP6WRxGREQamrAw98e2qqoqi5OISKAc+fd+5N+/v6k4lOBw6BD5Ue7isEl0pMVhTpFh0Of5xwFYVhWrb3ZFRMSnbDYbhmFQVlZmdRQRCRCn04lhGNhstoAcT8WhBIdDhyiwu4vDhDiHxWFOXY9WCdjCDLKLytidW2p1HBERaUDCwsJwOBwUFxdbHUVEAqSgoIDY2Fj1HEojk5fn6TlMiA/d4jAqIpxuLeMBWLYz1+I0IiLS0MTGxlJcXEx5ebnVUUTEz4qLi3E6ncTHxwfsmCoOJSiYuXnkOeIAaJIQa3Ga09M7PRGA5QvXWZxEREQamoSEBGw2G7t376aystLqOCLiJ8XFxezatYuYmBhiYwP32TgwN6+K1KE4N5/KsBQAEpLiLE5zenp/8V/ebTaMZQvWwo0DIYDf9oiISMNms9lo3bo1mZmZbNmyhYSEBGJjYwkPD8cwDKvjicgpMk2TqqoqnE4nBQUFOJ1OYmJiSEtLC9gtpaDiUIJEfr77+YlIVwVRiQkWpzk9faJdAKxLbYtzwSKiLhhlcSIREWlI7HY7bdu2JS8vj/z8fHJz9RiDSENhGAaxsbE0bdo0oM8aHqHiUIJCXkwTKIOE8mKMpi2tjnNa0gb1JmXRIQ7GJrFqwWoGqDgUEREfi4yMJDU1lZSUFFwul24xFWkAwsLCsNlsAS8Iq1NxKEEh/6pr4d+LSGiTBgMGWB3ntBhnDabP5Lf5vtNZLN+eQ2j/NiIiEswMwyAiIoKIiAiro4hIA6ABaSQoFJRWAJDgaAD/c2vXjj75uwBY5owATVYsIiIiIiFAxaEEhbySBlQcGgZ9UqMAWJbSHnPDBosDiYiIiIjUTcWhBIX8wz2HTRpCcQj0OLMDtkoXB2OT2D1nsdVxRERERETqpOJQgkL+B58AEL9orsVJfCPqrEF0PbANgGWrMq0NIyIiIiLiBRWHEhTy92UDkLB1k8VJfKRfP/rsdf8uy7PLLQ4jIiIiIlI3FYdivaoq8sLtADQJNy0O4yPR0fSOKAVgWVIGlKtAFBEREZHgpuJQrFdQQIE9BoB4e8M5Jfs8/QcA1iW2xmmEW5xGREREROTkGs4ncQldeXlHi8OohjP1ZlrvLqTE2XFVmazOyrc6joiIiIjISak4FOvl5VFojwYgLtpucRjfMQyD3q2bALBsR661YURERERE6qDiUKyXl0fh4Z7DuNgoi8P4Vp+MRACW7VRxKCIiIiLBTcWhWC8/39NzGB8fbXEY3zoyYumyJZswV6+2OI2IiIiIyImpOBTLVeTm4Yxw9xjGxcdanMa3eu5ci63SxUFHArvn/GJ1HBERERGRE1JxKJYrzC3wLMcmxVuYxPeizhpE1wPbAFi+OtPaMCIiIiIiJ6HiUCxX2K0XANGmC1vXLhan8bG+fY/eWpqtuQ5FREREJHipOBTLHSkO4xJioHdvi9P4mMNBb3sZAMsdzeDQIYsDiYiIiIjUTsWhWK6gtAKAuKgIi5P4R58OqQCsbdYO57wFFqcREREREamdikOxXIHTBUBclM3iJP6RNrA3yUW5uMJtrF641uo4IiIiIiK1UnEoljsyIE1D7Tk0zhpMnz0bAFiWmWNxGhERERGR2qk4FMsV/v0lAOK+/RpM09ow/tC6NX2K9gCwrDwKXC6LA4mIiIiIHE/FoViu0OUuCONdTjAMi9P4gWHQp4V7/sZlzTpgrlhhbR4RERERkVqoOBTLFZrhAMQblRYn8Z+eV1+ADZODsUlkpXe0Oo6IiIiIyHFUHIq1TJNCwz0QTVxYA7yl9LCoyy6ha1oTAJbtLbY2jIiIiIhILVQcirXKyiiMcAAQ1zAHK/Xo3boJAMt25FobRERERESkFioOxVoFBRTaowGIi2zYp2OfjEQAlu9UcSgiIiIiwadhfxqX4FdQQIE9BoA4e8PuOjwyKM3a3Xk4Z/xkcRoRERERkZpUHIq1qvccRjXs4jBt3TKSi3JxYbBm8g9WxxERERERqcFvn8azsrJYt24dO3bs4ODBgxQXuwfhiImJISUlhYyMDLp160bLli39FUFCQWEhhUd6DmPsFofxL2PgQPq8+gw/dBzEsl359LM6kIiIiIhINT4rDnNzc/nyyy/5/vvvmTVrFgcOHPCqXWpqKsOHD+eCCy5g9OjRNG3a1FeRJBRU6zmMj3VYHMbPoqPpE1bED8AyWyJkZ0NystWpREREREQAH9xWOm3aNK644gpatGjBnXfeyf/+9z/279+PaZpevfbv389nn33GXXfdRcuWLbn88sv55ptvfPG7SQioOGsIzogoAOKuHG1xGv/rm5EEwJK0rpjz5lucRkRERETkqFPqOayqquK9997j2WefZcuWLQCY5vFz1Nntdlq2bEliYiIOhwPTNCktLSU3N5e9e/dSVlZWo21FRQVTp05l6tSptGvXjj/+8Y+MHTuW8PDwU/39JMgVRsV6lmO7NPzJ4Xuc1YPI+eVkxySyfd5i2l3e8AtiEREREQkN9S4OP/vsMx599FG2bdsGHC3soqKiGDJkCMOHD6d///706NGjzucJs7KyWL16NUuWLOHnn39m3rx5OJ1OALZt28Y999zD//t//49nnnmGa6+9tr5RJQQUOisAiI4Mxxbe8MdHiho6hDMnvc3i1t35ZcsB2lkdSERERETksHoVh8OHD2fu3LmAuyi02Wxccskl3HTTTVx00UXExMTU6+CtWrWiVatWXHjhhTz22GOUlJQwbdo0PvroI6ZOnUpFRQXbt2/nhhtu4NVXX2X27Nn12r8Ev0KnC2j4I5V6pKTQv2Qfi+nOoooYristBUcDf9ZSREREREJCvbpq5syZg2maJCcn89RTT5GVlcUXX3zB1VdfXe/CsDbR0dFcddVVTJo0iaysLCZMmEBqaiqmaTJv3rzT3r8En4KlKwGIc5VBUZHFaQKjf3N3MfhLqy7wyy8WpxERERERcatXcZiamspLL73Ezp07efzxx0lJSfFXLpKTk3nsscfYsWMHL774ol+PJdYpmPodAHGbN0BWlsVpAqNvv06EVVWyq0lz9s1eaHUcERERERGgnsXh1q1beeCBB7DbAzcfnd1u58EHH/Q84ygNS1HZ4dtKy4ohLs7iNIERN3wIXfPchfDi1t0tTiMiIiIi4lav4tAXt46equjoaMuOLf5TVF4JQFx5CcTHW5wmQDp0oP9lwwD4JSHd4jAiIiIiIm4Nf3hICWrFLvdotzHlpWDhlw+BNqCNe77DXzIPWZxERERERMRNxaFYqsjdcUgMlWAY1oYJoH6Hi8MN+wrJKym3OI2IiIiIiIpDsVhxlbsgjA0zLU4SWClxdtqluHtKl8xaZnEaEREREZF6znN4MtnZ2cydO5d58+axfv16tmzZwt69eykrK8M0TZo1a0Z6ejr9+/dnyJAhjBgxgsTERF8dXkJUEeEAxIRbHCTQTJMB6xayLaUHv7z2ESMvGtioek5FREREJPj4rDhMTU3FqPbh1jRr9gTt3r2brKwsFixYwMsvv4zNZmPkyJHcc889XH755b6KIaGkspJiIwJohMWhYdC/ZB+f0IPFTTJgyxbo2NHqVCIiIiLSiPn8tlLTNI8rDKv/7Mh/Kyoq+O677xgzZgy9e/dm8eLFvo4iwa6wkGK7e0L42MjGVh3CgO5pAKxu3oHSmT9bnEZEREREGjuf9RyCu+hLT0+nbdu2tGrVipSUFAzDwDRN9uzZw86dO1m9ejUlJSWe7QFWrlzJ2WefzT/+8Q/uv/9+X0aSYFZaSmFMAgAxdp+eiiEh7ZzBNP9oC/viklm+aD1n3WN1IhERERFpzHz2ify7776jX79+JCUlnXS7yspKli9fztSpU/n444/ZvHkzhmHgcrn47W9/S1xcHGPHjvVVLAlmLVpQ3K0nHCgi9rE/WZ0m4Iz+/Rnw96l81elsFu8t4SzT1HOHIiIiImIZn91Wev7559dZGAKEh4fTr18/xo8fz8aNG/niiy9o27Yt4O5JfPDBB9m3b5+vYkmQKy5zARATFWFxEgtERtI/qgyAX+LTIDPT2jwiIiIi0qhZPpXF5ZdfztKlSxk0aBAARUVFvP766xankkApOlwcxkY1vttKAQZ0bgnAspadqZg5y9owIiIiItKoWV4cAiQkJPDxxx9js7kLhKlTp1qcSALBNE1Pz2FsI3zmEKDjuQNIKC2kNDKKNQtWWx1HRERERBqxoCgOATIyMujTpw+mabJ9+3ar40gAOH+YQdXhgW1jVi23NoxFwgYOZMCe9QAs2l1ocRoRERERacyCpjgEcDqdAJ7RTKVhK1qzwbMcvWuHhUksZLczyFYEwIKWXaGgwOJAIiIiItJYBaQ4rKioYOHChRQVFdX6c5fLxbPPPsuqVaswDIP09PRAxBKLFRe6vwSIKSshLCHe4jTWGfzAWAB+adWFiphYa8OIiIiISKMVkAe9CgoKOOusszAMg+bNm5OWlkZSUhIRERHk5OSwZs0aioqKMA4P43/dddcFIpZYrKjYCeEQU14Ksc2sjmOZzgO702T6XvJKKli1O5++GYlWRxIRERGRRiigo4CYpsm+ffuOm6rCNE3P8pgxY3jssccCGUssUlxaDrEQW14KcXFWx7FMWJjBwLZJfL92Pwu35ag4FBERERFLBOS2UofDwVVXXUVGRgamaXpeAIZhcN555/Hmm2+yevVqPv/8cyIjIwMRSyxW7KwAjvQcNu7bKQe3awrAwm05UO3LEhERERGRQAlIcRgdHc1nn33G9u3b2b17N++88w5XXnklkZGRVFVVMXPmTP7xj39w4MCBQMSRIFFYXgkcLg4bcc8hwOCKbACWrM+i/IOPLE4jIiIiIo1RwEcrbdmyJbfddhuTJk1iz549jB8/npiYGDZu3MioUaN4++23Ax1JLFJcUQUcvq20kfccdnTlk1SST6nNzirNdygiIiIiFvBZcehyuerdJjExkSeeeILVq1fTtWtXqqqquPfee1m1apWvYkkQK3a5b5+MKS+F6GiL01gr7KyzGLR7LQALNN+hiIiIiFjAZ8Vh9+7d+e67706pbUZGBlOnTsVut+Nyufj73//uq1gSxIoq3aPTxlaVQ1hQTbkZeNHRDA5zF4ULoltBVpbFgURERESksfHZJ/JNmzZxySWXcOmll7J8+fJ6t2/Tpg19+/bFNE1++uknX8WSIFZ8RhcAYtu0tjhJcBjcpTkAS1t1pmz6jxanEREREZHGxufdNdOmTaNfv35cdtllzJ49u15tDx06BMDBgwd9HUuCUPGAQQDEjLnc4iTBof2Is0guyqUsws6KeSutjiMiIiIijYzPisMnnniCyMhIzzQV3377Leeeey4dOnRgwoQJLFq0iKqqqhO2f+ONN9iwYQMASUlJvoolQayo7PBopfaATrcZtIzBgxm0Zx0AC3YXa0oLEREREQkon30qHz9+PDfddBP33XcfM2bM8MxjuG3bNp566imeeuopYmJi6N69O506daJZs2bY7XYOHTrE3LlzPYPQGIbBmWee6atYEsSKy9yDGMXawy1OEiTsdgZHlTEVWNgkAzZtgk6drE4lIiIiIo2ET7tsOnbsyA8//MD06dN5/PHHWbx4sednpmlSVFTEokWLWLRoUY125jE9JOPGjfNlLAlSRYeLQ/UcHjWoR2vIh2WtOuOc8SNRKg5FREREJED8MkTkqFGjWLhwITNmzOCqq67CZjv64f/YQhDcvYVH/O53v+OKK67wRywJJllZFC9eCkDMf960OEzwaDfqbFILcyi3RbLsl41WxxERERGRRsSvXTbnnXce5513HocOHeLHH39k5syZrFmzhs2bN7N//37AXRimpqYydOhQ7rnnHkaOHOnPSBIsioooDrcDEFuief2OMHr35qwp65nigvnXjuMsqwOJiIiISKMRkPv5kpKSuOaaa7jmmms875mmidPpJDw8nMjIyEDEkGBSVESR3QFAjEN//x7h4Qy54hymfL6KOdtz+T+r84iIiIhIo2HZzOOGYeBwOFQYNlZFRRRFuovD2Gi7xWGCy9COKQCs3p1HfkmFxWlEREREpLGwrDiUxq2yoBBnRBQAMTFRFqcJLs0TouiQGkuVCQu2ZVsdR0REREQaCRWHYomSgiLPckysw8Ikwenspu7pPea8+iGUlVmcRkREREQaAxWHYonSwhIADLMKe2yMxWmCz9nzpgIw10yAhQstTiMiIiIijUG9isMJEyZQXFzsrywnVFxczIQJEwJ+XPGfkqJSAKLLnRhxsRanCT6DBnclvKqSHYkt2TVjrtVxRERERKQRqFdxOH78eNq3b8+zzz5LXl6enyIdlZeXxzPPPEO7du146qmn/H48CZySYicA0RVOiFVxeKzY80fQe88GAOau22NxGhERERFpDOp9W+nBgwf585//THp6Or/+9a9ZsmSJz0MtXryYcePGkZ6ezmOPPcbBgwd9fgyxVkmp+zk6FYcn0KoVZxftBmBuVTwUFFgcSEREREQaunoVh7NmzaJnz56YpklRURFvvPEGAwcOpFOnTvzxj39k5syZOJ3OeocoKSlh+vTp/N///R8dOnRg8ODBvPXWWxQVFWGaJr169WLmzJn13q8Er5LzRgLgaJYCnTtbnCY4DW3TBIB56T2p/PEna8OIiIiISINnmKZp1qeBaZq8//77/OUvf2HLli3unRiG5+cRERF06dKF7t27065dO1q1akWTJk1wOByeie9zc3PJyspi69atrFmzhg0bNuByuWocA6BDhw48/vjj3HzzzTWOIXUrKCggISGB/Px84uPjrY5znO/W7ONXHyylb0Yik+49y+o4Qck19RvO/LGEIns0XxfOpsc/n7M6koiIiIiEIG9rA1t9d2wYBrfeeis333wz//vf/3j55ZdZWG00xfLyclatWsWqVau83uex9emgQYN48MEHueaaawgL04CqDVFJufvLgOjIcIuTBC/buecw6L2/MaP9AOZsy6WHaYK+JBERERERPznlyissLIzrr7+e+fPns2rVKh555BE6duwIuIu9Y19HnOhnHTp04A9/+AMrV65k/vz5XHfddSoMG7CS8koAHBEqDk8oJoahNvd8kHMT2sDmzdbmEREREZEGrd49h7Xp3r07zzzzDM888wyZmZn8/PPPLF26lHXr1rFjxw6ys7M9U2DExMSQnJxMRkYGXbt2pW/fvgwbNoy2bdv6IopX5s+fz3vvvcecOXPIysrCNE3S0tI4++yzue222xgyZIjPj3kqt8W+9tpr/OpXv/J5lmBQun0HADHOwE+NEkrO7pUOebAkrSul037AccYZVkcSERERkQbKJ8VhdW3atKFNmzbcdtttvt71aSsuLuaBBx7g7bffPu5n69evZ/369fz73//m9ttv55VXXiEmRpOz+0vx519Ax5E4pkyCe4ZbHSdotbt0BC3fWcueiFgW9R/BOVYHEhEREZEGy+fFYbCqrKxkzJgx/PDDD573HA4H3bp1w2azsW7dOgoOTxfwzjvvkJWVxbfffkt4uO9vexw2bBgOh6PO7dLT031+7GBR6r6rlGijytogQc7o3p1hZ1XxyS+7mJVdpeJQRERERPym0RSHjz/+eI3C8O677+bZZ58lKSkJcPcqPvfcczz99NMA/PDDDzzxxBP89a9/9XmW9957jzZt2vh8v6GkxHQ/Txpt1Guw3EbpnE4pfPLLLn7epPk+RURERMR/6j3iy/bt2/2Rw6/27NnDiy++6Fm/5ZZbePPNNz2FIbifhZwwYQKPPfaY570XXniBPXv2BDRrY1GMu0c2WuPR1GlIh2RsYQbbs4vZkaNnNEVERETEP+pdHLZv357ExETOPfdcHnroId5//31Wr15NZWWlP/L5xEsvvYTT6QQgOjqal1566YTbPv7447Ru3RoAp9PJxIkTAxGxcamspDTM3WkdHa6pGeoSFxVBv/QEAGb982OL04iIiIhIQ3VKc0UUFBQwe/ZsJk6cyNixYznzzDOJjY2lf//+3HPPPbz22mssWLCAkpISX+c9JV988YVn+dprr63RY3isyMhIbr/9ds/65MmT/ZqtUSopoSTCDkB0hKYr8cY5s9zn8KzVu0G92SIiIiLiB6f0zGH1eQsNw8A0TcrKyli2bBnLli2r8bOOHTvSu3dvevfuzZlnnknv3r1JTk4+/eRe2rhxI1u2bPGsX3jhhXW2ueiii5gwYQIAW7ZsYePGjXTq1MlvGRudoiJKItwD8kRH6r5Sb5zTIYlnXbAgvQfO734g6o6xVkcSERERkQam3sXhV199xYoVKzyv6s8gHikajxSMpmmyadMmNm3axKeffurZrmXLlscVjP4aoGXlypU11gcPHlxnmz59+hAZGUl5eTkAq1atUnHoS0VFnp5Dhz3C4jChodMFQ2n+yTb2xSWzaPZcht9hdSIRERERaWjqXRxeeumlXHrppZ71wsJCT6G4fPlyVqxYwbp16zyF1bEFI0BWVhZ79uzhm2++8ewnISGBM888kwEDBjB06FCGDRtGXFzcaf1y4J6/8IjIyEjP84Qnc2S7rVu3HrcPX/j973/PunXr2LVrFxUVFTRt2pSOHTsyfPhwbrvtNtq2bevT4wWdoiJKI6IAiI5ScegNY/Bgzvn7l3zS5VxmHqxkuMsFtkYz2LCIiIiIBMBpf7qMi4tj6NChDB061POey+Vi3bp1nmJxxYoVrFy5kry8PM82xxaNeXl5/Pzzz/z88888//zz2O12Ro8ezf3338+QIUNOOV9mZqZnOS0tDcPwbgCU9PR0T3FYfR++8Pnnn9dYz8rKIisri1mzZvHXv/6VO++8kxdffNGruRBDUnExxZHu3y1GxaF3bDbOSYJPgJ9bdYf582HYMKtTiYiIiEgD4peuB5vNRs+ePenZsye33Xab5/3MzMwaPYwrVqxg165dnp9XLxidTiefffYZn332GWPGjOGtt94iISGh3lkKCws9y/VpHx8fX+s+fCE5OZn27dsTGxtLfn4+GzZsoKioCHAX1m+88QaLFy9m5syZXmcuKyujrKzMs15QUODTzD511lmUTC+Cskoc995jdZqQMWR4L2xrXWxPasWOqTPIUHEoIiIiIj4U0PvS2rRpQ5s2bbjiiis87+Xm5tYoFpcuXcqGDRuAo8Xi5MmTWb16NXPnzq33YDZHii6AqKgor9tV77Wrvo9T1bVrV+655x4uu+wy2rVrV+NnLpeL77//nkcffZRVq1YBsHz5cq6//nqmTZvm1f6feeYZnnrqqdPOGRBhYZRWVAEQnZxocZjQEXfpRfT74d8sTO/JrDV7uK3uJiIiIiIiXrN8HoHExETOO+88HnroIf773/+ydu1acnJy+PDDDxkxYoRnu02bNnHTTTfVe/8ul8uzbKvHM1rVt62oqKj3cY+1du1aHnzwweMKwyPHuuSSS1i0aBGXXHKJ5/3vvvuOr7/+2qv9/+lPfyI/P9/zqt4jG2zKXVW4qtyFf3SknpvzWnIy51RmAzArJg22bbM4kIiIiIg0JJYXh7Vp0qQJN9xwA9OnT2fq1KnExMQAMGPGDGbOnFmvfUVHR3uWnU6n1+2qb3vk+P4WFRXFxx9/TLNmzTzvvfLKK161tdvtxMfH13gFq5LyowW7prKon3N6pAEwP70npV9/a3EaEREREWlIgrI4rO7iiy/mtdde86x/9NFH9WofGxvrWS4tLfW6XUlJSa378Le4uDjuvfdez/qcOXPqVdSGgpLZ8wCIoIqIrVvq2Fqq6zR6BK2KcyiLsDO3XW+r44iIiIhIAxL0xSHAjTfeSNOmTQGYP39+vdpWf0Zx7969Xrfbt2+fZ/nIsQPl3HPP9Sw7nc6gvkX0VJQsXgJAdGkxbNxocZrQYvTowahRfQGYUXb6U72IiIiIiBwREsWhYRh07doV0zTZs2dPvdpWn7w+JyenRo/gyVQvyDp37lyvY56u5s2b11jPzs4O6PH9raTU/QxndIUTAnTLboNhGIzq6r7t+McN+6k6/OymiIiIiMjpConiEI4+O1jfaSW6dOlSY33FihV1tsnKyuLgwYMn3Ie/HVvAVn9usiEocZYD4KhwQgP73QJhQNsk4qJsZBeVs2J3ntVxRERERKSBCJnicOLEibz55pvceeed9Wo3YMAA7Ha7Z33u3Ll1tpkzZ45nOSoqigEDBtTrmKdr7dq1NdZTU1MDenx/Ky1zD0gTU66ew1MRER7GOZ3c58SMT6ZDVZXFiURERESkIQiZ4vCMM87grrvu4o033qhXu9jY2BpTYnz44Yd1tqm+zYgRIwI2WukRn3zyiWe5TZs2tGjRIqDH97fiw6OVqufw1I1c8j0AMzZmw9KlFqcRERERkYYgZIrD0zF27FjP8qpVq046d+CyZctqTDxfvW0gfPXVV0ydOtWzfsUVVwT0+IFQUuHu6YquKFPP4Sk6p2MytkoXm1Iy2PHl91bHEREREZEGoFEUh1dffTW9evXyrI8bN44NGzYct93evXu5+eabqaysBODMM8/kqquuqnWfmZmZGIbheY0fP77W7fLz87nqqqtY6kXvzscff8yNN97oWY+OjuaRRx6ps12oKT1cHMZUlKrn8BQlXH4JA3a7bz+evnK3xWlEREREpCGwWR0gEAzD4N///jfDhw+ntLSUvXv3MnDgQO69916GDRuGzWZj8eLFvPrqq+zfvx8Ah8PBm2++iWEYp3Vs0zSZPHkykydPpnPnzlxwwQWceeaZtGjRgpiYGAoLC1m9ejWff/45v/zyS43M77zzznEjlzYExe7aW7eVno4WLRhZsZ/5wIzYDO7avBk6drQ6lYiIiIiEsEZRHAL079+fDz74gJtvvpnS0lIKCgp47rnneO65547b1uFw8MEHH9C/f3+fZtiwYUOtPZbHiouL44033uDaa6/16fGDRenh4jC6sgJsjeYU9LmR/doyIQ9+ad2NvElf0eSPD1sdSURERERCWKO4rfSIMWPGsHTpUkaOHFlrj6BhGIwYMYIlS5YwZswYnxzT4XBwzz330K1btzp7IRMSEnjggQdYs2YNN9xwg0+OH4xKWrQCIDo50eIkoS396kvpdDCTyrBwZi6o+0sHEREREZGTaXTdNl26dGH69Ons2rWLefPmkZWVBUCrVq0YMmQIrVu39mo/bdq0wTTrnoDcbrd7RljNzc1lxYoVHDhwgOzsbPLy8oiOjiYpKYmePXvSs2dPwsPDT/2XCxEl546ExTtxjL3N6iihrUMHRuW9xsaUNnwflsKVe/ZAy5ZWpxIRERGRENXoisMjWrduzfXXXx/QYyYmJnLuuecG9JjByFnhvq80OrLhF8L+dmGXFF51wax2fSn54iui7/uV1ZFEREREJEQ1qttKJTgcKQ6jInT6na5uV11A67x9OCOimDVrpdVxRERERCSE6dO5BFyppzhUz+HpMs48k4sPrANgWs/zLE4jIiIiIqFMxaEEVkEBpfMXAeD47BOLwzQAhsGFz/8RgJ8q4jy9siIiIiIi9aXiUAKrqAhnmQsAR9Yui8M0DGe2S6ZlQhTF5ZXM2ZxtdRwRERERCVEqDiWwSkpwRkQCEGWPsDhMw2AYBhd2bwHAtNV7LU4jIiIiIqFKxaEEVnExpTY7AFH2RjtYrs9d1KM5ANNXZ1G++BeL04iIiIhIKFJxKIFVUkJphLs4dERFWhym4ehbnk1qSR6FLpj3ygdWxxERERGREKTiUAKruBinikOfC2vXjgt2LANgWl44FBZanEhEREREQo2KQwmskhKctsPPHEZHWRymAbHZuCgjGoAf2vWn4quvLQ4kIiIiIqFGxaEEVEVRMRXh7oFoHNF2i9M0LAOuHEFycS55jnjmfTPP6jgiIiIiEmJUHEpAOYtKPctRMQ4LkzQ8tmFDuWTXcgC+KoqGvDxrA4mIiIhISFFxKAFVWuwuDg2zCntMtMVpGpjwcEa3jQHg+/YDcE75yuJAIiIiIhJKVBxKQJX16QdAlGFi9OppcZqGp8/VF5CWt49iezQ/fr/E6jgiIiIiEkJUHEpAlXbuCoAjxgE9elicpuExBg/msqwVAHxVHg+HDlkbSERERERChopDCajS8koAHBHhFidpoMLCuPyMRABmtu1H/udTrM0jIiIiIiFDxaEEVGmFuziMitCp5y+dr7+MMw7tptwWwfcpna2OIyIiIiIhQp/QJaCcObkARIUbYJoWp2mg+vXj8mvOAeCrHPXQioiIiIh3VBxKQDlffQ0Ax5JFsGePxWkaKMNgdN/WAMzfms2BQqfFgUREREQkFKg4lIAqLXcB4Kgog2hNZeEvrZOi6Z3ehCoTpq7ca3UcEREREQkBKg4loEpd7ltJoyrKICbG4jQN2xVntgJg8vfLYe1ai9OIiIiISLBTcSgB5XRVARBVWQERERanadhGH9pARGUFayrsrH/rY6vjiIiIiEiQU3EoAVVa6e45dFAJhmFxmoYt8exBjNy2BIDPNxeAy2VxIhEREREJZioOJaCc7o5DHFRZG6QxaNKEq2OLAZjSdiAV331vcSARERERCWYqDiWgSk13b2FUmKaxCIRhV55LclEuOTFNmDV5ltVxRERERCSIqTiUgCo13fPuOXRHaUBEXHwhV25bCMDnBQ7Iy7M2kIiIiIgELRWHEjimidNwn3JRmps9MCIjueqMBAB+bNuXnPc/sTiQiIiIiAQrFYcSOE4nzgg7AA6deQHT+Y7r6LF3M65wG1/9uMrqOCIiIiISpPQRXQInMpLSEecD4LjhOovDNCI9e3J14RYAPkvsjLl0qcWBRERERCQYqTiUwAkPp9QeDUBU23SLwzQuo88/k0hXOeuatWfVu5OsjiMiIiIiQUjFoQSU01UJQJRNp14gJd50HRfnbATgo0FXWpxGRERERIKRPqFLQJWWu4tDR6RGpAmouDhufOIeAL7akEOBs8LiQCIiIiISbFQcSuDs348zJxcAx47tFodpfPq3SaRDaiylFZV8uTzL6jgiIiIiEmRUHErgbNhA6aF8AKJmfG9xmMbHMAxuHOB+1vPDRTsxTdPiRCIiIiISTFQcSuCUlOC0RQIQZY+0OEzjdFWfNOzhBhv2FbL8z89aHUdEREREgoiKQwmc0lJKj8xzGKXi0AoJrlIuWT0TgI/W5UJRkcWJRERERCRYqDiUgKksKaX8cM+hw6Hi0BLx8dzUwr04tf1A8v/7kbV5RERERCRoqDiUgHGWOD3Ljmi7hUkatz73XE/nA9txRkTx2XfLQc8eioiIiAgqDiWAnMWlnmV7tMPCJI2b0bcvt+atB+C9lv2onD3b4kQiIiIiEgxUHErAlJaWA2CvKCNMxaGlrrzyLJqUFrCrSXN+fPdrq+OIiIiISBBQcSgB43S6i0OHqwyioy1O07g5rr2a6zfPBeCdsqawZ4/FiURERETEaioOJWBKyyoAcFSUgUM9h5ay27mldzPCqypZkN6T9a/91+pEIiIiImIxFYcSME67u7cwqsoFMTEWp5FWv7qdCzYvBODdNblQWlpHCxERERFpyFQcSsCU3nwrAPbOZ0C/fhanEVq35vYE9zyHUzoM4tD6LRYHEhERERErqTiUgHFWVALgiAy3OIkc0e/hu+keXUVZeCQf5UVZHUdERERELKTiUAKmzFUFgN2m0y5YGD16cMelvQF4d/4OTwEvIiIiIo2PPqVLwBwpPKIi1HMYTC7r1ZKWCVFkF5Uxadluq+OIiIiIiEVUHErAlE36AgD7yhXgclkbRjwiwsO4a2g7AN6cvoHKDRstTiQiIiIiVlBxKAHj3OQe8MS+bQuEq/cwmFzfKowmrlJ2FLmY9re3rY4jIiIiIhZQcSgBU1ZpAhBlVoJhWJxGqotu0YzbVv8AwOtVLTE3bbI4kYiIiIgEmopDCZgy93g02I0qa4PI8aKiuO3sdjjKnaxp3oG5L7xjdSIRERERCTAVhxIwR4rDKExrg0itkh64l+s3zATgtfx4yMy0NpCIiIiIBJSKQwmYMtN9K6k9TMVhUEpI4K6+zbFVupif0ZOlf3/T6kQiIiIiEkAqDiVgnIdPt6gwPW8YrFr97tdcs+FnAF7MiYWsLIsTiYiIiEigqDiUwKiqosxwj1Bq10ClwSspifu6xRFRWcHcjF78ot5DERERkUZDxaEERmkpzgg7AFHh6jkMZmkP3cc169zPHr54wAH79lmcSEREREQCQcWhBEZpKWXhEQDYbTrtglpqKved4SCisoL5rXuw6N0vrE4kIiIiIgGgT+kSGDYbZRltAbB36WxxGKlLq0d+y7UHVgPwYtKZ1oYRERERkYBQcSiB0aQJzg5nAGC//DKLw0idmjfnvon/R2R4GAu3HWL+1myrE4mIiIiIn6k4lIApc1UCYI/QaRcKWjZxcMOA1gA8N20DpqkpSEREREQaMn1Kl4BxVlQBEGXTcKWh4v4RHYmJDGfl7ny+mbrI6jgiIiIi4kcqDiVg1HMYepJj7dzTIQqAv01dQ/niXyxOJCIiIiL+ok/pEhjTpuHctQeAqE8/tjiM1MddBetJKTrEzsQWfPTCx6DbS0VEREQaJBWHEhjFxUensqh0WRxG6iPmnjv57cbpALyc2o/CSV9anEhERERE/EHFoQRGaSlOWyQAdkekxWGkXiIjue7eK2mXs4tD0Qm89sEsKCuzOpWIiIiI+JiKQwkIs6SU8sPFYZTDbnEaqS/blVfwx5wlALzV4Rx2THzD4kQiIiIi4msqDiUgykqdnmV7dJSFSeSUGAajHr+PoduXU26L4OmluXDwoNWpRERERMSHVBxKQJSVHC0Oo6IdFiaRU2X07s2TTXKwVbqY0bYfs5562epIIiIiIuJDKg4lIMpK3c+ohVVVYotRcRiqOjz1CGNXfwfAhPI0TW0hIiIi0oCoOJSAcDrLAYhylWNER1ucRk5Z8+Y8cF5Hkoty2ZaUxrtTl1udSERERER8RMWhBESZswIAu6scHOo5DGXxv7ufR0rWAjCRdPbklVqcSERERER8QcWhBISz7GjPoYrDEBcRwVVv/T/6ZSRSXF7J41PWYJqm1alERERE5DSpOJSAKLv6WgDsyUnQurXFaeR0hYUZPDOmBxHhBj9uOMA3q/daHUlERERETpOKQwmIsnYdALAnNoH4eGvDiE90bBbHr89x/72O/2QJeVO+sTiRiIiIiJwOFYcSEM6KSgCiInTKNSS/7teMDqU5ZFeF8//enwOHDlkdSUREREROkT6pS0CUuaoAsNvCLU4ivmRvEs8ze34G4H8dhzL3D//P4kQiIiIicqpUHEpAODdtAcBeWmRxEvEpw6D/SxO4Zc10AH4f2Z38SV9aHEpEREREToWKQwmIsrffBcA+f561QcT3WrXiT1f3oc2hPeyNT+HJT36BnByrU4mIiIhIPak4lIBwVhkARJkui5OIP0Tfdgsv5C0krKqSKe0H881Dz4CmtxAREREJKSoOJSDKDtcJdqqsDSL+YRj0eelpfr1yKgB/btKXA6/9x+JQIiIiIlIfKg4lIMoO14R21JvUYLVsyQPjLqbbvi3kOeJ5aO4BKlevsTqViIiIiHhJxaEEhPPwqRZlqOewIYsccwUT47JwlDuZm96Lf06cbHUkEREREfGSikMJiLLDp5rdsDiI+F2Hvz3F0+u+AuCllL7M35ptcSIRERER8YaKQ/G/qiqchg2AqDDdVtrgORxc/b9XuLpvGlUmPPjJCg4WllmdSkRERETqoOJQ/K+sjDJbBAD2MHUdNgrx8Tx9eXfOaBbLwcIyHvxkOa5K3VIsIiIiEsxUHIr/OZ04bZEARIVbnEUCxhEZzj9v7IMjIpz5W3N4ZtyzkK1bTEVERESClYpD8T+nk7LDxaHdplOuMenYLI5/9HD/3f8nuRef/eZpcGmuSxEREZFgpE/q4n/Nm1N24cUARN19p8VhJNAuHtKJB1a4B6j5c9q5LP3dE2Dq2VMRERGRYKPiUPzPMCirdC/a42KszSKB16oVv330Ji7YvJByWwS/Mruw5/mXrU4lIiIiIsdQcSgBUeZyV4e6rbRxChs2jBdGd6Lzge0cjE3i9o028j+dZHUsEREREalGn9QlIJwV7pEqoyI0Ik1jFXPbzbzVupDUwhw2prThnq+34py3wOpYIiIiInKYikPxv82bKdt/AAD7mlUWhxErpY3/I+86lxBXVsyitG48/NI0qlautDqWiIiIiKDiUAJh82achcUARK1YanEYsZRh0PW1v/NG5jQiKiv4pv1AnnriPcycHKuTiYiIiDR6Kg7F/5xOysIjALDbIy0OI5aLjOSs91/hHxu/BuC9LiN4ZtEBTI1gKiIiImIpFYfif04nTpsdgCh7hMVhJCjExjL63ef5a+syAN6cvY2//7BRBaKIiIiIhVQcit+ZpU7KbId7DqPUcyiHJSVx031jGH9ZVwD+OXMrL/+4BcrKLA4mIiIi0jipOBS/czmdVIW5Rym1R0dZnEaCzdghbfnzxV0AeHHGJv5+zf9hbtpkcSoRERGRxkfFofids7Tcs2x32C1MIsHq7mHt+GMH9xcIr3a9kCf/8AZVa9ZYnEpERESkcVFxKH5XVnr0NkG7Qz2HUrtfje7D02umYJhV/LfzeTz8xIdU/DTT6lgiIiIijYaKQ/E7Z5m759BeUYah4lBOJDWVW957lpfWfkF4VSVfnHE297w+h6IPPrY6mYiIiEijoOJQ/K6srAIAe2UFRKk4lJNo2pTLP3mZN3dMw15Rxsx2fbn6pxz2PPMCaCRTEREREb9ScSh+52zZGoAow4TERIvTSNCLjWXEh6/wackCkoty2ZDalsuzUlh1+wNQUmJ1OhEREZEGS8Wh+F3ZVVcDYG+RCt27W5xGQoLNxpn/+htTUrPodDCTg7FJXNv0HL68+89WJxMRERFpsFQcit85KyoBiLKFW5xEQophkPbEI3x+YQuG71iOMyKKB1uP5Ikv11DmqrQ6nYiIiEiDo+JQ/K7MVQWAPUKnm9Rf3DVjePvRK/hNc/fARv9dsINr31hIVl6pxclEREREGhZ9Whe/K6s4XByq51BOUXiP7vzfb6/k7bH9SHBEsHJXHhdPnM0390+AzZutjiciIiLSIKg4FL8re+0NAKJWLINS9fbIqTuvczOm3n82PdMSyC91cV9MXx76/VsU/OsNqKqyOp6IiIhISFNxKH5Xtu8AAPacgxARYXEaCXWtk6L5fGwffrNxBmFVlUzuPIyLVkcy/4qxsGWL1fFEREREQpaKQ/E7Z5V7frqoygqw2SxOIw1BZGw0//f+03xWNI/03L1kJaRyY9frePihNzj0txehosLqiCIiIiIhR8Wh+F3Z4YEl7aZGmBQfio2l77+eY9qVGdyyeTaGWcWkrucwYnczPr/0TswZM6xOKCIiIhJSVByK3zkPPwoWhZ4JE9+LuXAUT7/3OJ+X/0LnA9vJjU7g/3pfxzXvLmPFHQ+CaVodUURERCQkqDgUvyszDQDsKg7FX2Jj6fviBL6+pz+PbJ1BVIWTJWnduCL1fH776QpNeyEiIiLiBRWH4nee4tBQD474V8Sggdz76T+Y2bmYMftWATBlxR7O+/ssnvl2Pdm790N+vsUpRURERIKTRgcRv3Me/g5CxaEERFgYLe6+lRfuMrk9q4C/fLOORdsP8cbsbfz3583cuuo77u7bjOQHfw0pKVanFREREQka6jkUvyszDheHOtskkAyDHmkJfHLPIN4Z259eqQ5KjXDe6HUJQ0u6M+HmJ9h1/+9h+3ark4qIiIgEBX1cF/9yuXCGu+c2jNLZJhYwDINzO6cy5dozeCd3Lr32bqI0Moq3e1/KcMdQfv3Qv1l6469g+nSo0nOxIiIi0njp47r4l2lSdmYfAOyDBlgcRhozIy2Nc994hikTxvBu+XKG7lxJVVg433YawlXpl3H5+6v59IJbKf77C1BebnVcERERkYBTcSj+FRGBs017AKKGD7U4jAgYGRmc88JjvP/qr/jOsY5rN88h0lXBypadeKTvDfTf14ZHvlrPsp25mJoGQ0RERBoRDUgjflfmqgTAbtN3ERJEkpLo/OTv+VtFBX/4/EsmTV3Ap9Ht2NY0jU+X7ObTJbtpmxzDpT1bcMmcL+g0vB/G8OEQEWF1chERERG/aJSf1ufPn8+4cePo2rUrCQkJxMfH07VrV+655x7mzZvn9+Nv27aNJ554gr59+5KSkoLD4aB9+/ZceeWVfP7557hcLr9nCCRnhfs5rqiIcIuTiNQiIoLkG65m3IfP8ePDw/ns5h5c1SeNqIgwtmcX88pPW7iwogejPtnMSxf9irV3PIA5aTIUF1udXERERMSnDLMR3TdVXFzMAw88wNtvv33S7W6//XZeeeUVYmJifJ5h4sSJPPLII5SVlZ1wm0GDBvHhhx/Srl27Uz5OQUEBCQkJ5OfnEx8ff8r7OW3l5Vz08lzWZ5fy31t6M6xbS+uyiNRDUZmLH9fv5+tPfmS2K55y29Eew+aF2ZybuZxzY8s5u38Hos8fCT16gGFYmFhERESkdt7WBo2mOKysrOTiiy/mhx9+8LzncDjo1q0bNpuNdevWUVBQ4PnZ+eefz7fffkt4uO96u55++mmeeOIJz3pYWBhdu3YlKSmJzZs3s3fvXs/P0tLSWLx4MS1atDilYwVNcfjLL5z35lK2NW3Np6WLGDhxgnVZRE5FcTEFX05l+sxVTCu0M69lN0ojozw/jnSV0zdrPYMqshn0tz9zZnoT7Db1kouIiEjwUHF4jEcffZRnnnnGs3733Xfz7LPPkpSUBLh7FZ977jmefvrpGm3++te/+uT433//PRdddJFngIvBgwfz7rvvcsYZZwBQVVXFZ599xl133UVRUREAQ4YMYe7cuad0vKApDufMYchHW8hKSOXLyqX0ev6JutuIBKvycpwzfmLhtPnM3FXMT827sqtJ8xqb2G1h9M1IZEDbJM789lN6pThIHDoI+vYFh8Oi4CIiItKYqTisZs+ePbRv3x6n0wnALbfcwn//+99at3388cf5y1/+AkBUVBRbt26lZcvTuxXSNE169+7NypUrAejUqRPLli0jOjr6uG1nzJjBqFGjPOuTJ0/myiuvrPcxg6Y4nD6dfl/tJzsmke9sK+n8l0etyyLiS6aJuW4dW7/7mYUrMlnY7SwWumLJLjr+lvGM3D302reZXlUF9Ex10KlLBvF9e8GZZ0JiYuCzi4iISKOi4rCaP/zhDzz//PMAREdHs2vXLk+P4bHKy8vp0KEDu3bt8rR97rnnTuv43377LZdccoln/bvvvuOCCy444fbXX389n376KQADBgxg0aJF9T5m0BSHX39N95+cFNmjmRWzjjaP/966LCJ+ZpomWw8Ws2BbDssWrmPl2p1sa5pW67YtCw7Q6eAOOpXl0vm+2+jUswNtk2PcAze5XBAermcYRURExCe8rQ0axVQWX3zxhWf52muvPWFhCBAZGcntt9/OhAnuZ+MmT5582sXh5MmTPctt27bl/PPPP+n248aN8xSHixcvZvfu3aSl1f4BM+g5nZQdHsjDbo+0OIyIfxmGQYfUWDqkxnLLgNawYQP5cxawatlmVhx0siK8CetS2rA3PoU98ansiU9lJsDMve4X0CIhijbF2bRZ/QttIitpE2ejbWo8rdo0J6ZdBrRtC+npYLdb+ruKiIhIw9Pgi8ONGzeyZcsWz/qFF15YZ5uLLrrIUxxu2bKFjRs30qlTp1PO8M0333iWL7jgAow6egOGDh1KTEwMxYeHyv/mm28YN27cKR/fSpWlTirC3bfPRkWpOJRGJCwMunYloWtXhgJDAcrKYO1a8pesYOOa7Wzck8eGiCZsGn4RG/cVUuB0sTffyV5iWdDl3Jr7y4T4Dbm0LNhEy8JsWlSW0LJtS1refDUtEhykxNlJjrUT/8sCjIQEaNECkpPdOURERES80OCLwyPP+R0xePDgOtv06dOHyMhIysvLAVi1atUpF4cHDhxg37599Tq+zWajf//+zJo1y3P8UFVWevT5K7tDPR3SyNnt0KcPCX36MAAYUO1HpmmSW1LB9uxidkx8g8yNO9keHsuOxBZsT2xJYVQsBYdfG1LbHm34ac1rXGRlBU2Lt5FcPJPkkjySXaU0NVwk26poEhVOQkwUCXEOEkZfREKPriQ4IoiKCMMoLYW9eyE+3v1Sz6SIiEij0+CLw/Xr13uWIyMjad26dZ1tjmy3devW4/ZxOscHaN++vVft2rdv7ykOT+f4VlNxKOIdwzBIiokkKSaSvi/80f2m0wk7d8L27RRu28HeHfvYsy+XPXlO9jpN9vQZxN62ndmb7+RgYRlFZS7KwyPYG5/C3viUkx9w6l73C4gMDyM+vIqEvbtIcBYRW1ZCTGU5MaaLmDCT6DCIsRlER4YRazOIvv8+Yuw2oiPDibHbcCxfin3LJuzRUdij7UTFRBMR48CIiYHoaHehabe7i86UOnKJiIiIZRp8cZiZmelZTktLq/OWziPS09M9xWH1fZzO8Y/s19vjn2gfocTpdPe+RlRWEO6IqmNrEakhKgrOOAPOOIM4IA44o/rPTbPGoDXO3Hyy//Ey2dkF5OQWkV1aSXZlGAdNGzm2aPKjYsk/3PuY3zyNfBdUVpmUV1aRXQnZTev+8gyAj5fX8mbNtoZZht1ViN1Vgd1VTpSrHHu0HXu7Ntht4dhtYdhtYUT89CMRxUXYqMKGSYRhYjMgAtzLYYZ7/ewh2Lp3IyLccL9XmE/Ehx9iC8P98zCDsDCDcMMgPDwMI8z93/CwMIyxtxGekEB4mIFhGISvW0vYwgWEhYURbuBpFxZmEGZAeFgYYQaEJSYSfu01h993b2PM+AEjKwvDMDAOb2eEhcHhdcMwMMLDMDp1wujbFwP3X5FhGDB5svs2X8M4+t8af2iH1wcMqFlEZ2fDkiXHb1e9/ZHlkSNrvr9pExweYO2kbRMToVevmnmWLoWSkhMf68h/27SB6qN6l5XBihUcp7b//3bv7v4C4YiDB8Gb/+dFRLhH+61uyxbIza27bXKy+9nd6pYtg8rKutu2bw/Vxy0oKgJvv8Dt3Rts1T52ZWXBnj11t4uNhS5dar63fj0cfvTkpFq2rPl3U1kJy2v791uLzp3dxz7i0CHYtq3udmFh0KdPzfcyM93ncV0SE91/xtWtWgUVFXW3zchw/90eUVoK69bV3Q6gRw+IrPboy/79sHt33e2ioqBbt5rvbd4M1ebNPqHUVDi2w2Lp0rrbAXToAAkJR9cLCtznvzeO/bvZvRsOHKi7XVwcdOxY871169xfYtalVSto1uzoenk5rFlTdztwn/vVp4HKznZ/aVqXiAj332t127ZBXl7dbZs2dZ9P1a1YAVVVdbdt2zbkRyFv8MVhYWGhZzmh+j+kOlQfxaf6Pk7n+PXJUN/jl5WVUVZ2tJeuwJsLUwCUlbkv6HZXBUQdP3WHiJyGYz5sRyUmkPaXx6l1+KrycsjJcb+ys6FvX8zYWIrLK8kvrSBv4RLy/zeZAmclReUuSsorKXaZlFRCcZVBsc1OcaSDkug4is8Z4f55uYviMheleYWUVZqURRy9O8A0wnBGROGMOOZLoaxjrk2tenr3u+4Gdm84pu053rX9tLYP713rbpcD/L8fa/lBq7rbrtkPk76t8ZZhRmCYprtgNKswMDFMAPPw++71sPmLMWw2DAADDJcLo7Dw8M+PtDePrh95DxNjyU81c+TlYRQWHJOjlrxRe6FZTs3t9mS5z5sj67U0M0wTmu6HJk3c6wZQ4YIdmSc9nsHhNzNywW4/uu/8fPA8imEe06Yamw065NX8wnf3bigoOLrvEx0/sQlGq10131u31rsPfq331fxQXlIC27bW3Q6gaxGEhR9dP7Dfuw/ljmhof7Dme1u3QmlJ3W1TUyG12ofyqkrvC6Z22ZjR1T6U5xfU/KLhRMLCoOsxn1uy9nhXuCfEQ+tjirKNG93nVF1a7ar5obysDDZ7WTB1ynUXE0dk51Q7D49nHjntIu3Q4Ziid8cOKC6q+5hJTaF5zXlyWbfWu7zp+2oW7kVFsHOHd22P/bvZtw8O5dS+bXUxsZBxzJcZW7e4/5wPM2u9SuAuDJs2PbpeUeEuor3RPrvmYw55ed59qWKzwRmHar53+BpRpyZNoGVmzfc2bPDuGpGW5b5Lppo7z27LDQO86xwKBg2+ODwyoTy45y30lqPatxTV93E6x69Phvoe/5lnnuGpp56qX7gAcF5zHfx3DVHxMXBW3c9bioifREa6B6lp0cLzlgHE2m3E2m20unAoXDi09ram6f4AUFDg/pb42DsgFi+GTZswi7MpLymlrNSJs6SMstLDL1clZa4qyrp0wzn6CsoqqtzvVVRRPnEirsJiXJUmFaaJqwoqTHAdflWYBi7DwHXueVR0OANXZRUVVSauQ3m4Zv1MRVg4rjAbrvBwKo0wKsPCMHH/t9IIoyosjKpOnakMD6eqyqTKhMq8PKry8qkyqm1jGO7lw6/KsDCqwsKpDA/HVxM+mUbY0Q+VJ1MJVB7zYdgR591B8o/5Ft+Igngv/9+XV1pzPToJvPlOrwo4dEyhkujl/MCFLvfLwwYnmP7lOAeP6TmLbALJTbxru/+YD8hNvfzg5gScx7RNaeNd24PHFnPR3rc9Nm9sKsTWvmkNZi1tvT1mYSUUVv/8EQYpGSfcvIb9x3xuscVDipfTah3btokXX8YAlNfS1tu8h8qA6nPU2r1ve+CYYzqaul+n0jbZy2OWACWn2PbYY4bFQrI3J1MtbeOau29pqUtlLW2Tvfw3l18BVO85tnnf9thj1ucacWzbJC+vS07AWbNtbkl57dsGqQZfHLpcR/+nY7N5/+tW37bCm9sZvDh+fTLU9/h/+tOfeOihhzzrBQUFXj1f6W/pHVox6d54wAz5bnaRRssw3LdPnejLrQEDYMAADMB++OX17Kof/L3ubUzT/ao+8qrLBdf1cP+3stL9OrJ87Hs9e9a8ZWz7dvdteVVVR/d9ZNnz3wqIi4KLLsI0DxeVVSZV06fDnj2YpuluWlXl/vnh/1Jlun/WrRvmoMHuZcCsMjH/9S8wTXebqsPv466tjhSgJmBedhlm63T3smli7tiJ+fXXh/8oDrcza7Y1j7S+99c1/+jmznXflnf4z9E8cpBqf7YmQHpruGz00bcBPvzQfSvhkW1qtK323tlnQ58+R4vowkL4z39qNqh2zBq19i23YFbvUVizBmbMOKbvr5YeiZgYzDvvrPnetGmYx/RG1NqT0bULnDeiRh7jzTdr9JKe0AUX1Ly1bt8++PzzutsB3HO3u6fpiEWL3F+s1KV5c7jmmprvffbZSXu2PAYMgIEDj66Xl8Ebb3qX95pr3Mc+8ke4eQtMm1Z3u8hI+NUxI6z/+COs9aLHskMHuPiimu/9523veuJGjKh5i2fOIfjwg7rbAdx+B8RVK5CWL4c5c07axABITIJbbqn5gylTvOvFO7M3DBtW872JL3kRFrjiCshoc3R9R6b7uN548Lc112fPhuXL6m6XngFXXlnzvfffP67Xsdbvv4YOg77VbmetcY2ow80317xd+PA1ok6xsXDXXTXf++Yb73osu3WDUaNqvvevf3l3jbj4YvfjINW0TgqtO+cM0/TVd6LBafTo0Xx9+H+q/fv3Z7E3F2Lgmmuu4fPDF/zRo0fz5ZdfntLxX3jhBR5++GHPenFxMdHRdZ8k//znP/nNb34DuG8xzc/Pr9dxvZ3oUkREREREGjZva4MGPwFWbLV7sktLS0+yZU0lJUdv/6i+j9M5fn0y+Or4IiIiIiIi3mjwxWFyta7ovXv3et2u+tyETZt6ee94HcevTwZfHV9ERERERMQbDb44rD55fU5OTo0euZPZVW1Ers6dO/vk+AA7vRl+14fHFxERERER8UaDLw67HDM30Ira5l46RlZWFgcPHh02+th91EfHjh1rDC7jzfEBllebh+h0ji8iIiIiIuKNBl8cDhgwAHu1+VHmzp1bZ5s51UaoioqKYsCAAad8/MjISAZWGynMm+Pv27ePLdUmMx127GhWIiIiIiIiPtbgi8PY2FhGjDg6ZPWHH35YZ5vq24wYMYKYmJjTynD55Zd7lmfMmMH+/fu9Pn6TJk1UHIqIiIiIiN81+OIQYOzYsZ7lVatWeaa2qM2yZcuYVm0en+ptT9UNN9zg6b2sqKjgb3/72wm3LSoq4uWXX/as33TTTURERJx2BhERERERkZNpFMXh1VdfTa9evTzr48aNY8OGDcdtt3fvXm6++WYqKysBOPPMM7nqqqtq3WdmZiaGYXhe48ePP+Hx09LSGDfu6ISwEydOZNKkScdtV1FRwe233+4ZtMbhcPDoo4969TuKiIiIiIicDlvdm4Q+wzD497//zfDhwyktLWXv3r0MHDiQe++9l2HDhmGz2Vi8eDGvvvqq55ZPh8PBm2++iWEYPskwfvx4pk2bxubNm6msrOTaa6/lxhtv5IorriApKYmNGzfy2muvsWrVKk+b559/npYtW/rk+CIiIiIiIidjmKZpWh0iUCZPnszNN99c50T0DoeDDz74gDFjxpxwm8zMTNq2betZf/LJJ0/aewiwadMmRo4cWWOaihP5wx/+wHPPPVfndidSUFBAQkIC+fn5xMfHn/J+REREREQktHlbGzSK20qPGDNmDEuXLmXkyJG19ggahsGIESNYsmTJSQvDU3XGGWewatUq7rzzThwOR63bdOnShS+//PK0CkMREREREZH6alQ9h9Xt2rWLefPmkZWVBUCrVq0YMmQIrVu3DsjxCwsL+emnn9i1axfFxcW0aNGCHj160Lt3b5/sXz2HIiIiIiIC3tcGjbY4bOhUHIqIiIiICOi2UhEREREREamHRjFaaWN0pEO4oKDA4iQiIiIiImKlIzVBXTeNqjhsoAoLCwEC9gyliIiIiIgEt8LCQhISEk74cz1z2EBVVVWxZ88e4uLifDZX46kqKCigdevW7Nq1S88/ild0zkh96ZyR+tI5I/Wlc0bqI9jOF9M0KSwspGXLloSFnfjJQvUcNlBhYWGkpaVZHaOG+Pj4oPjHIaFD54zUl84ZqS+dM1JfOmekPoLpfDlZj+ERGpBGREREREREVByKiIiIiIiIikMJALvdzpNPPondbrc6ioQInTNSXzpnpL50zkh96ZyR+gjV80UD0oiIiIiIiIh6DkVERERERETFoYiIiIiIiKDiUERERERERFBxKCIiIiIiIqg4FD+ZP38+48aNo2vXriQkJBAfH0/Xrl255557mDdvntXxJAjMmjULwzDq/dqwYYPV0cUPDh48yLRp05gwYQKjR4+mRYsWNf7e33333VPe9+rVq3nooYfo2bMnSUlJxMbG0qlTJ2666Sa+++473/0SElC+PGcyMzNP6Xqk8yd05OXl8cUXX/DAAw8wbNgwmjdvjt1uJzY2lvT0dC677DJeeuklcnNzT2n/us40PL4+Z0LmOmOK+FBRUZF5xx13mMBJX7fffrtZVFRkdVyx0MyZM+s8T2p7rV+/3uro4kN79+41MzIy6vx7f+edd+q974qKCvNPf/qTGRYWdtJ9X3LJJeaBAwd8/8uJX/jjnNm+ffspXY+mTZvmv19UfGL9+vXmpZdeakZGRnr1dxodHW2++OKLZlVVlVf713Wm4fHXORMq1xmbF/WjiFcqKysZM2YMP/zwg+c9h8NBt27dsNlsrFu3joKCAgDeeecdsrKy+PbbbwkPD7cqsgSJqKgohg8f7tW2sbGxfk4jgeR0OtmxY4df9j1u3Djefvttz3pERARdu3YlNjaWDRs2kJOTA8A333zDyJEjmTdvns6vEODPc+aICy64wKvtUlJS/JpDTt+aNWuYOnVqjffCw8Pp0KEDzZo1o7KykvXr13Po0CEASkpK+N3vfsfatWt58803MQzjpPvXdabh8fc5c0TQXmcCWopKg/anP/2pxjcdd999t5mTk+P5eVFRkfn444/X2ObRRx+1MLFYqXrPYUZGhtVxxCLVv0lNSUkxL7zwQvOxxx4zp0yZclo9h2+88UaN9qNHjzZ3797t+Xl5ebn5yiuvmDabzbPNjTfe6OPfTvzBH+fMsd/oS8Px2WefmYBps9nMK664wpwyZYqZn59fY5uqqipzypQpZqtWrWqcB//6179Oum9dZxomf50zoXKdCd5kElKysrLMqKgozwl/yy23nHDbxx57zLNdVFSUmZWVFcCkEixUHIppmmZ+fr752WefmZmZmcf97FQ/6BcXF5vNmzf3tD3nnHNMl8tV67ZvvfWWZzvDMMylS5ee6q8iAeKPcyZUPrRJ/U2ZMsW86667zB07dtS57c6dO2tcO5KTk83y8vJat9V1puHy1zkTKtcZDUgjPvHSSy/hdDoBiI6O5qWXXjrhto8//jitW7cG3LcHTZw4MRARRSQIxcfHc/XVV5ORkeGzfb777rvs27cPAMMw+Ne//nXC29fvvPNOBg4cCIBpmjz33HM+yyH+4Y9zRhquyy+/nH//+9+kp6fXuW3r1q156qmnPOvZ2dnMnj271m11nWm4/HXOhAoVh+ITX3zxhWf52muvJSkp6YTbRkZGcvvtt3vWJ0+e7NdsItK4VL+mDB8+nC5dupx0+3HjxnmWv/32W8rKyvyWTUSC22WXXVZj/UQjZOs6I0d4e86EChWHcto2btzIli1bPOsXXnhhnW0uuugiz/KWLVvYuHGjX7KJSONSVFRU41vb+l6PioqKmDVrlj+iiUgIOPbL7SMD6VWn64xU5805E0pUHMppW7lyZY31wYMH19mmT58+REZGetZXrVrl81wi0visW7eOiooKz7o316PmzZvTpk0bz7quRyKN17Ej4aamph63ja4zUp0350woUXEop239+vWe5cjISM/zhCdz7HbV9yGNT15eHtdeey1t2rTB4XAQFxdH27ZtueKKK3j11VdD/ls4CZxjryXt27f3ql317XQ9kltvvZWOHTsSExNDTEwM6enpXHjhhfztb3/jwIEDVscTPzr2UZfaCj9dZ6Q6b86Z2gTrdUbFoZy2zMxMz3JaWprX87tUf9C3+j6k8cnPz+ezzz5jx44dOJ1OioqKyMzM5Msvv+T+++8nPT2dV155xeqYEgKqX0tsNhstWrTwqp2uR1Ld+++/z5YtWygpKaGkpIRdu3bx/fff88gjj5CRkcHjjz9OZWWl1THFx/Lz82sMktezZ0+6du163Ha6zsgR3p4ztQnW64wt4EeUBqewsNCznJCQ4HW7+Pj4WvchjVObNm1o1aoVdrud7Oxs1q1bh8vlAtwX3wceeIAVK1bwn//8x+KkEsyqX0vi4uIIC/PuO1Bdj6S6Fi1aeO5kyM3NZf369Z4RuZ1OJ3/5y1/45Zdf+Prrr4mIiLA4rfjKww8/7BmBFOAvf/lLrdvpOiNHeHvO1CZYrzPqOZTTVlRU5FmOioryup3D4ah1H9I4hIWFMXLkSD788ENycnLYvn07c+fO5ccff2TlypXk5uby2muvkZyc7Gnz9ttvawhwOSldj+RUGIbBgAED+Pe//82ePXvYs2cP8+fP58cff2TZsmXk5eXx0Ucf1Xhm7Pvvv+eBBx6wLrT41FtvvVXjy8frrrvuuFEoj9B1RqB+5wyEznVGxaGctiO9O+C+vcJb1bet/mC3NA7Dhg1j+vTp3HjjjbVOfRIbG8uvfvUrli1bVuNCOWHCBPbv3x/ApBJKdD2SU5GRkcGiRYu46667ar1F0G63c8MNN7Bs2TL69u3ref+NN97QwCINwOzZs7nvvvs8623btuWNN9444fa6zkh9zxkIneuMikM5bdHR0Z7lI93h3qi+bUxMjE8zScPRunVrPv30U896SUmJbi2VE9L1SPwpMTGRyZMne3qLTNPk1VdftTiVnI4VK1YwevRoysvLAfdIk999991JH5PRdaZxO5Vzpj6svs6oOJTTFhsb61kuLS31ul1JSUmt+xA51oABAzjnnHM869OnT7cujAQ1XY/E39LT07n++us967oeha6NGzdywQUXkJ+fD7g/lP/www+cccYZJ22n60zjdarnTH1ZeZ1RcSinrfozYXv37vW6XfUHeJs2berTTNLwnHvuuZ7lTZs2WZhEgln161FRUZHXz/XoeiT1Uf16lJmZ6elBkNCxfft2Ro4c6ZkyIC4ujmnTptGrV6862+o60zidzjlzKqy6zqg4lNPWqVMnz3JOTk6Nb8ZOZteuXZ7lzp07+zyXNCzNmzf3LGdnZ1uYRIJZ9esRwM6dO71qp+uR1Ef16xG4/98noWP37t2MGDGC3bt3A+7bRKdOncrAgQO9aq/rTONzuufMqbDqOqPiUE5bly5daqyvWLGizjZZWVkcPHjwhPsQOVb1Lx2qP+8hUt2pXI8qKipYu3btCfchcqxjvwTVNSl07N+/n5EjR7J9+3bAPQjIlClTGDZsmNf70HWmcfHFOXMqrLrOqDiU0zZgwADsdrtnfe7cuXW2mTNnjmc5KiqKAQMG+CWbNBzV/6eamppqYRIJZu3atSMtLc2z7s31aOnSpTX+J+zv/+FL6Kt+PbLb7T4biEL8Kycnh5EjR7Jx40YAIiIi+Pzzzxk1alS99qPrTOPhq3PmVFh1nVFxKKctNjaWESNGeNY//PDDOttU32bEiBEatUtOqqSkhK+++sqzftZZZ1mYRoLd6NGjPcufffZZnc9pVL8edevWjfbt2/stm4Q+0zT53//+51kfPHiwhWnEW/n5+VxwwQWsWbMGgPDwcD766CMuvfTSU9qfrjMNn6/Pmfqw8jqj4lB8YuzYsZ7lVatW8fXXX59w22XLljFt2rRa24rU5vHHH/c8AA5wxRVXWBdGgl71a0p2dvZJ557avXs37733Xq1tRWrz6quv1phzTNej4FdcXMwll1zC0qVLAQgLC+O9997j6quvPuV96jrTsPnjnKkPS68zpogPVFVVmb169TIBEzBbtGhhrl+//rjt9uzZY3bp0sWz3ZlnnmlWVVVZkFis9P3335sPPfSQuWvXrpNuV15ebj7yyCOe8wUw+/Tpo3Omkaj+9/7OO+/Uq+3o0aM9bWNjY825c+cet01+fr45dOhQz3bNmzc3S0pKfJRerHAq58yaNWvMO+64w9ywYcNJt6uqqjJfeuklMzw83HOMli1b6pwJck6n0xw5cqTn78wwDPM///mPT/at60zD5I9zJpSuM4ZpmmZAqlBp8H755ReGDx/umfMnPj6ee++9l2HDhmGz2Vi8eDGvvvoq+/fvB8DhcPDzzz/Tv39/K2OLBaZMmcKVV15JWFgYQ4YMYfjw4XTv3p3k5GQiIyPJzs5m8eLFfPjhhzVGd0tKSmL+/PnHjRQnoe3uu+/m/fffP+79srIyz7LNZiM8PPy4bU40AXVmZib9+/f3jGxrt9u58847Of/884mNjWXVqlW88sorngEGwsLCmDJlCpdddpkvfiXxM1+eMytWrKB3794A9O3bl/POO49evXqRmpqKw+EgNzeX5cuX8/HHH7NhwwZPO7vdzvTp0xk6dKivfi3xg7/97W888sgjnvXExMR6jXMwatQoHn744Vp/putMw+SPcyakrjMBK0OlUZg0aZLpcDhqfHtb28vhcJiTJk2yOq5Y5IsvvqjzHDn21bFjR3PZsmVWRxc/uO222+p9Phx5ncy8efPMpKSkOvcRHh5uvvLKKwH6bcUXfHnOLF++vN77aN68uTl9+nQLfnOpryeffPKUzxXAvO222066f11nGh5/nDOhdJ3RM4fiU2PGjGHp0qWMHDkSwzCO+7lhGIwYMYIlS5YwZswYCxJKMOjcuTPXXXddjdHeTqRNmzb87W9/Y/ny5Z5v3US8cdZZZ7Fq1SquuuoqbDZbrdv079+f2bNn85vf/CbA6SRYtGjRgltvvdWrAUKaNWvGY489xurVqxk5cmQA0kmw03VGvBFK1xndVip+s2vXLubNm0dWVhYArVq1YsiQIbRu3driZBJMdu7cybp168jOziY7O5vi4mLi4+NJTU2lX79+GtFNfOLgwYPMnj2b3bt3U15eTsuWLenXr59uUZYa9u/fz6pVqzh48CDZ2dkUFhYSGxtLcnIyvXv3pkuXLrV+8SkCus6Id4L9OqPiUERERERERDSVhYiIiIiIiKg4FBEREREREVQcioiIiIiICCoORUREREREBBWHIiIiIiIigopDERERERERQcWhiIiIiIiIoOJQREREREREUHEoIiIiIiIiqDgUERERERERVByKiIiIiIgIKg5FREREREQEFYciIiIiIiKCikMRERERERFBxaGIiIiIiIig4lBERERERERQcSgiIiIiIiKoOBQREQlp48ePxzAMDMPgjDPOoLy8vF7tv//+e097wzA4cOCAn5KKiEiwU3EoIiISojZv3syzzz7rWX/xxReJjIys1z769etXY33u3Lk+ySYiIqFHxaGIiEiIuu+++ygrKwPgwgsv5JJLLqn3Ppo2bUp6erpnfd68eT7LJyIioUXFoYiISAiaPn0606dP96w//fTTp7yvtm3bepbXr19/WrlERCR0qTgUEREJQY8//rhn+aKLLjru9tD6aNWqlWd5y5Ytp5VLRERCl4pDERGREPPjjz+yaNEiz/rvf//709pfSkqKZ3nv3r2ntS8REQldKg5FRERCzOuvv+5Zbtu2Leecc85p7c8wDM/ykWcYRUSk8bFZHUBERES8l5OTw5dffulZv/XWW2sUd9UVFxdTWloKQHx8/AlHMjVNs9ZlERFpXNRzKCIiEkJ+/PFHKioqPOsXXHDBCbcdO3YsKSkppKSksGTJkhNut2fPHs9ys2bNfBNURERCjopDERGREDJz5kzPckxMDP379z/htr/88otnuXv37ifcbufOnZ7l6tNaiIhI46LiUEREJISsWbPGs9y9e3dsttqfEMnKymLHjh0ANG/enPj4+Fq3c7lcrF692rN+smJTREQaNhWHIiIiIWTz5s2e5U6dOp1wu+pzIKalpZ1wu+XLl1NSUuJZHzJkyGkmFBGRUKXiUEREJERUVVWxf/9+z/rJng/86quvPMtJSUkn3G7q1KmeZZvNxogRI04zpYiIhCoVhyIiIiHC6XTWWLfb7bVud+jQIb799lvPekRERK3bmabJxx9/7FkfOXIkTZs29UFSEREJRSoORUREQkR4eHiNaSsOHTpU63avvvoqZWVlnm1zcnJq3e6rr76qcZvq3Xff7cO0IiISagxTExqJiIiEjObNm3tuLe3ZsycrV66s8fMdO3bQvXt3ioqKOPfcc5k5cyaxsbHk5OTUmOcwLy+Pvn37sm3bNuD/t3e/uIlEARzHf0kRTYMiISF1NQ0G0gsgMAiQmHpUHSHhBkgSUKQcAcMB4AwlbU9AewAUAoFYN8kmXbm7ZffzUTNvnpgnv3nzJ2k0Gnl7e/vlPxMB+PfZOQSAC9JqtYrj9/f3PD8/F+f7/T69Xi/H4zH39/d5fHxMkhyPx0yn02Lex8dHut1uEYZXV1dZLpfCEOA/Z+cQAC7IdrtNp9P5aaxer6dSqeTl5aV4nHSz2aRWq6XRaBTzms1mrq+vs9vtcj6fi/HZbJbhcPinlgDANyUOAeDCjEajzGazL6+VSqUsFovi/cF+v5/1ev3l3HK5nPl8nsFg8NvuFYDLIQ4B4AKt1+ssl8u8vr7mcDikWq2m3W5nPB7n4eGhmHc6nTKZTLJarfL5+Zmbm5vc3d2l1+vl6ekpt7e3f28RAHwr4hAAAAAfpAEAAEAcAgAAEHEIAABAxCEAAAARhwAAAEQcAgAAEHEIAABAxCEAAAARhwAAAEQcAgAAEHEIAABAxCEAAAARhwAAAEQcAgAAEHEIAABAxCEAAABJfgDyWiGrSNLbjAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the components of the fit separately:\n", - "plt.rcParams[\"font.size\"] = 25\n", - "plt.rcParams[\"figure.figsize\"] = (10, 5)\n", - "\n", - "\n", - "def plot_fit(func, J, w, lam, gamma, w0):\n", - " \"\"\"Plot the individual components of a fit to the spectral density.\n", - " and how they contribute to the full fit one by one\"\"\"\n", - " total = 0\n", - " for i in range(len(lam)):\n", - " component = func(w, lam[i], gamma[i], w0[i])\n", - " total += component\n", - " plt.plot(w, J, \"r--\", linewidth=2, label=\"original\")\n", - " plt.plot(w, total, label=rf\"$k={i+1}$\")\n", - " plt.xlabel(r\"$\\omega$\")\n", - " plt.ylabel(r\"$J(\\omega)$\")\n", - " plt.legend()\n", - " plt.pause(1)\n", - " plt.show()\n", - "\n", - "\n", - "def plot_fit_components(func, J, w, lam, gamma, w0):\n", - " \"\"\"Plot the individual components of a fit to the spectral density.\n", - " and how they contribute to the full fit\"\"\"\n", - " plt.plot(w, J, \"r--\", linewidth=2, label=\"original\")\n", - " for i in range(len(lam)):\n", - " component = func(w, lam[i], gamma[i], w0[i])\n", - " plt.plot(w, component, label=rf\"$k={i+1}$\")\n", - " plt.xlabel(r\"$\\omega$\")\n", - " plt.ylabel(r\"$J(\\omega)$\")\n", - " plt.legend(bbox_to_anchor=(1.04, 1))\n", - " plt.show()\n", - "\n", - "\n", - "lam=fitinfo[\"params\"][:,0]\n", - "gamma=fitinfo[\"params\"][:,1] \n", - "w0 = fitinfo[\"params\"][:,2]\n", - "def _sd_fit_model(wlist, a, b, c):\n", - " return (\n", - " 2 * a * b * wlist / ((wlist + c)**2 + b**2) / ((wlist - c)**2 + b**2)\n", - " )\n", - "plot_fit(_sd_fit_model, J, w, lam, gamma, w0)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "c05f2af0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_fit_components(_sd_fit_model, J, w, lam, gamma, w0)" - ] - }, - { - "cell_type": "markdown", - "id": "27fa30a5", - "metadata": {}, - "source": [ - "And let's also compare the power spectrum of the fit and the analytical spectral density:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "72deb34d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_power_spectrum(alpha, wc, beta, save=True):\n", - " \"\"\"Plot the power spectrum of a fit against the actual power spectrum.\"\"\"\n", - " w = np.linspace(-10, 10, 50000)\n", - " s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta)\n", - " s_fit = bath.power_spectrum(w)\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " axes.plot(w, s_orig, \"r\", linewidth=2, label=\"original\")\n", - " axes.plot(w, np.real(s_fit), \"b\", linewidth=2, label=\"fit\")\n", - "\n", - " axes.set_xlabel(r\"$\\omega$\", fontsize=28)\n", - " axes.set_ylabel(r\"$S(\\omega)$\", fontsize=28)\n", - " axes.legend()\n", - "\n", - " if save:\n", - " fig.savefig(\"powerspectrum.eps\")\n", - "\n", - "\n", - "plot_power_spectrum(alpha, wc, 1 / T, save=False)" - ] - }, - { - "cell_type": "markdown", - "id": "5bb8eb36", - "metadata": {}, - "source": [ - "Now if we want to see the systems's behaviour as we change the number of terms in the fit, we may use this auxiliary function." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "5a8a930d", - "metadata": {}, - "outputs": [], - "source": [ - "def generate_spectrum_results(Q, N, Nk, max_depth):\n", - " \"\"\"Run the HEOM with the given bath parameters and\n", - " and return the results of the evolution.\n", - " \"\"\"\n", - " # sigma = 0.0001\n", - " # J_max = abs(max(J, key=abs))\n", - " # lower = [-100*J_max, 0.1*wc, 0.1*wc]\n", - " # guess = [J_max, wc, wc]\n", - " # upper = [100*J_max, 100*wc, 100*wc]\n", - " bath, fitinfo= sd_env.approximate(\"sd\",w,Nmax=N,Nk=Nk,target_rmse=None)#,lower=lower,upper=upper,guess=guess,sigma=sigma)\n", - " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - "\n", - " # This problem is a little stiff, so we use the BDF method to solve\n", - " # the ODE ^^^\n", - " print(f\"Starting calculations for N={N}, Nk={Nk} and max_depth={max_depth} ... \")\n", - "\n", - " HEOM_spectral_fit = HEOMSolver(\n", - " Hsys,\n", - " (bath,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - " )\n", - " results_spectral_fit = HEOM_spectral_fit.run(rho0, tlist)\n", - " return results_spectral_fit" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "0273c6cb", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_result_expectations(plots, axes=None):\n", - " \"\"\"Plot the expectation values of operators as functions of time.\n", - "\n", - " Each plot in plots consists of (solver_result,\n", - " measurement_operation, color, label).\n", - " \"\"\"\n", - " if axes is None:\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " fig_created = True\n", - " else:\n", - " fig = None\n", - " fig_created = False\n", - "\n", - " # add kw arguments to each plot if missing\n", - " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", - " for result, m_op, color, label, kw in plots:\n", - " exp = np.real(expect(result.states, m_op))\n", - " kw.setdefault(\"linewidth\", 2)\n", - " if color == \"rand\":\n", - " axes.plot(\n", - " result.times,\n", - " exp,\n", - " c=np.random.rand(\n", - " 3,\n", - " ),\n", - " label=label,\n", - " **kw,\n", - " )\n", - " else:\n", - " axes.plot(result.times, exp, color, label=label, **kw)\n", - "\n", - " if fig_created:\n", - " axes.legend(loc=0, fontsize=12)\n", - " axes.set_xlabel(\"t\", fontsize=28)\n", - "\n", - " return fig" - ] - }, - { - "cell_type": "markdown", - "id": "9ea58304", - "metadata": {}, - "source": [ - "Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "96b86c48", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting calculations for N=1, Nk=1 and max_depth=5 ... \n", - "10.0%. Run time: 0.15s. Est. time left: 00:00:00:01\n", - "20.0%. Run time: 0.21s. Est. time left: 00:00:00:00\n", - "30.1%. Run time: 0.26s. Est. time left: 00:00:00:00\n", - "40.1%. Run time: 0.31s. Est. time left: 00:00:00:00\n", - "50.1%. Run time: 0.38s. Est. time left: 00:00:00:00\n", - "60.1%. Run time: 0.42s. Est. time left: 00:00:00:00\n", - "70.1%. Run time: 0.45s. Est. time left: 00:00:00:00\n", - "80.1%. Run time: 0.49s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 0.53s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 0.57s. Est. time left: 00:00:00:00\n", - "Total run time: 0.57s\n", - "Starting calculations for N=2, Nk=1 and max_depth=5 ... \n", - "10.0%. Run time: 0.37s. Est. time left: 00:00:00:03\n", - "20.0%. Run time: 0.52s. Est. time left: 00:00:00:02\n", - "30.1%. Run time: 0.67s. Est. time left: 00:00:00:01\n", - "40.1%. Run time: 0.84s. Est. time left: 00:00:00:01\n", - "50.1%. Run time: 0.96s. Est. time left: 00:00:00:00\n", - "60.1%. Run time: 1.06s. Est. time left: 00:00:00:00\n", - "70.1%. Run time: 1.17s. Est. time left: 00:00:00:00\n", - "80.1%. Run time: 1.28s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 1.47s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 1.58s. Est. time left: 00:00:00:00\n", - "Total run time: 1.59s\n", - "Starting calculations for N=3, Nk=1 and max_depth=5 ... \n", - "10.0%. Run time: 1.16s. Est. time left: 00:00:00:10\n", - "20.0%. Run time: 1.73s. Est. time left: 00:00:00:06\n", - "30.1%. Run time: 2.20s. Est. time left: 00:00:00:05\n", - "40.1%. Run time: 2.67s. Est. time left: 00:00:00:03\n", - "50.1%. Run time: 3.09s. Est. time left: 00:00:00:03\n", - "60.1%. Run time: 3.43s. Est. time left: 00:00:00:02\n", - "70.1%. Run time: 3.77s. Est. time left: 00:00:00:01\n", - "80.1%. Run time: 4.10s. Est. time left: 00:00:00:01\n", - "90.2%. Run time: 4.40s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 4.69s. Est. time left: 00:00:00:00\n", - "Total run time: 4.69s\n", - "Starting calculations for N=4, Nk=1 and max_depth=5 ... \n", - "10.0%. Run time: 4.25s. Est. time left: 00:00:00:38\n", - "20.0%. Run time: 6.36s. Est. time left: 00:00:00:25\n", - "30.1%. Run time: 8.33s. Est. time left: 00:00:00:19\n", - "40.1%. Run time: 10.59s. Est. time left: 00:00:00:15\n", - "50.1%. Run time: 12.36s. Est. time left: 00:00:00:12\n", - "60.1%. Run time: 14.07s. Est. time left: 00:00:00:09\n", - "70.1%. Run time: 15.62s. Est. time left: 00:00:00:06\n", - "80.1%. Run time: 17.43s. Est. time left: 00:00:00:04\n", - "90.2%. Run time: 18.94s. Est. time left: 00:00:00:02\n", - "100.0%. Run time: 20.40s. Est. time left: 00:00:00:00\n", - "Total run time: 20.40s\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# # Generate results for different number of lorentzians in fit:\n", - "\n", - "results_spectral_fit_pk = [\n", - " generate_spectrum_results(Q, n, Nk=1, max_depth=max_depth) for n in range(1, 5)\n", - "]\n", - "\n", - "plot_result_expectations(\n", - " [\n", - " (\n", - " result,\n", - " P11p,\n", - " \"rand\",\n", - " f\"P11 (spectral fit) $k_J$={pk + 1}\",\n", - " )\n", - " for pk, result in enumerate(results_spectral_fit_pk)\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "980af0cd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting calculations for N=4, Nk=2 and max_depth=5 ... \n", - "10.0%. Run time: 4.81s. Est. time left: 00:00:00:43\n", - "20.0%. Run time: 7.06s. Est. time left: 00:00:00:28\n", - "30.1%. Run time: 9.17s. Est. time left: 00:00:00:21\n", - "40.1%. Run time: 11.71s. Est. time left: 00:00:00:17\n", - "50.1%. Run time: 14.19s. Est. time left: 00:00:00:14\n", - "60.1%. Run time: 16.66s. Est. time left: 00:00:00:11\n", - "70.1%. Run time: 18.92s. Est. time left: 00:00:00:08\n", - "80.1%. Run time: 21.31s. Est. time left: 00:00:00:05\n", - "90.2%. Run time: 23.83s. Est. time left: 00:00:00:02\n", - "100.0%. Run time: 26.74s. Est. time left: 00:00:00:00\n", - "Total run time: 26.74s\n", - "Starting calculations for N=4, Nk=3 and max_depth=5 ... \n", - "10.0%. Run time: 15.37s. Est. time left: 00:00:02:18\n", - "20.0%. Run time: 25.95s. Est. time left: 00:00:01:43\n", - "30.1%. Run time: 35.67s. Est. time left: 00:00:01:23\n", - "40.1%. Run time: 45.77s. Est. time left: 00:00:01:08\n", - "50.1%. Run time: 54.57s. Est. time left: 00:00:00:54\n", - "60.1%. Run time: 62.19s. Est. time left: 00:00:00:41\n", - "70.1%. Run time: 67.18s. Est. time left: 00:00:00:28\n", - "80.1%. Run time: 75.76s. Est. time left: 00:00:00:18\n", - "90.2%. Run time: 80.96s. Est. time left: 00:00:00:08\n", - "100.0%. Run time: 85.12s. Est. time left: 00:00:00:00\n", - "Total run time: 85.12s\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# generate results for different number of Matsubara terms per Lorentzian\n", - "# for max number of Lorentzians:\n", - "\n", - "Nk_list = range(2, 4)\n", - "results_spectral_fit_nk = [\n", - " generate_spectrum_results(Q, 4, Nk=Nk, max_depth=max_depth) for Nk in Nk_list\n", - "]\n", - "\n", - "plot_result_expectations(\n", - " [\n", - " (\n", - " result,\n", - " P11p,\n", - " \"rand\",\n", - " f\"P11 (spectral fit) K={nk+1}\",\n", - " )\n", - " for nk, result in zip(Nk_list, results_spectral_fit_nk)\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "eb904688", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting calculations for N=4, Nk=1 and max_depth=2 ... \n", - "10.0%. Run time: 0.07s. Est. time left: 00:00:00:00\n", - "20.0%. Run time: 0.10s. Est. time left: 00:00:00:00\n", - "30.1%. Run time: 0.13s. Est. time left: 00:00:00:00\n", - "40.1%. Run time: 0.17s. Est. time left: 00:00:00:00\n", - "50.1%. Run time: 0.19s. Est. time left: 00:00:00:00\n", - "60.1%. Run time: 0.22s. Est. time left: 00:00:00:00\n", - "70.1%. Run time: 0.25s. Est. time left: 00:00:00:00\n", - "80.1%. Run time: 0.27s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 0.29s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 0.32s. Est. time left: 00:00:00:00\n", - "Total run time: 0.32s\n", - "Starting calculations for N=4, Nk=1 and max_depth=3 ... \n", - "10.0%. Run time: 0.17s. Est. time left: 00:00:00:01\n", - "20.0%. Run time: 0.24s. Est. time left: 00:00:00:00\n", - "30.1%. Run time: 0.30s. Est. time left: 00:00:00:00\n", - "40.1%. Run time: 0.36s. Est. time left: 00:00:00:00\n", - "50.1%. Run time: 0.41s. Est. time left: 00:00:00:00\n", - "60.1%. Run time: 0.47s. Est. time left: 00:00:00:00\n", - "70.1%. Run time: 0.54s. Est. time left: 00:00:00:00\n", - "80.1%. Run time: 0.60s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 0.66s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 0.71s. Est. time left: 00:00:00:00\n", - "Total run time: 0.71s\n", - "Starting calculations for N=4, Nk=1 and max_depth=4 ... \n", - "10.0%. Run time: 0.59s. Est. time left: 00:00:00:05\n", - "20.0%. Run time: 0.85s. Est. time left: 00:00:00:03\n", - "30.1%. Run time: 1.09s. Est. time left: 00:00:00:02\n", - "40.1%. Run time: 1.32s. Est. time left: 00:00:00:01\n", - "50.1%. Run time: 1.55s. Est. time left: 00:00:00:01\n", - "60.1%. Run time: 1.76s. Est. time left: 00:00:00:01\n", - "70.1%. Run time: 1.98s. Est. time left: 00:00:00:00\n", - "80.1%. Run time: 2.21s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 2.47s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 2.70s. Est. time left: 00:00:00:00\n", - "Total run time: 2.70s\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Generate results for different depths:\n", - "\n", - "Nc_list = range(2, max_depth)\n", - "results_spectral_fit_nc = [\n", - " generate_spectrum_results(Q, 4, Nk=1, max_depth=Nc) for Nc in Nc_list\n", - "]\n", - "\n", - "plot_result_expectations(\n", - " [\n", - " (\n", - " result,\n", - " P11p,\n", - " \"rand\",\n", - " f\"P11 (spectral fit) $N_C={nc}$\",\n", - " )\n", - " for nc, result in zip(Nc_list, results_spectral_fit_nc)\n", - " ]\n", - " );" - ] - }, - { - "cell_type": "markdown", - "id": "844af288", - "metadata": {}, - "source": [ - "#### We now combine the fitting and correlation function data into one large plot. Here we define a function to plot everything together" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "7fc617a1", - "metadata": {}, - "outputs": [], - "source": [ - "def gen_plots(fs, w, J, t, C, w2, S):\n", - " def plot_cr_fit_vs_actual(t, C, func, axes):\n", - " \"\"\"Plot the C_R(t) fit.\"\"\"\n", - " yR = func(t)\n", - "\n", - " axes.plot(\n", - " t,\n", - " np.real(C),\n", - " \"r\",\n", - " linewidth=3,\n", - " label=\"Original\",\n", - " )\n", - " axes.plot(\n", - " t,\n", - " np.real(yR),\n", - " \"g\",\n", - " dashes=[3, 3],\n", - " linewidth=2,\n", - " label=\"Reconstructed\",\n", - " )\n", - "\n", - " axes.set_ylabel(r\"$C_R(t)$\", fontsize=28)\n", - " axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=28)\n", - " axes.locator_params(axis=\"y\", nbins=4)\n", - " axes.locator_params(axis=\"x\", nbins=4)\n", - " axes.text(0.15, 0.85, \"(a)\", fontsize=28, transform=axes.transAxes)\n", - "\n", - " def plot_ci_fit_vs_actual(t, C, func, axes):\n", - " \"\"\"Plot the C_I(t) fit.\"\"\"\n", - " yI = func(t)\n", - "\n", - " axes.plot(\n", - " t,\n", - " np.imag(C),\n", - " \"r\",\n", - " linewidth=3,\n", - " )\n", - " axes.plot(\n", - " t,\n", - " np.real(yI),\n", - " \"g\",\n", - " dashes=[3, 3],\n", - " linewidth=2,\n", - " )\n", - "\n", - " axes.set_ylabel(r\"$C_I(t)$\", fontsize=28)\n", - " axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=28)\n", - " axes.locator_params(axis=\"y\", nbins=4)\n", - " axes.locator_params(axis=\"x\", nbins=4)\n", - " axes.text(0.80, 0.80, \"(b)\", fontsize=28, transform=axes.transAxes)\n", - "\n", - " def plot_jw_fit_vs_actual(w, J, axes):\n", - " \"\"\"Plot the J(w) fit.\"\"\"\n", - " J_fit = fs.spectral_density(w)\n", - "\n", - " axes.plot(\n", - " w,\n", - " J,\n", - " \"r\",\n", - " linewidth=3,\n", - " )\n", - " axes.plot(\n", - " w,\n", - " J_fit,\n", - " \"g\",\n", - " dashes=[3, 3],\n", - " linewidth=2,\n", - " )\n", - "\n", - " axes.set_ylabel(r\"$J(\\omega)$\", fontsize=28)\n", - " axes.set_xlabel(r\"$\\omega/\\omega_c$\", fontsize=28)\n", - " axes.locator_params(axis=\"y\", nbins=4)\n", - " axes.locator_params(axis=\"x\", nbins=4)\n", - " axes.text(0.15, 0.85, \"(c)\", fontsize=28, transform=axes.transAxes)\n", - "\n", - " def plot_sw_fit_vs_actual(axes):\n", - " \"\"\"Plot the S(w) fit.\"\"\"\n", - "\n", - " # avoid the pole in the fit around zero:\n", - " s_fit = fs.power_spectrum(w2)\n", - "\n", - " axes.plot(w2, S, \"r\", linewidth=3)\n", - " axes.plot(w2, s_fit, \"g\", dashes=[3, 3], linewidth=2)\n", - "\n", - " axes.set_ylabel(r\"$S(\\omega)$\", fontsize=28)\n", - " axes.set_xlabel(r\"$\\omega/\\omega_c$\", fontsize=28)\n", - " axes.locator_params(axis=\"y\", nbins=4)\n", - " axes.locator_params(axis=\"x\", nbins=4)\n", - " axes.text(0.15, 0.85, \"(d)\", fontsize=28, transform=axes.transAxes)\n", - "\n", - " def plot_matsubara_spectrum_fit_vs_actual(t, C):\n", - " \"\"\"Plot the Matsubara fit of the spectrum .\"\"\"\n", - " fig = plt.figure(figsize=(12, 10))\n", - " grid = plt.GridSpec(2, 2, wspace=0.4, hspace=0.3)\n", - "\n", - " plot_cr_fit_vs_actual(\n", - " t,\n", - " C,\n", - " lambda t: fs.correlation_function(t),\n", - " axes=fig.add_subplot(grid[0, 0]),\n", - " )\n", - " plot_ci_fit_vs_actual(\n", - " t,\n", - " C,\n", - " lambda t: np.imag(fs.correlation_function(t)),\n", - " axes=fig.add_subplot(grid[0, 1]),\n", - " )\n", - " plot_jw_fit_vs_actual(\n", - " w,\n", - " J,\n", - " axes=fig.add_subplot(grid[1, 0]),\n", - " )\n", - " plot_sw_fit_vs_actual(\n", - " axes=fig.add_subplot(grid[1, 1]),\n", - " )\n", - " fig.legend(loc=\"upper center\", ncol=2, fancybox=True, shadow=True)\n", - "\n", - " return plot_matsubara_spectrum_fit_vs_actual(t, C)" - ] - }, - { - "cell_type": "markdown", - "id": "674d5498", - "metadata": {}, - "source": [ - "#### And finally plot everything together" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "26209a1b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "t = np.linspace(0, 15, 1000)\n", - "C = ohmic_correlation(t, alpha, wc, 1 / T)\n", - "w2 = np.concatenate((-np.linspace(10, 1e-2, 100), np.linspace(1e-2, 10, 100)))\n", - "S = ohmic_power_spectrum(w2, alpha, wc, 1 / T)\n", - "gen_plots(bath, w, J, t, C, w2, S)" - ] - }, - { - "cell_type": "markdown", - "id": "a72989f8", - "metadata": {}, - "source": [ - "## Obtaining an decaying exponential description via the Correlation function" - ] - }, - { - "cell_type": "markdown", - "id": "81acee08", - "metadata": {}, - "source": [ - "Having successfully fitted the spectral density and used the result to calculate the Matsubara expansion and terminator for the HEOM bosonic bath, we now proceed to the second case of fitting the correlation function itself instead.\n", - "\n", - "Here we fit the real and imaginary parts separately, using the following ansatz \n", - "\n", - "$$C_R^F(t) = \\sum_{i=1}^{k_R} c_R^ie^{-\\gamma_R^i t}\\cos(\\omega_R^i t)$$\n", - "\n", - "$$C_I^F(t) = \\sum_{i=1}^{k_I} c_I^ie^{-\\gamma_I^i t}\\sin(\\omega_I^i t)$$\n", - "\n", - "Analogously to the spectral density case, one may use the `approx_by_cf_fit` method, the main difference with respect to the spectral density fit, is that now we are perfoming two fits, one for the real part and another one for the imaginary part" - ] - }, - { - "cell_type": "markdown", - "id": "b8c32d8a", - "metadata": {}, - "source": [ - "The ansatz used is not good for functions where\n", - "\n", - "$$C_I^F(0) \\neq 0$$\n", - "\n", - "The keyword `full_ansatz` which defaults to False. allows for the usage of a \n", - "more general ansatz, the fit however tends to be significantly slower, never\n", - "the less it can reach a similar level of accuracy with a lower amount of exponents\n", - "\n", - "When full_ansatz is True. the ansatz used corresponds to \n", - "\n", - "\\begin{align}\n", - "\\operatorname{Re}[C(t)] = \\sum_{k=1}^{N_r} \\operatorname{Re}\\Bigl[\n", - " (a_k + \\mathrm i d_k) \\mathrm e^{(b_k + \\mathrm i c_k) t}\\Bigl]\n", - " ,\n", - "\\\\\n", - "\\operatorname{Im}[C(t)] = \\sum_{k=1}^{N_i} \\operatorname{Im}\\Bigl[\n", - " (a'_k + \\mathrm i d'_k) \\mathrm e^{(b'_k + \\mathrm i c'_k) t}\n", - " \\Bigr].\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "57d768ee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2\n", - "10.0%. 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Est. time left: 00:00:00:00\n", - "Total run time: 8.87s\n" - ] - } - ], - "source": [ - "def generate_corr_results(N, max_depth):\n", - " tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - " bath_corr ,fitinfo= sd_env.approximate(\"cf\",tlist=t,Ni_max=N,Nr_max=N,maxfev=1e8,target_rsme=None)\n", - " HEOM_corr_fit = HEOMSolver(\n", - " Hsys,\n", - " (bath_corr,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - " )\n", - "\n", - " results_corr_fit = HEOM_corr_fit.run(rho0, tlist)\n", - "\n", - " return results_corr_fit\n", - "\n", - "\n", - "# # Generate results for different number of exponentials in fit:\n", - "results_corr_fit_pk = [\n", - " print(f\"{i + 1}\")\n", - " or generate_corr_results(\n", - " i,\n", - " max_depth=max_depth,\n", - " )\n", - " for i in range(1, 4)]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "91d1be7c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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lqcAMBgNu3LiBjRs3AjD1ihWM1c3MzMSuXbsAmGZAKDg2ISEBOp2uzI+1Q0JCMHv27CJz837++efo3bs3unXrhnfffRe+vr64ffs2tm3bhiVLlkCv12PmzJnYsWMH+vbtixkzZsDR0RGrV6/Gzp078emnn5rdFFdRzz77LFavXo0nnngCb7zxBoKCgqBUKhETE4OwsDAMGzasxPtgBg0ahM8//xzPP/88Jk2ahMTERCxYsKDYmR/atm2LdevWYf369WjevDk0Gg3atm1b7Hnnz5+PAQMGoG/fvnjrrbegUqmwePFinD9/HmvXri3z09ua0LZtWxw4cADbt2+Hm5sb9Ho9WrduLb1eMJVqRWrz9/fHwYMH0b9/f/Tp0wf79u2r8nADBwcHTJs2DVOnTsWaNWukaeB8fHyqvLLaG2+8AVtbW0yaNAn37t3DV199VaWfReHvX0VcvHgRFy9eBGBadyIzM1P6NxsQEFBkBpYaI9YAANJj+fLl1X7+FStWmF3j77//LtdxH374oXRMs2bNKnTN1NRUEYCYmppamZIrZN/ZGDFo7mnRe/oV8ZmvT9b49YiobsrKyhIvXrwoZmVlWbuUard8+XIRgHjs2LEy2wYHB5v9n1D4ERYWJrWLiooqsV3Tpk3LvE5kZKQoCIL4008/FXnt4sWL4jPPPCM6OTmJKpVK9Pb2FseNGydmZ2dLbc6dOycOGTJEtLe3F1Uqldi+ffsi/0eGhYWJAMQNGzYUucbYsWNFnU5XbG15eXniggULxPbt24sajUa0tbUV/fz8xMmTJ4tXr141+14FBwebHfv999+LrVu3FtVqtdi8eXNx/vz54rJly0QAYlRUlNQuOjpaHDhwoKjX682+ZwXf14e/loMHD4r9+vUTdTqdqNVqxe7du4vbt283a1PSz7ng+1D451ecko4fO3ZskZ/p6dOnxV69eok2NjYiALPvQ2RkpAhA3LRpU6nXKzj3wz+HmJgY0c/PT/Tx8RGvXbtW5jlKq10UTf+2vb29xZYtW4r5+fnlOl9JAIivvvqq2b61a9eKCoVCHD9+vGgwGMp1nuDgYLFNmzZVqqXAzJkzS/y3OHPmzFKPLev3XkXymiCK5RjRXEGF/6pYvnx5ieu4V9bXX39ttmRlampqkRsFivPll1/izTffBGDqmi9tEvGcnByzSc7T0tLg5eVV7mtVxfo/ojDxQCMAgEduPC7NstBfRERUp2RnZyMqKgrNmjUrcsMW1YyCGRgevmmJ6rbp06dj5cqVuHbtWrEr81HtUdbvvbS0NNjb25crr9XO2/XKcO/ePbPt8v7yLzxZ+MPneNj8+fNhb28vPSy5jOLjHdyB+xNiJxkttzQnERGVbv78+di3bx+OHTtm7VKomqSkpOCbb77BvHnzGIAbmDoZggvPgwig3G/awu3y8vJKbTtt2jSkpqZKj5s3b1a80Eqys1FDk5sJAMhS2SIzJ9di1yYiopIFBgZi+fLlZjdnU90WFRWFadOm4fnnn6+W8xmNRuTn55f6qE0KFuso6WEwGKxdYo2pkyH44UUxsrOzy3Vc4XZlrQajVqthZ2dn9rCkRrL7PdUyGX47z1+2RES1xejRozFkyBBrl0HVpGPHjnj77ber7Wa92bNnQ6lUlvqIjo6ulmtVhxYtWpRaa2hoqLVLrDF1st+/8Ao0gGk6l5JWiyssMzOzxHPUNp42Obh1/4/F8MspGNLZ27oFERERUZkmTZqEwYMHl9qm8DR51rZ9+3aze6AeVto803VdnQzBD883Fx8fX2S1meIU/viqPO2tKdBVwPFY0/Oz8UbrFkNERETl4u7uXqtCbllKmuauIaiTwyEenpfu77//Ltdxhcf1Fl6zvDbq2fLB8IvYDKUVKyEiIiKqf+pkCG7ZsqXZTW6nT58u13GnTp2Snvv7+1d3WdWqZ6sHvd1phqKTlhMRERFR5dXJEKxSqcyWhjx06FCZx9y6dQuRkZHSdp8+fWqkturi7ayHLN80g0WWoC2jNRERERFVRJ0MwQAwbNgw6fm+fftw+/btUtuvXr1aet6oUaNaH4IBQJ1vWuM8V6lBXi2bUoWIiIioLquzIfi5556T1jbPy8vDp59+WmLbgvWxC7zwwgtQKmv/OFsd7k/pJpPh3I1k6xZDREREVI/UqhAcHR0NQRCkx6xZs0ps6+npicmTJ0vbCxcuxKZNm4q0y8vLw/jx46Wb57RaLd57771qr70mNFI+WCTjRDRDMBEREVF1qVIInjhxIjQaTZFHRdtU1qxZs9CyZUsAphVPRo4ciTFjxmDTpk0ICwvDt99+iy5dumDjxo3SMZ999lmdmbrE1ebBEIiLcVlWrISIiIiofqlSCM7Ly0NOTk6RR2H5+flltqksBwcH7NixA15eXgBMSxX++OOPGDFiBPr164cpU6bg7NmzUvupU6fi1VdfrZZrW4K3w4PVa6KTOFcwETUcK1asMPtkUKFQwNPTE+PHj0dsbKxZ2w8++ACDBw+Gh4cHBEHAuHHjij3nhQsX8Morr6BHjx7Q6XQQBAEHDhyoUF15eXnw8/PDxx9/XMmvzLpCQkIQEhJSqWMXL16MFStWFNlf8Cluca9ZwqJFi+Dr6wuVSgVBEJCSkoJx48bBx8fHrN28efOwdevWIsf/9ttvsLW1LfK+Ksm4ceOqZcGt4t7jbm5uePbZZ3H16tUqn7+AIAh47bXXqnyekJAQBAYGVvk88fHx+OCDD9CjRw84OzvDzs4OnTt3xnfffWfxJZpr1XCIymjVqhXOnj2Ll156CVpt8bMo+Pv74+eff8Ynn3xi4eqqprXrg6nRYtLlVqyEiMg6li9fjoiICPz666+YOHEi1q5di0ceeQQZGRlSmy+++AKJiYkYOnQoVCpViec6fvw4tm7dCkdHx0ovBbt48WIkJyfj9ddfr9TxdVlJIdjNzQ0REREYNGiQxWs6ffo0/vGPf6Bv377Yv38/IiIioNfrMX36dGzZssWsbUkhODQ0FEFBQVYbKlnwHt+3bx9ee+01bNu2Db1790Zycv0cBnnixAmsXLkSoaGhWLlyJTZt2oTg4GBMmTIFEydOtGgtVVoxbsWKFdX6l5+Pjw9EUazwcY0aNcL//vc/fPHFF9i/fz9u3ryJjIwMuLm5oW3btujYsWO11WhJj7V3xawzpufxOTrrFkNEZAWBgYHo0qULAKBv374wGAz46KOPsHXrVrzwwgsAgPT0dMhkpj6dVatWlXiuMWPGYOzYsQCAjRs3Yvv27RWqJT8/H5999hkmTJgAnc5yv5Pz8vKknsKHZWZmwsbGxmK1FEetVqN79+5WufaFCxcAmIZeBgUFSftbtGhRofO8+uqrGDVqFObMmSN9umwphd/jISEhMBgMmDlzJrZu3Yrx48dbtBZL6NWrF65du2Y2QcGAAQOQm5uLb775Bh9++KHFfgZ1vie4ML1ej2HDhuG1117DO++8gxdffLHOBmAACPB0hCLXNBb4nlLPadKIqMErCFs3btyQ9hUE4LKUt11Jtm3bhtjYWIwZM6bIa5cvX8Zzzz0HV1dXqNVqeHt748UXXzQb/nf+/HkMGzYMDg4O0Gg06NChA3744Qez8xw4cACCIGDVqlX497//DQ8PD6jVakRGRkofw587dw4DBw6EXq+XerRzc3MxZ84c+Pn5Qa1Wo3Hjxhg/fjzu3r1b5tf14Ycfolu3bnB0dISdnR06deqEZcuWmXVK+fj44MKFCwgPD5c+vi8YblDScIhDhw4hNDQUer0eNjY26NmzJ3bu3GnWpmBIQFhYGKZMmQJnZ2c4OTlh+PDhiIuLK7XukJAQjB49GgDQrVs3s6EwDw+HEAQBGRkZ+OGHH6T6Cw8LGTJkCGxtbbF06dIyv1/FOXz4MJydnTF48GCzTykqoyAQlzX1a2WJooj33nsPSqWy0l9vVTg4OBQ7Q1fBHzExMTEWq6VKPcFU8xzEdNyFFkaFEvvP38KjHTytXRIR1XIjX/8NicnVc+9FdXByUOOnRZUbfvCwgkWPGjduXC3nq4idO3fCxcUFAQEBZvvPnDmD3r17w9nZGbNnz0bLli0RHx+Pbdu2ITc3F2q1GleuXEHPnj3h4uKCr776Ck5OTvjxxx8xbtw43L59G1OnTjU757Rp09CjRw98++23kMlkcHFxAWAKu0OHDsXkyZPx7rvvIj8/H0ajEcOGDcPBgwcxdepU9OzZEzdu3MDMmTMREhKC48ePlzhcEDCF2MmTJ8Pb2xsAcOTIEbz++uuIjY3FjBkzAABbtmzBiBEjYG9vj8WLFwOANE1pccLDwzFgwAC0a9cOy5Ytg1qtxuLFizFkyBCsXbsWo0aNMmv/8ssvY9CgQVizZg1u3ryJt99+G6NHj8b+/ftLvMbixYuxdu1azJkzB8uXL4efn1+J74uIiAj069cPffv2xfTp0wEAdnZ20usqlUoK6bNnzy7xmsX56aef8OKLL2LChAlYtGgR5PKqDV+MiooCYBruWZjRaITRWPb9QYIglFhDTk4Oxo0bh507d2L79u147LHHqlSrwWAo1yf4MpmszD9C9+/fD4VCUeTrrkkMwbVcU10W7t7vAN5zLpEhmIjKlJicg9sJ9WNGGYPBgPz8fGRnZyM8PBxz5syBXq/H0KFDLV5LREQEOnXqVGT/v/71LygUChw9etQshBUM1wBMsxnl5uYiLCxM+qj3iSeeQEpKCj788ENMnjwZ9vb2UvsWLVpgw4YNRa6Vl5eHGTNmmH1Mvm7dOuzZswebNm3C8OHDpf3t27dH165dsWLFCkyZMqXEr2v58uXSc6PRiJCQEIiiiIULF2L69OkQBAEdO3aEVquFnZ1duYY+vPvuu3BwcMCBAwekm8gGDx6MDh064K233sLIkSMhCA9u/n7sscfM5vNPSkrC1KlTcevWLTRp0qTYawQEBEjDHgoPKShO9+7dIZPJ0Lhx4xLr79SpE+bPn4+MjIxyD3f55JNP8P7772PevHlF/pApr8Lv8cOHD2POnDno06dPkff4hAkTinxyUJzg4OBib/hMSkrCsGHDEBUVhYMHD6J9+/aVqrew0NBQhIeHl9lu7NixpQ6f3bt3L1atWoU33ngDTk5OVa6rvBiCa7nOXnIcN/1RiCM3Kz5emogaHieHknvorKEq9TwcWNq2bYv//ve/cHV1rWpZFRYXF4euXbua7cvMzER4eDheeumlUnun9+/fj9DQ0CJjHceNG4fdu3cjIiLCrFfu6aefLvFcD7+2Y8cONGrUCEOGDEF+oWFzHTp0QJMmTXDgwIFSQ/D+/fsxb948HDt2DGlpaWav3blzp8Lf64yMDPz555+YMmWK2SwKcrkcY8aMwTvvvIMrV67Az89Peu3hwNeuXTsApmEvJYXg6ubi4gKj0Yhbt26VOaZYFEVMnjwZP/zwA9asWYORI0dW+roPv8cLbuZ/eAz4rFmzyjXLg16vL7IvKioKPXr0gEajwZEjR+DpWT0dakuWLEF6enqZ7ZydnUt87eTJkxg5ciS6d++O+fPnV0td5cUQXMtN6OOFJdeNgCDgWrYDRFE0++uZiOhh1TX0oDZYuXIl/P39oVAo4OrqCjc3N6vVkpWVVWSe++TkZBgMhjJDRWJiYrG1F8xbn5iYaLa/pK/TxsbG7GN8wDR2NCUlpcSZMRISEkqs6+jRoxg4cCBCQkKwdOlSeHp6QqVSYevWrZg7dy6ysir+iUJycjJEUazQ1/tw71/BUIvKXL+yCn625blmbm4u1q9fjzZt2uDxxx+v0nUL3uPp6elYv349lixZgueeew67d+82a+ft7V2u8FpcRjh69CgSEhIwd+7cagvAAODr61vu4RDFOXXqFAYMGICWLVti165dpQ6xqQkMwbWcv0cj2OZG4Z66EbI1dth/Lgqh7ZpbuywiIovw9/cv9WNuS3J2dkZSUpLZPkdHR8jl8jJv5nFyckJ8fHyR/QU3fz3cU1ZSZ0dx+wtuJtuzZ0+xxxTXM1hg3bp1UCqV2LFjh1nAL24qsfJycHCATCar0NdbGxT8bMtTm1qtRlhYGB599FH0798fe/bsgYODQ6WuW/g9XjADyv/+9z9s3LgRI0aMkNpVZTjEqFGj0KRJE7z//vswGo344IMPKlXrw6oyHOLUqVPo378/mjZtir1795oNB7IUhuA6INA+A0eyGwEAvj2QwBBMRGQFfn5+uHbtmtk+rVaL4OBgbNiwAXPnzi0xQIWGhmLLli2Ii4szW7V05cqVsLGxqdIUY4MHD8a6detgMBjQrVu3Ch1bMPVa4RupsrKyip1qTq1Wl6uXVKfToVu3bti8eTMWLFgg3ZRXsKCVp6enRW9+KlBW/devX4eTk1O5h3907NgR4eHh6N+/P0JCQvDrr79KNzBWxaeffopNmzZhxowZGD58uNSLWpXhEIBpURm9Xo8333wTGRkZ1TL0oLLDIU6fPo3+/fvD09MTv/76a6X/gKgqhuA6YHx3Jxw5YHp+5E4ja5ZCRFTrhIeHS1OBGQwG3LhxAxs3bgRg6hUrGKubmZmJXbt2ATDNgFBwbEJCAnQ6XZkfa4eEhGD27NlF5ub9/PPP0bt3b3Tr1g3vvvsufH19cfv2bWzbtg1LliyBXq/HzJkzsWPHDvTt2xczZsyAo6MjVq9ejZ07d+LTTz+tUi/Ys88+i9WrV+OJJ57AG2+8gaCgICiVSsTExCAsLAzDhg3DU089VeyxgwYNwueff47nn38ekyZNQmJiIhYsWFDsx9Jt27bFunXrsH79ejRv3hwajQZt27Yt9rzz58/HgAED0LdvX7z11ltQqVRYvHgxzp8/j7Vr11plWF/btm1x4MABbN++HW5ubtDr9WjdurX0+pEjRxAcHFyh2vz9/XHw4EH0798fffr0wb59+6o83MDBwQHTpk3D1KlTsWbNGmkaOB8fnyKr4FXUG2+8AVtbW0yaNAn37t3DV199VaWfReHvX3lduXIF/fv3BwDMnTsXV69eNVshr0WLFpab/UWkcklNTRUBiKmpqRa/tsFgEJ1mxoj6uUmifk6CGHH5b4vXQES1T1ZWlnjx4kUxKyvL2qVUu+XLl4sAxGPHjpXZNjg4WARQ7CMsLExqFxUVVWK7pk2blnmdyMhIURAE8aeffiry2sWLF8VnnnlGdHJyElUqlejt7S2OGzdOzM7OltqcO3dOHDJkiGhvby+qVCqxffv24vLly83OExYWJgIQN2zYUOQaY8eOFXU6XbG15eXliQsWLBDbt28vajQa0dbWVvTz8xMnT54sXr161ex7FRwcbHbs999/L7Zu3VpUq9Vi8+bNxfnz54vLli0TAYhRUVFSu+joaHHgwIGiXq83+54VfF8f/loOHjwo9uvXT9TpdKJWqxW7d+8ubt++3axNST/ngu9D4Z9fcUo6fuzYsUV+pqdPnxZ79eol2tjYiADMvg+RkZEiAHHTpk2lXq/g3A//HGJiYkQ/Pz/Rx8dHvHbtWpnnKK12UTT92/b29hZbtmwp5ufnl+t8JQEgvvrqq2b71q5dKyoUCnH8+PGiwWAo13mCg4PFNm3aVKkWUXzwdZf0ePh99LCyfu9VJK8JoliJJdoaoLS0NNjb2yM1NbXITQmW0HfBOZzINf112d/mGjb/s3aMkSMi68nOzkZUVBSaNWtW5IYtqhkFMzA8fNMS1W3Tp0/HypUrce3atWJX5qPao6zfexXJa/Vqxbj67LU+D8bL/JnQyHqFEBE1YPPnz8e+fftw7Ngxa5dC1SQlJQXffPMN5s2bxwDcwDAE1xHDu3pAnWtaijFd64jfztwo4wgiIqpugYGBWL58OW7dumXtUqiaREVFYdq0aXj++eer5XxGoxH5+fmlPmqTgsU6SnoYDAZrl1hjGILrCEEQ0Mk+uWAD/9lb8ryPRERUc0aPHo0hQ4ZYuwyqJh07dsTbb79dbTfrzZ49G0qlstRHdHR0tVyrOrRo0aLUWkND68+84w9jv38d8vbAxhj+s+n58TRnLpxBRERUy0yaNAmDBw8utU3hafKsbfv27cjJySnx9dLmma7rGILrkP5tmkC/IQrpqkbI1tphVdh1vNiv9KUdiYiIyHLc3d1rVcgtS0nT3DUEHA5RxwS7pUrPFx++Z8VKiIiIiOouhuA6ZvpgL0A0AgD+ym+MjKw8K1dEREREVPcwBNcx/h5OcMk3rYyUr9biPzuuW7kiIiIiorqHIbgOGtIyU3q+/lz9nbqEiIiIqKYwBNdB0wY3h8xgGgYRo2iMmLuZZRxBRERERIUxBNdBLnaN4CPEAwBEhQJztnLhDCIiIqKKYAiuo17o9OCGuN3RnOmOiOqXFStWQBAE6aFQKODp6Ynx48cjNjbWrO0HH3yAwYMHw8PDA4IgYNy4ccWe88KFC3jllVfQo0cP6HQ6CIKAAwcOVKiuvLw8+Pn54eOPP67kV2ZdISEhCAkJqdSxixcvxooVK4rsj46OhiAIxb5mCYsWLYKvry9UKhUEQUBKSgrGjRsHHx8fs3bz5s3D1q1bixz/22+/wdbWtsj7qiTjxo2Dra1tlesu7j3u5uaGZ599FlevXq3y+QsIgoDXXnutyucJCQlBYGBgNVQEvPzyywgMDESjRo2g1WrRqlUrvP3220hIsOxCYAzBddQroa2hzMsCACTbOOLo5RTrFkREVAOWL1+OiIgI/Prrr5g4cSLWrl2LRx55BBkZGVKbL774AomJiRg6dChUKlWJ5zp+/Di2bt0KR0fHSq+CtXjxYiQnJ+P111+v1PF1WUkh2M3NDRERERg0aJDFazp9+jT+8Y9/oG/fvti/fz8iIiKg1+sxffp0bNmyxaxtSSE4NDQUQUFBeO+99yxUtbmC9/i+ffvw2muvYdu2bejduzeSk5OtUo8lZGRkYNKkSVizZg127tyJl19+Gd999x2Cg4ORm5trsTrYhVhH6dR6tLG5hNN5LQFBhvm747DFr5G1yyIiqlaBgYHo0qULAKBv374wGAz46KOPsHXrVrzwwgsAgPT0dMhkpj6dVatWlXiuMWPGYOzYsQCAjRs3Yvv27RWqJT8/H5999hkmTJgAnU5XmS+nUvLy8qSewodlZmbCxsbGYrUUR61Wo3v37la59oULFwAAEydORFBQkLS/RYuKLST16quvYtSoUZgzZw68vLyqtcayFH6Ph4SEwGAwYObMmdi6dSvGjx9v0VosZe3atWbb/fr1g16vxyuvvIJDhw6hX79+FqmDPcF12JTeD358fyTqYDCIVqyGiKjmFYStGzce3AtREIDLUt52Jdm2bRtiY2MxZsyYIq9dvnwZzz33HFxdXaFWq+Ht7Y0XX3zRbDna8+fPY9iwYXBwcIBGo0GHDh3www8/mJ3nwIEDEAQBq1atwr///W94eHhArVYjMjJS+hj+3LlzGDhwIPR6vdSjnZubizlz5sDPzw9qtRqNGzfG+PHjcffu3TK/rg8//BDdunWDo6Mj7Ozs0KlTJyxbtgyi+OD/FB8fH1y4cAHh4eHSx/cFww1KGg5x6NAhhIaGQq/Xw8bGBj179sTOnTvN2hQMCQgLC8OUKVPg7OwMJycnDB8+HHFxcaXWHRISgtGjRwMAunXrZjYU5uHhEIIgICMjAz/88INUf+FhIUOGDIGtrS2WLl1a5verOIcPH4azszMGDx5s9ilFZRQE4tu3b1fpPCURRRHvvfcelEplpb/emtC4cWMAKPaPvZrCnuA67KnOAfjXL7eQoXJAlo0dNh2+g5F9XK1dFhFRjYmMjATw4D9MS9q5cydcXFwQEBBgtv/MmTPo3bs3nJ2dMXv2bLRs2RLx8fHYtm0bcnNzoVarceXKFfTs2RMuLi746quv4OTkhB9//BHjxo3D7du3MXXqVLNzTps2DT169MC3334LmUwGFxcXAKawO3ToUEyePBnvvvsu8vPzYTQaMWzYMBw8eBBTp05Fz549cePGDcycORMhISE4fvw4tFptiV9XdHQ0Jk+eDG9vbwDAkSNH8PrrryM2NhYzZswAAGzZsgUjRoyAvb09Fi9eDMDUA1yS8PBwDBgwAO3atcOyZcugVquxePFiDBkyBGvXrsWoUaPM2r/88ssYNGgQ1qxZg5s3b+Ltt9/G6NGjsX///hKvsXjxYqxduxZz5szB8uXL4efnV+L7IiIiAv369UPfvn0xffp0AICdnZ30ukqlkkL67NmzS7xmcX766Se8+OKLmDBhAhYtWgS5XF6h4x8WFRUFAGjVqpXZfqPRCKPRWObxgiCUWENOTg7GjRuHnTt3Yvv27XjssceqVKvBYDD7Y6kkMpms2D9C8/PzkZOTg9OnT2P69Ono3bs3evXqVaWaKoIhuA7TqGwQ5BSDsHQHAMCig0kMwUSE4O/TcDuj7P8sLcVVJ0P4BLuyGxbDYDAgPz8f2dnZCA8Px5w5c6DX6zF06NBqrrJsERER6NSpU5H9//rXv6BQKHD06FGzEFYwXAMAZs2ahdzcXISFhUkftz/xxBNISUnBhx9+iMmTJ8Pe3l5q36JFC2zYsKHItfLy8jBjxgyzj8nXrVuHPXv2YNOmTRg+fLi0v3379ujatStWrFiBKVOmlPh1LV++XHpuNBoREhICURSxcOFCTJ8+HYIgoGPHjtBqtbCzsyvX0Id3330XDg4OOHDggHQT2eDBg9GhQwe89dZbGDlyJARBkNo/9thj+Oqrr6TtpKQkTJ06Fbdu3UKTJk2KvUZAQIA07KHwkILidO/eHTKZDI0bNy6x/k6dOmH+/PnIyMgo93CXTz75BO+//z7mzZtX5A+Z8ir8Hj98+DDmzJmDPn36FHmPT5gwocgnB8UJDg4u9obPpKQkDBs2DFFRUTh48CDat29fqXoLCw0NRXh4eJntxo4dW+STgiNHjqBHjx7S9hNPPIF169ZV+Y+IimAIruNeD7VB2BYREARcyLHHvcw82NoorV0WEVnR7Qwj4tJr0/CoygfyhwNL27Zt8d///heurpb/gz8uLg5du3Y125eZmYnw8HC89NJLpfZO79+/H6GhoUXGm44bNw67d+9GRESEWa/c008/XeK5Hn5tx44daNSoEYYMGYL8/Hxpf4cOHdCkSRMcOHCg1BC8f/9+zJs3D8eOHUNaWprZa3fu3Knw9zojIwN//vknpkyZYjaLglwux5gxY/DOO+/gypUr8PPzk157OPC1a9cOgGnYS0khuLq5uLjAaDTi1q1bZY4pFkURkydPxg8//IA1a9Zg5MiRlb7uw+9xf39//Pzzz0WGBcyaNatcszzo9foi+6KiotCjRw9oNBocOXIEnp6ela63sCVLliA9Pb3Mds7OzkX2tW3bFseOHUNmZiZOnz6Njz/+GAMGDMD+/fstNs6dIbiOe6RlGzjlXUeiyh35Gi0W/3ILU5+y7KB+IqpdXHUyVCV4VjdTPZWzcuVK+Pv7Q6FQwNXVFW5ubtVYWcVkZWVBo9GY7UtOTobBYCgzVCQmJhZbu7u7u/R6YSV9nTY2NmYf4wOmsaMpKSklzoxR2rRTR48excCBAxESEoKlS5fC09MTKpUKW7duxdy5c5GVlVXq11Wc5ORkiKJYoa/XycnJbLtgqEVlrl9ZBT/b8lwzNzcX69evR5s2bfD4449X6boF7/H09HSsX78eS5YswXPPPYfdu3ebtfP29i5XeC3cw17g6NGjSEhIwNy5c6stAAOAr69vuYdDPEyn00m993369EG3bt3QvXt3LFmyBG+++Wa11VgahuA6Tq3Uop93HDbcMv1iWXUqE1OfsnJRRGRVlR16UBv5+/uX+jG3JTk7OyMpKclsn6OjI+RyOWJiYko91snJCfHx8UX2F9z89XBPWXFBpqT9BTeT7dmzp9hjiusZLLBu3ToolUrs2LHDLOAXN5VYeTk4OEAmk1Xo660NCn625alNrVYjLCwMjz76KPr37489e/bAwcGhUtct/B4vmAHlf//7HzZu3IgRI0ZI7aoyHGLUqFFo0qQJ3n//fRiNRnzwwQeVqvVhVRkO8bAuXbpAJpPhr7/+qpbayoMhuB54ta8jNv2YD6NcgZtyB/x9KxPeTaw7ZQ4RUX3j5+eHa9eume3TarUIDg7Ghg0bMHfu3BIDVGhoKLZs2YK4uDipNxQw9QLa2NhUaYqxwYMHY926dTAYDOjWrVuFji2Yeq3wOMysrKxip5pTq9Xl6iXV6XTo1q0bNm/ejAULFkg35RmNRvz444/w9PQsctOXJZRV//Xr1+Hk5FTu4R8dO3ZEeHg4+vfvj5CQEPz666/SDYxV8emnn2LTpk2YMWMGhg8fLvWiVmU4BGBaVEav1+PNN99ERkYG5s+fX+VaqzIc4mHh4eEwGo3w9fWtcl3lxRBcDwR6BcID53ATLWBUKvHZzttY9FIza5dFRGQR4eHh0lRgBoMBN27cwMaNGwGYesUKxupmZmZi165dAEw35RQcm5CQAJ1OV+bH2iEhIZg9e3aRuXk///xz9O7dG926dcO7774LX19f3L59G9u2bcOSJUug1+sxc+ZM7NixA3379sWMGTPg6OiI1atXY+fOnfj000/NboqrqGeffRarV6/GE088gTfeeANBQUFQKpWIiYlBWFgYhg0bhqeeKv4jwkGDBuHzzz/H888/j0mTJiExMRELFiwoduaHtm3bYt26dVi/fj2aN28OjUaDtm3bFnve+fPnY8CAAejbty/eeustqFQqLF68GOfPn8fatWtL7OmuSW3btsWBAwewfft2uLm5Qa/Xo3Xr1tLrR44cQXBwcIVq8/f3x8GDB9G/f3/06dMH+/btq/JwAwcHB0ybNg1Tp07FmjVrpGngfHx8iqyCV1FvvPEGbG1tMWnSJNy7dw9fffVVlX4Whb9/5bVjxw4sXboUQ4cORdOmTZGXl4fjx4/jyy+/hK+vL15++eVK11NhIpVLamqqCEBMTU21dinFevenHaJ+bpKon5skNn3vqmg0Gq1dEhHVsKysLPHixYtiVlaWtUupdsuXLxcBiMeOHSuzbXBwsAig2EdYWJjULioqqsR2TZs2LfM6kZGRoiAI4k8//VTktYsXL4rPPPOM6OTkJKpUKtHb21scN26cmJ2dLbU5d+6cOGTIENHe3l5UqVRi+/btxeXLl5udJywsTAQgbtiwocg1xo4dK+p0umJry8vLExcsWCC2b99e1Gg0oq2trejn5ydOnjxZvHr1qtn3Kjg42OzY77//XmzdurWoVqvF5s2bi/PnzxeXLVsmAhCjoqKkdtHR0eLAgQNFvV5v9j0r+L4+/LUcPHhQ7Nevn6jT6UStVit2795d3L59u1mbkn7OBd+Hwj+/4pR0/NixY4v8TE+fPi326tVLtLGxEQGYfR8iIyNFAOKmTZtKvV7BuR/+OcTExIh+fn6ij4+PeO3atTLPUVrtomj6t+3t7S22bNlSzM/PL9f5SgJAfPXVV832rV27VlQoFOL48eNFg8FQrvMEBweLbdq0qVItoiiKly5dEkeMGCE2bdpU1Gg0okajEf38/MS3335bTExMLPP4sn7vVSSvCaJYjhHNhLS0NNjb2yM1NbXITQm1wZW4s+j5P2fkKbSA0YgdQwX0aedo7bKIqAZlZ2cjKioKzZo1K3LDFtWMghkYHr5pieq26dOnY+XKlbh27ZpFF2ugiivr915F8hpXjKsnWrgGoJXqqmlDJsOCvYmlH0BERBU2f/587Nu3D8eOHbN2KVRNUlJS8M0332DevHkMwA0MQ3A9oZAr8GJQqrQdkaxGdo7BihUREdU/gYGBWL58OW7dumXtUqiaREVFYdq0aXj++eer5XxGoxH5+fmlPmqTgsU6SnoYDPU3SzAE1yOD2/nBLs80H2SOTo/vf+UvaSKi6jZ69GgMGTLE2mVQNenYsSPefvvtartZb/bs2VAqlaU+oqOjq+Va1aFFixal1hoaGmrtEmsM+/3rEQ8nX3Rx2oH9aY8AAJYdy8Arg61cFBERUQMyadIkDB5c+n++hafJs7bt27cjJyenxNdLm2e6rmMIrkdkggwv9wTCdhkgyuS4JrPHjfgMNHUr3xroREREVDXu7u61KuSWpaRp7hoCDoeoZ7o27wh38QYAwKhUYcHO21auiIiIiKj2YQiuZ1zsPRHa7LK0ve26WK51vYmo7uK/cSJqKKrz9x1DcD0jCAKe6+oIZb5pachkm0b47USClasioppQMJ1TbbvbnIiopuTl5QGA2VLflcUQXA8FeHRGK/Ul04ZMhi9/S7FqPURUM+RyOeRyOdLS0qxdChFRjRNFEampqVCr1VAqlVU+H2+Mq4ccbF3wVGAULpzpBAD4M02NzKx82Gj54yaqTwRBgIuLC+Lj46FWq6HT6aptmiciotpCFEXk5eUhNTUV9+7dg4eHR7Wcl6monurv3wKLjicgVemMHJ0e3/0Sh38+6W3tsoiomtnb2yMrKwsJCQm4e/eutcshIqoxarUaHh4eZS6HXF4MwfVUK/dO6Oi4FwfS+wMAfjiZjX8+ad2aiKj6CYIANzc3uLi4SGPliIjqG7lcXi1DIApjCK6nbDX2eLpdMsIPmuYMjpLZ49rNdLTwqr+TXhM1ZAXjg4mIqHx4Y1w91qVZO3ggGgBgVKmwYMcd6xZEREREVEswBNdjLdzaoZfncWl75w3AYOB8okREREQMwfWYRmmDwW2UUN2fMzjFthH2/MkV5IiIiIgYguu5AI/OaK29aNqQybAwLNW6BRERERHVAgzB9Vwz1zYIbX5K2j6RZYPE5GwrVkRERERkfQzB9ZxCrkQfXy845JuGQeTZ2OLrnXFWroqIiIjIuhiCGwB/z67o2viEtL3mfB5EkTfIERERUcPFENwAuDs2Q2iLG5AZTRPp37JxxOGziVauioiIiMh6GIIbAEGQoX3T9miu+AsAICoU+PwXhmAiIiJquBiCGwh/j67o431E2j6crEZ6BpdYJSIiooaJIbiBcLB1QTcvGWwNKQCALL09lu2JtW5RRERERFbCENyABHh1RcdGJ6Xt709wqjQiIiJqmBiCG5BW7p3Q2/skBNEIAPhb5YBzfyVbuSoiIiIiy2MIbkC0Kh3auDeDpxAFADCqVPhs5x0rV0VERERkeQzBDUyAZ1f09nxwg9y+WwpkZedbsSIiIiIiy2MIbmB8XNqgnUsc1IYMAMA9Owes388V5IiIiKhhYQhuYBRyBfzc26Ot/qxphyDg24gM6xZFREREZGEMwQ2Qv2cQenv9CdxfOvkK7HHt7zQrV0VERERkOQzBDZCbgw+aOSjgCtM8wQatFl/suGXlqoiIiIgshyG4ARIEAf6eXdDd7ai0b3sUkJdntGJVRERERJbDENxA+Xl0RXvnC1AYcwAAyfaO+Pl33iBHREREDQNDcAPVSOcMLydv+GsvmHbI5FgYnmrdooiIiIgshCG4AQvwDEKw9x/S9gWDPa7zBjkiIiJqABiCG7BW7h3hrkuFsxgPAMjX6fDFdg6JICIiovqPIbgBUyu18HVrh55N/pT2/RwlICfXYMWqiIiIiGoeQ3ADF+DVDe0bn5dukEtp5IRN+2OtXBURERFRzWIIbuC8nFrCUadHG5vzph0yOb4+yHHBREREVL8xBDdwgiBDgGcQHvGKkPZdFhrhahRniiAiIqL6iyGY4O8ZBBdtAhqLppvi8m10+A9vkCMiIqJ6jCGY0EjnDA/HFujp/uAGuZ03ZMjKzrdiVUREREQ1hyGYAJhukGvndAFKYzYAINXBGRt5gxwRERHVUwzBBABo6dYBNkoZ2tgW3CAn4w1yREREVG8xBBMAQKVQo6V7R/T2OCLtuyp3wMWryVasioiIiKhmMASTJOD+DXIueHCD3Oc74q1cFREREVH1YwgmiYdjC9jbOKOn+4Pe4D0xcmRk5lmxKiIiIqLqxxBMEkEQEOAVhHaOD26QS3NwxtpfY6xcGREREVH1YggmM/6eXaGQGdFWf860QybD4kPpEEXRuoURERERVSOGYDJjp3WEt3NL9PZ8MCQiWuOE4+cTrVgVERERUfViCKYiAry6wVmdiCbCTQBAvtYG/9nOG+SIiIio/mAIpiJ8m7SDSqFFH88/pH0HEtW4m5hlxaqIiIiIqg9DMBWhkKvg59EZ/o2uQGPMAABkOjpj6Y6/rVwZERERUfVgCKZiBXr3gFwworPjCdMOQcAPp3OQl2+0bmFERERE1YAhmIrlYu8JF3tP9HQ7CkE0Bd/b9q7YezjOypURERERVR1DMJUo0LsH9Mp7aKaKBAAYVSp8sZezRBAREVHdVy0h+I8//sDkyZMREBAAe3t72NnZISAgAJMmTcLhw4er4xIlSk9Px8KFC/HYY4/B09MTGo0GTk5OaNeuHd58802cOnWqRq9fn7V27wyFTIlgzwc/wzP5doiMTrNiVURERERVJ4hVWAUhIyMD//jHP/D999+X2m78+PFYtGgRdDpdZS9VrHXr1uHVV19FUlJSiW0EQcCrr76KTz/9FFqtttLXSktLg729PVJTU2FnZ1fp89Q1v5xejYs3j+I/Z/+BNMERADBeF4uFbwRauTIiIiIicxXJa5XuCTYYDBg+fLhZANZqtejSpQu6d+9uduHly5dj+PDhMBgMlb1cEV988QWee+45swDs7u6O4OBgdO/eHTY2NgAAURTx9ddf45lnnqnW6zcUgd7dIQhAT9c/pX1bogTcy8izYlVEREREVVPpEDx9+nTs3btX2p44cSJiYmJw7NgxREREIC4uDtOnT5de37t3L2bMmFG1au8LDw/HW2+9JW17eXlhz549iI2NxYEDBxAREYHExETMnz8fcrkcALBz585qu35D4u7QHA46F3RqfBpy0RR8Ux1d8NNeTpdGREREdVelhkPExcWhRYsWyM7OBgCMGTMGK1euLLbt9OnTMWfOHACARqPBtWvX4O7uXoWSge7du+PPP009k/b29jh16hSaNWtWbNulS5di0qRJAAC1Wo2rV6/Cy8urwtdsqMMhAODEtf04eOlnbIgchnOZHQEALROjcXxBBwiCYOXqiIiIiExqfDjEl19+KQVgGxsbfPnllyW2nT59uhQ6s7OzsXDhwspcUnL+/HkpAAPAe++9V2IABkw91EFBQQCAnJwcLFiwoErXb4j8PbtCJsjwiMcRaV+0xhl/nr5rxaqIiIiIKq9SIXjLli3S85EjR8LR0bHEtiqVCuPHj5e2N2/eXJlLSsLCwsy2n3322TKPee6558yuX4V7ARskG7UezV3boon2NlxkpnmC83S2+HIH5wwmIiKiuqnCIfjKlSuIjIyUth977LEyj3n88cel55GRkbhy5UpFLyu5ceOG9NzOzg7e3t5lHtOuXTvpeUxMDE6ePFnp6zdUgd49AAC93SOkfeFJGsTfzbRWSURERESVVuEQfObMGbPtHj16lHlMp06doFKppO2zZ89W9LKS1NRU6blery/XMQ+PCTl9+nSlr99QeTduDb3WAYGNLkItZgEAMpwa438/R1u3MCIiIqJKqHAIvnTpkvRcpVKV6yazh9sVPkdFFQ6+6enp5TomLc18cYeLFy9W+voNlUyQoY1XNyhkBnRyuN+TLpPhh7N5yM7h1HNERERUt1Q4BEdHR0vPPT09yz07QOFhC4XPUVGenp7S87S0NPz9d9lTdZ07d85suzzXz8nJQVpamtmjoQvw6gZAQM8mf0IQjQCABEdX/Pwbp0sjIiKiuqXCIbhw76u9vX25jys8JKG8PbjF6d27t9n2+vXryzxm3bp1Ztvluf78+fNhb28vPSozrVp9Y6d1RNPGfrBXpaGF5ioAwKhS48t9SbzZkIiIiOqUCofge/fuSc81Gk25jyu8ZHHhc1RUly5d0KpVK2l7/vz5ZjfLPWz58uU4cuSI2b7yXH/atGlITU2VHjdv3qx0zfVJ2/s3yPXxPCztu6pywrGzCdYqiYiIiKjCKhyC8/PzpecKhaLcxxVum5dX+SV3ZTIZPvzwQ2k7OTkZffr0wa+//mrWLjs7G5999pm0UEZhhb+GkqjVatjZ2Zk9CGjmGgid2g5Nbf6Go2CaJzjXzh5f/Bxj5cqIiIiIyq/CIdjGxkZ6XrBgRnkUbqvT6Sp6WTPPPvss/u///k/a/vvvvzFw4EB4enqib9++6NmzJ5ydnTF16lTk5+ejefPmZlO5VWQYB5mTy+Ro490dggA84vaHtP9AkgYxtzKsWBkRERFR+VU4BNva2krPs7Kyyn1cZuaD+WQLn6OyFi9ejI8++shs6rXY2FgcOHAAERERyMgwBbIuXbpg7969MBgezGDQqFGjKl+/IQv07gEBAto5noNKNP1xk+Hsgu+2Rlm5MiIiIqLyqXAIdnZ2lp7Hx8eX+7hbt25Jz52cnCp62SIEQcAHH3yAyMhIfPDBB+jevTucnZ2hVCrh5uaG/v37Y/ny5fjjjz/QokULsxkhyrPABpXMTusIH5cAKGX56Ox4wrRTJsPq8/nIzCp7qAkRERGRtZV/UO99rVu3lp4nJiYiMzPTbIhESQrfWObn51fRy5bIy8sLH330ET766KMS26SmppqtctelS5dqu35D1a5pL0TduYCern/iSFIPiIIMic5NsPnXGxg9tIW1yyMiIiIqVYV7gv39/c22y7P6WmxsLO7evVviOWra77//bjaFV7du3Sx6/fqoqYs/9FoH03Rp2gfTpX21PxlGI6dLIyIiotqtwiE4KCgIarVa2j506FCZxxw8eFB6rtFoEBQUVNHLVsmaNWuk50FBQWjevLlFr18fyQQZAgumS/N4MF3adY0zIk7dsVZZREREROVSqRvjQkNDpe3Vq1eXeUzhNqGhoVWeHaIirly5gk2bNknbEydOtNi167tAr+4QBBma2vwNJ1kiANN0aV/+HGvlyoiIiIhKV+EQDADjxo2Tnp89exbbt28vse3Jkyexe/fuYo+taTk5OZg4caI0L7Gfnx9efPFFi12/vtNp7NHCNRCCAPRye/CJwMFULW7EVn5VQCIiIqKaVqkQPGLECLRv317anjx5Mi5fvlykXXx8PEaPHi1NT9ahQwc8/fTTxZ4zOjoagiBIj1mzZpVaw6pVq5Cbm1vi67dv38aTTz4pDcUQBAFLliwxm1KNqq5t014AgPYO56BGDgAgs7ELlmzhdGlERERUe1V4dgjAFCiXLl2K4OBgZGVlIT4+Ht26dcOUKVPQp08fKBQKHD16FF9//TVu374NwLRs8nfffQdBEKql8IkTJ+KNN97A0KFD0atXLzRr1gxKpRLx8fEICwvDTz/9hJSUFKn94sWL0adPn2q5Nj3g7dwK9jbOSM1MQGeH4/gjuRcgk2HtJQPevZcLO1v+0UFERES1jyAWnjahgjZv3ozRo0eXuWiGVqvFjz/+iOHDh5fYJjo6Gs2aNZO2Z86cWWpvsEajQU5OTpk16nQ6LFy4EC+99FKZbUuTlpYGe3t7pKamcgnlhxy/9hsOXdqG1Fw7fH7pnxAFGWS5OfjUPxWTRrUu+wRERERE1aAiea1SwyEKDB8+HCdOnED//v2L7eEVBAGhoaE4fvx4qQG4Mp566ik4OjqW+LpOp8MLL7yACxcuVDkAU+kCPLtBLpPDXpWG1rbXAJimS/vm9zTk5RutXB0RERFRUVXqCS7s5s2bOHz4MGJjTTMDeHh4oFevXvDy8qqO0xfLaDTizJkziIyMxO3bt5Geno7GjRvDy8sLffr0gVarrbZrsSe4dLtP/oArcSfxd4Yn/nftZQCA8t49/NBfxOB+XKGPiIiIal5F8lq1heD6jiG4dDGJkdgYsQgAsOTKa4jNMS2v3TXhKvb9p2u1jQUnIiIiKonFhkMQFfBwbAFHW1cAQE+XMGn/OcEBx88lWKssIiIiomIxBFO1EAQB7X0eAQAENLoEO7npZslsJ2cs2nzDmqURERERFcEQTNXGz6MrlHI15IIRPVz+kPbvS1QjOoaLZxAREVHtwRBM1Uat1CDAMwgA0MnxGJQwLZKS4eKG7zZft2ZpRERERGYYgqlatfPpDQDQyrMR1PgCAECUy7HuUj5S0sqe15mIiIjIEhiCqVo56ZvAy6klAKCrYxgA0+QjyS4eWLOTSykTERFR7cAQTNWu4AY5R3Uy2jWKAwAY1Gr89/dU5OYarFkaEREREQCGYKoBzV0DYatpBADo4rRX2h/XqAl2HYixUlVEREREDzAEU7WTyeRo17QXAKCpzQ14aU0zQ+Tp7bBwVzy4PgsRERFZG0Mw1Yg23t0hE+QQBKCXa7i0/6LMERGn7lixMiIiIiKGYKohOrUdWrl3AAC0sj0Fe0UuACDLyRlfbYq2XmFEREREYAimGtTu/g1yCpkBIR5nTDsFAb+n2uDi1WQrVkZEREQNHUMw1Ri3Rj5wsfcEAATofoNKMAIAMlzdsHhDpDVLIyIiogaOIZhqjCAIaN/U1BusVWQj1DMagGnxjG03ZIi5lWHF6oiIiKghYwimGtXaoxM0ShsAQBv9DsjuL56R5uaJ/228as3SiIiIqAFjCKYapZCr0MarOwDATpGE3u6JAACjSoUfz+QgKYVLKRMREZHlMQRTjWvn0xsCBABAF4c90v6kJp5Yve2atcoiIiKiBowhmGqcvY0TmrsGAgAaySPRwdk0Fjhfa4MlB1OQmZ1vzfKIiIioAWIIJovo0CxYeh7c5JD0/JaTOzbvibZCRURERNSQMQSTRXg6+cJZ7w4AcBQi0FyfBwDItbPH17tvId9gtGZ5RERE1MAwBJNFCIKAjs2D7z8HHm92Tnrtuq0rfvk9xlqlERERUQPEEEwW09q9M7QqWwBAY+yCs8bU+5vt5IyvtvwNURStWR4RERE1IAzBZDEKuRJtm/YEAMiQjyd9o6XXzsIBf5y8Y6XKiIiIqKFhCCaLate0N2SCHADgrdgGG7mp9zfDpQm+2cjp0oiIiMgyGILJomw19mjl3sG0YUzBU61Mi2dAJsPvyTY4/1ey1WojIiKihoMhmCyu8HRpbWx3QSGYeoPT3T2wZAOXUiYiIqKaxxBMFtekUVO4OfgAAPJzrmOQbxYAQFQo8HO0gOiYdCtWR0RERA0BQzBZRcdmIdLzXs7hEGDqDU7z8MK36/+yTlFERETUYDAEk1W0aNIOtppGAIDsjKN4tJlpujSjSo31F/MQdzvDitURERFRfccQTFYhl8nR3qf3/S0Rj3qfkF5L8fDGsg3sDSYiIqKawxBMVhPo3RMKmRIAkJ32Gx7xNO03aLRYeSILCUnZVqyOiIiI6jOGYLIarUoHf8+uAIA8Qw6Gt3zQ+5vk7oUVmzhTBBEREdUMhmCyqo7NQ6Tnxnu70NFFAADk6Wyx7I8UpKbnWqkyIiIiqs8YgsmqHG1d0dw1EACQkZOC0W3ipNfuunrhx62R1iqNiIiI6jGGYLK6Ts37Ss81uTvQspHpea6dPZb8dhcZmXnWKYyIiIjqLYZgsjoPxxZwtfcCACSmx+LljmnSa7ecPbB+53VrlUZERET1FEMwWZ0gCOjUvJ+07SL8Ag9b0/NsRyf8d1ccsnMMVqqOiIiI6iOGYKoVWrq1h17rAAC4mXAB/9flwRCIvxu5Y8sv0VaqjIiIiOojhmCqFWQyOTo2C5a2W2oOwFFjep7l3BjfbPsbeflGK1VHRERE9Q1DMNUabbx6QKUwJd9rt45gSlfTdGkQBFyzccWO/X9bsToiIiKqTxiCqdZQKzVo690TAGAwGtDZ4U/YKkUAQIarK77eFAWDQbRmiURERFRPMARTrdKhWR/IBNPbMjI2HK8EqUwvCDJcUjXG3kMxVqyOiIiI6guGYKpV9FoHtHTrCADIzstAiNtZ2MhNvb/3XN3w5bprMBrZG0xERERVwxBMtU7nFg8Wz4iM3Y8pQWrThkyGczIn7D0Ua6XKiIiIqL5gCKZax8XeC55OLQEAKRl3MahZNDQFvcFubli4LpK9wURERFQlDMFUK3UutJTytdhfManL/fnSZHKchQP2HWZvMBEREVUeQzDVSj4u/nDSNwEAxCdH4ZnWt6CWFfQGe+DLtVfZG0xERESVxhBMtZIgyNC5Rai0HR33K17qbOoNFuVynBEd8NsfcdYqj4iIiOo4hmCqtVq7d4Ze0wgAEHXnIsa0SYGyoDfY3QNfrvmLvcFERERUKQzBVGvJZXJ0KjQ2OOb2foztYJopQpQrcMbYiL3BREREVCkMwVSrBXr3gEZpAwC4HHcCEzvkQCGYen/T3T3xFXuDiYiIqBIYgqlWUyrUaO/TBwAgikbcTgjDC+3v9wYrFDidb4f9EewNJiIioophCKZar0OzR6CQKQEA528ewStdjJDd7w1O8/DCojVX2BtMREREFcIQTLWeVmWLQO8eAIB8Qy5Skg/hucD7vcFKJU7m2iHsCHuDiYiIqPwYgqlO6NS8LwTB9HY9E/073ugmgwz3xwZ7mnqDRZG9wURERFQ+DMFUJ9jZOMLPvTMAIDsvE1n3/sSINioAgFGpwokcO4QdibdmiURERFSHMARTndG5RT/p+cmoA3irp6pQb7A3vlrN3mAiIiIqH4ZgqjOc7dzRzCUAAJCelQwx5wyebVvQG6zEiVw77DvMscFERERUNoZgqlO6+PaXnh+/tg9v91I/mCnC0wtf/HgZBgN7g4mIiKh0DMFUp3g4toCbQzMAQGL6LSD3Ml5oVzBvsBJnRQfsOnDTmiUSERFRHcAQTHVO10K9wUev7sXbPdUPVpHz8MLCNX8hL99orfKIiIioDmAIpjqnmUsbONt5AABup/4NIT8SYzs8WEXustIJP/96w5olEhERUS3HEEx1jiAI6OY7UNo+enUv/t1LC6WsoDfYE4vWX0VursFaJRIREVEtxxBMdZKvWzs42LoCAGKTrgF51zG+owYAIMoViNS6YMPuKGuWSERERLUYQzDVSYIgQ5DvAGn7aOSv+HdPDdRy0/Y9d098s/EaMrPzrVQhERER1WYMwVRntXbvBDsbJwDA33cvQzDcxMud748NlstxQ++GdduvWbNEIiIiqqUYgqnOksnk6Nqi8EwRv+LN7hpo7vcGp7u749st0biXkWelComIiKi2YgimOs3fMwi2GnsAwPXb5yAz3sLkrqbeYMjkiHFwx8otV61YIREREdVGDMFUpynkCnRu3k/aPhr5K97opoFWYdq+5+aOZTv+RkpajpUqJCIiotqIIZjqvMCmPaFV2QIA/oo7BZl4F68GmWaKgEyGuMaeWL7xLytWSERERLUNQzDVeUq5Cp2a972/JeJ45D681k0NW6VpT0YTNyz/JQ4JSdlWq5GIiIhqF4ZgqhfaNe0NtVILALgcexxyMRn/7HG/N1iQ4Y5bU3y37rIVKyQiIqLahCGY6gW1UoMOPsEAAKNoxPFrv+GVIA2cTLkYmS6uWBV+B3/H3bNilURERFRbMARTvdGxWR8o5aaZIS78fQSiIQXv9NZKryd6NcfXKy9aqzwiIiKqRRiCqd7QqHTo0KwPAMAoGnAs8leM76iGp14AAGQ7OmHzqXRcikyxYpVERERUGzAEU73SqXlfqTf4/N9HkJuXgveDH/QGp/i0wBfLz1urPCIiIqolGIKpXtEW0xv8bKAKrZ1Mb/Vce3vsi8rHn6fvWLNMIiIisjKGYKp3Hu4NzsxJwcyQB73Bqc1a4PPvz0MURWuVSERERFbGEEz1TnG9wYNaKdHFXQ4AyNPZ4miKCr8eirVmmURERGRFDMFULz3cG3wv27w3OKVpc3yx4iLy8o3WKpGIiIisiCGY6qXieoODfZQI8VEAAAxaLS4a7bDll2grVklERETWwhBM9dbDvcHpWcnmY4O9ffD1mivIzM63VolERERkJQzBVG8V1xvc2V2BYa2Vpn1qNaJsGuPHrZHWLJOIiIisgCGY6rXieoM/CNZCZlo/A2le3li66RpS0nKsWCURERFZGkMw1WvF9Qa3dpbjhXYqAICoUCKusSeWrr9izTKJiIjIwhiCqd57uDc4LSsJ7z2ihdo0YxrS3T3xwy+xiL2VYcUqiYiIyJIYgqnee7g3+OjVvfCwk+G1bhpTA5kMCZ4++JLLKRMRETUYDMHUIHRu3hcqhSn0Xrj5J1Iy7uKf3TVwvD9ZRKZLE2w9kYqzl5OsWCURERFZCkMwNQgalQ6dmvcFAIiiEUf+2g17jYB3exdaQKO5Lz797iyXUyYiImoAqiUE//HHH5g8eTICAgJgb28POzs7BAQEYNKkSTh8+HB1XKJE2dnZWLVqFZ555hn4+vrCzs4OKpUKzs7O6NKlC1599VVERETUaA1UN3RsFgKNUgcAuBx7EglpcZjQSY3mDqZ/BjmNHBBxC9h3OM6KVRIREZElCGIVur0yMjLwj3/8A99//32p7caPH49FixZBp9NV9lLF2rdvHyZMmICbN2+W2XbQoEFYtmwZXF1dK3WttLQ02NvbIzU1FXZ2dpU6B1nfiWv7cfDSzwAA3ybtMLjLS9h6KRcvbjHdFKfMuIeucRew/buBUCr5QQkREVFdUpG8Vun/5Q0GA4YPH24WgLVaLbp06YLu3bubXXj58uUYPnw4DAZDZS9XxI4dO/D444+bBWA7Ozt0794d/fr1Q0BAAGSyB1/ezp07ERwcjISEhGqrgeqedj69YaM2vTcjb53F7ZSbGOanRBd301QReTpbXBbtsG7HNWuWSURERDWs0iF4+vTp2Lt3r7Q9ceJExMTE4NixY4iIiEBcXBymT58uvb53717MmDGjatXel5KSggkTJiA/37TcrV6vx/fff4+EhARERETgt99+w4ULF3Djxg08//zz0nFXrlzBW2+9VS01UN2klKvQreVAafuPKzshCALmhtpI+1KbNsc3a68gNT3XGiUSERGRBVQqBMfFxeGLL76QtseMGYPvvvsOjo6O0j6dTofZs2fjgw8+kPZ9/vnniIur+njLdevW4e7du9L22rVrMX78eCiVSrN2np6eWL16NZ588klp35o1a5CSklLlGqjuCvTuAb3W9F69cfcSYpOuoYeXAoNbmd4/BrUasY3csGTNJWuWSURERDWoUiH4yy+/RHZ2NgDAxsYGX375ZYltp0+fDi8vLwCmm9gWLlxYmUuaOXjwoPQ8MDAQgwYNKrX9+++/Lz3Py8vDsWPHqlwD1V1ymQLdWz0qbf9xeRdEUcSHfbWQFyyn7OmNlXv+xt9x96xUJREREdWkSoXgLVu2SM9Hjhxp1gP8MJVKhfHjx0vbmzdvrswlzRTuBQ4MDCyz/cNtCh9PDZO/R1c46FwAALFJkfg74QpaOskxrqNpZTlRoUCShw++/J4LaBAREdVHFQ7BV65cQWRkpLT92GOPlXnM448/Lj2PjIzElStXKnpZM7a2ttLz3Nyyx23m5OSYbTs4OFTp+lT3yWRydG/14H35xxVTb/C03hrY3B9Vc8/NHTtPJOPkBd5MSUREVN9UOASfOXPGbLtHjx5lHtOpUyeoVCpp++zZsxW9rJmgoCDpeUREhHSDXEnCw8Ol50ql0ux4arhauXeAs94NAHA75Qau3z4PF1sZ/tn9/nLKggwpzVpgwdJzXECDiIionqlwCL506cHNQiqVShrvW5qH2xU+R2WMHTsWNjamu/nj4+Mxd+7cEtumpKRg2rRp0va4cePg5ORUpetT/SAIMvRo/WA8ecSVnTCKRrzeTYMmtqbBwVnOjXH0lojd4THWKpOIiIhqQIVDcHR0tPTc09MTgiCU6zhvb+9iz1EZbm5u+P7776XZIGbNmoVnn30Wv//+O9LT05Gfn4+YmBisWLECnTt3xsWLFwEAISEhWLBgQZWuTfVLc9dAuDZqCgBISI/HldgT0KkEzAx5sJxycnNf/GfZOWRll/6JAxEREdUdFQ7B6enp0nN7e/tyH1d48YzC56isUaNGYe/evfD39wcArF+/HsHBwbCzs4NSqYSXlxfGjx+P69evw8nJCe+++y5++eWXcq/2lpOTg7S0NLMH1T+CIKC33xBp+48ru5BvyMdzbVXo0OT+Ahq2elyTO2D5xr+sVSYRERFVswqH4Hv3HkwZpdFoyn2cVvugZ63wOaoiJCQEu3btwuDBg0tso1QqMWHCBEyZMsVsXHJZ5s+fD3t7e+lRnmEfVDd5ObdE08Z+AID0rCScu3EIMkHAx/0fvGdTfJpj6eZriL+Taa0yiYiIqBpVOAQXvglNoVCU+7jCbfPy8ip62SKysrLw2muvoWXLltixYwcA05zFQUFB6NevH9q2bQu5XI68vDx89tln8PX1xbx588p9/mnTpiE1NVV6FF6emeqfXoV6g49G7kVOXjZ6eivxlJ9pyI1RpcIdVy98vuyctUokIiKialThEFxwQxoAacGM8ijcVqfTVfSyZnJzczFo0CB88803yM/Ph729PZYtW4bk5GT8+eef+O2333D27Fncvn0bb7/9NgRBQF5eHt5//32zhTNKo1arYWdnZ/ag+svF3hOt3TsDALJyM3Dy+n4AwIf9tFCbRkUg3cML2/5MxInznDKNiIiorqtwCC48R29WVla5j8vMfPAxcuFzVMacOXMQFhYGwDTMIiwsDBMmTCgy3MHJyQmffvopvvrqK2nf/PnzcfTo0Spdn+qnnq2fgEwwJd4T18OQkZ0Gn0ZyvNbt/rAfmWnKtI//ewYGA6dMIyIiqssqHIKdnZ2l5/Hx8eU+7tatW9LzqkxR9vDSy5MmTULHjh1LPea1115D+/btAQCiKGLRokWVvj7VX/Y6Z7Rr2gsAkG/IxZ9XfwEA/KuHBi66+1OmNXbBqQRg66/R1iqTiIiIqkGFQ3Dr1q2l54mJiWY9vKUpPKbWz8+vopeVHD161GymhqFDh5bruCFDHoz5/P333yt9farfgloOhFJuWjr5/N9/ICXjLvTqh6dMa4kvV1xAekbVx7YTERGRdVQ4BBdMSVbg9OnTZR4TGxuLu3fvlniOioiNjTXbLu+sDYXbFe6VJirMRq1H5xZ9AQBG0Yg/ruwEADzfVoV2rvenTNPrcVPtiCVrqrboCxEREVlPhUNwUFAQ1Gq1tH3o0KEyjzl48KD0XKPRVGnZ4sLXBso/Lrlwj3Xh6dqIHtapeV9oVaZx63/FncLtlJuQywTMLzxlWrPmWLk9CtExVZ/zmoiIiCyvUjfGhYaGSturV68u85jCbUJDQ6s0O4Sbm5vZ9okTJ8p1XOF2Hh4elb4+1X8qhQbdWj4qbR++vA0A8EhTJYa2LpgyTY0kd298tvSsVWokIiKiqqlwCAaAcePGSc/Pnj2L7du3l9j25MmT2L17d7HHVkbnzp3NQvTixYthNBpLPebmzZvYtGmTtB0cHFylGqj+a9u0J+xtTDdw/p3wF27cvQwAmN1PC9X9KdPSPL3w25kUHDrO4TVERER1TaVC8IgRI6TZFgBg8uTJuHz5cpF28fHxGD16NAwGAwCgQ4cOePrpp4s9Z3R0NARBkB6zZs0qtp1KpcILL7wgbR8/fhz/93//V+ICHHFxcRg2bJjZsIkJEyaU+TVSwyaXKdCj9SBp++DFn2EUjWjuIMcrXe8PyZHJkdy8JT5ZchZ5eaX/IUZERES1S6VCsCAIWLp0qTS2Nj4+Ht26dcO7776LXbt2Ye/evZgzZw46duyIS5dMNw9ptVp89913EAShykXPmjULrq6u0vbSpUvRpk0bzJs3D3v27MGhQ4ewZcsWvPnmmwgICMCpU6ekti+//DK6dOlS5Rqo/mvt3hEu9qYbKhPS43Ap5hgA4K1eWjSxvT9lmnNjXLynxKqtV61WJxEREVWcIIpipWf937x5M0aPHl3mzWlarRY//vgjhg8fXmKb6OhoNGvWTNqeOXNmib3BgGlWiiFDhiAmJqbc9Y4cORI//vgjlEpluY8pkJaWBnt7e6SmpnL1uAbkZsJVbDryNQBAp7bHuL7vQ6lQY/35HEzcZrrZUpGZiWYXjmPndwPQpLFNaacjIiKiGlSRvFapnuACw4cPx4kTJ9C/f/9ie3gFQUBoaCiOHz9eagCujA4dOuDcuXN466234OjoWGrbzp07Y926dVi/fn2lAjA1XF7OLdHcNRAAkJGTipPXDwAARrZRoYenAgCQb2ODu07uWPC/c9Yqk4iIiCqoSj3Bhd28eROHDx+W5vH18PBAr169yj2Pb1Xk5+fjzJkzOHv2LBITE5GTkwM7Ozt4eHiga9eu1VIDe4IbrqR7t7Eq/GOIohFKuQrj+k6HTmOHs7fz0ef7dBhFQDDkw+3YEaz4sBu6d3SxdslEREQNUkXyWrWF4PqOIbhh239uA87eMM2JHejdE/3bjQIA/GtPJv53MgcAYHPnFrpm3MTmxf2hVFbpQxYiIiKqBIsNhyBqKLq3ehwqhWlWiAt/RyAhLQ4A8EGwBo5a01CgTJcmuJQm501yREREdQBDMFE52Kht0dV3AABAhIhDl0wLaDhqZZgZ8mAlueQWrbB49SXcuptZ7HmIiIiodmAIJiqnjs2Codc0AgBE370kLaDxYnsVOjYxraCRZ2uLu45u+Gwpb5IjIiKqzRiCicpJIVehp99gabtgAQ25TMBnjz6YGi2laTPsiriNiJO3rVEmERERlQNDMFEF+Hl0fmgBjaMAgCAPBV5opwIAiEolUnxaYN5/z3AlOSIiolqKIZioAgRBhkf8h0nbf1zehdx80+wQH4ZoYXd/ReWMJm64nALeJEdERFRLMQQTVdDDC2gcv7YPAOBiK8O0R+7fJCcISPZtzZvkiIiIaimGYKJKeMR/GGSC6Wa4E9fCkJqZCACY1FmNgMamf1a5ejvcdWiCj789Y7U6iYiIqHgMwUSV4GDrgg7N+gAADMY8aco0pVzAF4/ppHYpzVrgl6MJOPBnvFXqJCIiouIxBBNVUreWj0KrsgUAXI0/jZjESABADy8FRhfcJKdQILmFL+Z+cxqZ2flWq5WIiIjMMQQTVZJaqUXP1oOk7fALm2EUTbNBfNRPCwfNg5XkonLVWLzqolXqJCIioqIYgomqoI13dzjbeQAA7qbF4uLNPwEATjYyfNTvwUpySb6tsHLrNVy+nmKNMomIiOghDMFEVSATZAhpM1za/uPyDuTkZQEARrdXoZun6ea5fBsdkj28MfurUzAYRKvUSkRERA8wBBNVkaeTL1q6dQAAZObew9GrewEAMkHAF4/ZQG4aFYFU76Y4EZ2FDbuvW6lSIiIiKsAQTFQNevsPhVymAACcigpH8r07AIBAFwVeCbq/goZMjmTfVvji+/O4m5hlrVKJiIgIDMFE1cLexgmdm/cDABhFAw5e+ll6bdojWnjoTd3B2Y5OuGvjgI+XnLVKnURERGTCEExUTbr49odObQ8AuH77PG7cvQwAsFUJ+HSgjdQuuUUr7D4cj4PHblmlTiIiImIIJqo2KoUavf2HSNsHLmyGwWiaG3hwKyUe9VUCAAxqNVKbNsOcr08hi3MHExERWQVDMFE18vPojCaNfAAAyfdu43TU7wAAQRDw2UAttKZhw0j38EJUhgzfrrlspUqJiIgaNoZgomokCDL0DRwBwDQG+Mhfe3AvOxUA4NNIjqm9NQUNkdjSHys2XeXcwURERFbAEExUzVwbeaFt054AgDxDDg5e3Cq99no3Ddo0Ns0dnKfXI9nNEzO+OIF8g9EapRIRETVYDMFENaBX60HQKHUAgCtxJxGTGAkAUMkFfPWEzf1+YiC1aTOcvZmNlZuvWqlSIiKihokhmKgGaFQ69PIbLG2Hnd8Ig9EAAOjqocDkLqa5g0W5HEkt/fD1qov4O+6eVWolIiJqiBiCiWpIG+/ucLX3AgAkpsfjTPRB6bXpwVp42Zn++WU7OCLJwQUzvzwJUeSSykRERJbAEExUQ2SCDH0Dn5G2j/y1GxnZaQAAvVrA5489mDs4pXlLHLmUgk17oi1dJhERUYPEEExUg5o4NEUbr+4AgNz8bBy6vE167VFfJZ4OMM0dbFQqkdyiJRYsPYvbCVxSmYiIqKYxBBPVsF5+Q6BWagEAl2KOIS7puvTapwNs4KA13SaX6dIEd9V2mPP1KQ6LICIiqmEMwUQ1zEZti56tB0nb+89vhPH+TXKNdTLMC9VKryW19MNvR+/gl4OxFq+TiIioIWEIJrKAtk17obGdBwAgIS0WZ24ckl57vq0KIT6mpeQMGg1SfZpj3jenkZKWY5VaiYiIGgKGYCILkAky9G374Ca5iCs7cS8rBYBpSeUvH7eBRlpS2RPxRhU+/e6sFSolIiJqGBiCiSzE3aEZAr17AABy83MQfnGL9FpzBznee+T+sAhBQFIrf/z8200cOn7LGqUSERHVewzBRBbU228ItCpbAMDV+NOIunNReu21bmq0d72/pLLOFqnePpj55UmkZ+RZpVYiIqL6jCGYyII0Kh36BDwpbYed34g8Qy4AQCETsHiwDRT3/1WmeTfFzWw5Pl3CYRFERETVjSGYyML8PLrA08kXAJCWmYijV/dKr7V1VeDtXhrThiBDYit/bP71Bn4/Gm+NUomIiOothmAiCxMEAf3ajoRMMA19OHFtPxLTH4z9/XdPDQJd7g+LsNUj1dsHsxaeRGp6rlXqJSIiqo8YgomswNHWFV18QwEARtGA/ed+khbIUMkF/HewDeSmNTSQ5tUUMdkKfLLkjLXKJSIiqncYgomsJMh3AOxtnAEAsUnXcCnmmPRa+yYK/Lvn/WERMhkSW5tmiwiLiLNGqURERPUOQzCRlSjkKvQNHCFtH7y0FVm5GdL21N4atGn8YFhEmldTfPjVKS6iQUREVA0YgomsyMfFH63cOwIAsnIzcOjSz9JrKrlptoiCYRGp3j6Iy5Fj/n85LIKIiKiqGIKJrCw44CmoFKahDxdu/ombCVel1zq6KfBmD/NhETsOxODXQ7HWKJWIiKjeYAgmsjKdxh69/IZI27+dXYd8w4OZIN7prYGfs+mfap7eDmme3vho0SkkpXBYBBERUWUxBBPVAu2a9oS7Y3MAQEpmAo78tUd6Ta0Q8N/BOsgKhkU0bYZbeQrM/eaUNUolIiKqFxiCiWoBQZChf7tnIZfdnzv4ehjupMZIr3d2V+CN7mrThkyGRL8A7DkUh11hN61RLhERUZ3HEExUSzjauiKo5aMAAFE0Yt/ZtTAaDdLr0x7RIqDx/WERtnqkNm2GOd+cQvzdTKvUS0REVJcxBBPVIl1ahMJJ7wYAuJMag5NRB6TXNAoB3w3VQXn/X22aV1PcFbT44D/HYTSKVqiWiIio7mIIJqpF5DIF+rd7FoBpAHDEld1Iybgrvd7OVYFpj9yfLUIQkOgXgIiziVj9c6QVqiUiIqq7GIKJahk3Bx90aNYHAGAw5uG3s+ulJZUB4J89NOjqYRo7nK+1QUpzX3zx/XlERqdZpV4iIqK6iCGYqBbq2XoQ9FpHAMDNxKu4GPOn9JpCJmDJEB1slKbte+6eSLVthHc/O4q8PKM1yiUiIqpzGIKJaiGVQo3QtiOl7d8vbkVG9oOeXl9HOT7qp5W2k1r54+KNDHy96qJF6yQiIqqrGIKJaikfF3/4eXQBAOTkZeHAhU1mr7/cSY2+zRQAAINajSTfVvh+wxWcOJ9g8VqJiIjqGoZgolosuM1T0Kp0AICr8acReeus9JogCFg8SIdGGtNNdJkuTXDP2QXvfXYM9zLyrFIvERFRXcEQTFSLaVW2CA4YLm3vP/cTsnIzpG0POxkWPGojbSf7tsaN5Hx8/O0Zi9ZJRERU1zAEE9VyrT06o7lrIAAgMycdB85vNHv9mQAlnvIz3SVnVCqR1MofW369gV8PxVq8ViIiorqCIZiolhMEAaFtR0KtNPX4Xok7icj4M2avf/6YDVx1pmER2Y5OSHf3xKyFJ3GLq8kREREViyGYqA7QaewR0qbQsIjzG8yGRTjZyPDfITppO6W5L+4alXj302MwGLiaHBER0cMYgonqCD+PLqUOi+jfXIlXuqpNGzIZEv3a4OiFJPxv/WVLl0pERFTrMQQT1RFlDYsAgFl9tQh0Ma0ml6ezRUozXyz+8RJOX0y0eL1ERES1GUMwUR2i09ijb5unpW3TbBH3pG2NQsD3w3TQmKYPxj0PT9xr5Ih3PjmKdE6bRkREJGEIJqpjTLNFtAUAZObeQ9h580U0/BrLMTf0wWpyia388XdSPj786iREkeODiYiIAIZgojqnYFiE5v6wiL+KGRbxcic1Hm95f9o0lQqJrf2xOzwGW3+9YfF6iYiIaiOGYKI6SKexQ0hgycMiBEHAN08UnTZt3uLTiI5Jt3i9REREtQ1DMFEd1dq9M1oUGhax/5z5bBHOOhm+fWjatFS5BlM/PorcXINFayUiIqptGIKJ6ihBENCv0LCIq/GncDn2hFmb0OZKvBpkPm3a+etpWPjDBUuXS0REVKswBBPVYTqNHfq1HSlth53fgPSsZLM2s0K0aFt42rQWLfHDpqv4/Wi8RWslIiKqTRiCieq4Vu4d4efRGQCQk5eFvWfWQBSN0utqhYDvn9RBWzBtmpsHMhq7YNpnxxHPZZWJiKiBYggmqgdCAkfAVtMIAHAz4S+cjvrd7PXWznL851EbaTuppR8S8uR4e/5R5OUbQURE1NAwBBPVAxqlDR7t8IK0fejydiSmmw93eKGdCqMCVQAAUaFAgn8bnLqUhEUcH0xERA0QQzBRPeHl3Aodm4UAAAzGfOw5tQoGY770uiAI+PxRG/g6mv7Z5+ntkNzcF99v+AsH/uT4YCIialgYgonqkV5+g+Fo2wQAcDctFkf+2m32ul4tYMVTOqhN98nhnocXMp2c8f6C44i/w/HBRETUcDAEE9UjCrkSj3UcA5lgSrnHI39DbNI1szbtXBWY3//B+ODEVv5IzJXhrfl/cnwwERE1GAzBRPWMi70nerR+HAAgQsQvp1cjNz/brM1LnVR40s+0rLKoVCLBrw1OX07GwuXnLV4vERGRNTAEE9VDnVuEwt2hGQAgLTMR4Re2mL0uCAK+esIGPo1MvwJy7e2R2rQ5Vmy6igNH4ixeLxERkaUxBBPVQzJBhkc7jIZSblot7sLNI4iMP2PWppFGhuVP6qC8/1sgzbspshwc8R7HBxMRUQPAEExUT9nrnBHSZri0ve/sOqRnpZi16eyuwIf9tNJ2ol8AkvJk+NfcI8jNNViqVCIiIotjCCaqxwK8usG3SXsAQHZeJvacWgWjaH7z26td1Xi8pWl8sFGpQoJ/IM7+lYJPvjtr8XqJiIgshSGYqB4TBAH92z0L/f3V5GKTInE8cl+RNt8OLjQ+2M4eyc19sX7Hdfz86w1Ll0xERGQRDMFE9ZxGZYPHOr4IAQIAIOKv3YhLjjJr46CVYeVw8/mDMxq7Yvaik7h8LcXCFRMREdU8hmCiBsDDqQWCWg4EAIiiEXtOrkROXpZZmw5NFFjw6IP5g5Na+eGeQoN/zjmC1PRci9ZLRERU0xiCiRqIbi0ffTBtWlYSfjv3E0RRNGvzYnsVXminAgCIcjnuBrTF33eyMe2zYzAaxSLnJCIiqqsYgokaCJlMjsc6vgiVwjQbxF9xJ3Ex5qhZG0EQ8J9HbRDoYhoXkW+jQ1JLP4QfvYXv1l62eM1EREQ1hSGYqAGxs3FE/3ajpO0D5zci+d4dszY2SgGrhutgZ5piGJkurrjn7olvfryIw8dvWbJcIiKiGsMQTNTAtHLviDZe3QEAeYZc7D71A/IN+WZtWjjK8e1gnbSd3NwX2bZ2mPrJMcTeyrBovURERDWBIZioAQppMxwOOhcAwJ3UGPxxZXuRNoNbq/BG9/vdwTIZEgICkZQt4s25R5Cdw4U0iIiobmMIJmqAlAo1Hu80FnKZaezvyesHcP32+SLtZoZo0ctLAQAwqDVI8A/EhchUfPjVySI31REREdUlDMFEDZSLvSd6+w+Ttn85vRppmUlmbRQyASue0sHN1jTHcE4jByQ398X23/7Gqi2RFq2XiIioOjEEEzVgHXz6oEWTdgCAnLxM7Dq5Agaj+fhgV1sZVj1tC1WhhTTuuTTBgv+dRcTJ25YumYiIqFowBBM1YIIgYED752Bn4wQAuJVyA4cuFR0fHOShwH/MFtJojSwbPd6afxQ34+9ZrF4iIqLqwhBM1MBplDYY1GmcND74VNQBRN46W6Td2A5qTOhoWkgDMjkS2rRFUraIf3wYgcys/CLtiYiIajOGYCKCayNvPOL/pLT965k1SM1MLNLu04E26OZpCssFN8r9dSMd7//nOFeUIyKiOoUhmIgAAO19HkFLtw4AgJy8LOw6UXR8sEouYNVw2yI3yv16KJYryhERUZ1SLSH4jz/+wOTJkxEQEAB7e3vY2dkhICAAkyZNwuHDh6vjEmZ8fHwgCEKlHytWrKj2mojqOkEQ0L/ds7C3cQYA3E79Gwcv/lykXZMSbpT7etVFhEXEWbJkIiKiSqtSCM7IyMBLL72EXr164bvvvsOlS5eQlpaG9PR0XLp0CUuXLkXv3r0xYcIEZGTUnlWmnJycrF0CUa2kVmoxqPN4yGWmuYFPR/+Oq/Gni7Qr7ka5HFs93v3sGK7dSLNUuURERJUmiJWc8d5gMOCJJ57A3r17pX1arRZt2rSBQqHAxYsXkZb24D/DgQMHYteuXZDL5VUueuzYsbh9u/xTMx05cgSpqakAAEdHR8THx0OlUlXommlpabC3t0dqairs7OwqdCxRXXP2xiHsP7cBAKBSaPD8I2+jkc65SLs3d2dg2alcAIA8JxtNTh2Hj5MSaxf2RSM7tUVrJiIiqkheq3QIfu+99zB//nxpe+LEifj444/h6OgIwNRL/Mknn+Cjjz4yO2bu3LmVuVylpaamokmTJsjOzgYAvPLKK/jmm28qfB6GYGpIRFHE7lMr8VfcSQCAs50HRvX6J5Ry8z8ecw0iBq9Ox5EY0zLKqrRUuJw9haA2jlg67xEolbztgIiILKciea1S/0PFxcXhiy++kLbHjBmD7777TgrAAKDT6TB79mx88MEH0r7PP/8ccXGWHTP4008/SQEYAMaNG2fR6xPVRabxwaPgoHMBACSkxeK3s+uLLJWskgv4cbgtvOxMv0py7eyR1MoPx84lYPaiU1xamYiIaq1KheAvv/xSCpY2Njb48ssvS2w7ffp0eHl5AQCys7OxcOHCylyy0n744Qfpub+/P7p27WrR6xPVVSqFBoO7vCT1/l6OPY4z0QeLtHOxlWHdMzrolKbtTJcmSPNqii17o7Fi01VLlkxERFRulQrBW7ZskZ6PHDnSrAf4YSqVCuPHj5e2N2/eXJlLVkpkZKTZ7BRjx4612LWJ6gMnfRMMaP+8tP37xS2IS7pepF1bVwWWDtVJ26nNWiDTyRmfLzvHGSOIiKhWqnAIvnLlCiIjI6Xtxx57rMxjHn/8cel5ZGQkrly5UtHLVsrKlSul5zKZDGPGjLHIdYnqk1buHdGpeV8AgFE0YueJ5cjILjoDxODWKswM0UjbiX5tkGNji6mfHMXl6ymWKpeIiKhcKhyCz5w5Y7bdo0ePMo/p1KmT2WwMZ88WXZK1uomiiFWrVknbAwYMgLu7e41fl6g+6u03BJ5OvgCAjJw07Dq5AgajoUi7f/XQYGQb0791US7H3TbtcM8gx2sz/0BCUnaR9kRERNZS4RB86dIl6blKpZLG+5bm4XaFz1FTwsPDER0dLW3zhjiiypPJ5Hii0zjYauwBALFJ13DoUtGFNARBwNeDbNDF/f7SyhoN7rZpi/iEHPxjdgSyc4oGZyIiImuocAguHCw9PT0hCEK5jvP29i72HDWl8A1x9vb2ePLJJ2v8mkT1mY1aj0GdJ0AmmALuqahwXI49UaSdRiFg7QhbeOhNvxty7eyR2Ko1zlxOwowvTnDGCCIiqhUqHILT09Ol5/b29uU+rvBcbYXPURMyMjKwceNGaXvkyJHQaDSlHFFUTk4O0tLSzB5EDZ2bgw+C2wyXtvedXYeEtKI3vrnayrDuGVvYFMwY4eqGNK+m2HXgJr5eedFS5RIREZWowiH43r170vOKBEutVlvsOWrC5s2bza5RmaEQ8+fPh729vfQoz7APooagXdNe8PcMAgDkG3Kx4/gyZOdmFmnXvokC3w0xnzEio7Erlqy9jM2/RFuqXCIiomJVOATn5+dLzxUKRbmPK9w2Ly+vopetkMJDIXx9fdGzZ88Kn2PatGlITU2VHjdv3qzOEonqLEEQENr2GTS28wAApGQmYNepH2As5ka5oX4qzAp58AdwYmt/ZNvZ48OFJ3H4RPmXPiciIqpuFQ7BNjY20vPCK7GVpXBbnU5XSsuquXnzJsLCwqTtys4NrFarYWdnZ/YgIhOFXIXBXV6CVmX6t/z33cs4dHlbsW3f7KHG2A73Z4eRyZDQph2yVRr8a+4RTp1GRERWU+EQbGtrKz3Pysoq93GZmQ8+Li18juq2atUqGI1GAKYeqxdffLHGrkXUkNnbON2/Uc70a+Tk9QO4GHO0SDtBEPD5ozbo18z0aZBRqcTdtu2Rlivg1Rl/4NbdokMpiIiIalqFQ7Czs7P0PD4+vtzH3bp1S3ru5ORU0cuWW+EFMvr27Ws2KwURVS9PJ1+EBD4tbf92dj3ik6OLtFPKBfzwlC0CGpt+5eRrbZDQpi1uJebglRl/4F5GzQ6RIiIieliFQ3Dr1q2l54mJiWY9vKUpPKbWz8+vopctlyNHjpitRsdlkolqXrumvdG2aS8AgMGYjx3Hl+FedmqRdvYaARtG6uGqM02dlmPfCImt/XElKhVvzj2CvHyjResmIqKGrcIh2N/f32z79OnTZR4TGxuLu3fvlniO6lL4hjhbW1s8/fTTpbQmouoS0mY4PBxbADCtKLf9+DLkG4r27nrZy/DTyEJTp7m4ItWnOSJO3sHsr05xDmEiIrKYCofgoKAgqNVqafvQoUNlHnPw4EHpuUajQVBQUEUvW6acnBysX79e2h4xYkSN3oBHRA/IZQoM6jwBeq0jAOB2yg38dnZ9saG2o5sCy5/UQXZ/nZ00bx/ca+KGLXuj8e2ay5Ysm4iIGrBK3RgXGhoqba9evbrMYwq3CQ0NrZFwum3bNiQnJ0vbHApBZFk2alsM7foyFHLTTBCXYo/h5PWwYts+3lKFj/s/mDotqWVrZDk44ZtVF7Fxd5RF6iUiooatwiEYMF984uzZs9i+fXuJbU+ePIndu3cXe2x1KjwUwsfHB8HBwTVyHSIqWWM7Dzza/gVp+9ClbYi6U/wKcf/XVYMpXe9/qiTIkBAQiBy9HrMXncRvf8RaolwiImrAKhWCR4wYgfbt20vbkydPxuXLRT/GjI+Px+jRo2EwmCbR79ChQ4njdKOjoyEIgvSYNWtWueu5ffs2fvnlF2n7xRdfhCAI5T6eiKpPS/cO6NbyUQCACBG7T64odmllAJjfX4un/EwDhEW5HHfbtEeOWou35x/FifMJFquZiIgankqFYEEQsHTpUmkp5Pj4eHTr1g3vvvsudu3ahb1792LOnDno2LEjLl26BMC0bPJ3331XI+F09erVZivZcW5gIuvq3uox+DYx/aGcm5+Dn499h4zstCLtZIKAJUN16O19fw5hlQp3AjsgCwq8PusP/BVVdJYJIiKi6lCpEAwAXbt2xY8//igF4bS0NHzyyScYNGgQHn30UUyfPh23b5uWRdVqtfjxxx/RtWvX6qn6IYWHQjzyyCNo0aJFjVyHiMpHEGR4tONouNp7AQDSs5Kx/fj/kG/ILdJWoxCwZoQObRrLAQAGrRZ3AtsjJcuI//vgEOJuZ1i0diIiahgqHYIBYPjw4Thx4gT69+9fbA+vIAgIDQ3F8ePHMXz48KpcqkSnT5/G2bNnpW3eEEdUOyjlKgztOhG2mkYAgFspN/DL6dUQxaLzATfSyLDpWVt42Zl+JeXp9UgIaIvbSTmY/P4hJKfmWLJ0IiJqAASxmibmvHnzJg4fPozYWNMNLR4eHujVqxe8vLyq4/RWl5aWBnt7e6SmpsLOzs7a5RDVGXfTYvHT4YXIM5iCbJDvQPT0G1Rs2ysJBgxYmY6UbNOvJZs7t+B0+SLatXbAsk/6wEajsFjdRERU91Qkr1VbCK7vGIKJKi/q9gVsO7YUIky/bga2fwEBXsXPF37kZj6Grk1H9v1h/vqbN+AQdQ2PdHXFVzN7Qqmo0gdYRERUj1Ukr/F/EyKqcc1c26BPm6ek7X1n1yEmMbLYtt29zBfTSPdqijRPbxw8dhvvfXYMBgP/bicioqpjCCYii+jg0wftmvYGABhFA3YcX4bke3eKbTuolQqfP2ojbac098W9Jm7YHR6DOV9zeWUiIqo6hmAisghBEBDSZjiaNvYHAGTnZeLnY98hK/dese0ndFLj/T4aaTuppR8ynRtjw+4ofL7sPIMwERFVCUMwEVmMTCbHE53GwUnvBgBIybiLbceWIq+YqdMAYGovDV4NKlhVTkCCXxtkOThi+ca/sHT9FUuVTURE9RBDMBFZlFqpwbCuk6BTm25YiE+Oxp6TK2EsZuo0QRAwN1SLF9qpTDtkMiQEtEWO3g5frbiANduKH1dMRERUFoZgIrI4OxtHDAuaDKXc1Mt77fY5hJ/fXOwQB5kgYNETNhjS6sHyyncC2yNXp8O8xWfw874bFq2diIjqB4ZgIrIKF3tPDO4yATLB9GvozI2DOHFtf7FtFTIBy57UIcTHNE+wqFTiTmAH5Gm0mPH5Cew7HGuxuomIqH5gCCYiq2na2A/92z8nbR+6vA2XY48X21ajELD6aVt0djctr2xUq3GnbQfkKJR4++Oj+OPkbYvUTERE9QNDMBFZVYBnEHq2frCC3N7Ta3Az4a9i2+rVAjaOtIW/s+lXl0Grxd22HZANOf7xYQSOnb1rkZqJiKjuYwgmIqvr6jsAbb17AjDNIbz9+DIkpMUV29bJRoatz+nR1N706ytPZ4s7bTsg0yDglRmHcfJCgsXqJiKiuoshmIisThAE9A0cgeaugQCA3PxsbD36LdKzkott76aXYdvztnCzNS0rl2erx53ADsjIA6ZMP4xzV5IsVjsREdVNDMFEVCvIZHI83vFFuDZqCgC4l52KLX/+F1m5GcW2b+Ygx44X9HDRmYJwrp0d7ga2R3q2iEnvHcLFq8UHaCIiIoAhmIhqEaVCjWFdJ6KRjTMAIOnebWw9ugS5+TnFtm/pJMe25/Rw1JqCcI59I9wNbIfULAMmvncIV66nWqx2IiKqWxiCiahWsVHr8VT3V6TFNG6n3MCO48uQb8gvtn2Aixxbn7NFI839INzIAQkBbZFyLw8vT/sdkdFpFqudiIjqDoZgIqp17G2c8FS3KVArtQCAvxOu4JfTq4pdVQ4AOjRRYNMoW+jvLyyX7eiEBP9AJKXl4aVpvyPqZrqlSiciojqCIZiIaiVnO3cM6zoZCplppbir8acRdm5DsavKAUBXDwU2jLSFjak5spwbI9EvAAnJuXjp3d9xI5ZBmIiIHmAIJqJay92xGQYVWlXu3N9/IOKvXSW27+mtxPpnbKExLSyHzMauSPTzx+3EHIyfyh5hIiJ6gCGYiGq1Zi4BGNjhBWn76NW9OHn9QIntg32UWP20LZT3f7tlujRBol+AFISv/c0xwkRExBBMRHWAn0cXhLR5Wtr+/eIWXIo5VmL7AS2U+PFpHVSmFZaR6eKKRL8A3E02BWHeLEdERAzBRFQndGjWB91aPiZt7z2zBpHxZ0ps/3hLFX582rZIEE5MycX4d8I5fRoRUQPHEExEdUb3Vo+hfdNHAACiaMSukz8g6s7FEts/5msaGmEWhP0DTLNGvPs7Ll9LsUDVRERUGzEEE1GdIQgCQgKHw9+zKwDAKBqw4/j3uJlwtcRjHn04CDd2RYJfGySn52HCO79zZTkiogaKIZiI6hRBkGFAu+fQ0q0DAMBgzMO2Y98hLimqxGMe9VVizQhbqO8H4azGLkjwa4PUjHy8PO0gzv/FIExE1NAwBBNRnSOTyfFYxzFo5tIGAJBnyMXWo9/idsrNEo8Z2KLkIDxx2kGcvZxkidKJiKiWYAgmojpJLlNgUOfx8HZuBQDIzc/Glj8XIyEtrsRjBrRQYu0z5kH4bkBbpGYZ8PK0gzh65q4lSiciolqAIZiI6iyFXIkhXV6Gu2NzAEB2XiY2/7kYyffulHhM/+ZKrCu0oEa2kzPutmmHezkipkw/hN+PxluidCIisjKGYCKq05QKNYZ1nQxXe28AQGZOOjYd+QapmYklHhPaXInNo2xhqzJt5zg44k7b9sgyCPjHhxHY83uMJUonIiIrYggmojpPrdTgqW7/B2e9OwDgXnYKNkZ8XWoQ7t1Uia3P6dFIIwAAcu0b4U67jsgR5Jj68Z/Y/Eu0JUonIiIrYQgmonpBo9JhePdX4GDrCgBIz0oqMwgHeSiw/XlbOGnvB2G9He6074Q8uQozvjiBVVtKnnqNiIjqNoZgIqo3bNR6jOj+qlkQ3lRGEG7fRIHdo/VoYmsKwnk6W9xu3xH5ajU+WXIW366+BFEULVI/ERFZDkMwEdUrOo29KQjrXAAAafeDcFpmyVOg+TWWY/doPbzsTL8S8210uN2+E/I1Gny96iL+879zDMJERPUMQzAR1Ts6jT1G9HjNLAhvjFhUahBu4SjHnjF6NHcw/Vo0aLS43b4z8rQ2WLHpKmYvOgWDgUGYiKi+YAgmonqpMkHYy16GPWP08HO+H4TVatzu0Bk5ejts2BWFt+f/idxcg0XqJyKimsUQTET1VrFB+EjpQyOa2Mqwe7QeHZqYVtQwKpW4064DshwcsfdQLP5v+mGkZ+RZpH4iIqo5DMFEVK8VCcKZidgYsajUm+WcbGTY8YIefZqaVtQQ5QrcbdMOGY1dcPTMXYyfGo6EpGyL1E9ERDWDIZiI6j2dxh5PP9QjvOGPr0pdWc5OLWDjKFsMba007ZDJkOjXBuluHrh8LRWj/xWGG7HpliifiIhqAEMwETUItgVB+P70afeyU7Ah4iskpMWVeIxGIeCHp3QY1+H+0nKCgOSWrZHStBlu3srEmH+F4/xfyZYon4iIqhlDMBE1GLYaezzT43VpZbnMnHRsjPgad1JLXiZZLhOw8HEbvNVTI+1La9oMyb6tkJiagwnv/I4/Tt6u8dqJiKh6MQQTUYNio9ZjRI/X4GrvBQDIzsvAxoivEZ8cXeIxgiBgRogW8/trpX333D2R6NcGGdkGvDLjMHYduFnTpRMRUTViCCaiBse0xPKrcHdoBgDIzc/C5iOLEZMYWepxrwZpsGSIDeSmxeWQ6eKKu4HtkSvKMPXjo1i5mcssExHVFQzBRNQgqZVaPNltCrycWgIA8gw52Prnt7hx93Kpxz3XVo21z9hCa5o4AtkOjqbV5VQqfPrdWcz/72kuqkFEVAcwBBNRg6VSqDEsaBJ8GvsDAPKNedh27Dv8f3t3Hh5ldegP/Dv7kslM9n1fCAl7IGGTRUFAVLRIcUWl7rXXttprS28t9v7sora2VWtrr1pti7VqFXdElB1kCUJYQiCQkH2yTDLJzGT29/fHhEkmZJkJDCHM9/M88zjvO+ecOePjO/P15LznnGo8PGi9JTkyfHhbOCJVniFhhyYc+inTYFeHYd0Hp/CD/7cbFqsz6P0nIqLhYwgmopAmlchx3bR7kZ0wEQDgcrvwcclrOFa7d9B601Ok2HRnODIizu4up0TT5EJYdRHY/HUDvvP4NrS0cS1hIqJLFUMwEYU8qUSKpYV3Iy9pKgBAENzYeHAdDpzeMmi93GgJNt0ZjimJ3bvLSWVomjAZ5rh4HDnRhtt/sBmnqjuC3X0iIhoGhmAiIgASsQRLptyBSelzvOe2HXsfu8o/gSAMPMc3TiPGp7eHY0mO76YaxtR01OotWPXoFuw91Bzs7hMRUYAYgomIuolEYswffxOm5y7xntt7ciM2H3kXguAesF6YXIQ3V4Thnily7zljZjbacsbAaHLi/v/Zjo++rA5q34mIKDAMwUREvYhEIszMuwbzxi33nis9swOfffMPuNwD3+wmFYvw3BI1npzvu5Zw87gJsLvFWPPsPvxlXdmgo8pERHTxMAQTEfVjSuY8LJm8CiKR52vyRP0BfLjvFTictgHriEQiPDpLif9bpoas+9vVGh0D/eRCOOUKvPiPY/jJM/tgs7suxkcgIqJBMAQTEQ1gbMo0XD/tHkjEnvm+Z5rL8N6el9BlNw9a7+bxCrx3iwY6he8SajZNOD7ZXONZOcLAlSOIiEYSQzAR0SCy4sfjW9MfhFyqBAA0tFXh7Z1/gNHSOmi9eRkyfOGzhJoCTZMLYYmJxaHjBtzy/a9QVtEe7O4TEdEAGIKJiIaQEp2DFTP/C2qFFgDQZm7Cv3f+AU3G2kHrjY2VYPPd4ZiV6tleThBL0FIwAca0DDQ0d+HOx7bgix11we4+ERH1gyGYiMgPcboU3Dz7B4gMiwMAWGwdeHf380NusxytFuODWzW4bUKvlSMystCaVwCLXcAPn/oaL//rOG+YIyK6yBiCiYj8pFNHY+XsHyAxMgMAYHfa8MHel1FWu2/QegqpCH++To1fXNmzcoQlPgH6iZPhksnwwhtH8eOn98Fq4w1zREQXC0MwEVEAVPIw3DTjYWTHTwAAuAU3Pj/4T+yr+GLQ0VyRSIQfzlRi3U1hUHfvq2HXRaBxyjTY1WH4dEsNVj++Fc2tXRfjYxARhTyGYCKiAEklclw77TuYmH6F99zO4x9j85F34R5kUw0AuD5Pjg2rwpGo8awc4VKqoJ8yFZboGBwub8PNj3yFQ2WD33RHRETnjyGYiGgYxCIxrhy/ArPGXuc9V3pmBz7e/yrsg6wlDACTE6TYvFqLKQkSAIAgkaJl3ES0p2dC32rFXf+9Fe9+VhnU/hMRhTqGYCKiYRKJRCjOuRqLJt8OcfemGqf1R/DOrj+is6t90LpJ4WJ8tiocKwpk3nMd6ZloKZgAuyDGk388gF88fwB2bqxBRBQUDMFEROepIKUYNxY/CLnUc+Nbc0cd3trxHJqMNYPWU8tEePWGMPy/q1QQe2ZHoCsmFo2Tp8GhVOGdTyux+sfbOE+YiCgIGIKJiC6AtNg83Dz7B9CqogAAZpsRb+96HqcaDw9aTyQS4fszlHj3Zg0ilJ4k7AwLg76wCF2RUThUZsDK//oK3xzjPGEioguJIZiI6AKJDk/ALVc86l1Czemy46P9r+LA6c1DrgO8MEuGzXeHY2yM52vZLZWiefwkdKSkoclgxerHt+KdT08H+yMQEYUMhmAiogtIrQjHTTO+hzFJhd1nBGw7th5fHX4bLvfg83uzoyT48i4trhvTPU9YJEJ7Vg5ax46D3S3CL57/Bk/+sQQ2zhMmIjpvDMFERBeYVCLDNVPuxPTcxd5zh6t34YO9L8PqsAxaN1whwj9vCsOaOUrvOUtcPPRTpsGhUuPdz6qw6tEtqG00B63/REShgCGYiCgIRCIRZuYtxeLJd0Ai9iyFVt1Sjrd3/gHt5uZB64pFIqyZo8K/VoQhvHu3ZUeYBvop02CJicWxinas/N6X2PJ1fbA/BhHRZYshmIgoiPJTirB8xsNQysIAAAaTHv/a8TucaT4+ZN1rx8ixZbXWZ55wS8EEtGXmwGh24ntP7sbvXzsMp2vwDTqIiOhcDMFEREGWHJWNW654FFGaeACAzdGF9Xv+4tcNc7nREnx1t9ZnPeHO1DQ0TZwCl1yOV98+gXt/sh0tBmtQPwMR0eWGIZiI6CKICIvBzbMfRVb8eACA0H3D3MZD6+B0OQatq5F71hP+7SIVZN3f2jZdBBoLi2DVRWD/4RasePhL7CsdfJoFERH1YAgmIrpIFDIlrp92D4pzF3nPldXuwzu7nodpiB3mRCIR7p+mxGerwpEc7llP2CVXoGniFHSkpKK5zYp7frINr75dDrd78NFlIiJiCCYiuqhEIjFm5V2LpYV3Qyrx3PWmN1bjXzt+h4a2qiHrFydLsf07WlyZIT3bINqzctFSMAFOsRS/f+0IHl67C4Z2WxA/BRHR6McQTEQ0AsYkTcHKWd9HuHeHuQ68u/t5HK35esi6MWFivHeLBj+a1bOMWldMLBoKi2AL12L7vkbc9N1N2HuoKWj9JyIa7UTCUHdlEACgo6MDOp0ORqMRWq12pLtDRJcJi82ET0r+hjpDhffcpIw5mFtwIyRi6ZD1Pztpx4MfW9DW1f1VLgiIqDyF8NpqiEXAA7eOxYO350Mq4ZgHEV3+AslrDMF+YggmomBxuV3YdvR9HDqz3XsuMTIDSwtXI1wVMWT92g43vrPehK9re3aSUxpaEV1+DBKHA4XjovH0T4qRGKsORveJiC4ZgeQ1Dg0QEY0wiViCKyeswMKJt3g31mhoq8Kb259FTcuJIeunaMX49I5w/GiWEqLuc9aoaDQWFsOqi8CBo6246aFN+Go3N9cgIjqLI8F+4kgwEV0M+vZqfFzyN3R2GQAAIogwa+x1mJa9ACKRaIjawFeVDtz3gRnNlp7pEbozldBWV0EE4LZl2Xjs3glQyCVB/BRERCOD0yGCgCGYiC6WLrsZG775u8+uctnxE7Bo8u1QyFRD1teb3Lj3AzO2nnF6zyna2xB9/CikdjvysnR4+vFi5GTwu4yILi8MwUHAEExEF5NbcGPPic+x5+TnADxf0xFhsbhu6ncQo00asr7LLeB3u6z41XYrzi4bLHY6EHXiONQtzVDIxXj0ngm4bVm2XyPMRESjAUNwEDAEE9FIqNQfxYaD/4TNYQEASMUyLJh4M/JTivyqv7PagXs+MKO+s+erPqyhHpGnTkLsdmH21Hg89ehUxEYPPcJMRHSpYwgOAoZgIhopRksrPil5DU3GWu+5cakzMH/8TZB1b7gxGEOXG9//1IIPynu2Z5Z2WRBddhQKUycitHI8+f1CLJydHJT+ExFdLAzBQcAQTEQjyelyYPORd30204gOT8DSwtWIDk8Ysr4gCFhXasd/b7TAfDYLC27oqiqhrTkDEYBvLcrAmocmQa0aen1iIqJLEUNwEDAEE9Gl4FjNXnx15B04XXYAgFQix5XjV6Agpdivub2nDC7c+6EZJfU9aworjO2IPn4MUpsVqYlh+M3jRZiUHx20z0BEFCwMwUHAEExElwpDZyM+PfA6WjobvOfyk4tw5YRvQy5VDFnf4RLwmx1W/G5X75vmnIisKIe6SQ+pWIR7VubhodvzIZNxOXkiGj0YgoOAIZiILiVOlx1bjr6PI9W7vOciw+KwdOrdiNX6N7d3d40T931oRrXR7T2nam5CVEU5JA4HxmTq8KsfTcPY7IgL3X0ioqBgCA4ChmAiuhSV1x3Al4ffgt1pAwBIxFLMG7ccE9Jm+TU9wmgV8NjnFrx91O49J7HbEXnyONStLZBKRHjgtnzce3MeZFKOChPRpY0hOAgYgonoUtVubsanB173WT0iJ2EiFky8BSp5mF9tvF9mxw83WGDo6rWUmr4BkRUnIXY5UZATgV/+aBpyM3QXvP9ERBcKQ3AQMAQT0aXM6XJiR9kHOFi1zXsuTKHD4im3Iy0mz682mkxuPPKZBZ+e7FlKTWKzIqq8DKr2NkilIjx8RwFWf3sMpBKOChPRpYchOAgYgoloNKhoLMWmQ/+CtXtzDQCYmnUVZuZdC6lk6KXPBEHAm4ft+PEXFnTYes5r6msRcboCYrcbE/Ii8dRj05Cdxu9CIrq0MAQHAUMwEY0WJqsRGw/+E9UtJ7znYrXJuGbKnYjyY01hAKgxuvHwJ2ZsqXJ6z0m7uhB1ogxKYzvkMjG+d2cB7lyey1FhIrpkMAQHAUMwEY0mguDGN5VbsfP4R3C5PWsCS8QyzC24ERPTZ/t105xbEPBKiQ0/39wFS88MCWga6jyjwi4XCnIj8L8/mMoVJIjoksAQHAQMwUQ0GjV31OGzA3+HwdToPZcZNw5XT7oVakW4X21UGFx4+GMLdtf2jApLbFZEnSyHytAKiViEu1eMwUO350OpkFzwz0BE5C+G4CBgCCai0crpsmN72Yc4VLXde04t1+CqiTcjJ2GiX22cHRVeu7mrZ9tlAGp9IyJPnYTE6UB6sgZPfr8QRRNjL/RHICLyC0NwEDAEE9FoV6k/ii8OvQmL3eQ9l59chHnjl0MpU/vVRrXRhUc+teCryp5RYbHDjsiKE1A3N0EE4NvXZOKH94yHViO/0B+BiGhQFz0E79q1C2+88Qa2b9+Ouro6CIKAlJQUXHHFFbjrrrswe/bs832LITkcDmzcuBHvvPMO9u/fj4aGBlgsFsTHxyMxMRHTpk3DlVdeiSuvvBKRkZEBt88QTESXA7OtA1+W/hun9Ue85zRKHRZOvBUZcfl+tXF2BYk1m7rQbu35CVG1NCOyohxSux2xUUr8z8OTsXC2f7vXERFdCBctBJvNZjzyyCN47bXXBi23evVqvPDCCwgL82/R9kDt2rUL999/P44ePTpk2YcffhgvvvhiwO/BEExElwtBEFBWtw9bjrwHu7PLe35C2izMKbgBcqnSr3b0Jjce+9yCD8t75keInU7oKiugaaiHCMDVVyTjJw9OQnyM6kJ/DCKic1yUEOxyubB06VJs3LjRe06lUmHcuHGQSqU4duwYOjo6vK8tWrQIn376KSSSC3vTxN///nesXr0abrfbey4iIgJZWVmIiIiA0WjE8ePHYTabATAEExGd1dnVhi8O/QvVLeXec1p1NBZNug0p0Tl+t/PBcTse+9yCJnPPz4ncaETUyeOQW8wIU0vxvTsLcOv12VxOjYiCKpC8NuxvoyeeeMInAN93332ora3Fvn37sHv3btTX1+OJJ57wvr5x40b8/Oc/H+7b9evdd9/1CcBTpkzB559/jubmZpSUlODLL7/E/v370dHRgT179uBHP/oRoqOjL2gfiIhGq3BVJL41/SFcNWElZBLP/N0OSyve3f0ith59Dw6X3a92bhgrx977tbhtQs8cYLtOh8apxWjLzEan1Y2n/1KKWx75CofKWoPyWYiIAjWskeD6+npkZ2fDarUCAFatWoW///3v/ZZ94okn8NRTTwEAlEolTp06haSkpPPosoder0d+fj7a2toAADfddBPeeustSKVD74g0HBwJJqLLmdHcgo2H3kSd4ZT3nE4dg4UTb0FqTK7f7WytcuCHGyyoMPT8dU5i7UJUxQmoDK0QiYAVSzLxg++Mhy6cN84R0YUV9OkQjz/+OJ599lkAgFqtRk1NDaKiovota7fbkZOTg5qaGm/dp59+OtC3PMcdd9yBdevWAQDGjBmD0tJSKBSK8253IAzBRHS582ywsQ07j38Ml7tnnu/4tFmYk78MCpl/83ptTgHP7bbid7ussLt6zquamxB56gSkdjuidAo8dt8ELFuQ5tfGHURE/gj6dIj333/f+3zlypUDBmAAkMvlWL16tff4vffeG85b+mhsbMS///1v7/EzzzwT1ABMRBQKRCIxCrPm4/a5/42kqCzv+SPVu/CPrb/2WVFiMAqpCGvmqLD7Xi3mpff8da4rNg6NRTPQmZSCVqMN//Pb/Vj9+DacOtMxSGtERMERcAguLy9HRUWF93jJkiVD1rnmmmu8zysqKlBeXj5I6aG9/vrrcDo9a1QmJibiuuuuO6/2iIioR5QmHt+e+V+4cvwKyCSeAQaT1YgP9/0fPjvwBiw20xAteORGS/DhbRr8dZkaMWrPaK9bIkVbzhg0TpkGW7gW+w+34KbvbsJzrx6GuffezEREQRZwCD506JDP8cyZM4esU1hYCLm8Z+5XaWlpoG/ro/cNeUuWLLngK04QEYU6kUiMSRlzsGreT5Ae27N+cHn9Afx9y69wvG4//JlNJxKJcMt4BfY/oMXdk3t+BxzhWuinTEPrmHzYxFK89s4JXHfvRny46Qzcbu7hRETBF3AILisr8z6Xy+VITU0dsk7fcr3bCJQgCCgpKfEez5gxAwBQWVmJNWvWYMKECdDpdNBoNMjKysKtt96Kd955x2cJNSIi8o9WHYUbix/A4sl3eHeVszrM2PDNP/DBvr/CaPFvtYcolRjPLw3DxlXhGBfbM3BhTkhEQ9FMdCSnoKnNhp/+dj9WPbYFR060BePjEBF5BRyCq6qqvM9TUlL8vqEhLS2t3zYCVVlZ6bP+cG5uLl566SWMGzcOv/nNb3DkyBF0dHTAbDajsrISb731FlauXInCwkKcOHFi2O9LRBSqRCIR8lOKsGr+GuQmTvGer2o6hn9s+TX2VWyCy+0apIUeM1Kl2H5POJ5dpEKEsnuKhFSK9uwxaCwsglUXgUNlBtz6/a/w89+XoLXdGpTPREQUcAju7Oz0PtfpdH7X632HXu82AtXa6jvqsH79ejz88MPo6vLsepSeno758+dj2rRpPlMwDh06hJkzZ+LYsWN+vY/NZkNHR4fPg4golIUptLh26t24bto90Cg93/9OtwM7j3+EN7c/g3rDab/akYpFeGCaEiUPaHHnJDnODqU4wjRomlSIlrHj4JAr8N7nVbjuno34x/sn4XDyr3lEdGEFHIJNpp4bIpRK/7bWBDy7yfXXRqDa29t9jp9//nkAQF5eHrZt24aqqips3rwZ+/btQ1NTE374wx96yxoMBqxYsQJ2+9ALwP/617+GTqfzPvyZ9kFEFApyEiZi1byfYnLmPIi6I2xrZyPe3vVHbDr0Fqx2s1/txIaJ8eK1Yfjy7nAUJvZMkbDExaNh2gwYU9PRYXHh6ZdLcdN3N2HXAX1QPg8RhaaAQ/DZVRkABLQxRe+yDsfw7wC22WznnEtKSsK2bdswZ84cn/M6nQ7PPfccfvazn3nPlZWV4Y033hjyfdasWQOj0eh9nF3nmIiIAIVMifnjluOWKx5DnK5nkOBIzW68seWXOFa7168b5wBgWpIUX90djj9d27OKhCCRwJiZjfqi6TDHxOFUdSfu/+kOPPTETi6pRkQXRMAhWK1We5+f3THOH73LhoWFBfq2g9b95S9/ibi4uAHrrF271mck97XXXhvyfRQKBbRarc+DiIh8xUek4pYrHsX8cTdBLvUsp9ZlN2PjwXX4z9cvorWz0a92xCIRVk1S4MCDWjw4TQFx9xwJl1KF1oLxaJpUCFt4OLbva8Tyhzbhf184wPnCRHReAg7BGo3G+/zsPFx/WCyWfts4n/cHAJlMhpUrVw5aRyqV4pZbbvEe79+/H2azf3+uIyKiwYlFYkzOnIs75/0UuYmTvedrWyuwbtvT2Hr0fdgc/v1eRCjFeGaRGjvv0eLKzJ6/INp0EdBPKUJLXgFsMjne/qQSS7/zOf7vreOw2vy7KY+IqLeAQ3BMTIz3eUNDg9/1Ght7RgOio6MDfVuv2NhYn+OxY8f6jE4PpLCw0Pvc6XRyegMR0QWmUUXg2qmrcUPR/dCqPDuJugU3vqnc4pkiUbMXguDfDW7j4iRYf4sG76zUYEx0z0+VJT4BDdNmoD09E51WAX98/Siuv28jPtlczfWFiSggAYfgvLw87/PW1lafEd7B9A6dY8eODfRtvdLS0nxC72BbNvfWN3i3tXENSiKiYMiMH4c756/BjDFLIBHLAAAWWyc2HlqHt3f9EU1G/wYhRCIRFufIsPteLZ5dpEKkqme+cEd6JhqKZsAUn4j6Jgt+/PQ+3PaDzThwpCVon4uILi8Bh+D8/Hyf44MHDw5Zp66uDs3NzQO2EQixWOwTovu7Ua4/fecvB7KyBRERBUYqkWPGmGtw5/w1yE6Y6D3f0FaFN7f/DptK/40uu38rBckkniXVDj6oxfeKFZB1/3K5FAoY8vLRWFiErsgoHD7Rhjt/tBWP/GIXKqp48xwRDS7gEFxcXAyFQuE93rFjx5B1tm/f7n2uVCpRXFwc6Nv6mDdvnvd5ZWWlX3X6louPjz+vPhAR0dB06mhcP+0efGv6Q4jUnP3eFXCkehde3/xLHKraDrefUyQiVWL8aqEae+7X4voxMu95hyYczRMmo2niFNg04fhqdwOWf/cL/Ox3+1Gv5/0fRNS/Yd0Yt2DBAu/xunXrhqzTu8yCBQvOa3UIAFi+fLn3uV6vx+HDh4ess3HjRu/z1NRUJCUlnVcfiIjIf+mxY3HH3McxJ/8GyCSegRSbw4LNR97Fm9ueQXVzud9t5URJsG6FBp/crsGUXusL2yIioS8sQkv+ONjkKqz/4gyuvXcjnnn5ENqM/v3VkIhCR8AhGADuvvtu7/PS0lJ89NFHA5Y9cOAAPvvss37rDtesWbN8pkT8+te/HrT8wYMHffpwww03nHcfiIgoMBKxFFOzr8LdV/4P8pOLvOdbOhvw3p6X8MHel2Hwc0k1AJiTLsPmu8Px+o1hyIzsdfNcbDwaiqbDkDMGVkjx9/crsGT1BvxlXRksXc5BWiSiUCIS/F3NvBdBEDBlyhQcOnQIAJCYmIivvvrqnBveGhoasGDBApSVlQEAJk+ejAMHDkAkEp3TZlVVFTIzM73Ha9euxZNPPjlgH95//32fEeHnnnvOZ3e4s6qrq7FgwQJUVFQAAORyOU6cOIH09HT/PzCAjo4O6HQ6GI1GrhlMRHQB1BtOY+vR96DvdaOcSCTGxLTZmJG3BCq5/8tpOlwCXj9ow2+2W9Fs6flZE7lcCK+thra2GmKXC1ERCjx421h8+5osyGTDGgcioktYIHltWCEYAPbt24d58+Z51wrWarV46KGHMHfuXEilUuzduxcvvvgi9HrPNpcqlQpbt25FUVFRv+0FGoIBYNWqVfjnP//pPb7qqquwatUqZGZmwmw2Y9u2bfjzn/+Mjo6eGyReeuklPPTQQwF/XoZgIqILTxDcOF5Xgp3HP4bJ2u49L5eqMD13ESZlzIVU4v/upCa7gBf3WPH8HitM9p7zYocd2poz0NTXQex2IylOjQduG4tlC9MhkzIME10uLkoIBoD33nsPd9xxx5CbZqhUKvzzn//0Gbntazgh2G6349vf/jY+/PDDIfsqEonwy1/+EmvWrBmybH8YgomIgsfhsuPA6c3YX7EJDldPetWqozFn7DLkJE7q96+IA2k2u/H0Dite+8YGZ6/77sQ2G3Q1VdA01EMkCEhNDMODt+fj2itTIZUwDBONdoHktfO64pcvX46SkhIsXLiw3y8nkUiEBQsWYP/+/YMG4OGSy+X44IMP8Ne//tUnQPc1Z84cbNmyZdgBmIiIgksmkWN67mLcfeXPMC51BgDPb0qHpRWfHPgb3tn1R9QbTvvdXmyYGL9drMb+B7RYUSDD2V8ot0KBtpw81BfNhCkhCdWNFvzPb/fjxvu/wCebq+FyccMNolBxXiPBvdXU1GDnzp2oq6sDACQnJ2P27NlITU29EM37Zd++fTh69CgaGxuhUCiQmJiIOXPmIDk5+bzb5kgwEdHF09xRh23H1qOm5YTP+az48ZiVdy1itIGt8HOsyYVfbe/Ch+UOn/PSri5oz1QirKkRIgBZaeF46PZ8LJ6TArHY/5FnIro0XLTpEKGEIZiI6OISBAFVTcewrewDtJn0vV4RIT+lCDPHXAOt2r9dQ8862OjEL7dZ8XlFnzBsMUNXVQl1SxNEAHIztPjuHQVYMCuJYZhoFGEIDgKGYCKikeF2u3Csdi++PvEZTFaj97xELMHE9CtQnLsooJUkAGBPrRO/2taFzVW+S6bJzCZoq6ugbvaE4bwsHR64dSwWzk5mGCYaBRiCg4AhmIhoZDlddhys2o59FZtgc1i85+VSBaZmLcCUrPmQSxWDtHCu7WcceGqrFbtrfcOw1GKGtvoMwpr0EEFAZmo47rs5D0t5Ax3RJY0hOAgYgomILg1WuwX7T32Jg5Vb4XT3TGtQyzUoyrkaE9JnQyqRDdKCL0EQ8GWlE7/Z3oW9dS6f16RdXdDWVCFM3wiRICAlQY3vfDsPN16dDrlcMkCLRDRSGIKDgCGYiOjSYrIa8fWJDTha8zUEoWcdtDCFDsW5V2Nc6syA1hgWBAFbq5x4ZqcVO6p9R4YlVqtnneFGz9Jq8TEq3H1TLlZckwmV0v/3IKLgYggOAoZgIqJLk8Gkx+7yT3Gy4aDP+XBVJIpzFqEgdTok4sBGbXdWO/DMDus5c4YlNiu0NdUIa6yH2O1GlE6BO5fn4pbrsqAJ83/0mYiCgyE4CBiCiYgubU3GWnx9YgNO6w/7nNeqozE9dxHyk4sgDjAM76l14tmdXdh4yjcMi+12hNfVILyhDmKnE1qNDDdfl4Xbl+UgJkp53p+FiIaHITgIGIKJiEYHfXs1vj7xGSqbjvmcj1DHYPqYJchLngqxKLCb2w40OPHsTis+OeG7tJrI6YSmsR7htTWQ2m2Qy8RYtjANdy0fg8zU8PP+LEQUGIbgIGAIJiIaXRraqrD7xGeobj7ucz4yLA5FOVcjL3lqwNMkDuud+N0uK9Yfd8Dd+9fT7UZYkx7amjOQdVkgEgFXzkjE6m/nYUpB9AX4NETkD4bgIGAIJiIaneoMp/B1+WeoaT3pc16risK0nIUoSJke0A10AHC6zYUX9tiwrtQGq+9MCahamqGtPQNFRwcAYHJBNFavyMWVM7jxBlGwMQQHAUMwEdHoVtNyEl+f2IA6Q4XP+TCFDtOyr8L49FmQSeQBtdlsduPP+2x45YAN7Vbfn1OFsR3hNWegMrRCBCAzRYO7bhqD6xekQcHl1YiCgiE4CBiCiYguD3WGU9h78gucaS7zOa+Sa1CYNR8T0+dAIQvs5rZOm4A3Dtrw4l4r6jt9f1alFjPC62oRpm/wriix8ros3HJtFm+iI7rAGIKDgCGYiOjyom+vxt6TG3Gqz2oSCpkKkzPmYnLm3IC3Y7a7BLxz1I4/7LaivNXt85rY4YCmoR6a+lpI7TZIpSJcMy8Vd9yYg3G5kef9eYiIITgoGIKJiC5PLR312FvxBU7UfwOg5ydRKpZhXOoMFGbNhy4sJqA23YKAzysceHGPDdv7bLwBtxvqliaE19ZAYeoEABSOi8YdN+bgqllJ3JaZ6DwwBAcBQzAR0eXNYNJjf8UmlNXt99mBTgQRchInY1r2VYiPSAu43VK9E3/aa8O7R+1w+A4Oe+YN19VA1dIMEYDEODVuvT4LNy3JhC48sPnJRMQQHBQMwUREocFoacU3p7fgSM3XcLrsPq+lROdiWvYCpMeOhUgU2EoPjSY3/q/EhtcO2NDa5fvTK+nqQnhDLcIaGyBxOqFSSHD9wjTcen02cjN05/2ZiEIFQ3AQMAQTEYWWLrsZpWd24GDlNnTZTT6vxYQnYmr2AoxJKgx4reEuh4B/H7HjpX1WHG/xHRoWuVxQN+sRXl8LucnznlPHx+Dm67Jw9exkyGScKkE0GIbgIGAIJiIKTU6XHcdq9+HA6c1oNzf7vKZRRmBy5lyMT50JpVwdULuCIOCrSif+tNeKTaed57wu7zAivL4W6uYmiAQBUREK3LQkAyuXZiExLrD3IgoVDMFBwBBMRBTa3IIbpxsPY/+pL9HYfsbnNalEjvyUIkzJmIuo8ISA2z7Z6sIrB2x4s9QOo833Z1lst0PTWA9NQx2kNhvEYmBuUSJuuT4LswrjuQEHUS8MwUHAEExERIBnBLfecBolp7/Caf2Rc15Pjx2LyZnzkBE7FiJRYNMXzHYBbx+14/9KbDjS5Or7xlC1tkBTXwtlextEAFISw7ByaSa+tSgDkTrFeXwqossDQ3AQMAQTEVFfbaYmHKzahmM1e+DocxNdZFgcJmfORX5KMeTSwAKqIAjYXePEX0ts+LDcAWefVSUkXV2e0WF9AyR2O+QyMa6+Ihk3LcnAtAmxHB2mkMUQHAQMwURENBCrw4JjNXtwsHIbOroMPq/JpSqMT5uOSRlzoVNHB9x2o8mN17+x4W/f2NBg6vOTLbiham2FpqEOyjYDRABSE8OwfHEGbrg6HXHRqvP4VESjD0NwEDAEExHRUNyCG6f1R3CwcitqWyv6vCpCZlw+JqZfgfS4fIgDnCrhcAn49KQDrx+04avTTvT98ZZYrdA01iOssQFSuw0SsQhzixNw05IMXFGUwE04KCQwBAcBQzAREQWiuaMOByu34nhdCVxu39UfwlVRmJA+C+NTZ0CtCA+47ap2F/5+0I5/ltrQeM7osAClwTM6rDIYIIKA2Cglbrg6HcsXZyAtKbCtoIlGE4bgIGAIJiKi4bDYTDhSvQuHq3ehs6vN5zWxSIKcxEmYlH4FkqKyAt6Aw+n2bM/8t29s+OJUP6PDNhvC9A0I0zdC1mUBABRPisW3FmVgwewkqJXS8/loRJcchuAgYAgmIqLz4RbcqGo6htKqHahqPg70iazR4QmYmH4FxiYXQSFTBtx+jdGNfxyy4R+HbKjrPPenXd5h9ATipiaIXU6oVVIsuiIZyxam8WY6umwwBAcBQzAREV0oRnMLSqt34VjN1+iym31ek0nkGJNUiHFpM5AYkRHw6LDLLeCL0068/o0Nn1c44Or7K+92Qd3SgjB9g/dmusQ4Na6/Kg3XL0hDZmrg0zOILhUMwUHAEExERBea0+VERcNBlJ7Zgfq2ynNej9IkYHzaDIxNLoJaEfhc3iaTG28fs2PdITuONrvOeb2/6RITx0Zh2cI0XDMvFbpweeAfimgEMQQHAUMwEREFU3NHHQ6f2YnjdSWwO60+r4lFEmQnTMC41BlIi80LeGUJQRBQqnfhzcN2vH3EjtauAaZLNDVC3dwEicMBmUyMecUJWLYwHVdMjYdcLjmvz0d0MTAEBwFDMBERXQwOpw0nGw7haM3XqDOcOuf1cGUEClJnYFzqdGjVUQG3b3cJ2FjhwLrDdnxece5GHBDcULYZENakh6qlBWK3C1qNDAtnJ2Pp/FQUTYyFRML5w3RpYggOAoZgIiK62AwmPY7W7EFZzR5Y7KY+r4qQFpOL/JRiZCdMDHhXOgBoNrvx9lE73iy143DfbZoBiFwuqFqbEdak98wfFgTERCqxZF4Kls5PxYS8yIDnLBMFE0NwEDAEExHRSHG5XajUH8HRmq9R1VQGoc/KEjKJHDkJk5CfUoSUmNyAp0sAwNEmF94+asM7R+2o7Tg3GogddqibmxDWpIe8wwgRgJTEMCydl4ql81ORk8HfRhp5DMFBwBBMRESXgs6udpTV7sXRmj0wWlrOeV2j1GFs8jTkpxQjOjwh4PbdgoCva5x4+6gd7x93oK2f+cMSaxfCmpqgbtFDZjJBBCA3Q4ul81OxeG4KN+SgEcMQHAQMwUREdCkRBAENbVUoq92LEw3fwOboOqdMnC4V+SlFyEuaOqzVJewuAZtOOfD2UTs+PemA1XluGWlXF9TNeqhbmryBeGy2DovmpGDRFcnISOGSa3TxMAQHAUMwERFdqpwuByr1R1FWtw9VTcfgFnzvdhOLxEiPHYu8pKnISpgwrPnDnTYBH52w452jdmyudMLdT3qQdlmgbm6CurkJMnPPCPHZQJydzt9PCi6G4CBgCCYiotHAYutEef0BlNXuQ5Ox5pzXpWIZMuPHIS+pEBlxBZBKZAG/R7PZjY/KHXi/zI7t1f4H4qy0cG8gzs3Q8qY6uuAYgoOAIZiIiEab1s4GlNXuw/G6/TBZjee8LpcqkZ0wEXlJhUiNGQOJOPC1gAMJxKrWZsg7OyECkJGswdVzkrFwdjIKciIYiOmCYAgOAoZgIiIarQTBjTrDaZyoP4CTDQfP2aoZAFTyMOQkTkZeUiGSo7IgGsYKE81mNz4sd2D9IIFYYrNC1doCVUszlMZ2iAQB8TEqXDkjEVfOTELxxFjIZIG/NxHAEBwUDMFERHQ5cLldqGk5gfL6AzjVWHrO7nSAZ4WJnIRJyEmchKSorGEtueZPIBY5HVC1tkLd2gylwQCx2wWNWoo5RQm4cmYS5hQlIDws8OkaFLoYgoOAIZiIiC43TpcDVU3HUF7/DSr1R+B0O84po5ZrkJ0wETmJk5ASnTvsKROfnXTg4xMObK50wHbuvhyA2wVlWxvUrc1QtbZA4nBAKhWheGIsrpqZhPkzEpEQqx7Gp6RQwhAcBAzBRER0ObM7bTitP4zyugOobjkOl/vcpKqUqZGVMAG5CZOQGpMHqUQa8Pt02gR8edqBj044sLHCAaOtnxgiCFB0GKFqbYHS0AqZxQwRgHG5kZg/IxFzixOQnx0BsZjziMkXQ3AQMAQTEVGosDm6UNl0FCcbDuFMU1m/I8RyqRKZceOQmzgJ6bFjIRvGsmt2l4Ad1U58VO7AJyfsaDT1H0kk1i6oWluhMrRA2d4OkeBGTKQSVxTFY15xImZOiYOG0yYIDMFBwRBMREShyOG0oaqpDCcbD6FSfxQOl+2cMhKxDGkxY5AVPx5Z8eMRpgz8d9ItCCipd+GTEw58VG7HSYO733IilwvKNgNUhlYoDS2Q2u2QSkQoHB+DucUJmFOUgKzUcK42EaIYgoOAIZiIiEKd0+XAmebjqGg8hNP6I/3uUgcACRHp3kAcHZ44rEBaYXDh8wrPlIkd1U44+s/EkHV2QmVogcrQCnlnB0QAUhLUmFOUgLnFiSiaGAulIvB5zDQ6MQQHAUMwERFRD5fbiZqWk6hoLEWl/gjMto5+y+nU0d2BeAKSorKGdWNdh03AlkoHNlQ48HmFA82W/qOL2GGHsq0NyjYDlG2tkNrtUMjFmDo+BrOmxmNWYTw36bjMMQQHAUMwERFR/wTBDb2xBqf1R3C68QhaOuv7LaeQqZARW4DM+HFIjx0LlTws4PdyCwK+aXBhQ/co8TeN/S014SEzm6A0eAKx0miESHAjNkqJmYVxmD01HjOmxCE6QhlwH+jSxRAcBAzBRERE/jFaWj2BWH8Eda0VcAvnzmUQQYSEyHRkxBYgI64AcbrkYW3Q0dDpxsZTDnxxyoGtVc7+V5uAZy6xwtgOZVsrVAYDpF0WiADkZ0dg1tQ4zCqMx5SCaMjlnDoxmjEEBwFDMBERUeCsDguqmspwWn8EVU3H+t2cA/CsR5wel4+MuAKkx4yFUh74msBOt4D9dS5sOu3Al5UOHKh3YaCQI7FaPdMm2g1QtrdB4nBAqZCgaGIMZk6JR/HkWIzJ0HEZtlGGITgIGIKJiIjOj8vtRJ3hFKqaylDVVAaDqbHfcp5R4gxkxOUjM64AsdrhjRIbutzYUunEl5UOfHnagfrOgSOPzGSCst0ARXsblMZ2iF0uRGjlKJoYi+mTYlE8ORaZKVx14lLHEBwEDMFEREQXVofFgKrmY6hqKkNNywk4XPZ+y6nlGqTG5CEtdgzSYsYiXBUR8HsJgoDjLW58edqBTacd2FXjhNU5YGHIOzugbG+Dsr0N8g4jxG7PfOLiSbHeR0pCGEPxJYYhOAgYgomIiILH6XKi3nDKG4oNJv2AZSM18UiLGYO0mDykROdCIQv85rYuh4BdNU5sO+OZS3yw0QX3QInI7Yaiw9gdig2Qd3ZCJAhIilOjeHIsiid6QjG3dR55DMFBwBBMRER08RgtrahqKsOZ5jLUtJzsd5MOABCJxEiISEda90hxQkTGsJZha7e6sbPaia1VTmytcqCsZYCFidF9k12HEQpjOxTGdsg7OyB2u5GSGIap42JQOD4a0ybEIC1Jw5Hii4whOAgYgomIiEaGy+2Cvv0MzjSXo7qlHI3tZyD0s+IEAMilCiRH5SA1Jhcp0TmI0SZDPIz5xHqTG9vO9IwUV7UPHIrhdkPR2eENxYoOI8QuF6IjFZg6LgZTJ8Rg6vgY5GboIJEwFAcTQ3AQMAQTERFdGmwOK2pbT6Km5QTOtJSjbZCpEwqZCslR2UiJzkFKdC5itEnDCsVV7S5sq3Ji6xkndlU7UDfITXYQBMhNnT2h2NgOidOJ8DAZJhdEY+p4z2jx+NxILsl2gTEEBwFDMBER0aWps6sN1S0nUN1cjpqWcljspgHLXohQLAgCqo1u7KxxYle1EztrnDhlGGSkGJ6NOzyjxB2QdxghtXZBKRdjQl4UJhdEY3J+FCblRyNSpwioL+SLITgIGIKJiIgufYIgwGBqRG1rRffjJLrs5gHL9w7FyVHZiNUmQzyMOcWNJrc3EO+qduJo88A72QGA2G73zCvuMELeYYTc1Amx2420pDBMyo/G5IJoTMqPQm46p1AEgiE4CBiCiYiIRh9BcMNg0qOm5SRqWytQZ6gYNBTLJHIkRGYgKTILSVGZSIzMgFwa+OoThi43vq7xhOKd1U4canTBNVjicrs9Uyi8wbgDUrsNapUUE/IiMWmsJxRPyo9ChJajxQNhCA4ChmAiIqLRTxDcaO3sPVJcAatj4FAsggixuuTuUOx5aJS6gN/XZBdQUu/E3jon9tQ6sbfOhXbr4BFMYrV6AnGnEYrODshMJojdbmQkazApPxoTxkZi/Jgo5GXqIJMFPs/5csQQHAQMwURERJefs6G4znAK9YbTqDOchsnaPmgdrToayd2hODEyA1HhCQHPK3YLAk62urGn1ok9dU7srXWivHXwecUQ3JCZzVB0dkDe/ZCZLZDLRBibpcP4MZ5QPH5MJDJTw0Nyy2eG4CBgCCYiIgoNHV0G1BtOo95QiXrDabR0NgAYZMtliQLxEWlIjExHQkQGEiLSEaYMPCsYutzYV+fCnlon9tU5sb/eCbNj8DoilwtyU6c3FCs6OyCxWqFRS1GQE9kdjD2PpHj1Zb9uMUNwEDAEExERhSarw4KGtipvMG5sPwOXe/B0Gq6K6g7F6UiIzECcNgVSiSyg93W6BRxtcqGk3oWSBidK6p043uIeeGe7bmK7HXJTB+Sdnd3TKDohsdsRrVNgXHcgHpcbgbHZEYiPUV1WwZghOAgYgomIiAgAXG4nmoy1qDecRmP7GTS2VaFziCkUYpEEsdpkJEZ6RorjI9IQERYDUYDTKEx2AYcanSipd+FAg+efZ4xDTKMAILbbPCPGJhPknZ2QmzshsVoRrVNgbLYO+TkRyM+JRH62DqmJmlE7lYIhOAgYgomIiGggJqsRjW1VaGg/g8a2M9Abq+F02QetI5cqEadLRbwuFXERnn/q1DEBj8y2mN0oaXChpN6JkgYnDtS70No1dLwTOxyQmTp7wrGpE9IuCzRqKcZmRXjCcbYnHGelhUMmvfRvvmMIDgKGYCIiIvKX2+1Cq6kRDW1VaGyrQmP7GRgG2dnuLIVMhThtCuIi0hDfHZC16uiAgrEgCDhjdKOk3oVDjZ7l2Q7qXWjzIxiLXE7ITabucGyC3GyC1GKGUgLkZGhRkBOBvMwIjMnSYUymDuFhgU3xCDaG4CBgCCYiIqLzYXVYoG+v7h4prkGTsRomq3HIegqZGvG6FMTp0hCnS0GsNjngqRSCIKCmw41DjS7PQ+8JyI0mP2KgIEDaZfGEY7PnITebIbFZkRynxphMTyD2BGMt0pI0kEpGZtSYITgIGIKJiIjoQjNbO9BkrOkOxTXQt1fDbOsYsp5MIkeMNgkx2mTEdj9itEmQSeQBvX+jyROMS/VOb0D2Z44xAIicDsjNZsi6R4w9AdkMlVRAdroWYzJ1yOsOyPk5EdBqAuvbcDAEBwFDMBEREV0MJquxOxB7Rov1xhpYbJ1+1BQhMiwWsbpkbziO0yZDrdAGNJ3C0OVGaaMLR5o8j6NNLpS1uGAffCdoL2mXxRuI5d3//NnduVh1Q47ffRguhuAgYAgmIiKikSAIAsxWI/TGGjR31HkfHZZWv+qr5BrvSHF0eCJiwhMRpYmHTOr/9ssOl4BTBrcnFDd3B2S9E3Wd/sXI745z4Tc3xPj9fsPFEBwEDMFERER0KbE5utDSUe8TjFs7G+ByO/2oLYJOHY3o8EREhyd4w3GkJg4SsdTvPhi63Dh2dsS4Oxwfa3Khq08X3vqWEkvzVYF9wGFgCA4ChmAiIiK61LndLrSZm9BsPBuMa9HcUYcuu9mv+mKRGBFhcd5QfDYg68Ji/N4a2uUWcLrNjbJmF441u3C8xYVnF6kRGxb8m+UYgoOAIZiIiIhGI0EQYLZ1oLWzAa2dDWjpbEBrRwNaTY1DrmV8lkQsQ6QmDlGaeERp4hClSUCkJg6RYXEB74QXTIHkNf/Hu4mIiIho1BGJRNAoddAodUiPHes9LwhudFgMaDU19gTjzgYYTHq4Bd+74FxuB1o66tDSUde3dejUUd3hOKE7KCcgShMHpTzsIny64eNIsJ84EkxEREShwOV2wWhu9gTjzga0djaitbMB7ZYWCIJ/y6cBgFquQaQmHlGaeOSnFCMpKjOIvfbgSDARERERDYtELEFUeAKiwhMATPGed7mdaDe3oM2kh6HXo82kh6OfaRUWuwkWgwl1hlNIjsq+KCE4EAzBRERERDQkiVjafaNcgs95QXDDZDWeE4wNJr13feNITfxIdHlQDMFERERENGwikRjhqkiEqyJ95hwDgNVugcGsPyc4XwoYgomIiIgoKJRyNZLkl9Y0iLOCv2AbEREREdElhiGYiIiIiEIOQzARERERhRyGYCIiIiIKOQzBRERERBRyGIKJiIiIKOQwBBMRERFRyGEIJiIiIqKQwxBMRERERCGHIZiIiIiIQg5DMBERERGFHIZgIiIiIgo5DMFEREREFHIYgomIiIgo5DAEExEREVHIYQgmIiIiopDDEExEREREIYchmIiIiIhCDkMwEREREYUchmAiIiIiCjkMwUREREQUchiCiYiIiCjkMAQTERERUchhCCYiIiKikCMd6Q6MFoIgAAA6OjpGuCdERERE1J+zOe1sbhsMQ7CfOjs7AQCpqakj3BMiIiIiGkxnZyd0Ot2gZUSCP1GZ4Ha7UV9fj/DwcIhEoqC/X0dHB1JTU1FTUwOtVhv09yOiC4PXLtHoxet39BMEAZ2dnUhKSoJYPPisX44E+0ksFiMlJeWiv69Wq+WFSDQK8dolGr14/Y5uQ40An8Ub44iIiIgo5DAEExEREVHIYQi+RCkUCqxduxYKhWKku0JEAeC1SzR68foNLbwxjoiIiIhCDkeCiYiIiCjkMAQTERERUchhCCYiIiKikMMQTEREREQhhyH4ErJr1y488MADKCgogE6ng1arRUFBAe6//37s3LlzpLtHNGq1t7fj/fffxyOPPIK5c+ciISEBCoUCGo0GaWlpuP766/GHP/wBbW1tw2r/8OHDePTRRzFx4kRERUVBo9EgLy8Pt99+OzZs2DDsfp8+fRo///nPMXXqVMTGxkKlUiE7Oxvf+ta38O6778LpdA67baLRrqqqCmFhYRCJRN7Hk08+GVAbvHZDnEAjzmQyCd/5zncEAIM+Vq9eLZhMppHuLtGoUVZWJlx33XWCXC4f8voCIKjVauH3v/+94Ha7/Wrf4XAIa9asEcRi8aDtXnvttUJTU1NAff/DH/4gKBSKQdudMWOGcOrUqeH8qyEa9RYvXnzONbF27Vq/6vLaJUEQBIbgEeZ0OoVFixb5XBwqlUqYNm2aMGPGDEGr1fq8tmjRIsHpdI50t4lGhXfeeeecHx+JRCLk5eUJc+fOFWbPni1ERUWdU+bee+/1Kwj3/Z9XmUwmTJo0SZg9e7YQHR3t89rEiROFzs5Ov/r9v//7vz51xWKxMH78eGHu3LlCYmKiz2spKSlCfX39+f6rIhpV/vGPf/QbLv0Nwbx2SRAYgkfcmjVrfC6K++67T2htbfW+bjKZhCeeeMKnzE9/+tMR7DHR6HE2BEulUuHGG28U1q9fLxiNRp8ybrdbWL9+vZCcnOxznb300kuDtv3yyy/7lF+2bJlQW1vrfd1utwsvvPCCIJVKvWVuu+22Ifu8YcMGQSQSeevMnDlTKC8v977ucrmEt956S9BoNN4ys2fPDvDfDNHo1dzcLMTExAgAhPz8fCEpKSmgEMxrl85iCB5BdXV1glKp9F4Mq1atGrDsz372M285pVIp1NXVXcSeEo1O69evF+69917hzJkzQ5atrq4WEhISvNdZTEyMYLfb+y1rNpt9ys6fP3/Av9C88sor3nIikUgoKSkZsA9ut1uYNGmSt3xeXp5gNpv7LfvFF1/4/JC/9957Q35GosvBHXfc4f3vfuvWrUJ6errfIZjXLvXGEDyC/vu//9t7EajVap8R4L5sNpuQmprqLf/4449fxJ4ShYa+I0SbNm3qt9yf/vQnnx/HY8eODdru9OnTveVXrlw5YLlPPvnE5/03bNgwaLs333yzt2xxcfHQH5BolPv888+9/82vXr1aEAQhoBDMa5d64+oQI+j999/3Pl+5ciWioqIGLCuXy7F69Wrv8XvvvRfUvhGFouuvv97n+Pjx4/2W6339zZs3D/n5+YO2+8ADD3iff/rpp7DZbEO2m5mZiUWLFvnd7t69e1FbWztoeaLRzGKx4MEHHwQAxMTE4Nlnnw24DV671BtD8AgpLy9HRUWF93jJkiVD1rnmmmu8zysqKlBeXh6UvhGFqr7/I9rR0XFOGZPJhG3btnmPA712TSYTtmzZ0m+5Tz75xPt88eLFEIlEg7Y7Z84chIWF9Vuf6HLzxBNPoLKyEgDw29/+FtHR0QHV57VLfTEEj5BDhw75HM+cOXPIOoWFhZDL5d7j0tLSC94volB25swZn+O4uLhzyhw7dgwOh8N77M+1m5CQgIyMDO9xf9duU1MTGhsbA2pXKpWiqKho0HaJLgclJSX44x//CMAzgnvXXXcF3AavXeqLIXiElJWVeZ/L5XKkpqYOWadvud5tENH56zvNqL8fs77XXXZ2tl9t9y7X37UbrHaJRjun04l7770XLpcLcrkcf/nLX4bVDq9d6osheIRUVVV5n6ekpAz5p5Oz0tLS+m2DiM6P0Wj0jjQBwMSJE1FQUHBOud7XnVQqRWJiol/tD3Xt9j3Xu/z5tEs02v3ud7/DwYMHAQA//vGPMXbs2GG1w2uX+mIIHiGdnZ3e5zqdzu96Wq223zaI6Pw89thjPn/SfOqpp/ot1/u6Cw8Ph1js39foUNdu33P+fi/wO4EuZ6dOncIvfvELAEBOTg5++tOfDrstXrvUF0PwCDGZTN7nSqXS73oqlarfNoho+F555RW8+uqr3uObb775nJUizgrWtdv3nL9t8zuBLmcPPPAAurq6AAB//vOfA7rm+uK1S30xBI8Qp9PpfS6VSv2u17ts7wn+RDQ827Ztw8MPP+w9zszMxMsvvzxg+WBdu73bDaRtfifQ5epvf/sbvvzySwDA7bffjoULF55Xe7x2qS+G4BGiVqu9z61Wq9/1epftvbwKEQXu4MGDWLZsGex2OwDPahAbNmwY9M+Zwbp2e7cbSNv8TqDLUVNTE370ox8BACIjI/Hcc8+dd5u8dqkvhuARotFovM/P/qnHHxaLpd82iCgw5eXlWLx4MYxGIwDPD+3GjRsxZsyYQesF69rte87ftvmdQJejRx55BAaDAQDwm9/8pt/lCgPFa5f6YggeITExMd7nDQ0NftfrfeNOoAuFE5FHZWUlFi5ciKamJgCem2Q+++wzTJo0aci6va9dk8nk91y+oa7d3u0C/n8v8DuBLje7d+/Gv//9bwCeZQrvu+++C9Iur13qiyF4hOTl5Xmft7a2+vwf4WBqamq8z4e7TAxRKKutrcWCBQu825Sq1Wp8/PHHmD59ul/1e1+7AFBdXe1XvaGu3WC1SzTa6PV67/Pdu3dDLBZDJBIN+Oi9yc0vfvELn9d6Lz3Ga5f6YggeIX33Kz+7BuJg6urq0NzcPGAbRDQ4vV6PhQsXerdeVSgUWL9+PebOnet3G8O5dh0OB44ePTpgGwCQm5vrc6OMP+0CwDfffDNou0TkwWuX+mIIHiHFxcVQKBTe4x07dgxZZ/v27d7nSqUSxcXFQekb0eWotbUVCxcuRHl5OQBAJpPh3XffxdVXXx1QO1lZWUhJSfEe+3PtlpSU+Py1p7/QLZfLfUaj/Wm3sbERFRUVg7ZLNNrIZDLodDq/H703m1IoFD6v9V4LmNcu9cUQPEI0Gg0WLFjgPV63bt2QdXqXWbBgAe8mJfKT0WjE4sWLceTIEQCARCLBm2++ieuuu25Y7S1btsz7/J133vGuLjGQ3tfuuHHjBtxW9YYbbvA+37Rpk8+fhYdqNyIigj+kdFm49tpr0d7e7vej985rP/nJTwZ8DeC1S74YgkfQ3Xff7X1eWlqKjz76aMCyBw4cwGeffdZvXSIamNlsxrXXXouSkhIAgFgsxhtvvIEVK1YMu83e119LS8ug6wrX1tbijTfe6LduX7feeqv3L0QOhwPPPPPMgGVNJhOef/557/Htt98OmUzmR++JQhevXfIh0Ihxu93CpEmTBAACACExMVEoKys7p1x9fb2Qn5/vLTd58mTB7XaPQI+JRher1SosXLjQe+2IRCLh1VdfvSBtL1u2zNuuRqMRduzYcU4Zo9EozJkzx1suISFBsFgsg7b7yCOPeMtLJBLh3XffPaeM3W4XVqxY4S2nUqmEurq6C/K5iEab9PR077Wwdu3aIcvz2qWzRIIgCBcvclNf+/btw7x587zrCmq1Wjz00EOYO3cupFIp9u7dixdffNH7pxWVSoWtW7eiqKhoJLtNNCo888wz+PGPf+w9joyMDGgu/dVXX43HHnus39eqqqpQVFSElpYWAJ65iPfccw8WLVoEjUaD0tJSvPDCC96b8MRiMdavXz/gdsxntbW1Yfr06Th58qS33m233YYbb7wRUVFRKC8vx5///GeUlpZ667z44os+u94RhZKMjAzvChFr167Fk08+OWh5XrvkNdIpnAThP//5j6BSqbz/ZzjQQ6VSCf/5z39GurtEo8batWuHvK4Ge9x1112Dtr9z504hKipqyHYkEonwwgsv+N3v8vJyITU11a8+Pv744+f5b4lodAt0JFgQeO2SB+cEXwKWL1+OkpISLFy40Ocu17NEIhEWLFiA/fv3Y/ny5SPQQyLqz6xZs1BaWoqbbrrJZ4mk3oqKirBt2zZ873vf87vdMWPGoLS0FPfccw9UKlW/ZfLz8/HBBx/g6aefHlbfiUIZr10CAE6HuMTU1NRg586dqKurAwAkJydj9uzZSE1NHeGeEdFgmpubsW3bNtTW1sJutyMpKQnTpk07ZyH9QHV2duKrr75CTU0NzGYzEhMTMWHCBEyZMuUC9ZwotPHaDV0MwUREREQUcjgdgoiIiIhCDkMwEREREYUchmAiIiIiCjkMwUREREQUchiCiYiIiCjkMAQTERERUchhCCYiIiKikMMQTEREREQhhyGYiIiIiEIOQzARERERhRyGYCIiIiIKOQzBRERERBRypCPdASIiungOHjyI9evXe49/8IMfICIiYsT6Q0Q0UkSCIAgj3QkiIro4Xn/9daxevdp7XFlZiYyMjJHrEBHRCOF0CCIiIiIKOQzBRERERBRyGIKJiIiIKOQwBBMRERFRyGEIJiIiIqKQw9UhiIhCgEgkCrjO5s2bMX/+/AvfGSKiSwBHgomIiIgo5HCzDCKiECCRSAAAgiDA7Xafc74/wxk9JiIaLTgSTEQUApxOJ5xOJ1599VWf8xUVFd7X+j7mzZs3Qr0lIgo+hmAiIiIiCjkMwUREREQUchiCiYiIiCjkMAQTERERUchhCCYiIiKikMMQTEREREQhhyGYiIiIiEIOQzARERERhRyGYCIiIiIKOQzBRERERBRyGIKJiEKITCbzOXa5XCPUEyKikcUQTEQUQsLDw32O29raRqgnREQjiyGYiCiEZGRk+Bzv27dvZDpCRDTCRIIgCCPdCSIiujicTidiYmJgNBoBAElJSXjllVcwf/58qFSqEe4dEdHFw5FgIqIQIpVKsXr1au9xfX09li5dCrVaDbVaDY1G431s3759BHtKRBRcDMFERCHmqaeewhVXXHHO+a6uLpjNZu+DN80R0eWMIZiIKMSEhYVhy5YteOutt7By5UqMGTMG4eHhEIv5k0BEoYNzgomIiIgo5PB/+4mIiIgo5DAEExEREVHIYQgmIiIiopDDEExEREREIYchmIiIiIhCDkMwEREREYUchmAiIiIiCjkMwUREREQUchiCiYiIiCjkMAQTERERUchhCCYiIiKikMMQTEREREQhhyGYiIiIiEIOQzARERERhRyGYCIiIiIKOQzBRERERBRyGIKJiIiIKOT8fzWdQ9jOgCL5AAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_result_expectations(\n", - " [\n", - " (\n", - " result,\n", - " P11p,\n", - " \"rand\",\n", - " f\"P11 (correlation fit) k_R=k_I={pk + 1}\",\n", - " )\n", - " for pk, result in enumerate(results_corr_fit_pk)\n", - " ]\n", - ");" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "4770c53b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", - "\n", - "plot_result_expectations(\n", - " [\n", - " (\n", - " results_corr_fit_pk[0],\n", - " P11p,\n", - " \"y\",\n", - " \"Correlation Function Fit $k_R=k_I=1$\",\n", - " ),\n", - " (\n", - " results_corr_fit_pk[2],\n", - " P11p,\n", - " \"k\",\n", - " \"Correlation Function Fit $k_R=k_I=3$\",\n", - " ),\n", - " (results_spectral_fit_pk[0], P11p, \"b\", \"Spectral Density Fit $k_J=1$\"),\n", - " (results_spectral_fit_pk[3], P11p, \"r-.\", \"Spectral Density Fit $k_J=4$\"),\n", - " ],\n", - " axes=axes,\n", - ")\n", - "\n", - "axes.set_yticks([0.6, 0.8, 1])\n", - "axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", - "axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", - "axes.legend(loc=0, fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "id": "63716f70", - "metadata": {}, - "source": [ - "# Using the Ohmic Bath class\n", - "\n", - " As the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced quickly by using the OhmicBath class. This allows for rapid implementation of fitted ohmic baths via the correlation function or spectral density" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "4883e1cc", - "metadata": {}, - "outputs": [], - "source": [ - "obs = OhmicEnvironment(T, alpha, wc,s=1)\n", - "tlist = np.linspace(0, 30 * np.pi / Del, 600)" - ] - }, - { - "cell_type": "markdown", - "id": "005418f5", - "metadata": {}, - "source": [ - "Just like the other `BosonicEnvironment` we can obtain a decaying exponential \n", - "representation of the environment via the `approx_by_cf_fit` and \n", - "`approx_by_sd_fit` methods. " - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "e0924e70", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlation function fit:\n", - "\n", - "Result of fitting the real part of |Result of fitting the imaginary part \n", - "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", - " | \n", - " Parameters| a | b | c | Parameters| a | b | c \n", - " 1 | 3.24e-01 |-5.34e-01 |3.32e-23 | 1 |-8.92e+00 |-3.49e-01 |7.57e-04 \n", - " 2 | 2.84e+00 |-2.76e+00 |6.88e-08 | 2 | 5.44e-01 |-4.30e+00 |4.00e+00 \n", - " 3 |-1.67e+00 |-4.72e+00 |2.77e+00 | 3 |-1.34e+01 |-1.04e+00 |2.50e-02 \n", - " 4 | 2.49e-02 |-1.09e-01 |1.08e-41 | 4 |-1.34e+01 |-2.29e+00 |2.90e-01 \n", - " | \n", - "A 1-R2 coefficient of 5.94e-06 was obtained for the the real part of |A 1-R2 coefficient of 9.78e-07 was obtained for the the imaginary part\n", - "the correlation function. |of the correlation function. \n", - "The current fit took 9.391188 seconds. |The current fit took 57.800778 seconds. \n", - "\n", - "10.0%. Run time: 11.20s. Est. time left: 00:00:01:40\n", - "20.0%. Run time: 17.92s. Est. time left: 00:00:01:11\n", - "30.0%. Run time: 23.99s. Est. time left: 00:00:00:55\n", - "40.0%. Run time: 33.36s. Est. time left: 00:00:00:50\n", - "50.0%. Run time: 46.94s. Est. time left: 00:00:00:46\n", - "60.0%. Run time: 59.50s. Est. time left: 00:00:00:39\n", - "70.0%. Run time: 70.89s. Est. time left: 00:00:00:30\n", - "80.0%. Run time: 83.90s. Est. time left: 00:00:00:20\n", - "90.0%. Run time: 93.27s. Est. time left: 00:00:00:10\n", - "100.0%. Run time: 101.26s. Est. time left: 00:00:00:00\n", - "Total run time: 101.26s\n" - ] - } - ], - "source": [ - "tlist = np.linspace(0, 30 * np.pi / Del, 5000)\n", - "\n", - "Obath, fitinfo = obs.approximate(method=\"cf\",tlist=tlist,Nr_max=4,Ni_max=4,maxfev=1e9,target_rsme=None)\n", - "print(fitinfo[\"summary\"])\n", - "HEOM_ohmic_corr_fit = HEOMSolver(\n", - " Hsys,\n", - " (Obath,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - ")\n", - "results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "ddbaebf2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result of fitting the spectral density with 4 terms: \n", - " \n", - " Parameters| a | b | c \n", - " 1 |-4.41e+00 | 4.30e+00 |3.98e+00\n", - " 2 | 7.92e+00 | 2.30e+00 |1.00e-01\n", - " 3 | 6.01e-01 | 1.00e+00 |1.00e-01\n", - " 4 | 1.06e-02 | 3.07e-01 |1.00e-01\n", - " \n", - "A 1-R2 coefficient of 1.38e-06 was obtained for the the spectral density.\n", - "The current fit took 34.405920 seconds.\n", - "10.0%. Run time: 7.40s. Est. time left: 00:00:01:06\n", - "20.0%. Run time: 12.02s. Est. time left: 00:00:00:47\n", - "30.1%. Run time: 17.17s. Est. time left: 00:00:00:39\n", - "40.1%. Run time: 21.92s. Est. time left: 00:00:00:32\n", - "50.1%. Run time: 26.92s. Est. time left: 00:00:00:26\n", - "60.1%. Run time: 32.22s. Est. time left: 00:00:00:21\n", - "70.1%. Run time: 39.81s. Est. time left: 00:00:00:16\n", - "80.1%. Run time: 46.32s. Est. time left: 00:00:00:11\n", - "90.2%. Run time: 60.56s. Est. time left: 00:00:00:06\n", - "100.0%. Run time: 74.12s. Est. time left: 00:00:00:00\n", - "Total run time: 74.12s\n" - ] - } - ], - "source": [ - "Obath2, fitinfo = obs.approximate(method=\"sd\",wlist=w,Nmax=4,Nk=3)\n", - "print(fitinfo[\"summary\"])\n", - "tlist = np.linspace(0, 30 * np.pi / Del, 600)\n", - "HEOM_ohmic_sd_fit = HEOMSolver(\n", - " Hsys,\n", - " (Obath2,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - ")\n", - "results_ohmic_sd_fit2 = HEOM_ohmic_sd_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "50b833b1", - "metadata": {}, - "source": [ - "# Methods based on the Prony Polinomial \n", - "\n", - "The Prony polynomial forms the mathematical foundation for many spectral analysis techniques that estimate frequencies, damping factors, and amplitudes of signals. These methods work by interpreting a given signal as a sum of complex exponentials and deriving a polynomial whose roots correspond to the frequencies or poles of the system.\n", - "\n", - "The methods consider a signal \n", - "\n", - "$$f(t)=\\sum_{k=0}^{N-1} c_{k} e^{-\\nu_{k} t} =\\sum_{k=0}^{N-1} c_{k} z_{k}^{t} $$\n", - "\n", - "The $z_{k}$ can be seen as the solution of the Prony Polynomial, which we write in terms of Hankel matrices as \n", - "\n", - "\\begin{align}\n", - " H_{N,M}=V_{N,M-1}(z) diag((c_k)_{k=1}^{M-1}) V_{M,M-1}(z)^{T}\n", - "\\end{align}\n", - "\n", - "where $V_{N,M}(z)$ is the Vandermonde matrix given by\n", - "\n", - "\n", - "$$V_{M,N}(z)=\\begin{pmatrix} \n", - "1 &1 &\\dots &1 \\\\\n", - "z_{1} & z_{2} &\\dots & z_{N} \\\\\n", - "z_{1}^{2} & z_{2}^{2} &\\dots & z_{N}^{2} \\\\\n", - "\\vdots & \\vdots & \\ddots & \\vdots \\\\\n", - "z_{1}^{M} & z_{2}^{M} &\\dots & z_{N}^{M} \\\\\n", - "\\end{pmatrix}$$\n", - "\n", - "By obtaining the roots of this polynomial one can obtain the damping rate and the frequency of each mode, the amplitude can lated be obtained by solving the least-squares Vandermonde system given by\n", - "\n", - "$$ V_{N,M}(z)c = f $$\n", - "\n", - "Where $M$ is the length, of the signal, and $f=f(t_{sample})$ is the signal evaluated in the sampling points,is a vector $c = (c_{1}, \\dots, c_{N})$.\n", - "\n", - "The main difference between the methods is the way one obtains the roots of the polynomial, typically whether this system is solved or a low rank approximation is found for the polynomial, [this article](https://academic.oup.com/imajna/article-abstract/43/2/789/6525860?redirectedFrom=fulltext) is a good reference, the QuTiP implementations are based on it, and the matlab implementations made available by the authors\n" - ] - }, - { - "cell_type": "markdown", - "id": "f85ab699", - "metadata": {}, - "source": [ - "## Using the Original Prony Method on the Correlation Function\n", - "\n", - "The method is available via `approx_by_prony`. Compared to the other approaches showed so far. The Prony based methods, shine on their simplicity no information needs to be known about the function, and one just needs to provide the sampling points, and the Number of Exponents one desires" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "b75d4072", - "metadata": {}, - "outputs": [], - "source": [ - "tlist2=np.linspace(0,40,100)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "4e24e35b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4\n", - "Correlation function fit:\n", - "\n", - "Result of fitting the real part of |Result of fitting the imaginary part \n", - "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", - " | \n", - " Parameters| a | b | c | d | Parameters| a | b | c | d \n", - " 1 | 1.80e-01 | 8.81e-01 | 0.00e+00 |3.74e-19 | 1 |-1.52e-01 | 8.26e-01 | 0.00e+00 |-6.12e-16 \n", - " 2 | 9.09e-01 | 5.58e-01 | 0.00e+00 |-9.72e-17 | 2 |-1.86e+00 | 5.61e-01 | 0.00e+00 |2.44e-15 \n", - " 3 | 2.14e-01 | 2.00e-01 | 2.27e-01 |-7.86e-01 | 3 | 1.00e+00 | 1.27e-01 | 1.25e-01 |-1.17e+00 \n", - " 4 | 2.14e-01 | 2.00e-01 |-2.27e-01 |7.86e-01 | 4 | 1.00e+00 | 1.27e-01 |-1.25e-01 |1.17e+00 \n", - " | \n", - "A 1-R2 coefficient of 7.55e-05-8.86e-23j was obtained for the the real part of |A 1-R2 coefficient of 1.01e-05+1.31e-22j was obtained for the the imaginary part\n", - "the correlation function. |of the correlation function. \n", - "The current fit took 0.300285 seconds. |The current fit took 0.269018 seconds. \n", - "\n", - "10.0%. Run time: 1.33s. Est. time left: 00:00:00:11\n", - "20.0%. Run time: 2.35s. Est. time left: 00:00:00:09\n", - "30.1%. Run time: 3.27s. Est. time left: 00:00:00:07\n", - "40.1%. Run time: 4.32s. Est. time left: 00:00:00:06\n", - "50.1%. Run time: 5.19s. Est. time left: 00:00:00:05\n", - "60.1%. Run time: 5.96s. Est. time left: 00:00:00:03\n", - "70.1%. Run time: 6.57s. Est. time left: 00:00:00:02\n", - "80.1%. Run time: 7.16s. Est. time left: 00:00:00:01\n", - "90.2%. Run time: 7.76s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 8.32s. Est. time left: 00:00:00:00\n", - "Total run time: 8.32s\n" - ] - } - ], - "source": [ - "pbath,fitinfo=obs.approximate(\"prony\",tlist2,Nr=4,Ni=4)\n", - "print(fitinfo[\"summary\"])\n", - "HEOM_ohmic_prony_fit = HEOMSolver(\n", - " Hsys,\n", - " (pbath,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - ")\n", - "results_ohmic_prony_fit = HEOM_ohmic_prony_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "10e50bf0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "gen_plots(pbath, w, J, t, C, w2, S)" - ] - }, - { - "cell_type": "markdown", - "id": "1d4ffc81", - "metadata": {}, - "source": [ - "## Using the matrix Pencil Method on the Correlation Function\n" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "7f14b9cb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result of fitting Correlation Function with 6 terms: \n", - " \n", - " Parameters| a | b | c | d \n", - " 1 |-5.55e-02 |-6.25e-03 |-8.46e-02 |-4.36e-01\n", - " 2 |-1.32e+00 | 1.45e-01 |-1.18e-01 |1.69e+00\n", - " 3 | 3.00e+00 | 3.44e-01 |-6.84e-02 |6.96e-02\n", - " 4 | 6.46e-02 | 9.13e-01 | 2.32e-02 |-6.85e-02\n", - " 5 | 6.68e-02 | 7.28e-01 | 7.36e-02 |-4.58e-01\n", - " 6 |-2.40e-01 | 5.41e-01 | 1.08e-01 |-7.95e-01\n", - " \n", - "A 1-R2 coefficient of 3.18e-05-5.05e-06j was obtained for the Correlation Function.\n", - "The current fit took 0.218197 seconds.\n", - "10.0%. Run time: 5.71s. Est. time left: 00:00:00:51\n", - "20.0%. Run time: 9.38s. Est. time left: 00:00:00:37\n", - "30.1%. Run time: 13.38s. Est. time left: 00:00:00:31\n", - "40.1%. Run time: 17.24s. Est. time left: 00:00:00:25\n", - "50.1%. Run time: 21.54s. Est. time left: 00:00:00:21\n", - "60.1%. Run time: 25.52s. Est. time left: 00:00:00:16\n", - "70.1%. Run time: 29.99s. Est. time left: 00:00:00:12\n", - "80.1%. Run time: 33.85s. Est. time left: 00:00:00:08\n", - "90.2%. Run time: 38.35s. Est. time left: 00:00:00:04\n", - "100.0%. Run time: 42.36s. Est. time left: 00:00:00:00\n", - "Total run time: 42.36s\n" - ] - } - ], - "source": [ - "mpbath,fitinfo=obs.approximate(method=\"mp\",tlist=tlist2,Nr=6,separate=False)\n", - "print(fitinfo[\"summary\"])\n", - "HEOM_ohmic_mp_fit = HEOMSolver(\n", - " Hsys,\n", - " (mpbath,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - ")\n", - "results_ohmic_mp_fit = HEOM_ohmic_mp_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "3ed89ed7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "gen_plots(mpbath, w, J, t, C, w2, S)" - ] - }, - { - "cell_type": "markdown", - "id": "c04f6f61", - "metadata": {}, - "source": [ - "## Using the ESPRIT Method on the Correlation Function\n" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "7708f4f1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result of fitting Correlation Function with 4 terms: \n", - " \n", - " Parameters| a | b | c | d \n", - " 1 |-5.34e-01 | 1.63e-02 |-1.84e-01 |1.79e-01\n", - " 2 | 1.48e+00 | 3.20e-01 |-1.83e-01 |9.86e-01\n", - " 3 | 4.75e-01 | 6.57e-01 | 2.51e-02 |-1.12e+00\n", - " 4 | 9.95e-02 | 9.08e-01 | 9.49e-03 |-4.96e-02\n", - " \n", - "A 1-R2 coefficient of 3.09e-05+9.93e-06j was obtained for the Correlation Function.\n", - "The current fit took 0.321152 seconds.\n", - "10.0%. Run time: 0.86s. Est. time left: 00:00:00:07\n", - "20.0%. Run time: 1.41s. Est. time left: 00:00:00:05\n", - "30.1%. Run time: 1.94s. Est. time left: 00:00:00:04\n", - "40.1%. Run time: 2.42s. Est. time left: 00:00:00:03\n", - "50.1%. Run time: 2.89s. Est. time left: 00:00:00:02\n", - "60.1%. Run time: 3.38s. Est. time left: 00:00:00:02\n", - "70.1%. Run time: 3.87s. Est. time left: 00:00:00:01\n", - "80.1%. Run time: 4.39s. Est. time left: 00:00:00:01\n", - "90.2%. Run time: 4.89s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 5.37s. Est. time left: 00:00:00:00\n", - "Total run time: 5.37s\n" - ] - } - ], - "source": [ - "\n", - "esbath,fitinfo=obs.approximate(\"esprit\",tlist2,Nr=4,separate=False)\n", - "print(fitinfo[\"summary\"])\n", - "HEOM_ohmic_es_fit = HEOMSolver(\n", - " Hsys,\n", - " (esbath,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - ")\n", - "results_ohmic_es_fit = HEOM_ohmic_es_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "0d282401", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "gen_plots(esbath, w, J, t, C, w2, S)" - ] - }, - { - "cell_type": "markdown", - "id": "413f223a", - "metadata": {}, - "source": [ - "## Using the AAA Algorithm" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "5a685a80", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/mcditoos/qutip_gsoc_app/qutip/utilities.py:55: RuntimeWarning: overflow encountered in exp\n", - " result[non_zero] = 1 / (np.exp(w[non_zero] / w_th) - 1)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result of fitting the spectral density with 6 terms: \n", - " \n", - " Parameters| a | b | c | d \n", - " 1 | 2.73e-01 | 1.16e+00 | 2.80e+00 |2.39e+00\n", - " 2 | 5.16e-01 |-6.60e-01 | 1.17e+00 |6.45e-01\n", - " 3 | 6.73e-01 |-3.62e-01 | 6.98e-01 |1.53e-02\n", - " 4 | 3.79e-02 |-1.24e-02 | 1.57e-01 |-1.63e-02\n", - " 5 | 1.42e-03 |-5.98e-04 | 2.56e-02 |-5.36e-03\n", - " 6 | 9.23e-06 | 3.15e-06 | 1.54e-03 |1.88e-04\n", - " \n", - "A 1-R2 coefficient of 1.48e-05 was obtained for the the spectral density.\n", - "The current fit took 6.453628 seconds.\n" - ] - } - ], - "source": [ - "aaabath,fitinfo=obs.approximate(\"aaa\",np.concatenate((-np.logspace(3,-8,3500),np.logspace(-8,3,3500))),N_max=8,tol=1e-15)\n", - "print(fitinfo[\"summary\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "787b1ae6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10.0%. Run time: 3.70s. Est. time left: 00:00:00:33\n", - "20.0%. Run time: 5.97s. Est. time left: 00:00:00:23\n", - "30.1%. Run time: 8.22s. Est. time left: 00:00:00:19\n", - "40.1%. Run time: 10.23s. Est. time left: 00:00:00:15\n", - "50.1%. Run time: 12.55s. Est. time left: 00:00:00:12\n", - "60.1%. Run time: 14.56s. Est. time left: 00:00:00:09\n", - "70.1%. Run time: 17.38s. Est. time left: 00:00:00:07\n", - "80.1%. Run time: 21.86s. Est. time left: 00:00:00:05\n", - "90.2%. Run time: 24.94s. Est. time left: 00:00:00:02\n", - "100.0%. Run time: 27.39s. Est. time left: 00:00:00:00\n", - "Total run time: 27.40s\n" - ] - } - ], - "source": [ - "HEOM_ohmic_aaa_fit = HEOMSolver(\n", - " Hsys,\n", - " (aaabath,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - ")\n", - "results_ohmic_aaa_fit = HEOM_ohmic_aaa_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "80f55ad6", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "gen_plots(aaabath, w, J, t, C, w2, S)" - ] - }, - { - "cell_type": "markdown", - "id": "220c8552", - "metadata": {}, - "source": [ - "ESPIRA I" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "e3d9800d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4\n", - "Correlation function fit:\n", - "\n", - "Result of fitting the real part of |Result of fitting the imaginary part \n", - "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", - " | \n", - " Parameters| a | b | c | d | Parameters| a | b | c | d \n", - " 1 | 2.18e-01 | 9.37e-01 |-2.85e-02 |1.58e+00 | 1 | 7.39e-01 | 9.28e-01 | 4.54e-02 |-5.14e-01 \n", - " 2 | 8.01e-01 | 9.44e-01 | 2.50e-02 |-1.54e+00 | 2 | 9.27e-01 | 9.30e-01 |-4.16e-02 |4.43e-01 \n", - " 3 | 3.92e-01 | 9.87e-01 | 9.25e-04 |-4.15e-02 | 3 |-1.60e+00 | 9.75e-01 | 8.73e-04 |5.98e-02 \n", - " 4 | 4.45e-02 | 9.97e-01 | 1.24e-04 |-4.58e-03 | 4 |-7.70e-02 | 9.93e-01 | 5.62e-04 |1.68e-02 \n", - " | \n", - "A 1-R2 coefficient of 1.48e-04-5.04e-06j was obtained for the the real part of |A 1-R2 coefficient of 8.87e-06-9.73e-06j was obtained for the the imaginary part\n", - "the correlation function. |of the correlation function. \n", - "The current fit took 1.392402 seconds. |The current fit took 1.676065 seconds. \n", - "\n" - ] - } - ], - "source": [ - "tlist4=np.linspace(0,20,1000)\n", - "espibath,fitinfo=obs._approx_by_prony(\"espira-I\",tlist4,Nr=4,Ni=4)\n", - "print(fitinfo[\"summary\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "d5e92a0b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10.0%. Run time: 0.99s. Est. time left: 00:00:00:08\n", - "20.0%. Run time: 1.60s. Est. time left: 00:00:00:06\n", - "30.1%. Run time: 2.04s. Est. time left: 00:00:00:04\n", - "40.1%. Run time: 2.46s. Est. time left: 00:00:00:03\n", - "50.1%. Run time: 2.85s. Est. time left: 00:00:00:02\n", - "60.1%. Run time: 3.23s. Est. time left: 00:00:00:02\n", - "70.1%. Run time: 3.60s. Est. time left: 00:00:00:01\n", - "80.1%. Run time: 3.99s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 4.37s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 4.72s. Est. time left: 00:00:00:00\n", - "Total run time: 4.72s\n" - ] - } - ], - "source": [ - "HEOM_ohmic_espira_fit = HEOMSolver(\n", - " Hsys,\n", - " (espibath,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - ")\n", - "results_ohmic_espira_fit = HEOM_ohmic_espira_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "835ffab8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4\n", - "Correlation function fit:\n", - "\n", - "Result of fitting the real part of |Result of fitting the imaginary part \n", - "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", - " | \n", - " Parameters| a | b | c | d | Parameters| a | b | c | d \n", - " 1 | 3.75e-01 | 9.44e-01 | 3.15e-02 |-1.14e+00 | 1 | 7.81e-01 | 9.30e-01 |-4.43e-02 |4.62e-01 \n", - " 2 | 6.32e-01 | 9.48e-01 |-2.93e-02 |1.10e+00 | 2 | 8.75e-01 | 9.31e-01 | 4.27e-02 |-4.10e-01 \n", - " 3 | 5.12e-02 | 9.97e-01 |-1.24e-04 |4.19e-03 | 3 |-1.59e+00 | 9.75e-01 |-6.95e-04 |-3.86e-02 \n", - " 4 | 4.45e-01 | 9.86e-01 |-8.18e-04 |4.12e-02 | 4 |-7.57e-02 | 9.93e-01 |-4.99e-04 |-1.47e-02 \n", - " | \n", - "A 1-R2 coefficient of 4.02e-05-5.77e-06j was obtained for the the real part of |A 1-R2 coefficient of 7.44e-06+1.93e-06j was obtained for the the imaginary part\n", - "the correlation function. |of the correlation function. \n", - "The current fit took 2.052613 seconds. |The current fit took 1.619823 seconds. \n", - "\n" - ] - } - ], - "source": [ - "tlist4=np.linspace(0,20,1000)\n", - "espibath2,fitinfo=obs._approx_by_prony(\"espira-II\",tlist4,Nr=4,Ni=4)\n", - "print(fitinfo[\"summary\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "6c9c87ab", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10.0%. Run time: 0.71s. Est. time left: 00:00:00:06\n", - "20.0%. Run time: 1.36s. Est. time left: 00:00:00:05\n", - "30.1%. Run time: 1.70s. Est. time left: 00:00:00:03\n", - "40.1%. Run time: 2.05s. Est. time left: 00:00:00:03\n", - "50.1%. Run time: 2.41s. Est. time left: 00:00:00:02\n", - "60.1%. Run time: 2.78s. Est. time left: 00:00:00:01\n", - "70.1%. Run time: 3.14s. Est. time left: 00:00:00:01\n", - "80.1%. Run time: 3.49s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 3.84s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 4.18s. Est. time left: 00:00:00:00\n", - "Total run time: 4.18s\n" - ] - } - ], - "source": [ - "HEOM_ohmic_espira_fit2 = HEOMSolver(\n", - " Hsys,\n", - " (espibath2,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - ")\n", - "results_ohmic_espira2_fit = HEOM_ohmic_espira_fit2.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "0f305b40", - "metadata": {}, - "source": [ - "Finally we plot the dynamics obtained by the different methods" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "5ba2889a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", - "\n", - "plot_result_expectations(\n", - " [\n", - " (\n", - " results_corr_fit_pk[2],\n", - " P11p,\n", - " \"b\",\n", - " \"Correlation Function Fit $k_R=k_I=3$\",\n", - " ),\n", - " (results_spectral_fit_pk[3], P11p, \"r-.\", \"Spectral Density Fit $k_J=4$\"),\n", - " (results_ohmic_corr_fit, P11p, \"r\", \"Correlation Fit Ohmic Bath\"),\n", - " (results_ohmic_sd_fit2, P11p, \"g--\", \"Spectral Density Fit Ohmic Bath\"),\n", - " (results_ohmic_prony_fit, P11p, \"k\", \" Prony Fit\"),\n", - " (results_ohmic_mp_fit, P11p, \"r\", \"Matrix Pencil Fit\"),\n", - " (results_ohmic_es_fit, P11p, \"b-.\", \"ESPRIT Fit\"),\n", - " (results_ohmic_aaa_fit, P11p, \"r-.\", \"Matrix AAA Fit\"),\n", - " (results_ohmic_espira_fit, P11p, \"k\", \"ESPIRA I Fit\"),\n", - " (results_ohmic_espira2_fit, P11p, \"--\", \"ESPIRA II Fit\"),\n", - "\n", - " ],\n", - " axes=axes,\n", - ")\n", - "axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", - "axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", - "axes.legend(loc=0, fontsize=20);\n", - "axes.set_yscale(\"log\")" - ] - }, - { - "cell_type": "markdown", - "id": "d0fc9218", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "e1eb99ec", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "QuTiP: Quantum Toolbox in Python\n", - "================================\n", - "Copyright (c) QuTiP team 2011 and later.\n", - "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", - "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", - "Original developers: R. J. Johansson & P. D. Nation.\n", - "Previous lead developers: Chris Granade & A. Grimsmo.\n", - "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", - "\n", - "QuTiP Version: 5.2.0.dev0+daa7d68\n", - "Numpy Version: 1.26.4\n", - "Scipy Version: 1.14.1\n", - "Cython Version: 3.0.9\n", - "Matplotlib Version: 3.9.2\n", - "Python Version: 3.12.7\n", - "Number of CPUs: 16\n", - "BLAS Info: Generic\n", - "INTEL MKL Ext: None\n", - "Platform Info: Linux (x86_64)\n", - "Installation path: /home/mcditoos/qutip_gsoc_app/qutip\n", - "\n", - "Installed QuTiP family packages\n", - "-------------------------------\n", - "\n", - "No QuTiP family packages installed.\n", - "\n", - "================================================================================\n", - "Please cite QuTiP in your publication.\n", - "================================================================================\n", - "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" - ] - } - ], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "725e989d", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "fa50ddbb", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "assert np.allclose(\n", - " expect(P11p, results_spectral_fit_pk[2].states),\n", - " expect(P11p, results_spectral_fit_pk[3].states),\n", - " rtol=1e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(P11p, results_ohmic_aaa_fit.states),\n", - " expect(P11p, results_spectral_fit_pk[3].states),\n", - " rtol=1e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(P11p, results_ohmic_mp_fit.states),\n", - " expect(P11p, results_spectral_fit_pk[3].states),\n", - " rtol=1e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(P11p, results_ohmic_prony_fit.states),\n", - " expect(P11p, results_spectral_fit_pk[3].states),\n", - " rtol=1e-2,\n", - ")\n", - "\n", - "assert np.allclose(\n", - " expect(P11p, results_ohmic_es_fit.states),\n", - " expect(P11p, results_spectral_fit_pk[3].states),\n", - " rtol=1e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(P11p, results_ohmic_espira_fit.states),\n", - " expect(P11p, results_spectral_fit_pk[3].states),\n", - " rtol=1e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(P11p, results_ohmic_espira2_fit.states),\n", - " expect(P11p, results_spectral_fit_pk[3].states),\n", - " rtol=1e-2,\n", - ")" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "qutip-dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md index c4a6144b..05c508da 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 + jupytext_version: 1.16.1 kernelspec: display_name: qutip-dev language: python @@ -39,7 +39,7 @@ In each case we will use the fit parameters to determine the correlation functio ## Setup -```{code-cell} +```{code-cell} ipython3 import numpy as np from matplotlib import pyplot as plt import qutip @@ -50,11 +50,9 @@ from qutip import ( sigmaz, ) from qutip.solver.heom import ( - HEOMSolver, - SpectralFitter, - CorrelationFitter, - OhmicBath, + HEOMSolver ) +from qutip.core.environment import BosonicEnvironment,OhmicEnvironment # Import mpmath functions for evaluation of gamma and zeta # functions in the expression for the correlation: @@ -67,28 +65,15 @@ mp.pretty = True %matplotlib inline ``` -```{code-cell} -# Solver options: - -options = { - "nsteps": 15000, - "store_states": True, - "rtol": 1e-14, - "atol": 1e-14, - "method": "vern9", - "progress_bar": "enhanced", -} -``` - ## System and bath definition -And let us set up the system Hamiltonian, bath and system measurement operators: +Let us set up the system Hamiltonian, bath and system measurement operators: +++ ### System Hamiltonian -```{code-cell} +```{code-cell} ipython3 # Defining the system Hamiltonian eps = 0 # Energy of the 2-level system. Del = 0.2 # Tunnelling term @@ -98,7 +83,7 @@ rho0 = basis(2, 0) * basis(2, 0).dag() ### System measurement operators -```{code-cell} +```{code-cell} ipython3 # Define some operators with which we will measure the system # 1,1 element of density matrix - corresonding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -134,7 +119,7 @@ The corresponding spectral density for the Ohmic case is: J(\omega) = \omega \alpha e^{- \frac{\omega}{\omega_c}} \end{equation} -```{code-cell} +```{code-cell} ipython3 def ohmic_correlation(t, alpha, wc, beta, s=1): """The Ohmic bath correlation function as a function of t (and the bath parameters). @@ -154,7 +139,7 @@ def ohmic_correlation(t, alpha, wc, beta, s=1): ) ``` -```{code-cell} +```{code-cell} ipython3 def ohmic_spectral_density(w, alpha, wc): """The Ohmic bath spectral density as a function of w (and the bath parameters). @@ -162,13 +147,15 @@ def ohmic_spectral_density(w, alpha, wc): return w * alpha * np.e ** (-w / wc) ``` -```{code-cell} +```{code-cell} ipython3 def ohmic_power_spectrum(w, alpha, wc, beta): """The Ohmic bath power spectrum as a function of w (and the bath parameters). + It is obtained naively using the Fluctuation-Dissipation Theorem + but, this fails at w=0 where the limit should be taken properly """ bose = (1 / (np.e ** (w * beta) - 1)) + 1 - return w * alpha * np.e ** (-abs(w) / wc) * bose * 2 + return w * alpha * np.e ** (-abs(w) / wc) * 2*bose ``` ### Bath and HEOM parameters @@ -177,7 +164,7 @@ def ohmic_power_spectrum(w, alpha, wc, beta): Finally, let's set the bath parameters we will work with and write down some measurement operators: -```{code-cell} +```{code-cell} ipython3 Q = sigmaz() alpha = 3.25 T = 0.5 @@ -187,12 +174,17 @@ s = 1 And set the cut-off for the HEOM hierarchy: -```{code-cell} +```{code-cell} ipython3 # HEOM parameters: # The max_depth defaults to 5 so that the notebook executes more # quickly. Change it to 11 to wait longer for more accurate results. max_depth = 5 +# options used for the differential equation solver, while default works it +# is way slower than using bdf +options = { + "nsteps":15000, "store_states":True, "rtol":1e-12, "atol":1e-12, "method":"bdf", +} ``` ## Building the HEOM bath by fitting the spectral density @@ -211,91 +203,195 @@ where $a, b$ and $c$ are the fit parameters and each is a vector of length $k$. With the spectral density approximation $J_{\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. This can be done quickly using the `SpectralFitter` class, which takes the target spectral density as an array and fits it to the series of **k** underdamped harmonic oscillators with the Meier-Tannor form -```{code-cell} -w = np.linspace(0, 15, 20000) +```{code-cell} ipython3 +w = np.linspace(0, 25, 20000) J = ohmic_spectral_density(w, alpha, wc) ``` -We first initialize our SpectralFitter +The `BosonicEnviroment` class has special construtors that can be used to +create enviroments from arbitrary spectral densities, correlation functions, or +power spectrums. Below we show how to construct a `BosonicEnvironment` from a +user specified function or array + +```{code-cell} ipython3 +# From an array +sd_env=BosonicEnvironment.from_spectral_density(J=J,wlist=w) +``` + +The resulting `BosonicEnvironment` cannot compute the power spectrum, or +correlation function because the temperature of the environment has not been +specified. So the `BosonicEnvironment` is not fully characterized by the +parameters provided + +```{code-cell} ipython3 +# sd_env.power_spectrum(w) +``` + +If we want access to these properties we need to provide the Temperature at Initialization -```{code-cell} -fs = SpectralFitter(T, Q, w, J) +```{code-cell} ipython3 +# From an array +sd_env=BosonicEnvironment.from_spectral_density(J=J,wlist=w,T=T) ``` -To obtain a fit we simply pass our desired spectral density and range, into the ``get_fit`` method. The number of exponents we'll use in our bath is given by Nk +Now our bosonic environment can compute the Power Spectrum of the spectral +density provided -```{code-cell} -bath, fitinfo = fs.get_fit(Nk=1) +```{code-cell} ipython3 +# Here we avoid w=0 +np.allclose(sd_env.power_spectrum(w[1:]),ohmic_power_spectrum(w[1:],alpha,wc,1/T)) +``` + +Specifying the Temperature also gives the `BosonicEnvironment` access to the +correlation function + +```{code-cell} ipython3 +tlist=np.linspace(0,10,500) +plt.plot(tlist,sd_env.correlation_function(tlist),label="BosonicEnvironment (Real Part)") +plt.plot(tlist,ohmic_correlation(tlist,alpha,wc,1/T),"--",label="Original (Real Part)") +plt.plot(tlist,np.imag(sd_env.correlation_function(tlist)),label="BosonicEnvironment (Imaginary Part)") +plt.plot(tlist,np.imag(ohmic_correlation(tlist,alpha,wc,1/T)),"--",label="Original (Imaginary Part)") +plt.ylabel("C(t)") +plt.xlabel("t") +plt.legend() +plt.show() +``` + +One important optional parameter is WMax, when passing arrays to the constructor +it defaults to the maximum value of the array, however when passing a function +we don't need to specify the values on which it is evaluated, and in this case +WMax needs to be specified, Wmax is the cutoff frequency for which the +spectral density, or power spectrum has effectively decayed to zero, after this value the function can be +considered to be essentialy zero + +```{code-cell} ipython3 +# From a function +sd_env2=BosonicEnvironment.from_spectral_density(ohmic_spectral_density,T=T,wMax=10*wc,args={"alpha":alpha,"wc":wc}) +``` + +```{code-cell} ipython3 +tlist=np.linspace(0,10,500) +plt.plot(tlist,sd_env2.correlation_function(tlist)) +plt.plot(tlist,ohmic_correlation(tlist,alpha,wc,1/T),"--") +plt.plot(tlist,np.imag(sd_env2.correlation_function(tlist))) +plt.plot(tlist,np.imag(ohmic_correlation(tlist,alpha,wc,1/T)),"--") +``` + +In this example we considered how to obtain a `BosonicEnvironment` from the spectral density, it can be done analogously from the power spectrum or correlation function using the `from_correlation_function` and `from_power_spectrum` methods. + ++++ + +# Obtaining a decaying Exponential description of the environment + +In order to carry out our HEOM simulation, we need to express the correlation +function as a sum of decaying exponentials, that is we need to express it as + +$$C(\tau)= \sum_{k=0}^{N-1}c_{k}e^{-\nu_{k}t}$$ + +As the correlation function of the environment is tied to it's power spectrum via +a Fourier transform, such a representation of the correlation function implies a +power spectrum of the form + +$$S(\omega)= \sum_{k}2 Re\left( \frac{c_{k}}{\nu_{k}- i \omega}\right)$$ + +There are several ways one can obtain such a decomposition, in this tutorial we +will cover the following approaches: + +- Non-Linear Least Squares: + - On the Spectral Density (`sd`) + - On the Correlation function (`cf`) +- Methods based on the Prony Polynomial + - Prony on the correlation function(`prony`) + - The Matrix Pencil method on the correlation function (`mp`) + - ESPRIT on the correlation function(`esprit`) +- Methods based on rational Approximations + - The AAA algorithm on the Power Spectrum (`aaa`) + ++++ + +# Non-Linear Least Squares +## Obtaining an decaying Exponential Description via the spectral density + ++++ + +Once our `BosonicEnvironment` has been constructed, we can obtain a Decaying +exponnetial representation of the environment, via fitting either the spectral +density, power spectrum or the correlation function. + +First we will show how to do it via fitting the spectral density with the +Nonlinear-Least-Squares method. + +The idea here is that we express our arbitrary spectral density as a sum of +underdamped spectral densities with different coefficients, for which a the +Matsubara decomposition is available. The number of exponents to be kept in the +Matsubara decomposition of each underdamped spectral density needs to be specified + +The output of the fit is a tuple containing an `ExponentialBosonicEnvironment` +and a dictionary that has all the relevant information about the fit performed. +The goodness of the feed is measured via the normalized root mean squared error, +by default the number of terms in the fit increased until the target accuracy +is reached or the maximum number allowed `Nmax` is reached. The default target +is a normalized root mean squared error of $5\times 10^{-6}$, if set to None +the fit is performed only with the maximum number of exponents specified + + +```{code-cell} ipython3 +bath, fitinfo = sd_env.approximate("sd",w,Nmax=4) ``` To obtain an overview of the results of the fit we may take a look at the summary from the ``fitinfo`` -```{code-cell} +```{code-cell} ipython3 print(fitinfo["summary"]) ``` We may see how the number of exponents chosen affects the fit since the approximated functions are available: -```{code-cell} -fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5)) +```{code-cell} ipython3 +fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5)) ax1.plot(w, J, label="Original spectral density") -ax1.plot(w, bath.spectral_density_approx(w), label="Effective fitted SD") +ax1.plot(w, bath.spectral_density(w), "--",label="Effective fitted SD") ax1.set_xlabel(r'$\omega$') ax1.set_ylabel(r'$J$') ax1.legend() -ax2.plot(w, np.abs(J - bath.spectral_density_approx(w)), label="Error") +ax2.plot(w, np.abs(J - bath.spectral_density(w)), label="Error") ax2.set_xlabel(r'$\omega$') -ax2.set_ylabel(r'$J$') +ax2.set_ylabel(r'$|J-J_{approx}|$') ax2.legend() plt.show() ``` -Here we see a surprisingly large discrepancy in our approximated or effective spectral density. This happens because we are not using enough exponentials from each of the underdamped modes to have an appropiate fit. All modes have the same number of exponents, and we set it to 1 which is not enough to model a bath with the temperature considered, let us repeat this with a higher number of exponents. +Here we see a surprisingly large discrepancy in our approximated or effective spectral density. This happens because we are not using enough exponentials from each of the underdamped modes to have an appropiate fit. All modes have the same number of exponents, when not specified it defaults to $1$ which is not enough to model a bath with the temperature considered, let us repeat this with a higher number of exponents. -```{code-cell} -bath, fitinfo = fs.get_fit(Nk=5) -print(fitinfo["summary"]) +```{code-cell} ipython3 +bath, fitinfo = sd_env.approximate("sd",w,Nmax=4,Nk=3) fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5)) ax1.plot(w, J, label="Original spectral density") -ax1.plot(w, bath.spectral_density_approx(w), label="Effective fitted SD") +ax1.plot(w, bath.spectral_density(w), "--",label="Effective fitted SD") ax1.set_xlabel(r'$\omega$') ax1.set_ylabel(r'$J$') ax1.legend() -ax2.plot(w, np.abs(J - bath.spectral_density_approx(w)), label="Error") +ax2.plot(w, np.abs(J - bath.spectral_density(w)), label="Error") ax2.set_xlabel(r'$\omega$') -ax2.set_ylabel(r'$J$') +ax2.set_ylabel(r'$|J-J_{approx}|$') ax2.legend() plt.show() ``` -Since the number of exponents increases simulation time one should go with the least amount of exponents that correctly describe the bath properties (Power spectrum, Spectral density and the correlation function). When the number of exponents is not specified it defaults to 5. +Since the number of exponents increases simulation time one should go with the least amount of exponents that correctly describe the bath properties (Power spectrum, Spectral density and the correlation function). +++ -By default the ``get_fit`` method, has a threshold normalized root mean squared error (NRMSE) of $5\times 10^{-6}$ and selects the number of oscillators automatically to obtain that value. One may also specify the number of oscillators that is used with the optional argument N, or may want a more accurate NRMSE, which can be specified with the final_rmse optional argument - -```{code-cell} -bath, fitinfo = fs.get_fit(final_rmse=1e-6) -print(fitinfo["summary"]) -``` - -Alternatively one may choose the number of oscillators in the fit instead of a desired NRMSE - -```{code-cell} -fittedbath, fitinfo = fs.get_fit(N=4) -print(fitinfo["summary"]) -``` - Let's take a closer look at our last fit by plotting the contribution of each term of the fit: -```{code-cell} +```{code-cell} ipython3 # Plot the components of the fit separately: plt.rcParams["font.size"] = 25 plt.rcParams["figure.figsize"] = (10, 5) @@ -306,7 +402,7 @@ def plot_fit(func, J, w, lam, gamma, w0): and how they contribute to the full fit one by one""" total = 0 for i in range(len(lam)): - component = func(w, [lam[i]], [gamma[i]], [w0[i]]) + component = func(w, lam[i], gamma[i], w0[i]) total += component plt.plot(w, J, "r--", linewidth=2, label="original") plt.plot(w, total, label=rf"$k={i+1}$") @@ -322,7 +418,7 @@ def plot_fit_components(func, J, w, lam, gamma, w0): and how they contribute to the full fit""" plt.plot(w, J, "r--", linewidth=2, label="original") for i in range(len(lam)): - component = func(w, [lam[i]], [gamma[i]], [w0[i]]) + component = func(w, lam[i], gamma[i], w0[i]) plt.plot(w, component, label=rf"$k={i+1}$") plt.xlabel(r"$\omega$") plt.ylabel(r"$J(\omega)$") @@ -330,22 +426,28 @@ def plot_fit_components(func, J, w, lam, gamma, w0): plt.show() -lam, gamma, w0 = fitinfo["params"] -plot_fit(SpectralFitter._meier_tannor_SD, J, w, lam, gamma, w0) +lam=fitinfo["params"][:,0] +gamma=fitinfo["params"][:,1] +w0 = fitinfo["params"][:,2] +def _sd_fit_model(wlist, a, b, c): + return ( + 2 * a * b * wlist / ((wlist + c)**2 + b**2) / ((wlist - c)**2 + b**2) + ) +plot_fit(_sd_fit_model, J, w, lam, gamma, w0) ``` -```{code-cell} -plot_fit_components(SpectralFitter._meier_tannor_SD, J, w, lam, gamma, w0) +```{code-cell} ipython3 +plot_fit_components(_sd_fit_model, J, w, lam, gamma, w0) ``` And let's also compare the power spectrum of the fit and the analytical spectral density: -```{code-cell} +```{code-cell} ipython3 def plot_power_spectrum(alpha, wc, beta, save=True): """Plot the power spectrum of a fit against the actual power spectrum.""" w = np.linspace(-10, 10, 50000) s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta) - s_fit = fittedbath.power_spectrum_approx(w) + s_fit = bath.power_spectrum(w) fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) axes.plot(w, s_orig, "r", linewidth=2, label="original") axes.plot(w, np.real(s_fit), "b", linewidth=2, label="fit") @@ -361,36 +463,28 @@ def plot_power_spectrum(alpha, wc, beta, save=True): plot_power_spectrum(alpha, wc, 1 / T, save=False) ``` -Now that we have a good fit to the spectral density, Let us obtain its dynamics, by passing our ``FitSpectral`` bath specifications into the ``HEOMSolver`` - -```{code-cell} -tlist = np.linspace(0, 30 * np.pi / Del, 600) -HEOM_spectral_fit = HEOMSolver( - Hsys, - fittedbath, - max_depth=4, - options=options, -) -result_spectral = HEOM_spectral_fit.run(rho0, tlist) -``` - -Now if we want to see the systems's behaviour as we change the Number of terms in the fit, we may use this auxiliary function +Now if we want to see the systems's behaviour as we change the number of terms in the fit, we may use this auxiliary function. -```{code-cell} +```{code-cell} ipython3 def generate_spectrum_results(Q, N, Nk, max_depth): """Run the HEOM with the given bath parameters and and return the results of the evolution. """ - fs = SpectralFitter(T, Q, w, J) - bath, _ = fs.get_fit(N, Nk=Nk) + # sigma = 0.0001 + # J_max = abs(max(J, key=abs)) + # lower = [-100*J_max, 0.1*wc, 0.1*wc] + # guess = [J_max, wc, wc] + # upper = [100*J_max, 100*wc, 100*wc] + bath, fitinfo= sd_env.approximate("sd",w,Nmax=N,Nk=Nk,target_rmse=None)#,lower=lower,upper=upper,guess=guess,sigma=sigma) tlist = np.linspace(0, 30 * np.pi / Del, 600) # This problem is a little stiff, so we use the BDF method to solve # the ODE ^^^ print(f"Starting calculations for N={N}, Nk={Nk} and max_depth={max_depth} ... ") + HEOM_spectral_fit = HEOMSolver( Hsys, - bath, + (bath,Q), max_depth=max_depth, options=options, ) @@ -398,9 +492,7 @@ def generate_spectrum_results(Q, N, Nk, max_depth): return results_spectral_fit ``` -Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow. - -```{code-cell} +```{code-cell} ipython3 def plot_result_expectations(plots, axes=None): """Plot the expectation values of operators as functions of time. @@ -439,8 +531,10 @@ def plot_result_expectations(plots, axes=None): return fig ``` -```{code-cell} -# Generate results for different number of lorentzians in fit: +Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow. + +```{code-cell} ipython3 +# # Generate results for different number of lorentzians in fit: results_spectral_fit_pk = [ generate_spectrum_results(Q, n, Nk=1, max_depth=max_depth) for n in range(1, 5) @@ -459,7 +553,7 @@ plot_result_expectations( ); ``` -```{code-cell} +```{code-cell} ipython3 # generate results for different number of Matsubara terms per Lorentzian # for max number of Lorentzians: @@ -474,14 +568,14 @@ plot_result_expectations( result, P11p, "rand", - f"P11 (spectral fit) K={nk}", + f"P11 (spectral fit) K={nk+1}", ) for nk, result in zip(Nk_list, results_spectral_fit_nk) ] ); ``` -```{code-cell} +```{code-cell} ipython3 # Generate results for different depths: Nc_list = range(2, max_depth) @@ -499,12 +593,12 @@ plot_result_expectations( ) for nc, result in zip(Nc_list, results_spectral_fit_nc) ] -); + ); ``` #### We now combine the fitting and correlation function data into one large plot. Here we define a function to plot everything together -```{code-cell} +```{code-cell} ipython3 def gen_plots(fs, w, J, t, C, w2, S): def plot_cr_fit_vs_actual(t, C, func, axes): """Plot the C_R(t) fit.""" @@ -558,7 +652,7 @@ def gen_plots(fs, w, J, t, C, w2, S): def plot_jw_fit_vs_actual(w, J, axes): """Plot the J(w) fit.""" - J_fit = fs.spectral_density_approx(w) + J_fit = fs.spectral_density(w) axes.plot( w, @@ -584,7 +678,7 @@ def gen_plots(fs, w, J, t, C, w2, S): """Plot the S(w) fit.""" # avoid the pole in the fit around zero: - s_fit = fs.power_spectrum_approx(w2) + s_fit = fs.power_spectrum(w2) axes.plot(w2, S, "r", linewidth=3) axes.plot(w2, s_fit, "g", dashes=[3, 3], linewidth=2) @@ -603,13 +697,13 @@ def gen_plots(fs, w, J, t, C, w2, S): plot_cr_fit_vs_actual( t, C, - lambda t: fs.correlation_function_approx(t), + lambda t: fs.correlation_function(t), axes=fig.add_subplot(grid[0, 0]), ) plot_ci_fit_vs_actual( t, C, - lambda t: np.imag(fs.correlation_function_approx(t)), + lambda t: np.imag(fs.correlation_function(t)), axes=fig.add_subplot(grid[0, 1]), ) plot_jw_fit_vs_actual( @@ -627,53 +721,57 @@ def gen_plots(fs, w, J, t, C, w2, S): #### And finally plot everything together -```{code-cell} +```{code-cell} ipython3 t = np.linspace(0, 15, 1000) C = ohmic_correlation(t, alpha, wc, 1 / T) w2 = np.concatenate((-np.linspace(10, 1e-2, 100), np.linspace(1e-2, 10, 100))) S = ohmic_power_spectrum(w2, alpha, wc, 1 / T) -gen_plots(fittedbath, w, J, t, C, w2, S) +gen_plots(bath, w, J, t, C, w2, S) ``` -## Building the HEOM bath by fitting the correlation function +## Obtaining an decaying exponential description via the Correlation function +++ Having successfully fitted the spectral density and used the result to calculate the Matsubara expansion and terminator for the HEOM bosonic bath, we now proceed to the second case of fitting the correlation function itself instead. -Here we fit the real and imaginary parts separately, using the following ansatz +Here we fit the real and imaginary parts separately, using the following ansatz $$C_R^F(t) = \sum_{i=1}^{k_R} c_R^ie^{-\gamma_R^i t}\cos(\omega_R^i t)$$ $$C_I^F(t) = \sum_{i=1}^{k_I} c_I^ie^{-\gamma_I^i t}\sin(\omega_I^i t)$$ -Analogously to the spectral density case, one may use the `CorrelationFitter` class +Analogously to the spectral density case, one may use the `approx_by_cf_fit` method, the main difference with respect to the spectral density fit, is that now we are perfoming two fits, one for the real part and another one for the imaginary part -```{code-cell} -t = np.linspace(0, 15, 1500) -C = ohmic_correlation(t, alpha=alpha, wc=wc, beta=1 / T) -``` ++++ -```{code-cell} -fc = CorrelationFitter(Q, T, t, C) -``` +The ansatz used is not good for functions where -```{code-cell} -bath, fitinfo = fc.get_fit(Ni=4, Nr=4) -print(fitinfo["summary"]) -``` +$$C_I^F(0) \neq 0$$ -```{code-cell} -gen_plots(bath, w, J, t, C, w2, S) -``` +The keyword `full_ansatz` which defaults to False. allows for the usage of a +more general ansatz, the fit however tends to be significantly slower, never +the less it can reach a similar level of accuracy with a lower amount of exponents + +When full_ansatz is True. the ansatz used corresponds to + +\begin{align} +\operatorname{Re}[C(t)] = \sum_{k=1}^{N_r} \operatorname{Re}\Bigl[ + (a_k + \mathrm i d_k) \mathrm e^{(b_k + \mathrm i c_k) t}\Bigl] + , +\\ +\operatorname{Im}[C(t)] = \sum_{k=1}^{N_i} \operatorname{Im}\Bigl[ + (a'_k + \mathrm i d'_k) \mathrm e^{(b'_k + \mathrm i c'_k) t} + \Bigr]. +\end{align} -```{code-cell} +```{code-cell} ipython3 def generate_corr_results(N, max_depth): tlist = np.linspace(0, 30 * np.pi / Del, 600) - bath, _ = fc.get_fit(Ni=N, Nr=N) + bath_corr ,fitinfo= sd_env.approximate("cf",tlist=t,Ni_max=N,Nr_max=N,maxfev=1e8,target_rsme=None) HEOM_corr_fit = HEOMSolver( Hsys, - bath, + (bath_corr,Q), max_depth=max_depth, options=options, ) @@ -683,18 +781,17 @@ def generate_corr_results(N, max_depth): return results_corr_fit -# Generate results for different number of exponentials in fit: +# # Generate results for different number of exponentials in fit: results_corr_fit_pk = [ print(f"{i + 1}") or generate_corr_results( i, max_depth=max_depth, ) - for i in range(1, 4) -] + for i in range(1, 4)] ``` -```{code-cell} +```{code-cell} ipython3 plot_result_expectations( [ ( @@ -708,7 +805,7 @@ plot_result_expectations( ); ``` -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) plot_result_expectations( @@ -741,74 +838,228 @@ axes.legend(loc=0, fontsize=20); As the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced quickly by using the OhmicBath class. This allows for rapid implementation of fitted ohmic baths via the correlation function or spectral density -```{code-cell} -obs = OhmicBath(T, Q, alpha, wc, s) +```{code-cell} ipython3 +obs = OhmicEnvironment(T, alpha, wc,s=1) +tlist = np.linspace(0, 30 * np.pi / Del, 600) ``` -```{code-cell} -Obath, fitinfo = obs.make_correlation_fit(t, rmse=2e-4) +Just like the other `BosonicEnvironment` we can obtain a decaying exponential +representation of the environment via the `approx_by_cf_fit` and +`approx_by_sd_fit` methods. + +```{code-cell} ipython3 +tlist = np.linspace(0, 30 * np.pi / Del, 5000) + +Obath, fitinfo = obs.approximate(method="cf",tlist=tlist,Nr_max=4,Ni_max=4,maxfev=1e9,target_rsme=None) print(fitinfo["summary"]) +HEOM_ohmic_corr_fit = HEOMSolver( + Hsys, + (Obath,Q), + max_depth=max_depth, + options=options, +) +results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 +Obath2, fitinfo = obs.approximate(method="sd",wlist=w,Nmax=4,Nk=3) +print(fitinfo["summary"]) tlist = np.linspace(0, 30 * np.pi / Del, 600) -HEOM_ohmic_corr_fit = HEOMSolver( +HEOM_ohmic_sd_fit = HEOMSolver( Hsys, - Obath, - max_depth=5, + (Obath2,Q), + max_depth=max_depth, options=options, ) -results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist) +results_ohmic_sd_fit2 = HEOM_ohmic_sd_fit.run(rho0, tlist) +``` + +# Methods based on the Prony Polinomial + +The Prony polynomial forms the mathematical foundation for many spectral analysis techniques that estimate frequencies, damping factors, and amplitudes of signals. These methods work by interpreting a given signal as a sum of complex exponentials and deriving a polynomial whose roots correspond to the frequencies or poles of the system. + +The methods consider a signal + +$$f(t)=\sum_{k=0}^{N-1} c_{k} e^{-\nu_{k} t} =\sum_{k=0}^{N-1} c_{k} z_{k}^{t} $$ + +The $z_{k}$ can be seen as the solution of the Prony Polynomial, which we write in terms of Hankel matrices as + +\begin{align} + H_{N,M}=V_{N,M-1}(z) diag((c_k)_{k=1}^{M-1}) V_{M,M-1}(z)^{T} +\end{align} + +where $V_{N,M}(z)$ is the Vandermonde matrix given by + + +$$V_{M,N}(z)=\begin{pmatrix} +1 &1 &\dots &1 \\ +z_{1} & z_{2} &\dots & z_{N} \\ +z_{1}^{2} & z_{2}^{2} &\dots & z_{N}^{2} \\ +\vdots & \vdots & \ddots & \vdots \\ +z_{1}^{M} & z_{2}^{M} &\dots & z_{N}^{M} \\ +\end{pmatrix}$$ + +By obtaining the roots of this polynomial one can obtain the damping rate and the frequency of each mode, the amplitude can lated be obtained by solving the least-squares Vandermonde system given by + +$$ V_{N,M}(z)c = f $$ + +Where $M$ is the length, of the signal, and $f=f(t_{sample})$ is the signal evaluated in the sampling points,is a vector $c = (c_{1}, \dots, c_{N})$. + +The main difference between the methods is the way one obtains the roots of the polynomial, typically whether this system is solved or a low rank approximation is found for the polynomial, [this article](https://academic.oup.com/imajna/article-abstract/43/2/789/6525860?redirectedFrom=fulltext) is a good reference, the QuTiP implementations are based on it, and the matlab implementations made available by the authors + ++++ + +## Using the Original Prony Method on the Correlation Function + +The method is available via `approx_by_prony`. Compared to the other approaches showed so far. The Prony based methods, shine on their simplicity no information needs to be known about the function, and one just needs to provide the sampling points, and the Number of Exponents one desires + +```{code-cell} ipython3 +tlist2=np.linspace(0,40,100) ``` -```{code-cell} -Obath, fitinfo = obs.make_spectral_fit(w, rmse=2e-4) +```{code-cell} ipython3 +pbath,fitinfo=obs.approximate("prony",tlist2,Nr=4,Ni=4) print(fitinfo["summary"]) +HEOM_ohmic_prony_fit = HEOMSolver( + Hsys, + (pbath,Q), + max_depth=max_depth, + options=options, +) +results_ohmic_prony_fit = HEOM_ohmic_prony_fit.run(rho0, tlist) +``` + +```{code-cell} ipython3 +gen_plots(pbath, w, J, t, C, w2, S) ``` -```{code-cell} -HEOM_ohmic_spectral_fit = HEOMSolver( +## Using the matrix Pencil Method on the Correlation Function + +```{code-cell} ipython3 +mpbath,fitinfo=obs.approximate(method="mp",tlist=tlist2,Nr=6,separate=False) +print(fitinfo["summary"]) +HEOM_ohmic_mp_fit = HEOMSolver( Hsys, - Obath, - max_depth=5, + (mpbath,Q), + max_depth=max_depth, options=options, ) -results_ohmic_spectral_fit = HEOM_ohmic_spectral_fit.run(rho0, tlist) +results_ohmic_mp_fit = HEOM_ohmic_mp_fit.run(rho0, tlist) +``` + +```{code-cell} ipython3 +gen_plots(mpbath, w, J, t, C, w2, S) +``` + +## Using the ESPRIT Method on the Correlation Function + +```{code-cell} ipython3 + +esbath,fitinfo=obs.approximate("esprit",tlist2,Nr=4,separate=False) +print(fitinfo["summary"]) +HEOM_ohmic_es_fit = HEOMSolver( + Hsys, + (esbath,Q), + max_depth=max_depth, + options=options, +) +results_ohmic_es_fit = HEOM_ohmic_es_fit.run(rho0, tlist) +``` + +```{code-cell} ipython3 +gen_plots(esbath, w, J, t, C, w2, S) +``` + +## Using the AAA Algorithm + +```{code-cell} ipython3 +aaabath,fitinfo=obs.approximate("aaa",np.concatenate((-np.logspace(3,-8,3500),np.logspace(-8,3,3500))),N_max=8,tol=1e-15) +print(fitinfo["summary"]) +``` + +```{code-cell} ipython3 +HEOM_ohmic_aaa_fit = HEOMSolver( + Hsys, + (aaabath,Q), + max_depth=max_depth, + options=options, +) +results_ohmic_aaa_fit = HEOM_ohmic_aaa_fit.run(rho0, tlist) +``` + +```{code-cell} ipython3 +gen_plots(aaabath, w, J, t, C, w2, S) +``` + +ESPIRA I + +```{code-cell} ipython3 +tlist4=np.linspace(0,20,1000) +espibath,fitinfo=obs._approx_by_prony("espira-I",tlist4,Nr=4,Ni=4) +print(fitinfo["summary"]) ``` -```{code-cell} +```{code-cell} ipython3 +HEOM_ohmic_espira_fit = HEOMSolver( + Hsys, + (espibath,Q), + max_depth=max_depth, + options=options, +) +results_ohmic_espira_fit = HEOM_ohmic_espira_fit.run(rho0, tlist) +``` + +```{code-cell} ipython3 +tlist4=np.linspace(0,20,1000) +espibath2,fitinfo=obs._approx_by_prony("espira-II",tlist4,Nr=4,Ni=4) +print(fitinfo["summary"]) +``` + +```{code-cell} ipython3 +HEOM_ohmic_espira_fit2 = HEOMSolver( + Hsys, + (espibath2,Q), + max_depth=max_depth, + options=options, +) +results_ohmic_espira2_fit = HEOM_ohmic_espira_fit2.run(rho0, tlist) +``` + +Finally we plot the dynamics obtained by the different methods + +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) plot_result_expectations( [ - # ( - # results_corr_fit_pk[0], P11p, - # 'y', "Correlation Function Fit $k_R=k_I=1$", - # ), ( results_corr_fit_pk[2], P11p, - "y-.", - "Correlation Function Fit $k_R=k_I=3$", + "b", + "Correlation Function Fit $k_R=k_I=4$", ), - (results_spectral_fit_pk[0], P11p, "b", "Spectral Density Fit $k_J=1$"), - (results_spectral_fit_pk[2], P11p, "g--", "Spectral Density Fit $k_J=3$"), (results_spectral_fit_pk[3], P11p, "r-.", "Spectral Density Fit $k_J=4$"), - (results_ohmic_spectral_fit, P11p, "g-.", "Spectral Density Fit Ohmic Bath"), - (results_ohmic_corr_fit, P11p, "k-.", "Correlation Fit Ohmic Bath"), + (results_ohmic_corr_fit, P11p, "r", "Correlation Fit Ohmic Bath"), + (results_ohmic_sd_fit2, P11p, "g--", "Spectral Density Fit Ohmic Bath"), + (results_ohmic_prony_fit, P11p, "k", " Prony Fit"), + (results_ohmic_mp_fit, P11p, "r", "Matrix Pencil Fit"), + (results_ohmic_es_fit, P11p, "b-.", "ESPRIT Fit"), + (results_ohmic_aaa_fit, P11p, "r-.", "Matrix AAA Fit"), + (results_ohmic_espira_fit, P11p, "k", "ESPIRA I Fit"), + (results_ohmic_espira2_fit, P11p, "--", "ESPIRA II Fit"), + ], axes=axes, ) - -axes.set_yticks([0.6, 0.8, 1]) axes.set_ylabel(r"$\rho_{11}$", fontsize=30) axes.set_xlabel(r"$t\;\omega_c$", fontsize=30) axes.legend(loc=0, fontsize=20); +axes.set_yscale("log") ``` ## About -```{code-cell} +```{code-cell} ipython3 qutip.about() ``` @@ -816,10 +1067,46 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 + assert np.allclose( expect(P11p, results_spectral_fit_pk[2].states), expect(P11p, results_spectral_fit_pk[3].states), rtol=1e-2, ) +assert np.allclose( + expect(P11p, results_ohmic_aaa_fit.states), + expect(P11p, results_spectral_fit_pk[3].states), + rtol=1e-2, +) +assert np.allclose( + expect(P11p, results_ohmic_mp_fit.states), + expect(P11p, results_spectral_fit_pk[3].states), + rtol=1e-2, +) +assert np.allclose( + expect(P11p, results_ohmic_prony_fit.states), + expect(P11p, results_spectral_fit_pk[3].states), + rtol=1e-2, +) + +assert np.allclose( + expect(P11p, results_ohmic_es_fit.states), + expect(P11p, results_spectral_fit_pk[3].states), + rtol=1e-2, +) +assert np.allclose( + expect(P11p, results_ohmic_espira_fit.states), + expect(P11p, results_spectral_fit_pk[3].states), + rtol=1e-2, +) +assert np.allclose( + expect(P11p, results_ohmic_espira2_fit.states), + expect(P11p, results_spectral_fit_pk[3].states), + rtol=1e-2, +) +``` + +```{code-cell} ipython3 + ``` diff --git a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb deleted file mode 100644 index a6d13a83..00000000 --- a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.ipynb +++ /dev/null @@ -1,743 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e19a5cae", - "metadata": {}, - "source": [ - "# HEOM 1e: Spin-Bath model (pure dephasing)" - ] - }, - { - "cell_type": "markdown", - "id": "8a180f04", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded in a set of auxiliary density matrices.\n", - "\n", - "In this example we show the evolution of a single two-level system in contact with a single Bosonic environment. The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", - "\n", - "The Bosonic environment is implicitly assumed to obey a particular Hamiltonian (see paper), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", - "\n", - "In the example below we show how to model the overdamped Drude-Lorentz Spectral Density, commonly used with the HEOM. We show how to do the Matsubara and Pade analytical decompositions, as well as how to fit the latter with a finite set of approximate exponentials. This differs from examble 1a in that we assume that the system and coupling parts of the Hamiltonian commute, hence giving an analytically solvable ''pure dephasing'' model. This is a useful example to look at when introducing other approximations (e.g., fitting of correlation functions) to check for validity/convergence against the analytical results. (Note that, generally, for the fitting examples, the pure dephasing model is the 'worst possible case'. \n", - "\n", - "### Drude-Lorentz spectral density\n", - "\n", - "The Drude-Lorentz spectral density is:\n", - "\n", - "$$J(\\omega)=\\omega \\frac{2\\lambda\\gamma}{{\\gamma}^2 + \\omega^2}$$\n", - "\n", - "where $\\lambda$ scales the coupling strength, and $\\gamma$ is the cut-off frequency.\n", - "We use the convention,\n", - "\\begin{equation*}\n", - "C(t) = \\int_0^{\\infty} d\\omega \\frac{J_D(\\omega)}{\\pi}[\\coth(\\beta\\omega) \\cos(\\omega \\tau) - i \\sin(\\omega \\tau)]\n", - "\\end{equation*}\n", - "\n", - "With the HEOM we must use an exponential decomposition:\n", - "\n", - "\\begin{equation*}\n", - "C(t)=\\sum_{k=0}^{k=\\infty} c_k e^{-\\nu_k t}\n", - "\\end{equation*}\n", - "\n", - "The Matsubara decomposition of the Drude-Lorentz spectral density is given by:\n", - "\n", - "\\begin{equation*}\n", - " \\nu_k = \\begin{cases}\n", - " \\gamma & k = 0\\\\\n", - " {2 \\pi k} / {\\beta \\hbar} & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "\\begin{equation*}\n", - " c_k = \\begin{cases}\n", - " \\lambda \\gamma (\\cot(\\beta \\gamma / 2) - i) / \\hbar & k = 0\\\\\n", - " 4 \\lambda \\gamma \\nu_k / \\{(nu_k^2 - \\gamma^2)\\beta \\hbar^2 \\} & k \\geq 1\\\\\n", - " \\end{cases}\n", - "\\end{equation*}\n", - "\n", - "Note that in the above, and the following, we set $\\hbar = k_\\mathrm{B} = 1$." - ] - }, - { - "cell_type": "markdown", - "id": "2d6bb5b5", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "469bffd5", - "metadata": {}, - "outputs": [], - "source": [ - "import contextlib\n", - "import time\n", - "\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "import scipy\n", - "from scipy.optimize import curve_fit\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " basis,\n", - " expect,\n", - " liouvillian,\n", - " sigmax,\n", - " sigmaz,\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver\n", - ")\n", - "from qutip.core.environment import (\n", - " DrudeLorentzEnvironment,\n", - " system_terminator\n", - ")\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "fa8e2803", - "metadata": {}, - "source": [ - "## Helper functions\n", - "\n", - "Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "69d1848b", - "metadata": {}, - "outputs": [], - "source": [ - "def cot(x):\n", - " \"\"\" Vectorized cotangent of x. \"\"\"\n", - " return 1. / np.tan(x)\n", - "\n", - "\n", - "def coth(x):\n", - " \"\"\" Vectorized hyperbolic cotangent of x. \"\"\"\n", - " return 1. / np.tanh(x)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3f6fc1c4", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_result_expectations(plots, axes=None):\n", - " \"\"\" Plot the expectation values of operators as functions of time.\n", - "\n", - " Each plot in plots consists of (solver_result, measurement_operation,\n", - " color, label).\n", - " \"\"\"\n", - " if axes is None:\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " fig_created = True\n", - " else:\n", - " fig = None\n", - " fig_created = False\n", - "\n", - " # add kw arguments to each plot if missing\n", - " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", - " for result, m_op, color, label, kw in plots:\n", - " if m_op is None:\n", - " t, exp = result\n", - " else:\n", - " t = result.times\n", - " exp = np.real(expect(result.states, m_op))\n", - " kw.setdefault(\"linewidth\", 2)\n", - " axes.plot(t, exp, color, label=label, **kw)\n", - "\n", - " if fig_created:\n", - " axes.legend(loc=0, fontsize=12)\n", - " axes.set_xlabel(\"t\", fontsize=28)\n", - "\n", - " return fig" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a78edcad", - "metadata": {}, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\" Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "399060b9", - "metadata": {}, - "outputs": [], - "source": [ - "# Solver options:\n", - "\n", - "options = {\n", - " \"nsteps\": 15000,\n", - " \"store_states\": True,\n", - " \"rtol\": 1e-14,\n", - " \"atol\": 1e-14,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "8de86a56", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "And let us set up the system Hamiltonian, bath and system measurement operators:" - ] - }, - { - "cell_type": "markdown", - "id": "f480170d", - "metadata": {}, - "source": [ - "Here we set $H_{sys}=0$, which means the interaction Hamiltonian and the system Hamiltonian commute, and we can compare the numerical results to a known analytical one. We could in principle keep $\\epsilon \\neq 0$, but it just introduces fast system oscillations, so it is more convenient to set it to zero." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "08793af6", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the system Hamiltonian\n", - "eps = 0.0 # Energy of the 2-level system.\n", - "Del = 0.0 # Tunnelling term\n", - "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "89bebf47", - "metadata": {}, - "outputs": [], - "source": [ - "# System-bath coupling (Drude-Lorentz spectral density)\n", - "Q = sigmaz() # coupling operator\n", - "\n", - "# Bath properties:\n", - "gamma = 0.5 # cut off frequency\n", - "lam = 0.1 # coupling strength\n", - "T = 0.5\n", - "beta = 1. / T\n", - "\n", - "# HEOM parameters:\n", - "# cut off parameter for the bath:\n", - "NC = 6\n", - "# number of exponents to retain in the Matsubara expansion\n", - "# of the correlation function:\n", - "Nk = 3\n", - "\n", - "# Times to solve for\n", - "tlist = np.linspace(0, 50, 1000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "40892dda", - "metadata": {}, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresponding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresponding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "6973de51", - "metadata": {}, - "source": [ - "To get a non-trivial result we prepare the initial state in a superposition, and see how the bath destroys the coherence." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "00b82c7e", - "metadata": {}, - "outputs": [], - "source": [ - "# Initial state of the system.\n", - "psi = (basis(2, 0) + basis(2, 1)).unit()\n", - "rho0 = psi * psi.dag()" - ] - }, - { - "cell_type": "markdown", - "id": "71b927f6", - "metadata": {}, - "source": [ - "We then define our environment, from which all the different simulations will \n", - "be obtained" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ac7b875e", - "metadata": {}, - "outputs": [], - "source": [ - "env = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=Nk)" - ] - }, - { - "cell_type": "markdown", - "id": "4741a085", - "metadata": {}, - "source": [ - "## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c968a2e", - "metadata": {}, - "outputs": [], - "source": [ - "with timer(\"RHS construction time\"):\n", - " env_mats=env.approx_by_matsubara(Nk=Nk)\n", - " HEOMMats = HEOMSolver(Hsys, (env_mats,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMats = HEOMMats.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1721076c", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot the results so far\n", - "plot_result_expectations([\n", - " (resultMats, P11p, 'b', \"P11 Matsubara\"),\n", - " (resultMats, P12p, 'r', \"P12 Matsubara\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "ff8fd434", - "metadata": {}, - "source": [ - "## Simulation 2: Matsubara decomposition (including terminator)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "851f6695", - "metadata": {}, - "outputs": [], - "source": [ - "with timer(\"RHS construction time\"):\n", - " env_mats,delta=env.approx_by_matsubara(Nk=Nk,compute_delta=True)\n", - " Ltot = liouvillian(Hsys) + system_terminator(Q,delta)\n", - " HEOMMatsT = HEOMSolver(Ltot, (env_mats,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultMatsT = HEOMMatsT.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a3c97d27", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot the results\n", - "plot_result_expectations([\n", - " (resultMats, P11p, 'b', \"P11 Matsubara\"),\n", - " (resultMats, P12p, 'r', \"P12 Matsubara\"),\n", - " (resultMatsT, P11p, 'r--', \"P11 Matsubara and terminator\"),\n", - " (resultMatsT, P12p, 'b--', \"P12 Matsubara and terminator\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "84382ad4", - "metadata": {}, - "source": [ - "## Simulation 3: Pade decomposition\n", - "\n", - "As in example 1a, we can compare to Pade and Fitting approaches." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b3dc619a", - "metadata": {}, - "outputs": [], - "source": [ - "with timer(\"RHS construction time\"):\n", - " env_pade=env.approx_by_pade(Nk=Nk)\n", - " HEOMPade = HEOMSolver(Hsys, (env_pade,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultPade = HEOMPade.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "81b33bc8", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot the results\n", - "plot_result_expectations([\n", - " (resultMatsT, P11p, 'b', \"P11 Matsubara (+term)\"),\n", - " (resultMatsT, P12p, 'r', \"P12 Matsubara (+term)\"),\n", - " (resultPade, P11p, 'r--', \"P11 Pade\"),\n", - " (resultPade, P12p, 'b--', \"P12 Pade\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "17a35b89", - "metadata": {}, - "source": [ - "## Simulation 4: Fitting approach" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "73bc0976", - "metadata": {}, - "outputs": [], - "source": [ - "tfit=np.linspace(0,10,1000)\n", - "with timer(\"RHS construction time\"):\n", - " bath,_ = env.approx_by_cf_fit(tfit,Ni_max=1,Nr_max=3,target_rsme=None)\n", - " HEOMFit = HEOMSolver(Hsys, (bath,Q), NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " resultFit = HEOMFit.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "3e7e6de0", - "metadata": {}, - "source": [ - "## Analytic calculations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b4633a28", - "metadata": {}, - "outputs": [], - "source": [ - "def pure_dephasing_evolution_analytical(tlist, wq, ck, vk):\n", - " \"\"\"\n", - " Computes the propagating function appearing in the pure dephasing model.\n", - "\n", - " Parameters\n", - " ----------\n", - " t: float\n", - " A float specifying the time at which to calculate the integral.\n", - "\n", - " wq: float\n", - " The qubit frequency in the Hamiltonian.\n", - "\n", - " ck: ndarray\n", - " The list of coefficients in the correlation function.\n", - "\n", - " vk: ndarray\n", - " The list of frequencies in the correlation function.\n", - "\n", - " Returns\n", - " -------\n", - " integral: float\n", - " The value of the integral function at time t.\n", - " \"\"\"\n", - " evolution = np.array([\n", - " np.exp(-1j * wq * t - correlation_integral(t, ck, vk))\n", - " for t in tlist\n", - " ])\n", - " return evolution\n", - "\n", - "\n", - "def correlation_integral(t, ck, vk):\n", - " r\"\"\"\n", - " Computes the integral sum function appearing in the pure dephasing model.\n", - "\n", - " If the correlation function is a sum of exponentials then this sum\n", - " is given by:\n", - "\n", - " .. math:\n", - "\n", - " \\int_0^{t}d\\tau D(\\tau) = \\sum_k\\frac{c_k}{\\mu_k^2}e^{\\mu_k t}\n", - " + \\frac{\\bar c_k}{\\bar \\mu_k^2}e^{\\bar \\mu_k t}\n", - " - \\frac{\\bar \\mu_k c_k + \\mu_k \\bar c_k}{\\mu_k \\bar \\mu_k} t\n", - " + \\frac{\\bar \\mu_k^2 c_k + \\mu_k^2 \\bar c_k}{\\mu_k^2 \\bar \\mu_k^2}\n", - "\n", - " Parameters\n", - " ----------\n", - " t: float\n", - " A float specifying the time at which to calculate the integral.\n", - "\n", - " ck: ndarray\n", - " The list of coefficients in the correlation function.\n", - "\n", - " vk: ndarray\n", - " The list of frequencies in the correlation function.\n", - "\n", - " Returns\n", - " -------\n", - " integral: float\n", - " The value of the integral function at time t.\n", - " \"\"\"\n", - " t1 = np.sum(\n", - " (ck / vk**2) *\n", - " (np.exp(vk * t) - 1)\n", - " )\n", - " t2 = np.sum(\n", - " (ck.conj() / vk.conj()**2) *\n", - " (np.exp(vk.conj() * t) - 1)\n", - " )\n", - " t3 = np.sum(\n", - " (ck / vk + ck.conj() / vk.conj()) * t\n", - " )\n", - " return 2 * (t1 + t2 - t3)" - ] - }, - { - "cell_type": "markdown", - "id": "1286c92b", - "metadata": {}, - "source": [ - "For the pure dephasing analytics, we just sum up as many matsubara terms as we can:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7935ec7e", - "metadata": {}, - "outputs": [], - "source": [ - "lmaxmats2 = 15000\n", - "\n", - "vk = [complex(-gamma)]\n", - "vk.extend([\n", - " complex(-2. * np.pi * k * T)\n", - " for k in range(1, lmaxmats2)\n", - "])\n", - "\n", - "ck = [complex(lam * gamma * (-1.0j + cot(gamma * beta / 2.)))]\n", - "ck.extend([\n", - " complex(4 * lam * gamma * T * (-v) / (v**2 - gamma**2))\n", - " for v in vk[1:]\n", - "])\n", - "\n", - "P12_ana = 0.5 * pure_dephasing_evolution_analytical(\n", - " tlist, 0, np.asarray(ck), np.asarray(vk)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "8ed59c50", - "metadata": {}, - "source": [ - "Alternatively, we can just do the integral of the propagator directly, without using the correlation functions at all" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "780b82b0", - "metadata": {}, - "outputs": [], - "source": [ - "def JDL(omega, lamc, omega_c):\n", - " return 2. * lamc * omega * omega_c / (omega_c**2 + omega**2)\n", - "\n", - "\n", - "def integrand(omega, lamc, omega_c, Temp, t):\n", - " return (\n", - " (-4. * JDL(omega, lamc, omega_c) / omega**2) *\n", - " (1. - np.cos(omega*t)) * (coth(omega/(2.*Temp)))\n", - " / np.pi\n", - " )\n", - "\n", - "\n", - "P12_ana2 = [\n", - " 0.5 * np.exp(\n", - " scipy.integrate.quad(integrand, 0, np.inf, args=(lam, gamma, T, t))[0]\n", - " )\n", - " for t in tlist\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "28a0ad50", - "metadata": {}, - "source": [ - "## Compare results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c903876f", - "metadata": {}, - "outputs": [], - "source": [ - "plot_result_expectations([\n", - " (resultMats, P12p, 'r', \"P12 Mats\"),\n", - " (resultMatsT, P12p, 'r--', \"P12 Mats + Term\"),\n", - " (resultPade, P12p, 'b--', \"P12 Pade\"),\n", - " (resultFit, P12p, 'g', \"P12 Fit\"),\n", - " ((tlist, np.real(P12_ana)), None, 'b', \"Analytic 1\"),\n", - " ((tlist, np.real(P12_ana2)), None, 'y--', \"Analytic 2\"),\n", - "]);" - ] - }, - { - "cell_type": "markdown", - "id": "35474f66", - "metadata": {}, - "source": [ - "We can't see much difference in the plot above, so let's do a log plot instead:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c6ef7e4f", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - "\n", - "plot_result_expectations([\n", - " (resultMats, P12p, 'r', \"P12 Mats\"),\n", - " (resultMatsT, P12p, 'r--', \"P12 Mats + Term\"),\n", - " (resultPade, P12p, 'b-.', \"P12 Pade\"),\n", - " (resultFit, P12p, 'g', \"P12 Fit\"),\n", - " ((tlist, np.real(P12_ana)), None, 'b', \"Analytic 1\"),\n", - " ((tlist, np.real(P12_ana2)), None, 'y--', \"Analytic 2\"),\n", - "], axes)\n", - "\n", - "axes.set_yscale('log')\n", - "axes.legend(loc=0, fontsize=12);" - ] - }, - { - "cell_type": "markdown", - "id": "00259e47", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e2a4b6c9", - "metadata": {}, - "outputs": [], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "945917f5", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "98a3271d", - "metadata": {}, - "outputs": [], - "source": [ - "assert np.allclose(\n", - " expect(P12p, resultMats.states[:15]), np.real(P12_ana)[:15],\n", - " rtol=1e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(P12p, resultMatsT.states[:100]), np.real(P12_ana)[:100],\n", - " rtol=1e-3,\n", - ")\n", - "assert np.allclose(\n", - " expect(P12p, resultPade.states[:100]), np.real(P12_ana)[:100],\n", - " rtol=1e-3,\n", - ")\n", - "assert np.allclose(\n", - " expect(P12p, resultFit.states[:50]), np.real(P12_ana)[:50],\n", - " rtol=1e-3,\n", - ")\n", - "assert np.allclose(P12_ana, P12_ana2, rtol=1e-3)" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "qutip-dev", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md index bd130e8f..fe1409c3 100644 --- a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md +++ b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 + jupytext_version: 1.16.1 kernelspec: display_name: qutip-dev language: python @@ -66,7 +66,7 @@ Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. ## Setup -```{code-cell} ipython3 +```{code-cell} import contextlib import time @@ -98,7 +98,7 @@ from qutip.core.environment import ( Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: -```{code-cell} ipython3 +```{code-cell} def cot(x): """ Vectorized cotangent of x. """ return 1. / np.tan(x) @@ -109,7 +109,7 @@ def coth(x): return 1. / np.tanh(x) ``` -```{code-cell} ipython3 +```{code-cell} def plot_result_expectations(plots, axes=None): """ Plot the expectation values of operators as functions of time. @@ -141,7 +141,7 @@ def plot_result_expectations(plots, axes=None): return fig ``` -```{code-cell} ipython3 +```{code-cell} @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -155,7 +155,7 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} ipython3 +```{code-cell} # Solver options: options = { @@ -176,14 +176,14 @@ And let us set up the system Hamiltonian, bath and system measurement operators: Here we set $H_{sys}=0$, which means the interaction Hamiltonian and the system Hamiltonian commute, and we can compare the numerical results to a known analytical one. We could in principle keep $\epsilon \neq 0$, but it just introduces fast system oscillations, so it is more convenient to set it to zero. -```{code-cell} ipython3 +```{code-cell} # Defining the system Hamiltonian eps = 0.0 # Energy of the 2-level system. Del = 0.0 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` -```{code-cell} ipython3 +```{code-cell} # System-bath coupling (Drude-Lorentz spectral density) Q = sigmaz() # coupling operator @@ -204,7 +204,7 @@ Nk = 3 tlist = np.linspace(0, 50, 1000) ``` -```{code-cell} ipython3 +```{code-cell} # Define some operators with which we will measure the system # 1,1 element of density matrix - corresponding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -215,7 +215,7 @@ P12p = basis(2, 0) * basis(2, 1).dag() To get a non-trivial result we prepare the initial state in a superposition, and see how the bath destroys the coherence. -```{code-cell} ipython3 +```{code-cell} # Initial state of the system. psi = (basis(2, 0) + basis(2, 1)).unit() rho0 = psi * psi.dag() @@ -224,13 +224,13 @@ rho0 = psi * psi.dag() We then define our environment, from which all the different simulations will be obtained -```{code-cell} ipython3 +```{code-cell} env = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=Nk) ``` ## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator -```{code-cell} ipython3 +```{code-cell} with timer("RHS construction time"): env_mats=env.approx_by_matsubara(Nk=Nk) HEOMMats = HEOMSolver(Hsys, (env_mats,Q), NC, options=options) @@ -239,7 +239,7 @@ with timer("ODE solver time"): resultMats = HEOMMats.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} # Plot the results so far plot_result_expectations([ (resultMats, P11p, 'b', "P11 Matsubara"), @@ -249,7 +249,7 @@ plot_result_expectations([ ## Simulation 2: Matsubara decomposition (including terminator) -```{code-cell} ipython3 +```{code-cell} with timer("RHS construction time"): env_mats,delta=env.approx_by_matsubara(Nk=Nk,compute_delta=True) Ltot = liouvillian(Hsys) + system_terminator(Q,delta) @@ -259,7 +259,7 @@ with timer("ODE solver time"): resultMatsT = HEOMMatsT.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} # Plot the results plot_result_expectations([ (resultMats, P11p, 'b', "P11 Matsubara"), @@ -273,7 +273,7 @@ plot_result_expectations([ As in example 1a, we can compare to Pade and Fitting approaches. -```{code-cell} ipython3 +```{code-cell} with timer("RHS construction time"): env_pade=env.approx_by_pade(Nk=Nk) HEOMPade = HEOMSolver(Hsys, (env_pade,Q), NC, options=options) @@ -282,7 +282,7 @@ with timer("ODE solver time"): resultPade = HEOMPade.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} # Plot the results plot_result_expectations([ (resultMatsT, P11p, 'b', "P11 Matsubara (+term)"), @@ -294,7 +294,7 @@ plot_result_expectations([ ## Simulation 4: Fitting approach -```{code-cell} ipython3 +```{code-cell} tfit=np.linspace(0,10,1000) with timer("RHS construction time"): bath,_ = env.approx_by_cf_fit(tfit,Ni_max=1,Nr_max=3,target_rsme=None) @@ -306,7 +306,7 @@ with timer("ODE solver time"): ## Analytic calculations -```{code-cell} ipython3 +```{code-cell} def pure_dephasing_evolution_analytical(tlist, wq, ck, vk): """ Computes the propagating function appearing in the pure dephasing model. @@ -383,7 +383,7 @@ def correlation_integral(t, ck, vk): For the pure dephasing analytics, we just sum up as many matsubara terms as we can: -```{code-cell} ipython3 +```{code-cell} lmaxmats2 = 15000 vk = [complex(-gamma)] @@ -405,7 +405,7 @@ P12_ana = 0.5 * pure_dephasing_evolution_analytical( Alternatively, we can just do the integral of the propagator directly, without using the correlation functions at all -```{code-cell} ipython3 +```{code-cell} def JDL(omega, lamc, omega_c): return 2. * lamc * omega * omega_c / (omega_c**2 + omega**2) @@ -428,7 +428,7 @@ P12_ana2 = [ ## Compare results -```{code-cell} ipython3 +```{code-cell} plot_result_expectations([ (resultMats, P12p, 'r', "P12 Mats"), (resultMatsT, P12p, 'r--', "P12 Mats + Term"), @@ -441,7 +441,7 @@ plot_result_expectations([ We can't see much difference in the plot above, so let's do a log plot instead: -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) plot_result_expectations([ @@ -459,7 +459,7 @@ axes.legend(loc=0, fontsize=12); ## About -```{code-cell} ipython3 +```{code-cell} qutip.about() ``` @@ -467,7 +467,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 +```{code-cell} assert np.allclose( expect(P12p, resultMats.states[:15]), np.real(P12_ana)[:15], rtol=1e-2, diff --git a/tutorials-v5/heom/heom-2-fmo-example.ipynb b/tutorials-v5/heom/heom-2-fmo-example.ipynb deleted file mode 100644 index 9c2fd3e5..00000000 --- a/tutorials-v5/heom/heom-2-fmo-example.ipynb +++ /dev/null @@ -1,658 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7fd70470", - "metadata": {}, - "source": [ - "# HEOM 2: Dynamics in Fenna-Mathews-Olsen complex (FMO)" - ] - }, - { - "cell_type": "markdown", - "id": "a477c71b", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "In this example notebook we outline how to employ the HEOM to\n", - "solve the FMO photosynthetic complex dynamics.\n", - "\n", - "We aim to replicate the results in reference [https://www.pnas.org/content/106/41/17255](https://pubmed.ncbi.nlm.nih.gov/19815512/)\n", - "and compare them to a Bloch-Redfield (perturbative) solution.\n", - "\n", - "This demonstrates how to to employ the solver for multiple baths, as well as showing how a\n", - "quantum environment reduces the effect of pure dephasing." - ] - }, - { - "cell_type": "markdown", - "id": "9d341c87", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "107ebbba", - "metadata": {}, - "outputs": [], - "source": [ - "import contextlib\n", - "import time\n", - "\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " Qobj,\n", - " basis,\n", - " brmesolve,\n", - " expect,\n", - " liouvillian,\n", - " mesolve,\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver,\n", - ")\n", - "from qutip.core.environment import (\n", - " DrudeLorentzEnvironment,\n", - " system_terminator\n", - ")\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "972ebdfd", - "metadata": {}, - "source": [ - "## Helper functions\n", - "\n", - "Let's define some helper functions for calculating correlation functions, spectral densities, thermal energy level occupations, and for plotting results and timing how long operations take:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "03d58afe", - "metadata": {}, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\" Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "80b0393c", - "metadata": {}, - "outputs": [], - "source": [ - "# Solver options:\n", - "\n", - "options = {\n", - " \"nsteps\": 15000,\n", - " \"store_states\": True,\n", - " \"rtol\": 1e-12,\n", - " \"atol\": 1e-12,\n", - " \"min_step\": 1e-18,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "30d71cb7", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "And let us set up the system Hamiltonian and bath parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4baf9e45", - "metadata": {}, - "outputs": [], - "source": [ - "# System Hamiltonian:\n", - "#\n", - "# We use the Hamiltonian employed in\n", - "# https://www.pnas.org/content/106/41/17255 and operate\n", - "# in units of Hz:\n", - "\n", - "Hsys = 3e10 * 2 * np.pi * Qobj([\n", - " [200, -87.7, 5.5, -5.9, 6.7, -13.7, -9.9],\n", - " [-87.7, 320, 30.8, 8.2, 0.7, 11.8, 4.3],\n", - " [5.5, 30.8, 0, -53.5, -2.2, -9.6, 6.0],\n", - " [-5.9, 8.2, -53.5, 110, -70.7, -17.0, -63.3],\n", - " [6.7, 0.7, -2.2, -70.7, 270, 81.1, -1.3],\n", - " [-13.7, 11.8, -9.6, -17.0, 81.1, 420, 39.7],\n", - " [-9.9, 4.3, 6.0, -63.3, -1.3, 39.7, 230],\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b8a1a8e7", - "metadata": {}, - "outputs": [], - "source": [ - "# Bath parameters\n", - "\n", - "lam = 35 * 3e10 * 2 * np.pi\n", - "gamma = 1 / 166e-15\n", - "T = 300 * 0.6949 * 3e10 * 2 * np.pi\n", - "beta = 1 / T" - ] - }, - { - "cell_type": "markdown", - "id": "a285d0cf", - "metadata": {}, - "source": [ - "## Plotting the environment spectral density and correlation functions\n", - "\n", - "Let's quickly plot the spectral density and environment correlation functions so that we can see what they look like." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "08612b77", - "metadata": {}, - "outputs": [], - "source": [ - "env=DrudeLorentzEnvironment(T=T,lam=lam,gamma=gamma)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "23de3d8a", - "metadata": {}, - "outputs": [], - "source": [ - "wlist = np.linspace(0, 200 * 3e10 * 2 * np.pi, 100)\n", - "tlist = np.linspace(0, 1e-12, 1000)\n", - "\n", - "J = env.spectral_density(wlist) / (3e10*2*np.pi)\n", - "\n", - "fig, axes = plt.subplots(1, 2, sharex=False, figsize=(10, 3))\n", - "\n", - "fig.subplots_adjust(hspace=0.1) # reduce space between plots\n", - "\n", - "# Spectral density plot:\n", - "\n", - "axes[0].plot(wlist / (3e10 * 2 * np.pi), J, color='r', ls='--', label=\"J(w)\")\n", - "axes[0].set_xlabel(r'$\\omega$ (cm$^{-1}$)', fontsize=20)\n", - "axes[0].set_ylabel(r\"$J(\\omega)$ (cm$^{-1}$)\", fontsize=16)\n", - "axes[0].legend()\n", - "\n", - "# Correlation plot:\n", - "\n", - "axes[1].plot(\n", - " tlist, np.real(env.correlation_function(tlist, 10)),\n", - " color='r', ls='--', label=\"C(t) real\",\n", - ")\n", - "axes[1].plot(\n", - " tlist, np.imag(env.correlation_function(tlist, 10)),\n", - " color='g', ls='--', label=\"C(t) imaginary\",\n", - ")\n", - "axes[1].set_xlabel(r'$t$', fontsize=20)\n", - "axes[1].set_ylabel(r\"$C(t)$\", fontsize=16)\n", - "axes[1].legend();" - ] - }, - { - "cell_type": "markdown", - "id": "41507215", - "metadata": {}, - "source": [ - "## Solve for the dynamics with the HEOM\n", - "\n", - "Now let us solve for the evolution of this system using the HEOM." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dbd9661a", - "metadata": {}, - "outputs": [], - "source": [ - "# We start the excitation at site 1:\n", - "rho0 = basis(7, 0) * basis(7, 0).dag()\n", - "\n", - "# HEOM solver options:\n", - "#\n", - "# Note: We set Nk=0 (i.e. a single correlation expansion term\n", - "# per bath) and rely on the terminator to correct detailed\n", - "# balance.\n", - "NC = 4 # Use NC=8 for more precise results\n", - "Nk = 0\n", - "\n", - "Q_list = []\n", - "baths = []\n", - "Ltot = liouvillian(Hsys)\n", - "env_approx,delta=env.approx_by_matsubara(Nk=Nk,compute_delta=True)\n", - "for m in range(7):\n", - " Q = basis(7, m) * basis(7, m).dag()\n", - " Q_list.append(Q)\n", - " Ltot += system_terminator(Q,delta)\n", - " baths.append((env_approx,Q))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c7b9447", - "metadata": {}, - "outputs": [], - "source": [ - "with timer(\"RHS construction time\"):\n", - " HEOMMats = HEOMSolver(Hsys, baths, NC, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " outputFMO_HEOM = HEOMMats.run(rho0, tlist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "199d025a", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", - "\n", - "colors = ['r', 'g', 'b', 'y', 'c', 'm', 'k']\n", - "linestyles = [\n", - " '-', '--', ':', '-.',\n", - " (0, (1, 10)), (0, (5, 10)), (0, (3, 10, 1, 10)),\n", - "]\n", - "\n", - "for m in range(7):\n", - " Q = basis(7, m) * basis(7, m).dag()\n", - " axes.plot(\n", - " np.array(tlist) * 1e15,\n", - " np.real(expect(outputFMO_HEOM.states, Q)),\n", - " label=m + 1,\n", - " color=colors[m % len(colors)],\n", - " linestyle=linestyles[m % len(linestyles)],\n", - " )\n", - " axes.set_xlabel(r'$t$ (fs)', fontsize=30)\n", - " axes.set_ylabel(r\"Population\", fontsize=30)\n", - " axes.locator_params(axis='y', nbins=6)\n", - " axes.locator_params(axis='x', nbins=6)\n", - "\n", - "axes.set_title('HEOM solution', fontsize=24)\n", - "axes.legend(loc=0)\n", - "axes.set_xlim(0, 1000)\n", - "plt.yticks([0., 0.5, 1], [0, 0.5, 1])\n", - "plt.xticks([0., 500, 1000], [0, 500, 1000]);" - ] - }, - { - "cell_type": "markdown", - "id": "e5f1f8d3", - "metadata": {}, - "source": [ - "## Comparison with Bloch-Redfield solver\n", - "\n", - "Now let us solve the same problem using the Bloch-Redfield solver. We will see that the Bloch-Redfield technique fails to model the oscillation of population of the states that we saw in the HEOM.\n", - "\n", - "In the next section, we will examine the role of pure dephasing in the evolution to understand why this happens." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "47169b4e", - "metadata": {}, - "outputs": [], - "source": [ - "with timer(\"BR ODE solver time\"):\n", - " outputFMO_BR = brmesolve(\n", - " Hsys, rho0, tlist,\n", - " a_ops=[[Q, env] for Q in Q_list],\n", - " options=options,\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "4c1a445a", - "metadata": {}, - "source": [ - "And now let's plot the Bloch-Redfield solver results:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "45802918", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", - "\n", - "for m, Q in enumerate(Q_list):\n", - " axes.plot(tlist * 1e15, expect(outputFMO_BR.states, Q), label=m + 1)\n", - "\n", - "axes.set_xlabel(r'$t$ (fs)', fontsize=30)\n", - "axes.set_ylabel(r\"Population\", fontsize=30)\n", - "\n", - "axes.set_title('Bloch-Redfield solution ', fontsize=24)\n", - "axes.legend()\n", - "axes.set_xlim(0, 1000)\n", - "plt.yticks([0, 0.5, 1], [0, 0.5, 1])\n", - "plt.xticks([0, 500, 1000], [0, 500, 1000]);" - ] - }, - { - "cell_type": "markdown", - "id": "db49306a", - "metadata": {}, - "source": [ - "Notice how the oscillations are gone and the populations decay much more rapidly.\n", - "\n", - "Next let us try to understand why." - ] - }, - { - "cell_type": "markdown", - "id": "95e6620b", - "metadata": {}, - "source": [ - "## Role of pure dephasing\n", - "\n", - "It is useful to construct the various parts of the Bloch-Redfield master equation explicitly and to solve them using the Master equation solver, `mesolve`. We will do so and show that it is the pure-dephasing terms which suppresses coherence in these oscillations.\n", - "\n", - "First we will write a function to return the list of collapse operators for a given system, either with or without the dephasing operators:" - ] - }, - { - "cell_type": "markdown", - "id": "6344f4ac", - "metadata": {}, - "source": [ - "TODO: Maybe power spectrum at zero is wrong, by a factor 2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3fb0f126", - "metadata": {}, - "outputs": [], - "source": [ - "def J0_dephasing():\n", - " \"\"\" Under-damped brownian oscillator dephasing probability.\n", - "\n", - " This returns the limit as w -> 0 of J0(w) * n_th(w, T) / T.\n", - " \"\"\"\n", - " return 2 * lam * gamma / gamma**2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f8f11c93", - "metadata": {}, - "outputs": [], - "source": [ - "env.power_spectrum(0)/2 -J0_dephasing()*T" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9a90f9d0", - "metadata": {}, - "outputs": [], - "source": [ - "def get_collapse(H, T, dephasing=1):\n", - " \"\"\" Calculate collapse operators for a given system H and\n", - " temperature T.\n", - " \"\"\"\n", - " all_energy, all_state = H.eigenstates(sort=\"low\")\n", - " Nmax = len(all_energy)\n", - "\n", - " Q_list = [\n", - " basis(Nmax, n) * basis(Nmax, n).dag()\n", - " for n in range(Nmax)\n", - " ]\n", - "\n", - " collapse_list = []\n", - "\n", - " for Q in Q_list:\n", - " for j in range(Nmax):\n", - " for k in range(j + 1, Nmax):\n", - " Deltajk = abs(all_energy[k] - all_energy[j])\n", - " if abs(Deltajk) > 0:\n", - " rate = (\n", - " np.abs(Q.matrix_element(\n", - " all_state[j].dag(), all_state[k]\n", - " ))**2 *\n", - " env.power_spectrum(Deltajk)\n", - " )\n", - " if rate > 0.0:\n", - " # emission:\n", - " collapse_list.append(\n", - " np.sqrt(rate) * all_state[j] * all_state[k].dag()\n", - " )\n", - "\n", - " rate = (\n", - " np.abs(Q.matrix_element(\n", - " all_state[k].dag(), all_state[j]\n", - " ))**2 *\n", - " env.power_spectrum(-Deltajk)\n", - " )\n", - " if rate > 0.0:\n", - " # absorption:\n", - " collapse_list.append(\n", - " np.sqrt(rate) * all_state[k] * all_state[j].dag()\n", - " )\n", - "\n", - " if dephasing:\n", - " for j in range(Nmax):\n", - " rate = (\n", - " np.abs(Q.matrix_element(\n", - " all_state[j].dag(), all_state[j])\n", - " )**2 * env.power_spectrum(0)/2\n", - " )\n", - " if rate > 0.0:\n", - " # emission:\n", - " collapse_list.append(\n", - " np.sqrt(rate) * all_state[j] * all_state[j].dag()\n", - " )\n", - "\n", - " return collapse_list" - ] - }, - { - "cell_type": "markdown", - "id": "ebc084a1", - "metadata": {}, - "source": [ - "Now we are able to switch the pure dephasing terms on and off.\n", - "\n", - "Let us starting by including the dephasing operators. We expect to see the same behaviour that we saw when using the Bloch-Redfield solver." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "baf32f2a", - "metadata": {}, - "outputs": [], - "source": [ - "# dephasing terms on, we recover the full BR solution:\n", - "\n", - "with timer(\"Building the collapse operators\"):\n", - " collapse_list = get_collapse(Hsys, T=T, dephasing=True)\n", - "\n", - "with timer(\"ME ODE solver\"):\n", - " outputFMO_ME = mesolve(Hsys, rho0, tlist, collapse_list)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "62a6840b", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", - "\n", - "for m, Q in enumerate(Q_list):\n", - " axes.plot(tlist * 1e15, expect(outputFMO_ME.states, Q), label=m + 1)\n", - "\n", - "axes.set_xlabel(r'$t$', fontsize=20)\n", - "axes.set_ylabel(r\"Population\", fontsize=16)\n", - "axes.set_xlim(0, 1000)\n", - "axes.set_title('With pure dephasing', fontsize=24)\n", - "plt.yticks([0, 0.5, 1], [0, 0.5, 1])\n", - "plt.xticks([0, 500, 1000], [0, 500, 1000])\n", - "axes.legend(fontsize=18);" - ] - }, - { - "cell_type": "markdown", - "id": "d0d24fb3", - "metadata": {}, - "source": [ - "We see similar results to before.\n", - "\n", - "Now let us examine what happens when we remove the dephasing collapse operators:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "762960a5", - "metadata": {}, - "outputs": [], - "source": [ - "# dephasing terms off\n", - "\n", - "with timer(\"Building the collapse operators\"):\n", - " collapse_list = get_collapse(Hsys, T, dephasing=False)\n", - "\n", - "with timer(\"ME ODE solver\"):\n", - " outputFMO_ME_nodephase = mesolve(Hsys, rho0, tlist, collapse_list)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c2712875", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 1, figsize=(12, 8))\n", - "for m, Q in enumerate(Q_list):\n", - " axes.plot(\n", - " tlist * 1e15,\n", - " expect(outputFMO_ME_nodephase.states, Q),\n", - " label=m + 1,\n", - " )\n", - "\n", - "axes.set_xlabel(r'$t$', fontsize=20)\n", - "axes.set_ylabel(r\"Population\", fontsize=16)\n", - "axes.set_xlim(0, 1000)\n", - "axes.set_title('Without pure dephasing', fontsize=24)\n", - "plt.yticks([0, 0.5, 1], [0, 0.5, 1])\n", - "plt.xticks([0, 500, 1000], [0, 500, 1000])\n", - "axes.legend(fontsize=18);" - ] - }, - { - "cell_type": "markdown", - "id": "6162e3d6", - "metadata": {}, - "source": [ - "And now we see that without the dephasing, the oscillations reappear. The full dynamics capture by the HEOM are still not capture by this simpler model, however." - ] - }, - { - "cell_type": "markdown", - "id": "436a1179", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a28c22db", - "metadata": {}, - "outputs": [], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "a362e374", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d94c95c1", - "metadata": {}, - "outputs": [], - "source": [ - "assert np.allclose(\n", - " expect(outputFMO_BR.states, Q_list[0]),\n", - " expect(outputFMO_ME.states, Q_list[0]),\n", - " rtol=2e-2,\n", - ")\n", - "assert np.allclose(\n", - " expect(outputFMO_BR.states, Q_list[1]),\n", - " expect(outputFMO_ME.states, Q_list[1]),\n", - " rtol=2e-2,\n", - ")" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "qutip-dev", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-2-fmo-example.md b/tutorials-v5/heom/heom-2-fmo-example.md index 9b05297e..93c59b1d 100644 --- a/tutorials-v5/heom/heom-2-fmo-example.md +++ b/tutorials-v5/heom/heom-2-fmo-example.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 + jupytext_version: 1.16.1 kernelspec: display_name: qutip-dev language: python @@ -31,7 +31,7 @@ quantum environment reduces the effect of pure dephasing. ## Setup -```{code-cell} ipython3 +```{code-cell} import contextlib import time @@ -62,7 +62,7 @@ from qutip.core.environment import ( Let's define some helper functions for calculating correlation functions, spectral densities, thermal energy level occupations, and for plotting results and timing how long operations take: -```{code-cell} ipython3 +```{code-cell} @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -76,7 +76,7 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} ipython3 +```{code-cell} # Solver options: options = { @@ -94,7 +94,7 @@ options = { And let us set up the system Hamiltonian and bath parameters: -```{code-cell} ipython3 +```{code-cell} # System Hamiltonian: # # We use the Hamiltonian employed in @@ -112,7 +112,7 @@ Hsys = 3e10 * 2 * np.pi * Qobj([ ]) ``` -```{code-cell} ipython3 +```{code-cell} # Bath parameters lam = 35 * 3e10 * 2 * np.pi @@ -125,11 +125,11 @@ beta = 1 / T Let's quickly plot the spectral density and environment correlation functions so that we can see what they look like. -```{code-cell} ipython3 +```{code-cell} env=DrudeLorentzEnvironment(T=T,lam=lam,gamma=gamma) ``` -```{code-cell} ipython3 +```{code-cell} wlist = np.linspace(0, 200 * 3e10 * 2 * np.pi, 100) tlist = np.linspace(0, 1e-12, 1000) @@ -165,7 +165,7 @@ axes[1].legend(); Now let us solve for the evolution of this system using the HEOM. -```{code-cell} ipython3 +```{code-cell} # We start the excitation at site 1: rho0 = basis(7, 0) * basis(7, 0).dag() @@ -188,7 +188,7 @@ for m in range(7): baths.append((env_approx,Q)) ``` -```{code-cell} ipython3 +```{code-cell} with timer("RHS construction time"): HEOMMats = HEOMSolver(Hsys, baths, NC, options=options) @@ -196,7 +196,7 @@ with timer("ODE solver time"): outputFMO_HEOM = HEOMMats.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, figsize=(12, 8)) colors = ['r', 'g', 'b', 'y', 'c', 'm', 'k'] @@ -232,7 +232,7 @@ Now let us solve the same problem using the Bloch-Redfield solver. We will see t In the next section, we will examine the role of pure dephasing in the evolution to understand why this happens. -```{code-cell} ipython3 +```{code-cell} with timer("BR ODE solver time"): outputFMO_BR = brmesolve( Hsys, rho0, tlist, @@ -243,7 +243,7 @@ with timer("BR ODE solver time"): And now let's plot the Bloch-Redfield solver results: -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, figsize=(12, 8)) for m, Q in enumerate(Q_list): @@ -275,7 +275,7 @@ First we will write a function to return the list of collapse operators for a gi TODO: Maybe power spectrum at zero is wrong, by a factor 2 -```{code-cell} ipython3 +```{code-cell} def J0_dephasing(): """ Under-damped brownian oscillator dephasing probability. @@ -284,11 +284,11 @@ def J0_dephasing(): return 2 * lam * gamma / gamma**2 ``` -```{code-cell} ipython3 +```{code-cell} env.power_spectrum(0)/2 -J0_dephasing()*T ``` -```{code-cell} ipython3 +```{code-cell} def get_collapse(H, T, dephasing=1): """ Calculate collapse operators for a given system H and temperature T. @@ -352,7 +352,7 @@ Now we are able to switch the pure dephasing terms on and off. Let us starting by including the dephasing operators. We expect to see the same behaviour that we saw when using the Bloch-Redfield solver. -```{code-cell} ipython3 +```{code-cell} # dephasing terms on, we recover the full BR solution: with timer("Building the collapse operators"): @@ -362,7 +362,7 @@ with timer("ME ODE solver"): outputFMO_ME = mesolve(Hsys, rho0, tlist, collapse_list) ``` -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, figsize=(12, 8)) for m, Q in enumerate(Q_list): @@ -381,7 +381,7 @@ We see similar results to before. Now let us examine what happens when we remove the dephasing collapse operators: -```{code-cell} ipython3 +```{code-cell} # dephasing terms off with timer("Building the collapse operators"): @@ -391,7 +391,7 @@ with timer("ME ODE solver"): outputFMO_ME_nodephase = mesolve(Hsys, rho0, tlist, collapse_list) ``` -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, figsize=(12, 8)) for m, Q in enumerate(Q_list): axes.plot( @@ -415,7 +415,7 @@ And now we see that without the dephasing, the oscillations reappear. The full d ## About -```{code-cell} ipython3 +```{code-cell} qutip.about() ``` @@ -423,7 +423,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 +```{code-cell} assert np.allclose( expect(outputFMO_BR.states, Q_list[0]), expect(outputFMO_ME.states, Q_list[0]), diff --git a/tutorials-v5/heom/heom-3-quantum-heat-transport.ipynb b/tutorials-v5/heom/heom-3-quantum-heat-transport.ipynb deleted file mode 100644 index 4861dfc9..00000000 --- a/tutorials-v5/heom/heom-3-quantum-heat-transport.ipynb +++ /dev/null @@ -1,693 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "07cef890", - "metadata": {}, - "source": [ - "# HEOM 3: Quantum Heat Transport" - ] - }, - { - "cell_type": "markdown", - "id": "c812d416", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "In this notebook, we apply the QuTiP HEOM solver to a quantum system coupled to two bosonic baths and demonstrate how to extract information about the system-bath heat currents from the auxiliary density operators (ADOs).\n", - "We consider the setup described in Ref. \\[1\\], which consists of two coupled qubits, each connected to its own heat bath.\n", - "The Hamiltonian of the qubits is given by\n", - "\n", - "$$ \\begin{aligned} H_{\\text{S}} &= H_1 + H_2 + H_{12} , \\quad\\text{ where }\\\\\n", - "H_K &= \\frac{\\epsilon}{2} \\bigl(\\sigma_z^K + 1\\bigr) \\quad (K=1,2) \\quad\\text{ and }\\quad H_{12} = J_{12} \\bigl( \\sigma_+^1 \\sigma_-^2 + \\sigma_-^1 \\sigma_+^2 \\bigr) . \\end{aligned} $$\n", - "\n", - "Here, $\\sigma^K_{x,y,z,\\pm}$ denotes the usual Pauli matrices for the K-th qubit, $\\epsilon$ is the eigenfrequency of the qubits and $J_{12}$ the coupling constant.\n", - "\n", - "Each qubit is coupled to its own bath; therefore, the total Hamiltonian is\n", - "\n", - "$$ H_{\\text{tot}} = H_{\\text{S}} + \\sum_{K=1,2} \\bigl( H_{\\text{B}}^K + Q_K \\otimes X_{\\text{B}}^K \\bigr) , $$\n", - "\n", - "where $H_{\\text{B}}^K$ is the free Hamiltonian of the K-th bath and $X_{\\text{B}}^K$ its coupling operator, and $Q_K = \\sigma_x^K$ are the system coupling operators.\n", - "We assume that the bath spectral densities are given by Drude distributions\n", - "\n", - "$$ J_K(\\omega) = \\frac{2 \\lambda_K \\gamma_K \\omega}{\\omega^2 + \\gamma_K^2} , $$\n", - "\n", - "where $\\lambda_K$ is the free coupling strength and $\\gamma_K$ the cutoff frequency.\n", - "\n", - "We begin by defining the system and bath parameters.\n", - "We use the parameter values from Fig. 3(a) of Ref. \\[1\\].\n", - "Note that we set $\\hbar$ and $k_B$ to one and we will measure all frequencies and energies in units of $\\epsilon$.\n", - "\n", - "References:\n", - "\n", - "   \\[1\\] Kato and Tanimura, [J. Chem. Phys. **143**, 064107](https://doi.org/10.1063/1.4928192) (2015)." - ] - }, - { - "cell_type": "markdown", - "id": "83c6db96", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cc247fbe", - "metadata": {}, - "outputs": [], - "source": [ - "import dataclasses\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import qutip as qt\n", - "from qutip.solver.heom import (\n", - " HEOMSolver,\n", - " DrudeLorentzPadeBath\n", - ")\n", - "from qutip.core.environment import (\n", - " CFExponent,\n", - " DrudeLorentzEnvironment,\n", - " system_terminator,\n", - ")\n", - "\n", - "from ipywidgets import IntProgress\n", - "from IPython.display import display\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "7c732d88", - "metadata": {}, - "source": [ - "## Helpers" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5287dddb", - "metadata": {}, - "outputs": [], - "source": [ - "# Solver options:\n", - "\n", - "options = {\n", - " \"nsteps\": 15000,\n", - " \"store_states\": True,\n", - " \"rtol\": 1e-12,\n", - " \"atol\": 1e-12,\n", - " \"min_step\": 1e-18,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "9c3bc48f", - "metadata": {}, - "source": [ - "## System and bath definition" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3c30ef0f", - "metadata": {}, - "outputs": [], - "source": [ - "@dataclasses.dataclass\n", - "class SystemParams:\n", - " \"\"\" System parameters and Hamiltonian. \"\"\"\n", - " epsilon: float = 1.0\n", - " J12: float = 0.1\n", - "\n", - " def H(self):\n", - " \"\"\" Return the Hamiltonian for the system.\n", - "\n", - " The system consists of two qubits with Hamiltonians (H1 and H2)\n", - " and an interaction term (H12).\n", - " \"\"\"\n", - " H1 = self.epsilon / 2 * (\n", - " qt.tensor(qt.sigmaz() + qt.identity(2), qt.identity(2))\n", - " )\n", - " H2 = self.epsilon / 2 * (\n", - " qt.tensor(qt.identity(2), qt.sigmaz() + qt.identity(2))\n", - " )\n", - " H12 = self.J12 * (\n", - " qt.tensor(qt.sigmap(), qt.sigmam()) +\n", - " qt.tensor(qt.sigmam(), qt.sigmap())\n", - " )\n", - " return H1 + H2 + H12\n", - "\n", - " def replace(self, **kw):\n", - " return dataclasses.replace(self, **kw)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "73addcde", - "metadata": {}, - "outputs": [], - "source": [ - "@dataclasses.dataclass\n", - "class BathParams:\n", - " \"\"\" Bath parameters. \"\"\"\n", - " sign: str # + or -\n", - " qubit: int # 0 or 1\n", - "\n", - " gamma: float = 2.0\n", - " lam: float = 0.05\n", - " Tbar: float = 2.0\n", - " Tdelta: float = 0.01\n", - "\n", - " def __post_init__(self):\n", - " # T = Tbar +- Tdelta * Tbar:\n", - " assert self.sign in (\"+\", \"-\")\n", - " sign = +1 if self.sign == \"+\" else -1\n", - " self.T = self.Tbar + sign * self.Tdelta * self.Tbar\n", - " # qubit\n", - " assert self.qubit in (0, 1)\n", - "\n", - " def Q(self):\n", - " \"\"\" Coupling operator for the bath. \"\"\"\n", - " Q = [qt.identity(2), qt.identity(2)]\n", - " Q[self.qubit] = qt.sigmax()\n", - " return qt.tensor(Q)\n", - "\n", - " def bath(self, Nk, tag=None):\n", - " env=DrudeLorentzEnvironment(\n", - " lam=self.lam, gamma=self.gamma, T=self.T, tag=tag\n", - " )\n", - " env_approx,delta=env.approx_by_pade(Nk=Nk,compute_delta=True,tag=tag)\n", - " return (env_approx,self.Q()),system_terminator(self.Q(),delta),delta\n", - "\n", - " def replace(self, **kw):\n", - " return dataclasses.replace(self, **kw)" - ] - }, - { - "cell_type": "markdown", - "id": "b55f26f8", - "metadata": {}, - "source": [ - "## Heat currents\n", - "\n", - "Following Ref. \\[2\\], we consider two possible definitions of the heat currents from the qubits into the baths.\n", - "The so-called bath heat currents are $j_{\\text{B}}^K = \\partial_t \\langle H_{\\text{B}}^K \\rangle$ and the system heat currents are $j_{\\text{S}}^K = \\mathrm i\\, \\langle [H_{\\text{S}}, Q_K] X_{\\text{B}}^K \\rangle$.\n", - "As shown in Ref. \\[2\\], they can be expressed in terms of the HEOM ADOs as follows:\n", - "$$ \\begin{aligned} \\mbox{} \\\\\n", - " j_{\\text{B}}^K &= \\!\\!\\sum_{\\substack{\\mathbf n\\\\ \\text{Level 1}\\\\ \\text{Bath $K$}}}\\!\\! \\nu[\\mathbf n] \\operatorname{tr}\\bigl[ Q_K \\rho_{\\mathbf n} \\bigr] - 2 C_I^K(0) \\operatorname{tr}\\bigl[ Q_k^2 \\rho \\bigr] + \\Gamma_{\\text{T}}^K \\operatorname{tr}\\bigl[ [[H_{\\text{S}}, Q_K], Q_K]\\, \\rho \\bigr] , \\\\[.5em]\n", - " j_{\\text{S}}^K &= \\mathrm i\\!\\! \\sum_{\\substack{\\mathbf n\\\\ \\text{Level 1}\\\\ \\text{Bath $k$}}}\\!\\! \\operatorname{tr}\\bigl[ [H_{\\text{S}}, Q_K]\\, \\rho_{\\mathbf n} \\bigr] + \\Gamma_{\\text{T}}^K \\operatorname{tr}\\bigl[ [[H_{\\text{S}}, Q_K], Q_K]\\, \\rho \\bigr] . \\\\ \\mbox{}\n", - "\\end{aligned} $$\n", - "The sums run over all level-$1$ multi-indices $\\mathbf n$ with one excitation corresponding to the K-th bath, $\\nu[\\mathbf n]$ is the corresponding (negative) exponent of the bath auto-correlation function $C^K(t)$, and $\\Gamma_{\\text{T}}^K$ is the Ishizaki-Tanimura terminator (i.e., a correction term accounting for the error introduced by approximating the correlation function with a finite sum of exponential terms).\n", - "In the expression for the bath heat currents, we left out terms involving $[Q_1, Q_2]$, which is zero in this example.\n", - "\n", - "   \\[2\\] Kato and Tanimura, [J. Chem. Phys. **145**, 224105](https://doi.org/10.1063/1.4971370) (2016)." - ] - }, - { - "cell_type": "markdown", - "id": "adc4be74", - "metadata": {}, - "source": [ - "In QuTiP, these currents can be conveniently calculated as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "696f36e3", - "metadata": {}, - "outputs": [], - "source": [ - "def bath_heat_current(bath_tag, ado_state, hamiltonian, coupling_op, delta=0):\n", - " \"\"\"\n", - " Bath heat current from the system into the heat bath with the given tag.\n", - "\n", - " Parameters\n", - " ----------\n", - " bath_tag : str, tuple or any other object\n", - " Tag of the heat bath corresponding to the current of interest.\n", - "\n", - " ado_state : HierarchyADOsState\n", - " Current state of the system and the environment (encoded in the ADOs).\n", - "\n", - " hamiltonian : Qobj\n", - " System Hamiltonian at the current time.\n", - "\n", - " coupling_op : Qobj\n", - " System coupling operator at the current time.\n", - "\n", - " delta : float\n", - " The prefactor of the \\\\delta(t) term in the correlation function (the\n", - " Ishizaki-Tanimura terminator).\n", - " \"\"\"\n", - " l1_labels = ado_state.filter(level=1, tags=[bath_tag])\n", - " a_op = 1j * (hamiltonian * coupling_op - coupling_op * hamiltonian)\n", - "\n", - " result = 0\n", - " cI0 = 0 # imaginary part of bath auto-correlation function (t=0)\n", - " for label in l1_labels:\n", - " [exp] = ado_state.exps(label)\n", - " result += exp.vk * (coupling_op * ado_state.extract(label)).tr()\n", - "\n", - " if exp.type == CFExponent.types['I']:\n", - " cI0 += exp.ck\n", - " elif exp.type == CFExponent.types['RI']:\n", - " cI0 += exp.ck2\n", - "\n", - " result -= 2 * cI0 * (coupling_op * coupling_op * ado_state.rho).tr()\n", - " if delta != 0:\n", - " result -= (\n", - " 1j * delta *\n", - " ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr()\n", - " )\n", - " return result\n", - "\n", - "\n", - "def system_heat_current(\n", - " bath_tag, ado_state, hamiltonian, coupling_op, delta=0,\n", - "):\n", - " \"\"\"\n", - " System heat current from the system into the heat bath with the given tag.\n", - "\n", - " Parameters\n", - " ----------\n", - " bath_tag : str, tuple or any other object\n", - " Tag of the heat bath corresponding to the current of interest.\n", - "\n", - " ado_state : HierarchyADOsState\n", - " Current state of the system and the environment (encoded in the ADOs).\n", - "\n", - " hamiltonian : Qobj\n", - " System Hamiltonian at the current time.\n", - "\n", - " coupling_op : Qobj\n", - " System coupling operator at the current time.\n", - "\n", - " delta : float\n", - " The prefactor of the \\\\delta(t) term in the correlation function (the\n", - " Ishizaki-Tanimura terminator).\n", - " \"\"\"\n", - " l1_labels = ado_state.filter(level=1, tags=[bath_tag])\n", - " a_op = 1j * (hamiltonian * coupling_op - coupling_op * hamiltonian)\n", - "\n", - " result = 0\n", - " for label in l1_labels:\n", - " result += (a_op * ado_state.extract(label)).tr()\n", - "\n", - " if delta != 0:\n", - " result -= (\n", - " 1j * delta *\n", - " ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr()\n", - " )\n", - " return result" - ] - }, - { - "cell_type": "markdown", - "id": "089c2dd0", - "metadata": {}, - "source": [ - "Note that at long times, we expect $j_{\\text{B}}^1 = -j_{\\text{B}}^2$ and $j_{\\text{S}}^1 = -j_{\\text{S}}^2$ due to energy conservation. At long times, we also expect $j_{\\text{B}}^1 = j_{\\text{S}}^1$ and $j_{\\text{B}}^2 = j_{\\text{S}}^2$ since the coupling operators commute, $[Q_1, Q_2] = 0$. Hence, all four currents should agree in the long-time limit (up to a sign). This long-time value is what was analyzed in Ref. \\[2\\]." - ] - }, - { - "cell_type": "markdown", - "id": "e091142d", - "metadata": {}, - "source": [ - "## Simulations" - ] - }, - { - "cell_type": "markdown", - "id": "dc0ed322", - "metadata": {}, - "source": [ - "For our simulations, we will represent the bath spectral densities using the first term of their Padé decompositions, and we will use $7$ levels of the HEOM hierarchy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7afc97cb", - "metadata": {}, - "outputs": [], - "source": [ - "Nk = 1\n", - "NC = 7" - ] - }, - { - "cell_type": "markdown", - "id": "399ea6c2", - "metadata": {}, - "source": [ - "### Time Evolution\n", - "\n", - "We fix $J_{12} = 0.1 \\epsilon$ (as in Fig. 3(a-ii) of Ref. \\[2\\]) and choose the fixed coupling strength $\\lambda_1 = \\lambda_2 = J_{12}\\, /\\, (2\\epsilon)$ (corresponding to $\\bar\\zeta = 1$ in Ref. \\[2\\]).\n", - "Using these values, we will study the time evolution of the system state and the heat currents." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1d7eb5ba", - "metadata": {}, - "outputs": [], - "source": [ - "# fix qubit-qubit and qubit-bath coupling strengths\n", - "sys = SystemParams(J12=0.1)\n", - "bath_p1 = BathParams(qubit=0, sign=\"+\", lam=sys.J12 / 2)\n", - "bath_p2 = BathParams(qubit=1, sign=\"-\", lam=sys.J12 / 2)\n", - "\n", - "# choose arbitrary initial state\n", - "rho0 = qt.tensor(qt.identity(2), qt.identity(2)) / 4\n", - "\n", - "# simulation time span\n", - "tlist = np.linspace(0, 50, 250)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3f469adc", - "metadata": {}, - "outputs": [], - "source": [ - "H = sys.H()\n", - "\n", - "bath1,b1term,b1delta = bath_p1.bath(Nk, tag='bath 1')\n", - "Q1 = bath_p1.Q()\n", - "\n", - "bath2,b2term,b2delta = bath_p2.bath(Nk, tag='bath 2')\n", - "Q2 = bath_p2.Q()\n", - "\n", - "\n", - "solver = HEOMSolver(\n", - " qt.liouvillian(H) + b1term + b2term,\n", - " [bath1, bath2],\n", - " max_depth=NC,\n", - " options=options,\n", - ")\n", - "\n", - "result = solver.run(rho0, tlist, e_ops=[\n", - " qt.tensor(qt.sigmaz(), qt.identity(2)),\n", - " lambda t, ado: bath_heat_current('bath 1', ado, H, Q1, b1delta),\n", - " lambda t, ado: bath_heat_current('bath 2', ado, H, Q2, b2delta),\n", - " lambda t, ado: system_heat_current('bath 1', ado, H, Q1, b1delta),\n", - " lambda t, ado: system_heat_current('bath 2', ado, H, Q2, b2delta),\n", - "])" - ] - }, - { - "cell_type": "markdown", - "id": "fa0b201d", - "metadata": {}, - "source": [ - "We first plot $\\langle \\sigma_z^1 \\rangle$ to see the time evolution of the system state:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0bf29a8f", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(figsize=(8, 8))\n", - "axes.plot(tlist, result.expect[0], 'r', linewidth=2)\n", - "axes.set_xlabel('t', fontsize=28)\n", - "axes.set_ylabel(r\"$\\langle \\sigma_z^1 \\rangle$\", fontsize=28);" - ] - }, - { - "cell_type": "markdown", - "id": "ab79c89c", - "metadata": {}, - "source": [ - "We find a rather quick thermalization of the system state. For the heat currents, however, it takes a somewhat longer time until they converge to their long-time values:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "987ef90d", - "metadata": {}, - "outputs": [], - "source": [ - "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8))\n", - "\n", - "ax1.plot(\n", - " tlist, -np.real(result.expect[1]),\n", - " color='darkorange', label='BHC (bath 1 -> system)',\n", - ")\n", - "ax1.plot(\n", - " tlist, np.real(result.expect[2]),\n", - " '--', color='darkorange', label='BHC (system -> bath 2)',\n", - ")\n", - "ax1.plot(\n", - " tlist, -np.real(result.expect[3]),\n", - " color='dodgerblue', label='SHC (bath 1 -> system)',\n", - ")\n", - "ax1.plot(\n", - " tlist, np.real(result.expect[4]),\n", - " '--', color='dodgerblue', label='SHC (system -> bath 2)',\n", - ")\n", - "\n", - "ax1.set_xlabel('t', fontsize=28)\n", - "ax1.set_ylabel('j', fontsize=28)\n", - "ax1.set_ylim((-0.05, 0.05))\n", - "ax1.legend(loc=0, fontsize=12)\n", - "\n", - "ax2.plot(\n", - " tlist, -np.real(result.expect[1]),\n", - " color='darkorange', label='BHC (bath 1 -> system)',\n", - ")\n", - "ax2.plot(\n", - " tlist, np.real(result.expect[2]),\n", - " '--', color='darkorange', label='BHC (system -> bath 2)',\n", - ")\n", - "ax2.plot(\n", - " tlist, -np.real(result.expect[3]),\n", - " color='dodgerblue', label='SHC (bath 1 -> system)',\n", - ")\n", - "ax2.plot(\n", - " tlist, np.real(result.expect[4]),\n", - " '--', color='dodgerblue', label='SHC (system -> bath 2)',\n", - ")\n", - "\n", - "ax2.set_xlabel('t', fontsize=28)\n", - "ax2.set_xlim((20, 50))\n", - "ax2.set_ylim((0, 0.0002))\n", - "ax2.legend(loc=0, fontsize=12);" - ] - }, - { - "cell_type": "markdown", - "id": "52228d72", - "metadata": {}, - "source": [ - "### Steady-state currents\n", - "\n", - "Here, we try to reproduce the HEOM curves in Fig. 3(a) of Ref. \\[1\\] by varying the coupling strength and finding the steady state for each coupling strength." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f4560df1", - "metadata": {}, - "outputs": [], - "source": [ - "def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options):\n", - " \"\"\" Calculate the steady sate heat currents for the given system and\n", - " bath.\n", - " \"\"\"\n", - "\n", - " bath1,b1term,b1delta = bath_p1.bath(Nk, tag='bath 1')\n", - " Q1 = bath_p1.Q()\n", - "\n", - " bath2,b2term,b2delta = bath_p2.bath(Nk, tag='bath 2')\n", - " Q2 = bath_p2.Q()\n", - "\n", - " solver = HEOMSolver(\n", - " qt.liouvillian(sys.H()) + b1term + b2term,\n", - " [bath1, bath2],\n", - " max_depth=NC,\n", - " options=options\n", - " )\n", - "\n", - " _, steady_ados = solver.steady_state()\n", - "\n", - " return (\n", - " bath_heat_current('bath 1', steady_ados, sys.H(), Q1, b1delta),\n", - " bath_heat_current('bath 2', steady_ados, sys.H(), Q2, b2delta),\n", - " system_heat_current('bath 1', steady_ados, sys.H(), Q1, b1delta),\n", - " system_heat_current('bath 2', steady_ados, sys.H(), Q2, b2delta),\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9846b0b1", - "metadata": {}, - "outputs": [], - "source": [ - "# Define number of points to use for the plot\n", - "plot_points = 10 # use 100 for a smoother curve\n", - "\n", - "# Range of relative coupling strengths\n", - "# Chosen so that zb_max is maximum, centered around 1 on a log scale\n", - "zb_max = 4 # use 20 to see more of the current curve\n", - "zeta_bars = np.logspace(\n", - " -np.log(zb_max),\n", - " np.log(zb_max),\n", - " plot_points,\n", - " base=np.e,\n", - ")\n", - "\n", - "# Setup a progress bar\n", - "progress = IntProgress(min=0, max=(3 * plot_points))\n", - "display(progress)\n", - "\n", - "\n", - "def calculate_heat_current(J12, zb, Nk, progress=progress):\n", - " \"\"\" Calculate a single heat current and update the progress bar. \"\"\"\n", - " # Estimate appropriate HEOM max_depth from coupling strength\n", - " NC = 7 + int(max(zb * J12 - 1, 0) * 2)\n", - " NC = min(NC, 20)\n", - " # the four currents are identical in the steady state\n", - " j, _, _, _ = heat_currents(\n", - " sys.replace(J12=J12),\n", - " bath_p1.replace(lam=zb * J12 / 2),\n", - " bath_p2.replace(lam=zb * J12 / 2),\n", - " Nk, NC, options=options,\n", - " )\n", - " progress.value += 1\n", - " return j\n", - "\n", - "\n", - "# Calculate steady state currents for range of zeta_bars\n", - "# for J12 = 0.01, 0.1 and 0.5:\n", - "j1s = [\n", - " calculate_heat_current(0.01, zb, Nk)\n", - " for zb in zeta_bars\n", - "]\n", - "j2s = [\n", - " calculate_heat_current(0.1, zb, Nk)\n", - " for zb in zeta_bars\n", - "]\n", - "j3s = [\n", - " calculate_heat_current(0.5, zb, Nk)\n", - " for zb in zeta_bars\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "edefdc0a", - "metadata": {}, - "source": [ - "## Create Plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "adc2b5b6", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(figsize=(12, 7))\n", - "\n", - "axes.plot(\n", - " zeta_bars, -1000 * 100 * np.real(j1s),\n", - " 'b', linewidth=2, label=r\"$J_{12} = 0.01\\, \\epsilon$\",\n", - ")\n", - "axes.plot(\n", - " zeta_bars, -1000 * 10 * np.real(j2s),\n", - " 'r--', linewidth=2, label=r\"$J_{12} = 0.1\\, \\epsilon$\",\n", - ")\n", - "axes.plot(\n", - " zeta_bars, -1000 * 2 * np.real(j3s),\n", - " 'g-.', linewidth=2, label=r\"$J_{12} = 0.5\\, \\epsilon$\",\n", - ")\n", - "\n", - "axes.set_xscale('log')\n", - "axes.set_xlabel(r\"$\\bar\\zeta$\", fontsize=30)\n", - "axes.set_xlim((zeta_bars[0], zeta_bars[-1]))\n", - "\n", - "axes.set_ylabel(\n", - " r\"$j_{\\mathrm{ss}}\\; /\\; (\\epsilon J_{12}) \\times 10^3$\",\n", - " fontsize=30,\n", - ")\n", - "axes.set_ylim((0, 2))\n", - "\n", - "axes.legend(loc=0);" - ] - }, - { - "cell_type": "markdown", - "id": "3aab0a7c", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8e769307", - "metadata": {}, - "outputs": [], - "source": [ - "qt.about()" - ] - }, - { - "cell_type": "markdown", - "id": "ef5b9bd4", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e3e19455", - "metadata": {}, - "outputs": [], - "source": [ - "assert 1 == 1" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "qutip-dev", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-3-quantum-heat-transport.md b/tutorials-v5/heom/heom-3-quantum-heat-transport.md index 00e5a524..05d1e5df 100644 --- a/tutorials-v5/heom/heom-3-quantum-heat-transport.md +++ b/tutorials-v5/heom/heom-3-quantum-heat-transport.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 + jupytext_version: 1.16.1 kernelspec: display_name: qutip-dev language: python @@ -50,7 +50,7 @@ References: ## Setup -```{code-cell} ipython3 +```{code-cell} import dataclasses import numpy as np @@ -75,7 +75,7 @@ from IPython.display import display ## Helpers -```{code-cell} ipython3 +```{code-cell} # Solver options: options = { @@ -91,7 +91,7 @@ options = { ## System and bath definition -```{code-cell} ipython3 +```{code-cell} @dataclasses.dataclass class SystemParams: """ System parameters and Hamiltonian. """ @@ -120,7 +120,7 @@ class SystemParams: return dataclasses.replace(self, **kw) ``` -```{code-cell} ipython3 +```{code-cell} @dataclasses.dataclass class BathParams: """ Bath parameters. """ @@ -175,7 +175,7 @@ In the expression for the bath heat currents, we left out terms involving $[Q_1, In QuTiP, these currents can be conveniently calculated as follows: -```{code-cell} ipython3 +```{code-cell} def bath_heat_current(bath_tag, ado_state, hamiltonian, coupling_op, delta=0): """ Bath heat current from the system into the heat bath with the given tag. @@ -270,7 +270,7 @@ Note that at long times, we expect $j_{\text{B}}^1 = -j_{\text{B}}^2$ and $j_{\t For our simulations, we will represent the bath spectral densities using the first term of their Padé decompositions, and we will use $7$ levels of the HEOM hierarchy. -```{code-cell} ipython3 +```{code-cell} Nk = 1 NC = 7 ``` @@ -280,7 +280,7 @@ NC = 7 We fix $J_{12} = 0.1 \epsilon$ (as in Fig. 3(a-ii) of Ref. \[2\]) and choose the fixed coupling strength $\lambda_1 = \lambda_2 = J_{12}\, /\, (2\epsilon)$ (corresponding to $\bar\zeta = 1$ in Ref. \[2\]). Using these values, we will study the time evolution of the system state and the heat currents. -```{code-cell} ipython3 +```{code-cell} # fix qubit-qubit and qubit-bath coupling strengths sys = SystemParams(J12=0.1) bath_p1 = BathParams(qubit=0, sign="+", lam=sys.J12 / 2) @@ -293,7 +293,7 @@ rho0 = qt.tensor(qt.identity(2), qt.identity(2)) / 4 tlist = np.linspace(0, 50, 250) ``` -```{code-cell} ipython3 +```{code-cell} H = sys.H() bath1,b1term,b1delta = bath_p1.bath(Nk, tag='bath 1') @@ -321,7 +321,7 @@ result = solver.run(rho0, tlist, e_ops=[ We first plot $\langle \sigma_z^1 \rangle$ to see the time evolution of the system state: -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(figsize=(8, 8)) axes.plot(tlist, result.expect[0], 'r', linewidth=2) axes.set_xlabel('t', fontsize=28) @@ -330,7 +330,7 @@ axes.set_ylabel(r"$\langle \sigma_z^1 \rangle$", fontsize=28); We find a rather quick thermalization of the system state. For the heat currents, however, it takes a somewhat longer time until they converge to their long-time values: -```{code-cell} ipython3 +```{code-cell} fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8)) ax1.plot( @@ -382,7 +382,7 @@ ax2.legend(loc=0, fontsize=12); Here, we try to reproduce the HEOM curves in Fig. 3(a) of Ref. \[1\] by varying the coupling strength and finding the steady state for each coupling strength. -```{code-cell} ipython3 +```{code-cell} def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options): """ Calculate the steady sate heat currents for the given system and bath. @@ -411,7 +411,7 @@ def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options): ) ``` -```{code-cell} ipython3 +```{code-cell} # Define number of points to use for the plot plot_points = 10 # use 100 for a smoother curve @@ -464,7 +464,7 @@ j3s = [ ## Create Plot -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(figsize=(12, 7)) axes.plot( @@ -495,7 +495,7 @@ axes.legend(loc=0); ## About -```{code-cell} ipython3 +```{code-cell} qt.about() ``` @@ -503,6 +503,6 @@ qt.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 +```{code-cell} assert 1 == 1 ``` diff --git a/tutorials-v5/heom/heom-4-dynamical-decoupling.ipynb b/tutorials-v5/heom/heom-4-dynamical-decoupling.ipynb deleted file mode 100644 index 0800041e..00000000 --- a/tutorials-v5/heom/heom-4-dynamical-decoupling.ipynb +++ /dev/null @@ -1,751 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b9711696", - "metadata": {}, - "source": [ - "# HEOM 4: Dynamical decoupling of a non-Markovian environment" - ] - }, - { - "cell_type": "markdown", - "id": "e97d7161", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "Following [Lorenza Viola and Seth Lloyd](https://arxiv.org/abs/quant-ph/9803057) we consider an example of dynamical decoupling.\n", - "We choose a drive which performs pi rotations, interspersed with short periods where the bath causes dephasing.\n", - "\n", - "We first show the standard example of equally spaced pulses, and then consider the 'optimal' Uhrig spacing ([Götz S. Uhrig Phys. Rev. Lett. 98, 100504 (2007)](https://arxiv.org/abs/quant-ph/0609203))." - ] - }, - { - "cell_type": "markdown", - "id": "1052001d", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a118b8ac", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " QobjEvo,\n", - " basis,\n", - " expect,\n", - " ket2dm,\n", - " sigmax,\n", - " sigmaz,\n", - " DrudeLorentzEnvironment\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver\n", - ")\n", - "\n", - "from ipywidgets import IntProgress\n", - "from IPython.display import display\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "0e69dfa1", - "metadata": {}, - "source": [ - "## Solver options" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "018c1d26", - "metadata": {}, - "outputs": [], - "source": [ - "# Solver options:\n", - "\n", - "# The max_step must be set to a short time than the\n", - "# length of the shortest pulse, otherwise the solver\n", - "# might skip over a pulse.\n", - "\n", - "options = {\n", - " \"nsteps\": 1500,\n", - " \"store_states\": True,\n", - " \"rtol\": 1e-12,\n", - " \"atol\": 1e-12,\n", - " \"max_step\": 1 / 20.0,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "97e33657", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "Now we define the system and bath properties and the HEOM parameters. The system is a single stationary qubit with $H = 0$ and the bath is a bosonic bath with a Drude-Lorentz spectrum." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b46a6b55", - "metadata": {}, - "outputs": [], - "source": [ - "# Define the system Hamlitonian.\n", - "#\n", - "# The system isn't evolving by itself, so the Hamiltonian is 0 (with the\n", - "# correct dimensions):\n", - "\n", - "H_sys = 0 * sigmaz()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "56dda9dc", - "metadata": {}, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresponding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresponding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fbb0a230", - "metadata": {}, - "outputs": [], - "source": [ - "# Properties for the Drude-Lorentz bath\n", - "\n", - "lam = 0.0005\n", - "gamma = 0.005\n", - "T = 0.05\n", - "\n", - "# bath-system coupling operator:\n", - "Q = sigmaz()\n", - "\n", - "# number of terms to keep in the expansion of the bath correlation function:\n", - "Nk = 3\n", - "\n", - "env = DrudeLorentzEnvironment(lam=lam, gamma=gamma,T=T)\n", - "env_approx=env.approx_by_pade(Nk=Nk)\n", - "bath=(env_approx,Q)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2e14cabf", - "metadata": {}, - "outputs": [], - "source": [ - "# HEOM parameters\n", - "\n", - "# number of layers to keep in the hierarchy:\n", - "NC = 6" - ] - }, - { - "cell_type": "markdown", - "id": "31a1f196", - "metadata": {}, - "source": [ - "To perform the dynamic decoupling from the environment, we will drive the system with a time-dependent pulse that couples to the system via the $\\sigma_x$ operator. The area under the pulse will usual be set to $\\pi / 2$ so that the pulse flips the qubit state.\n", - "\n", - "Below we define a function that returns the pulse (which is itself a function):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "706ad7b4", - "metadata": {}, - "outputs": [], - "source": [ - "def drive(amplitude, delay, integral):\n", - " \"\"\" Coefficient of the drive as a function of time.\n", - "\n", - " The drive consists of a series of constant pulses with\n", - " a fixed delay between them.\n", - "\n", - " Parameters\n", - " ----------\n", - " amplitude : float\n", - " The amplitude of the drive during the pulse.\n", - " delay : float\n", - " The time delay between successive pulses.\n", - " integral : float\n", - " The integral of the pulse. This determines\n", - " the duration of each pulse with the duration\n", - " equal to the integral divided by the amplitude.\n", - " \"\"\"\n", - " duration = integral / amplitude\n", - " period = duration + delay\n", - "\n", - " def pulse(t):\n", - " t = t % period\n", - " if t < duration:\n", - " return amplitude\n", - " return 0\n", - "\n", - " return pulse\n", - "\n", - "\n", - "H_drive = sigmax()" - ] - }, - { - "cell_type": "markdown", - "id": "f90ae7f8", - "metadata": {}, - "source": [ - "## Plot the spectral density\n", - "\n", - "Let's start by plotting the spectral density of our Drude-Lorentz bath:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aef565f9", - "metadata": {}, - "outputs": [], - "source": [ - "wlist = np.linspace(0, 0.5, 1000)\n", - "J = env.spectral_density(wlist)\n", - "J_approx = env_approx.spectral_density(wlist)\n", - "\n", - "fig, axes = plt.subplots(1, 1, figsize=(8, 8))\n", - "axes.plot(wlist, J, 'r', linewidth=2)\n", - "axes.plot(wlist, J_approx, 'b--', linewidth=2)\n", - "\n", - "axes.set_xlabel(r'$\\omega$', fontsize=28)\n", - "axes.set_ylabel(r'J', fontsize=28);" - ] - }, - { - "cell_type": "markdown", - "id": "d657a2bd", - "metadata": {}, - "source": [ - "## Dynamic decoupling with fast and slow pulses\n", - "\n", - "Now we are ready to explore dynamic decoupling from the environment.\n", - "\n", - "First we will drive the system with fast, large amplitude pulses. Then we will drive the system with slower, smaller amplitude pulses. The faster pulses decoupling the system more effectively and retain the coherence longer, but the slower pulses help too.\n", - "\n", - "Let's start by simulating the fast pulses:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "13b69602", - "metadata": {}, - "outputs": [], - "source": [ - "# Fast driving (quick, large amplitude pulses)\n", - "\n", - "tlist = np.linspace(0, 400, 1000)\n", - "\n", - "# start with a superposition so there is something to dephase!\n", - "rho0 = (basis(2, 1) + basis(2, 0)).unit()\n", - "rho0 = ket2dm(rho0)\n", - "\n", - "# without pulses\n", - "hsolver = HEOMSolver(H_sys, bath, NC, options=options)\n", - "outputnoDD = hsolver.run(rho0, tlist)\n", - "\n", - "# with pulses\n", - "drive_fast = drive(amplitude=0.5, delay=20, integral=np.pi / 2)\n", - "H_d = qutip.QobjEvo([H_sys, [H_drive, drive_fast]])\n", - "\n", - "hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", - "outputDD = hsolver.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "e109b42c", - "metadata": {}, - "source": [ - "And now the longer slower pulses:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "29168059", - "metadata": {}, - "outputs": [], - "source": [ - "# Slow driving (longer, small amplitude pulses)\n", - "\n", - "# without pulses\n", - "hsolver = HEOMSolver(H_sys, bath, NC, options=options)\n", - "outputnoDDslow = hsolver.run(rho0, tlist)\n", - "\n", - "# with pulses\n", - "drive_slow = drive(amplitude=0.01, delay=20, integral=np.pi/2)\n", - "H_d = QobjEvo([H_sys, [H_drive, drive_slow]])\n", - "\n", - "hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", - "outputDDslow = hsolver.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "7e9d89a1", - "metadata": {}, - "source": [ - "Now let's plot all of the results and the shapes of the pulses:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1fcc6fd2", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_dd_results(outputnoDD, outputDD, outputDDslow):\n", - " fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 12))\n", - "\n", - " # Plot the dynamic decoupling results:\n", - "\n", - " tlist = outputDD.times\n", - "\n", - " P12 = basis(2, 1) * basis(2, 0).dag()\n", - " P12DD = qutip.expect(outputDD.states, P12)\n", - " P12noDD = qutip.expect(outputnoDD.states, P12)\n", - " P12DDslow = qutip.expect(outputDDslow.states, P12)\n", - "\n", - " plt.sca(axes[0])\n", - " plt.yticks([0, 0.25, 0.5], [0, 0.25, 0.5])\n", - "\n", - " axes[0].plot(\n", - " tlist, np.real(P12DD),\n", - " 'green', linestyle='-', linewidth=2, label=\"HEOM with fast DD\",\n", - " )\n", - " axes[0].plot(\n", - " tlist, np.real(P12DDslow),\n", - " 'blue', linestyle='-', linewidth=2, label=\"HEOM with slow DD\",\n", - " )\n", - " axes[0].plot(\n", - " tlist, np.real(P12noDD),\n", - " 'orange', linestyle='--', linewidth=2, label=\"HEOM no DD\",\n", - " )\n", - "\n", - " axes[0].locator_params(axis='y', nbins=3)\n", - " axes[0].locator_params(axis='x', nbins=3)\n", - "\n", - " axes[0].set_ylabel(r\"$\\rho_{01}$\", fontsize=30)\n", - "\n", - " axes[0].legend(loc=4)\n", - " axes[0].text(0, 0.4, \"(a)\", fontsize=28)\n", - "\n", - " # Plot the drive pulses:\n", - "\n", - " pulse = [drive_fast(t) for t in tlist]\n", - " pulseslow = [drive_slow(t) for t in tlist]\n", - "\n", - " plt.sca(axes[1])\n", - " plt.yticks([0., 0.25, 0.5], [0, 0.25, 0.5])\n", - "\n", - " axes[1].plot(\n", - " tlist, pulse,\n", - " 'green', linestyle='-', linewidth=2, label=\"Drive fast\",\n", - " )\n", - " axes[1].plot(\n", - " tlist, pulseslow,\n", - " 'blue', linestyle='--', linewidth=2, label=\"Drive slow\",\n", - " )\n", - "\n", - " axes[1].locator_params(axis='y', nbins=3)\n", - " axes[1].locator_params(axis='x', nbins=3)\n", - "\n", - " axes[1].set_xlabel(r'$t\\bar{V}_{\\mathrm{f}}$', fontsize=30)\n", - " axes[1].set_ylabel(r'Drive amplitude/$\\bar{V}_{\\mathrm{f}}$', fontsize=30)\n", - "\n", - " axes[1].legend(loc=1)\n", - " axes[1].text(0, 0.4, \"(b)\", fontsize=28)\n", - "\n", - " fig.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "21bcc1e1", - "metadata": {}, - "outputs": [], - "source": [ - "plot_dd_results(outputnoDD, outputDD, outputDDslow)" - ] - }, - { - "cell_type": "markdown", - "id": "1f964ec6", - "metadata": {}, - "source": [ - "## Non-equally spaced pulses" - ] - }, - { - "cell_type": "markdown", - "id": "3916fabb", - "metadata": {}, - "source": [ - "Next we consider non-equally spaced pulses.\n", - "\n", - "Rather than plot as a function of time we just consider the final coherence after time $T$ and 100 pulses. We change the width of the environment to demonstate that the Uhrig sequence (i.e. the evenly spaced pulses) can be sub-optimal when the bath is very broad.\n", - "\n", - "Instead of evenly spaced pulses, we will use pulses where the cummulative delay after $j$ pulses is given by:\n", - "\n", - "$$\n", - " \\sin^2(\\frac{\\pi}{2} \\frac{j}{N + 1})\n", - "$$\n", - "\n", - "This is just a convenient way to describe the varying delay. We could have chosen another monotonically increasing function to represent the cummulative delay (although it might not be as effective)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4776a07f", - "metadata": {}, - "outputs": [], - "source": [ - "def cummulative_delay_fractions(N):\n", - " \"\"\" Return an array of N + 1 cummulative delay\n", - " fractions.\n", - "\n", - " The j'th entry in the array should be the sum of\n", - " all delays before the j'th pulse. The last entry\n", - " should be 1 (i.e. the entire cummulative delay\n", - " should have been used once the sequence of pulses\n", - " is complete).\n", - "\n", - " The function should be monotonically increasing,\n", - " strictly greater than zero and the last value\n", - " should be 1.\n", - "\n", - " This implementation returns:\n", - "\n", - " sin((pi / 2) * (j / (N + 1)))**2\n", - "\n", - " as the cummulative delay after the j'th pulse.\n", - " \"\"\"\n", - " return np.array([\n", - " np.sin((np.pi / 2) * (j / (N + 1)))**2\n", - " for j in range(0, N + 1)\n", - " ])\n", - "\n", - "\n", - "def drive_opt(amplitude, avg_delay, integral, N):\n", - " \"\"\" Return an optimized distance pulse function.\n", - "\n", - " Our previous pulses were evenly spaced. Here we\n", - " instead use a varying delay after the j'th pulse.\n", - "\n", - " The cummulative delay is described by the function\n", - " ``cummulative_delay_fractions`` above.\n", - " \"\"\"\n", - " duration = integral / amplitude\n", - " cummulative_delays = N * avg_delay * cummulative_delay_fractions(N)\n", - "\n", - " t_start = cummulative_delays + duration * np.arange(0, N + 1)\n", - " t_end = cummulative_delays + duration * np.arange(1, N + 2)\n", - "\n", - " def pulse(t):\n", - " if any((t_start <= t) & (t <= t_end)):\n", - " return amplitude\n", - " return 0.0\n", - "\n", - " return pulse" - ] - }, - { - "cell_type": "markdown", - "id": "fb41f0a9", - "metadata": {}, - "source": [ - "Let's plot the cummulative delays and see what they look like. Note that the cummulative delay starts at $0$, ends at $1$ and is monotonically increasing, as required.\n", - "\n", - "On the same axes we plot the individual $j^{th}$ delays as a fraction of the average delay." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cb19ec7b", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_cummulative_delay_fractions(N):\n", - " cummulative = cummulative_delay_fractions(N)\n", - " individual = (cummulative[1:] - cummulative[:-1]) * N\n", - " plt.plot(np.arange(0, N + 1), cummulative, label=\"Cummulative delay\")\n", - " plt.plot(np.arange(0, N), individual, label=\"j'th delay\")\n", - " plt.xlabel(\"j\")\n", - " plt.ylabel(\"Fraction of delay\")\n", - " plt.legend()\n", - "\n", - "\n", - "plot_cummulative_delay_fractions(100)" - ] - }, - { - "cell_type": "markdown", - "id": "b2e7bc06", - "metadata": {}, - "source": [ - "And now let us plot the first ten even and optimally spaced pulses together to compare them:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bd019617", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_even_and_optimally_spaced_pulses():\n", - " amplitude = 10.0\n", - " integral = np.pi / 2\n", - " duration = integral / amplitude\n", - " delay = 1.0 - duration\n", - "\n", - " tlist = np.linspace(0, 10, 1000)\n", - "\n", - " pulse_opt = drive_opt(amplitude, delay, integral, 100)\n", - " pulse_eq = drive(amplitude, delay, integral)\n", - "\n", - " plt.plot(\n", - " tlist, [pulse_opt(t) for t in tlist], label=\"opt\",\n", - " )\n", - " plt.plot(\n", - " tlist, [pulse_eq(t) for t in tlist], label=\"eq\",\n", - " )\n", - " plt.legend(loc=4)\n", - "\n", - "\n", - "plot_even_and_optimally_spaced_pulses()" - ] - }, - { - "cell_type": "markdown", - "id": "dea669bb", - "metadata": {}, - "source": [ - "Now let's simulate the effectiveness of the two sets of delays by comparing how well they maintain coherence after a hundred pulses.\n", - "\n", - "We'll perform the simulation over a range of lambdas and gammas to show how the non-evenly spaced delays become optimal as the width of the bath spectral function increases." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5ae0eec", - "metadata": {}, - "outputs": [], - "source": [ - "# Bath parameters to simulate over:\n", - "\n", - "# We use only two lambdas and two gammas so that the notebook executes\n", - "# quickly:\n", - "\n", - "lams = [0.005, 0.0005]\n", - "gammas = np.linspace(0.005, 0.05, 2)\n", - "\n", - "# But one can also extend the lists to larger ones:\n", - "#\n", - "# lams = [0.01, 0.005, 0.0005]\n", - "# gammas = np.linspace(0.005, 0.05, 10)\n", - "\n", - "# Setup a progress bar:\n", - "\n", - "progress = IntProgress(min=0, max=(2 * len(lams) * len(gammas)))\n", - "display(progress)\n", - "\n", - "\n", - "def simulate_100_pulses(lam, gamma, T, NC, Nk):\n", - " \"\"\" Simulate the evolution of 100 evenly and optimally spaced pulses.\n", - "\n", - " Returns the expectation value of P12p from the final state of\n", - " each evolution.\n", - " \"\"\"\n", - " rho0 = (basis(2, 1) + basis(2, 0)).unit()\n", - " rho0 = ket2dm(rho0)\n", - "\n", - " N = 100 # number of pulses to simulate\n", - " avg_cycle_time = 1.0 # average time from one pulse to the next\n", - " t_max = N * avg_cycle_time\n", - "\n", - " tlist = np.linspace(0, t_max, 100)\n", - "\n", - " amplitude = 10.0\n", - " integral = np.pi / 2\n", - " duration = integral / amplitude\n", - " delay = avg_cycle_time - duration\n", - "\n", - " env = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T)\n", - " env_approx = env.approx_by_pade(Nk=Nk)\n", - " bath=(env_approx,Q)\n", - " # Equally spaced pulses:\n", - "\n", - " pulse_eq = drive(amplitude, delay, integral)\n", - " H_d = QobjEvo([H_sys, [H_drive, pulse_eq]])\n", - "\n", - " hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", - " result = hsolver.run(rho0, tlist)\n", - "\n", - " P12_eq = expect(result.states[-1], P12p)\n", - " progress.value += 1\n", - "\n", - " # Non-equally spaced pulses:\n", - "\n", - " pulse_opt = drive_opt(amplitude, delay, integral, N)\n", - " H_d = QobjEvo([H_sys, [H_drive, pulse_opt]])\n", - "\n", - " hsolver = HEOMSolver(H_d, bath, NC, options=options)\n", - " result = hsolver.run(rho0, tlist)\n", - "\n", - " P12_opt = expect(result.states[-1], P12p)\n", - " progress.value += 1\n", - "\n", - " return P12_opt, P12_eq\n", - "\n", - "\n", - "# We use NC=2 and Nk=2 to speed up the simulation:\n", - "\n", - "P12_results = [\n", - " list(zip(*(\n", - " simulate_100_pulses(lam=lam_, gamma=gamma_, T=0.5, NC=2, Nk=2)\n", - " for gamma_ in gammas\n", - " )))\n", - " for lam_ in lams\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "c3d0f4ab", - "metadata": {}, - "source": [ - "Now that we have the expectation values of $\\rho_{01}$ let's plot them as a function of gamma for each lambda. Note how in each case the non-evenly spaced pulses become optimal once gamma is sufficiently small:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1f4c2ac9", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=False, figsize=(10, 7))\n", - "colors = [\"green\", \"red\", \"blue\"]\n", - "\n", - "for i in range(len(lams)):\n", - " color = colors[i % len(colors)]\n", - " axes.plot(\n", - " gammas, np.real(P12_results[i][0]),\n", - " color, linestyle='-', linewidth=2,\n", - " label=f\"Optimal DD [$\\\\lambda={lams[i]}$]\",\n", - " )\n", - " axes.plot(\n", - " gammas, np.real(P12_results[i][1]),\n", - " color, linestyle='-.', linewidth=2,\n", - " label=f\"Even DD [$\\\\lambda={lams[i]}$]\",\n", - " )\n", - "\n", - "axes.set_ylabel(r\"$\\rho_{01}$\")\n", - "axes.set_xlabel(r\"$\\gamma$\")\n", - "axes.legend(fontsize=16)\n", - "\n", - "fig.tight_layout();" - ] - }, - { - "cell_type": "markdown", - "id": "ad8f3047", - "metadata": {}, - "source": [ - "And now you know about dynamically decoupling a qubit from its environment!" - ] - }, - { - "cell_type": "markdown", - "id": "47cedf88", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1cedbf02", - "metadata": {}, - "outputs": [], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "663ced6a", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a64d82a4", - "metadata": {}, - "outputs": [], - "source": [ - "assert 1 == 1" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "qutip-dev", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-4-dynamical-decoupling.md b/tutorials-v5/heom/heom-4-dynamical-decoupling.md index e20ed51c..0eb761d5 100644 --- a/tutorials-v5/heom/heom-4-dynamical-decoupling.md +++ b/tutorials-v5/heom/heom-4-dynamical-decoupling.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 + jupytext_version: 1.16.1 kernelspec: display_name: qutip-dev language: python @@ -27,7 +27,7 @@ We first show the standard example of equally spaced pulses, and then consider t ## Setup -```{code-cell} ipython3 +```{code-cell} import numpy as np import matplotlib.pyplot as plt @@ -53,7 +53,7 @@ from IPython.display import display ## Solver options -```{code-cell} ipython3 +```{code-cell} # Solver options: # The max_step must be set to a short time than the @@ -75,7 +75,7 @@ options = { Now we define the system and bath properties and the HEOM parameters. The system is a single stationary qubit with $H = 0$ and the bath is a bosonic bath with a Drude-Lorentz spectrum. -```{code-cell} ipython3 +```{code-cell} # Define the system Hamlitonian. # # The system isn't evolving by itself, so the Hamiltonian is 0 (with the @@ -84,7 +84,7 @@ Now we define the system and bath properties and the HEOM parameters. The system H_sys = 0 * sigmaz() ``` -```{code-cell} ipython3 +```{code-cell} # Define some operators with which we will measure the system # 1,1 element of density matrix - corresponding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -93,7 +93,7 @@ P22p = basis(2, 1) * basis(2, 1).dag() P12p = basis(2, 0) * basis(2, 1).dag() ``` -```{code-cell} ipython3 +```{code-cell} # Properties for the Drude-Lorentz bath lam = 0.0005 @@ -111,7 +111,7 @@ env_approx=env.approx_by_pade(Nk=Nk) bath=(env_approx,Q) ``` -```{code-cell} ipython3 +```{code-cell} # HEOM parameters # number of layers to keep in the hierarchy: @@ -122,7 +122,7 @@ To perform the dynamic decoupling from the environment, we will drive the system Below we define a function that returns the pulse (which is itself a function): -```{code-cell} ipython3 +```{code-cell} def drive(amplitude, delay, integral): """ Coefficient of the drive as a function of time. @@ -159,7 +159,7 @@ H_drive = sigmax() Let's start by plotting the spectral density of our Drude-Lorentz bath: -```{code-cell} ipython3 +```{code-cell} wlist = np.linspace(0, 0.5, 1000) J = env.spectral_density(wlist) J_approx = env_approx.spectral_density(wlist) @@ -180,7 +180,7 @@ First we will drive the system with fast, large amplitude pulses. Then we will d Let's start by simulating the fast pulses: -```{code-cell} ipython3 +```{code-cell} # Fast driving (quick, large amplitude pulses) tlist = np.linspace(0, 400, 1000) @@ -203,7 +203,7 @@ outputDD = hsolver.run(rho0, tlist) And now the longer slower pulses: -```{code-cell} ipython3 +```{code-cell} # Slow driving (longer, small amplitude pulses) # without pulses @@ -220,7 +220,7 @@ outputDDslow = hsolver.run(rho0, tlist) Now let's plot all of the results and the shapes of the pulses: -```{code-cell} ipython3 +```{code-cell} def plot_dd_results(outputnoDD, outputDD, outputDDslow): fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 12)) @@ -286,7 +286,7 @@ def plot_dd_results(outputnoDD, outputDD, outputDDslow): fig.tight_layout() ``` -```{code-cell} ipython3 +```{code-cell} plot_dd_results(outputnoDD, outputDD, outputDDslow) ``` @@ -306,7 +306,7 @@ $$ This is just a convenient way to describe the varying delay. We could have chosen another monotonically increasing function to represent the cummulative delay (although it might not be as effective). -```{code-cell} ipython3 +```{code-cell} def cummulative_delay_fractions(N): """ Return an array of N + 1 cummulative delay fractions. @@ -360,7 +360,7 @@ Let's plot the cummulative delays and see what they look like. Note that the cum On the same axes we plot the individual $j^{th}$ delays as a fraction of the average delay. -```{code-cell} ipython3 +```{code-cell} def plot_cummulative_delay_fractions(N): cummulative = cummulative_delay_fractions(N) individual = (cummulative[1:] - cummulative[:-1]) * N @@ -376,7 +376,7 @@ plot_cummulative_delay_fractions(100) And now let us plot the first ten even and optimally spaced pulses together to compare them: -```{code-cell} ipython3 +```{code-cell} def plot_even_and_optimally_spaced_pulses(): amplitude = 10.0 integral = np.pi / 2 @@ -404,7 +404,7 @@ Now let's simulate the effectiveness of the two sets of delays by comparing how We'll perform the simulation over a range of lambdas and gammas to show how the non-evenly spaced delays become optimal as the width of the bath spectral function increases. -```{code-cell} ipython3 +```{code-cell} # Bath parameters to simulate over: # We use only two lambdas and two gammas so that the notebook executes @@ -485,7 +485,7 @@ P12_results = [ Now that we have the expectation values of $\rho_{01}$ let's plot them as a function of gamma for each lambda. Note how in each case the non-evenly spaced pulses become optimal once gamma is sufficiently small: -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, sharex=False, figsize=(10, 7)) colors = ["green", "red", "blue"] @@ -515,7 +515,7 @@ And now you know about dynamically decoupling a qubit from its environment! ## About -```{code-cell} ipython3 +```{code-cell} qutip.about() ``` @@ -523,6 +523,6 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 +```{code-cell} assert 1 == 1 ``` diff --git a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.ipynb b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.ipynb deleted file mode 100644 index 5b331f1b..00000000 --- a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.ipynb +++ /dev/null @@ -1,963 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "294cf5cd", - "metadata": {}, - "source": [ - "# HEOM 5a: Fermionic single impurity model" - ] - }, - { - "cell_type": "markdown", - "id": "1e7f2b4f", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc). Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications.\n", - "\n", - "Notation:\n", - "\n", - "* $K=L/R$ refers to left or right leads.\n", - "* $\\sigma=\\pm$ refers to input/output\n", - "\n", - "We choose a Lorentzian spectral density for the leads, with a peak at the chemical potential. The latter simplifies a little the notation required for the correlation functions, but can be relaxed if neccessary.\n", - "\n", - "$$J(\\omega) = \\frac{\\Gamma W^2}{((\\omega-\\mu_K)^2 +W^2 )}$$\n", - "\n", - "The Fermi distribution function is:\n", - "\n", - "$$f_F (x) = (\\exp(x) + 1)^{-1}$$\n", - "\n", - "Together these allow the correlation functions to be expressed as:\n", - "\n", - "$$C^{\\sigma}_K(t) = \\frac{1}{2\\pi} \\int_{-\\infty}^{\\infty} d\\omega e^{\\sigma i \\omega t} \\Gamma_K(\\omega) f_F[\\sigma\\beta(\\omega - \\mu)]$$\n", - "\n", - "As with the bosonic case we can expand these in an exponential series using Matsubara, Pade, or fitting approaches.\n", - "\n", - "The Pade decomposition approximates the Fermi distubition as\n", - "\n", - "$$f_F(x) \\approx f_F^{\\mathrm{approx}}(x) = \\frac{1}{2} - \\sum_l^{l_{max}} \\frac{2k_l x}{x^2 + \\epsilon_l^2}$$\n", - "\n", - "where $k_l$ and $\\epsilon_l$ are co-efficients defined in J. Chem Phys 133,10106.\n", - "\n", - "Evaluating the integral for the correlation functions gives,\n", - "\n", - "$$C_K^{\\sigma}(t) \\approx \\sum_{l=0}^{l_{max}} \\eta_K^{\\sigma_l} e^{-\\gamma_{K,\\sigma,l}t}$$\n", - "\n", - "where:\n", - "\n", - "$$\\eta_{K,0} = \\frac{\\Gamma_KW_K}{2} f_F^{approx}(i\\beta_K W)$$\n", - "\n", - "$$\\gamma_{K,\\sigma,0} = W_K - \\sigma i\\mu_K$$ \n", - "\n", - "$$\\eta_{K,l\\neq 0} = -i\\cdot \\frac{k_m}{\\beta_K} \\cdot \\frac{\\Gamma_K W_K^2}{-\\frac{\\epsilon^2_m}{\\beta_K^2} + W_K^2}$$\n", - "\n", - "$$\\gamma_{K,\\sigma,l\\neq 0}= \\frac{\\epsilon_m}{\\beta_K} - \\sigma i \\mu_K$$\n", - "\n", - "In this notebook we:\n", - "\n", - "* compare the Matsubara and Pade approximations and contrast them with the analytical result for the current between the system and the leads.\n", - "\n", - "* plot the current through the qubit as a function of the different between the voltages of the leads." - ] - }, - { - "cell_type": "markdown", - "id": "2e344631", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "f3413b8c", - "metadata": {}, - "outputs": [], - "source": [ - "import contextlib\n", - "import dataclasses\n", - "import time\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy.integrate import quad\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " basis,\n", - " destroy,\n", - " expect,\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver,\n", - ")\n", - "from qutip.core.environment import LorentzianEnvironment\n", - "from ipywidgets import IntProgress\n", - "from IPython.display import display\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "3a83b998", - "metadata": {}, - "source": [ - "## Helpers" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "44710770", - "metadata": {}, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\" Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "1fefdfa4", - "metadata": {}, - "outputs": [], - "source": [ - "# Solver options:\n", - "\n", - "# We set store_ados to True so that we can\n", - "# use the auxilliary density operators (ADOs)\n", - "# to calculate the current between the leads\n", - "# and the system.\n", - "\n", - "options = {\n", - " \"nsteps\": 1500,\n", - " \"store_states\": True,\n", - " \"store_ados\": True,\n", - " \"rtol\": 1e-12,\n", - " \"atol\": 1e-12,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "3219912b", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "And let us set up the system Hamiltonian, bath and system measurement operators:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "48867a7b", - "metadata": {}, - "outputs": [], - "source": [ - "# Define the system Hamiltonian:\n", - "\n", - "# The system is a single fermion with energy level split e1:\n", - "d1 = destroy(2)\n", - "e1 = 1.0\n", - "H = e1 * d1.dag() * d1" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "bb765344", - "metadata": {}, - "outputs": [], - "source": [ - "# Define parameters for left and right fermionic baths.\n", - "# Each bath is a lead (i.e. a wire held at a potential)\n", - "# with temperature T and chemical potential mu.\n", - "\n", - "@dataclasses.dataclass\n", - "class LorentzianBathParameters:\n", - " lead: str\n", - " Q: object # coupling operator\n", - " gamma: float = 0.01 # coupling strength\n", - " W: float = 1.0 # cut-off\n", - " T: float = 0.025851991 # temperature\n", - " theta: float = 2.0 # bias\n", - "\n", - " def __post_init__(self):\n", - " assert self.lead in (\"L\", \"R\")\n", - " self.beta = 1 / self.T\n", - " if self.lead == \"L\":\n", - " self.mu = self.theta / 2.0\n", - " else:\n", - " self.mu = - self.theta / 2.0\n", - "\n", - " def J(self, w):\n", - " \"\"\" Spectral density. \"\"\"\n", - " return self.gamma * self.W**2 / ((w - self.mu)**2 + self.W**2)\n", - "\n", - " def fF(self, w, sign=1.0):\n", - " \"\"\" Fermi distribution for this bath. \"\"\"\n", - " x = sign * self.beta * (w - self.mu)\n", - " return fF(x)\n", - "\n", - " def lamshift(self, w):\n", - " \"\"\" Return the lamshift. \"\"\"\n", - " return 0.5 * (w - self.mu) * self.J(w) / self.W\n", - "\n", - " def replace(self, **kw):\n", - " return dataclasses.replace(self, **kw)\n", - "\n", - "\n", - "def fF(x):\n", - " \"\"\" Return the Fermi distribution. \"\"\"\n", - " # in units where kB = 1.0\n", - " return 1 / (np.exp(x) + 1)\n", - "\n", - "\n", - "bath_L = LorentzianBathParameters(Q=d1, lead=\"L\")\n", - "bath_R = LorentzianBathParameters(Q=d1, lead=\"R\")" - ] - }, - { - "cell_type": "markdown", - "id": "5e3f3457", - "metadata": {}, - "source": [ - "## Spectral density\n", - "\n", - "Let's plot the spectral density." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a1dec0a7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "w_list = np.linspace(-2, 2, 100)\n", - "\n", - "fig, ax = plt.subplots(figsize=(12, 7))\n", - "\n", - "spec_L = bath_L.J(w_list)\n", - "spec_R = bath_R.J(w_list)\n", - "\n", - "ax.plot(\n", - " w_list, spec_L,\n", - " \"b--\", linewidth=3,\n", - " label=r\"J_L(w)\",\n", - ")\n", - "ax.plot(\n", - " w_list, spec_R,\n", - " \"r--\", linewidth=3,\n", - " label=r\"J_R(w)\",\n", - ")\n", - "\n", - "ax.set_xlabel(\"w\")\n", - "ax.set_ylabel(r\"$J(\\omega)$\")\n", - "ax.legend();" - ] - }, - { - "cell_type": "markdown", - "id": "f877e6d4", - "metadata": {}, - "source": [ - "## Emission and absorption by the leads\n", - "\n", - "Next let's plot the emission and absorption by the leads." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a1c95fd8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "w_list = np.linspace(-2, 2, 100)\n", - "\n", - "fig, ax = plt.subplots(figsize=(12, 7))\n", - "\n", - "# Left lead emission and absorption\n", - "\n", - "gam_L_in = bath_L.J(w_list) * bath_L.fF(w_list, sign=1.0)\n", - "gam_L_out = bath_L.J(w_list) * bath_L.fF(w_list, sign=-1.0)\n", - "\n", - "ax.plot(\n", - " w_list, gam_L_in,\n", - " \"b--\", linewidth=3,\n", - " label=r\"S_L(w) input (absorption)\",\n", - ")\n", - "ax.plot(\n", - " w_list, gam_L_out,\n", - " \"r--\", linewidth=3,\n", - " label=r\"S_L(w) output (emission)\",\n", - ")\n", - "\n", - "# Right lead emission and absorption\n", - "\n", - "gam_R_in = bath_R.J(w_list) * bath_R.fF(w_list, sign=1.0)\n", - "gam_R_out = bath_R.J(w_list) * bath_R.fF(w_list, sign=-1.0)\n", - "\n", - "ax.plot(\n", - " w_list, gam_R_in,\n", - " \"b\", linewidth=3,\n", - " label=r\"S_R(w) input (absorption)\",\n", - ")\n", - "ax.plot(\n", - " w_list, gam_R_out,\n", - " \"r\", linewidth=3,\n", - " label=r\"S_R(w) output (emission)\",\n", - ")\n", - "\n", - "ax.set_xlabel(\"w\")\n", - "ax.set_ylabel(r\"$S(\\omega)$\")\n", - "ax.legend();" - ] - }, - { - "cell_type": "markdown", - "id": "ebfdbd43", - "metadata": {}, - "source": [ - "## Comparing the Matsubara and Pade approximations\n", - "\n", - "Let's start by solving for the evolution using a Pade expansion of the correlation function of the Lorentzian spectral density:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "97f3271c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.3457450866699219\n", - " Total run time: 14.46s*] Elapsed 14.45s / Remaining 00:00:00:00**** 19% ] Elapsed 1.25s / Remaining 00:00:00:05[*********66%*** ] Elapsed 7.09s / Remaining 00:00:00:03[*********67%*** ] Elapsed 7.27s / Remaining 00:00:00:03[*********82%******* ] Elapsed 9.66s / Remaining 00:00:00:02\n", - "ODE solver time: 14.470469951629639\n", - "Steady state solver time: 190.69628143310547\n" - ] - } - ], - "source": [ - "# HEOM dynamics using the Pade approximation:\n", - "\n", - "# Times to solve for and initial system state:\n", - "tlist = np.linspace(0, 100, 1000)\n", - "rho0 = basis(2, 0) * basis(2, 0).dag()\n", - "\n", - "Nk = 10 # Number of exponents to retain in the expansion of each bath\n", - "\n", - "envL = LorentzianEnvironment(\n", - " bath_L.T,bath_L.mu,bath_L.gamma, bath_L.W,\n", - ")\n", - "envL_pade= envL.approx_by_pade(Nk=Nk, tag=\"L\")\n", - "envR =LorentzianEnvironment(\n", - " bath_R.T,bath_R.mu,bath_R.gamma, bath_R.W,\n", - ")\n", - "envR_pade= envR.approx_by_pade(Nk=Nk, tag=\"L\")\n", - "\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " solver_pade = HEOMSolver(H, [(envL_pade,bath_L.Q), (envR_pade,bath_R.Q)], max_depth=2, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " result_pade = solver_pade.run(rho0, tlist)\n", - "\n", - "with timer(\"Steady state solver time\"):\n", - " rho_ss_pade, ado_ss_pade = solver_pade.steady_state()" - ] - }, - { - "cell_type": "markdown", - "id": "e0188db5", - "metadata": {}, - "source": [ - "Now let us plot the result which shows the decay of the initially excited impurity. This is not very illuminating, but we will compare it with the Matsubara expansion and analytic solution sortly:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "70f5d901", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the Pade results\n", - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - "\n", - "axes.plot(\n", - " tlist, expect(result_pade.states, rho0),\n", - " 'r--', linewidth=2,\n", - " label=\"P11 (Pade)\",\n", - ")\n", - "axes.axhline(\n", - " expect(rho_ss_pade, rho0),\n", - " color='r', linestyle=\"dotted\", linewidth=1,\n", - " label=\"P11 (Pade steady state)\",\n", - ")\n", - "\n", - "axes.set_xlabel('t', fontsize=28)\n", - "axes.legend(fontsize=12);" - ] - }, - { - "cell_type": "markdown", - "id": "3e030af0", - "metadata": {}, - "source": [ - "Now let us do the same for the Matsubara expansion:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "24fa4a52", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RHS construction time: 0.2509186267852783\n", - " Total run time: 8.65s*] Elapsed 8.65s / Remaining 00:00:00:00[****** 25% ] Elapsed 1.99s / Remaining 00:00:00:05[*********43% ] Elapsed 3.70s / Remaining 00:00:00:04[*********55%* ] Elapsed 4.72s / Remaining 00:00:00:03[*********67%*** ] Elapsed 5.80s / Remaining 00:00:00:02[*********68%**** ] Elapsed 5.85s / Remaining 00:00:00:02\n", - "ODE solver time: 8.654348611831665\n", - "Steady state solver time: 189.28271412849426\n" - ] - } - ], - "source": [ - "# HEOM dynamics using the Matsubara approximation:\n", - "\n", - "envL_mats= envL.approx_by_matsubara(Nk=Nk, tag=\"L\")\n", - "envR_mats= envR.approx_by_matsubara(Nk=Nk, tag=\"R-\")\n", - "\n", - "\n", - "with timer(\"RHS construction time\"):\n", - " solver_mats = HEOMSolver(H, [(envL_mats,bath_L.Q), (envR_mats,bath_R.Q)], max_depth=2, options=options)\n", - "\n", - "with timer(\"ODE solver time\"):\n", - " result_mats = solver_mats.run(rho0, tlist)\n", - "\n", - "with timer(\"Steady state solver time\"):\n", - " rho_ss_mats, ado_ss_mats = solver_mats.steady_state()" - ] - }, - { - "cell_type": "markdown", - "id": "c06cee47", - "metadata": {}, - "source": [ - "We see a marked difference in the Matsubara vs Pade results:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "90c30fab", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the Pade results\n", - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - "\n", - "axes.plot(\n", - " tlist, expect(result_pade.states, rho0),\n", - " 'r--', linewidth=2,\n", - " label=\"P11 (Pade)\",\n", - ")\n", - "axes.axhline(\n", - " expect(rho_ss_pade, rho0),\n", - " color='r', linestyle=\"dotted\", linewidth=1,\n", - " label=\"P11 (Pade steady state)\",\n", - ")\n", - "\n", - "axes.plot(\n", - " tlist, expect(result_mats.states, rho0),\n", - " 'b--', linewidth=2,\n", - " label=\"P11 (Mats)\",\n", - ")\n", - "axes.axhline(\n", - " expect(rho_ss_mats, rho0),\n", - " color='b', linestyle=\"dotted\", linewidth=1,\n", - " label=\"P11 (Mats steady state)\",\n", - ")\n", - "\n", - "axes.set_xlabel('t', fontsize=28)\n", - "axes.legend(fontsize=12);" - ] - }, - { - "cell_type": "markdown", - "id": "2fe28818", - "metadata": {}, - "source": [ - "But which is more correct? The Matsubara or the Pade result?\n", - "\n", - "One advantage of this simple model is that the steady state current to the baths is analytically solvable, so we can check convergence of the result by calculating it analytically (the sum of the currents to and from the system in the steady state must be zero, so the current from one bath is the same as the current to the other).\n", - "\n", - "See the [QuTiP-BoFiN paper](https://arxiv.org/abs/2010.10806) for a detailed description and references for the analytic result. Below we just perform the required integration numerically." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "76d31675", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analytical steady state current: (0.0008130726698792024+0j)\n" - ] - } - ], - "source": [ - "def analytical_steady_state_current(bath_L, bath_R, e1):\n", - " \"\"\" Calculate the analytical steady state current. \"\"\"\n", - "\n", - " def integrand(w):\n", - " return (2 / np.pi) * (\n", - " bath_L.J(w) * bath_R.J(w) * (bath_L.fF(w) - bath_R.fF(w)) /\n", - " (\n", - " (bath_L.J(w) + bath_R.J(w))**2 +\n", - " 4*(w - e1 - bath_L.lamshift(w) - bath_R.lamshift(w))**2\n", - " )\n", - " )\n", - "\n", - " def real_part(x):\n", - " return np.real(integrand(x))\n", - "\n", - " def imag_part(x):\n", - " return np.imag(integrand(x))\n", - "\n", - " # in principle the bounds for the integral should be rechecked if\n", - " # bath or system parameters are changed substantially:\n", - " bounds = [-10, 10]\n", - "\n", - " real_integral, _ = quad(real_part, *bounds)\n", - " imag_integral, _ = quad(imag_part, *bounds)\n", - "\n", - " return real_integral + 1.0j * imag_integral\n", - "\n", - "\n", - "curr_ss_analytic = analytical_steady_state_current(bath_L, bath_R, e1)\n", - "\n", - "print(f\"Analytical steady state current: {curr_ss_analytic}\")" - ] - }, - { - "cell_type": "markdown", - "id": "181fd371", - "metadata": {}, - "source": [ - "To compare the analytical result above with the result from the HEOM, we need to be able to calculate the current from the system to the bath from the HEOM result. In the HEOM description, these currents are captured in the first level auxilliary density operators (ADOs).\n", - "\n", - "In the function `state_current(...)` below, we extract the first level ADOs for the specified bath and sum the contributions to the current from each:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "2376c586", - "metadata": {}, - "outputs": [], - "source": [ - "def state_current(ado_state, bath_tag):\n", - " \"\"\" Determine current from the given bath (either \"R\" or \"L\") to\n", - " the system in the given ADO state.\n", - " \"\"\"\n", - " level_1_aux = [\n", - " (ado_state.extract(label), ado_state.exps(label)[0])\n", - " for label in ado_state.filter(level=1, tags=[bath_tag])\n", - " ]\n", - "\n", - " def exp_sign(exp):\n", - " return 1 if exp.type == exp.types[\"+\"] else -1\n", - "\n", - " def exp_op(exp):\n", - " return exp.Q if exp.type == exp.types[\"+\"] else exp.Q.dag()\n", - "\n", - " return -1.0j * sum(\n", - " exp_sign(exp) * (exp_op(exp) * aux).tr()\n", - " for aux, exp in level_1_aux\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "2986b96b", - "metadata": {}, - "source": [ - "Now we can calculate the steady state currents from the Pade and Matsubara HEOM results:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "efed55fc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pade steady state current (L): (1.6805133673525319e-18-4.336808689942018e-19j)\n", - "Pade steady state current (R): -0j\n" - ] - } - ], - "source": [ - "curr_ss_pade_L = state_current(ado_ss_pade, \"L\")\n", - "curr_ss_pade_R = state_current(ado_ss_pade, \"R\")\n", - "\n", - "print(f\"Pade steady state current (L): {curr_ss_pade_L}\")\n", - "print(f\"Pade steady state current (R): {curr_ss_pade_R}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "09df1f63", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Matsubara steady state current (L): (-0.0011018485316349584-1.0842021724855044e-18j)\n", - "Matsubara steady state current (R): -0j\n" - ] - } - ], - "source": [ - "curr_ss_mats_L = state_current(ado_ss_mats, \"L\")\n", - "curr_ss_mats_R = state_current(ado_ss_mats, \"R\")\n", - "\n", - "print(f\"Matsubara steady state current (L): {curr_ss_mats_L}\")\n", - "print(f\"Matsubara steady state current (R): {curr_ss_mats_R}\")" - ] - }, - { - "cell_type": "markdown", - "id": "16fe795e", - "metadata": {}, - "source": [ - "Note that the currents from each bath balance as is required by the steady state, but the value of the current is different for the Pade and Matsubara results.\n", - "\n", - "Now let's compare all three:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "20c32bd5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pade current (R): -0j\n", - "Matsubara current (R): -0j\n", - "Analytical curernt: (0.0008130726698792024+0j)\n" - ] - } - ], - "source": [ - "print(f\"Pade current (R): {curr_ss_pade_R}\")\n", - "print(f\"Matsubara current (R): {curr_ss_mats_R}\")\n", - "print(f\"Analytical curernt: {curr_ss_analytic}\")" - ] - }, - { - "cell_type": "markdown", - "id": "292927df", - "metadata": {}, - "source": [ - "In this case we observe that the Pade approximation has converged more closely to the analytical current than the Matsubara.\n", - "\n", - "The Matsubara result could be improved by increasing the number of terms retained in the Matsubara expansion (i.e. increasing `Nk`)." - ] - }, - { - "cell_type": "markdown", - "id": "f494ba8c", - "metadata": {}, - "source": [ - "## Current as a function of bias voltage" - ] - }, - { - "cell_type": "markdown", - "id": "88dbf381", - "metadata": {}, - "source": [ - "Now lets plot the current as a function of bias voltage (the bias voltage is the parameter `theta` for the two baths).\n", - "\n", - "We will calculate the steady state current for each `theta` both analytically and using the HEOM with the Pade correlation expansion approximation." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "3077adba", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9d44343dc5d14d54828d6a5b7a5eba7e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "IntProgress(value=0, max=200)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "NameError", - "evalue": "name 'LorentzianPadeBath' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[17], line 54\u001b[0m\n\u001b[1;32m 46\u001b[0m curr_ss_analytic_thetas \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 47\u001b[0m current_analytic_for_theta(e1, bath_L, bath_R, theta)\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m theta \u001b[38;5;129;01min\u001b[39;00m thetas\n\u001b[1;32m 49\u001b[0m ]\n\u001b[1;32m 51\u001b[0m \u001b[38;5;66;03m# The number of expansion terms has been dropped to Nk=6 to speed\u001b[39;00m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;66;03m# up notebook execution. Increase to Nk=10 for more accurate results.\u001b[39;00m\n\u001b[1;32m 53\u001b[0m curr_ss_pade_theta \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m---> 54\u001b[0m \u001b[43mcurrent_pade_for_theta\u001b[49m\u001b[43m(\u001b[49m\u001b[43mH\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbath_L\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbath_R\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtheta\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNk\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m theta \u001b[38;5;129;01min\u001b[39;00m thetas\n\u001b[1;32m 56\u001b[0m ]\n", - "Cell \u001b[0;32mIn[17], line 29\u001b[0m, in \u001b[0;36mcurrent_pade_for_theta\u001b[0;34m(H, bath_L, bath_R, theta, Nk)\u001b[0m\n\u001b[1;32m 26\u001b[0m bath_L \u001b[38;5;241m=\u001b[39m bath_L\u001b[38;5;241m.\u001b[39mreplace(theta\u001b[38;5;241m=\u001b[39mtheta)\n\u001b[1;32m 27\u001b[0m bath_R \u001b[38;5;241m=\u001b[39m bath_R\u001b[38;5;241m.\u001b[39mreplace(theta\u001b[38;5;241m=\u001b[39mtheta)\n\u001b[0;32m---> 29\u001b[0m bathL \u001b[38;5;241m=\u001b[39m \u001b[43mLorentzianPadeBath\u001b[49m(\n\u001b[1;32m 30\u001b[0m bath_L\u001b[38;5;241m.\u001b[39mQ, bath_L\u001b[38;5;241m.\u001b[39mgamma, bath_L\u001b[38;5;241m.\u001b[39mW, bath_L\u001b[38;5;241m.\u001b[39mmu, bath_L\u001b[38;5;241m.\u001b[39mT,\n\u001b[1;32m 31\u001b[0m Nk, tag\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mL\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 32\u001b[0m )\n\u001b[1;32m 33\u001b[0m bathR \u001b[38;5;241m=\u001b[39m LorentzianPadeBath(\n\u001b[1;32m 34\u001b[0m bath_R\u001b[38;5;241m.\u001b[39mQ, bath_R\u001b[38;5;241m.\u001b[39mgamma, bath_R\u001b[38;5;241m.\u001b[39mW, bath_R\u001b[38;5;241m.\u001b[39mmu, bath_R\u001b[38;5;241m.\u001b[39mT,\n\u001b[1;32m 35\u001b[0m Nk, tag\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mR\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 36\u001b[0m )\n\u001b[1;32m 38\u001b[0m solver_pade \u001b[38;5;241m=\u001b[39m HEOMSolver(H, [bathL, bathR], max_depth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, options\u001b[38;5;241m=\u001b[39moptions)\n", - "\u001b[0;31mNameError\u001b[0m: name 'LorentzianPadeBath' is not defined" - ] - } - ], - "source": [ - "# Theta (bias voltages)\n", - "\n", - "thetas = np.linspace(-4, 4, 100)\n", - "\n", - "# Setup a progress bar:\n", - "\n", - "progress = IntProgress(min=0, max=2 * len(thetas))\n", - "display(progress)\n", - "\n", - "# Calculate the current for the list of thetas\n", - "\n", - "\n", - "def current_analytic_for_theta(e1, bath_L, bath_R, theta):\n", - " \"\"\" Return the analytic current for a given theta. \"\"\"\n", - " current = analytical_steady_state_current(\n", - " bath_L.replace(theta=theta),\n", - " bath_R.replace(theta=theta),\n", - " e1,\n", - " )\n", - " progress.value += 1\n", - " return np.real(current)\n", - "\n", - "\n", - "def current_pade_for_theta(H, bath_L, bath_R, theta, Nk):\n", - " \"\"\" Return the steady state current using the Pade approximation. \"\"\"\n", - " bath_L = bath_L.replace(theta=theta)\n", - " bath_R = bath_R.replace(theta=theta)\n", - "\n", - " bathL = LorentzianPadeBath(\n", - " bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", - " Nk, tag=\"L\",\n", - " )\n", - " bathR = LorentzianPadeBath(\n", - " bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", - " Nk, tag=\"R\",\n", - " )\n", - "\n", - " solver_pade = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options)\n", - " rho_ss_pade, ado_ss_pade = solver_pade.steady_state()\n", - " current = state_current(ado_ss_pade, bath_tag=\"R\")\n", - "\n", - " progress.value += 1\n", - " return np.real(current)\n", - "\n", - "\n", - "curr_ss_analytic_thetas = [\n", - " current_analytic_for_theta(e1, bath_L, bath_R, theta)\n", - " for theta in thetas\n", - "]\n", - "\n", - "# The number of expansion terms has been dropped to Nk=6 to speed\n", - "# up notebook execution. Increase to Nk=10 for more accurate results.\n", - "curr_ss_pade_theta = [\n", - " current_pade_for_theta(H, bath_L, bath_R, theta, Nk=6)\n", - " for theta in thetas\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "bf13bd67", - "metadata": {}, - "source": [ - "Below we plot the results and see that even with `Nk=6`, the HEOM Pade approximation gives good results for the steady state current. Increasing `Nk` to `10` gives very accurate results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c6ae487", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(12, 7))\n", - "\n", - "ax.plot(\n", - " thetas, 2.434e-4 * 1e6 * np.array(curr_ss_analytic_thetas),\n", - " color=\"black\", linewidth=3,\n", - " label=r\"Analytical\",\n", - ")\n", - "ax.plot(\n", - " thetas, 2.434e-4 * 1e6 * np.array(curr_ss_pade_theta),\n", - " 'r--', linewidth=3,\n", - " label=r\"HEOM Pade $N_k=10$, $n_{\\mathrm{max}}=2$\",\n", - ")\n", - "\n", - "\n", - "ax.locator_params(axis='y', nbins=4)\n", - "ax.locator_params(axis='x', nbins=4)\n", - "\n", - "ax.set_xticks([-2.5, 0, 2.5])\n", - "ax.set_xticklabels([-2.5, 0, 2.5])\n", - "ax.set_xlabel(r\"Bias voltage $\\Delta \\mu$ ($V$)\", fontsize=28)\n", - "ax.set_ylabel(r\"Current ($\\mu A$)\", fontsize=28)\n", - "ax.legend(fontsize=25);" - ] - }, - { - "cell_type": "markdown", - "id": "05a64866", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6a48e113", - "metadata": {}, - "outputs": [], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "d0bfad09", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "226849e3", - "metadata": {}, - "outputs": [], - "source": [ - "assert np.allclose(curr_ss_pade_L + curr_ss_pade_R, 0)\n", - "assert np.allclose(curr_ss_mats_L + curr_ss_mats_R, 0)\n", - "assert np.allclose(curr_ss_pade_R, curr_ss_analytic, rtol=1e-4)" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "qutip-dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md index b032e330..bef402ee 100644 --- a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md +++ b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md @@ -5,9 +5,9 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 + jupytext_version: 1.16.1 kernelspec: - display_name: Python 3 (ipykernel) + display_name: qutip-dev language: python name: python3 --- @@ -69,7 +69,7 @@ In this notebook we: ## Setup -```{code-cell} +```{code-cell} ipython3 import contextlib import dataclasses import time @@ -86,10 +86,8 @@ from qutip import ( ) from qutip.solver.heom import ( HEOMSolver, - LorentzianBath, - LorentzianPadeBath, ) - +from qutip.core.environment import LorentzianEnvironment from ipywidgets import IntProgress from IPython.display import display @@ -98,7 +96,7 @@ from IPython.display import display ## Helpers -```{code-cell} +```{code-cell} ipython3 @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -112,7 +110,7 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} +```{code-cell} ipython3 # Solver options: # We set store_ados to True so that we can @@ -135,7 +133,7 @@ options = { And let us set up the system Hamiltonian, bath and system measurement operators: -```{code-cell} +```{code-cell} ipython3 # Define the system Hamiltonian: # The system is a single fermion with energy level split e1: @@ -144,7 +142,7 @@ e1 = 1.0 H = e1 * d1.dag() * d1 ``` -```{code-cell} +```{code-cell} ipython3 # Define parameters for left and right fermionic baths. # Each bath is a lead (i.e. a wire held at a potential) # with temperature T and chemical potential mu. @@ -197,7 +195,7 @@ bath_R = LorentzianBathParameters(Q=d1, lead="R") Let's plot the spectral density. -```{code-cell} +```{code-cell} ipython3 w_list = np.linspace(-2, 2, 100) fig, ax = plt.subplots(figsize=(12, 7)) @@ -225,7 +223,7 @@ ax.legend(); Next let's plot the emission and absorption by the leads. -```{code-cell} +```{code-cell} ipython3 w_list = np.linspace(-2, 2, 100) fig, ax = plt.subplots(figsize=(12, 7)) @@ -271,7 +269,7 @@ ax.legend(); Let's start by solving for the evolution using a Pade expansion of the correlation function of the Lorentzian spectral density: -```{code-cell} +```{code-cell} ipython3 # HEOM dynamics using the Pade approximation: # Times to solve for and initial system state: @@ -280,17 +278,18 @@ rho0 = basis(2, 0) * basis(2, 0).dag() Nk = 10 # Number of exponents to retain in the expansion of each bath -bathL = LorentzianPadeBath( - bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T, - Nk, tag="L", +envL = LorentzianEnvironment( + bath_L.T,bath_L.mu,bath_L.gamma, bath_L.W, ) -bathR = LorentzianPadeBath( - bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T, - Nk, tag="R", +envL_pade= envL.approx_by_pade(Nk=Nk, tag="L") +envR =LorentzianEnvironment( + bath_R.T,bath_R.mu,bath_R.gamma, bath_R.W, ) +envR_pade= envR.approx_by_pade(Nk=Nk, tag="L") + with timer("RHS construction time"): - solver_pade = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options) + solver_pade = HEOMSolver(H, [(envL_pade,bath_L.Q), (envR_pade,bath_R.Q)], max_depth=2, options=options) with timer("ODE solver time"): result_pade = solver_pade.run(rho0, tlist) @@ -301,7 +300,7 @@ with timer("Steady state solver time"): Now let us plot the result which shows the decay of the initially excited impurity. This is not very illuminating, but we will compare it with the Matsubara expansion and analytic solution sortly: -```{code-cell} +```{code-cell} ipython3 # Plot the Pade results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -322,20 +321,15 @@ axes.legend(fontsize=12); Now let us do the same for the Matsubara expansion: -```{code-cell} +```{code-cell} ipython3 # HEOM dynamics using the Matsubara approximation: -bathL = LorentzianBath( - bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T, - Nk, tag="L", -) -bathR = LorentzianBath( - bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T, - Nk, tag="R", -) +envL_mats= envL.approx_by_matsubara(Nk=Nk, tag="L") +envR_mats= envR.approx_by_matsubara(Nk=Nk, tag="R-") + with timer("RHS construction time"): - solver_mats = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options) + solver_mats = HEOMSolver(H, [(envL_mats,bath_L.Q), (envR_mats,bath_R.Q)], max_depth=2, options=options) with timer("ODE solver time"): result_mats = solver_mats.run(rho0, tlist) @@ -346,7 +340,7 @@ with timer("Steady state solver time"): We see a marked difference in the Matsubara vs Pade results: -```{code-cell} +```{code-cell} ipython3 # Plot the Pade results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -382,7 +376,7 @@ One advantage of this simple model is that the steady state current to the baths See the [QuTiP-BoFiN paper](https://arxiv.org/abs/2010.10806) for a detailed description and references for the analytic result. Below we just perform the required integration numerically. -```{code-cell} +```{code-cell} ipython3 def analytical_steady_state_current(bath_L, bath_R, e1): """ Calculate the analytical steady state current. """ @@ -420,7 +414,7 @@ To compare the analytical result above with the result from the HEOM, we need to In the function `state_current(...)` below, we extract the first level ADOs for the specified bath and sum the contributions to the current from each: -```{code-cell} +```{code-cell} ipython3 def state_current(ado_state, bath_tag): """ Determine current from the given bath (either "R" or "L") to the system in the given ADO state. @@ -444,7 +438,7 @@ def state_current(ado_state, bath_tag): Now we can calculate the steady state currents from the Pade and Matsubara HEOM results: -```{code-cell} +```{code-cell} ipython3 curr_ss_pade_L = state_current(ado_ss_pade, "L") curr_ss_pade_R = state_current(ado_ss_pade, "R") @@ -452,7 +446,7 @@ print(f"Pade steady state current (L): {curr_ss_pade_L}") print(f"Pade steady state current (R): {curr_ss_pade_R}") ``` -```{code-cell} +```{code-cell} ipython3 curr_ss_mats_L = state_current(ado_ss_mats, "L") curr_ss_mats_R = state_current(ado_ss_mats, "R") @@ -464,7 +458,7 @@ Note that the currents from each bath balance as is required by the steady state Now let's compare all three: -```{code-cell} +```{code-cell} ipython3 print(f"Pade current (R): {curr_ss_pade_R}") print(f"Matsubara current (R): {curr_ss_mats_R}") print(f"Analytical curernt: {curr_ss_analytic}") @@ -484,7 +478,7 @@ Now lets plot the current as a function of bias voltage (the bias voltage is the We will calculate the steady state current for each `theta` both analytically and using the HEOM with the Pade correlation expansion approximation. -```{code-cell} +```{code-cell} ipython3 # Theta (bias voltages) thetas = np.linspace(-4, 4, 100) @@ -545,7 +539,7 @@ curr_ss_pade_theta = [ Below we plot the results and see that even with `Nk=6`, the HEOM Pade approximation gives good results for the steady state current. Increasing `Nk` to `10` gives very accurate results. -```{code-cell} +```{code-cell} ipython3 fig, ax = plt.subplots(figsize=(12, 7)) ax.plot( @@ -572,7 +566,7 @@ ax.legend(fontsize=25); ## About -```{code-cell} +```{code-cell} ipython3 qutip.about() ``` @@ -580,7 +574,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 assert np.allclose(curr_ss_pade_L + curr_ss_pade_R, 0) assert np.allclose(curr_ss_mats_L + curr_ss_mats_R, 0) assert np.allclose(curr_ss_pade_R, curr_ss_analytic, rtol=1e-4) diff --git a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.ipynb b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.ipynb deleted file mode 100644 index 2ffe620d..00000000 --- a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.ipynb +++ /dev/null @@ -1,528 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4436eb27", - "metadata": {}, - "source": [ - "# HEOM 5b: Discrete boson coupled to an impurity and fermionic leads" - ] - }, - { - "cell_type": "markdown", - "id": "83c4d5b9", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc), also coupled to a discrete bosonic (vibronic) mode.\n", - "\n", - "Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his Dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications. In particular this example reproduces some results from https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.201407\n", - "\n", - "Notation:\n", - "\n", - "* $K=L/R$ refers to left or right leads.\n", - "* $\\sigma=\\pm$ refers to input/output\n", - "\n", - "We choose a Lorentzian spectral density for the leads, with a peak at the chemical potential. The latter simplifies a little the notation required for the correlation functions, but can be relaxed if neccessary.\n", - "\n", - "$$J(\\omega) = \\frac{\\Gamma W^2}{((\\omega-\\mu_K)^2 +W^2 )}$$\n", - "\n", - "The Fermi distribution function is:\n", - "\n", - "$$f_F (x) = (\\exp(x) + 1)^{-1}$$\n", - "\n", - "Together these allow the correlation functions to be expressed as:\n", - "\n", - "$$C^{\\sigma}_K(t) = \\frac{1}{2\\pi} \\int_{-\\infty}^{\\infty} d\\omega e^{\\sigma i \\omega t} \\Gamma_K(\\omega) f_F[\\sigma\\beta(\\omega - \\mu)]$$\n", - "\n", - "As with the Bosonic case we can treat these with Matsubara, Pade, or fitting approaches.\n", - "\n", - "The Pade decomposition approximates the Fermi distubition as \n", - "\n", - "$$f_F(x) \\approx f_F^{\\mathrm{approx}}(x) = \\frac{1}{2} - \\sum_l^{l_{max}} \\frac{2k_l x}{x^2 + \\epsilon_l^2}$$\n", - "\n", - "$k_l$ and $\\epsilon_l$ are co-efficients defined in J. Chem Phys 133,10106\n", - "\n", - "Evaluating the integral for the correlation functions gives,\n", - "\n", - "\n", - "$$C_K^{\\sigma}(t) \\approx \\sum_{l=0}^{l_{max}} \\eta_K^{\\sigma_l} e^{-\\gamma_{K,\\sigma,l}t}$$\n", - "\n", - "where\n", - "\n", - "$$\\eta_{K,0} = \\frac{\\Gamma_KW_K}{2} f_F^{approx}(i\\beta_K W)$$\n", - "\n", - "$$\\gamma_{K,\\sigma,0} = W_K - \\sigma i\\mu_K$$ \n", - "\n", - "$$\\eta_{K,l\\neq 0} = -i\\cdot \\frac{k_m}{\\beta_K} \\cdot \\frac{\\Gamma_K W_K^2}{-\\frac{\\epsilon^2_m}{\\beta_K^2} + W_K^2}$$\n", - "\n", - "$$\\gamma_{K,\\sigma,l\\neq 0}= \\frac{\\epsilon_m}{\\beta_K} - \\sigma i \\mu_K$$" - ] - }, - { - "cell_type": "markdown", - "id": "3aca80cf", - "metadata": {}, - "source": [ - "## Differences from Example 5a" - ] - }, - { - "cell_type": "markdown", - "id": "7e38bec0", - "metadata": {}, - "source": [ - "The system we study here has two big differences from the HEOM 5a example:\n", - "\n", - "* the system now includes a discrete bosonic mode,\n", - "* and the electronic leads have $W$ set to $10^4$ (i.e. the wide-band limit).\n", - "\n", - "The new system Hamiltonian is:\n", - "\n", - "$$\n", - "H_{\\mathrm{vib}} = H_{\\mathrm{SIAM}} + \\Omega a^{\\dagger}a + \\lambda (a+a^{\\dagger})c{^\\dagger}c.\n", - "$$\n", - "\n", - "where $H_{\\mathrm{SIAM}}$ is the Hamiltonian of the single impurity, and the remaining terms are the Hamiltonian of the bosonic mode and its interaction with the impurity.\n", - "\n", - "The complete setup now consists of four parts:\n", - "\n", - "* the single impurity\n", - "* a discrete bosonic mode\n", - "* two fermionic leads.\n", - "\n", - "**Note**: This example is quite numerically challenging and has many system and bath components. For an easier introduction into the fermionic case, see example 5a.\n", - "\n", - "**Note**: We've reduced the cut-off of the bosonic mode to 2 modes to facilitate faster execution of the notebooks when the outputs are being checked and compiled. A more accurate result may be obtained by increasing the number of bosonic modes to, for example, 16." - ] - }, - { - "cell_type": "markdown", - "id": "f1de2d6b", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "05b4b6bd", - "metadata": {}, - "outputs": [], - "source": [ - "import contextlib\n", - "import dataclasses\n", - "import time\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "import qutip\n", - "from qutip import (\n", - " destroy,\n", - " qeye,\n", - " tensor,\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver,\n", - " LorentzianPadeBath,\n", - ")\n", - "\n", - "from ipywidgets import IntProgress\n", - "from IPython.display import display\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "3014f9b6", - "metadata": {}, - "source": [ - "## Helpers" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bc7bacf1", - "metadata": {}, - "outputs": [], - "source": [ - "@contextlib.contextmanager\n", - "def timer(label):\n", - " \"\"\" Simple utility for timing functions:\n", - "\n", - " with timer(\"name\"):\n", - " ... code to time ...\n", - " \"\"\"\n", - " start = time.time()\n", - " yield\n", - " end = time.time()\n", - " print(f\"{label}: {end - start}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f309aa7a", - "metadata": {}, - "outputs": [], - "source": [ - "def state_current(ado_state, bath_tag):\n", - " \"\"\" Determine current from the given bath (either \"R\" or \"L\") to\n", - " the system in the given ADO state.\n", - " \"\"\"\n", - " level_1_aux = [\n", - " (ado_state.extract(label), ado_state.exps(label)[0])\n", - " for label in ado_state.filter(level=1, tags=[bath_tag])\n", - " ]\n", - "\n", - " def exp_sign(exp):\n", - " return 1 if exp.type == exp.types[\"+\"] else -1\n", - "\n", - " def exp_op(exp):\n", - " return exp.Q if exp.type == exp.types[\"+\"] else exp.Q.dag()\n", - "\n", - " return -1.0j * sum(\n", - " exp_sign(exp) * (exp_op(exp) * aux).tr()\n", - " for aux, exp in level_1_aux\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "90e77c67", - "metadata": {}, - "outputs": [], - "source": [ - "# Solver options:\n", - "\n", - "# We set store_ados to True so that we can\n", - "# use the auxilliary density operators (ADOs)\n", - "# to calculate the current between the leads\n", - "# and the system.\n", - "\n", - "options = {\n", - " \"nsteps\": 1500,\n", - " \"store_states\": True,\n", - " \"store_ados\": True,\n", - " \"rtol\": 1e-12,\n", - " \"atol\": 1e-12,\n", - " \"method\": \"vern9\",\n", - " \"progress_bar\": \"enhanced\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "9104d150", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "Let us set up the system Hamiltonian and specify the properties of the two reservoirs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cb1fc1b3", - "metadata": {}, - "outputs": [], - "source": [ - "# Define the system Hamiltonian:\n", - "\n", - "@dataclasses.dataclass\n", - "class SystemParameters:\n", - " e1: float = 0.3 # fermion mode energy splitting\n", - " Omega: float = 0.2 # bosonic mode energy splitting\n", - " Lambda: float = 0.12 # coupling between fermion and boson\n", - " Nbos: int = 2\n", - "\n", - " def __post_init__(self):\n", - " d = tensor(destroy(2), qeye(self.Nbos))\n", - " a = tensor(qeye(2), destroy(self.Nbos))\n", - " self.H = (\n", - " self.e1 * d.dag() * d +\n", - " self.Omega * a.dag() * a +\n", - " self.Lambda * (a + a.dag()) * d.dag() * d\n", - " )\n", - " self.Q = d\n", - "\n", - " def replace(self, **kw):\n", - " return dataclasses.replace(self, **kw)\n", - "\n", - "\n", - "sys_p = SystemParameters()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4d761f65", - "metadata": {}, - "outputs": [], - "source": [ - "# Define parameters for left and right fermionic baths.\n", - "# Each bath is a lead (i.e. a wire held at a potential)\n", - "# with temperature T and chemical potential mu.\n", - "\n", - "@dataclasses.dataclass\n", - "class LorentzianBathParameters:\n", - " lead: str\n", - " gamma: float = 0.01 # coupling strength\n", - " W: float = 1.0 # cut-off\n", - " T: float = 0.025851991 # temperature (in eV)\n", - " theta: float = 2.0 # bias\n", - "\n", - " def __post_init__(self):\n", - " assert self.lead in (\"L\", \"R\")\n", - " self.beta = 1 / self.T\n", - " if self.lead == \"L\":\n", - " self.mu = self.theta / 2.0\n", - " else:\n", - " self.mu = - self.theta / 2.0\n", - "\n", - " def J(self, w):\n", - " \"\"\" Spectral density. \"\"\"\n", - " return self.gamma * self.W**2 / ((w - self.mu)**2 + self.W**2)\n", - "\n", - " def fF(self, w, sign=1.0):\n", - " \"\"\" Fermi distribution for this bath. \"\"\"\n", - " x = sign * self.beta * (w - self.mu)\n", - " return fF(x)\n", - "\n", - " def lamshift(self, w):\n", - " \"\"\" Return the lamshift. \"\"\"\n", - " return 0.5 * (w - self.mu) * self.J(w) / self.W\n", - "\n", - " def replace(self, **kw):\n", - " return dataclasses.replace(self, **kw)\n", - "\n", - "\n", - "def fF(x):\n", - " \"\"\" Return the Fermi distribution. \"\"\"\n", - " # in units where kB = 1.0\n", - " return 1 / (np.exp(x) + 1)\n", - "\n", - "\n", - "# We set W = 1e4 to investigate the wide-band limit:\n", - "\n", - "bath_L = LorentzianBathParameters(W=10**4, lead=\"L\")\n", - "bath_R = LorentzianBathParameters(W=10**4, lead=\"R\")" - ] - }, - { - "cell_type": "markdown", - "id": "0acaafc6", - "metadata": {}, - "source": [ - "## Emission and absorption by the leads\n", - "\n", - "Next let's plot the emission and absorption by the leads." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a10714ec", - "metadata": {}, - "outputs": [], - "source": [ - "w_list = np.linspace(-2, 2, 100)\n", - "\n", - "fig, ax = plt.subplots(figsize=(12, 7))\n", - "\n", - "# Left lead emission and absorption\n", - "\n", - "gam_L_in = bath_L.J(w_list) * bath_L.fF(w_list, sign=1.0)\n", - "gam_L_out = bath_L.J(w_list) * bath_L.fF(w_list, sign=-1.0)\n", - "\n", - "ax.plot(\n", - " w_list, gam_L_in,\n", - " \"b--\", linewidth=3,\n", - " label=r\"S_L(w) input (absorption)\",\n", - ")\n", - "ax.plot(\n", - " w_list, gam_L_out,\n", - " \"r--\", linewidth=3,\n", - " label=r\"S_L(w) output (emission)\",\n", - ")\n", - "\n", - "# Right lead emission and absorption\n", - "\n", - "gam_R_in = bath_R.J(w_list) * bath_R.fF(w_list, sign=1.0)\n", - "gam_R_out = bath_R.J(w_list) * bath_R.fF(w_list, sign=-1.0)\n", - "\n", - "ax.plot(\n", - " w_list, gam_R_in,\n", - " \"b\", linewidth=3,\n", - " label=r\"S_R(w) input (absorption)\",\n", - ")\n", - "ax.plot(\n", - " w_list, gam_R_out,\n", - " \"r\", linewidth=3,\n", - " label=r\"S_R(w) output (emission)\",\n", - ")\n", - "\n", - "ax.set_xlabel(\"w\")\n", - "ax.set_ylabel(r\"$S(\\omega)$\")\n", - "ax.legend();" - ] - }, - { - "cell_type": "markdown", - "id": "aaf83d87", - "metadata": {}, - "source": [ - "## Below we give one example data set from Paper\n", - "\n", - "Here we just give one example of the current as a function of bias voltage, but in general one can try different cut-offs of the bosonic Fock space and the expansion of the correlation functions until convergence is found.\n", - "\n", - "One note: for very large problems, this can be slow." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a9c84981", - "metadata": {}, - "outputs": [], - "source": [ - "def steady_state_pade_for_theta(sys_p, bath_L, bath_R, theta, Nk, Nc, Nbos):\n", - " \"\"\" Return the steady state current using the Pade approximation. \"\"\"\n", - "\n", - " sys_p = sys_p.replace(Nbos=Nbos)\n", - " bath_L = bath_L.replace(theta=theta)\n", - " bath_R = bath_R.replace(theta=theta)\n", - "\n", - " bathL = LorentzianPadeBath(\n", - " sys_p.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T,\n", - " Nk, tag=\"L\",\n", - " )\n", - " bathR = LorentzianPadeBath(\n", - " sys_p.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T,\n", - " Nk, tag=\"R\",\n", - " )\n", - "\n", - " solver_pade = HEOMSolver(\n", - " sys_p.H, [bathL, bathR], max_depth=2, options=options,\n", - " )\n", - " rho_ss_pade, ado_ss_pade = solver_pade.steady_state()\n", - " current = state_current(ado_ss_pade, bath_tag=\"R\")\n", - "\n", - " return np.real(2.434e-4 * 1e6 * current)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3996e24c", - "metadata": {}, - "outputs": [], - "source": [ - "# Parameters:\n", - "\n", - "Nk = 6\n", - "Nc = 2\n", - "Nbos = 2 # Use Nbos = 16 for more accurate results\n", - "\n", - "thetas = np.linspace(0, 2, 30)\n", - "\n", - "# Progress bar:\n", - "\n", - "progress = IntProgress(min=0, max=len(thetas))\n", - "display(progress)\n", - "\n", - "currents = []\n", - "\n", - "for theta in thetas:\n", - " currents.append(steady_state_pade_for_theta(\n", - " sys_p, bath_L, bath_R, theta,\n", - " Nk=Nk, Nc=Nc, Nbos=Nbos,\n", - " ))\n", - " progress.value += 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "66cc7a25", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(12, 10))\n", - "\n", - "ax.plot(\n", - " thetas, currents,\n", - " color=\"green\", linestyle='-', linewidth=3,\n", - " label=f\"Nk = {5}, max_depth = {Nc}, Nbos = {Nbos}\",\n", - ")\n", - "\n", - "ax.set_yticks([0, 0.5, 1])\n", - "ax.set_yticklabels([0, 0.5, 1])\n", - "\n", - "ax.locator_params(axis='y', nbins=4)\n", - "ax.locator_params(axis='x', nbins=4)\n", - "\n", - "ax.set_xlabel(r\"Bias voltage $\\Delta \\mu$ ($V$)\", fontsize=30)\n", - "ax.set_ylabel(r\"Current ($\\mu A$)\", fontsize=30)\n", - "ax.legend(loc=4);" - ] - }, - { - "cell_type": "markdown", - "id": "5d0ea686", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "34a54211", - "metadata": {}, - "outputs": [], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "d8d53b43", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cc978722", - "metadata": {}, - "outputs": [], - "source": [ - "assert 1 == 1" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md index 35837558..de466f2a 100644 --- a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md +++ b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 + jupytext_version: 1.16.1 kernelspec: display_name: Python 3 (ipykernel) language: python diff --git a/tutorials-v5/heom/heom-index.ipynb b/tutorials-v5/heom/heom-index.ipynb deleted file mode 100644 index f10432a8..00000000 --- a/tutorials-v5/heom/heom-index.ipynb +++ /dev/null @@ -1,56 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "262d3c3b", - "metadata": {}, - "source": [ - "# Hierarchical Equation of Motion Examples\n", - "\n", - "The \"hierarchical equations of motion\" (HEOM) method is a powerful numerical approach to solve the dynamics and steady-state of a quantum system coupled to a non-Markovian and non-perturbative environment. Originally developed in the context of physical chemistry, it has also been extended and applied to problems in solid-state physics, optics, single-molecule electronics, and biological physics.\n", - "\n", - "QuTiP's implementation of the HEOM is described in detail in https://arxiv.org/abs/2010.10806.\n", - "\n", - "This collection of examples from the paper illustrates how to use QuTiP's HEOM to model and investigate the dynamics of a variety of systems coupled to bosonic or fermionic baths.\n", - "\n", - "## Overview of the notebooks\n", - "\n", - "\n", - "\n", - "* [Example 1a: Spin-Bath model (introduction)](./heom-1a-spin-bath-model-basic.ipynb)\n", - "\n", - "* [Example 1b: Spin-Bath model (very strong coupling)](./heom-1b-spin-bath-model-very-strong-coupling.ipynb)\n", - "\n", - "* [Example 1c: Spin-Bath model (underdamped case)](./heom-1c-spin-bath-model-underdamped-sd.ipynb)\n", - "\n", - "* [Example 1d: Spin-Bath model, fitting of spectrum and correlation functions](./heom-1d-spin-bath-model-ohmic-fitting.ipynb)\n", - "\n", - "* [Example 1e: Spin-Bath model (pure dephasing)](./heom-1e-spin-bath-model-pure-dephasing.ipynb)\n", - "\n", - "* [Example 2: Dynamics in Fenna-Mathews-Olsen complex (FMO)](./heom-2-fmo-example.ipynb)\n", - "\n", - "* [Example 3: Quantum Heat Transport](./heom-3-quantum-heat-transport.ipynb)\n", - "\n", - "* [Example 4: Dynamical decoupling of a non-Markovian environment](./heom-4-dynamical-decoupling.ipynb)\n", - "\n", - "* [Example 5a: Fermionic single impurity model](./heom-5a-fermions-single-impurity-model.ipynb)\n", - "\n", - "* [Example 5b: Discrete boson coupled to an impurity + fermionic leads](./heom-5b-fermions-discrete-boson-model.ipynb)\n", - "\n", - "" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/heom/heom-index.md b/tutorials-v5/heom/heom-index.md index f16fbb10..4bc08d9d 100644 --- a/tutorials-v5/heom/heom-index.md +++ b/tutorials-v5/heom/heom-index.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.4 + jupytext_version: 1.16.1 kernelspec: display_name: Python 3 (ipykernel) language: python From a9e5112764ab225dbc749c4a86c6f11ed1f7c1a5 Mon Sep 17 00:00:00 2001 From: Gerardo Suarez Date: Thu, 27 Feb 2025 10:03:15 +0100 Subject: [PATCH 20/44] fitting summary --- tutorials-v5/fitting_summary.ipynb | 1075 ++++++++++++++++++++++++++++ 1 file changed, 1075 insertions(+) create mode 100644 tutorials-v5/fitting_summary.ipynb diff --git a/tutorials-v5/fitting_summary.ipynb b/tutorials-v5/fitting_summary.ipynb new file mode 100644 index 00000000..411f553d --- /dev/null +++ b/tutorials-v5/fitting_summary.ipynb @@ -0,0 +1,1075 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a2772b9b", + "metadata": {}, + "source": [ + "# HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions" + ] + }, + { + "cell_type": "markdown", + "id": "d4f3eb64", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded \n", + "in a set of auxiliary density matrices.\n", + "\n", + "In this example we show the evolution of a single two-level system in contact with a single bosonic environment.\n", + "\n", + "The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", + "\n", + "The bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", + "\n", + "In the example below we show how to model an Ohmic environment with exponential cut-off in three ways:\n", + "\n", + "* First we fit the spectral density with a set of underdamped brownian oscillator functions.\n", + "* Second, we evaluate the correlation functions, and fit those with a certain choice of exponential functions.\n", + "* Third, we use the available OhmicBath class \n", + "\n", + "In each case we will use the fit parameters to determine the correlation function expansion co-efficients needed to construct a description of the bath (i.e. a `BosonicBath` object) to supply to the `HEOMSolver` so that we can solve for the system dynamics." + ] + }, + { + "cell_type": "markdown", + "id": "0e624b73", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0028e918", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "import qutip\n", + "from qutip import (\n", + " basis,\n", + " expect,\n", + " sigmax,\n", + " sigmaz,\n", + ")\n", + "from qutip.solver.heom import (\n", + " HEOMSolver\n", + ")\n", + "from qutip.core.environment import BosonicEnvironment,OhmicEnvironment\n", + "\n", + "# Import mpmath functions for evaluation of gamma and zeta\n", + "# functions in the expression for the correlation:\n", + "\n", + "from mpmath import mp\n", + "\n", + "mp.dps = 15\n", + "mp.pretty = True\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "9d44adc6", + "metadata": {}, + "source": [ + "## System and bath definition\n", + "\n", + "Let us set up the system Hamiltonian, bath and system measurement operators:" + ] + }, + { + "cell_type": "markdown", + "id": "7c36a423", + "metadata": {}, + "source": [ + "### System Hamiltonian" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "122d942d", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the system Hamiltonian\n", + "eps = 0 # Energy of the 2-level system.\n", + "Del = 0.2 # Tunnelling term\n", + "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()\n", + "rho0 = basis(2, 0) * basis(2, 0).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "2f90386e", + "metadata": {}, + "source": [ + "### System measurement operators" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "bcf86ec7", + "metadata": {}, + "outputs": [], + "source": [ + "# Define some operators with which we will measure the system\n", + "# 1,1 element of density matrix - corresonding to groundstate\n", + "P11p = basis(2, 0) * basis(2, 0).dag()\n", + "P22p = basis(2, 1) * basis(2, 1).dag()\n", + "# 1,2 element of density matrix - corresonding to coherence\n", + "P12p = basis(2, 0) * basis(2, 1).dag()" + ] + }, + { + "cell_type": "markdown", + "id": "7e0d0038", + "metadata": {}, + "source": [ + "### Bath and HEOM parameters" + ] + }, + { + "cell_type": "markdown", + "id": "2f4f5bbb", + "metadata": {}, + "source": [ + "Finally, let's set the bath parameters we will work with and write down some measurement operators:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "db22f152", + "metadata": {}, + "outputs": [], + "source": [ + "Q = sigmaz()\n", + "alpha = 3.25\n", + "T = 0.5\n", + "wc = 1.0\n", + "s = 1" + ] + }, + { + "cell_type": "markdown", + "id": "dc0db3ff", + "metadata": {}, + "source": [ + "And set the cut-off for the HEOM hierarchy:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "596c6e81", + "metadata": {}, + "outputs": [], + "source": [ + "# HEOM parameters:\n", + "\n", + "# The max_depth defaults to 5 so that the notebook executes more\n", + "# quickly. Change it to 11 to wait longer for more accurate results.\n", + "max_depth = 5 #could not do 11 my laptop rans out of ram\n", + "# I used 7 because I wanted to make sure things were working correctly\n", + "# cf is terribly slow at 7, probably can be done better by changing guess, lower\n", + "# upper, use 5 to play around :)\n", + "\n", + "# options used for the differential equation solver, while default works it \n", + "# is way slower than using bdf\n", + "options = {\n", + " \"nsteps\":15000, \"store_states\":True, \"rtol\":1e-12, \"atol\":1e-12, \"method\":\"bdf\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "242a054c", + "metadata": {}, + "source": [ + "#### Plotting function" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a0b1eb30", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_result_expectations(plots, axes=None):\n", + " \"\"\"Plot the expectation values of operators as functions of time.\n", + "\n", + " Each plot in plots consists of (solver_result,\n", + " measurement_operation, color, label).\n", + " \"\"\"\n", + " if axes is None:\n", + " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", + " fig_created = True\n", + " else:\n", + " fig = None\n", + " fig_created = False\n", + "\n", + " # add kw arguments to each plot if missing\n", + " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", + " for result, m_op, color, label, kw in plots:\n", + " exp = np.real(expect(result.states, m_op))\n", + " kw.setdefault(\"linewidth\", 2)\n", + " if color == \"rand\":\n", + " axes.plot(\n", + " result.times,\n", + " exp,\n", + " c=np.random.rand(\n", + " 3,\n", + " ),\n", + " label=label,\n", + " **kw,\n", + " )\n", + " else:\n", + " axes.plot(result.times, exp, color, label=label, **kw)\n", + "\n", + " if fig_created:\n", + " axes.legend(loc=0, fontsize=12)\n", + " axes.set_xlabel(\"t\", fontsize=28)\n", + "\n", + " return fig" + ] + }, + { + "cell_type": "markdown", + "id": "d0b9d0fb", + "metadata": {}, + "source": [ + "# Obtaining a decaying Exponential description of the environment\n", + "\n", + "In order to carry out our HEOM simulation, we need to express the correlation \n", + "function as a sum of decaying exponentials, that is we need to express it as \n", + "\n", + "$$C(\\tau)= \\sum_{k=0}^{N-1}c_{k}e^{-\\nu_{k}t}$$\n", + "\n", + "As the correlation function of the environment is tied to it's power spectrum via \n", + "a Fourier transform, such a representation of the correlation function implies a \n", + "power spectrum of the form\n", + "\n", + "$$S(\\omega)= \\sum_{k}2 Re\\left( \\frac{c_{k}}{\\nu_{k}- i \\omega}\\right)$$\n", + "\n", + "There are several ways one can obtain such a decomposition, in this tutorial we \n", + "will cover the following approaches:\n", + "\n", + "- Non-Linear Least Squares:\n", + " - On the Spectral Density (`sd`)\n", + " - On the Correlation Function (`cf`)\n", + " - On the Power Spectrum (`ps`)\n", + "- Methods based on the Prony Polynomial\n", + " - Prony on the correlation function(`prony`)\n", + " - The Matrix Pencil method on the correlation function (`mp`) :question:\n", + " - ESPRIT on the correlation function(`esprit`)\n", + "- Methods based on rational Approximations\n", + " - The AAA algorithm on the Power Spectrum (`aaa`)\n", + " - ESPIRA-I (`espira-I`) :question:\n", + " - ESPIRA-II (`espira-II`)\n", + "\n", + "the ones with a question mark are the ones I think maybe can be deleted.\n", + "Here's a quick high level comparison between the three different families \n", + "of methods\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ClassRequires Extra informationFastResilient to NoiseAllows constraitnsStable
Non-Linear Least Squares✔️❌❌✔️❌
Prony Polynomial❌✔️❗❌❗
Rational Approximations ❌✔️❗❗✔️
\n", + "\n", + "Legend:\n", + "\n", + "❌: NO ✔️: Yes ❗: Partially" + ] + }, + { + "cell_type": "markdown", + "id": "45bdec8e", + "metadata": {}, + "source": [ + "# Non-Linear Least Squares" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "08ce309d", + "metadata": {}, + "outputs": [], + "source": [ + "obs = OhmicEnvironment(T, alpha, wc,s=1)\n", + "tlist = np.linspace(0, 30 * np.pi / Del, 600)" + ] + }, + { + "cell_type": "markdown", + "id": "a8c1920b", + "metadata": {}, + "source": [ + "## Correlation Function" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3bbd9ae1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation function fit:\n", + "\n", + "Result of fitting the real part of |Result of fitting the imaginary part \n", + "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", + " | \n", + " Parameters| a | b | c | Parameters| a | b | c \n", + " 1 |-1.98e+00 |-4.66e+00 |2.58e+00 | 1 |-1.34e+01 |-1.05e+00 |2.56e-02 \n", + " 2 | 3.89e-02 |-1.43e-01 |5.74e-32 | 2 |-8.73e+00 |-3.57e-01 |8.36e-04 \n", + " 3 | 3.45e-01 |-5.95e-01 |1.62e-06 | 3 | 5.48e-01 |-4.30e+00 |3.99e+00 \n", + " 4 | 3.11e+00 |-2.93e+00 |4.65e-04 | 4 |-1.34e+01 |-2.30e+00 |2.90e-01 \n", + " | \n", + "A 1-R2 coefficient of 8.50e-07 was obtained for the the real part of |A 1-R2 coefficient of 7.13e-07 was obtained for the the imaginary part\n", + "the correlation function. |of the correlation function. \n", + "The current fit took 0.361273 seconds. |The current fit took 2.255287 seconds. \n", + "\n", + "10.0%. Run time: 18.30s. Est. time left: 00:00:02:44\n", + "20.0%. Run time: 30.19s. Est. time left: 00:00:02:00\n", + "30.1%. Run time: 41.27s. Est. time left: 00:00:01:36\n", + "40.1%. Run time: 52.42s. Est. time left: 00:00:01:18\n", + "50.1%. Run time: 63.79s. Est. time left: 00:00:01:03\n", + "60.1%. Run time: 74.86s. Est. time left: 00:00:00:49\n", + "70.1%. Run time: 86.01s. Est. time left: 00:00:00:36\n", + "80.1%. Run time: 97.09s. Est. time left: 00:00:00:24\n", + "90.2%. Run time: 108.06s. Est. time left: 00:00:00:11\n", + "100.0%. Run time: 118.88s. Est. time left: 00:00:00:00\n", + "Total run time: 118.88s\n" + ] + } + ], + "source": [ + "t=np.linspace(0,20,500)\n", + "Obath, fitinfo = obs.approximate(method=\"cf\",tlist=t,Nr_max=4,Ni_max=4,maxfev=1e9,target_rsme=None)\n", + "print(fitinfo[\"summary\"])\n", + "HEOM_ohmic_corr_fit = HEOMSolver(\n", + " Hsys,\n", + " (Obath,Q),\n", + " max_depth=max_depth,\n", + " options=options,\n", + ")\n", + "results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "f3e2ea45", + "metadata": {}, + "source": [ + "## Spectral Density" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5cafea21", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result of fitting the spectral density with 4 terms: \n", + " \n", + " Parameters| a | b | c \n", + " 1 | 1.06e-02 | 3.06e-01 |1.02e-01\n", + " 2 |-4.41e+00 | 4.30e+00 |3.98e+00\n", + " 3 | 6.01e-01 | 1.00e+00 |1.02e-01\n", + " 4 | 7.92e+00 | 2.30e+00 |1.02e-01\n", + " \n", + "A 1-R2 coefficient of 1.30e-06 was obtained for the the spectral density.\n", + "The current fit took 0.340964 seconds.\n", + "10.0%. Run time: 6.95s. Est. time left: 00:00:01:02\n", + "20.0%. Run time: 11.80s. Est. time left: 00:00:00:47\n", + "30.1%. Run time: 16.72s. Est. time left: 00:00:00:38\n", + "40.1%. Run time: 21.69s. Est. time left: 00:00:00:32\n", + "50.1%. Run time: 26.73s. Est. time left: 00:00:00:26\n", + "60.1%. Run time: 31.86s. Est. time left: 00:00:00:21\n", + "70.1%. Run time: 37.40s. Est. time left: 00:00:00:15\n", + "80.1%. Run time: 43.64s. Est. time left: 00:00:00:10\n", + "90.2%. Run time: 49.99s. Est. time left: 00:00:00:05\n", + "100.0%. Run time: 56.89s. Est. time left: 00:00:00:00\n", + "Total run time: 56.89s\n" + ] + } + ], + "source": [ + "w=np.linspace(0,30,500)\n", + "Obath2, fitinfo = obs.approximate(method=\"sd\",wlist=w,Nmax=4,Nk=3)\n", + "print(fitinfo[\"summary\"])\n", + "HEOM_ohmic_sd_fit = HEOMSolver(\n", + " Hsys,\n", + " (Obath2,Q),\n", + " max_depth=max_depth,\n", + " options=options,\n", + ")\n", + "results_ohmic_sd_fit = HEOM_ohmic_sd_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "a126d52a", + "metadata": {}, + "source": [ + "## Power Spectrum" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "dfabd825", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result of fitting the power spectrum with 5 terms: \n", + " \n", + " Parameters| a | b | c | d \n", + " 1 | 4.87e+00 |-4.87e+00 | 6.25e-01 |-5.50e-02\n", + " 2 | 6.51e-01 | 4.82e+00 | 5.00e-01 |1.87e-02\n", + " 3 |-4.87e+00 |-9.32e-01 | 5.00e-01 |-9.85e-02\n", + " 4 | 1.50e+00 | 7.36e-01 | 2.45e+00 |1.41e+00\n", + " 5 |-6.40e-01 | 2.47e-01 | 3.98e+00 |3.14e+00\n", + " \n", + "A 1-R2 coefficient of 1.08e-04 was obtained for the the power spectrum.\n", + "The current fit took 3.187300 seconds.\n", + "10.0%. Run time: 3.44s. Est. time left: 00:00:00:30\n", + "20.0%. Run time: 5.72s. Est. time left: 00:00:00:22\n", + "30.1%. Run time: 8.00s. Est. time left: 00:00:00:18\n", + "40.1%. Run time: 10.33s. Est. time left: 00:00:00:15\n", + "50.1%. Run time: 12.61s. Est. time left: 00:00:00:12\n", + "60.1%. Run time: 14.88s. Est. time left: 00:00:00:09\n", + "70.1%. Run time: 16.46s. Est. time left: 00:00:00:07\n", + "80.1%. Run time: 17.92s. Est. time left: 00:00:00:04\n", + "90.2%. Run time: 19.40s. Est. time left: 00:00:00:02\n", + "100.0%. Run time: 20.86s. Est. time left: 00:00:00:00\n", + "Total run time: 20.86s\n" + ] + } + ], + "source": [ + "w=np.linspace(-50,30,500)\n", + "Obath3, fitinfo = obs.approximate(method=\"ps\",wlist=w,Nmax=5)\n", + "print(fitinfo[\"summary\"])\n", + "HEOM_ohmic_ps_fit = HEOMSolver(\n", + " Hsys,\n", + " (Obath3,Q),\n", + " max_depth=max_depth,\n", + " options=options,\n", + ")\n", + "results_ohmic_ps_fit = HEOM_ohmic_ps_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "d337fbc3", + "metadata": {}, + "source": [ + "# Methods based on the Prony Polinomial" + ] + }, + { + "cell_type": "markdown", + "id": "7f788d72", + "metadata": {}, + "source": [ + "## Prony" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "46fb17a5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result of fitting Correlation Function with 4 terms: \n", + " \n", + " Parameters| a | b | c | d \n", + " 1 | 4.97e-02 |-2.48e-02 |-4.97e-02 |-2.48e-02\n", + " 2 | 2.38e-01 |-5.58e-01 |-2.38e-01 |-5.58e-01\n", + " 3 | 7.39e-01 | 4.93e-01 |-7.39e-01 |4.93e-01\n", + " 4 |-2.67e-01 | 8.95e-02 | 2.67e-01 |8.95e-02\n", + " \n", + "A 1-R2 coefficient of 3.09e-05+9.93e-06j was obtained for the Correlation Function.\n", + "The current fit took 0.170541 seconds.\n", + "10.0%. Run time: 0.92s. Est. time left: 00:00:00:08\n", + "20.0%. Run time: 1.51s. Est. time left: 00:00:00:06\n", + "30.1%. Run time: 2.10s. Est. time left: 00:00:00:04\n", + "40.1%. Run time: 2.68s. Est. time left: 00:00:00:04\n", + "50.1%. Run time: 3.23s. Est. time left: 00:00:00:03\n", + "60.1%. Run time: 3.79s. Est. time left: 00:00:00:02\n", + "70.1%. Run time: 4.37s. Est. time left: 00:00:00:01\n", + "80.1%. Run time: 4.94s. Est. time left: 00:00:00:01\n", + "90.2%. Run time: 5.53s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 6.09s. Est. time left: 00:00:00:00\n", + "Total run time: 6.09s\n" + ] + } + ], + "source": [ + "tlist2=np.linspace(0,40,100)\n", + "pbath,fitinfo=obs.approximate(\"prony\",tlist2,Nr=4)\n", + "print(fitinfo[\"summary\"])\n", + "HEOM_ohmic_prony_fit = HEOMSolver(\n", + " Hsys,\n", + " (pbath,Q),\n", + " max_depth=max_depth,\n", + " options=options,\n", + ")\n", + "results_ohmic_prony_fit = HEOM_ohmic_prony_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "920b8b0f", + "metadata": {}, + "source": [ + "## Matrix Pencil" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "efd0eae9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation function fit:\n", + "\n", + "Result of fitting the real part of |Result of fitting the imaginary part \n", + "the correlation function with 5 terms: |of the correlation function with 5 terms: \n", + " | \n", + " Parameters| a | b | c | d | Parameters| a | b | c | d \n", + " 1 | 1.94e+00 | 8.61e+00 |-0.00e+00 |0.00e+00 | 1 | 9.91e-17 |-6.25e-01 |-7.78e+00 |0.00e+00 \n", + " 2 |-1.37e+01 | 4.65e+00 |-0.00e+00 |0.00e+00 | 2 | 1.07e+00 | 4.39e+00 |-1.75e+00 |-1.46e+00 \n", + " 3 | 1.29e+01 | 3.77e+00 |-0.00e+00 |0.00e+00 | 3 | 1.07e+00 | 4.39e+00 | 1.75e+00 |1.46e+00 \n", + " 4 | 3.78e-01 | 6.67e-01 |-0.00e+00 |0.00e+00 | 4 |-1.92e+00 | 1.51e+00 |-0.00e+00 |2.43e-15 \n", + " 5 | 5.30e-02 | 1.66e-01 |-0.00e+00 |0.00e+00 | 5 |-2.13e-01 | 5.44e-01 |-0.00e+00 |-2.22e-16 \n", + " | \n", + "A 1-R2 coefficient of 8.56e-06+0.00e+00j was obtained for the the real part of |A 1-R2 coefficient of 2.40e-05-5.11e-22j was obtained for the the imaginary part\n", + "the correlation function. |of the correlation function. \n", + "The current fit took 0.171659 seconds. |The current fit took 0.204456 seconds. \n", + "\n", + "10.0%. Run time: 3.40s. Est. time left: 00:00:00:30\n", + "20.0%. Run time: 6.14s. Est. time left: 00:00:00:24\n", + "30.1%. Run time: 9.27s. Est. time left: 00:00:00:21\n", + "40.1%. Run time: 12.41s. Est. time left: 00:00:00:18\n", + "50.1%. Run time: 15.86s. Est. time left: 00:00:00:15\n", + "60.1%. Run time: 19.38s. Est. time left: 00:00:00:12\n", + "70.1%. Run time: 22.92s. Est. time left: 00:00:00:09\n", + "80.1%. Run time: 26.45s. Est. time left: 00:00:00:06\n", + "90.2%. Run time: 30.00s. Est. time left: 00:00:00:03\n", + "100.0%. Run time: 33.53s. Est. time left: 00:00:00:00\n", + "Total run time: 33.53s\n" + ] + } + ], + "source": [ + "mpbath,fitinfo=obs.approximate(method=\"mp\",tlist=tlist2,Nr=5,Ni=5,separate=True)\n", + "print(fitinfo[\"summary\"])\n", + "HEOM_ohmic_mp_fit = HEOMSolver(\n", + " Hsys,\n", + " (mpbath,Q),\n", + " max_depth=max_depth,\n", + " options=options,\n", + ")\n", + "results_ohmic_mp_fit = HEOM_ohmic_mp_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "db22b481", + "metadata": {}, + "source": [ + "## ESPRIT" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c4db8362", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result of fitting Correlation Function with 4 terms: \n", + " \n", + " Parameters| a | b | c | d \n", + " 1 |-2.67e-01 | 8.95e-02 | 2.67e-01 |8.95e-02\n", + " 2 | 7.39e-01 | 4.93e-01 |-7.39e-01 |4.93e-01\n", + " 3 | 2.38e-01 |-5.58e-01 |-2.38e-01 |-5.58e-01\n", + " 4 | 4.97e-02 |-2.48e-02 |-4.97e-02 |-2.48e-02\n", + " \n", + "A 1-R2 coefficient of 3.09e-05+9.93e-06j was obtained for the Correlation Function.\n", + "The current fit took 0.167535 seconds.\n", + "10.0%. Run time: 0.93s. Est. time left: 00:00:00:08\n", + "20.0%. Run time: 1.54s. Est. time left: 00:00:00:06\n", + "30.1%. Run time: 2.16s. Est. time left: 00:00:00:05\n", + "40.1%. Run time: 2.77s. Est. time left: 00:00:00:04\n", + "50.1%. Run time: 3.35s. Est. time left: 00:00:00:03\n", + "60.1%. Run time: 3.92s. Est. time left: 00:00:00:02\n", + "70.1%. Run time: 4.48s. Est. time left: 00:00:00:01\n", + "80.1%. Run time: 5.08s. Est. time left: 00:00:00:01\n", + "90.2%. Run time: 5.66s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 6.21s. Est. time left: 00:00:00:00\n", + "Total run time: 6.21s\n" + ] + } + ], + "source": [ + "esbath,fitinfo=obs.approximate(\"esprit\",tlist2,Nr=4)\n", + "print(fitinfo[\"summary\"])\n", + "HEOM_ohmic_es_fit = HEOMSolver(\n", + " Hsys,\n", + " (esbath,Q),\n", + " max_depth=max_depth,\n", + " options=options,\n", + ")\n", + "results_ohmic_es_fit = HEOM_ohmic_es_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "3fafdfdc", + "metadata": {}, + "source": [ + "# Rational Approximations" + ] + }, + { + "cell_type": "markdown", + "id": "c2e3d835", + "metadata": {}, + "source": [ + "## AAA" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "9c6e828f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/gerardo/Documents/gsuarezr/qutip_gsoc_app/qutip/utilities.py:55: RuntimeWarning: overflow encountered in exp\n", + " result[non_zero] = 1 / (np.exp(w[non_zero] / w_th) - 1)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result of fitting the power spectrum with 5 terms: \n", + " \n", + " Parameters| a | b | c | d \n", + " 1 | 9.81e-01 | 1.27e+00 | 2.27e+00 |1.79e+00\n", + " 2 | 5.53e-01 |-1.05e+00 | 9.41e-01 |-1.62e-01\n", + " 3 | 3.92e-02 |-3.15e-02 | 1.52e-01 |-3.28e-02\n", + " 4 | 4.13e-04 |-1.91e-04 | 1.13e-02 |-1.67e-03\n", + " 5 | 4.66e-08 |-2.57e-07 | 1.38e-04 |-1.27e-04\n", + " \n", + "A 1-R2 coefficient of 1.09e-04 was obtained for the the power spectrum.\n", + "The current fit took 10.199787 seconds.\n", + "10.0%. Run time: 1.56s. Est. time left: 00:00:00:14\n", + "20.0%. Run time: 2.42s. Est. time left: 00:00:00:09\n", + "30.1%. Run time: 3.26s. Est. time left: 00:00:00:07\n", + "40.1%. Run time: 4.11s. Est. time left: 00:00:00:06\n", + "50.1%. Run time: 4.95s. Est. time left: 00:00:00:04\n", + "60.1%. Run time: 5.80s. Est. time left: 00:00:00:03\n", + "70.1%. Run time: 6.64s. Est. time left: 00:00:00:02\n", + "80.1%. Run time: 7.49s. Est. time left: 00:00:00:01\n", + "90.2%. Run time: 8.33s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 9.16s. Est. time left: 00:00:00:00\n", + "Total run time: 9.16s\n" + ] + } + ], + "source": [ + "aaabath,fitinfo=obs.approximate(\"aaa\",np.concatenate((-np.logspace(3,-8,3500),np.logspace(-8,3,3500))),N_max=6,tol=1e-15)\n", + "print(fitinfo[\"summary\"])\n", + "HEOM_ohmic_aaa_fit = HEOMSolver(\n", + " Hsys,\n", + " (aaabath,Q),\n", + " max_depth=max_depth,\n", + " options=options,\n", + ")\n", + "results_ohmic_aaa_fit = HEOM_ohmic_aaa_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "ba03829b", + "metadata": {}, + "source": [ + "# ESPIRA I" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "381bb1ee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation function fit:\n", + "\n", + "Result of fitting the real part of |Result of fitting the imaginary part \n", + "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", + " | \n", + " Parameters| a | b | c | d | Parameters| a | b | c | d \n", + " 1 | 2.18e-01 | 3.22e+00 | 1.52e+00 |1.58e+00 | 1 | 7.39e-01 | 3.68e+00 |-2.44e+00 |-5.14e-01 \n", + " 2 | 8.01e-01 | 2.84e+00 |-1.32e+00 |-1.54e+00 | 2 | 9.27e-01 | 3.57e+00 | 2.23e+00 |4.43e-01 \n", + " 3 | 3.92e-01 | 6.42e-01 |-4.68e-02 |-4.15e-02 | 3 |-1.60e+00 | 1.26e+00 |-4.47e-02 |5.98e-02 \n", + " 4 | 4.45e-02 | 1.54e-01 |-6.22e-03 |-4.58e-03 | 4 |-7.70e-02 | 3.63e-01 |-2.83e-02 |1.68e-02 \n", + " | \n", + "A 1-R2 coefficient of 1.48e-04-5.04e-06j was obtained for the the real part of |A 1-R2 coefficient of 8.87e-06-9.73e-06j was obtained for the the imaginary part\n", + "the correlation function. |of the correlation function. \n", + "The current fit took 1.521248 seconds. |The current fit took 1.571012 seconds. \n", + "\n", + "10.0%. Run time: 0.71s. Est. time left: 00:00:00:06\n", + "20.0%. Run time: 1.26s. Est. time left: 00:00:00:05\n", + "30.1%. Run time: 1.79s. Est. time left: 00:00:00:04\n", + "40.1%. Run time: 2.33s. Est. time left: 00:00:00:03\n", + "50.1%. Run time: 2.87s. Est. time left: 00:00:00:02\n", + "60.1%. Run time: 3.40s. Est. time left: 00:00:00:02\n", + "70.1%. Run time: 3.93s. Est. time left: 00:00:00:01\n", + "80.1%. Run time: 4.49s. Est. time left: 00:00:00:01\n", + "90.2%. Run time: 5.02s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 5.53s. Est. time left: 00:00:00:00\n", + "Total run time: 5.53s\n" + ] + } + ], + "source": [ + "tlist4=np.linspace(0,20,1000)\n", + "espibath,fitinfo=obs._approx_by_prony(\"espira-I\",tlist4,Nr=4,Ni=4,separate=True)\n", + "print(fitinfo[\"summary\"])\n", + "HEOM_ohmic_espira_fit = HEOMSolver(\n", + " Hsys,\n", + " (espibath,Q),\n", + " max_depth=max_depth,\n", + " options=options,\n", + ")\n", + "results_ohmic_espira_fit = HEOM_ohmic_espira_fit.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "a25d9385", + "metadata": {}, + "source": [ + "# ESPIRA II" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "1756c0ab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation function fit:\n", + "\n", + "Result of fitting the real part of |Result of fitting the imaginary part \n", + "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", + " | \n", + " Parameters| a | b | c | d | Parameters| a | b | c | d \n", + " 1 | 3.75e-01 | 2.85e+00 |-1.66e+00 |-1.14e+00 | 1 | 7.81e-01 | 3.59e+00 | 2.38e+00 |4.62e-01 \n", + " 2 | 6.32e-01 | 2.62e+00 | 1.54e+00 |1.10e+00 | 2 | 8.75e-01 | 3.51e+00 |-2.29e+00 |-4.10e-01 \n", + " 3 | 5.12e-02 | 1.63e-01 | 6.22e-03 |4.19e-03 | 3 |-1.59e+00 | 1.26e+00 | 3.56e-02 |-3.86e-02 \n", + " 4 | 4.45e-01 | 7.02e-01 | 4.15e-02 |4.12e-02 | 4 |-7.57e-02 | 3.62e-01 | 2.51e-02 |-1.47e-02 \n", + " | \n", + "A 1-R2 coefficient of 4.02e-05-5.77e-06j was obtained for the the real part of |A 1-R2 coefficient of 7.44e-06+1.93e-06j was obtained for the the imaginary part\n", + "the correlation function. |of the correlation function. \n", + "The current fit took 1.758265 seconds. |The current fit took 1.723816 seconds. \n", + "\n", + "10.0%. Run time: 0.69s. Est. time left: 00:00:00:06\n", + "20.0%. Run time: 1.14s. Est. time left: 00:00:00:04\n", + "30.1%. Run time: 1.60s. Est. time left: 00:00:00:03\n", + "40.1%. Run time: 2.06s. Est. time left: 00:00:00:03\n", + "50.1%. Run time: 2.47s. Est. time left: 00:00:00:02\n", + "60.1%. Run time: 2.86s. Est. time left: 00:00:00:01\n", + "70.1%. Run time: 3.26s. Est. time left: 00:00:00:01\n", + "80.1%. Run time: 3.65s. Est. time left: 00:00:00:00\n", + "90.2%. Run time: 4.07s. Est. time left: 00:00:00:00\n", + "100.0%. Run time: 4.48s. Est. time left: 00:00:00:00\n", + "Total run time: 4.48s\n" + ] + } + ], + "source": [ + "espibath2,fitinfo=obs._approx_by_prony(\"espira-II\",tlist4,Nr=4,Ni=4,separate=True)\n", + "print(fitinfo[\"summary\"])\n", + "HEOM_ohmic_espira_fit2 = HEOMSolver(\n", + " Hsys,\n", + " (espibath2,Q),\n", + " max_depth=max_depth,\n", + " options=options,\n", + ")\n", + "results_ohmic_espira2_fit = HEOM_ohmic_espira_fit2.run(rho0, tlist)" + ] + }, + { + "cell_type": "markdown", + "id": "d486d3f6", + "metadata": {}, + "source": [ + "Finally we plot the dynamics obtained by the different methods" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "1a701be0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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4dfPmTSZPnsyKFSv466+/HpuM879t00rs51qtWrU4RZH/ypcvH0WKFCEgICDRn8WTVnfJmTMnISEhcYpeyTVnzpx4V1+RlFOBQjK8LzbO4cDbXxDdYiAP7bPRpOVH7Ns19ckHioiIiBjNw8PoBOkjjd5n7ty5rc8DAwNT1NedO3esz590u4idnR25c+fm2rVr3L59O8F2OXPmTPL5E2t7/fr1JPfzqNhbJJLq0KFDtGjRIsm3bjx48CA5sZIk9nNNyq07Hh4eBAQEJPqzSOzWFYhZdhYgKirqKVJKelOBQjK8Eo2b0sJpOhvsswFwtW4LXu/QnZ9+XmBwMhEREZEnSIPbHrKSR1fQOHz4cKr1G988BcmR1Ns7ntT20YvmH374gbp16yapz6cpkDx8+JBOnTpx69Yt7OzsePvtt2nTpg2lSpUiZ86c1tsjzp07R/HixQESHV2RWlLrZyHPBhUoJFP4Yf/P1G84gIt1W2Ey27A5X3N+Wz6LFzu+YXQ0EREREUkj5cuXt863sHPnToKDg3F1dU1WX49ezD9pNEZkZKR1lEGuXLmSdb6n8ehIkezZs1OhQoVUP8fWrVut8y9MmzaNN96I/7+jExulkJpy5crF1atXkzQyJvZWlvT4WYixtIqHZA4mEytnDMb2wh8xL11y886Ka9wLuvOEA0VEREQkszKZTPTs2ROImYMiduLK5HBwcKBkyZIA7N+/P9G2R44cISIiAiBNigX/VaVKFetIgt27d6fJOU6cOGF93rlz5wTb+T1h1E9qjXiI/VwPHz5MZGRkgu2uX7/OhQsX4hzzLNEIkrhUoJBMw71cOb6qZ8ISElPVjShSlcZd3jM4lYiIiIikpSFDhljnFxg1ahSnTp1K0nHR0dEsXLgwzramTZsCMRfrBw4cSPDYRwshscekpTx58vDcc88BsGjRIm7cuJHq53i0CJDQhKPR0dHMnDkz0X4cHR2tz8PDw5OdJ/ZzvXv3Lr/88kuC7X766SfrrSbp8bNIb7GfZ0o+y2eJChSSqbQf9BEvXl6CJTrmPr0bldvTsMGbBqcSERERkbTi5eXFlClTgJgL64YNG7J9+/ZEjzl58iQvvPACEydOjLN9wIAB1skS+/XrR3Bw8GPHbty4kZ9++gmAWrVqJbjEaGqLXUozODiYDh06cPfu3QTbhoeHM3XqVMLCwpLcf+zoEQAfH5942wwfPvyJc33kz5/f+vzvv/9O8vn/q3fv3tbC03vvvcfleFa7OXbsGF988QUQ83vQtm3bZJ8vo4r9PM+dO5cuc35kdCpQSKYzfeU6Su73sb4OqNmcN7sNMS6QiIiIiKSp3r1789lnnwExQ/69vb1p0aIF06ZNY9u2bRw5coQtW7Ywffp0Xn75ZSpVqsSmTZse66dixYq8917MCNxjx45RrVo1Zs6ciZ+fH9u3b2fYsGG8/PLLREVFYW9vzw8//JBu7/HFF1/knXfeAWDHjh2ULVuWMWPGsGXLFo4ePcru3buZO3cub7zxBvnz5+ett95K9NaI/2rRooV1xYwRI0bw5ptvsmHDBg4dOsTSpUtp2rQpEyZM4Pnnn0+0n0cn8BwyZAg7duzgr7/+4uzZs5w9ezbJmfLkyWMtIF26dInq1avz7bffcuDAAfbs2cNnn31GvXr1CAkJwWQy8eOPPya6HGlmFft5Xr9+naFDh3Lo0CHrZxl7a0tWokkyJVP6bdEU6vadxM1KTcDWjjUn/qbnGh/qtO5ldDQRERERSQMjR46kfPnyvPfeewQEBLBx40Y2btyYYPvy5cszYcKEx7aPHz+e0NBQpk2bxt9//02/fv0ea+Pm5sayZcuoUqVKar6FJ/rmm2/IlSsXY8eO5dq1a4wePTrBtk5OTk+1ioiTkxPz5s2jbdu2hIWF8cMPPzxWgPH29mbKlCmJzvVQokQJOnXqxLJly+L9GZw/f54iRYokKdPAgQO5e/cuI0eOJDAwkCFDHv/S0cHBgR9//JEXX3wxSX1mNl26dOHLL7/k3LlzfPvtt3z77bfWfYULFyYgIMCwbEbQCArJlOy9PFnyajVszx3m+s+fcfXYrwx6pTdBN64YHU1ERERE0sgrr7zC6dOnWbhwId27d6d06dLkzJkTW1tbcuXKRbVq1Rg4cCBbt27F39+f5s2bP9aH2Wxm6tSp7Nixg27dulGoUCEcHBxwdXWlSpUqfPzxx/z111/xHpvWTCYTo0aN4syZM3zwwQfUqFGDXLlyYWNjg4uLC+XKlaNbt27MnTuXq1evki1btqfqv0WLFvj5+dG9e3c8PT2xs7MjT548NGzYkB9//JEtW7bg5OT0xH4WLFjAhAkTqFWrFm5ubtbbZpLj448/5siRI/Tt25fixYuTLVs2nJycKFu2LO+88w6nTp3itddeS3b/GZ2zszN79uzhnXfeoWzZstbbXrIqk0U3umQZwcHBuLm5ERQUlOzlmTKa3ycNp9f747n+7+sXXE38HhRtaCYRERF5toWFhXH+/HmKFi0aZ8JAEZFnWXL/9j3NdahGUEim1nLYlwxpU5XYu9HWB1voWLGMoZlERERERETk6alAIZneR6sO07NIzFAolxqt2d/sc7q1H2xwKhEREREREXkaKlDIM2HmuRAalm9Jrib9MNs7sjN/LWZ8Pc3oWCIiIiIiIpJEKlDIs8FkYvE7r+Bw4Q8AzM45+cLfhL/fIYODiYiIiIiISFKoQCHPjPx932Bq+FEsd68BYPYoRKtxmwh/EG5wMhEREREREXkSFSjkmdJ0xRI+3vkT0eH3YzaUqUill8YYG0pERERERESeSAUKebaYTPQ/uJmuv32PJToKgPBaz1O30dsGBxMREREREZHEqEAhz57s2Rm/aQG1t821brpcvTG9uw41MJSIiIiIiIgkRgUKeTYVKsSCLwaQ79hGAEy29mxyKsvi76cYHExERERERETiowKFPLPsmzVlRTUX7P/xJ+LuNa6vGsdbgydz9vgfRkcTERERERGR/1CBQp5pBT4fx+xLm7GdN5SIm/9wm/N4V+5NxMMIo6OJiIiIiIjII1SgkGdevc2/8VM2R5z+fX05+jBVczcxNJOIiIiIiIjEpQKFPPvs7Wl66jg/2GbDBGC25Wq9KtRpMtjoZCIiIiIiIvIvFSgka8iTh25HDjDKNhv5Oo/FpXILrlRtRO8e7xmdTERERERERFCBQrKSChX4ZOkiity+DMSs7LHFrRpL5n5ncDARERERERFRgUKyFLtX2vJLeXvs//EHwOycg+G+cPaUVvYQERERERExkgoUkuV4TprEd16BWO5cAcCSrzgvjVxG2IMwg5OJiIiIiIhkXSpQSNZjMvHCxG/oH7GR6PBQAMKL16F2x3cMDiYiIiIiIpJ1qUAhWdbHs5fT3P8HLNFRAARVaE2VxkMNTiUiIiIiIpI1qUAhWdqszVuo7OtjfX2nhjet271tXCAREREREWD06NGYTCZMJpPRUUTSjQoUkuWtHNmHYgd+ASD80kl2rJ/L3IlvGZxKRERERB4VGhrKjBkzePHFF/Hy8sLR0REHBwfy5MlDzZo16dOnDzNnzuTixYvxHt+rVy/rBf+jD0dHR/Lly0fp0qVp164dn3/+OYcPH0613D4+PvGeN6GHr69vqp1bJLNRgUKyPJtWL7Oirheum+YQuHQkQWH3+OSDqezf8rPR0UREREQE2Lt3L+XKlWPAgAH8/vvvXLlyhfDwcB4+fMjNmzfx8/Njzpw59OvXj5o1az5V3+Hh4Vy/fp0zZ86watUqRowYQfXq1alVqxbbtm1Lo3eUct7e3phMJry9vY2OIpJqbI0OIJIR5Bz3GUtqzadjmxWcBC4DvV/oyI5/zuOev4jB6URERESyrjNnztCiRQvu3bsHQOvWrenQoQOlSpXC3t6emzdvcuzYMTZt2pTkgsKGDRvw9PQEIDo6mrt37xIYGMi+fftYtWoV586d4+DBgzRt2pQRI0YwZsyYVHkv48aNo02bNom2KVq0KBBzi8fo0aNT5bwimYUKFCL/Kte6B199vIO+X8ziGnDazpUanb/m9MavcXC0NzqeiIiISJb0ySefWIsTc+bMoVevXo+1adasGcOGDePGjRssW7bsiX2WKlWKIkWKPLa9Y8eOTJw4kblz5/LWW29x//59PvvsM/Lly8fAgQNT+lbw8vKiQoUKKe5H5FmlWzxEHvHy5zMZ8UJlnHIXxOO1yVD3Bcq1Tp2KuYiIiIg8naioKNatWwdAjRo14i1OPCpPnjwMGjQoRec0m8307t2bDRs2YGsb833u0KFDuXr1aor6FZEnU4FC5D8GrT7AuzkKYOuaB4CoanWp3vxdY0OJiIiIZEE3btzgwYMHAJQoUSJdz12vXj2GDBkCxMxT8c0336Tr+RNaxSN2ss/t27cDsH379scm2oxvdIhIZqAChch/2dszbtYX1N8y07rpZuVGdOg61MBQIiIiIlmPvf3/32b7559/pvv5Bw8ejNkcc8m0atWqdD+/SFajAoVIfLy98en7MgX91gBgsrHlQJ7ajB07yeBgIiIiIllHrly5KFy4MADHjh3jq6++Ijo6Ot3OX6BAAcqUKQPAX3/9xbVr19Lt3An5/PPP8ff3p0aNGkDMrS/+/v5xHhs3bjQ4pUjyaJJMkQTYvtmflYf60+Dvg9wvXhNzNmd+vJSbWhs30qJ5c6PjiYiIiGQJb7/9NsOGDQPgo48+YsaMGbRu3Zq6detSq1Yt66oXaaVatWqcPHkSiClSeHh4JLuvy5cvc/z48QT3FyhQgBw5ciTah5eXF15eXjg5OQHg5OSkiTflmaERFCKJcP9xBksvbsZ0/TwANjnz8vq8M1zTJEkiIiIi6WLIkCH06dPH+jogIIDvvvuOLl26UKxYMTw8POjSpQu//vorFosl1c+fO3du6/M7d+6kqK8RI0ZQsWLFBB+6jUSyOo2gEEmMyURF381MK1ON/i8Px+ycC9sCRan1pg/nfh6GrZ2d0QlFREQkA6vxYw2uhRh/W0Ba83D2wK+fX5r0bTab+emnn+jSpQuTJ09m8+bNREZGWvcHBgaydOlSli5dSo0aNViyZAnFixdPtfM7Oztbn8cudyoiaUMFCpEncXSkpd8OPqnaiC86jcZk50jog4tU9GzCnzd2GJ1OREREMrBrIde4fO+y0TGeCc2aNaNZs2YEBweze/duDh48iJ+fHzt27CAoKAgAPz8/6tevz6FDh8ifP3+qnPfRooSrq2uK+pozZ84Tl0oVycpUoBBJCnd3+m9ZwbmX+zAjZz6CD/zCbcC7fHN8T2gSIhEREYmfh3Py5yvITNLzfbq6utKyZUtatmwJxCwBumjRIt577z3u3LnD1atXGTlyJLNmzUqV8928edP6PFeuXKnSp4jETwUKkaQqVYqvZnyGS5OmjPx3066TW+nd+lXmrFlsaDQRERHJmNLqtgf5fw4ODvTu3RtPT09eeOEFAH755Rd+/PFH6xKhKXHkyBHr81KlSqW4PxFJmCbJFHkajRvz0ZQp9P33ZRRRLNvvzwcff25oLBEREZGsrkWLFhQsWBCImczy1q1bKe7z4sWLnD59GoDSpUuTJ0+eFPcpIglTgULkKdkOGsikPm/wAuBYtBq5u37K4qBCLFy01OhoIiIiIlmap6en9bnJZEpxf9999x3R0dEAtGvXLsX9pabUeH8iGY0KFCLJ4DrrR36oWRvPup0xO2THxiUXH24O4tSfR42OJiIiIpIl3b9/n5MnTwIx81Q8ujxocuzatYtvv/0WAEdHR959990UJkxdjo6OQMwcHCLPChUoRJLDZKLQDl/m75qN5c5VAGzzevHi2F+5dy/I4HAiIiIiz4aQkBBq167N2rVrrSMZ4hMdHc3bb79tXXGjdevWyR5hEB0djY+PDy1atLAuZ/q///2PfPnyJau/tBK7Ssm5c+ewWCwGpxFJHZokUyS5HB2p67eLz2o1Z2Sr9zFncyW6UDVq9vyUk8u/xmxjY3RCERERkUzvwIEDtGrVCi8vL9q2bUudOnUoXLgwLi4u3L17lyNHjjB79mz8/f0BcHNzY+zYsYn2eebMGUJCQoCYgkRQUBDXrl1j//79rFy5knPnzgFgNpv59NNP6devX9q+yWSoW7cuc+bM4fr16wwdOpTu3bvj5uYGgJ2dHYULFzY4ocjTU4FCJCXc3el57jAnnq/H0jrvYbK1J6xUM2q1743fqnlGpxMRERHJ1GxtbfHw8ODatWtcvnyZqVOnMnXq1ATblyxZksWLF1OkSJFE+23RosUTz12rVi0mTJhAw4YNnzZ2uujSpQtffvkl586d49tvv7XejgJQuHBhAgICDMsmklwqUIikggk7dxBYuwnbmwwD4GaZzjz38rvsW/utscFEREREMjFHR0cuX77Mvn372Lx5M/v27eP06dMEBgYSFhaGk5MTnp6eVK5cmTZt2tC+fXvs7e2f6hz29va4ubmRI0cOypUrR82aNXnppZeoUqVK2rypVOLs7MyePXv48ssv2bhxIxcuXOD+/ftGxxJJEZNFNyxlGcHBwbi5uREUFISrq6vRcZ49Dx7wQpPXONWgFwCWqAhq39zNsjkTjc0lIiIiqSosLIzz589TtGhR60SFIiLPuuT+7Xua61BNkimSWrJlY3Wb2ngeXhfz2mRm86HjLP5huLG5REREREREMgEVKERSkcMH77HK4TJOJ3dwfcVn3PFfz5AB4zm4/Rejo4mIiIiIiGRoKlCIpCaTibwrV7CsejTFzx0CINACnZq159rFMwaHExERERERybhUoBBJbba2lP90IpP7voLnv5sCIqBO0z7WtblFREREREQkLhUoRNJI8x9X8FW9sjgDLjXbYWk3nPLd/kd0VJTR0URERERERDIcFShE0lD3HScY6VGWnA1eA8BcriqlWo0wOJWIiIiIiEjGowKFSFoymfhg0VTar5uMxRINQGSlelR9eYjBwURERERERDIWFShE0lqjRkx6vzd1t8yybrpdrhEvvKoihYiIiIiISCwVKETSgblPb+Y1LUex/SsAMJlt+NOzPv2GjDQ4mYiIiIiISMagAoVIOrH78gtW5Qgi54ltAJjsHFgfXZ6J304xOJmIiIiIiIjxVKDIxNq1a0fOnDnp0KGD0VEkKUwmXH9extp/fHEIOAqAOZsL35124dd1a43NJiIiIiIiYjAVKDKxd955h3nz5hkdQ56GjQ1ee3fwy66fMAX+HbPN3oEefb/h0rnTxmYTERERERExkAoUmZi3tzcuLi5Gx5CnlT075Q/vY/6aiURf+INrC98n6OpWKpTuxP17QUanExERERERMUSWK1Dcu3ePd999l8KFC5MtWzbq1q3LwYMHU/UcO3bsoFWrVnh6emIymVi1alW87aZOnUqRIkVwdHSkdu3aHDhwIFVzSAaWNy/19m9j2bLRZLt1CYCgyD8olr8JUZGRBocTERERERFJf1muQPHGG2+wadMm5s+fj7+/P82bN6dp06Zcvnw53va7d+8mIiLise0nT54kMDAw3mNCQ0OpXLkyU6dOTTDH0qVLGTp0KJ9++imHDx+mcuXKtGjRguvXr1vbVKlShQoVKjz2uHLlylO+a8mQSpTguT07WIYZu383BYYeptRzvYxMJSIiIiIiYogsVaB48OABK1asYMKECTRo0IASJUowevRoSpQowfTp0x9rHx0dzaBBg+jatStRUVHW7adPn6Zx48bMnTs33vO0bNmScePG0a5duwSzTJ48mb59+9K7d2/KlSvHjBkzyJ49O7Nnz7a2OXr0KMePH3/s4enpmYJPQTKU2rV5YfVKZmMCsy25XxpCVNNXqdRqqNHJRERERERE0lWWKlBERkYSFRWFo6NjnO3ZsmVj165dj7U3m8389ttvHDlyhNdee43o6Gj+/vtvGjduTNu2bfnggw+SlePhw4ccOnSIpk2bxjlX06ZN2bt3b7L6TMzUqVMpV64cNWvWTPW+JRW0bk33qVMYWLQazhUaAxBUrhFNXx1scDAREREREZH0k6UKFC4uLtSpU4exY8dy5coVoqKiWLBgAXv37uXq1avxHuPp6cnWrVvZtWsXXbt2pXHjxjRt2jTeERdJdfPmTaKiosiXL1+c7fny5ePatWtJ7qdp06Z07NiR3377jQIFCiRY3Bg0aBAnT55M9bk2JBUNHMi3bRtQevdiAEwmM395NWbwx28bHExERERERCR9ZKkCBcD8+fOxWCx4eXnh4ODAd999x6uvvorZnPBHUahQIebPn8/SpUuxtbXlp59+wmQypWPq+G3evJkbN25w//59Ll26RJ06dYyOJClgN3ECK/OE4fLHdgBMtnaselCfCf/7zOBkIiIiIiIiaS/LFSiKFy/O9u3bCQkJ4eLFixw4cICIiAiKFSuW4DGBgYH069ePVq1acf/+fYYMGZKiDO7u7tjY2Dw2yWZgYCAeHh4p6lsyMZOJ7CuW82vHEmQ7G7Oii9nBiSl/FWPRslkGhxMRERGRtBIQEIDJZMJkMuHj42N0HBHDZLkCRSwnJyfy58/PnTt32LBhA23atIm33c2bN2nSpAlly5bll19+YcuWLSxdupRhw4Yl+9z29vZUr16dLVu2WLdFR0ezZcsWjYLI6kwmivTux4IyIdhcOgmA2TknH26xYePmDQaHExEREUl/vr6+1ov3/z6yZ89O4cKFadu2LYsWLSLyCcu1X7p0idGjR1O/fn3y5MmDnZ0d2bJlo0CBAjRo0IB33nmHn3/+maCgoGTnTShrfI9evXol+zwiz6IsV6DYsGED69ev5/z582zatIlGjRpRpkwZevfu/Vjb6OhoWrZsSeHCha23d5QrV45NmzYxZ84cvvnmm3jPERISwtGjRzl69CgA58+f5+jRo/zzzz/WNkOHDmXmzJnMnTuXP//8kwEDBhAaGhpvDsl6qo/7mh+i92G5cQEAm5x56b0wgFN/Hjc4mYiIiEjG8eDBA/755x9Wr15Nt27dqFu3boJzus2cOZPSpUszZswYdu3axc2bN4mMjCQsLIzLly+zc+dOvvvuOzp27Ej//v3T+Z0kzsfHx1rUCAgIMDqOSJqxNTpAegsKCmL48OFcunSJXLly0b59ez7//HPs7Owea2s2m/niiy+oX78+9vb21u2VK1dm8+bN5MmTJ95z+Pn50ahRI+vroUNjlozs2bOndchW586duXHjBqNGjeLatWtUqVKF9evXPzZxpmRdTX0WMzt/Cfp0GoPJLS8WcyTNGjXh1JmTOLnmNjqeiIiISLobMGAAAwcOtL4OCQnBz8+Pr7/+moCAAA4ePEibNm3Yt29fnDnjFi9eTL9+/QBwdHSkd+/etGjRggIFCmCxWLhy5Qp+fn6sXbuWI0eOpErWGjVqMGfOnETb5MyZE4AiRYpgsVhS5bwimZnJon8JWUZwcDBubm4EBQXh6upqdBxJiuPHWen9EgMbvMb1374hOjyUUvlMnPgnDNtHimYiIiKSfsLCwjh//jxFixZ9bPl6SX2+vr7WL/8+/fRTRo8e/VibO3fuUKtWLc6ePQvAmjVraNWqFQBRUVEUKFCAa9eu4eLiwq5du6hUqVKC5/vzzz/x9/enU6dOycobWxhp2LAhvr6+yerjv3x8fKwjrc+fP0+RIkVSpV+Rp5Hcv31Pcx2a5W7xEMlUKlSg3dZfmbjrW+zCQwE4E2ihVjlnUG1RREREBIgZiTB8+HDr6/Xr11uf79+/33rbR//+/RMtTgCULVs22cUJEUkZFShEMrpKlej+9xW+dbG3/oM9+o8NZZtpvhIRERGRWLVq1bI+v3DhgvX5o/PAlShRIl0zJVVCq3jEThD66Dx1RYsWfWyyzdQaqSFiNBUoRDIDFxfe/PsS4+1NmLO5kq/LFzyo0ZHKbYcanUxEREQkQ3h0TrmoqCjr80fnkvvzzz/TNZOIPB0VKEQyizx5GLb7AJ2LPYeDZykAgso0wfvVd43NJSIiIpIB+Pv7W597enpan1etWtX6/IcffmDr1q3pmislatasib+/P+PGjbNu27BhA/7+/nEeNWvWNDClSOrJcqt4iGRmpurVmVvGjZd3L+L0810BOF+gEZ3ffJ+lMyYanE5ERETEGJGRkXz99dfW197e3tbnRYsW5eWXX2bt2rWEhYXRpEkTatasScuWLXnuueeoWbMm7u7uqZ4pNDSU48cTXiLeycmJokWLJtqHk5MTFSpUwM/Pz7qtVKlSmiRTnlkqUIhkJiYTdsuWsNK7EY2O/EZg1Rcx2diyN/tzvPfpWL4eM9LohCIiIiLpJjQ0FD8/P0aPHs2+ffsAKFy48GOTXM6ZM4cXX3yRgwcPAnDw4EHrc4i56G/evDm9e/emWrVqqZLNz8+PihUrJrg/NVf5EHlWqEAhktmYzWTfvInfKlWjYTZXQsrUw2zvyPK7pSg8fTqDBwwwOqGIiIj8y8+vBg8fXjM6Rpqzt/egRg2/JzdMoTFjxjBmzJgE9+fNm5dVq1bh4OAQZ7u7uzu7d+/Gx8eHGTNmcPjw4Tj7z5w5w5kzZ5gyZQrdu3dnxowZODk5pcl7EJGEqUAhkhnZ25P70H5+L1GOJo4uPCxSGXM2Zyb6P6Twr6to06qt0QlFREQEePjwGg8fXjY6xjOvaNGidOjQgWHDhpE3b95429jZ2dG3b1/69u3LlStX2LlzJ35+fuzfv599+/YREREBwIIFC7hy5QobN27ExsYm2Zk0QkLk6alAIZJZOTlR8PhhVpWowEttPsLiUQIb11wM+jWQvLm3U6duQ6MTioiIZHn29h5GR0gX6fU+BwwYwMCBAwEwmUw4Ojri7u6Om5vbU/Xj6elJ586d6dy5MwC3b99m0qRJfPXVV0RHR7N161YWL15M9+7dU/09iEjCVKAQycxy56bcHwdYUqEmnTp/himXF+E3ztKk0UTOnVmNR+FSRicUERHJ0tLjtoesJG/evFSoUCHV+82VKxdffPEFFouF8ePHA7B8+XIVKETSmZYZFcnsChak9t6t/LR0NA/3r+Dm6q948PAUJUu1Jejms3/Pq4iIiEhq6du3r/X52bNnDUwikjWpQCHyLChXjqa//8xK33m4WqIBCHn4J4ULN+Phg1CDw4mIiIhkDp6entbnJpPJwCRxZaQsImlJBQqRZ0XdutRes5JVmHD8d1Oo3Q2Kth/Hw/CHhkYTERERMYrFYklyWz+//78lp1ixYmkRJ1kcHR2tz8PDww1MIpK2VKAQeZa0akWjn2bxM+DoXph83SZgV6keJV/9kuioKKPTiYiIiKS733//nU6dOnHkyJFE292+fZvBgwdbX7dp0yatoyVZ/vz5rc///vtvA5OIpC1NkinyrOnTh5du3OCjH5YxO3vMjNamUjWo8Oq7nFj6nYYIioiISJYSHR3N8uXLWb58OZUrV+all16iZs2a5M+fH3t7e65fv86uXbv48ccfuX79OgDVq1enZ8+eBif/f1WrVsXR0ZGwsDBGjhyJnZ0dhQsXxmyO+b7Zy8uLbNmyGZxSJOVUoBB5Fn34ISOvXePO2q9Z2fp9TCYz94u9QO3ufTiwcI7R6URERETSTc6cOXFyciI0NJRjx45x7NixRNs3a9aMxYsXY2ubcS6VXFxcGDx4MBMmTODw4cM0b948zv5t27bh7e1tTDiRVKRbPESeUebJk5lcvwzVNvx/QeJ6wQ407p1xvg0QERERSWvPP/88N27cYM2aNQwdOpSGDRvi6emJg4MDtra25MqVi2rVqtG/f3+2bdvGxo0byZ07t9GxHzN+/HhmzpxJ/fr1yZUrFzY2NkZHEkl1JsvTzBojmVpwcDBubm4EBQXh6upqdBxJJxHbtvHKR1Pxb9QbAIslmoq3f2XtzB8NTiYiIpI5hYWFcf78eYoWLRpn8kIRkWdZcv/2Pc11qEZQiDzj7Bo1YtkHXSi6dxkAJpMZ/5wv0/HNYQYnExERERER+X8qUIhkAdnad2Bll5rkOLwJAJPZhgPOzzN8xLvGBhMREREREfmXChQiWUSOXq/je3IVbid9AQjau4z/ffE/Fs/40NhgIiIiIiIiaBUPkawjWzZyHD/KhpIVaPznIS6c3QZA30ETcMuZhxc765YPERERERExjkZQiGQlOXLgcTWAlZWzU+3fTaHR0Ln7+/j+OtPQaCIiIiIikrWpQCGS1djaUnr5r/g0qEXZfzdFelWm+xoTa379xdBoIiIiIiKSdalAIZIVmUxU3LKbhVXKULBwdfJ2+BTb3PkZuC4Y362bjU4nIiIiIiJZkAoUIlmVrS1V123h55sXICgwZlOOPHRfHMDhQ/sMDiciIiIiIlmNChQiWZmnJ7V+Xcb8JaOw3L0GgG3u/LSa+gdnTvkbHE5ERERERLISFShEsrpGjWiw6CdmLhlBdPANAOzyetHoy51c/Oe8weFERERERCSrUIFCRKBVK5pPncT3S0cQHXoHALv8hXnuk1+5dTPQ4HAiIiIiIpIVqEAhIjG6daPNZ8OZsGQE0Q+CAbDzKk6lwQsJuXfP4HAiIiIiIvKsU4FCRP7foEF0GdqfMUtHEh0eCoBtgRIUf64bkeHhBocTEREREZFnmQoUIhLX8OH0fr0z7y8fTfSDe9xYNZ7rJ3+lUKFqREVGGp1ORERERESeUbZGBxCRDOjzz3k7NJSc01+n58P7AFy9fpLiRaty/sIxTGbVNkVEREREJHXpKkNE4vftt3Tr8SpzAdO/my5cOk7pOm2xWCxGJhMRERERkWeQChQikiDTzJl069yFGf++dqn2Mg8b9ad0p+EqUoiIiIiISKpSgUJEEmYyYVq8iNdfbsUI90LkbNoPgIfF61O+0wcqUoiIiIiISKpRgUJEEmcyYbN6FaMrlaTBhqnWzfeLe1OrZ38Dg4mIiIiIyLNEBQoReTKzGZuNG5jtGEyFjXOtm294tuG5Xn0NDCYiIiIiGVGvXr0wmUwUKVLE6CiSiahAISJJY2OD3a4drOj+HNU3/2jdfM2jLfV6v25gMBEREcnqfHx8MJlM8T6cnZ0pUaIEXbp0Ye3atUZHzXRGjx6d4Gcb3yMgIMDoyJKJqUAhIklna4vD0KEs7FyPStt+sm6+lO8Var36rnG5RERERBIQGhrK33//zdKlS2nVqhUtW7YkJCTE6FhZXpEiRTCZTPTq1cvoKJKB2BodQEQyH8f332fZw89pvXY+Zxr2AOB64WbU7TqYPYu+MzidiIiIZGXjxo2jTZs21td3795l9+7dfP3119y4cYP169fTp08fli1bZmDKzGn27NnUrFkz0TZeXl5AzKgWHx+fdEglzxIVKEQkWRw/+YTVi6vQducCTtfvTtSDexza+ht9u21h5sITRscTERGRLMrLy4sKFSrE2VavXj06d+5M9erVuX37NsuXL+ePP/6gUqVKBqXMnIoWLfrYZyuSmnSLh4gkW7a9O/n17x0U8V1I4JKPeRj4Nz8tOsnAnlWMjiYiIiISR5EiRRg0aJD19YYNGwxMIyLxUYFCRJLPxQX7P4+zrrwjr10/D4AF+GHeMd59o5ax2URERET+o1at///vkwsXLlifPzrJZkBAAOHh4Xz77bc899xzuLu7YzKZGD16dJy+Hj58yLRp02jUqBF58uTB3t4eDw8PXnzxRRYsWEB0dHSCOf67wsXdu3cZNWoU5cuXx8nJiRw5ctCgQQMWLlwY7/HfffedNe++ffue+L7bt2+PyWQiV65chIWFPbF9akhoFQ9vb29MJpP18587d+5jE216e3unS0bJeFSgEJGUyZkTp1k/8U3/vrz276ZoTMy/UJ2X+75rZDIRERGROOzs7KzPo6Ki4m1z8+ZNnnvuOYYMGcL+/fu5devWY20CAgKoXLkygwYNwtfXl5s3bxIREUFgYCC///47PXr0oGHDhty+ffuJmU6fPk3VqlUZO3YsJ0+e5P79+wQFBbFz5066d+/OW2+99dgx3bt3x8HBAeCJ8zzcvHmTX3/9FYCuXbvi6Oj4xEwiRlGBQkRSzmTCZfoPfNezB10wkeuFt3Cp/jL+ORvT7s2hRqcTERERAcDf39/63NPTM942r7/+OseOHeO1115j3bp1HDp0iJUrV1K7dm0AQkJCaNKkCadOnQKgbdu2rFmzBj8/P5YvX07Dhg0B2LVrF61atUqwEAJw//59WrVqxa1btxgxYgS+vr74+fkxc+ZMChQoAMDUqVMfux0lV65cvPLKKwAsWbKEBw8eJHiOhQsXEhERAUCfPn0S/XzSw5w5c/D397d+/m3atMHf3z/OY86cOQanFKNokkwRSR0mE25z5jJ1lx8vREVyHTCZbTjs6k3nQe+xdOrXRicUERGRLOzevXtMmzbN+jqh2wj++OMPZs2axeuvv27dVq1aNevzMWPGcO7cOQBGjBjB2LFjrfuqV69O+/bt6dGjBwsXLmTPnj38+OOPDBgwIN5z3bhxg4cPH7J3717Kly8fpx9vb28qVqxIWFgY06ZNo0WLFnGOfeONN1i8eDFBQUGsXLmSrl27xnuO2Iv9ypUrx3kfyXH+/Hnc3d0T3F+0aFGcnJwS7aNo0aLA/49myZEjhybeFCuNoBCR1GMykWv3NjbvXUzuYzGVfpPZhn3ZG9D9nfcNDiciIiJZUXBwML/99hv169fn/PmYObOee+456tevH2/7xo0bxylOPCo8PJxZs2YBUL58+cfmpQAwmUxMmzaN3LlzAzBlypRE840dOzZOcSJWiRIlaNu2LRAzGuO/GjVqRPHixQESHHFw+PBhjh07BqTO6Ik+ffpQsWLFBB8HDx5M8Tkka9MIChFJXfny4XrmJFtLlMXbbOZOxWaYbGzZaV+PboOHsfC7SUYnFBERSTc1atTg2rVrRsdIcx4eHvj5+RkdA4DevXvTu3fvBPcXL16cZcuWJbi/W7duCe47dOgQd+/eBWImgbSxsYm3naurK506dWL69OmcPHmSq1evkj9//sfamUymBEc+QMxIiiVLlnD79m3u3r1Ljhw54hzbp08fPvnkE7Zu3co///xDoUKF4hwfW7iwt7dP9H2JZBQqUIhI6sufH7czJ9hSqhyNTDYEVWiMycaWXQ716Tr4PRZ9p9s9REQka7h27RqXL182OkaWZzKZKFOmDF27duXdd9/F2dk5wbaVKlVKcN/x48etz2PnpEhI7dq1mT59uvW4+AoU7u7u1pEW8cmVK5f1+b179+IUKCCmSDJq1CiioqKYO3cuI0eOtO4LDw9n0aJFQMw8D4mdJ6m2bdumFTYkTalAISJpw8uLXKeO41u6PI0sFu5WbILJxpbdjg3p+vYwFn2vkRQiIvLs8/DwMDpCushI73PcuHG0adMGiClMZM+enTx58iRalHhUzpw5E9z36KocefPmTbSfRz+ThFbzyJ49e6J9mM3/f0d+fJNtenp68uKLL/Lrr7/i4+PDiBEjMJlMAKxevdp63owwOaZIUqhAISJpp2BBcp78g21lK9KY6JjbPcw2+N4vxpD+Hfjmh5+NTigiIpKmMsptD1mJl5dXiiZdTOi2jf+KLQQY7Y033uDXX3/l3Llz7Nixw7qKSOztHQUKFKB58+ZGRhRJMk2SKSJpq0gRcp44xrYts8j5xyYibl3k+tIR/O/HtQzt94rR6URERESS7NFbLgIDAxNt++jcI48el9peeukl6+0jsUWJy5cvs3HjRgB69uwZZySGSEam31QRSXvFipHjxDG2b5lFlQUfEBV6BwvhfDtznYoUIiIikmk8OjJj//79ibY9cOBAvMelNhsbG3r16gXAzz//TEhICHPnziU6OhqTyZTohKFGyigjUCRjUYFCRNJH8eK4Hj/C0uhI2vy7ycJD/uezma4DBhoaTURERCQpqlevbp2oMrYIEJ979+5ZVwopV65cvBNkpqbXX38dk8lEaGgoS5cuxcfHB4AGDRpYlyLNaBwdHYGYyTxFYqlAISLpp2RJnP2PstAuG20Bk60Dedp/xG6XF+gwcIjR6UREREQS5eDgwBtvvAHErMwxduzYx9pYLBbeeustbt68CcBbb72V5rmKFy9uXV1jxIgR/PXXX0DGnhwztmjz999/G5xEMhIVKEQkfZUqhZP/ERbYZaNu5RY4Fq6MyWzDQedGdHr7baPTiYiIiCRq1KhRFCtWDIDRo0fToUMH1q1bx+HDh1mxYgWNGzdm3rx5ANSpU4d+/fqlS67Ywkns3Beurq506NAhXc6dHHXr1gXg4MGDjB8/nmPHjnH27FnOnj2rpXmzMBUoRCT9lS6N0/Gj/O6/GfejvwNgMtuwP1szug0ZYHA4ERERkYS5uLiwZcsWypQpA8CKFSt4+eWXqV69Oh06dMDX1xeA559/nrVr1yZ5VZCUeuWVV+Iskdq5c+cnLmNqpAEDBlgnDx0+fDhVqlShZMmSlCxZkm7duhmcToyiAoWIGKNUKVxOHGObrw9OR7YCMUWKXXYtefVdFSlEREQk4ypSpAjHjh1jypQpNGzYkNy5c2NnZ0e+fPl44YUXmD9/Pjt27EjT1Tv+y9HRkY4dO1pfZ+TbOyBmOdgDBw7w+uuvU6JECeucFJK1mSwWi8XoEJI+goODcXNzIygoCFdXV6PjiMS4e5fbbVrRwqEiN6q9BIAlOoo64b+z5H/TDQ4nIiLyuLCwMM6fP0/RokV1USUZyvPPP8+ePXsoV64cJ06cMDqOPGOS+7fvaa5DNYJCRIyVIwe5Nm5mY7g/eQ6vA2JGUux1fJGX+r5rbDYRERGRTOL06dPs2bMHyPijJ0QSogKFiBjPwYGc/xYpch7aAIDJZOZE7ma06pUx1+4WERERyUi++uorIOZWj169ehkbRiSZbI0OICICxBQpft/Ajjz5aW4J42qNNjw4f5h1KxbSN+IAMxdqmKKIiIhIrAcPHnD58mXu37/PqlWr8PHxAaBfv37kzp3b2HAiyaQChYhkHNmz43L6BJtLlaPpzdvsP7EWS1QEsxadJOxhCeYvP2t0QhEREZEMYf/+/TRq1CjOtoIFCzJ69GhjAomkAt3iISIZS4ECOAWcZUvu2/SNDLduXvDz37zStpSBwUREREQyHpPJhKenJ927d2fXrl1xlhoVyWw0gkJEMp68ecn++3q+bdsGh9/X8z1g45Kb/fk+oHLXYRxdOBGTyWR0ShERERHDeHt7owUZ5VmjERQikjHZ25Nt9RomtG5Nf1s38nUeh13O/AQVakSFrh/p/5BFRERERJ4xKlCISMZlZ4fjihX8L7czdQ+ttW4OLdyAsq9+QnRUtIHhREREREQkNalAISIZm60tDn/6s/ifg9Rb/z0WS0xRIqzI85Tu/ilRkVEGBxQRERERkdSgAoWIZHw5c2J77i98rvnTdN23WKJjihIRhZ+j5GtjiYyINDigiIiIiIiklAoUIpI5uLpi+/cZfgg6y8u/TrIWKaIL1aRkzy8JCwt/QgciIiIiIpKRqUAhIpmHkxO2f53mu/ArtF81HktUBACWQtUo/frXhN1/YHBAERERERFJLhUoRCRzcXTE5tRJJprv0vWXz7FEPgTgXmAAxUpW4OH9EIMDioiIiIhIcqhAISKZj709Nsf/YJxzJH1WfMa9g6u4s2UmV6+cw6twRULvXDc6oYiIiIiIPCUVKEQkc7K1xebIIUbkceDHrbOw+3fzzZsBeBWtxe3L5wyNJyIiIiIiT0cFChHJvMxmbPbvpV39BqwGHP/d/MDJkYofr+X4H4eMTCciIiIiIk9BBQoRydxMJszbfXmhRQvWA255ipK34xjs8hfnhanH2L17q9EJRUREREQkCVSgyMTatWtHzpw56dChg9FRRIxlMmFav56Gr7yCT2Q4pof3AbDNmY8uCy+w/veVBgcUEREREZEnUYEiE3vnnXeYN2+e0TFEMo4VK2jbqilzFnxI9O0rANi45qXvb6EsWT7X4HAiIiIiIpIYFSgyMW9vb1xcXIyOIZKxzJ1Lk96v8uOiz7FcPw+AjVNOPthlz/RZ/zM4nIiIiMjTMZlMmEwmRo8ebXSULM3Hx8f6swgICHhsf69evTCZTBQpUiTdsz1LvyNZrkARFRXFyJEjKVq0KNmyZaN48eKMHTsWi8WSaufYsWMHrVq1wtPTE5PJxKpVq+JtN3XqVIoUKYKjoyO1a9fmwIEDqZZBJEv73/94YeVMVv08AtOV0wCYs7ny5UkvPpv4hcHhRERE5Gn4+vpaL8BMJhMuLi7cv3//icc9ePAANze3OMf6+vqmfeBnUJEiReJ8jrEPOzs73N3dqVevHqNHj+bKlStGR83UEvqc43t4e3sbHTdNZLkCxVdffcX06dOZMmUKf/75J1999RUTJkzg+++/j7f97t27iYiIeGz7yZMnCQwMjPeY0NBQKleuzNSpUxPMsXTpUoYOHcqnn37K4cOHqVy5Mi1atOD69evWNlWqVKFChQqPPfQPXyQJmjWj6m9r+X3VGGwu/AGA2T4bs66VZcinIw0OJyIiIskVEhKS4BeAj1q9ejXBwcFpH4gnf7v+rIqMjOTWrVvs3r2bMWPGULZsWVau1NxfGcmjBb7MUKCzNTpAetuzZw9t2rThpZdeAmKqVIsXL4539EJ0dDSDBg2iZMmSLFmyBBsbGwBOnz5N48aNGTp0KB988MFjx7Vs2ZKWLVsmmmPy5Mn07duX3r17AzBjxgzWrVvH7Nmz+eijjwA4evRoSt6qiNSrR5ltW1n3fAtat3qLh8VrYrazZ9aKvXhFtWHYuNVGJxQREZGn4OjoSFhYGPPnz6dr166Jtp0/f36cYzKr1BzpnVKenp5s2LDB+joiIoKAgADmzJnDr7/+SnBwMF26dGHfvn1UrVrVwKSpr1evXvTq1StdzvXfzzk+Tk5O1ucZ6XckpbLcCIq6deuyZcsWzpw5A8CxY8fYtWtXvAUFs9nMb7/9xpEjR3jttdeIjo7m77//pnHjxrRt2zbe4kRSPHz4kEOHDtG0adM452ratCl79+5N3htLxNSpUylXrhw1a9ZM9b5FMrzq1Snz0//Y+ct4sv+5k1sbpxFyYgsffrGGkYMbGp1OREREnkLr1q0B2LRpE9euXUuw3fXr19m4cSMAbdq0SZdsWYGdnV2ckd1Vq1alXbt2rFmzhqFDhwIx1zrjxo0zOGnm9t/POb5H0aJFjY6ZJrJcgeKjjz6iS5culClTBjs7O6pWrcq7775Lt27d4m3v6enJ1q1b2bVrF127dqVx48Y0bdqU6dOnJzvDzZs3iYqKIl++fHG258uXL9E/tP/VtGlTOnbsyG+//UaBAgUSLG4MGjSIkydPcvDgwWRnFsnUunUj3+qf2bBtHvWP/AZAtAU+/34Hb/WqbHA4ERERSarmzZvj4eFBVFQUixcvTrDd4sWLiYyMxMPDg2bNmqVjwqxrzJgxZMuWDYCNGzcSHR1tcCLJjLJcgWLZsmUsXLiQRYsWcfjwYebOncukSZOYOzfhJQgLFSrE/PnzWbp0Kba2tvz000+YTKZ0TB2/zZs3c+PGDe7fv8+lS5eoU6eO0ZFEMq6XX6bgif0sKViAjv9usgA+h/NSo8d7z9TQOBERkWeVjY0Nr776KvD/t3DEZ968eQB07drVept2Yo4fP864ceNo0aIFBQoUwMHBAWdnZ0qWLEnPnj3Zt29fvMfF3t8fe9s2QNGiRR+b0PDRe///u9rD1atX+fDDDylfvjwuLi6PtU9ohYZdu3ZhY2ODyWSy3r4en+DgYGumvHnzJjiPXko5OztTrlw5IGaekNu3bz/W5vDhw7z55puULl0aZ2dnnJycKF26NAMGDLCOcI/Pf+f4iI6O5scff6Ru3brkzJkTJycnKlWqxOeff56kCVSjo6NZvHgx7du3p1ChQmTLlo1s2bJRqlQpunXrxs8///zYPIQZeZ6R+H5HAgICMJlMNGrUyLqtUaNGj/1u+vj4pH/gRGS5AsX7779vHUVRsWJFevTowZAhQ/jyyy8TPCYwMJB+/frRqlUr7t+/z5AhQ1KUwd3dHRsbm8f+OAQGBuLh4ZGivkUkEQUL4nrgID4li9MHyFbyOXK/8DY3vRpTrvsnREZGGZ1QREREnqBHjx4AHDlyhBMnTjy2/+TJkxw+fDhO28T4+vpSsWJFRo4cycaNG7l8+TIPHz4kNDSUs2fPMm/ePOrUqcPw4cNT940A+/bto1KlSkyYMIGTJ08SEhKS5GPr1atnnbvut99+Y9q0afG2GzRokPWC+qeffnpsFHdqsrOzsz6Pivr//66Kjo5m6NCh1KhRgx9++IEzZ84QGhrK/fv3OXPmDDNmzKB8+fL8+OOPTzzH/fv3ad68Of3792fv3r3cvXuX+/fv4+/vz4gRI2jUqBGhoaEJHh8QEED16tXp2rUrv/zyCxcvXiQsLIywsDD++usvFi1aRMeOHdm9e3fKPgxJlixXoLh//z5mc9y3bWNjk+AQpJs3b9KkSRPKli3LL7/8wpYtW1i6dCnDhg1LdgZ7e3uqV6/Oli1brNuio6PZsmWLRkGIpDUPD7LvO8C0AoVo4ZwbkznmW5UHBZ+nVK/PuR/6wOCAIiIikpiqVatSvnx5IP5RFLHbKlSoQJUqVZ7YX2RkJE5OTnTq1IkZM2bg6+vL4cOHWb9+PV9//TWFCxcGYPz48cyZMyfOsTVr1sTf3z/OnAsbNmzA398/ziO+ueBCQkJo3749YWFhfPLJJ/j6+nLgwAF++ukn8ufPn6TPYvTo0dSoUQOAYcOGcerUqTj7lyxZwoIFCwDo378/rVq1SlK/yREZGWk9v729Pblz57bue/vtt/nmm2+wWCw0aNCA2bNnW9/vzJkzKV++PJGRkfTv3581a9Ykep6+ffuybds2evbsybp16zh06BArV660XkcdOHAgwTkwAgMDef75562LETRu3Ji5c+eyf/9+Dhw4wNKlS+nfvz+5cuVKhU/EWF5eXvj7+zN79mzrttmzZz/2u9m2bVvjQsbHksX07NnT4uXlZVm7dq3l/Pnzll9++cXi7u5u+eCDDx5rGxUVZalRo4blxRdftISHh1u3Hz161JIrVy7L5MmT4z3HvXv3LEeOHLEcOXLEAlgmT55sOXLkiOXChQvWNkuWLLE4ODhYfHx8LCdPnrT069fPkiNHDsu1a9dS/03/KygoyAJYgoKC0uwcIpnGxYuWcJecltfLeVsKvb/aUvjDtZbCH661FOwxwXL75i2j04mISAb24MEDy8mTJy0PHjwwOkqWsG3bNgsxd2Za5syZY7FYLJavvvrKAlgKFixoiY6OtraNjo62FCxY0AJYJkyYYLFYLJY5c+ZYj9+2bdtj/d+4ccNy586dBM8fHh5uadasmQWwFC5c2BIZGflYm0fPcf78+UTfT8+ePa1tnZ2dLUePHk20fWzbTz/9NN79p0+ftmTPnt0CWKpWrWq9bvnnn38sOXLksACWUqVKWUJDQxM9T2IKFy5sff8JmTJlijVr48aNrds3btxo3T5r1qx4j33w4IGlcePG1nNERETE2f/o5wtY5s+f/1gfYWFhlgoVKlgAS+7cuR/rw2KxWNq1a2ft46uvvkrwvdy7d89y+/btBDPE9zOO/bkm9hk9Sezn7OnpafH390/wcfr06TjHJfY78ui/n/h+/59Gcv/2Pc11aJYbQfH999/ToUMHBg4cSNmyZRk2bBj9+/dn7Nixj7U1m8188cUXrFixAnt7e+v2ypUrs3nzZjp27PjYMQB+fn5UrVrVurTO0KFDqVq1KqNGjbK26dy5M5MmTWLUqFFUqVKFo0ePsn79+jQdciUijyhQAPvzfzHj+p90WTGW6IhwAMye5ag0bCEB584aHFBEREQS0q1bN8xmMxcvXowzX4Ovry8XL17EbDY/cRnSWO7u7uTIkSPB/fb29kycOBGACxcuWL99Tw0ffPABlSunbMLuUqVK8c033wAxt72MHDmS6OhoevTowd27d7Gzs2PhwoVkz549NSLHERkZydmzZ/nkk0945513rNvff/996/Px48cD0L59e15//fV4+3F0dGTKlClAzGe8bdu2BM/5yiuv0L1798e2Ozg48NZbbwFw69YtTp48GWf/6dOnWbVqFcATV2R0dnYmZ86cCe5Pa1euXKFixYoJPpo3b25YtrRma3SA9Obi4sK3337Lt99+m6T2Cc36m9i6vt7e3kmacO+tt96y/iMSEQPkzo3thXN8UbYceZaO5LsOozA7OmOTrxj1vtzKmjdvUq36c0anFBGRzKxGDXiKVdoyLQ8P8PNLt9N5eXnRqFEjtmzZwvz5860TAcbe3tG4cWO8vLyS1Xd4eDiBgYGEhIRYbwN/9L/tjx07RvXq1VP4DmIktJLg0+rXrx/r1q1jzZo1TJo0iQsXLrB9+3YAPv30U+ttICl14cKFRBcLMJlMjB07lhdeeAGImaAztoDUoUOHRPsuW7Ys7u7u3Lx5k7179yZ4HZbYZ/boz+XcuXNUqlTJ+nrdunXWn2NK5xSUtJPlChQiInFkz47N2b8YWrUa7ouGM6rTZ5idc2Kb24vWs07z083rtGjR2uiUIiKSWV27BpcvG53imfTaa6+xZcsWVqxYwdSpUwH4+eefrfueRmhoKN999x1LlizhxIkTcSZ4/K+bN28mP/QjnJ2dKVasWKr0BTBr1iwqVarEtWvXWLp0KRB3Is205OrqSuPGjRk6dCj169e3bj9y5Ii1yPPqq69aV2B5kmuJFPXKlCmT4L5H5464d+9enH1HjhwBYibyfO65jP0FVOHChTPcSiHpRQUKERE7O8x/HKNn/QbkXvg+gzqPw5zDA7Njdtr1+ZzfplzEu90go1OKiEhmlFVWaDPgfb7yyisMGDCA4OBgVq9ejcVi4d69ezg5OfHKK68kuZ+AgAAaN27M+fPnk9T+wYPUmVA7sdtKkiNPnjx8+eWX1iVP7ezsmD9/fpKWWU0qT09PNmzYYH1ta2uLm5sbHh4e8Y6suH79erLOk9hSoYndqvLoYgj/LTLFFpZy5coV5/Z9yVhUoBARATCbMe3eRau2bcm14H26th/JDV8fwq78QZP2f7F42lU6vRn/jNAiIiIJSsfbHrIaZ2dn2rVrx8KFC5k/f751+H67du1wcnJKcj89evTg/PnzmEwmevfuTZcuXShbtix58uTB3t4ek8lEdHS09UI/KbdyJ0VqFg4gZj6I6dOnW19HRETg6+tLr169Uu0cdnZ2VKhQIcntHy0S/PDDD9StWzdJxxk5/4MYSwUKEZFHrVrF82++ybYf3uNFLJwGoi136DLgf1y9dpV3Rv9kdEIRERH512uvvcbChQvZuHFjnG1JderUKXbt2gXAxx9/nODylLdv305Z0HTw2WefceDAASDmlovg4GAGDx5Mw4YNKVq0qCGZHl1qNHv27E9V3Eht7u7uQMzP8uHDhxpFkUFluVU8RESeaMYMio0aiS8mYqeUshDCmJ12dH7nPUOjiYiIyP9r0qQJ+fPnJzIyksjISDw9PWnSpEmSjz9x4oT1eefOnRNs5/eEkTCJTRyZHvbu3csXX3wBQPPmzdm6dSt2dnbcu3ePHj16JDqnRlqqUqWK9bPZvXu3IRliVatWDYgZWbJ3715Ds6Qno383n5YKFCIi8RkzBo+pU9iADY0Bt7pdcK3Ziv3ZGtPo9XdTbXiniIiIJJ+NjQ09evTAwcEBBwcHevToEWcegieJjIy0Pg8NDU2w3YwZMxLtx9HR0fo8PDw8yedPDSEhIXTv3p2oqChy587NnDlzqF69OmPHjgViCgOxS32mtzx58lgnpFy0aBE3btwwJAfASy+9ZL1YT+qKjs8CI383k0MFChGRhAwcSK7lS/gFO0pmc7VuPp+nGTVff4foqGgDw4mIiAjAV199RVhYGGFhYU99IV6yZEnrcx8fn3jbTJ8+ndWrVyfaT/78+a3P//7776fKkFKDBw/m3LlzAPz44494enoC8P7779OwYUMAxowZw6FDh9I1V6wRI0YAMUuOdujQgbt37ybYNjw8nKlTpxIWFpbqOUqVKkW7du0AWLVqFRMnTkywbWhoKHfu3En1DEYw8nczOVSgEBFJTIcOuG3byLZtcymxY5518828LSj3xieEhqbOTN4iIiKS/qpWrWqdF+GHH36gc+fOrF27lkOHDrF69Wo6duzIwIEDef7555/YT+w31SNHjmTTpk2cOXOGs2fPcvbs2VRb+eO/Vq5cyZw5cwDo3bt3nNVLzGYz8+bNw83NjYiICLp165ZmORLz4osv8s477wCwY8cOypYty5gxY9iyZQtHjx5l9+7dzJ07lzfeeIP8+fPz1ltvxRnZkpqmTZtmLeB88MEHNGnShPnz53Pw4EH8/Pz4+eefGTRoEIUKFeLYsWNpkiG9FSpUiAIFCgAwadIk1qxZw+nTp62/m/9djtVomiRTRORJvL1xPnKAFfVeoG/IPfa/8CYmsw1h+epRYfA37B/Xh7z5s8gyciIiIs8Qk8nE/Pnzady4MXfu3GHZsmUsW7YsTpuKFSuyfPly64VtfFxcXBg8eDATJkzg8OHDNG/ePM7+bdu24e3tnarZr169St++fQEoVqwY33333WNtChUqxNSpU+nevTunT5/mvffeY9q0aamaIym++eYbcuXKxdixY7l27RqjR49OsK2Tk1Oqr3ASK1++fOzcuZM2bdpw/Phxtm7dytatW9PkXBnJxx9/zMCBAzl//jxt2rSJs2/OnDmputJLSmkEhYhIUlSqhNu5P1hsF0jnFWOJjogZemjJU5nqI1dy/A9jhk2KiIhIylSpUoWjR4/y5ptvUrhwYezs7MiVKxe1atVi0qRJHDhwIM4w+YSMHz+emTNnUr9+fXLlypVmF9kQs9Rp7969uXXrFjY2NixYsABnZ+d423br1o1XX30ViLld5bfffkuzXAkxmUyMGjWKM2fO8MEHH1CjRg3rZ+Ti4kK5cuXo1q0bc+fO5erVq2TLli3NshQrVoyjR4/i4+PDSy+9RP78+bGzsyNbtmyUKlWK1157jdWrV1O/fv00y5DeBgwYwIoVK2jevDl58+bF1jbjjlMwWTTTW5YRHByMm5sbQUFBuLq6PvkAEXlcSAjRr7zC9yf+4esOn2L+d26KqKAbzGqbkxYt2jyhAxERyezCwsI4f/48RYsWjTMBnYjIsyy5f/ue5jpUIyhERJ6GszPmdesY/Hwlxi0YQXRQIADR0RG80rUXW5Z9bXBAEREREZHMSQUKEZGnZWeHafFiXqtQgAUL3ifq/FGuLxvF/dt3adl9GHMn9zM6oYiIiIhIpqMChYhIctjYwPbtNGj7IpuXjaP03WsARERAn/dnMua9ZgYHFBERERHJXFSgEBFJifnzKb3wJ3zNZmKnUoo22TH9SmMavj4ETfMjIiIiIpI0KlCIiKRU1664r1/POkdHWmPC/eX3cCxciQt5mlK553CioqKNTigiIiIikuGpQCEikhqaNcNl1y7m2rlS9dpZ6+Zgz/qU6v05IcEhBoYTEREREcn4VKAQEUkt1auTY+cG1hz+jQbrvsESHQVAlGc1yr37E+fOnjE4oIiIiIhIxqUChYhIaqpdG8fTx5l14TBdV3xG9MMHAJjzlqDhpJ1s991ocEARERERkYxJBQoRkdRWtCj2F/5m7INAPln0EdEhtwGwyeFB919uMXP2VIMDioiIiIhkPCpQiIikhZw5sT1/lr55nZg5/z2ib14AwCa7K2NP5GfkmPcMDigiIiIikrGoQCEiklYcHDAfPUKLutVZt+ADbC78AUD4pRN8/tkMBvdsYHBAEREREZGMQwUKEZG0ZDbD779Tsc9r7Fw+mhx7lnBj1ZdYou/z/bxdtGxUFiwWo1OKiIiIiBhOBQoRkfQwbRqe4z9n184ldPt34kywsN73FOVqVif0npYhFREREZGsTQUKEZH0MmwYzr8sY5bJnk/+3WTjnIvgWm9T9p2fOK9lSEVEREQkC1OBQkQkPbVrh8OB3Xzq4MQPQJ5W72Prmhdz3hI00DKkIiIiIpKFqUAhIpLeatTA7uwp+uTKw+its+IsQ9rjlxvMmjPN4IAiIiIiIulPBQoRESMUKIDtP+fp62bLD/OHEXXzIgDm7Dn47Hg+3vtsuMEBRURERETSlwoUIiJGcXLCfPIELdu+wPoFw6zLkJrtHPk5tC4vDBiMRSt8iIiIiEgWoQKFiIiRbGzAx4fyn49h7/JROB/fCoDJZOaUWwsq9BzBw4cRBocUERERSX0BAQGYTCZMJhM+Pj5Gx5EMQAUKERGjmUzw3nvkXbyIgxu/w3PncuuuUM+6lG/RnNBb1wwMKCIiYhxfX1/rRWxSH++++26C/Z05c4b333+fWrVqkStXLuzs7HBycqJIkSI0adKEDz74gHXr1nH//v14j0/onPb29uTLl48mTZowadIk7ty589TvJ3v27BQsWJCXX36Z2bNnEx4e/sTPJ/ZYb29v67ZevXo99Wf238fo0aOfeO6kfC7xPXr16vVUfUvWoQKFiEhG0bEj2TZuYsvepTRdMwFL5EOC/VZz1teX4hXz8/fhrUYnFBERydTGjBlD+fLlmTRpEgcPHuTOnTtERkZy//59Lly4wNatW5k4cSIvv/wyn3322VP1HRERwfXr19m6dSvvv/8+5cqVY9euXU/Vx4MHD7h06RLr1q3j9ddfp3r16gQEBDxVH88SHx8fa1EjK38OWYmt0QFEROQR3t5kO7yX6XUb8sHc/zHj1k4AAq9C5YZNWPXjlzR99SODQ4qIiBhjwIABDBw48Int3N3dH9s2fvx466gANzc3+vfvT8OGDcmfPz8PHz7k0qVL7N+/n19//ZVTp0498Rw1atRgzpw51tcPHz7kzJkzTJs2jZ07d3Lt2jVatWrF8ePH8fLyStL7uX79OsePH2fixIlcunSJEydO0Lp1a44cOYKNjc0TM8X6/PPPGTZsWLz7pk2bxvTp0wGYPXs2NWvWjLdd3rx5k3y+R/33c4lPzpw5AShSpIjm25I4VKAQEcloqlTB7vwZJrdty6s3o2kNBAKhIdB2zFpePXaHmeO/MjqliIhIusubNy8VKlR46uNu3rzJmDFjAChQoAB79uyhYMGCcdrUrl2b9u3bM2HCBA4cOMCtW7cS7dPJyemxLNWqVaNTp0506dKF5cuXc/fuXSZPnszXX3+d5PfTuHFjevfuTaVKlQgICMDf35+VK1fSoUOHJL9fLy+vBIsijxYeihYtmqzPMzHxfS4iSaVbPEREMqJ8+TBt20atV19lP1ARsPcoQa6XhrCJBjR4Y6i+cRAREUmijRs3EhYWBsBHH330WHHiv2rVqkXLli2TdS6z2cz48eOtr9evX//Ufbi4uDBixAjr682bNycri0hmowKFiEhG5egICxdSeNQofDFRvtTzmO0cAfjHvQllen5K6L1Qg0OKiIhkfP/884/1eYkSJdL8fMWKFSN37twAXLhwIVl9VKxY0fr84sWLqZIro0loFY/YiUR79+5t3Va0aNHHJtv09fVN/9CSpjJVgWLFihUUK1aM4sWLGx1FRCR9mEwwZgy5vvicHTsWUXbbT1gs0QCEe9am3Dsz+fuv0waHFBERydjs7e2tz//88890OaednR0AUVFRyTr+0cyxfYk86zJVgSIkJISAgADN4CoiWc/w4TivWs6qQ+tpvfJLoiNihqma8pbEe/Ie1q9fY3BAERGRjKtatWrW51988QXHjh1L0/PduHGDwMBAADw9PZPVx6OFlCJFiqRGrEyjZs2a+Pv7M27cOOu2DRs24O/vH+eR0ASfknlpkkwRkcyiTRscjh5gcp36lFn0EeNfGYmNS25s3PLSb30ob574ko/fG250ShERkTQTu8rFk5QuXTrOqIMGDRpQqVIl/vjjD27cuEHVqlVp2LAhzZo147nnnqN69eq4ubmlWs4JEyZY54ry9vZ+6uOjoqKYOHGi9fXTTJBptNDQ0ER/Rk5OThQtWjTRPmIn2vTz87NuK1WqVJYr1GRFKlCIiGQmFSpgd/4v3qxRk9Lz3+P19qMw5SuG2dGJHwLLc+itt1kx5XujU4qIiKSJ6dOnW5fITMz58+fjXMyazWZWrFhBy5YtOXv2LBaLBV9fX+scBiaTiQoVKtCyZUtef/11SpUq9dTZYpcZnTJlCj/88AMAtra2DBkyJMl93LhxA39/f0aNGsWRI0eAmOJEvXr1njqPUfz8/OLMn/FfDRs21NwRkqB0KVA8OilNSty8eTNV+hERydTc3bE5fYqmLVqwftFwWrd6j4gStcBiYd2a7TQ5VozNvn9heor10kVEJO1MnhzzSKkFC+DRL+N9faF795jnQ4fGPGLduwdly6b8nA0bwsKFcbc1bgxnzoCzM5w6lfJzpJcSJUpw7Ngxpk6dyqxZszhz5ox1n8Visd428PXXX/POO+/w1VdfYWub8OXS9u3bMZlMCe63s7Nj5syZiS65OWbMGOvyp/+VPXt23nzzzTgrgog869KlQFGkSJFE//GKiMhTcnCAbdso+9Zb7Jn+FW0adMH/7jXCL/qz9SIUK1GAo3v24Za/sNFJRUSyvOBguHw55f2Ehz/+Orbf4OC4+yyW1DlnfN8PBgbG9O3ikvL+n9ann37K6NGjk3189uzZef/993n//ff5+++/2bNnD4cOHWLv3r34+fkRHR1NVFQUkydP5ubNm8ydO/epz+Hu7s4LL7zA+++/T6VKlZKdtUqVKgwePDjTTZCpERKSEul2i0fsPVgiIpJKTCaYOpU85cuzZdBQJhHOqH93BQRco0DpmqxePoPGLV4xNKaISFbn6gpeXinvx8Hh8dex/bq6xt1nMqXOOd3dH9+WLx8EBcWMoMjMihcvTvHixenRowcAly9fZvTo0cyaNQuAefPm0bdv3wRvr6hRowZz5syxvrazsyNnzpzkzZs3yRkGDBjAwIEDAYiMjOTSpUv8/PPPzJ8/nz179uDt7c2BAwfIkydPct+mSKaSLgWK2NETHh4eybqfK9a1a9c4fVrL6YmIxDFwINnKluXDF1pR7mEorwH3AZuKTei1PppeR8fw2YefGp1SRCTL+u/tF6nF2xsuXYp/n4tLwvtSauvWtOnXaF5eXsycOZOQkBCWLFkCwPLlyxMsUMRO5JgSefPmjdNHlSpVePnll2nUqBG9evUiICCAN954g9WrV6foPCKZRboUKEqUKMHZs2cpU6YMW7ZsSXY/c+fOpXfv3qmYTETkGdGoEfanj9O21nPsuhFIm2LVMdePuTF57u1qHBr4LmunfqPb7URERJ6gb9++1gLF2bNnDcnQs2dPfv31V1asWMGaNWvYunUrjRs3NiSLSHoyp8dJqlevjsVisc5EKyIiaaBIEWzOnaVq9epsvXgCx9O7ATCZzJxwbUblNz7kfugDg0OKiIhkbJ6entbnRhb2v/jiC2z+nfD6448/NiyHkfTFStaTLgWKGjVqABAUFMTff/+dHqcUEcmanJ3hwAFKdO3MvlWTcN+10rorOE9Dyr87lbOZacp1ERGRVPA08+H5+flZnxcrViwt4iRJqVKl6NSpEwD79+9n06ZNhmUxiqOjo/V5+H9niZVnUroWKCDuP3gREUkDZjP4+JDju6/Zt28uzVePJzoiDABL7rI0/n4vK35ZbHBIERGR9DNz5kz69ev3xFs2Lly4wCeffGJ93aZNm7SOlqiPP/7YOopg3LhxhmYxQv78+a3P9UV31pAuc1BUrVqVypUrA3Djxo1k91OvXr04M+WKiEgi3n4b21q1mNGmDdMXfsj49iOxcXHH7JKXIbvC2HdyFBNHfGZ0ShERkSS7fv06x48ff2K7bNmyUbx4cevrhw8fMnPmTGbOnMnzzz9P8+bNqV69Ovny5cNsNnP58mW2bdvGrFmzuHfvHhBTnGjSpEmavZekqFChAq1bt2b16tXs2LGDXbt2JThp57OoatWqODo6EhYWxsiRI7Gzs6Nw4cKYzTHfs3t5eZEtWzaDU0pqSpcChYuLS6rMPxG7FJCIiCRR7dqY/fwY2KYN5eYOodcrIzB5lsZs78jCc/kI7laWHxacjFmPTkREJIObPn0606dPf2K7ypUrc/ToUevrvHnzYm9vz8OHD9m9eze7d+9O9PiuXbtalxs12ieffGJdxWPs2LFs2LDB4ETpx8XFhcGDBzNhwgQOHz5M8+bN4+zftm0b3t7exoSTNJEut3iIiIiBChTAtHMnjWpWZtvikTid2EbUg3vcWDmOHxedou5z2QgPvmt0ShERkTTTqVMnrl+/zrJlyxg0aBB16tQhX7582NvbY29vj7u7O8899xxDhgzBz8+PhQsXZphv5mvWrEmzZs0A2LhxIwcPHjQ4UfoaP348M2fOpH79+uTKlcs6cag8m0yWp5kxRjK14OBg3NzcCAoKwtXV1eg4IpLeoqNh8GACp87nDbf8rA06bd3lVQC2/byBkrWbJ9KBiIgAhIWFcf78eYoWLRpnEj8RkWdZcv/2Pc11qEZQiIhkFWYzTJlCvqUzWR0WwA+A3b+7rlxzoMH/jjJ24udGJhQRERGRLEwFChGRrKZTJ8y7dtE3f362AfmA3C0H41CoPLNuVuKFAUOeajk2EREREZHUkKkKFCtWrKBYsWKaKFNEJKVq1MB06BB169Thdxt3ctnEjKUwmcyccmtKmd6fEXQn2OCQIiIiIpKVZKoCRUhICAEBAQQEBBgdRUQk88ufH9O2bVR9+Xn2rZpEkZ0LrLvCPWpQ8cOF7Nuz08CAIiIiIpKVZKoChYiIpDIHB1i5kpxfjOb3PStps+IzosPvA2DOVYhOSy/z9ZRJBocUERERkaxABQoRkazOZILhw8m2aS0TL57ky/lDib59GQBzNhe++6cUbQcP1bwUIiIiIpKmbNPjJP/880+q9HPz5s1U6UdEROLRtCn2f53k1efrUWHee7Rv9R6RxWtiMttwNHsTarZux77lS7DVknoiIiIikgbSpUBRpEgRTCZTepxKRERSomBBzKdPUbldO/as+Ip29TpwuW4XHvztx6F1v1KoeCEObFxLgfK1jE4qIiIiIs+YdLvFw2KxpMpDRETSWLZs8Pvv5B35ARt3/syLP3/GrV8ngiWaq1duUKJaC9bMHG50ShERERF5xqTLCIrY0RMeHh6UKlUq2f1cu3aN06dPp1YsERFJiMkEY8bgVKsWk9t1oU1EKF2B20D4w7t0GvMbDQ+H8fuUrzHbaDojEREREUm5dClQlChRgrNnz1KmTBm2bNmS7H7mzp1L7969UzGZiIgk6qWXcPzzKE0beLP3ylW6EMUxpxy4t3mP0y65Kf3GF+z+sh95PfIanVREREREMrl0+dqrevXqWCwWjhw5kh6nExGR1FS8ODanTlKqUQM24UjzwlWwcc4JQES+qtT4bDVr1640OKSIiIiIZHbpUqCoUaMGAEFBQfz999/pcUoREUlNLi6weTO5Px7Kzyf30Xn5aKIf3APA7OrJwG3RvDP6Y4NDioiIiEhmlq4FCgA/P7/0OKWIiKQ2sxk+/xyndSsYd+UMb8+diuXa2Zhddo6sDnueOv2G8PBhhMFBRURERCQzSpcCRdWqValcuTKVKlXixo0bye6nXr16zJkzh9mzZ6diOhEReSovvoj9KX+GFQtl58IPcPbfZN11NVdTSg2YwumTfxoYUEREREQyI5NFa3dmGcHBwbi5uREUFISrq6vRcUQks3vwAAYOJMLHh85VWnKoaT9MNnYARAUFMrdRMI07vWtsRhGRNBAWFsb58+cpWrQojo6ORscREUkXyf3b9zTXoVobTkREkidbNpg9G7vp01n+xyYGL/yQqHs3AbizayEtug3hs3caGBxSRERERDILFShERCT5TCZ4801sdu3gndAbLPIZQuSmOYQe30pkJHz63U6aebsQERJidFIRERERyeDSrEARFRXF8ePH2bNnD9u3b+fYsWMEBwen1elERMRIdepge+ZP6lcqzYnDK3jlkV2bt4dQpG1/1q5dZVQ6EREREckEUr1AsXr1apo2bYqrqyuVK1emfv36NG7cmGrVqpEzZ07y5ctHp06d+OGHH7h27Vpqn15ERIySLx/s3Inbe+/xMzABsAGyl6mPXY2uDNwWTf9PhhscUkREJHMxmUyYTCZGjx5tdBRJgV69emEymShSpIjRUTK0VCtQBAYG4u3tzSuvvMK2bdt48OABFovlsceNGzdYsWIFAwcOpHDhwnTs2JE9e/akVgwRETGSrS1MmoRp6VKGOTmxnBzkrvYSAGY7BzZE1aNyn48JuRdqcFAREcksfH19rRfpJpMJFxcX7t+//8TjHjx4gJubW5xjfX190z5wFmCxWChWrJj1c+3Xr1+K+gsICMBsNlv7W7RoUYr6mzt3rrUvGxsbLl26lKL+HjV69Og4v1NPegQEBKTaubOCVClQBAUFUadOHXbu3EnsoiCxP5BHPbrNYrEQERHBL7/8Qv369enevTtXr15NjTgiImK0Tp0w7d9PG8/sbFk2DtdjG6y7gvI+T7n35rFrxzYDA4qISGYVEhLCqlWrnthu9erV6XaLuY+PT5a6IN25cyfnz5+3vl6+fDlhYWHJ7m/evHk8urjkvHnzUpRv7ty51ufR0dEsWLAgRf2lhyJFimAymejVq5fRUQyVKgWKwYMHW/8hmkwm62gJT09PWrRoQYcOHWjbti1Vq1bFxcUlThEDYooVixcvpmrVquzduzc1IomIiNHKl8f855+UbFafnet/4vm1k4mOiPmPF3OuQnRdeZOPPh9tbEYREclUYpc2nD9//hPbxrbJ7EvBxl5bZaRbPGILCM7OzgDcvXuXNWvWJLu/2J9VbH+bN29O9pfXFy9etI6Uie0vKb8vyTF79mz8/f0TfXh5eQExRSyLxZIlClgpkeICxe3bt1m6dGmcwkTt2rXZvXs3Fy9e5Pfff2fZsmX88ssv+Pn5cefOHQ4fPsz48eMpU6YMFovFWqi4fv06jRs35tdff03xGxMRkQzA1RXWrcNt4hhmndzL6HnvEX3rIgBmh+wsuVeT2m+8T3h4hMFBRUQkM2jdujUAmzZtSnQ+u+vXr7Nx40YA2rRpky7ZsoqwsDB+/vlnAPr370+5cuWA5I962LNnD2fPngXgm2++wcbGhqioKBYuXJis/ubPn4/FYsHOzo5JkyYBcPLkSfz8/JLVX2KKFi1KhQoVEn3Y2dml+nmfZSkuUPj6+vLw4UMgZkSEt7c3u3btok6dOvG2N5lMVKlShQ8++IATJ07w22+/Ua5cOWuhIjw8nFdffTVNfoFERMQAJhMMG0a2PVvpGRXC73OHke2kr3V3oLs3pbt+QOCZo4ZFFBGRzKF58+Z4eHgQFRXF4sWLE2y3ePFiIiMj8fDwoFmzZumY8Nm3atUqgoKCAOjWrRvdu3cHYMOGDVy/fv2p+4stbLi7u9OzZ0+aNGkCJH/UQ+xxLVu2pGfPnri5ucU5j2RsKS5QxE44Envbxvfff4+NjU2Sj3/hhRc4cuQIAwcOtBYp7t+/T+fOna2FDxEReQY89xw2Z05RvmZldv86lSobpmKJjCAq9C6XNs6naOVGrPnxI6NTiohIBmZjY8Orr74KJH4BG3sx2rVr1yRdmxw/fpxx48bRokULChQogIODA87OzpQsWZKePXuyb9++eI+LncCzd+/e1m1FixZ9bKLERyfn/O9qDlevXuXDDz+kfPnyuLi4PNY+oVU8du3ahY2NDSaTiZdeeinB9xYcHGzNlDdvXgIDA5/4eSQm9rMtV64cVatWpVu3bphMJiIjI596csvw8HCWLVsGQKdOnbCzs6NHjx4A/PHHHxw9evSp+jtw4ACnTp0CoHv37jg6OtKhQwcAlixZQkSEcSM2E1rFw9vbG5PJxIULF4C4E3zGPry9vdM/sEFSXKC4d++e9XmBAgUoX778U/dha2vLlClTGDp0qLXQERAQwNdff53SeCIikpG4u8POneQa8R4Lj25n6ML3ub/6K6JCbvEg7C7t+k+mb+cK8MhEWSIiIo+KvYA9cuQIJ06ceGz/yZMnOXz4cJy2ifH19aVixYqMHDmSjRs3cvnyZR4+fEhoaChnz55l3rx51KlTh+HDU3+p7H379lGpUiUmTJjAyZMnCQkJSfKx9erV46OPYgr7v/32G9OmTYu33aBBg6zzHvz000/ky5cv2XkDAwOtt87EjpwoVKgQDRo0AJ5+lMKvv/7KnTt34vTXrl07nJycktVfbHs3NzdatWoVp98bN27w+++/P1V/kv5SXKBwcHAAYip7+fPnT1FfkyZNombNmkDMiIzp06enNJ6IiGQ0ZjOMHYvTptUMCr3Jzot/UuXfXdFEMHt1AMW6jeT4saMGhhQRkYyqatWq1i9F4xtFEbutQoUKVKlS5Yn9RUZG4uTkRKdOnZgxYwa+vr4cPnyY9evX8/XXX1O4cGEAxo8fz5w5c+IcW7NmTfz9/Rk3bpx124YNGx6bKDH2GudRISEhtG/fnrCwMD755BN8fX05cOAAP/30U5Kvq0aPHk2NGjUAGDZsmHX0QKwlS5ZYV7Do37+/9aI9uRYuXEhUVBQmk4lu3bpZt8cWARIqGiUktqBQvHhx6xQBTk5OtG3bFoBFixYRFRWVpL4iIiJYsmQJAO3bt7dOjtqwYUMKFiwY53wZyZw5c/D398fT0xOImTPlv78///29e5aluECRO3du6/Pbt2+ntDvGjh1rfX758mUOHjyY4j5FRCQDatoU21MnKFetEhtwou+/m3O1HEx0oTq8OPsU477+0tCIIiKSMb322mtAzAXso8tTWiwW6+SKsW2epEqVKly6dImlS5fSv39/GjZsSNWqVWnRogVDhw7lzJkz1nksxowZE+eC2cnJiQoVKlhXagAoVarUYxMlxo4IeNStW7cIDg5m165djBs3joYNG1KzZk369OlD6dKlk5Tdzs6OhQsXkj17dh48eEDXrl2tt8lfvHiRAQMGWDNNnjw5SX0mJvYCv379+hQqVMi6vWPHjtYvrh9d4jMxN27cYP369QBxih3w/wWPwMBANmzY8Nix8Vm3bh23bt2KczzEfJHetWtXANauXWsdsZEazp8/z/HjxxN8hIaGPrGP2Ik2YyfTzJEjx2O/P0WLFk21zBldigsUsffQxC6ZktIiRZMmTXB1dbW+/vPPP1PUn4iIZGCenrB/P3nf7cvXODPNKSeOHiUBMGdzYdaNStR/c6hW+RARkTi6deuG2WyOs6QkxNyucfHiRcxms/Wi9Enc3d3JkSNHgvvt7e2ZOHEiABcuXHjqeRES88EHH1C5cuUU9VGqVCm++eYbIGYEw8iRI4mOjqZHjx7cvXs3ThEjJfz9/Tl27BgQtwAAcW+pWLhwIdHR0U/sb/HixdY5If7bX7NmzfDw8ACSPuohtl2BAgUem7Mh9laf8PBwli5dmqT+kqJPnz5UrFgxwYe+bH96KS5Q1KxZk2zZsmEymVK0HEwsGxubOBOHJLZ8kIiIPANsbeGbb3BZvZA3oiNZ5jMUuzN7rbsv5mhCmcFTOLBvj4EhRURSYPJkKFAg5Y9HLsSBmNex+/777fi9e6lzzv98sw1A48Yx+8qUSatP7Im8vLxo1KgREPc2j9jnjRs3jjOq4WmEh4fzzz//cPLkSes34Y+O0oi9SE8N/x05kFz9+vWzLsE6adIkunbtyvbt2wH49NNPrbeBpETsyAgHBwc6duz42P7YIsOVK1fYsmXLE/uLLSjUqlWLkiVLxtlnY2NDly5dAFizZg3BwcGJ9nX79m3WrVsHxEyMajKZ4uwvX7689Xaf5K4OIukjxQUKZ2dn+vXrh8ViwWKxMGbMmBTPDPvoL9TTrAgiIiKZWOvW2J04Rp1invyychHlNv+IJTLmmxVLzlJ0WHqR4V+MNjajiEhyBAfD5cspf4SHx+03PPz/9/33As5iSZ1z3rz5+PsJDIzZd+VK2n1mSRB7C8eKFSt48OABDx484Oeff46zL6lCQ0P58ssvqVy5Mk5OThQuXJjy5ctbvwmvWrWqte3N+D6TZHB2dqZYsWKp0hfArFmz8PDwIDo62jpK4NGJNFPi0S+iX3rppXhHnLz44ovkypULePKohxMnTnDo0CHg8dETsWK3P3jwgOXLlyfa3+LFi623tjypvz179vD3338n2l9Sbdu2zXodHN8jK62+kVpSXKCAmHuxPDw8MJlM3L59mxdffJG7d+8mq6/IyEjOnz9vfR07KY2IiGQBRYvC4cNUfKc1vx1aw9AFw4i6E/MfwGYHZxYH16TG6x9yP+S+wUFFRJ6Cqyt4eaX88e89/lYODv+/75FbpAEwmVLnnO7uj7+ffPli9v07qZ9RXnnlFbJnz05wcDCrV69m1apV3Lt3DycnJ1555ZUk9xMQEEDFihX5+OOP+eOPP544KeODBw9SGh0g0dtKkiNPnjx8+eX/z91kZ2fH/PnzU+UL340bN1pHtidUALCzs6Nz584ArFy5MtEVSWILGLa2ttaREv9VvXp1ypYtG6f9k/qrVKkSFStWjLfNo0vOZsTJMiWGbWp04urqyk8//cRLL72EyWTiyJEj1KhRg8WLF8c7Y21ili9fbh3CYzabadiwYWpEFBGRzMLBAb79Fry9GdynD0193qFdi8E8LFcfgJt5GlDu5TZsnfgRxWo2MTariEhSDB0a80ht3t5w6VL8+1xcEt6XUlu3pk2/T8nZ2Zl27dqxcOFC5s+fb70N49FlKpOiR48enD9/HpPJRO/evenSpQtly5YlT5482NvbYzKZiI6Otl7cWlJpKezUHikeGRkZZxXEiIgIfH196dWrV4r7fvSCPinFn9DQUFasWEHPnj0f2xcdHW0djREZGUnevHmf2N/OnTsJCAiIMxVArNOnT3PgwAEA/vjjj8du74jPggULGD16dJLaSvpKlREUAC1btmTkyJFYLBZMJhPnzp2jTp06dOvWLcmTgxw5coS33noLk8lknW01T548qRVRREQyk7ZtMR09SvnqVdj96zRq/P4d0RHhhJ7ezYXtm6ng3ZQfx8b/rYuIiGQNsbdybNy4kU2bNsXZlhSnTp1i165dAHz88cf89NNPNGvWjAIFCuDg4GC9gE2N1QrT2meffWa9UI9ddGDw4MFxRqcnR+wIlaeV0CiFLVu2cPny5afqy2KxJDh3RHJGQ5w7d876c5eMJVVGUMQaM2YM9+7d49tvv7VWGpcsWcKSJUsoXbo0zZo1o169epQtW5bChQtjb2/PvXv3OH78OEuWLMHHx4eIiAgsFgslSpTg22+/Tc14IiKS2RQqBNu3k2f4cOZ+PZ0vroQz854fAA/uQ/9RS1m7ayNLf75ANhcXg8OKiEh6a9KkCfnz5+fq1asAeHp60qRJ0kfXnThxwvo89vaE+Pj5+SXaj9HfxO/du5cvvvgCgObNm/PFF19Qp04d7t27R48ePdi+fXuyR2wsX77celvLZ5999tiElv+1du1aFi5caF1RpWDBgnH2xxYUHBwcmD17NmZz4t+ZT5gwgSNHjjB//nxGjhwZZ5/FYmHBggVAzO0dw4cPT7Qvi8VCnz59CAsLY968edSvXz/R9unJ6N+hjCJVCxQAkydPpnz58gwZMsR635HFYuHUqVOcPn2aKVOmJHisxWLBbDbTsWNHpk6dSs6cOVM7noiIZDZ2djBpEk7NmjGue3cGhYfSDfD9d/ems8Up/f4SvmnmQvv2GlEhIpKV2NjY0KNHD/73v/8BMbdrPOmC91GRkZHW56GhoQm2mzFjRqL9ODo6Wp+H/3cy0zQWEhJC9+7diYqKInfu3MyZMwdPT0/Gjh3LRx99xO7duxk/fjyffPJJsvqPLSjkzJmTjz76CDs7u0TblylTxrrU6IIFC+IUDUJCQli5ciUQs5RoUpaCvXbtGkeOHOGvv/5i79691KlTx7rP19eXf/75B4gZOZPQfBaPWrp0KatXr2b58uV8//33cX52RorNkd6/PxlNqt3i8ajXX3+dY8eO0blzZ2slKPZ/E5vl1GQyUaBAAdzd3Vm5ciV+fn5Z/geUmHbt2pEzZ046dOhgdBQRkbTXogWmP/7As1EjNgOjATvn3Li/NARzDk+G7svGK++8R3TUk9deFxGRZ8dXX31FWFgYYWFhjB8//qmOfXQ0gI+PT7xtpk+f/sRbHPLnz299nlorRCTV4MGDOXfuHAA//vgjnv9OXvr+++9b5/MbM2aMddWMpxEQEMDOnTsBaNOmzROLEwBVqlShePHiwONLeq5YscJaCErqNUz79u2t15L/vZ3j0dft27dPUn+x5w0KCkrWrStpJfZ3KL1/fzKaNClQABQtWpTFixdz8uRJPvzwQwoVKvTYhDKxc03EPgAuXrzI9OnT6d+/P7Vr18bFxYUKFSrQvXt3Jk6cyKZNm7hx40Zaxc5U3nnnHc1AKyJZS/78sGkTNmPGMMpkYpaNE+agmKWtTTa2HM7WmFL9JnLmz9MGBxURkcygatWqVKhQAYAffviBzp07s3btWg4dOsTq1avp2LEjAwcO5Pnnn39iP7HfgI8cOZJNmzZx5swZzp49y9mzZ1Nt5Y//WrlyJXPmzAGgd+/ecSawNJvNzJs3Dzc3NyIiIujWrdtT53h08tGkFgAebfvnn3/GmY8w9trFzs6O1q1bJ6mvggULWhdeWLp0qXU50fv377NixQogZsWP+CbQjE+rVq2wt7ePkycjqFu3LgAHDx5k/PjxHDt2zPr787RzdmRmaVagiFWqVCm+/PJLzp8/z19//cXs2bN58803qVWrFo6Ojo+NoogV+zoyMpKTJ0+yePFiPvroI1544QU8PDzw8vLipZdeYsSIEWn9FjIsb29vXHTPtYhkNTY2MGoUpm3b6GZ7n80LxpBv33IslpiRE5F5KtB0xmHGff1036KJiEjWYzKZmD9/vvXW8mXLltGqVStq1KhB27Zt+fnnn6lYsSLLly9PtB8XFxcGDx4MwOHDh2nevDmlS5emZMmSlCxZkv3796d69qtXr9K3b18AihUrxnffffdYm0KFCjF16lQgZrWL995776nOETsCwtXVlebNmyf5uEdHR8QWAS5evIivry8AjRs3fqrb+WP7u3PnDr/++isQU5y5d+/eY+d7Ejc3N5o2bQrETK4aGBiY5GPT0oABA8iVKxcAw4cPp0qVKtbfn27duhmcLv2keYHiUcWLF6dXr15MmzaNffv2WSfInD9/PkOHDqVRo0bkyJEj3pEWsWILF1evXuX333+Ps9ZvUhQpUuSxkRsmk4lBgwalynsE2LFjB61atcLT0xOTycSqVavibTd16lSKFCmCo6MjtWvXts66KyIiSdCwITZ/nqRkoxr8vn0lXZeOJOreLQDM2VyZdaMitd74gPsh9w0OKiIiGVmVKlU4evQob775JoULF8bOzo5cuXJRq1YtJk2axIEDB+LcwpGQ8ePHM3PmTOrXr0+uXLlSfRnRR1ksFnr37s2tW7ewsbFhwYIFODs7x9u2W7duvPrqq0DM7Sq//fZbks6xd+9e/vrrLwBefvll66iDpKhZsyaFChUCYMmSJURERLBgwQKio2O+THia0Rj/bR9b8EjO7R3/bR8ZGcmiRYue6ti04uXlxYEDB3j99dcpUaJEhpkbI72ZLKm1kG8q+r/27js8imr/4/h7d9NIICEQEkoINTTpHQHpAiKCKHilCBZARH8IAmJHvSqi4BUpKiqCoCAgFor03ksCCSW00AkhAdLr7vz+iKxGQifZlM/refLcnZkzZ77nOk9gP5w5c/LkSYKCgjL9/Htai8lksq9bYbVab7nvixcvZmofGhpKhw4dWLt2La1bt76m/ebNm2ncuPE1z1sdOHCA4sWL4+fnd805y5YtY/PmzTRo0IAePXqwaNEiunfvnqnNvHnzeOqpp/jyyy9p0qQJ//vf/5g/fz5hYWH2dwHXrVs308I9V61YscL+bNm6deuYPHkyCxYsuOnYY2Nj8fLyIiYmxv7qIRGRPM9mg88+I3n0m+x0LcTTnYeSHtjUfti4fJrpj5bhwU5dHVikiOQnycnJhIeHU6FChQL7JUJECp47/d13O99Dc2VAkZWoqKhrQoujR49iGMZtBRT/9vLLL7N48WKOHDlyzatdbDYb9evXJzAwkLlz59pT0LCwMFq1asWIESMYPXr0Dfs3mUxZBhRNmjShUaNG9rea2Gw2ypYty0svvcSYMWNuuX4FFCIifwkKgh49uHAimsF1W7Cn7XOYnV2xJcdzcdZo3nm6GmMmLHd0lSKSDyigEJGCKCcCihx9xONu+Pj40KFDB0aPHs1PP/3EoUOHiImJsa8qeydSU1OZPXs2zzzzTJbvnTWbzSxdupSgoCCeeuopbDYbx44do23btnTv3v2m4cSNrrt79277s09Xr9W+fXu2bt16x+O5nilTplCjRg374jIiIvlSvXoQGorf04/zQ/BGxs4aji0ynOjlk0m+fIrXJq6gSUNf4i+ccXSlIiIiIpKFPBNQZMXDw8O+2umd+PXXX7ly5QoDBgy4bpvSpUuzZs0aNm3aRO/evWnbti3t27dn2rRpd3zdqKgorFbrNY+H+Pn5ERERccv9tG/fnp49e7J06VL8/f2vG24MHTqUAwcOZFpBV0QkX/LwgO++o8iC7+mfdJlVM1+n1aFN9sM7dl+kTI2GTJo20YFFioiIiEhW8nRAcbe+/fZbOnfubF/P4XoCAgL44YcfmDdvHk5OTnz77bdZzrjIaatWreLixYskJiZy5swZmjVr5uiSRERyh8cew3JwP1Wa1OQHvPgcuLq0l3PT3kw8WZXmg0aRnJjsyCpFRERE5B8KbEBx8uRJVq1axXPPPXfTthcuXGDQoEF07dqVxMREhg8fflfX9vHxwWKxXPNKmwsXLlCyZMm76ltERP5Stixs3Ejx90fynMmdTThRpVwdCtdqB8DZYq2p9sp3/P77IgcXKiIiIiJQgAOKGTNm4OvrS5cuXW7YLioqinbt2lG9enV++eUXVq9ezbx58xg5cuQdX9vFxYUGDRqwevVq+z6bzcbq1as1C0JE5F6yWODNN3HfspoGpf1YcfI01VZPx0hPzTjuVY6XNph47OXhWK02x9YqIiIiUsAVyIDCZrMxY8YM+vfvj5OT0w3bde7cmXLlytkf76hRowYrV65kxowZfPbZZ1meFx8fT3BwMMHBwQCEh4cTHBzMqVOn7G1GjBjB9OnTmTlzJgcPHmTIkCEkJCTw9NNP39OxiogI0LQp5oMHKPefB/lp13p6z/wc24XjAJicnNnt1p6qQz4heNcOBxcqIiIiUnDlmdeM3ksrVqygY8eOhIWFUaVKlRu2XblyJS1btrzmNSpBQUGUKFECf3//a85Zt24dbdq0uWZ///79+f777+3bkydP5pNPPiEiIoK6desyadIkmjRpcmeDugV6zaiICDB7NsaQIZxLSubRFn250KQHJlNGXm9LSaS7514+H/t+rlhrSERyJ71mVEQKopx4zWiBDCgKKgUUIiJ/OX4c+vXD2LKFd8rWZEaXEVi8fO2HSx38mNXfzsfdx+8GnYhIQaWAQkQKopwIKArkIx4iIlLAVawI69dj+u9/effcQeZ+N4ZCIRnrAiUd28W23zdS/r6S/DnzXQcXKiIiIlJwKKAQEZGCyckJ3ngD07ZtNPcvzIalM6izaDpXln0OwMVI6PLMWJ7qUYaUhETH1ioiIiJSACigEBGRgq1hQwgJocQLvfn18G9sTbhM9b8O2WwwP8iXKq/MZuHCeQ4tU0RERCS/U0AhIiLi7g5TpmBatox6pUqxG3gJMLsVpvhDwzEVLcOIbW50HjqC1NQ0R1crIiIiki8poBAREbmqUydMISEUeuwxJgFTXX0wJ1wGwGRx4mCRdlR58UtWLl/q2DpFRERE8iEFFCIiIv9UvDjMnw/ff8+zqRdZ9sN/Kb1lLobNmnG8WEWeW5lKj2EjsFptjq1VREREJB9RQCEiIvJvJhP074/TgVDuaxLIrxuX8+IPI7FFnco47OTMnkLtqPz8JDZtWO/gYkVERETyBwUUIiIi11O+PGzciO+4VxgWeZrF379Fie0LMYyMmRNG8UD6/BbNoFeeB8NwbK0iIiIieZwCChERkRuxWODVV3HZvZXaVf1YvO53npvzKtbL5wAwbFZmfPsrtWsX58KhPQ4uVkRERCTvUkAhIiJyK+rWhT178Ht9MKPPHuP3GWMouvsPLq/9lvSYC4SEXqZC3VZ88WZ3R1cqIiIikicpoBAREblVrq7wwQe4bt9IvXLFWL5qAZ/uXY7HX4eTUuIZNn451fqO4mDIfoeWKiIiUpCYTCZMJhNjx451dClyFxRQiIiI3K7GjWHfPvxG9Ocp3NiMB63+OuTd5hmS/VvT8bv9/N87b2FobQoRkbuybt06+5fPW/15+eWXr9vf4cOHGTVqFI0bN6ZYsWI4Ozvj4eFB+fLladeuHaNHj2bJkiUkJiZmef71runi4oKfnx/t2rXj008/5fLlyzcdz/W+TN/sGq1ateKDDz4gMjLytv6/NAyDihUr2vsbNGjQbZ1/M1f7bd269R33Ub58+Vv+73w315HcSQGFiIjInShUCCZMoNCGFdQp58McfBnn6oF75SYAmF09+D2lKTUGv6/ZFCIiucS7777Lfffdx6effsrOnTu5fPky6enpJCYmcvLkSdasWcMnn3zCww8/zHvvvXdbfaelpREZGcmaNWsYNWoUNWrUYNOmTfe0/qvX2LBhA2+++SbVq1dnxYoVt3z+xo0bCQ8Pt2/Pnz+f5OTke1pjbvTPUGjdunWOLkduwMnRBYiIiORpLVtCaChlXn2VF6d+T+PvRjGkfT+S72sDQFKxBnT6bj+d3Gfz5X8/xGQyObhgEZG8a8iQIbzwwgs3befj43PNvnHjxtlnLHh5eTF48GBatWpFqVKlSE1N5cyZM2zfvp0//viDQ4cO3fQaDRs2ZMaMGfbt1NRUDh8+zNSpU9m4cSMRERF07dqV0NBQypQpc+uDvME14uLiOHr0KFOnTmXbtm1cunSJHj16EBISQoUKFW7a36xZswAoXLgw8fHxXLlyhd9//51evXrdUX3ZqXTp0ixfvvyGbTw8POyfNWMxf1BAISIicrcKF4YpU/B49FHa9O/PgsWLmXhoBysffA5LkeKYXD1Ybm1BlUHj+XlQG+o1auzoikVE8iRfX19q1qx52+dFRUXx7rvvAuDv78+WLVsoW7ZspjZNmjThscceY/z48ezYsYPo6Ogb9unh4XFNLfXr16dXr1785z//Yf78+Vy5coWJEycyYcKE2675etdo1qwZffv2pVevXixYsICEhAQmTJjA5MmTb9hXcnIyCxYsAGDw4MEsW7aMAwcOMGvWrFwZUDg7O9/Rf2vJ2/SIh4iIyL3Svj0cOEDNZ5rz3dGN/PrtC3jsW2k/nFa8Jt1+Os3To18B/UuPiEiOWbFihf1RhjFjxlwTTvxb48aN6dy58x1dy2w2M27cOPv2n3/+eUf93IjJZMp0jVWrVt30nF9//ZWYmBgA+vTpQ9++fQFYvnz5ba9lIZJdFFCIiIjcS15e8O23sHgxDYp5ErLscx76+QOssRl/+TO7uLFw9WEaNnTh9L4tDi5WRKRgOHXqlP1z5cqVs/16FStWpHjx4gCcPHky265x9RGH06dP37T91cc7atSoQb169ejTpw8mk4n09HR+/PHHbKkxJ2W18OiJEycwmUy0adPGvq9NmzbXLLb5/fff53zBkiUFFCIiItmhSxfYvx/z00/zSfhefv72VTyDlpJ8OpS4PUvYvSedqs2a88GwBzSbQkQkm7m4uNg/Hzx4MEeu6ezsDIDVas2W/k0mE05OTpmudT0XLlywL6Z5deZEQEAADzzwAPB3eCHiaAooREREsou3N3z3HR7LFtLMx5lfVixjxLwPKUJGIJGUCG9O2kjgw71Yu+rWV2EXEZHbU79+ffvnDz/8kL1792br9S5evMiFCxeAjMUes8P58+ftj2yUL1/+hm3nzJmD1WrFZDLRp08f+/6rYUVQUBD79+e/N06VKVOGkJAQvvvuO/u+7777jpCQkEw/3bt3d1yRkokWyRQREclunTrBwYNUfvVVRn/5JU8Cg4A/AVf/+0irNYABfyZS9/cRLJgwHmdn/fEsIpKVyMhIQkNDb9quatWqmWYVPPDAA9SuXZt9+/Zx8eJF6tWrR6tWrejQoQNNmzalQYMGeHl53bM6x48fb3+rROvWre9Zv//00Ucf2T8//vjjN2x7dYZEy5YtCQgIsO/v2bMnL774IikpKcycOZPx48dnS613Ii0t7Yb/rV1cXKhSpcoN+7i60GZUVJR9X4UKFbT4Zi6mvwGJiIjkBE9PmDYNevak7HPPsTQ8nGkU4oMmjwFgcnJmr1M7Al/8mgkdivPY4084uGARkdxn2rRpTJs27abtwsPDM80qMJvNLFy4kM6dO3P06FEMw2DdunWsW7cOyHhcombNmnTu3Jlnn332pl98s3L1NaOTJ0/mq6++AsDJyYnhw4ffdl/XExcXx5EjR/jiiy+YOXMmAIGBgQwdOvS654SEhNhnjFydMXGVl5cXXbt2ZcGCBcyZM4dx48ZhNueOSfbnzp2jVq1a1z1erlw5Tpw4kXMFSY5QQCEiIpKT2raFkBBMb7zB059/SaPfvmZo81NcaPwoJrMFvMsxYoeV/60axZJxb+NZtIijKxaRu9CwYUMiIiIcXUa2K1myJLt27XJ0GTdUuXJl9u7dy5QpU/jmm284fPiw/ZhhGPbp/hMmTGDYsGF8/PHH9jUesrJ+/XpMJtN1jzs7OzN9+vS7+tf6G13DZDLRrVs3pk6dire393X7uBpkuLq60rNnz2uO9+3blwULFnDu3DlWr15Nhw4d7rhekbulgEJERCSneXjA//5HoZ49aTRgAAvXr2BO2Bamdn4Js28FTGYLp4u2puYbCxh+XwzDX3jZ0RWLyB2KiIjg7Nmzji4j33jnnXcyvaXhdrm7uzNq1ChGjRrFsWPH2LJlC7t372br1q3s2rULm82G1Wpl4sSJREVF2b/c3w4fHx86derEqFGjqF279h3XejOlS5fm5ZdfvuEaF1arlTlz5gDQpUsXihYtek2bhx56iGLFinHp0iVmzZqVawIKzZAomBRQiIiIOErz5rBvH2XHjuXl8Z/TaeY4RjS+n2PNn8Tk5IK5iC+fn/Ll++feZNXY5ynh7+/oikXkNpUsWdLRJeSIvDjOSpUqUalSJfr16wfA2bNnGTt2LN988w2QsW7DwIEDadGiRZbnN2zYkBkzZti3nZ2d8fb2xtfX957V+M9rGIZBREQEGzduZNKkSZw9e5ZOnTqxcuVKWrZsmeX5K1assM/g+ffjHf+s+4knnmDatGksWrSI+Ph4ChcufM/GIHI7FFCIiIg4UqFC8PHHuD3+OHUHDGDOtg0sD9vO2I7PQ7mMf3m7kOxF5eo1+Pz17gx4Ta+CE8lLcvtjD/K3MmXKMH36dOLj45k7dy4A8+fPv25A4eHhke2LLf77GrVq1aJDhw706tWL+++/n7i4OPr06UNoaCienp7XnP/P14f26NHjptdLSEhg4cKF9O/f/94MQOQ25Y4VUERERAq6Ro1gzx5KjR1C39gIls/9gkbLPscaf5lLf35BbHwcT7/+A40bFOPCwd2OrlZEJN8aOHCg/fPRo0cdWMn11axZkw8//BCA06dP88knn1zTJjY2lt9+++22+/5nqCGS0zSDQkREJLdwdYV33sGpVy+qPvccX23Zxd792xhpjWPPX0127rlMlc4D6fRoO+Z8Mg4nJ4tDSxYRyW/+uabDjRbBdLTBgwczceJEwsPD+eyzzxg2bBg+Pj724/PnzycpKQmA9957j8DAwBv2t3jxYubMmcO6des4ffo0ZcuWzdb6c1Ju/u8omSmgEBERyW2qV4eNGyn29de0GjmKnxP8+ZUzvAUkWZwo3GEQ213LEjh0Gp93KcMjjzzq6IpFRHI1wzBu+UvqPx/LqVixYnaVdNecnZ0ZM2YMgwcPJiEhgc8++4wPPvjAfvzqTAhvb2/GjBmDs7PzDfurVq0ac+bMwWazMXv2bF577bVsrT8nubm52T+npKQ4sBK5GT3iISIikhuZzfD885jDDlHp0UYMojDr8aZZubo4FSsDgOFdgZc2WWg15BVirsQ6uGARkdxr+vTpDBo06KaPbJw8eZI33njDvt2tW7fsLu2uDBgwgDJlMv5MmDJlCjExMQCcOHGCjRs3AhljuFk4AVC3bl0qVaoEwA8//JBNFTtGqVKl7J+PHTvmwErkZhRQiIiI5GZlysAvv1Bk4UwalXTlx+MXeG72q1gvngTAZLZw0qsttd/6hf9O+BjDMBxcsIhI9omMjCQ0NPSmP//+Epqamsr06dMJDAykRYsWvPfeeyxZsoRdu3axZ88e/vjjD0aMGEGtWrU4deoUkPHFvl27do4Y5i1zcXFh5MiRAMTExDBp0iQgI2C4+ufBY489dsv9XW178OBBdu7ceY+rdZyAgAD8/3oT1qeffsrvv/9OWFgYR48e5ejRo8TFxTm4QrlKj3iIiIjkBT16QNu2lB8zhlFfzaDG97P5oklZjjd7ArOzKyaPEnxzsQSzB/6X+UMfpla9eo6uWETknps2bRrTpk27abs6deoQHBxs3/b19cXFxYXU1FQ2b97M5s2bb3h+79697a8bze0GDRrEBx98QFRUFJ9//jnDhw+3z4Dw9PTkwQcfvOW+Hn/8ccaPHw9kPCLSqFGjbKnZEV5//XVeeOEFwsPDr5kZM2PGDAYMGOCYwiQTzaAQERHJK4oWhS+/xG3DKh6rcpl1W39m/HdDMYXvsTdJ9qnPw7OPM+DlQWCzOa5WEZFcpFevXkRGRvLzzz8zdOhQmjVrhp+fHy4uLri4uODj40PTpk0ZPnw4u3btYs6cORQqVMjRZd8Sd3d3hg8fDkB0dDSfffYZR44cAeDhhx/GxcXllvtq1KgRAQEBAMydO5e0tLR7X7CDDBkyhIULF/Lggw/i6+uLk5P+rT43MhmaC1pgxMbG4uXlRUxMTJbvSRYRkTwkJQU+/BDjo49ISUujX7WObGvbG0uR4hjWNM599xKVfM/yw/jpNOr6rKOrFclXkpOTCQ8Pp0KFCpkW3xMRyc/u9Hff7XwP1QwKERGRvMjVFd59F1NQEG4tWvD1oW3M+uY1vHb9QczW+aRfOkPYIYNmjz7Hk938SIq+6OiKRURERG5IAYWIiEhedt99sH493t99RpvCycxdvYapm3/E76/DVivMXXKZwBE/MOK9d7SIpoiIiORaCihERETyOrMZnn4awsKo/kxLegOHgBcAE+DVtCdOparyS2Jjqgz6lA3r1jq2XhEREZEsKKAQERHJL3x84NtvYcMGitaowRTgV0rgW7SkvUla8Rr0WxJDuxdeIeZKrONqFREREfkXBRQiIiL5TcuWEBQE48bxsGscvy75nUfmj8V6JQIAk8WZY55tqf3mr7z98Qd67ENERERyBQUUIiIi+ZGLC7z6KuZDB6nRpSofHD/ErG8/oNSWuRjpGa+NMxUuzqzLdak66GM99iEiIiIOp4BCREQkPytfHv74gyK/zKKNbwo/btzIW9+OwHxsl71JavFa9P3tIgP71CE9MdFxtYqIiEiBpoBCREQkvzOZ4NFH4dAhKox4jKdjTrFowY90Xvg+1pgLAMTuWMQ3P+6jTFk/FvzvJQcXLCIiIgWRAgoREZGCokgRmDABS9Bu6jQvwbijB5jzzXuUXfsdMdsXAhB5KZ5ew6fQqFFJNqzVYx8iIiKScxRQiIiIFDR16sDGjXjNnMwDxZKYvWMnv1stVPvrsIHBUfdO9FsaS5sXXuHKZb3tQ0RERLKfAgoREZGCyGSCp56Cw4cpN6wHD5nSmEcVPgaKeJfGs3F3TBYnwj3bUuedX3ntw/f1tg8RERHJVgooRERECjIvL/jf/7AE76F2Sz8G4clvcW74b5uPkZ4KgMm9OD/F1qfKkE9Z/PtvDi5YRERE8isFFCIiIgK1a8P69RSdPZU2xRP4YdMWnv/2Y0xH/37bR1rRGgzdbKbRoFc5feKUA4sVERGR/EgBhYiIiGQwmaBPHzh8mIrDH2VM3C4OLxzLI/PHYo0+81cTMxeLPUCLz7fRf+TLYLM5tmYRERHJNxRQiIiISGaenjBxIqbgYJxbtWLS8V1s++5FKq35DltKIgAmVw8WbThFlWpOrPnhIwcXLCIiIvmBAgoRERHJWs2asHYt/PgjZUr6Mm/nOiZ8/Sru+1aQFn2auN1/cOSIQfv+r9PuAXfOhWx3dMUiIiKShymgEBERkeszmeDJJyEsDJ/Xn+extLPMW7aKl77/CH9bOgCGAWs2JlF3+De0HfIKly/FOLhoERERyYsUUIiIiMjNFS4MH3yA+dBBavWoxpj0UxwC3gJcAWefchSq35XjXm2p+84fDBv7Fjar1qcQERGRW6eAQkRERG5dxYqwcCGsWoVHzZq8B+wHmvlVApsVAJOHN78lN6XyC1OYMXOGQ8sVERGRvEMBhYiIiNy+du0gKAgmT6ZS0aJ8s/8Ub3wzBqewzfYmNu+KvHvQl/sGvsuOLdscWKyIiBR0rVu3xmQy0bp1a0eXIjeggEJERETujJMTDB0KR48S+GInno09xk+/LuWxn97GeuG4vVlC8Yb0XHSBls+/QuT5iw4sWETyonXr1mEymW7r5+WXX75uf4cPH2bUqFE0btyYYsWK4ezsjIeHB+XLl6ddu3aMHj2aJUuWkJiYmOX517umi4sLfn5+tGvXjk8//ZTLly/fdDxjx469o2u0atWKDz74gMjIyNv6/9IwDCpWrGjvb9CgQbd1/s1c7fd6IcDVkMBkMt3xNQYMGHBb94LkLQooRERE5O4ULw5ffIFlXzCN2hbn3VOHmDXzOxot/R/WuGgATBYnThdtS+1uz/DpKx3ApvUpRCTnvfvuu9x33318+umn7Ny5k8uXL5Oenk5iYiInT55kzZo1fPLJJzz88MO89957t9V3WloakZGRrFmzhlGjRlGjRg02bdp0T+u/eo0NGzbw5ptvUr16dVasWHHL52/cuJHw8HD79vz580lOTr6nNeZVNwuNJGc4OboAERERySdq1oRVqyj822+0GTGC6iHn2HXoQz5q0oDTjXuQfvk8F3YtZdROG1PmevHF20N5ePA4R1ctInnIkCFDeOGFF27azsfH55p948aNs3/59PLyYvDgwbRq1YpSpUqRmprKmTNn2L59O3/88QeHDh266TUaNmzIjBl/r7OTmprK4cOHmTp1Khs3biQiIoKuXbsSGhpKmTJlbn2QN7hGXFwcR48eZerUqWzbto1Lly7Ro0cPQkJCqFChwk37mzVrFgCFCxcmPj6eK1eu8Pvvv9OrV687qs/Rli9fTunSpW+p7bp167K3GLknFFCIiIjIvWMyQffu0KkTJSdN4uH//pdqm9LZvHcHkwpZOW9kzJw4cS6eR57/mOrLTjPu6Z507dbdoWWLSN7g6+tLzZo1b/u8qKgo3n33XQD8/f3ZsmULZcuWzdSmSZMmPPbYY4wfP54dO3YQHR19wz49PDyuqaV+/fr06tWL//znP8yfP58rV64wceJEJkyYcNs1X+8azZo1o2/fvvTq1YsFCxaQkJDAhAkTmDx58g37Sk5OZsGCBQAMHjyYZcuWceDAAWbNmpVnA4oqVapQvnx5R5ch95Ae8RAREZF7z80NRo+Go0epPORB+sUfZ0akO9/hSsBfTZxLViahWm9e3GKh/sDXOXzg5v9iKSJyJ1asWGF/lGHMmDHXhBP/1rhxYzp37nxH1zKbzYwb9/fssD///POO+rkRk8mU6RqrVq266Tm//vorMTExAPTp04e+ffsCGbMQbnctC5HsooBCREREso+vL0ydijk0hNoPlaM3NuZSi/9ipliDRwAwmcxcKt6cDt/u56H/G0HMlVgHFy0i+c2pU6fsnytXrpzt16tYsSLFixcH4OTJk9l2DQ8PDwBOnz590/ZXH++oUaMG9erVo0+fPphMJtLT0/nxxx+zpcbc5Hpv8ShfvnymxTTffffdaxbaHDBgQM4WW4ApoBAREZHsV6MGLFmC64olNKsFQ/Bk5rKVVF/1NdakOABMzm4ccG9H7Xd+Z+Brr5GWlu7gokUkv3BxcbF/PnjwYI5c09nZGQCr1Zot/ZtMJpycnDJd63ouXLhgX0zz6syJgIAAHnjgAeDv8ELE0RRQiIiISM7p0AGCgig2/RMeLBHJl7uDGPP1VLx3/YZhzQgkTIW8WWm0IHDYDN79ZByGYTi4aBHJ6+rXr2///OGHH7J3795svd7Fixe5cOECwC0v4ni7zp8/b39k42brMMyZMwer1YrJZKJPnz72/VfDiqCgIPbv358tdeZ2K1asICQkxL49ZMgQQkJCMv188MEHDqywYNEimSIiIpKzLBZ47jl44gnKffwxQydMYOjqjczas4T3H3iKtGotMtp5lmZGdGnmP9iEGS8NpNEjAx1bt4g4XGRkJKGhoTdtV7Vq1UyzCh544AFq167Nvn37uHjxIvXq1aNVq1Z06NCBpk2b0qBBA7y8vO5ZnePHj7eHq/9+pOBe+eijj+yfH3/88Ru2vTpDomXLlgQEBNj39+zZkxdffJGUlBRmzpzJ+PHjs6XW7HL48GHi4+Ove/zf90FWqlSpkmn7ThdilXtDAYWIiIg4RpEi8N//wuDB8PrrPDV7Nn1/G8eoHU1Z0LobpoBapJwL4+SqnTRds5PWLYbx7SeLKd+4raMrFxEHmTZtGtOmTbtpu/Dw8EyzCsxmMwsXLqRz584cPXoUwzBYt26d/dWTJpOJmjVr0rlzZ5599tlrvrTeiquvGZ08eTJfffUVAE5OTgwfPvy2+7qeuLg4jhw5whdffMHMmTMBCAwMZOjQodc9JyQkxD5j5OqMiau8vLzo2rUrCxYsYM6cOYwbNw6zOe9Msu/YseMNj//7PpDcTwGFiIiIOFbZsvDDDzBsGObRo/lg7Wa6/mTlq4rrWJF0AgCbDdZsSKJqi3Y0fqIP3772JlVqVHNs3SK36JuNx/lmY/hN29Us48k3/Rtl2vfczJ2Enr35wrHPtazAcy0r2rfjU9JpP2H9LdU3/amG1PL/e/bA6oMXeGPRzWcpuLtaWPNK61u6Rm5QuXJl9u7dy5QpU/jmm284fPiw/ZhhGPbp/BMmTGDYsGF8/PHH9jUesrJ+/fpMiyv+m7OzM9OnT7+rf42/0TVMJhPdunVj6tSpeHt7X7ePq0GGq6srPXv2vOZ43759WbBgAefOnWP16tV06NDhjusVuVsKKERERCR3aNgQVq/G7c8/af3qqzQPWUUCVsYBnwPJgKlkTU6XeZIO3x2gctJ0fn7nDYr7FnNw4SI3FpecTkRs8k3blSrqds2+6ITUWzo3LjnzorKGYdzSeQCpVlum7eQ02y2dW9g1579KvPPOO4wdO/aOz3d3d2fUqFGMGjWKY8eOsWXLFnbv3s3WrVvZtWsXNpsNq9XKxIkTiYqKsn+5vx0+Pj506tSJUaNGUbt27Tuu9WZKly7Nyy+/fMM1LqxWK3PmzAGgS5cuFC1a9Jo2Dz30EMWKFePSpUvMmjUrTwUUmiGR/yigEBERkdzDZILOneHBB3GeM4eib77JuNOnGQq8iYU/m/XKaObkyrEiban/4Urut+xkxn8/wK2Qq2NrF7mOIm5OlPS8Nnz4t+IeLlnuu5Vzi7hl/mu9yWS6pfMAXCyZp/S7OZtv6Vx3V8st9Z9bVapUiUqVKtGvXz8Azp49y9ixY/nmm2+AjHUbBg4cSIsWLbI8v2HDhsyYMcO+7ezsjLe3N76+vvesxn9ewzAMIiIi2LhxI5MmTeLs2bN06tSJlStX0rJlyyzPX7FiBREREcC1j3f8s+4nnniCadOmsWjRIuLj4ylcuPA9G4PI7VBAISIiIrmPxQJPPQW9esHkyZT98EM+uexEp9/nMaHZCS7W74rJyRmTW2G20oZqI3+ih+8JPnnzbSyWvPP8tBQMz7WsmOnxi9vx70c+blVhVye2vd7ujs5tV92PdtX97ujcvKxMmTJMnz6d+Ph45s6dC8D8+fOvG1B4eHhk+2KK/75GrVq16NChA7169eL+++8nLi6OPn36EBoaiqen5zXn//P1oT169Ljp9RISEli4cCH9+/e/NwMQuU36E1xERERyLzc3GDkSjh3Dd9QAnrQd4fu12xk9/S3cQtdgGH9NTS9Sgl+SGlH5xem89+nHejWpiNyxgQP/fmPQ0aNHHVjJ9dWsWZMPP/wQgNOnT/PJJ59c0yY2Npbffvvttvv+Z6ghktM0g0JERERyP29vGD8eXnqJmm+/TY3vv6fBEjdW7FjNjNaPYlRsCIDh5c93Uf7MbdaQqS/2pHXfMQ4uXETymn+u6XCjRTAdbfDgwUycOJHw8HA+++wzhg0bho+Pj/34/PnzSUpKAuC9994jMDDwhv0tXryYOXPmsG7dOk6fPk3ZsmWztX6RrCigEBERkbyjbFmYMQPziBE0HTOGBkuX02K+B4vLLmHhA49j8r+P5JN7Obl9D+22B9Hg4w+Z9tFnNHj4WUdXLiIOZBjGLYcNu3btsn+uWPHOHs3JCc7OzowZM4bBgweTkJDAZ599xgcffGA/fnUmhLe3N2PGjMHZ2fmG/VWrVo05c+Zgs9mYPXs2r732WrbWn9u4ubmRnJxMSkqKo0sp0PSIh4iIiOQ9tWrBkiU4r11F28bpvH86lOlzfueh+f/FWPsdADYMdobG0bTrIGo/OYSli/9wcNEi4ijTp09n0KBBN31k4+TJk7zxxhv27W7dumV3aXdlwIABlClTBoApU6YQExMDwIkTJ9i4cSOQMYabhRMAdevWpVKlSgD88MMP2VRx7lWqVCkAjh075uBKCjYFFCIiIpJ3tW4N27bh8cscHqxxmY+O7+eHCz68jRNXl4tzqlif2HIP88ImM7Wee5stGzY4smIRuQuRkZGEhobe9OffXzJTU1OZPn06gYGBtGjRgvfee48lS5awa9cu9uzZwx9//MGIESOoVasWp06dAjK+2Ldrd2cLjeYUFxcXRo4cCUBMTAyTJk0CMgKGq2vxPPbYY7fc39W2Bw8eZOfOnfe42tzt/vvvB+D333/nq6++IjQ0lKNHj3L06FEiIyMdXF3BoUc8REREJG8zmeDRR+GRR/D68UcefOcd6od7cj8VWc1ufqjb2d40zqcJTy6+Qok5Y5g5pA/31a3lwMJF5HZNmzaNadOm3bRdnTp1CA4Otm/7+vri4uJCamoqmzdvZvPmzTc8v3fv3vbXjeZ2gwYN4oMPPiAqKorPP/+c4cOH22dAeHp68uCDD95yX48//jjjx48HMh4RadTozt4ikxeNHDmSBQsWkJKSwvPPP5/pWP/+/fn+++8dU1gBoxkUIiIikj9YLNCvHxw6hM+0D+hY6izD8WXab0upsfJLrAmXATCZLUR5t+ShOcdoMWQkJ4+dcGzdIpLtevXqRWRkJD///DNDhw6lWbNm+Pn54eLigouLCz4+PjRt2pThw4eza9cu5syZQ6FChRxd9i1xd3dn+PDhAERHR/PZZ59x5MgRAB5++GFcXFxuua9GjRoREBAAwNy5c0lLS7v3BedSdevWZevWrTz55JMEBATg6urq6JIKJJOh93AVGLGxsXh5eRETE5Ple5JFRETylcREmDoVPvqIk5c82Oxcim/qB3C06eOY3QrbmxnpKZSP38ycMS/jX16r1svNJScnEx4eToUKFXBzc3N0OSIiOeJOf/fdzvdQzaAQERGR/MndHUaOhPBwyr3zDL1dD7Bm+wIWfvkspbbMxZaa8fo9k5MrJ4u2pVb7drzQpxLJUXrWWERExBEUUIiIiEj+5ukJY8dCeDi88gqNTFa2bpzN6199TPEdi7ClpZB4dAdXjh1h2o/H8avsx0t9A0lSUCEiIpKjFFCIiIhIweDjA59+CkePwvPP83xqMNPX7ubpr76lxqpv7c1iY2DynGMEjphN+xdeIeKsggoREZGcoIBCRERECpYyZWDaNDh0iPr96zA26U/WxZxlK9Dxrybu1ZrjVLoqRz3b0mTCBh4cOoLI8xcdWbWIiEi+p4BCRERECqZKleD77zEdOgT9+tHEbOZPYAWFqO7pi5GesXq9yaUQh4u0o9H4dXR6cThRF6IdW7eIiEg+pYBCRERECrbAQJg1C9OBA9CnD+1I4qsd4Yz6+l089yzBsP4VVLi6c6hwexqMW81DLw4nOvKSgwsXERHJXxRQiIiIiABUrQqzZ2M+eJAGT1bjhbhgvl25k5e/+i9Fgpb9I6jw4EDh9tT/aBVPDHqC1MuXHVy4iIhI/qCAQkREROSfqlWDH3/EvH8/jZ6oxLC43UxfsZOhX39E4eA/MazpGe1cCrHol62ULlmSF56oRNKF846tW0REJI9TQCEiIiKSlRo1YO5czCEhNO1ZjlGxO/hq+S4GTR9P4eA/STywgbTo00SnpjLt5+OU9i/PY0+24ciBMEdXLiIikicpoBARERG5kZo14eefMe3bR/PHyvB6zBa+XL6HsYuX0uEfza5Y09nm2Yf2Mw7SZOBo9u4JcljJIiIieZECChEREZFbUasWLFiAKTiYFo+V4gUO8D6N+ZqadAU8qj+As3cpTBZnLhRvxSNzT9Ng8Bi2b9ri6MolmxiG4egSRERyTE78zlNAISIiInI76tTJCCpCQ2nSJ5CB5gOMpS4fhydTastcbCkJAJjMFqK9W9Lrj2jqDHqTNStWOrhwuVfM5oy/QttsNgdXIiKSc67+zrv6OzA7KKAQERERuRP33QezZ8OhQ9R/pj5D03bx1cZgBk2bQ9mNs7EmxQFgMpmJKdaMZ9akUvPZsSz+7TcHFy53y9nZGYvFQkJCgqNLERHJMcnJyZjNZpycnLLtGgooRERERO5GYCB8+y0cPUrtFx7gTeNPNm6Zy+Ivn6Hy2hlYE/5+DWl8iUYMmLaSB5q7sW3+ZAcWLXfDZDJRpEgRYmNj9ZiHiBQY8fHxuLu7awaFiIiISK5XrhxMmQLh4TB8OHUssGrHQuZ9OZKqq6ZjjYvGsFmJ2f4LG7ekcP8TL1GvnhOLpozGatWjAnmNl5cXaWlpnDt3TiGFiOR7ly9fJjExEU9Pz2y9jsnQb9QCIzY2Fi8vL2JiYrL9xhIRESnwIiPhs89g8mSOx/uw2lKFH8q6sOfEYv75YECRBo9QvNHDdPE5yYQ338HV1dlhJcvtiYuL48yZMzg7O+Pp6Ym7uzsWiwWTyeTo0kRE7pphGKSnpxMTE0NcXBze3t6ULFnytvu5ne+hCigKEAUUIiIiDnDpEnzxBfzvf3DlCpeAKcDnQLTZiTKDv8bJ0zejbcIlmrkEM+2NtylazMtxNcstS0xMtP/l3Wq1OrocEZF7ztXVlaJFi+Lt7X1HAawCCsmSAgoREREHio2FadMygoqICBKANwpXY9HD/TCVq5OpqZEcT/W0bXw7eiRlypVxSLlyewzDIC0tTW/2EJF8xWKx4OTkdFczwxRQSJYUUIiIiOQCyckwaxaMH0/8sfOsozlrS8Xza5M2WKven6mpkZZC2dgtfDW0P/fVreWggkVERO6cAgrJkgIKERGRXMRqhYULYdw4UoNCWE8LNhRPYUHjxiTe1waT5e/XuBkpidQLeomJH3xPxeadHFi0iIjI7bmd76F6i4eIiIiII1gs0KsX7N6Ny4qldGhr5t3obUxbtpuBX02k2M5fsaUmARAXuprf1l+geouHaH1fYdb/MM7BxYuIiNx7mkFRgGgGhYiISC63Ywd8/DG2Xxaxk4ZscPPit/ol2BG6mrTYSHszs4s75fv+l86lovn0jbdwK+TqwKJFRESuT494SJYUUIiIiOQRYWHwyScwaxYhaVXYRBn2sp45pBAPeDZ+FO82zwJgJF6mrmkPX786Br/Svo6tW0RE5F8UUEiWFFCIiIjkMWfPwmefwVdfcSq+KOupw3628GPX5zDXaJWpqZGWTJmErUx59knqNWrgoIJFREQyU0AhWVJAISIikkdduQJffw2TJnHlbByracH6UnH81rg1VGmIyWyxNzUMG54Xd/FWp+r06vUfh5UsIiICCijkOhRQiIiI5HGpqTBvHkyYQOre/ZzHjy1e6Yxv2I3o2h0wuxTK1NwjdDLjHqpJ1yHj4S7eYS8iInKnFFBIlhRQiIiI5BOGAWvWwIQJsGwZANtcPRhZtxsnG3TEUqQ4ttQkzk4dgC0lgfIVYGDXFoz+71KcihRxcPEiIlKQKKCQLCmgEBERyYf274eJE2H2bMJTS7HCXJOlNWBLITeidi7M1LR0m6cpX6kYE57qQdOW9zuoYBERKUgUUEiWFFCIiIjkYxERMGUKTJ1KxCUnXIlmDlYmAUcAk4s7/i98j9nVHQD3i8EMbVSMIYOex2zW4x8iIpI9bud7qDmHahIRERGR7FSyJLz/Ppw6Rckp7+BduQIvAoeAxUCjktUyrUORWKIun5wIoPLQ6QwcM4b4uERHVS4iIgJoBkWBohkUIiIiBYjVCn/8AZ9/DuvWsYWmbHQtyoraHhyu3xlL0ZKZmhvJ8VRK2sbUFwdSrVYNBxUtIiL5jWZQFBCPPvoo3t7ePP74444uRURERHIbiwW6d4e1ayE4mPufqcGrrOXrnUG8+/Uimv/yP4xTIfbmJrfCHPduT4cp23ikkRd7l85yXO0iIlIgaQZFHrZu3Tri4uKYOXMmCxYsuGl7zaAQEREp4C5ehK+/hqlTiTkXxyoeYJ1vEqsb1iKhemtMTs5cXjeD2O0LsQD1SxXi2ac70f+NH3Fzd3N09SIikgdpkcwCZN26dUyePFkBhYiIiNy61FRYuBA+/xzr9p2sozUb3d1YVceLHUF/kJYcb2/q5F2a0k9NpHzidj595gkaNW3iwMJFRCSv0SMeN3H27Fn69u1L8eLFKVSoELVq1WLXrl33rP8NGzbQtWtXSpcujclk4tdff82y3ZQpUyhfvjxubm40adKEHTt23LMaRERERK7LxQWefBK2bcOybQvtnvRjbOoKvty6l4nJ7Xme4vj91bRIvYcwuRXmZLF29Pw1imqDP+CTzz/DarU5dAgiIpL/FLiA4vLlyzRv3hxnZ2eWLVvGgQMHmDBhAt7e3lm237x5M2lpadfsP3DgABcuXMjynISEBOrUqcOUKVOuW8e8efMYMWIE77zzDnv27KFOnTp07NiRyMhIe5u6detSs2bNa37OnTt3m6MWERERuY4mTeDHH+HECWq+8Sgv+mzifQw+5TFeoyalAVtair15snddppyvQqWXv6fH8BFEnMn670MiIiK3q8A94jFmzBg2b97Mxo0bb9rWZrNRv359AgMDmTt3LhaLBYCwsDBatWrFiBEjGD169A37MJlMLFq0iO7du2fa36RJExo1asTkyZPt1ypbtiwvvfQSY8aMueXx6BEPERERuaeSkuCnn2DKFKx7gllLa7a6ubOgdhku1W2IxbtUpuZGeiolLm3n/W5N6NztUQcVLSIiuZUe8biB33//nYYNG9KzZ098fX2pV68e06dPz7Kt2Wxm6dKlBAUF8dRTT2Gz2Th27Bht27ale/fuNw0nric1NZXdu3fTvn37TNdq3749W7duvaM+b2TKlCnUqFGDRo0a3fO+RUREJJ8pVAieeQZ27cKydTPt+5bmLdsKdu34lr1fD6Lb/LGYju3EMDIe8TA5uRDl25LHx0ygbl0nvv9vX4wsZp+KiIjcTIELKI4fP860adMIDAxk+fLlDBkyhP/7v/9j5syZWbYvXbo0a9asYdOmTfTu3Zu2bdvSvn17pk2bdsc1REVFYbVa8fPzy7Tfz8+PiIiIW+6nffv29OzZk6VLl+Lv73/dcGPo0KEcOHCAnTt33nHNIiIiUsCYTNC0KfzwA5w+jfOH71EsoCyfH9/FsQXvMvDr9/HZsQhrUhzpcdEkHt7G3r1Wnn5rDr5lXOjdsxGb1998xqqIiMhVTo4uIKfZbDYaNmzIhx9+CEC9evUIDQ3lyy+/pH///lmeExAQwA8//ECrVq2oWLEi3377LSaTKSfLztKqVascXYKIiIgUBL6+8NprMHo0LF6MeepU3lixggfXGqzfeInNxc6RbLMS/VfzqIuwytaZLcticZ31Kd0C4nlv9Gu4FXJ16DBERCR3K3AzKEqVKkWNGjUy7atevTqnTp267jkXLlxg0KBBdO3alcTERIYPH35XNfj4+GCxWK5ZZPPChQuULFnyrvoWERERyTYWC3TrBsuXYwoLo/HLLRjlsYZfI7dzBpgJ3A9YivhQqFJDAFJKVOfnpEZUG7OI1gOHs3PrdkeOQEREcrECF1A0b96csLCwTPsOHz5MuXLlsmwfFRVFu3btqF69Or/88gurV69m3rx5jBw58o5rcHFxoUGDBqxevdq+z2azsXr1apo1a3bH/YqIiIjkmCpV4LPP4OxZ+Ppr3OrU4SlgM/BJSi0C18zCGnXy7/aFinCieHt6/hZFtWc+5o0P3iclVWtViIjI3wpcQDF8+HC2bdvGhx9+yNGjR/nxxx/5+uuvGTp06DVtbTYbnTt3ply5csybNw8nJydq1KjBypUrmTFjBp999lmW14iPjyc4OJjg4GAAwsPDCQ4OzjRLY8SIEUyfPp2ZM2dy8OBBhgwZQkJCAk8//XS2jFtEREQkW3h4wMCBEBQEmzZBv34MN23hm907efvb9bSZPRm30DUY6an2U5J9azInrj5VR87n+cdqEhW214EDEBGR3KLAvWYUYPHixbz22mscOXKEChUqMGLECAYOHJhl25UrV9KyZUvc3Nwy7Q8KCqJEiRL4+/tfc866deto06bNNfv79+/P999/b9+ePHkyn3zyCREREdStW5dJkybRpEmTuxvcDeg1oyIiIpIjLl2CWbPgq69IO3SU1bRlu5sr62u5caROOyzFM/7+lHL2EBGzR+KOieaV3Bj8/NP0ePlzTE4Fbpk0EZF863a+hxbIgKKgUkAhIiIiOcowMmZVfPUVLFhAeEpJVtGYDWUT2F73Ps4d20nCgXWZTinX71PKOZ9h7JNdadO+rWPqFhGRe0YBhWRJAYWIiIg4THS0fVZFathxVtCBXUAY6/mDBBIAV//7KNnnY/splitHaON9nk9eeQ1vn6KOqlxERO6CAgrJkgIKERERcTjDgA0bMmZVLFzI0VR/lnE/h9jLhjqliWk3CLNz5teRGmnJFIvbyYgHatC3T79c8bp3ERG5NQooJEsKKERERCRXiYqCmTPh669JO3yclbRns6sna2sU4nTtelhKVr7mFHPMeRpYgpj15hsUKlnaAUWLiMjtUEAhWVJAISIiIrmSYcDGjfDddzB/PrGJFlyJY5pvBb6v/SCX7muD2a2wvXl8yCoS1vyPZo0KMbrPCDoNeh80q0JEJFdSQCFZUkAhIiIiuV5sLMydmxFWbN8OwCGLC2OqdCS4dhMoX5eIOa+Scma//ZRyvu5U7vIMY5/qSYvWDziqchERyYICCsmSAgoRERHJU0JDM4KKH34gJiqVP2nPRk8rF2N/41cg9a9mHrU64PPQMACco45xf5FwPn5lDCX9/RxVuYiI/EUBhWRJAYWIiIjkSamp8Mcf8O23sHw52GxEA3OA6Zi42Psj3MrWzHSKYU3D62IQ/7nPg5HDRuDi4uSQ0kVECjoFFJIlBRQiIiKS5505k7Gw5nffwfHjHCeAyYXuJ6i6wdGaTbGUCrz2nKRYysbt5K0e7Xjw4S45X7OISAGmgEKypIBCRERE8g2bLeN1pd9+CwsWcDbZm2W0ZYtPIntrFiOyxgNYihTPdErMuu+pdHQx3R+pwSsf/qG3gIiI5AAFFJIlBRQiIiKSL8XEwIIFMHMmxsaN7KEe60w12F4+jr01q5AU2AyTkzNnpz2NNS4aAE+TmYY1y9G4Rz/eHv0ahdzdHDwIEZH8SQGFZEkBhYiIiOR7x4/DDz/ArFmkHj/NatqyzaUwe/yvsOf4as79o2nR1k/j1eQxjJQEfOJ280KzKgwY8DQWi9lh5YuI5DcKKCRLCihERESkwDAM2LIlY72KefOIinVmOW0J4hJH2cJyUwrFh8zA6V+PgRiJ0ZRLC+L1hx+k40OdMJlMDhqAiEj+oIBCsqSAQkRERAqkpCT4/feMsGL5cg7YqrCCxmwum8j+mv7EV70fs6v7NadZLp/hPvbz8QsDqV6nlgMKFxHJ+xRQSJYUUIiIiEiBFxEBP/4IM2di3RfKWloT5RTLqkqurK3RmvRKDTFZnDOdEr3oPSq776Zv+za89No8XIoVc1DxIiJ5jwIKyZICChEREZF/2Ls3Y1bFTz9BRAQGsNmtMO9XfZBDNRphCqiFNSmWM5OfAls6AG6FoHG9ejRp14V3Rr+OR+FCjh2DiEgup4BCsqSAQkRERCQLViusXQtz5sDChVyKc2IpD7K1iJW9PrHsD1/BlX80L9HjLdwDm2CkJVMiche965fgxaH/h4uLk6NGICKSaymgkCwpoBARERG5iaQkWLIk4zGQJUsgNZUUYCnwI/CHmyd+L87CZPlXGJEcR8lLuxnQrCLPDRyEk5PFAcWLiOQ+CigkSwooRERERG7D5cvwyy8ZYcXatWAYLKA18/x9OFijJDHVmmMpVOTa8xJjKBOzm9cfrEaX3s+B3gQiIgWYAgrJkgIKERERkTt09izMmwdz5mDbE8Rm7meNuQo7KiZytFpZkio3vuZNIGe/Gkil5Eu0aurFi6M/pVbnfg4qXkTEcRRQSJYUUIiIiIjcA4cOZcyq+PFHUo+dYg1t2ORUmt0VEzlavSIplRqSFn2GiJkv208xAZWbPop/1bK83K0NXbt3w6SZFSJSACigkCwpoBARERG5hwwDdu7MmFnx888kn7nIatqy2dmHw0WOcejSFvb/o3npgV/hXKxMxqnxEVSyhfBa18607/SgwgoRybcUUEiWFFCIiIiIZBObDbZtywgr5s8n8fwVVtGO3RQinJ1s80oiZeBX1y6uCRB/jsDEEP6vazse7v6IwgoRyVcUUEiWFFCIiIiI5ACbDTZtyggrFiwgPjKB5bRns1thgqvYOFKtJuZyNTGZr33ThynuIo2uTOPtfsOo2fFJLbApInmeAgrJkgIKERERkRxmtcL69fDzz7BwITFRqZiwsrOQhSlVm7OnaguMgL/DCltqEme+6IORnkqpMtC2kR9P93qXVj2f06tLRSRPUkAhWVJAISIiIuJA6ekZryudNy/j9aWXL2MAHxdqw5+BpThZtQpxibFEL5mY6TSfbq/iEVCbUpd306dJeQYPHoKzcxaPioiI5EIKKCRLCihEREREconUVFi1ChYsgN9+I+ZSOstpT0lWsZpYFgL7AZOTK/4vzcHs4vb3uUlx+EXv4fHaxXnp/4bj5ubiqFGIiNyUAgrJkgIKERERkVwoLS3jMZCFC2HRIrhwAYBDwLtF6rOrXUdSKjTIHFL8xUhNonhkMO0DrLw+YjRFfbxzuHgRkRtTQCFZUkAhIiIikstZrbB1a0ZYsXAhyacjWUMbtjj5satiKuFV/Emq3Aizq/s1pyZ8M4QGbhfp9FB9Br41B4+y5XO+fhGRf1FAIVlSQCEiIiKShxgG7NplDyvSjp5gHa1Yb6nArvKJnKxakvhKDbC4e5EWfZpz3wyxn+qOiSrNOhNQozyvPPk4Ldu21utLRcQhFFBIlhRQiIiIiORRhgEhIRmLay5cSHroQTbRnA2mauz2TyDS5RTBxzaS/I9TSg2YhItfxYyN+AjKWkN5/v76PPFkb70RRERyjAIKyZICChEREZF84vDhjJkVv/2GsX07+6jFGhpzkDOcZydbC5soPHRmlqcayTGUvBRE92pFGfZ/I3AvXCiHixeRgkQBhWRJAYWIiIhIPnTuHPzxB/z2G6xezcnUkqykFduLpBMWaCY8sCbmgBqYzNfOmjDSUvDb/y5P1ivNoJe+xqN0WQcMQETyMwUUkiUFFCIiIiL5XFwc/PlnRlixZAmXrpgwMDjkmsbXlRqxJbApKRXq2xfZNNLTOD3pSYy0ZJycoHKgiQeqN6FL75E83P1RzBazgwckInmdAgrJkgIKERERkQIkLQ02boRff80ILE6dAmC2pSk/lAvkdGAxLlrMRC39LNNpPl1H4lGjNSRcxu/yXh6qWoQRw16hiKdHzo9BRPI8BRSSJQUUIiIiIgWUYcDevfawwhq8jx00woej7CWaxcASIMpkxv+lOVgKFcl8ujWNwhcO0MDjHCOfe47aDeo6YBAikhcpoJAsKaAQEREREQBOnsxYt2LJElizBlJTsQITLc1YfF9lzlYqR2r5uphd3LI83XLpDPef/4nh/XpSv+cLoFeYish1KKCQLCmgEBEREZFrxMfD6tUZYcWSJSSeu8w6WrHFUprggGROVSrG5cr1sHj5ZTrt3IyXSIsMp7LFiXoBLtz/UAceHT6JcpUCHDQQEcmNFFBIlhRQiIiIiMgNGQYEB8PixbBkCbbtO9hHLdbRgO0+aRyr5ExEpWrYPH04++WzmU71avYERR/oh1NUOGXNh+l/fz369OmHs7OTY8YiIrmCAgrJkgIKEREREbktFy7AsmUZsyuWL+dCXCFW0pZtFlfOWvdygv3sJQ0D8OvzMW7+92U63UhNxCMuhPu943m1//ME3lfFMeMQEYdRQCFZUkAhIiIiIncsNRU2bcqYXbF4MSlHTrKJ5mymKmGEc6h5WSIr3YelVOB1u7BcOk3VlOW80bElzZ8YBhZLDg5ARBxBAYVkSQGFiIiIiNwzhw/D0qWwfDmsW8fpZB9W04pw98ucKJ/IjooNia9QD4u7V6bTLv7+CYkH1+PpBbVruNK1dlM6PjOROo3rO2ggIpKdFFBIlhRQiIiIiEi2SEqCDRvgzz8zfg4dAjJeWzrZrxZLKjbkQsVqmEoGcmbKU9iSYu2nFgpsim+PNzFfiaBUbCjtKrrxfy+NxMevuKNGIyL3kAIKyZICChERERHJESdOZMys+PNPWLWK8/GFWUMrLrhEkJq6nmXAFiAdKNZxKEXqds50umGz4nrxOJXSw3iiZV16D3gWFxcttimSFymgkCwpoBARERGRHJeaClu3/j27IjgYgBjgJ3yYUedBLtSohVGmGiaLc5ZdGKnJ+BxZQS/3rfQbNYXSDVvmXP0iclcUUEiWFFCIiIiIiMOdPw8rVmSEFStWEH0J1tKarc4lCCmbSkR5dy6Xr46lRPlMp8VsmcuVjbMxA9WdnalbwYWSXZ7lpRdGUK5yOYcMRURuTgGFZEkBhYiIiIjkKlYr7N4Nq1bBypXYNm3meHoAa2nODg8njpSzElHeh4Tytbn4xyeknA61n+pUzJ8yA78EwHz5FCUJ4+EqJRny9BC8i3ld74oiksMUUEiWFFCIiIiISK6WkAAbN9oDi/R9+9lFAzZTk4NcJIJQjnCSw9goUv9hinV4/pouDJsVy5WjBCYcpWv9ijw9eCgehQs5YDAiAgoo5DoUUIiIiIhInnLhAqxeDStXwsqVxJ+9wnoeYBtl2FsigVPVvIguH4i5ZGVMZkuWXdgSruC1/ila1a3EU92G07j7ILBk3VZE7j0FFJIlBRQiIiIikmcZBoSF2WdXsHYtZ+OKsJaWVHRdzI/larM1oDaRAbWxlPh7TYrEw1u5uOgD+7aXO1R84DnK+1p4skNzuv+nN85OCixEsosCCsmSAgoRERERyTfS02HHjoywYs0ajG3bMKWmkg686fEo2wKKciHAm4jTe7hyYN3f51mcKDtsLmZnt4zt1CSKRB7kPqfT9HvoQTo91gOLxeyIEYnkSwooJEsKKEREREQk30pMhC1bYO1aWLsW2/YdHLZVpiJh7ATW/PWzo0x1SvT95Pr9pCRQODKM6pZT/N/DzWj5+AAwK7AQuVMKKCRLCihEREREpMCIi4NNm+yBhbFnDyabjR/pylIvH8LLmbgYUIKkcvdhKVwsyy7OTO1P6fgrVPd2oVG94rTuPYLmT75AoUKuOTwYkbxLAYVkSQGFiIiIiBRYV67Ahg32wCJt73520ZAN1GZnMRuny5mIDvAlpWw1LB7epF0+z7mvB2bqwrvdQIrUfYhCF49SqtApHq1Vmaf7PUsRTw/HjEkkD1BAIVlSQCEiIiIi8pfoaFi/HtasgbVrST1whF00ZDM12VUMIj0uEnd6OSEkkfrXKaUGfI6LX6VM3Rg2K+aYY5RPOU6nct4898IwivsVz/nxiORSCigkSwooRERERESuIzIy45GQDRtgwwbSgkPZZ9RiK3UIJZHzHOVwx5bEBdTAqVjp63ZjGDbMwTNo6LKRjk078J+nPqRw2fI5Nw6RXEYBhWRJAYWIiIiIyC2KiclYdPOvwMK6YxcH0wPZTAPOFI4kwj+BXWVrElW2ZqbXmgJc+PltksP3ABnra1YoGYBXyz7c5xFF7y4P0f6Rrjjp1aZSQCigkCwpoBARERERuUOJibB9uz2wMLZuxZSUhBVYUqgUE/0f4mJZd2L8K3Fu7msYqUn2UwvX7kDxzsP+7is1CffII1TgBJ3rVaT/c0Mp4lU458ckkgMUUEiWFFCIiIiIiNwjqamwezds3JgRWmzaxMkYL67giSuhbAI2//UT3fFFitTtdN2uDJsVl+hTlL2yl2dKn+PRVybhUb7SdduL5CUKKCRLCihERERERLKJ1QohIRlhxZYtsHkznDlDIm6MN/dnr6+Vc2VMXPIvQXKZqliKXLuQZuLRHVxc+B6uQDUnZ6r7OlO0VWc6dn6Eh57ojYuLU86PS+QuKaCQLCmgEBERERHJQadO/R1WbNlCalAoe43abKMmuz1NhPtDVBkvYv0rYS5RjivrZxG7fcHf55stlB02D7OLG0ZaCpbYY5Q0naZFWU8GPd6fStUqYzKZHDc+kVuggEKypIBCRERERMSB4uMz1rH4K7SwbdnKybhibKYJO12KcMp0nssp+zjJOU5gxaVkZUr1/991uzPFReMZfZTAQhEM7lSf9j2exeTsnHPjEbkFCigkSwooRERERERyEasVDhzImGHx1yyLK8ej2UYTtlCBgx4JnKxamCuli5JYuhJO3qWu29WZqf1xSoumjL+JOlW8aR7Qkja9R1OvRTPNshCHUkAhWVJAISIiIiKSy50///djIdu3k74riLDUChQngrBC6fxRuio7SlflVOnq2EoFYnZ1Jz0uirNTB2Tqxrvtc3g26g5JsRS5eJTytlM8EOjLU4Nfws+/pEOGJgWTAgrJkgIKEREREZE8JjUV9u2DbdsyHg/Ztg2OHuUoFZlvas3e4hDvcY70k3+yDYj567SSfT/FtUy1LLs0X4nA8/JxKjtH0L1RIH0Gv4zJzS2nRiQFjAIKyZICChERERGRfCAqCnbssAcWxvbtmGJisAFhwI+UZ0njDlwpW4a00oFY3L2u21XMlrkYG+dQ2cWFyn7O1GsYQMVuI+n6RB/c3FxybEiSfymgkCwpoBARERERyYdsNjh8ONMsi8R9Rwi21WYz9xHkZeFUaYPLpdyJL1Uaw68iZueMGRORC98n6eh2e1dOxfwpM/BLDGs6lsunKEo4dYsb9G7dkdYdOuDkZHHUKCWPUkAhWVJAISIiIiJSQCQkwO7dGaHFzp2wcydRJ+PZTX12msqzt7jBuVIm0o+u4ETSKSLJ+FrocV8bfB5+JcsujbRk3KJOUioxnNqlbLzy1H8o16AFaBFOuQEFFJIlBRQiIiIiIgXYxYuwa9ffPzt3cva8iR00YC/FOUEUJ/xNRNSuRkLJ8ph9ymIymbPsypoYw5kv+uBRGEqVMlGzghc1Sz3A/Q/1pV33R3FxccrhwUlupYBCsqSAQkREREREMjl3LmOGxV+BhW3nLsIvebGD+lR0XsFKv7JsLVWF4yUrE1cqECfv0gAkhe8h8ue3M3Xl958PcStXGyM9DZfoUxSPP0m1IjF0eaA5D/d6kkLuWoizIFJAIVlSQCEiIiIiIjdkGHDyZKbQwti9G1NsLGtozs9u93GspAkXWyhXTm0mGEgEwETZl+didvXIulubFedLZ/COOUELI5j/tG1O46dHYypUKOfGJg6hgEKypIBCRERERERum80GR47YAwv27MEIDsYUF4cVOAx8a6nDuvr3E+vnRZJfAObi/td9POTib+NIPLQJLyDQyZnSZfxwatqBJpVK8ES/gQRUrYRJ61rkGwooJEsKKERERERE5J6w2eD4cdizB4KCYM8erHuCCY8qwm7qEORclEO+EOHnxBU/LxL8ymL2CcBkceLsVwNJv3Le3pV71eaU6P7a310nXcEp/gQlzBHU9TbzWOMHaP1IN61rkUcpoJAsKaAQEREREZFsYxhw9mxGYPFXaGHbE8SZ0wZ7qMMuiy/7S1hIjAjmCic4RTQR2Cj6QH+8mvW8cdfWNMwXwigX/h6NqlemXbPutOv+Ms7e3jk0OLlTCigkSwooREREREQkx0VHZwotCArifFgMwdRlJ2XY7wVnyjhx2dedZN9ipPmWx+JxbfCQfCqECz/9PdPCYoLyD4/GtXARSqedoY4PPNqlK006dcLJyZKTI5QbUEAhWVJAISIiIiIiuUJ8POzdmxFa7N0L+/aRuO8I7smXSQY2exRljW9FVpZowBXf4iT5liU+fDdX1n6XqRv/l+ZgcffKtM9IS8El+jRecWcp73yRRhV86Nv3GcrcVz0HByhXKaCQLCmgEBERERGRXMtqhaNHYd8+e2hhC97LydNmgqlFdVZwhBT2AnuB7W7FMQZNxlKoyE27jv7tY3zDtlDW1YUAX2cq16mIX4tePNr3WXxL+2b70AoyBRSSJQUUIiIiIiKS51y5khFa/CO4MEJCOJLkz3KaElTElZO+ZqJ9nYn39SalhD9m71KYzH8/5nHu2xdIizpl33avcj8lHn0dACPhMpbEE/iYLlCjmInOdRvSpUsPCnsVzumR5ksKKCRLCihERERERCRfsFrh2DF7YMHevViD93H8tDOhVGe3UwkOFnMiooSJ2BJupG34lhO2eFL+Ot2r+ZMUbdHnhpcwX4nA9fIRGtlm0+S+RnRo+zSB9z+EyUlvE7kdCigkSwooREREREQkX4uJgf37ITTU/r/JIYc5crEoIVRlP26cIYYT5QsRVdmfBB9frCUCrlnH4qrk0/u58OOr9m03JyjbcQSuHkXwTYugmncarRo1oPOTT1FEMy6ypIBCsqSAQkRERERECqTIyIzA4mp4ERpKXEg4h2JLcx+72OZRlA0+5QguUY5DPlVJLFESfAJI2L+WSyumZuqqzNBZOBUuds0lTLEXKXTlLMVTIgj0TKBL7fI8MuClAv8qVAUUkiUFFCIiIiIiIn8xDDh3zh5YXJ11ER16jtCkSgQRQEXnxZxLu8JB4CCw17kIri/OwOzidtPuL/7+CdaD6ylncsLfzYUSZUrg1KgNjSqVpvt/niKgRhVMJlO2D9PRFFBIlhRQiIiIiIiI3ITNBidOZHpMxDh4EA4d4kiSP8toQoinK6d9LEQXdyK+eGGSipfA8CmL2e3vxzzOzXiJtMhw+3ahyk3wfeytvy+TkoA5/jTuaRGUdYqhgauZ9g+0pXm37ri6ueTkiLOVAgrJkgIKERERERGRO2SzwalTcPBgpp9LoWc4HFOSEMqzz8ON8OJORBV3whSylIvpFzhJKumAZ9OeeLfqf/PLJMaSNLc3JUpYqOTvRf3KlalSqhUtHx9E2aqV8tysCwUUkiUFFCIiIiIiIveYYcDFi9cEF/Ghxzlyvgj7qcQBXAkrZuGcvwexxQuRXLwoqT7+WLz8ruku5ewhImaPzLTPt9d7FKpQH1IScL58jiJxEZTmIg/VjqHbE69RplqDnBrtbbud76F6P4qIiIiIiIjInTKZwNc346dVK/vuwkC9uDjqHTqUKbhI3X8Yp/WHwWbliJMrm7xL82vxZpwu5kNiscKkXj51zSWci/lnfHD1IK1kIJdKBhIZF82Ssf1Zv3cf8345nEODzV4KKERERERERESyQ5Ei0KhRxs9fXABSU+HYMaoePkzVsDCeDQuDsN1EbrmI56WTpAOHgTDgTwLYdTiEpOLFSCtWCrOXLyaTmfRLZwHo4lzGAQPLHgooRERERERERHKSiwtUr57x8w++AJcuweHD1A8Lo/7hwzwZFkbqwTUc35hOWFo5Qpy8OVTUlQRLFP5A274vOWIE2UJrUBQgWoNCREREREQkj7q6SGdYGBw+nPG/YWHw66/g4eHo6q5La1CIiIiIiIiI5CdmM5Qvn/HTsaOjq8kWZkcXICIiIiIiIiKigEJEREREREREHE4BhYiIiIiIiIg4nAIKEREREREREXE4BRQiIiIiIiIi4nAKKERERERERETE4RRQiIiIiIiIiIjDKaAQEREREREREYdTQCEiIiIiIiIiDqeAQkREREREREQcTgGFiIiIiIiIiDicAgoRERERERERcTgFFCIiIiIiIiLicAooRERERERERMThFFCIiIiIiIiIiMMpoBARERERERERh1NAISIiIiIiIiIOp4BCRERERERERBxOAYWIiIiIiIiIOJwCChERERERERFxOAUUIiIiIiIiIuJwTo4uQHKOYRgAxMbGOrgSERERERERKQiufv+8+n30RhRQFCBxcXEAlC1b1sGViIiIiIiISEESFxeHl5fXDduYjFuJMSRfsNlsnDt3jiJFimAymRxdznXFxsZStmxZTp8+jaenp6PLEbkrup8lv9C9LPmJ7mfJL3QvS15gGAZxcXGULl0as/nGq0xoBkUBYjab8ff3d3QZt8zT01O/aCXf0P0s+YXuZclPdD9LfqF7WXK7m82cuEqLZIqIiIiIiIiIwymgEBERERERERGHU0AhuY6rqyvvvPMOrq6uji5F5K7pfpb8Qvey5Ce6nyW/0L0s+Y0WyRQRERERERERh9MMChERERERERFxOAUUIiIiIiIiIuJwCihERERERERExOEUUIiIiIiIiIiIwymgkFxnypQplC9fHjc3N5o0acKOHTscXZJIJhs2bKBr166ULl0ak8nEr7/+mum4YRi8/fbblCpVikKFCtG+fXuOHDmSqc2lS5fo06cPnp6eFC1alGeffZb4+PgcHIUIfPTRRzRq1IgiRYrg6+tL9+7dCQsLy9QmOTmZoUOHUrx4cQoXLsxjjz3GhQsXMrU5deoUXbp0wd3dHV9fX0aNGkV6enpODkWEadOmUbt2bTw9PfH09KRZs2YsW7bMflz3suRV48aNw2Qy8fLLL9v36X6W/EoBheQq8+bNY8SIEbzzzjvs2bOHOnXq0LFjRyIjIx1dmohdQkICderUYcqUKVkeHz9+PJMmTeLLL79k+/bteHh40LFjR5KTk+1t+vTpw/79+1m5ciWLFy9mw4YNDBo0KKeGIALA+vXrGTp0KNu2bWPlypWkpaXx4IMPkpCQYG8zfPhw/vjjD+bPn8/69es5d+4cPXr0sB+3Wq106dKF1NRUtmzZwsyZM/n+++95++23HTEkKcD8/f0ZN24cu3fvZteuXbRt25Zu3bqxf/9+QPey5E07d+7kq6++onbt2pn2636WfMsQyUUaN25sDB061L5ttVqN0qVLGx999JEDqxK5PsBYtGiRfdtmsxklS5Y0PvnkE/u+K1euGK6ursZPP/1kGIZhHDhwwACMnTt32tssW7bMMJlMxtmzZ3OsdpF/i4yMNABj/fr1hmFk3LvOzs7G/Pnz7W0OHjxoAMbWrVsNwzCMpUuXGmaz2YiIiLC3mTZtmuHp6WmkpKTk7ABE/sXb29v45ptvdC9LnhQXF2cEBgYaK1euNFq1amUMGzbMMAz9bpb8TTMoJNdITU1l9+7dtG/f3r7PbDbTvn17tm7d6sDKRG5deHg4ERERme5jLy8vmjRpYr+Pt27dStGiRWnYsKG9Tfv27TGbzWzfvj3Haxa5KiYmBoBixYoBsHv3btLS0jLdz9WqVSMgICDT/VyrVi38/PzsbTp27EhsbKz9X65FcprVamXu3LkkJCTQrFkz3cuSJw0dOpQuXbpkum9Bv5slf3NydAEiV0VFRWG1WjP9IgXw8/Pj0KFDDqpK5PZEREQAZHkfXz0WERGBr69vpuNOTk4UK1bM3kYkp9lsNl5++WWaN29OzZo1gYx71cXFhaJFi2Zq++/7Oav7/eoxkZwUEhJCs2bNSE5OpnDhwixatIgaNWoQHByse1nylLlz57Jnzx527tx5zTH9bpb8TAGFiIiIMHToUEJDQ9m0aZOjSxG5Y1WrViU4OJiYmBgWLFhA//79Wb9+vaPLErktp0+fZtiwYaxcuRI3NzdHlyOSo/SIh+QaPj4+WCyWa1YgvnDhAiVLlnRQVSK35+q9eqP7uGTJktcs/Jqens6lS5d0r4tDvPjiiyxevJi1a9fi7+9v31+yZElSU1O5cuVKpvb/vp+zut+vHhPJSS4uLlSuXJkGDRrw0UcfUadOHT7//HPdy5Kn7N69m8jISOrXr4+TkxNOTk6sX7+eSZMm4eTkhJ+fn+5nybcUUEiu4eLiQoMGDVi9erV9n81mY/Xq1TRr1syBlYncugoVKlCyZMlM93FsbCzbt2+338fNmjXjypUr7N69295mzZo12Gw2mjRpkuM1S8FlGAYvvvgiixYtYs2aNVSoUCHT8QYNGuDs7Jzpfg4LC+PUqVOZ7ueQkJBModvKlSvx9PSkRo0aOTMQkeuw2WykpKToXpY8pV27doSEhBAcHGz/adiwIX369LF/1v0s+ZajV+kU+ae5c+carq6uxvfff28cOHDAGDRokFG0aNFMKxCLOFpcXJwRFBRkBAUFGYAxceJEIygoyDh58qRhGIYxbtw4o2jRosZvv/1m7Nu3z+jWrZtRoUIFIykpyd5Hp06djHr16hnbt283Nm3aZAQGBhpPPvmko4YkBdSQIUMMLy8vY926dcb58+ftP4mJifY2zz//vBEQEGCsWbPG2LVrl9GsWTOjWbNm9uPp6elGzZo1jQcffNAIDg42/vzzT6NEiRLGa6+95oghSQE2ZswYY/369UZ4eLixb98+Y8yYMYbJZDJWrFhhGIbuZcnb/vkWD8PQ/Sz5lwIKyXW++OILIyAgwHBxcTEaN25sbNu2zdEliWSydu1aA7jmp3///oZhZLxq9K233jL8/PwMV1dXo127dkZYWFimPqKjo40nn3zSKFy4sOHp6Wk8/fTTRlxcnANGIwVZVvcxYMyYMcPeJikpyXjhhRcMb29vw93d3Xj00UeN8+fPZ+rnxIkTRufOnY1ChQoZPj4+xiuvvGKkpaXl8GikoHvmmWeMcuXKGS4uLkaJEiWMdu3a2cMJw9C9LHnbvwMK3c+SX5kMwzAcM3dDRERERERERCSD1qAQEREREREREYdTQCEiIiIiIiIiDqeAQkREREREREQcTgGFiIiIiIiIiDicAgoRERERERERcTgFFCIiIiIiIiLicAooRERERERERMThFFCIiIiIiIiIiMMpoBARERERERERh1NAISIiIiIiIiIOp4BCRERE8oSPP/4Yk8lk/1m5cqWjSxIREZF7SAGFiIiI5Al79+7NtF27dm0HVSIiIiLZQQGFiIiI5An79u2zfy5RogR+fn4OrEZERETuNQUUIiIikuulpKQQFhZm365Vq5YDqxEREZHsoIBCREREcr0DBw6Qnp5u31ZAISIikv8ooBAREZFc75+Pd4ACChERkfxIAYWIiIjkegooRERE8j8FFCIiIpIrVa9e3f5K0YkTJ2Y61qRJk0yvHP3nzxtvvHHX1z5w4ACurq72PitWrEhycvId99e4ceNMNa5Zs+auaxQREclvFFCIiIhIrpOUlMSRI0fu6Ny7ff2oYRgMGjSI1NRU+77PP/8cNze3O+6zUaNGmbY3bNhwx32JiIjkVwooREREJNcJDQ3FarXe0bl3+/jHl19+yebNm+3bXbp0oWvXrnfV578Dio0bN95VfyIiIvmRyTAMw9FFiIiIiPzTxYsX2bt3LwAhISGMGDHCfqxfv3489dRT1z23TZs2WCyWO7ru5cuXqVixIleuXAHAYrGwf/9+qlatekf9XbV7924aNmxo3y5SpAixsbF31aeIiEh+4+ToAkRERET+rUSJErRv3x6Aw4cPZzr2yCOP2I/da+PGjbOHEwC9e/e+63ACoGzZspm24+LiuHDhAn5+fnfdt4iISH6hRzxEREQkV9uzZ0+m7QYNGmTLdc6dO8cXX3xh37ZYLLz11lv3pO8SJUrg6uqaad/Ro0fvSd8iIiL5hQIKERERydV2795t/1ysWDEqVKiQLdf57LPPSEpKsm93796dwMDAe9K3yWSiWLFimfZFRETck75FRETyCwUUIiIikmulpqayf/9++3b9+vWz5TopKSnMmDEj077nnnvunl7DZDJl2v7nW0JEREREAYWIiIjkYiEhIaSlpdm3s+vxjvnz5xMdHW3fDggI4MEHH7yn1/h3QPHPcYmIiIgWyRQREZFcLKfWn/jll18ybXft2hWz+cb/jvPII4+QmJgIQN++fRkwYMAN21++fDnTtre39+0XKiIiko8poBAREZFc698BRXY84mG1Wlm7dm2mfTd7S8ixY8f4448/7NtPP/30DdtHRUXZw4yr/v1mDxERkYJOj3iIiIhIrvXPgKJo0aJUqlTpnl8jJCQk06tFAR544IEbnrNjx45M27Vq1bph++PHj2faNpvNVKxY8daLFBERKQAUUIiIiEiulJ6ezr59++zb2bVA5sGDBzNtlypV6po3bvzbv2dc3Cw42bp1a6btmjVr4unpeRtVioiI5H8KKERERCRXOnDgAMnJyfbt7Fp/4vDhw5m2q1WrdtNzVq5caf/s7e2Nh4fHDdtv3Lgx03bLli1vqba0tDR+/PFHevfuTeXKlfH09MTFxQV/f3+6du3K9OnTiYyMvKW+REREcjutQSEiIiK5Uk4tkHnmzJlM235+fjdsv3fvXk6cOGHf9vHxuWH7lJQUVqxYkWnfrbwh5M8//2TgwIHX1Adw9uxZzp49y+LFizl37hzvvPPOTfsTERHJ7RRQiIiISK4UHBycabtevXrZcp2kpKRM20WKFLlh+9mzZ2fadnV1vWH7pUuXEhcXZ9/29vamU6dONzxnypQpvPTSSxiGgaurK08++SQPPfQQ5cuXx2q1cuTIEZYsWcKiRYto1KjRDfsSERHJKxRQiIiISK506NAh+2cXF5dsWSATIDU1NdP2v9+28U/x8fHMmDHjltsDTJo0KdN2z549cXFxuW77BQsW8H//938YhkHNmjX57bffrllQs2nTpvTr148jR45QokSJG15fREQkr9AaFCIiIpIr/XNtBRcXFywWS7Zcx9fXN9N2WFjYddt+/vnnREdHA9iDgXPnzpGWlpZl+6VLl7Ju3Tr7tpOTE6NGjbpu/xcuXODZZ5/FZrPh7+/PihUrbvi2j8DAQIoWLXrd4yIiInmJAgoRERHJlQoVKmT/HB8fz5YtW7LlOhUqVMi0HRQURHh4+DXt9u7dy/vvvw9A7dq17a8WTU5OZv369de0P3PmDAMHDsy0r3///lSuXPm6tbzxxhvExsYCMH36dEqVKnV7gxEREcnDTIZhGI4uQkREROTfhgwZwpdffmnf9vHxYciQIdSqVQtvb2/7fovFQps2be74Ovv376dmzZqZ9jVr1oyFCxfaA4JVq1bRu3dvLl68CMDPP//MwoULmTdvHpARWKxYscK+wObKlSt57rnnOHXqlL3PgIAAgoODM9X+TwkJCfj4+JCcnEzLli3ZsGHDHY9JREQkL1JAISIiIrnSnj17aNiwITf7q0qNGjXYv3//XV2refPm18zQcHV1pWrVqsTFxWWaUdG5c2eWLFnCF198wbBhw+z73d3dqVq1KhEREZw/fz5TX0WKFGHlypU0adLkujX88ssvPPbYYwBMnTqVIUOG3NWYRERE8ho94iEiIiK5Uv369ZkyZQrOzs43bHcv3u4xY8aMa97ekZKSwr59+zKFE3Xr1mX27NmYTCb69euXaf2KxMREgoKCrgknSpYsydq1a28YTgDs27fP/rlFixZ3MxwREZE8SQGFiIiI5FpDhgxh3759jBgxggYNGlC0aNFrFsusW7fuXV+nSpUq7Nq1i+bNm2d53NXVlRdffJFNmzZRrFgxION1oUuXLr3uIpbe3t6MHDmSsLAwGjRocNMaIiIi7J+19oSIiBREesRDRERE5B8OHz7M9u3b7YFBxYoVad26NcWLF8+yfVpaGuvXryc0NJSkpCRKlChB5cqVadGiBU5Ot/5G9wEDBjBz5kwg480gCilERKSgufU/NUVEREQKgCpVqlClSpVbbu/s7Ez79u1p3779XV23dOnS9s9bt26lR48ed9WfiIhIXqNHPERERERygQ4dOtg/jx07lri4uOu2PX36NJcvX86JskRERHKMHvEQERERySVatWplf71oxYoVeemll2jYsCGFCxcmOjqa4OBgli1bxvr164mOjsbT09PBFYuIiNw7CihEREREcomLFy/y8MMPs2PHjhu2CwwM5PDhwzlUlYiISM5QQCEiIiKSi6Snp/PTTz8xb948goKCiIqKwsXFBT8/P8qXL0/Hjh3p1q3bba2TISIikhcooBARERERERERh9MimSIiIiIiIiLicAooRERERERERMThFFCIiIiIiIiIiMMpoBARERERERERh1NAISIiIiIiIiIOp4BCRERERERERBxOAYWIiIiIiIiIOJwCChERERERERFxOAUUIiIiIiIiIuJwCihERERERERExOEUUIiIiIiIiIiIwymgEBERERERERGH+39nIK+SgJqrmwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", + "\n", + "plot_result_expectations(\n", + " [\n", + "\n", + " (results_ohmic_corr_fit, P11p, \"r\", \"Correlation Fit\"),\n", + " (results_ohmic_sd_fit, P11p, \"g\", \"SD Fit\"),\n", + " (results_ohmic_sd_fit, P11p, \"y\", \"PS Fit\"),\n", + " (results_ohmic_prony_fit, P11p, \"k\", \" Prony Fit\"),\n", + " (results_ohmic_mp_fit, P11p, \"r\", \"Matrix Pencil Fit\"),\n", + " (results_ohmic_es_fit, P11p, \"b-.\", \"ESPRIT Fit\"),\n", + " (results_ohmic_aaa_fit, P11p, \"r-.\", \"Matrix AAA Fit\"),\n", + " (results_ohmic_espira_fit, P11p, \"k\", \"ESPIRA I Fit\"),\n", + " (results_ohmic_espira2_fit, P11p, \"--\", \"ESPIRA II Fit\"),\n", + "\n", + " ],\n", + " axes=axes,\n", + ")\n", + "axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", + "axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", + "axes.legend(loc=0, fontsize=20);\n", + "axes.set_yscale(\"log\")" + ] + }, + { + "cell_type": "markdown", + "id": "ef38be59", + "metadata": {}, + "source": [ + "## About" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "14ae80bc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "QuTiP: Quantum Toolbox in Python\n", + "================================\n", + "Copyright (c) QuTiP team 2011 and later.\n", + "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", + "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", + "Original developers: R. J. Johansson & P. D. Nation.\n", + "Previous lead developers: Chris Granade & A. Grimsmo.\n", + "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", + "\n", + "QuTiP Version: 5.2.0.dev0+678a16d\n", + "Numpy Version: 2.2.1\n", + "Scipy Version: 1.15.0\n", + "Cython Version: 3.0.11\n", + "Matplotlib Version: 3.10.0\n", + "Python Version: 3.13.0\n", + "Number of CPUs: 8\n", + "BLAS Info: Generic\n", + "INTEL MKL Ext: None\n", + "Platform Info: Linux (x86_64)\n", + "Installation path: /home/gerardo/Documents/gsuarezr/qutip_gsoc_app/qutip\n", + "\n", + "Installed QuTiP family packages\n", + "-------------------------------\n", + "\n", + "No QuTiP family packages installed.\n", + "\n", + "================================================================================\n", + "Please cite QuTiP in your publication.\n", + "================================================================================\n", + "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" + ] + } + ], + "source": [ + "qutip.about()" + ] + }, + { + "cell_type": "markdown", + "id": "9e328c08", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "44448c73", + "metadata": {}, + "outputs": [], + "source": [ + "tol=1e-2\n", + "assert np.allclose(\n", + " expect(P11p, results_ohmic_ps_fit.states),\n", + " expect(P11p, results_ohmic_sd_fit.states),\n", + " rtol=tol,\n", + ")\n", + "assert np.allclose(\n", + " expect(P11p, results_ohmic_corr_fit.states),\n", + " expect(P11p, results_ohmic_sd_fit.states),\n", + " rtol=tol,\n", + ")\n", + "assert np.allclose(\n", + " expect(P11p, results_ohmic_aaa_fit.states),\n", + " expect(P11p, results_ohmic_sd_fit.states),\n", + " rtol=tol,\n", + ")\n", + "assert np.allclose(\n", + " expect(P11p, results_ohmic_mp_fit.states),\n", + " expect(P11p, results_ohmic_sd_fit.states),\n", + " rtol=tol,\n", + ")\n", + "assert np.allclose(\n", + " expect(P11p, results_ohmic_prony_fit.states),\n", + " expect(P11p, results_ohmic_sd_fit.states),\n", + " rtol=tol,\n", + ")\n", + "\n", + "assert np.allclose(\n", + " expect(P11p, results_ohmic_es_fit.states),\n", + " expect(P11p, results_ohmic_sd_fit.states),\n", + " rtol=tol,\n", + ")\n", + "assert np.allclose(\n", + " expect(P11p, results_ohmic_espira_fit.states),\n", + " expect(P11p, results_ohmic_sd_fit.states),\n", + " rtol=tol,\n", + ")\n", + "assert np.allclose(\n", + " expect(P11p, results_ohmic_espira2_fit.states),\n", + " expect(P11p, results_ohmic_sd_fit.states),\n", + " rtol=tol,\n", + ")" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "qutip-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 1b1659a5b93331c0d14aacc102f5f6a7eecfae6d Mon Sep 17 00:00:00 2001 From: mcditooss Date: Tue, 22 Apr 2025 17:43:15 +0200 Subject: [PATCH 21/44] minor --- tutorials-v5/fitting_summary.ipynb | 1075 ----------------- tutorials-v5/fitting_summary.md | 17 +- ...1b-spin-bath-model-very-strong-coupling.md | 50 +- .../heom-1d-spin-bath-model-ohmic-fitting.md | 13 +- .../heom-5a-fermions-single-impurity-model.md | 40 +- 5 files changed, 59 insertions(+), 1136 deletions(-) delete mode 100644 tutorials-v5/fitting_summary.ipynb diff --git a/tutorials-v5/fitting_summary.ipynb b/tutorials-v5/fitting_summary.ipynb deleted file mode 100644 index 411f553d..00000000 --- a/tutorials-v5/fitting_summary.ipynb +++ /dev/null @@ -1,1075 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a2772b9b", - "metadata": {}, - "source": [ - "# HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions" - ] - }, - { - "cell_type": "markdown", - "id": "d4f3eb64", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded \n", - "in a set of auxiliary density matrices.\n", - "\n", - "In this example we show the evolution of a single two-level system in contact with a single bosonic environment.\n", - "\n", - "The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment.\n", - "\n", - "The bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions.\n", - "\n", - "In the example below we show how to model an Ohmic environment with exponential cut-off in three ways:\n", - "\n", - "* First we fit the spectral density with a set of underdamped brownian oscillator functions.\n", - "* Second, we evaluate the correlation functions, and fit those with a certain choice of exponential functions.\n", - "* Third, we use the available OhmicBath class \n", - "\n", - "In each case we will use the fit parameters to determine the correlation function expansion co-efficients needed to construct a description of the bath (i.e. a `BosonicBath` object) to supply to the `HEOMSolver` so that we can solve for the system dynamics." - ] - }, - { - "cell_type": "markdown", - "id": "0e624b73", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "0028e918", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "import qutip\n", - "from qutip import (\n", - " basis,\n", - " expect,\n", - " sigmax,\n", - " sigmaz,\n", - ")\n", - "from qutip.solver.heom import (\n", - " HEOMSolver\n", - ")\n", - "from qutip.core.environment import BosonicEnvironment,OhmicEnvironment\n", - "\n", - "# Import mpmath functions for evaluation of gamma and zeta\n", - "# functions in the expression for the correlation:\n", - "\n", - "from mpmath import mp\n", - "\n", - "mp.dps = 15\n", - "mp.pretty = True\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "9d44adc6", - "metadata": {}, - "source": [ - "## System and bath definition\n", - "\n", - "Let us set up the system Hamiltonian, bath and system measurement operators:" - ] - }, - { - "cell_type": "markdown", - "id": "7c36a423", - "metadata": {}, - "source": [ - "### System Hamiltonian" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "122d942d", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the system Hamiltonian\n", - "eps = 0 # Energy of the 2-level system.\n", - "Del = 0.2 # Tunnelling term\n", - "Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax()\n", - "rho0 = basis(2, 0) * basis(2, 0).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "2f90386e", - "metadata": {}, - "source": [ - "### System measurement operators" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "bcf86ec7", - "metadata": {}, - "outputs": [], - "source": [ - "# Define some operators with which we will measure the system\n", - "# 1,1 element of density matrix - corresonding to groundstate\n", - "P11p = basis(2, 0) * basis(2, 0).dag()\n", - "P22p = basis(2, 1) * basis(2, 1).dag()\n", - "# 1,2 element of density matrix - corresonding to coherence\n", - "P12p = basis(2, 0) * basis(2, 1).dag()" - ] - }, - { - "cell_type": "markdown", - "id": "7e0d0038", - "metadata": {}, - "source": [ - "### Bath and HEOM parameters" - ] - }, - { - "cell_type": "markdown", - "id": "2f4f5bbb", - "metadata": {}, - "source": [ - "Finally, let's set the bath parameters we will work with and write down some measurement operators:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "db22f152", - "metadata": {}, - "outputs": [], - "source": [ - "Q = sigmaz()\n", - "alpha = 3.25\n", - "T = 0.5\n", - "wc = 1.0\n", - "s = 1" - ] - }, - { - "cell_type": "markdown", - "id": "dc0db3ff", - "metadata": {}, - "source": [ - "And set the cut-off for the HEOM hierarchy:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "596c6e81", - "metadata": {}, - "outputs": [], - "source": [ - "# HEOM parameters:\n", - "\n", - "# The max_depth defaults to 5 so that the notebook executes more\n", - "# quickly. Change it to 11 to wait longer for more accurate results.\n", - "max_depth = 5 #could not do 11 my laptop rans out of ram\n", - "# I used 7 because I wanted to make sure things were working correctly\n", - "# cf is terribly slow at 7, probably can be done better by changing guess, lower\n", - "# upper, use 5 to play around :)\n", - "\n", - "# options used for the differential equation solver, while default works it \n", - "# is way slower than using bdf\n", - "options = {\n", - " \"nsteps\":15000, \"store_states\":True, \"rtol\":1e-12, \"atol\":1e-12, \"method\":\"bdf\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "242a054c", - "metadata": {}, - "source": [ - "#### Plotting function" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a0b1eb30", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_result_expectations(plots, axes=None):\n", - " \"\"\"Plot the expectation values of operators as functions of time.\n", - "\n", - " Each plot in plots consists of (solver_result,\n", - " measurement_operation, color, label).\n", - " \"\"\"\n", - " if axes is None:\n", - " fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8))\n", - " fig_created = True\n", - " else:\n", - " fig = None\n", - " fig_created = False\n", - "\n", - " # add kw arguments to each plot if missing\n", - " plots = [p if len(p) == 5 else p + ({},) for p in plots]\n", - " for result, m_op, color, label, kw in plots:\n", - " exp = np.real(expect(result.states, m_op))\n", - " kw.setdefault(\"linewidth\", 2)\n", - " if color == \"rand\":\n", - " axes.plot(\n", - " result.times,\n", - " exp,\n", - " c=np.random.rand(\n", - " 3,\n", - " ),\n", - " label=label,\n", - " **kw,\n", - " )\n", - " else:\n", - " axes.plot(result.times, exp, color, label=label, **kw)\n", - "\n", - " if fig_created:\n", - " axes.legend(loc=0, fontsize=12)\n", - " axes.set_xlabel(\"t\", fontsize=28)\n", - "\n", - " return fig" - ] - }, - { - "cell_type": "markdown", - "id": "d0b9d0fb", - "metadata": {}, - "source": [ - "# Obtaining a decaying Exponential description of the environment\n", - "\n", - "In order to carry out our HEOM simulation, we need to express the correlation \n", - "function as a sum of decaying exponentials, that is we need to express it as \n", - "\n", - "$$C(\\tau)= \\sum_{k=0}^{N-1}c_{k}e^{-\\nu_{k}t}$$\n", - "\n", - "As the correlation function of the environment is tied to it's power spectrum via \n", - "a Fourier transform, such a representation of the correlation function implies a \n", - "power spectrum of the form\n", - "\n", - "$$S(\\omega)= \\sum_{k}2 Re\\left( \\frac{c_{k}}{\\nu_{k}- i \\omega}\\right)$$\n", - "\n", - "There are several ways one can obtain such a decomposition, in this tutorial we \n", - "will cover the following approaches:\n", - "\n", - "- Non-Linear Least Squares:\n", - " - On the Spectral Density (`sd`)\n", - " - On the Correlation Function (`cf`)\n", - " - On the Power Spectrum (`ps`)\n", - "- Methods based on the Prony Polynomial\n", - " - Prony on the correlation function(`prony`)\n", - " - The Matrix Pencil method on the correlation function (`mp`) :question:\n", - " - ESPRIT on the correlation function(`esprit`)\n", - "- Methods based on rational Approximations\n", - " - The AAA algorithm on the Power Spectrum (`aaa`)\n", - " - ESPIRA-I (`espira-I`) :question:\n", - " - ESPIRA-II (`espira-II`)\n", - "\n", - "the ones with a question mark are the ones I think maybe can be deleted.\n", - "Here's a quick high level comparison between the three different families \n", - "of methods\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ClassRequires Extra informationFastResilient to NoiseAllows constraitnsStable
Non-Linear Least Squares✔️❌❌✔️❌
Prony Polynomial❌✔️❗❌❗
Rational Approximations ❌✔️❗❗✔️
\n", - "\n", - "Legend:\n", - "\n", - "❌: NO ✔️: Yes ❗: Partially" - ] - }, - { - "cell_type": "markdown", - "id": "45bdec8e", - "metadata": {}, - "source": [ - "# Non-Linear Least Squares" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "08ce309d", - "metadata": {}, - "outputs": [], - "source": [ - "obs = OhmicEnvironment(T, alpha, wc,s=1)\n", - "tlist = np.linspace(0, 30 * np.pi / Del, 600)" - ] - }, - { - "cell_type": "markdown", - "id": "a8c1920b", - "metadata": {}, - "source": [ - "## Correlation Function" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "3bbd9ae1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlation function fit:\n", - "\n", - "Result of fitting the real part of |Result of fitting the imaginary part \n", - "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", - " | \n", - " Parameters| a | b | c | Parameters| a | b | c \n", - " 1 |-1.98e+00 |-4.66e+00 |2.58e+00 | 1 |-1.34e+01 |-1.05e+00 |2.56e-02 \n", - " 2 | 3.89e-02 |-1.43e-01 |5.74e-32 | 2 |-8.73e+00 |-3.57e-01 |8.36e-04 \n", - " 3 | 3.45e-01 |-5.95e-01 |1.62e-06 | 3 | 5.48e-01 |-4.30e+00 |3.99e+00 \n", - " 4 | 3.11e+00 |-2.93e+00 |4.65e-04 | 4 |-1.34e+01 |-2.30e+00 |2.90e-01 \n", - " | \n", - "A 1-R2 coefficient of 8.50e-07 was obtained for the the real part of |A 1-R2 coefficient of 7.13e-07 was obtained for the the imaginary part\n", - "the correlation function. |of the correlation function. \n", - "The current fit took 0.361273 seconds. |The current fit took 2.255287 seconds. \n", - "\n", - "10.0%. Run time: 18.30s. Est. time left: 00:00:02:44\n", - "20.0%. Run time: 30.19s. Est. time left: 00:00:02:00\n", - "30.1%. Run time: 41.27s. Est. time left: 00:00:01:36\n", - "40.1%. Run time: 52.42s. Est. time left: 00:00:01:18\n", - "50.1%. Run time: 63.79s. Est. time left: 00:00:01:03\n", - "60.1%. Run time: 74.86s. Est. time left: 00:00:00:49\n", - "70.1%. Run time: 86.01s. Est. time left: 00:00:00:36\n", - "80.1%. Run time: 97.09s. Est. time left: 00:00:00:24\n", - "90.2%. Run time: 108.06s. Est. time left: 00:00:00:11\n", - "100.0%. Run time: 118.88s. Est. time left: 00:00:00:00\n", - "Total run time: 118.88s\n" - ] - } - ], - "source": [ - "t=np.linspace(0,20,500)\n", - "Obath, fitinfo = obs.approximate(method=\"cf\",tlist=t,Nr_max=4,Ni_max=4,maxfev=1e9,target_rsme=None)\n", - "print(fitinfo[\"summary\"])\n", - "HEOM_ohmic_corr_fit = HEOMSolver(\n", - " Hsys,\n", - " (Obath,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - ")\n", - "results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "f3e2ea45", - "metadata": {}, - "source": [ - "## Spectral Density" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "5cafea21", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result of fitting the spectral density with 4 terms: \n", - " \n", - " Parameters| a | b | c \n", - " 1 | 1.06e-02 | 3.06e-01 |1.02e-01\n", - " 2 |-4.41e+00 | 4.30e+00 |3.98e+00\n", - " 3 | 6.01e-01 | 1.00e+00 |1.02e-01\n", - " 4 | 7.92e+00 | 2.30e+00 |1.02e-01\n", - " \n", - "A 1-R2 coefficient of 1.30e-06 was obtained for the the spectral density.\n", - "The current fit took 0.340964 seconds.\n", - "10.0%. Run time: 6.95s. Est. time left: 00:00:01:02\n", - "20.0%. Run time: 11.80s. Est. time left: 00:00:00:47\n", - "30.1%. Run time: 16.72s. Est. time left: 00:00:00:38\n", - "40.1%. Run time: 21.69s. Est. time left: 00:00:00:32\n", - "50.1%. Run time: 26.73s. Est. time left: 00:00:00:26\n", - "60.1%. Run time: 31.86s. Est. time left: 00:00:00:21\n", - "70.1%. Run time: 37.40s. Est. time left: 00:00:00:15\n", - "80.1%. Run time: 43.64s. Est. time left: 00:00:00:10\n", - "90.2%. Run time: 49.99s. Est. time left: 00:00:00:05\n", - "100.0%. Run time: 56.89s. Est. time left: 00:00:00:00\n", - "Total run time: 56.89s\n" - ] - } - ], - "source": [ - "w=np.linspace(0,30,500)\n", - "Obath2, fitinfo = obs.approximate(method=\"sd\",wlist=w,Nmax=4,Nk=3)\n", - "print(fitinfo[\"summary\"])\n", - "HEOM_ohmic_sd_fit = HEOMSolver(\n", - " Hsys,\n", - " (Obath2,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - ")\n", - "results_ohmic_sd_fit = HEOM_ohmic_sd_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "a126d52a", - "metadata": {}, - "source": [ - "## Power Spectrum" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "dfabd825", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result of fitting the power spectrum with 5 terms: \n", - " \n", - " Parameters| a | b | c | d \n", - " 1 | 4.87e+00 |-4.87e+00 | 6.25e-01 |-5.50e-02\n", - " 2 | 6.51e-01 | 4.82e+00 | 5.00e-01 |1.87e-02\n", - " 3 |-4.87e+00 |-9.32e-01 | 5.00e-01 |-9.85e-02\n", - " 4 | 1.50e+00 | 7.36e-01 | 2.45e+00 |1.41e+00\n", - " 5 |-6.40e-01 | 2.47e-01 | 3.98e+00 |3.14e+00\n", - " \n", - "A 1-R2 coefficient of 1.08e-04 was obtained for the the power spectrum.\n", - "The current fit took 3.187300 seconds.\n", - "10.0%. Run time: 3.44s. Est. time left: 00:00:00:30\n", - "20.0%. Run time: 5.72s. Est. time left: 00:00:00:22\n", - "30.1%. Run time: 8.00s. Est. time left: 00:00:00:18\n", - "40.1%. Run time: 10.33s. Est. time left: 00:00:00:15\n", - "50.1%. Run time: 12.61s. Est. time left: 00:00:00:12\n", - "60.1%. Run time: 14.88s. Est. time left: 00:00:00:09\n", - "70.1%. Run time: 16.46s. Est. time left: 00:00:00:07\n", - "80.1%. Run time: 17.92s. Est. time left: 00:00:00:04\n", - "90.2%. Run time: 19.40s. Est. time left: 00:00:00:02\n", - "100.0%. Run time: 20.86s. Est. time left: 00:00:00:00\n", - "Total run time: 20.86s\n" - ] - } - ], - "source": [ - "w=np.linspace(-50,30,500)\n", - "Obath3, fitinfo = obs.approximate(method=\"ps\",wlist=w,Nmax=5)\n", - "print(fitinfo[\"summary\"])\n", - "HEOM_ohmic_ps_fit = HEOMSolver(\n", - " Hsys,\n", - " (Obath3,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - ")\n", - "results_ohmic_ps_fit = HEOM_ohmic_ps_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "d337fbc3", - "metadata": {}, - "source": [ - "# Methods based on the Prony Polinomial" - ] - }, - { - "cell_type": "markdown", - "id": "7f788d72", - "metadata": {}, - "source": [ - "## Prony" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "46fb17a5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result of fitting Correlation Function with 4 terms: \n", - " \n", - " Parameters| a | b | c | d \n", - " 1 | 4.97e-02 |-2.48e-02 |-4.97e-02 |-2.48e-02\n", - " 2 | 2.38e-01 |-5.58e-01 |-2.38e-01 |-5.58e-01\n", - " 3 | 7.39e-01 | 4.93e-01 |-7.39e-01 |4.93e-01\n", - " 4 |-2.67e-01 | 8.95e-02 | 2.67e-01 |8.95e-02\n", - " \n", - "A 1-R2 coefficient of 3.09e-05+9.93e-06j was obtained for the Correlation Function.\n", - "The current fit took 0.170541 seconds.\n", - "10.0%. Run time: 0.92s. Est. time left: 00:00:00:08\n", - "20.0%. Run time: 1.51s. Est. time left: 00:00:00:06\n", - "30.1%. Run time: 2.10s. Est. time left: 00:00:00:04\n", - "40.1%. Run time: 2.68s. Est. time left: 00:00:00:04\n", - "50.1%. Run time: 3.23s. Est. time left: 00:00:00:03\n", - "60.1%. Run time: 3.79s. Est. time left: 00:00:00:02\n", - "70.1%. Run time: 4.37s. Est. time left: 00:00:00:01\n", - "80.1%. Run time: 4.94s. Est. time left: 00:00:00:01\n", - "90.2%. Run time: 5.53s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 6.09s. Est. time left: 00:00:00:00\n", - "Total run time: 6.09s\n" - ] - } - ], - "source": [ - "tlist2=np.linspace(0,40,100)\n", - "pbath,fitinfo=obs.approximate(\"prony\",tlist2,Nr=4)\n", - "print(fitinfo[\"summary\"])\n", - "HEOM_ohmic_prony_fit = HEOMSolver(\n", - " Hsys,\n", - " (pbath,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - ")\n", - "results_ohmic_prony_fit = HEOM_ohmic_prony_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "920b8b0f", - "metadata": {}, - "source": [ - "## Matrix Pencil" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "efd0eae9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlation function fit:\n", - "\n", - "Result of fitting the real part of |Result of fitting the imaginary part \n", - "the correlation function with 5 terms: |of the correlation function with 5 terms: \n", - " | \n", - " Parameters| a | b | c | d | Parameters| a | b | c | d \n", - " 1 | 1.94e+00 | 8.61e+00 |-0.00e+00 |0.00e+00 | 1 | 9.91e-17 |-6.25e-01 |-7.78e+00 |0.00e+00 \n", - " 2 |-1.37e+01 | 4.65e+00 |-0.00e+00 |0.00e+00 | 2 | 1.07e+00 | 4.39e+00 |-1.75e+00 |-1.46e+00 \n", - " 3 | 1.29e+01 | 3.77e+00 |-0.00e+00 |0.00e+00 | 3 | 1.07e+00 | 4.39e+00 | 1.75e+00 |1.46e+00 \n", - " 4 | 3.78e-01 | 6.67e-01 |-0.00e+00 |0.00e+00 | 4 |-1.92e+00 | 1.51e+00 |-0.00e+00 |2.43e-15 \n", - " 5 | 5.30e-02 | 1.66e-01 |-0.00e+00 |0.00e+00 | 5 |-2.13e-01 | 5.44e-01 |-0.00e+00 |-2.22e-16 \n", - " | \n", - "A 1-R2 coefficient of 8.56e-06+0.00e+00j was obtained for the the real part of |A 1-R2 coefficient of 2.40e-05-5.11e-22j was obtained for the the imaginary part\n", - "the correlation function. |of the correlation function. \n", - "The current fit took 0.171659 seconds. |The current fit took 0.204456 seconds. \n", - "\n", - "10.0%. Run time: 3.40s. Est. time left: 00:00:00:30\n", - "20.0%. Run time: 6.14s. Est. time left: 00:00:00:24\n", - "30.1%. Run time: 9.27s. Est. time left: 00:00:00:21\n", - "40.1%. Run time: 12.41s. Est. time left: 00:00:00:18\n", - "50.1%. Run time: 15.86s. Est. time left: 00:00:00:15\n", - "60.1%. Run time: 19.38s. Est. time left: 00:00:00:12\n", - "70.1%. Run time: 22.92s. Est. time left: 00:00:00:09\n", - "80.1%. Run time: 26.45s. Est. time left: 00:00:00:06\n", - "90.2%. Run time: 30.00s. Est. time left: 00:00:00:03\n", - "100.0%. Run time: 33.53s. Est. time left: 00:00:00:00\n", - "Total run time: 33.53s\n" - ] - } - ], - "source": [ - "mpbath,fitinfo=obs.approximate(method=\"mp\",tlist=tlist2,Nr=5,Ni=5,separate=True)\n", - "print(fitinfo[\"summary\"])\n", - "HEOM_ohmic_mp_fit = HEOMSolver(\n", - " Hsys,\n", - " (mpbath,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - ")\n", - "results_ohmic_mp_fit = HEOM_ohmic_mp_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "db22b481", - "metadata": {}, - "source": [ - "## ESPRIT" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "c4db8362", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result of fitting Correlation Function with 4 terms: \n", - " \n", - " Parameters| a | b | c | d \n", - " 1 |-2.67e-01 | 8.95e-02 | 2.67e-01 |8.95e-02\n", - " 2 | 7.39e-01 | 4.93e-01 |-7.39e-01 |4.93e-01\n", - " 3 | 2.38e-01 |-5.58e-01 |-2.38e-01 |-5.58e-01\n", - " 4 | 4.97e-02 |-2.48e-02 |-4.97e-02 |-2.48e-02\n", - " \n", - "A 1-R2 coefficient of 3.09e-05+9.93e-06j was obtained for the Correlation Function.\n", - "The current fit took 0.167535 seconds.\n", - "10.0%. Run time: 0.93s. Est. time left: 00:00:00:08\n", - "20.0%. Run time: 1.54s. Est. time left: 00:00:00:06\n", - "30.1%. Run time: 2.16s. Est. time left: 00:00:00:05\n", - "40.1%. Run time: 2.77s. Est. time left: 00:00:00:04\n", - "50.1%. Run time: 3.35s. Est. time left: 00:00:00:03\n", - "60.1%. Run time: 3.92s. Est. time left: 00:00:00:02\n", - "70.1%. Run time: 4.48s. Est. time left: 00:00:00:01\n", - "80.1%. Run time: 5.08s. Est. time left: 00:00:00:01\n", - "90.2%. Run time: 5.66s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 6.21s. Est. time left: 00:00:00:00\n", - "Total run time: 6.21s\n" - ] - } - ], - "source": [ - "esbath,fitinfo=obs.approximate(\"esprit\",tlist2,Nr=4)\n", - "print(fitinfo[\"summary\"])\n", - "HEOM_ohmic_es_fit = HEOMSolver(\n", - " Hsys,\n", - " (esbath,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - ")\n", - "results_ohmic_es_fit = HEOM_ohmic_es_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "3fafdfdc", - "metadata": {}, - "source": [ - "# Rational Approximations" - ] - }, - { - "cell_type": "markdown", - "id": "c2e3d835", - "metadata": {}, - "source": [ - "## AAA" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "9c6e828f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/gerardo/Documents/gsuarezr/qutip_gsoc_app/qutip/utilities.py:55: RuntimeWarning: overflow encountered in exp\n", - " result[non_zero] = 1 / (np.exp(w[non_zero] / w_th) - 1)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result of fitting the power spectrum with 5 terms: \n", - " \n", - " Parameters| a | b | c | d \n", - " 1 | 9.81e-01 | 1.27e+00 | 2.27e+00 |1.79e+00\n", - " 2 | 5.53e-01 |-1.05e+00 | 9.41e-01 |-1.62e-01\n", - " 3 | 3.92e-02 |-3.15e-02 | 1.52e-01 |-3.28e-02\n", - " 4 | 4.13e-04 |-1.91e-04 | 1.13e-02 |-1.67e-03\n", - " 5 | 4.66e-08 |-2.57e-07 | 1.38e-04 |-1.27e-04\n", - " \n", - "A 1-R2 coefficient of 1.09e-04 was obtained for the the power spectrum.\n", - "The current fit took 10.199787 seconds.\n", - "10.0%. Run time: 1.56s. Est. time left: 00:00:00:14\n", - "20.0%. Run time: 2.42s. Est. time left: 00:00:00:09\n", - "30.1%. Run time: 3.26s. Est. time left: 00:00:00:07\n", - "40.1%. Run time: 4.11s. Est. time left: 00:00:00:06\n", - "50.1%. Run time: 4.95s. Est. time left: 00:00:00:04\n", - "60.1%. Run time: 5.80s. Est. time left: 00:00:00:03\n", - "70.1%. Run time: 6.64s. Est. time left: 00:00:00:02\n", - "80.1%. Run time: 7.49s. Est. time left: 00:00:00:01\n", - "90.2%. Run time: 8.33s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 9.16s. Est. time left: 00:00:00:00\n", - "Total run time: 9.16s\n" - ] - } - ], - "source": [ - "aaabath,fitinfo=obs.approximate(\"aaa\",np.concatenate((-np.logspace(3,-8,3500),np.logspace(-8,3,3500))),N_max=6,tol=1e-15)\n", - "print(fitinfo[\"summary\"])\n", - "HEOM_ohmic_aaa_fit = HEOMSolver(\n", - " Hsys,\n", - " (aaabath,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - ")\n", - "results_ohmic_aaa_fit = HEOM_ohmic_aaa_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "ba03829b", - "metadata": {}, - "source": [ - "# ESPIRA I" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "381bb1ee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlation function fit:\n", - "\n", - "Result of fitting the real part of |Result of fitting the imaginary part \n", - "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", - " | \n", - " Parameters| a | b | c | d | Parameters| a | b | c | d \n", - " 1 | 2.18e-01 | 3.22e+00 | 1.52e+00 |1.58e+00 | 1 | 7.39e-01 | 3.68e+00 |-2.44e+00 |-5.14e-01 \n", - " 2 | 8.01e-01 | 2.84e+00 |-1.32e+00 |-1.54e+00 | 2 | 9.27e-01 | 3.57e+00 | 2.23e+00 |4.43e-01 \n", - " 3 | 3.92e-01 | 6.42e-01 |-4.68e-02 |-4.15e-02 | 3 |-1.60e+00 | 1.26e+00 |-4.47e-02 |5.98e-02 \n", - " 4 | 4.45e-02 | 1.54e-01 |-6.22e-03 |-4.58e-03 | 4 |-7.70e-02 | 3.63e-01 |-2.83e-02 |1.68e-02 \n", - " | \n", - "A 1-R2 coefficient of 1.48e-04-5.04e-06j was obtained for the the real part of |A 1-R2 coefficient of 8.87e-06-9.73e-06j was obtained for the the imaginary part\n", - "the correlation function. |of the correlation function. \n", - "The current fit took 1.521248 seconds. |The current fit took 1.571012 seconds. \n", - "\n", - "10.0%. Run time: 0.71s. Est. time left: 00:00:00:06\n", - "20.0%. Run time: 1.26s. Est. time left: 00:00:00:05\n", - "30.1%. Run time: 1.79s. Est. time left: 00:00:00:04\n", - "40.1%. Run time: 2.33s. Est. time left: 00:00:00:03\n", - "50.1%. Run time: 2.87s. Est. time left: 00:00:00:02\n", - "60.1%. Run time: 3.40s. Est. time left: 00:00:00:02\n", - "70.1%. Run time: 3.93s. Est. time left: 00:00:00:01\n", - "80.1%. Run time: 4.49s. Est. time left: 00:00:00:01\n", - "90.2%. Run time: 5.02s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 5.53s. Est. time left: 00:00:00:00\n", - "Total run time: 5.53s\n" - ] - } - ], - "source": [ - "tlist4=np.linspace(0,20,1000)\n", - "espibath,fitinfo=obs._approx_by_prony(\"espira-I\",tlist4,Nr=4,Ni=4,separate=True)\n", - "print(fitinfo[\"summary\"])\n", - "HEOM_ohmic_espira_fit = HEOMSolver(\n", - " Hsys,\n", - " (espibath,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - ")\n", - "results_ohmic_espira_fit = HEOM_ohmic_espira_fit.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "a25d9385", - "metadata": {}, - "source": [ - "# ESPIRA II" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "1756c0ab", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlation function fit:\n", - "\n", - "Result of fitting the real part of |Result of fitting the imaginary part \n", - "the correlation function with 4 terms: |of the correlation function with 4 terms: \n", - " | \n", - " Parameters| a | b | c | d | Parameters| a | b | c | d \n", - " 1 | 3.75e-01 | 2.85e+00 |-1.66e+00 |-1.14e+00 | 1 | 7.81e-01 | 3.59e+00 | 2.38e+00 |4.62e-01 \n", - " 2 | 6.32e-01 | 2.62e+00 | 1.54e+00 |1.10e+00 | 2 | 8.75e-01 | 3.51e+00 |-2.29e+00 |-4.10e-01 \n", - " 3 | 5.12e-02 | 1.63e-01 | 6.22e-03 |4.19e-03 | 3 |-1.59e+00 | 1.26e+00 | 3.56e-02 |-3.86e-02 \n", - " 4 | 4.45e-01 | 7.02e-01 | 4.15e-02 |4.12e-02 | 4 |-7.57e-02 | 3.62e-01 | 2.51e-02 |-1.47e-02 \n", - " | \n", - "A 1-R2 coefficient of 4.02e-05-5.77e-06j was obtained for the the real part of |A 1-R2 coefficient of 7.44e-06+1.93e-06j was obtained for the the imaginary part\n", - "the correlation function. |of the correlation function. \n", - "The current fit took 1.758265 seconds. |The current fit took 1.723816 seconds. \n", - "\n", - "10.0%. Run time: 0.69s. Est. time left: 00:00:00:06\n", - "20.0%. Run time: 1.14s. Est. time left: 00:00:00:04\n", - "30.1%. Run time: 1.60s. Est. time left: 00:00:00:03\n", - "40.1%. Run time: 2.06s. Est. time left: 00:00:00:03\n", - "50.1%. Run time: 2.47s. Est. time left: 00:00:00:02\n", - "60.1%. Run time: 2.86s. Est. time left: 00:00:00:01\n", - "70.1%. Run time: 3.26s. Est. time left: 00:00:00:01\n", - "80.1%. Run time: 3.65s. Est. time left: 00:00:00:00\n", - "90.2%. Run time: 4.07s. Est. time left: 00:00:00:00\n", - "100.0%. Run time: 4.48s. Est. time left: 00:00:00:00\n", - "Total run time: 4.48s\n" - ] - } - ], - "source": [ - "espibath2,fitinfo=obs._approx_by_prony(\"espira-II\",tlist4,Nr=4,Ni=4,separate=True)\n", - "print(fitinfo[\"summary\"])\n", - "HEOM_ohmic_espira_fit2 = HEOMSolver(\n", - " Hsys,\n", - " (espibath2,Q),\n", - " max_depth=max_depth,\n", - " options=options,\n", - ")\n", - "results_ohmic_espira2_fit = HEOM_ohmic_espira_fit2.run(rho0, tlist)" - ] - }, - { - "cell_type": "markdown", - "id": "d486d3f6", - "metadata": {}, - "source": [ - "Finally we plot the dynamics obtained by the different methods" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "1a701be0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7))\n", - "\n", - "plot_result_expectations(\n", - " [\n", - "\n", - " (results_ohmic_corr_fit, P11p, \"r\", \"Correlation Fit\"),\n", - " (results_ohmic_sd_fit, P11p, \"g\", \"SD Fit\"),\n", - " (results_ohmic_sd_fit, P11p, \"y\", \"PS Fit\"),\n", - " (results_ohmic_prony_fit, P11p, \"k\", \" Prony Fit\"),\n", - " (results_ohmic_mp_fit, P11p, \"r\", \"Matrix Pencil Fit\"),\n", - " (results_ohmic_es_fit, P11p, \"b-.\", \"ESPRIT Fit\"),\n", - " (results_ohmic_aaa_fit, P11p, \"r-.\", \"Matrix AAA Fit\"),\n", - " (results_ohmic_espira_fit, P11p, \"k\", \"ESPIRA I Fit\"),\n", - " (results_ohmic_espira2_fit, P11p, \"--\", \"ESPIRA II Fit\"),\n", - "\n", - " ],\n", - " axes=axes,\n", - ")\n", - "axes.set_ylabel(r\"$\\rho_{11}$\", fontsize=30)\n", - "axes.set_xlabel(r\"$t\\;\\omega_c$\", fontsize=30)\n", - "axes.legend(loc=0, fontsize=20);\n", - "axes.set_yscale(\"log\")" - ] - }, - { - "cell_type": "markdown", - "id": "ef38be59", - "metadata": {}, - "source": [ - "## About" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "14ae80bc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "QuTiP: Quantum Toolbox in Python\n", - "================================\n", - "Copyright (c) QuTiP team 2011 and later.\n", - "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", - "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", - "Original developers: R. J. Johansson & P. D. Nation.\n", - "Previous lead developers: Chris Granade & A. Grimsmo.\n", - "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", - "\n", - "QuTiP Version: 5.2.0.dev0+678a16d\n", - "Numpy Version: 2.2.1\n", - "Scipy Version: 1.15.0\n", - "Cython Version: 3.0.11\n", - "Matplotlib Version: 3.10.0\n", - "Python Version: 3.13.0\n", - "Number of CPUs: 8\n", - "BLAS Info: Generic\n", - "INTEL MKL Ext: None\n", - "Platform Info: Linux (x86_64)\n", - "Installation path: /home/gerardo/Documents/gsuarezr/qutip_gsoc_app/qutip\n", - "\n", - "Installed QuTiP family packages\n", - "-------------------------------\n", - "\n", - "No QuTiP family packages installed.\n", - "\n", - "================================================================================\n", - "Please cite QuTiP in your publication.\n", - "================================================================================\n", - "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" - ] - } - ], - "source": [ - "qutip.about()" - ] - }, - { - "cell_type": "markdown", - "id": "9e328c08", - "metadata": {}, - "source": [ - "## Testing\n", - "\n", - "This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "44448c73", - "metadata": {}, - "outputs": [], - "source": [ - "tol=1e-2\n", - "assert np.allclose(\n", - " expect(P11p, results_ohmic_ps_fit.states),\n", - " expect(P11p, results_ohmic_sd_fit.states),\n", - " rtol=tol,\n", - ")\n", - "assert np.allclose(\n", - " expect(P11p, results_ohmic_corr_fit.states),\n", - " expect(P11p, results_ohmic_sd_fit.states),\n", - " rtol=tol,\n", - ")\n", - "assert np.allclose(\n", - " expect(P11p, results_ohmic_aaa_fit.states),\n", - " expect(P11p, results_ohmic_sd_fit.states),\n", - " rtol=tol,\n", - ")\n", - "assert np.allclose(\n", - " expect(P11p, results_ohmic_mp_fit.states),\n", - " expect(P11p, results_ohmic_sd_fit.states),\n", - " rtol=tol,\n", - ")\n", - "assert np.allclose(\n", - " expect(P11p, results_ohmic_prony_fit.states),\n", - " expect(P11p, results_ohmic_sd_fit.states),\n", - " rtol=tol,\n", - ")\n", - "\n", - "assert np.allclose(\n", - " expect(P11p, results_ohmic_es_fit.states),\n", - " expect(P11p, results_ohmic_sd_fit.states),\n", - " rtol=tol,\n", - ")\n", - "assert np.allclose(\n", - " expect(P11p, results_ohmic_espira_fit.states),\n", - " expect(P11p, results_ohmic_sd_fit.states),\n", - " rtol=tol,\n", - ")\n", - "assert np.allclose(\n", - " expect(P11p, results_ohmic_espira2_fit.states),\n", - " expect(P11p, results_ohmic_sd_fit.states),\n", - " rtol=tol,\n", - ")" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "qutip-dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials-v5/fitting_summary.md b/tutorials-v5/fitting_summary.md index dfab0ee9..feee81ad 100644 --- a/tutorials-v5/fitting_summary.md +++ b/tutorials-v5/fitting_summary.md @@ -113,7 +113,11 @@ And set the cut-off for the HEOM hierarchy: # The max_depth defaults to 5 so that the notebook executes more # quickly. Change it to 11 to wait longer for more accurate results. -max_depth = 7 +max_depth = 5 #could not do 11 my laptop rans out of ram +# I used 7 because I wanted to make sure things were working correctly +# cf is terribly slow at 7, probably can be done better by changing guess, lower +# upper, use 5 to play around :) + # options used for the differential equation solver, while default works it # is way slower than using bdf options = { @@ -191,6 +195,7 @@ will cover the following approaches: - ESPIRA-I (`espira-I`) :question: - ESPIRA-II (`espira-II`) +the ones with a question mark are the ones I think maybe can be deleted. Here's a quick high level comparison between the three different families of methods @@ -248,7 +253,8 @@ tlist = np.linspace(0, 30 * np.pi / Del, 600) ## Correlation Function ```{code-cell} ipython3 -Obath, fitinfo = obs.approximate(method="cf",tlist=tlist,Nr_max=3,Ni_max=2,maxfev=1e9,target_rsme=None) +t=np.linspace(0,20,500) +Obath, fitinfo = obs.approximate(method="cf",tlist=t,Nr_max=4,Ni_max=4,maxfev=1e9,target_rsme=None) print(fitinfo["summary"]) HEOM_ohmic_corr_fit = HEOMSolver( Hsys, @@ -289,7 +295,7 @@ HEOM_ohmic_ps_fit = HEOMSolver( results_ohmic_ps_fit = HEOM_ohmic_ps_fit.run(rho0, tlist) ``` -# Methods based on the Prony Polinomial +# Methods based on the Prony Polinomial +++ @@ -311,7 +317,7 @@ results_ohmic_prony_fit = HEOM_ohmic_prony_fit.run(rho0, tlist) ## Matrix Pencil ```{code-cell} ipython3 -mpbath,fitinfo=obs.approximate(method="mp",tlist=tlist2,Nr=5) +mpbath,fitinfo=obs.approximate(method="mp",tlist=tlist2,Nr=5,Ni=5,separate=True) print(fitinfo["summary"]) HEOM_ohmic_mp_fit = HEOMSolver( Hsys, @@ -322,10 +328,9 @@ HEOM_ohmic_mp_fit = HEOMSolver( results_ohmic_mp_fit = HEOM_ohmic_mp_fit.run(rho0, tlist) ``` -## ESPRIT +## ESPRIT ```{code-cell} ipython3 - esbath,fitinfo=obs.approximate("esprit",tlist2,Nr=4) print(fitinfo["summary"]) HEOM_ohmic_es_fit = HEOMSolver( diff --git a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md index 6b303b0b..2f063fca 100644 --- a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md +++ b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md @@ -81,7 +81,7 @@ Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. ## Setup -```{code-cell} ipython3 +```{code-cell} import contextlib import time @@ -112,13 +112,13 @@ from qutip.solver.heom import ( Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: -```{code-cell} ipython3 +```{code-cell} def cot(x): """ Vectorized cotangent of x. """ return 1. / np.tan(x) ``` -```{code-cell} ipython3 +```{code-cell} @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -132,7 +132,7 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} ipython3 +```{code-cell} # Solver options: options = { @@ -149,19 +149,19 @@ options = { And let us set up the system Hamiltonian, bath and system measurement operators: -```{code-cell} ipython3 +```{code-cell} # Defining the system Hamiltonian eps = .0 # Energy of the 2-level system. Del = .2 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` -```{code-cell} ipython3 +```{code-cell} # Initial state of the system. rho0 = basis(2, 0) * basis(2, 0).dag() ``` -```{code-cell} ipython3 +```{code-cell} # System-bath coupling (Drude-Lorentz spectral density) Q = sigmaz() # coupling operator @@ -184,7 +184,7 @@ NC = 13 tlist = np.linspace(0, np.pi / Del, 600) ``` -```{code-cell} ipython3 +```{code-cell} # Define some operators with which we will measure the system # 1,1 element of density matrix - corresonding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -197,7 +197,7 @@ P12p = basis(2, 0) * basis(2, 1).dag() Let us briefly inspect the spectral density. -```{code-cell} ipython3 +```{code-cell} bath = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=500) w = np.linspace(0, 5, 1000) J = bath.spectral_density(w) @@ -211,7 +211,7 @@ axes.set_ylabel(r'J', fontsize=28); ## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator -```{code-cell} ipython3 +```{code-cell} with timer("RHS construction time"): matsBath=bath.approx_by_matsubara(Nk=Nk) HEOMMats = HEOMSolver(Hsys, (matsBath,Q), NC, options=options) @@ -222,7 +222,7 @@ with timer("ODE solver time"): ## Simulation 2: Matsubara decomposition (including terminator) -```{code-cell} ipython3 +```{code-cell} with timer("RHS construction time"): matsBath,delta=bath.approx_by_matsubara(Nk=Nk,compute_delta=True) terminator = system_terminator(Q,delta) @@ -233,7 +233,7 @@ with timer("ODE solver time"): resultMatsT = HEOMMatsT.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} # Plot the results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -256,7 +256,7 @@ axes.legend(loc=0, fontsize=12); ## Simulation 3: Pade decomposition -```{code-cell} ipython3 +```{code-cell} # First, compare Matsubara and Pade decompositions padeBath = bath.approx_by_pade(Nk=Nk) @@ -296,7 +296,7 @@ ax2.set_xlabel(r't', fontsize=28) ax2.legend(loc=0, fontsize=12); ``` -```{code-cell} ipython3 +```{code-cell} with timer("RHS construction time"): HEOMPade = HEOMSolver(Hsys, (padeBath,Q), NC, options=options) @@ -304,7 +304,7 @@ with timer("ODE solver time"): resultPade = HEOMPade.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} # Plot the results fig, axes = plt.subplots(figsize=(8, 8)) @@ -333,7 +333,7 @@ In `HEOM 1a: Spin-Bath model (introduction)` a fit is performed manually, here we will use the built-in tools. More details about them can be seen in `HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions` -```{code-cell} ipython3 +```{code-cell} tfit=np.linspace(0,10,10000) lower = [0, -np.inf, -1e-6, -3] guess = [np.real(bath.correlation_function(0))/10, -10, 0, 0] @@ -344,13 +344,13 @@ envfit,fitinfo = bath.approximate("cf",tlist=tfit,Nr_max=2,Ni_max=1,full_ansatz= lower=lower,upper=upper,guess=guess) ``` -```{code-cell} ipython3 +```{code-cell} print(fitinfo['summary']) ``` We can quickly compare the result of the Fit with the Pade expansion -```{code-cell} ipython3 +```{code-cell} fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8)) ax1.plot( @@ -388,7 +388,7 @@ ax2.set_ylabel(r"$C_I(t)$", fontsize=28) ax2.legend(loc=0, fontsize=12) ``` -```{code-cell} ipython3 +```{code-cell} with timer("RHS construction time"): # We reduce NC slightly here for speed of execution because we retain # 3 exponents in ckAR instead of 1. Please restore full NC for @@ -401,7 +401,7 @@ with timer("ODE solver time"): ## Simulation 5: Bloch-Redfield -```{code-cell} ipython3 +```{code-cell} with timer("ODE solver time"): resultBR = brmesolve( Hsys, rho0, tlist, @@ -413,7 +413,7 @@ with timer("ODE solver time"): Finally, let's plot all of our different results to see how they shape up against each other. -```{code-cell} ipython3 +```{code-cell} # Calculate expectation values in the bases: P11_mats = np.real(expect(resultMats.states, P11p)) P11_matsT = np.real(expect(resultMatsT.states, P11p)) @@ -422,7 +422,7 @@ P11_fit = np.real(expect(resultFit.states, P11p)) P11_br = np.real(expect(resultBR.states, P11p)) ``` -```{code-cell} ipython3 +```{code-cell} rcParams = { "axes.titlesize": 25, "axes.labelsize": 30, @@ -439,7 +439,7 @@ rcParams = { } ``` -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) with plt.rc_context(rcParams): @@ -479,7 +479,7 @@ with plt.rc_context(rcParams): ## About -```{code-cell} ipython3 +```{code-cell} qutip.about() ``` @@ -487,7 +487,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 +```{code-cell} assert np.allclose(P11_matsT, P11_pade, rtol=1e-3) assert np.allclose(P11_matsT, P11_fit, rtol=1e-3) ``` diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md index 05c508da..91e8c87b 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md @@ -334,7 +334,6 @@ is reached or the maximum number allowed `Nmax` is reached. The default target is a normalized root mean squared error of $5\times 10^{-6}$, if set to None the fit is performed only with the maximum number of exponents specified - ```{code-cell} ipython3 bath, fitinfo = sd_env.approximate("sd",w,Nmax=4) ``` @@ -845,7 +844,7 @@ tlist = np.linspace(0, 30 * np.pi / Del, 600) Just like the other `BosonicEnvironment` we can obtain a decaying exponential representation of the environment via the `approx_by_cf_fit` and -`approx_by_sd_fit` methods. +`approx_by_sd_fit` methods. ```{code-cell} ipython3 tlist = np.linspace(0, 30 * np.pi / Del, 5000) @@ -954,7 +953,6 @@ gen_plots(mpbath, w, J, t, C, w2, S) ## Using the ESPRIT Method on the Correlation Function ```{code-cell} ipython3 - esbath,fitinfo=obs.approximate("esprit",tlist2,Nr=4,separate=False) print(fitinfo["summary"]) HEOM_ohmic_es_fit = HEOMSolver( @@ -1010,8 +1008,8 @@ results_ohmic_espira_fit = HEOM_ohmic_espira_fit.run(rho0, tlist) ``` ```{code-cell} ipython3 -tlist4=np.linspace(0,20,1000) -espibath2,fitinfo=obs._approx_by_prony("espira-II",tlist4,Nr=4,Ni=4) +tlist4=np.linspace(0,40,500) +espibath2,fitinfo=obs._approx_by_prony("espira-II",tlist4,Nr=4,Ni=4,separate=True) print(fitinfo["summary"]) ``` @@ -1068,7 +1066,6 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. ```{code-cell} ipython3 - assert np.allclose( expect(P11p, results_spectral_fit_pk[2].states), expect(P11p, results_spectral_fit_pk[3].states), @@ -1106,7 +1103,3 @@ assert np.allclose( rtol=1e-2, ) ``` - -```{code-cell} ipython3 - -``` diff --git a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md index bef402ee..7341c606 100644 --- a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md +++ b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md @@ -69,7 +69,7 @@ In this notebook we: ## Setup -```{code-cell} ipython3 +```{code-cell} import contextlib import dataclasses import time @@ -96,7 +96,7 @@ from IPython.display import display ## Helpers -```{code-cell} ipython3 +```{code-cell} @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -110,7 +110,7 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} ipython3 +```{code-cell} # Solver options: # We set store_ados to True so that we can @@ -133,7 +133,7 @@ options = { And let us set up the system Hamiltonian, bath and system measurement operators: -```{code-cell} ipython3 +```{code-cell} # Define the system Hamiltonian: # The system is a single fermion with energy level split e1: @@ -142,7 +142,7 @@ e1 = 1.0 H = e1 * d1.dag() * d1 ``` -```{code-cell} ipython3 +```{code-cell} # Define parameters for left and right fermionic baths. # Each bath is a lead (i.e. a wire held at a potential) # with temperature T and chemical potential mu. @@ -195,7 +195,7 @@ bath_R = LorentzianBathParameters(Q=d1, lead="R") Let's plot the spectral density. -```{code-cell} ipython3 +```{code-cell} w_list = np.linspace(-2, 2, 100) fig, ax = plt.subplots(figsize=(12, 7)) @@ -223,7 +223,7 @@ ax.legend(); Next let's plot the emission and absorption by the leads. -```{code-cell} ipython3 +```{code-cell} w_list = np.linspace(-2, 2, 100) fig, ax = plt.subplots(figsize=(12, 7)) @@ -269,7 +269,7 @@ ax.legend(); Let's start by solving for the evolution using a Pade expansion of the correlation function of the Lorentzian spectral density: -```{code-cell} ipython3 +```{code-cell} # HEOM dynamics using the Pade approximation: # Times to solve for and initial system state: @@ -300,7 +300,7 @@ with timer("Steady state solver time"): Now let us plot the result which shows the decay of the initially excited impurity. This is not very illuminating, but we will compare it with the Matsubara expansion and analytic solution sortly: -```{code-cell} ipython3 +```{code-cell} # Plot the Pade results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -321,7 +321,7 @@ axes.legend(fontsize=12); Now let us do the same for the Matsubara expansion: -```{code-cell} ipython3 +```{code-cell} # HEOM dynamics using the Matsubara approximation: envL_mats= envL.approx_by_matsubara(Nk=Nk, tag="L") @@ -340,7 +340,7 @@ with timer("Steady state solver time"): We see a marked difference in the Matsubara vs Pade results: -```{code-cell} ipython3 +```{code-cell} # Plot the Pade results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -376,7 +376,7 @@ One advantage of this simple model is that the steady state current to the baths See the [QuTiP-BoFiN paper](https://arxiv.org/abs/2010.10806) for a detailed description and references for the analytic result. Below we just perform the required integration numerically. -```{code-cell} ipython3 +```{code-cell} def analytical_steady_state_current(bath_L, bath_R, e1): """ Calculate the analytical steady state current. """ @@ -414,7 +414,7 @@ To compare the analytical result above with the result from the HEOM, we need to In the function `state_current(...)` below, we extract the first level ADOs for the specified bath and sum the contributions to the current from each: -```{code-cell} ipython3 +```{code-cell} def state_current(ado_state, bath_tag): """ Determine current from the given bath (either "R" or "L") to the system in the given ADO state. @@ -438,7 +438,7 @@ def state_current(ado_state, bath_tag): Now we can calculate the steady state currents from the Pade and Matsubara HEOM results: -```{code-cell} ipython3 +```{code-cell} curr_ss_pade_L = state_current(ado_ss_pade, "L") curr_ss_pade_R = state_current(ado_ss_pade, "R") @@ -446,7 +446,7 @@ print(f"Pade steady state current (L): {curr_ss_pade_L}") print(f"Pade steady state current (R): {curr_ss_pade_R}") ``` -```{code-cell} ipython3 +```{code-cell} curr_ss_mats_L = state_current(ado_ss_mats, "L") curr_ss_mats_R = state_current(ado_ss_mats, "R") @@ -458,7 +458,7 @@ Note that the currents from each bath balance as is required by the steady state Now let's compare all three: -```{code-cell} ipython3 +```{code-cell} print(f"Pade current (R): {curr_ss_pade_R}") print(f"Matsubara current (R): {curr_ss_mats_R}") print(f"Analytical curernt: {curr_ss_analytic}") @@ -478,7 +478,7 @@ Now lets plot the current as a function of bias voltage (the bias voltage is the We will calculate the steady state current for each `theta` both analytically and using the HEOM with the Pade correlation expansion approximation. -```{code-cell} ipython3 +```{code-cell} # Theta (bias voltages) thetas = np.linspace(-4, 4, 100) @@ -539,7 +539,7 @@ curr_ss_pade_theta = [ Below we plot the results and see that even with `Nk=6`, the HEOM Pade approximation gives good results for the steady state current. Increasing `Nk` to `10` gives very accurate results. -```{code-cell} ipython3 +```{code-cell} fig, ax = plt.subplots(figsize=(12, 7)) ax.plot( @@ -566,7 +566,7 @@ ax.legend(fontsize=25); ## About -```{code-cell} ipython3 +```{code-cell} qutip.about() ``` @@ -574,7 +574,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 +```{code-cell} assert np.allclose(curr_ss_pade_L + curr_ss_pade_R, 0) assert np.allclose(curr_ss_mats_L + curr_ss_mats_R, 0) assert np.allclose(curr_ss_pade_R, curr_ss_analytic, rtol=1e-4) From af5dec7e884257ac9e2ccf52fe00505cf90be768 Mon Sep 17 00:00:00 2001 From: Gerardo Suarez Date: Wed, 23 Apr 2025 00:06:04 +0200 Subject: [PATCH 22/44] new PR --- .../heom/heom-1a-spin-bath-model-basic.md | 123 ++++--- ...1b-spin-bath-model-very-strong-coupling.md | 91 ++--- .../heom-1c-spin-bath-model-underdamped-sd.md | 340 +++++++++--------- .../heom-1d-spin-bath-model-ohmic-fitting.md | 265 ++++++++------ .../heom-1e-spin-bath-model-pure-dephasing.md | 252 +++++++------ tutorials-v5/heom/heom-2-fmo-example.md | 65 ++-- .../heom/heom-3-quantum-heat-transport.md | 49 +-- .../heom/heom-4-dynamical-decoupling.md | 50 +-- .../heom-5a-fermions-single-impurity-model.md | 80 +++-- .../heom-5b-fermions-discrete-boson-model.md | 51 +-- tutorials-v5/heom/heom-index.md | 2 +- 11 files changed, 725 insertions(+), 643 deletions(-) diff --git a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md index 2783a8e5..be97014d 100644 --- a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md +++ b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md @@ -5,9 +5,9 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.16.1 + jupytext_version: 1.17.0 kernelspec: - display_name: qutip-dev + display_name: qutip-tutorials language: python name: python3 --- @@ -285,7 +285,7 @@ options = {**default_options} with timer("RHS construction time"): env = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI) - HEOMMats = HEOMSolver(Hsys, (env,Q), NC, options=options) + HEOMMats = HEOMSolver(Hsys, (env, Q), NC, options=options) with timer("ODE solver time"): resultMats = HEOMMats.run(rho0, tlist) @@ -302,22 +302,28 @@ plot_result_expectations( In practice, one would not perform this laborious expansion for the Drude-Lorentz correlation function, because QuTiP already has a class, -`DrudeLorentzBath`, that can construct this bath for you. Nevertheless, +`DrudeLorentzEnvironment`, that can construct this bath for you. Nevertheless, knowing how to perform this expansion will allow you to construct your own baths for other spectral densities. -Below we show how to use this built-in functionality: +The `DrudeLorentzEnvironment` computes the correlation function using the Pade +approximation, the number of terms in the correlation function +expansion is specified using the $N_{k}$ parameter, it defaults to $10$, when +simulating, low temperatures $10$ Pade exponents may noy be enough, more details +about the Pade approximation are provided below . Next we show how to use this +built-in functionality: ```python # Compare to built-in Drude-Lorentz bath: with timer("RHS construction time"): - dlenv = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T,Nk=100) - dlenv_approx=dlenv.approx_by_matsubara(Nk=Nk) - HEOM_dlbath = HEOMSolver(Hsys, (dlenv_approx,Q), NC, options=options) + dlenv = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=100) + # 100 Terms in the Pade expansion for the correlation funtion + dlenv_approx = dlenv.approximate(method="matsubara", Nk=Nk) + HEOM_dlbath = HEOMSolver(Hsys, (dlenv_approx, Q), NC, options=options) with timer("ODE solver time"): - result_dlbath = HEOM_dlbath.run(rho0, tlist) # normal 115 + result_dlbath = HEOM_dlbath.run(rho0, tlist) ``` ```python @@ -334,6 +340,7 @@ The `DrudeLorentzEnvironment` class also allows us to easily obtain the power sp ```python w = np.linspace(-10, 20, 1000) w2 = np.linspace(0, 20, 1000) + fig, axs = plt.subplots(2, 2) axs[0, 0].plot(w, dlenv.power_spectrum(w)) @@ -405,7 +412,7 @@ Lindblad form. This is clearer if we plot the correlation function with a large number of Matsubara terms. To create the plot, we use the utility function of the -`DrudeLorentzBath` class mentioned above. +`DrudeLorentzEnvironment` class mentioned above. ```python def plot_correlation_expansion_divergence(): @@ -414,8 +421,8 @@ def plot_correlation_expansion_divergence(): """ t = np.linspace(0, 2, 100) - # correlation coefficients with 15k and 2 terms - corr_15k = dlenv.correlation_function(t) + # correlation coefficients with 100 and 2 terms + corr_100 = dlenv.correlation_function(t) corr_2 = dlenv_approx.correlation_function(t) fig, ax1 = plt.subplots(figsize=(12, 7)) @@ -427,16 +434,17 @@ def plot_correlation_expansion_divergence(): t, np.imag(corr_2), color="r", linewidth=3, label=rf"Mats = {Nk} imag" ) ax1.plot( - t, np.real(corr_15k), "b--", linewidth=3, label=r"Mats = 15000 real" + t, np.real(corr_100), "b--", linewidth=3, label=r"Mats = 15000 real" ) ax1.plot( - t, np.imag(corr_15k), "r--", linewidth=3, label=r"Mats = 15000 imag" + t, np.imag(corr_100), "r--", linewidth=3, label=r"Mats = 15000 imag" ) ax1.set_xlabel("t") ax1.set_ylabel(r"$C$") ax1.legend() + plot_correlation_expansion_divergence() ``` @@ -448,8 +456,9 @@ Let us evaluate the result including this Ishizaki-Tanimura terminator: # Notes: # # * when using the built-in DrudeLorentzEnvironment the terminator (L_bnd) is -# available from bath.terminator(). -# +# available from by setting the parameter compute_delta to True in the +# approximate method +# # * in the legacy HSolverDL function the terminator is included automatically # if the parameter bnd_cut_approx=True is used. @@ -472,7 +481,7 @@ options = {**default_options, "rtol": 1e-14, "atol": 1e-14} with timer("RHS construction time"): bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI) - HEOMMatsT = HEOMSolver(Ltot, (bath,Q), NC, options=options) + HEOMMatsT = HEOMSolver(Ltot, (bath, Q), NC, options=options) with timer("ODE solver time"): resultMatsT = HEOMMatsT.run(rho0, tlist) @@ -493,9 +502,9 @@ Or using the built-in Drude-Lorentz environment we can write simply: options = {**default_options, "rtol": 1e-14, "atol": 1e-14} with timer("RHS construction time"): - bath,delta = dlenv.approx_by_matsubara(Nk=Nk,compute_delta=True) - Ltot = liouvillian(Hsys) + system_terminator(Q,delta) - HEOM_dlbath_T = HEOMSolver(Ltot, (bath,Q), NC, options=options) + bath, delta = dlenv.approx_by_matsubara(Nk=Nk, compute_delta=True) + Ltot = liouvillian(Hsys) + system_terminator(Q, delta) + HEOM_dlbath_T = HEOMSolver(Ltot, (bath, Q), NC, options=options) with timer("ODE solver time"): result_dlbath_T = HEOM_dlbath_T.run(rho0, tlist) @@ -639,7 +648,7 @@ def pade_corr(tlist, lmax): tlist_corr = np.linspace(0, 2, 100) cppLP, etapLP, gampLP = pade_corr(tlist_corr, 2) -corr_15k = dlenv.correlation_function(tlist_corr, Nk=15) +corr_100 = dlenv.correlation_function(tlist_corr, Nk=15) corr_2k = dlenv.correlation_function(tlist_corr, Nk=2) fig, ax1 = plt.subplots(figsize=(12, 7)) @@ -652,7 +661,7 @@ ax1.plot( ) ax1.plot( tlist_corr, - np.real(corr_15k), + np.real(corr_100), "r--", linewidth=3, label=r"real pade 15 terms", @@ -673,14 +682,14 @@ fig, ax1 = plt.subplots(figsize=(12, 7)) ax1.plot( tlist_corr, - np.real(cppLP) - np.real(corr_15k), + np.real(cppLP) - np.real(corr_100), color="b", linewidth=3, label=r"pade error", ) ax1.plot( tlist_corr, - np.real(corr_2k) - np.real(corr_15k), + np.real(corr_2k) - np.real(corr_100), "r--", linewidth=3, label=r"mats error", @@ -702,7 +711,7 @@ options = {**default_options, "rtol": 1e-14, "atol": 1e-14} with timer("RHS construction time"): bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI) - HEOMPade = HEOMSolver(Hsys, (bath,Q), NC, options=options) + HEOMPade = HEOMSolver(Hsys, (bath, Q), NC, options=options) with timer("ODE solver time"): resultPade = HEOMPade.run(rho0, tlist) @@ -735,9 +744,9 @@ the correlation function well): options = {**default_options, "rtol": 1e-14, "atol": 1e-14} with timer("RHS construction time"): - env_approx,delta = dlenv.approx_by_pade(Nk=2,compute_delta=True) - Ltot = liouvillian(Hsys) + system_terminator(Q,delta) - HEOM_dlpbath_T = HEOMSolver(Ltot, (env_approx,Q), NC, options=options) + env_approx, delta = dlenv.approximate("pade", Nk=2, compute_delta=True) + Ltot = liouvillian(Hsys) + system_terminator(Q, delta) + HEOM_dlpbath_T = HEOMSolver(Ltot, (env_approx, Q), NC, options=options) with timer("ODE solver time"): result_dlpbath_T = HEOM_dlpbath_T.run(rho0, tlist) @@ -759,16 +768,17 @@ can be extremely useful in situations where large number of exponents are needed (e.g., near zero temperature). We will perform the fitting manually below, and then show how to do it with the built-in tools -For the manual fit we First we collect a large sum of Matsubara terms for +For the manual fit we First we collect a large sum of Pade terms for many time steps: ```python tlist2 = np.linspace(0, 2, 10000) -corr_15k_t10k = dlenv.correlation_function(tlist2, Nk=100) +corr_100_t10k = dlenv.correlation_function(tlist2, Nk=100) +# Nk specifies the number of pade terms to be used for the correlation function -corrRana = np.real(corr_15k_t10k) -corrIana = np.imag(corr_15k_t10k) +corrRana = np.real(corr_100_t10k) +corrIana = np.imag(corr_100_t10k) ``` We then fit this sum with standard least-squares approach: @@ -846,7 +856,8 @@ And plot the results of the fits: ```python # Define line styles and colors linestyles = ["-", "--", "-.", ":", (0, (3, 1, 1, 1)), (0, (5, 1))] -colors = ["blue", "green", "purple", "orange", "red", "brown", "cyan", "magenta"] +colors = ["blue", "green", "purple", "orange", + "red", "brown", "cyan", "magenta"] # Define a larger linewidth linewidth = 2.5 @@ -855,32 +866,36 @@ linewidth = 2.5 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6)) # Plot the real part on the first subplot (ax1) -ax1.plot(tlist2, corrRana, label="Analytic", color=colors[0], linestyle=linestyles[0], linewidth=linewidth) -ax1.plot(tlist2, corrRMats, label="Matsubara", color=colors[1], linestyle=linestyles[1], linewidth=linewidth) +ax1.plot(tlist2, corrRana, label="Analytic", + color=colors[0], linestyle=linestyles[0], linewidth=linewidth) +ax1.plot(tlist2, corrRMats, label="Matsubara", + color=colors[1], linestyle=linestyles[1], linewidth=linewidth) for i in range(kR): y = fit_func(tlist2, *poptR[i]) - ax1.plot(tlist2, y, label=f"Fit with {i} terms", color=colors[(i + 2) % len(colors)], linestyle=linestyles[(i + 2) % len(linestyles)], linewidth=linewidth) + ax1.plot(tlist2, y, label=f"Fit with {i} terms", color=colors[( + i + 2) % len(colors)], linestyle=linestyles[(i + 2) % len(linestyles)], linewidth=linewidth) ax1.set_ylabel(r"$C_{R}(t)$") ax1.set_xlabel(r"$t$") ax1.legend() # Plot the imaginary part on the second subplot (ax2) -ax2.plot(tlist2, corrIana, label="Analytic", color=colors[0], linestyle=linestyles[0], linewidth=linewidth) +ax2.plot(tlist2, corrIana, label="Analytic", + color=colors[0], linestyle=linestyles[0], linewidth=linewidth) for i in range(kI): y = fit_func(tlist2, *poptI[i]) - ax2.plot(tlist2, y, label=f"Fit with {i} terms", color=colors[(i + 3) % len(colors)], linestyle=linestyles[(i + 1) % len(linestyles)], linewidth=linewidth) + ax2.plot(tlist2, y, label=f"Fit with {i} terms", color=colors[( + i + 3) % len(colors)], linestyle=linestyles[(i + 1) % len(linestyles)], linewidth=linewidth) ax2.set_ylabel(r"$C_{I}(t)$") ax2.set_xlabel(r"$t$") ax2.legend() # Add overall plot title and show the figure -fig.suptitle("Comparison of Analytic and Fit to Correlations (Real and Imaginary Parts)", fontsize=16) +fig.suptitle( + "Comparison of Analytic and Fit to Correlations (Real and Imaginary Parts)", fontsize=16) plt.show() - - ``` ```python @@ -914,7 +929,7 @@ NC = 4 with timer("RHS construction time"): bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI) - HEOMFit = HEOMSolver(Hsys, (bath,Q), NC, options=options) + HEOMFit = HEOMSolver(Hsys, (bath, Q), NC, options=options) with timer("ODE solver time"): resultFit = HEOMFit.run(rho0, tlist) @@ -934,8 +949,13 @@ method that performs this fit automatically. More information on how the built-in functios work can be found in `HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions` ```python -tlist3 = np.linspace(0, 2, 200) -envfit, fitinfo =dlenv.approximate("cf",tlist=tlist3,full_ansatz=True,maxfev=1e6,Ni_max=1,Nr_max=3) +max_val = dlenv.correlation_function(0).real +guess = [max_val / 3, 0, 0, 0] +lower = [-max_val, -np.inf, -np.inf, -np.inf] +upper = [max_val, 0, 0, 0] +envfit, fitinfo = dlenv.approximate("cf", tlist=tlist2, full_ansatz=True, + Ni_max=1, Nr_max=3, + upper=upper, lower=lower, guess=guess) ``` The approx_by_cf_fit method outputs a `ExponentialBosonicEnvironment` object, @@ -957,7 +977,8 @@ fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6)) # Plot the real part on the first subplot (ax1) ax1.plot(tlist2, corrRana, label="Original", marker="o", markevery=500) ax1.plot(tlist2, fit_func(tlist2, *poptR[-1]), color="r", label="Manual Fit") -ax1.plot(tlist2, np.real(envfit.correlation_function(tlist2)), "k--", label="Built-in fit") +ax1.plot(tlist2, np.real(envfit.correlation_function(tlist2)), + "k--", label="Built-in fit") ax1.set_ylabel(r"$C_{R}(t)$") ax1.set_xlabel(r"$t$") ax1.legend() @@ -965,23 +986,21 @@ ax1.legend() # Plot the imaginary part on the second subplot (ax2) ax2.plot(tlist2, corrIana, label="Original", marker="o", markevery=500) ax2.plot(tlist2, fit_func(tlist2, *poptI[-1]), color="r", label="Manual Fit") -ax2.plot(tlist2, np.imag(envfit.correlation_function(tlist2)), "k--", label="Built-in fit") +ax2.plot(tlist2, np.imag(envfit.correlation_function(tlist2)), + "k--", label="Built-in fit") ax2.set_ylabel(r"$C_{I}(t)$") ax2.set_xlabel(r"$t$") ax2.legend() # Add an overall title and adjust layout plt.tight_layout(rect=[0, 0.03, 1, 0.95]) plt.show() - ``` ```python options = {**default_options} -NC = 4 - with timer("RHS construction time"): - HEOMFit_2 = HEOMSolver(Hsys, (envfit,Q), NC, options=options) + HEOMFit_2 = HEOMSolver(Hsys, (envfit, Q), NC, options=options) with timer("ODE solver time"): resultFit_2 = HEOMFit_2.run(rho0, tlist) @@ -1094,7 +1113,7 @@ with plt.rc_context(rcParams): resultFit, P11p, "r", - r"Fit $N_f = 4$, $N_k=15\times 10^3$", + r"Fit $N_f = 4$, Pade $N_k=100$", {"dashes": [3, 2]}, ), ( @@ -1131,7 +1150,7 @@ with plt.rc_context(rcParams): resultFit, P12p, "r", - r"Fit $N_f = 4$, $N_k=15\times 10^3$", + r"Fit $N_f = 4$, Pade $N_k=100$", {"dashes": [3, 2]}, ), ( diff --git a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md index 2f063fca..97368cbb 100644 --- a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md +++ b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md @@ -5,9 +5,9 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.1 + jupytext_version: 1.17.0 kernelspec: - display_name: qutip-dev + display_name: qutip-tutorials language: python name: python3 --- @@ -81,7 +81,7 @@ Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. ## Setup -```{code-cell} +```{code-cell} ipython3 import contextlib import time @@ -112,13 +112,13 @@ from qutip.solver.heom import ( Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: -```{code-cell} +```{code-cell} ipython3 def cot(x): """ Vectorized cotangent of x. """ return 1. / np.tan(x) ``` -```{code-cell} +```{code-cell} ipython3 @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -132,7 +132,7 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} +```{code-cell} ipython3 # Solver options: options = { @@ -149,19 +149,19 @@ options = { And let us set up the system Hamiltonian, bath and system measurement operators: -```{code-cell} +```{code-cell} ipython3 # Defining the system Hamiltonian eps = .0 # Energy of the 2-level system. Del = .2 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` -```{code-cell} +```{code-cell} ipython3 # Initial state of the system. rho0 = basis(2, 0) * basis(2, 0).dag() ``` -```{code-cell} +```{code-cell} ipython3 # System-bath coupling (Drude-Lorentz spectral density) Q = sigmaz() # coupling operator @@ -184,7 +184,7 @@ NC = 13 tlist = np.linspace(0, np.pi / Del, 600) ``` -```{code-cell} +```{code-cell} ipython3 # Define some operators with which we will measure the system # 1,1 element of density matrix - corresonding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -197,7 +197,7 @@ P12p = basis(2, 0) * basis(2, 1).dag() Let us briefly inspect the spectral density. -```{code-cell} +```{code-cell} ipython3 bath = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=500) w = np.linspace(0, 5, 1000) J = bath.spectral_density(w) @@ -211,10 +211,10 @@ axes.set_ylabel(r'J', fontsize=28); ## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator -```{code-cell} +```{code-cell} ipython3 with timer("RHS construction time"): - matsBath=bath.approx_by_matsubara(Nk=Nk) - HEOMMats = HEOMSolver(Hsys, (matsBath,Q), NC, options=options) + matsBath = bath.approximate(method="matsubara", Nk=Nk) + HEOMMats = HEOMSolver(Hsys, (matsBath, Q), NC, options=options) with timer("ODE solver time"): resultMats = HEOMMats.run(rho0, tlist) @@ -222,18 +222,19 @@ with timer("ODE solver time"): ## Simulation 2: Matsubara decomposition (including terminator) -```{code-cell} +```{code-cell} ipython3 with timer("RHS construction time"): - matsBath,delta=bath.approx_by_matsubara(Nk=Nk,compute_delta=True) - terminator = system_terminator(Q,delta) + matsBath, delta = bath.approximate( + method="matsubara", Nk=Nk, compute_delta=True) + terminator = system_terminator(Q, delta) Ltot = liouvillian(Hsys) + terminator - HEOMMatsT = HEOMSolver(Ltot, (matsBath,Q), NC, options=options) + HEOMMatsT = HEOMSolver(Ltot, (matsBath, Q), NC, options=options) with timer("ODE solver time"): resultMatsT = HEOMMatsT.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 # Plot the results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -256,9 +257,9 @@ axes.legend(loc=0, fontsize=12); ## Simulation 3: Pade decomposition -```{code-cell} +```{code-cell} ipython3 # First, compare Matsubara and Pade decompositions -padeBath = bath.approx_by_pade(Nk=Nk) +padeBath = bath.approximate("pade", Nk=Nk) fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(16, 8)) @@ -296,15 +297,15 @@ ax2.set_xlabel(r't', fontsize=28) ax2.legend(loc=0, fontsize=12); ``` -```{code-cell} +```{code-cell} ipython3 with timer("RHS construction time"): - HEOMPade = HEOMSolver(Hsys, (padeBath,Q), NC, options=options) + HEOMPade = HEOMSolver(Hsys, (padeBath, Q), NC, options=options) with timer("ODE solver time"): resultPade = HEOMPade.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 # Plot the results fig, axes = plt.subplots(figsize=(8, 8)) @@ -333,24 +334,26 @@ In `HEOM 1a: Spin-Bath model (introduction)` a fit is performed manually, here we will use the built-in tools. More details about them can be seen in `HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions` -```{code-cell} -tfit=np.linspace(0,10,10000) +```{code-cell} ipython3 +tfit = np.linspace(0, 10, 10000) lower = [0, -np.inf, -1e-6, -3] guess = [np.real(bath.correlation_function(0))/10, -10, 0, 0] -upper = [5, 0, 1e-6, 0] # for better fits increase the first element -# that makes the simuation slower though -envfit,fitinfo = bath.approximate("cf",tlist=tfit,Nr_max=2,Ni_max=1,full_ansatz=True, - sigma=0.1,maxfev=1e6,target_rsme=None, - lower=lower,upper=upper,guess=guess) +upper = [5, 0, 1e-6, 0] +# for better fits increase the first element in upper, or change approximate +# method that makes the simulation much slower (Larger C(t) as C(0) is fit +# better) +envfit, fitinfo = bath.approximate("cf", tlist=tfit, Nr_max=2, Ni_max=1, full_ansatz=True, + sigma=0.1, maxfev=1e6, target_rmse=None, + lower=lower, upper=upper, guess=guess) ``` -```{code-cell} +```{code-cell} ipython3 print(fitinfo['summary']) ``` We can quickly compare the result of the Fit with the Pade expansion -```{code-cell} +```{code-cell} ipython3 fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8)) ax1.plot( @@ -359,7 +362,7 @@ ax1.plot( ) ax1.plot( tlist, np.real(envfit.correlation_function(tlist)), - "g--", linewidth=2, label=f"Fit",marker="o",markevery=50 + "g--", linewidth=2, label=f"Fit", marker="o", markevery=50 ) ax1.plot( tlist, np.real(padeBath.correlation_function(tlist)), @@ -376,7 +379,7 @@ ax2.plot( ) ax2.plot( tlist, np.imag(envfit.correlation_function(tlist)), - "g--", linewidth=2, label=f"Fit",marker="o",markevery=50 + "g--", linewidth=2, label=f"Fit", marker="o", markevery=50 ) ax2.plot( tlist, np.imag(padeBath.correlation_function(tlist)), @@ -388,12 +391,12 @@ ax2.set_ylabel(r"$C_I(t)$", fontsize=28) ax2.legend(loc=0, fontsize=12) ``` -```{code-cell} +```{code-cell} ipython3 with timer("RHS construction time"): # We reduce NC slightly here for speed of execution because we retain # 3 exponents in ckAR instead of 1. Please restore full NC for # convergence though: - HEOMFit = HEOMSolver(Hsys, (envfit,Q), int(NC*0.7), options=options) + HEOMFit = HEOMSolver(Hsys, (envfit, Q), int(NC*0.7), options=options) with timer("ODE solver time"): resultFit = HEOMFit.run(rho0, tlist) @@ -401,11 +404,11 @@ with timer("ODE solver time"): ## Simulation 5: Bloch-Redfield -```{code-cell} +```{code-cell} ipython3 with timer("ODE solver time"): resultBR = brmesolve( Hsys, rho0, tlist, - a_ops=[[sigmaz(),bath]], sec_cutoff=0, options=options, + a_ops=[[sigmaz(), bath]], sec_cutoff=0, options=options, ) ``` @@ -413,7 +416,7 @@ with timer("ODE solver time"): Finally, let's plot all of our different results to see how they shape up against each other. -```{code-cell} +```{code-cell} ipython3 # Calculate expectation values in the bases: P11_mats = np.real(expect(resultMats.states, P11p)) P11_matsT = np.real(expect(resultMatsT.states, P11p)) @@ -422,7 +425,7 @@ P11_fit = np.real(expect(resultFit.states, P11p)) P11_br = np.real(expect(resultBR.states, P11p)) ``` -```{code-cell} +```{code-cell} ipython3 rcParams = { "axes.titlesize": 25, "axes.labelsize": 30, @@ -439,7 +442,7 @@ rcParams = { } ``` -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) with plt.rc_context(rcParams): @@ -479,7 +482,7 @@ with plt.rc_context(rcParams): ## About -```{code-cell} +```{code-cell} ipython3 qutip.about() ``` @@ -487,7 +490,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 assert np.allclose(P11_matsT, P11_pade, rtol=1e-3) assert np.allclose(P11_matsT, P11_fit, rtol=1e-3) ``` diff --git a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md index 9f45428b..870f203b 100644 --- a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md +++ b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md @@ -5,9 +5,9 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.1 + jupytext_version: 1.17.0 kernelspec: - display_name: qutip-dev + display_name: qutip-tutorials language: python name: python3 --- @@ -76,31 +76,18 @@ Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. ## Setup -```{code-cell} +```{code-cell} ipython3 import contextlib import time import numpy as np -from matplotlib import pyplot as plt - import qutip -from qutip import ( - basis, - brmesolve, - destroy, - expect, - qeye, - sigmax, - sigmaz, - tensor, -) -from qutip.solver.heom import ( - HEOMSolver, -) -from qutip.core.environment import ( - UnderDampedEnvironment, - ExponentialBosonicEnvironment -) +from matplotlib import pyplot as plt +from qutip import (basis, brmesolve, destroy, expect, qeye, sigmax, sigmaz, + tensor) +from qutip.core.environment import (ExponentialBosonicEnvironment, + UnderDampedEnvironment) +from qutip.solver.heom import HEOMSolver %matplotlib inline ``` @@ -109,47 +96,48 @@ from qutip.core.environment import ( Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: -```{code-cell} +```{code-cell} ipython3 def cot(x): - """ Vectorized cotangent of x. """ - return 1. / np.tan(x) + """Vectorized cotangent of x.""" + return 1.0 / np.tan(x) ``` -```{code-cell} +```{code-cell} ipython3 def coth(x): - """ Vectorized hyperbolic cotangent of x. """ - return 1. / np.tanh(x) + """Vectorized hyperbolic cotangent of x.""" + return 1.0 / np.tanh(x) ``` -```{code-cell} +```{code-cell} ipython3 def underdamped_matsubara_params(lam, gamma, T, nk): - """ Calculation of the real and imaginary expansions of the - underdamped correlation functions. + """Calculation of the real and imaginary expansions of the + underdamped correlation functions. """ - Om = np.sqrt(w0**2 - (gamma / 2)**2) - Gamma = gamma / 2. - beta = 1. / T + Om = np.sqrt(w0**2 - (gamma / 2) ** 2) + Gamma = gamma / 2.0 + beta = 1.0 / T ckAR = [ - (lam**2 / (4*Om)) * coth(beta * (Om + 1.0j * Gamma) / 2), - (lam**2 / (4*Om)) * coth(beta * (Om - 1.0j * Gamma) / 2), + (lam**2 / (4 * Om)) * coth(beta * (Om + 1.0j * Gamma) / 2), + (lam**2 / (4 * Om)) * coth(beta * (Om - 1.0j * Gamma) / 2), ] ckAR.extend( - (-2 * lam**2 * gamma / beta) * (2 * np.pi * k / beta) / - (((Om + 1.0j * Gamma)**2 + (2 * np.pi * k / beta)**2) * - ((Om - 1.0j * Gamma)**2 + (2 * np.pi * k / beta)**2)) + 0.j + (-2 * lam**2 * gamma / beta) + * (2 * np.pi * k / beta) + / ( + ((Om + 1.0j * Gamma) ** 2 + (2 * np.pi * k / beta) ** 2) + * ((Om - 1.0j * Gamma) ** 2 + (2 * np.pi * k / beta) ** 2) + ) + + 0.0j for k in range(1, nk + 1) ) vkAR = [ -1.0j * Om + Gamma, 1.0j * Om + Gamma, ] - vkAR.extend( - 2 * np.pi * k * T + 0.j - for k in range(1, nk + 1) - ) + vkAR.extend(2 * np.pi * k * T + 0.0j for k in range(1, nk + 1)) - factor = 1. / 4 + factor = 1.0 / 4 ckAI = [ -factor * lam**2 * 1.0j / Om, @@ -163,12 +151,12 @@ def underdamped_matsubara_params(lam, gamma, T, nk): return ckAR, vkAR, ckAI, vkAI ``` -```{code-cell} +```{code-cell} ipython3 def plot_result_expectations(plots, axes=None): - """ Plot the expectation values of operators as functions of time. + """Plot the expectation values of operators as functions of time. - Each plot in plots consists of: (solver_result, measurement_operation, - color, label). + Each plot in plots consists of: (solver_result, measurement_operation, + color, label). """ if axes is None: fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -191,13 +179,13 @@ def plot_result_expectations(plots, axes=None): return fig ``` -```{code-cell} +```{code-cell} ipython3 @contextlib.contextmanager def timer(label): - """ Simple utility for timing functions: + """Simple utility for timing functions: - with timer("name"): - ... code to time ... + with timer("name"): + ... code to time ... """ start = time.time() yield @@ -205,9 +193,10 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} +```{code-cell} ipython3 # Solver options: + options = { "nsteps": 15000, "store_states": True, @@ -222,28 +211,28 @@ options = { And let us set up the system Hamiltonian, bath and system measurement operators: -```{code-cell} +```{code-cell} ipython3 # Defining the system Hamiltonian -eps = .5 # Energy of the 2-level system. -Del = 1.0 # Tunnelling term +eps = 0.5 # Energy of the 2-level system. +Del = 1.0 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` -```{code-cell} +```{code-cell} ipython3 # Initial state of the system. rho0 = basis(2, 0) * basis(2, 0).dag() ``` -```{code-cell} +```{code-cell} ipython3 # System-bath coupling (underdamed spectral density) Q = sigmaz() # coupling operator # Bath properties: -gamma = .1 # cut off frequency -lam = .5 # coupling strength -w0 = 1. # resonance frequency -T = 1. -beta = 1. / T +gamma = 0.1 # cut off frequency +lam = 0.5 # coupling strength +w0 = 1.0 # resonance frequency +T = 1.0 +beta = 1.0 / T # HEOM parameters: @@ -258,7 +247,7 @@ NC = 10 tlist = np.linspace(0, 50, 1000) ``` -```{code-cell} +```{code-cell} ipython3 # Define some operators with which we will measure the system # 1,1 element of density matrix - corresonding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -269,16 +258,16 @@ P12p = basis(2, 0) * basis(2, 1).dag() ### First let us look at what the underdamped spectral density looks like: -```{code-cell} +```{code-cell} ipython3 def plot_spectral_density(): - """ Plot the underdamped spectral density """ + """Plot the underdamped spectral density""" w = np.linspace(0, 5, 1000) - J = lam**2 * gamma * w / ((w0**2 - w**2)**2 + (gamma**2) * (w**2)) + J = lam**2 * gamma * w / ((w0**2 - w**2) ** 2 + (gamma**2) * (w**2)) fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - axes.plot(w, J, 'r', linewidth=2) - axes.set_xlabel(r'$\omega$', fontsize=28) - axes.set_ylabel(r'J', fontsize=28) + axes.plot(w, J, "r", linewidth=2) + axes.set_xlabel(r"$\omega$", fontsize=28) + axes.set_ylabel(r"J", fontsize=28) plot_spectral_density() @@ -290,50 +279,47 @@ The correlation functions are now very oscillatory, because of the Lorentzian pe ### So next, let us plot the correlation functions themselves: -```{code-cell} +```{code-cell} ipython3 def Mk(t, k, gamma, w0, beta): - """ Calculate the Matsubara terms for a given t and k. """ - Om = np.sqrt(w0**2 - (gamma / 2)**2) - Gamma = gamma / 2. + """Calculate the Matsubara terms for a given t and k.""" + Om = np.sqrt(w0**2 - (gamma / 2) ** 2) + Gamma = gamma / 2.0 ek = 2 * np.pi * k / beta return ( - (-2 * lam**2 * gamma / beta) * ek * np.exp(-ek * np.abs(t)) - / (((Om + 1.0j * Gamma)**2 + ek**2) * ((Om - 1.0j * Gamma)**2 + ek**2)) + (-2 * lam**2 * gamma / beta) + * ek + * np.exp(-ek * np.abs(t)) + / (((Om + 1.0j * Gamma) ** 2 + ek**2) * ((Om - 1.0j * Gamma) ** 2 + ek**2)) ) def c(t, Nk, lam, gamma, w0, beta): - """ Calculate the correlation function for a vector of times, t. """ - Om = np.sqrt(w0**2 - (gamma / 2)**2) - Gamma = gamma / 2. + """Calculate the correlation function for a vector of times, t.""" + Om = np.sqrt(w0**2 - (gamma / 2) ** 2) + Gamma = gamma / 2.0 - Cr = ( - coth(beta * (Om + 1.0j * Gamma) / 2) * np.exp(1.0j * Om * t) - + coth(beta * (Om - 1.0j * Gamma) / 2) * np.exp(-1.0j * Om * t) - ) + Cr = coth(beta * (Om + 1.0j * Gamma) / 2) * np.exp(1.0j * Om * t) + coth( + beta * (Om - 1.0j * Gamma) / 2 + ) * np.exp(-1.0j * Om * t) Ci = np.exp(-1.0j * Om * t) - np.exp(1.0j * Om * t) - return ( - (lam**2 / (4 * Om)) * np.exp(-Gamma * np.abs(t)) * (Cr + Ci) + - np.sum([ - Mk(t, k, gamma=gamma, w0=w0, beta=beta) - for k in range(1, Nk + 1) - ], 0) + return (lam**2 / (4 * Om)) * np.exp(-Gamma * np.abs(t)) * (Cr + Ci) + np.sum( + [Mk(t, k, gamma=gamma, w0=w0, beta=beta) for k in range(1, Nk + 1)], 0 ) def plot_correlation_function(): - """ Plot the underdamped correlation function. """ + """Plot the underdamped correlation function.""" t = np.linspace(0, 20, 1000) corr = c(t, Nk=3, lam=lam, gamma=gamma, w0=w0, beta=beta) fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - axes.plot(t, np.real(corr), '-', color="black", label="Re[C(t)]") - axes.plot(t, np.imag(corr), '-', color="red", label="Im[C(t)]") - axes.set_xlabel(r't', fontsize=28) - axes.set_ylabel(r'C', fontsize=28) + axes.plot(t, np.real(corr), "-", color="black", label="Re[C(t)]") + axes.plot(t, np.imag(corr), "-", color="red", label="Im[C(t)]") + axes.set_xlabel(r"t", fontsize=28) + axes.set_ylabel(r"C", fontsize=28) axes.legend(loc=0, fontsize=12) @@ -342,26 +328,24 @@ plot_correlation_function() It is useful to look at what the Matsubara contributions do to this spectral density. We see that they modify the real part around $t=0$: -```{code-cell} +```{code-cell} ipython3 def plot_matsubara_correlation_function_contributions(): - """ Plot the underdamped correlation function. """ + """Plot the underdamped correlation function.""" t = np.linspace(0, 20, 1000) - M_Nk2 = np.sum([ - Mk(t, k, gamma=gamma, w0=w0, beta=beta) - for k in range(1, 2 + 1) - ], 0) + M_Nk2 = np.sum( + [Mk(t, k, gamma=gamma, w0=w0, beta=beta) for k in range(1, 2 + 1)], 0 + ) - M_Nk100 = np.sum([ - Mk(t, k, gamma=gamma, w0=w0, beta=beta) - for k in range(1, 100 + 1) - ], 0) + M_Nk100 = np.sum( + [Mk(t, k, gamma=gamma, w0=w0, beta=beta) for k in range(1, 100 + 1)], 0 + ) fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - axes.plot(t, np.real(M_Nk2), '-', color="black", label="Re[M(t)] Nk=2") - axes.plot(t, np.real(M_Nk100), '--', color="red", label="Re[M(t)] Nk=100") - axes.set_xlabel(r't', fontsize=28) - axes.set_ylabel(r'M', fontsize=28) + axes.plot(t, np.real(M_Nk2), "-", color="black", label="Re[M(t)] Nk=2") + axes.plot(t, np.real(M_Nk100), "--", color="red", label="Re[M(t)] Nk=100") + axes.set_xlabel(r"t", fontsize=28) + axes.set_ylabel(r"M", fontsize=28) axes.legend(loc=0, fontsize=12) @@ -376,32 +360,37 @@ Next we calculate the exponents using the Matsubara decompositions. Here we spli The HEOM code will optimize these, and reduce the number of exponents when real and imaginary parts have the same exponent. This is clearly the case for the first term in the vkAI and vkAR lists. -```{code-cell} +```{code-cell} ipython3 ckAR, vkAR, ckAI, vkAI = underdamped_matsubara_params( - lam=lam, gamma=gamma, T=T, nk=Nk, + lam=lam, + gamma=gamma, + T=T, + nk=Nk, ) ``` -Having created the lists which specify the bath correlation functions, we create a `BosonicBath` from them and pass the bath to the `HEOMSolver` class. +Having created the lists which specify the bath correlation functions, we create a `ExponentialBosonicEnvironment` from them and pass the bath to the `HEOMSolver` class. The solver constructs the "right hand side" (RHS) determinining how the system and auxiliary density operators evolve in time. This can then be used to solve for dynamics or steady-state. Below we create the bath and solver and then solve for the dynamics by calling `.run(rho0, tlist)`. -```{code-cell} +```{code-cell} ipython3 with timer("RHS construction time"): bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI) - HEOMMats = HEOMSolver(Hsys, (bath,Q), NC, options=options) + HEOMMats = HEOMSolver(Hsys, (bath, Q), NC, options=options) with timer("ODE solver time"): resultMats = HEOMMats.run(rho0, tlist) ``` -```{code-cell} -plot_result_expectations([ - (resultMats, P11p, 'b', "P11 Mats"), - (resultMats, P12p, 'r', "P12 Mats"), -]); +```{code-cell} ipython3 +plot_result_expectations( + [ + (resultMats, P11p, "b", "P11 Mats"), + (resultMats, P12p, "r", "P12 Mats"), + ] +); ``` In practice, one would not perform this laborious expansion for the underdamped correlation function, because @@ -410,49 +399,51 @@ to perform this expansion is an useful skill. Below we show how to use this built-in functionality: -```{code-cell} +```{code-cell} ipython3 # Compare to built-in under-damped bath: with timer("RHS construction time"): bath = UnderDampedEnvironment(lam=lam, gamma=gamma, w0=w0, T=T) - bath_approx=bath.approx_by_matsubara(Nk=Nk) - HEOM_udbath = HEOMSolver(Hsys, (bath_approx,Q), NC, options=options) + bath_approx = bath.approximate(method="matsubara", Nk=Nk) + HEOM_udbath = HEOMSolver(Hsys, (bath_approx, Q), NC, options=options) with timer("ODE solver time"): result_udbath = HEOM_udbath.run(rho0, tlist) ``` -```{code-cell} -plot_result_expectations([ - (result_udbath, P11p, 'b', "P11 (UnderDampedEnvironment)"), - (result_udbath, P12p, 'r', "P12 (UnderDampedEnvironment)"), - (resultMats, P11p, 'r--', "P11 Mats"), - (resultMats, P12p, 'b--', "P12 Mats"), -]); +```{code-cell} ipython3 +plot_result_expectations( + [ + (result_udbath, P11p, "b", "P11 (UnderDampedEnvironment)"), + (result_udbath, P12p, "r", "P12 (UnderDampedEnvironment)"), + (resultMats, P11p, "r--", "P11 Mats"), + (resultMats, P12p, "b--", "P12 Mats"), + ] +); ``` The `UnderDampedEnvironment` class also allows us to easily evaluate analytical expressions for the power spectrum, correlation function, and spectral density. In the following plots, the solid lines are the exact expressions, and the dashed lines are based on our approximation of the correlation function with a finite number of exponents. In this case, there is an excellent agreement. -```{code-cell} +```{code-cell} ipython3 w = np.linspace(-3, 3, 1000) w2 = np.linspace(0, 3, 1000) t = np.linspace(0, 10, 1000) -bath_cf = bath.correlation_function(t) # uses numerical integration +bath_cf = bath.correlation_function(t) fig, axs = plt.subplots(2, 2) axs[0, 0].plot(w, bath.power_spectrum(w)) -axs[0, 0].plot(w, bath_approx.power_spectrum(w), '--') -axs[0, 0].set(xlabel=r'$\omega$', ylabel=r'$S(\omega)$') +axs[0, 0].plot(w, bath_approx.power_spectrum(w), "--") +axs[0, 0].set(xlabel=r"$\omega$", ylabel=r"$S(\omega)$") axs[0, 1].plot(w2, bath.spectral_density(w2)) -axs[0, 1].plot(w2, bath_approx.spectral_density(w2), '--') -axs[0, 1].set(xlabel=r'$\omega$', ylabel=r'$J(\omega)$') +axs[0, 1].plot(w2, bath_approx.spectral_density(w2), "--") +axs[0, 1].set(xlabel=r"$\omega$", ylabel=r"$J(\omega)$") axs[1, 0].plot(t, np.real(bath_cf)) -axs[1, 0].plot(t, np.real(bath_approx.correlation_function(t)), '--') -axs[1, 0].set(xlabel=r'$t$', ylabel=r'$C_{R}(t)$') +axs[1, 0].plot(t, np.real(bath_approx.correlation_function(t)), "--") +axs[1, 0].set(xlabel=r"$t$", ylabel=r"$C_{R}(t)$") axs[1, 1].plot(t, np.imag(bath_cf)) -axs[1, 1].plot(t, np.imag(bath_approx.correlation_function(t)), '--') -axs[1, 1].set(xlabel=r'$t$', ylabel=r'$C_{I}(t)$') +axs[1, 1].plot(t, np.imag(bath_approx.correlation_function(t)), "--") +axs[1, 1].set(xlabel=r"$t$", ylabel=r"$C_{I}(t)$") fig.tight_layout() plt.show() @@ -464,21 +455,26 @@ plt.show() ### We can compare these results to those of the Bloch-Redfield solver in QuTiP: -```{code-cell} +```{code-cell} ipython3 with timer("ODE solver time"): resultBR = brmesolve( - Hsys, rho0, tlist, - a_ops=[[sigmaz(), bath]], options=options, + Hsys, + rho0, + tlist, + a_ops=[[sigmaz(), bath]], + options=options, ) ``` -```{code-cell} -plot_result_expectations([ - (resultMats, P11p, 'b', "P11 Mats"), - (resultMats, P12p, 'r', "P12 Mats"), - (resultBR, P11p, 'g--', "P11 Bloch Redfield"), - (resultBR, P12p, 'g--', "P12 Bloch Redfield"), -]); +```{code-cell} ipython3 +plot_result_expectations( + [ + (resultMats, P11p, "b", "P11 Mats"), + (resultMats, P12p, "r", "P12 Mats"), + (resultBR, P11p, "g--", "P11 Bloch Redfield"), + (resultBR, P12p, "g--", "P12 Bloch Redfield"), + ] +); ``` ### Lastly, let us calculate the analytical steady-state result and compare all of the results: @@ -487,7 +483,7 @@ plot_result_expectations([ The thermal state of a reaction coordinate (treating the environment as a single damped mode) should, at high temperatures and small gamma, tell us the steady-state: -```{code-cell} +```{code-cell} ipython3 dot_energy, dot_state = Hsys.eigenstates() deltaE = dot_energy[1] - dot_energy[0] @@ -503,14 +499,14 @@ a = tensor(destroy(NRC), qeye(2)) H0 = wa * a.dag() * a + Hsys_exp # interaction -H1 = (g * (a.dag() + a) * Q_exp) +H1 = g * (a.dag() + a) * Q_exp H = H0 + H1 energies, states = H.eigenstates() rhoss = 0 * states[0] * states[0].dag() for kk, energ in enumerate(energies): - rhoss += (states[kk] * states[kk].dag() * np.exp(-beta * energies[kk])) + rhoss += states[kk] * states[kk].dag() * np.exp(-beta * energies[kk]) rhoss = rhoss / rhoss.norm() P12RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 1).dag()) @@ -520,7 +516,7 @@ P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag()) P11RC = expect(rhoss, P11RC) ``` -```{code-cell} +```{code-cell} ipython3 rcParams = { "axes.titlesize": 25, "axes.labelsize": 30, @@ -537,27 +533,33 @@ rcParams = { } ``` -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) with plt.rc_context(rcParams): plt.yticks([P11RC, 0.6, 1.0], [0.38, 0.6, 1]) - plot_result_expectations([ - (resultBR, P11p, 'y-.', "Bloch-Redfield"), - (resultMats, P11p, 'b', "Matsubara $N_k=3$"), - ], axes=axes) + plot_result_expectations( + [ + (resultBR, P11p, "y-.", "Bloch-Redfield"), + (resultMats, P11p, "b", "Matsubara $N_k=3$"), + ], + axes=axes, + ) axes.plot( - tlist, [P11RC for t in tlist], - color='black', linestyle="-.", linewidth=2, + tlist, + [P11RC for t in tlist], + color="black", + linestyle="-.", + linewidth=2, label="Thermal state", ) - axes.set_xlabel(r'$t \Delta$', fontsize=30) - axes.set_ylabel(r'$\rho_{11}$', fontsize=30) + axes.set_xlabel(r"$t \Delta$", fontsize=30) + axes.set_ylabel(r"$\rho_{11}$", fontsize=30) - axes.locator_params(axis='y', nbins=4) - axes.locator_params(axis='x', nbins=4) + axes.locator_params(axis="y", nbins=4) + axes.locator_params(axis="x", nbins=4) axes.legend(loc=0) @@ -566,7 +568,7 @@ with plt.rc_context(rcParams): ## About -```{code-cell} +```{code-cell} ipython3 qutip.about() ``` @@ -574,11 +576,15 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 assert np.allclose( - expect(P11p, resultMats.states[-100:]), P11RC, rtol=1e-2, + expect(P11p, resultMats.states[-100:]), + P11RC, + rtol=1e-2, ) assert np.allclose( - expect(P11p, resultBR.states[-100:]), P11RC, rtol=1e-2, + expect(P11p, resultBR.states[-100:]), + P11RC, + rtol=1e-2, ) ``` diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md index 91e8c87b..fcaaa963 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md @@ -5,9 +5,9 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.1 + jupytext_version: 1.17.0 kernelspec: - display_name: qutip-dev + display_name: qutip-tutorials language: python name: python3 --- @@ -31,7 +31,7 @@ In the example below we show how to model an Ohmic environment with exponential * First we fit the spectral density with a set of underdamped brownian oscillator functions. * Second, we evaluate the correlation functions, and fit those with a certain choice of exponential functions. -* Third, we use the available OhmicBath class +* Third, we use the available OhmicBath class, and explore the other approximation methods QutiP offers In each case we will use the fit parameters to determine the correlation function expansion co-efficients needed to construct a description of the bath (i.e. a `BosonicBath` object) to supply to the `HEOMSolver` so that we can solve for the system dynamics. @@ -52,11 +52,12 @@ from qutip import ( from qutip.solver.heom import ( HEOMSolver ) -from qutip.core.environment import BosonicEnvironment,OhmicEnvironment +from qutip.core.environment import BosonicEnvironment, OhmicEnvironment # Import mpmath functions for evaluation of gamma and zeta # functions in the expression for the correlation: - +# mpmath is required for this tutorial you can install it +# with !pip install mpmath from mpmath import mp mp.dps = 15 @@ -155,7 +156,7 @@ def ohmic_power_spectrum(w, alpha, wc, beta): but, this fails at w=0 where the limit should be taken properly """ bose = (1 / (np.e ** (w * beta) - 1)) + 1 - return w * alpha * np.e ** (-abs(w) / wc) * 2*bose + return w * alpha * np.e ** (-abs(w) / wc) * 2*bose ``` ### Bath and HEOM parameters @@ -180,10 +181,10 @@ And set the cut-off for the HEOM hierarchy: # The max_depth defaults to 5 so that the notebook executes more # quickly. Change it to 11 to wait longer for more accurate results. max_depth = 5 -# options used for the differential equation solver, while default works it +# options used for the differential equation solver, while default works it # is way slower than using bdf options = { - "nsteps":15000, "store_states":True, "rtol":1e-12, "atol":1e-12, "method":"bdf", + "nsteps": 15000, "store_states": True, "rtol": 1e-12, "atol": 1e-12, "method": "bdf", } ``` @@ -215,7 +216,7 @@ user specified function or array ```{code-cell} ipython3 # From an array -sd_env=BosonicEnvironment.from_spectral_density(J=J,wlist=w) +sd_env = BosonicEnvironment.from_spectral_density(J=J, wlist=w) ``` The resulting `BosonicEnvironment` cannot compute the power spectrum, or @@ -231,7 +232,7 @@ If we want access to these properties we need to provide the Temperature at Init ```{code-cell} ipython3 # From an array -sd_env=BosonicEnvironment.from_spectral_density(J=J,wlist=w,T=T) +sd_env = BosonicEnvironment.from_spectral_density(J=J, wlist=w, T=T) ``` Now our bosonic environment can compute the Power Spectrum of the spectral @@ -239,18 +240,23 @@ density provided ```{code-cell} ipython3 # Here we avoid w=0 -np.allclose(sd_env.power_spectrum(w[1:]),ohmic_power_spectrum(w[1:],alpha,wc,1/T)) +np.allclose(sd_env.power_spectrum(w[1:]), + ohmic_power_spectrum(w[1:], alpha, wc, 1/T)) ``` Specifying the Temperature also gives the `BosonicEnvironment` access to the correlation function ```{code-cell} ipython3 -tlist=np.linspace(0,10,500) -plt.plot(tlist,sd_env.correlation_function(tlist),label="BosonicEnvironment (Real Part)") -plt.plot(tlist,ohmic_correlation(tlist,alpha,wc,1/T),"--",label="Original (Real Part)") -plt.plot(tlist,np.imag(sd_env.correlation_function(tlist)),label="BosonicEnvironment (Imaginary Part)") -plt.plot(tlist,np.imag(ohmic_correlation(tlist,alpha,wc,1/T)),"--",label="Original (Imaginary Part)") +tlist = np.linspace(0, 10, 500) +plt.plot(tlist, sd_env.correlation_function(tlist).real, + label="BosonicEnvironment (Real Part)") +plt.plot(tlist, ohmic_correlation(tlist, alpha, wc, 1/T).real, + "--", label="Original (Real Part)") +plt.plot(tlist, np.imag(sd_env.correlation_function(tlist)), + label="BosonicEnvironment (Imaginary Part)") +plt.plot(tlist, np.imag(ohmic_correlation(tlist, alpha, wc, 1/T)), + "--", label="Original (Imaginary Part)") plt.ylabel("C(t)") plt.xlabel("t") plt.legend() @@ -266,15 +272,16 @@ considered to be essentialy zero ```{code-cell} ipython3 # From a function -sd_env2=BosonicEnvironment.from_spectral_density(ohmic_spectral_density,T=T,wMax=10*wc,args={"alpha":alpha,"wc":wc}) +sd_env2 = BosonicEnvironment.from_spectral_density( + ohmic_spectral_density, T=T, wMax=10*wc, args={"alpha": alpha, "wc": wc}) ``` ```{code-cell} ipython3 -tlist=np.linspace(0,10,500) -plt.plot(tlist,sd_env2.correlation_function(tlist)) -plt.plot(tlist,ohmic_correlation(tlist,alpha,wc,1/T),"--") -plt.plot(tlist,np.imag(sd_env2.correlation_function(tlist))) -plt.plot(tlist,np.imag(ohmic_correlation(tlist,alpha,wc,1/T)),"--") +tlist = np.linspace(0, 10, 500) +plt.plot(tlist, sd_env2.correlation_function(tlist)) +plt.plot(tlist, ohmic_correlation(tlist, alpha, wc, 1/T), "--") +plt.plot(tlist, np.imag(sd_env2.correlation_function(tlist))) +plt.plot(tlist, np.imag(ohmic_correlation(tlist, alpha, wc, 1/T)), "--") ``` In this example we considered how to obtain a `BosonicEnvironment` from the spectral density, it can be done analogously from the power spectrum or correlation function using the `from_correlation_function` and `from_power_spectrum` methods. @@ -335,7 +342,7 @@ is a normalized root mean squared error of $5\times 10^{-6}$, if set to None the fit is performed only with the maximum number of exponents specified ```{code-cell} ipython3 -bath, fitinfo = sd_env.approximate("sd",w,Nmax=4) +bath, fitinfo = sd_env.approximate("sd", w, Nmax=4) ``` To obtain an overview of the results of the fit we may take a look at the summary from the ``fitinfo`` @@ -350,7 +357,7 @@ We may see how the number of exponents chosen affects the fit since the approxim fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5)) ax1.plot(w, J, label="Original spectral density") -ax1.plot(w, bath.spectral_density(w), "--",label="Effective fitted SD") +ax1.plot(w, bath.spectral_density(w), "--", label="Effective fitted SD") ax1.set_xlabel(r'$\omega$') ax1.set_ylabel(r'$J$') ax1.legend() @@ -366,12 +373,12 @@ plt.show() Here we see a surprisingly large discrepancy in our approximated or effective spectral density. This happens because we are not using enough exponentials from each of the underdamped modes to have an appropiate fit. All modes have the same number of exponents, when not specified it defaults to $1$ which is not enough to model a bath with the temperature considered, let us repeat this with a higher number of exponents. ```{code-cell} ipython3 -bath, fitinfo = sd_env.approximate("sd",w,Nmax=4,Nk=3) +bath, fitinfo = sd_env.approximate("sd", w, Nmax=4, Nk=3) fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5)) ax1.plot(w, J, label="Original spectral density") -ax1.plot(w, bath.spectral_density(w), "--",label="Effective fitted SD") +ax1.plot(w, bath.spectral_density(w), "--", label="Effective fitted SD") ax1.set_xlabel(r'$\omega$') ax1.set_ylabel(r'$J$') ax1.legend() @@ -425,13 +432,17 @@ def plot_fit_components(func, J, w, lam, gamma, w0): plt.show() -lam=fitinfo["params"][:,0] -gamma=fitinfo["params"][:,1] -w0 = fitinfo["params"][:,2] +lam = fitinfo["params"][:, 0] +gamma = fitinfo["params"][:, 1] +w0 = fitinfo["params"][:, 2] + + def _sd_fit_model(wlist, a, b, c): return ( 2 * a * b * wlist / ((wlist + c)**2 + b**2) / ((wlist - c)**2 + b**2) ) + + plot_fit(_sd_fit_model, J, w, lam, gamma, w0) ``` @@ -474,16 +485,19 @@ def generate_spectrum_results(Q, N, Nk, max_depth): # lower = [-100*J_max, 0.1*wc, 0.1*wc] # guess = [J_max, wc, wc] # upper = [100*J_max, 100*wc, 100*wc] - bath, fitinfo= sd_env.approximate("sd",w,Nmax=N,Nk=Nk,target_rmse=None)#,lower=lower,upper=upper,guess=guess,sigma=sigma) + # ,lower=lower,upper=upper,guess=guess,sigma=sigma) + bath, fitinfo = sd_env.approximate( + "sd", w, Nmax=N, Nk=Nk, target_rmse=None) tlist = np.linspace(0, 30 * np.pi / Del, 600) # This problem is a little stiff, so we use the BDF method to solve # the ODE ^^^ - print(f"Starting calculations for N={N}, Nk={Nk} and max_depth={max_depth} ... ") + print( + f"Starting calculations for N={N}, Nk={Nk} and max_depth={max_depth} ... ") HEOM_spectral_fit = HEOMSolver( Hsys, - (bath,Q), + (bath, Q), max_depth=max_depth, options=options, ) @@ -592,7 +606,7 @@ plot_result_expectations( ) for nc, result in zip(Nc_list, results_spectral_fit_nc) ] - ); +); ``` #### We now combine the fitting and correlation function data into one large plot. Here we define a function to plot everything together @@ -767,10 +781,11 @@ When full_ansatz is True. the ansatz used corresponds to ```{code-cell} ipython3 def generate_corr_results(N, max_depth): tlist = np.linspace(0, 30 * np.pi / Del, 600) - bath_corr ,fitinfo= sd_env.approximate("cf",tlist=t,Ni_max=N,Nr_max=N,maxfev=1e8,target_rsme=None) + bath_corr, fitinfo = sd_env.approximate( + "cf", tlist=t, Ni_max=N, Nr_max=N, maxfev=1e8, target_rmse=None) HEOM_corr_fit = HEOMSolver( Hsys, - (bath_corr,Q), + (bath_corr, Q), max_depth=max_depth, options=options, ) @@ -821,8 +836,10 @@ plot_result_expectations( "k", "Correlation Function Fit $k_R=k_I=3$", ), - (results_spectral_fit_pk[0], P11p, "b", "Spectral Density Fit $k_J=1$"), - (results_spectral_fit_pk[3], P11p, "r-.", "Spectral Density Fit $k_J=4$"), + (results_spectral_fit_pk[0], P11p, + "b", "Spectral Density Fit $k_J=1$"), + (results_spectral_fit_pk[3], P11p, + "r-.", "Spectral Density Fit $k_J=4$"), ], axes=axes, ) @@ -838,22 +855,21 @@ axes.legend(loc=0, fontsize=20); As the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced quickly by using the OhmicBath class. This allows for rapid implementation of fitted ohmic baths via the correlation function or spectral density ```{code-cell} ipython3 -obs = OhmicEnvironment(T, alpha, wc,s=1) +obs = OhmicEnvironment(T, alpha, wc, s=1) tlist = np.linspace(0, 30 * np.pi / Del, 600) ``` Just like the other `BosonicEnvironment` we can obtain a decaying exponential -representation of the environment via the `approx_by_cf_fit` and -`approx_by_sd_fit` methods. +representation of the environment via the `approximate`, let us do the same +methods we explored before ```{code-cell} ipython3 -tlist = np.linspace(0, 30 * np.pi / Del, 5000) - -Obath, fitinfo = obs.approximate(method="cf",tlist=tlist,Nr_max=4,Ni_max=4,maxfev=1e9,target_rsme=None) +Obath, fitinfo = obs.approximate( + method="cf", tlist=tlist, Nr_max=4, Ni_max=4, maxfev=1e9, target_rmse=None) print(fitinfo["summary"]) HEOM_ohmic_corr_fit = HEOMSolver( Hsys, - (Obath,Q), + (Obath, Q), max_depth=max_depth, options=options, ) @@ -861,19 +877,19 @@ results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist) ``` ```{code-cell} ipython3 -Obath2, fitinfo = obs.approximate(method="sd",wlist=w,Nmax=4,Nk=3) +Obath2, fitinfo = obs.approximate(method="sd", wlist=w, Nmax=4, Nk=3) print(fitinfo["summary"]) -tlist = np.linspace(0, 30 * np.pi / Del, 600) HEOM_ohmic_sd_fit = HEOMSolver( Hsys, - (Obath2,Q), + (Obath2, Q), max_depth=max_depth, options=options, ) results_ohmic_sd_fit2 = HEOM_ohmic_sd_fit.run(rho0, tlist) ``` -# Methods based on the Prony Polinomial +## Other Approximation methods +### Methods based on the Prony Polinomial The Prony polynomial forms the mathematical foundation for many spectral analysis techniques that estimate frequencies, damping factors, and amplitudes of signals. These methods work by interpreting a given signal as a sum of complex exponentials and deriving a polynomial whose roots correspond to the frequencies or poles of the system. @@ -904,119 +920,168 @@ $$ V_{N,M}(z)c = f $$ Where $M$ is the length, of the signal, and $f=f(t_{sample})$ is the signal evaluated in the sampling points,is a vector $c = (c_{1}, \dots, c_{N})$. -The main difference between the methods is the way one obtains the roots of the polynomial, typically whether this system is solved or a low rank approximation is found for the polynomial, [this article](https://academic.oup.com/imajna/article-abstract/43/2/789/6525860?redirectedFrom=fulltext) is a good reference, the QuTiP implementations are based on it, and the matlab implementations made available by the authors +The main difference between the methods is the way one obtains the roots of the polynomial, typically whether this system is solved or a low rank approximation is found for the polynomial, [this article](https://academic.oup.com/imajna/article-abstract/43/2/789/6525860?redirectedFrom=fulltext) is a good reference, the QuTiP implementations are based on it, and the matlab implementations made available by the authors. + +The prony like methods include: + +- Prony +- ESPRIT +- ESPIRA + +Though ESPIRA is prony like, since it is based on rational polynomial approximations +we group it with other methods +++ -## Using the Original Prony Method on the Correlation Function +##### Using the Original Prony Method on the Correlation Function -The method is available via `approx_by_prony`. Compared to the other approaches showed so far. The Prony based methods, shine on their simplicity no information needs to be known about the function, and one just needs to provide the sampling points, and the Number of Exponents one desires +The method is available via `approximate` passing "prony" as method. Compared to the other approaches showed so far. The Prony based methods, shine on their simplicity no information needs to be known about the function, and one just needs to provide the sampling points, and the Number of Exponents one desires ```{code-cell} ipython3 -tlist2=np.linspace(0,40,100) +tlist2 = np.linspace(0, 40, 100) ``` ```{code-cell} ipython3 -pbath,fitinfo=obs.approximate("prony",tlist2,Nr=4,Ni=4) +pbath, fitinfo = obs.approximate("prony", tlist2, Nr=4) print(fitinfo["summary"]) HEOM_ohmic_prony_fit = HEOMSolver( Hsys, - (pbath,Q), + (pbath, Q), max_depth=max_depth, options=options, ) results_ohmic_prony_fit = HEOM_ohmic_prony_fit.run(rho0, tlist) ``` -```{code-cell} ipython3 -gen_plots(pbath, w, J, t, C, w2, S) -``` - -## Using the matrix Pencil Method on the Correlation Function +Similar to how we approximated via prony we can use ESPRIT, the main difference +between both methods lies in the construction of the pencil matrix ```{code-cell} ipython3 -mpbath,fitinfo=obs.approximate(method="mp",tlist=tlist2,Nr=6,separate=False) +esbath, fitinfo = obs.approximate("esprit", tlist2, Nr=4, separate=False) print(fitinfo["summary"]) -HEOM_ohmic_mp_fit = HEOMSolver( +HEOM_ohmic_es_fit = HEOMSolver( Hsys, - (mpbath,Q), + (esbath, Q), max_depth=max_depth, options=options, ) -results_ohmic_mp_fit = HEOM_ohmic_mp_fit.run(rho0, tlist) +results_ohmic_es_fit = HEOM_ohmic_es_fit.run(rho0, tlist) ``` -```{code-cell} ipython3 -gen_plots(mpbath, w, J, t, C, w2, S) -``` +## Fitting the power spectrum + +So far all the methods covered fitted either the spectral density or the +correlation function. Here we will fit the power spectrum. + +### AAA algorithm + +The Adaptive Antoulas–Anderson algorithm (AAA) is a method for the +approximation of a function in terms of a quotient of polynomials + +\begin{align} + f(z) =\frac{q(z)}{p(z)} \approx \sum_{j=1}^{m} \frac{residues}{z-poles} +\end{align} + +We don't use this method on the correlation function directly, but on the power spectrum . After obtaining this rational polynomial form of the power spectrum one can recover the correlation function by noticing that + +\begin{align} + s(\omega) = \int_{-\infty}^{\infty} dt e^{i \omega t} C(t) = 2 \Re \left(\sum_{k} \frac{c_{k}}{\nu_{k}-i \omega} \right) +\end{align} + +Which allows us to identify + +\begin{align} + \nu_{k}= i \times poles \\ + c_{k} = -i \times residues +\end{align} -## Using the ESPRIT Method on the Correlation Function +this method works best when the sampling points provided are in the logarithmic scale ```{code-cell} ipython3 -esbath,fitinfo=obs.approximate("esprit",tlist2,Nr=4,separate=False) +wlist = np.concatenate((-np.logspace(3, -8, 3500), np.logspace(-8, 3, 3500))) +aaabath, fitinfo = obs.approximate("aaa", wlist, Nmax=8, tol=1e-15) print(fitinfo["summary"]) -HEOM_ohmic_es_fit = HEOMSolver( +``` + +```{code-cell} ipython3 +HEOM_ohmic_aaa_fit = HEOMSolver( Hsys, - (esbath,Q), + (aaabath, Q), max_depth=max_depth, options=options, ) -results_ohmic_es_fit = HEOM_ohmic_es_fit.run(rho0, tlist) +results_ohmic_aaa_fit = HEOM_ohmic_aaa_fit.run(rho0, tlist) ``` -```{code-cell} ipython3 -gen_plots(esbath, w, J, t, C, w2, S) -``` +### NLSQ on the power spectrum + +On the first part of the tutorials we dealt with methods based on non-linear +least squares. This is another one of those methods, but applied on the power +spectrum, compared to fitting the spectral density this is advantageous since +we don't need to specify $N_k$, while compared to the correlation fit, it is +easier to obtain approximations that hold the KMS relation. -## Using the AAA Algorithm +we fit the power spectrum to a function of the form + +$$S(\omega) = \sum_{k=1}^{N}\frac{2(a_k c_k + b_k (d_k - \omega))} +{(\omega - d_k)^2 + c_k^2}= 2 \Re \left(\sum_{k} \frac{c_{k}}{\nu_{k}-i \omega} \right)$$ ```{code-cell} ipython3 -aaabath,fitinfo=obs.approximate("aaa",np.concatenate((-np.logspace(3,-8,3500),np.logspace(-8,3,3500))),N_max=8,tol=1e-15) +psbath, fitinfo = obs.approximate("ps", w2, Nmax=4) print(fitinfo["summary"]) ``` ```{code-cell} ipython3 -HEOM_ohmic_aaa_fit = HEOMSolver( +HEOM_ohmic_ps_fit = HEOMSolver( Hsys, - (aaabath,Q), + (psbath, Q), max_depth=max_depth, options=options, ) -results_ohmic_aaa_fit = HEOM_ohmic_aaa_fit.run(rho0, tlist) +results_ohmic_ps_fit = HEOM_ohmic_ps_fit.run(rho0, tlist) ``` -```{code-cell} ipython3 -gen_plots(aaabath, w, J, t, C, w2, S) -``` +### ESPIRA + +ESPIRA is a Prony-like method, but while it takes a correlation function as +input. It exploits the relationship between parameter estimation (what we do +in Prony) and rational approximations, the rational approximation is done on the +DFT via the AAA algorithm, effectively using both information about the +power spectrum and the correlation function in the same fit. + +We have two implementations of ESPIRA, ESPIRA-I and ESPIRA-II. ESPIRA-I +is typically better, but in many cases especially when `separate=True`, +ESPIRA-II will yield better results. ESPIRA-II is recommended if +extremely slowly decaying exponents are expected. Otherwise ESPIRA-I is +recommended. + ++++ ESPIRA I ```{code-cell} ipython3 -tlist4=np.linspace(0,20,1000) -espibath,fitinfo=obs._approx_by_prony("espira-I",tlist4,Nr=4,Ni=4) +tlist4 = np.linspace(0, 20, 1000) +espibath, fitinfo = obs.approximate("espira-I", tlist4, Nr=4) print(fitinfo["summary"]) -``` - -```{code-cell} ipython3 HEOM_ohmic_espira_fit = HEOMSolver( Hsys, - (espibath,Q), + (espibath, Q), max_depth=max_depth, options=options, ) results_ohmic_espira_fit = HEOM_ohmic_espira_fit.run(rho0, tlist) ``` -```{code-cell} ipython3 -tlist4=np.linspace(0,40,500) -espibath2,fitinfo=obs._approx_by_prony("espira-II",tlist4,Nr=4,Ni=4,separate=True) -print(fitinfo["summary"]) -``` +ESPIRA-II ```{code-cell} ipython3 +tlist4 = np.linspace(0, 20, 1000) +espibath2, fitinfo = obs.approximate( + "espira-II", tlist4, Nr=4, Ni=4, separate=True) +print(fitinfo["summary"]) HEOM_ohmic_espira_fit2 = HEOMSolver( Hsys, - (espibath2,Q), + (espibath2, Q), max_depth=max_depth, options=options, ) @@ -1036,11 +1101,12 @@ plot_result_expectations( "b", "Correlation Function Fit $k_R=k_I=4$", ), - (results_spectral_fit_pk[3], P11p, "r-.", "Spectral Density Fit $k_J=4$"), + (results_spectral_fit_pk[3], P11p, + "r-.", "Spectral Density Fit $k_J=4$"), (results_ohmic_corr_fit, P11p, "r", "Correlation Fit Ohmic Bath"), (results_ohmic_sd_fit2, P11p, "g--", "Spectral Density Fit Ohmic Bath"), + (results_ohmic_ps_fit, P11p, "g--", "Power Spectrum Fit Ohmic Bath"), (results_ohmic_prony_fit, P11p, "k", " Prony Fit"), - (results_ohmic_mp_fit, P11p, "r", "Matrix Pencil Fit"), (results_ohmic_es_fit, P11p, "b-.", "ESPRIT Fit"), (results_ohmic_aaa_fit, P11p, "r-.", "Matrix AAA Fit"), (results_ohmic_espira_fit, P11p, "k", "ESPIRA I Fit"), @@ -1051,7 +1117,7 @@ plot_result_expectations( ) axes.set_ylabel(r"$\rho_{11}$", fontsize=30) axes.set_xlabel(r"$t\;\omega_c$", fontsize=30) -axes.legend(loc=0, fontsize=20); +axes.legend(loc=0, fontsize=20) axes.set_yscale("log") ``` @@ -1076,11 +1142,6 @@ assert np.allclose( expect(P11p, results_spectral_fit_pk[3].states), rtol=1e-2, ) -assert np.allclose( - expect(P11p, results_ohmic_mp_fit.states), - expect(P11p, results_spectral_fit_pk[3].states), - rtol=1e-2, -) assert np.allclose( expect(P11p, results_ohmic_prony_fit.states), expect(P11p, results_spectral_fit_pk[3].states), diff --git a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md index fe1409c3..3fe6a3ac 100644 --- a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md +++ b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md @@ -5,9 +5,9 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.1 + jupytext_version: 1.17.0 kernelspec: - display_name: qutip-dev + display_name: qutip-tutorials language: python name: python3 --- @@ -66,31 +66,19 @@ Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. ## Setup -```{code-cell} +```{code-cell} ipython3 import contextlib import time import numpy as np -from matplotlib import pyplot as plt +import qutip import scipy +from matplotlib import pyplot as plt +from qutip import basis, expect, liouvillian, sigmax, sigmaz +from qutip.core.environment import DrudeLorentzEnvironment, system_terminator +from qutip.solver.heom import HEOMSolver from scipy.optimize import curve_fit -import qutip -from qutip import ( - basis, - expect, - liouvillian, - sigmax, - sigmaz, -) -from qutip.solver.heom import ( - HEOMSolver -) -from qutip.core.environment import ( - DrudeLorentzEnvironment, - system_terminator -) - %matplotlib inline ``` @@ -98,23 +86,23 @@ from qutip.core.environment import ( Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: -```{code-cell} +```{code-cell} ipython3 def cot(x): - """ Vectorized cotangent of x. """ - return 1. / np.tan(x) + """Vectorized cotangent of x.""" + return 1.0 / np.tan(x) def coth(x): - """ Vectorized hyperbolic cotangent of x. """ - return 1. / np.tanh(x) + """Vectorized hyperbolic cotangent of x.""" + return 1.0 / np.tanh(x) ``` -```{code-cell} +```{code-cell} ipython3 def plot_result_expectations(plots, axes=None): - """ Plot the expectation values of operators as functions of time. + """Plot the expectation values of operators as functions of time. - Each plot in plots consists of (solver_result, measurement_operation, - color, label). + Each plot in plots consists of (solver_result, measurement_operation, + color, label). """ if axes is None: fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -141,13 +129,13 @@ def plot_result_expectations(plots, axes=None): return fig ``` -```{code-cell} +```{code-cell} ipython3 @contextlib.contextmanager def timer(label): - """ Simple utility for timing functions: + """Simple utility for timing functions: - with timer("name"): - ... code to time ... + with timer("name"): + ... code to time ... """ start = time.time() yield @@ -155,9 +143,10 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} +```{code-cell} ipython3 # Solver options: + options = { "nsteps": 15000, "store_states": True, @@ -176,14 +165,14 @@ And let us set up the system Hamiltonian, bath and system measurement operators: Here we set $H_{sys}=0$, which means the interaction Hamiltonian and the system Hamiltonian commute, and we can compare the numerical results to a known analytical one. We could in principle keep $\epsilon \neq 0$, but it just introduces fast system oscillations, so it is more convenient to set it to zero. -```{code-cell} +```{code-cell} ipython3 # Defining the system Hamiltonian eps = 0.0 # Energy of the 2-level system. Del = 0.0 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` -```{code-cell} +```{code-cell} ipython3 # System-bath coupling (Drude-Lorentz spectral density) Q = sigmaz() # coupling operator @@ -191,7 +180,7 @@ Q = sigmaz() # coupling operator gamma = 0.5 # cut off frequency lam = 0.1 # coupling strength T = 0.5 -beta = 1. / T +beta = 1.0 / T # HEOM parameters: # cut off parameter for the bath: @@ -204,7 +193,7 @@ Nk = 3 tlist = np.linspace(0, 50, 1000) ``` -```{code-cell} +```{code-cell} ipython3 # Define some operators with which we will measure the system # 1,1 element of density matrix - corresponding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -215,7 +204,7 @@ P12p = basis(2, 0) * basis(2, 1).dag() To get a non-trivial result we prepare the initial state in a superposition, and see how the bath destroys the coherence. -```{code-cell} +```{code-cell} ipython3 # Initial state of the system. psi = (basis(2, 0) + basis(2, 1)).unit() rho0 = psi * psi.dag() @@ -224,81 +213,89 @@ rho0 = psi * psi.dag() We then define our environment, from which all the different simulations will be obtained -```{code-cell} +```{code-cell} ipython3 env = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=Nk) ``` ## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator -```{code-cell} +```{code-cell} ipython3 with timer("RHS construction time"): - env_mats=env.approx_by_matsubara(Nk=Nk) - HEOMMats = HEOMSolver(Hsys, (env_mats,Q), NC, options=options) + env_mats = env.approximate(method="matsubara", Nk=Nk) + HEOMMats = HEOMSolver(Hsys, (env_mats, Q), NC, options=options) with timer("ODE solver time"): resultMats = HEOMMats.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 # Plot the results so far -plot_result_expectations([ - (resultMats, P11p, 'b', "P11 Matsubara"), - (resultMats, P12p, 'r', "P12 Matsubara"), -]); +plot_result_expectations( + [ + (resultMats, P11p, "b", "P11 Matsubara"), + (resultMats, P12p, "r", "P12 Matsubara"), + ] +); ``` ## Simulation 2: Matsubara decomposition (including terminator) -```{code-cell} +```{code-cell} ipython3 with timer("RHS construction time"): - env_mats,delta=env.approx_by_matsubara(Nk=Nk,compute_delta=True) - Ltot = liouvillian(Hsys) + system_terminator(Q,delta) - HEOMMatsT = HEOMSolver(Ltot, (env_mats,Q), NC, options=options) + env_mats, delta = env.approximate(method="matsubara", Nk=Nk, compute_delta=True) + Ltot = liouvillian(Hsys) + system_terminator(Q, delta) + HEOMMatsT = HEOMSolver(Ltot, (env_mats, Q), NC, options=options) with timer("ODE solver time"): resultMatsT = HEOMMatsT.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 # Plot the results -plot_result_expectations([ - (resultMats, P11p, 'b', "P11 Matsubara"), - (resultMats, P12p, 'r', "P12 Matsubara"), - (resultMatsT, P11p, 'r--', "P11 Matsubara and terminator"), - (resultMatsT, P12p, 'b--', "P12 Matsubara and terminator"), -]); +plot_result_expectations( + [ + (resultMats, P11p, "b", "P11 Matsubara"), + (resultMats, P12p, "r", "P12 Matsubara"), + (resultMatsT, P11p, "r--", "P11 Matsubara and terminator"), + (resultMatsT, P12p, "b--", "P12 Matsubara and terminator"), + ] +); ``` ## Simulation 3: Pade decomposition As in example 1a, we can compare to Pade and Fitting approaches. -```{code-cell} +```{code-cell} ipython3 with timer("RHS construction time"): - env_pade=env.approx_by_pade(Nk=Nk) - HEOMPade = HEOMSolver(Hsys, (env_pade,Q), NC, options=options) + env_pade = env.approximate(method="pade", Nk=Nk) + HEOMPade = HEOMSolver(Hsys, (env_pade, Q), NC, options=options) with timer("ODE solver time"): resultPade = HEOMPade.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 # Plot the results -plot_result_expectations([ - (resultMatsT, P11p, 'b', "P11 Matsubara (+term)"), - (resultMatsT, P12p, 'r', "P12 Matsubara (+term)"), - (resultPade, P11p, 'r--', "P11 Pade"), - (resultPade, P12p, 'b--', "P12 Pade"), -]); +plot_result_expectations( + [ + (resultMatsT, P11p, "b", "P11 Matsubara (+term)"), + (resultMatsT, P12p, "r", "P12 Matsubara (+term)"), + (resultPade, P11p, "r--", "P11 Pade"), + (resultPade, P12p, "b--", "P12 Pade"), + ] +); ``` ## Simulation 4: Fitting approach -```{code-cell} -tfit=np.linspace(0,10,1000) +```{code-cell} ipython3 +tfit = np.linspace(0, 10, 1000) with timer("RHS construction time"): - bath,_ = env.approx_by_cf_fit(tfit,Ni_max=1,Nr_max=3,target_rsme=None) - HEOMFit = HEOMSolver(Hsys, (bath,Q), NC, options=options) + bath, _ = env.approximate( + method="cf", tlist=tfit, Ni_max=1, Nr_max=3, target_rmse=None + ) + HEOMFit = HEOMSolver(Hsys, (bath, Q), NC, options=options) with timer("ODE solver time"): resultFit = HEOMFit.run(rho0, tlist) @@ -306,7 +303,7 @@ with timer("ODE solver time"): ## Analytic calculations -```{code-cell} +```{code-cell} ipython3 def pure_dephasing_evolution_analytical(tlist, wq, ck, vk): """ Computes the propagating function appearing in the pure dephasing model. @@ -330,10 +327,9 @@ def pure_dephasing_evolution_analytical(tlist, wq, ck, vk): integral: float The value of the integral function at time t. """ - evolution = np.array([ - np.exp(-1j * wq * t - correlation_integral(t, ck, vk)) - for t in tlist - ]) + evolution = np.array( + [np.exp(-1j * wq * t - correlation_integral(t, ck, vk)) for t in tlist] + ) return evolution @@ -367,36 +363,22 @@ def correlation_integral(t, ck, vk): integral: float The value of the integral function at time t. """ - t1 = np.sum( - (ck / vk**2) * - (np.exp(vk * t) - 1) - ) - t2 = np.sum( - (ck.conj() / vk.conj()**2) * - (np.exp(vk.conj() * t) - 1) - ) - t3 = np.sum( - (ck / vk + ck.conj() / vk.conj()) * t - ) + t1 = np.sum((ck / vk**2) * (np.exp(vk * t) - 1)) + t2 = np.sum((ck.conj() / vk.conj() ** 2) * (np.exp(vk.conj() * t) - 1)) + t3 = np.sum((ck / vk + ck.conj() / vk.conj()) * t) return 2 * (t1 + t2 - t3) ``` For the pure dephasing analytics, we just sum up as many matsubara terms as we can: -```{code-cell} +```{code-cell} ipython3 lmaxmats2 = 15000 vk = [complex(-gamma)] -vk.extend([ - complex(-2. * np.pi * k * T) - for k in range(1, lmaxmats2) -]) +vk.extend([complex(-2.0 * np.pi * k * T) for k in range(1, lmaxmats2)]) -ck = [complex(lam * gamma * (-1.0j + cot(gamma * beta / 2.)))] -ck.extend([ - complex(4 * lam * gamma * T * (-v) / (v**2 - gamma**2)) - for v in vk[1:] -]) +ck = [complex(lam * gamma * (-1.0j + cot(gamma * beta / 2.0)))] +ck.extend([complex(4 * lam * gamma * T * (-v) / (v**2 - gamma**2)) for v in vk[1:]]) P12_ana = 0.5 * pure_dephasing_evolution_analytical( tlist, 0, np.asarray(ck), np.asarray(vk) @@ -405,61 +387,65 @@ P12_ana = 0.5 * pure_dephasing_evolution_analytical( Alternatively, we can just do the integral of the propagator directly, without using the correlation functions at all -```{code-cell} +```{code-cell} ipython3 def JDL(omega, lamc, omega_c): - return 2. * lamc * omega * omega_c / (omega_c**2 + omega**2) + return 2.0 * lamc * omega * omega_c / (omega_c**2 + omega**2) def integrand(omega, lamc, omega_c, Temp, t): return ( - (-4. * JDL(omega, lamc, omega_c) / omega**2) * - (1. - np.cos(omega*t)) * (coth(omega/(2.*Temp))) + (-4.0 * JDL(omega, lamc, omega_c) / omega**2) + * (1.0 - np.cos(omega * t)) + * (coth(omega / (2.0 * Temp))) / np.pi ) P12_ana2 = [ - 0.5 * np.exp( - scipy.integrate.quad(integrand, 0, np.inf, args=(lam, gamma, T, t))[0] - ) + 0.5 * np.exp(scipy.integrate.quad(integrand, 0, np.inf, args=(lam, gamma, T, t))[0]) for t in tlist ] ``` ## Compare results -```{code-cell} -plot_result_expectations([ - (resultMats, P12p, 'r', "P12 Mats"), - (resultMatsT, P12p, 'r--', "P12 Mats + Term"), - (resultPade, P12p, 'b--', "P12 Pade"), - (resultFit, P12p, 'g', "P12 Fit"), - ((tlist, np.real(P12_ana)), None, 'b', "Analytic 1"), - ((tlist, np.real(P12_ana2)), None, 'y--', "Analytic 2"), -]); +```{code-cell} ipython3 +plot_result_expectations( + [ + (resultMats, P12p, "r", "P12 Mats"), + (resultMatsT, P12p, "r--", "P12 Mats + Term"), + (resultPade, P12p, "b--", "P12 Pade"), + (resultFit, P12p, "g", "P12 Fit"), + ((tlist, np.real(P12_ana)), None, "b", "Analytic 1"), + ((tlist, np.real(P12_ana2)), None, "y--", "Analytic 2"), + ] +); ``` We can't see much difference in the plot above, so let's do a log plot instead: -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) -plot_result_expectations([ - (resultMats, P12p, 'r', "P12 Mats"), - (resultMatsT, P12p, 'r--', "P12 Mats + Term"), - (resultPade, P12p, 'b-.', "P12 Pade"), - (resultFit, P12p, 'g', "P12 Fit"), - ((tlist, np.real(P12_ana)), None, 'b', "Analytic 1"), - ((tlist, np.real(P12_ana2)), None, 'y--', "Analytic 2"), -], axes) +plot_result_expectations( + [ + (resultMats, P12p, "r", "P12 Mats"), + (resultMatsT, P12p, "r--", "P12 Mats + Term"), + (resultPade, P12p, "b-.", "P12 Pade"), + (resultFit, P12p, "g", "P12 Fit"), + ((tlist, np.real(P12_ana)), None, "b", "Analytic 1"), + ((tlist, np.real(P12_ana2)), None, "y--", "Analytic 2"), + ], + axes, +) -axes.set_yscale('log') +axes.set_yscale("log") axes.legend(loc=0, fontsize=12); ``` ## About -```{code-cell} +```{code-cell} ipython3 qutip.about() ``` @@ -467,21 +453,25 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 assert np.allclose( - expect(P12p, resultMats.states[:15]), np.real(P12_ana)[:15], + expect(P12p, resultMats.states[:15]), + np.real(P12_ana)[:15], rtol=1e-2, ) assert np.allclose( - expect(P12p, resultMatsT.states[:100]), np.real(P12_ana)[:100], + expect(P12p, resultMatsT.states[:100]), + np.real(P12_ana)[:100], rtol=1e-3, ) assert np.allclose( - expect(P12p, resultPade.states[:100]), np.real(P12_ana)[:100], + expect(P12p, resultPade.states[:100]), + np.real(P12_ana)[:100], rtol=1e-3, ) assert np.allclose( - expect(P12p, resultFit.states[:50]), np.real(P12_ana)[:50], + expect(P12p, resultFit.states[:50]), + np.real(P12_ana)[:50], rtol=1e-3, ) assert np.allclose(P12_ana, P12_ana2, rtol=1e-3) diff --git a/tutorials-v5/heom/heom-2-fmo-example.md b/tutorials-v5/heom/heom-2-fmo-example.md index 93c59b1d..7fda60e7 100644 --- a/tutorials-v5/heom/heom-2-fmo-example.md +++ b/tutorials-v5/heom/heom-2-fmo-example.md @@ -5,9 +5,9 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.1 + jupytext_version: 1.17.0 kernelspec: - display_name: qutip-dev + display_name: qutip-tutorials language: python name: python3 --- @@ -31,7 +31,7 @@ quantum environment reduces the effect of pure dephasing. ## Setup -```{code-cell} +```{code-cell} ipython3 import contextlib import time @@ -62,7 +62,7 @@ from qutip.core.environment import ( Let's define some helper functions for calculating correlation functions, spectral densities, thermal energy level occupations, and for plotting results and timing how long operations take: -```{code-cell} +```{code-cell} ipython3 @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -76,7 +76,7 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} +```{code-cell} ipython3 # Solver options: options = { @@ -94,7 +94,7 @@ options = { And let us set up the system Hamiltonian and bath parameters: -```{code-cell} +```{code-cell} ipython3 # System Hamiltonian: # # We use the Hamiltonian employed in @@ -112,7 +112,7 @@ Hsys = 3e10 * 2 * np.pi * Qobj([ ]) ``` -```{code-cell} +```{code-cell} ipython3 # Bath parameters lam = 35 * 3e10 * 2 * np.pi @@ -125,11 +125,11 @@ beta = 1 / T Let's quickly plot the spectral density and environment correlation functions so that we can see what they look like. -```{code-cell} -env=DrudeLorentzEnvironment(T=T,lam=lam,gamma=gamma) +```{code-cell} ipython3 +env = DrudeLorentzEnvironment(T=T, lam=lam, gamma=gamma) ``` -```{code-cell} +```{code-cell} ipython3 wlist = np.linspace(0, 200 * 3e10 * 2 * np.pi, 100) tlist = np.linspace(0, 1e-12, 1000) @@ -165,7 +165,7 @@ axes[1].legend(); Now let us solve for the evolution of this system using the HEOM. -```{code-cell} +```{code-cell} ipython3 # We start the excitation at site 1: rho0 = basis(7, 0) * basis(7, 0).dag() @@ -180,15 +180,16 @@ Nk = 0 Q_list = [] baths = [] Ltot = liouvillian(Hsys) -env_approx,delta=env.approx_by_matsubara(Nk=Nk,compute_delta=True) +env_approx, delta = env.approximate( + method="matsubara", Nk=Nk, compute_delta=True) for m in range(7): Q = basis(7, m) * basis(7, m).dag() Q_list.append(Q) - Ltot += system_terminator(Q,delta) - baths.append((env_approx,Q)) + Ltot += system_terminator(Q, delta) + baths.append((env_approx, Q)) ``` -```{code-cell} +```{code-cell} ipython3 with timer("RHS construction time"): HEOMMats = HEOMSolver(Hsys, baths, NC, options=options) @@ -196,7 +197,7 @@ with timer("ODE solver time"): outputFMO_HEOM = HEOMMats.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, figsize=(12, 8)) colors = ['r', 'g', 'b', 'y', 'c', 'm', 'k'] @@ -232,7 +233,7 @@ Now let us solve the same problem using the Bloch-Redfield solver. We will see t In the next section, we will examine the role of pure dephasing in the evolution to understand why this happens. -```{code-cell} +```{code-cell} ipython3 with timer("BR ODE solver time"): outputFMO_BR = brmesolve( Hsys, rho0, tlist, @@ -243,7 +244,7 @@ with timer("BR ODE solver time"): And now let's plot the Bloch-Redfield solver results: -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, figsize=(12, 8)) for m, Q in enumerate(Q_list): @@ -271,11 +272,7 @@ It is useful to construct the various parts of the Bloch-Redfield master equatio First we will write a function to return the list of collapse operators for a given system, either with or without the dephasing operators: -+++ - -TODO: Maybe power spectrum at zero is wrong, by a factor 2 - -```{code-cell} +```{code-cell} ipython3 def J0_dephasing(): """ Under-damped brownian oscillator dephasing probability. @@ -284,11 +281,11 @@ def J0_dephasing(): return 2 * lam * gamma / gamma**2 ``` -```{code-cell} -env.power_spectrum(0)/2 -J0_dephasing()*T +```{code-cell} ipython3 +env.power_spectrum(0)/2 - J0_dephasing()*T ``` -```{code-cell} +```{code-cell} ipython3 def get_collapse(H, T, dephasing=1): """ Calculate collapse operators for a given system H and temperature T. @@ -312,7 +309,7 @@ def get_collapse(H, T, dephasing=1): np.abs(Q.matrix_element( all_state[j].dag(), all_state[k] ))**2 * - env.power_spectrum(Deltajk) + env.power_spectrum(Deltajk) ) if rate > 0.0: # emission: @@ -352,7 +349,7 @@ Now we are able to switch the pure dephasing terms on and off. Let us starting by including the dephasing operators. We expect to see the same behaviour that we saw when using the Bloch-Redfield solver. -```{code-cell} +```{code-cell} ipython3 # dephasing terms on, we recover the full BR solution: with timer("Building the collapse operators"): @@ -362,7 +359,7 @@ with timer("ME ODE solver"): outputFMO_ME = mesolve(Hsys, rho0, tlist, collapse_list) ``` -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, figsize=(12, 8)) for m, Q in enumerate(Q_list): @@ -381,7 +378,7 @@ We see similar results to before. Now let us examine what happens when we remove the dephasing collapse operators: -```{code-cell} +```{code-cell} ipython3 # dephasing terms off with timer("Building the collapse operators"): @@ -391,7 +388,7 @@ with timer("ME ODE solver"): outputFMO_ME_nodephase = mesolve(Hsys, rho0, tlist, collapse_list) ``` -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, figsize=(12, 8)) for m, Q in enumerate(Q_list): axes.plot( @@ -409,13 +406,13 @@ plt.xticks([0, 500, 1000], [0, 500, 1000]) axes.legend(fontsize=18); ``` -And now we see that without the dephasing, the oscillations reappear. The full dynamics capture by the HEOM are still not capture by this simpler model, however. +And now we see that without the dephasing, the oscillations reappear. The full dynamics captured by the HEOM are still not capture by this simpler model, however. +++ ## About -```{code-cell} +```{code-cell} ipython3 qutip.about() ``` @@ -423,7 +420,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 assert np.allclose( expect(outputFMO_BR.states, Q_list[0]), expect(outputFMO_ME.states, Q_list[0]), diff --git a/tutorials-v5/heom/heom-3-quantum-heat-transport.md b/tutorials-v5/heom/heom-3-quantum-heat-transport.md index 05d1e5df..6b219d8a 100644 --- a/tutorials-v5/heom/heom-3-quantum-heat-transport.md +++ b/tutorials-v5/heom/heom-3-quantum-heat-transport.md @@ -5,9 +5,9 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.1 + jupytext_version: 1.17.0 kernelspec: - display_name: qutip-dev + display_name: qutip-tutorials language: python name: python3 --- @@ -50,7 +50,7 @@ References: ## Setup -```{code-cell} +```{code-cell} ipython3 import dataclasses import numpy as np @@ -75,7 +75,7 @@ from IPython.display import display ## Helpers -```{code-cell} +```{code-cell} ipython3 # Solver options: options = { @@ -91,7 +91,7 @@ options = { ## System and bath definition -```{code-cell} +```{code-cell} ipython3 @dataclasses.dataclass class SystemParams: """ System parameters and Hamiltonian. """ @@ -120,7 +120,7 @@ class SystemParams: return dataclasses.replace(self, **kw) ``` -```{code-cell} +```{code-cell} ipython3 @dataclasses.dataclass class BathParams: """ Bath parameters. """ @@ -147,11 +147,12 @@ class BathParams: return qt.tensor(Q) def bath(self, Nk, tag=None): - env=DrudeLorentzEnvironment( + env = DrudeLorentzEnvironment( lam=self.lam, gamma=self.gamma, T=self.T, tag=tag ) - env_approx,delta=env.approx_by_pade(Nk=Nk,compute_delta=True,tag=tag) - return (env_approx,self.Q()),system_terminator(self.Q(),delta),delta + env_approx, delta = env.approximate( + "pade", Nk=Nk, compute_delta=True, tag=tag) + return (env_approx, self.Q()), system_terminator(self.Q(), delta), delta def replace(self, **kw): return dataclasses.replace(self, **kw) @@ -175,7 +176,7 @@ In the expression for the bath heat currents, we left out terms involving $[Q_1, In QuTiP, these currents can be conveniently calculated as follows: -```{code-cell} +```{code-cell} ipython3 def bath_heat_current(bath_tag, ado_state, hamiltonian, coupling_op, delta=0): """ Bath heat current from the system into the heat bath with the given tag. @@ -270,7 +271,7 @@ Note that at long times, we expect $j_{\text{B}}^1 = -j_{\text{B}}^2$ and $j_{\t For our simulations, we will represent the bath spectral densities using the first term of their Padé decompositions, and we will use $7$ levels of the HEOM hierarchy. -```{code-cell} +```{code-cell} ipython3 Nk = 1 NC = 7 ``` @@ -280,7 +281,7 @@ NC = 7 We fix $J_{12} = 0.1 \epsilon$ (as in Fig. 3(a-ii) of Ref. \[2\]) and choose the fixed coupling strength $\lambda_1 = \lambda_2 = J_{12}\, /\, (2\epsilon)$ (corresponding to $\bar\zeta = 1$ in Ref. \[2\]). Using these values, we will study the time evolution of the system state and the heat currents. -```{code-cell} +```{code-cell} ipython3 # fix qubit-qubit and qubit-bath coupling strengths sys = SystemParams(J12=0.1) bath_p1 = BathParams(qubit=0, sign="+", lam=sys.J12 / 2) @@ -293,13 +294,13 @@ rho0 = qt.tensor(qt.identity(2), qt.identity(2)) / 4 tlist = np.linspace(0, 50, 250) ``` -```{code-cell} +```{code-cell} ipython3 H = sys.H() -bath1,b1term,b1delta = bath_p1.bath(Nk, tag='bath 1') +bath1, b1term, b1delta = bath_p1.bath(Nk, tag='bath 1') Q1 = bath_p1.Q() -bath2,b2term,b2delta = bath_p2.bath(Nk, tag='bath 2') +bath2, b2term, b2delta = bath_p2.bath(Nk, tag='bath 2') Q2 = bath_p2.Q() @@ -321,7 +322,7 @@ result = solver.run(rho0, tlist, e_ops=[ We first plot $\langle \sigma_z^1 \rangle$ to see the time evolution of the system state: -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(figsize=(8, 8)) axes.plot(tlist, result.expect[0], 'r', linewidth=2) axes.set_xlabel('t', fontsize=28) @@ -330,7 +331,7 @@ axes.set_ylabel(r"$\langle \sigma_z^1 \rangle$", fontsize=28); We find a rather quick thermalization of the system state. For the heat currents, however, it takes a somewhat longer time until they converge to their long-time values: -```{code-cell} +```{code-cell} ipython3 fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8)) ax1.plot( @@ -382,16 +383,16 @@ ax2.legend(loc=0, fontsize=12); Here, we try to reproduce the HEOM curves in Fig. 3(a) of Ref. \[1\] by varying the coupling strength and finding the steady state for each coupling strength. -```{code-cell} +```{code-cell} ipython3 def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options): """ Calculate the steady sate heat currents for the given system and bath. """ - bath1,b1term,b1delta = bath_p1.bath(Nk, tag='bath 1') + bath1, b1term, b1delta = bath_p1.bath(Nk, tag='bath 1') Q1 = bath_p1.Q() - bath2,b2term,b2delta = bath_p2.bath(Nk, tag='bath 2') + bath2, b2term, b2delta = bath_p2.bath(Nk, tag='bath 2') Q2 = bath_p2.Q() solver = HEOMSolver( @@ -411,7 +412,7 @@ def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options): ) ``` -```{code-cell} +```{code-cell} ipython3 # Define number of points to use for the plot plot_points = 10 # use 100 for a smoother curve @@ -464,7 +465,7 @@ j3s = [ ## Create Plot -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(figsize=(12, 7)) axes.plot( @@ -495,7 +496,7 @@ axes.legend(loc=0); ## About -```{code-cell} +```{code-cell} ipython3 qt.about() ``` @@ -503,6 +504,6 @@ qt.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 assert 1 == 1 ``` diff --git a/tutorials-v5/heom/heom-4-dynamical-decoupling.md b/tutorials-v5/heom/heom-4-dynamical-decoupling.md index 0eb761d5..217e1617 100644 --- a/tutorials-v5/heom/heom-4-dynamical-decoupling.md +++ b/tutorials-v5/heom/heom-4-dynamical-decoupling.md @@ -5,9 +5,9 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.1 + jupytext_version: 1.17.0 kernelspec: - display_name: qutip-dev + display_name: qutip-tutorials language: python name: python3 --- @@ -27,7 +27,7 @@ We first show the standard example of equally spaced pulses, and then consider t ## Setup -```{code-cell} +```{code-cell} ipython3 import numpy as np import matplotlib.pyplot as plt @@ -53,7 +53,7 @@ from IPython.display import display ## Solver options -```{code-cell} +```{code-cell} ipython3 # Solver options: # The max_step must be set to a short time than the @@ -75,7 +75,7 @@ options = { Now we define the system and bath properties and the HEOM parameters. The system is a single stationary qubit with $H = 0$ and the bath is a bosonic bath with a Drude-Lorentz spectrum. -```{code-cell} +```{code-cell} ipython3 # Define the system Hamlitonian. # # The system isn't evolving by itself, so the Hamiltonian is 0 (with the @@ -84,7 +84,7 @@ Now we define the system and bath properties and the HEOM parameters. The system H_sys = 0 * sigmaz() ``` -```{code-cell} +```{code-cell} ipython3 # Define some operators with which we will measure the system # 1,1 element of density matrix - corresponding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -93,7 +93,7 @@ P22p = basis(2, 1) * basis(2, 1).dag() P12p = basis(2, 0) * basis(2, 1).dag() ``` -```{code-cell} +```{code-cell} ipython3 # Properties for the Drude-Lorentz bath lam = 0.0005 @@ -106,12 +106,12 @@ Q = sigmaz() # number of terms to keep in the expansion of the bath correlation function: Nk = 3 -env = DrudeLorentzEnvironment(lam=lam, gamma=gamma,T=T) -env_approx=env.approx_by_pade(Nk=Nk) -bath=(env_approx,Q) +env = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T) +env_approx = env.approximate(method="pade", Nk=Nk) +bath = (env_approx, Q) ``` -```{code-cell} +```{code-cell} ipython3 # HEOM parameters # number of layers to keep in the hierarchy: @@ -122,7 +122,7 @@ To perform the dynamic decoupling from the environment, we will drive the system Below we define a function that returns the pulse (which is itself a function): -```{code-cell} +```{code-cell} ipython3 def drive(amplitude, delay, integral): """ Coefficient of the drive as a function of time. @@ -159,7 +159,7 @@ H_drive = sigmax() Let's start by plotting the spectral density of our Drude-Lorentz bath: -```{code-cell} +```{code-cell} ipython3 wlist = np.linspace(0, 0.5, 1000) J = env.spectral_density(wlist) J_approx = env_approx.spectral_density(wlist) @@ -180,7 +180,7 @@ First we will drive the system with fast, large amplitude pulses. Then we will d Let's start by simulating the fast pulses: -```{code-cell} +```{code-cell} ipython3 # Fast driving (quick, large amplitude pulses) tlist = np.linspace(0, 400, 1000) @@ -203,7 +203,7 @@ outputDD = hsolver.run(rho0, tlist) And now the longer slower pulses: -```{code-cell} +```{code-cell} ipython3 # Slow driving (longer, small amplitude pulses) # without pulses @@ -220,7 +220,7 @@ outputDDslow = hsolver.run(rho0, tlist) Now let's plot all of the results and the shapes of the pulses: -```{code-cell} +```{code-cell} ipython3 def plot_dd_results(outputnoDD, outputDD, outputDDslow): fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 12)) @@ -286,7 +286,7 @@ def plot_dd_results(outputnoDD, outputDD, outputDDslow): fig.tight_layout() ``` -```{code-cell} +```{code-cell} ipython3 plot_dd_results(outputnoDD, outputDD, outputDDslow) ``` @@ -306,7 +306,7 @@ $$ This is just a convenient way to describe the varying delay. We could have chosen another monotonically increasing function to represent the cummulative delay (although it might not be as effective). -```{code-cell} +```{code-cell} ipython3 def cummulative_delay_fractions(N): """ Return an array of N + 1 cummulative delay fractions. @@ -360,7 +360,7 @@ Let's plot the cummulative delays and see what they look like. Note that the cum On the same axes we plot the individual $j^{th}$ delays as a fraction of the average delay. -```{code-cell} +```{code-cell} ipython3 def plot_cummulative_delay_fractions(N): cummulative = cummulative_delay_fractions(N) individual = (cummulative[1:] - cummulative[:-1]) * N @@ -376,7 +376,7 @@ plot_cummulative_delay_fractions(100) And now let us plot the first ten even and optimally spaced pulses together to compare them: -```{code-cell} +```{code-cell} ipython3 def plot_even_and_optimally_spaced_pulses(): amplitude = 10.0 integral = np.pi / 2 @@ -404,7 +404,7 @@ Now let's simulate the effectiveness of the two sets of delays by comparing how We'll perform the simulation over a range of lambdas and gammas to show how the non-evenly spaced delays become optimal as the width of the bath spectral function increases. -```{code-cell} +```{code-cell} ipython3 # Bath parameters to simulate over: # We use only two lambdas and two gammas so that the notebook executes @@ -446,7 +446,7 @@ def simulate_100_pulses(lam, gamma, T, NC, Nk): env = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T) env_approx = env.approx_by_pade(Nk=Nk) - bath=(env_approx,Q) + bath = (env_approx, Q) # Equally spaced pulses: pulse_eq = drive(amplitude, delay, integral) @@ -485,7 +485,7 @@ P12_results = [ Now that we have the expectation values of $\rho_{01}$ let's plot them as a function of gamma for each lambda. Note how in each case the non-evenly spaced pulses become optimal once gamma is sufficiently small: -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, sharex=False, figsize=(10, 7)) colors = ["green", "red", "blue"] @@ -515,7 +515,7 @@ And now you know about dynamically decoupling a qubit from its environment! ## About -```{code-cell} +```{code-cell} ipython3 qutip.about() ``` @@ -523,6 +523,6 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 assert 1 == 1 ``` diff --git a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md index 7341c606..106b025f 100644 --- a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md +++ b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md @@ -5,9 +5,9 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.1 + jupytext_version: 1.17.0 kernelspec: - display_name: qutip-dev + display_name: qutip-tutorials language: python name: python3 --- @@ -69,7 +69,7 @@ In this notebook we: ## Setup -```{code-cell} +```{code-cell} ipython3 import contextlib import dataclasses import time @@ -96,7 +96,7 @@ from IPython.display import display ## Helpers -```{code-cell} +```{code-cell} ipython3 @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -110,7 +110,7 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} +```{code-cell} ipython3 # Solver options: # We set store_ados to True so that we can @@ -133,7 +133,7 @@ options = { And let us set up the system Hamiltonian, bath and system measurement operators: -```{code-cell} +```{code-cell} ipython3 # Define the system Hamiltonian: # The system is a single fermion with energy level split e1: @@ -142,7 +142,7 @@ e1 = 1.0 H = e1 * d1.dag() * d1 ``` -```{code-cell} +```{code-cell} ipython3 # Define parameters for left and right fermionic baths. # Each bath is a lead (i.e. a wire held at a potential) # with temperature T and chemical potential mu. @@ -195,7 +195,7 @@ bath_R = LorentzianBathParameters(Q=d1, lead="R") Let's plot the spectral density. -```{code-cell} +```{code-cell} ipython3 w_list = np.linspace(-2, 2, 100) fig, ax = plt.subplots(figsize=(12, 7)) @@ -223,7 +223,7 @@ ax.legend(); Next let's plot the emission and absorption by the leads. -```{code-cell} +```{code-cell} ipython3 w_list = np.linspace(-2, 2, 100) fig, ax = plt.subplots(figsize=(12, 7)) @@ -269,7 +269,7 @@ ax.legend(); Let's start by solving for the evolution using a Pade expansion of the correlation function of the Lorentzian spectral density: -```{code-cell} +```{code-cell} ipython3 # HEOM dynamics using the Pade approximation: # Times to solve for and initial system state: @@ -279,17 +279,18 @@ rho0 = basis(2, 0) * basis(2, 0).dag() Nk = 10 # Number of exponents to retain in the expansion of each bath envL = LorentzianEnvironment( - bath_L.T,bath_L.mu,bath_L.gamma, bath_L.W, + bath_L.T, bath_L.mu, bath_L.gamma, bath_L.W, ) -envL_pade= envL.approx_by_pade(Nk=Nk, tag="L") -envR =LorentzianEnvironment( - bath_R.T,bath_R.mu,bath_R.gamma, bath_R.W, +envL_pade = envL.approx_by_pade(Nk=Nk, tag="L") +envR = LorentzianEnvironment( + bath_R.T, bath_R.mu, bath_R.gamma, bath_R.W, ) -envR_pade= envR.approx_by_pade(Nk=Nk, tag="L") +envR_pade = envR.approx_by_pade(Nk=Nk, tag="R") with timer("RHS construction time"): - solver_pade = HEOMSolver(H, [(envL_pade,bath_L.Q), (envR_pade,bath_R.Q)], max_depth=2, options=options) + solver_pade = HEOMSolver( + H, [(envL_pade, bath_L.Q), (envR_pade, bath_R.Q)], max_depth=2, options=options) with timer("ODE solver time"): result_pade = solver_pade.run(rho0, tlist) @@ -300,7 +301,7 @@ with timer("Steady state solver time"): Now let us plot the result which shows the decay of the initially excited impurity. This is not very illuminating, but we will compare it with the Matsubara expansion and analytic solution sortly: -```{code-cell} +```{code-cell} ipython3 # Plot the Pade results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -321,15 +322,16 @@ axes.legend(fontsize=12); Now let us do the same for the Matsubara expansion: -```{code-cell} +```{code-cell} ipython3 # HEOM dynamics using the Matsubara approximation: -envL_mats= envL.approx_by_matsubara(Nk=Nk, tag="L") -envR_mats= envR.approx_by_matsubara(Nk=Nk, tag="R-") +envL_mats = envL.approx_by_matsubara(Nk=Nk, tag="L") +envR_mats = envR.approx_by_matsubara(Nk=Nk, tag="R") with timer("RHS construction time"): - solver_mats = HEOMSolver(H, [(envL_mats,bath_L.Q), (envR_mats,bath_R.Q)], max_depth=2, options=options) + solver_mats = HEOMSolver( + H, [(envL_mats, bath_L.Q), (envR_mats, bath_R.Q)], max_depth=2, options=options) with timer("ODE solver time"): result_mats = solver_mats.run(rho0, tlist) @@ -340,7 +342,7 @@ with timer("Steady state solver time"): We see a marked difference in the Matsubara vs Pade results: -```{code-cell} +```{code-cell} ipython3 # Plot the Pade results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -376,7 +378,7 @@ One advantage of this simple model is that the steady state current to the baths See the [QuTiP-BoFiN paper](https://arxiv.org/abs/2010.10806) for a detailed description and references for the analytic result. Below we just perform the required integration numerically. -```{code-cell} +```{code-cell} ipython3 def analytical_steady_state_current(bath_L, bath_R, e1): """ Calculate the analytical steady state current. """ @@ -414,7 +416,7 @@ To compare the analytical result above with the result from the HEOM, we need to In the function `state_current(...)` below, we extract the first level ADOs for the specified bath and sum the contributions to the current from each: -```{code-cell} +```{code-cell} ipython3 def state_current(ado_state, bath_tag): """ Determine current from the given bath (either "R" or "L") to the system in the given ADO state. @@ -438,7 +440,7 @@ def state_current(ado_state, bath_tag): Now we can calculate the steady state currents from the Pade and Matsubara HEOM results: -```{code-cell} +```{code-cell} ipython3 curr_ss_pade_L = state_current(ado_ss_pade, "L") curr_ss_pade_R = state_current(ado_ss_pade, "R") @@ -446,7 +448,7 @@ print(f"Pade steady state current (L): {curr_ss_pade_L}") print(f"Pade steady state current (R): {curr_ss_pade_R}") ``` -```{code-cell} +```{code-cell} ipython3 curr_ss_mats_L = state_current(ado_ss_mats, "L") curr_ss_mats_R = state_current(ado_ss_mats, "R") @@ -458,7 +460,7 @@ Note that the currents from each bath balance as is required by the steady state Now let's compare all three: -```{code-cell} +```{code-cell} ipython3 print(f"Pade current (R): {curr_ss_pade_R}") print(f"Matsubara current (R): {curr_ss_mats_R}") print(f"Analytical curernt: {curr_ss_analytic}") @@ -478,7 +480,7 @@ Now lets plot the current as a function of bias voltage (the bias voltage is the We will calculate the steady state current for each `theta` both analytically and using the HEOM with the Pade correlation expansion approximation. -```{code-cell} +```{code-cell} ipython3 # Theta (bias voltages) thetas = np.linspace(-4, 4, 100) @@ -507,16 +509,18 @@ def current_pade_for_theta(H, bath_L, bath_R, theta, Nk): bath_L = bath_L.replace(theta=theta) bath_R = bath_R.replace(theta=theta) - bathL = LorentzianPadeBath( - bath_L.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T, - Nk, tag="L", + envL = LorentzianEnvironment( + bath_L.T, bath_L.mu, bath_L.gamma, bath_L.W ) - bathR = LorentzianPadeBath( - bath_R.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T, - Nk, tag="R", + bathL = envL.approx_by_pade(Nk=Nk) + envR = LorentzianEnvironment( + bath_R.T, bath_R.mu, bath_R.gamma, bath_R.W ) - solver_pade = HEOMSolver(H, [bathL, bathR], max_depth=2, options=options) + bathR = envR.approx_by_pade(Nk=Nk, tag="R") + + solver_pade = HEOMSolver(H, [(bathL,bath_L.Q), (bathR,bath_R.Q)], + max_depth=2, options=options) rho_ss_pade, ado_ss_pade = solver_pade.steady_state() current = state_current(ado_ss_pade, bath_tag="R") @@ -539,7 +543,7 @@ curr_ss_pade_theta = [ Below we plot the results and see that even with `Nk=6`, the HEOM Pade approximation gives good results for the steady state current. Increasing `Nk` to `10` gives very accurate results. -```{code-cell} +```{code-cell} ipython3 fig, ax = plt.subplots(figsize=(12, 7)) ax.plot( @@ -566,7 +570,7 @@ ax.legend(fontsize=25); ## About -```{code-cell} +```{code-cell} ipython3 qutip.about() ``` @@ -574,7 +578,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 assert np.allclose(curr_ss_pade_L + curr_ss_pade_R, 0) assert np.allclose(curr_ss_mats_L + curr_ss_mats_R, 0) assert np.allclose(curr_ss_pade_R, curr_ss_analytic, rtol=1e-4) diff --git a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md index 6b1756ef..d2f798f7 100644 --- a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md +++ b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md @@ -5,9 +5,9 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.1 + jupytext_version: 1.17.0 kernelspec: - display_name: Python 3 (ipykernel) + display_name: qutip-tutorials language: python name: python3 --- @@ -20,7 +20,7 @@ kernelspec: Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc), also coupled to a discrete bosonic (vibronic) mode. -Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his Dissertation https://open.fau.de/items/36fdd708-a467-4b59-bf4e-4a2110fbc431 and related publications. In particular this example reproduces some results from https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.201407 +Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his Dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications. In particular this example reproduces some results from https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.201407 Notation: @@ -95,7 +95,7 @@ The complete setup now consists of four parts: ## Setup -```{code-cell} +```{code-cell} ipython3 import contextlib import dataclasses import time @@ -111,8 +111,8 @@ from qutip import ( ) from qutip.solver.heom import ( HEOMSolver, - LorentzianPadeBath, ) +from qutip.core.environment import LorentzianEnvironment from ipywidgets import IntProgress from IPython.display import display @@ -122,7 +122,7 @@ from IPython.display import display ## Helpers -```{code-cell} +```{code-cell} ipython3 @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -136,7 +136,7 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} +```{code-cell} ipython3 def state_current(ado_state, bath_tag): """ Determine current from the given bath (either "R" or "L") to the system in the given ADO state. @@ -158,7 +158,7 @@ def state_current(ado_state, bath_tag): ) ``` -```{code-cell} +```{code-cell} ipython3 # Solver options: # We set store_ados to True so that we can @@ -181,7 +181,7 @@ options = { Let us set up the system Hamiltonian and specify the properties of the two reservoirs. -```{code-cell} +```{code-cell} ipython3 # Define the system Hamiltonian: @dataclasses.dataclass @@ -208,7 +208,7 @@ class SystemParameters: sys_p = SystemParameters() ``` -```{code-cell} +```{code-cell} ipython3 # Define parameters for left and right fermionic baths. # Each bath is a lead (i.e. a wire held at a potential) # with temperature T and chemical potential mu. @@ -262,7 +262,7 @@ bath_R = LorentzianBathParameters(W=10**4, lead="R") Next let's plot the emission and absorption by the leads. -```{code-cell} +```{code-cell} ipython3 w_list = np.linspace(-2, 2, 100) fig, ax = plt.subplots(figsize=(12, 7)) @@ -310,25 +310,26 @@ Here we just give one example of the current as a function of bias voltage, but One note: for very large problems, this can be slow. -```{code-cell} +```{code-cell} ipython3 def steady_state_pade_for_theta(sys_p, bath_L, bath_R, theta, Nk, Nc, Nbos): """ Return the steady state current using the Pade approximation. """ sys_p = sys_p.replace(Nbos=Nbos) bath_L = bath_L.replace(theta=theta) bath_R = bath_R.replace(theta=theta) + envR = LorentzianEnvironment( + bath_R.T, bath_R.mu, bath_R.gamma, bath_R.W + ) + envL = LorentzianEnvironment( + bath_L.T, bath_L.mu, bath_L.gamma, bath_L.W + ) - bathL = LorentzianPadeBath( - sys_p.Q, bath_L.gamma, bath_L.W, bath_L.mu, bath_L.T, - Nk, tag="L", - ) - bathR = LorentzianPadeBath( - sys_p.Q, bath_R.gamma, bath_R.W, bath_R.mu, bath_R.T, - Nk, tag="R", - ) + + bathL=envL.approx_by_matsubara(Nk,tag="L") + bathR=envR.approx_by_matsubara(Nk,tag="R") solver_pade = HEOMSolver( - sys_p.H, [bathL, bathR], max_depth=2, options=options, + sys_p.H, [(bathL,sys_p.Q), (bathR,sys_p.Q)], max_depth=2, options=options, ) rho_ss_pade, ado_ss_pade = solver_pade.steady_state() current = state_current(ado_ss_pade, bath_tag="R") @@ -336,7 +337,7 @@ def steady_state_pade_for_theta(sys_p, bath_L, bath_R, theta, Nk, Nc, Nbos): return np.real(2.434e-4 * 1e6 * current) ``` -```{code-cell} +```{code-cell} ipython3 # Parameters: Nk = 6 @@ -360,7 +361,7 @@ for theta in thetas: progress.value += 1 ``` -```{code-cell} +```{code-cell} ipython3 fig, ax = plt.subplots(figsize=(12, 10)) ax.plot( @@ -382,7 +383,7 @@ ax.legend(loc=4); ## About -```{code-cell} +```{code-cell} ipython3 qutip.about() ``` @@ -390,6 +391,6 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 assert 1 == 1 ``` diff --git a/tutorials-v5/heom/heom-index.md b/tutorials-v5/heom/heom-index.md index 4bc08d9d..52409f83 100644 --- a/tutorials-v5/heom/heom-index.md +++ b/tutorials-v5/heom/heom-index.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.1 + jupytext_version: 1.17.0 kernelspec: display_name: Python 3 (ipykernel) language: python From 447211e85cfeb8bcd51ccc2204c29502bda312c3 Mon Sep 17 00:00:00 2001 From: Gerardo Suarez Date: Wed, 23 Apr 2025 00:11:02 +0200 Subject: [PATCH 23/44] flake8 --- tutorials-v5/fitting_summary.md | 472 ------------------ .../heom/heom-1a-spin-bath-model-basic.md | 198 ++++---- ...1b-spin-bath-model-very-strong-coupling.md | 254 ++++++---- .../heom-1d-spin-bath-model-ohmic-fitting.md | 115 +++-- .../heom-1e-spin-bath-model-pure-dephasing.md | 1 - tutorials-v5/heom/heom-2-fmo-example.md | 157 +++--- .../heom/heom-3-quantum-heat-transport.md | 216 ++++---- .../heom/heom-4-dynamical-decoupling.md | 196 ++++---- .../heom-5a-fermions-single-impurity-model.md | 185 ++++--- .../heom-5b-fermions-discrete-boson-model.md | 121 +++-- tutorials-v5/template.md | 5 +- 11 files changed, 796 insertions(+), 1124 deletions(-) delete mode 100644 tutorials-v5/fitting_summary.md diff --git a/tutorials-v5/fitting_summary.md b/tutorials-v5/fitting_summary.md deleted file mode 100644 index feee81ad..00000000 --- a/tutorials-v5/fitting_summary.md +++ /dev/null @@ -1,472 +0,0 @@ ---- -jupytext: - formats: ipynb,md:myst - text_representation: - extension: .md - format_name: myst - format_version: 0.13 - jupytext_version: 1.16.1 -kernelspec: - display_name: qutip-dev - language: python - name: python3 ---- - -# HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions - -+++ - -## Introduction - -The HEOM method solves the dynamics and steady state of a system and its environment, the latter of which is encoded -in a set of auxiliary density matrices. - -In this example we show the evolution of a single two-level system in contact with a single bosonic environment. - -The properties of the system are encoded in Hamiltonian, and a coupling operator which describes how it is coupled to the environment. - -The bosonic environment is implicitly assumed to obey a particular Hamiltonian ([see paper](https://arxiv.org/abs/2010.10806)), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions. - -In the example below we show how to model an Ohmic environment with exponential cut-off in three ways: - -* First we fit the spectral density with a set of underdamped brownian oscillator functions. -* Second, we evaluate the correlation functions, and fit those with a certain choice of exponential functions. -* Third, we use the available OhmicBath class - -In each case we will use the fit parameters to determine the correlation function expansion co-efficients needed to construct a description of the bath (i.e. a `BosonicBath` object) to supply to the `HEOMSolver` so that we can solve for the system dynamics. - -+++ - -## Setup - -```{code-cell} ipython3 -import numpy as np -from matplotlib import pyplot as plt -import qutip -from qutip import ( - basis, - expect, - sigmax, - sigmaz, -) -from qutip.solver.heom import ( - HEOMSolver -) -from qutip.core.environment import BosonicEnvironment,OhmicEnvironment - -# Import mpmath functions for evaluation of gamma and zeta -# functions in the expression for the correlation: - -from mpmath import mp - -mp.dps = 15 -mp.pretty = True - -%matplotlib inline -``` - -## System and bath definition - -Let us set up the system Hamiltonian, bath and system measurement operators: - -+++ - -### System Hamiltonian - -```{code-cell} ipython3 -# Defining the system Hamiltonian -eps = 0 # Energy of the 2-level system. -Del = 0.2 # Tunnelling term -Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() -rho0 = basis(2, 0) * basis(2, 0).dag() -``` - -### System measurement operators - -```{code-cell} ipython3 -# Define some operators with which we will measure the system -# 1,1 element of density matrix - corresonding to groundstate -P11p = basis(2, 0) * basis(2, 0).dag() -P22p = basis(2, 1) * basis(2, 1).dag() -# 1,2 element of density matrix - corresonding to coherence -P12p = basis(2, 0) * basis(2, 1).dag() -``` - -### Bath and HEOM parameters - -+++ - -Finally, let's set the bath parameters we will work with and write down some measurement operators: - -```{code-cell} ipython3 -Q = sigmaz() -alpha = 3.25 -T = 0.5 -wc = 1.0 -s = 1 -``` - -And set the cut-off for the HEOM hierarchy: - -```{code-cell} ipython3 -# HEOM parameters: - -# The max_depth defaults to 5 so that the notebook executes more -# quickly. Change it to 11 to wait longer for more accurate results. -max_depth = 5 #could not do 11 my laptop rans out of ram -# I used 7 because I wanted to make sure things were working correctly -# cf is terribly slow at 7, probably can be done better by changing guess, lower -# upper, use 5 to play around :) - -# options used for the differential equation solver, while default works it -# is way slower than using bdf -options = { - "nsteps":15000, "store_states":True, "rtol":1e-12, "atol":1e-12, "method":"bdf", -} -``` - -#### Plotting function - -```{code-cell} ipython3 -def plot_result_expectations(plots, axes=None): - """Plot the expectation values of operators as functions of time. - - Each plot in plots consists of (solver_result, - measurement_operation, color, label). - """ - if axes is None: - fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - fig_created = True - else: - fig = None - fig_created = False - - # add kw arguments to each plot if missing - plots = [p if len(p) == 5 else p + ({},) for p in plots] - for result, m_op, color, label, kw in plots: - exp = np.real(expect(result.states, m_op)) - kw.setdefault("linewidth", 2) - if color == "rand": - axes.plot( - result.times, - exp, - c=np.random.rand( - 3, - ), - label=label, - **kw, - ) - else: - axes.plot(result.times, exp, color, label=label, **kw) - - if fig_created: - axes.legend(loc=0, fontsize=12) - axes.set_xlabel("t", fontsize=28) - - return fig -``` - -# Obtaining a decaying Exponential description of the environment - -In order to carry out our HEOM simulation, we need to express the correlation -function as a sum of decaying exponentials, that is we need to express it as - -$$C(\tau)= \sum_{k=0}^{N-1}c_{k}e^{-\nu_{k}t}$$ - -As the correlation function of the environment is tied to it's power spectrum via -a Fourier transform, such a representation of the correlation function implies a -power spectrum of the form - -$$S(\omega)= \sum_{k}2 Re\left( \frac{c_{k}}{\nu_{k}- i \omega}\right)$$ - -There are several ways one can obtain such a decomposition, in this tutorial we -will cover the following approaches: - -- Non-Linear Least Squares: - - On the Spectral Density (`sd`) - - On the Correlation Function (`cf`) - - On the Power Spectrum (`ps`) -- Methods based on the Prony Polynomial - - Prony on the correlation function(`prony`) - - The Matrix Pencil method on the correlation function (`mp`) :question: - - ESPRIT on the correlation function(`esprit`) -- Methods based on rational Approximations - - The AAA algorithm on the Power Spectrum (`aaa`) - - ESPIRA-I (`espira-I`) :question: - - ESPIRA-II (`espira-II`) - -the ones with a question mark are the ones I think maybe can be deleted. -Here's a quick high level comparison between the three different families -of methods - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
ClassRequires Extra informationFastResilient to NoiseAllows constraitnsStable
Non-Linear Least Squares✔️❌❌✔️❌
Prony Polynomial❌✔️❗❌❗
Rational Approximations ❌✔️❗❗✔️
- -Legend: - -❌: NO ✔️: Yes ❗: Partially - -+++ - -# Non-Linear Least Squares - -```{code-cell} ipython3 -obs = OhmicEnvironment(T, alpha, wc,s=1) -tlist = np.linspace(0, 30 * np.pi / Del, 600) -``` - -## Correlation Function - -```{code-cell} ipython3 -t=np.linspace(0,20,500) -Obath, fitinfo = obs.approximate(method="cf",tlist=t,Nr_max=4,Ni_max=4,maxfev=1e9,target_rsme=None) -print(fitinfo["summary"]) -HEOM_ohmic_corr_fit = HEOMSolver( - Hsys, - (Obath,Q), - max_depth=max_depth, - options=options, -) -results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist) -``` - -## Spectral Density - -```{code-cell} ipython3 -w=np.linspace(0,30,500) -Obath2, fitinfo = obs.approximate(method="sd",wlist=w,Nmax=4,Nk=3) -print(fitinfo["summary"]) -HEOM_ohmic_sd_fit = HEOMSolver( - Hsys, - (Obath2,Q), - max_depth=max_depth, - options=options, -) -results_ohmic_sd_fit = HEOM_ohmic_sd_fit.run(rho0, tlist) -``` - -## Power Spectrum - -```{code-cell} ipython3 -w=np.linspace(-50,30,500) -Obath3, fitinfo = obs.approximate(method="ps",wlist=w,Nmax=5) -print(fitinfo["summary"]) -HEOM_ohmic_ps_fit = HEOMSolver( - Hsys, - (Obath3,Q), - max_depth=max_depth, - options=options, -) -results_ohmic_ps_fit = HEOM_ohmic_ps_fit.run(rho0, tlist) -``` - -# Methods based on the Prony Polinomial - -+++ - -## Prony - -```{code-cell} ipython3 -tlist2=np.linspace(0,40,100) -pbath,fitinfo=obs.approximate("prony",tlist2,Nr=4) -print(fitinfo["summary"]) -HEOM_ohmic_prony_fit = HEOMSolver( - Hsys, - (pbath,Q), - max_depth=max_depth, - options=options, -) -results_ohmic_prony_fit = HEOM_ohmic_prony_fit.run(rho0, tlist) -``` - -## Matrix Pencil - -```{code-cell} ipython3 -mpbath,fitinfo=obs.approximate(method="mp",tlist=tlist2,Nr=5,Ni=5,separate=True) -print(fitinfo["summary"]) -HEOM_ohmic_mp_fit = HEOMSolver( - Hsys, - (mpbath,Q), - max_depth=max_depth, - options=options, -) -results_ohmic_mp_fit = HEOM_ohmic_mp_fit.run(rho0, tlist) -``` - -## ESPRIT - -```{code-cell} ipython3 -esbath,fitinfo=obs.approximate("esprit",tlist2,Nr=4) -print(fitinfo["summary"]) -HEOM_ohmic_es_fit = HEOMSolver( - Hsys, - (esbath,Q), - max_depth=max_depth, - options=options, -) -results_ohmic_es_fit = HEOM_ohmic_es_fit.run(rho0, tlist) -``` - -# Rational Approximations - -+++ - -## AAA - -```{code-cell} ipython3 -aaabath,fitinfo=obs.approximate("aaa",np.concatenate((-np.logspace(3,-8,3500),np.logspace(-8,3,3500))),N_max=6,tol=1e-15) -print(fitinfo["summary"]) -HEOM_ohmic_aaa_fit = HEOMSolver( - Hsys, - (aaabath,Q), - max_depth=max_depth, - options=options, -) -results_ohmic_aaa_fit = HEOM_ohmic_aaa_fit.run(rho0, tlist) -``` - -# ESPIRA I - -```{code-cell} ipython3 -tlist4=np.linspace(0,20,1000) -espibath,fitinfo=obs._approx_by_prony("espira-I",tlist4,Nr=4,Ni=4,separate=True) -print(fitinfo["summary"]) -HEOM_ohmic_espira_fit = HEOMSolver( - Hsys, - (espibath,Q), - max_depth=max_depth, - options=options, -) -results_ohmic_espira_fit = HEOM_ohmic_espira_fit.run(rho0, tlist) -``` - -# ESPIRA II - -```{code-cell} ipython3 -espibath2,fitinfo=obs._approx_by_prony("espira-II",tlist4,Nr=4,Ni=4,separate=True) -print(fitinfo["summary"]) -HEOM_ohmic_espira_fit2 = HEOMSolver( - Hsys, - (espibath2,Q), - max_depth=max_depth, - options=options, -) -results_ohmic_espira2_fit = HEOM_ohmic_espira_fit2.run(rho0, tlist) -``` - -Finally we plot the dynamics obtained by the different methods - -```{code-cell} ipython3 -fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) - -plot_result_expectations( - [ - - (results_ohmic_corr_fit, P11p, "r", "Correlation Fit"), - (results_ohmic_sd_fit, P11p, "g", "SD Fit"), - (results_ohmic_sd_fit, P11p, "y", "PS Fit"), - (results_ohmic_prony_fit, P11p, "k", " Prony Fit"), - (results_ohmic_mp_fit, P11p, "r", "Matrix Pencil Fit"), - (results_ohmic_es_fit, P11p, "b-.", "ESPRIT Fit"), - (results_ohmic_aaa_fit, P11p, "r-.", "Matrix AAA Fit"), - (results_ohmic_espira_fit, P11p, "k", "ESPIRA I Fit"), - (results_ohmic_espira2_fit, P11p, "--", "ESPIRA II Fit"), - - ], - axes=axes, -) -axes.set_ylabel(r"$\rho_{11}$", fontsize=30) -axes.set_xlabel(r"$t\;\omega_c$", fontsize=30) -axes.legend(loc=0, fontsize=20); -axes.set_yscale("log") -``` - -## About - -```{code-cell} ipython3 -qutip.about() -``` - -## Testing - -This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. - -```{code-cell} ipython3 -tol=1e-2 -assert np.allclose( - expect(P11p, results_ohmic_ps_fit.states), - expect(P11p, results_ohmic_sd_fit.states), - rtol=tol, -) -assert np.allclose( - expect(P11p, results_ohmic_corr_fit.states), - expect(P11p, results_ohmic_sd_fit.states), - rtol=tol, -) -assert np.allclose( - expect(P11p, results_ohmic_aaa_fit.states), - expect(P11p, results_ohmic_sd_fit.states), - rtol=tol, -) -assert np.allclose( - expect(P11p, results_ohmic_mp_fit.states), - expect(P11p, results_ohmic_sd_fit.states), - rtol=tol, -) -assert np.allclose( - expect(P11p, results_ohmic_prony_fit.states), - expect(P11p, results_ohmic_sd_fit.states), - rtol=tol, -) - -assert np.allclose( - expect(P11p, results_ohmic_es_fit.states), - expect(P11p, results_ohmic_sd_fit.states), - rtol=tol, -) -assert np.allclose( - expect(P11p, results_ohmic_espira_fit.states), - expect(P11p, results_ohmic_sd_fit.states), - rtol=tol, -) -assert np.allclose( - expect(P11p, results_ohmic_espira2_fit.states), - expect(P11p, results_ohmic_sd_fit.states), - rtol=tol, -) -``` diff --git a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md index be97014d..26f9858a 100644 --- a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md +++ b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md @@ -81,33 +81,16 @@ import contextlib import time import numpy as np +import qutip from matplotlib import pyplot as plt +from qutip import (basis, brmesolve, destroy, expect, liouvillian, qeye, + sigmax, sigmaz, spost, spre, tensor) +from qutip.core.environment import (DrudeLorentzEnvironment, + ExponentialBosonicEnvironment, + system_terminator) +from qutip.solver.heom import HEOMSolver, HSolverDL from scipy.optimize import curve_fit -import qutip -from qutip import ( - basis, - brmesolve, - destroy, - expect, - liouvillian, - qeye, - sigmax, - sigmaz, - spost, - spre, - tensor, -) -from qutip.core.environment import ( - DrudeLorentzEnvironment, - ExponentialBosonicEnvironment, - system_terminator -) -from qutip.solver.heom import ( - HEOMSolver, - HSolverDL, -) - %matplotlib inline ``` @@ -129,8 +112,7 @@ def dl_matsubara_params(lam, gamma, T, nk): """ ckAR = [lam * gamma * cot(gamma / (2 * T))] ckAR.extend( - (8 * lam * gamma * T * np.pi * k * T / - ((2 * np.pi * k * T)**2 - gamma**2)) + (8 * lam * gamma * T * np.pi * k * T / ((2 * np.pi * k * T) ** 2 - gamma**2)) for k in range(1, nk + 1) ) vkAR = [gamma] @@ -187,6 +169,7 @@ def timer(label): ```python # Default solver options: + default_options = { "nsteps": 1500, "store_states": True, @@ -323,7 +306,7 @@ with timer("RHS construction time"): HEOM_dlbath = HEOMSolver(Hsys, (dlenv_approx, Q), NC, options=options) with timer("ODE solver time"): - result_dlbath = HEOM_dlbath.run(rho0, tlist) + result_dlbath = HEOM_dlbath.run(rho0, tlist) ``` ```python @@ -344,17 +327,17 @@ w2 = np.linspace(0, 20, 1000) fig, axs = plt.subplots(2, 2) axs[0, 0].plot(w, dlenv.power_spectrum(w)) -axs[0, 0].plot(w, dlenv_approx.power_spectrum(w), '--') -axs[0, 0].set(xlabel=r'$\omega$', ylabel=r'$S(\omega)$') +axs[0, 0].plot(w, dlenv_approx.power_spectrum(w), "--") +axs[0, 0].set(xlabel=r"$\omega$", ylabel=r"$S(\omega)$") axs[0, 1].plot(w2, dlenv.spectral_density(w2)) -axs[0, 1].plot(w2, dlenv_approx.spectral_density(w2), '--') -axs[0, 1].set(xlabel=r'$\omega$', ylabel=r'$J(\omega)$') +axs[0, 1].plot(w2, dlenv_approx.spectral_density(w2), "--") +axs[0, 1].set(xlabel=r"$\omega$", ylabel=r"$J(\omega)$") axs[1, 0].plot(w2, np.real(dlenv.correlation_function(w2))) -axs[1, 0].plot(w2, np.real(dlenv_approx.correlation_function(w2)), '--') -axs[1, 0].set(xlabel=r'$t$', ylabel=r'$C_{R}(t)$') +axs[1, 0].plot(w2, np.real(dlenv_approx.correlation_function(w2)), "--") +axs[1, 0].set(xlabel=r"$t$", ylabel=r"$C_{R}(t)$") axs[1, 1].plot(w2, np.imag(dlenv.correlation_function(w2))) -axs[1, 1].plot(w2, np.imag(dlenv_approx.correlation_function(w2)), '--') -axs[1, 1].set(xlabel=r'$t$', ylabel=r'$C_{I}(t)$') +axs[1, 1].plot(w2, np.imag(dlenv_approx.correlation_function(w2)), "--") +axs[1, 1].set(xlabel=r"$t$", ylabel=r"$C_{I}(t)$") fig.tight_layout() plt.show() @@ -427,18 +410,10 @@ def plot_correlation_expansion_divergence(): fig, ax1 = plt.subplots(figsize=(12, 7)) - ax1.plot( - t, np.real(corr_2), color="b", linewidth=3, label=rf"Mats = {Nk} real" - ) - ax1.plot( - t, np.imag(corr_2), color="r", linewidth=3, label=rf"Mats = {Nk} imag" - ) - ax1.plot( - t, np.real(corr_100), "b--", linewidth=3, label=r"Mats = 15000 real" - ) - ax1.plot( - t, np.imag(corr_100), "r--", linewidth=3, label=r"Mats = 15000 imag" - ) + ax1.plot(t, np.real(corr_2), color="b", linewidth=3, label=rf"Mats = {Nk} real") + ax1.plot(t, np.imag(corr_2), color="r", linewidth=3, label=rf"Mats = {Nk} imag") + ax1.plot(t, np.real(corr_100), "b--", linewidth=3, label=r"Mats = 15000 real") + ax1.plot(t, np.imag(corr_100), "r--", linewidth=3, label=r"Mats = 15000 imag") ax1.set_xlabel("t") ax1.set_ylabel(r"$C$") @@ -525,9 +500,7 @@ We can compare the solution obtained from the QuTiP Bloch-Redfield solver: options = {**default_options} with timer("ODE solver time"): - resultBR = brmesolve( - Hsys, rho0, tlist, a_ops=[[sigmaz(), dlenv]], options=options - ) + resultBR = brmesolve(Hsys, rho0, tlist, a_ops=[[sigmaz(), dlenv]], options=options) ``` ```python @@ -587,7 +560,7 @@ def pade_chi(lmax): ) eigvalsAP = np.linalg.eigvalsh(AlphaP) - chi = [-2 / val for val in eigvalsAP[0:lmax - 1]] + chi = [-2 / val for val in eigvalsAP[0 : lmax - 1]] return chi @@ -636,12 +609,7 @@ def pade_corr(tlist, lmax): c_tot = [] for t in tlist: c_tot.append( - sum( - [ - eta_list[ll] * np.exp(-gamma_list[ll] * t) - for ll in range(lmax + 1) - ] - ) + sum([eta_list[ll] * np.exp(-gamma_list[ll] * t) for ll in range(lmax + 1)]) ) return c_tot, eta_list, gamma_list @@ -785,17 +753,17 @@ We then fit this sum with standard least-squares approach: ```python def wrapper_fit_func(x, N, args): - """ Fit function wrapper that unpacks its arguments. """ + """Fit function wrapper that unpacks its arguments.""" x = np.array(x) a = np.array(args[:N]) - b = np.array(args[N:2 * N]) + b = np.array(args[N : 2 * N]) return fit_func(x, a, b) def fit_func(x, a, b): - """ Fit function. Calculates the value of the - correlation function at each x, given the - fit parameters in a and b. + """Fit function. Calculates the value of the + correlation function at each x, given the + fit parameters in a and b. """ return np.sum( a[:, None] * np.exp(np.multiply.outer(b, x)), @@ -804,23 +772,14 @@ def fit_func(x, a, b): def fitter(ans, tlist, k): - """ Compute fit with k exponents. """ + """Compute fit with k exponents.""" upper_a = abs(max(ans, key=abs)) * 10 # sets initial guesses: - guess = ( - [upper_a / k] * k + # guesses for a - [0] * k # guesses for b - ) + guess = [upper_a / k] * k + [0] * k # guesses for a # guesses for b # sets lower bounds: - b_lower = ( - [-upper_a] * k + # lower bounds for a - [-np.inf] * k # lower bounds for b - ) + b_lower = [-upper_a] * k + [-np.inf] * k # lower bounds for a # lower bounds for b # sets higher bounds: - b_higher = ( - [upper_a] * k + # upper bounds for a - [0] * k # upper bounds for b - ) + b_higher = [upper_a] * k + [0] * k # upper bounds for a # upper bounds for b param_bounds = (b_lower, b_higher) p1, p2 = curve_fit( lambda x, *params_0: wrapper_fit_func(x, k, params_0), @@ -856,8 +815,7 @@ And plot the results of the fits: ```python # Define line styles and colors linestyles = ["-", "--", "-.", ":", (0, (3, 1, 1, 1)), (0, (5, 1))] -colors = ["blue", "green", "purple", "orange", - "red", "brown", "cyan", "magenta"] +colors = ["blue", "green", "purple", "orange", "red", "brown", "cyan", "magenta"] # Define a larger linewidth linewidth = 2.5 @@ -866,27 +824,57 @@ linewidth = 2.5 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6)) # Plot the real part on the first subplot (ax1) -ax1.plot(tlist2, corrRana, label="Analytic", - color=colors[0], linestyle=linestyles[0], linewidth=linewidth) -ax1.plot(tlist2, corrRMats, label="Matsubara", - color=colors[1], linestyle=linestyles[1], linewidth=linewidth) +ax1.plot( + tlist2, + corrRana, + label="Analytic", + color=colors[0], + linestyle=linestyles[0], + linewidth=linewidth, +) +ax1.plot( + tlist2, + corrRMats, + label="Matsubara", + color=colors[1], + linestyle=linestyles[1], + linewidth=linewidth, +) for i in range(kR): y = fit_func(tlist2, *poptR[i]) - ax1.plot(tlist2, y, label=f"Fit with {i} terms", color=colors[( - i + 2) % len(colors)], linestyle=linestyles[(i + 2) % len(linestyles)], linewidth=linewidth) + ax1.plot( + tlist2, + y, + label=f"Fit with {i} terms", + color=colors[(i + 2) % len(colors)], + linestyle=linestyles[(i + 2) % len(linestyles)], + linewidth=linewidth, + ) ax1.set_ylabel(r"$C_{R}(t)$") ax1.set_xlabel(r"$t$") ax1.legend() # Plot the imaginary part on the second subplot (ax2) -ax2.plot(tlist2, corrIana, label="Analytic", - color=colors[0], linestyle=linestyles[0], linewidth=linewidth) +ax2.plot( + tlist2, + corrIana, + label="Analytic", + color=colors[0], + linestyle=linestyles[0], + linewidth=linewidth, +) for i in range(kI): y = fit_func(tlist2, *poptI[i]) - ax2.plot(tlist2, y, label=f"Fit with {i} terms", color=colors[( - i + 3) % len(colors)], linestyle=linestyles[(i + 1) % len(linestyles)], linewidth=linewidth) + ax2.plot( + tlist2, + y, + label=f"Fit with {i} terms", + color=colors[(i + 3) % len(colors)], + linestyle=linestyles[(i + 1) % len(linestyles)], + linewidth=linewidth, + ) ax2.set_ylabel(r"$C_{I}(t)$") ax2.set_xlabel(r"$t$") @@ -894,7 +882,9 @@ ax2.legend() # Add overall plot title and show the figure fig.suptitle( - "Comparison of Analytic and Fit to Correlations (Real and Imaginary Parts)", fontsize=16) + "Comparison of Analytic and Fit to Correlations (Real and Imaginary Parts)", + fontsize=16, +) plt.show() ``` @@ -953,9 +943,16 @@ max_val = dlenv.correlation_function(0).real guess = [max_val / 3, 0, 0, 0] lower = [-max_val, -np.inf, -np.inf, -np.inf] upper = [max_val, 0, 0, 0] -envfit, fitinfo = dlenv.approximate("cf", tlist=tlist2, full_ansatz=True, - Ni_max=1, Nr_max=3, - upper=upper, lower=lower, guess=guess) +envfit, fitinfo = dlenv.approximate( + "cf", + tlist=tlist2, + full_ansatz=True, + Ni_max=1, + Nr_max=3, + upper=upper, + lower=lower, + guess=guess, +) ``` The approx_by_cf_fit method outputs a `ExponentialBosonicEnvironment` object, @@ -965,7 +962,7 @@ The dictionary contains a summary of the fir information and the normalized root mean squared error, which assesses how good the fit is. ```python -print(fitinfo['summary']) +print(fitinfo["summary"]) ``` We can then compare the result of the built-in fit with the manual fit @@ -977,8 +974,9 @@ fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6)) # Plot the real part on the first subplot (ax1) ax1.plot(tlist2, corrRana, label="Original", marker="o", markevery=500) ax1.plot(tlist2, fit_func(tlist2, *poptR[-1]), color="r", label="Manual Fit") -ax1.plot(tlist2, np.real(envfit.correlation_function(tlist2)), - "k--", label="Built-in fit") +ax1.plot( + tlist2, np.real(envfit.correlation_function(tlist2)), "k--", label="Built-in fit" +) ax1.set_ylabel(r"$C_{R}(t)$") ax1.set_xlabel(r"$t$") ax1.legend() @@ -986,8 +984,9 @@ ax1.legend() # Plot the imaginary part on the second subplot (ax2) ax2.plot(tlist2, corrIana, label="Original", marker="o", markevery=500) ax2.plot(tlist2, fit_func(tlist2, *poptI[-1]), color="r", label="Manual Fit") -ax2.plot(tlist2, np.imag(envfit.correlation_function(tlist2)), - "k--", label="Built-in fit") +ax2.plot( + tlist2, np.imag(envfit.correlation_function(tlist2)), "k--", label="Built-in fit" +) ax2.set_ylabel(r"$C_{I}(t)$") ax2.set_xlabel(r"$t$") ax2.legend() @@ -1034,9 +1033,7 @@ wa = 2 * np.pi * gamma2 * gamma # reaction coordinate frequency g = np.sqrt(np.pi * wa * lam / 2.0) # reaction coordinate coupling # reaction coordinate coupling factor over 2 because of diff in J(w) # (it is 2 lam now): -g = np.sqrt( - np.pi * wa * lam / 4.0 -) # +g = np.sqrt(np.pi * wa * lam / 4.0) # NRC = 10 @@ -1119,7 +1116,8 @@ with plt.rc_context(rcParams): ( resultRC, P11RC, - "--", "Thermal", + "--", + "Thermal", {"linewidth": 2, "color": "black"}, ), ], diff --git a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md index 97368cbb..7851105a 100644 --- a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md +++ b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md @@ -85,25 +85,12 @@ Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. import contextlib import time -import numpy as np import matplotlib.pyplot as plt - +import numpy as np import qutip -from qutip import ( - basis, - brmesolve, - expect, - liouvillian, - sigmax, - sigmaz, -) -from qutip.core.environment import ( - DrudeLorentzEnvironment, - system_terminator -) -from qutip.solver.heom import ( - HEOMSolver, -) +from qutip import basis, brmesolve, expect, liouvillian, sigmax, sigmaz +from qutip.core.environment import DrudeLorentzEnvironment, system_terminator +from qutip.solver.heom import HEOMSolver %matplotlib inline ``` @@ -114,17 +101,17 @@ Let's define some helper functions for calculating correlation function expansio ```{code-cell} ipython3 def cot(x): - """ Vectorized cotangent of x. """ - return 1. / np.tan(x) + """Vectorized cotangent of x.""" + return 1.0 / np.tan(x) ``` ```{code-cell} ipython3 @contextlib.contextmanager def timer(label): - """ Simple utility for timing functions: + """Simple utility for timing functions: - with timer("name"): - ... code to time ... + with timer("name"): + ... code to time ... """ start = time.time() yield @@ -135,6 +122,7 @@ def timer(label): ```{code-cell} ipython3 # Solver options: + options = { "nsteps": 15000, "store_states": True, @@ -151,8 +139,8 @@ And let us set up the system Hamiltonian, bath and system measurement operators: ```{code-cell} ipython3 # Defining the system Hamiltonian -eps = .0 # Energy of the 2-level system. -Del = .2 # Tunnelling term +eps = 0.0 # Energy of the 2-level system. +Del = 0.2 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` @@ -166,10 +154,10 @@ rho0 = basis(2, 0) * basis(2, 0).dag() Q = sigmaz() # coupling operator # Bath properties (see Shi et al., J. Chem. Phys. 130, 084105 (2009)): -gamma = 1. # cut off frequency -lam = 2.5 # coupling strength -T = 1. # in units where Boltzmann factor is 1 -beta = 1. / T +gamma = 1.0 # cut off frequency +lam = 2.5 # coupling strength +T = 1.0 # in units where Boltzmann factor is 1 +beta = 1.0 / T # HEOM parameters: @@ -204,9 +192,9 @@ J = bath.spectral_density(w) # Plot the results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) -axes.plot(w, J, 'r', linewidth=2) -axes.set_xlabel(r'$\omega$', fontsize=28) -axes.set_ylabel(r'J', fontsize=28); +axes.plot(w, J, "r", linewidth=2) +axes.set_xlabel(r"$\omega$", fontsize=28) +axes.set_ylabel(r"J", fontsize=28); ``` ## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator @@ -224,8 +212,7 @@ with timer("ODE solver time"): ```{code-cell} ipython3 with timer("RHS construction time"): - matsBath, delta = bath.approximate( - method="matsubara", Nk=Nk, compute_delta=True) + matsBath, delta = bath.approximate(method="matsubara", Nk=Nk, compute_delta=True) terminator = system_terminator(Q, delta) Ltot = liouvillian(Hsys) + terminator HEOMMatsT = HEOMSolver(Ltot, (matsBath, Q), NC, options=options) @@ -240,18 +227,23 @@ fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) P11_mats = np.real(expect(resultMats.states, P11p)) axes.plot( - tlist, np.real(P11_mats), - 'b', linewidth=2, label="P11 (Matsubara)", + tlist, + np.real(P11_mats), + "b", + linewidth=2, + label="P11 (Matsubara)", ) P11_matsT = np.real(expect(resultMatsT.states, P11p)) axes.plot( - tlist, np.real(P11_matsT), - 'b--', linewidth=2, + tlist, + np.real(P11_matsT), + "b--", + linewidth=2, label="P11 (Matsubara + Terminator)", ) -axes.set_xlabel(r't', fontsize=28) +axes.set_xlabel(r"t", fontsize=28) axes.legend(loc=0, fontsize=12); ``` @@ -265,35 +257,48 @@ padeBath = bath.approximate("pade", Nk=Nk) fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(16, 8)) ax1.plot( - tlist, np.real(bath.correlation_function(tlist)), - "r", linewidth=2, label=f"Exact", + tlist, + np.real(bath.correlation_function(tlist)), + "r", + linewidth=2, + label=f"Exact", ) ax1.plot( - tlist, np.real(matsBath.correlation_function(tlist)), - "g--", linewidth=2, label=f"Mats (Nk={Nk})", + tlist, + np.real(matsBath.correlation_function(tlist)), + "g--", + linewidth=2, + label=f"Mats (Nk={Nk})", ) ax1.plot( - tlist, np.real(padeBath.correlation_function(tlist)), - "b--", linewidth=2, label=f"Pade (Nk={Nk})", + tlist, + np.real(padeBath.correlation_function(tlist)), + "b--", + linewidth=2, + label=f"Pade (Nk={Nk})", ) -ax1.set_xlabel(r't', fontsize=28) +ax1.set_xlabel(r"t", fontsize=28) ax1.set_ylabel(r"$C_R(t)$", fontsize=28) ax1.legend(loc=0, fontsize=12) tlist2 = tlist[0:50] ax2.plot( - tlist2, np.abs(matsBath.correlation_function(tlist2) - - bath.correlation_function(tlist2)), - "g", linewidth=2, label="Mats Error", + tlist2, + np.abs(matsBath.correlation_function(tlist2) - bath.correlation_function(tlist2)), + "g", + linewidth=2, + label="Mats Error", ) ax2.plot( - tlist2, np.abs(padeBath.correlation_function(tlist2) - - bath.correlation_function(tlist2)), - "b--", linewidth=2, label="Pade Error", + tlist2, + np.abs(padeBath.correlation_function(tlist2) - bath.correlation_function(tlist2)), + "b--", + linewidth=2, + label="Pade Error", ) -ax2.set_xlabel(r't', fontsize=28) +ax2.set_xlabel(r"t", fontsize=28) ax2.legend(loc=0, fontsize=12); ``` @@ -310,21 +315,30 @@ with timer("ODE solver time"): fig, axes = plt.subplots(figsize=(8, 8)) axes.plot( - tlist, np.real(P11_mats), - 'b', linewidth=2, label="P11 (Matsubara)", + tlist, + np.real(P11_mats), + "b", + linewidth=2, + label="P11 (Matsubara)", ) axes.plot( - tlist, np.real(P11_matsT), - 'b--', linewidth=2, label="P11 (Matsubara + Terminator)", + tlist, + np.real(P11_matsT), + "b--", + linewidth=2, + label="P11 (Matsubara + Terminator)", ) P11_pade = np.real(expect(resultPade.states, P11p)) axes.plot( - tlist, np.real(P11_pade), - 'r', linewidth=2, label="P11 (Pade)", + tlist, + np.real(P11_pade), + "r", + linewidth=2, + label="P11 (Pade)", ) -axes.set_xlabel(r't', fontsize=28) +axes.set_xlabel(r"t", fontsize=28) axes.legend(loc=0, fontsize=12); ``` @@ -337,18 +351,28 @@ we will use the built-in tools. More details about them can be seen in ```{code-cell} ipython3 tfit = np.linspace(0, 10, 10000) lower = [0, -np.inf, -1e-6, -3] -guess = [np.real(bath.correlation_function(0))/10, -10, 0, 0] +guess = [np.real(bath.correlation_function(0)) / 10, -10, 0, 0] upper = [5, 0, 1e-6, 0] # for better fits increase the first element in upper, or change approximate # method that makes the simulation much slower (Larger C(t) as C(0) is fit # better) -envfit, fitinfo = bath.approximate("cf", tlist=tfit, Nr_max=2, Ni_max=1, full_ansatz=True, - sigma=0.1, maxfev=1e6, target_rmse=None, - lower=lower, upper=upper, guess=guess) +envfit, fitinfo = bath.approximate( + "cf", + tlist=tfit, + Nr_max=2, + Ni_max=1, + full_ansatz=True, + sigma=0.1, + maxfev=1e6, + target_rmse=None, + lower=lower, + upper=upper, + guess=guess, +) ``` ```{code-cell} ipython3 -print(fitinfo['summary']) +print(fitinfo["summary"]) ``` We can quickly compare the result of the Fit with the Pade expansion @@ -357,36 +381,58 @@ We can quickly compare the result of the Fit with the Pade expansion fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8)) ax1.plot( - tlist, np.real(bath.correlation_function(tlist)), - "r", linewidth=2, label=f"Exact", + tlist, + np.real(bath.correlation_function(tlist)), + "r", + linewidth=2, + label=f"Exact", ) ax1.plot( - tlist, np.real(envfit.correlation_function(tlist)), - "g--", linewidth=2, label=f"Fit", marker="o", markevery=50 + tlist, + np.real(envfit.correlation_function(tlist)), + "g--", + linewidth=2, + label=f"Fit", + marker="o", + markevery=50, ) ax1.plot( - tlist, np.real(padeBath.correlation_function(tlist)), - "b--", linewidth=2, label=f"Pade (Nk={Nk})", + tlist, + np.real(padeBath.correlation_function(tlist)), + "b--", + linewidth=2, + label=f"Pade (Nk={Nk})", ) -ax1.set_xlabel(r't', fontsize=28) +ax1.set_xlabel(r"t", fontsize=28) ax1.set_ylabel(r"$C_R(t)$", fontsize=28) ax1.legend(loc=0, fontsize=12) ax2.plot( - tlist, np.imag(bath.correlation_function(tlist)), - "r", linewidth=2, label=f"Exact", + tlist, + np.imag(bath.correlation_function(tlist)), + "r", + linewidth=2, + label=f"Exact", ) ax2.plot( - tlist, np.imag(envfit.correlation_function(tlist)), - "g--", linewidth=2, label=f"Fit", marker="o", markevery=50 + tlist, + np.imag(envfit.correlation_function(tlist)), + "g--", + linewidth=2, + label=f"Fit", + marker="o", + markevery=50, ) ax2.plot( - tlist, np.imag(padeBath.correlation_function(tlist)), - "b--", linewidth=2, label=f"Pade (Nk={Nk})", + tlist, + np.imag(padeBath.correlation_function(tlist)), + "b--", + linewidth=2, + label=f"Pade (Nk={Nk})", ) -ax2.set_xlabel(r't', fontsize=28) +ax2.set_xlabel(r"t", fontsize=28) ax2.set_ylabel(r"$C_I(t)$", fontsize=28) ax2.legend(loc=0, fontsize=12) ``` @@ -396,7 +442,7 @@ with timer("RHS construction time"): # We reduce NC slightly here for speed of execution because we retain # 3 exponents in ckAR instead of 1. Please restore full NC for # convergence though: - HEOMFit = HEOMSolver(Hsys, (envfit, Q), int(NC*0.7), options=options) + HEOMFit = HEOMSolver(Hsys, (envfit, Q), int(NC * 0.7), options=options) with timer("ODE solver time"): resultFit = HEOMFit.run(rho0, tlist) @@ -407,8 +453,12 @@ with timer("ODE solver time"): ```{code-cell} ipython3 with timer("ODE solver time"): resultBR = brmesolve( - Hsys, rho0, tlist, - a_ops=[[sigmaz(), bath]], sec_cutoff=0, options=options, + Hsys, + rho0, + tlist, + a_ops=[[sigmaz(), bath]], + sec_cutoff=0, + options=options, ) ``` @@ -449,32 +499,46 @@ with plt.rc_context(rcParams): # Plot the results plt.yticks([0.99, 1.0], [0.99, 1]) axes.plot( - tlist, np.real(P11_mats), - 'b', linewidth=2, label=f"Matsubara $N_k={Nk}$", + tlist, + np.real(P11_mats), + "b", + linewidth=2, + label=f"Matsubara $N_k={Nk}$", ) axes.plot( - tlist, np.real(P11_matsT), - 'g--', linewidth=3, + tlist, + np.real(P11_matsT), + "g--", + linewidth=3, label=f"Matsubara $N_k={Nk}$ & terminator", ) axes.plot( - tlist, np.real(P11_pade), - 'y-.', linewidth=2, label=f"Padé $N_k={Nk}$", + tlist, + np.real(P11_pade), + "y-.", + linewidth=2, + label=f"Padé $N_k={Nk}$", ) axes.plot( - tlist, np.real(P11_fit), - 'r', dashes=[3, 2], linewidth=2, + tlist, + np.real(P11_fit), + "r", + dashes=[3, 2], + linewidth=2, label=r"Fit $N_f = 3$, $N_k=15 \times 10^3$", ) axes.plot( - tlist, np.real(P11_br), - 'b-.', linewidth=1, label="Bloch Redfield", + tlist, + np.real(P11_br), + "b-.", + linewidth=1, + label="Bloch Redfield", ) - axes.locator_params(axis='y', nbins=6) - axes.locator_params(axis='x', nbins=6) - axes.set_ylabel(r'$\rho_{11}$', fontsize=30) - axes.set_xlabel(r'$t\;\gamma$', fontsize=30) + axes.locator_params(axis="y", nbins=6) + axes.locator_params(axis="x", nbins=6) + axes.set_ylabel(r"$\rho_{11}$", fontsize=30) + axes.set_xlabel(r"$t\;\gamma$", fontsize=30) axes.set_xlim(tlist[0], tlist[-1]) axes.set_ylim(0.98405, 1.0005) axes.legend(loc=0) diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md index fcaaa963..d935a9fb 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md @@ -41,24 +41,16 @@ In each case we will use the fit parameters to determine the correlation functio ```{code-cell} ipython3 import numpy as np -from matplotlib import pyplot as plt import qutip -from qutip import ( - basis, - expect, - sigmax, - sigmaz, -) -from qutip.solver.heom import ( - HEOMSolver -) -from qutip.core.environment import BosonicEnvironment, OhmicEnvironment - +from matplotlib import pyplot as plt # Import mpmath functions for evaluation of gamma and zeta # functions in the expression for the correlation: -# mpmath is required for this tutorial you can install it +# mpmath is required for this tutorial you can install it # with !pip install mpmath from mpmath import mp +from qutip import basis, expect, sigmax, sigmaz +from qutip.core.environment import BosonicEnvironment, OhmicEnvironment +from qutip.solver.heom import HEOMSolver mp.dps = 15 mp.pretty = True @@ -156,7 +148,7 @@ def ohmic_power_spectrum(w, alpha, wc, beta): but, this fails at w=0 where the limit should be taken properly """ bose = (1 / (np.e ** (w * beta) - 1)) + 1 - return w * alpha * np.e ** (-abs(w) / wc) * 2*bose + return w * alpha * np.e ** (-abs(w) / wc) * 2 * bose ``` ### Bath and HEOM parameters @@ -184,7 +176,11 @@ max_depth = 5 # options used for the differential equation solver, while default works it # is way slower than using bdf options = { - "nsteps": 15000, "store_states": True, "rtol": 1e-12, "atol": 1e-12, "method": "bdf", + "nsteps": 15000, + "store_states": True, + "rtol": 1e-12, + "atol": 1e-12, + "method": "bdf", } ``` @@ -240,8 +236,7 @@ density provided ```{code-cell} ipython3 # Here we avoid w=0 -np.allclose(sd_env.power_spectrum(w[1:]), - ohmic_power_spectrum(w[1:], alpha, wc, 1/T)) +np.allclose(sd_env.power_spectrum(w[1:]), ohmic_power_spectrum(w[1:], alpha, wc, 1 / T)) ``` Specifying the Temperature also gives the `BosonicEnvironment` access to the @@ -249,14 +244,28 @@ correlation function ```{code-cell} ipython3 tlist = np.linspace(0, 10, 500) -plt.plot(tlist, sd_env.correlation_function(tlist).real, - label="BosonicEnvironment (Real Part)") -plt.plot(tlist, ohmic_correlation(tlist, alpha, wc, 1/T).real, - "--", label="Original (Real Part)") -plt.plot(tlist, np.imag(sd_env.correlation_function(tlist)), - label="BosonicEnvironment (Imaginary Part)") -plt.plot(tlist, np.imag(ohmic_correlation(tlist, alpha, wc, 1/T)), - "--", label="Original (Imaginary Part)") +plt.plot( + tlist, + sd_env.correlation_function(tlist).real, + label="BosonicEnvironment (Real Part)", +) +plt.plot( + tlist, + ohmic_correlation(tlist, alpha, wc, 1 / T).real, + "--", + label="Original (Real Part)", +) +plt.plot( + tlist, + np.imag(sd_env.correlation_function(tlist)), + label="BosonicEnvironment (Imaginary Part)", +) +plt.plot( + tlist, + np.imag(ohmic_correlation(tlist, alpha, wc, 1 / T)), + "--", + label="Original (Imaginary Part)", +) plt.ylabel("C(t)") plt.xlabel("t") plt.legend() @@ -273,15 +282,16 @@ considered to be essentialy zero ```{code-cell} ipython3 # From a function sd_env2 = BosonicEnvironment.from_spectral_density( - ohmic_spectral_density, T=T, wMax=10*wc, args={"alpha": alpha, "wc": wc}) + ohmic_spectral_density, T=T, wMax=10 * wc, args={"alpha": alpha, "wc": wc} +) ``` ```{code-cell} ipython3 tlist = np.linspace(0, 10, 500) plt.plot(tlist, sd_env2.correlation_function(tlist)) -plt.plot(tlist, ohmic_correlation(tlist, alpha, wc, 1/T), "--") +plt.plot(tlist, ohmic_correlation(tlist, alpha, wc, 1 / T), "--") plt.plot(tlist, np.imag(sd_env2.correlation_function(tlist))) -plt.plot(tlist, np.imag(ohmic_correlation(tlist, alpha, wc, 1/T)), "--") +plt.plot(tlist, np.imag(ohmic_correlation(tlist, alpha, wc, 1 / T)), "--") ``` In this example we considered how to obtain a `BosonicEnvironment` from the spectral density, it can be done analogously from the power spectrum or correlation function using the `from_correlation_function` and `from_power_spectrum` methods. @@ -358,13 +368,13 @@ fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5)) ax1.plot(w, J, label="Original spectral density") ax1.plot(w, bath.spectral_density(w), "--", label="Effective fitted SD") -ax1.set_xlabel(r'$\omega$') -ax1.set_ylabel(r'$J$') +ax1.set_xlabel(r"$\omega$") +ax1.set_ylabel(r"$J$") ax1.legend() ax2.plot(w, np.abs(J - bath.spectral_density(w)), label="Error") -ax2.set_xlabel(r'$\omega$') -ax2.set_ylabel(r'$|J-J_{approx}|$') +ax2.set_xlabel(r"$\omega$") +ax2.set_ylabel(r"$|J-J_{approx}|$") ax2.legend() plt.show() @@ -379,13 +389,13 @@ fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5)) ax1.plot(w, J, label="Original spectral density") ax1.plot(w, bath.spectral_density(w), "--", label="Effective fitted SD") -ax1.set_xlabel(r'$\omega$') -ax1.set_ylabel(r'$J$') +ax1.set_xlabel(r"$\omega$") +ax1.set_ylabel(r"$J$") ax1.legend() ax2.plot(w, np.abs(J - bath.spectral_density(w)), label="Error") -ax2.set_xlabel(r'$\omega$') -ax2.set_ylabel(r'$|J-J_{approx}|$') +ax2.set_xlabel(r"$\omega$") +ax2.set_ylabel(r"$|J-J_{approx}|$") ax2.legend() plt.show() @@ -438,9 +448,7 @@ w0 = fitinfo["params"][:, 2] def _sd_fit_model(wlist, a, b, c): - return ( - 2 * a * b * wlist / ((wlist + c)**2 + b**2) / ((wlist - c)**2 + b**2) - ) + return 2 * a * b * wlist / ((wlist + c) ** 2 + b**2) / ((wlist - c) ** 2 + b**2) plot_fit(_sd_fit_model, J, w, lam, gamma, w0) @@ -486,14 +494,12 @@ def generate_spectrum_results(Q, N, Nk, max_depth): # guess = [J_max, wc, wc] # upper = [100*J_max, 100*wc, 100*wc] # ,lower=lower,upper=upper,guess=guess,sigma=sigma) - bath, fitinfo = sd_env.approximate( - "sd", w, Nmax=N, Nk=Nk, target_rmse=None) + bath, fitinfo = sd_env.approximate("sd", w, Nmax=N, Nk=Nk, target_rmse=None) tlist = np.linspace(0, 30 * np.pi / Del, 600) # This problem is a little stiff, so we use the BDF method to solve # the ODE ^^^ - print( - f"Starting calculations for N={N}, Nk={Nk} and max_depth={max_depth} ... ") + print(f"Starting calculations for N={N}, Nk={Nk} and max_depth={max_depth} ... ") HEOM_spectral_fit = HEOMSolver( Hsys, @@ -549,6 +555,7 @@ Below we generate results for different convergence parameters (number of terms ```{code-cell} ipython3 # # Generate results for different number of lorentzians in fit: + results_spectral_fit_pk = [ generate_spectrum_results(Q, n, Nk=1, max_depth=max_depth) for n in range(1, 5) ] @@ -782,7 +789,8 @@ When full_ansatz is True. the ansatz used corresponds to def generate_corr_results(N, max_depth): tlist = np.linspace(0, 30 * np.pi / Del, 600) bath_corr, fitinfo = sd_env.approximate( - "cf", tlist=t, Ni_max=N, Nr_max=N, maxfev=1e8, target_rmse=None) + "cf", tlist=t, Ni_max=N, Nr_max=N, maxfev=1e8, target_rmse=None + ) HEOM_corr_fit = HEOMSolver( Hsys, (bath_corr, Q), @@ -802,7 +810,8 @@ results_corr_fit_pk = [ i, max_depth=max_depth, ) - for i in range(1, 4)] + for i in range(1, 4) +] ``` ```{code-cell} ipython3 @@ -836,10 +845,8 @@ plot_result_expectations( "k", "Correlation Function Fit $k_R=k_I=3$", ), - (results_spectral_fit_pk[0], P11p, - "b", "Spectral Density Fit $k_J=1$"), - (results_spectral_fit_pk[3], P11p, - "r-.", "Spectral Density Fit $k_J=4$"), + (results_spectral_fit_pk[0], P11p, "b", "Spectral Density Fit $k_J=1$"), + (results_spectral_fit_pk[3], P11p, "r-.", "Spectral Density Fit $k_J=4$"), ], axes=axes, ) @@ -865,7 +872,8 @@ methods we explored before ```{code-cell} ipython3 Obath, fitinfo = obs.approximate( - method="cf", tlist=tlist, Nr_max=4, Ni_max=4, maxfev=1e9, target_rmse=None) + method="cf", tlist=tlist, Nr_max=4, Ni_max=4, maxfev=1e9, target_rmse=None +) print(fitinfo["summary"]) HEOM_ohmic_corr_fit = HEOMSolver( Hsys, @@ -1076,8 +1084,7 @@ ESPIRA-II ```{code-cell} ipython3 tlist4 = np.linspace(0, 20, 1000) -espibath2, fitinfo = obs.approximate( - "espira-II", tlist4, Nr=4, Ni=4, separate=True) +espibath2, fitinfo = obs.approximate("espira-II", tlist4, Nr=4, Ni=4, separate=True) print(fitinfo["summary"]) HEOM_ohmic_espira_fit2 = HEOMSolver( Hsys, @@ -1101,8 +1108,7 @@ plot_result_expectations( "b", "Correlation Function Fit $k_R=k_I=4$", ), - (results_spectral_fit_pk[3], P11p, - "r-.", "Spectral Density Fit $k_J=4$"), + (results_spectral_fit_pk[3], P11p, "r-.", "Spectral Density Fit $k_J=4$"), (results_ohmic_corr_fit, P11p, "r", "Correlation Fit Ohmic Bath"), (results_ohmic_sd_fit2, P11p, "g--", "Spectral Density Fit Ohmic Bath"), (results_ohmic_ps_fit, P11p, "g--", "Power Spectrum Fit Ohmic Bath"), @@ -1111,7 +1117,6 @@ plot_result_expectations( (results_ohmic_aaa_fit, P11p, "r-.", "Matrix AAA Fit"), (results_ohmic_espira_fit, P11p, "k", "ESPIRA I Fit"), (results_ohmic_espira2_fit, P11p, "--", "ESPIRA II Fit"), - ], axes=axes, ) diff --git a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md index 3fe6a3ac..042a7784 100644 --- a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md +++ b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md @@ -77,7 +77,6 @@ from matplotlib import pyplot as plt from qutip import basis, expect, liouvillian, sigmax, sigmaz from qutip.core.environment import DrudeLorentzEnvironment, system_terminator from qutip.solver.heom import HEOMSolver -from scipy.optimize import curve_fit %matplotlib inline ``` diff --git a/tutorials-v5/heom/heom-2-fmo-example.md b/tutorials-v5/heom/heom-2-fmo-example.md index 7fda60e7..a39f6660 100644 --- a/tutorials-v5/heom/heom-2-fmo-example.md +++ b/tutorials-v5/heom/heom-2-fmo-example.md @@ -36,24 +36,11 @@ import contextlib import time import numpy as np -from matplotlib import pyplot as plt - import qutip -from qutip import ( - Qobj, - basis, - brmesolve, - expect, - liouvillian, - mesolve, -) -from qutip.solver.heom import ( - HEOMSolver, -) -from qutip.core.environment import ( - DrudeLorentzEnvironment, - system_terminator -) +from matplotlib import pyplot as plt +from qutip import Qobj, basis, brmesolve, expect, liouvillian, mesolve +from qutip.core.environment import DrudeLorentzEnvironment, system_terminator +from qutip.solver.heom import HEOMSolver %matplotlib inline ``` @@ -65,10 +52,10 @@ Let's define some helper functions for calculating correlation functions, spectr ```{code-cell} ipython3 @contextlib.contextmanager def timer(label): - """ Simple utility for timing functions: + """Simple utility for timing functions: - with timer("name"): - ... code to time ... + with timer("name"): + ... code to time ... """ start = time.time() yield @@ -79,6 +66,7 @@ def timer(label): ```{code-cell} ipython3 # Solver options: + options = { "nsteps": 15000, "store_states": True, @@ -101,15 +89,22 @@ And let us set up the system Hamiltonian and bath parameters: # https://www.pnas.org/content/106/41/17255 and operate # in units of Hz: -Hsys = 3e10 * 2 * np.pi * Qobj([ - [200, -87.7, 5.5, -5.9, 6.7, -13.7, -9.9], - [-87.7, 320, 30.8, 8.2, 0.7, 11.8, 4.3], - [5.5, 30.8, 0, -53.5, -2.2, -9.6, 6.0], - [-5.9, 8.2, -53.5, 110, -70.7, -17.0, -63.3], - [6.7, 0.7, -2.2, -70.7, 270, 81.1, -1.3], - [-13.7, 11.8, -9.6, -17.0, 81.1, 420, 39.7], - [-9.9, 4.3, 6.0, -63.3, -1.3, 39.7, 230], -]) +Hsys = ( + 3e10 + * 2 + * np.pi + * Qobj( + [ + [200, -87.7, 5.5, -5.9, 6.7, -13.7, -9.9], + [-87.7, 320, 30.8, 8.2, 0.7, 11.8, 4.3], + [5.5, 30.8, 0, -53.5, -2.2, -9.6, 6.0], + [-5.9, 8.2, -53.5, 110, -70.7, -17.0, -63.3], + [6.7, 0.7, -2.2, -70.7, 270, 81.1, -1.3], + [-13.7, 11.8, -9.6, -17.0, 81.1, 420, 39.7], + [-9.9, 4.3, 6.0, -63.3, -1.3, 39.7, 230], + ] + ) +) ``` ```{code-cell} ipython3 @@ -133,7 +128,7 @@ env = DrudeLorentzEnvironment(T=T, lam=lam, gamma=gamma) wlist = np.linspace(0, 200 * 3e10 * 2 * np.pi, 100) tlist = np.linspace(0, 1e-12, 1000) -J = env.spectral_density(wlist) / (3e10*2*np.pi) +J = env.spectral_density(wlist) / (3e10 * 2 * np.pi) fig, axes = plt.subplots(1, 2, sharex=False, figsize=(10, 3)) @@ -141,22 +136,28 @@ fig.subplots_adjust(hspace=0.1) # reduce space between plots # Spectral density plot: -axes[0].plot(wlist / (3e10 * 2 * np.pi), J, color='r', ls='--', label="J(w)") -axes[0].set_xlabel(r'$\omega$ (cm$^{-1}$)', fontsize=20) +axes[0].plot(wlist / (3e10 * 2 * np.pi), J, color="r", ls="--", label="J(w)") +axes[0].set_xlabel(r"$\omega$ (cm$^{-1}$)", fontsize=20) axes[0].set_ylabel(r"$J(\omega)$ (cm$^{-1}$)", fontsize=16) axes[0].legend() # Correlation plot: axes[1].plot( - tlist, np.real(env.correlation_function(tlist, 10)), - color='r', ls='--', label="C(t) real", + tlist, + np.real(env.correlation_function(tlist, 10)), + color="r", + ls="--", + label="C(t) real", ) axes[1].plot( - tlist, np.imag(env.correlation_function(tlist, 10)), - color='g', ls='--', label="C(t) imaginary", + tlist, + np.imag(env.correlation_function(tlist, 10)), + color="g", + ls="--", + label="C(t) imaginary", ) -axes[1].set_xlabel(r'$t$', fontsize=20) +axes[1].set_xlabel(r"$t$", fontsize=20) axes[1].set_ylabel(r"$C(t)$", fontsize=16) axes[1].legend(); ``` @@ -180,8 +181,7 @@ Nk = 0 Q_list = [] baths = [] Ltot = liouvillian(Hsys) -env_approx, delta = env.approximate( - method="matsubara", Nk=Nk, compute_delta=True) +env_approx, delta = env.approximate(method="matsubara", Nk=Nk, compute_delta=True) for m in range(7): Q = basis(7, m) * basis(7, m).dag() Q_list.append(Q) @@ -200,10 +200,15 @@ with timer("ODE solver time"): ```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, figsize=(12, 8)) -colors = ['r', 'g', 'b', 'y', 'c', 'm', 'k'] +colors = ["r", "g", "b", "y", "c", "m", "k"] linestyles = [ - '-', '--', ':', '-.', - (0, (1, 10)), (0, (5, 10)), (0, (3, 10, 1, 10)), + "-", + "--", + ":", + "-.", + (0, (1, 10)), + (0, (5, 10)), + (0, (3, 10, 1, 10)), ] for m in range(7): @@ -215,16 +220,16 @@ for m in range(7): color=colors[m % len(colors)], linestyle=linestyles[m % len(linestyles)], ) - axes.set_xlabel(r'$t$ (fs)', fontsize=30) + axes.set_xlabel(r"$t$ (fs)", fontsize=30) axes.set_ylabel(r"Population", fontsize=30) - axes.locator_params(axis='y', nbins=6) - axes.locator_params(axis='x', nbins=6) + axes.locator_params(axis="y", nbins=6) + axes.locator_params(axis="x", nbins=6) -axes.set_title('HEOM solution', fontsize=24) +axes.set_title("HEOM solution", fontsize=24) axes.legend(loc=0) axes.set_xlim(0, 1000) -plt.yticks([0., 0.5, 1], [0, 0.5, 1]) -plt.xticks([0., 500, 1000], [0, 500, 1000]); +plt.yticks([0.0, 0.5, 1], [0, 0.5, 1]) +plt.xticks([0.0, 500, 1000], [0, 500, 1000]); ``` ## Comparison with Bloch-Redfield solver @@ -236,7 +241,9 @@ In the next section, we will examine the role of pure dephasing in the evolution ```{code-cell} ipython3 with timer("BR ODE solver time"): outputFMO_BR = brmesolve( - Hsys, rho0, tlist, + Hsys, + rho0, + tlist, a_ops=[[Q, env] for Q in Q_list], options=options, ) @@ -250,10 +257,10 @@ fig, axes = plt.subplots(1, 1, figsize=(12, 8)) for m, Q in enumerate(Q_list): axes.plot(tlist * 1e15, expect(outputFMO_BR.states, Q), label=m + 1) -axes.set_xlabel(r'$t$ (fs)', fontsize=30) +axes.set_xlabel(r"$t$ (fs)", fontsize=30) axes.set_ylabel(r"Population", fontsize=30) -axes.set_title('Bloch-Redfield solution ', fontsize=24) +axes.set_title("Bloch-Redfield solution ", fontsize=24) axes.legend() axes.set_xlim(0, 1000) plt.yticks([0, 0.5, 1], [0, 0.5, 1]) @@ -274,29 +281,26 @@ First we will write a function to return the list of collapse operators for a gi ```{code-cell} ipython3 def J0_dephasing(): - """ Under-damped brownian oscillator dephasing probability. + """Under-damped brownian oscillator dephasing probability. - This returns the limit as w -> 0 of J0(w) * n_th(w, T) / T. + This returns the limit as w -> 0 of J0(w) * n_th(w, T) / T. """ return 2 * lam * gamma / gamma**2 ``` ```{code-cell} ipython3 -env.power_spectrum(0)/2 - J0_dephasing()*T +env.power_spectrum(0) / 2 - J0_dephasing() * T ``` ```{code-cell} ipython3 def get_collapse(H, T, dephasing=1): - """ Calculate collapse operators for a given system H and - temperature T. + """Calculate collapse operators for a given system H and + temperature T. """ all_energy, all_state = H.eigenstates(sort="low") Nmax = len(all_energy) - Q_list = [ - basis(Nmax, n) * basis(Nmax, n).dag() - for n in range(Nmax) - ] + Q_list = [basis(Nmax, n) * basis(Nmax, n).dag() for n in range(Nmax)] collapse_list = [] @@ -305,24 +309,18 @@ def get_collapse(H, T, dephasing=1): for k in range(j + 1, Nmax): Deltajk = abs(all_energy[k] - all_energy[j]) if abs(Deltajk) > 0: - rate = ( - np.abs(Q.matrix_element( - all_state[j].dag(), all_state[k] - ))**2 * - env.power_spectrum(Deltajk) - ) + rate = np.abs( + Q.matrix_element(all_state[j].dag(), all_state[k]) + ) ** 2 * env.power_spectrum(Deltajk) if rate > 0.0: # emission: collapse_list.append( np.sqrt(rate) * all_state[j] * all_state[k].dag() ) - rate = ( - np.abs(Q.matrix_element( - all_state[k].dag(), all_state[j] - ))**2 * - env.power_spectrum(-Deltajk) - ) + rate = np.abs( + Q.matrix_element(all_state[k].dag(), all_state[j]) + ) ** 2 * env.power_spectrum(-Deltajk) if rate > 0.0: # absorption: collapse_list.append( @@ -332,9 +330,9 @@ def get_collapse(H, T, dephasing=1): if dephasing: for j in range(Nmax): rate = ( - np.abs(Q.matrix_element( - all_state[j].dag(), all_state[j]) - )**2 * env.power_spectrum(0)/2 + np.abs(Q.matrix_element(all_state[j].dag(), all_state[j])) ** 2 + * env.power_spectrum(0) + / 2 ) if rate > 0.0: # emission: @@ -352,6 +350,7 @@ Let us starting by including the dephasing operators. We expect to see the same ```{code-cell} ipython3 # dephasing terms on, we recover the full BR solution: + with timer("Building the collapse operators"): collapse_list = get_collapse(Hsys, T=T, dephasing=True) @@ -365,10 +364,10 @@ fig, axes = plt.subplots(1, 1, figsize=(12, 8)) for m, Q in enumerate(Q_list): axes.plot(tlist * 1e15, expect(outputFMO_ME.states, Q), label=m + 1) -axes.set_xlabel(r'$t$', fontsize=20) +axes.set_xlabel(r"$t$", fontsize=20) axes.set_ylabel(r"Population", fontsize=16) axes.set_xlim(0, 1000) -axes.set_title('With pure dephasing', fontsize=24) +axes.set_title("With pure dephasing", fontsize=24) plt.yticks([0, 0.5, 1], [0, 0.5, 1]) plt.xticks([0, 500, 1000], [0, 500, 1000]) axes.legend(fontsize=18); @@ -397,10 +396,10 @@ for m, Q in enumerate(Q_list): label=m + 1, ) -axes.set_xlabel(r'$t$', fontsize=20) +axes.set_xlabel(r"$t$", fontsize=20) axes.set_ylabel(r"Population", fontsize=16) axes.set_xlim(0, 1000) -axes.set_title('Without pure dephasing', fontsize=24) +axes.set_title("Without pure dephasing", fontsize=24) plt.yticks([0, 0.5, 1], [0, 0.5, 1]) plt.xticks([0, 500, 1000], [0, 500, 1000]) axes.legend(fontsize=18); diff --git a/tutorials-v5/heom/heom-3-quantum-heat-transport.md b/tutorials-v5/heom/heom-3-quantum-heat-transport.md index 6b219d8a..ab6a6937 100644 --- a/tutorials-v5/heom/heom-3-quantum-heat-transport.md +++ b/tutorials-v5/heom/heom-3-quantum-heat-transport.md @@ -53,22 +53,14 @@ References: ```{code-cell} ipython3 import dataclasses -import numpy as np import matplotlib.pyplot as plt - +import numpy as np import qutip as qt -from qutip.solver.heom import ( - HEOMSolver, - DrudeLorentzPadeBath -) -from qutip.core.environment import ( - CFExponent, - DrudeLorentzEnvironment, - system_terminator, -) - -from ipywidgets import IntProgress from IPython.display import display +from ipywidgets import IntProgress +from qutip.core.environment import (CFExponent, DrudeLorentzEnvironment, + system_terminator) +from qutip.solver.heom import DrudeLorentzPadeBath, HEOMSolver %matplotlib inline ``` @@ -94,25 +86,25 @@ options = { ```{code-cell} ipython3 @dataclasses.dataclass class SystemParams: - """ System parameters and Hamiltonian. """ + """System parameters and Hamiltonian.""" + epsilon: float = 1.0 J12: float = 0.1 def H(self): - """ Return the Hamiltonian for the system. + """Return the Hamiltonian for the system. - The system consists of two qubits with Hamiltonians (H1 and H2) - and an interaction term (H12). + The system consists of two qubits with Hamiltonians (H1 and H2) + and an interaction term (H12). """ - H1 = self.epsilon / 2 * ( - qt.tensor(qt.sigmaz() + qt.identity(2), qt.identity(2)) + H1 = ( + self.epsilon / 2 * (qt.tensor(qt.sigmaz() + qt.identity(2), qt.identity(2))) ) - H2 = self.epsilon / 2 * ( - qt.tensor(qt.identity(2), qt.sigmaz() + qt.identity(2)) + H2 = ( + self.epsilon / 2 * (qt.tensor(qt.identity(2), qt.sigmaz() + qt.identity(2))) ) H12 = self.J12 * ( - qt.tensor(qt.sigmap(), qt.sigmam()) + - qt.tensor(qt.sigmam(), qt.sigmap()) + qt.tensor(qt.sigmap(), qt.sigmam()) + qt.tensor(qt.sigmam(), qt.sigmap()) ) return H1 + H2 + H12 @@ -123,7 +115,8 @@ class SystemParams: ```{code-cell} ipython3 @dataclasses.dataclass class BathParams: - """ Bath parameters. """ + """Bath parameters.""" + sign: str # + or - qubit: int # 0 or 1 @@ -141,17 +134,14 @@ class BathParams: assert self.qubit in (0, 1) def Q(self): - """ Coupling operator for the bath. """ + """Coupling operator for the bath.""" Q = [qt.identity(2), qt.identity(2)] Q[self.qubit] = qt.sigmax() return qt.tensor(Q) def bath(self, Nk, tag=None): - env = DrudeLorentzEnvironment( - lam=self.lam, gamma=self.gamma, T=self.T, tag=tag - ) - env_approx, delta = env.approximate( - "pade", Nk=Nk, compute_delta=True, tag=tag) + env = DrudeLorentzEnvironment(lam=self.lam, gamma=self.gamma, T=self.T, tag=tag) + env_approx, delta = env.approximate("pade", Nk=Nk, compute_delta=True, tag=tag) return (env_approx, self.Q()), system_terminator(self.Q(), delta), delta def replace(self, **kw): @@ -208,22 +198,27 @@ def bath_heat_current(bath_tag, ado_state, hamiltonian, coupling_op, delta=0): [exp] = ado_state.exps(label) result += exp.vk * (coupling_op * ado_state.extract(label)).tr() - if exp.type == CFExponent.types['I']: + if exp.type == CFExponent.types["I"]: cI0 += exp.ck - elif exp.type == CFExponent.types['RI']: + elif exp.type == CFExponent.types["RI"]: cI0 += exp.ck2 result -= 2 * cI0 * (coupling_op * coupling_op * ado_state.rho).tr() if delta != 0: result -= ( - 1j * delta * - ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr() + 1j + * delta + * ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr() ) return result def system_heat_current( - bath_tag, ado_state, hamiltonian, coupling_op, delta=0, + bath_tag, + ado_state, + hamiltonian, + coupling_op, + delta=0, ): """ System heat current from the system into the heat bath with the given tag. @@ -255,8 +250,9 @@ def system_heat_current( if delta != 0: result -= ( - 1j * delta * - ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr() + 1j + * delta + * ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr() ) return result ``` @@ -297,10 +293,10 @@ tlist = np.linspace(0, 50, 250) ```{code-cell} ipython3 H = sys.H() -bath1, b1term, b1delta = bath_p1.bath(Nk, tag='bath 1') +bath1, b1term, b1delta = bath_p1.bath(Nk, tag="bath 1") Q1 = bath_p1.Q() -bath2, b2term, b2delta = bath_p2.bath(Nk, tag='bath 2') +bath2, b2term, b2delta = bath_p2.bath(Nk, tag="bath 2") Q2 = bath_p2.Q() @@ -311,21 +307,25 @@ solver = HEOMSolver( options=options, ) -result = solver.run(rho0, tlist, e_ops=[ - qt.tensor(qt.sigmaz(), qt.identity(2)), - lambda t, ado: bath_heat_current('bath 1', ado, H, Q1, b1delta), - lambda t, ado: bath_heat_current('bath 2', ado, H, Q2, b2delta), - lambda t, ado: system_heat_current('bath 1', ado, H, Q1, b1delta), - lambda t, ado: system_heat_current('bath 2', ado, H, Q2, b2delta), -]) +result = solver.run( + rho0, + tlist, + e_ops=[ + qt.tensor(qt.sigmaz(), qt.identity(2)), + lambda t, ado: bath_heat_current("bath 1", ado, H, Q1, b1delta), + lambda t, ado: bath_heat_current("bath 2", ado, H, Q2, b2delta), + lambda t, ado: system_heat_current("bath 1", ado, H, Q1, b1delta), + lambda t, ado: system_heat_current("bath 2", ado, H, Q2, b2delta), + ], +) ``` We first plot $\langle \sigma_z^1 \rangle$ to see the time evolution of the system state: ```{code-cell} ipython3 fig, axes = plt.subplots(figsize=(8, 8)) -axes.plot(tlist, result.expect[0], 'r', linewidth=2) -axes.set_xlabel('t', fontsize=28) +axes.plot(tlist, result.expect[0], "r", linewidth=2) +axes.set_xlabel("t", fontsize=28) axes.set_ylabel(r"$\langle \sigma_z^1 \rangle$", fontsize=28); ``` @@ -335,45 +335,65 @@ We find a rather quick thermalization of the system state. For the heat currents fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8)) ax1.plot( - tlist, -np.real(result.expect[1]), - color='darkorange', label='BHC (bath 1 -> system)', + tlist, + -np.real(result.expect[1]), + color="darkorange", + label="BHC (bath 1 -> system)", ) ax1.plot( - tlist, np.real(result.expect[2]), - '--', color='darkorange', label='BHC (system -> bath 2)', + tlist, + np.real(result.expect[2]), + "--", + color="darkorange", + label="BHC (system -> bath 2)", ) ax1.plot( - tlist, -np.real(result.expect[3]), - color='dodgerblue', label='SHC (bath 1 -> system)', + tlist, + -np.real(result.expect[3]), + color="dodgerblue", + label="SHC (bath 1 -> system)", ) ax1.plot( - tlist, np.real(result.expect[4]), - '--', color='dodgerblue', label='SHC (system -> bath 2)', + tlist, + np.real(result.expect[4]), + "--", + color="dodgerblue", + label="SHC (system -> bath 2)", ) -ax1.set_xlabel('t', fontsize=28) -ax1.set_ylabel('j', fontsize=28) +ax1.set_xlabel("t", fontsize=28) +ax1.set_ylabel("j", fontsize=28) ax1.set_ylim((-0.05, 0.05)) ax1.legend(loc=0, fontsize=12) ax2.plot( - tlist, -np.real(result.expect[1]), - color='darkorange', label='BHC (bath 1 -> system)', + tlist, + -np.real(result.expect[1]), + color="darkorange", + label="BHC (bath 1 -> system)", ) ax2.plot( - tlist, np.real(result.expect[2]), - '--', color='darkorange', label='BHC (system -> bath 2)', + tlist, + np.real(result.expect[2]), + "--", + color="darkorange", + label="BHC (system -> bath 2)", ) ax2.plot( - tlist, -np.real(result.expect[3]), - color='dodgerblue', label='SHC (bath 1 -> system)', + tlist, + -np.real(result.expect[3]), + color="dodgerblue", + label="SHC (bath 1 -> system)", ) ax2.plot( - tlist, np.real(result.expect[4]), - '--', color='dodgerblue', label='SHC (system -> bath 2)', + tlist, + np.real(result.expect[4]), + "--", + color="dodgerblue", + label="SHC (system -> bath 2)", ) -ax2.set_xlabel('t', fontsize=28) +ax2.set_xlabel("t", fontsize=28) ax2.set_xlim((20, 50)) ax2.set_ylim((0, 0.0002)) ax2.legend(loc=0, fontsize=12); @@ -385,30 +405,30 @@ Here, we try to reproduce the HEOM curves in Fig. 3(a) of Ref. \[1\] by varying ```{code-cell} ipython3 def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options): - """ Calculate the steady sate heat currents for the given system and - bath. + """Calculate the steady sate heat currents for the given system and + bath. """ - bath1, b1term, b1delta = bath_p1.bath(Nk, tag='bath 1') + bath1, b1term, b1delta = bath_p1.bath(Nk, tag="bath 1") Q1 = bath_p1.Q() - bath2, b2term, b2delta = bath_p2.bath(Nk, tag='bath 2') + bath2, b2term, b2delta = bath_p2.bath(Nk, tag="bath 2") Q2 = bath_p2.Q() solver = HEOMSolver( qt.liouvillian(sys.H()) + b1term + b2term, [bath1, bath2], max_depth=NC, - options=options + options=options, ) _, steady_ados = solver.steady_state() return ( - bath_heat_current('bath 1', steady_ados, sys.H(), Q1, b1delta), - bath_heat_current('bath 2', steady_ados, sys.H(), Q2, b2delta), - system_heat_current('bath 1', steady_ados, sys.H(), Q1, b1delta), - system_heat_current('bath 2', steady_ados, sys.H(), Q2, b2delta), + bath_heat_current("bath 1", steady_ados, sys.H(), Q1, b1delta), + bath_heat_current("bath 2", steady_ados, sys.H(), Q2, b2delta), + system_heat_current("bath 1", steady_ados, sys.H(), Q1, b1delta), + system_heat_current("bath 2", steady_ados, sys.H(), Q2, b2delta), ) ``` @@ -432,7 +452,7 @@ display(progress) def calculate_heat_current(J12, zb, Nk, progress=progress): - """ Calculate a single heat current and update the progress bar. """ + """Calculate a single heat current and update the progress bar.""" # Estimate appropriate HEOM max_depth from coupling strength NC = 7 + int(max(zb * J12 - 1, 0) * 2) NC = min(NC, 20) @@ -441,7 +461,9 @@ def calculate_heat_current(J12, zb, Nk, progress=progress): sys.replace(J12=J12), bath_p1.replace(lam=zb * J12 / 2), bath_p2.replace(lam=zb * J12 / 2), - Nk, NC, options=options, + Nk, + NC, + options=options, ) progress.value += 1 return j @@ -449,18 +471,9 @@ def calculate_heat_current(J12, zb, Nk, progress=progress): # Calculate steady state currents for range of zeta_bars # for J12 = 0.01, 0.1 and 0.5: -j1s = [ - calculate_heat_current(0.01, zb, Nk) - for zb in zeta_bars -] -j2s = [ - calculate_heat_current(0.1, zb, Nk) - for zb in zeta_bars -] -j3s = [ - calculate_heat_current(0.5, zb, Nk) - for zb in zeta_bars -] +j1s = [calculate_heat_current(0.01, zb, Nk) for zb in zeta_bars] +j2s = [calculate_heat_current(0.1, zb, Nk) for zb in zeta_bars] +j3s = [calculate_heat_current(0.5, zb, Nk) for zb in zeta_bars] ``` ## Create Plot @@ -469,19 +482,28 @@ j3s = [ fig, axes = plt.subplots(figsize=(12, 7)) axes.plot( - zeta_bars, -1000 * 100 * np.real(j1s), - 'b', linewidth=2, label=r"$J_{12} = 0.01\, \epsilon$", + zeta_bars, + -1000 * 100 * np.real(j1s), + "b", + linewidth=2, + label=r"$J_{12} = 0.01\, \epsilon$", ) axes.plot( - zeta_bars, -1000 * 10 * np.real(j2s), - 'r--', linewidth=2, label=r"$J_{12} = 0.1\, \epsilon$", + zeta_bars, + -1000 * 10 * np.real(j2s), + "r--", + linewidth=2, + label=r"$J_{12} = 0.1\, \epsilon$", ) axes.plot( - zeta_bars, -1000 * 2 * np.real(j3s), - 'g-.', linewidth=2, label=r"$J_{12} = 0.5\, \epsilon$", + zeta_bars, + -1000 * 2 * np.real(j3s), + "g-.", + linewidth=2, + label=r"$J_{12} = 0.5\, \epsilon$", ) -axes.set_xscale('log') +axes.set_xscale("log") axes.set_xlabel(r"$\bar\zeta$", fontsize=30) axes.set_xlim((zeta_bars[0], zeta_bars[-1])) diff --git a/tutorials-v5/heom/heom-4-dynamical-decoupling.md b/tutorials-v5/heom/heom-4-dynamical-decoupling.md index 217e1617..872ba642 100644 --- a/tutorials-v5/heom/heom-4-dynamical-decoupling.md +++ b/tutorials-v5/heom/heom-4-dynamical-decoupling.md @@ -28,25 +28,14 @@ We first show the standard example of equally spaced pulses, and then consider t ## Setup ```{code-cell} ipython3 -import numpy as np import matplotlib.pyplot as plt - +import numpy as np import qutip -from qutip import ( - QobjEvo, - basis, - expect, - ket2dm, - sigmax, - sigmaz, - DrudeLorentzEnvironment -) -from qutip.solver.heom import ( - HEOMSolver -) - -from ipywidgets import IntProgress from IPython.display import display +from ipywidgets import IntProgress +from qutip import (DrudeLorentzEnvironment, QobjEvo, basis, expect, ket2dm, + sigmax, sigmaz) +from qutip.solver.heom import HEOMSolver %matplotlib inline ``` @@ -124,21 +113,21 @@ Below we define a function that returns the pulse (which is itself a function): ```{code-cell} ipython3 def drive(amplitude, delay, integral): - """ Coefficient of the drive as a function of time. - - The drive consists of a series of constant pulses with - a fixed delay between them. - - Parameters - ---------- - amplitude : float - The amplitude of the drive during the pulse. - delay : float - The time delay between successive pulses. - integral : float - The integral of the pulse. This determines - the duration of each pulse with the duration - equal to the integral divided by the amplitude. + """Coefficient of the drive as a function of time. + + The drive consists of a series of constant pulses with + a fixed delay between them. + + Parameters + ---------- + amplitude : float + The amplitude of the drive during the pulse. + delay : float + The time delay between successive pulses. + integral : float + The integral of the pulse. This determines + the duration of each pulse with the duration + equal to the integral divided by the amplitude. """ duration = integral / amplitude period = duration + delay @@ -165,11 +154,11 @@ J = env.spectral_density(wlist) J_approx = env_approx.spectral_density(wlist) fig, axes = plt.subplots(1, 1, figsize=(8, 8)) -axes.plot(wlist, J, 'r', linewidth=2) -axes.plot(wlist, J_approx, 'b--', linewidth=2) +axes.plot(wlist, J, "r", linewidth=2) +axes.plot(wlist, J_approx, "b--", linewidth=2) -axes.set_xlabel(r'$\omega$', fontsize=28) -axes.set_ylabel(r'J', fontsize=28); +axes.set_xlabel(r"$\omega$", fontsize=28) +axes.set_ylabel(r"J", fontsize=28); ``` ## Dynamic decoupling with fast and slow pulses @@ -211,7 +200,7 @@ hsolver = HEOMSolver(H_sys, bath, NC, options=options) outputnoDDslow = hsolver.run(rho0, tlist) # with pulses -drive_slow = drive(amplitude=0.01, delay=20, integral=np.pi/2) +drive_slow = drive(amplitude=0.01, delay=20, integral=np.pi / 2) H_d = QobjEvo([H_sys, [H_drive, drive_slow]]) hsolver = HEOMSolver(H_d, bath, NC, options=options) @@ -237,20 +226,32 @@ def plot_dd_results(outputnoDD, outputDD, outputDDslow): plt.yticks([0, 0.25, 0.5], [0, 0.25, 0.5]) axes[0].plot( - tlist, np.real(P12DD), - 'green', linestyle='-', linewidth=2, label="HEOM with fast DD", + tlist, + np.real(P12DD), + "green", + linestyle="-", + linewidth=2, + label="HEOM with fast DD", ) axes[0].plot( - tlist, np.real(P12DDslow), - 'blue', linestyle='-', linewidth=2, label="HEOM with slow DD", + tlist, + np.real(P12DDslow), + "blue", + linestyle="-", + linewidth=2, + label="HEOM with slow DD", ) axes[0].plot( - tlist, np.real(P12noDD), - 'orange', linestyle='--', linewidth=2, label="HEOM no DD", + tlist, + np.real(P12noDD), + "orange", + linestyle="--", + linewidth=2, + label="HEOM no DD", ) - axes[0].locator_params(axis='y', nbins=3) - axes[0].locator_params(axis='x', nbins=3) + axes[0].locator_params(axis="y", nbins=3) + axes[0].locator_params(axis="x", nbins=3) axes[0].set_ylabel(r"$\rho_{01}$", fontsize=30) @@ -263,22 +264,30 @@ def plot_dd_results(outputnoDD, outputDD, outputDDslow): pulseslow = [drive_slow(t) for t in tlist] plt.sca(axes[1]) - plt.yticks([0., 0.25, 0.5], [0, 0.25, 0.5]) + plt.yticks([0.0, 0.25, 0.5], [0, 0.25, 0.5]) axes[1].plot( - tlist, pulse, - 'green', linestyle='-', linewidth=2, label="Drive fast", + tlist, + pulse, + "green", + linestyle="-", + linewidth=2, + label="Drive fast", ) axes[1].plot( - tlist, pulseslow, - 'blue', linestyle='--', linewidth=2, label="Drive slow", + tlist, + pulseslow, + "blue", + linestyle="--", + linewidth=2, + label="Drive slow", ) - axes[1].locator_params(axis='y', nbins=3) - axes[1].locator_params(axis='x', nbins=3) + axes[1].locator_params(axis="y", nbins=3) + axes[1].locator_params(axis="x", nbins=3) - axes[1].set_xlabel(r'$t\bar{V}_{\mathrm{f}}$', fontsize=30) - axes[1].set_ylabel(r'Drive amplitude/$\bar{V}_{\mathrm{f}}$', fontsize=30) + axes[1].set_xlabel(r"$t\bar{V}_{\mathrm{f}}$", fontsize=30) + axes[1].set_ylabel(r"Drive amplitude/$\bar{V}_{\mathrm{f}}$", fontsize=30) axes[1].legend(loc=1) axes[1].text(0, 0.4, "(b)", fontsize=28) @@ -308,39 +317,36 @@ This is just a convenient way to describe the varying delay. We could have chose ```{code-cell} ipython3 def cummulative_delay_fractions(N): - """ Return an array of N + 1 cummulative delay - fractions. + """Return an array of N + 1 cummulative delay + fractions. - The j'th entry in the array should be the sum of - all delays before the j'th pulse. The last entry - should be 1 (i.e. the entire cummulative delay - should have been used once the sequence of pulses - is complete). + The j'th entry in the array should be the sum of + all delays before the j'th pulse. The last entry + should be 1 (i.e. the entire cummulative delay + should have been used once the sequence of pulses + is complete). - The function should be monotonically increasing, - strictly greater than zero and the last value - should be 1. + The function should be monotonically increasing, + strictly greater than zero and the last value + should be 1. - This implementation returns: + This implementation returns: - sin((pi / 2) * (j / (N + 1)))**2 + sin((pi / 2) * (j / (N + 1)))**2 - as the cummulative delay after the j'th pulse. + as the cummulative delay after the j'th pulse. """ - return np.array([ - np.sin((np.pi / 2) * (j / (N + 1)))**2 - for j in range(0, N + 1) - ]) + return np.array([np.sin((np.pi / 2) * (j / (N + 1))) ** 2 for j in range(0, N + 1)]) def drive_opt(amplitude, avg_delay, integral, N): - """ Return an optimized distance pulse function. + """Return an optimized distance pulse function. - Our previous pulses were evenly spaced. Here we - instead use a varying delay after the j'th pulse. + Our previous pulses were evenly spaced. Here we + instead use a varying delay after the j'th pulse. - The cummulative delay is described by the function - ``cummulative_delay_fractions`` above. + The cummulative delay is described by the function + ``cummulative_delay_fractions`` above. """ duration = integral / amplitude cummulative_delays = N * avg_delay * cummulative_delay_fractions(N) @@ -389,10 +395,14 @@ def plot_even_and_optimally_spaced_pulses(): pulse_eq = drive(amplitude, delay, integral) plt.plot( - tlist, [pulse_opt(t) for t in tlist], label="opt", + tlist, + [pulse_opt(t) for t in tlist], + label="opt", ) plt.plot( - tlist, [pulse_eq(t) for t in tlist], label="eq", + tlist, + [pulse_eq(t) for t in tlist], + label="eq", ) plt.legend(loc=4) @@ -425,10 +435,10 @@ display(progress) def simulate_100_pulses(lam, gamma, T, NC, Nk): - """ Simulate the evolution of 100 evenly and optimally spaced pulses. + """Simulate the evolution of 100 evenly and optimally spaced pulses. - Returns the expectation value of P12p from the final state of - each evolution. + Returns the expectation value of P12p from the final state of + each evolution. """ rho0 = (basis(2, 1) + basis(2, 0)).unit() rho0 = ket2dm(rho0) @@ -475,10 +485,14 @@ def simulate_100_pulses(lam, gamma, T, NC, Nk): # We use NC=2 and Nk=2 to speed up the simulation: P12_results = [ - list(zip(*( - simulate_100_pulses(lam=lam_, gamma=gamma_, T=0.5, NC=2, Nk=2) - for gamma_ in gammas - ))) + list( + zip( + *( + simulate_100_pulses(lam=lam_, gamma=gamma_, T=0.5, NC=2, Nk=2) + for gamma_ in gammas + ) + ) + ) for lam_ in lams ] ``` @@ -492,13 +506,19 @@ colors = ["green", "red", "blue"] for i in range(len(lams)): color = colors[i % len(colors)] axes.plot( - gammas, np.real(P12_results[i][0]), - color, linestyle='-', linewidth=2, + gammas, + np.real(P12_results[i][0]), + color, + linestyle="-", + linewidth=2, label=f"Optimal DD [$\\lambda={lams[i]}$]", ) axes.plot( - gammas, np.real(P12_results[i][1]), - color, linestyle='-.', linewidth=2, + gammas, + np.real(P12_results[i][1]), + color, + linestyle="-.", + linewidth=2, label=f"Even DD [$\\lambda={lams[i]}$]", ) diff --git a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md index 106b025f..7c3fa208 100644 --- a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md +++ b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md @@ -74,22 +74,15 @@ import contextlib import dataclasses import time -import numpy as np import matplotlib.pyplot as plt -from scipy.integrate import quad - +import numpy as np import qutip -from qutip import ( - basis, - destroy, - expect, -) -from qutip.solver.heom import ( - HEOMSolver, -) -from qutip.core.environment import LorentzianEnvironment -from ipywidgets import IntProgress from IPython.display import display +from ipywidgets import IntProgress +from qutip import basis, destroy, expect +from qutip.core.environment import LorentzianEnvironment +from qutip.solver.heom import HEOMSolver +from scipy.integrate import quad %matplotlib inline ``` @@ -99,10 +92,10 @@ from IPython.display import display ```{code-cell} ipython3 @contextlib.contextmanager def timer(label): - """ Simple utility for timing functions: + """Simple utility for timing functions: - with timer("name"): - ... code to time ... + with timer("name"): + ... code to time ... """ start = time.time() yield @@ -118,6 +111,7 @@ def timer(label): # to calculate the current between the leads # and the system. + options = { "nsteps": 1500, "store_states": True, @@ -147,6 +141,7 @@ H = e1 * d1.dag() * d1 # Each bath is a lead (i.e. a wire held at a potential) # with temperature T and chemical potential mu. + @dataclasses.dataclass class LorentzianBathParameters: lead: str @@ -162,19 +157,19 @@ class LorentzianBathParameters: if self.lead == "L": self.mu = self.theta / 2.0 else: - self.mu = - self.theta / 2.0 + self.mu = -self.theta / 2.0 def J(self, w): - """ Spectral density. """ - return self.gamma * self.W**2 / ((w - self.mu)**2 + self.W**2) + """Spectral density.""" + return self.gamma * self.W**2 / ((w - self.mu) ** 2 + self.W**2) def fF(self, w, sign=1.0): - """ Fermi distribution for this bath. """ + """Fermi distribution for this bath.""" x = sign * self.beta * (w - self.mu) return fF(x) def lamshift(self, w): - """ Return the lamshift. """ + """Return the lamshift.""" return 0.5 * (w - self.mu) * self.J(w) / self.W def replace(self, **kw): @@ -182,7 +177,7 @@ class LorentzianBathParameters: def fF(x): - """ Return the Fermi distribution. """ + """Return the Fermi distribution.""" # in units where kB = 1.0 return 1 / (np.exp(x) + 1) @@ -204,13 +199,17 @@ spec_L = bath_L.J(w_list) spec_R = bath_R.J(w_list) ax.plot( - w_list, spec_L, - "b--", linewidth=3, + w_list, + spec_L, + "b--", + linewidth=3, label=r"J_L(w)", ) ax.plot( - w_list, spec_R, - "r--", linewidth=3, + w_list, + spec_R, + "r--", + linewidth=3, label=r"J_R(w)", ) @@ -234,13 +233,17 @@ gam_L_in = bath_L.J(w_list) * bath_L.fF(w_list, sign=1.0) gam_L_out = bath_L.J(w_list) * bath_L.fF(w_list, sign=-1.0) ax.plot( - w_list, gam_L_in, - "b--", linewidth=3, + w_list, + gam_L_in, + "b--", + linewidth=3, label=r"S_L(w) input (absorption)", ) ax.plot( - w_list, gam_L_out, - "r--", linewidth=3, + w_list, + gam_L_out, + "r--", + linewidth=3, label=r"S_L(w) output (emission)", ) @@ -250,13 +253,17 @@ gam_R_in = bath_R.J(w_list) * bath_R.fF(w_list, sign=1.0) gam_R_out = bath_R.J(w_list) * bath_R.fF(w_list, sign=-1.0) ax.plot( - w_list, gam_R_in, - "b", linewidth=3, + w_list, + gam_R_in, + "b", + linewidth=3, label=r"S_R(w) input (absorption)", ) ax.plot( - w_list, gam_R_out, - "r", linewidth=3, + w_list, + gam_R_out, + "r", + linewidth=3, label=r"S_R(w) output (emission)", ) @@ -279,18 +286,25 @@ rho0 = basis(2, 0) * basis(2, 0).dag() Nk = 10 # Number of exponents to retain in the expansion of each bath envL = LorentzianEnvironment( - bath_L.T, bath_L.mu, bath_L.gamma, bath_L.W, + bath_L.T, + bath_L.mu, + bath_L.gamma, + bath_L.W, ) envL_pade = envL.approx_by_pade(Nk=Nk, tag="L") envR = LorentzianEnvironment( - bath_R.T, bath_R.mu, bath_R.gamma, bath_R.W, + bath_R.T, + bath_R.mu, + bath_R.gamma, + bath_R.W, ) envR_pade = envR.approx_by_pade(Nk=Nk, tag="R") with timer("RHS construction time"): solver_pade = HEOMSolver( - H, [(envL_pade, bath_L.Q), (envR_pade, bath_R.Q)], max_depth=2, options=options) + H, [(envL_pade, bath_L.Q), (envR_pade, bath_R.Q)], max_depth=2, options=options + ) with timer("ODE solver time"): result_pade = solver_pade.run(rho0, tlist) @@ -306,17 +320,21 @@ Now let us plot the result which shows the decay of the initially excited impuri fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) axes.plot( - tlist, expect(result_pade.states, rho0), - 'r--', linewidth=2, + tlist, + expect(result_pade.states, rho0), + "r--", + linewidth=2, label="P11 (Pade)", ) axes.axhline( expect(rho_ss_pade, rho0), - color='r', linestyle="dotted", linewidth=1, + color="r", + linestyle="dotted", + linewidth=1, label="P11 (Pade steady state)", ) -axes.set_xlabel('t', fontsize=28) +axes.set_xlabel("t", fontsize=28) axes.legend(fontsize=12); ``` @@ -331,7 +349,8 @@ envR_mats = envR.approx_by_matsubara(Nk=Nk, tag="R") with timer("RHS construction time"): solver_mats = HEOMSolver( - H, [(envL_mats, bath_L.Q), (envR_mats, bath_R.Q)], max_depth=2, options=options) + H, [(envL_mats, bath_L.Q), (envR_mats, bath_R.Q)], max_depth=2, options=options + ) with timer("ODE solver time"): result_mats = solver_mats.run(rho0, tlist) @@ -347,28 +366,36 @@ We see a marked difference in the Matsubara vs Pade results: fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) axes.plot( - tlist, expect(result_pade.states, rho0), - 'r--', linewidth=2, + tlist, + expect(result_pade.states, rho0), + "r--", + linewidth=2, label="P11 (Pade)", ) axes.axhline( expect(rho_ss_pade, rho0), - color='r', linestyle="dotted", linewidth=1, + color="r", + linestyle="dotted", + linewidth=1, label="P11 (Pade steady state)", ) axes.plot( - tlist, expect(result_mats.states, rho0), - 'b--', linewidth=2, + tlist, + expect(result_mats.states, rho0), + "b--", + linewidth=2, label="P11 (Mats)", ) axes.axhline( expect(rho_ss_mats, rho0), - color='b', linestyle="dotted", linewidth=1, + color="b", + linestyle="dotted", + linewidth=1, label="P11 (Mats steady state)", ) -axes.set_xlabel('t', fontsize=28) +axes.set_xlabel("t", fontsize=28) axes.legend(fontsize=12); ``` @@ -380,14 +407,16 @@ See the [QuTiP-BoFiN paper](https://arxiv.org/abs/2010.10806) for a detailed des ```{code-cell} ipython3 def analytical_steady_state_current(bath_L, bath_R, e1): - """ Calculate the analytical steady state current. """ + """Calculate the analytical steady state current.""" def integrand(w): return (2 / np.pi) * ( - bath_L.J(w) * bath_R.J(w) * (bath_L.fF(w) - bath_R.fF(w)) / - ( - (bath_L.J(w) + bath_R.J(w))**2 + - 4*(w - e1 - bath_L.lamshift(w) - bath_R.lamshift(w))**2 + bath_L.J(w) + * bath_R.J(w) + * (bath_L.fF(w) - bath_R.fF(w)) + / ( + (bath_L.J(w) + bath_R.J(w)) ** 2 + + 4 * (w - e1 - bath_L.lamshift(w) - bath_R.lamshift(w)) ** 2 ) ) @@ -418,8 +447,8 @@ In the function `state_current(...)` below, we extract the first level ADOs for ```{code-cell} ipython3 def state_current(ado_state, bath_tag): - """ Determine current from the given bath (either "R" or "L") to - the system in the given ADO state. + """Determine current from the given bath (either "R" or "L") to + the system in the given ADO state. """ level_1_aux = [ (ado_state.extract(label), ado_state.exps(label)[0]) @@ -433,8 +462,7 @@ def state_current(ado_state, bath_tag): return exp.Q if exp.type == exp.types["+"] else exp.Q.dag() return -1.0j * sum( - exp_sign(exp) * (exp_op(exp) * aux).tr() - for aux, exp in level_1_aux + exp_sign(exp) * (exp_op(exp) * aux).tr() for aux, exp in level_1_aux ) ``` @@ -494,7 +522,7 @@ display(progress) def current_analytic_for_theta(e1, bath_L, bath_R, theta): - """ Return the analytic current for a given theta. """ + """Return the analytic current for a given theta.""" current = analytical_steady_state_current( bath_L.replace(theta=theta), bath_R.replace(theta=theta), @@ -505,22 +533,19 @@ def current_analytic_for_theta(e1, bath_L, bath_R, theta): def current_pade_for_theta(H, bath_L, bath_R, theta, Nk): - """ Return the steady state current using the Pade approximation. """ + """Return the steady state current using the Pade approximation.""" bath_L = bath_L.replace(theta=theta) bath_R = bath_R.replace(theta=theta) - envL = LorentzianEnvironment( - bath_L.T, bath_L.mu, bath_L.gamma, bath_L.W - ) + envL = LorentzianEnvironment(bath_L.T, bath_L.mu, bath_L.gamma, bath_L.W) bathL = envL.approx_by_pade(Nk=Nk) - envR = LorentzianEnvironment( - bath_R.T, bath_R.mu, bath_R.gamma, bath_R.W - ) + envR = LorentzianEnvironment(bath_R.T, bath_R.mu, bath_R.gamma, bath_R.W) bathR = envR.approx_by_pade(Nk=Nk, tag="R") - - solver_pade = HEOMSolver(H, [(bathL,bath_L.Q), (bathR,bath_R.Q)], - max_depth=2, options=options) + + solver_pade = HEOMSolver( + H, [(bathL, bath_L.Q), (bathR, bath_R.Q)], max_depth=2, options=options + ) rho_ss_pade, ado_ss_pade = solver_pade.steady_state() current = state_current(ado_ss_pade, bath_tag="R") @@ -529,15 +554,13 @@ def current_pade_for_theta(H, bath_L, bath_R, theta, Nk): curr_ss_analytic_thetas = [ - current_analytic_for_theta(e1, bath_L, bath_R, theta) - for theta in thetas + current_analytic_for_theta(e1, bath_L, bath_R, theta) for theta in thetas ] # The number of expansion terms has been dropped to Nk=6 to speed # up notebook execution. Increase to Nk=10 for more accurate results. curr_ss_pade_theta = [ - current_pade_for_theta(H, bath_L, bath_R, theta, Nk=6) - for theta in thetas + current_pade_for_theta(H, bath_L, bath_R, theta, Nk=6) for theta in thetas ] ``` @@ -547,19 +570,23 @@ Below we plot the results and see that even with `Nk=6`, the HEOM Pade approxima fig, ax = plt.subplots(figsize=(12, 7)) ax.plot( - thetas, 2.434e-4 * 1e6 * np.array(curr_ss_analytic_thetas), - color="black", linewidth=3, + thetas, + 2.434e-4 * 1e6 * np.array(curr_ss_analytic_thetas), + color="black", + linewidth=3, label=r"Analytical", ) ax.plot( - thetas, 2.434e-4 * 1e6 * np.array(curr_ss_pade_theta), - 'r--', linewidth=3, + thetas, + 2.434e-4 * 1e6 * np.array(curr_ss_pade_theta), + "r--", + linewidth=3, label=r"HEOM Pade $N_k=10$, $n_{\mathrm{max}}=2$", ) -ax.locator_params(axis='y', nbins=4) -ax.locator_params(axis='x', nbins=4) +ax.locator_params(axis="y", nbins=4) +ax.locator_params(axis="x", nbins=4) ax.set_xticks([-2.5, 0, 2.5]) ax.set_xticklabels([-2.5, 0, 2.5]) diff --git a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md index d2f798f7..2772384a 100644 --- a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md +++ b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md @@ -102,20 +102,12 @@ import time import matplotlib.pyplot as plt import numpy as np - import qutip -from qutip import ( - destroy, - qeye, - tensor, -) -from qutip.solver.heom import ( - HEOMSolver, -) -from qutip.core.environment import LorentzianEnvironment - -from ipywidgets import IntProgress from IPython.display import display +from ipywidgets import IntProgress +from qutip import destroy, qeye, tensor +from qutip.core.environment import LorentzianEnvironment +from qutip.solver.heom import HEOMSolver %matplotlib inline ``` @@ -125,10 +117,10 @@ from IPython.display import display ```{code-cell} ipython3 @contextlib.contextmanager def timer(label): - """ Simple utility for timing functions: + """Simple utility for timing functions: - with timer("name"): - ... code to time ... + with timer("name"): + ... code to time ... """ start = time.time() yield @@ -138,8 +130,8 @@ def timer(label): ```{code-cell} ipython3 def state_current(ado_state, bath_tag): - """ Determine current from the given bath (either "R" or "L") to - the system in the given ADO state. + """Determine current from the given bath (either "R" or "L") to + the system in the given ADO state. """ level_1_aux = [ (ado_state.extract(label), ado_state.exps(label)[0]) @@ -153,8 +145,7 @@ def state_current(ado_state, bath_tag): return exp.Q if exp.type == exp.types["+"] else exp.Q.dag() return -1.0j * sum( - exp_sign(exp) * (exp_op(exp) * aux).tr() - for aux, exp in level_1_aux + exp_sign(exp) * (exp_op(exp) * aux).tr() for aux, exp in level_1_aux ) ``` @@ -166,6 +157,7 @@ def state_current(ado_state, bath_tag): # to calculate the current between the leads # and the system. + options = { "nsteps": 1500, "store_states": True, @@ -184,6 +176,7 @@ Let us set up the system Hamiltonian and specify the properties of the two reser ```{code-cell} ipython3 # Define the system Hamiltonian: + @dataclasses.dataclass class SystemParameters: e1: float = 0.3 # fermion mode energy splitting @@ -195,9 +188,9 @@ class SystemParameters: d = tensor(destroy(2), qeye(self.Nbos)) a = tensor(qeye(2), destroy(self.Nbos)) self.H = ( - self.e1 * d.dag() * d + - self.Omega * a.dag() * a + - self.Lambda * (a + a.dag()) * d.dag() * d + self.e1 * d.dag() * d + + self.Omega * a.dag() * a + + self.Lambda * (a + a.dag()) * d.dag() * d ) self.Q = d @@ -213,6 +206,7 @@ sys_p = SystemParameters() # Each bath is a lead (i.e. a wire held at a potential) # with temperature T and chemical potential mu. + @dataclasses.dataclass class LorentzianBathParameters: lead: str @@ -227,19 +221,19 @@ class LorentzianBathParameters: if self.lead == "L": self.mu = self.theta / 2.0 else: - self.mu = - self.theta / 2.0 + self.mu = -self.theta / 2.0 def J(self, w): - """ Spectral density. """ - return self.gamma * self.W**2 / ((w - self.mu)**2 + self.W**2) + """Spectral density.""" + return self.gamma * self.W**2 / ((w - self.mu) ** 2 + self.W**2) def fF(self, w, sign=1.0): - """ Fermi distribution for this bath. """ + """Fermi distribution for this bath.""" x = sign * self.beta * (w - self.mu) return fF(x) def lamshift(self, w): - """ Return the lamshift. """ + """Return the lamshift.""" return 0.5 * (w - self.mu) * self.J(w) / self.W def replace(self, **kw): @@ -247,7 +241,7 @@ class LorentzianBathParameters: def fF(x): - """ Return the Fermi distribution. """ + """Return the Fermi distribution.""" # in units where kB = 1.0 return 1 / (np.exp(x) + 1) @@ -273,13 +267,17 @@ gam_L_in = bath_L.J(w_list) * bath_L.fF(w_list, sign=1.0) gam_L_out = bath_L.J(w_list) * bath_L.fF(w_list, sign=-1.0) ax.plot( - w_list, gam_L_in, - "b--", linewidth=3, + w_list, + gam_L_in, + "b--", + linewidth=3, label=r"S_L(w) input (absorption)", ) ax.plot( - w_list, gam_L_out, - "r--", linewidth=3, + w_list, + gam_L_out, + "r--", + linewidth=3, label=r"S_L(w) output (emission)", ) @@ -289,13 +287,17 @@ gam_R_in = bath_R.J(w_list) * bath_R.fF(w_list, sign=1.0) gam_R_out = bath_R.J(w_list) * bath_R.fF(w_list, sign=-1.0) ax.plot( - w_list, gam_R_in, - "b", linewidth=3, + w_list, + gam_R_in, + "b", + linewidth=3, label=r"S_R(w) input (absorption)", ) ax.plot( - w_list, gam_R_out, - "r", linewidth=3, + w_list, + gam_R_out, + "r", + linewidth=3, label=r"S_R(w) output (emission)", ) @@ -312,24 +314,22 @@ One note: for very large problems, this can be slow. ```{code-cell} ipython3 def steady_state_pade_for_theta(sys_p, bath_L, bath_R, theta, Nk, Nc, Nbos): - """ Return the steady state current using the Pade approximation. """ + """Return the steady state current using the Pade approximation.""" sys_p = sys_p.replace(Nbos=Nbos) bath_L = bath_L.replace(theta=theta) bath_R = bath_R.replace(theta=theta) - envR = LorentzianEnvironment( - bath_R.T, bath_R.mu, bath_R.gamma, bath_R.W - ) - envL = LorentzianEnvironment( - bath_L.T, bath_L.mu, bath_L.gamma, bath_L.W - ) - + envR = LorentzianEnvironment(bath_R.T, bath_R.mu, bath_R.gamma, bath_R.W) + envL = LorentzianEnvironment(bath_L.T, bath_L.mu, bath_L.gamma, bath_L.W) - bathL=envL.approx_by_matsubara(Nk,tag="L") - bathR=envR.approx_by_matsubara(Nk,tag="R") + bathL = envL.approx_by_matsubara(Nk, tag="L") + bathR = envR.approx_by_matsubara(Nk, tag="R") solver_pade = HEOMSolver( - sys_p.H, [(bathL,sys_p.Q), (bathR,sys_p.Q)], max_depth=2, options=options, + sys_p.H, + [(bathL, sys_p.Q), (bathR, sys_p.Q)], + max_depth=2, + options=options, ) rho_ss_pade, ado_ss_pade = solver_pade.steady_state() current = state_current(ado_ss_pade, bath_tag="R") @@ -340,6 +340,7 @@ def steady_state_pade_for_theta(sys_p, bath_L, bath_R, theta, Nk, Nc, Nbos): ```{code-cell} ipython3 # Parameters: + Nk = 6 Nc = 2 Nbos = 2 # Use Nbos = 16 for more accurate results @@ -354,10 +355,17 @@ display(progress) currents = [] for theta in thetas: - currents.append(steady_state_pade_for_theta( - sys_p, bath_L, bath_R, theta, - Nk=Nk, Nc=Nc, Nbos=Nbos, - )) + currents.append( + steady_state_pade_for_theta( + sys_p, + bath_L, + bath_R, + theta, + Nk=Nk, + Nc=Nc, + Nbos=Nbos, + ) + ) progress.value += 1 ``` @@ -365,16 +373,19 @@ for theta in thetas: fig, ax = plt.subplots(figsize=(12, 10)) ax.plot( - thetas, currents, - color="green", linestyle='-', linewidth=3, + thetas, + currents, + color="green", + linestyle="-", + linewidth=3, label=f"Nk = {5}, max_depth = {Nc}, Nbos = {Nbos}", ) ax.set_yticks([0, 0.5, 1]) ax.set_yticklabels([0, 0.5, 1]) -ax.locator_params(axis='y', nbins=4) -ax.locator_params(axis='x', nbins=4) +ax.locator_params(axis="y", nbins=4) +ax.locator_params(axis="x", nbins=4) ax.set_xlabel(r"Bias voltage $\Delta \mu$ ($V$)", fontsize=30) ax.set_ylabel(r"Current ($\mu A$)", fontsize=30) diff --git a/tutorials-v5/template.md b/tutorials-v5/template.md index b4f54353..1daf01b0 100644 --- a/tutorials-v5/template.md +++ b/tutorials-v5/template.md @@ -5,7 +5,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.13.8 + jupytext_version: 1.17.0 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -92,8 +92,7 @@ so it's not interfering with the user experience. Please, define the tests using `assert`, so that the cell execution fails if a wrong output is generated. ```python -assert np.allclose(result.expect[0][0], 0), \ - "Expectation value does not start at 1" +assert np.allclose(result.expect[0][0], 0), "Expectation value does not start at 1" assert 1 == 1 ``` From 3cbe188fb2c15ae497f98fedc21a07ea194d151b Mon Sep 17 00:00:00 2001 From: Gerardo Suarez Date: Wed, 23 Apr 2025 00:57:30 +0200 Subject: [PATCH 24/44] flake8 --- ...1b-spin-bath-model-very-strong-coupling.md | 8 +- .../heom-1d-spin-bath-model-ohmic-fitting.md | 101 ++++++++++-------- .../heom/heom-3-quantum-heat-transport.md | 2 +- 3 files changed, 59 insertions(+), 52 deletions(-) diff --git a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md index 7851105a..5fed1e11 100644 --- a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md +++ b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md @@ -261,7 +261,7 @@ ax1.plot( np.real(bath.correlation_function(tlist)), "r", linewidth=2, - label=f"Exact", + label="Exact", ) ax1.plot( tlist, @@ -385,7 +385,7 @@ ax1.plot( np.real(bath.correlation_function(tlist)), "r", linewidth=2, - label=f"Exact", + label="Exact", ) ax1.plot( tlist, @@ -413,14 +413,14 @@ ax2.plot( np.imag(bath.correlation_function(tlist)), "r", linewidth=2, - label=f"Exact", + label="Exact", ) ax2.plot( tlist, np.imag(envfit.correlation_function(tlist)), "g--", linewidth=2, - label=f"Fit", + label="Fit", marker="o", markevery=50, ) diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md index d935a9fb..b52303be 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md @@ -184,21 +184,43 @@ options = { } ``` -## Building the HEOM bath by fitting the spectral density +# Obtaining a decaying Exponential description of the environment -+++ +In order to carry out our HEOM simulation, we need to express the correlation +function as a sum of decaying exponentials, that is we need to express it as -We begin by fitting the spectral density, using a series of $k$ underdamped harmonic oscillators case with the Meier-Tannor form (J. Chem. Phys. 111, 3365 (1999); https://doi.org/10.1063/1.479669): +$$C(\tau)= \sum_{k=0}^{N-1}c_{k}e^{-\nu_{k}t}$$ -\begin{equation} -J_{\mathrm approx}(\omega; a, b, c) = \sum_{i=0}^{k-1} \frac{2 a_i b_i w}{((w + c_i)^2 + b_i^2) ((w - c_i)^2 + b_i^2)} -\end{equation} +As the correlation function of the environment is tied to it's power spectrum via +a Fourier transform, such a representation of the correlation function implies a +power spectrum of the form -where $a, b$ and $c$ are the fit parameters and each is a vector of length $k$. +$$S(\omega)= \sum_{k}2 Re\left( \frac{c_{k}}{\nu_{k}- i \omega}\right)$$ + +There are several ways one can obtain such a decomposition, in this tutorial we +will cover the following approaches: + +- Non-Linear Least Squares: + - On the Spectral Density (`sd`) + - On the Correlation function (`cf`) + - On the Power spectrum (`ps`) +- Methods based on the Prony Polynomial + - Prony on the correlation function(`prony`) + - ESPRIT on the correlation function(`esprit`) +- Methods based on rational Approximations + - The AAA algorithm on the Power Spectrum (`aaa`) + - ESPIRA-I on the correlation function and its FFT (`espira-I`) + - ESPIRA-II on the correlation function and its FFT (`espira-II`) +++ -With the spectral density approximation $J_{\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. This can be done quickly using the `SpectralFitter` class, which takes the target spectral density as an array and fits it to the series of **k** underdamped harmonic oscillators with the Meier-Tannor form +## Building User defined Bosonic Environments + +Before obtaining exponential approximations, we first need to construct a +`BosonicEnviroment`, here we will briefly explain how to create an user defined +`BosonicEnviroment` by specifying the spectral density, the same can be done +using the correlation function and the power spectrum. For this example we will +use the Ohmic Spectral density we defined above ```{code-cell} ipython3 w = np.linspace(0, 25, 20000) @@ -221,7 +243,7 @@ specified. So the `BosonicEnvironment` is not fully characterized by the parameters provided ```{code-cell} ipython3 -# sd_env.power_spectrum(w) +sd_env.power_spectrum(w) ``` If we want access to these properties we need to provide the Temperature at Initialization @@ -272,10 +294,10 @@ plt.legend() plt.show() ``` -One important optional parameter is WMax, when passing arrays to the constructor +One important optional parameter is wMax, when passing arrays to the constructor it defaults to the maximum value of the array, however when passing a function we don't need to specify the values on which it is evaluated, and in this case -WMax needs to be specified, Wmax is the cutoff frequency for which the +WMax needs to be specified, wMax is the cutoff frequency for which the spectral density, or power spectrum has effectively decayed to zero, after this value the function can be considered to be essentialy zero @@ -288,8 +310,8 @@ sd_env2 = BosonicEnvironment.from_spectral_density( ```{code-cell} ipython3 tlist = np.linspace(0, 10, 500) -plt.plot(tlist, sd_env2.correlation_function(tlist)) -plt.plot(tlist, ohmic_correlation(tlist, alpha, wc, 1 / T), "--") +plt.plot(tlist, sd_env2.correlation_function(tlist).real) +plt.plot(tlist, ohmic_correlation(tlist, alpha, wc, 1 / T).real, "--") plt.plot(tlist, np.imag(sd_env2.correlation_function(tlist))) plt.plot(tlist, np.imag(ohmic_correlation(tlist, alpha, wc, 1 / T)), "--") ``` @@ -298,36 +320,17 @@ In this example we considered how to obtain a `BosonicEnvironment` from the spec +++ -# Obtaining a decaying Exponential description of the environment - -In order to carry out our HEOM simulation, we need to express the correlation -function as a sum of decaying exponentials, that is we need to express it as +## Building the Exponential environment by fitting the spectral density -$$C(\tau)= \sum_{k=0}^{N-1}c_{k}e^{-\nu_{k}t}$$ - -As the correlation function of the environment is tied to it's power spectrum via -a Fourier transform, such a representation of the correlation function implies a -power spectrum of the form - -$$S(\omega)= \sum_{k}2 Re\left( \frac{c_{k}}{\nu_{k}- i \omega}\right)$$ - -There are several ways one can obtain such a decomposition, in this tutorial we -will cover the following approaches: +We begin by fitting the spectral density, using a series of $k$ underdamped harmonic oscillators case with the Meier-Tannor form (J. Chem. Phys. 111, 3365 (1999); https://doi.org/10.1063/1.479669): -- Non-Linear Least Squares: - - On the Spectral Density (`sd`) - - On the Correlation function (`cf`) -- Methods based on the Prony Polynomial - - Prony on the correlation function(`prony`) - - The Matrix Pencil method on the correlation function (`mp`) - - ESPRIT on the correlation function(`esprit`) -- Methods based on rational Approximations - - The AAA algorithm on the Power Spectrum (`aaa`) +\begin{equation} +J_{\mathrm approx}(\omega; a, b, c) = \sum_{i=0}^{k-1} \frac{2 a_i b_i w}{((w + c_i)^2 + b_i^2) ((w - c_i)^2 + b_i^2)} +\end{equation} -+++ +where $a, b$ and $c$ are the fit parameters and each is a vector of length $k$. -# Non-Linear Least Squares -## Obtaining an decaying Exponential Description via the spectral density +With the spectral density approximation $J_{\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. This can be done quickly using the `approximate` method, which fits the spectral density to the series of **k** underdamped harmonic oscillators with the Meier-Tannor form +++ @@ -448,7 +451,7 @@ w0 = fitinfo["params"][:, 2] def _sd_fit_model(wlist, a, b, c): - return 2 * a * b * wlist / ((wlist + c) ** 2 + b**2) / ((wlist - c) ** 2 + b**2) + return 2 * a * b * wlist / (((wlist + c) ** 2 + b**2) * ((wlist - c) ** 2 + b**2)) plot_fit(_sd_fit_model, J, w, lam, gamma, w0) @@ -905,12 +908,20 @@ The methods consider a signal $$f(t)=\sum_{k=0}^{N-1} c_{k} e^{-\nu_{k} t} =\sum_{k=0}^{N-1} c_{k} z_{k}^{t} $$ -The $z_{k}$ can be seen as the solution of the Prony Polynomial, which we write in terms of Hankel matrices as +The $z_{k}$ can be seen as the generalized eigenvalues of the matrix pencil \begin{align} - H_{N,M}=V_{N,M-1}(z) diag((c_k)_{k=1}^{M-1}) V_{M,M-1}(z)^{T} +z_{j} {\mathbf H}_{2N-L,L}(0) - {\mathbf H}_{2N-L,L}(1) = {\mathbf V}_{2N-L,M} +({\mathbf z}) \mathrm{diag} \Big( \left( (z_{j} - z_{k})\gamma_{k} +\right)_{k=1}^{M} \Big) {\mathbf V}_{L,M}({\mathbf z})^{T} \end{align} + + +The amplitudes ($c_{k}$) can later be obtained by solving the least-squares Vandermonde system given by + +$$ V_{N,M}(z)c = f $$ + where $V_{N,M}(z)$ is the Vandermonde matrix given by @@ -922,11 +933,7 @@ z_{1}^{2} & z_{2}^{2} &\dots & z_{N}^{2} \\ z_{1}^{M} & z_{2}^{M} &\dots & z_{N}^{M} \\ \end{pmatrix}$$ -By obtaining the roots of this polynomial one can obtain the damping rate and the frequency of each mode, the amplitude can lated be obtained by solving the least-squares Vandermonde system given by - -$$ V_{N,M}(z)c = f $$ - -Where $M$ is the length, of the signal, and $f=f(t_{sample})$ is the signal evaluated in the sampling points,is a vector $c = (c_{1}, \dots, c_{N})$. +and $M$ is the length of the signal, and $N$ the number of exponents, and $f=f(t_{sample})$ is the signal evaluated in the sampling points,is a vector $c = (c_{1}, \dots, c_{N})$. The main difference between the methods is the way one obtains the roots of the polynomial, typically whether this system is solved or a low rank approximation is found for the polynomial, [this article](https://academic.oup.com/imajna/article-abstract/43/2/789/6525860?redirectedFrom=fulltext) is a good reference, the QuTiP implementations are based on it, and the matlab implementations made available by the authors. diff --git a/tutorials-v5/heom/heom-3-quantum-heat-transport.md b/tutorials-v5/heom/heom-3-quantum-heat-transport.md index ab6a6937..85d11eae 100644 --- a/tutorials-v5/heom/heom-3-quantum-heat-transport.md +++ b/tutorials-v5/heom/heom-3-quantum-heat-transport.md @@ -60,7 +60,7 @@ from IPython.display import display from ipywidgets import IntProgress from qutip.core.environment import (CFExponent, DrudeLorentzEnvironment, system_terminator) -from qutip.solver.heom import DrudeLorentzPadeBath, HEOMSolver +from qutip.solver.heom import HEOMSolver %matplotlib inline ``` From f323319c6ff3cfb03550ac79ecabdfb81231daca Mon Sep 17 00:00:00 2001 From: Gerardo Suarez Date: Wed, 23 Apr 2025 01:02:09 +0200 Subject: [PATCH 25/44] floake8 --- .../heom/heom-1b-spin-bath-model-very-strong-coupling.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md index 5fed1e11..f9262b37 100644 --- a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md +++ b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md @@ -392,7 +392,7 @@ ax1.plot( np.real(envfit.correlation_function(tlist)), "g--", linewidth=2, - label=f"Fit", + label="Fit", marker="o", markevery=50, ) From 085c5e95405afe6faa3e85f3cef0935cb3e82930 Mon Sep 17 00:00:00 2001 From: Gerardo Suarez Date: Wed, 23 Apr 2025 01:11:07 +0200 Subject: [PATCH 26/44] hook --- .../heom/heom-1a-spin-bath-model-basic.md | 67 +++++++++++++++---- ...1b-spin-bath-model-very-strong-coupling.md | 14 +++- .../heom-1c-spin-bath-model-underdamped-sd.md | 9 ++- .../heom-1d-spin-bath-model-ohmic-fitting.md | 58 +++++++++++++--- .../heom-1e-spin-bath-model-pure-dephasing.md | 13 +++- tutorials-v5/heom/heom-2-fmo-example.md | 7 +- .../heom/heom-3-quantum-heat-transport.md | 25 +++++-- .../heom/heom-4-dynamical-decoupling.md | 4 +- .../heom-5a-fermions-single-impurity-model.md | 10 ++- 9 files changed, 165 insertions(+), 42 deletions(-) diff --git a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md index 26f9858a..f64fcbff 100644 --- a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md +++ b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md @@ -112,7 +112,16 @@ def dl_matsubara_params(lam, gamma, T, nk): """ ckAR = [lam * gamma * cot(gamma / (2 * T))] ckAR.extend( - (8 * lam * gamma * T * np.pi * k * T / ((2 * np.pi * k * T) ** 2 - gamma**2)) + ( + 8 + * lam + * gamma + * T + * np.pi + * k + * T + / ((2 * np.pi * k * T) ** 2 - gamma**2) + ) for k in range(1, nk + 1) ) vkAR = [gamma] @@ -410,10 +419,18 @@ def plot_correlation_expansion_divergence(): fig, ax1 = plt.subplots(figsize=(12, 7)) - ax1.plot(t, np.real(corr_2), color="b", linewidth=3, label=rf"Mats = {Nk} real") - ax1.plot(t, np.imag(corr_2), color="r", linewidth=3, label=rf"Mats = {Nk} imag") - ax1.plot(t, np.real(corr_100), "b--", linewidth=3, label=r"Mats = 15000 real") - ax1.plot(t, np.imag(corr_100), "r--", linewidth=3, label=r"Mats = 15000 imag") + ax1.plot( + t, np.real(corr_2), color="b", linewidth=3, label=rf"Mats = {Nk} real" + ) + ax1.plot( + t, np.imag(corr_2), color="r", linewidth=3, label=rf"Mats = {Nk} imag" + ) + ax1.plot( + t, np.real(corr_100), "b--", linewidth=3, label=r"Mats = 15000 real" + ) + ax1.plot( + t, np.imag(corr_100), "r--", linewidth=3, label=r"Mats = 15000 imag" + ) ax1.set_xlabel("t") ax1.set_ylabel(r"$C$") @@ -500,7 +517,9 @@ We can compare the solution obtained from the QuTiP Bloch-Redfield solver: options = {**default_options} with timer("ODE solver time"): - resultBR = brmesolve(Hsys, rho0, tlist, a_ops=[[sigmaz(), dlenv]], options=options) + resultBR = brmesolve( + Hsys, rho0, tlist, a_ops=[[sigmaz(), dlenv]], options=options + ) ``` ```python @@ -609,7 +628,12 @@ def pade_corr(tlist, lmax): c_tot = [] for t in tlist: c_tot.append( - sum([eta_list[ll] * np.exp(-gamma_list[ll] * t) for ll in range(lmax + 1)]) + sum( + [ + eta_list[ll] * np.exp(-gamma_list[ll] * t) + for ll in range(lmax + 1) + ] + ) ) return c_tot, eta_list, gamma_list @@ -777,9 +801,13 @@ def fitter(ans, tlist, k): # sets initial guesses: guess = [upper_a / k] * k + [0] * k # guesses for a # guesses for b # sets lower bounds: - b_lower = [-upper_a] * k + [-np.inf] * k # lower bounds for a # lower bounds for b + b_lower = [-upper_a] * k + [ + -np.inf + ] * k # lower bounds for a # lower bounds for b # sets higher bounds: - b_higher = [upper_a] * k + [0] * k # upper bounds for a # upper bounds for b + b_higher = [upper_a] * k + [ + 0 + ] * k # upper bounds for a # upper bounds for b param_bounds = (b_lower, b_higher) p1, p2 = curve_fit( lambda x, *params_0: wrapper_fit_func(x, k, params_0), @@ -815,7 +843,16 @@ And plot the results of the fits: ```python # Define line styles and colors linestyles = ["-", "--", "-.", ":", (0, (3, 1, 1, 1)), (0, (5, 1))] -colors = ["blue", "green", "purple", "orange", "red", "brown", "cyan", "magenta"] +colors = [ + "blue", + "green", + "purple", + "orange", + "red", + "brown", + "cyan", + "magenta", +] # Define a larger linewidth linewidth = 2.5 @@ -975,7 +1012,10 @@ fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6)) ax1.plot(tlist2, corrRana, label="Original", marker="o", markevery=500) ax1.plot(tlist2, fit_func(tlist2, *poptR[-1]), color="r", label="Manual Fit") ax1.plot( - tlist2, np.real(envfit.correlation_function(tlist2)), "k--", label="Built-in fit" + tlist2, + np.real(envfit.correlation_function(tlist2)), + "k--", + label="Built-in fit", ) ax1.set_ylabel(r"$C_{R}(t)$") ax1.set_xlabel(r"$t$") @@ -985,7 +1025,10 @@ ax1.legend() ax2.plot(tlist2, corrIana, label="Original", marker="o", markevery=500) ax2.plot(tlist2, fit_func(tlist2, *poptI[-1]), color="r", label="Manual Fit") ax2.plot( - tlist2, np.imag(envfit.correlation_function(tlist2)), "k--", label="Built-in fit" + tlist2, + np.imag(envfit.correlation_function(tlist2)), + "k--", + label="Built-in fit", ) ax2.set_ylabel(r"$C_{I}(t)$") ax2.set_xlabel(r"$t$") diff --git a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md index f9262b37..f97ece41 100644 --- a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md +++ b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md @@ -212,7 +212,9 @@ with timer("ODE solver time"): ```{code-cell} ipython3 with timer("RHS construction time"): - matsBath, delta = bath.approximate(method="matsubara", Nk=Nk, compute_delta=True) + matsBath, delta = bath.approximate( + method="matsubara", Nk=Nk, compute_delta=True + ) terminator = system_terminator(Q, delta) Ltot = liouvillian(Hsys) + terminator HEOMMatsT = HEOMSolver(Ltot, (matsBath, Q), NC, options=options) @@ -285,14 +287,20 @@ ax1.legend(loc=0, fontsize=12) tlist2 = tlist[0:50] ax2.plot( tlist2, - np.abs(matsBath.correlation_function(tlist2) - bath.correlation_function(tlist2)), + np.abs( + matsBath.correlation_function(tlist2) + - bath.correlation_function(tlist2) + ), "g", linewidth=2, label="Mats Error", ) ax2.plot( tlist2, - np.abs(padeBath.correlation_function(tlist2) - bath.correlation_function(tlist2)), + np.abs( + padeBath.correlation_function(tlist2) + - bath.correlation_function(tlist2) + ), "b--", linewidth=2, label="Pade Error", diff --git a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md index 870f203b..96ad956c 100644 --- a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md +++ b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md @@ -290,7 +290,10 @@ def Mk(t, k, gamma, w0, beta): (-2 * lam**2 * gamma / beta) * ek * np.exp(-ek * np.abs(t)) - / (((Om + 1.0j * Gamma) ** 2 + ek**2) * ((Om - 1.0j * Gamma) ** 2 + ek**2)) + / ( + ((Om + 1.0j * Gamma) ** 2 + ek**2) + * ((Om - 1.0j * Gamma) ** 2 + ek**2) + ) ) @@ -305,7 +308,9 @@ def c(t, Nk, lam, gamma, w0, beta): Ci = np.exp(-1.0j * Om * t) - np.exp(1.0j * Om * t) - return (lam**2 / (4 * Om)) * np.exp(-Gamma * np.abs(t)) * (Cr + Ci) + np.sum( + return (lam**2 / (4 * Om)) * np.exp(-Gamma * np.abs(t)) * ( + Cr + Ci + ) + np.sum( [Mk(t, k, gamma=gamma, w0=w0, beta=beta) for k in range(1, Nk + 1)], 0 ) diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md index b52303be..a9413d0e 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md @@ -258,7 +258,9 @@ density provided ```{code-cell} ipython3 # Here we avoid w=0 -np.allclose(sd_env.power_spectrum(w[1:]), ohmic_power_spectrum(w[1:], alpha, wc, 1 / T)) +np.allclose( + sd_env.power_spectrum(w[1:]), ohmic_power_spectrum(w[1:], alpha, wc, 1 / T) +) ``` Specifying the Temperature also gives the `BosonicEnvironment` access to the @@ -451,7 +453,13 @@ w0 = fitinfo["params"][:, 2] def _sd_fit_model(wlist, a, b, c): - return 2 * a * b * wlist / (((wlist + c) ** 2 + b**2) * ((wlist - c) ** 2 + b**2)) + return ( + 2 + * a + * b + * wlist + / (((wlist + c) ** 2 + b**2) * ((wlist - c) ** 2 + b**2)) + ) plot_fit(_sd_fit_model, J, w, lam, gamma, w0) @@ -497,12 +505,16 @@ def generate_spectrum_results(Q, N, Nk, max_depth): # guess = [J_max, wc, wc] # upper = [100*J_max, 100*wc, 100*wc] # ,lower=lower,upper=upper,guess=guess,sigma=sigma) - bath, fitinfo = sd_env.approximate("sd", w, Nmax=N, Nk=Nk, target_rmse=None) + bath, fitinfo = sd_env.approximate( + "sd", w, Nmax=N, Nk=Nk, target_rmse=None + ) tlist = np.linspace(0, 30 * np.pi / Del, 600) # This problem is a little stiff, so we use the BDF method to solve # the ODE ^^^ - print(f"Starting calculations for N={N}, Nk={Nk} and max_depth={max_depth} ... ") + print( + f"Starting calculations for N={N}, Nk={Nk} and max_depth={max_depth} ... " + ) HEOM_spectral_fit = HEOMSolver( Hsys, @@ -560,7 +572,8 @@ Below we generate results for different convergence parameters (number of terms results_spectral_fit_pk = [ - generate_spectrum_results(Q, n, Nk=1, max_depth=max_depth) for n in range(1, 5) + generate_spectrum_results(Q, n, Nk=1, max_depth=max_depth) + for n in range(1, 5) ] plot_result_expectations( @@ -582,7 +595,8 @@ plot_result_expectations( Nk_list = range(2, 4) results_spectral_fit_nk = [ - generate_spectrum_results(Q, 4, Nk=Nk, max_depth=max_depth) for Nk in Nk_list + generate_spectrum_results(Q, 4, Nk=Nk, max_depth=max_depth) + for Nk in Nk_list ] plot_result_expectations( @@ -848,8 +862,18 @@ plot_result_expectations( "k", "Correlation Function Fit $k_R=k_I=3$", ), - (results_spectral_fit_pk[0], P11p, "b", "Spectral Density Fit $k_J=1$"), - (results_spectral_fit_pk[3], P11p, "r-.", "Spectral Density Fit $k_J=4$"), + ( + results_spectral_fit_pk[0], + P11p, + "b", + "Spectral Density Fit $k_J=1$", + ), + ( + results_spectral_fit_pk[3], + P11p, + "r-.", + "Spectral Density Fit $k_J=4$", + ), ], axes=axes, ) @@ -1091,7 +1115,9 @@ ESPIRA-II ```{code-cell} ipython3 tlist4 = np.linspace(0, 20, 1000) -espibath2, fitinfo = obs.approximate("espira-II", tlist4, Nr=4, Ni=4, separate=True) +espibath2, fitinfo = obs.approximate( + "espira-II", tlist4, Nr=4, Ni=4, separate=True +) print(fitinfo["summary"]) HEOM_ohmic_espira_fit2 = HEOMSolver( Hsys, @@ -1115,9 +1141,19 @@ plot_result_expectations( "b", "Correlation Function Fit $k_R=k_I=4$", ), - (results_spectral_fit_pk[3], P11p, "r-.", "Spectral Density Fit $k_J=4$"), + ( + results_spectral_fit_pk[3], + P11p, + "r-.", + "Spectral Density Fit $k_J=4$", + ), (results_ohmic_corr_fit, P11p, "r", "Correlation Fit Ohmic Bath"), - (results_ohmic_sd_fit2, P11p, "g--", "Spectral Density Fit Ohmic Bath"), + ( + results_ohmic_sd_fit2, + P11p, + "g--", + "Spectral Density Fit Ohmic Bath", + ), (results_ohmic_ps_fit, P11p, "g--", "Power Spectrum Fit Ohmic Bath"), (results_ohmic_prony_fit, P11p, "k", " Prony Fit"), (results_ohmic_es_fit, P11p, "b-.", "ESPRIT Fit"), diff --git a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md index 042a7784..8baa1ad5 100644 --- a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md +++ b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md @@ -241,7 +241,9 @@ plot_result_expectations( ```{code-cell} ipython3 with timer("RHS construction time"): - env_mats, delta = env.approximate(method="matsubara", Nk=Nk, compute_delta=True) + env_mats, delta = env.approximate( + method="matsubara", Nk=Nk, compute_delta=True + ) Ltot = liouvillian(Hsys) + system_terminator(Q, delta) HEOMMatsT = HEOMSolver(Ltot, (env_mats, Q), NC, options=options) @@ -377,7 +379,9 @@ vk = [complex(-gamma)] vk.extend([complex(-2.0 * np.pi * k * T) for k in range(1, lmaxmats2)]) ck = [complex(lam * gamma * (-1.0j + cot(gamma * beta / 2.0)))] -ck.extend([complex(4 * lam * gamma * T * (-v) / (v**2 - gamma**2)) for v in vk[1:]]) +ck.extend( + [complex(4 * lam * gamma * T * (-v) / (v**2 - gamma**2)) for v in vk[1:]] +) P12_ana = 0.5 * pure_dephasing_evolution_analytical( tlist, 0, np.asarray(ck), np.asarray(vk) @@ -401,7 +405,10 @@ def integrand(omega, lamc, omega_c, Temp, t): P12_ana2 = [ - 0.5 * np.exp(scipy.integrate.quad(integrand, 0, np.inf, args=(lam, gamma, T, t))[0]) + 0.5 + * np.exp( + scipy.integrate.quad(integrand, 0, np.inf, args=(lam, gamma, T, t))[0] + ) for t in tlist ] ``` diff --git a/tutorials-v5/heom/heom-2-fmo-example.md b/tutorials-v5/heom/heom-2-fmo-example.md index a39f6660..b1dd8ebf 100644 --- a/tutorials-v5/heom/heom-2-fmo-example.md +++ b/tutorials-v5/heom/heom-2-fmo-example.md @@ -181,7 +181,9 @@ Nk = 0 Q_list = [] baths = [] Ltot = liouvillian(Hsys) -env_approx, delta = env.approximate(method="matsubara", Nk=Nk, compute_delta=True) +env_approx, delta = env.approximate( + method="matsubara", Nk=Nk, compute_delta=True +) for m in range(7): Q = basis(7, m) * basis(7, m).dag() Q_list.append(Q) @@ -330,7 +332,8 @@ def get_collapse(H, T, dephasing=1): if dephasing: for j in range(Nmax): rate = ( - np.abs(Q.matrix_element(all_state[j].dag(), all_state[j])) ** 2 + np.abs(Q.matrix_element(all_state[j].dag(), all_state[j])) + ** 2 * env.power_spectrum(0) / 2 ) diff --git a/tutorials-v5/heom/heom-3-quantum-heat-transport.md b/tutorials-v5/heom/heom-3-quantum-heat-transport.md index 85d11eae..0b40b542 100644 --- a/tutorials-v5/heom/heom-3-quantum-heat-transport.md +++ b/tutorials-v5/heom/heom-3-quantum-heat-transport.md @@ -98,13 +98,18 @@ class SystemParams: and an interaction term (H12). """ H1 = ( - self.epsilon / 2 * (qt.tensor(qt.sigmaz() + qt.identity(2), qt.identity(2))) + self.epsilon + / 2 + * (qt.tensor(qt.sigmaz() + qt.identity(2), qt.identity(2))) ) H2 = ( - self.epsilon / 2 * (qt.tensor(qt.identity(2), qt.sigmaz() + qt.identity(2))) + self.epsilon + / 2 + * (qt.tensor(qt.identity(2), qt.sigmaz() + qt.identity(2))) ) H12 = self.J12 * ( - qt.tensor(qt.sigmap(), qt.sigmam()) + qt.tensor(qt.sigmam(), qt.sigmap()) + qt.tensor(qt.sigmap(), qt.sigmam()) + + qt.tensor(qt.sigmam(), qt.sigmap()) ) return H1 + H2 + H12 @@ -140,9 +145,17 @@ class BathParams: return qt.tensor(Q) def bath(self, Nk, tag=None): - env = DrudeLorentzEnvironment(lam=self.lam, gamma=self.gamma, T=self.T, tag=tag) - env_approx, delta = env.approximate("pade", Nk=Nk, compute_delta=True, tag=tag) - return (env_approx, self.Q()), system_terminator(self.Q(), delta), delta + env = DrudeLorentzEnvironment( + lam=self.lam, gamma=self.gamma, T=self.T, tag=tag + ) + env_approx, delta = env.approximate( + "pade", Nk=Nk, compute_delta=True, tag=tag + ) + return ( + (env_approx, self.Q()), + system_terminator(self.Q(), delta), + delta, + ) def replace(self, **kw): return dataclasses.replace(self, **kw) diff --git a/tutorials-v5/heom/heom-4-dynamical-decoupling.md b/tutorials-v5/heom/heom-4-dynamical-decoupling.md index 872ba642..474392bf 100644 --- a/tutorials-v5/heom/heom-4-dynamical-decoupling.md +++ b/tutorials-v5/heom/heom-4-dynamical-decoupling.md @@ -336,7 +336,9 @@ def cummulative_delay_fractions(N): as the cummulative delay after the j'th pulse. """ - return np.array([np.sin((np.pi / 2) * (j / (N + 1))) ** 2 for j in range(0, N + 1)]) + return np.array( + [np.sin((np.pi / 2) * (j / (N + 1))) ** 2 for j in range(0, N + 1)] + ) def drive_opt(amplitude, avg_delay, integral, N): diff --git a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md index 7c3fa208..1554f160 100644 --- a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md +++ b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md @@ -303,7 +303,10 @@ envR_pade = envR.approx_by_pade(Nk=Nk, tag="R") with timer("RHS construction time"): solver_pade = HEOMSolver( - H, [(envL_pade, bath_L.Q), (envR_pade, bath_R.Q)], max_depth=2, options=options + H, + [(envL_pade, bath_L.Q), (envR_pade, bath_R.Q)], + max_depth=2, + options=options, ) with timer("ODE solver time"): @@ -349,7 +352,10 @@ envR_mats = envR.approx_by_matsubara(Nk=Nk, tag="R") with timer("RHS construction time"): solver_mats = HEOMSolver( - H, [(envL_mats, bath_L.Q), (envR_mats, bath_R.Q)], max_depth=2, options=options + H, + [(envL_mats, bath_L.Q), (envR_mats, bath_R.Q)], + max_depth=2, + options=options, ) with timer("ODE solver time"): From ce0e96433c4ee001f4ea70944a5b16f804ade15c Mon Sep 17 00:00:00 2001 From: Gerardo Suarez Date: Wed, 23 Apr 2025 01:16:06 +0200 Subject: [PATCH 27/44] hook --- .../heom/heom-1a-spin-bath-model-basic.md | 2 +- ...1b-spin-bath-model-very-strong-coupling.md | 52 +++++----- .../heom-1c-spin-bath-model-underdamped-sd.md | 56 +++++------ .../heom-1d-spin-bath-model-ohmic-fitting.md | 98 +++++++++---------- .../heom-1e-spin-bath-model-pure-dephasing.md | 50 +++++----- tutorials-v5/heom/heom-2-fmo-example.md | 44 ++++----- .../heom/heom-3-quantum-heat-transport.md | 32 +++--- .../heom/heom-4-dynamical-decoupling.md | 40 ++++---- .../heom-5a-fermions-single-impurity-model.md | 42 ++++---- .../heom-5b-fermions-discrete-boson-model.md | 26 ++--- tutorials-v5/heom/heom-index.md | 2 +- 11 files changed, 222 insertions(+), 222 deletions(-) diff --git a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md index f64fcbff..7cf03ceb 100644 --- a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md +++ b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md @@ -5,7 +5,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.17.0 + jupytext_version: 1.14.4 kernelspec: display_name: qutip-tutorials language: python diff --git a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md index f97ece41..fc60d1a3 100644 --- a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md +++ b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.17.0 + jupytext_version: 1.14.4 kernelspec: display_name: qutip-tutorials language: python @@ -81,7 +81,7 @@ Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. ## Setup -```{code-cell} ipython3 +```{code-cell} import contextlib import time @@ -99,13 +99,13 @@ from qutip.solver.heom import HEOMSolver Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: -```{code-cell} ipython3 +```{code-cell} def cot(x): """Vectorized cotangent of x.""" return 1.0 / np.tan(x) ``` -```{code-cell} ipython3 +```{code-cell} @contextlib.contextmanager def timer(label): """Simple utility for timing functions: @@ -119,7 +119,7 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} ipython3 +```{code-cell} # Solver options: @@ -137,19 +137,19 @@ options = { And let us set up the system Hamiltonian, bath and system measurement operators: -```{code-cell} ipython3 +```{code-cell} # Defining the system Hamiltonian eps = 0.0 # Energy of the 2-level system. Del = 0.2 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` -```{code-cell} ipython3 +```{code-cell} # Initial state of the system. rho0 = basis(2, 0) * basis(2, 0).dag() ``` -```{code-cell} ipython3 +```{code-cell} # System-bath coupling (Drude-Lorentz spectral density) Q = sigmaz() # coupling operator @@ -172,7 +172,7 @@ NC = 13 tlist = np.linspace(0, np.pi / Del, 600) ``` -```{code-cell} ipython3 +```{code-cell} # Define some operators with which we will measure the system # 1,1 element of density matrix - corresonding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -185,7 +185,7 @@ P12p = basis(2, 0) * basis(2, 1).dag() Let us briefly inspect the spectral density. -```{code-cell} ipython3 +```{code-cell} bath = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=500) w = np.linspace(0, 5, 1000) J = bath.spectral_density(w) @@ -199,7 +199,7 @@ axes.set_ylabel(r"J", fontsize=28); ## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator -```{code-cell} ipython3 +```{code-cell} with timer("RHS construction time"): matsBath = bath.approximate(method="matsubara", Nk=Nk) HEOMMats = HEOMSolver(Hsys, (matsBath, Q), NC, options=options) @@ -210,7 +210,7 @@ with timer("ODE solver time"): ## Simulation 2: Matsubara decomposition (including terminator) -```{code-cell} ipython3 +```{code-cell} with timer("RHS construction time"): matsBath, delta = bath.approximate( method="matsubara", Nk=Nk, compute_delta=True @@ -223,7 +223,7 @@ with timer("ODE solver time"): resultMatsT = HEOMMatsT.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} # Plot the results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -251,7 +251,7 @@ axes.legend(loc=0, fontsize=12); ## Simulation 3: Pade decomposition -```{code-cell} ipython3 +```{code-cell} # First, compare Matsubara and Pade decompositions padeBath = bath.approximate("pade", Nk=Nk) @@ -310,7 +310,7 @@ ax2.set_xlabel(r"t", fontsize=28) ax2.legend(loc=0, fontsize=12); ``` -```{code-cell} ipython3 +```{code-cell} with timer("RHS construction time"): HEOMPade = HEOMSolver(Hsys, (padeBath, Q), NC, options=options) @@ -318,7 +318,7 @@ with timer("ODE solver time"): resultPade = HEOMPade.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} # Plot the results fig, axes = plt.subplots(figsize=(8, 8)) @@ -356,7 +356,7 @@ In `HEOM 1a: Spin-Bath model (introduction)` a fit is performed manually, here we will use the built-in tools. More details about them can be seen in `HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions` -```{code-cell} ipython3 +```{code-cell} tfit = np.linspace(0, 10, 10000) lower = [0, -np.inf, -1e-6, -3] guess = [np.real(bath.correlation_function(0)) / 10, -10, 0, 0] @@ -379,13 +379,13 @@ envfit, fitinfo = bath.approximate( ) ``` -```{code-cell} ipython3 +```{code-cell} print(fitinfo["summary"]) ``` We can quickly compare the result of the Fit with the Pade expansion -```{code-cell} ipython3 +```{code-cell} fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8)) ax1.plot( @@ -445,7 +445,7 @@ ax2.set_ylabel(r"$C_I(t)$", fontsize=28) ax2.legend(loc=0, fontsize=12) ``` -```{code-cell} ipython3 +```{code-cell} with timer("RHS construction time"): # We reduce NC slightly here for speed of execution because we retain # 3 exponents in ckAR instead of 1. Please restore full NC for @@ -458,7 +458,7 @@ with timer("ODE solver time"): ## Simulation 5: Bloch-Redfield -```{code-cell} ipython3 +```{code-cell} with timer("ODE solver time"): resultBR = brmesolve( Hsys, @@ -474,7 +474,7 @@ with timer("ODE solver time"): Finally, let's plot all of our different results to see how they shape up against each other. -```{code-cell} ipython3 +```{code-cell} # Calculate expectation values in the bases: P11_mats = np.real(expect(resultMats.states, P11p)) P11_matsT = np.real(expect(resultMatsT.states, P11p)) @@ -483,7 +483,7 @@ P11_fit = np.real(expect(resultFit.states, P11p)) P11_br = np.real(expect(resultBR.states, P11p)) ``` -```{code-cell} ipython3 +```{code-cell} rcParams = { "axes.titlesize": 25, "axes.labelsize": 30, @@ -500,7 +500,7 @@ rcParams = { } ``` -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) with plt.rc_context(rcParams): @@ -554,7 +554,7 @@ with plt.rc_context(rcParams): ## About -```{code-cell} ipython3 +```{code-cell} qutip.about() ``` @@ -562,7 +562,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 +```{code-cell} assert np.allclose(P11_matsT, P11_pade, rtol=1e-3) assert np.allclose(P11_matsT, P11_fit, rtol=1e-3) ``` diff --git a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md index 96ad956c..416e1cba 100644 --- a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md +++ b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.17.0 + jupytext_version: 1.14.4 kernelspec: display_name: qutip-tutorials language: python @@ -76,7 +76,7 @@ Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. ## Setup -```{code-cell} ipython3 +```{code-cell} import contextlib import time @@ -96,19 +96,19 @@ from qutip.solver.heom import HEOMSolver Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: -```{code-cell} ipython3 +```{code-cell} def cot(x): """Vectorized cotangent of x.""" return 1.0 / np.tan(x) ``` -```{code-cell} ipython3 +```{code-cell} def coth(x): """Vectorized hyperbolic cotangent of x.""" return 1.0 / np.tanh(x) ``` -```{code-cell} ipython3 +```{code-cell} def underdamped_matsubara_params(lam, gamma, T, nk): """Calculation of the real and imaginary expansions of the underdamped correlation functions. @@ -151,7 +151,7 @@ def underdamped_matsubara_params(lam, gamma, T, nk): return ckAR, vkAR, ckAI, vkAI ``` -```{code-cell} ipython3 +```{code-cell} def plot_result_expectations(plots, axes=None): """Plot the expectation values of operators as functions of time. @@ -179,7 +179,7 @@ def plot_result_expectations(plots, axes=None): return fig ``` -```{code-cell} ipython3 +```{code-cell} @contextlib.contextmanager def timer(label): """Simple utility for timing functions: @@ -193,7 +193,7 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} ipython3 +```{code-cell} # Solver options: @@ -211,19 +211,19 @@ options = { And let us set up the system Hamiltonian, bath and system measurement operators: -```{code-cell} ipython3 +```{code-cell} # Defining the system Hamiltonian eps = 0.5 # Energy of the 2-level system. Del = 1.0 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` -```{code-cell} ipython3 +```{code-cell} # Initial state of the system. rho0 = basis(2, 0) * basis(2, 0).dag() ``` -```{code-cell} ipython3 +```{code-cell} # System-bath coupling (underdamed spectral density) Q = sigmaz() # coupling operator @@ -247,7 +247,7 @@ NC = 10 tlist = np.linspace(0, 50, 1000) ``` -```{code-cell} ipython3 +```{code-cell} # Define some operators with which we will measure the system # 1,1 element of density matrix - corresonding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -258,7 +258,7 @@ P12p = basis(2, 0) * basis(2, 1).dag() ### First let us look at what the underdamped spectral density looks like: -```{code-cell} ipython3 +```{code-cell} def plot_spectral_density(): """Plot the underdamped spectral density""" w = np.linspace(0, 5, 1000) @@ -279,7 +279,7 @@ The correlation functions are now very oscillatory, because of the Lorentzian pe ### So next, let us plot the correlation functions themselves: -```{code-cell} ipython3 +```{code-cell} def Mk(t, k, gamma, w0, beta): """Calculate the Matsubara terms for a given t and k.""" Om = np.sqrt(w0**2 - (gamma / 2) ** 2) @@ -333,7 +333,7 @@ plot_correlation_function() It is useful to look at what the Matsubara contributions do to this spectral density. We see that they modify the real part around $t=0$: -```{code-cell} ipython3 +```{code-cell} def plot_matsubara_correlation_function_contributions(): """Plot the underdamped correlation function.""" t = np.linspace(0, 20, 1000) @@ -365,7 +365,7 @@ Next we calculate the exponents using the Matsubara decompositions. Here we spli The HEOM code will optimize these, and reduce the number of exponents when real and imaginary parts have the same exponent. This is clearly the case for the first term in the vkAI and vkAR lists. -```{code-cell} ipython3 +```{code-cell} ckAR, vkAR, ckAI, vkAI = underdamped_matsubara_params( lam=lam, gamma=gamma, @@ -380,7 +380,7 @@ The solver constructs the "right hand side" (RHS) determinining how the system a Below we create the bath and solver and then solve for the dynamics by calling `.run(rho0, tlist)`. -```{code-cell} ipython3 +```{code-cell} with timer("RHS construction time"): bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI) HEOMMats = HEOMSolver(Hsys, (bath, Q), NC, options=options) @@ -389,7 +389,7 @@ with timer("ODE solver time"): resultMats = HEOMMats.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} plot_result_expectations( [ (resultMats, P11p, "b", "P11 Mats"), @@ -404,7 +404,7 @@ to perform this expansion is an useful skill. Below we show how to use this built-in functionality: -```{code-cell} ipython3 +```{code-cell} # Compare to built-in under-damped bath: with timer("RHS construction time"): @@ -416,7 +416,7 @@ with timer("ODE solver time"): result_udbath = HEOM_udbath.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} plot_result_expectations( [ (result_udbath, P11p, "b", "P11 (UnderDampedEnvironment)"), @@ -429,7 +429,7 @@ plot_result_expectations( The `UnderDampedEnvironment` class also allows us to easily evaluate analytical expressions for the power spectrum, correlation function, and spectral density. In the following plots, the solid lines are the exact expressions, and the dashed lines are based on our approximation of the correlation function with a finite number of exponents. In this case, there is an excellent agreement. -```{code-cell} ipython3 +```{code-cell} w = np.linspace(-3, 3, 1000) w2 = np.linspace(0, 3, 1000) t = np.linspace(0, 10, 1000) @@ -460,7 +460,7 @@ plt.show() ### We can compare these results to those of the Bloch-Redfield solver in QuTiP: -```{code-cell} ipython3 +```{code-cell} with timer("ODE solver time"): resultBR = brmesolve( Hsys, @@ -471,7 +471,7 @@ with timer("ODE solver time"): ) ``` -```{code-cell} ipython3 +```{code-cell} plot_result_expectations( [ (resultMats, P11p, "b", "P11 Mats"), @@ -488,7 +488,7 @@ plot_result_expectations( The thermal state of a reaction coordinate (treating the environment as a single damped mode) should, at high temperatures and small gamma, tell us the steady-state: -```{code-cell} ipython3 +```{code-cell} dot_energy, dot_state = Hsys.eigenstates() deltaE = dot_energy[1] - dot_energy[0] @@ -521,7 +521,7 @@ P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag()) P11RC = expect(rhoss, P11RC) ``` -```{code-cell} ipython3 +```{code-cell} rcParams = { "axes.titlesize": 25, "axes.labelsize": 30, @@ -538,7 +538,7 @@ rcParams = { } ``` -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) with plt.rc_context(rcParams): @@ -573,7 +573,7 @@ with plt.rc_context(rcParams): ## About -```{code-cell} ipython3 +```{code-cell} qutip.about() ``` @@ -581,7 +581,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 +```{code-cell} assert np.allclose( expect(P11p, resultMats.states[-100:]), P11RC, diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md index a9413d0e..359857d1 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.17.0 + jupytext_version: 1.14.4 kernelspec: display_name: qutip-tutorials language: python @@ -39,7 +39,7 @@ In each case we will use the fit parameters to determine the correlation functio ## Setup -```{code-cell} ipython3 +```{code-cell} import numpy as np import qutip from matplotlib import pyplot as plt @@ -66,7 +66,7 @@ Let us set up the system Hamiltonian, bath and system measurement operators: ### System Hamiltonian -```{code-cell} ipython3 +```{code-cell} # Defining the system Hamiltonian eps = 0 # Energy of the 2-level system. Del = 0.2 # Tunnelling term @@ -76,7 +76,7 @@ rho0 = basis(2, 0) * basis(2, 0).dag() ### System measurement operators -```{code-cell} ipython3 +```{code-cell} # Define some operators with which we will measure the system # 1,1 element of density matrix - corresonding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -112,7 +112,7 @@ The corresponding spectral density for the Ohmic case is: J(\omega) = \omega \alpha e^{- \frac{\omega}{\omega_c}} \end{equation} -```{code-cell} ipython3 +```{code-cell} def ohmic_correlation(t, alpha, wc, beta, s=1): """The Ohmic bath correlation function as a function of t (and the bath parameters). @@ -132,7 +132,7 @@ def ohmic_correlation(t, alpha, wc, beta, s=1): ) ``` -```{code-cell} ipython3 +```{code-cell} def ohmic_spectral_density(w, alpha, wc): """The Ohmic bath spectral density as a function of w (and the bath parameters). @@ -140,7 +140,7 @@ def ohmic_spectral_density(w, alpha, wc): return w * alpha * np.e ** (-w / wc) ``` -```{code-cell} ipython3 +```{code-cell} def ohmic_power_spectrum(w, alpha, wc, beta): """The Ohmic bath power spectrum as a function of w (and the bath parameters). @@ -157,7 +157,7 @@ def ohmic_power_spectrum(w, alpha, wc, beta): Finally, let's set the bath parameters we will work with and write down some measurement operators: -```{code-cell} ipython3 +```{code-cell} Q = sigmaz() alpha = 3.25 T = 0.5 @@ -167,7 +167,7 @@ s = 1 And set the cut-off for the HEOM hierarchy: -```{code-cell} ipython3 +```{code-cell} # HEOM parameters: # The max_depth defaults to 5 so that the notebook executes more @@ -222,7 +222,7 @@ Before obtaining exponential approximations, we first need to construct a using the correlation function and the power spectrum. For this example we will use the Ohmic Spectral density we defined above -```{code-cell} ipython3 +```{code-cell} w = np.linspace(0, 25, 20000) J = ohmic_spectral_density(w, alpha, wc) ``` @@ -232,7 +232,7 @@ create enviroments from arbitrary spectral densities, correlation functions, or power spectrums. Below we show how to construct a `BosonicEnvironment` from a user specified function or array -```{code-cell} ipython3 +```{code-cell} # From an array sd_env = BosonicEnvironment.from_spectral_density(J=J, wlist=w) ``` @@ -242,13 +242,13 @@ correlation function because the temperature of the environment has not been specified. So the `BosonicEnvironment` is not fully characterized by the parameters provided -```{code-cell} ipython3 +```{code-cell} sd_env.power_spectrum(w) ``` If we want access to these properties we need to provide the Temperature at Initialization -```{code-cell} ipython3 +```{code-cell} # From an array sd_env = BosonicEnvironment.from_spectral_density(J=J, wlist=w, T=T) ``` @@ -256,7 +256,7 @@ sd_env = BosonicEnvironment.from_spectral_density(J=J, wlist=w, T=T) Now our bosonic environment can compute the Power Spectrum of the spectral density provided -```{code-cell} ipython3 +```{code-cell} # Here we avoid w=0 np.allclose( sd_env.power_spectrum(w[1:]), ohmic_power_spectrum(w[1:], alpha, wc, 1 / T) @@ -266,7 +266,7 @@ np.allclose( Specifying the Temperature also gives the `BosonicEnvironment` access to the correlation function -```{code-cell} ipython3 +```{code-cell} tlist = np.linspace(0, 10, 500) plt.plot( tlist, @@ -303,14 +303,14 @@ WMax needs to be specified, wMax is the cutoff frequency for which the spectral density, or power spectrum has effectively decayed to zero, after this value the function can be considered to be essentialy zero -```{code-cell} ipython3 +```{code-cell} # From a function sd_env2 = BosonicEnvironment.from_spectral_density( ohmic_spectral_density, T=T, wMax=10 * wc, args={"alpha": alpha, "wc": wc} ) ``` -```{code-cell} ipython3 +```{code-cell} tlist = np.linspace(0, 10, 500) plt.plot(tlist, sd_env2.correlation_function(tlist).real) plt.plot(tlist, ohmic_correlation(tlist, alpha, wc, 1 / T).real, "--") @@ -356,19 +356,19 @@ is reached or the maximum number allowed `Nmax` is reached. The default target is a normalized root mean squared error of $5\times 10^{-6}$, if set to None the fit is performed only with the maximum number of exponents specified -```{code-cell} ipython3 +```{code-cell} bath, fitinfo = sd_env.approximate("sd", w, Nmax=4) ``` To obtain an overview of the results of the fit we may take a look at the summary from the ``fitinfo`` -```{code-cell} ipython3 +```{code-cell} print(fitinfo["summary"]) ``` We may see how the number of exponents chosen affects the fit since the approximated functions are available: -```{code-cell} ipython3 +```{code-cell} fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5)) ax1.plot(w, J, label="Original spectral density") @@ -387,7 +387,7 @@ plt.show() Here we see a surprisingly large discrepancy in our approximated or effective spectral density. This happens because we are not using enough exponentials from each of the underdamped modes to have an appropiate fit. All modes have the same number of exponents, when not specified it defaults to $1$ which is not enough to model a bath with the temperature considered, let us repeat this with a higher number of exponents. -```{code-cell} ipython3 +```{code-cell} bath, fitinfo = sd_env.approximate("sd", w, Nmax=4, Nk=3) fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5)) @@ -412,7 +412,7 @@ Since the number of exponents increases simulation time one should go with the l Let's take a closer look at our last fit by plotting the contribution of each term of the fit: -```{code-cell} ipython3 +```{code-cell} # Plot the components of the fit separately: plt.rcParams["font.size"] = 25 plt.rcParams["figure.figsize"] = (10, 5) @@ -465,13 +465,13 @@ def _sd_fit_model(wlist, a, b, c): plot_fit(_sd_fit_model, J, w, lam, gamma, w0) ``` -```{code-cell} ipython3 +```{code-cell} plot_fit_components(_sd_fit_model, J, w, lam, gamma, w0) ``` And let's also compare the power spectrum of the fit and the analytical spectral density: -```{code-cell} ipython3 +```{code-cell} def plot_power_spectrum(alpha, wc, beta, save=True): """Plot the power spectrum of a fit against the actual power spectrum.""" w = np.linspace(-10, 10, 50000) @@ -494,7 +494,7 @@ plot_power_spectrum(alpha, wc, 1 / T, save=False) Now if we want to see the systems's behaviour as we change the number of terms in the fit, we may use this auxiliary function. -```{code-cell} ipython3 +```{code-cell} def generate_spectrum_results(Q, N, Nk, max_depth): """Run the HEOM with the given bath parameters and and return the results of the evolution. @@ -526,7 +526,7 @@ def generate_spectrum_results(Q, N, Nk, max_depth): return results_spectral_fit ``` -```{code-cell} ipython3 +```{code-cell} def plot_result_expectations(plots, axes=None): """Plot the expectation values of operators as functions of time. @@ -567,7 +567,7 @@ def plot_result_expectations(plots, axes=None): Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow. -```{code-cell} ipython3 +```{code-cell} # # Generate results for different number of lorentzians in fit: @@ -589,7 +589,7 @@ plot_result_expectations( ); ``` -```{code-cell} ipython3 +```{code-cell} # generate results for different number of Matsubara terms per Lorentzian # for max number of Lorentzians: @@ -612,7 +612,7 @@ plot_result_expectations( ); ``` -```{code-cell} ipython3 +```{code-cell} # Generate results for different depths: Nc_list = range(2, max_depth) @@ -635,7 +635,7 @@ plot_result_expectations( #### We now combine the fitting and correlation function data into one large plot. Here we define a function to plot everything together -```{code-cell} ipython3 +```{code-cell} def gen_plots(fs, w, J, t, C, w2, S): def plot_cr_fit_vs_actual(t, C, func, axes): """Plot the C_R(t) fit.""" @@ -758,7 +758,7 @@ def gen_plots(fs, w, J, t, C, w2, S): #### And finally plot everything together -```{code-cell} ipython3 +```{code-cell} t = np.linspace(0, 15, 1000) C = ohmic_correlation(t, alpha, wc, 1 / T) w2 = np.concatenate((-np.linspace(10, 1e-2, 100), np.linspace(1e-2, 10, 100))) @@ -802,7 +802,7 @@ When full_ansatz is True. the ansatz used corresponds to \Bigr]. \end{align} -```{code-cell} ipython3 +```{code-cell} def generate_corr_results(N, max_depth): tlist = np.linspace(0, 30 * np.pi / Del, 600) bath_corr, fitinfo = sd_env.approximate( @@ -831,7 +831,7 @@ results_corr_fit_pk = [ ] ``` -```{code-cell} ipython3 +```{code-cell} plot_result_expectations( [ ( @@ -845,7 +845,7 @@ plot_result_expectations( ); ``` -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) plot_result_expectations( @@ -888,7 +888,7 @@ axes.legend(loc=0, fontsize=20); As the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced quickly by using the OhmicBath class. This allows for rapid implementation of fitted ohmic baths via the correlation function or spectral density -```{code-cell} ipython3 +```{code-cell} obs = OhmicEnvironment(T, alpha, wc, s=1) tlist = np.linspace(0, 30 * np.pi / Del, 600) ``` @@ -897,7 +897,7 @@ Just like the other `BosonicEnvironment` we can obtain a decaying exponential representation of the environment via the `approximate`, let us do the same methods we explored before -```{code-cell} ipython3 +```{code-cell} Obath, fitinfo = obs.approximate( method="cf", tlist=tlist, Nr_max=4, Ni_max=4, maxfev=1e9, target_rmse=None ) @@ -911,7 +911,7 @@ HEOM_ohmic_corr_fit = HEOMSolver( results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} Obath2, fitinfo = obs.approximate(method="sd", wlist=w, Nmax=4, Nk=3) print(fitinfo["summary"]) HEOM_ohmic_sd_fit = HEOMSolver( @@ -976,11 +976,11 @@ we group it with other methods The method is available via `approximate` passing "prony" as method. Compared to the other approaches showed so far. The Prony based methods, shine on their simplicity no information needs to be known about the function, and one just needs to provide the sampling points, and the Number of Exponents one desires -```{code-cell} ipython3 +```{code-cell} tlist2 = np.linspace(0, 40, 100) ``` -```{code-cell} ipython3 +```{code-cell} pbath, fitinfo = obs.approximate("prony", tlist2, Nr=4) print(fitinfo["summary"]) HEOM_ohmic_prony_fit = HEOMSolver( @@ -995,7 +995,7 @@ results_ohmic_prony_fit = HEOM_ohmic_prony_fit.run(rho0, tlist) Similar to how we approximated via prony we can use ESPRIT, the main difference between both methods lies in the construction of the pencil matrix -```{code-cell} ipython3 +```{code-cell} esbath, fitinfo = obs.approximate("esprit", tlist2, Nr=4, separate=False) print(fitinfo["summary"]) HEOM_ohmic_es_fit = HEOMSolver( @@ -1036,13 +1036,13 @@ Which allows us to identify this method works best when the sampling points provided are in the logarithmic scale -```{code-cell} ipython3 +```{code-cell} wlist = np.concatenate((-np.logspace(3, -8, 3500), np.logspace(-8, 3, 3500))) aaabath, fitinfo = obs.approximate("aaa", wlist, Nmax=8, tol=1e-15) print(fitinfo["summary"]) ``` -```{code-cell} ipython3 +```{code-cell} HEOM_ohmic_aaa_fit = HEOMSolver( Hsys, (aaabath, Q), @@ -1065,12 +1065,12 @@ we fit the power spectrum to a function of the form $$S(\omega) = \sum_{k=1}^{N}\frac{2(a_k c_k + b_k (d_k - \omega))} {(\omega - d_k)^2 + c_k^2}= 2 \Re \left(\sum_{k} \frac{c_{k}}{\nu_{k}-i \omega} \right)$$ -```{code-cell} ipython3 +```{code-cell} psbath, fitinfo = obs.approximate("ps", w2, Nmax=4) print(fitinfo["summary"]) ``` -```{code-cell} ipython3 +```{code-cell} HEOM_ohmic_ps_fit = HEOMSolver( Hsys, (psbath, Q), @@ -1098,7 +1098,7 @@ recommended. ESPIRA I -```{code-cell} ipython3 +```{code-cell} tlist4 = np.linspace(0, 20, 1000) espibath, fitinfo = obs.approximate("espira-I", tlist4, Nr=4) print(fitinfo["summary"]) @@ -1113,7 +1113,7 @@ results_ohmic_espira_fit = HEOM_ohmic_espira_fit.run(rho0, tlist) ESPIRA-II -```{code-cell} ipython3 +```{code-cell} tlist4 = np.linspace(0, 20, 1000) espibath2, fitinfo = obs.approximate( "espira-II", tlist4, Nr=4, Ni=4, separate=True @@ -1130,7 +1130,7 @@ results_ohmic_espira2_fit = HEOM_ohmic_espira_fit2.run(rho0, tlist) Finally we plot the dynamics obtained by the different methods -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) plot_result_expectations( @@ -1171,7 +1171,7 @@ axes.set_yscale("log") ## About -```{code-cell} ipython3 +```{code-cell} qutip.about() ``` @@ -1179,7 +1179,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 +```{code-cell} assert np.allclose( expect(P11p, results_spectral_fit_pk[2].states), expect(P11p, results_spectral_fit_pk[3].states), diff --git a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md index 8baa1ad5..caad1ad5 100644 --- a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md +++ b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.17.0 + jupytext_version: 1.14.4 kernelspec: display_name: qutip-tutorials language: python @@ -66,7 +66,7 @@ Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. ## Setup -```{code-cell} ipython3 +```{code-cell} import contextlib import time @@ -85,7 +85,7 @@ from qutip.solver.heom import HEOMSolver Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: -```{code-cell} ipython3 +```{code-cell} def cot(x): """Vectorized cotangent of x.""" return 1.0 / np.tan(x) @@ -96,7 +96,7 @@ def coth(x): return 1.0 / np.tanh(x) ``` -```{code-cell} ipython3 +```{code-cell} def plot_result_expectations(plots, axes=None): """Plot the expectation values of operators as functions of time. @@ -128,7 +128,7 @@ def plot_result_expectations(plots, axes=None): return fig ``` -```{code-cell} ipython3 +```{code-cell} @contextlib.contextmanager def timer(label): """Simple utility for timing functions: @@ -142,7 +142,7 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} ipython3 +```{code-cell} # Solver options: @@ -164,14 +164,14 @@ And let us set up the system Hamiltonian, bath and system measurement operators: Here we set $H_{sys}=0$, which means the interaction Hamiltonian and the system Hamiltonian commute, and we can compare the numerical results to a known analytical one. We could in principle keep $\epsilon \neq 0$, but it just introduces fast system oscillations, so it is more convenient to set it to zero. -```{code-cell} ipython3 +```{code-cell} # Defining the system Hamiltonian eps = 0.0 # Energy of the 2-level system. Del = 0.0 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` -```{code-cell} ipython3 +```{code-cell} # System-bath coupling (Drude-Lorentz spectral density) Q = sigmaz() # coupling operator @@ -192,7 +192,7 @@ Nk = 3 tlist = np.linspace(0, 50, 1000) ``` -```{code-cell} ipython3 +```{code-cell} # Define some operators with which we will measure the system # 1,1 element of density matrix - corresponding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -203,7 +203,7 @@ P12p = basis(2, 0) * basis(2, 1).dag() To get a non-trivial result we prepare the initial state in a superposition, and see how the bath destroys the coherence. -```{code-cell} ipython3 +```{code-cell} # Initial state of the system. psi = (basis(2, 0) + basis(2, 1)).unit() rho0 = psi * psi.dag() @@ -212,13 +212,13 @@ rho0 = psi * psi.dag() We then define our environment, from which all the different simulations will be obtained -```{code-cell} ipython3 +```{code-cell} env = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=Nk) ``` ## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator -```{code-cell} ipython3 +```{code-cell} with timer("RHS construction time"): env_mats = env.approximate(method="matsubara", Nk=Nk) HEOMMats = HEOMSolver(Hsys, (env_mats, Q), NC, options=options) @@ -227,7 +227,7 @@ with timer("ODE solver time"): resultMats = HEOMMats.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} # Plot the results so far plot_result_expectations( [ @@ -239,7 +239,7 @@ plot_result_expectations( ## Simulation 2: Matsubara decomposition (including terminator) -```{code-cell} ipython3 +```{code-cell} with timer("RHS construction time"): env_mats, delta = env.approximate( method="matsubara", Nk=Nk, compute_delta=True @@ -251,7 +251,7 @@ with timer("ODE solver time"): resultMatsT = HEOMMatsT.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} # Plot the results plot_result_expectations( [ @@ -267,7 +267,7 @@ plot_result_expectations( As in example 1a, we can compare to Pade and Fitting approaches. -```{code-cell} ipython3 +```{code-cell} with timer("RHS construction time"): env_pade = env.approximate(method="pade", Nk=Nk) HEOMPade = HEOMSolver(Hsys, (env_pade, Q), NC, options=options) @@ -276,7 +276,7 @@ with timer("ODE solver time"): resultPade = HEOMPade.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} # Plot the results plot_result_expectations( [ @@ -290,7 +290,7 @@ plot_result_expectations( ## Simulation 4: Fitting approach -```{code-cell} ipython3 +```{code-cell} tfit = np.linspace(0, 10, 1000) with timer("RHS construction time"): bath, _ = env.approximate( @@ -304,7 +304,7 @@ with timer("ODE solver time"): ## Analytic calculations -```{code-cell} ipython3 +```{code-cell} def pure_dephasing_evolution_analytical(tlist, wq, ck, vk): """ Computes the propagating function appearing in the pure dephasing model. @@ -372,7 +372,7 @@ def correlation_integral(t, ck, vk): For the pure dephasing analytics, we just sum up as many matsubara terms as we can: -```{code-cell} ipython3 +```{code-cell} lmaxmats2 = 15000 vk = [complex(-gamma)] @@ -390,7 +390,7 @@ P12_ana = 0.5 * pure_dephasing_evolution_analytical( Alternatively, we can just do the integral of the propagator directly, without using the correlation functions at all -```{code-cell} ipython3 +```{code-cell} def JDL(omega, lamc, omega_c): return 2.0 * lamc * omega * omega_c / (omega_c**2 + omega**2) @@ -415,7 +415,7 @@ P12_ana2 = [ ## Compare results -```{code-cell} ipython3 +```{code-cell} plot_result_expectations( [ (resultMats, P12p, "r", "P12 Mats"), @@ -430,7 +430,7 @@ plot_result_expectations( We can't see much difference in the plot above, so let's do a log plot instead: -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) plot_result_expectations( @@ -451,7 +451,7 @@ axes.legend(loc=0, fontsize=12); ## About -```{code-cell} ipython3 +```{code-cell} qutip.about() ``` @@ -459,7 +459,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 +```{code-cell} assert np.allclose( expect(P12p, resultMats.states[:15]), np.real(P12_ana)[:15], diff --git a/tutorials-v5/heom/heom-2-fmo-example.md b/tutorials-v5/heom/heom-2-fmo-example.md index b1dd8ebf..f1adda69 100644 --- a/tutorials-v5/heom/heom-2-fmo-example.md +++ b/tutorials-v5/heom/heom-2-fmo-example.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.17.0 + jupytext_version: 1.14.4 kernelspec: display_name: qutip-tutorials language: python @@ -31,7 +31,7 @@ quantum environment reduces the effect of pure dephasing. ## Setup -```{code-cell} ipython3 +```{code-cell} import contextlib import time @@ -49,7 +49,7 @@ from qutip.solver.heom import HEOMSolver Let's define some helper functions for calculating correlation functions, spectral densities, thermal energy level occupations, and for plotting results and timing how long operations take: -```{code-cell} ipython3 +```{code-cell} @contextlib.contextmanager def timer(label): """Simple utility for timing functions: @@ -63,7 +63,7 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} ipython3 +```{code-cell} # Solver options: @@ -82,7 +82,7 @@ options = { And let us set up the system Hamiltonian and bath parameters: -```{code-cell} ipython3 +```{code-cell} # System Hamiltonian: # # We use the Hamiltonian employed in @@ -107,7 +107,7 @@ Hsys = ( ) ``` -```{code-cell} ipython3 +```{code-cell} # Bath parameters lam = 35 * 3e10 * 2 * np.pi @@ -120,11 +120,11 @@ beta = 1 / T Let's quickly plot the spectral density and environment correlation functions so that we can see what they look like. -```{code-cell} ipython3 +```{code-cell} env = DrudeLorentzEnvironment(T=T, lam=lam, gamma=gamma) ``` -```{code-cell} ipython3 +```{code-cell} wlist = np.linspace(0, 200 * 3e10 * 2 * np.pi, 100) tlist = np.linspace(0, 1e-12, 1000) @@ -166,7 +166,7 @@ axes[1].legend(); Now let us solve for the evolution of this system using the HEOM. -```{code-cell} ipython3 +```{code-cell} # We start the excitation at site 1: rho0 = basis(7, 0) * basis(7, 0).dag() @@ -191,7 +191,7 @@ for m in range(7): baths.append((env_approx, Q)) ``` -```{code-cell} ipython3 +```{code-cell} with timer("RHS construction time"): HEOMMats = HEOMSolver(Hsys, baths, NC, options=options) @@ -199,7 +199,7 @@ with timer("ODE solver time"): outputFMO_HEOM = HEOMMats.run(rho0, tlist) ``` -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, figsize=(12, 8)) colors = ["r", "g", "b", "y", "c", "m", "k"] @@ -240,7 +240,7 @@ Now let us solve the same problem using the Bloch-Redfield solver. We will see t In the next section, we will examine the role of pure dephasing in the evolution to understand why this happens. -```{code-cell} ipython3 +```{code-cell} with timer("BR ODE solver time"): outputFMO_BR = brmesolve( Hsys, @@ -253,7 +253,7 @@ with timer("BR ODE solver time"): And now let's plot the Bloch-Redfield solver results: -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, figsize=(12, 8)) for m, Q in enumerate(Q_list): @@ -281,7 +281,7 @@ It is useful to construct the various parts of the Bloch-Redfield master equatio First we will write a function to return the list of collapse operators for a given system, either with or without the dephasing operators: -```{code-cell} ipython3 +```{code-cell} def J0_dephasing(): """Under-damped brownian oscillator dephasing probability. @@ -290,11 +290,11 @@ def J0_dephasing(): return 2 * lam * gamma / gamma**2 ``` -```{code-cell} ipython3 +```{code-cell} env.power_spectrum(0) / 2 - J0_dephasing() * T ``` -```{code-cell} ipython3 +```{code-cell} def get_collapse(H, T, dephasing=1): """Calculate collapse operators for a given system H and temperature T. @@ -350,7 +350,7 @@ Now we are able to switch the pure dephasing terms on and off. Let us starting by including the dephasing operators. We expect to see the same behaviour that we saw when using the Bloch-Redfield solver. -```{code-cell} ipython3 +```{code-cell} # dephasing terms on, we recover the full BR solution: @@ -361,7 +361,7 @@ with timer("ME ODE solver"): outputFMO_ME = mesolve(Hsys, rho0, tlist, collapse_list) ``` -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, figsize=(12, 8)) for m, Q in enumerate(Q_list): @@ -380,7 +380,7 @@ We see similar results to before. Now let us examine what happens when we remove the dephasing collapse operators: -```{code-cell} ipython3 +```{code-cell} # dephasing terms off with timer("Building the collapse operators"): @@ -390,7 +390,7 @@ with timer("ME ODE solver"): outputFMO_ME_nodephase = mesolve(Hsys, rho0, tlist, collapse_list) ``` -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, figsize=(12, 8)) for m, Q in enumerate(Q_list): axes.plot( @@ -414,7 +414,7 @@ And now we see that without the dephasing, the oscillations reappear. The full d ## About -```{code-cell} ipython3 +```{code-cell} qutip.about() ``` @@ -422,7 +422,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 +```{code-cell} assert np.allclose( expect(outputFMO_BR.states, Q_list[0]), expect(outputFMO_ME.states, Q_list[0]), diff --git a/tutorials-v5/heom/heom-3-quantum-heat-transport.md b/tutorials-v5/heom/heom-3-quantum-heat-transport.md index 0b40b542..98e09b33 100644 --- a/tutorials-v5/heom/heom-3-quantum-heat-transport.md +++ b/tutorials-v5/heom/heom-3-quantum-heat-transport.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.17.0 + jupytext_version: 1.14.4 kernelspec: display_name: qutip-tutorials language: python @@ -50,7 +50,7 @@ References: ## Setup -```{code-cell} ipython3 +```{code-cell} import dataclasses import matplotlib.pyplot as plt @@ -67,7 +67,7 @@ from qutip.solver.heom import HEOMSolver ## Helpers -```{code-cell} ipython3 +```{code-cell} # Solver options: options = { @@ -83,7 +83,7 @@ options = { ## System and bath definition -```{code-cell} ipython3 +```{code-cell} @dataclasses.dataclass class SystemParams: """System parameters and Hamiltonian.""" @@ -117,7 +117,7 @@ class SystemParams: return dataclasses.replace(self, **kw) ``` -```{code-cell} ipython3 +```{code-cell} @dataclasses.dataclass class BathParams: """Bath parameters.""" @@ -179,7 +179,7 @@ In the expression for the bath heat currents, we left out terms involving $[Q_1, In QuTiP, these currents can be conveniently calculated as follows: -```{code-cell} ipython3 +```{code-cell} def bath_heat_current(bath_tag, ado_state, hamiltonian, coupling_op, delta=0): """ Bath heat current from the system into the heat bath with the given tag. @@ -280,7 +280,7 @@ Note that at long times, we expect $j_{\text{B}}^1 = -j_{\text{B}}^2$ and $j_{\t For our simulations, we will represent the bath spectral densities using the first term of their Padé decompositions, and we will use $7$ levels of the HEOM hierarchy. -```{code-cell} ipython3 +```{code-cell} Nk = 1 NC = 7 ``` @@ -290,7 +290,7 @@ NC = 7 We fix $J_{12} = 0.1 \epsilon$ (as in Fig. 3(a-ii) of Ref. \[2\]) and choose the fixed coupling strength $\lambda_1 = \lambda_2 = J_{12}\, /\, (2\epsilon)$ (corresponding to $\bar\zeta = 1$ in Ref. \[2\]). Using these values, we will study the time evolution of the system state and the heat currents. -```{code-cell} ipython3 +```{code-cell} # fix qubit-qubit and qubit-bath coupling strengths sys = SystemParams(J12=0.1) bath_p1 = BathParams(qubit=0, sign="+", lam=sys.J12 / 2) @@ -303,7 +303,7 @@ rho0 = qt.tensor(qt.identity(2), qt.identity(2)) / 4 tlist = np.linspace(0, 50, 250) ``` -```{code-cell} ipython3 +```{code-cell} H = sys.H() bath1, b1term, b1delta = bath_p1.bath(Nk, tag="bath 1") @@ -335,7 +335,7 @@ result = solver.run( We first plot $\langle \sigma_z^1 \rangle$ to see the time evolution of the system state: -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(figsize=(8, 8)) axes.plot(tlist, result.expect[0], "r", linewidth=2) axes.set_xlabel("t", fontsize=28) @@ -344,7 +344,7 @@ axes.set_ylabel(r"$\langle \sigma_z^1 \rangle$", fontsize=28); We find a rather quick thermalization of the system state. For the heat currents, however, it takes a somewhat longer time until they converge to their long-time values: -```{code-cell} ipython3 +```{code-cell} fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8)) ax1.plot( @@ -416,7 +416,7 @@ ax2.legend(loc=0, fontsize=12); Here, we try to reproduce the HEOM curves in Fig. 3(a) of Ref. \[1\] by varying the coupling strength and finding the steady state for each coupling strength. -```{code-cell} ipython3 +```{code-cell} def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options): """Calculate the steady sate heat currents for the given system and bath. @@ -445,7 +445,7 @@ def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options): ) ``` -```{code-cell} ipython3 +```{code-cell} # Define number of points to use for the plot plot_points = 10 # use 100 for a smoother curve @@ -491,7 +491,7 @@ j3s = [calculate_heat_current(0.5, zb, Nk) for zb in zeta_bars] ## Create Plot -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(figsize=(12, 7)) axes.plot( @@ -531,7 +531,7 @@ axes.legend(loc=0); ## About -```{code-cell} ipython3 +```{code-cell} qt.about() ``` @@ -539,6 +539,6 @@ qt.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 +```{code-cell} assert 1 == 1 ``` diff --git a/tutorials-v5/heom/heom-4-dynamical-decoupling.md b/tutorials-v5/heom/heom-4-dynamical-decoupling.md index 474392bf..605a1cd4 100644 --- a/tutorials-v5/heom/heom-4-dynamical-decoupling.md +++ b/tutorials-v5/heom/heom-4-dynamical-decoupling.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.17.0 + jupytext_version: 1.14.4 kernelspec: display_name: qutip-tutorials language: python @@ -27,7 +27,7 @@ We first show the standard example of equally spaced pulses, and then consider t ## Setup -```{code-cell} ipython3 +```{code-cell} import matplotlib.pyplot as plt import numpy as np import qutip @@ -42,7 +42,7 @@ from qutip.solver.heom import HEOMSolver ## Solver options -```{code-cell} ipython3 +```{code-cell} # Solver options: # The max_step must be set to a short time than the @@ -64,7 +64,7 @@ options = { Now we define the system and bath properties and the HEOM parameters. The system is a single stationary qubit with $H = 0$ and the bath is a bosonic bath with a Drude-Lorentz spectrum. -```{code-cell} ipython3 +```{code-cell} # Define the system Hamlitonian. # # The system isn't evolving by itself, so the Hamiltonian is 0 (with the @@ -73,7 +73,7 @@ Now we define the system and bath properties and the HEOM parameters. The system H_sys = 0 * sigmaz() ``` -```{code-cell} ipython3 +```{code-cell} # Define some operators with which we will measure the system # 1,1 element of density matrix - corresponding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -82,7 +82,7 @@ P22p = basis(2, 1) * basis(2, 1).dag() P12p = basis(2, 0) * basis(2, 1).dag() ``` -```{code-cell} ipython3 +```{code-cell} # Properties for the Drude-Lorentz bath lam = 0.0005 @@ -100,7 +100,7 @@ env_approx = env.approximate(method="pade", Nk=Nk) bath = (env_approx, Q) ``` -```{code-cell} ipython3 +```{code-cell} # HEOM parameters # number of layers to keep in the hierarchy: @@ -111,7 +111,7 @@ To perform the dynamic decoupling from the environment, we will drive the system Below we define a function that returns the pulse (which is itself a function): -```{code-cell} ipython3 +```{code-cell} def drive(amplitude, delay, integral): """Coefficient of the drive as a function of time. @@ -148,7 +148,7 @@ H_drive = sigmax() Let's start by plotting the spectral density of our Drude-Lorentz bath: -```{code-cell} ipython3 +```{code-cell} wlist = np.linspace(0, 0.5, 1000) J = env.spectral_density(wlist) J_approx = env_approx.spectral_density(wlist) @@ -169,7 +169,7 @@ First we will drive the system with fast, large amplitude pulses. Then we will d Let's start by simulating the fast pulses: -```{code-cell} ipython3 +```{code-cell} # Fast driving (quick, large amplitude pulses) tlist = np.linspace(0, 400, 1000) @@ -192,7 +192,7 @@ outputDD = hsolver.run(rho0, tlist) And now the longer slower pulses: -```{code-cell} ipython3 +```{code-cell} # Slow driving (longer, small amplitude pulses) # without pulses @@ -209,7 +209,7 @@ outputDDslow = hsolver.run(rho0, tlist) Now let's plot all of the results and the shapes of the pulses: -```{code-cell} ipython3 +```{code-cell} def plot_dd_results(outputnoDD, outputDD, outputDDslow): fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 12)) @@ -295,7 +295,7 @@ def plot_dd_results(outputnoDD, outputDD, outputDDslow): fig.tight_layout() ``` -```{code-cell} ipython3 +```{code-cell} plot_dd_results(outputnoDD, outputDD, outputDDslow) ``` @@ -315,7 +315,7 @@ $$ This is just a convenient way to describe the varying delay. We could have chosen another monotonically increasing function to represent the cummulative delay (although it might not be as effective). -```{code-cell} ipython3 +```{code-cell} def cummulative_delay_fractions(N): """Return an array of N + 1 cummulative delay fractions. @@ -368,7 +368,7 @@ Let's plot the cummulative delays and see what they look like. Note that the cum On the same axes we plot the individual $j^{th}$ delays as a fraction of the average delay. -```{code-cell} ipython3 +```{code-cell} def plot_cummulative_delay_fractions(N): cummulative = cummulative_delay_fractions(N) individual = (cummulative[1:] - cummulative[:-1]) * N @@ -384,7 +384,7 @@ plot_cummulative_delay_fractions(100) And now let us plot the first ten even and optimally spaced pulses together to compare them: -```{code-cell} ipython3 +```{code-cell} def plot_even_and_optimally_spaced_pulses(): amplitude = 10.0 integral = np.pi / 2 @@ -416,7 +416,7 @@ Now let's simulate the effectiveness of the two sets of delays by comparing how We'll perform the simulation over a range of lambdas and gammas to show how the non-evenly spaced delays become optimal as the width of the bath spectral function increases. -```{code-cell} ipython3 +```{code-cell} # Bath parameters to simulate over: # We use only two lambdas and two gammas so that the notebook executes @@ -501,7 +501,7 @@ P12_results = [ Now that we have the expectation values of $\rho_{01}$ let's plot them as a function of gamma for each lambda. Note how in each case the non-evenly spaced pulses become optimal once gamma is sufficiently small: -```{code-cell} ipython3 +```{code-cell} fig, axes = plt.subplots(1, 1, sharex=False, figsize=(10, 7)) colors = ["green", "red", "blue"] @@ -537,7 +537,7 @@ And now you know about dynamically decoupling a qubit from its environment! ## About -```{code-cell} ipython3 +```{code-cell} qutip.about() ``` @@ -545,6 +545,6 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 +```{code-cell} assert 1 == 1 ``` diff --git a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md index 1554f160..70702873 100644 --- a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md +++ b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.17.0 + jupytext_version: 1.14.4 kernelspec: display_name: qutip-tutorials language: python @@ -69,7 +69,7 @@ In this notebook we: ## Setup -```{code-cell} ipython3 +```{code-cell} import contextlib import dataclasses import time @@ -89,7 +89,7 @@ from scipy.integrate import quad ## Helpers -```{code-cell} ipython3 +```{code-cell} @contextlib.contextmanager def timer(label): """Simple utility for timing functions: @@ -103,7 +103,7 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} ipython3 +```{code-cell} # Solver options: # We set store_ados to True so that we can @@ -127,7 +127,7 @@ options = { And let us set up the system Hamiltonian, bath and system measurement operators: -```{code-cell} ipython3 +```{code-cell} # Define the system Hamiltonian: # The system is a single fermion with energy level split e1: @@ -136,7 +136,7 @@ e1 = 1.0 H = e1 * d1.dag() * d1 ``` -```{code-cell} ipython3 +```{code-cell} # Define parameters for left and right fermionic baths. # Each bath is a lead (i.e. a wire held at a potential) # with temperature T and chemical potential mu. @@ -190,7 +190,7 @@ bath_R = LorentzianBathParameters(Q=d1, lead="R") Let's plot the spectral density. -```{code-cell} ipython3 +```{code-cell} w_list = np.linspace(-2, 2, 100) fig, ax = plt.subplots(figsize=(12, 7)) @@ -222,7 +222,7 @@ ax.legend(); Next let's plot the emission and absorption by the leads. -```{code-cell} ipython3 +```{code-cell} w_list = np.linspace(-2, 2, 100) fig, ax = plt.subplots(figsize=(12, 7)) @@ -276,7 +276,7 @@ ax.legend(); Let's start by solving for the evolution using a Pade expansion of the correlation function of the Lorentzian spectral density: -```{code-cell} ipython3 +```{code-cell} # HEOM dynamics using the Pade approximation: # Times to solve for and initial system state: @@ -318,7 +318,7 @@ with timer("Steady state solver time"): Now let us plot the result which shows the decay of the initially excited impurity. This is not very illuminating, but we will compare it with the Matsubara expansion and analytic solution sortly: -```{code-cell} ipython3 +```{code-cell} # Plot the Pade results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -343,7 +343,7 @@ axes.legend(fontsize=12); Now let us do the same for the Matsubara expansion: -```{code-cell} ipython3 +```{code-cell} # HEOM dynamics using the Matsubara approximation: envL_mats = envL.approx_by_matsubara(Nk=Nk, tag="L") @@ -367,7 +367,7 @@ with timer("Steady state solver time"): We see a marked difference in the Matsubara vs Pade results: -```{code-cell} ipython3 +```{code-cell} # Plot the Pade results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -411,7 +411,7 @@ One advantage of this simple model is that the steady state current to the baths See the [QuTiP-BoFiN paper](https://arxiv.org/abs/2010.10806) for a detailed description and references for the analytic result. Below we just perform the required integration numerically. -```{code-cell} ipython3 +```{code-cell} def analytical_steady_state_current(bath_L, bath_R, e1): """Calculate the analytical steady state current.""" @@ -451,7 +451,7 @@ To compare the analytical result above with the result from the HEOM, we need to In the function `state_current(...)` below, we extract the first level ADOs for the specified bath and sum the contributions to the current from each: -```{code-cell} ipython3 +```{code-cell} def state_current(ado_state, bath_tag): """Determine current from the given bath (either "R" or "L") to the system in the given ADO state. @@ -474,7 +474,7 @@ def state_current(ado_state, bath_tag): Now we can calculate the steady state currents from the Pade and Matsubara HEOM results: -```{code-cell} ipython3 +```{code-cell} curr_ss_pade_L = state_current(ado_ss_pade, "L") curr_ss_pade_R = state_current(ado_ss_pade, "R") @@ -482,7 +482,7 @@ print(f"Pade steady state current (L): {curr_ss_pade_L}") print(f"Pade steady state current (R): {curr_ss_pade_R}") ``` -```{code-cell} ipython3 +```{code-cell} curr_ss_mats_L = state_current(ado_ss_mats, "L") curr_ss_mats_R = state_current(ado_ss_mats, "R") @@ -494,7 +494,7 @@ Note that the currents from each bath balance as is required by the steady state Now let's compare all three: -```{code-cell} ipython3 +```{code-cell} print(f"Pade current (R): {curr_ss_pade_R}") print(f"Matsubara current (R): {curr_ss_mats_R}") print(f"Analytical curernt: {curr_ss_analytic}") @@ -514,7 +514,7 @@ Now lets plot the current as a function of bias voltage (the bias voltage is the We will calculate the steady state current for each `theta` both analytically and using the HEOM with the Pade correlation expansion approximation. -```{code-cell} ipython3 +```{code-cell} # Theta (bias voltages) thetas = np.linspace(-4, 4, 100) @@ -572,7 +572,7 @@ curr_ss_pade_theta = [ Below we plot the results and see that even with `Nk=6`, the HEOM Pade approximation gives good results for the steady state current. Increasing `Nk` to `10` gives very accurate results. -```{code-cell} ipython3 +```{code-cell} fig, ax = plt.subplots(figsize=(12, 7)) ax.plot( @@ -603,7 +603,7 @@ ax.legend(fontsize=25); ## About -```{code-cell} ipython3 +```{code-cell} qutip.about() ``` @@ -611,7 +611,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 +```{code-cell} assert np.allclose(curr_ss_pade_L + curr_ss_pade_R, 0) assert np.allclose(curr_ss_mats_L + curr_ss_mats_R, 0) assert np.allclose(curr_ss_pade_R, curr_ss_analytic, rtol=1e-4) diff --git a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md index 2772384a..a2843578 100644 --- a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md +++ b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.17.0 + jupytext_version: 1.14.4 kernelspec: display_name: qutip-tutorials language: python @@ -95,7 +95,7 @@ The complete setup now consists of four parts: ## Setup -```{code-cell} ipython3 +```{code-cell} import contextlib import dataclasses import time @@ -114,7 +114,7 @@ from qutip.solver.heom import HEOMSolver ## Helpers -```{code-cell} ipython3 +```{code-cell} @contextlib.contextmanager def timer(label): """Simple utility for timing functions: @@ -128,7 +128,7 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} ipython3 +```{code-cell} def state_current(ado_state, bath_tag): """Determine current from the given bath (either "R" or "L") to the system in the given ADO state. @@ -149,7 +149,7 @@ def state_current(ado_state, bath_tag): ) ``` -```{code-cell} ipython3 +```{code-cell} # Solver options: # We set store_ados to True so that we can @@ -173,7 +173,7 @@ options = { Let us set up the system Hamiltonian and specify the properties of the two reservoirs. -```{code-cell} ipython3 +```{code-cell} # Define the system Hamiltonian: @@ -201,7 +201,7 @@ class SystemParameters: sys_p = SystemParameters() ``` -```{code-cell} ipython3 +```{code-cell} # Define parameters for left and right fermionic baths. # Each bath is a lead (i.e. a wire held at a potential) # with temperature T and chemical potential mu. @@ -256,7 +256,7 @@ bath_R = LorentzianBathParameters(W=10**4, lead="R") Next let's plot the emission and absorption by the leads. -```{code-cell} ipython3 +```{code-cell} w_list = np.linspace(-2, 2, 100) fig, ax = plt.subplots(figsize=(12, 7)) @@ -312,7 +312,7 @@ Here we just give one example of the current as a function of bias voltage, but One note: for very large problems, this can be slow. -```{code-cell} ipython3 +```{code-cell} def steady_state_pade_for_theta(sys_p, bath_L, bath_R, theta, Nk, Nc, Nbos): """Return the steady state current using the Pade approximation.""" @@ -337,7 +337,7 @@ def steady_state_pade_for_theta(sys_p, bath_L, bath_R, theta, Nk, Nc, Nbos): return np.real(2.434e-4 * 1e6 * current) ``` -```{code-cell} ipython3 +```{code-cell} # Parameters: @@ -369,7 +369,7 @@ for theta in thetas: progress.value += 1 ``` -```{code-cell} ipython3 +```{code-cell} fig, ax = plt.subplots(figsize=(12, 10)) ax.plot( @@ -394,7 +394,7 @@ ax.legend(loc=4); ## About -```{code-cell} ipython3 +```{code-cell} qutip.about() ``` @@ -402,6 +402,6 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} ipython3 +```{code-cell} assert 1 == 1 ``` diff --git a/tutorials-v5/heom/heom-index.md b/tutorials-v5/heom/heom-index.md index 52409f83..dcb1fa89 100644 --- a/tutorials-v5/heom/heom-index.md +++ b/tutorials-v5/heom/heom-index.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.17.0 + jupytext_version: 1.14.4 kernelspec: display_name: Python 3 (ipykernel) language: python From 6ba397755e5f9d3f9edd924235fcc7702d8465dc Mon Sep 17 00:00:00 2001 From: Gerardo Suarez Date: Wed, 23 Apr 2025 01:22:16 +0200 Subject: [PATCH 28/44] remove unrelated changes --- .../heom/heom-1a-spin-bath-model-basic.md | 2 +- ...1b-spin-bath-model-very-strong-coupling.md | 52 +- .../heom-1c-spin-bath-model-underdamped-sd.md | 52 +- .../heom-1d-spin-bath-model-ohmic-fitting.md | 6 +- .../heom-1e-spin-bath-model-pure-dephasing.md | 56 +- tutorials-v4/heom/heom-2-fmo-example.md | 62 ++- .../heom/heom-3-quantum-heat-transport.md | 36 +- .../heom/heom-4-dynamical-decoupling.md | 68 ++- .../heom-5a-fermions-single-impurity-model.md | 62 ++- .../heom-5b-fermions-discrete-boson-model.md | 42 +- tutorials-v4/heom/heom-index.md | 2 +- .../Lecture-0-Introduction-to-QuTiP.md | 10 +- .../lectures/Lecture-11-Charge-Qubits.md | 2 +- .../lectures/Lecture-2B-Single-Atom-Lasing.md | 12 +- .../lectures/Lecture-3A-Dicke-model.md | 4 +- ...Cumming-model-with-ultrastrong-coupling.md | 6 +- .../Lecture-4-Correlation-Functions.md | 4 +- ...ation-number-restricted-states-jc-chain.md | 269 --------- .../single-photon-interference-setup.jpg | Bin 140373 -> 0 bytes .../single-photon-interference.md | 524 ------------------ .../qip-customize-device.md | 2 +- .../qip-optpulseprocessor.md | 2 +- .../qip-scheduler.md | 2 +- .../quantum-circuits/qip-toffoli-cnot.md | 8 +- .../quantum-circuits/quantum-gates.md | 54 +- .../time-evolution/002_larmor-precession.md | 22 +- .../time-evolution/003_qubit-dynamics.md | 2 +- .../time-evolution/004_rabi-oscillations.md | 4 +- .../time-evolution/006_photon_birth_death.md | 2 +- .../time-evolution/007_brmesolve_tls.md | 4 +- .../008_brmesolve_time_dependence.md | 8 +- .../009_brmesolve-cavity-QED.md | 2 +- .../time-evolution/011_floquet_solver.md | 2 +- .../time-evolution/012_floquet_formalism.md | 27 +- .../016_smesolve-inefficient-detection.md | 7 +- 35 files changed, 362 insertions(+), 1057 deletions(-) delete mode 100644 tutorials-v4/miscellaneous/excitation-number-restricted-states-jc-chain.md delete mode 100644 tutorials-v4/miscellaneous/images/single-photon-interference-setup.jpg delete mode 100644 tutorials-v4/miscellaneous/single-photon-interference.md diff --git a/tutorials-v4/heom/heom-1a-spin-bath-model-basic.md b/tutorials-v4/heom/heom-1a-spin-bath-model-basic.md index d0f90968..5bd42149 100644 --- a/tutorials-v4/heom/heom-1a-spin-bath-model-basic.md +++ b/tutorials-v4/heom/heom-1a-spin-bath-model-basic.md @@ -5,7 +5,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.16.1 + jupytext_version: 1.14.4 kernelspec: display_name: Python 3 (ipykernel) language: python diff --git a/tutorials-v4/heom/heom-1b-spin-bath-model-very-strong-coupling.md b/tutorials-v4/heom/heom-1b-spin-bath-model-very-strong-coupling.md index 88ca082c..dffef365 100644 --- a/tutorials-v4/heom/heom-1b-spin-bath-model-very-strong-coupling.md +++ b/tutorials-v4/heom/heom-1b-spin-bath-model-very-strong-coupling.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.1 + jupytext_version: 1.14.4 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -81,7 +81,7 @@ Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. ## Setup -```{code-cell} +```{code-cell} ipython3 import contextlib import time @@ -114,13 +114,13 @@ from qutip.nonmarkov.heom import ( Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: -```{code-cell} +```{code-cell} ipython3 def cot(x): """ Vectorized cotangent of x. """ return 1. / np.tan(x) ``` -```{code-cell} +```{code-cell} ipython3 @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -138,19 +138,19 @@ def timer(label): And let us set up the system Hamiltonian, bath and system measurement operators: -```{code-cell} +```{code-cell} ipython3 # Defining the system Hamiltonian eps = .0 # Energy of the 2-level system. Del = .2 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` -```{code-cell} +```{code-cell} ipython3 # Initial state of the system. rho0 = basis(2, 0) * basis(2, 0).dag() ``` -```{code-cell} +```{code-cell} ipython3 # System-bath coupling (Drude-Lorentz spectral density) Q = sigmaz() # coupling operator @@ -173,7 +173,7 @@ NC = 13 tlist = np.linspace(0, np.pi / Del, 600) ``` -```{code-cell} +```{code-cell} ipython3 # Define some operators with which we will measure the system # 1,1 element of density matrix - corresonding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -186,7 +186,7 @@ P12p = basis(2, 0) * basis(2, 1).dag() Let us briefly inspect the spectral density. -```{code-cell} +```{code-cell} ipython3 w = np.linspace(0, 5, 1000) J = w * 2 * lam * gamma / ((gamma**2 + w**2)) @@ -199,7 +199,7 @@ axes.set_ylabel(r'J', fontsize=28); ## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator -```{code-cell} +```{code-cell} ipython3 options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14) with timer("RHS construction time"): @@ -212,7 +212,7 @@ with timer("ODE solver time"): ## Simulation 2: Matsubara decomposition (including terminator) -```{code-cell} +```{code-cell} ipython3 options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14) with timer("RHS construction time"): @@ -225,7 +225,7 @@ with timer("ODE solver time"): resultMatsT = HEOMMatsT.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 # Plot the results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -248,7 +248,7 @@ axes.legend(loc=0, fontsize=12); ## Simulation 3: Pade decomposition -```{code-cell} +```{code-cell} ipython3 # First, compare Matsubara and Pade decompositions matsBath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) padeBath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) @@ -316,7 +316,7 @@ ax2.set_xlabel(r't', fontsize=28) ax2.legend(loc=0, fontsize=12); ``` -```{code-cell} +```{code-cell} ipython3 options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14) with timer("RHS construction time"): @@ -327,7 +327,7 @@ with timer("ODE solver time"): resultPade = HEOMPade.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 # Plot the results fig, axes = plt.subplots(figsize=(8, 8)) @@ -352,7 +352,7 @@ axes.legend(loc=0, fontsize=12); ## Simulation 4: Fitting approach -```{code-cell} +```{code-cell} ipython3 def wrapper_fit_func(x, N, args): """ Fit function wrapper that unpacks its arguments. """ x = np.array(x) @@ -404,7 +404,7 @@ def fitter(ans, tlist, k): return (a, b) ``` -```{code-cell} +```{code-cell} ipython3 # Fitting the real part of the correlation function: # Correlation function values to fit: @@ -419,7 +419,7 @@ with timer("Correlation (real) fitting time"): poptR.append(fitter(corrRana, tlist_fit, i + 1)) ``` -```{code-cell} +```{code-cell} ipython3 plt.plot(tlist_fit, corrRana, label="Analytic") for i in range(kR): @@ -431,7 +431,7 @@ plt.legend() plt.show() ``` -```{code-cell} +```{code-cell} ipython3 # Set the exponential coefficients from the fit parameters ckAR1 = poptR[-1][0] @@ -446,7 +446,7 @@ ckAI = [lam * gamma * (-1.0) + 0j] vkAI = [gamma + 0j] ``` -```{code-cell} +```{code-cell} ipython3 options = Options(nsteps=1500, store_states=True, rtol=1e-12, atol=1e-12) with timer("RHS construction time"): @@ -462,7 +462,7 @@ with timer("ODE solver time"): ## Simulation 5: Bloch-Redfield -```{code-cell} +```{code-cell} ipython3 DL = ( "2 * pi * 2.0 * {lam} / (pi * {gamma} * {beta}) if (w==0) " "else 2 * pi * (2.0 * {lam} * {gamma} * w / (pi * (w**2 + {gamma}**2))) " @@ -480,7 +480,7 @@ resultBR = brmesolve( Finally, let's plot all of our different results to see how they shape up against each other. -```{code-cell} +```{code-cell} ipython3 # Calculate expectation values in the bases: P11_mats = np.real(expect(resultMats.states, P11p)) P11_matsT = np.real(expect(resultMatsT.states, P11p)) @@ -489,7 +489,7 @@ P11_fit = np.real(expect(resultFit.states, P11p)) P11_br = np.real(expect(resultBR.states, P11p)) ``` -```{code-cell} +```{code-cell} ipython3 rcParams = { "axes.titlesize": 25, "axes.labelsize": 30, @@ -506,7 +506,7 @@ rcParams = { } ``` -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) with plt.rc_context(rcParams): @@ -546,7 +546,7 @@ with plt.rc_context(rcParams): ## About -```{code-cell} +```{code-cell} ipython3 qutip.about() ``` @@ -554,7 +554,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 assert np.allclose(P11_matsT, P11_pade, rtol=1e-3) assert np.allclose(P11_matsT, P11_fit, rtol=1e-3) ``` diff --git a/tutorials-v4/heom/heom-1c-spin-bath-model-underdamped-sd.md b/tutorials-v4/heom/heom-1c-spin-bath-model-underdamped-sd.md index 1e6e4595..7828fbf6 100644 --- a/tutorials-v4/heom/heom-1c-spin-bath-model-underdamped-sd.md +++ b/tutorials-v4/heom/heom-1c-spin-bath-model-underdamped-sd.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.1 + jupytext_version: 1.14.4 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -76,7 +76,7 @@ Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. ## Setup -```{code-cell} +```{code-cell} ipython3 import contextlib import time @@ -104,19 +104,19 @@ from qutip.nonmarkov.heom import ( %matplotlib inline ``` -```{code-cell} +```{code-cell} ipython3 def cot(x): """ Vectorized cotangent of x. """ return 1. / np.tan(x) ``` -```{code-cell} +```{code-cell} ipython3 def coth(x): """ Vectorized hyperbolic cotangent of x. """ return 1. / np.tanh(x) ``` -```{code-cell} +```{code-cell} ipython3 def underdamped_matsubara_params(lam, gamma, T, nk): """ Calculation of the real and imaginary expansions of the underdamped correlation functions. @@ -158,7 +158,7 @@ def underdamped_matsubara_params(lam, gamma, T, nk): return ckAR, vkAR, ckAI, vkAI ``` -```{code-cell} +```{code-cell} ipython3 def plot_result_expectations(plots, axes=None): """ Plot the expectation values of operators as functions of time. @@ -186,7 +186,7 @@ def plot_result_expectations(plots, axes=None): return fig ``` -```{code-cell} +```{code-cell} ipython3 @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -200,19 +200,19 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} +```{code-cell} ipython3 # Defining the system Hamiltonian eps = .5 # Energy of the 2-level system. Del = 1.0 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` -```{code-cell} +```{code-cell} ipython3 # Initial state of the system. rho0 = basis(2, 0) * basis(2, 0).dag() ``` -```{code-cell} +```{code-cell} ipython3 # System-bath coupling (underdamed spectral density) Q = sigmaz() # coupling operator @@ -236,7 +236,7 @@ NC = 10 tlist = np.linspace(0, 50, 1000) ``` -```{code-cell} +```{code-cell} ipython3 # Define some operators with which we will measure the system # 1,1 element of density matrix - corresonding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -247,7 +247,7 @@ P12p = basis(2, 0) * basis(2, 1).dag() ### First let us look at what the underdamped spectral density looks like: -```{code-cell} +```{code-cell} ipython3 def plot_spectral_density(): """ Plot the underdamped spectral density """ w = np.linspace(0, 5, 1000) @@ -268,7 +268,7 @@ The correlation functions are now very oscillatory, because of the Lorentzian pe ### So next, let us plot the correlation functions themselves: -```{code-cell} +```{code-cell} ipython3 def Mk(t, k, gamma, w0, beta): """ Calculate the Matsubara terms for a given t and k. """ Om = np.sqrt(w0**2 - (gamma / 2)**2) @@ -320,7 +320,7 @@ plot_correlation_function() It is useful to look at what the Matsubara contributions do to this spectral density. We see that they modify the real part around $t=0$: -```{code-cell} +```{code-cell} ipython3 def plot_matsubara_correlation_function_contributions(): """ Plot the underdamped correlation function. """ t = np.linspace(0, 20, 1000) @@ -354,7 +354,7 @@ Next we calculate the exponents using the Matsubara decompositions. Here we spli The HEOM code will optimize these, and reduce the number of exponents when real and imaginary parts have the same exponent. This is clearly the case for the first term in the vkAI and vkAR lists. -```{code-cell} +```{code-cell} ipython3 ckAR, vkAR, ckAI, vkAI = underdamped_matsubara_params( lam=lam, gamma=gamma, T=T, nk=Nk, ) @@ -366,7 +366,7 @@ The solver constructs the "right hand side" (RHS) determinining how the system a Below we create the bath and solver and then solve for the dynamics by calling `.run(rho0, tlist)`. -```{code-cell} +```{code-cell} ipython3 options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14) with timer("RHS construction time"): @@ -377,7 +377,7 @@ with timer("ODE solver time"): resultMats = HEOMMats.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 plot_result_expectations([ (resultMats, P11p, 'b', "P11 Mats"), (resultMats, P12p, 'r', "P12 Mats"), @@ -390,7 +390,7 @@ to perform this expansion will allow you to construct your own baths for other s Below we show how to use this built-in functionality: -```{code-cell} +```{code-cell} ipython3 # Compare to built-in under-damped bath: with timer("RHS construction time"): @@ -401,7 +401,7 @@ with timer("ODE solver time"): result_udbath = HEOM_udbath.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 plot_result_expectations([ (result_udbath, P11p, 'b', "P11 (UnderDampedBath)"), (result_udbath, P12p, 'r', "P12 (UnderDampedBath)"), @@ -410,7 +410,7 @@ plot_result_expectations([ ### We can compare these results to those of the Bloch-Redfield solver in QuTiP: -```{code-cell} +```{code-cell} ipython3 UD = ( f"2 * {lam}**2 * {gamma} / ( {w0}**4 * {beta}) if (w==0)" " else " @@ -427,7 +427,7 @@ with timer("ODE solver time"): ) ``` -```{code-cell} +```{code-cell} ipython3 plot_result_expectations([ (resultMats, P11p, 'b', "P11 Mats"), (resultMats, P12p, 'r', "P12 Mats"), @@ -442,7 +442,7 @@ plot_result_expectations([ The thermal state of a reaction coordinate (treating the environment as a single damped mode) should, at high temperatures and small gamma, tell us the steady-state: -```{code-cell} +```{code-cell} ipython3 dot_energy, dot_state = Hsys.eigenstates() deltaE = dot_energy[1] - dot_energy[0] @@ -475,7 +475,7 @@ P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag()) P11RC = expect(rhoss, P11RC) ``` -```{code-cell} +```{code-cell} ipython3 rcParams = { "axes.titlesize": 25, "axes.labelsize": 30, @@ -492,7 +492,7 @@ rcParams = { } ``` -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) with plt.rc_context(rcParams): @@ -521,7 +521,7 @@ with plt.rc_context(rcParams): ## About -```{code-cell} +```{code-cell} ipython3 qutip.about() ``` @@ -529,7 +529,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 assert np.allclose( expect(P11p, resultMats.states[-100:]), P11RC, rtol=1e-2, ) diff --git a/tutorials-v4/heom/heom-1d-spin-bath-model-ohmic-fitting.md b/tutorials-v4/heom/heom-1d-spin-bath-model-ohmic-fitting.md index 33e49619..0a9f84ee 100644 --- a/tutorials-v4/heom/heom-1d-spin-bath-model-ohmic-fitting.md +++ b/tutorials-v4/heom/heom-1d-spin-bath-model-ohmic-fitting.md @@ -5,9 +5,9 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.1 + jupytext_version: 1.14.5 kernelspec: - display_name: qutip-dev + display_name: Python 3 (ipykernel) language: python name: python3 --- @@ -59,7 +59,7 @@ from qutip import ( spost, spre, ) -from qutip.solver.heom import ( +from qutip.nonmarkov.heom import ( HEOMSolver, BosonicBath, ) diff --git a/tutorials-v4/heom/heom-1e-spin-bath-model-pure-dephasing.md b/tutorials-v4/heom/heom-1e-spin-bath-model-pure-dephasing.md index e9eedff9..129e04f2 100644 --- a/tutorials-v4/heom/heom-1e-spin-bath-model-pure-dephasing.md +++ b/tutorials-v4/heom/heom-1e-spin-bath-model-pure-dephasing.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.1 + jupytext_version: 1.14.4 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -66,7 +66,7 @@ Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. ## Setup -```{code-cell} +```{code-cell} ipython3 import contextlib import time @@ -98,7 +98,7 @@ from qutip.nonmarkov.heom import ( Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: -```{code-cell} +```{code-cell} ipython3 def cot(x): """ Vectorized cotangent of x. """ return 1. / np.tan(x) @@ -109,7 +109,7 @@ def coth(x): return 1. / np.tanh(x) ``` -```{code-cell} +```{code-cell} ipython3 def plot_result_expectations(plots, axes=None): """ Plot the expectation values of operators as functions of time. @@ -141,7 +141,7 @@ def plot_result_expectations(plots, axes=None): return fig ``` -```{code-cell} +```{code-cell} ipython3 @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -163,14 +163,14 @@ And let us set up the system Hamiltonian, bath and system measurement operators: Here we set $H_{sys}=0$, which means the interaction Hamiltonian and the system Hamiltonian commute, and we can compare the numerical results to a known analytical one. We could in principle keep $\epsilon \neq 0$, but it just introduces fast system oscillations, so it is more convenient to set it to zero. -```{code-cell} +```{code-cell} ipython3 # Defining the system Hamiltonian eps = 0.0 # Energy of the 2-level system. Del = 0.0 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` -```{code-cell} +```{code-cell} ipython3 # System-bath coupling (Drude-Lorentz spectral density) Q = sigmaz() # coupling operator @@ -191,7 +191,7 @@ Nk = 3 tlist = np.linspace(0, 50, 1000) ``` -```{code-cell} +```{code-cell} ipython3 # Define some operators with which we will measure the system # 1,1 element of density matrix - corresonding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -202,7 +202,7 @@ P12p = basis(2, 0) * basis(2, 1).dag() To get a non-trivial result we prepare the initial state in a superposition, and see how the bath destroys the coherence. -```{code-cell} +```{code-cell} ipython3 # Initial state of the system. psi = (basis(2, 0) + basis(2, 1)).unit() rho0 = psi * psi.dag() @@ -210,7 +210,7 @@ rho0 = psi * psi.dag() ## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator -```{code-cell} +```{code-cell} ipython3 options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14) with timer("RHS construction time"): @@ -221,7 +221,9 @@ with timer("ODE solver time"): resultMats = HEOMMats.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 +:tags: [] + # Plot the results so far plot_result_expectations([ (resultMats, P11p, 'b', "P11 Matsubara"), @@ -231,7 +233,7 @@ plot_result_expectations([ ## Simulation 2: Matsubara decomposition (including terminator) -```{code-cell} +```{code-cell} ipython3 options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14) with timer("RHS construction time"): @@ -244,7 +246,7 @@ with timer("ODE solver time"): resultMatsT = HEOMMatsT.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 # Plot the results plot_result_expectations([ (resultMats, P11p, 'b', "P11 Matsubara"), @@ -258,7 +260,7 @@ plot_result_expectations([ As in example 1a, we can compare to Pade and Fitting approaches. -```{code-cell} +```{code-cell} ipython3 options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14) with timer("RHS construction time"): @@ -269,7 +271,7 @@ with timer("ODE solver time"): resultPade = HEOMPade.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 # Plot the results plot_result_expectations([ (resultMatsT, P11p, 'b', "P11 Matsubara (+term)"), @@ -281,7 +283,9 @@ plot_result_expectations([ ## Simulation 4: Fitting approach -```{code-cell} +```{code-cell} ipython3 +:tags: [] + def c(t, Nk): """ Calculates real and imag. parts of the correlation function using Nk Matsubara terms. @@ -309,7 +313,7 @@ corr_ana = c(tlist_fit, lmaxmats) corrRana, corrIana = np.real(corr_ana), np.imag(corr_ana) ``` -```{code-cell} +```{code-cell} ipython3 def wrapper_fit_func(x, N, *args): """ Wrapper for fitting function. """ a, b = args[0][:N], args[0][N:2*N] @@ -377,7 +381,7 @@ for i in range(1): plt.show() ``` -```{code-cell} +```{code-cell} ipython3 # Set the exponential coefficients from the fit parameters ckAR = popt1[-1][0] @@ -393,7 +397,7 @@ vkAI = -1 * popt2[-1][1] # vkAI = [complex(gamma)] ``` -```{code-cell} +```{code-cell} ipython3 options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14) with timer("RHS construction time"): @@ -406,7 +410,7 @@ with timer("ODE solver time"): ## Analytic calculations -```{code-cell} +```{code-cell} ipython3 def pure_dephasing_evolution_analytical(tlist, wq, ck, vk): """ Computes the propagating function appearing in the pure dephasing model. @@ -483,7 +487,7 @@ def correlation_integral(t, ck, vk): For the pure dephasing analytics, we just sum up as many matsubara terms as we can: -```{code-cell} +```{code-cell} ipython3 lmaxmats2 = 15000 vk = [complex(-gamma)] @@ -505,7 +509,7 @@ P12_ana = 0.5 * pure_dephasing_evolution_analytical( Alternatively, we can just do the integral of the propagator directly, without using the correlation functions at all -```{code-cell} +```{code-cell} ipython3 def JDL(omega, lamc, omega_c): return 2. * lamc * omega * omega_c / (omega_c**2 + omega**2) @@ -528,7 +532,7 @@ P12_ana2 = [ ## Compare results -```{code-cell} +```{code-cell} ipython3 plot_result_expectations([ (resultMats, P12p, 'r', "P12 Mats"), (resultMatsT, P12p, 'r--', "P12 Mats + Term"), @@ -541,7 +545,7 @@ plot_result_expectations([ We can't see much difference in the plot above, so let's do a log plot instead: -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) plot_result_expectations([ @@ -559,7 +563,7 @@ axes.legend(loc=0, fontsize=12); ## About -```{code-cell} +```{code-cell} ipython3 qutip.about() ``` @@ -567,7 +571,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 assert np.allclose( expect(P12p, resultMats.states[:15]), np.real(P12_ana)[:15], rtol=1e-2, diff --git a/tutorials-v4/heom/heom-2-fmo-example.md b/tutorials-v4/heom/heom-2-fmo-example.md index ce279d48..81c8fc50 100644 --- a/tutorials-v4/heom/heom-2-fmo-example.md +++ b/tutorials-v4/heom/heom-2-fmo-example.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.1 + jupytext_version: 1.14.4 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -31,7 +31,7 @@ quantum environment reduces the effect of pure dephasing. ## Setup -```{code-cell} +```{code-cell} ipython3 import contextlib import time @@ -60,19 +60,21 @@ from qutip.nonmarkov.heom import ( Let's define some helper functions for calculating correlation functions, spectral densities, thermal energy level occupations, and for plotting results and timing how long operations take: -```{code-cell} +```{code-cell} ipython3 def cot(x): """ Vectorized cotangent of x. """ return 1 / np.tan(x) ``` -```{code-cell} +```{code-cell} ipython3 def J0(energy): """ Under-damped brownian oscillator spectral density. """ return 2 * lam * gamma * energy / (energy**2 + gamma**2) ``` -```{code-cell} +```{code-cell} ipython3 +:tags: [] + def J0_dephasing(): """ Under-damped brownian oscillator dephasing probability. @@ -81,13 +83,13 @@ def J0_dephasing(): return 2 * lam * gamma / gamma**2 ``` -```{code-cell} +```{code-cell} ipython3 def n_th(energy, T): """ The average occupation of a given energy level at temperature T. """ return 1 / (np.exp(energy / T) - 1) ``` -```{code-cell} +```{code-cell} ipython3 def dl_corr_approx(t, nk): """ Drude-Lorenz correlation function approximation. @@ -100,7 +102,9 @@ def dl_corr_approx(t, nk): return c ``` -```{code-cell} +```{code-cell} ipython3 +:tags: [] + @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -118,7 +122,9 @@ def timer(label): And let us set up the system Hamiltonian and bath parameters: -```{code-cell} +```{code-cell} ipython3 +:tags: [] + # System Hamiltonian: # # We use the Hamiltonian employed in @@ -136,7 +142,9 @@ Hsys = 3e10 * 2 * np.pi * Qobj([ ]) ``` -```{code-cell} +```{code-cell} ipython3 +:tags: [] + # Bath parameters lam = 35 * 3e10 * 2 * np.pi @@ -149,7 +157,7 @@ beta = 1 / T Let's quickly plot the spectral density and environment correlation functions so that we can see what they look like. -```{code-cell} +```{code-cell} ipython3 wlist = np.linspace(0, 200 * 3e10 * 2 * np.pi, 100) tlist = np.linspace(0, 1e-12, 1000) @@ -185,7 +193,7 @@ axes[1].legend(); Now let us solve for the evolution of this system using the HEOM. -```{code-cell} +```{code-cell} ipython3 # We start the excitation at site 1: rho0 = basis(7, 0) * basis(7, 0).dag() @@ -214,7 +222,7 @@ for m in range(7): Ltot += terminator ``` -```{code-cell} +```{code-cell} ipython3 with timer("RHS construction time"): HEOMMats = HEOMSolver(Hsys, baths, NC, options=options) @@ -222,7 +230,7 @@ with timer("ODE solver time"): outputFMO_HEOM = HEOMMats.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, figsize=(12, 8)) colors = ['r', 'g', 'b', 'y', 'c', 'm', 'k'] @@ -258,7 +266,7 @@ Now let us solve the same problem using the Bloch-Redfield solver. We will see t In the next section, we will examine the role of pure dephasing in the evolution to understand why this happens. -```{code-cell} +```{code-cell} ipython3 DL = ( f"2 * pi * 2.0 * {lam} / (pi * {gamma} * {beta}) if (w == 0) else " f"2 * pi * (2.0*{lam}*{gamma} *w /(pi*(w**2+{gamma}**2))) * " @@ -277,7 +285,9 @@ with timer("BR ODE solver time"): And now let's plot the Bloch-Redfield solver results: -```{code-cell} +```{code-cell} ipython3 +:tags: [] + fig, axes = plt.subplots(1, 1, figsize=(12, 8)) for m, Q in enumerate(Q_list): @@ -305,7 +315,7 @@ It is useful to construct the various parts of the Bloch-Redfield master equatio First we will write a function to return the list of collapse operators for a given system, either with or without the dephasing operators: -```{code-cell} +```{code-cell} ipython3 def get_collapse(H, T, dephasing=1): """ Calculate collapse operators for a given system H and temperature T. @@ -370,7 +380,7 @@ Now we are able to switch the pure dephasing tersms on and off. Let us starting by including the dephasing operators. We expect to see the same behaviour that we saw when using the Bloch-Redfield solver. -```{code-cell} +```{code-cell} ipython3 # dephasing terms on, we recover the full BR solution: with timer("Building the collapse operators"): @@ -380,7 +390,9 @@ with timer("ME ODE solver"): outputFMO_ME = mesolve(Hsys, rho0, tlist, collapse_list) ``` -```{code-cell} +```{code-cell} ipython3 +:tags: [] + fig, axes = plt.subplots(1, 1, figsize=(12, 8)) for m, Q in enumerate(Q_list): @@ -399,7 +411,7 @@ We see similar results to before. Now let us examine what happens when we remove the dephasing collapse operators: -```{code-cell} +```{code-cell} ipython3 # dephasing terms off with timer("Building the collapse operators"): @@ -409,7 +421,9 @@ with timer("ME ODE solver"): outputFMO_ME_nodephase = mesolve(Hsys, rho0, tlist, collapse_list) ``` -```{code-cell} +```{code-cell} ipython3 +:tags: [] + fig, axes = plt.subplots(1, 1, figsize=(12, 8)) for m, Q in enumerate(Q_list): axes.plot( @@ -433,7 +447,7 @@ And now we see that without the dephasing, the oscillations reappear. The full d ## About -```{code-cell} +```{code-cell} ipython3 qutip.about() ``` @@ -441,7 +455,9 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 +:tags: [] + assert np.allclose( expect(outputFMO_BR.states, Q_list[0]), expect(outputFMO_ME.states, Q_list[0]), diff --git a/tutorials-v4/heom/heom-3-quantum-heat-transport.md b/tutorials-v4/heom/heom-3-quantum-heat-transport.md index c4b448e2..155bee73 100644 --- a/tutorials-v4/heom/heom-3-quantum-heat-transport.md +++ b/tutorials-v4/heom/heom-3-quantum-heat-transport.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.1 + jupytext_version: 1.14.4 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -50,7 +50,7 @@ References: ## Setup -```{code-cell} +```{code-cell} ipython3 import dataclasses import numpy as np @@ -71,7 +71,9 @@ from IPython.display import display ## System and bath definition -```{code-cell} +```{code-cell} ipython3 +:tags: [] + @dataclasses.dataclass class SystemParams: """ System parameters and Hamiltonian. """ @@ -100,7 +102,9 @@ class SystemParams: return dataclasses.replace(self, **kw) ``` -```{code-cell} +```{code-cell} ipython3 +:tags: [] + @dataclasses.dataclass class BathParams: """ Bath parameters. """ @@ -153,7 +157,7 @@ In the expression for the bath heat currents, we left out terms involving $[Q_1, In QuTiP, these currents can be conveniently calculated as follows: -```{code-cell} +```{code-cell} ipython3 def bath_heat_current(bath_tag, ado_state, hamiltonian, coupling_op, delta=0): """ Bath heat current from the system into the heat bath with the given tag. @@ -248,7 +252,7 @@ Note that at long times, we expect $j_{\text{B}}^1 = -j_{\text{B}}^2$ and $j_{\t For our simulations, we will represent the bath spectral densities using the first term of their Padé decompositions, and we will use $7$ levels of the HEOM hierarchy. -```{code-cell} +```{code-cell} ipython3 Nk = 1 NC = 7 options = qt.Options(nsteps=1500, store_states=False, atol=1e-12, rtol=1e-12) @@ -259,7 +263,7 @@ options = qt.Options(nsteps=1500, store_states=False, atol=1e-12, rtol=1e-12) We fix $J_{12} = 0.1 \epsilon$ (as in Fig. 3(a-ii) of Ref. \[2\]) and choose the fixed coupling strength $\lambda_1 = \lambda_2 = J_{12}\, /\, (2\epsilon)$ (corresponding to $\bar\zeta = 1$ in Ref. \[2\]). Using these values, we will study the time evolution of the system state and the heat currents. -```{code-cell} +```{code-cell} ipython3 # fix qubit-qubit and qubit-bath coupling strengths sys = SystemParams(J12=0.1) bath_p1 = BathParams(qubit=0, sign="+", lam=sys.J12 / 2) @@ -272,7 +276,7 @@ rho0 = qt.tensor(qt.identity(2), qt.identity(2)) / 4 tlist = np.linspace(0, 50, 250) ``` -```{code-cell} +```{code-cell} ipython3 H = sys.H() bath1 = bath_p1.bath(Nk, tag='bath 1') @@ -301,7 +305,7 @@ result = solver.run(rho0, tlist, e_ops=[ We first plot $\langle \sigma_z^1 \rangle$ to see the time evolution of the system state: -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(figsize=(8, 8)) axes.plot(tlist, result.expect[0], 'r', linewidth=2) axes.set_xlabel('t', fontsize=28) @@ -310,7 +314,7 @@ axes.set_ylabel(r"$\langle \sigma_z^1 \rangle$", fontsize=28); We find a rather quick thermalization of the system state. For the heat currents, however, it takes a somewhat longer time until they converge to their long-time values: -```{code-cell} +```{code-cell} ipython3 fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8)) ax1.plot( @@ -362,7 +366,7 @@ ax2.legend(loc=0, fontsize=12); Here, we try to reproduce the HEOM curves in Fig. 3(a) of Ref. \[1\] by varying the coupling strength and finding the steady state for each coupling strength. -```{code-cell} +```{code-cell} ipython3 def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options): """ Calculate the steady sate heat currents for the given system and bath. @@ -393,7 +397,7 @@ def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options): ) ``` -```{code-cell} +```{code-cell} ipython3 # Define number of points to use for the plot plot_points = 10 # use 100 for a smoother curve @@ -446,7 +450,7 @@ j3s = [ ## Create Plot -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(figsize=(12, 7)) axes.plot( @@ -477,7 +481,7 @@ axes.legend(loc=0); ## About -```{code-cell} +```{code-cell} ipython3 qt.about() ``` @@ -485,6 +489,8 @@ qt.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 +:tags: [] + assert 1 == 1 ``` diff --git a/tutorials-v4/heom/heom-4-dynamical-decoupling.md b/tutorials-v4/heom/heom-4-dynamical-decoupling.md index d8675e18..4c4da69a 100644 --- a/tutorials-v4/heom/heom-4-dynamical-decoupling.md +++ b/tutorials-v4/heom/heom-4-dynamical-decoupling.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.1 + jupytext_version: 1.14.4 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -27,7 +27,7 @@ We first show the standard example of equally spaced pulses, and then consider t ## Setup -```{code-cell} +```{code-cell} ipython3 import numpy as np import matplotlib.pyplot as plt @@ -56,7 +56,9 @@ from IPython.display import display Let's define some helper functions for calculating the spectral density: -```{code-cell} +```{code-cell} ipython3 +:tags: [] + def dl_spectrum(w, lam, gamma): """ Return the Drude-Lorentz spectral density. """ J = w * 2 * lam * gamma / (gamma**2 + w**2) @@ -67,7 +69,9 @@ def dl_spectrum(w, lam, gamma): Now we define the system and bath properties and the HEOM parameters. The system is a single stationary qubit with $H = 0$ and the bath is a bosonic bath with a Drude-Lorentz spectrum. -```{code-cell} +```{code-cell} ipython3 +:tags: [] + # Define the system Hamlitonian. # # The system isn't evolving by itself, so the Hamiltonian is 0 (with the @@ -76,7 +80,9 @@ Now we define the system and bath properties and the HEOM parameters. The system H_sys = 0 * sigmaz() ``` -```{code-cell} +```{code-cell} ipython3 +:tags: [] + # Define some operators with which we will measure the system # 1,1 element of density matrix - corresonding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -85,7 +91,9 @@ P22p = basis(2, 1) * basis(2, 1).dag() P12p = basis(2, 0) * basis(2, 1).dag() ``` -```{code-cell} +```{code-cell} ipython3 +:tags: [] + # Properties for the Drude-Lorentz bath lam = 0.0005 @@ -101,7 +109,9 @@ Nk = 3 bath = DrudeLorentzPadeBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) ``` -```{code-cell} +```{code-cell} ipython3 +:tags: [] + # HEOM parameters # number of layers to keep in the hierarchy: @@ -112,7 +122,9 @@ To perform the dynamic decoupling from the environment, we will drive the system Below we define a function that returns the pulse (which is itself a function): -```{code-cell} +```{code-cell} ipython3 +:tags: [] + def drive(amplitude, delay, integral): """ Coefficient of the drive as a function of time. @@ -149,7 +161,7 @@ H_drive = sigmax() Let's start by plotting the spectral density of our Drude-Lorentz bath: -```{code-cell} +```{code-cell} ipython3 wlist = np.linspace(0, 0.5, 1000) J = dl_spectrum(wlist, lam, gamma) @@ -167,7 +179,9 @@ First we will drive the system with fast, large amplitude pulses. Then we will d Let's start by simulating the fast pulses: -```{code-cell} +```{code-cell} ipython3 +:tags: [] + # Fast driving (quick, large amplitude pulses) # The max_step must be set to a short time than the @@ -201,7 +215,9 @@ outputDD = hsolver.run(rho0, tlist, ado_return=True) And now the longer slower pulses: -```{code-cell} +```{code-cell} ipython3 +:tags: [] + # Slow driving (longer, small amplitude pulses) # without pulses @@ -218,7 +234,9 @@ outputDDslow = hsolver.run(rho0, tlist, ado_return=True) Now let's plot all of the results and the shapes of the pulses: -```{code-cell} +```{code-cell} ipython3 +:tags: [] + def plot_dd_results(outputnoDD, outputDD, outputDDslow): fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 12)) @@ -284,7 +302,9 @@ def plot_dd_results(outputnoDD, outputDD, outputDDslow): fig.tight_layout() ``` -```{code-cell} +```{code-cell} ipython3 +:tags: [] + plot_dd_results(outputnoDD, outputDD, outputDDslow) ``` @@ -304,7 +324,9 @@ $$ This is just a convenient way to describe the varying delay. We could have chosen another monotonically increasing function to represent the cummulative delay (although it might not be as effective). -```{code-cell} +```{code-cell} ipython3 +:tags: [] + def cummulative_delay_fractions(N): """ Return an array of N + 1 cummulative delay fractions. @@ -358,7 +380,7 @@ Let's plot the cummulative delays and see what they look like. Note that the cum On the same axes we plot the individual $j^{th}$ delays as a fraction of the average delay. -```{code-cell} +```{code-cell} ipython3 def plot_cummulative_delay_fractions(N): cummulative = cummulative_delay_fractions(N) individual = (cummulative[1:] - cummulative[:-1]) * N @@ -374,7 +396,7 @@ plot_cummulative_delay_fractions(100) And now let us plot the first ten even and optimally spaced pulses together to compare them: -```{code-cell} +```{code-cell} ipython3 def plot_even_and_optimally_spaced_pulses(): amplitude = 10.0 integral = np.pi / 2 @@ -398,11 +420,13 @@ def plot_even_and_optimally_spaced_pulses(): plot_even_and_optimally_spaced_pulses() ``` ++++ {"tags": []} + Now let's simulate the effectiveness of the two sets of delays by comparing how well they maintain coherence after a hundred pulses. We'll perform the simulation over a range of lambdas and gammas to show how the non-evenly spaced delays become optimal as the width of the bath spectral function increases. -```{code-cell} +```{code-cell} ipython3 # Bath parameters to simulate over: # We use only two lambdas and two gammas so that the notebook executes @@ -482,7 +506,7 @@ P12_results = [ Now that we have the expectation values of $\rho_{01}$ let's plot them as a function of gamma for each lambda. Note how in each case the non-evenly spaced pulses become optimal once gamma is sufficiently small: -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, sharex=False, figsize=(10, 7)) colors = ["green", "red", "blue"] @@ -512,7 +536,9 @@ And now you know about dynamically decoupling a qubit from its environment! ## About -```{code-cell} +```{code-cell} ipython3 +:tags: [] + qutip.about() ``` @@ -520,6 +546,8 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 +:tags: [] + assert 1 == 1 ``` diff --git a/tutorials-v4/heom/heom-5a-fermions-single-impurity-model.md b/tutorials-v4/heom/heom-5a-fermions-single-impurity-model.md index d249fe78..623af4db 100644 --- a/tutorials-v4/heom/heom-5a-fermions-single-impurity-model.md +++ b/tutorials-v4/heom/heom-5a-fermions-single-impurity-model.md @@ -5,13 +5,15 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.1 + jupytext_version: 1.14.4 kernelspec: display_name: Python 3 (ipykernel) language: python name: python3 --- ++++ {"tags": []} + # HEOM 5a: Fermionic single impurity model +++ @@ -69,7 +71,7 @@ In this notebook we: ## Setup -```{code-cell} +```{code-cell} ipython3 import contextlib import dataclasses import time @@ -99,7 +101,9 @@ from IPython.display import display ## Helpers -```{code-cell} +```{code-cell} ipython3 +:tags: [] + @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -113,11 +117,15 @@ def timer(label): print(f"{label}: {end - start}") ``` ++++ {"tags": []} + ## System and bath definition And let us set up the system Hamiltonian, bath and system measurement operators: -```{code-cell} +```{code-cell} ipython3 +:tags: [] + # Define the system Hamiltonian: # The system is a single fermion with energy level split e1: @@ -126,7 +134,9 @@ e1 = 1.0 H = e1 * d1.dag() * d1 ``` -```{code-cell} +```{code-cell} ipython3 +:tags: [] + # Define parameters for left and right fermionic baths. # Each bath is a lead (i.e. a wire held at a potential) # with temperature T and chemical potential mu. @@ -179,7 +189,9 @@ bath_R = LorentzianBathParameters(Q=d1, lead="R") Let's plot the spectral density. -```{code-cell} +```{code-cell} ipython3 +:tags: [] + w_list = np.linspace(-2, 2, 100) fig, ax = plt.subplots(figsize=(12, 7)) @@ -207,7 +219,7 @@ ax.legend(); Next let's plot the emission and absorption by the leads. -```{code-cell} +```{code-cell} ipython3 w_list = np.linspace(-2, 2, 100) fig, ax = plt.subplots(figsize=(12, 7)) @@ -253,7 +265,7 @@ ax.legend(); Let's start by solving for the evolution using a Pade expansion of the correlation function of the Lorentzian spectral density: -```{code-cell} +```{code-cell} ipython3 # HEOM dynamics using the Pade approximation: # Solver options, times to solve for and initial system state: @@ -284,7 +296,7 @@ with timer("Steady state solver time"): Now let us plot the result which shows the decay of the initially excited impurity. This is not very illuminating, but we will compare it with the Matsubara expansion and analytic solution sortly: -```{code-cell} +```{code-cell} ipython3 # Plot the Pade results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -305,7 +317,7 @@ axes.legend(fontsize=12); Now let us do the same for the Matsubara expansion: -```{code-cell} +```{code-cell} ipython3 # HEOM dynamics using the Matsubara approximation: bathL = LorentzianBath( @@ -329,7 +341,9 @@ with timer("Steady state solver time"): We see a marked difference in the Matsubara vs Pade results: -```{code-cell} +```{code-cell} ipython3 +:tags: [] + # Plot the Pade results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -365,7 +379,7 @@ One advantage of this simple model is that the steady state current to the baths See the [QuTiP-BoFiN paper](https://arxiv.org/abs/2010.10806) for a detailed description and references for the analytic result. Below we just perform the required integration numerically. -```{code-cell} +```{code-cell} ipython3 def analytical_steady_state_current(bath_L, bath_R, e1): """ Calculate the analytical steady state current. """ @@ -403,7 +417,7 @@ To compare the analytical result above with the result from the HEOM, we need to In the function `state_current(...)` below, we extract the first level ADOs for the specified bath and sum the contributions to the current from each: -```{code-cell} +```{code-cell} ipython3 def state_current(ado_state, bath_tag): """ Determine current from the given bath (either "R" or "L") to the system in the given ADO state. @@ -427,7 +441,9 @@ def state_current(ado_state, bath_tag): Now we can calculate the steady state currents from the Pade and Matsubara HEOM results: -```{code-cell} +```{code-cell} ipython3 +:tags: [] + curr_ss_pade_L = state_current(ado_ss_pade, "L") curr_ss_pade_R = state_current(ado_ss_pade, "R") @@ -435,7 +451,9 @@ print(f"Pade steady state current (L): {curr_ss_pade_L}") print(f"Pade steady state current (R): {curr_ss_pade_R}") ``` -```{code-cell} +```{code-cell} ipython3 +:tags: [] + curr_ss_mats_L = state_current(ado_ss_mats, "L") curr_ss_mats_R = state_current(ado_ss_mats, "R") @@ -447,7 +465,7 @@ Note that the currents from each bath balance as is required by the steady state Now let's compare all three: -```{code-cell} +```{code-cell} ipython3 print(f"Pade current (R): {curr_ss_pade_R}") print(f"Matsubara current (R): {curr_ss_mats_R}") print(f"Analytical curernt: {curr_ss_analytic}") @@ -467,7 +485,7 @@ Now lets plot the current as a function of bias voltage (the bias voltage is the We will calculate the steady state current for each `theta` both analytically and using the HEOM with the Pade correlation expansion approximation. -```{code-cell} +```{code-cell} ipython3 # Theta (bias voltages) thetas = np.linspace(-4, 4, 100) @@ -528,7 +546,7 @@ curr_ss_pade_theta = [ Below we plot the results and see that even with `Nk=6`, the HEOM Pade approximation gives good results for the steady state current. Increasing `Nk` to `10` gives very accurate results. -```{code-cell} +```{code-cell} ipython3 fig, ax = plt.subplots(figsize=(12, 7)) ax.plot( @@ -555,7 +573,9 @@ ax.legend(fontsize=25); ## About -```{code-cell} +```{code-cell} ipython3 +:tags: [] + qutip.about() ``` @@ -563,7 +583,9 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 +:tags: [] + assert np.allclose(curr_ss_pade_L + curr_ss_pade_R, 0) assert np.allclose(curr_ss_mats_L + curr_ss_mats_R, 0) assert np.allclose(curr_ss_pade_R, curr_ss_analytic, rtol=1e-4) diff --git a/tutorials-v4/heom/heom-5b-fermions-discrete-boson-model.md b/tutorials-v4/heom/heom-5b-fermions-discrete-boson-model.md index 2acc7a6c..19d63126 100644 --- a/tutorials-v4/heom/heom-5b-fermions-discrete-boson-model.md +++ b/tutorials-v4/heom/heom-5b-fermions-discrete-boson-model.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.1 + jupytext_version: 1.14.4 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -60,13 +60,13 @@ $$\gamma_{K,\sigma,0} = W_K - \sigma i\mu_K$$ $$\eta_{K,l\neq 0} = -i\cdot \frac{k_m}{\beta_K} \cdot \frac{\Gamma_K W_K^2}{-\frac{\epsilon^2_m}{\beta_K^2} + W_K^2}$$ -$$\gamma_{K,\sigma,l\neq 0}= \frac{\epsilon_m}{\beta_K} - \sigma i \mu_K$$ +$$\gamma_{K,\sigma,l\neq 0}= \frac{\epsilon_m}{\beta_K} - \sigma i \mu_K$$ +++ ## Differences from Example 5a -+++ ++++ {"tags": []} The system we study here has two big differences from the HEOM 5a example: @@ -95,7 +95,7 @@ The complete setup now consists of four parts: ## Setup -```{code-cell} +```{code-cell} ipython3 import contextlib import dataclasses import time @@ -123,7 +123,9 @@ from IPython.display import display ## Helpers -```{code-cell} +```{code-cell} ipython3 +:tags: [] + @contextlib.contextmanager def timer(label): """ Simple utility for timing functions: @@ -137,7 +139,9 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} +```{code-cell} ipython3 +:tags: [] + def state_current(ado_state, bath_tag): """ Determine current from the given bath (either "R" or "L") to the system in the given ADO state. @@ -163,7 +167,9 @@ def state_current(ado_state, bath_tag): Let us set up the system Hamiltonian and specify the properties of the two reservoirs. -```{code-cell} +```{code-cell} ipython3 +:tags: [] + # Define the system Hamiltonian: @dataclasses.dataclass @@ -190,7 +196,9 @@ class SystemParameters: sys_p = SystemParameters() ``` -```{code-cell} +```{code-cell} ipython3 +:tags: [] + # Define parameters for left and right fermionic baths. # Each bath is a lead (i.e. a wire held at a potential) # with temperature T and chemical potential mu. @@ -244,7 +252,9 @@ bath_R = LorentzianBathParameters(W=10**4, lead="R") Next let's plot the emission and absorption by the leads. -```{code-cell} +```{code-cell} ipython3 +:tags: [] + w_list = np.linspace(-2, 2, 100) fig, ax = plt.subplots(figsize=(12, 7)) @@ -292,7 +302,7 @@ Here we just give one example of the current as a function of bias voltage, but One note: for very large problems, this can be slow. -```{code-cell} +```{code-cell} ipython3 def steady_state_pade_for_theta(sys_p, bath_L, bath_R, theta, Nk, Nc, Nbos): """ Return the steady state current using the Pade approximation. """ options = Options(nsteps=15000, store_states=True, rtol=1e-14, atol=1e-14) @@ -319,7 +329,7 @@ def steady_state_pade_for_theta(sys_p, bath_L, bath_R, theta, Nk, Nc, Nbos): return np.real(2.434e-4 * 1e6 * current) ``` -```{code-cell} +```{code-cell} ipython3 # Parameters: Nk = 6 @@ -343,7 +353,7 @@ for theta in thetas: progress.value += 1 ``` -```{code-cell} +```{code-cell} ipython3 fig, ax = plt.subplots(figsize=(12, 10)) ax.plot( @@ -365,7 +375,9 @@ ax.legend(loc=4); ## About -```{code-cell} +```{code-cell} ipython3 +:tags: [] + qutip.about() ``` @@ -373,6 +385,8 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 +:tags: [] + assert 1 == 1 ``` diff --git a/tutorials-v4/heom/heom-index.md b/tutorials-v4/heom/heom-index.md index 4bc08d9d..dcb1fa89 100644 --- a/tutorials-v4/heom/heom-index.md +++ b/tutorials-v4/heom/heom-index.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.16.1 + jupytext_version: 1.14.4 kernelspec: display_name: Python 3 (ipykernel) language: python diff --git a/tutorials-v4/lectures/Lecture-0-Introduction-to-QuTiP.md b/tutorials-v4/lectures/Lecture-0-Introduction-to-QuTiP.md index a5ade5ef..533ed6b4 100644 --- a/tutorials-v4/lectures/Lecture-0-Introduction-to-QuTiP.md +++ b/tutorials-v4/lectures/Lecture-0-Introduction-to-QuTiP.md @@ -40,7 +40,7 @@ It includes facilities for representing and doing calculations with quantum obje It also includes solvers for a time-evolution of quantum systems, according to: Schrodinger equation, von Neuman equation, master equations, Floquet formalism, Monte-Carlo quantum trajectors, experimental implementations of the stochastic Schrodinger/master equations. For more information see the project web site at [qutip.org](https://qutip.org), and the -[QuTiP documentation](https://qutip.readthedocs.io/en/latest/index.html). +[QuTiP documentation](https://qutip.org/docs/latest/index.html). ### Installation @@ -51,7 +51,11 @@ You can install QuTiP directly from `pip` by running: For further installation details, refer to the [GitHub repository](https://github.com/qutip/qutip). -To use QuTiP in a Python program, first include the relevant functionality from the `qutip` module as shown above. +To use QuTiP in a Python program, first inlude the relevant functionality from the `qutip` module: + +```python + +``` This will make the functions and classes in QuTiP available in the rest of the program. @@ -148,7 +152,7 @@ H.tr() H.eigenenergies() ``` -For a complete list of methods and properties of the `Qobj` class, see the [QuTiP documentation](https://qutip.readthedocs.io/en/latest/index.html) or try `help(Qobj)` or `dir(Qobj)`. +For a complete list of methods and properties of the `Qobj` class, see the [QuTiP documentation](https://qutip.org/docs/latest/index.html) or try `help(Qobj)` or `dir(Qobj)`. ## States and operators diff --git a/tutorials-v4/lectures/Lecture-11-Charge-Qubits.md b/tutorials-v4/lectures/Lecture-11-Charge-Qubits.md index 70ed1617..dbc8e279 100644 --- a/tutorials-v4/lectures/Lecture-11-Charge-Qubits.md +++ b/tutorials-v4/lectures/Lecture-11-Charge-Qubits.md @@ -39,7 +39,7 @@ where $E_C$ is the charge energy, $E_J$ is the Josephson energy, and $\left| n\r #### References - * [J. Koch et al, Phys. Rec. A 76, 042319 (2007)](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.76.042319) + * [J. Koch et al, Phys. Rec. A 76, 042319 (2007)](http://link.aps.org/doi/10.1103/PhysRevA.76.042319) * [Y.A. Pashkin et al, Quantum Inf Process 8, 55 (2009)](http://dx.doi.org/10.1007/s11128-009-0101-5) diff --git a/tutorials-v4/lectures/Lecture-2B-Single-Atom-Lasing.md b/tutorials-v4/lectures/Lecture-2B-Single-Atom-Lasing.md index f9efd6ee..f2cc1c2c 100644 --- a/tutorials-v4/lectures/Lecture-2B-Single-Atom-Lasing.md +++ b/tutorials-v4/lectures/Lecture-2B-Single-Atom-Lasing.md @@ -55,17 +55,17 @@ In addition to the coherent dynamics the following incoherent processes are also The Lindblad master equation for the model is: -$$\frac{d}{dt}\rho = -i[H, \rho] + \Gamma\left(\sigma_+\rho\sigma_- - \frac{1}{2}\sigma_-\sigma_+\rho - \frac{1}{2}\rho\sigma_-\sigma_+\right) +$\frac{d}{dt}\rho = -i[H, \rho] + \Gamma\left(\sigma_+\rho\sigma_- - \frac{1}{2}\sigma_-\sigma_+\rho - \frac{1}{2}\rho\sigma_-\sigma_+\right) + \kappa (1 + n_{\rm th}) \left(a\rho a^\dagger - \frac{1}{2}a^\dagger a\rho - \frac{1}{2}\rho a^\dagger a\right) -+ \kappa n_{\rm th} \left(a^\dagger\rho a - \frac{1}{2}a a^\dagger \rho - \frac{1}{2}\rho a a^\dagger\right)$$ ++ \kappa n_{\rm th} \left(a^\dagger\rho a - \frac{1}{2}a a^\dagger \rho - \frac{1}{2}\rho a a^\dagger\right)$ in units where $\hbar = 1$. References: - * [Yi Mu, C.M. Savage, Phys. Rev. A 46, 5944 (1992)](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.46.5944) + * [Yi Mu, C.M. Savage, Phys. Rev. A 46, 5944 (1992)](http://dx.doi.org/10.1103/PhysRevA.46.5944) - * [D.A. Rodrigues, J. Imbers, A.D. Armour, Phys. Rev. Lett. 98, 067204 (2007)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.98.067204) + * [D.A. Rodrigues, J. Imbers, A.D. Armour, Phys. Rev. Lett. 98, 067204 (2007)](http://dx.doi.org/10.1103/PhysRevLett.98.067204) * [S. Ashhab, J.R. Johansson, A.M. Zagoskin, F. Nori, New J. Phys. 11, 023030 (2009)](http://dx.doi.org/10.1088/1367-2630/11/2/023030) @@ -102,6 +102,10 @@ sx = tensor(qeye(N), sigmax()) H = w0 * a.dag() * a + wa * sm.dag() * sm + g * (a.dag() + a) * sx ``` +```python +H +``` + ### Create a list of collapse operators that describe the dissipation ```python diff --git a/tutorials-v4/lectures/Lecture-3A-Dicke-model.md b/tutorials-v4/lectures/Lecture-3A-Dicke-model.md index 3aaae2ab..cdee9f4b 100644 --- a/tutorials-v4/lectures/Lecture-3A-Dicke-model.md +++ b/tutorials-v4/lectures/Lecture-3A-Dicke-model.md @@ -49,7 +49,7 @@ $\displaystyle J_\pm = \sum_{i=1}^N \sigma_\pm^{(i)}$ ### References - * [R.H. Dicke, Phys. Rev. 93, 99–110 (1954)](https://journals.aps.org/pr/abstract/10.1103/PhysRev.93.99) + * [R.H. Dicke, Phys. Rev. 93, 99–110 (1954)](http://dx.doi.org/10.1103/PhysRev.93.99) ## Setup problem in QuTiP @@ -198,7 +198,7 @@ fig.tight_layout() ### References -* [Lambert et al., Phys. Rev. Lett. 92, 073602 (2004)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.92.073602). +* [Lambert et al., Phys. Rev. Lett. 92, 073602 (2004)](http://dx.doi.org/10.1103/PhysRevLett.92.073602). ```python def calulcate_entropy(M, N, g_vec): diff --git a/tutorials-v4/lectures/Lecture-3B-Jaynes-Cumming-model-with-ultrastrong-coupling.md b/tutorials-v4/lectures/Lecture-3B-Jaynes-Cumming-model-with-ultrastrong-coupling.md index cce8fc10..37d822b6 100644 --- a/tutorials-v4/lectures/Lecture-3B-Jaynes-Cumming-model-with-ultrastrong-coupling.md +++ b/tutorials-v4/lectures/Lecture-3B-Jaynes-Cumming-model-with-ultrastrong-coupling.md @@ -51,11 +51,11 @@ The regime $g$ is large compared with all other energy scales in the Hamiltonian References: - * [P. Nataf et al., Phys. Rev. Lett. 104, 023601 (2010)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.104.023601) + * [P. Nataf et al., Phys. Rev. Lett. 104, 023601 (2010)](http://dx.doi.org/10.1103/PhysRevLett.104.023601) - * [J. Casanova et al., Phys. Rev. Lett. 105, 26360 (2010)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.105.263603). + * [J. Casanova et al., Phys. Rev. Lett. 105, 26360 (2010)](http://dx.doi.org/10.1103/PhysRevLett.105.263603). - * [S. Ashhab et al., Phys. Rev. A 81, 042311 (2010)](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.81.042311) + * [S. Ashhab et al., Phys. Rev. A 81, 042311 (2010)](http://dx.doi.org/10.1103/PhysRevA.81.042311) diff --git a/tutorials-v4/lectures/Lecture-4-Correlation-Functions.md b/tutorials-v4/lectures/Lecture-4-Correlation-Functions.md index fb585463..64313244 100644 --- a/tutorials-v4/lectures/Lecture-4-Correlation-Functions.md +++ b/tutorials-v4/lectures/Lecture-4-Correlation-Functions.md @@ -152,7 +152,7 @@ $L(\tau) = 2\langle Q(\tau)Q(0)\rangle - \langle Q(2\tau)Q(0)\rangle \leq 1$ ### References -* [A. J. Leggett and A. Garg, Phys. Rev. Lett. 54, 857 (1985)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.54.857) +* [A. J. Leggett and A. Garg, Phys. Rev. Lett. 54, 857 (1985)](http://dx.doi.org/10.1103/PhysRevLett.54.857) * [A. J. Leggett, J. Phys. Condens. Matter 14, R415 (2002)](http://dx.doi.org/10.1088/0953-8984/14/15/201) @@ -186,7 +186,7 @@ def leggett_garg(c_mat): References: - * [N. Lambert, J.R. Johansson, F. Nori, Phys. Rev. B 82, 245421 (2011)](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.84.245421). + * [N. Lambert, J.R. Johansson, F. Nori, Phys. Rev. B 82, 245421 (2011)](http://dx.doi.org/10.1103/PhysRevB.84.245421). ```python diff --git a/tutorials-v4/miscellaneous/excitation-number-restricted-states-jc-chain.md b/tutorials-v4/miscellaneous/excitation-number-restricted-states-jc-chain.md deleted file mode 100644 index 8cdd9e0d..00000000 --- a/tutorials-v4/miscellaneous/excitation-number-restricted-states-jc-chain.md +++ /dev/null @@ -1,269 +0,0 @@ ---- -jupyter: - jupytext: - text_representation: - extension: .md - format_name: markdown - format_version: '1.3' - jupytext_version: 1.13.8 - kernelspec: - display_name: qutip-tutorials - language: python - name: python3 ---- - - -# Excitation-number-restricted states: Jaynes-Cummings Chain - -Authors: Robert Johansson (jrjohansson@gmail.com), Neill Lambert (nwlambert@gmail.com), Maximilian Meyer-Mölleringhof (m.meyermoelleringhof@gmail.com) - -## Introduction - -The ENR functions construct a basis set for multipartite systems which contains only states that have an overall number of excitations. -This is particularly useful for systems where the model conserves excitation number, as in the JC-chain example below. - -We can see this by considering a system consisting of 4 modes, each with 5 states. -The total hilbert space size is $5^4 = 625$. -If we are only interested in states that contain up to 2 excitations, we only need to include states such as - - - (0, 0, 0, 0) - (0, 0, 0, 1) - (0, 0, 0, 2) - (0, 0, 1, 0) - (0, 0, 1, 1) - (0, 0, 2, 0) - ... - -The ENR fucntions create operators and states for the 4 modes that act within this state space. -For example, - -```python -a1, a2, a3, a4 = enr_destroy([5, 5, 5, 5], excitations=2) -``` - -creates destruction operators for each mode. -From this point onwards, the annihiltion operators a1, ..., a4 can be used to setup a Hamiltonian, collapse operators and expectation-value operators, etc., following the usual patterne. - -In this example we outline the advantage of ENR states by comparing them with the regular qutip implementation. -For this we calculate the time evolution and the partial trace for each and see consistent results with notable performance improvements. - -#### Be aware! - -Many default functions in QuTiP will fail on states and operators constructed with this method. -Additionally, using this formalism, annihilation and creation operators of different sub-systems no longer commute. -Therefore, when constructing Hamiltonians, annihilation operators must be on the right and creation operators on the left (see the offical publication for QuTiP v5 for more info). -To find all available functions to work with ENR states see [Energy Restricted Operators in the official documentation](https://qutip.readthedocs.io/en/qutip-5.0.x/apidoc/functions.html#module-qutip.core.energy_restricted). - - -```python -import numpy as np -from qutip import (Options, Qobj, about, basis, destroy, enr_destroy, enr_fock, - enr_state_dictionaries, identity, liouvillian_ref, mesolve, - plot_expectation_values, tensor) - -%matplotlib inline -``` - -## The Jaynes-Cumming Chain - -The general Jaynes-Cumming model describes a single two-level atom interacting with a single electromagnetic cavity mode. -For this example, we put multiple of these systems in a chain and let them interact with neighbouring systems via their cavities. -We use $a_i$ ($a^\dag_i$) as annihilation (creation) operators for the cavity $i$ and $s_i$ ($s^\dag_i$) for the atoms. -We then model the complete Hamiltonian by splitting it into the individual systems: - -$H_0 = \sum_{i=0}^{N} a_i^\dag a_i + s_i^\dag s_i$, - -the atom-cavity interactions: - -$H_{int,AC} = \sum_{i=0}^{N} = \frac{1}{2} (a_i^\dag s_i + s_i^\dag a_i)$, - -and the cavity-cavity interactions: - -$H_{int,CC} = \sum_{i=0}^{N-1} 0.9 \cdot (a_i^\dag a_{i+1} + a_{i+1}^\dag a_{i})$, - -where the interaction strength of $0.9$ was chosen arbitrarily. - - -### Problem paramters - -```python -N = 4 # number of systems -M = 2 # number of cavity states -dims = [M, 2] * N # dimensions of JC spin chain -excite = 1 # total number of excitations -init_excite = 1 # initial number of excitations -``` - -### Setup to Calculate Time Evolution - -```python -def solve(d, psi0): - # annihilation operators for cavity modes - a = d[::2] - # atomic annihilation operators - sm = d[1::2] - - # notice the ordering of annihilation and creation operators - H0 = sum([aa.dag() * aa for aa in a]) + sum([s.dag() * s for s in sm]) - - # atom-cavity couplings - Hint_ac = 0 - for n in range(N): - Hint_ac += 0.5 * (a[n].dag() * sm[n] + sm[n].dag() * a[n]) - - # cavity-cavity couplings - Hint_cc = 0 - for n in range(N - 1): - Hint_cc += 0.9 * (a[n].dag() * a[n + 1] + a[n + 1].dag() * a[n]) - - H = H0 + Hint_ac + Hint_cc - - e_ops = [x.dag() * x for x in d] - c_ops = [0.01 * x for x in a] - - times = np.linspace(0, 250, 1000) - L = liouvillian_ref(H, c_ops) - opt = Options(nsteps=5000, store_states=True) - result = mesolve(H, psi0, times, c_ops, e_ops, options=opt) - return result, H, L -``` - -### Regular QuTiP States and Operators - -```python -d = [ - tensor( - [ - destroy(dim1) if idx1 == idx2 else identity(dim1) - for idx1, dim1 in enumerate(dims) - ] - ) - for idx2, _ in enumerate(dims) -] -psi0 = tensor( - [ - basis(dim, init_excite) if idx == 1 else basis(dim, 0) - for idx, dim in enumerate(dims) - ] -) -``` - -Regular operators of different systems commute as they belong to different Hilbert spaces. -Example: - -```python -d[0].dag() * d[1] == d[1] * d[0].dag() -``` - -Solving the time evolution: - -```python -res1, H1, L1 = solve(d, psi0) -``` - -### Using ENR States and Operators - -```python -d_enr = enr_destroy(dims, excite) -init_enr = [init_excite if n == 1 else 0 for n in range(2 * N)] -psi0_enr = enr_fock(dims, excite, init_enr) -``` - -Using ENR states forces us to give up on the standard tensor structure of multiple Hilbert spaces. -Operators for different systems therefore generally no longer commute: - -```python -d_enr[0].dag() * d_enr[1] == d_enr[1] * d_enr[0].dag() -``` - -Solving the time evolution: - -```python -res2, H2, L2 = solve(d_enr, psi0_enr) -``` - -### Comparison of Expectation Values - -```python -fig, axes = plot_expectation_values([res1, res2], figsize=(10, 8)) -for idx, ax in enumerate(axes[:, 0]): - if idx % 2: - ax.set_ylabel(f"Atom {idx//2}") - else: - ax.set_ylabel(f"Cavity {idx//2}") - ax.set_ylim(-0.1, 1.1) - ax.grid() -fig.tight_layout() -``` - -### Calculation of Partial Trace - -The usage of ENR states makes many standard QuTiP features fail. -*ptrace* is one of those. -Below we demonstrate how the partial trace for ENR states can be calculated and show the corresponding result together with the standrad QuTiP approach. - -```python -def ENR_ptrace(rho, sel, excitations): - if isinstance(sel, int): - sel = np.array([sel]) - else: - sel = np.asarray(sel) - - if (sel < 0).any() or (sel >= len(rho.dims[0])).any(): - raise TypeError("Invalid selection index in ptrace.") - - drho = rho.dims[0] - _, state2idx, idx2state = enr_state_dictionaries(drho, excitations) - - dims_short = np.asarray(drho).take(sel).tolist() - nstates2, state2idx2, _ = enr_state_dictionaries(dims_short, excitations) - - # construct new density matrix - rhout = np.zeros((nstates2, nstates2), dtype=np.complex64) - # dimensions of traced out system - rest = np.setdiff1d(np.arange(len(drho)), sel) - for state in idx2state: - for state2 in idx2state: - # add diagonal elements to the new density matrix - state_red = np.asarray(state).take(rest) - state2_red = np.asarray(state2).take(rest) - if np.all(state_red == state2_red): - rhout[ - state2idx2[tuple(np.asarray(state).take(sel))], - state2idx2[tuple(np.asarray(state2).take(sel))], - ] += rho.data[state2idx[state], state2idx[state2]] - - new_dims = np.asarray(drho).take(sel).tolist() - return Qobj(rhout, dims=[new_dims, new_dims], shape=[nstates2, nstates2]) -``` - -```python -res1.states[10].ptrace(1) -``` - -```python -ENR_ptrace(res2.states[10], 1, excite) -``` - -```python -res1.states[10].ptrace([0, 1, 4]) -``` - -```python -ENR_ptrace(res2.states[10], [0, 1, 4], excite) -``` - -## About - -```python -about() -``` - -## Testing - -```python -assert np.allclose( - res1.states[10].ptrace([1]), ENR_ptrace(res2.states[10], [1], excite) -), "The approaches do not yield the same result." -``` diff --git a/tutorials-v4/miscellaneous/images/single-photon-interference-setup.jpg b/tutorials-v4/miscellaneous/images/single-photon-interference-setup.jpg deleted file mode 100644 index 50dd42eef28bf0b2fa524417d5495f8912f67630..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 140373 zcmeFacRXC*yFa=|4-&oCC_zN;ZH6QWB1D2{5uJ!mbfW|zS_l#mB!YzKy~OAxAw&(M z3=$G`Mjd91yYusilQ``IVoPEY`?u9l7# z0D(Y&d*B~%vLMu@8Q|gw0Q&mCMF0RO05X6IAOb@dzzbUlJwObGA>bbXk^1NT#}Mhi z!i*qIS*!~{@)r$2(Xq-2kc0QA!D|x$pwr@91w$0ze!86^aEibw0;dR^B5;bp|40N( 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Based on the project Simulating Single Photon Interference using QuTiP (Quantum Toolbox in Python) carried out in June 2021 at Maastricht University, Maastricht Science Programme. -
Last updated: 09/11/21. - -```python -import matplotlib.pyplot as plt -import qutip -from IPython.display import Image -from numpy import cos, exp, pi, random, sin, sqrt -from qutip import Qobj, basis - -%matplotlib inline -``` - -### Table of Contents -* [Introduction](#section0) -* [1. Theoretical Background](#section1) -* [2. Coding the constitutive elements](#section2) -* [3. Simulation of Single-Photon Interference Experiment](#section3) -* [4. Variations of Single-Photon Interference Experiment](#section4) -* [Conclusion and Takeaways](#section5) - - -### Introduction - - -Wave-particle duality is one of the key concepts of quantum mechanics and it illustrates the fact that every quantum particle or object can be described using both particle and wave properties. Neither of these classical concepts can fully describe quantum objects on its own. - -In a similar manner to Thomas Young's double-slit experiment, originally carried out in 1801, the wave-particle duality of light can be observed by performing a single-photon interference experiment, or in this case, simulation. The individual *particle*-like photons interfere with themselves, which is an intrinsically *wave*-like property, thus exhibiting both particle and wave characteristics at the same time. - - -This lecture investigates the phenomenon of single-photon interference by creating a simulation of a quantum optical experiment in which single photons go through a polarisation interferometer. -* As mentionned, the experiment to be simulated is similar to Young‘s, in that it also produces interference by allowing a photon to take two separate paths. In a classical sense, a beam of light is split into two separate waves, one of which will travel a different path length, to then be recombined into a single wave by using beam splitters and a polarization analyzer. -* Varying the path lengths results in a phase shift, which creates an interference pattern. -* Applying this classical understanding to the case under study, a single photon, which is an indivisible packet of light, would have to be ‘split’ into two ‘waves’ for an interference pattern to occur. Because the photons are sent through the path individually and cannot be split any further, it seems that they cannot interfere with each other. -* But they do interfere! The canonical understanding of the single-photon interference is that the photon’s probability wave interferes with itself, in opposition to interference happening between two distinct states. - - -### 1. Theoretical Background - - -This section presents the setup of the single-photon interference experiment from a theoretical perspective, and details the different optical elements that must be simulated. The mathematical expression of each element will also be presented, in the form of Jones matrices acting as operators on the given quantum state. - -The setup and corresponding theory of this single photon interference experiment closely follows the work of Mark Beck in "Quantum Mechanics, Theory and Experiment" (*Beck, M. (2012). Quantum mechanics, theory and experiment. Oxford University Press.*), more specifically Experiment 6 of Section 3. - - -#### Half-wave plate $\frac{\lambda}{2}$ - - -A wave plate is used to modify the polarization of a wave, using the fact that its effective index of refraction depends on the polarization of the incident wave. -* A wave plate has two orthogonal axes. The fast axis represents the direction with the lower index of refraction as light polarized along that direction propagates faster, and the slow axis, orthogonal to the fast axis, represents the direction with the higher index of refraction. -* This difference in indices of refraction leads to the orthogonal components of the wave (polarization of a wave can always be decomposed into orthogonal components) acquiring different phase shifts when propagating through the material, resulting in an overall relative phase shift between the two components. -* A wave plate makes use of this relative phase shift between the components of the wave to modify its overall polarization. - -In this case, a half-wave plate is used, for which the relative phase shift between the fast and slow axes corresponds to a half wavelength shift. -* The fast axis of the half-wave plate is positioned in order to create a $45^\circ$ angle with the horizontal plane. -* Half of the incident wave is thus polarized along the fast axis and result in a $|+45\rangle$ polarization state, while the other half is polarized along the slow axis into a $|-45\rangle$ state, with a relative phase shift of $\pi$. -* The purpose of a half-wave plate is to rotate the linear polarization of some wave by an arbitrary angle (wave does *not* get split into its orthogonal components), depending on the orientation of the wave plate. - -The mathematical expression of a half-wave plate at $45^\circ$ is the following: - -\begin{equation} -J_{\lambda/2 \hspace{1mm} 45^\circ} = \left[\begin{array}{cc} \cos(2\cdot45) & \sin(2\cdot45) \\ \sin(2\cdot45) & -\cos(2\cdot45) \end{array}\right] = \left[\begin{array}{cc} 0 & 1 \\ 1 & 0 \end{array}\right] -\end{equation} - - -#### Polarization analyzers PA$_{HV}$ and PA$_{45}$ - - -A polarization analyzer, also called beam displacing polarizer or polarizing beam splitter, has the property that it can both *split* a beam into two orthogonal components, and *displace* each of these components differently. -* A general wave composed of orthogonal polarization components gets split into two waves travelling in different directions. When the faces of the polarization analyzer are parallel, the outgoing waves will emerge parallel, with a displacement separating them. - -If the polarization analyzer is positioned perpendicular to the horizontal plane, in such a way that waves propagating horizontally are normally incident, the general waves get split into vertical and horizontal components and it is referred to as a PA$_{HV}$. -* In this case, the $|V\rangle$ polarization state gets transmitted without bending, while the $|H\rangle$ gets bent when entering and leaving the PA$_{HV}$ such as to emerge parallel to $|V\rangle$, but displaced and phase-shifted. -* This polarization analyzer can also be used to recombine orthogonal components of a beam. The process simply gets reversed. -* The Jones matrix representing a PA$_{HV}$ is composed of three other Jones matrices: the horizontal polarizer, vertical polarizer and phase-shifting matrices. This combination is made such that it represents the splitting and the phase-shifting caused by this element, and is as follows: - -\begin{equation} -J_{PA_{HV}} = J_{V} + J_{\phi}J_{H} = \left[\begin{array}{cc} 0 & 0 \\ 0 & 1 \end{array}\right] + \left[\begin{array}{cc} e^{i \phi} & 0 \\ 0 & 1 \end{array}\right]\left[\begin{array}{cc} 1 & 0 \\ 0 & 0 \end{array}\right] = \left[\begin{array}{cc} e^{i \phi} & 0 \\ 0 & 1 \end{array}\right] -\end{equation} - -If one were to rotate a PA$_{HV}$ by $45^\circ$, a general wave incident on such a polarization analyzer would be analyzed into $|+45\rangle$ and $|-45\rangle$ polarization components, making it a PA$_{45}$. -* In this case, the $|+45\rangle$ polarization state gets transmitted without bending, while the $|-45\rangle$ gets bent when entering and leaving the PA$_{45}$ such as to emerge displaced and parallel to $|+45\rangle$. -* The Jones matrix for PA$_{45}$ is also a combination of other matrices: the linear polarizer with transmission axis at $45^\circ$, linear polarizer with transmission axis at $-45^\circ$ and phase-shifting matrices. The mathematical expression is thus: - -\begin{equation} -\begin{split} -J_{PA_{45}} &= J_{+45} + J_{\phi}J_{-45} \\ -&= \left[\begin{array}{cc} \cos^2(45) & \cos(45)\sin(45) \\ \cos(45)\sin(45) & \sin^2(45) \end{array}\right] -+ \left[\begin{array}{cc} e^{i \phi} & 0 \\ 0 & 1 \end{array}\right]\left[\begin{array}{cc} \cos^2(-45) & \cos(-45)\sin(-45) \\ \cos(-45)\sin(-45) & \sin^2(-45) \end{array}\right] \\ -&= \left[\begin{array}{cc} \frac{1}{2}(e^{i \phi} +1) & \frac{1}{2}(1-e^{i \phi}) \\ 0 & 1 \end{array}\right] -\end{split} -\end{equation} - - -#### Complete Setup - - -The setup of the experiment, represented in the figure below, is as follows. -* A photon is prepared in the $|+45\rangle$ state and is sent through an interferometer, composed of a PA$_{HV}$, a half-wave plate, another PA$_{HV}$, and finally a PA$_{45}$. -* The first polarization analyzer PA$_{HV}$ is used to split the incident photon into two orthogonal polarization components $|H\rangle$ and $|V\rangle$, and displace them accordingly. -* Each of the components emerges parallel to the other and then goes through the half-wave plate. Inserting the half-wave plate in the polarization interferometer allows for the equalization of the path lengths of the two arms. -* As already mentioned, a half-wave plate with its fast axis rotated to $45^\circ$ causes half of the incident wave to be polarized into a $|+45\rangle$ state (fast axis) and the other half into a $|-45\rangle$ state (slow axis), with a phase shift of $\pi$. In this specific setup, this implies that the polarization of the two components gets "switched" from $|H\rangle$ to $|V\rangle$ and vice-versa as the linear polarizations are rotated by $90^\circ$, making the overall behaviour of the two arms symmetric with equalized path lengths and no relative phase shift (for now). -* The second polarization analyzer PA$_{HV}$ is then used to recombine the components. -* Finally, the use of a PA$_{45}$ at the end of the interferometer allows for the splitting of the final photon into $|+45\rangle$ and $|-45\rangle$ polarization states, providing two different paths and two different ports for the output. In this way, it is easy to measure how the intensity of each of the outputs gets modulated. - -This apparatus acts as an interferometer, and the final super-positioned states should interfere producing an interference pattern and output intensities that depend on the relative phase between them (equivalently, on the path length difference). -* This relative phase (initially zero) can be modified using the PA$_{HV}$'s. -* One (or both) of the two polarization analyzers can be tilted in order to vary the relative phase between the two arms, as the relative phase shift is proportional to the tilt angle of the PA$_{HV}$ (because it modifies the path lengths). -* When the phase is $\phi = 0$, there should be constructive interference, with all of the output coming from the $+45^\circ$ port, while when the phase is $\phi = \pi$, there should be destructive interference, with all of the output coming from the $-45^\circ$ port. -* Note that this interference is visible because of the last element, the PA$_{45}$, as it projects the two, initially orthogonal (no possibility of displaying interference), polarization states onto the $+45^\circ$ and $-45^\circ$ axes where they do interfere. - - -```python -Image(filename="images/single-photon-interference-setup.jpg", width=700, embed=True) -``` - - -Note that a single photon is sent through these optical elements, making the process quantum mechanical in nature. This requires for an important distinction, when it comes to the splitting of the state into orthogonal components. -* A classical wave is split into its orthogonal components deterministically, with the wave actually getting divided into two components, each *in proportion* to the coefficient of the corresponding polarization state in the general polarization ($\psi = c_H |H\rangle + c_V |V\rangle$). -* On the other hand, when it comes to single photons, as they cannot be broken down any further, they are 'split' randomly and it is not possible to determine with certainty which port any photon will emerge from. -* The proportion of the basis states $|H\rangle$ and $|V\rangle$ in a general state $\psi$ describes the *probability* of the photon taking that corresponding path. -* Note also that the state of the photon never actually gets 'split' and it stays as one single state throughout the entire interferometer. - - -### 2. Coding the constitutive elements - - -#### Basis vectors -Code the polarization basis vectors, for the bases $H/V$ and $+/-45$. They can then be used to code any polarization state, since any state can be expressed as a linear combination of basis vectors. - -```python -# HV basis - -# horizontal polarization -H = basis(2, 0) -# vertical polarization -V = basis(2, 1) -``` - -```python -# +45/-45 basis (in terms of HV basis) - -# +45 polarization -p45 = 1 / sqrt(2) * (H + V) -# -45 polarization -n45 = 1 / sqrt(2) * (H - V) -``` - -#### Polarization analyzer (HV) n°1 -It induces a phase shift on the H polarization relative to V polarization, because of the difference in the path lengths of the two arms of the interferometer. -
In the case of a single photon, the state doesn't actually get split and there is only a single state throughout the apparatus, so the PA$_{HV}$ only affects the phase of the components. - -```python -# Polarization analyzer (HV) n°1 - -phaseshift1 = pi / 4 # CONSTANT -# should depend on real size of the setup (here: arbitrarily chosen) -PA_HV1 = Qobj([[exp(1j * phaseshift1), 0], [0, 1]]) -PA_HV1 -``` - -#### Half-wave plate $\frac{\lambda}{2}$ -In this case, the fast axis makes an angle of $45^\circ$ with the horizontal. -
For a single photon, the effect of going through such a half-wave plate is that the $|H\rangle$ and $|V\rangle$ polarization components are switched. - -```python -# Half-wave plate - -θ = pi / 4 # fast axis orientation -# (!) numpy calculates with rad -halfwave = Qobj([[cos(2 * θ), sin(2 * θ)], [sin(2 * θ), -cos(2 * θ)]]) - -""" -removes very small elements -(numerical artifacts from the finite precision of the computer) -""" -halfwave.tidyup() -``` - -#### Polarization analyzer (HV) n°2 -As before, it induces a phase shift on the H polarization relative to V polarization. -* Initially, the value of the phase shift induced by PA$_{HV2}$ should be set to the same value as for PA$_{HV1}$. The initial state of our setup has both of the PA$_{HV}$s in the exact same position (both tilted in the same way), with the overall relative phase shift being zero. -* The phase shift induced by PA$_{HV2}$ can then be changed in order to observe different levels of interference. -* A difference in the phase shifts induced by the two polarization analyzers causes the *relative* phase shift between the components to change, and it is this *relative* phase shift that is responsible for the interference. - -```python -# Polarization analyzer (HV) n°2 - -phaseshift2 = pi / 4 # CHANGE TO CHANGE INTERFERENCE -# should depend on real size of the setup -PA_HV2 = Qobj([[exp(1j * phaseshift2), 0], [0, 1]]) - -PA_HV2 -``` - -#### Polarization analyzer (45) -The PA$_{45}$ acts on the photon such that it has to come out as a $|+45\rangle$ polarized photon or as a $|-45\rangle$ polarized photon. -* Based on the coefficients of each polarization basis components constituting the general polarization state, the probability of the photon getting out in that corresponding state can be computed. -* It should also induce a phase shift on $|-45\rangle$ component with respect to $|+45\rangle$ as a result of different path lengths (as seen in the theory section), but this effect is ignored as this is the last optical element, and the goal is to determine whether the final state of the photon is $|-45\rangle$ or $|+45\rangle$, not which one "gets out first". -* The role of this polarization analyzer is to determine the outcome of the experiment. -* The single photon interference happens just before the photon goes through this last polarization analyzer, e.g. when the states are 'recombined' after the second PA$_{HV}$ (this is not to be interpreted too literally because, as previously mentioned, the state of the photon never actually gets split and it stays as one state throughout the apparatus), and thus phase shift does not have an importance anymore. - -```python -# Polarization analyzer (45) - -# linear Polarizer, transmission axis +45 wrt horizontal -θ = pi / 4 -Pp45 = Qobj([[cos(θ) ** 2, cos(θ) * sin(θ)], [cos(θ) * sin(θ), sin(θ) ** 2]]) -# linear Polarizer, transmission axis -45 wrt horizontal -θ = -pi / 4 -Pn45 = Qobj([[cos(θ) ** 2, cos(θ) * sin(θ)], [cos(θ) * sin(θ), sin(θ) ** 2]]) - - -def PA_45(vector): - p45_comp = Pp45 * vector # retrieve only +45 component - n45_comp = Pn45 * vector # retrieve only -45 component - return p45_comp, n45_comp -``` - -### 3. Simulation of Single-Photon Interference Experiment - - -**Define initial variables:** -* One has to choose the initial minimal and maximal values of the phase shift induced by PA$_{HV2}$, along with the number of steps to be effectuated between these values. These different values will be run through during the simulation and the experiment will be carried out for each of them in order to collect data to determine what the relationship is between the interference and the relative phase shift. The higher the number of steps is, the more precise the final result will be. -* The number of iterations, for one value of the phase shift, also has to be defined. Since the photon getting out of the interferometer in a certain polarization state is governed by probabilities, it is important to carry out the same process numerous times to get a good sample and a good representation of the results. - -```python -psi_0 = p45 # define the initial state (+45 vector) - -phaseshift2_init = pi / 4 # initial value -phaseshift2_max = 8 * pi -n = 100 # resolution of φ (amount of steps) -step = ( - phaseshift2_max - phaseshift2_init -) / n # interval divided by number of small steps we want -N_init = 1000 # number of iterations (range(N) -> 0 to N-1, both included) - -# create x and y coords arrays to store the values needed to plot output graph -x_coords = [] # relative phase shift -y1_coords = [] # amount of photons in +45 -y2_coords = [] # amount of photons in -45 -``` - -**Create a *for loop* to run through all of the possible values for the phase shift considered for PA$_{HV2}$:** -
In this loop, the passage of the photon through the interferometer is simulated using an effective matrix. The PA$_{45}$ is used after that to compute the probability related to each of these polarization states, and eventually to get the results of the experiment. -* 'Output' values are first defined to keep track of the results of the numerous iterations that are performed to measure the final state of the photon for a given phase shift. They are used to count the amount of photons that come out as $|+45\rangle$ and the amount that come out as $|-45\rangle$. -* The phase shift is also defined and directly added to the array of x-coordinates. -* With this phase shift, PA$_{HV2}$ can be defined, which allows for the definition of the final effective matrix. The effective matrix regroups all of the operators acting on the photon (in the correct order) in a single matrix. -* This is then multiplied with the initial state of the photon, to get the final state that will be passed through the PA$_{45}$. -* The $|+45\rangle$ and $|-45\rangle$ components are then retrieved and used to compute the final probabilities. To do so, the norm of the vector of each component is computed and then squared. - -Inside the first *for loop* is another loop performing what can be considered as the final measurement. Whether the photon comes out in a $|+45\rangle$ or $|-45\rangle$ polarization state is a random process that depends on the corresponding probabilities. -* To simulate this, a random number is generated between 1 and 100 (100 because $100\%$ of the photons need to come out in either one of the states). -* If the number is smaller or equal to the probability (in percentage form) related to the $|+45\rangle$ component, the photon is measured to have come out in the $|+45\rangle$ state. Otherwise, it is measured in the $|-45\rangle$ state. -* These measurements are then accounted for and stored in the 'output' values that were previously defined. -* When all of the iterations are performed, the output values are added to the arrays corresponding to different y-coordinates (one for $|+45\rangle$ photons and one for $|-45\rangle$ photons). -* They are then reinitialized at the beginning of the first *for loop*, before the entire process starts anew for another value of the phase shift. - -```python -for i in range(n + 1): - output_p45 = 0 - output_n45 = 0 - - phaseshift2 = phaseshift2_init + i * step - x_coords.append( - (phaseshift2 - phaseshift1) / pi - ) # add realtive phase shift to x coords - # create corresponding PA_HV2 - PA_HV2 = Qobj([[exp(1j * phaseshift2), 0], [0, 1]]) - EffM = PA_HV2 * halfwave * PA_HV1 # define the effective matrix - - # apply the effective matrix to the initial state to get the final state - psi_final = EffM * psi_0 - - psi_p45 = PA_45(psi_final)[0] # retrieve +45 and -45 components - psi_n45 = PA_45(psi_final)[1] - - # probab is rounded up to 5 decimals to avoid machine precision artifacts - proba_p45 = round(psi_p45.norm() ** 2, 5) - proba_n45 = round(psi_n45.norm() ** 2, 5) - - for j in range(N_init): - """ - generates random number between 1 and 100 (both included), - 100 because 100% of the photons need to come out in either - +45 or -45 state - """ - a = random.randint(1, 100) - if a <= proba_p45 * 100: - output_p45 = output_p45 + 1 - else: - output_n45 = output_n45 + 1 - - y1_coords.append(output_p45) - y2_coords.append(output_n45) -``` - -**Create output plot:** -
Using the arrays (which are collections of coordinates) that are created as the simulation goes on for different values and setups, a plot can be created to visualize the dependency of the interference on the relative phase shift, or similarly, on the different in path lengths of the two arms of the interferometer. - -```python -plt.plot(x_coords, y1_coords, "b.", markersize=9, label="Photons in state +45") -plt.plot(x_coords, y2_coords, "r.", markersize=9, label="Photons in state -45") -legend = plt.legend(loc="upper center", fontsize="x-large") -plt.ylim([-100, N_init + 500]) -plt.xlabel("Relative phase shift (multiples of π)") -plt.ylabel("Amount of photons detected") -plt.title("Amount of photons existing in |+/-45> states"); -``` - -The data shows a clear interference pattern. The measurements oscillate between only measuring photons in the state $|+45\rangle$ or $|-45\rangle$ as the relative phase shift increases. - - -### 4. Variations of Single-Photon Interference Experiment - - -Alternative cases can be investigated to get a better understanding of what is happening, why this interference pattern appears, and of the quantum nature of this experiment. - -In this case, it is shown that, when the superpositon of states collapses (here the superposition refers to the photon taking both paths "at the same time"), the interference pattern disappears. This is done by blocking either the vertical or the horizontal output port of the very first polarization analyzer, thus preventing the photon to be in the previously mentionned superposition of states as it can take only one path. This comes with some modifications, with respect to how the simulation was implemented previously: -* The main element that has to be modified is the first polarization analyzer. Depending on which port is blocked, the corresponding polarization component as to be put to zero for any further computation. -* The other big difference with the full interference simulation concerns the amount of photons that goes through the entire interferometer. When the initial $|+45\rangle$ polarized photon goes through PA$_{HV1}$, it has $50\%$ chance of coming out $|V\rangle$ polarized and $50\%$ chance of coming out $|H\rangle$ polarized. Under the assumption that the experiment is carried out enough times to actually get half of the photons to come out from each of the ports, it implies that only half of the photons will get to the other end of the interferometer, as the other half will have been blocked. Where, for the original interference simulation, 1000 photons got out the interferometer, here, only 500 will do so. Because of how it was coded in this simulation, this requires that the number of iterations (for one phase shift value) is halved. This is directly linked to the final probabilities of the photon coming out in state $|+45\rangle$ or $|-45\rangle$. The sum of these probabilities is not 1 anymore (or $100\%$), but 0.5 because half of the photons are not concerned by this. The missing 0.5 is thus the probability that the photon was blocked at the first polarization analyzer. -* The rest of the code is otherwise unchanged. - - -#### Output port 'V' of the first PA$_{HV}$ is blocked - -```python -# Polarization analyzer (HV) n°1, with V output port BLOCKED - -phaseshift1 = pi / 4 # CONSTANT -# should depend on real size of the setup (here: arbitrarily chosen) -PA_HV1vb = Qobj([[exp(1j * phaseshift1), 0], [0, 0]]) -``` - -```python -psi_0 = p45 # Defining the initial state (+45 vector) - -phaseshift2_init = pi / 4 # initial value -phaseshift2_max = 8 * pi -n = 100 # resolution of φ (amount of steps) -step = ( - phaseshift2_max - phaseshift2_init -) / n # interval divided by number of small steps we want - -# number of iterations (range(N) -> 0 to N-1, both included) -N_init = 1000 - -x_coords = [] # create x- and y- coords. arrays -# (x = phase shift of 2nd PA_HV, -# y1 = amount of photons in +45, -# y2 = amount of photons in -45) -y1_coords = [] -y2_coords = [] - -for i in range(n + 1): - output_p45 = 0 - output_n45 = 0 - - phaseshift2 = phaseshift2_init + i * step - # add realtive phase shift to x coords - x_coords.append((phaseshift2 - phaseshift1) / pi) - # create corresponding PA_HV2 - PA_HV2 = Qobj([[exp(1j * phaseshift2), 0], [0, 1]]) - EffM = PA_HV2 * halfwave * PA_HV1vb # Defining the effective matrix - - """ - Applying the effective matrix to the initial state to get the final state - """ - psi_final = EffM * psi_0 - psi_p45 = PA_45(psi_final)[0] # Determining the probabilities - psi_n45 = PA_45(psi_final)[1] - proba_p45 = round(psi_p45.norm() ** 2, 5) - proba_n45 = round(psi_n45.norm() ** 2, 5) - - if (proba_p45 + proba_n45) == 1: - N = N_init # all of the photons get to the end - else: - """ - half of the photons are blocked (should only get N/2 in the ouput) - -> total prob should be 0.5 - """ - N = int(N_init / 2) - - for j in range(N): - """ - generates random number between 1 and 50 (both included), - 50 because 50% of the photons need to come out in either - +45 or -45 state (since other 50% was blocked) - """ - a = random.randint(1, 50) - if a <= proba_p45 * 100: - output_p45 = output_p45 + 1 - else: - output_n45 = output_n45 + 1 - - y1_coords.append(output_p45) - y2_coords.append(output_n45) - -plt.plot(x_coords, y1_coords, "b.", markersize=9, label="Photons in state +45") -plt.plot(x_coords, y2_coords, "r.", markersize=9, label="Photons in state -45") -legend = plt.legend(loc="upper center", fontsize="x-large") -plt.ylim([0, 1000]) -plt.xlabel("Relative phase shift (multiples of π)") -plt.ylabel("Amount of photons detected") -plt.title("Amount of photons existing in |+/-45> states (V port blocked)"); -``` - -#### Output port 'H' of the first PA$_{HV}$ is blocked - -```python -# Polarization analyzer (HV) n°1, with H output port BLOCKED - -PA_HV1hb = Qobj([[0, 0], [0, 1]]) -``` - -```python -psi_0 = p45 # Defining the initial state (+45 vector) - -phaseshift2_init = pi / 4 # initial value -phaseshift2_max = 8 * pi -n = 100 # resolution of φ (amount of steps) -step = ( - phaseshift2_max - phaseshift2_init -) / n # interval divided by number of small steps we want - -# number of iterations (range(N) -> 0 to N-1, both included) -N_init = 1000 - -""" -create x- and y- coords. arrays (x = phase shift of 2nd PA_HV, -y1 = amount of photons in +45, y2 = amount of photons in -45) -""" -x_coords = [] -y1_coords = [] -y2_coords = [] - -for i in range(n + 1): - output_p45 = 0 - output_n45 = 0 - - phaseshift2 = phaseshift2_init + i * step - # add realtive phase shift to x coords - x_coords.append((phaseshift2 - phaseshift1) / pi) - # create corresponding PA_HV2 - PA_HV2 = Qobj([[exp(1j * phaseshift2), 0], [0, 1]]) - # Defining the effective matrix - EffM = PA_HV2 * halfwave * PA_HV1vb - - """ - Applying the effective matrix to the initial state to get the final state - """ - psi_final = EffM * psi_0 - psi_p45 = PA_45(psi_final)[0] # Determining the probabilities - psi_n45 = PA_45(psi_final)[1] - proba_p45 = round(psi_p45.norm() ** 2, 5) - proba_n45 = round(psi_n45.norm() ** 2, 5) - - if (proba_p45 + proba_n45) == 1: - N = N_init # all of the photons get to the end - else: - """ - half of the photons are blocked (should only get N/2 in the ouput) - -> total probability should be 0.5 - """ - N = int(N_init / 2) - """ - # generates random number between 1 and 50 (both included), - 50 because 50% of the photons need to come out in either - +45 or -45 state (since other 50% was blocked) - """ - for j in range(N): - a = random.randint(1, 50) - if a <= proba_p45 * 100: - output_p45 = output_p45 + 1 - else: - output_n45 = output_n45 + 1 - - y1_coords.append(output_p45) - y2_coords.append(output_n45) - -plt.plot(x_coords, y1_coords, "b.", markersize=9, label="Photons in state +45") -plt.plot(x_coords, y2_coords, "r.", markersize=9, label="Photons in state -45") -legend = plt.legend(loc="upper center", fontsize="x-large") -plt.ylim([0, 1000]) -plt.xlabel("Relative phase shift (multiples of π)") -plt.ylabel("Amount of photons detected") -plt.title("Amount of photons existing in |+/-45> states (H port blocked)"); -``` - -### Conclusion and Takeaways - - -The results show that, when both pathways are unobstructed, an interference pattern emerges. As the relative phase varies, the detected photons oscillate between the $|+45\rangle$ and $|-45\rangle$ states, clearly displaying interference. Note that, while inside the interferometer, the beams are in orthogonal polarizations, so no interference should occur there, though the components get affected and phase-shifted differently. Interference "happens", or rather becomes visible, at the PA$_{45}$ where both the horizontal and vertical polarisation are projected onto $+45^\circ$ and $-45^\circ$ axes. The degree of interference oscillates with increasing phase difference between the two paths. - -When one of the output port from the first polarization analyzer is blocked, the amount of photons detected is constant (apart from some random fluctuations) under a changing relative phase shift and there is no interference pattern. The results are identical for both setups, with the V beam and the H beam blocked. This illustrates that when the superposition collapses, no interference is displayed. - - -**Important takeaways:** -* This experiment demonstrates that photons can behave as particles and waves at the same time. This illustrates that the wave-particle duality is not an either-or concept, and that the transition is fluid. -* *So, which path did the photon take before being 'reunited' with its other half?* It was mentioned that the photon never actually gets split nor reunited and remains in one (evolving) state throughout. However, the interference pattern is a function of phase length variance between the two arms of the interferometer, and thus a function of both paths. Hence there is no way to describe the measured interference other than by considering that the photon travels through both paths at the same time. This is done by considering the *wavefunction* of the photon. Its wavefunction is in a superposition of travelling down both paths simultaneously, and will remain in this state until it collapses due to a measurement or an other external factor that causes quantum decoherence. This superposition results in the observed interference pattern. -* By blocking one of the beam’s paths, the photon is prevented from being in superposition and therefore can't interfere with itself. It further results in only half the amount of photons exiting the interferometer. This is due to the photons originally having a 50/50 chance of passing through either path. We still observe photons in both polarization states with a 50/50 distribution, demonstrating that a quantum state can collapse from one superposition and still remain in another superposition at the same time. While the obstruction of one path permanently determines which path the photons are taking (thus collapsing the superposition that allowed for the interference), their polarisation is still in a superposition of two basis states afterwards. -* The simulation shows that single-particle quantum interference can be described using an *event-by-event* basis. The use of frameworks such as wave fields or time-dependent system evolutions is not necessary to demonstrate the basic workings of the physical mechanism. This is possible, as the mathematical essence of single photon interference can be described with simple vector and matrix functions. However, when intending to use the simulation to make actual predictions about physical mechanisms, it is important to take into account physical limitations to ensure the results of the simulation are meaningful. One example of such a limitation is coherence length, which puts a real limit on the relative phase differences that would still result in interference. - -```python -qutip.about() -``` diff --git a/tutorials-v4/pulse-level-circuit-simulation/qip-customize-device.md b/tutorials-v4/pulse-level-circuit-simulation/qip-customize-device.md index 470d8f81..787a5664 100644 --- a/tutorials-v4/pulse-level-circuit-simulation/qip-customize-device.md +++ b/tutorials-v4/pulse-level-circuit-simulation/qip-customize-device.md @@ -403,7 +403,7 @@ plt.show() ``` ## Customizing the noise -Apart from pre-defined noise such as T1, T2 noise and random noise in the control pulse amplitude (see this [guide](https://qutip-qip.readthedocs.io/en/stable/qip-processor.html), one can also define custom noise. Here we will see two examples of customizing noise, one systematic (pulse-independent) noise and one pulse-dependent noise. +Apart from pre-defined noise such as T1, T2 noise and random noise in the control pulse amplitude (see this [guide](https://qutip.org/docs/latest/guide/qip/qip-processor.html), one can also define custom noise. Here we will see two examples of customizing noise, one systematic (pulse-independent) noise and one pulse-dependent noise. To understand how noise is processed, we briefly introduced the data structure of the simulation framework. The control elements are stored as a list of `Pulse` objects in the Processor. In each Pulse contains the idea pulse, the control noise part and the decoherence part. For systematic noise, it is saved under the `Pulse` representation labelled `"system"`, which represents the intrinsic dynamics of the quantum system. For pulse-dependent noise, we will add them to their corresponding control `Pulse`. diff --git a/tutorials-v4/pulse-level-circuit-simulation/qip-optpulseprocessor.md b/tutorials-v4/pulse-level-circuit-simulation/qip-optpulseprocessor.md index 9f91ec10..0721ef79 100644 --- a/tutorials-v4/pulse-level-circuit-simulation/qip-optpulseprocessor.md +++ b/tutorials-v4/pulse-level-circuit-simulation/qip-optpulseprocessor.md @@ -27,7 +27,7 @@ from qutip.ipynbtools import version_table import qutip_qip ``` -The `qutip.OptPulseProcessor` is a noisy quantum device simulator integrated with the optimal pulse algorithm from the `qutip.control` module. It is a subclass of `qutip.Processor` and is equipped with a method to find the optimal pulse sequence (hence the name `OptPulseProcessor`) for a `qutip.QubitCircuit` or a list of `qutip.Qobj`. For the user guide of `qutip.Processor`, please refer to [the introductory guide](https://qutip-qip.readthedocs.io/en/stable/qip-processor.html). +The `qutip.OptPulseProcessor` is a noisy quantum device simulator integrated with the optimal pulse algorithm from the `qutip.control` module. It is a subclass of `qutip.Processor` and is equipped with a method to find the optimal pulse sequence (hence the name `OptPulseProcessor`) for a `qutip.QubitCircuit` or a list of `qutip.Qobj`. For the user guide of `qutip.Processor`, please refer to [the introductory guide](https://qutip.org/docs/latest/guide/qip/qip-processor.html). ## Single-qubit gate Like in the parent class `Processor`, we need to first define the available Hamiltonians in the system. The `OptPulseProcessor` has one more parameter, the drift Hamiltonian, which has no time-dependent coefficients and thus won't be optimized. diff --git a/tutorials-v4/pulse-level-circuit-simulation/qip-scheduler.md b/tutorials-v4/pulse-level-circuit-simulation/qip-scheduler.md index a8f9d7ae..7f1ea974 100644 --- a/tutorials-v4/pulse-level-circuit-simulation/qip-scheduler.md +++ b/tutorials-v4/pulse-level-circuit-simulation/qip-scheduler.md @@ -97,7 +97,7 @@ for gate in circuit.gates: scheduler.schedule(instructions) ``` -The scheduled execution time for each gate can no longer be assigned to gate cycles. But we can see this through the [noisy circuit simulator](https://qutip-qip.readthedocs.io/en/stable/qip-processor.html) of qutip, where the circuit is compiled to control signals: (Notice that the execution time follows the hardware parameter of spin chain and the Y gate is decomposed into a Z-X-Z rotation). +The scheduled execution time for each gate can no longer be assigned to gate cycles. But we can see this through the [noisy circuit simulator](https://qutip.org/docs/latest/guide/qip/qip-processor.html) of qutip, where the circuit is compiled to control signals: (Notice that the execution time follows the hardware parameter of spin chain and the Y gate is decomposed into a Z-X-Z rotation). ```python device = LinearSpinChain(3) diff --git a/tutorials-v4/quantum-circuits/qip-toffoli-cnot.md b/tutorials-v4/quantum-circuits/qip-toffoli-cnot.md index 9c0d1f64..3d933926 100644 --- a/tutorials-v4/quantum-circuits/qip-toffoli-cnot.md +++ b/tutorials-v4/quantum-circuits/qip-toffoli-cnot.md @@ -5,9 +5,9 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.16.4 + jupytext_version: 1.13.8 kernelspec: - display_name: qutip-dev + display_name: Python 3 (ipykernel) language: python name: python3 --- @@ -39,7 +39,7 @@ q.add_gate("TOFFOLI", controls=[0, 2], targets=[1]) ``` ```python -q.draw() +q.png ``` ```python @@ -53,7 +53,7 @@ q2 = q.resolve_gates() ``` ```python -q2.draw() +q2.png ``` ```python diff --git a/tutorials-v4/quantum-circuits/quantum-gates.md b/tutorials-v4/quantum-circuits/quantum-gates.md index be7a4d0b..c35df148 100644 --- a/tutorials-v4/quantum-circuits/quantum-gates.md +++ b/tutorials-v4/quantum-circuits/quantum-gates.md @@ -5,7 +5,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.16.4 + jupytext_version: 1.13.8 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -63,7 +63,7 @@ cphase(pi / 2) ```python q = QubitCircuit(2, reverse_states=False) q.add_gate("CSIGN", controls=[0], targets=[1]) -q.draw() +q.png ``` ### Rotation about X-axis @@ -74,8 +74,8 @@ rx(pi / 2) ```python q = QubitCircuit(1, reverse_states=False) -q.add_gate("RX", targets=[0], arg_value=pi / 2, style={"showarg": True}) -q.draw() +q.add_gate("RX", targets=[0], arg_value=pi / 2, arg_label=r"\frac{\pi}{2}") +q.png ``` ### Rotation about Y-axis @@ -86,8 +86,8 @@ ry(pi / 2) ```python q = QubitCircuit(1, reverse_states=False) -q.add_gate("RY", targets=[0], arg_value=pi / 2, style={"showarg": True}) -q.draw() +q.add_gate("RY", targets=[0], arg_value=pi / 2, arg_label=r"\frac{\pi}{2}") +q.png ``` ### Rotation about Z-axis @@ -98,8 +98,8 @@ rz(pi / 2) ```python q = QubitCircuit(1, reverse_states=False) -q.add_gate("RZ", targets=[0], arg_value=pi / 2, style={"showarg": True}) -q.draw() +q.add_gate("RZ", targets=[0], arg_value=pi / 2, arg_label=r"\frac{\pi}{2}") +q.png ``` ### CNOT @@ -111,7 +111,7 @@ cnot() ```python q = QubitCircuit(2, reverse_states=False) q.add_gate("CNOT", controls=[0], targets=[1]) -q.draw() +q.png ``` ### CSIGN @@ -123,7 +123,7 @@ csign() ```python q = QubitCircuit(2, reverse_states=False) q.add_gate("CSIGN", controls=[0], targets=[1]) -q.draw() +q.png ``` ### Berkeley @@ -135,7 +135,7 @@ berkeley() ```python q = QubitCircuit(2, reverse_states=False) q.add_gate("BERKELEY", targets=[0, 1]) -q.draw() +q.png ``` ### SWAPalpha @@ -162,7 +162,7 @@ toffoli() swap() q = QubitCircuit(2, reverse_states=False) q.add_gate("SWAP", targets=[0, 1]) -q.draw() +q.png ``` ### ISWAP @@ -171,7 +171,7 @@ q.draw() iswap() q = QubitCircuit(2, reverse_states=False) q.add_gate("ISWAP", targets=[0, 1]) -q.draw() +q.png ``` ### SQRTiSWAP @@ -234,7 +234,7 @@ cnot(N=3) ```python q = QubitCircuit(3, reverse_states=False) q.add_gate("CNOT", controls=[1], targets=[2]) -q.draw() +q.png ``` Furthermore, the control and target qubits (when applicable) can also be similarly specified using keyword arguments `control` and `target` (or in some cases `controls` or `targets`): @@ -246,7 +246,7 @@ cnot(N=3, control=2, target=0) ```python q = QubitCircuit(3, reverse_states=False) q.add_gate("CNOT", controls=[0], targets=[2]) -q.draw() +q.png ``` ## Setup of a Qubit Circuit @@ -261,7 +261,7 @@ In the following example, we take a SWAP gate. It is known that a swap gate is e N = 2 qc0 = QubitCircuit(N) qc0.add_gate("ISWAP", [0, 1], None) -qc0.draw() +qc0.png ``` ```python @@ -275,7 +275,7 @@ qc1 = QubitCircuit(N) qc1.add_gate("CNOT", 0, 1) qc1.add_gate("CNOT", 1, 0) qc1.add_gate("CNOT", 0, 1) -qc1.draw() +qc1.png ``` ```python @@ -288,7 +288,7 @@ In place of manually converting the SWAP gate to CNOTs, it can be automatically ```python qc2 = qc0.resolve_gates("CNOT") -qc2.draw() +qc2.png ``` ```python @@ -301,7 +301,7 @@ From QuTiP 4.4, we can also add gate at arbitrary position in a circuit. ```python qc1.add_gate("CSIGN", index=[1], targets=[0], controls=[1]) -qc1.draw() +qc1.png ``` ## Example of basis transformation @@ -313,7 +313,7 @@ qc3.add_gate("RX", 0, None, pi / 2, r"\pi/2") qc3.add_gate("RY", 1, None, pi / 2, r"\pi/2") qc3.add_gate("RZ", 2, None, pi / 2, r"\pi/2") qc3.add_gate("ISWAP", [1, 2]) -qc3.draw() +qc3.png ``` ```python @@ -325,7 +325,7 @@ U3 ```python qc4 = qc3.resolve_gates("CNOT") -qc4.draw() +qc4.png ``` ```python @@ -335,7 +335,7 @@ U4 ```python qc5 = qc3.resolve_gates("ISWAP") -qc5.draw() +qc5.png ``` ```python @@ -347,7 +347,7 @@ U5 ```python qc6 = qc3.resolve_gates(["ISWAP", "RX", "RY"]) -qc6.draw() +qc6.png ``` ```python @@ -357,7 +357,7 @@ U6 ```python qc7 = qc3.resolve_gates(["CNOT", "RZ", "RX"]) -qc7.draw() +qc7.png ``` ```python @@ -373,7 +373,7 @@ Interactions between non-adjacent qubits can be resolved by QubitCircuit to a se ```python qc8 = QubitCircuit(3) qc8.add_gate("CNOT", 2, 0) -qc8.draw() +qc8.png ``` ```python @@ -393,7 +393,7 @@ U9 ```python qc10 = qc9.resolve_gates("CNOT") -qc10.draw() +qc10.png ``` ```python @@ -418,7 +418,7 @@ From QuTiP 4.4 on, user defined gates can be defined by a python function that t ```python def user_gate1(arg_value): # controlled rotation X - mat = np.zeros((4, 4), dtype=complex) + mat = np.zeros((4, 4), dtype=np.complex) mat[0, 0] = mat[1, 1] = 1.0 mat[2:4, 2:4] = rx(arg_value) return Qobj(mat, dims=[[2, 2], [2, 2]]) diff --git a/tutorials-v4/time-evolution/002_larmor-precession.md b/tutorials-v4/time-evolution/002_larmor-precession.md index 8aecd8fc..949d000c 100644 --- a/tutorials-v4/time-evolution/002_larmor-precession.md +++ b/tutorials-v4/time-evolution/002_larmor-precession.md @@ -18,12 +18,12 @@ Author: C. Staufenbiel, 2022 ### Introduction -This notebook guides you through the process of setting up a Schrödinger -equation in QuTiP and using the corresponding solver to obtain the time -evolution. We will investigate the example of the Larmor precession to -explore the functionality of [`qutip.sesolve()`](https://qutip.readthedocs.io/en/latest/apidoc/solver.html#module-qutip.solver.sesolve). +This notebook guides you through the process of setting up a Schrödinger +equation in QuTiP and using the corresponding solver to obtain the time +evolution. We will investigate the example of the Larmor precession to +explore the functionality of [`qutip.sesolve()`](https://qutip.org/docs/latest/apidoc/functions.html?highlight=sesolve#module-qutip.sesolve). -You can also find more on time evolutions with QuTiP [here](https://qutip.readthedocs.io/en/latest/guide/guide-dynamics.html). +You can also find more on time evolutions with QuTiP [here](https://qutip.org/docs/latest/guide/guide-dynamics.html). ### Setup @@ -49,8 +49,8 @@ b.show() ### Simulation with constant magnetic field Let's define a simple Hamiltonian and use `qutip.sesolve` to solve the -Schrödinger equation. The Hamiltonian describes a constant magnetic field -along the z-axis. We can describe this magnetic field by the corresponding +Schrödinger equation. The Hamiltonian describes a constant magnetic field +along the z-axis. We can describe this magnetic field by the corresponding Pauli matrix, which is defined as `qutip.sigmaz()` in QuTiP. To solve the Schrödinger equation for this particular Hamiltonian, we have to pass the Hamiltonian, the initial state, the times for which we want to simulate the system, and a set of observables that we evaluate at these times. @@ -64,7 +64,7 @@ times = np.linspace(0, 10, 100) result = sesolve(H, psi, times, [sigmay()]) ``` -`result.expect` holds the expecation values for the times that we passed to `sesolve`. `result.expect` is a two dimensional array, where the first dimension refers to the different expectation operators that we passed to `sesolve` before. +`result.expect` holds the expecation values for the times that we passed to `sesolve`. `result.expect` is a two dimensional array, where the first dimension refers to the different expectation operators that we passed to `sesolve` before. Above we passed `sigmay()` as the only expectation operator and therefore we can access its values by `result.expect[0]`. Below we plot the evolution of the expecation value. @@ -85,7 +85,7 @@ b.show() ## Simulation with varying magnetic field -Above we passed a constant Hamiltonian to `sesolve`. In QuTiP these constant operators are represented by `Qobj`. However, `sesolve` can also take time-dependent operators as an argument, which are represented by [`QobjEvo`](https://qutip.readthedocs.io/en/latest/apidoc/time_dep.html#qutip.core.cy.qobjevo.QobjEvo) in QuTiP. In this section we define the magnetic field with a linear and a periodic field strength, and observe the changes in the expecation value of $\sigma_y$. +Above we passed a constant Hamiltonian to `sesolve`. In QuTiP these constant operators are represented by `Qobj`. However, `sesolve` can also take time-dependent operators as an argument, which are represented by [`QobjEvo`](https://qutip.org/docs/latest/apidoc/classes.html?highlight=qobjevo#qutip.QobjEvo) in QuTiP. In this section we define the magnetic field with a linear and a periodic field strength, and observe the changes in the expecation value of $\sigma_y$. You can find more information on `QobjEvo` in [this notebook](https://nbviewer.jupyter.org/github/qutip/qutip-notebooks/blob/master/examples/qobjevo.ipynb). We start by defining two functions for the field strength of the magnetic field. To be passed on to `QobjEvo` the functions need two arguments: the times and optional arguments. @@ -127,8 +127,8 @@ plt.show() ``` ### Conclusion -We can use `sesolve` to solve unitary time evolutions. This is not only -limited to constant Hamiltonians, but we can also make use of time-dependent Hamiltonians using `QobjEvo`. +We can use `sesolve` to solve unitary time evolutions. This is not only +limited to constant Hamiltonians, but we can also make use of time-dependent Hamiltonians using `QobjEvo`. ### About diff --git a/tutorials-v4/time-evolution/003_qubit-dynamics.md b/tutorials-v4/time-evolution/003_qubit-dynamics.md index 2592d178..2ae5d0ff 100644 --- a/tutorials-v4/time-evolution/003_qubit-dynamics.md +++ b/tutorials-v4/time-evolution/003_qubit-dynamics.md @@ -21,7 +21,7 @@ Modified by: C. Staufebiel (2022) ### Introduction In this notebook we will explore the dynamics of a single-qubit interacting with an environment. The evolution of the qubit state is governed by the Master equation. We will make use of the master equation solver `qutip.mesolve` implemented in qutip, to obtain the time-evolution of the qubit for different settings. -You can read more about the master equation solver (and the theory behind it) in the [QuTiP docs](https://qutip.readthedocs.io/en/latest/apidoc/time_dep.html#qutip.core.cy.qobjevo.QobjEvo). +You can read more about the master equation solver (and the theory behind it) in the [QuTiP docs](https://qutip.org/docs/latest/apidoc/functions.html?highlight=sesolve#module-qutip.sesolve). ### Import Here we import the required modules for this example. diff --git a/tutorials-v4/time-evolution/004_rabi-oscillations.md b/tutorials-v4/time-evolution/004_rabi-oscillations.md index cc469c33..82b6e270 100644 --- a/tutorials-v4/time-evolution/004_rabi-oscillations.md +++ b/tutorials-v4/time-evolution/004_rabi-oscillations.md @@ -25,7 +25,7 @@ Jaynes-Cumming model, i.e., the cavity and the atom are coupled to an environment. -For more information on the theory behind the Master Equation Solver see [the documentation](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-master.html#non-unitary-evolution). +For more information on the theory behind the Master Equation Solver see [the documentation](https://qutip.org/docs/latest/guide/dynamics/dynamics-master.html#non-unitary-evolution). ### Package import @@ -50,7 +50,7 @@ $H_{\rm RWA} = \hbar \omega_c a^\dagger a + \frac{1}{2}\hbar\omega_a\sigma_z + \ where $\omega_c$ and $\omega_a$ are the frequencies of the cavity and atom, respectively, and $g$ is the interaction strength. -In this example we also consider the coupling of the Jaynes-Cummings model to an external environment, i.e., we need to solve the system using the Master Equation Solver `qutip.mesolve`. The coupling to the environment is described by the collapse operators (as described in [the docs](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-master.html#non-unitary-evolution)). Here, we consider two collapse operators for the cavity $C_1, C_2$, describing creation and annihilation of photons, and one collapse operator for the atom $C_3$. +In this example we also consider the coupling of the Jaynes-Cummings model to an external environment, i.e., we need to solve the system using the Master Equation Solver `qutip.mesolve`. The coupling to the environment is described by the collapse operators (as described in [the docs](https://qutip.org/docs/latest/guide/dynamics/dynamics-master.html#non-unitary-evolution)). Here, we consider two collapse operators for the cavity $C_1, C_2$, describing creation and annihilation of photons, and one collapse operator for the atom $C_3$. $C_1 = \sqrt{\kappa (1+\langle n \rangle)} \; a$ diff --git a/tutorials-v4/time-evolution/006_photon_birth_death.md b/tutorials-v4/time-evolution/006_photon_birth_death.md index 45782cba..72a8f866 100644 --- a/tutorials-v4/time-evolution/006_photon_birth_death.md +++ b/tutorials-v4/time-evolution/006_photon_birth_death.md @@ -21,7 +21,7 @@ Modifications: C. Staufenbiel (2022) ### Introduction -In this tutorial we demonstrate the *Monte Carlo Solver* functionality implemented in `qutip.mcsolve()`. For more information on the *MC Solver* refer to the [QuTiP documentation](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-monte.html). +In this tutorial we demonstrate the *Monte Carlo Solver* functionality implemented in `qutip.mcsolve()`. For more information on the *MC Solver* refer to the [QuTiP documentation](https://qutip.org/docs/latest/guide/dynamics/dynamics-monte.html). We aim to reproduce the experimental results from: diff --git a/tutorials-v4/time-evolution/007_brmesolve_tls.md b/tutorials-v4/time-evolution/007_brmesolve_tls.md index f9508d11..716af725 100644 --- a/tutorials-v4/time-evolution/007_brmesolve_tls.md +++ b/tutorials-v4/time-evolution/007_brmesolve_tls.md @@ -24,7 +24,7 @@ with inspirations from the [`brmesolve notebook`](https://github.com/qutip/qutip The Bloch-Redfield solver is another method to solve a master equation. In comparison to the Lindblad Master equation solver `qutip.mesolve()` the Bloch-Redfield solver `qutip.brmesolve()` differs in the description of the interaction with the environment. In `qutip.mesolve()` we described the dissipation by collapse operators, which do not necessarily have a physical interpretation. The `qutip.brmesolve()` function requires the a dissipation description by the so-called *noise-power-spectrum*, which gives the intensity of the dissipation depending on the frequency $\omega$. -In this notebook we will introduce the basic usage of `qutip.brmesolve()` and compare it to `qutip.mesolve()`. For more information on the Bloch-Redfield solver see the follow-up notebooks and the [QuTiP Documentation of the functionality](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-bloch-redfield.html). +In this notebook we will introduce the basic usage of `qutip.brmesolve()` and compare it to `qutip.mesolve()`. For more information on the Bloch-Redfield solver see the follow-up notebooks and the [QuTiP Documentation of the functionality](https://qutip.org/docs/latest/guide/dynamics/dynamics-bloch-redfield.html). ### Imports @@ -111,7 +111,7 @@ plt.legend(), plt.xlabel("time"), plt.ylabel(""); ## Bloch-Redfield Tensor -We described the dynmamics of the system by the Bloch-Redfield master equation, which is constructed from the Bloch-Redfield tensor $R_{abcd}$ (see [documentation of Bloch-Redfield master equation](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-bloch-redfield.html)). Hence the dynamics are determined by this tensor. We can calculate the tensor in QuTiP using the `qutip.bloch_redfield_tensor()` function. We have to pass the Hamiltonian of the system and the dissipation description in `a_ops` to construct $R_{abcd}$. Furthermore, the function gives us the **eigenstates of the Hamiltonian**, as they are calculated along the way. +We described the dynmamics of the system by the Bloch-Redfield master equation, which is constructed from the Bloch-Redfield tensor $R_{abcd}$ (see [documentation of Bloch-Redfield master equation](https://qutip.org/docs/latest/guide/dynamics/dynamics-bloch-redfield.html)). Hence the dynamics are determined by this tensor. We can calculate the tensor in QuTiP using the `qutip.bloch_redfield_tensor()` function. We have to pass the Hamiltonian of the system and the dissipation description in `a_ops` to construct $R_{abcd}$. Furthermore, the function gives us the **eigenstates of the Hamiltonian**, as they are calculated along the way. ```python diff --git a/tutorials-v4/time-evolution/008_brmesolve_time_dependence.md b/tutorials-v4/time-evolution/008_brmesolve_time_dependence.md index 1b621755..de645102 100644 --- a/tutorials-v4/time-evolution/008_brmesolve_time_dependence.md +++ b/tutorials-v4/time-evolution/008_brmesolve_time_dependence.md @@ -16,10 +16,10 @@ jupyter: Authors: C. Staufenbiel, 2022 -following the instructions in the [Bloch-Redfield documentation](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-bloch-redfield.html?#time-dependent-bloch-redfield-dynamics). +following the instructions in the [Bloch-Redfield documentation](https://qutip.org/docs/latest/guide/dynamics/dynamics-bloch-redfield.html?#time-dependent-bloch-redfield-dynamics). ### Introduction -This notebook introduces the usage of time-dependent operators in the Bloch-Redfield solver, which is also described in the [corresponding documentation](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-bloch-redfield.html?#time-dependent-bloch-redfield-dynamics). +This notebook introduces the usage of time-dependent operators in the Bloch-Redfield solver, which is also described in the [corresponding documentation](https://qutip.org/docs/latest/guide/dynamics/dynamics-bloch-redfield.html?#time-dependent-bloch-redfield-dynamics). We will discuss time-dependent Hamiltonians and time-dependent dissipations. The Bloch-Redfield solver is especially efficient since it uses Cython internally. For correct functioning we have to pass the time dependence in a string-based format. @@ -55,7 +55,7 @@ result_const = brmesolve(H, psi0, times, e_ops=[a.dag() * a]) plot_expectation_values(result_const, ylabels=[""]); ``` -Next we define a string, which describes some time-dependence. We can use functions that are supported by the Cython implementation. A list of all supported functions can be found in the [docs](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-time.html#time). For example, supported functions are `sin` or `exp`. The time variable is denoted by `t`. +Next we define a string, which describes some time-dependence. We can use functions that are supported by the Cython implementation. A list of all supported functions can be found in the [docs](https://qutip.org/docs/latest/guide/dynamics/dynamics-time.html#time). For example, supported functions are `sin` or `exp`. The time variable is denoted by `t`. ```python time_dependence = "sin(t)" @@ -63,7 +63,7 @@ time_dependence = "sin(t)" ### Time-dependent Hamiltonian -As a first example, we define a time-dependent Hamiltonian (as described [here](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-time.html)). +As a first example, we define a time-dependent Hamiltonian (as described [here](https://qutip.org/docs/latest/guide/dynamics/dynamics-time.html)). $$ H = \hat{n} + sin(t) \hat{x} $$ diff --git a/tutorials-v4/time-evolution/009_brmesolve-cavity-QED.md b/tutorials-v4/time-evolution/009_brmesolve-cavity-QED.md index 499d1945..02ec6d79 100644 --- a/tutorials-v4/time-evolution/009_brmesolve-cavity-QED.md +++ b/tutorials-v4/time-evolution/009_brmesolve-cavity-QED.md @@ -21,7 +21,7 @@ with inspirations from the [`brmesolve notebook`](https://github.com/qutip/qutip ### Introduction -This notebook does not introduce the usage of the Bloch-Redfield solver `qutip.brmesolve()` in detail. For a more detailed introduction to this solver see the [*Bloch-Redfield Solver: Two Level System* notebook](007_brmesolve_tls.md) and the [documentation of the function](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-bloch-redfield.html). +This notebook does not introduce the usage of the Bloch-Redfield solver `qutip.brmesolve()` in detail. For a more detailed introduction to this solver see the [*Bloch-Redfield Solver: Two Level System* notebook](007_brmesolve_tls.md) and the [documentation of the function](https://qutip.org/docs/latest/guide/dynamics/dynamics-bloch-redfield.html). The Lindblad master equation solver, implemented in `qutip.mesolve()`, deals with dissipation using collapse operators which can act on subsystems of the general system. For example, we can define dissipation for the atom-cavity system for the cavity and the atom separately, by the corresponding annihilation operator. In this example, we will see the limitations of this approach when it comes to strong coupling between atom and cavity. diff --git a/tutorials-v4/time-evolution/011_floquet_solver.md b/tutorials-v4/time-evolution/011_floquet_solver.md index aad4d484..9a75b434 100644 --- a/tutorials-v4/time-evolution/011_floquet_solver.md +++ b/tutorials-v4/time-evolution/011_floquet_solver.md @@ -20,7 +20,7 @@ Author: C. Staufenbiel, 2022 The *Floquet formalism* deals with periodic time-dependent systems. The Floquet approach can be more efficient for such problems than using the standard master equation solver `qutip.mesolve()` and it has a broader range of validity for periodic driving. -In this notebook, we will discuss the solver functionality of the Floquet formalism implemented in QuTiP using an example quantum system. A more detailed introduction into the Floquet formalism can be found in the [documentation](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-floquet.html). +In this notebook, we will discuss the solver functionality of the Floquet formalism implemented in QuTiP using an example quantum system. A more detailed introduction into the Floquet formalism can be found in the [documentation](https://qutip.org/docs/latest/guide/dynamics/dynamics-floquet.html). A more in depth introduction into the internal functions of the Floquet formalism, used also by the solvers `fsesolve` and `fmmesolve`, is given in the [*floquet formalism notebook*](012_floquet_formalism.md). diff --git a/tutorials-v4/time-evolution/012_floquet_formalism.md b/tutorials-v4/time-evolution/012_floquet_formalism.md index ad3b32df..90659223 100644 --- a/tutorials-v4/time-evolution/012_floquet_formalism.md +++ b/tutorials-v4/time-evolution/012_floquet_formalism.md @@ -18,14 +18,14 @@ Author: C. Staufenbiel, 2022 inspirations taken from the [Floquet notebook](https://github.com/qutip/qutip-notebooks/blob/master/examples/floquet-dynamics.ipynb) by P.D. Nation and J.R. Johannson, -and the [qutip documentation](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-floquet.html). +and the [qutip documentation](https://qutip.org/docs/latest/guide/dynamics/dynamics-floquet.html). ### Introduction In the [floquet_solver notebook](011_floquet_solver.md) we introduced the two functions to solve the Schrödinger and Master equation using the Floquet formalism. In this notebook, we will focus on the internal functions of these solvers, that implement the Floquet formalism in QuTiP. Here, we will focus on the `Floquet modes` and the `quasienergies`. -More information on the implementation of the Floquet Formalism in QuTiP can be found in the [documentation](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-floquet.html). +More information on the implementation of the Floquet Formalism in QuTiP can be found in the [documentation](https://qutip.org/docs/latest/guide/dynamics/dynamics-floquet.html). -### Imports +### Imports ```python import matplotlib.pyplot as plt @@ -40,7 +40,7 @@ from qutip import (about, expect, floquet_markov_mesolve, ``` ### System setup -For consistency with the documentation we consider the driven system with the following Hamiltonian: +For consistency with the documentation we consider the driven system with the following Hamiltonian: $$ H = - \frac{\Delta}{2} \sigma_x - \frac{\epsilon_0}{2} \sigma_z + \frac{A}{2} \sigma_x sin(\omega t) $$ @@ -130,13 +130,13 @@ assert np.allclose(psi_t.full(), psi_t_direct.full()) ### Precomputing and reusing the Floquet modes of one period -The Floquet modes have the same periodicity as the Hamiltonian: +The Floquet modes have the same periodicity as the Hamiltonian: $$ \phi_\alpha(t + T) = \phi_\alpha(t) $$ -Hence it is enough to evaluate the modes at times $t \in [0,T]$. From these modes we can extrapolate the system state $\psi(t)$ for any time $t$. +Hence it is enough to evaluate the modes at times $t \in [0,T]$. From these modes we can extrapolate the system state $\psi(t)$ for any time $t$. -The function `floquet_modes_table` allows to calculate the Floquet modes for multiple times in the first period. +The function `floquet_modes_table` allows to calculate the Floquet modes for multiple times in the first period. ```python @@ -172,15 +172,14 @@ plt.legend(loc="upper right") plt.xlabel("Time"), plt.ylabel("Occupation prob."); ``` - ### Floquet Markov formalism -We can also solve a master equation using the Floquet formalism. A detailed derivation of the Floquet-Markov formalism used here is given in [Grifoni et al., Physics Reports 304, 299 (1998)](https://www.sciencedirect.com/science/article/pii/S0370157398000222) and in the [QuTiP docs](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-floquet.html). Note that the functionality described here is summarised in the function `fmmesolve` described in the [floquet solver notebook](011_floquet_solver.md). +We can also solve a master equation using the Floquet formalism. A detailed derivation of the Floquet-Markov formalism used here is given in [Grifoni et al., Physics Reports 304, 299 (1998)](https://www.sciencedirect.com/science/article/abs/pii/S0370157398000222) and in the [QuTiP docs](https://qutip.org/docs/latest/guide/dynamics/dynamics-floquet.html). Note that the functionality described here is summarised in the function `fmmesolve` described in the [floquet solver notebook](011_floquet_solver.md). The interaction with the bath is described by a noise spectrum, which does not include the temperature dependency. The temperature dependency can be passed to `fmmesolve` using the keyword `w_th` in the `args` parameter: `args[w_th]`. Hence, the definition is slightly different to the one in the Bloch-Redfield formalism. For details see the derivation of the formalism. - -Here we define a simple linear noise spectrum: + +Here we define a simple linear noise spectrum: $$ S(\omega) = \frac{\gamma \cdot \omega}{4 \pi} $$ @@ -203,9 +202,9 @@ temp = 10.0 args = {"w_th": temp} ``` -The Floquet Markov approach starts by calculating rates, that describe the dissipation process of the system with the given spectrum and temperature of the bath. Especially important is `Amat`, which is later used to calculate the Floquet tensor for the master equation. +The Floquet Markov approach starts by calculating rates, that describe the dissipation process of the system with the given spectrum and temperature of the bath. Especially important is `Amat`, which is later used to calculate the Floquet tensor for the master equation. -In theory the matrix is defined as an infinite sum (see [docs](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-floquet.html)). However, in QuTiP the sidebands need to be truncated to create a finite sum. This is done with the `kmax` argument. +In theory the matrix is defined as an infinite sum (see [docs](https://qutip.org/docs/latest/guide/dynamics/dynamics-floquet.html)). However, in QuTiP the sidebands need to be truncated to create a finite sum. This is done with the `kmax` argument. ```python kmax = 20 @@ -222,7 +221,7 @@ Together with the quasienergies, the tensor for the Floquet master equation can R = floquet_master_equation_tensor(Amat, f_energies) ``` -We can pass in the tensor, initial state, expectation value and expectation operator into the `floquet_markov_mesolve` function and obtain the time evolution of the system (i.e. expectation operator) using the Floquet formalism. +We can pass in the tensor, initial state, expectation value and expectation operator into the `floquet_markov_mesolve` function and obtain the time evolution of the system (i.e. expectation operator) using the Floquet formalism. ```python res_fme_manual = floquet_markov_mesolve( diff --git a/tutorials-v4/time-evolution/016_smesolve-inefficient-detection.md b/tutorials-v4/time-evolution/016_smesolve-inefficient-detection.md index 886d3555..420cdc62 100644 --- a/tutorials-v4/time-evolution/016_smesolve-inefficient-detection.md +++ b/tutorials-v4/time-evolution/016_smesolve-inefficient-detection.md @@ -63,12 +63,13 @@ and $dN(t)$ is a Poisson distributed increment with $E[dN(t)] = \eta \langle a^\ In QuTiP, the photocurrent stochastic master equation is written in the form: -$\displaystyle d\rho(t) = -i[H, \rho] dt + \mathcal{D}[B] \rho dt - \frac{1}{2}\mathcal{H}[A^\dagger A]\rho(t) dt + \mathcal{G}[A]\rho(t) d\xi$ +$\displaystyle d\rho(t) = -i[H, \rho] dt + \mathcal{D}[B] \rho dt +- \frac{1}{2}\mathcal{H}[A^\dagger A] \rho(t) dt ++ \mathcal{G}[A]\rho(t) d\xi$ where the first two term gives the deterministic master equation (Lindblad form with collapse operator $B$ (c_ops)) and $A$ the stochastic collapse operator (sc_ops). -Here $A = \sqrt{\eta\gamma}a $ and $B = \sqrt{(1-\eta)\gamma} a$ - +Here $A = \sqrt{\eta\gamma} a$ and $B = \sqrt{(1-\eta)\gamma} $a. ```python N = 15 From 402288b06360359ffd50f8b7542c119cd23fb001 Mon Sep 17 00:00:00 2001 From: Gerardo Suarez Date: Wed, 23 Apr 2025 01:25:21 +0200 Subject: [PATCH 29/44] remove unrelated changes --- .../Lecture-0-Introduction-to-QuTiP.md | 10 +- .../lectures/Lecture-11-Charge-Qubits.md | 2 +- .../lectures/Lecture-2B-Single-Atom-Lasing.md | 12 +- .../lectures/Lecture-3A-Dicke-model.md | 4 +- ...Cumming-model-with-ultrastrong-coupling.md | 6 +- .../Lecture-4-Correlation-Functions.md | 4 +- ...ation-number-restricted-states-jc-chain.md | 269 +++++++++ .../single-photon-interference-setup.jpg | Bin 0 -> 140373 bytes .../single-photon-interference.md | 524 ++++++++++++++++++ .../qip-customize-device.md | 2 +- .../qip-optpulseprocessor.md | 2 +- .../qip-scheduler.md | 2 +- .../quantum-circuits/qip-toffoli-cnot.md | 8 +- .../quantum-circuits/quantum-gates.md | 54 +- .../time-evolution/002_larmor-precession.md | 22 +- .../time-evolution/003_qubit-dynamics.md | 2 +- .../time-evolution/004_rabi-oscillations.md | 4 +- .../time-evolution/006_photon_birth_death.md | 2 +- .../time-evolution/007_brmesolve_tls.md | 4 +- .../008_brmesolve_time_dependence.md | 8 +- .../009_brmesolve-cavity-QED.md | 2 +- .../time-evolution/011_floquet_solver.md | 2 +- .../time-evolution/012_floquet_formalism.md | 27 +- .../016_smesolve-inefficient-detection.md | 7 +- 24 files changed, 882 insertions(+), 97 deletions(-) create mode 100644 tutorials-v4/miscellaneous/excitation-number-restricted-states-jc-chain.md create mode 100644 tutorials-v4/miscellaneous/images/single-photon-interference-setup.jpg create mode 100644 tutorials-v4/miscellaneous/single-photon-interference.md diff --git a/tutorials-v4/lectures/Lecture-0-Introduction-to-QuTiP.md b/tutorials-v4/lectures/Lecture-0-Introduction-to-QuTiP.md index 533ed6b4..a5ade5ef 100644 --- a/tutorials-v4/lectures/Lecture-0-Introduction-to-QuTiP.md +++ b/tutorials-v4/lectures/Lecture-0-Introduction-to-QuTiP.md @@ -40,7 +40,7 @@ It includes facilities for representing and doing calculations with quantum obje It also includes solvers for a time-evolution of quantum systems, according to: Schrodinger equation, von Neuman equation, master equations, Floquet formalism, Monte-Carlo quantum trajectors, experimental implementations of the stochastic Schrodinger/master equations. For more information see the project web site at [qutip.org](https://qutip.org), and the -[QuTiP documentation](https://qutip.org/docs/latest/index.html). +[QuTiP documentation](https://qutip.readthedocs.io/en/latest/index.html). ### Installation @@ -51,11 +51,7 @@ You can install QuTiP directly from `pip` by running: For further installation details, refer to the [GitHub repository](https://github.com/qutip/qutip). -To use QuTiP in a Python program, first inlude the relevant functionality from the `qutip` module: - -```python - -``` +To use QuTiP in a Python program, first include the relevant functionality from the `qutip` module as shown above. This will make the functions and classes in QuTiP available in the rest of the program. @@ -152,7 +148,7 @@ H.tr() H.eigenenergies() ``` -For a complete list of methods and properties of the `Qobj` class, see the [QuTiP documentation](https://qutip.org/docs/latest/index.html) or try `help(Qobj)` or `dir(Qobj)`. +For a complete list of methods and properties of the `Qobj` class, see the [QuTiP documentation](https://qutip.readthedocs.io/en/latest/index.html) or try `help(Qobj)` or `dir(Qobj)`. ## States and operators diff --git a/tutorials-v4/lectures/Lecture-11-Charge-Qubits.md b/tutorials-v4/lectures/Lecture-11-Charge-Qubits.md index dbc8e279..70ed1617 100644 --- a/tutorials-v4/lectures/Lecture-11-Charge-Qubits.md +++ b/tutorials-v4/lectures/Lecture-11-Charge-Qubits.md @@ -39,7 +39,7 @@ where $E_C$ is the charge energy, $E_J$ is the Josephson energy, and $\left| n\r #### References - * [J. Koch et al, Phys. Rec. A 76, 042319 (2007)](http://link.aps.org/doi/10.1103/PhysRevA.76.042319) + * [J. Koch et al, Phys. Rec. A 76, 042319 (2007)](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.76.042319) * [Y.A. Pashkin et al, Quantum Inf Process 8, 55 (2009)](http://dx.doi.org/10.1007/s11128-009-0101-5) diff --git a/tutorials-v4/lectures/Lecture-2B-Single-Atom-Lasing.md b/tutorials-v4/lectures/Lecture-2B-Single-Atom-Lasing.md index f2cc1c2c..f9efd6ee 100644 --- a/tutorials-v4/lectures/Lecture-2B-Single-Atom-Lasing.md +++ b/tutorials-v4/lectures/Lecture-2B-Single-Atom-Lasing.md @@ -55,17 +55,17 @@ In addition to the coherent dynamics the following incoherent processes are also The Lindblad master equation for the model is: -$\frac{d}{dt}\rho = -i[H, \rho] + \Gamma\left(\sigma_+\rho\sigma_- - \frac{1}{2}\sigma_-\sigma_+\rho - \frac{1}{2}\rho\sigma_-\sigma_+\right) +$$\frac{d}{dt}\rho = -i[H, \rho] + \Gamma\left(\sigma_+\rho\sigma_- - \frac{1}{2}\sigma_-\sigma_+\rho - \frac{1}{2}\rho\sigma_-\sigma_+\right) + \kappa (1 + n_{\rm th}) \left(a\rho a^\dagger - \frac{1}{2}a^\dagger a\rho - \frac{1}{2}\rho a^\dagger a\right) -+ \kappa n_{\rm th} \left(a^\dagger\rho a - \frac{1}{2}a a^\dagger \rho - \frac{1}{2}\rho a a^\dagger\right)$ ++ \kappa n_{\rm th} \left(a^\dagger\rho a - \frac{1}{2}a a^\dagger \rho - \frac{1}{2}\rho a a^\dagger\right)$$ in units where $\hbar = 1$. References: - * [Yi Mu, C.M. Savage, Phys. Rev. A 46, 5944 (1992)](http://dx.doi.org/10.1103/PhysRevA.46.5944) + * [Yi Mu, C.M. Savage, Phys. Rev. A 46, 5944 (1992)](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.46.5944) - * [D.A. Rodrigues, J. Imbers, A.D. Armour, Phys. Rev. Lett. 98, 067204 (2007)](http://dx.doi.org/10.1103/PhysRevLett.98.067204) + * [D.A. Rodrigues, J. Imbers, A.D. Armour, Phys. Rev. Lett. 98, 067204 (2007)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.98.067204) * [S. Ashhab, J.R. Johansson, A.M. Zagoskin, F. Nori, New J. Phys. 11, 023030 (2009)](http://dx.doi.org/10.1088/1367-2630/11/2/023030) @@ -102,10 +102,6 @@ sx = tensor(qeye(N), sigmax()) H = w0 * a.dag() * a + wa * sm.dag() * sm + g * (a.dag() + a) * sx ``` -```python -H -``` - ### Create a list of collapse operators that describe the dissipation ```python diff --git a/tutorials-v4/lectures/Lecture-3A-Dicke-model.md b/tutorials-v4/lectures/Lecture-3A-Dicke-model.md index cdee9f4b..3aaae2ab 100644 --- a/tutorials-v4/lectures/Lecture-3A-Dicke-model.md +++ b/tutorials-v4/lectures/Lecture-3A-Dicke-model.md @@ -49,7 +49,7 @@ $\displaystyle J_\pm = \sum_{i=1}^N \sigma_\pm^{(i)}$ ### References - * [R.H. Dicke, Phys. Rev. 93, 99–110 (1954)](http://dx.doi.org/10.1103/PhysRev.93.99) + * [R.H. Dicke, Phys. Rev. 93, 99–110 (1954)](https://journals.aps.org/pr/abstract/10.1103/PhysRev.93.99) ## Setup problem in QuTiP @@ -198,7 +198,7 @@ fig.tight_layout() ### References -* [Lambert et al., Phys. Rev. Lett. 92, 073602 (2004)](http://dx.doi.org/10.1103/PhysRevLett.92.073602). +* [Lambert et al., Phys. Rev. Lett. 92, 073602 (2004)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.92.073602). ```python def calulcate_entropy(M, N, g_vec): diff --git a/tutorials-v4/lectures/Lecture-3B-Jaynes-Cumming-model-with-ultrastrong-coupling.md b/tutorials-v4/lectures/Lecture-3B-Jaynes-Cumming-model-with-ultrastrong-coupling.md index 37d822b6..cce8fc10 100644 --- a/tutorials-v4/lectures/Lecture-3B-Jaynes-Cumming-model-with-ultrastrong-coupling.md +++ b/tutorials-v4/lectures/Lecture-3B-Jaynes-Cumming-model-with-ultrastrong-coupling.md @@ -51,11 +51,11 @@ The regime $g$ is large compared with all other energy scales in the Hamiltonian References: - * [P. Nataf et al., Phys. Rev. Lett. 104, 023601 (2010)](http://dx.doi.org/10.1103/PhysRevLett.104.023601) + * [P. Nataf et al., Phys. Rev. Lett. 104, 023601 (2010)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.104.023601) - * [J. Casanova et al., Phys. Rev. Lett. 105, 26360 (2010)](http://dx.doi.org/10.1103/PhysRevLett.105.263603). + * [J. Casanova et al., Phys. Rev. Lett. 105, 26360 (2010)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.105.263603). - * [S. Ashhab et al., Phys. Rev. A 81, 042311 (2010)](http://dx.doi.org/10.1103/PhysRevA.81.042311) + * [S. Ashhab et al., Phys. Rev. A 81, 042311 (2010)](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.81.042311) diff --git a/tutorials-v4/lectures/Lecture-4-Correlation-Functions.md b/tutorials-v4/lectures/Lecture-4-Correlation-Functions.md index 64313244..fb585463 100644 --- a/tutorials-v4/lectures/Lecture-4-Correlation-Functions.md +++ b/tutorials-v4/lectures/Lecture-4-Correlation-Functions.md @@ -152,7 +152,7 @@ $L(\tau) = 2\langle Q(\tau)Q(0)\rangle - \langle Q(2\tau)Q(0)\rangle \leq 1$ ### References -* [A. J. Leggett and A. Garg, Phys. Rev. Lett. 54, 857 (1985)](http://dx.doi.org/10.1103/PhysRevLett.54.857) +* [A. J. Leggett and A. Garg, Phys. Rev. Lett. 54, 857 (1985)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.54.857) * [A. J. Leggett, J. Phys. Condens. Matter 14, R415 (2002)](http://dx.doi.org/10.1088/0953-8984/14/15/201) @@ -186,7 +186,7 @@ def leggett_garg(c_mat): References: - * [N. Lambert, J.R. Johansson, F. Nori, Phys. Rev. B 82, 245421 (2011)](http://dx.doi.org/10.1103/PhysRevB.84.245421). + * [N. Lambert, J.R. Johansson, F. Nori, Phys. Rev. B 82, 245421 (2011)](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.84.245421). ```python diff --git a/tutorials-v4/miscellaneous/excitation-number-restricted-states-jc-chain.md b/tutorials-v4/miscellaneous/excitation-number-restricted-states-jc-chain.md new file mode 100644 index 00000000..8cdd9e0d --- /dev/null +++ b/tutorials-v4/miscellaneous/excitation-number-restricted-states-jc-chain.md @@ -0,0 +1,269 @@ +--- +jupyter: + jupytext: + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.13.8 + kernelspec: + display_name: qutip-tutorials + language: python + name: python3 +--- + + +# Excitation-number-restricted states: Jaynes-Cummings Chain + +Authors: Robert Johansson (jrjohansson@gmail.com), Neill Lambert (nwlambert@gmail.com), Maximilian Meyer-Mölleringhof (m.meyermoelleringhof@gmail.com) + +## Introduction + +The ENR functions construct a basis set for multipartite systems which contains only states that have an overall number of excitations. +This is particularly useful for systems where the model conserves excitation number, as in the JC-chain example below. + +We can see this by considering a system consisting of 4 modes, each with 5 states. +The total hilbert space size is $5^4 = 625$. +If we are only interested in states that contain up to 2 excitations, we only need to include states such as + + + (0, 0, 0, 0) + (0, 0, 0, 1) + (0, 0, 0, 2) + (0, 0, 1, 0) + (0, 0, 1, 1) + (0, 0, 2, 0) + ... + +The ENR fucntions create operators and states for the 4 modes that act within this state space. +For example, + +```python +a1, a2, a3, a4 = enr_destroy([5, 5, 5, 5], excitations=2) +``` + +creates destruction operators for each mode. +From this point onwards, the annihiltion operators a1, ..., a4 can be used to setup a Hamiltonian, collapse operators and expectation-value operators, etc., following the usual patterne. + +In this example we outline the advantage of ENR states by comparing them with the regular qutip implementation. +For this we calculate the time evolution and the partial trace for each and see consistent results with notable performance improvements. + +#### Be aware! + +Many default functions in QuTiP will fail on states and operators constructed with this method. +Additionally, using this formalism, annihilation and creation operators of different sub-systems no longer commute. +Therefore, when constructing Hamiltonians, annihilation operators must be on the right and creation operators on the left (see the offical publication for QuTiP v5 for more info). +To find all available functions to work with ENR states see [Energy Restricted Operators in the official documentation](https://qutip.readthedocs.io/en/qutip-5.0.x/apidoc/functions.html#module-qutip.core.energy_restricted). + + +```python +import numpy as np +from qutip import (Options, Qobj, about, basis, destroy, enr_destroy, enr_fock, + enr_state_dictionaries, identity, liouvillian_ref, mesolve, + plot_expectation_values, tensor) + +%matplotlib inline +``` + +## The Jaynes-Cumming Chain + +The general Jaynes-Cumming model describes a single two-level atom interacting with a single electromagnetic cavity mode. +For this example, we put multiple of these systems in a chain and let them interact with neighbouring systems via their cavities. +We use $a_i$ ($a^\dag_i$) as annihilation (creation) operators for the cavity $i$ and $s_i$ ($s^\dag_i$) for the atoms. +We then model the complete Hamiltonian by splitting it into the individual systems: + +$H_0 = \sum_{i=0}^{N} a_i^\dag a_i + s_i^\dag s_i$, + +the atom-cavity interactions: + +$H_{int,AC} = \sum_{i=0}^{N} = \frac{1}{2} (a_i^\dag s_i + s_i^\dag a_i)$, + +and the cavity-cavity interactions: + +$H_{int,CC} = \sum_{i=0}^{N-1} 0.9 \cdot (a_i^\dag a_{i+1} + a_{i+1}^\dag a_{i})$, + +where the interaction strength of $0.9$ was chosen arbitrarily. + + +### Problem paramters + +```python +N = 4 # number of systems +M = 2 # number of cavity states +dims = [M, 2] * N # dimensions of JC spin chain +excite = 1 # total number of excitations +init_excite = 1 # initial number of excitations +``` + +### Setup to Calculate Time Evolution + +```python +def solve(d, psi0): + # annihilation operators for cavity modes + a = d[::2] + # atomic annihilation operators + sm = d[1::2] + + # notice the ordering of annihilation and creation operators + H0 = sum([aa.dag() * aa for aa in a]) + sum([s.dag() * s for s in sm]) + + # atom-cavity couplings + Hint_ac = 0 + for n in range(N): + Hint_ac += 0.5 * (a[n].dag() * sm[n] + sm[n].dag() * a[n]) + + # cavity-cavity couplings + Hint_cc = 0 + for n in range(N - 1): + Hint_cc += 0.9 * (a[n].dag() * a[n + 1] + a[n + 1].dag() * a[n]) + + H = H0 + Hint_ac + Hint_cc + + e_ops = [x.dag() * x for x in d] + c_ops = [0.01 * x for x in a] + + times = np.linspace(0, 250, 1000) + L = liouvillian_ref(H, c_ops) + opt = Options(nsteps=5000, store_states=True) + result = mesolve(H, psi0, times, c_ops, e_ops, options=opt) + return result, H, L +``` + +### Regular QuTiP States and Operators + +```python +d = [ + tensor( + [ + destroy(dim1) if idx1 == idx2 else identity(dim1) + for idx1, dim1 in enumerate(dims) + ] + ) + for idx2, _ in enumerate(dims) +] +psi0 = tensor( + [ + basis(dim, init_excite) if idx == 1 else basis(dim, 0) + for idx, dim in enumerate(dims) + ] +) +``` + +Regular operators of different systems commute as they belong to different Hilbert spaces. +Example: + +```python +d[0].dag() * d[1] == d[1] * d[0].dag() +``` + +Solving the time evolution: + +```python +res1, H1, L1 = solve(d, psi0) +``` + +### Using ENR States and Operators + +```python +d_enr = enr_destroy(dims, excite) +init_enr = [init_excite if n == 1 else 0 for n in range(2 * N)] +psi0_enr = enr_fock(dims, excite, init_enr) +``` + +Using ENR states forces us to give up on the standard tensor structure of multiple Hilbert spaces. +Operators for different systems therefore generally no longer commute: + +```python +d_enr[0].dag() * d_enr[1] == d_enr[1] * d_enr[0].dag() +``` + +Solving the time evolution: + +```python +res2, H2, L2 = solve(d_enr, psi0_enr) +``` + +### Comparison of Expectation Values + +```python +fig, axes = plot_expectation_values([res1, res2], figsize=(10, 8)) +for idx, ax in enumerate(axes[:, 0]): + if idx % 2: + ax.set_ylabel(f"Atom {idx//2}") + else: + ax.set_ylabel(f"Cavity {idx//2}") + ax.set_ylim(-0.1, 1.1) + ax.grid() +fig.tight_layout() +``` + +### Calculation of Partial Trace + +The usage of ENR states makes many standard QuTiP features fail. +*ptrace* is one of those. +Below we demonstrate how the partial trace for ENR states can be calculated and show the corresponding result together with the standrad QuTiP approach. + +```python +def ENR_ptrace(rho, sel, excitations): + if isinstance(sel, int): + sel = np.array([sel]) + else: + sel = np.asarray(sel) + + if (sel < 0).any() or (sel >= len(rho.dims[0])).any(): + raise TypeError("Invalid selection index in ptrace.") + + drho = rho.dims[0] + _, state2idx, idx2state = enr_state_dictionaries(drho, excitations) + + dims_short = np.asarray(drho).take(sel).tolist() + nstates2, state2idx2, _ = enr_state_dictionaries(dims_short, excitations) + + # construct new density matrix + rhout = np.zeros((nstates2, nstates2), dtype=np.complex64) + # dimensions of traced out system + rest = np.setdiff1d(np.arange(len(drho)), sel) + for state in idx2state: + for state2 in idx2state: + # add diagonal elements to the new density matrix + state_red = np.asarray(state).take(rest) + state2_red = np.asarray(state2).take(rest) + if np.all(state_red == state2_red): + rhout[ + state2idx2[tuple(np.asarray(state).take(sel))], + state2idx2[tuple(np.asarray(state2).take(sel))], + ] += rho.data[state2idx[state], state2idx[state2]] + + new_dims = np.asarray(drho).take(sel).tolist() + return Qobj(rhout, dims=[new_dims, new_dims], shape=[nstates2, nstates2]) +``` + +```python +res1.states[10].ptrace(1) +``` + +```python +ENR_ptrace(res2.states[10], 1, excite) +``` + +```python +res1.states[10].ptrace([0, 1, 4]) +``` + +```python +ENR_ptrace(res2.states[10], [0, 1, 4], excite) +``` + +## About + +```python +about() +``` + +## Testing + +```python +assert np.allclose( + res1.states[10].ptrace([1]), ENR_ptrace(res2.states[10], [1], excite) +), "The approaches do not yield the same result." +``` diff --git a/tutorials-v4/miscellaneous/images/single-photon-interference-setup.jpg b/tutorials-v4/miscellaneous/images/single-photon-interference-setup.jpg new file mode 100644 index 0000000000000000000000000000000000000000..50dd42eef28bf0b2fa524417d5495f8912f67630 GIT binary patch literal 140373 zcmeFacRXC*yFa=|4-&oCC_zN;ZH6QWB1D2{5uJ!mbfW|zS_l#mB!YzKy~OAxAw&(M z3=$G`Mjd91yYusilQ``IVoPEY`?u9l7# z0D(Y&d*B~%vLMu@8Q|gw0Q&mCMF0RO05X6IAOb@dzzbUlJwObGA>bbXk^1NT#}Mhi z!i*qIS*!~{@)r$2(Xq-2kc0QA!D|x$pwr@91w$0ze!86^aEibw0;dR^B5;bp|40N( zUEJ+G{C#*0ojrU#cs1=^d_BB*r6k2A#ib-ArKH5A1_rM5b5aPe<6l9>qgdHFS?}4@xx@QoM 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b/tutorials-v4/miscellaneous/single-photon-interference.md new file mode 100644 index 00000000..a4676ddb --- /dev/null +++ b/tutorials-v4/miscellaneous/single-photon-interference.md @@ -0,0 +1,524 @@ +--- +jupyter: + jupytext: + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.16.1 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +# Lecture: Single-photon Interference + + +Authors: Amélie Orban (Editor), Inga Schöyen, Alessandro Delmonte, Alexander Rihane. +
Based on the project Simulating Single Photon Interference using QuTiP (Quantum Toolbox in Python) carried out in June 2021 at Maastricht University, Maastricht Science Programme. +
Last updated: 09/11/21. + +```python +import matplotlib.pyplot as plt +import qutip +from IPython.display import Image +from numpy import cos, exp, pi, random, sin, sqrt +from qutip import Qobj, basis + +%matplotlib inline +``` + +### Table of Contents +* [Introduction](#section0) +* [1. Theoretical Background](#section1) +* [2. Coding the constitutive elements](#section2) +* [3. Simulation of Single-Photon Interference Experiment](#section3) +* [4. Variations of Single-Photon Interference Experiment](#section4) +* [Conclusion and Takeaways](#section5) + + +### Introduction + + +Wave-particle duality is one of the key concepts of quantum mechanics and it illustrates the fact that every quantum particle or object can be described using both particle and wave properties. Neither of these classical concepts can fully describe quantum objects on its own. + +In a similar manner to Thomas Young's double-slit experiment, originally carried out in 1801, the wave-particle duality of light can be observed by performing a single-photon interference experiment, or in this case, simulation. The individual *particle*-like photons interfere with themselves, which is an intrinsically *wave*-like property, thus exhibiting both particle and wave characteristics at the same time. + + +This lecture investigates the phenomenon of single-photon interference by creating a simulation of a quantum optical experiment in which single photons go through a polarisation interferometer. +* As mentionned, the experiment to be simulated is similar to Young‘s, in that it also produces interference by allowing a photon to take two separate paths. In a classical sense, a beam of light is split into two separate waves, one of which will travel a different path length, to then be recombined into a single wave by using beam splitters and a polarization analyzer. +* Varying the path lengths results in a phase shift, which creates an interference pattern. +* Applying this classical understanding to the case under study, a single photon, which is an indivisible packet of light, would have to be ‘split’ into two ‘waves’ for an interference pattern to occur. Because the photons are sent through the path individually and cannot be split any further, it seems that they cannot interfere with each other. +* But they do interfere! The canonical understanding of the single-photon interference is that the photon’s probability wave interferes with itself, in opposition to interference happening between two distinct states. + + +### 1. Theoretical Background + + +This section presents the setup of the single-photon interference experiment from a theoretical perspective, and details the different optical elements that must be simulated. The mathematical expression of each element will also be presented, in the form of Jones matrices acting as operators on the given quantum state. + +The setup and corresponding theory of this single photon interference experiment closely follows the work of Mark Beck in "Quantum Mechanics, Theory and Experiment" (*Beck, M. (2012). Quantum mechanics, theory and experiment. Oxford University Press.*), more specifically Experiment 6 of Section 3. + + +#### Half-wave plate $\frac{\lambda}{2}$ + + +A wave plate is used to modify the polarization of a wave, using the fact that its effective index of refraction depends on the polarization of the incident wave. +* A wave plate has two orthogonal axes. The fast axis represents the direction with the lower index of refraction as light polarized along that direction propagates faster, and the slow axis, orthogonal to the fast axis, represents the direction with the higher index of refraction. +* This difference in indices of refraction leads to the orthogonal components of the wave (polarization of a wave can always be decomposed into orthogonal components) acquiring different phase shifts when propagating through the material, resulting in an overall relative phase shift between the two components. +* A wave plate makes use of this relative phase shift between the components of the wave to modify its overall polarization. + +In this case, a half-wave plate is used, for which the relative phase shift between the fast and slow axes corresponds to a half wavelength shift. +* The fast axis of the half-wave plate is positioned in order to create a $45^\circ$ angle with the horizontal plane. +* Half of the incident wave is thus polarized along the fast axis and result in a $|+45\rangle$ polarization state, while the other half is polarized along the slow axis into a $|-45\rangle$ state, with a relative phase shift of $\pi$. +* The purpose of a half-wave plate is to rotate the linear polarization of some wave by an arbitrary angle (wave does *not* get split into its orthogonal components), depending on the orientation of the wave plate. + +The mathematical expression of a half-wave plate at $45^\circ$ is the following: + +\begin{equation} +J_{\lambda/2 \hspace{1mm} 45^\circ} = \left[\begin{array}{cc} \cos(2\cdot45) & \sin(2\cdot45) \\ \sin(2\cdot45) & -\cos(2\cdot45) \end{array}\right] = \left[\begin{array}{cc} 0 & 1 \\ 1 & 0 \end{array}\right] +\end{equation} + + +#### Polarization analyzers PA$_{HV}$ and PA$_{45}$ + + +A polarization analyzer, also called beam displacing polarizer or polarizing beam splitter, has the property that it can both *split* a beam into two orthogonal components, and *displace* each of these components differently. +* A general wave composed of orthogonal polarization components gets split into two waves travelling in different directions. When the faces of the polarization analyzer are parallel, the outgoing waves will emerge parallel, with a displacement separating them. + +If the polarization analyzer is positioned perpendicular to the horizontal plane, in such a way that waves propagating horizontally are normally incident, the general waves get split into vertical and horizontal components and it is referred to as a PA$_{HV}$. +* In this case, the $|V\rangle$ polarization state gets transmitted without bending, while the $|H\rangle$ gets bent when entering and leaving the PA$_{HV}$ such as to emerge parallel to $|V\rangle$, but displaced and phase-shifted. +* This polarization analyzer can also be used to recombine orthogonal components of a beam. The process simply gets reversed. +* The Jones matrix representing a PA$_{HV}$ is composed of three other Jones matrices: the horizontal polarizer, vertical polarizer and phase-shifting matrices. This combination is made such that it represents the splitting and the phase-shifting caused by this element, and is as follows: + +\begin{equation} +J_{PA_{HV}} = J_{V} + J_{\phi}J_{H} = \left[\begin{array}{cc} 0 & 0 \\ 0 & 1 \end{array}\right] + \left[\begin{array}{cc} e^{i \phi} & 0 \\ 0 & 1 \end{array}\right]\left[\begin{array}{cc} 1 & 0 \\ 0 & 0 \end{array}\right] = \left[\begin{array}{cc} e^{i \phi} & 0 \\ 0 & 1 \end{array}\right] +\end{equation} + +If one were to rotate a PA$_{HV}$ by $45^\circ$, a general wave incident on such a polarization analyzer would be analyzed into $|+45\rangle$ and $|-45\rangle$ polarization components, making it a PA$_{45}$. +* In this case, the $|+45\rangle$ polarization state gets transmitted without bending, while the $|-45\rangle$ gets bent when entering and leaving the PA$_{45}$ such as to emerge displaced and parallel to $|+45\rangle$. +* The Jones matrix for PA$_{45}$ is also a combination of other matrices: the linear polarizer with transmission axis at $45^\circ$, linear polarizer with transmission axis at $-45^\circ$ and phase-shifting matrices. The mathematical expression is thus: + +\begin{equation} +\begin{split} +J_{PA_{45}} &= J_{+45} + J_{\phi}J_{-45} \\ +&= \left[\begin{array}{cc} \cos^2(45) & \cos(45)\sin(45) \\ \cos(45)\sin(45) & \sin^2(45) \end{array}\right] ++ \left[\begin{array}{cc} e^{i \phi} & 0 \\ 0 & 1 \end{array}\right]\left[\begin{array}{cc} \cos^2(-45) & \cos(-45)\sin(-45) \\ \cos(-45)\sin(-45) & \sin^2(-45) \end{array}\right] \\ +&= \left[\begin{array}{cc} \frac{1}{2}(e^{i \phi} +1) & \frac{1}{2}(1-e^{i \phi}) \\ 0 & 1 \end{array}\right] +\end{split} +\end{equation} + + +#### Complete Setup + + +The setup of the experiment, represented in the figure below, is as follows. +* A photon is prepared in the $|+45\rangle$ state and is sent through an interferometer, composed of a PA$_{HV}$, a half-wave plate, another PA$_{HV}$, and finally a PA$_{45}$. +* The first polarization analyzer PA$_{HV}$ is used to split the incident photon into two orthogonal polarization components $|H\rangle$ and $|V\rangle$, and displace them accordingly. +* Each of the components emerges parallel to the other and then goes through the half-wave plate. Inserting the half-wave plate in the polarization interferometer allows for the equalization of the path lengths of the two arms. +* As already mentioned, a half-wave plate with its fast axis rotated to $45^\circ$ causes half of the incident wave to be polarized into a $|+45\rangle$ state (fast axis) and the other half into a $|-45\rangle$ state (slow axis), with a phase shift of $\pi$. In this specific setup, this implies that the polarization of the two components gets "switched" from $|H\rangle$ to $|V\rangle$ and vice-versa as the linear polarizations are rotated by $90^\circ$, making the overall behaviour of the two arms symmetric with equalized path lengths and no relative phase shift (for now). +* The second polarization analyzer PA$_{HV}$ is then used to recombine the components. +* Finally, the use of a PA$_{45}$ at the end of the interferometer allows for the splitting of the final photon into $|+45\rangle$ and $|-45\rangle$ polarization states, providing two different paths and two different ports for the output. In this way, it is easy to measure how the intensity of each of the outputs gets modulated. + +This apparatus acts as an interferometer, and the final super-positioned states should interfere producing an interference pattern and output intensities that depend on the relative phase between them (equivalently, on the path length difference). +* This relative phase (initially zero) can be modified using the PA$_{HV}$'s. +* One (or both) of the two polarization analyzers can be tilted in order to vary the relative phase between the two arms, as the relative phase shift is proportional to the tilt angle of the PA$_{HV}$ (because it modifies the path lengths). +* When the phase is $\phi = 0$, there should be constructive interference, with all of the output coming from the $+45^\circ$ port, while when the phase is $\phi = \pi$, there should be destructive interference, with all of the output coming from the $-45^\circ$ port. +* Note that this interference is visible because of the last element, the PA$_{45}$, as it projects the two, initially orthogonal (no possibility of displaying interference), polarization states onto the $+45^\circ$ and $-45^\circ$ axes where they do interfere. + + +```python +Image(filename="images/single-photon-interference-setup.jpg", width=700, embed=True) +``` + + +Note that a single photon is sent through these optical elements, making the process quantum mechanical in nature. This requires for an important distinction, when it comes to the splitting of the state into orthogonal components. +* A classical wave is split into its orthogonal components deterministically, with the wave actually getting divided into two components, each *in proportion* to the coefficient of the corresponding polarization state in the general polarization ($\psi = c_H |H\rangle + c_V |V\rangle$). +* On the other hand, when it comes to single photons, as they cannot be broken down any further, they are 'split' randomly and it is not possible to determine with certainty which port any photon will emerge from. +* The proportion of the basis states $|H\rangle$ and $|V\rangle$ in a general state $\psi$ describes the *probability* of the photon taking that corresponding path. +* Note also that the state of the photon never actually gets 'split' and it stays as one single state throughout the entire interferometer. + + +### 2. Coding the constitutive elements + + +#### Basis vectors +Code the polarization basis vectors, for the bases $H/V$ and $+/-45$. They can then be used to code any polarization state, since any state can be expressed as a linear combination of basis vectors. + +```python +# HV basis + +# horizontal polarization +H = basis(2, 0) +# vertical polarization +V = basis(2, 1) +``` + +```python +# +45/-45 basis (in terms of HV basis) + +# +45 polarization +p45 = 1 / sqrt(2) * (H + V) +# -45 polarization +n45 = 1 / sqrt(2) * (H - V) +``` + +#### Polarization analyzer (HV) n°1 +It induces a phase shift on the H polarization relative to V polarization, because of the difference in the path lengths of the two arms of the interferometer. +
In the case of a single photon, the state doesn't actually get split and there is only a single state throughout the apparatus, so the PA$_{HV}$ only affects the phase of the components. + +```python +# Polarization analyzer (HV) n°1 + +phaseshift1 = pi / 4 # CONSTANT +# should depend on real size of the setup (here: arbitrarily chosen) +PA_HV1 = Qobj([[exp(1j * phaseshift1), 0], [0, 1]]) +PA_HV1 +``` + +#### Half-wave plate $\frac{\lambda}{2}$ +In this case, the fast axis makes an angle of $45^\circ$ with the horizontal. +
For a single photon, the effect of going through such a half-wave plate is that the $|H\rangle$ and $|V\rangle$ polarization components are switched. + +```python +# Half-wave plate + +θ = pi / 4 # fast axis orientation +# (!) numpy calculates with rad +halfwave = Qobj([[cos(2 * θ), sin(2 * θ)], [sin(2 * θ), -cos(2 * θ)]]) + +""" +removes very small elements +(numerical artifacts from the finite precision of the computer) +""" +halfwave.tidyup() +``` + +#### Polarization analyzer (HV) n°2 +As before, it induces a phase shift on the H polarization relative to V polarization. +* Initially, the value of the phase shift induced by PA$_{HV2}$ should be set to the same value as for PA$_{HV1}$. The initial state of our setup has both of the PA$_{HV}$s in the exact same position (both tilted in the same way), with the overall relative phase shift being zero. +* The phase shift induced by PA$_{HV2}$ can then be changed in order to observe different levels of interference. +* A difference in the phase shifts induced by the two polarization analyzers causes the *relative* phase shift between the components to change, and it is this *relative* phase shift that is responsible for the interference. + +```python +# Polarization analyzer (HV) n°2 + +phaseshift2 = pi / 4 # CHANGE TO CHANGE INTERFERENCE +# should depend on real size of the setup +PA_HV2 = Qobj([[exp(1j * phaseshift2), 0], [0, 1]]) + +PA_HV2 +``` + +#### Polarization analyzer (45) +The PA$_{45}$ acts on the photon such that it has to come out as a $|+45\rangle$ polarized photon or as a $|-45\rangle$ polarized photon. +* Based on the coefficients of each polarization basis components constituting the general polarization state, the probability of the photon getting out in that corresponding state can be computed. +* It should also induce a phase shift on $|-45\rangle$ component with respect to $|+45\rangle$ as a result of different path lengths (as seen in the theory section), but this effect is ignored as this is the last optical element, and the goal is to determine whether the final state of the photon is $|-45\rangle$ or $|+45\rangle$, not which one "gets out first". +* The role of this polarization analyzer is to determine the outcome of the experiment. +* The single photon interference happens just before the photon goes through this last polarization analyzer, e.g. when the states are 'recombined' after the second PA$_{HV}$ (this is not to be interpreted too literally because, as previously mentioned, the state of the photon never actually gets split and it stays as one state throughout the apparatus), and thus phase shift does not have an importance anymore. + +```python +# Polarization analyzer (45) + +# linear Polarizer, transmission axis +45 wrt horizontal +θ = pi / 4 +Pp45 = Qobj([[cos(θ) ** 2, cos(θ) * sin(θ)], [cos(θ) * sin(θ), sin(θ) ** 2]]) +# linear Polarizer, transmission axis -45 wrt horizontal +θ = -pi / 4 +Pn45 = Qobj([[cos(θ) ** 2, cos(θ) * sin(θ)], [cos(θ) * sin(θ), sin(θ) ** 2]]) + + +def PA_45(vector): + p45_comp = Pp45 * vector # retrieve only +45 component + n45_comp = Pn45 * vector # retrieve only -45 component + return p45_comp, n45_comp +``` + +### 3. Simulation of Single-Photon Interference Experiment + + +**Define initial variables:** +* One has to choose the initial minimal and maximal values of the phase shift induced by PA$_{HV2}$, along with the number of steps to be effectuated between these values. These different values will be run through during the simulation and the experiment will be carried out for each of them in order to collect data to determine what the relationship is between the interference and the relative phase shift. The higher the number of steps is, the more precise the final result will be. +* The number of iterations, for one value of the phase shift, also has to be defined. Since the photon getting out of the interferometer in a certain polarization state is governed by probabilities, it is important to carry out the same process numerous times to get a good sample and a good representation of the results. + +```python +psi_0 = p45 # define the initial state (+45 vector) + +phaseshift2_init = pi / 4 # initial value +phaseshift2_max = 8 * pi +n = 100 # resolution of φ (amount of steps) +step = ( + phaseshift2_max - phaseshift2_init +) / n # interval divided by number of small steps we want +N_init = 1000 # number of iterations (range(N) -> 0 to N-1, both included) + +# create x and y coords arrays to store the values needed to plot output graph +x_coords = [] # relative phase shift +y1_coords = [] # amount of photons in +45 +y2_coords = [] # amount of photons in -45 +``` + +**Create a *for loop* to run through all of the possible values for the phase shift considered for PA$_{HV2}$:** +
In this loop, the passage of the photon through the interferometer is simulated using an effective matrix. The PA$_{45}$ is used after that to compute the probability related to each of these polarization states, and eventually to get the results of the experiment. +* 'Output' values are first defined to keep track of the results of the numerous iterations that are performed to measure the final state of the photon for a given phase shift. They are used to count the amount of photons that come out as $|+45\rangle$ and the amount that come out as $|-45\rangle$. +* The phase shift is also defined and directly added to the array of x-coordinates. +* With this phase shift, PA$_{HV2}$ can be defined, which allows for the definition of the final effective matrix. The effective matrix regroups all of the operators acting on the photon (in the correct order) in a single matrix. +* This is then multiplied with the initial state of the photon, to get the final state that will be passed through the PA$_{45}$. +* The $|+45\rangle$ and $|-45\rangle$ components are then retrieved and used to compute the final probabilities. To do so, the norm of the vector of each component is computed and then squared. + +Inside the first *for loop* is another loop performing what can be considered as the final measurement. Whether the photon comes out in a $|+45\rangle$ or $|-45\rangle$ polarization state is a random process that depends on the corresponding probabilities. +* To simulate this, a random number is generated between 1 and 100 (100 because $100\%$ of the photons need to come out in either one of the states). +* If the number is smaller or equal to the probability (in percentage form) related to the $|+45\rangle$ component, the photon is measured to have come out in the $|+45\rangle$ state. Otherwise, it is measured in the $|-45\rangle$ state. +* These measurements are then accounted for and stored in the 'output' values that were previously defined. +* When all of the iterations are performed, the output values are added to the arrays corresponding to different y-coordinates (one for $|+45\rangle$ photons and one for $|-45\rangle$ photons). +* They are then reinitialized at the beginning of the first *for loop*, before the entire process starts anew for another value of the phase shift. + +```python +for i in range(n + 1): + output_p45 = 0 + output_n45 = 0 + + phaseshift2 = phaseshift2_init + i * step + x_coords.append( + (phaseshift2 - phaseshift1) / pi + ) # add realtive phase shift to x coords + # create corresponding PA_HV2 + PA_HV2 = Qobj([[exp(1j * phaseshift2), 0], [0, 1]]) + EffM = PA_HV2 * halfwave * PA_HV1 # define the effective matrix + + # apply the effective matrix to the initial state to get the final state + psi_final = EffM * psi_0 + + psi_p45 = PA_45(psi_final)[0] # retrieve +45 and -45 components + psi_n45 = PA_45(psi_final)[1] + + # probab is rounded up to 5 decimals to avoid machine precision artifacts + proba_p45 = round(psi_p45.norm() ** 2, 5) + proba_n45 = round(psi_n45.norm() ** 2, 5) + + for j in range(N_init): + """ + generates random number between 1 and 100 (both included), + 100 because 100% of the photons need to come out in either + +45 or -45 state + """ + a = random.randint(1, 100) + if a <= proba_p45 * 100: + output_p45 = output_p45 + 1 + else: + output_n45 = output_n45 + 1 + + y1_coords.append(output_p45) + y2_coords.append(output_n45) +``` + +**Create output plot:** +
Using the arrays (which are collections of coordinates) that are created as the simulation goes on for different values and setups, a plot can be created to visualize the dependency of the interference on the relative phase shift, or similarly, on the different in path lengths of the two arms of the interferometer. + +```python +plt.plot(x_coords, y1_coords, "b.", markersize=9, label="Photons in state +45") +plt.plot(x_coords, y2_coords, "r.", markersize=9, label="Photons in state -45") +legend = plt.legend(loc="upper center", fontsize="x-large") +plt.ylim([-100, N_init + 500]) +plt.xlabel("Relative phase shift (multiples of π)") +plt.ylabel("Amount of photons detected") +plt.title("Amount of photons existing in |+/-45> states"); +``` + +The data shows a clear interference pattern. The measurements oscillate between only measuring photons in the state $|+45\rangle$ or $|-45\rangle$ as the relative phase shift increases. + + +### 4. Variations of Single-Photon Interference Experiment + + +Alternative cases can be investigated to get a better understanding of what is happening, why this interference pattern appears, and of the quantum nature of this experiment. + +In this case, it is shown that, when the superpositon of states collapses (here the superposition refers to the photon taking both paths "at the same time"), the interference pattern disappears. This is done by blocking either the vertical or the horizontal output port of the very first polarization analyzer, thus preventing the photon to be in the previously mentionned superposition of states as it can take only one path. This comes with some modifications, with respect to how the simulation was implemented previously: +* The main element that has to be modified is the first polarization analyzer. Depending on which port is blocked, the corresponding polarization component as to be put to zero for any further computation. +* The other big difference with the full interference simulation concerns the amount of photons that goes through the entire interferometer. When the initial $|+45\rangle$ polarized photon goes through PA$_{HV1}$, it has $50\%$ chance of coming out $|V\rangle$ polarized and $50\%$ chance of coming out $|H\rangle$ polarized. Under the assumption that the experiment is carried out enough times to actually get half of the photons to come out from each of the ports, it implies that only half of the photons will get to the other end of the interferometer, as the other half will have been blocked. Where, for the original interference simulation, 1000 photons got out the interferometer, here, only 500 will do so. Because of how it was coded in this simulation, this requires that the number of iterations (for one phase shift value) is halved. This is directly linked to the final probabilities of the photon coming out in state $|+45\rangle$ or $|-45\rangle$. The sum of these probabilities is not 1 anymore (or $100\%$), but 0.5 because half of the photons are not concerned by this. The missing 0.5 is thus the probability that the photon was blocked at the first polarization analyzer. +* The rest of the code is otherwise unchanged. + + +#### Output port 'V' of the first PA$_{HV}$ is blocked + +```python +# Polarization analyzer (HV) n°1, with V output port BLOCKED + +phaseshift1 = pi / 4 # CONSTANT +# should depend on real size of the setup (here: arbitrarily chosen) +PA_HV1vb = Qobj([[exp(1j * phaseshift1), 0], [0, 0]]) +``` + +```python +psi_0 = p45 # Defining the initial state (+45 vector) + +phaseshift2_init = pi / 4 # initial value +phaseshift2_max = 8 * pi +n = 100 # resolution of φ (amount of steps) +step = ( + phaseshift2_max - phaseshift2_init +) / n # interval divided by number of small steps we want + +# number of iterations (range(N) -> 0 to N-1, both included) +N_init = 1000 + +x_coords = [] # create x- and y- coords. arrays +# (x = phase shift of 2nd PA_HV, +# y1 = amount of photons in +45, +# y2 = amount of photons in -45) +y1_coords = [] +y2_coords = [] + +for i in range(n + 1): + output_p45 = 0 + output_n45 = 0 + + phaseshift2 = phaseshift2_init + i * step + # add realtive phase shift to x coords + x_coords.append((phaseshift2 - phaseshift1) / pi) + # create corresponding PA_HV2 + PA_HV2 = Qobj([[exp(1j * phaseshift2), 0], [0, 1]]) + EffM = PA_HV2 * halfwave * PA_HV1vb # Defining the effective matrix + + """ + Applying the effective matrix to the initial state to get the final state + """ + psi_final = EffM * psi_0 + psi_p45 = PA_45(psi_final)[0] # Determining the probabilities + psi_n45 = PA_45(psi_final)[1] + proba_p45 = round(psi_p45.norm() ** 2, 5) + proba_n45 = round(psi_n45.norm() ** 2, 5) + + if (proba_p45 + proba_n45) == 1: + N = N_init # all of the photons get to the end + else: + """ + half of the photons are blocked (should only get N/2 in the ouput) + -> total prob should be 0.5 + """ + N = int(N_init / 2) + + for j in range(N): + """ + generates random number between 1 and 50 (both included), + 50 because 50% of the photons need to come out in either + +45 or -45 state (since other 50% was blocked) + """ + a = random.randint(1, 50) + if a <= proba_p45 * 100: + output_p45 = output_p45 + 1 + else: + output_n45 = output_n45 + 1 + + y1_coords.append(output_p45) + y2_coords.append(output_n45) + +plt.plot(x_coords, y1_coords, "b.", markersize=9, label="Photons in state +45") +plt.plot(x_coords, y2_coords, "r.", markersize=9, label="Photons in state -45") +legend = plt.legend(loc="upper center", fontsize="x-large") +plt.ylim([0, 1000]) +plt.xlabel("Relative phase shift (multiples of π)") +plt.ylabel("Amount of photons detected") +plt.title("Amount of photons existing in |+/-45> states (V port blocked)"); +``` + +#### Output port 'H' of the first PA$_{HV}$ is blocked + +```python +# Polarization analyzer (HV) n°1, with H output port BLOCKED + +PA_HV1hb = Qobj([[0, 0], [0, 1]]) +``` + +```python +psi_0 = p45 # Defining the initial state (+45 vector) + +phaseshift2_init = pi / 4 # initial value +phaseshift2_max = 8 * pi +n = 100 # resolution of φ (amount of steps) +step = ( + phaseshift2_max - phaseshift2_init +) / n # interval divided by number of small steps we want + +# number of iterations (range(N) -> 0 to N-1, both included) +N_init = 1000 + +""" +create x- and y- coords. arrays (x = phase shift of 2nd PA_HV, +y1 = amount of photons in +45, y2 = amount of photons in -45) +""" +x_coords = [] +y1_coords = [] +y2_coords = [] + +for i in range(n + 1): + output_p45 = 0 + output_n45 = 0 + + phaseshift2 = phaseshift2_init + i * step + # add realtive phase shift to x coords + x_coords.append((phaseshift2 - phaseshift1) / pi) + # create corresponding PA_HV2 + PA_HV2 = Qobj([[exp(1j * phaseshift2), 0], [0, 1]]) + # Defining the effective matrix + EffM = PA_HV2 * halfwave * PA_HV1vb + + """ + Applying the effective matrix to the initial state to get the final state + """ + psi_final = EffM * psi_0 + psi_p45 = PA_45(psi_final)[0] # Determining the probabilities + psi_n45 = PA_45(psi_final)[1] + proba_p45 = round(psi_p45.norm() ** 2, 5) + proba_n45 = round(psi_n45.norm() ** 2, 5) + + if (proba_p45 + proba_n45) == 1: + N = N_init # all of the photons get to the end + else: + """ + half of the photons are blocked (should only get N/2 in the ouput) + -> total probability should be 0.5 + """ + N = int(N_init / 2) + """ + # generates random number between 1 and 50 (both included), + 50 because 50% of the photons need to come out in either + +45 or -45 state (since other 50% was blocked) + """ + for j in range(N): + a = random.randint(1, 50) + if a <= proba_p45 * 100: + output_p45 = output_p45 + 1 + else: + output_n45 = output_n45 + 1 + + y1_coords.append(output_p45) + y2_coords.append(output_n45) + +plt.plot(x_coords, y1_coords, "b.", markersize=9, label="Photons in state +45") +plt.plot(x_coords, y2_coords, "r.", markersize=9, label="Photons in state -45") +legend = plt.legend(loc="upper center", fontsize="x-large") +plt.ylim([0, 1000]) +plt.xlabel("Relative phase shift (multiples of π)") +plt.ylabel("Amount of photons detected") +plt.title("Amount of photons existing in |+/-45> states (H port blocked)"); +``` + +### Conclusion and Takeaways + + +The results show that, when both pathways are unobstructed, an interference pattern emerges. As the relative phase varies, the detected photons oscillate between the $|+45\rangle$ and $|-45\rangle$ states, clearly displaying interference. Note that, while inside the interferometer, the beams are in orthogonal polarizations, so no interference should occur there, though the components get affected and phase-shifted differently. Interference "happens", or rather becomes visible, at the PA$_{45}$ where both the horizontal and vertical polarisation are projected onto $+45^\circ$ and $-45^\circ$ axes. The degree of interference oscillates with increasing phase difference between the two paths. + +When one of the output port from the first polarization analyzer is blocked, the amount of photons detected is constant (apart from some random fluctuations) under a changing relative phase shift and there is no interference pattern. The results are identical for both setups, with the V beam and the H beam blocked. This illustrates that when the superposition collapses, no interference is displayed. + + +**Important takeaways:** +* This experiment demonstrates that photons can behave as particles and waves at the same time. This illustrates that the wave-particle duality is not an either-or concept, and that the transition is fluid. +* *So, which path did the photon take before being 'reunited' with its other half?* It was mentioned that the photon never actually gets split nor reunited and remains in one (evolving) state throughout. However, the interference pattern is a function of phase length variance between the two arms of the interferometer, and thus a function of both paths. Hence there is no way to describe the measured interference other than by considering that the photon travels through both paths at the same time. This is done by considering the *wavefunction* of the photon. Its wavefunction is in a superposition of travelling down both paths simultaneously, and will remain in this state until it collapses due to a measurement or an other external factor that causes quantum decoherence. This superposition results in the observed interference pattern. +* By blocking one of the beam’s paths, the photon is prevented from being in superposition and therefore can't interfere with itself. It further results in only half the amount of photons exiting the interferometer. This is due to the photons originally having a 50/50 chance of passing through either path. We still observe photons in both polarization states with a 50/50 distribution, demonstrating that a quantum state can collapse from one superposition and still remain in another superposition at the same time. While the obstruction of one path permanently determines which path the photons are taking (thus collapsing the superposition that allowed for the interference), their polarisation is still in a superposition of two basis states afterwards. +* The simulation shows that single-particle quantum interference can be described using an *event-by-event* basis. The use of frameworks such as wave fields or time-dependent system evolutions is not necessary to demonstrate the basic workings of the physical mechanism. This is possible, as the mathematical essence of single photon interference can be described with simple vector and matrix functions. However, when intending to use the simulation to make actual predictions about physical mechanisms, it is important to take into account physical limitations to ensure the results of the simulation are meaningful. One example of such a limitation is coherence length, which puts a real limit on the relative phase differences that would still result in interference. + +```python +qutip.about() +``` diff --git a/tutorials-v4/pulse-level-circuit-simulation/qip-customize-device.md b/tutorials-v4/pulse-level-circuit-simulation/qip-customize-device.md index 787a5664..470d8f81 100644 --- a/tutorials-v4/pulse-level-circuit-simulation/qip-customize-device.md +++ b/tutorials-v4/pulse-level-circuit-simulation/qip-customize-device.md @@ -403,7 +403,7 @@ plt.show() ``` ## Customizing the noise -Apart from pre-defined noise such as T1, T2 noise and random noise in the control pulse amplitude (see this [guide](https://qutip.org/docs/latest/guide/qip/qip-processor.html), one can also define custom noise. Here we will see two examples of customizing noise, one systematic (pulse-independent) noise and one pulse-dependent noise. +Apart from pre-defined noise such as T1, T2 noise and random noise in the control pulse amplitude (see this [guide](https://qutip-qip.readthedocs.io/en/stable/qip-processor.html), one can also define custom noise. Here we will see two examples of customizing noise, one systematic (pulse-independent) noise and one pulse-dependent noise. To understand how noise is processed, we briefly introduced the data structure of the simulation framework. The control elements are stored as a list of `Pulse` objects in the Processor. In each Pulse contains the idea pulse, the control noise part and the decoherence part. For systematic noise, it is saved under the `Pulse` representation labelled `"system"`, which represents the intrinsic dynamics of the quantum system. For pulse-dependent noise, we will add them to their corresponding control `Pulse`. diff --git a/tutorials-v4/pulse-level-circuit-simulation/qip-optpulseprocessor.md b/tutorials-v4/pulse-level-circuit-simulation/qip-optpulseprocessor.md index 0721ef79..9f91ec10 100644 --- a/tutorials-v4/pulse-level-circuit-simulation/qip-optpulseprocessor.md +++ b/tutorials-v4/pulse-level-circuit-simulation/qip-optpulseprocessor.md @@ -27,7 +27,7 @@ from qutip.ipynbtools import version_table import qutip_qip ``` -The `qutip.OptPulseProcessor` is a noisy quantum device simulator integrated with the optimal pulse algorithm from the `qutip.control` module. It is a subclass of `qutip.Processor` and is equipped with a method to find the optimal pulse sequence (hence the name `OptPulseProcessor`) for a `qutip.QubitCircuit` or a list of `qutip.Qobj`. For the user guide of `qutip.Processor`, please refer to [the introductory guide](https://qutip.org/docs/latest/guide/qip/qip-processor.html). +The `qutip.OptPulseProcessor` is a noisy quantum device simulator integrated with the optimal pulse algorithm from the `qutip.control` module. It is a subclass of `qutip.Processor` and is equipped with a method to find the optimal pulse sequence (hence the name `OptPulseProcessor`) for a `qutip.QubitCircuit` or a list of `qutip.Qobj`. For the user guide of `qutip.Processor`, please refer to [the introductory guide](https://qutip-qip.readthedocs.io/en/stable/qip-processor.html). ## Single-qubit gate Like in the parent class `Processor`, we need to first define the available Hamiltonians in the system. The `OptPulseProcessor` has one more parameter, the drift Hamiltonian, which has no time-dependent coefficients and thus won't be optimized. diff --git a/tutorials-v4/pulse-level-circuit-simulation/qip-scheduler.md b/tutorials-v4/pulse-level-circuit-simulation/qip-scheduler.md index 7f1ea974..a8f9d7ae 100644 --- a/tutorials-v4/pulse-level-circuit-simulation/qip-scheduler.md +++ b/tutorials-v4/pulse-level-circuit-simulation/qip-scheduler.md @@ -97,7 +97,7 @@ for gate in circuit.gates: scheduler.schedule(instructions) ``` -The scheduled execution time for each gate can no longer be assigned to gate cycles. But we can see this through the [noisy circuit simulator](https://qutip.org/docs/latest/guide/qip/qip-processor.html) of qutip, where the circuit is compiled to control signals: (Notice that the execution time follows the hardware parameter of spin chain and the Y gate is decomposed into a Z-X-Z rotation). +The scheduled execution time for each gate can no longer be assigned to gate cycles. But we can see this through the [noisy circuit simulator](https://qutip-qip.readthedocs.io/en/stable/qip-processor.html) of qutip, where the circuit is compiled to control signals: (Notice that the execution time follows the hardware parameter of spin chain and the Y gate is decomposed into a Z-X-Z rotation). ```python device = LinearSpinChain(3) diff --git a/tutorials-v4/quantum-circuits/qip-toffoli-cnot.md b/tutorials-v4/quantum-circuits/qip-toffoli-cnot.md index 3d933926..9c0d1f64 100644 --- a/tutorials-v4/quantum-circuits/qip-toffoli-cnot.md +++ b/tutorials-v4/quantum-circuits/qip-toffoli-cnot.md @@ -5,9 +5,9 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.13.8 + jupytext_version: 1.16.4 kernelspec: - display_name: Python 3 (ipykernel) + display_name: qutip-dev language: python name: python3 --- @@ -39,7 +39,7 @@ q.add_gate("TOFFOLI", controls=[0, 2], targets=[1]) ``` ```python -q.png +q.draw() ``` ```python @@ -53,7 +53,7 @@ q2 = q.resolve_gates() ``` ```python -q2.png +q2.draw() ``` ```python diff --git a/tutorials-v4/quantum-circuits/quantum-gates.md b/tutorials-v4/quantum-circuits/quantum-gates.md index c35df148..be7a4d0b 100644 --- a/tutorials-v4/quantum-circuits/quantum-gates.md +++ b/tutorials-v4/quantum-circuits/quantum-gates.md @@ -5,7 +5,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.13.8 + jupytext_version: 1.16.4 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -63,7 +63,7 @@ cphase(pi / 2) ```python q = QubitCircuit(2, reverse_states=False) q.add_gate("CSIGN", controls=[0], targets=[1]) -q.png +q.draw() ``` ### Rotation about X-axis @@ -74,8 +74,8 @@ rx(pi / 2) ```python q = QubitCircuit(1, reverse_states=False) -q.add_gate("RX", targets=[0], arg_value=pi / 2, arg_label=r"\frac{\pi}{2}") -q.png +q.add_gate("RX", targets=[0], arg_value=pi / 2, style={"showarg": True}) +q.draw() ``` ### Rotation about Y-axis @@ -86,8 +86,8 @@ ry(pi / 2) ```python q = QubitCircuit(1, reverse_states=False) -q.add_gate("RY", targets=[0], arg_value=pi / 2, arg_label=r"\frac{\pi}{2}") -q.png +q.add_gate("RY", targets=[0], arg_value=pi / 2, style={"showarg": True}) +q.draw() ``` ### Rotation about Z-axis @@ -98,8 +98,8 @@ rz(pi / 2) ```python q = QubitCircuit(1, reverse_states=False) -q.add_gate("RZ", targets=[0], arg_value=pi / 2, arg_label=r"\frac{\pi}{2}") -q.png +q.add_gate("RZ", targets=[0], arg_value=pi / 2, style={"showarg": True}) +q.draw() ``` ### CNOT @@ -111,7 +111,7 @@ cnot() ```python q = QubitCircuit(2, reverse_states=False) q.add_gate("CNOT", controls=[0], targets=[1]) -q.png +q.draw() ``` ### CSIGN @@ -123,7 +123,7 @@ csign() ```python q = QubitCircuit(2, reverse_states=False) q.add_gate("CSIGN", controls=[0], targets=[1]) -q.png +q.draw() ``` ### Berkeley @@ -135,7 +135,7 @@ berkeley() ```python q = QubitCircuit(2, reverse_states=False) q.add_gate("BERKELEY", targets=[0, 1]) -q.png +q.draw() ``` ### SWAPalpha @@ -162,7 +162,7 @@ toffoli() swap() q = QubitCircuit(2, reverse_states=False) q.add_gate("SWAP", targets=[0, 1]) -q.png +q.draw() ``` ### ISWAP @@ -171,7 +171,7 @@ q.png iswap() q = QubitCircuit(2, reverse_states=False) q.add_gate("ISWAP", targets=[0, 1]) -q.png +q.draw() ``` ### SQRTiSWAP @@ -234,7 +234,7 @@ cnot(N=3) ```python q = QubitCircuit(3, reverse_states=False) q.add_gate("CNOT", controls=[1], targets=[2]) -q.png +q.draw() ``` Furthermore, the control and target qubits (when applicable) can also be similarly specified using keyword arguments `control` and `target` (or in some cases `controls` or `targets`): @@ -246,7 +246,7 @@ cnot(N=3, control=2, target=0) ```python q = QubitCircuit(3, reverse_states=False) q.add_gate("CNOT", controls=[0], targets=[2]) -q.png +q.draw() ``` ## Setup of a Qubit Circuit @@ -261,7 +261,7 @@ In the following example, we take a SWAP gate. It is known that a swap gate is e N = 2 qc0 = QubitCircuit(N) qc0.add_gate("ISWAP", [0, 1], None) -qc0.png +qc0.draw() ``` ```python @@ -275,7 +275,7 @@ qc1 = QubitCircuit(N) qc1.add_gate("CNOT", 0, 1) qc1.add_gate("CNOT", 1, 0) qc1.add_gate("CNOT", 0, 1) -qc1.png +qc1.draw() ``` ```python @@ -288,7 +288,7 @@ In place of manually converting the SWAP gate to CNOTs, it can be automatically ```python qc2 = qc0.resolve_gates("CNOT") -qc2.png +qc2.draw() ``` ```python @@ -301,7 +301,7 @@ From QuTiP 4.4, we can also add gate at arbitrary position in a circuit. ```python qc1.add_gate("CSIGN", index=[1], targets=[0], controls=[1]) -qc1.png +qc1.draw() ``` ## Example of basis transformation @@ -313,7 +313,7 @@ qc3.add_gate("RX", 0, None, pi / 2, r"\pi/2") qc3.add_gate("RY", 1, None, pi / 2, r"\pi/2") qc3.add_gate("RZ", 2, None, pi / 2, r"\pi/2") qc3.add_gate("ISWAP", [1, 2]) -qc3.png +qc3.draw() ``` ```python @@ -325,7 +325,7 @@ U3 ```python qc4 = qc3.resolve_gates("CNOT") -qc4.png +qc4.draw() ``` ```python @@ -335,7 +335,7 @@ U4 ```python qc5 = qc3.resolve_gates("ISWAP") -qc5.png +qc5.draw() ``` ```python @@ -347,7 +347,7 @@ U5 ```python qc6 = qc3.resolve_gates(["ISWAP", "RX", "RY"]) -qc6.png +qc6.draw() ``` ```python @@ -357,7 +357,7 @@ U6 ```python qc7 = qc3.resolve_gates(["CNOT", "RZ", "RX"]) -qc7.png +qc7.draw() ``` ```python @@ -373,7 +373,7 @@ Interactions between non-adjacent qubits can be resolved by QubitCircuit to a se ```python qc8 = QubitCircuit(3) qc8.add_gate("CNOT", 2, 0) -qc8.png +qc8.draw() ``` ```python @@ -393,7 +393,7 @@ U9 ```python qc10 = qc9.resolve_gates("CNOT") -qc10.png +qc10.draw() ``` ```python @@ -418,7 +418,7 @@ From QuTiP 4.4 on, user defined gates can be defined by a python function that t ```python def user_gate1(arg_value): # controlled rotation X - mat = np.zeros((4, 4), dtype=np.complex) + mat = np.zeros((4, 4), dtype=complex) mat[0, 0] = mat[1, 1] = 1.0 mat[2:4, 2:4] = rx(arg_value) return Qobj(mat, dims=[[2, 2], [2, 2]]) diff --git a/tutorials-v4/time-evolution/002_larmor-precession.md b/tutorials-v4/time-evolution/002_larmor-precession.md index 949d000c..8aecd8fc 100644 --- a/tutorials-v4/time-evolution/002_larmor-precession.md +++ b/tutorials-v4/time-evolution/002_larmor-precession.md @@ -18,12 +18,12 @@ Author: C. Staufenbiel, 2022 ### Introduction -This notebook guides you through the process of setting up a Schrödinger -equation in QuTiP and using the corresponding solver to obtain the time -evolution. We will investigate the example of the Larmor precession to -explore the functionality of [`qutip.sesolve()`](https://qutip.org/docs/latest/apidoc/functions.html?highlight=sesolve#module-qutip.sesolve). +This notebook guides you through the process of setting up a Schrödinger +equation in QuTiP and using the corresponding solver to obtain the time +evolution. We will investigate the example of the Larmor precession to +explore the functionality of [`qutip.sesolve()`](https://qutip.readthedocs.io/en/latest/apidoc/solver.html#module-qutip.solver.sesolve). -You can also find more on time evolutions with QuTiP [here](https://qutip.org/docs/latest/guide/guide-dynamics.html). +You can also find more on time evolutions with QuTiP [here](https://qutip.readthedocs.io/en/latest/guide/guide-dynamics.html). ### Setup @@ -49,8 +49,8 @@ b.show() ### Simulation with constant magnetic field Let's define a simple Hamiltonian and use `qutip.sesolve` to solve the -Schrödinger equation. The Hamiltonian describes a constant magnetic field -along the z-axis. We can describe this magnetic field by the corresponding +Schrödinger equation. The Hamiltonian describes a constant magnetic field +along the z-axis. We can describe this magnetic field by the corresponding Pauli matrix, which is defined as `qutip.sigmaz()` in QuTiP. To solve the Schrödinger equation for this particular Hamiltonian, we have to pass the Hamiltonian, the initial state, the times for which we want to simulate the system, and a set of observables that we evaluate at these times. @@ -64,7 +64,7 @@ times = np.linspace(0, 10, 100) result = sesolve(H, psi, times, [sigmay()]) ``` -`result.expect` holds the expecation values for the times that we passed to `sesolve`. `result.expect` is a two dimensional array, where the first dimension refers to the different expectation operators that we passed to `sesolve` before. +`result.expect` holds the expecation values for the times that we passed to `sesolve`. `result.expect` is a two dimensional array, where the first dimension refers to the different expectation operators that we passed to `sesolve` before. Above we passed `sigmay()` as the only expectation operator and therefore we can access its values by `result.expect[0]`. Below we plot the evolution of the expecation value. @@ -85,7 +85,7 @@ b.show() ## Simulation with varying magnetic field -Above we passed a constant Hamiltonian to `sesolve`. In QuTiP these constant operators are represented by `Qobj`. However, `sesolve` can also take time-dependent operators as an argument, which are represented by [`QobjEvo`](https://qutip.org/docs/latest/apidoc/classes.html?highlight=qobjevo#qutip.QobjEvo) in QuTiP. In this section we define the magnetic field with a linear and a periodic field strength, and observe the changes in the expecation value of $\sigma_y$. +Above we passed a constant Hamiltonian to `sesolve`. In QuTiP these constant operators are represented by `Qobj`. However, `sesolve` can also take time-dependent operators as an argument, which are represented by [`QobjEvo`](https://qutip.readthedocs.io/en/latest/apidoc/time_dep.html#qutip.core.cy.qobjevo.QobjEvo) in QuTiP. In this section we define the magnetic field with a linear and a periodic field strength, and observe the changes in the expecation value of $\sigma_y$. You can find more information on `QobjEvo` in [this notebook](https://nbviewer.jupyter.org/github/qutip/qutip-notebooks/blob/master/examples/qobjevo.ipynb). We start by defining two functions for the field strength of the magnetic field. To be passed on to `QobjEvo` the functions need two arguments: the times and optional arguments. @@ -127,8 +127,8 @@ plt.show() ``` ### Conclusion -We can use `sesolve` to solve unitary time evolutions. This is not only -limited to constant Hamiltonians, but we can also make use of time-dependent Hamiltonians using `QobjEvo`. +We can use `sesolve` to solve unitary time evolutions. This is not only +limited to constant Hamiltonians, but we can also make use of time-dependent Hamiltonians using `QobjEvo`. ### About diff --git a/tutorials-v4/time-evolution/003_qubit-dynamics.md b/tutorials-v4/time-evolution/003_qubit-dynamics.md index 2ae5d0ff..2592d178 100644 --- a/tutorials-v4/time-evolution/003_qubit-dynamics.md +++ b/tutorials-v4/time-evolution/003_qubit-dynamics.md @@ -21,7 +21,7 @@ Modified by: C. Staufebiel (2022) ### Introduction In this notebook we will explore the dynamics of a single-qubit interacting with an environment. The evolution of the qubit state is governed by the Master equation. We will make use of the master equation solver `qutip.mesolve` implemented in qutip, to obtain the time-evolution of the qubit for different settings. -You can read more about the master equation solver (and the theory behind it) in the [QuTiP docs](https://qutip.org/docs/latest/apidoc/functions.html?highlight=sesolve#module-qutip.sesolve). +You can read more about the master equation solver (and the theory behind it) in the [QuTiP docs](https://qutip.readthedocs.io/en/latest/apidoc/time_dep.html#qutip.core.cy.qobjevo.QobjEvo). ### Import Here we import the required modules for this example. diff --git a/tutorials-v4/time-evolution/004_rabi-oscillations.md b/tutorials-v4/time-evolution/004_rabi-oscillations.md index 82b6e270..cc469c33 100644 --- a/tutorials-v4/time-evolution/004_rabi-oscillations.md +++ b/tutorials-v4/time-evolution/004_rabi-oscillations.md @@ -25,7 +25,7 @@ Jaynes-Cumming model, i.e., the cavity and the atom are coupled to an environment. -For more information on the theory behind the Master Equation Solver see [the documentation](https://qutip.org/docs/latest/guide/dynamics/dynamics-master.html#non-unitary-evolution). +For more information on the theory behind the Master Equation Solver see [the documentation](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-master.html#non-unitary-evolution). ### Package import @@ -50,7 +50,7 @@ $H_{\rm RWA} = \hbar \omega_c a^\dagger a + \frac{1}{2}\hbar\omega_a\sigma_z + \ where $\omega_c$ and $\omega_a$ are the frequencies of the cavity and atom, respectively, and $g$ is the interaction strength. -In this example we also consider the coupling of the Jaynes-Cummings model to an external environment, i.e., we need to solve the system using the Master Equation Solver `qutip.mesolve`. The coupling to the environment is described by the collapse operators (as described in [the docs](https://qutip.org/docs/latest/guide/dynamics/dynamics-master.html#non-unitary-evolution)). Here, we consider two collapse operators for the cavity $C_1, C_2$, describing creation and annihilation of photons, and one collapse operator for the atom $C_3$. +In this example we also consider the coupling of the Jaynes-Cummings model to an external environment, i.e., we need to solve the system using the Master Equation Solver `qutip.mesolve`. The coupling to the environment is described by the collapse operators (as described in [the docs](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-master.html#non-unitary-evolution)). Here, we consider two collapse operators for the cavity $C_1, C_2$, describing creation and annihilation of photons, and one collapse operator for the atom $C_3$. $C_1 = \sqrt{\kappa (1+\langle n \rangle)} \; a$ diff --git a/tutorials-v4/time-evolution/006_photon_birth_death.md b/tutorials-v4/time-evolution/006_photon_birth_death.md index 72a8f866..45782cba 100644 --- a/tutorials-v4/time-evolution/006_photon_birth_death.md +++ b/tutorials-v4/time-evolution/006_photon_birth_death.md @@ -21,7 +21,7 @@ Modifications: C. Staufenbiel (2022) ### Introduction -In this tutorial we demonstrate the *Monte Carlo Solver* functionality implemented in `qutip.mcsolve()`. For more information on the *MC Solver* refer to the [QuTiP documentation](https://qutip.org/docs/latest/guide/dynamics/dynamics-monte.html). +In this tutorial we demonstrate the *Monte Carlo Solver* functionality implemented in `qutip.mcsolve()`. For more information on the *MC Solver* refer to the [QuTiP documentation](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-monte.html). We aim to reproduce the experimental results from: diff --git a/tutorials-v4/time-evolution/007_brmesolve_tls.md b/tutorials-v4/time-evolution/007_brmesolve_tls.md index 716af725..f9508d11 100644 --- a/tutorials-v4/time-evolution/007_brmesolve_tls.md +++ b/tutorials-v4/time-evolution/007_brmesolve_tls.md @@ -24,7 +24,7 @@ with inspirations from the [`brmesolve notebook`](https://github.com/qutip/qutip The Bloch-Redfield solver is another method to solve a master equation. In comparison to the Lindblad Master equation solver `qutip.mesolve()` the Bloch-Redfield solver `qutip.brmesolve()` differs in the description of the interaction with the environment. In `qutip.mesolve()` we described the dissipation by collapse operators, which do not necessarily have a physical interpretation. The `qutip.brmesolve()` function requires the a dissipation description by the so-called *noise-power-spectrum*, which gives the intensity of the dissipation depending on the frequency $\omega$. -In this notebook we will introduce the basic usage of `qutip.brmesolve()` and compare it to `qutip.mesolve()`. For more information on the Bloch-Redfield solver see the follow-up notebooks and the [QuTiP Documentation of the functionality](https://qutip.org/docs/latest/guide/dynamics/dynamics-bloch-redfield.html). +In this notebook we will introduce the basic usage of `qutip.brmesolve()` and compare it to `qutip.mesolve()`. For more information on the Bloch-Redfield solver see the follow-up notebooks and the [QuTiP Documentation of the functionality](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-bloch-redfield.html). ### Imports @@ -111,7 +111,7 @@ plt.legend(), plt.xlabel("time"), plt.ylabel(""); ## Bloch-Redfield Tensor -We described the dynmamics of the system by the Bloch-Redfield master equation, which is constructed from the Bloch-Redfield tensor $R_{abcd}$ (see [documentation of Bloch-Redfield master equation](https://qutip.org/docs/latest/guide/dynamics/dynamics-bloch-redfield.html)). Hence the dynamics are determined by this tensor. We can calculate the tensor in QuTiP using the `qutip.bloch_redfield_tensor()` function. We have to pass the Hamiltonian of the system and the dissipation description in `a_ops` to construct $R_{abcd}$. Furthermore, the function gives us the **eigenstates of the Hamiltonian**, as they are calculated along the way. +We described the dynmamics of the system by the Bloch-Redfield master equation, which is constructed from the Bloch-Redfield tensor $R_{abcd}$ (see [documentation of Bloch-Redfield master equation](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-bloch-redfield.html)). Hence the dynamics are determined by this tensor. We can calculate the tensor in QuTiP using the `qutip.bloch_redfield_tensor()` function. We have to pass the Hamiltonian of the system and the dissipation description in `a_ops` to construct $R_{abcd}$. Furthermore, the function gives us the **eigenstates of the Hamiltonian**, as they are calculated along the way. ```python diff --git a/tutorials-v4/time-evolution/008_brmesolve_time_dependence.md b/tutorials-v4/time-evolution/008_brmesolve_time_dependence.md index de645102..1b621755 100644 --- a/tutorials-v4/time-evolution/008_brmesolve_time_dependence.md +++ b/tutorials-v4/time-evolution/008_brmesolve_time_dependence.md @@ -16,10 +16,10 @@ jupyter: Authors: C. Staufenbiel, 2022 -following the instructions in the [Bloch-Redfield documentation](https://qutip.org/docs/latest/guide/dynamics/dynamics-bloch-redfield.html?#time-dependent-bloch-redfield-dynamics). +following the instructions in the [Bloch-Redfield documentation](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-bloch-redfield.html?#time-dependent-bloch-redfield-dynamics). ### Introduction -This notebook introduces the usage of time-dependent operators in the Bloch-Redfield solver, which is also described in the [corresponding documentation](https://qutip.org/docs/latest/guide/dynamics/dynamics-bloch-redfield.html?#time-dependent-bloch-redfield-dynamics). +This notebook introduces the usage of time-dependent operators in the Bloch-Redfield solver, which is also described in the [corresponding documentation](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-bloch-redfield.html?#time-dependent-bloch-redfield-dynamics). We will discuss time-dependent Hamiltonians and time-dependent dissipations. The Bloch-Redfield solver is especially efficient since it uses Cython internally. For correct functioning we have to pass the time dependence in a string-based format. @@ -55,7 +55,7 @@ result_const = brmesolve(H, psi0, times, e_ops=[a.dag() * a]) plot_expectation_values(result_const, ylabels=[""]); ``` -Next we define a string, which describes some time-dependence. We can use functions that are supported by the Cython implementation. A list of all supported functions can be found in the [docs](https://qutip.org/docs/latest/guide/dynamics/dynamics-time.html#time). For example, supported functions are `sin` or `exp`. The time variable is denoted by `t`. +Next we define a string, which describes some time-dependence. We can use functions that are supported by the Cython implementation. A list of all supported functions can be found in the [docs](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-time.html#time). For example, supported functions are `sin` or `exp`. The time variable is denoted by `t`. ```python time_dependence = "sin(t)" @@ -63,7 +63,7 @@ time_dependence = "sin(t)" ### Time-dependent Hamiltonian -As a first example, we define a time-dependent Hamiltonian (as described [here](https://qutip.org/docs/latest/guide/dynamics/dynamics-time.html)). +As a first example, we define a time-dependent Hamiltonian (as described [here](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-time.html)). $$ H = \hat{n} + sin(t) \hat{x} $$ diff --git a/tutorials-v4/time-evolution/009_brmesolve-cavity-QED.md b/tutorials-v4/time-evolution/009_brmesolve-cavity-QED.md index 02ec6d79..499d1945 100644 --- a/tutorials-v4/time-evolution/009_brmesolve-cavity-QED.md +++ b/tutorials-v4/time-evolution/009_brmesolve-cavity-QED.md @@ -21,7 +21,7 @@ with inspirations from the [`brmesolve notebook`](https://github.com/qutip/qutip ### Introduction -This notebook does not introduce the usage of the Bloch-Redfield solver `qutip.brmesolve()` in detail. For a more detailed introduction to this solver see the [*Bloch-Redfield Solver: Two Level System* notebook](007_brmesolve_tls.md) and the [documentation of the function](https://qutip.org/docs/latest/guide/dynamics/dynamics-bloch-redfield.html). +This notebook does not introduce the usage of the Bloch-Redfield solver `qutip.brmesolve()` in detail. For a more detailed introduction to this solver see the [*Bloch-Redfield Solver: Two Level System* notebook](007_brmesolve_tls.md) and the [documentation of the function](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-bloch-redfield.html). The Lindblad master equation solver, implemented in `qutip.mesolve()`, deals with dissipation using collapse operators which can act on subsystems of the general system. For example, we can define dissipation for the atom-cavity system for the cavity and the atom separately, by the corresponding annihilation operator. In this example, we will see the limitations of this approach when it comes to strong coupling between atom and cavity. diff --git a/tutorials-v4/time-evolution/011_floquet_solver.md b/tutorials-v4/time-evolution/011_floquet_solver.md index 9a75b434..aad4d484 100644 --- a/tutorials-v4/time-evolution/011_floquet_solver.md +++ b/tutorials-v4/time-evolution/011_floquet_solver.md @@ -20,7 +20,7 @@ Author: C. Staufenbiel, 2022 The *Floquet formalism* deals with periodic time-dependent systems. The Floquet approach can be more efficient for such problems than using the standard master equation solver `qutip.mesolve()` and it has a broader range of validity for periodic driving. -In this notebook, we will discuss the solver functionality of the Floquet formalism implemented in QuTiP using an example quantum system. A more detailed introduction into the Floquet formalism can be found in the [documentation](https://qutip.org/docs/latest/guide/dynamics/dynamics-floquet.html). +In this notebook, we will discuss the solver functionality of the Floquet formalism implemented in QuTiP using an example quantum system. A more detailed introduction into the Floquet formalism can be found in the [documentation](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-floquet.html). A more in depth introduction into the internal functions of the Floquet formalism, used also by the solvers `fsesolve` and `fmmesolve`, is given in the [*floquet formalism notebook*](012_floquet_formalism.md). diff --git a/tutorials-v4/time-evolution/012_floquet_formalism.md b/tutorials-v4/time-evolution/012_floquet_formalism.md index 90659223..ad3b32df 100644 --- a/tutorials-v4/time-evolution/012_floquet_formalism.md +++ b/tutorials-v4/time-evolution/012_floquet_formalism.md @@ -18,14 +18,14 @@ Author: C. Staufenbiel, 2022 inspirations taken from the [Floquet notebook](https://github.com/qutip/qutip-notebooks/blob/master/examples/floquet-dynamics.ipynb) by P.D. Nation and J.R. Johannson, -and the [qutip documentation](https://qutip.org/docs/latest/guide/dynamics/dynamics-floquet.html). +and the [qutip documentation](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-floquet.html). ### Introduction In the [floquet_solver notebook](011_floquet_solver.md) we introduced the two functions to solve the Schrödinger and Master equation using the Floquet formalism. In this notebook, we will focus on the internal functions of these solvers, that implement the Floquet formalism in QuTiP. Here, we will focus on the `Floquet modes` and the `quasienergies`. -More information on the implementation of the Floquet Formalism in QuTiP can be found in the [documentation](https://qutip.org/docs/latest/guide/dynamics/dynamics-floquet.html). +More information on the implementation of the Floquet Formalism in QuTiP can be found in the [documentation](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-floquet.html). -### Imports +### Imports ```python import matplotlib.pyplot as plt @@ -40,7 +40,7 @@ from qutip import (about, expect, floquet_markov_mesolve, ``` ### System setup -For consistency with the documentation we consider the driven system with the following Hamiltonian: +For consistency with the documentation we consider the driven system with the following Hamiltonian: $$ H = - \frac{\Delta}{2} \sigma_x - \frac{\epsilon_0}{2} \sigma_z + \frac{A}{2} \sigma_x sin(\omega t) $$ @@ -130,13 +130,13 @@ assert np.allclose(psi_t.full(), psi_t_direct.full()) ### Precomputing and reusing the Floquet modes of one period -The Floquet modes have the same periodicity as the Hamiltonian: +The Floquet modes have the same periodicity as the Hamiltonian: $$ \phi_\alpha(t + T) = \phi_\alpha(t) $$ -Hence it is enough to evaluate the modes at times $t \in [0,T]$. From these modes we can extrapolate the system state $\psi(t)$ for any time $t$. +Hence it is enough to evaluate the modes at times $t \in [0,T]$. From these modes we can extrapolate the system state $\psi(t)$ for any time $t$. -The function `floquet_modes_table` allows to calculate the Floquet modes for multiple times in the first period. +The function `floquet_modes_table` allows to calculate the Floquet modes for multiple times in the first period. ```python @@ -172,14 +172,15 @@ plt.legend(loc="upper right") plt.xlabel("Time"), plt.ylabel("Occupation prob."); ``` + ### Floquet Markov formalism -We can also solve a master equation using the Floquet formalism. A detailed derivation of the Floquet-Markov formalism used here is given in [Grifoni et al., Physics Reports 304, 299 (1998)](https://www.sciencedirect.com/science/article/abs/pii/S0370157398000222) and in the [QuTiP docs](https://qutip.org/docs/latest/guide/dynamics/dynamics-floquet.html). Note that the functionality described here is summarised in the function `fmmesolve` described in the [floquet solver notebook](011_floquet_solver.md). +We can also solve a master equation using the Floquet formalism. A detailed derivation of the Floquet-Markov formalism used here is given in [Grifoni et al., Physics Reports 304, 299 (1998)](https://www.sciencedirect.com/science/article/pii/S0370157398000222) and in the [QuTiP docs](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-floquet.html). Note that the functionality described here is summarised in the function `fmmesolve` described in the [floquet solver notebook](011_floquet_solver.md). The interaction with the bath is described by a noise spectrum, which does not include the temperature dependency. The temperature dependency can be passed to `fmmesolve` using the keyword `w_th` in the `args` parameter: `args[w_th]`. Hence, the definition is slightly different to the one in the Bloch-Redfield formalism. For details see the derivation of the formalism. + - -Here we define a simple linear noise spectrum: +Here we define a simple linear noise spectrum: $$ S(\omega) = \frac{\gamma \cdot \omega}{4 \pi} $$ @@ -202,9 +203,9 @@ temp = 10.0 args = {"w_th": temp} ``` -The Floquet Markov approach starts by calculating rates, that describe the dissipation process of the system with the given spectrum and temperature of the bath. Especially important is `Amat`, which is later used to calculate the Floquet tensor for the master equation. +The Floquet Markov approach starts by calculating rates, that describe the dissipation process of the system with the given spectrum and temperature of the bath. Especially important is `Amat`, which is later used to calculate the Floquet tensor for the master equation. -In theory the matrix is defined as an infinite sum (see [docs](https://qutip.org/docs/latest/guide/dynamics/dynamics-floquet.html)). However, in QuTiP the sidebands need to be truncated to create a finite sum. This is done with the `kmax` argument. +In theory the matrix is defined as an infinite sum (see [docs](https://qutip.readthedocs.io/en/latest/guide/dynamics/dynamics-floquet.html)). However, in QuTiP the sidebands need to be truncated to create a finite sum. This is done with the `kmax` argument. ```python kmax = 20 @@ -221,7 +222,7 @@ Together with the quasienergies, the tensor for the Floquet master equation can R = floquet_master_equation_tensor(Amat, f_energies) ``` -We can pass in the tensor, initial state, expectation value and expectation operator into the `floquet_markov_mesolve` function and obtain the time evolution of the system (i.e. expectation operator) using the Floquet formalism. +We can pass in the tensor, initial state, expectation value and expectation operator into the `floquet_markov_mesolve` function and obtain the time evolution of the system (i.e. expectation operator) using the Floquet formalism. ```python res_fme_manual = floquet_markov_mesolve( diff --git a/tutorials-v4/time-evolution/016_smesolve-inefficient-detection.md b/tutorials-v4/time-evolution/016_smesolve-inefficient-detection.md index 420cdc62..886d3555 100644 --- a/tutorials-v4/time-evolution/016_smesolve-inefficient-detection.md +++ b/tutorials-v4/time-evolution/016_smesolve-inefficient-detection.md @@ -63,13 +63,12 @@ and $dN(t)$ is a Poisson distributed increment with $E[dN(t)] = \eta \langle a^\ In QuTiP, the photocurrent stochastic master equation is written in the form: -$\displaystyle d\rho(t) = -i[H, \rho] dt + \mathcal{D}[B] \rho dt -- \frac{1}{2}\mathcal{H}[A^\dagger A] \rho(t) dt -+ \mathcal{G}[A]\rho(t) d\xi$ +$\displaystyle d\rho(t) = -i[H, \rho] dt + \mathcal{D}[B] \rho dt - \frac{1}{2}\mathcal{H}[A^\dagger A]\rho(t) dt + \mathcal{G}[A]\rho(t) d\xi$ where the first two term gives the deterministic master equation (Lindblad form with collapse operator $B$ (c_ops)) and $A$ the stochastic collapse operator (sc_ops). -Here $A = \sqrt{\eta\gamma} a$ and $B = \sqrt{(1-\eta)\gamma} $a. +Here $A = \sqrt{\eta\gamma}a $ and $B = \sqrt{(1-\eta)\gamma} a$ + ```python N = 15 From 14f3c5293426dfed43f584913c370654895c9ed3 Mon Sep 17 00:00:00 2001 From: Gerardo Suarez Date: Wed, 23 Apr 2025 01:28:09 +0200 Subject: [PATCH 30/44] hook --- tutorials-v5/heom/heom-1a-spin-bath-model-basic.md | 2 +- .../heom/heom-1b-spin-bath-model-very-strong-coupling.md | 2 +- tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md | 2 +- tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md | 2 +- tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md | 2 +- tutorials-v5/heom/heom-2-fmo-example.md | 2 +- tutorials-v5/heom/heom-3-quantum-heat-transport.md | 2 +- tutorials-v5/heom/heom-4-dynamical-decoupling.md | 2 +- tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md | 2 +- tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md | 2 +- tutorials-v5/heom/heom-index.md | 2 +- tutorials-v5/template.md | 5 +++-- 12 files changed, 14 insertions(+), 13 deletions(-) diff --git a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md index 7cf03ceb..85ee467e 100644 --- a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md +++ b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md @@ -5,7 +5,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.14.4 + jupytext_version: 1.14.5 kernelspec: display_name: qutip-tutorials language: python diff --git a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md index fc60d1a3..ee42ba98 100644 --- a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md +++ b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.4 + jupytext_version: 1.14.5 kernelspec: display_name: qutip-tutorials language: python diff --git a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md index 416e1cba..52921c73 100644 --- a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md +++ b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.4 + jupytext_version: 1.14.5 kernelspec: display_name: qutip-tutorials language: python diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md index 359857d1..1108f982 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.4 + jupytext_version: 1.14.5 kernelspec: display_name: qutip-tutorials language: python diff --git a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md index caad1ad5..17e0028d 100644 --- a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md +++ b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.4 + jupytext_version: 1.14.5 kernelspec: display_name: qutip-tutorials language: python diff --git a/tutorials-v5/heom/heom-2-fmo-example.md b/tutorials-v5/heom/heom-2-fmo-example.md index f1adda69..a26276fb 100644 --- a/tutorials-v5/heom/heom-2-fmo-example.md +++ b/tutorials-v5/heom/heom-2-fmo-example.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.4 + jupytext_version: 1.14.5 kernelspec: display_name: qutip-tutorials language: python diff --git a/tutorials-v5/heom/heom-3-quantum-heat-transport.md b/tutorials-v5/heom/heom-3-quantum-heat-transport.md index 98e09b33..7864f58f 100644 --- a/tutorials-v5/heom/heom-3-quantum-heat-transport.md +++ b/tutorials-v5/heom/heom-3-quantum-heat-transport.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.4 + jupytext_version: 1.14.5 kernelspec: display_name: qutip-tutorials language: python diff --git a/tutorials-v5/heom/heom-4-dynamical-decoupling.md b/tutorials-v5/heom/heom-4-dynamical-decoupling.md index 605a1cd4..8bb2dac7 100644 --- a/tutorials-v5/heom/heom-4-dynamical-decoupling.md +++ b/tutorials-v5/heom/heom-4-dynamical-decoupling.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.4 + jupytext_version: 1.14.5 kernelspec: display_name: qutip-tutorials language: python diff --git a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md index 70702873..75091d42 100644 --- a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md +++ b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.4 + jupytext_version: 1.14.5 kernelspec: display_name: qutip-tutorials language: python diff --git a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md index a2843578..55bfdc0f 100644 --- a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md +++ b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.4 + jupytext_version: 1.14.5 kernelspec: display_name: qutip-tutorials language: python diff --git a/tutorials-v5/heom/heom-index.md b/tutorials-v5/heom/heom-index.md index dcb1fa89..e30e2ecf 100644 --- a/tutorials-v5/heom/heom-index.md +++ b/tutorials-v5/heom/heom-index.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.4 + jupytext_version: 1.14.5 kernelspec: display_name: Python 3 (ipykernel) language: python diff --git a/tutorials-v5/template.md b/tutorials-v5/template.md index 1daf01b0..b4f54353 100644 --- a/tutorials-v5/template.md +++ b/tutorials-v5/template.md @@ -5,7 +5,7 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.17.0 + jupytext_version: 1.13.8 kernelspec: display_name: Python 3 (ipykernel) language: python @@ -92,7 +92,8 @@ so it's not interfering with the user experience. Please, define the tests using `assert`, so that the cell execution fails if a wrong output is generated. ```python -assert np.allclose(result.expect[0][0], 0), "Expectation value does not start at 1" +assert np.allclose(result.expect[0][0], 0), \ + "Expectation value does not start at 1" assert 1 == 1 ``` From 87399f859ad9e66fab9e9d99dff198e6f1d77dc5 Mon Sep 17 00:00:00 2001 From: Gerardo Suarez Date: Wed, 23 Apr 2025 01:31:12 +0200 Subject: [PATCH 31/44] hook --- tutorials-v5/heom/heom-index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials-v5/heom/heom-index.md b/tutorials-v5/heom/heom-index.md index e30e2ecf..dcb1fa89 100644 --- a/tutorials-v5/heom/heom-index.md +++ b/tutorials-v5/heom/heom-index.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.5 + jupytext_version: 1.14.4 kernelspec: display_name: Python 3 (ipykernel) language: python From d6fdd794bdbcfceb18a571c2d7b7c7eb1d36a9be Mon Sep 17 00:00:00 2001 From: Gerardo Suarez Date: Wed, 23 Apr 2025 01:34:21 +0200 Subject: [PATCH 32/44] hook --- tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md index 55bfdc0f..4a987b7f 100644 --- a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md +++ b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md @@ -20,7 +20,7 @@ kernelspec: Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc), also coupled to a discrete bosonic (vibronic) mode. -Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his Dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications. In particular this example reproduces some results from https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.201407 +Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his Dissertation https://open.fau.de/items/36fdd708-a467-4b59-bf4e-4a2110fbc431 and related publications. In particular this example reproduces some results from https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.201407 Notation: From 493a167f65f56d5db17f240dcc98a6fc5fe7bc03 Mon Sep 17 00:00:00 2001 From: Paul Menczel Date: Thu, 24 Apr 2025 13:44:40 +0900 Subject: [PATCH 33/44] HEOM 1a notebook final pass --- .../heom/heom-1a-spin-bath-model-basic.md | 254 ++++++++---------- 1 file changed, 108 insertions(+), 146 deletions(-) diff --git a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md index 85ee467e..05b0b4d0 100644 --- a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md +++ b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md @@ -5,9 +5,9 @@ jupyter: extension: .md format_name: markdown format_version: '1.3' - jupytext_version: 1.14.5 + jupytext_version: 1.17.0 kernelspec: - display_name: qutip-tutorials + display_name: Python 3 (ipykernel) language: python name: python3 --- @@ -81,15 +81,17 @@ import contextlib import time import numpy as np -import qutip +from scipy.optimize import curve_fit from matplotlib import pyplot as plt -from qutip import (basis, brmesolve, destroy, expect, liouvillian, qeye, - sigmax, sigmaz, spost, spre, tensor) -from qutip.core.environment import (DrudeLorentzEnvironment, - ExponentialBosonicEnvironment, - system_terminator) + +from qutip import ( + about, basis, brmesolve, destroy, expect, liouvillian, + qeye, sigmax, sigmaz, spost, spre, tensor +) +from qutip.core.environment import ( + DrudeLorentzEnvironment, ExponentialBosonicEnvironment, system_terminator +) from qutip.solver.heom import HEOMSolver, HSolverDL -from scipy.optimize import curve_fit %matplotlib inline ``` @@ -112,15 +114,8 @@ def dl_matsubara_params(lam, gamma, T, nk): """ ckAR = [lam * gamma * cot(gamma / (2 * T))] ckAR.extend( - ( - 8 - * lam - * gamma - * T - * np.pi - * k - * T - / ((2 * np.pi * k * T) ** 2 - gamma**2) + 8 * lam * gamma * T * np.pi * k * T / ( + (2 * np.pi * k * T) ** 2 - gamma**2 ) for k in range(1, nk + 1) ) @@ -178,7 +173,6 @@ def timer(label): ```python # Default solver options: - default_options = { "nsteps": 1500, "store_states": True, @@ -298,19 +292,15 @@ Drude-Lorentz correlation function, because QuTiP already has a class, knowing how to perform this expansion will allow you to construct your own baths for other spectral densities. -The `DrudeLorentzEnvironment` computes the correlation function using the Pade -approximation, the number of terms in the correlation function -expansion is specified using the $N_{k}$ parameter, it defaults to $10$, when -simulating, low temperatures $10$ Pade exponents may noy be enough, more details -about the Pade approximation are provided below . Next we show how to use this -built-in functionality: +Below we show how to use this built-in functionality: ```python # Compare to built-in Drude-Lorentz bath: with timer("RHS construction time"): - dlenv = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=100) - # 100 Terms in the Pade expansion for the correlation funtion + # Abstract representation of D-L Environment + dlenv = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T) + # Matsubara approximation of D-L Environment dlenv_approx = dlenv.approximate(method="matsubara", Nk=Nk) HEOM_dlbath = HEOMSolver(Hsys, (dlenv_approx, Q), NC, options=options) @@ -321,13 +311,15 @@ with timer("ODE solver time"): ```python plot_result_expectations( [ - (result_dlbath, P11p, "b", "P11 (DrudeLorentzBath)"), - (result_dlbath, P12p, "r", "P12 (DrudeLorentzBath)"), + (result_dlbath, P11p, "b", "P11 (DrudeLorentzEnvironment)"), + (result_dlbath, P12p, "r", "P12 (DrudeLorentzEnvironment)"), ] ); ``` -The `DrudeLorentzEnvironment` class also allows us to easily obtain the power spectrum, correlation function, and spectral density. The approximated Environment is a `BosonicEnvironment` where the effective power spectrum, correlation function, and spectral density from the approximation is also accessible. In the following plots, the solid lines are the exact expressions, and the dashed lines are based on our approximation of the correlation function with a finite number of exponents. +The `DrudeLorentzEnvironment` class also allows us to easily obtain the power spectrum, correlation function, and spectral density. The approximated Environment is a `BosonicEnvironment` where the approximated correlation function, and the corresponding effective power spectrum and spectral density are also accessible. In the following plots, the solid lines are the exact expressions, and the dashed lines are based on our Matsubara approximation of the correlation function with a finite number of exponents. + +The `DrudeLorentzEnvironment` computes the exact correlation function using the Pade approximation. The number of terms to use defaults to $10$, but when simulating low temperatures, $10$ Pade exponents may noy be enough. More details about the Pade approximation are provided below. Next we show how to use this built-in functionality: ```python w = np.linspace(-10, 20, 1000) @@ -341,10 +333,10 @@ axs[0, 0].set(xlabel=r"$\omega$", ylabel=r"$S(\omega)$") axs[0, 1].plot(w2, dlenv.spectral_density(w2)) axs[0, 1].plot(w2, dlenv_approx.spectral_density(w2), "--") axs[0, 1].set(xlabel=r"$\omega$", ylabel=r"$J(\omega)$") -axs[1, 0].plot(w2, np.real(dlenv.correlation_function(w2))) +axs[1, 0].plot(w2, np.real(dlenv.correlation_function(w2, Nk=100))) # 100 Pade axs[1, 0].plot(w2, np.real(dlenv_approx.correlation_function(w2)), "--") axs[1, 0].set(xlabel=r"$t$", ylabel=r"$C_{R}(t)$") -axs[1, 1].plot(w2, np.imag(dlenv.correlation_function(w2))) +axs[1, 1].plot(w2, np.imag(dlenv.correlation_function(w2, Nk=100))) axs[1, 1].plot(w2, np.imag(dlenv_approx.correlation_function(w2)), "--") axs[1, 1].set(xlabel=r"$t$", ylabel=r"$C_{I}(t)$") @@ -378,6 +370,20 @@ plot_result_expectations( ); ``` + +Another legacy class kept for convenience is the `DrudeLorentzBath`. The code +```python +dlenv = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T) +dlenv_approx = dlenv.approximate(method="matsubara", Nk=Nk) # Computes Matsubara exponents +HEOM_dlbath = HEOMSolver(Hsys, (dlenv_approx, Q), NC, options=options) +``` +that we used above is equivalent to the following code: +```python +dlbath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) +HEOM_dlbath = HEOMSolver(Hsys, dlbath, NC, options=options) +``` + + ## Ishizaki-Tanimura Terminator To speed up convergence (in terms of the number of exponents kept in the @@ -413,8 +419,8 @@ def plot_correlation_expansion_divergence(): """ t = np.linspace(0, 2, 100) - # correlation coefficients with 100 and 2 terms - corr_100 = dlenv.correlation_function(t) + # correlation coefficients with 100 pade and with 2 matsubara terms + corr_100 = dlenv.correlation_function(t, Nk=100) corr_2 = dlenv_approx.correlation_function(t) fig, ax1 = plt.subplots(figsize=(12, 7)) @@ -426,10 +432,10 @@ def plot_correlation_expansion_divergence(): t, np.imag(corr_2), color="r", linewidth=3, label=rf"Mats = {Nk} imag" ) ax1.plot( - t, np.real(corr_100), "b--", linewidth=3, label=r"Mats = 15000 real" + t, np.real(corr_100), "b--", linewidth=3, label=r"Pade = 100 real" ) ax1.plot( - t, np.imag(corr_100), "r--", linewidth=3, label=r"Mats = 15000 imag" + t, np.imag(corr_100), "r--", linewidth=3, label=r"Pade = 100 imag" ) ax1.set_xlabel("t") @@ -443,10 +449,12 @@ plot_correlation_expansion_divergence() Let us evaluate the result including this Ishizaki-Tanimura terminator: ```python -# Run HEOM solver and include the Ishizaki-Tanimura terminator +# Run HEOM solver including the Ishizaki-Tanimura terminator # Notes: # +# * here, we will first show how to compute the terminator manually +# # * when using the built-in DrudeLorentzEnvironment the terminator (L_bnd) is # available from by setting the parameter compute_delta to True in the # approximate method @@ -472,8 +480,8 @@ Ltot = liouvillian(Hsys) + L_bnd options = {**default_options, "rtol": 1e-14, "atol": 1e-14} with timer("RHS construction time"): - bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI) - HEOMMatsT = HEOMSolver(Ltot, (bath, Q), NC, options=options) + env = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI) + HEOMMatsT = HEOMSolver(Ltot, (env, Q), NC, options=options) with timer("ODE solver time"): resultMatsT = HEOMMatsT.run(rho0, tlist) @@ -494,21 +502,21 @@ Or using the built-in Drude-Lorentz environment we can write simply: options = {**default_options, "rtol": 1e-14, "atol": 1e-14} with timer("RHS construction time"): - bath, delta = dlenv.approx_by_matsubara(Nk=Nk, compute_delta=True) + dlenv_approx, delta = dlenv.approximate( + "matsubara", Nk=Nk, compute_delta=True + ) Ltot = liouvillian(Hsys) + system_terminator(Q, delta) - HEOM_dlbath_T = HEOMSolver(Ltot, (bath, Q), NC, options=options) + HEOM_dlbath_T = HEOMSolver(Ltot, (dlenv_approx, Q), NC, options=options) with timer("ODE solver time"): result_dlbath_T = HEOM_dlbath_T.run(rho0, tlist) ``` ```python -plot_result_expectations( - [ - (result_dlbath_T, P11p, "b", "P11 Mats (DrudeLorentzBath + Term)"), - (result_dlbath_T, P12p, "r", "P12 Mats (DrudeLorentzBath + Term)"), - ] -); +plot_result_expectations([ + (result_dlbath_T, P11p, "b", "P11 Mats (DrudeLorentzEnvironment + Term)"), + (result_dlbath_T, P12p, "r", "P12 Mats (DrudeLorentzEnvironment + Term)"), +]); ``` We can compare the solution obtained from the QuTiP Bloch-Redfield solver: @@ -579,7 +587,7 @@ def pade_chi(lmax): ) eigvalsAP = np.linalg.eigvalsh(AlphaP) - chi = [-2 / val for val in eigvalsAP[0 : lmax - 1]] + chi = [-2 / val for val in eigvalsAP[0:(lmax - 1)]] return chi @@ -640,8 +648,8 @@ def pade_corr(tlist, lmax): tlist_corr = np.linspace(0, 2, 100) cppLP, etapLP, gampLP = pade_corr(tlist_corr, 2) -corr_100 = dlenv.correlation_function(tlist_corr, Nk=15) -corr_2k = dlenv.correlation_function(tlist_corr, Nk=2) +corr_100 = dlenv.correlation_function(tlist_corr, Nk=100) +corr_2 = dlenv.approximate("matsubara", Nk=2).correlation_function(tlist_corr) fig, ax1 = plt.subplots(figsize=(12, 7)) ax1.plot( @@ -656,11 +664,11 @@ ax1.plot( np.real(corr_100), "r--", linewidth=3, - label=r"real pade 15 terms", + label=r"real pade 100 terms", ) ax1.plot( tlist_corr, - np.real(corr_2k), + np.real(corr_2), "g--", linewidth=3, label=r"real mats 2 terms", @@ -681,7 +689,7 @@ ax1.plot( ) ax1.plot( tlist_corr, - np.real(corr_2k) - np.real(corr_100), + np.real(corr_2) - np.real(corr_100), "r--", linewidth=3, label=r"mats error", @@ -722,8 +730,8 @@ plot_result_expectations( ); ``` -The Padé decomposition of the Drude-Lorentz bath is also available via a -built-in class, `DrudeLorentzEnvironment` bath. Similarly to the terminator +As mentioned previously, the Padé decomposition of the Drude-Lorentz bath is also available via the +built-in `DrudeLorentzEnvironment`. Similarly to the terminator section when approximating by Padé one can calculate the terminator easily by requesting the approximation function to compute delta @@ -747,8 +755,8 @@ with timer("ODE solver time"): ```python plot_result_expectations( [ - (result_dlpbath_T, P11p, "b", "P11 Padé (DrudeLorentzBath + Term)"), - (result_dlpbath_T, P12p, "r", "P12 Padé (DrudeLorentzBath + Term)"), + (result_dlpbath_T, P11p, "b", "P11 Padé + Term"), + (result_dlpbath_T, P12p, "r", "P12 Padé + Term"), ] ); ``` @@ -758,9 +766,9 @@ plot_result_expectations( Fitting the correlation function is not efficient for this example, but can be extremely useful in situations where large number of exponents are needed (e.g., near zero temperature). We will perform the fitting -manually below, and then show how to do it with the built-in tools +manually below, and then show how to do it with the built-in tools. -For the manual fit we First we collect a large sum of Pade terms for +For the manual fit we first we collect a large sum of Pade terms for many time steps: ```python @@ -771,6 +779,8 @@ corr_100_t10k = dlenv.correlation_function(tlist2, Nk=100) corrRana = np.real(corr_100_t10k) corrIana = np.imag(corr_100_t10k) + +corrRMats = np.real(dlenv_approx.correlation_function(tlist2)) ``` We then fit this sum with standard least-squares approach: @@ -780,7 +790,7 @@ def wrapper_fit_func(x, N, args): """Fit function wrapper that unpacks its arguments.""" x = np.array(x) a = np.array(args[:N]) - b = np.array(args[N : 2 * N]) + b = np.array(args[N:(2 * N)]) return fit_func(x, a, b) @@ -799,15 +809,20 @@ def fitter(ans, tlist, k): """Compute fit with k exponents.""" upper_a = abs(max(ans, key=abs)) * 10 # sets initial guesses: - guess = [upper_a / k] * k + [0] * k # guesses for a # guesses for b + guess = ( + [upper_a / k] * k + # guesses for a + [0] * k # guesses for b + ) # sets lower bounds: - b_lower = [-upper_a] * k + [ - -np.inf - ] * k # lower bounds for a # lower bounds for b + b_lower = ( + [-upper_a] * k + # lower bounds for a + [-np.inf] * k # lower bounds for b + ) # sets higher bounds: - b_higher = [upper_a] * k + [ - 0 - ] * k # upper bounds for a # upper bounds for b + b_higher = ( + [upper_a] * k + # upper bounds for a + [0] * k # upper bounds for b + ) param_bounds = (b_lower, b_higher) p1, p2 = curve_fit( lambda x, *params_0: wrapper_fit_func(x, k, params_0), @@ -829,8 +844,6 @@ with timer("Correlation (real) fitting time"): for i in range(kR): poptR.append(fitter(corrRana, tlist2, i + 1)) -corrRMats = np.real(dlenv_approx.correlation_function(tlist2)) - kI = 1 # number of exponents for imaginary part poptI = [] with timer("Correlation (imaginary) fitting time"): @@ -843,16 +856,7 @@ And plot the results of the fits: ```python # Define line styles and colors linestyles = ["-", "--", "-.", ":", (0, (3, 1, 1, 1)), (0, (5, 1))] -colors = [ - "blue", - "green", - "purple", - "orange", - "red", - "brown", - "cyan", - "magenta", -] +colors = ["blue", "green", "purple", "orange", "red", "brown"] # Define a larger linewidth linewidth = 2.5 @@ -861,65 +865,37 @@ linewidth = 2.5 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6)) # Plot the real part on the first subplot (ax1) -ax1.plot( - tlist2, - corrRana, - label="Analytic", - color=colors[0], - linestyle=linestyles[0], - linewidth=linewidth, -) -ax1.plot( - tlist2, - corrRMats, - label="Matsubara", - color=colors[1], - linestyle=linestyles[1], - linewidth=linewidth, -) +ax1.plot(tlist2, corrRana, label="Analytic", color=colors[0], + linestyle=linestyles[0], linewidth=linewidth) +ax1.plot(tlist2, corrRMats, label="Matsubara", color=colors[1], + linestyle=linestyles[1], linewidth=linewidth) for i in range(kR): y = fit_func(tlist2, *poptR[i]) - ax1.plot( - tlist2, - y, - label=f"Fit with {i} terms", - color=colors[(i + 2) % len(colors)], - linestyle=linestyles[(i + 2) % len(linestyles)], - linewidth=linewidth, - ) + ax1.plot(tlist2, y, label=f"Fit with {i+1} terms", color=colors[i + 2], + linestyle=linestyles[i + 2], linewidth=linewidth) + ax1.set_ylabel(r"$C_{R}(t)$") ax1.set_xlabel(r"$t$") ax1.legend() # Plot the imaginary part on the second subplot (ax2) -ax2.plot( - tlist2, - corrIana, - label="Analytic", - color=colors[0], - linestyle=linestyles[0], - linewidth=linewidth, -) +ax2.plot(tlist2, corrIana, label="Analytic", color=colors[0], + linestyle=linestyles[0], linewidth=linewidth) for i in range(kI): y = fit_func(tlist2, *poptI[i]) - ax2.plot( - tlist2, - y, - label=f"Fit with {i} terms", - color=colors[(i + 3) % len(colors)], - linestyle=linestyles[(i + 1) % len(linestyles)], - linewidth=linewidth, - ) + ax2.plot(tlist2, y, label=f"Fit with {i+1} terms", color=colors[i + 3], + linestyle=linestyles[i + 3], linewidth=linewidth) + ax2.set_ylabel(r"$C_{I}(t)$") ax2.set_xlabel(r"$t$") - ax2.legend() # Add overall plot title and show the figure fig.suptitle( - "Comparison of Analytic and Fit to Correlations (Real and Imaginary Parts)", + "Comparison of Analytic and Fit to Correlations" + " (Real and Imaginary Parts)", fontsize=16, ) plt.show() @@ -971,7 +947,7 @@ plot_result_expectations( ); ``` -Now we use the built-in functions. The `BosonicEnvironment` class, includes a +Now we use the built-in fitting functions. The `BosonicEnvironment` class includes a method that performs this fit automatically. More information on how the built-in functios work can be found in `HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions` @@ -981,18 +957,11 @@ guess = [max_val / 3, 0, 0, 0] lower = [-max_val, -np.inf, -np.inf, -np.inf] upper = [max_val, 0, 0, 0] envfit, fitinfo = dlenv.approximate( - "cf", - tlist=tlist2, - full_ansatz=True, - Ni_max=1, - Nr_max=3, - upper=upper, - lower=lower, - guess=guess, -) + "cf", tlist=tlist2, full_ansatz=True, Ni_max=1, Nr_max=3, + upper=upper, lower=lower, guess=guess) ``` -The approx_by_cf_fit method outputs a `ExponentialBosonicEnvironment` object, +The `approximate("cf", ...)` method outputs an `ExponentialBosonicEnvironment` object, which contains a decaying exponential representation of the original environment , and a dictionary containing all information related to the fit. The dictionary contains a summary of the fir information and the normalized @@ -1011,12 +980,8 @@ fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6)) # Plot the real part on the first subplot (ax1) ax1.plot(tlist2, corrRana, label="Original", marker="o", markevery=500) ax1.plot(tlist2, fit_func(tlist2, *poptR[-1]), color="r", label="Manual Fit") -ax1.plot( - tlist2, - np.real(envfit.correlation_function(tlist2)), - "k--", - label="Built-in fit", -) +ax1.plot(tlist2, np.real(envfit.correlation_function(tlist2)), "k--", + label="Built-in fit") ax1.set_ylabel(r"$C_{R}(t)$") ax1.set_xlabel(r"$t$") ax1.legend() @@ -1024,16 +989,13 @@ ax1.legend() # Plot the imaginary part on the second subplot (ax2) ax2.plot(tlist2, corrIana, label="Original", marker="o", markevery=500) ax2.plot(tlist2, fit_func(tlist2, *poptI[-1]), color="r", label="Manual Fit") -ax2.plot( - tlist2, - np.imag(envfit.correlation_function(tlist2)), - "k--", - label="Built-in fit", -) +ax2.plot(tlist2, np.imag(envfit.correlation_function(tlist2)), "k--", + label="Built-in fit") ax2.set_ylabel(r"$C_{I}(t)$") ax2.set_xlabel(r"$t$") ax2.legend() -# Add an overall title and adjust layout + +# Adjust layout plt.tight_layout(rect=[0, 0.03, 1, 0.95]) plt.show() ``` @@ -1216,7 +1178,7 @@ And that's the end of a detailed first dive into modeling bosonic environments w ## About ```python -qutip.about() +about() ``` ## Testing From dabc2996e2c0ecb0b471857628ac11e41b7cdce0 Mon Sep 17 00:00:00 2001 From: Paul Menczel Date: Thu, 24 Apr 2025 15:41:36 +0900 Subject: [PATCH 34/44] HEOM 1b notebook final pass --- .../heom/heom-1a-spin-bath-model-basic.md | 10 +- ...1b-spin-bath-model-very-strong-coupling.md | 307 +++++++----------- 2 files changed, 118 insertions(+), 199 deletions(-) diff --git a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md index 05b0b4d0..102ade46 100644 --- a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md +++ b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md @@ -787,7 +787,7 @@ We then fit this sum with standard least-squares approach: ```python def wrapper_fit_func(x, N, args): - """Fit function wrapper that unpacks its arguments.""" + """ Fit function wrapper that unpacks its arguments. """ x = np.array(x) a = np.array(args[:N]) b = np.array(args[N:(2 * N)]) @@ -795,9 +795,9 @@ def wrapper_fit_func(x, N, args): def fit_func(x, a, b): - """Fit function. Calculates the value of the - correlation function at each x, given the - fit parameters in a and b. + """ Fit function. Calculates the value of the + correlation function at each x, given the + fit parameters in a and b. """ return np.sum( a[:, None] * np.exp(np.multiply.outer(b, x)), @@ -806,7 +806,7 @@ def fit_func(x, a, b): def fitter(ans, tlist, k): - """Compute fit with k exponents.""" + """ Compute fit with k exponents. """ upper_a = abs(max(ans, key=abs)) * 10 # sets initial guesses: guess = ( diff --git a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md index ee42ba98..57a29e32 100644 --- a/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md +++ b/tutorials-v5/heom/heom-1b-spin-bath-model-very-strong-coupling.md @@ -1,15 +1,14 @@ --- jupytext: - formats: ipynb,md:myst text_representation: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.5 + jupytext_version: 1.17.0 kernelspec: - display_name: qutip-tutorials - language: python name: python3 + display_name: Python 3 (ipykernel) + language: python --- # HEOM 1b: Spin-Bath model (very strong coupling) @@ -81,14 +80,14 @@ Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. ## Setup -```{code-cell} +```{code-cell} ipython3 import contextlib import time -import matplotlib.pyplot as plt import numpy as np -import qutip -from qutip import basis, brmesolve, expect, liouvillian, sigmax, sigmaz +import matplotlib.pyplot as plt + +from qutip import about, basis, brmesolve, expect, liouvillian, sigmax, sigmaz from qutip.core.environment import DrudeLorentzEnvironment, system_terminator from qutip.solver.heom import HEOMSolver @@ -99,19 +98,19 @@ from qutip.solver.heom import HEOMSolver Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: -```{code-cell} +```{code-cell} ipython3 def cot(x): - """Vectorized cotangent of x.""" + """ Vectorized cotangent of x. """ return 1.0 / np.tan(x) ``` -```{code-cell} +```{code-cell} ipython3 @contextlib.contextmanager def timer(label): - """Simple utility for timing functions: + """ Simple utility for timing functions: - with timer("name"): - ... code to time ... + with timer("name"): + ... code to time ... """ start = time.time() yield @@ -119,10 +118,9 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} +```{code-cell} ipython3 # Solver options: - options = { "nsteps": 15000, "store_states": True, @@ -137,26 +135,26 @@ options = { And let us set up the system Hamiltonian, bath and system measurement operators: -```{code-cell} +```{code-cell} ipython3 # Defining the system Hamiltonian eps = 0.0 # Energy of the 2-level system. Del = 0.2 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` -```{code-cell} +```{code-cell} ipython3 # Initial state of the system. rho0 = basis(2, 0) * basis(2, 0).dag() ``` -```{code-cell} +```{code-cell} ipython3 # System-bath coupling (Drude-Lorentz spectral density) Q = sigmaz() # coupling operator # Bath properties (see Shi et al., J. Chem. Phys. 130, 084105 (2009)): gamma = 1.0 # cut off frequency -lam = 2.5 # coupling strength -T = 1.0 # in units where Boltzmann factor is 1 +lam = 2.5 # coupling strength +T = 1.0 # in units where Boltzmann factor is 1 beta = 1.0 / T # HEOM parameters: @@ -172,7 +170,7 @@ NC = 13 tlist = np.linspace(0, np.pi / Del, 600) ``` -```{code-cell} +```{code-cell} ipython3 # Define some operators with which we will measure the system # 1,1 element of density matrix - corresonding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -185,24 +183,24 @@ P12p = basis(2, 0) * basis(2, 1).dag() Let us briefly inspect the spectral density. -```{code-cell} -bath = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=500) +```{code-cell} ipython3 +env = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=500) w = np.linspace(0, 5, 1000) -J = bath.spectral_density(w) +J = env.spectral_density(w) # Plot the results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) -axes.plot(w, J, "r", linewidth=2) -axes.set_xlabel(r"$\omega$", fontsize=28) -axes.set_ylabel(r"J", fontsize=28); +axes.plot(w, J, 'r', linewidth=2) +axes.set_xlabel(r'$\omega$', fontsize=28) +axes.set_ylabel(r'J', fontsize=28); ``` ## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator -```{code-cell} +```{code-cell} ipython3 with timer("RHS construction time"): - matsBath = bath.approximate(method="matsubara", Nk=Nk) - HEOMMats = HEOMSolver(Hsys, (matsBath, Q), NC, options=options) + matsEnv = env.approximate(method="matsubara", Nk=Nk) + HEOMMats = HEOMSolver(Hsys, (matsEnv, Q), NC, options=options) with timer("ODE solver time"): resultMats = HEOMMats.run(rho0, tlist) @@ -210,206 +208,155 @@ with timer("ODE solver time"): ## Simulation 2: Matsubara decomposition (including terminator) -```{code-cell} +```{code-cell} ipython3 with timer("RHS construction time"): - matsBath, delta = bath.approximate( + matsEnv, delta = env.approximate( method="matsubara", Nk=Nk, compute_delta=True ) terminator = system_terminator(Q, delta) Ltot = liouvillian(Hsys) + terminator - HEOMMatsT = HEOMSolver(Ltot, (matsBath, Q), NC, options=options) + HEOMMatsT = HEOMSolver(Ltot, (matsEnv, Q), NC, options=options) with timer("ODE solver time"): resultMatsT = HEOMMatsT.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 # Plot the results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) P11_mats = np.real(expect(resultMats.states, P11p)) axes.plot( - tlist, - np.real(P11_mats), - "b", - linewidth=2, - label="P11 (Matsubara)", + tlist, np.real(P11_mats), + 'b', linewidth=2, label="P11 (Matsubara)", ) P11_matsT = np.real(expect(resultMatsT.states, P11p)) axes.plot( - tlist, - np.real(P11_matsT), - "b--", - linewidth=2, + tlist, np.real(P11_matsT), + 'b--', linewidth=2, label="P11 (Matsubara + Terminator)", ) -axes.set_xlabel(r"t", fontsize=28) +axes.set_xlabel(r't', fontsize=28) axes.legend(loc=0, fontsize=12); ``` ## Simulation 3: Pade decomposition -```{code-cell} +```{code-cell} ipython3 # First, compare Matsubara and Pade decompositions -padeBath = bath.approximate("pade", Nk=Nk) +padeEnv = env.approximate("pade", Nk=Nk) fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(16, 8)) ax1.plot( - tlist, - np.real(bath.correlation_function(tlist)), - "r", - linewidth=2, - label="Exact", + tlist, np.real(env.correlation_function(tlist)), + "r", linewidth=2, label="Exact", ) ax1.plot( - tlist, - np.real(matsBath.correlation_function(tlist)), - "g--", - linewidth=2, - label=f"Mats (Nk={Nk})", + tlist, np.real(matsEnv.correlation_function(tlist)), + "g--", linewidth=2, label=f"Mats (Nk={Nk})", ) ax1.plot( - tlist, - np.real(padeBath.correlation_function(tlist)), - "b--", - linewidth=2, - label=f"Pade (Nk={Nk})", + tlist, np.real(padeEnv.correlation_function(tlist)), + "b--", linewidth=2, label=f"Pade (Nk={Nk})", ) -ax1.set_xlabel(r"t", fontsize=28) +ax1.set_xlabel(r't', fontsize=28) ax1.set_ylabel(r"$C_R(t)$", fontsize=28) ax1.legend(loc=0, fontsize=12) tlist2 = tlist[0:50] ax2.plot( - tlist2, - np.abs( - matsBath.correlation_function(tlist2) - - bath.correlation_function(tlist2) - ), - "g", - linewidth=2, - label="Mats Error", + tlist2, np.abs(matsEnv.correlation_function(tlist2) + - env.correlation_function(tlist2)), + "g", linewidth=2, label="Mats Error", ) ax2.plot( - tlist2, - np.abs( - padeBath.correlation_function(tlist2) - - bath.correlation_function(tlist2) - ), - "b--", - linewidth=2, - label="Pade Error", + tlist2, np.abs(padeEnv.correlation_function(tlist2) + - env.correlation_function(tlist2)), + "b--", linewidth=2, label="Pade Error", ) -ax2.set_xlabel(r"t", fontsize=28) +ax2.set_xlabel(r't', fontsize=28) ax2.legend(loc=0, fontsize=12); ``` -```{code-cell} +```{code-cell} ipython3 with timer("RHS construction time"): - HEOMPade = HEOMSolver(Hsys, (padeBath, Q), NC, options=options) + HEOMPade = HEOMSolver(Hsys, (padeEnv, Q), NC, options=options) with timer("ODE solver time"): resultPade = HEOMPade.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 # Plot the results fig, axes = plt.subplots(figsize=(8, 8)) axes.plot( - tlist, - np.real(P11_mats), - "b", - linewidth=2, - label="P11 (Matsubara)", + tlist, np.real(P11_mats), + 'b', linewidth=2, label="P11 (Matsubara)", ) axes.plot( - tlist, - np.real(P11_matsT), - "b--", - linewidth=2, - label="P11 (Matsubara + Terminator)", + tlist, np.real(P11_matsT), + 'b--', linewidth=2, label="P11 (Matsubara + Terminator)", ) P11_pade = np.real(expect(resultPade.states, P11p)) axes.plot( - tlist, - np.real(P11_pade), - "r", - linewidth=2, - label="P11 (Pade)", + tlist, np.real(P11_pade), + 'r', linewidth=2, label="P11 (Pade)", ) -axes.set_xlabel(r"t", fontsize=28) +axes.set_xlabel(r't', fontsize=28) axes.legend(loc=0, fontsize=12); ``` ## Simulation 4: Fitting approach -In `HEOM 1a: Spin-Bath model (introduction)` a fit is performed manually, here -we will use the built-in tools. More details about them can be seen in +In `HEOM 1a: Spin-Bath model (introduction)` there is an example of a manually performed fit, here +we will only use the built-in tools. More details about them can be seen in `HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions` -```{code-cell} +```{code-cell} ipython3 tfit = np.linspace(0, 10, 10000) lower = [0, -np.inf, -1e-6, -3] -guess = [np.real(bath.correlation_function(0)) / 10, -10, 0, 0] +guess = [np.real(env.correlation_function(0)) / 10, -10, 0, 0] upper = [5, 0, 1e-6, 0] # for better fits increase the first element in upper, or change approximate # method that makes the simulation much slower (Larger C(t) as C(0) is fit # better) -envfit, fitinfo = bath.approximate( - "cf", - tlist=tfit, - Nr_max=2, - Ni_max=1, - full_ansatz=True, - sigma=0.1, - maxfev=1e6, - target_rmse=None, - lower=lower, - upper=upper, - guess=guess, + +envfit, fitinfo = env.approximate( + "cf", tlist=tfit, Nr_max=2, Ni_max=1, full_ansatz=True, + sigma=0.1, maxfev=1e6, target_rmse=None, + lower=lower, upper=upper, guess=guess, ) ``` -```{code-cell} +```{code-cell} ipython3 print(fitinfo["summary"]) ``` -We can quickly compare the result of the Fit with the Pade expansion +We can quickly compare the result of the fit with the Pade expansion -```{code-cell} +```{code-cell} ipython3 fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8)) ax1.plot( - tlist, - np.real(bath.correlation_function(tlist)), - "r", - linewidth=2, - label="Exact", + tlist, np.real(env.correlation_function(tlist)), + "r", linewidth=2, label="Exact", ) ax1.plot( - tlist, - np.real(envfit.correlation_function(tlist)), - "g--", - linewidth=2, - label="Fit", - marker="o", - markevery=50, + tlist, np.real(envfit.correlation_function(tlist)), + "g--", linewidth=2, label="Fit", marker="o", markevery=50, ) ax1.plot( - tlist, - np.real(padeBath.correlation_function(tlist)), - "b--", - linewidth=2, - label=f"Pade (Nk={Nk})", + tlist, np.real(padeEnv.correlation_function(tlist)), + "b--", linewidth=2, label=f"Pade (Nk={Nk})", ) ax1.set_xlabel(r"t", fontsize=28) @@ -417,35 +364,25 @@ ax1.set_ylabel(r"$C_R(t)$", fontsize=28) ax1.legend(loc=0, fontsize=12) ax2.plot( - tlist, - np.imag(bath.correlation_function(tlist)), - "r", - linewidth=2, - label="Exact", + tlist, np.imag(env.correlation_function(tlist)), + "r", linewidth=2, label="Exact", ) ax2.plot( - tlist, - np.imag(envfit.correlation_function(tlist)), - "g--", - linewidth=2, - label="Fit", - marker="o", - markevery=50, + tlist, np.imag(envfit.correlation_function(tlist)), + "g--", linewidth=2, label="Fit", marker="o", markevery=50, ) ax2.plot( - tlist, - np.imag(padeBath.correlation_function(tlist)), - "b--", - linewidth=2, - label=f"Pade (Nk={Nk})", + tlist, np.imag(padeEnv.correlation_function(tlist)), + "b--", linewidth=2, label=f"Pade (Nk={Nk})", ) ax2.set_xlabel(r"t", fontsize=28) ax2.set_ylabel(r"$C_I(t)$", fontsize=28) ax2.legend(loc=0, fontsize=12) +plt.show() ``` -```{code-cell} +```{code-cell} ipython3 with timer("RHS construction time"): # We reduce NC slightly here for speed of execution because we retain # 3 exponents in ckAR instead of 1. Please restore full NC for @@ -458,15 +395,11 @@ with timer("ODE solver time"): ## Simulation 5: Bloch-Redfield -```{code-cell} +```{code-cell} ipython3 with timer("ODE solver time"): resultBR = brmesolve( - Hsys, - rho0, - tlist, - a_ops=[[sigmaz(), bath]], - sec_cutoff=0, - options=options, + Hsys, rho0, tlist, + a_ops=[[sigmaz(), env]], sec_cutoff=0, options=options, ) ``` @@ -474,7 +407,7 @@ with timer("ODE solver time"): Finally, let's plot all of our different results to see how they shape up against each other. -```{code-cell} +```{code-cell} ipython3 # Calculate expectation values in the bases: P11_mats = np.real(expect(resultMats.states, P11p)) P11_matsT = np.real(expect(resultMatsT.states, P11p)) @@ -483,7 +416,7 @@ P11_fit = np.real(expect(resultFit.states, P11p)) P11_br = np.real(expect(resultBR.states, P11p)) ``` -```{code-cell} +```{code-cell} ipython3 rcParams = { "axes.titlesize": 25, "axes.labelsize": 30, @@ -500,53 +433,39 @@ rcParams = { } ``` -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) with plt.rc_context(rcParams): # Plot the results plt.yticks([0.99, 1.0], [0.99, 1]) axes.plot( - tlist, - np.real(P11_mats), - "b", - linewidth=2, - label=f"Matsubara $N_k={Nk}$", + tlist, np.real(P11_mats), + 'b', linewidth=2, label=f"Matsubara $N_k={Nk}$", ) axes.plot( - tlist, - np.real(P11_matsT), - "g--", - linewidth=3, + tlist, np.real(P11_matsT), + 'g--', linewidth=3, label=f"Matsubara $N_k={Nk}$ & terminator", ) axes.plot( - tlist, - np.real(P11_pade), - "y-.", - linewidth=2, - label=f"Padé $N_k={Nk}$", + tlist, np.real(P11_pade), + 'y-.', linewidth=2, label=f"Padé $N_k={Nk}$", ) axes.plot( - tlist, - np.real(P11_fit), - "r", - dashes=[3, 2], - linewidth=2, + tlist, np.real(P11_fit), + 'r', dashes=[3, 2], linewidth=2, label=r"Fit $N_f = 3$, $N_k=15 \times 10^3$", ) axes.plot( - tlist, - np.real(P11_br), - "b-.", - linewidth=1, - label="Bloch Redfield", + tlist, np.real(P11_br), + 'b-.', linewidth=1, label="Bloch Redfield", ) - axes.locator_params(axis="y", nbins=6) - axes.locator_params(axis="x", nbins=6) - axes.set_ylabel(r"$\rho_{11}$", fontsize=30) - axes.set_xlabel(r"$t\;\gamma$", fontsize=30) + axes.locator_params(axis='y', nbins=6) + axes.locator_params(axis='x', nbins=6) + axes.set_ylabel(r'$\rho_{11}$', fontsize=30) + axes.set_xlabel(r'$t\;\gamma$', fontsize=30) axes.set_xlim(tlist[0], tlist[-1]) axes.set_ylim(0.98405, 1.0005) axes.legend(loc=0) @@ -554,15 +473,15 @@ with plt.rc_context(rcParams): ## About -```{code-cell} -qutip.about() +```{code-cell} ipython3 +about() ``` ## Testing This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 assert np.allclose(P11_matsT, P11_pade, rtol=1e-3) assert np.allclose(P11_matsT, P11_fit, rtol=1e-3) ``` From 2dfcd81f82ccb2abce63bb5ab73b8e6c81ad05c6 Mon Sep 17 00:00:00 2001 From: Paul Menczel Date: Thu, 24 Apr 2025 16:19:28 +0900 Subject: [PATCH 35/44] HEOM 1c notebook final pass --- .../heom-1c-spin-bath-model-underdamped-sd.md | 291 ++++++++---------- 1 file changed, 130 insertions(+), 161 deletions(-) diff --git a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md index 52921c73..af48a007 100644 --- a/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md +++ b/tutorials-v5/heom/heom-1c-spin-bath-model-underdamped-sd.md @@ -1,15 +1,14 @@ --- jupytext: - formats: ipynb,md:myst text_representation: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.5 + jupytext_version: 1.17.0 kernelspec: - display_name: qutip-tutorials - language: python name: python3 + display_name: Python 3 (ipykernel) + language: python --- # HEOM 1c: Spin-Bath model (Underdamped Case) @@ -76,17 +75,18 @@ Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. ## Setup -```{code-cell} +```{code-cell} ipython3 import contextlib import time import numpy as np -import qutip from matplotlib import pyplot as plt -from qutip import (basis, brmesolve, destroy, expect, qeye, sigmax, sigmaz, - tensor) -from qutip.core.environment import (ExponentialBosonicEnvironment, - UnderDampedEnvironment) + +from qutip import (about, basis, brmesolve, destroy, expect, qeye, + sigmax, sigmaz, tensor) +from qutip.core.environment import ( + ExponentialBosonicEnvironment, UnderDampedEnvironment +) from qutip.solver.heom import HEOMSolver %matplotlib inline @@ -96,39 +96,35 @@ from qutip.solver.heom import HEOMSolver Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: -```{code-cell} +```{code-cell} ipython3 def cot(x): - """Vectorized cotangent of x.""" + """ Vectorized cotangent of x. """ return 1.0 / np.tan(x) ``` -```{code-cell} +```{code-cell} ipython3 def coth(x): - """Vectorized hyperbolic cotangent of x.""" + """ Vectorized hyperbolic cotangent of x. """ return 1.0 / np.tanh(x) ``` -```{code-cell} +```{code-cell} ipython3 def underdamped_matsubara_params(lam, gamma, T, nk): - """Calculation of the real and imaginary expansions of the - underdamped correlation functions. + """ Calculation of the real and imaginary expansions of the + underdamped correlation functions. """ - Om = np.sqrt(w0**2 - (gamma / 2) ** 2) + Om = np.sqrt(w0**2 - (gamma / 2)**2) Gamma = gamma / 2.0 beta = 1.0 / T ckAR = [ - (lam**2 / (4 * Om)) * coth(beta * (Om + 1.0j * Gamma) / 2), - (lam**2 / (4 * Om)) * coth(beta * (Om - 1.0j * Gamma) / 2), + (lam**2 / (4*Om)) * coth(beta * (Om + 1.0j * Gamma) / 2), + (lam**2 / (4*Om)) * coth(beta * (Om - 1.0j * Gamma) / 2), ] ckAR.extend( - (-2 * lam**2 * gamma / beta) - * (2 * np.pi * k / beta) - / ( - ((Om + 1.0j * Gamma) ** 2 + (2 * np.pi * k / beta) ** 2) - * ((Om - 1.0j * Gamma) ** 2 + (2 * np.pi * k / beta) ** 2) - ) - + 0.0j + (-2 * lam**2 * gamma / beta) * (2 * np.pi * k / beta) / + (((Om + 1.0j * Gamma)**2 + (2 * np.pi * k / beta)**2) * + ((Om - 1.0j * Gamma)**2 + (2 * np.pi * k / beta)**2)) + 0.j for k in range(1, nk + 1) ) vkAR = [ @@ -151,12 +147,12 @@ def underdamped_matsubara_params(lam, gamma, T, nk): return ckAR, vkAR, ckAI, vkAI ``` -```{code-cell} +```{code-cell} ipython3 def plot_result_expectations(plots, axes=None): - """Plot the expectation values of operators as functions of time. + """ Plot the expectation values of operators as functions of time. - Each plot in plots consists of: (solver_result, measurement_operation, - color, label). + Each plot in plots consists of: (solver_result, measurement_operation, + color, label). """ if axes is None: fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -179,13 +175,13 @@ def plot_result_expectations(plots, axes=None): return fig ``` -```{code-cell} +```{code-cell} ipython3 @contextlib.contextmanager def timer(label): - """Simple utility for timing functions: + """ Simple utility for timing functions: - with timer("name"): - ... code to time ... + with timer("name"): + ... code to time ... """ start = time.time() yield @@ -193,10 +189,9 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} +```{code-cell} ipython3 # Solver options: - options = { "nsteps": 15000, "store_states": True, @@ -211,26 +206,26 @@ options = { And let us set up the system Hamiltonian, bath and system measurement operators: -```{code-cell} +```{code-cell} ipython3 # Defining the system Hamiltonian eps = 0.5 # Energy of the 2-level system. Del = 1.0 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` -```{code-cell} +```{code-cell} ipython3 # Initial state of the system. rho0 = basis(2, 0) * basis(2, 0).dag() ``` -```{code-cell} +```{code-cell} ipython3 # System-bath coupling (underdamed spectral density) Q = sigmaz() # coupling operator # Bath properties: gamma = 0.1 # cut off frequency -lam = 0.5 # coupling strength -w0 = 1.0 # resonance frequency +lam = 0.5 # coupling strength +w0 = 1.0 # resonance frequency T = 1.0 beta = 1.0 / T @@ -247,7 +242,7 @@ NC = 10 tlist = np.linspace(0, 50, 1000) ``` -```{code-cell} +```{code-cell} ipython3 # Define some operators with which we will measure the system # 1,1 element of density matrix - corresonding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -258,16 +253,16 @@ P12p = basis(2, 0) * basis(2, 1).dag() ### First let us look at what the underdamped spectral density looks like: -```{code-cell} +```{code-cell} ipython3 def plot_spectral_density(): - """Plot the underdamped spectral density""" + """ Plot the underdamped spectral density """ w = np.linspace(0, 5, 1000) - J = lam**2 * gamma * w / ((w0**2 - w**2) ** 2 + (gamma**2) * (w**2)) + J = lam**2 * gamma * w / ((w0**2 - w**2)**2 + (gamma**2) * (w**2)) fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - axes.plot(w, J, "r", linewidth=2) - axes.set_xlabel(r"$\omega$", fontsize=28) - axes.set_ylabel(r"J", fontsize=28) + axes.plot(w, J, 'r', linewidth=2) + axes.set_xlabel(r'$\omega$', fontsize=28) + axes.set_ylabel(r'J', fontsize=28) plot_spectral_density() @@ -279,52 +274,50 @@ The correlation functions are now very oscillatory, because of the Lorentzian pe ### So next, let us plot the correlation functions themselves: -```{code-cell} +```{code-cell} ipython3 def Mk(t, k, gamma, w0, beta): - """Calculate the Matsubara terms for a given t and k.""" - Om = np.sqrt(w0**2 - (gamma / 2) ** 2) + """ Calculate the Matsubara terms for a given t and k. """ + Om = np.sqrt(w0**2 - (gamma / 2)**2) Gamma = gamma / 2.0 ek = 2 * np.pi * k / beta return ( - (-2 * lam**2 * gamma / beta) - * ek - * np.exp(-ek * np.abs(t)) - / ( - ((Om + 1.0j * Gamma) ** 2 + ek**2) - * ((Om - 1.0j * Gamma) ** 2 + ek**2) - ) + (-2 * lam**2 * gamma / beta) * ek * np.exp(-ek * np.abs(t)) + / (((Om + 1.0j * Gamma)**2 + ek**2) * ((Om - 1.0j * Gamma)**2 + ek**2)) ) def c(t, Nk, lam, gamma, w0, beta): - """Calculate the correlation function for a vector of times, t.""" - Om = np.sqrt(w0**2 - (gamma / 2) ** 2) + """ Calculate the correlation function for a vector of times, t. """ + Om = np.sqrt(w0**2 - (gamma / 2)**2) Gamma = gamma / 2.0 - Cr = coth(beta * (Om + 1.0j * Gamma) / 2) * np.exp(1.0j * Om * t) + coth( - beta * (Om - 1.0j * Gamma) / 2 - ) * np.exp(-1.0j * Om * t) + Cr = ( + coth(beta * (Om + 1.0j * Gamma) / 2) * np.exp(1.0j * Om * t) + + coth(beta * (Om - 1.0j * Gamma) / 2) * np.exp(-1.0j * Om * t) + ) Ci = np.exp(-1.0j * Om * t) - np.exp(1.0j * Om * t) - return (lam**2 / (4 * Om)) * np.exp(-Gamma * np.abs(t)) * ( - Cr + Ci - ) + np.sum( - [Mk(t, k, gamma=gamma, w0=w0, beta=beta) for k in range(1, Nk + 1)], 0 + return ( + (lam**2 / (4 * Om)) * np.exp(-Gamma * np.abs(t)) * (Cr + Ci) + + np.sum([ + Mk(t, k, gamma=gamma, w0=w0, beta=beta) + for k in range(1, Nk + 1) + ], 0) ) def plot_correlation_function(): - """Plot the underdamped correlation function.""" + """ Plot the underdamped correlation function. """ t = np.linspace(0, 20, 1000) corr = c(t, Nk=3, lam=lam, gamma=gamma, w0=w0, beta=beta) fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - axes.plot(t, np.real(corr), "-", color="black", label="Re[C(t)]") - axes.plot(t, np.imag(corr), "-", color="red", label="Im[C(t)]") - axes.set_xlabel(r"t", fontsize=28) - axes.set_ylabel(r"C", fontsize=28) + axes.plot(t, np.real(corr), '-', color="black", label="Re[C(t)]") + axes.plot(t, np.imag(corr), '-', color="red", label="Im[C(t)]") + axes.set_xlabel(r't', fontsize=28) + axes.set_ylabel(r'C', fontsize=28) axes.legend(loc=0, fontsize=12) @@ -333,9 +326,9 @@ plot_correlation_function() It is useful to look at what the Matsubara contributions do to this spectral density. We see that they modify the real part around $t=0$: -```{code-cell} +```{code-cell} ipython3 def plot_matsubara_correlation_function_contributions(): - """Plot the underdamped correlation function.""" + """ Plot the underdamped correlation function. """ t = np.linspace(0, 20, 1000) M_Nk2 = np.sum( @@ -347,10 +340,10 @@ def plot_matsubara_correlation_function_contributions(): ) fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) - axes.plot(t, np.real(M_Nk2), "-", color="black", label="Re[M(t)] Nk=2") - axes.plot(t, np.real(M_Nk100), "--", color="red", label="Re[M(t)] Nk=100") - axes.set_xlabel(r"t", fontsize=28) - axes.set_ylabel(r"M", fontsize=28) + axes.plot(t, np.real(M_Nk2), '-', color="black", label="Re[M(t)] Nk=2") + axes.plot(t, np.real(M_Nk100), '--', color="red", label="Re[M(t)] Nk=100") + axes.set_xlabel(r't', fontsize=28) + axes.set_ylabel(r'M', fontsize=28) axes.legend(loc=0, fontsize=12) @@ -365,37 +358,32 @@ Next we calculate the exponents using the Matsubara decompositions. Here we spli The HEOM code will optimize these, and reduce the number of exponents when real and imaginary parts have the same exponent. This is clearly the case for the first term in the vkAI and vkAR lists. -```{code-cell} +```{code-cell} ipython3 ckAR, vkAR, ckAI, vkAI = underdamped_matsubara_params( - lam=lam, - gamma=gamma, - T=T, - nk=Nk, + lam=lam, gamma=gamma, T=T, nk=Nk, ) ``` -Having created the lists which specify the bath correlation functions, we create a `ExponentialBosonicEnvironment` from them and pass the bath to the `HEOMSolver` class. +Having created the lists which specify the bath correlation functions, we create an `ExponentialBosonicEnvironment` from them and pass the bath to the `HEOMSolver` class. The solver constructs the "right hand side" (RHS) determinining how the system and auxiliary density operators evolve in time. This can then be used to solve for dynamics or steady-state. Below we create the bath and solver and then solve for the dynamics by calling `.run(rho0, tlist)`. -```{code-cell} +```{code-cell} ipython3 with timer("RHS construction time"): - bath = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI) - HEOMMats = HEOMSolver(Hsys, (bath, Q), NC, options=options) + env = ExponentialBosonicEnvironment(ckAR, vkAR, ckAI, vkAI) + HEOMMats = HEOMSolver(Hsys, (env, Q), NC, options=options) with timer("ODE solver time"): resultMats = HEOMMats.run(rho0, tlist) ``` -```{code-cell} -plot_result_expectations( - [ - (resultMats, P11p, "b", "P11 Mats"), - (resultMats, P12p, "r", "P12 Mats"), - ] -); +```{code-cell} ipython3 +plot_result_expectations([ + (resultMats, P11p, 'b', "P11 Mats"), + (resultMats, P12p, 'r', "P12 Mats"), +]); ``` In practice, one would not perform this laborious expansion for the underdamped correlation function, because @@ -404,50 +392,46 @@ to perform this expansion is an useful skill. Below we show how to use this built-in functionality: -```{code-cell} +```{code-cell} ipython3 # Compare to built-in under-damped bath: with timer("RHS construction time"): - bath = UnderDampedEnvironment(lam=lam, gamma=gamma, w0=w0, T=T) - bath_approx = bath.approximate(method="matsubara", Nk=Nk) - HEOM_udbath = HEOMSolver(Hsys, (bath_approx, Q), NC, options=options) + env = UnderDampedEnvironment(lam=lam, gamma=gamma, w0=w0, T=T) + env_approx = env.approximate("matsubara", Nk=Nk) + HEOM_udbath = HEOMSolver(Hsys, (env_approx, Q), NC, options=options) with timer("ODE solver time"): result_udbath = HEOM_udbath.run(rho0, tlist) ``` -```{code-cell} -plot_result_expectations( - [ - (result_udbath, P11p, "b", "P11 (UnderDampedEnvironment)"), - (result_udbath, P12p, "r", "P12 (UnderDampedEnvironment)"), - (resultMats, P11p, "r--", "P11 Mats"), - (resultMats, P12p, "b--", "P12 Mats"), - ] -); +```{code-cell} ipython3 +plot_result_expectations([ + (result_udbath, P11p, 'b', "P11 (UnderDampedEnvironment)"), + (result_udbath, P12p, 'r', "P12 (UnderDampedEnvironment)"), +]); ``` The `UnderDampedEnvironment` class also allows us to easily evaluate analytical expressions for the power spectrum, correlation function, and spectral density. In the following plots, the solid lines are the exact expressions, and the dashed lines are based on our approximation of the correlation function with a finite number of exponents. In this case, there is an excellent agreement. -```{code-cell} +```{code-cell} ipython3 w = np.linspace(-3, 3, 1000) w2 = np.linspace(0, 3, 1000) t = np.linspace(0, 10, 1000) -bath_cf = bath.correlation_function(t) +env_cf = env.correlation_function(t) fig, axs = plt.subplots(2, 2) -axs[0, 0].plot(w, bath.power_spectrum(w)) -axs[0, 0].plot(w, bath_approx.power_spectrum(w), "--") +axs[0, 0].plot(w, env.power_spectrum(w)) +axs[0, 0].plot(w, env_approx.power_spectrum(w), "--") axs[0, 0].set(xlabel=r"$\omega$", ylabel=r"$S(\omega)$") -axs[0, 1].plot(w2, bath.spectral_density(w2)) -axs[0, 1].plot(w2, bath_approx.spectral_density(w2), "--") +axs[0, 1].plot(w2, env.spectral_density(w2)) +axs[0, 1].plot(w2, env_approx.spectral_density(w2), "--") axs[0, 1].set(xlabel=r"$\omega$", ylabel=r"$J(\omega)$") -axs[1, 0].plot(t, np.real(bath_cf)) -axs[1, 0].plot(t, np.real(bath_approx.correlation_function(t)), "--") +axs[1, 0].plot(t, np.real(env_cf)) +axs[1, 0].plot(t, np.real(env_approx.correlation_function(t)), "--") axs[1, 0].set(xlabel=r"$t$", ylabel=r"$C_{R}(t)$") -axs[1, 1].plot(t, np.imag(bath_cf)) -axs[1, 1].plot(t, np.imag(bath_approx.correlation_function(t)), "--") +axs[1, 1].plot(t, np.imag(env_cf)) +axs[1, 1].plot(t, np.imag(env_approx.correlation_function(t)), "--") axs[1, 1].set(xlabel=r"$t$", ylabel=r"$C_{I}(t)$") fig.tight_layout() @@ -460,26 +444,21 @@ plt.show() ### We can compare these results to those of the Bloch-Redfield solver in QuTiP: -```{code-cell} +```{code-cell} ipython3 with timer("ODE solver time"): resultBR = brmesolve( - Hsys, - rho0, - tlist, - a_ops=[[sigmaz(), bath]], - options=options, + Hsys, rho0, tlist, + a_ops=[[sigmaz(), env]], options=options, ) ``` -```{code-cell} -plot_result_expectations( - [ - (resultMats, P11p, "b", "P11 Mats"), - (resultMats, P12p, "r", "P12 Mats"), - (resultBR, P11p, "g--", "P11 Bloch Redfield"), - (resultBR, P12p, "g--", "P12 Bloch Redfield"), - ] -); +```{code-cell} ipython3 +plot_result_expectations([ + (resultMats, P11p, 'b', "P11 Mats"), + (resultMats, P12p, 'r', "P12 Mats"), + (resultBR, P11p, 'g--', "P11 Bloch Redfield"), + (resultBR, P12p, 'g--', "P12 Bloch Redfield"), +]); ``` ### Lastly, let us calculate the analytical steady-state result and compare all of the results: @@ -488,7 +467,7 @@ plot_result_expectations( The thermal state of a reaction coordinate (treating the environment as a single damped mode) should, at high temperatures and small gamma, tell us the steady-state: -```{code-cell} +```{code-cell} ipython3 dot_energy, dot_state = Hsys.eigenstates() deltaE = dot_energy[1] - dot_energy[0] @@ -521,7 +500,7 @@ P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag()) P11RC = expect(rhoss, P11RC) ``` -```{code-cell} +```{code-cell} ipython3 rcParams = { "axes.titlesize": 25, "axes.labelsize": 30, @@ -538,33 +517,27 @@ rcParams = { } ``` -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) with plt.rc_context(rcParams): plt.yticks([P11RC, 0.6, 1.0], [0.38, 0.6, 1]) - plot_result_expectations( - [ - (resultBR, P11p, "y-.", "Bloch-Redfield"), - (resultMats, P11p, "b", "Matsubara $N_k=3$"), - ], - axes=axes, - ) + plot_result_expectations([ + (resultBR, P11p, 'y-.', "Bloch-Redfield"), + (resultMats, P11p, 'b', "Matsubara $N_k=3$"), + ], axes=axes) axes.plot( - tlist, - [P11RC for t in tlist], - color="black", - linestyle="-.", - linewidth=2, + tlist, [P11RC for t in tlist], + color='black', linestyle="-.", linewidth=2, label="Thermal state", ) - axes.set_xlabel(r"$t \Delta$", fontsize=30) - axes.set_ylabel(r"$\rho_{11}$", fontsize=30) + axes.set_xlabel(r'$t \Delta$', fontsize=30) + axes.set_ylabel(r'$\rho_{11}$', fontsize=30) - axes.locator_params(axis="y", nbins=4) - axes.locator_params(axis="x", nbins=4) + axes.locator_params(axis='y', nbins=4) + axes.locator_params(axis='x', nbins=4) axes.legend(loc=0) @@ -573,23 +546,19 @@ with plt.rc_context(rcParams): ## About -```{code-cell} -qutip.about() +```{code-cell} ipython3 +about() ``` ## Testing This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 assert np.allclose( - expect(P11p, resultMats.states[-100:]), - P11RC, - rtol=1e-2, + expect(P11p, resultMats.states[-100:]), P11RC, rtol=1e-2, ) assert np.allclose( - expect(P11p, resultBR.states[-100:]), - P11RC, - rtol=1e-2, + expect(P11p, resultBR.states[-100:]), P11RC, rtol=1e-2, ) ``` From 24512ed852182853085430fdd68c5ebb7974d4a6 Mon Sep 17 00:00:00 2001 From: Paul Menczel Date: Thu, 24 Apr 2025 16:21:09 +0900 Subject: [PATCH 36/44] Converted to MYST format for consistency with other HEOM notebooks --- .../heom/heom-1a-spin-bath-model-basic.md | 131 +++++++++--------- 1 file changed, 67 insertions(+), 64 deletions(-) diff --git a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md index 102ade46..292ff8aa 100644 --- a/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md +++ b/tutorials-v5/heom/heom-1a-spin-bath-model-basic.md @@ -1,19 +1,19 @@ --- -jupyter: - jupytext: - text_representation: - extension: .md - format_name: markdown - format_version: '1.3' - jupytext_version: 1.17.0 - kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.17.0 +kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 --- # HEOM 1a: Spin-Bath model (introduction) ++++ ## Introduction @@ -73,10 +73,11 @@ density is given by: Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. ++++ ## Setup -```python +```{code-cell} ipython3 import contextlib import time @@ -96,18 +97,17 @@ from qutip.solver.heom import HEOMSolver, HSolverDL %matplotlib inline ``` - ## Helper functions Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: -```python +```{code-cell} ipython3 def cot(x): """Vectorized cotangent of x.""" return 1.0 / np.tan(x) ``` -```python +```{code-cell} ipython3 def dl_matsubara_params(lam, gamma, T, nk): """Calculation of the real and imaginary expansions of the Drude-Lorenz correlation functions. @@ -128,7 +128,7 @@ def dl_matsubara_params(lam, gamma, T, nk): return ckAR, vkAR, ckAI, vkAI ``` -```python +```{code-cell} ipython3 def plot_result_expectations(plots, axes=None): """Plot the expectation values of operators as functions of time. @@ -156,7 +156,7 @@ def plot_result_expectations(plots, axes=None): return fig ``` -```python +```{code-cell} ipython3 @contextlib.contextmanager def timer(label): """Simple utility for timing functions: @@ -170,7 +170,7 @@ def timer(label): print(f"{label}: {end - start}") ``` -```python +```{code-cell} ipython3 # Default solver options: default_options = { @@ -187,19 +187,19 @@ default_options = { And let us set up the system Hamiltonian, bath and system measurement operators: -```python +```{code-cell} ipython3 # Defining the system Hamiltonian eps = 0.5 # Energy of the 2-level system. Del = 1.0 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` -```python +```{code-cell} ipython3 # Initial state of the system. rho0 = basis(2, 0) * basis(2, 0).dag() ``` -```python +```{code-cell} ipython3 # System-bath coupling (Drude-Lorentz spectral density) Q = sigmaz() # coupling operator @@ -217,7 +217,7 @@ Nk = 2 # terms in the Matsubara expansion of the correlation function tlist = np.linspace(0, 50, 1000) ``` -```python +```{code-cell} ipython3 # Define some operators with which we will measure the system # 1,1 element of density matrix - corresonding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -230,7 +230,7 @@ P12p = basis(2, 0) * basis(2, 1).dag() Now we are ready to begin. Let's look at the shape of the spectral density given the bath parameters we defined above: -```python +```{code-cell} ipython3 def plot_spectral_density(): """Plot the Drude-Lorentz spectral density""" w = np.linspace(0, 5, 1000) @@ -252,7 +252,7 @@ The HEOM code will optimize these, and reduce the number of exponents when real and imaginary parts have the same exponent. This is clearly the case for the first term in the vkAI and vkAR lists. -```python +```{code-cell} ipython3 ckAR, vkAR, ckAI, vkAI = dl_matsubara_params(nk=Nk, lam=lam, gamma=gamma, T=T) ``` @@ -266,7 +266,7 @@ to solve for dynamics or steady-state. Below we create the bath and solver and then solve for the dynamics by calling `.run(rho0, tlist)`. -```python +```{code-cell} ipython3 options = {**default_options} with timer("RHS construction time"): @@ -277,7 +277,7 @@ with timer("ODE solver time"): resultMats = HEOMMats.run(rho0, tlist) ``` -```python +```{code-cell} ipython3 plot_result_expectations( [ (resultMats, P11p, "b", "P11 Mats"), @@ -294,7 +294,7 @@ baths for other spectral densities. Below we show how to use this built-in functionality: -```python +```{code-cell} ipython3 # Compare to built-in Drude-Lorentz bath: with timer("RHS construction time"): @@ -308,7 +308,7 @@ with timer("ODE solver time"): result_dlbath = HEOM_dlbath.run(rho0, tlist) ``` -```python +```{code-cell} ipython3 plot_result_expectations( [ (result_dlbath, P11p, "b", "P11 (DrudeLorentzEnvironment)"), @@ -321,7 +321,7 @@ The `DrudeLorentzEnvironment` class also allows us to easily obtain the power sp The `DrudeLorentzEnvironment` computes the exact correlation function using the Pade approximation. The number of terms to use defaults to $10$, but when simulating low temperatures, $10$ Pade exponents may noy be enough. More details about the Pade approximation are provided below. Next we show how to use this built-in functionality: -```python +```{code-cell} ipython3 w = np.linspace(-10, 20, 1000) w2 = np.linspace(0, 20, 1000) @@ -348,7 +348,7 @@ We also provide a legacy class, `HSolverDL`, which calculates the Drude-Lorentz correlation functions automatically, to be backwards compatible with the previous HEOM solver in QuTiP: -```python +```{code-cell} ipython3 # Compare to legacy class: # The legacy class performs the above collation of coefficients automatically, @@ -361,7 +361,7 @@ with timer("ODE solver time"): resultLegacy = HEOMlegacy.run(rho0, tlist) # normal 115 ``` -```python +```{code-cell} ipython3 plot_result_expectations( [ (resultLegacy, P11p, "b", "P11 Legacy"), @@ -370,7 +370,6 @@ plot_result_expectations( ); ``` - Another legacy class kept for convenience is the `DrudeLorentzBath`. The code ```python dlenv = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T) @@ -382,7 +381,8 @@ that we used above is equivalent to the following code: dlbath = DrudeLorentzBath(Q, lam=lam, gamma=gamma, T=T, Nk=Nk) HEOM_dlbath = HEOMSolver(Hsys, dlbath, NC, options=options) ``` - + ++++ ## Ishizaki-Tanimura Terminator @@ -412,7 +412,7 @@ This is clearer if we plot the correlation function with a large number of Matsubara terms. To create the plot, we use the utility function of the `DrudeLorentzEnvironment` class mentioned above. -```python +```{code-cell} ipython3 def plot_correlation_expansion_divergence(): """We plot the correlation function with a large number of Matsubara terms to show that the real part is slowly diverging at t = 0. @@ -448,7 +448,7 @@ plot_correlation_expansion_divergence() Let us evaluate the result including this Ishizaki-Tanimura terminator: -```python +```{code-cell} ipython3 # Run HEOM solver including the Ishizaki-Tanimura terminator # Notes: @@ -487,7 +487,7 @@ with timer("ODE solver time"): resultMatsT = HEOMMatsT.run(rho0, tlist) ``` -```python +```{code-cell} ipython3 plot_result_expectations( [ (resultMatsT, P11p, "b", "P11 Mats + Term"), @@ -498,7 +498,7 @@ plot_result_expectations( Or using the built-in Drude-Lorentz environment we can write simply: -```python +```{code-cell} ipython3 options = {**default_options, "rtol": 1e-14, "atol": 1e-14} with timer("RHS construction time"): @@ -512,7 +512,7 @@ with timer("ODE solver time"): result_dlbath_T = HEOM_dlbath_T.run(rho0, tlist) ``` -```python +```{code-cell} ipython3 plot_result_expectations([ (result_dlbath_T, P11p, "b", "P11 Mats (DrudeLorentzEnvironment + Term)"), (result_dlbath_T, P12p, "r", "P12 Mats (DrudeLorentzEnvironment + Term)"), @@ -521,7 +521,7 @@ plot_result_expectations([ We can compare the solution obtained from the QuTiP Bloch-Redfield solver: -```python +```{code-cell} ipython3 options = {**default_options} with timer("ODE solver time"): @@ -530,7 +530,7 @@ with timer("ODE solver time"): ) ``` -```python +```{code-cell} ipython3 plot_result_expectations( [ (resultMats, P11p, "b", "P11 Mats"), @@ -545,11 +545,12 @@ plot_result_expectations( ## Padé decomposition ++++ The Matsubara decomposition is not the only option. We can also use the faster-converging Pade decomposition. -```python +```{code-cell} ipython3 def deltafun(j, k): if j == k: return 1.0 @@ -700,7 +701,7 @@ ax1.set_ylabel(r"Error") ax1.legend(); ``` -```python +```{code-cell} ipython3 # put pade parameters in lists for heom solver ckAR = [np.real(eta) + 0j for eta in etapLP] ckAI = [np.imag(etapLP[0]) + 0j] @@ -717,7 +718,7 @@ with timer("ODE solver time"): resultPade = HEOMPade.run(rho0, tlist) ``` -```python +```{code-cell} ipython3 plot_result_expectations( [ (resultMats, P11p, "b", "P11 Mats"), @@ -740,7 +741,7 @@ Padé decomposition approximation and its terminator (although the terminator does not provide much improvement here,because the Padé expansion already fits the correlation function well): -```python +```{code-cell} ipython3 options = {**default_options, "rtol": 1e-14, "atol": 1e-14} with timer("RHS construction time"): @@ -752,7 +753,7 @@ with timer("ODE solver time"): result_dlpbath_T = HEOM_dlpbath_T.run(rho0, tlist) ``` -```python +```{code-cell} ipython3 plot_result_expectations( [ (result_dlpbath_T, P11p, "b", "P11 Padé + Term"), @@ -771,7 +772,7 @@ manually below, and then show how to do it with the built-in tools. For the manual fit we first we collect a large sum of Pade terms for many time steps: -```python +```{code-cell} ipython3 tlist2 = np.linspace(0, 2, 10000) corr_100_t10k = dlenv.correlation_function(tlist2, Nk=100) @@ -785,7 +786,7 @@ corrRMats = np.real(dlenv_approx.correlation_function(tlist2)) We then fit this sum with standard least-squares approach: -```python +```{code-cell} ipython3 def wrapper_fit_func(x, N, args): """ Fit function wrapper that unpacks its arguments. """ x = np.array(x) @@ -837,7 +838,7 @@ def fitter(ans, tlist, k): return (a, b) ``` -```python +```{code-cell} ipython3 kR = 4 # number of exponents to use for real part poptR = [] with timer("Correlation (real) fitting time"): @@ -853,7 +854,7 @@ with timer("Correlation (imaginary) fitting time"): And plot the results of the fits: -```python +```{code-cell} ipython3 # Define line styles and colors linestyles = ["-", "--", "-.", ":", (0, (3, 1, 1, 1)), (0, (5, 1))] colors = ["blue", "green", "purple", "orange", "red", "brown"] @@ -901,7 +902,7 @@ fig.suptitle( plt.show() ``` -```python +```{code-cell} ipython3 # Set the exponential coefficients from the fit parameters ckAR1 = poptR[-1][0] @@ -917,7 +918,7 @@ vkAI1 = poptI[-1][1] vkAI = [-x + 0j for x in vkAI1] ``` -```python +```{code-cell} ipython3 # overwrite imaginary fit with analytical value (not much reason to use the # fit for this) @@ -925,7 +926,7 @@ ckAI = [lam * gamma * (-1.0) + 0.0j] vkAI = [gamma + 0.0j] ``` -```python +```{code-cell} ipython3 options = {**default_options} NC = 4 @@ -938,7 +939,7 @@ with timer("ODE solver time"): resultFit = HEOMFit.run(rho0, tlist) ``` -```python +```{code-cell} ipython3 plot_result_expectations( [ (resultFit, P11p, "b", "P11 Fit"), @@ -951,7 +952,7 @@ Now we use the built-in fitting functions. The `BosonicEnvironment` class includ method that performs this fit automatically. More information on how the built-in functios work can be found in `HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions` -```python +```{code-cell} ipython3 max_val = dlenv.correlation_function(0).real guess = [max_val / 3, 0, 0, 0] lower = [-max_val, -np.inf, -np.inf, -np.inf] @@ -965,15 +966,15 @@ The `approximate("cf", ...)` method outputs an `ExponentialBosonicEnvironment` o which contains a decaying exponential representation of the original environment , and a dictionary containing all information related to the fit. The dictionary contains a summary of the fir information and the normalized -root mean squared error, which assesses how good the fit is. +root mean squared error, which assesses how good the fit is. -```python +```{code-cell} ipython3 print(fitinfo["summary"]) ``` We can then compare the result of the built-in fit with the manual fit -```python +```{code-cell} ipython3 # Create a single figure with two subplots fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6)) @@ -1000,7 +1001,7 @@ plt.tight_layout(rect=[0, 0.03, 1, 0.95]) plt.show() ``` -```python +```{code-cell} ipython3 options = {**default_options} with timer("RHS construction time"): @@ -1010,7 +1011,7 @@ with timer("ODE solver time"): resultFit_2 = HEOMFit_2.run(rho0, tlist) ``` -```python +```{code-cell} ipython3 plot_result_expectations( [ (resultFit, P11p, "b", "P11 Fit"), @@ -1023,13 +1024,14 @@ plot_result_expectations( ## A reaction coordinate approach ++++ Here we construct a reaction coordinate inspired model to capture the steady-state behavior, and compare to the HEOM prediction. This result is more accurate for narrow spectral densities. We will use the population and coherence from this cell in our final plot below. -```python +```{code-cell} ipython3 dot_energy, dot_state = Hsys.eigenstates() deltaE = dot_energy[1] - dot_energy[0] @@ -1076,7 +1078,7 @@ P11RC = tensor(qeye(NRC), basis(2, 0) * basis(2, 0).dag()) Finally, let's plot all of our different results to see how they shape up against each other. -```python +```{code-cell} ipython3 rcParams = { "axes.titlesize": 25, "axes.labelsize": 30, @@ -1093,7 +1095,7 @@ rcParams = { } ``` -```python +```{code-cell} ipython3 fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 15)) with plt.rc_context(rcParams): @@ -1174,10 +1176,11 @@ with plt.rc_context(rcParams): And that's the end of a detailed first dive into modeling bosonic environments with the HEOM. ++++ ## About -```python +```{code-cell} ipython3 about() ``` @@ -1185,7 +1188,7 @@ about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```python +```{code-cell} ipython3 # Check P11p assert np.allclose( expect(P11p, resultMatsT.states), From 59656ef3e5f2b7db11ea4d862ce7a86464251d7a Mon Sep 17 00:00:00 2001 From: Paul Menczel Date: Thu, 24 Apr 2025 16:34:50 +0900 Subject: [PATCH 37/44] HEOM 1e notebook final pass --- .../heom-1d-spin-bath-model-ohmic-fitting.md | 103 +++++----- .../heom-1e-spin-bath-model-pure-dephasing.md | 183 ++++++++---------- 2 files changed, 133 insertions(+), 153 deletions(-) diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md index 1108f982..dd75ec1a 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md @@ -1,15 +1,14 @@ --- jupytext: - formats: ipynb,md:myst text_representation: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.5 + jupytext_version: 1.17.0 kernelspec: - display_name: qutip-tutorials - language: python name: python3 + display_name: Python 3 (ipykernel) + language: python --- # HEOM 1d: Spin-Bath model, fitting of spectrum and correlation functions @@ -39,7 +38,7 @@ In each case we will use the fit parameters to determine the correlation functio ## Setup -```{code-cell} +```{code-cell} ipython3 import numpy as np import qutip from matplotlib import pyplot as plt @@ -66,7 +65,7 @@ Let us set up the system Hamiltonian, bath and system measurement operators: ### System Hamiltonian -```{code-cell} +```{code-cell} ipython3 # Defining the system Hamiltonian eps = 0 # Energy of the 2-level system. Del = 0.2 # Tunnelling term @@ -76,7 +75,7 @@ rho0 = basis(2, 0) * basis(2, 0).dag() ### System measurement operators -```{code-cell} +```{code-cell} ipython3 # Define some operators with which we will measure the system # 1,1 element of density matrix - corresonding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -112,7 +111,7 @@ The corresponding spectral density for the Ohmic case is: J(\omega) = \omega \alpha e^{- \frac{\omega}{\omega_c}} \end{equation} -```{code-cell} +```{code-cell} ipython3 def ohmic_correlation(t, alpha, wc, beta, s=1): """The Ohmic bath correlation function as a function of t (and the bath parameters). @@ -132,7 +131,7 @@ def ohmic_correlation(t, alpha, wc, beta, s=1): ) ``` -```{code-cell} +```{code-cell} ipython3 def ohmic_spectral_density(w, alpha, wc): """The Ohmic bath spectral density as a function of w (and the bath parameters). @@ -140,7 +139,7 @@ def ohmic_spectral_density(w, alpha, wc): return w * alpha * np.e ** (-w / wc) ``` -```{code-cell} +```{code-cell} ipython3 def ohmic_power_spectrum(w, alpha, wc, beta): """The Ohmic bath power spectrum as a function of w (and the bath parameters). @@ -157,7 +156,7 @@ def ohmic_power_spectrum(w, alpha, wc, beta): Finally, let's set the bath parameters we will work with and write down some measurement operators: -```{code-cell} +```{code-cell} ipython3 Q = sigmaz() alpha = 3.25 T = 0.5 @@ -167,7 +166,7 @@ s = 1 And set the cut-off for the HEOM hierarchy: -```{code-cell} +```{code-cell} ipython3 # HEOM parameters: # The max_depth defaults to 5 so that the notebook executes more @@ -222,7 +221,7 @@ Before obtaining exponential approximations, we first need to construct a using the correlation function and the power spectrum. For this example we will use the Ohmic Spectral density we defined above -```{code-cell} +```{code-cell} ipython3 w = np.linspace(0, 25, 20000) J = ohmic_spectral_density(w, alpha, wc) ``` @@ -232,7 +231,7 @@ create enviroments from arbitrary spectral densities, correlation functions, or power spectrums. Below we show how to construct a `BosonicEnvironment` from a user specified function or array -```{code-cell} +```{code-cell} ipython3 # From an array sd_env = BosonicEnvironment.from_spectral_density(J=J, wlist=w) ``` @@ -242,13 +241,13 @@ correlation function because the temperature of the environment has not been specified. So the `BosonicEnvironment` is not fully characterized by the parameters provided -```{code-cell} +```{code-cell} ipython3 sd_env.power_spectrum(w) ``` If we want access to these properties we need to provide the Temperature at Initialization -```{code-cell} +```{code-cell} ipython3 # From an array sd_env = BosonicEnvironment.from_spectral_density(J=J, wlist=w, T=T) ``` @@ -256,7 +255,7 @@ sd_env = BosonicEnvironment.from_spectral_density(J=J, wlist=w, T=T) Now our bosonic environment can compute the Power Spectrum of the spectral density provided -```{code-cell} +```{code-cell} ipython3 # Here we avoid w=0 np.allclose( sd_env.power_spectrum(w[1:]), ohmic_power_spectrum(w[1:], alpha, wc, 1 / T) @@ -266,7 +265,7 @@ np.allclose( Specifying the Temperature also gives the `BosonicEnvironment` access to the correlation function -```{code-cell} +```{code-cell} ipython3 tlist = np.linspace(0, 10, 500) plt.plot( tlist, @@ -303,14 +302,14 @@ WMax needs to be specified, wMax is the cutoff frequency for which the spectral density, or power spectrum has effectively decayed to zero, after this value the function can be considered to be essentialy zero -```{code-cell} +```{code-cell} ipython3 # From a function sd_env2 = BosonicEnvironment.from_spectral_density( ohmic_spectral_density, T=T, wMax=10 * wc, args={"alpha": alpha, "wc": wc} ) ``` -```{code-cell} +```{code-cell} ipython3 tlist = np.linspace(0, 10, 500) plt.plot(tlist, sd_env2.correlation_function(tlist).real) plt.plot(tlist, ohmic_correlation(tlist, alpha, wc, 1 / T).real, "--") @@ -356,19 +355,19 @@ is reached or the maximum number allowed `Nmax` is reached. The default target is a normalized root mean squared error of $5\times 10^{-6}$, if set to None the fit is performed only with the maximum number of exponents specified -```{code-cell} +```{code-cell} ipython3 bath, fitinfo = sd_env.approximate("sd", w, Nmax=4) ``` To obtain an overview of the results of the fit we may take a look at the summary from the ``fitinfo`` -```{code-cell} +```{code-cell} ipython3 print(fitinfo["summary"]) ``` We may see how the number of exponents chosen affects the fit since the approximated functions are available: -```{code-cell} +```{code-cell} ipython3 fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5)) ax1.plot(w, J, label="Original spectral density") @@ -387,7 +386,7 @@ plt.show() Here we see a surprisingly large discrepancy in our approximated or effective spectral density. This happens because we are not using enough exponentials from each of the underdamped modes to have an appropiate fit. All modes have the same number of exponents, when not specified it defaults to $1$ which is not enough to model a bath with the temperature considered, let us repeat this with a higher number of exponents. -```{code-cell} +```{code-cell} ipython3 bath, fitinfo = sd_env.approximate("sd", w, Nmax=4, Nk=3) fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5)) @@ -412,7 +411,7 @@ Since the number of exponents increases simulation time one should go with the l Let's take a closer look at our last fit by plotting the contribution of each term of the fit: -```{code-cell} +```{code-cell} ipython3 # Plot the components of the fit separately: plt.rcParams["font.size"] = 25 plt.rcParams["figure.figsize"] = (10, 5) @@ -465,13 +464,13 @@ def _sd_fit_model(wlist, a, b, c): plot_fit(_sd_fit_model, J, w, lam, gamma, w0) ``` -```{code-cell} +```{code-cell} ipython3 plot_fit_components(_sd_fit_model, J, w, lam, gamma, w0) ``` And let's also compare the power spectrum of the fit and the analytical spectral density: -```{code-cell} +```{code-cell} ipython3 def plot_power_spectrum(alpha, wc, beta, save=True): """Plot the power spectrum of a fit against the actual power spectrum.""" w = np.linspace(-10, 10, 50000) @@ -494,7 +493,7 @@ plot_power_spectrum(alpha, wc, 1 / T, save=False) Now if we want to see the systems's behaviour as we change the number of terms in the fit, we may use this auxiliary function. -```{code-cell} +```{code-cell} ipython3 def generate_spectrum_results(Q, N, Nk, max_depth): """Run the HEOM with the given bath parameters and and return the results of the evolution. @@ -526,7 +525,7 @@ def generate_spectrum_results(Q, N, Nk, max_depth): return results_spectral_fit ``` -```{code-cell} +```{code-cell} ipython3 def plot_result_expectations(plots, axes=None): """Plot the expectation values of operators as functions of time. @@ -567,7 +566,7 @@ def plot_result_expectations(plots, axes=None): Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow. -```{code-cell} +```{code-cell} ipython3 # # Generate results for different number of lorentzians in fit: @@ -589,7 +588,7 @@ plot_result_expectations( ); ``` -```{code-cell} +```{code-cell} ipython3 # generate results for different number of Matsubara terms per Lorentzian # for max number of Lorentzians: @@ -612,7 +611,7 @@ plot_result_expectations( ); ``` -```{code-cell} +```{code-cell} ipython3 # Generate results for different depths: Nc_list = range(2, max_depth) @@ -635,7 +634,7 @@ plot_result_expectations( #### We now combine the fitting and correlation function data into one large plot. Here we define a function to plot everything together -```{code-cell} +```{code-cell} ipython3 def gen_plots(fs, w, J, t, C, w2, S): def plot_cr_fit_vs_actual(t, C, func, axes): """Plot the C_R(t) fit.""" @@ -758,7 +757,7 @@ def gen_plots(fs, w, J, t, C, w2, S): #### And finally plot everything together -```{code-cell} +```{code-cell} ipython3 t = np.linspace(0, 15, 1000) C = ohmic_correlation(t, alpha, wc, 1 / T) w2 = np.concatenate((-np.linspace(10, 1e-2, 100), np.linspace(1e-2, 10, 100))) @@ -802,7 +801,7 @@ When full_ansatz is True. the ansatz used corresponds to \Bigr]. \end{align} -```{code-cell} +```{code-cell} ipython3 def generate_corr_results(N, max_depth): tlist = np.linspace(0, 30 * np.pi / Del, 600) bath_corr, fitinfo = sd_env.approximate( @@ -831,7 +830,7 @@ results_corr_fit_pk = [ ] ``` -```{code-cell} +```{code-cell} ipython3 plot_result_expectations( [ ( @@ -845,7 +844,7 @@ plot_result_expectations( ); ``` -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) plot_result_expectations( @@ -888,7 +887,7 @@ axes.legend(loc=0, fontsize=20); As the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced quickly by using the OhmicBath class. This allows for rapid implementation of fitted ohmic baths via the correlation function or spectral density -```{code-cell} +```{code-cell} ipython3 obs = OhmicEnvironment(T, alpha, wc, s=1) tlist = np.linspace(0, 30 * np.pi / Del, 600) ``` @@ -897,7 +896,7 @@ Just like the other `BosonicEnvironment` we can obtain a decaying exponential representation of the environment via the `approximate`, let us do the same methods we explored before -```{code-cell} +```{code-cell} ipython3 Obath, fitinfo = obs.approximate( method="cf", tlist=tlist, Nr_max=4, Ni_max=4, maxfev=1e9, target_rmse=None ) @@ -911,7 +910,7 @@ HEOM_ohmic_corr_fit = HEOMSolver( results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 Obath2, fitinfo = obs.approximate(method="sd", wlist=w, Nmax=4, Nk=3) print(fitinfo["summary"]) HEOM_ohmic_sd_fit = HEOMSolver( @@ -976,11 +975,11 @@ we group it with other methods The method is available via `approximate` passing "prony" as method. Compared to the other approaches showed so far. The Prony based methods, shine on their simplicity no information needs to be known about the function, and one just needs to provide the sampling points, and the Number of Exponents one desires -```{code-cell} +```{code-cell} ipython3 tlist2 = np.linspace(0, 40, 100) ``` -```{code-cell} +```{code-cell} ipython3 pbath, fitinfo = obs.approximate("prony", tlist2, Nr=4) print(fitinfo["summary"]) HEOM_ohmic_prony_fit = HEOMSolver( @@ -995,7 +994,7 @@ results_ohmic_prony_fit = HEOM_ohmic_prony_fit.run(rho0, tlist) Similar to how we approximated via prony we can use ESPRIT, the main difference between both methods lies in the construction of the pencil matrix -```{code-cell} +```{code-cell} ipython3 esbath, fitinfo = obs.approximate("esprit", tlist2, Nr=4, separate=False) print(fitinfo["summary"]) HEOM_ohmic_es_fit = HEOMSolver( @@ -1036,13 +1035,13 @@ Which allows us to identify this method works best when the sampling points provided are in the logarithmic scale -```{code-cell} +```{code-cell} ipython3 wlist = np.concatenate((-np.logspace(3, -8, 3500), np.logspace(-8, 3, 3500))) aaabath, fitinfo = obs.approximate("aaa", wlist, Nmax=8, tol=1e-15) print(fitinfo["summary"]) ``` -```{code-cell} +```{code-cell} ipython3 HEOM_ohmic_aaa_fit = HEOMSolver( Hsys, (aaabath, Q), @@ -1065,12 +1064,12 @@ we fit the power spectrum to a function of the form $$S(\omega) = \sum_{k=1}^{N}\frac{2(a_k c_k + b_k (d_k - \omega))} {(\omega - d_k)^2 + c_k^2}= 2 \Re \left(\sum_{k} \frac{c_{k}}{\nu_{k}-i \omega} \right)$$ -```{code-cell} +```{code-cell} ipython3 psbath, fitinfo = obs.approximate("ps", w2, Nmax=4) print(fitinfo["summary"]) ``` -```{code-cell} +```{code-cell} ipython3 HEOM_ohmic_ps_fit = HEOMSolver( Hsys, (psbath, Q), @@ -1098,7 +1097,7 @@ recommended. ESPIRA I -```{code-cell} +```{code-cell} ipython3 tlist4 = np.linspace(0, 20, 1000) espibath, fitinfo = obs.approximate("espira-I", tlist4, Nr=4) print(fitinfo["summary"]) @@ -1113,7 +1112,7 @@ results_ohmic_espira_fit = HEOM_ohmic_espira_fit.run(rho0, tlist) ESPIRA-II -```{code-cell} +```{code-cell} ipython3 tlist4 = np.linspace(0, 20, 1000) espibath2, fitinfo = obs.approximate( "espira-II", tlist4, Nr=4, Ni=4, separate=True @@ -1130,7 +1129,7 @@ results_ohmic_espira2_fit = HEOM_ohmic_espira_fit2.run(rho0, tlist) Finally we plot the dynamics obtained by the different methods -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) plot_result_expectations( @@ -1171,7 +1170,7 @@ axes.set_yscale("log") ## About -```{code-cell} +```{code-cell} ipython3 qutip.about() ``` @@ -1179,7 +1178,7 @@ qutip.about() This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 assert np.allclose( expect(P11p, results_spectral_fit_pk[2].states), expect(P11p, results_spectral_fit_pk[3].states), diff --git a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md index 17e0028d..af1fb51d 100644 --- a/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md +++ b/tutorials-v5/heom/heom-1e-spin-bath-model-pure-dephasing.md @@ -1,15 +1,14 @@ --- jupytext: - formats: ipynb,md:myst text_representation: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.5 + jupytext_version: 1.17.0 kernelspec: - display_name: qutip-tutorials - language: python name: python3 + display_name: Python 3 (ipykernel) + language: python --- # HEOM 1e: Spin-Bath model (pure dephasing) @@ -24,7 +23,7 @@ In this example we show the evolution of a single two-level system in contact wi The Bosonic environment is implicitly assumed to obey a particular Hamiltonian (see paper), the parameters of which are encoded in the spectral density, and subsequently the free-bath correlation functions. -In the example below we show how to model the overdamped Drude-Lorentz Spectral Density, commonly used with the HEOM. We show how to do the Matsubara and Pade analytical decompositions, as well as how to fit the latter with a finite set of approximate exponentials. This differs from examble 1a in that we assume that the system and coupling parts of the Hamiltonian commute, hence giving an analytically solvable ''pure dephasing'' model. This is a useful example to look at when introducing other approximations (e.g., fitting of correlation functions) to check for validity/convergence against the analytical results. (Note that, generally, for the fitting examples, the pure dephasing model is the 'worst possible case'. +In the example below we show how to model the overdamped Drude-Lorentz Spectral Density, commonly used with the HEOM. We show how to do the Matsubara and Pade analytical decompositions, as well as how to fit the latter with a finite set of approximate exponentials. This differs from example 1a in that we assume that the system and coupling parts of the Hamiltonian commute, hence giving an analytically solvable ''pure dephasing'' model. This is a useful example to look at when introducing other approximations (e.g., fitting of correlation functions) to check for validity/convergence against the analytical results. (Note that, generally, for the fitting examples, the pure dephasing model is the 'worst possible case'.) ### Drude-Lorentz spectral density @@ -66,15 +65,15 @@ Note that in the above, and the following, we set $\hbar = k_\mathrm{B} = 1$. ## Setup -```{code-cell} +```{code-cell} ipython3 import contextlib import time import numpy as np -import qutip import scipy from matplotlib import pyplot as plt -from qutip import basis, expect, liouvillian, sigmax, sigmaz + +from qutip import about, basis, expect, liouvillian, sigmax, sigmaz from qutip.core.environment import DrudeLorentzEnvironment, system_terminator from qutip.solver.heom import HEOMSolver @@ -85,23 +84,23 @@ from qutip.solver.heom import HEOMSolver Let's define some helper functions for calculating correlation function expansions, plotting results and timing how long operations take: -```{code-cell} +```{code-cell} ipython3 def cot(x): - """Vectorized cotangent of x.""" + """ Vectorized cotangent of x. """ return 1.0 / np.tan(x) def coth(x): - """Vectorized hyperbolic cotangent of x.""" + """ Vectorized hyperbolic cotangent of x. """ return 1.0 / np.tanh(x) ``` -```{code-cell} +```{code-cell} ipython3 def plot_result_expectations(plots, axes=None): - """Plot the expectation values of operators as functions of time. + """ Plot the expectation values of operators as functions of time. - Each plot in plots consists of (solver_result, measurement_operation, - color, label). + Each plot in plots consists of (solver_result, measurement_operation, + color, label). """ if axes is None: fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) @@ -128,13 +127,13 @@ def plot_result_expectations(plots, axes=None): return fig ``` -```{code-cell} +```{code-cell} ipython3 @contextlib.contextmanager def timer(label): - """Simple utility for timing functions: + """ Simple utility for timing functions: - with timer("name"): - ... code to time ... + with timer("name"): + ... code to time ... """ start = time.time() yield @@ -142,10 +141,9 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} +```{code-cell} ipython3 # Solver options: - options = { "nsteps": 15000, "store_states": True, @@ -164,14 +162,14 @@ And let us set up the system Hamiltonian, bath and system measurement operators: Here we set $H_{sys}=0$, which means the interaction Hamiltonian and the system Hamiltonian commute, and we can compare the numerical results to a known analytical one. We could in principle keep $\epsilon \neq 0$, but it just introduces fast system oscillations, so it is more convenient to set it to zero. -```{code-cell} +```{code-cell} ipython3 # Defining the system Hamiltonian eps = 0.0 # Energy of the 2-level system. Del = 0.0 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() ``` -```{code-cell} +```{code-cell} ipython3 # System-bath coupling (Drude-Lorentz spectral density) Q = sigmaz() # coupling operator @@ -192,7 +190,7 @@ Nk = 3 tlist = np.linspace(0, 50, 1000) ``` -```{code-cell} +```{code-cell} ipython3 # Define some operators with which we will measure the system # 1,1 element of density matrix - corresponding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -203,7 +201,7 @@ P12p = basis(2, 0) * basis(2, 1).dag() To get a non-trivial result we prepare the initial state in a superposition, and see how the bath destroys the coherence. -```{code-cell} +```{code-cell} ipython3 # Initial state of the system. psi = (basis(2, 0) + basis(2, 1)).unit() rho0 = psi * psi.dag() @@ -212,13 +210,13 @@ rho0 = psi * psi.dag() We then define our environment, from which all the different simulations will be obtained -```{code-cell} +```{code-cell} ipython3 env = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T, Nk=Nk) ``` ## Simulation 1: Matsubara decomposition, not using Ishizaki-Tanimura terminator -```{code-cell} +```{code-cell} ipython3 with timer("RHS construction time"): env_mats = env.approximate(method="matsubara", Nk=Nk) HEOMMats = HEOMSolver(Hsys, (env_mats, Q), NC, options=options) @@ -227,19 +225,17 @@ with timer("ODE solver time"): resultMats = HEOMMats.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 # Plot the results so far -plot_result_expectations( - [ - (resultMats, P11p, "b", "P11 Matsubara"), - (resultMats, P12p, "r", "P12 Matsubara"), - ] -); +plot_result_expectations([ + (resultMats, P11p, 'b', "P11 Matsubara"), + (resultMats, P12p, 'r', "P12 Matsubara"), +]); ``` ## Simulation 2: Matsubara decomposition (including terminator) -```{code-cell} +```{code-cell} ipython3 with timer("RHS construction time"): env_mats, delta = env.approximate( method="matsubara", Nk=Nk, compute_delta=True @@ -251,23 +247,21 @@ with timer("ODE solver time"): resultMatsT = HEOMMatsT.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 # Plot the results -plot_result_expectations( - [ - (resultMats, P11p, "b", "P11 Matsubara"), - (resultMats, P12p, "r", "P12 Matsubara"), - (resultMatsT, P11p, "r--", "P11 Matsubara and terminator"), - (resultMatsT, P12p, "b--", "P12 Matsubara and terminator"), - ] -); +plot_result_expectations([ + (resultMats, P11p, 'b', "P11 Matsubara"), + (resultMats, P12p, 'r', "P12 Matsubara"), + (resultMatsT, P11p, 'b--', "P11 Matsubara and terminator"), + (resultMatsT, P12p, 'r--', "P12 Matsubara and terminator"), +]); ``` ## Simulation 3: Pade decomposition As in example 1a, we can compare to Pade and Fitting approaches. -```{code-cell} +```{code-cell} ipython3 with timer("RHS construction time"): env_pade = env.approximate(method="pade", Nk=Nk) HEOMPade = HEOMSolver(Hsys, (env_pade, Q), NC, options=options) @@ -276,27 +270,25 @@ with timer("ODE solver time"): resultPade = HEOMPade.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 # Plot the results -plot_result_expectations( - [ - (resultMatsT, P11p, "b", "P11 Matsubara (+term)"), - (resultMatsT, P12p, "r", "P12 Matsubara (+term)"), - (resultPade, P11p, "r--", "P11 Pade"), - (resultPade, P12p, "b--", "P12 Pade"), - ] -); +plot_result_expectations([ + (resultMatsT, P11p, 'b', "P11 Matsubara (+term)"), + (resultMatsT, P12p, 'r', "P12 Matsubara (+term)"), + (resultPade, P11p, 'b--', "P11 Pade"), + (resultPade, P12p, 'r--', "P12 Pade"), +]); ``` ## Simulation 4: Fitting approach -```{code-cell} +```{code-cell} ipython3 tfit = np.linspace(0, 10, 1000) with timer("RHS construction time"): - bath, _ = env.approximate( + env_fit, _ = env.approximate( method="cf", tlist=tfit, Ni_max=1, Nr_max=3, target_rmse=None ) - HEOMFit = HEOMSolver(Hsys, (bath, Q), NC, options=options) + HEOMFit = HEOMSolver(Hsys, (env_fit, Q), NC, options=options) with timer("ODE solver time"): resultFit = HEOMFit.run(rho0, tlist) @@ -304,7 +296,7 @@ with timer("ODE solver time"): ## Analytic calculations -```{code-cell} +```{code-cell} ipython3 def pure_dephasing_evolution_analytical(tlist, wq, ck, vk): """ Computes the propagating function appearing in the pure dephasing model. @@ -365,14 +357,14 @@ def correlation_integral(t, ck, vk): The value of the integral function at time t. """ t1 = np.sum((ck / vk**2) * (np.exp(vk * t) - 1)) - t2 = np.sum((ck.conj() / vk.conj() ** 2) * (np.exp(vk.conj() * t) - 1)) + t2 = np.sum((ck.conj() / vk.conj()**2) * (np.exp(vk.conj() * t) - 1)) t3 = np.sum((ck / vk + ck.conj() / vk.conj()) * t) return 2 * (t1 + t2 - t3) ``` For the pure dephasing analytics, we just sum up as many matsubara terms as we can: -```{code-cell} +```{code-cell} ipython3 lmaxmats2 = 15000 vk = [complex(-gamma)] @@ -390,23 +382,21 @@ P12_ana = 0.5 * pure_dephasing_evolution_analytical( Alternatively, we can just do the integral of the propagator directly, without using the correlation functions at all -```{code-cell} +```{code-cell} ipython3 def JDL(omega, lamc, omega_c): return 2.0 * lamc * omega * omega_c / (omega_c**2 + omega**2) def integrand(omega, lamc, omega_c, Temp, t): return ( - (-4.0 * JDL(omega, lamc, omega_c) / omega**2) - * (1.0 - np.cos(omega * t)) - * (coth(omega / (2.0 * Temp))) + (-4.0 * JDL(omega, lamc, omega_c) / omega**2) * + (1.0 - np.cos(omega*t)) * (coth(omega/(2*Temp))) / np.pi ) P12_ana2 = [ - 0.5 - * np.exp( + 0.5 * np.exp( scipy.integrate.quad(integrand, 0, np.inf, args=(lam, gamma, T, t))[0] ) for t in tlist @@ -415,69 +405,60 @@ P12_ana2 = [ ## Compare results -```{code-cell} -plot_result_expectations( - [ - (resultMats, P12p, "r", "P12 Mats"), - (resultMatsT, P12p, "r--", "P12 Mats + Term"), - (resultPade, P12p, "b--", "P12 Pade"), - (resultFit, P12p, "g", "P12 Fit"), - ((tlist, np.real(P12_ana)), None, "b", "Analytic 1"), - ((tlist, np.real(P12_ana2)), None, "y--", "Analytic 2"), - ] -); +```{code-cell} ipython3 +plot_result_expectations([ + (resultMats, P12p, 'r', "P12 Mats"), + (resultMatsT, P12p, 'r--', "P12 Mats + Term"), + (resultPade, P12p, 'b--', "P12 Pade"), + (resultFit, P12p, 'g', "P12 Fit"), + ((tlist, np.real(P12_ana)), None, 'b', "Analytic 1"), + ((tlist, np.real(P12_ana2)), None, 'y--', "Analytic 2"), +]); ``` We can't see much difference in the plot above, so let's do a log plot instead: -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) -plot_result_expectations( - [ - (resultMats, P12p, "r", "P12 Mats"), - (resultMatsT, P12p, "r--", "P12 Mats + Term"), - (resultPade, P12p, "b-.", "P12 Pade"), - (resultFit, P12p, "g", "P12 Fit"), - ((tlist, np.real(P12_ana)), None, "b", "Analytic 1"), - ((tlist, np.real(P12_ana2)), None, "y--", "Analytic 2"), - ], - axes, -) +plot_result_expectations([ + (resultMats, P12p, 'r', "P12 Mats"), + (resultMatsT, P12p, 'r--', "P12 Mats + Term"), + (resultPade, P12p, 'b-.', "P12 Pade"), + (resultFit, P12p, 'g', "P12 Fit"), + ((tlist, np.real(P12_ana)), None, 'b', "Analytic 1"), + ((tlist, np.real(P12_ana2)), None, 'y--', "Analytic 2"), +], axes) -axes.set_yscale("log") +axes.set_yscale('log') axes.legend(loc=0, fontsize=12); ``` ## About -```{code-cell} -qutip.about() +```{code-cell} ipython3 +about() ``` ## Testing This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 assert np.allclose( - expect(P12p, resultMats.states[:15]), - np.real(P12_ana)[:15], + expect(P12p, resultMats.states[:15]), np.real(P12_ana)[:15], rtol=1e-2, ) assert np.allclose( - expect(P12p, resultMatsT.states[:100]), - np.real(P12_ana)[:100], + expect(P12p, resultMatsT.states[:100]), np.real(P12_ana)[:100], rtol=1e-3, ) assert np.allclose( - expect(P12p, resultPade.states[:100]), - np.real(P12_ana)[:100], + expect(P12p, resultPade.states[:100]), np.real(P12_ana)[:100], rtol=1e-3, ) assert np.allclose( - expect(P12p, resultFit.states[:50]), - np.real(P12_ana)[:50], + expect(P12p, resultFit.states[:50]), np.real(P12_ana)[:50], rtol=1e-3, ) assert np.allclose(P12_ana, P12_ana2, rtol=1e-3) From 3a6e8a02d9084ee293ef1b4b5bc55f9a14bca6cd Mon Sep 17 00:00:00 2001 From: Paul Menczel Date: Thu, 24 Apr 2025 16:50:42 +0900 Subject: [PATCH 38/44] HEOM 2 notebook final pass --- tutorials-v5/heom/heom-2-fmo-example.md | 167 ++++++++++-------------- 1 file changed, 68 insertions(+), 99 deletions(-) diff --git a/tutorials-v5/heom/heom-2-fmo-example.md b/tutorials-v5/heom/heom-2-fmo-example.md index a26276fb..185da1ab 100644 --- a/tutorials-v5/heom/heom-2-fmo-example.md +++ b/tutorials-v5/heom/heom-2-fmo-example.md @@ -1,15 +1,14 @@ --- jupytext: - formats: ipynb,md:myst text_representation: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.5 + jupytext_version: 1.17.0 kernelspec: - display_name: qutip-tutorials - language: python name: python3 + display_name: Python 3 (ipykernel) + language: python --- # HEOM 2: Dynamics in Fenna-Mathews-Olsen complex (FMO) @@ -31,14 +30,14 @@ quantum environment reduces the effect of pure dephasing. ## Setup -```{code-cell} +```{code-cell} ipython3 import contextlib import time import numpy as np -import qutip from matplotlib import pyplot as plt -from qutip import Qobj, basis, brmesolve, expect, liouvillian, mesolve + +from qutip import Qobj, about, basis, brmesolve, expect, liouvillian, mesolve from qutip.core.environment import DrudeLorentzEnvironment, system_terminator from qutip.solver.heom import HEOMSolver @@ -47,15 +46,13 @@ from qutip.solver.heom import HEOMSolver ## Helper functions -Let's define some helper functions for calculating correlation functions, spectral densities, thermal energy level occupations, and for plotting results and timing how long operations take: - -```{code-cell} +```{code-cell} ipython3 @contextlib.contextmanager def timer(label): - """Simple utility for timing functions: + """ Simple utility for timing functions: - with timer("name"): - ... code to time ... + with timer("name"): + ... code to time ... """ start = time.time() yield @@ -63,10 +60,9 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} +```{code-cell} ipython3 # Solver options: - options = { "nsteps": 15000, "store_states": True, @@ -82,32 +78,25 @@ options = { And let us set up the system Hamiltonian and bath parameters: -```{code-cell} +```{code-cell} ipython3 # System Hamiltonian: # # We use the Hamiltonian employed in # https://www.pnas.org/content/106/41/17255 and operate # in units of Hz: -Hsys = ( - 3e10 - * 2 - * np.pi - * Qobj( - [ - [200, -87.7, 5.5, -5.9, 6.7, -13.7, -9.9], - [-87.7, 320, 30.8, 8.2, 0.7, 11.8, 4.3], - [5.5, 30.8, 0, -53.5, -2.2, -9.6, 6.0], - [-5.9, 8.2, -53.5, 110, -70.7, -17.0, -63.3], - [6.7, 0.7, -2.2, -70.7, 270, 81.1, -1.3], - [-13.7, 11.8, -9.6, -17.0, 81.1, 420, 39.7], - [-9.9, 4.3, 6.0, -63.3, -1.3, 39.7, 230], - ] - ) -) +Hsys = 3e10 * 2 * np.pi * Qobj([ + [200, -87.7, 5.5, -5.9, 6.7, -13.7, -9.9], + [-87.7, 320, 30.8, 8.2, 0.7, 11.8, 4.3], + [5.5, 30.8, 0, -53.5, -2.2, -9.6, 6.0], + [-5.9, 8.2, -53.5, 110, -70.7, -17.0, -63.3], + [6.7, 0.7, -2.2, -70.7, 270, 81.1, -1.3], + [-13.7, 11.8, -9.6, -17.0, 81.1, 420, 39.7], + [-9.9, 4.3, 6.0, -63.3, -1.3, 39.7, 230], +]) ``` -```{code-cell} +```{code-cell} ipython3 # Bath parameters lam = 35 * 3e10 * 2 * np.pi @@ -120,11 +109,11 @@ beta = 1 / T Let's quickly plot the spectral density and environment correlation functions so that we can see what they look like. -```{code-cell} +```{code-cell} ipython3 env = DrudeLorentzEnvironment(T=T, lam=lam, gamma=gamma) ``` -```{code-cell} +```{code-cell} ipython3 wlist = np.linspace(0, 200 * 3e10 * 2 * np.pi, 100) tlist = np.linspace(0, 1e-12, 1000) @@ -136,28 +125,22 @@ fig.subplots_adjust(hspace=0.1) # reduce space between plots # Spectral density plot: -axes[0].plot(wlist / (3e10 * 2 * np.pi), J, color="r", ls="--", label="J(w)") -axes[0].set_xlabel(r"$\omega$ (cm$^{-1}$)", fontsize=20) +axes[0].plot(wlist / (3e10 * 2 * np.pi), J, color='r', ls='--', label="J(w)") +axes[0].set_xlabel(r'$\omega$ (cm$^{-1}$)', fontsize=20) axes[0].set_ylabel(r"$J(\omega)$ (cm$^{-1}$)", fontsize=16) axes[0].legend() # Correlation plot: axes[1].plot( - tlist, - np.real(env.correlation_function(tlist, 10)), - color="r", - ls="--", - label="C(t) real", + tlist, np.real(env.correlation_function(tlist, 10)), + color='r', ls='--', label="C(t) real", ) axes[1].plot( - tlist, - np.imag(env.correlation_function(tlist, 10)), - color="g", - ls="--", - label="C(t) imaginary", + tlist, np.imag(env.correlation_function(tlist, 10)), + color='g', ls='--', label="C(t) imaginary", ) -axes[1].set_xlabel(r"$t$", fontsize=20) +axes[1].set_xlabel(r'$t$', fontsize=20) axes[1].set_ylabel(r"$C(t)$", fontsize=16) axes[1].legend(); ``` @@ -166,7 +149,7 @@ axes[1].legend(); Now let us solve for the evolution of this system using the HEOM. -```{code-cell} +```{code-cell} ipython3 # We start the excitation at site 1: rho0 = basis(7, 0) * basis(7, 0).dag() @@ -179,7 +162,6 @@ NC = 4 # Use NC=8 for more precise results Nk = 0 Q_list = [] -baths = [] Ltot = liouvillian(Hsys) env_approx, delta = env.approximate( method="matsubara", Nk=Nk, compute_delta=True @@ -188,29 +170,24 @@ for m in range(7): Q = basis(7, m) * basis(7, m).dag() Q_list.append(Q) Ltot += system_terminator(Q, delta) - baths.append((env_approx, Q)) ``` -```{code-cell} +```{code-cell} ipython3 with timer("RHS construction time"): - HEOMMats = HEOMSolver(Hsys, baths, NC, options=options) + HEOMMats = HEOMSolver(Hsys, [(env_approx, Q) for Q in Q_list], + NC, options=options) with timer("ODE solver time"): outputFMO_HEOM = HEOMMats.run(rho0, tlist) ``` -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, figsize=(12, 8)) -colors = ["r", "g", "b", "y", "c", "m", "k"] +colors = ['r', 'g', 'b', 'y', 'c', 'm', 'k'] linestyles = [ - "-", - "--", - ":", - "-.", - (0, (1, 10)), - (0, (5, 10)), - (0, (3, 10, 1, 10)), + '-', '--', ':', '-.', + (0, (1, 10)), (0, (5, 10)), (0, (3, 10, 1, 10)), ] for m in range(7): @@ -222,12 +199,12 @@ for m in range(7): color=colors[m % len(colors)], linestyle=linestyles[m % len(linestyles)], ) - axes.set_xlabel(r"$t$ (fs)", fontsize=30) + axes.set_xlabel(r'$t$ (fs)', fontsize=30) axes.set_ylabel(r"Population", fontsize=30) - axes.locator_params(axis="y", nbins=6) - axes.locator_params(axis="x", nbins=6) + axes.locator_params(axis='y', nbins=6) + axes.locator_params(axis='x', nbins=6) -axes.set_title("HEOM solution", fontsize=24) +axes.set_title('HEOM solution', fontsize=24) axes.legend(loc=0) axes.set_xlim(0, 1000) plt.yticks([0.0, 0.5, 1], [0, 0.5, 1]) @@ -240,12 +217,10 @@ Now let us solve the same problem using the Bloch-Redfield solver. We will see t In the next section, we will examine the role of pure dephasing in the evolution to understand why this happens. -```{code-cell} +```{code-cell} ipython3 with timer("BR ODE solver time"): outputFMO_BR = brmesolve( - Hsys, - rho0, - tlist, + Hsys, rho0, tlist, a_ops=[[Q, env] for Q in Q_list], options=options, ) @@ -253,16 +228,16 @@ with timer("BR ODE solver time"): And now let's plot the Bloch-Redfield solver results: -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, figsize=(12, 8)) for m, Q in enumerate(Q_list): axes.plot(tlist * 1e15, expect(outputFMO_BR.states, Q), label=m + 1) -axes.set_xlabel(r"$t$ (fs)", fontsize=30) +axes.set_xlabel(r'$t$ (fs)', fontsize=30) axes.set_ylabel(r"Population", fontsize=30) -axes.set_title("Bloch-Redfield solution ", fontsize=24) +axes.set_title('Bloch-Redfield solution ', fontsize=24) axes.legend() axes.set_xlim(0, 1000) plt.yticks([0, 0.5, 1], [0, 0.5, 1]) @@ -281,23 +256,19 @@ It is useful to construct the various parts of the Bloch-Redfield master equatio First we will write a function to return the list of collapse operators for a given system, either with or without the dephasing operators: -```{code-cell} +```{code-cell} ipython3 def J0_dephasing(): - """Under-damped brownian oscillator dephasing probability. + """ Under-damped brownian oscillator dephasing probability. - This returns the limit as w -> 0 of J0(w) * n_th(w, T) / T. + This returns the limit as w -> 0 of J0(w) * n_th(w, T) / T. """ return 2 * lam * gamma / gamma**2 ``` -```{code-cell} -env.power_spectrum(0) / 2 - J0_dephasing() * T -``` - -```{code-cell} +```{code-cell} ipython3 def get_collapse(H, T, dephasing=1): - """Calculate collapse operators for a given system H and - temperature T. + """ Calculate collapse operators for a given system H and + temperature T. """ all_energy, all_state = H.eigenstates(sort="low") Nmax = len(all_energy) @@ -332,10 +303,9 @@ def get_collapse(H, T, dephasing=1): if dephasing: for j in range(Nmax): rate = ( - np.abs(Q.matrix_element(all_state[j].dag(), all_state[j])) - ** 2 - * env.power_spectrum(0) - / 2 + np.abs( + Q.matrix_element(all_state[j].dag(), all_state[j]) + ) ** 2 * env.power_spectrum(0) / 2 ) if rate > 0.0: # emission: @@ -350,10 +320,9 @@ Now we are able to switch the pure dephasing terms on and off. Let us starting by including the dephasing operators. We expect to see the same behaviour that we saw when using the Bloch-Redfield solver. -```{code-cell} +```{code-cell} ipython3 # dephasing terms on, we recover the full BR solution: - with timer("Building the collapse operators"): collapse_list = get_collapse(Hsys, T=T, dephasing=True) @@ -361,16 +330,16 @@ with timer("ME ODE solver"): outputFMO_ME = mesolve(Hsys, rho0, tlist, collapse_list) ``` -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, figsize=(12, 8)) for m, Q in enumerate(Q_list): axes.plot(tlist * 1e15, expect(outputFMO_ME.states, Q), label=m + 1) -axes.set_xlabel(r"$t$", fontsize=20) +axes.set_xlabel(r'$t$', fontsize=20) axes.set_ylabel(r"Population", fontsize=16) axes.set_xlim(0, 1000) -axes.set_title("With pure dephasing", fontsize=24) +axes.set_title('With pure dephasing', fontsize=24) plt.yticks([0, 0.5, 1], [0, 0.5, 1]) plt.xticks([0, 500, 1000], [0, 500, 1000]) axes.legend(fontsize=18); @@ -380,7 +349,7 @@ We see similar results to before. Now let us examine what happens when we remove the dephasing collapse operators: -```{code-cell} +```{code-cell} ipython3 # dephasing terms off with timer("Building the collapse operators"): @@ -390,7 +359,7 @@ with timer("ME ODE solver"): outputFMO_ME_nodephase = mesolve(Hsys, rho0, tlist, collapse_list) ``` -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, figsize=(12, 8)) for m, Q in enumerate(Q_list): axes.plot( @@ -399,10 +368,10 @@ for m, Q in enumerate(Q_list): label=m + 1, ) -axes.set_xlabel(r"$t$", fontsize=20) +axes.set_xlabel(r'$t$', fontsize=20) axes.set_ylabel(r"Population", fontsize=16) axes.set_xlim(0, 1000) -axes.set_title("Without pure dephasing", fontsize=24) +axes.set_title('Without pure dephasing', fontsize=24) plt.yticks([0, 0.5, 1], [0, 0.5, 1]) plt.xticks([0, 500, 1000], [0, 500, 1000]) axes.legend(fontsize=18); @@ -414,15 +383,15 @@ And now we see that without the dephasing, the oscillations reappear. The full d ## About -```{code-cell} -qutip.about() +```{code-cell} ipython3 +about() ``` ## Testing This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 assert np.allclose( expect(outputFMO_BR.states, Q_list[0]), expect(outputFMO_ME.states, Q_list[0]), From 8252b20b49378f454299c2e5c610dbb6298d2336 Mon Sep 17 00:00:00 2001 From: Paul Menczel Date: Thu, 24 Apr 2025 17:04:17 +0900 Subject: [PATCH 39/44] HEOM 3 notebook final pass --- .../heom/heom-3-quantum-heat-transport.md | 220 +++++++----------- 1 file changed, 83 insertions(+), 137 deletions(-) diff --git a/tutorials-v5/heom/heom-3-quantum-heat-transport.md b/tutorials-v5/heom/heom-3-quantum-heat-transport.md index 7864f58f..95611961 100644 --- a/tutorials-v5/heom/heom-3-quantum-heat-transport.md +++ b/tutorials-v5/heom/heom-3-quantum-heat-transport.md @@ -1,15 +1,14 @@ --- jupytext: - formats: ipynb,md:myst text_representation: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.5 + jupytext_version: 1.17.0 kernelspec: - display_name: qutip-tutorials - language: python name: python3 + display_name: Python 3 (ipykernel) + language: python --- # HEOM 3: Quantum Heat Transport @@ -50,24 +49,26 @@ References: ## Setup -```{code-cell} +```{code-cell} ipython3 import dataclasses -import matplotlib.pyplot as plt import numpy as np +import matplotlib.pyplot as plt + import qutip as qt -from IPython.display import display -from ipywidgets import IntProgress from qutip.core.environment import (CFExponent, DrudeLorentzEnvironment, system_terminator) from qutip.solver.heom import HEOMSolver +from ipywidgets import IntProgress +from IPython.display import display + %matplotlib inline ``` ## Helpers -```{code-cell} +```{code-cell} ipython3 # Solver options: options = { @@ -83,33 +84,29 @@ options = { ## System and bath definition -```{code-cell} +```{code-cell} ipython3 @dataclasses.dataclass class SystemParams: - """System parameters and Hamiltonian.""" + """ System parameters and Hamiltonian. """ epsilon: float = 1.0 J12: float = 0.1 def H(self): - """Return the Hamiltonian for the system. + """ Return the Hamiltonian for the system. - The system consists of two qubits with Hamiltonians (H1 and H2) - and an interaction term (H12). + The system consists of two qubits with Hamiltonians (H1 and H2) + and an interaction term (H12). """ - H1 = ( - self.epsilon - / 2 - * (qt.tensor(qt.sigmaz() + qt.identity(2), qt.identity(2))) + H1 = self.epsilon / 2 * ( + qt.tensor(qt.sigmaz() + qt.identity(2), qt.identity(2)) ) - H2 = ( - self.epsilon - / 2 - * (qt.tensor(qt.identity(2), qt.sigmaz() + qt.identity(2))) + H2 = self.epsilon / 2 * ( + qt.tensor(qt.identity(2), qt.sigmaz() + qt.identity(2)) ) H12 = self.J12 * ( - qt.tensor(qt.sigmap(), qt.sigmam()) - + qt.tensor(qt.sigmam(), qt.sigmap()) + qt.tensor(qt.sigmap(), qt.sigmam()) + + qt.tensor(qt.sigmam(), qt.sigmap()) ) return H1 + H2 + H12 @@ -117,11 +114,10 @@ class SystemParams: return dataclasses.replace(self, **kw) ``` -```{code-cell} +```{code-cell} ipython3 @dataclasses.dataclass class BathParams: - """Bath parameters.""" - + """ Bath parameters. """ sign: str # + or - qubit: int # 0 or 1 @@ -139,7 +135,7 @@ class BathParams: assert self.qubit in (0, 1) def Q(self): - """Coupling operator for the bath.""" + """ Coupling operator for the bath. """ Q = [qt.identity(2), qt.identity(2)] Q[self.qubit] = qt.sigmax() return qt.tensor(Q) @@ -179,7 +175,7 @@ In the expression for the bath heat currents, we left out terms involving $[Q_1, In QuTiP, these currents can be conveniently calculated as follows: -```{code-cell} +```{code-cell} ipython3 def bath_heat_current(bath_tag, ado_state, hamiltonian, coupling_op, delta=0): """ Bath heat current from the system into the heat bath with the given tag. @@ -219,19 +215,14 @@ def bath_heat_current(bath_tag, ado_state, hamiltonian, coupling_op, delta=0): result -= 2 * cI0 * (coupling_op * coupling_op * ado_state.rho).tr() if delta != 0: result -= ( - 1j - * delta - * ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr() + 1j * delta * + ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr() ) return result def system_heat_current( - bath_tag, - ado_state, - hamiltonian, - coupling_op, - delta=0, + bath_tag, ado_state, hamiltonian, coupling_op, delta=0, ): """ System heat current from the system into the heat bath with the given tag. @@ -263,9 +254,8 @@ def system_heat_current( if delta != 0: result -= ( - 1j - * delta - * ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr() + 1j * delta * + ((a_op * coupling_op - coupling_op * a_op) * ado_state.rho).tr() ) return result ``` @@ -280,7 +270,7 @@ Note that at long times, we expect $j_{\text{B}}^1 = -j_{\text{B}}^2$ and $j_{\t For our simulations, we will represent the bath spectral densities using the first term of their Padé decompositions, and we will use $7$ levels of the HEOM hierarchy. -```{code-cell} +```{code-cell} ipython3 Nk = 1 NC = 7 ``` @@ -290,7 +280,7 @@ NC = 7 We fix $J_{12} = 0.1 \epsilon$ (as in Fig. 3(a-ii) of Ref. \[2\]) and choose the fixed coupling strength $\lambda_1 = \lambda_2 = J_{12}\, /\, (2\epsilon)$ (corresponding to $\bar\zeta = 1$ in Ref. \[2\]). Using these values, we will study the time evolution of the system state and the heat currents. -```{code-cell} +```{code-cell} ipython3 # fix qubit-qubit and qubit-bath coupling strengths sys = SystemParams(J12=0.1) bath_p1 = BathParams(qubit=0, sign="+", lam=sys.J12 / 2) @@ -303,7 +293,7 @@ rho0 = qt.tensor(qt.identity(2), qt.identity(2)) / 4 tlist = np.linspace(0, 50, 250) ``` -```{code-cell} +```{code-cell} ipython3 H = sys.H() bath1, b1term, b1delta = bath_p1.bath(Nk, tag="bath 1") @@ -312,7 +302,6 @@ Q1 = bath_p1.Q() bath2, b2term, b2delta = bath_p2.bath(Nk, tag="bath 2") Q2 = bath_p2.Q() - solver = HEOMSolver( qt.liouvillian(H) + b1term + b2term, [bath1, bath2], @@ -320,93 +309,69 @@ solver = HEOMSolver( options=options, ) -result = solver.run( - rho0, - tlist, - e_ops=[ - qt.tensor(qt.sigmaz(), qt.identity(2)), - lambda t, ado: bath_heat_current("bath 1", ado, H, Q1, b1delta), - lambda t, ado: bath_heat_current("bath 2", ado, H, Q2, b2delta), - lambda t, ado: system_heat_current("bath 1", ado, H, Q1, b1delta), - lambda t, ado: system_heat_current("bath 2", ado, H, Q2, b2delta), - ], -) +result = solver.run(rho0, tlist, e_ops=[ + qt.tensor(qt.sigmaz(), qt.identity(2)), + lambda t, ado: bath_heat_current('bath 1', ado, H, Q1, b1delta), + lambda t, ado: bath_heat_current('bath 2', ado, H, Q2, b2delta), + lambda t, ado: system_heat_current('bath 1', ado, H, Q1, b1delta), + lambda t, ado: system_heat_current('bath 2', ado, H, Q2, b2delta), +]) ``` We first plot $\langle \sigma_z^1 \rangle$ to see the time evolution of the system state: -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(figsize=(8, 8)) -axes.plot(tlist, result.expect[0], "r", linewidth=2) -axes.set_xlabel("t", fontsize=28) +axes.plot(tlist, np.real(result.expect[0]), 'r', linewidth=2) +axes.set_xlabel('t', fontsize=28) axes.set_ylabel(r"$\langle \sigma_z^1 \rangle$", fontsize=28); ``` We find a rather quick thermalization of the system state. For the heat currents, however, it takes a somewhat longer time until they converge to their long-time values: -```{code-cell} +```{code-cell} ipython3 fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8)) ax1.plot( - tlist, - -np.real(result.expect[1]), - color="darkorange", - label="BHC (bath 1 -> system)", + tlist, -np.real(result.expect[1]), + color='darkorange', label='BHC (bath 1 -> system)', ) ax1.plot( - tlist, - np.real(result.expect[2]), - "--", - color="darkorange", - label="BHC (system -> bath 2)", + tlist, np.real(result.expect[2]), + '--', color='darkorange', label='BHC (system -> bath 2)', ) ax1.plot( - tlist, - -np.real(result.expect[3]), - color="dodgerblue", - label="SHC (bath 1 -> system)", + tlist, -np.real(result.expect[3]), + color='dodgerblue', label='SHC (bath 1 -> system)', ) ax1.plot( - tlist, - np.real(result.expect[4]), - "--", - color="dodgerblue", - label="SHC (system -> bath 2)", + tlist, np.real(result.expect[4]), + '--', color='dodgerblue', label='SHC (system -> bath 2)', ) -ax1.set_xlabel("t", fontsize=28) -ax1.set_ylabel("j", fontsize=28) +ax1.set_xlabel('t', fontsize=28) +ax1.set_ylabel('j', fontsize=28) ax1.set_ylim((-0.05, 0.05)) ax1.legend(loc=0, fontsize=12) ax2.plot( - tlist, - -np.real(result.expect[1]), - color="darkorange", - label="BHC (bath 1 -> system)", + tlist, -np.real(result.expect[1]), + color='darkorange', label='BHC (bath 1 -> system)', ) ax2.plot( - tlist, - np.real(result.expect[2]), - "--", - color="darkorange", - label="BHC (system -> bath 2)", + tlist, np.real(result.expect[2]), + '--', color='darkorange', label='BHC (system -> bath 2)', ) ax2.plot( - tlist, - -np.real(result.expect[3]), - color="dodgerblue", - label="SHC (bath 1 -> system)", + tlist, -np.real(result.expect[3]), + color='dodgerblue', label='SHC (bath 1 -> system)', ) ax2.plot( - tlist, - np.real(result.expect[4]), - "--", - color="dodgerblue", - label="SHC (system -> bath 2)", + tlist, np.real(result.expect[4]), + '--', color='dodgerblue', label='SHC (system -> bath 2)', ) -ax2.set_xlabel("t", fontsize=28) +ax2.set_xlabel('t', fontsize=28) ax2.set_xlim((20, 50)) ax2.set_ylim((0, 0.0002)) ax2.legend(loc=0, fontsize=12); @@ -416,10 +381,10 @@ ax2.legend(loc=0, fontsize=12); Here, we try to reproduce the HEOM curves in Fig. 3(a) of Ref. \[1\] by varying the coupling strength and finding the steady state for each coupling strength. -```{code-cell} +```{code-cell} ipython3 def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options): - """Calculate the steady sate heat currents for the given system and - bath. + """ Calculate the steady sate heat currents for the given system and + bath. """ bath1, b1term, b1delta = bath_p1.bath(Nk, tag="bath 1") @@ -432,20 +397,20 @@ def heat_currents(sys, bath_p1, bath_p2, Nk, NC, options): qt.liouvillian(sys.H()) + b1term + b2term, [bath1, bath2], max_depth=NC, - options=options, + options=options ) _, steady_ados = solver.steady_state() return ( - bath_heat_current("bath 1", steady_ados, sys.H(), Q1, b1delta), - bath_heat_current("bath 2", steady_ados, sys.H(), Q2, b2delta), - system_heat_current("bath 1", steady_ados, sys.H(), Q1, b1delta), - system_heat_current("bath 2", steady_ados, sys.H(), Q2, b2delta), + bath_heat_current('bath 1', steady_ados, sys.H(), Q1, b1delta), + bath_heat_current('bath 2', steady_ados, sys.H(), Q2, b2delta), + system_heat_current('bath 1', steady_ados, sys.H(), Q1, b1delta), + system_heat_current('bath 2', steady_ados, sys.H(), Q2, b2delta), ) ``` -```{code-cell} +```{code-cell} ipython3 # Define number of points to use for the plot plot_points = 10 # use 100 for a smoother curve @@ -465,7 +430,7 @@ display(progress) def calculate_heat_current(J12, zb, Nk, progress=progress): - """Calculate a single heat current and update the progress bar.""" + """ Calculate a single heat current and update the progress bar. """ # Estimate appropriate HEOM max_depth from coupling strength NC = 7 + int(max(zb * J12 - 1, 0) * 2) NC = min(NC, 20) @@ -474,9 +439,7 @@ def calculate_heat_current(J12, zb, Nk, progress=progress): sys.replace(J12=J12), bath_p1.replace(lam=zb * J12 / 2), bath_p2.replace(lam=zb * J12 / 2), - Nk, - NC, - options=options, + Nk, NC, options=options, ) progress.value += 1 return j @@ -491,32 +454,23 @@ j3s = [calculate_heat_current(0.5, zb, Nk) for zb in zeta_bars] ## Create Plot -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(figsize=(12, 7)) axes.plot( - zeta_bars, - -1000 * 100 * np.real(j1s), - "b", - linewidth=2, - label=r"$J_{12} = 0.01\, \epsilon$", + zeta_bars, -1000 * 100 * np.real(j1s), + 'b', linewidth=2, label=r"$J_{12} = 0.01\, \epsilon$", ) axes.plot( - zeta_bars, - -1000 * 10 * np.real(j2s), - "r--", - linewidth=2, - label=r"$J_{12} = 0.1\, \epsilon$", + zeta_bars, -1000 * 10 * np.real(j2s), + 'r--', linewidth=2, label=r"$J_{12} = 0.1\, \epsilon$", ) axes.plot( - zeta_bars, - -1000 * 2 * np.real(j3s), - "g-.", - linewidth=2, - label=r"$J_{12} = 0.5\, \epsilon$", + zeta_bars, -1000 * 2 * np.real(j3s), + 'g-.', linewidth=2, label=r"$J_{12} = 0.5\, \epsilon$", ) -axes.set_xscale("log") +axes.set_xscale('log') axes.set_xlabel(r"$\bar\zeta$", fontsize=30) axes.set_xlim((zeta_bars[0], zeta_bars[-1])) @@ -531,14 +485,6 @@ axes.legend(loc=0); ## About -```{code-cell} +```{code-cell} ipython3 qt.about() ``` - -## Testing - -This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. - -```{code-cell} -assert 1 == 1 -``` From 791b18e2240c2e58d28ee284ee27bf206304fe88 Mon Sep 17 00:00:00 2001 From: Paul Menczel Date: Thu, 24 Apr 2025 17:16:23 +0900 Subject: [PATCH 40/44] HEOM 4 notebook final pass --- .../heom/heom-4-dynamical-decoupling.md | 252 ++++++++---------- 1 file changed, 104 insertions(+), 148 deletions(-) diff --git a/tutorials-v5/heom/heom-4-dynamical-decoupling.md b/tutorials-v5/heom/heom-4-dynamical-decoupling.md index 8bb2dac7..538c5b4d 100644 --- a/tutorials-v5/heom/heom-4-dynamical-decoupling.md +++ b/tutorials-v5/heom/heom-4-dynamical-decoupling.md @@ -1,15 +1,14 @@ --- jupytext: - formats: ipynb,md:myst text_representation: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.5 + jupytext_version: 1.17.0 kernelspec: - display_name: qutip-tutorials - language: python name: python3 + display_name: Python 3 (ipykernel) + language: python --- # HEOM 4: Dynamical decoupling of a non-Markovian environment @@ -27,22 +26,23 @@ We first show the standard example of equally spaced pulses, and then consider t ## Setup -```{code-cell} -import matplotlib.pyplot as plt +```{code-cell} ipython3 import numpy as np -import qutip +import matplotlib.pyplot as plt + +from qutip import (DrudeLorentzEnvironment, QobjEvo, about, + basis, expect, ket2dm, sigmax, sigmaz) +from qutip.solver.heom import HEOMSolver + from IPython.display import display from ipywidgets import IntProgress -from qutip import (DrudeLorentzEnvironment, QobjEvo, basis, expect, ket2dm, - sigmax, sigmaz) -from qutip.solver.heom import HEOMSolver %matplotlib inline ``` ## Solver options -```{code-cell} +```{code-cell} ipython3 # Solver options: # The max_step must be set to a short time than the @@ -64,7 +64,7 @@ options = { Now we define the system and bath properties and the HEOM parameters. The system is a single stationary qubit with $H = 0$ and the bath is a bosonic bath with a Drude-Lorentz spectrum. -```{code-cell} +```{code-cell} ipython3 # Define the system Hamlitonian. # # The system isn't evolving by itself, so the Hamiltonian is 0 (with the @@ -73,7 +73,7 @@ Now we define the system and bath properties and the HEOM parameters. The system H_sys = 0 * sigmaz() ``` -```{code-cell} +```{code-cell} ipython3 # Define some operators with which we will measure the system # 1,1 element of density matrix - corresponding to groundstate P11p = basis(2, 0) * basis(2, 0).dag() @@ -82,7 +82,7 @@ P22p = basis(2, 1) * basis(2, 1).dag() P12p = basis(2, 0) * basis(2, 1).dag() ``` -```{code-cell} +```{code-cell} ipython3 # Properties for the Drude-Lorentz bath lam = 0.0005 @@ -97,10 +97,9 @@ Nk = 3 env = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T) env_approx = env.approximate(method="pade", Nk=Nk) -bath = (env_approx, Q) ``` -```{code-cell} +```{code-cell} ipython3 # HEOM parameters # number of layers to keep in the hierarchy: @@ -111,23 +110,23 @@ To perform the dynamic decoupling from the environment, we will drive the system Below we define a function that returns the pulse (which is itself a function): -```{code-cell} +```{code-cell} ipython3 def drive(amplitude, delay, integral): - """Coefficient of the drive as a function of time. - - The drive consists of a series of constant pulses with - a fixed delay between them. - - Parameters - ---------- - amplitude : float - The amplitude of the drive during the pulse. - delay : float - The time delay between successive pulses. - integral : float - The integral of the pulse. This determines - the duration of each pulse with the duration - equal to the integral divided by the amplitude. + """ Coefficient of the drive as a function of time. + + The drive consists of a series of constant pulses with + a fixed delay between them. + + Parameters + ---------- + amplitude : float + The amplitude of the drive during the pulse. + delay : float + The time delay between successive pulses. + integral : float + The integral of the pulse. This determines + the duration of each pulse with the duration + equal to the integral divided by the amplitude. """ duration = integral / amplitude period = duration + delay @@ -148,7 +147,7 @@ H_drive = sigmax() Let's start by plotting the spectral density of our Drude-Lorentz bath: -```{code-cell} +```{code-cell} ipython3 wlist = np.linspace(0, 0.5, 1000) J = env.spectral_density(wlist) J_approx = env_approx.spectral_density(wlist) @@ -169,7 +168,7 @@ First we will drive the system with fast, large amplitude pulses. Then we will d Let's start by simulating the fast pulses: -```{code-cell} +```{code-cell} ipython3 # Fast driving (quick, large amplitude pulses) tlist = np.linspace(0, 400, 1000) @@ -179,37 +178,37 @@ rho0 = (basis(2, 1) + basis(2, 0)).unit() rho0 = ket2dm(rho0) # without pulses -hsolver = HEOMSolver(H_sys, bath, NC, options=options) +hsolver = HEOMSolver(H_sys, (env_approx, Q), NC, options=options) outputnoDD = hsolver.run(rho0, tlist) # with pulses drive_fast = drive(amplitude=0.5, delay=20, integral=np.pi / 2) -H_d = qutip.QobjEvo([H_sys, [H_drive, drive_fast]]) +H_d = QobjEvo([H_sys, [H_drive, drive_fast]]) -hsolver = HEOMSolver(H_d, bath, NC, options=options) +hsolver = HEOMSolver(H_d, (env_approx, Q), NC, options=options) outputDD = hsolver.run(rho0, tlist) ``` And now the longer slower pulses: -```{code-cell} +```{code-cell} ipython3 # Slow driving (longer, small amplitude pulses) # without pulses -hsolver = HEOMSolver(H_sys, bath, NC, options=options) +hsolver = HEOMSolver(H_sys, (env_approx, Q), NC, options=options) outputnoDDslow = hsolver.run(rho0, tlist) # with pulses drive_slow = drive(amplitude=0.01, delay=20, integral=np.pi / 2) H_d = QobjEvo([H_sys, [H_drive, drive_slow]]) -hsolver = HEOMSolver(H_d, bath, NC, options=options) +hsolver = HEOMSolver(H_d, (env_approx, Q), NC, options=options) outputDDslow = hsolver.run(rho0, tlist) ``` Now let's plot all of the results and the shapes of the pulses: -```{code-cell} +```{code-cell} ipython3 def plot_dd_results(outputnoDD, outputDD, outputDDslow): fig, axes = plt.subplots(2, 1, sharex=False, figsize=(12, 12)) @@ -218,40 +217,28 @@ def plot_dd_results(outputnoDD, outputDD, outputDDslow): tlist = outputDD.times P12 = basis(2, 1) * basis(2, 0).dag() - P12DD = qutip.expect(outputDD.states, P12) - P12noDD = qutip.expect(outputnoDD.states, P12) - P12DDslow = qutip.expect(outputDDslow.states, P12) + P12DD = expect(outputDD.states, P12) + P12noDD = expect(outputnoDD.states, P12) + P12DDslow = expect(outputDDslow.states, P12) plt.sca(axes[0]) plt.yticks([0, 0.25, 0.5], [0, 0.25, 0.5]) axes[0].plot( - tlist, - np.real(P12DD), - "green", - linestyle="-", - linewidth=2, - label="HEOM with fast DD", + tlist, np.real(P12DD), + 'green', linestyle='-', linewidth=2, label="HEOM with fast DD", ) axes[0].plot( - tlist, - np.real(P12DDslow), - "blue", - linestyle="-", - linewidth=2, - label="HEOM with slow DD", + tlist, np.real(P12DDslow), + 'blue', linestyle='-', linewidth=2, label="HEOM with slow DD", ) axes[0].plot( - tlist, - np.real(P12noDD), - "orange", - linestyle="--", - linewidth=2, - label="HEOM no DD", + tlist, np.real(P12noDD), + 'orange', linestyle='--', linewidth=2, label="HEOM no DD", ) - axes[0].locator_params(axis="y", nbins=3) - axes[0].locator_params(axis="x", nbins=3) + axes[0].locator_params(axis='y', nbins=3) + axes[0].locator_params(axis='x', nbins=3) axes[0].set_ylabel(r"$\rho_{01}$", fontsize=30) @@ -264,30 +251,22 @@ def plot_dd_results(outputnoDD, outputDD, outputDDslow): pulseslow = [drive_slow(t) for t in tlist] plt.sca(axes[1]) - plt.yticks([0.0, 0.25, 0.5], [0, 0.25, 0.5]) + plt.yticks([0, 0.25, 0.5], [0, 0.25, 0.5]) axes[1].plot( - tlist, - pulse, - "green", - linestyle="-", - linewidth=2, - label="Drive fast", + tlist, pulse, + 'green', linestyle='-', linewidth=2, label="Drive fast", ) axes[1].plot( - tlist, - pulseslow, - "blue", - linestyle="--", - linewidth=2, - label="Drive slow", + tlist, pulseslow, + 'blue', linestyle='--', linewidth=2, label="Drive slow", ) - axes[1].locator_params(axis="y", nbins=3) - axes[1].locator_params(axis="x", nbins=3) + axes[1].locator_params(axis='y', nbins=3) + axes[1].locator_params(axis='x', nbins=3) - axes[1].set_xlabel(r"$t\bar{V}_{\mathrm{f}}$", fontsize=30) - axes[1].set_ylabel(r"Drive amplitude/$\bar{V}_{\mathrm{f}}$", fontsize=30) + axes[1].set_xlabel(r'$t\bar{V}_{\mathrm{f}}$', fontsize=30) + axes[1].set_ylabel(r'Drive amplitude/$\bar{V}_{\mathrm{f}}$', fontsize=30) axes[1].legend(loc=1) axes[1].text(0, 0.4, "(b)", fontsize=28) @@ -295,7 +274,7 @@ def plot_dd_results(outputnoDD, outputDD, outputDDslow): fig.tight_layout() ``` -```{code-cell} +```{code-cell} ipython3 plot_dd_results(outputnoDD, outputDD, outputDDslow) ``` @@ -315,40 +294,40 @@ $$ This is just a convenient way to describe the varying delay. We could have chosen another monotonically increasing function to represent the cummulative delay (although it might not be as effective). -```{code-cell} +```{code-cell} ipython3 def cummulative_delay_fractions(N): - """Return an array of N + 1 cummulative delay - fractions. + """ Return an array of N + 1 cummulative delay + fractions. - The j'th entry in the array should be the sum of - all delays before the j'th pulse. The last entry - should be 1 (i.e. the entire cummulative delay - should have been used once the sequence of pulses - is complete). + The j'th entry in the array should be the sum of + all delays before the j'th pulse. The last entry + should be 1 (i.e. the entire cummulative delay + should have been used once the sequence of pulses + is complete). - The function should be monotonically increasing, - strictly greater than zero and the last value - should be 1. + The function should be monotonically increasing, + strictly greater than zero and the last value + should be 1. - This implementation returns: + This implementation returns: - sin((pi / 2) * (j / (N + 1)))**2 + sin((pi / 2) * (j / (N + 1)))**2 - as the cummulative delay after the j'th pulse. + as the cummulative delay after the j'th pulse. """ return np.array( - [np.sin((np.pi / 2) * (j / (N + 1))) ** 2 for j in range(0, N + 1)] + [np.sin((np.pi / 2) * (j / (N + 1)))**2 for j in range(0, N + 1)] ) def drive_opt(amplitude, avg_delay, integral, N): - """Return an optimized distance pulse function. + """ Return an optimized distance pulse function. - Our previous pulses were evenly spaced. Here we - instead use a varying delay after the j'th pulse. + Our previous pulses were evenly spaced. Here we + instead use a varying delay after the j'th pulse. - The cummulative delay is described by the function - ``cummulative_delay_fractions`` above. + The cummulative delay is described by the function + ``cummulative_delay_fractions`` above. """ duration = integral / amplitude cummulative_delays = N * avg_delay * cummulative_delay_fractions(N) @@ -368,11 +347,11 @@ Let's plot the cummulative delays and see what they look like. Note that the cum On the same axes we plot the individual $j^{th}$ delays as a fraction of the average delay. -```{code-cell} +```{code-cell} ipython3 def plot_cummulative_delay_fractions(N): cummulative = cummulative_delay_fractions(N) individual = (cummulative[1:] - cummulative[:-1]) * N - plt.plot(np.arange(0, N + 1), cummulative, label="Cummulative delay") + plt.plot(np.arange(0, N + 1), cummulative, label="Cumulative delay") plt.plot(np.arange(0, N), individual, label="j'th delay") plt.xlabel("j") plt.ylabel("Fraction of delay") @@ -384,7 +363,7 @@ plot_cummulative_delay_fractions(100) And now let us plot the first ten even and optimally spaced pulses together to compare them: -```{code-cell} +```{code-cell} ipython3 def plot_even_and_optimally_spaced_pulses(): amplitude = 10.0 integral = np.pi / 2 @@ -397,14 +376,10 @@ def plot_even_and_optimally_spaced_pulses(): pulse_eq = drive(amplitude, delay, integral) plt.plot( - tlist, - [pulse_opt(t) for t in tlist], - label="opt", + tlist, [pulse_opt(t) for t in tlist], label="opt", ) plt.plot( - tlist, - [pulse_eq(t) for t in tlist], - label="eq", + tlist, [pulse_eq(t) for t in tlist], label="eq", ) plt.legend(loc=4) @@ -416,7 +391,7 @@ Now let's simulate the effectiveness of the two sets of delays by comparing how We'll perform the simulation over a range of lambdas and gammas to show how the non-evenly spaced delays become optimal as the width of the bath spectral function increases. -```{code-cell} +```{code-cell} ipython3 # Bath parameters to simulate over: # We use only two lambdas and two gammas so that the notebook executes @@ -437,10 +412,10 @@ display(progress) def simulate_100_pulses(lam, gamma, T, NC, Nk): - """Simulate the evolution of 100 evenly and optimally spaced pulses. + """ Simulate the evolution of 100 evenly and optimally spaced pulses. - Returns the expectation value of P12p from the final state of - each evolution. + Returns the expectation value of P12p from the final state of + each evolution. """ rho0 = (basis(2, 1) + basis(2, 0)).unit() rho0 = ket2dm(rho0) @@ -457,14 +432,13 @@ def simulate_100_pulses(lam, gamma, T, NC, Nk): delay = avg_cycle_time - duration env = DrudeLorentzEnvironment(lam=lam, gamma=gamma, T=T) - env_approx = env.approx_by_pade(Nk=Nk) - bath = (env_approx, Q) + env_approx = env.approximate("pade", Nk=Nk) # Equally spaced pulses: pulse_eq = drive(amplitude, delay, integral) H_d = QobjEvo([H_sys, [H_drive, pulse_eq]]) - hsolver = HEOMSolver(H_d, bath, NC, options=options) + hsolver = HEOMSolver(H_d, (env_approx, Q), NC, options=options) result = hsolver.run(rho0, tlist) P12_eq = expect(result.states[-1], P12p) @@ -475,7 +449,7 @@ def simulate_100_pulses(lam, gamma, T, NC, Nk): pulse_opt = drive_opt(amplitude, delay, integral, N) H_d = QobjEvo([H_sys, [H_drive, pulse_opt]]) - hsolver = HEOMSolver(H_d, bath, NC, options=options) + hsolver = HEOMSolver(H_d, (env_approx, Q), NC, options=options) result = hsolver.run(rho0, tlist) P12_opt = expect(result.states[-1], P12p) @@ -487,40 +461,30 @@ def simulate_100_pulses(lam, gamma, T, NC, Nk): # We use NC=2 and Nk=2 to speed up the simulation: P12_results = [ - list( - zip( - *( - simulate_100_pulses(lam=lam_, gamma=gamma_, T=0.5, NC=2, Nk=2) - for gamma_ in gammas - ) - ) - ) + list(zip(*( + simulate_100_pulses(lam=lam_, gamma=gamma_, T=0.5, NC=2, Nk=2) + for gamma_ in gammas + ))) for lam_ in lams ] ``` Now that we have the expectation values of $\rho_{01}$ let's plot them as a function of gamma for each lambda. Note how in each case the non-evenly spaced pulses become optimal once gamma is sufficiently small: -```{code-cell} +```{code-cell} ipython3 fig, axes = plt.subplots(1, 1, sharex=False, figsize=(10, 7)) colors = ["green", "red", "blue"] for i in range(len(lams)): color = colors[i % len(colors)] axes.plot( - gammas, - np.real(P12_results[i][0]), - color, - linestyle="-", - linewidth=2, + gammas, np.real(P12_results[i][0]), + color, linestyle='-', linewidth=2, label=f"Optimal DD [$\\lambda={lams[i]}$]", ) axes.plot( - gammas, - np.real(P12_results[i][1]), - color, - linestyle="-.", - linewidth=2, + gammas, np.real(P12_results[i][1]), + color, linestyle='-.', linewidth=2, label=f"Even DD [$\\lambda={lams[i]}$]", ) @@ -537,14 +501,6 @@ And now you know about dynamically decoupling a qubit from its environment! ## About -```{code-cell} -qutip.about() -``` - -## Testing - -This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. - -```{code-cell} -assert 1 == 1 +```{code-cell} ipython3 +about() ``` From edf808781568935b3a04a75b48f4c2af10eb75e4 Mon Sep 17 00:00:00 2001 From: Paul Menczel Date: Thu, 24 Apr 2025 17:32:55 +0900 Subject: [PATCH 41/44] HEOM 5 notebooks final pass --- .../heom-5a-fermions-single-impurity-model.md | 197 +++++++----------- .../heom-5b-fermions-discrete-boson-model.md | 135 +++++------- 2 files changed, 132 insertions(+), 200 deletions(-) diff --git a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md index 75091d42..2c5278e1 100644 --- a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md +++ b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md @@ -1,15 +1,14 @@ --- jupytext: - formats: ipynb,md:myst text_representation: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.5 + jupytext_version: 1.17.0 kernelspec: - display_name: qutip-tutorials - language: python name: python3 + display_name: Python 3 (ipykernel) + language: python --- # HEOM 5a: Fermionic single impurity model @@ -69,33 +68,34 @@ In this notebook we: ## Setup -```{code-cell} +```{code-cell} ipython3 import contextlib import dataclasses import time -import matplotlib.pyplot as plt import numpy as np -import qutip -from IPython.display import display -from ipywidgets import IntProgress -from qutip import basis, destroy, expect +from scipy.integrate import quad +import matplotlib.pyplot as plt + +from qutip import about, basis, destroy, expect from qutip.core.environment import LorentzianEnvironment from qutip.solver.heom import HEOMSolver -from scipy.integrate import quad + +from IPython.display import display +from ipywidgets import IntProgress %matplotlib inline ``` ## Helpers -```{code-cell} +```{code-cell} ipython3 @contextlib.contextmanager def timer(label): - """Simple utility for timing functions: + """ Simple utility for timing functions: - with timer("name"): - ... code to time ... + with timer("name"): + ... code to time ... """ start = time.time() yield @@ -103,7 +103,7 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} +```{code-cell} ipython3 # Solver options: # We set store_ados to True so that we can @@ -111,7 +111,6 @@ def timer(label): # to calculate the current between the leads # and the system. - options = { "nsteps": 1500, "store_states": True, @@ -127,7 +126,7 @@ options = { And let us set up the system Hamiltonian, bath and system measurement operators: -```{code-cell} +```{code-cell} ipython3 # Define the system Hamiltonian: # The system is a single fermion with energy level split e1: @@ -136,12 +135,11 @@ e1 = 1.0 H = e1 * d1.dag() * d1 ``` -```{code-cell} +```{code-cell} ipython3 # Define parameters for left and right fermionic baths. # Each bath is a lead (i.e. a wire held at a potential) # with temperature T and chemical potential mu. - @dataclasses.dataclass class LorentzianBathParameters: lead: str @@ -160,16 +158,16 @@ class LorentzianBathParameters: self.mu = -self.theta / 2.0 def J(self, w): - """Spectral density.""" - return self.gamma * self.W**2 / ((w - self.mu) ** 2 + self.W**2) + """ Spectral density. """ + return self.gamma * self.W**2 / ((w - self.mu)**2 + self.W**2) def fF(self, w, sign=1.0): - """Fermi distribution for this bath.""" + """ Fermi distribution for this bath. """ x = sign * self.beta * (w - self.mu) return fF(x) def lamshift(self, w): - """Return the lamshift.""" + """ Return the lamb shift. """ return 0.5 * (w - self.mu) * self.J(w) / self.W def replace(self, **kw): @@ -177,7 +175,7 @@ class LorentzianBathParameters: def fF(x): - """Return the Fermi distribution.""" + """ Return the Fermi distribution. """ # in units where kB = 1.0 return 1 / (np.exp(x) + 1) @@ -190,7 +188,7 @@ bath_R = LorentzianBathParameters(Q=d1, lead="R") Let's plot the spectral density. -```{code-cell} +```{code-cell} ipython3 w_list = np.linspace(-2, 2, 100) fig, ax = plt.subplots(figsize=(12, 7)) @@ -199,17 +197,13 @@ spec_L = bath_L.J(w_list) spec_R = bath_R.J(w_list) ax.plot( - w_list, - spec_L, - "b--", - linewidth=3, + w_list, spec_L, + "b--", linewidth=3, label=r"J_L(w)", ) ax.plot( - w_list, - spec_R, - "r--", - linewidth=3, + w_list, spec_R, + "r--", linewidth=3, label=r"J_R(w)", ) @@ -222,7 +216,7 @@ ax.legend(); Next let's plot the emission and absorption by the leads. -```{code-cell} +```{code-cell} ipython3 w_list = np.linspace(-2, 2, 100) fig, ax = plt.subplots(figsize=(12, 7)) @@ -233,17 +227,13 @@ gam_L_in = bath_L.J(w_list) * bath_L.fF(w_list, sign=1.0) gam_L_out = bath_L.J(w_list) * bath_L.fF(w_list, sign=-1.0) ax.plot( - w_list, - gam_L_in, - "b--", - linewidth=3, + w_list, gam_L_in, + "b--", linewidth=3, label=r"S_L(w) input (absorption)", ) ax.plot( - w_list, - gam_L_out, - "r--", - linewidth=3, + w_list, gam_L_out, + "r--", linewidth=3, label=r"S_L(w) output (emission)", ) @@ -253,17 +243,13 @@ gam_R_in = bath_R.J(w_list) * bath_R.fF(w_list, sign=1.0) gam_R_out = bath_R.J(w_list) * bath_R.fF(w_list, sign=-1.0) ax.plot( - w_list, - gam_R_in, - "b", - linewidth=3, + w_list, gam_R_in, + "b", linewidth=3, label=r"S_R(w) input (absorption)", ) ax.plot( - w_list, - gam_R_out, - "r", - linewidth=3, + w_list, gam_R_out, + "r", linewidth=3, label=r"S_R(w) output (emission)", ) @@ -276,7 +262,7 @@ ax.legend(); Let's start by solving for the evolution using a Pade expansion of the correlation function of the Lorentzian spectral density: -```{code-cell} +```{code-cell} ipython3 # HEOM dynamics using the Pade approximation: # Times to solve for and initial system state: @@ -286,21 +272,14 @@ rho0 = basis(2, 0) * basis(2, 0).dag() Nk = 10 # Number of exponents to retain in the expansion of each bath envL = LorentzianEnvironment( - bath_L.T, - bath_L.mu, - bath_L.gamma, - bath_L.W, + bath_L.T, bath_L.mu, bath_L.gamma, bath_L.W, ) envL_pade = envL.approx_by_pade(Nk=Nk, tag="L") envR = LorentzianEnvironment( - bath_R.T, - bath_R.mu, - bath_R.gamma, - bath_R.W, + bath_R.T, bath_R.mu, bath_R.gamma, bath_R.W, ) envR_pade = envR.approx_by_pade(Nk=Nk, tag="R") - with timer("RHS construction time"): solver_pade = HEOMSolver( H, @@ -318,32 +297,28 @@ with timer("Steady state solver time"): Now let us plot the result which shows the decay of the initially excited impurity. This is not very illuminating, but we will compare it with the Matsubara expansion and analytic solution sortly: -```{code-cell} +```{code-cell} ipython3 # Plot the Pade results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) axes.plot( - tlist, - expect(result_pade.states, rho0), - "r--", - linewidth=2, + tlist, expect(result_pade.states, rho0), + 'r--', linewidth=2, label="P11 (Pade)", ) axes.axhline( expect(rho_ss_pade, rho0), - color="r", - linestyle="dotted", - linewidth=1, + color='r', linestyle="dotted", linewidth=1, label="P11 (Pade steady state)", ) -axes.set_xlabel("t", fontsize=28) +axes.set_xlabel('t', fontsize=28) axes.legend(fontsize=12); ``` Now let us do the same for the Matsubara expansion: -```{code-cell} +```{code-cell} ipython3 # HEOM dynamics using the Matsubara approximation: envL_mats = envL.approx_by_matsubara(Nk=Nk, tag="L") @@ -367,41 +342,33 @@ with timer("Steady state solver time"): We see a marked difference in the Matsubara vs Pade results: -```{code-cell} +```{code-cell} ipython3 # Plot the Pade results fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) axes.plot( - tlist, - expect(result_pade.states, rho0), - "r--", - linewidth=2, + tlist, expect(result_pade.states, rho0), + 'r--', linewidth=2, label="P11 (Pade)", ) axes.axhline( expect(rho_ss_pade, rho0), - color="r", - linestyle="dotted", - linewidth=1, + color='r', linestyle="dotted", linewidth=1, label="P11 (Pade steady state)", ) axes.plot( - tlist, - expect(result_mats.states, rho0), - "b--", - linewidth=2, + tlist, expect(result_mats.states, rho0), + 'b--', linewidth=2, label="P11 (Mats)", ) axes.axhline( expect(rho_ss_mats, rho0), - color="b", - linestyle="dotted", - linewidth=1, + color='b', linestyle="dotted", linewidth=1, label="P11 (Mats steady state)", ) -axes.set_xlabel("t", fontsize=28) +axes.set_xlabel('t', fontsize=28) axes.legend(fontsize=12); ``` @@ -411,18 +378,16 @@ One advantage of this simple model is that the steady state current to the baths See the [QuTiP-BoFiN paper](https://arxiv.org/abs/2010.10806) for a detailed description and references for the analytic result. Below we just perform the required integration numerically. -```{code-cell} +```{code-cell} ipython3 def analytical_steady_state_current(bath_L, bath_R, e1): - """Calculate the analytical steady state current.""" + """ Calculate the analytical steady state current. """ def integrand(w): return (2 / np.pi) * ( - bath_L.J(w) - * bath_R.J(w) - * (bath_L.fF(w) - bath_R.fF(w)) - / ( - (bath_L.J(w) + bath_R.J(w)) ** 2 - + 4 * (w - e1 - bath_L.lamshift(w) - bath_R.lamshift(w)) ** 2 + bath_L.J(w) * bath_R.J(w) * (bath_L.fF(w) - bath_R.fF(w)) / + ( + (bath_L.J(w) + bath_R.J(w))**2 + + 4 * (w - e1 - bath_L.lamshift(w) - bath_R.lamshift(w))**2 ) ) @@ -451,10 +416,10 @@ To compare the analytical result above with the result from the HEOM, we need to In the function `state_current(...)` below, we extract the first level ADOs for the specified bath and sum the contributions to the current from each: -```{code-cell} +```{code-cell} ipython3 def state_current(ado_state, bath_tag): - """Determine current from the given bath (either "R" or "L") to - the system in the given ADO state. + """ Determine current from the given bath (either "R" or "L") to + the system in the given ADO state. """ level_1_aux = [ (ado_state.extract(label), ado_state.exps(label)[0]) @@ -474,7 +439,7 @@ def state_current(ado_state, bath_tag): Now we can calculate the steady state currents from the Pade and Matsubara HEOM results: -```{code-cell} +```{code-cell} ipython3 curr_ss_pade_L = state_current(ado_ss_pade, "L") curr_ss_pade_R = state_current(ado_ss_pade, "R") @@ -482,7 +447,7 @@ print(f"Pade steady state current (L): {curr_ss_pade_L}") print(f"Pade steady state current (R): {curr_ss_pade_R}") ``` -```{code-cell} +```{code-cell} ipython3 curr_ss_mats_L = state_current(ado_ss_mats, "L") curr_ss_mats_R = state_current(ado_ss_mats, "R") @@ -494,7 +459,7 @@ Note that the currents from each bath balance as is required by the steady state Now let's compare all three: -```{code-cell} +```{code-cell} ipython3 print(f"Pade current (R): {curr_ss_pade_R}") print(f"Matsubara current (R): {curr_ss_mats_R}") print(f"Analytical curernt: {curr_ss_analytic}") @@ -514,7 +479,7 @@ Now lets plot the current as a function of bias voltage (the bias voltage is the We will calculate the steady state current for each `theta` both analytically and using the HEOM with the Pade correlation expansion approximation. -```{code-cell} +```{code-cell} ipython3 # Theta (bias voltages) thetas = np.linspace(-4, 4, 100) @@ -528,7 +493,7 @@ display(progress) def current_analytic_for_theta(e1, bath_L, bath_R, theta): - """Return the analytic current for a given theta.""" + """ Return the analytic current for a given theta. """ current = analytical_steady_state_current( bath_L.replace(theta=theta), bath_R.replace(theta=theta), @@ -539,7 +504,7 @@ def current_analytic_for_theta(e1, bath_L, bath_R, theta): def current_pade_for_theta(H, bath_L, bath_R, theta, Nk): - """Return the steady state current using the Pade approximation.""" + """ Return the steady state current using the Pade approximation. """ bath_L = bath_L.replace(theta=theta) bath_R = bath_R.replace(theta=theta) @@ -572,27 +537,23 @@ curr_ss_pade_theta = [ Below we plot the results and see that even with `Nk=6`, the HEOM Pade approximation gives good results for the steady state current. Increasing `Nk` to `10` gives very accurate results. -```{code-cell} +```{code-cell} ipython3 fig, ax = plt.subplots(figsize=(12, 7)) ax.plot( - thetas, - 2.434e-4 * 1e6 * np.array(curr_ss_analytic_thetas), - color="black", - linewidth=3, + thetas, 2.434e-4 * 1e6 * np.array(curr_ss_analytic_thetas), + color="black", linewidth=3, label=r"Analytical", ) ax.plot( - thetas, - 2.434e-4 * 1e6 * np.array(curr_ss_pade_theta), - "r--", - linewidth=3, + thetas, 2.434e-4 * 1e6 * np.array(curr_ss_pade_theta), + 'r--', linewidth=3, label=r"HEOM Pade $N_k=10$, $n_{\mathrm{max}}=2$", ) -ax.locator_params(axis="y", nbins=4) -ax.locator_params(axis="x", nbins=4) +ax.locator_params(axis='y', nbins=4) +ax.locator_params(axis='x', nbins=4) ax.set_xticks([-2.5, 0, 2.5]) ax.set_xticklabels([-2.5, 0, 2.5]) @@ -603,15 +564,15 @@ ax.legend(fontsize=25); ## About -```{code-cell} -qutip.about() +```{code-cell} ipython3 +about() ``` ## Testing This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. -```{code-cell} +```{code-cell} ipython3 assert np.allclose(curr_ss_pade_L + curr_ss_pade_R, 0) assert np.allclose(curr_ss_mats_L + curr_ss_mats_R, 0) assert np.allclose(curr_ss_pade_R, curr_ss_analytic, rtol=1e-4) diff --git a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md index 4a987b7f..372a78b5 100644 --- a/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md +++ b/tutorials-v5/heom/heom-5b-fermions-discrete-boson-model.md @@ -1,15 +1,14 @@ --- jupytext: - formats: ipynb,md:myst text_representation: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.5 + jupytext_version: 1.17.0 kernelspec: - display_name: qutip-tutorials - language: python name: python3 + display_name: Python 3 (ipykernel) + language: python --- # HEOM 5b: Discrete boson coupled to an impurity and fermionic leads @@ -95,32 +94,33 @@ The complete setup now consists of four parts: ## Setup -```{code-cell} +```{code-cell} ipython3 import contextlib import dataclasses import time -import matplotlib.pyplot as plt import numpy as np -import qutip -from IPython.display import display -from ipywidgets import IntProgress -from qutip import destroy, qeye, tensor +import matplotlib.pyplot as plt + +from qutip import about, destroy, qeye, tensor from qutip.core.environment import LorentzianEnvironment from qutip.solver.heom import HEOMSolver +from IPython.display import display +from ipywidgets import IntProgress + %matplotlib inline ``` ## Helpers -```{code-cell} +```{code-cell} ipython3 @contextlib.contextmanager def timer(label): - """Simple utility for timing functions: + """ Simple utility for timing functions: - with timer("name"): - ... code to time ... + with timer("name"): + ... code to time ... """ start = time.time() yield @@ -128,10 +128,10 @@ def timer(label): print(f"{label}: {end - start}") ``` -```{code-cell} +```{code-cell} ipython3 def state_current(ado_state, bath_tag): - """Determine current from the given bath (either "R" or "L") to - the system in the given ADO state. + """ Determine current from the given bath (either "R" or "L") to + the system in the given ADO state. """ level_1_aux = [ (ado_state.extract(label), ado_state.exps(label)[0]) @@ -149,7 +149,7 @@ def state_current(ado_state, bath_tag): ) ``` -```{code-cell} +```{code-cell} ipython3 # Solver options: # We set store_ados to True so that we can @@ -157,7 +157,6 @@ def state_current(ado_state, bath_tag): # to calculate the current between the leads # and the system. - options = { "nsteps": 1500, "store_states": True, @@ -173,10 +172,9 @@ options = { Let us set up the system Hamiltonian and specify the properties of the two reservoirs. -```{code-cell} +```{code-cell} ipython3 # Define the system Hamiltonian: - @dataclasses.dataclass class SystemParameters: e1: float = 0.3 # fermion mode energy splitting @@ -188,9 +186,9 @@ class SystemParameters: d = tensor(destroy(2), qeye(self.Nbos)) a = tensor(qeye(2), destroy(self.Nbos)) self.H = ( - self.e1 * d.dag() * d - + self.Omega * a.dag() * a - + self.Lambda * (a + a.dag()) * d.dag() * d + self.e1 * d.dag() * d + + self.Omega * a.dag() * a + + self.Lambda * (a + a.dag()) * d.dag() * d ) self.Q = d @@ -201,12 +199,11 @@ class SystemParameters: sys_p = SystemParameters() ``` -```{code-cell} +```{code-cell} ipython3 # Define parameters for left and right fermionic baths. # Each bath is a lead (i.e. a wire held at a potential) # with temperature T and chemical potential mu. - @dataclasses.dataclass class LorentzianBathParameters: lead: str @@ -224,16 +221,16 @@ class LorentzianBathParameters: self.mu = -self.theta / 2.0 def J(self, w): - """Spectral density.""" - return self.gamma * self.W**2 / ((w - self.mu) ** 2 + self.W**2) + """ Spectral density. """ + return self.gamma * self.W**2 / ((w - self.mu)**2 + self.W**2) def fF(self, w, sign=1.0): - """Fermi distribution for this bath.""" + """ Fermi distribution for this bath. """ x = sign * self.beta * (w - self.mu) return fF(x) def lamshift(self, w): - """Return the lamshift.""" + """ Return the lamb shift. """ return 0.5 * (w - self.mu) * self.J(w) / self.W def replace(self, **kw): @@ -241,7 +238,7 @@ class LorentzianBathParameters: def fF(x): - """Return the Fermi distribution.""" + """ Return the Fermi distribution. """ # in units where kB = 1.0 return 1 / (np.exp(x) + 1) @@ -256,7 +253,7 @@ bath_R = LorentzianBathParameters(W=10**4, lead="R") Next let's plot the emission and absorption by the leads. -```{code-cell} +```{code-cell} ipython3 w_list = np.linspace(-2, 2, 100) fig, ax = plt.subplots(figsize=(12, 7)) @@ -267,17 +264,13 @@ gam_L_in = bath_L.J(w_list) * bath_L.fF(w_list, sign=1.0) gam_L_out = bath_L.J(w_list) * bath_L.fF(w_list, sign=-1.0) ax.plot( - w_list, - gam_L_in, - "b--", - linewidth=3, + w_list, gam_L_in, + "b--", linewidth=3, label=r"S_L(w) input (absorption)", ) ax.plot( - w_list, - gam_L_out, - "r--", - linewidth=3, + w_list, gam_L_out, + "r--", linewidth=3, label=r"S_L(w) output (emission)", ) @@ -287,17 +280,13 @@ gam_R_in = bath_R.J(w_list) * bath_R.fF(w_list, sign=1.0) gam_R_out = bath_R.J(w_list) * bath_R.fF(w_list, sign=-1.0) ax.plot( - w_list, - gam_R_in, - "b", - linewidth=3, + w_list, gam_R_in, + "b", linewidth=3, label=r"S_R(w) input (absorption)", ) ax.plot( - w_list, - gam_R_out, - "r", - linewidth=3, + w_list, gam_R_out, + "r", linewidth=3, label=r"S_R(w) output (emission)", ) @@ -312,15 +301,16 @@ Here we just give one example of the current as a function of bias voltage, but One note: for very large problems, this can be slow. -```{code-cell} +```{code-cell} ipython3 def steady_state_pade_for_theta(sys_p, bath_L, bath_R, theta, Nk, Nc, Nbos): - """Return the steady state current using the Pade approximation.""" + """ Return the steady state current using the Pade approximation. """ sys_p = sys_p.replace(Nbos=Nbos) bath_L = bath_L.replace(theta=theta) bath_R = bath_R.replace(theta=theta) - envR = LorentzianEnvironment(bath_R.T, bath_R.mu, bath_R.gamma, bath_R.W) + envL = LorentzianEnvironment(bath_L.T, bath_L.mu, bath_L.gamma, bath_L.W) + envR = LorentzianEnvironment(bath_R.T, bath_R.mu, bath_R.gamma, bath_R.W) bathL = envL.approx_by_matsubara(Nk, tag="L") bathR = envR.approx_by_matsubara(Nk, tag="R") @@ -337,10 +327,9 @@ def steady_state_pade_for_theta(sys_p, bath_L, bath_R, theta, Nk, Nc, Nbos): return np.real(2.434e-4 * 1e6 * current) ``` -```{code-cell} +```{code-cell} ipython3 # Parameters: - Nk = 6 Nc = 2 Nbos = 2 # Use Nbos = 16 for more accurate results @@ -355,37 +344,27 @@ display(progress) currents = [] for theta in thetas: - currents.append( - steady_state_pade_for_theta( - sys_p, - bath_L, - bath_R, - theta, - Nk=Nk, - Nc=Nc, - Nbos=Nbos, - ) - ) + currents.append(steady_state_pade_for_theta( + sys_p, bath_L, bath_R, theta, + Nk=Nk, Nc=Nc, Nbos=Nbos, + )) progress.value += 1 ``` -```{code-cell} +```{code-cell} ipython3 fig, ax = plt.subplots(figsize=(12, 10)) ax.plot( - thetas, - currents, - color="green", - linestyle="-", - linewidth=3, + thetas, currents, + color="green", linestyle='-', linewidth=3, label=f"Nk = {5}, max_depth = {Nc}, Nbos = {Nbos}", ) ax.set_yticks([0, 0.5, 1]) ax.set_yticklabels([0, 0.5, 1]) -ax.locator_params(axis="y", nbins=4) -ax.locator_params(axis="x", nbins=4) +ax.locator_params(axis='y', nbins=4) +ax.locator_params(axis='x', nbins=4) ax.set_xlabel(r"Bias voltage $\Delta \mu$ ($V$)", fontsize=30) ax.set_ylabel(r"Current ($\mu A$)", fontsize=30) @@ -394,14 +373,6 @@ ax.legend(loc=4); ## About -```{code-cell} -qutip.about() -``` - -## Testing - -This section can include some tests to verify that the expected outputs are generated within the notebook. We put this section at the end of the notebook, so it's not interfering with the user experience. Please, define the tests using assert, so that the cell execution fails if a wrong output is generated. - -```{code-cell} -assert 1 == 1 +```{code-cell} ipython3 +about() ``` From 80d6794faec863d1230b9dca60956b04847d3cbb Mon Sep 17 00:00:00 2001 From: Paul Menczel Date: Fri, 25 Apr 2025 15:27:34 +0900 Subject: [PATCH 42/44] HEOM 1d notebook final pass --- .../heom-1d-spin-bath-model-ohmic-fitting.md | 766 +++++------------- 1 file changed, 216 insertions(+), 550 deletions(-) diff --git a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md index dd75ec1a..bf06c0f8 100644 --- a/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md +++ b/tutorials-v5/heom/heom-1d-spin-bath-model-ohmic-fitting.md @@ -30,31 +30,31 @@ In the example below we show how to model an Ohmic environment with exponential * First we fit the spectral density with a set of underdamped brownian oscillator functions. * Second, we evaluate the correlation functions, and fit those with a certain choice of exponential functions. -* Third, we use the available OhmicBath class, and explore the other approximation methods QutiP offers +* Third, we use the built-in OhmicBath class, and explore the other approximation methods QuTiP offers -In each case we will use the fit parameters to determine the correlation function expansion co-efficients needed to construct a description of the bath (i.e. a `BosonicBath` object) to supply to the `HEOMSolver` so that we can solve for the system dynamics. +In each case we will use the fit parameters to determine the correlation function expansion co-efficients needed to construct a description of the bath (i.e. a `BosonicEnvironment` object) to supply to the `HEOMSolver` so that we can solve for the system dynamics. +++ ## Setup ```{code-cell} ipython3 -import numpy as np -import qutip -from matplotlib import pyplot as plt -# Import mpmath functions for evaluation of gamma and zeta +# mpmath is required for this tutorial, +# for the evaluation of gamma and zeta # functions in the expression for the correlation: -# mpmath is required for this tutorial you can install it -# with !pip install mpmath from mpmath import mp -from qutip import basis, expect, sigmax, sigmaz + +import numpy as np +from matplotlib import pyplot as plt + +from qutip import about, basis, expect, sigmax, sigmaz from qutip.core.environment import BosonicEnvironment, OhmicEnvironment from qutip.solver.heom import HEOMSolver +%matplotlib inline + mp.dps = 15 mp.pretty = True - -%matplotlib inline ``` ## System and bath definition @@ -67,9 +67,11 @@ Let us set up the system Hamiltonian, bath and system measurement operators: ```{code-cell} ipython3 # Defining the system Hamiltonian -eps = 0 # Energy of the 2-level system. +eps = 0 # Energy of the 2-level system. Del = 0.2 # Tunnelling term Hsys = 0.5 * eps * sigmaz() + 0.5 * Del * sigmax() + +# Initial state of the system. rho0 = basis(2, 0) * basis(2, 0).dag() ``` @@ -113,8 +115,8 @@ J(\omega) = \omega \alpha e^{- \frac{\omega}{\omega_c}} ```{code-cell} ipython3 def ohmic_correlation(t, alpha, wc, beta, s=1): - """The Ohmic bath correlation function as a function of t - (and the bath parameters). + """ The Ohmic bath correlation function as a function of t + (and the bath parameters). """ corr = (1 / np.pi) * alpha * wc ** (1 - s) corr *= beta ** (-(s + 1)) * mp.gamma(s + 1) @@ -133,18 +135,18 @@ def ohmic_correlation(t, alpha, wc, beta, s=1): ```{code-cell} ipython3 def ohmic_spectral_density(w, alpha, wc): - """The Ohmic bath spectral density as a function of w - (and the bath parameters). + """ The Ohmic bath spectral density as a function of w + (and the bath parameters). """ return w * alpha * np.e ** (-w / wc) ``` ```{code-cell} ipython3 def ohmic_power_spectrum(w, alpha, wc, beta): - """The Ohmic bath power spectrum as a function of w - (and the bath parameters). - It is obtained naively using the Fluctuation-Dissipation Theorem - but, this fails at w=0 where the limit should be taken properly + """ The Ohmic bath power spectrum as a function of w + (and the bath parameters). + We here obtain it naively using the Fluctuation-Dissipation Theorem, + but this fails at w=0 where the limit should be taken properly """ bose = (1 / (np.e ** (w * beta) - 1)) + 1 return w * alpha * np.e ** (-abs(w) / wc) * 2 * bose @@ -154,7 +156,7 @@ def ohmic_power_spectrum(w, alpha, wc, beta): +++ -Finally, let's set the bath parameters we will work with and write down some measurement operators: +Finally, let's set the bath parameters we will work with ```{code-cell} ipython3 Q = sigmaz() @@ -164,7 +166,7 @@ wc = 1.0 s = 1 ``` -And set the cut-off for the HEOM hierarchy: +and set the cut-off for the HEOM hierarchy: ```{code-cell} ipython3 # HEOM parameters: @@ -172,8 +174,9 @@ And set the cut-off for the HEOM hierarchy: # The max_depth defaults to 5 so that the notebook executes more # quickly. Change it to 11 to wait longer for more accurate results. max_depth = 5 -# options used for the differential equation solver, while default works it -# is way slower than using bdf + +# options used for the differential equation solver +# "bdf" integration method is faster here options = { "nsteps": 15000, "store_states": True, @@ -216,44 +219,27 @@ will cover the following approaches: ## Building User defined Bosonic Environments Before obtaining exponential approximations, we first need to construct a -`BosonicEnviroment`, here we will briefly explain how to create an user defined -`BosonicEnviroment` by specifying the spectral density, the same can be done -using the correlation function and the power spectrum. For this example we will -use the Ohmic Spectral density we defined above +`BosonicEnviroment` describing the exact environment. +Here, we will briefly explain how to create a user-defined +`BosonicEnviroment` by specifying the spectral density. The same can be done +using either the correlation function or the power spectrum. For this example, we will +use the Ohmic Spectral density we defined above: ```{code-cell} ipython3 w = np.linspace(0, 25, 20000) J = ohmic_spectral_density(w, alpha, wc) ``` -The `BosonicEnviroment` class has special construtors that can be used to -create enviroments from arbitrary spectral densities, correlation functions, or -power spectrums. Below we show how to construct a `BosonicEnvironment` from a -user specified function or array - -```{code-cell} ipython3 -# From an array -sd_env = BosonicEnvironment.from_spectral_density(J=J, wlist=w) -``` - -The resulting `BosonicEnvironment` cannot compute the power spectrum, or -correlation function because the temperature of the environment has not been -specified. So the `BosonicEnvironment` is not fully characterized by the -parameters provided - -```{code-cell} ipython3 -sd_env.power_spectrum(w) -``` - -If we want access to these properties we need to provide the Temperature at Initialization +The `BosonicEnvironment` class has special constructors that can be used to +create environments from arbitrary spectral densities, correlation functions, or +power spectrums. For example: ```{code-cell} ipython3 # From an array sd_env = BosonicEnvironment.from_spectral_density(J=J, wlist=w, T=T) ``` -Now our bosonic environment can compute the Power Spectrum of the spectral -density provided +Specifying the temperature is optional, but it allows us to automatically compute the corresponding power spectrum and correlation function. For example, the automatically computed power spectrum matches the analytically defined `ohmic_power_spectrum` function from above: ```{code-cell} ipython3 # Here we avoid w=0 @@ -262,32 +248,31 @@ np.allclose( ) ``` -Specifying the Temperature also gives the `BosonicEnvironment` access to the -correlation function +Specifying the Temperature also allows QuTiP to automatically compute the correlation function by fast Fourier transformation: ```{code-cell} ipython3 tlist = np.linspace(0, 10, 500) plt.plot( tlist, sd_env.correlation_function(tlist).real, - label="BosonicEnvironment (Real Part)", + label="BosonicEnvironment FFT (Real Part)", ) plt.plot( tlist, ohmic_correlation(tlist, alpha, wc, 1 / T).real, "--", - label="Original (Real Part)", + label="Analytical (Real Part)", ) plt.plot( tlist, np.imag(sd_env.correlation_function(tlist)), - label="BosonicEnvironment (Imaginary Part)", + label="BosonicEnvironment FFT (Imaginary Part)", ) plt.plot( tlist, np.imag(ohmic_correlation(tlist, alpha, wc, 1 / T)), "--", - label="Original (Imaginary Part)", + label="Analytical (Imaginary Part)", ) plt.ylabel("C(t)") plt.xlabel("t") @@ -295,68 +280,47 @@ plt.legend() plt.show() ``` -One important optional parameter is wMax, when passing arrays to the constructor -it defaults to the maximum value of the array, however when passing a function -we don't need to specify the values on which it is evaluated, and in this case -WMax needs to be specified, wMax is the cutoff frequency for which the -spectral density, or power spectrum has effectively decayed to zero, after this value the function can be -considered to be essentialy zero +Note that above, we constructed the `BosonicEnvironment` from the arrays `w` and `J`. +Instead, one can also use a pure Python function. +In that case, it is important to specify the parameter `wMax`, which is the cutoff frequency where the +spectral density or power spectrum has effectively decayed to zero. That is, for $\omega > \omega_{max}$, the function can be +considered to be essentially zero. The following is therefore equivalent to the environment that we used above: ```{code-cell} ipython3 # From a function sd_env2 = BosonicEnvironment.from_spectral_density( - ohmic_spectral_density, T=T, wMax=10 * wc, args={"alpha": alpha, "wc": wc} + ohmic_spectral_density, T=T, wMax=25 * wc, args={"alpha": alpha, "wc": wc} ) ``` -```{code-cell} ipython3 -tlist = np.linspace(0, 10, 500) -plt.plot(tlist, sd_env2.correlation_function(tlist).real) -plt.plot(tlist, ohmic_correlation(tlist, alpha, wc, 1 / T).real, "--") -plt.plot(tlist, np.imag(sd_env2.correlation_function(tlist))) -plt.plot(tlist, np.imag(ohmic_correlation(tlist, alpha, wc, 1 / T)), "--") -``` - -In this example we considered how to obtain a `BosonicEnvironment` from the spectral density, it can be done analogously from the power spectrum or correlation function using the `from_correlation_function` and `from_power_spectrum` methods. - -+++ - ## Building the Exponential environment by fitting the spectral density -We begin by fitting the spectral density, using a series of $k$ underdamped harmonic oscillators case with the Meier-Tannor form (J. Chem. Phys. 111, 3365 (1999); https://doi.org/10.1063/1.479669): +Once our `BosonicEnvironment` has been constructed, we can obtain a decaying +exponential representation of the environment, via fitting either the spectral +density, power spectrum or the correlation function. + +We begin with a nonlinear-least-squares fit of the spectral density, using a series of $k$ underdamped harmonic oscillators case with the Meier-Tannor form (J. Chem. Phys. 111, 3365 (1999); https://doi.org/10.1063/1.479669): \begin{equation} J_{\mathrm approx}(\omega; a, b, c) = \sum_{i=0}^{k-1} \frac{2 a_i b_i w}{((w + c_i)^2 + b_i^2) ((w - c_i)^2 + b_i^2)} \end{equation} where $a, b$ and $c$ are the fit parameters and each is a vector of length $k$. - -With the spectral density approximation $J_{\mathrm approx}(w; a, b, c)$ implemented above, we can now perform the fit and examine the results. This can be done quickly using the `approximate` method, which fits the spectral density to the series of **k** underdamped harmonic oscillators with the Meier-Tannor form - -+++ - -Once our `BosonicEnvironment` has been constructed, we can obtain a Decaying -exponnetial representation of the environment, via fitting either the spectral -density, power spectrum or the correlation function. - -First we will show how to do it via fitting the spectral density with the -Nonlinear-Least-Squares method. - The idea here is that we express our arbitrary spectral density as a sum of -underdamped spectral densities with different coefficients, for which a the -Matsubara decomposition is available. The number of exponents to be kept in the -Matsubara decomposition of each underdamped spectral density needs to be specified +underdamped spectral densities with different coefficients, for which the +Matsubara decomposition is available. -The output of the fit is a tuple containing an `ExponentialBosonicEnvironment` +The fit can be done easily using the `approximate` method. Its output is a tuple containing an `ExponentialBosonicEnvironment` and a dictionary that has all the relevant information about the fit performed. -The goodness of the feed is measured via the normalized root mean squared error, -by default the number of terms in the fit increased until the target accuracy -is reached or the maximum number allowed `Nmax` is reached. The default target -is a normalized root mean squared error of $5\times 10^{-6}$, if set to None -the fit is performed only with the maximum number of exponents specified +The goodness of the fit is measured via the normalized root mean squared error. + +By default, the number of terms in the fit is increased automatically until the target accuracy +is reached or the maximum number allowed terms `Nmax` is reached. (The target accuracy can be set to None, +then the fit is performed only with the specified number `Nmax` of exponents.) ```{code-cell} ipython3 -bath, fitinfo = sd_env.approximate("sd", w, Nmax=4) +# adding a small uncertainty "sigma" helps the fit routine +approx_env, fitinfo = sd_env.approximate("sd", w, Nmax=4, sigma=0.0001) ``` To obtain an overview of the results of the fit we may take a look at the summary from the ``fitinfo`` @@ -365,161 +329,117 @@ To obtain an overview of the results of the fit we may take a look at the summar print(fitinfo["summary"]) ``` -We may see how the number of exponents chosen affects the fit since the approximated functions are available: +Since the effective spectral density and power spectrum corresponding to the approximated correlation function are available through the `approx_env` object, we can compare them to the original: ```{code-cell} ipython3 fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5)) ax1.plot(w, J, label="Original spectral density") -ax1.plot(w, bath.spectral_density(w), "--", label="Effective fitted SD") +ax1.plot(w, approx_env.spectral_density(w), "--", label="Effective fitted SD") ax1.set_xlabel(r"$\omega$") ax1.set_ylabel(r"$J$") ax1.legend() -ax2.plot(w, np.abs(J - bath.spectral_density(w)), label="Error") +ax2.plot(w, np.abs(J - approx_env.spectral_density(w)), label="Error") ax2.set_xlabel(r"$\omega$") ax2.set_ylabel(r"$|J-J_{approx}|$") ax2.legend() +fig.tight_layout() plt.show() ``` -Here we see a surprisingly large discrepancy in our approximated or effective spectral density. This happens because we are not using enough exponentials from each of the underdamped modes to have an appropiate fit. All modes have the same number of exponents, when not specified it defaults to $1$ which is not enough to model a bath with the temperature considered, let us repeat this with a higher number of exponents. +Here we see a surprisingly large discrepancy in our approximated or effective spectral density. This happens because we are not using enough exponentials (i.e., not enough Matsubara terms) from each of the underdamped modes to have an appropiate fit. All modes use the same number of Matsubara terms; when not specified, the number defaults to $1$, which is not enough to model a bath with the temperature considered here. Let us repeat this with a larger number of exponents. ```{code-cell} ipython3 -bath, fitinfo = sd_env.approximate("sd", w, Nmax=4, Nk=3) +# 3 Matsubara terms per mode instead of one (default) +approx_env, fitinfo = sd_env.approximate("sd", w, Nmax=4, Nk=3, sigma=0.0001) fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5)) ax1.plot(w, J, label="Original spectral density") -ax1.plot(w, bath.spectral_density(w), "--", label="Effective fitted SD") +ax1.plot(w, approx_env.spectral_density(w), "--", label="Effective fitted SD") ax1.set_xlabel(r"$\omega$") ax1.set_ylabel(r"$J$") ax1.legend() -ax2.plot(w, np.abs(J - bath.spectral_density(w)), label="Error") +ax2.plot(w, np.abs(J - approx_env.spectral_density(w)), label="Error") ax2.set_xlabel(r"$\omega$") ax2.set_ylabel(r"$|J-J_{approx}|$") ax2.legend() +fig.tight_layout() plt.show() ``` -Since the number of exponents increases simulation time one should go with the least amount of exponents that correctly describe the bath properties (Power spectrum, Spectral density and the correlation function). - -+++ +Since the number of exponents increases simulation time, one should go with the least amount of exponents that correctly describe the bath properties (Power spectrum, Spectral density and the correlation function). Let's take a closer look at our last fit by plotting the contribution of each term of the fit: ```{code-cell} ipython3 -# Plot the components of the fit separately: -plt.rcParams["font.size"] = 25 -plt.rcParams["figure.figsize"] = (10, 5) - - -def plot_fit(func, J, w, lam, gamma, w0): - """Plot the individual components of a fit to the spectral density. - and how they contribute to the full fit one by one""" - total = 0 - for i in range(len(lam)): - component = func(w, lam[i], gamma[i], w0[i]) - total += component - plt.plot(w, J, "r--", linewidth=2, label="original") - plt.plot(w, total, label=rf"$k={i+1}$") - plt.xlabel(r"$\omega$") - plt.ylabel(r"$J(\omega)$") - plt.legend() - plt.pause(1) - plt.show() - - def plot_fit_components(func, J, w, lam, gamma, w0): - """Plot the individual components of a fit to the spectral density. - and how they contribute to the full fit""" + """ Plot the individual components of a fit to the spectral density + and how they contribute to the full fit""" + plt.figure(figsize=(10, 5)) plt.plot(w, J, "r--", linewidth=2, label="original") for i in range(len(lam)): component = func(w, lam[i], gamma[i], w0[i]) plt.plot(w, component, label=rf"$k={i+1}$") - plt.xlabel(r"$\omega$") - plt.ylabel(r"$J(\omega)$") + plt.xlabel(r"$\omega$", fontsize=20) + plt.ylabel(r"$J(\omega)$", fontsize=20) plt.legend(bbox_to_anchor=(1.04, 1)) plt.show() - lam = fitinfo["params"][:, 0] gamma = fitinfo["params"][:, 1] w0 = fitinfo["params"][:, 2] - def _sd_fit_model(wlist, a, b, c): return ( - 2 - * a - * b - * wlist + 2 * a * b * wlist / (((wlist + c) ** 2 + b**2) * ((wlist - c) ** 2 + b**2)) ) - -plot_fit(_sd_fit_model, J, w, lam, gamma, w0) -``` - -```{code-cell} ipython3 plot_fit_components(_sd_fit_model, J, w, lam, gamma, w0) ``` And let's also compare the power spectrum of the fit and the analytical spectral density: ```{code-cell} ipython3 -def plot_power_spectrum(alpha, wc, beta, save=True): - """Plot the power spectrum of a fit against the actual power spectrum.""" +def plot_power_spectrum(alpha, wc, beta): + """ Plot the power spectrum of a fit against the actual power spectrum. """ w = np.linspace(-10, 10, 50000) s_orig = ohmic_power_spectrum(w, alpha=alpha, wc=wc, beta=beta) - s_fit = bath.power_spectrum(w) - fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) + s_fit = approx_env.power_spectrum(w) + fig, axes = plt.subplots(1, 1, sharex=True) axes.plot(w, s_orig, "r", linewidth=2, label="original") axes.plot(w, np.real(s_fit), "b", linewidth=2, label="fit") - axes.set_xlabel(r"$\omega$", fontsize=28) - axes.set_ylabel(r"$S(\omega)$", fontsize=28) + axes.set_xlabel(r"$\omega$", fontsize=20) + axes.set_ylabel(r"$S(\omega)$", fontsize=20) axes.legend() - if save: - fig.savefig("powerspectrum.eps") - - -plot_power_spectrum(alpha, wc, 1 / T, save=False) +plot_power_spectrum(alpha, wc, 1 / T) ``` -Now if we want to see the systems's behaviour as we change the number of terms in the fit, we may use this auxiliary function. +Now if we want to see the systems's behaviour as we change the parameters of the fit and the simulation, we may use this auxiliary function. ```{code-cell} ipython3 -def generate_spectrum_results(Q, N, Nk, max_depth): - """Run the HEOM with the given bath parameters and - and return the results of the evolution. +def generate_spectrum_results(N, Nk, max_depth): + """ Run the HEOM with the given bath parameters and + and return the results of the evolution. """ - # sigma = 0.0001 - # J_max = abs(max(J, key=abs)) - # lower = [-100*J_max, 0.1*wc, 0.1*wc] - # guess = [J_max, wc, wc] - # upper = [100*J_max, 100*wc, 100*wc] - # ,lower=lower,upper=upper,guess=guess,sigma=sigma) - bath, fitinfo = sd_env.approximate( - "sd", w, Nmax=N, Nk=Nk, target_rmse=None + approx_env, fitinfo = sd_env.approximate( + "sd", w, Nmax=N, Nk=Nk, sigma=0.0001, target_rmse=None ) tlist = np.linspace(0, 30 * np.pi / Del, 600) - # This problem is a little stiff, so we use the BDF method to solve - # the ODE ^^^ - print( - f"Starting calculations for N={N}, Nk={Nk} and max_depth={max_depth} ... " - ) + print(f"Starting calculations for N={N}, Nk={Nk}" + f" and max_depth={max_depth} ... ") HEOM_spectral_fit = HEOMSolver( - Hsys, - (bath, Q), - max_depth=max_depth, - options=options, + Hsys, (approx_env, Q), max_depth=max_depth, + options={**options, 'progress_bar': False}, ) results_spectral_fit = HEOM_spectral_fit.run(rho0, tlist) return results_spectral_fit @@ -527,13 +447,13 @@ def generate_spectrum_results(Q, N, Nk, max_depth): ```{code-cell} ipython3 def plot_result_expectations(plots, axes=None): - """Plot the expectation values of operators as functions of time. + """ Plot the expectation values of operators as functions of time. - Each plot in plots consists of (solver_result, - measurement_operation, color, label). + Each plot in plots consists of (solver_result, + measurement_operation, color, label). """ if axes is None: - fig, axes = plt.subplots(1, 1, sharex=True, figsize=(8, 8)) + fig, axes = plt.subplots(1, 1, sharex=True) fig_created = True else: fig = None @@ -545,21 +465,14 @@ def plot_result_expectations(plots, axes=None): exp = np.real(expect(result.states, m_op)) kw.setdefault("linewidth", 2) if color == "rand": - axes.plot( - result.times, - exp, - c=np.random.rand( - 3, - ), - label=label, - **kw, - ) + axes.plot(result.times, exp, color=np.random.rand(3), + label=label, **kw) else: axes.plot(result.times, exp, color, label=label, **kw) if fig_created: axes.legend(loc=0, fontsize=12) - axes.set_xlabel("t", fontsize=28) + axes.set_xlabel("t", fontsize=20) return fig ``` @@ -567,205 +480,50 @@ def plot_result_expectations(plots, axes=None): Below we generate results for different convergence parameters (number of terms in the fit, number of matsubara terms, and depth of the hierarchy). For the parameter choices here, we need a relatively large depth of around '11', which can be a little slow. ```{code-cell} ipython3 -# # Generate results for different number of lorentzians in fit: - +# Generate results for different number of lorentzians in fit: results_spectral_fit_pk = [ - generate_spectrum_results(Q, n, Nk=1, max_depth=max_depth) + generate_spectrum_results(n, Nk=1, max_depth=max_depth) for n in range(1, 5) ] -plot_result_expectations( - [ - ( - result, - P11p, - "rand", - f"P11 (spectral fit) $k_J$={pk + 1}", - ) - for pk, result in enumerate(results_spectral_fit_pk) - ] -); +plot_result_expectations([ + (result, P11p, "rand", f"P11 (spectral fit) $k$={pk + 1}") + for pk, result in enumerate(results_spectral_fit_pk) +]); ``` ```{code-cell} ipython3 -# generate results for different number of Matsubara terms per Lorentzian -# for max number of Lorentzians: +# generate results for different number of Matsubara terms per Lorentzian: -Nk_list = range(2, 4) +Nk_list = range(1, 4) results_spectral_fit_nk = [ - generate_spectrum_results(Q, 4, Nk=Nk, max_depth=max_depth) + generate_spectrum_results(4, Nk=Nk, max_depth=max_depth) for Nk in Nk_list ] -plot_result_expectations( - [ - ( - result, - P11p, - "rand", - f"P11 (spectral fit) K={nk+1}", - ) - for nk, result in zip(Nk_list, results_spectral_fit_nk) - ] -); +plot_result_expectations([ + (result, P11p, "rand", f"P11 (spectral fit) N_k={nk}") + for nk, result in zip(Nk_list, results_spectral_fit_nk) +]); ``` ```{code-cell} ipython3 -# Generate results for different depths: +# generate results for different hierarchy depths: -Nc_list = range(2, max_depth) +Nc_list = range(3, max_depth+1) results_spectral_fit_nc = [ - generate_spectrum_results(Q, 4, Nk=1, max_depth=Nc) for Nc in Nc_list + generate_spectrum_results(4, Nk=1, max_depth=Nc) + for Nc in Nc_list ] -plot_result_expectations( - [ - ( - result, - P11p, - "rand", - f"P11 (spectral fit) $N_C={nc}$", - ) - for nc, result in zip(Nc_list, results_spectral_fit_nc) - ] -); -``` - -#### We now combine the fitting and correlation function data into one large plot. Here we define a function to plot everything together - -```{code-cell} ipython3 -def gen_plots(fs, w, J, t, C, w2, S): - def plot_cr_fit_vs_actual(t, C, func, axes): - """Plot the C_R(t) fit.""" - yR = func(t) - - axes.plot( - t, - np.real(C), - "r", - linewidth=3, - label="Original", - ) - axes.plot( - t, - np.real(yR), - "g", - dashes=[3, 3], - linewidth=2, - label="Reconstructed", - ) - - axes.set_ylabel(r"$C_R(t)$", fontsize=28) - axes.set_xlabel(r"$t\;\omega_c$", fontsize=28) - axes.locator_params(axis="y", nbins=4) - axes.locator_params(axis="x", nbins=4) - axes.text(0.15, 0.85, "(a)", fontsize=28, transform=axes.transAxes) - - def plot_ci_fit_vs_actual(t, C, func, axes): - """Plot the C_I(t) fit.""" - yI = func(t) - - axes.plot( - t, - np.imag(C), - "r", - linewidth=3, - ) - axes.plot( - t, - np.real(yI), - "g", - dashes=[3, 3], - linewidth=2, - ) - - axes.set_ylabel(r"$C_I(t)$", fontsize=28) - axes.set_xlabel(r"$t\;\omega_c$", fontsize=28) - axes.locator_params(axis="y", nbins=4) - axes.locator_params(axis="x", nbins=4) - axes.text(0.80, 0.80, "(b)", fontsize=28, transform=axes.transAxes) - - def plot_jw_fit_vs_actual(w, J, axes): - """Plot the J(w) fit.""" - J_fit = fs.spectral_density(w) - - axes.plot( - w, - J, - "r", - linewidth=3, - ) - axes.plot( - w, - J_fit, - "g", - dashes=[3, 3], - linewidth=2, - ) - - axes.set_ylabel(r"$J(\omega)$", fontsize=28) - axes.set_xlabel(r"$\omega/\omega_c$", fontsize=28) - axes.locator_params(axis="y", nbins=4) - axes.locator_params(axis="x", nbins=4) - axes.text(0.15, 0.85, "(c)", fontsize=28, transform=axes.transAxes) - - def plot_sw_fit_vs_actual(axes): - """Plot the S(w) fit.""" - - # avoid the pole in the fit around zero: - s_fit = fs.power_spectrum(w2) - - axes.plot(w2, S, "r", linewidth=3) - axes.plot(w2, s_fit, "g", dashes=[3, 3], linewidth=2) - - axes.set_ylabel(r"$S(\omega)$", fontsize=28) - axes.set_xlabel(r"$\omega/\omega_c$", fontsize=28) - axes.locator_params(axis="y", nbins=4) - axes.locator_params(axis="x", nbins=4) - axes.text(0.15, 0.85, "(d)", fontsize=28, transform=axes.transAxes) - - def plot_matsubara_spectrum_fit_vs_actual(t, C): - """Plot the Matsubara fit of the spectrum .""" - fig = plt.figure(figsize=(12, 10)) - grid = plt.GridSpec(2, 2, wspace=0.4, hspace=0.3) - - plot_cr_fit_vs_actual( - t, - C, - lambda t: fs.correlation_function(t), - axes=fig.add_subplot(grid[0, 0]), - ) - plot_ci_fit_vs_actual( - t, - C, - lambda t: np.imag(fs.correlation_function(t)), - axes=fig.add_subplot(grid[0, 1]), - ) - plot_jw_fit_vs_actual( - w, - J, - axes=fig.add_subplot(grid[1, 0]), - ) - plot_sw_fit_vs_actual( - axes=fig.add_subplot(grid[1, 1]), - ) - fig.legend(loc="upper center", ncol=2, fancybox=True, shadow=True) - - return plot_matsubara_spectrum_fit_vs_actual(t, C) -``` - -#### And finally plot everything together - -```{code-cell} ipython3 -t = np.linspace(0, 15, 1000) -C = ohmic_correlation(t, alpha, wc, 1 / T) -w2 = np.concatenate((-np.linspace(10, 1e-2, 100), np.linspace(1e-2, 10, 100))) -S = ohmic_power_spectrum(w2, alpha, wc, 1 / T) -gen_plots(bath, w, J, t, C, w2, S) +plot_result_expectations([ + (result, P11p, "rand", f"P11 (spectral fit) $N_C={nc}$") + for nc, result in zip(Nc_list, results_spectral_fit_nc) +]); ``` -## Obtaining an decaying exponential description via the Correlation function +## Obtaining a decaying exponential description by fitting the correlation function +++ @@ -777,115 +535,65 @@ $$C_R^F(t) = \sum_{i=1}^{k_R} c_R^ie^{-\gamma_R^i t}\cos(\omega_R^i t)$$ $$C_I^F(t) = \sum_{i=1}^{k_I} c_I^ie^{-\gamma_I^i t}\sin(\omega_I^i t)$$ -Analogously to the spectral density case, one may use the `approx_by_cf_fit` method, the main difference with respect to the spectral density fit, is that now we are perfoming two fits, one for the real part and another one for the imaginary part +Also this fit can easily be performed using the `approximate` method. The main difference with respect to the spectral density fit is that now we are perfoming two fits, one for the real part and another one for the imaginary part. +++ -The ansatz used is not good for functions where - -$$C_I^F(0) \neq 0$$ - -The keyword `full_ansatz` which defaults to False. allows for the usage of a -more general ansatz, the fit however tends to be significantly slower, never -the less it can reach a similar level of accuracy with a lower amount of exponents - -When full_ansatz is True. the ansatz used corresponds to - -\begin{align} -\operatorname{Re}[C(t)] = \sum_{k=1}^{N_r} \operatorname{Re}\Bigl[ - (a_k + \mathrm i d_k) \mathrm e^{(b_k + \mathrm i c_k) t}\Bigl] - , -\\ -\operatorname{Im}[C(t)] = \sum_{k=1}^{N_i} \operatorname{Im}\Bigl[ - (a'_k + \mathrm i d'_k) \mathrm e^{(b'_k + \mathrm i c'_k) t} - \Bigr]. -\end{align} +Note that the ansatz is not good if $C_I^F(0) \neq 0$. In this case, the option `full_ansatz=True` allows for the usage of a +more general ansatz. The fit however tends to be significantly slower. We refer to the documentation for details. ```{code-cell} ipython3 def generate_corr_results(N, max_depth): tlist = np.linspace(0, 30 * np.pi / Del, 600) - bath_corr, fitinfo = sd_env.approximate( - "cf", tlist=t, Ni_max=N, Nr_max=N, maxfev=1e8, target_rmse=None + approx_env, fitinfo = sd_env.approximate( + "cf", tlist=tlist, Ni_max=N, Nr_max=N, maxfev=1e8, target_rmse=None ) + + print(f"Starting calculations for N={N}" + f" and max_depth={max_depth} ... ") + HEOM_corr_fit = HEOMSolver( - Hsys, - (bath_corr, Q), - max_depth=max_depth, - options=options, + Hsys, (approx_env, Q), max_depth=max_depth, + options={**options, 'progress_bar': False}, ) - results_corr_fit = HEOM_corr_fit.run(rho0, tlist) - return results_corr_fit +``` - -# # Generate results for different number of exponentials in fit: +```{code-cell} ipython3 +# Generate results for different number of exponentials in fit: results_corr_fit_pk = [ - print(f"{i + 1}") - or generate_corr_results( - i, - max_depth=max_depth, - ) + generate_corr_results(i, max_depth=max_depth) for i in range(1, 4) ] -``` -```{code-cell} ipython3 -plot_result_expectations( - [ - ( - result, - P11p, - "rand", - f"P11 (correlation fit) k_R=k_I={pk + 1}", - ) - for pk, result in enumerate(results_corr_fit_pk) - ] -); +plot_result_expectations([ + (result, P11p, "rand", f"P11 (correlation fit) k_R=k_I={pk + 1}") + for pk, result in enumerate(results_corr_fit_pk) +]); ``` ```{code-cell} ipython3 -fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) - -plot_result_expectations( - [ - ( - results_corr_fit_pk[0], - P11p, - "y", - "Correlation Function Fit $k_R=k_I=1$", - ), - ( - results_corr_fit_pk[2], - P11p, - "k", - "Correlation Function Fit $k_R=k_I=3$", - ), - ( - results_spectral_fit_pk[0], - P11p, - "b", - "Spectral Density Fit $k_J=1$", - ), - ( - results_spectral_fit_pk[3], - P11p, - "r-.", - "Spectral Density Fit $k_J=4$", - ), - ], - axes=axes, -) +# Comparison plot + +fig, axes = plt.subplots(1, 1, sharex=True, figsize=(10, 6)) + +plot_result_expectations([ + (results_corr_fit_pk[0], P11p, "y", "Correlation Fct. Fit $k_R=k_I=1$"), + (results_corr_fit_pk[2], P11p, "k", "Correlation Fct. Fit $k_R=k_I=3$"), + (results_spectral_fit_pk[0], P11p, "b", "Spectral Density Fit $k_J=1$"), + (results_spectral_fit_pk[3], P11p, "r-.", "Spectral Density Fit $k_J=4$"), +], axes=axes) axes.set_yticks([0.6, 0.8, 1]) -axes.set_ylabel(r"$\rho_{11}$", fontsize=30) -axes.set_xlabel(r"$t\;\omega_c$", fontsize=30) -axes.legend(loc=0, fontsize=20); +axes.set_ylabel(r"$\rho_{11}$", fontsize=20) +axes.set_xlabel(r"$t\;\omega_c$", fontsize=20) +axes.legend(loc=0, fontsize=15); ``` -# Using the Ohmic Bath class +# Using the Ohmic Environment class - As the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class, the results above can be reproduced quickly by using the OhmicBath class. This allows for rapid implementation of fitted ohmic baths via the correlation function or spectral density +As the ohmic spectrum is popular in the modeling of open quantum systems, it has its own dedicated class. The results above can be reproduced quickly by using the `OhmicEnvironment` class. This allows for rapid implementation of fitted Ohmic baths. ```{code-cell} ipython3 obs = OhmicEnvironment(T, alpha, wc, s=1) @@ -893,37 +601,33 @@ tlist = np.linspace(0, 30 * np.pi / Del, 600) ``` Just like the other `BosonicEnvironment` we can obtain a decaying exponential -representation of the environment via the `approximate`, let us do the same -methods we explored before +representation of the environment via the `approximate` function. Let us first do the same +methods we explored before: ```{code-cell} ipython3 -Obath, fitinfo = obs.approximate( - method="cf", tlist=tlist, Nr_max=4, Ni_max=4, maxfev=1e9, target_rmse=None +sd_approx_env, fitinfo = obs.approximate( + method="sd", wlist=w, Nmax=4, Nk=3, sigma=0.0001, target_rmse=None ) print(fitinfo["summary"]) -HEOM_ohmic_corr_fit = HEOMSolver( - Hsys, - (Obath, Q), - max_depth=max_depth, - options=options, +HEOM_ohmic_sd_fit = HEOMSolver( + Hsys, (sd_approx_env, Q), max_depth=max_depth, options=options ) -results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist) +results_ohmic_sd_fit = HEOM_ohmic_sd_fit.run(rho0, tlist) ``` ```{code-cell} ipython3 -Obath2, fitinfo = obs.approximate(method="sd", wlist=w, Nmax=4, Nk=3) +cf_approx_env, fitinfo = obs.approximate( + method="cf", tlist=tlist, Nr_max=4, Ni_max=4, maxfev=1e8, target_rmse=None +) print(fitinfo["summary"]) -HEOM_ohmic_sd_fit = HEOMSolver( - Hsys, - (Obath2, Q), - max_depth=max_depth, - options=options, +HEOM_ohmic_corr_fit = HEOMSolver( + Hsys, (cf_approx_env, Q), max_depth=max_depth, options=options ) -results_ohmic_sd_fit2 = HEOM_ohmic_sd_fit.run(rho0, tlist) +results_ohmic_corr_fit = HEOM_ohmic_corr_fit.run(rho0, tlist) ``` ## Other Approximation methods -### Methods based on the Prony Polinomial +### Methods based on the Prony Polynomial The Prony polynomial forms the mathematical foundation for many spectral analysis techniques that estimate frequencies, damping factors, and amplitudes of signals. These methods work by interpreting a given signal as a sum of complex exponentials and deriving a polynomial whose roots correspond to the frequencies or poles of the system. @@ -958,7 +662,7 @@ z_{1}^{M} & z_{2}^{M} &\dots & z_{N}^{M} \\ and $M$ is the length of the signal, and $N$ the number of exponents, and $f=f(t_{sample})$ is the signal evaluated in the sampling points,is a vector $c = (c_{1}, \dots, c_{N})$. -The main difference between the methods is the way one obtains the roots of the polynomial, typically whether this system is solved or a low rank approximation is found for the polynomial, [this article](https://academic.oup.com/imajna/article-abstract/43/2/789/6525860?redirectedFrom=fulltext) is a good reference, the QuTiP implementations are based on it, and the matlab implementations made available by the authors. +The main difference between the methods is the way one obtains the roots of the polynomial, typically whether this system is solved or a low rank approximation is found for the polynomial. [This article](https://academic.oup.com/imajna/article-abstract/43/2/789/6525860?redirectedFrom=fulltext) is a good reference, the QuTiP implementations are based on it and on the matlab implementations made available by the authors. The prony like methods include: @@ -967,7 +671,7 @@ The prony like methods include: - ESPIRA Though ESPIRA is prony like, since it is based on rational polynomial approximations -we group it with other methods +we group it with other methods. +++ @@ -980,13 +684,10 @@ tlist2 = np.linspace(0, 40, 100) ``` ```{code-cell} ipython3 -pbath, fitinfo = obs.approximate("prony", tlist2, Nr=4) +prony_approx_env, fitinfo = obs.approximate("prony", tlist2, Nr=4) print(fitinfo["summary"]) HEOM_ohmic_prony_fit = HEOMSolver( - Hsys, - (pbath, Q), - max_depth=max_depth, - options=options, + Hsys, (prony_approx_env, Q), max_depth=max_depth, options=options ) results_ohmic_prony_fit = HEOM_ohmic_prony_fit.run(rho0, tlist) ``` @@ -995,13 +696,12 @@ Similar to how we approximated via prony we can use ESPRIT, the main difference between both methods lies in the construction of the pencil matrix ```{code-cell} ipython3 -esbath, fitinfo = obs.approximate("esprit", tlist2, Nr=4, separate=False) +esprit_approx_env, fitinfo = obs.approximate( + "esprit", tlist2, Nr=4, separate=False +) print(fitinfo["summary"]) HEOM_ohmic_es_fit = HEOMSolver( - Hsys, - (esbath, Q), - max_depth=max_depth, - options=options, + Hsys, (esprit_approx_env, Q), max_depth=max_depth, options=options ) results_ohmic_es_fit = HEOM_ohmic_es_fit.run(rho0, tlist) ``` @@ -1033,20 +733,15 @@ Which allows us to identify c_{k} = -i \times residues \end{align} -this method works best when the sampling points provided are in the logarithmic scale +This method works best when the sampling points provided are in the logarithmic scale: ```{code-cell} ipython3 wlist = np.concatenate((-np.logspace(3, -8, 3500), np.logspace(-8, 3, 3500))) -aaabath, fitinfo = obs.approximate("aaa", wlist, Nmax=8, tol=1e-15) -print(fitinfo["summary"]) -``` -```{code-cell} ipython3 +aaa_aprox_env, fitinfo = obs.approximate("aaa", wlist, Nmax=8, tol=1e-15) +print(fitinfo["summary"]) HEOM_ohmic_aaa_fit = HEOMSolver( - Hsys, - (aaabath, Q), - max_depth=max_depth, - options=options, + Hsys, (aaa_aprox_env, Q), max_depth=max_depth, options=options ) results_ohmic_aaa_fit = HEOM_ohmic_aaa_fit.run(rho0, tlist) ``` @@ -1065,25 +760,21 @@ $$S(\omega) = \sum_{k=1}^{N}\frac{2(a_k c_k + b_k (d_k - \omega))} {(\omega - d_k)^2 + c_k^2}= 2 \Re \left(\sum_{k} \frac{c_{k}}{\nu_{k}-i \omega} \right)$$ ```{code-cell} ipython3 -psbath, fitinfo = obs.approximate("ps", w2, Nmax=4) -print(fitinfo["summary"]) -``` +w2 = np.concatenate((-np.linspace(10, 1e-2, 100), np.linspace(1e-2, 10, 100))) -```{code-cell} ipython3 +ps_approx_env, fitinfo = obs.approximate("ps", w2, Nmax=4) +print(fitinfo["summary"]) HEOM_ohmic_ps_fit = HEOMSolver( - Hsys, - (psbath, Q), - max_depth=max_depth, - options=options, + Hsys, (ps_approx_env, Q), max_depth=max_depth, options=options ) results_ohmic_ps_fit = HEOM_ohmic_ps_fit.run(rho0, tlist) ``` ### ESPIRA -ESPIRA is a Prony-like method, but while it takes a correlation function as -input. It exploits the relationship between parameter estimation (what we do -in Prony) and rational approximations, the rational approximation is done on the +ESPIRA is a Prony-like method. While it takes a correlation function as +input, it exploits the relationship between parameter estimation (what we do +in Prony) and rational approximations. The rational approximation is done on the DFT via the AAA algorithm, effectively using both information about the power spectrum and the correlation function in the same fit. @@ -1095,34 +786,28 @@ recommended. +++ -ESPIRA I +##### ESPIRA I ```{code-cell} ipython3 tlist4 = np.linspace(0, 20, 1000) -espibath, fitinfo = obs.approximate("espira-I", tlist4, Nr=4) + +espi_approx_env, fitinfo = obs.approximate("espira-I", tlist4, Nr=4) print(fitinfo["summary"]) HEOM_ohmic_espira_fit = HEOMSolver( - Hsys, - (espibath, Q), - max_depth=max_depth, - options=options, + Hsys, (espi_approx_env, Q), max_depth=max_depth, options=options ) results_ohmic_espira_fit = HEOM_ohmic_espira_fit.run(rho0, tlist) ``` -ESPIRA-II +##### ESPIRA-II ```{code-cell} ipython3 -tlist4 = np.linspace(0, 20, 1000) -espibath2, fitinfo = obs.approximate( +espi2_approx_env, fitinfo = obs.approximate( "espira-II", tlist4, Nr=4, Ni=4, separate=True ) print(fitinfo["summary"]) HEOM_ohmic_espira_fit2 = HEOMSolver( - Hsys, - (espibath2, Q), - max_depth=max_depth, - options=options, + Hsys, (espi2_approx_env, Q), max_depth=max_depth, options=options ) results_ohmic_espira2_fit = HEOM_ohmic_espira_fit2.run(rho0, tlist) ``` @@ -1130,48 +815,29 @@ results_ohmic_espira2_fit = HEOM_ohmic_espira_fit2.run(rho0, tlist) Finally we plot the dynamics obtained by the different methods ```{code-cell} ipython3 -fig, axes = plt.subplots(1, 1, sharex=True, figsize=(12, 7)) - -plot_result_expectations( - [ - ( - results_corr_fit_pk[2], - P11p, - "b", - "Correlation Function Fit $k_R=k_I=4$", - ), - ( - results_spectral_fit_pk[3], - P11p, - "r-.", - "Spectral Density Fit $k_J=4$", - ), - (results_ohmic_corr_fit, P11p, "r", "Correlation Fit Ohmic Bath"), - ( - results_ohmic_sd_fit2, - P11p, - "g--", - "Spectral Density Fit Ohmic Bath", - ), - (results_ohmic_ps_fit, P11p, "g--", "Power Spectrum Fit Ohmic Bath"), - (results_ohmic_prony_fit, P11p, "k", " Prony Fit"), - (results_ohmic_es_fit, P11p, "b-.", "ESPRIT Fit"), - (results_ohmic_aaa_fit, P11p, "r-.", "Matrix AAA Fit"), - (results_ohmic_espira_fit, P11p, "k", "ESPIRA I Fit"), - (results_ohmic_espira2_fit, P11p, "--", "ESPIRA II Fit"), - ], - axes=axes, -) -axes.set_ylabel(r"$\rho_{11}$", fontsize=30) -axes.set_xlabel(r"$t\;\omega_c$", fontsize=30) -axes.legend(loc=0, fontsize=20) +fig, axes = plt.subplots(1, 1, sharex=True, figsize=(10, 6)) + +plot_result_expectations([ + (results_ohmic_corr_fit, P11p, "r", "Correlation Fct. Fit"), + (results_ohmic_sd_fit, P11p, "g--", "Spectral Density Fit"), + (results_ohmic_ps_fit, P11p, "g--", "Power Spectrum Fit Ohmic Bath"), + (results_ohmic_prony_fit, P11p, "k", " Prony Fit"), + (results_ohmic_es_fit, P11p, "b-.", "ESPRIT Fit"), + (results_ohmic_aaa_fit, P11p, "r-.", "Matrix AAA Fit"), + (results_ohmic_espira_fit, P11p, "k", "ESPIRA I Fit"), + (results_ohmic_espira2_fit, P11p, "--", "ESPIRA II Fit"), +], axes=axes) + +axes.set_ylabel(r"$\rho_{11}$", fontsize=20) +axes.set_xlabel(r"$t\;\omega_c$", fontsize=20) +axes.legend(loc=0, fontsize=15) axes.set_yscale("log") ``` ## About ```{code-cell} ipython3 -qutip.about() +about() ``` ## Testing From 0503a8f9540428f5fbb1d881c36dbdb06963a37b Mon Sep 17 00:00:00 2001 From: Paul Menczel Date: Fri, 25 Apr 2025 15:36:21 +0900 Subject: [PATCH 43/44] Fixed dead link --- tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md index 2c5278e1..7746cb15 100644 --- a/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md +++ b/tutorials-v5/heom/heom-5a-fermions-single-impurity-model.md @@ -17,7 +17,7 @@ kernelspec: ## Introduction -Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc). Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications. +Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc). Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his dissertation https://open.fau.de/items/36fdd708-a467-4b59-bf4e-4a2110fbc431 and related publications. Notation: From 8eae881c0bb8a22a02e190f231fe39da055012fa Mon Sep 17 00:00:00 2001 From: Paul Menczel Date: Fri, 25 Apr 2025 15:40:36 +0900 Subject: [PATCH 44/44] Fixed more dead links --- tutorials-v4/heom/heom-5a-fermions-single-impurity-model.md | 2 +- tutorials-v4/heom/heom-5b-fermions-discrete-boson-model.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorials-v4/heom/heom-5a-fermions-single-impurity-model.md b/tutorials-v4/heom/heom-5a-fermions-single-impurity-model.md index 623af4db..d56b4219 100644 --- a/tutorials-v4/heom/heom-5a-fermions-single-impurity-model.md +++ b/tutorials-v4/heom/heom-5a-fermions-single-impurity-model.md @@ -20,7 +20,7 @@ kernelspec: ## Introduction -Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc). Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications. +Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc). Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his dissertation https://open.fau.de/items/36fdd708-a467-4b59-bf4e-4a2110fbc431 and related publications. Notation: diff --git a/tutorials-v4/heom/heom-5b-fermions-discrete-boson-model.md b/tutorials-v4/heom/heom-5b-fermions-discrete-boson-model.md index 19d63126..f2afc4f8 100644 --- a/tutorials-v4/heom/heom-5b-fermions-discrete-boson-model.md +++ b/tutorials-v4/heom/heom-5b-fermions-discrete-boson-model.md @@ -20,7 +20,7 @@ kernelspec: Here we model a single fermion coupled to two electronic leads or reservoirs (e.g., this can describe a single quantum dot, a molecular transistor, etc), also coupled to a discrete bosonic (vibronic) mode. -Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his Dissertation https://opus4.kobv.de/opus4-fau/files/10984/DissertationChristianSchinabeck.pdf and related publications. In particular this example reproduces some results from https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.201407 +Note that in this implementation we primarily follow the definitions used by Christian Schinabeck in his Dissertation https://open.fau.de/items/36fdd708-a467-4b59-bf4e-4a2110fbc431 and related publications. In particular this example reproduces some results from https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.201407 Notation: