notebooks/dc-resistivity/inv_dcr_1d.ipynb

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   "source": [
    "---\n",
    "title: \"1D Inversion for a Single Sounding\"\n",
    "authors:\n",
    "  - id: devincowan\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Intermediate notebook\n",
    ":class: caution\n",
    "This tutorial focusses on intermediate level functionality within SimPEG. Basic functionality within SimPEG is not discussed in detail, as we assume the user is already familiar. \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Light-weight notebook\n",
    ":class: hint\n",
    "This tutorial requires minimal computational resources and can be executed quickly in the background while other computer processes are running.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Keywords:** DC resistivity, 1D sounding, inversion, parametric, sparse norm, apparent resistivity, wires mapping.\n",
    "\n",
    "</br>\n",
    "\n",
    "**Summary:** Here we invert DC resistivity data for a single Wenner sounding. We demonstrate 3 approaches that can be used to invert 1D data in SimPEG:\n",
    "\n",
    "1. A weighted least-squares inversion where the number of layers and their thicknesses are fixed.\n",
    "2. An iteratively reweighted least-squares (IRLS) inversion to recover sparse and/or blocky structures.\n",
    "3. A parametric inversion where we invert for the layer thicknesses and electrical properties assuming a 3-layered Earth.\n",
    "\n",
    "The *weighted least-squares* approach is a great introduction to geophysical inversion with SimPEG. One drawback however, is that it recovers smooth structures which may not be representative of the true model. To recover sparse and/or blocky 1D structures, we demonstrate an *iteratively reweighted least-squares* approach. If the number of layers is known, but their depths, thicknesses and conductivities are not, we can use a *parametric inversion* approach.\n",
    "\n",
    "</br>\n",
    "\n",
    "**Learning Objectives:** Because this tutorial focusses primarily on inversion-related functionality, we urge the reader to become familiar with functionality explained in the [1D Forward Simulation of DC Resistivity Data](fwd_dcr_1d.ipynb) tutorial before working through this one. For this tutorial, we focus on:\n",
    "\n",
    "- General approaches for 1D inversion with SimPEG.\n",
    "- How to assign appropriate uncertainties to apparent resistivity or normalized voltage data.\n",
    "- Choosing suitable parameters for the inversion.\n",
    "- Specifying directives that are applied throughout the inversion.\n",
    "- Weighted least-squares, sparse-norm and parametric inversion.\n",
    "- Analyzing inversion outputs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Modules\n",
    "\n",
    "Here, we import all of the functionality required to run the notebook for the tutorial exercise.\n",
    "All of the functionality specific to the forward simulation of 1D DC resistivity data are imported from the [simpeg.electromagnetics.static.resistivity](xref:simpeg#simpeg.electromagnetics.static.resistivity) module. Classes required to define the data misfit, regularization, optimization, etc... are imported from elsewhere within SimPEG. We also import some useful utility functions from [simpeg.utils](xref:simpeg#simpeg.utils). To generate the mesh used for the inversion, we use the [discretize](https://discretize.simpeg.xyz/en/main) package."
   ]
  },
  {
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   "execution_count": 1,
   "metadata": {
    "execution": {
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   "source": [
    "# SimPEG functionality\n",
    "from simpeg.electromagnetics.static import resistivity as dc\n",
    "from simpeg.utils import plot_1d_layer_model, download\n",
    "from simpeg import (\n",
    "    maps,\n",
    "    data,\n",
    "    data_misfit,\n",
    "    regularization,\n",
    "    optimization,\n",
    "    inverse_problem,\n",
    "    inversion,\n",
    "    directives,\n",
    ")\n",
    "\n",
    "# discretize functionality\n",
    "from discretize import TensorMesh\n",
    "\n",
    "# Basic Python functionality\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import tarfile\n",
    "\n",
    "mpl.rcParams.update({\"font.size\": 14})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download Tutorial Files\n",
    "\n",
    "For this tutorial, the observed data for the Wenner sounding are stored within a tar-file. Here, we download and extract the data file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:18.066289Z",
     "iopub.status.busy": "2024-11-20T01:23:18.065858Z",
     "iopub.status.idle": "2024-11-20T01:23:18.566944Z",
     "shell.execute_reply": "2024-11-20T01:23:18.565823Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "overwriting /home/ssoler/git/user-tutorials/notebooks/dc-resistivity/inv_dcr_1d_files.tar.gz\n",
      "Downloading https://github.com/simpeg/user-tutorials/raw/main/assets/dc-resistivity/inv_dcr_1d_files.tar.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   saved to: /home/ssoler/git/user-tutorials/notebooks/dc-resistivity/inv_dcr_1d_files.tar.gz\n",
      "Download completed!\n"
     ]
    }
   ],
   "source": [
    "# URL to download from repository assets\n",
    "data_source = \"https://github.com/simpeg/user-tutorials/raw/main/assets/dc-resistivity/inv_dcr_1d_files.tar.gz\"\n",
    "\n",
    "# download the data\n",
    "downloaded_data = download(data_source, overwrite=True)\n",
    "\n",
    "# unzip the tarfile\n",
    "tar = tarfile.open(downloaded_data, \"r\")\n",
    "tar.extractall()\n",
    "tar.close()\n",
    "\n",
    "# path to the directory containing our data\n",
    "dir_path = downloaded_data.split(\".\")[0] + os.path.sep\n",
    "\n",
    "# files to work with\n",
    "data_filename = dir_path + \"app_res_1d_data.dobs\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and Plot the Data\n",
    "\n",
    "Here, we load and parse the data file containing observed data and survey geometry. The data file contains the xyz locations for the A, B, M and N electrodes, in order, as well as the observed data. We then compute the AB/2 (half separation distance) and plot the Wenner sounding data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:18.570947Z",
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     "iopub.status.idle": "2024-11-20T01:23:18.575464Z",
     "shell.execute_reply": "2024-11-20T01:23:18.574740Z"
    }
   },
   "outputs": [],
   "source": [
    "# Load data\n",
    "dobs = np.loadtxt(str(data_filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:18.579242Z",
     "iopub.status.busy": "2024-11-20T01:23:18.578927Z",
     "iopub.status.idle": "2024-11-20T01:23:18.584180Z",
     "shell.execute_reply": "2024-11-20T01:23:18.583196Z"
    }
   },
   "outputs": [],
   "source": [
    "# Extract A, B, M and N electrode locations and the observed data\n",
    "A_electrodes = dobs[:, 0:3]\n",
    "B_electrodes = dobs[:, 3:6]\n",
    "M_electrodes = dobs[:, 6:9]\n",
    "N_electrodes = dobs[:, 9:12]\n",
    "dobs = dobs[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:18.587448Z",
     "iopub.status.busy": "2024-11-20T01:23:18.587037Z",
     "iopub.status.idle": "2024-11-20T01:23:18.592120Z",
     "shell.execute_reply": "2024-11-20T01:23:18.591058Z"
    }
   },
   "outputs": [],
   "source": [
    "# Compute the AB/2 separation for the 1D Wenner array\n",
    "AB_separations = np.sqrt(np.sum((A_electrodes - B_electrodes) ** 2, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-11-20T01:23:18.932761Z"
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   "outputs": [
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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot apparent resistivity as a function of AB/2\n",
    "fig = plt.figure(figsize=(8, 4))\n",
    "mpl.rcParams.update({\"font.size\": 14})\n",
    "ax1 = fig.add_axes([0.15, 0.1, 0.7, 0.85])\n",
    "ax1.semilogy(AB_separations / 2, dobs, \"b-o\")\n",
    "ax1.grid(which=\"both\")\n",
    "ax1.set_xlabel(\"AB/2 (m)\")\n",
    "ax1.set_ylabel(r\"Apparent Resistivity ($\\Omega m$)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assign Uncertainties\n",
    "\n",
    "Inversion with SimPEG requires that we define the uncertainties on our data; that is, an estimate of the standard deviation of the noise on our data assuming it is uncorrelated Gaussian with zero mean. An online resource explaining uncertainties and their role in the inversion can be found [here](https://giftoolscookbook.readthedocs.io/en/latest/content/fundamentals/Uncertainties.html).\n",
    "\n",
    "**For 1D apparent resistivity data,** we generally apply a percent uncertainty to all data. A percent uncertainty will fit conductive and resistive structures equally; unlike a floor uncertainty which will prioritize fitting more resistive structures. For this tutorial, we apply a 2% uncertainty to all data. Depending on the quality of the data, we may choose a percent uncertainty between 2% - 10%.\n",
    "\n",
    "**For 1D normalized voltage data,** we also tend to apply a percent uncertainty to all data. Differences in electrode spacing and subsurface conductivity result in measured voltages that span many orders of magnitude. A percent uncertainty ensures all data are fit equally. Like apparent resistivity, a percent uncertainty of 2% - 10% is a good initial guess.\n",
    "\n",
    "*NOTE: if the data contain very small values that you feel could be erroneous, a small floor value can be added to ensure stability of the inversion.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:18.937661Z",
     "iopub.status.busy": "2024-11-20T01:23:18.937387Z",
     "iopub.status.idle": "2024-11-20T01:23:18.940940Z",
     "shell.execute_reply": "2024-11-20T01:23:18.940398Z"
    }
   },
   "outputs": [],
   "source": [
    "# Add 2.5% uncertainties to all data.\n",
    "uncertainties = 0.025 * np.abs(dobs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the Survey\n",
    "\n",
    "Here, we define the survey geometry. Although the survey consists of a single 1D Wenner sounding, the approach is generalized for 2D and 3D surveys where the electrode locations may not be sorted. For a comprehensive description of constructing DC resistivity surveys in SimPEG, see the [1D Forward Simulation of DC Resistivity Data](fwd_dcr_1d.ipynb) tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:18.944408Z",
     "iopub.status.busy": "2024-11-20T01:23:18.944059Z",
     "iopub.status.idle": "2024-11-20T01:23:18.948540Z",
     "shell.execute_reply": "2024-11-20T01:23:18.947913Z"
    }
   },
   "outputs": [],
   "source": [
    "# Sort by unique current electrode locations\n",
    "unique_tx, k = np.unique(np.c_[A_electrodes, B_electrodes], axis=0, return_index=True)\n",
    "n_sources = len(k)\n",
    "k = np.sort(k)\n",
    "k = np.r_[k, len(k) + 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:18.951002Z",
     "iopub.status.busy": "2024-11-20T01:23:18.950732Z",
     "iopub.status.idle": "2024-11-20T01:23:18.960279Z",
     "shell.execute_reply": "2024-11-20T01:23:18.959492Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define empty list for sources to live\n",
    "source_list = []\n",
    "\n",
    "# Loop over all sources\n",
    "for ii in range(0, n_sources):\n",
    "    # MN electrode locations for receivers. Each is an (N, 3) numpy array\n",
    "    M_locations = M_electrodes[k[ii] : k[ii + 1], :]\n",
    "    N_locations = N_electrodes[k[ii] : k[ii + 1], :]\n",
    "    receiver_list = [\n",
    "        dc.receivers.Dipole(\n",
    "            M_locations,\n",
    "            N_locations,\n",
    "            data_type=\"apparent_resistivity\",\n",
    "        )\n",
    "    ]\n",
    "\n",
    "    # AB electrode locations for source. Each is a (1, 3) numpy array\n",
    "    A_location = A_electrodes[k[ii], :]\n",
    "    B_location = B_electrodes[k[ii], :]\n",
    "    source_list.append(dc.sources.Dipole(receiver_list, A_location, B_location))\n",
    "\n",
    "# Define survey\n",
    "survey = dc.Survey(source_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the Data\n",
    "\n",
    "The SimPEG [Data](xref:simpeg#simpeg.data.Data) class is required for inversion and connects the observed data, uncertainties and survey geometry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:18.963474Z",
     "iopub.status.busy": "2024-11-20T01:23:18.963124Z",
     "iopub.status.idle": "2024-11-20T01:23:18.967164Z",
     "shell.execute_reply": "2024-11-20T01:23:18.966545Z"
    }
   },
   "outputs": [],
   "source": [
    "data_object = data.Data(survey, dobs=dobs, standard_deviation=uncertainties)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weighted Least-Squares Inversion\n",
    "\n",
    "Here, we use the weighted least-squares inversion approach to recover the log-conductivities on a 1D layered Earth. We impose no a-priori information about the number of layers (geological units) or their thicknesses. Instead, we define a large number of layers with exponentially increasing thicknesses. And the depth, thickness and electrical properties of the Earth are inferred from the recovered model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Design a 1D Layered Earth\n",
    "\n",
    "When the spacings between the electrodes are small, we are obtaining higher resolution information about the conductivity near the Earth's surface. As a result, we define much thinner layers near the Earth's surface. When the spacings between electrodes are large, we are obtaining lower resolution information about the conductivity at depth. So at depth, we can define layers that are much thicker. The thickness defining the top layer is ultimately determined by the minimum electrode spacing. And the thickness and depth to the lower layer is determined by the largest electrode spacing. The rate at which the layer thicknesses increase with depth is determined by the user.\n",
    "\n",
    "**For Wenner soundings:** For the 1D Wenner array, the layer thicknesses and largest depth are determined by the AB/2 spacing. A reasonable minimum layer thickness is 10% the minimum AB/2 spacing. And the depth to the bottom layer should be roughly equal to the largest AB/2 spacing.\n",
    "\n",
    "**For dipole-dipole, pole-dipole, etc...:** For these surveys, the source electrodes AB and receiver electrodes MN are offset from one another. For a particular set of electrodes ABMN, we consider the distance between the average source location and average receiver location. For a dipole-dipole survey, this would be:\n",
    "\n",
    "$$\n",
    "d = \\Bigg | \\frac{\\vec{r}_A+\\vec{r}_B}{2} - \\frac{\\vec{r}_M+\\vec{r}_N}{2} \\Bigg | \n",
    "$$\n",
    "\n",
    "For a pole-dipole survey, this would be:\n",
    "\n",
    "$$\\, \n",
    "d = \\Bigg | \\, \\vec{r}_A - \\frac{\\vec{r}_M+\\vec{r}_N}{2} \\Bigg | \n",
    "$$\n",
    "\n",
    "Pole sources penetrate the Earth more deeply and produce smoother fields within the source region, so we can get away with a larger layer thickness for the top layer but we need to discretize deeper. But in general, it is easy to make your your top layer equal to 10% of your smallest distance. And to make the depth to the lower layer equal to the largest distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:18.970385Z",
     "iopub.status.busy": "2024-11-20T01:23:18.970063Z",
     "iopub.status.idle": "2024-11-20T01:23:18.974677Z",
     "shell.execute_reply": "2024-11-20T01:23:18.974030Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AB separations: [  60.  120.  180.  240.  300.  360.  420.  480.  540.  600.  660.  720.\n",
      "  780.  840.  900.  960. 1020. 1080. 1140. 1200. 1260. 1320. 1380. 1440.\n",
      " 1500.]\n"
     ]
    }
   ],
   "source": [
    "# Use Wenner electrode spacings to set discretization parameters\n",
    "print(\"AB separations: {}\".format(AB_separations))\n",
    "depth_min = 5.0  # top layer thickness\n",
    "depth_max = np.max(AB_separations / 2)  # depth to lowest layer\n",
    "geometric_factor = 1.1  # rate of thickness increase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:18.977676Z",
     "iopub.status.busy": "2024-11-20T01:23:18.977324Z",
     "iopub.status.idle": "2024-11-20T01:23:18.982176Z",
     "shell.execute_reply": "2024-11-20T01:23:18.981329Z"
    }
   },
   "outputs": [],
   "source": [
    "# Increase subsequent layer thicknesses by the geometric factors until\n",
    "# it reaches the maximum layer depth.\n",
    "layer_thicknesses = [depth_min]\n",
    "while np.sum(layer_thicknesses) < depth_max:\n",
    "    layer_thicknesses.append(geometric_factor * layer_thicknesses[-1])\n",
    "\n",
    "n_layers = len(layer_thicknesses) + 1  # Number of layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define a Model and Mapping\n",
    "\n",
    "Recall from the [Forward Simulation of 1D Sounding DC Resistivity Data](fwd_dcr_1d.ipynb) tutorial that the 'model' is not necessarily synonymous with physical property values. And that we need to define a mapping from the model to the set of input parameters required by the forward simulation. When inverting to recover electrical resistivities (or conductivities), it is best to use the log-value, as electrical resistivities (or conductivities) of rocks span many order of magnitude.\n",
    "\n",
    "Here, the model defines the log-resistivity values for a defined set of subsurface layers. And we use the [simpeg.maps.ExpMap](xref:simpeg#simpeg.maps.ExpMap) to map from the model parameters to the resistivity values required by the forward simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:18.985303Z",
     "iopub.status.busy": "2024-11-20T01:23:18.985018Z",
     "iopub.status.idle": "2024-11-20T01:23:18.988890Z",
     "shell.execute_reply": "2024-11-20T01:23:18.988183Z"
    }
   },
   "outputs": [],
   "source": [
    "log_resistivity_map = maps.ExpMap(nP=n_layers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Starting/Reference Models\n",
    "\n",
    "The **starting model** defines a reasonable starting point for the inversion. Because electromagnetic problems are non-linear, your choice in starting model does have an impact on the recovered model. For DC resistivity inversion, we generally choose our starting model based on apparent resistivities. For the tutorial example, the apparent resistivities were near 1000 $\\Omega m$. It should be noted that the starting model **cannot be vector of zeros,** otherwise the inversion will be unable to compute a gradient direction at the first iteration.\n",
    "\n",
    "The **reference model** is used to include a-priori information. The impact of the reference model on the inversion will be discussed in another tutorial. The reference model for basic inversion approaches is either zero or equal to the starting model.\n",
    "\n",
    "Notice that the length of the starting and reference models is equal to the number of model parameters!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:18.991997Z",
     "iopub.status.busy": "2024-11-20T01:23:18.991637Z",
     "iopub.status.idle": "2024-11-20T01:23:18.995588Z",
     "shell.execute_reply": "2024-11-20T01:23:18.994917Z"
    }
   },
   "outputs": [],
   "source": [
    "# Starting model is log-resistivity values (Ohmm)\n",
    "starting_resistivity_model = np.log(1e3 * np.ones(n_layers))\n",
    "\n",
    "# Reference model is also log-resistivity values (Ohmm)\n",
    "reference_resistivity_model = starting_resistivity_model.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the Forward Simulation\n",
    "\n",
    "A simulation object defining the forward problem is required in order to predict data and calculate misfits for recovered models. A comprehensive description of the simulation object for 1D DC resistivity was discussed in the [1D Forward Simulation of DC Resistivity Data for a Single Sounding](fwd_dcr_1d.ipynb) tutorial. Here, we use the [Simulation1DLayers](xref:simpeg#simpeg.electromagnetics.static.resistivity.Simulation1DLayers) which simulates the data according to a 1D Hankel transform solution.\n",
    "\n",
    "The layer thicknesses are a static property of the simulation, and we set them using the ``thicknessess`` keyword argument. Since our model consists of log-resistivities, we use ``rhoMap`` to set the mapping from the model to the layer resistivities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:18.998497Z",
     "iopub.status.busy": "2024-11-20T01:23:18.998134Z",
     "iopub.status.idle": "2024-11-20T01:23:19.005078Z",
     "shell.execute_reply": "2024-11-20T01:23:19.004311Z"
    }
   },
   "outputs": [],
   "source": [
    "simulation_L2 = dc.simulation_1d.Simulation1DLayers(\n",
    "    survey=survey,\n",
    "    rhoMap=log_resistivity_map,\n",
    "    thicknesses=layer_thicknesses,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the Data Misfit\n",
    "\n",
    "To understand the role of the data misfit in the inversion, please visit [this online resource](https://giftoolscookbook.readthedocs.io/en/latest/content/fundamentals/Uncertainties.html).\n",
    "Here, we use the [L2DataMisfit](xref:simpeg#simpeg.data_misfit.L2DataMisfit) class to define the data misfit. In this case, the data misfit is the L2 norm of the weighted residual between the observed data and the data predicted for a given model. When instantiating the data misfit object within SimPEG, we must assign an appropriate *data object* and *simulation object* as properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:19.008410Z",
     "iopub.status.busy": "2024-11-20T01:23:19.008074Z",
     "iopub.status.idle": "2024-11-20T01:23:19.012563Z",
     "shell.execute_reply": "2024-11-20T01:23:19.011895Z"
    }
   },
   "outputs": [],
   "source": [
    "dmis_L2 = data_misfit.L2DataMisfit(simulation=simulation_L2, data=data_object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the Regularization\n",
    "\n",
    "To understand the role of the regularization in the inversion, please visit [this online resource](https://giftoolscookbook.readthedocs.io/en/latest/content/fundamentals/ObjectiveFunction.html). \n",
    "\n",
    "To define the regularization within SimPEG, we must define a 1D [tensor mesh](xref:discretize#discretize.TensorMesh). Meshes are designed using the [discretize package](https://discretize.simpeg.xyz). Whereas layer *thicknesses* and our *model* are defined from our top-layer down, tensor meshes are defined from the bottom up. So to define a 1D tensor mesh for the regularization, we:\n",
    "\n",
    "- add an extra layer to the end of our thicknesses so that the number of cells in the 1D mesh equals the number of model parameters\n",
    "- reverse the order so that the model parameters in the regularization match up with the appropriate cell\n",
    "- define the tensor mesh from the cell widths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:19.015937Z",
     "iopub.status.busy": "2024-11-20T01:23:19.015628Z",
     "iopub.status.idle": "2024-11-20T01:23:19.020674Z",
     "shell.execute_reply": "2024-11-20T01:23:19.019806Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  TensorMesh: 31 cells\n",
      "\n",
      "                      MESH EXTENT             CELL WIDTH      FACTOR\n",
      "  dir    nC        min           max         min       max      max\n",
      "  ---   ---  ---------------------------  ------------------  ------\n",
      "   x     31       -901.79         -0.00      5.00     79.32    1.10\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Define 1D cell widths\n",
    "h = np.r_[layer_thicknesses, layer_thicknesses[-1]]\n",
    "h = np.flipud(h)\n",
    "\n",
    "# Create regularization mesh\n",
    "regularization_mesh = TensorMesh([h], \"N\")\n",
    "print(regularization_mesh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the regularization acts on the model parameters. In this case, the model parameters are the log-resistivities, not the electric resistivities!!! Here, we use the [WeightedLeastSquares](xref:simpeg#simpeg.regularization.WeightedLeastSquares) regularization class to constrain the inversion result. Here, length scale along x are used to balance the smallness and smoothness terms; yes, x is smoothness along the vertical direction. And the reference model is only applied to the smallness term. If we wanted to apply the regularization to a function of the model parameters, we would need to set an approprate mapping object using the ``mapping`` keyword argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:19.024284Z",
     "iopub.status.busy": "2024-11-20T01:23:19.023871Z",
     "iopub.status.idle": "2024-11-20T01:23:19.031716Z",
     "shell.execute_reply": "2024-11-20T01:23:19.030897Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_L2 = regularization.WeightedLeastSquares(\n",
    "    regularization_mesh,\n",
    "    length_scale_x=1.0,\n",
    "    reference_model=reference_resistivity_model,\n",
    "    reference_model_in_smooth=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the Optimization Algorithm\n",
    "\n",
    "Here, we use the [InexactGaussNewton](xref:simpeg#simpeg.optimization.InexactGaussNewton) class to solve the optimization problem using the inexact Gauss Newton with conjugate gradient solver. Reasonable default values have generally been set for the properties of each optimization class. However, the user may choose to set custom values; e.g. the accuracy tolerance for the conjugate gradient solver or the number of line searches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:19.034500Z",
     "iopub.status.busy": "2024-11-20T01:23:19.034195Z",
     "iopub.status.idle": "2024-11-20T01:23:19.037612Z",
     "shell.execute_reply": "2024-11-20T01:23:19.036986Z"
    }
   },
   "outputs": [],
   "source": [
    "opt_L2 = optimization.InexactGaussNewton(\n",
    "    maxIter=100, maxIterLS=20, maxIterCG=20, tolCG=1e-3\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the Inverse Problem\n",
    "\n",
    "We use the [BaseInvProblem](xref:simpeg#simpeg.inverse_problem.BaseInvProblem) class to fully define the inverse problem that is solved at each beta (trade-off parameter) iteration. The inverse problem requires appropriate *data misfit*, *regularization* and *optimization* objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:19.040754Z",
     "iopub.status.busy": "2024-11-20T01:23:19.040485Z",
     "iopub.status.idle": "2024-11-20T01:23:19.044489Z",
     "shell.execute_reply": "2024-11-20T01:23:19.043792Z"
    }
   },
   "outputs": [],
   "source": [
    "inv_prob_L2 = inverse_problem.BaseInvProblem(dmis_L2, reg_L2, opt_L2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Provide Inversion Directives\n",
    "\n",
    "Directives represent operations that are carried out during the inversion. Here, we apply common directives for weighted least-squares inversion of DC resistivity data and describe their roles. These are:\n",
    "\n",
    "- [UpdateSensitivityWeights](xref:simpeg#simpeg.directives.UpdateSensitivityWeights): Apply sensitivity weighting to counteract the natural tendency of DC resistivity inversion to place materials near the electrodes. Since the problem is non-linear and the sensitivities are updated with every model, we set `every_iteration=True`.\n",
    "\n",
    "- [UpdatePreconditioner](xref:simpeg#simpeg.directives.UpdatePreconditioner): Apply Jacobi preconditioner when solving optimization problem to reduce the number of conjugate gradient iterations. We set `update_every_iteration=True` because the ideal preconditioner is model-dependent.\n",
    "\n",
    "- [BetaEstimate_ByEig](xref:simpeg#simpeg.directives.BetaEstimate_ByEig): Compute and set starting trade-off parameter (beta) based on largest eigenvalues.\n",
    "\n",
    "- [BetaSchedule](xref:simpeg#simpeg.directives.BetaSchedule): Size reduction of the trade-off parameter at every beta iteration, and the number of Gauss-Newton iterations for each beta. In general, a `coolingFactor` between 1.5 and 2.5, and `coolingRate` of 2 or 3 works well for DC resistivity inversion. Cooling beta too quickly will result in portions of the model getting trapped in local minima. And we will not be finding the solution that minimizes the optimization problem if the cooling rate is too small.\n",
    "\n",
    "- [TargetMisfit](xref:simpeg#simpeg.directives.TargetMisfit): Terminates the inversion when the data misfit equals the target misfit. A `chifact=1` terminates the inversion when the data misfit equals the number of data.\n",
    "\n",
    "\n",
    "The directive objects are organized in a ``list``. Upon starting the inversion or updating the recovered model at each iteration, the inversion will call each directive within the list **in order**. The order of the directives matters, and SimPEG will throw an error if directives are organized into an improper order. Some directives, like the ``BetaEstimate_ByEig`` are only used when starting the inversion. Other directives, like ``UpdatePreconditionner``, are used whenever the model is updated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:19.047746Z",
     "iopub.status.busy": "2024-11-20T01:23:19.047364Z",
     "iopub.status.idle": "2024-11-20T01:23:19.053133Z",
     "shell.execute_reply": "2024-11-20T01:23:19.052405Z"
    }
   },
   "outputs": [],
   "source": [
    "sensitivity_weights = directives.UpdateSensitivityWeights(every_iteration=True)\n",
    "update_jacobi = directives.UpdatePreconditioner(update_every_iteration=True)\n",
    "starting_beta = directives.BetaEstimate_ByEig(beta0_ratio=5)\n",
    "beta_schedule = directives.BetaSchedule(coolingFactor=2.0, coolingRate=2)\n",
    "target_misfit = directives.TargetMisfit(chifact=1.0)\n",
    "\n",
    "directives_list_L2 = [\n",
    "    sensitivity_weights,\n",
    "    update_jacobi,\n",
    "    starting_beta,\n",
    "    beta_schedule,\n",
    "    target_misfit,\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define and Run the Inversion\n",
    "\n",
    "We define the inversion using the [BaseInversion](xref:simpeg#simpeg.inversion.BaseInversion) class. The inversion class must be instantiated with an appropriate *inverse problem* object and *directives list*. The ``run`` method, along with a starting model, is respondible for running the inversion. The output is a 1D numpy.ndarray containing the recovered model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:19.056503Z",
     "iopub.status.busy": "2024-11-20T01:23:19.056181Z",
     "iopub.status.idle": "2024-11-20T01:23:22.046416Z",
     "shell.execute_reply": "2024-11-20T01:23:22.045567Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running inversion with SimPEG v0.23.0\n",
      "\n",
      "                        simpeg.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
      "                        ***Done using same Solver, and solver_opts as the Simulation1DLayers problem***\n",
      "                        \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model has any nan: 0\n",
      "============================ Inexact Gauss Newton ============================\n",
      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
      "-----------------------------------------------------------------------------\n",
      "x0 has any nan: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/t40array/ssoler/miniforge3/envs/simpeg-user-tutorials/lib/python3.10/site-packages/simpeg/simulation.py:197: DefaultSolverWarning: Using the default solver: Pardiso. \n",
      "\n",
      "If you would like to suppress this notification, add \n",
      "warnings.filterwarnings('ignore', simpeg.utils.solver_utils.DefaultSolverWarning)\n",
      " to your script.\n",
      "  return get_default_solver(warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  4.30e+02  1.42e+03  0.00e+00  1.42e+03    1.88e+03      0              \n",
      "   1  4.30e+02  1.07e+03  3.57e-01  1.22e+03    4.74e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   2  2.15e+02  1.06e+03  3.73e-01  1.14e+03    7.50e+02      0   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   3  2.15e+02  8.74e+02  1.00e+00  1.09e+03    2.34e+01      0              \n",
      "   4  1.07e+02  8.70e+02  1.02e+00  9.80e+02    6.25e+02      0   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   5  1.07e+02  6.78e+02  2.33e+00  9.29e+02    1.91e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   6  5.37e+01  6.75e+02  2.36e+00  8.02e+02    4.79e+02      0   Skip BFGS  \n",
      "   7  5.37e+01  5.03e+02  4.79e+00  7.60e+02    1.87e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   8  2.69e+01  5.05e+02  4.76e+00  6.32e+02    3.44e+02      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   9  2.69e+01  3.56e+02  9.18e+00  6.03e+02    2.50e+01      0              \n",
      "  10  1.34e+01  3.64e+02  8.85e+00  4.83e+02    2.38e+02      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  11  1.34e+01  2.41e+02  1.66e+01  4.64e+02    3.32e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  12  6.71e+00  2.54e+02  1.55e+01  3.58e+02    1.62e+02      0              \n",
      "  13  6.71e+00  1.59e+02  2.84e+01  3.49e+02    4.06e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  14  3.36e+00  1.73e+02  2.57e+01  2.59e+02    1.09e+02      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  15  3.36e+00  1.04e+02  4.56e+01  2.57e+02    4.64e+01      0              \n",
      "  16  1.68e+00  1.17e+02  4.02e+01  1.85e+02    7.27e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  17  1.68e+00  7.04e+01  6.94e+01  1.87e+02    5.07e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  18  8.39e-01  8.10e+01  6.00e+01  1.31e+02    4.72e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  19  8.39e-01  5.04e+01  1.01e+02  1.35e+02    5.38e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  20  4.20e-01  5.81e+01  8.64e+01  9.44e+01    2.95e+01      0              \n",
      "  21  4.20e-01  3.85e+01  1.34e+02  9.46e+01    5.26e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  22  2.10e-01  4.21e+01  1.24e+02  6.82e+01    1.63e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  23  2.10e-01  3.14e+01  1.52e+02  6.32e+01    3.91e+01      0              \n",
      "  24  1.05e-01  3.12e+01  1.50e+02  4.69e+01    9.05e+00      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  25  1.05e-01  2.64e+01  1.73e+02  4.45e+01    2.93e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  26  5.24e-02  2.62e+01  1.72e+02  3.52e+01    4.18e+00      0              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 1.4241e+02\n",
      "1 : |xc-x_last| = 4.8633e-01 <= tolX*(1+|x0|) = 3.9461e+00\n",
      "0 : |proj(x-g)-x|    = 4.1800e+00 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 4.1800e+00 <= 1e3*eps       = 1.0000e-02\n",
      "0 : maxIter   =     100    <= iter          =     27\n",
      "------------------------- DONE! -------------------------\n"
     ]
    }
   ],
   "source": [
    "# Here we combine the inverse problem and the set of directives\n",
    "inv_L2 = inversion.BaseInversion(inv_prob_L2, directives_list_L2)\n",
    "\n",
    "# Run the inversion\n",
    "recovered_model_L2 = inv_L2.run(starting_resistivity_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Observed and Predicted Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:22.050444Z",
     "iopub.status.busy": "2024-11-20T01:23:22.050186Z",
     "iopub.status.idle": "2024-11-20T01:23:22.286181Z",
     "shell.execute_reply": "2024-11-20T01:23:22.285578Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the true and apparent resistivities on a sounding curve\n",
    "fig = plt.figure(figsize=(11, 5))\n",
    "ax1 = fig.add_axes([0.2, 0.1, 0.6, 0.8])\n",
    "ax1.semilogy(AB_separations / 2, dobs, \"k\")\n",
    "ax1.semilogy(AB_separations / 2, simulation_L2.dpred(recovered_model_L2), \"b\")\n",
    "ax1.grid(which=\"both\")\n",
    "ax1.set_xlabel(\"AB/2 (m)\")\n",
    "ax1.set_ylabel(r\"Apparent Resistivity ($\\Omega m$)\")\n",
    "ax1.legend([\"Observed\", \"L2 Inversion\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the Recovered Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:22.290395Z",
     "iopub.status.busy": "2024-11-20T01:23:22.290115Z",
     "iopub.status.idle": "2024-11-20T01:23:22.294365Z",
     "shell.execute_reply": "2024-11-20T01:23:22.293499Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define true model and layer thicknesses\n",
    "true_resistivities = np.r_[1e3, 4e3, 2e2]\n",
    "true_layers = np.r_[100.0, 100.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:22.297926Z",
     "iopub.status.busy": "2024-11-20T01:23:22.297632Z",
     "iopub.status.idle": "2024-11-20T01:23:22.493034Z",
     "shell.execute_reply": "2024-11-20T01:23:22.492424Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot true model and recovered model\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "\n",
    "ax1 = fig.add_axes([0.2, 0.15, 0.7, 0.7])\n",
    "plot_1d_layer_model(true_layers, true_resistivities, ax=ax1, color=\"k\")\n",
    "plot_1d_layer_model(\n",
    "    layer_thicknesses, log_resistivity_map * recovered_model_L2, ax=ax1, color=\"b\"\n",
    ")\n",
    "x_min, x_max = true_resistivities.min(), true_resistivities.max()\n",
    "ax1.set_xlim(0.9 * x_min, 1.1 * x_max)\n",
    "ax1.grid()\n",
    "ax1.set_xlabel(r\"Resistivity ($\\Omega m$)\")\n",
    "ax1.legend([\"True Model\", \"L2-Model\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iteratively Re-weighted Least-Squares Inversion\n",
    "\n",
    "Here, we use the iteratively reweighted least-squares (IRLS) inversion approach to recover sparse and/or blocky models on the set layers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the Forward Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:22.496814Z",
     "iopub.status.busy": "2024-11-20T01:23:22.496543Z",
     "iopub.status.idle": "2024-11-20T01:23:22.500757Z",
     "shell.execute_reply": "2024-11-20T01:23:22.500132Z"
    }
   },
   "outputs": [],
   "source": [
    "simulation_irls = dc.simulation_1d.Simulation1DLayers(\n",
    "    survey=survey,\n",
    "    rhoMap=log_resistivity_map,\n",
    "    thicknesses=layer_thicknesses,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the Data Misfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:22.504437Z",
     "iopub.status.busy": "2024-11-20T01:23:22.504143Z",
     "iopub.status.idle": "2024-11-20T01:23:22.508951Z",
     "shell.execute_reply": "2024-11-20T01:23:22.508052Z"
    }
   },
   "outputs": [],
   "source": [
    "dmis_irls = data_misfit.L2DataMisfit(simulation=simulation_irls, data=data_object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the Regularization\n",
    "\n",
    "Here, we use the [Sparse](xref:simpeg#simpeg.regularization.Sparse) regularization class to constrain the inversion result using an IRLS approach. Here, the scaling constants that balance the smallness and smoothness terms are set directly. Equal emphasis on smallness and smoothness is generally applied by using the inverse square of the smallest cell dimension. The reference model is only applied to the smallness term; which is redundant for the tutorial example since we have set the reference model to an array of zeros. Here, we apply a 1-norm to the smallness term and a 1-norm to first-order smoothness along the x (vertical direction)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:22.511692Z",
     "iopub.status.busy": "2024-11-20T01:23:22.511273Z",
     "iopub.status.idle": "2024-11-20T01:23:22.519019Z",
     "shell.execute_reply": "2024-11-20T01:23:22.518236Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_irls = regularization.Sparse(\n",
    "    regularization_mesh,\n",
    "    alpha_s=0.1,\n",
    "    alpha_x=1,\n",
    "    reference_model_in_smooth=False,\n",
    "    norms=[1.0, 1.0],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the Optimization Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:22.522054Z",
     "iopub.status.busy": "2024-11-20T01:23:22.521717Z",
     "iopub.status.idle": "2024-11-20T01:23:22.526183Z",
     "shell.execute_reply": "2024-11-20T01:23:22.525565Z"
    }
   },
   "outputs": [],
   "source": [
    "opt_irls = optimization.InexactGaussNewton(\n",
    "    maxIter=100, maxIterLS=20, maxIterCG=30, tolCG=1e-3\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the Inverse Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:22.529210Z",
     "iopub.status.busy": "2024-11-20T01:23:22.528956Z",
     "iopub.status.idle": "2024-11-20T01:23:22.532790Z",
     "shell.execute_reply": "2024-11-20T01:23:22.532121Z"
    }
   },
   "outputs": [],
   "source": [
    "inv_prob_irls = inverse_problem.BaseInvProblem(dmis_irls, reg_irls, opt_irls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Provide Inversion Directives\n",
    "\n",
    "Here, we create common directives for IRLS inversion of DC resistivity data and describe their roles. In additon to the [UpdateSensitivityWeights](xref:simpeg#simpeg.directives.UpdateSensitivityWeights), [UpdatePreconditioner](xref:simpeg#simpeg.directives.UpdatePreconditioner) and [BetaEstimate_ByEig](xref:simpeg#simpeg.directives.BetaEstimate_ByEig)  directives (described before), inversion with sparse-norms requires the [`UpdateIRLS`](xref:simpeg#simpeg.directives.UpdateIRLS) directive.\n",
    "\n",
    "You will notice that we don't use the [BetaSchedule](xref:simpeg#simpeg.directives.BetaSchedule) and [TargetMisfit](xref:simpeg#simpeg.directives.TargetMisfit) directives. Here, the beta cooling schedule is set in the [`UpdateIRLS`](xref:simpeg#simpeg.directives.UpdateIRLS) directive using the `coolingFactor` and `coolingRate` properties. The target misfit for the L2 portion of the IRLS approach is set with the `chifact_start` property. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:22.535940Z",
     "iopub.status.busy": "2024-11-20T01:23:22.535600Z",
     "iopub.status.idle": "2024-11-20T01:23:22.541712Z",
     "shell.execute_reply": "2024-11-20T01:23:22.540966Z"
    }
   },
   "outputs": [],
   "source": [
    "sensitivity_weights_irls = directives.UpdateSensitivityWeights(every_iteration=True)\n",
    "starting_beta_irls = directives.BetaEstimate_ByEig(beta0_ratio=1)\n",
    "update_jacobi_irls = directives.UpdatePreconditioner(update_every_iteration=True)\n",
    "update_irls = directives.UpdateIRLS(\n",
    "    cooling_factor=2,\n",
    "    cooling_rate=2,\n",
    "    f_min_change=1e-4,\n",
    "    max_irls_iterations=30,\n",
    "    chifact_start=1.0,\n",
    ")\n",
    "\n",
    "directives_list_irls = [\n",
    "    update_irls,\n",
    "    sensitivity_weights_irls,\n",
    "    starting_beta_irls,\n",
    "    update_jacobi_irls,\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define and Run the Inversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:22.545227Z",
     "iopub.status.busy": "2024-11-20T01:23:22.544920Z",
     "iopub.status.idle": "2024-11-20T01:23:42.225873Z",
     "shell.execute_reply": "2024-11-20T01:23:42.225045Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running inversion with SimPEG v0.23.0\n",
      "simpeg.InvProblem will set Regularization.reference_model to m0.\n",
      "simpeg.InvProblem will set Regularization.reference_model to m0.\n",
      "\n",
      "                        simpeg.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
      "                        ***Done using same Solver, and solver_opts as the Simulation1DLayers problem***\n",
      "                        \n",
      "model has any nan: 0\n",
      "============================ Inexact Gauss Newton ============================\n",
      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
      "-----------------------------------------------------------------------------\n",
      "x0 has any nan: 0\n",
      "   0  8.47e+02  1.42e+03  0.00e+00  1.42e+03    1.88e+03      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1  8.47e+02  6.18e+02  2.98e-01  8.70e+02    4.38e+01      0              \n",
      "   2  4.24e+02  6.13e+02  3.04e-01  7.42e+02    4.29e+02      0   Skip BFGS  \n",
      "   3  4.24e+02  4.49e+02  6.03e-01  7.04e+02    2.02e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   4  2.12e+02  4.53e+02  5.93e-01  5.78e+02    3.04e+02      0              \n",
      "   5  2.12e+02  3.13e+02  1.13e+00  5.53e+02    2.79e+01      0              \n",
      "   6  1.06e+02  3.23e+02  1.08e+00  4.37e+02    2.09e+02      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   7  1.06e+02  2.09e+02  2.01e+00  4.22e+02    3.61e+01      0              \n",
      "   8  5.30e+01  2.23e+02  1.85e+00  3.21e+02    1.42e+02      0              \n",
      "   9  5.30e+01  1.37e+02  3.36e+00  3.15e+02    4.29e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  10  2.65e+01  1.51e+02  3.01e+00  2.31e+02    9.52e+01      0              \n",
      "  11  2.65e+01  9.05e+01  5.30e+00  2.31e+02    4.81e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  12  1.32e+01  1.03e+02  4.63e+00  1.64e+02    6.29e+01      0              \n",
      "  13  1.32e+01  6.23e+01  7.93e+00  1.67e+02    5.20e+01      0              \n",
      "  14  6.62e+00  7.19e+01  6.82e+00  1.17e+02    4.04e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  15  6.62e+00  4.56e+01  1.14e+01  1.21e+02    5.45e+01      0              \n",
      "  16  3.31e+00  5.24e+01  9.73e+00  8.46e+01    2.48e+01      0              \n",
      "  17  3.31e+00  3.57e+01  1.40e+01  8.20e+01    5.08e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  18  1.66e+00  3.74e+01  1.33e+01  5.94e+01    1.35e+01      0              \n",
      "  19  1.66e+00  2.92e+01  1.59e+01  5.55e+01    3.61e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  20  8.28e-01  2.90e+01  1.58e+01  4.20e+01    6.96e+00      0              \n",
      "  21  8.28e-01  2.54e+01  1.80e+01  4.02e+01    2.68e+01      0              \n",
      "  22  4.14e-01  2.53e+01  1.78e+01  3.27e+01    3.09e+00      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reached starting chifact with l2-norm regularization: Start IRLS steps...\n",
      "irls_threshold 2.266062153902932\n",
      "  23  4.14e-01  2.38e+01  2.66e+01  3.48e+01    2.65e+01      0              \n",
      "  24  4.14e-01  2.47e+01  2.45e+01  3.49e+01    3.66e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  25  4.14e-01  2.43e+01  2.98e+01  3.66e+01    4.05e+00      0              \n",
      "  26  4.14e-01  2.48e+01  2.84e+01  3.66e+01    1.18e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  27  4.14e-01  2.47e+01  3.42e+01  3.89e+01    2.12e+00      0              \n",
      "  28  4.14e-01  2.53e+01  3.25e+01  3.88e+01    9.72e+00      0              \n",
      "  29  4.14e-01  2.52e+01  3.96e+01  4.16e+01    2.36e+00      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  30  4.14e-01  2.60e+01  3.74e+01  4.15e+01    1.01e+01      0              \n",
      "  31  4.14e-01  2.59e+01  4.60e+01  4.49e+01    2.86e+00      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  32  4.14e-01  2.69e+01  4.33e+01  4.48e+01    1.03e+01      0              \n",
      "  33  4.14e-01  2.67e+01  5.34e+01  4.88e+01    3.44e+00      0              \n",
      "  34  2.66e-01  2.79e+01  5.03e+01  4.13e+01    6.33e+00      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  35  2.66e-01  2.57e+01  6.87e+01  4.40e+01    1.22e+01      0              \n",
      "  36  1.70e-01  2.68e+01  6.42e+01  3.77e+01    7.88e+00      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  37  1.70e-01  2.51e+01  8.63e+01  3.98e+01    1.10e+01      0              \n",
      "  38  1.09e-01  2.58e+01  8.15e+01  3.47e+01    6.97e+00      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  39  1.09e-01  2.46e+01  1.04e+02  3.60e+01    9.16e+00      0              \n",
      "  40  7.02e-02  2.50e+01  9.86e+01  3.19e+01    5.85e+00      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  41  7.02e-02  2.41e+01  1.17e+02  3.23e+01    7.82e+00      0              \n",
      "  42  4.51e-02  2.43e+01  1.12e+02  2.93e+01    5.27e+00      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  43  4.51e-02  2.37e+01  1.20e+02  2.91e+01    7.27e+00      0              \n",
      "  44  2.89e-02  2.37e+01  1.17e+02  2.71e+01    4.60e+00      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  45  2.89e-02  2.34e+01  9.78e+01  2.62e+01    1.31e+01      0              \n",
      "  46  1.86e-02  2.33e+01  9.99e+01  2.52e+01    1.56e+01      1   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  47  1.86e-02  2.33e+01  7.65e+01  2.47e+01    1.61e+01      3   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  48  1.19e-02  2.32e+01  7.84e+01  2.42e+01    1.98e+01      3   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  49  1.19e-02  2.32e+01  6.78e+01  2.40e+01    2.14e+01      4   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  50  7.65e-03  2.32e+01  6.92e+01  2.37e+01    2.33e+01      4   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  51  7.65e-03  2.31e+01  6.33e+01  2.36e+01    2.66e+01      4   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  52  4.91e-03  2.30e+01  6.52e+01  2.34e+01    2.95e+01      4   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  53  4.91e-03  2.30e+01  5.96e+01  2.33e+01    3.30e+01      4   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  54  3.15e-03  2.29e+01  6.20e+01  2.31e+01    3.62e+01      4   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  55  3.15e-03  2.29e+01  5.63e+01  2.31e+01    4.03e+01      4   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  56  2.02e-03  2.28e+01  5.97e+01  2.30e+01    4.52e+01      4   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  57  2.02e-03  2.28e+01  5.43e+01  2.29e+01    4.64e+01      5   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  58  1.30e-03  2.28e+01  5.65e+01  2.28e+01    4.81e+01      5   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  59  1.30e-03  2.28e+01  5.29e+01  2.28e+01    5.13e+01      5   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  60  8.33e-04  2.27e+01  5.43e+01  2.28e+01    5.18e+01      6   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  61  8.33e-04  2.27e+01  5.20e+01  2.27e+01    5.23e+01      6   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  62  5.35e-04  2.27e+01  5.37e+01  2.27e+01    5.28e+01      6   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  63  5.35e-04  2.27e+01  5.04e+01  2.27e+01    5.38e+01      6   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  64  3.43e-04  2.27e+01  5.25e+01  2.27e+01    5.51e+01      6   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  65  3.43e-04  2.26e+01  5.26e+01  2.27e+01    5.54e+01      7   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  66  2.20e-04  2.26e+01  5.38e+01  2.26e+01    5.59e+01      7   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  67  2.20e-04  2.26e+01  5.51e+01  2.26e+01    5.67e+01      7   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  68  1.41e-04  2.26e+01  5.58e+01  2.26e+01    5.69e+01      8   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  69  1.41e-04  2.26e+01  5.68e+01  2.26e+01    5.72e+01      8   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  70  9.08e-05  2.26e+01  5.75e+01  2.26e+01    5.75e+01      8   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  71  9.08e-05  2.26e+01  5.88e+01  2.26e+01    5.79e+01      8   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  72  5.82e-05  2.26e+01  5.97e+01  2.26e+01    5.85e+01      8   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  73  5.82e-05  2.26e+01  6.13e+01  2.26e+01    5.91e+01      8   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  74  3.74e-05  2.26e+01  6.18e+01  2.26e+01    5.92e+01      9   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  75  3.74e-05  2.26e+01  6.27e+01  2.26e+01    5.94e+01      9   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  76  2.40e-05  2.26e+01  6.32e+01  2.26e+01    5.96e+01      9   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  77  2.40e-05  2.26e+01  6.44e+01  2.26e+01    5.99e+01      9   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  78  1.54e-05  2.26e+01  6.50e+01  2.26e+01    6.02e+01      9   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  79  1.54e-05  2.26e+01  6.85e+01  2.26e+01    6.05e+01      9   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  80  9.89e-06  2.26e+01  6.90e+01  2.26e+01    6.09e+01      9   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  81  9.89e-06  2.26e+01  7.20e+01  2.26e+01    6.10e+01     10   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  82  6.35e-06  2.26e+01  7.23e+01  2.26e+01    6.11e+01     10   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reach maximum number of IRLS cycles: 30\n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 1.4241e+02\n",
      "1 : |xc-x_last| = 6.1065e-02 <= tolX*(1+|x0|) = 3.9461e+00\n",
      "0 : |proj(x-g)-x|    = 6.1085e+01 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 6.1085e+01 <= 1e3*eps       = 1.0000e-02\n",
      "0 : maxIter   =     100    <= iter          =     83\n",
      "------------------------- DONE! -------------------------\n"
     ]
    }
   ],
   "source": [
    "# Here we combine the inverse problem and the set of directives\n",
    "inv_irls = inversion.BaseInversion(inv_prob_irls, directives_list_irls)\n",
    "\n",
    "# Run the inversion\n",
    "recovered_model_irls = inv_irls.run(starting_resistivity_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Observed and Predicted Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:42.229927Z",
     "iopub.status.busy": "2024-11-20T01:23:42.229675Z",
     "iopub.status.idle": "2024-11-20T01:23:42.474262Z",
     "shell.execute_reply": "2024-11-20T01:23:42.473289Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the true and apparent resistivities on a sounding curve\n",
    "fig = plt.figure(figsize=(11, 5))\n",
    "ax1 = fig.add_axes([0.2, 0.1, 0.6, 0.8])\n",
    "ax1.semilogy(AB_separations / 2, dobs, \"k\")\n",
    "ax1.semilogy(AB_separations / 2, simulation_L2.dpred(recovered_model_L2), \"b\")\n",
    "ax1.semilogy(AB_separations / 2, simulation_irls.dpred(recovered_model_irls), \"r\")\n",
    "ax1.grid(which=\"both\")\n",
    "ax1.set_xlabel(\"AB/2 (m)\")\n",
    "ax1.set_ylabel(r\"Apparent Resistivity ($\\Omega m$)\")\n",
    "ax1.legend([\"Observed\", \"L2 Inversion\", \"IRLS Inversion\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Recovered Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:42.479164Z",
     "iopub.status.busy": "2024-11-20T01:23:42.478898Z",
     "iopub.status.idle": "2024-11-20T01:23:42.688972Z",
     "shell.execute_reply": "2024-11-20T01:23:42.688330Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot true model and recovered model\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "\n",
    "ax1 = fig.add_axes([0.2, 0.15, 0.7, 0.7])\n",
    "plot_1d_layer_model(true_layers, true_resistivities, ax=ax1, color=\"k\")\n",
    "plot_1d_layer_model(\n",
    "    layer_thicknesses, log_resistivity_map * recovered_model_L2, ax=ax1, color=\"b\"\n",
    ")\n",
    "plot_1d_layer_model(\n",
    "    layer_thicknesses, log_resistivity_map * recovered_model_irls, ax=ax1, color=\"r\"\n",
    ")\n",
    "x_min, x_max = true_resistivities.min(), true_resistivities.max()\n",
    "ax1.set_xlim(0.9 * x_min, 1.1 * x_max)\n",
    "ax1.grid()\n",
    "ax1.set_xlabel(r\"Resistivity ($\\Omega m$)\")\n",
    "ax1.legend([\"True Model\", \"L2 Model\", \"IRLS Model\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parametric Inversion\n",
    "\n",
    "Here, we assume the subsurface is defined by a 3-layered Earth. However, the electrical properties and thicknesses of the layers are unknown. Here, we define our model to include log-conductivities and log-thicknesses. When including quantities that span different scales, it is frequently best to define the model in terms of log-values so that each quantity influences the predicted data evenly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Models and Mappings\n",
    "\n",
    "For a 3-layered Earth model, the model consists of 2 log-thicknesses and 3 log-conductivities. Similar to the [1D Forward Simulation of DC Resistivity Data](fwd_dcr_1d.ipynb) tutorial, need a mapping that extract log-thicknesses and log-conductivities from the model, and mappings that convert log-values to property values. For this, we require the [simpeg.maps.Wires](xref:simpeg#simpeg.maps.Wires) mapping and [simpeg.maps.ExpMap](xref:simpeg#simpeg.maps.ExpMap) mapping classes. Note that successive mappings can be chained together using the $*$ operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:42.693213Z",
     "iopub.status.busy": "2024-11-20T01:23:42.692944Z",
     "iopub.status.idle": "2024-11-20T01:23:42.698480Z",
     "shell.execute_reply": "2024-11-20T01:23:42.697719Z"
    }
   },
   "outputs": [],
   "source": [
    "# Wire maps to extract log-thicknesses and log-conductivities\n",
    "wire_map = maps.Wires((\"log_thicknesses\", 2), (\"log_conductivity\", 3))\n",
    "\n",
    "# Maping for layer thicknesses\n",
    "log_thicknesses_map = maps.ExpMap() * wire_map.log_thicknesses\n",
    "\n",
    "# Mapping for conductivities\n",
    "log_conductivity_map = maps.ExpMap() * wire_map.log_conductivity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Starting and Reference Models\n",
    "\n",
    "This problem is highly non-linear so it is important to have a reasonable estimate of the true model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:42.701223Z",
     "iopub.status.busy": "2024-11-20T01:23:42.700979Z",
     "iopub.status.idle": "2024-11-20T01:23:42.705418Z",
     "shell.execute_reply": "2024-11-20T01:23:42.704459Z"
    }
   },
   "outputs": [],
   "source": [
    "starting_parametric_model = np.log(np.r_[125.0, 50.0, 1e-3, 2e-3, 5e-2])\n",
    "\n",
    "reference_parametric_model = starting_parametric_model.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the Forward Simulation\n",
    "\n",
    "Because the layer thicknesses are part of the model, we define the ``thicknessesMap``. Because we are working in terms of electrical conductivity, we must define the ``sigmaMap``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:42.708236Z",
     "iopub.status.busy": "2024-11-20T01:23:42.707900Z",
     "iopub.status.idle": "2024-11-20T01:23:42.712339Z",
     "shell.execute_reply": "2024-11-20T01:23:42.711576Z"
    }
   },
   "outputs": [],
   "source": [
    "simulation_parametric = dc.simulation_1d.Simulation1DLayers(\n",
    "    survey=survey,\n",
    "    sigmaMap=log_conductivity_map,\n",
    "    thicknessesMap=log_thicknesses_map,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the Data Misfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:42.715501Z",
     "iopub.status.busy": "2024-11-20T01:23:42.715094Z",
     "iopub.status.idle": "2024-11-20T01:23:42.719656Z",
     "shell.execute_reply": "2024-11-20T01:23:42.718923Z"
    }
   },
   "outputs": [],
   "source": [
    "dmis_parametric = data_misfit.L2DataMisfit(\n",
    "    simulation=simulation_parametric, data=data_object\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define a (Combo) Regularization\n",
    "\n",
    "We need to define a regularization for each model parameter type. In this case, we have log-thicknesses and log-conductivities. For each model parameter type, we create a 1D [tensor mesh](xref:discretize#discretize.TensorMesh) with length equal to the number of parameters. In the ``mapping`` keyword argument, we used the wire map that extracts the specific model parameters from the model.\n",
    "\n",
    "Using the $*$ operator, separate regularizations can be summed to form a regularization that is also a [ComboObjectiveFunction](xref:simpeg#simpeg.objective_function.ComboObjectiveFunction). By setting the ``multipliers`` property, we can emphasis the relative contributions of the log-thicknesses and log-conductivities regularizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:42.722894Z",
     "iopub.status.busy": "2024-11-20T01:23:42.722588Z",
     "iopub.status.idle": "2024-11-20T01:23:42.729083Z",
     "shell.execute_reply": "2024-11-20T01:23:42.728308Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_1 = regularization.Smallness(\n",
    "    TensorMesh([(np.ones(2))], \"0\"),\n",
    "    mapping=wire_map.log_thicknesses,\n",
    "    reference_model=reference_parametric_model,\n",
    ")\n",
    "\n",
    "reg_2 = regularization.Smallness(\n",
    "    TensorMesh([(np.ones(3))], \"0\"),\n",
    "    mapping=wire_map.log_conductivity,\n",
    "    reference_model=reference_parametric_model,\n",
    ")\n",
    "\n",
    "reg_parametric = reg_1 + reg_2\n",
    "reg_parametric.multipliers = [1.0, 0.1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the Optimization Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:42.732497Z",
     "iopub.status.busy": "2024-11-20T01:23:42.732169Z",
     "iopub.status.idle": "2024-11-20T01:23:42.736932Z",
     "shell.execute_reply": "2024-11-20T01:23:42.736181Z"
    }
   },
   "outputs": [],
   "source": [
    "opt_parametric = optimization.InexactGaussNewton(\n",
    "    maxIter=100, maxIterLS=20, maxIterCG=20, tolCG=1e-3\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the Inverse Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:42.740490Z",
     "iopub.status.busy": "2024-11-20T01:23:42.740153Z",
     "iopub.status.idle": "2024-11-20T01:23:42.743691Z",
     "shell.execute_reply": "2024-11-20T01:23:42.743054Z"
    }
   },
   "outputs": [],
   "source": [
    "inv_prob_parametric = inverse_problem.BaseInvProblem(\n",
    "    dmis_parametric, reg_parametric, opt_parametric\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Provide Inversion Directives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:42.747384Z",
     "iopub.status.busy": "2024-11-20T01:23:42.747010Z",
     "iopub.status.idle": "2024-11-20T01:23:42.752954Z",
     "shell.execute_reply": "2024-11-20T01:23:42.752205Z"
    }
   },
   "outputs": [],
   "source": [
    "sensitivity_weights = directives.UpdateSensitivityWeights(every_iteration=True)\n",
    "update_jacobi = directives.UpdatePreconditioner(update_every_iteration=True)\n",
    "starting_beta = directives.BetaEstimate_ByEig(beta0_ratio=10)\n",
    "beta_schedule = directives.BetaSchedule(coolingFactor=2.0, coolingRate=2)\n",
    "target_misfit = directives.TargetMisfit(chifact=1.0)\n",
    "\n",
    "directives_list_parametric = [\n",
    "    sensitivity_weights,\n",
    "    update_jacobi,\n",
    "    starting_beta,\n",
    "    beta_schedule,\n",
    "    target_misfit,\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define and Run Inversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:42.756176Z",
     "iopub.status.busy": "2024-11-20T01:23:42.755774Z",
     "iopub.status.idle": "2024-11-20T01:23:44.663728Z",
     "shell.execute_reply": "2024-11-20T01:23:44.662934Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running inversion with SimPEG v0.23.0\n",
      "\n",
      "                        simpeg.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
      "                        ***Done using same Solver, and solver_opts as the Simulation1DLayers problem***\n",
      "                        \n",
      "model has any nan: 0\n",
      "============================ Inexact Gauss Newton ============================\n",
      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
      "-----------------------------------------------------------------------------\n",
      "x0 has any nan: 0\n",
      "   0  1.36e+05  1.95e+04  0.00e+00  1.95e+04    1.72e+04      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1  1.36e+05  1.35e+04  2.04e-02  1.63e+04    3.54e+03      0              \n",
      "   2  6.81e+04  1.24e+04  2.92e-02  1.43e+04    8.80e+03      0   Skip BFGS  \n",
      "   3  6.81e+04  7.15e+03  9.98e-02  1.39e+04    1.25e+03      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   4  3.41e+04  6.84e+03  1.04e-01  1.04e+04    7.29e+03      0   Skip BFGS  \n",
      "   5  3.41e+04  2.56e+03  2.27e-01  1.03e+04    1.31e+03      0              \n",
      "   6  1.70e+04  2.75e+03  2.17e-01  6.45e+03    4.96e+03      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   7  1.70e+04  8.86e+02  3.22e-01  6.37e+03    9.63e+02      0              \n",
      "   8  8.51e+03  9.84e+02  3.26e-01  3.76e+03    2.94e+03      0              \n",
      "   9  8.51e+03  3.41e+02  4.05e-01  3.79e+03    5.31e+02      0              \n",
      "  10  4.26e+03  3.56e+02  4.29e-01  2.18e+03    1.71e+03      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  11  4.26e+03  1.37e+02  4.99e-01  2.26e+03    2.65e+02      0              \n",
      "  12  2.13e+03  1.38e+02  5.17e-01  1.24e+03    9.96e+02      0              \n",
      "  13  2.13e+03  6.26e+01  5.76e-01  1.29e+03    1.34e+02      0              \n",
      "  14  1.06e+03  6.34e+01  5.82e-01  6.82e+02    5.64e+02      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  15  1.06e+03  3.67e+01  6.28e-01  7.06e+02    6.75e+01      0              \n",
      "  16  5.32e+02  3.72e+01  6.29e-01  3.72e+02    3.08e+02      0              \n",
      "  17  5.32e+02  2.79e+01  6.64e-01  3.81e+02    2.69e+01      0              \n",
      "  18  2.66e+02  2.80e+01  6.64e-01  2.05e+02    1.63e+02      0              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 1.9497e+03\n",
      "1 : |xc-x_last| = 4.6199e-02 <= tolX*(1+|x0|) = 1.2573e+00\n",
      "0 : |proj(x-g)-x|    = 1.6254e+02 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 1.6254e+02 <= 1e3*eps       = 1.0000e-02\n",
      "0 : maxIter   =     100    <= iter          =     19\n",
      "------------------------- DONE! -------------------------\n"
     ]
    }
   ],
   "source": [
    "inv_parametric = inversion.BaseInversion(\n",
    "    inv_prob_parametric, directives_list_parametric\n",
    ")\n",
    "recovered_model_parametric = inv_parametric.run(starting_parametric_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Observed and Predicted Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:44.667516Z",
     "iopub.status.busy": "2024-11-20T01:23:44.667247Z",
     "iopub.status.idle": "2024-11-20T01:23:44.917148Z",
     "shell.execute_reply": "2024-11-20T01:23:44.916425Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the true and apparent resistivities on a sounding curve\n",
    "fig = plt.figure(figsize=(11, 5))\n",
    "ax1 = fig.add_axes([0.2, 0.1, 0.6, 0.8])\n",
    "ax1.semilogy(AB_separations / 2, dobs, \"k\")\n",
    "ax1.semilogy(AB_separations / 2, simulation_L2.dpred(recovered_model_L2), \"b\")\n",
    "ax1.semilogy(AB_separations / 2, simulation_irls.dpred(recovered_model_irls), \"r\")\n",
    "ax1.semilogy(\n",
    "    AB_separations / 2, simulation_parametric.dpred(recovered_model_parametric), \"g\"\n",
    ")\n",
    "ax1.grid(which=\"both\")\n",
    "ax1.set_xlabel(\"AB/2 (m)\")\n",
    "ax1.set_ylabel(r\"Apparent Resistivity ($\\Omega m$)\")\n",
    "ax1.legend([\"Observed\", \"L2 Inversion\", \"IRLS Inversion\", \"Parametric Inversion\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Recovered Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-20T01:23:44.921292Z",
     "iopub.status.busy": "2024-11-20T01:23:44.921018Z",
     "iopub.status.idle": "2024-11-20T01:23:45.158968Z",
     "shell.execute_reply": "2024-11-20T01:23:45.158363Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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M/kRERDaGA/6IiBqacgvgGLoWjlIJaCaxWdOaNkpluXYptf+lGmPwJyJqSCosgCMBYMhYeBnKxXwrehKtdrusqGH1HLv9iYgaEkstgGMJDWjRnbrGK38iogZK+Wc2vNqXXfdf/NOw6fDWto6NUqmE1/0r/pvZ2doP9rG2xtYjDP5ERA2VTIa8+53+Mq/6uxaOZsgCF/QxGXb7ExER2RgGfyIiIhvD4E9ERGRjGPyJiIhsDIM/ERGRjeFofyKyDEOXnrNhNTpFSqVmUR8uhEeVYfAnorpXYRU60s/Q1fkq0669qVpCDQ27/Ymo7tnSKnQWchDByLu/Kj4XwqOKeOVPRJaVnc2FW/RQKgGv+0vZG7o6X3mPuLpCcX/1Oy6ERxUx+BORZXHVtkqpb/fX59X5yDox+NcxIQTyVBzkRA2XSqVCQUkBlEVKSIVUf6EiJSDV8540lEXQnJfy722Nstz/D2U9+r+iVFn3aEuJEEJYuhH1mVwuh4eHB3Jzc+Hu7l5lWSEE+ib3xaHLvNdJRGQrFNEKyBzN33VjTDzigL86lKfKY+AnIrIhwa2D4Sq1vtGW7Pa3kOxZ2ZBJeROPGh6VSoWUlBSEhYVBKq2kj1apBO4/ppUD/vTjKSqjVCrhff9EZFd8pG894Cp1hcQKR1sy+FuITCqrk24gorqmkqjgbO8MmaOs8uCvuv8CAEdZ2Yu0lTtHMseyl03SOg/8u2kq7PYnIiKyMbzyJyLDGLrWrEoF+4KCsn7rqrr9GxBzrFTcwE4RWRkGfyKqnhHL8UoBDDV/i6wGVyqm+ojd/kRUPXMtx9sA1p0190rFDeAUkRXilT8RGaeaoecGjfZXa2DrzppjVH4DO0VkJRj8icg41S3Hq1KhxNm5rEx1wb+B4UrFVF+w25+IiMjGWF3wv3r1KpYsWYLQ0FC0adMGjo6O8PHxwbPPPoujR4/q3UYulyMyMhJ+fn5wcnKCn58fIiMjIZfLK93P+vXr0bNnT8hkMnh6eiI8PBzHjh0z12ERERFZDasL/h9//DEiIiLw559/YsiQIXjjjTfQt29ffPfdd+jTpw82b96sVV6pVCIkJASLFy9Gx44dERERgYceegiLFy9GSEgIlHrmy8ybNw8TJkxAdnY2pk+fjjFjxiAtLQ3BwcFITU2toyMlIiKyEGFlvv76a7F//36d9P379wupVCqaNGkiCgoKNOmxsbECgIiKitIqr06PjY3VSj937pxwcHAQgYGB4u7du5r0kydPCldXV+Hv7y9UKpXB7c3NzRUARG5ubrVlFYUKgXgIxEMoChUG74PI4hQKIcpmtZW9r0JRUZHYunWrKCoqqqPGWZYRp4ZqQKFQCAACgFDwBFfJmHhkdVf+I0eORL9+/XTS+/Xrh8cffxy3b9/GiRMnAJQ9JW/58uVwc3NDbGysVvno6Gh4enpixYoVEOUeXJicnIzi4mLExMTAw8NDkx4UFIRJkybhwoUL2LNnj5mOjoiIyPLq1Wh/9bQhB4eyZmdmZuLatWsICwvTediDs7Mz+vfvj++++w7nz59HQEAAAGi69UNDQ3XqDwsLw2effYZ9+/bpzbcEIQTyTL10GJGxlErING+rXnpOpVKhoKAASqWy+ql+DUDZ6ZDdf89l+UyN59Q86k3w/+uvv/Dzzz/Dx8cHnTp1AlAW/AFoAntF6vTMzEyt925ubvDx8amyfGUKCwtRWFio+Vk9qFClUkGlUlW2maZM+fcqSdXlhRAYMGAADh8+XGU5sk11ue6LDMDN+++9vL1R/76OmvNs/XN2vL29gHp4duoLQ/7O2jJjzk29CP4qlQrPP/88CgsLMX/+fNjb2wMAcnNzAUCr+748d3d3rXLq915eXgaXrygpKQkJCQk66Tt37oRrNctwFZQUaN6npKTA2d656vIFBQz8pNdBAMGWbkS9wbPVEDz44INITU21ysfjWgtjeomtPviXlpbihRdewP79+/HSSy/h+eeft2h7oqOjERkZqflZLpejdevWCA0N1Xx5qIyySAmUDVcou1VRzaMpy3d3Xblypd49x5rMRKlE41atLLLr4l69cHXHjiqXnFOpVNizZw8GDhxo8W5/pRJo1apxneyrV69i7NhxlavxmYmrqysDfzWqmt5ekVUHfyEEXnrpJaxbtw4TJ07EZ599ppWvvuKv7EpdfSLK9wx4eHgYVb4iJycnODk56aRLpdJq/9BJxT/5BpUvl9+4cWMGfypT/v+NOdaTrYKDqysaV/MHWKVSwdnZGY0bN7Z48K/LU+Xq6gCJpLH5dkBUDWM+b1Yb/EtLSzFt2jQkJydj3LhxWLVqFezstCcnVHePXt+YgICAABw+fBg3btzQue9f3RgCIqvD9WQNxlNF9A+rm+oHaAf+5557DmvXrtXc5y8vICAAvr6+SEtL0xkRWlBQgP3798PX1xcdOnTQpIeEhAAou0dfUUpKilYZIiKihsjqgn9paSlefPFFJCcnY/To0Vi3bp3ewA8AEokE06ZNg0KhQGJiolZeUlIS7ty5g2nTpmndJ5o6dSocHBwwd+5cre7/U6dOYc2aNfD398fAgQPNc3BERERWwOq6/RMTE7Fq1Sq4ubkhMDAQ7733nk6ZESNGoGvXrgCAqKgofP/995g/fz6OHz+Obt26ISMjAzt27EDXrl0RFRWltW1gYCDi4+MxZ84cdO7cGaNGjYJSqcSGDRugUqmwbNkyzToCREREDZHVRbmsrCwAgEKhwNy5c/WWadu2rSb4y2QypKamIiEhAVu2bEFqaip8fHwQERGBuLg4vYPkYmJi0LZtWyxZsgRLly6Fo6Mj+vTpg8TERPTo0cNch0ZERGQVJKL82rdkNLlcrplBYMhUP7ckNwCAIlph0FQ/N7f75RUKjvanMkolcP//BRQKqxvFplKpsH37doSHh1t8tL+VnyoikzImHlndPX8iIiIyLwZ/IiIiG8PgT0REZGOsbsAfUZ0RAqiPT0xsAE85q6tT3wBOFZFZMPiTbRIC6NsXOHTI0i2xOTz1RJbHbn+yTXl59T/6BAcD1TxJ0hpZ4tTX01NFZDa88ieq44fjmIyra5VP16sP6urUN4BTRWRSDP5EfOKLxfDUE1kGu/2JiIhsDIM/ERGRjWHwJyIisjEM/kRERDaGwZ+IiMjGMPgTERHZGAZ/IiIiG8PgT0REZGMY/ImIiGwMV/gj87PGp+fxcW86TPVrUqmAggJ7KJWAVKqbz1NPZHkM/mRefIRbvWDaX5MUwFBTVEREZsJufzIva396Hh/3BoBP2iOyNbzyp7pjjU/P4+PedNT216RSqZCSkoKwsDBI9fX738dTT2Q5DP5Ud/gIt3qhtr8mlQpwdi6BTKb/nj8RWR67/YmIiGwMgz8REZGNYfAnIiKyMQz+RERENobBn4iIyMYw+BMREdkYBn8iIiIbw+BPRERkYxj8iYiIbAyDPxERkY1h8CciIrIxXNufqlbbh7zz4e01VttTbwz+mohsC4M/Vc60D3knI/DUE5E5sdufKmfKh7zz4e1GMeWpNwZ/TUS2weqC/927dzFz5kw89thj8PHxgZOTE1q2bImBAwfi66+/hhBCZxu5XI7IyEj4+fnByckJfn5+iIyMhFwur3Q/69evR8+ePSGTyeDp6Ynw8HAcO3bMnIdWv2VnAwpFzV8HDvDh7TVU21PPXxMRVWR13f45OTlYuXIlevfujREjRqBJkya4efMmfvjhB4waNQovvfQSvvjiC015pVKJkJAQpKenY8iQIRg3bhwyMjKwePFi7N27FwcPHoSswsPJ582bh5iYGLRp0wbTp0+HQqHAxo0bERwcjJSUFAwYMKCOj7oeqO1D3qnGeOqJyOSElSkuLhYqlUonXS6Xi4ceekgAECdPntSkx8bGCgAiKipKq7w6PTY2Viv93LlzwsHBQQQGBoq7d+9q0k+ePClcXV2Fv7+/3v1XJjc3VwAQubm51ZZVFCoE4iEQD6EoVFRfXqEQAAQAoVBUX97kFAohym4/l72nOlOfT31RUZHYunWrKCoqsnRTiGyKMfHI6rr97e3t4eCg2yHRqFEjhIWFAQDOnz8PABBCYPny5XBzc0NsbKxW+ejoaHh6emLFihVatwqSk5NRXFyMmJgYeHh4aNKDgoIwadIkXLhwAXv27DHHoREREVkFqwv+lSkoKMCePXsgkUjw0EMPAQAyMzNx7do1BAcH63TtOzs7o3///rh69armywIApKamAgBCQ0N19qH+crFv3z4zHQUREZHlWd09f7W7d+9iyZIlKC0txc2bN7F9+3ZcvnwZcXFxCAgIAFAW/AFofq6ofLny793c3ODj41Nl+coUFhaisLBQ87N6UKFKpYJKparymMrnq1QqqCRGlq+mfpNTqSAt35a63r8NKzvV0vvvVfXq1Kv/n9b5/1ciG2fMZ86qg39CQoLmZ6lUig8//BBvvPGGJi03NxcAtLrvy3N3d9cqp37v5eVlcPmKkpKStNqltnPnTrhWM0eqoKRA8z4lJQXO9s5Vly+oUN656vKmZl9QgKHl9l9Sx/u3ZQUF9sD9s1/2uy+xbINqYNeuXZZuApFNyTNiVTCrDf5t27aFEAIlJSW4fPkyNm7ciJiYGBw6dAibN2/WOy6gLkRHRyMyMlLzs1wuR+vWrREaGqr58lAZZZESOFH2PiwsDDLHqodwK8stuxYWFqZza8PsKuyfQ87rTvkV98p+95Zri7FUKhV27dqFIUOGQCqVVr8BEZlEVdPbK7La4K9mb2+Ptm3b4u2334a9vT2ioqKwbNkyvPrqq5or/squ1NUnonzPgIeHh1HlK3JycoKTk5NOulQqrfYPnVT8k29Qealx5U2uwv7BP+R1pvypLvvdW64tNWWR/7NENsyYz1u9GfAH/DNITz1or7p79PrGBAQEBEChUODGjRsGlSciImpo6lXwv3btGgBouvwDAgLg6+uLtLQ0rS5yoOx++f79++Hr64sOHTpo0kNCQgCU3aOvKCUlRasMERFRQ2R1wT89PV1vt/zt27cxe/ZsAMCTTz4JAJBIJJg2bRoUCgUSExO1yiclJeHOnTuYNm0aJOXWK506dSocHBwwd+5crf2cOnUKa9asgb+/PwYOHGiOQyMiIrIKVnfPf9WqVVi+fDkef/xx+Pn5QSaT4dKlS/jxxx+hUCjw7LPPYvz48ZryUVFR+P777zF//nwcP34c3bp1Q0ZGBnbs2IGuXbsiKipKq/7AwEDEx8djzpw56Ny5M0aNGgWlUokNGzZApVJh2bJlFhtMSEREVBesLsqNGjUKubm5OHLkCPbv34+8vDw0adIEffv2xaRJkzB27FitK3mZTIbU1FQkJCRgy5YtSE1NhY+PDyIiIhAXF6d3hHxMTAzatm2LJUuWYOnSpXB0dESfPn2QmJiIHj161OXhEhER1TmJEHoek0cGk8vlmhkEhkz1c0tyAwAoohUGTfVzc7tfXqGwzFS/+/vHvXv/vLdRQpQ9arcuKJWAt3fZe4Wifs2yVKlU2L59O8LDwznan6gOGROPrO7Kn6xUv37Ab7/Z7PNehQD69gUOHbJ0S4iIaq/GwT8zMxOHDh3ClStXkJOTA1dXVzRv3hydOnVCnz594OLiYsp2kiW4ugJduwLp6WWvvLz6dQlqQnl5lgn8wcFlvwYiIlMyKvhfvnwZy5Ytw6pVq3D16lUAQMW7BhKJBA4ODggNDcUrr7yCp556SusePdUjEglw4ADQqJGlW2JVsrPr7juQq6vNdrYQkRkZFPxv3bqF2NhYrFixAsXFxQgICMDzzz+Pbt26wdvbG02aNEF+fj5u376Ns2fP4siRI9izZw+2b9+OgIAAzJ8/H8OGDTP3sZA5MPLokMlstgOEiBoIg4K/v78/7O3t8frrr2PixIno0qVLtdsolUps2bIFK1aswDPPPIMFCxYgIiKi1g0mIiKi2jEo+EdGRiIiIqLKNe8rkslkmDx5MiZPnozU1NQqn5RHREREdceg4B8fH1+rnQwYMKBW2xMREZHpWN3yvkRERGReDP5EREQ2psbB/9ChQ3jmmWfQvn17ODk5wd7eXufFNfKJiIisT42i87p16zB58mQIIdC+fXv07NmTgZ6IiKieqFHEfvfdd+Hp6YkdO3bwQThERET1TI26/f/66y+MHTuWgZ+IiKgeqlHwb9u2LYqKikzdFiIiIqoDNQr+06dPx7Zt23D79m1Tt4eIiIjMrEb3/F9//XWcP38ewcHBmDNnDrp06VLps4PbtGlTqwYSERGRadV4iH7Xrl2xbt06TJo0qdIyEokExcXFNd0FERERmUGNgv/HH3+Mf//735BKpXj88cfRokULTvUjIiKqJ2oUsRcvXoyWLVvi0KFDaNWqlanbRNZKqTSsXB0/hF4IIC/PvPsw9NCJiOqDGgX/Gzdu4JVXXmHgtzXe3oaVCw4GDhyoky8AQgB9+wKHDpl9V0REDUaNRvt36NABd+/eNXFTyCq5upYFc2OkpZn/Uvy+vLy6DfzBwWWnhIioPqvRlX9ERATeeOMNXLp0CX5+fqZuE1kTiaTsKt6QYK5UGt47YAbZ2YBMZt591PEdDSIis6hR8Pf390dISAi6d++O119/HV27dq10ql///v1r1UCyAhKJ+aOqCchk9aKZREQWV6PgP2DAAEgkEgghEBsbC0kVl0IlJSU1bhwRERGZXo2Cf3UBn4iIiKxXjYJ/fHy8iZtBREREdaVGo/2JiIio/jIo+OeZYNqWKeogIiKi2jMo+Ldr1w6LFi2qUQD/9ddfMXToUCxcuNDobYmIiMj0DAr+o0ePxttvvw0fHx9MmTIF27ZtQ05Ojt6yJSUlSE9Px4IFC9C1a1f07t0bFy9exBNPPGHShhMREVHNGDTg75NPPsHMmTORkJCATZs2Ye3atQCAli1bwtvbG56ensjPz8ft27eRlZWFgoICCCHwwAMP4LPPPsOLL74IOzsOLyAiIrIGBo/2DwwMxJdffom///4b69evx+7du3H48GH873//05SRSqV4+OGHERISghEjRnCBHyIiIitk9FS/pk2bYsaMGZgxYwYAQKVS4e+//4aLiws8PDxM3kAiIiIyrRrN8y9PKpXCx8fHFG0hIiKiOsAb8URERDaGwZ+IiMjG1IvgP3/+fEgkEkgkEhw5ckRvGblcjsjISPj5+cHJyQl+fn6IjIyEXC6vtN7169ejZ8+ekMlk8PT0RHh4OI4dO2auwyAiIrIKVh/8//jjD8TGxkJWxbNalUolQkJCsHjxYnTs2BERERF46KGHsHjxYoSEhECpVOpsM2/ePEyYMAHZ2dmYPn06xowZg7S0NAQHByM1NdWMR0RERGRZVh38S0pKMHnyZHTp0gXPPPNMpeXmz5+P9PR0REVFYefOnXj//fexY8cOxMbGIj09HfPnz9cqn5mZibi4OAQGBuL333/HwoUL8fnnn+PQoUNwcHDAtGnTUFxcbO7DIyIisgirDv4ffPABMjIysHLlStjb2+stI4TA8uXL4ebmhtjYWK286OhoeHp6YsWKFRBCaNKTk5NRXFyMmJgYremJQUFBmDRpEi5cuIA9e/aY56BshVKpeQmFEsqbVbwUonxxo19ERGQcqw3+J0+eREJCAubMmYOgoKBKy2VmZuLatWsIDg7WuTXg7OyM/v374+rVqzh//rwmXd2tHxoaqlNfWFgYAGDfvn0mOAob5u0NuLkBbm6QNHKDzLvy1/FG/eDmJtTFjXp5e1v6QImI6p9azfP/5Zdf8Ouvv+Lu3bsoKSnRyZdIJHjnnXeMrre4uBhTpkzBgw8+iLfffrvKspmZmQCAgIAAvfnq9MzMTK33bm5uetcnKF9Gn8LCQhQWFmp+Vg8oVKlUUKlUVba1fL5KpYJKYmT5auq3OKkU9n36wO7QIaM264s0uCIPeah8XEd1+vQphVRaAms/RbZA/f/U6v+/EjUwxnzmahT8b9++jREjRiAtLU2rO72imgb/efPmISMjA0ePHoVUKq2ybG5uLgBUurqgu7u7Vjn1ey8vL4PLl5eUlISEhASd9J07d8LV1bXKthaUFGjep6SkwNneueryBRXKO1dd3iq8+Sbsy305Kiiwx+QpTwIAvvh8J5yc/vmS6FBYgBGvPA8AWL1qB0pqcXxOTiXYsaPGm5MZ7Nq1y9JNILIpxjx5t0bBPzIyEgcPHsSAAQMwefJktGrVCg4OtV4sEACQkZGB9957D7NmzcKjjz5qkjpNKTo6GpGRkZqf5XI5WrdujdDQUM0Xh8ooi5TAibL3YWFhkDlWfaVbfpZCWFhYlTMerJVSCeSh7AvcU2OGQusQlErglbK3w4cPBurh8ZEulUqFXbt2YciQIdV+eSci06lqantFNYrY27ZtQ8+ePbF7925IJJKaVFGpyZMnw9/fH/Hx8QaVV1/xV3alrj4Z5XsGPDw8jCpfnpOTE5ycnHTSpVJptX/opOKffIPKS40rb43KN7nsGPRnSqVS7cJU79XX/7NE9ZUxn7caDfgrKChA//79TR74gbIr/zNnzsDZ2VmzsI9EIsHq1asBAI899hgkEgm2bt0KoPp79PrGBAQEBEChUODGjRsGlSciImpIanTl/8gjjyArK8vETSnz4osv6k3fv38/MjMzMWzYMDRv3hxt27YFUBakfX19kZaWBqVSqdU1XlBQgP3798PX1xcdOnTQpIeEhODw4cPYuXMnJk2apLWflJQUTRkiIqIGSdTAzz//LJycnMThw4drsnmNTJ48WQDQu8/Y2FgBQERFRelNj42N1Uo/e/ascHBwEIGBgeLu3bua9JMnTwpXV1fh7+8vVCqVQe3Kzc0VAERubm61ZRWFCoF4CMRDKAoV1ZdXKAQAAUAoFNWXt0YKhRBA2UvnEKrMpPqqqKhIbN26VRQVFVm6KUQ2xZh4ZNCV/5o1a3TShg4dipCQEEyYMAGPPPJIpffIK15Zm0NUVBS+//57zJ8/H8ePH0e3bt2QkZGBHTt2oGvXroiKitIqHxgYiPj4eMyZMwedO3fGqFGjoFQqsWHDBqhUKixbtsxkAxiJiIisjUERbsqUKTr398X9KX6rVq3CqlWr9OZLJJI6Cf4ymQypqalISEjAli1bkJqaCh8fH0RERCAuLk7vKPmYmBi0bdsWS5YswdKlS+Ho6Ig+ffogMTERPXr0MHubiYiILEUiRBUT9e9TD7aricmTJ9d42/pALpdrZg8YMtXPLckNAKCIVhg01c/N7X55haLeTvW7fwhQKKA71a/STKqvVCoVtm/fjvDwcI72J6pDxsQjg678G3oAJyIisiU1muq3Zs0a/P7771WWOXXqlN6xAkRERGRZNQr+U6ZM0cyzr8y2bdswderUmlRPREREZmS2p/qVlJTAzs5qHxpIRERks8wWnY8fP44mTZqYq3oiIiKqIYMnsw8cOFDr51WrViE1NVWnXElJCa5cuYKsrCyMGTOm1g0kIiIi0zI4+JcP9BKJBFlZWXqX+LWzs0OTJk0wevRoLFmyxARNJCIiIlMyOPiXlpZq3tvZ2SE+Ph6xsbFmaRQRERGZT43WsN27d6/mwTpERERUv9Qo+Fd84p1SqYRcLoe7u3u9XIWOiIjIltR4tH9hYSHmzp2LwMBAuLu7o1WrVnB3d0dgYCDmzZuHwsJCU7aTiIiITKRGV/53797FwIEDkZGRAQcHB3Ts2BHe3t7Izs7GhQsX8M477+Drr7/G7t270bhxYxM3mcxFCCAvz7R1KpW1LOjqClR4aBQREdVOja78Z8+ejfT0dEyZMgVZWVk4ffo09u7di9OnTyMrKwuTJ0/G8ePHERMTY+r2kpkIAfTtW/acHVO+vL0NbIC3t/4K+vUraxwREZlMjYL/t99+iz59+mDFihVo0aKFVl6LFi2wcuVK9O7dG998841JGknml5cHHDpkvvqDg8su4rW4upZlVCUtzfTdEURENq5G3f5yuVxn0F9FAwYMQEZGRo0aRZaVnW36p+vq7b2XSIADB/QHd6XSiG4DIiIyRo2Cf1BQEC5fvlxlmcuXLyMoKKhGjSLLkslMH/wrJZHU4c6IiAioYbd/dHQ0vvrqK73L+wLAnj178NVXX/GePxERkRWq0ZX/vXv3MHjwYAwaNAihoaHo27cvvLy8cPPmTRw4cAC7du3CU089hdzcXKxZs0Zr20mTJpmk4URERFQzNQr+U6ZMgUQigRACKSkpSElJ0Smzbds2/Pjjj5qfhRCQSCQM/kRERBZWo+CfnJxs6nYQERFRHalR8J88ebKp20FERER1pMbL+xIREVH9VKvg/+2332LMmDHo3LkzOnTooEk/c+YM5s+fj6tXr9a6gURERGRaNer2Ly0txbhx47BlyxYAgIuLC/Lz8zX5np6eiImJQUlJCaKjo03TUiIiIjKJGl35L168GF999RVeeeUV3LlzB7NmzdLK9/b2Rr9+/bRG+xMREZF1qFHwX7VqFbp3745PP/0U7u7ukOh56lqHDh1w8eLFWjeQiIiITKtGwf/8+fPo379/lWWaNm2Kv//+u0aNIiIiIvOpUfB3cXGBXC6vssylS5fQuHHjmlRPREREZlSj4P/II48gJSUFhYWFevNv376Nn376Cb17965V44iIiMj0ahT8Z86cicuXL2PUqFE60/kuXLiAZ555Brm5uZg5c6ZJGklERESmU6OpfsOHD8fbb7+N999/H23atIHs/iNZvby88Pfff0MIgXfeeQcDBw40aWOJiIio9mq8yM+8efOQkpKCoUOHwtXVFfb29igtLcUTTzyBHTt2ICEhwZTtJCIiIhOp0ZW/2pAhQzBkyBBTtYWIiIjqANf2JyIisjE1uvK/evUqtm7dil9//RU5OTkAgObNm6NHjx545pln0KJFC5M2koiIiEzH6OAfFxeH+fPno6ioCEIIrbw1a9Zg1qxZmD17NubMmWOyRhIREZHpGNXtHxMTg3fffRcA8Pzzz2PFihXYsWMHtm/fjhUrVmDChAkQQiAuLg7x8fE1blTbtm0hkUj0vqZPn65TXi6XIzIyEn5+fnBycoKfnx8iIyOrXIho/fr16NmzJ2QyGTw9PREeHo5jx47VuM2WIASgVJruZVUqfLEkIiLTMfjK/88//8T8+fPRrl07/PTTTwgICNApM3XqVMyZMwdhYWGYN28eJk+ejHbt2tWoYR4eHvj3v/+tk969e3etn5VKJUJCQpCeno4hQ4Zg3LhxyMjIwOLFi7F3714cPHhQMxVRbd68eYiJiUGbNm0wffp0KBQKbNy4EcHBwUhJScGAAQNq1Oa6JATQty9w6JClW2IGQgD9+lm6FUREDZcwUGxsrLCzsxMHDx6stuyBAweERCIR8fHxhlavxc/PT/j5+RncLgAiKipKb3psbKxW+rlz54SDg4MIDAwUd+/e1aSfPHlSuLq6Cn9/f6FSqQxua25urgAgcnNzqy2rKFQIxEMgHkJRqKi+vEIhAAgAQqFQVMgToixKmvYVHCxEaanBh28e5Q+ua1craBAZo6ioSGzdulUUFRVZuilENsWYeGRwt39aWhoefvhhBAcHV1u2b9++ePjhh3HgwIEafiUxjBACy5cvh5ubG2JjY7XyoqOj4enpiRUrVmiNTUhOTkZxcTFiYmLg4eGhSQ8KCsKkSZNw4cIF7Nmzx6ztNrXsbEChMM3rwAFAz0MaLcfqGkREVP8ZHPz/+OMP9OzZ0+CKe/XqhTNnztSoUQBQWFiI1atXY968eVi6dCkyMjJ0ymRmZuLatWsIDg7W6dp3dnZG//79cfXqVZw/f16TnpqaCgAIDQ3VqS8sLAwAsG/fvhq32xJkMtO9rC7OWl2DiIjqP4Pv+d+9exdeXl4GV+zl5YW7d+/WpE0AgBs3bmDKlClaaU888QTWrl2LZs2aASgL/gD0jj8on56Zman13s3NDT4+PlWWr0xhYaHWA43UgwpVKhVUKlWVx1Q+X6VSQSUxsrzWzwAgLZdXZVX1i0p1/8jun4MGdXANn/r/aXWfByIyLWM+cwYH//z8fDg5ORlcsaOjI/Lz8w0uX94LL7yAkJAQBAUFwcnJCadPn0ZCQgJ27NiBYcOGIS0tDRKJBLm5uQCg1X1fnru7OwBoyqnfV/YlRl/5ipKSkvQuXbxz5064urpWeVwFJQWa9ykpKXC2d666fEGF8s7O5fLsAQwtl1dSZV31iX1Bwf0jKzu2EueqzxNZp127dlm6CUQ2JS8vz+CytVre11wq3r/v1asXtm3bhpCQEBw8eBDbt2/HU089ZZG2RUdHIzIyUvOzXC5H69atERoaqvnyUBllkRI4UfY+LCwMMkdZ1eXLzb8LCwvTurVRfmpeWZ4RB2HtKhx3wzq4hk+lUmHXrl0YMmQIpFJp9RsQkUlUNb29IqOC/7p163DkyBGDypa/z24KdnZ2mDp1Kg4ePIi0tDQ89dRTmiv+yq7U1SeifM+Ah4eHUeUrcnJy0tsDIpVKq/1DJxX/5BtUXlp5+fKbluVVWVX9UuG4G9bB2Q5D/o8TkekY83kzKvifP3/eqKAuMfFgLfW9fnXXRnX36PWNCQgICMDhw4dx48YNnfv+1Y0hICIiaggMDv4XL140ZzsMcvToUQBlKwACZUHa19cXaWlpUCqVWt3iBQUF2L9/P3x9fdGhQwdNekhICA4fPoydO3di0qRJWvWnpKRoyhARETVUBk/18/Pzq9HLWKdPn9Y7S+DgwYNYtGgRnJycMHLkSABlPQvTpk2DQqFAYmKiVvmkpCTcuXMH06ZN0+qBmDp1KhwcHDB37lyt7v9Tp05hzZo18Pf3x8CBA41uNxERUX1hdQP+Nm/ejPnz52PQoEFo27YtnJyccPLkSezcuRN2dnb47LPP0KZNG035qKgofP/995g/fz6OHz+Obt26ISMjAzt27EDXrl0RFRWlVX9gYCDi4+MxZ84cdO7cGaNGjYJSqcSGDRugUqmwbNkyODhY3WkhIiIyGauLco8//jj++OMP/Pbbb9i3bx8KCgrg7e2N5557DhEREToLDclkMqSmpiIhIQFbtmxBamoqfHx8EBERgbi4OJ3Ff4CyBxS1bdsWS5YswdKlS+Ho6Ig+ffogMTERPXr0qKtDJSIisgiJEHx8Wm3I5XLNDAJDpvq5JbkBABTRCoOm+rm53S+vUOhM9bufBYWigc2Ga9AH1/CpVCps374d4eHhHO1PVIeMiUdGPdKXiIiI6j8GfyIiIhvD4E9ERGRjGPyJiIhsDIM/ERGRjWHwJyIisjEM/kRERDaGwZ+IiMjGMPgTERHZGAZ/IiIiG8PgT0REZGMY/ImIiGwMgz8REZGNsbpH+toKpRKAyoAycC33vmJePSYEkJenP6/eHxwRkXVj8LcQb29UG/wBGQDlP+UbCiGAvn2BQ4cs3RIiIpvEbv96LjgYcHW1dCuMlJdnWOCvlwdHRGT9eOVvQdnZgExWeb5SqYS3t9f9sjch01PY1RWQSMzVwjpQ1Umo9wdHRGSdGPwtSCarOviXyTOibD3UYA+MiMh6sdufiIjIxjD4ExER2RgGfyIiIhvD4E9ERGRjGPyJiIhsDIM/ERGRjeFUPyIbpFKpUFJSYra6HRwcUFBQYLZ9ENkKe3t7SKVSk9fL4E9kQ+RyOXJyclBYWGi2fQgh4OPjg8uXL0PCRZqIas3JyQnNmjWDu7u7yepk8CeyEXK5HFevXoWbmxuaNWsGqVRqluBcWloKhUIBNzc32NnxziJRTQkhoFKpkJubi6tXrwKAyb4AMPgT2YicnBy4ubmhVatWZr0iLy0tRVFREZydnRn8iWrJxcUFjRo1wpUrV5CTk2Oy4M9PJpENUKlUKCwshIeHB7viieoZiUQCDw8PFBYWQqWq9nGwBmHwJ7IB6oF35hg4RETmp/7smmoQLYM/kQ3hVT9R/WTqzy6DPxERkY1h8CciIrIxDP5EREQ2hsGfiIiMNmDAgFrfh161ahUkEglWrVplmkaRwRj8iajBk0gkRr2sUXx8vKZ9b7/9dqXlIiMjNeXef//9Omwh1SdWvcjPt99+i08//RS//fYb8vLy4OPjg969e2P+/Plo3bq1ppxcLkd8fDy+/vpr3LhxAz4+Pnj22WcRHx9f6YII69evx5IlS3Dq1Ck4OjriscceQ2JiIrp3715Xh0dEdSQuLk4nLSEhAR4eHvj3v/9d9w2qBQcHB6xZswZz586Fvb29Vp5KpcK6devg4OCA4uJiC7WQ6gOrDP5CCEyfPh1ffPEF/P39MXbsWDRq1AjXrl3Dvn37cOnSJU3wVyqVCAkJQXp6OoYMGYJx48YhIyMDixcvxt69e3Hw4EHIZDKt+ufNm4eYmBi0adMG06dPh0KhwMaNGxEcHIyUlBQMGDDAAkdNROYSHx+vk5aQkIDGjRvrzbNmTz75JH744Qfs2LEDQ4cO1cr74YcfcOvWLQwbNgzff/+9hVpI9YFVdvt//PHH+OKLL/Cvf/0LZ8+exX//+1+8//77WLNmDS5duoTevXtrys6fPx/p6emIiorCzp078f7772PHjh2IjY1Feno65s+fr1V3ZmYm4uLiEBgYiN9//x0LFy7E559/jkOHDsHBwQHTpk3jN2YiG5WVlQWJRIIpU6bgzJkzGDlyJJo1awaJRIKsrCytfH0kEonei4d79+4hLi4OQUFBcHFxQePGjfHEE0/g4MGDRrdx5MiRaNy4MVauXKmTt3LlSjRv3lznS0F5p06dwnPPPQcvLy84OTmhXbt2iIiIwO3bt/WWP3jwIEJCQiCTydC0aVM899xzuHz5cqX1CyGwcuVKBAcHw93dHa6urujevbve9pLlWN2Vf35+PhISEtC+fXssWbJEp1sLKOv2Asr+ky1fvhxubm6IjY3VKhMdHY2PP/4YK1as0NwrA4Dk5GQUFxcjJiYGHh4emvJBQUGYNGkSPvvsM+zZswehoaFmPEoi6yGEQF5ensnqKy0thVKphL29vUnX9nd1da2z+/Hnz59H7969ERQUhMmTJ+P27dtwdHREUVGR0XXdvn0b/fv3x6lTp9CvXz+EhYUhNzcX3333HR5//HF89dVXGDFihMH1OTs7Y+zYsVixYgVu3bqF5s2bAwCuXbuGn376CTNnzqx0JcdDhw4hNDQUhYWFGDVqFNq2bYsjR45gyZIl+PHHH3H48GE0bdpUU3737t148sknYWdnh+eeew6+vr7YvXs3goOD4enpqVO/EAITJ07E+vXrERgYiPHjx8PR0RG7du3Ciy++iNOnT2PBggXGnUAyD2FlvvvuOwFAREZGioKCAvH111+LpKQksXTpUpGZmalV9uzZswKACAsL01vX8OHDBQBx7tw5Tdpjjz0mAIjr16/rlP/2228FADF79myD25ubmysAiNzc3GrLKgoVAvEoe0kVQqGoprxCIQAIAEJRXeH6RKEQAih7NaTjsmL5+fni9OnTIj8/Xyev/P8za36Z+jMAQPj5+WmlXbx4UbO/d955R2cbdf7kyZMrrTMkJEQrbfz48QKAWLlypVb6jRs3ROvWrUXz5s31/l4qiouLEwDEhg0bxC+//CIAiEWLFmny582bJwCIEydOiOTkZAFAJCUlafJLSkpEQECAACB++uknrbqjo6MFAPHiiy9qlW/fvr2QSCTiwIEDmvTS0lLNMVUMIV988YWmHpVKpUkvLCwUTz/9tAAgjh07pklXtzM5Obna47d1VX2G1YyJR1Z35X/s2DEAZVf3Xbp0wdmzZzV5dnZ2iIiI0HxzzMzMBAAEBATorUudnpmZqfXezc0NPj4+VZavTGFhodaz0OVyOYCygTbVPXChYn7ZNoaVN6T+ekOlglTzVoUqTwKZhEqlghACpaWlKC0t1cqr+LO10td2U9Vb8b2Pjw9mz55d6blSn8vq6szJycGmTZswaNAgTJ48WWub5s2bY9asWXj99dexc+fOKrvq1ftU192tWzd06tQJK1euxOuvvw6gbNpcjx498NBDD+GXX37RaeeBAweQmZmJJ554AkOGDNFqS3R0NL744gusX78en3zyCRwdHbF//378+eefGDp0KPr06aNV/r333sOmTZtQUlKilf7JJ59AJpPho48+gp2dnSbPwcEB7777Ln744QesX78ejzzyiNZ5MtfvtiEpLS3VPOJXX484oBtjqmJ1wf/mzZsAgIULF+LRRx/FL7/8ggcffBDHjx/Hyy+/jIULF8Lf3x+vvvoqcnNzAUCr+7489Uh/dTn1ey8vL4PLV5SUlISEhASd9J07d8LV1bXKYysoKdD6OSUlBc7OlT+koaCgoEJZ5yrrry/sCwqg/jOXkpKCkgZyXNbMwcEBPj4+UCgUOl3XQghcuXLFQi0zXHFxsebLtqmUlpZq1alQKACU3QYsKCjQ+gyWz1epVJW2pXw79+3bh5KSEigUCsyePVun7IULFwAAGRkZ6N+/f5VtVV905OfnQy6XY+zYsYiJiUFqaioKCgpw7tw5LFy4EHK5XNPugoICTVuOHDkCAOjdu7fetnft2hW7d+/Gb7/9pvUFokePHjrlPT090bJlS/z111+avLy8PJw4cQItWrRAYmKiTv3qwHTy5EnNNvraSfoVFRUhPz8f+/fvr3RcmjG376wu+Ku//Tk6OmLr1q3w9fUFAPTr1w9btmxB586dsXDhQrz66qsWaV90dDQiIyM1P8vlcrRu3RrBwaHVPmdZqVICJ/75OSwsDBUmImiXVyorlK2isKkJAZjwPrCWCsdV5UkgkygoKMDly5fh5uam90tkZV+ga0IIgXv37qFRo0ZWO2dezc7OTutz6+bmBgDw9fXV+3lW50ul0ko/7w4ODpo8dXA7evQojh49Wmk7SkpKqv374eTkBKDs+e7u7u548cUXER8fj82bN6OgoADOzs6YMmUK3N3dNb9jZ2dnTb3qL31t2rTRu6+WLVtqtUX9ZaN169Z6y7do0QJ//fWXJu/evXsQQuDatWv44IMPKj2OoqIizTb62kn6FRQUwMXFBf3796/0QtCYL1BWF/zVf4S6d++uCfxqQUFBaN++Pc6fP4+7d+9qylZ2pa4+EeX/sHl4eBhVviInJyfNh7A8Pz8pgGoelyqVAjHlf5Siqieslh+0U1a2jh7HKgTQty9w6JDZdyWVSlHlSSCTKCkpgUQigZ2dnUkH4emj/gKv3p+1K99G9fvKzpN6sHFJSYlOfvm/K+q8xo0bAwDeeOONWg90U3+RUrfN29sbQ4cOxcaNG1FcXIyRI0eiSZMmWvsv/ztQ/127deuW3mNT97o2btwYdnZ2mrbn5OToLZ+dna33WLt166a5fVud6s43/cPOzg4SiaTKWGBMjLC6s92xY0cA//xHqkidnp+fX+09en1jAgICAqBQKHDjxg2DyptL795ANXcJLCcvr04CP4KDrfgkEOlS//25evWqTt7x48d10nr06AGJRILDhw+bpT0vvPACcnNzoVQq8cILL1RZVn2fPTU1VScvLy8Px44dg4uLi+ZvcJcuXQCUjRWo6NKlSzrT/Ro1aoQHH3wQf/zxB+7evVuDo6G6ZHXB//HHHwcA/PHHHzp5KpUK58+fh0wmQ/PmzREQEABfX1+kpaVpdZEDZV0k+/fvh6+vLzp06KBJDwkJAVB2j76ilJQUrTLGuHYNUCiqft3/ogwA2LULsPIe0TLZ2dUfWE1fBw7Uk5NAVMbd3R2BgYE4ePAgzp8/r0m/d+8eoqOjdcr7+PhgzJgxOHToED788EPNoL3yjh49WuOplk8++SS2bt2KrVu3YuDAgVWWDQ4Ohr+/P3bs2IGff/5ZKy8pKQk5OTkYN24cHB0dAQB9+/ZFu3btsG3bNq31CIQQmD17NkpKdMcrzZw5E3l5eXjppZd0/iYDwMWLF5GVlVWDIyVTs7puf39/f4SGhmLnzp1Yvnw5pk2bpsl7//33cffuXUycOFHT/TZt2jQkJiYiMTFR6z5TUlIS7ty5gxkzZmjdd5w6dSoWLFiAuXPnYvjw4ZqusFOnTmHNmjXw9/ev9kOkj0xmwK3rcj0y9SbmGXRgRLYjMjIS06dPx2OPPYbRo0ejtLQUO3bsqHRp8E8//RRnz55FVFQU1q5di8ceewweHh64fPky/ve//yEzMxPXr1+vdsCwPvb29hg+fLhBZe3s7LBq1SqEhYUhPDwco0ePhp+fH44ePYo9e/bA399f61kAdnZ2+OKLLxAeHo7Bgwdr5vnv2bMH169fR+fOnfH7779r7eOVV17BkSNHsHr1aqSlpWHw4MHw9fVFdnY2zpw5g6NHj2L9+vVo27at0cdKJlb72Yemd/78eeHl5SUAiKeeekq88cYbYuDAgZp5ueXn6CsUCtG1a1cBQAwZMkS8/fbb4sknnxQARNeuXfXODX7vvfcEANGmTRsRGRkpXnnlFeHu7i6kUqnYs2ePUW2t6Tx/RWH1c5YtNs+f8/AbHEPmCJtKSUmJuHPnjigpKTH7vmpD/fekvOrm8at9/PHHokOHDkIqlYo2bdqI2NhYUVRUpHeevxBC5OXlifnz54tu3boJmUwmXFxcRLt27cSIESPEmjVrtObEV6b8PP/q6Jvnr/b777+LUaNGiWbNmgmpVCr8/PzEzJkzxa1bt/TWtX//ftG/f3/h4uIimjRpIkaPHi0uXbokQkJCdOb5q23atEkMHjxYeHp6CqlUKlq2bCkGDBggFi5cqLUfzvM3nKnn+Vtl8BdCiL/++ktMmTJF+Pj4CKlUKlq3bi3+9a9/iezsbJ2yd+/eFREREaJ169aashEREeLu3buV1r9u3TrRvXt34eLiIjw8PMQTTzwhfvnlF6PbyeBP9QGDP1H9ZurgLxFCz00oMphcLtfMIKh2ql+REm5JZVOFFNEKyByr7k5XKpWaqUUKhaLupvoplcD9/UKhYLd/A1BQUICLFy+iXbt2Zl8vQj133t3dnSO4iUzEkM+wMfGIn0wiIiIbw+BPRERkYxj8iYiIbAyDPxERkY1h8CciIrIxDP5EREQ2hsGfiIjIxjD4ExER2RgGfyIiIhvD4E9ERGRjGPyJiIhsDIM/ERGRjWHwJyKyEVlZWZBIJJgyZUqt6hkwYAAkEolpGkUWweBPRDZBHfieeOKJasump6fjnXfeQe/eveHl5QUnJye0b98er732Gq5evWrUftu2bQuJRAKJRIIzZ87oLVNcXAwfHx9NuRs3bhi1DyJjMfgTEVUwffp0zJ07F0IIjB07FjNnzkSrVq2wdOlSdO3atdIgXhk7OzvY2dlh5cqVevO3bduG7OxsODg4mKL5RNVi8CciqmDixInIzMzE0aNH8dFHH+HDDz/E/v378f777yMnJwdvvPGGUfVJpVIMGjQIa9euRXFxsU7+ypUr0axZM/To0cNUh0BUJQZ/IqIK/u///g/+/v466bNmzYKrqyv27dtndJ1Tp07FjRs3sH37dq30GzduYMeOHZgwYQIcHR0r3X716tXo3bs33Nzc4Obmht69e2P16tV6y5aUlOCDDz5Ahw4d4OzsjA4dOiApKQmlpaWV1n/z5k1ERESgQ4cOcHJyQrNmzfDss8/i5MmTRh8rWT/2MRHZOCGAvDzT1VdaCiiVgL09YGfCywtXV8DSY8wkEgns7e1hV4MDe+aZZ+Dp6Ynk5GQMGzZMk75mzRoUFxfjhRdewMyZM/VuGxERgSVLlqBly5Z48cUXIZFI8PXXX2PKlCnIyMjAokWLtMq//PLLWLlyJdq1a4d//etfKCgowKJFi3Do0CG99V+4cAEDBgzA1atXERoaihEjRuDmzZv4+uuvkZKSgt27d6NXr15GHzNZMUG1kpubKwCI3NzcassqChUC8RCIh1AUKqovr1AIAAKAUCiqL28yCoUQZTGh7D3Ve/n5+eL06dMiPz9fJ6/8r9uaX7X9r3jx4kUBQISFhdW4jk2bNgkAYvTo0QZv4+fnJ5ycnIQQQrz22mtCKpWK7OxsTX7Hjh1Ft27dhBBChISECADi+vXrmvz9+/cLAOLBBx8Ud+/e1aTfvXtXPPDAAwKAOHDggCZ97969AoDo0qWL1t+NK1euiGbNmgkAYvLkyVpt7NOnj3BwcBA7d+7USj979qxo1KiR6NSpk1a6up1Ud6r6DKsZE4/Y7U9EZIDLly9j5syZcHFxwbvvvlujOl544QWoVCqsXbsWAJCWloazZ8/ihRdeqHSbVatWAQDi4+Ph4eGhSffw8EBcXJxWGaCsJwEAYmNjIZPJNOktW7bE66+/rlP/8ePHcejQIUyePBlDhgzRygsMDMRLL72EEydOsPu/gWG3P5GNc3UFFArT1VdaWgq5XA53d/cadY9XxtXVZFUZ7fbt2wgPD8fNmzexZs0adOzYsUb1dOvWDZ07d0ZycjLeeOMNrFy5Es7Ozhg/fnyl2xw/fhxA2dz6itRp6enpmrSMjAwAQL9+/XTK60s7cuQIgLKxB/Hx8Tr56pkNZ86cwcMPP1xpO6l+YfAnsnESCVDuArHWSkuBkpKyOk15z99S7ty5g8GDB+PUqVNYunQpJk6cWKv6pk6dioiICOzZswebN2/GiBEj0Lhx40rLy+Vy2NnZoXnz5jp53t7esLOzQ25uriYtNzcXdnZ2aNasmd7yFd2+fRsA8OOPP+LHH3+stB1KpbKqw6J6pgF8NImIzOP27dsYNGgQjh8/jk8++QSvvPJKreucOHEiHB0dMWnSJCgUiiq7/AHA3d0dpaWluHXrlk7ezZs3UVpaCnd3d02ah4cHSktLkZOTo1M+Oztbb/0A8PHHH0MIUelr8uTJxh4qWTEGfyIiPW7fvo3Bgwfj+PHj+Pjjj/Haa6+ZpN5mzZrh6aefxtWrV9GmTRsMGjSoyvKPPPIIACA1NVUnTz3lsGvXrpq0Ll26AAAOHDigU15fmnoU/+HDhw1qPzUMDP5ERBWUv+L/z3/+g//7v/8zaf0ffvghvv32W3z77bfVjotQX3EnJCRALpdr0uVyORISErTKAMCkSZMAAImJiVpd9VevXsV//vMfnfp79uyJXr16YcOGDdi0aZNOfmlpaY3WNSDrxnv+llbVJGulEq7l3tcZ3tujBuzEiROVPtjm0UcfxcyZMzFy5Eikp6fjgQcewO3bt/UOhPv3v/9d5b36qrRr1w7t2rUzqGz//v0xY8YMfPzxx3j44Yfx7LPPQgiBb775RjMDoX///pryAwYMwNSpU5GcnIxOnTrhmWeeQWFhITZt2oTevXtj27ZtOvvYsGEDHn/8cYwdOxZLlixBt27d4OzsjL/++guHDx/GrVu3UFBQUKNjJevE4G9JQgB9+wKVLLwhA6AJw3oG6hCR8a5du1bpynh3797FzJkzkZWVBaBshLv66rqiKVOm1Dj4G+ujjz7CI488gqVLl+KLL74AAAQFBSEhIQFTp07VKb9s2TIEBgZi2bJl+OSTT9CqVStERkZizJgxeoN/u3btcPz4cSxatAhbt27FypUrYW9vjxYtWqB///4YNWqU2Y+R6pZECCEs3Yj6TC6Xw8PDA7m5uVqDbvRRFinhluQGAFBEKyBTAXBzq4NW1lBwMHDggOWXVaNaKygowMWLF9GuXTs4OzubdV/mmupHZMsM+QwbE4945W8tsrN15lsplUp43b/iv5mdrbVgR52whvVUiYjI5Bj8rYVMpneydV41+URERMZinxwREZGNYfAnIiKyMQz+RERENobBn4iIyMYw+BMREdkYBn8iIiIbY3XBf9WqVZBIJFW+Kj4IQy6XIzIyEn5+fnBycoKfnx8iIyO11sGuaP369ejZsydkMhk8PT0RHh6OY8eOmfvwiIiILM7q5vl37doVcXFxevO2bNmCU6dOISwsTJOmVCoREhKC9PR0DBkyBOPGjUNGRgYWL16MvXv34uDBgzqL48ybNw8xMTFo06YNpk+fDoVCgY0bNyI4OBgpKSkYMGCAOQ+RiIjIskQ9UVhYKJo2bSocHBzEjRs3NOmxsbECgIiKitIqr06PjY3VSj937pxwcHAQgYGB4u7du5r0kydPCldXV+Hv7y9UKpXB7crNzRUARG5ubrVlFYUKgXgIxEMoChVCKBRClK3wX/a+YnmFQgAQAIRCTz6RofLz88Xp06dFfn6+2fdVUlIi7ty5I0pKSsy+LyJbYchn2Jh4ZHXd/pX59ttv8ffff2Po0KHwvr/krRACy5cvh5ubG2JjY7XKR0dHw9PTEytWrIAo9/iC5ORkFBcXIyYmBh4eHpr0oKAgTJo0CRcuXMCePXvq5qCIiIgsoN4E/xUrVgAApk2bpknLzMzEtWvXEBwcrNO17+zsjP79++Pq1as4f/68Jj01NRUAEBoaqrMP9e0EPruaiKh6Eomk1rdJp0yZAolEonmSItUNq7vnr8+lS5ewe/dutGzZEk888YQmPTMzEwAQEBCgdzt1emZmptZ7Nzc3+Pj4VFm+MoWFhSgsLNT8rB5UqFKpoFKpqjyO8vll5QFp+bwK2+uWr7p+osqoVCoIIVBaWorS0lKz7kvd06ben7XIysqCv78/QkNDsWPHDk16QkICEhMTtcq6uLigffv2GDZsGKKiovQ+Ic3e3h4dO3bE6dOnq93333//jQULFmDbtm24ePEiAKB58+bo0KED+vXrh1deeUXTo1mVgQMHai5OfvzxR62/h+V1794dx48fBwCkpaWhd+/e1dZdG7X5Pav/v9TF/836rLS0FEIIqFQq2Nvb6y1jTIyoF8E/OTkZpaWlmDp1qtZB5+bmAoBW93156g+supz6vZeXl8HlK0pKStL7fO+dO3fC1dW1yuMoKCnQvE9JSYFMBQwt93NJhcc0FhRolzf3o1ip4XJwcICPjw8UCgWKiorqZJ/37t2rk/0YSqFQAABKSkq0ZgKpv8wPGzYMDz74IAAgOzsbP//8M5KSkvD9999j9+7dcHJy0qlT/fjiqly9ehVhYWG4evUqOnXqhPHjx0Mmk+Hy5cs4efIkEhIS0LVrV4OuoIuLiwGU/T6XLVuGPn366JQ5ceIEjh8/DgcHBxQXF0OpVFbbxtooLi6uVf3qgKVQKMzazvquqKgI+fn52L9/v+b/QUV5eXl60/Wx+uBfWlqK5ORkSCQSvPDCC5ZuDqKjoxEZGan5WS6Xo3Xr1ggNDa32+cnKIiVwoux9WFgYZOW+pIWFhel9pG/5/Dp/pC81GAUFBbh8+TLc3NzM/iVSCIF79+6hUaNGkFjRI6Hd3NwAlF2xl/+sqoP6c889h7Fjx2rSCwoK0KdPH2RkZGDbtm2YOnWqTp12dnbVfu4jIyNx9epVJCQkYM6cOTr5J06cQOPGjautBygL+gDwxBNPYPv27VCpVGjatKlWmc2bN8PBwQGDBw/GTz/9BJlMZlDdNeXg4FCr+qXSsv5PNzc3s7azvisoKICLiwv69+9f6WfYmC9PVh/8d+3ahb/++guDBg1Cu3bttPLUV/yVXamrT0T5ngEPDw+jylfk5OSk9wpAKpVq/hNXRir+yZdKpShfWiqVAhW2L1+fIfUTVaakpAQSiQR2dnawszPvUB911616f9ZC3ZaK7VJ/Qal4blxdXTFhwgRkZGTg+PHjePHFF6ustzJHjhwBAMycOVNv2S5duhh3IABeeOEFbNu2DRs2bMDMmTM16UVFRdiwYQOeeuopNG7cWNO+ivvdtm0bFi5ciN9++w1FRUXo2LEjpkyZghkzZujtUl6+fDmWLFmC8+fPo3nz5hg3bpzWrZKK9d+7dw8LFizAli1b8Oeff8LJyQm9e/fGnDlz0LdvX62ylZ1/0mZnZweJRFJlLDAmRlj9mdY30E+tunv0+sYEBAQEQKFQ4MaNGwaVJyLbpb4frb7irokmTZoAgNbA49p67LHH8OCDDyI5OVkrfevWrfj777+r7CX9z3/+g6effhq///47xo8fj3/961/Iz89HREQExowZozU7CgDeffddvPTSS8jJycFLL72E0aNHY9OmTRg9erTe+m/fvo3HHnsMiYmJaNq0KV599VU8++yzOHbsGB5//HFs3bq11sdPtWfVwf/vv//Gd999hyZNmuCZZ57RyQ8ICICvry/S0tK0usiBsi6S/fv3w9fXFx06dNCkh4SEACi7R19RSkqKVhkimyAEoFRa/6tCUDK3/Px8rFu3DgB0rlaNoQ6STz/9NN577z0cPHhQM/6gNqZOnYr09HTNwD6g7GLJ29sb4eHherf5888/MWvWLHh5eSE9PR1Lly7FggULcOLECYSEhOCbb77Bl19+qSl//vx5JCYmomXLlvj999/x8ccfY9GiRThx4kSlF10zZszAqVOnsHLlSuzfvx+LFi3CihUrcOrUKbRo0QIvv/yy1ngmsgyrDv5r165FUVERJk6cqLerXSKRYNq0aVAoFDqjdZOSknDnzh1MmzZN677j1KlT4eDggLlz52p1/586dQpr1qyBv78/Bg4caL6DIrI2eXmAm5vJXnbu7mjcqhXs3N1NWi+MGMxUE1u2bEF8fDzi4+Px6quvIjAwECdOnMDw4cMxcuTIGtc7Y8YMREZG4vbt23jnnXfQr18/uLu7IygoCG+//TauX79eo3onTZoEBwcHrFy5EgBw+fJl/Pzzz5p0fb788ksUFxfjjTfeQOvWrTXpjo6OeP/99wGULbGutn79ehQXFyMyMlJroLS7u7ve8Qs5OTnYtGkTBg0apDNGwtvbG2+++SZu3bqFn3/+uUbHTKZj1ff8q+ryV4uKisL333+P+fPn4/jx4+jWrRsyMjKwY8cOdO3aFVFRUVrlAwMDER8fjzlz5qBz584YNWoUlEolNmzYAJVKhWXLltWqi4+I6qevv/4aX3/9tVbayJEjsWXLlloNXLSzs8PChQsRHR2N7du348iRIzh27Bj+97//4fTp0/j888/x008/oVevXkbVq77CX79+PRYsWIBVq1ZpZkVVRt1LoG9mQe/eveHi4oL09HRNWkZGBgCgX79+OuX1pf36668oKSlBQUEB4uPjdfLVvQVnzpzB0KFDdfKp7lhtlPvll19w8uRJ9OzZE506daq0nEwmQ2pqKhISErBlyxakpqbCx8cHERERiIuL0ztCPiYmBm3btsWSJUuwdOlSODo6ok+fPkhMTESPHj3MeVhE1sfVFTBBN7Saevqbu7u7aQdwVTOVtrY2bNiAsWPHori4GGfPnsWsWbPwzTffIDY2Fu+++26t62/WrBkmTZqESZMmAQBu3LiB//u//8PXX3+Nl19+WRNojTF16lR8//33+Pbbb7Fq1Sr07t1bM11RH/Wg5srWFPDy8sLVq1c1P6t7R/VNj9ZXx+3btwGUrS2QlpZWaTsq3qalume1wb9nz546A08q4+HhgUWLFmHRokUG1z9hwgRMmDChps0jajgkEp1pprVSWgqUlJTVWQ9Hbzs4OCAoKAjffvstOnXqhLlz5+KZZ57Bo48+atL9+Pj4YO3atdi2bRt+//13/P333zrT9qqjXu581qxZuHr1Kt5+++0qy6un0mVnZ8PPz08n/+bNm1rT7dQzn27evKlTPjs7u9L633jjDSxYsMCoY6G6Vf8+mUREdcDZ2RkLFiyAEKLaoFpTTk5OtZrC6+DggIkTJ+Lq1atwdXXFc889V2X5Rx55BMA/y5yX98svvyA/Px9du3bVpKmnIR44cECnvL60Hj16QCKR4PDhw0YcBVkCgz8RUSWGDx+ORx99FLt27dIb7AyxcOFCnDlzRm/eRx99BIVCgQceeMDoq361N998E99++y1SUlKqXSRn/PjxcHBwwKJFi3Dt2jVNukql0nzBmTJlilZ5e3t7LFq0CDdv3tSky+VyvPfeezr1+/j4YMyYMTh06BA+/PBDvb23R48eNWolOjIPq+32JyKyBvHx8Rg2bBhiY2Oxd+9erbzr169rBcvy2rRpg8TERKxduxazZs1Cp06d0KtXL3h5eeHu3bs4fPgwjh8/DhcXFyxdurTG7fP29saIESMMKuvv748PPvgAb7zxBjp37owxY8ZAJpNh27ZtOHPmDIYPH46JEydqynfo0AGxsbGIi4vTlHdwcMDXX3+NTp064ezZszr7+PTTT3H27FlERUVh7dq1eOyxx+Dh4YHLly/jf//7HzIzM3H9+vVql0Mn82LwJyKqwtNPP43u3bsjNTUVe/bs0ZoKLJfLsXr1ar3bdenSBYmJiUhOTsYPP/yAPXv2ICUlBdnZ2bC3t4efnx9effVVRERE1OnCYpGRkejQoQMWLVqEdevWoaioCIGBgVi4cCFmzpypM7MhNjYWvr6+WLx4MT7//HN4eXlh7NixSExM1BvAmzRpgkOHDuGTTz7Bpk2b8OWXX6K0tBQ+Pj7o0qUL3nnnHTRr1qyuDpcqIRGGjqojveRyuWbJYEPW9ndLKltfXBGtKFvb//5641Ao9K7tr16PXKFQcG1/qrGCggJcvHgR7dq1M/va/mYb7U9kwwz5DBsTj/jJJCIisjEM/kRERDaGwZ+IiMjGMPgTERHZGAZ/IiIiG8PgT0REZGM4z99UlErA3r7qMkXlHmahVAIq8zaJiIhIHwZ/U/H1rb6MFEDM/ffe3gz+RERkEez2twbBwWZ/XCkREZEar/xN5do1oJoVlVCkBD66/wzs7GzA8f6Kfa6uZY9VJSIiqgMM/qYik1X/TPTyT+6Uyf4J/kRERHWI3f5EREQ2hsGfiIjIxjD4ExGRybRt2xZt27a1dDPMbsCAATqPPzbWqlWrIJFIsGrVKtM0yggM/kRkE7KysiCRSLRejo6OaN26NcaPH4/ff//d0k20CqYIauYQHx+v+b29/fbblZaLjIzUlHv//ffrsIX1Cwf8EZFN8ff3x8SJEwEACoUCR44cwYYNG/DNN99gz5496NOnj4VbWL/t3r3brPU7ODhgzZo1mDt3LuwrLKymUqmwbt06ODg4oLi42KztqO945U9ENqVDhw6Ij49HfHw8FixYgIMHDyImJgaFhYWIiYmpvgKqkr+/P/z9/c1W/5NPPonr169jx44dOnk//PADbt26hfDwcLPtv6Fg8CcimzdjxgwAwK+//goAuHbtGuLi4tC7d294eXnByckJbdu2xWuvvYabN2/qbD9lyhRIJBL8+eefWLx4MYKCguDk5IQpU6bUur4FCxYgMDAQLi4ueOihh7Bx40YAZVe5sbGxaNeuHZydndG5c2ekpKToPb579+4hLi4OQUFBcHFxQePGjfHEE0/g4MGDWuUkEgn27dunea9+qY9DfetkypQpOHPmDEaOHIlmzZpBIpEgKysLQOX3/IUQWL16Nfr374/GjRvD1dUVAQEBmD59Ov7666+qf0HljBw5Eo0bN8bKlSt18lauXInmzZtj6NChlW5/6tQpPPfcc5rfQ7t27RAREYHbt2/rLX/w4EGEhIRAJpOhadOmeO6553D58uVK6xdCYOXKlQgODoa7uztcXV3RvXt3ve21JHb7E5HNq3iPe//+/Vi4cCEGDRqEXr16QSqV4vjx41i6dClSUlLw22+/wcPDQ6eeGTNm4MiRI3jqqacwdOhQeHt716q+yMhIHD16FE8//TTs7e2xceNGjB8/Hp6envjvf/+LkydPIjw8HAUFBVi/fj2GDRuGM2fOoF27dpo6bt++jf79++PUqVPo168fwsLCkJubi++++w6PP/44vvrqK4wYMQIAEBcXh1WrVuHSpUuIi4vT1NG1a1etdp0/fx69e/dGUFAQJk+ejNu3b8PR0bHS8yuEwLhx47Bp0ya0bNkS48aNg7u7O7KysrBp0yY88cQTaNOmTbW/JwBwdnbG2LFjsWLFCty6dQvNmzcHUPYF66effsLMmTMhlUr1bnvo0CGEhoaisLAQo0aNQtu2bXHkyBEsWbIEP/74Iw4fPoymTZtqyu/evRtPPvkk7Ozs8Nxzz8HX1xe7d+9GcHAwPD099R7nxIkTsX79egQGBmL8+PFwdHTErl278OKLL+L06dNYsGCBQcdpdoJqJTc3VwAQubm51ZZVFCoE4iEQD6EoVFRfXqEQAAQAoVBUX56oMvn5+eL06dMiPz9fJ6+0tFQoChUme8nz5eLKzStCni83ab2lpaW1OgcXL14UAERYWJhOXkxMjAAgBgwYIIQQIjs7W9y7d0+n3OrVqwUA8d5772mlT548WQAQrVq1EpcuXdLZrqb1BQQEiJs3b2rSjxw5IgCIxo0bi759+2r9Xdi0aZMAIGbOnKlV1/jx4wUAsXLlSq30GzduiNatW4vmzZtr/b8ICQkRlYUG9TkEIN555x29Zfz8/ISfn59W2n//+18BQAwaNEjk5eVp5eXl5Ym///5bb13lxcXFCQBiw4YN4pdffhEAxKJFizT58+bNEwDEiRMnRHJysgAgkpKSNPklJSUiICBAABA//fSTVt3R0dECgHjxxRe1yrdv315IJBJx4MABTXppaanmnFY8T1988YWmHpVKpUkvLCwUTz/9tAAgjh07pklXtzM5Obna46/qM6xmTDzilT+RjctT5cEtyc3SzaiWIloBmQlWxTx//jzi4+PL6rw/4C8tLQ3Ozs6YN28eAMDLy0vvts8//zxmzJiBn3/+We/4gDfffFPvFWxN64uJidFc2QJAr1690L59e/z555+YO3cuZOVWFX322WchlUqRkZGhScvJycGmTZswaNAgTJ06Vatub29vvPnmm5g5cyZ+/vnnKrvKK/Lx8cGcOXMMLv/f//4X9vb2WLp0KVxcXLTyXFxcdNKq06NHD3Tq1AkrV65EREQEgLJpcz169MDDDz+MY8eO6WyTlpaGzMxMPPnkkwgLC9PKi4mJwRdffIH169fj008/haOjIw4ePIg///wTTz/9NPr27aspK5FIMG/ePGzatAklJSVa9XzyySeQyWT45JNP4ODwT3h1dHTE3Llz8cMPP2DDhg3o1q2bUcdrDgz+RGRTLly4gISEBACAVCqFt7c3xo8fj7fffhudOnXSlPvmm2/w+eef47fffsOdO3e0/tBfu3ZNb909e/asdL81qe+RRx7RSWvRogX+/PNPna54e3t7eHl54erVq5q0X3/9FSUlJSgoKNB84SkvMzMTAHDmzBmjgn+XLl2q7OYvT6lU4vTp0+jQoQMCAgIM3kd1pk6disjISPz6668oKCjAuXPnsHTp0krLHz9+HEDZVMaKZDIZunfvjpSUFJw7dw4PP/yw5ktUv379dMr7+fmhdevWmnEOAJCXl4cTJ07A19dX7xRDlarsMa5nzpwx5jDNhsGfyMa5Sl2hiFaYrL7S0lLI78nh3sgddnamG1PsKjXNky/DwsLw008/VVlm4cKFmDVrFpo3b47Q0FC0atVKc3W6ZMkSFBYW6t1OfY/fVPW563lYmPqKsrI8dZABoBnElpaWhrS0tMoOF0qlstI8fSo7Tn3u3r0LAGjZsqVR+6jOxIkT8dZbb2HlypUoKCjQjAWojFwuB1B52318fAAAubm5Wv9W1mvj7e2tFfzv3LkDIQSuXr2q+XKpj7Hn2lwY/IlsnEQiMUl3ulppaSlKpCWQOcpMGvzrSnFxMd599134+voiPT1dq9tdCIH58+dXuq2+xXFqU19tqb8gvPHGGyYdaGbMIkDqgYzleyRMQT2qf8OGDSguLtbMAqiM+lxkZ2frzVenq8up261vNoa+etTbdevWTe9tB2tT/z6ZRERmlJOTg9zcXPTu3VsrUAPAsWPHkJ+fb9H6jNGjRw9IJBIcPnzY4G3UC+dUvJ9dU25ubnjooYdw8eJFzW0GU3nhhReQm5sLpVKJF154ocqy6lsoqampOnl5eXk4duwYXFxc0LFjRwBltzYA4MCBAzrlL126pDPdr1GjRnjwwQfxxx9/aHo7rBmDPxFROV5eXnBxccFvv/2GvLw8TfqdO3c06wFYsj5j+Pj4YMyYMTh06BA+/PBDCCF0yhw9elSrXU2aNAEAXLlyxWTt+Ne//oWSkhK89tprOl92CgoKKp1jX50nn3wSW7duxdatWzFw4MAqywYHB8Pf3x87duzAzz//rJWXlJSEnJwcjBs3TjOWoW/fvmjXrh22bdumtR6CEAKzZ8/W++Vo5syZyMvLw0svvaS3e//ixYtatwosid3+RETl2NnZ4bXXXsPChQvRpUsXPP3005DL5dixYwf8/Pzg6+tr0fqM9emnn+Ls2bOIiorC2rVr8dhjj8HDwwOXL1/G//73P2RmZuL69etwdS0bUzFw4EBs2bIFo0ePRnh4OJydndGpUyc89dRTNW7Dq6++in379mHz5s0ICAjAsGHD4O7ujr/++gspKSlYsWKFZq0BY9jb22P48OEGlbWzs8OqVasQFhaG8PBwjB49Gn5+fjh69Cj27NkDf39/rYF6dnZ2+OKLLxAeHo7Bgwdr5vnv2bMH169fR+fOnXWeB/HKK6/gyJEjWL16NdLS0jB48GD4+voiOzsbZ86cwdGjR7F+/XqrePARgz8RUQVJSUlo0qQJVq1ahU8//RTe3t4YO3YsEhIS8PDDD1u8PmM0adIEhw4dwieffIJNmzbhyy+/RGlpKXx8fNClSxe88847aNasmab8Sy+9hKysLGzcuBFz585FcXExJk+eXKvgL5FIsHHjRoSGhmL58uVYs2YNhBBo2bIlxowZU2dT3/r27YsjR44gMTERO3fuRG5uLnx9fTFz5kyd8wAAgwcPxu7duzFnzhx89dVXcHFxwaBBg/DVV19h0qRJeo9z1apVCA8Px7Jly7Bt2zYoFAp4eXkhICAACxYswODBg+vkWKsjEfr6gchgcrkcHh4eyM3N1Tv6tjxlkVIzn9qQOctKpRJubvfLKxRac3qJjFFQUICLFy9qloI1p9LSUsjlcri7m3a0P5EtM+QzbEw84ieTiIjIxjD4ExER2RgGfyIiIhtjlcFfCIFvvvkGjz/+OFq0aAFXV1d07NgRr7zyCv7880+d8nK5HJGRkfDz84OTkxP8/PwQGRmpWdFJn/Xr16Nnz56QyWTw9PREeHh4vViYgYiIqLasMvjPmjULzz77LM6ePYsRI0ZgxowZaNeuHZYtW4auXbvi5MmTmrJKpRIhISFYvHgxOnbsiIiICDz00ENYvHgxQkJC9M61nDdvHiZMmIDs7GxMnz4dY8aMQVpaGoKDg/UuAEFERNSgVPvcvzp2/fp1YWdnJ9q2bavzWMLFixcLAGLq1KmatNjYWAFAREVFaZVVp8fGxmqlnzt3Tjg4OIjAwEBx9+5dTfrJkyeFq6ur8Pf313oUY3X4SF+qDwx5HKiplJSUiDt37oiSkhKz74vIVpj6kb5Wd+WflZWF0tJSBAcH60xVUM8zVa+1LITA8uXL4ebmhtjYWK2y0dHR8PT0xIoVK7RWtUpOTkZxcTFiYmI0azcDQFBQECZNmoQLFy5gz5495jo8IosSnNlLVC+Z+rNrdYv8BAQEwNHREWlpabh37x4aNWqkydu+fTsAaJZxzMzMxLVr1xAWFqYzB97Z2Rn9+/fHd999h/Pnz2seJanu1g8NDdXZd1hYGD777DPs27dPbz4AFBYWaj2BSz2uQKVSaT1NS5/y+SqVCiqJkeWrqZ+oMkIICCFQVFQEJycns+9L/W9paalZ90VkKwoLCzWf48pigTExwuqCf9OmTTF37ly8+eabePDBBzFs2DA0atQIJ06cwM8//4yXX35Zsx62+iERlT0jWp2emZmp9d7NzU3z+MbKylcmKSlJ7+Mad+7cqVkeszIFJQWa9ykpKXC2r3qxlYKCCuXNvDgLNWzNmjWDVCpFaWmpUU9lq6l79+6ZfR9EtkAIgZycHNy+fbvK+FT+GQ3VsbrgD5QN+PP19cUrr7yCpUuXatL79OmDiRMnQiqVAvjnecvlu+/LU982UJdTv6/s+cz6ylcUHR2NyMhIzc9yuRytW7dGaGioQSv84UTZ+7CwMINW+FPT17tBZIx79+7hxo0buHfvHtzd3SGVSs3yJUAIAaVSCZlMVidfMogaKvVV/r1796BSqfDQQw9p9YZXVNUMt4qsMvi/9957SExMRHx8PCZNmgRPT0+kp6cjMjISjz/+ODZv3oyRI0dapG1OTk56u02lUqnmS0llpOKffIPKS40rT1SVJk2awMHBATk5Obh+/brZ9iOEQH5+PlxcXBj8iUzAyckJrVq1qvYC05gYYXXBf8+ePXjnnXcQERGB2bNna9KDg4Oxbds2tG/fHhERERg5cqTmir+yK3X1t6DyPQPqdY8NLU/UkLi7u8Pd3R0qlcpkz2uvSKVSYf/+/ejfvz+/sBLVkr29vVk+R1YX/H/88UcAwOOPP66T17x5c3Tq1AmHDx9GTk5Otffo9Y0JCAgIwOHDh3Hjxg2d+/7VjSEgaijM2ZNkb2+P4uJiODs7M/gTWSmrm+pXVFQEALh165befHW6k5MTAgIC4Ovri7S0NJ3FfAoKCrB//374+vqiQ4cOmvSQkBAAZQP0KkpJSdEqQ0RE1BBZXfAPDg4GACxatEine3716tU4f/48unXrhkaNGkEikWDatGlQKBRITEzUKpuUlIQ7d+5g2rRpWvcdp06dCgcHB8ydO1er/lOnTmHNmjXw9/fXTCUkIiJqiKyu23/06NH4/PPPkZqaioCAAAwbNgyenp7IyMjArl274OTkhCVLlmjKR0VF4fvvv8f8+fNx/PhxdOvWDRkZGdixYwe6du2KqKgorfoDAwMRHx+POXPmoHPnzhg1ahSUSiU2bNgAlUqFZcuWwcHB6k4LERGRyVjdlb+9vT1++uknfPDBB2jdujU2bNiAJUuW4PTp0xg/fjyOHTuGvn37asrLZDKkpqYiIiICZ86cwcKFC3Hy5ElEREQgNTVV7/S4mJgYrFu3Dl5eXli6dCk2btyIPn36IC0tTe9YAyIiooZEIrjeZ63I5XLNDAJD5vm7JbkBABTRCoPm+bu53S+vUHCeP9ULKpUK27dvR3h4OAf8EdUhY+KR1V35ExERkXnx5nYtqTtODFlZSVmkBO6v2CuXy1HiWPU86/IzGORyudnmZROZkkqlQl5eHuRyOa/8ieqQOg4Z0qHPbv9aunLlClq3bm3pZhAREQEALl++jFatWlVZhsG/lkpLS3Ht2jXN1ENz6NGjB3799Vez1G0OlmyvufdtyvpNUVdN66jJdoZuo37exeXLl6u970j16/PNz3bd1VeT7YUQ6NatG86dOwc7u6rv6rPbv5bs7Oyq/YZVW/b29vXqj6gl22vufZuyflPUVdM6arKdsduolxKmqtWnzzc/23VXX023d3R0rDbwAxzwVy/861//snQTjGLJ9pp736as3xR11bSOmmxX3/4f1hf16bzys1139Zn7s81ufyIyKWOmGxGRZfDKn4hMysnJCXFxcXoffU1E1oFX/kRERDaGV/5EREQ2hsGfiIjIxjD4ExER2RgGfyKyiP/85z8ICgqCm5sbGjdujEGDBuHo0aOWbhaRTWDwJyKLaNOmDRYtWoSMjAwcOnQI/v7+CAsLw99//23pphE1eBztT0RWQb0+QGpqKkJCQizdHKIGjVf+RFSpdevW4ZVXXkH37t3h5OQEiUSCVatWVbnNr7/+ivDwcHh6ekImk6Fnz55Yv359ldsUFRXhiy++gKenJzp16mTCIyAifbi2PxFVas6cObh06RKaNWuGFi1a4NKlS1WWT01NRVhYGBwdHTF27Fh4eHjgm2++wYQJE5CVlYXZs2drlT9w4ACefPJJ5Ofnw8fHB7t27UKTJk3MeUhEBF75E1EVli9fjqysLNy6dQvTp0+vsmxxcTGmTZsGiUSC/fv3Y9myZViwYAEyMjIQFBSEuLg4ZGZmam3TvXt3pKen49ChQ3jyyScxZswY5OTkmPOQiAgM/kRUhcGDB8PPz8+gsnv27MGFCxcwfvx4PPLII5r0Ro0a4Z133kFxcTGSk5O1tnFxcUGHDh3Qq1cvLF++HHZ2djpliMj0GPyJyCRSU1MBAKGhoTp56rR9+/ZVWYcQAoWFhSZvGxFp4z1/IjIJdZd+QECATp6npyeaNWum1e3/1ltvYdiwYWjVqhVu376NTz/9FFeuXMGzzz5bZ20mslUM/kRkErm5uQAADw8Pvfnu7u64cuWK5udr165h7NixuHnzJpo0aYIePXrgwIEDePDBB+ukvUS2jMGfiCxi7dq1lm4Ckc3iPX8iMgn1Fb+6B6Ai9SI+RGR5DP5EZBLqe/0Vp/MBwJ07d5CTk6N3PAAR1T0GfyIyCfWSvDt37tTJU6dx2V4i68DgT0QmMWjQILRv3x7r169Henq6Jv3evXt499134eDggClTplisfUT0Dz7Yh4gqtXz5chw8eBAAcOLECfz2228IDg5Ghw4dAAAjRozAiBEjNOX37t2LsLAwODk5Ydy4cXB3d8c333yDixcv4r333kNMTIwlDoOIKmDwJ6JKTZkyBatXr640Py4uDvHx8Vppv/zyC+Li4nD48GEUFRUhKCgI//73vzFhwgQzt5aIDMXgT0REZGN4z5+IiMjGMPgTERHZGAZ/IiIiG8PgT0REZGMY/ImIiGwMgz8REZGNYfAnIiKyMQz+RERENobBn4iIyMYw+BMREdkYBn8iIiIbw+BPRERkYxj8iWxEVlYWJBIJpkyZYpV1m7N9hiotLUWXLl0QHh5usTZU5vz583BwcMCnn35q6aZQA8DgT2RG6oBW/iWVStGyZUuMGTMGx44ds3QTDZaamgqJRKLzCN+GtN9Vq1bh999/17uvkpISrFq1CkOGDEHz5s3h6OiIFi1aYPjw4di6davZ29ahQwdMmDAB8fHxkMvlZt8fNWwOlm4AkS3w9/fHxIkTAQBKpRL/+9//8NVXX2Hr1q34+eef0b9/f7O3oWXLlvjjjz/g4eFhlXWbs32GKCkpQUJCAkJCQtCzZ0+tvOzsbIwYMQJHjhxBy5YtMWLECDRv3hzXrl3Djz/+iO+//x7Dhg3Dl19+CTc3N7O18c0338SaNWvw0UcfYc6cOWbbD9kAQURmc/HiRQFAhIWF6eQlJSUJAKJ///4WaJnx9u7dKwCIuLi4Brnf77//XgAQy5cv10ovKCgQPXr0EADE66+/LgoKCrTy7927J8aNGycAiGHDhpm1jUII0aVLF9GmTRtRUlJi9n1Rw8XgT2RGVQX/mzdvCgBCJpPp3Xbfvn1i6NChomnTpsLR0VF06NBBxMTECKVSqVN2y5Yton///qJ58+bCyclJtGrVSoSFhYlvv/1Wpy2TJ082evu4uDgBQO/r4sWLeuvet2+fACBeeOEFvcd3+fJlYWdnJwYOHFhp+6ra78qVKwUA8dprr+mt/9SpU0YF5JEjRwqJRCLu3Lmjlf7RRx9VW49KpRLdu3cXAMTWrVu18jZv3iwAiI8//lhs375dhISECDc3N+Ht7S3eeustTRBfv3696NWrl5DJZMLPz0/85z//0buvuXPnCgBi586dBh0XkT7s9ieyMAcH3Y/hZ599htdeew2enp54+umn0bx5c/z666+YO3cu9u7di71798LR0REAsHTpUrz22mto0aIFnnnmGTRt2hTXr1/HL7/8gq1bt2LEiBFV7t+Q7QcMGICsrCysXr0aISEhGDBggGb7xo0b4+7duzr19uvXD23btsXXX3+N//73v3B2dtbK//LLL1FaWornn3++0rZVtd9nnnkG77//Pr788kssWLAALi4uWtsuX74cAPDSSy9VefwAIIRAamoqHnjgATRu3Fgrb+XKlQBQZTe7g4MD3nrrLYwePRrJyckYPny4Ji89PR0AsGvXLsyZMwdPP/00HnnkEXz55Zf44IMP0KZNG5w4cQJbt27FU089hW7dumHNmjV4/fXX8fDDD2PgwIFa+3rssccAAHv27MGQIUOqPTYivSz97YOoIavqyv/dd98VAMRTTz2llX7q1Cnh4OAgHnnkEfH3339r5alvFSxYsECT9uijjwpHR0dx8+ZNnX3k5OTotKXilb+h21fV/V5Z3TExMQKA2Lx5s842nTp1Ei4uLkIul1dZR1X7/fDDDwUAsXr1aq30wsJC0axZM9GyZUtRXFyss11F6l6CCRMmaKXn5+cLiUQiXF1dq+1mv3HjhgAgfHx8tNLDw8MFAPHQQw+Jv/76S5O+f/9+AUA0atRI9O/fX+t3vXbtWgFAvP322zr7kcvl9ep2EVknjvYnqgPnz59HfHw84uPj8eabb2LAgAF455134OXlhQ8//FCr7Oeff47i4mJ89NFHaNKkiVZeVFQUmjdvjg0bNmilS6VSSKVSnf02bdrUoPbVdvvKqK/q161bp5WekZGBEydOYPjw4WjUqFGN658yZQqcnJw0V/lq3333HXJycjB16lTY29tXW8+VK1cAAN7e3lrpt2/fhhACjRs3hp1d1X8uPT09NduUd/z4cdjZ2WHz5s1o3bq1Jr1z584AynodNm7cqPW77tSpEwBAoVDo7KdRo0ZwdnbWtJmoJtjtT1QHLly4gISEBK00Ly8vHDhwAIGBgVrpR44cAQD89NNP+Pnnn3XqkkqlOHPmjObnMWPG4O2338bDDz+MsWPHYsCAAejbt69O93Vlart9VTp27Iju3btjx44duH37tibArV27FgCq7PI3RLNmzTBy5Ehs2LAB586d05zLFStWQCKR4MUXXzSonr///hvAPwFcTX0Obt68ieLiYr23aNSuXr2qU8etW7dw/fp19OvXD0FBQVrlL126BAB4+umn0aJFC608dWAv/2WhvCZNmiAnJ6e6wyKqFK/8iepAWFgYRNkAW9y8eRMffvghcnJyMGLECJ2rO/WV49y5c5GQkKDzunbtGpRKpaZ8VFQUli1bBh8fHyxatEgzRmD48OG4ePFitW2r7fbVef7556FSqbB582YAZQvpbNiwAV5eXggNDa11/S+//DKAf+7x//XXX9i1axcGDx6Mtm3bGlSHerxAfn6+Vrqrqyvatm2L4uJinDhxoso6jh8/DgB46KGHdNIGDx5caXl9eRkZGQCALl266N1Xfn4+XF1dq2wPUVUY/InqWPPmzTFr1izMnj0bf/zxh85AMnd3dwCAXC7XfGHQ91KTSCSYNm0ajh07hlu3buHbb7/FyJEj8f333+Opp55CSUlJle2p7fbVGTt2LBwcHDRd/3v27MG1a9cwbty4Kq+kDTVgwAB07NgRa9asgUqlwsqVK1FaWmrQQD+15s2bA9DtsgeA8ePHAwD++9//VlmHeuW9sWPHatLUAf6RRx7RKf/bb78BAB599FGdPPV2+vJKS0uRm5uraTNRTTD4E1nI7Nmz4evri08//RRZWVma9F69egH4p/vfGE2bNsWIESOwadMmDBw4EH/88QfOnz9vku3V986N/TKgvsI/dOgQLl68qPkSoF70qDqG7Pell15CdnY2vvvuOyQnJ6NZs2ZaI+6rExQUBDs7O2RmZurkvfnmm/D19cXq1auRmpqqd/t169Zh9+7d6NSpE6ZOnapJV4/01xf8jx8/DicnJ53bAeq8li1b6g3wmZmZKC0t1YwLIKoJBn8iC3FxccFbb70FlUqFd999V5P+2muvwcHBATNmzMDly5d1trt7967myhAAUlJSUFxcrFVGpVJprmIrToGryNDt1ffrazLQ7Pnnn4cQAsuXL8c333yDBx54AN27dzdoW0P2qx749/rrr+Ovv/7C5MmTNVMhDdG4cWN07twZx44d0+pVUedt2rQJTk5OGD16NA4dOqSV/8MPP+Cll16Cl5cXNm3apDVwMj09Hc2bN0erVq20thFCID09HQ8//LDOQEu5XI4///xT7xcGADh69CgAICQkxODjI6qIA/6ILOjll1/GBx98gDVr1mD27Nnw9/fHww8/jE8//RSvvvoqOnbsiPDwcPj7+2uCwr59+zBlyhR89tlnAIDnnnsOrq6u6Nu3L/z8/KBSqbBr1y6cPn0azz33HNq0aVNlGwzd/oEHHoCvry82btwIV1dXtGrVChKJBK+++mq1xzl8+HC4u7vjww8/hEqlMmqgX1X7VS8F3LRpUzz77LNYv349AGDatGkG1682YsQIxMfH49dff9Us75uVlYVVq1YBAIKDg7Fz5070798f69evx5gxY7Bw4ULMmjULADBs2DBs2rQJAPDvf/8bjo6OOHfunN57+pmZmbh3757ebv309HQIIfTmAWXrBdjb22Po0KFGHyORhgWmFxLZjKrm+at9/PHHAoB4/vnntdJ/+eUXMXbsWOHr6yukUqlo1qyZePTRR8Xbb78t/vjjD025Tz/9VAwbNkz4+fkJZ2dn0bRpU9GrVy/x+eefC5VKpdOWivPoDd1eCCGOHDkiQkJCRKNGjapd4a+iqVOnCgBCIpGIrKysSs+Vvjoq2295KSkpAoDo27dvpW2oypUrV4S9vb2YMWOGJk29xkDFl3rNgcmTJ1e66uHhw4cFAPHWW2/p7Gvjxo0CgFi6dKlO3pIlSwQArdUZ1ZRKpXBzcxMjRoyo0TESqUmEqNDHRURUD82fPx9vvfUWVq9ejUmTJtWojvHjx2Pnzp24dOkSZDKZiVtYeytXrsSLL76Iffv21cnDoKjhYvAnonqvoKAAHTt2hEKhwJUrV6od51CZixcv4sEHH0RiYiKioqJM3MraKS4uxgMPPICgoCB89913lm4O1XO8509E9dbBgwexb98+pKSk4K+//sL7779f48APAO3atcPq1autcgGdK1euYOLEibVeGIkI4JU/EdVj8fHxSEhIQLNmzfD8889j/vz5Jlk7gKihY/AnIiKyMZznT0REZGMY/ImIiGwMgz8REZGNYfAnIiKyMQz+RERENobBn4iIyMYw+BMREdkYBn8iIiIbw+BPRERkYxj8iYiIbMz/Ax/8XYXnWYjzAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 6))\n",
    "\n",
    "ax1 = fig.add_axes([0.2, 0.15, 0.7, 0.7])\n",
    "plot_1d_layer_model(true_layers, true_resistivities, ax=ax1, color=\"k\")\n",
    "plot_1d_layer_model(\n",
    "    layer_thicknesses, log_resistivity_map * recovered_model_L2, ax=ax1, color=\"b\"\n",
    ")\n",
    "plot_1d_layer_model(\n",
    "    layer_thicknesses, log_resistivity_map * recovered_model_irls, ax=ax1, color=\"r\"\n",
    ")\n",
    "plot_1d_layer_model(\n",
    "    log_thicknesses_map * recovered_model_parametric,\n",
    "    1 / (log_conductivity_map * recovered_model_parametric),\n",
    "    ax=ax1,\n",
    "    color=\"g\",\n",
    ")\n",
    "x_min, x_max = true_resistivities.min(), true_resistivities.max()\n",
    "ax1.set_xlim(0.8 * x_min, 2 * x_max)\n",
    "ax1.set_ylim([np.sum(layer_thicknesses), 0])\n",
    "ax1.grid()\n",
    "ax1.set_xlabel(r\"Resistivity ($\\Omega m$)\")\n",
    "ax1.legend([\"True Model\", \"L2 Model\", \"IRLS Model\", \"Parametric Model\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.10.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}