BilevelPSR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0cf39405",
   "metadata": {},
   "source": [
    "# Broadband bi-level taper polarization rotator-splitter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f37fcb",
   "metadata": {},
   "source": [
    "**This notebook contains large simulations. Running the entire notebook will cost about 30 FlexCredits.**\n",
    "\n",
    "A polarization splitter-rotator (PSR) is a key component in silicon photonics that is used to manage the polarization state of light. It is a critical component in silicon photonic integrated circuits, enabling more complex and versatile photonic devices and systems. As the name suggests, the PSR has two main functions:\n",
    "\n",
    "**Splitting**: It separates incoming light into its TE and TM components. This is useful because it allows each polarization to be processed separately, which can be important for certain applications.\n",
    "\n",
    "**Rotating**: It can convert light from one polarization to another. For example, it can convert TE-polarized light to TM-polarized light, or vice versa. This is important because it allows the light to be correctly interfaced with other devices that might require a specific polarization.\n",
    "\n",
    "In another case study, we have introduced a [compact PSR](../notebooks/PolarizationSplitterRotator.html), which has a small footprint but narrow bandwidth. In the present study, we delve into the exploration of a broadband PSR. This device employs a bi-level taper to transition the TM0 mode input light into the TE1 mode. Subsequently, a long adiabatic coupler transforms this TE1 mode into the TE0 mode. Conversely, the TE0 input light primarily passes through the PSR with minimal alterations. The broadband PSR demonstrates superior bandwidth and insertion loss compared to the compact design, marking a significant improvement. However, it's worth noting that this enhanced performance comes with a trade-off in terms of size. The broadband PSR has a considerably larger footprint, with a length longer than **0.5 mm**, which causes a difficult simulation in conventional numerical tools. For Tidy3D, even such a large 3D full-wave simulation is fast.\n",
    "\n",
    "The design is based on `Wesley D. Sacher, Tymon Barwicz, Benjamin J. F. Taylor, and Joyce K. S. Poon, \"Polarization rotator-splitters in standard active silicon photonics platforms,\" Opt. Express 22, 3777-3786 (2014)`, [DOI: 10.1364/OE.22.003777](https://doi.org/10.1364/OE.22.003777).\n",
    "\n",
    "<img src=\"img/bilevel_PSR.png\" width=\"500\" alt=\"Schematic of the PSR\">\n",
    "\n",
    "For more integrated photonic examples such as the [8-Channel mode and polarization de-multiplexer](https://www.flexcompute.com/tidy3d/examples/notebooks/8ChannelDemultiplexer/), the [90 degree optical hybrid](https://www.flexcompute.com/tidy3d/examples/notebooks/90OpticalHybrid/), and the [broadband directional coupler](https://www.flexcompute.com/tidy3d/examples/notebooks/BroadbandDirectionalCoupler/), please visit our [examples page](https://www.flexcompute.com/tidy3d/examples/). If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. FDTD simulations can diverge due to various reasons. If you run into any simulation divergence issues, please follow the steps outlined in our [troubleshooting guide](https://www.flexcompute.com/tidy3d/examples/notebooks/DivergedFDTDSimulation/) to resolve it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8d1a8953",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import gdstk\n",
    "\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web\n",
    "from tidy3d.plugins.mode import ModeSolver"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "413e6b41",
   "metadata": {},
   "source": [
    "## Simulation Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94b310dd",
   "metadata": {},
   "source": [
    "The simulation wavelength range is from 1500 nm to 1580 nm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7f7d6cae",
   "metadata": {},
   "outputs": [],
   "source": [
    "lda0 = 1.54  # central wavelength\n",
    "ldas = np.linspace(1.5, 1.58, 101)  # wavelength range of interest\n",
    "freq0 = td.C_0 / lda0  # central frequency\n",
    "freqs = td.C_0 / ldas  # frequency range of interest\n",
    "fwidth = 0.4 * (np.max(freqs) - np.min(freqs))  # frequency width of the source"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c729fe1e",
   "metadata": {},
   "source": [
    "In this wavelength range, silicon and silicon oxide have minimal dispersion. Therefore, we use a constant refractive index for both. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0da6e324",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define silicon and silicon dioxide media from material library\n",
    "si = td.Medium(permittivity=3.47**2)\n",
    "sio2 = td.Medium(permittivity=1.44**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d3e4044",
   "metadata": {},
   "source": [
    "Define geometric parameters. The parameters are indicated in the figure below. In addition, we add a side wall angle to mimic realistic fabrication and see if this will degrade the device performance. \n",
    "\n",
    "<img src=\"img/bilevel_PSR_2.png\" width=\"500\" alt=\"Top view of the PSR\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a6c80d2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "L_blt = 100  # length of the bi-level taper\n",
    "L_ac = 300  # length of the adiabatic coupler\n",
    "L_s = 5  # length of the straight waveguide between the bi-level taper and the adiabatic coupler\n",
    "L_t = 30  # length of the top waveguide taper after the adiabatic coupler\n",
    "\n",
    "w_1 = 0.45  # input waveguide width\n",
    "w_2 = 0.55  # waveguide width at the center of the bi-level taper\n",
    "w_pes = 1.55  # width of the widest part of the partially etched slab\n",
    "w_3 = 0.85  # waveguide width at the output of the bi-level taper\n",
    "w_4 = 0.2  # waveguide width at the beginning of the bottom waveguide\n",
    "w_5 = 0.65  # top waveguide width at the end of the adiabatic coupler\n",
    "w_6 = 0.5  # output waveguide width\n",
    "gap = 0.2  # gap size of the adiabatic coupler\n",
    "\n",
    "t_pes = 0.09  # thickness of the partially etched slab\n",
    "t_si = 0.22  # thickness of the silicon layer\n",
    "\n",
    "inf_eff = 1e5  # effective infinity\n",
    "\n",
    "sidewall_angle = 10 * np.pi / 180  # side wall angle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c334bcd2",
   "metadata": {},
   "source": [
    "Define the partially etched slab structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e4af6ae6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the partially etched slab\n",
    "vertices = [\n",
    "    (0, w_1 / 2),\n",
    "    (L_blt / 2, w_pes / 2),\n",
    "    (L_blt, w_3 / 2),\n",
    "    (L_blt, -w_3 / 2),\n",
    "    (L_blt / 2, -w_pes / 2),\n",
    "    (0, -w_1 / 2),\n",
    "]\n",
    "\n",
    "partially_etched_slab = td.Structure(\n",
    "    geometry=td.PolySlab(\n",
    "        vertices=vertices,\n",
    "        axis=2,\n",
    "        slab_bounds=(0, t_pes),\n",
    "        sidewall_angle=sidewall_angle,\n",
    "    ),\n",
    "    medium=si,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb211d2d",
   "metadata": {},
   "source": [
    "Define the waveguide structures using `gdstk`. For structures with bending parts, using `gdstk` is easy and convenient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e9cdeabb",
   "metadata": {},
   "outputs": [],
   "source": [
    "R = 300  # bend radius\n",
    "theta = np.pi / 30  # s bend angle\n",
    "\n",
    "# define a gds cell\n",
    "cell = gdstk.Cell(\"device\") \n",
    "\n",
    "# define the top waveguide using a path\n",
    "top_wg = gdstk.RobustPath((-inf_eff, 0), w_1,layer=1, datatype=0)\n",
    "top_wg.horizontal(0)\n",
    "top_wg.horizontal(L_blt / 2, w_2)\n",
    "top_wg.horizontal(L_blt, w_3)\n",
    "top_wg.horizontal(L_blt + L_s)\n",
    "top_wg.segment((L_blt + L_s + L_ac, (w_5-w_3)/2), w_5)\n",
    "top_wg.horizontal(L_blt + L_s + L_ac + L_t)\n",
    "top_wg.arc(R, -np.pi / 2, -np.pi / 2 + theta)\n",
    "top_wg.arc(R, np.pi / 2 + theta, np.pi / 2)\n",
    "top_wg.horizontal(inf_eff)\n",
    "cell.add(top_wg)\n",
    "\n",
    "# define the bottom waveguide using a path\n",
    "bottom_wg = gdstk.RobustPath((L_blt + L_s, (-w_4-w_3-2*gap)/2), w_4, layer=1, datatype=0)\n",
    "bottom_wg.segment((L_blt + L_s + L_ac, (-w_3-2*gap-w_6)/2), w_6)\n",
    "bottom_wg.arc(R, np.pi / 2, np.pi / 2 - theta)\n",
    "bottom_wg.arc(R, -np.pi / 2 - theta, -np.pi / 2)\n",
    "bottom_wg.horizontal(inf_eff)\n",
    "cell.add(bottom_wg)   \n",
    "\n",
    "# define the waveguide tidy3d geometries\n",
    "wg_geos = td.PolySlab.from_gds(\n",
    "    cell,\n",
    "    gds_layer=1,\n",
    "    axis=2,\n",
    "    slab_bounds=(0, t_si),\n",
    "    sidewall_angle=sidewall_angle,\n",
    ")\n",
    "\n",
    "# define the waveguide tidy3d structures\n",
    "wgs = [td.Structure(geometry=wg_geo, medium=si) for wg_geo in wg_geos]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e61f0135",
   "metadata": {},
   "source": [
    "Define source, monitors, and simulation. Here we will define a [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html) that launches the TE0 mode into the input waveguide. Two [ModeMonitors](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) are added to the top and bottom waveguides to monitor the transmission. A [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) is added in the $xy$ plane to visualize the field distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "72a73176",
   "metadata": {},
   "outputs": [
    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[16:02:13] </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Default value for the field monitor </span><span style=\"color: #008000; text-decoration-color: #008000\">'colocate'</span><span style=\"color: #800000; text-decoration-color: #800000\"> setting has  </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">changed to </span><span style=\"color: #008000; text-decoration-color: #008000\">'True'</span><span style=\"color: #800000; text-decoration-color: #800000\"> in Tidy3D </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.4</span><span style=\"color: #800000; text-decoration-color: #800000\">.</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"color: #800000; text-decoration-color: #800000\">. All field components will be      </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">colocated to the grid boundaries. Set to </span><span style=\"color: #008000; text-decoration-color: #008000\">'False'</span><span style=\"color: #800000; text-decoration-color: #800000\"> to get the raw      </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">fields on the Yee grid instead.                                      </span>\n",
       "</pre>\n"
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       "\u001b[2;36m[16:02:13]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Default value for the field monitor \u001b[0m\u001b[32m'colocate'\u001b[0m\u001b[31m setting has  \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mchanged to \u001b[0m\u001b[32m'True'\u001b[0m\u001b[31m in Tidy3D \u001b[0m\u001b[1;36m2.4\u001b[0m\u001b[31m.\u001b[0m\u001b[1;36m0\u001b[0m\u001b[31m. All field components will be      \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mcolocated to the grid boundaries. Set to \u001b[0m\u001b[32m'False'\u001b[0m\u001b[31m to get the raw      \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mfields on the Yee grid instead.                                      \u001b[0m\n"
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_time = 15e-12  # simulation run time\n",
    "\n",
    "Lx = 510  # simulation domain size in x\n",
    "Ly = 11  # simulation domain size in y\n",
    "Lz = 9 * t_si  # simulation domain size in z\n",
    "\n",
    "x0 = 250  # simulation domain center in x\n",
    "y0 = -0.5  # simulation domain center in y\n",
    "z0 = t_si / 2  # simulation domain center in z\n",
    "\n",
    "# define a mode source that launches the TE0 mode\n",
    "mode_spec = td.ModeSpec(num_modes=2, target_neff=3.47)\n",
    "mode_source_te = td.ModeSource(\n",
    "    center=(-lda0 / 2, 0, t_si / 2),\n",
    "    size=(0, 4 * w_1, 6 * t_si),\n",
    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "    direction=\"+\",\n",
    "    mode_spec=mode_spec,\n",
    "    mode_index=0,\n",
    ")\n",
    "\n",
    "# define a mode monitor at the top waveguide output\n",
    "mode_monitor_top = td.ModeMonitor(\n",
    "    center=(x0 + Lx / 2 - lda0, 3.2, t_si / 2),\n",
    "    size=mode_source_te.size,\n",
    "    freqs=freqs,\n",
    "    mode_spec=mode_spec,\n",
    "    name=\"mode_top\",\n",
    ")\n",
    "\n",
    "# define a mode monitor at the bottom waveguide output\n",
    "mode_monitor_bottom = td.ModeMonitor(\n",
    "    center=(x0 + Lx / 2 - lda0, -4.2, t_si / 2),\n",
    "    size=mode_source_te.size,\n",
    "    freqs=freqs,\n",
    "    mode_spec=mode_spec,\n",
    "    name=\"mode_bottom\",\n",
    ")\n",
    "\n",
    "# define a field monitor to visualize field intensity distribution in the xy plane\n",
    "field_monitor = td.FieldMonitor(\n",
    "    center=(0, 0, t_pes),\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    freqs=[freq0],\n",
    "    interval_space=(2, 2, 1),\n",
    "    name=\"field\",\n",
    ")\n",
    "\n",
    "# define simulation\n",
    "sim_te = td.Simulation(\n",
    "    center=(x0, y0, z0),\n",
    "    size=(Lx, Ly, Lz),\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=12, wavelength=lda0),\n",
    "    structures=[partially_etched_slab] + wgs,\n",
    "    sources=[mode_source_te],\n",
    "    monitors=[mode_monitor_top, mode_monitor_bottom, field_monitor],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    "    medium=sio2,\n",
    ")\n",
    "\n",
    "# visualize the simulation\n",
    "ax = sim_te.plot(z=t_pes)\n",
    "ax.set_aspect(\"auto\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc157aec",
   "metadata": {},
   "source": [
    "We can also visualize the cross sections at different $x$ positions of the bi-level taper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fee64d30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 1, figsize=(4, 7), tight_layout=True)\n",
    "\n",
    "for i in range(3):\n",
    "    sim_te.plot_eps(x=i * L_blt / 2, freq=freq0, ax=ax[i])\n",
    "    ax[i].set_xlim(-1, 1)\n",
    "    ax[i].set_ylim(-0.1, 0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abaa2ce9",
   "metadata": {},
   "source": [
    "## Mode Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5abca817",
   "metadata": {},
   "source": [
    "We can intuitively visualize the mode evolution alone the device to see physically why it functions as a PSR. This requires us to perform mode analysis at different $x$ positions. First, let's perform the mode analysis at the input waveguide ($x=0$) and solve for two modes. \n",
    "\n",
    "As expected, the first mode is TE0 mode and the second mode is the TM0 mode. Note that the grid looks a bit coarse. If a finer resolution for the mode profile is needed, either use a finer grid or use Tidy3D's [waveguide plugin](https://www.flexcompute.com/tidy3d/examples/notebooks/WaveguidePluginDemonstration/) for the same analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9a1b4c64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[16:02:15] </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Use the remote mode solver with subpixel averaging for      </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">better accuracy through </span><span style=\"color: #008000; text-decoration-color: #008000\">'tidy3d.plugins.mode.web.run(...)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.          </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:02:15]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Use the remote mode solver with subpixel averaging for      \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mbetter accuracy through \u001b[0m\u001b[32m'tidy3d.plugins.mode.web.run\u001b[0m\u001b[32m(\u001b[0m\u001b[32m...\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.          \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>wavelength</th>\n",
       "      <th>n eff</th>\n",
       "      <th>k eff</th>\n",
       "      <th>TE (Ey) fraction</th>\n",
       "      <th>wg TE fraction</th>\n",
       "      <th>wg TM fraction</th>\n",
       "      <th>mode area</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <th>mode_index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.946704e+14</th>\n",
       "      <th>0</th>\n",
       "      <td>1.54</td>\n",
       "      <td>2.386117</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.975661</td>\n",
       "      <td>0.729618</td>\n",
       "      <td>0.821577</td>\n",
       "      <td>0.194031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.54</td>\n",
       "      <td>1.758549</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.046269</td>\n",
       "      <td>0.699664</td>\n",
       "      <td>0.880206</td>\n",
       "      <td>0.355355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         wavelength     n eff  k eff  TE (Ey) fraction  \\\n",
       "f            mode_index                                                  \n",
       "1.946704e+14 0                 1.54  2.386117    0.0          0.975661   \n",
       "             1                 1.54  1.758549    0.0          0.046269   \n",
       "\n",
       "                         wg TE fraction  wg TM fraction  mode area  \n",
       "f            mode_index                                             \n",
       "1.946704e+14 0                 0.729618        0.821577   0.194031  \n",
       "             1                 0.699664        0.880206   0.355355  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mode_solver = ModeSolver(\n",
    "    simulation=sim_te,\n",
    "    plane=td.Box(center=(0, 0, t_si/2), size=(0, 4*w_1, 5*t_si)),\n",
    "    mode_spec=mode_spec,\n",
    "    freqs=[freq0],\n",
    ")\n",
    "mode_data = mode_solver.solve()\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8,3), tight_layout=True)\n",
    "mode_data.intensity.sel(mode_index=0).plot(x='y', y='z', cmap='magma', ax=ax1)\n",
    "mode_data.intensity.sel(mode_index=1).plot(x='y', y='z', cmap='magma', ax=ax2)\n",
    "\n",
    "mode_data.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d24f12d8",
   "metadata": {},
   "source": [
    "Similarly, we move the mode analysis plane to the bi-level taper section at $x=L_{blt}/2$. The first mode is still the TE0 while the second mode becomes a hybrid mode due to the fact that the partially etched slab breaks the vertical mirror symmetry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ddb50db8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>wavelength</th>\n",
       "      <th>n eff</th>\n",
       "      <th>k eff</th>\n",
       "      <th>TE (Ey) fraction</th>\n",
       "      <th>wg TE fraction</th>\n",
       "      <th>wg TM fraction</th>\n",
       "      <th>mode area</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <th>mode_index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.946704e+14</th>\n",
       "      <th>0</th>\n",
       "      <td>1.54</td>\n",
       "      <td>2.623751</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.992292</td>\n",
       "      <td>0.884681</td>\n",
       "      <td>0.807455</td>\n",
       "      <td>0.197804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.54</td>\n",
       "      <td>2.140926</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.905734</td>\n",
       "      <td>0.821026</td>\n",
       "      <td>0.802903</td>\n",
       "      <td>0.385694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         wavelength     n eff  k eff  TE (Ey) fraction  \\\n",
       "f            mode_index                                                  \n",
       "1.946704e+14 0                 1.54  2.623751    0.0          0.992292   \n",
       "             1                 1.54  2.140926    0.0          0.905734   \n",
       "\n",
       "                         wg TE fraction  wg TM fraction  mode area  \n",
       "f            mode_index                                             \n",
       "1.946704e+14 0                 0.884681        0.807455   0.197804  \n",
       "             1                 0.821026        0.802903   0.385694  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mode_solver = mode_solver.copy(update={\"plane\": td.Box(center=(L_blt/2, 0, t_si/2), size=(0, 2*w_pes, 5*t_si))})\n",
    "mode_data = mode_solver.solve()\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8,3), tight_layout=True)\n",
    "mode_data.intensity.sel(mode_index=0).plot(x='y', y='z', cmap='magma', ax=ax1)\n",
    "mode_data.intensity.sel(mode_index=1).plot(x='y', y='z', cmap='magma', ax=ax2)\n",
    "\n",
    "mode_data.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80c2faf4",
   "metadata": {},
   "source": [
    "The same mode analysis at the end of the bi-level taper section shows the first mode as the TE0 and the second mode as the TE1. That is, when TM0 mode is launched at the input waveguide, it adiabatically converts into the TE1 mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fd969dee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>wavelength</th>\n",
       "      <th>n eff</th>\n",
       "      <th>k eff</th>\n",
       "      <th>TE (Ey) fraction</th>\n",
       "      <th>wg TE fraction</th>\n",
       "      <th>wg TM fraction</th>\n",
       "      <th>mode area</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <th>mode_index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.946704e+14</th>\n",
       "      <th>0</th>\n",
       "      <td>1.54</td>\n",
       "      <td>2.724055</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.997053</td>\n",
       "      <td>0.906866</td>\n",
       "      <td>0.810590</td>\n",
       "      <td>0.209982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.54</td>\n",
       "      <td>2.284869</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.977161</td>\n",
       "      <td>0.673375</td>\n",
       "      <td>0.848376</td>\n",
       "      <td>0.330191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         wavelength     n eff  k eff  TE (Ey) fraction  \\\n",
       "f            mode_index                                                  \n",
       "1.946704e+14 0                 1.54  2.724055    0.0          0.997053   \n",
       "             1                 1.54  2.284869    0.0          0.977161   \n",
       "\n",
       "                         wg TE fraction  wg TM fraction  mode area  \n",
       "f            mode_index                                             \n",
       "1.946704e+14 0                 0.906866        0.810590   0.209982  \n",
       "             1                 0.673375        0.848376   0.330191  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cEbjWOE4T5FRr2taDI6kX+BzwOmAdcJ+kVWb2aFm+WcD7gHsn30rH6WBy2m082bjWOE6T5FRr2tmDcyKw1syeNLNB4CZgeYV8HwU+DgxMpnGO0+kYYAWruDkjcK1xnCbIq9a0s4GzGHgmc7wuppWQdAJwpJl9dzINc5xcYIS3qkpbHSQdKelOSY9KekTS+2L6PEm3SVoTP+fGdEn6bBzieTA+m0lZ58X8aySdN1G32wSuNY7TDE1oTTupOkQlaWedawVsMLMXt9akUv09wKeA8xvIeyFw4UTY4TidjI1fX4aBPzezn8Shmfsl3UZ43u4ws6ujr8qlwCXA64GlcTsJuBY4SdI84CPAMoIM3h+Hf7Y1aohrjeN0Pk1oTduo1YPzczM7uMY2C9jTRN3rgSMzx0fEtIRZwEuBuyQ9BZwMrKrk/GdmK8xsmZm5Y6Bz4GDAsFXe6l1qtsHMfhL3dwGPEXo1lgM3xGw3AG+I+8uBGy1wDzBH0iLgdOA2M9saGzW3AWeM8U5caxynk2lCa9pJLSfjNzdwfSN5qnEfsFTS0QSxOQt4e3LSzHYA85NjSXcBHzKz1U3U6TjdgxlWXWDmS8o+KyvMbEWljJKWAC8nONcuNLMN8dRzwMK4X22Yp+7wTwO41jhOJ1NbazqWqg0cM3syeyzp4Gz++Mb25KgLG8TMhiVdDNwK9AIrzewRSVcCq81s1XjLPpCoG91zVDTP0Z12I8pQC9yyKvZlZh+OsvMeQXR8GLWc/DY30ssgaSbwTeD9ZrZTmd+LmZmkCf/yXWsmn9IzX/p795Sl94xID1mT/d5MSdUXYLSKOhDT6mrEaCpqXQU7K17bgK6NtLcY09L7U8n20XZ2vUbV1pqOpe40cUnvBv6KMLMguUMDjmm2cjP7HvC9srS/rJL3lGbrc5yuo4lxcUn9hMbNV83sWzF5o6RFZrYhDkFtiunVhnnWA6eUpd81TntcaxynU+kyH5yEDwEvNbMlZnZ03JoWHMdxmsTAhitv9VDoqrkOeMzMPpU5tQpIZkKdB9ySST83zqY6GdgRh7JuBU6TNDfOuDotpo0H1xrH6USa0Jp20kgD5+fA3ok2xHGcsWHNic4rgT8GXiPpgbidCVwNvE7SGuDUeAyh9+NJYC3w98B7gw22lRA/5r64XRnTxoNrjeN0IM1ojaSVkjZJejiTdoWk9WXak5y7LIajeFzS6Zn0hqKRZ2kkkvFlwH9IuhfYn96w/VkjFTiOM3EUq7tA1MTMfgRVHbheWyG/ARdVKWslsHJ8lozAtcZxOpTxag1wPXANcGNZ+qfN7BPZBEnHEiYBHAccDtwuKQkPUTcaeTmNNHC+CPwb8BC5HIVznC7F6LYn0rXGcTqRJrTGzO6OMzUbYTlwk5ntB34haS0hEjnEaOQAkpJo5E03cPrN7IMNGuc4ziTSxFtVJ+Ja4zgdSg2taTgkRRkXSzoXWE0IOrqNEGLinkyebNiJ8nAUJ9WroJEGzvdj9M5/YWS38XjH2Z0mqDa9s2IeKE2jlJI/dTrFsyempeegpyemVSq3wlTL7NRKi038YjE7MBvTMoO1ZkPxc3hEnlDG6KmIlaZndv20zAYI4+J1wgTkC9eaCWSkLiimRT2IGtDTMzV8agoA/X0HlS7pjedGPPM2+n89q/Cqn1yT6EA9jahsf7mWVdYrlTSvuovpyPzhOygUSz85hgsDMS1dlix0KoCNmBofbM8GU+hGbaqjNQ2FpCjjWoLvnsXPTwIXjN/CyjTSwDk7fl6WSWvJ1E3HcZrARLHQVQ0c1xrH6URarDVmtjHZl/T3wHfiYa2o47WikVekbgPHzI6ul8dxnPaQx/VhquFa4zidSyu1Jom1FQ/fCCQzrFYBX5P0KYKT8VLgvwgTIqpGI69GI4H+zq2UbmblHtGO40wiZlAstCDydIfgWuM4nUkzWiPp64RgoPMlrSMsznuKpOMJPbRPAe8O9dgjkm4mOA8PAxdZHAetFI28Xt2NDFH9ZmZ/GmEK6U8YPeXLcZxJpljsqiEq1xrH6VDGqzVmdnaF5Otq5L8KuKpC+qho5PVoZIjqf2aPJc0BbhpLJU7zlDsXlzsHhlOj14rp7QnOgj3xs69nWulcf+/0mGdqmtYT0+gvpfUo3S+nGB2GAQqE/eGss15xHwBDhX1pvuJg/BwYcRzuq4LTYjxvyqwLE/34utGhbyxYFzVwXGsmhtETE6A0OaHMubi/dyYA06fMBWBm32GlKw7hCAB2KfX5HrbwDBczjsU9sewioycgJPkraURhxESE6LybcRTujbb2RY0K+8HuPqW6VmmChJWtLdWnVPNmxnVWd/BcKW33UNjfN7StlDZUMi+1PZ33kF2zSrHO7tKmPGpNIz045ewBfKzccdqMmbqtB6cc1xrH6QDyqjWN+OD8C+nCdz3AscDNE2mU4zgNYFDoollUrjWO06HkVGsa6cHJhlIeBp42s3UTZI/jOA1iQLHYPU7GuNY4TkeSV61pxAfnh5NhiDOaioG5oj9MT+Irk/GpScat+3rTtKl9swCY1jcHgBk980vnZtk8AA4uziqlHUTw1ZnWk/40pvSGH3b2552Mrg8V03H2gUIYh96r1C9nV98uAHZOScft9xS3ALBvOHzuH95VOpcE2MqOvReV+OzsSe81+vt0e4CtehQtf29V1XCtmTyS4HaJnvT1hoB+ie/Ngv6XAPCynqWlaxZMD5rw+M65o8oboRc94dnty/j8DEdnlUQj9ij1Y9ndF57rfUqf74KCw0uvpeVOtxkAzIyfADOYGuvPBDCNulnIBAYdiIEF91rQkmLm3NKDZgOwc/AFpbT/nvIzADbzRCktCU5YyIb0jZprWf8m604dyqPWVG2SSfpOtXNjyeM4zsRgMfhWpS1PuNY4TmeTV62p1YPzKkmrapwXYYx83Eg6A/gMYdrPl8zs6rLzHwTeReiufh64wMyebqZOx+kmCjnsNq6Aa43jdDh51JpaDZzlDVw/WD9LZRT6SOstf/5TYJmZ7ZX0p8DfAG8bb52O002Y5VN0KuBa4zgdTF61pmoDZxLGw0+kzvLnZnZnJv89wDkTbJPj5IpuWKnBtcZxOp88as144uC0isWMbfnzdwLfr3QirkB8YetMay+1AnMlK+kmzsVT+2ePun5m/8LS/jyF9ckOK4a0Rf1pkKzDDgplLkx9klkwNTjQze1PHekO6gsvz31Kf+KF6HC2t5A69+0cCg6LmwfTAp/bdzAAG/ctKqVt2BcciZ/tfx6ALX3pSMDuobAGWzZI2GB0Qi5aNsBWYt/owIAHCoZy+VbVBlxrgJEul2E/CQTa3xscd2f0HQrAokII6vfCOWmQz6NmhGdyVn/q5HtofNQXTk2fwzn9wRl3am/6DA/FGCrbBoN+PZ/RiE0DcwDYPpg65w4Wwn5/T6qBs6eE/QXT0nxJvUmd4ZpQ7/7Ms7F1MNzncwNB/zamcUc5PC6Yvm0wvdetm5cAsK8vDfQ3WAhO0MWhtDPRojM0FVZV7ybyqjW5sFjSOcAy4G8rnTezFWa2bBxLtjtOrikUVXGrh6SVkjZJejiTdoWk9ZIeiNuZmXOXSVor6XFJp2fSz4hpayVd2vIbnGRcaxynMuPVmnbSzh6cWsuil5B0KnA58LtmmVd4xznAMYOCjfsd5XrgGkav8/RpM8vGo0HSsYTVe48jrPB7u6QXx9P1fFs6Adcax2mCJrWmbTQSyfiVwBXAUTG/ADOzY5qs+z7qLH8u6eXAF4EzzGxTk/U5Ttcx3pAbZna3pCUNZl8O3BT/0/+FpLUEvxao49syFlxrHKdzyWN4n0Z6cK4DPgDcTwsdHsxsuNLy55KuBFab2SpCN/FM4J8U/FF+aWZ/0Cob8sTowFzRB6c3DdKXLDx3iI4qpR3N4QAsmR3GoJfOygS4mhl8YY6evbOUtnBR8HeZfniar3duqFPT0p+LDYafQnFHGtRvYEMY+97y7MxS2lPbg4/QE7tS35+1u8P+9J3BL6evkJZb7A9lDBZ3l9JKi/dlFuW0uDCfZXx1DrQFOA0x3Pq3qoslnQusBv7czLYRfFjuyeRZF9NgbL4t9XCtaREVg4Rm0tKAoUEXkoCgswmLax4+NTimvHBm+mf4lYP3AnDIlHShymPn7gDgyBdsL6VNOyoGBp2R0Yv9oZz968Jzu/mZ1I/nl9uCn96z+1K/nL2FUMa0nrT+hdPC839URq8OPSLo1bQjM4H+Yr3FPalfzsAzoZxnngpBCh/aOie1N/oKZZ+kLfuDhj2/J+3429MbfAaHhlNtKpauyl7dff44E6Q1E04jDZwdZlbR4a5ZKi1/bmZ/mdk/dSLqdZxuwKDWGPh8SaszxyvMbEWdIq8FPhqL/ijwSeCCZu0cA641jtOB1NGajqWRBs6dkv4W+BaZdeLN7CcTZpXjOPWxdDZbBTaP1RHWzDYm+5L+HkiiB9fyYanr2zIGXGscpxOprTUdSyMNnKTLOSuWBrym9eY4jtMoRmvXh5G0yMw2xMM3AskMq1XA1yR9iuBkvBT4L4KPTE3fljHiWuM4HUirtWayaGSxzVdPhiFOJSrFrQjj1FNi3IrE7wZgmoK/y7xiuhjeoTPCWPsL4pD3S2YNlM4du3AzAAtemk4Y6X/pgrBz1GGlNDs0LtA5Na1LQ8H3pm9rGidi5i+fA2D6YxvTtAdDff3rF5TShuJieXti3Jxdu+eUzu3sOQQY6YOTxOiY0pfaWSiGQBY2Iv5E941910bjfquS9HXgFMJQ1jrgI8Apko4n6NlTwLsBor/KzQTn4WHgIotffCXflvHejWvNBJNZwLanFE8rWVw3+MHML4bn9JhZ4fxxs3eUrpk5JTzzM/tTX7hjXhqe/6m/leoFv3o0ADb74LS+PcF/56BfBJetxf+ddvTNeij4dM/dmMb0SrRhWm/qR3PYvOBvM+/YNK3v5dEVbGm6UGZSb9+uVENmrgmxtl50T6i39/7UT++BLUEvf/2QVMtOs2DL2jWpbm3sDX450pZSWrIocCUH3KzPU/79AsevNe2kkVlUswni9zsx6YfAlWa2o/pVjuNMNMb4u43N7OwKydfVyH8VcFWF9FG+LePFtcZxOpNmtKadNOIWvRLYBbw1bjuBL0+kUY7jNEbRVHHLKa41jtOh5FFrGvHBeaGZvTlz/FeSHpggexzHaRDLqeNfDVxrHKcDyavWNNLA2SfpVWb2IygF49pX5xpnHJSvQVUpbkWy9lRfT4glM1RM/xQz+oKvTH/mzzolruVyUG8YA57Vn8atmXFwGEvvXZBZjOrQOQDY4el6VnZo3M/44DA4OCpNA8FHpmfzrlLa9DkhZsXMTem4/czeEGNjel+wbarSGBZ9FmPuZDoXkxhAPT3pWjHB5cPJo+jUwLWmRWR9PlIdyT5TiQ9OeH6nKPiXHNoXdOWMGA9r0ezUj+XBjUFfjj/s+VJa/6L4TB4+v5RWXBz9YmanPjW2J6zj1BM1onfD9tK5aXPD/qydo4NHT8/o1fRZYb93QRpTi0OD/0xxUbrWHXNDmu1Lfzo9g9FncF3ws5mzZm9af4zVlR3OmDsl+PlM60m1tNfCvlRn4CNZQzCPkfFqkEetaaSB86fADXF8XMBW4PyJNMpxnPoYMNxdGupa4zgdSF61ppFZVA8AL5N0cDzeWfsKx3EmB2Hk762qGq41jtOp5FNrqjZwJJ1jZl+R9MGydADM7FMTbJuTobxbtFhaqiCdGl2w0A07RCZEeSE0u/cUwt9tx+CU0rld28PQ1PRn0+7aKXPD1HH1pUNA2hemelt2OCoZotq6PTVqfei6LqxPu7X3bAn17czUuzvaMhBNH7J0uYViTzHeV2YJhjgcZZl8B96U8NHkdWZDOa41E0wyVDJi9Ya4nEIcmOmJw8C98Tsvxku27D6odM22oZBn175UB4afD9ox5dnNpbSeWetCtTvTyW+K08R5JoSSKDybDmPv3Rz+G9q+Jx0q3z0U9GL/cKpD07YFfZv2bDr01L8waE7P9PRa2x3KTobMAdgUpnYXd4S0oaF0uDu5177eVFP2xnoLGc0ZqT8HFs1ojaSVwO8Bm8zspTFtHvCPwBJCSIq3mtk2hYf+M8CZwF7g/CTQp6TzgA/HYj9mZjfUq7vWYGKyWMisCtvMahc5jjM5mMFwsfKWM1xrHKeDaVJrrgfOKEu7FLjDzJYCd8RjgNcTAokuBS4kLB+TNIg+QggGeiLwEUlzqUPVHhwz+2Lcvd3Mfpw9F53/HMdpM93Qg+Na4zidTxMxt+6WtKQseTkh0CjADcBdwCUx/UYzM+AeSXMkLYp5bzOzrQCSbiM0mr5eq+5G4uD8vwbTHMeZRAwoVtlyimuN43QgdbRmvqTVme3CBopcmFkW5jkgmba7GHgmk29dTKuWXpNaPjivAH4LWFA2Nn4wPkd3Yolj5ibLJIWfUqEYp2LHtmnBMv42vWHMe3tPOva9eSBMqXxmTxKSPR0/N8KyCPv2p+PRh8bpmwctTsOW986NP5Op6c/FBsN4dWFHOo1z/8Zg75Zn01GFp+IUzLW70zHyX+4JbwLP7wu2byMdj99vu0bcL8BQXLZhcDjNl57P8X/nTdJFPjiuNZPCaH+SRD+GLfjZ7S2E53rz/qAXe4bTZ/65gfCn+PmOdAmGKQ+F/It2PVdKm7om+vHNyPi5xGd9cF3Qi+efnlE699TWoBHPZnx79hSCvk3pSTVw80DQkCV7Ux04fFNY5mHaz1IfoN65oZxiZvyksCno5o4nw/1s2D6rdG7YQl1PZ+7rp9tDGdttSyZfKMPq+eV02fRwqKs1Y17Yd0TZZiZpQr60Wj04Uwjj332MHBPfCfzhRBjjOM7YKFjlLWe41jhOh9NirdkYh56In5ti+nrgyEy+I2JatfSa1PLB+SHwQ0nXm9nTY7O9MSSdQfCY7gW+ZGZXl52fCtwI/AawBXibmT01EbY4Tt7olh4c1xrH6WwmQGtWAecBV8fPWzLpF0u6ieBQvMPMNki6FfjrjGPxacBl9SqpNUT1d2b2fuCaSt1HZvYHY7iZSuX3Ap8DXkcYT7tP0iozezST7Z3ANjN7kaSzgI8Db2umXsfpGiyd4ppnXGscp8NpQmskfZ3gJDxf0jrCbKirgZslvRN4mrD2HISFe88E1hKmib8DwMy2SvoocF/Md2XicFyLWoH+/iF+fmJMd9M4JwJrzexJgNhiWw5kRWc5cEXc/wZBABU9rLuOJLy6KgRUshjjplAM8Wd64tINiU8OwMDwdgC29GVegmNR+3cuAGDnYOoL8+y+4J/zRMY/ZsHz8wCY+0Tq2zOjL+z392TGtOO49d5MnIodQ+Hn9Pz+9Ge1aX/It2Fveu2GveEeno2/zy1Kexp3F0Jci/3Do2O8WcbfiOz+AUp4q2q3FS3BtWYyGOE7Ep6fRD8GiuF520xYUuHJPeFF+Yjp6XO2IYafGSikMa02DBwKwMKt6YzdOQ8FrZqaiSuzvxB0Yutg0K1NGb+/5waCRmwZSL/qJH5Xf0/qRTF7StCVn+1K9eqwLaHe+Y9llqDpS/cT9gz3j6h/8/5Ut7YMBpE8Zkaqu9timK8dPen/ocOD4QsYoUM1fACzy2XknWa0xszOrnLqtRXyGnBRlXJWEhbkbZhaQ1T3x88fJmmxe+hIM3twLJVUoZJX9EnV8pjZsKQdwCHAZhzngEfdMkTlWuM4HU0+tabuUg2S7gL+IOa9H9gk6cdm9sGaF04icVpaI1PTHKdryOv6MNVwrXGcziSvWtNIHJzZcU2YNxEC8JwEnNqCuhvxii7lUVj+djbBAXAEZrbCzJY1M1XNcfKIVdlyimuN43QoedSaRlYT74vTuN4KXN7Cuu8Dlko6miAuZwFvL8uTeFr/J2G66L911Zh4PSwdw05uenAoxKcpFOL6UJkx4OFCWO9lfyZezJ6+4NOysTfEeFiz/5DSuVlxf+aWNCbEQQrxH6Yr/Wn0xdg5fUq7KBOHs+w6UgPFMDa91/aU0nb3hBg2u7U9tSnGlhgohHsZHE7zD8V7KEZfo+w9Fov7Mmnxu8n8HLppzLsRzKDQXWGAXGsmkOzzUYjPkmK8md2DGwH4xdQwInjzhl8F4IX9qV48P5g+pwn9cU2raT2pXkzpDT4y2bfnoSgYewtBI/Zk1lHd2RP0aoCMDij4BvWS+upM2RN8Bg8uzi6lzVSoa0ZvWv/UnuAjlJGrkv/IUDE8MMPF1E+nGL+Xh7amfjlrCmE9recLa0tpA8Mhvlgho00cIPG48qo1jfTgXAncCvzczO6TdAywptmKLXhqXRzLfgy42cwekXSlpGTWxHXAIZLWAh8kXa/CcQ54km7jSls9JK2UtEnSw5m0eZJuk7Qmfs6N6ZL0WUlrJT0o6YTMNefF/GviYnjN4FrjOB1IM1rTTur24JjZPwH/lDl+EnhzKyo3s+8RpoVl0/4ysz8AvKUVdTlON9KEvlwPXEOI/ZKQLIB3taRL4/EljFwA7yTCAngnZRbAWxZNuT9Ov97GOHCtcZzOpcPbMhWp24Mj6QhJ345ve5skfVPSEZNhnOM41UmCb1Xa6l5rdjdQHkdiOWHhO+LnGzLpN1rgHiBZAO904gJ4sVGTLIA3LlxrHKczaUZr2kkjPjhfBr5G+nZzTkx73UQZdaBTMR5O9MdJWtGFOJaN0jZqMVmfKjNGPDQcfGD29YaX6l09G0rntvaEMe3ezPpUyX4faVpPHKcP8dKiOdGeYmbsuRhj9SRrtgAUhsL+UCH1n0nsKxQH4nVpXIliKa5PZsA3jnOX/G6CATEtj+8VrWO4+rj4fEmrM8crzGxFneImZQG8GrjWTCjZZyqJgxOey8H4CO7glwDs7ws+MutI12dK5Kgn816caIIsTesZHv3eXCCueUV45rMaMTwUbBjO+t1l/A8TEl/ArF71Rw3rLx5USust9kU7U/+dxI8v0ZCsbpXODad17i0EP8F9g2lnZOLjaBnbS/rTXe5aFamhNR1LIw2cBWb25czx9ZLeP0H2OI7TIFZ7LZiOXQCvBq41jtOB1NGajqURJ+Mtks6R1Bu3c6gwfdJxnMnHqvwbJ5OyAF4NXGscp0NpsdZMCo00cC4gTNt8Lm5/SFwfwnGc9pGET2/hCr/JVGkYvQDeuXE21cnEBfAIs5JOkzQ3zrg6LaaNF9cax+lAJkBrJoVGZlE9TYgu6jhOhzHe2BTtXACvGq41jtO55DEOTiNLNRwDfAY4mdCQ+0/gA8nCdc7EUan7T2XOxiOyxMhWycKc4XRIKxSTIFppp92AEufh1BlPFTr1pEY6+sBKzsDF0WmWXQAvni/lt9HnRhY8OqnDu0Yng2bCp7dzAbxquNZMDKVJC5nfiinqSHTqtzKn4/1D2wHoUfa/iEQvGtODETaUBcTLTixIzxUqpKWohl4pY2eab4y6ZdkJE4MxLaOl0Ql6hPZUcIbuRm3q5qUavgbcDCwCDifEqfj6RBrlOE4DWGj7VdpyimuN43QiOdWaRho4B5nZP5jZcNy+Akyre5XjOBNKXsfFa+Ba4zgdSF61ppFp4t+PUU1vItzn24DvxSimNDPm7jjO+DGMQqe/Qo0N1xrH6UDyqjWNNHASR8N3l6WfRRChY1pqkeM4DdPpb1BjxLXGcTqUPGpNI7Oojp4MQxzHGRtGuqp7N+Ba4zidSV61ppEeHMdxOhEjl93GjuPkjJxqjTdwHCenGPmMTeE4Tr7Iq9Z4Aydn1IyxUGphZ3+JSi6sUFaSZRJWhK3R+u/GuBGTRR7fqpz2MDJ+S7Kgb1y0NomvpSTuS4iPMzrKS9kiwI3WV6LG/5J1fsu19apC/K4G7UzLr2PvAR6PK49aU3eauKQ7JJ1ZllZvVWLHcSYYI8hvpS2PuNY4TmeSV61pJA7O0cAlkj6SSRv3KsUAkuZJuk3Smvg5t0Ke4yX9p6RHJD0o6W3N1Ok4XYdBoWgVt5ziWuM4nUhOtaaRBs52Qvj2hZL+RdLsFtR7KXCHmS0F7ojH5ewFzjWz44AzgL+TNKcFdTtOVxCCb1nFLadsx7XGcTqOvGpNIw0cxaii7wW+CfwIOLTJepcDN8T9G4A3lGcwsyfMbE3cfxbYBCxosl7H6RoMo1hlyymuNY7TgTSrNZKekvSQpAckrY5pFXtXFfispLWxR/WE8drdSAPnC8mOmV0PnA/8YLwVRhaa2Ya4/xywsFZmSScCU4CfVzl/oaTVyRfnOAcKeXyrqoFrjeN0KC3Qmleb2fFmlgw7V+tdfT2wNG4XAteO1+ZGAv19sez4fuCCetdJuh04rMKpy8vKM0lVvyVJi4B/AM6zSkvMhjJWACti/tyqu+OMhdBt3Olufo3jWuM4nckEac1y4JS4fwNwF3BJTL/RzAy4R9IcSYsyLyoNM2HTxM3s1GrnJG1MDI6isqlKvoOB7wKXm9k9E2Sq4+QSA4bzOxzVMlxrHGdiaYHWGPCD+FLwxfiiUK13dTHwTObadTFtzA2cRoaoJoJVwHlx/zzglvIMkqYA3ya05L4xibY5Tk4wzCpvTgnXGsdpmppaMz8Zto3bhRUKeJWZnUAYfrpI0u+MKD0U1HLhalegv6uBmyW9E3iauMiepGXAe8zsXTHtd4BDJJ0frzvfzB6YfHPzS0OBqPw/xFwS3qq6Z4hqgnCtqcEofagYLLT8mjZTUa9G21spV6XgfwdSsL7xUkdrNmf8aipfb7Y+fm6S9G3gRKBa7+p64MjM5UfEtDHTlgaOmW0hTActT18NvCvufwX4yiSb5jg5wihUjDXrJLjWOE4rGL/WSJoB9JjZrrh/GnAlae/q1YzsXV0FXCzpJuAkYMd4/G/Al2pwnNwS3qrG38CR9BSwixCRf9jMlkmaB/wjsAR4CnirmW2TJOAzwJmEuDHnm9lPmrHfcZx80KTWLAS+HSSEPuBrZvavku6jQu8q8D2CzqwlaM07xluxN3AcJ7cYpqaHqF5tZpszx8nUzaslXRqPL2Hk1M2TCFM3T2q2csdx8sD4tcbMngReViG9Wu+qAReNq7Iy2uVk7DhOkyRvVZW2JqgWGK80dTPOMpoTx80dx+lyJkhrJhzvwXGcnGIYBQ1XOz2/LBjdijg1c2QRbZi66ThOvqijNR2LN3AcJ7cYw1QVnbozGwhTN9dLOhS4TdLPRpReJzCe4zgHCjW1pmPxISrHySkhcETlFWIauj4zdZMQB6Y0dRNKkX1bPnXTcZx80azWtAtv4DhObjEKDFXc6iFphqRZyT5h6ubDVA+Mtwo4Ny6EdzJNTN10HCdvjF9r2okPUTlOTjGMYY1bYNo2ddNxnHzRpNa0DW/gOE5OMYyCjU902jl103GcfNGM1rQTb+A4Tm6xjh8Dd5xOwpdlGC/51Bpv4DhObrGOHwN3HKcbyKfWeAPHcXKKYRRz2G3sOE6+yKvWeAPHcXJMMYfdxo7j5I88ao03cBwnp+T1rcpxnHyRV63xBo7j5BXL58wGx3FyRk61pi2B/iTNk3SbpDXxc26NvAdLWifpmsm00XE6nfBWNVxxcwKuNY7TPHnVmnZFMr4UuMPMlgJ3xONqfBS4e1KscpxcYRiFiptTwrXGcZomn1rTrgbOcuCGuH8D8IZKmST9BiHi6g8mxyzHyRfF4nDFzSnhWuM4LSCPWtMuH5yFmXVsniMIywgk9QCfBM4BTq1VmKQLgQtbbaTjdDIhumhnC0wH4FrjOE2SV62ZsAaOpNuBwyqcujx7YGYmqVJ4yfcC3zOzdXG9nKqY2QpgRazXQ1U6BwaWz5kNrca1xnEmmJxqzYQ1cMys6puQpI2SFpnZBkmLgE0Vsr0C+G1J7wVmAlMk7TazWmPojnMAYZjlLzZFq3GtcZyJJp9a064hqlXAecDV8fOW8gxm9kfJvqTzgWUuOI6TYtDxsxg6ANcax2mSvGpNu5yMrwZeJ2kNYcz7agBJyyR9qU02OU7OsFw6/k0yrjWO0zT51BqZddcwchgX9/iFTl4Zvt/MljWSs0f91tc3r+K5oeFNDZfjjA/XGiffdL/W+NPpODnFAMvh+jCO4+SLvGqNN3AcJ7cYlsOZDY7j5I18ao03cBwntxiWQ8c/x3HyRj61pl1Oxo7jNI1hVqi4NYKkMyQ9LmmtJJ815DhOFZrTmnbhDRzHyTXFKlttJPUCnwNeDxwLnC3p2Imz03GcfDM+rWknPkTlOLmlqeBbJwJrzexJAEk3EdZterRFxjmO0zV4oL9OYTMMPz0B5c4PZTv4d5Gl1d/FUWPIeysMz69ybpqk1ZnjFXGZgYTFwDOZ43XASWOo25kYrfFnK8W/i5SJ+C5apTUd+zfqugaOmS2YiHIlre7Uuf6TjX8XKe38LszsjHbU6wQmQmv82Urx7yKl3d9FXrXGfXAc58BkPXBk5viImOY4jtMVeAPHcQ5M7gOWSjpa0hTgLMK6TY7jOF1B1w1RTSAr6mc5YPDvIiWX34WZDUu6GLgV6AVWmtkjbTbLyenvaYLw7yLFv4tx0HVrUTmO4ziO4/gQleM4juM4XYc3cBzHcRzH6Tq8gdMgkt4i6RFJRUkH5NRFD+2fImmlpE2SHm63LU534VrjWpPgOtMc3sBpnIeBNwF3t9uQduCh/UdxPZDL2BBOx+Na41qTcD2uM+PGGzgNYmaPmdnj7bajjZRC+5vZIJCE9j8gMbO7ga3ttsPpPlxrXGsSXGeawxs4TqNUCu2/uE22OI7TvbjWOC3B4+BkkHQ7cFiFU5eb2S2TbY/jON2Ja43jTDzewMlgZqe224YOxkP7O06LcK2piWuN0xJ8iMppFA/t7zjOZOBa47QEb+A0iKQ3SloHvAL4rqRb223TZGJmw0AS2v8x4OYDObS/pK8D/wm8RNI6Se9st01Od+Ba41qT4DrTHL5Ug+M4juM4XYf34DiO4ziO03V4A8dxHMdxnK7DGziO4ziO43Qd3sBxHMdxHKfr8AaO4ziO4zhdhzdwHCS9R9K5cf98SYdnzn2pFQvdSbpC0npJVzZbVizvbXGl4e+0ojzHcSYe1xpnMvFp4s4IJN0FfMjMVre43CuA3Wb2iRaWeQrB1t9rVZmO40wOrjXOROM9OB2KpCslvT9zfJWk95XlWSLpZ5K+KukxSd+QdFA891pJP5X0kKSVkqbG9KslPSrpQUmfiGlXSPqQpD8ElgFflfSApOmS7pK0LOY7O5b3sKSPZ+zYHe37b0n3SFrYwP1dIelDmeOH4/0k93S9pCfivZ0q6ceS1kg6sakv1nGcEbjWuNZ0K97A6VxWAklXbg8hXPlXKuR7CfB5M/tVYCfwXknTgOuBt5nZrxHWHPtTSYcAbwSOM7NfBz6WLcjMvgGsBv7IzI43s33JudiV/HHgNcDxwG9KekM8PQO4x8xeBtwN/EmT9/4i4JPAr8Tt7cCrgA8Bf9Fk2Y7jjMS1xrWmK/EGTodiZk8BWyS9HDgN+KmZbamQ9Rkz+3Hc/wrh4XwJ8AszeyKm3wD8DrADGACuk/QmYO8YTPpN4C4zez6GUv9qLBNgEEjGp+8Hloyh3Er8wsweMrMi8Ahwh4Wx1IdaULbjOBlca1xruhVv4HQ2XwLOB95BeMuqRLkTVVWnqigWJwLfAH4P+NfmTQRgyFJnrgKNrVI/zMjf37TM/v7MfjFzXGywbMdxxoZrjWtN1+ENnM7m28AZhDeaagvuvUDSK+L+24EfAY8DSyS9KKb/MfBDSTOB2Wb2PeADwMsqlLcLmFUh/b+A35U0X1IvcDbww3HcU8JTwAkAkk4Ajm6iLMdxmsO1xuk6vIXawZjZoKQ7ge1mVqiS7XHgIkkrgUeBa81sQNI7gH+S1AfcB3wBmAfcEsfNBXywQnnXA1+QtI+wmnFiywZJlwJ3xmu/a2a3NHF73wTOlfQIcC/wRJ38juNMEK41Tjfi08Q7mOjw9xPgLWa2psL5JcB3zOylk23bWJFP3XScjsW1pm6Zp+Bakzt8iKpDUQh4tZbg9DZKcHLIbuBCtTD4FvB5YFsrynOcAxXXmtq41uQX78FxHMdxHKfr8B4cx3Ecx3G6Dm/gOI7jOI7TdXgDx3Ecx3GcrsMbOI7jOI7jdB3ewHEcx3Ecp+v4/w6qW79bLPoTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mode_solver = mode_solver.copy(update={\"plane\": td.Box(center=(L_blt + L_s, 0, t_si/2), size=(0, 3*w_3, 5*t_si))})\n",
    "mode_data = mode_solver.solve()\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8,3), tight_layout=True)\n",
    "mode_data.intensity.sel(mode_index=0).plot(x='y', y='z', cmap='magma', ax=ax1)\n",
    "mode_data.intensity.sel(mode_index=1).plot(x='y', y='z', cmap='magma', ax=ax2)\n",
    "\n",
    "mode_data.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "891c6fc0",
   "metadata": {},
   "source": [
    "Lastly, mode analysis at the end of the adiabatic coupler section shows the first mode as the TE0 at the top waveguide and the second mode as the TE0 at the bottom waveguide, which indicates that with an appropriate choice of coupling length and gap size, the TE1 mode on the top waveguide will be converted into the TE0 mode at the bottom waveguide. The TE0 mode launched in the input waveguide however stays as TE0 throughout the device. \n",
    "\n",
    "For a more detailed analysis, please refer to the [reference](https://doi.org/10.1364/OE.22.003777). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "55b0c905",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>wavelength</th>\n",
       "      <th>n eff</th>\n",
       "      <th>k eff</th>\n",
       "      <th>TE (Ey) fraction</th>\n",
       "      <th>wg TE fraction</th>\n",
       "      <th>wg TM fraction</th>\n",
       "      <th>mode area</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <th>mode_index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.946704e+14</th>\n",
       "      <th>0</th>\n",
       "      <td>1.54</td>\n",
       "      <td>2.629170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.992861</td>\n",
       "      <td>0.850426</td>\n",
       "      <td>0.815152</td>\n",
       "      <td>0.189568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.54</td>\n",
       "      <td>2.477716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.983444</td>\n",
       "      <td>0.772801</td>\n",
       "      <td>0.820322</td>\n",
       "      <td>0.188611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         wavelength     n eff  k eff  TE (Ey) fraction  \\\n",
       "f            mode_index                                                  \n",
       "1.946704e+14 0                 1.54  2.629170    0.0          0.992861   \n",
       "             1                 1.54  2.477716    0.0          0.983444   \n",
       "\n",
       "                         wg TE fraction  wg TM fraction  mode area  \n",
       "f            mode_index                                             \n",
       "1.946704e+14 0                 0.850426        0.815152   0.189568  \n",
       "             1                 0.772801        0.820322   0.188611  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mode_solver = mode_solver.copy(update={\"plane\": td.Box(center=(L_blt + L_s + L_ac, 0, t_si/2), size=(0, 5*w_5, 5*t_si))})\n",
    "mode_data = mode_solver.solve()\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8,3), tight_layout=True)\n",
    "mode_data.intensity.sel(mode_index=0).plot(x='y', y='z', cmap='magma', ax=ax1)\n",
    "mode_data.intensity.sel(mode_index=1).plot(x='y', y='z', cmap='magma', ax=ax2)\n",
    "\n",
    "mode_data.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d603044",
   "metadata": {},
   "source": [
    "## Run a Batch Simulation "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed81f5e5",
   "metadata": {},
   "source": [
    "We also want to investigate the TM mode as input. To do so, we only need to define a new [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html) and set `mode_index=1`. Then a simulation is created by copying the previous simulation with the sources updated. To run both the TE and TM input simulations simultaneously, we made a simulation batch. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "56499f75",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:08:57.985486Z",
     "iopub.status.busy": "2023-08-18T17:08:57.985342Z",
     "iopub.status.idle": "2023-08-18T17:08:59.309346Z",
     "shell.execute_reply": "2023-08-18T17:08:59.307367Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[10:08:58] </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'TE input'</span> with task_id                    <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#188\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">188</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-5277b0c0-5b50-438c-8c80-397d3ee45361v1'</span>.          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[10:08:58]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'TE input'\u001b[0m with task_id                    \u001b]8;id=77720;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=371356;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[32m'fdve-5277b0c0-5b50-438c-8c80-397d3ee45361v1'\u001b[0m.          \u001b[2m             \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>View task using web UI at                               <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#190\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">190</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-5277b0c0-5b50-438c-8c80-397d3ee45361v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-</span></a> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-5277b0c0-5b50-438c-8c80-397d3ee45361v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">5277b0c0-5b50-438c-8c80-397d3ee45361v1'</span></a>.                <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
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      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                               \u001b]8;id=558913;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=674050;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#190\u001b\\\u001b[2m190\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b]8;id=844579;https://tidy3d.simulation.cloud/workbench?taskId=fdve-5277b0c0-5b50-438c-8c80-397d3ee45361v1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=348624;https://tidy3d.simulation.cloud/workbench?taskId=fdve-5277b0c0-5b50-438c-8c80-397d3ee45361v1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=844579;https://tidy3d.simulation.cloud/workbench?taskId=fdve-5277b0c0-5b50-438c-8c80-397d3ee45361v1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=292364;https://tidy3d.simulation.cloud/workbench?taskId=fdve-5277b0c0-5b50-438c-8c80-397d3ee45361v1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=844579;https://tidy3d.simulation.cloud/workbench?taskId=fdve-5277b0c0-5b50-438c-8c80-397d3ee45361v1\u001b\\\u001b[32m-\u001b[0m\u001b]8;;\u001b\\ \u001b[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b]8;id=844579;https://tidy3d.simulation.cloud/workbench?taskId=fdve-5277b0c0-5b50-438c-8c80-397d3ee45361v1\u001b\\\u001b[32m5277b0c0-5b50-438c-8c80-397d3ee45361v1'\u001b[0m\u001b]8;;\u001b\\.                \u001b[2m             \u001b[0m\n"
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       "Output()"
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
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       "\n"
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     },
     "metadata": {},
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'TM input'</span> with task_id                    <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#188\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">188</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-c68f038c-18b2-4b35-8bfb-19f0ce0a999bv1'</span>.          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'TM input'\u001b[0m with task_id                    \u001b]8;id=817644;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=639356;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[32m'fdve-c68f038c-18b2-4b35-8bfb-19f0ce0a999bv1'\u001b[0m.          \u001b[2m             \u001b[0m\n"
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>View task using web UI at                               <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#190\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">190</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-c68f038c-18b2-4b35-8bfb-19f0ce0a999bv1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-</span></a> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-c68f038c-18b2-4b35-8bfb-19f0ce0a999bv1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">c68f038c-18b2-4b35-8bfb-19f0ce0a999bv1'</span></a>.                <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                               \u001b]8;id=184999;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=600939;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#190\u001b\\\u001b[2m190\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b]8;id=602534;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c68f038c-18b2-4b35-8bfb-19f0ce0a999bv1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=761474;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c68f038c-18b2-4b35-8bfb-19f0ce0a999bv1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=602534;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c68f038c-18b2-4b35-8bfb-19f0ce0a999bv1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=386980;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c68f038c-18b2-4b35-8bfb-19f0ce0a999bv1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=602534;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c68f038c-18b2-4b35-8bfb-19f0ce0a999bv1\u001b\\\u001b[32m-\u001b[0m\u001b]8;;\u001b\\ \u001b[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b]8;id=602534;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c68f038c-18b2-4b35-8bfb-19f0ce0a999bv1\u001b\\\u001b[32mc68f038c-18b2-4b35-8bfb-19f0ce0a999bv1'\u001b[0m\u001b]8;;\u001b\\.                \u001b[2m             \u001b[0m\n"
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       "Output()"
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       "</pre>\n"
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       "\n"
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   ],
   "source": [
    "# define a new mode source that launches the tm mode\n",
    "mode_source_tm = mode_source_te.copy(update={\"mode_index\": 1})\n",
    "\n",
    "# define a new simulation and update the source\n",
    "sim_tm = sim_te.copy(update={\"sources\": [mode_source_tm]})\n",
    "\n",
    "# define a simulation batch\n",
    "sims = {\n",
    "    \"TE input\": sim_te,\n",
    "    \"TM input\": sim_tm,\n",
    "}\n",
    "batch = web.Batch(simulations=sims)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aed077f2",
   "metadata": {},
   "source": [
    "Submit the simulation batch to the server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "29dde0cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:08:59.350785Z",
     "iopub.status.busy": "2023-08-18T17:08:59.350621Z",
     "shell.execute_reply": "2023-08-18T17:17:15.063215Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[10:09:00] </span>Started working on Batch.                            <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">container.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py#475\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">475</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[10:09:00]\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch.                            \u001b]8;id=669205;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py\u001b\\\u001b[2mcontainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=221199;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py#475\u001b\\\u001b[2m475\u001b[0m\u001b]8;;\u001b\\\n"
      ]
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[10:09:12] </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">27.324</span> for the whole batch. <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">container.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py#479\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">479</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>cost after the Batch has completed.                  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[10:09:12]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m27.324\u001b[0m for the whole batch. \u001b]8;id=334788;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py\u001b\\\u001b[2mcontainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=928728;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py#479\u001b\\\u001b[2m479\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit \u001b[2m                \u001b[0m\n",
       "\u001b[2;36m           \u001b[0mcost after the Batch has completed.                  \u001b[2m                \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6287ebd582c24d31bac5f8aedca990fd",
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_results = batch.run(path_dir=\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b306f17",
   "metadata": {},
   "source": [
    "## Result Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0432517",
   "metadata": {},
   "source": [
    "We define a helper function to plot the transmission spectra of the TE and TM modes at the top and bottom output waveguides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a4afff44",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:17:22.334126Z",
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    }
   },
   "outputs": [],
   "source": [
    "def plot_transmission(sim_data):\n",
    "\n",
    "    # get te mode amplitude in the top waveguide\n",
    "    amp_top_te = sim_data[\"mode_top\"].amps.sel(mode_index=0, direction=\"+\")\n",
    "    # calculate te mode transmission in the top waveguide\n",
    "    T_top_te = np.abs(amp_top_te) ** 2\n",
    "    # get tm mode amplitude in the top waveguide\n",
    "    amp_top_tm = sim_data[\"mode_top\"].amps.sel(mode_index=1, direction=\"+\")\n",
    "    # calculate tm mode transmission in the top waveguide\n",
    "    T_top_tm = np.abs(amp_top_tm) ** 2\n",
    "    # get te mode amplitude in the bottom waveguide\n",
    "    amp_bottom_te = sim_data[\"mode_bottom\"].amps.sel(mode_index=0, direction=\"+\")\n",
    "    # calculate te mode transmission in the bottom waveguide\n",
    "    T_bottom_te = np.abs(amp_bottom_te) ** 2\n",
    "    # get tm mode amplitude in the bottom waveguide\n",
    "    amp_bottom_tm = sim_data[\"mode_bottom\"].amps.sel(mode_index=1, direction=\"+\")\n",
    "    # calculate tm mode transmission in the bottom waveguide\n",
    "    T_bottom_tm = np.abs(amp_bottom_tm) ** 2\n",
    "\n",
    "    # plotting transmission spectra in dB\n",
    "    plt.plot(ldas, 10 * np.log10(T_top_te), label=\"Top TE\")\n",
    "    plt.plot(ldas, 10 * np.log10(T_top_tm), label=\"Top TM\")\n",
    "    plt.plot(ldas, 10 * np.log10(T_bottom_te), label=\"Bottom TE\")\n",
    "    plt.plot(ldas, 10 * np.log10(T_bottom_tm), label=\"Bottom TM\")\n",
    "\n",
    "    # formatting the plot\n",
    "    plt.xlim(1.5, 1.58)\n",
    "    plt.ylim(-60, 1)\n",
    "    plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
    "    plt.ylabel(\"Transmission (dB)\")\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e266de59",
   "metadata": {},
   "source": [
    "Plot the transmission spectra for the TE input case. From the transmission plot, we can see that when the input mode is TE, all power is transmitted to the TE mode in the top waveguide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5f646c10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:17:22.356645Z",
     "iopub.status.busy": "2023-08-18T17:17:22.356503Z",
     "iopub.status.idle": "2023-08-18T17:17:25.539492Z",
     "shell.execute_reply": "2023-08-18T17:17:25.539005Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a55968ea3eb340c98e41a0eafcf77106",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
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     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>data/fdve-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">5277b0c0-5b50-438c-8c80-397d3ee45361</span>v1.hdf5   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
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       "\u001b[2;36m[10:17:25]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from                             \u001b]8;id=997748;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=466425;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#590\u001b\\\u001b[2m590\u001b[0m\u001b]8;;\u001b\\\n",
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data_te = batch_results[\"TE input\"]\n",
    "plot_transmission(sim_data_te)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6704eca",
   "metadata": {},
   "source": [
    "Plot the field intensity distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f3e2606c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:17:25.592874Z",
     "iopub.status.busy": "2023-08-18T17:17:25.592698Z",
     "iopub.status.idle": "2023-08-18T17:17:31.209780Z",
     "shell.execute_reply": "2023-08-18T17:17:31.209277Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sim_data_te.plot_field(\n",
    "    field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\", vmin=0, vmax=4e3\n",
    ")\n",
    "ax.set_aspect(\"auto\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9754d7b1",
   "metadata": {},
   "source": [
    "Plot the transmission spectra for the TM input case. From the transmission plot, we can see that when the input mode is TM, all power is transmitted to the TE mode in the bottom waveguide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "faa4c7e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:17:31.213822Z",
     "iopub.status.busy": "2023-08-18T17:17:31.213634Z",
     "iopub.status.idle": "2023-08-18T17:17:33.732518Z",
     "shell.execute_reply": "2023-08-18T17:17:33.731975Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4fe2f8c9270c4a6bb155b0358fe77a54",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
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     "metadata": {},
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      "text/plain": [
       "\n"
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data_tm = batch_results[\"TM input\"]\n",
    "plot_transmission(sim_data_tm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7be25aa0",
   "metadata": {},
   "source": [
    "Plot the field intensity distribution as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2166ffd3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:17:33.766629Z",
     "iopub.status.busy": "2023-08-18T17:17:33.766382Z",
     "iopub.status.idle": "2023-08-18T17:17:39.381684Z",
     "shell.execute_reply": "2023-08-18T17:17:39.381167Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yo0ePtqlUyu6888527ty5Np/PW2vbpy3pbmoTa900JbvvvrtNJBJ27Nix9pxzzrEbNmyI97/55pv29NNPt7vssotNp9N2xIgR9vDDD7f33XdfXOb++++3n/nMZ+yECRNsMpm0EyZMsF/4whfsq6++2mPde3tcoVCwP/zhD+2ee+5pU6mUHT58uN13333tt7/9bdvY2FhW9re//a390Ic+FJc79NBD7aJFi+L9q1atsrNnz7a1tbUWiKcw6TxtScmtt94an2/EiBH2lFNOscuXLy8rM2fOHFtdXd3l+V1++eW2N/8kPfbYY/bAAw+0mUzGTpgwwV500UV24cKFXerT0tJiTz75ZDts2DAL9DiFyaGHHrrRKUguv/zyTdZpU8IwtN///vft5MmTbTKZtHvuuaf9wx/+0KVcaUqXt956q2z7+vXr7RlnnGFHjhxpq6qq7KGHHtrte/SCCy6wO++8s02lUnb06NH25JNPtm+88Uaf6y+E2PbJ0l9CCCGEEIOc5OCFEEIIIQY5CeiEEEIIIQY5CeiEEEIIIQY5CeiEEEIIIQY5CeiEEEIIIQa5QRXQrVixgi9+8YuMHDmSTCbD9OnTu50kVgghhBBiKBk0Ewtv2LCBj370oxx++OH8/e9/Z/To0bz22mvdLvmzMcYY3nvvPWprazd7WSAhhBBiMLLW0tzczIQJE2SC6e3YoJmH7pvf/CaPPfYYjzzyyBafY/ny5dvM4uRCCCFEf3r33XeZOHHiQFcDgJtuuon//M/zeOqp57aZOg12gyagmzZtGrNmzWL58uU89NBD7LDDDnzta1/jzDPP3Ogx+Xy+bF3JxsZGdtxxR1xLs2TohBBCDAUWMDQ0NFBfXz/QlaFQKLD77jsRBCFHH/0RfvnLvwx0lbYLgyb3+uabb3L99dez6667snDhQs455xy+/vWvc+ONN270mHnz5lFfXx/fXDAHLpiTm9zkJje5yW2o3Nhmuhr96n8vIp1OsuBvP+TGG+/h9ddfH+gqbRcGTYYumUyy33778fjjj8fbvv71r/Pkk0+yZMmSbo/pnKFramqKmlw9Sm9wIYQQYvtmgZDGxkbq6uoGtCZtbW3sssskfv7z/+C44w/hzK9cRS5X4Oab7xvQem0PBk2Gbvz48UybNq1s2x577MGyZcs2ekwqlaKurq7sJoQQQoiB8T8/v4CJE0dz7HEHA/B/LpvDHXc8wnPPPTfANRv8Bk1A99GPfpRXXnmlbNurr77K5MmTB6hGQgghhOithoYGrvrhH/nOd78SN//uuONYzjrrU3zrW+cMcO0Gv0ET0F1wwQX84x//4Pvf/z6vv/46N998M7/61a+YO3fuQFdNCCGEEJvwox9/nQ/uM5WZM/ct2/7NS05h8eJnN9p9SvTOoOlDB3D33XdzySWX8NprrzFlyhQuvPDCHke5dtbU1BSN8JE+dEIIIYaKge9Dt3r1anbZZSfuWfhjZszYs8v+yy77LY899jwPPvDMNjN4Y7AZVAFdX0lAJ4QQYugZ+IDuP/7jRN58cyV33vW9bvc3NLSw69STufXW/8cnPvGJfq7d9kECOiGEEGK7NrAB3bJly9htt115fMl17L33Lhst96Mf/ZH/96eHeOKJf0uWbgsMmj50QgghhBh8rrjibI477uAegzmAuXOPY8WKddxxxx39VLPti2TohBBCiO3awGXoXnnlFT74wen867nfMXXqDpssf8P1d/GLX9zB88+/ged5/VDD7Ydk6IQQQgixVfyf/3M2c+Yc1atgDuD0Mz5JLlfg97+/dCvXbPsjAZ0QQgghKu6ZZ57h7ruX8K1LT+31Mclkgiuu+DLf/vZ8CoXCVqzd9kcCOiGEEEJU3Le+9TXO+dqx7LDD6M067qQvfJzq6jS//NU3+nT9H/zgByilOP/88+NtuVyOuXPnMnLkSGpqajjhhBNYvXp12XHLli1j9uzZVFVVMWbMGL7xjW8QBEFZmcWLF/PhD3+YVCrF1KlTmT9/fp/qWgkS0AkhhBCioh555BEee+x5LrroC5t9rOd5XHnlGXzvu7+ntbV1i67/5JNP8stf/pK99967bPsFF1zAX//6V26//XYeeugh3nvvPY4//vh4fxiGzJ49m0KhwOOPP86NN97I/Pnzueyyy+Iyb731FrNnz+bwww9n6dKlnH/++XzlK19h4cKFW1TXSpFBEUIIIcR2rX8HRVhrOfiQDzJz5r78n/8zZ4vPcdBBX+PYYw/mvy/51WYd29LSwoc//GGuu+46vvvd77LPPvvws5/9jMbGRkaPHs3NN9/MiSeeCMDLL7/MHnvswZIlSzjwwAP5+9//zjHHHMN7773H2LFjAbjhhhu4+OKLWbt2LclkkosvvpgFCxbwwgsvxNc86aSTaGho4J577tmi51sJkqETQgghRMXcc889vPLyMs4//7NbfA6lFN/97lf40VV/pKGhYbOOnTt3LrNnz2bmzJll259++mmKxWLZ9t13350dd9wxXnZsyZIlTJ8+PQ7mAGbNmkVTUxMvvvhiXKbzuWfNmjXgS5f5A3p1IYQQQmxXfnjVf/Nf3ziJ2tqqPp3niCP2Ze8P7sINN9zA1772tbJ9qVSKVCrV5ZhbbrmFZ555hieffLLLvlWrVpFMJhk2bFjZ9rFjx7Jq1aq4TMdgrrS/tK+nMk1NTWSzWTKZzOY90QqRDJ0QQgghKqaxAXbbbceKnGuP3SezYMEC6uvry27z5s3rUvbdd9/lP/7jP7jppptIp9MVuf5gIhk6IYQQQlSWNWBMBc5jOeigg1iwYEHZ5u6yc08//TRr1qzhwx/+cLwtDEMefvhhrr32WhYuXEihUKChoaEsS7d69WrGjRsHwLhx43jiiSfKzlsaBduxTOeRsatXr6aurm7AsnMgGTohhBBCVJqxLqDr681afN+nrq6u7NZdQHfEEUfw/PPPs3Tp0vi23377ccopp8T3E4kE999/f3zMK6+8wrJly5gxYwYAM2bM4Pnnn2fNmjVxmUWLFlFXV8e0adPiMh3PUSpTOsdAkQydEEIIIQa92tpa9tprr7Jt1dXVjBw5Mt5+xhlncOGFFzJixAjq6uo477zzmDFjBgceeCAARx55JNOmTePUU0/lqquuYtWqVVx66aXMnTs3DiLPPvtsrr32Wi666CJOP/10HnjgAW677bYuWcT+JgGdEEIIISrL2so0uVLZmdWuvvpqtNaccMIJ5PN5Zs2axXXXXRfv9zyPu+++m3POOYcZM2ZQXV3NnDlzuPLKK+MyU6ZMYcGCBVxwwQVcc801TJw4kV//+tfMmjWronXdXBLQCSGEEKKyrHW3Pp+nb4cvXry47HE6neYXv/gFv/jFLzZ6zOTJk/nb3/7W43kPO+wwnn322b5VrsKkD50QQgghxCAnGTohhBBCVFalmlyHzmJWfSYBnRBCCCEqq1LTllS4D932TJpchRBCCCEGOcnQCSGEEKKyjDS59jcJ6IQQQghRWRVbKaLvpxgqpMlVCCGEEGKQkwydEEIIISpLmlz7nQR0QgghhKgohUVZGeXanySgE0IIIURlmUr1oZOArrekD50QQgghxCAnGTohhBBCVJbF9aOrxHlEr0hAJ4QQQojKkibXfidNrkIIIYQQg5xk6IQQQghRWVamLelvEtAJIYQQorKscbe+n6gC5xgapMlVCCGEEGKQkwydEEIIISpLmlz7nQR0QgghhKgsa2Xakn4mAZ0QQgghKqtSa7lKRNdr0odOCCGEEGKQkwydEEIIISrLysTC/U0COiGEEEJUlDIWVZGAru+nGCqkyVUIIYQQYpCTDJ0QQgghKsxWprlUmlx7TQI6IYQQQlSWjHLtd9LkKoQQQggxyEmGTgghhBCVJStF9DsJ6IQQQghRWUZWiuhvEtAJIYQQorJkHrp+J33ohBBCCCEGuUEb0P3gBz9AKcX5558/0FURQgghREelPnR9vUmGrtcGZZPrk08+yS9/+Uv23nvvga6KEEIIITqzFZqHTvTaoMvQtbS0cMopp/C///u/DB8+fKCrI4QQQggx4AZdQDd37lxmz57NzJkzB7oqQgghhOiONLn2u0EV0N1yyy0888wzzJs3r1fl8/k8TU1NZTchhBBCbGWlaUv6etvMeO76669n7733pq6ujrq6OmbMmMHf//73eP9hhx2GUqrsdvbZZ5edY9myZcyePZuqqirGjBnDN77xDYIgKCuzePFiPvzhD5NKpZg6dSrz58/f0leqYgZNH7p3332X//iP/2DRokWk0+leHTNv3jy+/e1vb+WaCSGEEGJbMHHiRH7wgx+w6667Yq3lxhtv5DOf+QzPPvsse+65JwBnnnkmV155ZXxMVVVVfD8MQ2bPns24ceN4/PHHWblyJV/60pdIJBJ8//vfB+Ctt95i9uzZnH322dx0003cf//9fOUrX2H8+PHMmjWrf59wB8rawZHPvPPOOznuuOPwPC/eFoYhSim01uTz+bJ94DJ0+Xw+ftzU1MSkSZMAD1D9VHMhhBBiIFkgpLGxkbq6uq1+tQ996EN8++R9+NTHpvX5XOf86M+M3OPjfO9739vic4wYMYIf/ehHnHHGGRx22GHss88+/OxnP+u27N///neOOeYY3nvvPcaOHQvADTfcwMUXX8zatWtJJpNcfPHFLFiwgBdeeCE+7qSTTqKhoYF77rlni+vZV4OmyfWII47g+eefZ+nSpfFtv/3245RTTmHp0qVdgjmAVCoVp11LNyGEEEJsZZXoPxf1oQuCoEv3qY7Jmo0Jw5BbbrmF1tZWZsyYEW+/6aabGDVqFHvttReXXHIJbW1t8b4lS5Ywffr0OJgDmDVrFk1NTbz44otxmc79+GfNmsWSJUv6+qr1yaBpcq2trWWvvfYq21ZdXc3IkSO7bBdCCCHEALJUbOmvxx9/nPr6+rLNl19+OVdccUW3hzz//PPMmDGDXC5HTU0Nd9xxB9OmuWzhySefzOTJk5kwYQLPPfccF198Ma+88gp//vOfAVi1alVZMAfEj1etWtVjmaamJrLZLJlMps9Pe0sMmoBOCCGEEEPPQQcdxIIFC8q2pVKpjZbfbbfdWLp0KY2NjfzpT39izpw5PPTQQ0ybNo2zzjorLjd9+nTGjx/PEUccwRtvvMEuu+yy1Z5DfxjUAd3ixYsHugpCCCGE6Mwad+vzeSy+729Wl6lkMsnUqVMB2HfffXnyySe55ppr+OUvf9ml7Ec+8hEAXn/9dXbZZRfGjRvHE088UVZm9erVAIwbNy7+WdrWsUxdXd2AZedgEPWhE0IIIcQgUWpy7eutAowxG+1zt3TpUgDGjx8PwIwZM3j++edZs2ZNXGbRokXU1dXFzbYzZszg/vvvLzvPokWLyvrpDYRBnaETQgghhCi55JJLOProo9lxxx1pbm7m5ptvZvHixSxcuJA33niDm2++mU9+8pOMHDmS5557jgsuuIBDDjkkXkr0yCOPZNq0aZx66qlcddVVrFq1iksvvZS5c+fGzbxnn3021157LRdddBGnn346DzzwALfddluXZuH+JgGdEEIIISqrNEq1rzZzZrU1a9bwpS99iZUrV1JfX8/ee+/NwoUL+cQnPsG7777Lfffdx89+9jNaW1uZNGkSJ5xwApdeeml8vOd53H333ZxzzjnMmDGD6upq5syZUzZv3ZQpU1iwYAEXXHAB11xzDRMnTuTXv/71gM5BBxLQCSGEEKLSKjbKdfPO8Zvf/Gaj+yZNmsRDDz20yXNMnjyZv/3tbz2WOeyww3j22Wc3q25bm/ShE0IIIYQY5CRDJ4QQQojKsgPT5DqUSUAnhBBCiMqq1ChVied6TQI6IYQQQlSYrcw8dBLR9ZoEdEIIIcQglEgkqKuro7q6murqapLJZLflwjDkhReW9m/lRL+TgE4IIYQYZMaOHcuECRNQSg10VbonTa79TgI6IYQQYpBIp9PstNNOVFVVDXRVejZA05YMZRLQCSGEEINATU0Nu+6667ablRMDSgI6IYQQYhuXyWSYOnXq4AnmKrVShOg1CeiEEEKIbVgqlWLXXXdF60G0FoA0ufa7QfTuEEIIIYYW3/fZdddd8X3Jv4ieyTtECCGE2EZNnjx5o9ORbNOsjHLtbxLQCSGEENugESNGUF9fP9DV2DLGytJf/UwCOiGEEGIb4/s+kyZNGuhq9IGVYKyfSR86IYQQYhszadIkPM8b6GqIQUQydEIIIcQ2ZNiwYQwfPnygq9E3FVspQrJ8vSUBnRBCCLGNUEoxceLEga5G38mgiH4nTa5CCCHENmLUqFGDc1SrGHCSoRNCCCG2AUopxo0bN9DVqAwZ5drvJKATQgghtgGjRo0ikUgMdDUqQ5pc+500uQohhBADbLvKzokBIRk6IYQQYoCNHj16+8nOQeXWcpUUXa9JQCeEEEIMoO0yO1exaUv6foqhQgI6IYQQYgCNHDkS39+yj+NcLkdLSwutra0UCoVuy9gBGFhgrcXKPHT9SgI6IYQQYgCNHj16s49paWnh7bff3mgQV06CoqFAAjohhBBigFRXV5PJZHpd3hjD8uXLWbdu3VasVQVYJLvWzySgE0IIIQbI5mTnCoUCr776ai+zcgOsYtOWSFDYWzJtiRBCCDEAfN/v9ZqtQRDw2muvDY5gTgwIydAJIYQQA2DUqFEopTZZzhjDa6+9Rj6f74daVYiMcu13EtAJIYQQA2DUqFGbLGOt5Y033iCbzfZDjSpIVorod9LkKoQQQvSz+vp6ksnkJsutWLGC5ubmfqiRGOwkQyeEEEL0s95k51pbW1mzZk0/1GYrMMigiH4mAZ0QQgjRjzzPo66urscy1lreeeedfqpR5VVuYuG+n2KokIBOCCGE6EfDhw/f5GCIlStXksvl+qlGW0Gl+tBJRNdr0odOCCGE6EcjRozocX82m2XVqlX9VBuxvZCATgghhOgniUSCmpqaHsssW7asn2qzFZX60PX1tpkJuuuvv569996buro66urqmDFjBn//+9/j/blcjrlz5zJy5Ehqamo44YQTWL16ddk5li1bxuzZs6mqqmLMmDF84xvfIAiCsjKLFy/mwx/+MKlUiqlTpzJ//vwtfKEqRwI6IYQQop9saiLhxsZGWltb+6k2W5GtQDC3BQHdxIkT+cEPfsDTTz/NU089xcc//nE+85nP8OKLLwJwwQUX8Ne//pXbb7+dhx56iPfee4/jjz8+Pj4MQ2bPnk2hUODxxx/nxhtvZP78+Vx22WVxmbfeeovZs2dz+OGHs3TpUs4//3y+8pWvsHDhwoq8dFtKWTt0hpA0NTVRX18PeMCmJ3MUQgghKmn33Xenqqpqo/tffvll2traKnxVC4Q0NjZucjBGJXzoQx/i8n1G8alpO/b5XHP//DgjP3483/ve97b4HCNGjOBHP/oRJ554IqNHj+bmm2/mxBNPBNzrvccee7BkyRIOPPBA/v73v3PMMcfw3nvvMXbsWABuuOEGLr74YtauXUsymeTiiy9mwYIFvPDCC/E1TjrpJBoaGrjnnnv69oT7QDJ0QgghRD9IpVI9BnONjY1bIZgbINZW5oYlCAKamprKbr1ZNSMMQ2655RZaW1uZMWMGTz/9NMVikZkzZ8Zldt99d3bccUeWLFkCwJIlS5g+fXoczAHMmjWLpqamOMu3ZMmSsnOUypTOMVAkoBNCCCH6waYGQ7z33nv9VJN+YMGaCtwsPP7449TX15fd5s2bt9FLP//889TU1JBKpTj77LO54447mDZtGqtWrSKZTDJs2LCy8mPHjo0HoaxataosmCvtL+3rqUxTU9OArugh05YIIYQQ/aCngK6hoWHwLe/VTw466CAWLFhQti2VSm20/G677cbSpUtpbGzkT3/6E3PmzOGhhx7a2tUccBLQCSGEEFtZOp3uMQjZrrJzUMGVIsD3/c3q+5dMJpk6dSoA++67L08++STXXHMNn//85ykUCjQ0NJRl6VavXs24ceMAGDduHE888UTZ+UqjYDuW6TwydvXq1dTV1ZHJZDb7KVaKNLkKIYQQW1nnZr6OGhsbB/ckwt2p2CjXvgeFxhjy+Tz77rsviUSC+++/P973yiuvsGzZMmbMmAHAjBkzeP7558uWXFu0aBF1dXVMmzYtLtPxHKUypXMMFMnQCSGEEFuZm2Ghe2vXru3HmvQPG/Wh6/uJNq/4JZdcwtFHH82OO+5Ic3MzN998M4sXL2bhwoXU19dzxhlncOGFFzJixAjq6uo477zzmDFjBgceeCAARx55JNOmTePUU0/lqquuYtWqVVx66aXMnTs3zrCeffbZXHvttVx00UWcfvrpPPDAA9x2221dmoX726DJ0M2bN4/999+f2tpaxowZw7HHHssrr7wy0NUSQggheuT7PtXV1d3uy+fzNDU19XONtl9r1qzhS1/6ErvtthtHHHEETz75JAsXLuQTn/gEAFdffTXHHHMMJ5xwAocccgjjxo3jz3/+c3y853ncfffdeJ7HjBkz+OIXv8iXvvQlrrzyyrjMlClTWLBgAYsWLeKDH/wgP/nJT/j1r3/NrFmz+v35djRo5qE76qijOOmkk9h///0JgoD//u//5oUXXuCll17a6B9KZzIPnRBCiP42cuRIJk+e3O2+FStWdOmPVXn9Pw/dZbsP55jdJvX5XOfe/Q9GzTqxT/PQDRWDpsm182R98+fPZ8yYMTz99NMccsghA1QrIYQQomcb6z9nrWXdunX9W5n+YnEDI0S/GTQBXWeNjY1Az8PA8/l82eSDktYWQgjRn5RS1NbWdrtv/fr1hGHYzzUS26tB04euI2MM559/Ph/96EfZa6+9Nlpu3rx5ZRMRTprU9/SvEEII0Vt1dXVo3f1H7fY4GKLEGluZ26DoFLZtGJQB3dy5c3nhhRe45ZZbeix3ySWX0NjYGN/efffdfqqhEEIIsfHRrW1tbdvPMl/dKTW59vUmAV2vDbom13PPPZe7776bhx9+mIkTJ/ZYNpVK9TiRoxBCCLE1bSyg2277zokBM2gCOmst5513HnfccQeLFy9mypQpA10lIYQQYqOqqqpIJBJdtltr2bBhwwDUqB9ZJLvWzwZNQDd37lxuvvlm7rrrLmpra+NFcuvr6wd0qQ0hhBCiOxsb3drU1LT9D4awrh9d388jUWFvDZo+dNdffz2NjY0cdthhjB8/Pr7deuutA101IYQQoouNNbeuX7++n2syAKQPXb8bNBm6QTL/sRBCCEEymey29cgYQ0NDQ/9XSGz3Bk1AJ4QQQgwWG8vONTQ0DIkEhTUDs5brUCYBnRBCCFFhG+s/NySaW6FyK0VIQNdrg6YPnRBCCDEYaK2pqanpsj0IAlmxSGw1kqETQgghKqi+vh6lVJft2/1UJR1UqslVEnS9JwGdEEIIUUFDenRriTS59jtpchVCCCEqqLuALggCWltbB6A2YqiQDJ0QQghRITU1NXie12X7kJuqxFZoTmDJ0PWaBHRCCCFEhWxsdGtjY2P/VmSAWVuhPnQS0PWaBHRCCCFEhXTX3GqMGXqjW0srPYh+I33ohBBCiAqoqqoilUp12d7c3DwkJhMWA0sydEIIIUQFjBgxotvtQ67/HJVrcpU+dL0nAZ0QQghRAcOHD+92+1DrPwfIoIgBIE2uQgghRB/V1taSSCS6bG9tbSUIggGokRhqJEMnhBBC9NHGmluHZHYOoomFu66WsfkqcY6hQQI6IYQQog+UUhudrmQo9p8DmbZkIEiTqxBCCNEHdXV13U4mXCgUyOVyA1AjMRRJhk4IIYToA2lu7cpahbUVaC6VDF2vSUAnhBBCbCGttTS3dsOaCjW59v0UQ4YEdEIIIcQWGj58OEp1zUSFYUhzc/MA1GgbIfPQ9TvpQyeEEEJsodGjR3e7fcgt9SUGnGTohBBCiC1QVVVFVVVVt/uGcv85qFwfOhnl2nsS0AkhhBBbYMyYMRvdJwGdwso8dP1KmlyFEEKIzeR53kaX+mppaSEMw36ukRjqJEMnhBBCbKZRo0Z1OxgChvbo1pis5drvJKATQgghNtPGBkOANLeCi8Mq0oeu71UZMqTJdRuneviv434hhBD9o66ujmQy2e2+XC5HPp/v5xqJknnz5rH//vtTW1vLmDFjOPbYY3nllVfKyhx22GEopcpuZ599dlmZZcuWMXv2bKqqqhgzZgzf+MY3CIKgrMzixYv58Ic/TCqVYurUqcyfP39rP70eSUAnhBBCbAYZDLFp1qiK3NjMLN9DDz3E3Llz+cc//sGiRYsoFosceeSRtLa2lpU788wzWblyZXy76qqr4n1hGDJ79mwKhQKPP/44N954I/Pnz+eyyy6Ly7z11lvMnj2bww8/nKVLl3L++efzla98hYULF/bthesDaXIdID1m1cr6Zej28kqjlI+1BmuLKEyH/cZ1WFCqvOOCUlhr4uvZKIGtUPF9IYQQvVNdXU1dXd1G90v/uUiF+tBt7jnuueeessfz589nzJgxPP300xxyyCHx9qqqKsaNG9ftOe69915eeukl7rvvPsaOHcs+++zDd77zHS6++GKuuOIKkskkN9xwA1OmTOEnP/kJAHvssQePPvooV199NbNmzdq8SleIZOiEEEKIXho/fvxG9xUKhS6ZoKHKouK56Ppy66tSxrTzers33XQTo0aNYq+99uKSSy6hra0t3rdkyRKmT5/O2LFj422zZs2iqamJF198MS4zc+bMsnPOmjWLJUuW9LnOW0oydBW00axbKWvWTeYtPk51eKxSHe677dYaqqtT1NTU09raTEtLM9ZatE5F+4tYG0THhtiyNVfyKDx3DVuMr99tVq9zhi9isXFWT7J7QoihaFPZuQ0bNvRjbYaOIAi6rLyRSqVIpVIbOcIxxnD++efz0Y9+lL322ivefvLJJzN58mQmTJjAc889x8UXX8wrr7zCn//8ZwBWrVpVFswB8eNVq1b1WKapqYlsNksmk9myJ9sHEtBt41KpFCNHDmf48HoSCdfkCmMwxpDNZmlrK9LS0kpzcwPFYrDJ8wkhhNgyEyZM6HH/+vXr+6km2z5jwFRgYmFr4fHHH6e+vr5s++WXX84VV1zR47Fz587lhRde4NFHHy3bftZZZ8X3p0+fzvjx4zniiCN444032GWXXfpc54EiAd02yE1YOYyRI0dQU1MNgLUWa9sDNqUUVVVV1NQkGDNmFNbuQC6XpbW1QEtLE83NzTLSSgghKqSmpoba2tqN7s/lcmSz2X6s0bavUst2HXTQQSxYsKBs26ayc+eeey533303Dz/8MBMnTuyx7Ec+8hEAXn/9dXbZZRfGjRvHE088UVZm9erVAHG/u3HjxsXbOpapq6sbkOwcSEC3WbptUu2pGTV+4INyAxiUSkSHabRKxveV0tTX1zF8eC21dZnotBpVGvSgQOk0WnkYG2JsETCo0q9QQTqdIZOpZeTIEUBIGCqybQENjatpaw0o5KEQuLR1wq+hGLQAIUolMCYb1U9jTSFqno2abeOmWh0/KzcgYwt1bObtPDFn533d/RT9ortmdWluF0OVZOcGju/7PTZ1d2St5bzzzuOOO+5g8eLFTJkyZZPHLF26FGjvHzljxgy+973vsWbNmnhE86JFi6irq2PatGlxmb/97W9l51m0aBEzZszo7dOqOAnoBlhVVYZRo0YyfPhwfN/DmBBLcdMH9oLve9TXV1FT66FUEk2Shqa1tLS0ks9BQ2MbxsjyNEII0ZP6+npqamp6LCMBXblKDWrY3HPMnTuXm2++mbvuuova2tq4z1t9fT2ZTIY33niDm2++mU9+8pOMHDmS5557jgsuuIBDDjmEvffeG4AjjzySadOmceqpp3LVVVexatUqLr30UubOnRtnBs8++2yuvfZaLrroIk4//XQeeOABbrvtti6ZxP405AO6Xk/K28NUIvHjKPsG4Ok0WruX19dplPLcduXjJTTDh9dTV58imfLRyoumJFFonQDSKDRa+W7a4GhghFY+WiUxtkBg8hgb4EeDItzjEE8lsBiMKaK0h8ZNc6K1h6dT1NcNp75uOJ6XJggnkMsVCPIJNjSuIdsWYEJNIWgm4VVjbYixAVr5FIKmeMoUrX2MKcQDLywGhcbETcKdg0Qv2uZ+Wmui12rjwaQro8t+Ri96N0rTt3T3k41s2wRr2rOUpQErG9sG5dttH7KXtA9A6fgY+j87trG/jC3OzvYiuyrZP7Gt0Vqz44479limtbWVQqHQTzUaHCoV0G2u66+/HnCTB3f0u9/9jtNOO41kMsl9993Hz372M1pbW5k0aRInnHACl156aVzW8zzuvvtuzjnnHGbMmEF1dTVz5szhyiuvjMtMmTKFBQsWcMEFF3DNNdcwceJEfv3rXw/YlCUgAV2/UUoxfHgdo0aOorrGta8HYRZjBzZDlkmnSdXWUz88hdYJwkCxoXEt+aylpbmZtqwMtBBCDF0TJ04kkUj0WEayc9sOu4kvjpMmTeKhhx7a5HkmT57cpUm1s8MOO4xnn312s+q3NUlAt5HFldt1zcYplXD93tD4XhWedn3hkn41vq7C2hBfp1B4VNemqKpT1NSl8bWP1j4KjVIeCa8KAA8fT7UvI1PKyHkkowydF5fzbYqQAnmvBUNIUlWj8cjZJkIbkFRVWEKKNk9CpfBUihZ/DZ5KktHDaPZdJ84qPYzWcB3WGqr8kWT1BpTy8f0EqUQSz0uDNRSKOQo5aGlpweQyZNvyJKkmb1vwSZGiKnp9NE12FSFFQhvgKR+FhyXEI0FIEV+lCW2RwObxVSrqB9jORJmfUmbQ0ylCk0cpjbFFtEoQmjxaJeJjldIYE6C1T2AKeMqPrx9GGUONJrQBvk4SmELZVDDu+CJaJ8qmerE2dAFudD1rQywmvnapb6OxQZfzuYmfS5nM8oC4lM0s3W+/nik7T2eb2t+znstvNBvaqY7gfs82/j1tqh7tX1asNVHKLzqm7Brt/wCrjvXdyPQ5QvSX2tpaRo0atclyMl1JVxYwFVnLVZa27C0J6LaCVCrJ6FGjGDFiOJ6vKJpWgnBwjjj1PE1tXYaauirSuh6sJchqNrSsJ99iMG2aMOxbE6MQQmxrlFJMnjx5k+Wampq6rPEp2pf+6vuJ+n6KoWLIB3QKr9OGjotn6Cgr4uHpJEr5KKVJeNWk/Fq0SpBUGXyVwvM0mWGa2mEpqqtqouxYAo1PlT8c61sSNoVPEs96ZX2kNJqEdSl9i43fwAn8eFypVgoPRUJ7GGtpM0UMhhqdQiloNUUCQtK48wTWkFY+aaVZxzhSJBjhJVkdjsdgGetVscK6Ea87JupZZ/IktUe15/E+eWr9JMZasmFAUns0mxwpkyDj+aRrNS2ZgMRY99q1ZHPYthDye+LlA4qFEF8rAuO+W3laUQgtaU9RNJZsYKlJanKBRSswFnwNRWPxooxpLjRU+5psYPG1IrRuXz40ZHxNLjRoFElPkQ3atyW1JjCWtK/Ih+6YhIbWwDAs6dFUMKR99+oXjKtTLrQktaJoIKHdy1+M9rUWLRnf7SsYQ5WvaS4aktrVMxsYElH9StfPmZCiNSSUps0U8ZQmtAaDJeyQtSqqIsrq+L5Go1HkVR7Ptv9pWgyBCkjZNCEBgQrQUZZPW02gAgyGpE0SKPfBUsp2+vgUKaDQhNFgG42HwWUcjTKEFOMsqkIT2iI2qq3pJrtXyo6GFLHWdFumlNHsWD6I+l2WMrDWmrjfpY0eu/KlbW4kd+l80Kn/XsdR0pLNExU2adIkksnkJsutXbu2H2ozCA3Q0l9D2ZAP6Pqqvr6GkSNGUltXTZEsgR2cmbi+SKZ8kpk0KU+R8TRBEBJmixQKIdrTeFpRDC2pUkBXNFT5inxgUVhCA56yFEOLBqyxJEJDlVZ4gcVTEBi3TxcNaU9hA4PGBWBB0eB7Cq+o8JTChBalo5lihBBiM40bN65XTa2FQiFeWkqIgTbkAzqlk3QchVnqG1eiddKNNo3mivN0ipqqakaMqGf06Alk/BrSqg7fpvDxSJDEtz7VJElqD1+pOAuV8jSeAq9DFtpY0AoS2uUeTPRtJDQuACqpjn5TSc/1M2oLEyigPgFJbWkOEhgLGc+dY3QqpDYRMiyRY2U2Q40fsuuoNby2bjgGxbRxa3hp1Ug8LFNGrOP19cOo8QMmDFvPG+uGM6GmiVzgsz6XYlg6T2M+SdozvNriU+MZ1uZTREkqWgMfX0MhdK+iSfroap9c4DJvWrlZw73oZW0LoMqHoml/vr6CgoWkgsC6W5UPubB8jGpgIe257QoX0OWjYLEQddOy0fnyocvuKQW5wFDlQVvRorGAIgwNvlIUQ4O1rjOtjl57FZWpD1wAGYSWMHSZueGhIaUUWllyIWS0xRpL0UCND4XQfTVNaUUuMG4/CmstHhZrLUkF+cCS0JaEgpaiJa0hoSytRfccQ9s+0jQwUO1bckYRGvc+CK17LxVC9/pUedAWum1F416Hah9aA3ee0Lr+KBnPUoiC3dC67GNSW4pGkfYsBaMIo99LwbjsZSlD6inIRZnPbFTIWHfutOeum/Laf7fWQjZ0mdqWookzmVkbEFpDi2rDYMirHAWVJaRIzjS6bWETockT2oBi2OoyeqbgsofW0HXktHaTb8f986JsaMdl7TqytlP/va4jizvvk6zf9m/UqFGbnHOuRLJzG2dQFelDJ3pvyAd0vZVIJBg5agSjR40hnXRThXja28RRYluhUGit0J7L+CmlUJ7GL0XbEU8rQuOagpVSJEJLylcuaImC7GQUQGoFaeMCGaIgtNqHYhSIJTUUjAuyS1coBcEJ5collAt6q0NIaXe/NnTHhqWIyEIQWjKeJR9CaMDXljDKaBZCi7GQ1pZkABqX7bQWMp7FKwLWElr3VNPKkAhdABsalzVNKIsfQlIZVOgCOGuB0JBQCm0MOvry4QUmmvLaBcLKtHcTEGIwGzZs2CanKCmx1vL+++9v5RoNXhWbh04GRfTakA/oPJ2hYw7I2iAazehGpI4cOZIxo8YzetgOpHQdGWqpMXXU6jQjkgmGpRS1CajyLL6CKt/gK8voVIGMnyfhGRJeQD7wqUm7eYp832BM+xs+DDWpVEAQaKxVeF5IEPik00WUsliraGpJExiFry3pZJFcIUEqEVBdk0d50NqUxFpFOlMgVR3y4htj0Mp9ymoF7xcSsG44q3JJihbMylG80+b62yW8OtblfZqKHrlwGCtzSVpDD09ZGooeLaHnMjZFGOaH1PohSkFKW9I6pCXwSGjrMmGhwleQ9gxNRbfdV5ac0dT4IaFVNBU9anxDaCFvFGnt6pkNNdW+oWDc/WGJkJwpjfi1hCjyoaIuYWgJXO/Cat/QVNSkPBMFLC6XktKGtlCT1K6HVyHU1CaCuE4JbcgGmoQ2WCAwLuPma0M+1HjKApaCVVR5IflQY6LztgaatGdQUZ3TnqFoFaFVVHtuv6ctHpCLnl/QIeAJrNtWMAoUJJWNfrfgK4uvFL5y0VOpD6XV4PuWIFRg24PFpAcqytCloinwfOUyZqF1WTuirF0poMtEGU4dbQs6ZNVKP4Mo21mw7ryBdecFyBsXiGY7ZE+Ndb+jgnFZxmIIJgpGW4sGrCVdCAmtoRCG5EyAsYYWlcAYSxstFI2PsYa81VhryQcJgqBI0eQJwiqMCTGmSGgCF4yGecB3k3Fbl1E1phg9DgnDYrRknnV98KLzGmuwxmJViCr9XuLsnXtGqnOAGu1XtnxewJLO2zpn/raFVTdklY+ejRkzhh122KHX5Tds2CCDIcQ2ZcgHdN2pra1h9KjR1A8bhu8l8PXArMsmxGCilELpaACPam8u9rVCoUj5GmMtngnR1nPBhXbNoq6pPYHFki8NFDFJjAkIbZHQ5OPAzNI+kMJNdm3j67vBFRbiZtkSEw+AKk3cbAmxJmpupxT4KTdoIzQdgkEXBllrscYQlgJIS7zfWBNvM8bEj+Nt0WNjut/eeV/nW2mfqLxUKsVOO+1EdXX1Zh0nza09s7ZC05bI277XhnxAl/BrUGgymSrGj5nEuJETySRryNhaqkwV1SpJrZ8ABdW+JqEVGd/1lar1LSkvjPvEBUZhLbSGGmuThKQIrfvO3xpqEsqSj4ZxB9Y14xnrMi1KQTFsn7mrY/8pgLqEy6pYCwnPlfWjJjo3YtP9dFke4n57ac9G11B4yqdo3bUDq0h5rlktF2qMVRjcB1ops5fWhoTWJLSlRrnMiacsGT8kbzVpHZLxQkIUaW1I6pA25ZP2Q9JeiEeSpGdIeiGNhSTDkwUCo/BUglrffSi3BD61fhGLoiXwqU8WKIQebaHHiGSBXOhRNIpqPyAX+uSNZniyQFXRvXVrEwFpnSDlGbSyUXYNV5fA1cVYRT7U1CaLVHsJEtqS9gMaC65foKcM2cAj6Vl8ZWiL7gMUQnft1sBHK0vGC9lQTFLrB2hlaS4mqE0UyQYeCqjyAzYUk1R5IVpBW+BR5YcUQ4VSrim3LfSoibaFqCib6VPlBXgKmgOfWj8gbzQ6anBoDT2GJ4vkQ03eaDJeSDb0yHghRaspGk2NH9BU9KNspeu/UuW5bWnPut+7gVo/pDl6jkWjKBpFlW9oCzRVvqEQ1ctXluaiq2veaPwo29oaaFKepamo0ao0KlhR5RlyoSal3fu8FE41FzW+huaCT2AhH/pkA7c3HbjVUFpNFUVVJCSkRbtteXyKKo9HQCFKhIQmjzFu9RQDKHy3xG80v5/FolSItTrqB+lYdFmWrcQFoZ1XgfG6fop0GE3rcoHl4kRfVLZj8KU67C87ZaftGyvXWedgD0scZPYYDHYKIHsbRPZ136YMRObQ8zyqq6upra1lzJgxqE3OR1oum83S2tq6lWq3fXD9hivRXCpNrr01JAO6qVN3xfPct/XqzEQ8zyedSpP0a/D1kHxJhBCDhFKqLABRuOlpNnkcA9fVsaegr2Og2R9834/X49xSkp0T26IhGb1UV1fHAV0mnS4b1SqEEKKyOgehZfsYXGNqCoUC69atG+hqbPNcx4fKnEf0zqAL6H7xi1/wox/9iFWrVvHBD36Qn//85xxwwAFbfL7d/IPxooDOx8OPmkJ9pUn4Ou70nVCuuSqIpqfIBopGT6GjZbmiL5poFXUy165JqNQ8UzTgKTdFRGn6jXhyBWNRyk3EW5qKotRUS3T9hjwE0cjDpKficoloWg4TNdF60ahJcNuSUXtwEELeeBRCN7oSfJqLUT8HkjQWIOV55EPN+wVFc6Cp8TUNRe2a2LQlazS+sjR6XrTNc82FgUdGW9KecYMEokESLYHbn9I+6/I+BaMxFtpC10RYul8wmsBAzmiKRmFRtIUKSJINNIFVFK2O70OStkBHz1vRUPRJGzfQIRe6Ovo6Kh+l/dtC15TcGnr4xjXwtASuqdlXipzxMLjRm22hG0lgUBSMJmEsOeNF5yVupgQVTfGhCaxGKUvRegRGESoFun1kaYDCs8TN2dZCGA3rd8+4/Z+t0qDbUnOrpy2E7tiOv2Nl3H1jLaFyz9/XloQyeCgK6GgAiCWpQ7TVKBQJz5IwbkCLpzSe0lR5oZu2RIdoPEJrSWlD0SgyvoEAEtptsxYyvtvnK9eU6wE1vsFaRY0fokNNGA1QKXqKlIaCBylcH7vAuqZx97oqgjCBtooQQ96m0GgClXN/H1YRKNcMayiidbQEmfXjZfGwoJSHIsD9sxYNhCibeLj0FxVtt10nK3S/Dd2hTDw+OT62LCyJm3Ftt82nqsNjOm2jU/nutlk2v2m2EgHS5p5jsAVlfbFq1aqBrsKgUKlRrkPmjVUBgyo1deutt3LhhRdy+eWX88wzz/DBD36QWbNmsWbNmoGumhBCiO2cZOfEtmxQZeh++tOfcuaZZ/LlL38ZgBtuuIEFCxbw29/+lm9+85tbdM5dqmrRSpV9CSiGbgoOP5qTrPQtuTTNg7EuwxZGHbUt0ZxhEWOhRZWyX24EXRDtD6PlrQxueamOSQBjLaE10Wi99vOFtI+yM1iSUYdwjcsoeNFyZaVkhB/dMbhpV5VSBNaSKWhC6+rSmPfceS2syytyoSGlNQlP0VIMSXuatKdpDdyca75SBNZlV3zluSkztIoyYZDSioSnCS0kovqEuPuegqYiZHI+CqIcipveN7C45bqiaTZSWkcrQ8BK7ZGPEigZzyOa/YKk58WDRnzt0xq4ueASyg0OKfVxL0ZzwIGbYqPa9ylEWa2UTtASuOk/Oi4/VvrdprUPCvKhu3YudPPOVXs+TYEi43kugxdCc6DJRRnHtLa0hYq05+EBWaNIat9lsZTFpzT/nE/eum78VZ5HS6hJKT/KIuo4e6iirFw21OSizGBgFdWBJhtqmnSUEcQN4GgJPHztXp/AKnKhoi3waPW0K2cVOeOyl1kvyn4aRd4oWqIMaD50fw8prWkMvOgYl41LaHfdgnVZ3FKGrmgAvCiz6pE3Kp6qpS2AwHOvJbgJn4uhoaggb0JCpQlsSJEAowyBKuBZ341uJYgG65h4WT7rJr+LBkKURq623++dUu690/2yrFw3jzd6rnDjjzuco3O+orvsG1QmKdEla9apHh2zhb293uZk4jZ1jS0dFDKQVq5cOdBVGDQqNspVBkX02qDJ0BUKBZ5++mlmzpwZb9NaM3PmTJYsWTKANRNCCLG9y+fzMpHwZiiNcu3zbaCfyCAyaDJ069atIwxDxo4dW7Z97NixvPzyy90ek8/nyefb11ZtamrqUqY64TIvNsoQgctCgZv+I1Aq7uMGxNMW2Gjm/dKbzfWJsvGX+TCaPd9Numrjny5rV1qEPPpJx/O4LB20T07qlkg3aDQGQxDN4VUa7u9bz62EgJt6xFcu+1Va0ExBtKJA+6SneWPwVWm+rxA/WtQ+KLo6Foylxa2pTjbKRPrKTS5WykYmtI6zgm24BECiQ8dnL0qVlcq3FqO+X8qt7Qrl/f1Kr4Gbt8z1VzSWKGtYKmfj31Gpr2FgLAlPlfU5LPUpbI02Fgw0F0u/3/ZrliQ9tzyZy0K6W2k5s9LkvgZIRf0XNxRK7wdoCzQh7f0k4/rhspHWquh1UvjR61TKSCaUokm7DJfCdRwvWmhCR0ulqThr2Bx4cZ2btBe9Zm4pMKWgLfAxRMt5heXbCDwCEy3RFbgMnhf9NBaaovttgUcQ/S68KEPZpl32UXV4zVsCj1zg6ueeo5tUOij9NMTb2wJQgVvqzADF0JANDaE1ZG0BZRV5lSdQAQY371yBLEWbxxBgrdvupucounewVbi55kqZMIOxxQ6Z7U7LfsW/wdI8dKZ9f8d+dJ2zcfHjUk6crvu7LBpsupTp3HduY33pOusuo7excpvqV1dWpS04z5bkSTZ1zGDKvUh2bvNYW/5v7JafqALnGCIGTYZuS8ybN4/6+vr4NmnSpIGukhBCiEEmm82yfv36ga6GED0aNBm6UaNG4Xkeq1evLtu+evVqxo0b1+0xl1xyCRdeeGH8uKmpqUtQ11ywdFzOU5f6YQGhjhY0t26EoafLv0/6HR5bQBuwLokV9X9qz9yByyRpVNRXzqKURxBlAEqJAIvb57bZOBMXYvGin9Defw7AUy5TVsrYlbJkSU+TjRagT3mafHTf025B+rTvck/ZQFGb9LBANjBURTP6BwZqE24R9tBCTULFEyGnO3wVSHluOSgsVCdc/zd3fTcBctEqqn23cLyPy3y1RdnCjNe+zJTq0AcuqSEbqqi/G+RMtASVUeQ79I0rRlku9zq7MnlTmlSZOHtVTbRUVjQZcy5eL9Udl4n6eCVw24NoWa10h2uXzpXxXJ88L3ouFrfSQbSkK8noOfkakriyaa/9PZbSLmullXvtjG3/aYGMdq9bMjp3Pjq+dM5SFiUZPVelIKFdXsiP+h+qKMPo+igS90V0o3Sj59jhPV/aVlqHFty2Uvn2ybPbtynlypsoq1laEiwf/f1Y216uEGVki8YQRH1B81F2raiKWKzLPquiy8TZApbQZedMgEJjTDH6G2nPgEW57ygzF0/LHWfqysaedsi+la0iUdquShPcmi7lnY1PwrA5SYSeytpO9/uSwepynV7M87Yl19xY+c0517aeqXvnnXcGugqDTuXWchW9NWgydMlkkn333Zf7778/3maM4f7772fGjBndHpNKpairqyu7CSGEEL21evVq2traBroag477aqT6fJNBEb03aDJ0ABdeeCFz5sxhv/3244ADDuBnP/sZra2t8ajXLfF6rgFlFRqNhyYRrQ3pK0VCe1G/JkhrTbQaFJ5yfaQyfvvoV9evrr3vnRvh6bIXJpq6OLDEc8YFUSYqHy2cHljirELYKalQyiC6vldR/zXl+v4ppfBVeQ8fX4HWLmvUFmqSnluqrKnokdIuw9QaeFT77tzNRY9RaUsYjXQclTK0BG7E6sikuw9QnwgpRCMi6xJuHjJj3fxj2dCVqfHDaI424qWl8kYxLOGWm3IjJS0tgZvjL+MZckbjYePMncvKRUtMaRPPaZfxTDx3XY3v7ueNpto3blSlcXOgtUZ1qfYMrdFSYCntlqqq8gy+dnPQZTwTLYfljmsJPJSybs69UJPWxr0+gbteqWy1716ThLZ4WNpCV4fSCNFqP5oPL1oqq6noUeu7JdJKS2S1BKV6mfj40gjWpHbLj9UmQmzUX63KD12dojdhLlTURPVQQMqzZdvALfuWDXT8miggGT236uh8Kno/Z42iNjo2pW302ioyno3nIQT3vk5rS1OgSWpLW6Di/nYFAzW+jTOrgYVcAJ6GxoLC16CLmqK2+EZhioYUKVqtIkECD48cxmWkVQFIu354yuCRiOag8zDGRqNaE1hCQEfZ8DAaIR71Xiwb1dn+oVC21FS3c811o2N2Lz5P5+N7LtuxVHcjPzvvt3TNTnSXrehtmZ4yaZs6fku29+a6nfsVbmvy+TzvvffeQFdDiF4ZVAHd5z//edauXctll13GqlWr2Geffbjnnnu6DJQQQggh+uqdd97ptyXJtjfW9qqVvxcnqsA5hohBFdABnHvuuZx77rkVO99rhcfi+0mviqSuxtdVpFUNGVNDlc1Q66VIeYrapKLKgyrfZauKRpHSloxn4jGnoVUktevZk9Zu4fR86BYxz0X9xkLrVhgwEM9vVjRu3rDSaNtS97ywQ18layEfKmoTrs9Vd998S996PeUydRkDVb6lxjNoPGp9S5UfsqHoMywRorAktcfoZBA9H48dMgXeLySwFkanijQWfXxtqfLCqA+bZnjSLUgfoqjxA3JRJiypQwLrVglIdbhf7Qckte/6jemAKs/dTyi3yHzp2BrjxWUyOkHSM3jKktaGtB9Gff48qvyAIHptaxNFsqFPaBQ1ySLVgXtbZ/yAVCGBry0ZPyCtE2T8EI0lrX2qfFe/wEC1H5Dx/LgezUGCtGdIqJC0l6Amul4xVFT5ARntk/QMWllaAp8aP8BYRT7ULpsWeCQ8i68MKZ2gLlEksJpcqKnxA6o8t5qGr018fGDcXHFpP6S5mKA2UcRYRdqzVPsB2dAjpV0/rtbQY1iiSMZzM/olPZfFrPUD0p6HAtKeoVm7TF868NDKrfTQHPjU+gGtoVsjxVeG1tCjPhGQ0j5p7VbJaIkykwntsqPuvamo8kKU8klrSyIaUV3KatYlQpqLHr62BEZF/Tfdu9KLRvoGRlE0lsD4+Fph3dIlFKxHSEiCFEWVw1duxYh8lB0MbD6ea86YMMrWufe9iuZmVGhQQfRBoqNcnG3PpJWtHOHKlFaFaM/cRStIdOhb11HnVSF6a1MjUbeGjtfsTXZva+htn8Ftzdq1a2lpaRnoagxaFlWheehEbw26gK4S1q1bh9aueSWTTuBpj/r6WjZrXlIhhBDbpcbGRt59992BroYQm2VIBnQrViynlN+qSrufSilGjxzPuFETGVGf2faHXQkhhKi4lpYW3nzzzYGuxqBXGtTQVzIooveGZEDXURC2ARprA95bneW91a+jPRg5YiQjR46gqqoGz0uSKGbwsilSuoakqqba1DM5MYLahEd9wiPtuyZOX0O9H5L2LBkvpN5zzWYpP8BaRTpRJJUMCEMPaxX5wKMqVcBahTGa0ABW4fuGRCLEGvB8S7GoCUOPMFS0FJPUp/P4foAC/ITB8w3GKPI5H8+zVNUXKGY1zywby4hkAYBcWMWwZOgWiC+AhyVEkQ3dclC+iiZtsMQDG3LGLfWUxtAQurdLwSoSgU9btFRUYBWFqOlYK58w6lCf1n48NUZL4MdNzhnPi6+n8MkZN2gk7fnkjcLDkvQ82gJNMrTRxLqKROiaKd0yWNEyVUZRsDo+d95qikZFE916tETLUxWMpjnwyBsdP6+CdctrhRYKxi1X5QYYaDdxbqhJeZrWQGPxo6ZCRYAiF2oS1g3saI0Wogf3uoXRfs9AUruBGVr58QAR8MmGmoS1eMadn+j8FkUYKNpChVY+xpbuu/oY295U3xZt85QlxA2K8ONt7nXPG4Uf6qhp3w1gCIwbTFKaaNrXXlQ3TcG4ZsvQKopGkwvdcyotJxdGA1GK0e+s9LwL0fQveeMmGqb0MyoTRit3Gdu+fJ6CuD2ltEReiVJeh0mDy6kuS211Uyaqr7WlARG6wwTArimW0jVVdx8YPV8jHhDR8XE3ZWzHspvoUNRTk2x3gyq6KzsQzbrbk2w2y+uvvy795ipA+tD1vyEf0HUnCALWrFnL2rXvk8nUMGbMOMaMHIeXSg101YQQQmwFra2tvPHGGxiz8fkGRe9ZK33o+tuQD+hCk21/YF1OQcWLgBuy2TaWv7uc5e8up66ujtGjxjBsWD3vK1hpq0kUM6R1HQmbQuOTsAlSNkUCnxovFWdKSstmJbR2y0eZ0hJikPTaJyF229wkxsnoWEM0aCI6pi6pSOlatHKDJnzdPnFtPpoEtsqD5gCqfaj2QxLaoJRlbDpHyg/IhTXsNqyJotFALXsMa2JdNoMCdqhtwdpa0n5IbbKAoor6ZIHGQpKMF/JSU4aiUTQWdTT5rEdrlI4pTY5bmri3NMmwH903QNrz3H7VvkwXuAl5C1FSJBVN9OtHWZ3S5LUpzy1FlvS8ePqXtO8RRGucafcyu9fFI96e0tHEwdFxoYGU5xFG1/dUez0SCgrR9TzcgJRUNBGvse0T8Hq0T0TtKzcQoePkxtA+KXJae/FyWOloYl6t3HVLE/iWBsAkdPlkxGE0yXFpgt9Sli3j+9GSYdHAmRDSfvtUOL5un5Q4G83ikYrO03ECZk+VzufF9Sq9l1JakzeQ9nQ8YCehoTVwr1PRlpYJU9F5PfKm/fdWCEtT41g0kA0NRWMx1tIWTRScVTlCQoq6SF61ApAzTVgMgc0ThFmsNfHEwsaEWMJo+pIA0FEqwGAxWBuUZfcsFtUx26dMh2ygKSvXrhcf6h0HWfSmrNgmGWNYsWIFa9euHeiqCNEnQz6g2xzNzS20tRZZvnw19cNqGDtqAsPrMgNdLSGEEJspCAJaWlpYvnw5hUJhoKuz3XHzKEr/t/602QHdnDlzOOOMMzjkkEO2Rn36nTVd/5Ct6jjBqCY0CZTSaJUkNAV06NO0QVNoXsfqRDNjRo5h5IgRqESRvGlGKQ8PH99W4Vn3EnsqAUAySJOwqQ7XMiRsEs9G0zFEmYEEPgnljrXWErply1Eo/KxHMqpjIVrCKKU8QixZW8BDk1EJWmyegirwzPv1WOux1q5n5Oo6NCnW2A2MeW8YgQ1ZrVYyasUoPKDNZhn23jCawhw+HkldRYvJk1YJWm0bGk0imvajYEIMNq7L8FQSz1hMrsiGxjzaGJRWURZNkQ0sCQ3VCeWyZR2WVrO0P9a4TFNL0U0Rkw1clilvoCYBbUVI+UT95Nq3VScUhdCS9BS50JLyov5aBmoTsKEAw5PQGiVrMlqRNZZqD5oNpKK6AiS0pS2E+gS0BYq8cpP3NhZheAJyxvULq0ta2grKTeviQ1tRYT2LUu44nbAYA224TJinXMbOEC03ZlwdU76b5qNgIJlwE++W+qD5CjxlyQaKhOdew7xRlCbLSWm3fJ0NFZ62ZErXAkwICc/9oScApSxZo0iraJtykx/njZsoOGcUSdW+HF7ac5MkJ6LJhkOrSHuGgtGkPUM+6juZ0JZ8qEh5hqJx/fpsNJFyaaJkX7trh1Gmr7Wo8DS0BR7G+hRtirag2k2mXBztlj0zllwQElhDi80TEhKqkKIqoq0mUEE0ybAmwP0tF1WewGYxGIrWZfdU2d80hKYQZfMMoS2gUHg6hbXGZf9sSGDy7vXXqfjv0mUKC1hr0DqB1gmMyWOMe1N1vI6xQXxt1x8ywJgCoPF0MnpcRCmF1kmMCTC2iFYeSvlu2TMbopWH6+cbxs/VTaBsMTZAKz9+Ljr6N8PYPEol0MojCNtQyv0bZky+w/0cSnmAh7VFlErglk0Loz6Mpqy1IvoXi649BkvbbPQaeFhTROkE1hRd50kU2LDDNg9KWVOlwbpOltaGpQmg4qzmlk0Ss3FhGNLS0iJB3FZW6o9difOI3tnsgK6xsZGZM2cyefJkvvzlLzNnzhx22GGHrVG3QaFYDFi7egPvr27BywRk6i0jR4zAGyJToBQLAbnWgFxrgWyoUMWQhFa0BoZENJleYKDoK7LRNptQ7WupRgGdof2xBkIfmotgooAuiIIflYDWIgS++8ciF7ZvI6HIh5aU59afDaKALjTgJaElD6kU7c3DUTnluybO0HPNhuBWVGgNIJGE1sDNoRZ6ltYCpJIuoAsM+ElLa0GR0BZ8F6SUArrWKKArndOLmoTDKKCjQ0Dn+dYN8DDgJywF0/6h6SkIlFuVAc8SaqKBJO7j3WqLVm6wiNU2agK1bgWI6BgLhMoFdLlQg7YEFkJtSShLzrhjc2HpfNHABc+QDzWmQ0CnooBOdQjoTBTQlfZ1DOisNuQCN6AlFypCqwisC369aF1bNwgDWgOLta6J1mLJhZZsFNA12xxBHNAV4oDOYNBoirgArKjyFG1bFNC1dRvQBSbfHtCZPErp8oDOBBsN6EKTx1qDp5NonSA0eYwp764BvQ3oCm5/HNAV0MrvENAFUZDmBm61B3TaBZebDOh8grB1IwFdFqV8ugZ0QXT97gK6jmvS0GmbiV4DH2sKKJ10X5iVdvtt0GGbH3dx6e+ATojt1WYHdHfeeSdr167l97//PTfeeCOXX345M2fO5IwzzuAzn/kMiURia9Rzq7HdjWQrG13n/iGygFFZiP4xLYYttKq1aO2T9GsB8IMkydYa1q5sob6+hpHDR1BVl6QQNsdn83QSpbx4glRtFUr5eNr9ox2Xw8dTCXRULsT94+6WIvPwbQKrLEWbAyCh0oQEFEwLSmmSqpqsaSSwOZoZgyGg1b5PgxqGwZI177PBG0lIgWxxPQ1eHQZLaHMkwgxFm3UfHMYjCLOkPPccw7xPS2sbtKRoacsSBCFVtpYcreRpo4g7LrB5NDoerahVgsDmSaoMqVwthhDfJigq96EZ2iK+SkWvuEfSZsiqJveTJjLUUVBZqu0wCtE1AAoqS8bWkVVN1JtRtKlmkmTIqdY4E1pUeerNSBr1+9Q3jaRFNaLxSJAkT5Y6O4wm1UCNraeoCgQEVNtqmlQDw80o2rTr11VlqmnUG6g19YQqJK9y1Jk6WnQL2mpqbDUtqpWkTZHEp0k3U2WqAcirPBmbxsOjTWXxrEeNytBiXR/OGpWhYIvkVYFaqrHWkqeIRpGMsrttNk+GJGntk7chRRvio6nSbn+LKZBQHgmlSShNaC1ZG5AqbdOKwFpyYRFfaTylSHqubMG4pdSKNiSlNb5WeEqR0D5FY10H5+iDNandPxtp3703PdX+cV7qj6eibCRAle9F2T4VlW8PGBPRMQltSXsuEwtQn9Rxv8sgyl7nwrTro2nbl8cLrY37VxZNaRStJTA26ntqCK1FR1klgwsYA+360QVYijpEo/CUJrTGldGGAoFbdsz6GAw2GiVbpIiJtmt0/J4pBVsaTUhAGJXTVruRwyqgaHNo5ZGwKQJVpGjbUHh4KoGxYfz345MiIB9l7BLo6G+qFLyW/q5CitGyaK6/YelvqGBa3MTMKuH+TUDjqQRFk0Ur929LPmzC06loVHMeT6VckBlds+O/jaWArnOQp5QmtIF7zjZwgbHyKYZZEl6GYuje36XAN6EzFE0WT6cIo4BZq0QcVBeDtujcAaHJukATN8mzBHaDS8UGRVTgHENFD4sXbtzo0aO58MIL+de//sU///lPpk6dyqmnnsqECRO44IILeO211ypdz0GnsbGFd95eyUsvvMGKFavJZnMDXaXNZoyhpbmN1avW8eaby3nh+dd549XlrFyxjqbGNoKg56kjhBBCDE0WVZHb9uqZZ57hkksuYcOGDQBceumlfT5nnwZFrFy5kkWLFrFo0SI8z+OTn/wkzz//PNOmTeOqq67iggsu6HMFt7pNjj4L3fdCpeLMnbUaRcFl7EJNMXDLwyil4+YQrZMuG4cmW6zm/XUNpFIJRowYzvAR9VhdcM0MgNKeOy6Kr915FJ5ORs0VgDVxBsCV8bBYwtAFiqUmnI7NREWTxZgiRa8NYwOKQRt5vxGAYtBK3msBawhMG4HJEwbW9S3JWhqa1pPPh/g6STHM4esk2eIGSg19WvmuH1LcPOOaT1wW0ZUBaG8YdD89nY6ylFHWIurDWDpn6du/p1MUg1Y8nSQwOXydJjQFEn51PNoRXF8o38sQhFneT9RTCFvjLICOMlvGFlnr15LLNbIuMYJ80OT6OSqfosmSSYwgW1xP2h9GaPKENiDl15ItrmdNYgRBwfXHSvvDaMutJZ0YhjWGQthKJjGCQqEJgLQ/jFyxAU+nSOgMbYX1pLxalPIoFJtI+nV4yicXNODrDClVQ9Y0AFDlj6Ro2yiELVR5I0FB3rSglEda1WFskZxtIqlrSFNHUWXJW5eFqVLDMTYkp5rQaNKqnoRNkaeNAi34pMjYepJhhiJ5siqqr62hqliLh09RFciTpajypMMaMkEVmWi0dpGAAoHrt4YmZZOkVZIanSChFb52795SBi+MRs4mtSbtK9IWqnxFXcJS7Vlq/ZAa35D2QuqSRVI6JJUISPsBSkEyGeB5hlRVgPYsyrfohGuhU37UQhe4Px8bKkyoUMpiovvWKEzUZB0GmiAoZZQUYagJjSI0Oso6QiGIsuUKisaN5jVWUTCpaBSwddujD5d8NO+gVm5LwWiKURbBxJnD9qZlcN+ci9GSfwC+svE8iuCuYS20hSpetq/UHK+jf34Kpv38WrU3U3vKlSsaG3dzaI36q/pK0VJ095Vy3Rd87c7fXHT9Td1zsiS1IrQ27t9qy55P9Ls14OlStwGX+Qyt62IQRIV97bpXpD1NLjTR81PkTEiV59ESuOXpXFbYktCanAnJ2QJr0qswypC3zbyff5N8sZHQ5LAmC1EzrGTqxPbgrLPO4rjjjuO4447jrrvu4oEHHujzOTc7oCsWi/zlL3/hd7/7Hffeey977703559/PieffDJ1dXUA3HHHHZx++umDI6DrR7lcnpUr17By5RqSGcuw4bUMH1aPpwemw10+l6e5pYWGxjUUch5B0QWNSa+aYphD68HVfC6EEGLbUJpuqq+21/A9nU7zrW99iyOPPJIzzjijIpNZb3ZAN378eIwxfOELX+CJJ55gn3326VLm8MMPZ9iwYX2uXH/o7bc9VVYsLI3ncuewxfKyqPaOwEC2UMqqJaIMnk+umKSpqYlly5YzYvhwRowYTnVNumz+LKW8eMZ7dx0bj27TysNiOnTG9qIO3a4uRXyMLbqO1SbEWktLayP5nKWlpY0NDe+hVQ3WGoKwDU+nMbaAtUXyqgETZfqcqNNzlIWz0ejK7l+/8g7S7ffdz3Fjaxg5cngvXnDApqMHtRvZ3lFVtK8++pns5nzD3S/S1rYP1LPJaFt99EtOdr8NgEJ0rBuQj60CVeiwPxfdV9H9NG5mujDaXkBRxOIDIYomSovBK9UQ9VPSKJpAqehxiFLro8xEiKIFVBaiTvCKYvQY6uJRg60opUm5FJZbeF41ucyxNSSty4oqtQF0ghDQ1pCyISkMSvmEKkGb8tDKw1g36hMMIZDFo+ClyKoUWiejUZjt74VS3ypfp/BJk1I1VNkaqmwVVSpFje+R8d18jDW+Iu25/nMZz5DWlpRnqPJC0tqQ0iFpLyClA/yMwffDeOo37bmslo1WBnHPCTzPxssypzyDtVHWzipM4LJ5rp4K7VtsSJSxA2vKe6EoZQkCTWg0SllCq/GUIbQu06cUcTYQIIxW+zC2fW7J0Ko466eVJbDuGsa2NynFmTDj6metoojCxxKgIMomatyqKa7foBsZXTpeK5cFLK1xYa2K6lx+jVK/psC2Z/dK41QN7a9lx5n4AtM+Z6LuMGdm/LzjY6IhDVbF/SRLq4LYuGzp9Sd63zheaCi27UgxW+D9hjyrmvfj1cI6VqrXeT/7Gvni+25Qhw0lSzdIVKLJdHttds1k3JRn+++/P0cffTTnnHNOn8+52QHd1VdfzWc/+1nS6e4+VJ1hw4bx1ltv9aliQ4W1lvXrG1i/vgHPVwwfXsOIkcNJpzb++vZGaEKam5tpbm4gl1W0trYRhFkSfo3bH4boAZiFUGuN7/ciI7mxpZJ6WkKptK+7Mp33laICa9unUOh2m0dZcNpxW6lct8Er3d5Xcb+QTvejUYvuI9HrENBFIydtKYz24mu6gE5Fj4FS5+Hoi4C12m1TbuBNaVv7ef14VKTFoK2bGrk0aEdrN32GjqZ+oUNzv+d5eMrD01409UWH4N66LzSecmV85eEZD896eFqjfY32NNpTaA+0r9DaPQ3l2ei+jR8rT6M8z83D4tEejHsmikR0e1Sgou0lOpqJWSmUUa69ULXXU+nS5C/RF5ROo2FdYB99sVIWZXS0ipj7nSnV4b1EackxhbIKVCn2VygLKgqwVBTQKaviJ1OqklJRwGlV/A7RKBecRaVVdD6tbPyFr7RPR4FkfF1lo99r+59E6XHpK6el45+G6vTltf1lVAq07nBMh/3xn5t1X2htFGCWCnYM6Eof0J0DukRCk8z4QBXJsZbWtxpgZde6iMGh46TxfbKZ55g3bx5//vOfefnll8lkMhx00EH88Ic/ZLfddovL5HI5/vM//5NbbrmFfD7PrFmzuO666xg7dmxcZtmyZZxzzjk8+OCD1NTUMGfOHObNm4fvt39wLl68mAsvvJAXX3yRSZMmcemll3Laaaf1qp7f/e53CYIA3/c544wzGDly5OY90W5s9kf6qaee2ueLDkbdfiO0pUxV2Kks8YdIVCzisl6K9jnGSpk8pTQEHvlVraxctZLqqhpGjhzB8OF1aK/8GqVMXcf7QUHR3OICuJaWVgp5i7F5rA3QOoMxeawtUijmo+OKFAqF6HkZjGmjPUjpISDq+Bw3+jp1/G5vyn72ugfMxoK2HtLSykbn7qZMaV/HMqUPGfeN3+2jy7awfRvdbOu0AoG7dhgFZBbV6b6NX8dO94HS629VCFa399nstE/Z6HW27hxxfBK9R9x+FWf1sBq3RoVyGeC4H1IBA3HW1/WDNC4DrF0Q57JFJjq3KV0Ia0JCXQQDWvtl/SatNWh8l89TJsrrufd91MvUrYTiuRUw0trNd1ftGTK+IeMZqv0iVYki6VSRVDogkTbolMVLgUrp9mjE4Cbbi/4lUwpUQoOvUZ5un9ww+mSxxSgI9FS034/3W2uh4JbUUEmvvQnEBNhCFAR7UWRjjOsCay02tCg/CrqLNlqY1rqlNAIodaSz1qK0wkZTs3QUz+5R+l2G0X0PSg0A1iiUtpjABXjai+5HbzvtQVBszwxiVdkIwTBQbj3d6Ndooqxj6adbT7o8m1f6khBaHWXeNFpZl+3s8BxCo+Pso8bGWcDS2sOl51Xa7jq8U3buQqjJGo9cqGgJNf7U4UwZX836FeN5Z/1HeDT4J+vaXqEQbMCYbIepVIRo99BDDzF37lz2339/giDgv//7vznyyCN56aWXqK52sw5ccMEFLFiwgNtvv536+nrOPfdcjj/+eB577DHAJTxmz57NuHHjePzxx1m5ciVf+tKXSCQSfP/73wfgrbfeYvbs2Zx99tncdNNN3H///XzlK19h/PjxzJo1a5P1/MhHPlL2+Nhjj+3zc5eVIrZRbW1tZLM53n13ObV1VYwcWU99fX2cDMhmc7S0tNLc3Exraxth4D5QS82/Wsm6s0KIwS1VnWTs1NGsemENNA10bcTmGKi1XO+5556yx/Pnz2fMmDE8/fTTHHLIITQ2NvKb3/yGm2++mY9//OMA/O53v2OPPfbgH//4BwceeCD33nsvL730Evfddx9jx45ln3324Tvf+Q4XX3wxV1xxBclkkhtuuIEpU6bwk5/8BIA99tiDRx99lKuvvnqTAd3DDz+8mc/K2Wmnndhxxx03ul8Cuj7qMd/U6Wt46Ttp2dYok9e530ppnjqUpqEhS0PD+3ieoqqqhtbW5g5NZ6bDZJzt2TFDPs7whFGQZ0t9quJ62/i+6pg76y4LNgjWotzcGnZXfkueZcfWvlJfpE2V3/x/5ko9o/pa3m1XcVOv6lK2tM01KUb7bcd3eqlJNVE2r2IpQ2iVdesaqxQ+KdK2hrRNk9EJqn2P6oSmJgG1PtQlDDW+odoLqfYDMn5AdapIVVWBdH2AX6vQtR6qKoPK+FCdhpo0eNFCt6UsTSrlmlONBd+HTBqbSbs2wjB05bRbb5dCEXyNzVSDVqgwxCqNsgbyeff3WFMTTWoXgueh83mwBpuOlvqzxjVzBi5LaTNpKAboqE8rocGmUqgwhCAgnjzX0+5PNCi2t2ECFApRu2b0uBhEi/16bl+HyXdVGGVZPY0KjdturCsbxLl/l4orda4zFopFl5EMwvK/5+g5lh0bhu3XBNeJTuvouXTI1sed/8LotTbuzV1aoqVjGYAwei3C6P1mwQYG5WtstojNBZjWkLDZsH5FFcsaa3gvmyK/+2jGvnUwb60/gEeCh1nb8iJh2OKy1NKfbpvUuVm+L+fpi8bGRgBGjBgBwNNPP02xWGTmzJlxmd13350dd9yRJUuWcOCBB7JkyRKmT59e1gQ7a9YszjnnHF588UU+9KEPsWTJkrJzlMqcf/75m6zTnDlzNvt5KKU4//zz+frXv77RMhLQDSJh6PrFQdeZ74UQYnulPc2oKSNZ1rwagk2XF9uXIAhoaipP0aZSKVKpnluijDGcf/75fPSjH2WvvfYCYNWqVSSTyS4DN8eOHcuqVaviMh2DudL+0r6eyjQ1NZHNZuNBD93ZWmMMJKDrRxv9Jtlt9qtTj+ESVeobVeiudHyNjWXcOtZhY/e3ppUrV7J27Vq0dtkfrXV86/i4t/s2Vq7j6OC+6vUr0/H32Jv7Hc7dPht+KXvXuX+Qifd17adY6lPn+j+6jKvp9Ds1bpBEh+0KE/Wb86DURy7qX6eUy/zauM9TlAmOMnaldUaVDaJO8mFcDsBE86R5KkFAgaJKUrQJCsaQCBVFrchryIUaT7V36gfQ2qJzFu0Z0Abfhqgwj7bW9Rc01i1Qa0r94aIlJXztsj9BlN0qZbysQRmL9Ux7Rg5QJoj6KpoOGbBSz3/dPp4lkcCGocvgpVPuulFmzRY8dz+VgmSINR1+L4mEe6VL2S6IRgp02FZ63HGCudJj3f172G7kfvuv2pZfp7QOYbEIiUR7ZrOU5QwDt71YBM93+0oTzplO78POFyxl8EqvScfynbN84F57P4EKiu1Z0dBgPfd7U0rjFfP4+SLj161n/Ir3CZat5b2Xq3n2/WGEe45h/JtH8Bc9iuWtT1Iovg+m0G//foneq1STK8Djjz9OfX192bbLL7+cK664osfj5s6dywsvvMCjjz5akXps6ySgE/0qCAKCoH++YpcFe0qjtHKjMrXa6D6UG4lbKlfa17mc1hoUZefztC4b8SiEqCw/5TNsfD00DHRNxKZ0/dq55ec56KCDWLBgQdn2TWXnzj33XO6++24efvhhJk6cGG8fN24chUKBhoaGsizd6tWrGTduXFzmiSeeKDvf6tWr432ln6VtHcvU1dX1mJ0rue222zj22GNJJt20V8uXL2fChAnuswXXj/7aa6/loosu2uS5SiSg20ZtXjZvM44fQqy1hGFIGG5O3zOn1CexvW9i+TZgo/u1irKFUUBYuu+CP+3mSutwX2uN5/kuS6WVu69UXE57vhu9GR/nuQSSiqb/ULo9vdVhBHTpMaq7BdW7fcGiO1HmLm7WN/EoWJcEdCM5A7IY3Mog8Sk6rBASqgQBRUJVpKDayFFHLqihNUzSFvikix4ZX5H2NBlPk9I+GT9JlWeo8iyZDSEZPyTlh2T8gHSySCKRRXuteJ6Nq6d0dF9ZSksidzsatrSEBW6bTkYBeLTNBiZO1qmU374kglbY0GCNRaV9l+XTGmtsnBlUidKKLkT92VR7ZqyUofK8qD6eG02rFST9aLI24w5OJd01g9BlITtmvUrnK9FRNjEIXV29UtbSQjLhrlsMIZ102bLAQCrK0IXR/dLyfb7n7pemFOqQhXTXiurheXEmuIuO9fF9lyktO5/rp6eSCfezNKG6icoDtqrK9W+sqcEOG4baZSe8tjZ2XL6KCc8sZ8UL1TxTPYzT2w7gqWV78GD+rzS2vQkmJ//mbcd8348XLtgUay3nnXced9xxB4sXL2bKlCll+/fdd18SiQT3338/J5xwAgCvvPIKy5YtY8aMGQDMmDGD733ve6xZs4YxY8YAsGjRIurq6pg2bVpc5m9/+1vZuRctWhSfY1O+8IUvsHLlyvj806ZNY+nSpey8884ANDc3c8kll0hAJ8RAKgWSNuwa/JXml+t4H+gw15ylbPHzeJ65zvPOtU8x4wYk2HheMs9Loj0fMGhl0Z6H56VQygWCKi6XwPMSbq45bVEEUfDp4XkJfC/tAktdmh8tjI4D3/e2bCFoISqgfkI9ennLQFdD9MBC2bQ5W3yezTzH3Llzufnmm7nrrruora2N+7zV19eTyWSor6/njDPO4MILL2TEiBHU1dVx3nnnMWPGDA488EAAjjzySKZNm8app57KVVddxapVq7j00kuZO3dunBk8++yz4wza6aefzgMPPMBtt93WJZO48edle3y8JSSgE6Ib8aoH3fQz3FQ/xB73W9vlvtvQ3ucxDtyich3nuuv4uP0c7rh4vc0w7NDXshRIthCnqjosqxCPpqa9H5wb4eomFSbeH3YIPt3o1tK6vC4ojCbN1TZqsk6S8BIkEzWkdQ0Zfxg1KqSVgBqdJp9IUJ30KPqaYlIR+JowASoBXgK8hMXzDMlkiDGG0BpCZaMg0j0PZeKnEf0sTRrtnp+2hniQbggmR/xauR+mvQtYoCjmPMJQu8Ghxv0DWyj6FI1bKSIwur0JyUJg3TZdul48P5vC1x36jeFF/QldOU+FhNa9rp4OwJZWjdAkdCFaXUHh66Bs3RVfFUsnjPnKEFiNh0FrN5ectZDw3QoZoVEkvZAwmszYU6VR8G57MfRQWHzPYIxCa1fHUje8eFUObTBG43khxmi0NvGKGx0/brXn1tT1E4Yw1HheaW5ChfYMJtR4flh2LWsVnu/eO4l6i05r9LAEeofhMH40tq4WM203vMk7MHmfd9nhqeU8+dQY1kwdSd3bx3G3t4h1zc9Jlm4bY6hMk+vmuv766wE47LDDyrb/7ne/iyf9vfrqq9Fac8IJJ5RNLFzieR53330355xzDjNmzKC6upo5c+Zw5ZVXxmWmTJnCggULuOCCC7jmmmuYOHEiv/71r3s1B93WIgGdEKJPjCl9aIdxk6tWIZ4qkvAUBa0oaJ/A+gRGEaiA0EtQTGiKvibvQ95TFHxLGN2Mb7BeiE4UwQ9QiQAvWYREgPUNnm9QUROf0jb6CSrlmjtVWmHT0cAIT2GNxuSioM+1z0JppQ6lsIHG5n2s8aIpgTwsGluaT7lCnbtF5VQPryK9rg3aBromojtuubsKnGezr7vpI9LpNL/4xS/4xS9+sdEykydP7tKk2tlhhx3Gs88+u5k13HokoBNiG9A5s9DTCORePe48stl2mmsQyrJ7Hc+jsC44U8VOZaO7Ko8x2q0xCh362tHeX6zDCihKJdDKRykfTyfRysfXKbxCCk+n8FV0I0VCpUnaDEmbJGlTpEiSUikSKkNSa3yl8LXC69CHqzSgU6Hi+54CT6uyZuFStksp9zOho65nivh8noKkdmvLprQloYt4qhCVsW6bX+oupgjQ+Bp8T2GVy3wZ10aNpxVWucchKmq+dgFkEddc7nsaiytnlSLheYAmVIpUtMyZ0m61BS9+nu29N0trsWraVzmzFhLaradqovtFo6Isn/uQDawioS15Ey0rpqIy0coOpfVijW1fp7UYDQIOSguG2G4G4ROtXBwNPC79Pqx1g5DD6GdpfdeOv0OtoNq3pLWl1g+ZWN3GuLEvkpms8PYYj915R8yeu+HttAMHTnyBHe5r5T49DLJHcqdKsbr5aUzYJlk6MWgsXLgwHr1rjOH+++/nhRdeAKChoWGzzycBnRBCbA7jAmNr3D/CobJuNTBKAVC0XJZqfxx2yvDlTSmAjJbcio5NRM2mgXUBZXxJIOG5qEcrsEqjo0AytOB5Ck8DWkfNqRaj3P3QsxTxXHCpwShNgEZ5bqkulMtuGlx5W2qnHgQjtv2kT/WwDEh3um2OLWXA+3ye7VfnCYa/+tWvlj3e3Om3JKATQohBwEYpMzeToPu/ijJhRoFR0QyEFtAmDiSNNhSMjgPG0CqKRlH0LNmwPUNXMCrO6hWtCxTd4GCXWSxY5RaDwI3atsp9YCsd3RRopTBKkUooAuuyqUq7NXISPlg0odcekLomcBeY6oS3iVege3Vja2B5JV9pUQkVa3LdTiM603mOxwqQgE6IIaK7pqget23sX1JbmmGq1J7W7cXcD6WArAsCUBTj5lkdjeb1XLNs1Diqyppq3faO912Z8g/+zqum6LJztZextnyFldKAkM7Hq2hQSOfzdSxbOtYSovDQaEyHLuCa8nN2rLdyy9ej0O11jc7nqURc3iOBtjo+RuOVTRGjo/8Mxt2zGhMNevCsj8VglInvu3P6cT0TNoFVBmU1SXwMlgQ+ntJoFB6KhNZ4SuFHmYKk1hgsnnLN3h2n8Smx0f6ise3zG0fNtaF1+wJj4wSgta7JW3uKUfUp6mqTDBuW5qlEPSPW1DPh9ZBpTzeww4few5s+EbvzjnDY/uw47nVOvG8ZC16YxBk7HcWN76RY0fS4NLuKIUsCOiGEEAPOGku+tUBzrkCwvpWwNsPwyb2be6xubC28s5UrKDZLpZpct2Tl623dlClTtmg1I1nLVQixVfQqC2I7DLRwBzlKdUoAds6SdfjHbpPrFm/ZjHhbvh5yxwxh2Olx5eqgevG8uss8dn5crn2yaNhEBrObc6huspW9rVfHc5QylB0zoZ7ySfp18SCZfTfsy6r3s+y563BeaBjBnhvqmb70fSbs/x764D0xe+1B1diRfOauf7HwiYmcufPH+d83YXnjozKFyTZgoEa5Dgbz58/fouN22mmnHvdLQCeEEGKbFBRC1r29nsyE4VCf6LFs7ehaeLOfKiZEHxx66KFb5bwS0Akhtrou2ZJO/fNUl6lXNvZgM2yiSWN77WztlKYj7u7+1td5mbzeHVRap81l+FZ7z5NK1FOXH8+MpoPIJEfz76aR7Lkh4OBl/6LqyB2xH9iZxIn7c3T6aR58dBxnTTmcX7zZxuqmp8AUJEs3gEojt/tKfoO9J6v3CCGE2KZZa3n/nQ0UssUey1WPqO6nGoneKPWj68tN9J5k6IQQA26rZFK27xTcJphe3K8s1d5TkvZek5vxgWzL7xZMnmLxfVr1cu72X2VU+AEObv0omeQ4Vi3dkSNWv88OR7wPB++Ld+z+HFH1NNw7hpNHH8lvgwYaW1/F2p4DQLH1mEr1oRvKf8abSTJ0QgghBoUwMGxY0dBjmZpRkqUTQ5Nk6IQQQvRZd8vV9SnzagNAYcOAvMmzMmjkL/6rPB58gM+nZrMhP5KP3JnnIy3/QH1iX9QnD+Tj5h80/30cxy87ntvMLbRk38LaoI/PTGyJ0myVlTiP6B3J0AkhhBhU1r/XRBiE3e5TSlEzPNPPNRKdWasqchO9JwGdEEKIbZIt/WcDTNhGvrCGlc1PcmfTn3nxtfUsbUjx4MJRhH/9JxQK6E8ewKc+tpwPTqnhMzWfI5kYtXkjbYUYxCSgE0IIMehkm/NkG3Pd7vOTPunqZD/XSHRUanLt602aXHtPAjohhBDbvI6Zusa21/lL0//lN/9+nFebfRY+OJ7g/z0BQYB3/P6csM8ypu9Uw1E1c9BelWTpBkBpHrq+3mSUa+9JQCeEEGJQCoKQ1vVt3e5L16a3aL1MIQYrCeiEEEIMWi1rW7qd8ExpRXV9egBqJEpsBW6i92TaEiGEEIOGxYIpkCus4q3wQf6vbSO17AjydhKfVk/ifW4GmVM+xGfbnmPN+pFM2HAgKxofxZr8QFd9SDFWYSowSlVWi+g9ydAJIYQY1JrXtWK76WyVrknhefIxN1AkQ9e/JEMnhBBiULFYsAFh2MS7TUv4g83jv/VJtJrIp2ueQB/7MepP/QDHNaygdfXHuSa/ig0tL2Lt1lv6TIiBJgGdEEKIQa95XQt2SlWX7Zl6mWR4IFRsLde+n2LIkFy0EEKIQclagwmbWdn8JLduWMRz7+a4774x8MATmPHjmXJKDXuN8/jciONI+CNk+pJ+JPPQ9T8J6IQQQmwXWta1dru9SrJ0YggYFAHd22+/zRlnnMGUKVPIZDLssssuXH755RQKhYGumhBCiAFksZiwjTXNS7l57T28tNry+F/q0EuexUzfg0/MXM2ek6rYp/oElJbVI/qLxU0K3NebpOh6b1D0oXv55ZcxxvDLX/6SqVOn8sILL3DmmWfS2trKj3/844GunhBCiG1E6/pW2KG8L10inSCRHBQfd9sNi8JUoIlb4rneGxTv8KOOOoqjjjoqfrzzzjvzyiuvcP3110tAJ4QQQ5ybmy7Hmual/O/bVYyYcDzVfwrYZ/Qb2E8ewAkrHuetdybycnYP1rc8J6NdxXZpUAR03WlsbGTEiBE9lsnn8+Tz7ZNJNjU1be1qCSGEGGCtG9pgbHl2KFMnq0b0p7jJtK/n6fsphoxB0Yeus9dff52f//znfPWrX+2x3Lx586ivr49vkyZN6qcaCiGE6E8WizU53mv6Bze+8U8eW1fFyt+vAROSOfmDfHzHkM8N/zSeVyejXftBpUa5it4b0IDum9/8JkqpHm8vv/xy2TErVqzgqKOO4rOf/Sxnnnlmj+e/5JJLaGxsjG/vvvvu1nw6QgghtgHFYkiuqdNSX0qRqUsNTIWGoNI8dH29VSLLN1QMaJPrf/7nf3Laaaf1WGbnnXeO77/33nscfvjhHHTQQfzqV7/a5PlTqRSplPwBCyHEUNO2oa3LtkytNLuK7deABnSjR49m9OjRvSq7YsUKDj/8cPbdd19+97vfofWgbC0WQgixlZSmMHmr6UH+8s4Ial77EOf9vyfwTj6YD5/4Nv/+TS0fqD2KlxvvwJr8pk8o+qQSyTVJ0PXeoBgUsWLFCg477DAmT57Mj3/8Y9auXRvvGzdu3ADWTAghxLYq25Qre6y0pqomDY0DVKEhpFJLf4neGxQB3aJFi3j99dd5/fXXmThxYtk+Kw3sQgghIhZLGDbxXMsd7Ll8Igse3YFPj38Kc8SBHP+vB3h35Z68vn5nGttekelLxHZlULRbnnbaaVhru70JIYQQ3cm1FjBBedCWrpV+1f2hUitFyMd87w2KgE4IIYToLWsNQdjAX5t/z/Mr8m4psNWrSZ/0IQ4YE/K50Z+T6Uu2skpNW7K58dzDDz/Mpz71KSZMmIBSijvvvLNs/2mnndZlNo2OCxcArF+/nlNOOYW6ujqGDRvGGWecQUtLS1mZ5557joMPPph0Os2kSZO46qqrNrOmlScBnRBCiO1WW0O2y7YqmWR4u9Xa2soHP/hBfvGLX2y0zFFHHcXKlSvj2x//+Mey/aeccgovvvgiixYt4u677+bhhx/mrLPOivc3NTVx5JFHMnnyZJ5++ml+9KMfccUVV/Rq9o2taVD0oRNCCCE2h7WGXGEVv1+1kPFrPs/ev/83NRd+jAOPbeHFNbXstPxQ3tzwN6wtDnRVt0t2gAZFHH300Rx99NE9lkmlUhsdUPnvf/+be+65hyeffJL99tsPgJ///Od88pOf5Mc//jETJkzgpptuolAo8Nvf/pZkMsmee+7J0qVL+elPf1oW+PU3ydAJIYTYrnXO0iWqkniefPxtTbaCtyAIaGpqKrt1XNZzcy1evJgxY8aw2267cc455/D+++/H+5YsWcKwYcPiYA5g5syZaK355z//GZc55JBDSCaTcZlZs2bxyiuvsGHDhi2uV1/JO1oIIcR2ydqA1U3PcMs7S7n1xYlw/1Nw2P6cOP1dTpl0AFXpSSglH4NbQylD1+eVIoDHH3+8bBnP+vp65s2bt0X1Ouqoo/i///f/cv/99/PDH/6Qhx56iKOPPpowDAFYtWoVY8aMKTvG931GjBjBqlWr4jJjx44tK1N6XCozEKTJVQghxHatkC8S5IOybRnpRzdoHHTQQSxYsKBs25auAnXSSSfF96dPn87ee+/NLrvswuLFizniiCP6VM+BJl9NhBBCbLesyfFG0328vDLLY3fXod5fT/UXp7PfDorP1H1BRrtuJa65VPX5Bgrf96mrqyu7VWpZz5133plRo0bx+uuvA26xgjVr1pSVCYKA9evXx/3uxo0bx+rVq8vKlB4P5GIHEtAJIYTY7rU1lq8agVaka5LdFxZ9ZqhQk+tWHlixfPly3n//fcaPHw/AjBkzaGho4Omnn47LPPDAAxhj+MhHPhKXefjhhykW2wfULFq0iN12243hw4dv3Qr3QAI6IYQQ263SyhG3rv4Dz65L0PbH5zCjRnHIJzew5w7V7FI3E6UlsNtetLS0sHTpUpYuXQrAW2+9xdKlS1m2bBktLS184xvf4B//+Advv/02999/P5/5zGeYOnUqs2bNAmCPPfbgqKOO4swzz+SJJ57gscce49xzz+Wkk05iwoQJAJx88skkk0nOOOMMXnzxRW699VauueYaLrzwwoF62oD0oRNCCDFE5JrLs3Rp6Ue31VRq2pLNPcVTTz3F4YcfHj8uBVlz5szh+uuv57nnnuPGG2+koaGBCRMmcOSRR/Kd73ynrAn3pptu4txzz+WII45Aa80JJ5zA//zP/8T76+vruffee5k7dy777rsvo0aN4rLLLhvQKUtAAjohhBBDRK65fKoLz9ekM0loGJj6bM9KU470t8MOO6zHZUEXLly4yXOMGDGCm2++uccye++9N4888shm129rkiZXIYQQ2zVrDa3Zt7l5+VP8aekk9ENPwpEH8Lk9VvD5SftQXzUVpSS/IQY3eQcLIYQYEoyxFFoLZdtk+pKto2JNrgOR5hukJEMnhBBiu2dtwFtND/LvFVn+eUcG1dRC/akfYL9xhs+PPpFkYpRMMlxBrsm17/8NTMPt4CQZOiGEEENGtjkHlGflqodlYMXA1Gd7ZRmYQRFDmXwdEUIIsd2Lpy95///x+HseuVufwYwfz0GfbmLaxCqOqpmD1jUyybAYtCRDJ4QQYkjpPH2Jl/BJVSWgcYAqtB2qVGOpZOh6TzJ0QgghhgRrDY1tr3Pnsle586kd0UuehY8fwIn7LGP3HerZse5jKJ2ULF0F2M1cEWJjN9F7EtAJIYQYUnLZAiYIy7ZV1ctoVzG4SUAnhBBiyLCmwMvNC3jxvRz/+pOH2rCBqi/szeemNnH6jgcxsmY6SvmSpesjayt3E70jfeiEEEIMOdnmPJ1Hu2bqMwNTme2QBUyFziN6RzJ0QgghhgyLJQgb+MOK/8fi1Rlyt/wLO3IEO51cy4cmak6sP5rqzE6ycoQYdOQdK4QQYsix1pJrKV/bVWlNpj4N7w9QpbYjMqih/0mGTgghxJCUa8p12VY7onoAarJ9kj50/UsCOiGEEEOKtYYNrf/mwXdW8adndoTFT2Km78Ehn1zPbmOTnDjqZDLpHaTZtQ9MhW4Sz/WeBHRCCCGGpDA0FNsKXbbXjZQsnRh8JKATQggx5FhT4Mm2W3h5ZZZ/3FmDfuU11Cc+wnH7L2PPHav5VN0ppJJjJEu3paS5td/JO1UIIcSQ1daUo/P0JUoraoZnoGFAqrRdKDWZ9pXEdL0nGTohhBBDjsVSDNbzx7V/5h8rNe/9fh2qpYXUZz/M5/dazp471vHp2jlkUhNkomExKEiGTgghxJCWbch22eanfVLVSWgZgAptB6y1WGkz7VeSoRNCCDEkWWtoaH2V2975F7e9NYr87c9ga2qoOWUP/n979x8VVZn/Afx9Z4YZQJwZEJyBFIRSDPNXkBP92EpZyfXbV8vvbrpsa+rRUwt7Kt1Kz5Zg22apx8xOm3vObtl2aktrs+9q+hWxtBJNUQx/rpoGJgMZwfB7ftzn+wfM1VGwUS4zDLxfnHuYe5+Hy3Ofw8lPn+fHnT7MjmGDjZjS/zfM0l0DgQt70XXlYEzoPwZ0RETUp7lcHrQ2tl52PcIUgbBwDmRRaGBAR0REfZaQnThRvxWnzzXi472DIf5vN8TAOAz47RD8T1IN0pJN+K+oHERFpkDShEOS+M+mP4SKB/mH/+tBRER9XmNdS4fjexGmCOgjwoDLp9nRFQi++ivg+L8aRETUZwkIeDwO/OvHtThqd2LHllhIX+yHnDQYib8x41fXV2P09dH4737/A4vxZmg0UZxP5wfhx/w4zqFTFwM6IiIiAI0drHYFgHCjoS1LR9SDcciViIgIQHNdC4Qc1WGZ2dIfqAtwg0KYaP9S4z7kH2boiIioTxNCRovTjnV1m3Ggwo19/wqHZk8p5BGpsDwUj+nD7Ljxugj8Jv5eJJvuaXslmEYPSdJw6LUTMtQZciX/MaAjIiJq11TT2GlZzHUmSBIDOOqZGNAREVGfJ4QbPzQcxpvnvsQXZ7U4sk5Ac/QE5BtSMOChJPwq7SyGxWgxd/jt+O/+M2GMSIFOa25bIMFM3eXaFzR0+Qj2c4QQBnREREQXaahp6rQsakA/GCK5QOKnyBCqHAzo/MeAjoiICICQW1DhKMa/zhzH1u8iUf6PGmhKD0NOHIz+D92I/7q9AiPNTowYFosp/R/AqKj7ERXZnqnThPtk6y798rr4vKPPVyr7qXpX+jnq/RjQERERXUQIgcYfO8/S6Qw6RMcbA9iiEKTSkCtTdP7jtiVERERo2yJDlhtwpP7f+OJUHCIGpOC+d+owtPUgRMZI6KaNw23XHULSpxK2GQYgVjcUx7614qC+HHbPUTS02uHyNECW298LK9zKvaX2+1/Invn+Zt9M2tXXu1KZEDIAz7V1yjWS24+uYjznPwZ0REREl3A5PWipb7liHVO8Ef3q3MD3AWpUKBFtmU4KHAZ0RERE7YSQ4fbU4oumd3Ddt7/Dx1ED8Yt1DUhr2Q8x7ibgngwMGnQGv/q/Chw9E4eItGjEf5MKe80QnG9244h8GrXiHNyiFU63A265FaI9VyWEDEnSKN8v/p2dnXf2uaMyAB3W9XicaHGWd1eXUQ/BOXREREQdaG5ohbPJeeVKkoSYxBj0i44ITKNChFobC19tkm/nzp247777kJCQAEmSsGHDBp9yIQQWL16M+Ph4REREICsrCydOnPCpU1NTg5ycHBiNRpjNZsyZMwcNDQ0+db7++mvceeedCA8Px+DBg7Fs2bJr6CV1hVyGrrW1FTabDQcPHsSBAwcwZsyYYDeJiIh6ESFkuNznsbHhLUinZiOyXwKa/tWCsee/gvbONMg3DkXkADPSj51B6t7zOFoei9gIMwbERGBEdTjqm4aixQM4nDI87RGJjLZgQqeR4LkoSNFKgNMjEKa5MP/N00k9tywgSZKSifEIAW37Rsce0VamlQCPuPC7hABqXU3Y7FzRzb3mS7RvO6LGfa5GY2MjRo8ejdmzZ+OBBx64rHzZsmVYvXo13nrrLSQnJ+PZZ59FdnY2jhw5gvDwcABATk4OKisrUVhYCJfLhVmzZmHevHl49913AQAOhwMTJ05EVlYW1qxZg7KyMsyePRtmsxnz5s3r8jNfq5AL6J566ikkJCTg4MGDwW4KERH1cs0NTrQ0tgJxP103PMoAkzEOkr0eqHeivrYVPlEZdbtJkyZh0qRJHZYJIbBq1So888wzmDJlCgDgH//4BywWCzZs2IDp06fj6NGj2LJlC/bu3YuMjAwAwKuvvopf/OIXWLFiBRISEvDOO+/A6XTijTfegF6vx4gRI1BaWoqVK1cyoPPX5s2bsXXrVnz44YfYvHlzsJtDRES9lBAyWp3V2CqvhebkHJiNg3F+mwU/O/s1IrMGQaSmALffjKjUH3DLsW8x6lg1as5G4qwjCt9F9oNTjkKTR0JTkxsut4CEtthOpwHcMpRMmvdcp2kbYtRIF847qqeV2lZ+eu/nPfdqW03r+7mmtQmbPw90/139cGln3G43HA6HzzWDwQCDwXBV9zl9+jTsdjuysrKUayaTCTabDcXFxZg+fTqKi4thNpuVYA4AsrKyoNFosGfPHtx///0oLi7Gz372M+j1eqVOdnY2XnrpJfz444+Ijo6+xiftmpAJ6KqqqjB37lxs2LABkZGRwW4OERH1Ec0NTrQ2OoEo7VX/bFi4DpLcHoAB0EmARgDaS8510oWAznveUT0tLgrocOHcq6OAzqDRI9DUG3IFdu3aBZPJ5HM9Pz8fBQUFV3Uvu90OALBYLD7XLRaLUma32zFw4ECfcp1Oh5iYGJ86ycnJl93DW8aA7gqEEHj44YfxyCOPICMjA2fOnPHr51pbW9Ha2qqcXxrhExER+aO+uh6wmIPdjD7ptttuw6ZNm3yuXW12ri8IakC3cOFCvPTSS1esc/ToUWzduhX19fVYtGjRVd1/6dKlWLJkSVeaSEREfZQQbrjc57Gj8W3Yz2bDEDMG1S2JuMv+IxLH74OUlgRhtQB3DIBhpAPx52sQX1mDsRXfQ24WcDo0cDslyB4NJAmQZQlarQyPRwONRijnbpcGurC265eeX1zPey4ElPt5z70k6cJQp/dz5Y8CzwS879QZchWiLUNmNHb9zRxWqxVA24hffHy8cr2qqkpZYGm1WlFdXe3zc263GzU1NcrPW61WVFVV+dTxnnvrBENQty1ZsGABjh49esUjJSUF27dvR3FxMQwGA3Q6HW644QYAQEZGBmbOnNnp/RctWoS6ujrlqKioCNSjERFRL1N3rg6yR433H/R+AoDcPuzalUPNd0UkJyfDarWiqKhIueZwOLBnzx5kZmYCADIzM1FbW4uSkhKlzvbt2yHLMmw2m1Jn586dcLlcSp3CwkKkpqYGbbgVCHKGLi4uDnFxP710aPXq1Xj++eeV83PnziE7Oxvvv/++0sEduZZJk0RERF7eLUyO1W9CLcZi6n/uwXlXDG7/31aklfwHxlsqIA1PhIiLhYiJhnT9EGhbmqF1uaFvdQJuN+ByX0iXaTWAR778vLPvnf3cxd8vNPbC+UWfdacrgb8Ftt9kCMgqpOiu9g4NDQ04efKkcn769GmUlpYiJiYGiYmJePzxx/H8889j6NChyrYlCQkJmDp1KgDgxhtvxL333ou5c+dizZo1cLlcyMvLw/Tp05GQkAAA+PWvf40lS5Zgzpw5ePrpp3Ho0CG88sorePnll7v8vF0REnPoEhMTfc6joqIAANdffz0GDRoUjCYREVEf1Fjb0vZKsFjppytTwO3btw/33HOPcj5//nwAwMyZM7F27Vo89dRTaGxsxLx581BbW4s77rgDW7ZsUfagA4B33nkHeXl5mDBhAjQaDaZNm4bVq1cr5SaTCVu3bkVubi7S09MRGxuLxYsXB3XLEiBEAjoiIqJg8b4O7JxjNz5y2THxP1MRa07AYcd1GHmuBWklx9DvRh00iQOA2GiIaBMQEQER1Q9C0wP+mXW6frqOykT7V9fvc3XuvvvuK75DVpIkPPfcc3juuec6rRMTE6NsItyZUaNG4fPPA7wXzE/oAX9pV2/IkCF86S8REQWFxy2j9rs6xCaag92UHqttDh0FUkgGdERERIEkhAzZ04S6ppPY6HoLB8/YML45A55h0TjkGISUb90Y3K8RltgqRCQBGoMWUn8DNAYtEKZti24u3jX40l2Evec/dV0WbcsZvffznnvJuHDe/lk+fT7AvUXBwICOiIjoGjjON6IhOgxmS1Swm9LjeFe5dv0+HI3zFwM6IiIiPwgIQHai1VmN055PURl2EKVHMzCxJQPOQVE48KMJpmoTrN94YNAIRGk90Gtl6DUeeIQGWo0Mt6yFTiPDI3dwLsmd1vN+l2UJGkko9TyyBlJ70KORBGQhKZ89oq3M3hT4rTSEEKpMjWI457+g7kNHREQU6n44W4c6ez3ndlNQMUNHRETkJwEBCDc8HgeaPI044arBWf0B7DwxBLbycUhKHoDmuHBoAeg0OrR4AE97nNfZFLqw9i3mNBpAbt+CztVZPaE0RKkP6cL9vecXf65taQ5KP6kx5Mocnf8Y0BEREanA7fTAfuoHRDVHIjLKgIh+YRBhOigRVx8iQ605dOQvBnREREQqanG0wFXfiiYN0OwRkCXvvLa2zFvYRZk3lwzoNW0ZOO/LIHQawNlJPW+G7tKXR3jv7z2/+POPzpYg9QQFEgM6IiKiqySEDEDA43GgsbkRTa3nUKUtg17XHybnddBIYYiQTDDJA2AQemggQYIGLrihu2j6uhsyDAiDC27lmhbaDuuFQQcBWcl8aaGFBx7ls7fM+7u8ZS2iKQA94sv7Ntau34c5On8xoCMiIiJVqTWHjuGc/xjQERERXYO2BRJtoQs8LsieJjidVWhoPgXvJhJSD5g/F4zVt2rtQ8eQzn/ctoSIiIgoxDFDR0RERKqS27+6ivk5/zGgIyIiInVJAkLioohA4pArERERUYhjho6IiEgFSjZJtC0JaLvWEwS+FTLfFBFwDOiIiIhIVaJ9Vzw17kP+4ZArERERUYhjho6IiIhUpd6bIshfDOiIiIhIVQICsgqrXBnS+Y8BHREREalK5hy6gOMcOiIiIqIQxwwdERERqUqo9qYIZuj8xYCOiIiIVCXaNy5R407kHw65EhEREYU4ZuiIiIhIVW1Drh4V7sMMnb8Y0BEREZGq1NuHjgGdvzjkSkRERBTimKEjIiIiVQlJnY2FhaRCY/oIBnRERESkKlmlOXRQZaVs38CAjoiIiFTGOXSBxjl0RERERCGOGToiIiJSlSxkyEKFbUsEM3T+YkBHREREKuObIgKNQ65ERETUKxQUFECSJJ9j+PDhSnlLSwtyc3MxYMAAREVFYdq0aaiqqvK5R3l5OSZPnozIyEgMHDgQTz75JNxud6Af5aoxQ0dERESqattYWI1VrlefoRsxYgS2bdumnOt0F0KdJ554Aps2bcL69ethMpmQl5eHBx54AF9++SUAwOPxYPLkybBardi1axcqKyvx29/+FmFhYXjhhRe6/jjdiAEdERERqUpu/+qqa1nlqtPpYLVaL7teV1eHv//973j33Xcxfvx4AMCbb76JG2+8Ebt378att96KrVu34siRI9i2bRssFgvGjBmDP/3pT3j66adRUFAAvV7f5WfqLhxyJSIiol7jxIkTSEhIQEpKCnJyclBeXg4AKCkpgcvlQlZWllJ3+PDhSExMRHFxMQCguLgYI0eOhMViUepkZ2fD4XDg8OHDgX2Qq8QMHREREalMnUURAoDb7YbD4fC5bjAYYDAYLqtvs9mwdu1apKamorKyEkuWLMGdd96JQ4cOwW63Q6/Xw2w2+/yMxWKB3W4HANjtdp9gzlvuLevJGNARERGRqgRkCBW2LQFk7Nq1CyaTyedqfn4+CgoKLqs9adIk5fOoUaNgs9mQlJSEdevWISIiQoX29FwM6IiIiEhVbfk5dTJ0t912GzZt2uRzvaPsXEfMZjOGDRuGkydP4uc//zmcTidqa2t9snRVVVXKnDur1YqvvvrK5x7eVbAdzcvrSTiHjoiIiHosnU4Ho9Hoc/gb0DU0NODUqVOIj49Heno6wsLCUFRUpJQfP34c5eXlyMzMBABkZmairKwM1dXVSp3CwkIYjUakpaWp+2AqY4aOiIiIVCXgUWnbkqvL8v3hD3/Afffdh6SkJJw7dw75+fnQarWYMWMGTCYT5syZg/nz5yMmJgZGoxG///3vkZmZiVtvvRUAMHHiRKSlpeGhhx7CsmXLYLfb8cwzzyA3N9fvIDJYGNARERGRqoQQECLw25acPXsWM2bMwA8//IC4uDjccccd2L17N+Li4gAAL7/8MjQaDaZNm4bW1lZkZ2fjL3/5i/LzWq0WGzduxKOPPorMzEz069cPM2fOxHPPPdflZ+lukuhDL0pzOBztEyu1AKRgN4eIiCgABAAP6urqYDQau/23jR07Fmf+40R42IAu36uu+QSe+MNs/PnPf1ahZb0bM3RERESkKrUWRZD/GNARERGRqoRQZ9sSNYZt+wquciUiIiIKcczQERERkaoEZFXeFEH+C6kM3aZNm2Cz2RAREYHo6GhMnTo12E0iIiKiy4j2YdcuHle5yrUvC5kM3Ycffoi5c+fihRdewPjx4+F2u3Ho0KFgN4uIiIgu0ZahU2MfOgZ0/gqJgM7tduOxxx7D8uXLMWfOHOV6T9+1mYiIiCgQQmLIdf/+/fjuu++g0WgwduxYxMfHY9KkST+ZoWttbYXD4fA5iIiIqHupMtwqZPShrXK7LCQCum+++QYAUFBQgGeeeQYbN25EdHQ07r77btTU1HT6c0uXLoXJZFKOwYMHB6rJREREfZdQZw4dh1z9F9SAbuHChZAk6YrHsWPHIMttK2X++Mc/Ytq0aUhPT8ebb74JSZKwfv36Tu+/aNEi1NXVKUdFRUWgHo2IiIgoYII6h27BggV4+OGHr1gnJSUFlZWVAHznzBkMBqSkpKC8vLzTnzUYDD3+ZbpERES9jazSmyK4ytV/QQ3o4uLilBfmXkl6ejoMBgOOHz+OO+64AwDgcrlw5swZJCUldXcziYiI6GooQ6ZdvpEK9+gbQmKVq9FoxCOPPIL8/HwMHjwYSUlJWL58OQDgl7/8ZZBbR0RERBRcIRHQAcDy5cuh0+nw0EMPobm5GTabDdu3b0d0dHSwm0ZEREQXEVDrXa7M0PkrZAK6sLAwrFixAitWrAh2U4iIiOgKBIRKr/5iQOevkAnoiIiIKDQIzqELuJDYh46IiIiIOscMHREREamrfWNhFW5DfmJAR0RERKoS7TvRqXEn8g+HXImIiIhCHDN0REREpCoBlYZcmaHzGwM6IiIiUpdKc+g45Oo/DrkSERERhThm6IiIiEhlcvvRVczQ+YsBHREREalKqDXkynjObwzoiIiISFVqbVvCRRH+4xw6IiIiohDHDB0RERGpjKtcA40BHREREalMBuBR4T4M6PzFIVciIiKiEMcMHREREalKCAE1ti1puw/5gwEdERERqYz70AUah1yJiIiIQhwzdERERKQulYZcmaHzHwM6IiIiUlXbhsAMxgKJQ65ERESkMm+GrqvHtQWFr732GoYMGYLw8HDYbDZ89dVXXXqaUMCAjoiIiHqN999/H/Pnz0d+fj7279+P0aNHIzs7G9XV1cFuWrdiQEdEREQqE23z6Lp6XIOVK1di7ty5mDVrFtLS0rBmzRpERkbijTfeUPkZexYGdERERKQyocrX1Q65Op1OlJSUICsrS7mm0WiQlZWF4uJilZ+xZ+lTiyIubFDIiZpERNRXtP2bF6hNeiMiIqDePnRAWFgYHA6HzzWDwQCDwXBZ3fPnz8Pj8cBisfhct1gsOHbsmCrt6an6VEBXX1/f/kmdPzIiIqJQUV9fD5PJ1O2/Z926dTh8+LBq9/v8888va3d+fj4KCgpU+x29QZ8K6BISElBRUYH+/ftDkiQAgMPhwODBg1FRUQGj0RjkFvYu7Nvuxf7tPuzb7sX+7T4d9a0QAvX19UhISAhIGwYNGoRBgwapdr+7774bTz31lM+1jrJzABAbGwutVouqqiqf61VVVbBaraq1qSfqUwGdRqPp9I/MaDTyPyzdhH3bvdi/3Yd9273Yv93n0r4NRGauu3Q2vNoRvV6P9PR0FBUVYerUqQAAWZZRVFSEvLy8bmxl8PWpgI6IiIh6t/nz52PmzJnIyMjAuHHjsGrVKjQ2NmLWrFnBblq3YkBHREREvcaDDz6I77//HosXL4bdbseYMWOwZcuWyxZK9DZ9PqAzGAzIz8/3O51L/mPfdi/2b/dh33Yv9m/3Yd+2ycvL6/VDrJeSRKDWMRMRERFRt+DGwkREREQhjgEdERERUYhjQEdEREQU4vp0QPfaa69hyJAhCA8Ph81mw1dffRXsJvV4O3fuxH333YeEhARIkoQNGzb4lAshsHjxYsTHxyMiIgJZWVk4ceKET52amhrk5OTAaDTCbDZjzpw5aGhoCOBT9ExLly7FLbfcgv79+2PgwIGYOnUqjh8/7lOnpaUFubm5GDBgAKKiojBt2rTLNtAsLy/H5MmTERkZiYEDB+LJJ5+E2+0O5KP0SK+//jpGjRql7M+VmZmJzZs3K+XsW/W8+OKLkCQJjz/+uHKN/XvtCgoKIEmSzzF8+HClnH1LQB8O6N5//33Mnz8f+fn52L9/P0aPHo3s7GxUV1cHu2k9WmNjI0aPHo3XXnutw/Jly5Zh9erVWLNmDfbs2YN+/fohOzsbLS0tSp2cnBwcPnwYhYWF2LhxI3bu3Il58+YF6hF6rB07diA3Nxe7d+9GYWEhXC4XJk6ciMbGRqXOE088gX//+99Yv349duzYgXPnzuGBBx5Qyj0eDyZPngyn04ldu3bhrbfewtq1a7F48eJgPFKPMmjQILz44osoKSnBvn37MH78eEyZMkV5RRH7Vh179+7FX//6V4waNcrnOvu3a0aMGIHKykrl+OKLL5Qy9i0BAEQfNW7cOJGbm6ucezwekZCQIJYuXRrEVoUWAOKjjz5SzmVZFlarVSxfvly5VltbKwwGg/jnP/8phBDiyJEjAoDYu3evUmfz5s1CkiTx3XffBaztoaC6uloAEDt27BBCtPVlWFiYWL9+vVLn6NGjAoAoLi4WQgjxySefCI1GI+x2u1Ln9ddfF0ajUbS2tgb2AUJAdHS0+Nvf/sa+VUl9fb0YOnSoKCwsFHfddZd47LHHhBD82+2q/Px8MXr06A7L2Lfk1SczdE6nEyUlJcjKylKuaTQaZGVlobi4OIgtC22nT5+G3W736VeTyQSbzab0a3FxMcxmMzIyMpQ6WVlZ0Gg02LNnT8Db3JPV1dUBAGJiYgAAJSUlcLlcPv07fPhwJCYm+vTvyJEjfTbQzM7OhsPhUPVl2aHO4/HgvffeQ2NjIzIzM9m3KsnNzcXkyZN9+hHg364aTpw4gYSEBKSkpCAnJwfl5eUA2Ld0QZ/cWPj8+fPweDyX7RptsVhw7NixILUq9NntdgDosF+9ZXa7HQMHDvQp1+l0iImJUepQ27sHH3/8cdx+++246aabALT1nV6vh9ls9ql7af921P/esr6urKwMmZmZaGlpQVRUFD766COkpaWhtLSUfdtF7733Hvbv34+9e/deVsa/3a6x2WxYu3YtUlNTUVlZiSVLluDOO+/EoUOH2Lek6JMBHVFPl5ubi0OHDvnMk6GuS01NRWlpKerq6vDBBx9g5syZ2LFjR7CbFfIqKirw2GOPobCwEOHh4cFuTq8zadIk5fOoUaNgs9mQlJSEdevWISIiIogto56kTw65xsbGQqvVXrYKqKqqClarNUitCn3evrtSv1qt1ssWnrjdbtTU1LDv2+Xl5WHjxo349NNPMWjQIOW61WqF0+lEbW2tT/1L+7ej/veW9XV6vR433HAD0tPTsXTpUowePRqvvPIK+7aLSkpKUF1djZtvvhk6nQ46nQ47duzA6tW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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sim_data_tm.plot_field(\n",
    "    field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\", vmin=0, vmax=4e3\n",
    ")\n",
    "ax.set_aspect(\"auto\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac1bf36a",
   "metadata": {},
   "source": [
    "In conclusion, we see that the device functions as a PSR. The TE0 input is transmitted to the top waveguide at the output while staying as the TE0. The TM0 input is rotated to TE0 and transmitted to the bottom waveguide at the output. The loss is small in both cases. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "591b9651",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components"
  ],
  "description": "This notebook demonstrates how to model a broadband polarization rotator-splitter in Tidy3D FDTD.",
  "feature_image": "./img/bilevel_PSR.png",
  "features": [
   "GDS component"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "polarization rotator-splitter, PSR, broadband, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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   "version": "3.10.9"
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