{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "59eadfa3-93e4-447d-bf83-c6c3230ca278",
   "metadata": {},
   "source": [
    "# Bragg grating filter with phase change material\n",
    "\n",
    "A waveguide Bragg grating filter is a photonic device that reflects specific wavelengths of light using periodic variations along a waveguide. It’s commonly used in optical communications and sensing for its compact size and precise wavelength selectivity. In the work `Boshu Sun, Maoliang Wei, Kunhao Lei, Zequn Chen, Chunlei Sun, Junying Li, Lan Li, and Hongtao Lin, \"Integrated Bragg grating filters based on silicon-Sb2Se3 with non-volatile bandgap engineering capability,\" Opt. Express 31, 27905-27913 (2023)` [DOI: 10.1364/OE.495196](https://doi.org/10.1364/OE.495196), a compact and low-loss reconfigurable Bragg grating filter using phase change material Sb₂Se₃ on silicon is demonstrated. With a footprint of 0.5 µm × 43.5 µm and >30 dB extinction ratio, it enables efficient switching without static power, ideal for scalable photonic systems.\n",
    "\n",
    "In this notebook, we design a Bragg grating filter using both FDTD and EME methods. While 3D FDTD offers fully rigorous simulations, it can become computationally expensive for long waveguide gratings. In contrast, EME efficiently handles periodic structures analytically, enabling fast and cost-effective simulations. We compare results from FDTD and EME to validate the accuracy and reliability of the EME approach.\n",
    "\n",
    "<img src=\"img/periodic_eme_grating.png\" width=\"400\" alt=\"Schematic of the Bragg grating filter.\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8a63314b-b7f0-40aa-bd68-9cb30f9b8c97",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ae76d41-9a69-4b85-bb2e-28b4f918d1a0",
   "metadata": {},
   "source": [
    "## Simulation Setup\n",
    "\n",
    "We focus on the wavelength range from 1450 nm to 1550 nm. The goal is to design the waveguide Bragg grating to have the stop band at around 1500 nm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ca707bf8-cf0e-4f56-9bea-6def483c484c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lda0 = 1.5  # central wavelength\n",
    "freq0 = td.C_0 / lda0  # central frequency\n",
    "ldas = np.linspace(1.45, 1.55, 100)  # wavelength range\n",
    "freqs = td.C_0 / ldas  # frequency range\n",
    "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))  # width of the source frequency range"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4e74640-cfc8-475f-8dcb-5965e0892495",
   "metadata": {},
   "source": [
    "The filtering band can be tuned by changing the Sb₂Se₃ between the crystalline phase and the amorphous phase. Crystalline Sb₂Se₃ has a refractive index of 4.16, while the amorphous Sb₂Se₃ has a refractive index of 3.32. The waveguide is made of silicon on oxide. In this case, we model all mediums as nondispersive since the dispersion within the wavelength range of interest is relatively weak. They can be modeled as dispersive mediums if needed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9c60bc9a-b9a3-4d71-ba23-7894e626fa4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_aSb2Se3 = 3.32  # refractive index of amorphous Sb₂Se₃\n",
    "n_cSb2Se3 = 4.16  # refractive index of crystalline Sb₂Se₃\n",
    "n_Si = 3.47  # refractive index of silicon\n",
    "n_SiO2 = 1.44  # refractive index of oxide\n",
    "\n",
    "# define the mediums\n",
    "medium_aSb2Se3 = td.Medium(permittivity=n_aSb2Se3**2)\n",
    "medium_cSb2Se3 = td.Medium(permittivity=n_cSb2Se3**2)\n",
    "medium_Si = td.Medium(permittivity=n_Si**2)\n",
    "medium_SiO2 = td.Medium(permittivity=n_SiO2**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef80aa18-5d44-4bea-b314-50036c824cbb",
   "metadata": {},
   "source": [
    "The waveguide Bragg grating design is a silicon rib waveguide with periodic Sb₂Se₃ blocks on top. For the target Bragg wavelength of 1550 nm, we can calculate the appropriate grating period by the Bragg condition\n",
    "\n",
    "$$ \n",
    "\\lambda_B = 2pn_{eff},\n",
    "$$\n",
    "\n",
    "where $p$ is the grating period, $n_{eff} = ff*n_{SOI-SbSe} + (1-ff)*n_{SOI} $, $ff$ is the filling fraction, $n_{SOI-SbSe}$ is the effective index of the waveguide mode with the Sb₂Se₃ block, and $n_{SOI}$ is the effective index of the waveguide mode without the Sb₂Se₃ block. In this case, we use a filling fraction of 50%. $n_{SOI-SbSe}$ and $n_{SOI}$ can be determined from mode solving. For the sake of keeping this notebook short, we don't show the mode solving here and only use the calculated effective indices directly. The grating period is calculated to be around 280 nm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0163e7fa-f23b-4d9b-929e-b96543a9128b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the Bragg wavelength of 1500 nm, the grating period is 283.55 nm.\n"
     ]
    }
   ],
   "source": [
    "w_wg = 0.5  # waveguide width\n",
    "h_wg = 0.22  # waveguide thickness\n",
    "h_etch = 0.13  # etching depth\n",
    "h_pcm = 0.06  # Sb₂Se₃ thickness\n",
    "w_pcm = 0.35  # Sb₂Se₃ width\n",
    "\n",
    "ff = 0.5  # filling fraction\n",
    "\n",
    "n_SOI_SbSe = 2.54  # effective index of the waveguide mode with the Sb₂Se₃ block\n",
    "n_SOI = 2.75  # effective index of the waveguide mode without the Sb₂Se₃ block\n",
    "\n",
    "# calculate the Bragg wavelength\n",
    "n_eff = ff * n_SOI_SbSe + (1 - ff) * n_SOI\n",
    "lambda_B = 1.5\n",
    "p = lambda_B / (2 * n_eff)\n",
    "print(f\"For the Bragg wavelength of 1500 nm, the grating period is {p * 1e3:.2f} nm.\")\n",
    "\n",
    "inf_eff = 1e3  # effective infinity to help with model setup\n",
    "buffer = 0.6 * lda0  # buffer spacing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1c51d19-720a-40c6-bc1b-4cb7544f7a0a",
   "metadata": {},
   "source": [
    "Next we define a function `make_structures` to create all the structures in the simulation. This function takes the Sb₂Se₃ medium as an argument so we can switch between the crystalline and amorphous phase. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4a403972-4c72-46a2-9cf7-18a4debfcb8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_structures(n: int, medium_Sb2Se3: td.Medium) -> list[td.Structure]:\n",
    "    \"\"\"\n",
    "    Creates a list of Tidy3D structures representing a waveguide with Bragg gratings.\n",
    "\n",
    "    Args:\n",
    "        n (int): Number of grating periods.\n",
    "        medium_Sb2Se3 (td.Medium): Material used for the gratings.\n",
    "\n",
    "    Returns:\n",
    "        List[td.Structure]: List of Tidy3D structures [box, slab, rib, gratings].\n",
    "    \"\"\"\n",
    "\n",
    "    p * n  # length of the grating\n",
    "\n",
    "    # silicon slab structure\n",
    "    slab = td.Structure(\n",
    "        geometry=td.Box.from_bounds(\n",
    "            rmin=(-inf_eff, -inf_eff, 0), rmax=(inf_eff, inf_eff, h_wg - h_etch)\n",
    "        ),\n",
    "        medium=medium_Si,\n",
    "    )\n",
    "\n",
    "    # box structure\n",
    "    box = td.Structure(\n",
    "        geometry=td.Box.from_bounds(\n",
    "            rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, 0)\n",
    "        ),\n",
    "        medium=medium_SiO2,\n",
    "    )\n",
    "\n",
    "    # rib waveguide structure\n",
    "    rib = td.Structure(\n",
    "        geometry=td.Box.from_bounds(\n",
    "            rmin=(-inf_eff, -w_wg / 2, h_wg - h_etch), rmax=(inf_eff, w_wg / 2, h_wg)\n",
    "        ),\n",
    "        medium=medium_Si,\n",
    "    )\n",
    "\n",
    "    # create the geometries for the gratings\n",
    "    grating_geometries = 0\n",
    "    for i in range(n):\n",
    "        grating_geometries += td.Box.from_bounds(\n",
    "            rmin=(i * p, -w_pcm / 2, h_wg), rmax=(i * p + ff * p, w_pcm / 2, h_wg + h_pcm)\n",
    "        )\n",
    "\n",
    "    # grating structure\n",
    "    gratings = td.Structure(\n",
    "        geometry=grating_geometries,\n",
    "        medium=medium_Sb2Se3,\n",
    "    )\n",
    "\n",
    "    return [box, slab, rib, gratings]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e091f434-721c-4eb4-9a11-20b1dd490c37",
   "metadata": {},
   "source": [
    "## FDTD Simulations\n",
    "\n",
    "First we try to simulate the designed waveguide Bragg grating using FDTD. Since we need to simulate both the crystalline and amorphous Sb₂Se₃ cases, we can create a helper function `make_sim` here and then create a [Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Batch.html) later to run two simulations in parallel to save time. \n",
    "\n",
    "To ensure accuracy, we use a pretty fine grid with 30 steps per wavelength. Since the grating can be long, a long `run_time` is needed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "02766fe2-39df-4daa-a91e-8783b1a0f0e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_sim(n: int, medium_Sb2Se3: td.Medium) -> td.Simulation:\n",
    "    \"\"\"\n",
    "    Constructs a Tidy3D simulation for a Bragg grating waveguide filter.\n",
    "\n",
    "    Args:\n",
    "        n (int): Number of grating periods.\n",
    "        medium_Sb2Se3 (td.Medium): Material used for the phase change gratings.\n",
    "\n",
    "    Returns:\n",
    "        td.Simulation: Configured Tidy3D simulation object.\n",
    "    \"\"\"\n",
    "\n",
    "    l = p * n  # length of the grating\n",
    "\n",
    "    # create a mode source for excitation\n",
    "    mode_spec = td.ModeSpec(num_modes=1, target_neff=n_Si)\n",
    "    mode_source = td.ModeSource(\n",
    "        center=(-buffer / 2, 0, h_wg - h_etch),\n",
    "        size=(0, 4 * w_wg, 4 * h_wg + h_pcm),\n",
    "        source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "        direction=\"+\",\n",
    "        mode_spec=mode_spec,\n",
    "        mode_index=0,\n",
    "    )\n",
    "\n",
    "    # add a mode monitor to measure transmission at the output waveguide\n",
    "    mode_monitor = td.ModeMonitor(\n",
    "        center=(l + buffer / 2, 0, h_wg - h_etch),\n",
    "        size=mode_source.size,\n",
    "        freqs=freqs,\n",
    "        mode_spec=mode_spec,\n",
    "        name=\"mode\",\n",
    "    )\n",
    "\n",
    "    run_time = 2e-11  # simulation run time\n",
    "\n",
    "    # construct simulation\n",
    "    sim = td.Simulation(\n",
    "        center=(l / 2, 0, h_wg - h_etch),\n",
    "        size=(l + 2 * buffer, w_wg + 2 * buffer, h_wg + h_pcm + 2 * buffer),\n",
    "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=30, wavelength=lda0),\n",
    "        structures=make_structures(n, medium_Sb2Se3),\n",
    "        sources=[mode_source],\n",
    "        monitors=[mode_monitor],\n",
    "        run_time=run_time,\n",
    "        boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    "        symmetry=(0, -1, 0),\n",
    "    )\n",
    "\n",
    "    return sim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c46dcc3-18ea-46bf-ae64-332be4bf91a4",
   "metadata": {},
   "source": [
    "Now we simulate 150 grating periods. Due to the cost consideration, it might be hard to simulate very long waveguide grating using 3D FDTD. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9eb44360-3f9c-46b2-90e7-31de327f1826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ab775Jn355ZeWO2RUNE7P/N7FxsbSwoULaerUqeTv70/NmjWzGstoHqdX2ftblczMTKpXrx41bdqUPvzwQ5o/fz516dKF7r777nLxzJs3jwDQyy+/TOvWraPvvvuuymWbdejQgQBQ27Ztbapvq6VLlxIAeuyxx2jlypU0cuRIAkBvv/22VT3z+3DnGMghQ4ZYxuB9/PHHdN9995FSqaTdu3db1Tt69ChpNBqrO7JotVqbxxpOmDCBGjZsWG58HADy8/Ojhx9+mJYvX04zZswgnU5HwcHB1LJlS1q3bh0RVd6HzNt159XHlX0eOnToQE8++aRNMbOKcdJjdOnSJRo5ciSFh4eTRqOh5s2b0/jx48sNTq8o6RGVDFFo06YNqVQqioiIoHHjxll9oV+4cIFGjx5NLVq0IK1WS/Xr16cHHniAfvrpJ0udHTt20KBBgyg6OprUajVFR0fTsGHD6Pfff68ydlvnMxgM9N5771H79u1Jo9FQvXr1qFu3bjRnzpxyg5s///xz6tKli6Venz59aPv27ZbpaWlplJiYSEFBQTYNTt+wYYNlefXr169ycPqdbEl6RET79++ne++9l/z8/Cg6OpomT55M27ZtKxdPXl4e/etf/6KQkJBqB6eXZU6W77zzjk317fHJJ5/QXXfdRWq1mlq0aEELFiwol1wqS3qFhYX06quvUmRkJGk0GurRowdt3bq1wvXs3buX7rvvPtJqtRQeHk7jx48nvV5vU4zHjh2zDC4vCwAlJSXRkCFDyM/Pj6Kiouijjz6iFStWkL+/Pz377LNE5Jikd/r0aQJg1W+Y/QQiJ+yDM8bqlEWLFuHll1/Gn3/+We4KRF/Rt29fREdHY82aNZYyQRAwa9YszJ492+nrnzRpEvbs2YOjR4/y1Zu1wOf0GGNVIiJ89tln6NOnj88mPAB45513sGHDBrc9WujTTz/FW2+9xQmvlnjIAmOsQvn5+fjuu++wc+dOnDhxAps2bXJ3SG4VGxtb6bhCZwsNDbXrMUiscpz0GGMVunHjBv71r38hJCQEb7zxBgYOHOjukBirNT6nxxhjzGfwOT3GGGM+gw9vehBZlnHt2jUEBQXxyWrGmEcgIuTm5iI6OrrK++B6C056HuTatWtYsWKF5YNlvpchESErK8spd3gwUyqVUCqVMJlMMJlMUKvVljumuOoIuCAI0Gg0kGUZBoOhXEyuUnbbAVjF5CrcHqW4PUq4sz0+/vhjXLlyBY0aNXLqelyBk54HCQoKgkKhgCiKUKlUlr09IkKzZs1qfENiWxUUFFhuySWKIoKDg6FUuvYjYjKZkJOTY7kNWEBAgOWho64iyzL0ej2MRiMAQKVSQafTufxXLrdHCW6PUu5oD/OP76CgIKeux1U46XkQc5JTqVSW54zJsgxZlqHRaCp99pijaLVay69prVZb7onUrmI0Gi2Ph6lfv75bDqmoVCrLM8tCQ0MrfBabs3F7lOL2KOXq9jD/2Kgrp1y8/wBtHUNELj98BJQk15s3b0IQBGi1WhQVFVV493hny83NRVFREbRaLQRBwM2bN2v1aJyaMD/aRqVSQaVS4ebNmy4fn8XtUYrbo5QntIe346TnYcxPcTYYDC5LfOYObTQaERYWhtDQUOh0Ouj1epd27NzcXOj1euh0OoSGhiIsLAxGo9GlHdtgMCAzMxMqlQphYWEICwuz7GW46ouW26MUt0cpT2iPuoCTngfSaDQQBAEGg8GmB3PWxp0d2nwINSgoyKUdu2yHNp87UKvVLu3YZb9gQ0NDIYoiRFFEaGioy75ouT1KcXuU8oT2qCs46Xkg81Va5sTnrA9zZR3azFUdu6IObeaqjl3RF6yZq75ouT1KcXuU8oT2qEs46XkoURQtic9kMllOJjtKdR3azNkdu6oObebsjl3VF6yZs79ouT1KcXuU8oT2cPR3j7tx0vNgZRNfVlaWwzq2rR3azFkd25YObeasjm3LF6yZs75ouT1KcXuU8pT2yMrKcsiyPAUnPQ8nCAIUCgWUSqVDOra9HdrM0R3bng5t5uiObc8XrJmjv2i5PUpxe5TypPZw9VhEZ+Ok5wVEUURISEitO3ZNO7SZozp2TTq0maM6dk2+YM0c9UXL7VGK26OUp7VHSEhIjZbhqTjpeYnaduzadmiz2nbs2nRos9p27Np8wZpxe5Ti9ijF7eH56tbW1HE17diO6tBmNe3YjujQZjXt2I7s0Nwepbg9SnF7eLa6t0V1nL0d29Ed2szeju3IDm1mb8d2Rofm9ijF7VGK28Nz1c2tquNs7djO6tBmtnZsZ3RoM1s7tjM7NLdHKW6PUtwenqnublkdV13HdnaHNquuYzuzQ5tV17Fd0aG5PUpxe5Ti9vA8dXvr6rjKOrarOrRZZR3bFR3arLKO7coOze1RitujFLeHZ6n7W1jH3dmxi4uLXdqhze7s2K7s0GZ3duzi4mKXd2huj1LcHqW4PTxH3Rp16KPMHTszM9PyzLHw8HCXdWgzc+fV6/UA4NIObWbu2Ddu3HBbh+b2KMXtUYrbwzP4zpYyxhjzeZz06gDzOQqTyWQ5ZOPK542ZlT1k447njQGl5yjMv2hNJpPL7z7P7VGK26MUt4dn4KTn5e48Ka/RaFz6vDGzO89RuPp5Y0D5k/Iajcblj13h9ijF7VGK28NzcNLzYpVdhebqB21WdlLelR27sqvQXPm8MW6PUtwepbg9PAsnPS9V3WXXrurY1V2F5oqOXd1l167o2Nwepbg9SnF7eB5Oel7I1nFGzu7Ytl527cyObes4I2d2bG6PUtwepbg9PBMnPS9j78BaZ3Vse8cZOaNj2zuw1hkdm9ujFLdHKW4Pz8VJz4vU9E4Sju7YNR1Y68iOXdM7STiyY3N7lOL2KMXt4dk46XmJ2t46yVEdu7Z3knBEx67trZMc0bG5PUpxe5Ti9vB8nPS8gCzLyM7OrvWtk2rbsR1166TadGxH3SuwNh3bUfdu5PYoxe1RytPaIzs7u0bL8FR8GzIPIwiCVYeXJAkmkwkGgwGhoaEAUOvDLzqdDllZWcjIyEC9evWgUqmqnSc/Px95eXkIDAyERqOpdQwajQb+/v7Izs6GyWRCQEBAtfMYjUZkZWVBqVRCp9PBZDLVKgYACA4OtnovBEGosj4RISsrCyaTCfXq1QPA7cHtUbfb49atW7VejifhpOdhFAoFiAiyLIOIYDQaAQABAQGQJAmSJDlkPX5+fjAYDMjPz4dara7yF6HJZILRaERAQAAUCgWKioocEoNCoUBAQAAMBgOICEpl5R9HWZZhMBig0WigVqtRXFzskBgAIDAwEAaDAXq9Hmq1utIvWiKCwWCAKIoIDAzk9uD2AFD328OWhOtNOOl5mGvXrqFly5ZQKBTIysqCKIrw9/dHYmIigoOD3R0eY8zH5OXlYcKECe4Ow2E46XkYo9EIhUIBvV4PWZYRGhoKSZIQHByM+vXruzs8xpiPqWoP0xv55IUse/bswYABAxAdHQ1BEJCSklLtPLt27ULXrl2h0WjQsmVLrF69ulydpUuXolmzZtBqtYiNjcXhw4ftjk0QBKuLVmw5n8AYY8w2Ppn08vPzcffdd2Pp0qU21b948SISExPxwAMP4Pjx45g0aRKeffZZbNu2zVJnw4YNSEpKwqxZs3Ds2DHcfffdSEhIQEZGhl2xhYeHW90NnjHGmOMIRETuDsKdBEHAt99+i8GDB1da5/XXX8fmzZtx8uRJS9kTTzyB7OxsbN26FQAQGxuLHj164KOPPgJQcmK5cePGePHFFzFlypQKl1tcXGx1wlmv12PRokVo3bo1AgMDAZRciVZUVIQhQ4bw4U3GmMvp9XoEBwcjJycHOp3O3eHUmk/u6dnr4MGDiI+PtypLSEjAwYMHAZQkpqNHj1rVEUUR8fHxljoVSU5ORnBwsOXVuHFjACV7onVtQChjjHkCTno2SEtLQ0REhFVZREQE9Ho9CgsLkZmZCUmSKqyTlpZW6XKnTp2KnJwcy+vKlSvIyMjwyQc7MsaYK9Sty3K8jEajgUajsSozGo2oV68esrOzcfPmzTpxOIExxjwF7+nZIDIyEunp6VZl6enp0Ol08PPzQ1hYGBQKRYV1IiMj7V6fSqWy3AIoKysLPn7alTHGHIaTng3i4uKwY8cOq7Lt27cjLi4OQMl96rp162ZVR5Zl7Nixw1LHXuZ735lvQcYYY6z2fDLp5eXl4fjx4zh+/DiAkiEJx48fx+XLlwGUnGsbOXKkpf7YsWNx4cIFTJ48GWfOnMGyZcvw1Vdf4eWXX7bUSUpKwsqVK/HFF1/g9OnTGDduHPLz8/H000/XOE61Wo169erxnh5jjDmIT57TO3LkCB544AHL30lJSQCAUaNGYfXq1bh+/bolAQJATEwMNm/ejJdffhmLFi1Co0aN8OmnnyIhIcFSZ+jQobhx4wZmzpyJtLQ0dO7cGVu3bi13cYu9VCoVj9djjDEH8flxep5Er9dj7NixeOihhyx3YuFxeowxd+JxeowxxpiX4qTHGGPMZ3DSY4wx5jM46THGGPMZnPQYY4z5DE56jDHGfAYnPcYYYz6Dkx5jjDGfwUmPMcaYz+CkxxhjzGdw0mOMMeYzOOkxxhjzGZz0GGOM+QyPe7SQLMvYvXs39u7di0uXLqGgoADh4eHo0qUL4uPj0bhxY3eHyBhjzEt5zJ5eYWEh3nrrLTRu3Bj9+/fHDz/8gOzsbCgUCvzxxx+YNWsWYmJi0L9/f/zyyy/uDtdr6U16fJP5jdVLb9K7OyzGGHMJj9nTa926NeLi4rBy5Ur8/e9/tzxPrqxLly5h3bp1eOKJJzBt2jSMGTPGDZEyxhjzVh6T9H788Ue0bdu2yjpNmzbF1KlT8eqrr1o92Zwxxhizhccc3qwu4ZWlUqnQokULJ0bDGGOsLvKYPb07FRUV4X//+x8yMjIgy7LVtIEDB7opKvcwmUzuDoExxuoEj0x6W7duxciRI5GZmVlumiAIkCTJDVG5R35+PoxGo7vDYIyxOsFjDm+W9eKLL2LIkCG4fv06ZFm2evlSwsvNzUVeXl6FF/Uwxhizn0fu6aWnpyMpKQkRERHuDsVtcnNzodfrERgYCIVC4e5wGGOsTvDIPb3HHnsMu3btcncYbpOXlwe9Xg+dToeAgAB3h8MYY3WGR+7pffTRRxgyZAj27t2Ljh07lju899JLL7kpMucLCgpCXl4eQkJCEBQUBIPB4O6QGGOszvDIpPfll1/ixx9/hFarxa5duyAIgmWaIAh1OunpdDoEBgYiKCjI3aEwxlid45FJb9q0aZgzZw6mTJkCUfTII7CMMca8kEdmFIPBgKFDh/pkwtPr9cjLy0Nubq67Q2GMsTrHI/f0Ro0ahQ0bNuCNN95wdygul5ubi8DAQOj1JTeB1mg0bo7IfqMmflPptC8WPcrzefB83hAjz+fa+Za8GV/pNG/kkUlPkiTMmzcP27ZtQ6dOncpdyDJ//nw3ReYagYGBUCqV0Ov18Pf35yELjDHmIB6Z9E6cOIEuXboAAE6ePOnmaNzDfCFLdna2RwxbMJpMMEhGBGj83B0KY4zVmEBE5O4gWAm9Xo+xY8eiZ5M2UIgle3f5ogRJBHplBSBAqv05znx1MX6NsX5CRY+LTRBgqPow6nrpPDbKF/A3IQr9FA3RBiFWV9UyxuqmPEMRen32BnJycqDT6dwdTq151ZUily5dwoQJE9wdhktpZBEKcn9yyYMRmSjCJrqEJNMvmCQdxDb5LxQQ3wybMeY9PPLw5gMPPFDhXsT169dx/fp1fPTRR26Iyn0UHrIvroIC4dCgCBJOUhZ+k7LwGc4gXmyIeLEhmgve/yuQMVa3eWTS69y5s9XfkiThwoUL+OOPP7B69Wq3xFSX7TJexylTTpV1LiEPQMnNAfyghB+UMJGMXBixXr6AFPkSOgr18bDYCHFCBDQCX3zDGPM8Hpn0FixYUGH5p59+io8++gjDhw93cUR1224pDQcoA2pUnaj875iuFETUgwZEhAJIOEI3cEzKRANBi35CI8SLDdFQcP9FOIwxZuaRSa8yffv2xYsvvujuMOokPygRLmhrNK8gCAiAEgFQwkgyblIx/k3nsFG+iG5CGB4SG6G7EA6V4FWnkBljdZBXJb2ff/4ZDzzwgLvDYFVQCSLq3977y4cJO+kaDkrpaC0EY7HiPoh8xSdjzI08Muk9+mj5Owekp6fj0KFDeOCBB6ymf/NN5XcSYO4hEyEPRhRCgh+UaCYEIlFsAk53jDF388jjTcHBweVerVu3xogRI9CoUSOr8tpYunQpmjVrBq1Wi9jYWBw+fLjK+hs3bkSbNm2g1WrRsWNHbNmyxWo6EWHmzJmIioqCn58f4uPjce7cuVrF6CoyCEaSq3zJ1QzpNJCETCrCDRRBhIB+QiO8q7gHyxW98LDYmMf1McbcziP39FatWuX0dWzYsAFJSUlYsWIFYmNjsXDhQiQkJODs2bNo0KBBufoHDhzAsGHDkJycjEceeQTr1q3D4MGDcezYMXTo0AEAMG/ePCxevBhffPEFYmJiMGPGDCQkJODUqVPQamt2vswVtIICSojQw1hlPQkyGsD6jizmw5j5MEEBAdFCAB4WGuFBMRrhAt+9hTHmWTzmjixE5NI9gdjYWPTo0cMy5k+WZTRu3BgvvvgipkyZUq7+0KFDkZ+fj++//95Sdu+996Jz585YsWIFiAjR0dF45ZVX8OqrrwIAcnJyEBERgdWrV+OJJ54ot8zi4mIUFxdb/tbr9Zg8ebLVHVlMIJhEwj05/k67I8tdFyKRbah6kPn38hVsk/9Cg9sXuxhJhh5GmCDDD0q+YIWxOqqu3ZHFY/b02rdvj5kzZ+LRRx+FWq2utN65c+cwf/58NG3atMLkZAuDwYCjR49i6tSpljJRFBEfH4+DBw9WOM/BgweRlJRkVZaQkICUlBQAwMWLF5GWlob4+NI7kgcHByM2NhYHDx6sMOklJydjzpw5VmXDhg3DFV0QhNs3mRZkCSJJWNfkLkjK2u85mRQFuBUiWZWdDOkEpeRf5Xzn/tqD/LQ0XFWIMEgGCBAQoK6HVqHt0Ty0HXTaevgvgP/WOkLGmCcxFOa7OwSH8pikt2TJErz++ut44YUX8Pe//x3du3dHdHQ0tFotsrKycOrUKezbtw+//fYbJkyYgHHjxtV4XZmZmZAkCREREVblEREROHPmTIXzpKWlVVg/LS3NMt1cVlmdO02dOtUqkZr39DwVkQyZZETrmqJVWEc0CmkBpaiqfkbGGPMQHpP0+vbtiyNHjmDfvn3YsGED1q5di0uXLqGwsBBhYWHo0qULRo4cieHDh6NevXruDtchNBqN1zwvLzo4BoIgolm9u1DPP9zd4TDGWI14TNIz69WrF3r16uXUdYSFhUGhUCA9Pd2qPD09HZGRkRXOExkZWWV98//T09MRFRVlVefO26p5o8igxogMauzuMBhjrFZ88ooDtVqNbt26YceOHZYyWZaxY8cOxMXFVThPXFycVX0A2L59u6V+TEwMIiMjrero9XocOnSo0mUyxhhzLY/b03OVpKQkjBo1Ct27d8c999yDhQsXIj8/H08//TQAYOTIkWjYsCGSk5MBABMnTkSfPn3w4YcfIjExEevXr8eRI0fwySefACi5FdekSZPw1ltvoVWrVpYhC9HR0Rg8eLC7NpMxxlgZPpv0hg4dihs3bmDmzJlIS0tD586dsXXrVsuFKJcvX4Yolu4I33fffVi3bh2mT5+ON954A61atUJKSopljB4ATJ48Gfn5+XjuueeQnZ2NXr16YevWrR49Ro8xxnyJx4zTY6VPTm/SoUe5IQvZYW0cOGThf1Zl9bOrH7LAGPNNhsJ8fD5hUJ0Zp+eT5/QYY4z5Jo9Meg8++GC5QdsAkJWVhQcffNANETHGGKsLPPKc3q5du3DixAmkpqZi7dq1CAgoeRCpwWDA7t273RydG/ARaMYYcwiP3NMDgJ9++glpaWm499578eeff7o7HPchGQJJ1ddjjDFWLY9NelFRUdi9ezc6duyIHj16YNeuXe4OyeUEWYLSVATwk+gYY8whPDLpmZ+2oNFosG7dOkycOBEPPfQQli1b5ubIXKck4RWCBBF0+4kLjDHGascjz+ndOYpi+vTpaNu2LUaNGuWmiFxLIAlKyQASREgKNUSS3R0SY4zVCR6Z9C5evIjwcOubGv/zn/9EmzZtcOTIETdF5RoqlQpKqRgkKmBS+kHghMcYYw7jkUmvadOmFZa3b98e7du3d3E0rtWgQYOSPTylHyAIAF+4yRhjDuOR5/R8nSSqShIeY4wxh+Kk52GMRiOUUjEEmYcpMMaYo3nk4U1fduPGDQSGRkBpKoTJAffavJNS8keDm/c6fLmMMeYNeE/PwxARTKIGJIhQmgoBvpCFMcYchpOeJxIEmJR+txNfEd+GjDHGHISTnqcqk/j4NmSMMeYYnPQ8mSBAUmrAtyFjjDHH4KTn8QS+DRljjDkIJz3GGGM+g5MeY4wxn8FJjzHGmM/gpMcYY8xncNJjjDHmMzjpMcYY8xmc9BhjjPkMTnqMMcZ8Bic9xhhjPoOTHmOMMZ/BSY8xxpjP4KTHGGPMZ3DSY4wx5jM46THGGPMZnPQYY4z5DE56jDHGfAYnPcYYYz6Dkx5jjDGfwUmPMcaYz+Ck5/EIgiy5OwjGGKsTOOl5MiIoTMUAyN2RMMZYnaB0dwCu9s0332DFihU4evQobt26hdTUVHTu3Lna+TZu3IgZM2bgzz//RKtWrfDee++hf//+lulEhFmzZmHlypXIzs5Gz549sXz5crRq1cqu+FQqFQSSIcglCU+QJcgKFURTsb2byhhjtaaU6tZ3j88lvfz8fPTq1QuPP/44xowZY9M8Bw4cwLBhw5CcnIxHHnkE69atw+DBg3Hs2DF06NABADBv3jwsXrwYX3zxBWJiYjBjxgwkJCTg1KlT0Gq1NscXHR0NtakAAmQAAEGAQjJAl3UeEJyzY15y+JRAggIQhNIJJEMgGSSITlt3tesigkASAAEkKpwcQ+XrqvQ9cgJuj+rXxe1hLnd+e5AsO2W57iIQkU8eO/vzzz8RExNj057e0KFDkZ+fj++//95Sdu+996Jz585YsWIFiAjR0dF45ZVX8OqrrwIAcnJyEBERgdWrV+OJJ56ocLnFxcUoLi79FaXX67F48WL4BwQAAGSIAAQIIBjV/nDe0ejbe5Ukw6TUAoIIQTZCIRkhKVQgUeWk9Vort06SoTQVgQQRklIDwLlfbgAqWCfKvTcuCILbw4zbw8Jd7UGyjHenv4qcnBzodDqnrMOVfG5PryYOHjyIpKQkq7KEhASkpKQAAC5evIi0tDTEx8dbpgcHByM2NhYHDx6sNOklJydjzpw5VmUzZswAAMhQAKIImH+TCIqSX3lOYlIpoDQVQikVQxYVEGUJklIDWaF22jrvRKICEEQoJANkEERZAokKmJR+Tv81X0oBkyBCaSqEQjIAAAQQTCp/5+/ZlMHtYcbtYeau9qhru0V8IYsN0tLSEBERYVUWERGBtLQ0y3RzWWV1KjJ16lTk5ORYXleuXIEgCCV7eKKLm0YQSjoPAFGWIIsKl3ZoM1mhtnypAHDxF2yJki8Sbcm51du/7l35BQuA26MMbo9SntAe3q5OJ721a9ciMDDQ8tq7d6+7Q7Ki0Wig0+msXgAgQgbI9cfRRdlY5t+SW4ZKCLJk6dB3xuQyRJa9CgAl/3bDz11uj9u4PSw8oj28XJ0+vDlw4EDExsZa/m7YsGGNlhMZGYn09HSrsvT0dERGRlqmm8uioqKs6thyZWhZRUVF8PP3h0jS7UtZXPMrTpQMUEgGSAo1ZFFVcijHVAiT0s9lv6oFWYLSVAgSRJiUfhBlo+XLzmW/qomgNBXe3qMo+WVf9r1w1a9qbo/buD0sPKI96oA6vacXFBSEli1bWl5+fn41Wk5cXBx27NhhVbZ9+3bExcUBAGJiYhAZGWlVR6/X49ChQ5Y6tpJl+fYFLIBIkkv2+Kw6tEJtOZRDt8+luOIX7Z0dGoIAWaGGpFBDIRkglvml7zR3fMGSqLCcMxFIhtJU6JI9DG6P27g9LDyiPeqIOr2nV5Fbt27h8uXLuHbtGgDg7NmzAEr21sx7bCNHjkTDhg2RnJwMAJg4cSL69OmDDz/8EImJiVi/fj2OHDmCTz75BAAgCAImTZqEt956C61atbIMWYiOjsbgwYPtD1IQIZMAERJEyCBy3m+Tch3aEkNJx3bFL9qKOrSZOSan/6Kt4AvWMun2F60r9jC4PW7j9ihdlSe0Rx1Sp/f0KvLdd9+hS5cuSExMBAA88cQT6NKlC1asWGGpc/nyZVy/ft3y93333Yd169bhk08+wd13342vv/4aKSkpljF6ADB58mS8+OKLeO6559CjRw/k5eVh69atdo3RsyKKJVdwAiVj9pzwi7bSDm3mgl+0VXVoM6f/oq3iC9ZSxQV7GNwet3F7lK7CQ9qjLvHZcXqeSK/X480334RfUEjph1su2duTFGqYlP4O+0VbbYcuy4YvoZqwpUPXOGZb2blt9sZsK26P27g9LDylPRTGArw98406M07P5/b0vI4ggiBAuN2xHPGry+7O4YRftDX5snL4L9oafFk5Yw+D2+M2bo/SRXpYe9QlnPS8gSBCUmgc0rFr/GvQgR27Nr/OHdaxa/Hr3JFftNwet3F7lC7K09pDoan5cjwQJz0v4YiOXevDHw7o2I44HFXrju2Aw1HcHqW4PW6rq+0huPhGAE7GSc+L1KZjO+x4fy06tiPPv9S4Yzvw/Au3Ryluj9u4PTweJz0vU5OO7fAT3DXo2M644MDuju2EDs3tUYrb4zZuD4/GSc8L2dOxnXJFF2BXx3bWFXaAHR3biR2a26MUt8dt3B4ei5Oel7KlYzutQ5vZ0LGd2aHNqu3YLujQ3B6luD1u4/bwSJz0vFhVHdvpHdqsio7tig5tVmnHdmGH5vYoxe1xG7eHx+Gk5+Uq6tgu69BmFXRsV3Zos3Id2w0dmtujFLfHbdweHsXn7r3pyYgIoigCJAFk7gS3f51KRlT2G4UAmAQlFJIBKim3pLqohEwCYHLdo0dMUEJBBigN+QAAWRAhCUpAMrksBhkCABEKYxEUKCp5bxRqkCwDsmsG2XJ7lOL2KOWt7UG3907rys27+DZkHuTChQto0aKFu8NgjLFyzp8/j+bNm7s7jFrjPT0PUr9+fQAlN7wODg52czQ1o9fr0bhxY1y5csVr79PH2+AZeBs8Q05ODpo0aWL5fvJ2nPQ8iCiWHL4MDg722g5iVvZJ8N6Kt8Ez8DZ4BvP3k7erG1vBGGOM2YCTHmOMMZ/BSc+DaDQazJo1CxqN997VnLfBM/A2eAbeBs/DV28yxhjzGbynxxhjzGdw0mOMMeYzOOkxxhjzGZz0GGOM+QxOeowxxnwGJz0n++abb9CvXz+EhoZCEAQcP37cpvk2btyINm3aQKvVomPHjtiyZYvVdCLCzJkzERUVBT8/P8THx+PcuXNO2AJg6dKlaNasGbRaLWJjY3H48GGviX3Pnj0YMGAAoqOjIQgCUlJSqp1n165d6Nq1KzQaDVq2bInVq1eXq2Pve1IbycnJ6NGjB4KCgtCgQQMMHjwYZ8+erXY+T2qH5cuXo1OnTpY7k8TFxeGHH37wmvgr8u6770IQBEyaNKnKep60HbNnz4YgCFavNm3aeE38DkHMqf7973/TnDlzaOXKlQSAUlNTq51n//79pFAoaN68eXTq1CmaPn06qVQqOnHihKXOu+++S8HBwZSSkkL//e9/aeDAgRQTE0OFhYUOjX/9+vWkVqvp888/p99++43GjBlDISEhlJ6e7vGxExFt2bKFpk2bRt988w0BoG+//bbK+hcuXCB/f39KSkqiU6dO0ZIlS0ihUNDWrVstdex9T2orISGBVq1aRSdPnqTjx49T//79qUmTJpSXl1fpPJ7WDt999x1t3ryZfv/9dzp79iy98cYbpFKp6OTJk14R/50OHz5MzZo1o06dOtHEiRMrredp2zFr1ixq3749Xb9+3fK6ceOG18TvCJz0XOTixYs2J73HH3+cEhMTrcpiY2Pp+eefJyIiWZYpMjKS3n//fcv07Oxs0mg09OWXXzo07nvuuYfGjx9v+VuSJIqOjqbk5GSPj/1OtiS9yZMnU/v27a3Khg4dSgkJCZa/7X1PHC0jI4MA0O7duyut48ntYFavXj369NNPK5zmyfHn5uZSq1ataPv27dSnT58qk56nbcesWbPo7rvvtrm+p8XvCHx40wMdPHgQ8fHxVmUJCQk4ePAgAODixYtIS0uzqhMcHIzY2FhLHUcwGAw4evSo1XpEUUR8fHyl6/GU2Guquvhr8p44Wk5ODgBUedd7T24HSZKwfv165OfnIy4ursI6nhz/+PHjkZiYWC6+injidpw7dw7R0dFo3rw5hg8fjsuXL3tV/LXFT1nwQGlpaYiIiLAqi4iIQFpammW6uayyOo6QmZkJSZIqXM+ZM2cqnMdTYq+pyuLX6/UoLCxEVlaW3e+JI8myjEmTJqFnz57o0KFDpfU8sR1OnDiBuLg4FBUVITAwEN9++y3atWtXYV1PjB8A1q9fj2PHjuHXX3+1qb6nbUdsbCxWr16Nu+66C9evX8ecOXPQu3dvnDx5EkFBQR4fvyPwnp4DrV27FoGBgZbX3r173R0Sq2PGjx+PkydPYv369e4OxW533XUXjh8/jkOHDmHcuHEYNWoUTp065e6wbHblyhVMnDgRa9euhVardXc4NfLwww9jyJAh6NSpExISErBlyxZkZ2fjq6++cndoLsNJz4EGDhyI48ePW17du3ev0XIiIyORnp5uVZaeno7IyEjLdHNZZXUcISwsDAqFwq71eErsNVVZ/DqdDn5+fjV6TxxlwoQJ+P7777Fz5040atSoyrqe2A5qtRotW7ZEt27dkJycjLvvvhuLFi2qsK4nxn/06FFkZGSga9euUCqVUCqV2L17NxYvXgylUglJkrxiO8oKCQlB69at8ccff1Q43dPjrwlOeg4UFBSEli1bWl5+fn41Wk5cXBx27NhhVbZ9+3bL+Y+YmBhERkZa1dHr9Th06FCl50hqQq1Wo1u3blbrkWUZO3bsqHQ9nhJ7TVUXf03ek9oiIkyYMAHffvstfv75Z8TExFQ7jze0gyzLKC4urnCaJ8bft29fnDhxotwP2+HDh+P48eNQKBResR1l5eXl4fz584iKiqpwuqfHXyPuvpKmrrt58yalpqbS5s2bCQCtX7+eUlNT6fr165Y6I0aMoClTplj+3r9/PymVSvrggw/o9OnTNGvWrAovEw4JCaFNmzbR//73Pxo0aJDThixoNBpavXo1nTp1ip577jkKCQmhtLQ0j4+dqORKu9TUVEpNTSUANH/+fEpNTaVLly4REdGUKVNoxIgRlvrmIQuvvfYanT59mpYuXVrhkIWq3hNHGzduHAUHB9OuXbusLjUvKCiw1PH0dpgyZQrt3r2bLl68SP/73/9oypQpJAgC/fjjj14Rf2XuvHrT07fjlVdeoV27dtHFixdp//79FB8fT2FhYZSRkeEV8TsCJz0nW7VqFQEo95o1a5alTp8+fWjUqFFW83311VfUunVrUqvV1L59e9q8ebPVdFmWacaMGRQREUEajYb69u1LZ8+edco2LFmyhJo0aUJqtZruuece+uWXX7wm9p07d1b4/ptjHjVqFPXp06fcPJ07dya1Wk3NmzenVatWlVtuVe+Jo1UUPwCruDy9HUaPHk1NmzYltVpN4eHh1LdvX0vC84b4K3Nn0vP07Rg6dChFRUWRWq2mhg0b0tChQ+mPP/7wmvgdgZ+nxxhjzGfwOT3GGGM+g5MeY4wxn8FJjzHGmM/gpMcYY8xncNJjjDHmMzjpMcYY8xmc9BhjjPkMTnqMeanPPvsM/fr1c/p6tm7dis6dO0OWZaevizFn46THmBcqKirCjBkzMGvWLKev66GHHoJKpcLatWudvi7GnI2THmNe6Ouvv4ZOp0PPnj1dsr6nnnoKixcvdsm6GHMmTnqMudGNGzcQGRmJd955x1J24MABqNXqcne3L2v9+vUYMGCAVdn999+PSZMmWZUNHjwYTz31lOXvZs2a4a233sLIkSMRGBiIpk2b4rvvvsONGzcwaNAgBAYGolOnTjhy5IjVcgYMGIAjR47g/PnzNd9YxjwAJz3G3Cg8PByff/45Zs+ejSNHjiA3NxcjRozAhAkT0Ldv30rn27dvX42f17hgwQL07NkTqampSExMxIgRIzBy5Eg8+eSTOHbsGFq0aIGRI0ei7G15mzRpgoiICH4wMvN6nPQYc7P+/ftjzJgxGD58OMaOHYuAgAAkJydXWj87Oxs5OTmIjo6u8fqef/55tGrVCjNnzoRer0ePHj0wZMgQtG7dGq+//jpOnz5d7sGg0dHRuHTpUo3WyZin4KTHmAf44IMPYDKZsHHjRqxduxYajabSuoWFhQAArVZbo3V16tTJ8u+IiAgAQMeOHcuVZWRkWM3n5+eHgoKCGq2TMU/BSY8xD3D+/Hlcu3YNsizjzz//rLJuaGgoBEFAVlZWtcuVJKlcmUqlsvxbEIRKy+4conDr1i2Eh4dXu07GPBknPcbczGAw4Mknn8TQoUPx5ptv4tlnny23l1WWWq1Gu3btcOrUqXLT7jwkeeHCBYfEWFRUhPPnz6NLly4OWR5j7sJJjzE3mzZtGnJycrB48WK8/vrraN26NUaPHl3lPAkJCdi3b1+58k2bNuGbb77B+fPn8fbbb+PUqVO4dOkSrl69WqsYf/nlF2g0GsTFxdVqOYy5Gyc9xtxo165dWLhwIdasWQOdTgdRFLFmzRrs3bsXy5cvr3S+Z555Blu2bEFOTo5VeWJiIubNm4d27dphz549WLZsGQ4fPow1a9bUKs4vv/wSw4cPh7+/f62Ww5i7CVT2umTGmNcYMmQIunbtiqlTpwIoGafXuXNnLFy40KHryczMxF133YUjR44gJibGoctmzNV4T48xL/X+++8jMDDQ6ev5888/sWzZMk54rE7gPT3G6ghn7ekxVpdw0mOMMeYz+PAmY4wxn8FJjzHGmM/gpMcYY8xncNJjjDHmMzjpMcYY8xmc9BhjjPkMTnqMMcZ8Bic9xhhjPoOTHmOMMZ/BSY8xxpjP4KTHGGPMZ3DSY4wx5jM46THGGPMZnPQYY4z5DE56jDnIrl27IAgCdu3a5e5QmINduXIFWq0W+/fvd/m6jUYjGjdujGXLlrl83XURJz3G7LRs2TKsXr3a3WHUyLp16zzmIbOyLGPevHmIiYmBVqtFp06d8OWXX9o0744dOzB69Gi0bt0a/v7+aN68OZ599llcv37dql5BQQGWLl2Kfv36ISoqCkFBQejSpQuWL18OSZJsjnXu3LmIjY1Fz5497dpGR1CpVEhKSsLbb7+NoqIil6+/ruGHyDJmpw4dOiAsLKzcHp0syzAYDFCr1RBFz/w9+cgjj+DkyZP4888/3R0Kpk6dinfffRdjxoxBjx49sGnTJmzevBlffvklnnjiiSrn7d69O27duoUhQ4agVatWuHDhAj766CP4+/vj+PHjiIyMBACcPHkSnTp1Qt++fdGvXz/odDps27YN3377LUaOHIkvvvii2jhv3LiBhg0b4osvvsCwYcMcsu32ys7ORkREBJYvX47Ro0e7JYY6gxirgby8PHeH4Dbt27enPn36uDuMGklMTKSmTZu6Owz666+/SKVS0fjx4y1lsixT7969qVGjRmQymaqcf/fu3SRJUrkyADRt2jRL2Y0bN+jkyZPl5n/66acJAJ07d67aWOfPn09+fn6Um5tbbV1neuSRR6h3795ujaEu4KTH6K+//qLRo0dTVFQUqdVqatasGY0dO5aKi4uJiGjVqlUEgHbt2kXjxo2j8PBwCgkJscy/dOlSateuHanVaoqKiqIXXniBsrKyrNbx+++/06OPPkoRERGk0WioYcOGNHToUMrOzrbU+fHHH6lnz54UHBxMAQEB1Lp1a5o6dWq18dsyX1FREc2cOZNatGhBarWaGjVqRK+99hoVFRWVW96aNWuoR48e5OfnRyEhIdS7d2/atm0bERE1bdqUAFi9zAlw586dBIB27txptbyvvvqKunbtSlqtlkJDQ2n48OH0119/WdUZNWoUBQQE0F9//UWDBg2igIAACgsLo1deeaXaBEBElJKSQv3797e0YfPmzWnu3LlW8/bp06dc7FUlwFGjRpWrb37NmjWr2piqsnTpUgJAv/32m1X5unXrCADt3bu3RsutX78+Pfroo9XW++677wgAfffdd9XW/dvf/kb3339/ufKmTZvSqFGjypX36dPH6keR+XOxYcMGmj17NkVHR1NgYCD985//pOzsbCoqKqKJEydSeHg4BQQE0FNPPVXh53LRokUkCALdvHmz2phZ5ZSu3KtknufatWu45557kJ2djeeeew5t2rTB1atX8fXXX6OgoABqtdpS94UXXkB4eDhmzpyJ/Px8AMDs2bMxZ84cxMfHY9y4cTh79iyWL1+OX3/9Ffv374dKpYLBYEBCQgKKi4vx4osvIjIyElevXsX333+P7OxsBAcH47fffsMjjzyCTp06Ye7cudBoNPjjjz+qvXDAlvlkWcbAgQOxb98+PPfcc2jbti1OnDiBBQsW4Pfff0dKSoql7pw5czB79mzcd999mDt3LtRqNQ4dOoSff/4Z/fr1w8KFC/Hiiy8iMDAQ06ZNAwBERERUGt/q1avx9NNPo0ePHkhOTkZ6ejoWLVqE/fv3IzU1FSEhIZa6kiQhISEBsbGx+OCDD/DTTz/hww8/RIsWLTBu3Lgq34fVq1cjMDAQSUlJCAwMxM8//4yZM2dCr9fj/fffBwBMmzYNOTk5+Ouvv7BgwQIAQGBgYKXLfP755xEfH29VtnXrVqxduxYNGjSwlGVmZlYZm1lQUBA0Gg0AIDU1FQEBAWjbtq1VnXvuuccyvVevXjYt1ywvLw95eXkICwurtm5aWhoAVFvXaDTi119/rfb9t0VycjL8/PwwZcoU/PHHH1iyZAlUKhVEUURWVhZmz56NX375BatXr0ZMTAxmzpxpNX+3bt1ARDhw4AAeeeSRWsfjs9yddZl7jRw5kkRRpF9//bXcNFmWiah0T69Xr15Wew4ZGRmkVqupX79+VoeaPvroIwJAn3/+ORERpaamEgDauHFjpXEsWLCAANCNGzfsit+W+dasWUOiKJbbe1ixYgUBoP379xMR0blz50gURfrHP/5R7tCZ+b0gqvzw5p17egaDgRo0aEAdOnSgwsJCS73vv/+eANDMmTMtZea9qrlz51ots0uXLtStW7eq3wQiKigoKFf2/PPPk7+/v9VeQ20Ob547d46Cg4Pp73//u9XnAJXsDd75WrVqlVUczZs3L7eO/Px8AkBTpkyxO74333yTANCOHTuqrFdcXEzt2rWjmJgYMhqNVdb9448/CAAtWbKk3DR79/Q6dOhABoPBUj5s2DASBIEefvhhq/nj4uIqbKNr164RAHrvvfeqjJlVzTPPtjOXkGUZKSkpGDBgALp3715uuiAIVn+PGTMGCoXC8vdPP/0Eg8GASZMmWV24MWbMGOh0OmzevBkAEBwcDADYtm0bCgoKKozFvMezadMmyLJs8zbYMt/GjRvRtm1btGnTBpmZmZbXgw8+CADYuXMnACAlJQWyLGPmzJnlLkS5872wxZEjR5CRkYEXXngBWq3WUp6YmIg2bdpY3p+yxo4da/V37969ceHChWrX5efnZ/l3bm4uMjMz0bt3bxQUFODMmTN2x36n/Px8/OMf/0C9evXw5ZdfWn0Otm/fbtMrISHBMk9hYaFlr68s8/tUWFhoV3x79uzBnDlz8Pjjj1vatTITJkzAqVOn8NFHH0GprPpg182bNwEA9erVsyueiowcORIqlcryd2xsLIio3IUpsbGxuHLlCkwmk1W5OQZb96xZxfjwpg+7ceMG9Ho9OnToYFP9mJgYq78vXboEALjrrrusytVqNZo3b26ZHhMTg6SkJMyfPx9r165F7969MXDgQDz55JOWhDh06FB8+umnePbZZzFlyhT07dsXjz76KB577LEqr4S0Zb5z587h9OnTCA8Pr3AZGRkZAIDz589DFEW0a9fOpvejOpW9PwDQpk0b7Nu3z6pMq9WWi7FevXrIysqqdl2//fYbpk+fjp9//hl6vd5qWk5Ojr2hlzNmzBicP38eBw4cQGhoqNW0Ow+B2sLPzw/FxcXlys2X5JdN4tU5c+YM/vGPf6BDhw749NNPq6z7/vvvY+XKlXjzzTfRv39/m9dBDrjIvUmTJlZ/mz/7jRs3LlcuyzJycnKs3mtzDDX5AcZKcdJjNrPni+hOH374IZ566ils2rQJP/74I1566SUkJyfjl19+QaNGjeDn54c9e/Zg586d2Lx5M7Zu3YoNGzbgwQcfxI8//mi1Z3FnTNXNJ8syOnbsiPnz51e4jDu/dNylsm2sTnZ2Nvr06QOdToe5c+eiRYsW0Gq1OHbsGF5//XW79pwrsmjRInz55Zf4z3/+g86dO5ebbj4/Vp3g4GDLZygqKgo7d+4EEVl9iZvH2UVHR9u0zCtXrqBfv34IDg7Gli1bEBQUVGnd1atX4/XXX8fYsWMxffp0m5ZvTjq2/PAwu3ObzCpr38rK70y05hhsOWfJKseHN31YeHg4dDodTp48WaP5mzZtCgA4e/asVbnBYMDFixct0806duyI6dOnY8+ePdi7dy+uXr2KFStWWKaLooi+ffti/vz5OHXqFN5++238/PPPlsOPlaluvhYtWuDWrVvo27cv4uPjy73Me2ItWrSALMs4depUleuz9Zd2Ze+PuezO96emdu3ahZs3b2L16tWYOHEiHnnkEcTHx1d4SM7evYS9e/fi1VdfxaRJkzB8+PAK60RFRdn02rBhg2Wezp07o6CgAKdPn7Za1qFDhyzTq3Pz5k3069cPxcXF2LZtG6Kioiqtu2nTJjz77LN49NFHsXTpUhu2vESTJk3g5+eHixcvVjg9Nze3XFl6errNy7eHOYY7L/5h9uGk58NEUcTgwYPxf//3fzhy5Ei56dUd0omPj4darcbixYut6n722WfIyclBYmIiAECv15c7P9GxY0eIomg5xHXr1q1yyzd/8VV0GMzMlvkef/xxXL16FStXrixXt7Cw0HIl6uDBgyGKIubOnVtu76js9gUEBCA7O7vSmMy6d++OBg0aYMWKFVbb8MMPP+D06dOW96e2zHsKZWM0GAwV3rYqICDA5sOd169fx+OPP45evXpZrgCtSE3O6Q0aNAgqlcoqRiLCihUr0LBhQ9x3331WcZw5cwZGo9FSlp+fj/79++Pq1avYsmULWrVqVWl8e/bswRNPPIG//e1vWLt2rV03DlCpVOjevXuF/QMADh48aHWXlN9++w3nzp1zyOHQOx09ehSCICAuLs7hy/YlfHjTx73zzjv48ccf0adPH8vl/NevX8fGjRuxb98+q0vq7xQeHo6pU6dizpw5eOihhzBw4ECcPXsWy5YtQ48ePfDkk08CAH7++WdMmDABQ4YMQevWrWEymbBmzRooFAr885//BFBym6c9e/YgMTERTZs2RUZGBpYtW4ZGjRpVeem6LfONGDECX331FcaOHYudO3eiZ8+ekCQJZ86cwVdffYVt27ahe/fuaNmyJaZNm4Y333wTvXv3xqOPPgqNRoNff/0V0dHRSE5OBlBy6fjy5cvx1ltvoWXLlmjQoEGFF0+oVCq89957ePrpp9GnTx8MGzbMMmShWbNmePnll2vabFbuu+8+1KtXD6NGjcJLL70EQRCwZs2aCr94u3Xrhg0bNiApKQk9evRAYGAgBgwYUOFyX3rpJdy4cQOTJ0/G+vXrraZ16tQJnTp1AlCzc3qNGjXCpEmT8P7778NoNKJHjx5ISUnB3r17sXbtWqtDflOnTsUXX3yBixcvolmzZgCA4cOH4/Dhwxg9ejROnz5ttccYGBiIwYMHAyg5rzpw4EAIgoDHHnsMGzdurHQ7KjNo0CBMmzYNer0eOp3Oalp2djYefPBBDB8+HHq9HkuWLEFQUBBOnjyJjz/+GM8//7zd701ltm/fjp49e5Y7p8rs5JZrRplHuXTpEo0cOZLCw8NJo9FQ8+bNafz48eUGp1c0rIGoZIhCmzZtSKVSUUREBI0bN85qcPqFCxdo9OjR1KJFC9JqtVS/fn164IEH6KeffrLU2bFjBw0aNIiio6NJrVZTdHQ0DRs2jH7//fcqY7d1PoPBQO+99x61b9+eNBoN1atXj7p160Zz5syhnJwcq7qff/45denSxVKvT58+tH37dsv0tLQ0SkxMpKCgIJsGp2/YsMGyvPr161c5OP1Os2bNIlu66f79++nee+8lPz8/io6OpsmTJ9O2bdvKxZOXl0f/+te/KCQkpNrB6RUNZje/ajs4nYhIkiR65513qGnTpqRWq6l9+/b0n//8p1w983COixcvWsoqukmA+VV2m8xtUpvtSE9PJ6VSSWvWrLEqb9q0KQ0fPpzGjh1LQUFBVL9+fZo+fTp99913FBQURP369bOK4c4hO5X1K3Oblx2Gk52dTWq1mj799NNq42VV43tvMsZYNZ555hn8/vvv2Lt3r6WsWbNmuP/++11y8/GFCxdi3rx5OH/+fK0uKGN8To8xxqo1a9Ysy12GXM1oNGL+/PmYPn06JzwH4HN6jDFWjSZNmrjtsT4qlQqXL192y7rrIt7TY4wx5jP4nB5jjDGfwXt6jDHGfAYnPcYYYz6DL2SxkSzLuHbtGoKCgviGr4wx5mBEhNzcXERHR9t11xx7cdKz0bVr17B8+fIKpxERsrKyanTrIUEQoNFoIMsyDAYDgJKnFJhv0eWqU653rlOpVEKpVMJkMpW7hZiz3LnOit4bZ+P2KMXtUYrbo4Sz2+Pjjz/GlStX0KhRI0eEWyFOejYKCgqCyWSq8LZcRIRmzZrV+C75JpMJOTk5lvklSUJwcHC1z/pyJFmWodfrIUkSlEolDAYDAgIC4O/v77IYAKCgoAD5+flQq9UwmUxQKBTQ6XRO/eV3J26PUtweJbg9SjmrPSRJAoAqn5ThCJz0bCQIAmRZhkKhsPqAybIMWZah0WigVqtrvHyNRmN5OGSDBg0qfMCms/n7++P69euQZRmBgYFuucefv78/BEFAUVERRFFEZGSkSzu0GbdHaQzcHiW4PUo5oz3MNxR39ukjvpDFA5h/RZrp9fpaPwOtJsxPGwBKnlDgqkMmZRkMBqsnEpSNyVW4PUpxe5Ti9ijhKe1RU5z03EyWZdy8eRNGoxHh4eEIDw+H0WjEzZs3XfpBys3NtdxFPioqCiqVCpmZmS7t2AaDAZmZmVCpVIiKioJOp4Ner6/wmWXOwu1RitujFLdHCU9pj9rgpOdGZT9AYWFhUKvVUKvVCAsLc+kHqWyHDgoKgiiKCA0NdWnHLtuhQ0NDIYoigoKCXNqxuT1KcXuU4vYo4SntUVuc9Nykog+QmSs/SHd2aDNXduyKOrSZqzo2t0cpbo9S3B4lPKU9HIGTnhtU9QEyc8UHqbIObeaKjl1VhzZzdsfm9ijF7VGK26OEp7SHo3DSczFbPkBmzvwgVdehzZzZsW3p0GbO6tjcHqW4PUpxe5TwlPZwJB6yYAfzsIWyZFkGEVkut62KeRC7yWRCvXr1AMCmThIcHIysrCxkZGSgXr16tb6kNz8/H3l5eQgMDIRGo7EpBp1OZxWDSqWqVQxGoxFZWVlQKpXQ6XQ2DfDVaDTw9/dHdnY2TCYTAgICahUDt0cpbo9S3B4lXN0erhrkz09ZsJFer8e8efMQEBBg1YjmpBcSElLl4EwigsFgABFZ7mBgD/MdEARBgFqtrnHHNplMMBqNUKlUdg8mre02mNV2W2qzDWbcHqW4PUpxe5RwR3tIkoTx48cjJycHOp2uRnHbgvf07GAwGBAREWHVIEajEcXFxejfvz+Cg4PdGB1jjHmvvLw8jB8/3unr4aRnh5ycHISFhUGj0Vgd5zffhqd+/fpujI4xxryXq24rxxey2CE3NxeBgYEuHxDKGGPMMXhPz06BgYFQKpWW2/C44x6AjDHGasbj9vT27NmDAQMGIDo6GoIgICUlpdp5du3aha5du0Kj0aBly5ZYvXp1uTpLly5Fs2bNoNVqERsbi8OHD9c4xrKXB7vj3neMMcZqxuOSXn5+Pu6++24sXbrUpvoXL15EYmIiHnjgARw/fhyTJk3Cs88+i23btlnqbNiwAUlJSZg1axaOHTuGu+++GwkJCcjIyKhxnObEl5eX57JLbRljjNWOxx3efPjhh/Hwww/bXH/FihWIiYnBhx9+CABo27Yt9u3bhwULFiAhIQEAMH/+fIwZMwZPP/20ZZ7Nmzfj888/x5QpUypcbnFxsdXdzMveVdzM/Iw9d9xtnTHGmP08bk/PXgcPHkR8fLxVWUJCAg4ePAigZJjB0aNHreqIooj4+HhLnYokJycjODjY8mrcuHGF9QICAmo9ELUqepMe32R+Y/XSm8onYMYYY9Xz+qSXlpaGiIgIq7KIiAjo9XoUFhYiMzMTkiRVWCctLa3S5U6dOhU5OTmW15UrVyqtW9MnpjPGGHMtjzu86Sk0Go1NV2aa71zAGGPM83l90ouMjER6erpVWXp6OnQ6Hfz8/KBQKKBQKCqsExkZWat1y7KMrKysGt9uiDHGmGt5/bd1XFwcduzYYVW2fft2xMXFASi583e3bt2s6siyjB07dljq1IT57uMmk6nKO48zxhjzHB6X9PLy8nD8+HEcP34cQMmQhOPHj+Py5csASs61jRw50lJ/7NixuHDhAiZPnowzZ85g2bJl+Oqrr/Dyyy9b6iQlJWHlypX44osvcPr0aYwbNw75+fmWqzntVfZxG/Xq1eM9PcYY8xIed3jzyJEjeOCBByx/JyUlAQBGjRqF1atX4/r165YECAAxMTHYvHkzXn75ZSxatAiNGjXCp59+ahmuAABDhw7FjRs3MHPmTKSlpaFz587YunVruYtbbCHLMrKzsy3PlwJK7r3JGGPM8/GjhWyk1+sxbtw49OjRA5IkWR6oaDAYUFRUhCFDhjjlhtN6kx4/Zf9kVRYfEg+d0nmP3mCMMVfT6/UIDg52+qOF+LicHcLCwmAymap9gjBjjDHPxEnPDkqlEvXq1eOExxhjXoqTnh0yMzOdevcVxhhjzsVJzw5Go9HdITDGGKsFTnqMMcZ8Bic9xhhjPoOTHmOMMZ/BSY8xxpjP4KTHGGPMZ3DSY4wx5jM46THGGPMZnPQYY4z5DE56jDHGfAYnPcYYYz6Dkx5jjDGfwUmPMcaYz+CkxxhjzGdw0rMDP1aIMca8Gyc9O4SFhfHjhRhjzItx0rODyWRCVlYWDAaDu0NhjDFWA5z07JCZmQmlUonMzExOfIwx5oU46dmBiBASEgKVSsWJjzHGvBAnPTuJoojQ0FBL4uNzfIwx5j046dVA2cSXlZUFWZbdHRJjjDEbcNKrIXPiUyqVfJiTMca8BCe9WhBFEfXq1YMgCO4OhTHGmA046dWSIAhQq9XuDoMxxpgNOOk5gCRJ7g6BMcaYDTjp1VJ+fj5fwckYY16Ck14t5ObmIi8vj+/JyRhjXkJp7wwXL17E3r17cenSJRQUFCA8PBxdunRBXFwctFqtM2L0SLm5udDr9QgMDIRCoXB3OIwxxmxgc9Jbu3YtFi1ahCNHjiAiIgLR0dHw8/PDrVu3cP78eWi1WgwfPhyvv/46mjZt6syY3c6c8HQ6HTQaDYqKitwdEmOMMRvYlPS6dOkCtVqNp556Cv/v//0/NG7c2Gp6cXExDh48iPXr16N79+5YtmwZhgwZ4pSA3S0vLw8FBQXQ6XQICgriMXqMMeZFbEp67777LhISEiqdrtFocP/99+P+++/H22+/jT///NNR8XmUoKAg5OXlISQkBEFBQe4OhzHGmJ1sSnpVJbw7hYaGIjQ0tMYBeTKdTofAwEBOeIwx5qXsvpDFLCMjAxkZGeXuO9mpU6daB+Wp9Ho96HImcv7KspSZQDCJhDOzViFAcvzFsPnqYlyJuWxVdvLi7wgwaBy+LsYYc5c8g2uujbA76R09ehSjRo3C6dOnQUQASu5KQkQQBKFOD9TOzc0F6rk7CsYYYzVld9IbPXo0Wrdujc8++wwRERF830nGGGNew+6kd+HCBfy///f/0LJlS2fEwxhjjDmN3Seh+vbti//+97/OiIUxxhhzKrv39D799FOMGjUKJ0+eRIcOHcrdgmvgwIEOC4653nxV9TcWSDJeqtUyPH1+T4jB2fN7Qgzcjr6xDY74LDqS3Unv4MGD2L9/P3744Ydy0+r6hSyMMca8m92HN1988UU8+eSTuH79OmRZtnpxwvMcecRPfmCMsTvZvad38+ZNvPzyy4iIiHBGPMwB/qJ8TDQdQAtBh4fExrhPiIBW4JtiM8aY3Unv0Ucfxc6dO9GiRQtnxMMcIA9G5MGEX+kGjkk30QBa9BMbIl5shEZCgLvDY4wxt7E76bVu3RpTp07Fvn370LFjx3IXsrz00ksOC47VTig0ECDgFoqxRv4DG+WL6C6EI0FshB5COFQCP06RMeZbanT1ZmBgIHbv3o3du3dbTRMEwSFJb+nSpXj//feRlpaGu+++G0uWLME999xTaf2NGzdixowZ+PPPP9GqVSu899576N+/v2U6EWHWrFlYuXIlsrOz0bNnTyxfvhytWrWqdayudtNgwEeG01BUccuz3DLn81SCiPrQgIiQDxP2URoOSOloKPjjIaExHhSj0UDwc0XojDHmdjV6iKwzbdiwAUlJSVixYgViY2OxcOFCJCQk4OzZs2jQoEG5+gcOHMCwYcOQnJyMRx55BOvWrcPgwYNx7NgxdOjQAQAwb948LF68GF988QViYmIwY8YMJCQk4NSpU1734Nu/Cgux1ZgGAVXfCUcEoCpznZIgCAiECoFQwUASrlEBVtIZrJP/QJwQgQSxEToLdfNG4YwxZuaw41vXr1/HvHnzar2c+fPnY8yYMXj66afRrl07rFixAv7+/vj8888rrL9o0SI89NBDeO2119C2bVu8+eab6Nq1Kz766CMAJXt5CxcuxPTp0zFo0CB06tQJ//73v3Ht2jWkpKTUOl53iRT8qnw1EPwqvUWcWlAgTNAiHFoQgJ/oKqZKv2KstA/nTCdRRIWu3RjGGHORGt17syKXLl3C4cOHMXny5BoHYzAYcPToUUydOtVSJooi4uPjcfDgwQrnOXjwIJKSkqzKEhISLAnt4sWLSEtLQ3x8vGV6cHAwYmNjcfDgQTzxxBMVLre4uBjFxcWWv/V6PbRqARAlCCJZygXzf9R5EGTH34dUVBVDK5YmIZ2yGBEaE8IERz68VgEDycjBTRRgJ27RL+im/BvqK8IrrC2IuVUuTafMqnK6p8/vCTE4e35PiIHb0Te2wdbPoigUV1vPEexOellZ1hsgSRIuXLiA06dPY9myZbUKJjMzE5IklRsOERERgTNnzlQ4T1paWoX109LSLNPNZZXVqUhycjLmzJljXTbjaSi1NyGWObQokgiBVFCHn4Ra4fg9JFGQ0dGv9JEbTbUmdA0qhgaOG4IgQUbJCEsCQQGt4I9AYQfESoY5qKvZE0ys5hyhp8/vCTE4e35PiIHb0Te2wdbPonjHY+qcxe6k9+2331ZY/vbbbyMlJQXPP/98rYPyBFOnTrXag9Tr9fj84w9BkgQSS5MekQiQANmohixRRYuqFVLIMFDpoP9CCcgxGqGp5si0AFT5BAwiwAQZEggiRAQKKkQjANlifYhQoKonW8ly1dtZIPpXOd3T5/eEGJw9vyfEwO3oG9tg62eRyDU3N6nxQ2TvNGzYMLz11lu1WkZYWBgUCgXS09OtytPT0xEZGVnhPJGRkVXWN/8/PT0dUVFRVnU6d+5caSwajQYajfWDWglK5MlaBKEIlnxCIgABkNUAnJD0BAkSlTaTBBlGWYliqnpdEgh+ECHekfgkIhhQ8otKBQUiBT80EgJQnzQABNyEP6r96MlV3+3FiGoecOvh83tCDE6f3xNi4Hb0iW2w9bNIsqn6eg7gsAtZ/vvf/6JLly61WoZarUa3bt2wY8cOS5ksy9ixYwfi4uIqnCcuLs6qPgBs377dUj8mJgaRkZFWdfR6PQ4dOlTpMitlyoIEEbmkRTU5x2lCVCr0UIThPjGi0lcnsT4UECwpmAgwkoxCkmCEjCBBhfZiCPqIUegkhKI+tEA1V4MyxlhdYPee3p0XjQAle02bNm1CYmKi1fT58+fbHVBSUhJGjRqF7t2745577sHChQuRn5+Pp59+GgAwcuRINGzYEMnJyQCAiRMnok+fPvjwww+RmJiI9evX48iRI/jkk08AlBzimzRpEt566y20atXKMmQhOjoagwcPti84MiFQKEQe/JBLJXt8riZAQLCghtKGc3oyCEaSQQCUENBQ8EcjIQCh0FY75IExxuoiu5NeampqheU9evRARkYGMjIyAFR9PqkqQ4cOxY0bNzBz5kykpaWhc+fO2Lp1q+VClMuXL0MUS3dQ77vvPqxbtw7Tp0/HG2+8gVatWiElJcUyRg8AJk+ejPz8fDz33HPIzs5Gr169sHXrVrvH6On8RahUhdCREXopELmCCv5iIUAqiP6ZEJUFNdrmqihFGcFK68MHyoACiHLlO+n+MKEtFYFQMjhdBzWCoIQSEgD97VfFooTqr7QSqeorR6tbhqfP7wkxOHt+T4iB29E3tsHWz6KSXHMhi0DkrgN13kWv12P5BxMRog2AAAEmEpErBUCADLVoQs+gkwh0wtWbkihB72e9R6kr1EIhV76nJ0HGNRQgCCoEQ23XXt0lsfofAk3lqvdwq1uGp8/vCTE4e35PiIHb0Te2wdbPokQSOr60Hzk5OdDpdNXOU1MOu5DFFxQZCFApAUGAEkCQUIwcWQuTLIAMgSBRVe0y7CUrJBRqrPfqAg2BEKXKk54IoBFCLH/b86smRwystg7JebVahqfP7wkxOHt+T4iB29E3tsHWz6LsSReyPPTQQ/jll1+qrZebm4v33nsPS5curXVg3kApyAgUikF8fowxxryCTXt6Q4YMwT//+U8EBwdjwIAB6N69O6Kjo6HVapGVlYVTp05h37592LJlCxITE/H+++87O26PoRAISheNL2GMMVY7Np/TKy4uxsaNG7Fhwwbs27cPOTk5JQsQBLRr1w4JCQl45pln0LZtW6cG7C56vR4L3nkJkQEhVhfpmEhEMSnR0+88AkXH30bHpJBwI8j68EF4biCUVRzeZIwxb2OSTbh78lbPOaen0Wjw5JNP4sknnwQA5OTkoLCwEKGhoeWeqedLJBJgcuAtwRhjjDlPjS9kCQ4ORnBwsCNj8TomEpFHGqgE11xqyxhjrHb46k07aNUCoDAB5iELsh8UggSlaIKgzoPgjBtOixL8ROtLgkW1DKGKIQuMMeZtRG+796YvUCsFKP2VMMoK5BVpoVTLCFAZYZQV0DYJgL/W8W+nTBKkOz4LAYqgSp+AwBhj3ki+84vOSTjp2UFfIMM/JAi5RSJUSiA4gCDJKkgmAZqQu6ANcPw6jbIBhYarVmWh6oZQiWrHr4wxxtzEaDAA2Or09TjshtM+QVAiJ1+EUixJeCIPz2OMMa9id9IbNWoU9uzZ44xYPJ+yHpQKTniMMeat7E56OTk5iI+PR6tWrfDOO+/g6tWr1c9UV5AJOn+ZEx5jjHkpu5NeSkoKrl69inHjxmHDhg1o1qwZHn74YXz99dcwGqt/IKJXM2VzwmOMMS9Wo3N64eHhSEpKwn//+18cOnQILVu2xIgRIxAdHY2XX34Z586dc3ScHoIfSMEYY96sVldvXr9+Hdu3b8f27duhUCjQv39/nDhxAu3atcO8efPw8ssvOypOj6AQAcgSqMzuHhFAJIJMhZCNjh+kLpMRosn69mayWABZqON71YwxnyKbXPOdZnfSMxqN+O6777Bq1Sr8+OOP6NSpEyZNmoR//etflvulffvttxg9enSdS3pBfiJkUx7KPh+XJBEkqVCsP4fiYsc/SV0mGX5yvlWZScyGLPCFt4yxukOWXXNnK7uTXlRUFGRZxrBhw3D48GF07ty5XJ0HHngAISEhDgjPs+QWyggKCYRQZk9PEAFBEKHRtYLG3/GNZiQjCg1pVmXB6kioBN+93yljrO4puSZkp9PXY3fSW7BgAYYMGQKttvKn4YaEhODixYu1CswTSTIAUWH1lAVBKHnShKD0gxOeIQtRNkCWNdZlSn+IPDidMVaHiGRwyXrsTnojRoxwRhyMMcaY0/GJIcYYYz6Dkx5jjDGfwUmPMcaYz+CkxxhjzGfwo4XsoPMXQcZcSGWu3pQlEbKkQHH2eRQVFVcxd83IJMFPyrUqMyqyIfHz9BhjdQg/T88DGUwEiCrrIQskQJBFKNQ6KDQmx6+UTJBM1uP/RGUIFAI3HWOsDjE54fuzAvzNaYciA0FQaCGWTXooSXxK/yionPAQWcgGGO4YvqLgh8gyxuqaO7/onITP6THGGPMZnPTsoXDGrhxjjDFX4aRnD0UgCor5gXqMMeatOOnZQ8pDfpGA/CJOfIwx5o046dlDykeAlpBfzImPMca8EV+9aQetWoCfshCQlcgrUoJkE9RKGSSJMBWkwUiOv+RWIhPUplvWZUYAPGSBMVaHSDxkwfOolQIgG+GnMoFIhfxiNUySDIUoQTLoIYnOGZyukKwfIitLIsCD0xljdQgPTvdA+gIZwaFBEAUBQSpAVAjILVLCT0HQhNwFrRMu7jTKBhQarlqVhfI4PcZYHWM0GABsdfp6OOnVQoCWIMmA0TU/UBhjjNUSX8hSS34agpKPNDLGmFfgpOcACn4XGWPMK/DXdS0R8eFNxhjzFpz0akEmILdAAJG7I2GMMWYLvpDFDgoRgCyBRAEyATkFCphkgr+SQKZCyEa52mXYSyYjRJP1UAhZLIAsGB2+LsYYcxfZ5JrvNE56dgjyEyGb8gAAOUX+kIgQoCoCyQoU68+huLjI4euUSYafbD1OzyRmQxZ4J50xVnfIsuN3GirCSc8OuYUyAoIDoS9SQgYQEigB8IPBJEKjawWNv+MbzUhGFBrSrMqC1ZFQCSqHr4sxxtzFaDQC2On09XDSs4MkC9AXqSDJQEgAQaVUwCgBgiBAUPpBdEIeEmUDZFljXab0h8iD0xljdYhI/BBZz6MMgUkCQvwJKv65wBhjXoeTnj0EJYIDZE54jDHmpTjp2cOUBRXffYUxxrwWJz17OOHRQYwxxlzHo5LeN998g379+iE0NBSCIOD48eM2zbdx40a0adMGWq0WHTt2xJYtW6ymExFmzpyJqKgo+Pn5IT4+HufOnXPCFjDGGPNkHnV2Kj8/H7169cLjjz+OMWPG2DTPgQMHMGzYMCQnJ+ORRx7BunXrMHjwYBw7dgwdOnQAAMybNw+LFy/GF198gZiYGMyYMQMJCQk4deoUtFqtzfHp/EWQMReSUPrUdFkSIUsKFGefR1GRc56n5yflWpUZFdmQ+Hl6jLE6xFXP0xOIPO8mWn/++SdiYmKQmpqKzp07V1l36NChyM/Px/fff28pu/fee9G5c2esWLECRITo6Gi88sorePXVVwEAOTk5iIiIwOrVq/HEE09UuNzi4mIUF5cmMb1ej7WfzEBYWBiEMknPJAkwmEQ82DYNOj/nPDn95h1PTg9V1oeCn5zOGKtDJJMJzRLWIycnBzqdzmnr8fpvzoMHDyIpKcmqLCEhASkpKQCAixcvIi0tDfHx8ZbpwcHBiI2NxcGDBytNesnJyZgzZ45V2ezXR0FQaCGWSXoCAIEEKP2joHLCQ2RVABoixvELZowxT2LgcXo2SUtLQ0REhFVZREQE0tLSLNPNZZXVqcjUqVORk5NjeV25csXBkTPGGHM1tyW9tWvXIjAw0PLau3evu0KpkEajgU6ns3oxxhjzbm47vDlw4EDExsZa/m7YsGGNlhMZGYn09HSrsvT0dERGRlqmm8uioqKs6lR3vpAxxljd4rY9vaCgILRs2dLy8vPzq9Fy4uLisGPHDquy7du3Iy4uDgAQExODyMhIqzp6vR6HDh2y1GGMMeYbPOpCllu3buHy5cu4du0aAODs2bMASvbWzHtsI0eORMOGDZGcnAwAmDhxIvr06YMPP/wQiYmJWL9+PY4cOYJPPvkEQMnNoCdNmoS33noLrVq1sgxZiI6OxuDBg12/kYwxxtzGo5Led999h6efftryt/nKylmzZmH27NkAgMuXL0MUS3dQ77vvPqxbtw7Tp0/HG2+8gVatWiElJcUyRg8AJk+ejPz8fDz33HPIzs5Gr169sHXrVrvG6AGAVi2ApCLIZa7eJEkASSJMBWkw8h1bGGOsRiSTa74/PXKcnifS6/X4bOGkkgtarMbpiTBICtzf8jx0WscPTmeMMV8gSxKaD9jK4/Q8ib5QREBIkNXgdJkAIgEGzV0otm/HkTHG2G0moxHAVqevh5OePdQNkKm3vv0XASAC9p9SQWHDncFMUslLqSh52aIm81RFlnH74beASmG14+rQeapCVLI8opLliTZcUlWTearD7VGC26MUt0cpV7YHya5JR5z07EGAIJDVnh4RIANQKanaBi4sFlBoEOCvJvhpbD+qrFaWzFtQg3nvZDIBeQYBSgEI9Ce7OqfSBOiLBMgSEGTnvGURAbkFAkwE6LQEpR2fQrWyZN4Cyf5578TtUYLboxS3RylXt4erzrRx0rOHqAIBUJT5MMsouRWZUoEqn7WXXySgyCggSEsI0NrfuCp/gkIE8osFKETUaBlGE5BXLECtAIIDCKKdnVKlAJQKQnaBgPwioUbLkAnIyRcgA6gfULMn0NcLIuTkC8grFhCiqNkyuD1KcHuU4vYo5Y72kF10dYnX34bMtQhGkwCTbN9c+UUC8osFBGhq9gEyC9ASAjSE/OKSTmUPownILhCgFGvWoc1USiDEn2CSb3dOOzbH3KFNcskyavoEelEo2QalWLJNRjsv+uL2KMHtUYrbo5QntIczcdKzh1wMASUdxNbE56gPkFlNPkiO6tBmNenYjurQZjXt2NweJbg9SnF7lPKE9nA2PrxpJ7VShsEklnyIlLc7CAEgudwx6fxiEflFAgK0Evw1Mhx1yNpfAxBE5BWJIBACNJVnYKME5OQroFAQdP4SBMAhcSgVQLB/ybKz84Fgf6nSLwuZgJwCBSSJEBwgQalwTAwCAJ1/ybKz84HgAKnqQ8zcHgC4Pcri9ijl9vZw0fFNTnp2UCoAARLUCgkGSQmjCVAKEgRBgGzKhyyXPgSxwKBGgVEDf1Uh/EQDZKNjY/ETAVKqkV+oAZmK4a8u/1gOoyRCX+wPhWCATlUAmErOQTqKAkCQumQd2bkygrUF5U7eEwE5Rf6QiKDTFEAhy5AdGQQAnQrIkfyRnSdCpymASlF+BdweJbg9SnF7lPKE9jCYXHPgkZOeHUwSoFQpIAgC1AJgMAkwkRJKgSAqAyAqSn6p5BeLKDCJCPCTEaDRANA4JZ5AFSAUi8gvCoCg9LP6BWWUgNxCBZRK86/MIKfEoFEBISogJ18NvVFt9YtWJkBfoIAMICRQgkrhhAcO3haiKvlFm2vQlftFy+1RMo3bg9vDo9vD6IDxJjbgpGc3AYIgQBBKLs01mABJFmCSRaiUJcfEC4oFBGoJAVoBJb/3nCdQW3IYI79YAQEiArQEownIKRBKDrEEEETBuTGolUBIQMm5A32BEsEBJclfXyBAkoGQAILKEQOoqqAQSmLIyReQU6C0nBfh9uD24PbwnvZwBU56Nio5XydCkgkCSo89i4IAiQRk5wMFRQSDBPirJKhE2VUPAoZKBDQKEfpCEUUGglEWoBRk+KkkmBx8mKIq/ioBucUK3NSX/C0RIUgjgWRy2XvhpwLyihS4mSdAJXJ7cHtwe5Tl0e2hLLnaxtnj9fjemza6cOECWrRo4e4wGGOsTjt//jyaN2/utOXznp6N6tevD6DkKQ/BwcFujsY59Ho9GjdujCtXrtTpJ8X7wnb6wjYCvrGdvrCNAJCTk4MmTZpYvmudhZOejcyPMwoODq7THzwA0Ol0dX4bAd/YTl/YRsA3ttMXthGA1aPjnLJ8py6dMcYY8yCc9BhjjPkMTno20mg0mDVrFjQa54xh8QS+sI2Ab2ynL2wj4Bvb6QvbCLhuO/nqTcYYYz6D9/QYY4z5DE56jDHGfAYnPcYYYz6Dkx5jjDGf4dNJ75tvvkG/fv0QGhoKQRBw/Phxm+bbuHEj2rRpA61Wi44dO2LLli1W04kIM2fORFRUFPz8/BAfH49z5845YQuqt3TpUjRr1gxarRaxsbE4fPhwlfW9adsAYM+ePRgwYACio6MhCAJSUlKqnWfXrl3o2rUrNBoNWrZsidWrV5erY+/75kzJycno0aMHgoKC0KBBAwwePBhnz56tdj5va8vly5ejU6dOlkHYcXFx+OGHH6qcx9u28U7vvvsuBEHApEmTqqznTds5e/bs2zflL321adOmynlcun3kw/7973/TnDlzaOXKlQSAUlNTq51n//79pFAoaN68eXTq1CmaPn06qVQqOnHihKXOu+++S8HBwZSSkkL//e9/aeDAgRQTE0OFhYVO3Jry1q9fT2q1mj7//HP67bffaMyYMRQSEkLp6ekV1vembTPbsmULTZs2jb755hsCQN9++22V9S9cuED+/v6UlJREp06doiVLlpBCoaCtW7da6tj7vjlbQkICrVq1ik6ePEnHjx+n/v37U5MmTSgvL6/SebyxLb/77jvavHkz/f7773T27Fl64403SKVS0cmTJyus743bWNbhw4epWbNm1KlTJ5o4cWKl9bxtO2fNmkXt27en69evW143btyotL6rt8+nk57ZxYsXbU56jz/+OCUmJlqVxcbG0vPPP09ERLIsU2RkJL3//vuW6dnZ2aTRaOjLL790aNzVueeee2j8+PGWvyVJoujoaEpOTq6wvjdtW0VsSXqTJ0+m9u3bW5UNHTqUEhISLH/b+765WkZGBgGg3bt3V1rH29vSrF69evTpp59WOM2btzE3N5datWpF27dvpz59+lSZ9LxtO2fNmkV33323zfVdvX0+fXizJg4ePIj4+HirsoSEBBw8eBAAcPHiRaSlpVnVCQ4ORmxsrKWOKxgMBhw9etQqDlEUER8fX2kc3rJttVHdNtbkfXO1nJwcAKjyxrze3paSJGH9+vXIz89HXFxchXW8eRvHjx+PxMTEcvFXxBu389y5c4iOjkbz5s0xfPhwXL58udK6rt4+vuG0ndLS0hAREWFVFhERgbS0NMt0c1lldVwhMzMTkiRVGMeZM2cqnMdbtq02KttGvV6PwsJCZGVl2f2+uZIsy5g0aRJ69uyJDh06VFrPW9vyxIkTiIuLQ1FREQIDA/Htt9+iXbt2Fdb11m1cv349jh07hl9//dWm+t62nbGxsVi9ejXuuusuXL9+HXPmzEHv3r1x8uRJBAWVf0K9q7fPZ/b01q5di8DAQMtr79697g6JMbuNHz8eJ0+exPr1690dilPcddddOH78OA4dOoRx48Zh1KhROHXqlLvDcpgrV65g4sSJWLt2LbRarbvDcYqHH34YQ4YMQadOnZCQkIAtW7YgOzsbX331lbtDA+BDSW/gwIE4fvy45dW9e/caLScyMhLp6elWZenp6YiMjLRMN5dVVscVwsLCoFAo7IrDW7atNirbRp1OBz8/vxq9b64yYcIEfP/999i5cycaNWpUZV1vbUu1Wo2WLVuiW7duSE5Oxt13341FixZVWNcbt/Ho0aPIyMhA165doVQqoVQqsXv3bixevBhKpRKSJJWbxxu3s6yQkBC0bt0af/zxR4XTXb19PpP0goKC0LJlS8vLz8+vRsuJi4vDjh07rMq2b99uOe8QExODyMhIqzp6vR6HDh2q9NyEM6jVanTr1s0qDlmWsWPHjkrj8JZtq43qtrEm75uzEREmTJiAb7/9Fj///DNiYmKqnaeutKUsyyguLq5wmjduY9++fXHixIlyP8CHDx+O48ePQ6FQlJvHG7ezrLy8PJw/fx5RUVEVTnf59tl96UsdcvPmTUpNTaXNmzcTAFq/fj2lpqbS9evXLXVGjBhBU6ZMsfy9f/9+UiqV9MEHH9Dp06dp1qxZFV5eGxISQps2baL//e9/NGjQILcNWdBoNLR69Wo6deoUPffccxQSEkJpaWlev21mubm5lJqaSqmpqQSA5s+fT6mpqXTp0iUiIpoyZQqNGDHCUt88ZOG1116j06dP09KlSyscslDV++Zq48aNo+DgYNq1a5fVZeAFBQWWOnWhLadMmUK7d++mixcv0v/+9z+aMmUKCYJAP/74IxHVjW2syJ1Xb3r7dr7yyiu0a9cuunjxIu3fv5/i4+MpLCyMMjIyPGL7fDrprVq1igCUe82aNctSp0+fPjRq1Cir+b766itq3bo1qdVqat++PW3evNlquizLNGPGDIqIiCCNRkN9+/als2fPumCLyluyZAk1adKE1Go13XPPPfTLL79Ypnn7thER7dy5s8I2NG/XqFGjqE+fPuXm6dy5M6nVamrevDmtWrWq3HKret9craLtA2AVd11oy9GjR1PTpk1JrVZTeHg49e3b15LwiOrGNlbkzqTn7ds5dOhQioqKIrVaTQ0bNqShQ4fSH3/8YZnu7u3jRwsxxhjzGT5zTo8xxhjjpMcYY8xncNJjjDHmMzjpMcYY8xmc9BhjjPkMTnqMMcZ8Bic9xhhjPoOTHmOMMZ/BSY8xL/XZZ5+hX79+Tl/P1q1b0blzZ8iy7PR1MeZsnPQY80JFRUWYMWMGZs2a5fR1PfTQQ1CpVFi7dq3T18WYs3HSY8wLff3119DpdOjZs6dL1vfUU09h8eLFLlkXY87ESY8xN7px4wYiIyPxzjvvWMoOHDgAtVpd7nErZa1fvx4DBgywKrv//vsxadIkq7LBgwfjqaeesvzdrFkzvPXWWxg5ciQCAwPRtGlTfPfdd7hx4wYGDRqEwMBAdOrUCUeOHLFazoABA3DkyBGcP3++5hvLmAfgpMeYG4WHh+Pzzz/H7NmzceTIEeTm5mLEiBGYMGEC+vbtW+l8+/btq/GDkBcsWICePXsiNTUViYmJGDFiBEaOHIknn3wSx44dQ4sWLTBy5EiUvRd9kyZNEBERgb1799ZonYx5Ck56jLlZ//79MWbMGAwfPhxjx45FQEAAkpOTK62fnZ2NnJwcREdH13h9zz//PFq1aoWZM2dCr9ejR48eGDJkCFq3bo3XX38dp0+fLvek6ujoaFy6dKlG62TMU3DSY8wDfPDBBzCZTNi4cSPWrl0LjUZTad3CwkIAgFarrdG6OnXqZPl3REQEAKBjx47lyjIyMqzm8/PzQ0FBQY3WyZin4KTHmAc4f/48rl27BlmW8eeff1ZZNzQ0FIIgICsrq9rlSpJUrkylUln+LQhCpWV3DlG4desWwsPDq10nY56Mkx5jbmYwGPDkk09i6NChePPNN/Hss8+W28sqS61Wo127djh16lS5aXcekrxw4YJDYiwqKsL58+fRpUsXhyyPMXfhpMeYm02bNg05OTlYvHgxXn/9dbRu3RqjR4+ucp6EhATs27evXPmmTZvwzTff4Pz583j77bdx6tQpXLp0CVevXq1VjL/88gs0Gg3i4uJqtRzG3I2THmNutGvXLixcuBBr1qyBTqeDKIpYs2YN9u7di+XLl1c63zPPPIMtW7YgJyfHqjwxMRHz5s1Du3btsGfPHixbtgyHDx/GmjVrahXnl19+ieHDh8Pf379Wy2HM3QQqe10yY8xrDBkyBF27dsXUqVMBlIzT69y5MxYuXOjQ9WRmZuKuu+7CkSNHEBMT49BlM+ZqvKfHmJd6//33ERgY6PT1/Pnnn1i2bBknPFYn8J4eY3WEs/b0GKtLOOkxxhjzGXx4kzHGmM/gpMcYY8xncNJjjDHmMzjpMcYY8xmc9BhjjPkMTnqMMcZ8Bic9xhhjPoOTHmOMMZ/x/wFHMiY3wE36IgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 150  # number of grating periods\n",
    "\n",
    "# create simulations\n",
    "sims = {\"crystalline\": make_sim(n, medium_cSb2Se3), \"amorphous\": make_sim(n, medium_aSb2Se3)}\n",
    "\n",
    "# visualize the simulation setup\n",
    "fig, ax = plt.subplots(2, 1, tight_layout=True)\n",
    "sims[\"crystalline\"].plot(y=0, ax=ax[0])\n",
    "ax[0].set_xlim(-1, 5)\n",
    "\n",
    "sims[\"crystalline\"].plot(z=h_wg, ax=ax[1])\n",
    "ax[1].set_xlim(-1, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d79bf8fc-28c2-4202-867a-d82527277bd1",
   "metadata": {},
   "source": [
    "Run the simulations in parallel. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d69b11c5-fb60-407c-ae07-7f6d57f31f86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c9368522a703495ea061e410915a4f79",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:41:46 CEST </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span> tasks.                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:41:46 CEST\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m2\u001b[0m tasks.                      \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\">14:41:49 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">15.516</span> for the whole batch.              \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:41:49 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m15.516\u001b[0m for the whole batch.              \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>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after   \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>the Batch has completed.                                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\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 cost after   \n",
       "\u001b[2;36m              \u001b[0mthe Batch has completed.                                          \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3ba12c8b2844e549a5915aead98db65",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:50:16 CEST </span>Batch complete.                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:50:16 CEST\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                   \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\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b415ff5a78b34b82bfce3679e1ad67c2",
       "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"
    }
   ],
   "source": [
    "batch = web.Batch(simulations=sims, verbose=True)\n",
    "batch_results = batch.run(path_dir=\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31062368-0fc5-4d10-8c82-cdbf202e94c6",
   "metadata": {},
   "source": [
    "Check the cost of the simulations. We can see that the cost is substantial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a093d413-eaf9-4969-89f4-f9b8e9702cdb",
   "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\">14:50:25 CEST </span>Total billed flex credit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9.370</span>.                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:50:25 CEST\u001b[0m\u001b[2;36m \u001b[0mTotal billed flex credit cost: \u001b[1;36m9.370\u001b[0m.                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = batch.real_cost()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad092949-a068-4964-8647-ef63b4eb31f7",
   "metadata": {},
   "source": [
    "Plot the transmission spectra. We can see that in the crystalline phase, the Bragg wavelength is at 1500 nm as designed. In the amorphous phase, the Bragg wavelength is shifted to a shorter wavelength. Thus, this filter can effectively be tuned between these two spectra. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9d9a0095-2016-412b-b283-5fbb90a7e804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for phase in [\"crystalline\", \"amorphous\"]:\n",
    "    amp = batch_results[phase][\"mode\"].amps.sel(mode_index=0, direction=\"+\")\n",
    "    T = np.abs(amp) ** 2  # transmission to the top waveguide\n",
    "\n",
    "    plt.plot(ldas, 10 * np.log10(T), linewidth=2, label=phase)\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel(\"Wavelength (μm)\")\n",
    "plt.ylabel(\"Transmission (dB)\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3800566f-fb3d-4c3a-b542-317167df653c",
   "metadata": {},
   "source": [
    "## EME Simulations\n",
    "\n",
    "Now we demonstrate that EME can be an effective and low-cost tool in waveguide Bragg grating design. Tidy3D's EME solver supports periodic structures and can perform a sweep of the number of periods very quickly. Here we only simulate the amorphous phase but the crystalline phase can be simulated in the same way.\n",
    "\n",
    "A few things to note regarding the EME simulation settings:\n",
    "\n",
    "1. For the EME grid, we first use [EMEExplicitGrid](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.EMEExplicitGrid.html) to define the unit cell that will be periodic and give it a name. This name will be used later to specify the periodicity sweep. Outside of the unit cell, we only need one grid and can use a [EMEUniformGrid](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.EMEUniformGrid.html) since the waveguide is translationally invariant. The overall EME grid is then the composite of them. Here we will simulate 50, 150, and 1000 periods. We will compare the EME result to the FDTD result in the 150-period case to verify the accuracy of EME. \n",
    "\n",
    "2. We set `num_mode=1` in the mode specification and set `constraint=\"unitary\"` in the [EMESimulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.EMESimulation.html). since we are primarily interested in the propagating mode instead of the radiation or leakage. Using too many modes can worsen results by introducing unstable radiative modes, causing mode mismatch between cells, or creating numerical instabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b204ee88-92de-49e4-890b-99377761706c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the periodic grid for the grating\n",
    "grating_grid = td.EMEExplicitGrid(\n",
    "    mode_specs=[td.EMEModeSpec(num_modes=1)] * 2, boundaries=[p * ff], name=\"unit cell\"\n",
    ")\n",
    "\n",
    "# define the grid for the waveguide\n",
    "wg_grid = td.EMEUniformGrid(num_cells=1, mode_spec=td.EMEModeSpec(num_modes=1))\n",
    "\n",
    "# define the overall EME grid as a composite\n",
    "eme_grid_spec = td.EMECompositeGrid(\n",
    "    subgrids=[wg_grid, grating_grid, wg_grid], subgrid_boundaries=[0, p]\n",
    ")\n",
    "\n",
    "# using the same in-plane grid spec as the previous FDTD simulations\n",
    "grid_spec = td.GridSpec.auto(min_steps_per_wvl=30, wavelength=lda0)\n",
    "\n",
    "# we will simulate three cases with 50, 150, and 1000 grating periods\n",
    "n_list = [50, 150, 1000]\n",
    "sweep_spec = td.EMEPeriodicitySweep(num_reps=[{\"unit cell\": n} for n in n_list])\n",
    "\n",
    "# define the EME simulation\n",
    "eme_sim = td.EMESimulation(\n",
    "    center=(p / 2, 0, h_wg - h_etch),\n",
    "    size=(p + 2 * buffer, w_wg + 2 * buffer, h_wg + h_pcm + 2 * buffer),\n",
    "    structures=make_structures(1, medium_aSb2Se3),\n",
    "    grid_spec=grid_spec,\n",
    "    eme_grid_spec=eme_grid_spec,\n",
    "    freqs=freqs,\n",
    "    axis=0,\n",
    "    port_offsets=(buffer / 2, buffer / 2),\n",
    "    sweep_spec=sweep_spec,\n",
    "    store_port_modes=False,\n",
    "    constraint=\"unitary\",\n",
    "    symmetry=(0, -1, 0),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "433ef0fe-03ed-4c58-bccb-dfc0b4ed8dab",
   "metadata": {},
   "source": [
    "Visualize the EME simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "64913f3f-2704-403c-af22-c91cee298e4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eme_sim.plot(z=h_wg)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5de6fd5f-2545-464d-a151-b8d363b408c3",
   "metadata": {},
   "source": [
    "Run the EME simulations with different numbers of periods. We can see that the cost is over an order of magnitude lower compared to FDTD, making it a very affordable option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "17f5ad7b-2144-4d6a-a3c6-371ae655f93c",
   "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\">14:50:27 CEST </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Simulation has </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.00e+02</span><span style=\"color: #800000; text-decoration-color: #800000\"> frequencies. Mode solving is     </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">repeated at each frequency, so EME simulations with too many      </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">frequencies can be slower and more expensive than FDTD            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">simulations. Consider using an </span><span style=\"color: #008000; text-decoration-color: #008000\">'EMEFreqSweep'</span><span style=\"color: #800000; text-decoration-color: #800000\"> instead for a faster</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">approximate solution.                                             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:50:27 CEST\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Simulation has \u001b[0m\u001b[1;36m1.00e+02\u001b[0m\u001b[31m frequencies. Mode solving is     \u001b[0m\n",
       "\u001b[2;36m              \u001b[0m\u001b[31mrepeated at each frequency, so EME simulations with too many      \u001b[0m\n",
       "\u001b[2;36m              \u001b[0m\u001b[31mfrequencies can be slower and more expensive than FDTD            \u001b[0m\n",
       "\u001b[2;36m              \u001b[0m\u001b[31msimulations. Consider using an \u001b[0m\u001b[32m'EMEFreqSweep'\u001b[0m\u001b[31m instead for a faster\u001b[0m\n",
       "\u001b[2;36m              \u001b[0m\u001b[31mapproximate solution.                                             \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>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'eme periodic'</span> with task_id                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'eme-45a7b3f0-aacd-49b3-9bc9-b91142c49721'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'EME'</span>.   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'eme periodic'\u001b[0m with task_id                          \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'eme-45a7b3f0-aacd-49b3-9bc9-b91142c49721'\u001b[0m and task_type \u001b[32m'EME'\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>Tidy3D's EME solver is currently in the beta stage. Cost of EME   \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>simulations is subject to change in the future.                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTidy3D's EME solver is currently in the beta stage. Cost of EME   \n",
       "\u001b[2;36m              \u001b[0msimulations is subject to change in the future.                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "150787259b224970b0d1949a76d8adf0",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:50:30 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.314</span>. Minimum cost depends on task      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>FlexCredit cost after a simulation run.                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:50:30 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.314\u001b[0m. Minimum cost depends on task      \n",
       "\u001b[2;36m              \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
       "\u001b[2;36m              \u001b[0mFlexCredit cost after a simulation run.                           \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\">14:50:31 CEST </span>status = queued                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:50:31 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                   \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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or             \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Terminating the Python script will not stop the job running on the\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>cloud.                                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or             \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.     \n",
       "\u001b[2;36m              \u001b[0mTerminating the Python script will not stop the job running on the\n",
       "\u001b[2;36m              \u001b[0mcloud.                                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2ca3a2f309af45b98821af3b979233b9",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:50:36 CEST </span>starting up solver                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:50:36 CEST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                \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>running solver                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f208c42d7a91496fac236e4a5d1a34f2",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:52:59 CEST </span>status = success                                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:52:59 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \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\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c99c1e06b8794315891c0111bf8b506b",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:53:00 CEST </span>loading simulation from simulation_data.hdf5                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:53:00 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                      \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eme_sim_data = web.run(eme_sim, task_name=\"eme periodic\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b49504ee-d46a-4780-aa64-907f35151910",
   "metadata": {},
   "source": [
    "Plot the transmission of the EME simulations. As expected. the extinction ratio is increasing with the number of periods. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "25cba781-8ed7-4917-87aa-b8b084444606",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, n in enumerate(n_list):\n",
    "    # extract transmission\n",
    "    T = (\n",
    "        eme_sim_data.smatrix.S21.isel(\n",
    "            sweep_index=i, mode_index_out=0, mode_index_in=0\n",
    "        ).abs.squeeze()\n",
    "        ** 2\n",
    "    )\n",
    "\n",
    "    # plot the spectrum\n",
    "    plt.plot(ldas, 10 * np.log10(T), label=f\"n={n}\")\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel(\"Wavelength (μm)\")\n",
    "plt.ylabel(\"Transmission (dB)\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e63f49fe-6353-46a5-be43-d20b515247aa",
   "metadata": {},
   "source": [
    "Compare the EME result with the previous FDTD result. They match very well, validating the efficacy of the EME method in modeling waveguide Bragg gratings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cdf2a237-ad5f-4146-a21d-cc264a6e00a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Co1kw41HG9h2hfuhaK2HNy/DzM+rYPlCpM/Kfkgi+MIex2+pPx6hE/u/W5xhYtAu+man+wwPQ6cia/G+u+flnth/dA0CwfxA/PLSI1IRO9b+xP96Dr+5Uy/5h5EZ0Ze3RAwToFM7xKwagvO8lHDjzEe599yl2HFPb77c5n9AtPsW1xqsoAWMg6PWs2PEb1742C1D/2t/30k8eS/6UwKcWB9fAO2rgyUWvwrDrapwy/5s3eem7d7THC298iotOG1/3NYuzUf45AJ2lBMXgh+7WH2HD/8Hm97QvoeNWE2fld6UYE0v/8Sa9OqRy2Ut3sPnQTgD6duzOV7MW1p6AXJQJC8+CguPqY70BRs+Csf8Aox8/71zHtFfvI4hKrg/I46HQXAzVPUIGE4ybDWfcCaZAdhzdw+wPX2DToT+1y4/udRqvzHic+MM/wQ9PQFGG/d5+wXD6rWT3n8YT373PFxuXa09FBIUx5/J7uGzEJAwnD8L2T2Hbx5B7qL7/A5gVPf519LLk2/R8aQ7nY3MEWysDcOxpHdKlL5cNn0TQSRuXXniJ8+90USYcXA2H10L6Fsj806VA5LDNny0Wf7ZYAvm9Mog/KwMIDgojKjicPh1T+VvPYZzRcyjdEzrX/++00gxZu+DEX5B/DPKOQv5RyDuifp6ZAsCo/ih+QVSEJZMfnMQJ/xiO6EIp8QsnKjSS6JAIIoPDiAqJICK4Cd8TNiuUnlQDgeqAAMDoX/UTAH6BEN4RQuLV36VauOWzw2ZTgxR06u+h3gStNAez3QY+H3/8MdOnT+eNN95g+PDhvPzyy3zyySfs3r2b+PiGZ0p5IvBpNEWBtE2w5UP486tae4IcFdn0rLUE8X55JIfCezE0dSAjUgcyefC4+v9RlpyEZY/CFufejOzQzryZVcGXZUEct/lhRGFyVADXd4hgSPkR/PIOauf+ZQvi9oIE9ljVfJ1xfU/ntevnEBVSlTNRnAVf32PvqTEFUT7jG+5Y9iX/27ISUHtU/vfg/xFUVzLnyYPw2hlqYAKYr3yXv330HsdOZuCPjb2Jx/C3lKgfFg/uZeFv3/HkZ/8G4MEptzDzPBe6Uw/9Cu9eAlGd4bZV/HPFh7zw7Vv29/niD/b35GYS+NRiw9vw7X1qOaE/3PFrjV6fB5c8z7urv9AeXz/mMp69+v66r/n9I2oPCWA97SYMFy5Qjx/fhPLlHehO/AXAN+Ywtp3+II9dehcA2YW5nP/8zRzJUXN1xvc/g/f+/qLzF2xFCbx9nvplDhCTCpe9CR2HAXDwxFEmPXcDhWVqkD5r8o08cNoo+OwWcOyBDQiHgZfD0OuwxPfjxW/f4pUf3iVFV8EAYxm3hxQyWO/Qa2MwUTn8Fn6KHMF/t6zh553rsdqs2tMTBvyNF6bNJj48xrktFEXtad78Afz5JZide4xrU2jTs84SxBfmcJZVhGJ2SBQekNKTKUPPYcrQs0mJSXL9d7rSDCd2QtpW9Q8kvUH90elBb4TYntBhEEpABBn52RSVlRAVEk5kcBhGF3Kf2jr57HDm6vd3m/vNufLKK8nOzubxxx8nMzOTQYMGsWzZMpeCnhZDp1M/MDsOg/NfhOIT6l9GJ/5Su2xLT0J8HyxxfdlviGRbUTmhQaG81G0AsWHRrt8nOBoufR2GTINv7tV6mWKLDvNIIDwSCH9W+tNJbyFUbwOHPzCtCrxSFsM/S2OxoEOn03HfeTcwa/KNzjO2QuJg6hL49Cb1L01LKQEfXcsrN3zPvszD7M04xK60Azzy0T956bpHatbRWql+OVQFPQy9juf3pXHspFqZoT2G4ddjjNp7VVkO2z/l/CEXaIHPN5t+ci3w+f1t9fVZu2Hfj2w7ssvp6WMnMzwW+IhaVPcSghoYHP8Dkp1nbeUWFzg9/v3AtrqvV5QJG9RA1qozYTvzXrS/oTsO5f86XsGlGXOJ1NuY4l/IuR3tyf2xYVF8cOcCzn/+ZvJLC1mx4zfe+vljbjn7KvUEm1X9/a4OeiJS4MbvIVT9zCkqK2H66//Qgp5zB45h1uQb1b+qb1sJK5+D1QvU4YbyAjXo2/A2psQBPOQfygMJhzHWkk+zvCKUD8NHsu6nTeSXrnR6LiIojKevnMmlwyfV3gOi00Gn09Wf85+H3d+pny0lOfafsjyI7gpdzsTWaRR7y/WcPHGUs/QGJhlNGPVG/IxGeiZ1pXNsx7rbvj5Gf+gwRP2phw5IioyDyKbdRghHbS7wAbjzzju58847fV0N99DpIDRB/el+jtNTJqB31U+zdPkb3LEWfn8LtnwEGfYvkH5G59kaNgU2VQYypySePyqDCPYPYnL/M5g+5hJGdh9c93u4+DW1O/nY71CUQdCnM/i/619nwot3UFZRzodrv2VE6kCuGnXKtOHVC9TXAER2ZvfAm1j44h0A+BlNPD/1QXQUqYEPwB/v0XHEzQzr2p8/Du5gV9oB9mYcqn+tIkVRc6yqHf6V7Uf2OJ1yNCedgZ161X0N4V5luc6Pf3+nRuCTV+Ic+PyVtp/CsmLCAkNqXm/1AjWwBQ7FnU2n0ETtqeLyEub9/DVrdUm8E6YOU5mWPQJdzoQ4dd2p1IRO/OfGOUx9ZSYAT33xKiNSB6m/E8setfdo+ofBtZ9qQY/VZuWuxXPYl3kYgB6JXXjl+sftfxwY/WD849B3CqxbqPbwVgc5GdvVU055K3sr/Xi8JIFVlhAoPO70XFJkHJeNOJebxl1BXLiLfwSZAqH/pepPHfTAMGBY6iDXrilEC9Y6B/KE+xn9YNQdcMcauHcTnPMYSnxfAJSgaMp6X8iKntdzeeC53KofQbfTLue9v7/Azhe/Z+FNT9Ud9FQzBcC0DyGiKpcnfSvdf32GF6Y+oJ3y0IcvsCttv/01aZvVv4YBdHoqL13IfZ++onXl33PuDDU3KKGf/S/GjG2QvpULh9qDxG82/VR/3fKPqcl/VSz7fyGzwHl4sbqHSXhJ6SlTlXd87pSPBpBbnO/0WFEUNh38kxoK0mDjIvUcUxD7E52D65U7N1BmMfNdRRi/hQ9SD1rK4JPrwWJP5j2r70j+Pn6a+rS1ktvefhTzr6/C2tfUE/RGuPp9iFf/FKm0VnLnojlacnR4UCjv3v587YvVJQ2CSxfCg3vhwn9Bx6H258I6QK/zYNxsCi9bxL7LltBl1Ax6Vi3+Fmjy59LhE/nknn+zcd6XPHzR7a4HPUK0Q22yx0c0U0x3GPsAlWfcy/JvP2fC+RcT6OfPeKCe1NGGhcTCtZ/Am+eomf5/fctlwJ7Tx/HK+pWUWczc9ObDLL5tPt2P/wLLHtESqItP/zvTPntXSzJNje/EnROutV972Aw1UAL4413OP/MhbcbPN5t+4v7zb6q7XkecF7c0Zu8mQteDfIe1PY6eTD/1VcKTTu3xqSxXc95G/V07dOpQF6jDXeP6nu588JcX1VwSwHbajZgtzkOWP2xfbb/NhKfgl3+oCbCZf8Kyh+H8f2r5RbMvvI21e7ew+8ifXFOyCdOyFfYLTXkJUscBUFFp4fb/e1zLYzMZjCy88Sm6xDWwYFtAmDqL7bTr1Vk4BhME2/NzwoDJwORhEwB1VXQ/o4kAk3/91xVCaKTHR9Sr0hCoJhq6S3xvuHKx/Zp/fcvDh9/huQQbAdioyN5PxstnwNd3q8ERUBzTm9G/bGHDfnUIzmQw8s9rH8LfZF/kkQGXqjNbALZ9SmJwMCNSBwKwN+MQu9PtCdk1HF3v9FCHwgiTc07F0RwJfLzq1B4fgI3vaLOvFEXRhrqiQ+2JH9W/I5ribNhUtaSBXwi2M+5yetpireTHHWsBdfbe6X1GwhXvqLknoObbvDoKtn0C1kr8jCYWn3cxP0cd5o6gk+ir18w6cyYMmw6A2VLBTW8+pAU9fkYT/3frczUDsoaEJToFPbWeEhgiQY8QjSSBj/C+HuPhykUQpHbH6yrLmFG5m/Uxh1gZcZDRpmLt1M1RQzhtn470QvWLMCkyjq/vX8iIU3MN/EOh3yVq2VwIf37FBUPP1p6ud7jrlB4fgJGmEqfHMtTlZdU9PgHh0PkMtZy9V50GDZSayzBXquvX9E7qRseoBAC2HNqJxeqwaOCfX9qnSw+/Qfudq7Zh/zZtXamz+o5Ug+mEvnDec/aTTuxUk5dfHgKf3ULCZ9fQVa8OgZkVHc+WJjBp0xHuWjyXV354j+mvP8Dy7b+q1Tf5897fX2TCgL+5rWmEEM0jgY/wjX4Xw8zNMPJ2bY2KBMyEVC2PnmY1cnVBCuftLSevanbu33oOY/nD7zKkS7/ar+m41sum9zh/8DhtRsu3m36k1pUbyvLU2XIAUV20IY2RplLCAkPoXrW+0PGTGbW/XnhGdY9PYCSc5jArb6O6AvpJh/yeyOBwrXevzGJmx1GHxPQd9unuDLq6xm1+2GYf5po0cLT9ieE3qknKVdPRAXURuq0fab1OhwOSOCe/K/8qjWLr0d18uv475n35H1b9pa4QH+gXwH/v+Cdj+4xoxBsXQniaBD7CdwIjYfJ8uHMddBunHS7oexkPxV7GSot9ds5dE6/jo7tfJia0nvmsycMhrmqO25F1JFjytJ6hfZlHnBOnqx112Mak5yQsMeosnn6Gck7v2JXkaHXByTKLmZyi3JqvF+5ns9kTmYMi1RlP1T01O7+G4mzySuzrzkSFhHNatwHaY21ae0EaHFF7iIjrBfHO2z5U78AO6urIZ/cb5VyPnhPh1p/ghqVOv58YA2DSPOLu38RZY6fTObZjjSnjIQFBfHT3y/yt1zCEEC2LJDcL34vrBTO+guMbwRREeEI/3lUUvt38Mz9sW83Fp03knP6jGrwMOp3a6/PdQ+rjTe8xZejZrN+nrq/yzaaf6NOxu/NrjjgEPp1Gkp6dRqfs3Rh0cG6kiW2B9pW2j+ZkNG6dJNE05gL7EvpBUWq+zdBr1RXDrRbY8gG5UfZ8maiQCK3HB2Djge3q3mp/fmm/Zv9LayyAuDv9gDaEObLHYMKDQmvWRaeDrqPVn+Ob4PBv0HsyRHcjCJhz+b3MufxeyirKOXDiKHszDpGWe4KJA8+sfwkFIYTPSI+PaBl0OrXHJqFf1UMdU4aezWs3zHEt6Kk28Cp1JgzA1o84f8DftB3av9n0U83hKsf8nk6ns1Vn71Eapi8kOdq+3ovk+XiJY2JzYNX/j2Ez7Mc2LiLPYagrKjicnoldtfV7Nuzfpv5/3u6waWn/S2rcZpnDMNdEx2GuunQcCn+7G6Jrbqwb6BdAv+QeXDJ8IndNuk6CHiFaMAl8RNsSHA29qvZ4Ks4i7sRmRvZQ1xg6mHWMrY4rMlea1a1BACI7Q2gCPxTYl/tPKTpISrRDj49MafcOx6nsQVHqf6O72oebcg9hTN+knRIVEo5er9eGu3KK8ji27zf78gZJg9QlGk6xbNsarTzRlY1yhRBtggQ+ou0Zeo29vPl9Ljltgvbwsw3f259L36qt70KnkQD8lnaUPZXq9GC/7L/o5LDfi/T4eIlTj0+UvTzoSq3Y8Zi9tyYqJAKA4Q55PnnrF9tfV8uKxBn52dq2JP2Sezj17Akh2jYJfETbk3o2hFX11OxZxpSe/fE3qmv+fLlxBRWVVdObnYa5RpKZn82JghzWWYIA0NmsdClL006RtXy8xHGfriCHZPY+F6jbKwA9T27FVLWGTmTVHmrDHfJ8Yg877F3V7+Iat1ix41etLL09QrQvEviItkdvgMFT1bLNSuiepUwapOZw5Bbns3JnVcDjuD9Xp5FsP7obQAt8AEIytxDsrz6WHh8vKaujx8c/VN26AQi2ljHWT13vKSpYDXwGdeqNyWCkp6GcpIqqLUeSh0NkSo1bLHcIfCa5kt8jhGgzJPARbdOQafbypve5fPi52sNP1n+vTpmuXrE5KApie7DtiBr4rLXY91LSHf5NGwY5npuJzWbzfN3bu7p6fMBpuOtSf3Xl5qiqHp9AvwAGdurNhf72qe4MqDnMVWYxs3avmv/TISqBfsk93FRxIURrIIGPaJuiuzms+LuHsWF6bQ2gFTt+peDYZvsXbMoI0OnYVtXjk60YsUR0Vp87vonUKHXbgIpKCycKcrz5Ltqn2pKbq6Wera3pM9GviCijXuuRAzhv4Bgt8LFBrcNcO3MOaqs7Txzwtxpr8Agh2jYJfETbNcSe5Gzc+iGXDp8IqAHMzt/es59Xldi8varHJyIoDGPqWPU5WyWjAu1bIMjMLi+obTp7NYNJm5oeqFO4NMTiFLhM796NbgZ1K4t1lhBO2JyXKlMUhR8P/a49lmEuIdofCXxE29X3QvCrWv15x+dcMdS++m7ZvlX286oSm7MKTwLQP6UHui72vZUG2k5qZcnz8YL6enwAZaB9uOtC00mHJxSCN/2f9vDL8lDe/Okjp9d+9ccKDhWowWv3hM6M6jHETZUWQrQWEviItss/xL5wnbmIPgV/cUZSByb6FdGn/Lh63OgPSYO0qc0AAzv1BofAp1Op48wuCXw8rrrHR6cH//CaT8f145BVXaRyiJIDhRnq/llL71d3UQfKFR3fVYTy7uovKCgtAqDEXMYz3yzUrjP38nsxGmTxeiHaGwl8RNs29FqtqPvyDj6vWMG7YcdINFQNX3UYyh9H9/L0l69p5w1I6aVOh6+aOh1ite/ULj0+XlCdexUYAfqaH1G5JQV8YVYDIj2oKzT/7wHY8JZ6gk7Hlx0uIFcxUlxeynur1a0rXlv+XzLz1dleZ/cdybi+p9e4thCi7ZPAR7RtycPtq/baKp2esirwqS2RC164hX2ZRwCIDo3kbz2rNpYMUL9c/SpLtddIjo8XlOWr/61lmAsgr6SAz8sdeoJ+nAvr31TLOh1cspARlz6j5f68+fNHHDxxlP8s/y8Aep2exy6+01O1F0K0cBL4iLZNp4OJc9UhLf9Q6DKab4L6c1NhR4bmdeeuLbu0/bsGdurNZ/e+ok2Prg589OYibQNL6fHxMGsllOer5VMTm6vkFhdw0ObPFkuAeqB69W2dDi55HQZfTdf4FCYPVnO6sgtzuWTBHZRb1PPGdRpKt/iaa/sIIdoHGeAWbV/vyfB4JqADvR7rxuUs/b/HtacDTP7844JbuOXsK51zPqoCH8xFdIpKYHtpEWm5J6i0VkpuiKeUF9jLdfT45FZtUPq5OZzBpnL1oE4HF//HvnAlcOfEa1m6+WcAMgvUIa7I4HAmp57h/noLIVoN6fER7YPeoOWLTBw4mpQYdUuLkd0Hs/Kx//L3CdNqBjMB9n26ekSpa8dYbVYyqvJEhAc4Ll5YV49PiRocfWqOoDgoTs3Fuvg/zotWoq7krA1bVrl/8o0EV+VuCSHaJ/mzVbQ7QX4BrHj4XY7mpNO3Y3f0tSTQAvYeH6BbmL18NCddNrX0lAamsoM61AVQoBj4/YLFnNV9gFOQ6uiuidfy654/AOiV1I2poy5g+Q/L3VtnIUSrIoGPaJfCg0Lpn9Kz/pMCI7Ri57BQrSx5Ph7kSo9P1VAXQGRoVJ1BD8Do3sOZed71bNi/jXlXzpIhSiGEBD5C1Mmhx6djUIBWPiqBj+eUNtzjk1dizwOKComo93I6nY4Hp9yqPbZYLM2qnhCi9ZMcHyHq4hD4JAQ4BD45MqXdY8rq2a6iimOPT/XO7EII4SoJfISoi8MQSpzJ/k/luPT4eI4LPT7VOT4mg5GQgKBazxFCiLpI4CNEXRx6fPytZdqwiixi6EGOPT4NDHVFhUTIzupCiEaTwEeIujgEPpQVaDO5MvKzqaiUXBGPqG9n9irVPT6RMswlhGgCCXyEqEugwxdreQEp0eraP4qikJab6aNKtXENDHWVVpRrKzBrK2wLIUQjSOAjRF0CIuzl8kKSoxO0h5Lg7CHVQ116I/iF1HjaaSq79PgIIZpAAh8h6uK4Pkx5AYkRcdrDnKK8Wl4gmq26xycoSt2G4hSOgU90A1PZhRCiNhL4CFGXAOehrtDAYO1hYVmxDyrUDlT3+NSR35NX7LiGj/T4CCEaTwIfIepiDACDn1ouLyAs0D70UlRe4qNKtWGVFVBRFVDWNZXdYfFCGeoSQjSFBD5C1EWns/f6lDkHPtLj4wEuLF540nHxQhnqEkI0gQQ+QtSnemZXeQFhQRL4eJQr21UUS4+PEKJ5JPARoj7+VQnO5kLCHFYJLiqToS63a8SqzSA5PkKIppHAR4j6VA91KTbCDPZZRtLj4wGu7NNVkq+VZVaXEKIpJPARoj6BEVoxFKtWLpLAx/0aO9QlPT5CiCaQwEeI+jhMaTdZigk0+QNQKENd7udSj48a+Bj1BkIDgms9Rwgh6iOBjxD1OWW/rtCqmV3S4+MBLuX45AOyQakQoukk8BGiPo6rN5sLtSntkuPjAS70+FQPdckwlxCiqdpU4NO5c2d0Op3Tz3PPPefraonWzKnHJ19bvbnYXIrNZvNRpdooFzYoLaveoFSmsgshmsjo6wq429y5c7n55pu1x6GhoT6sjWj1Ttm2orrHR1EUis2lTosaimZyDHxq6fGR7SqEEO7Q5gKf0NBQEhISGj6xitlsxmw2a48LCwsBsFgsWCwWt9evNal+/+25HXSmEO0fibUkjxB/+1o+uYX5BBr9m30PaWeVsTQXHaAYA6jUmeCU9sjKz9HKEUFhTWovaWvvkHb2DmlnZ662g05RFMXDdfGazp07U15ejsViISUlhalTpzJz5kyMxrrjuyeffJI5c+bUOL5kyRKCgoJqeYVoT6KK9nLm7qcA2B9/Lg8WRPDb8W0APP63G+kQGlffy0UjTNh6N4GWPMpMkSwf9O8az/+Vc4h/bfwIgHO7jeKiHmO8XUUhRAtWWlrK1KlTKSgoICwsrM7z2lSPz913382QIUOIiopi7dq1PPTQQ2RkZLBgwYI6X/PQQw9x3333aY8LCwtJTk5mwoQJ9TZce2CxWFixYgXjx4/HZDL5ujq+kdUFqgKfrknR9InrrgU+A4cNYXi3Ac2+hbSzyrhFHaIOiEzkvPPOq/G8ZdOPsFEtDxswmPPOqnlOQ6StvUPa2TuknZ1Vj9g0pMUHPrNnz2b+/Pn1nrNr1y569erlFMAMGDAAPz8/br31Vp599ln8/WsfkvD396/1OZPJJL9IVdp1W4REa0V9RRERDsFwSUWZW9ulXbdzRSlUlgOgC46utR0KHGbSxYbXfo6r2nVbe5G0s3dIO6tcbYMWH/jMmjWLGTNm1HtO165daz0+YsQIKisrOXz4MD179vRA7USbd8o6PmFx9mRmWcvHjVxZvNBhZ3bZoFQI0VQtPvCJjY0lNja2Sa/dunUrer2euDjJwxBN5BcMegPYrFBeoE1nB1nLx61c2a6ixD6rK1pmdQkhmqjFBz6uWrduHRs2bGDcuHGEhoaybt06Zs6cyTXXXENkZO1/QQrRIJ1O7fUpzYXyfKfp60Xlsm2F2zQwlR1O3Zk9wsMVEkK0VW0m8PH39+ejjz7iySefxGw206VLF2bOnOmU9yNEk/iHVQU+hU6Bj/T4uJHjUFcD21WADHUJIZquzQQ+Q4YMYf369b6uhmiLAiMgD3WoK0CGujzChR6f6qEug94gC0cKIZqsTW1ZIYRHVCc42yoJN9n/yRTJDu3u40KPT/WsrvCgUNmgVAjRZBL4CNEQh5ldYTr7/lzS4+NGLiQ3F5QWARAuvT1CiGaQwEeIhjgEPqFUamWZzu5GDUxnt9lsWqAZHtS+FxYVQjSPBD5CNCTA/kVrqiwlwKQueFkoQ13u00CPT1F5CdW764QHSY+PEKLpJPARoiFOixjma2v5SI+PGzWQ3Fw9zAUQFhjqjRoJIdooCXyEaEigQ+BTXqDNKJIcHzeqHuryCwGjX42nHds6IlgCHyFE00ngI0RDApwDn9AANfApNpdis9nqeJFolOrAp46p7PlOPT4y1CWEaDoJfIRoSECEvVxWQFjVUJeiKBSbS31Tp7ZEUeyBTx0zugodAp8ISW4WQjSDBD5CNMQhuVndr0tWb3arihKwWtRyYEStpzj1+EhysxCiGSTwEaIhTkNdzttWSIKzG7iwM3thmT3wCZfkZiFEM0jgI0RDAk5NbpZtK9zKhcCnoNTezuFBEvgIIZpOAh8hGuI0qyv/lKEuWcun2Uodt6uoK/Ap1Mqyjo8Qojkk8BGiIX6hUL03lMN0dpChLrcoz7eX6xzqcuzxkeRmIUTTSeAjREP0evCv+rKV5Gb3c+zxcSW5WaazCyGaQQIfIVxRnedTXuiU4yM7tLuBK8nNDoGP5PgIIZpDAh8hXKEFPs5DXdLj4wauJDdXtXOgXwB+RpM3aiWEaKMk8BHCFdWBT6WZMJP9i1cCHzdwJbm5RO3xkansQojmksBHCFc4zOyK0Nm3qZDkZjdwIbm5oGodn3DZp0sI0UwS+AjhCn/7TKIwvVUrF5ZLjk+zNZDcXFFpoayiHIBwSWwWQjSTBD5CuMKhxyeESq0sPT5uUJ3jY/ADU1CNpwuctquQHh8hRPNI4COEKxxWbzZZSggw+QOS4+MWjjuzV6+X5MCxjSMk8BFCNJMEPkK44pRtK0KrprTLys1uoO3M3vCqzbKGjxCiuSTwEcIVARH2cpl9SrsMdTVTZYW6Ozu4uE+XrNoshGgeCXyEcEWAwxdueQGhAVWBT3kJNputjheJBjnN6Iqo9ZQCx53ZZZ8uIUQzSeAjhCuchrrsqzcrikKJucxHlWoDSl3Zmd0x8JEeHyFE80jgI4Qr6t2hXYa7msyl7Sochrokx0cI0UwS+AjhilOSm2XbCjdxIfDJd0xullldQohmksBHCFfUCHwcNyqVwKfJSnPt5Tpmdcl0diGEO0ngI4QrHFZupqzglKEumdLeZK5sV+G4gKEMdQkhmkkCHyFcYTCCX9WXrrnQ6Qu4qFx6fJqsge0qwDnwiQiW5GYhRPNI4COEq6qHu2r0+Ejg02Qu5PhUr+Oj0+kI8a+5pYUQQjSGBD5CuKp6ZtcpOT4S+DSDS4GPmtwcFhiCXi8fWUKI5pFPESFcVd3jYykl3M9fO1wkOT5N5xj41LVlRVVgGS6JzUIIN5DARwhXOczsCjfYD0uPTzOU5av/1enAP7zG04qiUFiV4yNr+Agh3EECHyFc5bBtRbjOqpVlOnszVPf4BIRDLcNYpeYyKm1qW8uqzUIId5DARwhXOfT4hGIPfKTHpxmqZ3XVld9T5rhBqfT4CCGaTwIfIVzlEPgEY9HKkuPTRDabfR0fl9bwkRwfIUTzSeAjhKsc1pnxsxTjb/QDpMenycwFoChqua7EZqcNSiXwEUI0nwQ+QrgqMMpeLs0ltGpKuwQ+TeTCzuyFZY6Bjwx1CSGaTwIfIVwVHG0vl5zUVm8uKpehriZx2q4iotZT8p16fCS5WQjRfBL4COGqIMcen5Pa6s1FZSUo1UM2wnWu9Pg4Bj4ynV0I4QatJvCZN28eo0aNIigoiIiIiFrPOXr0KJMnTyYoKIi4uDgeeOABKisrvVtR0XYFOfT4lJ7UVm+2KTZKzKU+qlQr5sqqzQ7DiGGS4yOEcINWE/hUVFRw+eWXc/vtt9f6vNVqZfLkyVRUVLB27VreffddFi9ezOOPP+7lmoo2K/jUwEd2aG8WVwKfEocNSiXwEUK4QasJfObMmcPMmTPp379/rc8vX76cv/76i//+978MGjSIc889l6eeeorXXnuNiooKL9dWtEn+4aCvWrK5JFc2Km0ux6Eux2FEBwVlMp1dCOFeRl9XwF3WrVtH//79iY+P145NnDiR22+/nZ07dzJ48OBaX2c2mzGbzdrjwkJ1Q0SLxYLFYqn1Ne1F9ftv7+3gyBgYha4kG6Ukh+CoQO14XlF+k9upvbazviSX6p0/Kk0hKLW8/7ziAq0c7BfQ7DZqr23tbdLO3iHt7MzVdmgzgU9mZqZT0ANojzMzM+t83bPPPsucOXNqHF++fDlBQUHurWQrtWLFCl9XocUYZ/UjDLAWZXHieIZ2/Oc1q8jafaxZ125v7Tz40A5SqsqrN+6g6M/cGuccTjuqldet/g0/g8kt925vbe0r0s7eIe2sKi11LdfSp4HP7NmzmT9/fr3n7Nq1i169enmsDg899BD33Xef9riwsJDk5GQmTJhAWFj7nj5rsVhYsWIF48ePx2RyzxdOa2fIeh2OpGG0mTmtTx+W7v8VgJ79enPe0HOadM322s6Gjz6AHLV85sQpEBJf45x/bf8UAD+jiQvPn4JOp2vWPdtrW3ubtLN3SDs7qx6xaYhPA59Zs2YxY8aMes/p2rWrS9dKSEjg999/dzp24sQJ7bm6+Pv74+/vX+O4yWSSX6Qq0hYOHBKc4/zsKXKlFeXNbqN2187l9mEsU2gsGGu+9+pZXeFBofj5+bnt1u2urX1E2tk7pJ1VrrZBswMfs9lca+DgitjYWGJjY5tbBQBGjhzJvHnzyMrKIi4uDlC7/8LCwujTp49b7iGE45T2KL1sVNos1bO6/ILBWPtnSPU6PuGS2CyEcJNGz+r6/vvvmT59Ol27dsVkMhEUFERYWBhjxoxh3rx5pKene6KeHD16lK1bt3L06FGsVitbt25l69atFBerXzgTJkygT58+XHvttWzbto0ffviBRx99lDvuuKPJgZkQNTgEPpE6x41KJfBptAZ2ZrfarNqq2LJPlxDCXVwOfL788kt69OjBDTfcgNFo5MEHH+SLL77ghx9+4O2332bMmDH8+OOPdO3aldtuu43s7Gy3VvTxxx9n8ODBPPHEExQXFzN48GAGDx7MH3/8AYDBYGDp0qUYDAZGjhzJNddcw3XXXcfcuXPdWg/RzjkMdYXaHGYDyjo+jaMoDjuzR9R6SqHT4oWyarMQwj1cHup6/vnneemllzj33HPR62vGS1dccQUAaWlpvPLKK/z3v/9l5syZbqvo4sWLWbx4cb3ndOrUie+++85t9xSiBof1ZkJs5Vq5qFx6fBrFUgaVVYFjndtV2NtUhrqEEO7icuCzbt06l87r0KEDzz33XJMrJESL5jDUFVRp7+WRHJ9GcmHVZucNSiXwEUK4R6tZuVmIFsFhqMu/wv7FLENdjeRC4FNYJoGPEML9Gh347Nu3j88//5xDhw4B8L///Y/Ro0dz2mmnMW/ePNmlWrRtDj0+xvJ8/I3qFGtJbm4kp+0q6ujxKZHARwjhfo0KfL788kv69OnD1KlT6d27N++99x6XXXYZwcHBxMfH8+STT/L88897qq5C+J7TDu25hFbt0F7gMCwjXFCd2AwQEFHrKU49PoGS3CyEcI9GBT7z5s3jH//4B+Xl5bz++uvcdtttPPvss3z//fcsXbqU1157rcEEZCFaNf9QqN42ofQkkcHhAOSVuLZiqKjiQo9PQanjrC7p8RFCuEejAp89e/Zwww03oNPpmD59OhUVFZxzjn2Z/gkTJnDkyBG3V1KIFkOns/f6lJwkOiRCLZpLKbeY636dcOZCjk9BqT2YjJDARwjhJo0KfEpKSggNVT+A9Ho9gYGBTht5BgYGOu10LkSbVD2lvfQkUcH2/dwcdxIXDXAl8CmTHh8hhPs1KvDR6XROmwSe+liIdqG6x6eynITgYO1wbokEPi5zYair0CFvSnp8hBDu0qi9uhRFoUePHlqwU72CcvWChjKjS7QLDgnOSf72TfFyi/N9UJlWyrHHp47kZsd1fMJkAUMhhJs0KvBZtGiRp+ohROvhsJZPgp+9x/OkBD6uK8u3l+vq8XEc6goMrvUcIYRorEYFPtOnT/dUPYRoPRy2rYgzOgQ+Rfk+qEwrVd3jozeCX+1T1auTm0MCgjAaGvVRJYQQdZKVm4VoLIehriidVSvLUFcjlDnszF5HnmD1dHbZp0sI4U4u/xkVGRnpciJzbm5ukyskRIvnEPhEUKGVc2VWl+uqA586hrnAPtQlqzYLIdzJ5cDn5Zdf1sonT57k6aefZuLEiYwcORJQNzH94YcfeOyxx9xeSSFaFIccnxDFvnxDbkm+DyrTClktYK5KXK4jsbncYtbWRZKp7EIId3I58HHM77n00kuZO3cud955p3bs7rvv5tVXX+XHH39k5syZ7q2lEC2JQ49PsMMO7TLU5aJyh56xOqey2xObZbsKIYQ7NSnH54cffmDSpEk1jk+aNIkff/yx2ZUSokVz2qi0AD+jOqVdkptdVNrw4oX5Dqs2y1CXEMKdmhT4REdH8/XXX9c4/vXXXxMdHV3LK4RoQxyGunSludq2FZLj46IyhxzAOgIfx6nsEvgIIdypSXNE58yZw0033cSqVasYMWIEABs2bGDZsmW89dZbbq2gEC2OKQiM/lBpVretCEklIz+b3JJ8FEWR1cwb4tI+XQ47s0vgI4Rwoyb1+MyYMYPffvuNsLAwvvjiC7744gvCwsL49ddfmTFjhpurKEQL47hRaelJokLUHdorKi2UmEt9WLFWwmmoK6LWU5wCH5nOLoRwoyavCjZixAg++OADd9ZFiNYjKBoK09XAJypcO3yyuICQAFlluF4u9Pg45ktFh0Z4tj5CiHbF5R6fkpKShk9qxvlCtCrVPT5WC4mBQdphmdnlgpIce9lhFWxH2UX2PKCY0LrX+hFCiMZyOfBJTU3lueeeIyMjo85zFEVhxYoVnHvuufz73/92SwWFaJGC7V/YSf4GrSwzu1xQnGUvhybUekqOBD5CCA9xeahr1apVPPzwwzz55JMMHDiQYcOGkZSUREBAAHl5efz111+sW7cOo9HIQw89xK233urJegvhW0GOG5Xa/36QRQxd4BT4xNd6Sk6RfTgsJrT2XiEhhGgKlwOfnj178vnnn3P06FE+/fRT1qxZw9q1aykrKyMmJobBgwfz1ltvce6552IwGBq+oBCtmUPgE2uwaWUZ6nJB8Qn1vzq9Uzs6cgx8qpPHhRDCHRqd3JySksKsWbOYNWuWJ+ojROvgsJZPlN4x8JG1fBpUVNXjExwL+tr/SKoOfKKCw2VndiGEW8nu7EI0hUNPRbjiuFFpvg8q04rYbFBSFfjUMcwF9sAnWvJ7hBBuJoGPEE3hEPiE2Mq0sgQ+DSjPUzcpBQiJq/WUEnMZpWa1TWPDJL9HCOFeEvgI0RQOQ12BFvvSDScl8KlfkUNic0jtPT4nnRKbpcdHCOFeEvgI0RSB9p4IQ3k+IQHqWj7S49OA6sRmqLPHR6ayCyE8SQIfIZrCceG90pNEVW1UelKSm+tX5BD4hNYV+MhUdiGE5zR5ukR+fj6///47WVlZ2Gw2p+euu+66ZldMiBbNL0jdrNRSqgY+wf04mpNOfkkhVpsVQx2zldq94oaHunJkqEsI4UFNCny+/fZbpk2bRnFxMWFhYU67Uet0Ogl8RPsQFA0FpVBi36jUptgoKC2WtWfq4jTUJYGPEML7mjTUNWvWLG644QaKi4vJz88nLy9P+8nNzW34AkK0BdUJzmW5RAfbAx3J86mH01BXHYFPoQQ+QgjPaVLgk5aWxt13301QUFDDJwvRVlXn+disJAX6a4dPFufV8QLhPNQlyc1CCO9rUuAzceJE/vjjD3fXRYjWxWEtn6QA+6ixrN5cj+rAx+gPAbUPBzoOdcWG1b6lhRBCNFWTcnwmT57MAw88wF9//UX//v0xmUxOz0+ZMsUtlROiRXNYyyfe4V+SDHXVozrHJyQeHHIDHWVX9fj4G/20ZQKEEMJdmhT43HzzzQDMnTu3xnM6nQ6r1dq8WgnRGjj0+MToFa0sPT51sFqg9KRarmOYC+w9PjGhkU4TJ4QQwh2aFPicOn1diHbJIfCJ1FVqZVm9uQ4lOaBUBYh1JDbbbDZOFuUDsk+XEMIzZAFDIZrKYagrDPtGpRL41MGFqex5pYXYFPUPK0lsFkJ4QpMDn19++YULLriA1NRUUlNTmTJlCmvWrHFn3YRo2Ry2rQi2ykalDSpyYbsKmcouhPCwJgU+//3vfznnnHMICgri7rvv5u677yYwMJCzzz6bJUuWuLuOQrRMDj0+AZZiLR9FAp86yFR2IUQL0KQcn3nz5vH8888zc+ZM7djdd9/NggULeOqpp5g6darbKihEi+WQ46MvyyUyKIzckgJJbq6LK4sXyj5dQggPa1KPz8GDB7ngggtqHJ8yZQqHDh1qdqVqM2/ePEaNGkVQUBARERG1nqPT6Wr8fPTRRx6pjxBOG5WW2DcqlR6fOri0M7vjGj4S+Agh3K9JgU9ycjI//fRTjeM//vgjycnJza5UbSoqKrj88su5/fbb6z1v0aJFZGRkaD8XXXSRR+ojBEZ/8A9Vy6U52v5cReUlVFRafFixFsqlDUplqEsI4VlNGuqaNWsWd999N1u3bmXUqFEA/PbbbyxevJh//etfbq1gtTlz5gCwePHies+LiIggISHBI3UQoobgGDAXQXE2UdER2uHc4nwSImJ9V6+WyKUcH0luFkJ4VpMCn9tvv52EhAT++c9/8sknnwDQu3dvPv74Yy688EK3VrCx7rjjDm666Sa6du3KbbfdxvXXX1/vImhmsxmz2aw9LiwsBMBisWCxtO+/2qvff3tvh/oYQhPR5x6C8gLiAwK041n5OUQHR7h0jfbSzsaiTHSA4h9Kpc4EtbzfrIKTWjk8MNTtbdJe2trXpJ29Q9rZmavt0KTAB+Diiy/m4osvburLPWLu3LmcddZZBAUFsXz5cv7+979TXFzM3XffXedrnn32Wa03ydHy5ctlE9YqK1as8HUVWqyhRTY6Vj/IPK4d//7n5RyK3tuoa7X1dj4vPx0TUKIL5qfvvqv1nP1HD2rljb+ux6g3eKQubb2tWwppZ++QdlaVlpa6dF6TAx93mD17NvPnz6/3nF27dtGrVy+XrvfYY49p5cGDB1NSUsILL7xQb+Dz0EMPcd9992mPCwsLSU5OZsKECYSFhbl037bKYrGwYsUKxo8fX2M/NqHSL18P69YDcHqXDrx75C8AuvftxXlDznLpGu2inS2lmDaqax0FxXflvPPOq/W0Z/94H4DwwBCmnF9zAkWzq9Ee2roFkHb2DmlnZ9UjNg1xOfCJiopi7969xMTEEBlZ/x46ubm5dT7naNasWcyYMaPec7p27epqFWsYMWIETz31FGazGX9//1rP8ff3r/U5k8kkv0hVpC3qEdFBKyYa7dtWFJQVNbrN2nQ7O+Tu6EMT0NfxPk9W79MVFuXRtmjTbd2CSDt7h7SzytU2cDnweemllwgNDdXK7tg8MDY2lthYzyWAbt26lcjIyDqDHiGaLSxRK0Yp5Vo5t0TW8nHiOJU9tPbE5nKLmaLyEkASm4UQnuNy4DN9+nSt3FAvjSccPXqU3Nxcjh49itVqZevWrQCkpqYSEhLCt99+y4kTJzj99NMJCAhgxYoVPPPMM9x///1er6toR0LtgU9EZZFWPunQwyFwcSq7LF4ohPC8JuX4bN68GZPJRP/+/QH4+uuvWbRoEX369OHJJ5/Ez8/PrZUEePzxx3n33Xe1x4MHDwZg5cqVjB07FpPJxGuvvcbMmTNRFIXU1FQWLFjAzTff7Pa6CKFx6PEJrrCPL8vqzadwZZ8umcouhPCCJi1geOutt7J3rzpj5eDBg1x55ZUEBQXx6aef8o9//MOtFay2ePFiFEWp8TN27FgAJk2axJYtWygqKqK4uJitW7dy6623otfLBvTCgxx6fALK7FOxZfXmU7iyarNsUCqE8IImRQV79+5l0KBBAHz66aeMGTOGJUuWsHjxYj7//HN31k+Ils0UAIHql7S++AQmg9qJKjk+pyhyGOqqc58uWbVZCOF5TQp8FEXBZrMB6jYV1VNTk5OTycnJcV/thGgNwpIA0BVlattWSI/PKZx6fCTHRwjhO00KfIYNG8bTTz/N+++/zy+//MLkyZMBOHToEPHxtX+oCdFmhVVtkWKtoEtwIKDm+CiK4sNKtTDVyc06nbrNRy2cAp8w6fERQnhGkwKfl19+mc2bN3PnnXfyyCOPkJqaCsBnn32m7d0lRLsRmqQVuwaq60iUW8yUmst8VaOWpzrwCYoGQ+1rbUhysxDCG5o0q2vAgAHs2LGjxvEXXngBg8EzS8wL0WI5zOzq7G//W+JkcT7BAbLtCYpiH+qqY5gLnJcAkMBHCOEpTerxOXbsGMeP2/cl+v3337n33nt57733ZPVI0f44BD4djPbhLUlwrlJeAJVVGwHXMaMLILsqudmoNxAeGOqNmgkh2qEmBT5Tp05l5cqVAGRmZjJ+/Hh+//13HnnkEebOnevWCgrR4oXZh7ridRVaWRKcqxQ3PKML7NPZY0KjZBkKIYTHNOnT5c8//2T48OEAfPLJJ/Tr14+1a9fywQcfsHjxYnfWT4iWLzRBK8Y4blshgY+qqOEZXYqiaNPZJbFZCOFJTQp8LBaLtv/Vjz/+yJQpUwDo1asXGRkZ7qudEK2BQ49PhLVYK2cXurZZb5vntE9X7YFPQWkRlTYrIPk9QgjPalLg07dvXxYuXMiaNWtYsWIFkyZNAiA9PZ3o6Gi3VlCIFi84BvRqUn+Iw7YVmQWyphXg2qrNktgshPCSJgU+8+fP54033mDs2LFcffXVDBw4EIBvvvlGGwITot3QGyBEHe4KLLd/gWfmZ/uqRi2L46rNLgQ+0RL4CCE8qEnT2ceOHUtOTg6FhYVERto/pG655RaCgmT6rmiHwhKhMA1D2UlMxGFBJ4FPNZdWbZbtKoQQ3tGkwAfAYDA4BT0AnTt3bm59hGidHKa0pwYHsKvETIYEPiqnHJ/ae3wc86FkuwohhCe5HPgMGTKEn376icjISAYPHoxOp6vz3M2bN7ulckK0Gg6BT+/QIHaVmDmRn4OiKPX+W2kXiqsCQIMJAmrvzZEcHyGEt7gc+Fx44YXaTK6LLrrIU/URonUKtQc+3YP8ADBXVpBbUkB0SISPKtVCVE9nD46DOtbncQx8YsOkx0cI4TkuBz5PPPFErWUhBE49Pp0ctq3IzM9u34GPzQolVT0+9azaLD0+QghvaXKOT7Xi4mJsNpvTsbCwsOZeVojWxWEtnySjVStn5GXTt2N3X9SoZShMB6Xq8yG8Q52nOe7T1a4DRSGExzVpOvuhQ4eYPHkywcHBhIeHExkZSWRkJBERETUSnoVoFxyGumKxb1uRmZ9V29ntR/4xezkiuc7Tqnt8QgKCCPQL8HSthBDtWJN6fK655hoUReGdd94hPj5ekjeFcBjqinRYvbndz+zKP2ovR6bUedqJqsUeZUaXEMLTmhT4bNu2jU2bNtGzZ09310eI1sk/FPyCoaKEYHM+EALIIobkOQQ+EbUHPkVlJRSVlwCQFFl3HpAQQrhDk4a6TjvtNI4dO9bwiUK0Fzqd1utjKj2pHZYen4aHutLz7MOBEvgIITytST0+b7/9NrfddhtpaWn069cPk8nk9PyAAQPcUjkhWpXQRMjZj95SQqRBR55VkR6f/IZ7fNLz7AscJkXWvrKzEEK4S5MCn+zsbA4cOMD111+vHdPpdNpibVartZ5XC9FGOeT59AkP4bfcIgl8qgMfvxAIrH3ig3PgIz0+QgjPalLgc8MNNzB48GA+/PBDSW4WoprDlPZeIYH8lltEbkkB5RYzASZ/H1bMR2w2+1BXZIo6HFiLNKehLunxEUJ4VpMCnyNHjvDNN9+Qmprq7voI0XqFJmjFroH24d8T+Tl0iq17DZs2qyQLrFVT++uZyu7Y49MhSgIfIYRnNSm5+ayzzmLbtm3urosQrZtDj0+KSdHK7TbB2Sm/p57AJ1eGuoQQ3tOkHp8LLriAmTNnsmPHDvr3718juXnKlCluqZwQrYpDjk+8vlIrt9s8H6ep7J3qPK16VlegXwARQbLquxDCs5oU+Nx2220AzJ07t8Zzktws2i2H1ZujlTKtnNFeV292YSq7oiikVQ11dYiUfEEhhOc1KfA5dW8uIQROOT5hliKt3G57fFxYtTm/tJCyinJAhrmEEN7RpByf2uTn57vrUkK0TkY/CI4BILA8VzssOT7Us4aPw4wuSWwWQnhBkwKf+fPn8/HHH2uPL7/8cqKioujQoYMkPYv2rSrB2VCagw41wbn99vhUDXUZAyA4ttZT0nJl8UIhhHc1KfBZuHAhycnqmP2KFSv48ccfWbZsGeeeey4PPPCAWysoRKtSNdyls1WSGhwItNMeH0WxJzdHJNe5ho/TVHYZ6hJCeEGTcnwyMzO1wGfp0qVcccUVTJgwgc6dOzNixAi3VlCIVsVhSnvvsGD2lZRzoiBHW9W83SjNBUupWq53DR9ZvFAI4V1N6vGJjIzUNildtmwZ55xzDqDO0JAZXaJdc0hw7h6srtZcUWnhZHG+jyrkI/lH7OV6p7LLUJcQwrua1ONzySWXMHXqVLp3787Jkyc599xzAdiyZYus5izaN4cen87+9r8rMvOziQmtfa+qNsmFqezgnOPTIUqGuoQQntekwOell16ic+fOHDt2jOeff56QkBAAMjIy+Pvf/+7WCgrRqkR01IrJeotWzsjPpl9yD1/UyDfyGp7KDvahrrDAEEICgj1dKyGEaFrgYzKZuP/++2scnzlzZrMrJESrFtlZKybYSrRyu5vZ5cJUdkVRtMUdZZhLCOEtTQp8APbt28fKlSvJysqqsaDh448/3uyKCdEqRaSATg+KjaiKPEDdzqXdzexyHOqqo8cnpyiPikq1V0wWLxRCeEuTAp+33nqL22+/nZiYGBISEpxmq+h0Ogl8RPtl9IPwjpB/lOCSTEDNb8lsb9tWVPf4GEwQklDrKY4zumRXdiGEtzQp8Hn66aeZN28eDz74oLvrI0TrF9UF8o9iqCgiXGelQDG03x6fsA6gr33yqPOMLunxEUJ4R5Oms+fl5XH55Ze7uy5CtA0OeT7d/dRh4BP5OT6qjA+U5UN5gVqOrHsqe5pMZRdC+ECTAp/LL7+c5cuXu7sudTp8+DA33ngjXbp0ITAwkG7duvHEE09QUVHhdN727ds588wzCQgIIDk5meeff95rdRRCE9VZK/YPUdfyaVfJzS5OZU93nMougY8QwkuaNNSVmprKY489xvr16+nfvz8mk8np+bvvvtstlau2e/dubDYbb7zxBqmpqfz555/cfPPNlJSU8OKLLwJQWFjIhAkTOOecc1i4cCE7duzghhtuICIigltuucWt9RGiXlFdtGKvAD1gI7ekgHKLmQCTv+/q5S1OgU/DU9lBhrqEEN7TpMDnzTffJCQkhF9++YVffvnF6TmdTuf2wGfSpElMmjRJe9y1a1f27NnD66+/rgU+H3zwARUVFbzzzjv4+fnRt29ftm7dyoIFCyTwEd7l0OPT1VSplU/k59AptoMPKuRlTqs217ddhb3HJ1ECHyGElzQp8Dl06JC769FoBQUFREVFaY/XrVvH6NGj8fPz045NnDiR+fPnk5eXR2Rk7avmms1mzGaz9riwsBAAi8WCxWKp9TXtRfX7b+/t0GihyVT3gSYpZYC6WemxnAySImp+wbe1dtafPIKhqlwZ2gGljvdVvWpzZHA4Rp3BK++/rbV1SyXt7B3Szs5cbYcmr+PjS/v37+eVV17RentA3Ti1S5cuTufFx8drz9UV+Dz77LPMmTOnxvHly5cTFBTkxlq3XitWrPB1FVqdcw1B+FlLCS/LoXpK+7JVKzi5N63O17SVdj5t/+9Ub9zx85b9lP1VUOMcm2LTFi8M0Qfw3XffebGGbaetWzppZ++QdlaVlpa6dF6TA5/jx4/zzTffcPTo0RpJxgsWLHDpGrNnz2b+/Pn1nrNr1y569eqlPU5LS2PSpElcfvnl3HzzzY2v+Ckeeugh7rvvPu1xYWEhycnJTJgwgbCwsGZfvzWzWCysWLGC8ePH18jjEvUzHu8OGduIthVjQsGCjoQuHTnv7PNqnNvW2tnwpvoHiaLTM27KVHUtn1Nk5GdjW6YA0LNTKuedV7NdPKGttXVLJe3sHdLOzqpHbBrSpMDnp59+YsqUKXTt2pXdu3fTr18/Dh8+jKIoDBkyxOXrzJo1ixkzZtR7TteuXbVyeno648aNY9SoUbz55ptO5yUkJHDixAmnY9WPExJqX0ANwN/fH3//mgmnJpNJfpGqSFs0QXRXyNiGDoUOeguHbX5kF52stx3bTDsXqMnNurAOmAJq7zXNLsrVyh2jE7z+vttMW7dw0s7eIe2scrUNmhT4PPTQQ9x///3MmTOH0NBQPv/8c+Li4pg2bZpTEnJDYmNjiY2NdenctLQ0xo0bx9ChQ1m0aBH6UxZFGzlyJI888ggWi0V78ytWrKBnz551DnMJ4TEOa/l0MlRw2ObXPhYxrCiB0qqgpr5d2fNkKrsQwjeatI7Prl27uO666wAwGo2UlZUREhLC3LlzGxy6aoq0tDTGjh1LSkoKL774ItnZ2WRmZpKZmamdM3XqVPz8/LjxxhvZuXMnH3/8Mf/617+chrGE8BqHmV2dDepQcLtYy8eFPbpAVm0WQvhOk3p8goODtbyexMREDhw4QN++fQHIyXH/CrUrVqxg//797N+/n44dOzo9pyhqnkB4eDjLly/njjvuYOjQocTExPD444/LVHbhGw5r+fTw10F5O9moNM/Fqey5Dmv4yD5dQggvalLgc/rpp/Prr7/Su3dvzjvvPGbNmsWOHTv44osvOP30091dR2bMmNFgLhDAgAEDWLNmjdvvL0SjOW5bUZVClpmfjc1mqzFM26Y4LV4o21UIIVqeJgU+CxYsoLi4GIA5c+ZQXFzMxx9/TPfu3V2e0SVEmxbeEfRGsFXSSa/2jlqslWTkZ7ftncird2WHent8qqey63Q6EiNcy/MTQgh3aHTgY7VaOX78OAMGDADUYa+FCxe6vWJCtGoGo/rFn3uIeFsxEAfoOJx9vG0HPjn77WWHPKdTVQ91xYZG4WeU2ShCCO9pdJ+7wWBgwoQJ5OXleaI+QrQdVcNdATYz0TorAEdy6l7AsE3I2av+1xhQ5z5dFmslJwrVXEAZ5hJCeFuTkg369evHwYMH3V0XIdqWaPsaVClVM7sOZ7fhwMdqgZNVnwsx3UFvqPW0zPxsbVJChyiZ0SWE8K4mBT5PP/00999/P0uXLiUjI4PCwkKnHyEEzmv56NU9ZA5nH/dRZbwg9xDYqjZlje1e52npktgshPChRuX4zJ07l1mzZmnLy0+ZMgWdTqc9rygKOp0Oq9Xq3loK0Ro5TGnvYrRARRvv8cneYy/H9qzztPQ8h6nssoaPEMLLGhX4zJkzh9tuu42VK1d6qj5CtB0Oyb29Aw1Q2sZzfLL32suxPeo87XiufeFR6fERQnhbowKf6nH5MWPGeKQyQrQpDkNd3Uw2AApKi8grKSAyONxHlfIgxx6fmLoDn8NZ9uG+TrEdPFkjIYSoodE5Po5DW0KIegSEQVA0AB0o1Q632eGu6h4fnQ5iUus87UCWfZHDrnF1r/UjhBCe0Oh1fHr06NFg8JObm1vv80K0G1GdofQk4ZXF+GPDjJ4j2WkM7tzH1zVzL0WBnH1qOaITmALrPPVQVeATExpJWGCIN2onhBCaRgc+c+bMITy8DXbTC+EJkZ3h+CZ0KHQ0WDhg9W+bM7uKMsBcpJbrye8pKS/lRIG6ho/09gghfKHRgc9VV11FXJzMxBDCJQ4zuzrrK9TApy0mOGc5zuiqO/A55BD0dZHARwjhA43K8ZH8HiEaySHw6WSoXsunDQY+OY4zuuqeyn5Q8nuEED7WqMCnelaXEMJFDoFPjwD1D4ejbbHHx8UZXYccAh/p8RFC+EKjhrpsNpun6iFE2+Qwpb1nVeCTkZ9NucVMgMnfR5XygOx99nJc3YGP9PgIIXytSVtWCCFcFJYIBj8AUnRmQO05PZqT7stauV91j09QtDaFvzZOPT6xHT1dKyGEqEECHyE8SW+AyE4AxFkL0aEOF7epPJ/yAiiqWo25nvwesPf4xIfHEBwQ5OmaCSFEDRL4COFpMeqGnSabhY5Vm5UeaUuBT47DMFc9M7oKy4rJKcoDJL9HCOE7EvgI4Wnx9sUKexnU4a7DOW1oLR8Xp7I75/fIMJcQwjck8BHC0+J6a8WexqrApx32+MiMLiFESyCBjxCe5tDj08/UBoe6HKeyyxo+QogWTgIfITwtJlVNcgb6+lsBOHoyHavN6stauU/15qTGAAivO6A5JIGPEKIFkMBHCE8z+kO0ult5Z6UYAwoVlRYy8rN9XDE3qKyA3INqOaY76Ov+SHHs8eksU9mFED4igY8Q3lA13GXCRhdDBdBG8nxyD0F1z1U9+T1g7/HpEBlPoF+Ap2smhBC1ksBHCG9wSHCuntl1pC3s0p7t2oyuvJIC8koKAUlsFkL4lgQ+QniD45R2YznQRnp8sl3bnFTye4QQLYUEPkJ4Q3zNHp+2Efg0fg2fLrKGjxDChyTwEcIborqqSc5Ar6q1fI60hUUMc6p6fHQ6LYG7Ngez7O9VenyEEL4kgY8Q3qA3QGwvADrrLfhj43B2Goqi+LhizaAo9l3ZIzqBqe6EZVm8UAjRUkjgI4S3VA13GXUK3QwVFJYVawm/rVLBcagoVstx9W9OeuDEUQD0Oj2dYjp4umZCCFEnCXyE8BanBOfqPJ9WPNyVvs1eju9b52mKotinskfF42/y83TNhBCiThL4COEtTlPa1ZldR3JacYJz2mZ7ucOQOk/LKcqjqLwEkPweIYTvSeAjhLe0uR6fLfZyh8F1nib5PUKIlkQCHyG8Jbwj+IcC9int+zOP+LJGTacokFYV+ATHqO+tDrI5qRCiJZHARwhv0em04a4Ug4VgnZXd6Qd9XKkmyj8KpblqucNg9b3VwXnxwhRP10wIIeolgY8Q3uQw3NXDYGZf5mEqrZU+rFATpTkMcyXVPcwFp67hI4sXCiF8SwIfIbwprpdW7GUwU1FpaZ0rODsGPvUkNgMcylZ7fAx6A8kxSZ6slRBCNEgCHyG8qZYE5z0ZrXC4K821xGZ1Krva45MSnYjJYPR0zYQQol4S+AjhTY6Bj6E68Dnkq9o0jaLYZ3SFJkBYYp2nHs/NpMRcCkC3hE7eqJ0QQtRLAh8hvCk4FoKigVbc45N7EMoL1HI9vT0Afx3fp5X7dKh7Ly8hhPAWCXyE8CadTuv1iddXEqWrZE96K+vxaURi819p+7Vyn44S+AghfE8CHyG8Ld6+gnNPg5lD2cepsFp8WKFGcjG/B2DncXvg07djd0/VSAghXNYqAp/Dhw9z44030qVLFwIDA+nWrRtPPPEEFRUVTufodLoaP+vXr/dhzYWoRZxzgrNNsZFZfNKHFWqkRgQ+1UNdASZ/usTKVHYhhO+1iikWu3fvxmaz8cYbb5Camsqff/7JzTffTElJCS+++KLTuT/++CN9+9o3TIyOjvZ2dYWoX4L997O/Ud2zK70421e1aRybDTKqNicN7wghcXWeWmIu41DVlhw9k7pilBldQogWoFV8Ek2aNIlJkyZpj7t27cqePXt4/fXXawQ+0dHRJCQkeLuKQrgucQDojWCrZKixDIC0ohwfV8pFJw+AuUgtN9Dbsyf9AIqiAJLYLIRoOVpF4FObgoICoqKiahyfMmUK5eXl9OjRg3/84x9MmTKl3uuYzWbMZrP2uLCwEACLxYLF0oryLjyg+v2393ZwPyOGhH7o07fS02gmTGclvTi7VbSz7uhG7UPDmjAAWz113nFkj1buldi1Rbw/+Z32Dmln75B2duZqO7TKwGf//v288sorTr09ISEh/POf/+SMM85Ar9fz+eefc9FFF/HVV1/VG/w8++yzzJkzp8bx5cuXExQU5JH6tzYrVqzwdRXanP6VsXStKg8xlrG9KLtVtHO/o1/Sraq84biF7O++q/Pc73f+pJULj+XwXT3neltraOu2QNrZO6SdVaWlpS6dp1Oq+6J9YPbs2cyfP7/ec3bt2kWvXvZl/tPS0hgzZgxjx47l7bffrve11113HYcOHWLNmjV1nlNbj09ycjI5OTmEhYW5+E7aJovFwooVKxg/fjwmk8nX1WlTdDs+x/jFLQC8WBrDi6VxbHvmW6JCI3xbsQYYFk1Gf1SdMGB5YB8E1ex1rXbJS3ew8eB2AHbM/46IoFCv1LE+8jvtHdLO3iHt7KywsJCYmBgKCgrq/f72aY/PrFmzmDFjRr3ndO3aVSunp6czbtw4Ro0axZtvvtng9UeMGNFgJOzv74+/v3+N4yaTSX6RqkhbeECXkVqxOs/nUM5x4qNifVWjhtmskKEGMkR2xhQeX+epiqKwO/0AAB0i44kNrztA8gX5nfYOaWfvkHZWudoGPg18YmNjiY117YM+LS2NcePGMXToUBYtWoRe3/BM/K1bt5KYWPdy+kL4TEQKhMRD8QmGGMvQobAn/SCn96g/YdinsveCpaoruYHE5mMnMygqLwFk4UIhRMvSKnJ80tLSGDt2LJ06deLFF18kO9s+9bd6Bte7776Ln58fgwerH8hffPEF77zzToPDYUL4hE4HKcPhr28J19voYTC3/D270jbbyw0EPrscV2yWGV1CiBakVQQ+K1asYP/+/ezfv5+OHZ0XQXNMUXrqqac4cuQIRqORXr168fHHH3PZZZd5u7pCuCb5NPjrW0Ad7mrxe3alu75VheOKzX1kxWYhRAvSKgKfGTNmNJgLNH36dKZPn+6dCgnhDsnDteIwUxnLW3qPz+G16n91OkgaWO+pskeXEKKlahVbVgjRJnUYrC5kCAwzlpJTlEd2Ya6PK1WH4izI/FMtJw2CwIh6T6/eqiLQ5E/XuGTP1k0IIRpBAh8hfMUUCIn9AehhrCBcZ2VPegsd7jrwi73cbVy9pzpvVdENg97gyZoJIUSjSOAjhC85DHcNMZZpU8BbnAOr7OVuY+s91Wmrio7d6j1XCCG8TQIfIXzJKc+nlN0tscdHUeyBj9EfUkbUe7okNgshWjIJfITwpRR74DPUWNYyA5/cg1BwTC2njFSH6Orx13GZyi6EaLkk8BHClyJSUELUFZCHGMvYk74fH+4iU7tGDHMB/JW2TytL4COEaGkk8BHCl3Q6lI7DAAjT2+hgyeNoTrqPK3UKx8AndWy9pyqKovX4dIhKICK4fe93J4RoeSTwEcLHlI6naeWhxjI2HfrTh7U5hc0KB1er5YAISKx//R6nrSo6SGKzEKLlkcBHCB9Tku2BzzBTCwt8MrZDWZ5a7joaGpia7pjf01cSm4UQLZAEPkL4mJI4EBtqQDHUWMofB1tQ4NPI/J6dDvk9vSW/RwjRAkngI4SvmQIpCO4EqAsZph//i7KKch9XqkojA58th//Syv2Te7i/PkII0UwS+AjRAuSE9tLKo40FbD+624e1qWIphyPr1HJ4R4iuP2fHZrOx8cB2AKJDI+kiW1UIIVogCXyEaAFORNh3O5/gV9wyhruOboDKqp6nbmPVzUnrsTfzMAWlRQAM7zoAXQPnCyGEL0jgI0QLkBvSnUp/der3OFMx2w5u83GNaPQw1+/77XUenjrA/fURQgg3kMBHiBZA0RnQdR8PqOv5KIfX+n4hwwMr7WVXAp+qYS6A4d3qn/YuhBC+IoGPEC2E0mOiVh5qSSMt74TvKlOWB+lb1HJ8HwiJa/Alvx9Qe3wCTP70T+npydoJIUSTSeAjRAuhpJ6Nreqf5AS/Yv5w6EHxut3fq5uTgku9PZn52dqK04M698HPaPJg5YQQoukk8BGipQiMoCCuHwBdDRUc3fWL7+qy7RN7ue9FDZ7uOMw1opvk9wghWi4JfIRoQfz7TdHKQUd8FPgUnbAnNkd0gpQRDb5ko0Pgc5rk9wghWjAJfIRoQYIGXKyV+5UcwGyp8H4ldnwOik0tD7y8wWnsABscZnQN69rPUzUTQohmk8BHiJYkpjtZpkgAhhlK+Gv/Ru/XwXGYa+AVDZ5eUl7KzuPqVhW9krrJjuxCiBZNAh8hWpjsJHVoyaiD3M2feffmOfshbbNaThwAcb3qPx/YfHgnVpsVgOGS3yOEaOEk8BGihXEc7go5ssa7N29kbw+csn6PLFwohGjhJPARooVJGXIxhYq6W3vvkoNgtXjnxooC26sCH50OBlzm0sscV2yWxGYhREsngY8QLYzBFMB2f3WDzzBdJSd3LvPOjdM2wcmDarnLaAhLavAlVpuVTYfUfcUSwmNJiU70ZA2FEKLZJPARogXK7XC6Vi7Y/JF3btqEYa5daQcoLi8F4LRusjGpEKLlk8BHiBYoZMDFmBU1iEg49CNUlHj2htZK2P65Wjb6Q98p9Z9fxXEa+wjJ7xFCtAIS+AjRAg3sPZKvzeq08CBrGWz1cK/PgZVQkq2We06CgHCXXiYLFwohWhsJfIRogaJDIlgdOUx7bPn1VbDZPHdDx8DKxWEusG9MGuQfSN+Oqe6ulRBCuJ3R1xVojaxWKxaLl2ba+JDFYsFoNFJeXo7VavXovfz8/NDrJQ531HngZNatWcdIUymm3ANw4Gfofo77b5SzH/78Qi0HRkCPCS697Eh2Gul5WQAM7dIXo0E+ToQQLZ98UjWCoihkZmaSn5/v66p4haIoJCQkcOzYMY8nrer1erp06YKfn59H79OanN1vFK8uj2KkSU0eZt3rngl8Vj4HVQsQMupONcfHBcu3/6qVz+x1mvvrJYQQHiCBTyNUBz1xcXEEBQW1+RksNpuN4uJiQkJCPNobY7PZSE9PJyMjg5SUlDbfrq4amNKLjf4pHLOeINlggb0rIHsfxHZ3301O7ILtn6rloCgYdbvLL122fbVWnjhgtPvqJIQQHiSBj4usVqsW9ERHR/u6Ol5hs9moqKggICDA48NQsbGxpKenU1lZiclk8ui9Wgu9Xs+YvqN4Z9t+nghWh5RY/zpcsMB9N/n5GXXhQoDR94F/qEsvyy8pZP2+rQB0ju1Ij8TO7quTEEJ4kCRVuKg6pycoKMjHNWmbqoe4PJ1L1Nqc3W8kS8ojKama2s7mJVCW556Lp2+FnV+r5ZB4GH6Tyy/9eec6bX+uiQP+Jr10QohWQwKfRpIPeM+Qdq3d2N4jKMLEJ+UR6gFLKWx63z0X/2mew43uBz/Xg/pl2xyGuQbKMJcQovWQwEeIFiwiOIzTuvXn7fIo+8H1bzZ//65jv8OeH9RyeEcYNsPll1ZUWvh55zq1fkFhsiO7EKJVkcBHiBbu7H6jOGD156eKEPVA/lHn3pqm+NHh9eMedHkmF8C6vZu1bSrO6T9KprELIVoVCXzagRkzZqDT6Wr8TJo0CYDOnTuj0+n46KOaqwOPHDkSg8HA4sWLtWPV55/689xzz3nrLbUrZ/cbBcD8klgqqRoSXL0A9v3YtAtu+1RdqRkgsjMMntqoly/btkYry2wuIURrI4FPOzFp0iQyMjKcfj788EPt+eTkZBYtWuT0mvXr13PixAmCg4NrXG/u3Lk1rnfXXXd5/H20R306pJIQHst2ayDPljnsmP7ZLVCY0biLHfoNvnCYsn72w2BwfRadoigs364GPn5GE+P6jmjc/YUQwsck8Gkn/P39SUhIcPqJjIzUnp82bRq//PILx44d044tWrSIyy+/HKOx5lBGaGhojevVFiCJ5tPpdJzdbyQAr5WEkZM0XH2iJAc+vdm++GBDsvbAB1eDtUJ9PPQ6GHhlo+qy8/g+0vJOAHBGj6GEBMj/cyFE6yKD88004ZkZZBee9Pp9Y8OiWf7wYrddLz4+nokTJ/Luu+/y6KOPUlpayieffMLSpUv5+OOP3XYf0TRn9xvFB799A+hYGDaaR4uPQ2E6HFoNq16As2bXf4GiE/DeZVCerz7ufjZMeQkaOZvOeTbXmY17E0II0QK0mh6fKVOmkJKSQkBAAImJiVx77bWkp6c7nbN9+3bOPPNMAgICSE5O5vnnn/d4vbILT5KRn+31n8YGW0uXLiUkJMTp55lnnnE654YbbmDx4sUoisJnn31Gt27d6N+/f63Xe/DBB2tcb82aNbWeK5rvzF6nYapKIv529zaUy/8PdFX/fFc+B5s/qLvnp6IE3r8C8o+ojxMHwFXvNWqIq1r1MBfAhAES+AghWp9W0+Mzbtw4Hn74YRITE0lLS+P+++/nsssuY+3atQAUFhYyYcIEzjnnHBYuXMiOHTu44YYbiIiI4JZbbvFYvWLDfLOKc2PvO27cOF5//XWnY1FRUU6PJ0+ezK233srq1at55513uP766+u83gMPPMCMGTOcjnXo0KFRdRKuCw0MZkTqIH7d8wdHctLY7ZdA77MfgR+fAsWm5u388iKceS8MugoMfuoChTs+h+2fQ2GaeqHwjnDtpy6v0OwoPS+L7Uf3ADAgpSdJkXHue4NCCOElrSbwmTlzplbu1KkTs2fP5qKLLsJisWAymfjggw+oqKjgnXfewc/Pj759+7J161YWLFjg0cDHncNNnhQcHExqamq95xiNRq699lqeeOIJNmzYwOeff17nuTExMQ1eT7jXpEGj+XXPHwB8tPZb5lx6H2Rst6++fPIAfHWXOtXdL1h97CggHK77HMISm3R/x94eWbRQCNFatZrAx1Fubi4ffPABo0aN0vZ1WrduHaNHj3ba3XvixInMnz+fvLw8p0ReR2azGbPZrD0uLCwE1C0qqrepqH6sKAo2mw2bzeaJt+UxiqJoda/vHJvNxowZM3jxxRe54ooriIiIoKioCKDG+27oeo1ls9lQFAWLxYLBYHDbdVuD6t8zx9+32kwZfDZPff4q5soKPl73HfdPvomAS/8P3ZDr0P/6MvpDVYFJUabT6xS9EaXbOKzjHoaoVGjgPnX5fMMPWvnsPiMbrG9L5Gpbi+aRdvYOaWdnrrZDqwp8HnzwQV599VVKS0s5/fTTWbp0qfZcZmYmXbp0cTo/Pj5ee66uwOfZZ59lzpw5NY4vX77caV8uo9FIQkICxcXFVFRUuOPteI3FYqGkpIR9+/Y5HTcajURHR2Oz2SgvL6ewsJAOHTpw4MABAgMDtaBHURTteVCDlJycnBrXCwwMJCwsrEl1rKiooKysjNWrV1NZWdmka7R2K1asaPCcQXHd2ZC+k/zSQp57998MT+qrPhFzCxEBZ9M9YylJ+X+goCMntBdp0SNJjxyGxRgKW46rP01wvDCLjQe3A5AYHM3hbXs5sn1fA69quVxpa9F80s7eIe2sKi0tdek8nwY+s2fPZv78+fWes2vXLnr16gWoeSU33ngjR44cYc6cOVx33XUsXbq0Wfs8PfTQQ9x3333a48LCQpKTk5kwYYLTl3h5eTnHjh0jJCSEgICAJt/PF0wmEz/99JPWjtV69uzJX3/9hV6vJyAgQHu/1f9VFIWioiJ0Op3T83q9nmeeeaZGcvQtt9xSI4/IVeXl5QQGBjJ69OhW177NZbFYWLFiBePHj29wZ/qoHklc/i91vaS/So/x5HkPnHLGXVjK8gGFiMBIIoC+bqjj7I9e0Mp/P+86Jo+e7Iarel9j2lo0nbSzd0g7O6v+47whPg18Zs2aVSNB9lRdu3bVyjExMcTExNCjRw969+5NcnIy69evZ+TIkSQkJHDixAmn11Y/TkhIqPP6/v7++PvXXK7fZDI5/SJZrVZ0Oh16vR69vtVMhgPg3Xff5d13363z+cOHD9d6vHooKzc31+k913V+c+j1enQ6XY12b09cee9/6zWM1PhO7D9xhHX7tnA0N4Nu8SmnXCjWrfUqKC3ii43LAQj2D+KqUee3+v9H7fn3zJuknb1D2lnlahv4NPCJjY0lNrZpH9LVX8rV+TkjR47kkUce0ZKdQe3+69mzZ53DXEK0Njqdjml/m8Kcz18B4INfv+bxSz27YvYn676jrKIcgCtOP4/QQFm0UAjRerWKrosNGzbw6quvsnXrVo4cOcLPP//M1VdfTbdu3Rg5Ul3RdurUqfj5+XHjjTeyc+dOPv74Y/71r385DWMJ0RZcMXIyfkY1uP9o3f8wWzyXc2az2Vj0y2fa4xljLvXYvYQQwhtaReATFBTEF198wdlnn03Pnj258cYbGTBgAL/88os2TBUeHs7y5cs5dOgQQ4cOZdasWTz++OMencouhC9Eh0Rw3qCxAOQW5zutpuxuq3dv5GCWuo3JGT2H0jOpSwOvEEKIlq1VzOrq378/P//8c4PnDRgwQFYPFu3CNX+7kK/+UGdyfPDr11w47ByP3GfRKntvz/XS2yOEaANaRY+PEMLZqB5D6BLbEVB7ZQ5nN22aen2O5qSzYsdvACRFxjFJFi0UQrQBEvgI0Qrp9Xqm/e1C7fH7a75y+z3eW/MlNkWdRHDtmRdhNLSKDmIhhKiXBD5CtFJXjpysbVz6zspPOXYyw23XLqsoZ8lv3wJgMhi5xiHIEkKI1kwCHyFaqdiwKG2WVZnFzOOfvOy2a7/83SJyi/MBOH/IWT7bjFcIIdxNAh8hWrEHLriZuKqg5Pttv/DjjrXNvubO4/t4bfl/AbW3555zZzT7mkII0VJI4CNEKxYWGMITDgsYPvrJAsot5npeUb9KayUz35tHpc0KwF2TptMrqWsDrxJCiNZDAp92YMaMGeh0uho/+/fvd3rOZDIRHx/P+PHjeeedd7TVsVetWlXr6x1/Vq1axeLFi7XHBoOByMhIRowYwdy5cykoKPBxK7RdlwyfyMjugwE4nH1c661pijd/+pjtR3cD0D2hM/dMmu6WOgohREshgU87MWnSJDIyMpx+qnezr37u8OHDfP/994wbN4577rmHCy64gMrKSkaNGuX0uiuuuKLG9UaNGgWoG5xmZGRw/Phx1q5dyy233MJ7773HoEGDSE9P92UTtFk6nY5nr34Ag94AwCvL3uNIdlqjr3M4+zgvfPumds2XrnsEf5OfW+sqhBC+JoFPO+Hv709CQoLTj8FgcHquQ4cODBkyhIcffpivv/6aZcuWsWTJEvz8/JxeFxgYWON6fn7qF6ROpyMhIYHExER69+7NjTfeyNq1aykuLuYf//iHL5ugTeuV1JWbz7oSgHKLmUc+WYCiKC6/XlEU7v/vc5RVDZPdOPZyhnXt75G6CiGEL8nCHM31nzFQfKLh89wtJB7+/ovHLn/WWWcxcOBAvv32W+68885mXSsuLo5p06bxzjvvYLVatYBLuNf959/IVxtXkFmQzY87fmP2hy/w9JX3aVPe6/PGTx/y654/AOgYlcBDF97m6eoKIYRPSODTXMUnoLDlD+EsXbqUkJAQ7fG5557Lp59+Wu9revbsybZt29xy/169elFUVMTJkyeJi4tzyzWFs5CAYJ6+ciY3vfkwAO+u/oKDWUd58+Z5RAaH1/qakvJSHvzwBT7b8L127IVpswkOCPJKnYUQwtsk8GmukPhWcd9x48bx+uuva4+Dg4MbfI2iKOh0ukZXra5rAW67nqjd+UPO4pUZTzDrv89QUWlhze4/mDz/Jt77+4ukJnRyOvfPY3u59e1HOXDiqHbs7+OnMa7v6d6uthBCeI0EPs3lweEmdwoODiY1NbVRr9m9ezcpKSluuf+uXbsICwsjOloWwvO0y08/l86xHZix8EFOFuVxMOsYk5+/ifH9/0awfyBB/oFYrVbeXf0F5soKAIL9g3jxmtlcfNoEH9deCCE8SwIfUauff/6ZHTt2cOuttzb7WllZWSxZsoSLLroIvV7y6b3htG4DWDb7Ha77z/3sSjtAQWmR03CWowEpPXnjpqfpEpfs5VoKIYT3ybeQwGw2k5mZSVpaGps3b+aZZ57hwgsvZPLkyVx11VWNupaiKGRmZpKRkcGuXbt45513GDVqFOHh4Tz33HMeegeiNsnRiXz7wJucP+SsOs+5adwVfPvAWxL0CCHaDenxESxbtozExESMRiORkZEMHDiQf//731x77bUUFxc36lqFhYUkJiai0+kICwujZ8+eTJ8+nXvuuYewsDAPvQNRl5CAYN6+5RlyivIoKC2ixFxKibmMkvIyusR1pFu8e4YyhRCitZDApx1YvHhxvc/V9Xz1ys2uXm/GjBnMmDGjcZUTXhETGklMaKSvqyGEED4nQ11CCCGEaDck8BFCCCFEuyGBjxBCCCHaDQl8hBBCCNFuSODTSI3Z+FG4TtpVCCGEN0jg4yKTyQRAaWmpj2vSNlVUqCsIywamQgghPEmms7vIYDAQERFBVlYWAEFBQW1+3ymbzUZFRQXl5eUeXXHZZrORnZ1NUFAQRqP8SgohhPAc+ZZphISEBAAt+GnrFEWhrKyMwMBAjwd5er2elJSUNh9MCiGE8C0JfBpBp9ORmJhIXFwcFovF19XxOIvFwurVqxk9erQ21Ocpfn5+so+XEEIIj5PApwkMBkO7yEUxGAxUVlYSEBDg8cBHCCGE8Ab5E1sIIYQQ7YYEPkIIIYRoNyTwEUIIIUS7ITk+p6heSK+wsNDHNfE9i8VCaWkphYWFkuPjQdLO3iNt7R3Szt4h7eys+nu7oQVxJfA5RVFREQDJyck+rokQQgghGquoqIjw8PA6n9cpsleAE5vNRnp6OqGhoe1+TZnCwkKSk5M5duwYYWFhvq5OmyXt7D3S1t4h7ewd0s7OFEWhqKiIpKSkepdHkR6fU+j1ejp27OjrarQoYWFh8o/KC6SdvUfa2juknb1D2tmuvp6eapLcLIQQQoh2QwIfIYQQQrQbEviIOvn7+/PEE0/g7+/v66q0adLO3iNt7R3Szt4h7dw0ktwshBBCiHZDenyEEEII0W5I4COEEEKIdkMCHyGEEEK0GxL4CCGEEKLdkMCnnVi9ejUXXHABSUlJ6HQ6vvrqK5df+9tvv2E0Ghk0aFCN59LS0rjmmmuIjo4mMDCQ/v3788cff7iv4q2MJ9rZarXy2GOP0aVLFwIDA+nWrRtPPfVUg/vRtHWNbetVq1ah0+lq/GRmZjqd99prr9G5c2cCAgIYMWIEv//+uwffRcvniXZ+9tlnOe200wgNDSUuLo6LLrqIPXv2ePidtGye+n2u9txzz6HT6bj33nvdX/lWRgKfdqKkpISBAwfy2muvNep1+fn5XHfddZx99tk1nsvLy+OMM87AZDLx/fff89dff/HPf/6TyMhId1W71fFEO8+fP5/XX3+dV199lV27djF//nyef/55XnnlFXdVu1Vqalvv2bOHjIwM7ScuLk577uOPP+a+++7jiSeeYPPmzQwcOJCJEyeSlZXl7uq3Gp5o519++YU77riD9evXs2LFCiwWCxMmTKCkpMTd1W81PNHO1TZu3Mgbb7zBgAED3FXd1k0R7Q6gfPnlly6de+WVVyqPPvqo8sQTTygDBw50eu7BBx9U/va3v7m/gm2Eu9p58uTJyg033OB07JJLLlGmTZvmppq2fq609cqVKxVAycvLq/Oc4cOHK3fccYf22Gq1KklJScqzzz7rppq2bu5q51NlZWUpgPLLL780r4JthDvbuaioSOnevbuyYsUKZcyYMco999zjtnq2VtLjI+q0aNEiDh48yBNPPFHr89988w3Dhg3j8ssvJy4ujsGDB/PWW295uZatX0PtPGrUKH766Sf27t0LwLZt2/j1118599xzvVnNNmPQoEEkJiYyfvx4fvvtN+14RUUFmzZt4pxzztGO6fV6zjnnHNatW+eLqrZqdbVzbQoKCgCIioryRtXalIba+Y477mDy5MlOv9ftnWxSKmq1b98+Zs+ezZo1azAaa/81OXjwIK+//jr33XcfDz/8MBs3buTuu+/Gz8+P6dOne7nGrZMr7Tx79mwKCwvp1asXBoMBq9XKvHnzmDZtmpdr27olJiaycOFChg0bhtls5u2332bs2LFs2LCBIUOGkJOTg9VqJT4+3ul18fHx7N6920e1bn0aaudT2Ww27r33Xs444wz69evngxq3Tq6080cffcTmzZvZuHGjj2vbskjgI2qwWq1MnTqVOXPm0KNHjzrPs9lsDBs2jGeeeQaAwYMH8+eff7Jw4UIJfFzgajt/8sknfPDBByxZsoS+ffuydetW7r33XpKSkqSdG6Fnz5707NlTezxq1CgOHDjASy+9xPvvv+/DmrUtjW3nO+64gz///JNff/3Vm9Vs9Rpq52PHjnHPPfewYsUKAgICfFjTlkcCH1FDUVERf/zxB1u2bOHOO+8E1CBHURSMRiPLly/nrLPOIjExkT59+ji9tnfv3nz++ee+qHar42o7P/DAA8yePZurrroKgP79+3PkyBGeffZZCXyaafjw4doXbkxMDAaDgRMnTjidc+LECRISEnxRvTbDsZ0d3XnnnSxdupTVq1fTsWNHH9SsbXFs502bNpGVleXUy2a1Wlm9ejWvvvoqZrMZg8Hgq6r6lAQ+ooawsDB27NjhdOw///kPP//8M5999hldunQB4IwzzqgxBXXv3r106tTJa3VtzVxt59LSUvR653Q8g8GAzWbzWl3bqq1bt5KYmAiAn58fQ4cO5aeffuKiiy4C1ED0p59+0gJT0TSO7QygKAp33XUXX375JatWrdJ+10XzOLbz2WefXePz5frrr6dXr148+OCD7TboAQl82o3i4mL279+vPT506BBbt24lKiqKlJQUHnroIdLS0njvvffQ6/U1xtrj4uIICAhwOj5z5kxGjRrFM888wxVXXMHvv//Om2++yZtvvum199XSeKKdL7jgAubNm0dKSgp9+/Zly5YtLFiwgBtuuMFr76slakxbA7z88st06dKFvn37Ul5ezttvv83PP//M8uXLtWvcd999TJ8+nWHDhjF8+HBefvllSkpKuP76673+/loKT7TzHXfcwZIlS/j6668JDQ3V1p4JDw8nMDDQu2+whXB3O4eGhtb4fAkODiY6OlpyqXw8q0x4SfXUx1N/pk+friiKokyfPl0ZM2ZMna+vbZq1oijKt99+q/Tr10/x9/dXevXqpbz55pueeQOthCfaubCwULnnnnuUlJQUJSAgQOnatavyyCOPKGaz2XNvpBVobFvPnz9f6datmxIQEKBERUUpY8eOVX7++eca133llVeUlJQUxc/PTxk+fLiyfv16L72jlskT7Vzb9QBl0aJF3ntjLYynfp8dyXR2lU5R2vnyr0IIIYRoN2QdHyGEEEK0GxL4CCGEEKLdkMBHCCGEEO2GBD5CCCGEaDck8BFCCCFEuyGBjxBCCCHaDQl8hBBCCNFuSOAjhBBCiHZDAh8hRIv15JNPMmjQIF9XQ6PT6fjqq68a/bo9e/aQkJBAUVGR+yvlICcnh7i4OI4fP+7R+wjRmkngI0Q7t3DhQkJDQ6msrNSOFRcXYzKZGDt2rNO5q1atQqfTceDAAS/X0rvcHXA99NBD3HXXXYSGhrrtmrWJiYnhuuuu44knnvDofYRozSTwEaKdGzduHMXFxfzxxx/asTVr1pCQkMCGDRsoLy/Xjq9cuZKUlBS6devmi6q2SkePHmXp0qXMmDHDK/e7/vrr+eCDD8jNzfXK/YRobSTwEaKd69mzJ4mJiaxatUo7tmrVKi688EK6dOnC+vXrnY6PGzcOgPfff59hw4YRGhpKQkICU6dOJSsrCwCbzUbHjh15/fXXne61ZcsW9Ho9R44cASA/P5+bbrqJ2NhYwsLCOOuss9i2bVu99X377bfp3bs3AQEB9OrVi//85z/ac4cPH0an0/HFF18wbtw4goKCGDhwIOvWrXO6xltvvUVycjJBQUFcfPHFLFiwgIiICAAWL17MnDlz2LZtGzqdDp1Ox+LFi7XX5uTkcPHFFxMUFET37t355ptv6q3vJ598wsCBA+nQoYN2rLYepZdffpnOnTtrj2fMmMFFF13EM888Q3x8PBEREcydO5fKykoeeOABoqKi6NixI4sWLXK6Tt++fUlKSuLLL7+st15CtFcS+AghGDduHCtXrtQer1y5krFjxzJmzBjteFlZGRs2bNACH4vFwlNPPcW2bdv46quvOHz4sNarodfrufrqq1myZInTfT744APOOOMMOnXqBMDll19OVlYW33//PZs2bWLIkCGcffbZdfZWfPDBBzz++OPMmzePXbt28cwzz/DYY4/x7rvvOp33yCOPcP/997N161Z69OjB1VdfrQ3l/fbbb9x2223cc889bN26lfHjxzNv3jzttVdeeSWzZs2ib9++ZGRkkJGRwZVXXqk9P2fOHK644gq2b9/Oeeedx7Rp0+rtXVmzZg3Dhg2rt/3r8vPPP5Oens7q1atZsGABTzzxBOeffz6RkZFs2LCB2267jVtvvbVGTs/w4cNZs2ZNk+4pRJvn6+3hhRC+99ZbbynBwcGKxWJRCgsLFaPRqGRlZSlLlixRRo8erSiKovz0008KoBw5cqTWa2zcuFEBlKKiIkVRFGXLli2KTqfTzrdarUqHDh2U119/XVEURVmzZo0SFhamlJeXO12nW7duyhtvvKEoiqI88cQTysCBA52eW7JkidP5Tz31lDJy5EhFURTl0KFDCqC8/fbb2vM7d+5UAGXXrl2KoijKlVdeqUyePNnpGtOmTVPCw8O1x6fetxqgPProo9rj4uJiBVC+//77WttEURRl4MCByty5c52O1Xb9l156SenUqZP2ePr06UqnTp0Uq9WqHevZs6dy5plnao8rKyuV4OBg5cMPP3S61syZM5WxY8fWWSch2jPp8RFCMHbsWEpKSti4cSNr1qyhR48exMbGMmbMGC3PZ9WqVXTt2pWUlBQANm3axAUXXEBKSgqhoaGMGTMGUHNaAAYNGkTv3r21Xp9ffvmFrKwsLr/8cgC2bdtGcXEx0dHRhISEaD+HDh2qNXm6pKSEAwcOcOONNzqd//TTT9c4f8CAAVo5MTERQBuG27NnD8OHD3c6/9TH9XG8dnBwMGFhYdq1a1NWVkZAQIDL13fUt29f9Hr7x3R8fDz9+/fXHhsMBqKjo2vcPzAwkNLS0ibdU4i2zujrCgghfC81NZWOHTuycuVK8vLytCAmKSmJ5ORk1q5dy8qVKznrrLMANQiZOHEiEydO5IMPPiA2NpajR48yceJEKioqtOtOmzaNJUuWMHv2bJYsWcKkSZOIjo4G1Jljp+YWVavOt3FUXFwMqPk5I0aMcHrOYDA4PTaZTFpZp9MBat6ROzheu/r69V07JiaGvLy8Bq9rtVpdupcr98/NzSU2NrbBewrRHkngI4QA1DyfVatWkZeXxwMPPKAdHz16NN9//z2///47t99+OwC7d+/m5MmTPPfccyQnJwM4zQqrNnXqVB599FE2bdrEZ599xsKFC7XnhgwZQmZmJkaj0Smpty7x8fEkJSVx8OBBpk2b1uT32bNnTzZu3Oh07NTHfn5+tQYiTTF48GD++uuvGsdPnDjh9PjgwYNuuR/An3/+WWMpAiGESoa6hBCAGvj8+uuvbN26VevxARgzZgxvvPEGFRUVWmJzSkoKfn5+vPLKKxw8eJBvvvmGp556qsY1O3fuzKhRo7jxxhuxWq1MmTJFe+6cc85h5MiRXHTRRSxfvpzDhw+zdu1aHnnkkVqDKFATi5999ln+/e9/s3fvXnbs2MGiRYtYsGCBy+/zrrvu4rvvvmPBggXs27ePN954g++//17rGaqu96FDh9i6dSs5OTmYzWaXr3+qiRMnsm7duhqBVGZmJnPnzuXgwYN8/vnnvP/+++Tl5bF79+4m3wugtLSUTZs2MWHChGZdR4i2SgIfIQSgBj5lZWWkpqYSHx+vHR8zZgxFRUXatHeA2NhYFi9ezKeffkqfPn147rnnePHFF2u97rRp09i2bRsXX3wxgYGB2nGdTsd3333H6NGjuf766+nRowdXXXUVR44ccbq/o5tuuom3336bRYsW0b9/f8aMGcPixYvp0qWLy+/zjDPOYOHChSxYsICBAweybNkyZs6c6ZSHc+mllzJp0iTGjRtHbGwsH374ocvXP9W5556L0Wjkxx9/dDrer18/9u7dS9++fXnsscd4++238fPz4/7772/yvQC+/vprUlJSOPPMM5t1HSHaKp2iKIqvKyGEEL508803s3v3bo9NAX/ttdf45ptv+OGHHwB1HZ+vvvqKrVu3uv1ep59+OnfffTdTp051+7WFaAskx0cI0e68+OKLjB8/nuDgYL7//nveffddp4UQ3e3WW28lPz+foqIij25bkZOTwyWXXMLVV1/tsXsI0dpJj48Qot254oorWLVqFUVFRXTt2pW77rqL2267zWv392SPjxCifhL4CCGEEKLdkORmIYQQQrQbEvgIIYQQot2QwEcIIYQQ7YYEPkIIIYRoNyTwEUIIIUS7IYGPEEIIIdoNCXyEEEII0W5I4COEEEKIduP/AQfkcYHsA2B9AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T = (\n",
    "    eme_sim_data.smatrix.S21.isel(sweep_index=1, mode_index_out=0, mode_index_in=0).abs.squeeze()\n",
    "    ** 2\n",
    ")\n",
    "\n",
    "plt.plot(ldas, 10 * np.log10(T), linewidth=2, label=\"EME\")\n",
    "\n",
    "amp = batch_results[\"amorphous\"][\"mode\"].amps.sel(mode_index=0, direction=\"+\")\n",
    "T = np.abs(amp) ** 2  # transmission to the top waveguide\n",
    "\n",
    "plt.plot(ldas, 10 * np.log10(T), linewidth=2, label=\"FDTD\")\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel(\"Wavelength (μm)\")\n",
    "plt.ylabel(\"Transmission (dB)\")\n",
    "plt.grid()\n",
    "plt.show()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7a9fad5-cd0e-4d87-99d3-dafe41af0d37",
   "metadata": {},
   "source": [
    "## Final Remarks\n",
    "\n",
    "EME offers a powerful and efficient method for simulating long mode propagation problems including those with periodic features like Bragg gratings. By analytically solving for the modes within each section and propagating them through the structure, EME significantly reduces computational time and FlexCredit cost compared to full 3D FDTD simulations—especially for long devices. This makes EME an ideal tool for rapid design iteration and parameter sweeps. However, it is recommended to validate key results with rigorous FDTD simulations to ensure accuracy, particularly in complex or highly non-uniform structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f80bc0e7-4d53-4f3e-a620-6bd0a3650a1c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components"
  ],
  "description": "This notebook demonstrates how to simulate a Bragg waveguide grating filter using Tidy3D FDTD and EME solvers.",
  "feature_image": "./img/periodic_eme_grating.png",
  "features": [
   "Eigenmode expansion (EME)",
   "Parameter sweep",
   "Mode analysis"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "Bragg grating, phase change material, waveguide, grating, filter, integrated photonics, Tidy3D, FDTD, EME",
  "language_info": {
   "codemirror_mode": {
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