{ "cells": [ { "cell_type": "markdown", "id": "a6675262", "metadata": {}, "source": [ "# Design of 1D photonic crystal cavity" ] }, { "cell_type": "markdown", "id": "fb75afd3", "metadata": {}, "source": [ "**Author: Amberly Xie, Harvard University**\n", "\n", "1D photonic crystal cavities enhance light-matter interaction for point defects, improving zero phonon line emission, which is essential for many quantum applications involving solid-state point defects. This notebook contains code for designing 1D photonic crystal cavities in 4H-SiC.\n", "\n", "First, we will perform a band structure calculation to examine the photonic band gap. Next, we will simulate the full cavity and use the [ResonanceFinder](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.resonance.ResonanceFinder.html#tidy3d.plugins.resonance.ResonanceFinder.html) plugin to obtain the resonance frequency and quality factor.\n", "\n", "The cavity consists of two stacks of unit cells with the photonic band gap tuned to the desired resonance frequency, and a tapered region where the unit cell parameters are gradually modified to confine the resonance mode.\n", "\n", "\"Schematic\"\n", "\n", "This notebook is associated with the publication `Amberly H. Xie, Aaron M. Day, Jonathan R. Dietz, Chang Jin, Chaoshen Zhang, Eliana Mann, Zhujing Xu, Marko Loncar, and Evelyn L. Hu, \n", "Nano Letters 2025 25 (43), 15637-15642.` [DOI: 10.1021/acs.nanolett.5c04169](https://pubs.acs.org/doi/10.1021/acs.nanolett.5c04169)." ] }, { "cell_type": "markdown", "id": "b8fe2c7b", "metadata": {}, "source": [ "## Importing Packages and Defining Useful Functions" ] }, { "cell_type": "code", "execution_count": 1, "id": "fd3d4c1c", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import tidy3d as td\n", "from tidy3d import web\n", "from tidy3d.plugins.resonance import ResonanceFinder" ] }, { "cell_type": "code", "execution_count": 2, "id": "ef8d5cf7", "metadata": {}, "outputs": [], "source": [ "# --- Function for filtering resonances ---\n", "def filter_resonances(resonance_data, minQ, minamp, maxerr):\n", " resonance_data = resonance_data.where(abs(resonance_data.Q) > minQ, drop=True)\n", " resonance_data = resonance_data.where(resonance_data.amplitude > minamp, drop=True)\n", " resonance_data = resonance_data.where(resonance_data.error < maxerr, drop=True)\n", " resonance_data = resonance_data.where(resonance_data.freq > 0, drop=True)\n", " return resonance_data" ] }, { "cell_type": "code", "execution_count": 3, "id": "c05ca36b", "metadata": {}, "outputs": [], "source": [ "# --- Function to convert from freq to nm ---\n", "def freq_nm(freq):\n", " return (td.C_0 / freq) * 1e3" ] }, { "cell_type": "code", "execution_count": 4, "id": "d0d63b4d", "metadata": {}, "outputs": [], "source": [ "# --- Function to find the cavity mode ---\n", "def cavitymode(resonance_data):\n", " freq = resonance_data.freq\n", " nm = freq_nm(freq)\n", " Q = resonance_data.Q\n", " amplitude = resonance_data.amplitude\n", " cost = (amplitude * Q) / (np.pi * nm)\n", " idx = cost.argmax()\n", " return float(freq[idx]), float(Q[idx])" ] }, { "cell_type": "code", "execution_count": 5, "id": "a9cadc52", "metadata": {}, "outputs": [], "source": [ "# --- Function to calculate mode volume ---\n", "def findvmode(sim, data, field_mnt, eps_mnt, freq0):\n", " # Get grid points\n", " xaxis = np.array(data[eps_mnt].eps_xx.x)\n", " yaxis = np.array(data[eps_mnt].eps_xx.y)\n", " zaxis = np.array(data[eps_mnt].eps_xx.z)\n", "\n", " # Get E-field data\n", " Ex = np.array(data[field_mnt].Ex).squeeze()\n", " Ey = np.array(data[field_mnt].Ey).squeeze()\n", " Ez = np.array(data[field_mnt].Ez).squeeze()\n", " E_sqr = np.abs(Ex) ** 2 + np.abs(Ey) ** 2 + np.abs(Ez) ** 2\n", " E_sqr_pad = np.pad(E_sqr, ((0, 1), (0, 1), (0, 1)), \"constant\")\n", " eps = (np.array(data[eps_mnt].eps_xx).squeeze()).real\n", "\n", " # Calculate mode volume\n", " vmode = (\n", " np.trapz(\n", " np.trapz(np.trapz(eps * E_sqr_pad, xaxis, axis=0), yaxis, axis=0),\n", " zaxis,\n", " axis=0,\n", " )\n", " ) / (np.max(eps * E_sqr_pad))\n", " n_sic = 2.59\n", "\n", " # Normalize mode volume\n", " wv = freq_nm(freq0) * 1e-3\n", " vmode = vmode / ((wv / n_sic) ** 3)\n", "\n", " return vmode" ] }, { "cell_type": "markdown", "id": "9c11355b", "metadata": {}, "source": [ "## Simulating Mirror Cells" ] }, { "cell_type": "markdown", "id": "9bd8f444", "metadata": {}, "source": [ "We begin by simulating the band structure for the mirror cells, targeting a bandgap centered around surface state emission (700nm)." ] }, { "cell_type": "code", "execution_count": 6, "id": "efc26255", "metadata": {}, "outputs": [], "source": [ "# --- Simulation parameters ---\n", "epAir = 1\n", "epSic = 2.6**2\n", "\n", "# Frequency range of interest\n", "ss1 = td.C_0 / (400e-3)\n", "ss2 = td.C_0 / (1000e-3)\n", "ss0 = td.C_0 / (700e-3)\n", "ssWidth = 149999999997498 / 2.35\n", "\n", "# Runtime\n", "runtime = 800 / ssWidth\n", "tstart = 5 / ssWidth" ] }, { "cell_type": "markdown", "id": "0a980594", "metadata": {}, "source": [ "### Building the Simulation" ] }, { "cell_type": "code", "execution_count": 7, "id": "7f0efd46", "metadata": {}, "outputs": [], "source": [ "# --- Function for creating ellipse unit cells ---\n", "def ellipse_uc(center, x_axis, y_axis, height=0.5):\n", " theta = np.linspace(0, np.pi * 2, 1001)\n", " x = x_axis * np.cos(theta) + center[0]\n", " y = y_axis * np.sin(theta) + center[1]\n", "\n", " geometry = td.PolySlab(\n", " vertices=np.array([x, y]).T,\n", " slab_bounds=(-height / 2 + center[2], height / 2 + center[2]),\n", " axis=2,\n", " )\n", " return geometry" ] }, { "cell_type": "code", "execution_count": 8, "id": "4ae9d995", "metadata": {}, "outputs": [], "source": [ "# --- Setting up simulation ---\n", "def makecells(params):\n", " a = params[0] # Lattice constant\n", " a1 = params[1] # X width of holes\n", " a2 = params[2] # Y width of holes\n", " w = params[3] # Width of beam\n", " h = 0.3 # Thickness\n", "\n", " matSlab = td.Medium(permittivity=epSic, name=\"matSlab\")\n", " matHole = td.Medium(permittivity=epAir, name=\"matHole\")\n", "\n", " slab = td.Structure(\n", " geometry=td.Box(\n", " center=(0, 0, 0),\n", " size=(td.inf, w, h),\n", " ),\n", " medium=matSlab,\n", " name=\"slab\",\n", " )\n", "\n", " hole = td.Structure(\n", " geometry=ellipse_uc((0, 0, 0), a1, a2, height=0.5), medium=matHole, name=\"hole\"\n", " )\n", "\n", " structures = [slab, hole]\n", "\n", " # Sources and monitors\n", "\n", " numDipoles = 5\n", " numMonitors = 2\n", "\n", " polarization = \"Ey\"\n", "\n", " rng = np.random.default_rng(12345)\n", " dipolePos = rng.uniform([-a / 2, -w / 2, 0], [a / 2, w / 2, 0], [numDipoles, 3])\n", " monitorPos = rng.uniform([-a / 2, -w / 2, 0], [a / 2, w / 2, 0], [numDipoles, 3])\n", "\n", " dipolePhase = rng.uniform(0, 2 * np.pi, numDipoles)\n", "\n", " pulses = []\n", " dipoles = []\n", " for i in range(numDipoles):\n", " pulse = td.GaussianPulse(freq0=ss0, fwidth=ssWidth, phase=dipolePhase[i])\n", " pulses.append(pulse)\n", " dipoles.append(\n", " td.PointDipole(\n", " source_time=pulse,\n", " center=tuple(dipolePos[i]),\n", " polarization=polarization,\n", " name=\"dipole-\" + str(i),\n", " )\n", " )\n", "\n", " monitorsTime = []\n", " for i in range(numMonitors):\n", " monitorsTime.append(\n", " td.FieldTimeMonitor(\n", " fields=[\"Ey\"],\n", " center=tuple(monitorPos[i]),\n", " size=(0, 0, 0),\n", " start=tstart,\n", " name=\"monitor-time-\" + str(i),\n", " )\n", " )\n", "\n", " # Boundary conditions\n", "\n", " bspecs = td.BoundarySpec(\n", " x=td.Boundary.bloch(0.5), y=td.Boundary.pml(), z=td.Boundary.pml()\n", " )\n", "\n", " simSize = (a, 2, 4)\n", "\n", " return [structures, dipoles, monitorsTime, simSize, bspecs]" ] }, { "cell_type": "markdown", "id": "92b51b5c", "metadata": {}, "source": [ "### Running the Simulation" ] }, { "cell_type": "code", "execution_count": 9, "id": "c136c706", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "params = [0.198, 0.066, 0.066, 0.345]\n", "structures = makecells(params)[0]\n", "dipoles = makecells(params)[1]\n", "monitorsTime = makecells(params)[2]\n", "simSize = makecells(params)[3]\n", "bspecs = makecells(params)[4]\n", "symmetry = (0, 0, 1)\n", "\n", "\n", "sim = td.Simulation(\n", " center=(0, 0, 0),\n", " size=simSize,\n", " grid_spec=td.GridSpec.auto(),\n", " structures=structures,\n", " sources=dipoles,\n", " monitors=monitorsTime,\n", " run_time=runtime,\n", " shutoff=0,\n", " boundary_spec=bspecs,\n", " normalize_index=None,\n", " symmetry=symmetry,\n", ")\n", "\n", "ax = sim.plot(z=0)\n", "ax.set_ylim(-0.5, 0.5)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "id": "0d80dd02", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
15:26:14 -03 Created task 'NBBands' with task_id                                \n",
       "             'fdve-bc57a594-ed6c-43a3-8600-342ba02c6374' and task_type 'FDTD'.  \n",
       "
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             View task using web UI at                                          \n",
       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-bc57a594-ed6\n",
       "             c-43a3-8600-342ba02c6374'.                                         \n",
       "
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             Task folder: 'default'.                                            \n",
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
15:26:17 -03 Maximum FlexCredit cost: 0.025. Minimum cost depends on task       \n",
       "             execution details. Use 'web.real_cost(task_id)' to get the billed  \n",
       "             FlexCredit cost after a simulation run.                            \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m15:26:17 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\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": [
       "
15:26:18 -03 Created task 'NBBands' with task_id                                \n",
       "             'fdve-607246f3-115c-43e2-afe7-7a4ab07811e5' and task_type 'FDTD'.  \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m15:26:18 -03\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'NBBands'\u001b[0m with task_id                                \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-607246f3-115c-43e2-afe7-7a4ab07811e5'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             View task using web UI at                                          \n",
       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-607246f3-115\n",
       "             c-43e2-afe7-7a4ab07811e5'.                                         \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=841960;https://tidy3d.simulation.cloud/workbench?taskId=fdve-607246f3-115c-43e2-afe7-7a4ab07811e5\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=585893;https://tidy3d.simulation.cloud/workbench?taskId=fdve-607246f3-115c-43e2-afe7-7a4ab07811e5\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=841960;https://tidy3d.simulation.cloud/workbench?taskId=fdve-607246f3-115c-43e2-afe7-7a4ab07811e5\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=966928;https://tidy3d.simulation.cloud/workbench?taskId=fdve-607246f3-115c-43e2-afe7-7a4ab07811e5\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=841960;https://tidy3d.simulation.cloud/workbench?taskId=fdve-607246f3-115c-43e2-afe7-7a4ab07811e5\u001b\\\u001b[32m-607246f3-115\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=841960;https://tidy3d.simulation.cloud/workbench?taskId=fdve-607246f3-115c-43e2-afe7-7a4ab07811e5\u001b\\\u001b[32mc-43e2-afe7-7a4ab07811e5'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             Task folder: 'data'.                                               \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=798922;https://tidy3d.simulation.cloud/folders/folder-8bb21a81-8cca-44d8-937d-19c5ff541e80\u001b\\\u001b[32m'data'\u001b[0m\u001b]8;;\u001b\\.                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81ecf630f5864507a1258736dfdf79f8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
15:26:20 -03 Maximum FlexCredit cost: 0.025. Minimum cost depends on task       \n",
       "             execution details. Use 'web.real_cost(task_id)' to get the billed  \n",
       "             FlexCredit cost after a simulation run.                            \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m15:26:20 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\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": [
       "
15:26:22 -03 status = success                                                   \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m15:26:22 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c520bd251b7541b8a6f5541c79fa2955",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
15:26:28 -03 loading simulation from data/NBBands.hdf5                          \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m15:26:28 -03\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/NBBands.hdf5                          \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Upload task\n",
    "task_id = web.upload(sim, task_name=\"NBBands\")\n",
    "# Run simulation\n",
    "NBBandsData = web.run(\n",
    "    sim, \"NBBands\", folder_name=\"data\", path=\"data/NBBands.hdf5\", verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b8656979",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wavelength 1: 780.02\n",
      "Wavelength 2: 621.99\n"
     ]
    }
   ],
   "source": [
    "# Plotting E field as a function of time\n",
    "plt.plot(\n",
    "    NBBandsData.monitor_data[\"monitor-time-0\"].Ey.t,\n",
    "    np.real(NBBandsData.monitor_data[\"monitor-time-0\"].Ey.squeeze()),\n",
    ")\n",
    "plt.xlabel(\"Time (sec)\")\n",
    "plt.ylabel(\"Field value\")\n",
    "plt.title(\"E-field\")\n",
    "plt.show()\n",
    "\n",
    "# Plot spectra\n",
    "time_series = np.real(NBBandsData.monitor_data[\"monitor-time-0\"].Ey.squeeze())\n",
    "dt = NBBandsData.simulation.dt\n",
    "fmesh = np.fft.fftshift(np.fft.fftfreq(time_series.size, dt))\n",
    "spectrum = np.fft.fftshift(np.fft.fft(time_series))\n",
    "plt.plot(fmesh, np.abs(spectrum))\n",
    "\n",
    "time_series = np.real(NBBandsData.monitor_data[\"monitor-time-0\"].Ey.squeeze())\n",
    "dt = NBBandsData.simulation.dt\n",
    "fmesh = np.fft.fftshift(np.fft.fftfreq(time_series.size, dt))\n",
    "spectrum = np.fft.fftshift(np.fft.fft(time_series))\n",
    "plt.plot(fmesh, np.abs(spectrum))\n",
    "plt.xlim(1e14, 7e14)\n",
    "plt.title(\"Spectrum\")\n",
    "plt.xlabel(\"Frequency (Hz)\")\n",
    "plt.ylabel(\"Amplitude\")\n",
    "plt.show()\n",
    "\n",
    "# Run resonance finder\n",
    "resonance_finder = ResonanceFinder(freq_window=tuple((ss2, ss1)))\n",
    "resonance_data = resonance_finder.run(signals=NBBandsData.data)\n",
    "\n",
    "# Filter resonances\n",
    "resonance_data_filtered = filter_resonances(\n",
    "    resonance_data=resonance_data, minQ=100, minamp=0.001, maxerr=100\n",
    ")\n",
    "freqs = resonance_data_filtered.freq.to_numpy()\n",
    "\n",
    "# Target frequency\n",
    "v0 = 428274940000000\n",
    "\n",
    "# Center frequency\n",
    "v1 = None\n",
    "v2 = None\n",
    "for freq in freqs:\n",
    "    if freq < v0:\n",
    "        v1 = freq\n",
    "    elif freq > v0:\n",
    "        v2 = freq\n",
    "        break\n",
    "\n",
    "# Calculating band edges\n",
    "print(f\"Wavelength 1: {freq_nm(v1):.2f}\")\n",
    "print(f\"Wavelength 2: {freq_nm(v2):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1930547f",
   "metadata": {},
   "source": [
    "## Simulating 1D Photonic Crystal Cavity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab2b63d4",
   "metadata": {},
   "source": [
    "### Building the Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "44d59cfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- Set up structures ---\n",
    "def makecells(params):\n",
    "    a = params[0]  # Lattice constant\n",
    "    a1 = params[1]  # X width\n",
    "    a2 = params[2]  # Y width\n",
    "    w = params[3]  # Width of beam\n",
    "    t = params[4]  # Taper constant\n",
    "    nummirr = 12  # Number of mirror cells on each side\n",
    "    numtaper = 8  # Half the number of taper cells\n",
    "    h = 0.3  # Thickness\n",
    "\n",
    "    matSlab = td.Medium(permittivity=epSic, name=\"matSlab\")\n",
    "    matHole = td.Medium(permittivity=epAir, name=\"matHole\")\n",
    "\n",
    "    # Array to hold lattice constants\n",
    "    latticeconsttemp = []\n",
    "    a1temp = []\n",
    "    a2temp = []\n",
    "\n",
    "    # Adding mirror cell lattice constants\n",
    "    for i in np.arange(0, nummirr):\n",
    "        latticeconsttemp.append(a)\n",
    "        a1temp.append(a1)\n",
    "        a2temp.append(a2)\n",
    "\n",
    "    # Adding taper cell lattice constants\n",
    "    for i, num in enumerate(np.arange(0, numtaper)):\n",
    "        latticeconsttemp.append((t ** (i + 1)) * a)\n",
    "        a1temp.append((t ** (i + 1)) * a1)\n",
    "        a2temp.append((t ** (i + 1)) * a2)\n",
    "\n",
    "    latticeconstL = latticeconsttemp\n",
    "    latticeconstR = latticeconsttemp[::-1]\n",
    "    a1L = a1temp\n",
    "    a1R = a1temp[::-1]\n",
    "    a2L = a2temp\n",
    "    a2R = a2temp[::-1]\n",
    "\n",
    "    structures = []\n",
    "\n",
    "    slab = td.Structure(\n",
    "        geometry=td.Box(\n",
    "            center=(0, 0, 0),\n",
    "            size=(td.inf, w, h),\n",
    "        ),\n",
    "        medium=matSlab,\n",
    "        name=\"slab\",\n",
    "    )\n",
    "\n",
    "    structures.append(slab)\n",
    "\n",
    "    # Setting up nanobeam\n",
    "    for i, const in enumerate(latticeconstL):\n",
    "        center = (-np.sum(latticeconstL[i + 1 :]) - const / 2, 0, 0)\n",
    "        hole = td.Structure(\n",
    "            geometry=ellipse_uc(center, a1L[i], a2L[i], height=0.5),\n",
    "            medium=matHole,\n",
    "            name=\"holeL\" + str(i),\n",
    "        )\n",
    "\n",
    "        structures.append(hole)\n",
    "\n",
    "    for i, const in enumerate(latticeconstR):\n",
    "        center = (np.sum(latticeconstR[:i]) + const / 2, 0, 0)\n",
    "        hole = td.Structure(\n",
    "            geometry=ellipse_uc(center, a1R[i], a2R[i], height=0.5),\n",
    "            medium=matHole,\n",
    "            name=\"holeR\" + str(i),\n",
    "        )\n",
    "\n",
    "        structures.append(hole)\n",
    "\n",
    "    # Set up sources and monitors\n",
    "\n",
    "    source = td.PointDipole(\n",
    "        center=(0, 0, 0),\n",
    "        source_time=td.GaussianPulse(freq0=ss0, fwidth=ssWidth),\n",
    "        polarization=\"Ey\",\n",
    "    )\n",
    "\n",
    "    point_t_mnt = td.FieldTimeMonitor(\n",
    "        center=[0, 0, 0],\n",
    "        size=[0, 0, 0],\n",
    "        start=tstart,\n",
    "        name=\"point\",\n",
    "        colocate=True,  # Colocate so field is colocated to grid cell centers\n",
    "    )\n",
    "\n",
    "    field_t_mnt = td.FieldTimeMonitor(\n",
    "        center=[0, 0, 0],\n",
    "        size=[td.inf, td.inf, td.inf],\n",
    "        start=runtime,\n",
    "        name=\"field\",\n",
    "        colocate=True,\n",
    "    )\n",
    "\n",
    "    eps_mnt = td.PermittivityMonitor(\n",
    "        center=[0, 0, 0], size=[td.inf, td.inf, td.inf], freqs=[ss0], name=\"eps\"\n",
    "    )\n",
    "\n",
    "    # Boundary conditions\n",
    "    bspecs = td.BoundarySpec(\n",
    "        x=td.Boundary.pml(), y=td.Boundary.pml(), z=td.Boundary.pml()\n",
    "    )\n",
    "\n",
    "    simSize = (a * 2 * len(latticeconstL) * 1.1, w * 5, 1.5)\n",
    "\n",
    "    return structures, simSize, bspecs, source, point_t_mnt, field_t_mnt, eps_mnt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f5b247d",
   "metadata": {},
   "source": [
    "## Running the simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8361d244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PSZKk+dwf/uEfsmPHDj73uc81n9u5cyc33ngjL37xi5tz6o488kgOO+wwPvjBD3akfvnABz6AEII//MM/nPMaHHPMMQRB0PXzAcz60fHv//7vzXMZNnfeeSdHHnlkc/qCw7EScLdiHY4Vxjvf+U6+/OUvc+qppzZTgWzbto0bb7yRb33rWx3pOGayZs0aLr74Yi677DJ+7/d+j9NPP52f/exnXHvttRx33HG8+tWvBuCrX/0qb3jDG3j5y1/O05/+dNI05V//9V9RSvGyl70MgMsvv5xvfOMbnHbaaRx00EE8/PDDXHvttRxwwAFzpr3o53V/9Ed/xKc//Wn+9E//lK997Wv81m/9FlprfvrTn/LpT3+a//zP/+TYY4/l4IMP5q1vfSt/8zd/w3Oe8xxe+tKXEoYh//M//8OGDRu48sorgayz/8AHPsAVV1zBwQcfzP77799ciNGO7/v8/d//Peeeey6nnnoqZ511VjPdyVOe8hTe9KY3LVS2Dk4++WRWr17NOeecw5//+Z8jhOBf//Vfu5qLY445hhtuuIGtW7dy3HHHMTIywotf/OKu9f75n/85jzzyCBdddBGf+tSnOo4961nP4lnPehawsDl2BxxwABdeeCHvfve7SZKE4447jptuuolvfvObfOITn+i4RXnxxRfz0Y9+lHvvvbeZd+8P//APOfHEEzn33HO5++67mztPaK257LLLOmK9+93v5vTTT+cFL3gBZ555Jj/60Y94//vfz+te97pZ6VZmUigUeMELXsBXvvIVLr/88lnHb7nlFl71qlfx27/92/z85z/ngx/8IKVSiS9/+cscd9xxvOhFLxr42nQjSZJmHkmHY0WxW9biOhyOOfnNb35jzz77bLtmzRobhqF96lOfav/sz/7MRlFkrW2lO+mWEsXaLL3JYYcdZn3ft2vXrrUXXHCBffzxx5vHf/WrX9k//uM/tk972tNsoVCw++yzj33e855nv/KVrzTL3HbbbfYlL3mJ3bBhgw2CwG7YsMGeddZZ9uc///mcbe/3dXEc27//+7+3Rx55pA3D0K5evdoec8wx9rLLLrMTExMdZT/ykY/Yo48+ulnu1FNPtbfeemvz+Pbt2+1pp51mR0dHLdBMfTIz3UnODTfc0Kxvn332sa961avsAw880FHmnHPOseVyedb55aky5uPb3/62PfHEE22xWLQbNmywF110kf3P//zPWe2Znp62/9//9//ZVatWWWDO1Cennnpqz9Ql7ek8ForW2r7zne+0Bx10kA2CwB555JH24x//+KxyeSqYe++9t+P5xx57zL72ta+1++67ry2VSvbUU0/t+R7993//d7tp0yYbhqE94IAD7Nve9jYbx3Ff7fzc5z5nhRAdKVPydCfvfOc77ebNm20Yhnbjxo32M5/5jH3LW95iS6WSveyyy6y1LQ1npuTppfmpp55qjzzyyI7nvvSlL1nA3nPPPX212eFYLtxesQ6Hw+F4QqG15ogjjuAVr3gFf/M3fwNkO09s3LiRf/mXf+E1r3nNkrfhjDPOQAjRvNXrcKwU3Bw7h8PhcDyhUEpx+eWXc8011zA9Pb3s8X/yk5/w+c9/vmkqHY6VhDN2DofD4XjC8cpXvpLHHntszi3YlorDDz+cNE37zjfpcCwnztg5HA6Hw+Fw7CG4OXYOh8PhcDgcewhuxM7hcDgcDodjD8EZO4fD4XA4HI49BJeguE+MMTz00EOMjo66DZ8dDofD4XAsG9Zapqam2LBhA1LOPSbnjF2fPPTQQ3zow/9M7ukE2T8s3acoiub/dishQHqNzdAt1hqweZ7RxnEhEEIipejYdqezFoFUCqN1z3Z0xOhWR3uMjjYAQg4/Rj/n0VFPdi2GHmO+83B6OD1mnsci9UjThEcf3jZra7RBEEI0t+YCSNN01tZwy0EQBM3OxRhDHMfL3gbP8/C8VhcWRdGSbBs2F06PFk6PFu16WGspl8tDGRB65zvfyf33388BBxwwZzln7PokG6kDJQVSCCyCrOvo/SVtkEhm729pgZGyB6qAQCOtJtWaWj37MBYLPp5SGKGwKJTt9SEVpCLAszH06FS0CJoxuraxLYa1lulqHQBPKYqFYOgx+jmPWj0mbXTWI6UQIcTQY8x3Hk4Pp8fM81icHhZBwJPHx5EsrqOZqPvEJtvea99iHbkbbiDEWjIRBQCMhzGBCud5xfAxFh6tFQAIpGa84C97G8DpkeP0aNGux4ifMBl52WCPWLjRNSY7kdHR0XnLOmPXJ7nblkIgpcA2pifmI3ddXoFANsf1cizZAEjgCYxSSMDqlLiW4nvZmzGOU4KyzH7po/B7xMjaoPCRPQ1mgmqU6n5ck8XwLExXE5SUeJ4kSTTGpI1fP8OJ0c95RFGEMYYw8EhTQ5wkjJQChBhejPnOw+nh9Jh5HovVQ2AQCIoIVNdS/TEVeRirGAk0USqppQH7luJl7bxiLdhV9yl62flWE59SEBOo5RudMRYerQb40hJ6hnqi0NYyGi7v6IzTI8Pp0WKmHrU0M7hK5a5hYSQNH9HPyJ9bPLGb0VozXYlRSjBaDhgtByglmK7EPW8xDZtsJCJGa8tIOWCkFFAseNTqKfUoWZY2ANSjhFo9pVjwGCkFjJQDtM7atlxD+k6PFk6PjJWix1TkMVn3GCuk7FtK2K8ck2jBo9UAs0x9eKwFOysBvrLsV47ZrxzjK8vOSkCsl6f3zE1EogX7lWP2LSWMFVIm6x5T0fKNVTg9MpweLbrp4UlLaiTaLJ+7dMZuAMSsf4k5HjNf2XnMIki1plKpo5SkXCqAUCAU5VIBpSSVSp1Uayyyx6NV11xleh+TGCuoVqtobSmXQ5TysEjCMKRQCKjXE+KovqgY85cRxFGdej2hUAgIwxCLRCmPcjlEa0u1WsXY+eLMHWO+a+X0cHospR6Lob3TykdBgkbnsVydV3unlY+CSAH7lpbPTMw0Efmo1GiYLquZcHpkOD1a9NJjVTFBkN2eTZfJYLpbsQMhMEhE25f0fF/YM49bLBZDpD2iehWpFEGpTNo+vCogKIXoaoVapQrlEkr1liolmLMNBolh9pwLay21agWjDcXyKFZ5tI8/qDDEp05cj4CQICwMHKOdpMfxOMpiBIUiKix0tAEFYTmgVqlSqdYoluaehNorRk6va6V1Sq3i9ACnR84w9bDMnG3bP906rZy889pZCXi0unS3nbp1Wjm5mXi0GrCzEnR08MOkl4nIya/NZN3r+HvYOD0ynB4t5tNDSYM1kjhV4Gm8JR68c8ZuILKv52yt6/yLJyzd5t1YjDXEtWmkF1AuFfFEl8nfAmSpSKVaI6pMUS6HeErNqEmQEuARI+j+wU0IkRhUZ/eMtZZKNcJqKJbLFJQGZt/a8kIJhCT1GoqUQjh7QmyvGDkaH4PEJ5p1rB4lJPWEoFCkFEpElzK+Asol6pUKcXWScmMCf78xYO5rlWpNVImQynd6OD2y1w5Zj+wn4eC3jufqtHKWuvOaq9PKWWozMZ+JyFlqM+H0yHB6tOhXj1BpYq2WxdytuFux3/jGN3jxi1/Mhg0bEEJw0003zVn+61//OkKIWY/t27d3lLvmmmt4ylOeQqFQ4IQTTuC73/3uwG2zs/5l53jMfGX2MMZgjEVJQalUQgrbmFg9+yGFpVQqoZSgUonQOp1RpjGZkt51dDuG1VSqdbQ2lMsFPKXmfH0QFigUfOr1mCiK+orRT5koiqjXYwoFnyAszFmPpxTlcgGtDZVqHawesB3dr5XWKZVKhFJOD6fH0uoxKP10WjlLddupn04rZ6luA/ZrInKW6jag0yPD6dFiED2EhNDTCCBO1ZLell1xxq5SqXDUUUdxzTXXDPS6n/3sZ2zbtq352H///ZvHbrjhBrZu3cqll17K97//fY466ii2bNnCww8/POzmz4mxBq2zsb5S0e9rdYsQgpFSa8J4qheeAwtmTwRXqr81eoXQH+qE8faJ+d1GObqhlBrqBP5Um+bE/Gylp9PD6bEy9Bik08oZduc1SKeVM2wzMaiJyBm2mXB6ZDg9WixUj+UwdyvO2P3+7/8+V1xxBX/wB38w0Ov2339/1q1b13y0Z2Z+73vfy3nnnce5557LEUccwXXXXUepVOIjH/nIwO0zPRKZ9vO6tKGilGKgZIXD6rxmdlqeGkz+YXVeCzEROZ6SQzETCzEROU6PFk6PFsPSYyGdVs6wOq+FdFo5wzITCzUROcMyE06PDKdHi8XqsdTmbsUZu4WyadMm1q9fz+/+7u/y7W9/u/l8HMfceeedbN68ufmclJLNmzdz++2396wviiImJyc7HgDGMHDn1W7qPDWYqctZbOe12E4rZ7Gd12JMRM5izcRiTESO06OF06PFYvVYTKeVs9jOazGdVs5izcRiTUTOYs2E0yPD6dFiWHospbl7whu79evXc9111/HZz36Wz372sxx44IE897nP5fvf/z4AO3fuRGvN2rVrO163du3aWfPw2rnyyisZHx9vPg488MDmsSQ1Pbcgmkm7qfM9iVjErM3ZnVd/k7GH1WnlLLTzGoaJyFmomUjb8qIt1ETkOD1aOD1aLFSPYXRaOQvtvIbRaeUs1EwMy0TkLNRMOD0ynB4thq3HUpm7J7yxO/TQQ/mTP/kTjjnmGE4++WQ+8pGPcPLJJ3PVVVctqt6LL76YiYmJ5uP+++8HaAqZpnreziszdZkB9D05lL3i2juvfML4XFhrqVZrQ+u0cto7ryjqvvKxnSiKhmYictrNRLVam1eP9on5izUROU6PFk6PFoPqMcxOK2fQzmuYnVbOoGZi2CYiZ1Az4fTIcHq0WCo9lsLc7ZHpTo4//ni+9a1vAbDffvuhlGLHjh0dZXbs2MG6det61hGGYcdmwpAlNxFSoQToxubCnud17ZCMtejG5sOe54EQ7WtjG2kWBBoP02OToTzBasKM/FgCwlKBarXazOPlqdm5urJOq4rRUCqPYtXsBBg9Y8w6HjIzZYQKC/hEjTxeEPbI4xVFUSMvWgkVhnO0YXaM9jJdr5WCQjmkWqlSrVahVOqqR6p1Iy+aT1gqdeZFmy/GrHY6PZweM4/Pp4foaaCXotPK6TfVw1J0Wjn9pt5YKhOR02/qDadHhtOjxVLrEXqaKJ07Fcog0/uf8CN23bjrrrtYv349AEEQcMwxx3Dbbbc1jxtjuO222zjppJMWULtFCoHysjdjmqazbstaazpMXXvHltu79pxWEt31MddxJQzlUhGpJPVKBavjjuPCptSr043kqiV81b2e+dqQp1WVmK7Hi6FHUAiJ6xFJVJt1PIlqDRMRUgy9BcWYr52+gmK5hNGGenUaYdOO41bH1CsVpJKUS0WU6B7H6eH0WDo96kRRnZksZaeVM9/IxFJ2WjnzjRQttYnImW+kyOmR4fRosVx6zDVyZywktv9dplfciN309DS/+MUvmn/fe++93HXXXeyzzz48+clP5uKLL+bBBx/kYx/7GABXX301Gzdu5Mgjj6Rer/PhD3+Yr371q3z5y19u1rF161bOOeccjj32WI4//niuvvpqKpUK55577kBts43NgbIcWhI8rzFyp/E9ixCiY06d8rzGm8DMqAMUKYYg64R6JJLNXql6HpdCUiyViauTVCvV5q2kfM6Q0ZZSeRRfseAYWUrmEEXSMxdXGBaQGKJ6HdmWpLUeJUT1lEKhgB+GKGZ3bP3GMKg5r1WgfES5RL0yRa1aad7aS7WhWonxlCQolfFEvOAYeRmnh9Mjp189BIaoHjONZLxxI2A5Oq2cXiMTy9Fp5fQaKVouE5HTa6TI6eH02N16dBu5MxaitH9TByvQ2H3ve9/jec97XvPvrVu3AnDOOedw/fXXs23bNu67777m8TiO+cu//EsefPBBSqUSz3rWs/jKV77SUccrX/lKHnnkES655BK2b9/Opk2buOWWW2YtqBgUKQTCE6SpJUkNnhKkurVQAiGhx5f9sBBCUC6FVKp1pisx5ZJPPUqbc4asUnTLmD9swjBAklKrtz6A+RwuPwyW+Cpk5HnVpisx09WYQuhRqSYolV2jbrf7ho3To4XTo0UYhghgql5HNr52l6vTypnZeY2FKY9W/WXptHJmmol9SwmTkbdsJiJnppnI/+30cHrsbj06zJ0yJFpiAU/0/z0l7GKziu4lTE5O8t6rrsJXEilbm3oLTMcoHeQLJWTblmLte1ZYrIFVYwWMKs05IpFty6Tw5xhZSQizbZusZqoSoxtpHkZHQjwlSSgMLUavEYn2GPlKS6A5MX/YMeY7j1QbpqazeWZKSUbLAQg11BjznYfTw+nRfh75DhQqepjpetZDLGen1U6sBY9MZ8OGvjLsV16eTqsdY2FnJSDR2ffompFo2UxEO/moEDg9nB4Zw9Yj1pJtkyFSzrezfAtjoZ6qpnMIPY218Ja3X8HExARjY2Nzvn6PnGPncDgcDofDsTfijN0imZl8GAbLczcMmnOGTCNlgyeHsr3SoLTnRRvm9kqDkCe79bws9YYxw9nuahCcHi2cHi2iKGaqLhkrLM3emf2QzxkKvGwkIjXD3TuzH/I5XKlp3O7zzFD3Mu2X9jlcTg+nB6wcPfI5daEySLIFFWZvXxW7XBhrO5IPSynxvJa5W+j2Y4NgraVSjZpzhnxPdSRp1X0maV0sURR35EXrzOMVL0sb9Ixkt77X2su0Uo2WxUw4PVo4PVpEUUS9HjNaMIyG6ZJtjD4XMyeCh43Oa9gbo8/FzIn5oWeWZKP6+Zg5Md/p4fRYKXpEjVuwgafxpG2ulk2fyKtiVzKisSbWImfkqVMgsgmOQkg8z5CmGp2mCM9DiHb/bLEYNB408nT1Ij+m6Z7/ylhBrVrBaiiXSwilsmngAooln2q1RrVSpVguEajudcwXI58VoPF7ziHK8qIljdWWQXMquh/6GGLq9QSDRPbI49VPDDvPtYo11CpVPKUoloqYxsR8oaBU9qhU6uhqBVkqIsXcudl64fRoL+P0yNrYvx5hIWAknG4+328er2HQa3Vfv3m8hkGv1Zb95lUbFr1WWzo9nB67W48OU9eIky+oqA2wMtYZu4ERs5IPz0zAKoREeaJrEuMs2YlopHLI6JWAlTmOt5KrGorlMkKpzm5FQKE00kzSKsollOodZ74ksAaJYPY7upXsNsQPw1ldmx8WMci2pLnhrDrmizFfO3Uz2a2kUCphRWeqWKEUhbKiVqlSqdYo9UiaO1eM+Y47PVo4PVq09CgQdjHSy9F5zZeyYTk6r/lSaCyXmZgvhYbTI8Pp0WK59Ohm6nKkAH+AVbHO2A1AtqI1bQ7Jep7XGG2Y/YZXQrTluUvbthRr3LolmjdPV68VefmcIbSlWB6loDSiWx0CKJWoV6epV6a6bpm0mFV/9SghqacEhRLF0OuZFy0fGUrqVTziWRn4F7MKM9WGeiXGU4pCqUQgum/f5CkJ5RJRZYqoOtl1C6uFrpB0erRwerRo16MQej2v5VJ2Xv3m4VrKzqvfvGhLbSb6zYvm9MhwerRYaj3mMnU5YoCJc26O3YC0TJ2ad29LKSSel13ibEHF4j8U7RuWl8shSs3tzYUQlErFto3RhzPvr31ifrdRn5mEYTj0Cfz5xHylsnOcTw+lPMrlcOCN6ufC6dHC6dFiUD2WYk7RoMlVF7ox+lwMmux2oRvVz8egyW6dHhlOjxZLpUc/pm5QnLFbAHmeun7IzF3batlFvBvaO61sdKG/e+7tG6MPo/Nq77QG2UC+fQL/Ys1Eu4kYZAN5T7Um8C/WTDg9Wjg9WixUj2F2XgvNmD/MzmuhOxgM20wsdAcDp0eG06PFsPVYClMHztgNjJT03Wk1X9Nm7lJtF9R5ze60BpNuWJ3XQjutnGGYiYWaiBxPyUWbCadHC6dHi8XqMYzOa7HbIA2j81rstlTDMhOL3ZbK6ZHh9GgxLD2WytSBM3YDIwe50T3jdbm5M2Ywc7fYTitnsZ3XYjutnMWYicWaiJzFmAmnRwunR4th6bGYzmtYe1supvMa1l6jizUTw9pr1OmR4fRosVg9ltLUgTN2y4oUEqWyFYLVWtJX5zWsTitnZufVbx6vYXVaOQsxEzPzoi3UROQsxEw4PVo4PVoMW4+FdF7D3rB8IZ3XsDeQX6iZGPYG8k6PDKdHi4XqsdSmDpyxGwgx619ijsfMV2YPKbO9ZrVppGOwWV68bg9jBdVqtWMieGeZVgqVXnV0O4ZQlEsFlJJUKnVSred8fRzVqdcTCoWAMAz7itFPmTAMKRQC6vWEOKrPWU+qNZVKHaUk5VIBhBqwHd2vVfsEfqeH02Mp9RiUQTqvYXdaOYN0XsM2ETmDmolhm4gcp0eG06PFIHpYszymDly6kwERjXxVrS/p+b6wZx63WISAoDhCVK9TqdYolsqzRjustdSqlUYerlGs8nokfYCUYM42GCSGGSvzBASlEF2tUKtUoVzquoIwjuqNPFxFVFjo2YauMWaQdDmuwhCfeiOvWkgQFmaV0Tpt5EVTBKUy6RwjQ91itNP1WikIy0Ezr5rTw+kxbD0sskeyk7npJ9XDUnVaOf2kelgqE5HTb+qNpTIROU6PDKdHi371SE22mGupTR04Yzcg2ddzlo0uG4HrlZsqLz37eJb3LlQpslyiXqkQVycpl8JWEuPGNkhWQ7FcpqA0MPuWkEWQEuAR05kCtkVCiMR0zwUmQJaKVKo1osoU5XLYsZIwy8OVEBSKlEKJoHtOsjljkOcCk/g9Xu+FEghJ6jUUacetrFRrokqEVD7lUhFPdN8Oa74Y810rXwFOD8Dp0WSIemQ/CRe2fdlcnddSd1o5c3VeS20icuYzE0ttInKcHhlOjxbz6aGNxJLtILHUpg7crdiBsLP+Zed4zHxl5zGBxVOKcrmA1oZKtQ5Wg9VUqnW0NpTLBTylEJgej1Zdc5XpfcwghaVUKqGUoFKJ0DpFYJp7WxYKPkFYWFSM+ctYgrBAoeBTr8dEUZbsVeuUSiVq5EUrIcV8ceaOMd+1cno4PZZSj8XQ7bbTcnVaOd1uOy2XicjpdRtwuUxEjtMjw+nRopceu2p+dvtVmWUxdeBG7HY7qpHHa7oSM1XJRj+MySaCz9oGaYnIJ4xPV2OmKzGeJ0kS3Uiu6ve8vTRsCmG232atnpJqS5qa5sT8uW73DROnRwunR8ZK0aN9ZCLWgiiVy9Zp5bSPTOysZLe4U7M8JiJn5khR6BnqiVo2E5Hj9MhwerTopYcnDUoujx7gRuxWBJ6SlEs+WptsJKLkL3p136DknZe1liTR+L4ayuq+QSmEPr6vSBKNtXYoqy0HxenRwumRsVL0GA1TCr6mniisFcvaaeUEyrJvKSHRkkRL9i0ly2YicnIzYa2gnigKvl5WE5Hj9MhwerSYqceqQrLsbXDGbgVgraUetT4E9SgdyvZKgxLFrTakqRna9kqDkGpDmrbitrdpuXB6tHB6tFgJeuQjETmVePlvuhgLk22rECcjbyjbKw1K+7lHqRzadleD4PRo4fTImKnHdLz8eqw4Y/eNb3yDF7/4xWzYsAEhBDfddNO8r/n617/Os5/9bMIw5OCDD+b666+fVeaaa67hKU95CoVCgRNOOIHvfve7w2/8AmjPwzU6EjI6Mty9M/ulPQ/XqrFCW5LWhU34XghpW160VWOFoe9l2g9OjxZOjxYrQY/2OUPrx+pD3zuzH9rncK0ZiVgzEg1178x+aZ/DtX6sPvS9TPvB6dHC6ZHRTY/UiGzxxDL+Dlxxxq5SqXDUUUdxzTXX9FX+3nvv5bTTTuN5z3sed911FxdeeCGve93r+M///M9mmRtuuIGtW7dy6aWX8v3vf5+jjjqKLVu28PDDDy/VafSFtZZqtdaRXLU9SWu1WluWziuKoo7kqu1JWvMJ40tN+8T8/HZfe9LcKOq+4nCYOD1aOD1arAQ9uk0EX4qN0eei28T8pdgYfT5mTsxfqo3q58Lp0cLpkdFLj9XFBAtEWi2b2RZ2d9zT6BMhBP/+7//OGWec0bPMX/3VX/GFL3yBH/3oR83nzjzzTHbt2sUtt9wCwAknnMBxxx3H+9//fgCMMRx44IG88Y1v5K//+q/7asvk5CRXXXU1SimkbE9C3Ovytb+ZO9fTWmMoj44SRRFGG0rlEmrGhuVaa6qVKlJJSqVS13lNti1Gr3QOeZlex6MoauThCgnDzlxeWcdabeQKK/XcVH2+GPMdT7Vu5EXrfq5ztXHQNvQq036uTg+nR87w9BBINKt4qGcaml7Mt7pvOVYgzrfacrlWIM51rsu1ItTp0cLpkTHXucZa8uBESGIkkizlyUL0SAy85e1XMDExwdjY2JxlV9yI3aDcfvvtbN68ueO5LVu2cPvttwMQxzF33nlnRxkpJZs3b26W6UYURUxOTnY8Mvp9c9oZ/58hsFhriGqtDsFXINEdD19BsVzCaEO9Oo2w6awyecpTielyTHfkzep2LIlqzU6rGHqzjithKJeKSCWpVypYHQ8cY77jVsfUKxWkkpRLRZSYfS7F0CMohMT1iCSqLagNc10rYVPq1Wmnh9NjSfXo/7ujRT8d9FKPTPTTQS/HSNF8HfRyjBQ5PVo4PTL60UOKLN2JJdt9YqlH7p7w6U62b9/O2rVrO55bu3Ytk5OT1Go1Hn/8cbTWXcv89Kc/7VnvlVdeyWWXXdbx3KXveEczJ1W+o0Tv/FRZSTEjr50xBmss1mhKI2P4ip6/4APlI8ol6pUpatXKrBWJWbrkEEXSsx0GlXVCM2LUo4SonlIoFPDDEEW96+ulkBRLZeLqJNVKtet+nL1izCwz83iqDdVKjKckQamMJ+Ke5xGGBSSGqF5HzkiaO1eMnF7XKp/DZbSlVB51ejg9gOHrIdA9RwV7McioSz8Z+BfCIKMu/WTgXyj9jrr0uyPCQnB6tHB6ZAyih5IWITRxqohSteCRu354wo/YLRUXX3wxExMTzcf999+/6DqttaTaYoFy0Z91e6kbeR6vYU4Yb58IHoZzb7cE2S3xcilsmzC++FmgqTbNifntuwrMRRgGQ53AP3MDeaeH0wNWhh4LuZU27JGJhdxKW4qRokFvpS3FSJHTo4XTI2Mhengi21JsqUfunvDGbt26dezYsaPjuR07djA2NkaxWGS//fZDKdW1zLp163rWG4YhY2NjHY/FYK0laaSNUFKgBsjD1T5hfLGdV3unNUgervYJ44vtvNpNxKB50don8C/GTMw0EYPkRXN6tHB6ZAxTj8XMjxpW57WY+VHDNBMLnR81TDPh9Gjh9MhYjB7LYe6e8MbupJNO4rbbbut47tZbb+Wkk04CIAgCjjnmmI4yxhhuu+22ZplBWMgXdoepU2JBCV6H0XkttNPKGUbntRgTkbNYM7EYE5Hj9Gjh9MgYhh7DmPS+2M5rGJPeh2EmFjvpfRhmwunRwumRMQw9ltrcrThjNz09zV133cVdd90FZOlM7rrrLu677z4gu0V69tlnN8v/6Z/+Kb/61a+46KKL+OlPf8q1117Lpz/9ad70pjc1y2zdupUPfehDfPSjH+UnP/kJF1xwAZVKhXPPPXdBbWxP2Dof1pqmqfM80VhRuzAW03ktttPKWUznNQwTkbNQMzEME5Hj9Gjh9MhYjB7DXMm40M5rmCsZF2MmhrWScTFmwunRwumRMUw9ltLcrThj973vfY+jjz6ao48+GshM2dFHH80ll1wCwLZt25omD2Djxo184Qtf4NZbb+Woo47iPe95Dx/+8IfZsmVLs8wrX/lK/uEf/oFLLrmETZs2cdddd3HLLbfMWlDRL8Za0nT+N7eZaerE4i93e+dVqUZ9dV5RFA+l08qZ2XnpPpK06rZkt8Palqozr1o8b3lrs2s2DBOR4/Ro4fTIWIgeS5GeYtDOaynSUyzETAw7PcVCzITTo4XTI2Mp9Fgqc7ei89itJFp57GS2stVahBB4Xvc3hG0zf56nEE1TZzHGMDY2AirLi6Xo/mHRjUXL3Y5rralU6gilspQLoruMtSglrkcUCn7XieBzxchamyWLkJhZKwvzBLKpzlJPBD3muscaapUqnoJSqTjLRMwVo592RlFMvZ4001J0w1hBpVrDak25XOg6MX++a+H06K+dTo/GsQ49yvgKVrFt1grcpc451k/HvNQ5x/o9x6XMOdbvOTo9BmvrQtlb9Ii1ZNtkiJS250haaiFOFYLeee4GyWP3hE93stxYm63ES9O0sSF4MsvcWWubv9KlUoBojhzYRh2JppnjqtfNGjvncUVQKFCv1ZmerlAozu6g4zgmiWL8MEB6AUmXgYO5Y0C2hlej6Uy5nCHwwyK6VqM6XcEUZ3fQWmvqtTpSZmVT0y2R7Fwx5m+n9AK8AKJaHWMCgqCzg7bWUq9VMcZSKBYwKMyCroXTo592Oj1y2vWYplgMSRTotpKJFjxe8/GkZSxMuuqxWEJPUwoEu2oeqRGUg87Oy1p4vOaTGsHqYmY6lyLn2Hgh4fGaz8PTAauLCTMHiSuxx3SkGAk1oaeXpA1jYWcb/BkdtNOjhdMjY7F6JFpgLXOOxknAV5pYK+qpIlCzzV2qB1hQ1ndJB5ZGhnprUUo1R+6MTpFSNspYjM4UlFIiG+U7awGh62gbZglZ6X6rxqIwKDy639aSCIIgwOoIHVVQSjY/nFpbMAY/CPCkhbS6oBgg0PiNUYbZ70wB+L6P0QkmriGVJB+ctAaMNvieRCofoWsLigGgCea8Vp5UEPigI3SUoFR2IawFrQ1KgBeESBtnP48WEMPp0cLpkdOfHlqnJHFMVcns2gDGCGItCD1LoAyRnj+9y0JR0lIODLGW2MjHU5lVtTYbUZACRgKDtpKl3CFtJDDEWjAZ+QTKNPVItSQxgnJgUNJST5fuWhT97DpUYo9AWaTMdHN6OD2WQo/UCAyCHjcNmgjAl4bUSBKt8JRp/gTM6+gXZ+wGQSjKJR/fFxg8DAodV4iilDD0CHxFtZZgpKVYDLCqiEdnkletDak2PPPQdSSFAwioUWSya7gaY8QUGWdH1+MpPlPszygP4/VI4jrBWhfDxXAxdnMMRYyH5jC+QlFMdS3ncDj2PCZqHl/82RpCpWeNSHajfZRydTGhmmQjp4Gaf65ujjN2gyCy/HOeEmgUAkWhGKCkoFZPiWONEILRkRClPBIUHnKWz7bWUir4xKUSIZZSj6z2ggKKEmW6J0lNCUgpUSbE6+HmY1wMF8PF2N0xPBQeKavRlMTikzk7HI4nDp60+Mr2nVTaV9mOITsr2Tz8VcUUJQbIG7jgljqahEHLH3ueHMrqPofD4XA4HHsfgbKEXutO38x5f/PhHMgiyfNwCSHwfUWS6KFsr+RwOBwOh2PvYyryqCeKgq8Rwg6cd9Ddil0E3ZKr5olOLRIVhru7iQ6Hw+FwOJ4gzEy/kqd7SfT8+1bnOGM3AAJBti5WYqygWq2CtpTLIUopLNkesxZJvZ7gU8cLJe0DoxawGFJ8DB4pIWnP+Ttho0zv+T3t/98NF8PFcDF2fwzdyHjncDgcveiWUy9PKr1jyhm7JUEqRSoCrJXUqhWMNhTLo1jVuR5OhSE+deJ6BIQEYaF5TKPRCKbYnyKQUGCC9XPGne94hX3nPO5iuBguxu6O4UbvHQ5Hb+ZKlBwoy3hh/t18cpyxGwCjNcpExFGCbWSTLygNXfJsZSN1IUm9hiJtblWUpT5JGeVhUp6CT50Sj3eNV2U1CQXG2db1eEpAhX0p82jPPFsTrHcxXAwXY7fHiPB7rLx1OBx7N/3sfuHJ/ifZOWM3ABZLrR4BUC6XEEoheuSuAkkQFlCk1OtZLrtC6JOlGbR4JBhSPKKeHYJHhMabIzlqXi6eI0mri+FiuBi7O4YidrdiHQ7HLJZi2zZn7AbBWoy1jI2ECKX6+prOzVyt3to31uFwOBwOx97NUu3F64zdQFiKYbb6tf8c0DRvw9bqKUFgUcPexdjhcDgcDscThqUydeCM3WAIiVpg8uHc3FVrSUdCY4fD4XA4HHsPS2nqwBm7ZaUQ+mhj0drNtXE4HA6HY29jqU0duJ0nlp0w8FDSXXaHw+FwOPYmlsPUgRuxGwilFFoEJChsY8PvhELXsq3jIYLWMmWNxirFBGsJ8agxTkS5ax2mIc/jPGnO4xOsR9L9TWJcDBfDxdjtMeoEBC7dicOx11KJPaqxWnJTB87YDYQxFoFGArpx6Xoto8jyzEskppG7Ln9eI9EE1IAxAEIqXeuoMT7n8ZSQhEK2wwXRgupwMVwMF2N5YvQyfg6HY88m1ZJKLFlVXHpTByvU2F1zzTW8+93vZvv27Rx11FH83//7fzn++OO7lr3++us599xzO54Lw5B6vfXr2FrLpZdeyoc+9CF27drFb/3Wb/GBD3yAQw45ZKB2WWuQVpMnOjEoVI88dpmxC1EkM4ydQaIpMknMvoRUKLGrZ8yIcs/jKQETrKfE4z1zaEWUXQwXw8XYzTECIjxn7ByOvZLECEZCvSymDlbgHLsbbriBrVu3cumll/L973+fo446ii1btvDwww/3fM3Y2Bjbtm1rPn7zm990HH/Xu97F+973Pq677jruuOMOyuUyW7Zs6TB/DofD4XA4HMPGl5ZysHw/7FacsXvve9/Leeedx7nnnssRRxzBddddR6lU4iMf+UjP1wghWLduXfOxdu3a5jFrLVdffTVve9vbeMlLXsKznvUsPvaxj/HQQw9x0003LcMZORwOh8Ph2Fvx1PJmwlhRxi6OY+688042b97cfE5KyebNm7n99tt7vm56epqDDjqIAw88kJe85CX8+Mc/bh6799572b59e0ed4+PjnHDCCXPWGUURk5OTHQ+Hw+FwOByOlcyKMnY7d+5Ea90x4gawdu1atm/f3vU1hx56KB/5yEe4+eab+fjHP44xhpNPPpkHHngAoPm6QeoEuPLKKxkfH28+DjzwwMWcmsPhcDgcjr2QVC+v1VpRxm4hnHTSSZx99tls2rSJU089lc997nOsWbOGf/qnf1pUvRdffDETExPNx/333z+kFjscjr0Na6Geen3tL+1wOPYsEiOoxMu3VnVFrYrdb7/9UEqxY8eOjud37NjBunXr+qrD932OPvpofvGLXwA0X7djxw7Wr1/fUeemTZt61hOGIWEYdjwnhMQIhUY1051o/K6vtw3PrPE7VsUaNAaoMYZo5NDqRX6syqqux1PCxvHVPVMtGBfDxXAxdnuMyBTRacJEUmakNNEznsPh2PPwpWU6UnjS7n3pToIg4JhjjuG2227jjDPOAMAYw2233cYb3vCGvurQWvPDH/6QF77whQBs3LiRdevWcdtttzWN3OTkJHfccQcXXHDBQO2TUmBRGFTzufZ/t5MnKDZIROPf2d9gsMQUyW1jr+SozHM8T46aUGgazUHrcDFcDBdj6WOkRmJRPFwZ40mlh+aM5XA49iw8ZRgJLZP17Dtir0tQvHXrVs455xyOPfZYjj/+eK6++moqlUozV93ZZ5/Nk570JK688koALr/8ck488UQOPvhgdu3axbvf/W5+85vf8LrXvQ7IVsxeeOGFXHHFFRxyyCFs3LiRt7/97WzYsKFpHvtFa42yMT4CjY9B4ffIJm+RJIT4RB0jdgKD1inj7CCmNGcOrSqriCizmge7Hs9zaI2zrWcOrcd5kovhYrgYuzHGo+ZA0HWk0UzUy1STgJLfvazD4dgzKQcpnlwec7fijN0rX/lKHnnkES655BK2b9/Opk2buOWWW5qLH+677z5k216rjz/+OOeddx7bt29n9erVHHPMMXznO9/hiCOOaJa56KKLqFQqnH/++ezatYtTTjmFW265hUKh+3ZgS0kUp2jjZto4HHsLiVZZYnJh0VayfWoVT92nd15Oh8OxZ5KbuaU2dyvO2AG84Q1v6Hnr9etf/3rH31dddRVXXXXVnPUJIbj88su5/PLLh9XEBVGPEqIoJQxW5GV3OBxDRhtJahQCgwCksGyvrOKgVY+gpJ339Q6HY89iOczdE35V7LJiDVovbLStHiXU6ilh6KGUmP8FDofjCU89DbDQnGWrZEqceuysju3OZjkcjt3IaJgyVkiZrHtMRcMf6HHGbiAEtSgmHdDc5aauWPDcaJ3DsRdRTYKOv6XIFlZtm1qNdQN2Dsdey1Kau4Fru/fee/nmN7/Jb37zG6rVKmvWrOHoo4/mpJNO2i1z1pYVIZACpisxpbKHUN1XxLZTjxLqDVNXCP2BTaHD4XhiEqV+4zZsJ57UTERFpuMCo6Hbr9rh2FtZqtuyfRu7T3ziE/zjP/4j3/ve91i7di0bNmygWCzy2GOP8ctf/pJCocCrXvUq/uqv/oqDDjpoKI1baQgExUJAnCRUKnUKZYWnug96WgRxVCepJxQKAWHoY6HxMKT4GDxSQlKCrnWkhI0yvY4HHf/fDRfDxXAxdk+M6bSIER5IiTXZAgoAJQyp8dkxPe6MncOxl7MU5q4vY3f00UcTBAGvec1r+OxnPztre60oirj99tv51Kc+xbHHHsu1117Ly1/+8kU3bqUhlULLkKBURFcr1CpVKJdQavZljKM6cT0iKBRRYYGk8bxGoxFMsT9FsvxXE6yf9fp25jteYd85j7sYLoaLsbwxrBXUZAABCCxW+libjd0JAVIYdlRW8ZTVj+BJN4rvcOzN9GPuUtP/3Py+jN3f/d3fsWXLlp7HwzDkuc99Ls997nP527/9W37961/33YAnEkZrPBvjC4ksFalUa0SVKcrlEK/ttmw9SkjqCUGhSCmUiLaM9FlOu5RRHiblKfjUKfF413hVVpNQYJxtXY+nBFTYlzKP9syhNcF6F8PFcDGWOUYlDomSQjY6F+yDMAnCJs3jvtRE2ufhyhgbRnd1rdvhcOw9zGXuYi2YqPe+8zCTvozdXKZuJvvuuy/77jv3L+QnKrZxI1U0clKVSiWi6iSVSsRIOcBTsjmnrlAIUGEB0SVBscDikWBI8Yh6djoeERqv5/FWubhnGeliuBguxrLGsECUFJE2zT77ViOsRti27wGRldw2tZr1I7safzscjr2ZbuYu1oKdlWCg9EgLXorx8MMP8/DDD2NmJNt91rOetdAqn3AIIRgpBUxXY6YrMZ4nSRKdrX4NfZL5q3A4HHsYUeKjrUSKub+IfamZjotMRkXGC7Vlap3D4VjJtJu7WAuiVOIrS9Hrf7eagY3dnXfeyTnnnMNPfvITbGO9vhACay1CCLTWg1b5hCY3d7sm6ySJxvcVhcZCCYfDsfdRTcLG53/ubwEpLIkRbJ9e5Yydw+Foko/U1ZNsite+pYj6ACNFAxu7P/7jP+bpT386//zP/8zatWsR7h4CUdy6F56mhlQbVI/Vsg6HY88l1ZJIewhoLpaYibWgbev74ZHKOBtXP0yg9q4fxQ6Hozv5SF1OJfZQov/VsgMbu1/96ld89rOf5eCDDx70pXskM5MP57dly2UB86e5czgcexCpaZi6xoxcEDDD4KVGYRBILEpkU1mqSUigqsvdXIfDscLI59T5yrJvKaISe0zWPcIB3NrAxu75z38+//u//+uMHVmal6Qt+TDQnHNXqUSE5QDfmTuHY6+h4Mfs7yXN0bpHKmMdW4pBdoM2VAnPXn8vAEJYN1rncDhmmLoYKVpz7h6r9W/XhLWDbWyzc+dOzjnnHI4//nie8Yxn4Pt+x/HTTz99kOqeMExOTvKP7/8ApWIBpSRxnJDEdfwgIAjCjrLWWur1OtZowmIZJVtf61prtE552mFHEpay/SLb0yB01CP8vo4Pow4Xw8VwMYYdQ6Apg1BYQNqEcMdXIY2RxIzy313rdDgcew7VpMAPHj4cXySoOXJWpkZSTUtIYSh71Vkr5afjgL96+3uYmJhgbGzuvaYHHrG7/fbb+fa3v82XvvSlWcf29MUTxliwmiSOSOIUPygQBgLovPctBBQKIfV6RFSdplAMUI08dwKDsAZpKkAmjtRTXeNpb585j1tZwMgS0lQRpnsG+/nqcDFcDBdjqWIIDB7WKzUK77nfjQ6HY+HMZ+oAAtX/6omBjd0b3/hGXv3qV/P2t7+dtWvXDvryJzTWGpI4wuiUIAzxAx9huq9mk0JSKGZ57uq1OsXQQymJMAYwqPRxhN0PqadQ6aM9Yxo12vO4ESEmfDIy2Ym0UfcyatTFcDFcjN0SQ2Dw0SpEmNgZO4fDMYt+TN2gDLx089FHH+VNb3rTXmfqALCWNE0JfEUQ+PMWF0JQKPhIIahFKVq7rYMcDofD4XAsjamDBRi7l770pXzta18bTvQnHBbP8wiCASYxCkGx4DXNnTEuw53D4XA4HHszS2XqYAG3Yp/+9Kdz8cUX861vfYtnPvOZsxZP/Pmf//nQGrfyEPgLWOaam7taPaUepwuqw+FwPHERWIQwSBLcuL3DsXezlKYOFjBi9+EPf5iRkRH+67/+i/e///1cddVVzcfVV189lEZdc801POUpT6FQKHDCCSfw3e9+d87yN954I4cddhiFQoFnPvOZfPGLX+w4bq3lkksuYf369RSLRTZv3sw999wzeMMWcfXbR+7MCr4l6xlLqDWB1oh5MucLIDAaZS2qj8XVyrbqlvOUF4BvDKHW+Gb+6yXb6p+vLTPrnk9V2ajX66MsbXUHfZQXWAKtG/XPfw09k13rwPSvT7/XsKmP0X2dp7QW35h5tczLetb2Va/AzntuTxyyvaEl2XUKiPGJkKL7nL+VgugzWYKnDcr2894yFJMUv4/FdYHWjEYxxSSdb/MOAq1ZVYtYVYsI5qvbWkbimH1qdcaieN73ra814/WI1bU6hXT+5LDlOGF1rc5YFM37HaSMadZdTOavu5ikrK7VGa9HqHk+y9JaxqKY1bU65Xj+bagKaVb3qno0rz7CWkaj7BqOxPG875OVpM9KYalNHSxgxO7ee+8dfivauOGGG9i6dSvXXXcdJ5xwAldffTVbtmzhZz/7Gfvvv/+s8t/5znc466yzuPLKK3nRi17EJz/5Sc444wy+//3v84xnPAOAd73rXbzvfe/jox/9KBs3buTtb387W7Zs4e6776ZQKCzp+bQjhCAMFXGy8oydAEpJ0mEuiqlmOgjo9pHxjKWUJAggDQx+qvGThErQfTs1zxhG4vZVPZq6p4jU7NFLZbO62/c8TgNNr6+FQqqxNiXxDYVUA5pIKere7Lpl48ujvW4joOp339s31LpRJyANQmrCNKXmzf7o5IaxmGqUyV5TIKs7lbM/vb4xlDq+1DUoTa3LgG6uj0AybS2F1KBMQtX3SOTs32fSwmgUdxgpLQSVHnNDS2mK3/jBoaUhNQbfaJIuXzrKWoTWBKmm0Gh/rGSPa2IpJWnH+6ruKbqlZFLWUkzSZqc46Zmeo1uh1lhSUjTlOKbu9bgOQDlJ8HWMEYK65/XUItAaYUHLrNzM97Eg66g8YzBCECuF6XJ9PGPxjQavimU1MWBRgCWlhKRC2U6xv70HW3kSk4WAepdrV0xSxqMIaS1V32dXGDLTGStj2K9aJ0xTUiV5vFCg5s+uqxzH7FOLCLQh8iQ7S8WuMceimH1qNXxtSaXg0VKRyTCYVa6QpqybruDr7CrVfMX2cpm0y447a6pVxmut92Il8Ng+UsbM6NGEtaybrjDStpNPogQPjo6QdPme2LdaY59am0Guwq5iwCOl0qyyvjZsmJomaPtRva8UbBstd70O4/WINdUa7dv9ToU+28vlWRpIa3nS5HTrewIwosb2kXLXz9tIHLN2utrxHVT1PbaNdr8m66crlNuuyZpqjYfLpa66FJOU9VOVDmMZeREPjpXRYrY2a6erjEUt87cf8GipwOPF2f1ioDVPmpzu+CwPrk/II6XirLIL0qdS65Cilz4rieUwdbCAPHa92LZtG//6r//KRRddtKh6TjjhBI477jje//73A2CM4cADD+SNb3wjf/3Xfz2r/Ctf+UoqlQqf//znm8+deOKJbNq0ieuuuw5rLRs2bOAv//IvefOb3wzAxMQEa9eu5frrr+fMM8/sq12Tk5Nc9b5rGSkFeJ7AiuxDJWz3X0QWBUKB1Yg2S6K1IU01hxz6VPyR9QCo9LGudeSpFnodHySdw3wxivFOvC4jiVoVqAVjiBkxikna/PxE4X4AhNFOUpl1eB2o1dloULRzVv1130PPOI9imjJz//Qo3A8LGLOrs2prCVPd0YbmazyFbnxy8mtVSKbw09krma2A6UL2wyG/VtLS8UuwPUasJOkMI+FbH6PKeOk0SreulYVmZ5tfbz99rGmKZp6nEQKrH+94PjAGTxu0KpB6Ix0xan6nCTHePtmoYZfrnUpBLVzTcZ6eMR1fqO0xEpnOMlfFNCUOZl/v9muSn2e5/kjXX971oESiRjreuzN176V53t6Zmtd9r6OtQoRIUZylR3s57e2DxFKuPdIRIzOBquPzE2qNmjGqWg3KaFVunkf7tVThFAafSK3HoAj0Yxy+828p6IfxmUCKCaoPb0JYybbREtNBq6MuxwkbpiodsaZCn+0j5ebf0lqePDHZNFeQvdceHBvpMHfd6rIC7hsf7fisjsQx66dm734xs22eNhw0MdlhTAAiJblvfLTjzsY+tTr7Vmd/N1UCj4dGRzqeW1Opsqo++/s0UYJfj493dNi92gqwY2S26TlwYqrDeOWkUvDrVWPYtjYX0pQDJ6a71v1oqcBjM0zPuukKo9Hsn4VGwK9XjaHbvid8rTloYmrW9xvARBjw8EinKV1TrbGqNnt01wL3rerUT1rLxscnu37eul3vVbXMvHbjwbERqu0/EKzlKTPeazmRpzLd21hJ+uxu8jx2Ak2kCws2dbEWvOkt71uaPHZ//Md/3PX53/zmN3z3u99dlLGL45g777yTiy++uPmclJLNmzdz++23d33N7bffztatWzue27JlCzfddBOQjTBu376dzZs3N4+Pj49zwgkncPvtt/c0dlEUEUWtD9Tk5CRSWhAGkG2GrrsvFui29AbtZWzj1kyMp3c2ynYfRhbzHLfWIHV1UXXkx6WNMF1uzFsBQRqhhWjWoazFStM8K9Wow8gECcgZe6mp9BFE4/hMlEmxonUeyiZYYWbuwtSMoYXpuJrKGoy0HW1oHUuxjV+p1hqUriIwXdsBEOhHMLSfZ1b3zDYYmaAsmBnnKUhRuoYVs8/Vtzq7ho06lEkwXZJVKr0TBaSi85i0GiMzPZSud8TwjEa3jUJ5aWamup2nBHy9s7EbQn6eukP79hjKpNDWMUlrscL0uN6tayL0zqwfFnHXUS1lqljqrevWqLdd9zyGRTcNent7Z7bBM2mH2VYmRVCZpUd7OaF3ZiNwXa6VZ2VLL5si0LM+I76pIKg1z8PLdZK6cZsqxtfbMSh8O02JBymKHfnVpD7yGEFlNfvUp6kGrc5xdVRBy86OrpRoAu01R8VG4xhpU/SMNq2KKkR+yyCMx7PrAlhVr7Kz3OoEx6Nq13LjUZVq0Lr+Y3EdKzR6hq6e1ZTTeoepHI2r6JkOkGyU3TMBJn/fWhhJarPOBbIfWCVd7xiBH41rXduax5wOW43ztcE3cde6BVBOa1Tb5oqPRfWedY8kVXYVW+0QFkpJvWvdAKNJrcPEjMZ1jNBdR5XKaQ1p/RkGttq77rjaMbI2ksRYkc7SBbpc78a56B4Jc0fjKnW/NbJW0Lrrew2y759CWifuU5+RZDB9SmmN2gL1WQkIPKyFSBfwpF7wSN0gLxnY2D3+eOdIgtaaX/3qV/zkJz/h2muvHbS6Dnbu3InWelYqlbVr1/LTn/6062u2b9/etfz27dubx/PnepXpxpVXXslll13W8dy7/vb/EKYJntFkXaSAnjcIRaOMod3YpUaSap81k9+kFAuw6dxZ7YXXM1eeRWJlAWHqiB43raws9hXDTyvdj6NIvSK0xZDWdvwq1KqEsCnSZGZXy2yWVI7EwwoPpWf/grMIUqma56Fs9/l3eQxrk45fb15jvomRwawYFtE0PBYJsoCX1jpGUNtJvHLHtVKmc67XzBjtJkJgkUaivSKqSwwjRDYS19DDM93np+QxrO0c5cjP06JmxbBCdBgfRBFpk6YeM0lViG17X3kz5uy0xwDTYRpz7Wdq3qy7cU3mfV8JRaKKzffuzPcUtDTHJh23qHpp3q43gLICrWbr0fG+kEWU6X6tjBBoGUAjRrf35czzaOokNaoRM5GjSDQCwy6jmGLf5uuj0JCoChYoyZbmcSntOjWg6NWbWqvAUPFmf+6NgJJsXfeklNJloAUjq5Sk6ijX7edf1rbW50oUNJWg+w9aX9UQbZ+L+kjvuU9l1ToXAVRHe5cNVB3Zpm1a0lR63GzKzr/VXiUslbHe87s8VaPU1mZT0lQK3eueqZOwc9ctZWfdzHHtAMoqan7jzHdNmKGf5xsqY72n+ZRUveNzFJe7v8cgm7ZRkq1RMc+b+zxDr44nlkafme+pQfRZCRhZxlhQImHMn1jw7VdP9G9YBzZ2//7v/971+b/927/lpptu4k/+5E8GrXJFcvHFF3eMBE5OTvKpj/0j2cdNNUaCRNchdcg6j9at2PZCstERF0iFhxApwnSfSG1liMWjy5SgRgyJFWWEED2NnRHlvmIoYboPPgqFFiPQFsMCtE2Y1rKMNAk0Rmu0kDPGKAOs9BBdehcjBFqo5nlYYbrfEm7EMCLpqFsIg7CNRLHS74jRbnjya6UE2B6JYrUoQ9u1EsJ2mK+OGIKOOSuCxs4rYgTZJYYWEiPa9LCqq1HIY9gZw1wy16ehR3sMM8PYWVnGMzHQ/YsglcWO95WcqX1bDIPprBvAmlma5xchvyZWhoCH6vm+zGLk792Z7yloaW5JOkb98vbO1HyWwaW7Hu3ljCiDmHEezeskMY3Ph5V0fV/OPI/WtdRYmbZikI34ZT/32uoxEmEy098+AmwwsxfTCEhRzR9NGgi6LLgxckZd1sy6hZy1S84fk+yHWns5Ddkcwi5opTBta/KMNd0XEsw4FwCs6TkZX3tydr09PIyd0V6LbSSG746Z1WY768dO89gMnQS20e7udWs58xqD6HHtrBDoGe9Da3svTpqpX6/3Q6Oh6BnXe8665cy6zZzXUNO/PjPfn0upz0rAoBBYil4VxMKXhQ3yuoGNXS/OOussrrjiikXVsd9++6GUYseOHR3P79ixg3Xr1nV9zbp16+Ysn///jh07WL9+fUeZTZs29WxLGIaEYTjreYvEZjs/QvPf3chH7GxHGdt43go/6/xsQq9vBSvCrFyv0TaykS5sSq+Rw+z4/DG0jHt8+SuMLGQjJ40YWmSdU16lFYXM6AqLboxMtaNFASE8ELNHRbSSHedh0Fg5+4vSigJaqubm6jmpyobys/MIOmKkqmUw8xhaJtntxVnXgcZ5tq5VXnfntcpipFJ23Da0ZF/i2bWIO0eIBM15NrkeWtrG9nIz2xGiRYgQnW3U0qKMxQrVESOvu2OOnSxgrER20VwLMet9lcrOUbv2GFp2vsNz7ds1b17vtmuSxzCy++o2Lf2O9+7M91RWRyF7/814P+XtbdfDikb89nJCwQw9ZpazsoAWatZKw+YIa34eDc3aTUpWV+d5JPl7xoIVdQT5jycFNkUKmtdDA14aYpHUfY/2JAV1z2ckTjuMTs3zsG2/2hMlCUyn4bRCEHk+7TduIs/Hn1GXEYJIzS7nxckMDWjU12pbIgUBs1fCJ0qiZWeXEitvxgKhRiylOs4le87rukI0URItZtTr+QRx0rW3y86r1d5MJ9t1xaeWgnRGmyPlE+i461dm7HXqZAUkMpvnOxMj8rpb1ziRggK2q4Gtz6gbsuvX7ZpYIYhn6JdISShs1+/xrtfb8ynHs/sWK2afp5YSLW3XHzeJkpiZ+iifwHTXJ16kPvEA+qwMJJ7UCCFY3MqO/l87tCvwv//7vxx99NGLqiMIAo455hhuu+225nPGGG677TZOOumkrq856aSTOsoD3Hrrrc3yGzduZN26dR1lJicnueOOO3rWuTeiRedtLGh1gt2YaWx61ZHXYxAd70srsi+EbnYzlXLWvCzLbMOYPT+7jd06+fY2zqwn+wKefZ6WRhtnhO3WPoBUiFlle13DvN0zy5sudeTt7qVP1/OUs+vppY9pM54d59Oj7uz5tl/+Yo5rIkVHW6zIDHe339uplA3jmZWbeWu1vb1ZZyKa74tubbWNsrlJyzqJ7uUyU5KN5KVSdl05q6VolsvK9K4rlZLEzr07TcWOUPV8pgJ/1opFIyRTgU/N96h7HtOhP3tRElDxfaq+R+Qp6p7HVOB3eX9ndUWeIlGSyFNMB37HlAbIRlunA59Eqca5ZuVmts02Vldnq4JFYxWxotpl9WKiFFXfz3RsaFFvnNNMYqWyhUB5u0TjuS5ltZBU/M5ztUJQ9b2u7+Wqp2Zdv0RldczECkHF9ztHqoWg5ntdV3/Wvez6t3+/aZldo5nX2ArBdOB1vK+tyFaKd9M3Voq673V8lrO6vVl1I7L3Q8fKZEHzvTGTVEqqfmc9pnHupssK2qrnkcxY9Zz00kcOpk9tAH3MgPqsDBY6TrcwBh6xm7lQAbLRr5tvvpnTTjut4/h73/vegRu0detWzjnnHI499liOP/54rr76aiqVCueeey4AZ599Nk960pO48sorAfiLv/gLTj31VN7znvdw2mmn8alPfYrvfe97fPCDHwSy22MXXnghV1xxBYccckgz3cmGDRs444wzBm7fnowWAqM6b7v1ejtasl+IgkbHKSVijl8UVkDcKJ+/vmdZsi+d9tqM6DnoiBFgpQQheprFzrpFR1vnK5+fp5WZmeh1a7yzvB2o7ta59K5cC9G8pZul9pj7y2Jm3XOV1iIbPRVko9Km4cO7tSY3TFZmt1/m+8rSs26Rdicb9cxvWM6tuSX7Ms9M5dx1p0I0fi3PE1uIeX8UN8vNUybriH26TYBIgSlGSexYr7vlWT2iywrzLiRK9Zwr1V5Xt859JkZIqv78v/fzjrQfEiVJ1OzUHN2IVdbBC9sYKZ7jWqdSMhUGzTx63dJ5NGm0t+5lUyCyH1BzfNakZDoMkNY0ZlKLOdtS9zwipRqLi7r/CM0xQjIdNOq2zNuWSCliKRt109V05eSmVHi2r/NMlCKREtW4rzRf3dUB6h5EH7vE+uxurF3eUcSBjd0PfvCDrs8fd9xxPPzwwzz88MMA836R9uKVr3wljzzyCJdccgnbt29n06ZN3HLLLc3FD/fddx+yzfGffPLJfPKTn+Rtb3sbb3nLWzjkkEO46aabmjnsAC666CIqlQrnn38+u3bt4pRTTuGWW25Z1hx2TxQG/V2Rm79+1R6k/oW25YlY96Bl+73mS3meC6l/b8NYH4PfWAkPuxjDmFWs6GRbK4C5OvWZzGnoutTbz4+MnLmMzlDq7rP4oHUPVF60fkwtRVtWij67k9QqlFF4PVbzDpuh5bHb05mcnORD77+cwDd4EmgsnqDHRHyguXiindRIUqM45oBfUSr4WcqJHgsbaEzcFqb7ykIac6GErfdsh5VlF8PFcDF2c4xsMoImiH4FPVYrOxyOPY/ppMy3HzqFxPiMBRMLNneJlrzx4uuWJo/d3owUNksjYW1/QxVdykgrEdYnMI9T6JIot4NhbFBhZiepdTFcDBdjd8SwKKogludXu8Ph2P14wseTCcYKppNRRv1JPDn4FmhygJHMvozd7/3e7/GOd7yDE088cc5yU1NTXHvttYyMjPBnf/ZnfTfiiYKxAiP8bJL4Akfs8iXqsVxN3dt/jxmRcDFcDBejjxE7HUGP3WocDseeR2oDQFDyKtR1malknBF/cuCRO9NtVV0P+jJ2L3/5y3nZy17G+Pg4L37xizn22GPZsGEDhUKBxx9/nLvvvptvfetbfPGLX+S0007j3e9+90ANfqKQpyppzXDKExB3o71sa+hON27FIjysDMAKRI9f8Fb4WOEjbI8cdHmaEJ32nNPoYrgYLsbujyGszsqiECsuHYPD4Vg6GrvcCMGIP8V0Msp0MsaIPzWguRuysXvta1/Lq1/9am688UZuuOEGPvjBDzIxMdFs7BFHHMGWLVv4n//5Hw4//PABGvrEQwpQjZ0VLL2HR7PjopG4NCPVgloS4CtDwVd4SqJQeD1kSFFoJKHqftygiJEEykP2ED3CxXAxXIzdHaORzpx9Rwp4ztg5HHsNYVTAV5JAKXwJgVdlIipTTcdYFVbwVX/mbpAFqX3PsQvDkFe/+tW8+tWvBmBiYoJarca+++6L3yXXzJ6INgprB783Dpmpm4pClLR4PfbnczgcDofDseciBYyHFSaiMrui8kDmru8YC33h+Pg469at22tMHWQ3VKtxwKDriNtNXTmIV3K6HYfD4XA4HEtIbu48qdkVlUn0cBMru3sCA+BJjbaZSevX3LWbutEwcqbO4XA4HI69nKU0d87YDUg5iNFGMB3585q7VAumo8CZOofD4XA4HB0slblzeewGQAmDEgmjQcxUXKZSh9Ggu2FLjaQS+1l5v5ptjWQBI8H62Hhnc8Ohzm3WW8jG+rlex0GQb9TTq4zfmNDtYrgYLsbujSGwxEyQ4vLYORx7C1FsMdZgTIru8dkf9SeYiMd4PCoxFkzid1ktq3X/43DO2A2AtQJQKGUZCSOmowJT8QgjYa3D3KVGMh0XUcIwEsZZrqu8DiGxSKwsYhr7J0q6L8gwDXl6HbdkdQkMvdKuzFeHi+FiuBjLEUMg0UimUW47MYdjr0FJD5FnyOhx204IGA+nmIxGmYzHGA+nOsydsTCdjvYdc2Bjd8455/Da176W3/7t3x70pU94DAKEQgjwlKQcaiqRx3Rcbt5qzW6/NhZKhBYhvOaG8EBDXIn0ymgvQJEiu24VDoYQjYdH9+SoBkVCiYAqsscvgYSyi+FiuBi7OYbEYDB4TOLhEhQ7HHsLng4b/b5CzvGbTgpYVchSoUxGrVQoxsJUXEbb/m/TDjzHbmJigs2bN3PIIYfwzne+kwcffHDQKvYYPJXNndMmWyCRaNlcKDESutWvDofD4XA4+mPmnLtYe0xEZVKjGA167H7TrZ5BA9900008+OCDXHDBBdxwww085SlP4fd///f5zGc+Q5Ikg1b3hCc3d6nJTJ0UbqGEw+FwOByOwcnNnRImW1BhvGz0boD8twtaFbtmzRq2bt3K//7v/3LHHXdw8MEH80d/9Eds2LCBN73pTdxzzz0LqdbhcDgcDofDsQgWle5k27Zt3Hrrrdx6660opXjhC1/ID3/4Q4444giuuuqqYbVxRZPnqfOkYTSMMAPmuXM4HA6Hw+GAbKHERFRGW9kYqUsbI3f927WBjV2SJHz2s5/lRS96EQcddBA33ngjF154IQ899BAf/ehH+cpXvsKnP/1pLr/88kGrfsIxM/mwr0xzzt10NPgOFQ6Hw+FwOPZOclOXGsWqsEKg0uacu6m43Hc9A6+KXb9+PcYYzjrrLL773e+yadOmWWWe97znsWrVqkGrXvFILFiNtZbUKCqRjxKakaAGgLWgJIyEmumoSKUuGAlTsiR2NMpIrBWYtIJSkwCYHqkUoIaa47hF4jGNbay464ai5mK4GC7Gbo+RpTtJqWJ7xnI4HHsaaaqw1mKtxtjeOSyNhclolNRKxsNJlMxWxAKMBpOk9bG+Yw5s7K666ipe/vKXUygUepZZtWoV995776BVr3iEsIBGa8F0HKJEymhQzbJStY3O+UIzEmim4zLTUZCVaSymENYikAhTQ+osxUJ7OpR2ZFti0x4tambEWmgdLoaL4WIsXwxDAi5BscOx16BNisU2zF337whrYSIeQ1vFWDCJJ3THHT8BjHhTfccc2Nj90R/90aAv6RtrLZdeeikf+tCH2LVrF7/1W7/FBz7wAQ455JCer3nHO97BZZdd1vHcoYceyk9/+tPm3/V6nb/8y7/kU5/6FFEUsWXLFq699lrWrl07UPu0lWgbUkvyPHUgRNi1rCcFZWGpRD5TyVjbSlkBViGC/UjCLI+d1yOHlm7k0Ap75NCyKOJGDi3Ro7OIG3m6XAwXw8XYfTEkBoEhwLg8dg7HXkRECSkkUnqoLonsjIXJuIyxitVhBV8JulkzpfpPtbGi9op917vexfve9z6uu+467rjjDsrlMlu2bKFer8/5uiOPPJJt27Y1H9/61rc6jr/pTW/i//2//8eNN97If/3Xf/HQQw/x0pe+dEFtrMRBI09dMm9KE09l+ezyPHduzp3D4XA4HA6YPafOV8MZzV8xW4pZa7n66qt529vexkte8hIAPvaxj7F27VpuuukmzjzzzJ6v9TyPdevWdT02MTHBP//zP/PJT36S3/md3wHgX/7lXzj88MP57//+b0488cS+25gahRKW0TAGIXvenOloWyPP3VQUMhWFFH33a93hcDgcjr2ZpTJ1sIJG7O699162b9/O5s2bm8+Nj49zwgkncPvtt8/52nvuuYcNGzbw1Kc+lVe96lXcd999zWN33nknSZJ01HvYYYfx5Cc/ed56ZyKAUjD4jhLtO1RUYrda1uFwOByOvZWlNHWwgkbstm/fDjBr3tvatWubx7pxwgkncP3113PooYeybds2LrvsMp7znOfwox/9iNHRUbZv304QBLNW6c5XbxRFRFFr3s3kZLZKZaE7SuTmbqIekooV46cdDofD4XAsE0tt6mA3jth94hOfYGRkpPlY6HZkv//7v8/LX/5ynvWsZ7Flyxa++MUvsmvXLj796U8vqn1XXnkl4+PjzceBBx64qPqgMecuiLHW7TfmcDgcDsfexHKYOtiNI3ann346J5xwQvPvfHRsx44drF+/vvn8jh07uubK68WqVat4+tOfzi9+8QsA1q1bRxzH7Nq1q2PUbseOHT3n5QFcfPHFbN26tfn35OQkN3zsasBiO/63133V/HnTUUIqg2c1xmgwMRY9Z44sMHMcNwjqWJKeObTAxXAxXIzdHQMMohHHuHQnDsdegzVZ6hJjLVNxGW0U4+E0ntJ9zdNv1jNA4d1m7EZHRxkdHW3+ba1l3bp13HbbbU0jNzk5yR133MEFF1zQd73T09P88pe/bKZlOeaYY/B9n9tuu42XvexlAPzsZz/jvvvu46STTupZTxiGhGFnKhMpLMLECGHpZ8ytWxlhJNJaRPIIgajN+fr5xBFAME+Z+Y67GC6Gi7EcMQQCS8okuqcBdDgcexpRAsYaJqMCFsFYMIESKWbArwFrVd9lV8wcOyEEF154IVdccQWHHHIIGzdu5O1vfzsbNmzgjDPOaJZ7/vOfzx/8wR/whje8AYA3v/nNvPjFL+aggw7ioYce4tJLL0UpxVlnnQVkCzBe+9rXsnXrVvbZZx/GxsZ44xvfyEknnTTQilgAYwVWBliZ/5IHMceXtEXOOm4RWONh/TXEgUKiUT3yWmkCDAqf7gbQIkkJ8Yh6tiOh6GK4GC7Gbo9hkGg8LB4Lm3bicDieeIS2hLY+xvqsLkzhK1iI9RKm/ylcK8bYAVx00UVUKhXOP/98du3axSmnnMItt9zSscvFL3/5S3bu3Nn8+4EHHuCss87i0UcfZc2aNZxyyin893//N2vWrGmWueqqq5BS8rKXvawjQfGg5ON0rTzyome2+aysapbMEQiEACkVyABBiuzRIRh8wEP26AgMCksB0eg0uuNiuBguxu6OkU3JMEi8nrEcDseeh5AKayVjQZVAGbrfy+ujngFWbq4oYyeE4PLLL+fyyy/vWebXv/51x9+f+tSn5q23UChwzTXXcM011yy2iQ6Hw+FwOBx946sUTy3fDzqXd8PhcDgcDodjiZBieZPXOmPncDgcDofDsUQs96YEztg5HA6Hw+FwLBGJ8ZbV3Dlj53A4HA6Hw7FEWCuYiouYZTJ3K2rxxEpHCQM2IVdnvjUqXY8bCdbHxjvxyZIy90pyLBHIOY6DaObQ6lXGb7TCxXAxXIzdG0NgiZkgdQmKHY69hii2eDKmmhZ4vF5iPJhc0NakxvQ/DueM3QBkW4EprLAwT4fQbuvay1ghsUisLGJU1iXIHlntTUOeXsctsi1XXq90DXPX4WK4GC7GcsQQSDSSadQC0x04HI4nHkp6SAGjQYVKUmIyHmMsnEIO+DXwhE13stIxCBAKIfIvdNEzd5VFYFEI9Ixcd4LUeEivjPYCFCmyMXI3O16IxsOj0uO4IqFEQLVnOxLKLoaL4WLs5hgSg8HgMYnXIxmyw+HY8/B0iBCCQEEgq+yKykzFY4yHlYHM3SDGzs2xW0ashUocNEb+HA6Hw+Fw7C34SrMqrJAaxURUXrI5d87YDYBZhCGzFqaiEG3EsiYqdDgcDofDsTJYDnPnjN0AGKuoJ4PfvW43dSNBvOzJCh0Oh8PhcKwMltrcOWM3AFJo4tSjNoC5azd1o2GEUs7UORwOh8OxN7OU5s4ZuwGQwhJ4KbXEp5aoectbC9NR0DR1njN1DofD4XA4WDpz51bFDoDEEnoRymjqcQhWU/R7pEGwgkrkoS2MhFWUNFgL1kqsFZi0glKTAJgeqRSghprjuEXiMY1trLjrhqLmYrgYLsZuj5GtoE+pYnvGcjgcexppqrDWYq3G2Nkr55XUjIWaiWiUXfVSz1Qo1ro8dkuCEBbQlLwUrKGeFBBWU/Q70xdYC9NxCW0FI8E0vjDkGU+EzZKkCFND6izFQmc6lBayLbFpjxY1M2IttA4Xw8VwMZYvhiEBl6DY4dhr0CbFYhvmrvt3hCdSxoJJJuMxJqLRrkmMq2nYd0xn7AYgNRJlQwTg+xKNpZqW0AQUGiN3eUoTbQSlwIAMSdu+8DUCbRVV1pPgI0l75rVKCTB4BFS7Hs9yaBXwqffMoRVTcjFcDBdjt8fIEhhLHkSRdC3ncDj2PGIKWOs1vgF6Z8RQEkb9OpNxiYl4FaNBrWnuaklALQl6vnYmztgNQF0XiEzrFzpkKVDiKKQSB0hh0UZiybYfm44UMy+xJbtNe9cD60BkdYkeYmdJkGl0Ct2OCywSrCHVEiEsnjQYK9BGoqRByOHEEJieIxIGCRa0yc4tT+eSt0lJQCw+Rj/nYY1tnrsUltTIVpvEcGI4PZweM2P0p4dA2P0JVGbsEu0hhMWX6YK2GFoIqVGkRuJJgxK6sTm5wFfpsq3WN1Z0nLu2rTZ5cnlGM62l49zB6eH0WBo9tJVMxiWEsHOM/LewVhBpn0pSwJMabSXGisY3UX84YzcIVmQdYkNgi8AThkRnFz7Pc+cJjcy+y2cJaW228ZAnNUJKBHaOTsX2sbsFCDSe0EzHAYnOFnWEXkrBTzGoocXo3XE1ztMaKnHQTAmjpKHkJyD62aWjvxjznofS1BOPKPWaR0aCGKkYXgynh9OjS4y5zgPAt5Z6LKgk2S0VX2pG/PqydVoAgUqpJQHVNCDfxXYsqC57bk1PaCbjEpHO3hclL541pWWpCWzKVFx0euD0aLZhCfRITfbDT2Ib07nmQQDGkFqPxORbGmqU7L8NztgNiMB2vNEE4EtDbLIOQwiLUp3HZ9cBSlqktI1OpbvYWadCT6duG2UEFiEtsTHNjqscJAgBhiHG6NlxtY6Xg4SpKGy2wVd26DHmO49ykBA3vqB8ZQj9/NXDizHveTg9nB5t5wHZd4cX1JmMygCMBnUCtfzz7VRQo66z2zqBSij6y39r2JeayCTE2gdgJKgNvHfmMBgN6uxyejg92lgKPYTIHv1eU6ksRtvmYJEvdY9vlx6vH7yJjnasgaTN1FkrSPTyX9Za4pFoha+ykYOpKKTHPM0lI9WC6ShACdO4FR2Q6uX9dsjzBgosvtIkWg2Ud3BYOD0ynB4tUi2YjEp4IhtBnIhKTaO5XBgLE1EZgSVQWUeej5AsJ5UkJNY+gUoQ2CXdXqkXiVZMOD0Ap0fOStEj1gpjJVIYwBIbHzPAoOGKMnaf+9zneMELXsC+++6LEIK77rqrr9fdeOONHHbYYRQKBZ75zGfyxS9+seO4tZZLLrmE9evXUywW2bx5M/fcc8+i25ubOkvmqAOVzSMyy2zuaolHLfEp+gmjYcxoGKGNWNbOK9VZPCUtY4WIsUKEklkHulxmYmYy6NEwpugnjbyDy2cmnB4ZTo8Wucn2pGFVYZpVhWk8qdkVlZet88o7rdQoVoUVVoVVyn6dSlJY1s6rkoRUkgJlv86qsLose2fOJNGKXVEZT2qnh9MDWDl6xFqhrUIJTahSfJktv0zx+65jRRm7SqXCKaecwt///d/3/ZrvfOc7nHXWWbz2ta/lBz/4AWeccQZnnHEGP/rRj5pl3vWud/G+972P6667jjvuuINyucyWLVuo1+sLaGV288UYSBoLJTypkTJ73le6ae6yDtR2PGx7NRjsPP/NVQYM9URQTxQFP6bgJ1gsShlGGp3XdORh7OJiZOkZepdJtWUq8lHSMBLWQVgQlpGwjpKGqcgn1XPHny/GfNfKWMt05GXbtoURSmXlC35CwY+pJ4p6IhYVw+nh9FioHtORh5KGsXA6m0QtLGPhNKrRecVaznuui/lPW8uuRqc1Hk7jqSwFQ8mvU/JrTCcFppNgSdtgsUwnAdNJgZJfo+TXsVg8lTIeTpOarHPX8+ix2P9iLdkVlRv5w5weTo+l1aP916Od59Fu6nyV3X5VMltAMgjC9kqsshv59a9/zcaNG/nBD37Apk2b5iz7yle+kkqlwuc///nmcyeeeCKbNm3iuuuuw1rLhg0b+Mu//Eve/OY3AzAxMcHatWu5/vrrOfPMM/tq0+TkJP941T+ghEYIS2J8LOCLtGHqOkmMwliFFBq/bWWRtdn6udWFSbwBJkN2o5aE1NKQohdR9KNZx1OjmIpLKGEYDSpLMgl1vhjWwlRcRlvJaFBdklVW/cSY71oNA6dH/zH2Vj08kdBMakl2rSbiMbRVjAVTA3+B90M/MapJkWpapOTVKPm1obehnxiJ8ZiMR1FCd83jNQzmi+H0aOH0aLEYPVLj8Wh9NRIz5+KJxPgtUydnz+tLtOLt73gnExMTjI2NzRnzCb944vbbb2fr1q0dz23ZsoWbbroJgHvvvZft27ezefPm5vHx8XFOOOEEbr/99p7GLooioqjVGUxONnaJsAJjPSyiMVKXJUuYia8sibYYq0iMxG9MArWIxuraENMYL50r1UKv47XEo659CoGm4INl9lCxklAWmukoYCoZYySMZn9w5ojRXqbb8VRLpuMQKS3lMAUR0u1tWy4YpiOPqXiUkTDqusKoV4z52pklgw5JrWQkjFHKw3Z5WxdCsNJSS4pY6XXdMWS+a+H0mL+dTo8Ws/UodF1ksapQZSIaYSoeZzycbn5XDANjYTIewVjFqnAaX0G3r/2RMEFISSUpI6SkPGSzXUlCqrpIOahR9pOubQglrBLZtZhMVjEeTg91An+iFVPxCJ7UjIdVpOje/Tk9MpweLRajh7QKECBkdvekC8mMkbpuN1PVAOlWVtSt2IWwfft21q5d2/Hc2rVr2b59e/N4/lyvMt248sorGR8fbz4OPPBALKBRDVNn2kxd94enbNucu0zc5udCQJaTfu7/upWpJz71JKDgawq+nbMeTwlGwwRtJNNRAWx/MdqPgZpVRmvJdGMO10iYIkXvc5BCMBKmKGmZjkK0ln3FmK+NWMF0VEAbyWiY4Km5r2PBtxR8TT0JqCf+QNfB6eH0WIweQshG2dl1KCFYFVbwpGYiGiHV3rzn3c9/1gomoxF0Y85QoMyc5Uf8mBG/no1OJIWhtEEgqCYFqkmREb/OiB/PWTZQhlVhBW0Uk9EItoseC/kv1R4TUWYiVoUV1ByfD6dH6z+nR+u/herR/muxm1NoN3WB0l3LiKZn6I/dZuw+8YlPMDIy0nx885vf3F1N6crFF1/MxMRE83H//fdno20IfKlRXW6/dsMf8oKK9ongvfapnYmn7FAnjLdPzB/tMsrRDSFgNBzeBP6ZE/M91d9JFf10qBP4nR4ZTo8WC9FDChhvdF7DmDA+cyJ4v6McZT8a6oTx9on5/Y5yDHtj9PaJ+eNhpa9RJ6dHC6dHi2HrEc8wdcNitxm7008/nbvuuqv5OPbYYxdUz7p169ixY0fHczt27GDdunXN4/lzvcp0IwxDxsbGOh4Aiiz58CB0mDuz8Eu+kE4rZ1id10I6rZxhmYmFmoicYZkJp0eG06PFYvQYVue10E4rZ1id10JMRM6wzMRCTESO06OF06PFsPRYKlMHu9HYjY6OcvDBBzcfxWJxQfWcdNJJ3HbbbR3P3XrrrZx00kkAbNy4kXXr1nWUmZyc5I477miW6Zcs0emCmtk0d9YKtB28A11Mp5Wz2M5rMZ1WzmLNxGJNRM5izYTTI8Pp0WIYeiy281psp5Wz2M5rMSYiZ7FmYjEmIsfp0cLp0WKxeiylqYMVNsfuscce46677uLuu+8G4Gc/+xl33XVXx1y4s88+m4svvrj591/8xV9wyy238J73vIef/vSnvOMd7+B73/seb3jDGwAQQnDhhRdyxRVX8B//8R/88Ic/5Oyzz2bDhg2cccYZA7VvsfNGfWWaSYzrA3Rew+i0chbaeQ2j08pZqJkYlonIWaiZcHpkOD1aDFOPhXZew+q0chbaeQ3DROQs1EwMw0TkOD1aOD1aLFSPpTZ1sMKM3X/8x39w9NFHc9pppwFw5plncvTRR3Pdddc1y9x3331s27at+ffJJ5/MJz/5ST74wQ9y1FFH8ZnPfIabbrqJZzzjGc0yF110EW984xs5//zzOe6445ienuaWW26hUCgs38k18GVm7qLUo57M/4asJ2ponVZOe+c1Hfnzdl7NHQyG0GnltJuJfnZEsBamI39oJiKn3Uw4PZwesDL0GLTzGnanlTNo5zVME5EzqJkYponIcXq0cHq0GFSP5TB1sELz2K1E8jx2UmjUIuywtWCQFL06sQ4oeHVKPTZbriYB9bRA0at33ZC5XbhenxU7x/HUSKbiMkpoRoNq1w4pMZLpecrMFWO+41nOsxLaKkaCCn6X3H7tZUaDStf8f/22oVeZWhJQSwtOD6dHk6XQQ9GZx24+OnNsTXbkxBykzGKpJgWqaYmSV6Xkd0/s3k+ZxZAYxWQ8NmdetX7KLAanRwunR4u5rnVqFI/WV6ONxJDnqRv8R6g2grdeeuXekcdu+RFkX8z5O7TXl3T7O7gtIakQYKHga6RMqCdFEHLWRsO1xKeeho2M+RpLt18krRh2nnZ0O64UjIQR01GBqXiEkbDW8cFLjWQ6LqKEYSSMQageUXrHmPe4gJEwZjoqMN1oQ7tRyPKiFdFWNnZOEHNei/na0KtMIdBY4fRwerQYrh6iESMdaEqHEDAeTjEZjTIZjzEeTnV0TFkerlG0VY1jht42duGUgwghBJWkhBBiVueVdWxFyn6Nkh8tSRsCZRgPp5hoXIuxcKpj9CczEaN4QjeODb8NTo8WTo8Wc+khhFy0qYPBWu2M3aAI0cxLY7NsOD2Lto63JBG2kaRYKAqBAKGpJwWEUM1bSbXEo576FIKUgi8QPWTKUp0qBLpnOwwqW/TRI8GqpyTlUFOJPKbjcvNWUnZ7KZszVA4tQngLjmGRWASSHr+UhKBcsFQimI5KzVt7ebJbbQXlMMVTKssLtIAY/Vwrp0cDp0eTYekBjcVXJMgBRuwgu+2UJWktMxmNNW8lGZvv8KFYHVYayVWXbl/NkSBBCNnovFpJWitJSDUtMBLUG8lul64NoYLVYZVdUZmpeKx5ay/RismojN9Mdrt0bXB6tHB6tOilRz0tNEydadx+XaCxHOBlztjtZop+1unUktYGv/mcoYJvGrnzlpZ8TtFUFDIVhRT9pDlnKB+JWGpEc6QoYCoKGQljaklrDpdczP3vAXB6ZDg9WqwEPfI5RRNRmV1RmfGwSiUJhz5naD5a5qE1P3nYc7jmI5/jtSsqMxGVKfsRE1FpqHO45sPp0cLp0aKbHtU0RAqzJNs39sIZuxVAayTCb/ydTQS3y7i2Je+8JqNCtrpPGEbDCMRcY5LDJZ/AP1nPOlCAsbCOp+wcGzoNH6dHhtOjxUrQI++8dtVH2BWVAVg95C2W+mFm57WcJiInNxOPRyPsijw8sXwmIsfp0cLp0WKmHiUvYjpZ3oWaK2pVrMPhcDgcDodj4ThjtwLozMM1vO2VBiHPw+XJbCTC2OFsrzQIeV40YxspNKQZynZXg+L0yHB6tFgJeuQpG7SV2e0lmQ5le6VB6UyhMbztlQYhT6HhyzTby9TKoWx3NQhOjxZOjxYz9aimIdour9Vyxm43U5uRh6szSevyvCFnJlf1lWnL4xUsS+eV5UULmnO48jYMay/TfnF6ZDg9WqwEPWbm4QpUOtS9M/tlZl60Ye+d2Q8z86IFKh3qXqb94PRo4fRo0U2PkhdhrCQ1y2cw3Ry7QbE2ezRXvPZOo9CafdOW7iSf7G019VhQTxQFv07BS5odRMFLsVZTj0Oweo7EqwJB2qi3V6IFPefx1CgqkY8SmpGg1jxFJWEk1ExHRSp1wUiYZkt6FxCDxtrJXsetFVQiD21hJKyipGlei5EgzdoQKUbCZI4JqHPH6Oda1RPp9MDp0c7w9MivhsYMOCvPWJiMRkmtZDycREnd7CxHg0kmo1Eej0qzUj0Mm2rDLJT9KkWv3mxD0atirWE6LmKtWZKcaTmJUdnEfJEyGkwB2fVRUjMWaiaiUXbVS7NSbwwTp0cLp0eLXnoUvDoyKqKtItZqwelOGGArUmfsBma2WZu7XCfCWiyZoYu1T8GrUfLiWcVLngZrslQPVi9ZAtbpOESJNEuuOqNSX2hGAs10XGY6CpYsIe50XEJbwUgwjS9MRxsEMBpMMxWXmI7CJU2IW0+dHk6PFkujh53D7HZ5re1MruoJ3TFCKICxYJKJeIyJaHSJE7AWKXlZpzVzlLLo1bDWUklKWGuXMCHuKEpoxoLJzCq3tcMTKWPBJJONa7EcCXGdHk4PmFsPaw1KGjCgrQLDgszdID8HnbEbFCGbvUD/eew60UYS6QLFQFP0FdB9yLwYSpCWWlIC6XcZmWjl6eolu23k6WLGOsZUC6biPA8XCNG9DZ4UlIWlEvlMJWNdt0zqFaOFbFyLzg9UtoNBnhdN46mg63kIoFyASiSYikd7bGHVPUZ7Lb2uVS3xqGmfgtPD6dFg2Hrk/xKNDH/9kCVXLWOaebgEvb6yVzfyeE3F40NP71BJQmo6z4uW9mzDaJgiZUQlKSOlGurKzEQrpuL2vGjd26AkKNHIq5aMD3VlptOjhdOjxXx6ZHksBb4ypG3mbtAtxQYxxW6O3TKTGIm1gtBLKfjzC1vw9dAnjLfPGRoJk3nfMJ7K8nUNujH6XLRvID8SxvPuNZrlVUuGPserfWK+08PpAStDj0H3tlzoxujzMeheo0sxx2vQvUYXulH9XDg9Wjg9WgyqR6A0Sujmbdmlwhm7AVjs+zHRmakTwlIYYMPyzgnji+u8Zk4E7/dXQPvG6IvtvNpNxCAbyLdvVD8MM9G52tLp4fRYGXosdMPyYXdeC91AfphmYqEbyA/TTDg9Wjg9WixUj+Uwd87YDYBFYBeYmTXREtMwdarHJOu5GEbntdBOK2cYnddCTUTOsMzEQk1EjtMjw+nRYhh6LLTTyhlW57XQTitnGGZioSYiZxhmwunRwunRYrF6LLW5c8ZuQDQKM6C5y02dFLaxCfHCWEzntdhOK2cxnddiTUTOYs3EYk1EjtMjw+nRYjF6LLbTylls57XYTitnMWZisSYiZzFmwunRwunRYlh6LKW5c8ZuAPL0JolRaNPfO7vD1KnFb8S0kM5rWJ1WzkI6r2GZiJyFmolhmYgcp0eG06PFQvQYVqeVs9DOa1idVs5CzMSwTETOQsyE06OF06PFsPVYKnPnVsUOgAAkGoMiNRIPjZK935Vph6nL82U1l9RCM6NVrxV7lm7pEQp+gsVST3xAU/R7Z8ZKtWU6CvCkYSSMQHRG6xWjvURGZwylLCNhxHQUMh15jIQxssctZmMt05GPMYKRMEKpme3tHmNGLbOvlYCRsM50FDIV+YyEMX5Pg2KpJ14jL1pMwU+7xHJ6zBVjRi1Oj462za+HEN3ryjqtEbRRjIfTeEovej4vZGZ7LJxmIhppbIw+976ZrU6rRsmPhtIGgJJfx2KZTopY7JwdYqKzzj7LgzaNEL3UHwxPpYzPuBa9DIrTo4XTo8WC9Wj7ZdftNb7SoGmYO3q3YYCTdsZuQKSwKJGQGJ/USIRNkV3MXWJUw9TpLG9Oo4iwIjN3NkLOc093rh9GJRUhTEgtDhEGil0+nKlRVOIATySM+hUa/UrfMeYr4wsYDWKm4hKVumQ0qM1O9WChEpcx1jAaVPGE7pqFY7529DougFE/YiouU4kUo0HUNWluLQmpp4KiV6OooqG2AZwe7c87PTJm61HBirTD2LXycAnGggmUSAee6jEfY/4uJuIxdkUlxoKprjm0qkmRahpS8ioUVW3obSiqFGsMlbiINYaSX5tVJjEek3EJJRLG/EmwDHXXgiwfoWYyzpLmdsur5vRo4fRosRg9smtmySbod7+AfnueOw2+TGaV0bb/ET1n7AbECoGUAg9DYhSJ9fFs58hdolXbSJ2l/Y53ZuoEiBDTeFr0yDdmG6/rdbwQgpWWWlLESq/jVlaqs+SqUlrKYQoi7PqWmi9GXqbXcSWhLDTTUcBUMsZI262sLNltSGolI2GMUh62x1turhj9tLNcMExHHlPxKCNhhNd227uWeNQbedEKPtgeedHmi+H06L+dTo+MTj3GWRXuai6eyvJwjWCsYlU4ja9gqb6SVxWqTEQjTMXjs0YmKklIVRcpBzXKfrJkbRgJE4SUVJIyQsqOkaIsL9pI43Zf77xoiyWUsEpk12IyWdUxUuT0cHoshR7SZnnsshy4vZ1x+8gdpnPkThuBHiDuippj97nPfY4XvOAF7LvvvgghuOuuu+Z9zfXXX48QouNRKBQ6ylhrueSSS1i/fj3FYpHNmzdzzz33LLCVAhBImblsQfbL35js+XZTl81bEh0P0V4NEjHPf3OVAUnBtxR8TT0JqCd+lhJWS6abebhSpFhcDFBzlvGUYDRM0EYyHRUy42oF01EBbSSjYYKn5o4/X4z5rpUU2bZOSlqmoxCts/L1xKeeBBR8TcG3i4rh9HB6LFSPkTBFG8lkNIK1AmsFk43bS9nelmbec13Mf0oIVjXmFE1EI6TaQyCyjPlJkRG/zogfL2kbBIIRP2bEr2cjIEkBgSDVHhNRZiJWhRXUPHos9r9AmWyjeqOcHk4PBEurR/swpJjn0T7nLtEKQWbqEjOYmVxRI3aVSoVTTjmFV7ziFZx33nl9v25sbIyf/exnzb/FjPHcd73rXbzvfe/jox/9KBs3buTtb387W7Zs4e67755lAgdBSPDR2cidUdl2YW0LJZrz6ZaYop8isNSS7PZwqmVzIrgVy7PxcD5hfCoKmaxnIzDGZhPzlRLLci3yCfxTUchUFOIpQ6JVM9ltaweApcXpkeH0aJEnMa5Ekl31EQC0lUPPgj8X+YTxiajMrqiMr1Ji7Q9tIni/5LEqSSH77tTe0Cbm90s+gX9XVHZ6OD2AlaNHoDRxY+Qu0iJLkwYoZt+e7cWKMnZ/9Ed/BMCvf/3rgV4nhGDdunVdj1lrufrqq3nb297GS17yEgA+9rGPsXbtWm666SbOPPPMRbU5N3exUc3kw8NY/TooRT8lNbK5umc0rA9tomu/5J3XVBQ22pCttlwugwstM/F4rUiiFb7KNolfzjaA0yPH6dHCU5axsMpkVAZY1k4rJ++8dtbGibVPoJJl7bRyyn5EYrL9soFlNRE5vspuM+5yejg9GqwUPQKlG6Yuu6kayHig76oVdSt2oUxPT3PQQQdx4IEH8pKXvIQf//jHzWP33nsv27dvZ/Pmzc3nxsfHOeGEE7j99tt71hlFEZOTkx2PXqS2bQ6dFUOf3NkPqRakutWOerr8nt1aqCV+8+9a4g9le6VBaT/3VMuhbXc1CE6PFk6PDGuzzcJzKkk41Mno/VJLW3MaE+0NbXulQUh0NjLUrU3LhbF0pPxwejg9YGXokZpspK759wALJ2APMHaHHnooH/nIR7j55pv5+Mc/jjGGk08+mQceeACA7du3A7B27dqO161du7Z5rBtXXnkl4+PjzceBBx7YtVx7nrpAagQ08twN5/z6oT0P1+pibeh7Z/ZDe160sbDOWFgf6t6Z/dKeF211sTb0vUz7wenRwumRYS1MR9mt4NXhNKvD6aHundkv7Xm49itODH3vzH5oz4u2X3Fi6HuZ9kN7XjSnh9MjZyXokTbm1AkglHFrzp3pvw27zdh94hOfYGRkpPn45je/uaB6TjrpJM4++2w2bdrEqaeeyuc+9znWrFnDP/3TPy2qfRdffDETExPNx/333z+rTNq4/53PqRMS/Ia5yxZULKoJfZFqwXQUdCRXbU/SWk+W/g2Zd1rtyW7bk7ROR8szUlRPVEey2/akudNRsCxmwunRwumR0dJDMh5W8ZVeko3R52NmctWl2hh9Lrolu12KjernoluyW6eH02Ml6NFu6gKZIGVrQcUgc5J32xy7008/nRNOOKH595Oe9KSh1Ov7PkcffTS/+MUvAJpz73bs2MH69eub5Xbs2MGmTZt61hOGIWHY5U1tDVhITD5Sp7McNHmeOpGtlk2MR2oEWI1qt88WQIKJmSuFQs5cXV9qJJXYR4mEUb/akYcryw0WUIsLYDQlP15QjPmOZ3nRSmhrGQ2m8YRp5iTzmnm8ylTqMBr0zuq/mDYA1JKAeupT9KoUVdxsQyuvWolKpBgJ4p7bui0mZxk4PdpxemTkemR5Ayt4IkI3eikpUkaDlMl4jMd75PEaFtWk0MzDVVD1jjsKo/4EE/EYj0clxoLJLO/mEpAY1ciLljLqT2It6IYeBZVijGY6LmGMpuTXl6QNrbxokrFgAil081o4PZweS6GHMY0vHjtXonPQRpLY3NTF2RzgRnFfppgBwu42Yzc6Osro6OjQ69Va88Mf/pAXvvCFAGzcuJF169Zx2223NY3c5OQkd9xxBxdccMECIoiGqZNIYbp2SkKAJzWpUaTWB5s2c1fZfMa2UNnKC5hDbNHzeGok03ERJQwjYQxCzSpVCDRWJNSTIghJ0e+2qqZ3jPbj3cpkedGKaCsZCesoKWb9qlCKRgb+AlPxCCPh7KS5c8Xop521xKeeho0dDLr8shEwEsZMRwWmG23wupqJ/q6F02Pudjo9Gn936BGhpKQj5REQKMN4OMVENMpkPMZYODX0SetZp1VsZsyfaUWFgPFwislGG8bDqaGbicxEjOIJ3TjH2SdZDiKEEFSSEkKIoZuJLC/aKNqqxjkaZl4Lp0cLp0fGYvUQIvvcz5XDTtvZpm4mvug/5opaFfvYY49x33338dBDDwE0U5isW7euOfJ29tln86QnPYkrr7wSgMsvv5wTTzyRgw8+mF27dvHud7+b3/zmN7zuda8DshWzF154IVdccQWHHHJIM93Jhg0bOOOMMwZuY9pINpjlqYNed7OlAA9LagSp8RBSIxv5CS0i62iEJEs80TsBq0Ug6RQ0u72UzRkqhxYhPESPjqcQCBCaelJACDVrP85eMVrHM3Mg0B0x8mS32grKYYqn1Iwuq4WnJOVQU4k8puPyrP04e8Vox6C6Xqta4lFPfQpBSsEXiF5vaSEoFyyVCKajUtf9UXvFaLXT6ZHj9MiP96eHUnmuuwQ5oy2hgtWN1YBT8dhQVyRWkpBqWmAkqDeSq3a/nSNFnqS1zGQ0NtQViYlWTEZl/Gay2963lEaCBCFkw0zIoa1INBam4jLaKlaHlUay2+7tcHq0cHpkLEYPIXKPILoattm3Xxd4G6W9vf0XXXr+4z/+g6OPPprTTjsNgDPPPJOjjz6a6667rlnmvvvuY9u2bc2/H3/8cc477zwOP/xwXvjCFzI5Ocl3vvMdjjjiiGaZiy66iDe+8Y2cf/75HHfccUxPT3PLLbcMnMMu+zpuzambDyGzkbt8QcUw5ty1TwTP9p+c/zVFXw91wvhCNpBfyMbo89G5gfz8HzLRGCka5gR+p0cLp0fGQvRYijlFg25YvhRzihaygfyw53gtZAN5p0cLp0fGUujRbU7dMBDW7o4ECE88Jicnueqq9wAQeplDs4ieIwHtx63JjJ2FbG9OIVhdqiHlYCN27Z3WaBiBGGxkpbPjTbvG6HYO7TG6dVqDjKzMPIfszvTgI0Qzz2WQ88Darh3voCNETg+nR796AAgsPtVZI3btLKTj7cagnVY7C+l4u7HYc1nMOeQs9lycHi2cHhmDnkNiFI9Ux5DCIttuxw5q6rSBt1xyJRMTE4yNjc1ZdkWN2K10BLbHXKB5Xte+WtaqBY2OdOuAB6V9NeBCRiYWMhIxk2GMFHXrgAehfXXmQkeKnB4tnB4Zw9BjGCMTi+2AhzEyMYwOeLEjRcMwRE6PFk6PjGHosVQjdc02Dre6PZvF3CjKzR1kEyVT039tw+i0chbaeQ2j08pZjJlYrInIWYyZcHq0cHpkDFOPxXRewxhVgcV1XsMaVYGFm4lhjXKB06Mdp0fGYvRYalMHztgtK605d5Zq3F8er2F2Wjmdndf8b8gsD1cwlE4rpzOvWtCXmajNyIu2WBZiJpweLZweGUuhx0I6r2F1WjkL6byGaSJyBjUTwzQROU6PFk6PjIXosRymDlbYqtiVTD4V0VrQebKfOebKzHVcYMAapuqSsp+getze1cZSSTyU0BRUnbTr52G+DrX7h8hr5N+rRT5aawpejzlEFqqJh7aWsl/HWkMyK+R8H1Tds1TRM1SSkKmaoNRIYtuNeiqJU0ngRXgi6dmGuVvS/VoVVJWqKTg9cHrkDFuP5nzbxqy8/kgpetNMJyM8Wisz6k/31KOWhtTSAkWvhiciosX77CZFNc2UGeHRepkRf7pnqofEKKaTMkpoimqaZIgZOjxRJ5CWyahIoi1Fr3vHbC1MJSNoqxjxpzFWD/FaOD1ynB4t5tMjNZl/SA3ohuVSJA0v0X8c3RiH62dZhFs80ScPPPBAz23FHA6Hw+FwOJaa+++/nwMOOGDOMs7Y9YkxhoceeojR0VHEUqW/XiCTk5MceOCB3H///fOulnE8MXCa7lk4Pfc8nKZ7HitZU2stU1NTbNiwATnPPVx3K7ZPpJTzuuTdzdjY2Ip7MzoWh9N0z8LpuefhNN3zWKmajo+P91XOLZ5wOBwOh8Ph2ENwxs7hcDgcDodjD8EZuz2AMAy59NJLCcPFb/fiWBk4TfcsnJ57Hk7TPY89RVO3eMLhcDgcDodjD8GN2DkcDofD4XDsIThj53A4/v/27j8m6vqPA/jz5H6AnoKR3XXZASvJaZCJQhd/2IJFk4m2pq4h5CqVgiVbc9RKaesHpm4SLv0rdWPMK5o/WjkdoRIqRoeREsgc6fVDD3TFaROCuFd/9PXz5Q4OGN7B8fH52G5w78/r/Xm/P3uO+fJz3EFERCrBxo6IiIhIJdjYEREREakEG7sJIC8vDxqNBqWlpcPWfvLJJ4iNjUV4eDhSUlJQX1/vdby7uxv5+fmIjo6G0WjE888/j/b29iDtnG7r7e1FUVEREhISMGXKFFgsFuTm5uLKlSvDzmWmoWm4XHxVVlZi9uzZCA8PR0JCAg4fPux1XESwadMm3H///YiIiEB6ejouXrwYzEug/ykpKcHChQsxdepU3HfffVi2bBlaW1uHncdMJ4bNmzdDo9GgsLBwyDrV5CkU0vbv3y+PPfaYWCwW2b59+5C1drtd9Hq97N69W3766SdZs2aNREVFSXt7u1KTl5cnDz74oFRXV4vD4ZAnnnhCnnzyySBfBXV2dkp6erp89tlncuHCBamrq5Pk5GRJSkoach4zDU0jyaW/U6dOSVhYmGzZskWam5vlnXfeEZ1OJ+fPn1dqNm/eLJGRkXLw4EH58ccfJSsrS+Li4qSrq2usLuuulZGRIXv27JGmpiZpbGyUxYsXi9Vqlb/++svvHGY6MdTX10tsbKwkJibK+vXr/dapKU82diHst99+kwceeECampokJiZm2MYuOTlZ8vPzled9fX1isVikpKRERP5rLnQ6nVRWVio1LS0tAkDq6uqCcg3kX319vQAQp9Ppt4aZhqbhcvG1YsUKyczM9BpLSUmRdevWiYiIx+MRs9ksW7duVY53dnaKwWCQffv2BeEKaCgdHR0CQGpqavzWMNPQd/PmTZk1a5ZUVVXJokWLhmzs1JQnX4oNUR6PBzk5OdiwYQPmzp07bH1PTw8aGhqQnp6ujE2aNAnp6emoq6sDADQ0NKC3t9erZvbs2bBarUoNjR232w2NRoOoqKhBjzPT0DSSXHzV1dV51QNARkaGUn/p0iW4XC6vmsjISKSkpDDHceB2uwEA99xzj98aZhr68vPzkZmZOSCnwagpT+14b4AG99FHH0Gr1eL1118fUf3169fR19cHk8nkNW4ymXDhwgUAgMvlgl6vH9BImEwmuFyugOybRqa7uxtFRUV44YUX/P6xaWYamkaSiy+XyzVo/e2Mbn8dqobGhsfjQWFhIVJTU/Hoo4/6rWOmoc1ut+Ps2bP4/vvvR1Svpjx5xy4EVFRUwGg0Ko+amhp8/PHH2Lt3LzQazXhvj0bBN9Pa2lrlWG9vL1asWAERwa5du8Zxl0TkKz8/H01NTbDb7eO9FRqlX3/9FevXr0dFRQXCw8PHeztjjo1dCMjKykJjY6PyOH36NDo6OmC1WqHVaqHVauF0OvHGG28gNjZ20HPce++9CAsLG/BuyPb2dpjNZgCA2WxGT08POjs7/dZQYPhmumDBAgD/b+qcTieqqqr83q0DmGmoGkkuvsxm87A53h4b6Tkp8AoKCvDVV1/h+PHjmDlz5pC1zDR0NTQ0oKOjA/Pnz1f+Da2pqUFZWRm0Wi36+voGzFFTnmzsQsDUqVPx8MMPK4+1a9fi3LlzXo2BxWLBhg0bcPTo0UHPodfrkZSUhOrqamXM4/GguroaNpsNAJCUlASdTudV09rail9++UWpocDwzTQiIkJp6i5evIhvvvkG0dHRQ56DmYamkeTiy2azedUDQFVVlVIfFxcHs9nsVXPjxg189913zHEMiAgKCgpw4MABHDt2DHFxccPOYaahKy0tDefPnx/wn+vs7Gw0NjYiLCxswBxV5Tne796gkRnsXbFPP/207NixQ3lut9vFYDDI3r17pbm5WdauXStRUVHicrmUmry8PLFarXLs2DFxOBxis9nEZrON1WXctXp6eiQrK0tmzpwpjY2NcvXqVeXx999/K3XMdGIYLpecnBx58803lfpTp06JVquVbdu2SUtLixQXFw/6UQpRUVFy6NAhOXfunCxdujQkP0pBjV599VWJjIyUEydOeP1s3rp1S6lhphOb77ti1ZwnG7sJYrDGLiYmRoqLi73GduzYIVarVfR6vSQnJ8uZM2e8jnd1dclrr70m06dPl8mTJ8tzzz0nV69eDfLu6dKlSwJg0Mfx48eVOmY6cQyVy6JFi+TFF1/0qv/8888lPj5e9Hq9zJ07V77++muv4x6PRzZu3Cgmk0kMBoOkpaVJa2vrWFzKXc/fz+aePXuUGmY6sfk2dmrOUyMiMj73ComIiIgokPg7dkREREQqwcaOiIiISCXY2BERERGpBBs7IiIiIpVgY0dERESkEmzsiIiIiFSCjR0RERGRSrCxIyIKkE8//RTPPPNM0Nc5cuQI5s2bB4/HE/S1iGhiYWNHRBQA3d3d2LhxI4qLi4O+1rPPPgudToeKioqgr0VEEwsbOyKiAPjiiy8wbdo0pKamjsl6q1evRllZ2ZisRUQTBxs7IqJ+rl27BrPZjA8//FAZO336NPR6Paqrq/3Os9vtWLJkidfYU089hcLCQq+xZcuWYfXq1crz2NhYvP/++8jNzYXRaERMTAy+/PJLXLt2DUuXLoXRaERiYiIcDofXeZYsWQKHw4G2trbRXywRqQ4bOyKifmbMmIHdu3fj3XffhcPhwM2bN5GTk4OCggKkpaX5nXfy5EksWLBgVGtu374dqamp+OGHH5CZmYmcnBzk5uZi1apVOHv2LB566CHk5uai/5/2tlqtMJlMqK2tHdWaRKRObOyIiHwsXrwYa9asQXZ2NvLy8jBlyhSUlJT4re/s7ITb7YbFYhn1euvWrcOsWbOwadMm3LhxAwsXLsTy5csRHx+PoqIitLS0oL293WuexWKB0+kc1ZpEpE5s7IiIBrFt2zb8888/qKysREVFBQwGg9/arq4uAEB4ePio1kpMTFS+N5lMAICEhIQBYx0dHV7zIiIicOvWrVGtSUTqxMaOiGgQbW1tuHLlCjweDy5fvjxkbXR0NDQaDf78889hz9vX1zdgTKfTKd9rNBq/Y74fb/LHH39gxowZw65JRHcPNnZERD56enqwatUqrFy5Eu+99x5eeeWVAXfL+tPr9ZgzZw6am5sHHPN9+fTnn38OyB67u7vR1taGxx9/PCDnIyJ1YGNHROTj7bffhtvtRllZGYqKihAfH4+XXnppyDkZGRk4efLkgPFDhw5h//79aGtrwwcffIDm5mY4nU78/vvvd7THM2fOwGAwwGaz3dF5iEhd2NgREfVz4sQJlJaWory8HNOmTcOkSZNQXl6O2tpa7Nq1y++8l19+GYcPH4bb7fYaz8zMxJYtWzBnzhx8++232LlzJ+rr61FeXn5H+9y3bx+ys7MxefLkOzoPEamLRvq/f56IiEZt+fLlmD9/Pt566y0A/32O3bx581BaWhrQda5fv45HHnkEDocDcXFxAT03EU1svGNHRBQgW7duhdFoDPo6ly9fxs6dO9nUEdEAvGNHRBQkwbpjR0TkDxs7IiIiIpXgS7FEREREKsHGjoiIiEgl2NgRERERqQQbOyIiIiKVYGNHREREpBJs7IiIiIhUgo0dERERkUqwsSMiIiJSCTZ2RERERCrxLy17cBDxNHcKAAAAAElFTkSuQmCC",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = [0.198, 0.066, 0.066, 0.345, 0.97]\n",
    "\n",
    "simsetup = makecells(params)\n",
    "\n",
    "structures = simsetup[0]\n",
    "simSize = simsetup[1]\n",
    "bspecs = simsetup[2]\n",
    "source = simsetup[3]\n",
    "point_t_mnt = simsetup[4]\n",
    "field_t_mnt = simsetup[5]\n",
    "eps_mnt = simsetup[6]\n",
    "symmetry = (1, -1, 1)\n",
    "\n",
    "mesh_override = td.MeshOverrideStructure(\n",
    "    geometry=td.Box(center=(0, 0, 0), size=(0.5, 0.5, 0.5 * 1.5)), dl=[0.01, 0.01, 0.01]\n",
    ")\n",
    "\n",
    "# Define grid specification for the simulation\n",
    "grid_spec = td.GridSpec.auto(\n",
    "    min_steps_per_wvl=10,\n",
    "    override_structures=[mesh_override],\n",
    ")\n",
    "\n",
    "\n",
    "sim = td.Simulation(\n",
    "    center=(0, 0, 0),\n",
    "    size=simSize,\n",
    "    grid_spec=td.GridSpec.auto(),\n",
    "    structures=structures,\n",
    "    sources=[source],\n",
    "    monitors=[point_t_mnt, field_t_mnt, eps_mnt],\n",
    "    run_time=runtime,\n",
    "    shutoff=0,\n",
    "    boundary_spec=bspecs,\n",
    "    normalize_index=None,\n",
    "    symmetry=symmetry,\n",
    ")\n",
    "\n",
    "sim.plot(z=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ecd65df0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
15:26:36 -03 Created task 'NBSym' with task_id                                  \n",
       "             'fdve-1db7c742-a4c9-4f6c-9fe7-632e467dabf5' and task_type 'FDTD'.  \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m15:26:36 -03\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'NBSym'\u001b[0m with task_id                                  \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-1db7c742-a4c9-4f6c-9fe7-632e467dabf5'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             View task using web UI at                                          \n",
       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-1db7c742-a4c\n",
       "             9-4f6c-9fe7-632e467dabf5'.                                         \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=156701;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1db7c742-a4c9-4f6c-9fe7-632e467dabf5\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=566010;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1db7c742-a4c9-4f6c-9fe7-632e467dabf5\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=156701;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1db7c742-a4c9-4f6c-9fe7-632e467dabf5\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=50006;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1db7c742-a4c9-4f6c-9fe7-632e467dabf5\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=156701;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1db7c742-a4c9-4f6c-9fe7-632e467dabf5\u001b\\\u001b[32m-1db7c742-a4c\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=156701;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1db7c742-a4c9-4f6c-9fe7-632e467dabf5\u001b\\\u001b[32m9-4f6c-9fe7-632e467dabf5'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             Task folder: 'default'.                                            \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=685773;https://tidy3d.simulation.cloud/folders/folder-dc5128c7-562b-4bcf-a539-9356f1d308ca\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa0cdd2596bb4914875b647cad2cca27",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
15:26:41 -03 Maximum FlexCredit cost: 0.039. Minimum cost depends on task       \n",
       "             execution details. Use 'web.real_cost(task_id)' to get the billed  \n",
       "             FlexCredit cost after a simulation run.                            \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m15:26:41 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.039\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": [
       "
             Created task 'NBSym' with task_id                                  \n",
       "             'fdve-8e295aef-41f2-4968-b8cf-c00df61e8adf' and task_type 'FDTD'.  \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'NBSym'\u001b[0m with task_id                                  \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-8e295aef-41f2-4968-b8cf-c00df61e8adf'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             View task using web UI at                                          \n",
       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-8e295aef-41f\n",
       "             2-4968-b8cf-c00df61e8adf'.                                         \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=88919;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8e295aef-41f2-4968-b8cf-c00df61e8adf\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=946174;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8e295aef-41f2-4968-b8cf-c00df61e8adf\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=88919;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8e295aef-41f2-4968-b8cf-c00df61e8adf\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=793996;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8e295aef-41f2-4968-b8cf-c00df61e8adf\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=88919;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8e295aef-41f2-4968-b8cf-c00df61e8adf\u001b\\\u001b[32m-8e295aef-41f\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=88919;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8e295aef-41f2-4968-b8cf-c00df61e8adf\u001b\\\u001b[32m2-4968-b8cf-c00df61e8adf'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             Task folder: 'data'.                                               \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=911195;https://tidy3d.simulation.cloud/folders/folder-8bb21a81-8cca-44d8-937d-19c5ff541e80\u001b\\\u001b[32m'data'\u001b[0m\u001b]8;;\u001b\\.                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "617df38bc9f3447c86975cc4542fb913",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
15:26:46 -03 Maximum FlexCredit cost: 0.039. Minimum cost depends on task       \n",
       "             execution details. Use 'web.real_cost(task_id)' to get the billed  \n",
       "             FlexCredit cost after a simulation run.                            \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m15:26:46 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.039\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": [
       "
15:26:47 -03 status = success                                                   \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m15:26:47 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "207f6524f1584278ad64f2211334628d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
15:27:03 -03 loading simulation from data/NBSym.hdf5                            \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m15:27:03 -03\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/NBSym.hdf5                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Upload task\n",
    "task_id = web.upload(sim, task_name=\"NBSym\")\n",
    "# Run simulation\n",
    "NBSymData = web.run(\n",
    "    sim, \"NBSym\", folder_name=\"data\", path=\"data/NBSym.hdf5\", verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "64b19951",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting E field at very end of simulation\n",
    "final_time = NBSymData[\"field\"].Ey.t\n",
    "NBSymData.plot_field(\"field\", \"Ey\", val=\"abs\", t=final_time, z=0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c0801175",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resonance frequency: 691.01 nm\n",
      "Quality Factor: 1997968.1586556784\n"
     ]
    }
   ],
   "source": [
    "# Get y-component of E field from point monitor\n",
    "time_series = NBSymData[\"point\"].Ey.squeeze()\n",
    "\n",
    "# Run resonance finder\n",
    "resonance_finder = ResonanceFinder(freq_window=tuple((ss2, ss1)))\n",
    "resonance_data = resonance_finder.run_scalar_field_time(time_series)\n",
    "resonance_data_filtered = filter_resonances(\n",
    "    resonance_data=resonance_data, minQ=100, minamp=0.001, maxerr=100\n",
    ")\n",
    "\n",
    "# Cavity mode with highest Q from filtered resonances\n",
    "freqcav, Q = cavitymode(resonance_data_filtered)\n",
    "\n",
    "print(f\"Resonance frequency: {freq_nm(freqcav):.2f} nm\")\n",
    "print(f\"Quality Factor: {Q}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44719cfb",
   "metadata": {},
   "source": [
    "## Notes on Q-factor calculations\n",
    "\n",
    "In this example, a relatively broadband excitation was used. This method is useful when the exact resonance frequency is not known beforehand, but it can also excite modes other than the fundamental one, which increases the error in the Q-factor calculation.\n",
    "\n",
    "For a more precise Q-factor calculation, a second simulation with a narrow band around the resonance can be used."
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Photonic crystals"
  ],
  "description": "This notebook demonstrates how to design and simulate 1D photonic crystal cavities in 4H-SiC using Tidy3D.",
  "feature_image": "./img/NanobeamCavity.png",
  "features": [
   "Resonance finder"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "photonic crystal cavity, band structure, resonance frequency, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  },
  "redirect_from": "/tidy3d/kjFIWnQUtc",
  "title": "How to model a 1D SiC cavity using Tidy3D | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}