{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bb6c9859",
   "metadata": {},
   "source": [
    "# Graphene metamaterial absorber"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f286d274",
   "metadata": {},
   "source": [
    "Graphene, a single atomic sheet of carbon atoms arranged in a hexagonal lattice, has shown great promise for functional optical and optoelectronic devices. Compared to devices made of conventional materials, graphene-based devices have a unique tunability advantage since graphene's conductivity can be drastically modulated by electrostatic gating. \n",
    "\n",
    "Due to its atomic thickness, it is usually very difficult to model graphene in an FDTD simulation without using a large number of grid points. Fortunately, Tidy3D natively supports a surface conductivity model such that thin material layers can be accurately simulated even with grids much larger than the actual layer thickness. More specifically, you can model graphene directly using Tidy3D's [material library](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/material_library.html?highlight=material%20ibrary). The graphene's conductivity is described by the well established Kubo formula, which includes the  contributions from the intraband and interband electronic transitions. To define graphene, a few parameters, namely the scattering rate, chemical potential, and temperature, are required as user inputs.\n",
    "\n",
    "This notebook demonstrates how to model a graphene fishnet metamaterial absorber in the THz frequency range. By forming a Fabry-Perot resonator between the graphene layer and a metal mirror, high absorption is achieved across a bandwidth of ~ 2 THz. The design is adapted from the seminal work [Andrei Andryieuski and Andrei V. Lavrinenko, \"Graphene metamaterials based tunable terahertz absorber: effective surface conductivity approach,\" Opt. Express 21, 9144-9155 (2013)](https://opg.optica.org/oe/fulltext.cfm?uri=oe-21-7-9144&id=252404&ibsearch=false).\n",
    "\n",
    "<img src=\"img/graphene_metamaterial_schematic.png\" width=\"400\" alt=\"Schematic of the graphene metamaterial absorber\">\n",
    "\n",
    "If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. For more simulation examples, please visit our [examples page](https://www.flexcompute.com/tidy3d/examples/). FDTD simulations can diverge due to various reasons. If you run into any simulation divergence issues, please follow the steps outlined in our [troubleshooting guide](https://www.flexcompute.com/tidy3d/examples/notebooks/DivergedFDTDSimulation/) to resolve it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "96ba5d2f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62959f4c",
   "metadata": {},
   "source": [
    "The overall design of the absorber involves two layers of graphene sheets embedded in TOPAS polymer. The bottom of the device is a metallic mirror, which forms a Fabry-Perot resonator with graphene. This resonator greatly amplifies the absorption in graphene and achieves perfect absorption when the resonance condition is met. Two designs are explored in this notebook. The first one is a simple uniform graphene layer. When the chemical potential of graphene is tuned to 0.5 eV, perfect absorption is achieved but only within a narrow frequency band. Then, we test a fishnet structure, which shows a much wider absorption band. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16b4ccfc",
   "metadata": {},
   "source": [
    "## Uniform Graphene Sheet Absorber"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a95e5d2",
   "metadata": {},
   "source": [
    "We start with a simpler case where the absorber is made of two uniform graphene layers embedded in TOPAS polymer above a ground plate. \n",
    "\n",
    "First, define the basic simulation parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d76a88bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "THz = 1e12  # 1 THz = 1e12 Hz\n",
    "\n",
    "freq0 = 2.5 * THz  # central frequency\n",
    "freqs = np.linspace(0.1, 5, 300) * THz  # frequency range of interest\n",
    "\n",
    "lda0 = td.C_0 / freq0  # central wavelength"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4ba8e8f",
   "metadata": {},
   "source": [
    "The TOPAS polymer has a refractive index of 1.53 in the THz range with little absorption. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cd1055cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_topas = 1.53  # refractive index of the polymer\n",
    "topas = td.Medium(permittivity=n_topas**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ecca39e",
   "metadata": {},
   "source": [
    "To define graphene conductivity, we need to specify a few parameters. Here, we consider a relaxation time of $\\tau$=0.1 ps, which translates to a scattering rate $\\Gamma=\\frac{1}{2\\tau}=\\frac{6.58e-16}{2 \\times 1e-13}=$0.0033 eV, where 6.58e-16 is $\\hbar$ in the unit of eV*s. The temperature is at room temperature. Since there are two layers of graphene, the scaling parameter is set to 2. Note that this only works for graphene layers separated by a small distance. For real bilayer graphene or few-layer graphene in general, the conductivity is more complex as interlayer coupling and stacking order play an important role."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8f2e5355",
   "metadata": {},
   "outputs": [],
   "source": [
    "gamma = 0.0033  # scattering rate\n",
    "temp = 300  # temperature\n",
    "scaling = 2  # number of layers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f11672f",
   "metadata": {},
   "source": [
    "Furthermore, we consider a unit cell size of 15 $\\mu m$. In the uniform graphene sheet absorber, the distance between the graphene and the mirror is 35.9 $\\mu m$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3dc8f7a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 15  # unit cell size\n",
    "h = 35.9  # distance between graphene and the ground plate\n",
    "offset = lda0 / 2  # distance between the flux monitor and the graphene"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8aff35b",
   "metadata": {},
   "source": [
    "The periodic boundary condition is applied in both the $x$ and $y$ directions. In the $z$ direction, PML is applied in the plus direction and PEC is applied in the minus direction. The PEC mimics the metal mirror. For the grids, we use automatic nonuniform grids in all directions. The same [BoundarySpec](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.BoundarySpec.html?highlight=BOUNDARYSPEC) and [GridSpec](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.GridSpec.html?highlight=gridspec) will be used through the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1ef2e4e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a BoundarySpec\n",
    "boundary_spec = td.BoundarySpec(\n",
    "    x=td.Boundary.periodic(),\n",
    "    y=td.Boundary.periodic(),\n",
    "    z=td.Boundary(minus=td.PECBoundary(), plus=td.PML()),\n",
    ")\n",
    "\n",
    "# define a GridSpec\n",
    "grid_spec = td.GridSpec.auto(min_steps_per_wvl=200, wavelength=lda0)\n",
    "\n",
    "# simulation run time\n",
    "run_time = 1e-11"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5edb267",
   "metadata": {},
   "source": [
    "Next we define a function that returns a simulation at a given graphene chemical potential. This function will later be called repeatedly to construct a simulation batch to investigate how the chemical potential affects the absorption spectrum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b85bd993",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_sim_uniform(mu_c):\n",
    "    # define graphene\n",
    "    graphene = td.material_library[\"graphene\"](\n",
    "        gamma=gamma, mu_c=mu_c, temp=temp, scaling=scaling\n",
    "    ).medium\n",
    "\n",
    "    # define graphene structure\n",
    "    graphene_layer = td.Structure(\n",
    "        geometry=td.Box(center=(0, 0, h), size=(td.inf, td.inf, 0)), medium=graphene\n",
    "    )\n",
    "\n",
    "    # define a plane wave source\n",
    "    plane_wave = td.PlaneWave(\n",
    "        center=(0, 0, h + 0.1 * offset),\n",
    "        size=(td.inf, td.inf, 0),\n",
    "        source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0 / 2),\n",
    "        direction=\"-\",\n",
    "    )\n",
    "\n",
    "    # define a flux monitor to measure reflection\n",
    "    flux_monitor = td.FluxMonitor(\n",
    "        center=(0, 0, h + offset),\n",
    "        size=(td.inf, td.inf, 0),\n",
    "        freqs=freqs,\n",
    "        name=\"R\",\n",
    "    )\n",
    "\n",
    "    # simulation domain size in z\n",
    "    Lz = h + 1.1 * offset\n",
    "\n",
    "    # define simulation\n",
    "    sim = td.Simulation(\n",
    "        center=(0, 0, Lz / 2),\n",
    "        size=(a, a, Lz),\n",
    "        grid_spec=grid_spec,\n",
    "        structures=[graphene_layer],\n",
    "        sources=[plane_wave],\n",
    "        monitors=[flux_monitor],\n",
    "        run_time=run_time,\n",
    "        medium=topas,\n",
    "        boundary_spec=boundary_spec,\n",
    "        shutoff=1e-7,\n",
    "        symmetry=(-1, 1, 0),  # symmetry is used to reduce the computational load\n",
    "    )\n",
    "\n",
    "    return sim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1bbea24",
   "metadata": {},
   "source": [
    "Specifically, we study $\\mu_c=$0, 0.1, 0.2, and 0.5 eV. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9f32c260",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:48:13 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING:  ℹ️ ⚠️ RF simulations are subject to new license            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">requirements in the future. You are using RF-specific components in</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">this simulation.                                                   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\"> - Contains monitors defined for RF wavelengths.                   </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:48:13 UTC\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING:  ℹ️ ⚠️ RF simulations are subject to new license            \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mrequirements in the future. You are using RF-specific components in\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthis simulation.                                                   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31m - Contains monitors defined for RF wavelengths.                   \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu_cs = [0, 0.1, 0.2, 0.5]  # values of mu_c to be simulated\n",
    "\n",
    "# define a simulation batch\n",
    "sims = {f\"mu_c={mu_c:.2f}\": make_sim_uniform(mu_c) for mu_c in mu_cs}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4b6fb5c",
   "metadata": {},
   "source": [
    "Submit the simulation batch to the server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a706f2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "43349eba99394613a4c18cd5b846c1eb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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     "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\">12:48:17 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:48:17 UTC\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m4\u001b[0m tasks.                       \n"
      ]
     },
     "metadata": {},
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    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:48:21 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.100</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:48:21 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.100\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 the\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>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 the\n",
       "\u001b[2;36m             \u001b[0mBatch has completed.                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "Output()"
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     "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\">12:49:56 UTC </span>Batch complete.                                                    \n",
       "</pre>\n"
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       "\u001b[2;36m12:49:56 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
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     "metadata": {},
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
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      "text/plain": [
       "Output()"
      ]
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     "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, folder_name=\"default\")\n",
    "batch_results = batch.run(path_dir=\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b14e5299",
   "metadata": {},
   "source": [
    "After the batch of jobs is complete, we want to plot the absorption spectra. Since the bottom ground plane is a perfect mirror, absorption is simply $A=1-R$, where $R$ is the reflection. A function `plot_absorption` is defined to do this plotting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "75eb91f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_absorption(batch_results):\n",
    "    for i, mu_c in enumerate(mu_cs):\n",
    "        sim_data = batch_results[f\"mu_c={mu_c:.2f}\"]\n",
    "        A = 1 - sim_data[\"R\"].flux\n",
    "        plt.plot(freqs / THz, A, label=f\"{mu_c:.2f} eV\")\n",
    "        plt.xlim(0, 5)\n",
    "        plt.ylim(0, 1)\n",
    "        plt.xlabel(\"Frequency (THz)\")\n",
    "        plt.ylabel(\"Absorption\")\n",
    "        plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6516c046",
   "metadata": {},
   "source": [
    "At 0.5 eV chemical potential, perfect absorption is observed around 2.3 THz. However, the bandwidth is rather small."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "77f7b725",
   "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\">12:50:02 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:50:02 UTC\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Warning messages were found in the solver log. For more   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                     \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                       \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\">12:50:04 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:50:04 UTC\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Warning messages were found in the solver log. For more   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                     \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                       \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\">12:50:05 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:50:05 UTC\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Warning messages were found in the solver log. For more   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                     \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                       \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\">12:50:07 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:50:07 UTC\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Warning messages were found in the solver log. For more   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                     \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                       \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_absorption(batch_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f49c61a9",
   "metadata": {},
   "source": [
    "## Graphene Fishnet Absorber"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3ae8df1",
   "metadata": {},
   "source": [
    "To enhance the absorption bandwidth, a fishnet design is explored, as discussed in the [reference](https://opg.optica.org/oe/fulltext.cfm?uri=oe-21-7-9144&id=252404&ibsearch=false).\n",
    "\n",
    "Similarly, we define a function here that returns a simulation at a given graphene chemical potential. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9fe6635e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fishnet parameters\n",
    "b = 2\n",
    "w = 12\n",
    "\n",
    "h = 17.6  # distance between the graphene and the ground plate\n",
    "inf_eff = 1e3  # effective infinity\n",
    "\n",
    "\n",
    "def make_sim_fishnet(mu_c):\n",
    "    # define graphene\n",
    "    graphene = td.material_library[\"graphene\"](\n",
    "        gamma=gamma, mu_c=mu_c, temp=temp, scaling=scaling\n",
    "    ).medium\n",
    "\n",
    "    # define the fishnet structure\n",
    "    graphene_finishnet_center = td.Structure(\n",
    "        geometry=td.Box.from_bounds(rmin=(0, 0, h), rmax=(w / 2, w / 2, h)),\n",
    "        medium=graphene,\n",
    "    )\n",
    "    graphene_finishnet_right = td.Structure(\n",
    "        geometry=td.Box.from_bounds(rmin=(w / 2, 0, h), rmax=(inf_eff, b / 2, h)),\n",
    "        medium=graphene,\n",
    "    )\n",
    "    graphene_finishnet_top = td.Structure(\n",
    "        geometry=td.Box.from_bounds(rmin=(0, w / 2, h), rmax=(b / 2, inf_eff, h)),\n",
    "        medium=graphene,\n",
    "    )\n",
    "\n",
    "    # define a plane wave source\n",
    "    plane_wave = td.PlaneWave(\n",
    "        center=(0, 0, h + 0.1 * offset),\n",
    "        size=(td.inf, td.inf, 0),\n",
    "        source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0 / 2),\n",
    "        direction=\"-\",\n",
    "    )\n",
    "\n",
    "    # define a flux monitor to measure reflection\n",
    "    flux_monitor = td.FluxMonitor(\n",
    "        center=(0, 0, h + offset),\n",
    "        size=(td.inf, td.inf, 0),\n",
    "        freqs=freqs,\n",
    "        name=\"R\",\n",
    "    )\n",
    "\n",
    "    # simulation domain size in z\n",
    "    Lz = h + 1.1 * offset\n",
    "\n",
    "    # define simulation\n",
    "    sim = td.Simulation(\n",
    "        center=(0, 0, Lz / 2),\n",
    "        size=(a, a, Lz),\n",
    "        grid_spec=grid_spec,\n",
    "        structures=[\n",
    "            graphene_finishnet_center,\n",
    "            graphene_finishnet_right,\n",
    "            graphene_finishnet_top,\n",
    "        ],\n",
    "        sources=[plane_wave],\n",
    "        monitors=[flux_monitor],\n",
    "        run_time=run_time,\n",
    "        medium=topas,\n",
    "        boundary_spec=boundary_spec,\n",
    "        shutoff=1e-7,\n",
    "        symmetry=(-1, 1, 0),\n",
    "    )\n",
    "\n",
    "    return sim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62d74707",
   "metadata": {},
   "source": [
    "To make sure the structures are defined correctly, use the `plot` method to visualize the fishnet. Due to symmetry, only a quarter of it needs to be defined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cfc44984",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim = make_sim_fishnet(0.5)\n",
    "ax = sim.plot(z=h)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24fe60ce",
   "metadata": {},
   "source": [
    "A batch of simulations at different chemical potentials is defined and submitted to the server. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6c616915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aebf84afe26341039b2902cc48bd40ff",
       "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\">12:50:11 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:50:11 UTC\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m4\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\">12:50:15 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.100</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:50:15 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.100\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 the\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>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 the\n",
       "\u001b[2;36m             \u001b[0mBatch has completed.                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6544b362dc98418988eca9df9a35a5aa",
       "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\">13:02:47 UTC </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:02:47 UTC\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": "5ed0f23c3e63413bafdf95e3a91af160",
       "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": [
    "mu_cs = [0, 0.1, 0.2, 0.5]  # values of mu_c to be simulated\n",
    "\n",
    "# define a simulation batch\n",
    "sims = {f\"mu_c={mu_c:.2f}\": make_sim_fishnet(mu_c) for mu_c in mu_cs}\n",
    "\n",
    "# submit the batch to the server\n",
    "batch = web.Batch(simulations=sims, folder_name=\"default\")\n",
    "batch_results = batch.run(path_dir=\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aea2ae0",
   "metadata": {},
   "source": [
    "At 0.5 eV, the absorber shows an absorption bandwidth (defined as absorption above 0.9) of ~2 THz, much better than the uniform graphene absorber case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4c4965ac",
   "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\">13:02:53 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:02:53 UTC\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Warning messages were found in the solver log. For more   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                     \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                       \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\">13:02:55 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:02:55 UTC\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Warning messages were found in the solver log. For more   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                     \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                       \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\">13:02:57 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:02:57 UTC\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Warning messages were found in the solver log. For more   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                     \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                       \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\">13:02:58 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:02:58 UTC\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Warning messages were found in the solver log. For more   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                     \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                       \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_absorption(batch_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e4644e",
   "metadata": {},
   "source": [
    "## Additional Notes about Graphene "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d9b85e8",
   "metadata": {},
   "source": [
    "As briefly discussed in the introduction, graphene's conductivity includes the contributions from both the intraband and interband electronic transitions. The intraband transitions lead to a Drude-like response that is dominant at lower frequencies. The interband transitions give rise to the prominent feature around $f=2\\mu_c$.\n",
    "\n",
    "The conductivity of graphene can be inspected by using the `numerical_conductivity` method. Here we show an example of graphene conductivity at $\\Gamma$=0.0033 eV, $\\mu_c$=0.2 eV, and $T$=300 K. Below 20 THz, the conductivity is predominantly Drude-like, which comes from the intraband transitions. The interband transition feature shows up at around 100 THz (~0.4 eV). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fbd83cbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gamma = 0.0033  # scattering rate\n",
    "mu_c = 0.2  # chemical potential\n",
    "temp = 300  # temperature\n",
    "\n",
    "freqs = np.linspace(10, 250, 100) * THz\n",
    "graphene = td.material_library[\"graphene\"](gamma=gamma, mu_c=mu_c, temp=temp)\n",
    "sigma_analytical = graphene.numerical_conductivity(freqs)\n",
    "plt.plot(freqs / THz, np.real(sigma_analytical * 1e6), label=r\"Re($\\sigma$) ($\\mu$S)\")\n",
    "plt.plot(freqs / THz, np.imag(sigma_analytical * 1e6), label=r\"Im($\\sigma$) ($\\mu$S)\")\n",
    "plt.xlabel(\"frequency (THz)\")\n",
    "plt.title(\"analytically calculated surface conductivity\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d66f68dd",
   "metadata": {},
   "source": [
    "Like other dispersive materials used in FDTD, graphene's conductivity needs to be fitted, which is automatically done when you define a graphene medium. The plot generated by `numerical_conductivity` above is the analytical result. To inspect the fitting, one can use the `plot_sigma` method as shown below. The fitted conductivity coincides well with the analytical result in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c1591ef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pk5ubS2pqaqmuKq+Wwe7g4de4uDgFOxEREakVSnP6mS6eEBEREakhFOxEREREaggFOxEREZEaolqeYyciInIsgUCA4uLicJchUmputxun01khbSnYiYhIjWCMITMzk+zs7HCXIlJmCQkJJCcnn/T4vAp2IiJSIxwMdUlJSURFRWkAe6kWjDHk5+ezY8cOABo2bHhS7SnYiYhItRcIBIKhrl69euEuR6RMIiMjAdixYwdJSUkndVhWF0+IiEi1d/CcuqioqDBXIlI+B392T/b8UAU7ERGpMXT4VaqrivrZVbATERERqSEU7ERERERqCAU7ERGRGmT37t0kJSWxcePGUq8zaNAgnn766VPWfkXXIMemYCciIhJmQ4cOxbIsLMvC7XbTrFkz/vSnP1FYWFjmth577DEuv/xymjZtWup1Ro8ezWOPPUZOTs4paX/nzp3cdtttNGnSBK/XS3JyMn369OHbb78tVw1ybAp2x2Ev+obAJ//F5OwOdykiIlLD9e3bl+3bt/PLL78wYcIEXn75ZcaOHVumNvLz83n11Ve56aabyrRe69atad68OW+88cYpaX/gwIEsWbKEKVOm8PPPP/PRRx/RvXt3du/+7fdraWuQ41OwO47Cv02h8J9zsVf+EO5SRESkhjvYk5WamsoVV1xBz549mTFjRnC+bduMHz+eZs2aERkZSbt27Zg6dWpIG9OnT8fr9dK1a9eQ6fPnz6dHjx7Uq1cv2DN48JGbmwvApZdeyttvv33cGo/W/ty5c3G73SG9ixs3bsSyLDZt2kR2djZz5szhiSee4KKLLiItLY3OnTszatQoLrvsspD2S1PD4UrzvXTv3p0RI0YwYsQI4uPjSUxM5OGHH8YYA8DUqVNp06YNkZGR1KtXj549e5KXl1eq7R9v3ZNpt7w0QPFxWF6DKQKza0e4SxERkTIyxpBfVPZDmRUhyhNxUsNXrFixgu+++460tLTgtPHjx/PGG28wadIkTj/9dL755huuvfZa6tevT7du3QCYM2cOnTp1Cmlr2bJlwWAzceJEtmzZwtVXX02HDh245ZZbiIuLA6Bz58489thj+Hw+vF7vUes6WvtLly6lVatWREREBKctWbKEOnXqkJaWht/vJyYmhg8//JCuXbses+3S1nC40nwvAFOmTOGmm25i4cKF/PDDD9xyyy00adKESy65hMGDB/Pkk0/y+9//nn379jFnzpxg6Due7du3H3Pd4807lRTsjifCgn1gdutQrIhIdZNfVEjzuy4Ky7bXP/c10d7IMq0zbdo0YmJi8Pv9+Hw+HA4H//jHPwDw+Xw8/vjjzJw5k4yMDABOO+005s6dy8svvxwMMJs2bSIlJSWk3TvvvJMBAwbw97//HYD09HQGDx7MokWLuOqqq4LLpaSkUFRURGZmZkigPNTR2l+2bBkdOnQImbZ06VLatWsHgMvlYvLkyQwbNoxJkybRsWNHunXrxqBBg2jbtm3IeqWp4VCl/V4AUlNTmTBhApZl0bJlS3788UcmTJhAp06d8Pv9DBgwILjNNm3anHDbUBLsjrXuzz//XO52T4YOxR6HFe0ueaEbSouIyCl20UUXsXTpUhYsWMCQIUO44YYbGDhwIADr1q0jPz+fXr16ERMTE3y8/vrrrF+/PthGQUFBSM9ZVlYWc+fO5fbbbw/ZVnR09BE9igdva5Wfn3/MGg9vH0pCXPv27UOmLVmyJGTawIED2bZtGx999BF9+/Zl1qxZdOzYkcmTJ5e5hkOV9nsB6Nq1a8hnzsjIYO3atbRr144ePXrQpk0brrzySl555RX27t1bqu0fb92TafdkqMfuOKyYCKAYk7M/3KWIiEgZRXkiWP/c12HbdllFR0fTokULAP7973/Trl274IUK+/eX/B765JNPaNSoUch6hx6yTExMDAkPixYtwrbtYO/ZodPPPvvskGl79uwBoH79+ses8fD2A4EAK1asOKLHbvHixcFQelBERAS9evWiV69ePPzww9x8882MHTuWoUOHlqmGQ5X2ezmRGTNm8N133/HFF18wceJEHnroIRYsWECzZs2Ou57T6TzuuuVt92Sox+44rLhoAMy+gjBXIiIiZWVZFtHeyLA8Tvb2UA6HgwcffJDRo0dTUFBAeno6Xq+XzZs306JFi5BHampqcL0OHTqwatWq4HvbtgFCTthfvnw533zzDVdffXXINlesWEHjxo1JTEw8Zl2Ht79mzRoKCwtDDs/OmzePrVu3HtGLd7j09PQjLiQoTQ2Ht1Ga7wVgwYIFIe/nz5/P6aefjtPpxLIszjvvPMaNG8eSJUvweDx88MEHparheOueTLvlpWB3HFZCPAAmzxfmSkREpLa58sorcTqdvPDCC8TGxnLfffcxcuRIpkyZwvr161m8eDETJ05kypQpwXX69OnDypUrg71qXbp0ITIykvvvv5/Vq1fzySefcNlllzF8+PAjrpydM2cOvXv3Pm5Nh7e/dOlSACZOnMjatWv59NNPuf766wEoKioCSgY0/t3vfscbb7zB8uXL2bBhA++99x5PPvkkl19+eZlrOFRpvxeAzZs3c88997BmzRr++9//MnHiRO666y4WLFjA448/zg8//MDmzZt5//332blzJ61atTrh9o+37sm0e1JMGbz44oumTZs2JjY21sTGxpquXbua6dOnB+d369bNACGPP/7xjyFtbNq0yVx88cUmMjLS1K9f39x3332muLi4LGWYnJwcA5icnJwyrVdWxe++YvZfdrvJv/6GU7odERE5OQUFBWbVqlWmoKAg3KWUy5AhQ8zll19+xPTx48eb+vXrm/379xvbts2zzz5rWrZsadxut6lfv77p06ePmT17dsg6nTt3NpMmTQq+//jjj80ZZ5xh3G63ad68uXnqqadMIBAIWaegoMDEx8ebefPmnbDWQ9u///77TZ8+fczFF19svF6v6dChg3nzzTdNXFycufbaa40xxhQWFpo///nPpmPHjiY+Pt5ERUWZli1bmtGjR5v8/Pzj1vDaa6+ZE0WV0nwv3bp1M7fffru59dZbTVxcnKlTp4558MEHjW3bZtWqVaZPnz6mfv36xuv1mjPOOMNMnDjxhN+DMea465a13eP9DJcl95Qp2H300Ufmk08+MT///LNZs2aNefDBB43b7TYrVqwwxpR8ccOGDTPbt28PPg4twu/3m9atW5uePXuaJUuWmOnTp5vExEQzatSospRRacHOP/NDs/+y203eVQp2IiJVWXUPdhVp2rRpplWrVkeEt+N58cUXTa9evcrcfu/evc1DDz1U3lJPWMOYMWNMt27dTrrtbt26mbvuuuuk2zmVKirYleniiUsvvTTk/WOPPcZLL73E/PnzOeusswCIiooiOTn5qOt/8cUXrFq1ipkzZ9KgQQPat2/PX/7yFx544AEeeeQRPB5P2bobTzErqeRzmGJdYyIiItVD//79Wbt2LVu3bj3iPLNjcbvdTJw4scztL1u2jBtvvPFkyj1uDZ9++mlwyBcpnXKfYxcIBHj77bfJy8sLjh0D8Oabb5KYmEjr1q0ZNWpUyCXL8+bNo02bNjRo0CA4rU+fPuTm5rJy5cpjbsvn85GbmxvyqAxWgwNX2ATcmIJTO1K0iIhIRbn77rtLHeoAbr75Zlq2bFmm9t1uN1lZWRU2NtvRali4cCGdO3eukPbLY/PmzSHDqBz+2Lx5c9hqO5Yyd0X9+OOPZGRkUFhYSExMDB988AHp6ekAXH311aSlpZGSksLy5ct54IEHWLNmDe+//z4AmZmZIaEOCL7PzMw85jbHjx/PuHHjylrqyaubDNiAA5O1BavpmZVfg4iISBWUnJx8yu+iUFFmzZpVrvVSUlKCF4gca35VU+Zg17JlS5YuXUpOTg5Tp05lyJAhzJ49m/T0dG655Zbgcm3atKFhw4b06NGD9evX07x583IXOWrUKO65557g+9zc3DL9S6S8LJcLy12MKfZidmwDBTsREZFaw+VyBccWrC7KfCjW4/HQokULOnXqxPjx42nXrh3PPffcUZft0qULUDIyNJSk+6ysrJBlDr4/1nl5UDLIYFxcXMij0nhKxgBiZ9bxlxMREREJs5Mex862bXy+o4/zdrD7smHDhkDJ7Tt+/PFHduzYEVxmxowZxMXFBQ/nVjVWZMkgk2bPrjBXIiIiInJ8ZToUO2rUKPr160eTJk3Yt28fb731FrNmzeLzzz9n/fr1vPXWW1x88cXUq1eP5cuXM3LkSC688MLgTX579+5Neno61113HU8++SSZmZmMHj2a4cOHl+nWH5XJiir5isze7PAWIiIiInICZQp2O3bs4Prrr2f79u3Ex8fTtm1bPv/8c3r16sWWLVuYOXMmzz77LHl5eaSmpjJw4EBGjx4dXN/pdDJt2jRuu+02MjIyiI6OZsiQITz66KMV/sEqihXjBQKYnH3hLkVERETkuMoU7F599dVjzktNTWX27NknbCMtLY3p06eXZbNhZcVGA7mY3PwTLisiIiISTrpX7AlYCSUXauh+sSIiIlLVKdidgFWnDgCmIBDmSkRERESOT8HuBKx6iQCYgjAXIiIiInICCnYnYNU/cL/YImeYKxERETm+3bt3k5SUxMaNG0u9zqBBg3j66adPWfsVXYMcn4LdCVhJB+8X68H4CsNbjIiI1EhDhw7liiuuOOl2HnvsMS6//HKaNm1a6nVGjx7NY489Rk5Ozilpf+fOndx22200adIEr9dLcnIyffr04dtvvy1XDXJ8CnYnktQYKLkXntmxNby1iIiIHEN+fj6vvvoqN910U5nWa926Nc2bN+eNN944Je0PHDiQJUuWMGXKFH7++Wc++ugjunfvzu7du8tcg5yYgt0JWG43uIpL3ijYiYjIKda9e3fuuOMO7r77burUqUODBg145ZVXyMvL44YbbiA2NpYWLVrw6aefhqw3ffp0vF4vXbt2DZk+f/58evToQb169bAsK+SRm5sLwKWXXsrbb7993LqO1v7cuXNxu90UFv52RGvjxo1YlsWmTZvIzs5mzpw5PPHEE1x00UWkpaXRuXNnRo0axWWXXRbSfmlqOJxt24wfP55mzZoRGRlJu3btmDp1ash3OWLECEaMGEF8fDyJiYk8/PDDGGOCy0ydOpU2bdoQGRlJvXr16NmzJ3l5eSfc9onWK2+7J0vBrhQsT8kVsWZnZpgrERGRUjMGivLC8zgkOJTHlClTSExMZOHChdxxxx3cdtttXHnllZx77rksXryY3r17c91115Gf/9sYq3PmzKFTp04h7Sxbtozu3bvToUMH5syZw2effUbdunXp0aMH77zzTvDe6507d2bhwoXHvEXosdpfunQprVq1IiIiIjhtyZIl1KlTh7S0NGJiYoiJieHDDz88btulreFw48eP5/XXX2fSpEmsXLmSkSNHcu2114aMqztlyhRcLhcLFy7kueee45lnnuFf//oXANu3b2fw4MHceOON/PTTT8yaNYsBAwaEBL+jOdF65W23IpRpgOLayooAk6/7xYqIVCvF+fBow/Bse8x28ESXe/V27doF79w0atQo/va3v5GYmMiwYcNKmh8zhpdeeonly5cHe9A2bdpESkpKSDt33nknAwYM4O9//zsA6enpDB48mEWLFnHVVVcFl0tJSaGoqIjMzEzS0tKOWtPR2l+2bBkdOnQImbZ06VLatWsHgMvlYvLkyQwbNoxJkybRsWNHunXrxqBBg4K3Gy1LDYfy+Xw8/vjjzJw5k4yMDABOO+005s6dy8svv0y3bt2AkhsoTJgwAcuyaNmyJT/++CMTJkxg2LBhbN++Hb/fz4ABA4LbbNOmzQm3faL1yttuRVCPXSkE7xe7Z2+YKxERkdrg0NDjdDqpV69eSDBo0KABUHKrz4MKCgpCes6ysrKYO3cut99+e0jb0dHRWJYVMi0yMhIgpAfwcIe3DyUhrn379iHTlixZEjJt4MCBbNu2jY8++oi+ffsya9YsOnbsyOTJk8tcw6HWrVtHfn4+vXr1CvYMxsTE8Prrr7N+/frgcl27dg35vBkZGaxdu5ZAIEC7du3o0aMHbdq04corr+SVV15h794T/64/0XrlbbciqMeuFKxoL2BjcnLDXYqIiJSWO6qk5yxc2z6Z1d3ukPeWZYVMOxhUbNsOTktMTAwJD4sWLcK27WDv2aHTzz777JBpe/bsAaB+/frHrOnw9gOBACtWrDiix27x4sUMHDgwZFpERAS9evWiV69ePPzww9x8882MHTuWoUOHlqmGQ+3fvx+ATz75hEaNGoXM83q9pWrD6XQyY8YMvvvuO7744gsmTpzIQw89xIIFC2jWrFm51ytvuxVBPXalYMWV/CtC94sVEalGLKvkcGg4Hof1iFWGDh06sGrVquD7g6Hv0BP2ly9fzjfffMPVV18dsu6KFSto3LgxiYmJpW5/zZo1FBYWhhyenTdvHlu3bj2iF+9w6enpR1xIUJoaDm/D6/WyefNmWrRoEfJITU0NLrdgwYKQ9ebPn8/pp5+O01kyPq1lWZx33nmMGzeOJUuW4PF4+OCDD064/ROtV952T5Z67ErBio8D8nS/WBERqbL69OnDqFGj2Lt3L3Xq1KFLly5ERkZy//3389BDD7F+/XqGDx/O8OHDj7hyds6cOfTu3btM7S9duhSAiRMncuedd7Ju3TruvPNOAIqKioCSAY2vvPJKbrzxRtq2bUtsbCw//PADTz75JJdffnmZazhUbGws9913HyNHjsS2bc4//3xycnL49ttviYuLY8iQIQBs3ryZe+65hz/+8Y8sXryYiRMnBgdDXrBgAV9++SW9e/cmKSmJBQsWsHPnTlq1anXcbZ9ovfK2WxEU7ErBSkgAtmPy/eEuRURE5KjatGlDx44deffdd/njH/9I/fr1effdd7n33ntp27YtTZo0YcSIEdxzzz0h6xUWFvLhhx/y2Weflan9pUuX0qdPH3755RfatGlDeno648aN47bbbuP555/nP//5DzExMXTp0oUJEyawfv16iouLSU1NZdiwYTz44IPHrWHy5MnccMMNx72S9C9/+Qv169dn/Pjx/PLLLyQkJNCxY8eQtq+//noKCgro3LkzTqeTu+66i1tuuQWAuLg4vvnmG5599llyc3NJS0vj6aefpl+/fsf9Lk60XnnbrQiWqYxrbytYbm4u8fHx5OTkBC/VPpUC09+m8OU5WNEFRL3171O+PRERKZvCwkI2bNhAs2bNjjjBvzb55JNPuP/++1mxYgUOR+nOtnrppZf44IMP+OKLL8rUfr9+/TjnnHP461//erJlH7WGsWPHMnv2bGbNmlXudrt370779u159tlnT7rGU+14P8NlyT3qsSuN+iVXHxmfTkkUEZGqq3///qxdu5atW7eGnGd2PG63m4kTJ5a5/WXLlnHjjTeeTLnHreHTTz/lH//4R4W0X5so2JVC8H6xfjfG78dy6WsTEZGq6e677y7T8jfffHOZ28/MzCQrK6vCxmY7Wg0LFy6skLbLY/PmzaSnpx9z/qpVq2jSpEklVlR6SiilYCU1PvDKATu3QcOquTNFREQqQ3JycqXcReFklfcwbkpKSvDikGPNr6oU7ErBiowCZzEE3JgdW7EU7ERERGosl8tFixYtwl1GueiksVKyPCVXxOp+sSIiIlJVKdiVknVgEGuze2d4CxERERE5BgW7UrKiSkaoNgdueSIiIiJS1SjYlZIV7QHQ/WJFRESkylKwKyUr9uD9YvNOsKSIiIhIeCjYlZIVHwuA2V8Y5kpEREREjk7BrpSsOnUAMHm6X6yIiIhUTQp2pVWvXslzYdUfkFFERERqJwW7UrISdb9YERERqdqUUkrJql9y+xDjd2FsO8zViIiIHN3u3btJSkpi48aNpV5n0KBBPP3006es/YquQY5Nwa6UrOTUkhfGCXuywluMiIjUKEOHDsWyLCzLwu1206xZM/70pz9RWFj2C/Yee+wxLr/8cpo2bVrqdUaPHs1jjz1GTk7OKWl/586d3HbbbTRp0gSv10tycjJ9+vTh22+/LVcNVUn37t25++67TzitsijYlZIVHQuOYgBM1q9hrkZERGqavn37sn37dn755RcmTJjAyy+/zNixY8vURn5+Pq+++io33XRTmdZr3bo1zZs354033jgl7Q8cOJAlS5YwZcoUfv75Zz766CO6d+/O7t27y1yDHJ+CXRnofrEiInKqHOzJSk1N5YorrqBnz57MmDEjON+2bcaPH0+zZs2IjIykXbt2TJ06NaSN6dOn4/V66dq1a8j0+fPn06NHD+rVqxfsGTz4yM0tGXj/0ksv5e233z5ujUdrf+7cubjd7pDexY0bN2JZFps2bSI7O5s5c+bwxBNPcNFFF5GWlkbnzp0ZNWoUl112WUj7panhcKX5Xrp3786IESMYMWIE8fHxJCYm8vDDD2NMyQWRU6dOpU2bNkRGRlKvXj169uxJXt6Jx60dOnQos2fP5rnnngt+n0ebVtGHrY9Hwa4MgveL3aX7xYqIVHXGGIqL/WF5HAwM5bVixQq+++47PB5PcNr48eN5/fXXmTRpEitXrmTkyJFce+21zJ49O7jMnDlz6NSpU0hby5Yto3v37nTo0IE5c+bw2WefUbduXXr06ME777xDXFwcAJ07d2bhwoX4fL5j1nW09pcuXUqrVq2IiIgITluyZAl16tQhLS2NmJgYYmJi+PDDD4/bdmlrOFxpvheAKVOm4HK5WLhwIc899xzPPPMM//rXv9i+fTuDBw/mxhtv5KeffmLWrFkMGDCgVPvwueeeIyMjg2HDhrF9+3a2b99+1Gmpqaml/jwny1VpW6oBrEgH5IDZu/vEC4uISFj5/QFenfJBWLZ905Df43aX7VfstGnTiImJwe/34/P5cDgc/OMf/wDA5/Px+OOPM3PmTDIyMgA47bTTmDt3Li+//DLdunUDYNOmTaSkpIS0e+eddzJgwAD+/ve/A5Cens7gwYNZtGgRV111VXC5lJQUioqKyMzMJC0t7ag1Hq39ZcuW0aFDh5BpS5cupV27dgC4XC4mT57MsGHDmDRpEh07dqRbt24MGjSItm3bhqxXmhoOVdrvBSA1NZUJEyZgWRYtW7bkxx9/ZMKECXTq1Am/38+AAQOC22zTps0Jtw0QHx+Px+MhKiqK5OTk4PSjTass6rErg+D9YrOr14mdIiJS9V100UUsXbqUBQsWMGTIEG644QYGDhwIwLp168jPz6dXr17BHrCYmBhef/111q9fH2yjoKAgpOcsKyuLuXPncvvtt4dsKzo6GsuyQqZFRpbcOjM/P/+YNR7ePpSEuPbt24dMW7JkSci0gQMHsm3bNj766CP69u3LrFmz6NixI5MnTy5zDYcq7fcC0LVr15DPnJGRwdq1a2nXrh09evSgTZs2XHnllbzyyivs3bu3VNuvisr0z4mXXnqJl156KXis+KyzzmLMmDH069cPgMLCQu69917efvttfD4fffr04cUXX6RBgwbBNjZv3sxtt93G119/TUxMDEOGDGH8+PG4XFW/87DkfrGFmBzdL1ZEpKpzuZzcNOT3Ydt2WUVHR9OiRQsA/v3vf9OuXbvghQr79+8H4JNPPqFRo0Yh63m93uDrxMTEkFCyaNEibNsO9p4dOv3ss88OmbZnzx4A6tevf8waD28/EAiwYsWKI3rsFi9eHAylB0VERNCrVy969erFww8/zM0338zYsWMZOnRomWo4VGm/lxOZMWMG3333HV988QUTJ07koYceYsGCBTRr1qzUbVQVZeqxa9y4MX/7299YtGgRP/zwA7/73e+4/PLLWblyJQAjR47k448/5r333mP27Nls27aNAQMGBNcPBAL079+foqIivvvuO6ZMmcLkyZMZM2ZMxX6qUyUuBgCzvyDMhYiIyImUDB3iCsvj8N6wsnI4HDz44IOMHj2agoIC0tPT8Xq9bN68mRYtWoQ8Dj1/q0OHDqxatSr43j4w7uqhFwIsX76cb775hquvvjpkmytWrKBx48YkJiYes67D21+zZg2FhYUhh2fnzZvH1q1bj+jFO1x6evoRFyiUpobD2yjN9wKwYMGCkPfz58/n9NNPx+l0YlkW5513HuPGjWPJkiV4PB4++KB0h/E9Hg+BQOCE0yqNOUl16tQx//rXv0x2drZxu93mvffeC8776aefDGDmzZtnjDFm+vTpxuFwmMzMzOAyL730komLizM+n6/U28zJyTGAycnJOdnyy6To1WfM/stuNwU331ip2xURkeMrKCgwq1atMgUFBeEupVyGDBliLr/88pBpxcXFplGjRuapp54yxhjz0EMPmXr16pnJkyebdevWmUWLFpnnn3/eTJ48ObjO8uXLjcvlMnv27DHGGLNjxw4TGRlprr32WvPTTz+ZadOmmbS0NHP33XcftYYbbzz+77fD23/zzTcNYEaMGGF+/vlnM336dNOiRQsDmIULFxpjjNm1a5e56KKLzH/+8x+zbNky88svv5h3333XNGjQ4IjtlaaGw5Xme+nWrZuJiYkxI0eONKtXrzZvvfWWiY6ONpMmTTLz5883jz32mPn+++/Npk2bzLvvvms8Ho+ZPn16qbY/bNgwc84555gNGzaYnTt3mkAgcNRpJ3K8n+Gy5J5yBzu/32/++9//Go/HY1auXGm+/PJLA5i9e/eGLNekSRPzzDPPGGOMefjhh027du1C5v/yyy8GMIsXLz7mtgoLC01OTk7wsWXLlrAEu+IPJpv9l91u8q+9oVK3KyIix1cTg50xxowfP97Ur1/f7N+/39i2bZ599lnTsmVL43a7Tf369U2fPn3M7NmzQ9bp3LmzmTRpUvD9xx9/bM444wzjdrtN8+bNzVNPPXVE0CgoKDDx8fHBjpjjObT9+++/3/Tp08dcfPHFxuv1mg4dOpg333zTxMXFmWuvvdYYU/I7/M9//rPp2LGjiY+PN1FRUaZly5Zm9OjRJj8//7g1vPbaa+ZEfVCl+V66detmbr/9dnPrrbeauLg4U6dOHfPggw8a27bNqlWrTJ8+fUz9+vWN1+s1Z5xxhpk4ceIJv4eD1qxZY7p27WoiIyMNYDZs2HDUaScStmC3fPlyEx0dbZxOp4mPjzeffPKJMaYktXs8niOWP+ecc8yf/vQnY0xJqu3du3fI/Ly8PAMcNxmPHTvWAEc8KjvY+edMN/svu93kXakeOxGRqqS6B7uKNG3aNNOqVatS9RId9OKLL5pevXqVuf3evXubhx56qLylnrCGMWPGmG7dup102926dTN33XXXSbdzKlVUsCvzFQstW7Zk6dKl5OTkMHXqVIYMGXLEWDEVbdSoUdxzzz3B97m5uZU6JsxBVv2GAJgiN8a2sRy6qFhERKqW/v37s3btWrZu3Vrq35Vut5uJEyeWuf1ly5Zx4403nky5x63h008/DQ75IqVT5mDn8XiCV+106tSJ77//nueee44//OEPFBUVkZ2dTUJCQnD5rKys4DguycnJLFy4MKS9rKys4Lxj8Xq9Zbq65VQJuV/svr0QXy+8BYmIiBxFWe9TevPNN5e5/czMTLKysko95lt5ajg8M1S2zZs3k56efsz5q1atokmTJpVY0Ymd9Bgjtm3j8/no1KkTbrebL7/8MniJ85o1a9i8eXNw0MCMjAwee+wxduzYQVJSElByiXFcXNxxv7gqI7YOWAEwTkzmFiwFOxERqaWSk5NP+g4blWXWrFnlWi8lJYWlS5ced35VU6ZgN2rUKPr160eTJk3Yt28fb731FrNmzeLzzz8nPj6em266iXvuuYe6desSFxfHHXfcQUZGRvCecr179yY9PZ3rrruOJ598kszMTEaPHs3w4cOrRI/ciVgOB5anGONzYnZuh5btw12SiIiInCIulyt4lLK6KFOw27FjB9dffz3bt28nPj6etm3b8vnnn9OrVy8AJkyYgMPhYODAgSEDFB/kdDqZNm0at912GxkZGURHRzNkyBAeffTRiv1Up5DlNRgfmF07wl2KiIiISIgyBbtXX331uPMjIiJ44YUXeOGFF465TFpaGtOnTy/LZqsUK8KCXDAHRscWERERqSp0WWdZRbsBMHuzw1uHiIiIyGEU7MrIER8NgNmbE+ZKREREREIp2JWRVb8uAGZP3gmWFBEREalcCnZlZKU0AsDsC9PNfUVERESOQcGujKzUNADsfH11IiIiUrUonZSRo2nLkhd+L2ZfdlhrERERETmUgl1Z1UsGRzEAZuOaMBcjIiLym927d5OUlMTGjRtLvc6gQYN4+umnT1n7FV2DHJ+CXRlZDgdWpB8As/mXMFcjIiI1wdChQ7niiitOup3HHnuMyy+/nKZNm5Z6ndGjR/PYY4+Rk3Pi0R7K0/7OnTu57bbbaNKkCV6vl+TkZPr06cO3335brhqqiu7dux/1nrzHml5ZFOzKwRFT8rXZ234NcyUiIiIl8vPzefXVV7npppvKtF7r1q1p3rw5b7zxxilpf+DAgSxZsoQpU6bw888/89FHH9G9e3d2795d5hrkxBTsysGqEwmAydoV5kpERKSm6d69O3fccQd33303derUoUGDBrzyyivk5eVxww03EBsbS4sWLfj0009D1ps+fTperzd4f/aD5s+fT48ePahXrx6WZYU8cnNzAbj00kt5++23j1vX0dqfO3cubrebwsLC4LSNGzdiWRabNm0iOzubOXPm8MQTT3DRRReRlpZG586dGTVqFJdddllI+6Wp4XC2bTN+/HiaNWtGZGQk7dq1Y+rUqSHf5YgRIxgxYgTx8fEkJiby8MMPY4wJLjN16lTatGlDZGQk9erVo2fPnuTlHX9Is6FDhzJ79myee+654He5cePGY06vTAp25WAlxgNg9uSGuRIRETkWYwym0BeexyHBoTymTJlCYmIiCxcu5I477uC2227jyiuv5Nxzz2Xx4sX07t2b6667jvz8/OA6c+bMoVOnTiHtLFu2jO7du9OhQwfmzJnDZ599Rt26denRowfvvPMOcXFxAHTu3JmFCxfi8/mOWdPR2l+6dCmtWrUiIiIiOG3JkiXUqVOHtLQ0YmJiiImJ4cMPPzxu26Wt4XDjx4/n9ddfZ9KkSaxcuZKRI0dy7bXXMnv27OAyU6ZMweVysXDhQp577jmeeeYZ/vWvfwGwfft2Bg8ezI033shPP/3ErFmzGDBgwAn333PPPUdGRgbDhg1j+/btbN++ndTU1GNOr0xluleslHA0SAL2YmeX/odPREQqma+I/D/cE5ZNR73zDER4y71+u3btGD16NACjRo3ib3/7G4mJiQwbNgyAMWPG8NJLL7F8+fJgD9qmTZtISUkJaefOO+9kwIAB/P3vfwcgPT2dwYMHs2jRIq666qrgcikpKRQVFZGZmUlaWtpRazpa+8uWLaNDhw4h05YuXUq7du0AcLlcTJ48mWHDhjFp0iQ6duxIt27dGDRoEG3btg1ZrzQ1HMrn8/H4448zc+ZMMjIyADjttNOYO3cuL7/8Mt26dQMgNTWVCRMmYFkWLVu25Mcff2TChAnB8OX3+xkwYEBwm23atDnhtuPj4/F4PERFRZGcnHzC6ZVJPXblYDUq2flGN58QEZFT4NDQ43Q6qVevXkjgaNCgAQA7duwITisoKAjpOcvKymLu3LncfvvtIW1HR0djWVbItMjIklOMDu0BPNzh7UNJiGvfvn3ItCVLloRMGzhwINu2beOjjz6ib9++zJo1i44dOzJ58uQy13CodevWkZ+fT69evYI9gzExMbz++uusX78+uFzXrl1DPm9GRgZr164lEAjQrl07evToQZs2bbjyyit55ZVX2Lt3b6m2X1Wpx64crKbNATA+D8bvx3LpaxQRqXK8npKeszBt+2S43e6Q95ZlhUw7GFRs2w5OS0xMDAklixYtwrbtYO/ZodPPPvvskGl79uwBoH79+ses6fD2A4EAK1asOKLHbvHixQwcODBkWkREBL169aJXr148/PDD3HzzzYwdO5ahQ4eWqYZD7d+/H4BPPvmERo0ahczzekvXW+p0OpkxYwbfffcdX3zxBRMnTuShhx5iwYIFNGvWrFRtVDXqsSsHK/V0wAbjxGzVkCciIlWRZVlYEd7wPA7rEasMHTp0YNWqVcH3B0PfoRcCLF++nG+++Yarr746ZN0VK1bQuHFjEhMTS93+mjVrKCwsDDk8O2/ePLZu3XpEL97h0tPTj7hAoTQ1HN6G1+tl8+bNtGjRIuRx6HltCxYsCFlv/vz5nH766TidTqDk5+S8885j3LhxLFmyBI/HwwcffHDC7Xs8HgKBI28veqzplUXBrhwsjxfLWwSA2bQ2zNWIiIhAnz59WLlyZbBXrUuXLkRGRnL//fezevVqPvnkEy677DKGDx9+xJWzc+bMoXfv3mVqf+nSpQBMnDiRtWvX8umnn3L99dcDUFRU8jty9+7d/O53v+ONN95g+fLlbNiwgffee48nn3ySyy+/vMw1HCo2Npb77ruPkSNHMmXKFNavX8/ixYuZOHEiU6ZMCS63efNm7rnnHtasWcN///tfJk6cyF133QWUhL7HH3+cH374gc2bN/P++++zc+dOWrVqdcLtN23alAULFrBx40Z27doVDNLHml5ZFOzKyYouuWLG/Lo5zJWIiIiUnPTfsWNH3n33XaDkkOa7777LwoULadu2LXfddRcjRow44g4PhYWFfPjhh8ELM0rb/tKlS+nTpw+//PILbdq04aGHHmLcuHHExcXx/PPPAxATE0OXLl2YMGECF154Ia1bt+bhhx9m2LBh/OMf/zhuDZMnTz5hz+df/vIXHn74YcaPH0+rVq3o27cvn3zySchh1Ouvv56CggI6d+7M8OHDueuuu7jlllsAiIuL45tvvuHiiy/mjDPOYPTo0Tz99NP069fvRF839913H06nk/T0dOrXr8/mzZuPO72yWOZkr8kOg9zcXOLj48nJyQleql3ZCu+6lcBGJ+4LE/HcOy4sNYiISInCwkI2bNhAs2bNjjjBvzb55JNPuP/++1mxYgUOR+n6bl566SU++OADvvjiizK1369fP8455xz++te/nmzZR61h7NixzJ49m1mzZpW73e7du9O+fXueffbZk67xVDvez3BZco/O+i8nR91YAhvzMbuq99UzIiJSc/Tv35+1a9eydevWUo+f5na7mThxYpnbX7ZsGTfeeOPJlHvcGj799NOQXj0pHQW7crKS6gH5mOyCcJciIiISVNb7lN58881lbj8zM5OsrKxSjflW3hoWLlxYIW2Xx+bNm0lPTz/m/FWrVtGkSZNKrKj0FOzKyWqYAmzB5IbvyhcREZFwSE5OPum7a1SG8h7GTUlJCV4ccqz5VZWCXTk50k4DFmAXOMNdioiIiFQgl8tFixYtwl1Gueiq2HKy0s4oeRHwYLJ3hbcYERERERTsys2qmwTOA2PZbVgd5mpEREREFOxOiiOy5Pw6e8uGMFciIiIiomB3UqzYkq/PbNsW5kpERASo9FH+RSpKRf3s6uKJk2DViYLtAUyWzrETEQknj8eDw+Fg27Zt1K9fH4/HE5b7tYqUlTGGoqIidu7cicPhwOPxnFR7CnYnwUqsA+zC3rMv3KWIiNRqDoeDZs2asX37drbpKIpUQ1FRUTRp0qTUdww5FgW7k+Bo2ADYhcktCncpIiK1nsfjoUmTJvj9fgIBjTEq1YfT6cTlclVIL7OC3UmwGjUBVmLy1N0vIlIVWJaF2+3G7XaHuxSRsNDFEyfBaloylp3xeTBFvjBXIyIiIrWdgt1JsBqdBlYAcGC2rA93OSIiIlLLKdidBMvlwvIeGKR449owVyMiIiK1nYLdSbKiS57Nts3hLURERERqPQW7k+SI9wJgZ+4IcyUiIiJS25Up2I0fP55zzjmH2NhYkpKSuOKKK1izZk3IMt27d8eyrJDHrbfeGrLM5s2b6d+/P1FRUSQlJXH//ffj9/tP/tOEgVUvFgCzKzu8hYiIiEitV6bhTmbPns3w4cM555xz8Pv9PPjgg/Tu3ZtVq1YRHR0dXG7YsGE8+uijwfdRUVHB14FAgP79+5OcnMx3333H9u3buf7663G73Tz++OMV8JEql9WgPpCHyS4IdykiIiJSy5Up2H322Wch7ydPnkxSUhKLFi3iwgsvDE6PiooiOTn5qG188cUXrFq1ipkzZ9KgQQPat2/PX/7yFx544AEeeeSRk76VRmWzGjYCNmLv0/0JRUREJLxO6hy7nJwcAOrWrRsy/c033yQxMZHWrVszatQo8vPzg/PmzZtHmzZtaNCgQXBanz59yM3NZeXKlUfdjs/nIzc3N+RRVTianAaAKXBhdPNpERERCaNy33nCtm3uvvtuzjvvPFq3bh2cfvXVV5OWlkZKSgrLly/ngQceYM2aNbz//vsAZGZmhoQ6IPg+MzPzqNsaP34848aNK2+pp5TV7MySF7Yb9mRBYsPwFiQiIiK1VrmD3fDhw1mxYgVz584NmX7LLbcEX7dp04aGDRvSo0cP1q9fT/Pmzcu1rVGjRnHPPfcE3+fm5pKamlq+wiuYFZsALh/4vZiNP2Mp2ImIiEiYlOtQ7IgRI5g2bRpff/01jRs3Pu6yXbp0AWDdunUAJCcnk5WVFbLMwffHOi/P6/USFxcX8qhKHFElh2DtzRvCXImIiIjUZmUKdsYYRowYwQcffMBXX31Fs2bNTrjO0qVLAWjYsKQnKyMjgx9//JEdO34b923GjBnExcWRnp5elnKqDCvWCYDZvi3MlYiIiEhtVqZDscOHD+ett97if//7H7GxscFz4uLj44mMjGT9+vW89dZbXHzxxdSrV4/ly5czcuRILrzwQtq2bQtA7969SU9P57rrruPJJ58kMzOT0aNHM3z4cLxeb8V/wkpg1YmGrcWYnbvDXYqIiIjUYmXqsXvppZfIycmhe/fuNGzYMPh45513APB4PMycOZPevXtz5plncu+99zJw4EA+/vjjYBtOp5Np06bhdDrJyMjg2muv5frrrw8Z9666cdSvA4C9e3+YKxEREZHarEw9dsaY485PTU1l9uzZJ2wnLS2N6dOnl2XTVZrVMAXYgckpDncpIiIiUovpXrEVwHFGKwDs/RrLTkRERMJHwa4CWGd2BGwIuDGbfw53OSIiIlJLKdhVACsyCivKB4BZtTS8xYiIiEitpWBXQRx1SoY8sdevC3MlIiIiUlsp2FUQR4MEAOxfs46/oIiIiMgpomBXQRxNS25xZu/MC3MlIiIiUlsp2FUQ6+CVsTn6SkVERCQ8lEIqiOOss0te+L2YrC3hLUZERERqJQW7CmLF1cHyFgJgr1gc5mpERESkNlKwq0COhJJne92asNYhIiIitZOCXQWykuIAsLdsC3MlIiIiUhsp2FUgR1oKACZrX5grERERkdpIwa4COVq0BMDOCXMhIiIiUisp2FUgx1kdATC+CEz2rjBXIyIiIrWNgl0FspIaY7lL7hlrr/whzNWIiIhIbaNgV8GsOBsA++fVYa5EREREahsFuwrmqB8NgNn8a5grERERkdpGwa6COVKTAbAzdQWFiIiIVC4FuwrmaN4CAHtvIMyViIiISG2jYFfBrPQOAJgCL6YgL8zViIiISG2iYFfBrNQW4CwGHJhVi8JdjoiIiNQiCnYVzHI4cMT6AbDXrAxzNSIiIlKbKNidAo7ECADsDZvCXImIiIjUJgp2p4CjURIAdubeMFciIiIitYmC3SlgndYMAHtPcZgrERERkdpEwe4UcJzZFgCT58EU+cJcjYiIiNQWCnangNW8NVh+ME7M2uXhLkdERERqCQW7U8Byu3HElByGtVevCHM1IiIiUlso2J0iVj0PAPYvv4S5EhEREaktFOxOEUfDugCYbbvCXImIiIjUFgp2p4ijWVMA7N2F4S1EREREag0Fu1PEcWYbAOx9boxth7kaERERqQ0U7E4R68wOYAXAdmNWLw53OSIiIlILKNidIpY3Akd8EQD2onlhrkZERERqAwW7U8jROBaAwOr1Ya5EREREaoMyBbvx48dzzjnnEBsbS1JSEldccQVr1qwJWaawsJDhw4dTr149YmJiGDhwIFlZWSHLbN68mf79+xMVFUVSUhL3338/fr//5D9NFeNs2RwA+9fcMFciIiIitUGZgt3s2bMZPnw48+fPZ8aMGRQXF9O7d2/y8vKCy4wcOZKPP/6Y9957j9mzZ7Nt2zYGDBgQnB8IBOjfvz9FRUV89913TJkyhcmTJzNmzJiK+1RVhKNjVwDsHA/Gp6tjRURE5NSyjDGmvCvv3LmTpKQkZs+ezYUXXkhOTg7169fnrbfe4v/+7/8AWL16Na1atWLevHl07dqVTz/9lEsuuYRt27bRoEEDACZNmsQDDzzAzp078Xg8J9xubm4u8fHx5OTkEBcXV97yTznj95N/1W0Q8BAx6vc4u/YMd0kiIiJSzZQl95zUOXY5OTkA1K1bMhjvokWLKC4upmfP3wLMmWeeSZMmTZg3r+QCgnnz5tGmTZtgqAPo06cPubm5rFy58qjb8fl85ObmhjyqA8vlwlG3ZKgTe8kPYa5GREREarpyBzvbtrn77rs577zzaN26NQCZmZl4PB4SEhJClm3QoAGZmZnBZQ4NdQfnH5x3NOPHjyc+Pj74SE1NLW/Zlc6ZVg8Ae92WMFciIiIiNV25g93w4cNZsWIFb7/9dkXWc1SjRo0iJycn+NiypfqEJEd6KwAC2wvCXImIiIjUdOUKdiNGjGDatGl8/fXXNG7cODg9OTmZoqIisrOzQ5bPysoiOTk5uMzhV8kefH9wmcN5vV7i4uJCHtWF45zzATB5EZhs3TdWRERETp0yBTtjDCNGjOCDDz7gq6++olmzZiHzO3XqhNvt5ssvvwxOW7NmDZs3byYjIwOAjIwMfvzxR3bs2BFcZsaMGcTFxZGenn4yn6VKcjQ5HctbCFjY338T7nJERESkBnOVZeHhw4fz1ltv8b///Y/Y2NjgOXHx8fFERkYSHx/PTTfdxD333EPdunWJi4vjjjvuICMjg65dS4b+6N27N+np6Vx33XU8+eSTZGZmMnr0aIYPH47X6634T1gFOOo7CfwKgeXLcPYacOIVRERERMqhTMHupZdeAqB79+4h01977TWGDh0KwIQJE3A4HAwcOBCfz0efPn148cUXg8s6nU6mTZvGbbfdRkZGBtHR0QwZMoRHH3305D5JFeY4LZnAr7uxN2wPdykiIiJSg53UOHbhUl3GsTso8Pl7FL44C8tbSNS7r4a7HBEREalGKm0cOykdxzndAIPxRWC2/hLuckRERKSGUrCrBFbdJKzokluKBRbqAgoRERE5NRTsKokzOQIAe+VPYa5EREREaioFu0riaFFytwx7k8ayExERkVNDwa6SODqcDUBgtwNj22GuRkRERGoiBbtK4mh/HlgBCHgwqxeHuxwRERGpgRTsKokVGYUjvggAe9G8MFcjIiIiNZGCXSVyNI4FILB6fZgrERERkZpIwa4SOVs2B8D+NTfMlYiIiEhNpGBXiRwdS+6Xa+d4ML7CMFcjIiIiNY2CXSWyzuwIziIwTuwlc8NdjoiIiNQwCnaVyHK5cCaWDHViL/guzNWIiIhITaNgV8mcZ5YMVBxYtTnMlYiIiEhNo2BXyRznXwhAYIdD59mJiIhIhVKwq2SOjheCywe2G/u7L8JdjoiIiNQgCnaVzHK5cKY4AQjM00DFIiIiUnEU7MLA2boFAIG1WWGuRERERGoSBbswcHbvBYC9x4vJ3hXmakRERKSmULALA0fL9lgRBYCDwKxPwl2OiIiI1BAKdmHibBIFQOCHJWGuRERERGoKBbswcXZsDUDgF903VkRERCqGgl2YOLtfDBhMXiT2lvXhLkdERERqAAW7MLEaNsERVzJAsf21zrMTERGRk6dgF0bO0+oAEFi2OsyViIiISE2gYBdGjnM6ARDY4sPYdpirERERkepOwS6MnN0uBiuA8UVgVv0Q7nJERESkmlOwCyMrNgFHvWIAAt98GeZqREREpLpTsAszZ8uGAARW/BLmSkRERKS6U7ALM+e55wEQyLQwxcVhrkZERESqMwW7MHN06QnOYgi4sRfMDHc5IiIiUo0p2IWZ5XbjTDYABL6dG+ZqREREpDpTsKsCnG1bABBYuS3MlYiIiEh1pmBXBTj7/x9gsHOisNevCHc5IiIiUk0p2FUBjtTmOOqW3F4sMO39MFcjIiIi1ZWCXRXh6tAMAP/SjeEtRERERKqtMge7b775hksvvZSUlBQsy+LDDz8MmT906FAsywp59O3bN2SZPXv2cM011xAXF0dCQgI33XQT+/fvP6kPUt05+w8EwN4Tgb1lfZirERERkeqozMEuLy+Pdu3a8cILLxxzmb59+7J9+/bg47///W/I/GuuuYaVK1cyY8YMpk2bxjfffMMtt9xS9uprEEfzdBzxBYBF4ON3w12OiIiIVEOusq7Qr18/+vXrd9xlvF4vycnJR533008/8dlnn/H9999z9tlnAzBx4kQuvvhi/v73v5OSklLWkmoMZ7tU7G924V+0Dne4ixEREZFq55ScYzdr1iySkpJo2bIlt912G7t37w7OmzdvHgkJCcFQB9CzZ08cDgcLFiw4ans+n4/c3NyQR03k6n8FAPYuD2b75vAWIyIiItVOhQe7vn378vrrr/Pll1/yxBNPMHv2bPr160cgEAAgMzOTpKSkkHVcLhd169YlMzPzqG2OHz+e+Pj44CM1NbWiy64SHGd2wBFbADjwT9PhWBERESmbCg92gwYN4rLLLqNNmzZcccUVTJs2je+//55Zs2aVu81Ro0aRk5MTfGzZsqXiCq5inK1LDmEHvl8d5kpERESkujnlw52cdtppJCYmsm7dOgCSk5PZsWNHyDJ+v589e/Yc87w8r9dLXFxcyKOmcvW7FIDADhdm1/YwVyMiIiLVySkPdr/++iu7d++mYcOGAGRkZJCdnc2iRYuCy3z11VfYtk2XLl1OdTlVnqNdBlZUARgngU/eC3c5IiIiUo2UOdjt37+fpUuXsnTpUgA2bNjA0qVL2bx5M/v37+f+++9n/vz5bNy4kS+//JLLL7+cFi1a0KdPHwBatWpF3759GTZsGAsXLuTbb79lxIgRDBo0qFZfEXso11mJAPgX/BjmSkRERKQ6KXOw++GHH+jQoQMdOnQA4J577qFDhw6MGTMGp9PJ8uXLueyyyzjjjDO46aab6NSpE3PmzMHr9QbbePPNNznzzDPp0aMHF198Meeffz7//Oc/K+5TVXPO3iUDOge2OTE5u0+wtIiIiEgJyxhjwl1EWeXm5hIfH09OTk6NPN/O2DYFg2/GFEbiHdQG1+Bbw12SiIiIhElZco/uFVsFWQ4HrjPrAOD/bkmYqxEREZHqQsGuinL27gVAYAu6OlZERERKRcGuinJk9MaKLgDjwv/u5HCXIyIiItWAgl0VZTkcuLs0BcA/b214ixEREZFqQcGuCnNdNRSsAHZuJIEFX4a7HBEREaniFOyqMKthE5ypJRct+z/8X5irERERkapOwa6Kc/XpBoB/TQFmf26YqxEREZGqTMGuinP2vQrLWwgBD4H3J4e7HBEREanCFOyqOMvlwtU+CYDi2cvCXI2IiIhUZQp21YDrD9cABntXBPaqH8JdjoiIiFRRCnbVgKN5a5wNfAD433snzNWIiIhIVaVgV024ftcZAP+PezFFvjBXIyIiIlWRgl014bz8OnD5MMVeAtP+G+5yREREpApSsKsmrMgoXOmxAPhnfBvmakRERKQqUrCrRtwDrwQgsM2FveGnMFcjIiIiVY2CXTXiaH8ujnoFgAP/a6+EuxwRERGpYhTsqhn3ZSV3oij+cT9mT1aYqxEREZGqRMGumnFecg1WdAHYbopfezHc5YiIiEgVomBXzVguF+4erQHwz/8VU5Af5opERESkqlCwq4ZcV/8Ry+3DFEXg/+/L4S5HREREqggFu2rIiozG1aUhAMUzf8TYdpgrEhERkapAwa6act9wOziKMXmRBKa9Fe5yREREpApQsKumrMSGuM6KBqD4f1+HuRoRERGpChTsqjH3DTcBNvauCALzZ4a7HBEREQkzBbtqzNG8Nc6mBoDit94LczUiIiISbgp21Zz72j8AENjkwF6zNLzFiIiISFgp2FVzznMuwplUCDgofkW3GRMREanNFOxqAPd1AwDwr7Wxl88PczUiIiISLgp2NYDzwv44U4oAB0X//He4yxEREZEwUbCrITzDrgcMgS1uAvNmhLscERERCQMFuxrC0fECnM1K7kBR/O+3w1yNiIiIhIOCXQ3iufWPgE1gRwSBL6aGuxwRERGpZAp2NYjjzA64WrkAKHpzuu4hKyIiUsso2NUw7tvvBMuPnR1J4IMp4S5HREREKpGCXQ3jaHI67g5xABT9vzmY4uIwVyQiIiKVpczB7ptvvuHSSy8lJSUFy7L48MMPQ+YbYxgzZgwNGzYkMjKSnj17snbt2pBl9uzZwzXXXENcXBwJCQncdNNN7N+//6Q+iPzGfftIcBZh8iLxv/FCuMsRERGRSlLmYJeXl0e7du144YWjB4Ynn3yS559/nkmTJrFgwQKio6Pp06cPhYWFwWWuueYaVq5cyYwZM5g2bRrffPMNt9xyS/k/hYSw6qfgzkgGoHj6j5jsXWGuSERERCqDZYwx5V7Zsvjggw+44oorgJLeupSUFO69917uu+8+AHJycmjQoAGTJ09m0KBB/PTTT6Snp/P9999z9tlnA/DZZ59x8cUX8+uvv5KSknLC7ebm5hIfH09OTg5xcXHlLb9GM3n7KLjxLkxhJO72EXjGPR3ukkRERKQcypJ7KvQcuw0bNpCZmUnPnj2D0+Lj4+nSpQvz5s0DYN68eSQkJARDHUDPnj1xOBwsWLDgqO36fD5yc3NDHnJ8VnQsnsHdAChemo+99LswVyQiIiKnWoUGu8zMTAAaNGgQMr1BgwbBeZmZmSQlJYXMd7lc1K1bN7jM4caPH098fHzwkZqaWpFl11iuK4YEbzXmm/iqhj8RERGp4arFVbGjRo0iJycn+NiyZUu4S6o2PHcPByuAvSuCwNuTwl2OiIiInEIVGuySk0tO2M/KygqZnpWVFZyXnJzMjh07Qub7/X727NkTXOZwXq+XuLi4kIeUjqNle9xd6wJQ9MEiXUghIiJSg1VosGvWrBnJycl8+eWXwWm5ubksWLCAjIwMADIyMsjOzmbRokXBZb766its26ZLly4VWY4c4L5jFFZEAaYoguJnHg93OSIiInKKlDnY7d+/n6VLl7J06VKg5IKJpUuXsnnzZizL4u677+avf/0rH330ET/++CPXX389KSkpwStnW7VqRd++fRk2bBgLFy7k22+/ZcSIEQwaNKhUV8RK2YVcSLGsQBdSiIiI1FBlHu5k1qxZXHTRRUdMHzJkCJMnT8YYw9ixY/nnP/9JdnY2559/Pi+++CJnnHFGcNk9e/YwYsQIPv74YxwOBwMHDuT5558nJiamVDVouJPyKbxtGIFtHhz1Coj45z+xXK5wlyQiIiInUJbcc1Lj2IWLgl352D8vp+BPL4Bx4endCPfwB8NdkoiIiJxA2Maxk6rNcUZbPD1KhoopmrkJe9UPYa5IREREKpKCXS3juu3POBILwXbhe+pFTHFxuEsSERGRCqJgV8tYLhfeB+4CRzH2nkj8L+gqWRERkZpCwa4WcpzRFk/fFgAUzdqOvXx+mCsSERGRiqBgV0u5ht2HM6kQjBPf3/+JKfKFuyQRERE5SQp2tZTlcOD5873gLMbOiaT4ub+EuyQRERE5SQp2tZijeTqeS1oBUPztLgLzZ4a5IhERETkZCna1nGvoXThTikoOyU54C7Nnx4lXEhERkSpJwa6WsxwOvOPGYnkLMYWR+MY+jLHtcJclIiIi5aBgJ1hJjfGOGADYBDa78E96ItwliYiISDko2AkAzgv747koGYCiLzYRWPBlmCsSERGRslKwkyDXnQ/jbOg7cL7dmzrfTkREpJpRsJMgy+HA++gjJefbFeh8OxERkepGwU5CHHG+nW45JiIiUm0o2MkRQs63m7mVwPS3w1yRiIiIlIaCnRyV686HcZ1mAw4K//Ul9srvw12SiIiInICCnRyV5XDg+euTOBIKIOCh8K8vYfZkhbssEREROQ4FOzkmKzoW718fwvIUYvIj8f35QUxxcbjLEhERkWNQsJPjcqQ2x3vvYLD8BLIiKPrLA+EuSURERI5BwU5OyNm1J96r2gPgX+aj+NVnwluQiIiIHJWCnZSK6+rbcJ8TDUDRR2vxf/RGmCsSERGRwynYSam5//w4zqYBwIHvtTkEvv0s3CWJiIjIIRTspNQslwvv357BUb8QbBeFz0zFXvVDuMsSERGRAxTspEysyCgi/v4EjrgC8HspHPciZusv4S5LREREULCTcrASEon422isiAJMYSSFf/4rJntXuMsSERGp9RTspFysRqcRMeY2cPmwcyMpvO8BTN6+cJclIiJSqynYSbk5zjqHiHv+Dxx+7J0R+O4diSnID3dZIiIitZaCnZwU53l9ibi1J1gBAtu9+O67G1PkC3dZIiIitZIr3AVI9efscyVenw/fv+cS+NWN7/678P59IpbbHe7SRKqNYr+f7P255Oblsb8gj7zCAvILCinwFeIrKqLIX0yx309xsR+/348/ECBg29gBG9s22LaNMQZjG4wxABhD8DUHnoLPHOP98VgnmHbgtWUd+qbklWWVvLBKZmJZ1iGPkvcOh+O315YDy2HhsCwcjoPzHDidDizLwulw4HA4cDqcB15bOJ0unAeWLZnuxOVy4rAcuFxOXM4D0w55drvcuJwOXE4XTqcTj8uF48B6HqcLl9OFw6E+EKk+FOykQrguuxaKi/G9vpDARidFf74bzxPPYbn0Iya1SyAQIHPPLjL37GLn3j3szc1lX34eBYU+inzF+IsDmIAB28KJA5flwm258Dg9ZdiKExfO0v0Fbh32XNmOFSjLwD7wAJviA68qk21KQrONwRj7wLMh+N+hrwEOe1XyDFiHfAlHCcQHQzAHwi4HX3NoMD4YjkNDccmzAyxwhITmknkOqyQ0YzlwHBaeDy7nOFBAybIHplnWgYDtCG7j4HTHoetaDji4/KHPDgcWhyx3MLxzlCDPgc/hOPT9ge0c+PCOA2G/ZN6h6/+2Dfitbovf/iHhCM4/yvtD2j/43jrw2nFgW8Ah8w/unpLp7ir0u67qVFLFFPv9fDrvG37euIk7Bl2D112Wv3RrJ9fAG6DIh+/tZfjXOeChkXjGP4elf+1KDWHbNlt2bOeXbb+ybccOsnP3kV9QQLEvgBWw8FgeIp0RJb/kQlh4icBLRMlb54HHUQRMgGLbT8AESh4lMaIkFFiA48Avcsdvv1gdB3qsjni2HFgOB07Hb78ES36ZHggLB36RH/wFetTwd6DXz2BKXh+IKr/1Ch7oJTzw/QRDzoGeQ9scCD7GYNuh80oeHPLaHLI94MB8OPB8eEg8NCya4K/w4Gc7+B8HAkNJVHAEQ0zJsyMYLI7FcSAwHWOXnRoVEIgPb+5gU3ZwSqBiGq/ligLF3PnHq8NdRpCC3XGsX7mVGFcMc5ctosfZGeEup1pwDb4Vip7H9/5q/KttGHUXnscmqOdOqg3bttm6K4vVmzawJTOTPXtzKCooxmk7iXJG4Xb89rPsxE0s7pKzlQ/LBQX+QopMEQErAE5wuZy4PS4iIjxERUYSExlFTFQUsdHRxEXFUCc2jvjYWGIioir3AwtQst/9AT9FAT9+fwB/wE/xgUPeth0Ivi45BB4gELAJmAB+/4FD4nbJc/AQuR3Atm0CtimZF7BL3h/s/bNt7APPxjbYxg4JvgffY37rMTw8BP8WgH+bDoQE5IOCR+QPPzR/+OujvQ9Osw7L/sd+f8xXVuhav8XxQ/5vhU4Lvjv4j5BDp1uETDm4bm2m37bH4Ha5KHQVEkkEy9f8rGBXBq4hd2L8z1D00bqScPenO0sOy+qcO6licvP3s/Tn1azfspmdu/fiLwwQZUUR4fQGl4khJqSHzTY2ef58iq1iLLdFRISH2Jgo6sbF0yCxPimJ9UlJTFIvfzXjcDjwODx4tN9qhIO9x8CBXmP7QK/yb4fTgQO9zSW9ySUTDjuEfqDX+eDrY02rShTsjiO1UQP2b/VRmFsU7lKqHfdN92B5JuKbugr/egfm3hF4n3oOyxsR7tKklvIVF7FozUpWrV/Prl17sYocxLpigofg4ogL/o1ojGG/P49iRzGeSDd1EuJITqxHs5TGNGuYSqTXe5wtiUi4HXrBS6UeQq8CFOyOo1unc/hk61zquBPYnLWNJg1Swl1SteK67g7w/BPffxcT2OTCN/IOvE8/ixUZHe7SpBbI3pfLtz8u5ucNmyjY5yPWEYPbUdJrnEACHOhAzvcXUGgV4o3ykFy/HqenpdEqrTmxUfo5FZHqp8KD3SOPPMK4ceNCprVs2ZLVq1cDUFhYyL333svbb7+Nz+ejT58+vPjiizRo0KCiSzlpqUkN2VucTR13ArMWf8/1/S4Pd0nVjusPt4B3Cr7J8whs9eC7+y68Tz+DFRMX7tKkhskvLGDWkoWsXr+Bwn3FxLticVpOIoki0lVy3lphwEc++UTFRpDWKIVOZ6brH2wiUqOckh67s846i5kzZ/62kUNOnB85ciSffPIJ7733HvHx8YwYMYIBAwbw7bffnopSTlpkvAfyYevWHeEupdpyXTEEy+Ol8J9fE8j0Unjn3UT8fTxW3aoX5qV6WbVxHd8uXcLOrL3EWDF4HG6iiCbqQG/cvuL9BDx+GjasT/szzqR1s9NxOmvbgRkRqU1OSbBzuVwkJycfMT0nJ4dXX32Vt956i9/97ncAvPbaa7Rq1Yr58+fTtWvXU1HOSWnbsiU/L9lChD+CYr+/So1VU504Lx5EhMdL4YufYu+OpPCOUXjHj8LR5PRwlybViG3bzF2+mEUrVlK0z0+8u6Tnt66zDkDJRQ3uIhqlJNG5dVtapjYLZ7kiIpXulKSUtWvXkpKSQkREBBkZGYwfP54mTZqwaNEiiouL6dmzZ3DZM888kyZNmjBv3rxjBjufz4fP99ttqnJzc09F2Ud1ftuOLP9hHZGuCL77cQndOpxTaduuaZw9f09EXDy+J9/C3h9J4f3jiRhzG46z9J3KsRX7/Xy5aD4/rl6DVeAkxhVdcnjVDQE7QI6dS3y9GM5p3ZqzW7bWXQJEpFar8GDXpUsXJk+eTMuWLdm+fTvjxo3jggsuYMWKFWRmZuLxeEhISAhZp0GDBmRmZh6zzfHjxx9x3l5l8bg95DvyicDL0tWrFexOkrPz74j4ax0Kx07EFEZSMOZlIu7LxpnRK9ylSRVi2zbzVi5j3tKlWHkOol1RxB64arUoUESeI4+mTRvRp8t51I1LCHe5IiJVRoUHu379+gVft23bli5dupCWlsa7775LZGRkudocNWoU99xzT/B9bm4uqampJ11raaWk1Kdwu5/87MJK22ZN5jizA5HPjKbwgb9i74uk8Mn3iBi2G+fFg8JdmoTZ6k2/8MW8bynYW0S8O65kDDlXyUUPhe5CWjZvSq8u52oQXxGRYzjlJ4wlJCRwxhlnsG7dOnr16kVRURHZ2dkhvXZZWVlHPSfvIK/XizeM40Z163gOn38yjwRXPFt3ZtGovk76P1lWo9OIeP5vFN4/CntXBIUvz8aTuR33jSPDXZpUsvzCAv435ys2/LKVOo4EPFYEHncExbaf/dY+WrZoSt+MC4iKKN8/DEVEapNTfjLK/v37Wb9+PQ0bNqRTp0643W6+/PLL4Pw1a9awefNmMjKq7p0dmjVsTHZxDpZlMXvx9+Eup8aw6iYR8Y/ncab5AQdF/1tH0V//hLEr/ybfUvkWr1nF31//Ny9PnkrOpnzqOutgWRZ7/HuJbuThmkH9GDXsFgZc1FuhTkSklCq8x+6+++7j0ksvJS0tjW3btjF27FicTieDBw8mPj6em266iXvuuYe6desSFxfHHXfcQUZGRpW8IvZQnlgXFMLmX7eHu5QaxYqMxvvMCxQ//iDFi/Io/j4Pe+TteP/2tAYyroGKiov435yvWPvzZuq66hBDLLhgvz8PV5yDPueeR6umzcNdpohItVXhwe7XX39l8ODB7N69m/r163P++eczf/586tevD8CECRNwOBwMHDgwZIDiqq71Gafzy/JteIq9BAIBjYVVgSyXC8+YJ7H+9TRFH68jsNFJ4e13EPG3cVgNKu9cSjl1tuzI5MOvZ+Lb6yfGFU1dVx1sY5NtcjirVQsuzvi9hhISEakAlqmKd7A9gdzcXOLj48nJySEurnLuYOArLmLSv9/D6/TQunNzzm/bsVK2W9sEpr9N4b++goAbK7KAiAdvwdG2avfmyrEtWrOSmd/OI9ofjctREtzy/QVYsYZLunXj9MZNw1ugiEg1UJbco38il5LX7SHPysOLh8WrVinYnSLOiwcR2bAxheNfwxREUvDIv/EOXYvrsuvCXZqUkm3bfLZgDkuXr6Gusw7xxIMD9hZnk9qsAdd3v0TnzImInCIKdmWQ3DCRoqwA+/fmh7uUGs3R4XwiJzSicPRfsPdE4nv1O8z69bjuGoOlwWerrKLiIqZ+9QWbf8kkwR1PXeeBw63kkNGpHRd1vDLcJYqI1Hg6FFsGa3/dyJeffY9tbAYM7EFy3cRK23ZtZAryKXr4fvxrS967TrPx/PVJrOjY8BYmIfbl5/HOjE/Zu20fse4YAIoCxfgiCunf7ULSm7YIc4UiItVbWXKPgl0Z/e2lV0lwxxHfJIrBvftX6rZrI2Pb+F94nKKZWwEHjoQCvI/+GUfaGeEurdbblbOXdz7/FN+eYqJcJQMGF/gLcNVx8Ife/UiqUy/MFYqI1Aw6x+4U8h4Y9mTD+q3Ytq37Up5ilsOB+47ROJq/TeG/vsTOjqTw3ifx3nYJzh5XhLu8Wilzzy7e+fxTTK5FhNNLlMvNvuI84htGc1PvAcRFxYS7RBGRWks9dmW0KXMrH330DW6Hi8atErnkvIsqdfu1mb3qBwr/+iImLxKw8fRshGv4gzrvrpJs2ZHJe198hivPjcfpASCnOJeUpolc1bMfXrcnzBWKiNRMZck9+o1YRmnJjfBFlNwz9sfl67B1l4RK40g/m8hJT/92p4qZ2/HdcztmX3a4S6vRNmdt4+//eY0PP/yKqMJoPE4P2cU51G0aw323DOW6fpcr1ImIVBHqsSuHLTu28+GHX+N2uGnYsh6XX/C7Sq+hNjO2jf/5v1D0dSbgwBFTgPfhO3Cc2SHcpdUomzK38t4Xn+MtjMR9YAy6vcXZtGyVxhUX9tRpCCIilUTn2J1iqUkNKYoswu1zs2rFei49r7t+yVUiy+HAffdYHOlTKXz5M+z9kRQ8+BLewV1wXXlTuMur9n7ZtoX/N2MGkb5IYhyx4IA9xdm0adOCW84dqJ91EZEqTD125bR1Zxb/7/0v8TjdJLWIZ0D33mGpo7azN/yE75GnsLNLBrx1tfPieehxLG9EmCurftZv3cL/m/EFUUVRwbtE7PHvpV3bM+jX9UIFOhGRMNE5dpWgUf0GBKKLAVjz02adaxcmjmatiJj0D1xtSs7x8i/zUfjH27E3/BTmyqqPdb9u4snJr/LpJ98S54/D5XCxx7+XtNYN+PMtN9P/XPVIi4hUF+qxOwnbd+/kvalf4HF6SDwtjv/7XZ+w1SLgf+9f+P67EAJucPnw3nARrkuuCXdZVdaaLRv438yviC6OxulwAiU9dB07pNP7nHMV5kREqgidY1dJGtarjx3rh3wP69ZsIdAtgNPpDHdZtZbryptxtO6I77F/YO+LxPfKd9g/LME96q86NHuIFb+sZdqs2cQGYoiz4krOofPv5eyOZ3FrZ932S0SkOlOP3UnK3LOLd977DK/TS92mMVzVs19Y6xEwefsoevRB/KtLDo87EgrwPngnjpbtw1tYmC1d9xOfzp5LvB2Hwyrpjdvj38s5nVrT65xzw1ydiIgci3rsKlFy3USIM5AHm9dlsan1VtKSG4W7rFrNio7F+8REnO/8E98732NnR1Iw6kW8V3bENfjWcJdX6eavXMbX3y0ggQTqWAlgwZ7AXs7t3J7uHdRDJyJSk6jHrgLs3ZfDP9+YSrw7jj3F2dx5wzXERESFuywB7J+X43vs2d+umj3DwjPmr1ixCeEtrBLM/H4eCxf/SF1nneC0PfZeLux6Nue37RjGykREpCzKknsU7CrIil/WMnPGfCKcEey1snnghpt08nkVYXyFFP/tYYoX5wEWVlQB3ruvwdmlR7hLq3CBQICPv53FqlXrqesqCXS2scmxculxXhc6t2ob5gpFRKSsFOzC5LP5c/jlx204LAd2gp/b/29wuEuSQwSmv43v3zMxxV6wArgvaID7roexXNX/jIT9hflM/fJzsjbvId5d8mfCbwfId+dxyUXdSG/aIswViohIeSnYhdErH75HYBcYY0g+oy6/79Yz3CXJIUzWFnzjHiWwtWTcO0diId7R9+Fo1irMlZXP9t07mTrzc4r3BohylRz+9wWK8EcVM6BnT5o1bBzmCkVE5GQp2IWRbdv8fcprxAXi8AWKOL97ezq1PCvcZckhjG3jf/VpiqavA9sFziK8/9cR19W3hbu0Ulv403K+nr+QqOIo3A43APv9eUTX93Jlz74kxtc5QQsiIlJdKNiFma+4iKdfnUxdVx32FefRrXtHndtUBdkrFuJ7YhJ2bsmFFc6mAbyjx2DVTwlzZUdXVFzEh7O/ZN26LcHz5wD2FufQpFkD/q9HH7xuTxgrFBGRU0HBrgrYtnsHb7w7jTh3LEV2MfWbxjO4d/9wlyWHMb5Cip8aS/H3uYADy1OId2hPnP2rzvmRa3/dyPS5cyjODhDjigZKLojIJodz2remR6euulBHRKQGU7CrIrbv3slrUz8IDjeR583jjkFX41GvSpUTmDMd3wtTMQUHhkVJd+IZNQ4rLjyHNH3FRXw852t+XruJOo4ELMsCIN9fgCPOcGm339G8UWpYahMRkcqlYFeFFPv9THz7DaIKS3pa9vj3MvTKK0iplxTmyuRwZl82RY+Pwb8qAIAVWYB32OU4e1xeaTUs/Gk5c35YBHkOol2/jYW4x7+Xps1SuKzb7zRGoohILaNgVwW9M/NTstbvxeN0s684j2YtGzKgey8dQquC/NPeomjKl5iiCMDgauPB88Ajp2xQ43W/buLzed+yb1c+Ce744PR8fwHE2PTKOJc2zc84JdsWEZGqT8Guivph9Qq++nohce5YoKQX5rwuHejW4ZwwVyaHM7u2UzT+L/jXlRwCtSIL8P7xCpwXXVYh7a/fuoUvF37Hrqwc6jh/O9TqtwPkWrm0bNGUS87vroshREREwa4q27svhykf/w/nfldwmIo9Zi+X9biI1qedHubq5HD+D1+n6M3Zh/TeefH8aUy5zr1btXEds374nuyd+6nrTgiZt8e/l4aNE+l/XjeS6tSrmOJFRKRGULCrBn7ZtoV3P/uc+EAclmURsAPkOvbRoU0rep9zrg7RViFH9N5FFOAd2gdnvz8cd738wgK+XryAn9ZtIJBnB+8IASUDWO8NZFO3fhw9unTl9MZNT+VHEBGRakzBrhr5fvWPzJg9L+RG7bnF+4hOjOCybheRmtQwjNXJofwf/YeiN2ZhfBEAuFqA588PBce9CwQCLPp5JUtX/8SunTnEmBg8TndwfdvYZAdySGpYl15dMkhLbhSWzyEiItWLgl019O2PS/j2h8VEFEXgcZacVxUwAXLsXOo3qMMFHTpxZtppYa5STPYuip54NHjlrO0uZlmnRnxTN5lIE0WkKyJk+Tx/PsWeYk5La8Tvzu6qO0KIiEiZKdhVY9n7cvnwmy/ZtnnXEedhZRfn4o5xkH56c7qe1Y46sfFHb0QqnG3bbMraxuLVq9i0dRsNN++k24pM4vMKAdjSoB7ftz6dvV4n+9lPfJ0YOp11Fp1btdFhdREROSkKdjXE4jWrmLd8KTm795PgjMdh/RYQbGOT69+HI8KiQYN6tG1xBmc1a6HBjytAfmEBqzauZ83GDWTu3I0vv4gIE0GUKzJkOUcgQJs1v9D6l604DBhHANf5DfDe8SCWxxum6kVEpKZRsKuBduzdzZc/zGfj5m24ilzEumOOWMZv+9kfyMO4DbFxUaQk1adZSmNaNE4jNio6DFVXXcV+P79s38LGbVvZvmsnu/fk4MsvwmW7iHFFh4Togw6GaSsCGiTVo22LlrRr0RJr+Tx8z/8be09J8HPEFOC59f9wXnBxZX8sERGpgRTsaoFNmVv5ftUKNm3dhi+vmBhHDB6H+5jL5/nz8eHD4bbwRniIiY6iTlwcSXXrkpKYRKP6yUR6a0YvU4HPR+aenWzbtZOs3TvZk5PL/rw8CguLsYtsXMZNtCsSp+U8Zhu+gI88k48rwkn9xDq0SE2j/elnHjMgG78f/+TnKJq+GgIewOA6w4HnvgewGujWXyIiUn4KdrVQIBDg5y0bWb3pF7Zm7WBfbj4UW0Q5IvA6SxfYfAEfPruYAH6Mw2C5LFwuB263C7fbTYTXS1REBBFeDxFeL5HeCCK9EUR5I4j0evF6PHjdHjxuD5FeL26n64Tnl9m2jTGGgmIfRUVFFBYXUejz4SsupqCwkLzCAvIKCygoLKSwyIevqIjCQh+FviKKi/0E/AHsgIGAhQsnHoeHiFJ+Xr8dID+Qj9/hxx3hok5CHI2Tk0lvehqN6yeX69w4s30TRU/9Df/6A+s6i/D0ao7r5nux3McO3iIiIsdSLYLdCy+8wFNPPUVmZibt2rVj4sSJdO7cuVTrKtiVnm3bZO3dzfqtW/g1K5Pde7PJyy/AXxSAgIUbN1HOCJyOY/denXQNxsZgKPlJM1hYWJYVfD5V2ywIFFJkijBOg8vjJDLSS3xcLA0T69OsYSPSkhvhdrlOyfYDX35I0b8/wt5/4PBsXAGemwfg7HbJKdmeiIjUXFU+2L3zzjtcf/31TJo0iS5duvDss8/y3nvvsWbNGpKSkk64voJdxQoEAuzI3sPO7D3szslmb24OuXl55BcUUlR0sGfMLukZs8EyJaHMgROn5cBluXBaTlwVFA4DJkDAtgmYAH4TwCaAjY2xDFhgOS2cB3oSPR43kRERxEZHUSc2nsQ6CTSok0hSQl2czlMXVkvDFPnw//PvFH21CQIlvXWu02zcd92Fo+mZYa1NRESqjyof7Lp06cI555zDP/7xD6CkVyk1NZU77riDP//5zydcX8GuarJtm+KAnwKfj2J/MYVFRYDBtm0Ctl3ybGxcDhcOh4XT4cCyHDidDiLcHjweD5Fub9gDWUUzWzdQ9Mzf8a87MMHhx312Hdx3PFCuW5OJiEjtUqWDXVFREVFRUUydOpUrrrgiOH3IkCFkZ2fzv//974h1fD4fPp8v+D43N5fU1FQFO6lWAt9+RtEr72LvLTk8a3kKcfdrjev6O7BO0SFhEakcxrbBXwzFRRDwg7+o5LW/GOP3g99fMj34CEDAjzn42g4ceLbBHwBjlyxn22DbGGMHX2ObkudDpxnz27OxMbY58PrAr/iD8yg58gKHzD+4zKHPR5t26HPIhz9kWnCxUxctjnoKjxWceayVjv58rPnHeh3yfOC124X7xpGlKb3cyhLsKv23ya5duwgEAjRo0CBkeoMGDVi9evVR1xk/fjzjxo2rjPJEThnneX2JyOhN4N1XKPpgAaYwkqL/rcP/1S24B/XAefFgLA1mLHJUxrbBVwD7czB5uVCQDwV5mMJ8KCiAwgKMzwe+QigqwhT5oKi45HWxH4qLwR/AFAdKQpbfhoCNCdgQMHDg2diUvLeBA9kJm5LAYiywrQP5yDrwcJQ8oz+7tZbDj/vGcBfxm2rRTTBq1Cjuueee4PuDPXYi1Y3lcOAa9Eecl15N8aS/U/ztdux9kfhe+Q7H+1/iGToQ54X9w12myEkxRT7I3onZuwuTuxdyc0vC2P79mLw8KCjAFBRCoQ9TWARFfozPD0U2xm9DscEELPBbJc+2s+RBRV9s5aByApkN1oHeMuvAA8AyBzp+zCE9TgdeB98f0jlkcaAN67f3h34lFof0KB3aqWQd1uZvy4S8P9G0w6cfMu+4vWjlUYYOv2Dv4LHWOWov4wmWMaWcB+A8NRcBllelB7vExEScTidZWVkh07OyskhOTj7qOl6vF28NGWNNBMCKjsVz7zjcV2+m6MUJ+H8swN4dSeHT03H+90M8twzB0eH8cJcptZjZn4vZuRWzKxP27MJk78Xk5mBy98H+fEyeD1NQBD4/xmcwRUCxAxNwgl3WXy0WUJbhgGxwBMBhYzkNOAyWEwg+LCyXBS4HuBxYLic4HeB2lpz24HKC2wUuF9aBZ9xucLtL5rs9Je9dLiyPp+R9cBkPlst9YJr7wHIHp5W8Lpl3YL7TqZ54qVRhu3iic+fOTJw4ESg56b5JkyaMGDFCF09IrWSvX0Hxiy/hX2dT0oNgcDYJ4LnhWhwdLwh3eVIDmNy9mG0bMVlbMTuyMHt2Y7JzMLl5mH0FmHw/FBqMz8IUu8BUwL/7LT+4AlguG8sFuMHyOMDrxPK6wevGinBDRARWZMSB50iIisKKjILoGKzIaIiOheg4rOgYiI7H8kacfG0i1UiVvngCSoY7GTJkCC+//DKdO3fm2Wef5d1332X16tVHnHt3NAp2UlPZy+ZR9M/XCPz6W++Fs1ER7iGDcHbpEcbKpKoye3ZgtqzHbN2Enbkds3s3Zu8+TG4BZr8fUwCmyF2OXjQAG9zFWG4bywtWhAMr0g1RHqzoKKyYKIiJwYqNw4qPh/g6WPF1seokQly9knAmIietygc7gH/84x/BAYrbt2/P888/T5cuXUq1roKd1HT2D7MpmvwmgS0uDp6o4mzow/2Hy3B0u0SHdmoJsz8Xs+EnzOZfsLdtxezYhdmzDzunEJNnMIUusMtwCNPyY3n8WBEGK8qBFe3FiovEio+FhASsunWx6tXHqtcAKykFEurrZ02kCqgWwe5kKNhJbWEv/Y6i114nsPG3q+4c8QW4+56N8/9uwvLo3NPqzPgKS4LbL2uwt2zGbN+BvXsfJqcYk+/AFJdy/zqLsSL8WFEWjjgPVnw0Vt0ErMRErKSGWA0bYaU0hbi6Cmoi1ZCCnUgNY69YSPHkKfjXBcCUDOBseQtxZ6Tiuu6PWIkNw1yhHIvZn4tZuxx7/c+YLZuxM3dj787H5ILxeTjhVZkOP1ZkMY4YCys+EqteHFZSfRwpjbAaN8VKbY4VX69SPouIhIeCnUgNZbZuoHjKPyletBP8B3pzLD+u0xy4LrsYx4X91SMTBsbvx/yyCnv1csymjdjbdmDvzDsQ3rwcd9wHK4AVWYQjzoFVNwpHg3pYDVOwmjTFcdqZUL+R9qlILadgJ1LDmbx9+N96meKvV2HyIoPTregC3F2b4Rp0A1ZS4zBWWDOZ/bnYq37ArFmFvWkLduZezF4/dp4n2JN6VM5iHNF+rAQ3jqR4rEYNcaSdhnV6OlZKM915RESOS8FOpJYwto09Zzr+/03H/0vgtyEqrADOZD+uc9vivOxqrITE8BZazZg9O7BX/oD982rMpl+xs3Kxs21MYQTH7H2zAlhRRTgSnDgaxGM1TsFxWgscZ7aDBqnqdRORclOwE6mFzJ4s/G//G/93a7H3/daLhxXA2cjGdV4HnH0HYNU98ZBCtYXZ8Sv2j4uw163B3rwNO2sfJgdM0XHGSXMW4YgL4EiMxNGoPlazZjhatsFq0QbLXZZBdkVESkfBTqSWs5d+h/+T/+H/cSemIPLQOTjq+nCmN8LZ/Xc4OnWr8T1JxrYxv67HrFyMvX4d9q+Z2DvyMLnHv+rU8hRixYMjKQZH42Qcp7fEkd4BGqbV+O9MRKoWBTsRAQ4cqv1hFoHp0/Gv3nNYyCsJL46GLpxnNMFxdhccHS+otkOomNy92GuWYtb/jL1xE3bmHsxuH/Z+93HHerO8hTgSwGoQh6NJo5IA1/psXWksIlWGgp2IHJW9egmBLz8l8OMvBLKOck9Phx9HXT/OxglYaY1xnJmOo3VnrLg64Sn4EMbvx2z9BbNhDWbLRuztWZisvdh7CjD7TjTmm40V6cNRx4kjOR6rSWMcZ5yJ46yzdf6hiFR5CnYickImbx/2nE8JLF2K/UsWgV1OCBytZ8tgRRTiSHD8No5a/cSScdQapWHVrV9yh4Lo2LLX4PfD3h2YXZklt8bavQuy92B27cHek4PJycfsL8bkHxjz7XhXnkLJ+W8xARz1vFgp9XA0bYrj9HSslu1K7jkqIlINKdiJSJkZvx+zfB6B7+dhb/wVO2s/do7jt/HyTsQKgNOP5bJLxty1wLJKnrEAG0wAsC1MwALbAbaTEw7QG8LG8hZhxRgcCRFYifE4UlOwTjsdx+mtNcSLiNRIZck9GjxJRACwXC6sjhfg6HhByHSzfRP2j99jb9iA2bULs2f/b/cq9bkgcOB+tsYJfifGf8i6pd24w4/l9mO5DURYWDFuHPFRWHXjsZLql9wWq0lzrKYtq+05gCIilUHBTkSOy2qYhrNhGsc6CGr8fsjdjdm7E3L2YnL2QnER2DYEAhhjgz8ALhdWRCRERpYcFo2Mhph4rMRkHSYVEakgCnYiclIslwvqNtD4eCIiVYAGYxIRERGpIRTsRERERGoIBTsRERGRGkLBTkRERKSGULATERERqSEU7ERERERqCAU7ERERkRpCwU5ERESkhlCwExEREakhFOxEREREaggFOxEREZEaQsFOREREpIZQsBMRERGpIRTsRERERGoIV7gLKA9jDAC5ublhrkRERETk1DqYdw7mn+OplsFu3759AKSmpoa5EhEREZHKsW/fPuLj44+7jGVKE/+qGNu22bZtG7GxsViWRW5uLqmpqWzZsoW4uLhwlyeloH1W/WifVT/aZ9WL9lf1U1n7zBjDvn37SElJweE4/ll01bLHzuFw0Lhx4yOmx8XF6Q9DNaN9Vv1on1U/2mfVi/ZX9VMZ++xEPXUH6eIJERERkRpCwU5ERESkhqgRwc7r9TJ27Fi8Xm+4S5FS0j6rfrTPqh/ts+pF+6v6qYr7rFpePCEiIiIiR6oRPXYiIiIiomAnIiIiUmMo2ImIiIjUEDUi2L3wwgs0bdqUiIgIunTpwsKFC8NdkgCPPPIIlmWFPM4888zg/MLCQoYPH069evWIiYlh4MCBZGVlhbHi2uebb77h0ksvJSUlBcuy+PDDD0PmG2MYM2YMDRs2JDIykp49e7J27dqQZfbs2cM111xDXFwcCQkJ3HTTTezfv78SP0XtcqJ9NnTo0CP+3PXt2zdkGe2zyjN+/HjOOeccYmNjSUpK4oorrmDNmjUhy5Tm78LNmzfTv39/oqKiSEpK4v7778fv91fmR6k1SrPPunfvfsSfs1tvvTVkmXDts2of7N555x3uuecexo4dy+LFi2nXrh19+vRhx44d4S5NgLPOOovt27cHH3Pnzg3OGzlyJB9//DHvvfces2fPZtu2bQwYMCCM1dY+eXl5tGvXjhdeeOGo85988kmef/55Jk2axIIFC4iOjqZPnz4UFhYGl7nmmmtYuXIlM2bMYNq0aXzzzTfccsstlfURap0T7TOAvn37hvy5++9//xsyX/us8syePZvhw4czf/58ZsyYQXFxMb179yYvLy+4zIn+LgwEAvTv35+ioiK+++47pkyZwuTJkxkzZkw4PlKNV5p9BjBs2LCQP2dPPvlkcF5Y95mp5jp37myGDx8efB8IBExKSooZP358GKsSY4wZO3asadeu3VHnZWdnG7fbbd57773gtJ9++skAZt68eZVUoRwKMB988EHwvW3bJjk52Tz11FPBadnZ2cbr9Zr//ve/xhhjVq1aZQDz/fffB5f59NNPjWVZZuvWrZVWe211+D4zxpghQ4aYyy+//JjraJ+F144dOwxgZs+ebYwp3d+F06dPNw6Hw2RmZgaXeemll0xcXJzx+XyV+wFqocP3mTHGdOvWzdx1113HXCec+6xa99gVFRWxaNEievbsGZzmcDjo2bMn8+bNC2NlctDatWtJSUnhtNNO45prrmHz5s0ALFq0iOLi4pB9d+aZZ9KkSRPtuypiw4YNZGZmhuyj+Ph4unTpEtxH8+bNIyEhgbPPPju4TM+ePXE4HCxYsKDSa5YSs2bNIikpiZYtW3Lbbbexe/fu4Dzts/DKyckBoG7dukDp/i6cN28ebdq0oUGDBsFl+vTpQ25uLitXrqzE6munw/fZQW+++SaJiYm0bt2aUaNGkZ+fH5wXzn1WLe8Ve9CuXbsIBAIhXxxAgwYNWL16dZiqkoO6dOnC5MmTadmyJdu3b2fcuHFccMEFrFixgszMTDweDwkJCSHrNGjQgMzMzPAULCEO7oej/fk6OC8zM5OkpKSQ+S6Xi7p162o/hknfvn0ZMGAAzZo1Y/369Tz44IP069ePefPm4XQ6tc/CyLZt7r77bs477zxat24NUKq/CzMzM4/65/DgPDl1jrbPAK6++mrS0tJISUlh+fLlPPDAA6xZs4b3338fCO8+q9bBTqq2fv36BV+3bduWLl26kJaWxrvvvktkZGQYKxOpuQYNGhR83aZNG9q2bUvz5s2ZNWsWPXr0CGNlMnz4cFasWBFyrrFUbcfaZ4eek9qmTRsaNmxIjx49WL9+Pc2bN6/sMkNU60OxiYmJOJ3OI64eysrKIjk5OUxVybEkJCRwxhlnsG7dOpKTkykqKiI7OztkGe27quPgfjjen6/k5OQjLlTy+/3s2bNH+7GKOO2000hMTGTdunWA9lm4jBgxgmnTpvH111/TuHHj4PTS/F2YnJx81D+HB+fJqXGsfXY0Xbp0AQj5cxaufVatg53H46FTp058+eWXwWm2bfPll1+SkZERxsrkaPbv38/69etp2LAhnTp1wu12h+y7NWvWsHnzZu27KqJZs2YkJyeH7KPc3FwWLFgQ3EcZGRlkZ2ezaNGi4DJfffUVtm0H/6KT8Pr111/ZvXs3DRs2BLTPKpsxhhEjRvDBBx/w1Vdf0axZs5D5pfm7MCMjgx9//DEkkM+YMYO4uDjS09Mr54PUIifaZ0ezdOlSgJA/Z2HbZ6f00oxK8Pbbbxuv12smT55sVq1aZW655RaTkJAQciWKhMe9995rZs36/+3df0zU9R8H8Od1cQQccuLx4y5BtCOppHm6UVgSCaHIjGwpo4YeImXaDzZl5lbNWWtZamlsaEvBTTPdbFJtUZkcXlgmdf6gtYjboeiOfqkN5Gfe6/uHXz/fPh0q9AVOzudj+2x8Pvfm/X597r2x570/nw9nF7fbLXV1dZKZmSlGo1F+/fVXERFZsmSJxMfHy4EDB6S+vl5SU1MlNTXVz1XfWNra2sTpdIrT6RQAsmHDBnE6nXLy5EkREXn99dfFYDBIVVWVHD9+XHJzc2X8+PHS2dmp9DFr1iyxWq1y+PBh+eqrryQxMVHy8/P9dUoB72pz1tbWJitWrJCvv/5a3G637N+/X6ZMmSKJiYnS1dWl9ME5Gz5PP/20REREiN1uF4/Ho2wdHR1Km2v9Lfzrr79k0qRJkpWVJUePHpXq6mqJioqSVatW+eOUAt615qypqUnWrFkj9fX14na7paqqSiZMmCBpaWlKH/6csxEf7ERE3nnnHYmPjxedTicpKSnyzTff+LskEpG8vDwxmUyi0+nk1ltvlby8PGlqalJe7+zslKVLl8ro0aMlNDRU5s6dKx6Px48V33hqamoEgM+2cOFCEbn0L09eeukliYmJkeDgYMnIyJCffvpJ1ccff/wh+fn5otfrZdSoUVJYWChtbW1+OJsbw9XmrKOjQ7KysiQqKkqCgoJk3LhxUlxc7PNBl3M2fPqaKwBSUVGhtOnP38Lm5mbJzs6WkJAQMRqNsnz5cunt7R3ms7kxXGvOTp06JWlpaRIZGSnBwcFisViktLRU/vzzT1U//pozzX9PgoiIiIhGuBF9jx0RERE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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphene.medium.plot_sigma(freqs)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32145119",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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  "applications": [
   "Metamaterials, gratings, and other periodic structures"
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  "description": "This notebook demonstrates how to model a graphene metamaterial absorber in Tidy3D FDTD.",
  "feature_image": "./img/graphene_metamaterial_schematic.png",
  "features": [
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   "language": "python",
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   "nbconvert_exporter": "python",
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