{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Microwave frequency selective surface\n",
        "\n",
        "A Frequency Selective Surface (FSS) is a type of electromagnetic\n",
        "structure that allows certain frequencies to pass through while\n",
        "reflecting or blocking other frequencies. It is composed of a periodic\n",
        "array of conductive or dielectric elements that are spaced apart at a\n",
        "distance that is much smaller than the wavelength of the electromagnetic\n",
        "radiation. The FSS can be designed to selectively filter and manipulate\n",
        "the electromagnetic waves that pass through it based on the geometry,\n",
        "spacing, and material properties of its constituent elements. It is\n",
        "often used as a passive component in microwave and millimeter-wave\n",
        "devices, such as antennas, radars, filters, and absorbers.\n",
        "\n",
        "This notebook provides a demonstration of a microwave FSS composed of\n",
        "copper cross structures. The copper is defined as\n",
        "[LossyMetalMedium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.LossyMetalMedium.html),\n",
        "which is modeled as surface impedance boundary condition (SIBC). It\n",
        "works accurately even though the copper layer is very thin compared to\n",
        "the wavelength (~2.5 cm), and hence the grid step size around the copper\n",
        "layer, so it’s computational efficiency. The FSS has been designed to\n",
        "exhibit a stop band at 12 GHz, where the transmission (S21) reaches as\n",
        "low as -50 dB. By visualizing the field distribution at the resonant\n",
        "frequency, we can observe the dipolar resonance feature of the copper\n",
        "structure. This simulation showcases the effectiveness of FSSs as\n",
        "passive components in microwave devices, and their ability to manipulate\n",
        "electromagnetic waves with high precision.\n",
        "\n",
        "<img src=\"img/frequency_selective_surface.png\" width=\"500\" alt=\"Schematic of the FSS\">\n",
        "\n",
        "If you are new to the finite-difference time-domain (FDTD) method, we\n",
        "highly recommend going through our\n",
        "[FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. For more\n",
        "simulation examples, please visit our [examples\n",
        "page](https://www.flexcompute.com/tidy3d/examples/). FDTD simulations\n",
        "can diverge due to various reasons. If you run into any simulation\n",
        "divergence issues, please follow the steps outlined in our\n",
        "[troubleshooting\n",
        "guide](https://www.flexcompute.com/tidy3d/examples/notebooks/DivergedFDTDSimulation/)\n",
        "to resolve it."
      ],
      "id": "88c1ad95-bbb8-40d0-8f0a-e2408ddd9645"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tidy3d as td\n",
        "import tidy3d.rf as rf\n",
        "import tidy3d.web as web"
      ],
      "id": "6e6fd42d"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Simulation Setup\n",
        "\n",
        "The default frequency unit in `Tidy3D` is Hz. For convenience, we prefer\n",
        "to work with GHz in this example. A frequency range from 10 GHz to 14\n",
        "GHz is studied while the FSS is designed to resonate at 12 GHz."
      ],
      "id": "19233d98-6361-4fd6-b9ef-4c2db49a4203"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "GHz = 1e9  # 1 GHz = 1e9 Hz\n",
        "\n",
        "freq_low, freq_high = 10 * GHz, 14 * GHz\n",
        "freq0 = 12 * GHz  # central frequency\n",
        "freqs = np.linspace(freq_low, freq_high, 500)  # frequency range of interest\n",
        "\n",
        "fwidth = 0.5 * (freq_high - freq_low)  # width of the source spectrum\n",
        "\n",
        "lda0 = td.C_0 / freq0  # central wavelength"
      ],
      "id": "f2c1529b"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The default length unit in Tidy3D is $\\mu m$. For convenience, we prefer\n",
        "to work with mm in this example. Here we define the geometric parameters\n",
        "such as the length and width of the cross structure."
      ],
      "id": "4a56762b-0084-4cd2-b33a-c6a7b4fc020d"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "mm = 1e3  # 1 mm = 1e3 um\n",
        "P = 15 * mm  # periodicity of the unit cell\n",
        "L = 9.4 * mm  # length of the cross\n",
        "W = 2 * mm  # width of the cross\n",
        "t_sub = 2.2 * mm  # thickness of the substrate\n",
        "t_copper = 0.1 * mm  # thickness of the copper layer"
      ],
      "id": "cbc8ebac"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The copper is modeled as\n",
        "[LossyMetalMedium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.LossyMetalMedium.html),\n",
        "or SIBC internally. We don’t need to use very fine grid to resolve the\n",
        "actual thickness of the copper layer, as long as the copper layer is at\n",
        "the primal grid along the thickness dimension. The conductivity of\n",
        "copper is about $5\\times 10^7$ S/m, which is 50 S/$\\mu m$."
      ],
      "id": "817c9c10-46a1-4dca-a302-7937d67aa348"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "sigma_copper = 50  # copper conductivity in S/um\n",
        "copper = rf.LossyMetalMedium(conductivity=sigma_copper, frequency_range=(freq_low, freq_high))\n",
        "\n",
        "eps_sub = 2.5  # permittivity of the substrate\n",
        "sub_medium = td.Medium(permittivity=eps_sub)  # define substrate medium"
      ],
      "id": "8d014466"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "cross = []\n",
        "cross.append(\n",
        "    td.Structure(\n",
        "        geometry=td.Box.from_bounds(\n",
        "            rmin=(-L / 2, -W / 2, t_sub), rmax=(L / 2, W / 2, t_sub + t_copper)\n",
        "        ),\n",
        "        medium=copper,\n",
        "    )\n",
        ")\n",
        "cross.append(\n",
        "    td.Structure(\n",
        "        geometry=td.Box.from_bounds(\n",
        "            rmin=(-W / 2, -L / 2, t_sub), rmax=(W / 2, L / 2, t_sub + t_copper)\n",
        "        ),\n",
        "        medium=copper,\n",
        "    )\n",
        ")\n",
        "\n",
        "substrate = td.Structure(\n",
        "    geometry=td.Box(center=(0, 0, t_sub / 2), size=(td.inf, td.inf, t_sub)),\n",
        "    medium=sub_medium,\n",
        ")"
      ],
      "id": "cc2048f3"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "A\n",
        "[PlaneWave](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PlaneWave.html)\n",
        "source polarized in the x direction is added as the incident wave from\n",
        "the top of the FSS. To measure reflection (S11) and transmission (S21),\n",
        "two\n",
        "[FluxMonitors](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FluxMonitor.html)\n",
        "are added on the top and bottom of the simulation domain. Lastly, to\n",
        "visualize the resonant mode field, a\n",
        "[FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html)\n",
        "is added to the copper layer plane."
      ],
      "id": "4a212b7c-0420-4270-89a9-04fa1063d1cd"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "offset = lda0 / 2  # extra spacing added in the positive and negative z directions\n",
        "\n",
        "# define a plane wave source\n",
        "plane_wave = td.PlaneWave(\n",
        "    center=(0, 0, t_sub + 0.1 * offset),\n",
        "    size=(td.inf, td.inf, 0),\n",
        "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
        "    direction=\"-\",\n",
        ")\n",
        "\n",
        "# define a flux monitor to measure reflection\n",
        "S11_monitor = td.FluxMonitor(\n",
        "    center=(0, 0, t_sub + offset),\n",
        "    size=(td.inf, td.inf, 0),\n",
        "    freqs=freqs,\n",
        "    name=\"S11\",\n",
        ")\n",
        "\n",
        "# define a flux monitor to measure reflection\n",
        "S21_monitor = td.FluxMonitor(\n",
        "    center=(0, 0, -t_sub - offset),\n",
        "    size=(td.inf, td.inf, 0),\n",
        "    freqs=freqs,\n",
        "    name=\"S21\",\n",
        "    normal_dir=\"-\",\n",
        ")\n",
        "\n",
        "# define a field monitor to visualize field distribution\n",
        "field_monitor = td.FieldMonitor(\n",
        "    center=(0, 0, t_sub), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\"\n",
        ")"
      ],
      "id": "9ff5d910"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "With the previously defined structures, source, and monitors, we are\n",
        "ready to define a `Tidy3D`\n",
        "[Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html).\n",
        "Periodic boundary condition is applied in the $x$ and $y$ directions\n",
        "while\n",
        "[PML](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PML.html)\n",
        "is applied in the $z$ direction.\n",
        "\n",
        "We also set up automatic nonuniform grids. In addition, we use\n",
        "[LayerRefinementSpec](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.LayerRefinementSpec.html)\n",
        "to refine mesh around corners."
      ],
      "id": "491c58b6-02c8-4b58-b8b8-055640e23cf8"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">05:26:51 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: A structure has a nonzero dimension along axis z, which is</span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">however too small compared to the generated mesh step along that   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">direction. This could produce unpredictable results. We recommend  </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">increasing the resolution, or adding a mesh override structure to  </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">ensure that all geometries are at least one pixel thick along all  </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">dimensions.                                                        </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Suppressed </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #800000; text-decoration-color: #800000\"> WARNING message.                             </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: RF simulations and functionality will require new license </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">requirements in an upcoming release. All RF-specific classes are   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">now available within the sub-package </span><span style=\"color: #008000; text-decoration-color: #008000\">'tidy3d.rf'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                  </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\"> - Contains a </span><span style=\"color: #008000; text-decoration-color: #008000\">'LossyMetalMedium'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                  </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\"> - Contains sources defined for RF wavelengths.                    </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>"
            ]
          }
        }
      ],
      "source": [
        "# simulation domain size in z\n",
        "Lz = t_sub + 2.2 * offset\n",
        "\n",
        "# define a BoundarySpec\n",
        "boundary_spec = td.BoundarySpec(\n",
        "    x=td.Boundary.periodic(),\n",
        "    y=td.Boundary.periodic(),\n",
        "    z=td.Boundary(minus=td.PML(), plus=td.PML()),\n",
        ")\n",
        "\n",
        "# define a GridSpec\n",
        "layer_spec = rf.LayerRefinementSpec.from_structures(\n",
        "    axis=2,\n",
        "    structures=cross,\n",
        "    corner_refinement=td.GridRefinement(refinement_factor=5),\n",
        ")\n",
        "\n",
        "grid_spec = td.GridSpec.auto(\n",
        "    min_steps_per_wvl=30,\n",
        "    layer_refinement_specs=[layer_spec],\n",
        "    wavelength=lda0,\n",
        ")\n",
        "\n",
        "run_time = 1e-8  # simulation run time\n",
        "\n",
        "# define simulation\n",
        "sim = td.Simulation(\n",
        "    size=(P, P, Lz),\n",
        "    grid_spec=grid_spec,\n",
        "    structures=[substrate] + cross,\n",
        "    sources=[plane_wave],\n",
        "    monitors=[S11_monitor, S21_monitor, field_monitor],\n",
        "    run_time=run_time,\n",
        "    boundary_spec=boundary_spec,\n",
        "    symmetry=(-1, 1, 0),  # symmetry is used to reduce the computational load\n",
        "    subpixel=td.SubpixelSpec(\n",
        "        lossy_metal=td.SurfaceImpedance(edge_singularity_correction=True)\n",
        "    ),  # edge singularity correction for faster convergence\n",
        ")"
      ],
      "id": "47795dc9"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Nx': 58, 'Ny': 58, 'Nz': 64, 'grid_points': 215296, 'min_grid_size': 124.91352416666632, 'max_grid_size': 832.7568277777791, 'computational_complexity': 1723.5603705547571, 'fine_mesh_info': ['x=-4887 (size=124.9)', 'x=-4762 (size=124.9)', 'x=-4638 (size=124.9)', 'x=-4513 (size=124.9)', 'x=-1187 (size=124.9)', 'x=-1062 (size=124.9)', 'x=-937.5 (size=124.9)', 'x=-812.6 (size=124.9)', 'x=812.6 (size=124.9)', 'x=937.5 (size=124.9)']}"
          ]
        }
      ],
      "source": [
        "print(sim.grid_info)"
      ],
      "id": "a05742d7"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Before submitting the simulation job to the server, we can validate the\n",
        "simulation setup by plotting it. Here we overlay the grids on top to\n",
        "make sure the grid is sufficiently fine compared to the structure sizes."
      ],
      "id": "5997593c-f335-4bde-afd7-fce7cc63ebe9"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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cOdJpnKuvvlocP37crdz2798vRo8e7TSeRqMRY8aMqbZf27Zt3b478cI7TkeN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/8/mdWmQsUt3COu18fqfTcQCg5LnxeBNrjen7Vd9X7fdqFWuNOR2naNEE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\n9vBjN2ld3qpFxiLVLaxT6/JWp+MAQMnyeD3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H\ngoIC/P3vf0dAQIDsdXW3U7NmzeDg4GBwXNHXdkO3talryKGmrEqRlZWFP//8E3v37sXx48cRExNT\n6jpRUVFIS0sDIP//3ps3b0Kr1SquhhxqykpERERERERlxyYomURkZKT0xt/Pz8/oWF9fX+m6rhlZ\nnfNUV1aSJyIiAkDF7gul1JBDTVmV7O7du9Jei127doWTk1Ox5UW3V+7tlJKSUuzLDkqpIYeasipF\nfn4+du/ejd27d2Pnzp1Yvnw5Xn31Vbz00kvQaDR46623MGvWrBLryb2dbGxs4O3tXWIdJdWQQ01Z\nlWDhwoW4fPkyGjRogH/9619lWle3zRV5bVdKDTnUlFUpevToga5du2LQoEEICgqCj48PWrZsiTVr\n1kAIoXed8ry25+Tk4OHDh4qrIYeashIREREREVHZWZY+hKjy6b4tDQCurq5GxxZdXnS96pqnurJS\n6bKyspCfnw+g/PeFUmrIoaasSqbVajFx4kRoNBoAwMcff1xiTEWe57rD7CqlhhxqyqoUmZmZePXV\nV0v83tfXF+vXrzd4qGnd7WRtbQ07Ozujc7i6uiIuLq7E808pNeRQU1ZTu3HjBj777DMAwMqVK2Fv\nby97Xa1Wi4yMDADlf21XSg051JRVKSwsLFCvXj34+vrCzs4OERERCA8Px61btzBhwgSEhYUhLCys\nxKHMlfK/M98rEBERERERUWXgnqBkEllZWdJ1Q+eR0yn6AWhmZma1z1NdWal0Srk/+fhVl48++gjH\njh0DAIwePVo611tRSrmt+dhSLisrKwQHByM4OBiDBg1C27ZtYWFhgaioKAQHB+OLL77Qu57udirt\nNgL+dzs9fRsppYYcaspqShqNBuPGjUNeXh5GjhwpnZNZLqU8h/mapTy1a9fG2rVrkZCQgPv37+P3\n33/H/v37ce/ePVy5cgXPP/88gMLzo06ZMqXE+kq5rfnYIiIiIiIiosrAJiiZRNHzEhYUFBgdq9uL\nDZD3gWhlz1NdWal0Srk/+fhVj9WrV2PJkiUAgLZt22LlypV6xynltuZjS7kcHBykw+H+97//xaVL\nl/DgwQMMHToUGRkZmDFjBj755JMS6+lup9JuI+B/t9PTt5FSasihpqym9Pnnn+PcuXNwc3PDV199\nVeb1lfIc5muW8jRv3hxvv/023NzcSixr06YNjhw5grZt2wIA1q1bh7t37xYbo5Tbmo8tIiIiIiIi\nqgxsgpJJFD0fX2nfgi663NHRsdrnqa6sVLqit2l57wul1JBDTVmVaMeOHZg0aRIAoEmTJti/f7/B\n7VLKawVfG9XFx8cH27dvR+/evQEACxYsQHR0dLExutspJydHOr+0Ibrb6enbSCk15FBTVlO5c+cO\n5s2bBwBYunQpvLy8ylzD2tpaaryU9/mnlBpyqCmr0tnZ2eHTTz8FUHiI4F9++aXYcqX8feDfQyIi\nIiIiIqoMPCcomUSdOnWk648ePTI69uHDh9J1Hx+fap+nurJS6czNzVG7dm3ExcWV+75QSg051JRV\naX799Ve8+eab0Gq18PPzw6FDh+Dt7W1wfHme59bW1vD09FRcDTnUlFXpzM3N8be//Q2HDx+GRqPB\nwYMHMX78eGm57nYSQiAmJgZ169bVW0e3HCj5/FNKDTnUlNVUfvjhB+Tk5KBOnTpwd3fH7t27S4wp\n+nr8yy+/wNzcHC4uLujZs6f0+zp16uCvv/6q0Gu7UmrIoaasShcYGChdDw8PL7asPK/tZmZmxdZT\nSg051JSViP4nNTUVoaGhuHTpElJTU+Hl5YXu3btj4MCBxfbOVsI81ZW1ouLi4nD27FmcPXsWd+/e\nhVarhYeHB7777rsy1bl//z527dqFe/fuITc3F/Xq1cOgQYPQtWtX1dVQQ9bExEQcP34ct2/fRmxs\nLJKSkuDk5IQWLVqgf//+aN68uewcKSkp2LVrF65cuYLU1FR4e3sjKCgI/fv3l/1YVUoNtWS9c+cO\njh8/jgcPHiAmJgZ5eXmoVasWAgICMHjwYNSqVUtWjtzcXOzduxcnTpxAfHw8XF1d0a5dO4SEhJR6\nvnKl1VBz1l9++QUbN26Ufl61apWszz9OnTqFX3/9FQ8fPoSNjQ2aNGmC119/HfXr15c9t1JqKD3r\n6tWrcfDgwVLre3l5GTySXFH37t1DaGgowsPDUVBQAF9fXwwePBidOnWSuymKqVHTspYgiEzEw8ND\nABD9+vUzOu61114TAISdnZ3QaDQmmae6sj6Lrl27JgAIAGLhwoWlju/du7cAILy9vY2O+/TTT6W6\nt27dUmQNOdSUVSkOHTokbG1tBQBRp04dER4eXuo6KSkp0ra///77Rse2adNGABCtWrVSZA051JRV\nDa5cuSLdFvPnzy+2bP369dKy3377zWCNyMhIadzHH3+syBpyqCmrqcyePVvKXZZLQEBAsTqDBg0S\nAISrq6vQarUG51uyZIlU49KlS4qsIYeasipddna2tH1jx44ttiwnJ0dYWFjoXfa0bt26CQDCz89P\nkTXkUFNWIiq0ZcsW4eLiovdvZdOmTcW5c+cUM091Za2ILVu2CF9fX70ZfXx8ZNcpKCgQs2bNEubm\n5npr9e/fXzx+/FgVNdSwvYmJiaJ169bCzMzM6P+Pr732moiPjy91ezZt2iScnZ311mjWrJm4cOGC\namrIYeqsJ06cEHXr1jV631lZWYlZs2aJ/Px8oznOnDkjGjdurLeGq6ur2LZtW6nbopQacig1a3x8\nvKhVq1axWvfv3ze6TmxsrOjTp4/eHBYWFuKjjz4q9bNlpdSQQwlZJ06caPR5p7uU9r96Xl6emDp1\nqsHX4FdeeUUkJSWpooYcaspqCJugZDKvvvqqACCcnZ1Fbm6u3jEFBQXC09NTABB9+/Y12TzVlfVZ\nVNYm6P/93/9J44016nr16iUAiNq1a5f4A6iUGnKoKasSnDx5Ujg4OAgAwsvLS9y8eVP2uq1btxaA\n8UZdYmKi9CZ18uTJiq0hh5qyKt2RI0ek59jKlSuLLYuIiJCWGWvUrVu3Thp38OBBRdaQQ01ZTWXj\nxo0iODjY6KXohyJDhgwRwcHBYvr06cXqLF68WBpjrFE3cOBAAUC4u7uX+BBFKTXkUFNWpSv6xY05\nc+aUWN61a1cBQNSvX99gjfT0dGFtbS0AiDFjxii2hhxqykr0rNu+fbv04VmDBg3EokWLxMaNG8WH\nH34oNSNcXV3L9B6gquaprqwV9e9//1sAhV+Yffnll8Wnn34qWrRoIYCyNUEnT54s/W158cUXxTff\nfCPWrVsnRo8eLb0XaN++vcjIyFB8DTVsb3R0tLSui4uLGDFihFi+fLnYvHmz+OKLL8Tzzz8vLW/R\nooVITk42mGPLli3SY7VRo0Zi8eLFYuPGjWLGjBnCyclJ+p/ozp07iq8hhxKy7ty5U5ibm4uOHTuK\nyZMni0WLFolNmzaJlStXiunTpwt3d3fp/hs+fLjBHNevX5e+aOHq6ipmz54tNm3aJBYsWCD8/PwE\nAGFubi5CQ0MVX0MOJWd9/fXXBQDpC/mA8SZoenq6CAgIkOZ76623xPr168V//vMf6UtzAMS0adMU\nX0MOpWTVNUFr164tdu7cafCyd+9eo9szduxYab5evXqJb7/9Vqxdu1aMGDFC+v0LL7wgcnJyFF9D\nDjVlNYRNUDKZzZs3Sw/yH3/8Ue+YsLAwaczq1atNNk91ZX0WlbUJeuPGDWn8zJkz9Y6JiIgw2nRR\nSg051JTV1M6fPy99oODu7i6uXLlSpvWLNov//PNPvWOWLl0qjTly5Ihia8ihpqxKN2nSJGkbL168\nWGJ5u3btBADh6+sr8vLy9NZ48cUXBQDh4eGhd4xSasihpqxK9cYbb0iPKUNNuPDwcOnDlylTpugd\nEx0dLSwtLQUA8fbbbyu2hhxqyqp0RV+z9u3bV2L5559/Li0/dOiQ3hqrVq2Sxvzyyy+KrSGHmrIS\nPcuSk5OFm5ubAAqPjpCamlps+fXr16Xmw4svvmjSeaora2WIiIgQf/31V7HfBQUFCUB+E/TYsWPS\n69u4ceNKHG1h48aN0vK5c+cquoZatjc6Olp4enqK5cuX622SarVaMWfOHKmGof97EhMTpYZQ+/bt\nRVpaWrHlV69elb5k3KNHD0XXkEMpWePi4ozuoZueni66d+8u3X/Hjh3TO07X7HZxcRG3b98utiw5\nOVn4+/tL75eefh1SWg05lJo1NDRUes0s+n+2sSZo0aMDbd26tdgyjUYj3nzzTWn5yZMnFV1DDqVk\n1TVBGzVqVO5t2bNnj9HX1pUrV0rLFy1apOgacqgpqzFsgpLJ5Ofni6ZNm0p/KB4+fFhseVxcnGjQ\noIEACj8E1fdNgCdPnoiwsDARFhZm8Bv9lTFPZdQg/craBBXif3vmWltbl2i8ZGdni549e0rLDf3T\noZQaP//8swgLCzP4T62SsirZ9evXpcNWu7q6luswOUlJSdKbmXbt2pV4M3Pr1i2pydqtWzdF14iJ\niZFeG+/evavorEoWHR0t0tPTjY5Zt26ddBjE559/Xu8Y3ZsiAGLGjBklln/zzTfS8iVLlii6xoUL\nF6THlqHbRilZ1UxOE1QIIUaOHCkACEtLS3H8+PFiy3Jzc0X//v2l5Ya+ia6UGnv27BFhYWHi8OHD\nit9epcrLyxMRERFGx3z99ddSI7hVq1aioKCgxJj09HTh5eUlgMK9R54+LFFERIR0uK+AgAC9hxZW\nSo34+HjpNcvQ3lZKyUpExun2WAQgTp06pXdM0T3+jb2/qup5qitrVSlrE1R3+hV3d3eDez4OGDBA\nABAODg56xyilhhxKyJqdnV1qg0aj0Uh79bq5uen9mz9v3jzpcXj27Fm9debPn2+0saCUGnKoKevp\n06elGp988kmJ5b/99pu0/IsvvtBb4/fffzfaWFBKDTmUmjUpKUl4e3sLACIsLKxYo87QZ2tPnjwR\ndnZ2Aig86o+hMbrPWvr376/YGnIoKWtlNEG7dOkigMLTbxn6/D8wMFB6jdf3hW2l1JBDTVmNYROU\nTOrkyZPCxsZGAIWHX/n000/F1q1bxfz584WPj4/0IZShc4IVbaCNHz++yuaprBpUuFei7sOosLAw\nsXz5cuk+HD16dLFlhvYai4qKEs8995wAIOzt7cUHH3wgtmzZIpYvXy4dnhOA+OqrrwzmUEoNXfOk\nQ4cOis+qVHFxcdK2AYWH9iv6ONJ3MfSty6KHtWncuLFYtmyZ2Lp1q/joo4+Eq6urAAq/MXj9+nWD\neZRQo+ie6ca+XKCErEq2dOlSYWtrKwYNGiTmzp0r/vOf/4jt27eLzZs3i//7v/8TnTp1km5nb2/v\nEt8kLaroIT769+8v1q5dK3744QcxatQo6felHfpDCTWKHsLE2OG1lZBVzeQ2QWNiYkS9evUEUHjo\npWnTpoktW7aIr7/+WrRt21bWG3il1NB9Y97Y4bWVklWpEhISpNtw2rRpYsmSJWLjxo1i27ZtYsGC\nBaJjx47S9rm6uho9YsLPP/8sHSHCz89PLF68WGzdulX885//lL505ODgIM6fP6/oGkU/7DJ2eG0l\nZCUi43TnmG/ZsqXBMQkJCdLzsLRz1lflPNWVtaqUpQmakJAgvac1dkShol9w27lzpyJryKGmrEII\n8f7770s1nv4yvxBCtGzZUgAQbdq0MVgjLi5Oer+n7zCTSqkhh5qyPnr0yOj/MBMmTBBA4XkQjR3u\nuEmTJgY/c1JKDTmUmlX3vu2VV14RQghZTdAtW7ZIY4wdHeTdd98VQOH5YZ/Oq5Qacigpa0WboFFR\nUVKOWbNmGRz3ww8/SOOePvKOUmrIoaaspWETlEzu4MGDxRoYRS+enp5GX9zkNkErOk9l1njWFf2H\noLRLQECAwTo3btyQ3lw+fbG1tRVffvllqVmUUENOE1QpWZWq6Dck5V6MfVlh48aN0uGpnr40atRI\nnDlzptRMpq4htwmqhKxKtm3bNumbhIYuZmZmYvDgwSUOI/a0nJwc6U2XvsuQIUNKPQm8EmrIbYIq\nIauayW2CCiHEnTt3RPv27fXeRtbW1mLx4sWlzqeEGnKaoErJqlRPnjyRPrQxdunZs6fBowQUtWPH\nDulwjk9f6tWrJ37//XfF15DbBFVCViIyLCUlRXoulXbqDt0XPP39/U0yT3VlrUplaYLu3r1b2t4d\nO3YYHJeYmCiNe/pQeEqpIYeasgohiv0vHR0dbbD+1KlTjdbR7VHatm1bRdaQQ01ZhSj+OPnpp59K\nLG/evLkACg/Ja4yuKWRmZlbiSD5KqSGHErPqDvHp5OQkfclAThP0vffek+ob26O76KnZ/vvf/yqy\nhhxKylrRJuiPP/4o1Td23tAHDx5I4+bMmaPIGnKoKWtpLEFkYn369MHdu3exe/dunD59GomJiXB3\nd0fnzp0REhICZ2dng+s6OzsjODgYANCuXbsqm6cyazzrWrVqJd1npWnQoIHBZS1btsSFCxewf/9+\nHD58GLGxsXB0dETr1q0xbNgwPPfcc6XWV0KN4OBgaDQaNG7cWPFZlcrd3V32Y0rHy8vL4LLRo0dj\n4MCB2LlzJy5duoTU1FR4e3sjMDAQgwcPhq2tban1TV3jueeek26Tpk2bKjqrkg0fPhxDhw7Fn3/+\niVOnTiEmJgZxcXEwMzODh4cHWrdujb59+6J+/fql1rKxscF3332HqVOnIjQ0FBEREdBoNPD19cWQ\nIUPw/PPPq6JG+/btkZqaCgBwcnJSdFY169ixIzIyMgAA5ubmRsc2bdoUZ8+excGDB/Hbb78hJiYG\njo6OaNmyJYYNG4a6deuWOp8SagwePBjZ2dnw8/NTfFalcnJywt27dxEZGYlDhw7h/v37iImJQU5O\nDlxdXdGyZUu89NJLaNWqlax6r7/+Ovr06YNdu3bh/PnzSElJgaenJ1544QUEBwfD3t5e8TW8vLyk\nv4ctW7ZUdFYiMuzmzZvS9RYtWhgd27x5c1y7dg13796FRqOBhYVFtc5TXVmV4saNG9J1Y9vr4eEB\nT09PJCQkFLuNlFRDDjVlBYAjR44AAFxdXUu835ebAyh8rN66dQt37tyBVquV/j9VSg051JT1yJEj\nmDJlCgCgS5cuJT7vyMvLQ3h4uOwcACCEwK1bt9CpUydF1ZBDiVnT0tLw3nvvAQAWLFgAHx8f2duj\nexzVqVMHLi4upeYACv+2DBo0SHE15FBi1szMTCxduhS3b99GTk4OPDw80K5dOwwcOBDe3t6lbgtg\n/HHk6+sLe3t7ZGVllftvQFXXkENNWUtV6W1VIiIiIiIiIiKiSrBjxw6je0QVNX36dGlsTExMtc9T\nXVmrUln2BJ08ebK0DaUdJlF32PmmTZsqsoYcaspadO+bd999t8RyuYeXFEKIKVOmSGOLnlpGKTXk\nUGrWH3/8UYSEhIiQkBDRq1cvUb9+fQEUHp1kzJgx4smTJyXWkXt4SSEKj26k7zVJKTXkUGLWt99+\nWwAQXbp0ERqNRvq9nD1BGzZsKACIzp07G80RFxcn1Xp6z2Ol1JBDSVl1e4IautjY2Ii///3vBk+5\nM2bMGGlsaeetbNy4sQBK7nmslBpyqClrabgnKBERERERERERKVJ6erp03cHBwejYost1R1Woznmq\nK6tSlGd7n95WpdSQQy1Zo6KiMH36dACFR4r45JNPSoypyGPV09NTUTXkUGrWa9euITQ0tMTvR44c\niQ8//FDv0XeU8lr1rL42Hzx4EOvWrYOlpSVWr15dpr18i2apSA6l1JBDaVnr1q2Lvn37onHjxqhX\nrx5ycnJw6dIlbN26FSkpKfjyyy9x9epV7Nu3D1ZWVnpzWFlZlVhmKIuhbTF1DTnUlLU0bIISERER\nEREREZEi5eXlSdctLY1/jGVtbS1dz83NrfZ5qiurUpRne5/eVqXUkEMNWTMzM/HKK68gMTERAPDt\nt9/qPdS/Uh7vz/rz+80330THjh0BFDYLrl27hi1btuCHH37A5s2bsWLFCkyaNEmR2/Is3nfp6emY\nMGECAGDGjBlo06aN0Vr66LJUJIdSasihpKwfffQRVq5cqbdx/dlnn2HYsGE4dOgQDh8+jCVLluDj\njz8uV46iWcq7LVVdQw41ZS1N2b6qQEREREREREREVE2Knk+3tA/GsrOzpeul7TFSFfNUV1alKM/2\nPr2tSqkhh9Kz5ufnIyQkBJcuXQJQ+IH/qFGj9I5VyuP9WX9++/v7Y+jQoRg6dCjGjRuHL774Avfu\n3cOAAQNQUFCAyZMnY8+ePYrclmfxvps1axYePHiAhg0bYt68ecaDG6DLUpEcSqkhh5Ky+vn5Gdxz\n183NDWFhYahTpw4A4KuvvoJWqy1XjqJZyrstVV1DDjVlLQ2boEREREREREREpEguLi7S9dTUVKNj\n09LSpOvOzs7VPk91ZVWK8mzv09uqlBpyKDmrRqPBqFGjcODAAQDApEmTsGDBAoPjlfJ45/O7JCcn\nJ2zevFk6FO6SJUuKLVfKtjxr990ff/yB7777DgCwatUq2NnZGa1jiC5LRW4zpdSQQ01ZHR0dMWbM\nGABAYmIirl+/rjeHVqstdohlY1kMbYupa8ihpqylYROUiIiIiIiIiIgUqVGjRtL1Bw8eGB2rW+7m\n5gYPD49qn6e6sipFWbY3KioKANC4cWNF1pBDqVmFEBg/fjx27doFABgzZgy++eYbo+uU57Faq1at\nYs0kpdSQQ01ZgcLXhcDAQADAuXPnii3z9vaWGqRycwDFH0dKqSGHUrJeunQJQgg4Ojpi9erV0h68\nRS+7d++Wxk+ePBlDhw7F22+/Xay+7nGke36XNYeSasihpqwA0KxZM+l6bGys3hxPz/W0goICxMTE\n6M2hlBpyqClradgEJSIiIiIiIiIiRWratClsbGwAQDrMpyG65f7+/iaZp7qyKkXR8+EZ2947d+4g\nMzMTANC6dWtF1pBDqVnff/99/PDDDwCAYcOGYd26dTAzMzO6TvPmzWFlZVVqjqLLn86hlBpyqCmr\njm5vqPz8/BKH5dS9bpSW4+LFiwAAOzu7Eo0FpdSQQ0lZMzIyEBoaqvdy584dady+ffsQGhqKX375\npdj6uteA1NRU3L9/v9QcgOHXEVPXkENNWYHih20teo7RojkA44+jK1euQKPR6M2hlBpyqClradgE\nJSIiIiIiIiIiRbK2tkbv3r0BAIcPH0ZeXp7ecZcvX5b22hg4cKBJ5qmurErRtWtXuLq6AgD27t1r\ncNy+ffuk609vr1JqyKHErB9++CFWrVoFAAgODsbmzZthYWFhcLyOra0tevbsCQA4dOgQ8vPz9Y47\nf/48EhIS9OZQSg051JRVR9dQq1OnTonzGA4YMABA4Z5xN27c0Lt+Tk4Ojh07BgDo27dviceFUmrI\noYSsAwYMwM6dO41eXnnlFWn8ypUrsXPnTqxfv15vDkDea4CHhwe6dOmiyBpyqCkrAJw8eVK6XnQv\nRgDo0aOHdBjk8r5+K6WGHGrKWipBRERERERERESkUNu2bRMABACxatUqvWNGjRolAAgrKytx//59\nk81TXVmrSlBQkAAgfHx8ZI1/7733BABhYWEhrl+/XmJ5dna2aNSokQAgmjZtKrRarWJrqGl7hRBi\n3rx50mOtf//+Ijc3t0zb8uOPP0rrr1mzRu+YYcOGCQDC2tpaREVFKbaGHGrK+ttvv0nzvPPOOyWW\nh4eHC0tLSwFAjB07Vm+NFStWSDVCQ0MVW0MOtWSdPXu2tL6h13aNRlPs+Z2Tk1NizOXLl4W5ubkA\nIP72t78ptoYcasp66dIlYWVlJQCItm3b6h3z5ptvSs/v8PDwEsvT09NF3bp1BQDRrl07RdeQQ01Z\njWETlIiIiIiIiIiIFEur1YoOHToIAMLJyUkcOHBAWqbRaMTSpUulD56nTp2qt8bZs2dFSEiICAkJ\nEWFhYVU2T2XUMKWyNkEfPXokHB0dBQDRokULERERIS3LyMgQr7/+eqlNBaXUWL9+vfQYuXv3rqKz\nLlu2TBrTu3dvkZ2drXecMRqNRgQEBAgAwtnZWRw6dKjYskWLFklzfPDBB4qucfr0aem+++WXXxSd\n9d///rf4448/hEajKbEsNzdXrF+/Xjg7OwsAws7Ortjjo6hJkyZJc3311VfFmuV79+4VDg4OAoDo\n3LmzwUa6EmqkpKRI993ixYv1zqGUrKWR0wQVQojt27dL40aOHCkyMzOlZffu3RNNmzaV/obExsYq\nusbUqVNFSEiIGDNmjKK398iRI+L7778XcXFxJZZpNBqxY8cOUatWLQFAmJmZiX379unNERERIWxt\nbQUAERAQIKKjo6VlaWlp4uWXX5ZyFv37r8Qaq1atkp57hr6woZSsFcUmKBERERERERERKdr9+/dF\n7dq1pQ/K2rZtKwYNGiT8/Pyk373wwgsGm0FhYWHSuIULF1bZPJVVozokJSVJH4DqLroPge3s7Eos\nu3Xrlt46e/bskfawsrKyEt27dxf9+vUTbm5u0vbOnDnTaBYl1Jg2bZo07vTp04rNevnyZWkMADFg\nwIAS99XTF317nApR+OG0l5eXVKtdu3Zi0KBBwtfXV/pdUFCQ0b1MlVBj586d0rilS5cqOquHh4cA\nIFxcXESrVq1Er169RHBwsAgMDJSan7pmjrGGQFZWlujatas0vkGDBmLQoEGiTZs20u/q1KkjHjx4\noOgasbGx0rjg4GBFZy2N3CaoEEJMnz5dGuvu7i769+8vAgMDpdcFS0tLg404JdVo1qyZ9HhWctav\nv/5aABDm5ubCz89PdOnSRQwePFj06dOn2PPZ0tJS/Oc//zGaY/v27dJep9bW1qJHjx6ib9++xZ6/\n//rXvxRfY/z48dK4a9euKTprRbEJSkREREREREREihcTEyOGDRsmHa5Od3FxcRGzZs3Se4g8HblN\n0IrOU5k1qlp0dHSxbKVdTpw4YbDWmTNnxPPPP19iHT8/P7F+/XpZeUxdQ24T1NRZT5w4Uab7DYA4\nevSowXqPHj0SQ4cOLfFYdXV1FXPmzJH1WDV1DblNUCVk/c9//iP69u0r7Rn19MXd3V1MmjSp2N5S\nhmRnZ4uZM2cWayQAhY314cOH693rTWk15DZBlZC1NGVpggohxPfff1+sea67dO3aVZw7d07WnKau\nIbcJauqsN2/eFO+9955o0KCB3uedtbW1ePXVV8X58+dl5Thx4oR01Ieil4YNG4qtW7eqoobcJqgS\nslaUmRBCgIiIiIiIiIiISAVSUlJw/fp1pKamwtvbGwEBAbCxsTG6TkxMDE6dOgUAaN26NZo1a1Yl\n81RFjaqSnZ2NX3/9Vfb4nj17wsPDw+iYqKgo3Lt3D7m5uahXrx78/f1hZmZWplymqnHlyhXcu3cP\nANCrVy+4u7srMmtSUhKOHj1apjmCgoLg6elpdExycjJu3LhRoceqqWo8evQIp0+fBgC0adMGTZs2\nVWxWndzcXDx48ACxsbFITU2Fg4MD/Pz80Lhx4zI/hnJycnD16lU8fvwYrq6u8Pf3h5ubmypq5Obm\nYs+ePQAAHx8fdO3aVbFZS3P9+nXcvn0bADBw4EDY29uXuo4QAtevX0d0dDRsbGzQpEkT+Pr6lmle\nU9Y4ePAgnjx5AmtrawwZMkTRWXViY2Px6NEjxMXFwcLCAl5eXmjdujWsra3LlAMA7t+/j/DwcBQU\nFMDX1xetWrVSTY0LFy7g/v37AIC+ffvC2dlZsVkrik1QIiIiIiIiIiIiIiIiIqpRzE0dgIiIiIiI\niIiIiIiIiIioMrEJSkREREREREREREREREQ1iqWpAxAREREBQFZWFs6ePQtLS0u8+OKLpo5TbtnZ\n2Thz5gwsLCwQGBho6jhERERERERERETPJO4JSkRERIowf/589OzZE2vWrDF1lAqxtbXFtGnT0L17\nd+zatcvUcYiIiIiIiIiIiJ5JZkIIYeoQRERE9GyLjo5Gs2bNkJ+fj9u3b6NRo0amjlQhYWFheO21\n19CwYUPcunUL1tbWpo5ERERERERERET0TOGeoERERGRyH330EbKzszFmzBjVN0AB4JVXXkHbtm0R\nGRmJFStWmDoOERERERERERHRM4dNUCIiIjKpqKgobNu2DQDw3nvvmThN5TAzM8OECRMAAF988QUK\nCgpMnIiIiIiIiIiIiOjZwiYoERERmdTq1auh0WjQvHlzdOrUydRxKs3w4cNhbW2N2NhY/Pzzz6aO\nQ0RERERERERE9EyxNHUAIiIiUhYhBE6cOAGtVgt7e3t07tzZ4NgHDx7g/v37AIAWLVrA29u7THMV\nFBRg7dq1AIA33njD6LiTJ0+WOk9OTg7+/PNPAEDr1q3h4eFRbPmpU6eQl5cHX19fNGzYEACQn5+P\nyMhIpKeno0mTJnBxcdFbOyUlBZGRkbCyskKLFi1gZWVldNvc3d0xYMAA/Pzzz1i1ahVCQkKMjici\nIiIiIiIiIqLKwz1BiYiIqBgzMzP88ssv6NmzJ7p06YLdu3frHZeSkoLAwED07NkTEyZMgIODQ5nn\nOnv2LOLi4gAAvXr1MjguIyMDPXv2RM+ePfHbb78ZHBcXFyeNO336dInlr732Gnr27InVq1cDAD7/\n/HPUrVtX2gvV3d0d48aNw5MnT6R1wsPD8corr8DDwwMdO3ZEQEAAvL29sXLlylK3T7dNx44dK1aT\niIiIiIiIiIiIqhaboERERFTCwoULpUPTvv3224iKiioxZvz48YiOjoa1tTW2bt0KR0fHMs/z+++/\nAwCsrKzQvn37ioUuo6lTp2LmzJkwNzdH165dUbduXWi1WmzYsAGvvPIKNBoNbty4gRdeeAF79uyB\nv78/OnfuDBsbG6SkpOD999/Hd999Z3SOrl27AgA0Go20JysRERERERERERFVPTZBiYiIqAQrKyts\n27YNzs7OSElJwahRo6DRaKTl33zzDcLCwgAAixYtQocOHco1z/HjxwEAbdq0ga2tbcWDy3TgwAGs\nXbsWW7duRUxMDE6dOoXo6GgsWbIEAHD06FHs2LEDb775Jpo3b47IyEhcvXoVZ86cwV9//YWAgAAA\nwJw5c5CVlWVwnrZt28LGxgbA/xq+REREREREREREVPXYBCUiIiK9GjZsKO3p+Mcff2DevHkAgKtX\nr2LmzJkAgIEDB2L69OnlniMyMhIAULdu3YqFLaPLly/ju+++w4gRI2BmZib9/sMPP5TOgTp+/Hik\npKTg119/hZ+fnzSmdu3aWLFiBQAgNTUV+/fvNziPlZUVvLy8APxvW4mIiIiIiIiIiKjqsQlKRERE\nBo0YMQLjx48HUHiI3P/+978YMWIEcnJy8Nxzz2HDhg3FmohllZCQAABwd3evlLxy+fn54Y033tC7\nbODAgQCA7OxsTJs2Te9hfgMDA+Hk5AQAuHjxotG5PDw8AADx8fEViUxERERERERERERlwCYoERER\nGbVixQq0aNECWq0WQ4YMwa1bt2Bubo5NmzbB09Oz3HW1Wi1SU1MBAG5ubpWUVp7u3bsbbN4W3esz\nKChI7xgzMzPUq1cPQOnNTV2DNzExsTxRiYiIiIiIiIiIqBzYBCUiIiKj7O3tsX37dtjY2EAIAaDw\nXJi9e/euUF1zc3NYW1sDAHJyciqcsyy8vb0NLit6blJj4+zs7AAU7jFqjG65vb19WSISERERERGR\nguTl5eH999/Hu+++i6SkJFPHqZAFCxbgnXfewblz50wdhYioSrEJSkRERKV69OgR8vLypJ8ra89N\n3Z6kycnJlVJPLgsLi0obp2sMG6J7c1yRvWaJiIiIiIjItJYvX46VK1fi0aNH0mlP1KpOnTpYu3Yt\n3n///VLf0xIRqRmboERERGRUXFwcxo4dCyGE1BT8xz/+gfPnz1e4du3atQFU3qFilfjmTdcE1W0r\nERERERERqUtiYiLmz58PAPj0009NnKbiRo8ejSZNmuDcuXPYvHmzqeMQEVUZNkGJiIjIICEERo8e\njfj4eDg4OODEiROoV68e8vPzMXLkSKSnp1eofqdOnQAAN27ckL1OZmamwWUpKSkVylPZYmNjpSao\nbluJiIiIiIhIXebPn4+0tDT069cPHTt2NHWcCrOwsMDs2bMBFH7JuaCgwMSJiIiqBpugREREZNCi\nRYtw6NAhAMDXX3+Nrl27YvPmzbCwsEB4eDgmT55cofpBQUEACpuF0dHRsta5ffu2wWWnTp2qUJ7K\ndvr0aem6bluJiIiIiIhIPTIzM7Fu3ToAwLhx40ycpvIMGzYMdnZ2iI6Oxu7du00dh4ioSrAJSkRE\nRHr9+eef+OSTTwAAI0eOlN7sBQYG4p///CcA4Mcff8SmTZvKPUePHj1gbl7470jRhqEx69atw8OH\nD0v8Pj4+HkuXLpV+1mg05c5VWf78808AhYfCbdmypYnTEBERERERUVlt3rwZT548gbOzM4YMGWLq\nOJXGyckJwcHBAIBVq1aZOA0RUdWwNHUAIiIiUp60tDSMHDkSBQUFaNiwIb799ttiy+fOnYsjR47g\n+PHjmDx5Mrp27YrGjRuXeR5vb2/069cP+/btw08//YRhw4aVuk5mZiYCAgIwffp0tG7dGjY2Nrh0\n6RK++eYbJCQkwMzMDEIIbNq0Cc7OzujSpQvs7e3LnK0yhIWFAQDGjh1rkvmJiIiIiIhqmkePHmHe\nvHkAgDFjxqB79+5Gx2/cuBHHjx+HnZ0dli9fLn0RV661a9cCAAYPHgw7OzuD4z777DP89ddfeOGF\nF4zuMfqPf/wD8fHx6NOnD4YPH15irtOnT6N+/fqYO3cuAODKlSv49ddfERsbi3r16uHVV19FkyZN\niq2Xl5eHXbt24dq1a8jKyoK/vz+GDRsGFxcXo9s2fPhwbNu2DUeOHEFkZCQaNmxodDwRkdqwCUpE\nREQlTJgwAX/99ResrKywdetWODs7F1tuYWGBzZs3IyAgAMnJyRgxYgROnToFa2vrMs81adIk7Nu3\nDz///DPS0tJKfZM2Z84crF69WtpLVcfS0hLff/89Nm7ciCNHjiA0NBShoaG4desWmjdvXuZcFXX6\n9GmEh4fD3NwcEydOrPb5iYiIiIiIaiIfHx+cPHkSd+7cwaNHj4w2QQsKCjB79mzExcVh1KhRZW6A\npqWl4fz58wCAbt26GR27e/duXLhwAQUFBUaboDt27EBERAQcHR1LNEGPHj2KzZs3o0OHDpg9ezYm\nTpyI9evXFxvz8ccfY+nSpZg+fToA4MyZM3jttdcQExNTbNy8efOwb98+BAQEGMzywgsvSNcPHz7M\nJigR1ThsghIREVExp06dwuPHjxEUFIRhw4ahc+fOesfVrVsXP/zwA5YtWwYA+OmnnzBixIgyzzdo\n0CDUr18ff/31F3bs2IF3333X6PjmzZvj6tWrWLNmDS5cuIC8vDw0a9YM48aNQ5s2bdCrVy/MmzcP\niYmJ6N69O+rXry+t261bNyQnJxt9Y+fl5SWdv9NYU7dDhw5wdHREixYt9C7fuHEjAGDAgAFo0KCB\n0W0iIiIiIiIi+SZMmIAZM2bg4MGDiIqKgq+vr95xe/bsQVxcHABg/PjxZZ7nxIkT0Gq1AIDnn3++\n/IHL4W9/+xt+/PFHjB07Fh06dEBGRgY2bdqEW7du4YMPPkCLFi3g4+ODl156CbVr18bcuXPx3HPP\n4dq1a1i7di1iY2MRHByMW7duGdyD1dPTEw0bNkRkZCSOHTtW6vtxIiK1MRNCCFOHICIiomfbjz/+\niNGjR6NJkya4desWLCwsii1PTU2Fm5sbAGDTpk148803TRFTtri4ODRs2BC5ubm4cOEC2rZta+pI\nRERERERENUZSUhJ8fHyQm5uLefPm4V//+pfecQMHDsS+ffvQsGFDhIeHw8zMrEzzzJ07F/Pnz4e5\nuTlyc3NhaWl4n6KOHTviwoULGDt2LDZs2GBwXOPGjREREYFp06bhq6++KrbszTffxObNm+Ho6AgL\nCwscOnQIHTt2lJZnZWWhU6dOuHnzJrp16watVovatWtj+/btxb7EGxoaiqFDhwIoPMTu22+/bTDP\n0KFDERoaigYNGiAyMrKUW4SISF3Ktv8/ERERURV444030KlTJ9y7dw+bNm0ydZwKW7BgAbKzszFu\n3Dg2QImIiIiIiCqZh4eH1ORbv369tLdmUdHR0Thw4AAA4O233y5zAxQAYmNjAQAuLi5GG6CVLSMj\nA5999lmxBigA2Nvb44MPPgBQeBSne/fu4YcffihxFKOQkBDptDD79+83OpenpyeA/20rEVFNwiYo\nERERmZyZmRm++uorBAUF4fjx46aOUyFZWVm4e/cuevbsic8++8zUcYiIiIiIiGqkiRMnAgCioqKk\nZmdRa9euhVarhYWFBd56661yzZGQkACgsOlanSwsLDB27Fi9y7p06SJdDwkJgbOzs95xulPb3Lp1\ny+hcum3LyclBRkZGeeISESkWzwlKREREitCtWzccO3bM1DEqzN7evtRv2hIREREREVHFBAYGomXL\nlrh58ybWrFmDAQMGSMu0Wi3Wr18PAOjfvz98fHzKNYeuKejg4FDxwGXQsGFDODk56V1Wq1Yt6XpA\nQIDBGro9PFNTU43OVXTb0tPT4ejoWIakRETKxj1BiYiIiIiIiIiIiEh1dHuD7tmzB48fP5Z+f+DA\nAURFRQEAxo8fX+767u7uAEpvJFY2Q3t3Aih2WF454/Lz843OVXTbdNtLRFRTsAlKREREimdpaYmg\noCAEBQXB29vb1HGIiIiIiIhIAcaMGQM7OzsUFBRgw4YN0u/XrFkDAPD29sbgwYPLXV+3N2VycnKF\ncipZUlISAMDJyQk2NjYmTkNEVLnYBCUiIiLFc3R0xLFjx3Ds2DH06dPH1HGIiIiIiIhIAVxdXTFs\n2DAAhecABYC4uDjs2bMHQGGTtOiek2Xl5+cHoPAwsZmZmRVMW6igoKBS6lSWuLg4AP/bViKimoRN\nUCIiIiIiIiIiIiJSJd0hce/du4djx45hw4YNUqOxIofCBQrPO6pz4cIFWevomor6FBQUFDtsrxKc\nO3cOQPFtJSKqKdgEJSIiIiIiIiIiIiJV6tq1K9q0aQOg8DC4uj1CX3zxRTRr1qxCtTt16gR7e3sA\nwJkzZ2Stc+nSJWRlZeldtn//fuTk5FQoU2WKjIxEfHw8ACAoKMjEaYiIKh+boERERERERERERESk\nWrq9Qbdu3Yrw8HAAFd8LFACsra3Ru3dvAMDx48dlrRMfH4/333+/RCP02rVrmDx5svTzkydPKpyv\nonTbVHQ7iYhqEjZBiYiIiIiIiIiIiEi13nzzTTg4OEAIAQBwcnKSzhVaURMmTAAAHDhwAImJiaWO\n9/X1xYYNG9CwYUMMGjQIY8eORWBgINq2bYv4+Hi0bNkSQGHDdsyYMTh69Gil5CyPLVu2AABCQkJQ\nq1Ytk+UgIqoqbIISERERERERERERkWo5OztjxIgR0s8jR46UDmNbUQMHDoSfnx/y8/Oxbdu2Usf3\n7NkTX375JVJSUrB3715s3LgRJ0+eRK1atRAWFobPP/8cZmZmyMnJwaZNm3D//v1KyVlWsbGxOHz4\nMADgvffeM0kGIqKqZmnqAEREREREREREREREFeHv7y9dr4xD4eqYm5tj2rRp+OCDD/D9999jypQp\npa4zffp0jB07Fn/88QdSUlJQp04dBAYGwtraGkDhoXGvXLmCVq1aISAgQFrvnXfeQY8ePeDp6Wmw\ntpOTE9asWQOg8HyohoSEhKBx48YGm8Hr16+HVqtFx44d0b1791K3iYhIjcyE7hgBRERERERERERE\nREQq1KFDB1y8eBGtW7fG1atXK7V2Xl4eWrRogcjISPz000949dVXS4zp2LEjLly4gLFjx2LDhg2V\nOn9lS09PR4MGDZCUlITff/+dTVAiqrF4OFwiIiIiIiIiIiIiUq1Lly7h4sWLAP53Ds/KZG1tjcWL\nFwMA/vWvf0Ht+xUtX74cSUlJCAkJYQOUiGo0Hg6XiIiIiIiIiIiIiFRJo9Hgo48+AgC4ublh7Nix\nVTLP0KFDsXHjRuTm5iIlJQXu7u5VMk91aNGiBdasWYP+/fubOgoRUZViE5SIiIiIiIiIiIiIVGXK\nlClIT0/HmTNncOfOHQDAJ598Aicnpyqbc/To0VVWuzqFhISYOgIRUbVgE5SIiIiIiIiIiIiIVGXD\nhg3IzMwEAJibm2PixImYOnWqiVMREZGSsAlKRERERERERERERKryzTffID8/H+7u7mjbti0aNmxo\n0jz//Oc/kZCQgKZNm5o0BxER/Y+ZUPtZnImIiIiIiIiIiIiIiIiIijA3dQAiIiIiIiIiIiIiIiIi\nosrEJigRERERERERERERERER1ShsghIRERERERERERERERFRjcImKBERERERERERERERERHVKGyC\nEhEREREREREREREREVGNwiYoEREREREREREREREREdUobIISERERERERERERERERUY3CJigRERER\nERERERERERER1ShsghIRERERERERERERERFRjcImKBERERERERERERERERHVKGyCEhERERERERER\nEREREVGNwiYoEREREREREREREREREdUobIISERERERERERERERERUY3CJigRERERERERERERERER\n1ShsghIRERERERERERERERFRjcImKBERERERERERERERERHVKGyCEhEREREREREREREREVGNwiYo\nEREREREREREREREREdUo/w9sXZKUfZwV2QAAAABJRU5ErkJggg==\n"
          }
        }
      ],
      "source": [
        "f, ax = plt.subplots(1, 2, tight_layout=True, figsize=(10, 5))\n",
        "sim.plot(z=t_sub, ax=ax[0])\n",
        "sim.plot_grid(z=t_sub, ax=ax[0])\n",
        "ax[0].set_xlim(0, 5000)\n",
        "ax[0].set_ylim(0, 5000)\n",
        "\n",
        "sim.plot(x=0, ax=ax[1])\n",
        "sim.plot_grid(x=0, ax=ax[1])\n",
        "ax[1].set_xlim(0, 5000)\n",
        "ax[1].set_ylim(-2000, 5000)\n",
        "plt.show()"
      ],
      "id": "e49e7c21"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Submit the simulation job to the server."
      ],
      "id": "3ac292da-574f-482b-9268-2074b9721a12"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">05:26:52 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'frequency_selective_surface'</span> with resource_id        \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-253e48cb-37fb-4a5a-9a4d-11fee348663f'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-253e48cb-37fb-4a5a-9a4d-11fee348663f\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-253e48cb-37f</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-253e48cb-37fb-4a5a-9a4d-11fee348663f\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">b-4a5a-9a4d-11fee348663f'</span></a>.                                         \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"76bbf9c21482475397a1f04cd7821dfe\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">05:26:53 UTC </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. This assumes the FDTD solver runs\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>for the full simulation time; if early shutoff is reached, the     \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed cost can be lower. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the  \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed FlexCredit cost after a simulation run.                     \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">05:26:54 UTC </span>status = success                                                   \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"23dcd479a5e042c08caa338c5586991b\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">05:26:55 UTC </span>Loading results from data/simulation_data.hdf5                     \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">05:26:56 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: A structure has a nonzero dimension along axis z, which is</span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">however too small compared to the generated mesh step along that   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">direction. This could produce unpredictable results. We recommend  </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">increasing the resolution, or adding a mesh override structure to  </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">ensure that all geometries are at least one pixel thick along all  </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">dimensions.                                                        </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Suppressed </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #800000; text-decoration-color: #800000\"> WARNING message.                             </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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><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>"
            ]
          }
        }
      ],
      "source": [
        "job = web.Job(simulation=sim, task_name=\"frequency_selective_surface\")\n",
        "sim_data = job.run(path=\"data/simulation_data.hdf5\")"
      ],
      "id": "e0f162fa"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Result Visualization\n",
        "\n",
        "After the simulation is complete, we first visualize the S-parameters. A\n",
        "prominent resonance feature is observed at 12 GHz where S21 reaches -50\n",
        "dB."
      ],
      "id": "daa8c942-a035-4f6b-871c-6a213681966a"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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d45mnWgbbgjU7c7IunrhAF0+cr/5JfTxQPAB0H+EYLOd04diZ1uZKbOlv006I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\nZIm2b9+us2fPKiUlR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aWkpBh33323pc8vv/yy0LG6ak5qRs6Y5/3555+5Hp88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\nrYtlnB9++MGyZfO2224rtqLwsFauXDm7Wymz/3Tu3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J9X69atDUnGAw884LBNUZ7LmDFj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\nAACXOHr0qNavXy9Jevjhh52aGJMyV1JNmzZNkjRx4kSlpqY6tf/Sbvbs2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          }
        }
      ],
      "source": [
        "S11 = sim_data[\"S11\"].flux\n",
        "S21 = sim_data[\"S21\"].flux\n",
        "\n",
        "plt.plot(freqs / GHz, 10 * np.log10(S11), label=\"S11\")\n",
        "plt.plot(freqs / GHz, 10 * np.log10(S21), label=\"S21\")\n",
        "plt.xlim(10, 14)\n",
        "plt.ylim(-60, 0)\n",
        "plt.xlabel(\"Frequency (GHz)\")\n",
        "plt.ylabel(\"S-parameters (dB)\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "id": "e82d7b86"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Lastly, we plot the field intensity distribution at the resonant\n",
        "frequency. A strong dipolar field pattern is observed in the horizontal\n",
        "copper pattern. This dipolar resonance is responsible for the total\n",
        "reflection of the electromagnetic wave at this frequency."
      ],
      "id": "333e4017-1804-4bc2-ae75-664c30f02723"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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          }
        }
      ],
      "source": [
        "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\")\n",
        "plt.show()"
      ],
      "id": "b07b67b3"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  }
}