{
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      "source": [
        "# Building an AI surrogate model for a directional coupler\n",
        "\n",
        "**Note: the cost of running the entire notebook is higher than 2\n",
        "FlexCredits. It also requires at least tidy3d version 2.8 to run. To run\n",
        "the notebook, you will need pytorch installed ([pytorch\n",
        "information](https://pytorch.org)). The notebook has been tested with\n",
        "pytorch version 2.5.1.**\n",
        "\n",
        "In this notebook, we build a machine learning model of a directional\n",
        "coupler (DC) that splits power from an incoming waveguide into two\n",
        "output waveguides. Using the surrogate model, we design a DC with a\n",
        "75/25 splitting ratio. This coupling optimization boils down to choosing\n",
        "the gap size `wg_spacing_coup` and coupling length `coup_length` of the\n",
        "DC.\n",
        "\n",
        "Through the machine learning training process, we demonstrate how tidy3d\n",
        "can be used as a data source when training AI models for\n",
        "electromagnetics. We first run a batch of simulations with tidy3d where\n",
        "the two geometric parameters are randomly sampled for each simulation. A\n",
        "mode is injected into one waveguide and monitors on both waveguides\n",
        "characterize the final power split between the two outputs. From this,\n",
        "we define a fitness function describing the performance of a given\n",
        "directional coupler. We define the fitness as the mean squared error\n",
        "between desired output power in each waveguide and actual power. The\n",
        "desired output power fractions are 0.25 and 0.75 in the top and bottom\n",
        "waveguides, respectively. By plotting the fitness function, we can\n",
        "observe that we have found a variety of suitable solutions to the\n",
        "coupling problem. We have also collected a significant amount of data\n",
        "that can be used as input to a data driven model of the coupling. We use\n",
        "the broadband simulation data collected in the first step to train a\n",
        "fully connected neural network. The network is optimized to learn the\n",
        "mapping between the waveguide spacing, coupling length, and input\n",
        "frequency to the power fraction in the two output waveguides. We refer\n",
        "to this network as a surrogate solver because we can now use it as an\n",
        "approximate substitute for the full electromagnetic simulation. The\n",
        "final step of the optimization uses this surrogate solver inside of a\n",
        "Bayesian optimization routine to identify a more optimal set of\n",
        "parameters for the directional coupler. With this set of parameters, we\n",
        "can use tidy3d to run a final verification of the design and evaluate\n",
        "the fidelity of the surrogate solver when compared to ground truth\n",
        "simulation results. The general flow of the notebook is shown in the\n",
        "diagram below."
      ],
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      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<img src=\"img/directional_coupler_surrogate.svg\" alt=\"diagram\" width=\"700\"/>"
      ],
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        "</center>"
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Standard python and external package imports\n",
        "import pickle\n",
        "from pathlib import Path\n",
        "\n",
        "import gdstk\n",
        "import matplotlib.colors as mcolors\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# tidy3D imports\n",
        "import tidy3d as td\n",
        "import tidy3d.plugins.design as tdd\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from tidy3d import web\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "td.config.logging.level = \"ERROR\""
      ],
      "id": "afe0a5d2"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The first step in the design process will be to generate a dataset of\n",
        "simulation data for randomly sampled geometric parameters for the\n",
        "directional coupler. This part sets up a place to save the simulation\n",
        "data and the option to reuse it if you’ve already run through the\n",
        "notebook."
      ],
      "id": "01810d80-6bd0-48aa-a67f-8ce3d3bafbe8"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Configure notebook options for reusing simulation data if it exists\n",
        "use_previous_simulation_data = False\n",
        "\n",
        "output_dir = Path(\"./misc/dc_surrogate/\")\n",
        "mc_data_dir = output_dir / \"data\"\n",
        "mc_data_dir.mkdir(parents=True, exist_ok=True)"
      ],
      "id": "42af5243"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Simulation Setup\n",
        "\n",
        "Before running simulations, we need to set up general parameters that\n",
        "will remain unchanged for all of the simulations. We set up the desired\n",
        "frequency range to use, optical constants for the materials in the\n",
        "coupler, and parameters controlling fixed aspects of the coupler\n",
        "geometry. Finally, we specify the bounds for the parameters we will\n",
        "sample to create the training dataset."
      ],
      "id": "14de08de-9720-4b8d-b380-e42625dadf78"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Wavelength / frequency\n",
        "lambda0 = 1.550  # all length scales in microns\n",
        "freq0 = td.constants.C_0 / lambda0\n",
        "fwidth = freq0 / 10\n",
        "ldas = np.linspace(1.5, 1.6, 101)  # wavelength range\n",
        "freqs = td.C_0 / ldas  # frequency range\n",
        "\n",
        "# Permittivity of waveguide and substrate\n",
        "wg_n = 3.48\n",
        "sub_n = 1.45\n",
        "mat_wg = td.Medium(permittivity=wg_n**2)\n",
        "mat_sub = td.Medium(permittivity=sub_n**2)\n",
        "\n",
        "# Waveguide dimensions\n",
        "\n",
        "# Waveguide height\n",
        "wg_height = 0.22\n",
        "# Waveguide width\n",
        "wg_width = 0.45\n",
        "# Waveguide separation in the beginning/end\n",
        "wg_spacing_in = 8\n",
        "# Reference plane where the cross section of the device is defined\n",
        "reference_plane = \"bottom\"\n",
        "# Angle of the sidewall deviating from the vertical ones, positive values for the base larger than the top\n",
        "sidewall_angle = np.pi / 20.0\n",
        "# Total device length along propagation direction\n",
        "device_length = 100\n",
        "# Length of the bend region\n",
        "bend_length = 16\n",
        "# space between waveguide and PML\n",
        "pml_spacing = 1\n",
        "# resolution control: minimum number of grid cells per wavelength in each material\n",
        "grid_cells_per_wvl = 16\n",
        "\n",
        "# Coupler optimization parameter ranges to consider\n",
        "wg_spacing_coup_range = (0.05, 0.25)\n",
        "coup_length_range = (2, 16)\n",
        "top_waveguide_transmission_goal = 0.25\n",
        "bottom_waveguide_transmission_goal = 0.75\n",
        "\n",
        "# Optimization parameters\n",
        "num_monte_carlo_sims = 100\n",
        "\n",
        "filename_mc_data = mc_data_dir / f\"result_{num_monte_carlo_sims}.pkl\"\n",
        "if not filename_mc_data.exists() and use_previous_simulation_data:\n",
        "    print(\n",
        "        \"Warning, MC simulation data does not exist to be loaded. Reconfiguring use_previous_simulation_data flag to False and recomputing.\"\n",
        "    )\n",
        "    use_previous_simulation_data = False"
      ],
      "id": "151c82e5"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We now add functions to create the directional coupler in a tidy3d\n",
        "simulation. Each coupler from the Monte Carlo sweep will have a\n",
        "different waveguide spacing and coupler length. These are created as\n",
        "tidy3d objects in the `make_coupler` function."
      ],
      "id": "531b2b41-ab9e-4a7e-93e5-1ed8be56f35d"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "def tanh_interp(max_arg):\n",
        "    \"\"\"Interpolator for tanh with adjustable extension\"\"\"\n",
        "    scale = 1 / np.tanh(max_arg)\n",
        "    return lambda u: 0.5 * (1 + scale * np.tanh(max_arg * (u * 2 - 1)))\n",
        "\n",
        "\n",
        "def make_coupler(\n",
        "    length,\n",
        "    wg_spacing_in,\n",
        "    wg_width,\n",
        "    wg_spacing_coup,\n",
        "    coup_length,\n",
        "    bend_length,\n",
        "):\n",
        "    \"\"\"Make an integrated coupler using the gdstk RobustPath object.\"\"\"\n",
        "    # bend interpolator\n",
        "    interp = tanh_interp(3)\n",
        "    delta = wg_width + wg_spacing_coup - wg_spacing_in\n",
        "    offset = lambda u: wg_spacing_in + interp(u) * delta\n",
        "\n",
        "    coup = gdstk.RobustPath(\n",
        "        (-0.5 * length, 0),\n",
        "        (wg_width, wg_width),\n",
        "        wg_spacing_in,\n",
        "        simple_path=True,\n",
        "        layer=1,\n",
        "        datatype=[0, 1],\n",
        "    )\n",
        "    coup.segment((-0.5 * coup_length - bend_length, 0))\n",
        "    coup.segment(\n",
        "        (-0.5 * coup_length, 0),\n",
        "        offset=[lambda u: -0.5 * offset(u), lambda u: 0.5 * offset(u)],\n",
        "    )\n",
        "    coup.segment((0.5 * coup_length, 0))\n",
        "    coup.segment(\n",
        "        (0.5 * coup_length + bend_length, 0),\n",
        "        offset=[lambda u: -0.5 * offset(1 - u), lambda u: 0.5 * offset(1 - u)],\n",
        "    )\n",
        "    coup.segment((0.5 * length, 0))\n",
        "    return coup"
      ],
      "id": "d6646b40"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The `make_sim` function below sets up the full simulation in tidy3d for\n",
        "a user specified coupler length and waveguide spacing. It uses the\n",
        "`make_coupler` function above to create the geometry and adds mode\n",
        "sources to inject into the input waveguide and mode monitors to measure\n",
        "outputs from each waveguide. The DC simulation setup process follows\n",
        "this previous\n",
        "[tutorial](https://www.flexcompute.com/tidy3d/examples/notebooks/GDSImport/)."
      ],
      "id": "be68e75a-4b04-4887-b6f2-e0c63768e5ea"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "def make_sim(coup_length, wg_spacing_coup, add_field_monitor=False):\n",
        "    \"\"\"Make a simulation with a given length of the coupling region and\n",
        "    distance between the waveguides in that region. If ``add_field_monitor``\n",
        "    is True, a 2D in-plane field monitor will be added.\n",
        "    \"\"\"\n",
        "\n",
        "    # Geometry must be placed in GDS cells to import into Tidy3D\n",
        "    coup_cell = gdstk.Cell(\"Coupler\")\n",
        "\n",
        "    substrate = gdstk.rectangle(\n",
        "        (-device_length / 2, -wg_spacing_in / 2 - 10),\n",
        "        (device_length / 2, wg_spacing_in / 2 + 10),\n",
        "        layer=0,\n",
        "    )\n",
        "    coup_cell.add(substrate)\n",
        "\n",
        "    # Add the coupler to a gdstk cell\n",
        "    gds_coup = make_coupler(\n",
        "        device_length,\n",
        "        wg_spacing_in,\n",
        "        wg_width,\n",
        "        wg_spacing_coup,\n",
        "        coup_length,\n",
        "        bend_length,\n",
        "    )\n",
        "    coup_cell.add(gds_coup)\n",
        "\n",
        "    # Substrate\n",
        "    (oxide_geo,) = td.PolySlab.from_gds(\n",
        "        gds_cell=coup_cell,\n",
        "        gds_layer=0,\n",
        "        gds_dtype=0,\n",
        "        slab_bounds=(-10, 0),\n",
        "        reference_plane=reference_plane,\n",
        "        axis=2,\n",
        "    )\n",
        "\n",
        "    oxide = td.Structure(geometry=oxide_geo, medium=mat_sub)\n",
        "\n",
        "    # Waveguides (import all datatypes if gds_dtype not specified)\n",
        "    coupler1_geo, coupler2_geo = td.PolySlab.from_gds(\n",
        "        gds_cell=coup_cell,\n",
        "        gds_layer=1,\n",
        "        slab_bounds=(0, wg_height),\n",
        "        sidewall_angle=sidewall_angle,\n",
        "        reference_plane=reference_plane,\n",
        "        axis=2,\n",
        "    )\n",
        "\n",
        "    coupler1 = td.Structure(geometry=coupler1_geo, medium=mat_wg)\n",
        "    coupler2 = td.Structure(geometry=coupler2_geo, medium=mat_wg)\n",
        "\n",
        "    # Simulation size along propagation direction\n",
        "    sim_length = 2 + 2 * bend_length + coup_length\n",
        "\n",
        "    # Spacing between waveguides and PML\n",
        "    sim_size = [\n",
        "        sim_length,\n",
        "        wg_spacing_in + wg_width + 2 * pml_spacing,\n",
        "        wg_height + 2 * pml_spacing,\n",
        "    ]\n",
        "\n",
        "    # Source\n",
        "    src_pos = -sim_length / 2 + 0.5\n",
        "    msource = td.ModeSource(\n",
        "        center=[src_pos, wg_spacing_in / 2, wg_height / 2],\n",
        "        size=[0, 3, 2],\n",
        "        source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
        "        direction=\"+\",\n",
        "        mode_spec=td.ModeSpec(num_modes=1),\n",
        "        mode_index=0,\n",
        "    )\n",
        "\n",
        "    mon_in = td.ModeMonitor(\n",
        "        center=[(src_pos + 0.5), wg_spacing_in / 2, wg_height / 2],\n",
        "        size=[0, 3, 2],\n",
        "        freqs=freqs,\n",
        "        mode_spec=td.ModeSpec(num_modes=1),\n",
        "        name=\"in\",\n",
        "    )\n",
        "    mon_ref_bot = td.ModeMonitor(\n",
        "        center=[(src_pos + 0.5), -wg_spacing_in / 2, wg_height / 2],\n",
        "        size=[0, 3, 2],\n",
        "        freqs=freqs,\n",
        "        mode_spec=td.ModeSpec(num_modes=1),\n",
        "        name=\"reflect_bottom\",\n",
        "    )\n",
        "    mon_top = td.ModeMonitor(\n",
        "        center=[-(src_pos + 0.5), wg_spacing_in / 2, wg_height / 2],\n",
        "        size=[0, 3, 2],\n",
        "        freqs=freqs,\n",
        "        mode_spec=td.ModeSpec(num_modes=1),\n",
        "        name=\"top\",\n",
        "    )\n",
        "    mon_bot = td.ModeMonitor(\n",
        "        center=[-(src_pos + 0.5), -wg_spacing_in / 2, wg_height / 2],\n",
        "        size=[0, 3, 2],\n",
        "        freqs=freqs,\n",
        "        mode_spec=td.ModeSpec(num_modes=1),\n",
        "        name=\"bottom\",\n",
        "    )\n",
        "    monitors = [mon_in, mon_ref_bot, mon_top, mon_bot]\n",
        "\n",
        "    if add_field_monitor:\n",
        "        field_monitor = td.FieldMonitor(\n",
        "            center=[0, 0, wg_height / 2],\n",
        "            size=[td.inf, td.inf, 0],\n",
        "            freqs=freqs,\n",
        "            name=\"field\",\n",
        "        )\n",
        "        monitors.append(field_monitor)\n",
        "\n",
        "    # Initialize the simulation\n",
        "    sim = td.Simulation(\n",
        "        size=sim_size,\n",
        "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=grid_cells_per_wvl),\n",
        "        structures=[oxide, coupler1, coupler2],\n",
        "        sources=[msource],\n",
        "        monitors=monitors,\n",
        "        run_time=50 / fwidth,\n",
        "        boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
        "    )\n",
        "\n",
        "    return sim"
      ],
      "id": "3b7c64cf"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Below, we visualize an example grating coupler for a fixed coupling\n",
        "length and waveguide spacing."
      ],
      "id": "d66a3033-35ca-43dc-aa15-166da889a7ba"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
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QvDNHAX5OAuyCGrovyIY1Y4SqiumVBdM5376umoqqWzqoZYRyjpdUVbg9Q/9xb1z71Fxphi8hdU\nkDtnCs7MgRiZEhEREZH+CNW3dG9VWUm4MZDStV25mfjKS8goLyZj5Ag8xXm4C7IxHAMzwJ4IhtsL\n0xdCxQKMuj3YG1Zhb38HIjmHuri01IHTFe/iMkC65fSLFQMrCpaFSeK2ixURERERERERkeRRsEUS\nIhS1wGOCbeO3ugAHrXgBiEQi6S2uW9SKsb+lgUyPl9F5RWR6MsjPzOGq0xfxkTMWURINYz36w/id\niV3tYJjx9tsZfnC4MErHYcw4G8afFm/jPYyYbicZowrJGFVI7pmTAbBtm3B9Kx1VNd2PWjr31GFH\nkhdk6txTy/49tRz4y8vknDmZoiVzyRh54q2YRERERGRgs22b9s17qFu5lvYte1OypsPnwTe2hIzy\nEnzlxfjKi3HlZKZk7YHIMAwoLscoLsc+eyn2ljXYG1ZBSz1EQ/GAS3tz/GTHiTtBDmi2He9KY5rg\ndBHpGgJBHRERERERERGRYUDBFkmImmCIEn82GB6wLfxWFIhRN0D+iDkMkwJ/Dnm+LFxOJ3PKp3LN\n7PNYPPkMXM54jXbDgfjJLjf4ssHrA7cXY8ocjOlnYRSUpfEnGHgMw8BTlIunKJe8uVMBsGMWXdWN\ndFTVEtyxn7bK3UQDHQlf245ZPVsVZc0YR9GSuWROHJnwdUREREQkeWzLovXd7dQ9u5bOvXVJXctT\nnE/WtDH4xpfhKy/GPSJn0G4jlGyG14cxazH2aefBvm3YG1dh73ofMiPxbYqsQRoGMQwwneBwAga2\nJ4PmlqZ0VyUiIiIiIiIiIqdgYKQOZNBrDUUpzsiCvBxoa4KOAH7Lxm2mv7PJGWMm8/7+nWR6Mrhy\n1tlcc+Z5jB1RevSJuYXgi9+laYwoi3dnmXRmvD23nBLDYfZ0dik4ewa2bdO1v4G2yt0ENu4muOMA\nWFZC12zbsIu2DbvwTSijeMlcsmaM04cUIiIiIgOYFYnSvHoTdc+9Rbi+JSlrmB4XmVPGkD19LFkV\n5bgLcpKyzlBmGAaMnowxejJ2sBW78k3syjegvRWjdHy6y+sdlwciUXB7ITMXsvKI2QZde+vTXZmI\niIiIiIiIiJwCBVskoQyvD7w+7Fh833JXUwsuV3rDLbddcA0fmrmAUXmFeF2e455nOF2YN34zfhdi\nTqHCEQlgGEZP0KVoyVxinSHat+6jbeMuApVVRJoCCVurY8cBdv38KbxlBRQtmUvu7CkYDjNh84uI\niIhI/8Q6QzS+9j71L7xDtDWY8Pm9owrJqignu2IsvvFlmE5HwtcYrgx/DsbcS7BnXwTNtZBTmO6S\nesXw54DhhOISDLP7PUI4nN6iRERERERERETklCnYIklhOBzgiF9IdjrT+8fMMAwmFo06tXO9fvD6\nk1zR8OXI8JAzawI5syZg2zah2mYC7++kee1muvYl5m7JrgON7Pn936n52yoKL5pN/sLpmO70dw4S\nERERGa4ibR00vLCOhlfWY3WGEjexAZlTxpA7ZwrZFWNx5WYmbm45JsN0wGDcotUwwHQcCrWIiIiI\niIiIiMigomCLiKSFYRh4S/LxluRTdPEcOvfV07y6kua1m4kGOvo9f7gxwP5HX6R2xZsUXTqPgnNn\n6a5dERERkRSKdnRR+/RqGl9djx2JJWxeT3EeefMryJs3DXd+VsLmFRERERERERERkYFJwRYRGRDi\nWxadR+lVi2ir3E3T6koC7+3EjvbvQ5BoeycHHnuZxlfeo/TqRWTPHK9tpkRERESSyI7FaHz1fWpW\nvEEs2JWQOR0+D7lzppK/oIKM8mL9e05ERERERERERGQYUbBFRAYUw2GSPXM82TPHE+3oovXtrTS9\nWUnHrup+zRuqa2b3L/5K5pTRlF1zHhmjChNUsYiIiIgA2LZN28bdHPjLK4Rqm/o/oWmSPWMsefMr\nyJ4xDtOlt68iIiIiIiIiIiLDka4MisiA5fR5KVh0GgWLTqOrtomGF9bR9MbGfnVxad+yl63/8RD5\nZ02n5MqzcGX7E1ixiIiIyPDUdaCB/X95hfZNVf2ey+H3Unj+GeQvOg1Xli8B1YmIiIiIiIiIiMhg\npmCLiAwK3uJ8Rt14IcUfWkDDi+toeGU9Vle4b5PZNk2vb6Dl7a0UXzqPEeefoTuARURERPog2tZB\nzfI3aHztfbDtfs3lys+i8MLZ5J81A4fHlaAKRUREREREREREZLDTJ7kiMqi4cvyUXnUORZfMpfHV\n96h/4R2igY4+zWV1hal+8jUaX32P0o8sIueMSRiGkeCKRURERIYeKxKl4aV3qX1mdd/Dxt08pfkU\nXTyXvLlTMByOBFUoIiIiIiIiIiIiQ4WCLSIyKDkyPBQtmcuIxWfQ9OZG6v/xNuGG1j7NFW4MUPWb\nFfgnlDHqYxfhLS1IcLUiIiIiQ0egcjf7H32BcH3f/u11kG9cKUWXzCV7xngMU+FiERERERERERER\nOTYFWyTpotFoukuQIcx0Oxlx7iwKzp5Jy7pt1K1cS9e++j7NFdxxgK3/8RAlVyyk8MLZGA4zwdWK\niIiIDF6xzhAH/vIKTas29GuerOljKVoyF//EkeqWJyIiIiIiIiIiIielYIsklUItkiqGwyRvzhRy\nZ0+mdd02qp94lXBjoNfz2NEY1U++Ruu72xl98yV4S/KTUK2IiIjI4NK2qYq9Dz5HpLmtz3P4xpVS\ndu15+MeVJrAyERERERERERERGeoUbJGkCQaDhEIhnE79MZPUMQyD3DMnkz1zPPUvvEPds2uxusK9\nnqdjdw1b73qQkg+fTeEFZ2CY6t4iIiIiw0+sK0z146/Q+Nr7fZ7DlZdF6UcWkTt7sjq0iIiIiIiI\niIiISK8pcSBJEQwGaWlpSXcZMoyZLifFl8wjf+F0av62iqZVG8G2ezWHHY1R/fgrtL67jTHLLsFT\nnJekakVEREQGnrbNe+JdWpp63wUPwPS4KFoyl8ILZ2O69dZTRERERERERERE+kZXFyXhPhhqcblc\n6StGhj1Xtp/RN13MiPNO58BfXqZ9y95ez9Gxs5otdz1A6YfPYcT5p6t7i4iIiAxpsa4w1U++RuMr\n6/s2gQH5C6ZT8uGzcOVkJrY4ERERERERERERGXYUbJGE+mCoxePxaCsiGRAyRhUy/ovXEHh/J9WP\nv0qorrlX4+1IjAN/eZnWd7cxetkleIpyk1OoiIiISBq1b93L3gdWEm7sW5cW/6RRlF17Hr7RRQmu\nTERERERERERERIYrJQ4kYaLR6BGhltzcXNrb29NXkMgHGIZBzmkTyKoYS+Mr66ld8SaxzlCv5gju\nOMCWOx+g9KpzGHHe6RimkaRqRURERFInFopQ89RrNLz0bp/GuwtzKPvIuWTPmoBh6N9HIiK9FQ6H\neeqpp3jmmWfYt28fXq+XKVOmcOONN3LmmWf2e/6PfvSj7Nu376TnXXnllXzjG99Ia60iIiIiIiIi\nH6RgiySEw+EgHA7j9XqBeKjF7/cr2CIDkul0UHjBmeTOnsy+Pz5P4L2dvRpvR6Ic+PNLtL67ndHL\nLsYzIjc5hYqIiIikQPv2ffEuLfWtvR9sQOEFsym58ixMt95eioj0xZYtW7j++ut57733jnj9b3/7\nG3fffTe33norP/vZz/rVEfett95ix44dJz1v6tSpaa9VRERERERE5IP0LlMSwuVy9Tw/GGoRGehc\nOZmMvfXDtKzZzP4/vdj77i3b9rH1zgcpveocChbNUvcWERERGVSscITqv75Ow4vrwO79eHdRLmOW\nLcE/YWTiixMRGSZqa2tZsmQJe/bsISMjg6985SssWrSItrY2fv/737NixQp++ctfEo1G+c1vftPv\n9ZYuXcrXvva14x4vKSkZMLWKiIiIiIiIHKRgiySUQi0y2BiGQd78aWROGc3eh/9B24ZdvRpvhSLs\nf/RFWtfFu7e4C3KSVKmIiIhI4gR37GfPAysJ17X0frABI84/g9IPn43pdp38fBEROa5vfetb7Nmz\nB4fDwTPPPMN5553Xc+zaa6/lc5/7HL/85S/57W9/y8c//nEWL17cr/VKSko455xzBkWtIiIiIiIi\nIgcp2CIJ43a7ex1quXrE1ad24sTP9qEikVPnys1k3OeX0vxmJfsfexmrl91b2rfuZcsdD1B29bnk\nnzMTw1D3FhERERl4rHCUmr+tov6Ft/vWpaUwh9HLlpA5cVTii5PhSe/1ZBirqanhD3/4AwA333zz\nEUGRg370ox/x6KOP0tLSwn/8x3+kLSwymGoVERERERGRocdMdwEyNEQiEe2fLIOeYRjkL5zOlO8s\nI6tibK/HW6EI+/74PDv/73HCjYHEFygiIiLSD8Fd1Wz9jwepf75voZYRi09n8reWKdQiIpIgTz75\nJLFYDIBPfOITxzzH7/dz7bXXAvDiiy/S3NycqvKOMJhqFRERERERkaFHwRZJiIMXN0SGAndeFuO+\ncBWjbroY0+vu9fj2zXvYcucDNL72Prbdh0+NRERERBLIikQ58MSrbL/7UUK1vf+Q0V2QzYQvXcvI\n68/H4dHWQyIiifLaa68B8Q648+fPP+555557LhC/qWjNmjUpqe2DBlOtIiIiIiIiMvSoxYaIyDEY\nhkHB2TPImjaGvQ8+R/vmPb0ab3WF2ffwP2h9dxujPnYx7vysJFUqIiIicnwdu2vYc/+zhGqa+jS+\n4NxZlF51Do4+hH1FROTENm3aBEB5eTkej+e4502ePLnneWVlJZdcckmf13zvvff4zGc+w+7duzFN\nk5KSEhYuXMh1111HQUHBgKpVRERERERE5CAFW0RETsCdn834266m6fX3OfCXV7BCkV6Nb6usYssd\n9zPyusXkLajAMIwkVSoiIiJyiBWJUvv0m9StfAv60EHOlZ/F6I8vIWvqmCRUJyIiAAcOHACgrKzs\nhOcdfry6urpfa77xxhu88cYbR7x2//3385WvfIXbb7+db3zjG8d835qKWk+0dVFLS0uv5hIRERER\nEZGhRcEWEZGTMAyDgnNOI2taebx7y5a9vRpvdYXZ+8BKWt7ZxuibLsKVm5mkSkVERESgo6qGvfev\npKu6sU/j88+ZSdlHFuHIOP4d+SIi0n/t7e0A+P3+E57n8/mOGtNbLpeLSy65hMWLF1NeXk5xcTFN\nTU28/PLL/OEPf6CtrY1//dd/Zf/+/fzf//1fWmrNz8/v1fkiIiIiIiIyfCjYIiJyitwFOYy/7Roa\nX3uP6ide7X33lo272Pzv3d1b5k9T9xYRERFJKCsao/aZ1dQ9uwasPnRpycti9E0Xk1VRnoTqRETk\ngyKR+HtKh8NxwvOczkOX7w6O6a1Vq1aRl5d31OvXXnstX/va17j00kvZvHkzP/vZz1i6dCkXXXRR\n2moVERERERER+SAFW0REesEwDUacO4usinL2PvAcwW37ejXe6gyx9/5naV23jVEfuxBXjrq3iIiI\nSP917K1j7/3P0rW/oU/j88+aQdk156pLi4hICvn9fkKhEF1dXSc8r7Oz84gxfXGsUMtB5eXlPPbY\nY5x22mlYlsX//d//HRVsSUWtTU1Nxz3W0tLC+PHjezWfiIiIiIiIDB0KtoiI9IFnRC4T/vlaGl9Z\nT/WTr2KFo70aH3h/J1v+fT8jrz+f3LlT1b1FRERE+sSKxqj7+xpq/74GLKvX4125mYy66SKyp49L\nQnUiInIiubm5NDU10dzcfMLzDj+em5ublFqmT5/O/PnzeeONN3j11VePOp6KWk8UvhEREREREZHh\nzUx3ASIig5VhGoxYfDqTv7UM/4SyXo+PdYTY8/u/s/tXfyMSCCahQhERERnKOvfVs+2//0jt02/2\nKdSSt6CCKd9ZplCLiEiaTJo0CYDdu3ef8LyqqqqjxiTDhAkTgHg45YPbCA20WkVERERERGR4SWnH\nlsbGRt555x3q6uqor6+nq6uLwsJCCgsLmTRpEtOmTUtlOSIiCeEpymXCl6+n4aV1VD/1GnYk1qvx\ngfU72LJ9PyNvuIDc2ZPVvUVEREROyI7FqHt2LbXPrMaO9T7Q4szxM/pjF5E9U1s6iIik06xZs3j2\n2WdpaGigqqqK8vLyY563du3aI8YkS3t7OwAulwuXyzWgaxUREREREZHhJenBlnXr1nH//ffzwgsv\n8P7772Pb9nHPLSws5LzzzmPp0qVcd911eDza311EBgfDNCi84Eyyp49jzwPP0rGzulfjY8Eu9vzu\naVrXbWXkRy/EleVLUqUiIiIymHUeaGDvH56lc29dn8bnzZtK2XXn4/R7E1yZyODzwQ/uZeior69n\n3bp1PTdWhcPhnhurJk+ezJQpU9JdIgBXXHEF//3f/w3A3/72N/7pn/7pmOctX74cgPLycmbMmJGU\nWsLhMK+//joAkydPHtC1ioiIiIiIyPCTlGBLLBbj4Ycf5p577mH16tWnPK6+vp7HHnuMxx57jC9/\n+ct88pOf5Itf/CKjRo1KRpkiIgnnKc5j4leup/6FddT89XXsaO+6t7Su2077tv2M+ugF5J559MVE\nERERGZ7smEXdc29R+/Sbvf73BYAz28eoGy8iZ9aEJFQnMvh0dnbqZpohZs2aNTzwwAO8+OKLVFZW\nnvDGqpKSEs477zw+8pGPcPXVV6ct5HT22WczefJktm7dyk9+8hM+/elPk5GRccQ5zz//PGvWrAHg\nk5/8ZNJqufPOO6mvrwfgmmuuGdC1ioiIiIiIyPBj2Cd6p98Hzz77LF/96lfZuHFjz2ujRo1i4cKF\nLFiwgBkzZpCfn09BQQEej4fGxkaamprYs2cPb775Jm+++Sbvvfce0WgUgIyMDL72ta/xjW98A7/f\nn8hSJQGam5vJz8/n1ltvZfLkyZSWlh5xvLa2loyMDEaNGsXll1+epipF0qOrpom99z9Lx+6aPo3P\nnT2ZkTdcgDMz4+Qni4iIyJDVVd3InvufpbOqtk/jc+dOZeR1i/VvChmWVqxYwb59++js7KS4uBiA\nYDBIU1MTgUCAO++8k6amJvLy8tJcqfRFJBLhgQce4J577uGdd97p0xzFxcV8+tOf5rbbbqOkpCTB\nFZ7cU089xVVXXQXEu6Lcd999jBgxAoBXX32V6667jtraWsrKyti8eTNZWVlHzXHddddRXV1NQUEB\nTz311FHH7777brKzs7nyyiuPum5z4MAB7rzzTu69914gfg1vw4YN5OTkJKXWvjp4/QnQ/2ZFRERE\nRERO0VB6L5XQYMunP/1pfve73wFQVFTERz/6UZYtW8acOXN6NU9DQwOPPPIIDzzwQM+dHmVlZaxa\nteq4e/hKeijYInJidsyi/vm3qVn+Rt/urs7yMerGC8k5fWISqhMREZGBzLYs6v/Rj39HZGYw8sYL\nyT1jUhKqExkcVqxYwe7du4lEIhQXFxMMBmlpacGyLAVbBrm//e1vfO1rX2Pr1q09r5WXl7NgwQIW\nLFjA9OnTe26scrlcNDU10djYSFVVVc+NVe+//z6xWPzv18zMTL75zW/y1a9+Fa83tdu1fe973+MH\nP/gBAF6vl6lTp9LW1saOHTsAyM7O5oUXXmD27NnHHD927FiqqqooLi6mpuboGyuuuuqqnsBLVlYW\nJSUl5Ofn09jYyI4dO3q624wbN47ly5dTUVGRtFr7aihdjBUREREREUmVofReKqHBljlz5lBfX8+3\nv/1tPvnJTyakleu6deu4/fbbWb58OWvXru11SEaSS8EWkVPT7zut50xh5PXn605rERGRYaKrpom9\nD6ykY1d1n8bnnDmZUTecjzPLl+DKRAaXFStWsG3bNqLRKLm5ubS0tABgWRb19fXcfffdg/7CznB0\nww038Kc//QmA0tJSbrzxRpYtW8bpp5/eq3lqa2t5+OGHefDBB3s6vowZM4Y1a9b0dPhJlSeffJIf\n/OAHrFu3ruc1j8fDhz/8YX74wx+e8EavkwVbVq1axSOPPMLf//53tm3bdsQxwzCYMmUKN998M7fd\ndhuZmZlJrbWvhtLFWBERERERkVQZSu+lEhpseeaZZ7jwwgtxu92JmrLH2rVrGTlyJGVlZQmfW/pO\nwRaRU2fHLOqee4vaFW9gx6xej3dm+xj1sYvIOW1CEqoTERGRgcC2LOpfWEfNX1/vU5cWR2YGoz56\nAblnTk5CdSKDz8FgSzAYxOPx9LyemZnJ5s2b+d///d9Bf2FnOJo6dSrRaJTvfOc7LFu2DIfD0e85\nV69eze23387KlSvZtGkTU6dOTUClvVdbW8v+/fvxeDyMHTv2lLblfuutt+jq6sLtdjNv3rwTntvR\n0UF1dTUNDQ34fD5Gjx5Nbm5uymrtq6F0MVZERERERCRVhtJ7KWciJ7vssssSOd0R5s6dm7S5RURS\nwXCYFF86j+yZ49l7/7N07q3r1fhooIPdv/grefOmUXbdYpz+1LbHFhERkeQK1Taz54Fn6djZxy4t\nZ0xk5EcvxKUuLSInlJubm5AOs5I+P/rRj7jkkktwOhN3WWv+/Pk8++yzvP7664wYMSJh8/ZWcXFx\nr7vF9Ka7sc/nY8KECUyY0P8bJvpSq4iIiIiIiEhfJDTYIiIiJ5cxcgST/uWj1K18i9qn3+x195bm\nNZsIbNxFyRULKTjnNAyHmaRKRUREJBVinSFqn1lNw0vv9q1Li9/LyBsuIHf2ZAzDSEKFIiIDSzI7\nwp599tlJm1tERERERERE+kbBFhGRNDAcDoovm0/2zPHsuf9ZuvbV92p8LNjF/kdfpOHl9ZRdcy7Z\n08clqVIRERFJFjtm0fj6+9Quf4Noe2ef5sg+bQKjbrwQV07ytn8QGWpaWlrIzMxMdxkiIiIiIiIi\nInKK0hJsaWhoYMeOHQQCAWKxE9+ReNZZZ5GdnZ2iykREUitjVCGT/uVG6v6+htq/rwGrd91bQjVN\n7LrnSbIqyim7+ly8ZelrmS0iIiKnrq2yigN/eZmu6sY+jXf4PIy8/nxy505VlxaRU+DxeMjNzaWl\npQWAQCCg7YhERERERERERAaJlAVbLMvit7/9LT/96U/ZsGHDKY9bu3Ztr/YKFhEZbEyng5IrFpI9\nawJ7//AsXQcaej1HW2UVWzY/SME5p1FyxUKcmRlJqFRERET6q6umiQOPv0Lbhl19niN75nhGfexC\nXDnqOCFyKg4GWPz+eGejg+EWj8eTrpIkRerq6ti5cydtbW0nvbFq0aJFPX9GRERERERERGRgSUmw\nxbZtbrzxRv70pz+lYjkRkUHJN7qISd/8GLXPrKbu2TVg2b2bwLJpfGU9zWs3U3zZfEYsPh3T6UhO\nsSIiItIr0fZOap9+k4ZX3ut1h7aDzAwPI69bTN78aerSItILTqeTSCQCHAq3NDU1pbMkSaJYLMbP\nf/5z7rnnHjZv3nzK4zZt2sTUqVOTWJmIiIiIiIiI9FVKgi2PPPJIT6jljDPO4P/9v//HrFmzyMvL\nwzTNE44dO3ZsCioUERkYTKeD0ivPIue08ey9f2WftiewOkNUP/4Kja+up/SqReTMmohh6sMvERGR\ndLAiURpffY/ap98k1hHq8zxZ08cx+qaLcOWqS4tIf/n9fqLRKIFAIN2lSIJZlsVVV13F8uXL012K\niIiIiIiIiCRQSoItjz32GABLlixhxYoVOJ0p2wFJRGRQ8pWXxLu3PP0mdSvfAruX3VuAcH0rVb9e\njre0gMIlc8ibMwXDoQ4uIiIiqRDrDNH46nvUv/AO0UBHn+cxve54l5YFFerSIpJAGRkZhEJ9D5vJ\nwPTb3/62J9Qyb948PvWpT3HaaaeRk5Nz0hurxo8fn4oSRURERERERKQPUpIwqampAeCLX/yiQi0i\nIqfIdDkpXXoOObMmsv9PL9Kxu6ZP83RVN7L3D89S87dVFF44m4KzZ2C6XQmuVkRERAAirUEaXlxH\nwyvrsbrC/Zor58xJlF19Hu78rARVJyKHO7g9kQwdB2+suuqqq/jLX/5y0jCLiIiIiIiIiAwOKUmZ\nlJWVAZCbm5uK5UREhhTf2BImfu2jtLy9heonXyPS3NaneSJNbRz480vUPrOawsWnU3De6Tj93gRX\nKyIiMjyF6luo/8fbNL2xETsa69dcGWOKKbv2PDInjkxQdSIiw8PBG6u+9KUvKdQiIiIiIiIiMoSk\nJNhy0UUX8dhjj7F+/XrOPvvsVCwpIjKkGKZB3typ5MyaQP3z71C3ci1WqG93mMbaO6lZ/gZ1z71F\nwTkzGXHBmbjzdCe4iIhIX3Tuq6du5Vpa3t7ap60DD+fKzaRk6dnkzZ2GYWrbIRGR3iorK+O9997T\njVUiIiIiIiIiQ0xKbl+55ZZbmDx5MnfffTctLS2pWFJEZEgy3S6KL5vP1O99gryF06Efn3lZoQj1\nz7/D5tt/x94HVtJRVYPdzw/kREREhgM7ZhHYuIud9zzB1rsepOWtLf0KtZhuJ8WXL2DK9z5B/vwK\nhVpERProoosuAmD9+vVprkREREREREREEiklwRav18tzzz2H0+nk7LPP5oUXXkjFsiIiQ5YrN5Mx\ny5Yw6Rsfw9/PbQrsmEXTGxvZ9l9/ZMsd91O3ci2RlvYEVSoiIjJ0dB5o4MDjr1D57V+z654nadu4\nu99z5s2fxtR/+yQlly/E4XH1v0gRkWHss5/9LOXl5fznf/4n7e16TyMiIiIiIiIyVKRkKyKAMWPG\nsH79eq6++mouvPBCRowYwaRJk/D5fCccd++99zJ58uQUVSkiMrj4xhQz4cvX0frudqqfeJVwQ2u/\n5gtVN1H95GtUP/U6mVPHkL+ggpxZEzDd+qBNRESGp2hbB81vbaH5zUo699YlbF7/hDLKrj0PX3lJ\nwuYUERnusrKyeO6557j44otZtGgRP/3pT1m0aFG6yxIRERERERGRfkpZsGXPnj3cdNNNvPbaawA0\nNDTQ0NBw0nGBQCDZpYmIDGqGYZB7xiSyZ4yj8ZX11D33FtFAR/8mtW3aN1XRvqkK0+sm98zJ5C2Y\nhn/CSAxD2yOIiMjQZkVjBN7fSfPqSgIbdoNlJWxub2kBxZcvIOeMSfr/qSIiSTBp0iQ2bNjAFVdc\nwbnnnktRURETJ04kIyPjhOPuu+8+Ro8enaIqRURERERERKQ3UhJsaWlp4ayzzmL//v0A5OTkMG3a\nNPLy8jDNE++GlJubm4IKRUQGP9PlpPDC2RScO4vm1ZXUPfcW4fr+dXABsLrCNK3aQNOqDbgLssmZ\nPZnsinH4J5RiOBwJqFxERCT9Yl1h2rfsJbBxF63rthELdiV0ft/4UoovmUfW9HEYpgItIiLJsmPH\nDj72sY+xZs0aAOrq6qirO3nHrWAwmOzSRERERERERKSPUhJs+elPf8r+/fvx+Xz84he/4MYbb8Tp\nTFmzGBGRYcV0OSk45zTyz5pB67pt1K18K2FbJ4QbA9SvfIv6lW9het1kThlNdsVYsirG4i7ITsga\nIiIiqWDbNl37G2ir3E1g4246dh7AjiWuM8tBWTPGUbRkLpkTRyZ8bhEROVJdXR0LFy6kvr4eiN8s\nVVFRQU5OzklvrMrMzExFiSIiIiIiIiLSBylJl7z55psAfPe732XZsmWpWFJSzKGuDSIDjmGa5M6e\nQs6Zk2nbVEXds2sJbtuXsPmtrjCB9TsIrN8BgKc4n6zp5WRVjCVz4ihMtwKMIiIysESDXbRvriJQ\nWUVb5W6irUm6O98wyJ0zhaKL55AxqjA5a4iIyFHuvvtu6uvryc7O5te//jXXXHONrleIiIiIiIiI\nDAEp+dSxqyvexnvRokWpWE7SwOVyEY1G012GiByDYRhkV4wlu2IswV3V1K1c2xNGSaRQbROh2iYa\nXliH4XLgnzAS37hSfOXF+MqLceXoDkgREUkd27YJ17fSUVVDR1UtHbuq6dhdA7adtDUNp4P8s2ZQ\neNGZeEbkJm0dERE5toM3Vt15551cf/31aa5GRERERERERBIlJcGWKVOm8OKLL9LW1paK5SRNwuEw\nwWAQv9+f7lJE5Dj840oZd+uH6apupP4fb9PyzlasUCTh69iRGO2b99C+eU/Pa67cTHzlJWSUF+Mb\nW4xvTDEOnzfha4uIyPAUaWmPB1iqaujYXUPnnlpiHaGUrO3M9pG/cDojzj8DV7b+LSwiki66sUpE\nRERERERkaEpJsOVTn/oUv/jFL3jyySe59NJLU7GkpElLSwuAwi0iA5y3tIDRy5ZQdt1iWt/dTvPq\nStq37oXk3cROpKWd1pbttK7f3vOapyiPjPJivKUFeIrz4o/CXEyXtjESEZFji3WGCNU1E6qNPzoP\nNNCxuyZ52wodh+F0kDNrAnkLKsiaWo7hMFO6voiIHG3KlCmsXbtWN1aJiIiIiIiIDDEp+eRw7ty5\n/OAHP+D73/8+5513HjfeeGMqlpUUikQOdXxQuEVk8HB43eQvqCB/QQXhpgDNazbT/GYlobrmlKwf\nqms+ei0D3PnZ8ZBLUd4RX125WRimkZLaREQkfexYjHBDgK7aJkJ1Ld3b3cX/f0Y00JHW2nzjS8lf\nUEHumZPVeUxEZID51Kc+xYMPPsiTTz7JOeeck+5yRERERERERCRBUhJs2bFjBx/5yEfo6upi2bJl\nPPTQQ9xwww1MmTIFn893wrETJ07E69UF44EuFovhdrt7vj8YbhGRwcOdn03xpfMoumQuHbtraH6z\nkpa3t6RsG4ceNoQbA4QbA7RVVh15zDRxZmXgyvbjzPbhzPLhzPbFv8/yHXo924/D58EwFIIRERko\nbMsi2t5JNNBBNNBBJBAk2tZBNBAkEujoeR4NdBANdia1i1hvufKzyZ8/jbz5FXiKctNdjoiIHMf5\n55/Pv/zLv/DjH/+YRYsWsXTp0nSXJCIiIiIiIiIJkJJgyw033MDbb7/d8/2KFStYsWLFKY1du3Yt\nc+bMSVZpkkBOp5Ps7OyeUIvCLSKDk2EY+MeV4h9XStm15xF4fyfNazbTvmUPVihy8gmSybKItgZP\nabsJw2H2BF+cWX5c2b7DwjAHv/fj8HtxeFwYLqeCMCIivWBbNlY4ghUKE23v6gmlHAqsHPk82t4J\n9gBKq5yEM8tH1oxx5M+fhn/iKHUMExEZBLZt28ayZcuIRqNcffXVLF26lGuvvZZJkyaRkZFxwrGT\nJ08+4oYdERERERERERk4UhJskeHj4PZDB0MtoVDopBePRGTgMl1Ocs+cTO6Zk7EiUYI7D9C2cTdt\nlVV0HWhId3knZMcsIi3tRFraT22AAabbhenpfrgPfXV43JgeJ6bHfexzPIef68JwOzFMM/5wmGAa\nhz03MUwDw+HQh6QikjC2bYNtY8csbMuGWAzbOvi9BZaFHbOxYzGsSBQrFOkOpRz2CEeI9XwfxgpH\n418PO37weSwUwY5E0/1jJ5Zp4B9fRlbFWLIqyskYVaS/p0VEBpkrr7ySLVu29Hz/xBNP8MQTT5zS\n2E2bNjF16tRklSYiIiIiIiIi/ZCSYMu9995LIBDo09gpU6YkuJq+iUQi1NTU4PF4KCwsTNpd/YlY\nJ1W1Hs8Hwy2RSJo7PIhIQpguJ1lTxpA1ZQxcDZGWdgKVu2mr3E37pj3EOlO8ZVGi2fR8YJsyBhhm\nd9jF0R14MU1wdIdiTOOw54eFZBzmB4IyJobDOOlchsM8cj7TAHWpEUkM28Y+GB7pCZLEQyZ293O6\nQya2ZUH3eT3HDjsvPr47lHJEMOXgePuIueyYDZaV7t/AoOTKzSRr+th4mGXKaBw+bYEqIiIiIiIi\nIiIiMtCkJNgyb968VCyTFKtWreKOO+7gueeeIxqN35Wam5vL9ddfzw9+8AOKi4sHzDqpqvVUHAy3\n1NbWpmxNEUktV24mBWfNoOCsGdgxi47dNbRV7qZt8x4699ZhR2PpLnHgszn0YbcygCIiSWd63fjG\nlpBVMZbsinI8pQXahk5EZAi57777CAZPvm3psYwZMybB1YiIiIiIiIhIomgrohP45S9/yRe+8AVi\nsRiGYTBy5Eg6Ojpobm7mV7/6FY8//jgvvvgiM2bMSPs6qaq1N/x+Px6PJ2XriUj6GA4T/4Qy/BPK\nKLnyLKxojK4DDXTsrqGjqpbOqhq6qpvAttNdqoiIDBOG00HG6CJ85cVklBfjKy/BU5Sn7YVERIaw\nhQsXprsEEREREREREUmCARtsufnmm7FtmwceeCAt67/66qs9QZEFCxZw//33M2nSJACWL1/OLbfc\nQkNDA5dffjmVlZU9HUrSsU6qau0Lp9OJy+VK2XoiMjCYTge+McX4xhzqFBXrCtO5r46O3fGgS0dV\nLeGG1jRWKSIiQ4Zh4C0twFdejG9sCRnlxXjLRmA6HemuTEREREREZEiLdYaItLSnu4yEc2R4cOVm\nJm3+WFeYSHNb0uZPF9Pjwp2fne4yRERkCBqQwZbW1lb+8Y9/UF1dzQ9/+ENKSkpSXsPXvvY1YrEY\nxcXFrFixgvz8/J5jV1xxBQ8//DCXXnope/bs4X/+53/47ne/m7Z1UlVrXzmdA/KPmYikmMPrJnPi\nKDInjup5LdYZIlTbHH/UNdPV/TVU24wdiaaxWhERGYgcPg+e4nw8RblHfi3MxXTr35wiItJ71157\nLfn5+fzqV79KdykiIiKDUtOqDRz4yyvpLiPh/JNGMfHL1yVt/pZ3trLvweeSNn+6eEcVMuVbH093\nGSIiMgSl5Orv+eefz7p16075/Pb2dmKxGACBQCDlwZb169ezZs0aAG677bYjgiIHXXLJJcybN481\na9bwm9/8pk9hkUSsk6paRUSSwZHhwTe2BN/YI/+ety2bSEt7T8glVNtEuClANNBBtK2DSCCIHYml\nqWoREUkW0+vGme3DleXDme3HXZiDpygPb3EenuJ8HH4vhqGthEREJDEaGxt58cUXaWlp4e677yY7\nW3cXi4iI9FZXTVO6S0iKUG1yf67QkP29NWNbtrYBFhGRhEtJsKWtrY3W1t5vN3HxxRf3bKmTSn/7\n2996ni9duvS451111VWsWbOGPXv2sH79embNmpXydVJVq4hIKhmmgTs/C3d+FllTxxx13LZtrK5w\nT8glGuggGugg0tZB9OD3hx2zowrBiIiki+lx4cyOB1UOBlYOD684s324sn04s/zquiIiIv0yb948\ntm7desrnH35jVXt7u4ItIiIifTBUAxrRQAfRji6cPm9S5h+qvzc7EiXSHMBdkJPuUkREZIhJyZXj\n/+//+/84cODACc8JhULs2LGD5cuX09bWxkc/+lEefPDBtNyR+e677wLg9/uZPn36cc+bN2/eEWN6\nGxZJxDqpqlVEZCAxDANHhgdHhgdPUd4Jz7VtG6sz1B16iQdfIt1BmGh7B1ZXGCsUIRaKYIUjWKHu\nR/dzhWJERMBwOTE9LhweF6bbhek5/OHG4XXjzPJ1h1T83UGWeFjF4XGlu3wRERkmAoFAn26suuqq\nqygrK0tCRSIiIkNfV21zuktImlBNE87xyfk3wlD+vXXVNCvYIiIiCZeSYMunPvWpUz63oaGBK664\ngkceeYRLLrmET3ziE8kr7Di2bNkCwJgxY04YrBk7duxRY1K9TqpqFREZrAzDwOHz4vB5ofjo7dpO\nxo7FuoMuUWKh8FHBl8O/j4UiWKEwdtTCtiyIxb/alo0dO/x7K/69Zfc8t63u7w87J36+jR2LYVv2\nB8bbYFlJ+I2JSLoYDhNME8NhYpgGhmmCw8Toec0ER/z1g6/RfV7PWNPE6D6nZ66Dc5gGmCZmd0il\n53FYUOVQcMWN6XHGv7qd8flEREQGuC996Us0NDSc8JxQKMTWrVtZsWIFwWCQT3/60/zqV79KUYUi\nIiJDS7S9k1h7Z7rLSJqumib8SQi2WJEo4Ybeh3EHi1BNE0wfm+4yRERkiBlwvb5HjBjBfffdR0VF\nBbfffju33HJLyru2NDfHk7IFBQUnPO/w4wfHpHqdVNR6ovNbWlp6NZeIyGBjOBw4fA4cPhhoPQds\n2wb7YBgmHnQ5MiQTw47ZHwjKHBa0UTBGJKGODKZ0B0l6nneHTw47dkQYRXtPi4iI9NvnPve5Uz63\npqaGSy+9lN/+9rdceumlXHvttUmsTEREZGjqGqLb6RyUrO2CQvUtYNtJmXsg6Kod2n8uREQkPQZc\nsAVg2rRpjB49mr1791JVVXVEt5FUCAaDAHi9J947MSMjo+d5e3t7WtZJRa35+b3vcCAiIslnGAYY\nhjopiIiIiIj0UklJCb/+9a+ZN28e3/nOdxRsERER6YNkBT8GiqQFW/R7ExER6bUB+0lYdnY2cCi4\nkUoOhwMA6yR3ssdisZ7nTmfvM0KJWCdVtYqIiIiIiIiIDCVz584lPz+fLVu2nHQLIxERETlaaIh3\n5uiq7X2n/lMx1IMfQ/3nExGR9BiwCYeqqiqcTiejR49O+dqZmZk0NjbS2XnivSE7OjqOGJOOdVJR\na1PT8f8R0tLSwvjx4084PhqN9mo9EZHBxrZtrHAUKxzBCoWxox/Y+ufg9kCHbQsU3wrIPvL4B7YP\nOrS1kH2M+Q5tJ3RovkOvHzq/e84PztG9dZGIJNBhWw4ZpnFoW6LDtiPCYRz2vHtLooPbFB2+jVH3\neYe/Rvf2Rcbhrx1zjg+ONTAcjp6tkUynE9PjwvS4MJyOlG/7KSIiMpBkZWXR1NREMBhkxIgR6S5H\nRERkUBnqWxGFG1qxIlFMV2I/Shvqv7doeyfR9k6cmRknP1lEROQUDchgy5/+9Cfa29u57rrrejq3\npFJJSQlVVVVUV1ef8LzDjxcXF6dlnVTUmpeX16vzD6dQi4gMRHbMItreQbStk1hXuDuQctijO6Bi\nhaLxr+EIsaOOH/mVobstrogMZYbRE3IxPS5MtwvHB743PW5MtxPT6z503O06YpzD68aZ7cfh88YD\nNCIiIoNANBpl//79ZGRkUFpamu5yREREBp0h35nDtgnVt5BRltjwayhJnWAGklBtE87MkekuQ0RE\nhpABE2yxbZs9e/awYsUKvvWtbzFv3jzuueeetNQybdo0Vq9ezb59++js7CQj49ip0u3bt/c8r6io\nSMs6qaq1L4LBIKFQSFsfiUhK2JYVvxsg0EE00EGkLXjoeSBItK2j+/sg0WCngigiIgC2jdUVxuoK\nJ2Y+08SZlYEr248z24czy3foebYfV5av57nD51G3GBERSav777+faDTKsmXLcLvd6S5HRERkULHC\nUcJNgXSXkXShmqaEBltsyx7yHVsg3pXGP0HBFhERSZyUJA7mzJnD22+/3asxa9asoaio6LjH165d\ny5w5c/pb2jEtXLiQ3//+90SjUVavXs3ixYuPed5rr70GgGEYzJ8/Py3rpKrW3goGg7S0tCR9HREZ\n+uyYRbixlXBTgGhrB5G27nBK4LDnbfHuK9hKq4iIpJVlEW0NEm0NnvRUw2HiPCzocnjo5WAgxl2Y\ngys3UwEYERFJGMuy2LNnD08++STf/e53WbRoEf/zP/+T7rKkN3b+HnL86a5CRGTYizYFyC9ak+4y\nks6oqoXs8QmbL9beSV7+6wmbb6By7N8P2yenuwwRETmF67SDhVppHMPSpUv5/Oc/j2VZPPjgg8cM\ni0SjUR555BEA5s2bx8iRvU+eJmKdVNXaGx8MtbhcrqSuJyKDn23bRNs6CNU1E6rtftQ1E6ptIdzQ\ngh2z0l2iiIgkmB2ziLS0E2lpP+F5ptuJuygPb3EenqI8PId9dWR4UlStiIgMBlOnTmXLli29GvPq\nq6+ecAvmTZs2MXXq1P6WJiIiMuREAh3pLiElooHEfiAYSfB8A1WkdXj8+RARkdRJSbDlX/7lX6ir\nq0vonOXl5Qmd73DFxcUsW7aMP/zhD/zhD3/gM5/5zFFdTu644w4OHDgAwNe//vVjzrNr1y5CoRAO\nh4NJkyYlZZ1E1ZooHwy1eDwebUUkIkeIhSJ07q2js6qWzr11hOqa6aptxuoMpbs0EREZgKxwlK59\n9XTtqz/qmDPbFw+5lOTjG1NERnkJGWUFGA5HGioVEREREREZPhId+BioIgm+0z06TAIf0cCJb2IR\nERHprZQkDq6//vpULJNQd911FytXrqS6upolS5Zw++23s3jxYoLBIA888AC/+c1vALjiiiu45ppr\njjnHZZddxpYtW8jJyTnutjyJWCcRcyRCNBo94ufMzc2lvV3/eBEZzuxYjM79DXRW1dJRVUtHVQ1d\nBxq1ZZCIiCRENNBBNNBBcPt+Du5QbrgcZIwqwldejG9sCb7yEtyFuRimtjMSERnqvvvd79Lc3JzQ\nOUtKShI6n4iIyFAxXDpyRAMd2JadsPeUkbbhEQiKBruwojFMp248ERGRxFArjeMoKyvj73//O1df\nfTU7duzga1/72lHnXHnllfzxj39M+zqpqvVEHA4H4XAYr9cLxEMtfr9fwRaRYSbSGqR9yx46dtfQ\nUVVL57467Egs3WWJiMgwYkdidOyqpmNXdc9rZoYH35iieNBlXCmZk0fj8LrTWKWIiCTDTTfdlO4S\nREREho3h0rHFjlnEOrpwZmYkZL5ogjvADFg2RNs6cOdlpbsSEREZIhRsOYHTTjuN9evX8+CDD/L0\n00+zd+9ePB4PkydP5oYbbuBDH/rQCcePHz8egOzs7KSuk6g5+sPlcvU8PxhqEZGhz47FCO6spq1y\nN22VVXTuTey2c5JAhoHhMDEcJpgmhmlgGAYY6mAgkhC2jW3b2DELLAvb6n6uDlUDgtUZon3LXtq3\n7AXAcJj4xpeRPX0sWRVj8Y4cEf87UURERERERE7KtiyibcOjYwtAJBBMXLBlmASCIH4TpIItIiKS\nKAq2nITf7+fWW2/l1ltv7fXYp59+OiXrJHKO/lKoRWToCzcFaKusiodZNu/B6gqnu6SUMpwOTI/r\n0MMdfzh6XnNjuBzxEIkZf+DoDpKYhwVLDh53GN1Bk4OvGUceN01wGD3PDcfB+brn7AmqxOc6tN5h\n8xumtuAQSRPbssG2sGPdD8vGtiyIWdiW1f08/prd/RrdoZgjXjts7KHwjIUdsw97Hj8H6/D1jl7D\nikSxQmGsUPfXcAQrdOgRC0XAstL9q0sqO2YR3LaP4LZ9VD/5Gs4cP1kVY8muKCdzWjlOnzfdJYqI\niIiIiAxY0WBX/L3pMBFt7YCy/s8T6woT64r0f6JBYjiFeEREJPkSGmx58MEHufLKK8nJyUnktAAs\nX76cmTNnUl5envC5JTHcbrdCLSJDkG1ZtG/bR9uGXQQqdxOqbkp3SX1met04s324snw4s304s/04\nfd6jgyoeN6bHielx43AfecxwmOn+MURkEImHyhwYjsG1p7QVjcUDL12R7uDLkUGYWOjIMEw02Ek0\nECQa6CAS6CDaFhxUW9FFW4M0v7GR5jc2gmHgG1dCdsVYsmeOxzuqUN1cREREREREDjPcAguRtsT8\nvNEEzTNYRAPDp6uPiIgkX0KDLT/5yU/44he/yFe/+lVuu+22k27BcyqeffZZbr/9dtasWcPatWsV\nbBmgIpEITqcaAIkMJV3VjTStrqRlzWYiLe3pLue4TI8rHlLJ8uHK9uPM8uHM8cfDK93P48d8mG7X\nyScUERFMpwPT6YA+di6xbRurKxwPurTFA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P3+pxhx/hnQxw5qTW9sZMu/309bZVWCKxQZ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\nIxz2NsETCuFpd+CmEwtnd829YzlsMGxsA2IWEIv/vLZtwzB7XyYiIiIiI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          }
        }
      ],
      "source": [
        "# Length of the coupling region\n",
        "coup_length = 10\n",
        "\n",
        "# Waveguide separation in the coupling region\n",
        "wg_spacing_coup = 0.10\n",
        "\n",
        "sim = make_sim(coup_length, wg_spacing_coup, add_field_monitor=True)\n",
        "\n",
        "# Visualize geometry\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 4))\n",
        "sim.plot(z=wg_height / 2 + 0.01, ax=ax1)\n",
        "sim.plot(x=0.1, ax=ax2)\n",
        "ax2.set_xlim([-3, 3])\n",
        "plt.show()"
      ],
      "id": "8b9061c3"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Design Space Exploration\n",
        "\n",
        "In this section, we take advantage of the simplicity of the tidy3d\n",
        "design plugin to generate a Monte Carlo search over the geometric\n",
        "parameters of the coupler. The design plugin allows us to easily run\n",
        "optimizations that consider a set of user defined variables as\n",
        "optimization parameters. In this case, we leverage the plugin to\n",
        "generate a randomly sampled set of data that we can later use to train\n",
        "the neural network surrogate model. To do this, we set up the parameter\n",
        "names we are considering along with the ranges to sample values from for\n",
        "each. Then, we create a `MethodMonteCarlo` to specify the number of\n",
        "samples to evaluate and a `DesignSpace` to facilitate the running of the\n",
        "Monte Carlo method. Finally, we write a function, `fn_post` that will be\n",
        "used to postprocess the data from each from each simulation run. In this\n",
        "function, we use the above `measure_coupling_amplitude` function to\n",
        "collect the power flowing into all of the waveguide monitor ports. A\n",
        "tutorial on the design plugin can be found\n",
        "[here](https://www.flexcompute.com/tidy3d/examples/notebooks/Design/)."
      ],
      "id": "d3a7a531-aa3d-47d8-9a31-2416dc86af13"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Set up the parameters we want to sweep in the Monte Carlo search\n",
        "param_spc = tdd.ParameterFloat(name=\"wg_spacing_coup\", span=wg_spacing_coup_range)\n",
        "param_len = tdd.ParameterFloat(name=\"coup_length\", span=coup_length_range)\n",
        "parameters = [param_spc, param_len]\n",
        "\n",
        "monte_carlo = tdd.MethodMonteCarlo(\n",
        "    num_points=num_monte_carlo_sims,\n",
        "    seed=2,\n",
        ")\n",
        "design_space = tdd.DesignSpace(\n",
        "    method=monte_carlo,\n",
        "    parameters=parameters,\n",
        "    task_name=\"DC_MC_Surrogate\",\n",
        "    path_dir=str(mc_data_dir),\n",
        ")\n",
        "\n",
        "\n",
        "def measure_coupling_amplitude(sim_data):\n",
        "    \"\"\"Constructs a \"row\" of the scattering matrix when sourced from top left port\"\"\"\n",
        "\n",
        "    input_amp = sim_data[\"in\"].amps.sel(direction=\"+\")\n",
        "\n",
        "    amps_dict = {}\n",
        "    directions = (\"-\", \"-\", \"+\", \"+\")\n",
        "    for monitor, direction in zip(sim_data.simulation.monitors[:4], directions):\n",
        "        amp = sim_data[monitor.name].amps.sel(direction=direction)\n",
        "        amp_normalized = amp / input_amp\n",
        "        amps_dict[monitor.name] = amp_normalized\n",
        "\n",
        "    return amps_dict\n",
        "\n",
        "\n",
        "def fn_post(*args, **kwargs):\n",
        "    \"\"\"Post processing function, but label the outputs using a dictionary.\"\"\"\n",
        "    amps_dict = measure_coupling_amplitude(*args, **kwargs)\n",
        "    return amps_dict"
      ],
      "id": "6b6ec2be"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Here, we run the Monte Carlo simulations or load previous simulation\n",
        "data from a past run through the notebook. If the simulations are run\n",
        "again, we overwrite the simulation data file to be loaded again in the\n",
        "future. By setting the `use_previous_simulation_data` flag, you can opt\n",
        "to use past data or rerun the simulations. If any of the parameters\n",
        "controlling the general geometry or number of simulations are changed,\n",
        "you should rerun the simulations to ensure they are accurate for the\n",
        "result of the notebook."
      ],
      "id": "83572c05-16ae-454b-8148-266c3873b930"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "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\">14:09:14 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100</span> Simulations                                            \n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "if use_previous_simulation_data:\n",
        "    with open(filename_mc_data, \"rb\") as in_file:\n",
        "        results = pickle.load(in_file)\n",
        "        df = results.to_dataframe()\n",
        "else:\n",
        "    results = design_space.run(make_sim, fn_post, verbose=True)\n",
        "    df = results.to_dataframe()\n",
        "\n",
        "    with open(filename_mc_data, \"wb\") as out_file:\n",
        "        pickle.dump(results, out_file)"
      ],
      "id": "6acc4516"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can plot the fitness for each set of randomly sampled geometric\n",
        "parameters in the Monte Carlo batch of simulations. Better performing\n",
        "designs correspond to a larger fitness function with a maximum value of\n",
        "0. In the plot, the points are colored according to their fitness value.\n",
        "The fitness function is the mean squared error between the desired\n",
        "output power fraction into each of the two waveguides and the actual\n",
        "output power fraction computed from the simulation results. In this\n",
        "example, we set the desired power fraction to 0.25/0.75 for top/bottom\n",
        "waveguides. However, this notebook can be re-run with a variety of\n",
        "different splitting ratios by changing the\n",
        "`top_waveguide_transmission_goal` and\n",
        "`bottom_waveguide_transmission_goal` parameters. We can see from this\n",
        "plot that several randomly selected points correspond to acceptable\n",
        "performances in our metric. The data collected in this first step will\n",
        "become the training data for a surrogate model of the coupling problem.\n",
        "We will use the surrogate model as an input to further optimization to\n",
        "see if we can discover even better designs than the ones we found via\n",
        "random sampling."
      ],
      "id": "b433e144-1958-428a-8120-7dcdf890c771"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The best fitness value is -0.00043395327531710803 corresponding to:\n",
            "MC Top waveguide transmission: 0.261, Bottom waveguide transmission: 0.733"
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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qirPOOgvvv/8+jEZj3fWioiJMnjwZkydPjjkvERERERERtU6KTjYx1kMGalrKNXC0cMst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\ngUNEREREREREZOX+D6byaEAbDLXRAAAAAElFTkSuQmCC\n"
          }
        }
      ],
      "source": [
        "top_center_freq = abs(df[\"top\"].apply(lambda x: x.sel(f=freq0, method=\"nearest\"))) ** 2\n",
        "bot_center_freq = abs(df[\"bottom\"].apply(lambda x: x.sel(f=freq0, method=\"nearest\"))) ** 2\n",
        "\n",
        "\n",
        "# Compute MSE error function for fitness\n",
        "def fitness(top_wg_power, bottom_wg_power):\n",
        "    return -(\n",
        "        abs(top_wg_power - top_waveguide_transmission_goal) ** 2\n",
        "        + abs(bottom_wg_power - bottom_waveguide_transmission_goal) ** 2\n",
        "    )\n",
        "\n",
        "\n",
        "fitness_val = fitness(top_center_freq, bot_center_freq)\n",
        "list_fitness = [float(np.squeeze(val.data)) for val in fitness_val]\n",
        "\n",
        "best_fitness = np.max(fitness_val)\n",
        "best_fitness_location = np.argmax(fitness_val)\n",
        "best_top_power_mc = np.squeeze(top_center_freq[best_fitness_location].data)\n",
        "best_bottom_power_mc = np.squeeze(bot_center_freq[best_fitness_location].data)\n",
        "print(f\"The best fitness value is {np.squeeze(best_fitness.data)} corresponding to:\")\n",
        "print(\n",
        "    f\"MC Top waveguide transmission: {np.round(best_top_power_mc, 3)}, Bottom waveguide transmission: {np.round(best_bottom_power_mc, 3)}\"\n",
        ")\n",
        "\n",
        "scatter = plt.scatter(df[\"wg_spacing_coup\"], df[\"coup_length\"], c=list_fitness)\n",
        "plt.xlabel(\"wg_spacing_coup\")\n",
        "plt.ylabel(\"coup_length\")\n",
        "plt.title(\"Fitness\")\n",
        "plt.colorbar(scatter)\n",
        "plt.show()"
      ],
      "id": "02204d4f"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Neural Network Surrogate Model\n",
        "\n",
        "We can now leverage the Monte Carlos data we collected from tidy3d\n",
        "simulation. First, we clean and prepare the dataset for use in training\n",
        "a neural network surrogate model. The inputs to the model are the\n",
        "waveguide spacing (`wg_spacing_coup`), the coupling length\n",
        "(`coup_length`), and the frequency (`freq`). In optimizing the model, we\n",
        "aim to find the connection between these inputs and the output power\n",
        "fractions in the top and bottom waveguides (`top` and `bottom`)."
      ],
      "id": "c88aa098-e22b-4233-ae64-fb2f2a187527"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Monte Carlo data for training the neural network surrogate model\n",
            "       wg_spacing_coup  coup_length          freq       top    bottom\n",
            "0             0.137477    14.698211  1.998616e+14  0.222475  0.771632\n",
            "1             0.137477    14.698211  1.997285e+14  0.217822  0.776317\n",
            "2             0.137477    14.698211  1.995955e+14  0.213184  0.780979\n",
            "3             0.137477    14.698211  1.994627e+14  0.208552  0.785609\n",
            "4             0.137477    14.698211  1.993301e+14  0.203935  0.790223\n",
            "...                ...          ...           ...       ...       ...\n",
            "10095         0.055433     3.331761  1.878399e+14  0.014410  0.979482\n",
            "10096         0.055433     3.331761  1.877223e+14  0.012980  0.980936\n",
            "10097         0.055433     3.331761  1.876048e+14  0.011618  0.982321\n",
            "10098         0.055433     3.331761  1.874875e+14  0.010326  0.983623\n",
            "10099         0.055433     3.331761  1.873703e+14  0.009104  0.984866\n",
            "\n",
            "[10100 rows x 5 columns]"
          ]
        }
      ],
      "source": [
        "expanded_df = df.copy()\n",
        "# Removing the input and reflection amplitudes because we will run the\n",
        "# neural network optimization only over the output waveguide amplitudes\n",
        "# and thus assume reflected power is negligible.\n",
        "expanded_df.drop(\"in\", axis=1, inplace=True)\n",
        "expanded_df.drop(\"reflect_bottom\", axis=1, inplace=True)\n",
        "\n",
        "# Expand out multiple entries for top and bottom and re-index the dataframe.\n",
        "# then, we can add the frequency dimension to the dataframe.\n",
        "expanded_df = expanded_df.explode([\"top\", \"bottom\"]).reset_index(drop=True)\n",
        "expanded_df[\"freq\"] = expanded_df[\"top\"].apply(lambda x: float(np.squeeze(x.f)))\n",
        "\n",
        "# Convert the amplitude fractions into power fractions\n",
        "# We use .values to get the numpy array and .item() to safely convert a 1-element array to a float\n",
        "expanded_df[\"top\"] = expanded_df[\"top\"].apply(lambda x: float(np.abs(np.squeeze(x.values)) ** 2))\n",
        "expanded_df[\"bottom\"] = expanded_df[\"bottom\"].apply(\n",
        "    lambda x: float(np.abs(np.squeeze(x.values)) ** 2)\n",
        ")\n",
        "\n",
        "# Visualize the dataframe\n",
        "print(\"Monte Carlo data for training the neural network surrogate model\")\n",
        "print(expanded_df[[\"wg_spacing_coup\", \"coup_length\", \"freq\", \"top\", \"bottom\"]])\n",
        "\n",
        "# Read the dataframe back into a Result that we can use as a pytorch Dataset\n",
        "expanded_results = tdd.Result()\n",
        "expanded_results = expanded_results.from_dataframe(expanded_df, dims=list(results.dims) + [\"freq\"])"
      ],
      "id": "16037d5f"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can use the `Result` class directly as a pytorch dataset and take\n",
        "advantage of pytorch utilities to create a training, validation, and\n",
        "testing split of the data. Documentation on handling data in pytorch can\n",
        "be found [here](https://pytorch.org/docs/stable/data.html). The training\n",
        "data we will use directly to adjust network weights. Validation data is\n",
        "used to assess the quality of the model when applied to non-training\n",
        "data. It is a useful evaluation criteria when deciding between different\n",
        "network hyperparameters. We reserve testing data for the end to perform\n",
        "a final verification on the trained model."
      ],
      "id": "3dcd669a-09c4-4273-b0e0-e33a274ceb6d"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Neural Network parameters\n",
        "number_samples = len(expanded_results)\n",
        "train_samples = int(np.floor(0.6 * number_samples))\n",
        "validation_samples = int(np.floor(0.2 * number_samples))\n",
        "test_samples = number_samples - train_samples - validation_samples\n",
        "batch_size = 16\n",
        "epochs = 120\n",
        "final_training_epochs = 1000\n",
        "\n",
        "training_dataset, validation_dataset, test_dataset = torch.utils.data.random_split(\n",
        "    expanded_results, [train_samples, validation_samples, test_samples]\n",
        ")\n",
        "\n",
        "training_dataloader = DataLoader(training_dataset, batch_size=batch_size, shuffle=True)\n",
        "validation_dataloader = DataLoader(validation_dataset, batch_size=batch_size, shuffle=False)\n",
        "testing_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)"
      ],
      "id": "4efe0e35"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "In this section, we set up the general structure for a fully connected\n",
        "neural network with a variable number of hidden layers, ReLU activation\n",
        "functions, and the option to include dropout layers to prevent\n",
        "overfitting and improve generalization. The input to the network is\n",
        "given by two geometric parameters and the frequency, the three of which\n",
        "are normalized to fall between \\[-1, 1\\] based on their allowed ranges.\n",
        "The output of the network is the power fraction in the top and bottom\n",
        "waveguides. More information on how to build neural networks in pytorch\n",
        "can be found\n",
        "[here](https://pytorch.org/tutorials/beginner/basics/buildmodel_tutorial.html)."
      ],
      "id": "e85e59df-8dad-4346-ab36-be214b4a7de9"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Assuming the features are coming in as (wg_spacing_coup, coup_length, freq)\n",
        "# and we can normalize by the min/max possible values to center each feature in [-1, 1]\n",
        "min_freq = np.min(freqs)\n",
        "max_freq = np.max(freqs)\n",
        "\n",
        "subtract_min = torch.tensor(\n",
        "    [wg_spacing_coup_range[0], coup_length_range[0], min_freq], dtype=torch.float\n",
        ")\n",
        "normalize_max = torch.tensor(\n",
        "    [\n",
        "        wg_spacing_coup_range[1] - wg_spacing_coup_range[0],\n",
        "        coup_length_range[1] - coup_length_range[0],\n",
        "        max_freq - min_freq,\n",
        "    ],\n",
        "    dtype=torch.float,\n",
        ")\n",
        "\n",
        "network_input_size = 3\n",
        "\n",
        "\n",
        "# Define a fully connected model in PyTorch with ReLU activation\n",
        "# where we can set a variable number of hidden layers through\n",
        "# neurons list. Allow option to specify dropout percentage.\n",
        "class VariableANN(nn.Module):\n",
        "    def __init__(self, input_size, neurons, dropout) -> None:\n",
        "        super().__init__()\n",
        "        self.layers = nn.ModuleList()\n",
        "        self.layers.append(nn.Linear(input_size, neurons[0]))\n",
        "\n",
        "        for i in range(1, len(neurons)):\n",
        "            self.layers.append(nn.Linear(neurons[i - 1], neurons[i]))\n",
        "\n",
        "        self.layers.append(nn.Linear(neurons[-1], 2))\n",
        "\n",
        "        self.drop = nn.Dropout(dropout)\n",
        "        self.relu = nn.ReLU()\n",
        "\n",
        "    def forward(self, x):\n",
        "        subtract_min_unsqueeze = torch.unsqueeze(subtract_min, dim=0)\n",
        "        normalize_max_unsqueeze = torch.unsqueeze(normalize_max, dim=0)\n",
        "\n",
        "        # normalize inputs to be between [-1, 1]\n",
        "        x = x - subtract_min_unsqueeze\n",
        "        x = x / normalize_max_unsqueeze\n",
        "        x = 2.0 * (x - 0.5)\n",
        "\n",
        "        for layer in self.layers[:-1]:\n",
        "            x = self.drop(self.relu(layer(x)))\n",
        "\n",
        "        x = self.layers[-1](x)\n",
        "\n",
        "        return x"
      ],
      "id": "1362cd35"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We set up functions here to facilitate the training of the neural\n",
        "network. By default, we use an MSE loss function in the training that\n",
        "computes the mean squared error between the network’s output power\n",
        "fractions and the ground truth output power fractions. In each epoch of\n",
        "training, we run through all the batches in the training dataset,\n",
        "adjusting the weights in the network with an Adam optimizer after each\n",
        "batch. We also keep track of the validation loss from when the network\n",
        "is in evaluation mode and processing data from the validation dataset.\n",
        "This will be useful later for choosing the best network hyperparameters.\n",
        "More information on how to train neural networks in pytorch can be found\n",
        "[here](https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html)."
      ],
      "id": "35331ee9-50b9-4d99-a99b-b71d62822110"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "loss_fn = nn.MSELoss()\n",
        "\n",
        "\n",
        "def evaluate_loss(data, network, loss_fn_user=None):\n",
        "    \"\"\"Extract inputs/labels from the data and allow for loss evaluation on user defined loss\n",
        "    function (otherwise use the default loss_fn which is set to MSE).\"\"\"\n",
        "\n",
        "    inputs, labels = data\n",
        "\n",
        "    if loss_fn_user is None:\n",
        "        loss_fn_user = loss_fn\n",
        "\n",
        "    loss = loss_fn_user(network(inputs.type(torch.FloatTensor)), labels.type(torch.FloatTensor))\n",
        "\n",
        "    return loss\n",
        "\n",
        "\n",
        "def evaluate_loss_from_loader(data_loader, network, loss_fn_user=None):\n",
        "    \"\"\"Given a data_loader, run all the batches through the network and evaluate loss.\"\"\"\n",
        "    loss_by_iteration = []\n",
        "    for data_idx, data in enumerate(data_loader):\n",
        "        loss = evaluate_loss(data, network, loss_fn_user)\n",
        "        loss_by_iteration.append(loss.item())\n",
        "\n",
        "    avg_loss = np.mean(loss_by_iteration)\n",
        "    return avg_loss\n",
        "\n",
        "\n",
        "def train_network(hyperparameters, epochs):\n",
        "    \"\"\"Train a network according to hyperparameters specification for a given number of epochs.\"\"\"\n",
        "\n",
        "    # build network and set to training mode\n",
        "    network = VariableANN(network_input_size, hyperparameters[\"hidden\"], hyperparameters[\"dropout\"])\n",
        "    network.train()\n",
        "    opt = optim.Adam(\n",
        "        network.parameters(),\n",
        "        lr=hyperparameters[\"learning_rate\"],\n",
        "        weight_decay=hyperparameters[\"weight_decay\"],\n",
        "    )\n",
        "\n",
        "    training_loss_by_epoch = np.zeros(1 + epochs)\n",
        "    validation_loss_by_epoch = np.zeros(1 + epochs)\n",
        "\n",
        "    avg_training_loss = evaluate_loss_from_loader(training_dataloader, network)\n",
        "    avg_validation_loss = evaluate_loss_from_loader(validation_dataloader, network)\n",
        "\n",
        "    training_loss_by_epoch[0] = avg_training_loss\n",
        "    validation_loss_by_epoch[0] = avg_validation_loss\n",
        "\n",
        "    for epoch_idx in range(0, epochs):\n",
        "        training_loss_by_iteration = []\n",
        "\n",
        "        for data_idx, data in enumerate(training_dataloader):\n",
        "            opt.zero_grad()\n",
        "            loss = evaluate_loss(data, network)\n",
        "\n",
        "            training_loss_by_iteration.append(loss.item())\n",
        "\n",
        "            loss.backward()\n",
        "            opt.step()\n",
        "\n",
        "        # Remove training features like dropout for the evaluation section and then turn it back on for training\n",
        "        network.eval()\n",
        "        avg_validation_loss = evaluate_loss_from_loader(validation_dataloader, network)\n",
        "        network.train()\n",
        "\n",
        "        training_loss_by_epoch[1 + epoch_idx] = np.mean(training_loss_by_iteration)\n",
        "        validation_loss_by_epoch[1 + epoch_idx] = avg_validation_loss\n",
        "\n",
        "    return training_loss_by_epoch, validation_loss_by_epoch, network"
      ],
      "id": "23a96c3e"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Next, we perform a random search over the network and optimizer\n",
        "hyperparameters to find the best structure and optimization. The figure\n",
        "of merit for the search is the validation error for the trained model\n",
        "because we ultimately care both about good network performance and its\n",
        "ability to generalize outside the training set. This search is done by\n",
        "randomly sampling hidden layer dimensions, number of hidden layers, and\n",
        "dropout percentage for the network and the learning rate and weight\n",
        "decay for the Adam optimizer. In this search, we fix the network to two\n",
        "hidden layers and train 75 trial network and optimization parameter\n",
        "combinations searching for the best validation loss. The tidy3d design\n",
        "plugin once again is useful here as we can utilize the Monte Carlo\n",
        "method once again to randomly choose parameters to evaluate."
      ],
      "id": "8676e6bc-ae26-4f26-8099-5032ef958566"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "allowed_hidden_layer_sizes = np.arange(16, 257, 16)\n",
        "allowed_dropout = np.arange(0, 0.55, 0.05)\n",
        "num_hidden_layers = 2\n",
        "allowed_learning_rate_log_range = (-5, -3)\n",
        "allowed_weight_decay_log_range = (-5, -4)\n",
        "\n",
        "num_trial_networks = 75\n",
        "\n",
        "\n",
        "def hyperparemeter_fn(dropout, learning_rate_log, weight_decay_log, **hidden_layer_sizes):\n",
        "    hidden_layer_sizes = [\n",
        "        hidden_layer_sizes[f\"hidden_layer_size_{idx}\"] for idx in range(0, len(hidden_layer_sizes))\n",
        "    ]\n",
        "\n",
        "    hyperparam = {\n",
        "        \"hidden\": hidden_layer_sizes,\n",
        "        \"dropout\": dropout,\n",
        "        \"learning_rate\": 10**learning_rate_log,\n",
        "        \"weight_decay\": 10**weight_decay_log,\n",
        "    }\n",
        "\n",
        "    training_loss, validation_loss, network = train_network(hyperparam, epochs)\n",
        "\n",
        "    return -validation_loss[-1]\n",
        "\n",
        "\n",
        "param_dropout = tdd.ParameterAny(name=\"dropout\", allowed_values=tuple(allowed_dropout))\n",
        "param_lr_log = tdd.ParameterFloat(name=\"learning_rate_log\", span=allowed_learning_rate_log_range)\n",
        "param_wd_log = tdd.ParameterFloat(name=\"weight_decay_log\", span=allowed_weight_decay_log_range)\n",
        "params_hyperparameter = [\n",
        "    tdd.ParameterAny(\n",
        "        name=f\"hidden_layer_size_{idx}\",\n",
        "        allowed_values=tuple(allowed_hidden_layer_sizes),\n",
        "    )\n",
        "    for idx in range(0, num_hidden_layers)\n",
        "]\n",
        "hyperparameters = [param_dropout, param_lr_log, param_wd_log] + params_hyperparameter\n",
        "\n",
        "hyperparam_optimizer = tdd.MethodMonteCarlo(\n",
        "    num_points=num_trial_networks,\n",
        "    seed=2,\n",
        ")\n",
        "design_space = tdd.DesignSpace(\n",
        "    method=hyperparam_optimizer,\n",
        "    parameters=hyperparameters,\n",
        "    task_name=\"DC_Network_MC\",\n",
        "    path_dir=str(mc_data_dir),\n",
        ")\n",
        "network_hyperparameter_results = design_space.run(hyperparemeter_fn, verbose=True)"
      ],
      "id": "0b0561e5"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "After the hyperparameter search, we perform one final network training\n",
        "using the best hyperparameter settings and an increased number of\n",
        "training epochs. This network can now operate as the surrogate solver\n",
        "for the coupling problem."
      ],
      "id": "79f2c6c1-78e0-457d-9f1e-553bff06f50b"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best network hyperparameters:\n",
            "hidden : [144, 160]\n",
            "dropout : 0.0\n",
            "learning_rate : 0.0005177283857996054\n",
            "weight_decay : 1.4211205011036916e-05\n",
            "\n",
            "Average training loss: 2.0049918248932617e-05\n",
            "Average validation loss: 1.7024746649962982e-05\n",
            "Average test loss: 1.9087183762338986e-05\n",
            "Average L1 validation loss: 0.003073897396991631\n",
            "Average L1 test loss: 0.003029713447491719"
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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p8ccfV8OGDb12XqAipILpsSYVAAAAgl2tWo4vX4mJkdq08d39Kkvjxo116aWX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\noc2bN8tut+vKK6/UyZMn1a5dOz388MPasGGDWrduLbvdrhd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QfOngBVJOovP+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\nqQAAAAAoTqQCAAAAoDiRCgAAAIDi/gYA5tEP8WhIAwAAAABJRU5ErkJggg==\n"
          }
        }
      ],
      "source": [
        "# Extract the best parameters from the design space result and formulate the hyperparameter dictionary for the optimization\n",
        "best_parameters = dict(\n",
        "    zip(\n",
        "        network_hyperparameter_results.dims,\n",
        "        network_hyperparameter_results.coords[np.argmax(network_hyperparameter_results.values)],\n",
        "    )\n",
        ")\n",
        "best_hyperparam = {\n",
        "    \"hidden\": [best_parameters[f\"hidden_layer_size_{idx}\"] for idx in range(0, num_hidden_layers)],\n",
        "    \"dropout\": best_parameters[\"dropout\"],\n",
        "    \"learning_rate\": 10 ** best_parameters[\"learning_rate_log\"],\n",
        "    \"weight_decay\": 10 ** best_parameters[\"weight_decay_log\"],\n",
        "}\n",
        "print(\"Best network hyperparameters:\")\n",
        "for k, v in best_hyperparam.items():\n",
        "    print(f\"{k} : {v}\")\n",
        "avg_training_loss, avg_validation_loss, optimized_network = train_network(\n",
        "    best_hyperparam, final_training_epochs\n",
        ")\n",
        "optimized_network.eval()\n",
        "\n",
        "# Report the final average training loss from the final optimization as well as the validation and test losses for eval mode\n",
        "avg_test_loss = evaluate_loss_from_loader(testing_dataloader, optimized_network)\n",
        "print(f\"\\nAverage training loss: {avg_training_loss[-1]}\")\n",
        "print(f\"Average validation loss: {avg_validation_loss[-1]}\")\n",
        "print(f\"Average test loss: {avg_test_loss}\")\n",
        "\n",
        "# Compute the L1 validation and test loss in eval mode to get a better intuitive sense of the network performance by examining\n",
        "# absolute error instead of squared error\n",
        "l1_loss_fn = nn.L1Loss()\n",
        "avg_l1_validation_loss = evaluate_loss_from_loader(\n",
        "    validation_dataloader, optimized_network, l1_loss_fn\n",
        ")\n",
        "avg_l1_test_loss = evaluate_loss_from_loader(testing_dataloader, optimized_network, l1_loss_fn)\n",
        "\n",
        "print(f\"Average L1 validation loss: {avg_l1_validation_loss}\")\n",
        "print(f\"Average L1 test loss: {avg_l1_test_loss}\")\n",
        "\n",
        "# Plot log of loss curves for the network\n",
        "plt.plot(\n",
        "    np.arange(0, len(avg_training_loss)),\n",
        "    np.log(avg_training_loss),\n",
        "    color=\"g\",\n",
        "    linewidth=2,\n",
        "    label=\"training loss\",\n",
        ")\n",
        "plt.plot(\n",
        "    np.arange(0, len(avg_validation_loss)),\n",
        "    np.log(avg_validation_loss),\n",
        "    color=\"b\",\n",
        "    linewidth=2,\n",
        "    label=\"validation loss\",\n",
        "    linestyle=\"--\",\n",
        ")\n",
        "plt.axhline(y=np.log(avg_test_loss), color=\"k\", linewidth=2, label=\"final test loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Log(Loss)\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "id": "4d20686d"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## DC Optimization\n",
        "\n",
        "The final optimization step uses the neural network surrogate as part of\n",
        "a gradient based optimization. Instead of running simulations for each\n",
        "candidate set of geometric parameters, we use the neural network to\n",
        "estimate the output waveguide power fractions. We set as a loss function\n",
        "the mean squared error between the output of the network and our desired\n",
        "splitting ratio of 0.25/0.75. However, instead of optimizing the network\n",
        "weights as we did before, we optimize the network input (i.e. - the\n",
        "coupling geometric parameters) to reduce the loss. Below, we see this\n",
        "converges quickly and identifies a good set of parameters for the\n",
        "coupler."
      ],
      "id": "02092058-7043-41af-8827-d855617cb102"
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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AUA6EhoaqTp06btt169ZNK1euVI8ePUohKpRlDRo00FdffeXrMADAZxjpBQCA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\nXnlFr776aqH7ue666/THH39Ikt566y3deeedTuvqrF+/XnP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AAAAA\ngGdIegFllKOk1/Yz25Vr5PogGgAAAAAAyheSXkAZ5WgFx4tZF3Ug8UDpBwMAAAAAQDlD0gsooxyt\n4ChR1wsAAAAAAE+Q9ALKqNqVa6t6aHW7/azgCAAAAACAeyS9gDLKYrE4HO219czW0g8GAAAAAIBy\nhqQXUIY5KmbPSC8AAAAAANwj6QWUYY6K2R9LPabEjMTSDwYAAAAAgHKEpBdQhjka6SUx2gsAAAAA\nAHdIegFlWIvoFgr0D7TbzwqOAAAAAAC4RtILKMMq+VdS6+qt7fZvPb219IMBAAAAAKAcIekFlHGO\nVnBkpBcAAAAAAK6R9ALKOEd1veIS4pSZk+mDaAAAAAAAKB9IegFlnKMVHDNzMrXn3J7SDwYAAAAA\ngHKCpBdQxrGCIwAAAAAAhUfSCyjjIkMiFRsea7eful4AAAAAADhH0gsoBxxNcWQFRwAAAAAAnCPp\nBZQDjqY4bjuzTYZh+CAaAAAAAADKPpJeQDngaKTXufRzOpV2qvSDAQAAAACgHCDpBZQD7Ws5LmbP\nFEcAAAAAABwj6QWUA40iG6lyYGW7/X+e+tMH0QAAAAAAUPaR9ALKAT+Ln8O6Xn+eJukFAAAAAIAj\nJL2AcqJT7U52+7ac2uKDSAAAAAAAKPtIegHlhKOk16HkQ0rMSPRBNAAAAAAAlG0kvYByonPtzg73\nU9cLAAAAAAB7JL2AcqJl9ZYKDgi2288URwAAAAAA7JH0AsqJAL8AtavZzm7/5lObfRANAAAAAABl\nG0kvoBxxNMWRkV4AAAAAANgj6QWUI46K2ccnxiv1cqoPogEAAAAAoOwi6QWUI46SXhLF7AEAAAAA\nKIikF1COtKnRRpX8KtntZ4ojAAAAAAC2SHoB5Uigf6Da1mxrt3/LaZJeAAAAAABYI+kFlDOdatlP\ncdx8khUcAQAAAACwRtILKGc617FfwXHPuT26mHnRB9EAAAAAAFA2kfQCyhlHxewNGdp2ZpsPogEA\nAAAAoGwi6QWUM+1qtpO/xd9uP1McAQAAAAD4fyS9gHImOCBYrWu0tttPMXsAAAAAAP4fSS+gHHI0\nxXHLKZJeAAAAAADkI+kFlEOda9sXs991dpcysjJ8EA0AAAAAAGUPSS+gHHI00ivHyNGOszt8EA0A\nAAAAAGUPSS+gHGpfs738LPYfX6Y4AgAAAACQh6QXUA6FBYapRXQLu/2s4AgAAAAAQJ4AXwfgS2lp\naTpy5IgyMzMVGxur6OjoMnudhIQExcfHKzc3V8HBwbrqqqvcnnPo0CGdOHHCbbvAwEB16dKl0DHB\ntzrV7qS4hDibfazgCAAAAABAngqZ9Nq3b5+effZZLViwQFlZWeb+rl276pVXXtHAgQN9fp39+/dr\n7ty52rBhg/744w8dPnzYPFa/fn2b1868//77ev/99922q1mzpk6fPu22HcqWTrU6acb2GTb7dpzZ\nocycTAX6B/ooKgAAAAAAyoYKl/RasWKFhgwZovT0dElSs2bNFBwcrN27d+uPP/7QoEGD9O677+qJ\nJ57w6XVmzJihl19+2XwdHR2tS5cuKS0trdCxBAYG6uqrr3Z6vFq1aoXuE77XuY79Co5ZuVnaeXan\nw0L3AAAAAABUJBUq6ZWQkKCbbrpJ6enpqlevnmbNmqXOnfMSBydPntTtt9+u5cuX66mnnlLHjh3V\nu3dvn12nadOmeuKJJ9SlSxd16dJFjRo1Uo8ePbRmzZpCx1O9enWtXr26SPeCsqtDrQ4O9285tYWk\nFwAAAACgwqtQhexff/11JSUlyc/PTzNnzjQTUZJUp04dzZ49W7Vr15ZhGHrmmWd8ep3bb79d7777\nrkaNGqVGjRoVORZcucKDwtWsWjO7/azgCAAAAABABUp6ZWdna/r06ZKkAQMGOCwEHxERoYceekiS\ntGnTJu3cubPMXgeQ5HBE1+ZTrOAIAAAAAECFSXqtW7dOiYmJkqTrr7/eabthw4aZ2/PmzSuz1wGk\nvGL2BW07vU1ZOVkOWgMAAAAAUHFUmKTXtm3bzG1Ho6/ytW/fXoGBgXbnlLXrFFZGRoZ27typjRs3\n6sCBA8rNzS3xa6LkOSpmfznnsvac2+ODaAAAAAAAKDsqTNIrPj7e3G7QoIHTdn5+foqJibE7p6xd\npzBOnz6tqlWrqm3bturSpYuaNGmiqKgo3XnnnYqLiyvRa6NkdazV0eF+pjgCAAAAACq6CpP0Sk5O\nNrcjIyNdts0/bn1OWbtOYeTk5CgiIkKdOnVSmzZtFBgYqJSUFM2YMUOdOnXStGnTitRvUlKS05+S\nvifkiQyJVMOIhnb7KWYPAAAAAKjoAnwdQGm5ePGiJMlisZjTCp0JCQmxOacsXscTnTt31jfffKPr\nrrtOERER5v6MjAxNnTpVzz33nNLS0nTfffepfv366t27d6H6j4qK8m7AKJLOdTrrUPIhm30kvQAA\nAAAAFV2FGelVqVIlSZJhGG7rWWVnZ9ucUxav44k777xTo0ePtkl4SXnJtvHjx2vRokXy9/dXTk6O\nnnnmmRKJASXPUTH7P0//qZzcHB9EAwAAAABA2VBhkl6VK1c2t9PT0122zT9ufU5Zu443dO/eXbfd\ndpskadOmTTpy5Eihzk9MTHT6c/DgwZIIGQ50qm2f9ErPSte+8/t8EA0AAAAAAGVDhUl61alTx9w+\nefKky7b5x2vXrl1mr+Mtffv2Nbf37Cncin+RkZFOfwqOLkPJcZT0kpjiCAAAAACo2CpM0qtly5bm\n9r59zkfAJCYm6ty5c5KkVq1aldnreIv1KLOMjAyfxYGiqx5WXbHhsXb7SXoBAAAAACqyCpP06t69\nu7m9cuVKp+1WrVplbvfo0aPMXsdbrKch1qhRw2dxoHg61+lst2/zqc0+iAQAAAAAgLKhwiS9YmNj\n1aVLF0nSN998o6ysLIftpk2bJkkKDg7WkCFDyux1vCE7O1tfffWVJCk0NFSdOjmeJoeyz1kx+/9r\n777jo6ry/4+/Jz2kFwgt9CaKdBQQBFERsADiCqhrg3VX3VW/llXX/bprWcXd/bm6y+rXtqhYUGmy\niA3pRECQIiWEKpCEloT0ZJK5vz9iRsKdNMjMnfJ6Ph55zM05Z+79DNxMknfuOddh1H0zBQAAAAAA\n/FXAhF6S9Oijj0qSjhw5ov/93/819S9atEjz58+XJN1zzz2KiYkxjdm3b59Wr16t1atXq7Cw0G3H\nOVe5ubm1Bm6SVF5erunTp2vnzp2SpDvvvFMRERFNXgc8w9W6Xvll+dqXyw0FAAAAAACBKcTqAjxp\nwoQJuv766zV37lw9//zz2rt3r6ZOnarw8HAtXbpUL7/8siSpe/fueuKJJ1zu4//9v/+nmTNnSpI2\nbNigAQMGuOU4ZWVl2rBhQ422/Px8Z9/q1atr9PXu3btGeDZ37lw99NBDGjNmjAYNGqSWLVsqJSVF\nZWVl2rx5s/7zn/8oIyPD+dynn3663n8/eC9X0xslaWPmRnVJ7OLhagAAAAAAsF5AhV6S9O677yoi\nIkLvvfeePv74Y3388cc1+gcOHKiPP/74nO8+eK7HycrK0rBhw1z2ZWdnm/rS0tJ08cUXOz9PTExU\nSUmJPvzwQ3344Ycu92Oz2TR16lT985//VFxcXCNeHbxNy+iWahXdSlmFWTXaN2Vt0o0X3GhRVQAA\nAAAAWCfgQq/IyEjNnj1b99xzjz766CNlZGTIbrcrNTVV48aN0zXXXKOQkNr/WTp37uxcrL6uaYnn\nepyIiIgai+LXJzY2tsbnEydOVE5Ojr755hutWbNGR44cUXZ2toKCgtS8eXP169dP48ePV6dOnRp8\nDHi3fq36aXHG4hptm7K5gyMAAAAAIDDZDMMwrC4C/ic3N1eJiYmSpJycHCUkJFhckf97ctmTemrl\nUzXaEiISdPKRk7LZbBZVBQAAAABA/dyRIwTUQvaAP3O1mH1uaa4OnjpoQTUAAAAAAFiL0AvwE65C\nL6lqXS8AAAAAAAINoRfgJ9rGtlXzZs1N7RszN1pQDQAAAAAA1iL0AvyEzWZzebUXi9kDAAAAAAIR\noRfgR1yFXhszN4r7VQAAAAAAAg2hF+BH+rfqb2o7Xnxch/IPWVANAAAAAADWIfQC/MiA1gNctq8/\nst7DlQAAAAAAYC1CL8CPtItrpxZRLUztG45ssKAaAAAAAACsQ+gF+BGbzaZBbQaZ2tdncqUXAAAA\nACCwEHoBfmZQa3Po9V3md6p0VFpQDQAAAAAA1iD0AvzMwDYDTW2F5YXadWKXBdUAAAAAAGANQi/A\nzwxsbQ69JBazBwAAAAAEFkIvwM8kNUtS54TOpvYNmSxmDwAAAAAIHIRegB9yuZg9V3oBAAAAAAII\noRfgh1xNcdxydItKK0otqAYAAAAAAM8j9AL8kKsrvSocFdqcvdnzxQAAAAAAYAFCL8AP9W3VV8G2\nYFP7hiOs6wUAAAAACAyEXoAfahbaTL1Sepna12eyrhcAAAAAIDAQegF+ytW6XixmDwAAAAAIFIRe\ngJ9yta7X7pO7lVuSa0E1AAAAAAB4FqEX4KdchV6S9F3mdx6uBAAAAAAAzyP0AvxUz+Y91Sy0mal9\nQyaL2QMAAAAA/B+hF+CnQoJC1K9VP1M763oBAAAAAAIBoRfgxwa1Nk9xXHdknQzDsKAaAAAAAAA8\nh9AL8GOu1vXKLszWkYIjFlQDAAAAAIDnEHoBfqy2xew3HGFdLwAAAACAfyP0AvxYh/gOSopMMrWz\nrhcAAAAAwN8RegF+zGazubzaa30moRcAAAAAwL8RegF+zlXoteHIBjkMhwXVAAAAAADgGYRegJ9z\nFXoVlBco/US6BdUAAAAAAOAZhF6AnxvYeqDL9g2ZLGYPAAAAAPBfhF6An2se1Vwd4juY2lnMHgAA\nAADgzwi9gADgcjF7Qi8AAAAAgB8j9AICwKDW5tBrc/ZmlVWUWVANAAAAAADuR+gFBICBbczretkd\ndm09utWCagAAAAAAcD9CLyAA9GvVT0E285c7UxwBAAAAAP6K0AsIANFh0Tq/+fmm9vWZhF4AAAAA\nAP9E6AUECBazBwAAAAAEEkIvIEAMbG1e1yv9RLpOlZ6yoBoAAAAAANzLK0KvuXPn6pZbbtHEiRP1\n7LPP6tQpfgkHmpqrK70MGdqYtdGCagAAAAAAcC+PhF5Tp05VcnKyvv76a1PfY489pkmTJmn27Nma\nP3++nnjiCQ0YMEDHjh3zRGlAwLigxQWKCIkwtTPFEQAAAADgj9weep06dUrz5s2Tw+HQ8OHDa/Tt\n3LlTM2bMkCRFRkaqe/fustls2rNnjx588EF3lwYElNDgUPVr1c/UTugFAAAAAPBHbg+9li9frrKy\nMo0cOVJhYWE1+t566y0ZhqHk5GTt2rVLu3bt0oIFCyRJc+bMUW5urrvLAwKKq3W9CL0AAAAAAP7I\n7aHX4cOHJUmpqammvs8//1ySdOutt6pdu3aSpGuvvVZ9+/aV3W7XunXr3F0eEFBcret1pOCIMgsy\nLagGAAAAAAD3cXvolZ2dLUlq0aJFjfa8vDxt375dkjR69Ogafb1795YkHTx40N3lAQHFVeglSRuO\nbPBwJQAAAAAAuJfH7t6Yl5dX4/PVq1fLMAwFBwfroosuqtEXHR0tSSovL/dUeUBA6JzQWQkRCaZ2\npjgCAAAAAPyN20Ov6mmLGzdurNG+aNEiSVKfPn0UGxtbo6/6zo1nXh0G4NzYbDYNbONiXa9MQi8A\nAAAAgH9xe+g1bNgwSVUL2n/yySeSpE2bNmn27NmSpDFjxpiek5WVJUlq06aNu8sDAs6g1uYpjt9l\nfieH4bCgGgAAAAAA3MPtoVePHj00evRoORwO3XDDDYqNjVX//v1VXFysiIgITZ8+vcb4iooKbdmy\nRWFhYerTp4+7ywMCjqt1vfJK87QnZ48F1QAAAAAA4B4eWdNr1qxZ6tu3rySpoKBAUtW6XbNmzXJO\nf6y2Zs0a5efna+jQoc61vQA0HVfTGyXW9QIAAAAA+JcQTxykZcuW2rBhg77++mvt3LlTKSkpuvTS\nS9W6dWvT2IqKCt133326/PLLPVEaEHBaRrdUamyqDuUfqtG+7vA63XzhzRZVBQAAAABA0/JI6CVJ\nwcHBGj16tEaPHl3nuFGjRmnUqFEeqgoITIPaDDKFXmmH0yyqBgAAAACApueR6Y0AvMuQ1CGmts3Z\nm1VUXmRBNQAAAAAAND2vCL3mzp2rW265RRMnTtSzzz6rU6dOWV0S4NdchV6VRqW+y/zOgmoAAAAA\nAGh6Hgm9pk6dquTkZH399demvscee0yTJk3S7NmzNX/+fD3xxBMaMGCAjh075onSgIDUt2VfhQWH\nmdrXHlprQTUAAAAAADQ9t4dep06d0rx58+RwODR8+PAafTt37tSMGTMkSZGRkerevbtsNpv27Nmj\nBx980N2lAQErPCRcA1oPMLWvPUzoBQAAAADwD24PvZYvX66ysjKNHDlSYWE1ryx56623ZBiGkpOT\ntWvXLu3atUsLFiyQJM2ZM0e5ubnuLg8IWEPamqc4ph1Kk2EYFlQDAAAAAEDTcnvodfjwYUlSamqq\nqe/zzz+XJN16661q166dJOnaa69V3759ZbfbtW7dOneXBwSswamDTW0nS04qIyfDgmoAAAAAAGha\nbg+9srOzJUktWrSo0Z6Xl6ft27dLkkaPHl2jr3fv3pKkgwcPurs8IGANbmsOvSTW9QIAAAAA+AeP\n3b0xLy+vxuerV6+WYRgKDg7WRRddVKMvOjpaklReXu6p8oCA0yqmlTrGdzS1E3oBAAAAAPyB20Ov\n6mmLGzdurNG+aNEiSVKfPn0UGxtbo6/6zo1nXh0GoGm5muKYdjjNgkoAAAAAAGhabg+9hg0bJqlq\nQftPPvlEkrRp0ybNnj1bkjRmzBjTc7KysiRJbdq0cXd5QEBztZj99mPblVea5/liAAAAAABoQm4P\nvXr06KHRo0fL4XDohhtuUGxsrPr376/i4mJFRERo+vTpNcZXVFRoy5YtCgsLU58+fdxdHhDQhqSa\nQy9DhtYd5iYSAAAAAADf5pE1vWbNmqW+fftKkgoKCiRVrds1a9Ys5/THamvWrFF+fr6GDh3qXNsL\ngHv0SumlqNAoUztTHAEAAAAAvi7EEwdp2bKlNmzYoK+//lo7d+5USkqKLr30UrVu3do0tqKiQvfd\nd58uv/xyT5QGBLSQoBANajNIyw4sq9HOYvYAAAAAAF9nMwzDsLoI+J/c3FwlJiZKknJycpSQkGBx\nRajNE988oWdXPVujLSYsRrm/z1VwULBFVQEAAAAAAok7cgSPTG8E4L1cretVUF6gHcd3WFANAAAA\nAABNg9ALCHAXt73YZTtTHAEAAAAAvsyjoVdRUZH+/e9/a8yYMWrZsqVCQ0MVHh6uNm3a6LrrrtPb\nb7+t8vJyT5YEBLzEyET1SO5hal97mNALAAAAAOC7PBZ6rVixQt26ddM999yjzz//XEePHlVFRYXK\ny8uVmZmpTz/9VLfddpt69eqlLVu2eKosAJKGtDVPcUw7xB0cAQAAAAC+yyN3b1y/fr3GjBmjkpIS\nSVKLFi00ePBgtWrVSg6HQ0eOHNHatWuVm5ur3bt3a9SoUUpLS1PXrl09UR4Q8AanDtZbm9+q0ZaR\nk6HjRcfVPKq5RVUBAAAAAHD23B56GYahu+66SyUlJUpISNBLL72kqVOnKji45l3hysvL9cYbb+ih\nhx7SyZMn9bvf/U5Llixxd3kA5Hoxe0lKO5yma7tf6+FqAAAAAAA4d26f3rhhwwZt3rxZISEh+vLL\nL3XLLbeYAi9JCgsL09133625c+dKkj7//HMdPnzY3eUBkNQjuYfiI+JN7UxxBAAAAAD4KreHXmvX\nVi2GPWHCBA0YMKDe8WPGjNEll1xS47kA3CvIFuTyLo4sZg8AAAAA8FVuD72OHz8uSerdu3eDn1M9\n9tixY26pCYCZq8XsNxzZIHul3YJqAAAAAAA4N24PvSIiIiRJBQUFDX5O9djq5wJwP1frepVUlGjL\nUe6mCgAAAADwPW4PvTp37ixJ+vTTT1VZWVnv+JKSEn355Zc1ngvA/Qa1GaQgm/ktYe0hpjgCAAAA\nAHyP20OvK664QmFhYdq5c6ceeughGYZR69iKigr96le/UnZ2thISEjR06FB3lwfgJzHhMerVopep\nndALAAAAAOCL3B56NW/eXL/5zW8kSf/4xz908cUX65133tHevXtVXFysoqIi7d69W6+//rr69Omj\n2bNnS5Iee+wxhYWFubs8AKdxNcUx7TB3cAQAAAAA+B6bUdelV02krKxMY8eO1TfffNOg8VOmTNF7\n770nm83m5srgLrm5uUpMTJQk5eTkKCEhweKK0BCzt87WLfNvMbUfeuCQ2sa2taAiAAAAAEAgcEeO\n4PYrvSQpPDxcX3zxhZ5++mklJSXVOq5169b617/+ReAFWGRw28Eu29MOcbUXAAAAAMC3eORKr9OV\nlpYqLS1Nmzdv1smTJ2Wz2dS8eXP1799fF110kUJCQjxZDtyEK718k2EYavn3ljpWdKxG+/0X3a8X\nr3rRoqoAAAAAAP7OHTmCxxOmiIgIjRw5UiNHjqx1zLZt25Senq4LL7xQ3bp182B1QGCz2WwakjpE\nC3YtqNHOul4AAAAAAF/jkemNjfX222/rhhtu0Keffmp1KUDAcTXFcVPWJpXYSyyoBgAAAACAs+OV\noRcA67i6g6PdYdfGrI0WVAMAAAAAwNkh9AJQQ/9W/RUaFGpqZzF7AAAAAIAvIfQCUENkaKT6tupr\nal97eK0F1QAAAAAAcHYIvQCYDGlrnuK49tBaefhmrwAAAAAAnDVCLwAmrtb1OlZ0TPvz9ltQDQAA\nAAAAjUfoBcBkcKr5Do5S1dVeAAAAAAD4AkIvACZtY9sqNTbV1E7oBQAAAADwFYReAFxyNcUx7TB3\ncAQAAAAA+IaQptzZK6+8ojlz5pzzfvbu3dsE1QA4F0NSh2jO9ppfz1uPblVBWYFiwmMsqgoAAAAA\ngIZp0tBr7969WrFiRVPuEoBFBrc1r+vlMBxKO5ymKztfaUFFAAAAAAA0XJOGXrGxsUpJSWmy/UVF\nRTXZvgA0Tp+WfRQVGqUie1GN9pUHVxJ6AQAAAAC8ns0wDMPqIuB/cnNzlZiYKEnKyclRQkKCxRXh\nbFz57pX6at9XNdouaXeJVt2+yqKKAAAAAAD+yB05AgvZA6jV8PbDTW3rj6xXib3EgmoAAAAAAGg4\nQi8AtXIVepVXlmv9kfUWVAMAAAAAQMMRegGo1aA2gxQWHGZqX3lwpQXVAAAAAADQcIReAGoVERKh\ni9pcZGpf+SOhFwAAAADAuxF6AajTpe0vNbWtPbRW9kq7BdUAAAAAANAwhF4A6uRqXa9ie7E2Zm20\noBoAAAAAABqG0AtAnQanDlawLdjUzrpeAAAAAABvRugFoE7RYdHq37q/qZ3QCwAAAADgzQi9ANTL\n1bpeq35cpUpHpQXVAAAAAABQP0IvAPVyta5Xflm+th7dakE1AAAAAADUj9ALQL2Gpg6VTTZTO1Mc\nAQAAAADeitALQL0SIhN0YcqFpvaVPxJ6AQAAAAC8E6EXgAZxNcVx5cGVMgzDgmoAAAAAAKgboReA\nBnG1mP2J4hPaeWKnBdUAAAAAAFA3Qi8ADTKs/TCX7azrBQAAAADwRoReABqkRVQL9UjuYWon9AIA\nAAAAeCNCLwANNrydeV2vFQdXsK4XAAAAAMDrEHoBaDBXi9lnFmRqX+4+C6oBAAAAAKB2hF4AGsxV\n6CUxxREAAAAA4H0IvQA0WGpcqjrGdzS1r/yR0AsAAAAA4F0IvQA0iqurvVYcWGFBJQAAAAAA1C7E\n6gKs8t133+mjjz5SRkaGysvL1a5dO40bN05jxoxRcHCwVxzH4XBox44dWrdundavX6+MjAw5HA61\nbNlSH374ocfqAE43vP1wvb3l7Rpt+/P269CpQ0qNS7WoKgAAAAAAarIZAXbbtbKyMt111116++23\nXfYPHjxYc+bMUWrquf3yfq7Hee211/Tggw+qsLDQ1Ne+fXsdOHDAI3WcrdzcXCUmJkqScnJylJCQ\n0KT7h3X25OxR1392NbW/N/E9Te011YKKAAAAAAC+zh05QsBNb7z11ludAdCECRP00Ucf6dNPP9V9\n992nkJAQpaWl6aqrrlJ+fr6lx8nMzFRxcbHOP/983X777XrllVd0/vnne7wO4EydEzqrdUxrUzuL\n2QMAAAAAvElAXem1cOFCjR8/XpL00EMP6a9//WuN/gULFmjChAmSpIcfflgvvPCCZcfJyspSTEyM\noqOjnW2XXHKJ1qxZ0+ArvTz1el3hSi//NmXuFH34Q80ptucln6cd9+ywqCIAAAAAgC9zR44QUKHX\nxRdfrHXr1ql169bav3+/wsLCTGPGjx+vhQsXKjIyUseOHasROll9nMaGXp56va4Qevm3Vza8ors/\nu9vUfvSho2oR1cKCigAAAAAAvozpjefg8OHDWr9+vSRp6tSpLgMgSbrtttskSSUlJfrss8+89ji+\nUgf8k6s7OErSqoOrPFwJAAAAAACuBUzotWbNGlVf1DZ8uOtf2M/sW716tdcex1fqgH/q2bynkpsl\nm9pZ1wsAAAAA4C0CJvTauXOnc7t79+61jktMTFRSUpIkaceOxq9P5Knj+Eod8E82m03D2g0zta/8\nkdALAAAAAOAdAib0OnLkiHO7dWvznedO16ZNG0lVd1D01uN4Qx25ubm1fuTl5TW6ZvgWV1Mct2Rv\nUW5JrgXVAAAAAABQU4jVBXhKYWGhc7tZs2Z1jq3uP/053nYcb6ijeoE5BCZXoZchQ2sOrdHV3a62\noCIAAAAAAH4WMFd62e12SVXTsoKC6n7ZISEhNZ7jjcfxlTrgv3qn9FZseKypnXW9AAAAAADeIGCu\n9IqKipIkGYah8vLyWu9mKEmlpaU1nuONx/GGOnJycmrty8vLU6dOnRq1P/iW4KBgXdLuEn2WUfOu\nn4ReAAAAAABvEDBXesXHxzu3c3PrXnOouj8uLs5rj+MNdSQkJNT6cfrx4b+GtzNPcfwu8zsVljf9\nlF0AAAAAABojYEKvrl27OrcPHjxY6ziHw6FDhw5Jkrp16+a1x/GVOuDfXK3rVWlUKu1QmgXVAAAA\nAADws4AJvS688ELn9nfffVfruK1bt6q8vNz0HG87jq/UAf/Wv3V/RYZEmtqZ4ggAAAAAsFrAhF5D\nhgxx3m3wv//9b63jFi9e7NweN26c1x7HV+qAfwsLDtPg1MGm9mUHlllQDQAAAAAAPwuY0CskJEQ3\n33yzJOmLL77Qpk2bTGNOnTqlf//735Kk/v37n9WVT546jq/UAf83ov0IU9u6I+tUUFbg+WIAAAAA\nAPhJwIRekvSHP/xB8fHxcjgcmjRpkrZs2eLsO3r0qK6//nplZmbKZrPphRdecLmPl156SSNGjNCI\nESOUnp7utuM0BW+pA/5tVKdRprYKRwVTHAEAAAAAlgqxugBPatGihebNm6exY8dq//796tu3r3r2\n7Knw8HBt375dZWVlkqQZM2bosssuc7mPjIwMrVixQpJUUOD6SpamOE52drYmT55co23btm3OvhEj\nRtTo+/e//62ePXs2eR1AfQa2HqjosGjTHRuX7l+qcd2YMgsAAAAAsEZAhV6SNHLkSG3atEkPPfSQ\nvvzyS23fvt3Z169fPz399NMaO3as5ccpLS11hmtnKisrM/Xl5+e7pQ6gPqHBoRrRYYT+u7vm2nFf\n7/vaoooAAAAAAJBshmEYVhdhlVOnTmn//v2y2+1KTU1Vy5Yt631ORkaGjhw5IqlqHayYmBi3HKe0\ntFTffvtt/S/iJ/369VNsbGyT13G2cnNznQvp5+TkKCEhwW3HgvX+8e0/9MAXD5jasx/MVkp0igUV\nAQAAAAB8iTtyhIAOveA+hF6B5YdjP6jXK71M7e9PfF9Tek2xoCIAAAAAgC9xR44QUAvZA3CP85uf\nr5Qo8xVdS/cvtaAaAAAAAAAIvQA0AZvN5vIujl/v+1pcTAoAAAAAsAKhF4AmMaqjOfQ6eOqg9uXu\ns6AaAAAAAECgI/QC0CQu73S5y3bu4ggAAAAAsAKhF4Am0S6unbokdjG1s64XAAAAAMAKhF4Amszl\nHc1Xe32z/xs5DIcF1QAAAAAAAhmhF4Am42qK48mSk9qSvcWCagAAAAAAgYzQC0CTGdlxpGyymdpZ\n1wsAAAAA4GmEXgCaTGJkovq16mdqZ10vAAAAAICnEXoBaFKjOo4yta08uFJlFWUWVAMAAAAACFSE\nXgCalKt1vUoqSvTt4W8tqAYAAAAAEKgIvQA0qaHthiosOMzUzrpeAAAAAABPIvQC0KSahTbT0NSh\npnbW9QIAAAAAeBKhF4Am52pdr/VH1iu/LN+CagAAAAAAgYjQC0CTc7WuV6VRqRUHVlhQDQAAAAAg\nEBF6AWhy/Vv3V2x4rKmddb0AAAAAAJ5C6AWgyYUEhWhkh5Gmdtb1AgAAAAB4CqEXALdwta7X9uPb\nlVWQZUE1AAAAAIBAQ+gFwC1creslSd/s/8bDlQAAAAAAAhGhFwC36JHcQ62iW5nav97Pul4AAAAA\nAPcj9ALgFjabzeXVXkv3LZVhGBZUBAAAAAAIJIReANzG1bpeh/IPKSMnw4JqAAAAAACBhNALgNuM\n6mQOvaSqq70AAAAAAHAnQi8AbtM2tq26J3U3tbOuFwAAAADA3Qi9ALiVq3W9lu1fpkpHpQXVAAAA\nAAACBaEXALdyta5Xbmmuvs/+3oJqAAAAAACBgtALgFuN6DBCQTbzWw3regEAAAAA3InQC4BbJUQm\naEDrAaZ21vUCAAAAALgToRcAt3M1xXH1j6tVWlFqQTUAAAAAgEBA6AXA7VwtZl9aUarVP662oBoA\nAAAAQCAg9ALgdkNShygiJMLUviRjiQXVAAAAAAACAaEXALeLCInQyA4jTe1L9hB6AQAAAADcg9AL\ngEeM6TLG1LbzxE7tz91vQTUAAAAAAH9H6AXAI8Z0NYdeEld7AQAAAADcg9ALgEd0SeyibkndTO2E\nXgAAAAAAdyD0AuAxrqY4Lt23VKUVpRZUAwAAAADwZ4ReADxmbNexpraSihKtOLDCgmoAAAAAAP6M\n0AuAxwxvP1zNQpuZ2pniCAAAAABoaoReADwmIiRCl3W8zNT+WcZnFlQDAAAAAPBnhF4APGpsF/MU\nx4ycDGWczLCgGgAAAACAvyL0AuBRY7qaF7OXmOIIAAAAAGhahF4APKpDfAedl3yeqZ3QCwAAAADQ\nlAi9AHicq7s4Ltu/TMX2YguqAQAAAAD4I0IvAB7nKvQqqyzTsv3LLKgGAAAAAOCPCL0AeNwl7S5R\ndFi0qZ0pjgAAAACApkLoBcDjwoLDdHmny03tizMWyzAMCyoCAAAAAPgbQi8AlhjbxTzF8UDeAaWf\nTLegGgAAAACAvyH0AmCJMV3HuGxfksEURwAAAADAuSP0AmCJtrFt1atFL1P7Z3s+s6AaAAAAAIC/\nIfQCYBlXd3FceXClCssLLagGAAAAAOBPCL0AWGZMF/MUx/LKcn2z/xsLqgEAAAAA+BNCLwCWGZI6\nRLHhsab2zzKY4ggAAAAAODeEXgAsExocqis7X2lq/yzjMxmGYUFFAAAAAAB/QegFwFKupjgeyj+k\nHcd3WFANAAAAAMBfEHoBsNRVXa5y2c4URwAAAADAuSD0AmCp1jGt1bdlX1P7Z3sIvQAAAAAAZ4/Q\nC4DlXE1xXP3jauWX5VtQDQAAAADAHxB6AbDc2K5jTW0Vjgp9ve9rC6oBAAAAAPgDQi8Alruo7UVK\niEgwtbOuFwAAAADgbBF6AbBcSFCIrux8pal9yZ4lMgzDgooAAAAAAL6O0AuAV3A1xTGzIFNbj261\noBoAAAAAgK8j9ALgFa7qcpXLdqY4AgAAAADOBqEXAK/QIqqFBrQeYGpfnLHYgmoAAAAAAL6O0AuA\n1xjbxTzFce2htTpaeNSCagAAAAAAvozQC4DXuKb7NaY2Q4Y+Tf/UgmoAAAAAAL6M0AuA1+jfqr9S\nY1NN7fN3zbegGgAAAACALyP0AuA1bDabxvcYb2pfun+p8svyPV8QAAAAAMBnEXoB8CoTekwwtZVX\nlnMXRwAAAABAoxB6AfAqw9oPU1JkkqmdKY4AAAAAgMYg9ALgVUKCQlwuaP9ZxmcqrSi1oCIAAAAA\ngC8i9ALgdVxNcSwsL9TSfUstqAYAAAAA4IsIvQB4nSs6XaGo0ChTO1McAQAAAAANRegFwOtEhkbq\nqi5Xmdo/Tf9UlY5KCyoCAAAAAPgaQi8AXsnVFMfjxce15tAaC6oBAAAAAPgaQi8AXmlct3EKCQox\ntc/fyRRHAAAAAED9CL0AeKX4iHhd1vEyU/v8XfNlGIYFFQEAAAAAfAmhFwCv5WqK48FTB7U5e7Pn\niwEAAAAA+BRCLwBe67ru18kmm6mduzgCAAAAAOpD6AXAa7WKaaWL215saif0AgAAAADUh9ALgFcb\n32O8qe2HYz9oT84ezxcDAAAAAPAZhF4AvJqrdb0k7uIIAAAAAKgboRcAr9Y1qavOb36+qZ0pjgAA\nAACAuhB6AfB6rq72SjucpqyCLAuqAQAAAAD4AkIvAF5vwnmupzguTF/o4UoAAAAAAL6C0AuA1+vb\nsq/ax7U3tTPFEQAAAABQG0IvAF7PZrO5vIvjN/u/UV5pnsfrAQAAAAB4P0IvAD7B1bpeFY4KLd69\n2IJqAAAAAADejtALgE+4pN0lSm6WbGpniiMAAAAAwBVCLwA+ITgoWNd2u9bUvmTPEpXYSyyoCAAA\nAADgzQi9APgMV3dxLLYX66t9X1lQDQAAAADAmxF6AfAZl3e6XNFh0aZ2pjgCAAAAAM5E6AXAZ0SE\nRGhMlzGm9kXpi1ThqLCgIgAAAACAtyL0AuBTXN3F8WTJSa08uNKCagAAAAAA3orQC4BPGdt1rEKD\nQk3tc36YY0E1AAAAAABvRegFwKfERcTpis5XmNo/2fmJyivLLagIAAAAAOCNCL0A+JwpF0wxteWU\n5OjLvV9aUA0AAAAAwBsRegHwOdd1v06RIZGm9ve3vW9BNQAAAAAAb0ToBcDnxITH6Nru15raF6Yv\nVFF5kQUVAQAAAAC8DaEXAJ/kaopjsb1Yn6Z/akE1AAAAAABvQ+gFwCdd1eUqxUfEm9rf/4EpjgAA\nAAAAQi8APio8JFyTzptkav98z+c6WXzSgooAAAAAAN6E0AuAz5rSyzzFscJRobk751pQDQAAAADA\nmxB6AfBZl7a/VK2iW5nauYsjAAAAAIDQC4DPCg4K1uQLJpvaVx5cqcP5hy2oCAAAAADgLQi9APg0\nV3dxNGRozg9zLKgGAAAAAOAtCL0A+LQBrQeoS2IXUzt3cQQAAACAwEboBcCn2Ww2Tb1gqql9U9Ym\npZ9It6AiAAAAAIA3IPQC4PNc3cVRkj744QMPVwIAAAAA8BaEXgB8Xo/kHurbsq+p/f1t78swDAsq\nAgAAAABYjdALgF+Y2ss8xTEjJ0ObsjZZUA0AAAAAwGqEXgD8wo3n3+iy/f1tLGgPAAAAAIGI0AuA\nX0iNS9Xw9sNN7R9u/1CVjkoLKgIAAAAAWInQC4DfcHUXx8yCTK36cZUF1QAAAAAArEToBcBvTOo5\nSSFBIaZ2pjgCAAAAQOAh9ALgN5KaJWl059Gm9k92fKLyynILKgIAAAAAWIXQC4BfcXUXx9zSXH2x\n5wsLqgEAAAAAWMU8DyiAnDp1SgcOHFB5eblSU1PVsmVLrz3O2exjz549Onz4cL3jwsLCNGTIkEbX\nBHija7tfq8iQSJVUlNRof/+H93VN92ssqgoAAAAA4GkBGXrt3LlTDz/8sL744gtVVFQ42/v376+n\nnnpKY8eO9ZrjnMs+/vWvf+mll16q9xgpKSnKzs6udxzgC6LDonVdj+v04Q8f1mhfuGuhCssLFR0W\nbVFlAAAAAABPCrjQa9myZRo7dqxKS0tls9nUs2dPRUREaPv27dq4caPGjRunF154QQ8//LDlx2mq\nWsPCwjR48OBa+xMTE8/6dQLeaOoFU02hV0lFiT5N/9Tl9EcAAAAAgP+xGYZhWF2Epxw7dkzdu3dX\nXl6eOnTooAULFqh3796SpKNHj+qmm27S0qVLZbPZ9PXXX+uyyy6z7DhNsY/7779fL730ktq0adOg\naY5NKTc31xmm5eTkKCEhwaPHR2ArryxXy7+1VG5pbo32sV3HavHUxRZVBQAAAACojTtyhIBayP7Z\nZ59VXl6egoKCNHfuXGeIJFVN8Zs7d65at24twzD0yCOPWHocT9UK+KOw4DBN6jnJ1P7l3i91vOi4\nBRUBAAAAADwtYEKviooKzZ49W5I0evRo9evXzzQmLi5Od999tyRp48aN2rp1qyXH8VStgD9zNY2x\nwlGhOdvnWFANAAAAAMDTAib0Wrt2rXJyciRJV199da3jxo0b59xevLjx06Ca4jieqhXwZ8PbD1fb\n2Lam9tlbZ1tQDQAAAADA0wIm9Dr9SqgBAwbUOu7CCy9UWFiY6TmePI47ai0qKtLmzZuVlpam9PR0\nVVZW1jke8HVBtiDd1OsmU/u6I+u0++RuCyoCAAAAAHhSwIReGRkZzu327dvXOi4oKEipqamSpN27\nG/+LcVMcp6lrzcrKUlxcnPr27ashQ4aoR48eio+P15QpU7Rt27a6XxDgw26+8GaX7e9tfc/DlQAA\nAAAAPC1gQq+8vDzndn13AKjuP3XqlCXHcUetLVq00KBBg9SnTx9FRESosLBQH374oQYMGKC33nqr\nzufWJjc3t9aP018DYJULWlygPi37mNpnb5utALpxLQAAAAAEpIAJvYqKiiRJNpvNOSWwNhERETWe\n4+njNFWtAwcO1EcffaS8vDxlZmZq3bp1+v7775WXl6dXX31VMTExKi8v1/Tp07V06dKGvcDTJCYm\n1vrRqVOnRu8PcIebe5mv9tqXu09ph9MsqAYAAAAA4CkBE3qFhoZKkgzDkMPhqHNsRUVFjed4+jhN\nVetNN92kG264QTExMTXaw8PDddddd+mLL75QSEiIHA6HHnnkkTqPA/iqKb2mKMhmfqtjQXsAAAAA\n8G8BE3pFR0c7t4uLi+scW91/+nM8eRxP1Tp48GBNnjxZkrRp0yYdOHCgUc/Pycmp9WPfvn2Nrgdw\nh9YxrTWq4yhT+5ztc1ReWW5BRQAAAAAATwiY0KtNmzbO7czMzDrHHjlyRJLUunVrS47jqVolaeTI\nkc7t9PT0Rj03ISGh1o/4+PizqgdwB1cL2ueU5GhJxhILqgEAAAAAeELAhF7nnXeec7uucCcnJ0cn\nT56UJPXs2dOS43iqVkmKiopybpeUlJzVPgBvN/G8iWoW2szUPnsbUxwBAAAAwF8FTOg1dOhQ2Ww2\nSdLKlStrHXd63yWXXGLJcTxVqyTt3bvXud2iRYuz2gfg7aLDojW+x3hT+6L0RcorzfN4PQAAAAAA\n9wuY0Ktt27YaNGiQJOn9999XebnrtXxmzZolSYqMjNSYMWMsOY6naq2oqNA777wjSWrWrJn69+/f\n6H0AvsLVXRzLKsv0yY5PLKgGAAAAAOBuARN6SdJjjz0mqWqdrD/84Q+m/gULFmjhwoWSpHvvvdd0\n10NJysjI0PLly7V8+XIVFBS47Tjnuo+cnByVlZW5rE+SysrKdMcddzinT06fPl3h4eG1jgd83RWd\nr1CLKPPVjNzFEQAAAAD8k80wDMPqIjxp8uTJmjNnjiRpwoQJmjp1qsLDw7V06VLNnDlTFRUV6tmz\np9LS0hQbG2t6/r333quZM2dKkjZs2KABAwa45Tjnuo833nhDDzzwgK666ioNGjRILVu2VEpKisrK\nyrR582bNmjXLeYfFvn37avny5bXWcTZyc3OVmJgoqSqAS0hIaLJ9A2fr/s/v10vrXjK1H7jvgNrH\nt7egIgAAAACA5J4cIeSc9+BjZs2apYiICL399tuaP3++5s+fX6N/8ODBmjNnzjkHQE1xnHPZR3Jy\nsioqKvTJJ5/ok09cT98KCgrSLbfcon/84x9NGngB3uqWC29xGXq9v+19PTbsMQsqAgAAAAC4S8Bd\n6VVtw4YN+uijj5SRkSG73a7U1FSNGzdOY8eOVXBwcK3Pe+mll5zh0//93/+pe/fubjlOU+yjpKRE\ny5cv15o1a3TkyBFlZ2crKChIzZs3V79+/XTdddepfXv3XN3ClV7wRoZhqOe/e2rXiV012s9LPk/b\n797uvIEEAAAAAMCz3JEjBGzoBfci9IK3enbls3pi2ROm9o2/2qh+rfpZUBEAAAAAwB05QkAtZA8A\nN114k8t2FrQHAAAAAP9C6AUgoHSI76Bh7YaZ2j/44QNVOCosqAgAAAAA4A6EXgACzs0X3mxqyy7M\n1tJ9Sy2oBgAAAADgDoReAALODT1vUFhwmKl99jamOAIAAACAvyD0AhBwEiITdHW3q03t83bOU2F5\noQUVAQAAAACaGqEXgIB0y4W3mNqK7cVasGuB54sBAAAAADQ5Qi8AAWlMlzFKiDDfApe7OAIAAACA\nfyD0AhCQwkPC9Yvzf2Fq/2rfV8oqyLKgIgAAAABAUyL0AhCwXE1xdBgOvbftPQuqAQAAAAA0JUIv\nAAFrSOoQdYzvaGp/fdPrMgzDgooAAAAAAE2F0AtAwLLZbLq1962m9t0nd2v5geWeLwgAAAAA0GQI\nvQAEtDv73akgm/mt8P82/p8F1QAAAAAAmgqhF4CA1ja2rcZ1HWdqn7dzno4VHbOgIgAAAABAUyD0\nAhDw7up/l6nN7rBr1uZZni8GAAAAANAkCL0ABLyrulyldnHtTO2vbXxNDsNhQUUAAAAAgHNF6AUg\n4AUHBWt6v+mm9r25e/XN/m8sqAgAAAAAcK4IvQBA0h1971CwLdjUzoL2AAAAAOCbCL0AQFLrmNa6\ntvu1pvYFuxYouzDbgooAAAAAAOeC0AsAfuJqQfsKR4Xe+v4tC6oBAAAAAJwLQi8A+MkVna9Qx/iO\npvbXN73OgvYAAAAA4GMIvQDgJ0G2IJcL2h/IO6Av935pQUUAAAAAgLNF6AUAp7m97+0KCQoxtbOg\nPQAAAAD4FkIvADhNy+iWGt9jvKl9UfoiZRZker4gAAAAAMBZIfQCgDO4WtC+0qjUm5vetKAaAAAA\nAMDZIPQCgDNc1vEydU7obGp/fdPrqnRUWlARAAAAAKCxCL0A4AxBtiCXV3sdyj+kz/d8bkFFAAAA\nAIDGIvQCABdu63ObwoLDTO0saA8AAAAAvoHQCwBcaB7VXBPPm2hqX5yxWIdOHbKgIgAAAABAYxB6\nAUAtXE1xdBgOvfk9C9oDAAAAgLcj9AKAWlza/lJ1T+puan9j0xuqcFRYUBEAAAAAoKEIvQCgFjab\nTb/q/ytT+5GCI/os4zMLKgIAAAAANBShFwDU4dbetyo8ONzUzoL2AAAAAODdCL0AoA5JzZI0qeck\nU/uSjCVKP5FuQUUAAAAAgIYg9AKAerha0N6Qob+t/ZsF1QAAAAAAGoLQCwDqcUm7S9SnZR9T+ztb\n31FWQZbnCwIAAAAA1IvQCwDqY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eUZcXFxhiRjxIgRLsd8+umnzv38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\n6/LLL3eOfffdd3Xddddp2bJlmjFjhmbOnKmLL75YrVu3VlFRkbKzs/Xjj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          }
        }
      ],
      "source": [
        "# Set up coupler geometry in a pytorch tensor and initialize to the mean values\n",
        "# for both the coupling length and the waveguide spacing\n",
        "coupler_geometry = torch.Tensor([np.mean(wg_spacing_coup_range), np.mean(coup_length_range)])\n",
        "coupler_geometry.requires_grad = True  # we will track the gradient in the optimizer\n",
        "\n",
        "freq_tensor = torch.Tensor([freq0]).unsqueeze(dim=0)  # design coupler at center frequency\n",
        "\n",
        "# Set up bounds to clamp the design parameters between so we stay in the parameter\n",
        "# space learned by the neural network surrogate\n",
        "min_design_tensor = torch.Tensor([np.min(wg_spacing_coup_range), np.min(coup_length_range)])\n",
        "max_design_tensor = torch.Tensor([np.max(wg_spacing_coup_range), np.max(coup_length_range)])\n",
        "\n",
        "# Set up the transmission goal we can use in the loss function and define an MSE loss\n",
        "desired_transmission = torch.Tensor(\n",
        "    [top_waveguide_transmission_goal, bottom_waveguide_transmission_goal]\n",
        ")\n",
        "mse_loss = nn.MSELoss()\n",
        "\n",
        "# Create an Adam optimizer the focuses on optimizing the coupler geometry\n",
        "# based on the surrogate prediction\n",
        "opt = optim.Adam(\n",
        "    [coupler_geometry],\n",
        "    lr=1e-3,\n",
        "    weight_decay=1e-5,\n",
        ")\n",
        "\n",
        "coupler_optimization_iterations = 100\n",
        "coupler_loss_curve = np.zeros(coupler_optimization_iterations)\n",
        "\n",
        "for idx in range(0, coupler_optimization_iterations):\n",
        "    opt.zero_grad()\n",
        "\n",
        "    # Clamp the geometry between the min/max values allowed\n",
        "    clamp_geometry = torch.clamp(coupler_geometry, min_design_tensor, max_design_tensor)\n",
        "\n",
        "    # Combine the geometry parameters with the frequency\n",
        "    geometry_and_freq = torch.cat((clamp_geometry.unsqueeze(dim=0), freq_tensor), dim=1)\n",
        "\n",
        "    # Check the surrogate's prediction for the current coupler geometry\n",
        "    prediction = torch.squeeze(optimized_network(geometry_and_freq))\n",
        "\n",
        "    # Compute the loss based on the goal transmission in each waveguide\n",
        "    loss = mse_loss(prediction, desired_transmission)\n",
        "    coupler_loss_curve[idx] = loss.item()\n",
        "\n",
        "    loss.backward()  # backpropagate\n",
        "    opt.step()  # step parameters\n",
        "\n",
        "\n",
        "final_geometry = torch.clamp(coupler_geometry, min_design_tensor, max_design_tensor)\n",
        "final_geometry_numpy = final_geometry.detach().numpy()\n",
        "final_geometry_numpy_display = [float(np.round(float(num), 4)) for num in final_geometry_numpy]\n",
        "\n",
        "plt.plot(coupler_loss_curve, linewidth=2, color=\"g\")\n",
        "plt.xlabel(\"Iteration\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\n",
        "    \"Optimized coupler geometry\\n\"\n",
        "    rf\"wg_spacing={final_geometry_numpy_display[0]}$\\mu$m\"\n",
        "    \"\\n\"\n",
        "    rf\"coup_length={final_geometry_numpy_display[1]}$\\mu$m\"\n",
        ")\n",
        "plt.show()"
      ],
      "id": "2a97272c"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Using the parameters from our optimization, we run a final\n",
        "electromagnetic evaluation of the coupler. This allows us to assess one\n",
        "more time the quality of the surrogate model as well as evaluate the\n",
        "performance of the final design."
      ],
      "id": "99d15b43-0482-4202-a7cc-51201313651f"
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "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\">15:20:14 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'best_predicted'</span> with resource_id                     \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-f4117b9b-f843-4900-9866-20438e5b0586'</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-f4117b9b-f843-4900-9866-20438e5b0586\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-f4117b9b-f84</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-f4117b9b-f843-4900-9866-20438e5b0586\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">3-4900-9866-20438e5b0586'</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\":\"4b1e9112e7174e6f8432bfd8f90b47f9\",\"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\">15:20:16 UTC </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.612</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\">15:20:17 UTC </span>status = queued                                                    \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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"eb4242711b1247eeadd7e4c03195aaca\",\"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\">15:21:41 UTC </span>starting up solver                                                 \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>running solver                                                     \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"46c57f6bfd864e0a8888a96bbd2d0599\",\"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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:23:34 UTC </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">46</span>%, exiting.                            \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\"></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>status = postprocess                                               \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"a0e05db4e33b44f4ab8bbf3492d79de3\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
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          }
        },
        {
          "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\">15:23:37 UTC </span>status = success                                                   \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\"></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\">15:23:39 UTC </span>View simulation result at                                          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-f4117b9b-f843-4900-9866-20438e5b0586\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-f4117b9b-f84</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-f4117b9b-f843-4900-9866-20438e5b0586\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">3-4900-9866-20438e5b0586'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"8e4728cf39c340b4b4538b0695ca73b3\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
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          }
        },
        {
          "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\">15:23:40 UTC </span>Loading results from simulation_data.hdf5                          \n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "optimized_coup_length = final_geometry_numpy[1]\n",
        "optimized_wg_spacing = final_geometry_numpy[0]\n",
        "sim = make_sim(optimized_coup_length, optimized_wg_spacing)\n",
        "sim_data = web.run(sim, task_name=\"best_predicted\")"
      ],
      "id": "5fd2e6a8"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "For the optimal point, we report the power in both the top and bottom\n",
        "waveguides after the coupling. We can compare these powers to the best\n",
        "ones we found with Monte Carlo sampling of the coupler geometric\n",
        "parameters. They are improved, albeit not by a significant margin\n",
        "because the random sampler did a good job of finding adequate design\n",
        "parameters. For a low dimensional optimization problem like this, it is\n",
        "not entirely unexpected for this to be the case. However, this technique\n",
        "may improve in utility as the number of design parameters grows."
      ],
      "id": "73178443-2f94-48e7-9907-89302f4567ff"
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top simulated waveguide transmission: 0.24071, Bottom simulated waveguide transmission: 0.75219\n",
            "\n",
            "Compare these values to the predicted values from the surrogate model:\n",
            "Top predicted waveguide transmission: 0.24583, Bottom predicted waveguide transmission: 0.74744\n",
            "\n",
            "Compare these values to the ones found via Monte Carlo sampling:\n",
            "Top simulated MC waveguide transmission: 0.26143, Bottom simulated MC waveguide transmission: 0.73259"
          ]
        }
      ],
      "source": [
        "def make_prediction(network, wg_spacing_coup, coup_length, freq):\n",
        "    \"\"\"Use the neural network surrogate to estimate the output coupling power fractions.\"\"\"\n",
        "    feature_array = np.expand_dims(np.array([wg_spacing_coup, coup_length, freq]), axis=0)\n",
        "    input_tensor = torch.Tensor(feature_array)\n",
        "    prediction = np.squeeze(network(input_tensor).detach().numpy())\n",
        "\n",
        "    return prediction\n",
        "\n",
        "\n",
        "# Extract power in each waveguide from simulation data\n",
        "amps_dict = measure_coupling_amplitude(sim_data)\n",
        "top_simulated_power = abs(amps_dict[\"top\"].sel(f=freq0, method=\"nearest\").item()) ** 2\n",
        "bottom_simulated_power = abs(amps_dict[\"bottom\"].sel(f=freq0, method=\"nearest\").item()) ** 2\n",
        "print(\n",
        "    f\"Top simulated waveguide transmission: {np.round(top_simulated_power, 5)}, Bottom simulated waveguide transmission: {np.round(bottom_simulated_power, 5)}\\n\"\n",
        ")\n",
        "\n",
        "# Check the network prediction for this point to compare\n",
        "feature_array = np.array(\n",
        "    (\n",
        "        optimized_wg_spacing,\n",
        "        optimized_coup_length,\n",
        "        freq0,\n",
        "    )\n",
        ")\n",
        "\n",
        "prediction = make_prediction(optimized_network, *feature_array)\n",
        "print(\"Compare these values to the predicted values from the surrogate model:\")\n",
        "print(\n",
        "    f\"Top predicted waveguide transmission: {np.round(float(prediction[0]), 5)}, Bottom predicted waveguide transmission: {np.round(float(prediction[1]), 5)}\\n\"\n",
        ")\n",
        "\n",
        "# Compare the result above to the best point found via Monte Carlo sampling where geometric parameters were chosen at random\n",
        "print(\"Compare these values to the ones found via Monte Carlo sampling:\")\n",
        "print(\n",
        "    f\"Top simulated MC waveguide transmission: {np.round(best_top_power_mc, 5)}, Bottom simulated MC waveguide transmission: {np.round(best_bottom_power_mc, 5)}\"\n",
        ")"
      ],
      "id": "1bd6a6c4"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Since the neural network was trained to predict output powers for\n",
        "multiple frequencies, we can compare the spectrum prediction for our\n",
        "final design to the ground truth simulation data. We also summarize the\n",
        "mean squared and mean absolute error across the spectrum."
      ],
      "id": "ac60667a-cc71-43b3-bf3d-e760757aea05"
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error summary for optimal point:\n",
            "MSE for top: 1.4599926602053263e-05\n",
            "MSE for bottom: 1.6765073833559993e-05\n",
            "MAE for top: 0.003020117746933446\n",
            "MAE for bottom: 0.003173812417666478"
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
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2GjcVqS5dugCw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\n1UTIyw2eLXsRtLoHmt8BJSqc9tJpmel8u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          }
        }
      ],
      "source": [
        "top_amp_predict = np.zeros(len(freqs))\n",
        "top_amp_real = np.zeros(len(freqs))\n",
        "bot_amp_predict = np.zeros(len(freqs))\n",
        "bot_amp_real = np.zeros(len(freqs))\n",
        "\n",
        "for idx, freq_val in enumerate(freqs):\n",
        "    feature_array = np.array(\n",
        "        (\n",
        "            optimized_wg_spacing,\n",
        "            optimized_coup_length,\n",
        "            freq_val,\n",
        "        )\n",
        "    )\n",
        "\n",
        "    prediction = make_prediction(optimized_network, *feature_array)\n",
        "\n",
        "    top_amp_predict[idx] = prediction[0]\n",
        "    top_amp_real[idx] = abs(amps_dict[\"top\"].sel(f=freq_val, method=\"nearest\").item()) ** 2\n",
        "\n",
        "    bot_amp_predict[idx] = prediction[1]\n",
        "    bot_amp_real[idx] = abs(amps_dict[\"bottom\"].sel(f=freq_val, method=\"nearest\").item()) ** 2\n",
        "\n",
        "fig, ax = plt.subplots(1, tight_layout=True, figsize=(10, 5))\n",
        "\n",
        "\n",
        "mse_top = np.mean((top_amp_predict - top_amp_real) ** 2)\n",
        "mse_bottom = np.mean((bot_amp_predict - bot_amp_real) ** 2)\n",
        "mae_top = np.mean(np.abs(top_amp_predict - top_amp_real))\n",
        "mae_bottom = np.mean(np.abs(bot_amp_predict - bot_amp_real))\n",
        "\n",
        "print(\"Error summary for optimal point:\")\n",
        "print(f\"MSE for top: {mse_top}\")\n",
        "print(f\"MSE for bottom: {mse_bottom}\")\n",
        "print(f\"MAE for top: {mae_top}\")\n",
        "print(f\"MAE for bottom: {mae_bottom}\")\n",
        "\n",
        "ax.plot(td.C_0 / freqs, top_amp_predict, label=\"Predicted\")\n",
        "ax.plot(td.C_0 / freqs, top_amp_real, label=\"Actual\")\n",
        "ax.set_xlabel(r\"Wavelength ($\\mu$m)\")\n",
        "ax.set_ylabel(\"Amp$^2$\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "id": "2cb54966"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  }
}