GeneticAlgorithmReflector.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "89f983f5",
   "metadata": {},
   "source": [
    "# Genetic algorithm optimization of a silicon on-chip reflector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c129793b",
   "metadata": {},
   "source": [
    "**Note: the cost of running the entire notebook is larger than 10 FlexCredits.**\n",
    "\n",
    "A [genetic algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm) (GA) is a search heuristic that mimics the process of natural selection. This algorithm reflects the process of natural evolution where the fittest individuals are selected for reproduction in order to produce offspring of the next generation.\n",
    "\n",
    "The steps in a GA are typically as follows:\n",
    "\n",
    "1. **Initial Population**: The process begins with a set of individuals which is called a population. Each individual is a solution to the problem you want to solve.\n",
    "\n",
    "2. **Fitness Function**: Each individual in the population has a fitness score which indicates how good it is at solving the problem.\n",
    "\n",
    "3. **Selection**: The algorithm selects individuals, often the fittest among the population, to breed a new generation. The selection can be done in various ways, such as roulette wheel selection, tournament selection, etc.\n",
    "\n",
    "4. **Crossover**: During crossover, parts of two individuals' chromosome strings are swapped to get a new offspring, which may contain some parts of both parents' strings. This simulates reproduction and biological crossover.\n",
    "\n",
    "5. **Mutation**: In some new offspring, random genes are mutated or changed to maintain diversity within the population and to avoid premature convergence.\n",
    "\n",
    "6. **New Generation**: The new generation of population is formed by the offspring. This new generation is then used in the next iteration of the algorithm.\n",
    "\n",
    "7. **Termination**: The algorithm terminates if the population has converged (does not produce offspring that are significantly different from the previous generation), or a satisfactory solution has been found, or a set number of generations have been produced.\n",
    "\n",
    "GAs have become a powerful tool for optimizing photonic components, leveraging their ability to efficiently search large and complex design spaces. In photonics, where the performance of components like waveguides, photonic crystals, and fibers can be highly sensitive to geometrical and material parameters, GAs offer a way to find optimal solutions that might be difficult to discover using traditional design methods. \n",
    "\n",
    "In this notebook, we demonstrate the optimization of a compact silicon waveguide reflector using GA, utilizing the open source Python library [pyGAD](https://pygad.readthedocs.io/en/latest/). The idea follows `Zejie Yu, Haoran Cui, and Xiankai Sun, \"Genetically optimized on-chip wideband ultracompact reflectors and Fabry–Perot cavities,\" Photon. Res. 5, B15-B19 (2017)` [DOI:10.1364/PRJ.5.000B15](https://doi.org/10.1364/PRJ.5.000B15). The design region of the reflector consists of a grid divided into 18x18 pixels. Each pixel is a square measuring 120 nm by 120 nm. \n",
    "\n",
    "<img src=\"img/optimized_reflector.png\" width=\"450\" alt=\"Schematic of the reflector\">\n",
    "\n",
    "`Tidy3D` is a powerful tool for photonic design optimization due to its fast speed and high throughput. Besides GA, we have demonstrated particle swarm optimizations of a [polarization beam splitter](https://www.flexcompute.com/tidy3d/examples/notebooks/ParticleSwarmOptimizedPBS/) and a [bullseye cavity](https://www.flexcompute.com/tidy3d/examples/notebooks/BullseyeCavityPSO/), CMA-ES optimization of an [waveguide S-bend](https://www.flexcompute.com/tidy3d/examples/notebooks/SbendCMAES/), and direct binary search optimization of an [optical switch](https://www.flexcompute.com/tidy3d/examples/notebooks/OpticalSwitchDBS/). Furthermore, we also have a growing list of gradient-based adjoint optimization examples including\n",
    "\n",
    "1. [Mode converter](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin3InverseDesign/),\n",
    "\n",
    "2. [Waveguide taper](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin5BoundaryGradients/),\n",
    "\n",
    "3. [Metalens](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin7Metalens/),\n",
    "\n",
    "4. [Waveguide bend](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin8WaveguideBend/), \n",
    "\n",
    "5. [Multiplexer](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin9WDM/),\n",
    "\n",
    "6. [Y-branch](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin10YBranchLevelSet/),\n",
    "\n",
    "7. [MZI circuit](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin11CircuitMZI/),\n",
    "\n",
    "8. [Light extractor](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin12LightExtractor/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9979b1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment the following line to install pygad if it's not installed in your environment already\n",
    "# pip install pygad \n",
    "\n",
    "import pygad\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb19411a",
   "metadata": {},
   "source": [
    "## Simulation Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21fd3302",
   "metadata": {},
   "source": [
    "For simplicity, we will use the silicon and oxide media directly from the [material library](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/material_library.html#)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e01ad5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "Si = td.material_library[\"cSi\"][\"Palik_Lossless\"]\n",
    "SiO2 = td.material_library[\"SiO2\"][\"Palik_Lossless\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb7a4663",
   "metadata": {},
   "source": [
    "The simulation wavelength range is 1450 nm to 1650 nm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "531341c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "lda0 = 1.55  # central wavelength\n",
    "freq0 = td.C_0 / lda0  # central frequency\n",
    "ldas = np.linspace(1.45, 1.65, 101)  # wavelength range\n",
    "freqs = td.C_0 / ldas  # frequency range\n",
    "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))  # width of the source frequency range"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c58826ce",
   "metadata": {},
   "source": [
    "The waveguide has a 500 nm width and 220 nm thickness. The design region consists of 18 by 18 pixels. Each pixel is 120 nm by 120 nm. Due to the symmetry, we only consider symmetric design so the total number of tunable pixels is 18*9=162."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "70707ef4",
   "metadata": {},
   "outputs": [],
   "source": [
    "w = 0.5  # width of the waveguide\n",
    "t = 0.22  # thickness of the silicon\n",
    "l = 1  # length of the waveguide in the simulation\n",
    "Px = Py = 0.12  # pixel sizes in the x and y directions\n",
    "Nx = 18  # number of pixels in the x direction\n",
    "Ny = 9  # numbre of pixels in the y direction\n",
    "buffer = 0.8  # buffer spacing\n",
    "res = 15 # overall resolution setting (steps per wavelength)\n",
    "gsx = gsy = 5 # number of grid steps per pixel in override region"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f6e1510",
   "metadata": {},
   "source": [
    "We will use an array of length 162 to represent a design. Each element corresponds to each pixel in the design region. An element value of 1 means the pixel is silicon while an element value of 0 means the pixel is void. To facilitate the optimization, we define a helper function `create_design(pixels)` that takes the pixel array and creates the [Structures](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Structure.html) of the simulation, inlcuding the waveguide and the design region. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "04b74f92",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_design(pixels):\n",
    "    geo = 0\n",
    "    for i, pixel in enumerate(pixels):\n",
    "        if pixel == 1:\n",
    "            geo += td.Box(\n",
    "                center=(l + Px / 2 + Px * (i % Nx), Py * Ny - Py / 2 - Py * (i // Nx), t / 2),\n",
    "                size=(Px, Py, t),\n",
    "            )\n",
    "            geo += td.Box(\n",
    "                center=(l + Px / 2 + Px * (i % Nx), -(Py * Ny - Py / 2 - Py * (i // Nx)), t / 2),\n",
    "                size=(Px, Py, t),\n",
    "            )\n",
    "    geo = geo + td.Box(center=(0, 0, t / 2), size=(2 * l, w, t))\n",
    "\n",
    "    design = td.Structure(geometry=geo, medium=Si)\n",
    "\n",
    "    return design"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4a87700",
   "metadata": {},
   "source": [
    "As a quick check, we create a random array and plot the created structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3fc9d4e5",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pixels = np.random.choice([0, 1], size=Nx * Ny)\n",
    "design = create_design(pixels)\n",
    "design.plot(z=t / 2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93cfd3ca",
   "metadata": {},
   "source": [
    "Furthermore, we define a `make_sim(pixels)` function to define the entire simulation given the pixel array. The simulation includes a [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html) to launch the TE0 mode at the waveguide and a [ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) next to the source to measure the reflection. To minimize data download during the optimization, we will only record the reflection at the central wavelength of 1550 nm since the objective is to maximize this value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cd22e367",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_sim(pixels):\n",
    "\n",
    "    design = create_design(pixels)\n",
    "\n",
    "    # add a mode source as excitation\n",
    "    mode_spec = td.ModeSpec(num_modes=1, target_neff=3.5)\n",
    "    mode_source = td.ModeSource(\n",
    "        center=(l / 2, 0, t / 2),\n",
    "        size=(0, 4 * w, 6 * t),\n",
    "        source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "        direction=\"+\",\n",
    "        mode_spec=mode_spec,\n",
    "        mode_index=0,\n",
    "    )\n",
    "\n",
    "    # add a mode monitor to measure transmission at the output waveguide\n",
    "    mode_monitor = td.ModeMonitor(\n",
    "        center=(l / 4, 0, t / 2),\n",
    "        size=mode_source.size,\n",
    "        freqs=[freq0],\n",
    "        mode_spec=mode_spec,\n",
    "        name=\"mode\",\n",
    "    )\n",
    "\n",
    "    # define simulation domain size\n",
    "    Lx = l + Nx * Px + buffer\n",
    "    Ly = 2 * Ny * Py + 2 * buffer\n",
    "    Lz = 10 * t\n",
    "\n",
    "    eff_inf = 1e2  # effective infinity\n",
    "\n",
    "    # define substrate structure\n",
    "    substrate = td.Structure(\n",
    "        geometry=td.Box.from_bounds(\n",
    "            rmin=(-eff_inf, -eff_inf, -eff_inf), rmax=(eff_inf, eff_inf, 0)\n",
    "        ),\n",
    "        medium=SiO2,\n",
    "    )\n",
    "\n",
    "    run_time = 5e-13  # simulation run time\n",
    "\n",
    "    # simulation box\n",
    "    sim_box = td.Box.from_bounds(rmin=(0, -Ly / 2, -Lz / 2), rmax=(Lx, Ly / 2, Lz / 2))\n",
    "\n",
    "    # mesh override structure over the pixel region to ensure grid that conforms to the pixels\n",
    "    mesh_override = td.MeshOverrideStructure(\n",
    "        geometry=td.Box.from_bounds(rmin=(l, -Py * Ny, 0), rmax=(l + Px * Nx, Py * Ny, 0)),\n",
    "        dl=(Px / gsx, Py / gsy, 1), # the z-direction dl doesn't matter as the box size is 0 along z\n",
    "    )\n",
    "\n",
    "    # define simulation\n",
    "    sim = td.Simulation(\n",
    "        center=sim_box.center,\n",
    "        size=sim_box.size,\n",
    "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=res, wavelength=lda0),\n",
    "        structures=[design, substrate],\n",
    "        sources=[mode_source],\n",
    "        monitors=[mode_monitor],\n",
    "        run_time=run_time,\n",
    "        boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    "        symmetry=(0, -1, 0),\n",
    "    )\n",
    "\n",
    "    return sim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "174fe54c",
   "metadata": {},
   "source": [
    "Again, we test it by defining a simulation with a random pixel array. From the 3D view, we confirm that the simulation setup is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cc3e6ad7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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&quot;min_steps_per_wvl&quot;: 15.0, &quot;max_scale&quot;: 1.4, &quot;dl_min&quot;: 0.0, &quot;mesher&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;GradedMesher&quot;}}, &quot;grid_z&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;AutoGrid&quot;, &quot;min_steps_per_wvl&quot;: 15.0, &quot;max_scale&quot;: 1.4, &quot;dl_min&quot;: 0.0, &quot;mesher&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;GradedMesher&quot;}}, &quot;wavelength&quot;: 1.55, &quot;override_structures&quot;: [], &quot;snapping_points&quot;: [], &quot;type&quot;: &quot;GridSpec&quot;}, &quot;version&quot;: &quot;2.7.0&quot;, &quot;lumped_elements&quot;: [], &quot;subpixel&quot;: {&quot;attrs&quot;: {}, &quot;dielectric&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;PolarizedAveraging&quot;}, &quot;metal&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;Staircasing&quot;}, &quot;pec&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;PECConformal&quot;, &quot;timestep_reduction&quot;: 0.3}, &quot;type&quot;: &quot;SubpixelSpec&quot;}, &quot;courant&quot;: 0.99, &quot;normalize_index&quot;: 0, &quot;shutoff&quot;: 1e-05, &quot;run_time&quot;: 5e-13}\" />\n",
       "    <script>\n",
       "        \n",
       "        /**\n",
       "        * Simulation Viewer Injector\n",
       "        *\n",
       "        * Monitors the document for elements being added in the form:\n",
       "        *\n",
       "        *    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"{...}\" />\n",
       "        *\n",
       "        * This script will then inject an iframe to the viewer application, and pass it the simulation data\n",
       "        * via the postMessage API on request. The script may be safely included multiple times, with only the\n",
       "        * configuration of the first started script (e.g. viewer URL) applying.\n",
       "        *\n",
       "        */\n",
       "        (function() {\n",
       "            const TARGET_CLASS = \"simulation-viewer\";\n",
       "            const ACTIVE_CLASS = \"simulation-viewer-active\";\n",
       "            const VIEWER_URL = \"https://feature-simulation-viewer.d3a9gfg7glllfq.amplifyapp.com/simulation-viewer\";\n",
       "\n",
       "            class SimulationViewerInjector {\n",
       "                constructor() {\n",
       "                    for (var node of document.getElementsByClassName(TARGET_CLASS)) {\n",
       "                        this.injectViewer(node);\n",
       "                    }\n",
       "\n",
       "                    // Monitor for newly added nodes to the DOM\n",
       "                    this.observer = new MutationObserver(this.onMutations.bind(this));\n",
       "                    this.observer.observe(document.body, {childList: true, subtree: true});\n",
       "                }\n",
       "\n",
       "                onMutations(mutations) {\n",
       "                    for (var mutation of mutations) {\n",
       "                        if (mutation.type === 'childList') {\n",
       "                            /**\n",
       "                            * Have found that adding the element does not reliably trigger the mutation observer.\n",
       "                            * It may be the case that setting content with innerHTML does not trigger.\n",
       "                            *\n",
       "                            * It seems to be sufficient to re-scan the document for un-activated viewers\n",
       "                            * whenever an event occurs, as Jupyter triggers multiple events on cell evaluation.\n",
       "                            */\n",
       "                            var viewers = document.getElementsByClassName(TARGET_CLASS);\n",
       "                            for (var node of viewers) {\n",
       "                                this.injectViewer(node);\n",
       "                            }\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                injectViewer(node) {\n",
       "                    // (re-)check that this is a valid simulation container and has not already been injected\n",
       "                    if (node.classList.contains(TARGET_CLASS) && !node.classList.contains(ACTIVE_CLASS)) {\n",
       "                        // Mark node as injected, to prevent re-runs\n",
       "                        node.classList.add(ACTIVE_CLASS);\n",
       "\n",
       "                        var uuid;\n",
       "                        if (window.crypto && window.crypto.randomUUID) {\n",
       "                            uuid = window.crypto.randomUUID();\n",
       "                        } else {\n",
       "                            uuid = \"\" + Math.random();\n",
       "                        }\n",
       "\n",
       "                        var frame = document.createElement(\"iframe\");\n",
       "                        frame.width = node.dataset.width || 800;\n",
       "                        frame.height = node.dataset.height || 800;\n",
       "                        frame.src = VIEWER_URL + \"?uuid=\" + uuid;\n",
       "\n",
       "                        var postMessageToViewer;\n",
       "                        postMessageToViewer = event => {{\n",
       "                            if(event.data.type === 'viewer' && event.data.uuid===uuid){{\n",
       "                                var simulation = JSON.parse(node.dataset.simulation);\n",
       "                                frame.contentWindow.postMessage({ type: 'jupyter', uuid, value: simulation}, '*');\n",
       "\n",
       "                                // Run once only\n",
       "                                window.removeEventListener('message', postMessageToViewer);\n",
       "                            }}\n",
       "                        }};\n",
       "                        window.addEventListener(\n",
       "                            'message',\n",
       "                            postMessageToViewer,\n",
       "                            false\n",
       "                        );\n",
       "\n",
       "                        node.appendChild(frame);\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            if (!window.simulationViewerInjector) {\n",
       "                window.simulationViewerInjector = new SimulationViewerInjector();\n",
       "            }\n",
       "        })();\n",
       "    \n",
       "    </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim = make_sim(pixels)\n",
    "sim.plot_3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ada207d",
   "metadata": {},
   "source": [
    "## Fitness Function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "940dae3a",
   "metadata": {},
   "source": [
    "An important step in any optimization is to define the objective function, which is often called the fitness function in the context of GA. Here we define the fitness function according to the convention in [pyGAD](https://pygad.readthedocs.io/en/latest/). By default, the evaluation of the fitness function in each generation of the GA is done sequentially. To harness the concurrent run in Tidy3D, we will submit all simulations in each generation as a batch. In `fitness_function`, the argument `pixels_array` is a 2D array, where each row is a pixel array of size 162. The number of columns is the number of solutions in the population, which we will define later. \n",
    "\n",
    "In addition, we define the `on_generation` function, which is called after each generation, to provide a real time update on the optimization. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e1340e47",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fitness_function(ga_instance, pixels_array, solution_indices):\n",
    "    sims = {}\n",
    "    R = []\n",
    "\n",
    "    # define all simulations in the generation\n",
    "    for i in range(pixels_array.shape[0]):\n",
    "        pixels = pixels_array[i]\n",
    "        sim = make_sim(pixels)\n",
    "        sims[f\"sim_{i}\"] = sim\n",
    "\n",
    "    # define and submit a batch\n",
    "    batch = web.Batch(simulations=sims, verbose=False)\n",
    "    batch_results = batch.run(path_dir=\"data\")\n",
    "\n",
    "    # extract reflection results from all simulations\n",
    "    for i in range(pixels_array.shape[0]):\n",
    "        sim_data = batch_results[f\"sim_{i}\"]\n",
    "        R.append(abs(sim_data[\"mode\"].amps.sel(direction=\"-\").squeeze(drop=True).values) ** 2)\n",
    "    return R\n",
    "\n",
    "\n",
    "# function to be called in each generation\n",
    "def on_generation(ga_instance):\n",
    "    best_fitness = ga_instance.best_solution(ga_instance.last_generation_fitness)[1]\n",
    "    print(f\"Generation {ga_instance.generations_completed}: Best Fitness = {best_fitness:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a25227f1",
   "metadata": {},
   "source": [
    "## GA Optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e62a2da5",
   "metadata": {},
   "source": [
    "Now we are ready to create a GA optimizer. First we need to define some hyper-parameters. In this particular example, we will use 30 solutions per population and run the optimization for a total of 25 generations. There are a number of parameters we can tune to change the selection, crossover, and mutation processes. We only change a few and leave the most to the default settings. Users are encouraged to explore these settings and fine tune them for better results. For reproducibility, we also set a fixed random seed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f20c82cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/momchil/miniconda3/envs/tidy3d-test/lib/python3.10/site-packages/pygad/pygad.py:1139: UserWarning: The 'delay_after_gen' parameter is deprecated starting from PyGAD 3.3.0. To delay or pause the evolution after each generation, assign a callback function/method to the 'on_generation' parameter to adds some time delay.\n",
      "  warnings.warn(\"The 'delay_after_gen' parameter is deprecated starting from PyGAD 3.3.0. To delay or pause the evolution after each generation, assign a callback function/method to the 'on_generation' parameter to adds some time delay.\")\n"
     ]
    }
   ],
   "source": [
    "sol_per_pop = 30  # number of solutions in the population\n",
    "num_generations = 25  # number of generation\n",
    "\n",
    "num_parents_mating = 10  # number of mating parents\n",
    "parent_selection_type = \"sss\"  # parent selection rule\n",
    "\n",
    "crossover_type = \"single_point\"  # crossover rule\n",
    "crossover_probability = 0.7  # cross over probability\n",
    "\n",
    "mutation_type = \"inversion\"  # mutation rule\n",
    "\n",
    "# define the optimizer\n",
    "ga_instance = pygad.GA(\n",
    "    num_generations=num_generations,\n",
    "    num_parents_mating=num_parents_mating,\n",
    "    fitness_func=fitness_function,\n",
    "    parent_selection_type=parent_selection_type,\n",
    "    mutation_type=mutation_type,\n",
    "    crossover_type=crossover_type,\n",
    "    crossover_probability=crossover_probability,\n",
    "    sol_per_pop=sol_per_pop,\n",
    "    num_genes=Nx * Ny,\n",
    "    init_range_low=0,\n",
    "    init_range_high=2,\n",
    "    gene_type=int,\n",
    "    fitness_batch_size=sol_per_pop,\n",
    "    on_generation=on_generation,\n",
    "    random_seed=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b67c427",
   "metadata": {},
   "source": [
    "## Optimization and Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce8d115d",
   "metadata": {},
   "source": [
    "With the optimizer defined, we are ready to start the optimization. With the defined `on_generation` function, we will print out the best fitness in each generation. We should see a steady increase of the fitness function as the population evolves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "71f251d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 1: Best Fitness = 0.641\n",
      "Generation 2: Best Fitness = 0.755\n",
      "Generation 3: Best Fitness = 0.755\n",
      "Generation 4: Best Fitness = 0.755\n",
      "Generation 5: Best Fitness = 0.760\n",
      "Generation 6: Best Fitness = 0.832\n",
      "Generation 7: Best Fitness = 0.850\n",
      "Generation 8: Best Fitness = 0.918\n",
      "Generation 9: Best Fitness = 0.918\n",
      "Generation 10: Best Fitness = 0.918\n",
      "Generation 11: Best Fitness = 0.918\n",
      "Generation 12: Best Fitness = 0.931\n",
      "Generation 13: Best Fitness = 0.931\n",
      "Generation 14: Best Fitness = 0.931\n",
      "Generation 15: Best Fitness = 0.931\n",
      "Generation 16: Best Fitness = 0.938\n",
      "Generation 17: Best Fitness = 0.938\n",
      "Generation 18: Best Fitness = 0.938\n",
      "Generation 19: Best Fitness = 0.938\n",
      "Generation 20: Best Fitness = 0.938\n",
      "Generation 21: Best Fitness = 0.938\n",
      "Generation 22: Best Fitness = 0.938\n",
      "Generation 23: Best Fitness = 0.939\n",
      "Generation 24: Best Fitness = 0.939\n",
      "Generation 25: Best Fitness = 0.939\n"
     ]
    }
   ],
   "source": [
    "ga_instance.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "296adb3a",
   "metadata": {},
   "source": [
    "After the optimization is complete, we see that we achieved the best fitness (reflection) of 0.928. We can plot the best fitness as a function of generation to better visualize the improvement. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8dfdc377",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitness value of the best solution = 0.939\n"
     ]
    }
   ],
   "source": [
    "# Plotting the best fitness over generations\n",
    "best_fitness = ga_instance.best_solutions_fitness\n",
    "generations = range(len(best_fitness))\n",
    "\n",
    "plt.plot(generations, best_fitness)\n",
    "plt.title(\"Best Fitness over Generations\")\n",
    "plt.xlabel(\"Generation\")\n",
    "plt.ylabel(\"Best Fitness\")\n",
    "plt.show()\n",
    "\n",
    "pixels_final, solution_fitness, solution_idx = ga_instance.best_solution()\n",
    "print(f\"Fitness value of the best solution = {solution_fitness:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd16ffaf",
   "metadata": {},
   "source": [
    "Now that we have an optimized design, we will grab the best pixel array and re-simulate the design with a broadband [ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) and a [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) at the central wavelength. This final simulation can be defined by copying the original simulation and updating the monitors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2024c056",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:51:51 CEST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'final design'</span> with task_id                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-498121ce-d6bd-439f-8c07-d0c04b8414a2'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>. \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:51:51 CEST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'final design'\u001b[0m with task_id                          \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'fdve-498121ce-d6bd-439f-8c07-d0c04b8414a2'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>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-498121ce-d6bd-439f-8c07-d0c04b8414a2\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-498121ce-d6</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-498121ce-d6bd-439f-8c07-d0c04b8414a2\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">bd-439f-8c07-d0c04b8414a2'</span></a>.                                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                         \n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=700150;https://tidy3d.simulation.cloud/workbench?taskId=fdve-498121ce-d6bd-439f-8c07-d0c04b8414a2\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=887653;https://tidy3d.simulation.cloud/workbench?taskId=fdve-498121ce-d6bd-439f-8c07-d0c04b8414a2\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=700150;https://tidy3d.simulation.cloud/workbench?taskId=fdve-498121ce-d6bd-439f-8c07-d0c04b8414a2\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=556254;https://tidy3d.simulation.cloud/workbench?taskId=fdve-498121ce-d6bd-439f-8c07-d0c04b8414a2\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=700150;https://tidy3d.simulation.cloud/workbench?taskId=fdve-498121ce-d6bd-439f-8c07-d0c04b8414a2\u001b\\\u001b[32m-498121ce-d6\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=700150;https://tidy3d.simulation.cloud/workbench?taskId=fdve-498121ce-d6bd-439f-8c07-d0c04b8414a2\u001b\\\u001b[32mbd-439f-8c07-d0c04b8414a2'\u001b[0m\u001b]8;;\u001b\\.                                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "19d778e92a6543a5ae0050fac7bb0957",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:51:55 CEST </span>status = queued                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:51:55 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                   \n"
      ]
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     "metadata": {},
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    },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or             \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Terminating the Python script will not stop the job running on the\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>cloud.                                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or             \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.     \n",
       "\u001b[2;36m              \u001b[0mTerminating the Python script will not stop the job running on the\n",
       "\u001b[2;36m              \u001b[0mcloud.                                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:51:58 CEST </span>status = preprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:51:58 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:52:00 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to   \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>get the billed FlexCredit cost after a simulation run.            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:52:00 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to   \n",
       "\u001b[2;36m              \u001b[0mget the billed FlexCredit cost after a simulation run.            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>starting up solver                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:52:01 CEST </span>running solver                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:52:01 CEST\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:52:09 CEST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">40</span>%, exiting.                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:52:09 CEST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m40\u001b[0m%, exiting.                           \n"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>status = postprocess                                              \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                              \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "Output()"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:52:12 CEST </span>status = success                                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:52:12 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \n"
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "text/plain": []
     },
     "metadata": {},
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>View simulation result at                                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-498121ce-d6bd-439f-8c07-d0c04b8414a2\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-498121ce-d6</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-498121ce-d6bd-439f-8c07-d0c04b8414a2\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">bd-439f-8c07-d0c04b8414a2'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                         \n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=386553;https://tidy3d.simulation.cloud/workbench?taskId=fdve-498121ce-d6bd-439f-8c07-d0c04b8414a2\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=860801;https://tidy3d.simulation.cloud/workbench?taskId=fdve-498121ce-d6bd-439f-8c07-d0c04b8414a2\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=386553;https://tidy3d.simulation.cloud/workbench?taskId=fdve-498121ce-d6bd-439f-8c07-d0c04b8414a2\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=97253;https://tidy3d.simulation.cloud/workbench?taskId=fdve-498121ce-d6bd-439f-8c07-d0c04b8414a2\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=386553;https://tidy3d.simulation.cloud/workbench?taskId=fdve-498121ce-d6bd-439f-8c07-d0c04b8414a2\u001b\\\u001b[4;34m-498121ce-d6\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=386553;https://tidy3d.simulation.cloud/workbench?taskId=fdve-498121ce-d6bd-439f-8c07-d0c04b8414a2\u001b\\\u001b[4;34mbd-439f-8c07-d0c04b8414a2'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                       \n"
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       "Output()"
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
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     },
     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:52:15 CEST </span>loading simulation from simulation_data.hdf5                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:52:15 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                      \n"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "sim_final = make_sim(pixels_final)\n",
    "\n",
    "# update mode monitor's recording frequencies\n",
    "mode_monitor = sim.monitors[0].copy(update={\"freqs\": freqs})\n",
    "\n",
    "# add a field monitor to visualize field distribution\n",
    "field_monitor = td.FieldMonitor(\n",
    "    center=(0, 0, t / 2), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\"\n",
    ")\n",
    "\n",
    "# update simulation to use the new monitors\n",
    "sim_final = sim_final.copy(update={\"monitors\": [mode_monitor, field_monitor]})\n",
    "\n",
    "# submit the simulation to the server\n",
    "sim_data_final = web.run(simulation=sim_final, task_name=\"final design\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "645d4052",
   "metadata": {},
   "source": [
    "After the simulation, we visualize the electric field norm at the central wavelength. As expected, a strong reflection is observed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0eff6f90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EXbZt4+OPP0ZNTU2PKmkSQggpPkIItLS0oL6+PmdBRDIwGFSGxscff9xvil8RQgjJzebNmzF27NhSDyONM844Hh0dCTzyyHOlHsqAYFAZGjU1Nep/JgAqGoQQ0j8RAGzP7+z+w3PPPYdHH/03ysoiWLlyJU466aRSD6nfM6gygzY3N6Ourg5ABDQ0CCGkvyIAWGhqakJtbW2pB+MghMDxJ0zFZz4zGRUVcTz88DN48YXXuRSfBy5+EUIIIQWwYsUKbHh1ExYs+AouvvjL+PCDbU6xPZIdGhqEEEJIHoQQ+MlPLsYlP/4a6uqqUVVVgZ9c/k1cfvl8WJZV6uH1a2hoEEIIIXn4y1/+go8//gQXXTTb2fY//3MKWls78Mc//rF0AxsA0NAghBBCcmBZFq64YgEuv/ybqKwsd7bH4zEsXHg2rrrqEiQSiRKOsH9DQ4MQQgjJwV13XY5EIoVzz/vPtO/O+sZnUV4ew+9+9+MSjGxgQEODEEIIyUJXVxeuvXY5Fi48G7FYNO37SCSCa645B9dffzfa29tLMML+Dw0NQgghJAu/+e0lqK6uwJlfOzHrPv/15WNRXz8cv77l+304soEDDQ1CCCEkA21tbbjh+rvx05+eh0gkknU/wzDw0+v+B4tv/COampr6cIQDAxoahBBCSAaW/Go+xu89Cqd+6ei8+372szMw+bD9sPjnF/fByAYWzAxKCCGkn1H6zKC7d+/GvvuOx/1/uhonnTS9oGOef/41fP7kH+K99z7AyJEjizzCgQMVDUIIISTAjYvnYdr0/yjYyACAmTMPwQknTMP1119UxJENPGhoEEIIIR4aGhpw6y1/wU9/el63j732p+fit7/9Gz788MNuH7tq1SqceuqpqK+vh2EYeOihh3zfCyFw1VVXYcyYMaioqMCsWbPw9ttv+/bZtWsXzjrrLNTW1mLIkCE477zz0Nra2u2xhAkNDUIIIcTDddddhFmzZuDIIyd2+9jJk/fD7NnH4JprLuz2sW1tbTjssMNw2223Zfx+8eLF+PWvf42lS5fixRdfRFVVFU4++WR0dnY6+5x11ll47bXXsGLFCjzyyCNYtWoVLrjggm6PJUzoo0EIIaSfUTofjffffx8HH3wgXnhxKSZNmtCjNt55ZwsOm3wOXn75VRx44IE9asMwDDz44IOYPXs2AKlm1NfX4wc/+AF++MMfAgCampowatQoLF++HGeeeSbeeOMNTJw4EatXr8aMGTMAAI8++ij+8z//Ex999BHq6+t7NJbeQkWDEEIIUVx99YU4/fTjemxkAMD++38K3/rWybjyyu+ENq5NmzahoaEBs2bNcrbV1dXhyCOPxPPPPw8AeP755zFkyBDHyACAWbNmwTRNvPjii6GNpbuUlaxnQgghpB/R0NCAe+5Zgdff+N9et3XFld/CAfufhTfffDNNSYjH44jH490eGwCMGjXKt33UqFHOdw0NDWnRLmVlZRg2bJizTymgokEIIYQAaGlpQSRiYt99e7/E8KlPjUB1dQWWLFmCuro632vRokUhjHbgQEWDEEII8WLbITUkcO6552Lx4sW+rd1VMwBg9OjRAIBt27ZhzJgxzvZt27ZhypQpzj7bt2/3HZdKpbBr1y7n+FJARYMQQgjxYtvhvADEYjHU1tb6Xj0xNCZMmIDRo0dj5cqVzrbm5ma8+OKLmDlzJgBg5syZaGxsxJo1a5x9nnjiCdi2jSOPPLKXF6XnUNEghBBC+gGtra145513nM+bNm3C+vXrMWzYMIwfPx7z58/HddddhwMOOAATJkzAlVdeifr6eicy5eCDD8bnP/95nH/++Vi6dCmSySTmzZuHM888s2QRJwANDUIIIcRPaEsn3eOll17CCSec4HxesGABAGDOnDlYvnw5LrnkErS1teGCCy5AY2MjjjnmGDz66KMoLy93jrnnnnswb948nHTSSTBNE6effjp+/etf9/m5eGEeDUIIIf2M0uTRePvtt3HooYegbfffQmlv5NgzsHLlKseHYrBCHw1CCCGEFA0unRBCCCFeSrR0sqdCQ4MQQgjxEpahMWgcE3LDpRNCCCGEFA0qGoQQQogXLp2ECg0NQgghxAsNjVDh0gkhhBBCigYVDUIIIcRLiLVOCA0NQgghxIchuHQSJjQ0CCGEEC/00QgV+mgQQgghpGhQ0SCEEEK82PStCBMaGoQQQogXZgYNFS6dEEIIIaRoUNEghBBCvNAZNFRoaBBCCCFeGN4aKlw6IYQQQkjRoKJBCCGEaASYGTRkaGgQQgghDoLhrSFDQ4MQQgjxQmfQUKGPBiGEEEKKBhUNQgghxAsVjVAZMIrGokWLcPjhh6OmpgYjR47E7NmzsXHjxlIPixBCyB6GYduhvOgLKhkwhsbTTz+NuXPn4oUXXsCKFSuQTCbxuc99Dm1tbaUeGiGEEEKyMGCWTh599FHf5+XLl2PkyJFYs2YNjj322BKNihBCyB6HoBQRJgPG0AjS1NQEABg2bFiJR0IIIWSPgj4aoTIgDQ3btjF//nwcffTRmDRpUtb9urq60NXV5Xxubm7ui+ERQgghRDFgfDS8zJ07Fxs2bMB9992Xc79Fixahrq7OeY0bN66PRkgIIWTAYtvhvAiAAWhozJs3D4888giefPJJjB07Nue+l112GZqampzX5s2b+2iUhBBCBiy2COfFsBMAA2jpRAiBiy++GA8++CCeeuopTJgwIe8x8Xgc8Xi8D0ZHCCFkj4FqRKgMGENj7ty5uPfee/Hwww+jpqYGDQ0NAIC6ujpUVFSUeHSEEEIIycSAMTTuuOMOAMDxxx/v275s2TKcffbZfT8gQggheyZUNEJlwBgagnHNhBBC+gL+vQmVAecMSgghhJCBw4BRNAghhJA+IaylEwojAGhoEEIIIX5sWghhwqUTQgghhBQNKhqEEEKIF0adhAoNDUIIIcQLDY1QoaFBCCGEeAnNR4O+HgB9NAghhBBSRKhoEEIIIV4El07ChIYGIYQQohFgeGvI0NDYgzBglHoIAw7BNVRCCCkqNDQIIYQQL8wMGio0NAghhBAvXDoJFUadEEIIIaRoUNEghBBCHAQTdoUMDY0+go6a/ZNC7wudRkmYhP37gM9nyHDpJFRoaBBCCCFemEcjVOijQQghhJCiQUWDEEII8cKlk1ChoUEIIYR4oaERKjQ0egEdPAcP2e41nfAGB/39Zz3T+Phskv4CDQ1CCCHES2jhrTT2ABoahBBCiJ+wlk5oZwBg1AkhhBBCiggVDUIIIcQLnUFDhYZGD+jvjmGk7+gPTqJhPI+D1XFwT/5ZpoNoL2AK8lChoUEIIYR4ETTIwoQ+GoQQQggpGlQ0CCGEEC/00QgVKhqEEEKIF1uE8+oGlmXhyiuvxIQJE1BRUYH99tsPP/3pTyE8yzhCCFx11VUYM2YMKioqMGvWLLz99tthn33o0NDoBob6R0g+jBz/wmwrrOexGG32B/ri2hESBjfeeCPuuOMO3HrrrXjjjTdw4403YvHixbjlllucfRYvXoxf//rXWLp0KV588UVUVVXh5JNPRmdnZwlHnh8unRBCCCFeShB18txzz+G0007DKaecAgDYZ5998Mc//hH//ve/AUg1Y8mSJbjiiitw2mmnAQD+93//F6NGjcJDDz2EM888s8/HXChUNAghhBAvYS2ddGP15KijjsLKlSvx1ltvAQBefvllPPPMM/jCF74AANi0aRMaGhowa9Ys55i6ujoceeSReP7550M9/bChokEIIYQUiUQigebmZt+2eDyOeDzu23bppZeiubkZBx10ECKRCCzLwvXXX4+zzjoLANDQ0AAAGDVqlO+4UaNGOd/1V6hoEEIIIRqBUJ1B77zzTtTV1fleixYtSuv2T3/6E+655x7ce++9WLt2Le666y784he/wF133dXHFyB8qGgQ0sfQCbEweJ1IyQgtvFXg3HPPxeLFi31bg2oGAPzoRz/CpZde6vhaHHroofjggw+waNEizJkzB6NHjwYAbNu2DWPGjHGO27ZtG6ZMmRLSeIsDFQ1CCCHEg7BFKC8AiMViqK2t9b0yGRrt7e0wTf+f5EgkAls5pk6YMAGjR4/GypUrne+bm5vx4osvYubMmUW8Gr2HigYhhBBSYk499VRcf/31GD9+PA455BCsW7cON910E84991wAgGEYmD9/Pq677joccMABmDBhAq688krU19dj9uzZpR18HmhoEEIIIV5KUOvklltuwZVXXomLLroI27dvR319Pb797W/jqquucva55JJL0NbWhgsuuACNjY045phj8Oijj6K8vLzPx9sdDCEGT/WY5uZm1NXVAYgAPUycRMhgoD9U+eTPW/j0h/taGAKAhaamJtTW1vZZr2+//TYOnXgw2m+9IJT2RvzgTqx85oV+70NRbKhoFAB/4WXA4DUpiDDt+DCueQnmFXvEz0/Yz/vgmd8RQkODEEII8cGiaqFCQ4MQQgjxEpahQXsFAMNbCSGEEFJEqGgQQgghXrh0Eio0NEg6dPQMj0KuZdAxMO8x3REiA1Uog20XwSmx186f3Xn+ujv+/vJs92YcdCQtOoKGRqjQ0CCEEEK80NAIFfpoEEIIIaRoUNEghBBCvFDRCBUaGoQQQogXGhqhQkNjMNNfHOOykm1lz86yvRT0ZvVRnUfW++BvuydOliJtfIVdu1CyeYb2fGW6xlmuXbcdawcgmc6JDqKkH0NDgxBCCPFCwy1UaGgQQgghHkRYointFQA0NAYn/VZOzr0MoeX8ni4HhEvmseZccjD8x7iTpuD4TX9bRr7lmexLC4b6jdnjqp15l3U8Y88yCzQQyTOGzOfvHp8+BhHsnzNQQvotNDQIIYQQB0Fn0JChoUEIIYR4oaERKjQ0BhP9dskkM2mSuV5CEFbfDyYPWcfqQy2JOOehlzUChxoR3/5Z+8y5pGKqtlNy3yx95Wg84xjSzzPq/Fcgqf6je1HHGmXqWL3MEVzOybAM4+nLMGOq2ZT7XfB89Hh13wU/6yFEDZE9jtB8NAgAZgYlhBBCSBGhojEYGChKhlYqDO30Gfw+uCWMaUc3c3U4Y+juWN0ZvXDUhsz6glDXwd3fv5/jFNsNdbfbzqCOMuC/Dm4rSp3JOfXTCkYqMIbC7puzvz7ep2T11Ak0zLlVoW31wfSYuTXCQyDEpRPeA4CGBulHTJs2Vf4nR6TD2rXr+mw8uShkrEHcKBIDa9euL7iPbIZGIYRxvZxz9eFGxuTrI/080v/w5m9jCoTPqEk3NPrLs0H2ALh0EioDaulk1apVOPXUU1FfXw/DMPDQQw+Vekj9G8Po/2qGEM5LQNr/QogsL0ut09vw/yYws7yy0Z19AsN1/hUyVv/LFinYIgVhJyHshFRwhOW7BhACQqQg1L62SDnH6/319wW9YEHASr9mur98BMbmvtLHktZm4Hzc8wierwgYEYEh2AkIO+m5XpZ7nfV9QDbVptBnghBSLAbUT15bWxsOO+ww3HbbbaUeCiGEkD0UYYtQXkQyoJZOvvCFL+ALX/hCqYfRf+jvakU30WeT98ez4MiCYDRDT+xqfxtOJES2oeVoKfS7NcDuf9poDfdaGobpJPbSOAqFVuYKUWCyRMu4Y/CPoseJzLpFjlotpH/CzKChMqAMje7S1dWFrq4u53Nzc3MJR0MIIYQMPgbU0kl3WbRoEerq6pzXuHHjSj2k7qNnc5le/ZJs/hJZ1spDPB9D/QujjeA/Z9x5xmogt9pRyAQn73k4Yyj0WofwY17IM5hnn2znb5pxmGYFDDMmc2YYJmCYBd1Pw/dyr0n2+xg8vrB/ewp78rmFigjpRQDs4YbGZZddhqamJue1efPmUg+JEEJIP4c+GuGyRy+dxONxxOPxUg+je/RbpSIf3bVZM4R/GvpxDIRBqrV54Xwr/2foDYY/P0VPFljzZfY0Alk9DVM/V5n7MjL4E/jCOwvIe1Dok1DYrDRwbYKZNLM2nkdt8n2fuRicGSmXXdk6n4bOExJRTcRgGnFYdocaU6bMoabvgpjO/dH3pQxmpAY6v4kbxRK4P/0m5SP9Nvo1vBWhskcbGmRgofMgpOVbcAwNgWnTpjn7O393PH/o+iqXgpsHI1tir/Tt3U1Ytaewdu3LAGSYKpBuaEQiNZg6bTJsW/lTqcRcXkfN4H0Npmk3PKnQCSH9iwFlaLS2tuKdd95xPm/atAnr16/HsGHDMH78+BKOrBcMWAVD0z0lI7fvQbAmhsqAaag/TEIaF66yof+jFA5Y8NVBKeDa5q9R4v9DFlF1N9yu9ezbzrjd35eebauaHcGZuxG+AeJkEQ1twTh4fdwxu0qGuo+Ba+bcGWVwaCy7A7bdBSH824P9mGaFOwojrvqQfZmmfE9Z7WoPWXclTbAJPhIFKhyZS9VTGt9T6TfC1x7CgDI0XnrpJZxwwgnO5wULFgAA5syZg+XLl5doVIQQQvYoaGiEyoAyNI4//niIPszfX1SP7AGrZISoYAABBaF3P906O6TTZ7creXoJVFpV2J4KornQx/mVDe2ToMercnM4tUC652OSrsb0wFcja+OFXTMDETf/RUDJ0Ncg7Zo510ZpHCKzmuG2a8Awoo6KAQBmQFmy7eB90Usr+c4zw/NMlYOQUBlQhgYhhBBSbLh0Ei40NPqafq1khBft3D0lIx96XyvnXr2iwPFohSKzYuFpTkdcGOk/YoahfBZUlIVWMgzHZ6Ob9yFnVEiw7QKiTAA411zXJnG2+7OsGmaZzH0B+BSHzG0HHTb1NUzK/weq9+r3iFmNskgdomWVaU1qJcNKU0SK+KyQPR9mBg2VPTqPBiGEEEJKCxWNQUkJ7MtuKRiBQz3RDDLKQK/t+/NraJw8Gz3yscnsmxEkXcnw52/I5UqkfRjcNlRfjgqSJVdHd/yT9LURwZl9lracuiMBfwszrvJg+OuQuIcZzvm4vhjBXBaBvh3keUYi1YhEKmBZHapN2XfElApGebQOlbHh6Eq1OEdaOlRW9RnsOzvZo2bck+q53wbZM+hDV8BBAQ0NQgghxAPtynChoTGoKL6SUWheitzo7JXpx65d9yog/BkmvQm9wsc/kw8qHcFZu/Q3AKZNO8w3tkAjWLt2nUc9cLNbAq6vwysvv+3rQ8/a3T4mqwazR5IExxEczdq1a2XfASXDVGpKxCzHKy+/5/ia6EgZPaaU1e70oSNHsmflzKyimGYca9ascdSJskiNepeKhmmUYcrUQ9CVbPQ0lUhrc+3adc74CekVNDRChT4ahBBCCCkaNP8HBf3BnizM9wHwTlKDM2KtZPT9Amq2cbvRJ+ozAlEb3myfeichACEgDO1L4sfJMho4Ta0quC32xu9FvTtqivSz0EqGaehIkjL/fqrP9CgPb1pwhc7smsdvwraTsO0uRCLVaovqQ/lhJO1WJDz+Gd6+/PVj+sNz3htY/6S/ENbSCV09JDQ0CCGEEA90Bg0XGhp9Ta+yVRZKz6uY9pR8ER6uIhAJfPa2oTJJQhbXctb6s00v0qqdZsuU6a8qmksRyTZOIzDbTBu/6sru1aw64uvL7cPfZsRRGfSPr47y8O7nVxHSzjgQZeIoGoHaIdGyKn87aXlEUjB1FE3g+rs+NIE8KIG+zUglyiK1iJZV+47XioYtUhmiZ/S4vefek2sfrorAbKGEpENDgxBCCPFi9+fEigMPGhqlIhRlI98MLjAL70FeiV7PxpyMlDrPgY6cyKBopOWXyDaWLDNOp4iI8L8b2TNuOjk3ujFO2ZVf+dBKgBMpYZbL43Xpc7hVaN3x6XLoGrmvpaJKsqkrrt9ExNcc4Kn34RZUUR+ViqB9L5QvRjQ6XLWROeNpMtWmmsl8zU0z5qgflq330b4auhy8rsLrH0NEVWOtiA1DdfkYJ09GyupUY/DmxrCQOZeHq2wU5v9DnweSB8Hw1rAZ6N5ThBBCCOnHUNEYkIRUQVXPAHOY74X4NeTCOU4kMW3a1Jz7rl37sq/PtDbydiYwbdrUtL2DZ7923XrPJ7Pb48yE198AANatfQUAYAtX0bBt7XuSwrRp09xxpalaeZQoI1iZ1Ap8hiNzrF2z1jcunaMiqj7HyuRnS41N5+rQNUQS1m43F0cGTKMCth30n/B/XuPk6lB5MyJVaiyVaixVmDJlIjqTu9TQtZLhZk4VQjj+GBK/alJ0+iRbKKfR/QUhuHQSJjQ0CCGEEA+h2ZD0AwZAQ6N7BH0AwqBgX40QVrmyrWEHt3fjpyxfJlDH90HljshNvuqlxZjxZcheWcj9NTJHWFiW9GnQlVm97bs1W0zHZ0KSRcHI80zYdtLXh8//QPl/uHVDpD9EpmqymbYLw+tvkbnOCQAIJN3/O5lLg7VO/L4bOrNpymoHACRTTehMjpDPiH9U6hwyXYdAVI0QEMLO66eRL7MrIQB9NMKGPhqEEEIIKRpUNHpCMZSNrOS3BfNGk2TJx5CjQZdCTftsfRie/2SamXqvoZteMze9vu45roNhy3H2NCpI53vQfgYZ84VE3Noivi8CeT/y9J2mHKTlmgDKyoYCAKJlum5IVLWtIlicDKYqckT4I1yEsFWWUF0rxa9SuP4UuZQMfX7+iBbh8V+RPhj6fIPXxnL2ccn0XNoAohm2p7fVbQr8OejvOTP6+/j6C+H5aNDXA6ChQQghhPgQzKMRKjQ0CiHbzNK7PTDLzjdz6ElOi4KPTYsmyVZnJNvM0deZfHPyTOj6HK6/gb8P/4xZI0QcplmZoS+3MyfTZWD8adcyV5SGEYGZ5fqkjd3Tl+HkpogiEqnyjCv3DNjdT19rrYRkUE282zL4aKQrGJEs27UyoBUBrZ6497MsUgsAqIxJX5Gk8hlJWR2qzcLUrYgZQ8SMwnaUGt+bJzupey3c4SrVRPmH5PeH8J+v4fx6knk3hJH7XkRMN4tpsMosIaR00NAghBBCPLDWSbjQ0AiLgN9Gb/NPFJTVM9+s1AgqGdnrjPj79EYS6P9lmxkGqn8G6nDorJFlZjne2LDFmWnqPA22Z41f51fQUQk6S6erpmTrW59XGdavf91VVZwcGYHZrWcW7mbblJkyyyKVePXl9zxVTTNHKeh324m0SPm26/P3z6i9CpPbbtA3IZuCEWTdutdkq3arOs71T4hHRwAApkydCMBVMtIVGr+qZQT8RCJmDAJ2BmXA/bxW5QzRBLOM6mubVsMmC1rJ0PfVNKNYv25D5n099ycWrUUy1a4GoSv9UtEg3cVgHo2QYdQJIYQQQooGFQ1CCCHEA51Bw4WGRkEUIvxoWT7zEkpP+ypsySRzCXNXovYX/spWflyfg19s1ssY2kHTL72nF/rSCaJivvcyVcRLYxlS2ra8RcdEpv4BIRKBLelLJnIMsg9T9Rm8Du5yh3veQZlfE1HjDYaB2s7Sj1pesn1vcBwzRTBNeDr5wlezPXduGGkysN393JXcAQBIJD/xtRVMBuYOIXNSLr3ElS2sVdgp2CpJmaGuu5uYSy95JX2fPa37xpYN207CshPOsyTHnXlJy73n2fbro5TlZEBDH41woaFBCCGEeKCPRrjQ0MhAthTQudUJNYtDMOlUoc5ogRmn02n2ZFv5wlX18E3HMdM/0zcCbTqzVtvd7gRtepw2/ePyqwrZlAztFBps2Buy6MxYVblxy3He1GpK5hTVWsmIRMr97QRwS597kkwF7o8eZyTgzKoxHNXFryZkw3uPss+Sgk6gee6Pg18R8N2jNBXIGZB68983N5Q0c2p1Vwnwfxbqn/ygVQX1MYcDaeahBfr2HC+E7XMcDnoGB1WrTG0QQkoDDQ1CCCHEgx2SjwaXYCQ0NArAVThyXS41m9MfA/4RhXeWWcHwqxeZw1SDM2BbPeRmQGVw/Sn8Y9OzVAue2bBSF4QTrulf4zaDYawRmZypzOlTKRqGPzW0bSQzngPgztzd0FG9jyoUBsN3rJ6V65LnbrEx+M4zosaoi3nJxvX5yLbiqg3Hn8MOKDl6/IHwyWwz6r4gPXmaF70tmLzNr2To+5f+DJm+o90nXIeeGohEqgF4zz1D2XogPY13gUnDDMOEYZg5r23WtOf9loEyzsEJDYRwYXgrIYQQQooGFY2c+AtOBdNrZ55hqdlc1nXqzGTzt8g04zecWWke9UN17fhLKLUhqlJCB9u2VdRC0nKVjqC6AOgoEf/MuMyU/hFR5SdRptKNxwzdl189sXRf8Pow+PvSaolQ6gdEcObuj+xIpFowdeqkDJESasTq/q1+aUuGPqWK05lswpSpkxxVxL1//inOuiwJpDTuM+JVgII+FTplt94n249jNhnX9H1t9GAWluar4aT/1tE2UaxXicEyIWAjHh0CAEimZOIwy+7w7yP8SlTWttJSmAe/z/WzFFCWsigGBfmN7CEF1EjPoTNouNDQIIQQQjzQ0AgXGho5cH0B5EzYjWbwzyQlWbzdA89rcKYVXBN3tqcVKzPTvgseG0ybrTUIrWSUlw0B4KoMQb+JlCrSZRqtaeOxbTnjtwMnpK9JVPURM+V6fbkpi3pFDalsRIX01dDnnzRlX12eMejvUrZfNcmGPk+tgKSSLUimmtJ3VDNlN4LG68OhfTHkLLwz0YFEcozn+2xjyJwDIrfa5T3WG1Gh2wjO6INRT/q5y5wzIqdgIILRUH60kmEGfiXoexIrq/Jt16pRMqBeyK60uuD31RDByCrfz4jnejj+Tvr+JmHbiYILwfkZJHkz6FSwR7Blyxb8+Mc/xj/+8Q+0t7dj//33x7JlyzBjxgwAUvVbuHAhfve736GxsRFHH3007rjjDhxwwAElHnlu6KNBCCGEKAQAWxihvLrD7t27cfTRRyMajeIf//gHXn/9dfzyl7/E0KFDnX0WL16MX//611i6dClefPFFVFVV4eSTT0ZnZ2fIVyFcqGjkwnALS8l36X+QlhMC+eP38xV3yqdOZNrXXV/3Z68MzqrjERlJUWUOBwBUQKoNEQQUDUMqCR2RdFVAF+VKoUn14Vd54rosueqjEnUAgHJbKhpVQioetpq1dxjyB6PFdMdgmVJpSJhtgd6VL0Ygs6lpBDOAJmEYEV/pebmDTtohfPt7NjlFuJyNRqBkuRN1I3yf07OPqi6dgm5e3xQ9jhQg3PwThpN7xR/R4Qw/SxZM149H729l2CeAGqDIonCkPW/C71+j27XU9fJG5aRFfmhFIxjd5Jx38BexHr8uSFgIwaia4FjCoXf+GIwwGWiUIgX5jTfeiHHjxmHZsmXOtgkTJrhjEgJLlizBFVdcgdNOOw0A8L//+78YNWoUHnroIZx55pl9PuZCoaJBCCGEeBAinBcAJBIJNDc3+15dXV1pff71r3/FjBkzcMYZZ2DkyJGYOnUqfve73znfb9q0CQ0NDZg1a5azra6uDkceeSSef/75ol+T3kBFIxOBWaxWMnSOhUyKhqbQzIS6ZoaZx0fD9ceIpH8XUDayKRrVkZEAgGFC+h7UCHke8UBekE4VedFsuuvxdkRFdJRJlaEr6a9tEi/zKxl1kOXJK23ZRlypJjWRmBqTbDdqy+Mtj5LQgu2yTyfiw5/DoiyixyXHUBmTfer70Z4wURaphm3rrJV+HwHdbkRF3QCuf4ft3CbbF/bg5puQ51FWpsvJl6sx+qMcrEBGTr9CoFUOU/bhZNdUCP3caR8M2adpZM7FYqbldXE/2/BfO3c+rq6JOu9s9VYiSmmKGhWoiu7l+O9YQpd612NP960QASWjTKldlt3m/75ADJgF+Gfoa6vPW2cwzRNlwsyhpMjceeed+M1vfuPbtnDhQlx99dW+be+99x7uuOMOLFiwAD/5yU+wevVqfPe730UsFsOcOXPQ0NAAABg1apTvuFGjRjnf9VdoaBBCCCEeuutfkYtzzz0Xixcv9m2Lx+Np+9m2jRkzZuCGG24AAEydOhUbNmzA0qVLMWfOnNDGUwpoaORAzyijyuO+IjoMAFBmlGc9Jjhbs4NrxwEveCNL5lAz6IfhUT6c7+D/Ts98te+FVSZnd1rJGGNIBWBoXH5fHvH/MHWk5MO/K+H+ENhqdt0R2Q0AaNV9Kd+MyrLhvj6Ga9+MiByLpSSM7VYLDjxsrNNe1GiW755KqpVJqbQkkqMBAEKMUn3J8dp2F9auXY9h1YfI8yjbWx4n5Iy5LFKGxJvu+XQpP5A2yLG3WNLqr4yNcPZJqMqjbg6IoH+IxFWY5DU7dPIBaoz+mb3+rHNP+HxvPBE2hhFxo2Ec5SbL7Nrw31+oa6Zru2i8s3dD7fPyy2/LltV56ufPVKrO4TOOUsfqyByldKg16qTZgYMnj0OXuj5aFdI5V7yVdYXOX6LPR12rl19+V+/gH6/21RC2z2fFjZfSvjk2pkyd6FO4gn44hmFg7dqXu11fJRP0xyBhhrfGYjHU1tbm3W/MmDGYOHGib9vBBx+M/+//+/8AAKNHy9+L27Ztw5gxbmTctm3bMGXKlNDGWwzoo0EIIYSUmKOPPhobN270bXvrrbew995yQjVhwgSMHj0aK1eudL5vbm7Giy++iJkzZ/bpWLsLFY2M6GgTOWPU+SdqTWlFlosqtZe3yqk/b4YN/9p9oQSjT3Qf3r5MPaPVdSp0vg9VdbVMzQQTpqzpMUpIJWbvaqlUjFYuClWBu9+SlO1s63BnyqJVzv6bIlK52KVm17quyDCMBQCMUz4aIyuU74ZSS1qTStHosGAbNjoNOaYOFb3Sntzp9JVISpVDz1YrYtK3pDK6FwAgasQxvm4Ixtj7AQCGC5mzo6ZM9lkb81+7NtX3rkS9PC91/3ZEPnT3Se1QfSofC7sD8v5rhUJFaegcHympktjOTN7y7efUvDH8yo+3D9s2YRgxwFS+NTpII9CWYarcFlqp0pE+6l3fA423xovje6Lek1qBsP1OaLb2ubDUILR6pKrTppKdakwJ9a59PPR18Sh0AZVBn4dlNWf8XiseQrWjM5u6kTpaIclURyboCyVzoWRTCIsD1Ys9lTCXTgrl+9//Po466ijccMMN+MpXvoJ///vf+O1vf4vf/va3AKRqN3/+fFx33XU44IADMGHCBFx55ZWor6/H7Nmz+3y83YGGBiGEEOIhrKWT7izCHX744XjwwQdx2WWX4dprr8WECROwZMkSnHXWWc4+l1xyCdra2nDBBRegsbERxxxzDB599FGUl2dfzu8P0NDIgI7v15EFtRG5NjbGkrP3al0F1ZMHQOeHsIOzuiyPWnAuFFzD0mMwnYyNnkgItXMk8J2aADvb21WGy32q5XkcUifHMqFKzmKHxPwREru6pOKxKe7Owm1I+aOhTaoCm9WMUfurjIP0ozigTs6E96mSfdRF5Uy3NSUHW9E4EuOqhmFXp1yr3GZJVciKuuvuXSqrp85YWhOTfY6x9wEADI1UIGbW4lPV8vsq5fJQps63TF2X9pTOdSG3J1SES7vyAelQvioAIMq08qQq19oJRMxK2CI94yUAmEqFELaeOev74r/P0TKptujqtYA7I09ZHSgzy2Fpfwc1bmG3q/38ESM6ukT7ZOjsnVVK6XHaFa5aofOedKVa1LXQdXqUSqAzvaoxmGoMli75q1WyQL0f51yc6BWvD0ogX4l74ur7YJrcLNvTIkMK+HWt4glFoOBL3voqBf8poHpBis8Xv/hFfPGLX8z6vWEYuPbaa3Httdf24ah6Dw0NQgghxAPNynChoZEDnfUwIeRMs91Qs1y9nJ1htmQjqGj07JF1ZpIivS/HJyOoaDjHKkXDkLPZxoScfe7okrPwmFJkWlP+9ezGpPy8vdOdpe7ukteg1WiUwxH+qIN2NSNuTcZVG3qcso2oKU/goFpgRJVAmZoZd7VJRaPRcL2x9fq6zuFQq3JyjC+X6sDhe9mYuttEeUT2aSl5s8tS52uZgTFIOtV5VlvyvFuEmydE11zRPwnlVUMxpGIf7O5QkRLq/kUi8vy0v4Sbq0OdrROVIr+vjX9KHufJvqoVh06jEdFIJUyVCTWpBAxhaIXJ7x+i+4yqGjUVpozsGa22t5vyPncIN6Nrh4q00bVITFtXwtUZTlVfOmrGqYyrWyjUTzzD8234nwHDDMi6SrERhsffw6NGOKKEbke/Z8q/4VVFMuQE6VkEST//M8O6JkUnvKgTFmcDGHVCCCGEkCIyKBWNww6bgkgku3e6VgoiqkZIZUzOICuVE0BUZw7NaK0Gt+mMhd2bheRbW5YtB+qjBKzwMbauxyFn403mEPlFVG7fWRbMoyHH2JJwfQSMsZ8AAMZZso3adhnnXRmX/ipD4mqGbMm2W6MqA2hUjq0qKvsYWyHbrlSXvUJd/7jlZunUSoZ2QikTsu2oimCxBJASwMYWuZ+OkmlT425OqPekVAoaU1JB2G02yvOHjDDRs30A6FKRLinlsxCLVCEpOmCr6IxYmcoLEpXnV6auZY3KxVGlcniUq+q0caUyxMX7cmyG6+vREnH7jUdqHIXDycGRUmqRpSM9Ms+sTaX8tBi7AADttmw3YbtVd7VvhmUnAkf768Y4kR9paoG/1kva8U69Ek/GWlOrPqo2kFKB6ir3leNMyGcpZSlfFO2TghQEAMNREoI/m1otiiENR/QwYJoVrn+Lk7E1388d1QuSTmhRJ7x9AAapoUEGLsMOHgNLLQHVqL8pHepdGxxtKflHsyUl/8i2qJL1rZCGY6docdpLqgRd2jGzLOIaPrl4/eX35RiErKxYqYrGVaSlBS8ehxwm/4B32tJYSnocWJMpeV6r16wu+jjWrl0HADBUEjAdFq6dqeOBMNye9bE+5/c9KyFPSGbCTNhFaGhkQWdilDPneETOarXPQLklf4FGMsTs9y6rYPfIp3p0mHLNfpiqbTK6Qp7PXmrJvDKis0FqZUC+74y4fgVd7UMAALs99U8AoELlFhlhyaiT+opy1bZsQ+foiCkfjYhaeI84uSHk96bnEdQZQJMpOdNNKJ+FVuVj8mqjvN6Nym+kSxUo6VS+NC3KV6FZKRftQs74O7vkH2JLKwWeWaytfBiC6oGuz1Eek9E1NRF5nhXqWu6lomXGKImmVl0yJeRgU4u8Hp90VTpt7kqpaxiR9WfabDk+rTqk4I900ZEdtuNHIfvsFFK56LSkkpGydR0SV71w68Xo8/LXqLEDGWqDeTEctwiRWfkzPIqIoXx+dKSNzu8RU7VposoASapIGFudr+hWlVbA9ETwBPPNaPTZOjVPullXhRAAsKlEhAqnAYQQQggpGlQ0MmA42S/lDG0vjAcAjDWlTF4V15kavcf41YVgLYZikq0CZ6pdjvs/6uTselKdnOXuXSNnxDWVykdATQOb2uV+77dUO23Elfzd2Cp9MnbiFQDAEHMcAGC/SjljP6ROzhzHVclZa63K0ZFUSSK2tsv9Gjrl7Ddh+2trAECZmrF2CbmWn1BZRHW9lHJlFo+r9j+2XSqaZFenPP4DlbtDR5SkTF1xVPaV7rcgRwIAETULL49KFatKZTwdYsucFdUqr4iISTWhRvmglEf893tUhfZRcdWh2i7lc9K5N0bbY7DFVFk7TV1fJXM+CZ3d01LXKqI+x02pHJQpxcD2zN4tIdUbrS5YSvVIKVXHCGbWdOqO6M+W8w2Q3VfDMMrcHB3aH0lnQFWRSUmdNNdwj5H/Ub4XQmlzWlXR0ScI/kx5xpxFzMvm1+LST3wy6HvRfxFcOgkbGhqEEEKIB5thqaFCQyMjKrokImfhwyHX68cox4MhOiNlzoWn7j2oPVkTNPN0sbNTKhP7VcvZ6aGjZV2RIYeo/Ayja32dD90qfRlqNrgZJltT0i/l3RY5w9ezzL1U/ZR9q+Wxhw6XM/xRB0gVIjJUqgx2i5zVtq2RF21Hp3q3pD9Fu8rPAQAVEakYtYrNAICEdm5U56nKqGBEXNVCicjZacLWfiFyxvx+k65bonN+qLofOveFd90+WAVU5b3QWTjjkApHHeSzMLZKRVYo/8ZqlVm0LJCRsk4pHV22e5OsKvn/SaISI/cqx/99IhtptD+QQwn4TTgVVUVg3KpJXUU4qjbYhqsOWSoKRvtJJA2/2iAs7cOQJZOtk91T+274HzYnxQXKHb+PoEOm7iuqHGxTKgLHNNX+Tm4PCzAiGfwpdLSJVE3MHI62diCbarBSbK+hAkFIj6GhQQghhHigXRkuNDQyoNej46ZUBOrUOvtIFa2xV0zOnnTWyzAJ4wHXs833YvL2jq+UPgBDJsnt5syDAQD2WJm9Eip6I/KhVBKG226p4nGNUt2oU/kxdONDyqKqbfn96APlbLXsSOkXIkbLKI0yS85SJx+6FQCw38qPAACvfyCVkpcbJzl9bdgt22qJNQAAulTYpi6nUaECIIbH5fUfHpP7l6n7sE+V3GFEXFZ9/bhTjmFnpzy/T7qkb8YnHhVll1JPmjvluEQgwsPJcaFr2ajjXtut/RCg9ocPO4MvQEwVFPlUlYmKJLBvmbwGEXEYAGBH2fvy/Lu2AABSlvSlcSJIApVhdRXXMhHzfQ8AKZV1NB5xM68CgIj4x6VzxQRxqs9qvwpdaVZXDNYVZSPlqK1oVOOVz4ClK8aqz/o9kZLnY9kq+iejr4wXPVYLgOXzQUn3xQhRweBfmUFPWHk0+CRJaGiQvqP5Y9gfyBLtLRvlH8KtOz15K+qH9bqLtre2YGeTXC74uE3+stjaLv8INXRKw6TB2OHsv128AwDY1S5TjguRwtSpk/P2s/fkMWp/+N41wVT0ABBVhsbIcgMfv9aA91vkH+SNiW1yvHgLALC7YxMA4NDJ++QdR29Zt+7VjNtdQ0MZFqY2MHSpevm5kLwjU6YeAsANW7Z1yXlbJyzL/+t42rTDfJ/Tna3tvLk2CCGlgYaGB9eGlbO1MqgskCo5wnCVO2FkXGWNjBTPgz0Mi7paKzG1ciZpHiz/ONr77Zu5zwP/AwAQ2eFmsBz5crNqS/9BkdeiOiav0chKmauj7EDpX2FPkplDEZfXzvlzMH4cgFZUfUF+PPHPjwAA9lnm1ud4KCLH946qFNuSkMqGU4ckoivDSiNlWGWn6kreF8MQSI36BAeNlYbEtp1ytv5uk5zVv9Uqz+GdlvFOn2+1SOPmTZUPY1vLetgi6WQM7Yjo3BzSR8VSASIfm1IB0X4kWgGxHQUk/dkoU5ktqxPD8fquDzA9vg8AYKwpI1psIa9/Mq58GZQPho7e0LV3dPqWiMqcWob0asL6u5iqj6Ijb8pUW5pomT8/is42qw2LiMqEqiOCdO0WbXB4aRXyfiVTSslQhkVnlzSk0g0Kf7VXt26s3q6zlmp1KbdBkp7dtACoXpAM0Bk0XGhoEEIIIR5of4YLDY0MaB8NXdeiXF2lWqVo1JWruhhl4WUdLMaDXaXygFQNVVL12DGFHTh2lPPf2trtAIByXSLDMHyfa2tVZEi98veIu9kbfXR0AHYbzFc2AABSG6TqsLN9hLOLKiuCmCFn2VZg9q1Ls5Sr615RIb+PqM+tLeVItMfQkVSRLe1yNt+kqtJ2qttleUJ8tApQqXOklI9BRWwvdKjlDO0n0qFyXRgqiqTaHgIAsI0a9e7PC6KVDdvw5LZQ37WK3WjGdrzSpZQI5YTSYspMoUZAVXB8GgIzer2frgnjrX3jfKdUEb3UYemcFypCJejroINkDJ0LQ405pXaz1HGm0NlI3XukFRe3TSeBhhqTk25UnU9unNweCByfBe/3fZmhl+x5hFbrhAAYgJlBb7vtNuyzzz4oLy/HkUceiX//+9+lHhIhhBBCsjCgFI37778fCxYswNKlS3HkkUdiyZIlOPnkk7Fx40aMHDkyxJ7UjFLVeYjqPA4RNZOOS4WgrMydDWZJzpmXQpWMnmSq03U3ylTggRg6vLC+htQ5/49V2b62gm3HKq20YzJiWYCVAnY2AgASO2W7HSlPrRN1itq/QARyI5hKNYiask9T+ciogAjYtgHLNtGWkLP2NtV2p5qmp/Sav2e2qxWamCqKVl42BLFIDTogFQ0dSdEVke/lRqXaX6oRRqBNV8mQ7ym4M/4uVcskiXYI2E41WVOdQEK0+87XCOSNCKoPjj+F87x6bpK+ljovCLSS4T/G0j4lAf8G7WNiw68qGc52ie1VNJy2/O/ufCbot6K3F6gM5vshM8wM+TP6SSZQMqAI5o0hvaPbisacOXOwatWqYowlLzfddBPOP/98nHPOOZg4cSKWLl2KyspK3HnnnSUZDyGEkD0PW4TzIpJuGxpNTU2YNWsWDjjgANxwww3YsmVLMcaVRiKRwJo1azBr1ixnm2mamDVrFp5//vk+GQMhhBBCuke3l04eeugh7NixA3fffTfuuusuLFy4ELNmzcJ5552H0047DdFoethbGOzcuROWZWHUqFG+7aNGjcKbb76Z8Ziuri50dbnptJubm7vVp5aXdfE0UyWG0hK+GUJ4a1aBLm2ppPvmsbYiDe1FGSvs3oiopxx3mb8tjS4erpctUJbnUTJMwDSBhHKWVIq75TlPw3nX6bKDzo+qz8D90NjCgC0Mp82ketef9QzDO9PQzqAmdKG8MicZldzXn/5bLw9ox8NI4MpYjsOj/1yA9BTdKchnMyKi6hCdkMtfmj1fgT7DOQe3fT1O33JKN9DH6/Nz0qMHhuJdzslW0EwvTwk9FsMf1kpIf0KAzqBh0yMfjREjRmDBggVYsGAB1q5di2XLluGb3/wmqqur8Y1vfAMXXXQRDjjggLDH2m0WLVqEa665ptTDIIQQMoAYrD4a1157bY+OO/7443Hsscdm/b5XzqBbt27FihUrsGLFCkQiEfznf/4nXn31VUycOBGLFy/G97///d4072OvvfZCJBLBtm3bfNu3bduG0aNHZzzmsssuw4IFC5zPzc3NGDduXMF96lmdpWfCtp4Zq3fLO1stuFkfxXQG1fNLkVKdJJSMkEfZMJKuCqT9MYNzVe2+Z+v/pAJFrYIIW6Y6V2nRTTWEiKcYmXDe9cw+UCZc9xm4HxGP0mQawmkzqt71Z0cJ8TRrO06c8kQskfKlunZScKt3rRBo50/HmTKLM6gwPDP+wFXUCeF0HzpUNOiYmV6i3U+wby+2ox7kbCINM6DUGE5Z+OD27IqNMz7nIaeSQUh/ZtOmTT06bsqUKTm/77ahkUwm8de//hXLli3D448/jsmTJ2P+/Pn4+te/jtpaGd7w4IMP4txzzw3V0IjFYpg+fTpWrlyJ2bNnAwBs28bKlSsxb968jMfE43HEs+V1IIQQQjIQliPnQDOply1bVpR2u21ojBkzBrZt42tf+xr+/e9/Z7RkTjjhBAwZMiSE4flZsGAB5syZgxkzZuCII47AkiVL0NbWhnPOOSfknvzJlpLqaemw5KyuQyVailn9O2FXUk0gU8o1pWz3J7KvUZkVII3R6KYFT7SZvraCbSfa5TWpVMeIMVmSgkUi0uFjryEAgNheHwMAKspcJUT/cOukUMHwTq0kJW3Zp1aUDHWfTFMgYtqoiklloF2Ft5arxFe6lLtXH9Cz7YQpQ087U41IqBL2gFvLI67CX7WikTBkiLN+RlxlJH/CLggTBkzUQSYrM23ZZpO5EwDQgd1qbH6VKKgYOH04ibHc7yyt0KjwVP0uROAYp22/YuHWOtEpx8t8+7nhsV6/kFTGNrKHmHbTxynfD0lGH5GgysJwV5Kfwbp04qWtrQ1VVVX5dyyAbhsaN998M8444wyUl5dn3WfIkCE9lmBy8dWvfhU7duzAVVddhYaGBkyZMgWPPvpomoMoIYQQ0lMYmioDLb7yla/g3HPPxTHHHNOrtrptaHzzm9/sVYe9Zd68eVmXSsJCz3JTKs1yp5pYNifl5aroVIpGPy+q1qbG3bZbjrfiI1mqPZ+igY9cP5jmZmlQ6vTd+troz83NcqY/5GNVEXXfCfI9uGRVUQHAgj3zSABA2Ra5/16r3SRVOs11Qsh035FA4S7tatKZUspSR1R1pdKilydRVplAbVSm7MZONUaVkrw8oiJLPE4aWolot6WK0Na5FR2JkYiq8upxU75XCGnZV6qU3gUXVfP8wnKKqmE4ajESk+OyeFyjjsRRM/JGyNL1ThE1NSsPpuDWfaR0WnDP9zpRWErIa6FLt9vCr2wEVRKtVASrtxZSVM1SPy+GrdtU707K8e799k4/3zxF1QacUE1I/+UPf/gDli9fjhNPPBH77LMPzj33XHzrW99CfX19t9sacCnICSGEkGKiQ+V7+xrIzJ49Gw899BC2bNmC73znO7j33nux995744tf/CL+8pe/IJUvAMDDgEpBXmzc+ZCcrus8By3KIeGTpF6nlntFzfBnUGH4aujxtSrnku3NsrjasDekomGqdOH2WFUIzVY5Fz6UM2nrre1OW9vbhqu2nDgT+TkhP29vlym56zdKhaJsyOvyPEbL5SxD+7Fs3go0f4yWP38IAFi9Ufb97E5XXfmkXl7v5qT034jr3OmKTkueWJNSKMrapbpQ1inPs8uKoG3bcGxokurD+21y/63tcswNnVJ9aDB2uOdnvAMA2NX+LgAgYsZgGlHEo0rJgCyaVgup3HyqSva5V2pfeZ7qfgXvm51hdh01pV0/stzAAcPKsKlF/qB+ZEvppQFvAQBau2S5dVuogmimPB9dZE3j+LIIVTTP43+QMqRyoRUXy5bXNmXLfXVqdUt9DhJUNHTfWsnQRdq0DwvgKjB6n5gq6lcel8+CLhtv6/GqMQntO+NE2+h8G6qYmvYf8YwvPbeI7c8/otOg51M5MkX0MCpm0MMnwMWbzuKWW27Bj370I/zf//0f9tprL3znO9/BpZdeisrKypxt0NAgfUdtPcy95R+RGuUIOWaU6+vzye6ujId1h6r/+BT2apV//BKdqo5Jp/yjU90l/8ANNdwfilHKubO5U24LhpZm44NXpNFmd8PQiClDI1Vlov6Q0RDtsi8jKZclhitDoaVLGhYpq7WgsfSGqVMPzbjdNTTUH3lTO4GW+bZHIuV4ZX3mhHma9eteAwAkUvJ8nGq0tlp2gsC0aVNztrF27cu+z+nJwejkSUgx2LZtG+666y4sX74cH3zwAf77v/8b5513Hj766CPceOONeOGFF/D444/nbIOGRgb0bKnLlr8Ym1T+ie2d8g9CwtLr1uH12RPnIzOPMteoxv2hKpc+eoOMphgi3pDHj97i69zaKsNTPnndXX/f3C7PuSmpZr7q2jSmdNvy+9EbZR+jbKlYRIbKWbnVIvd7fc0wAMBft4wFALzb2Sj7UooC4EZGdCakolIbk/vq0+xQNsAnXfL6t6dUBIjKp9HQKT//q0n6mGy3pUNyZ0r6XwRn1IBnVq1UrLLIENmnjrII+EfoW37oUPm/6jJ5PcoM/w1MKQOmy3ZvkpOPRQjURIH3UlJZ2ZraIM8vKcvE25Ycp6H8IkyVdMTNmGr7rpcb6eJKmZZKvdplyXuaVH/UtZKRUn1YKsIm28zf8ZMIzPyd6BOjHM0d8p6XRaSxFlHjjkbcQnUA0GHK8+tMyQilZEplXbWzGZj6akcARBx1RQ3Mhy10iXpd5K0XhgdVjkFPaMseA3j55C9/+QuWLVuGxx57DBMnTsRFF12Eb3zjG76I0qOOOgoHH3xw3rZoaBBCCCEeqI8B55xzDs4880w8++yzOPzwwzPuU19fj8svvzxvWzQ0MiIfs6Sa9X1SJmeFFW1DAQDNUV0Xwz0iLYtlH86AsmWO/EQpMu+2ynFXNOwFANi7TW6vqZTvWoVuale+DS3VThubVB6N3YbKk6FmvjsNOTt9r1V6IFdEZB+NnXIppDYmVYOkLT83p+SsfES5HGtzUvo+dNhDnL62W2+p85HXN2a46/8A0KEm7FudWYLcr0spHbtUKIwhdI0afw4IXY5d2O5avtDXTi+B6LwTlpT3u0wZAdMEpQC0qQgKlWpjZIWpzt9/v1uSst3WlLu9sUv+f0tnBw4Y2YkOUzYSMVXJeUR8bej7GsxOqnEiSrSi4Vn2sdR3CUuOX/toaJ8Mx5/DOX+dYySoYOgolOAzpq4lUh41IZibQ+dgUf4gOvLF1vvrvCKWfKVhO/sJkfT98k/LUGpotUdv0BEvIeW66Wnq32xQISH9nK1bt+b1vaioqMDChQvztsWoE0IIIcSDEEYor4HIn/70JyQSCcfI+Oijj2Dbrpnf3t6OxYsXd6tNKhoZ0DMz7by2s0yuQVuqsEd5h4oGCMxAgb6N5Q/mGQiyw5TjfqtJKgMdKTnuTUqZqYzobJayHZ13Y2en28bmdvnhE3yktqhcD7aMUHm3dQgAoMuSfXzYLtWQKvVkxVRkTtypR6K3q9m67T6CKTXrNpWSERN6zV/uq3w60aCkjS718HeqGXKLUgiaVVRJUs3mdbtuXopMqEyXyoehE1LBiUTkNTMjcpxdQioxpoq6qSjzR0RElem+rUNFKnW5fe5KyWu5NfIRakwTnbZUytzIj+Cz44/8iEBHPcn3Tmu37/wsj++JrXw0EinlgyFcdUC+J+ElqGS46kn6M+5Fqw1y9P7cHDr6JGrK+9kpGn1j8EaGCLiqSbbn2qtiGFnmSK6ykU387ieZQgtVSKh8lITBvHTyta99DVu3bsXIkSMBABMnTsT69eux774y0q6lpQWXXXYZLrnkkoLbpKFBCCGEeBistU6A9GX/MNwAaGhkRNWDENpzX85um8vkWnqHmuXmUxS89DYrYibM4Jp+QKrrtOVsdpch36MdKqOmqgESL/Pv36H8CZoTnlm40QjAVQfcfeX2HXEZ4WF2jFbb5TWqUlP7qqjsY6xyt7DUVMGpwOqJlNCz8EhERi3EhMqkGZPjPnSIVBF2qKgTrcDoPttSFepd5v5oUUpBS1yef6uqIdKpHSwAJG3twyD71nkhmttVxEpC+qIgpvpS12NzUh73TqNSh1QtlAqlNrQrdaHN6HD6aonI/pusLWi1qpzMs/pdwK+4aJ8N10dD1W4xpGoULVNZW5UykhRuX8lUmzpGz/C1H0dmf4psSkY23yOvf4VQ1zmpFECtHKUicjzxMqkCaZ8nZwzd/Jmws0anpJ9n5ronhJBSQEODDCh2vbEVDcpg0g6XbY6BpN6T8o9NY0r+YdptNgIAmiCXVDrEbqc9Hf6pE1nFIlWYPOWgvOOYeNg+AIAqIRN7lQtpHMXVH+pX1r/fzTPrPq+9/B4AN316wnbzbnSlWjIeUwx0DgwnHFelWtcG4xuvfhBCH1Nyfm8YRlquDUJ6CouqhcugNDRefnk90gLxPei15XhMZjQcUSk/jxZyplypZnWZVomDiZpED1f7gmvQ3joW+v+GYfs+m/BHDmxVmSNHR+Uf3Lo6mYHyUxXyuLqo3yO/MSn/SG5ud5WSj5tkG5st2ca7jTLz57Bq2VfUkOt2o2saAQDVqtjfkKj6XvlmbLUMjJg4BipHFTpUxtAuz4xfqwoRlTdC1/BIqgQUEQMoM4ADa1RkiFJwulTG0HZVzbVRZQ79pEu+b++Qg9rZJZOEbcUnTp/NUZXbQSh/CZFE1KiAqf5opmw5C+9M+hNYNaWk/4vOv+Hk21Dff2DI43RtEABIWbp2TiO6rBZYSjFLKd8NZ8buqAuZ/RBs1WeNGOa7Xh2GW19Gj0ercrbjB6KL1uiZv3pmTL//R3ZfBn+GWAH3GRLqWtkqQiel7kdT+3v+ppyqtMqnIy1LSRC/f4m/LaWwGAZsuyNHG9nI/FPcb2BOj5Iw2IuqPfbYY6irUxmkbRsrV67Ehg0y309jY2O32xuUhgYhhBBCMjNnzhzf529/+9u+z9lSKmSDhkYOdH2HmEpZXWnLdfhqlffAqzJoJcMOOtJkWYcOzpmC8ypnjqdzKXj60vc4EvjODGyvVLkqhigfhxFxOZZPVSTUdn+di8ouOSP2ZrPc3imPrW4bovr2V++sVLPpajVx10pGXVTVRFHZO99sNtDWZmCXKoXbqCq0aiUBcGfHlppONKuljg87pSLR0lCB9z608SmlMFWpPsvU+epMre16KSWpP8t2W9WsvsN0/U10/20pmY20tXMbGjuiEE4Ehzx/S6kRQufiCMzsnRwWSkFo7pJZV3WWTMD1i0hZHUha7Y6C49T+UDN2Qy096B9m3bb2wbAseVyDkPVZhIq+0b4eug/A9ZdwfTMCqkDA/8NwfiXoZ0BXXi0kikNXrNXPvM78qcKYgr+c9H6GARhGWuRL2n6Z+vLmAREiTajM5+uU+eeTKsdgZzAvnXhDWcOChgYhhBDiYbAvnYQNDY0M6FlOSmWHbLZk3Y6tETmFLheqmqZn5mM7Myz/5+76aKT7ZphpfZm2ynypC1xpnw2VEbNMzcJ3mbLw1/utci3fMGSUwicJ+V5V5hY0AwBVlgTbXLcJfNAqP+w2PlZb5CxV1+XYrKJOok1jAABNCRXREpFj0BVkN3RsR0tbHJ3Kd6HVkH4S7YmdTl+6oJmtZuYtCdmnFZUz/m12HBvsd7G1eT8AwHBTRl/UlMk+a2P+a9em+m5V5YxbDB194vpotKfk/zsSKieF1QLLbofjPxDIxmmrXCpu8TX9Wf9mUllldQEx01NXRSkatp1Ayu502gjW+jAM/4+lViN0tlLdc1typ38/O5l2jHD8G5K+cZtpdVRUVVbT9SkBgJQugOYoIlq58Z+vf5v66J5Qxu+z55IIKApGUCnJQFAVKZBM+w9IlYOQEJgwYUK3l0UAYP78+fjud7+b9XsaGoQQQoiHwapoLF++vEfH7bPPPjm/p6GREVUdU83mOlXOiGadI8Eoz3gUkF5m3A7Merxe+kB6fQuNGYhi8CodpqErivq/07U8dKRDmyVn69tUXRKzVUbNNCeUMhPxW646j8auhDsL/1hl2dRtuVlTVY6OuMwYGrWlX0F7m/RULldjsdQsNCIiMIWJckh/F0tFlFhRTx4NlUcikZTqQoeq4tqlqq/adhc+bFqP1mo5pp3G3gCAypQMMS1L+h/nLkPevzZDHt+ilCntvwC4tUB0NVN5720Ea3zoCJBIRJ6nZem8DUqVMGz/Z11h1XLTrHrVBSESEAH/Cedeqz61SmIHnyn1OREIYfXmxkjLm+FEnfifR9PwKxtO+XcdeRWV17bLyZGh/Uq0QuKthOv3A9HnEYnINmx1rYOKgdQhIh5/EX8tG/d6uM9KMImQYRgwzYrul4/vVb4Nqhx7KoPVR+O4444rSrs0NAghhBAPg1XRKBY0NHKg17V1lsUORznIftmCM6psNRe00mFmifvXM2gjoF74t/lVD1cFke+JlIyo2BXbqgYj31o7ZTRKPOAL0Klmp82mO1PeDekn0WXpba6fAeD6OOwqkxE5upJopSX9WOJKXRkZqcHuDU3OMnurmhnvNt3rs01l7GzukspDV7JR9qVmshEzhrJIDZo7pIqSikllQt+P9sQnmDp1kuNHEVHqUaUa83Bbzt5feflDp0/bqSiaPesk4K0VIk/g1VfeDmzXKpg/ksfro+P6SaQghOXm4MiTN0PPum3nXUXneNSSIPq8pkw5UPUZ7CsW+Kwjl3SlW/keNSrwystvOREtugpsUqlC2ocDcFUhy3IThwFAmVI0ghVV3SgTU/pXOGPx5/IwYGLduleynKl7zSJmlSeCR1/r4P75FYfC/TZyj6c7fRKyJ0NDgxBCCPEQlmlIYURCQyMTgXoO2ldDF+LMpGjkUzLyRZ8Eo02Cikamfd01fX9kiqNoqDXxVkv6OmhRpMNoUx8DEQaGnK12qMqlANCufDO0quPOrlUFVaWatJsqkkMNN6miLcpVBVZLVXfV+UY6lP9Eu+HxM1A/lU4khJ7Zqr5Slr9+R7sSD/TnlNWClNWaoQiQP2rBsl0fDec+efNLePIxOH4B6nMylVB9dfr6zqZc+f0mdF+2GouesQfrjPjbTPM60Nk+0zJlerJ0BsZjOO/+vBnZCibpHB8p0YW25E5EI36/pEy+Q8HnUvuFpKxm9AYBG0LY2RUfAG6WUXXtnPMMtBU83UxL8ayTMqgRwIAt8d5f6W6+XkIIIYSQgqGikYss6+6WnX8dNusMF8GZZm4fDSBd2Qj6aKRtV+963Nq/Qs80kyqXhc7uqdHr8JmKc1m23x9Ar4G7ffhnrbapKt+aUtFoV0XH9PknlXrSJdy+dL/efBASf30O4dTxUPdF6DGl0qJ+1BeBj957oPOfeK6lYTjbhdDqgn9Mbl6JzDVB3PtspX3n+CQ402ndRuYIJHe8wdwVVuD7AmbiAZ+M7H3pHm3fNXWib5QiYnvUPcP2P7NwFBrbtz17rotIYD+tTuQ6r8zfuUpThuehB9BvY3DBuxMuNDQIIYQQD4w6CRcaGjlwZizO7F1+zL0u759BZVM2gqStPwu//4V38mSkqRyZlQ3bWSOXPglak7DMrox96rX/pCeaIan8ItxMk7pCrGpL9aGjEJy+1ZwgppSLzkCGTe0DkLTbnW1OfY5Adc9s6GsbUbVnyqLDES2rc+p7BGfCWsExPEqOVip0psxYWS1i0eGOqpKuJjit5RxTbkzf8cHID09reg/1yR/5kqZk5Oo7T7Y/J2eHym8SVMsSqba0Y7J35VdonJImaYVIvM9vuq+HPm/TiMI0Y3mubbbvgirRHjpXZf0T0o+hoUEIIYR4oIkWLjQ0cuKPSNAPX+aJQmCm1E3PdadNZ5Zn+7Z71QeBdI9/72d9jFMjI+BjomfrwToeej3bu79WRYI+Cu74VESI8uEwLD2LVRETylcj6A9iOyqRp+Ko+r8dzGqpc1VoNUVfW0OrR/K8Y2U1eO3VD9J8PAwn26V83LUCIvtXtT9UXonyaB3e3LA5LT9GkDIVheHm4fBHgGQ+Lqg8+TNgZifbrz2/khHMOlsIbiZTHWaj/SJ03RkLkybv59wvBFQVwzCwdu0rals2f4mIb5zZyFdjIVfUibcQrDoB9RbwiQq0kfFnucBIlN75bZD+jM2ok1ChoUEIIYR4oLkYLjQ0CsCdSQfzFmTf16VQZUP5XQTX3Q2/SuFvMzA7S5sZq1lpUNHIsg6vZ3/e2bmrKmQ+d6dOhzomhQ61vz8iwjT9KoPuI+VRNLSS4qoomaMpXN8Zv6qia394FQtvO1o98c+8/ZEROsomalaocft/RJzzcfJQ6CqvAX+ZPvxNpa+H97zc2XaWZyWQH8R2DvXP5IQT2RKsVqufLdvJBGo4PjCZfYd6Wj5CCJlHI1dGXndf/b/+7ovBSBQyeKChQQghhHhg1Em40NDIgKtgqA2Gf3tmgj4a3XxSdfXP4Dp+pll9WtSL368jGBWjq6I6Ikm2GadWNHxVMoO+GfoLv5+Em0U18+7BB80K+GHIYxOBbTp/RvAa+M/XVvk/nN1F5uvg+npknzlqpUVXl7Vtf02aYDXefGTM2ZF23fU11JFGepz50O1onxtvJEs0w/5wnsugP4fzzBv+3B6O6uWoEempNt3Mpv5oE9cHSJ9fcDDB3DN6DPoc/LljTE9tnnRfC3/Om0y+TXJ7OHk1yJ4NU5CHCzODEkIIIaRoUNEghBBCPDAFSbjQ0CiIbghpwXTXWcSz7GmY/fK6myDLs7+zbGEGjtFDCI5XhyrqvnMLWd7lEl8hsIz7+pdndMt6CSW4TBF0OPUunaSX+M7mfOsP/fVvBYQdLLAVWDrxnF+25GvacdRJ567bDizDBJ1l3XbyS/TeENHMZF5qcYvpRVU7Xb7PABCPjgAAxKLDAbjJ0NLHlTu0Vjtg2rY/lNip8B4pgxmp8h0TXLoy1K8ZG0Fn3MIEVdOMZnXwDabdz1fckJD8GLB76rlMMsKlE0IIIaSf8bOf/QyGYWD+/PnOts7OTsydOxfDhw9HdXU1Tj/9dGzbtq10gywQKhphUaCSUTiZlQ3Ao25km605acv9zo964uw6xGUOh83swJh7nFol0HsbWrmwVIryVCumTTssQ1/uea1d81KW8edOWKWbEMLCtGlTs6pFup11a9d7tmknT0nSsjFt2mTPuHIrE+5+WsKRn9eueznHUTa811UXZjOMoNOk3znURe4/deohav+EbzsAlEVqAQBvvLoZAJC0VXp3pSTlLrnuIoSNqVMnwTB0CG1A2YC8XrJtf1Ivzbp1G5y2CupTOy+r62BZNg6dfEDehGTrVOIweSiVDNJzSr10snr1avzmN7/B5MmTfdu///3v4+9//zseeOAB1NXVYd68efjyl7+MZ599tkQjLQwqGoQQQogHO6RXT2htbcVZZ52F3/3udxg6dKizvampCb///e9x00034cQTT8T06dOxbNkyPPfcc3jhhRd62FvfQEWjENLyGwe2ZyC7D0YIwwnM8NP6Sps5ZvZpyP6j4NmeRVVITweutjv+FcG+k7A9BdTchoTnv9onI09K7rQT8cx0hZV2VumlyjzHi2BCqg5YVlv2e54PR14J+tHAf19856DGECgbn65wBAn4OnhCN1Op3QCA9sQnAIBoWSUAoCxSodo21ZH+uYYbdizvY8rqVAXwdFisP9TY70fj919xwo9tXXBP+5Bkm9/4w3wFkoHt3guR/uxadhsMI56lbUIKJ8w8GolEAs3Nzb5t8Xgc8XjmZ3Xu3Lk45ZRTMGvWLFx33XXO9jVr1iCZTGLWrFnOtoMOOgjjx4/H888/j09/+tPhDTpkqGgQQgghReLOO+9EXV2d77Vo0aKM+953331Yu3Ztxu8bGhoQi8UwZMgQ3/ZRo0ahoaGhGEMPDSoaPaFPF/Dye+jn8wcJqg556YmnfrY+nKgVkf+6FaoeuM4mBQ/PT47zC44zWx/ZxqqTVDnJpdL7ErAC/gZakdARRlo9yHY9/AnXnMvh60S2r5UNPY6IWa7eZRSHMHQRPB2Z5C/EZ9mdTjRQJgwj+6+QtEunI0VUJIsbNZOEYRgexcIfbaSjcvzROcGCgALyehQpIVcmn5YBWGitv4+vvxDmFTn33HOxePFi37ZMasbmzZvxve99DytWrEB5eXmIIyg9NDQIIYQQD6EtnQggFouhtrY2765r1qzB9u3bMW3aNGebZVlYtWoVbr31Vjz22GNIJBJobGz0qRrbtm3D6NGjQxpwcaCh0R2KoWQU7APgnT31cMUrOAMLRHf0qMlgno9gHgN4fB3ynqvOuxAs7CV834dbfCpY+Mvuvl8GAEPNsM2I9IWIqKJsTnp0z/9lkTCPXmEEr1lQ4QiMR300Ta0IZMo3Yvi+sxw/CfkjH4E/L4UdSAnvzweSXSUwUOH5oK9lUAEJ5gGR4y5T1yoaqUJ5dAQ6k7sC56N9PrRyk0nRsJzvComkYV4N0l856aST8Oqrr/q2nXPOOTjooIPw4x//GOPGjUM0GsXKlStx+umnAwA2btyIDz/8EDNnzizFkAuGhgYhhBDioRThrTU1NZg0aZJvW1VVFYYPH+5sP++887BgwQIMGzYMtbW1uPjiizFz5sx+7QgK0NAYoHQvs2LG7KJAQUpGb9dvnT6NqJNPQZI+dtOUM1w3c6da08+bT0N3ZmDtuvXpvyUChcIy9d2dccom/QXEdMGvSESurU7ROS5sV9EQbtU3rF27ztNYsAiZP9ulm0/D9PU1Zeok3/e+lWWlkkyb7sqwsg2pZJimXCN+ef2bakiZM23GyoZiwysf5sxLMVWNw7KD+Vnk5+lKChaO+iDHoFWfitgwvLz+DXSlWgDISBc5Jq2ydKkcLJmikfx9FY0+UUJYOr6/0F+Lqt18880wTROnn346urq6cPLJJ+P2228PuZfwoaFBCCGE9EOeeuop3+fy8nLcdtttuO2220ozoB5CQ6NU9MAPIJ1sdnfQlyE9u2if4cyQ9aPmn537dnWydGbKUOqqDo4PQ3AWm0fJcI/PkGU1zziD9WGy+QOkLJkrxLY71ZZsuTOQpmToHBBasXD7yNyXm/sjw2w+mKlWl4d3/ERUxtakzLNhKHVBR6OY6j1alrmOic6zkUy1OtlG3XPNXILeLVGvIlosuV97wkZr51ZEy6oBAGVKFdKRL7aVQvbcKm6ODfpfkLAIM48GoaFBCCGE+KCdES40NPqaUJSMfPT9zC6rH4j+3vEB0J/TZ+EisK9DtuiYQD6NtIyp+qOhfTwKj0pIS9WRlhQ2c5VQYXeg5+gMofp89XZ/X8GKsZ5vsrash+806Vwz7TeiatWoCA/Y8j2ZapPHBxQcN89GwlE3XHXB9UXJOC7dt8p8alltSFnNjh+IqXw4vD4phhEJPDPBbKE66XN3I7LC/VlhToo9Ayoa4cLMoIQQQggpGlQ0BgXdi1Ip5hgKCxvLVpVW+S7oGX0IoyqUYFRGcLurRmSu5upD5RTJpv4YgagT1y9CKxnBKqndv6/p6pFSNAJNGkLXRClTx2VXAIQI+tYUphaYZhSmGXdqopgRFcGjsphGI5WIldWgK9mY1pc/i6udOYPngIE+Jv0BgVxZeUlPoKFBCCGEeODSSbjQ0BhUFF/ZyJop1AhGwhTSWJYqtPDPZou7Lu7PDZEvskFnvdS5IjJNjAyjDKZZ7VEm4GvbhooMsQPKQLBvp8JsrjFpaUK/+ZUWV1XR21XuCmc3PcbMUR+GYXoydmrfikzVVjON0430MYwobJVzJGW1+b6vjA1HxIgiGnEjYCxb1Wrx+IXIyJlAxAsjUQgpOTQ0yIBi2rSpGQwNl7Vr1/b9oDKwdu36rN+55dJzM23aFPW/HMswecexLrCl+21lT5YlU3+vXfuKajKwjJTFUHRRyzJmHNOmTYZlBdKkqwRu5dE6X0IvwHVGFYHU6YSEARWNcKGhMSjJl3+jCGSLJCnkUCcPgwUhUnn1i2y+D4Xh9yXJli8j6D/h5t3InX/D+53OpOlEjzi+GMqA8mQVDTSg2s6WW8Kzj8426mRX1QpHwOAIRII4n20LttWZIVpKGwlRR/UwDV2RUv1aCUbqONc2qd7lZ8tqlUaG9vFQ+1lKVulMAu2JTxAtq3R7VxVgdQ4PJ+eG0AaIXy1yKUDhoAoy6KGdES4D2XOKEEIIIf0cKhp9jTtVLu04MlLITK6X9VWcHYK+G70dVy8pcDxBhSNfxdCMkn4wOiOtlks3zzfXM1VonZi0tvT1CFbdDUTd2CkInVPD0JE52k8lGKGjlQwdPaP9KVQOjOD41VgsuxUpq8mXN0RnLs2OVnuoTpDuE9bSCZURCQ0NQgghxAMTr4ULDY0c5J2V96rx/qxs5CKzQ1820rJ15qog69QZ6dks1Ai236trG/TV8Fdp1TgZQQNjzhztkNkno6dKRnqETyG/HAvso8DnU8Byq7Jqdw/nW6ls6Poplvab0DlJnFCYOAwj5tRf8batGxQiCVt4fFYcwUXeD8dnw9JtFFjNtRf+GPxjtOdCZ9BwGTA+Gtdffz2OOuooVFZWYsiQIaUeDiGEEEIKYMAoGolEAmeccQZmzpyJ3//+96UeTnhkrTg6UOhebo5cs0A342fm7JoicLRzpbxVWo0IuqMMFJr3Q/sXuJEiebJfZpgp51UuehC+mo/wZ905MoPqa+ncRx09oq6ZDknVCojyo4iYFSozqG4omGUVAEwnzwYACCPlbAcAw9Yhw3nuTw8VDKoXgwve7XAZMIbGNddcAwBYvnx5aQdCisa0aVMB5DI0/HkyXOndXw6+L0jLcREkg9HgPa/0/BZ7LjIPh3QcBdINDTNSiXVrX4GlC9KlGZzus6FxHU3dpF9r175clPGTQYbg0knYDBhDY9AwYBWO7mYdzTDjL7CGSTYDQ29Pr9KafxabVdlwvvfPlLPmuHD2z44hGypgTBmOy7hfD3wzClVPhJCvbM+h/h7wVMnV38lrZludvu1OJVkng2gCtuhyc5Po++n4sFhy/J4x28Lyt4Uu2FaLUrSK5FcVKoyGIYOHPdrQ6OrqQleX+wehubm5hKMhhBAyECjCKuagpqTOoJdeeikMw8j5evPNN3vc/qJFi1BXV+e8xo0bF+Lo+wg9Y8z06pfYBb4UIZ6PSPPi6HkbwX/OuPOMVSC7mmEguyqRaQzZd9BjKPRahzB7LuQZzLNPtvO37S7YdgeEnZB+HMIGhF3Q/RS+l3tNst/H4PGF/dtT2JPPLUy685NVxJ+6PYaSKho/+MEPcPbZZ+fcZ9999+1x+5dddhkWLFjgfG5ubh6YxgYhhBAyQCmpoTFixAiMGDGiaO3H43HE4/H8Ow5U8qkAA8a/Q1Lw3Krg88pW1Ks7Ql7QVyO9kFuhBH0Uek2/VbUyk3b+HidfIWzPF4H70x3FK62ibSA7aUlm8JzbDjToDBouA8ZH48MPP8SuXbvw4YcfwrIsrF+/HgCw//77o7q6urSDI4QQsscwwGz4fs+AMTSuuuoq3HXXXc7nqVNluNuTTz6J448/vkSj6ucMhOyjnrGl58UIYjqZICV5cllkxbt/NnUjc5s6mqGQsWY7FoYBw4wFan149/NnIc2bXTUHuq20kGE97kJUsYzn6Eb6GM44/ZVf3Qqy+p5lziNiIHfNGMOMwTCjvmyrRoZy95mvS/cy2RIChKdB0V6RGEIMHtutubkZdXV1kAWXuvPLuh//oS6E/mxoeMlrGHU/bDU/3TM0Cg8/zmVo6BLzmQ2N9HDdnhsamqzJwnq8/OYfo+wji6GRzdjJSrBwnTpeeNOKpxsa3Rl331KipZOcuVz6OwKAhaamJtTW1vZZr2+//TYO/o+J+M64K0Np7/9t+RleWPMcpkyZEkp7A5UBo2iQXjAQlA3AyYHgfAz+QU37A+3P3tkzunlsluRg2cbqRyeXUt85eSKCXUT8+2cdSv4/mjo3iZGlr6z05DxFsL6I+k4pGq4KkdmASlc6goqIB92GEegz77wpTIWjH/leDJ75Yp/AyxkuNDQIIYQQD/3IhNwj4IIlIYQQQooGFY3BxEBZQlGk+Sb0oqR3scmXwlx9Kb9KKw8faMtJrx0s8BbcL1e4rl6myN1XVtLCRNVm5396+SeZfkzaGNJrl2TcLzgEvb+dyP5dtr4Lftb77zNFSoVAaK6LXIIBQEODEEII8cE8GuFCQ2Mw0m+VjdyOeoVHLfQFPUgIleYsme08tAqhVQOtcGS7X/lLt/eYrDO74LnkaKIb+0qCKkoOxUZTaLguIaTPoaFBCCGEeKC5Gi40NAghhBAPXDoJFxoahBBCiEKAhkbY0NAYzHQ7q2Jf0x98MfIRUgl2L871zxPxURA9G19vfDvcyJsetpH2/OU4h3x9FJzJdQBBPxQywKChQQghhHgYOKnaBwY0NAghhBAPYS2d0FyRMDMoIYQQQooGFQ2STiFrwHvCWndf0JP19Ly+M73wC+mD9f1eV5st5hhD8xsp0ThIn8DbEy40NAghhBAPNhc9QoVLJ4QQQggpGlQ0CCGEEA9cOgkXGhqEEEKIh4GQwWcgQUOjANJKgBOa/KVggF7znuYk6Fc/bwP02pOeEVqZeAKAPhqEEEIIKSJUNAghhBAPrHUSLjQ0CCGEEA8Mbw0XLp0QQgghpGhQ0SCkj+muc2S/corsQ8IobDVYrx3pHWH5glIXkdDQIIQQQjxw6SRcuHRCCCGEkKJBRYMQQgjxwDQa4UJDgxBCCPHApZNwoaHRDZghlBRKGI6MhbYVxvMY5nj7E31x7ciehx2apLFn/lx1F/poEEIIIaRoUNEghBBCPOypCl+poKFBCCGEeGD11nDh0gkhhBBCigYVjR5Ap1Ci6Q8Sa38Yw0Cl0Gs3EH/W+Vz0HEadhAsNDUIIIcSDYCKNUOHSCSGEEEKKBhUNQgghxENYSyfURSQ0NAghhBCFAH00woZLJ71AdOMfGdjwvg5uuvOzXorfB3w2Bz6LFi3C4YcfjpqaGowcORKzZ8/Gxo0bfft0dnZi7ty5GD58OKqrq3H66adj27ZtJRpx4dDQIIQQQjwI2KG8usPTTz+NuXPn4oUXXsCKFSuQTCbxuc99Dm1tbc4+3//+9/G3v/0NDzzwAJ5++ml8/PHH+PKXvxz26YcOl04IIYQQD6VYOnn00Ud9n5cvX46RI0dizZo1OPbYY9HU1ITf//73uPfee3HiiScCAJYtW4aDDz4YL7zwAj796U/3+ZgLhYoGIYQQ4iEcPaN3xkpTUxMAYNiwYQCANWvWIJlMYtasWc4+Bx10EMaPH4/nn3++V30VGyoahBBCSJFIJBJobm72bYvH44jH41mPsW0b8+fPx9FHH41JkyYBABoaGhCLxTBkyBDfvqNGjUJDQ0Po4w4TKhp9RNjOZHT+CgdeW1IK+LPfv7FD+gcAd955J+rq6nyvRYsW5ex/7ty52LBhA+67776+ON2iQ0WDEEII8SCMsMqqCZx77rlYvHixb2suNWPevHl45JFHsGrVKowdO9bZPnr0aCQSCTQ2NvpUjW3btmH06NEhjbc4UNEghBBCikQsFkNtba3vlcnQEEJg3rx5ePDBB/HEE09gwoQJvu+nT5+OaDSKlStXOts2btyIDz/8EDNnziz6efQGKhqEEEKIh1JkBp07dy7uvfdePPzww6ipqXH8Lurq6lBRUYG6ujqcd955WLBgAYYNG4ba2lpcfPHFmDlzZr+OOAFoaBBCCCE+tH9FX3LHHXcAAI4//njf9mXLluHss88GANx8880wTROnn346urq6cPLJJ+P222/v45F2HxoaexB0CiOEkIFJIRVjy8vLcdttt+G2227rgxGFBw0NQgghxEN3s3qS3NDQIIQQQjzYoUWdEICGBiGEEOKjFD4aezIMbyWEEEJI0aCiQQghhHigohEuNDQIIYQQB0Fn0JDh0gkhhBBCigYVDUIIIcSDDSuklpjbCKChQQghhPjg0km4cOmEEEIIIUWDigYhhBDiIayEXVw4kQwIReP999/HeeedhwkTJqCiogL77bcfFi5ciEQiUeqhEUII2cOwYYXyIpIBoWi8+eabsG0bv/nNb7D//vtjw4YNOP/889HW1oZf/OIXpR4eIYSQPQj6aITLgDA0Pv/5z+Pzn/+883nffffFxo0bcccdd9DQIIQQQvoxA8LQyERTUxOGDRuWc5+uri50dXU5n5ubm4s9LEIIIQMcWzC8NUwGhI9GkHfeeQe33HILvv3tb+fcb9GiRairq3Ne48aN66MREkIIGagI2KG8iKSkhsall14KwzByvt58803fMVu2bMHnP/95nHHGGTj//PNztn/ZZZehqanJeW3evLmYp0MIIYSQACVdOvnBD36As88+O+c+++67r/P/jz/+GCeccAKOOuoo/Pa3v83bfjweRzwe7+0wCSGEDBIEAMGIkVApqaExYsQIjBgxoqB9t2zZghNOOAHTp0/HsmXLYJoDctWHEEJIP4fVW8NlQDiDbtmyBccffzz23ntv/OIXv8COHTuc70aPHl3CkRFCCCEkFwPC0FixYgXeeecdvPPOOxg7dqzvOyHo1UsIISQ86MgZLgNi/eHss8+GECLjixBCCAkTIaxQXkQyIBQNQgghpK8Iy0dDMI8GgAGiaBBCCCFkYEJFgxBCCHEQDG8NGRoahBBCiAch6AwaJlw6IYQQQkjRoKJBCCGEeGDCrnChoUEIIYR4YGhquHDphBBCCCFFg4oGIYQQ4iG8zKDMowHQ0CCEEEJ8hBZ1QjsDAA0NQgghxAfzaIQLfTQIIYQQUjSoaBBCCCEemLArXGhoEEIIIR5oaIQLl04IIYQQUjSoaBBCCCEemBk0XGhoEEIIIR7CWjphdKuESyeEEEIIKRpUNAghhBAP4dU6oaYB0NAghBBCfISXgpwANDQIIYQQHwxvDRf6aBBCCCGkaFDRIIQQQhwEFY2QoaFBCCGEeKCPRrhw6YQQQgghRYOKBiGEEOKBSyfhQkODEEII8RCeocE8GgCXTgghhBBSRKhoEEIIIT64dBImNDQIIYQQD/TRCBcaGoQQQogHhreGC300CCGEEFI0qGgQQgghHrh0Ei40NAghhBAfYZWJJwCXTgghhBBSRKhoEEIIIR6YsCtcaGgQQgghPuijESZcOiGEEEJI0aCiQQghhHhh1Emo0NAghBBCPAj6VoQKl04IIYQQH3ZIr+5z2223YZ999kF5eTmOPPJI/Pvf/+7dqfQDaGgQQggh/YD7778fCxYswMKFC7F27VocdthhOPnkk7F9+/ZSD61X0NAghBBCvAgRzqub3HTTTTj//PNxzjnnYOLEiVi6dCkqKytx5513FuEk+w4aGoQQQogHEdK/7pBIJLBmzRrMmjXL2WaaJmbNmoXnn38+7FPsUwaVM6hwLEw6+hBCSP9F/o4WPVAFekM8Hlf/S4XWpmmaaG5uTuvH7Uuyc+dOWJaFUaNG+baPGjUKb775ZmjjKQWDytBoaWlR/2PoEiGE9HdaWlpQV1fXZ/2NHz8eq1evxieffBJKe0OGDMFf/vIXXHPNNb7tCxcuxNVXXx1KHwOBQWVo1NfXY/PmzaipqYFhGEXpo7m5GePGjcPmzZtRW1tblD4GIrwumeF1yQyvS3YGw7URQqClpQX19fV93veMGTNCbW/KlClYsGCBb1tQzQCAvfbaC5FIBNu2bfNt37ZtG0aPHh3qmPqaQWVomKaJsWPH9klftbW1e+wvgd7A65IZXpfM8LpkZ0+/Nn2pZBSTTMskmYjFYpg+fTpWrlyJ2bNnAwBs28bKlSsxb968Io+yuAwqQ4MQQgjpryxYsABz5szBjBkzcMQRR2DJkiVoa2vDOeecU+qh9QoaGoQQQkg/4Ktf/Sp27NiBq666Cg0NDZgyZQoeffTRNAfRgQYNjZCJx+NYuHBhQVLZYILXJTO8LpnhdckOr82ezbx58wb8UkkQQ/R1/BAhhBBCBg1M2EUIIYSQokFDgxBCCCFFg4YGIYQQQooGDY0Q2RPL+/aWVatW4dRTT0V9fT0Mw8BDDz1U6iH1CxYtWoTDDz8cNTU1GDlyJGbPno2NGzeWelgl54477sDkyZOdHBEzZ87EP/7xj1IPq9/xs5/9DIZhYP78+aUeCiF5oaEREntqed/e0tbWhsMOOwy33XZbqYfSr3j66acxd+5cvPDCC1ixYgWSySQ+97nPoa2trdRDKyljx47Fz372M6xZswYvvfQSTjzxRJx22ml47bXXSj20fsPq1avxm9/8BpMnTy71UAgpCEadhMSRRx6Jww8/HLfeeisAmdFt3LhxuPjii3HppZeWeHT9A8Mw8OCDDzpZ74jLjh07MHLkSDz99NM49thjSz2cfsWwYcPw85//HOedd16ph1JyWltbMW3aNNx+++247rrrMGXKFCxZsqTUwyIkJ1Q0QmBPLu9L+oampiYA8o8qkViWhfvuuw9tbW2YOXNmqYfTL5g7dy5OOeUU3+8aQvo7TNgVAntyeV9SfGzbxvz583H00Udj0qRJpR5OyXn11Vcxc+ZMdHZ2orq6Gg8++CAmTpxY6mGVnPvuuw9r167F6tWrSz0UQroFDQ1CSszcuXOxYcMGPPPMM6UeSr/gwAMPxPr169HU1IQ///nPmDNnDp5++ulBbWxs3rwZ3/ve97BixQqUl5eXejiEdAsaGiGwJ5f3JcVl3rx5eOSRR7Bq1ao+qyzc34nFYth///0BANOnT8fq1avxq1/9Cr/5zW9KPLLSsWbNGmzfvh3Tpk1ztlmWhVWrVuHWW29FV1cXIpFICUdISHbooxEC3vK+Gl3el2vLJBNCCMybNw8PPvggnnjiCUyYMKHUQ+q32LaNrq6uUg+jpJx00kl49dVXsX79euc1Y8YMnHXWWVi/fj2NDNKvoaIREntqed/e0trainfeecf5vGnTJqxfvx7Dhg3D+PHjSziy0jJ37lzce++9ePjhh1FTU4OGhgYAQF1dHSoqKko8utJx2WWX4Qtf+ALGjx+PlpYW3HvvvXjqqafw2GOPlXpoJaWmpibNf6eqqgrDhw+nXw/p99DQCIk9tbxvb3nppZdwwgknOJ8XLFgAAJgzZw6WL19eolGVnjvuuAMAcPzxx/u2L1u2DGeffXbfD6ifsH37dnzrW9/C1q1bUVdXh8mTJ+Oxxx7DZz/72VIPjRDSQ5hHgxBCCCFFgz4ahBBCCCkaNDQIIYQQUjRoaBBCCCGkaNDQIIQQQkjRoKFBCCGEkKJBQ4MQQgghRYOGBiGEEEKKBg0NQgghhBQNGhqEEEIIKRo0NAghhBBSNGhoEEIIIaRo0NAgZACyY8cOjB49GjfccIOz7bnnnkMsFsPKlStLODJCCPHDomqEDFD+7//+D7Nnz8Zzzz2HAw88EFOmTMFpp52Gm266qdRDI4QQBxoahAxg5s6di3/+85+YMWMGXn31VaxevRrxeLzUwyKEEAcaGoQMYDo6OjBp0iRs3rwZa9aswaGHHlrqIRFCiA/6aBAygHn33Xfx8ccfw7ZtvP/++6UeDiGEpEFFg5ABSiKRwBFHHIEpU6bgwAMPxJIlS/Dqq69i5MiRpR4aIYQ40NAgZIDyox/9CH/+85/x8ssvo7q6Gscddxzq6urwyCOPlHpohBDiwKUTQgYgTz31FJYsWYK7774btbW1ME0Td999N/71r3/hjjvuKPXwCCHEgYoGIYQQQooGFQ1CCCGEFA0aGoQQQggpGjQ0CCGEEFI0aGgQQgghpGjQ0CCEEEJI0aChQQghhJCiQUODEEIIIUWDhgYhhBBCigYNDUIIIYQUDRoahBBCCCkaNDQIIYQQUjRoaBBCCCGkaPz/8B0XzWLKnBoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data_final.plot_field(\"field\", \"Ey\", \"abs\", vmin=0, vmax=1e2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89311128",
   "metadata": {},
   "source": [
    "Finally plot the reflection spectrum, where we see a broadband reflection. Considering the very compact footprint, the performance is very good. With this reflector design, we can further construct other devices such as high-quality-factor Fabry–Perot cavities as demonstrated in the [publication](https://doi.org/10.1364/PRJ.5.000B15)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "20e9667f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "R = abs(sim_data_final[\"mode\"].amps.sel(direction=\"-\").squeeze(drop=True).values) ** 2\n",
    "\n",
    "plt.plot(ldas, R, \"red\", linewidth=2)\n",
    "plt.xlim(min(ldas), max(ldas))\n",
    "plt.ylim(0, 1)\n",
    "plt.xlabel(\"Wavelength (µm)\")\n",
    "plt.ylabel(\"Reflectivity\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cf44ce8",
   "metadata": {},
   "source": [
    "With the optimized design, we can directly export a GDS file of the reflector for fabrication. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "26cf87f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make the misc/ directory to store the GDS file if it doesnt exist already\n",
    "import os\n",
    "if not os.path.exists('./misc/'):\n",
    "    os.mkdir('./misc/')\n",
    "    \n",
    "sim_final.to_gds_file(fname=\"misc/optimized_reflector.gds\", z=t / 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a60323d-aac4-44a0-a855-e3f3e7057027",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components"
  ],
  "description": "This notebook demonstrates how to use genetic algorithm (GA) to optimize an on-chip reflector.",
  "feature_image": "./img/optimized_reflector.png",
  "features": [
   "Global optimization"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "genetic algorithm, optimization, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  },
  "title": "Genetic Algorithm Optimization of a Reflector | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}