Autograd6GratingCoupler.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inverse design optimization of a compact grating coupler\n",
    "\n",
    "> Note: native autograd support is an experimental feature in tidy3d 2.7. To see the original implementation of this notebook using jax and the adjoint plugin, refer to [this notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin6GratingCoupler/).\n",
    "\n",
    "**This notebook contains a long optimization. Running the entire notebook will cost about 10 FlexCredits and take a few hours.**\n",
    "\n",
    "The ability to couple light in and out of photonic integrated circuits (PICs) is crucial for developing wafer-scale systems and tests. This need makes designing efficient and compact grating couplers an important task in the PIC development cycle. In this notebook, we will demonstrate how to use `tidy3d` to perform the inverse design of a compact 3D grating coupler. We will show how to improve design fabricability by enhancing permittivity binarization and controlling the device's minimum feature size.\n",
    "\n",
    "<img src=\"img/adjoint_6.png\" width=400 alt=\"Schematic of the grating coupler\">\n",
    "\n",
    "In addition, if you are interested in more conventional designs, we modeled an [uniform grating coupler](https://www.flexcompute.com/tidy3d/examples/notebooks/GratingCoupler/) and a [Focusing apodized grating coupler](https://www.flexcompute.com/tidy3d/examples/notebooks/FocusedApodGC/) in previous case studies. For more integrated photonic examples, please visit our [examples page](https://www.flexcompute.com/tidy3d/examples/). If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. FDTD simulations can diverge due to various reasons. If you run into any simulation divergence issues, please follow the steps outlined in our [troubleshooting guide](https://www.flexcompute.com/tidy3d/examples/notebooks/DivergedFDTDSimulation/) to resolve it.\n",
    "\n",
    "We start by importing our typical python packages, plus `autograd` and `tidy3d`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard python imports.\n",
    "from typing import List\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "# Import autograd to be able to use automatic differentiation.\n",
    "import autograd.numpy as anp\n",
    "from autograd import value_and_grad\n",
    "\n",
    "# Import regular tidy3d.\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grating Coupler Inverse Design Configuration\n",
    "\n",
    "The grating coupler inverse design begins with a rectangular design region connected to a $Si$ waveguide. Throughout the optimization process, this initial structure evolves to convert a vertically incident Gaussian-like mode from an optical fiber into a guided mode and then funnel it into the $Si$ waveguide.\n",
    "\n",
    "We are considering a full-etched grating structure, so a $SiO_{2}$ BOX layer is included. To reduce backreflection, we adjusted the fiber tilt angle to $10^{\\circ}$ [[1](https://doi.org/10.1364/OE.23.022628), [2](https://doi.org/10.3390/mi11070666)].\n",
    "\n",
    "In the following block of code, you can find the parameters that can be modified to configure the grating coupler structure, optimization, and simulation setup. Special care should be devoted to the `it_per_step` and `opt_steps` variables bellow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Geometric parameters.\n",
    "w_thick = 0.22  # Waveguide thickness (um).\n",
    "w_width = 0.5  # Waveguide width (um).\n",
    "w_length = 1.0  # Waveguide length (um).\n",
    "box_thick = 1.6  # SiO2 BOX thickness (um).\n",
    "spot_size = 2.5  # Spot size of the input Gaussian field regarding a lensed fiber (um).\n",
    "fiber_tilt = 10.0  # Fiber tilt angle (degrees).\n",
    "src_offset = 0.05  # Distance between the source focus and device (um).\n",
    "\n",
    "# Material.\n",
    "nSi = 3.48  # Silicon refractive index.\n",
    "nSiO2 = 1.44  # Silica refractive index.\n",
    "\n",
    "# Design region parameters.\n",
    "gc_width = 4.0  # Grating coupler width (um).\n",
    "gc_length = 4.0  # Grating coupler length (um).\n",
    "dr_grid_size = 0.02  # Grid size within the design region (um).\n",
    "\n",
    "# Inverse design set up parameters.\n",
    "#################################################################\n",
    "# Total number of iterations = opt_steps x it_per_step.\n",
    "it_per_step = 1  # Number of iterations per optimization step.\n",
    "opt_steps = 75  # Number of optimization steps.\n",
    "#################################################################\n",
    "eta = 0.50  # Threshold value for the projection filter.\n",
    "fom_name = \"fom_field\"  # Name of the monitor used to compute the objective function.\n",
    "\n",
    "# Simulation wavelength.\n",
    "wl = 1.55  # Central simulation wavelength (um).\n",
    "bw = 0.06  # Simulation bandwidth (um).\n",
    "n_wl = 61  # Number of wavelength points within the bandwidth.\n",
    "\n",
    "# feature size\n",
    "min_feature_size = 0.080\n",
    "filter_radius = min_feature_size\n",
    "\n",
    "# Buffer layer thickness\n",
    "border_buffer = 0.16\n",
    "\n",
    "# projection\n",
    "beta_min = 1.0\n",
    "beta_max = 30.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total iterations = 75\n"
     ]
    }
   ],
   "source": [
    "total_iter = opt_steps * it_per_step\n",
    "print(f\"Total iterations = {total_iter}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inverse Design Optimization Set Up\n",
    "\n",
    "We will calculate the values of some parameters used throughout the inverse design set up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Minimum and maximum values for the permittivities.\n",
    "eps_max = nSi**2\n",
    "eps_min = 1.0\n",
    "\n",
    "# Material definitions.\n",
    "mat_si = td.Medium(permittivity=eps_max)  # Waveguide material.\n",
    "mat_sio2 = td.Medium(permittivity=nSiO2**2)  # Substrate material.\n",
    "\n",
    "# Wavelengths and frequencies.\n",
    "wl_max = wl + bw / 2\n",
    "wl_min = wl - bw / 2\n",
    "wl_range = np.linspace(wl_min, wl_max, n_wl)\n",
    "freq = td.C_0 / wl\n",
    "freqs = td.C_0 / wl_range\n",
    "freqw = 0.5 * (freqs[0] - freqs[-1])\n",
    "run_time = 5e-12\n",
    "\n",
    "# Computational domain size.\n",
    "pml_spacing = 0.6 * wl\n",
    "size_x = pml_spacing + w_length + gc_length + 2 * border_buffer\n",
    "size_y = gc_width + 2 * pml_spacing + 2 * border_buffer\n",
    "size_z = w_thick + box_thick + 2 * pml_spacing\n",
    "center_z = size_z / 2 - pml_spacing - w_thick / 2\n",
    "eff_inf = 1000\n",
    "\n",
    "# Inverse design variables.\n",
    "src_pos_z = w_thick / 2 + src_offset\n",
    "mon_pos_x = -size_x / 2 + 0.25 * wl\n",
    "mon_w = int(3 * w_width / dr_grid_size) * dr_grid_size\n",
    "mon_h = int(5 * w_thick / dr_grid_size) * dr_grid_size\n",
    "nx = int((gc_length + 2 * border_buffer) / dr_grid_size)\n",
    "ny = int((gc_width + 2 * border_buffer) / dr_grid_size / 2.0)\n",
    "npar = int(nx * ny)\n",
    "dr_size_x = nx * dr_grid_size\n",
    "dr_size_y = 2 * ny * dr_grid_size\n",
    "dr_center_x = -size_x / 2 + w_length + dr_size_x / 2\n",
    "n_border = int(border_buffer / dr_grid_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will introduce the simulation components that do not change during optimization, such as the $Si$ waveguide and $SiO_{2}$ BOX layer. Additionally, we will include a Gaussian source to drive the simulations, and a mode monitor to compute the objective function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input/output waveguide.\n",
    "waveguide = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-eff_inf, -w_width / 2, -w_thick / 2),\n",
    "        rmax=(-size_x / 2 + w_length, w_width / 2, w_thick / 2),\n",
    "    ),\n",
    "    medium=mat_si,\n",
    ")\n",
    "\n",
    "# SiO2 BOX layer.\n",
    "sio2_substrate = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-eff_inf, -eff_inf, -w_thick / 2 - box_thick),\n",
    "        rmax=(eff_inf, eff_inf, -w_thick / 2),\n",
    "    ),\n",
    "    medium=mat_sio2,\n",
    ")\n",
    "\n",
    "# Si substrate.\n",
    "si_substrate = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-eff_inf, -eff_inf, -eff_inf),\n",
    "        rmax=(eff_inf, eff_inf, -w_thick / 2 - box_thick),\n",
    "    ),\n",
    "    medium=mat_si,\n",
    ")\n",
    "\n",
    "# Gaussian source focused above the grating coupler.\n",
    "gauss_source = td.GaussianBeam(\n",
    "    center=(dr_center_x, 0, src_pos_z),\n",
    "    size=(dr_size_x - 2 * border_buffer, dr_size_y - 2 * border_buffer, 0),\n",
    "    source_time=td.GaussianPulse(freq0=freq, fwidth=freqw),\n",
    "    pol_angle=np.pi / 2,\n",
    "    angle_theta=fiber_tilt * np.pi / 180.0,\n",
    "    direction=\"-\",\n",
    "    num_freqs=7,\n",
    "    waist_radius=spot_size / 2,\n",
    ")\n",
    "\n",
    "# Monitor where we will compute the objective function from.\n",
    "mode_spec = td.ModeSpec(num_modes=1, target_neff=nSi)\n",
    "fom_monitor = td.ModeMonitor(\n",
    "    center=[mon_pos_x, 0, 0],\n",
    "    size=[0, mon_w, mon_h],\n",
    "    freqs=[freq],\n",
    "    mode_spec=mode_spec,\n",
    "    name=fom_name,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will define a random vector of initial design parameters or load a previously designed structure.\n",
    "\n",
    "> Note: if a previous optimization file is found, the optimizer will pick up where that left off instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_par = np.random.uniform(0, 1, int(npar))\n",
    "init_par = sp.ndimage.gaussian_filter(init_par, 1)\n",
    "init_par = init_par.reshape((nx, ny))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fabrication Constraints\n",
    "\n",
    "We will use the `tidy3d.plugins.autograd` plugin to introduce functions that improve device fabricability. A classical conic density filter, which is popular in topology optimization problems, is used to enforce a minimum feature size specified by the `filter_radius` variable. Next, a hyperbolic tangent projection function is applied to eliminate grayscale and obtain a binarized permittivity pattern. The `beta` parameter controls the sharpness of the transition in the projection function, and for better results, this parameter should be gradually increased throughout the optimization process. Finally, the design parameters are transformed into permittivity values. For a detailed review of these methods, refer to [[3](https://doi.org/10.1007/s00419-015-1106-4)].\n",
    "\n",
    "We will also introduce a buffer layer around the design region to enhance fabricability at the interfaces. The permittivity is enforced to lower values within the buffer layer, except at the output waveguide connection where we want a smooth transition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_eps(design_param: np.ndarray, beta: float = 1.00, binarize: bool = False) -> np.ndarray:\n",
    "    \"\"\"Returns the permittivities after applying a conic density filter on design parameters\n",
    "    to enforce fabrication constraints, followed by a binarization projection function\n",
    "    which reduces grayscale.\n",
    "    Parameters:\n",
    "        design_param: np.ndarray\n",
    "            Vector of design parameters.\n",
    "        beta: float = 1.0\n",
    "            Sharpness parameter for the projection filter.\n",
    "        binarize: bool = False\n",
    "            Enforce binarization.\n",
    "    Returns:\n",
    "        eps: np.ndarray\n",
    "            Permittivity vector.\n",
    "    \"\"\"\n",
    "\n",
    "    # Calculates the permittivities from the transformed design parameters.\n",
    "    eps = get_eps_values(design_param, beta=beta)\n",
    "    if binarize:\n",
    "        eps = anp.where(eps < (eps_min + eps_max) / 2, eps_min, eps_max)\n",
    "    else:\n",
    "        eps = anp.where(eps < eps_min, eps_min, eps)\n",
    "        eps = anp.where(eps > eps_max, eps_max, eps)\n",
    "    return eps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tidy3d.plugins.autograd import rescale, make_filter_and_project\n",
    "\n",
    "filter_project = make_filter_and_project(filter_radius, dr_grid_size, padding=\"constant\")\n",
    "\n",
    "\n",
    "def interface_buffer(params):\n",
    "    \"\"\"Introduce a buffer around design to enhance fabricability at the interfaces.\"\"\"\n",
    "    mask = anp.zeros_like(params)\n",
    "    mask[0:n_border, :] = 0\n",
    "    mask[nx - n_border :, :] = 0\n",
    "    mask[:, ny - n_border :] = 0\n",
    "    mask[0:n_border, 0 : int((w_width / 2) / dr_grid_size) + 1] = 1\n",
    "\n",
    "    return params * (1 - mask) + mask\n",
    "\n",
    "\n",
    "def pre_process(params, beta):\n",
    "    \"\"\"Get the permittivity values (1, eps_wg) array as a funciton of the parameters (0,1)\"\"\"\n",
    "    params1 = interface_buffer(params)\n",
    "    params2 = filter_project(params1, beta=beta)\n",
    "    params3 = filter_project(params2, beta=beta)\n",
    "    return params3\n",
    "\n",
    "\n",
    "def get_eps_values(params: np.ndarray, beta: float) -> np.ndarray:\n",
    "    \"\"\"Get the relative permittivity array given the parameters.\"\"\"\n",
    "    params = pre_process(params, beta=beta)\n",
    "    eps_values = rescale(params, eps_min, eps_max)\n",
    "    return eps_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The permittivity values obtained from the design parameters are then used to build a [CustomMedium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.CustomMedium.html). As we will consider symmetry about the x-axis in the simulations, only the upper-half part of the design region needs to be populated. A `Structure` built using the `CustomMedium` will be returned by the following function: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_design(eps, unfold: bool = False) -> List[td.Structure]:\n",
    "    \"\"\" Reflects the structure about the x-axis.\"\"\"\n",
    "    nyii = ny\n",
    "    y_min = 0\n",
    "    dr_s_y = dr_size_y / 2\n",
    "    dr_c_y = dr_s_y / 2\n",
    "    eps_val = anp.array(eps).reshape((nx, ny, 1))\n",
    "    if unfold:\n",
    "        nyii = 2 * ny\n",
    "        y_min = -dr_size_y / 2\n",
    "        dr_s_y = dr_size_y\n",
    "        dr_c_y = 0\n",
    "        eps_val = anp.concatenate((anp.fliplr(anp.copy(eps_val)), eps_val), axis=1)\n",
    "\n",
    "    # Definition of the coordinates x,y along the design region.\n",
    "    coords_x = [(dr_center_x - dr_size_x / 2) + ix * dr_grid_size for ix in range(nx)]\n",
    "    coords_y = [y_min + iy * dr_grid_size for iy in range(nyii)]\n",
    "    coords = dict(x=coords_x, y=coords_y, z=[0])\n",
    "\n",
    "    # Creation of a custom medium using the values of the design parameters.\n",
    "    permittivity = td.SpatialDataArray(eps_val, coords=coords)\n",
    "    eps_medium = td.CustomMedium(permittivity=permittivity)\n",
    "    box = td.Box(center=(dr_center_x, dr_c_y, 0), size=(dr_size_x, dr_s_y, w_thick))\n",
    "    design_structure = td.Structure(geometry=box, medium=eps_medium)\n",
    "    return [design_structure]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will write a function to return the `td.Simulation` object. Note that we are using a `MeshOverrideStructure` to obtain a uniform mesh over the design region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_adjoint_sim(\n",
    "    design_param : np.ndarray, beta: float = 1.00, unfold: bool = False, binarize: bool = False\n",
    ") -> td.Simulation:\n",
    "    # Builds the design region from the design parameters.\n",
    "    eps = get_eps(design_param, beta, binarize)\n",
    "    design_structure = update_design(eps, unfold=unfold)\n",
    "\n",
    "    # Creates a uniform mesh for the design region.\n",
    "    adjoint_dr_mesh = td.MeshOverrideStructure(\n",
    "        geometry=td.Box(center=(dr_center_x, 0, 0), size=(dr_size_x, dr_size_y, w_thick)),\n",
    "        dl=[dr_grid_size, dr_grid_size, dr_grid_size],\n",
    "        enforce=True,\n",
    "    )\n",
    "\n",
    "    return td.Simulation(\n",
    "        size=[size_x, size_y, size_z],\n",
    "        center=[0, 0, -center_z],\n",
    "        grid_spec=td.GridSpec.auto(\n",
    "            wavelength=wl_max,\n",
    "            min_steps_per_wvl=15,\n",
    "            override_structures=[adjoint_dr_mesh],\n",
    "        ),\n",
    "        symmetry=(0, -1, 0),\n",
    "        structures=[waveguide, sio2_substrate, si_substrate] + design_structure,\n",
    "        sources=[gauss_source],\n",
    "        monitors=[fom_monitor],\n",
    "        run_time=run_time,\n",
    "        subpixel=True,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the simulation set up and verify if all the elements are in their correct places."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "init_design = make_adjoint_sim(init_par, beta=beta_min)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(10, 10))\n",
    "init_design.plot_eps(z=0, ax=ax1)\n",
    "init_design.plot_eps(y=0, ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tidy3d.plugins.autograd import make_erosion_dilation_penalty\n",
    "\n",
    "erode_dilate_penalty = make_erosion_dilation_penalty(filter_radius, dr_grid_size)\n",
    "\n",
    "\n",
    "# Figure of Merit (FOM) calculation.\n",
    "def fom(sim_data: td.SimulationData) -> float:\n",
    "    \"\"\"Return the power at the mode index of interest.\"\"\"\n",
    "    output_amps = sim_data[fom_name].amps\n",
    "    amp = output_amps.sel(direction=\"-\", f=freq, mode_index=0).values\n",
    "    return anp.sum(anp.abs(amp) ** 2)\n",
    "\n",
    "\n",
    "def penalty(params, beta) -> float:\n",
    "    \"\"\"Penalty function based on amount of change in parameters after erosion and dilation.\"\"\"\n",
    "    params_processed = pre_process(params, beta=beta)\n",
    "    return erode_dilate_penalty(params_processed)\n",
    "\n",
    "\n",
    "# Objective function to be passed to the optimization algorithm.\n",
    "def obj(design_param, beta: float = 1.0, step_num: int = None, verbose: bool = False) -> float:\n",
    "    sim = make_adjoint_sim(design_param, beta)\n",
    "    task_name = \"inv_des\"\n",
    "    if step_num:\n",
    "        task_name += f\"_step_{step_num}\"\n",
    "    sim_data = web.run(sim, task_name=task_name, verbose=verbose)\n",
    "    fom_val = fom(sim_data)\n",
    "    feature_size_penalty = penalty(design_param, beta=beta)\n",
    "    J = fom_val - feature_size_penalty\n",
    "    return J\n",
    "\n",
    "\n",
    "# Function to calculate the objective function value and its\n",
    "# gradient with respect to the design parameters.\n",
    "obj_grad = value_and_grad(obj)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimization\n",
    "\n",
    "We need to provide an objective function and its gradients with respect to the design parameters of the optimization algorithm.\n",
    "\n",
    "Our figure-of-merit (FOM) is the coupling efficiency of the incident power into the fundamental transverse electric mode of the $Si$ waveguide. The optimization algorithm will call the objective function at each iteration step. Therefore, the objective function will create the adjoint simulation, run it, and return the FOM value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will define the optimizer using `optax`. We will save the optimization progress in a `pickle` file. If that file is found, it will pick up the optimization from the last state. Otherwise, we will create a blank history."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import optax\n",
    "import pickle\n",
    "\n",
    "# hyperparameters\n",
    "learning_rate = 0.2\n",
    "optimizer = optax.adam(learning_rate=learning_rate)\n",
    "\n",
    "# where to store history\n",
    "history_fname = \"misc/grating_coupler_history_autograd.pkl\"\n",
    "\n",
    "\n",
    "def save_history(history_dict: dict) -> None:\n",
    "    \"\"\"Convenience function to save the history to file.\"\"\"\n",
    "    with open(history_fname, \"wb\") as file:\n",
    "        pickle.dump(history_dict, file)\n",
    "\n",
    "\n",
    "def load_history() -> dict:\n",
    "    \"\"\"Convenience method to load the history from file.\"\"\"\n",
    "    with open(history_fname, \"rb\") as file:\n",
    "        history_dict = pickle.load(file)\n",
    "    return history_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Checking For a Previous Optimization\n",
    "\n",
    "If `history_fname` is a valid file, the results of a previous optimization are loaded, then the optimization will continue from the last iteration step. If the optimization was completed, only the final structure will be simulated. The pickle file used in this notebook can be downloaded from our documentation [repo](https://github.com/flexcompute/tidy3d-notebooks/tree/develop/misc)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded optimization checkpoint from file.\n",
      "Found 7 iterations previously completed out of 75 total.\n",
      "Will resume optimization.\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    history_dict = load_history()\n",
    "    opt_state = history_dict[\"opt_states\"][-1]\n",
    "    params = history_dict[\"params\"][-1]\n",
    "    num_iters_completed = len(history_dict[\"params\"])\n",
    "    print(\"Loaded optimization checkpoint from file.\")\n",
    "    print(f\"Found {num_iters_completed} iterations previously completed out of {total_iter} total.\")\n",
    "    if num_iters_completed < total_iter:\n",
    "        print(f\"Will resume optimization.\")\n",
    "    else:\n",
    "        print(\"Optimization completed, will return results.\")\n",
    "\n",
    "except FileNotFoundError:\n",
    "    params = np.array(init_par)\n",
    "    opt_state = optimizer.init(params)\n",
    "    history_dict = dict(\n",
    "        values=[],\n",
    "        params=[],\n",
    "        gradients=[],\n",
    "        opt_states=[opt_state],\n",
    "        data=[],\n",
    "        beta=[],\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration = (8 / 75)\n",
      "\tbeta = 3.7432432432432434\n",
      "\tJ = -1.0105e-01\n",
      "\tgrad_norm = 2.0684e+00\n",
      "iteration = (9 / 75)\n",
      "\tbeta = 4.135135135135135\n",
      "\tJ = -6.4229e-02\n",
      "\tgrad_norm = 5.6390e+00\n",
      "iteration = (10 / 75)\n",
      "\tbeta = 4.527027027027027\n",
      "\tJ = -5.4990e-02\n",
      "\tgrad_norm = 6.8322e+00\n",
      "iteration = (11 / 75)\n",
      "\tbeta = 4.918918918918919\n",
      "\tJ = 3.7188e-03\n",
      "\tgrad_norm = 6.0532e+00\n",
      "iteration = (12 / 75)\n",
      "\tbeta = 5.3108108108108105\n",
      "\tJ = 1.2623e-01\n",
      "\tgrad_norm = 4.8860e+00\n",
      "iteration = (13 / 75)\n",
      "\tbeta = 5.702702702702703\n",
      "\tJ = 1.3554e-01\n",
      "\tgrad_norm = 6.2354e+00\n",
      "iteration = (14 / 75)\n",
      "\tbeta = 6.094594594594595\n",
      "\tJ = 2.0426e-01\n",
      "\tgrad_norm = 2.8154e+00\n",
      "iteration = (15 / 75)\n",
      "\tbeta = 6.486486486486487\n",
      "\tJ = 2.1564e-01\n",
      "\tgrad_norm = 5.5530e+00\n",
      "iteration = (16 / 75)\n",
      "\tbeta = 6.878378378378379\n",
      "\tJ = 3.0087e-01\n",
      "\tgrad_norm = 2.9738e+00\n",
      "iteration = (17 / 75)\n",
      "\tbeta = 7.27027027027027\n",
      "\tJ = 2.8483e-01\n",
      "\tgrad_norm = 6.5081e+00\n",
      "iteration = (18 / 75)\n",
      "\tbeta = 7.662162162162162\n",
      "\tJ = 3.2850e-01\n",
      "\tgrad_norm = 5.6174e+00\n",
      "iteration = (19 / 75)\n",
      "\tbeta = 8.054054054054054\n",
      "\tJ = 3.4609e-01\n",
      "\tgrad_norm = 3.9949e+00\n",
      "iteration = (20 / 75)\n",
      "\tbeta = 8.445945945945946\n",
      "\tJ = 3.5675e-01\n",
      "\tgrad_norm = 3.3927e+00\n",
      "iteration = (21 / 75)\n",
      "\tbeta = 8.837837837837839\n",
      "\tJ = 3.9659e-01\n",
      "\tgrad_norm = 2.0726e+00\n",
      "iteration = (22 / 75)\n",
      "\tbeta = 9.229729729729728\n",
      "\tJ = 4.1460e-01\n",
      "\tgrad_norm = 2.4157e+00\n",
      "iteration = (23 / 75)\n",
      "\tbeta = 9.621621621621621\n",
      "\tJ = 4.2088e-01\n",
      "\tgrad_norm = 3.0004e+00\n",
      "iteration = (24 / 75)\n",
      "\tbeta = 10.013513513513514\n",
      "\tJ = 4.3382e-01\n",
      "\tgrad_norm = 1.2936e+00\n",
      "iteration = (25 / 75)\n",
      "\tbeta = 10.405405405405405\n",
      "\tJ = 4.4221e-01\n",
      "\tgrad_norm = 9.0443e-01\n",
      "iteration = (26 / 75)\n",
      "\tbeta = 10.797297297297296\n",
      "\tJ = 4.5096e-01\n",
      "\tgrad_norm = 6.8137e-01\n",
      "iteration = (27 / 75)\n",
      "\tbeta = 11.18918918918919\n",
      "\tJ = 4.5168e-01\n",
      "\tgrad_norm = 8.2429e-01\n",
      "iteration = (28 / 75)\n",
      "\tbeta = 11.58108108108108\n",
      "\tJ = 4.5227e-01\n",
      "\tgrad_norm = 5.7163e-01\n",
      "iteration = (29 / 75)\n",
      "\tbeta = 11.972972972972974\n",
      "\tJ = 4.5806e-01\n",
      "\tgrad_norm = 8.0114e-01\n",
      "iteration = (30 / 75)\n",
      "\tbeta = 12.364864864864865\n",
      "\tJ = 4.6744e-01\n",
      "\tgrad_norm = 7.7619e-01\n",
      "iteration = (31 / 75)\n",
      "\tbeta = 12.756756756756758\n",
      "\tJ = 4.7429e-01\n",
      "\tgrad_norm = 4.9001e-01\n",
      "iteration = (32 / 75)\n",
      "\tbeta = 13.148648648648647\n",
      "\tJ = 4.7699e-01\n",
      "\tgrad_norm = 3.9124e-01\n",
      "iteration = (33 / 75)\n",
      "\tbeta = 13.54054054054054\n",
      "\tJ = 4.7944e-01\n",
      "\tgrad_norm = 4.0417e-01\n",
      "iteration = (34 / 75)\n",
      "\tbeta = 13.932432432432433\n",
      "\tJ = 4.8034e-01\n",
      "\tgrad_norm = 5.4810e-01\n",
      "iteration = (35 / 75)\n",
      "\tbeta = 14.324324324324325\n",
      "\tJ = 4.8560e-01\n",
      "\tgrad_norm = 4.1935e-01\n",
      "iteration = (36 / 75)\n",
      "\tbeta = 14.716216216216216\n",
      "\tJ = 4.8965e-01\n",
      "\tgrad_norm = 4.2543e-01\n",
      "iteration = (37 / 75)\n",
      "\tbeta = 15.108108108108109\n",
      "\tJ = 4.9190e-01\n",
      "\tgrad_norm = 5.5886e-01\n",
      "iteration = (38 / 75)\n",
      "\tbeta = 15.5\n",
      "\tJ = 4.9426e-01\n",
      "\tgrad_norm = 4.2057e-01\n",
      "iteration = (39 / 75)\n",
      "\tbeta = 15.891891891891891\n",
      "\tJ = 4.9409e-01\n",
      "\tgrad_norm = 4.1136e-01\n",
      "iteration = (40 / 75)\n",
      "\tbeta = 16.283783783783782\n",
      "\tJ = 4.9623e-01\n",
      "\tgrad_norm = 6.7276e-01\n",
      "iteration = (41 / 75)\n",
      "\tbeta = 16.675675675675677\n",
      "\tJ = 5.0001e-01\n",
      "\tgrad_norm = 5.3285e-01\n",
      "iteration = (42 / 75)\n",
      "\tbeta = 17.06756756756757\n",
      "\tJ = 5.0185e-01\n",
      "\tgrad_norm = 2.8375e-01\n",
      "iteration = (43 / 75)\n",
      "\tbeta = 17.459459459459456\n",
      "\tJ = 5.0488e-01\n",
      "\tgrad_norm = 5.1513e-01\n",
      "iteration = (44 / 75)\n",
      "\tbeta = 17.85135135135135\n",
      "\tJ = 5.1086e-01\n",
      "\tgrad_norm = 5.6422e-01\n",
      "iteration = (45 / 75)\n",
      "\tbeta = 18.243243243243246\n",
      "\tJ = 5.1546e-01\n",
      "\tgrad_norm = 4.0330e-01\n",
      "iteration = (46 / 75)\n",
      "\tbeta = 18.635135135135133\n",
      "\tJ = 5.1873e-01\n",
      "\tgrad_norm = 3.8604e-01\n",
      "iteration = (47 / 75)\n",
      "\tbeta = 19.027027027027028\n",
      "\tJ = 5.2127e-01\n",
      "\tgrad_norm = 2.7575e-01\n",
      "iteration = (48 / 75)\n",
      "\tbeta = 19.41891891891892\n",
      "\tJ = 5.2196e-01\n",
      "\tgrad_norm = 2.3169e-01\n",
      "iteration = (49 / 75)\n",
      "\tbeta = 19.81081081081081\n",
      "\tJ = 5.2092e-01\n",
      "\tgrad_norm = 3.4853e-01\n",
      "iteration = (50 / 75)\n",
      "\tbeta = 20.202702702702705\n",
      "\tJ = 5.2071e-01\n",
      "\tgrad_norm = 2.5739e-01\n",
      "iteration = (51 / 75)\n",
      "\tbeta = 20.594594594594593\n",
      "\tJ = 5.1994e-01\n",
      "\tgrad_norm = 3.1624e-01\n",
      "iteration = (52 / 75)\n",
      "\tbeta = 20.986486486486484\n",
      "\tJ = 5.2340e-01\n",
      "\tgrad_norm = 4.3364e-01\n",
      "iteration = (53 / 75)\n",
      "\tbeta = 21.37837837837838\n",
      "\tJ = 5.2250e-01\n",
      "\tgrad_norm = 6.2767e-01\n",
      "iteration = (54 / 75)\n",
      "\tbeta = 21.77027027027027\n",
      "\tJ = 5.0786e-01\n",
      "\tgrad_norm = 1.9542e+00\n",
      "iteration = (55 / 75)\n",
      "\tbeta = 22.16216216216216\n",
      "\tJ = 4.7256e-01\n",
      "\tgrad_norm = 3.7736e+00\n",
      "iteration = (56 / 75)\n",
      "\tbeta = 22.554054054054053\n",
      "\tJ = 5.1516e-01\n",
      "\tgrad_norm = 5.1769e-01\n",
      "iteration = (57 / 75)\n",
      "\tbeta = 22.945945945945947\n",
      "\tJ = 5.1328e-01\n",
      "\tgrad_norm = 1.6166e+00\n",
      "iteration = (58 / 75)\n",
      "\tbeta = 23.33783783783784\n",
      "\tJ = 5.2419e-01\n",
      "\tgrad_norm = 6.7000e-01\n",
      "iteration = (59 / 75)\n",
      "\tbeta = 23.72972972972973\n",
      "\tJ = 5.1963e-01\n",
      "\tgrad_norm = 1.3484e+00\n",
      "iteration = (60 / 75)\n",
      "\tbeta = 24.12162162162162\n",
      "\tJ = 5.2764e-01\n",
      "\tgrad_norm = 7.8138e-01\n",
      "iteration = (61 / 75)\n",
      "\tbeta = 24.513513513513516\n",
      "\tJ = 5.2764e-01\n",
      "\tgrad_norm = 1.0723e+00\n",
      "iteration = (62 / 75)\n",
      "\tbeta = 24.905405405405407\n",
      "\tJ = 5.3763e-01\n",
      "\tgrad_norm = 4.6276e-01\n",
      "iteration = (63 / 75)\n",
      "\tbeta = 25.297297297297295\n",
      "\tJ = 5.3712e-01\n",
      "\tgrad_norm = 5.0109e-01\n",
      "iteration = (64 / 75)\n",
      "\tbeta = 25.68918918918919\n",
      "\tJ = 5.3661e-01\n",
      "\tgrad_norm = 6.7659e-01\n",
      "iteration = (65 / 75)\n",
      "\tbeta = 26.08108108108108\n",
      "\tJ = 5.4104e-01\n",
      "\tgrad_norm = 5.7816e-01\n",
      "iteration = (66 / 75)\n",
      "\tbeta = 26.472972972972972\n",
      "\tJ = 5.4545e-01\n",
      "\tgrad_norm = 5.3850e-01\n",
      "iteration = (67 / 75)\n",
      "\tbeta = 26.864864864864867\n",
      "\tJ = 5.4569e-01\n",
      "\tgrad_norm = 1.1178e+00\n",
      "iteration = (68 / 75)\n",
      "\tbeta = 27.256756756756754\n",
      "\tJ = 5.4433e-01\n",
      "\tgrad_norm = 7.2051e-01\n",
      "iteration = (69 / 75)\n",
      "\tbeta = 27.64864864864865\n",
      "\tJ = 5.4086e-01\n",
      "\tgrad_norm = 1.2716e+00\n",
      "iteration = (70 / 75)\n",
      "\tbeta = 28.040540540540544\n",
      "\tJ = 5.4005e-01\n",
      "\tgrad_norm = 5.5040e-01\n",
      "iteration = (71 / 75)\n",
      "\tbeta = 28.43243243243243\n",
      "\tJ = 5.4064e-01\n",
      "\tgrad_norm = 4.1068e-01\n",
      "iteration = (72 / 75)\n",
      "\tbeta = 28.824324324324323\n",
      "\tJ = 5.3792e-01\n",
      "\tgrad_norm = 1.6507e+00\n",
      "iteration = (73 / 75)\n",
      "\tbeta = 29.216216216216218\n",
      "\tJ = 5.2882e-01\n",
      "\tgrad_norm = 1.5308e+00\n",
      "iteration = (74 / 75)\n"
     ]
    },
    {
     "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\">17:46:51 EDT </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: No connection: Retrying for </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">180</span><span style=\"color: #800000; text-decoration-color: #800000\"> seconds.                  </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:46:51 EDT\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: No connection: Retrying for \u001b[0m\u001b[1;36m180\u001b[0m\u001b[31m seconds.                  \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tbeta = 29.60810810810811\n",
      "\tJ = 5.3590e-01\n",
      "\tgrad_norm = 1.5002e+00\n",
      "iteration = (75 / 75)\n",
      "\tbeta = 30.0\n",
      "\tJ = 5.4635e-01\n",
      "\tgrad_norm = 3.5577e-01\n"
     ]
    }
   ],
   "source": [
    "iter_done = len(history_dict[\"values\"])\n",
    "\n",
    "for i in range(iter_done, total_iter):\n",
    "    print(f\"iteration = ({i + 1} / {total_iter})\")\n",
    "\n",
    "    # compute gradient and current objective funciton value\n",
    "    perc_done = i / (total_iter - 1)\n",
    "    beta_i = beta_min * (1 - perc_done) + beta_max * perc_done\n",
    "    value, gradient = obj_grad(np.array(params), beta=beta_i)\n",
    "\n",
    "    # outputs\n",
    "    print(f\"\\tbeta = {beta_i}\")\n",
    "    print(f\"\\tJ = {value:.4e}\")\n",
    "    print(f\"\\tgrad_norm = {np.linalg.norm(gradient):.4e}\")\n",
    "\n",
    "    # compute and apply updates to the optimizer based on gradient (-1 sign to maximize obj_fn)\n",
    "    updates, opt_state = optimizer.update(-gradient, opt_state, params)\n",
    "    params = optax.apply_updates(params, updates)\n",
    "\n",
    "    # cap parameters between 0 and 1\n",
    "    params = anp.clip(params, 0.0, 1.0)\n",
    "\n",
    "    # save history\n",
    "    history_dict[\"values\"].append(value)\n",
    "    history_dict[\"params\"].append(params)\n",
    "    history_dict[\"beta\"].append(beta_i)\n",
    "    history_dict[\"gradients\"].append(gradient)\n",
    "    history_dict[\"opt_states\"].append(opt_state)\n",
    "    # history_dict[\"data\"].append(sim_data_i) # uncomment to store data, can create large files\n",
    "    save_history(history_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimization Results\n",
    "\n",
    "After 150 iterations, a coupling efficiency value of 0.71 (-1.48 dB) was achieved at the central wavelength."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj_vals = np.array(history_dict[\"values\"])\n",
    "final_par = history_dict[\"params\"][-1]\n",
    "final_beta = history_dict[\"beta\"][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nz9f461sIoZr58vjjj6NVq1ZYuXKlxpdN2fPs7OwAQOsXkjYhISFo2rQptm3bhm3btqF3795qXQouLi4ICgrCp59+qvWv4ps3b+p0n8q0adMGBQUFaqu3ZmVl6TwDqTwLCwuEh4dj9+7dWr8Yla+XPq/VoEGDkJ2djW3btqnSHj16hA8//BCNGzdG//79q1XWioSHh8PW1hYff/wxfvjhB0RERKito6Ns8Sj73h85cgQpKSlVXlvX11vXzyag37Tm4cOH49ixY2rvzcWLF7Fv3z7VGB2lCxcu4Pr166rnhYWFGt1JQgi8++67AKDWCqjts3n69Gl88803eOaZZzS696jusIWFGqTBgwdjxYoVCAsLw0svvYTc3FysWbMGbdu2rfby5Zs3b0ZwcDCGDh2Kl156CYGBgSguLkZiYiIOHDiAyMhIvP322xrntW3bFv369cO4ceNQXFyMlStXolmzZpg+fXqF92rTpg3effddzJo1C1evXkV4eDiaNGmC9PR0JCUlYezYsXjrrbdgYWGBTz75BM899xx69OiBUaNGwd3dHRcuXMDZs2fx448/AgB8fX0BKBa5Cw0NhVQqRVRUVIX3t7S0REREBLZu3YqioiKt+yGtWbMG/fr1Q9euXTFmzBi0bt0aOTk5SElJwY0bN3D69Gl9X2I1UVFRmDFjBp5//nlMnDgR9+7dwyeffIL27dtXewDpokWL8NNPP6F///4YO3YsOnbsiKysLGzfvh2//vorHB0d0aNHD0ilUixZsgQFBQWQyWR46qmn4OLionG9sWPH4tNPP0VsbCxOnDgBHx8f7NixA4cPH8bKlSur1SpQmcaNGyM8PFzVrVm2OwhQtAQmJibi+eefx+DBg5Geno61a9eiU6dOVa5NouvrretnE1AE5wMGDMDcuXOrHHj7xhtvYP369Rg8eDDeeustWFpaYsWKFXB1dcW0adPU8nbs2BH9+/dXDQw/efIkRowYgREjRqBt27a4f/8+kpKScPjwYYwdOxaPP/646tzIyEjY2NigT58+cHFxwblz57Bu3TrY2tqa/OJ7Jq/O5yUR6Ug5rVnbFFMhKp7WbGdnp5FX21TXDRs2iHbt2gmZTCY6dOggNm7cqDWfLtOale7cuSPmzZsnOnfuLGxsbESTJk1E3759RXx8vNo04rLlX7p0qVi+fLnw8vISMplMBAYGitOnT1dZfiGE+Prrr0W/fv2EnZ2dsLOzEx06dBDjx48XFy9eVMv366+/iqefflo0adJE2NnZiW7duqlN53306JF48803hbOzs5BIJGr3QgXTTvfs2SMACIlEIjIyMrS+HleuXBExMTHCzc1NWFpaihYtWohnn31W7Nixo8rXEoAYP358pXl++ukn0aVLF2FlZSUee+wx8cUXX1Q4rVnbtbS9t9euXRMxMTHC2dlZyGQy0bp1azF+/Hi16cDr168XrVu3FlKpVG2Kc/lpzUIIkZOTI0aNGiWaN28urKysRNeuXdU+s0Kofxa0vQ5VTfst67vvvhMAhLu7u9oUZyEUU7MXLVokvL29hUwmEz179hTffvut1inL2u6r6+sthG6fTV2nNStlZGSI4cOHC3t7e9G4cWPx7LPPikuXLmnkA6D2Pvz111/ihRdeED4+PsLa2lrY2toKX19fsXbtWo3/l6tWrRK9e/cWTZs2FY0aNRLu7u7i5Zdf1nofqlsSIYxoxBkRaTV79mzExcVx1U0iarDYGUdkArKystC8efP6LgYRUb3hGBYiI/bXX38hKSkJ27dvx7PPPlvfxSEiqjdsYSEyYr/88gvmz5+P/v37Y8WKFfVdHCKiemNSAcsvv/yC5557Dh4eHpBIJDpNQT1w4AAef/xxyGQytG3bVm1nX6U1a9bAx8cH1tbW8Pf3x9GjRw1feKJqiI2NRWFhIb755hu4urrWd3GIiOqNSQUsRUVF6N69O9asWaNT/vT0dAwePBgDBgxAamoqJk+ejNdee001lRNQLCk+depUzJ07FydPnkT37t0RGhqK3Nzc2qoGERER6clkZwlJJBIkJSUhPDy8wjwzZszAd999p7ZyZVRUFPLz85GcnAwA8Pf3R69evVTbm5eWlsLLywtvvvkmZs6cWat1ICIiIt2Y9aDblJQUjWXBQ0NDMXnyZABASUkJTpw4obYDp4WFBUJCQipd+bG4uFht1UTlrqfNmjWr9g64REREDZEQAnfu3IGHh0elKwmbdcCSnZ2t0e/v6uqKwsJC3L9/H//88w/kcrnWPMq9PrSJi4vD/Pnza6XMREREDVFGRgY8PT0rPG7WAUttmTVrFqZOnap6XlBQgJYtWyIjI4O7eBIREemhsLAQXl5eVW5VYdYBi5ubG3JyctTScnJyYG9vDxsbG0ilUkilUq153NzcKryuTCZT2+FXyd7engELERFRNVQ1pMKkZgnpKyAgAHv37lVL27NnDwICAgAAVlZW8PX1VctTWlqKvXv3qvIQERFR/TOpgOXu3btITU1FamoqAMW05dTUVNU24rNmzUJMTIwq/+uvv46//voL06dPx4ULF/Dxxx/jq6++wpQpU1R5pk6divXr12PTpk04f/48xo0bh6KiIowaNapO60ZEREQVM6kuoePHj2PAgAGq58pxJCNHjkR8fDyysrJUwQsAtGrVCt999x2mTJmCVatWwdPTE5999hlCQ0NVeSIjI3Hz5k3MmTMH2dnZ6NGjB5KTk7lIFxERkREx2XVYjElhYSEcHBxQUFDAMSxERER60PU71KS6hIiIiKhhYsBCRERERo8BCxERERk9BixERERk9BiwEBERkdFjwEJERERGjwELERERGT0GLERERGT0GLAQERGR0WPAQkREREaPAQsREREZPQYsREREZPQYsBAREZHRY8BCRERERo8BCxERERk9BixERERk9BiwEBERkdFjwEJERERGjwELERERGT0GLERERGT0GLAQERGR0WPAQkREREaPAQsREREZPQYsREREZPQYsBAREZHRY8BCRERERo8BCxERERk9kwtY1qxZAx8fH1hbW8Pf3x9Hjx6tMG9QUBAkEonGY/Dgwao8sbGxGsfDwsLqoipERESko0b1XQB9bNu2DVOnTsXatWvh7++PlStXIjQ0FBcvXoSLi4tG/sTERJSUlKie3759G927d8cLL7ygli8sLAwbN25UPZfJZLVXCSIiItKbSbWwrFixAmPGjMGoUaPQqVMnrF27Fra2tvj888+15m/atCnc3NxUjz179sDW1lYjYJHJZGr5nJyc6qI6REREpCOTCVhKSkpw4sQJhISEqNIsLCwQEhKClJQUna6xYcMGREVFwc7OTi39wIEDcHFxwWOPPYZx48bh9u3blV6nuLgYhYWFag8iIiKqPSYTsNy6dQtyuRyurq5q6a6ursjOzq7y/KNHj+LMmTN47bXX1NLDwsKwefNm7N27F0uWLMHBgwcxcOBAyOXyCq8VFxcHBwcH1cPLy6t6lSIiIiKdmNQYlprYsGEDunbtit69e6ulR0VFqf7dtWtXdOvWDW3atMGBAwcQHBys9VqzZs3C1KlTVc8LCwsZtBAREdUik2lhad68OaRSKXJyctTSc3Jy4ObmVum5RUVF2Lp1K0aPHl3lfVq3bo3mzZvj8uXLFeaRyWSwt7dXexAREVHtMZmAxcrKCr6+vti7d68qrbS0FHv37kVAQECl527fvh3FxcV4+eWXq7zPjRs3cPv2bbi7u9e4zERERGQYJhOwAMDUqVOxfv16bNq0CefPn8e4ceNQVFSEUaNGAQBiYmIwa9YsjfM2bNiA8PBwNGvWTC397t27ePvtt/H777/j6tWr2Lt3L4YOHYq2bdsiNDS0TupEREREVTOpMSyRkZG4efMm5syZg+zsbPTo0QPJycmqgbjXr1+HhYV6DHbx4kX8+uuv+OmnnzSuJ5VKkZaWhk2bNiE/Px8eHh545plnsHDhQq7FQkREZEQkQghR34UwdYWFhXBwcEBBQQHHsxAREelB1+9Qk+oSIiIiooaJAQsREREZPQYsREREZPQYsBAREZHRY8BCRERERo8BCxERERk9BixERERk9BiwEBERkdFjwEJERERGjwELERERGT0GLERERGT0GLAQERGR0WPAQkREREaPAQsREREZPQYsREREZPQYsBAREZHRY8BCRERERo8BCxERERk9BixERERk9BiwEBERkdFrVN8FICIiMmlyOXDoEJCVBbi7A4GBgFRqmHRAe159y1Kb9awjDFiIiKjhqmlQcesWMGUKcOPG/67p6QmMGAFs2VKz9GbNFD9v31bPu2oVMHSoZvl27QImTdK89ooVgLNzzYKeiq69ahUQEaHfa15NEiGEqJM7mbHCwkI4ODigoKAA9vb29V0cIqKGrS6DjbomkQBCKIKZsoFM+eeV0TfoqejaEoni544dNQpadP0OZcBiAAxYiKhOGKLLoKb3q6z1obboc7/ERO0tAcYQbBgLQwQ9Za/l6Qmkp1f7M8CApQ4xYCGiatEnIKjoL19A9y4DfcZJVNQFUFHrg75dA7oGIRUFINrqeOsW8OKLii9jqlv79wNBQdU6lQFLHWLAQkSV0mdMQEVjGXT9y7eiv571GSeh71/a+nYN6BqEXLoEzJunGYBUVEdlQEZ1LyFB8fmqBgYsdYgBC1EDVJNxEtVpejd2EgnQogUQHw/k5lbcapKYCAwfrnsQQqaBLSymgQELkZmoje6Shqx8V5FcDvj48PUxJ3U4hsXkFo5bs2YNfHx8YG1tDX9/fxw9erTCvPHx8ZBIJGoPa2trtTxCCMyZMwfu7u6wsbFBSEgILl26VNvVIKLqksuBAwcUgcGBA4rn2tIqyltRemKi4st0wADgpZcUP11dFY/yacOGaX7p3rgBLF3KL+OyMjMVrSmJiYrnhw7x9TEnyq7AlSvrZj0WYUK2bt0qrKysxOeffy7Onj0rxowZIxwdHUVOTo7W/Bs3bhT29vYiKytL9cjOzlbLs3jxYuHg4CB27twpTp8+LYYMGSJatWol7t+/r3O5CgoKBABRUFBQo/oRURmPHgmxf78QCQmKn48eCfH110J4egqh6DxQPJo1UzzKpnl6CvH225p5K0ovfz4fhntIJEJ4eSnev4SE+i9PXTy8vLR/zvRN1/bZVj6XSDRf57LHDf0earu2l5fi/2QN6fodihrfqQ717t1bjB8/XvVcLpcLDw8PERcXpzX/xo0bhYODQ4XXKy0tFW5ubmLp0qWqtPz8fCGTycSWLVt0LhcDFqJytAUbFaXrE5jU9xcRH9V/7N+veNR3Oap6VCfY+Oor3T/vtfX/Qxk8lM+/fXvF/5d0DXoquray3DWk63eoyYxhKSkpga2tLXbs2IHw8HBV+siRI5Gfn49du3ZpnBMfH4/XXnsNLVq0QGlpKR5//HEsWrQInTt3BgD89ddfaNOmDU6dOoUePXqozuvfvz969OiBVatWaS1LcXExiouLVc8LCwvh5eXFMSzU8Bhi9gtQ87UgyPhNmACEhwOxsYquotr86ik/W8jLC4iK0vz8eXkBy5drXwXWWNagqYg+5dD1/6mXl6J7p6Ip8bXE7Abd/v3332jRogV+++03BAQEqNKnT5+OgwcP4siRIxrnpKSk4NKlS+jWrRsKCgqwbNky/PLLLzh79iw8PT3x22+/oW/fvvj777/h7u6uOu/FF1+ERCLBtm3btJZl3rx5mD9/vkY6AxYyW/qsC8Jgw/AqW+gL0B7wKc8x5P0q+uLXhyHKV9E1lGMqtm3TLwhpqIzk9dA1YDHrvYQCAgLUgps+ffqgY8eO+PTTT7Fw4cJqX3fWrFmYOnWq6rmyhYXILNRkWi6DFd3oGxB4elb8ly+g+1/P2q5d0eJzlf2lHRf3v3QXF/1aTfLyFD+bNtUt0FI+nz8faNeu6pa8lSsrXgtGKq321FuzZGKvh8kELM2bN4dUKkVOTo5aek5ODtzc3HS6hqWlJXr27InLly8DgOq8nJwctRaWnJwctS6i8mQyGWQymZ41IKoD+izdrmuriTYMTGpGn4Cg/F++2r5gyqdFROh3bUC/+5X/olu1SjEbSJdWEyEU+WxsgJ9/Vl+zRZ8gpLI6knkyyIiZOtK7d28xYcIE1XO5XC5atGhR4aDb8h49eiQee+wxMWXKFCHE/wbdLlu2TJWnoKCAg27JNNV0Bg0Hter2qGxgor6DMs2Jts9fVY/9+zWvU0sDO8l4meUsoa1btwqZTCbi4+PFuXPnxNixY4Wjo6NqqvIrr7wiZs6cqco/f/588eOPP4orV66IEydOiKioKGFtbS3Onj2ryrN48WLh6Ogodu3aJdLS0sTQoUM5rZmMn7aZAOVH/PNR8yBEW8BX1YyJhvyFq6z7hAm6ve4JCfVdYjICun6HmkyXEABERkbi5s2bmDNnDrKzs9GjRw8kJyfD1dUVAHD9+nVYWPxvLbx//vkHY8aMQXZ2NpycnODr64vffvsNnTp1UuWZPn06ioqKMHbsWOTn56Nfv35ITk7WWGCOqF7o2m0jlSq+AsyZPgNPK5sVYogxIvp0lzQkZev+0UdV5y/TFU9UFZOZJWTMuDQ/1QptG8Q1hFk4lQ1I1TeoMPapqeZKuQR/RQNxDbCcO5kPs5vWbMwYsFCNaZuZ8+KLptdqou/sl4paR+phLQgyMOUmh4D651jfnZ3J7DFgqUMMWEgvukwbLr/wlbGpTitIRS0bAAMTc6WtlVD5GWGwQv/FgKUOMWAhnWn7BW6K2ApCumL3G1WBC8cRGRtlE7mx/o1QWatJRcuXAw13gCnppiEPQiaDYsBCVFvK/mXp4qJoWantYKV8V5I+M2iqWsyMiKgesUvIANglRBrquuunsj1UAM6UISKjxS4hovpSm10/FXXbVLWHCtcLISITx4CFyJDkcsN2/ZTv4mG3DRE1UAxYiAzp0CHDdAMpu3i2bOFgVyIiMGAhMqysLMNcp6ouHiKiBqZaAcvevXuxd+9e5ObmorS0VO3Y559/bpCCEZmMsoNXc3L0P7+qacNERKR/wDJ//nwsWLAAfn5+cHd3h0TZdE3UENR0lVqJBGjRAoiPB3JzGZwQEelI74Bl7dq1iI+PxyuvvFIb5SEyXrpOVa4sWAGAVauA4GDDlo2IyMxZ6HtCSUkJ+vTpUxtlITJeyqnK+gyoLd9q4unJDd+IiKpJ7xaW1157DQkJCZg9e3ZtlIfIOBhilVq5HPjgA8DVlV0/REQ1pHfA8uDBA6xbtw4///wzunXrBktLS7XjK1asMFjhiOqFIVepdXUFRoyo+XWIiBo4vQOWtLQ09OjRAwBw5swZtWMcgEsmz9Cr1Lq7G+Y6REQNnN4By/79+2ujHET1z5Cr1EokijEryr18iIioRmq0cNyN/zaZe3p6GqQwRLVGl43+DL1K7cqVHLNCRGQges8SKi0txYIFC+Dg4ABvb294e3vD0dERCxcu1FhEjsgoJCYCPj7AgAHASy8pfvr4KNLLMuQqtZwNRERkUHq3sLzzzjvYsGEDFi9ejL59+wIAfv31V8ybNw8PHjzAe++9Z/BCElVbRWNSMjMV6WUDi+qMN+EqtUREdUIihH4d9h4eHli7di2GDBmilr5r1y688cYbyMzMNGgBTUFhYSEcHBxQUFAAe3v7+i4OKcnlipaUirp5yq866+ICxMYqghlt/y24Si0RkcHp+h2qdwtLXl4eOnTooJHeoUMH5OXl6Xs5otpT1ZgUIRTHQ0L+l9asmSJdIlEPWrhKLRFRvdJ7DEv37t3x0UcfaaR/9NFH6N69u0EKRWQQ1RmTogy6mzZVT+e4FCKieqV3C8v777+PwYMH4+eff0ZAQAAAICUlBRkZGfj+++8NXkCiaqvOmBRl64qNDfDzz+z6ISIyEnq3sPTv3x9//vknnn/+eeTn5yM/Px8RERG4ePEiArnmBBmTwEBFy4i+Cxoqu4qkUsUqtUFBDFaIiOpZtdZh8fDw4GwgMn5SqWLMyfDhmmNSdGGoac5ERFRjOrWwpKWlqdZYSUtLq/RR29asWQMfHx9YW1vD398fR48erTDv+vXrERgYCCcnJzg5OSEkJEQjf2xsLCQSidojLCystqtBdSUiQjH2pEUL/c/lsvpEREZDp2nNFhYWyM7OhouLCywsLCCRSKDtNIlEArlcXisFBYBt27YhJiYGa9euhb+/P1auXInt27fj4sWLcHFx0cgfHR2Nvn37ok+fPrC2tsaSJUuQlJSEs2fPosV/v8BiY2ORk5ODjRs3qs6TyWRwcnLSuVyc1mwCyu++XNX0ZU9PID2dXUFERLVM1+9QnQKWa9euoWXLlpBIJLh27Vqleb29vfUvrY78/f3Rq1cv1Syl0tJSeHl54c0338TMmTOrPF8ul8PJyQkfffQRYmJiACgClvz8fOzcubPa5WLAYoKUC8oB2qcvc0YQEVGd0PU7VKcuIW9vb9VOzNeuXUOLFi1Uy/IrHy1atKgymKmJkpISnDhxAiFl1sywsLBASEgIUlJSdLrGvXv38PDhQzQtN2X1wIEDcHFxwWOPPYZx48bh9u3blV6nuLgYhYWFag8yMRV1FXH6MhGRUdJ7ltCAAQO0LhBXUFCAAQMGGKRQ2ty6dQtyuRyurq5q6a6ursjOztbpGjNmzICHh4da0BMWFobNmzdj7969WLJkCQ4ePIiBAwdW2rUVFxcHBwcH1cPLy6t6laL6FREBXL0K7N8PJCQofqanM1ghIjJCes8SEkKoWlvKun37Nuzs7AxSqNqwePFibN26FQcOHIC1tbUqPSoqSvXvrl27olu3bmjTpg0OHDiA4ApWNJ01axamTp2qel5YWMigxZjosjOzklSqmLZMRERGTeeAJeK/f3VKJBLExsZCJpOpjsnlcqSlpaFPnz6GL+F/NW/eHFKpFDk5OWrpOTk5cHNzq/TcZcuWYfHixfj555/RrVu3SvO2bt0azZs3x+XLlysMWGQymVr9qZ5oC0x27QImTVJfkt/TUzG9mS0nREQmS+eAxcHBAYCihaVJkyawsbFRHbOyssITTzyBMWPGGL6EZe7h6+uLvXv3Ijw8HIBi0O3evXsxYcKECs97//338d577+HHH3+En59flfe5ceMGbt++DXdOaTVuiYmagUmzZoC28UfadmYmIiKTonPAopz26+Pjg7fffhu2tra1VqiKTJ06FSNHjoSfnx969+6NlStXoqioCKNGjQIAxMTEoEWLFoiLiwMALFmyBHPmzEFCQgJ8fHxUY10aN26Mxo0b4+7du5g/fz6GDRsGNzc3XLlyBdOnT0fbtm0RGhpa5/UjHSln+JSf4FbRYGnlcvuTJwNDh3KqMhGRCdJ70G1MTAwyMzM10i9duoSrV68aokwVioyMxLJlyzBnzhz06NEDqampSE5OVg3EvX79OrLKrE76ySefoKSkBMOHD4e7u7vqsWzZMgCAVCpFWloahgwZgvbt22P06NHw9fXFoUOH2OVjrORyRcuKvqvWCgFkZCi6kIiIyOTotA5LWf3798err76KkSNHqqV/8cUX+Oyzz3DgwAFDls8kcB2WOnTgAFCT2WgJCYr9gYiIyCgYdB2Wsk6dOoW+fftqpD/xxBNITU3V93JE+qnp/j4cm0REZJL0ntYskUhw584djfSCgoJaXZafCED1Aw7lcvvcUZyIyCTp3cLy5JNPIi4uTi04kcvliIuLQ79+/QxaOCINgYGKwEPLWkAVUuZduZIDbomITJTeLSxLlizBk08+icceewyB//1r9dChQygsLMS+ffsMXkAiNVKpYk2V4cMVgUj5fYCE0Jze7OmpCFY4pZmIyGTp3cLSqVMnpKWl4cUXX0Rubi7u3LmDmJgYXLhwAV26dKmNMhKpq2wfoK+/BnJyuNw+EZGZ0XuWEGniLKF6os8S/EREZJR0/Q7Vu0sIAPLz83H06FHk5uaitLRU7VhMTEx1LklUsYoCE+4DRETUYOgdsOzevRvR0dG4e/cu7O3t1TZClEgkDFioZsoHJ7duAVOmcG8gIqIGTu8uofbt22PQoEFYtGhRvSzPb4zYJWQg2vYH0kYZJHNvICIik1drC8dlZmZi4sSJDFbIsJT7A1UVrAD/mxk0ebKiRYaIiMye3gFLaGgojh8/XhtloYaqOvsDcW8gIqIGRe8xLIMHD8bbb7+Nc+fOoWvXrrC0tFQ7PmTIEIMVjhqIQ4d0a1nRpqZL9RMRkUnQO2AZM2YMAGDBggUaxyQSCZfnJ/3VJOjg3kBERA2C3gFL+WnMRDVWnaCDewMRETUoeo9hITI4ffcH4t5AREQNjt4tLNq6gsqaM2dOtQtDDVRl+wNpw72BiIgaHL3XYenZs6fa84cPHyI9PR2NGjVCmzZtcPLkSYMW0BRwHRYD0bYOi5cXsHw54OzMJfiJiMxQrS3Nf+rUKa03i42NxfPPP6/v5Yj+JyICGDqU+wMREZEGg21++Mcff+C5557D1atXDXE5k8IWFiIiouqptZVuK1JQUICCggJDXY6IiIhIRe8uodWrV6s9F0IgKysL//nPfzBw4ECDFYyIiIhISe+A5YMPPlB7bmFhAWdnZ4wcORKzZs0yWMGIiIiIlHQKWNLS0tClSxdYWFggPT29tstEREREpEanMSw9e/bErVu3AACtW7fG7du3a7VQRERERGXpFLA4OjqqWlauXr3K5fmJiIioTunUJTRs2DD0798f7u7ukEgk8PPzg7SCtTH++usvgxaQiIiISKeAZd26dYiIiMDly5cxceJEjBkzBk2aNKntspE5k8u5QBwREelM51lCYWFhAIATJ05g0qRJDFio+rQtwe/pqdhPiPsDERGRFnovHLdx48Z6DVbWrFkDHx8fWFtbw9/fH0ePHq00//bt29GhQwdYW1uja9eu+P7779WOCyEwZ84cuLu7w8bGBiEhIbh06VJtVqFhS0xUbHJYNlgBgMxMRXpiYv2Ui4iIjJrBVrqtC9u2bcPUqVMxd+5cnDx5Et27d0doaChyc3O15v/tt98wYsQIjB49GqdOnUJ4eDjCw8Nx5swZVZ73338fq1evxtq1a3HkyBHY2dkhNDQUDx48qKtqNRxyuaJlRdtuEMq0yZMV+YiIiMow2F5CdcHf3x+9evXCRx99BAAoLS2Fl5cX3nzzTcycOVMjf2RkJIqKivDtt9+q0p544gn06NEDa9euhRACHh4emDZtGt566y0Aii0GXF1dER8fj6ioKJ3Kxb2EdHTgADBgQNX59u8HgoJquzRERGQE6nwvodpWUlKCEydOICQkRJVmYWGBkJAQpKSkaD0nJSVFLT8AhIaGqvKnp6cjOztbLY+DgwP8/f0rvCYAFBcXo7CwUO1BOsjKMmw+IiJqMEwmYLl16xbkcjlcXV3V0l1dXZGdna31nOzs7ErzK3/qc00AiIuLg4ODg+rh5eWld30aJHd3w+YjIqIGo1oBy3/+8x/07dsXHh4euHbtGgBg5cqV2LVrl0ELZ6xmzZql2p26oKAAGRkZ9V0k0xAYqJgNJJFoPy6RAF5einxERERl6B2wfPLJJ5g6dSoGDRqE/Px8yP87QNLR0RErV640dPlUmjdvDqlUipycHLX0nJwcuLm5aT3Hzc2t0vzKn/pcEwBkMhns7e3VHqQDqVQxdRnQDFqUz1eu5HosRESkQe+A5cMPP8T69evxzjvvqK126+fnhz/++MOghSvLysoKvr6+2Lt3ryqttLQUe/fuRUBAgNZzAgIC1PIDwJ49e1T5W7VqBTc3N7U8hYWFOHLkSIXXJD3J5YrBtlu2KH4OHQrs2AG0aKGez9NTkc51WIiISAudF45TSk9PR8+ePTXSZTIZioqKDFKoikydOhUjR46En58fevfujZUrV6KoqAijRo0CAMTExKBFixaIi4sDAEyaNAn9+/fH8uXLMXjwYGzduhXHjx/HunXrAAASiQSTJ0/Gu+++i3bt2qFVq1aYPXs2PDw8EB4eXqt1aRAqWyDu6lWudEtERDrTO2Bp1aoVUlNT4e3trZaenJyMjh07Gqxg2kRGRuLmzZuYM2cOsrOz0aNHDyQnJ6sGzV6/fh0WFv9rNOrTpw8SEhLw73//G//3f/+Hdu3aYefOnejSpYsqz/Tp01FUVISxY8ciPz8f/fr1Q3JyMqytrWu1Lman/FL7t24BL76oueaKcoE4tqYQEZEe9F6H5bPPPsO8efOwfPlyjB49Gp999hmuXLmCuLg4fPbZZzqvXWJOGvw6LNpaUqTSiheAk0gULS3p6WxVISJq4HT9DtW7heW1116DjY0N/v3vf+PevXt46aWX4OHhgVWrVjXIYKXBUy61Xz7urWy1WiGAjAxFiwwXiCMiIh3oHbAAQHR0NKKjo3Hv3j3cvXsXLi4uhi4XmYLKltrXBReIIyIiHek9S+jdd99Feno6AMDW1pbBSkN26JDmJob64AJxRESkI70Dlu3bt6Nt27bo06cPPv74Y9y6das2ykXGTDlV+euvq3c+F4gjIiI96R2wnD59GmlpaQgKCsKyZcvg4eGBwYMHIyEhAffu3auNMpIxSUwEfHwUmxj+dxNKvXCBOCIiqoYa79Z8+PBhJCQkYPv27Xjw4EGD3AiwwcwSqmiAbWXKzxby8lIEK5zSTEREqMVZQuXZ2dnBxsYGVlZWuHPnTk0vR8ZK3wG2ypaULVsAZ2cuEEdERDVSrYAlPT0dCQkJSEhIwMWLF9G/f3/Mnz8fw4cPN3T5yFjoO8DW05MtKUREZDB6ByxPPPEEjh07hm7dumHUqFEYMWIEWpTfF4bMj65TkCdMAIYNY0sKEREZlN4BS3BwMD7//HN06tSpNspDxkrXKcjDhnExOCIiMrgaD7qlBjLoVi5XzA7KzNQ+joXL7RMRUTUYdNDt1KlTsXDhQtjZ2WHq1KmV5l2xYoV+JSXTIJUqdlkePlwRnJQNWjhVmYiIaplOAcupU6fw8OFD1b+pgYqIUOyyXH6jQw6wJSKiWsYuIQNoEF1CZcnlillDnKpMREQ1pOt3qN4r3b766qta11spKirCq6++qu/lyBRJpYqBtSNGKH4yWCEiolqmd8CyadMm3L9/XyP9/v372Lx5s0EKRURERFSWztOaCwsLIYSAEAJ37tyBtbW16phcLsf333/PnZuJiIioVugcsDg6OkIikUAikaB9+/YaxyUSCebPn2/QwlE941gVIiIyEjoHLPv374cQAk899RS+/vprNG3aVHXMysoK3t7e8PDwqJVCUj1ITNQ+G2jVKs4GIiKiOqf3LKFr166hZcuWkCjX3iDzmyVU0a7Myvd8xw4GLUREZBC1Nkto37592LFjh0b69u3bsWnTJn0vR8amsl2ZlWmTJyvyERER1RG9A5a4uDg0b95cI93FxQWLFi0ySKGoHlW1K7MQQEaGIh8REVEd0TtguX79Olq1aqWR7u3tjevXrxukUFSPdN2VWdd8REREBqB3wOLi4oK0tDSN9NOnT6NZs2YGKRTVI113ZdY1HxERkQHoHbCMGDECEydOxP79+yGXyyGXy7Fv3z5MmjQJUVFRtVFGqkuBgYrZQBUNqpZIAC8vRT4iIqI6ovO0ZqWFCxfi6tWrCA4ORqNGitNLS0sRExPDMSzmgLsyExGREar25od//vknTp8+DRsbG3Tt2hXe3t6GLpvJMLtpzYD2dVi8vLgrMxERGVStTWtW8vHxQbdu3RAWFlYnwUpeXh6io6Nhb28PR0dHjB49Gnfv3q00/5tvvonHHnsMNjY2aNmyJSZOnIiCggK1fMrVe8s+tm7dWtvVMX4REcDVq8D+/UBCguJnejqDFSIiqhd6dwndu3cPb775pmrNlT///BOtW7fGm2++iRYtWmDmzJkGLyQAREdHIysrC3v27MHDhw8xatQojB07FgkJCVrz//333/j777+xbNkydOrUCdeuXcPrr7+Ov//+W2MdmY0bNyIsLEz13NHRsVbqYHKUuzITERHVN6GniRMnCl9fX3Ho0CFhZ2cnrly5IoQQYufOnaJHjx76Xk4n586dEwDEsWPHVGk//PCDkEgkIjMzU+frfPXVV8LKyko8fPhQlQZAJCUl1ah8BQUFAoAoKCio0XWIiIgaGl2/Q/XuEtq5cyc++ugj9OvXT215/s6dO+PKlSsGC6TKSklJgaOjI/z8/FRpISEhsLCwwJEjR3S+jrJ/TDlYWGn8+PFo3rw5evfujc8//xyiimE9xcXFKCwsVHsQERFR7dG7S+jmzZtwcXHRSC8qKqq1/YWys7M17tmoUSM0bdoU2dnZOl3j1q1bWLhwIcaOHauWvmDBAjz11FOwtbXFTz/9hDfeeAN3797FxIkTK7xWXFwcd6YmIiKqQ3q3sPj5+eG7775TPVcGKZ999hkCAgL0utbMmTO1Dnot+7hw4YK+RdRQWFiIwYMHo1OnTpg3b57asdmzZ6Nv377o2bMnZsyYgenTp2Pp0qWVXm/WrFkoKChQPTIyMmpcRiIiIqqY3i0sixYtwsCBA3Hu3Dk8evQIq1atwrlz5/Dbb7/h4MGDel1r2rRpiI2NrTRP69at4ebmhtzcXLX0R48eIS8vD25ubpWef+fOHYSFhaFJkyZISkqCpaVlpfn9/f2xcOFCFBcXQyaTac0jk8kqPEZERESGp3fA0q9fP6SmpmLx4sXo2rUrfvrpJzz++ONISUlB165d9bqWs7MznJ2dq8wXEBCA/Px8nDhxAr6+vgAUu0aXlpbC39+/wvMKCwsRGhoKmUyGb775BtbW1lXeKzU1FU5OTg0rIJHLFZsZZmUpltwPDOTCcEREZFSqvXBcXRs4cCBycnKwdu1a1bRmPz8/1bTmzMxMBAcHY/PmzejduzcKCwvxzDPP4N69e0hKSoKdnZ3qWs7OzpBKpdi9ezdycnLwxBNPwNraGnv27MFbb72Ft956S68xKia9cJy2BeI8PRWr3XLNFSIiqmW6fofq1MJSWFioukhVM2JsbW01ZuEYwpdffokJEyYgODgYFhYWGDZsGFavXq06/vDhQ1y8eBH37t0DAJw8eVI1g6ht27Zq10pPT4ePjw8sLS2xZs0aTJkyBUIItG3bFitWrMCYMWMMXn6jlJioWIK/fMyamalI37GDQQsRERkFnVpYpFIpsrKy4OLiAgsLi0pnA0kkErRr1w4ff/wxBgwYYNDCGiuTbGGRywEfH/WWlbIkEkVLS3o6u4eIiKjWGLSFZd++fWjatCkAYP/+/ZXmLS4uxs6dOzFu3DiDzPChWnLoUMXBCqBodcnIUOTjardERFTPdApY+vfvr/XfFenRoweOHj1a/VJR7cvKMmw+IiKiWlStwSZyuRxJSUk4f/48AKBTp04YOnSoauyKi4sLjh8/brhSkuG5uxs2HxERUS3Se5bQ2bNnMWTIEGRnZ+Oxxx4DoNgA0dnZGbt370aXLl1qpaDGzKTHsGRmag66BTiGhYiI6oSu36F6r3T72muvoXPnzrhx4wZOnjyJkydPIiMjA926ddNY9p6MmFSqmLoMKIKTspTPV65ksEJEREZB7xYWGxsbHD9+HJ07d1ZLP3PmDHr16oX79+8btICmwCRbWJS0rcPi5aUIVjilmYiIaplBZwmV1b59e+Tk5GgELLm5uRrrnZAJiIgAhg7lSrdERGTUdF44TikuLg4TJ07EvHnz8MQTTwAAfv/9dyxYsABLliypnVJS7ZJKOXWZiIiMmk5dQuUXi1Oeokwr+1wul9dGOY2aSXcJERER1SODdglVtVgcERERUW3Se+E4IiIiorpWrYXj8vPzsWHDBtXCcZ07d8arr74KBwcHgxaOiIiICKjGOizHjx9HmzZt8MEHHyAvLw95eXlYsWIF2rRpg5MnT9ZGGYmIiKiB03sdlsDAQLRt2xbr169XLcX/6NEjvPbaa/jrr7/wyy+/1EpBjRkH3RIREVWPrt+h1Vo47tSpU+jQoYNa+rlz5+Dn54d79+5Vr8QmjAELERFR9dTa0vz29va4fv26RnpGRgaaNGmi7+WIiIiIqqT3oNvIyEiMHj0ay5YtQ58+fQAAhw8fxttvv40RI0YYvIBkYHI5V7UlIiKTo3fAsmzZMkgkEsTExODRo0cAAEtLS4wbNw6LFy82eAHJgLTtG+TpqdgEkfsGERGREdN7DIvSvXv3cOXKFQBAmzZtYGtra9CCmRKTGMOSmAgMHw6Uf7uVKxjv2MGghYiI6lytDbolTUYfsMjlgI+PestKWRKJoqUlPZ3dQ0REVKdqbdAtmaBDhyoOVgBFq0tGhiIfERGREWLA0hBkZRk2HxERUR1jwNIQuLsbNh8REVEdY8DSEAQGKsaoKAfYlieRAF5einxERERGiAFLQyCVKqYuA5pBi/L5ypUccEtEREaLAUtDERGhmLrcooV6uqcnpzQTEZHR03vhODJhERHA0KFc6ZaIiEwOA5aGRioFgoLquxRERER6MZkuoby8PERHR8Pe3h6Ojo4YPXo07t69W+k5QUFBkEgkao/XX39dLc/169cxePBg2NrawsXFBW+//bZqywEiIiIyDibTwhIdHY2srCzs2bMHDx8+xKhRozB27FgkJCRUet6YMWOwYMEC1fOyWwjI5XIMHjwYbm5u+O2335CVlYWYmBhYWlpi0aJFtVYXIiIi0o9JLM1//vx5dOrUCceOHYOfnx8AIDk5GYMGDcKNGzfg4eGh9bygoCD06NEDK1eu1Hr8hx9+wLPPPou///4brq6uAIC1a9dixowZuHnzJqysrHQqn9EvzU9ERGSkzGpp/pSUFDg6OqqCFQAICQmBhYUFjhw5Uum5X375JZo3b44uXbpg1qxZuHfvntp1u3btqgpWACA0NBSFhYU4e/ZshdcsLi5GYWGh2oOIiIhqj0l0CWVnZ8PFxUUtrVGjRmjatCmys7MrPO+ll16Ct7c3PDw8kJaWhhkzZuDixYtITExUXbdssAJA9byy68bFxWH+/PnVrQ4RERHpqV4DlpkzZ2LJkiWV5jl//ny1rz927FjVv7t27Qp3d3cEBwfjypUraNOmTbWvO2vWLEydOlX1vLCwEF5eXtW+HhEREVWuXgOWadOmITY2ttI8rVu3hpubG3Jzc9XSHz16hLy8PLi5uel8P39/fwDA5cuX0aZNG7i5ueHo0aNqeXJycgCg0uvKZDLIZDKd70tEREQ1U68Bi7OzM5ydnavMFxAQgPz8fJw4cQK+vr4AgH379qG0tFQVhOgiNTUVAOD+303+AgIC8N577yE3N1fV5bRnzx7Y29ujU6dOetaGiIiIaotJDLrt2LEjwsLCMGbMGBw9ehSHDx/GhAkTEBUVpZohlJmZiQ4dOqhaTK5cuYKFCxfixIkTuHr1Kr755hvExMTgySefRLdu3QAAzzzzDDp16oRXXnkFp0+fxo8//oh///vfGD9+PFtQiIiIjIhJBCyAYrZPhw4dEBwcjEGDBqFfv35Yt26d6vjDhw9x8eJF1SwgKysr/Pzzz3jmmWfQoUMHTJs2DcOGDcPu3btV50ilUnz77beQSqUICAjAyy+/jJiYGLV1W4iIiKj+mcQ6LMaO67AQERFVj1mtw0JEREQNGwMWIiIiMnoMWIiIiMjoMWAhIiIio8eAhYiIiIyeSewlRNUglwOHDgFZWYC7OxAYCEil9V0qIiKiamHAYo4SE4FJk4AbN/6X5ukJrFoFRETUX7mIiIiqiV1C5iYxERg+XD1YAYDMTEX6f3eqJiIiMiUMWMyJXK5oWdG2FqAybfJkRT4iIiITwoDFnBw6pNmyUpYQQEaGIh8REZEJYcBiTrKyDJuPiIjISDBgMSfu7obNR0REZCQYsJiTwEDFbCCJRPtxiQTw8lLkIyIiMiEMWMyJVKqYugxoBi3K5ytXcj0WIiIyOQxYzE1EBLBjB9CihXq6p6cineuwEBGRCeLCceYoIgIYOpQr3RIRkdlgwGKupFIgKKi+S0FERGQQ7BIiIiIio8eAhYiIiIweAxYiIiIyegxYiIiIyOgxYCEiIiKjx4CFiIiIjB4DFiIiIjJ6DFiIiIjI6DFgISIiIqPHgIWIiIiMnskELHl5eYiOjoa9vT0cHR0xevRo3L17t8L8V69ehUQi0frYvn27Kp+241u3bq2LKhmGXA4cOABs2aL4KZfXd4mIiIgMzmT2EoqOjkZWVhb27NmDhw8fYtSoURg7diwSEhK05vfy8kJWVpZa2rp167B06VIMHDhQLX3jxo0ICwtTPXd0dDR4+WtFYiIwaRJw48b/0jw9gVWruCszERGZFZMIWM6fP4/k5GQcO3YMfn5+AIAPP/wQgwYNwrJly+Dh4aFxjlQqhZubm1paUlISXnzxRTRu3Fgt3dHRUSOv0UtMBIYPB4RQT8/MVKTv2MGghYiIzIZJdAmlpKTA0dFRFawAQEhICCwsLHDkyBGdrnHixAmkpqZi9OjRGsfGjx+P5s2bo3fv3vj8888hygcB5RQXF6OwsFDtUafkckXLirZyKtMmT2b3EBERmQ2TCFiys7Ph4uKiltaoUSM0bdoU2dnZOl1jw4YN6NixI/r06aOWvmDBAnz11VfYs2cPhg0bhjfeeAMffvhhpdeKi4uDg4OD6uHl5aVfhWrq0CH1bqDyhAAyMhT5iIiIzEC9BiwzZ86scGCs8nHhwoUa3+f+/ftISEjQ2roye/Zs9O3bFz179sSMGTMwffp0LF26tNLrzZo1CwUFBapHRkZGjcuol3Jjc2qcj4iIyMjV6xiWadOmITY2ttI8rVu3hpubG3Jzc9XSHz16hLy8PJ3GnuzYsQP37t1DTExMlXn9/f2xcOFCFBcXQyaTac0jk8kqPFYn3N0Nm4+IiMjI1WvA4uzsDGdn5yrzBQQEID8/HydOnICvry8AYN++fSgtLYW/v3+V52/YsAFDhgzR6V6pqalwcnKq34CkKoGBitlAmZnax7FIJIrjgYF1XzYiIqJaYBJjWDp27IiwsDCMGTMGR48exeHDhzFhwgRERUWpZghlZmaiQ4cOOHr0qNq5ly9fxi+//ILXXntN47q7d+/GZ599hjNnzuDy5cv45JNPsGjRIrz55pt1Uq9qk0oVU5cBRXBSlvL5ypWKfERERGbAJAIWAPjyyy/RoUMHBAcHY9CgQejXrx/WrVunOv7w4UNcvHgR9+7dUzvv888/h6enJ5555hmNa1paWmLNmjUICAhAjx498Omnn2LFihWYO3durdenxiIiFFOXW7RQT/f05JRmIiIyOxJR1RxeqlJhYSEcHBxQUFAAe3v7ur25XK6YDZSVpRizEhjIlhUiIjIZun6HmsTCcVQJqRQICqrvUhAREdUqk+kSIiIiooaLAQsREREZPQYsREREZPQYsBAREZHR46BbU8IZQURE1EAxYDEViYmKHZrLbnro6alYQI5rrhARkZljl5ApSEwEhg/X3KE5M1ORnphYP+UiIiKqIwxYjJ1crmhZ0ba+nzJt8mRFPiIiIjPFgMXYHTqk2bJSlhBARoYiHxERkZliwGLssrIMm4+IiMgEMWAxdu7uhs1HRERkghiwGLvAQMVsIIlE+3GJBPDyUuQjIiIyUwxYjJ1Uqpi6DGgGLcrnK1dyPRYiIjJrDFhMQUQEsGMH0KKFerqnpyKd67AQEZGZ48JxpiIiAhg6lCvdEhFRg8SAxZRIpUBQUH2XgoiIqM6xS4iIiIiMHgMWIiIiMnoMWIiIiMjoMWAhIiIio8eAhYiIiIweAxYiIiIyegxYiIiIyOgxYCEiIiKjx4CFiIiIjB4DFiIiIjJ6XJrfGMnl3DOIiIioDJNpYXnvvffQp08f2NrawtHRUadzhBCYM2cO3N3dYWNjg5CQEFy6dEktT15eHqKjo2Fvbw9HR0eMHj0ad+/erYUa6CgxEfDxAQYMAF56SfHTx0eRTkRE1ECZTMBSUlKCF154AePGjdP5nPfffx+rV6/G2rVrceTIEdjZ2SE0NBQPHjxQ5YmOjsbZs2exZ88efPvtt/jll18wduzY2qhC1RITgeHDgRs31NMzMxXpDFqIiKiBkgghRH0XQh/x8fGYPHky8vPzK80nhICHhwemTZuGt956CwBQUFAAV1dXxMfHIyoqCufPn0enTp1w7Ngx+Pn5AQCSk5MxaNAg3LhxAx4eHjqVqbCwEA4ODigoKIC9vX31KiaXK1pSygcrShIJ4OkJpKeze4iIiMyGrt+hZjuGJT09HdnZ2QgJCVGlOTg4wN/fHykpKYiKikJKSgocHR1VwQoAhISEwMLCAkeOHMHzzz+v9drFxcUoLi5WPS8oKACgeNGr7dChioMVABACyMgAkpMVY1qIiIjMgPK7s6r2E7MNWLKzswEArq6uaumurq6qY9nZ2XBxcVE73qhRIzRt2lSVR5u4uDjMnz9fI93Ly6umxa7as8/W/j2IiIjq2J07d+Dg4FDh8XoNWGbOnIklS5ZUmuf8+fPo0KFDHZVIN7NmzcLUqVNVz0tLS5GXl4dmzZpBIpEY5B6FhYXw8vJCRkZG9buZjBzraB5YR/PQEOoINIx6mlodhRC4c+dOlcMw6jVgmTZtGmJjYyvN07p162pd283NDQCQk5MDd3d3VXpOTg569OihypObm6t23qNHj5CXl6c6XxuZTAaZTKaWpuvMJX3Z29ubxAeuJlhH88A6moeGUEegYdTTlOpYWcuKUr0GLM7OznB2dq6Va7dq1Qpubm7Yu3evKkApLCzEkSNHVDONAgICkJ+fjxMnTsDX1xcAsG/fPpSWlsLf379WykVERET6M5lpzdevX0dqaiquX78OuVyO1NRUpKamqq2Z0qFDByQlJQEAJBIJJk+ejHfffRfffPMN/vjjD8TExMDDwwPh4eEAgI4dOyIsLAxjxozB0aNHcfjwYUyYMAFRUVE6zxAiIiKi2mcyg27nzJmDTZs2qZ737NkTALB//34EBQUBAC5evKiasQMA06dPR1FREcaOHYv8/Hz069cPycnJsLa2VuX58ssvMWHCBAQHB8PCwgLDhg3D6tWr66ZSlZDJZJg7d65G15M5YR3NA+toHhpCHYGGUU9zraPJrcNCREREDY/JdAkRERFRw8WAhYiIiIweAxYiIiIyegxYiIiIyOgxYDFCa9asgY+PD6ytreHv74+jR4/Wd5Fq5JdffsFzzz0HDw8PSCQS7Ny5U+24EAJz5syBu7s7bGxsEBISgkuXLtVPYashLi4OvXr1QpMmTeDi4oLw8HBcvHhRLc+DBw8wfvx4NGvWDI0bN8awYcOQk5NTTyXW3yeffIJu3bqpFqIKCAjADz/8oDpu6vXTZvHixarlEZTMoZ7z5s2DRCJRe5RdTdwc6ggAmZmZePnll9GsWTPY2Niga9euOH78uOq4qf/e8fHx0XgfJRIJxo8fD8B83seyGLAYmW3btmHq1KmYO3cuTp48ie7duyM0NFRjRV5TUlRUhO7du2PNmjVaj7///vtYvXo11q5diyNHjsDOzg6hoaF48OBBHZe0eg4ePIjx48fj999/x549e/Dw4UM888wzKCoqUuWZMmUKdu/eje3bt+PgwYP4+++/ERERUY+l1o+npycWL16MEydO4Pjx43jqqacwdOhQnD17FoDp16+8Y8eO4dNPP0W3bt3U0s2lnp07d0ZWVpbq8euvv6qOmUMd//nnH/Tt2xeWlpb44YcfcO7cOSxfvhxOTk6qPKb+e+fYsWNq7+GePXsAAC+88AIA83gfNQgyKr179xbjx49XPZfL5cLDw0PExcXVY6kMB4BISkpSPS8tLRVubm5i6dKlqrT8/Hwhk8nEli1b6qGENZebmysAiIMHDwohFPWxtLQU27dvV+U5f/68ACBSUlLqq5g15uTkJD777DOzq9+dO3dEu3btxJ49e0T//v3FpEmThBDm8z7OnTtXdO/eXesxc6njjBkzRL9+/So8bo6/dyZNmiTatGkjSktLzeZ9LI8tLEakpKQEJ06cQEhIiCrNwsICISEhSElJqceS1Z709HRkZ2er1dnBwQH+/v4mW2fl4oVNmzYFAJw4cQIPHz5Uq2OHDh3QsmVLk6yjXC7H1q1bUVRUhICAALOr3/jx4zF48GC1+gDm9T5eunQJHh4eaN26NaKjo3H9+nUA5lPHb775Bn5+fnjhhRfg4uKCnj17Yv369arj5vZ7p6SkBF988QVeffVVSCQSs3kfy2PAYkRu3boFuVwOV1dXtXRXV1dkZ2fXU6lql7Je5lLn0tJSTJ48GX379kWXLl0AKOpoZWWlsUGmqdXxjz/+QOPGjSGTyfD6668jKSkJnTp1Mpv6AcDWrVtx8uRJxMXFaRwzl3r6+/sjPj4eycnJ+OSTT5Ceno7AwEDcuXPHbOr4119/4ZNPPkG7du3w448/Yty4cZg4caJqtXRz+72zc+dO5OfnqzYTNpf3sTyTWZqfyBSMHz8eZ86cURsTYC4ee+wxpKamoqCgADt27MDIkSNx8ODB+i6WwWRkZGDSpEnYs2eP2vYd5mbgwIGqf3fr1g3+/v7w9vbGV199BRsbm3osmeGUlpbCz88PixYtAqDYyuXMmTNYu3YtRo4cWc+lM7wNGzZg4MCBZr8HHltYjEjz5s0hlUo1RnLn5OTAzc2tnkpVu5T1Moc6T5gwAd9++y32798PT09PVbqbmxtKSkqQn5+vlt/U6mhlZYW2bdvC19cXcXFx6N69O1atWmU29Ttx4gRyc3Px+OOPo1GjRmjUqBEOHjyI1atXo1GjRnB1dTWLepbn6OiI9u3b4/Lly2bzXrq7u6NTp05qaR07dlR1fZnT751r167h559/xmuvvaZKM5f3sTwGLEbEysoKvr6+2Lt3ryqttLQUe/fuRUBAQD2WrPa0atUKbm5uanUuLCzEkSNHTKbOQghMmDABSUlJ2LdvH1q1aqV23NfXF5aWlmp1vHjxIq5fv24yddSmtLQUxcXFZlO/4OBg/PHHH6qd4FNTU+Hn54fo6GjVv82hnuXdvXsXV65cgbu7u9m8l3379tVYWuDPP/+Et7c3APP4vaO0ceNGuLi4YPDgwao0c3kfNdT3qF9St3XrViGTyUR8fLw4d+6cGDt2rHB0dBTZ2dn1XbRqu3Pnjjh16pQ4deqUACBWrFghTp06Ja5duyaEEGLx4sXC0dFR7Nq1S6SlpYmhQ4eKVq1aifv379dzyXUzbtw44eDgIA4cOCCysrJUj3v37qnyvP7666Jly5Zi37594vjx4yIgIEAEBATUY6n1M3PmTHHw4EGRnp4u0tLSxMyZM4VEIhE//fSTEML061eRsrOEhDCPek6bNk0cOHBApKeni8OHD4uQkBDRvHlzkZubK4QwjzoePXpUNGrUSLz33nvi0qVL4ssvvxS2trbiiy++UOUx9d87QihmkbZs2VLMmDFD45g5vI/lMWAxQh9++KFo2bKlsLKyEr179xa///57fRepRvbv3y8AaDxGjhwphFBMMZw9e7ZwdXUVMplMBAcHi4sXL9ZvofWgrW4AxMaNG1V57t+/L9544w3h5OQkbG1txfPPPy+ysrLqr9B6evXVV4W3t7ewsrISzs7OIjg4WBWsCGH69atI+YDFHOoZGRkp3N3dhZWVlWjRooWIjIwUly9fVh03hzoKIcTu3btFly5dhEwmEx06dBDr1q1TO27qv3eEEOLHH38UALSW21zex7IkQghRL007RERERDriGBYiIiIyegxYiIiIyOgxYCEiIiKjx4CFiIiIjB4DFiIiIjJ6DFiIiIjI6DFgISIiIqPHgIWIiIiMHgMWIqp1QUFBmDx5cn0XQ41EIsHOnTvruxhEpCOudEtEtS4vLw+WlpZo0qQJfHx8MHny5DoLYObNm4edO3ciNTVVLT07OxtOTk6QyWR1Ug4iqplG9V0AIjJ/TZs2Nfg1S0pKYGVlVe3z3dzcDFgaIqpt7BIiolqn7BIKCgrCtWvXMGXKFEgkEkgkElWeX3/9FYGBgbCxsYGXlxcmTpyIoqIi1XEfHx8sXLgQMTExsLe3x9ixYwEAM2bMQPv27WFra4vWrVtj9uzZePjwIQAgPj4e8+fPx+nTp1X3i4+PB6DZJfTHH3/gqaeego2NDZo1a4axY8fi7t27quOxsbEIDw/HsmXL4O7ujmbNmmH8+PGqewHAxx9/jHbt2sHa2hqurq4YPnx4bbycRA0SAxYiqjOJiYnw9PTEggULkJWVhaysLADAlStXEBYWhmHDhiEtLQ3btm3Dr7/+igkTJqidv2zZMnTv3h2nTp3C7NmzAQBNmjRBfHw8zp07h1WrVmH9+vX44IMPAACRkZGYNm0aOnfurLpfZGSkRrmKiooQGhoKJycnHDt2DNu3b8fPP/+scf/9+/fjypUr2L9/PzZt2oT4+HhVAHT8+HFMnDgRCxYswMWLF5GcnIwnn3zS0C8hUcNVv5tFE1FD0L9/fzFp0iQhhBDe3t7igw8+UDs+evRoMXbsWLW0Q4cOCQsLC3H//n3VeeHh4VXea+nSpcLX11f1fO7cuaJ79+4a+QCIpKQkIYQQ69atE05OTuLu3buq4999952wsLAQ2dnZQgghRo4cKby9vcWjR49UeV544QURGRkphBDi66+/Fvb29qKwsLDKMhKR/jiGhYjq3enTp5GWloYvv/xSlSaEQGlpKdLT09GxY0cAgJ+fn8a527Ztw+rVq3HlyhXcvXsXjx49gr29vV73P3/+PLp37w47OztVWt++fVFaWoqLFy/C1dUVANC5c2dIpVJVHnd3d/zxxx8AgKeffhre3t5o3bo1wsLCEBYWhueffx62trZ6lYWItGOXEBHVu7t37+Jf//oXUlNTVY/Tp0/j0qVLaNOmjSpf2YACAFJSUhAdHY1Bgwbh22+/xalTp/DOO++gpKSkVsppaWmp9lwikaC0tBSAomvq5MmT2LJlC9zd3TFnzhx0794d+fn5tVIWooaGLSxEVKesrKwgl8vV0h5//HGcO3cObdu21etav/32G7y9vfHOO++o0q5du1bl/crr2LEj4uPjUVRUpAqKDh8+DAsLCzz22GM6l6dRo0YICQlBSEgI5s6dC0dHR+zbtw8RERF61IqItGELCxHVKR8fH/zyyy/IzMzErVu3AChm+vz222+YMGECUlNTcenSJezatUtj0Gt57dq1w/Xr17F161ZcuXIFq1evRlJSksb90tPTkZqailu3bqG4uFjjOtHR0bC2tsbIkSNx5swZ7N+/H2+++SZeeeUVVXdQVb799lusXr0aqampuHbtGjZv3ozS0lK9Ah4iqhgDFiKqUwsWLMDVq1fRpk0bODs7AwC6deuGgwcP4s8//0RgYCB69uyJOXPmwMPDo9JrDRkyBFOmTMGECRPQo0cP/Pbbb6rZQ0rDhg1DWFgYBgwYAGdnZ2zZskXjOra2tvjxxx+Rl5eHXr16Yfjw4QgODsZHH32kc70cHR2RmJiIp556Ch07dsTatWuxZcsWdO7cWedrEFHFuNItERERGT22sBAREZHRY8BCRERERo8BCxERERk9BixERERk9BiwEBERkdFjwEJERERGjwELERERGT0GLERERGT0GLAQERGR0WPAQkREREaPAQsREREZvf8HiVr0/j244hgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(6, 4))\n",
    "ax.plot(obj_vals, \"ro\")\n",
    "ax.set_xlabel(\"iterations\")\n",
    "ax.set_ylabel(\"objective function\")\n",
    "ax.set_ylim(-1, 1)\n",
    "ax.set_title(f\"Final Objective Function Value: {obj_vals[-1]:.2f}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final grating coupler structure is well binarized, with mostly black (`eps_max`) and white (`eps_min`) regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(4, 4))\n",
    "sim_final = make_adjoint_sim(final_par, beta=final_beta, unfold=True)\n",
    "sim_final.plot_eps(z=0, source_alpha=0, monitor_alpha=0, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the inverse design is complete, we can visualize the field distributions and the wavelength dependent coupling efficiency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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: #008000; text-decoration-color: #008000; font-weight: bold\">↓</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">simulation_data.hdf5.gz</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">3.0/3.0 MB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">8.1 MB/s</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1;32m↓\u001b[0m \u001b[1;34msimulation_data.hdf5.gz\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m3.0/3.0 MB\u001b[0m • \u001b[31m8.1 MB/s\u001b[0m • \u001b[36m0:00:00\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\"></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\">17:50:33 EDT </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:50:33 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Field monitors to visualize the final fields.\n",
    "field_xy = td.FieldMonitor(\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    freqs=[freq],\n",
    "    name=\"field_xy\",\n",
    ")\n",
    "\n",
    "field_xz = td.FieldMonitor(\n",
    "    size=(td.inf, 0, td.inf),\n",
    "    freqs=[freq],\n",
    "    name=\"field_xz\",\n",
    ")\n",
    "\n",
    "# Monitor to compute the grating coupler efficiency.\n",
    "gc_efficiency = td.ModeMonitor(\n",
    "    center=[mon_pos_x, 0, 0],\n",
    "    size=[0, mon_w, mon_h],\n",
    "    freqs=freqs,\n",
    "    mode_spec=mode_spec,\n",
    "    name=\"gc_efficiency\",\n",
    ")\n",
    "import tidy3d.web as web\n",
    "\n",
    "sim_final = sim_final.copy(update=dict(monitors=(field_xy, field_xz, gc_efficiency)))\n",
    "sim_data_final = web.run(sim_final, task_name=\"inv_des_final\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mode_amps = sim_data_final[\"gc_efficiency\"]\n",
    "coeffs_f = mode_amps.amps.sel(direction=\"-\")\n",
    "power_0 = np.abs(coeffs_f.sel(mode_index=0)) ** 2\n",
    "power_0_db = 10 * np.log10(power_0)\n",
    "\n",
    "sim_plot = sim_final.updated_copy(symmetry=(0, 0, 0), monitors=(field_xy, field_xz, gc_efficiency))\n",
    "sim_data_plot = sim_data_final.updated_copy(simulation=sim_plot)\n",
    "\n",
    "f, ax = plt.subplots(2, 2, figsize=(8, 6), tight_layout=True)\n",
    "sim_plot.plot_eps(z=0, source_alpha=0, monitor_alpha=0, ax=ax[0, 1])\n",
    "ax[1, 0].plot(wl_range, power_0_db, \"-k\")\n",
    "ax[1, 0].set_xlabel(\"Wavelength (um)\")\n",
    "ax[1, 0].set_ylabel(\"Power (db)\")\n",
    "ax[1, 0].set_ylim(-15, 0)\n",
    "ax[1, 0].set_xlim(wl - bw / 2, wl + bw / 2)\n",
    "ax[1, 0].set_title(\"Coupling Efficiency\")\n",
    "sim_data_plot.plot_field(\"field_xy\", \"E\", \"abs^2\", z=0, ax=ax[1, 1])\n",
    "ax[0, 0].plot(obj_vals, \"ro\")\n",
    "ax[0, 0].set_xlabel(\"iterations\")\n",
    "ax[0, 0].set_ylabel(\"objective function\")\n",
    "ax[0, 0].set_ylim(-1, 1)\n",
    "ax[0, 0].set_title(f\"Final Objective Function Value: {obj_vals[-1]:.2f}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "optimized loss of -1.46 dB\n"
     ]
    }
   ],
   "source": [
    "loss_db = max(power_0_db)\n",
    "print(f\"optimized loss of {loss_db:.2f} dB\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export to GDS\n",
    "The `Simulation` object has the [.to_gds_file](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html#tidy3d.Simulation.to_gds_file) convenience function to export the final design to a `GDS` file. In addition to a file name, it is necessary to set a cross-sectional plane (`z = 0` in this case) on which to evaluate the geometry, a `frequency` to evaluate the permittivity, and a `permittivity_threshold` to define the shape boundaries in custom mediums. See the [GDS export](https://www.flexcompute.com/tidy3d/examples/notebooks/GDSExport/) notebook for a detailed example on using `.to_gds_file` and other GDS related functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_final.to_gds_file(\n",
    "    fname=\"./misc/inverse_designed_gc.gds\",\n",
    "    z=0,\n",
    "    permittivity_threshold=(eps_max + eps_min) / 2,\n",
    "    frequency=freq,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components"
  ],
  "description": "This notebook demonstrates the inverse design of a compact 3D grating coupler with permittivity binarization and minimum feature size control.",
  "feature_image": "./img/adjoint_6.png",
  "features": [
   "Adjoint inverse design"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "inverse design, grating coupler, photonic integrated circuits, design optimization, adjoint, 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"
  },
  "nbdime-conflicts": {
   "local_diff": [
    {
     "diff": [
      {
       "diff": [
        {
         "diff": [
          {
           "key": 5,
           "op": "addrange",
           "valuelist": "9"
          },
          {
           "key": 5,
           "length": 1,
           "op": "removerange"
          }
         ],
         "key": 0,
         "op": "patch"
        }
       ],
       "key": "version",
       "op": "patch"
      }
     ],
     "key": "language_info",
     "op": "patch"
    }
   ],
   "remote_diff": [
    {
     "diff": [
      {
       "diff": [
        {
         "diff": [
          {
           "key": 5,
           "op": "addrange",
           "valuelist": "12"
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       "key": "version",
       "op": "patch"
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     ],
     "key": "language_info",
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    }
   ]
  },
  "title": "Inverse Design of a Grating Coupler in Tidy3D | Flexcompute",
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