{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ec818b9b",
   "metadata": {},
   "source": [
    "# Robust adjoint optimization for manufacturability\n",
    "\n",
    "> In the previous notebook, we discovered that our apodized grating is somewhat brittle: realistic ±20 nm fabrication errors can reduce efficiency by almost a decibel. A design that only works on paper is not a practical solution.\n",
    "\n",
    "> In this final notebook we incorporate fabrication awareness directly into the adjoint optimization loop. Instead of optimizing a single, nominal simulation, we will maximize the performance across multiple fabrication corners so the resulting device maintains high efficiency even when etched dimensions shift on the wafer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b47bfd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from copy import deepcopy\n",
    "from pathlib import Path\n",
    "\n",
    "import autograd.numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tidy3d as td\n",
    "from autograd import value_and_grad\n",
    "from optim import adam_update, apply_updates, clip_params, init_adam\n",
    "from setup import (\n",
    "    center_wavelength,\n",
    "    first_gap_sin,\n",
    "    get_mode_monitor_power,\n",
    "    make_simulation,\n",
    "    max_gap_si,\n",
    "    max_gap_sin,\n",
    "    max_width_si,\n",
    "    max_width_sin,\n",
    "    min_gap_si,\n",
    "    min_gap_sin,\n",
    "    min_width_si,\n",
    "    min_width_sin,\n",
    ")\n",
    "from tidy3d import web\n",
    "\n",
    "ETCH_BIAS = 0.02  # 20 nm fabrication bias expressed in microns.\n",
    "\n",
    "\n",
    "def apply_bias(param_dict, etch_bias):\n",
    "    \"\"\"Return a new parameter dictionary with widths widened by bias.\"\"\"\n",
    "    return {\n",
    "        \"widths_si\": param_dict[\"widths_si\"] + etch_bias,\n",
    "        \"gaps_si\": param_dict[\"gaps_si\"] - etch_bias,\n",
    "        \"widths_sin\": param_dict[\"widths_sin\"] + etch_bias,\n",
    "        \"gaps_sin\": param_dict[\"gaps_sin\"] - etch_bias,\n",
    "        \"first_gap_si\": param_dict[\"first_gap_si\"],\n",
    "        \"first_gap_sin\": param_dict[\"first_gap_sin\"],\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aab0e16",
   "metadata": {},
   "source": [
    "## Defining a Robust Multi-Objective Function\n",
    "\n",
    "We evaluate the design under three fabrication scenarios: nominal, over-etched (−20 nm), and under-etched (+20 nm). We then maximize the mean transmission and simultaneously minimize the standard deviation in performance between these different scenarios, which should lead to a more robust design overall. The amount of weight we place on the standard deviation minimization determines the tradeoff between nominal performance and robustness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f55644b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "STD_PENALTY = 2.0  # Penalty for standard deviation in power\n",
    "\n",
    "\n",
    "def robust_objective(params):\n",
    "    freq0 = td.C_0 / center_wavelength\n",
    "    scenarios = {\n",
    "        \"nominal\": params,\n",
    "        \"over\": apply_bias(params, -ETCH_BIAS),\n",
    "        \"under\": apply_bias(params, ETCH_BIAS),\n",
    "    }\n",
    "\n",
    "    sims = {\n",
    "        name: make_simulation(\n",
    "            scenario[\"widths_si\"],\n",
    "            scenario[\"gaps_si\"],\n",
    "            scenario[\"widths_sin\"],\n",
    "            scenario[\"gaps_sin\"],\n",
    "            first_gap_si=scenario[\"first_gap_si\"],\n",
    "            first_gap_sin=scenario[\"first_gap_sin\"],\n",
    "        )\n",
    "        for name, scenario in scenarios.items()\n",
    "    }\n",
    "\n",
    "    batch_data = web.run_async(sims, verbose=False, local_gradient=True)\n",
    "\n",
    "    powers = []\n",
    "    for name in (\"nominal\", \"over\", \"under\"):\n",
    "        sim_data = batch_data[name]\n",
    "        power_da = get_mode_monitor_power(sim_data)\n",
    "        target_power = power_da.sel(f=freq0, method=\"nearest\")\n",
    "        powers.append(target_power.item())\n",
    "\n",
    "    powers = np.array(powers)\n",
    "    mean_power = np.mean(powers)\n",
    "    variance = np.mean((powers - mean_power) ** 2)\n",
    "    std_power = np.sqrt(variance)\n",
    "    robust_metric = mean_power - STD_PENALTY * std_power\n",
    "\n",
    "    return -robust_metric"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f242f7e",
   "metadata": {},
   "source": [
    "### Starting Point and Bounds\n",
    "\n",
    "We seed the optimizer with the fabrication-sensitive adjoint design and enforce the same foundry limits as before so the updates remain manufacturable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d0e4d00e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = json.loads(Path(\"./results/gc_adjoint_best.json\").read_text(encoding=\"utf-8\"))\n",
    "\n",
    "num_iters = 40\n",
    "\n",
    "params0 = {\n",
    "    \"widths_si\": np.array(data[\"widths_si\"], dtype=float),\n",
    "    \"gaps_si\": np.array(data[\"gaps_si\"], dtype=float),\n",
    "    \"widths_sin\": np.array(data[\"widths_sin\"], dtype=float),\n",
    "    \"gaps_sin\": np.array(data[\"gaps_sin\"], dtype=float),\n",
    "    \"first_gap_si\": float(data[\"first_gap_si\"]),\n",
    "    \"first_gap_sin\": float(data.get(\"first_gap_sin\", first_gap_sin)),\n",
    "}\n",
    "\n",
    "bounds = {\n",
    "    \"widths_si\": (min_width_si, max_width_si),\n",
    "    \"gaps_si\": (min_gap_si, max_gap_si),\n",
    "    \"widths_sin\": (min_width_sin, max_width_sin),\n",
    "    \"gaps_sin\": (min_gap_sin, max_gap_sin),\n",
    "    \"first_gap_si\": (None, None),\n",
    "    \"first_gap_sin\": (min_gap_sin, None),\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93e0619d",
   "metadata": {},
   "source": [
    "## Running the Robust Optimization\n",
    "\n",
    "Starting from the adjoint-optimized design found earlier, we use Adam to minimize the robust objective."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd52c91f",
   "metadata": {},
   "outputs": [],
   "source": [
    "vg_fun = value_and_grad(robust_objective)\n",
    "params = deepcopy(params0)\n",
    "opt_state = init_adam(params, lr=1e-3)\n",
    "\n",
    "objective_history = []\n",
    "\n",
    "for n in range(num_iters):\n",
    "    value, grad = vg_fun(params)\n",
    "    objective_value = -value\n",
    "\n",
    "    objective_history.append(objective_value)\n",
    "    print(f\"iter {n}: objective={objective_value:.4f}\")\n",
    "\n",
    "    updates, opt_state = adam_update(grad, opt_state)\n",
    "    params = apply_updates(params, updates)\n",
    "    params = clip_params(params, bounds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a509ddc7",
   "metadata": {},
   "source": [
    "### Tracking Progress\n",
    "\n",
    "Plotting the objective over iterations lets us confirm that the robust objective steadily improves (higher is better)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3732b06c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAikAAAGJCAYAAABPZ6NtAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAbl9JREFUeJzt3XlcVPX6B/DPzACDiggCwyIIKmiiIgiJULlBilpZWmKZW0qaWiqmv9DcF1JMUdPw5nWprFxuWjdSU1wqBTMFd1EUd1mVRRTQmfP7I5jrwAwMODCLn/frxSvnnDPnPM+cSR6/57uIBEEQQERERGRgxPoOgIiIiEgdFilERERkkFikEBERkUFikUJEREQGiUUKERERGSQWKURERGSQWKQQERGRQWKRQkRERAaJRQoREREZJBYp9EyZM2cORCIRcnJy9B1KnfLw8MCIESOUrw8ePAiRSISDBw/W+Fzln5mpqYu8RowYAQ8PD52e05CvW/692r59e7XH6itGMm4sUshgbdy4ESKRSPljZmaGZs2aYcSIEbh165a+w6uR7777DrGxsfoOo94dOXIEc+bMQV5enl6u/+DBA8yZM6dWxZmhuX37NubMmYOUlBR9h1Jvzp07hzlz5uDq1av6DoX0hEUKGbx58+bhm2++QVxcHPr06YNvv/0W3bp1Q3Fxsb5D05q+i5SuXbvi4cOH6Nq1a43f++mnn+Lhw4e1uu6RI0cwd+5cvRYpc+fOVVukPE1emnz11VdITU3V6TnL3b59G3PnzlVbpNTldXWlNjGeO3cOc+fOZZHyDDPTdwBE1enTpw8CAgIAAKNHj4a9vT0WL16Mn3/+GYMGDdJ3eEZBLBbD0tKyVu81MzODmZnp/VVRF3mZm5vr9HyGft2aMKQYHzx4gIYNG+o7DNICW1LI6Lz00ksAgMuXL6ts379/P1566SU0atQINjY26N+/P86fP6/2HDk5ORg0aBCsra1hZ2eHiRMnqrTMXL16FSKRCBs3bqz0XpFIhDlz5ihfFxYWYtKkSfDw8IBUKoVMJsPLL7+MEydOAAC6d++O+Ph4XLt2Tfnoqrpn8xs2bEDPnj0hk8kglUrh7e2NL7/8stJxgiBgwYIFcHV1RcOGDdGjRw+cPXu20nGa+qRs27YN/v7+aNCgAezt7fHuu+9WepSmru+GSCTChAkTsHPnTrRv3x5SqRTt2rXD7t27Vd43depUAECLFi2UuVf3r2JtYhoxYgSsrKxw5coV9O7dG40aNYKLiwvmzZuH8oXdr169CgcHBwDA3Llzldcvv3dV5bVt2zZ4e3ujQYMGCAoKwunTpwEAa9euhaenJywtLdG9e/dKuVTsd9G9e3eVR5ZP/pR/t+7evYuPP/4YHTp0gJWVFaytrdGnTx+cPHlS5f49//zzAICRI0dWOoe6/h5FRUWYMmUK3NzcIJVK0aZNGyxduhQVF77X5l5WR6FQYOHChXB1dYWlpSVCQkKQlpZW5WcDAD/88AP8/f3RuHFjWFtbo0OHDlixYgVQ9rj3rbfeAgD06NFDmfOT3+E1a9agXbt2kEqlcHFxwfjx4yu12nXv3h3t27fH8ePH0bVrVzRs2BDTp0/H8OHDYW9vj0ePHlXKp1evXmjTpo3W+VPdMb1/HpHJK//FYGtrq9y2b98+9OnTBy1btsScOXPw8OFDrFq1Ci+88AJOnDhR6S/HQYMGwcPDA9HR0UhKSsLKlStx7949fP311zWOZ+zYsdi+fTsmTJgAb29v5Obm4s8//8T58+fRqVMnzJgxA/n5+bh58yaWL18OALCysqrynF9++SXatWuH1157DWZmZvjvf/+LcePGQaFQYPz48crjZs2ahQULFqBv377o27cvTpw4gV69eqG0tLTauDdu3IiRI0fi+eefR3R0NDIzM7FixQocPnwYycnJsLGxqfL9f/75J3788UeMGzcOjRs3xsqVKzFw4EBcv34ddnZ2GDBgAC5evIjvv/8ey5cvh729PQAoC4enjUkulyMsLAxdunTBkiVLsHv3bsyePRuPHz/GvHnz4ODggC+//BIffPAB3njjDQwYMAAA4OPjU2Vef/zxB37++Wfl5xwdHY1XXnkF06ZNw5o1azBu3Djcu3cPS5YswXvvvYf9+/drPNeMGTMwevRolW3ffvst9uzZA5lMBgC4cuUKdu7cibfeegstWrRAZmYm1q5di27duuHcuXNwcXFB27ZtMW/ePMyaNQvvv/++slAPDg5We11BEPDaa6/hwIEDGDVqFHx9fbFnzx5MnToVt27dUn4Ptb2X1fnss88gFovx8ccfIz8/H0uWLMGQIUNw9OhRje/Zu3cv3n77bYSEhGDx4sUAgPPnz+Pw4cOYOHEiunbtio8++ggrV67E9OnT0bZtWwBQ/nfOnDmYO3cuQkND8cEHHyA1NRVffvkljh07hsOHD6u03OTm5qJPnz4YPHgw3n33XTg6OqJRo0b4+uuvsWfPHrzyyivKYzMyMrB//37Mnj272rypHghEBmrDhg0CAGHfvn1Cdna2cOPGDWH79u2Cg4ODIJVKhRs3biiP9fX1FWQymZCbm6vcdvLkSUEsFgvDhg1Tbps9e7YAQHjttddUrjVu3DgBgHDy5ElBEAQhPT1dACBs2LChUlwAhNmzZytfN2nSRBg/fnyVufTr109wd3fXOvcHDx5U2ta7d2+hZcuWytdZWVmChYWF0K9fP0GhUCi3T58+XQAgDB8+XLntwIEDAgDhwIEDgiAIQmlpqSCTyYT27dsLDx8+VB73yy+/CACEWbNmKbeVf2YVPwMLCwshLS1Nue3kyZMCAGHVqlXKbTExMQIAIT09vdqcaxLT8OHDBQDChx9+qNymUCiEfv36CRYWFkJ2drYgCIKQnZ1d6X5Vl5dUKlWJd+3atQIAwcnJSSgoKFBuj4qKqpTb8OHDq7zPhw8fFszNzYX33ntPua24uFiQy+Uqx6WnpwtSqVSYN2+ectuxY8c0ficrXnfnzp0CAGHBggUqx7355puCSCRSuW/a3kt1yr9Xbdu2FUpKSpTbV6xYIQAQTp8+rTHGiRMnCtbW1sLjx481nn/btm0q39ty5d/9Xr16qXx2X3zxhQBAWL9+vXJbt27dBABCXFycyjnkcrng6uoqhIeHq2xftmyZIBKJhCtXrlSZO9UPPu4hgxcaGgoHBwe4ubnhzTffRKNGjfDzzz/D1dUVAHDnzh2kpKRgxIgRaNq0qfJ9Pj4+ePnll/Hrr79WOueTrREA8OGHHwKA2mOrY2Njg6NHj+L27du1yE69Bg0aKP+cn5+PnJwcdOvWDVeuXEF+fj5Q1npUWlqKDz/8UOWxxaRJk6o9/99//42srCyMGzdOpa9Kv3798NxzzyE+Pr7ac4SGhqJVq1bK1z4+PrC2tsaVK1dqlOvTxDRhwgTln8sfW5SWlmLfvn21igEAQkJCVFreAgMDAQADBw5E48aNK23XNt+MjAy8+eab8PX1xZo1a5TbpVIpxOJ//iqWy+XIzc2FlZUV2rRpo3xkWFO//vorJBIJPvroI5XtU6ZMgSAI2LVrl8r2p72XI0eOhIWFhfJ1eUtPVe+3sbFBUVER9u7dq3Ve5cq/+5MmTVJ+dgAQEREBa2vrSt8VqVSKkSNHqmwTi8UYMmQIfv75ZxQWFiq3b968GcHBwWjRokWN4yLdY5FCBm/16tXYu3cvtm/fjr59+yInJwdSqVS5/9q1awCg9hly27ZtkZOTg6KiIpXtXl5eKq9btWoFsVhcq1EES5YswZkzZ+Dm5obOnTtjzpw5tf5FXe7w4cMIDQ1V9q9xcHDA9OnTgbKiBU/kXTEXBwcHlUdh6lT1mT333HPK/VVp3rx5pW22tra4d+9ete/VRUxisRgtW7ZU2da6dWvgiUeCtVExryZNmgAA3Nzc1G7XJt/Hjx9j0KBBkMvl+PHHH1W+vwqFAsuXL4eXlxekUins7e3h4OCAU6dOKe91TV27dg0uLi4qRRWeeFRS8bN82ntZ8f3l37+q3j9u3Di0bt0affr0gaurK9577z2t+8Fo+q5YWFigZcuWlfJr1qyZShFVbtiwYXj48CF27NgBAEhNTcXx48cxdOhQreKguscihQxe586dERoaioEDB+Lnn39G+/bt8c477+D+/fs6u4a6DpTqyOXyStsGDRqEK1euYNWqVXBxcUFMTAzatWtX6V+r2rp8+TJCQkKQk5ODZcuWIT4+Hnv37sXkyZOBsl9qhkAikajdXrFjprHRlNfT5Dt16lQkJiZi69atyhbAcosWLUJkZCS6du2q7K+yd+9etGvXrt7u9dPey9q8XyaTISUlBT///LOy/0yfPn0wfPhwLaPW3pMtk0/y9vaGv78/vv32W6Csv5CFhQVHDRoQFilkVCQSCaKjo3H79m188cUXAAB3d3eg7F9BFV24cAH29vZo1KiRyvZLly6pvE5LS4NCoVA285f/S7DiSAFNLQzOzs4YN24cdu7cifT0dNjZ2WHhwoXK/TWZ2fS///0vSkpK8PPPP2PMmDHo27cvQkNDK/1FW553xVyys7Or/RdwVZ9Zamqqcv/TqkneNY1JoVBUarG6ePEiUDbjbk2vX1d++OEHxMbGYunSpejWrVul/du3b0ePHj3w73//G4MHD0avXr0QGhpa6btX08/y9u3bKo8xUPb/A574rPXNwsICr776KtasWYPLly9jzJgx+Prrr5UjgzTlrOm7UlpaivT09BrlN2zYMOzfvx937tzBd999h379+lXbEkn1h0UKGZ3u3bujc+fOiI2NRXFxMZydneHr64tNmzap/MV+5swZ/Pbbb+jbt2+lc6xevVrl9apVq4CyOVkAwNraGvb29vj9999VjnuyLwHKWlYqNsnLZDK4uLigpKREua1Ro0ZaN92X/6v0yX+F5ufnY8OGDSrHhYaGwtzcHKtWrVI5VptJ4wICAiCTyRAXF6cS565du3D+/Hn069dPq1irU14cajOZW21iKi9UUfZ5ffHFFzA3N0dISAgAKOfC0NdkcmfOnMHo0aPx7rvvYuLEiWqPkUgklVoctm3bVmnYdU0+y759+0Iul6t8PgCwfPlyiEQi5fdcn3Jzc1Vei8Vi5cir8vuvKefQ0FBYWFhg5cqVKp/dv//9b+Tn59fo+/v2229DJBJh4sSJuHLlCt59992nyot0i0OQyShNnToVb731FjZu3IixY8ciJiYGffr0QVBQEEaNGqUcgtykSROVOU3Kpaen47XXXkNYWBgSExPx7bff4p133kHHjh2Vx4wePRqfffYZRo8ejYCAAPz+++/Kf6mXKywshKurK95880107NgRVlZW2LdvH44dO4bPP/9ceZy/vz+2bNmCyMhIPP/887CyssKrr76qNrdevXop/4U5ZswY3L9/H1999RVkMhnu3LmjPM7BwQEff/yxcohs3759kZycjF27dimH+2pibm6OxYsXY+TIkejWrRvefvtt5XBfDw8P5aOlp+Xv7w+UDcUdPHgwzM3N8eqrr1Zq2apNTJaWlti9ezeGDx+OwMBA7Nq1C/Hx8Zg+fbpymHODBg3g7e2NLVu2oHXr1mjatCnat2+P9u3b6yS/6pR31ix/lPOk4OBgtGzZEq+88grmzZuHkSNHIjg4GKdPn8bmzZsr9bdp1aoVbGxsEBcXh8aNG6NRo0YIDAxU28Hz1VdfRY8ePTBjxgxcvXoVHTt2xG+//YaffvoJkyZNUukkqy+jR4/G3bt30bNnT7i6uuLatWtYtWoVfH19lX1nfH19IZFIsHjxYuTn50MqlSrnD4qKisLcuXMRFhaG1157DampqVizZg2ef/75GhUaDg4OCAsLw7Zt22BjY6OzAp10RN/Di4g0KR+CfOzYsUr75HK50KpVK6FVq1bKIYz79u0TXnjhBaFBgwaCtbW18Oqrrwrnzp1TeV/5sNNz584Jb775ptC4cWPB1tZWmDBhgsqwV6FsGPCoUaOEJk2aCI0bNxYGDRokZGVlqQxpLSkpEaZOnSp07NhRaNy4sdCoUSOhY8eOwpo1a1TOdf/+feGdd94RbGxsBADVDkf++eefBR8fH8HS0lLw8PAQFi9eLKxfv77SkFe5XC7MnTtXcHZ2Fho0aCB0795dOHPmjODu7l7lEORyW7ZsEfz8/ASpVCo0bdpUGDJkiHDz5k21n9mTAKgddl3xuoIgCPPnzxeaNWsmiMVirYYjaxPT8OHDhUaNGgmXL18WevXqJTRs2FBwdHQUZs+eXWk475EjRwR/f3/BwsJC5d5pm1f5cPSYmBiV7eWf6bZt21TievLeuru7CwDU/pQPJS4uLhamTJmivIcvvPCCkJiYKHTr1k3o1q2byjV/+uknwdvbWzAzM1M5h7qhz4WFhcLkyZMFFxcXwdzcXPDy8hJiYmJUhqtrylnQcC8rUvcZCBqG8FeMcfv27UKvXr0EmUwmWFhYCM2bNxfGjBkj3LlzR+VcX331ldCyZUtBIpFU+g5/8cUXwnPPPSeYm5sLjo6OwgcffCDcu3dP5f3dunUT2rVrV2UeW7duFQAI77//fpXHUf0TCcbey42IqpWQkIDQ0FD88ccfePHFF/UdzlMbMWIEtm/frtPO0/Ts+umnn/D666/j999/Vw6fJsPAPilEz4Dyx0TVPQYiehZ99dVXaNmypUkU8KaGfVKITFhRURE2b96MFStWwNXVVTmPCBH9M/Lq1KlTiI+Px4oVKwxiNBipYpFCZMKys7Px4YcfokOHDtiwYYPK7JxEz7q3334bVlZWGDVqFMaNG6fvcEgN9kkhIiIig8R/VhEREZFBYpFCREREBol9UmpJoVDg9u3baNy4MTtbERER1YAgCCgsLISLi0uVfeVYpNTS7du3K62KSkRERNq7ceNGpUU3n6T3ImX16tWIiYlBRkYGOnbsiFWrVqFz585qj924caNymulyUqkUxcXFytcjRozApk2bVI7p3bt3pSXA4+PjMW/ePJw6dQqWlpbo1q0bdu7cqXXc5Uug37hxA9bW1lq/ryoKhQLZ2dlwcHAwqVEYzMv4mGpuzMu4MC/jUpO8CgoK4Obmpvxdqolei5TytUzi4uIQGBiI2NhY9O7dG6mpqZDJZGrfY21trbLypbpHLWFhYSqLsUmlUpX9//nPfxAREYFFixahZ8+eePz4Mc6cOVOj2Muva21trdMipbi4GNbW1ib3xWVexsVUc2NexoV5GZfa5FVddwm9FinLli1DRESEsnUkLi4O8fHxWL9+PT755BO17xGJRHBycqryvFKpVOMxjx8/xsSJExETE4NRo0Ypt3t7ez9VLkRERKRbeitSSktLcfz4cURFRSm3icVihIaGIjExUeP77t+/D3d3dygUCnTq1AmLFi1Cu3btVI45ePAgZDIZbG1t0bNnTyxYsAB2dnYAgBMnTuDWrVsQi8Xw8/NDRkYGfH19ERMTU+XKqCUlJSrLxxcUFABllaNCoXiqz6KcQqGAIAg6O5+hYF7Gx1RzY17GhXkZl5rkpW3ueitScnJyIJfL4ejoqLLd0dERFy5cUPueNm3aYP369fDx8UF+fj6WLl2K4OBgnD17VtnxJiwsDAMGDECLFi1w+fJlTJ8+HX369EFiYiIkEgmuXLkCAJgzZw6WLVsGDw8PfP755+jevTsuXryIpk2bqr12dHQ05s6dW2l7dna2Sp+Yp6FQKJCfnw9BEEyuCZB5GRdTzY15GRfmZVxqkldhYaFW59R7x9maCAoKQlBQkPJ1cHAw2rZti7Vr12L+/PkAgMGDByv3d+jQAT4+PmjVqhUOHjyIkJAQZfU2Y8YMDBw4EACwYcMGuLq6Ytu2bRgzZozaa0dFRSEyMlL5urzTj4ODg077pIhEIpPsTMW8jIup5sa8jAvzMi41ycvS0lKrc+qtSLG3t4dEIkFmZqbK9szMzGr7nJQzNzeHn58f0tLSNB7TsmVL2NvbIy0tDSEhIXB2dgYq9EGRSqVo2bIlrl+/rvE8Uqm0UgdclD2i0uWXTCQS6fychoB5GR9TzY15GRfmZVy0zUvbvPX26VhYWMDf3x8JCQnKbQqFAgkJCSqtJVWRy+U4ffq0svBQ5+bNm8jNzVUe4+/vD6lUqjJC6NGjR7h69Src3d2fKiciIiLSHb0+7omMjMTw4cMREBCAzp07IzY2FkVFRcrRPsOGDUOzZs0QHR0NAJg3bx66dOkCT09P5OXlISYmBteuXcPo0aOBsk61c+fOxcCBA+Hk5ITLly9j2rRp8PT0RO/evYGyIcNjx47F7Nmz4ebmBnd3d8TExAAA3nrrLb19FkRERPVBrpAj6VIKsgpyIbO2QxcvX0jEEn2HpZZei5Tw8HBkZ2dj1qxZylE2u3fvVnamvX79ukqT0L179xAREYGMjAzY2trC398fR44cUT66kUgkOHXqFDZt2oS8vDy4uLigV69emD9/vsqjmpiYGJiZmWHo0KF4+PAhAgMDsX//ftja2urhUyAiIno62hYe8ckH8OmW5biTl6Xc5mwjw4Lwyejn16Oeo66eSBAEQd9BGKOCggI0adIE+fn5Ou04m5WVBZlMZlLPKZmX8THV3JiXcWFe2tG28IhPPoDRa6NQ8Zd++XRq68ZEP1WhUpO8tP0dajp3nYiI6BlTXng8WaAAQEZeFkavjUJ88gGgrKXl0y3LKxUoAJTbZm6NhVwhr4eotWdUQ5CJiIjoH9oUHlO//QwX76Qj8VJypUKm4vG372Ui6VIKXmjjX2cx1xSLFCIiIiOUdCmlysIDAO4W5WPxz//S+pxZBbk6iEx3+LiHiIjICGlbUHRu5YN3XnhVq2Nl1nZPGZVusSWFiIjICGlbUPzfa2PQxcsXB84eRUZeltrHQyIAzraO6OLlq/M4nwZbUoiIiIxQFy9fONvIlKNzKhIBcCkrPCRiCRaET1ZuV2f+oEkGN18KixQiIiIjVF54aGoZQYXCo59fD6wbEw0nG1ml498PGWyQ86SwSCEiIjJS/fx64MU2AZW2O9s6qp33pJ9fD/y9aAf+M3k1vhw1D4ODXgEA7D+bZHDDj8E+KURERMbrYWkxTl47DwCY99YkOFg3rXaqe4lYohxmHNI+GLtOHsKljKv474n9eD3g5XqNvzpsSSEiIjJSe079gcLiIrg2dcLoHoPwxvO98EIbf637llg3sML7IYMBAMvi10OhUNRxxDXDIoWIiMhIbUvaBQB4MzCs1lPsj+4xCNYNrHDxTjr+e2K/jiN8OixSiIiIjFB2QS4OnjsKAHgzsE+tz9OkYeP/tab8alitKSxSiIiIjNDOY/sgV8jh6+4NTyf3pzpXRM9wWDewQurtK/ilbL0fQ8AihYiIyAhtO/rPo563utS+FaVck4aNEdEzHADwefy/DaY1hUUKERGRkUm9nY5T1y/ATCzB68/rZkRORM9wNLZshNTbVxCffFAn53xaLFKIiIiMzPayVpSe7YNgZ2Wjk3PaNLJGRFnfFENpTWGRQkREZEQUCgV+/GsP8JQdZtV5v6w15cLty/g1Rf+tKSxSiIiIjMiRS8m4dS8T1g2s0MvnRZ2e26aRNUYbUN8UFilERERGZFvSrwCA1/xDYGku1fn53w8Jh5VlQ5y/dRnxyQdwOPU4dhz7DYdTj9f71PmcFp+IiMhIPCgtxi8n/hkirOtHPeVsGzVBRM9wLP91Az749yw8fqIwcbaRYUH45HpbjJAtKUREREZiz8nfUVTyAG52zujcyqfOruPh4AoAKgUKAGTkZWH02ijE19NcKixSiIiIjMT/psHvU+tp8KsjV8jx2U9r1e4Tyv47c2tsvTz6YZFCRERkBLLy/zcN/luBYXV2naRLKbiTl6VxvwDg9r1MJF1KqbMYyrFIISIiMgI7jv0GhaBApxbt0NKxeZ1dJ6sgV6fHPQ0WKUREREagfAK3t+qow2w5mbWdTo97GixSiIiIDNz5W5dx+sZFmEvM0D9AN9Pga9LFyxfONjKINOwXAXCxdUQXL986jQMsUoiIiAxfeStKSPtgNLVqUqfXkoglWBA+GSgrSJ5U/nr+oEmQiCV1GgdYpBARERk2uUL+xDT4dddh9kn9/Hpg3ZhoONnIVLY72zpi3ZjoepsnhZO5ERERGSC5Qo6kSyn4/fxfuJOXDesGVni5g26nwa9KP78eCOvYFUmXUpBVkAuZtR26ePnWSwtKORYpREREBiY++QA+3bJcZSjwY/lj7DtzuN5aMVD26OeFNv71dr2K+LiHiIjIgMQnH8DotVGV5ip5UFpcr7O9GgIWKURERAZCrpDj0y3LlTO7qlNfs70aAhYpREREBsKQZns1BCxSiIiIDIQhzfZqCFikEBER1RO5Qo4jF09g1+nfceTiCZXHNoIg4GrWLa3OUx+zvRoCgyhSVq9eDQ8PD1haWiIwMBB//fWXxmM3btwIkUik8mNpaalyzIgRIyodExamfmx5SUkJfH19IRKJkJLybDSfERFR/YtPPoCA6W/gzdgJiPrPMrwZOwEB099AfPIBnL91GeErPsLi/6pffbhcfc72agj0PgR5y5YtiIyMRFxcHAIDAxEbG4vevXsjNTUVMplM7Xusra2RmpqqfC0SVZ68NywsDBs2bFC+lkqlas81bdo0uLi44OTJkzrJh4iIqKLyETsVO8Rm5GVh1NooiCCCAAEWZuYIbf8CdqUcBMr6oJSr79leDYHeW1KWLVuGiIgIjBw5Et7e3oiLi0PDhg2xfv16je8RiURwcnJS/jg6OlY6RiqVqhxja2tb6Zhdu3bht99+w9KlS3WeFxEREaoZsSMo/yugr283/DH7B6wf+5lBzPZqCPTaklJaWorjx48jKipKuU0sFiM0NBSJiYka33f//n24u7tDoVCgU6dOWLRoEdq1a6dyzMGDByGTyWBra4uePXtiwYIFsLP73zO8zMxMREREYOfOnWjYsGG1sZaUlKCkpET5uqCgAACgUCigUChqnLs6CoUCgiDo7HyGgnkZH1PNjXkZF1PJK/FicpUjdsq91/0tuNk5Q6FQoE/HbujV4UUcTTuJzPwcODaxR6BnR0jEEoP9PGpyv7TNQa9FSk5ODuRyeaWWEEdHR1y4cEHte9q0aYP169fDx8cH+fn5WLp0KYKDg3H27Fm4uroCZY96BgwYgBYtWuDy5cuYPn06+vTpg8TEREgkEgiCgBEjRmDs2LEICAjA1atXq401Ojoac+fOrbQ9OzsbxcXFtf4MnqRQKJCfnw9BECAW672RS2eYl/Ex1dyYl3Exlbwu3bii9XGeNq4q2zxtXJXbcnMMe0RPTe5XYWGhVufUe5+UmgoKCkJQUJDydXBwMNq2bYu1a9di/vz5AIDBgwcr93fo0AE+Pj5o1aoVDh48iJCQEKxatQqFhYUqLTjViYqKQmRkpPJ1QUEB3Nzc4ODgAGtra53kplAoIBKJ4ODgYNT/Q1bEvIyPqebGvIyLqeTl5dZS6+M09cU0BjW5XxUHvGii1yLF3t4eEokEmZmZKtszMzPh5OSk1TnMzc3h5+eHtLQ0jce0bNkS9vb2SEtLQ0hICPbv34/ExMRKnWkDAgIwZMgQbNq0qdI5pFKp2s63YrFYp//ziEQinZ/TEDAv42OquTEv42IKeXXx8oWVtCHulzxQu19U1t8kqLWfUeeJGtwvbfPU66dhYWEBf39/JCQkKLcpFAokJCSotJZURS6X4/Tp03B2dtZ4zM2bN5Gbm6s8ZuXKlTh58iRSUlKQkpKCX3/9FSgbabRw4cKnzouIiAgAHskfY8q30VUWKHjGRuzUhN4f90RGRmL48OEICAhA586dERsbi6KiIowcORIAMGzYMDRr1gzR0dEAgHnz5qFLly7w9PREXl4eYmJicO3aNYwePRoo61Q7d+5cDBw4EE5OTrh8+TKmTZsGT09P9O7dGwDQvHlzlRisrKwAAK1atVL2ayEiInoaRSUP8f5XM5Bw5ggkYgmGvvQ69pz8Q6UTrbOtI+YPmvRMjdipCb0XKeHh4cjOzsasWbOQkZEBX19f7N69W9mZ9vr16yrNQvfu3UNERAQyMjJga2sLf39/HDlyBN7e3gAAiUSCU6dOYdOmTcjLy4OLiwt69eqF+fPna5wrhYiIqLbkCjmSLqUgqyAXMms7dPHyRd6DQgxdPQUn0s+igbkUayMWopfPi1gYHonEi8m4dOMKvNxaIqi1H1tQqiASBKGqxRZJg4KCAjRp0gT5+fk67TiblZUFmUxm9M8ln8S8jI+p5sa8jIsx5BWffACfblmu0jois7aDWCRGRn42bBtZ45vxnyOgZQflfmPIqzZqkpe2v0P13pJCRERkjDTNIlu++F/TRk2w8+M4tHZuoZf4TIHplHBERET1pKpZZMuZm5mjlWPzKo6g6rBIISIiqqGkSynVziKbmZ+DpEtcuPZpsEghIiKqofJHOro6jtRjnxQiIiI11I3aKR+Jk5WvXfEhs7bT4ijShEUKERFRBepG7TjbyDA29B38kXoM+04frvL95bPIdvHyrYdoTReLFCIioidoGrVzJy8Ls7fHAgDMxBK89FxnHDyXCAAqx3IWWd1hkUJERFRGm1E7luZS7J2+CV7OHupbXDiLrM6wSCEiIiqjzaid4kclyCrIhZezB/r59UBYx64a+67Q02GRQkREVKY2o3YkYgleaONfh1E9uzgEmYiIqGxBwD2n/tDqWI7aqR9sSSEiomeGpmHFe08fRtT3Mbh5N6PK93PUTv1ikUJERM8EtYsBNrGDa1MnnEg/CwBo1tQJA55/GV/s+QbgqB29Y5FCREQmT+NigPm5yMrPhVgkxtjQtzHlldFoJG0AXw9vjtoxACxSiIjIpGkzrNiusQ1mvDFO2ULCUTuGgUUKERGZNG2GFWcX3EXSpRSVUToctaN/LFKIiMioVbXGjkKhwG+n/tTqPFwM0PCwSCEiIqOlaY2d2QM/xIPSYny591tcyrim1bk4rNjwsEghIiKjVNUaO2P/PVP5urFlIwiCgKKSB2r7pXBYseHiZG5ERGR0tOkMKxaJ8ekb43Ei+mesGPFP0SKqcAyHFRs2FilERGRw5Ao5jlw8gV2nf8eRiycgV8hV9mvTGVYhKODn4Y3GDRqhn18PrBsTDScbmcoxzraOWDcmmsOKDRQf9xARkUHR1M9kQfhk9PPrgTv3srDx0H+0OteTnWE5rNj4sEghIiKDoamfSUZeFkatjUJH9+dw+vpFKASFVuer2BmWw4qNCx/3EBGRQaiqn0n5tpPXLkAhKNC5lQ9sGlpX6mNSTgTAhZ1hjR5bUoiIDExV836YMm36mQDAiuEzER7UT9nqIuIaOyaLRQoRkQGprj+GKdN2MjULM3OgrI/JujHRXGPHhLFIISKqB9q0jlTVH2P02iijH4VS1WeQeCkZa377VqvzPNnPhJ1hTRuLFCKiOqZN60h1/TFEAGZujUVYx65G+QtY02cw5MXXcDj1OBIvJVd7Dk2TrrEzrOlix1kiojpU3jpSsa9FeetIfPIBQIv+GAKA2/cykXQppc5j1jVNn8GdvCws/WUdEi8lw1xihmFd38Did6ZBxEnXqAxbUoiI6og2o1U+2jgfmw79iJPXLmh1TmNbBE+bmWEbSRvgwMzNaG7vAgCwb2zLfiYEsEghIqo72oxWKSp5gN8vHNP6nMa2CJ52n8FD3Mi9oyxSyvuZJF5MxqUbV+Dl1hJBrf3YgvIMYpFCRFRHtG31ePfF/hjUpR/e/2oGMvOzNbY6WJpL0dq5Ra3j0cfQZm0/g4rHScQSBLfuBE8bV8hkMojF7J3wLGKRQkRUS1X90pcr5Dhz46JW53nj+V7o7OmDhYMj1c77Ua74UQl6LRqOVSPn4MWyjqJyhVyrFoeaDm3WVUGjbcuPsbUQUf1gkUJEVAtV/dJvaNEA835chfO3Lld5joqjVTTN++Fi64jRPd7C5sM/43LmdbwVOwEf9R6Gdm6tMXvbimoLj5oObdblXC1dvHzhbCPT+MhH04gdIgAQCYJQVX8m0qCgoABNmjRBfn4+rK2tdXJOhUKBrKwsk2vaZF7Gx1Rz01Vemn7pV9SkYWP06vAith/dBWiYFVXd3CeaWjGKSh5i5tZl+O7wfzVes+J55Qo5Aqa/UW2RcGzhj5CIJRpzqyre6vxy4gBG/yuq2lgr4vfQuNQkL21/h7IlhYioBrQZrQIAESHhiOz7HmwbNUGYb9cajVbRNO9HI2kDLBs6A12f64wP1s+Cun9jlm+J/GYRTqSfxclrF7Qa2rz0l3/j5Q4vYsYPn+t8rhbbRup/CXHEDlXHIIqU1atXIyYmBhkZGejYsSNWrVqFzp07qz1248aNGDlypMo2qVSK4uJi5esRI0Zg06ZNKsf07t0bu3fvBgBcvXoV8+fPx/79+5GRkQEXFxe8++67mDFjBiwsLOokRyIyDdquLxPm0xW2jZoAdTArqoN1U7UFypPyHxRitZYzuALA8l/XY/mv66s85sm5WmoyedoXe74BAAzrOgD9/UM4MyxpTe9FypYtWxAZGYm4uDgEBgYiNjYWvXv3RmpqKmQymdr3WFtbIzU1VflaJKq8DmZYWBg2bNigfC2VSpV/vnDhAhQKBdauXQtPT0+cOXMGERERKCoqwtKlS3WeIxGZjqcZraKrWVG1jaFb285wt2+Gr//YUe2xbV1a4ea9DBQ+LNLZ9QHg1PULOHAuCRKxBON7vQv3smHGRNrQe5GybNkyREREKFtH4uLiEB8fj/Xr1+OTTz5R+x6RSAQnJ6cqzyuVSjUeExYWhrCwMOXrli1bIjU1FV9++SWLFCKqkqjSXKjq1eVoFW3P/VHYcHTx8sXe04eRkZel9jFOeZ+UfZ9+jaRLKRi4fLzOro8nWlH6+4ewQKEa02uRUlpaiuPHjyMq6n8dqsRiMUJDQ5GYmKjxfffv34e7uzsUCgU6deqERYsWoV27dirHHDx4EDKZDLa2tujZsycWLFgAOzvN/2Pl5+ejadOmGveXlJSgpKRE+bqgoAAo6yikUCi0zrkqCoUCgiDo7HyGgnkZH1PMrXyobtrNq/B09ahycjC5Qo6jaSeRmZ8Dxyb2CPTsCLlCgbUJ32NZfNWPRP75pS9D51Y+dfb5dW7lA2cbWTWFxz8xiCDC/LcmIeKr6ZWGNpeXW/PenAgRRDU6rza5Xcm6gV9O/DPt//heQ2v8eZji9xDMS3msNvRapOTk5EAul8PR0VFlu6OjIy5cUD9FdJs2bbB+/Xr4+PggPz8fS5cuRXBwMM6ePQtXV1egrKVkwIABaNGiBS5fvozp06ejT58+SExMhERS+S+ltLQ0rFq1qspWlOjoaMydO7fS9uzsbJX+ME9DoVAgPz8fgiCYXI9v5mVcTC23hHOJWLJ7HTKfeEzhaG2HaWGjEeIdVO2xtg2tYS4xR1bhP9ta2LsiPeemxl/6U14eidycup2+/uNeI/Hx1sVaxRDQzBtLB/1fpbxk1vaYFjYKAc28kZWVVeV5Ufa6Jrkt+3kdFIICL3kFwM68sfIa2jK172E55gUUFhZqdU69DkG+ffs2mjVrhiNHjiAo6H9/UUybNg2HDh3C0aNHqz3Ho0eP0LZtW7z99tuYP3++2mOuXLmCVq1aYd++fQgJCVHZd+vWLXTr1g3du3fHunXrNF5HXUuKm5sb7t27p9MhyNnZ2XBwcDC5Ly7zMi6mlNuvyQcR8dV0jUNqv4pYhL5+3as8tlxjy0ZYGB6JgZ3DsCvlEGZui60wn4kM896cpDxfXfs1+WCNYlDXQqSuNUndeQEgtP0L+HpcjFaxZeRlo8usN1H6+BF2TolD51Y+Nc7PlL6HT2Je//wOtbW1NewhyPb29pBIJMjMzFTZnpmZWW2fk3Lm5ubw8/NDWlqaxmNatmwJe3t7pKWlqRQpt2/fRo8ePRAcHIx//etfVV5HKpWqdL4tJxaLdfolE4lEOj+nIWBexscUcpMr5Ji5LbbqIbXbYhHo5YuHpcWI2rK06oXwLBtiYGAYJGIJXvHviT5+3ep9mvknlceg7Ro3YrEYLz4XoPV5y3O7mZuBhTvXIOHsERy9fBJBXn7VnuOrA1tR+vgRAj07PtVEbabwPVTnWc9L27z1WqRYWFjA398fCQkJeP3114GySiwhIQETJkzQ6hxyuRynT59G3759NR5z8+ZN5ObmwtnZWbnt1q1b6NGjB/z9/bFhwwaT+6IQUfXDhQUAd/Ky0GGa5r8/npSRl60y/FaXI3Zqq67WuKmYW3r2DXx3+L+YtGkB9s/8Fo2kDTS+N6+oAF///s+Iogm9h+kkHno26f03c2RkJL766its2rQJ58+fxwcffICioiLlaJ9hw4apdKydN28efvvtN1y5cgUnTpzAu+++i2vXrmH06NFAWafaqVOnIikpCVevXkVCQgL69+8PT09P9O7dGygrULp3747mzZtj6dKlyM7ORkZGBjIyMvT0KRBRXajJUFltW0Bqck5TMufNiWhm64hrObewcMfqKo/deOg/KCp5gLbNWiG0fXC9xUimR+9DkMPDw5GdnY1Zs2YhIyMDvr6+2L17t7Iz7fXr11X+ZXDv3j1EREQgIyMDtra28Pf3x5EjR+Dt7Q0AkEgkOHXqFDZt2oS8vDy4uLigV69emD9/vvJxzd69e5GWloa0tDRlZ9tyXCWAyHRoO1R268SVkIglOh9+a0qsG1hh2dDpCF85EesPbkdfvx7KRQ6f9KC0GP/avwUA8GHvYWrnsSLSFtfuqSWu3aM95mV8TCW3R48fwWtyKIoflajd/+S6NQAQMP2NaucTKV/jxpDU5/2atnkxvv5jB9zsnHFg5rewsmyksv/fB7ZhxpbP0dzeBUfmboWZpPb/FjaV72FFzEv736Gm8+kQEVWwcOeXVRYoADB/0CRIxBJIxBIsCJ+ssk/Tsc+yWQMnwLWpE27k3sG8H79Q2fdI/hhf7t0MABj38pCnKlCIwCKFiEzV+oPbEbfvOwBARM9wONuoLrPhbOtYafXdfn49sG5MNJy0OPZZZWXZCLHDPgUAfP37Dhw697+pInYe24ubdzNg39gW4UH99BglmQqWuURkcn479Qc+3bIMABDVfywm9hmBOW9+pNVQXV0vBmiKXnwuACO7vYkNh7Zj8tcLsWTI/yH/QSGW/PefqRzGhLyNBhaW+g6TTACLFCIyKSevXcCYdTOhEBQY8sJr+ChsOFDDobqGMLTY0H36xjj8cmI/budl4d3VU5TbRRDBydZBr7GR6eDjHiIyGTdy72Do6il4WFqM7t6B+OydaRxdUkcOnj+K7MK7lbYLEPDRhrmITz6gl7jItLAlhYiMllwhVz6WaSRtiPk/foGsglx4N/PEVxGLYM6Om3VCrpDj0y3Lqzxm5tZYhHXsysdk9FT4fzARGaX45AP4dMvySjPK2jS0xrcTlqFxg0Ya30tPR5uZfG/fy1SZnZeoNvi4h4iMTnzyAYxeG6X2F2XegwIkXz2rl7ieFdrOuvuszs5LusMihYiMSvmjBk2zUIrKHjXIFfJ6juzZoe2su8/q7LykOyxSiMio1ORRA9WNLl6+cLaRVZr0rpwIgIut41OtfkwEFilEZGz4qEH/ODsv1RcWKURkVPiowTBwdl6qDxzdQ0RGpYuXL2waWSOvqEDt/vKFAPmooe5xdl6qayxSiMioZBfcRemjR2r38VFD/ePsvFSX+LiHiIyGIAiY/M1CPCh9CHeHZnC2UZ1+nY8aiEwLW1KIyGh888dOHDibBKmZBb4ZtxStHJvzUQORCWORQkRGIT3rBmZvXwEAmPHGOLR2bgEAfNRAZML4uIeIDJ5cIcdHG+fhYWkxglt3wugeg/QdEhHVg6cqUtLS0rBnzx48fPgQKHteTESka2v2bsaxK6dhZdkQK4bPhFjMf18RPQtq9X96bm4uQkND0bp1a/Tt2xd37twBAIwaNQpTpkzRdYxE9Aw7d/MSlvz8LwDAgkGRcLNz1ndIRFRPalWkTJ48GWZmZrh+/ToaNmyo3B4eHo7du3frMj4iesbIFXIcTj2OHcd+w8FzRzF+/Rw8kj9GWMeuCA/qp+/wiKge1arj7G+//YY9e/bA1dVVZbuXlxeuXbumq9iI6BkTn3wAn25ZXmltHivLRogZ8glEIk2rxRCRKapVS0pRUZFKC0q5u3fvQiqV6iIuInrGxCcfwOi1UWoXD7xfXIS/Lp/US1xEpD+1KlJeeuklfP3118rXIpEICoUCS5YsQY8enESJiGpGrpDj0y3LoanrvQjAzK2xkCvk9RwZEelTrR73LFmyBCEhIfj7779RWlqKadOm4ezZs7h79y4OHz6s+yiJyKQlXUpR24JSTgBw+14mki6lcF4UomdIrVpS2rdvj4sXL+LFF19E//79UVRUhAEDBiA5ORmtWrXSfZREZNKyCnJ1ehwRmYZazzjbpEkTzJgxQ7fRENEzSWZtp9PjiMg01KolxdPTE3PmzMGlS5d0HxERPXO6ePnC2Uamcb8IgIutI7p4+dZrXESkX7UqUsaPH4/4+Hi0adMGzz//PFasWIGMjAzdR0dEzwSJWIKo/mPV7isfdDx/0CQuHkj0jKn1ZG7Hjh3DhQsX0LdvX6xevRpubm7o1auXyqgfIiJtpVw7BwAwq1CIONs6Yt2YaPTz48hBomfNU62C3Lp1a8ydOxdz585FUlISPvjgA4wcORLDhg3TXYREZPLO3byEjYd+BABsnrAcZhIJsgpyIbO2QxcvX7agED2jnqpIAYC//voL3333HbZs2YKCggK89dZbuomMiJ4JgiBg+pZlUAgKvNKpJ7p5d9Z3SERkIGpVpFy8eBGbN2/G999/j/T0dPTs2ROLFy/GgAEDYGVlpfsoichk/fT3PiRdSkYDcynmvPmRvsMhIgNSqyLlueeew/PPP4/x48dj8ODBcHR01H1kRGTyioofYO5/VgIAPgwbDtemTvoOiYgMSK2KlNTUVHh5eek+GiJ6pqzYvQl38rLR3N4F43oN0Xc4RGRgajW6R9cFyurVq+Hh4QFLS0sEBgbir7/+0njsxo0bIRKJVH4sLS1VjhkxYkSlY8LCwlSOuXv3LoYMGQJra2vY2Nhg1KhRuH//vk7zIiLN0rNuIG7fdwCAuW9OhKU5FyclIlVat6Q0bdoUFy9ehL29PWxtbatcMv3u3btaB7BlyxZERkYiLi4OgYGBiI2NRe/evZGamgqZTP3kTtbW1khNTVW+VhdLWFgYNmzYoHxdcXXmIUOG4M6dO9i7dy8ePXqEkSNH4v3338d3332ndexEVHszt8Wi9PEjdPcORFjHrvoOh4gMkNZFyvLly9G4cWPln6sqUmpi2bJliIiIwMiRIwEAcXFxiI+Px/r16/HJJ5+ofY9IJIKTU9XPrqVSqcZjzp8/j927d+PYsWMICAgAAKxatQp9+/bF0qVL4eLi8tR5EZFme08fxr7Th2EmlmD+oMk6+/uEiEyL1kXK8OHDlX8eMWKETi5eWlqK48ePIyoqSrlNLBYjNDQUiYmJGt93//59uLu7Q6FQoFOnTli0aBHatWuncszBgwchk8lga2uLnj17YsGCBbCz+2fdj8TERNjY2CgLFAAIDQ2FWCzG0aNH8cYbb1S6ZklJCUpKSpSvCwoKAAAKhQIKheIpPwkozyUIgs7OZyiYV92QK+Q4mnYSmfk5cGxij0DPjjqZT0SukCPxYjLSbl6Fp6sHglr76XSekpJHpZi1dTkAYHTPcLSSNa+3z1Df96yuMC/jwrygde616jgrkUhw586dSo9jcnNzIZPJIJfLtTpPTk4O5HJ5pdFBjo6OuHDhgtr3tGnTBuvXr4ePjw/y8/OxdOlSBAcH4+zZs3B1dQXKHvUMGDAALVq0wOXLlzF9+nT06dMHiYmJkEgkyMjIqBS7mZkZmjZtqnF6/+joaMydO7fS9uzsbBQXF2uVb3UUCgXy8/MhCALE4lp1FzJIzEv3Es4lYsnudch8YlVgR2s7TAsbjRDvoErHyxVynLh2Djn378Heyhad3L3VFh41Pa+2nrz+sfTTSM++CXsrW7wb8AqysrJqfd6a4nfRuDAv41KTvAoLC7U6Z62KFEEQ1G4vKSmBhYVFbU6ptaCgIAQF/e8vy+DgYLRt2xZr167F/PnzAQCDBw9W7u/QoQN8fHzQqlUrHDx4ECEhIbW6blRUFCIjI5WvCwoK4ObmBgcHB1hbWz9VTuUUCgVEIhEcHBxM7ovLvHTn1+SD+HjrYlT8vzCrIBcfb12MryIWoa9fd5XjZ26LxZ28/xUDzjYyzH9rUqXjanLemsRb8foA0D8gBC2be9T4fE+D30XjwryMS03yqjjgRZMaFSkrV/4zn4FIJMK6detUJm6Ty+X4/fff8dxzz2l9Pnt7e0gkEmRmZqpsz8zMrLbPSTlzc3P4+fkhLS1N4zEtW7aEvb090tLSEBISAicnp0r/env8+DHu3r2r8bpSqbRS51uUPZ7S5ZdMJBLp/JyGgHnphlwhx8xtsZUKCQAQyhbjm7V9Bfr4dYNELEF88gFEfDW90vEZeVmI+Gq6ck2cmp5XW5quDwDrD25HcBv/el+Th99F48K8jIu2eWmbd42KlOXL/3mOLAgC4uLiIJH87y8rCwsLeHh4IC4uTuvzWVhYwN/fHwkJCXj99deBskosISEBEyZM0Ooccrkcp0+fRt++fTUec/PmTeTm5sLZ2Rkoa43Jy8vD8ePH4e/vDwDYv38/FAoFAgMDtY6fqL4lXUqp1CLxJAHA7XuZmP/jF2jXzAuztq/QWHgAwJRvonHrbiYu3k7X6rxJl1LwQht/rWKVK+T4dMtytdcvN3NrLMI6duXaPESkVo2KlPT0dABAjx498OOPP8LW1vapA4iMjMTw4cMREBCAzp07IzY2FkVFRcrRPsOGDUOzZs0QHR0NAJg3bx66dOkCT09P5OXlISYmBteuXcPo0aOBsk61c+fOxcCBA+Hk5ITLly9j2rRp8PT0RO/evQEAbdu2RVhYGCIiIhAXF4dHjx5hwoQJGDx4MEf2kEHLeqKvSFXi9n2v1XF5Dwowa1uszq+PGhRUNSl8iOjZUqs+KQcOHNBZAOHh4cjOzsasWbOQkZEBX19f7N69W9mZ9vr16yrNQvfu3UNERAQyMjJga2sLf39/HDlyBN7e3kBZp95Tp05h06ZNyMvLg4uLC3r16oX58+erPK7ZvHkzJkyYgJCQEIjFYgwcOFD5OIvIUMms7bQ67vmWHVBYXIQLt69Ue2ynFu3QpGFjHDibVKPryxVyJF1K0bha8e172nWIrUnhQ0TPlloVKQMHDkTnzp3xf//3fyrblyxZgmPHjmHbtm01Ot+ECRM0Pt45ePCgyuvly5crHzup06BBA+zZs6faazZt2pQTt5HRMZeYQyQSaey8LgLgbOuInR/HIelSCgYuH1/tOWe8Pg5dvHwRMP0NZORlaXw8IxKJcO5mGjp7dsRvp/7Ap1uWV+qMuyB8Mnr7vITtR3dj4c41WuWkbeFFRM+eWhUpv//+O+bMmVNpe58+ffD555/rIi4iquDX5IMYt362skARPdG3pPw1AMwfNAkSsQRdvHzhbCPTWHiUFzTlLSALwidj9NqoSuctJwgCZm5bjtW/fYuM/OxK+zPysjBqbRScmjgo94tFYigE9fMhPHl9IiJ1atWt+P79+2qHGpubmysnOSMi3Vl3YCtG/SsKxY9KENrhBawZOQdONqpz/TjbOipH6wBQFh54ooApV7GgAYB+fj2wbkx0pfO62DriXxEL8dnbU9HUykZtgYInCpuM/GzYNGyMmQMm4Iv35kCk5fWJiCqqVUtKhw4dsGXLFsyaNUtl+w8//KDsG0JENVexn0fnVj5YuPNL5UJ8w156A4sGT4GZxAz9n3+5yj4heKLwqPRoxtYR8wdNqjT8t59fD4R17IrEi8m4dOMKvNxaqsw426ypI4au/rjaPL4YOQehHV4AAEjNzLW+PhHRk2pVpMycORMDBgzA5cuX0bNnTwBAQkICvv/++xr3RyGif8QnH6j0y9zSXIriR/8sxzDj9XGY0Huocp0biVii1aiY8sKjuoKmnEQsQXDrTvC0cYVMJlPpuH6/+IFWuRQWF9X6+kRE5WpVpLz66qvYuXMnFi1ahO3bt6NBgwbw8fHBvn370K1bN91HSWTi4pMPYPTaqEp9QcoLlIie4fgwbFitz69tQVMdbTu5VjxOV9cnomdLrYoUAOjXrx/69eun22iInkHaTHoWn3wQc978SO+tDzXpjEtE9LRqPR9vXl4e1q1bh+nTp+Pu3bsAgBMnTuDWrVu6jI/I5FU36RmemPRM32raGZeI6GnUqkg5deoUWrdujcWLFyMmJgZ5eXkAgB9//BFRUVG6jpHIpGk7mZmhTHqmaRRQxdFFRERPq1aPeyIjIzFixAgsWbIEjRs3Vm7v27cv3nnnHV3GR2TyatvPQ5/YGZaI6kOtipRjx45h7dq1lbY3a9YMGRkZuoiL6JnRxcsXto2sca9I/RxDhtrPg51hiaiu1epxj1QqVTtp28WLF+Hg4KCLuIieGVeybuBBSbHafeznQUTPsloVKa+99hrmzZuHR48eAWVrely/fh3/93//h4EDB+o6RiKTVVT8AKPWRqHkcSnaOLeAs41qkc9+HkT0LKvV457PP/8cb775JmQyGR4+fIhu3bohIyMDQUFBWLhwoe6jJDJBgiDg482f4eKddDg2scf2yV+gqZUN+3kQEZWpVZHSpEkT7N27F3/++SdOnTqF+/fvo1OnTggNDdV9hEQmav3B7dhx7DeYiSX4KmIhHMo6xrKfBxHRP2o9mRsAvPjii3jxxRd1Fw3RM+LvK6cxZ/sKAMCsgR+is2dHfYdERGRwtC5SVq5ciffffx+WlpZYuXJllcdaWVmhXbt2CAwM1EWMRCYlu+AuIv41HY/kj/FqpxBE9AzXd0hERAZJ6yJl+fLlGDJkCCwtLbF8+fIqjy0pKUFWVhYmT56MmJgYXcRJZLSeXNnYvrEtYn/diDt52fB0dMfyYdOVCwYSEZEqrYuU9PR0tX/WZO/evXjnnXdYpNAzTd3KxgBgYWaBf4/5DFaWjfQWGxGRoav12j3VefHFF/Hpp5/W1emJDF75ysbq1uUpfVyKtMyreomLiMhY1LpISUhIwCuvvIJWrVqhVatWeOWVV7Bv3z7l/gYNGmDixIm6ipPIqFS3srEIwMytsZAr5PUcGRGR8ahVkbJmzRqEhYWhcePGmDhxIiZOnAhra2v07dsXq1ev1n2UREamupWNBQNa2ZiIyFDVagjyokWLsHz5ckyYMEG57aOPPsILL7yARYsWYfz48bqMkcjoGNvKxkREhqhWLSl5eXkICwurtL1Xr17Iz8/XRVxERs0YVzYmIjI0tV67Z8eOHZW2//TTT3jllVd0EReRUevi5QtnG5nG/SIALga4sjERkSGp0WRu5by9vbFw4UIcPHgQQUFBAICkpCQcPnwYU6ZMqZtIiYyIRCxBVP+x+GjTvEr7uLIxEZF2ajSZ25NsbW1x7tw5nDt3TrnNxsYG69ev59BjIgAXbl8GAJiJJXj8xCgeZ1tHzB80iSsbExFVo1aTuZXLyckBANjb2+s2KiIjd/NuBv59YBsAYP2YxWhk2YArGxMR1VCNR/fk5eVhxowZ2LJlC+7duweUtaoMHjwYCxYsgI2NTV3ESWRUFv/8L5Q8LsULbfzxss8LnPqeiKgWalSk3L17F0FBQbh16xaGDBmCtm3bAgDOnTuHjRs3IiEhAUeOHIGtrW1dxUtk8M7cuIjtR3cBAGYOmMAChYiolmpUpMybNw8WFha4fPkyHB0dK+3r1asX5s2bV+0ChESmbMGO1RAEAa8HvAxf97b6DoeIyGjVaAjyzp07sXTp0koFCgA4OTlhyZIlaocmEz0rDp47ioPnjsJcYoao/mP1HQ4RkVGrUZFy584dtGvXTuP+9u3bIyMjQxdxERkdhUKBBTv+WRZiZLc34e7QTN8hEREZtRoVKfb29rh6VfPKrenp6WjatKku4iIyOj8e24MzNy6isWUjTOw7Qt/hEBEZvRoVKb1798aMGTNQWlpaaV9JSQlmzpypdrp8IlNX/KgE0T+tBQB8FDYcdlYc5UZE9LRq3HE2ICAAXl5eGD9+PJ577jkIgoDz589jzZo1KCkpwTfffFN30RIZqPUHtuPW3Qy42MowuucgfYdDRGQSalSkuLq6IjExEePGjUNUVBQEQQAAiEQivPzyy/jiiy/g5uZWV7ESGaR7RQVYsXsjAGDaq++jgYWlvkMiIjIJNV5gsEWLFti1axdycnKQlJSEpKQkZGdnY/fu3fD09KxxAKtXr4aHhwcsLS0RGBiIv/76S+OxGzduhEgkUvmxtNT8C2Hs2LEQiUSIjY1V2X7x4kX0798f9vb2sLa2xosvvogDBw7UOHZ6dskVchy5eAK7Tv+OT75fgvwHhWjbrBXe6tJH36EREZmMGs84W87W1hadO3d+qotv2bIFkZGRiIuLQ2BgIGJjY9G7d2+kpqZCJlO/gqy1tTVSU1OVrzVNlLVjxw4kJSXBxcWl0r5XXnkFXl5e2L9/Pxo0aIDY2Fi88soruHz5MpycnJ4qJzJ98ckH8OmW5biTl6WyvbfPS5zunohIh2rckqJLy5YtQ0REBEaOHAlvb2/ExcWhYcOGWL9+vcb3iEQiODk5KX/Uzdly69YtfPjhh9i8eTPMzc1V9uXk5ODSpUv45JNP4OPjAy8vL3z22Wd48OABzpw5Uyd5kumITz6A0WujKhUoALBi10bEJ7NFjohIV2rdkvK0SktLcfz4cURFRSm3icVihIaGIjExUeP77t+/D3d3dygUCnTq1AmLFi1SmbtFoVBg6NChmDp1qto5Xezs7NCmTRt8/fXX6NSpE6RSKdauXQuZTAZ/f3+N1y0pKUFJSYnydUFBgfJ6CoWiVp9BRQqFAoIg6Ox8hsJU8pIr5Ph0y3IIVRwzc+ty9OrwotG3qJjKPauIeRkX5mVcapKXtrnrrUjJycmBXC6v1BLi6OiICxcuqH1PmzZtsH79evj4+CA/Px9Lly5FcHAwzp49C1dXVwDA4sWLYWZmho8++kjtOUQiEfbt24fXX38djRs3hlgshkwmw+7du6tccyg6Ohpz586ttD07OxvFxcU1zF49hUKB/Px8CIIAsVivjVw6ZSp5HUs/rbYFpZwA4Pa9LOw+dhDPt+hQr7Hpmqncs4qYl3FhXsalJnkVFhZqdU69FSm1ERQUhKCgIOXr4OBgtG3bFmvXrsX8+fNx/PhxrFixAidOnNDYV0UQBIwfPx4ymQx//PEHGjRogHXr1uHVV1/FsWPH4OzsrPZ9UVFRiIyMVL4uKCiAm5sbHBwcYG1trZP8FAoFRCIRHBwcTO6Lawp5lV6Ta3ecWK6xT5WxMJV7VhHzMi7My7jUJK+qBr08SW9Fir29PSQSCTIzM1W2Z2Zmat151dzcHH5+fkhLSwMA/PHHH8jKykLz5s2Vx8jlckyZMgWxsbG4evUq9u/fj19++QX37t1TFhdr1qzB3r17sWnTJnzyySdqryWVSiGVSittF4vFOv2SiUQinZ/TEJhCXk42DlofZ8x5ljOFe6YO8zIuzMu4aJuXtnnr7dOxsLCAv78/EhISlNsUCgUSEhJUWkuqIpfLcfr0aWXrx9ChQ3Hq1CmkpKQof1xcXDB16lTs2bMHAPDgwQNAzQckFotN7vkg6VYXL98qCxURABdbR3Tx8q3XuIiITJVeH/dERkZi+PDhCAgIQOfOnREbG4uioiKMHDkSADBs2DA0a9YM0dHRQNmMt126dIGnpyfy8vIQExODa9euYfTo0UBZp1g7OzuVa5ibm8PJyQlt2rQByh4Z2draYvjw4Zg1axYaNGiAr776Cunp6ejXr1+9fwZkPCRiCdq6eCIjL7vSvvKHi/MHTTL6TrNERIZCr0VKeHg4srOzMWvWLGRkZMDX1xe7d+9Wdqa9fv26SovHvXv3EBERgYyMDNja2sLf3x9HjhyBt7e31te0t7fH7t27MWPGDPTs2ROPHj1Cu3bt8NNPP6Fjx451kieZhoQzR3Dg3D8jz5pa2eDu/TzlPmdbR8wfNAn9/HroMUIiItMiEsrntqcaKSgoQJMmTZCfn6/TjrNZWVmQyWQm9ZzSFPLKvZ+HHvOGIKsgF6N6vIV5b01C4sVkXLpxBV5uLRHU2s+kWlBM4Z6pw7yMC/MyLjXJS9vfoUY1uodIHwRBwMffRCOrIBdeTh749I3xkIglCG7dCZ42rib3Fw0RkaHg36xE1fj+yC/YdfIQzCVmWPPeXC4gSERUT1ikEFXhavZNfLp1GQBg2mvvo0PzNvoOiYjomcHHPWSU5Ao5ki6lIKsgFzJrO3Tx8tV5n5DH8seYsGEuHpQ8RBcvP4x7eYhOz09ERFVjkUJGR90qxM42MiwIn/xUo2sqFj6Jl5Lx95XTaGzZCKtGzDKpjrFERMaARQoZlfJViCsOScvIy8LotVFYNya6UqGiTauLusKnXPTbH8PNTv1yCUREVHdYpJDRqGoVYqFsQrWZW2MR1rGrsgjRptVFU+FTztKcHWWJiPSBHWfJaCRdStFiFeJMJF1KAZ4oPiq+p7zVJT75QJWFD8oKn1nbYiFXaLe4IBER6Q5bUshoZBXkanXcxE3z0cXLD7+d+kNjqwsATNu8GCfSz2pd+LzQxr+WkRMRUW2wSCGDUVXfkes5t/Hdnz9rdZ6bdzOw/eiuao/LvZ+H1b99q9U5tS2QiIhId1ikkEHQ1Hdk+utjcf7WZaw7sBWljx9VeQ4RAFkTe0SHf4z/HNuD+OQD1V63uZ0LrufervY4mbVdtccQEZFusU8K6Z2mviN38rLw4cZ5WLN3M0ofP8KLbQIwa8AEiJ5Ydbhc+etFg6egb6fueK/7m1pde+m7UXC2kVU635PndbF1RBcv31pkRkRET4NFCulVdR1XAcBMLMHXHyzFtkmrMK7Xu1g3JhpONjKVY5xtHVWGH3fx8tWq+HihTScsCJ+s3FbxGACYP2gS50ghItIDPu4hvapuxA4APFbI0ciyAUSif8qGfn49ENaxa5Vzn0jEEiwIn4zRa6MgeqKzLNQUH/38emDdmOjKj5tsHTF/0KSnmiCOiIhqj0UK6ZW2HVIrHicRS6odbVOT4kObwoeIiOoXixTSK207pNa242pNig9tCh8iIqo/LFJIr7p4+cLOyga59/PU7heVtXw8TcdVFh9ERMaJHWdJrzLzc1GiYWgxO64SET3bWKSQ3pQ8KsWotZ/gfnER3Oyc4WTjoLK/4ogdIiJ6tvBxD+nNjC2fI/nqOdg0tMb2SV/A1c6JHVeJiEiJRQrpxbd/7MS3f/4EkUiEL0fNg7tDMwBg3xEiIlLi4x6qdyfSz2D6ls8BAJ+8NgY92nXRd0hERGSAWKRQvcouyMWotVEoffwIfX274aOw4foOiYiIDBQf91CdkivkSLyYjEs3rqBFM3fE/roBd/Ky4eXkjhXDZylnkSUiIqqIRQrVGXUrGwOApbkF1o9djMYNGuktNiIiMnx83EN1QtPKxgBQ/KgUF++k6yUuIiIyHixSSOeqW9lYBGDm1ljIFfJ6joyIiIwJixTSuepWNhYA3L6XiaRLKfUaFxERGRcWKaRztV3ZmIiI6EksUkjn6nplYyIiejawSCGd6+LlC2cbmcb9IgAuT7myMRERmT4WKaRzErEEH786Wu0+rmxMRETaYpFCdSLl6jkAgLlEdSoermxMRETa4mRupHOXMq7iu8P/BQBsmbgSCoUCl25cgZdbSwS19mMLChERaYVFCuncoh1fQq6Qo7fPSwhu3QkKhQKeNq6QyWQQi9l4R0RE2tH7b4zVq1fDw8MDlpaWCAwMxF9//aXx2I0bN0IkEqn8WFpaajx+7NixEIlEiI2NrbQvPj4egYGBaNCgAWxtbfH666/rLKdn2V9pJ7Hr5CGIRWJMf32cvsMhIiIjpteWlC1btiAyMhJxcXEIDAxEbGwsevfujdTUVMhk6keHWFtbIzU1Vfla0wJ1O3bsQFJSElxcXCrt+89//oOIiAgsWrQIPXv2xOPHj3HmzBkdZvZsEgQB83/8AgDwdvAraOPSQt8hERGREdNrkbJs2TJERERg5MiRAIC4uDjEx8dj/fr1+OSTT9S+RyQSwcnJqcrz3rp1Cx9++CH27NmDfv36qex7/PgxJk6ciJiYGIwaNUq53dvbWyc5Pct2n/wdx66cRgNzKaa+GqHvcIiIyMjprUgpLS3F8ePHERUVpdwmFosRGhqKxMREje+7f/8+3N3doVAo0KlTJyxatAjt2rVT7lcoFBg6dCimTp2qsr3ciRMncOvWLYjFYvj5+SEjIwO+vr6IiYlB+/btNV63pKQEJSUlytcFBQXK6ykUilp9BhUpFAoIgqCz89Wnx/LHWLBjNQAgomc4ZNZ2yjyMOa+qmGpeMOHcmJdxYV7GpSZ5aZu73oqUnJwcyOVyODo6qmx3dHTEhQsX1L6nTZs2WL9+PXx8fJCfn4+lS5ciODgYZ8+ehaurKwBg8eLFMDMzw0cffaT2HFeuXAEAzJkzB8uWLYOHhwc+//xzdO/eHRcvXkTTpk3Vvi86Ohpz586ttD07OxvFxcU1zl8dhUKB/Px8CIJgdB1Mt/+9B5czr8OmQWO85dcbWVn/W7vHmPOqiqnmBRPOjXkZF+ZlXGqSV2FhoVbnNKrRPUFBQQgKClK+Dg4ORtu2bbF27VrMnz8fx48fx4oVK3DixAmNfVXKq7cZM2Zg4MCBAIANGzbA1dUV27Ztw5gxY9S+LyoqCpGRkcrXBQUFcHNzg4ODA6ytrXWSn0KhgEgkgoODg1F9cR+UPMS/ft8KAIjs9x5aNVfti2KseVXHVPOCCefGvIwL8zIuNcmrqkEvT9JbkWJvbw+JRILMzEyV7ZmZmdX2OSlnbm4OPz8/pKWlAQD++OMPZGVloXnz5spj5HI5pkyZgtjYWFy9ehXOzs5AhT4oUqkULVu2xPXr1zVeSyqVQiqVVtouFot1+iUTiUQ6P2dd+9f+LcgqyEVzexcM7zZQbezGmJc2TDUvmHBuzMu4MC/jom1e2uatt0/HwsIC/v7+SEhIUG5TKBRISEhQaS2pilwux+nTp5WFx9ChQ3Hq1CmkpKQof1xcXDB16lTs2bMHAODv7w+pVKoyQujRo0e4evUq3N3ddZ6nqcsuuIvVv30LAJje/wNIzS30HRIREZkIvT7uiYyMxPDhwxEQEIDOnTsjNjYWRUVFytE+w4YNQ7NmzRAdHQ0AmDdvHrp06QJPT0/k5eUhJiYG165dw+jR/6wTY2dnBzs71ZV1zc3N4eTkhDZt2gBlQ5jHjh2L2bNnw83NDe7u7oiJiQEAvPXWW/X8CRgnuUKOpEspyCrIxc9/70NRyQP4NH8Or/mH6Ds0IiIyIXotUsLDw5GdnY1Zs2YpR9ns3r1b2Zn2+vXrKk1C9+7dQ0REBDIyMmBrawt/f38cOXKkxsOHY2JiYGZmhqFDh+Lhw4cIDAzE/v37YWtrq/McTU188gF8umU57uRlqWzv5fOiyTVbEhGRfokEQRD0HYQxKigoQJMmTZCfn6/TjrNZWVkGO318fPIBjF4bBXVfGBGgceFAQ8+rtkw1L5hwbszLuDAv41KTvLT9HWo6nw7VKblCjk+3LFdboJSbuTUWcoW8HqMiIiJTxiKFtJJ0KaXSI54nCQBu38tE0qWUeo2LiIhMF4sU0kpWQa5OjyMiIqoOixTSiszaToujtD+OiIioOixSSCtdvHxh26iJxv0iAC62juji5VuvcRERkelikUJauXgnHUUlD9XuK1+AYP6gSZCIJfUaFxERmS4WKVStnMJ7GLZmKkofl+I5l1ZwtnFQ2e9s66hx+DEREVFtGdUCg1T/Sh8/wui1UbiRewceDq7YMWUNrBtYKWeclVnboYuXL1tQiIhI51ikkEaCIGD6D0uRlJaCxpaN8PW4GGW/lBfa+Os7PCIiMnF83EMa/fvgNnz7508QiUT4ctR8tHZuoe+QiIjoGcKWFAIqLBoos7ZD6aNSzNoaCwCY+cYEhHYI1neIRET0jGGRQmoXDRSJRBAEAW916YsPXn5Hr/EREdGziUXKM07TooHl606GtAuCSCRS+14iIqK6xD4pz7DqFg0UAZj34xdcNJCIiPSCRYqJkyvkOJx6HDuO/YbDqcdVCg4uGkhERIaMj3tMmLq+Js42MiwIn4y+vt21Lj64aCAREekDixQTpamvSUZeFkatjUIzW0fcupep1bm4aCAREekDH/eYoKr6mpRvu3UvExYSczSwsNR4Hi4aSERE+sQixQRV19ek3FfvL8IXI2dD9MQigeW4aCAREekbixQTpG0fkgelD9HPrwfWjYmGk41MZR8XDSQiIn1jnxQTpG0fkvLj+vn1QFjHrlw0kIiIDAqLFBPUxcsXzjYyjY98RGUtJU/2NZGIJVw0kIiIDAof95ggiViCBeGT1e5jXxMiIjIWLFJM1IttAmCmpghhXxMiIjIWfNxjouKTD+CxQo7Wzi0QPfhj9jUhIiKjwyLFRP3nrz0AgDcDw9jXhIiIjBIf95igO/eycOTiCQDAG8/30nc4REREtcIixQTtOLYXgiAg0LMj3Oyc9R0OERFRrbBIMUE/HvvnUc+Azr31HQoREVGtsUgxMam303HmxkWYiSV4tVOIvsMhIiKqNRYpJubHv3YDAHq2D0JTqyb6DoeIiKjWWKSYEEEQ8OOx3wA+6iEiIhPAIsWEHLt8Cjdy76CRtCF6+byk73CIiIieCosUE1I+N0pfv+5oaGGp73CIiIieCosUE/FI/hj/PZ4AABjIRz1ERGQCWKSYiANnk3C3KB8O1k3xImeYJSIiE2AQRcrq1avh4eEBS0tLBAYG4q+//tJ47MaNGyESiVR+LC01P9oYO3YsRCIRYmNj1e4vKSmBr68vRCIRUlJSdJKPPvxY9qjn9YCXYSbhagdERGT89F6kbNmyBZGRkZg9ezZOnDiBjh07onfv3sjKytL4Hmtra9y5c0f5c+3aNbXH7dixA0lJSXBxcdF4rmnTplW53xjcLy7CnpO/AxzVQ0REJkTvRcqyZcsQERGBkSNHwtvbG3FxcWjYsCHWr1+v8T0ikQhOTk7KH0dHx0rH3Lp1Cx9++CE2b94Mc3NztefZtWsXfvvtNyxdulSnOdW3X1MO4eGjErRybA5f97b6DoeIiEgn9PpcoLS0FMePH0dUVJRym1gsRmhoKBITEzW+7/79+3B3d4dCoUCnTp2waNEitGvXTrlfoVBg6NChmDp1qsr2J2VmZiIiIgI7d+5Ew4YNq421pKQEJSUlytcFBQXKaykUCq1zropCoYAgCDU+33+O/jOB2xvP94IgCBAEQSfx6Ept8zJ0ppoXTDg35mVcmJdxqUle2uau1yIlJycHcrm8UkuIo6MjLly4oPY9bdq0wfr16+Hj44P8/HwsXboUwcHBOHv2LFxdXQEAixcvhpmZGT766CO15xAEASNGjMDYsWMREBCAq1evVhtrdHQ05s6dW2l7dnY2iouLtcy4agqFAvn5+RAEAWKxdo1cOYX38MeFvwEAXVv4V/mYTF9qk5cxMNW8YMK5MS/jwryMS03yKiws1OqcRtfDMigoCEFBQcrXwcHBaNu2LdauXYv58+fj+PHjWLFiBU6cOAGRSKT2HKtWrUJhYaFKC051oqKiEBkZqXxdUFAANzc3ODg4wNra+imzAuQKORIvJiPt5lV4Ch4Iau0HiVhS7ft+PnMACkGBTh7tENC241PHURcUCgVEIhEcHBxM7n9IU8wLJpwb8zIuzMu41CSvqga8PEmvRYq9vT0kEgkyMzNVtmdmZsLJyUmrc5ibm8PPzw9paWkAgD/++ANZWVlo3ry58hi5XI4pU6YgNjYWV69exf79+5GYmAipVKpyroCAAAwZMgSbNm2qdB2pVFrpeJQ9nnraL1l88gF8umU57uT9rxXE2UaGBeGT0c+vR5Xv/fHYXgDAgMDeBv1lF4lEOvmsDI2p5gUTzo15GRfmZVy0zUvbvPX66VhYWMDf3x8JCQnKbQqFAgkJCSqtJVWRy+U4ffo0nJ2dAQBDhw7FqVOnkJKSovxxcXHB1KlTsWfPP8N0V65ciZMnTyr3//rrr0DZSKOFCxfWSa6axCcfwOi1USoFCgBk5GVh9NooxCcf0PjeK5nXkXLtHCRiCfr7h9ZDtERERPVH7497IiMjMXz4cAQEBKBz586IjY1FUVERRo4cCQAYNmwYmjVrhujoaADAvHnz0KVLF3h6eiIvLw8xMTG4du0aRo8eDQCws7ODnZ2dyjXMzc3h5OSENm3aAIBKKwsAWFlZAQBatWql7NdSH+QKOT7dshzqurkKAEQAZm6NRVjHrmof/ZRPg9+17fNwsG5aDxETERHVH70XKeHh4cjOzsasWbOQkZEBX19f7N69W9mZ9vr16yrNQvfu3UNERAQyMjJga2sLf39/HDlyBN7e3nrMonaSLqVUakF5kgDg9r1MJF1KwQtPzCIrV8iRdCkFX/++AwDwRsDL9RIvERFRfdJ7kQIAEyZMwIQJE9TuO3jwoMrr5cuXY/ny5TU6f3Wjdzw8PPQybDerIFer43af/B0BLTtAam6htv/Kop/i0MiyYbX9V4iIiIyJQRQpzyqZtZ0WRwFf7d+CbUm70NHjORw6V3nJgMy8bIxeG4V1Y6JZqBARkckwrW7FRqaLly+cbWRQP1D6H1aWDeHUxB55DwrUFigoeyyEsv4rcoW8TmIlIiKqbyxS9EgilmBB+GSgrJPsk0RlPyuGz8Tx6J8we+CHVZ7ryf4rREREpoBFip718+uBdWOi4WQjU9nubOuofHwjEUvgZOOg1fm07edCRERk6NgnxQD08+uBsI5dkXgxGZduXIGXW8tKM85q239F2+OIiIgMHYsUAyERSxDcuhM8bVwhk8kqzcZX3n8lIy9L7bwqorLWly5evvUWMxERUV3i4x4jUV3/FQCYP2iSVuv9EBERGQMWKUZEm/4rREREpoKPe4xMef+VpEspyCrIhczaDl28fNmCQkREJodFihGSiCUq0+QTERGZIj7uISIiIoPEIoWIiIgMEosUIiIiMkgsUoiIiMggsUghIiIig8QihYiIiAwShyDXkiD8Mzl9QUGBzs6pUChQWFgIS0vLStPiGzPmZXxMNTfmZVyYl3GpSV7lvzvLf5dqwiKllgoLCwEAbm5u+g6FiIjIKBUWFqJJkyYa94uE6soYUkuhUOD27dto3LgxRKKKq+nUTkFBAdzc3HDjxg1YW1vr5JyGgHkZH1PNjXkZF+ZlXGqSlyAIKCwshIuLS5WtLmxJqSWxWAxXV9c6Obe1tbVJfXHLMS/jY6q5MS/jwryMi7Z5VdWCUs50HoYRERGRSWGRQkRERAaJRYoBkUqlmD17NqRSqb5D0SnmZXxMNTfmZVyYl3Gpi7zYcZaIiIgMEltSiIiIyCCxSCEiIiKDxCKFiIiIDBKLFCIiIjJILFIMxOrVq+Hh4QFLS0sEBgbir7/+0ndIT23OnDkQiUQqP88995y+w6qx33//Ha+++ipcXFwgEomwc+dOlf2CIGDWrFlwdnZGgwYNEBoaikuXLuktXm1Vl9eIESMq3b+wsDC9xaut6OhoPP/882jcuDFkMhlef/11pKamqhxTXFyM8ePHw87ODlZWVhg4cCAyMzP1FrM2tMmre/fule7Z2LFj9RazNr788kv4+PgoJwALCgrCrl27lPuN8V6Vqy43Y7xfFX322WcQiUSYNGmScpsu7xmLFAOwZcsWREZGYvbs2Thx4gQ6duyI3r17IysrS9+hPbV27drhzp07yp8///xT3yHVWFFRETp27IjVq1er3b9kyRKsXLkScXFxOHr0KBo1aoTevXujuLi43mOtieryAoCwsDCV+/f999/Xa4y1cejQIYwfPx5JSUnYu3cvHj16hF69eqGoqEh5zOTJk/Hf//4X27Ztw6FDh3D79m0MGDBAr3FXR5u8ACAiIkLlni1ZskRvMWvD1dUVn332GY4fP46///4bPXv2RP/+/XH27FnASO9VuepygxHerycdO3YMa9euhY+Pj8p2nd4zgfSuc+fOwvjx45Wv5XK54OLiIkRHR+s1rqc1e/ZsoWPHjvoOQ6cACDt27FC+VigUgpOTkxATE6PclpeXJ0ilUuH777/XU5Q1VzEvQRCE4cOHC/3799dbTLqSlZUlABAOHTokCGX3x9zcXNi2bZvymPPnzwsAhMTERD1GWjMV8xIEQejWrZswceJEvcalC7a2tsK6detM5l49qTw3wcjvV2FhoeDl5SXs3btXJQ9d3zO2pOhZaWkpjh8/jtDQUOU2sViM0NBQJCYm6jU2Xbh06RJcXFzQsmVLDBkyBNevX9d3SDqVnp6OjIwMlfvXpEkTBAYGmsT9O3jwIGQyGdq0aYMPPvgAubm5+g6pxvLz8wEATZs2BQAcP34cjx49Urlnzz33HJo3b25U96xiXuU2b94Me3t7tG/fHlFRUXjw4IGeIqw5uVyOH374AUVFRQgKCjKZewU1uZUz1vs1fvx49OvXT+XeoA7+/+ICg3qWk5MDuVwOR0dHle2Ojo64cOGC3uLShcDAQGzcuBFt2rTBnTt3MHfuXLz00ks4c+YMGjdurO/wdCIjIwMou19PcnR0VO4zVmFhYRgwYABatGiBy5cvY/r06ejTpw8SExMhkUj0HZ5WFAoFJk2ahBdeeAHt27cHyu6ZhYUFbGxsVI41pnumLi8AeOedd+Du7g4XFxecOnUK//d//4fU1FT8+OOPeo23OqdPn0ZQUBCKi4thZWWFHTt2wNvbGykpKUZ/rzTlBiO+Xz/88ANOnDiBY8eOVdqn6/+/WKRQnenTp4/yzz4+PggMDIS7uzu2bt2KUaNG6TU2qt7gwYOVf+7QoQN8fHzQqlUrHDx4ECEhIXqNTVvjx4/HmTNnjLIvVFU05fX+++8r/9yhQwc4OzsjJCQEly9fRqtWrfQQqXbatGmDlJQU5OfnY/v27Rg+fDgOHTqk77B0QlNu3t7eRnm/bty4gYkTJ2Lv3r2wtLSs8+vxcY+e2dvbQyKRVOr5nJmZCScnJ73FVRdsbGzQunVrpKWl6TsUnSm/R8/C/WvZsiXs7e2N5v5NmDABv/zyCw4cOABXV1fldicnJ5SWliIvL0/leGO5Z5ryUicwMBAADP6eWVhYwNPTE/7+/oiOjkbHjh2xYsUKo79XqCI3dYzhfh0/fhxZWVno1KkTzMzMYGZmhkOHDmHlypUwMzODo6OjTu8ZixQ9s7CwgL+/PxISEpTbFAoFEhISVJ5bmoL79+/j8uXLcHZ21ncoOtOiRQs4OTmp3L+CggIcPXrU5O7fzZs3kZuba/D3TxAETJgwATt27MD+/fvRokULlf3+/v4wNzdXuWepqam4fv26Qd+z6vJSJyUlBQAM/p5VpFAoUFJSYrT3qirlualjDPcrJCQEp0+fRkpKivInICAAQ4YMUf5Zp/dMp919qVZ++OEHQSqVChs3bhTOnTsnvP/++4KNjY2QkZGh79CeypQpU4SDBw8K6enpwuHDh4XQ0FDB3t5eyMrK0ndoNVJYWCgkJycLycnJAgBh2bJlQnJysnDt2jVBEAThs88+E2xsbISffvpJOHXqlNC/f3+hRYsWwsOHD/UdepWqyquwsFD4+OOPhcTERCE9PV3Yt2+f0KlTJ8HLy0soLi7Wd+hV+uCDD4QmTZoIBw8eFO7cuaP8efDggfKYsWPHCs2bNxf2798v/P3330JQUJAQFBSk17irU11eaWlpwrx584S///5bSE9PF3766SehZcuWQteuXfUdepU++eQT4dChQ0J6erpw6tQp4ZNPPhFEIpHw22+/CYKR3qtyVeVmrPdLnYqjlHR5z1ikGIhVq1YJzZs3FywsLITOnTsLSUlJ+g7pqYWHhwvOzs6ChYWF0KxZMyE8PFxIS0vTd1g1duDAAQFApZ/hw4cLQtkw5JkzZwqOjo6CVCoVQkJChNTUVH2HXa2q8nrw4IHQq1cvwcHBQTA3Nxfc3d2FiIgIoyic1eUEQNiwYYPymIcPHwrjxo0TbG1thYYNGwpvvPGGcOfOHb3GXZ3q8rp+/brQtWtXoWnTpoJUKhU8PT2FqVOnCvn5+foOvUrvvfee4O7uLlhYWAgODg5CSEiIskARjPRelasqN2O9X+pULFJ0ec9Ewj9ffiIiIiKDwj4pREREZJBYpBAREZFBYpFCREREBolFChERERkkFilERERkkFikEBERkUFikUJEREQGiUUKERERGSQWKUT0zPLw8EBsbKy+wyAiDVikEFG9GDFiBF5//XUAQPfu3TFp0qR6u/bGjRthY2NTafuxY8fw/vvv11scRFQzZvoOgIiotkpLS2FhYVHr9zs4OOg0HiLSLbakEFG9GjFiBA4dOoQVK1ZAJBJBJBLh6tWrAIAzZ86gT58+sLKygqOjI4YOHYqcnBzle7t3744JEyZg0qRJsLe3R+/evQEAy5YtQ4cOHdCoUSO4ublh3LhxuH//PgDg4MGDGDlyJPLz85XXmzNnDqDmcc/169fRv39/WFlZwdraGoMGDUJmZqZy/5w5c+Dr64tvvvkGHh4eaNKkCQYPHozCwsJ6+/yIniUsUoioXq1YsQJBQUGIiIjAnTt3cOfOHbi5uSEvLw89e/aEn58f/v77b+zevRuZmZkYNGiQyvs3bdoECwsLHD58GHFxcQAAsViMlStX4uzZs9i0aRP279+PadOmAQCCg4MRGxsLa2tr5fU+/vjjSnEpFAr0798fd+/exaFDh7B3715cuXIF4eHhKsddvnwZO3fuxC+//IJffvkFhw4dwmeffVannxnRs4qPe4ioXjVp0gQWFhZo2LAhnJyclNu/+OIL+Pn5YdGiRcpt69evh5ubGy5evIjWrVsDALy8vLBkyRKVcz7Zv8XDwwMLFizA2LFjsWbNGlhYWKBJkyYQiUQq16soISEBp0+fRnp6Otzc3AAAX3/9Ndq1a4djx47h+eefB8qKmY0bN6Jx48YAgKFDhyIhIQELFy7U2WdERP9gSwoRGYSTJ0/iwIEDsLKyUv4899xzQFnrRTl/f/9K7923bx9CQkLQrFkzNG7cGEOHDkVubi4ePHig9fXPnz8PNzc3ZYECAN7e3rCxscH58+eV2zw8PJQFCgA4OzsjKyurVjkTUdXYkkJEBuH+/ft49dVXsXjx4kr7nJ2dlX9u1KiRyr6rV6/ilVdewQcffICFCxeiadOm+PPPPzFq1CiUlpaiYcOGOo3T3Nxc5bVIJIJCodDpNYjoHyxSiKjeWVhYQC6Xq2zr1KkT/vOf/8DDwwNmZtr/1XT8+HEoFAp8/vnnEIv/aRzeunVrtderqG3btrhx4wZu3LihbE05d+4c8vLy4O3tXYPsiEhX+LiHiOqdh4cHjh49iqtXryInJwcKhQLjx4/H3bt38fbbb+PYsWO4fPky9uzZg5EjR1ZZYHh6euLRo0dYtWoVrly5gm+++UbZofbJ692/fx8JCQnIyclR+xgoNDQUHTp0wJAhQ3DixAn89ddfGDZsGLp164aAgIA6+RyIqGosUoio3n388ceQSCTw9vaGg4MDrl+/DhcXFxw+fBhyuRy9evVChw4dMGnSJNjY2ChbSNTp2LEjli1bhsWLF6N9+/bYvHkzoqOjVY4JDg7G2LFjER4eDgcHh0odb1H22Oann36Cra0tunbtitDQULRs2RJbtmypk8+AiKonEgRB0HcQRERERBWxJYWIiIgMEosUIiIiMkgsUoiIiMggsUghIiIig8QihYiIiAwSixQiIiIySCxSiIiIyCCxSCEiIiKDxCKFiIiIDBKLFCIiIjJILFKIiIjIIP0/O6elkYnrp0oAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "ax.plot(np.arange(len(objective_history)), objective_history, marker=\"o\")\n",
    "ax.set_xlabel(\"Iteration\")\n",
    "ax.set_ylabel(\"Objective\")\n",
    "ax.set_title(\"Robust adjoint optimization history\")\n",
    "ax.grid(True, alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb0cbd5c",
   "metadata": {},
   "source": [
    "### Pre- and Post-Optimization Bias Sweeps\n",
    "\n",
    "To visualize the payoff, we re-run the ±20 nm fabrication corners for the original and robust designs. This mirrors the analysis step from the sensitivity notebook so we can compare apples to apples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "90b134cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_numpy_params(param_dict):\n",
    "    \"\"\"Detach autograd arrays into plain numpy arrays for analysis/export.\"\"\"\n",
    "    return {\n",
    "        \"widths_si\": np.array(param_dict[\"widths_si\"], dtype=float),\n",
    "        \"gaps_si\": np.array(param_dict[\"gaps_si\"], dtype=float),\n",
    "        \"widths_sin\": np.array(param_dict[\"widths_sin\"], dtype=float),\n",
    "        \"gaps_sin\": np.array(param_dict[\"gaps_sin\"], dtype=float),\n",
    "        \"first_gap_si\": float(param_dict[\"first_gap_si\"]),\n",
    "        \"first_gap_sin\": float(param_dict[\"first_gap_sin\"]),\n",
    "    }\n",
    "\n",
    "\n",
    "def run_bias_sweep(param_dict, task_prefix, bias=ETCH_BIAS):\n",
    "    \"\"\"Run nominal/over/under simulations and return spectra in linear scale.\"\"\"\n",
    "    scenarios = [\n",
    "        (\"Over-etched (-20 nm)\", apply_bias(param_dict, -bias)),\n",
    "        (\"Nominal\", param_dict),\n",
    "        (\"Under-etched (+20 nm)\", apply_bias(param_dict, bias)),\n",
    "    ]\n",
    "\n",
    "    sims = {\n",
    "        f\"{task_prefix}_{idx}\": make_simulation(\n",
    "            scenario[\"widths_si\"],\n",
    "            scenario[\"gaps_si\"],\n",
    "            scenario[\"widths_sin\"],\n",
    "            scenario[\"gaps_sin\"],\n",
    "            first_gap_si=scenario[\"first_gap_si\"],\n",
    "            first_gap_sin=scenario[\"first_gap_sin\"],\n",
    "        )\n",
    "        for idx, (_, scenario) in enumerate(scenarios)\n",
    "    }\n",
    "\n",
    "    batch_data = web.run_async(sims, verbose=False)\n",
    "\n",
    "    wavelengths = None\n",
    "    spectra = {}\n",
    "    for idx, (label, _) in enumerate(scenarios):\n",
    "        sim_data = batch_data[f\"{task_prefix}_{idx}\"]\n",
    "        power_da = get_mode_monitor_power(sim_data)\n",
    "        freqs = power_da.coords[\"f\"].values\n",
    "        wl = td.C_0 / freqs\n",
    "        power = np.asarray(power_da.data).squeeze()\n",
    "        order = np.argsort(wl)\n",
    "        wl = wl[order]\n",
    "        power = power[order]\n",
    "        if wavelengths is None:\n",
    "            wavelengths = wl\n",
    "        spectra[label] = power\n",
    "    return wavelengths, spectra\n",
    "\n",
    "\n",
    "params_initial = to_numpy_params(params0)\n",
    "params_robust = to_numpy_params(params)\n",
    "\n",
    "w_before, spectra_before = run_bias_sweep(params_initial, \"gc_robust_bias_before\", bias=ETCH_BIAS)\n",
    "w_after, spectra_after = run_bias_sweep(params_robust, \"gc_robust_bias_after\", bias=ETCH_BIAS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86af00bc",
   "metadata": {},
   "source": [
    "## The Final Payoff: Visualizing Robustness\n",
    "\n",
    "The left panel shows how sensitive the previous design is to ±20 nm fabrication bias, while the right panel show the spectrum after robust optimization. We can see observe a slight shift in the spectra, but to make any quantitative statement, we'll not to run another sensitivity analysis, which we'll do in the next notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5d3bb559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [\"Over-etched (-20 nm)\", \"Nominal\", \"Under-etched (+20 nm)\"]\n",
    "colors = {\n",
    "    \"Over-etched (-20 nm)\": \"tab:orange\",\n",
    "    \"Nominal\": \"tab:green\",\n",
    "    \"Under-etched (+20 nm)\": \"tab:blue\",\n",
    "}\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4), sharey=True)\n",
    "\n",
    "for label in labels:\n",
    "    axes[0].plot(\n",
    "        w_before,\n",
    "        10 * np.log10(spectra_before[label]),\n",
    "        label=label,\n",
    "        color=colors[label],\n",
    "        linewidth=2 if label == \"Nominal\" else 1.6,\n",
    "        alpha=0.9,\n",
    "    )\n",
    "\n",
    "axes[0].set_title(\"Before robust optimization\")\n",
    "axes[0].set_xlabel(\"Wavelength (µm)\")\n",
    "axes[0].set_ylabel(\"Transmission (dB)\")\n",
    "axes[0].grid(True, alpha=0.3)\n",
    "\n",
    "for label in labels:\n",
    "    axes[1].plot(\n",
    "        w_after,\n",
    "        10 * np.log10(spectra_after[label]),\n",
    "        label=label,\n",
    "        color=colors[label],\n",
    "        linewidth=2 if label == \"Nominal\" else 1.6,\n",
    "        alpha=0.9,\n",
    "    )\n",
    "\n",
    "axes[1].set_title(\"After robust optimization\")\n",
    "axes[1].set_xlabel(\"Wavelength (µm)\")\n",
    "axes[1].grid(True, alpha=0.3)\n",
    "axes[1].legend(loc=\"best\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a67bc8c6",
   "metadata": {},
   "source": [
    "### Exporting the Robust Design\n",
    "\n",
    "Finally we save the fabrication-aware geometry so downstream notebooks - or a GDS handoff - can reuse it without re-running the optimization loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "64b37506",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved robust design to /home/yannick/flexcompute/worktrees/seminar_notebooks/docs/notebooks/2025-10-09-invdes-seminar/results/gc_adjoint_robust_best.json\n"
     ]
    }
   ],
   "source": [
    "export_path = Path(\"./results/gc_adjoint_robust_best.json\")\n",
    "export_payload = {\n",
    "    \"widths_si\": params_robust[\"widths_si\"].tolist(),\n",
    "    \"gaps_si\": params_robust[\"gaps_si\"].tolist(),\n",
    "    \"widths_sin\": params_robust[\"widths_sin\"].tolist(),\n",
    "    \"gaps_sin\": params_robust[\"gaps_sin\"].tolist(),\n",
    "    \"first_gap_si\": params_robust[\"first_gap_si\"],\n",
    "    \"first_gap_sin\": params_robust[\"first_gap_sin\"],\n",
    "    \"etch_bias_modeled\": ETCH_BIAS,\n",
    "}\n",
    "\n",
    "export_path.parent.mkdir(parents=True, exist_ok=True)\n",
    "export_path.write_text(json.dumps(export_payload, indent=2), encoding=\"utf-8\")\n",
    "print(f\"Saved robust design to {export_path.resolve()}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
