{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "495ae8bd",
   "metadata": {},
   "source": [
    "# Fabrication Sensitivity Analysis: Is Our Design Robust?\n",
    "\n",
    "> The adjoint-optimized grating from the previous notebook delivers excellent nominal performance. In practice, however, fabrication variability means the manufactured device rarely matches the design exactly. Here we quantify how the current design responds to some assumed process deviations to see whether it is robust or brittle.\n",
    "\n",
    "> In the adjoint notebook we purposefully focused on maximizing performance at the nominal geometry. The natural follow-up question is: *how does that optimized design behave once it leaves the computer?* Photonic fabrication processes inevitably introduce small deviations in etched dimensions. Even a well-controlled foundry run can exhibit ±20 nm variations in tooth widths and gaps due to lithography or etch bias. A design that is overly sensitive to these changes might look great in simulation yet fail to meet targets on wafer, so our immediate goal is to measure that sensitivity before pursuing robustness improvements."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f10594b5",
   "metadata": {},
   "source": [
    "## Modeling Fabrication Errors with a Bias\n",
    "\n",
    "We begin by reloading the best adjoint design and defining a simple bias model. A ±20 nm shift in feature dimensions is a realistic foundry tolerance, so we will simulate three cases: the nominal geometry, an over-etched device (features narrower than intended), and an under-etched device (features wider than intended). This gives an intuitive first look at the design's sensitivity before launching a full Monte Carlo analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04f09f74",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from pathlib import Path\n",
    "\n",
    "import autograd.numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import tidy3d as td\n",
    "from autograd import value_and_grad\n",
    "from scipy.stats import norm\n",
    "from setup import (\n",
    "    center_wavelength,\n",
    "    default_spacer_thickness,\n",
    "    get_mode_monitor_power,\n",
    "    make_simulation,\n",
    ")\n",
    "from tidy3d import web"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6657cb9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_nominal_parameters(path):\n",
    "    \"\"\"Load a design JSON (Bayes or adjoint) into numpy-friendly fields.\"\"\"\n",
    "    data = json.loads(Path(path).read_text(encoding=\"utf-8\"))\n",
    "    return {\n",
    "        \"widths_si\": np.array(data[\"widths_si\"]),\n",
    "        \"gaps_si\": np.array(data[\"gaps_si\"]),\n",
    "        \"widths_sin\": np.array(data[\"widths_sin\"]),\n",
    "        \"gaps_sin\": np.array(data[\"gaps_sin\"]),\n",
    "        \"first_gap_si\": data[\"first_gap_si\"],\n",
    "        \"first_gap_sin\": data[\"first_gap_sin\"],\n",
    "        \"spacer_thickness\": default_spacer_thickness,\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "29aec207",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_variation_builder(nominal):\n",
    "    \"\"\"Return a closure that maps process deltas to a tidy3d Simulation.\"\"\"\n",
    "    base_widths_si = np.array(nominal[\"widths_si\"])\n",
    "    base_gaps_si = np.array(nominal[\"gaps_si\"])\n",
    "\n",
    "    def builder(overlay_delta=0.0, spacer_delta=0.0, etch_bias=0.0):\n",
    "        # Etch bias widens features when positive and narrows them when\n",
    "        # negative, so widths grow with the bias while gaps shrink, mirroring\n",
    "        # the fabrication effect of over/under etching.\n",
    "        pert_widths_si = base_widths_si + etch_bias\n",
    "        pert_gaps_si = base_gaps_si - etch_bias\n",
    "\n",
    "        return make_simulation(\n",
    "            pert_widths_si,\n",
    "            pert_gaps_si,\n",
    "            nominal[\"widths_sin\"],\n",
    "            nominal[\"gaps_sin\"],\n",
    "            first_gap_si=nominal[\"first_gap_si\"] + overlay_delta,\n",
    "            first_gap_sin=nominal[\"first_gap_sin\"],\n",
    "            spacer_thickness=nominal[\"spacer_thickness\"] + spacer_delta,\n",
    "        )\n",
    "\n",
    "    return builder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0505451c",
   "metadata": {},
   "outputs": [],
   "source": [
    "design_path = Path(\"./results\") / \"gc_adjoint_best.json\"\n",
    "\n",
    "# Load the best apodized design from the previous notebook.\n",
    "# This will be our nominal, or central, design point for the analysis.\n",
    "nominal = load_nominal_parameters(design_path)\n",
    "builder = make_variation_builder(nominal)\n",
    "\n",
    "# Define the fabrication bias in microns (20 nm).\n",
    "bias = 0.02\n",
    "\n",
    "# Create simulations for each fabrication scenario: over-etched, nominal,\n",
    "# and under-etched. Positive bias widens features, while a negative bias\n",
    "# corresponds to over-etching that narrows them.\n",
    "bias_cases = {\n",
    "    \"Over-etched (-20 nm)\": builder(etch_bias=-bias),\n",
    "    \"Nominal\": builder(),\n",
    "    \"Under-etched (+20 nm)\": builder(etch_bias=bias),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39d4febe",
   "metadata": {},
   "outputs": [],
   "source": [
    "bias_data = web.run_async(bias_cases, verbose=False)\n",
    "\n",
    "bias_wavelengths = None\n",
    "bias_spectra = {}\n",
    "\n",
    "for label, sim_data in bias_data.items():\n",
    "    power_da = get_mode_monitor_power(sim_data)\n",
    "    freqs = power_da.coords[\"f\"].values\n",
    "    wavelengths = td.C_0 / freqs\n",
    "    power = np.asarray(power_da.data).squeeze()\n",
    "    order = np.argsort(wavelengths)\n",
    "    wavelengths = wavelengths[order]\n",
    "    power = power[order]\n",
    "\n",
    "    if bias_wavelengths is None:\n",
    "        bias_wavelengths = wavelengths\n",
    "\n",
    "    bias_spectra[label] = power"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50c73781",
   "metadata": {},
   "source": [
    "## Interpreting the Sensitivity Plot\n",
    "\n",
    "The curves below compare the nominal spectrum to ±20 nm biased geometries. The separation between them conveys how quickly our high-efficiency design degrades under realistic fabrication shifts in tooth width and gap. Watch for both a drop in peak efficiency and a shift of the optimal wavelength."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6a994928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "colors = {\n",
    "    \"Over-etched (-20 nm)\": \"tab:orange\",\n",
    "    \"Nominal\": \"tab:green\",\n",
    "    \"Under-etched (+20 nm)\": \"tab:blue\",\n",
    "}\n",
    "\n",
    "for label, spectrum in bias_spectra.items():\n",
    "    ax.plot(\n",
    "        bias_wavelengths,\n",
    "        10 * np.log10(spectrum),\n",
    "        label=label,\n",
    "        color=colors.get(label, None),\n",
    "        linewidth=2 if label == \"Nominal\" else 1.8,\n",
    "        alpha=0.9,\n",
    "    )\n",
    "\n",
    "ax.set_xlabel(\"Wavelength (µm)\")\n",
    "ax.set_ylabel(\"Transmission (dB)\")\n",
    "ax.set_title(\"Impact of ±20 nm Fabrication Bias\")\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cb42ec87",
   "metadata": {},
   "outputs": [],
   "source": [
    "sigma_spec = {\n",
    "    \"overlay\": 0.025,\n",
    "    \"spacer\": 0.02,\n",
    "    \"widths_si\": 0.01,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8249bf45",
   "metadata": {},
   "source": [
    "## Monte Carlo\n",
    "\n",
    "After inspecting the deterministic bias sweep, we broaden the analysis with a Monte Carlo study. We randomly sample overlay, spacer, and width variations according to foundry-provided sigma values to estimate the distribution of coupling efficiency across a wafer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8abfdc5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 42\n",
    "num_mc_samples = 100\n",
    "design_path = Path(\"./results\") / \"gc_adjoint_best.json\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f6e0b847",
   "metadata": {},
   "outputs": [],
   "source": [
    "nominal = load_nominal_parameters(design_path)\n",
    "builder = make_variation_builder(nominal)\n",
    "\n",
    "sigma_vector = np.array([sigma_spec[\"overlay\"], sigma_spec[\"spacer\"], sigma_spec[\"widths_si\"]])\n",
    "rng = np.random.default_rng(seed)\n",
    "samples = rng.standard_normal(size=(num_mc_samples, len(sigma_vector))) * sigma_vector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3c6c19b",
   "metadata": {},
   "source": [
    "We draw overlay, spacer, and silicon-width perturbations from independent Gaussian models whose sigmas come straight from the (hypothetical) foundry tolerance table. Each row in the `samples` array represents one die that we will feed into the simulation pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1c93f5af",
   "metadata": {},
   "outputs": [],
   "source": [
    "sims = {\"nominal\": builder()}\n",
    "sims.update({f\"sample_{idx + 1}\": builder(*tuple(sample)) for idx, sample in enumerate(samples)})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75f5ca55",
   "metadata": {},
   "source": [
    "The closure returned by `make_variation_builder` maps each sampled triplet into a full tidy3d `Simulation`. We keep the nominal design in the dictionary so the subsequent analysis can always reference the baseline spectrum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "817a9dd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_data = web.run_async(sims, verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38c474f3",
   "metadata": {},
   "source": [
    "We submit the entire batch with `web.run_async` so Tidy3D executes the jobs in parallel since they are all independent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cae4cc32",
   "metadata": {},
   "outputs": [],
   "source": [
    "ordered_names = list(sims.keys())\n",
    "wavelengths = None\n",
    "linear_spectra = []\n",
    "\n",
    "for name in ordered_names:\n",
    "    sim_data = batch_data[name]\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",
    "\n",
    "    if wavelengths is None:\n",
    "        wavelengths = wl\n",
    "\n",
    "    linear_spectra.append(power)\n",
    "\n",
    "linear_array = np.vstack(linear_spectra)\n",
    "nominal_index = ordered_names.index(\"nominal\")\n",
    "nominal_spectrum = linear_array[nominal_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86096b73",
   "metadata": {},
   "source": [
    "Once the solver responses return, we stack them into a 2D array and compute statistics such as the mean trace, percentile envelope, and nominal curve for direct comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a5594acf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "\n",
    "for name, spectrum in zip(ordered_names, linear_array):\n",
    "    if name == \"nominal\":\n",
    "        continue\n",
    "    spectrum_db = 10 * np.log10(spectrum)\n",
    "    ax.plot(\n",
    "        wavelengths,\n",
    "        spectrum_db,\n",
    "        color=\"lightgray\",\n",
    "        alpha=0.6,\n",
    "        linewidth=1,\n",
    "        zorder=1,\n",
    "    )\n",
    "\n",
    "mean_spectrum = linear_array.mean(axis=0)\n",
    "p10_spectrum = np.percentile(linear_array, 10, axis=0)\n",
    "p90_spectrum = np.percentile(linear_array, 90, axis=0)\n",
    "\n",
    "ax.fill_between(\n",
    "    wavelengths,\n",
    "    10 * np.log10(p10_spectrum),\n",
    "    10 * np.log10(p90_spectrum),\n",
    "    color=\"tab:blue\",\n",
    "    alpha=0.18,\n",
    "    label=\"P10–P90\",\n",
    "    zorder=2,\n",
    ")\n",
    "ax.plot(\n",
    "    wavelengths,\n",
    "    10 * np.log10(mean_spectrum),\n",
    "    color=\"tab:blue\",\n",
    "    linewidth=2,\n",
    "    linestyle=\"--\",\n",
    "    label=\"Mean\",\n",
    "    zorder=3,\n",
    ")\n",
    "ax.plot(\n",
    "    wavelengths,\n",
    "    10 * np.log10(nominal_spectrum),\n",
    "    color=\"black\",\n",
    "    linewidth=2.5,\n",
    "    label=\"Nominal\",\n",
    "    zorder=4,\n",
    ")\n",
    "\n",
    "ax.set_xlabel(\"Wavelength (µm)\")\n",
    "ax.set_ylabel(\"Transmission (dB)\")\n",
    "ax.set_title(\"Monte Carlo Ensemble of Spectra\")\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1543f365",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>wavelength_um</th>\n",
       "      <td>1.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_linear</th>\n",
       "      <td>0.554907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_db</th>\n",
       "      <td>2.557796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_linear</th>\n",
       "      <td>0.027393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_db</th>\n",
       "      <td>0.219866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p10_linear</th>\n",
       "      <td>0.517121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p10_db</th>\n",
       "      <td>2.864077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p90_linear</th>\n",
       "      <td>0.587606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p90_db</th>\n",
       "      <td>2.309138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_overlay</th>\n",
       "      <td>0.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_spacer</th>\n",
       "      <td>0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_widths_si</th>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    value\n",
       "wavelength_um    1.550000\n",
       "mean_linear      0.554907\n",
       "mean_db          2.557796\n",
       "std_linear       0.027393\n",
       "std_db           0.219866\n",
       "p10_linear       0.517121\n",
       "p10_db           2.864077\n",
       "p90_linear       0.587606\n",
       "p90_db           2.309138\n",
       "sigma_overlay    0.025000\n",
       "sigma_spacer     0.020000\n",
       "sigma_widths_si  0.010000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def linear_to_loss_db(x):\n",
    "    return -10 * np.log10(x)\n",
    "\n",
    "\n",
    "idx_center = np.argmin(np.abs(wavelengths - center_wavelength))\n",
    "eta_center = linear_array[:, idx_center]\n",
    "\n",
    "mc_summary = {\n",
    "    \"wavelength_um\": wavelengths[idx_center],\n",
    "    \"mean_linear\": eta_center.mean(),\n",
    "    \"mean_db\": linear_to_loss_db(eta_center.mean()),\n",
    "    \"std_linear\": eta_center.std(ddof=1),\n",
    "    \"std_db\": linear_to_loss_db(eta_center.mean() - eta_center.std(ddof=1))\n",
    "    - linear_to_loss_db(eta_center.mean()),\n",
    "    \"p10_linear\": np.percentile(eta_center, 10),\n",
    "    \"p10_db\": linear_to_loss_db(np.percentile(eta_center, 10)),\n",
    "    \"p90_linear\": np.percentile(eta_center, 90),\n",
    "    \"p90_db\": linear_to_loss_db(np.percentile(eta_center, 90)),\n",
    "    \"sigma_overlay\": sigma_spec[\"overlay\"],\n",
    "    \"sigma_spacer\": sigma_spec[\"spacer\"],\n",
    "    \"sigma_widths_si\": sigma_spec[\"widths_si\"],\n",
    "}\n",
    "\n",
    "pd.Series(mc_summary).to_frame(\"value\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b23f290",
   "metadata": {},
   "source": [
    "The helper converts the center-wavelength transmission into dB loss and aggregates mean, standard deviation, and percentile values. These single-number metrics offer a quick dashboard before moving on to more detailed adjoint sensitivities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c34e6a8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eta_center_db = linear_to_loss_db(eta_center)\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "ax.hist(eta_center_db[1:], bins=\"auto\", color=\"tab:blue\", alpha=0.7, label=\"Samples\")\n",
    "ax.axvline(eta_center_db[0], color=\"black\", linewidth=2, label=\"Nominal\")\n",
    "ax.set_xlabel(f\"Transmission at {wavelengths[idx_center]:.3f} µm (dB)\")\n",
    "ax.set_ylabel(\"Count\")\n",
    "ax.set_title(\"Monte Carlo Transmission at Center Wavelength\")\n",
    "ax.grid(True, alpha=0.3)\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "134ec056",
   "metadata": {},
   "source": [
    "## Adjoint"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b74e025d",
   "metadata": {},
   "source": [
    "### Linearized Sensitivity via Adjoint\n",
    "\n",
    "Before launching a full robust optimization we want directional information: which fabrication knobs most strongly impact coupling efficiency near the nominal point? The objective below evaluates a single perturbed simulation and, through `value_and_grad`, returns both the power and its gradient with respect to the overlay, spacer, and silicon-width errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "12db8468",
   "metadata": {},
   "outputs": [],
   "source": [
    "nominal = load_nominal_parameters(design_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3ded3426",
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(params):\n",
    "    overlay_delta, spacer_delta, etch_bias = params\n",
    "    sim = make_simulation(\n",
    "        nominal[\"widths_si\"] + etch_bias,\n",
    "        nominal[\"gaps_si\"] - etch_bias,\n",
    "        nominal[\"widths_sin\"],\n",
    "        nominal[\"gaps_sin\"],\n",
    "        first_gap_si=nominal[\"first_gap_si\"] + overlay_delta,\n",
    "        first_gap_sin=nominal[\"first_gap_sin\"],\n",
    "        spacer_thickness=nominal[\"spacer_thickness\"] + spacer_delta,\n",
    "    )\n",
    "    sim_data = web.run(sim, task_name=\"gc_sensitivity_adj\", verbose=False)\n",
    "    power_da = get_mode_monitor_power(sim_data)\n",
    "    target_power = power_da.sel(f=td.C_0 / center_wavelength, method=\"nearest\")\n",
    "    return target_power.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2ad8a7d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "params0 = np.zeros(3)\n",
    "value, grad = value_and_grad(objective)(params0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "52b48b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_77239\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_77239_level0_col0\" class=\"col_heading level0 col0\" >adjoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_77239_level0_row0\" class=\"row_heading level0 row0\" >mean_linear</th>\n",
       "      <td id=\"T_77239_row0_col0\" class=\"data row0 col0\" >0.5682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77239_level0_row1\" class=\"row_heading level0 row1\" >std_linear</th>\n",
       "      <td id=\"T_77239_row1_col0\" class=\"data row1 col0\" >0.0373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77239_level0_row2\" class=\"row_heading level0 row2\" >p10_linear</th>\n",
       "      <td id=\"T_77239_row2_col0\" class=\"data row2 col0\" >0.5204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77239_level0_row3\" class=\"row_heading level0 row3\" >p90_linear</th>\n",
       "      <td id=\"T_77239_row3_col0\" class=\"data row3 col0\" >0.6160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77239_level0_row4\" class=\"row_heading level0 row4\" >mean_db</th>\n",
       "      <td id=\"T_77239_row4_col0\" class=\"data row4 col0\" >2.4551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77239_level0_row5\" class=\"row_heading level0 row5\" >p10_db</th>\n",
       "      <td id=\"T_77239_row5_col0\" class=\"data row5 col0\" >2.8368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_77239_level0_row6\" class=\"row_heading level0 row6\" >p90_db</th>\n",
       "      <td id=\"T_77239_row6_col0\" class=\"data row6 col0\" >2.1042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f08a0fc27b0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordered = (\"overlay\", \"spacer\", \"widths_si\")\n",
    "\n",
    "grads = dict(zip(ordered, grad))\n",
    "sigmas = {k: float(sigma_spec[k]) for k in ordered}\n",
    "\n",
    "grad_vec = np.fromiter((grads[k] for k in ordered), dtype=float)\n",
    "sigma_vec = np.fromiter((sigmas[k] for k in ordered), dtype=float)\n",
    "\n",
    "# Linearized error propagation\n",
    "scaled = grad_vec * sigma_vec\n",
    "variance = scaled @ scaled\n",
    "std = float(np.sqrt(variance))\n",
    "\n",
    "# 10th/90th percentiles for Gaussian assumption\n",
    "z = 1.28155\n",
    "p10 = value - z * std\n",
    "p90 = value + z * std\n",
    "\n",
    "# Normalized variance contribution per parameter\n",
    "if variance == 0.0:\n",
    "    importance_dict = dict.fromkeys(ordered, 0.0)\n",
    "else:\n",
    "    importance_dict = {k: (s**2) / variance for k, s in zip(ordered, scaled)}\n",
    "\n",
    "adj_summary = {\n",
    "    \"mean_linear\": value,\n",
    "    \"std_linear\": std,\n",
    "    \"p10_linear\": p10,\n",
    "    \"p90_linear\": p90,\n",
    "    \"mean_db\": linear_to_loss_db(value),\n",
    "    \"p10_db\": linear_to_loss_db(p10),\n",
    "    \"p90_db\": linear_to_loss_db(p90),\n",
    "    \"importance\": importance_dict,\n",
    "}\n",
    "\n",
    "result = {\n",
    "    \"center_wavelength_um\": center_wavelength,\n",
    "    \"sigmas\": sigmas,\n",
    "    \"gradients_linear\": grads,\n",
    "    \"summary\": adj_summary,\n",
    "}\n",
    "\n",
    "adjoint_stats = pd.Series(\n",
    "    {\n",
    "        k: adj_summary[k]\n",
    "        for k in (\n",
    "            \"mean_linear\",\n",
    "            \"std_linear\",\n",
    "            \"p10_linear\",\n",
    "            \"p90_linear\",\n",
    "            \"mean_db\",\n",
    "            \"p10_db\",\n",
    "            \"p90_db\",\n",
    "        )\n",
    "    },\n",
    "    name=\"adjoint\",\n",
    ")\n",
    "adjoint_stats.to_frame().style.format(\"{:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "31b94606",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_03e2b\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_03e2b_level0_col0\" class=\"col_heading level0 col0\" >importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_03e2b_level0_row0\" class=\"row_heading level0 row0\" >overlay</th>\n",
       "      <td id=\"T_03e2b_row0_col0\" class=\"data row0 col0\" >0.035%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03e2b_level0_row1\" class=\"row_heading level0 row1\" >spacer</th>\n",
       "      <td id=\"T_03e2b_row1_col0\" class=\"data row1 col0\" >99.836%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03e2b_level0_row2\" class=\"row_heading level0 row2\" >widths_si</th>\n",
       "      <td id=\"T_03e2b_row2_col0\" class=\"data row2 col0\" >0.129%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f08a0fcde50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance_ser = pd.Series(adj_summary[\"importance\"], name=\"importance\")\n",
    "display(importance_ser.to_frame().style.format(\"{:.3%}\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdb03b8b",
   "metadata": {},
   "source": [
    "### Interpreting Variance Contributions\n",
    "\n",
    "Normalizing the gradient-scaled sigmas reveals how much each parameter contributes to the linearized variance. Plotting the breakdown highlights the dominant sensitivities we should target when we redesign for robustness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f05343ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "ax.bar(importance_ser.index, importance_ser.values, alpha=0.75)\n",
    "ax.set_ylim(0, 1)\n",
    "ax.set_ylabel(\"Normalized variance contribution\")\n",
    "ax.set_title(\"Adjoint Sensitivity Importance\")\n",
    "ax.grid(axis=\"y\", alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9eb1445",
   "metadata": {},
   "source": [
    "## Comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dbc2987",
   "metadata": {},
   "source": [
    "### Monte Carlo vs. Adjoint View\n",
    "\n",
    "Finally we line up the Monte Carlo results with the adjoint prediction. Agreement between the two lenses justifies replacing expensive sampling with cheaper gradient estimates in the next notebook, while any mismatch would signal nonlinearity that the linearized model misses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c3dcafd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_6c347\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_6c347_level0_col0\" class=\"col_heading level0 col0\" >mean_linear</th>\n",
       "      <th id=\"T_6c347_level0_col1\" class=\"col_heading level0 col1\" >std_linear</th>\n",
       "      <th id=\"T_6c347_level0_col2\" class=\"col_heading level0 col2\" >p10_linear</th>\n",
       "      <th id=\"T_6c347_level0_col3\" class=\"col_heading level0 col3\" >p90_linear</th>\n",
       "      <th id=\"T_6c347_level0_col4\" class=\"col_heading level0 col4\" >mean_db</th>\n",
       "      <th id=\"T_6c347_level0_col5\" class=\"col_heading level0 col5\" >p10_db</th>\n",
       "      <th id=\"T_6c347_level0_col6\" class=\"col_heading level0 col6\" >p90_db</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_6c347_level0_row0\" class=\"row_heading level0 row0\" >Monte Carlo</th>\n",
       "      <td id=\"T_6c347_row0_col0\" class=\"data row0 col0\" >0.5549</td>\n",
       "      <td id=\"T_6c347_row0_col1\" class=\"data row0 col1\" >0.0274</td>\n",
       "      <td id=\"T_6c347_row0_col2\" class=\"data row0 col2\" >0.5171</td>\n",
       "      <td id=\"T_6c347_row0_col3\" class=\"data row0 col3\" >0.5876</td>\n",
       "      <td id=\"T_6c347_row0_col4\" class=\"data row0 col4\" >2.5578</td>\n",
       "      <td id=\"T_6c347_row0_col5\" class=\"data row0 col5\" >2.8641</td>\n",
       "      <td id=\"T_6c347_row0_col6\" class=\"data row0 col6\" >2.3091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6c347_level0_row1\" class=\"row_heading level0 row1\" >Adjoint</th>\n",
       "      <td id=\"T_6c347_row1_col0\" class=\"data row1 col0\" >0.5682</td>\n",
       "      <td id=\"T_6c347_row1_col1\" class=\"data row1 col1\" >0.0373</td>\n",
       "      <td id=\"T_6c347_row1_col2\" class=\"data row1 col2\" >0.5204</td>\n",
       "      <td id=\"T_6c347_row1_col3\" class=\"data row1 col3\" >0.6160</td>\n",
       "      <td id=\"T_6c347_row1_col4\" class=\"data row1 col4\" >2.4551</td>\n",
       "      <td id=\"T_6c347_row1_col5\" class=\"data row1 col5\" >2.8368</td>\n",
       "      <td id=\"T_6c347_row1_col6\" class=\"data row1 col6\" >2.1042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f0888f24cd0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison = pd.DataFrame(\n",
    "    {\n",
    "        \"Monte Carlo\": {\n",
    "            \"mean_linear\": mc_summary[\"mean_linear\"],\n",
    "            \"std_linear\": mc_summary[\"std_linear\"],\n",
    "            \"p10_linear\": mc_summary[\"p10_linear\"],\n",
    "            \"p90_linear\": mc_summary[\"p90_linear\"],\n",
    "            \"mean_db\": mc_summary[\"mean_db\"],\n",
    "            \"p10_db\": mc_summary[\"p10_db\"],\n",
    "            \"p90_db\": mc_summary[\"p90_db\"],\n",
    "        },\n",
    "        \"Adjoint\": {\n",
    "            \"mean_linear\": adj_summary[\"mean_linear\"],\n",
    "            \"std_linear\": adj_summary[\"std_linear\"],\n",
    "            \"p10_linear\": adj_summary[\"p10_linear\"],\n",
    "            \"p90_linear\": adj_summary[\"p90_linear\"],\n",
    "            \"mean_db\": adj_summary[\"mean_db\"],\n",
    "            \"p10_db\": adj_summary[\"p10_db\"],\n",
    "            \"p90_db\": adj_summary[\"p90_db\"],\n",
    "        },\n",
    "    }\n",
    ").T\n",
    "\n",
    "comparison.style.format(\"{:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6d639979",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3bzFq1CgcO3YMgwYN0n6xhoeH63x5FObZB5ocw8LCUKVKFW27QqFARESE9pd+SenrkHvjxo3x119/ISYmBp6enqhatSpSUlL0FqdG1apVMW3aNEybNg2PHz9GgwYNsHr1auzevTvX6Yua34cffggXFxfs378frVq1wtmzZ/HVV18VOF9+67Gzs0Pnzp3x008/YeDAgbh8+XKOi1KJYaJTHqTMWFhYYNOmTZg/fz66dOmS53SdOnWCWq3Gxo0bddq//fZbcBynvVOkKMzNzYGsW/Pe16NHD4jFYixYsCDHL1HGGOLj4/Nd7pdffglzc3OMGDECr169yjE+PDxce2ul5lfl++tJTExEUFBQkfN5X6dOnXD9+nWEhIRo21JTU7F161Z4eXnlemQgu759++Lq1avYsWMHXr9+neOQdfbtIBKJUK9ePSDrVs+8aE53zJgxA7169dL5GzlyJKpXr6497aHZr9nv+inMl4m/vz9kMhnWr1+vs323b9+OxMTEQt8pUpC8Xke5efnyZa7XrygUCpw5cwYikUhbiPXp0wchISE615RoJCQkQKVSFSnOtLQ0ZGRk6LRVrVoVlpaW+e6vouSHrNdBr1698Ouvv+LHH3+ESqUq1OmOgtYzePBg/PPPP5gxYwbEYnGed4wQw0JHKEiZGjp0aIHTdOnSBW3btsVXX32FyMhI1K9fH3/++Sd+/vlnTJ48Occ59sJo1KgRAOCrr75Cv379IJVK0aVLF1StWhWLFy9GYGAgIiMj0b17d1haWiIiIgJHjx7FqFGjtPfT56Zq1arYs2cP+vbti1q1amHIkCGoW7cuFAoFrly5goMHD2r73Wjfvr32V/7o0aORkpKCbdu2wdHRETExMUXOSWPWrFnaW3InTpwIOzs77Ny5ExERETh8+LDORYp56dOnD6ZPn47p06fDzs4uxy/lESNG4M2bN/joo49QuXJlREVFYcOGDWjQoEGOIw8amZmZOHz4MD7++OM8H2zUtWtXrFu3DrGxsWjQoAH69++P77//HomJiWjRogXOnDmDJ0+eFBi/g4MDAgMDsWDBAnTo0AFdu3ZFWFgYvv/+ezRp0kRvtxtqXkcTJ05EQEBAvl92z549Q9OmTfHRRx+hXbt2cHZ2RmxsLPbu3Ys7d+5g8uTJ2luYZ8yYgV9++QWffPIJhg0bhkaNGiE1NRX37t3DoUOHEBkZqZ22MB49eoR27dqhT58+qF27NiQSCY4ePYpXr17l++VctWpV2NjYYPPmzbC0tIS5uTmaNWuW7/U8ffv2xYYNGzBv3jz4+vrm+Xp4X0HbsXPnzrC3t8fBgwfRsWPHHM/sIAZK6NtMSPn1/m2j+cl+2yjLuu1xypQpzNXVlUmlUla9enW2cuXKHLeNAWDjxo3LdZnv31rJsm7Nc3NzYyKRKMctpIcPH2atWrVi5ubmzNzcnNWsWZONGzeOhYWFFSrXR48esZEjRzIvLy8mk8mYpaUla9myJduwYQPLyMjQTvfLL7+wevXqMRMTE+bl5cWWL1/OduzYkSOe3LZJfrmFh4ezXr16MRsbG2ZiYsKaNm3Kfvvtt0LFrtGyZUsGgI0YMSLHuEOHDrH27dszR0dHJpPJmIeHBxs9ejSLiYnJc3mHDx9mANj27dvznOb8+fMMAFu3bh1jjLH09HQ2ceJEZm9vz8zNzVmXLl3Y06dPC7xtVGPjxo2sZs2aTCqVMicnJzZmzBj29u1bnWnyum00t1sss69XpVKxCRMmMAcHB8ZxXL63kCYlJbF169axgIAAVrlyZSaVSpmlpSVr3rw527ZtW47XcnJyMgsMDGTVqlVjMpmMVapUibVo0YKtWrWKKRSKIsX6+vVrNm7cOFazZk1mbm7OrK2tWbNmzdiBAwd05sl+2yjLugW1du3aTCKR6NzumX27afA8z9zd3XO91ZvlcdtoYbbj2LFjGQC2Z8+ePLcxMSwc09cVR4QQUka2b9+OESNG4OnTp6hcubLQ4ZBSMGXKFGzfvh0vX77M96FgxHDQNRSEEKMTExMDjuN0nqZIyo+MjAzs3r0bPXv2pGLCiNA1FIQQo/Hq1SscOnQImzdvRvPmzenLppyJjY3F6dOncejQIcTHx1NPvUaGCgpCiNF4+PAhZsyYgaZNm2Lbtm1Ch0P07J9//sHAgQPh6OiI9evXo0GDBkKHRIqArqEghBBCSInRNRSEEEIIKTEqKAghhBBSYuX+Ggqe5/HixQtYWlpST3WEEEJIETDGkJycDFdX1wIfklfuC4oXL17A3d1d6DAIIYQQo1WYZ76U+4JC07nU06dPtd1NlwTP84iLi4ODg0OhHmlszCjX8qei5AnKtdyiXMtWUlIS3N3ddTpqzEu5Lyg0pzmsrKz0VlBkZGTAysqqQryYKdfypaLkCcq13KJchVGYSwbK994ghBBCSJmggoIQQgghJUYFBSGEEEJKrNxfQ1EYjDGoVCqo1eoCp+V5HkqlEhkZGYKf0yptlKswxGIxJBIJ3eZMCDEqFb6gUCgUiImJQVpaWqGmZ4yB53kkJyeX+w98ylU4ZmZmcHFxgUwmEzoUQggplApdUPA8j4iICIjFYri6ukImkxX4ZaI5mlERfkFSrsLEoVAoEBcXh4iICFSvXl3wIyaEEFIYFbqgUCgU4Hke7u7uhe4G2VC+eMoC5SoMU1NTSKVSREVFQaFQwMTERNB4CCGkMOinD0C/AInBodckIcTY0KcWIYQQQkqMCgpCCCGElBgVFKTc4TgOx44dEzoMQgipUKigyINarYZSqcz1jzGW57ji/BXm+RfvGzZsGDiOwxdffJFj3Lhx48BxHIYNG6bHrQHMnz8fDRo00Nvyzp07h06dOsHe3h5mZmaoXbs2pk2bhufPn+ttHYQYm/w+dwr6U6lUev1cKu7nE6m4KvRdHvnheR7x8fE53kyMMTDGwHGcXu4GEIvFsLe3h1gsLtJ87u7u2LdvH7799luYmpoCADIyMrBnzx54eHiUOK7StGXLFowdOxZDhw7F4cOH4eXlhejoaOzatQurV6/GmjVrirVchUJBz20gRi2vz52CMMaQmJgIFLITp8Iq7ucTqZjoCEU+1Go1VCpVqf4Vt/pv2LAh3N3dceTIEW3bkSNH4OHhAT8/P51pMzMzMXHiRDg6OsLExAStWrXCjRs3tOPPnz8PjuNw5swZNG7cGGZmZmjRogXCwsIAAMHBwViwYAHu3LmjLaSCg4MBAAkJCRgxYgQcHBxgZWWFjz76CHfu3Mkz7mfPnmHixImYOHEiduzYgTZt2sDLywsffvghfvjhB8ydOxcAEB8fj/79+8PNzQ1mZmbw9fXF3r17dZbVpk0bjB8/HpMnT0alSpUQEBCQ6zrv3buHjz76CKamprC3t8eoUaOQkpJSrO1OSGkr7udOaXxe0dEJUhRUUBixzz77DEFBQdrhHTt2YPjw4Tmm+/LLL3H48GHs3LkTt2/fRrVq1RAQEIA3b97oTPfVV19h9erVuHnzJiQSCT7//HMAQN++fTFt2jTUqVMHMTExiImJQd++fQEAvXv3RmxsLE6cOIFbt26hYcOGaNeuXY5laxw8eBAKhQJffvllruNtbGyArKMtjRo1wvHjx3H//n2MGjUKgwcPxvXr13Wm37lzJ2QyGS5fvozNmzfnWF5qaioCAgJga2uLGzdu4ODBgzh9+jTGjx9fiC1MCCGksAQtKC5cuIAuXbrA1dU1zwvpHj58iK5du8La2hrm5uZo0qQJoqOjBYnX0AwaNAiXLl1CVFQUoqKicPnyZQwaNEhnmtTUVGzatAkrV65Ex44dUbt2bWzbtg2mpqbYvn27zrRLlixB69atUbt2bcyaNQtXrlxBRkYGTE1NYWFhAYlEAmdnZzg7O8PU1BSXLl3C9evXcfDgQTRu3BjVq1fHqlWrYGNjg0OHDuUa8+PHj2FlZQUXF5d8c3Nzc8P06dPRoEEDVKlSBRMmTECHDh1w4MABnemqV6+OFStWoEaNGqhRo0aO5ezZswcZGRnYtWsX6tati48++ggbN27Ejz/+iFevXhVhaxNCCMmPoNdQpKamon79+vjss8/Qo0ePHOPDw8PRqlUrfP7551iwYAGsrKzw4MEDenJgFgcHB3Tu3BnBwcFgjKFz586oVKmSzjTh4eFQKpVo2bKltk0qlaJp06Z4+PChzrT16tXT/l/zhR8bGwsLC4tc13/nzh2kpKTA3t5epz09PR3h4eG5zqO5/qQgarUa33zzDQ4cOIDnz59DoVAgMzMzxxNNGzVqlO9yHj58iPr168Pc3Fzb1rJlS/A8j7CwMDg5ORUYCyGEkIIJWlB07NgRHTt2zHP8V199hU6dOmHFihXatqpVq+a7zMzMTGRmZmqHk5KSgKyLnXie15mW53ntRZaMMZ1xebVnn6akCrOe/OYdPnw4JkyYAADYuHGjznLeX25hcpRIJDmm0ZxDfX85GsnJyXBxccG5c+dyxGZjY5NrTtWrV0diYiJevHiR71GKFStWYN26dfj222/h6+sLc3NzTJkyBQqFQme5ZmZmua4ne27Zt0tu2yS3aYWiiS23121JaF7z+lymoTLGXN//TCqKknyOFLRcfb8GS8oY92txGUKuRVm3wd7lwfM8jh8/ji+//BIBAQH4+++/4e3tjcDAQHTv3j3P+ZYuXYoFCxbkaI+Li0NGRoZOm1KpBM/z2guQssvrDarPN61mWbmtPy+aN7hKpYK/vz8UCgU4jkO7du2gUql0xnt6ekImk+HChQvo378/kJX3jRs3MGHCBJ0Lr97fDpp/Nd16SySSHNupfv36ePnyJQDAy8srR5y55dS9e3cEBgZi+fLlWLVqVY7xCQkJsLGxwaVLl9ClSxf069dPG0dYWBhq1aqlXa5m/+S2Hs0FajVq1MDOnTuRmJioPUpx4cIFiEQiVK1aVWdZmu0gdF8eyNp2miv+pVKp3pbL8zwSExPBGCv3j/c2xlxVKhUSExOLdZdHeno6UAp3eSDrx4ahMMb9WlyGkGtycnKhpzWcV0k2sbGxSElJwbJly7B48WIsX74cf/zxB3r06IFz586hdevWuc4XGBiIqVOnaoeTkpLg7u6uvQvhfRkZGUhOToZEIsnxhlEqlZBKpbm+OQt72L4wNOstyhtWJBJBJBJp4/7nn38AAHK5PMd4a2trfPHFFwgMDISDgwM8PDywcuVKpKWlYeTIkZBIJDofGtnjEYlEkEqlqFKlCiIjI3H//n1UrlwZlpaWCAgIQPPmzdG7d28sX74cPj4+ePHiBY4fP45PP/0UjRs3zhG7t7c31qxZgwkTJiA5ORlDhgyBl5cXnj17hl27dsHCwgKrV6+Gj48PDh8+jOvXr8PW1hZr1qxBbGws6tSpo41Nc8dJbttOLBZDIpFg8ODBWLhwIUaMGIF58+YhLi4OU6ZMweDBg+Hm5pZjPn1+eZeERCKBSCSCvb29Xk/x8TwPjuPg4OBQIT6MjS1XpVIJFPEHBt77YWJlZaXXgkIikcDBwcFg3hcw0v1aXIaQa1E+fwy2oNAcZunWrRumTJkCAGjQoAGuXLmCzZs351lQyOVy7Rfr+zRfstnbNF9K2d+Emvuvs3v/6IS+3riaOIpKM4+1tXW+45cvXw7GGIYMGYLk5GQ0btwYJ0+ehJ2dnc5072+H7P/26tULR48exUcffYSEhAQEBQVh2LBh+P333/HVV1/hs88+Q1xcHJydnfHhhx/C2dk5z5zGjRuHGjVqYNWqVejRowfS09Ph5eWFTz75BFOnTgXHcZgzZw4iIiLQoUMHmJmZYdSoUejevTsSExN1lpvX80A07ebm5jh58iQmTZqEpk2bwszMDD179sSaNWt05nu/SDSEIxSa+HN73epj2aWxXENkbLm+/5lUVHl9lpWEoW4/Q42rNAida1HWyzFDOGGctdGOHj2qPZ2hUChgbm6OefPm4euvv9ZON3PmTFy6dAmXL18u1HKTkpJgbW2NxMTEXI9QREREwNvbu9BVmCF1c13aKFfhFOe1WRg8zyM2NhaOjo7l/sPYGHNVKpWIjY0t1hGKpKSkUjlC4ejoaHBHKIxtvxaXIeSa33dodga7N2QyGZo0aaJ9uJLGo0eP4OnpKVhchBBCCMlJ0FMeKSkpePLkiXY4IiICoaGhsLOzg4eHB2bMmIG+ffviww8/RNu2bfHHH3/g119/xfnz54UMmxBCCCHZCFpQ3Lx5E23bttUOay6mHDp0KIKDg/Hpp59i8+bNWLp0KSZOnIgaNWrg8OHDaNWqlYBRE0IIISQ7QQuKNm3aFHgL5meffYbPPvuszGIihBBCSNEZ7DUUhBBCCDEeVFAQQgghpMSooCCEEEJIiVFBQQghhJASo4KCEEIIISVGBUUFMn/+fDRo0CDP4YJERkaC4ziEhoaWUoSG7/z58+A4DgkJCUKHQgghBoUKCiMWEhICsViMzp07F2v+6dOn48yZM4We3t3dHTExMahbt26h5ylK0ZKUlIQ5c+agTp06MDU1hb29PZo0aYIVK1bg7du3hV5naWrRogViYmLy7D+FEEIqKioojNj27dsxYcIEXLhwAS9evCjy/BYWFrl2gJYXsVgMZ2fnUunK+M2bN/jf//6HoKAgTJ8+HdeuXcPt27exZMkS/P3339izZ4/e11kcMpks347PCCGkoqKCwkilpKRg//79GDNmDDp37ozg4OAc0yxbtgxOTk6wtLTE559/joyMDJ3x2Y8e8DyPhQsXonLlypDL5fDz88PJkye147Of8tAc/j9z5gwaN24MMzMztGjRQtv/SnBwMBYsWIA7d+5oe0HMLU4AmD17NqKjo3H9+nUMHz4c9erVg6enJ9q3b4+9e/di7Nix2ml//PFHNG7cGJaWlnB2dsaAAQMQGxurHR8cHAwbGxud5R87dkynCLhz5w7atm0LS0tLWFlZoXHjxrh16xYAICoqCl26dIGtrS3Mzc1Rp04d/P777zo5a055xMfHo3///nBzc4OZmRl8fX2xd+9enXW3adMGEydOxJdffgk7Ozs4Oztj/vz5+e5fQggxNgbbfbnQ4oOC8SaPL7/3u7o2qV0b7pu+1xn/dMxYZPzzT4HrsBs2DPbDhxUrvgMHDqBmzZqoUaMGBg0ahMmTJyMwMFAb14EDBzB//nx89913aNWqFX788UesX78eVapUyXOZ69atw+rVq7Flyxb4+flh+/bt6NGjB+7fvw8fH5885/vqq6+wevVqODg44IsvvsBnn32Gy5cvo2/fvrh//z7++OMPnD59Gsijq3We57F//34MGjQIrq6uua7j/WJAqVRi0aJFqFGjBmJjYzF16lRtV+qFNXDgQPj5+WHTpk0Qi8X4+++/tUdexo0bB4VCgQsXLsDc3Bz//PMPLCwscl1ORkYGGjVqhJkzZ8LKygrHjx/H4MGDUbVqVTRt2lQ73c6dOzF16lRcu3YNISEhGDZsGFq2bImPP/640DETQogho4IiD3xKClSvXhU4ndrZOWfbmzeFmpdPSSl2fNu3b8egQYMAAB06dEBiYiL++usvtGnTBgCwdu1afP755/j8888BAIsXL8bp06dzHKV436pVqzBz5kz069cPALB8+XKcO3cOa9euxffff5/nfEuWLEHr1q0BALNmzULnzp2RkZEBU1NTWFhYQCKRwDmX7aQRFxeHhIQE1KhRQ6e9UaNG2qMdXbp00f7yf/9R7FWqVMH69evRpEkTpKSk5PnFn110dDRmzJiBmjVrAgCqVaum7TI6OjoaPXv2hK+vr3YdeXFzc8P06dO1wxMmTMDJkydx4MABnYKiXr16mDdvHgCgevXq2LhxI86cOUMFBSGk3KBTHnkQWVhA4uSU65/Y0fH//29nl2NesZ1dnvO+/ycq5JdfdmFhYbh+/Tr69+8PAJBIJOjbty+2b9+unebhw4do1qyZznzNmzfPc5lJSUl48eIFWrZsqdPeokUL/Pvvv/nGU69ePe3/XVxcAEDnFERxHT16FKGhoQgICEB6erq2/datW+jSpQs8PDxgaWmpLWaio6MLveypU6dixIgR8Pf3x7JlyxAeHq4dN3HiRCxevBgtW7bEvHnzcPfu3TyXo1arsWjRIvj6+sLOzg4WFhY4efJkjlje30bI2k762EaEEGIo6AhFHuyH5346gjEGlUoFiUSS54V52U+B6Nv27duhUql0Tg8wxiCXy7Fx48YyvwNBKpVq/6/ZJjzPF3p+BwcH2NjYaI9GaHh4eAAALC0ttdcspKamIiAgAAEBAfjpp5/g4OCA6OhoBAQEQKFQAABEIlGOTueUSqXO8Pz58zFgwAAcP34cJ06cwLx587B792706tULI0aMQEBAAI4fP44///wTS5cuxerVqzFhwoQcsa9cuRLr1q3D2rVr4evrC3Nzc0yePFkbS27bSLOdirKNCCHE0NERCiOjUqmwa9curF69GqGhodq/O3fuwNXVVXtaoFatWrh27ZrOvFevXs1zuVZWVnB1dcXly5d12q9cuYJatWoVO16ZTAa1Wp3vNCKRCH369MHu3bsLvFvl33//RXx8PJYtW4YPPvgANWvWzPFL38HBAcnJyUhNTdW25fbsDB8fH0yZMgV//vknevTogZ07d2rHubu744svvsCRI0cwbdo0bNu2Ldd4Ll++jG7dumHQoEGoX78+qlSpgkePHuWbAyGElEdUUBiZ3377DW/fvsXnn3+OunXr6vz17NlTe9pj0qRJ2LFjB4KCgvDo0SPMmzcPDx48yHfZM2bMwPLly7F//36EhYVh1qxZuHPnDiZNmlTseL28vBAREYHQ0FC8fv0amZmZuU73zTffwM3NDU2bNsWOHTtw9+5dhIeH4+jRo9rnbSDrqIVMJsOGDRvw33//4ZdffsGiRYt0ltWsWTOYmZlh9uzZCA8Px549e3TuLklPT8f48eNx/vx5REVF4fLly7hx44b2eorJkyfj5MmTiIiIwO3bt3Hu3Lk8i6rq1avj1KlTuHLlCh4+fIjRo0fjVSGunyGEkPKGCgojs337dvj7++d6WqNnz564efMm7t69i759+2LOnDn48ssv0ahRI0RFRWHMmDH5LnvixImYOnUqpk2bBl9fX5w8eRJHjhxB9erVix1vz5490aFDB7Rt2xYODg45bqnUsLe3x/Xr1zFkyBCsXLkSTZs2ha+vL+bPn4++fftqjxA4ODggODgYBw8eRO3atbFs2TKsWrVKZ1l2dnbYvXs3fv/9d+1tnO/fpikWixEfH48hQ4bAx8cHffr0QYcOHbQXTarVaowbNw61atVChw4d4OPjk+dFqV9//TUaNmyIgIAAtGnTBs7OzujevXuxtxchhBgrjmU/2VzOJCUlwdraGomJibCystIZl5GRgYiICHh7e8PExKRQyyvMNRTGIjAwEBcvXsSlS5dyHV+eci2IoeVanNdmYfA8j9jYWDg6OkIkKt+/J4wxV6VSidjYWO0dR4XFGENSUhKsrKz0+vqVSCRwdHTMcQ2QkIxxvxaXIeSa33doduV7b5BcMcYQHh6OM2fOoE6dOkKHQwghpByggqICSkxMRO3atSGTyTB79myhwyGEEFIO0G2jFZCNjU2eF0cSQgghxUFHKAghhBBSYlRQZF1TQIghodckIcTYVOiCQnPlclpamtChEKJD85o0pKvrCSEkPxX6GgqxWAwbGxvtkxbNzMwKvOXK0G4vLE2UqzBxpKWlITY2FjY2NtoHehFCiKETtKC4cOECVq5ciVu3biEmJgZHjx7N86FAX3zxBbZs2YJvv/0WkydP1lsMml4wC9tRE2MMPM9DJBJViC9ZylUYNjY2+fbQSgghhkbQgiI1NRX169fHZ599hh49euQ53dGjR3H16lWdzrD0heM4uLi4wNHRMUcHUrnheR7x8fGwt7evEA9VoVzLnlQqpSMThBCjI2hB0bFjR3Ts2DHfaZ4/f44JEybg5MmT6Ny5c6nFIhaLC/UhzvM8pFIpTExMBP/iKW2UKyGEkMIy6GsoeJ7H4MGDMWPGjEI/0TEzM1PnGQtJSUnaZemju2ie57WHx8s7yrX8qSh5wkhz1cRc1Lt8NPPo++4gzfYzpG1ojPu1uAwh16Ks26ALiuXLl0MikWDixImFnmfp0qVYsGBBjva4uDhkZGSUOCae55GYmAjGWLn/JUu5lj8VJU8Yaa4qlQqJiYlQq9VFmo8xhvT0dCDrNK6+aI7aSiSG81VhjPu1uAwh1+Tk5EJPazivkmxu3bqFdevW4fbt20V6gwQGBmLq1Kna4aSkJLi7u8PBwaHAjk0Kg+d5cBwHBweHCvFiplzLl4qSJ4w0V811XMXpHAxAqXQO5uDgYFC3Lxvjfi0uQ8i1KJ0TGmxBcfHiRcTGxsLDw0PbplarMW3aNKxduxaRkZG5zieXyyGXy3O0i0Qive0QjuP0ujxDRrmWPxUlTxhhrpq7jIpTFGjm02dBYajbz1DjKg1C51qU9RpsQTF48GD4+/vrtAUEBGDw4MEYPny4YHERQgghJCdBC4qUlBQ8efJEOxwREYHQ0FDY2dnBw8MD9vb2OtNLpVI4OzujRo0aAkRLCCGEkLwIWlDcvHkTbdu21Q5rrn0YOnQogoODBYyMEEIIIUUhaEHRpk2bIt3mlNd1E4QQQggRVvm/ooUQQgghpY4KCkIIIYSUGBUUhBBCCCkxKigIIYQQUmJUUBBCCCGkxKigIIQQQkiJUUFBCCGEkBKjgoIQQgghJUYFBSGEEEJKjAoKQgghhJQYFRSEEEIIKTEqKAghhBBSYlRQEEIIIaTEqKAghBBCSIlRQUEIIYSQEqOCghBCCCElRgUFIYQQQkqMCgpCCCGElJhE6AAIIUQIarUaPM8LHYYOxpjQIRBSbFRQEEIqJJ7nER8fD7VaLXQoAACZTAZra2uhwyCk2KigIIRUWGq1GiqVSugwAABisVjoEAgpEbqGghBCCCElRgUFIYQQQkqMCgpCCCGElBgVFIQQQggpMUELigsXLqBLly5wdXUFx3E4duyYdpxSqcTMmTPh6+sLc3NzuLq6YsiQIXjx4oWQIRNCCCEkF4IWFKmpqahfvz6+++67HOPS0tJw+/ZtzJkzB7dv38aRI0cQFhaGrl27ChIrIYQQQvIm6G2jHTt2RMeOHXMdZ21tjVOnTum0bdy4EU2bNkV0dDQ8PDzKKEpCCCGEFMSonkORmJgIjuNgY2OT5zSZmZnIzMzUDiclJQFZD7HRx1PxeJ4HY8zgnrBXGijX8qei5IlC5KoZbyhPp9TEWpyYNPPoOxdNTIb0eqHXcNnHUFhGU1BkZGRg5syZ6N+/P6ysrPKcbunSpViwYEGO9ri4OGRkZJQ4Dp7nkZiYCMYYRKLyfU0r5Vr+VJQ8UYhcVSoVEhMTDeZJmVKpFCqVCklJSUWOiTGG9PR0AADHcXqLSfOwLYnEcL4q6DVctpKTkws9reG8SvKhVCrRp08fMMawadOmfKcNDAzE1KlTtcNJSUlwd3eHg4NDvoVIYfE8D47j4ODgUCFezJRr+VJR8kQhclUqlUBWYWEI5HI5bG1twXFckWPSHJmwsrLSa0EhkUjg4OAAqVSqt2WWFL2Gy5aJiUmhpzX4gkJTTERFReHs2bMFFgVyuRxyuTxHu0gk0tsO4ThOr8szZJRr+VNR8kQBuYpEInAcp9cv4JLQxFrcmDTz6TMfQ32tGGpcpUHoXIuyXoMuKDTFxOPHj3Hu3DnY29sLHRIhhBBCciFoQZGSkoInT55ohyMiIhAaGgo7Ozu4uLigV69euH37Nn777Teo1Wq8fPkSAGBnZweZTCZg5IQQQgh5n6AFxc2bN9G2bVvtsObah6FDh2L+/Pn45ZdfAAANGjTQme/cuXNo06ZNGUdLCCGEkLwIWlC0adMm39ucDOV2LkIIIYTkr/xf0UIIIYSQUkcFBSGEEEJKjAoKQgghhJQYFRSEEEIIKTEqKAghhBBSYlRQEEIIIaTEqKAghBBCSIlRQUEIIYSQEqOCghBCCCElRgUFIYQQQkqMCgpCCCGElBgVFIQQQggpMSooCCGEEFJiVFAQQgghpMSooCCEEEJIiUmEDoAQQso7lpEB/vET8JGRYDExYC9egL15A1HNGpCNHaszbdqChVBHR4OzsgTn6ASRqws4NzeIqlcH5+oKjuMEy4OQ/FBBQQgpdWq1GjzPl+k6eZ6HSqWCUqmESJTzYCxjrNTWzdLTwd+5C/WtW1DfuwsWFQ3klr+paY4m/ulTsMhIvIvuHtTvj7SwgKhGDYj9/CBu0RwiN7dSy4GQoqKCghBS6nieR3x8PNRqdSGm1g/GGBITEwEgx696mUwGa2vrUlu38oftUP32W/4TcRygUuVsl0kBuRzIzMw5LiUF/K1b4G/dApQKiAYM0F/QhJQQFRSEkDKhVquhyu0LtJQwxrTrzF5QiMViva2Hf/wYnLc3OMn/f5yKGjUENAWFSATOywuiGjUgqlYNospu705d2NuDyyUOi++/h0qlAktPB4uJAR8TAxYVBT7sEdSPwoA3b7PW0Uh3xuRkqM6eheTjj8FZWOgtP0IKiwoKQggpIsbzUF+5AtWBg+DDwiCb8zUkrVppx4vr14ekUyeIGjeCuEEDcObmRV4HZ2oKrkoViKpUAVq2fLdexsCePoM69G+IqlXTmV507jyUu3dDGRQMib8/JL17QeTioodsCSkcKigIIaSQmFoN9fnzUO7bDxYdrW1X/fqbTkHBmZtDNmmi3tfPcRw4D3eIPNx142IMotOn3w1kZkJ1/DhUJ05A3LYNpP37Q+TunvsCCdEjKigIIaQAjDHwN25AsWMHWESkzjjO2xuSNq3BGBPsDgyO46D6cgZMzv8F9alTQHo6wPNQnzkL9bnzkHToAOmggeDs7QWJj1QMVFAQQkg++KdPofjue/B//63TLqpbF9J+/SBq3MgwbuWsXBmysWOAIYOh+vVXKI8eA5KSAJ6H6vffoTpzBtJ+/SDp1ROcTCZ0tKQcooKCEELywEc/RcbYsYBSqW0T+fhAOuJziOvXFzS2vHCWlpAOGADJp59CdfQolAcPAWlpQGYmlHv2QNz6Q3B0uykpBVRQEEJIHjj3yhD5+YG/fh2ck9O7QuKDDwzjiEQBOFPTd4VFp05Q/rQHqt9+g6RXT3p2BSk1gj56+8KFC+jSpQtcs57+duzYMZ3xjDHMnTsXLi4uMDU1hb+/Px4/fixYvISQ8o1le04Gx3GQjRsLSb9+MNm6BZIPPzSKYuJ9nI0NZOPGwuT77yDt109nHFOpoL53X7DYSPkiaEGRmpqK+vXr47vvvst1/IoVK7B+/Xps3rwZ165dg7m5OQICApCRkVHmsRJCyjf+yRNkfDEG6lu3dNpFzs6QDR8GzsREsNj0QeTtnSMH1b59yJw+HYr1G8DS0wWLjZQPgp7y6NixIzp27JjrOMYY1q5di6+//hrdunUDAOzatQtOTk44duwY+mWrtAkhpDgYz0N1+DCUwTsBlQqZq1bBdPNmcKX4JE1DwEc/hXLPXgCA6vhxqO/dg/zrryDy9BQ6NGKkDPYaioiICLx8+RL+/v7aNmtrazRr1gwhISF5FhSZmZnIfO+RtUlJSUDWo3/10ZcAz/PvbiEr434JhEC5lj9C5alZb2n2n5GdZn25rVOzDfikJGR+sxT8jRvacZydPfjUVIisrMos1vdjKs52yi/XPLm5QvrFF1D+8AOQmQkWHY2MCRMhnTQRko8+0onJkN4XFeW9CgPJtSjrNtiC4uXLlwAAJycnnXYnJyftuNwsXboUCxYsyNEeFxenl1MlPM8jMTHx3YNkculwqDyhXMsfofJUqVRITEws87480rMO42e/7kEqlSLz8WOkzZ0HLuvzhHEc+K5dwfftA4VE8u6WyzIklUqhUqmQlJRU5O2UX675av0h4FMdkjVrwEVFv7sTZMVKZPwdCn7oEIizOi+TSAznq6KivFdhILkmJycXelrDeZXoSWBgIKZOnaodTkpKgru7OxwcHGClh18cPM+D4zg4ODhUiBcz5Vq+CJWnMuu2y7LuywMArKyscnzJcpevIH3lSnCaHxlWVpDPmgVxQ78yiy87uVwOW1vbdw+pKuJ2yi/XAllZga1fD+V330H95ykAgPjUKUiiomC2aCEcHBwglUqLtsxSVFHeqzCQXE2KcO1QsQqK//77D1WqVCnOrIXm7OwMAHj16hVc3nse/atXr9CgQYM855PL5ZDL5TnaRSKR3nYIx3F6XZ4ho1zLHyHyFIlE7x4bXcZ3SGjWqVkv43kog4Oh2n/g/6epVg3yuXMgynY0tKxp9ktxt1P2XIs0r4kJ5NOmQVW3LhQbvwMUCrBHj5A2YQJU236AvIZPkZdZmirKexUGkGtR1lusCKtVq4a2bdti9+7dpXbHhbe3N5ydnXHmzBltW1JSEq5du4bmzZuXyjoJIeUbe/YMqiNHtcPS9u1hsma14MWEoZAEBMBk7bfgHBwAAJypGcQOlYQOixiJYhUUt2/fRr169TB16lQ4Oztj9OjRuH79epGXk5KSgtDQUISGhgJZF2KGhoYiOjoaHMdh8uTJWLx4MX755Rfcu3cPQ4YMgaurK7p3716csAkhFZzIwwOyL2cAYjEqzZoJky9ngMvliGZFJqpaFSbr10HUtCnMFi+C2NJS6JCIkShWQdGgQQOsW7cOL168wI4dOxATE4NWrVqhbt26WLNmDeLi4gq1nJs3b8LPzw9+fu/OW06dOhV+fn6YO3cuAODLL7/EhAkTMGrUKDRp0gQpKSn4448/inROhxBC3if58ENY7d4N6wEDjO4hVWWFs7ODyaKFELm66rTzaWlleqcOMS4lOikjkUjQo0cPHDx4EMuXL8eTJ08wffp0uLu7Y8iQIYiJicl3/jZt2ujc7qT5Cw4OBrLOHS1cuBAvX75ERkYGTp8+DR8fwzqXRwgxYJGRUGV7Ai8AiF2cBQnHmPHp6YgeMRIxX38NVoYX1xLjUaKC4ubNmxg7dixcXFywZs0aTJ8+HeHh4Th16hRevHihfSAVIYSUNf7hQ0gWLIRy8xYof/5F6HCMGmMMz6dOQ/rt20g8fATPJk8G/97zfghBcQuKNWvWwNfXFy1atMCLFy+wa9cuREVFYfHixfD29sYHH3yA4OBg3L59W/8RE0JIAdShocgMnA0uNfXd8PnzOfrpIIXHcRysu3UFsm4fTTl9Bk9HjoI6JUXo0IgBKVZBsWnTJgwYMABRUVE4duwYPvnkkxy3ljg6OmL79u36ipMQQgpFffUaMr+eA2TdgSZqUB/yb5aAE4uFDs2oWXXoAPfNm8CZmQEA0q5fx9MRI6EuwoOPSPlWrILi1KlTmDlzps7zIZB1WCw6OhoAIJPJMHToUP1ESQghhaC6fBmZCxcCWQ/S4hs1gmzhQnBZT3wkJWPRsiU8g4MgzurnJD00FNEjRlBRQYDiFhRVq1bF69evc7S/efMG3t7e+oiLEEKKRBUSAsWSb4CsUxviNm2gnjYVnEwmdGjlimm9evDYGQyxjQ0AIOPOXUR/PgLqMn5UOTE8xSoo8rptKCUlhW7pJISUOfXVa1AsXvL/xYS/P6QzpgMG1AdFeWJSs+a7osLWFgCQcfcuoj/7nIqKCq5I7zZNHxkcx2Hu3LkwyzqXBgBqtRrXrl3L97HYhBCibywzE4r164GsWxnFH7WFbOoUoAI8lllIJjVqwGNnMKKHDYf6zRuIbWzoIWEVXJEKir///hvIOkJx7949yN47lCiTyVC/fn1Mnz5d/1ESQkgeOLkc8sWLkDErEGK/BpBNnw5OLKYHMJUBEx8feO4MxustW+GyeBFEVFBUaEUqKM6dOwcAGD58ONatW6eX3jsJIaSkRFWqwGT9OnAODnQ3RxmTV68Ot1UrhQ6DGIBiHRMMCgqiYoIQIhj25k2OIxAiZ2cqJgyE8lUsYletoidqVjCFPkLRo0cPBAcHw8rKCj169Mh32iNHjugjNkIIyYF/9QqZU6dB1KQJZBPGUxFhYBTPniN6+HAonz6F6s1buCxZTH2mVBCFLiisra21LwrrrHuQCSGkLLGEBGTOng32+jXUJ05AaWMN2bBhQodF3qOIioTy5UsAQOKRI5A4OsBx8mShwyJloNAFRVBQUK7/J4SQssBSU5Hx1ddgz54DALjKbpB27y50WCQbi5Yt4bZyBZ5PmQowhvjNWyB1coJt//5Ch0ZKWbGuoUhPT0daWpp2OCoqCmvXrsWff/6pz9gIIQQAwBQKZM5fAPbkCQCAq1QJ8qVLwWU9XIkYFqsOHeA0e7Z2+OWixUg+fVrQmEjpK1ZB0a1bN+zatQsAkJCQgKZNm2L16tXo1q0bNm3apO8YCSEVGON5KFavAX/37rsGKyvIl34DkaOj0KGRfNgNHgT7kSPeDfA8nk+bjjTqMLJcK1ZBcfv2bXzwwQcAgEOHDsHZ2RlRUVHYtWsX1q9fr+8YCSEVmHLXLqjPn383IJdDvmgRRB4eQodFCsFhyhRYde0CZD2A7OmYscgMDxc6LFJKilVQpKWlwdLSEgDw559/okePHhCJRPjf//6HqKgofcdICKmgVH/8AdXefe8GOA6ywFkQ16whdFikkDiRCK6LF8O8ZUsAAJ+YiOiRI6HKpS8oYvyKVVBUq1YNx44dw9OnT3Hy5Em0b98eABAbG0vPpyCE6AVTKqE8eEg7LB3zBSTNmwsaEyk6TiaD27p1MKldGwBg0bq1tmMxUr4Uq6CYO3cupk+fDi8vLzRr1gzNs97kf/75J/z8/PQdIyGkAuKkUpisXgVRrZqQfPoppN26CR0SKSaxhTnct2yG86KFcJ47Fxx12lYuFWuv9urVC61atUJMTAzq16+vbW/Xrh0+/fRTfcZHCKnAOBsbyJcvp15DywGJgwNse/cWOgxSior9LnV2doazs7NOW9OmTfUREyGkgmKZmYBYrPMLlnqwLL8ywsKgCA+HVadOQodC9KBYBUVqaiqWLVuGM2fOIDY2FjzP64z/77//9BUfIaSCYIxBsXIVWHIy5F99Bc7KUuiQSClKPncOL6ZNB69UQlypEszpB6nRK1ZBMWLECPz1118YPHgwXFxc6DnthJASU/20B+qLFwEAGYGzYLJhAzhRsS7zIkYg9UoI+KwHJD6fMBFe+/dB5uUldFikBIpVUJw4cQLHjx9Hy6xbgQghpCRUFy9B+eOP7wY4DrLBQ6iYKOecZn4JRVQkUi9chDoxEU+/GAOvA/shpjsFjVax3rG2traws7PTfzSEkAqHDw+HYuVK7bD0s88g/l8zQWMipY+TSOC2Zg3k1asBABSRkXg+fTqYWi10aKSYilVQLFq0CHPnztXpz6M0qNVqzJkzB97e3jA1NUXVqlWxaNEiMMZKdb2EkLLB3r5F5rz5QGYmAEDcrh0kvXsJHRYpI2ILC1TetBliW1sAQOqFi4hbu07osEgxFeuUx+rVqxEeHg4nJyd4eXlBKpXqjL+tp+e1L1++HJs2bcLOnTtRp04d3Lx5E8OHD4e1tTUmTpyol3UQQoTBFApkLlgIFhcHABDVrAnZ5El0TVYFI6vsBre1axH92WeAWo34bdtgUrsWrDp2FDo0UkTFKii6l1GXwVeuXEG3bt3QuXNnAICXlxf27t2L69evl8n6CSGlgzEGxXffgX/4END0Hjp3DjiZTOjQiADMmzWF06xZeLVkCQDgxeyvIPP2hknNmkKHRoqgWAXFvHnz9B9JLlq0aIGtW7fi0aNH8PHxwZ07d3Dp0iWsWbMmz3kyMzORmXX4FACSkpIAADzP57i9tTh4ngdjTC/LMnSUa/kjVJ6a9WpOV6ouXID6j5PvRspkkM2bC9jZ6fV0pmZ9uS1Tsw3yGi+EksSUX676iKksXi/WA/oj/cEDJB07BqZQIP3+A8h8fHJMV1HeqzCQXIuy7mI/2CohIQGHDh1CeHg4ZsyYATs7O9y+fRtOTk5wc3Mr7mJ1zJo1C0lJSahZsybEYjHUajWWLFmCgQMH5jnP0qVLsWDBghztcXFxyMjIKHFMPM8jMTERjDGIyvlV6JRr+SNUniqVComJiVBrLrirXRviNq0hOv8XVKNHQenkBGQV//rCGEN6ejoA5DiNIpVKoVKpkJSU9P8xCawkMeWXa0mIxWIAgKSMnlQqGjMGkpgYmPTpjcxGjRAbG5tjmoryXoWB5JqcnFzoaTlWjJL27t278Pf3h7W1NSIjIxEWFoYqVarg66+/RnR0NHbt2lXUReZq3759mDFjBlauXIk6deogNDQUkydPxpo1azB06NBc58ntCIW7uzvevn2rl47LeJ5HXFwcHBwcKsSLmXItX4TKU6lUIi4uDiqVStvGGAP/4AHEdeuWyjoZY0hKSoKVlVWOL1m5XA5bW1u8fv1aJyYhlSSm/HItCYlEAgcHhxzXyQmporxXYSC5JiUlwdbWFomJiQV+hxar7Jw6dSqGDRuGFStWaLsxB4BOnTphwIABxVlkrmbMmIFZs2ahX79+AABfX19ERUVh6dKleRYUcrkc8lwe1SsSifS2QziO0+vyDBnlWv4IkadIJALHcTpfdhzHQeTrW6rr1awz+5esZhvkNk4oJY0pr1z1EZPQ7wnV69eQVKpkcHGVBaFzLcp6ixXhjRs3MHr06Bztbm5uePnyZXEWmau0tLQcyYjF4gpx7oyQ8iZz7z7wz54JHQYxIowxvN62DU8+bo/0e/eFDocUoFgFhVwu117s+L5Hjx7BwcFBH3EBALp06YIlS5bg+PHjiIyMxNGjR7FmzRrq0ZQQI5P088/I/OEHZEyYCNXVq0KHQ4xE4pEjiFu9Biw9Hc8nTYLq7VuhQyL5KFZB0bVrVyxcuBBKpRLIOiQTHR2NmTNnomfPnnoLbsOGDejVqxfGjh2LWrVqYfr06Rg9ejQWLVqkt3UQQkpX+v0HiFuY9Z5NSwMSEoQOiRgJ6y5dYNqgAQBA+eIFXnw5k56kacCKVVCsXr0aKSkpcHBwQHp6Olq3bo1q1arB0tISS7LuI9YHS0tLrF27FlFRUUhPT0d4eDgWL14MGd2rTohRUL19i2cTJ4ApFAAASadOkHToIHRYxEhwMhnc1n4LcVZXD6kXLyJ+02ahwyJ5KNZFmdbW1jh16hQuX76MO3fuICUlBQ0bNoS/v7/+IySEGCWmVuPFtOlQvYgBAIhr1YJ0zBdCh0WKQHORqOZotCDs7eG0YjlejBoN8DziN22CmXtloFs34WIiuSpyQcHzPIKDg3HkyBFERkaC4zh4e3vD2dkZjDGDuWKaECKs199vQuqVKwAAsb09TOfPA09HF40Kx3HgeR5v3rwR9nkd3t6QfzYcmT9sBxhD2jdLoWzcGHJ3d+FiIjkU6ZQHYwxdu3bFiBEj8Pz5c/j6+qJOnTqIiorCsGHD6GJJQggAIOXiRbz+/vt3AyIRnFeuhOi92/6IcVGr1VCpVIL+iXr1grh583cBJSfj+eQp4N975hARXpEKiuDgYFy4cAFnzpzB33//jb1792Lfvn24c+cOTp8+jbNnz+rtoVaEEOOkfPECL2Z8CWQ9M89hymSYNmksdFjEyHEcB9mM6eDcXAEAmQ8eIHbFygLnI2WnSAXF3r17MXv2bLRt2zbHuI8++gizZs3CTz/9pM/4CCFGJjXkKtSJiQAAi7ZtYf/550KHRMoJztwcpvPmAXI5ZN7esO3fT+iQyHuKVFDcvXsXHfK5Qrtjx464c+eOPuIihBgpm5494L51C0x8feG6bCm4CvA0Q1J2xFWqwHTJEnjs3wd5tWpCh0PeU6SLMt+8eQMnJ6c8xzs5OeEtPXiEkArP4oMPYN6qFV2kTUqFpEF9iC0shA6DZFOknw5qtTrfXufEYrHBdLRDCCk7LJf3PRUTpKzwCgXe7t0LRt0yCKpIRygYYxg2bFiunW8hq6dPQkjFwqelIXLgINh82h22gwdTIUHKlPL5czybPAUZ9+5BnZiESl/k7GeKlI0iFRR59fD5viFDhpQkHkKIEWGMIWbefGQ+fIhXDx9CFfcajtOmCh0WqUAyIyORcf9dx2Fx69fDtH49mGtuLyVlqkgFRVBQUOlFQggxOgn79yPp118BACJzc1j3oGfRkLJl0bIlKo0fh9cbNgI8j+fTZ8D7yGFI87nej5QOuvyaEFIs6ffu49WSb7TDLksWQ+7tLWhMpGKqNGYMzFu1AgCo4+PxfMpUMCEfF15BFasvD0LIO2q1GryBXQgmEokgFotLdR3qhAQ8nzRJ+6FtO2QwrKjTLyIQTiSC68oViOjRE6qYGKTfvo3Y1WvgNGum0KFVKFRQEFICPM8jPj5e2H4O3iMWi2Fvb1+qBQXjebyYOQvKFy8AAKb168Np+vRSWx8hhSGxtUXldWsROXAQoFTiTXAwTBv6wap9e6FDqzCooCCkhDT9HFQU8dt+QMpffwEAxLa2cFv7LTjq9IsYANN69eA0ayZeLVoMAIiZ/RVMfHwg8/ISOrQKga6hIIQUWurVa4hbt+7dAMfBdeVKSF1chA6LEC3bAQNg1akTAIBPSUH8DrqZoKzQEQpCSKGJra0grVwZyuhoVBo7FhatWgodEiE6OI6Dy6KFyHgUBovWreE4ZYrQIVUYVFAQQgrNpFYteB8+hLc//QT7kSOFDoeQXInMzeF98CBEpqZCh1KhUEFBCCkSsaUlKn3xhdBhEJIvKibKHl1DQQjJV+bjx3RPPzF66Q8eIGr4cKgTE4UOpdyigoIQkiflixeIGjQYUcOGQ/nqldDhEFIsyadPI6r/AKSFXMWLWYHUiVgpoYKCEJIrplDg2ZQpUCcmIv3WLcStXSd0SIQUi0mtWtpTICnnziF++3ahQyqXqKAghOTq1apVyLhzFwAgrVyZnjpIjJbUzQ2uq1YCWT3hxn27FqnXrgsdVrlDBQUh5YhIJALHcVAqlXn+qVSqfMcrlUq8PX4cb3f9+G6hUimcVq0Cb2ZW4Hx5/THGhN40pIKz+OADVBqTdTExz+P5tGlQxsYKHVa5YvB3eTx//hwzZ87EiRMnkJaWhmrVqiEoKAiNGzcWOjRCDA7HceB5Hm/evMn1ceCMMSRmXZTGZf1ay0797BlS58zVDpuMHYMkh0pIKuaHr0wmg7W1dbHmJUSfKo0bh/TQO0i9cgXq16/xYuo0eAQHgZMY/FehUTDoIxRv375Fy5YtIZVKceLECfzzzz9YvXo1bG1thQ6NEIOmeRx4bn/5jVOmpiJtwUIgLQ0AIG7TBlzHjnlOX5g/Q+nnhBBOLIbrqpWQZHVtnnbzJuLWrhU6rHLDoMuy5cuXw93dHUFB///oVG/qHpmQUqP4fhPYf/8BADh3d8gmT8rzSAYhxkhiZwe3td8iavAQQKVC/A/bYernB8t27YQOzegZdEHxyy+/ICAgAL1798Zff/0FNzc3jB07FiPzeUJfZmYmMjMztcNJSUlAVq+Q+uhmmud5MMYMrsvq0kC5Fn4+Q7lGQJNDXjFp2nMbpw4JgfqPP94NyOWQffUVYGJS4twKiqm05JerUDHlpyQx5ZerUDGVFk0cJflcMqlfH44zpiN26TJAIoHy9WuD/JwzhM/goqzboAuK//77D5s2bcLUqVMxe/Zs3LhxAxMnToRMJsPQoUNznWfp0qVYsGBBjva4uDhkZGSUOCae55GYmAjGGEQigz5jVGKUa8FUKhUSExMN5rC+VCqFSqVCUlJSntdQpKenA7ldQ1GjBkQdOkD8xx9QjRwBpZ0tkFWQl2ZMpSW/XIWKKT8liSnf/SpQTKVF8/4Ui8Ul+lxi7dtD/ugxZB99BEWd2og1wAs0DeEzODk5udDTcsxQys5cyGQyNG7cGFeuXNG2TZw4ETdu3EBISEiu8+R2hMLd3R1v376FlZVViWPieR5xcXFwcHCoEF+ylGv+lEol4uLiDKb7crlcDltbW7x+/TrXmBhjSEpKgpWVVZ5fPPyjRxD5+JRZTKUlv1yFiik/JYmpMPu1rGMqLWKxGADg4uJCn0tlICkpCba2tkhMTCzwO9Sgj1C4uLigdu3aOm21atXC4cOH85xHLpdDLpfnaBeJRHrbIRzH6XV5hoxyzZ/mNk1Duc5Ak0N+MWnG5TVeXKNGmcdUWvLKVciY8lLSmArar0LEVBo0cZTW55IyNhZSR0e9L7e4hP4MLtLnYalGUkItW7ZEWFiYTtujR4/g6ekpWEyElCeq8+fBR0YKHQYhgmNqNWLXrkV4QAdk/Puv0OEYJYMuKKZMmYKrV6/im2++wZMnT7Bnzx5s3boV48aNEzo0Qoye+tEjKFatRsbESVCdOiV0OIQI6u1PPyF+8xaw9HQ8mzQJ6iJcO0DeMeiCokmTJjh69Cj27t2LunXrYtGiRVi7di0GDhwodGiEGDWWnAzF4iWAUglkZoJ/Ei50SIQIyqZvX5jUqQMAUEZF40VgoMHc2WIsDLqgAIBPPvkE9+7dQ0ZGBh4+fJjvLaOEkELgeShWrADL6j1UVKsmpCM+FzoqQgQlksvhtm4dRFlPdU05fQZvgoKFDsuoGHxBQQjRL9Hhw+Bv3Hw3YG0N2eyvwEmlQodFiOBkld3gunyZdjh29Wqk3bwpaEzGhAoKQioQ9fXrEB3KuktKJII8cBZEjg5Ch0WIwbBs0wb2o0e/G1Cr8XzKVKhevxY6LKNABQUhFQQfEwPFipXgss4LS4cNg9jPT+iwCDE4DhMnwOx//wMAqOLi8HzadDADeQ6HIaOCgpAKgGVmInPRYiAlBQAgatECkj69hQ6LEIPEicVwW7USEod3R+/Srl3D602bhQ7L4FFBQUgFwN+/DxYRAQBgLi6QTZtqMA8qIsQQSSpVgtvabwGxGPKaNWHdravQIRk8KigIqQDEjRpBvmI5OBcXqKZPA2duLnRIhBg8s0aN4L5lC7z27YXMw0PocAyeQT96mxCiP2JfX8h/2AZFaqrQoRBiNCxatRQ6BKNBRygIKadYLr1DclkdKxFCiodPT0fC0WNCh2GQ6AgFIeUQU6uROWcuxHXrQNKvH7gK0LkbIaUtMyICzydOQubjxwAH2HTvLnRIBoU+ZQgphzJ37AB/6xaUO3dBsW6d0OEQUi6k37nzrpgA8HL+AmSEPRI6JINCBQUh5UzyiRNQ7Nv/bkAkgsTfX+iQCCkXbLp3h03vXgAAlpGB5xMnQp11KzahgoKQckX1+DHi5s3XDku/GA2xr6+gMRFSnjh9/TXktWsBABRRUXgx40swnhc6LINABQUh5QRLSEDq13PAMjIAAOL27SHpSvfOE6JPIrkcldetg8jKCgCQcu4c4jZsEDosg0AFBSHlAFOpkLlkCfisHkTFtWpCNmE8PbyKkFIgc3eH25o1QNbFzvGbNiPpjz+EDktwVFAQUg4ot2wFf/ceAEBcqRJM588HJ5MJHRYh5ZZFq5ZwnDFDO/wicDYyHj4UNCahUUFBiJFTnToF1S+/vBuQSuH87RqIKlUSOixCyj27YUNh3a0bAIClpyPh4CGhQxIUPYeCECMnql0HnKcnWFQUzCZPgkn9+kiOixM6LELKPY7j4LxwARSRkTBv2RKVxo8TOiRBUUFBiJETubnCZO23UF+8CHnnzkKHQ0iFIpLL4fnjLjrFSKc8CCkfODMzSAIChA6DkAopt2KCVygEiUVIVFAQYmQYz0N55Ij29lBCiGFJu3UL4e0DkHbzptChlCkqKAgxMspdu6DcshUZ06aDf/1a6HAIIe9Ju/03ooYNh+rlSzwbPwGKqCihQyozVFAQYkRUp09DtXcfAID99x9YRKTQIRFC3mNazxfmTZsCANQJCXg6+guoExOFDqtMUEFBiJFQP3gAxdr/7+hLOmoUxE0aCxoTIUQXJ5HAbe23kFWrCgBQREbi2cRJYBXgmgoqKAgxAnxMDDIXLASUSgCApHNnSLp3EzosQkguxJaWcN+8BWJ7ewBA2rVriFmwAIwxoUMrVVRQEGLgWGoqMufOA7IOm4r8/CAdO4Yeq02IAZNVdoP7dxu1d4AkHj6C+B9+EDqsUmVUBcWyZcvAcRwmT54sdCiElAmmViNzyTdg0dEAAK5yZci//gqchB4hQ4ihM23QAK7LlmqH41avQdIfJwWNqTQZTUFx48YNbNmyBfXq1RM6FELKBGMMyu83gb91612DpSXkCxeCs7AQOjRCSCFZdeoEh8mTtMMvAgOhKqdPsjWKgiIlJQUDBw7Etm3bYGtrK3Q4hJQNtRrs7Zt3/5dIIJ87ByI3V6GjIoQUkf3o0bDu3h2cTAbXJYshcXAQOqRSYRTHTceNG4fOnTvD398fixcvznfazMxMZGZmaoeTkpIAADzPg+f5EsfC8zwYY3pZlqGjXAs/X6lcbCUWQzp7NrB1G0TVq0Pk61vgejQ55BWTpr0sLw4rKKbSXG9+20GImPJTkphKa78a6nZC1nvPmDjNnwebwYNgUqtWoWM3hM/goqzb4AuKffv24fbt27hx40ahpl+6dCkWLFiQoz0uLg4ZeniyIM/zSExMBGMMIpFRHOApNsq1YCqVComJiVCr1aUX3MAB7/7NKo7zI5VKoVKpkJSUlGtMjDGkp6cDWR0blYWCYiot+eUqVEz5KUlMpbVfDXE7ad6fYrHY+D6X7O2RFBur08QYy3OfGcJncHJycqGnNeiC4unTp5g0aRJOnToFExOTQs0TGBiIqVOnaoeTkpLg7u4OBwcHWFlZlTgmnufBcRwcHByM78VcRJRrwZRZt3GqVCr9xBERAZiYQOTiUqz55XI5bG1twXFcrjFpft1ZWVmVWUFRUEylJb9chYopPyWJqbT2qyFuJ7FYDABwdHQ0+s+lhP0HkBpyBa6rV4PLyut9hvAZXNjvXhh6QXHr1i3ExsaiYcOG2ja1Wo0LFy5g48aNyMzM1L64NORyOeRyeY5liUQive0QjuP0ujxDRrnmTyQSgeM4vXyI87GxUHw9B0ythnzRQoh9fIq8DE0O+cWkGVdWBUVhYirNdee2XiFjyktJYyqN/Wqo2wl6/kwXwuvNWxC3di0AIPabb+A8d26u21joz+AifR6WaiQl1K5dO9y7dw+hoaHav8aNG2PgwIEIDQ3NUUwQYqxYcjIyv/oaLD4eSEiAcvsOgzlnTQjRP5O6dYGs278T9u5D/JYtQodUYgZ9hMLS0hJ169bVaTM3N4e9vX2OdkKMFcvIQOacuf//rAlXV8gDZxnML0JCiP5ZtGoJ1yWL8WLmLABA3Np1kDg6wabHp0KHVmwGfYSCkPKOKZXIXLgQ/MOH7xpsbCBfvBicjY3QoRFCSpl1t25wmPb/1/zFzJmD5LNnBY2pJAz6CEVuzp8/L3QIhOgFU6uhWLES/K3b7xrMzGDyzRJ61gQhFYj9iBFQvYrF2927AbUazydPgfvWLTD/3/+EDq3I6AgFIQJgjEG5cSPUFy68a5DLIV+0EKKqVYUOjRBShjiOg9PsQFh98gkAgCkUeDp2HNLv3BE6tCKjgoIQASiDgqH6/cS7AbEY8q+/gpiuCyKkQuJEIrgu/QYWbdsCAFhaGp5PnwGWdVu6saCCghABcOZmWf/hIJsxHeKmTYUOiRAiIE4qhdvab2HWrBnElSqh8sYN4KRSocMqEqO7hoKQ8kDaty84S0swtRqSrF8lhJCKTSSXo/J330H9Jh4yDw+je7w4FRSECETSqZPQIRBCDIzYwhxiC3OdNsbzUCUmQmLgnWPSKQ9CyoDqjz+gCgkROgxCiJFhajVefj0HUQMGQpmtHxBDQwUFIaVM9eefUKxdB8WixVBdvix0OIQQI5KxfQeSjh2DIiIC0cOGQxUXJ3RIeaKCgpBSpDpzBoo13wKMAWo1+If/Ch0SIcSIyLp2gdTNDQCg+O8/RA0bDtXr10KHlSsqKAgpJapz56BYtfpdMQFA0r0bpJ9/JnRYhBAjInZ2hntwEKSu7x54pwgPR9TQYQZZVFBBQUgpUJ08CcXyFUDWVdqSTz6B9IsvqH8OQkiRSd3c4LFrJySuLoCmqBhmeEUFFRSE6Jnyl1/+/zRH1t0c0nFjqZgghBSbrHJleO7cCYlLVlHxJBxRgwZDGRMjdGhaVFAQokfKgweh/O577bDk008hnTgBnIjeaoSQkpG5u8Nz13tFRWQkogYOguLpU6FDA6igIER/+Jcvodz1o3ZY0r8fpKNH0ZEJQojeyNzd4bX7R0g9PQAAIktLiK2shA4LoIKCEP0ROTtDPncuIJVCOmwYZMOGUTFBCNE7qZsbvHbvhuXHH8Nj+w8QW1sLHRJAT8okRL/ETRrDZNtWiLIOSRJCSGmQODig8ob1OdoZY4L9kKEjFIQUE5+ejqSjR8GyLr7UoGKCECIEdUoK0gR8Ii8VFIQUg+rtW0QPG47YufOg2H9A6HAIIRUcn5GBtJs3Yda8uWAxUEFBSBEpnz9H1ICBSL9zBwCQuWcPWEKC0GERQiowTiyGRevWgl63RQUFIUWQERaGyH79oYiIAACIK1WC+ZrV4GxshA6NEFKBcVKp4BeBU0FBSCElnz+PqP4DtJ3zyLy8UHn3jxBXqyZ0aIQQIji6y4MYDbVaDT7rUdb6xvM8VCoVlEolRNkeQsUYQ8KuHxG/+v/75ZDX84Xrxo0Q2dgABtz7HyGElBUqKIjR4Hke8fHxUKvVel82YwyJiYkAoHPYkCmVyFi3HsoTJ7Rtkg8/hGzml0jmOBjG3d+EECI8KiiIUVGr1VCpVHpfLmNMu2ydgiIlBaqsiy8BQDJwIKSDBkItEpVKYUMIIcaKrqEgJB+cpSXkCxcAdraQzZoJ2ZDB1C8HIYTkgo5QEJINy8wEJ5drh0Xu7jANCgJnYiJoXIQQYsgM/qfW0qVL0aRJE1haWsLR0RHdu3dHWFiY0GGR8kilguL775E5fTqYQqEziooJQgjJn8EXFH/99RfGjRuHq1ev4tSpU1AqlWjfvj1SU1OFDo2UI3xcHMTz5kP9y6/gHz2GctNmoUMihBCjYvCnPP744w+d4eDgYDg6OuLWrVv48MMPBYuLlB/qv/9G5tKlECUmvWuQSiGqTs+WIISQojD4giI7za19dnZ2uY7PzMxEZmamdjgp6d2XBM/zenmGAc/zYIyV2vMQDImh5aqJJ3tnXMXFVCqoftwN1YED2udLwNER8q+/gsjHp8D1aLaNPmMqqYJi0rSXZbxCbaf8cjXGfVfQvKWRi6FuJ2R9HpR3hvAZXJR1G1VBwfM8Jk+ejJYtW6Ju3bq5TrN06VIsWLAgR3tcXBwyMjL0EkNiYiIYYzkegFTeGFquKpUKiYmJ+rldMyYG4vXrIQr/T9ukrOcLNmkSlJaWQFYhmh+pVAqVSoWkpCSDuYW0oJgYY0hPTweyPW9DyJhKS365GuO+y09p7VdD3E6azyKxWGwQn0ulyRA+g5OTkws9rVEVFOPGjcP9+/dx6dKlPKcJDAzE1KlTtcNJSUlwd3eHg4MDrKysShwDz/PgOA4ODg4V4sVsSLkqlUogq7AoCdWfp6D8/ntAU2CKxZAMGQxF+/awsrEp9AeyXC6Hra0tOI4rlWdjFEdBMWl+3VlZWZVZQSHUdsovV2Pcd/kprf1qiNtJLBYDABwdHQ3ic6k0GcJnsEkRLkg3moJi/Pjx+O2333DhwgVUrlw5z+nkcjnk793ypyESifS2QziO0+vyDJkh5SoSicBxXMk/MONitcUEV9kN8lmzwFWrBi4pqUjL12wbvcSkJ4WJSTOurGIWcjvllaux7ruC5td3Poa6naDnz3RDJvRncFHWa/AFBWMMEyZMwNGjR3H+/Hl4e3sLHRIxcpL+/aG+dRuchwdkY74AZ2JiMOeHCSHEWBl8QTFu3Djs2bMHP//8MywtLfHy5UsAgLW1NUxNTYUOjxg49vYt1KGhkLRtq23jxGLIly3VeXgVIYSQkjH4gmLTpk0AgDZt2ui0BwUFYdiwYQJFRQwdYwzqs2eh2LQZSE0F5+oKcY0a2vFUTBBCiH4ZfEFBh6JJUfGvXkGx8Tvw169r25Q/bId45QpB4yKEkPLM4AsKQgqLKRRQHTwI5f4DwHvPIhG3bg3Z2DGCxkYIIeUdFRSkXFBdvQrl5i1gMTHaNs7ODtIJEyBp0VzQ2AghpCKggoIYNZaSgszlK3ROb0AkgqR7d0gHDQRnbi5keIQQUmFQQUGMm5kZkPU4dgAQ1a8P2dgxEHl5CRoWIYRUNFRQEKPCMjOBrCflAQAnEkE6biwUi5dAOmIExB9+YDAP4CGEkIqk/D9mjJQLTKlE4v79SBk4COpbt3XGiWvUgEnQDkhaf0jFBCGECISOUBCDxisUSDxyBPFbt0H54gUAQLFjB0z8GoB775GwnIReyoQQIiT6FCYGiU9PR8LBg4j/YTtUsbE640QuzkB6OkAXXBJCiMGggoIYFHVSEt7u3483wTuhjo/XGWfWqhW4/v3AqlUTLD5CCCG5o4KCGAx1YiKefNQOfGqqTruFfztUGv0FJDVrIDY21mC6USaEEPL/qKAgBkNsbQ2zpk2Rcu4cwHGw6tgB9qO/gEkNHwCAUqkUOkRCCCF5oIKClDlVfDwSj/2M1MuX4b51i84FlXbDh0Hq4gzbQYMhr0Jd1RNCiLGggoKUCaZWI/XyZSQcPITkc+eArNMWyafPwKpDgHY686ZNYd60qYCREkIIKQ4qKEipYYwhMywMScePI/HX36B6+TLHNGm3b+kUFIQQQowTFRSkVLzdtw9vdv0IxX//5RgnrlQJNp92h3WPHpB702kNQggpD6igICXGGAMAnadUKmNe6hYTYjEsPvgANr17weLDD8FJpUKESgghpJRQQUGKhSkUSL1xAylnzyH53Fl4bNsGedWq2vFWnTohfssWmDZuBOvOnWHZvj0k9vaCxkwIIaT0UEFBcqVWq6FSqaBSqaBUKsFxHBRPniA95CrSrl5F+s2bYOnp2ukTT52GrYeHdlhcxRteZ05D4ugIAGB6uO1TcySEEEKI4aGCguSK53m8efMGiX+cRHro31Df/hvszZvcJ5ZIkPz8OZTZHpENAMitrRhkMhmsra31sixCCCH6RwUFAQDwmZnIfPwEpnXraNvUajVw/TpUFy7kmJ6zs4OoYUOI/9cM4oYNwZmbl+oTLMXvdVlOCCHE8FBBUQHx6enIDAtDxsOHyPjnH2Q8+AcZjx8DajV8rl2F2NLy/6etWROiCxcAExOI6tWD2M8P4oZ+4Dw9qatwQgghWlRQVAB8Whre7tmDjEePkPnwITLD/wN4Ptdp00PvwOKDVtph1qQxZDVrQOzjQ3dmEEIIyRMVFEaOMQb127dQPnsGxdOnUERFQV61GqwC2mun4SQSxK5dp306ZQ4cB5m3N0wbNIDY1lZ3nLU1xO7udDSCEEJIvqigMCLpDx4g/e9QKJ8+heL5MyifPoPy6VPwaWk601l27KBbUMhkkHl4vHsuhFQKebVqMKldCya1asOkdm2Y1PCByNxcgIwIIYSUF1RQCIAxBj4lBeo3b6B++xaqt2+hfvMWqjfxUMXGQfXqFVTx8fD8cRc4kUg7X9Kvv+FNcHCBy1c8Cc/R5rJgPkTm5pBVqwaRTKb3nAghhFRsRlFQfPfdd1i5ciVevnyJ+vXrY8OGDWgqcAdSvEIBPjX1XWGQnAI+JRnq5GTwKangk5OhTnn3f6sOATCtV087X/q9e4jsPyDv0w/vUb99q/MwKImTk+4EUimkri6QVXaH1L0yZJUrQ1rZHfLq1XIsy6xJk5KmTAghhOTJ4AuK/fv3Y+rUqdi8eTOaNWuGtWvXIiAgAGFhYXDMemiSECI6dMy1s6vsZB7uOgWF2NKyUMUEAKhev9YpKMxbtoDLksWQVnaHzL0yJE5O4Oh2SkIIIQbA4AuKNWvWYOTIkRg+fDgAYPPmzTh+/Dh27NiBWbNmCRZXYa85UCcn6wyL7e0hr1ULElsbiG3tILa1hdjOFhJbW4ht7SBxdITUyRFiB4ccpyZMfHxg4uOj1zwIIYQQfTDogkKhUODWrVsIDAzUtolEIvj7+yMkJCTXeTIzM5GZmakdTkxMBAAkJCSAz+NWyaLgeR5JSUlgdeuCd3KEyMwcYksLiMwtILIwh8jcHCILS+3/4e6OhIQEnWXYBe3IfdkAFFl/SEt79ycQpVKJ1NRUpKWlQSKRCH6Xh0qlgkgkQmpq6rsHbukZY6zIuZZ2TMVRUEzFybO0Yyot+eVqjPsuP6W1Xw1xO4lEIqjVapiZmUH03jVm5ZHm+0YmkwmWa1JSElDIrg8MuqB4/fo11Go1nLJdO+Dk5IR///0313mWLl2KBQsW5Gj39PQstTgJIYSQ8iw5ObnA7g8MuqAojsDAQEydOlU7rOmTwt7eXi+Ve1JSEtzd3fH06VNYWVmVeHmGjHItfypKnqBcyy3KtWwxxpCcnAxXV9cCpzXogqJSpUoQi8V49eqVTvurV6/g7Oyc6zxyuRxyuVynzcbGRu+xWVlZlfsXswblWv5UlDxBuZZblGvZKWzHjAZ9Akomk6FRo0Y4c+aMto3neZw5cwbNmzcXNDZCCCGE/D+DPkIBAFOnTsXQoUPRuHFjNG3aFGvXrkVqaqr2rg9CCCGECM/gC4q+ffsiLi4Oc+fOxcuXL9GgQQP88ccfOS7ULCtyuRzz5s3LcVqlPKJcy5+Kkico13KLcjVcHCvMvSCEEEIIIfkw6GsoCCGEEGIcqKAghBBCSIlRQUEIIYSQEqOCghBCCCElVuEKiu+++w5eXl4wMTFBs2bNcP369ULNt2/fPnAch+7du+u0Dxs2DBzH6fx16NBBZ5o3b95g4MCBsLKygo2NDT7//HOkpKToNa/c6DvX7Hlq/lauXKmdxsvLK8f4ZcuW6T237IqSa3BwcI4YTUxMdKZhjGHu3LlwcXGBqakp/P398fjxY51pjGG/FpSrUqnEzJkz4evrC3Nzc7i6umLIkCF48eKFznLKy34tL+/XwuRqqO/Xon4uJSQkYNy4cXBxcYFcLoePjw9+//33Ii0zIyMD48aNg729PSwsLNCzZ88cD0wsDfrOdenSpWjSpAksLS3h6OiI7t27IywsTGcZbdq0ybFPv/jii1LLUQerQPbt28dkMhnbsWMHe/DgARs5ciSzsbFhr169yne+iIgI5ubmxj744APWrVs3nXFDhw5lHTp0YDExMdq/N2/e6EzToUMHVr9+fXb16lV28eJFVq1aNda/f/9SyVGjNHJ9P8eYmBi2Y8cOxnEcCw8P107j6enJFi5cqDNdSkpKqeXJipFrUFAQs7Ky0onx5cuXOtMsW7aMWVtbs2PHjrE7d+6wrl27Mm9vb5aenq6dxhj2a0G5JiQkMH9/f7Z//37277//spCQENa0aVPWqFEjneWUl/1aXt6vhcnVEN+vRc0zMzOTNW7cmHXq1IldunSJRUREsPPnz7PQ0NAiLfOLL75g7u7u7MyZM+zmzZvsf//7H2vRokWp5VlauQYEBLCgoCB2//59Fhoayjp16sQ8PDx09lnr1q3ZyJEjdfZpYmJiqeaqUaEKiqZNm7Jx48Zph9VqNXN1dWVLly7Ncx6VSsVatGjBfvjhBzZ06NBcC4rsbe/7559/GAB248YNbduJEycYx3Hs+fPnJc4pL6WRa3bdunVjH330kU6bp6cn+/bbb/WQQeEVNdegoCBmbW2d5/J4nmfOzs5s5cqV2raEhAQml8vZ3r17GTOi/VpQrrm5fv06A8CioqK0beVhv7Jy9H4tzn41hPdrUfPctGkTq1KlClMoFMVeZkJCApNKpezgwYPaaR4+fMgAsJCQED1lVvS4sitMrtnFxsYyAOyvv/7StrVu3ZpNmjSphNEXT4U55aHpCt3f31/bVlBX6ACwcOFCODo64vPPP89zmvPnz8PR0RE1atTAmDFjEB8frx0XEhICGxsbNG7cWNvm7+8PkUiEa9eu6SW37EozV41Xr17h+PHjuU67bNky2Nvbw8/PDytXroRKpSpBNvkrbq4pKSnw9PSEu7s7unXrhgcPHmjHRURE4OXLlzrLtLa2RrNmzbTLNKb9ml+uuUlMTATHcTn6wDH2/apRXt6vRdmvhvB+LU6ev/zyC5o3b45x48bByckJdevWxTfffKPtSr0wy7x16xaUSqXONDVr1oSHh0e+29fQcs1NYmIiAMDOzk6n/aeffkKlSpVQt25dBAYGIi0tTW+55cfgn5SpL8XpCv3SpUvYvn07QkND81xuhw4d0KNHD3h7eyM8PByzZ89Gx44dERISArFYjJcvX8LR0VFnHolEAjs7O7x8+VJP2ekqrVzft3PnTlhaWqJHjx467RMnTkTDhg1hZ2eHK1euIDAwEDExMVizZk0JMspbcXKtUaMGduzYgXr16iExMRGrVq1CixYt8ODBA1SuXFm7X3JbpmacsezXgnLNLiMjAzNnzkT//v11OiMqD/sV5ej9WtT9agjv1+Lk+d9//+Hs2bMYOHAgfv/9dzx58gRjx46FUqnEvHnzCrXMly9fQiaT5SiQ338/61tp5Jodz/OYPHkyWrZsibp162rbBwwYAE9PT7i6uuLu3buYOXMmwsLCcOTIkVLIVFeFKSiKKjk5GYMHD8a2bdtQqVKlPKfr16+f9v++vr6oV68eqlativPnz6Ndu3ZlFG3JFDbX9+3YsQMDBw7McSHY+13H16tXDzKZDKNHj8bSpUsN5vGxzZs31+lcrkWLFqhVqxa2bNmCRYsWCRqbvhUlV6VSiT59+oAxhk2bNumMKy/7tTy8X1GM17Cxvl95noejoyO2bt0KsViMRo0a4fnz51i5cmWuX7LGrKi5jhs3Dvfv38elS5d02keNGqX9v6+vL1xcXNCuXTuEh4ejatWqpZpDhSkoitoVenh4OCIjI9GlSxdtG8/zQNYvlrCwsFx3TpUqVVCpUiU8efIE7dq1g7OzM2JjY3WmUalUePPmTZ5dsJdUaed68eJFhIWFYf/+/QXG0qxZM6hUKkRGRqJGjRolzCyn4nRxn51UKoWfnx+ePHkCANr5Xr16BRcXF51lNmjQQDuNoe/X3GTPVUNTTERFReHs2bMFdpVsjPs1N8b4fs1Nfrkayvu1OHm6uLhAKpVCLBZr22rVqoWXL19CoVAUapnOzs5QKBRISEjQOUpRlO1bVKWRq0wm07aPHz8ev/32Gy5cuJDrEan3NWvWDADw5MmTUi8oKsw1FEXtCr1mzZq4d+8eQkNDtX9du3ZF27ZtERoaCnd391zX8+zZM8THx2u/iJo3b46EhATcunVLO83Zs2fB87x2Rxtbrtu3b0ejRo1Qv379AmMJDQ2FSCTKcRhZX/TRxb1arca9e/e0+8zb2xvOzs46y0xKSsK1a9e0yzSG/Zqb7LnivWLi8ePHOH36NOzt7QtcjjHu19wY4/s1N/nlaijv1+Lk2bJlSzx58kT7AwcAHj16BBcXF8hkskIts1GjRpBKpTrThIWFITo6utDb1xByRdbt7OPHj8fRo0dx9uxZeHt7FxiL5jR2fu8DvRHkUlCB7Nu3j8nlchYcHMz++ecfNmrUKGZjY6O93Wrw4MFs1qxZec6f/Qrx5ORkNn36dBYSEsIiIiLY6dOnWcOGDVn16tVZRkaGdroOHTowPz8/du3aNXbp0iVWvXr1MrkNTZ+5aiQmJjIzMzO2adOmHOOuXLnCvv32WxYaGsrCw8PZ7t27mYODAxsyZIies9NV1FwXLFjATp48ycLDw9mtW7dYv379mImJCXvw4IF2mmXLljEbGxv2888/s7t377Ju3brletuooe/XgnJVKBSsa9eurHLlyiw0NFTnVrPMzEzGytF+LU/v18K8hpkBvl+Lmmd0dDSztLRk48ePZ2FhYey3335jjo6ObPHixYVeJsu6bdTDw4OdPXuW3bx5kzVv3pw1b9681PIsrVzHjBnDrK2t2fnz53Xeq2lpaYwxxp48ecIWLlzIbt68ySIiItjPP//MqlSpwj788MNSzVWjQhUUjDG2YcMG5uHhwWQyGWvatCm7evWqdlzr1q3Z0KFD85w3+5dsWloaa9++PXNwcGBSqZR5enqykSNH5rgfPD4+nvXv359ZWFgwKysrNnz4cJacnFxKGf4/feaqsWXLFmZqasoSEhJyjLt16xZr1qwZs7a2ZiYmJqxWrVrsm2++0fmwLi1FyXXy5MnaaZ2cnFinTp3Y7du3dZbH8zybM2cOc3JyYnK5nLVr146FhYXpTGMM+7WgXCMiIhiAXP/OnTvHWDnar+Xp/VqY1zAz0PdrUT+Xrly5wpo1a8bkcjmrUqUKW7JkCVOpVIVeJmOMpaens7FjxzJbW1tmZmbGPv30UxYTE1OqeRYUV3Fyzeu9GhQUxFhWUfLhhx8yOzs7JpfLWbVq1diMGTPK7DkU1H05IYQQQkqswlxDQQghhJDSQwUFIYQQQkqMCgpCCCGElBgVFIQQQggpMSooCCGEEFJiVFAQQgghpMSooCCEEEJIiVFBQQghhJASo4KCEAHNnz9f2+FYSQQHB+fonpkYr7CwMDg7OyM5ORnIZf/q63WjDwqFAl5eXrh586bQoRCBUUFBjA7Hcfn+zZ8/X+gQC2369Ok6HQgVV9++ffHo0SO9xFQS58+fB8dxSEhIyHe6jIwMDBs2DL6+vpBIJOjevXuhlu/l5ZVjfy9btkw7PjIyMtfXxNWrV3WWc/DgQdSsWRMmJibw9fXF77//XsyMS0dgYCAmTJgAS0vLXMfr63WjDzKZDNOnT8fMmTOFDoUIrMJ0X07Kj5iYGO3/9+/fj7lz5yIsLEzbZmFhof0/YwxqtRoSiWG+1C0sLHTiLS5TU1OYmprqJaayoFarYWpqiokTJ+Lw4cNFmnfhwoUYOXKkdji3L93Tp0+jTp062uH3e1C9cuUK+vfvj6VLl+KTTz7Bnj170L17d9y+fRt169Ytdk76Eh0djd9++w0bNmzIcxp9vW5KStOt9sCBAzFt2jQ8ePBAZ7uTioWOUBCj4+zsrP2ztrYGx3Ha4X///ReWlpY4ceIEGjVqBLlcjkuXLiE8PBzdunWDk5MTLCws0KRJE5w+fVpnuV5eXvjmm2/w2WefwdLSEh4eHti6dat2vEKhwPjx4+Hi4gITExN4enpi6dKl2vEcx2HLli345JNPYGZmhlq1aiEkJARPnjxBmzZtYG5ujhYtWiA8PFw7T/ZD1+fPn0fTpk1hbm4OGxsbtGzZElFRUQCAO3fuoG3btrC0tISVlRUaNWqkPcyc2ymPTZs2oWrVqpDJZKhRowZ+/PFHnfEcx+GHH37Ap59+CjMzM1SvXh2//PJLvtv+xx9/ROPGjWFpaQlnZ2cMGDAAsbGxQNbRgbZt2wIAbG1twXEchg0blutyzM3NsWnTJowcORLOzs75rjM7zbo1f+bm5jmmsbe315lGKpVqx61btw4dOnTAjBkzUKtWLSxatAgNGzbExo0b81znsGHDchxFmTx5Mtq0aaMdbtOmDSZMmIDJkyfD1tYWTk5O2LZtG1JTUzF8+HBYWlqiWrVqOHHiRL75HThwAPXr14ebm1ue02R/3WjiW7VqFVxcXGBvb49x48ZBqVRqp8nMzMT06dPh5uYGc3NzNGvWDOfPn9eOj4+PR//+/eHm5gYzMzP4+vpi7969Outt06YNxo8fj8mTJ6NSpUoICAgAsvZ3y5YtsW/fvnxzI+UbFRSkXJo1axaWLVuGhw8fol69ekhJSUGnTp1w5swZ/P333+jQoQO6dOmC6OhonflWr16Nxo0b4++//8bYsWMxZswY7dGP9evX45dffsGBAwcQFhaGn376CV5eXjrzL1q0CEOGDEFoaChq1qyJAQMGYPTo0QgMDMTNmzfBGMP48eNzjVmlUqF79+5o3bo17t69i5CQEIwaNQocxwEABg4ciMqVK+PGjRu4desWZs2apfNF+b6jR49i0qRJmDZtGu7fv4/Ro0dj+PDhOHfunM50CxYsQJ8+fXD37l106tQJAwcOxJs3b/LcrkqlEosWLcKdO3dw7NgxREZGaosGd3d37dGGsLAwxMTEYN26dYXYW0WzbNky2Nvbw8/PDytXroRKpcoxTdeuXeHo6IhWrVrlKJJCQkLg7++v0xYQEICQkJASx7Zz505UqlQJ169fx4QJEzBmzBj07t0bLVq0wO3bt9G+fXsMHjwYaWlpeS7j4sWLaNy4cZHXfe7cOYSHh+PcuXPYuXMngoODERwcrB0/fvx4hISEYN++fbh79y569+6NDh064PHjx0DWaahGjRrh+PHjuH//PkaNGoXBgwfj+vXrOXKUyWS4fPkyNm/erG1v2rQpLl68WOS4STlSJn2aElJKgoKCmLW1tXb43LlzDAA7duxYgfPWqVOHbdiwQTvs6enJBg0apB3meZ45OjqyTZs2McYYmzBhAvvoo48Yz/O5Lg8A+/rrr7XDISEhDADbvn27tm3v3r3MxMREOzxv3jxWv359xrK6zQbAzp8/n+vyLS0tWXBwcKG2Q4sWLdjIkSN1punduzfr1KlTnvGmpKQwAOzEiRO5riM3N27cYAC03Xtrtv/bt28LvYyhQ4eybt26FWra1atXs3PnzrE7d+6wTZs2MRsbGzZlyhTt+Li4OLZ69Wp29epVdv36dTZz5kzGcRz7+eeftdNIpVK2Z88eneV+9913zNHRsUgxTpo0ibVu3Vo73Lp1a9aqVSvtsEqlYubm5mzw4MHatpiYGAaAhYSE5Lmu+vXrs4ULF+q0Zd+/779uNPF5enrqdHXdu3dv1rdvX8YYY1FRUUwsFrPnz5/rLLddu3YsMDAwz1g6d+7Mpk2bppOjn59frtOuW7eOeXl55bksUv4Z5ollQkoo+y+8lJQUzJ8/H8ePH0dMTAxUKhXS09NzHKGoV6+e9v+aUymaQ/rDhg3Dxx9/jBo1aqBDhw745JNP0L59+zznd3JyAgD4+vrqtGVkZCApKQlWVlY689rZ2WHYsGEICAjAxx9/DH9/f/Tp0wcuLi4AgKlTp2LEiBH48ccf4e/vj969e6Nq1aq55v/w4UOMGjVKp61ly5Y5jhi8H6+5uTmsrKy0+ebm1q1bmD9/Pu7cuYO3b9+C53kg67x/7dq185xPX6ZOnar9f7169SCTyTB69GgsXboUcrkclSpV0pmmSZMmePHiBVauXImuXbuWenzvb0+xWAx7e/sc+x9Avts4PT0dJiYmRV53nTp1IBaLtcMuLi64d+8eAODevXtQq9Xw8fHRmSczM1N7fYlarcY333yDAwcO4Pnz51AoFMjMzISZmZnOPI0aNcp1/aampvkeeSHlH53yIOVS9vPq06dPx9GjR/HNN9/g4sWLCA0Nha+vLxQKhc502U8hcByn/dJs2LAhIiIisGjRIqSnp6NPnz7o1atXnvNrTlXk1qZZZnZBQUEICQlBixYtsH//fvj4+GjvUJg/fz4ePHiAzp074+zZs6hduzaOHj1arO1TmHyzS01NRUBAAKysrPDTTz/hxo0b2vVn345lpVmzZlCpVIiMjMx3midPnmiHnZ2d8erVK51pXr16VeRrOdRqdY623LZnUfY/AFSqVAlv374tUix5rVuznpSUFIjFYty6dQuhoaHav4cPH2qLzJUrV2LdunWYOXMmzp07h9DQUAQEBOTYt7ldswIAb968gYODQ5HjJuUHFRSkQrh8+TKGDRuGTz/9FL6+vnB2ds73SygvVlZW6Nu3L7Zt24b9+/fj8OHD+V5zUBx+fn4IDAzElStXULduXezZs0c7zsfHB1OmTMGff/6JHj16ICgoKNdl1KpVC5cvX9Zpu3z5comOIvz777+Ij4/HsmXL8MEHH6BmzZo5fmnLZDIgjy/b0hAaGgqRSARHR8d8p9Ec5QGA5s2b57jl8tSpU2jevHm+68pehPz333/Fjjs/fn5++Oeff/S+TLVajdjYWFSrVk3nT1NIXb58Gd26dcOgQYNQv359VKlSpUi3It+/fx9+fn56jZsYFzrlQSqE6tWr48iRI+jSpQs4jsOcOXPy/ZWYmzVr1sDFxQV+fn4QiUQ4ePAgnJ2d9fZAqYiICGzduhVdu3aFq6srwsLC8PjxYwwZMgTp6emYMWMGevXqBW9vbzx79gw3btxAz549c13WjBkz0KdPH/j5+cHf3x+//vorjhw5kuPOlqLw8PCATCbDhg0b8MUXX+D+/ftYtGiRzjSenp7gOA6//fYbOnXqBFNT0zxvb/znn3+gUCjw5s0bJCcnIzQ0FAC0dy9cv34dQ4YMwZkzZ+Dm5oaQkBBcu3ZNe6dLSEgIpkyZgkGDBsHW1hZ474JBzRfbkSNHsGPHDvzwww/a9U6aNAmtW7fG6tWr0blzZ+zbtw83b97UuaMnN9euXcO2bdvQrl07nD17FidPnkTVqlUREREBb2/vYm/X7AICAjBixAio1WqdUxgl4ePjg4EDB2LIkCFYvXo1/Pz8EBcXhzNnzqBevXro3LkzqlevjkOHDuHKlSuwtbXFmjVr8OrVq0IXoRcvXszxeiAVCx2hIBXCmjVrYGtrixYtWqBLly4ICAhAw4YNi7QMS0tLrFixAo0bN0aTJk0QGRmJ33//HSKRft5GZmZm+Pfff9GzZ0/4+Phg1KhRGDduHEaPHg2xWIz4+HgMGTIEPj4+6NOnDzp27IgFCxbkuqzu3btj3bp1WLVqFerUqYMtW7YgKChI5zbHonJwcEBwcDAOHjyI2rVrY9myZVi1apXONG5ubliwYAFmzZoFJyenPO9oAYBOnTrBz88Pv/76K86fPw8/Pz+dX7hpaWkICwvT3vool8uxb98+tG7dGnXq1MGSJUswZcqUHIXAokWL0KhRIzRr1gw///wz9u/fj+HDh2vHt2jRAnv27MHWrVtRv359HDp0CMeOHSvwGRRt27bF4cOHUatWLWzbtg1BQUGIiYnBypUri7wt89OxY0dIJJISFX+5CQoKwpAhQzBt2jTUqFED3bt3x40bN+Dh4QEA+Prrr9GwYUMEBASgTZs2cHZ2LvQDx0JCQpCYmJjjFCCpWDj27mpvQggheRg2bBgSEhJw7NixMlnfd999h19++QUnT54sk/WVVN++fVG/fn3Mnj1b6FCIgOiUByGEGJjRo0cjISEBycnJeT5+21AoFAr4+vpiypQpQodCBEZHKAghpABlfYSCEGNEBQUhhBBCSowuyiSEEEJIiVFBQQghhJASo4KCEEIIISVGBQUhhBBCSowKCkIIIYSUGBUUhBBCCCkxKigIIYQQUmJUUBBCCCGkxP4PyA7A3BV2h2wAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "ax.hist(\n",
    "    eta_center,\n",
    "    bins=\"auto\",\n",
    "    color=\"lightgray\",\n",
    "    edgecolor=\"white\",\n",
    "    alpha=0.7,\n",
    "    density=True,\n",
    "    label=\"Monte Carlo\",\n",
    ")\n",
    "\n",
    "x = np.linspace(eta_center.min() * 0.95, eta_center.max() * 1.05, 300)\n",
    "pdf = norm.pdf(x, loc=adj_summary[\"mean_linear\"], scale=adj_summary[\"std_linear\"])\n",
    "ax.plot(x, pdf, color=\"tab:red\", linewidth=2, linestyle=\"--\", label=\"Adjoint Gaussian\")\n",
    "\n",
    "ax.set_xlabel(f\"Transmission at {center_wavelength:.3f} µm (linear)\")\n",
    "ax.set_ylabel(\"Density\")\n",
    "ax.set_title(\"Monte Carlo vs Adjoint Sensitivity\")\n",
    "ax.grid(True, alpha=0.3)\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "025977d9",
   "metadata": {},
   "source": [
    "## Analysis and Conclusion\n",
    "\n",
    "The ±20 nm sweep already hinted that the design is somewhat brittle: the peak efficiency drops by roughly a dB and the optimal wavelength shifts under bias. The Monte Carlo and adjoint statistics confirm that fabrication variability will erode performance across a wafer. To address this we need to optimize directly for robustness.\n",
    "\n",
    "## Next Step: Designing for Robustness\n",
    "\n",
    "In the next notebook we will incorporate the process variations into the objective function itself, searching for geometries that maintain high efficiency across the biased scenarios rather than just at the nominal point."
   ]
  }
 ],
 "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
}