{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9bce848f",
   "metadata": {},
   "source": [
    "# Measurement Calibration: Bridging Simulation and Fabrication\n",
    "\n",
    "> Our robust adjoint design is ready for fabrication, but once real devices come back from the foundry, their spectral responses rarely match the nominal simulation exactly. In this notebook we demonstrate a way to calibrate the simulation model to match measured data using adjoint optimization, recovering the as-built geometry so subsequent optimization or analysis stays grounded in reality.\n",
    "\n",
    "Just as we used gradient-based optimization with adjoint derivatives to design the device, we can apply the same approach to calibrate fabrication parameters. Instead of optimizing geometric features to achieve a target performance, we now optimize fabrication corners (like width bias, etch depth, or sidewall angle) to match measured spectral data. Because we're using adjoint sensitivities, this approach scales efficiently to many parameters - real-world calibration often involves multiple fabrication variables simultaneously, and adjoint lets us handle that complexity with the same computational efficiency we saw during design."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0563acbd",
   "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 numpy as npl\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 get_mode_monitor_power, make_simulation, widths_gaps_to_centers\n",
    "from tidy3d import web"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "411c06f4",
   "metadata": {},
   "source": [
    "## Calibration Workflow Overview\n",
    "\n",
    "We assume access to three ingredients:\n",
    "1. The robust nominal design.\n",
    "2. A measured spectrum from fabricated hardware (here we synthesize one by applying a known bias and measurement noise).\n",
    "3. A differentiable simulation model we can tune so the simulated spectrum matches the measured data.\n",
    "\n",
    "The goal is to infer the effective SiN tooth widths that best reproduce the measurement, keeping the digital twin aligned with the hardware. We'll use gradient-based optimization driven by adjoint sensitivities - the same computational engine that powered our design optimization - to efficiently tune the fabrication parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "93eda25c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def centers_widths_to_spacing(centers, widths, *, last_gap):\n",
    "    \"\"\"Return (first_gap, gaps) that preserve the supplied centers.\"\"\"\n",
    "    centers = np.array(centers)\n",
    "    widths = np.array(widths)\n",
    "    if widths.size == 0:\n",
    "        return 0.0, np.zeros_like(widths)\n",
    "    left_edges = centers - widths / 2\n",
    "    right_edges = centers + widths / 2\n",
    "    first_gap = left_edges[0]\n",
    "    if widths.size == 1:\n",
    "        gaps = np.array([last_gap], dtype=widths.dtype)\n",
    "    else:\n",
    "        interior = left_edges[1:] - right_edges[:-1]\n",
    "        gaps = np.concatenate((interior, np.array([last_gap], dtype=widths.dtype)))\n",
    "    return first_gap, gaps\n",
    "\n",
    "\n",
    "def extract_spectrum(sim_data):\n",
    "    \"\"\"Return (freqs, power) from the mode monitor of `sim_data`.\"\"\"\n",
    "    power_da = get_mode_monitor_power(sim_data)\n",
    "    freqs = power_da.coords[\"f\"].values\n",
    "    power = np.array(power_da.data).squeeze()\n",
    "    return freqs, power\n",
    "\n",
    "\n",
    "def to_wavelength_db(freqs, power):\n",
    "    \"\"\"Convert a spectrum to (wavelength, power_db) sorted by wavelength.\"\"\"\n",
    "    wavelengths = td.C_0 / freqs\n",
    "    order = np.argsort(wavelengths)\n",
    "    wl = wavelengths[order]\n",
    "    power_lin = np.array(power)[order]\n",
    "    power_db = 10 * np.log10(np.clip(power_lin, 1e-12, None))\n",
    "    return wl, power_db"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "560fa555",
   "metadata": {},
   "source": [
    "## Generating Reference and Synthetic Measurement Data\n",
    "\n",
    "The baseline spectrum corresponds to the calibrated simulation before any fabrication shifts. To emulate a measured device we create a second spectrum with a uniform +20 nm SiN width bias and add multiplicative noise, representing typical measurement variability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "99b48095",
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = npl.random.default_rng(1234)\n",
    "\n",
    "robust_path = Path(\"./results\") / \"gc_adjoint_robust_best.json\"\n",
    "\n",
    "robust_data = json.loads(robust_path.read_text(encoding=\"utf-8\"))\n",
    "widths_si_nominal = np.array(robust_data[\"widths_si\"], dtype=float)\n",
    "gaps_si_nominal = np.array(robust_data[\"gaps_si\"], dtype=float)\n",
    "widths_sin_nominal = np.array(robust_data[\"widths_sin\"], dtype=float)\n",
    "gaps_sin_nominal = np.array(robust_data[\"gaps_sin\"], dtype=float)\n",
    "first_gap_si_nominal = float(robust_data[\"first_gap_si\"])\n",
    "first_gap_sin_nominal = float(robust_data[\"first_gap_sin\"])\n",
    "\n",
    "sin_centers, _ = widths_gaps_to_centers(\n",
    "    widths_sin_nominal, gaps_sin_nominal, first_gap=first_gap_sin_nominal\n",
    ")\n",
    "sin_last_gap_template = gaps_sin_nominal[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "10ffbfde",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Baseline spectrum from the robust design provides the \"uncalibrated\" reference.\n",
    "baseline_sim = make_simulation(\n",
    "    widths_si_nominal,\n",
    "    gaps_si_nominal,\n",
    "    widths_sin_nominal,\n",
    "    gaps_sin_nominal,\n",
    "    first_gap_si=first_gap_si_nominal,\n",
    "    first_gap_sin=first_gap_sin_nominal,\n",
    ")\n",
    "baseline_data = web.run(baseline_sim, task_name=\"gc_measurement_baseline\", verbose=False)\n",
    "base_freqs, base_power = extract_spectrum(baseline_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fde907a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Synthetic measurement: apply a uniform width bias to the SiN layer.\n",
    "global_bias = 0.02\n",
    "biased_widths_sin = widths_sin_nominal + global_bias\n",
    "biased_first_gap_sin, biased_gaps_sin = centers_widths_to_spacing(\n",
    "    sin_centers, biased_widths_sin, last_gap=sin_last_gap_template\n",
    ")\n",
    "\n",
    "measurement_sim = make_simulation(\n",
    "    widths_si_nominal,\n",
    "    gaps_si_nominal,\n",
    "    biased_widths_sin,\n",
    "    biased_gaps_sin,\n",
    "    first_gap_si=first_gap_si_nominal,\n",
    "    first_gap_sin=biased_first_gap_sin,\n",
    ")\n",
    "measurement_data = web.run(measurement_sim, task_name=\"gc_measurement_truth\", verbose=False)\n",
    "meas_freqs, meas_power = extract_spectrum(measurement_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4578da12",
   "metadata": {},
   "outputs": [],
   "source": [
    "tooth_indices = npl.arange(1, biased_widths_sin.size + 1)\n",
    "\n",
    "noise_sigma = 0.01\n",
    "noise = rng.normal(scale=noise_sigma, size=meas_power.shape)\n",
    "noisy_power = np.clip(meas_power * (1.0 + noise), 1e-12, None)\n",
    "\n",
    "target_power = np.array(noisy_power, dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7f44a851",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wl_base, base_db = to_wavelength_db(base_freqs, base_power)\n",
    "wl_meas, meas_db = to_wavelength_db(meas_freqs, noisy_power)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "ax.plot(wl_base, base_db, label=\"Simulated (nominal)\", linewidth=2)\n",
    "ax.plot(\n",
    "    wl_meas,\n",
    "    meas_db,\n",
    "    label=\"Measured (synthetic)\",\n",
    "    linewidth=2,\n",
    "    linestyle=\"--\",\n",
    ")\n",
    "ax.set_xlabel(\"Wavelength (um)\")\n",
    "ax.set_ylabel(\"Transmission (dB)\")\n",
    "ax.set_title(\"Simulated vs Measured Spectrum\")\n",
    "ax.grid(True, alpha=0.3)\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b8fde5",
   "metadata": {},
   "source": [
    "## Calibration Objective\n",
    "\n",
    "We adjust the SiN tooth widths so the simulated spectrum matches the measured one. The loss is the mean-squared error between spectra sampled at the monitor frequencies, optimized with Adam while respecting fabrication bounds.\n",
    "\n",
    "In this example we optimize a single global width bias, which is a one-dimensional problem that could also be solved with simpler techniques like bisection or line search. However, we're showcasing adjoint optimization here because real calibration scenarios often involve multiple correlated fabrication parameters (width bias, etch depth, sidewall angle, material index shifts, etc.) and adjoint derivatives make it practical to optimize all of them simultaneously. This demonstration establishes the workflow that scales naturally to those multi-parameter calibration problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d91af149",
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(params):\n",
    "    bias = params[\"bias\"]\n",
    "    widths_sin = widths_sin_nominal + bias\n",
    "    first_gap_sin, gaps_sin = centers_widths_to_spacing(\n",
    "        sin_centers, widths_sin, last_gap=sin_last_gap_template\n",
    "    )\n",
    "    sim = make_simulation(\n",
    "        widths_si_nominal,\n",
    "        gaps_si_nominal,\n",
    "        widths_sin,\n",
    "        gaps_sin,\n",
    "        first_gap_si=first_gap_si_nominal,\n",
    "        first_gap_sin=first_gap_sin,\n",
    "    )\n",
    "    sim_data = web.run(sim, task_name=\"gc_measurement_calibration_opt\", verbose=False)\n",
    "    power_da = get_mode_monitor_power(sim_data)\n",
    "    power = np.array(power_da.data).squeeze()\n",
    "    diff = power - target_power\n",
    "    return np.mean(diff**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a2b4a5fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter 0: mse=1.168650e-04, bias=0.000000\n",
      "iter 1: mse=9.655110e-05, bias=0.002000\n",
      "iter 2: mse=8.147495e-05, bias=0.003991\n",
      "iter 3: mse=6.712432e-05, bias=0.005965\n",
      "iter 4: mse=5.820263e-05, bias=0.007910\n",
      "iter 5: mse=4.864759e-05, bias=0.009820\n",
      "iter 6: mse=4.191141e-05, bias=0.011675\n",
      "iter 7: mse=3.766940e-05, bias=0.013464\n",
      "iter 8: mse=3.487550e-05, bias=0.015177\n",
      "iter 9: mse=3.235886e-05, bias=0.016812\n",
      "iter 10: mse=3.058458e-05, bias=0.018360\n",
      "iter 11: mse=2.997697e-05, bias=0.019815\n"
     ]
    }
   ],
   "source": [
    "params0 = {\"bias\": 0.0}\n",
    "bounds = {\"bias\": (-0.05, 0.05)}\n",
    "\n",
    "vg_fun = value_and_grad(objective)\n",
    "params = deepcopy(params0)\n",
    "opt_state = init_adam(params, lr=2e-3)\n",
    "\n",
    "mse_history = []\n",
    "bias_history = []\n",
    "best_value = float(\"inf\")\n",
    "best_params = deepcopy(params)\n",
    "num_iters = 12\n",
    "for n in range(num_iters):\n",
    "    value, grad = vg_fun(params)\n",
    "    value_float = float(value)\n",
    "    mse_history.append(value_float)\n",
    "    bias_history.append(float(params[\"bias\"]))\n",
    "    print(f\"iter {n}: mse={value_float:.6e}, bias={float(params['bias']):.6f}\")\n",
    "\n",
    "    if value_float < best_value:\n",
    "        best_value = value_float\n",
    "        best_params = deepcopy(params)\n",
    "\n",
    "    updates, opt_state = adam_update(grad, opt_state)\n",
    "    params = apply_updates(params, updates)\n",
    "    params = clip_params(params, bounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "702147d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimized_bias = best_params[\"bias\"]\n",
    "calibrated_widths_sin = widths_sin_nominal + optimized_bias\n",
    "calibrated_first_gap_sin, calibrated_gaps_sin = centers_widths_to_spacing(\n",
    "    sin_centers, calibrated_widths_sin, last_gap=sin_last_gap_template\n",
    ")\n",
    "\n",
    "calibrated_sim = make_simulation(\n",
    "    widths_si_nominal,\n",
    "    gaps_si_nominal,\n",
    "    calibrated_widths_sin,\n",
    "    calibrated_gaps_sin,\n",
    "    first_gap_si=first_gap_si_nominal,\n",
    "    first_gap_sin=calibrated_first_gap_sin,\n",
    ")\n",
    "calibrated_data = web.run(\n",
    "    calibrated_sim, task_name=\"gc_measurement_calibration_final\", verbose=False\n",
    ")\n",
    "calib_freqs, calib_power = extract_spectrum(calibrated_data)\n",
    "wl_calib, calib_db = to_wavelength_db(calib_freqs, calib_power)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "70ab2c7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TUVFhenp6rGvXruzMmTNi+8vJyWGenp5MW1ubaWpqMnd3d5acnFzq49cpKSklHs/BgwdZx44dmbq6OlNXV2dNmjRhkyZNYg8fPuTqODk5sebNm0vdFgDYpEmTxMqSkpLYpEmTmLm5OVNUVGQmJibs22+/ZVu3buXqFD/O/Omj5yU9bv7u3Ts2ePBgpqOjwwBI9Sh2UVER++OPP1inTp2YtrY2U1RUZPXr12cjR44UezT7zZs3bOTIkczAwIBpaGgwFxcXFhsby+rXr8+GDx8uEa80j1+vXLmSBQQEMHNzc6asrMw6derE/vnnH7H4hg8fztTV1UuM/f79+8zZ2ZlpaGgwAwMDNmbMGPbPP/9ItEtRURGbPHkyMzQ0ZDweT+xR7E/PDcYYu3XrFnNxcWEaGhpMTU2Nde3alV29elWsTmn/N0o6fkLKwmPUo4oQQgghXynqI0MIIYSQrxYlMoQQQgj5alEiQwghhJCvFiUyhBBCCPlqUSJDCCGEkK8WJTKEEEII+WrRgHiVSCQS4fXr19DU1JRp6HNCCCGktmOM4e3btzAzM5OYK+xjlMhUotevX9O8IoQQQsgXePnyZYmztxejRKYSaWpqAu9/CRU1v4hIJEJKSgoMDQ3LzFDJB9RmsqH2kh21meyozWRX29osKysL5ubm3HdpaSiRqUTFt5O0tLQqNJHJy8uDlpZWrTiRKwK1mWyovWRHbSY7ajPZ1dY2K69rRu1pCUIIIYTUOJTIEEIIIeSrRYkMIYQQQr5alMgQQggh5KtVLRKZTZs2wcLCAioqKnBwcMD169fLrB8SEoImTZpARUUFNjY2OHHiBLessLAQc+bMgY2NDdTV1WFmZgYPDw+8fv1abBvp6ekYMmQItLS0oKOjA09PT7x7906szu3bt9GpUyeoqKjA3NwcK1asqOAjJ4QQQsiXkHsiExwcDG9vb/j5+eHWrVto1aoVXFxckJycXGL9q1evYtCgQfD09ER0dDTc3Nzg5uaGu3fvAgBycnJw69YtLFiwALdu3cKhQ4fw8OFD9OnTR2w7Q4YMwb179xAWFoZjx47h4sWLGDt2LLc8KysLPXr0QP369REVFYWVK1di4cKF2Lp1ayW3CCGEEEKkxuSsbdu2bNKkSdx7oVDIzMzMmL+/f4n13d3dmaurq1iZg4MDGzduXKn7uH79OgPAXrx4wRhj7P79+wwAu3HjBlfn77//Zjwej7169YoxxtjmzZuZrq4uy8/P5+rMmTOHWVtbS31smZmZDADLzMyUep3yCIVClpCQwIRCYYVts6ajNpMNtZfsqM1kR20mu+rYZkXCInY59iY7dP0Uuxx7kxUJiyps29J+h8p1HJmCggJERUXBx8eHK+Pz+XB2dkZERESJ60RERMDb21uszMXFBaGhoaXuJzMzEzweDzo6Otw2dHR00KZNG66Os7Mz+Hw+IiMj0a9fP0RERKBz585QUlIS28+vv/6KN2/eQFdXV2I/+fn5yM/P595nZWUBANiGNmAqgrIbw7Ql2MC/xIp4+wYBCbfFy8BgKBKBx+eD4b9n61m7iYDjpI8CeQve5nZl7+89NmAPYGb7oeDRSfCOzyh/RSV1sEnitwB5YQuAu4fKX9eqO1jvteLr/t4VeFfyVTixeJ0XAjb9PxSkxoG3y63MdYrbTDT6LKBt9mFB1HbwLq4sP179hmAeR8W3eWgM8OJq+fG29gCc5oivu6Z5+fsEwPr9Blh0/FDw/DJ4h8dJt+70e+IFF34F79bO8les3x4it9/AGINIJPov3p19gLQn5e+z8yzAfsSHgreJ4P3xrXTxDgsFDKw+FNwJAe/MwvJX1DACG3NOrIh3bBoQF1b+ui1+AOu+RHzdTW2Bguzy43UNABr35N6LXt2C4b7BYv8vS1134jVA+aMBviI2gXdtc/nxSvkZUeI+q+FnREmfZZwq/ozg1h0dDmiafCioZp8Rn7aZvD4j2A+/AwBORJ/HgpC12MC7AQeFAgBAKp8PLVVNqCgqS+5Txs8IlieU6ljkmsikpqZCKBTC2NhYrNzY2BixsbElrpOYmFhi/cTExBLr5+XlYc6cORg0aBA3KF1iYiKMjIzE6ikoKEBPT4/bTmJiIiwtLSX2U7yspETG398fixYtkijnvUsEr7DsD7cCNSOkf3I7TS8zEUpvX0vU/TQlyk5PxLuP1uUVvINxCeuVJD01CYUKH9ZVTkuCrhTrihTVJW7/ab1JhJoU6+ZlJCLjk3UNsxIgyE4qd92s9GTkfrSuQnoyDKTYpwBAYkoykP/hlFdLT4KWFOsWKqgh7ZN4dTMSoSzFujlvEvH2k3VNpPzdvElLQoHah3WV0pKgJ+W6SZ/sU/NNItSlWDc/IxFpycnIzMwEYwx8Ph/6mQlQlGLdt+lJyPlov/x3yTCSMt601GQUibS596rpydCWYl2hSIiUT45VJyMRKlL+brI+Wdco6zX4heUnMhlpScj/aF1BahIMpTh/ASA5JQVMKZd7r5GeCA0p4pXlM+JT1fUzorQ/7+TxGQEAKSnJEOV+6HFRHT8jPm4zeX1GvElORvj9CMzc/ysYAEP9IpgJij5UyksD8iTXlfUzgpfPpDqWGj2yb2FhIdzd3cEYw5YtWyp9fz4+PmJXi4qHV2YaJuVekVHUNpFIrnjaJmA5Zp/U/O8v5f9GdfwvOVLTM4Hax+vmq4JpfrpeyXQNjIGP180wlmpdnpK6ZLy6JlKtq6xTwrFqmYLxy7lqBUBTzwiaH6/Lz5Rin/+1mYGhEfjaH62rJ92xKmibSsarYwKWWf66qromUP1kXWl/Nzr6n/xucqSLF4BEvJDyd6P0/nfD4/G4YdB52qZgRTnlrquhZwyNj/erKpI6Xj0DI8Dgo3WTjKRal69hVPLvRop1VXVNoCJxHpqBSXFFRvuT342o0BhCdWOx/5elMTQ0FL8ioyddvNJ/Rkiqnp8Rkp9lxar+M+I/BoZGgGZ1/owQbzN5fUboG+hj1ekgFKcZKSIFaApFYvUEfD6MtPTFfreyfkYwRSGAt+XGxGOMSZfyVIKCggKoqanhwIEDcHP7cOlv+PDhyMjIwJEjRyTWqVevHry9vTFt2jSuzM/PD6Ghofjnn3+4suIk5unTpzh79iz09fW5Zdu2bcOMGTPw5s0brqyoqAgqKioICQlBv3794OHhgaysLLFbVufOnUO3bt2Qnp5e4hWZT2VlZUFbWxuZmZkVOkVBcnIyjIyMatUQ1V+C2kw21F6yozaTHbWZ7KpLm115GIUf10wqt97B6ZvQwdr+s/cj7XeoXM8eJSUl2NvbIzw8nCsTiUQIDw+Ho6Njies4OjqK1QeAsLAwsfrFSUxcXBzOnDkjlsQUbyMjIwNRUVFc2dmzZyESieDg4MDVuXjxIgoLC8X2Y21tLVUSQwghhNREVx5GSVELSM5Kq/RYIO9EBgC8vb3x+++/Y8eOHXjw4AEmTJiA7OxsjBw5EgDg4eEh1hl46tSpOHnyJAICAhAbG4uFCxfi5s2b8PLyAt4nMT/99BNu3ryJPXv2QCgUIjExEYmJiSgo+K8zUtOmTdGzZ0+MGTMG169fx5UrV+Dl5YWBAwfCzOy/y1yDBw+GkpISPD09ce/ePQQHB2PdunUSHY0JIYSQ2uD+v3EYstEbq09sk6r+f7eWKp/c+8gMGDAAKSkp8PX1RWJiImxtbXHy5EmuY218fLzYJbT27dtj7969mD9/PubNmwcrKyuEhoaiRYsWAIBXr17h6NH/eo/b2tqK7evcuXPo0qULAGDPnj3w8vLCt99+Cz6fjx9//BHr16/n6mpra+P06dOYNGkS7O3tYWBgAF9fX7GxZgghhJCa7kXqa6z831YcvH4K0vRG4QEw1TVGOyvbcutWBLn2kanpqI9M9UBtJhtqL9lRm8mO2kx2Vd1mKVnpWPf3duy4eAiFwg9PJdXRNUaPlp2w/cIBAMDHSURx194/xvnD1a7rF+1f2u9QuV+RIYQQQkj18S4vG1vC9iLwzF/Izv/wxKKuuham9hqBEU4/QkVRGR2b2GN+8BokZHx4pNpU1xhL3Kd9cRIjC0pkCCGEEIL8wgLsunQYa/7ejrS3H57qVVVSwbhvB2Jij6HQUtXgyl3tuqJnq864FheD5Kw0GGnpo52VLQRSPCZfkSiRIYQQQmoxkUiEQzdO4dejW/EyLYErV+ALMKRjX3i7joKxtkGJ6wr4gi96xLoiUCJDCCGE1EKMMYTfjcDy0M24/+qx2LK+bZwxt884WBqZyy0+aVEiQwghhNQyN5/ewdLDm3EtLlqsvEszB/j0nYBW9ZvILTZZUSJDCCGE1BIPXz+D/5EtOPnPRbFy2/rNML/fRHRs0qbUdasrSmQIIYSQGu5VehJWHvsd+yNOQMQ+zIvU0Lge5vYZj96tu4LHK3uesOqKEhlCCCGkhkp/l4n1J3cg6PwB5BcVcOXG2gaY2Xs0BrbvDUXB150KfN3RE0IIIURCdn4ufg/fh02nd+Nt3ocZ3bVUNTDZxQOe3dyhpqQi1xgrCiUyhBBCSA1RKCzCnstHsPr4NrFJG1UUleHZtT+8XIZBV11brjFWNEpkCCGEkK+cSCTC0Vvh+PXIb3iW8i9XzufxMah9b8zoPRpmukZyjbGyUCJDCCGEVHNCkRARj6IR9/IprMwbwLGxHTeC7oX7kVgWuhm34x+KreNq1wVz+46HlYmFnKKuGpTIEEIIIdXY8ehzknMa6RjBs2t/XHgQiUuxN8Xqt2/cGvP7TURryxZyiLbqUSJDCCGEVFPHo89h9G8+YjNMA0BCRjKWHt4kVtbCvDHmuU1A12btvtpHqT8HJTKEEEJINSQUCTE/eI1EEvOpevpm8HEbj772zuDz+VUUXfVBiQwhhBBSDV2LixG7nVSalUPmwqlZ2yqJqTqqfakbIYQQ8hVIzEiRql56dkalx1KdUSJDCCGEVDM3n97BmhNBUtU10tKv9HiqM7q1RAghhFQTL9MSsOzwZoTeDCu3Lg+Aqa4x2lnZVkls1RUlMoQQQoicZeflYOPpXdgSthd5hflcuZmuMV6/SQIPEOv0W/xM0hL3adx4MrUVJTKEEEKInIhEIuy/dgL+RwKRlJnKletp6GBOn7EY0qEPTt2+JDmOjK4xlrhPg6tdVzlFXn1QIkMIIYTIwbW4GPiGrMXt+FiuTFGgAM+u7pj+3Uhoq2kCAFztuqJnq86ljuxb21EiQwghhFShF6mvsfTQJvzvVrhYea9WTljwwyQ0MK4nsY6AL0D7xq3RSKcujIyMauV4MaWhRIYQQgipAu/ysrHu7x3YGr4P+UUFXHmzOo2wuP80dGzSRq7xfa0okSGEEEIqkVAkRHDEcfgfCURKVjpXbqCpi7l9xmFQh+/pNtEXkPu1qU2bNsHCwgIqKipwcHDA9evXy6wfEhKCJk2aQEVFBTY2Njhx4oTY8kOHDqFHjx7Q19cHj8dDTEyM2PLnz5+Dx+OV+AoJCeHqlbR83759FXz0hBBCarIrD6Pg4j8S3ruWc0mMkoIivFyGIWLxAQzt5EZJzBeSayITHBwMb29v+Pn54datW2jVqhVcXFyQnFzykMxXr17FoEGD4OnpiejoaLi5ucHNzQ13797l6mRnZ6Njx4749ddfS9yGubk5EhISxF6LFi2ChoYGevXqJVY3KChIrJ6bm1sFtwAhhJCa6HnKvxgVOAc/rpmEuy8fceWudl1xyW8f5vebBE1VdbnGWFPwGGPlzUdVaRwcHPDNN99g48aNwPvH0MzNzTF58mTMnTtXov6AAQOQnZ2NY8eOcWXt2rWDra0tAgMDxeo+f/4clpaWiI6Ohq1t2YMF2dnZoXXr1vjzzz+5Mh6Ph8OHD8uUvOTn5yM//8Pz/1lZWTA3N8ebN2+gpaUl9XbKIhKJkJKSAkNDQ+rsJSVqM9lQe8mO2kx2NbXNsnLfYd3fO/Dn+f0oKCrkyluYN8aiH6fCsbHdZ2+7prZZabKysqCrq4vMzMwyv0Pl1kemoKAAUVFR8PHx4cr4fD6cnZ0RERFR4joRERHw9vYWK3NxcUFoaOhnxxEVFYWYmBhs2rRJYtmkSZMwevRoNGjQAOPHj8fIkSPLnBrd398fixYtkihPSUlBXl7eZ8f4MZFIhMzMTDDGasWJXBGozWRD7SU7ajPZ1bQ2E4qEOHzrDDad3Ys3OZlcuYGGLry+HYrvW3WBgC8o9Y6DNGpam5Xn7du3UtWTWyKTmpoKoVAIY2NjsXJjY2PExsaWuE5iYmKJ9RMTEz87jj///BNNmzZF+/btxcoXL16Mbt26QU1NDadPn8bEiRPx7t07TJkypdRt+fj4iCVaxVdkDA0NK/SKDI/HqzUZeUWgNpMNtZfsqM1kV5Pa7FLsTSw8sA4PXj/hypQVlDDu24HwchkGDZWKuYVUk9pMGioqKlLVq9VPLeXm5mLv3r1YsGCBxLKPy+zs7JCdnY2VK1eWmcgoKytDWVlZopzP51foScfj8Sp8mzUdtZlsqL1kR20mu6+9zZ4mxWPRwQ04dfuSWHnfNs742W0i6hmYVfg+v/Y2k4W0xyi3RMbAwAACgQBJSUli5UlJSTAxMSlxHRMTE5nql+fAgQPIycmBh4dHuXUdHBywZMkS5Ofnl5isEEIIqR0yc95i9Ylt2HYuBIXCIq68Vf2mWNJ/Gto2aiXX+GobuaV0SkpKsLe3R3j4h5ENRSIRwsPD4ejoWOI6jo6OYvUBICwsrNT65fnzzz/Rp08fGBoalls3JiYGurq6lMQQQkgtVSQsQtD5A3Bc8BN+O/MXl8SYaBti/Qhf/D3nT0pi5ECut5a8vb0xfPhwtGnTBm3btsXatWuRnZ2NkSNHAgA8PDxQp04d+Pv7AwCmTp0KJycnBAQEwNXVFfv27cPNmzexdetWbpvp6emIj4/H69evAQAPHz4E3l/N+fjKzePHj3Hx4kWJcWgA4H//+x+SkpLQrl07qKioICwsDMuXL8fMmTMrvU0IIYTIj1AkxLW4GCRnpcFISx/trGwh4Atw/n4kfEPW4lHCM66uiqIyJvYYikk9hkJdWVWucddmck1kBgwYgJSUFPj6+iIxMRG2trY4efIk16E3Pj5e7B5Z+/btsXfvXsyfPx/z5s2DlZUVQkND0aJFC67O0aNHuUQIAAYOHAgA8PPzw8KFC7nybdu2oW7duujRo4dEXIqKiti0aROmT58OxhgaNWqE1atXY8yYMZXWFoQQQuTrePQ5iVmmDbX0YKpjJDaxIwD88E0PzOs3EXX1Pq9rA6k4ch1HpqbLysqCtrZ2uc/Ay0IkEiE5OZkmDZMBtZlsqL1kR20mu+rWZsejz2H0bz4o7wuxtWVzLOk/HfYNWpRTs+JVtzarbNJ+h9bqp5YIIYQQoUiI+cFrykxi+Dw+1g9fgB8depY5nhipejU/pSOEEELKcC0uRux2UklETARTXSNKYqohSmQIIYTUah934C1LclZapcdCZEe3lgghhNRKhcIibDsXAv8jgVLUBoy09Cs9JiI7SmQIIYTUOhFx0fD5axViP5pWoDQ8AKa6xmhnVfYExEQ+ZEpkRCIRLly4gEuXLuHFixfIycmBoaEh7Ozs4OzsDHNz88qLlBBCCPlCSZmpWHJoIw5EnuTKeDweOjf5BhcfXAcAsU6/xT1ilrhPg4AvqOJoiTSk6iOTm5uLpUuXwtzcHN999x3+/vtvZGRkQCAQ4PHjx/Dz84OlpSW+++47XLt2rfKjJoQQQmRQJCzC1vB96ODnLpbEtKrfFCfm/Ingqevxxzh/mOgYia1nqmuMP8b5w9WuqxyiJtKQ6opM48aN4ejoiN9//x3du3eHoqKiRJ0XL15g7969GDhwIH7++WcaPI4QQki1cC0uBj77VuLBqw+3kXTVtTDPbSIGd/ieu9LiatcVPVt1LnFkX1J9SZXInD59Gk2bNi2zTv369eHj44OZM2ciPj6+ouIjhBBCPktyZhoWH9qIA5F/c2U8Hg9DOvSBj9sE6GvoSKwj4AvQwdq+iiMlX0KqRKa8JOZjioqKaNiw4ZfERAghhHy2/yZ3PIgV/9uKt3nZXHnLek3wy6CZaG1Z9aPyksrzWU8t5eXl4fbt20hOToZIJBJb1qdPn4qKjRBCCJFJ5OMY+Py1CvdfPebKdNS04OM2HkM79qXbRDWQzInMyZMn4eHhgdTUVIllPB4PQqGwomIjhBBCpJKSlYbFhzYh5NoJsfIhHfpgXr+JJd5GIjWDzInM5MmT0b9/f/j6+nKzVBNCCCHyUCQswvaLh7Di6FZk5b7jylvWs4b/wFlymdyRVC2ZE5mkpCR4e3tTEkMIIUSurj/+Bz77VuHev3FcmbaaJnz6jsewTm50G6mWkDmR+emnn3D+/Hnq0EsIIUQuUrLSsfTwJgRHHBcrH9T+e/zcbyIMNHXlFhupejInMhs3bkT//v1x6dIl2NjYSIwpM2XKlIqMjxBCCAHe30bacfEwfj36m9htpBbmjfHLoFlo08BGrvER+ZA5kfnrr79w+vRpqKio4Pz582JTmvN4PEpkCCGEVLgbT27DZ98q3H35iCvTVtPEnD7jMLxzP7qNVIvJnMj8/PPPWLRoEebOnQs+X6oZDgghhJDPkpKVjmWhm7Hv6jGx8gGOrpjfbxIMtfTkFhupHmROZAoKCjBgwABKYgghhFQaoUiInRcP45ejvyEz5y1X3ryuFfwHzkTbRq3kGh+pPmROZIYPH47g4GDMmzevciIihBBSq0U9vYu5f63AnY9uI2mpanC3kRQEnzWWK6mhZD4bhEIhVqxYgVOnTqFly5YSnX1Xr15dkfERQgipYYQiISIeRSPu5VNYmTeAY2M7CPgCpL59g+Whm7H3yv/E6ru3+w4LfpgEQy19ucVMqi+ZE5k7d+7Azs4OAHD37t3KiIkQQkgNdTz6HOYHr0FCRjJXZqpjiG9btMexW+eQkZPFlTer0wj+g2bCoZGtnKIlXwOZE5lz585VTiSEEEJqtOPR5zD6Nx+wT8oTMlKw+/IR7r2mijrm9BmLEU4/0m0kUq4K67H74sULeHl5VdTmCCGE1CBCkRDzg9dIJDGf+rFtT1xZtB+juw2gJIZIReZEpmvXrujWrZvEq2fPnti9e7fMAWzatAkWFhZQUVGBg4MDrl+/Xmb9kJAQNGnSBCoqKrCxscGJE+IThB06dAg9evSAvr4+eDweYmJiJLbRpUsX8Hg8sdf48ePF6sTHx8PV1RVqamowMjLCrFmzUFRUJPPxEUIIAa7FxYjdTirN4A7fw0ib+sIQ6cmc7trait+rFAqFePr0KR4/fozt27fLtK3g4GB4e3sjMDAQDg4OWLt2LVxcXPDw4UMYGRlJ1L969SoGDRoEf39/9O7dG3v37oWbmxtu3bqFFi3+mxgsOzsbHTt2hLu7O8aMGVPqvseMGYPFixdz79XU1MSOydXVFSYmJrh69SoSEhLg4eEBRUVFLF++XKZjJIQQAiRnpVVoPUKK8Rhj5V3pk8off/yBP//8ExEREVKv4+DggG+++QYbN24EAIhEIpibm2Py5MmYO3euRP0BAwYgOzsbx459GBipXbt2sLW1RWBgoFjd58+fw9LSEtHR0RLJV5cuXWBra4u1a9eWGNfff/+N3r174/Xr19zkmIGBgZgzZw5SUlKgpKQk1fFlZWVBW1sbmZmZ0NLSkmqd8ohEIiQnJ8PIyIjG8pEStZlsqL1kR21WtoKiQsze8yv2RRwrt+7B6ZvQwdq+SuL62tS280za79AKuwH57bffYvLkyVLXLygoQFRUFHx8fLgyPp8PZ2fnUpOhiIgIeHt7i5W5uLggNDRU5nj37NmD3bt3w8TEBN9//z0WLFjAXZWJiIiAjY2N2AzfLi4umDBhAu7du8c9tfWp/Px85Ofnc++zsv7rfS8SiSASiWSOsSQikQiMsQrbXm1AbSYbai/ZUZuVLvLxP5i991fEJT4vsx4PgKmuEdo2bEntWIradp5Je5wVlsicPXsWXbt2lbp+amoqhEKhWLIAAMbGxoiNjS1xncTExBLrJyYmyhTr4MGDUb9+fZiZmeH27duYM2cOHj58iEOHDpW5n+JlpfH398eiRYskylNSUpCXlydTjKURiUTIzMwEY6xWZOQVgdpMNtResqM2k5SV+w7rzuzEwajTXBkfPIhK6O5bPGPfjO4jkZZKt5ZKU9vOs7dv30pR6zMSmR9++EGiLCkpCZGRkejatavY8uLEoLoZO3Ys97ONjQ1MTU3x7bff4smTJ2jYsOFnb9fHx0fsilFWVhbMzc1haGhYobeWeDweDA0Na8WJXBGozWRD7SU7arMPGGM4GhUO3wNrkZKVzpXb1m+KFYPnID71NRaErBUfR0bXCIt/mobv7LrIKeqvQ207z1RUVKSqJ3Mio62tXWJZ48aNZdqOgYEBBAIBkpKSxMqTkpJgYmJS4jomJiYy1ZeWg4MDAODx48do2LAhTExMJJ6eKt5vWftSVlaGsrKyRDmfz6/Qk47H41X4Nms6ajPZUHvJjtoMiE99DZ99qxB+9ypXpq6sBp++4zCyy08Q8AVoWb8Jetk5lTiyLylfbTrPpD1GmROZoKCgz4lHgpKSEuzt7REeHg43NzfgfbYZHh5e6ng0jo6OCA8Px7Rp07iysLAwODo6flEsxY9om5qacvtZtmwZ16mqeD9aWlpo1qzZF+2LEEJqmiJhEX4/G4wV//sduQUfbqO7tOyE5QNnoo6e+K16AV+A9o1bo5FO3VrTcZVUHqkSGcYYeDyeFDVl4+3tjeHDh6NNmzZo27Yt1q5di+zsbIwcORIA4OHhgTp16sDf3x8AMHXqVDg5OSEgIACurq7Yt28fbt68ia1bt3LbTE9PR3x8PF6/fg0AePjwIfD+SoqJiQmePHmCvXv34rvvvoO+vj5u376N6dOno3PnzmjZsiUAoEePHmjWrBmGDRuGFStWIDExEfPnz8ekSZNKvOJCCCG11T8vYjFz93KxCR5NtA2xbKA3vrPtUinfHYSIYVJo2rQp++uvv1h+fn6Z9R49esTGjx/P/P39pdksY4yxDRs2sHr16jElJSXWtm1bdu3aNW6Zk5MTGz58uFj9/fv3s8aNGzMlJSXWvHlzdvz4cbHlQUFBDIDEy8/PjzHGWHx8POvcuTPT09NjysrKrFGjRmzWrFksMzNTbDvPnz9nvXr1YqqqqszAwIDNmDGDFRYWSn1cjDGWmZnJAEhs+0sIhUKWkJDAhEJhhW2zpqM2kw21l+xqY5u9y81mC4LXMNPxjsx4nAMzHufATMa3Y3P3rmSZOW/LXb82ttmXqm1tJu13qFTjyISHh2POnDl4+vQpunfvjjZt2sDMzAwqKip48+YN7t+/j8uXL+PevXvw8vLCvHnzSuxLU9vQODLVA7WZbKi9ZFfb2uz07Uvw+WsVXr350GexiVlDBAz1gX2DFlJto7a1WUWobW1WoePIfPvtt7h58yYuX76M4OBg7NmzBy9evEBubi4MDAxgZ2cHDw8PDBkyBLq6uhV5HIQQQqqJpMxU/By8GsduneXKVBSVMcPVE+O7D4YizY1E5ECms65jx47o2LFj5UVDCCGk2hGJRNh56TCWHd6Mt3nZXLlT07b4dfBsWBjWlWt8pHaj9JkQQkipHrx6gtl7fsGNp3e4Mj0NHSzuPxU/tu1JnXmJ3FEiQwghREJuQR7WngjCptO7USQScuUD2/eG7w+ToadB/SBJ9UCJDCGEEDGXYm9g9p5f8SzlX66sgZE5VgyZi440oSOpZiiRIYQQAgBIe5eBhQfWI+TaCa5MUaAALxcPTO01HCqKNI4WqX4okSGEkFqOMYaQyL+xMGQd0rMzufK2DVti5RAfWJtZyjU+QsryWYmMSCTC48ePkZycLDHNdufOnSsqNkIIIZXsaVI85vy1Apdib3JlWqoaWPCDF4Z06FMrxishXzeZE5lr165h8ODBePHiBT4dS4/H40EoFJa6LiGEkOqhoKgQW8L2YPXxbcgvKuDK+9h/iyXu02GsbSDX+AiRlsyJzPjx49GmTRscP34cpqam9OgdIYRUU0KRENfiYpCclQYjLX20s7KFgC/AjSe3MXPPL3j4+ilXt66eCX4ZNBvONu3lGjMhspI5kYmLi8OBAwfQqFGjyomIEELIFzsefQ7zg9cgISOZKzPRNoB1nQa4+OAGd0Wdz+Nj7LcDMev7MVBXVpVjxIR8HpkTGQcHBzx+/JgSGUIIqaaOR5/D6N988OlEeomZqUjMTOXet6zXBKuGzkXLek2qPEZCKorMiczkyZMxY8YMJCYmwsbGBoqKimLLW7ZsWZHxEUIIkYFQJMT84DUSSczHeAD8fpqC0V3doUDzI5GvnMxn8I8//ggAGDVqFFfG4/HAGKPOvoQQImfX4mLEbieVhAGwMbemJIbUCDKfxc+ePaucSAghhHyx5Ky0Cq1HSHUncyJTv379yomEEELIFxGKhIh4FC1VXSMt/UqPh5Cq8FnXFZ88eYK1a9fiwYMHAIBmzZph6tSpaNiwYUXHRwghRAqxr59i+s6liH5+v8x6PACmusZoZ2VbZbERUplkHrLx1KlTaNasGa5fv46WLVuiZcuWiIyMRPPmzREWFlY5URJCCClRobAIa08Eocfy4RJJzKejfBW/X+I+DQK+oMpiJKQyyXxFZu7cuZg+fTp++eUXifI5c+age/fuFRkfIYSQUtx9+QjTdy7FnZePuLJGxvWxxuNnJGelSYwjY6prjCXu0+Bq11VOERNS8WROZB48eID9+/dLlI8aNQpr166tqLgIIYSUIr+wAGv/3o4NJ3egSPTfk6J8Hh8TewzBzN6juVmqe7bqXOLIvoTUJDInMoaGhoiJiYGVlZVYeUxMDIyMjCoyNkIIIZ+Ifn4f03YuFZteoIlZQ6zx+Bl2Fs3E6gr4AnSwtpdDlIRUHZkTmTFjxmDs2LF4+vQp2rf/b06OK1eu4Ndff4W3t3dlxEgIIbVebkEeVh37A1vC9kLERAAABb4AU3qNwLReI6CkoFjuNgipiWROZBYsWABNTU0EBATAx8cHAGBmZoaFCxdiypQplREjIYTUatcf/wPvXcvxOOkFV2Zj3hhrhy9A87pWZa5LSE0ncyLD4/Ewffp0TJ8+HW/fvgUAaGpqVkZshBBSq2Xn5+KXI4H449x+bpJHJQVFzOw9GhO6D4EijcxLiOyPX39MU1Pzi5OYTZs2wcLCAioqKnBwcMD169fLrB8SEoImTZpARUUFNjY2OHHihNjyQ4cOoUePHtDX1wePx0NMTIzY8vT0dEyePBnW1tZQVVVFvXr1MGXKFGRmZorV4/F4Eq99+/Z90bESQoi0Lj+MQrclQ/H72WAuiWlt2Rxh83ZiSs/hlMQQ8p5U/xNat26N8PBw6Orqws7ODjzep6MTfHDr1i2pdx4cHAxvb28EBgbCwcEBa9euhYuLCx4+fFhix+GrV69i0KBB8Pf3R+/evbF37164ubnh1q1baNGiBQAgOzsbHTt2hLu7O8aMGSOxjdevX+P169dYtWoVmjVrhhcvXmD8+PF4/fo1Dhw4IFY3KCgIPXv25N7r6OhIfWyEEPI53uZmY8nhjdh58TBXpqKojLl9x2FMtwH01BEhn5Aqkenbty+UlZW5n8tKZGSxevVqjBkzBiNHjgQABAYG4vjx49i2bRvmzp0rUX/dunXo2bMnZs2aBQBYsmQJwsLCsHHjRgQGBgIAhg0bBgB4/vx5ifts0aIFDh48yL1v2LAhli1bhqFDh6KoqAgKCh+aREdHByYmJhVyrIQQUp5z965h5m5/vHqTxJW1a2SL1cPmoYFxPbnGRkh1JVUi4+fnx/28cOHCCtlxQUEBoqKiuA7DAMDn8+Hs7IyIiIgS14mIiJB4MsrFxQWhoaFfFEtmZia0tLTEkhgAmDRpEkaPHo0GDRpg/PjxGDlyZJlJXH5+PvLz87n3WVlZAACRSASRSPRFMRYTiURgjFXY9moDajPZUHvJ7kvbLCMnC4sObkBwxHGuTE1ZFfP6TsCIzj+Az+fXuN8HnWeyq21tJu1xynyTtUGDBrhx4wb09cUnHMvIyEDr1q3x9OnTUtf9WGpqKoRCIYyNjcXKjY2NERsbW+I6iYmJJdZPTEyU9TDE4liyZAnGjh0rVr548WJ069YNampqOH36NCZOnIh3796V+WSWv78/Fi1aJFGekpKCvLy8z47xYyKRCJmZmWCMgc//oi5OtQa1mWyovWT3JW12PvY6lh3bgpR3b7gyB8uW8O0zCXV0jZGamloJEcsfnWeyq21tVvxAUXlkTmSeP38OoVAoUZ6fn49///1X1s3JVVZWFlxdXdGsWTOJK00LFizgfrazs0N2djZWrlxZZiLj4+MjdsUoKysL5ubmMDQ0hJaWVoXELBKJwOPxYGhoWCtO5IpAbSYbai/ZfU6bpb3LgG/IWhy+cZor01RRh++PkzG4/fcVdgu/uqLzTHa1rc1UVFSkqid1InP06FHu51OnTkFbW5t7LxQKER4eDktLS6kDNDAwgEAgQFJSklh5UlJSqf1STExMZKpflrdv36Jnz57Q1NTE4cOHoahY9mBSDg4OWLJkCfLz87n+Qp9SVlYucRmfz6/Qk47H41X4Nms6ajPZUHvJTpY2+1/UWfjsW4nUtx+uwnRr7ohfB82GoYYuCgsLKzla+ROJRCgqKkJBQQGdZ1KqaW2mqKgIgaD0zuvSHqPUiYybmxvw/j/r8OHDJYKxsLBAQECAtJuDkpIS7O3tER4ezm1bJBIhPDwcXl5eJa7j6OiI8PBwTJs2jSsLCwuDo6Oj1PvF+yslLi4uUFZWxtGjR6XK+mJiYqCrq1tqEkMIIeVJyUrD3L9W4Xj0Oa5MR00LS9yno0O9VshMycC7lAy5xlhVivt6vH37tsZffaooNbHNih+q+ZLjkTqRKe50Y2lpiRs3bsDAwOCzd1rM29sbw4cPR5s2bdC2bVusXbsW2dnZ3FNMHh4eqFOnDvz9/QEAU6dOhZOTEwICAuDq6op9+/bh5s2b2Lp1K7fN9PR0xMfH4/Xr1wCAhw8fAu+v5piYmCArKws9evRATk4Odu/ejaysLK5TrqGhIQQCAf73v/8hKSkJ7dq1g4qKCsLCwrB8+XLMnDnzi4+ZEFL7MMZw6PopzN+/Gm+ys7jyXq2c8MvgWRDlFCIjIwNGRkZQU1OrMV9SZWGMcU+K1objrQg1qc0YY8jJyUFy8n+zs5uamn7RxuRqw4YNrF69ekxJSYm1bduWXbt2jVvm5OTEhg8fLlZ///79rHHjxkxJSYk1b96cHT9+XGx5UFAQAyDx8vPzY4wxdu7cuRKXA2DPnj1jjDH2999/M1tbW6ahocHU1dVZq1atWGBgIBMKhTIdW2ZmJgPAMjMzv6CFxAmFQpaQkCBzLLUZtZlsqL1kV1abvU5PYkM3ejPjcQ7cq+kMFxZ6I4yJRCJWVFTE7t+/z1JTU+USu7yIRCJWUFDARCKRvEP5atTENktNTWX3799nRUVFEsuk/Q7lseIhI2WQnZ2NCxcuID4+HgUFBWLLaL6lD7KysqCtrc093l0RRCIRkpOTYWRkVCPukVYFajPZUHvJrqQ2Y4zhr6vHsPDAOmTlvuPqurXpjqUDvGGgqQsAyMvLw7Nnz2BhYQFVVVW5HUNVq0lXF6pKTWyz3NxcPH/+HJaWlhLdPKT9DpX5qaXo6Gh89913yMnJQXZ2NvT09JCamgo1NTUYGRlRIkMIqVWEIiEiHkUj7uVTWJk3gGNjO7x+k4yZu/1x4cGHKVeMtPTx6+DZ6GXrVOJ2asoXEyGyqIjzXuZEZvr06fj+++8RGBgIbW1tXLt2DYqKihg6dCimTp36xQERQsjX4nj0OcwPXoOEjGSuTFtVA3mFBcgv+nC12r3dd1jUfyp01bVL2RIh5HPJfN04JiYGM2bMAJ/Ph0AgQH5+PszNzbFixQrMmzevcqIkhJBq5nj0OYz+zUcsiQGAzNx3XBJjpmuEPV6rsX6ELyUxn2HhwoWwtbX9om08f/68xAmEK9L27dtpLj45kjmRUVRU5O4BGxkZIT4+HgCgra2Nly9fVnyEhBBSzQhFQswPXoOyOhiqKasifP4ufNuifZXGdeVhFA7fOI0rD6MgFEkOXlrRXr58iVGjRsHMzAxKSkqoX78+pk6dirS0NJm2w+PxJKabmTlzJsLDw78oPnNzcyQkJHATC5OaR+ZbS3Z2drhx4wasrKzg5OQEX19fpKamYteuXXSiEEJqhWtxMRJXYj6Vk5+L+/8+Rgdr+yqJqaTbXKY6Rlg6YDpc7bpWyj6fPn0KR0dHNG7cGH/99RcsLS1x7949zJo1C3///TeuXbsGPT29z96+hoYGNDQ0vihGgUBAk//WcDJfkVm+fDn3vPeyZcugq6uLCRMmICUlRWw8F0IIqamSMqWb/yg5S7arEp+rtNtciRnJGP2bj9gAfBVp0qRJUFJSwunTp+Hk5IR69eqhV69eOHPmDF69eoWff/4ZAGBhYYElS5Zg0KBBUFdXR506dbBp0yZuOxYWFgCAfv36gcfjce8/vbU0YsQIuLm5Yfny5TA2NoaOjg4WL16MoqIizJo1C3p6eqhbty6CgoK4dT69tTRixAjweDyJ1/nz54H30+3MnDkTderUgbq6OhwcHLhlxbZv34569epBTU0N/fr1k/nqE6lYMicybdq0Qdeu/2X3RkZGOHnyJLKyshAVFYVWrVpVRoyEEFJtJGak4I+z+6Wqa6SlL0WtL1PWba7isgX711b4bab09HScOnUKEydOlHhs3MTEBEOGDEFwcDCKR/hYuXIlWrVqhejoaMydOxfTpk3DmTNnAAA3btwAAAQFBSEhIYF7X5KzZ8/i9evXuHjxIlavXg0/Pz/07t0burq6iIyMxPjx4zFu3LhS5/5bt24dEhISuNfUqVNhZGSEJk2aAAC8vLwQERGBffv24fbt2+jfvz969uyJuLg4AEBkZCQ8PT3h5eWFmJgYdO3aFUuXLq2gViWfQ+ZbS4QQUhsxxnDk5hnM/WslMnKyyqzLA2Cqa4x2Vp/fUbXH8hFIkeKKTn5hAdKzM0tdzgC8fpMEm1nfQVlRqdztGWrp4/S87eXWi4uLA2MMTZs2LXF506ZN8ebNG6SkpAAAOnTogLlz5wIAGjdujCtXrmD9+vXo2bMnDA0NgY+Gqy+Lnp4e1q9fDz6fD2tra6xYsQI5OTncwyY+Pj745ZdfcPnyZQwcOFBifW1tbW6uwEOHDuG3337DmTNnYGJigvj4eAQFBSE+Ph5mZmbA+346J0+eRFBQEJYvX45169ahZ8+emD17NncsV69excmTJ8ttM1I5pEpkWrdujfDwcOjq6sLOzq7M575v3bpVkfERQojcpb3LwNy9K/G/Wx86nmqpaiAr9x14H135wPskBgCWuE+DgF/6hHjlSclKQ0JGyhdELa6sZOdLSDum6qdz4rVr1w7r1q2TeX/NmzcXG6jR2NhYrH+mQCCAvr4+N/R9aaKjozFs2DBs3LgRHTp0AADcuXMHQqEQjRs3Fqubn58Pff3/rq49ePAA/fr1kzg2SmTkR6pEpm/fvtxkiX379qWBmwghtcbp25cwY7c/UrLSuTK3Nt2xfOBMRMTdkuxgq2uMJe7TvriDraGUt6XKuyJTTE9dW+orMtJo1KgReDxeiV/seP+Fr6ury11tqSiKiopi73k8XollxfMDliQxMRF9+vTB6NGj4enpyZW/e/cOAoEAUVFRErMyf2mnY1J5pEpk/Pz8uJ8XLlxYmfEQQki18DY3GwtC1mDf1WNcma66Fn4ZNBt92zgDAFztuqJnq84SI/t+yZWYYtLc3sH7PjJt5vVDYkZyif1kim9z3Vh2qELiKqavr4/u3btj8+bNmD59ulg/mcTEROzZswceHh7cH77Xrl0TWz8yMpLrl4L3CYpQWPmPi+fl5aFv375o0qQJVq9eLbbMzs4OQqEQycnJ6NSpU4nrN23aFJGRkWJlnx4bqVoyd/YdPXq0RA9uQgipSS7H3kSXJUPEkpjuNh1wwfcvLokpJuAL0L5xa/Sy6Yz2jVtXaLIgDQFfgKUDpgMf3dYqVlG3uUqzceNG5Ofnw8XFBRcvXsTLly9x8uRJdO/eHXXq1MGyZcu4uleuXMGKFSvw6NEjbNq0CSEhIfDy8uKWW1hYIDw8HImJiXjz5k2Fx1ps3LhxePnyJdavX4+UlBQkJiYiMTERBQUFaNy4MYYMGQIPDw8cOnQIz549w/Xr1+Hv74/jx48D7+cTPHnyJFatWoW4uDhs3LiRbivJmcyJTEpKCnr27Alzc3PMmjUL//zzT+VERgghVSynIA8/Bwfgp7VeeJWeCADQUFHD6mE/Y+fEVTDSrvynkD6Hq11X/DHOHyY6RmLlprrG+GOcf6WNI2NlZYWbN2+iQYMGcHd3R8OGDTF27Fh07doVERERYmPIzJgxAzdv3oSdnR2WLl2KgIAA9OjRg1seEBCAsLAwmJubw87OrlLiBYALFy4gISEBzZo1g6mpKfe6evUq8P7JKQ8PD8yYMQPW1tZwc3PDjRs3UK9ePeB9357ff/8d69atQ6tWrXD69GnMnz+/0uIl5fus2a/fvHmDkJAQ7N27F5cuXUKTJk0wZMgQDB48mHv+n9Ds19UFtZlsamt7RT29i8nbF+Fp8ocRyjtY22Otx3yY65uWue6XtFnx7Nclzf4rK6FIiGtxMUjOSoORlj7aWdlW+RWiklhYWGDatGmYNm0aV1YTZ3KubDWxzco6/ytt9msA0NXVxdixYzF27Fj8+++/+Ouvv7Bt2zb4+vqiqKjoczZJCCFykV9YgIDjf2LjqV0Qsf86iKooKmN+v4kY1aX/V5XMCfiCKhtJmJDq4ovGkSksLMTNmzcRGRmJ58+fw9jYuOIiI4SQSnbv3zhMDlqE+68ec2WtLZtj/XBfNDKpL9fYCCHS+axE5ty5c9i7dy8OHjwIkUiEH374AceOHUO3bt0qPkJCCKlgRcIibDq9G6uO/YFC4X9XkRUFCpjZezQm9RgKBQGNFVqRnj9/Lu8QSA0m8//WOnXqID09HT179sTWrVvx/fffc2PMEEJIdfc48QWm7FiMW8/ucWXN6jTChpF+aF7XSq6xEUJkJ3Mis3DhQvTv3x86OjqVExEhhFQCkUiEbedDsOzwZuQW5gMA+Dw+vFyGYYarp1SDxRFCqh+ZE5kxY8YAAB4/fownT56gc+fOUFVVBWOsxvSiJoTULC/TEjBt51JceRjFlTUwMseGEX6wb9CizHUJIdWbzIlMWloa3N3dce7cOfB4PMTFxaFBgwbw9PSErq4uAgICKidSQgiREWMMf109Bt+QNXiXl8OVe3btj5/7TYKa0pc97kwIkT+ZnyucPn06FBUVER8fDzU1Na58wIABNLohIaTaSMpMhcfmmfDetYxLYuromeDAtI1YNmAGJTGE1BAyX5E5ffo0Tp06hbp164qVW1lZ4cWLFxUZGyGEfJYjN89g7l8r8CY7iysb2L43FvefBi1VmvyPkJpE5kQmOztb7EpMsfT0dHp6iRAiV+nvMjFv3yqE3gzjygy19BAw1Ac9WpY8CSAh5Osm862lTp06YefOndz74unSV6xYga5dK2c+D0IIKc+ZO1fRZfFgsSTm+9bf4rzvXkpi5IjH4yE0NLTS92NhYYG1a9dW+n5Ksn37dqme5P3zzz/F5peSp8porxEjRsDNzY17P3DgwCrpNytzIrNixQps3boVvXr1QkFBAWbPno0WLVrg4sWL+PXXX2UOYNOmTbCwsICKigocHBxw/fr1MuuHhISgSZMmUFFRgY2NDU6cOCG2/NChQ+jRowf09fXB4/EQExMjsY28vDxMmjQJ+vr60NDQwI8//oikpCSxOvHx8XB1dYWamhqMjIwwa9Ysmn6BkGrobW42ZuxajqGbvJGclQYA0FHTwhbPxdg6Zin0NWioiMqSkpKCCRMmoF69elBWVoaJiQlcXFxw5coVrk5CQgJ69eol1zhLIm3yUVHy8vKwYMEC+Pn5Vdk+y3Ljxg2MHTu2Uvcxf/58LFu2DJmZmZW6H5kTmRYtWuDRo0fo2LEj+vbti+zsbPzwww+Ijo5Gw4YNZdpWcHAwvL294efnh1u3bqFVq1ZwcXFBcnJyifWvXr2KQYMGwdPTE9HR0XBzc4Obmxvu3r3L1cnOzkbHjh3LTKqmT5+O//3vfwgJCcGFCxfw+vVr/PDDD9xyoVAIV1dXFBQU4OrVq9ixYwe2b98OX19fmY6PEFJxhCIhrjyMwuEbp3HlYRT3vuuSIdhz5ShXr1tzR5z33YN+3/SgISEq2Y8//ojo6Gjs2LEDjx49wtGjR9GlSxekpaVxdUxMTKjbAYADBw5AS0sLHTp0kHcoAABDQ8MSu4lUpBYtWqBhw4bYvXt3pe4HTI7atm3LJk2axL0XCoXMzMyM+fv7l1jf3d2dubq6ipU5ODiwcePGSdR99uwZA8Cio6PFyjMyMpiioiILCQnhyh48eMAAsIiICMYYYydOnGB8Pp8lJiZydbZs2cK0tLRYfn5+qceTl5fHMjMzudfLly8ZAPbmzRsmFAor5FVYWMhev37NCgsLK2ybNf1Fbfb1t9f/boYz2znfM+NxDtyrwZQun7zvynZePMyKioq+qjbLzs5m9+7dYzk5OUwkEn01r/T0dAaAnTt3rsx6ANihQ4eYSCRiT58+ZQDYvn37WMeOHZmKigpr06YNi42NZZGRkcze3p6pq6uznj17sqSkJG4bTk5ObMqUKWLb7du3Lxs+fDj3vn79+mz16tXc+1WrVrEWLVowNTU1VrduXTZ+/HiWlZXFRCIRO3v2LAMg9vL19WUikYjl5uYyb29vZmZmxtTU1Fjbtm3Z2bNnxfa9bds2Zm5uzlRVVZmbmxtbuXIl09bWLrMdXF1d2YwZM8TKhg8fzvr27ctWrFjBTExMmJ6eHpswYQLLz8/n6qSlpbFhw4YxHR0dpqqqynr06MEePnwoFou2tjY7evQoa9y4MVNVVWU//vgje/fuHQsKCmL169dnOjo6zMvLixUWFpbaXgDY1q1bmZubG1NVVWWNGjVioaGh3PLCwkI2cuRIZmFhwVRUVFjjxo3ZmjVrSjyej8sWLlzIOnbsWGq75OTksHv37rHs7GyJ/xtv3rxhAFhmZmaZuYRUnX1v374tdWLUsmVLqeoVFBQgKioKPj4+XBmfz4ezszMiIiJKXCciIgLe3t5iZS4uLjLdf42KikJhYSGcnZ25siZNmqBevXqIiIhAu3btEBERARsbG7FJMF1cXDBhwgTcu3cPdnZ2JW7b398fixYtkihPSUlBXl6e1DGWRSQSITMzE4yxr2pWXnmiNpNNdWuv8PsRmLn/V7BPyrPzc7mf7es3x2K3Kaija4yUlJQqj/FL2qywsBAikQhFRUUSt6+3XXmObVfLfxq0uakWfhsq/rk0bnc07iVklbpOsVHt62NUBwuZYgYAFRUVaGho4PDhw2jTpk2ZV12EQqHY8fn5+WHVqlWoU6cOJkyYgMGDB0NTUxMBAQFQVVXFkCFDsGDBAmzcuBF4Px4QY0ysfUQiEdduH5d9/H716tWwsLDAs2fPMHnyZMyaNQsbNmxA27ZtERAQgEWLFnFX9DU0NFBUVIRJkybhwYMH2L17N0xNTXHkyBH06tULt27dgpWVFa5fv47Ro0dj6dKl6NOnD06fPo3FixcDQJndDy5fvoxBgwZJxHvu3DkYGxvj9OnTePLkCYYMGYKWLVvC09MTADB8+HA8fvwYhw4dgqamJnx8fPDdd9/h9u3bUFRUhEgkQk5ODtavX49du3bh3bt3cHd3R79+/aCtrY0jR47g2bNnGDBgANq1awd3d/dS22vx4sVYvnw5li9fjs2bN2Po0KF4/Pgx9PT0UFhYCDMzM/z111/Q09NDREQEJk6cCCMjI/Tv37/U34m9vT2WL1+O7OzsEs+RoqIiiEQipKWlQVFRUWzZ27dvS23Pj0mVyNja2oLH40mM3svYfx8tH5cJhUKpdpyamgqhUCgxY7axsTFiY2NLXCcxMbHE+omJiVLts3gbSkpKEvdGP95OafspXlYaHx8fsUQrKysL5ubmMDQ0hJaWltQxlkUkEoHH48HQ0LBafMl8DajNZFOd2ksoEmLV6SCJJOZjWqoaODRjMxQVFMuoVbm+pM3y8vLw9u1bKCgoQEFB/CM5p1CEpKz8crdhpl0ose6bnEKp1s0pFEmsKw0FBQUEBQVh7Nix2Lp1K1q3bo3OnTtj4MCBEn/QCgQCseObOXMmXF1dUVhYiKlTp2Lw4ME4c+YMOnfuDAAYNWoUduzYwdXn8Xjg8XhicfL5fPD5fImy4vcffxY3atQIS5cuxYQJE7BlyxYoKChAV1cXPB5PbCiR+Ph47NixAy9evICZmRkAYPbs2QgLC8OuXbuwfPlybNq0CT179sTcuXMBAM2aNUNkZCROnjxZajtmZGQgMzMT5ubmEvHq6upi06ZNEAgEaNGiBVxdXXH+/HmMGzcOcXFxOHbsGC5fvoz27dsDAHbu3ImGDRvi2LFj6N+/P/h8PgoLC7Flyxaue8ePP/6I3bt3IzExERoaGmjZsiW6du2KixcvYvDgwSW2F94nTUOHDgXe/2G+ceNG3Lp1Cz179oSCggKWLFnC1S1O6g4dOoRBgwaV+jsxNzdHQUEBUlNTUb++5IzyCgoK4PP50NfXh4qK+NhOn74vjVRn77Nnz7ifo6OjMXPmTMyaNQuOjo7A+yslAQEBWLFihVQ7ramUlZVLzDiLf7kVhcfjVfg2azpqM9lUl/aKiItGQkbJfeaKZeW+w81nd9HB2r7K4irJ57YZn8/nvqg/7dOjqaIIE63yP8z1NZQk1tXXUJJqXU0Vxc/uS/TTTz+hd+/euHTpEq5du4a///4bK1euxB9//IERI0Zw9T49vlatWnHlxX8ktmzZkltuYmKC5ORksbhKah988of0x3XOnDkDf39/xMbGIisrC0VFRcjLy0Nubi7U1NS4eh+vf/fuXQiFQlhbW4vtIz8/n3uA5MGDB+jXr5/Yeo6Ojjh58mSp7Vh8RV5VVVWiTvPmzcW++E1NTXHnzh3weDzExsZCQUEB7dq14y4mGBgYwNraGrGxsdzxqqmpoVGjRtw2TExMYGFhAU1NTa7M2Pi/q5VltWmrVq249xoaGtDS0hJbZ9OmTdi2bRvi4+ORm5uLgoIC7kJHab+T4n44ubm5pf7+Svu/I+3/JakSmY+zqP79+2P9+vX47rvvuLKWLVvC3NwcCxYsEHv0qiwGBgYQCAQSTwslJSXBxMSkxHVMTExkql/aNgoKCpCRkSF2Vebj7ZiYmEg8PVW8X1n2RQj5MkmZqVLVK35aqaYZ3akBRndq8Fnr/jH8mwqPpyQqKiro3r07unfvjgULFmD06NHw8/MTS2Q+9fEthOIvt0/LRCIR957P53N3AIoVFhaWuv3nz5+jd+/emDBhApYtWwY9PT1cvnwZnp6eKCgoKLWT67t37yAQCBAVFQWBQCC2TEPj8wdSLE6C3rx5I7Hs09spnx67NEraxudst6x19u3bh5kzZyIgIACOjo7Q1NTEypUrERkZWeY209PTgfediyuLzH9u3blzB5aWlhLllpaWuH//vtTbUVJSgr29PcLDw7kykUiE8PBw7krPpxwdHcXqA0BYWFip9Utib28PRUVFse08fPgQ8fHx3HYcHR1x584dsaenwsLCoKWlhWbNmkm9L0LI50vOTMPvZ4OlqmukpV/p8RDpNGvWDNnZ2RW6TUNDQyQkJHDvhUKh2NOqn4qKioJIJEJAQADatWuHxo0b4/Xr12J1lJSUJLpC2NnZQSgUIjk5GY0aNRJ7Ff8R27RpU4kv72vXrpUZv5KSEpo1aybTd2TxvoqKisT2l5aWhocPH1b5d9GVK1fQvn17TJw4EXZ2dmjUqBGePHlS7np3795F3bp1YWBgUGmxyZzING3aFP7+/igoKODKCgoK4O/vj6ZNm8q0LW9vb/z+++/YsWMHHjx4gAkTJiA7OxsjR44EAHh4eIh1Bp46dSpOnjyJgIAAxMbGYuHChbh58ya8vLy4Ounp6YiJieFOmIcPHyImJobr26KtrQ1PT094e3vj3LlziIqKwsiRI+Ho6Ih27doBAHr06IFmzZph2LBh+Oeff3Dq1CnMnz8fkyZNoscICakCf8dcQJclQxD9vOwPfh4AM11jtLOyrbLYyH/S0tLQrVs37N69G7dv38azZ88QEhKCFStWoG/fvhW6r27duuH48eM4fvw4YmNjMWHCBGRkZJRav1GjRigsLMSGDRvw9OlT7Nq1C4GBgWJ1LCws8O7dO4SHhyM1NRU5OTlo3LgxhgwZAg8PDxw6dAjPnj3D9evX4e/vj+PHjwMApkyZgpMnT2LVqlWIi4vDxo0bpZpn0MXFBZcvX5bpuK2srNC3b1+MGTMGly9fxj///IMRI0agTp06Fd7G0sRy8+ZNnDp1Co8ePcKCBQtw48aNcte7dOlSpQ8CKHMiExgYyM215OzsDGdnZ9StWxenTp2SOFHKM2DAAKxatQq+vr6wtbVFTEwMTp48yd0zjY+PF8vC27dvj71792Lr1q1o1aoVDhw4gNDQULRo0YKrc/ToUdjZ2cHV1RV4P7KgnZ2dWGxr1qxB79698eOPP6Jz584wMTHBoUOHuOUCgQDHjh2DQCCAo6Mjhg4dCg8PD65nOiGkcmTn5cB71zKMDJyD9Hf/fVEVz4306d314vdL3KdBwBeAVC0NDQ04ODhgzZo16Ny5M1q0aIEFCxZgzJgx3NNGFWXUqFEYPnw4PDw84OTkhAYNGpQ5knyrVq2wevVq/Prrr2jRogX27NkDf39/sTrt27fH+PHjMWDAABgaGnJ9PIOCguDh4YEZM2bA2toabm5uuHHjBurVqwcAaNeuHX7//XesW7cOrVq1wunTpzF//vxyj8HT0xMnTpyQeXC4oKAg2Nvbo3fv3mjfvj0YYzh+/LjEbaDKNm7cOPzwww8YMGAAHBwckJaWhokTJ5a5Tl5eHkJDQzFmzJhKjY3HPr3xKIXs7Gzs2bOHe7qoadOmGDx4MNTV1Ssjxq9WVlYWtLW1kZmZWaFPLSUnJ8PIyEjuHTG/FtRmspFXe918egdeQYvwPOVfrqxXKyesGuaDa3HRmB+8Rqzjr5muMZa4T4OrnfynRvmSNsvLy8OzZ89gaWkp9VMaNUHx49QKCgq1ZuDC/v37o3Xr1mJ3GmTxtbXZli1bcPjwYZw+fbrUOmWd/9J+h8r+zB0AdXX1Sh/amBBSOxQKi7DmRBDWngiCiP3XsVBNWRVL3b0xqH1v8Hg8uNp1Rc9WnXEtLgbJWWkw0tJHOytbuhJDviorV67E//73P3mHUWUUFRWxYcOGSt/PZyUyhBBSEZ4mxWNS0EKxvjD2li2wadRCWBjWFasr4Avk/og1IV/CwsICkydPlncYVWb06NFVsh9KZAghVY4xht2Xj8A3ZC1yC/4bY0PAF2CGqyem9PSAgoA+mggh0qFPC0JIlUrJSseM3ctx+vaHJzgsDeti06hFaG3ZXK6xEUK+PpTIEEKqTNidK5i+cylS334YGGxYJzcs/HEK1FUqdyZeQkjN9NmJTEFBAZKTkyVGCix+RI0QQopl5+di0cH12HnxMFemr6mLNcPmoUfLTnKNjRDydZM5kYmLi8OoUaNw9epVsfLiCSWlnTSSEFI7xLx4gEnb/PAkKZ4rc7bpgDXD5sGQRuQlhHwhmROZESNGQEFBAceOHYOpqelX8Sw7IaTqFQmLsPHULqw69geKRP/9gaOqqIyF/afCo1M/+uwghFQImROZmJgYREVFoUmTJpUTESHkq/ci5RW8ghbixtM7XFmr+k2xaeRCNDKpX+a6pHbYvn07pk+fXuJEiuXp0qULbG1tsXbt2kqJ7UuMGDECGRkZCA0NLbPesGHD0LRpU8ybN6/KYqsIJ0+exNy5c3Hr1q1qM8CozFE0a9YMqanSzUhLCKldGGPYd/UYui0dxiUxfB4f078biWOzf6ckpoYZMWIEeDyexOvx48eVut9Dhw5hyZIlFba9ESNGwM3NrcK2V55//vkHJ06cwJQpU7gyCwuLapeYlRRTz549oaioiD179sgtrk9JdUUmKyuL+/nXX3/F7NmzsXz5ctjY2EjM91BRQ/ETQr4u6e8yMWuPP45Hn+fK6hmYYdPIhfimYUu5xkYqT8+ePREUFCRWZmhoWCn7KigogJKSEvT09Cpl+1Vlw4YN6N+/PzQ0NCp828VtVJlGjBiB9evXY9iwYZW6H2lJdUVGR0cHurq60NXVRffu3XHt2jV8++23MDIy4sqL6xBCap9z966h65IhYknMwPa9Ef7zLkpiajhlZWWYmJiIvQQCAVavXg0bGxuoq6vD3NwcEydOxLt37yTWDw0NhZWVFVRUVODi4oKXL19yyxYuXAhbW1v88ccfYnPxdOnSBdOmTePq5efnY86cOTA3N4eysjIaNWqEP//8EwAgFArh6ekJS0tLqKqqwtraGuvWrRPbx44dO3DkyBHuitL58/+dxy9fvoS7uzt0dHSgp6eHvn374vnz59y6QqEQ3t7e0NHRgb6+PmbPno3ypi8UCoU4cOAAvv/+e66sS5cuePHiBaZPn87FgPczjA8aNAh16tSBmpoaWrZsiX379oltr0uXLvDy8sK0adNgYGAAFxcX4P0EysXt2rVrV+zYsQM8Hk9s1vDLly+jU6dOUFVVhbm5OaZMmYLs7OwyYwKA77//Hjdv3sSTJ0/KPNaqItUVmXPnzlV+JISQr05uQR6WHd6MP87t58p01bWwaqhPtZjMkcgPn8/H+vXrYWlpiadPn2LixImYPXs2Nm/ezNXJycnB8uXLsXPnTigpKWHixIkYOHAgrly5wtV5/PgxDh48iEOHDkEgKHluLQ8PD0RERGD9+vVo1aoVnj17xnWBEIlEqFu3LkJCQqCvr4+rV69i7NixMDU1hbu7O2bOnIkHDx4gKyuLu7Kkp6eHwsJCuLi4wNHREZcuXYKCggKWLl2Knj174vbt21BSUkJAQAC2b9+Obdu2oWnTpggICMDhw4fRrVu3Utvl9u3byMzMRJs2bbiyQ4cOoVWrVhg7dqzYTNF5eXmwt7fHnDlzoKWlhWPHjmHkyJFo3LgxHBwcuHo7duzAhAkTuHZ79uwZfvrpJ0ydOhWjR49GdHQ0Zs6cKRbHkydP0LNnTyxduhTbtm1DSkoKvLy84OXlhaCgoFJjwvthVoyNjXHp0iU0bNhQirOhckmVyDg5OXE/x8fHw9zcXOKJA8aYWCZNCKnZ7sQ/xKSghXiU8Iwr69LMAWs95sNEp3JuLdQ6VzcCEZvKr2faChgs/pc69g4EEv4pf13HSUB7r88O8dixY2K3SHr16oWQkBCxKyYWFhZYunQpxo8fL5bIFBYWYsOGDWjXrh3w/gu5adOmuH79Otq2bQu8v1Wyc+fOUm9XPXr0CPv370dYWBicnZ0BAA0aNOCWKyoqYtGiRdx7S0tLREREYP/+/XB3d4eGhgZUVVWRn58PExMTrt7u3bshEonwxx9/cN93QUFB0NHRwfnz59GjRw+sXbsWPj4++OGHHwAAgYGBOHXqVJnt9eLFCwgEAhgZGXFlenp6EAgE0NTUFIuhTp06YgnI5MmTcerUKezfv18skbGyssKKFSu493PnzoW1tTVWrlwJALC2tsbdu3exbNkyro6/vz+GDBnC/Z6srKywfv16ODk5YcuWLaXGVMzMzAwvXrwo81irisxPLVlaWiIhIUHslwAA6enpsLS0pHFkCKnhhCIhNoftwYqjW1EoLAIAqCgqY/4PkzDK6adq8yRDjZD/Fnj7uvx62nUky3JSpVs3/+3nxfZe165dsWXLFu69uro6AODMmTPw9/dHbGwssrKyUFRUhLy8POTk5EBN7b9RnBUUFPDNN99w6zZp0gQ6Ojp48OABl8jUr1+/zD43MTExEAgEYn9wf2rTpk3Ytm0b4uPjkZubi4KCAtja2pZ5XP/88w8eP34MTU1NsfK8vDw8efIEmZmZSEhIEEsoFBQU0KZNmzJvL+Xm5kJZWVmq4QeEQiGWL1+O/fv349WrVygoKEB+fj7XxsXs7cUnU3348KFYuwLg2vPj47t9+7ZYp13GGEQiEZ49e4amTZuWGZuqqipycnLKPYaqIHMiUzzw3afevXvH3b8khNRML9MSMHn7YlyLi+bKWpg3xsaRC9HErEGZ65LPoKwJaJqVX0/NoOQyadZV1iy/ThnU1dXRqFEjsbLnz5+jd+/emDBhApYtWwY9PT1cvnwZnp6eKCgo4BIZabdfFlVV1TKX79u3DzNnzkRAQAAcHR2hqamJlStXIjIyssz13r17B3t7+xKfzvmSzswGBgbIycmRqlPuypUrsW7dOqxduxY2NjZQU1PDtGnTUFBQIFavvDYqybt37zBu3DixJ6eKSTNCf3p6eqV16paV1ImMt7c3AIDH42HBggViJ6JQKERkZGS5GS4h5OvEGMOh66cw96+VeJv3X2dAHo+HST2GYlbvMVBWrNynJGqt9l6ff9vn01tNVSgqKgoikQgBAQHcFbr9+/dL1CsqKsLNmze5qxoPHz5ERkZGuVcDPmZjYwORSIQLFy5wt5Y+duXKFbRv3x4TJ07kyj7tpKqkpCRxN6F169YIDg6GkZFRqU/jmpqaIjIyEp07d+aOJyoqCq1bty413uLvyfv374t9Z5YUw5UrV9C3b18MHToUeP9d++jRIzRvXvbkqtbW1jhx4oRY2Y0bNySO7/79+xJJ6MdKigkfXZWys7MrM46qIvU14OjoaERHR4Mxhjt37nDvo6OjERsbi1atWmH79u2VGy0hpFIJRUJcfXQLf9+5iKuPbkEoEiIjOwvj/1yASUELuSSmjp4JDk7fhPn9JlESQyQ0atSI6//y9OlT7Nq1C4GBgRL1FBUVMWXKFERGRiIqKgojRoxAu3btJG6DlMXCwgLDhw/HqFGjEBoaimfPnuH8+fNc4mRlZYWbN2/i1KlTePToERYsWCDxpW5hYYHbt2/j4cOHSE1NRWFhIYYMGQIDAwP07dsXly5d4rY7ZcoU/PvvvwCAqVOn4pdffkFoaChiY2MxceJEsaeCSmJoaIjWrVvj8uXLYuUWFha4ePEiXr16xXVUtrKyQlhYGK5evYoHDx5g3LhxSE5OLrdNxo0bh9jYWMyZM4frQ1T8/Vx8R2XOnDm4evUqvLy8EBMTg7i4OBw5cgReXh8S55JiAoBr165BWVkZjo6O5cZSJZiMRowYwTIzM2VdrVbKzMxkACq0vYRCIUtISGBCobDCtlnTUZtJ59its8x2zvfMeJwD92o2w4U18e4uVjbxT1+WkZ0l73CrlS85x3Jzc9n9+/dZbm5upcRWmYYPH8769u1b4rLVq1czU1NTpqqqylxcXNjOnTsZAPbmzRvGGGPbtm1j2tra7MCBA6xBgwZMWVmZOTs7sxcvXnDb8PPzY61atZLYtpOTE5s6dSr3Pjc3l02fPp2ZmpoyJSUl1qhRI7Zt2zbGGGN5eXlsxIgRTFtbm+no6LAJEyawuXPnim03OTmZde/enWloaDAA7Ny5c4wxxhISEpiHhwczMDBgysrKrEGDBmzMmDHcZ3phYSGbOnUq09LSYjo6Oszb25t5eHiU2ibFNm/ezNq1aydWFhERwVq2bMmUlZVZ8VdzWloa69u3L9PQ0GBGRkbs559/ZkOHDhXb/qdtUezIkSOsUaNGTFlZmXXp0oVt2bKFARA7z65fv84dt7q6OmvZsiVbtmxZmTExxtjYsWPZuHHjyjxGaZV1/kv7Hcpj5T30Tj5bVlYWtLW1kZmZWWEDBYpEIiQnJ8PIyIg6VUqJ2qx8x6PPYfRvPijrw0BbTRO/DpoNt2+6V2FkX4cvOcfy8vLw7NkzsXFSagPGGIqKiqCgoFDr5t3Kzc2FtbU1goODZbqq8SVttmzZMgQGBn7x08WpqamwtrbGzZs3YWlp+UXbQjnnv7TfoVL1kSl+tEwahw4dkrouIUT+hCIh5gevKTOJUVJQRNi8HahnIEXnUUJImVRVVbFz585Kne5n8+bN+Oabb6Cvr48rV65g5cqVYreNPtfz58+xefPmCkliKopUiYy2tjb3M2MMhw8fhra2NjegT1RUFDIyMmRKeAgh1cO1uBgkZJR9372gqBAv0xIokSGkgnTp0qVStx8XF4elS5ciPT0d9erVw4wZM+Dj4/PF223Tpo3YYH7VgVSJzMfzaMyZMwfu7u4IDAzkRlkUCoWYOHEizbNEyFcoOSutQusRQuRvzZo1WLNmjbzDqBIydxjYtm0bZs6cKTZUtEAggLe3N7Zt21bR8RFCKtnLtESp6hlp6Vd6LIQQIiuZE5mioiLExsZKlMfGxkIkEn1WEJs2bYKFhQVUVFTg4OCA69evl1k/JCQETZo0gYqKCmxsbCSel2eMwdfXF6amplBVVYWzszPi4uK45efPny9x6nkej8c9lvf8+fMSl1+7du2zjpGQ6iavMB/zg1djeejmMuvxAJjpGqOdFY0TVZnouQtSG1XEeS9zIjNy5Eh4enpi9erVuHz5Mi5fvoyAgACMHj0aI0eOlDmA4OBgeHt7w8/PD7du3UKrVq3g4uJS6rPyV69exaBBg+Dp6Yno6Gi4ubnBzc0Nd+/e5eqsWLEC69evR2BgICIjI6Gurg4XFxfk5eUBANq3b4+EhASx1+jRo2FpaSlx7+/MmTNi9T4dCpqQr9GDV0/Q65dRYpM94n3SUtL7Je7TIOCXPGEf+TKKiorA+wkUCaltis/74v8Hn0Pmx69FIhFWrVqFdevWISEhAXg/uuHUqVMxY8aMUmcnLY2DgwO++eYbbNy4kdu+ubk5Jk+ejLlz50rUHzBgALKzs3Hs2DGurF27drC1tUVgYCAYYzAzM8OMGTO4ybYyMzNhbGyM7du3Y+DAgRLbLCwsRJ06dTB58mQsWLAAeH9FxtLSEtHR0Z89YjE9fl09UJt9wBjDtvMHsOTQRuQV5gMAlBWUsOBHL5hoG2LB/jViHX/NdI2xxH0azWRdji89xxISEpCRkQEjIyOoqanViseRa/Pj15+rJrUZYww5OTlITk6Gjo4OTE1NJepU6OPXH+Pz+Zg9ezZmz56NrKwsAPjsL+mCggJERUWJ9aTm8/lwdnZGREREietERERw0yUUc3FxQWhoKPB++vLExESxoaq1tbXh4OCAiIiIEhOZo0ePIi0trcQrSn369EFeXh4aN26M2bNno0+fPqUeT35+PvLz87n3xe0jEok++7bbp0QiETexF5EOtdl/Ut+mw3uXP87cvcKVWZtaYvOoxWhapyEAwKVlR0Q8isbjf5+jUV0LODa2g4AvqPVtV54vPceMjIzAGENSUlKFx1adiUSiWv/HhaxqWpvp6OjAyMioxP870v5/kjmR+diXXmVITU2FUCiEsbGxWLmxsXGJ/XAAIDExscT6iYmJ3PListLqfOrPP/+Ei4sL6taty5VpaGggICAAHTp0AJ/Px8GDB+Hm5obQ0NBSkxl/f3+x6eKLpaSkcLe1vpRIJEJmZiYYYzXqZK5M1GbA1cfRWHB4HdKyPwyfPrDtd5jWfThUFJXFbuU20qkLQ54mtLW1kZZKTypJoyLOMT6fDx0dnRLntqmJGGN4+/YtNDQ0vvqrC1WlprWZQCAAn89HSkpKicvfvpVuZnapEpnWrVsjPDwcurq6sLOzK7MBb926JdWOq4t///0Xp06dkpjQzMDAQOzKzzfffIPXr19j5cqVpSYyPj4+YutkZWXB3NwchoaGFXpricfjwdDQsNZ+KcuqNrdZXmE+/I8E4vezwVyZvoYO1gz7Gc42HUpcpza31+eiNpOdSCRCSkoKtZkMalubSTvStVSJTN++faGsrMz9XFGZoIGBAQQCgcTl1KSkJJiYmJS4jomJSZn1i/9NSkoSu+eWlJRUYl+XoKAg6Ovrl3nLqJiDgwPCwsJKXa6srMy108f4fH6FnnQ8Hq/Ct1nT1cY2e/j6GSb8uQD3Xz3myro2b4d1HgtgpF32o9S1sb2+FLWZ7KjNZFeb2kzaY5QqkfHz8+N+Xrhw4edH9QklJSXY29sjPDwcbm5uwPuMMzw8vNShlB0dHREeHo5p06ZxZWFhYdx8FZaWljAxMUF4eDiXuGRlZSEyMhITJkwQ2xZjDEFBQfDw8JCqx3RMTEyJHZIIqU4YY9hx8RAWHljPdehVUlDEgn5e8Ozav1Z8ABJCag+Z+8j4+vqia9eucHR0rJAJzry9vTF8+HC0adMGbdu2xdq1a5Gdnc11vPXw8ECdOnXg7+8PvJ823cnJCQEBAXB1dcW+fftw8+ZNbN26FXifrU6bNg1Lly6FlZUVLC0tsWDBApiZmXHJUrGzZ8/i2bNnGD16tERcO3bsgJKSEuzs7ID3c0ht27YNf/zxxxcfMyGVJfXtG8zYtRynbl/iyhqbWiLQczGa1bWSa2yEEFIZZE5kIiIisHr1ahQVFeGbb76Bk5MTunTpgg4dOkBVVVXmAAYMGICUlBT4+voiMTERtra2OHnyJNdZNz4+XuwvyPbt22Pv3r2YP38+5s2bBysrK4SGhqJFixZcndmzZyM7Oxtjx45FRkYGOnbsiJMnT0okXn/++Sfat2+PJk2alBjbkiVL8OLFCygoKKBJkyYIDg7GTz/9JPMxElIVLtyPxOTti8WmEhjp9BN8f/SCqlLtmVWZEFK7yDyODN6P7hsZGYmLFy/iwoULuHr1KvLz8/HNN9/g8uXLlRPpV4jGkakeanqb5RcWYPmRLfjtzF9cmZ6GDtZ6zEePlh1l3l5Nb6/KQG0mO2oz2dW2Nqu0cWQAQEFBAR06dIChoSH09PSgqamJ0NDQUh+ZJoRUjkcJzzBxmx/uvnzElXVp5oB1wxfAWNtArrERQkhVkDmR2bp1K86fP48LFy4gPz8fnTp1QpcuXTB//ny0bNmycqIkhIhhjGHXpVD4haxF7kcden92m4gx3QbUir/WCCEEn5PIjB8/HoaGhpgxYwYmTpwIDQ2NyomMEFKitHcZmLFrOU7+c5ErszKxwBbPxWhh3liusRFCSFWT+c+2Q4cOYciQIdi3bx8MDQ3Rvn17zJs3D6dPn6ZJzwipZBcfXEe3JUPFkpjhnX/AqXnbKYkhhNRKMl+RKZ5tGu8nY7x06RJCQkLQu3dv8Pn8ChuKnxDyQUFRIfyPBGJL2B6uTE9dG2s8foZLq85yjY0QQuTpszr7pqWl4cKFCzh//jzOnz+Pe/fuQVdXF506dar4CAmp5eISn2Pin76481GHXqembbF+hC916CWE1HoyJzI2NjZ48OABdHV10blzZ4wZMwZOTk7U0ZeQCsYYw+7LR+C7fw3XoVdRoICf+03E2G4DqUMvIYR8bmdfJycnsQHoCCEVK/1dJmbuXo4TMRe4MiuT+tg8ajFs6lnLNTZCCKlOZE5kJk2aVDmREEIAAJdjb2Ly9kVIyPgwtb1Hp35Y2H8q1GiEXkIIEfNZfWQIIV9GKBLiWlwMkrPSYKSlj3ZWthCKRPj16G/YHLYHxQNu66prYfWwn9HL1kneIRNCSLVEiQwhVex49DnMD16DhIxkrsxQSx9qSip4kfqKK+vUpA02jPCDiY6hnCIlhJDqjxIZQqrQ8ehzGP2bDz6d4Czlo4keFQUKmNt3PCY4D6YOvYQQUg5KZAipIkKREPOD10gkMR8T8AU4Omsr7CyaVWFkhBDy9ZIqkbl9+7bUG6THsAkp2bW4GLHbSSURioTIyc+tspgIIeRrJ1UiY2trCx6Px3VA/FTxMh6PB6FQWNExElIjJH90+6gi6hFCCJEykXn27FnlR0JIDccDT6p6Rlr6lR4LIYTUFFIlMvXr16/8SAipwY5GhWPWnl/KrMMDYKprjHZWtlUWFyGEfO0+u7Pv/fv3ER8fj4KCArHyPn36VERchNQI2fm58AtZi92Xj5RZr/hazRL3aRDwBVUSGyGE1AQyJzJPnz5Fv379cOfOHbF+Mzzefx/F1EeGkP/c+zcO4/+Yj7jEF1xZ3zbOcLbpgOWHt4h1/DXVNcYS92lwtesqp2gJIeTrJHMiM3XqVFhaWiI8PByWlpa4fv060tLSMGPGDKxatapyoiTkK8IYw5/nQ7D44AYUFBUCAFSVVLB84EwMdHQFj8fDD9/0kBjZl67EEEKI7GROZCIiInD27FkYGBiAz+eDz+ejY8eO8Pf3x5QpUxAdHV05kRLyFUh7l4HpO5fi9O3LXFkL88YI9FyCRiYf+poJ+AJ0sLaXU5SEEFJzyJzICIVCaGpqAgAMDAzw+vVrWFtbo379+nj48GFlxEjIV+Fy7E1MClqIpMxUrmxst4H4ud9EKCsqyTU2QgipqWROZFq0aIF//vkHlpaWcHBwwIoVK6CkpIStW7eiQYMGlRMlIdVYobAIq/73O9af2sn1GdPX1MU6jwVwtmkv7/AIIaRGkzmRmT9/PrKzswEAixcvRu/evdGpUyfo6+sjODi4MmIkpNp6kfoaE//0RdSzu1xZ5ybfYMNIPxhrG8g1NkIIqQ1knpHOxcUFP/zwAwCgUaNGiI2NRWpqKpKTk9GtW7fPCmLTpk2wsLCAiooKHBwccP369TLrh4SEoEmTJlBRUYGNjQ1OnDghtpwxBl9fX5iamkJVVRXOzs6Ii4sTq2NhYQEejyf2+uUX8XE+bt++jU6dOkFFRQXm5uZYsWLFZx0fqZlCb4TBeekwLolR4Aswv98k7JuyjpIYQgipIl80te7Lly/x8uVL6OnpcY9fyyo4OBje3t7w8/PDrVu30KpVK7i4uCA5ueQ5aa5evYpBgwbB09MT0dHRcHNzg5ubG+7e/fAX8YoVK7B+/XoEBgYiMjIS6urqcHFxQV5enti2Fi9ejISEBO41efJkbllWVhZ69OiB+vXrIyoqCitXrsTChQuxdevWzzpOUnNk5+Vg2s6lGP/nArzN++/qZH2DOjg6ayu8XIbRjNWEEFKVmIwKCwvZ/PnzmZaWFuPz+YzP5zMtLS32888/s4KCAlk3x9q2bcsmTZrEvRcKhczMzIz5+/uXWN/d3Z25urqKlTk4OLBx48YxxhgTiUTMxMSErVy5kluekZHBlJWV2V9//cWV1a9fn61Zs6bUuDZv3sx0dXVZfn4+VzZnzhxmbW0t9bFlZmYyACwzM1PqdcojFApZQkICEwqFFbbNmq4i2+z2i1jW3rc/Mx7nwL0m/unLsnLeVUis1QGdY7KjNpMdtZnsalubSfsdKnMfmcmTJ+PQoUNYsWIFHB0dgfePZC9cuBBpaWnYsmWL1NsqKChAVFQUfHx8uDI+nw9nZ2dERESUuE5ERAS8vb3FylxcXBAaGgq8nxcqMTERzs7O3HJtbW04ODggIiICAwcO5Mp/+eUXLFmyBPXq1cPgwYMxffp0KCgocPvp3LkzlJSUxPbz66+/4s2bN9DV1ZWILT8/H/n5+dz7rKwsAIBIJIJIJJK6XcoiEonAGKuw7dUGFdFmjDH8cW4/loVu5saGUVdWg//AGfjJoRe3n5qAzjHZUZvJjtpMdrWtzaQ9TpkTmb1792Lfvn3o1asXV9ayZUuYm5tj0KBBMiUyqampEAqFMDY2Fis3NjZGbGxsieskJiaWWD8xMZFbXlxWWh0AmDJlClq3bg09PT1cvXoVPj4+SEhIwOrVq7ntWFpaSmyjeFlJiYy/vz8WLVokUZ6SkiJxW+tziUQiZGZmgjFGtzCk9KVtlv4uA75HNuByXBRX1sy0Ifx/moH6+mal3gb9WtE5JjtqM9lRm8mutrXZ27dvpaoncyKjrKwMCwsLiXJLS0uxqxfV3cdXdVq2bAklJSWMGzcO/v7+UFZW/qxt+vj4iG03KysL5ubmMDQ0hJaWVoXELRKJwOPxYGhoWCtO5IrwJW124cF1TN2xBMlZaVzZeOfBmNtnHJQUFCshWvmjc0x21GayozaTXW1rMxUVFanqyZzIeHl5YcmSJQgKCuK+8PPz87Fs2TJ4eXnJtC0DAwMIBAIkJSWJlSclJcHExKTEdUxMTMqsX/xvUlISTE1NxerY2pY+q7CDgwOKiorw/PlzWFtbl7qfj/fxKWVl5RKToOIRkCsKj8er8G3WdLK2WaGwCL8cCcSm07u5MgNNXWwY4YeuzdtVYqTVA51jsqM2kx21mexqU5tJe4xS1frhhx+4V0xMDI4dO4a6devC2dkZzs7OqFu3Lv73v//hn3/+kSlIJSUl2NvbIzw8nCsTiUQIDw/n+t98ytHRUaw+AISFhXH1LS0tYWJiIlYnKysLkZGRpW4TAGJiYsDn82FkZMTt5+LFiygsLBTbj7W1dYm3lUjN8TzlX3y/YqxYEtO1WTucW7C7ViQxhBDyNZHqioy2trbY+x9//FHsvbm5+WcH4O3tjeHDh6NNmzZo27Yt1q5di+zsbIwcORIA4OHhgTp16sDf3x94P2mlk5MTAgIC4Orqin379uHmzZvcY9E8Hg/Tpk3D0qVLYWVlBUtLSyxYsABmZmZwc3MD3nfkjYyMRNeuXaGpqYmIiAhMnz4dQ4cO5ZKUwYMHY9GiRfD09MScOXNw9+5drFu3DmvWrPnsYyXV38HIk5jz1wq8y8sBACgKFPBzv4kY221grfgLiBBCvjZSJTJBQUGVFsCAAQOQkpICX19fJCYmwtbWFidPnuQ61sbHx4t9gbRv3x579+7F/PnzMW/ePFhZWSE0NBQtWrTg6syePRvZ2dkYO3YsMjIy0LFjR5w8eZK736asrIx9+/Zh4cKFyM/Ph6WlJaZPny7Wv0VbWxunT5/GpEmTYG9vDwMDA/j6+mLs2LGV1hZEft7lZcNnXwBCrn0YXNHSsC4CRy9Fq/pN5BobIYSQ0vFY8eQwMkpJSeEmibS2toahoWFFx/bVy8rKgra2NjIzMyu0s29ycjKMjIzoCoGUymuzmBcPMOGPBXiW8i9X5t7uOywfOAMaKupVHK380TkmO2oz2VGbya62tZm036Eyd/bNzs7G5MmTsXPnTu4Zb4FAAA8PD2zYsAFqampfFjkhVUQkEuG38L+wPHQLCoVFAAANFTX8Omg2fnToKe/wCCGESEHmlM7b2xsXLlzA//73P2RkZCAjIwNHjhzBhQsXMGPGjMqJkpAKlpyZhsEbp2PRwQ1cEmNn0Qxnft5JSQwhhHxFZL4ic/DgQRw4cABdunThyr777juoqqrC3d1dpgHxCKlsQpEQEY+iEffyKazMG8CxsR0uPriBydsXIfXtG+B9B3GvHsMwu89YKApk/i9BCCFEjmT+1M7JyZEYNRcAjIyMkJOTU1FxEfLFjkefw/zgNUjI+DDyrrqyGrLzP5ynRlr62DjSD52btpVTlIQQQr6EzLeWHB0d4efnJzbkfm5uLhYtWlTmOC2EVKXj0ecw+jcfsSQGgFgS822L9ji7YDclMYQQ8hWT+YrMunXr4OLigrp166JVq1YAgH/++QcqKio4depUZcRIiEyEIiHmB69BWY/jaatqYMeEFVCgW0mEEPJVk/lTvEWLFoiLi8OePXu4iR0HDRqEIUOGQFVVtTJiJEQm1+JiJK7EfCoz9x0iH/+DDtb2VRYXIYSQivdZf46qqalhzJgxFR8NIRXg4wkeK6IeIYSQ6kuqRObo0aNSb7BPnz5fEg8hX0QkEuFaXIxUdY209Cs9HkIIIZVLqkSmeI6i8vB4PAiFwi+NiZDPkpKVjqk7luDsvYgy6/EAmOoao51V6bOhE0II+TpIlcgUj+BLSHV1KfYGJm1bKHG7iAeIdfrlvf93ifs0CPiCKo2REEJIxaNHNshXrUhYhJX/+x3rT+1E8bRhBpq62DhyIbLzcyTGkTHVNcYS92lwtesqx6gJIYRUFKkTmdzcXISHh6N3794AAB8fH+Tn53PLBQIBlixZws0wTUhle5mWgIl/+uLG0ztcWZdmDtgwwheG7/u/9GzVWWJkX7oSQwghNYfUicyOHTtw/PhxLpHZuHEjmjdvzj1yHRsbCzMzM0yfPr3yoiXkvWO3zmLGbn9k5rwFACjwBZjbdzwmdh8iNiusgC9A+8at0Uinbq2ZMZYQQmoTqROZPXv2YPbs2WJle/fuRYMGDQAAu3fvxqZNmyiRIZUqtyAPfiHrsPPSYa7MXN8Uv41egtaWLeQaGyGEkKon9Z+njx8/ho2NDfdeRUVF7K/btm3b4v79+xUfISHvxb5+il6/eIolMX3bOCN8/i5KYgghpJaS+opMRkaGWJ+YlJQUseUikUhsOSEVhTGGPZePYMH+Ncgt/O8cU1VUxrKBMzCo/ffg8XjlboMQQkjNJHUiU7duXdy9exfW1tYlLr99+zbq1q1bkbERgsyct5i5+xf871Y4V9a0TkMEei6FtZmlXGMjhBAif1LfWvruu+/g6+srNut1seLZr11dXSs6PlKLRT29C+dlHmJJzPDOP+DEnD8piSGEEALIckVm3rx52L9/P6ytreHl5YXGjRsDAB4+fIiNGzeiqKgI8+bNq8xYSS0hEomw6fRu/HL0NwhF/40Ura2midXD5tH4L4QQQsRIncgYGxvj6tWrmDBhAubOncsNPsbj8dC9e3ds3rwZxsbGlRkrqQWSM9PgFbQQF2NvcGVtG7bEplGLYK5vKtfYCCGEVD8yjexraWmJkydPIj09HY8fPwYANGrUCHp6epUVH6lFzt27hsnbFyH17RvgfZI8rdcIzHD1hIKABqEmhBAi6bO+HfT09NC2bduKj4bUSgVFhfjlSCA2h+3hyoy1DbBp5EJ0bNJGrrERQgip3ujPXCJXL1JeYdwfCxDz4sMYRN+2aI91wxfAQFNXrrERQgip/iiRIXJz+MZpzNrzC97l5QAAFAUKmN9vEsZ+O5DGhiGEECKVajHxzKZNm2BhYQEVFRU4ODjg+vXrZdYPCQlBkyZNoKKiAhsbG5w4cUJsOWMMvr6+MDU1haqqKpydnREXF8ctf/78OTw9PWFpaQlVVVU0bNgQfn5+KCgoEKvD4/EkXteuXauEFqhdsvNzMX3nMkz405dLYiwN6+LY7D8wznkQJTGEEEKkJvdEJjg4GN7e3vDz88OtW7fQqlUruLi4IDk5ucT6V69exaBBg+Dp6Yno6Gi4ubnBzc0Nd+/e5eqsWLEC69evR2BgICIjI6Gurg4XFxduDJzY2FiIRCL89ttvuHfvHtasWYPAwMASHx8/c+YMEhISuJe9vX0ltkbNd//fOPT0H4G/rv6PK/vJoSfCft6BVvWbyDU2QgghXx8eK36OWk4cHBzwzTffYOPGjcD7MUTMzc0xefJkzJ07V6L+gAEDkJ2djWPHjnFl7dq1g62tLQIDA8EYg5mZGWbMmIGZM2cCADIzM2FsbIzt27dj4MCBJcaxcuVKbNmyBU+fPgXeX5GxtLREdHQ0bG1tpTqW/Px8sWkasrKyYG5ujjdv3kBLS0vGlimZSCRCSkoKDA0Nv6qZnBlj2H7xEBYf3ID8ov+ufKkpq8J/wEz0b9erUvf9tbaZvFB7yY7aTHbUZrKrbW2WlZUFXV1dZGZmlvkdKtc+MgUFBYiKioKPjw9Xxufz4ezsjIiIiBLXiYiIgLe3t1iZi4sLQkNDAQDPnj1DYmIinJ2dueXa2tpwcHBAREREqYlMZmZmiY+R9+nTB3l5eWjcuDFmz56NPn36lHo8/v7+WLRokUR5SkpKiSMifw6RSITMzEwwxr6aEzkz5y0WHt2Ic7GRXFkTkwb49acZqG9Qp9SrbxXla2wzeaL2kh21meyozWRX29rs7du3UtWTayKTmpoKoVAoMZCesbExYmNjS1wnMTGxxPqJiYnc8uKy0up86vHjx9iwYQNWrVrFlWloaCAgIAAdOnQAn8/HwYMH4ebmhtDQ0FKTGR8fH7Ekq/iKjKGhYYVekeHxeF9NRh75+B9MClqI12+SuLLRXd3xs9tEKCsqVUkMX1ubyRu1l+yozWRHbSa72tZmKioqUtWr9U8tvXr1Cj179kT//v0xZswYrtzAwEAsKfnmm2/w+vVrrFy5stRERllZGcrKyhLlfD6/Qk86Ho9X4dv8EkKRENfiYpCclQYjLX20s/rvVty6v3dg1bE/IGIiAICeujbWDp+PHi07VXmM1a3NqjtqL9lRm8mO2kx2tanNpD1GuSYyBgYGEAgESEpKEitPSkqCiYlJieuYmJiUWb/436SkJJiamorV+bSvy+vXr9G1a1e0b98eW7duLTdeBwcHhIWFyXCENd/x6HOYH7wGCRkfbg8Za+tDR00LDxOecWWOVnbYPGoRTHWN5BQpIYSQmkiuKZ2SkhLs7e0RHv5hdmORSITw8HA4OjqWuI6jo6NYfQAICwvj6ltaWsLExESsTlZWFiIjI8W2+erVK3Tp0gX29vYICgqSKvOLiYkRS45qu+PR5zD6Nx+xJAYAkjLTuCSGz+Nj1vdjcGD6RkpiCCGEVDi531ry9vbG8OHD0aZNG7Rt2xZr165FdnY2Ro4cCQDw8PBAnTp14O/vDwCYOnUqnJycEBAQAFdXV+zbtw83b97krqjweDxMmzYNS5cuhZWVFSwtLbFgwQKYmZnBzc0N+CiJqV+/PlatWoWUlBQunuIrOjt27ICSkhLs7OwAAIcOHcK2bdvwxx9/VHkbVUdCkRDzg9egrEfe+Dw+QqZtRAfr1lUYGSGEkNpE7onMgAEDkJKSAl9fXyQmJsLW1hYnT57kOuvGx8eLXS1p37499u7di/nz52PevHmwsrJCaGgoWrRowdWZPXs2srOzMXbsWGRkZKBjx444efIk13EoLCwMjx8/xuPHj1G3bl2xeD5+Gn3JkiV48eIFFBQU0KRJEwQHB+Onn36qglap/q7FxUhcifnUf31j5Pp0PyGEkBpO7uPI1GRZWVnQ1tYu9xl4WYhEIiQnJ8PIyEiunb0O3ziNCX/6lltvi+di9PumR5XEVJrq0mZfC2ov2VGbyY7aTHa1rc2k/Q6t+S1BKoWWioZU9Yy09Cs9FkIIIbWX3G8tka/P3ZePsCBkTZl1eABMdY25R7EJIYSQykCJDJEaYwxBFw5i0YH13DQDJSme8nGJ+zQI+IIqi48QQkjtQ7eWiFTeZGdiVOBczNu3iktiWtazxvIBM2CqI/5YtamuMf4Y5w9Xu65yipYQQkhtQVdkSLmuP/4HE7b54VX6hykexnQbgPn9JkFZUQnDnX6QGNmXrsQQQgipCpTIkFIJRUJsPLULK/73O4QiIQBAV10Laz3mw6VVZ66egC9AB2t7OUZKCCGktqJEhpQoOTMNk4L8cCn2JlfWrpEtNnsuhhmN0EsIIaSaoESGSDh37xomb1+E1LdvgPejJXt/NwrTvxsJBQGdMoQQQqoP+lYinEJhEX45EohNp3dzZSbahtg0aiHdOiKEEFItUSJDAAAvUl9jwp8LcOvZPa7s2xbtsW74Ahho6so1NkIIIaQ0lMgQHI0Kx8zd/sjKfQcAUBQo4Od+EzG228BaMQw2IYSQrxclMrVYbkEe/ELWYeelw1xZfYM6CBy9BHYWzeQaGyGEECINSmRqqYevn2HcH/MR+/oJV+bWpjtWDpkLTVV1ucZGCCGESIsSmVqGMYa/rv4PP+8LQG5hPgBAVVEZywbOwKD234PH45W7DUIIIaS6oESmFnmbm41Ze35B6M0wrqyJWUP8NnoprM0s5RobIYQQ8jkokaklYl48wLjf5+NF6iuuzKNzPyz6aSpUlVTkGhshhBDyuSiRqeFEIhG2nt2HZYc3o1BYBADQUtXAqqE+6GP/rbzDI4QQQr4IJTI1WOrbN5i6YwnC717lylpbNscWzyWob2Am19gIIYSQikCJTA115WEUJm1biMTMFK7My2UY5vQZB0WaZoAQQkgNQd9oNUyRsAirTwRhzYltYIwBAPQ1dbFxhB+6Nm8n7/AIIYSQCkWJTA3y+k0yJv7pi2uPY7iyTk3aYOPIhTDWNpBrbIQQQkhloESmhjh9+xKm7liCN9lZAAABX4DZ34+Bl8swCPgCeYdHCCGEVApKZL5y+YUFWHp4E34/G8yV1dEzwZZRi9C2USu5xkYIIYRUNkpkviJCkRARj6IR9/IprMwbwFjbAJOC/HA7/iFX5ztbJwQMmwdddW25xkoIIYRUhWoxtfGmTZtgYWEBFRUVODg44Pr162XWDwkJQZMmTaCiogIbGxucOHFCbDljDL6+vjA1NYWqqiqcnZ0RFxcnVic9PR1DhgyBlpYWdHR04OnpiXfv3onVuX37Njp16gQVFRWYm5tjxYoVFXjUsjkefQ5t5vXDT2u94HNwNX5a64XOiwZxSYyyghL8B87En+N+oSSGEEJIrSH3RCY4OBje3t7w8/PDrVu30KpVK7i4uCA5ObnE+levXsWgQYPg6emJ6OhouLm5wc3NDXfv3uXqrFixAuvXr0dgYCAiIyOhrq4OFxcX5OXlcXWGDBmCe/fuISwsDMeOHcP/27v7qCbOfA/g3wRMeA+gJCEKBVutolRRhIIetcLRuq5X3XOruFYpunbbQtdI3a22x9U9FRF79fparfa6Wq/t6u5e0dpTdymyuLoIiOKqRW0LHtkqROQlgAqSzP1DCUZeknTRycj3c47nOM/Mk/nNL+HJL89MMsePH8frr79uWW80GjFx4kQ888wzKCoqwocffoiVK1dix44djzkj7X15Nge/+HgZbtRa50TA/W8laVUB+PLdT5A0/j95ryQiIupRZELrd3RFEh0djVGjRmHLli3Ag1+iDQoKwttvv42lS5e2237WrFlobGzEkSNHLG0vvvgihg8fju3bt0MQBOh0OrzzzjtYsmQJAKCurg4ajQa7d+9GQkICSkpKEBYWhsLCQkRGRgIAjh49ip/85Cf417/+BZ1Oh23btuH9999HRUUFFAoFAGDp0qXIzMzEpUuX7Do2o9EIlUqFuro6+Pj4/Kj8mMwmRL43o10R87BA3wCcXp3Ji3o7YTabYTAYoFarIZeLXrs7PebLccyZ45gzx/W0nNn7HirqNTLNzc0oKirCsmXLLG1yuRzx8fHIy8vrsE9eXh5SU1Ot2iZNmoTMzEwAQFlZGSoqKhAfH29Zr1KpEB0djby8PCQkJCAvLw++vr6WIgYA4uPjIZfLkZ+fjxkzZiAvLw9jx461FDGt+8nIyEBNTQ38/PzaxdbU1ISmpibLstF4/xtEE/4rB65unl3mYohOhZ3zRlq1Lfy0CGeuVaHaPBvKLuogw51TyLtyFrEDRwAAGppaMPG/j3e5v1Yfzx2J8L5tp6KyLxmwPPNCl30AwEPhiq9Tx1q1pX91CV+cu26z70vPq5E2Y6hV239sPYmq+qZO+7R69+VBmDa87VeJS2824NX/6fpUpPBgADiUPBpalbul/fOCa9h87Dub+wzt44l9v4i2atPvL0ZBWbXNvrNGBWFR3ACrttg1x2z2A4D1M4fhxf69LcunSm8h9cA5u/r+Y+kEq+WN2d9if2G5zX5Rof5Y/8oLEAQBZrMZADDnk3yUVTXa7Pv2hOcwOyrYsmww3sX0j/7RZZ9W/7sgCv0DvCzLh4qvI+Oo7Q8MfbyVOJw82qrt/YMXkHO588K/1dRhOiybPMiqLX79cdxubrHZ94PpQxE3SG1Z/md5DRbu/SfkcjlszYn+dfFYeCnbht5PTpRh14kym/vsbIy4eL3OZt/5Y0LxizFtN4Z1hjGi9e+yo5w96TGiVeZbsVD7tN17ztnGiEdzJtYYsWHWcKu2xzVGtNy1/ZgQu5CpqqqCyWSCRqOxatdoNJ3OelRUVHS4fUVFhWV9a1tX26jVaqv1rq6u8Pf3t9omNDS03WO0ruuokElPT8fvfve7du2G+mbIm7tOdR+Pxnan0yprG1Fz2wyZvOvZHJlMiW/LS/Gcbz8AQGOTCRVG23/wAGC4eQuGXm3bGqpq7OrrobjXLt6KaqNdfStr69sfa90d3Gy4Zzve6loYDG25NNy6Y/+xGm5C3tQ2SFVW19nV191V1j7emkb7jrXa2D5P9sZbVQ2Dl+mhZfviBdA+Xnufm5r7r8O6ujoIggC5XI6K2tt2Hmud1X4N9c0OHOsteAm325ara+3qa3rwCdUqjtp6u/pWdPjc3MHtZrMd8dbg4a6VVfV2vX4B4KbhJm4r22ZPK2/Z97x2NkbY9dzcsn5uOEZ00vdmFXC37cMrx4gO4q1p/zp8XGOEuanZ5mNC7ELmabNs2TKr2SKj0YigoCCovRVwdVN22Vfj69muuNL4luMH4x1UN9R22VcQmjAgqL+lf0NTC7Q+Xe+vlTqgN9Tqtk9b6mrY1ddD4douXq1/NbQ+DZ32aaXx9W5/rCp3uNgxVar297Xq2yBrsBlv66cYtToA6odmZDT+d6H1udllXwDQ+nq0j9fvOrS1tv9wNf4+7fNk73PTxx9qddunLXWDi/19H43Xvw5aH9uf3DV+91+HMpkMAQEBkMvl0PqW4U6L7TPQGn+V9X7d7jpwrL2hfmhGRu3fAq1Phc1+fbyVHfzdGKD1uWOzr7bD58bdrhkZdR8/q76apl4I8Opl14xMgDrAakZG07sRWh/bn9w7GyOqbtuOV9Pb+rlxhjGiqxmZJz1GWPoG9LGakXG2MeLRnIk5RljF+5jGiJa7LbA9RyTyNTLNzc3w8PDAn/70J0yfPt3SnpiYiNraWhw6dKhdn+DgYKSmpkKv11vaVqxYgczMTJw7dw6lpaV49tlncfbsWQwf3jb9NW7cOAwfPhwbN27Erl278M4776CmpsayvqWlBW5ubvjjH/+IGTNmYN68eTAajZZTVgCQk5ODCRMmoLq6usMZmUd15zUyFbUGdPREyQAE+mlQmPZ/vEamEz3tvPK/i/lyHHPmOObMcT0tZ/a+h4qaCYVCgZEjRyI7O9vSZjabkZ2djZiYmA77xMTEWG0PAFlZWZbtQ0NDodVqrbYxGo3Iz8+3bBMTE4Pa2loUFRVZtjl27BjMZjOio6Mt2xw/fhz37t2z2s/zzz9vVxHTXVzkLlg1azHwoGh5WOvyBzP1LGKIiKhHEr2kS01Nxc6dO7Fnzx6UlJTgzTffRGNjI5KSkgAA8+bNs7oYeNGiRTh69CjWrVuHS5cuYeXKlTh9+jRSUlIAADKZDHq9HqtWrcLhw4dx/vx5zJs3DzqdzjLrM3jwYLz88stYuHAhCgoKcPLkSaSkpCAhIQE63f2LxH7+859DoVBgwYIFuHjxIvbv34+NGze2u9D4SZgS8RI++WU6tL7W03mBfhp88st0TIl46YnHRERE5BQEJ7B582YhODhYUCgUQlRUlHDq1CnLunHjxgmJiYlW2x84cEAYOHCgoFAohCFDhghffvml1Xqz2SwsX75c0Gg0glKpFOLi4oTLly9bbXPr1i1h9uzZgpeXl+Dj4yMkJSUJ9fX1VtucO3dOGDNmjKBUKoW+ffsKa9ascei46urqBABCXV2dQ/0602JqEf5eUijs+ut+4e8lhUKLqaVbHvdpZzKZhBs3bggmk0nsUCSB+XIcc+Y45sxxPS1n9r6Hiv47Mk+z7rhG5lE97Rxpd2DOHMN8OY45cxxz5rieljNJXCNDRERE9O9gIUNERESSxUKGiIiIJIuFDBEREUkWCxkiIiKSLBYyREREJFm819Jj1PrN9ta7YHcHs9mM+vp6uLm59Yiv33UH5swxzJfjmDPHMWeO62k5a33vtPUrMSxkHqP6+noAQFBQkNihEBERSVJ9fT1UKlWn6/mDeI+R2WzG9evX4e3tDZnM1j1x7dN6R+3y8vJu+5G9px1z5hjmy3HMmeOYM8f1tJwJgoD6+nrodLouZ6A4I/MYyeVy9OvX77E8to+PT494IXcn5swxzJfjmDPHMWeO60k562omptXTf5KNiIiInlosZIiIiEiyWMhIjFKpxIoVK6BUKsUORTKYM8cwX45jzhzHnDmOOesYL/YlIiIiyeKMDBEREUkWCxkiIiKSLBYyREREJFksZIiIiEiyWMhIyNatWxESEgI3NzdER0ejoKBA7JCcVnp6OkaNGgVvb2+o1WpMnz4dly9fFjssSVmzZg1kMhn0er3YoTi1H374Aa+++ip69+4Nd3d3hIeH4/Tp02KH5bRMJhOWL1+O0NBQuLu749lnn8UHH3xg8346Pcnx48cxdepU6HQ6yGQyZGZmWq0XBAG//e1vERgYCHd3d8THx+Pbb78VLV6xsZCRiP379yM1NRUrVqzAmTNnMGzYMEyaNAkGg0Hs0JxSbm4ukpOTcerUKWRlZeHevXuYOHEiGhsbxQ5NEgoLC/Hxxx/jhRdeEDsUp1ZTU4PRo0ejV69e+Oqrr/DNN99g3bp18PPzEzs0p5WRkYFt27Zhy5YtKCkpQUZGBtauXYvNmzeLHZrTaGxsxLBhw7B169YO169duxabNm3C9u3bkZ+fD09PT0yaNAl379594rE6BYEkISoqSkhOTrYsm0wmQafTCenp6aLGJRUGg0EAIOTm5ooditOrr68XBgwYIGRlZQnjxo0TFi1aJHZITuvdd98VxowZI3YYkjJlyhRh/vz5Vm0/+9nPhDlz5ogWkzMDIBw8eNCybDabBa1WK3z44YeWttraWkGpVAqff/65SFGKizMyEtDc3IyioiLEx8db2uRyOeLj45GXlydqbFJRV1cHAPD39xc7FKeXnJyMKVOmWL3eqGOHDx9GZGQkXnnlFajVakRERGDnzp1ih+XUYmNjkZ2djStXrgAAzp07hxMnTmDy5MlihyYJZWVlqKiosPr7VKlUiI6O7rHvB7xppARUVVXBZDJBo9FYtWs0Gly6dEm0uKTCbDZDr9dj9OjRGDp0qNjhOLU//OEPOHPmDAoLC8UORRJKS0uxbds2pKam4r333kNhYSF+9atfQaFQIDExUezwnNLSpUthNBoxaNAguLi4wGQyIS0tDXPmzBE7NEmoqKgAHoz/D9NoNJZ1PQ0LGXrqJScn48KFCzhx4oTYoTi18vJyLFq0CFlZWXBzcxM7HEkwm82IjIzE6tWrAQARERG4cOECtm/fzkKmEwcOHMC+ffvw2WefYciQISguLoZer4dOp2PO6EfhqSUJ6NOnD1xcXFBZWWnVXllZCa1WK1pcUpCSkoIjR44gJycH/fr1Ezscp1ZUVASDwYARI0bA1dUVrq6uyM3NxaZNm+Dq6gqTySR2iE4nMDAQYWFhVm2DBw/GtWvXRIvJ2f3617/G0qVLkZCQgPDwcMydOxeLFy9Genq62KFJQuuYz/eDNixkJEChUGDkyJHIzs62tJnNZmRnZyMmJkbU2JyVIAhISUnBwYMHcezYMYSGhoodktOLi4vD+fPnUVxcbPkXGRmJOXPmoLi4GC4uLmKH6HRGjx7d7mv9V65cwTPPPCNaTM7u9u3bkMut33pcXFxgNptFi0lKQkNDodVqrd4PjEYj8vPze+z7AU8tSURqaioSExMRGRmJqKgobNiwAY2NjUhKShI7NKeUnJyMzz77DIcOHYK3t7fl3LFKpYK7u7vY4Tklb2/vdtcQeXp6onfv3ry2qBOLFy9GbGwsVq9ejZkzZ6KgoAA7duzAjh07xA7NaU2dOhVpaWkIDg7GkCFDcPbsWaxfvx7z588XOzSn0dDQgO+++86yXFZWhuLiYvj7+yM4OBh6vR6rVq3CgAEDEBoaiuXLl0On02H69Omixi0asb82RfbbvHmzEBwcLCgUCiEqKko4deqU2CE5LQAd/vv9738vdmiSwq9f2/bFF18IQ4cOFZRKpTBo0CBhx44dYofk1IxGo7Bo0SIhODhYcHNzE/r37y+8//77QlNTk9ihOY2cnJwOx6/ExERBePAV7OXLlwsajUZQKpVCXFyccPnyZbHDFo1M4M8pEhERkUTxGhkiIiKSLBYyREREJFksZIiIiEiyWMgQERGRZLGQISIiIsliIUNERESSxUKGiIiIJIuFDBEREUkWCxkioi6EhIRgw4YNYodBRJ1gIUNETuO1116z3C9m/Pjx0Ov1T2zfu3fvhq+vb7v2wsJCvP76608sDiJyDG8aSURPtebmZigUih/dPyAgoFvjIaLuxRkZInI6r732GnJzc7Fx40bIZDLIZDJcvXoVAHDhwgVMnjwZXl5e0Gg0mDt3Lqqqqix9x48fj5SUFOj1evTp0weTJk0CAKxfvx7h4eHw9PREUFAQ3nrrLTQ0NAAA/va3vyEpKQl1dXWW/a1cuRLo4NTStWvXMG3aNHh5ecHHxwczZ85EZWWlZf3KlSsxfPhw7N27FyEhIVCpVEhISEB9ff0Tyx9RT8JChoiczsaNGxETE4OFCxfixo0buHHjBoKCglBbW4sJEyYgIiICp0+fxtGjR1FZWYmZM2da9d+zZw8UCgVOnjyJ7du3AwDkcjk2bdqEixcvYs+ePTh27Bh+85vfAABiY2OxYcMG+Pj4WPa3ZMmSdnGZzWZMmzYN1dXVyM3NRVZWFkpLSzFr1iyr7b7//ntkZmbiyJEjOHLkCHJzc7FmzZrHmjOinoqnlojI6ahUKigUCnh4eECr1Vrat2zZgoiICKxevdrStmvXLgQFBeHKlSsYOHAgAGDAgAFYu3at1WM+fL1NSEgIVq1ahTfeeAMfffQRFAoFVCoVZDKZ1f4elZ2djfPnz6OsrAxBQUEAgE8//RRDhgxBYWEhRo0aBTwoeHbv3g1vb28AwNy5c5GdnY20tLRuyxER3ccZGSKSjHPnziEnJwdeXl6Wf4MGDQIezIK0GjlyZLu+X3/9NeLi4tC3b194e3tj7ty5uHXrFm7fvm33/ktKShAUFGQpYgAgLCwMvr6+KCkpsbSFhIRYihgACAwMhMFg+FHHTERd44wMEUlGQ0MDpk6dioyMjHbrAgMDLf/39PS0Wnf16lX89Kc/xZtvvom0tDT4+/vjxIkTWLBgAZqbm+Hh4dGtcfbq1ctqWSaTwWw2d+s+iOg+FjJE5JQUCgVMJpNV24gRI/DnP/8ZISEhcHW1f/gqKiqC2WzGunXrIJffn4g+cOCAzf09avDgwSgvL0d5ebllVuabb75BbW0twsLCHDg6IuouPLVERE4pJCQE+fn5uHr1KqqqqmA2m5GcnIzq6mrMnj0bhYWF+P777/GXv/wFSUlJXRYhzz33HO7du4fNmzejtLQUe/futVwE/PD+GhoakJ2djaqqqg5POcXHxyM8PBxz5szBmTNnUFBQgHnz5mHcuHGIjIx8LHkgoq6xkCEip7RkyRK4uLggLCwMAQEBuHbtGnQ6HU6ePAmTyYSJEyciPDwcer0evr6+lpmWjgwbNgzr169HRkYGhg4din379iE9Pd1qm9jYWLzxxhuYNWsWAgIC2l0sjAeniA4dOgQ/Pz+MHTsW8fHx6N+/P/bv3/9YckBEtskEQRDEDoKIiIjox+CMDBEREUkWCxkiIiKSLBYyREREJFksZIiIiEiyWMgQERGRZLGQISIiIsliIUNERESSxUKGiIiIJIuFDBEREUkWCxkiIiKSLBYyREREJFn/D54CF1vfztqVAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "ax.plot(npl.arange(len(bias_history)), bias_history, marker=\"o\", label=\"Optimized\", linewidth=2)\n",
    "ax.axhline(0.0, color=\"tab:blue\", linestyle=\"--\", linewidth=2, label=\"Simulated (nominal)\")\n",
    "ax.axhline(\n",
    "    global_bias, color=\"tab:orange\", linestyle=\"--\", linewidth=2, label=\"Fabricated (target)\"\n",
    ")\n",
    "ax.set_xlabel(\"Iteration\")\n",
    "ax.set_ylabel(\"Global width deviation (µm)\")\n",
    "ax.set_title(\"Measurement Calibration\")\n",
    "ax.grid(True, alpha=0.3)\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "526a3744",
   "metadata": {},
   "source": [
    "After optimization, the width deviation matches the \"fabricated\" one very well. Note that the match is not perfect, although the remaining discrepancy is due to the convergence properties of the optimizer - running more iterations would further reduce the error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "16fcbb46",
   "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",
    "ax.plot(\n",
    "    wl_base,\n",
    "    base_db,\n",
    "    label=\"Before optimization\",\n",
    "    linewidth=2,\n",
    ")\n",
    "ax.plot(\n",
    "    wl_calib,\n",
    "    calib_db,\n",
    "    label=\"After optimization\",\n",
    "    linewidth=2,\n",
    ")\n",
    "ax.plot(\n",
    "    wl_meas,\n",
    "    meas_db,\n",
    "    label=\"Measured (synthetic)\",\n",
    "    linewidth=1.5,\n",
    "    linestyle=\"--\",\n",
    "    alpha=0.7,\n",
    ")\n",
    "ax.set_xlabel(\"Wavelength (um)\")\n",
    "ax.set_ylabel(\"Transmission (dB)\")\n",
    "ax.set_title(\"Spectrum Before vs After Calibration\")\n",
    "ax.grid(True, alpha=0.3)\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "030bb489",
   "metadata": {},
   "source": [
    "## Takeaways\n",
    "\n",
    "By calibrating the simulation to match measurement we keep the model and fabricated hardware in sync. Combined with robust optimization this closes the loop between design, fabrication, and test, enabling faster debug and higher-yield deployment of inverse-designed photonics.\n",
    "\n",
    "Key advantages of the gradient-based approach:\n",
    "- **Scalability**: Adjoint derivatives allow efficient optimization of many fabrication parameters simultaneously (width bias, etch depth, sidewall angle, material variations, etc.) without a combinatorial explosion in computational cost.\n",
    "- **Versatility**: The same optimization framework used for device design applies seamlessly to calibration, demonstrating the broad applicability of adjoint methods across the photonics workflow.\n",
    "- **Precision**: Gradient information enables faster convergence to accurate parameter estimates compared to gradient-free methods, especially important when calibration involves expensive simulations."
   ]
  }
 ],
 "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
}
