work force model/work_force_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7c0b7316",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "class Pedestrian:\n",
    "    def __init__(self, position, goal, speed=1.0):\n",
    "        self.position = np.array(position, dtype=np.float64)\n",
    "        self.goal = np.array(goal, dtype=np.float64)\n",
    "        self.speed = speed\n",
    "\n",
    "    def update_position(self):\n",
    "        direction = self.goal - self.position\n",
    "        direction_norm = np.linalg.norm(direction)\n",
    "        if direction_norm > 0:\n",
    "            direction_unit = direction / direction_norm\n",
    "            self.position += self.speed * direction_unit\n",
    "\n",
    "def simulate_pedestrians(pedestrians, steps, dt=1.0):\n",
    "    positions = []\n",
    "    for _ in range(steps):\n",
    "        step_positions = []\n",
    "        for pedestrian in pedestrians:\n",
    "            pedestrian.update_position()\n",
    "            step_positions.append(pedestrian.position.copy())\n",
    "        positions.append(step_positions)\n",
    "    return np.array(positions)\n",
    "\n",
    "def plot_pedestrians(positions):\n",
    "    for i, pos in enumerate(positions):\n",
    "        pos = np.array(pos)\n",
    "        plt.scatter(pos[:, 0], pos[:, 1], label=f'Step {i+1}')\n",
    "    plt.xlabel('X')\n",
    "    plt.ylabel('Y')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    # Initialize pedestrians\n",
    "    p1 = Pedestrian([0, 0], [10, 10])\n",
    "    p2 = Pedestrian([0, 5], [10, 5])\n",
    "    p3 = Pedestrian([5, 0], [5, 10])\n",
    "\n",
    "    pedestrians = [p1, p2, p3]\n",
    "\n",
    "    # Simulate and plot\n",
    "    positions = simulate_pedestrians(pedestrians, steps=10)\n",
    "    plot_pedestrians(positions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "89d59262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAHCCAYAAAAadEjjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAACqjklEQVR4nOzde1hU97nw/e8awEEcYFCBEYMcPASRkELwgBgTGwlYNSZhY2NLg3FLbJM+SerbNmoqimfZOyZpTKxJfYRGW6tPqhttixpTklgloMaI4lZi0aTKwRMgIAgz8/6BjI7AyGFgGLg/18WVrDX3mnUPIHPP76gYjUYjQgghhBB2SmXrBIQQQgghOkKKGSGEEELYNSlmhBBCCGHXpJgRQgghhF2TYkYIIYQQdk2KGSGEEELYNSlmhBBCCGHXpJgRQgghhF1ztHUCnc1gMHDp0iVcXV1RFMXW6QghhBCiFYxGIzdu3MDHxweVynLbS48vZi5duoSvr6+t0xBCCCFEO3z33Xc88MADFmN6fDHj6uoKNHwz3NzcbJyNEEIIIVqjoqICX19f0/u4JT2+mGnsWnJzc5NiRgghhLAzrRkiIgOAhRBCCGHXpJgRQgghhF2TYkYIIYQQdq3Hj5lpLb1eT11dna3TELc5OTnh4OBg6zSEEELYgV5fzBiNRoqLiykrK7N1KuIeWq0WnU4n6wMJIYSwqNcXM42FjJeXFy4uLvLG2Q0YjUaqq6spLS0FYNCgQTbOSAghRHfWq4sZvV5vKmQGDBhg63TEXfr27QtAaWkpXl5e0uUkhBCiRb16AHDjGBkXFxcbZyKa0/hzkbFMQgghLOnVxUwj6VrqnuTnIoQQojV6dTeTEEIIIdqnvq6Ofx38Ozcri+mr0RE4YQqOTk42yUVaZuxQaWkp8+bNY8iQIajVanQ6HTExMRw+fNgUoygKu3bt6pJ8Pv/8c6ZPn46Pj0+X3lcIIYRtnPxbOgf3R/Kd8Rdc6beW74y/4OD+SE7+Ld0m+UjLjB2Ki4ujrq6O9PR0AgMDKSkp4cCBA1y7ds0m+VRVVfHwww/zwgsvEBcXZ5MchBBCdI2Tf0unRL2syXm9+jolLIO/QcgPErs0J2mZsQK9wcjhc1f5n+MXOXzuKnqDsdPuVVZWxsGDB1m7di2TJk3Cz8+PMWPGsHDhQqZOnQqAv78/AM888wyKopiOAXbv3s0jjzyCs7MzgYGBpKSkUF9fb3pcURQ2bNjAlClT6Nu3LwEBAezYscNiTlOmTGHFihU8++yzVn+9Qgghuo/6ujqu8G7Dwb3DGm8fXzGup76LJ25IMdNBmSeLmLD2U2Z9mM2r244z68NsJqz9lMyTRZ1yP41Gg0ajYdeuXdTW1jYbk5ubC8DmzZspKioyHe/du5eEhAReeeUV8vPz2bhxI2lpaaxcudLs+sWLFxMXF8fXX39NQkICs2bN4vTp053yeoQQQtiPfx38O3rn600LmUYK6Pte418H/96leUkx0wGZJ4v42ZZjFJXXmJ0vLq/hZ1uOdUpB4+joSFpaGunp6Wi1WqKioli0aBEnTpwwxXh6egJ3VtBtPF65ciULFiwgMTGRwMBAoqOjWb58ORs3bjS7R3x8PHPnzmXEiBEsX76ciIgI3n33Xau/FiGEEPblZmWxVeOsRYqZdtIbjKTszqe5DqXGcym78zulyykuLo5Lly6RkZFBTEwMWVlZhIeHk5aWZvG6o0ePsmzZMlPrjkajISkpiaKiIqqrq01xkZGRZtdFRkZKy4wQQgj6anRWjbMWKWbaKafwWpMWmbsZgaLyGnIKO2dQrrOzM9HR0SQnJ3Po0CFmz57NkiVLLF5jMBhISUnh+PHjpq+8vDwKCgpwdna2eK2s+SKEECJwwhQcajxo9pM8gBEcbvYncMKULs1Lipl2Kr3RciHTnriOCg4OpqqqynTs5OSEXq83iwkPD+fMmTMMGzasyZdKdedXITs72+y67OxsgoKCOvcFCCGE6PYcnZwYyP9pOLi3oLl9PFD5eZevNyNTs9vJy9VyS0Zb41rr6tWrxMfHM2fOHEJDQ3F1deXIkSOkpqYyY8YMU5y/vz8HDhwgKioKtVqNh4cHycnJTJs2DV9fX+Lj41GpVJw4cYK8vDxWrFhhunbHjh1EREQwYcIEtm7dSk5ODps2bWoxp8rKSr755hvTcWFhIcePH6d///4MGTLEqq9fCCGEbYX8IBH+Bld4t2Ew8G0ONf0ZqPy8y6dlgxQz7TYmoD+D3J0pLq9ptrVNAXTuzowJ6G/V+2o0GsaOHctbb73FuXPnqKurw9fXl6SkJBYtWmSKe/PNN5k/fz4ffvghgwcP5vz588TExLBnzx6WLVtGamoqTk5OBAUFMXfuXLN7pKSksG3bNl566SV0Oh1bt24lODi4xZyOHDnCpEmTTMfz588HIDEx8b7jeIQQQtifkB8kUl/3I/MVgJ+03QrAitFo7LxFUe7j888/57/+6784evQoRUVF7Ny5k6efftr0uNFoJCUlhQ8++IDr168zduxY3nvvPUaNGtXqe1RUVODu7k55eTlubm5mj9XU1FBYWEhAQMB9x4w0p3E2E5i3tjWOLtmQEE5syKA2P68tKYrS5OdgKx39+QghhLBflt6/72XTMTONK8euX7++2cdTU1NZt24d69evJzc3F51OR3R0NDdu3OjiTJsXGzKIDQnh6NzN32h17s52WcgI0dX0RiP/vH6DnSXX+ef1G+ht99lKiF6npuYmv/vTDn6z6Q/87k87qKm5aeuU2s2m3UxTpkxhypTmRzwbjUbefvtt3njjDdPKsunp6Xh7e/PHP/6RefPmdWWqLYoNGUR0sI6cwmuU3qjBy7Wha8lBJbN/hLDkr5fL+E3BRYpq76wUOkjtxIrhg5nqqbVdYkL0Ass3f0SabihVuuGmc//1j6+YXXyOxS/8xIaZtU+3HTNTWFhIcXExTz75pOmcWq3mscce49ChQy0WM7W1tWYr41ZUVHR6rg4qhcihAzr9Pl3Bhr2Oohf56+Uy5p4832S8WXFtHXNPnuf3If5S0AjRSZZv/oj3/EKanK9S9204v/kjuytouu3U7OLihtUDvb29zc57e3ubHmvO6tWrcXd3N335+vp2ap5CiLbRG438puCixQUnFxdclC4nITpBTc1N0nRDGw7uXT/s9nG6d6DddTl122Km0b2LtRmNRosLuC1cuJDy8nLT13fffdfZKQoh2iC7rNKsa+leRuBSbR3ZZZVdl5QQvUTazj1UObs0LWQaKQqVffuRtnNP1ybWQd22m0mna1gKubi4mEGD7gykLS0tbdJacze1Wo1are70/IQQ7VN6q/7+QW2IE0K03r+rW9fi0tq47qLbtswEBASg0+nYv3+/6dytW7f47LPPGD9+vA0zE0J0hFef1n2Gam2cEKL1HnDpa9W47sKmxUxlZaVpjyC4s3Lst99+i6IovPbaa6xatYqdO3dy8uRJZs+ejYuLCz/60Y9smbYQogPGaTUMUjvRUmexAvionRin1XRlWkL0CrOfmUa/mmpoaUya0YjmZhWzn5nWtYl1kE2LmSNHjhAWFkZYWBjQsHJsWFgYycnJAPz617/mtdde46WXXiIiIoKLFy+yb98+XF1dbZm2EKIDHBSFFcMHAzQpaBqPlw8fjINsbiqE1Tk792V28bmGg3sLmtvHiSX/wtnZvlpmbLoCcFfozBWAReeSn0/P1tw6Mz5qJ5bLOjNCdDrTOjPOLqZzmptVJJb8q9tMy7abFYBF+5SWljJv3jyGDBmCWq1Gp9MRExPD4cOHTTGKorBr164uyWf16tWMHj0aV1dXvLy8ePrppzlz5kyX3FvYr6meWo5EBvPx94ayIdiPj783lNzIYClkhOgCi1/4CacmhbG0uIC5/zrB0uICTn4/vNsUMm0lI+zsUFxcHHV1daSnpxMYGEhJSQkHDhzg2rVrNsnns88+4+WXX2b06NHU19fzxhtv8OSTT5Kfn0+/fv1skpOwDw6KQpSHdBsLYQvOzn356ax4W6dhFdLNZI1uDIMeLhyCyhLQeIPfeFA5dDDz5pWVleHh4UFWVhaPPfZYszH+/v5cuHDBdOzn58f58+cB2L17N0uXLuXUqVP4+PiQmJjIG2+8gaNjQ12rKArvv/8+GRkZZGVlodPpSE1NJT6+9b/wly9fxsvLi88++4yJEye2+7VKN5MQQrRMb9BzrPQYl6sv4+niSbhXOA6d9N5jC23pZpKWmY7Kz4DM16Hi0p1zbj4QuxaCn7L67TQaDRqNhl27djFu3Lhm19TJzc3Fy8uLzZs3Exsbi4NDwy/33r17SUhI4Le//S2PPvoo586d48UXXwRgyZIlpusXL17MmjVreOedd/joo4+YNWsWISEhjBw5slU5lpeXA9C/f/+OvlwhhBDN+OTCJ6zJWUNJdYnpnLeLNwvGLGCy32QbZmYb0jLTkU/++Rmw/XlosjD77VkYM//QKQXNxx9/TFJSEjdv3iQ8PJzHHnuM5557jtDQ0DsZKAo7d+7k6aefNp2bOHEiU6ZMYeHChaZzW7Zs4de//jWXLl0yXffTn/6UDRs2mGLGjRtHeHg477///n1zMxqNzJgxg+vXr/PFF1906HVKy4wQQjT1yYVPmJ81H+M97z3K7feedY+v6xEFjQwA7goGfUOLjKUdZjIXNMRZWVxcHJcuXSIjI4OYmBiysrIIDw8nLS3N4nVHjx5l2bJlptYdjUZDUlISRUVFVFdXm+IiIyPNrouMjOT06dOtyu3nP/85J06c4E9/+lObX5cQQgjL9AY9a3LWNClkANO5tTlr0XfCe093JsVMe104ZN611IQRKi42xHUCZ2dnoqOjSU5O5tChQ8yePdusq6g5BoOBlJQU00KFx48fJy8vj4KCgvu2fFjaD6vR//k//4eMjAz+8Y9/8MADD7Tp9QghhLi/Y6XHzLqW7mXESHF1McdKj3VhVrYnxUx7Vbb8y9SuuA4KDg6mqqrKdOzk5IReb16Zh4eHc+bMGYYNG9bkS6W686uQnZ1tdl12djZBQUEt3ttoNPLzn/+cv/zlL3z66acEBARY6VWJnsxo1HP9ejbFxRlcv56N0di7PkkK0R6Xqy9bNa6nkAHA7aVpebPLdsW10tWrV4mPj2fOnDmEhobi6urKkSNHSE1NZcaMGaY4f39/Dhw4QFRUFGq1Gg8PD5KTk5k2bRq+vr7Ex8ejUqk4ceIEeXl5rFixwnTtjh07iIiIYMKECWzdupWcnBw2bdrUYk4vv/wyf/zjH/mf//kfXF1dKS4uBsDd3Z2+fe1rFUnRNUpL93K2YBm1tcWmc2q1jhHDk/HyirFhZkJ0b54unlaN6ymkZaa9/MY3zFqytMOM2+CGOCvSaDSMHTuWt956i4kTJxISEsLixYtJSkpi/fr1prg333yT/fv34+vra9ouIiYmhj179rB//35Gjx7NuHHjWLduHX5+fmb3SElJYdu2bYSGhpKens7WrVsJDg5uMacNGzZQXl7O448/zqBBg0xff/7zn6362kXPUFq6l7yTL5sVMgC1tSXknXyZ0tK9NspMiO4v3Cscbxdv02Dfeyko6Fx0hHuFd3FmtiWzmawymwnMBwJ37mymztTcLChbkdlMPY/RqOefhyY2KWTuUFCrdUSN/wxF6TnrZQhhTY2zmQCzgcAym0m0T/BTDQWL2yDz824+dlnICNHZyspyLRQyAEZqa4soK8vtspyEsDeT/Saz7vF1eLl4mZ33dvHuMYVMW8mYmY4KfgqCpnbZCsBC2LPa2lKrxgnRW032m8wk30k9egXgtpBixhpUDhDwqK2zsIoe3usobEyt9rp/UBvihOjNHFQOjNaNtnUa3YIUM0KILqPVjkat1lFbW0LzC042jJnRauUPtOgFunBfv55OihkhRJdRFAdGDE8m7+TLNAyUbzpwfsTwxTL4V/R8XbyvX08nA4CFEF3KyyuGh0LeQ602X4NJrdbxUMh7ss6M6PkaZ8Leu4p8RVHD+fwM2+Rlx6RlRgjR5by8YvD0nHx7dlMparUXWu1oaZERPd999/VTGvb1C5oqXU5tIMWMEMImFMUBD49xtk5DiK7Vln39esjEkq4g3UxCCCFEV+lm+/r1FFLMCCGEEF3FRvv69XRSzNih0tJS5s2bx5AhQ1Cr1eh0OmJiYjh8+LApRlEUdu3a1SX5bNiwgdDQUNzc3HBzcyMyMpK///3vXXJvIYSwKzba16+nkzEzdiguLo66ujrS09MJDAykpKSEAwcOcO3aNZvk88ADD7BmzRqGDRsGQHp6OjNmzOCrr75i1KhRNslJCCG6JZVDw/Tr7c/T0vIExK6Rwb9tJC0zVqA36MktzuVv//obucW56A36TrtXWVkZBw8eZO3atUyaNAk/Pz/GjBnDwoULmTp1KgD+/v4APPPMMyiKYjoG2L17N4888gjOzs4EBgaSkpJCfX296XFFUdiwYQNTpkyhb9++BAQEsGPHDos5TZ8+nR/84AeMGDGCESNGsHLlSjQaDdnZ2VZ//UIIYfdkXz+rk5aZDvrkwiesyVlDSfWdwVreLt4sGLOgUzb70mg0aDQadu3axbhx41Cr1U1icnNz8fLyYvPmzcTGxuLg0FDh7927l4SEBH7729/y6KOPcu7cOV588UUAlixZYrp+8eLFrFmzhnfeeYePPvqIWbNmERISwsiRI++bn16vZ8eOHVRVVREZGWmlVy16GoPBwIULF6isrESj0eDn54dKJZ+tRC8i+/pZlWLs4ZvxWNpCvKamhsLCQgICAnB2dm7zczduw268Z72Azt6G/eOPPyYpKYmbN28SHh7OY489xnPPPUdoaOidHBSFnTt38vTTT5vOTZw4kSlTprBw4ULTuS1btvDrX/+aS5cuma776U9/yoYNG0wx48aNIzw8nPfff7/FnPLy8oiMjKSmpgaNRsMf//hHfvCDH3TodXb05yO6p/z8fDIzM6moqDCdc3NzIzY2luDgYBtmJoToTiy9f99LPgq1k96gZ03OmiaFDGA6tzZnbad0OcXFxXHp0iUyMjKIiYkhKyuL8PBw0tLSLF539OhRli1bZmrd0Wg0JCUlUVRURHV1tSnu3haVyMhITp8+bfG5H3zwQY4fP052djY/+9nPSExMJD8/v92vUfRM+fn5bN++3ayQgYY/Wtu3b5ffGWFX9AYjh89d5X+OX+TwuavoDT26baBbk26mdjpWesysa+leRowUVxdzrPRYp+xq6uzsTHR0NNHR0SQnJzN37lyWLFnC7NmzW7zGYDCQkpLCs88+2+zzWaIoLY28b9CnTx/TAOCIiAhyc3N555132Lhx4/1fjOgVDAYDmZmZFmMyMzMJCgqSLifR7WWeLCJldz5F5TWmc4PcnVkyPZjYkEEWrhSdQf5itNPl6stWjeuo4OBgqqqqTMdOTk7o9eatQuHh4Zw5c4Zhw4Y1+br7zePegbvZ2dkEBQW1KR+j0UhtbW07XonoqS5cuNCkReZeFRUVXLhwoYsyEqJ9Mk8W8bMtx8wKGYDi8hp+tuUYmSeLbJRZ7yUtM+3k6eJp1bjWunr1KvHx8cyZM4fQ0FBcXV05cuQIqampzJgxwxTn7+/PgQMHiIqKQq1W4+HhQXJyMtOmTcPX15f4+HhUKhUnTpwgLy+PFStWmK7dsWMHERERTJgwga1bt5KTk8OmTZtazGnRokVMmTIFX19fbty4wbZt28jKyrrvp3DRu1RWVlo1Tghb0BuMpOzOt7SzEim784kO1uGgstyiLaxHWmbaKdwrHG8Xb9Ng33spKOhcdIR7hVv1vhqNhrFjx/LWW28xceJEQkJCWLx4MUlJSaxfv94U9+abb7J//358fX0JCwsDICYmhj179rB//35Gjx7NuHHjWLduHX5+fmb3SElJYdu2bYSGhpKens7WrVstDswsKSnhJz/5CQ8++CBPPPEEX375JZmZmURHR1v1tQv7ptForBonhC3kFF5r0iJzNyNQVF5DTqFt1v3qraRlpp0cVA4sGLOA+VnzUVDMBgI3Fjivj3kdBytPs1Or1axevZrVq1dbjJs+fTrTp09vcj4mJoaYmBiL1/r4+LBv375W52Sp1UaIRn5+fri5uVnsanJzc2tSXAvRnZTeaLmQaU+csA5pmemAyX6TWff4OrxcvMzOe7t4d9q0bCHslUqlIjY21mJMbGysDP4V3ZqXa+uWiWhtnLAOaZnpoMl+k5nkO4ljpce4XH0ZTxdPwr3Crd4iI0RPEBwczMyZM2WdGWG3xgT0Z5C7M8XlNc2Om1EAnbszYwL6d3VqvZoUM1bgoHLolOnXttDD11AU3UBwcDBBQUGyArCwSw4qhSXTg/nZlmMt7azEkunBMvi3i8lfDyFEl1OpVAQEBPDQQw8REBAghYywK7Ehg9iQEI7O3bwrSefuzIaEcFlnxgakZUYIIYRoo9iQQUQH68gpvEbpjRq8XBu6lqRFxjakmBFCCNEr6Y1GsssqKb1Vj1cfR8ZpNTjcZ7XzuzmoFCKHDujEDEVrSTEjhBCi1/nr5TJ+U3CRoto607lBaidWDB/MVE+t7RIT7SId1UIIIXqVv14uY+7J82aFDEBxbR1zT57nr5fLbJOYaDcpZoQQQvQaeqOR3xRcbHE7AoDFBRfRy8xOuyLFjBCiSxkNRmrOlVF9vJSac2UYDfKmIbpOdlllkxaZuxmBS7V1ZJfJHmH2RIoZO1RaWsq8efMYMmQIarUanU5HTEwMhw8fNsUoisKuXbu6PLfVq1ejKAqvvfZal99bdH83T16heG0OVz7M49q2M1z5MI/itTncPHnF1qmJXqL0Vr1V40T3IAOA7VBcXBx1dXWkp6cTGBhISUkJBw4c4No1225slpubywcffEBoaKhN8xDd082TV7i65XST8/ryW1zdcpoBCSPpGzLQBpmJ3sSrT+ve9lobJ7oHaZmxAqNeT9WXOZTv+StVX+Zg1Os77V5lZWUcPHiQtWvXMmnSJPz8/BgzZgwLFy5k6tSpAPj7+wPwzDPPoCiK6Rhg9+7dPPLIIzg7OxMYGEhKSgr19Xc+gSiKwoYNG5gyZQp9+/YlICCAHTt23DevyspKfvzjH/Phhx/i4eFh1dcs7J/RYKRs9zmLMWW7/yVdTqLTjdNqGKR2oqUJ2Argo3ZinFZ2b7cnUsx0UMW+fXzzxGS+TUzk0i9/ybeJiXzzxGQq2rDrdFtoNBo0Gg27du2itra22Zjc3FwANm/eTFFRkel47969JCQk8Morr5Cfn8/GjRtJS0tj5cqVZtcvXryYuLg4vv76axISEpg1axanTzf9RH23l19+malTpzJ5smyuKZqqLSxHX37LYoy+vJbawvIuykj0Vg6KworhgwGaFDSNx8uHD27TejPC9qSY6YCKffu4+Opr1BcXm52vLynh4quvdUpB4+joSFpaGunp6Wi1WqKioli0aBEnTpwwxXh6egKg1WrR6XSm45UrV7JgwQISExMJDAwkOjqa5cuXs3HjRrN7xMfHM3fuXEaMGMHy5cuJiIjg3XffbTGnbdu2cezYMVavXm311yt6BsMNy4VMW+OE6Iipnlp+H+KPTu1kdn6Q2onfh/jLOjN2SDoF28mo11OyajU0N33PaARFoWTValyfeALFwbo7aMfFxTF16lS++OILDh8+TGZmJqmpqfz+979n9uzZLV539OhRcnNzzVpi9Ho9NTU1VFdX4+LiAkBkZKTZdZGRkRw/frzZ5/zuu+949dVX2bdvH87OsuW9aJ7KtY9V44ToqKmeWmIHundoBWDRfUgx007VR442aZExYzRSX1xM9ZGj9Bs7xur3d3Z2Jjo6mujoaJKTk5k7dy5LliyxWMwYDAZSUlJ49tlnm30+S5QW/oEfPXqU0tJSHnnkEdM5vV7P559/zvr166mtrcXBysWcsD/qAHcc3PtY7GpycFejDnDvwqxEb+egKER5uNo6DWEFUsy0U/3ly1aN66jg4GCzqdhOTk7o7xmIHB4ezpkzZxg2bJjF58rOzub55583Ow4LC2s29oknniAvL8/s3AsvvEBQUBCvv/66FDICAEWloJ0+tNnZTI200wNRZJM+0QZGo56yslxqa0tRq73QakejKPI3pzeSYqadHG+PQ7FWXGtdvXqV+Ph45syZQ2hoKK6urhw5coTU1FRmzJhhivP39+fAgQNERUWhVqvx8PAgOTmZadOm4evrS3x8PCqVihMnTpCXl8eKFStM1+7YsYOIiAgmTJjA1q1bycnJYdOmTc3m4+rqSkhIiNm5fv36MWDAgCbnRe/WN2QgAxJGUrb7nFkLjYO7Gu30QJmWLdqktHQvZwuWUVt7p4VcrdYxYngyXl4xNsxM2IIUM+3kEvEIjjod9SUlzY+bURQcvb1xiXik6WMdoNFoGDt2LG+99Rbnzp2jrq4OX19fkpKSWLRokSnuzTffZP78+Xz44YcMHjyY8+fPExMTw549e1i2bBmpqak4OTkRFBTE3Llzze6RkpLCtm3beOmll9DpdGzdupXg4GCrvg7RO/UNGYhz8ABqC8sx3LiFyrUP6gB3aZERbVJaupe8ky/DPZsS1NaWkHfyZR4KeU8Kml5GMRp79gYUFRUVuLu7U15ejpubm9ljNTU1FBYWEhAQ0K7Bq42zmQDzgub2+JLB77yN25NPtjd1m1AUhZ07d/L000/bOpUO/3yEED2P0ajnn4cmmrXImFNQq3VEjf9MupzsnKX373vJ1OwOcHvySQa/8zaO3t5m5x29ve2ykBFCiO6uYYyMhckXGKmtLaKsLLfLchK2J91MHeT25JO4PvFEw+ymy5dx9PTEJeIRq0/HFkIIAbW1pVaNEz2DFDNWoDg4dMr0a1vo4b2OQgg7p1Z7WTVO9Azdupupvr6e3/zmNwQEBNC3b18CAwNZtmwZBoPB1qkJIYSwAa12NGq1jqabETRSUKsHodWO7sq0hI1165aZtWvX8rvf/Y709HRGjRrFkSNHeOGFF3B3d+fVV1+1dXpCCCG6mKI4MGJ48u3ZTArmM5oaCpwRwxfL4N9eplsXM4cPH2bGjBlmu0H/6U9/4siRIzbOTAjRHgaDnounT1FZdh2N1oPBI0ehUsmbjmgbL68YHgp5r4V1ZhbLtOxeqFsXMxMmTOB3v/sdZ8+eZcSIEXz99dccPHiQt99+29apCSHaqODLQ3ya9gGV166Yzmn6D+T7s19k+NjxNsxM2CMvrxg8PSfLCsAC6ObFzOuvv055eTlBQUE4ODig1+tZuXIls2bNavGa2tpaamtrTccVFRVdkaoQwoKCLw+RsW5Vk/OV166QsW4VT81fJAWNaDNFccDDY5yt0xDdQLceAPznP/+ZLVu28Mc//pFjx46Rnp7Of//3f5Oent7iNatXr8bd3d305evr24UZCyHuZTDo+TTtA4sx/0j/AINBbzFG9DwGg4HCwkLy8vIoLCyUyR2i3bp1y8yvfvUrFixYwHPPPQfAQw89xIULF1i9ejWJiYnNXrNw4ULmz59vOq6oqJCCRggbunj6lFnXUnNuXL3CxdOn8B0V2kVZCVvLz88nMzPTrPXczc2N2NhY2T5FtFm3bpmprq5GpTJP0cHBwWL1rlarcXNzM/vqaUpLS5k3bx5DhgxBrVaj0+mIiYnh8OHDphhFUcx20e5MS5cuRVEUsy+dTtcl9xbdX2XZdavGCfuXn5/P9u3bmwwDqKioYPv27eTn59soM2GvunXLzPTp01m5ciVDhgxh1KhRfPXVV6xbt445c+bYOjWbiouLo66ujvT0dAIDAykpKeHAgQNcu3bNZjmNGjWKTz75xHTsICsgi9s0Wg+rxgn7ZjAYyMzMtBiTmZlJUFBQkw+zQrSkW/+mvPvuu/zHf/wHL730EiNHjuSXv/wl8+bNY/ny5bZOzYzBYOTimeuczS3m4pnrGAydt4puWVkZBw8eZO3atUyaNAk/Pz/GjBnDwoULzaawAzzzzDMoimI6Bti9ezePPPIIzs7OBAYGkpKSQn19velxRVHYsGEDU6ZMoW/fvgQEBLBjx4775uXo6IhOpzN9eXp6WvV1C/s1eOQoNP0HWoxxHTCQwSNHdVFGwpYuXLhw34kZFRUVXLhwoYsyEj1Bt26ZcXV15e233+7WU7HPfVXKF38uoKrszgyqflo1j/5wOEPDrL+ctkajQaPRsGvXLsaNG4darW4Sk5ubi5eXF5s3byY2NtbUSrJ3714SEhL47W9/y6OPPsq5c+d48cUXAViyZInp+sWLF7NmzRreeecdPvroI2bNmkVISAgjR45sMa+CggJ8fHxQq9WMHTuWVatWERgYaOVXL+yRSuXA92e/2OxspkaTEl+U9WZ6icrKSqvGCQHdvGWmuzv3VSmZG0+aFTIAVWW1ZG48ybmvrL/RmaOjI2lpaaSnp6PVaomKimLRokWcOHHCFNPYKqLVas1aSVauXMmCBQtITEwkMDCQ6Oholi9fzsaNG83uER8fz9y5cxkxYgTLly8nIiKCd999t8Wcxo4dyx/+8Af27t3Lhx9+SHFxMePHj+fq1atWf/3CPg0fO56n5i9q0kLjOmCgTMvuZTQajVXjhIBu3jLTnRkMRr74c4HFmIPbCwh42BOVqqU9RNonLi6OqVOn8sUXX3D48GEyMzNJTU3l97//PbNnz27xuqNHj5Kbm8vKlStN5/R6PTU1NVRXV+Pi4gJAZGSk2XWRkZEcP368xeedMmWK6f8feughIiMjGTp0KOnp6WYzy0TvNnzseIaOHisrAPdyfn5+uLm5WexqcnNzw8/PrwuzEvZOipl2Kiooa9Iic6/K67UUFZQx+EHrD2x0dnYmOjqa6OhokpOTmTt3LkuWLLFYzBgMBlJSUnj22WebfT5LFKX1BVm/fv146KGHKCiwXOyJ3kelcpDp172cSqUiNjaW7du3txgTGxsrg39Fm8hvSztVVVguZNoa11HBwcFUVVWZjp2cnNDrzRchCw8P58yZMwwbNqzJ191/OLKzs82uy87OJigoqNW51NbWcvr0aQYNGtTOVyOE6MmCg4OZOXNmk6Uz3NzcmDlzpqwzI9pMWmbaqZ9b04G3HYlrratXrxIfH8+cOXMIDQ3F1dWVI0eOkJqayowZM0xx/v7+HDhwgKioKNRqNR4eHiQnJzNt2jR8fX2Jj49HpVJx4sQJ8vLyWLFihenaHTt2EBERwYQJE9i6dSs5OTls2rSpxZx++ctfMn36dIYMGUJpaSkrVqygoqKixYUNhRAiODiYoKAgLly4QGVlJRqNBj8/P2mREe0ixUw7DRqupZ9WbbGrSeOhZtBwrVXvq9FoGDt2LG+99Rbnzp2jrq4OX19fkpKSWLRokSnuzTffZP78+Xz44YcMHjyY8+fPExMTw549e1i2bBmpqak4OTkRFBTE3Llzze6RkpLCtm3beOmll9DpdGzdutXiJ6V///vfzJo1iytXruDp6cm4cePIzs6WPm8hhEUqlYqAgABbpyF6AMVoNHbeoijdQEVFBe7u7pSXlzdp0qypqaGwsJCAgID7jhlpTuNsppbEzgvplOnZnUlRFHbu3MnTTz9t61Q6/PMRQnQuo8FIbWE5hhu3ULn2QR3gjmLlCQ+i97L0/n0vaZnpgKFhXsTOC2myzozGQ82EmZ2zzowQ9spgMDYMnK+opZ9bQ6ultWf6ia5z8+QVynafQ19+y3TOwb0P2ulD6RtieZFEIaxNipkOGhrmRcDDnvJHWggLunpxSdG5bp68wtUtp5uc15ff4uqW0wxIGCkFjehSUsxYgUqldMr0a1vo4b2OwgZa6o5tXFzSHrtjezOjwUjZ7nMWY8p2/wvn4AHS5SS6jAwbF0J0mtYuLtmZ+5kJ66otLDfrWmqOvryW2sLyLspICClmhBCdqC2LSwr7YLhhuZBpa5wQ1iDFjBCi03S3xSVFx6lc+1g1TghrkGJGCNFpbLW4pOg86gB3HNwtFyoO7mrUAe5dlJEQUswIITpR4+KSlnTG4pKi8ygqBe30oRZjtNMDZfCv6FJSzAghOo1KpfDoD4dbjJkwc7gsZWBn+oYMZEDCyCYtNA7uapmWLWxCpmYLITqVLC7ZM/UNGYhz8ABZAVh0C9IyY4dKS0uZN28eQ4YMQa1Wo9PpiImJ4fDhw6YYRVHYtWtXl+V08eJFEhISGDBgAC4uLnzve9/j6NGjXXZ/0b0NDfPi+VXjefoXYUT/ZzBP/yKMn6wcL4WMnVNUCs5Dtbh8zwvnoVopZITNSMuMHYqLi6Ouro709HQCAwMpKSnhwIEDXLt2zSb5XL9+naioKCZNmsTf//53vLy8OHfuHFqt1ib5iO6pJy0u2VMYDHounj5FZdl1NFoPBo8chUrlYOu0hGgzKWasoCv/IJSVlXHw4EGysrJ47LHHAPDz82PMmDGmGH9/fwCeeeYZ0+Pnz58HYPfu3SxdupRTp07h4+NDYmIib7zxBo6ODb8KiqLw/vvvk5GRQVZWFjqdjtTUVOLj41vMae3atfj6+rJ58+YmOQghuqeCLw/xadoHVF67Yjqn6T+Q789+keFjx9swMyHaTrqZOqjgy0N8+PJ/sn3ZIv722/9i+7JFfPjyf1Lw5aFOuZ9Go0Gj0bBr1y5qa5tfmyM3NxeAzZs3U1RUZDreu3cvCQkJvPLKK+Tn57Nx40bS0tJYuXKl2fWLFy8mLi6Or7/+moSEBGbNmsXp0033YWmUkZFBREQE8fHxeHl5ERYWxocffmilVyyEsLb/zT7E+m1/Ire/D9/6BGBQGrqHKq9dIWPdqk77+yVEZ1GMPXwzHktbiNfU1FBYWEhAQADOzs5tfu6CLw+RsW5Vi48/NX9Rp3zC+fjjj0lKSuLmzZuEh4fz2GOP8dxzzxEaGmqKURSFnTt38vTTT5vOTZw4kSlTprBw4ULTuS1btvDrX/+aS5cuma776U9/yoYNG0wx48aNIzw8nPfff7/ZfBq/d/Pnzyc+Pp6cnBxee+01Nm7cyPPPP9/u19nRn48Qoqk9JdeYf+QUFS6upnOaynKe+OdfGVGYD4DrgIHMXb9JupyETVl6/76XtMy0k8Gg59O0DyzG/CP9AwwGvdXvHRcXx6VLl8jIyCAmJoasrCzCw8NJS0uzeN3Ro0dZtmyZqXVHo9GQlJREUVER1dXVprjIyEiz6yIjIy22zBgMBsLDw1m1ahVhYWHMmzePpKQks4JICGF7f71cxtz8b6noqzE7X9nPjf95chZnA4IBuHH1ChdPn7JFikK0ixQz7XTx9CmzvubmdOYfBGdnZ6Kjo0lOTubQoUPMnj2bJUuWWLzGYDCQkpLC8ePHTV95eXkUFBTct+VDUVqepTBo0CCCg4PNzo0cOZJvv/229S9ICNGp9EYjvym4CBjh3n/Pt48/Hf+DO11OZde7OEMh2k+KmXZq7T/0rvqDEBwcTFVVlenYyckJvd68VSg8PJwzZ84wbNiwJl8q1Z1fhezsbLPrsrOzCQoKavHeUVFRnDlzxuzc2bNn8fPz68hLEj2MUa+n6sscyvf8laovczDqrd9qKVqWXVZJUW0d0MIHE0XhhquWfw/yB0CjlZlnwn7IbKZ2au0/dGv/Qbh69Srx8fHMmTOH0NBQXF1dOXLkCKmpqcyYMcMU5+/vz4EDB4iKikKtVuPh4UFycjLTpk3D19eX+Ph4VCoVJ06cIC8vjxUrVpiu3bFjBxEREUyYMIGtW7eSk5PDpk2bWszpF7/4BePHj2fVqlXMnDmTnJwcPvjgAz74wHI3nOg9Kvbto2TVauqLi03nHHU6vBctxO3JJ22YWe9Requ+VXFVLq64DhjI4JGjOjkjIaxHWmbaafDIUWj6W16yuzP+IGg0GsaOHctbb73FxIkTCQkJYfHixSQlJbF+/XpT3Jtvvsn+/fvx9fUlLCwMgJiYGPbs2cP+/fsZPXo048aNY926dU1aUFJSUti2bRuhoaGkp6ezdevWJt1Idxs9ejQ7d+7kT3/6EyEhISxfvpy3336bH//4x1Z97cI+Vezbx8VXXzMrZADqS0q4+OprVOzbZ6PMehevPq377Nqv+gaTEl+Uwb/CrshsJjuczdSZmpsFZSsym8n+GfV6vnlicpNCxkRRcPT2ZtiBT1Ac5M2zM+mNRiIO51NcW0ezf/SNRtxuVpIxwIGgcfb1d0v0TDKbqYsMHzuep+YvatJC4zpgoF0WMkJYW/WRoy0XMgBGI/XFxVQfka0vOpuDorBi+GCguVEzDYOC10WMkkJG2CUZM9NBw8eOZ+josbIkuBDNqL982apxomOmemr5fYg/vym4eHswcAMfdR+WDx/MVE+t7ZITogOkmLEClcoB31Gh9w+0Az2811F0MUdPT6vGCXMGg5GigjKqKmrp56Zm0HAtqvts9jjVU0vsQHeyyyopvVWPVx9Hxmk1OFhYfkGI7k6KGSFEp3GJeARHnY76khJorlC+PWbGJeKRrk/Ozp37qpQv/lxAVdmdbU36adU8+sPh992N3EFRiPJwtRgjhD2RMTNCiE6jODjgvej29hktLNTmvWihDP5to2+OFfH5Xz5G5fo5Lp5nQDEAUFVWS+bGk5z7qtTGGQrRtaRlRgjRqdyefBLeebvpOjPe3rLOTDuUlGRyrngxQyZdM52rq/ag5KvnqLwYDsDB7QUEPOx53y4nIXoKKWaEEJ3O7ckncX3iiYbZTZcv4+jpiUvEI9Ii00alpXs5eeplHNTm5x37Xmfw+A1cPPQzKi+GU3m9lqKCMgY/KKv4it5BihkhRJdQHBzoN3aMrdOwW0ajnrMFy4Dme+yMRvAO20blpe+BUUVVRW3TJxGih5IxM0IIYQfKynKprW15zR5FASeX67gMLACgn5u6xVghehopZoQQwg7U1rZuUK+Dcxkaj4Zp2kL0FlLM2KHS0lLmzZvHkCFDUKvV6HQ6YmJiOHz4sClGURR27drVJfn4+/ujKEqTr5dffrlL7i9Eb6BWW55u3Uhfo2XCzOEy+Ff0KjJmxg7FxcVRV1dHeno6gYGBlJSUcODAAa5du3b/iztBbm4uer3edHzy5Emio6OJj4+3ST5C9ERa7WjUah21tSXQzO5KRiPoa/rz6DNP33edGSF6GmmZsQKjwUjNuTKqj5dSc64Mo6HzVtEtKyvj4MGDrF27lkmTJuHn58eYMWNYuHAhU6dOBRpaSgCeeeYZFEUxHQPs3r2bRx55BGdnZwIDA0lJSaG+vt70uKIobNiwgSlTptC3b18CAgLYsWOHxZw8PT3R6XSmrz179jB06FAee+wxq79+IXorRXFgxPDkxqNmHofvRSxnWPigrk1MiG5AipkOunnyCsVrc7jyYR7Xtp3hyod5FK/N4ebJK51yP41Gg0ajYdeuXdTWNj9bITc3F4DNmzdTVFRkOt67dy8JCQm88sor5Ofns3HjRtLS0li5cqXZ9YsXLyYuLo6vv/6ahIQEZs2axenTp1uV361bt9iyZQtz5sxBkeXRxW16g57c4lz+9q+/kVuci96gv/9FogkvrxgeCnkPtdrb7LxaPYiHQt7H2zvWRpkJYVuKsYdvxmNpC/GamhoKCwsJCAjA2dm5zc998+QVrm5p+U1+QMJI+oYMbPHx9vr4449JSkri5s2bhIeH89hjj/Hcc88RGnpnfyhFUdi5cydPP/206dzEiROZMmUKCxcuNJ3bsmULv/71r7l06ZLpup/+9Kds2LDBFDNu3DjCw8N5//3375vb9u3b+dGPfsS3336Lj49Ph15nR38+onv45MInrMlZQ0l1iemct4s3C8YsYLLfZBtmZntGvb5da+8Yjfrbs5tKUau90GpHoyiyZo/oWSy9f99LWmbayWgwUrb7nMWYst3/6pQup7i4OC5dukRGRgYxMTFkZWURHh5OWlqaxeuOHj3KsmXLTK07Go2GpKQkioqKqK6uNsVFRkaaXRcZGdnqlplNmzYxZcqUDhcyomf45MInzM+ab1bIAJRWlzI/az6fXPjERpnZXsW+fXzzxGS+TUzk0i9/ybeJiXzzxGQq9u2777WK4oCHxzh0uqfw8BgnhYzo9aSYaafawnL05bcsxujLa6ktLO+U+zs7OxMdHU1ycjKHDh1i9uzZLFmyxOI1BoOBlJQUjh8/bvrKy8ujoKDgvi0frekyunDhAp988glz585t02sRPZPeoGdNzhqMzQ1WvX1ubc7aXtnlVLZ3L1+tWMm5Pn0o9fLEcPvfV31JCRdffa1VBY0Q4g6ZzdROhhuWC5m2xnVUcHCw2VRsJycnsxlGAOHh4Zw5c4Zhw4ZZfK7s7Gyef/55s+OwsLD75rB582a8vLxMA5FF73as9FiTFpm7GTFSXF3MsdJjjNaN7sLMbCv/5En2/OMfVH9/kulc3+pqwo8d44F/XwRFoWTValyfeEK2exCilaSYaSeVax+rxrXW1atXiY+PZ86cOYSGhuLq6sqRI0dITU1lxowZpjh/f38OHDhAVFQUarUaDw8PkpOTmTZtGr6+vsTHx6NSqThx4gR5eXmsWLHCdO2OHTuIiIhgwoQJbN26lZycHDZt2mQxL4PBwObNm0lMTMTRUX6tBFyuvmzVuJ4gPz+f7f/v/4HafHXem3378s+oKKL++U8e+PdF6ouLqT5yVLZ/EKKVpJupndQB7ji4Wy5UHNzVqAPcrXpfjUbD2LFjeeutt5g4cSIhISEsXryYpKQk1q9fb4p788032b9/P76+vqZWlZiYGPbs2cP+/fsZPXo048aNY926dfj5+ZndIyUlhW3bthEaGkp6ejpbt24lODjYYl6ffPIJ3377LXPmzLHq6xX2y9PF06px9s5gMJCZmdlw0NzmSsCxsLA7XU6Xe0+RJ0RHyUfodlJUCtrpQy3OZtJOD0Sx8iqcarWa1atXs3r1aotx06dPZ/r06U3Ox8TEEBMTY/FaHx8f9rWxz/7JJ5+kh0+ME20U7hWOt4s3pdWlzY6bUVDwdvEm3CvcBtl1vQsXLlBRUdFygKJws18/rngOxKu0YXaTEKJ1pGWmA/qGDGRAwsgmLTQO7upOm5YthL1wUDmwYMwCoKFwuVvj8etjXsdB1TvGhVRWVrYq7mbfvjjqdLhEPNLJGQnRc0jLTAf1DRmIc/AAagvLMdy4hcq1D+oAd6u3yAhhjyb7TWbd4+uaXWfm9TGv96p1ZjQaTavi+t6swfs3b8jgXyHaQIoZK1BUCs5DtbZOwyqkq0hY22S/yUzyncSx0mNcrr6Mp4sn4V7hvaZFppGfnx9ubm4tdzUZjbjU1vLwG4twe/LJrk1OCDsnxYwQotM5qBx61fTr5qhUKmJjY9m+fXvzAYrCtIQEtCEhXZuYED2AjJkRQoguEhwczMyZM5ssze7m5sbMmTMJlkJGiHaRlhkhhGgnvUHf5u6z4OBggoKCuHDhApWVlWg0Gvz8/FCp5LOlEO0lxYwQQrRDRzbQVKlUBAQEdHaKQvQa8lFACCHa6JPCT9j81w0EFfnyUNVwVMaG2YuygaYQttHti5mLFy+SkJDAgAEDcHFx4Xvf+x5Hjx61dVpCiF6q8kQpXv+3hrXfvsaCS3NI/fYXpH2zgvEV3+v1G2gKYSvdupi5fv06UVFRODk58fe//538/HzefPNNtFqtrVMTQrSWQQ+FX0De/2v4rx2/yd88eYXrfzyDR535AN4B9Vp+czHJVNA0bqAphOga3bqYWbt2Lb6+vmzevJkxY8bg7+/PE088wdChQ22dmk2VlpYyb948hgwZglqtRqfTERMTw+HDh00xiqKY7aLdmerr6/nNb35DQEAAffv2JTAwkGXLlmEwGLrk/qIby8+At0MgfRp8/J8N/307pOG8nTEajJTtPgcYm6xorELBCMwr+Q9Tl1Nv2kBTCFvr1gOAMzIyiImJIT4+ns8++4zBgwfz0ksvkZSU1OI1tbW11NbWmo4t7oViJQaDoUtnJsTFxVFXV0d6ejqBgYGUlJRw4MABrl271mn3tGTt2rX87ne/Iz09nVGjRnHkyBFeeOEF3N3defXVV22Sk+gG8jNg+/Nw775MFUUN52f+AYKfsklq7VFbWI6+/FaTQqaRCgWv+v6Mqh5GXr+CXrOBphDdQbcuZv71r3+xYcMG5s+fz6JFi8jJyeGVV15BrVbz/PPPN3vN6tWrSUlJ6bIc8/PzyczMNCua3NzciI2Nve9O0+1RVlbGwYMHycrK4rHHHgMaVhYdM2aMKcbf3x+AZ555xvT4+fPnAdi9ezdLly7l1KlT+Pj4kJiYyBtvvIGjY8OvgqIovP/++2RkZJCVlYVOpyM1NZX4+PgWczp8+DAzZsxg6tSppvv/6U9/4siRI9Z++cJeGPSQ+TpNChm4fU6BzAUQNBXsZCVgw41brYobUO+OzkXXazbQFKI76NbdTAaDgfDwcFatWkVYWBjz5s0jKSmJDRs2tHjNwoULKS8vN3199913nZZffn4+27dvb9L6U1FRwfbt28nPz7f6PTUaDRqNhl27dpm1QN0tNzcXgM2bN1NUVGQ63rt3LwkJCbzyyivk5+ezceNG0tLSWLlypdn1ixcvJi4ujq+//pqEhARmzZrF6dMt7w4+YcIEDhw4wNmzZwH4+uuvOXjwID/4wQ+s8ZKFPbpwCCouWQgwQsXFhjg7oXLtc/8g4JpjRa/aQFOI7qBbFzODBg1q0roxcuRIvv322xavUavVuLm5mX11BoPBQGZmpsWYzMxMq48bcXR0JC0tjfT0dLRaLVFRUSxatIgTJ06YYjw9G5q3tVotOp3OdLxy5UoWLFhAYmIigYGBREdHs3z5cjZu3Gh2j/j4eObOncuIESNYvnw5ERERvPvuuy3m9PrrrzNr1iyCgoJwcnIiLCyM1157jVmzZln1tQs7Ully/5i2xHUD6gB3HNxbLmgMGLnqVM4LU37WqzbQFKI76NbFTFRUFGfOnDE7d/bsWfz8/GyU0R0XLly473iciooKLly4YPV7x8XFcenSJdOYoqysLMLDw0lLS7N43dGjR1m2bJmpdUej0ZCUlERRURHV1dWmuMjISLPrIiMjLbbM/PnPf2bLli388Y9/5NixY6Snp/Pf//3fpKend+h1Cjum8bZuXDegqBS005uffGC8PSh4aPwYJgdIISNEV+vWY2Z+8YtfMH78eFatWsXMmTPJycnhgw8+4IMPPrB1alRWVlo1rq2cnZ2Jjo4mOjqa5ORk5s6dy5IlS5g9e3aL1xgMBlJSUnj22WebfT5LFKX5QY8Av/rVr1iwYAHPPfccAA899BAXLlxg9erVJCYmtu4FiZ7Fbzy4+TQM9m123IzS8Ljf+K7OrEP6hgxkQMJIynafQ19+ZwyNo7sz2umB9A0ZaMPshOi9unUxM3r0aHbu3MnChQtZtmwZAQEBvP322/z4xz+2dWpoNBqrxnVUcHCw2VRsJycn9Hrz9TzCw8M5c+YMw4YNs/hc2dnZZgOss7OzCQsLazG+urq6yewtBwcHmZrdm6kcIHbt7dlMCuYFze3COHaN7Qf/GvQN43YqSxpaifzG3zenviEDcQ4eQG1hOYYbt1C59kEd4I6iarngF0J0rm5dzABMmzaNadOm2TqNJvz8/HBzc7PY1eTm5mb1LrGrV68SHx/PnDlzCA0NxdXVlSNHjpCamsqMGTNMcf7+/hw4cICoqCjUajUeHh4kJyczbdo0fH19iY+PR6VSceLECfLy8lixYoXp2h07dhAREcGECRPYunUrOTk5bNq0qcWcpk+fzsqVKxkyZAijRo3iq6++Yt26dcyZM8eqr13YmeCnGqZfZ75uPhjYzaehkLH1tOz8jBZyW3vf3BSVgvNQbefmJ4RoNcVoNDbXBtxjVFRU4O7uTnl5eZPBwDU1NRQWFhIQEHDfbpbmNM5masnMmTOtPj27traWpUuXsm/fPs6dO0ddXZ2pOFm0aBF9+/YFGqZgz58/n/PnzzN48GDT1Oy9e/eybNkyvvrqK5ycnAgKCmLu3LmmtXsUReG9995j165dfP755+h0OtasWWPqQmrOjRs3WLx4MTt37qS0tBQfHx9mzZpFcnIyffq0bgZIczr68xHdRDtaPzo9pZO7uJj+KpX1TmgcbzHYpZyGhpXbrSt2tgaOED2Rpffve0kx08E3y65eZ6azKYrCzp07efrpp22dihQzolMUZB/k0/dWUHnrTsO0xrGW73ufY7jbVUzjeV7Ls3nRJURv1pZiptt3M3V3wcHBBAUFdekKwEKI9in48hAZb60BzIuUyvo+ZFwcyVOcbihoGtfACXjUNokKIdpEihkrUKlUBAQE2DoNIYQFBoOeT9MaZ0LeO1i3YZDyP0oCGep6taHLyY7WwBGit5NiRpjp4b2Oohe7ePoUldeuWIhQuFHvzMVqd3z7ldvVGjhC9HbSFyKE6BUqy663Lq6+D7gNtrs1cITozaRlRghh9/QGIzmF1yi9UYOXqzNjAvrjcM+6LxqtR6ueS+NYB7HrZPCvEHZEihkhRKdpTZHRUZkni0jZnU9ReY3p3CB3Z5ZMDyY2ZJDp3OCRo9D0H2ihq8mIax89gxPfuTMtuxtOKxdCNCXFjBCiU7S2yOjoPX625ViTDROKy2v42ZZjbEgIN91LpXLg+7NfJGPdqhaeTWHSy79BFTKh4bADi+oJIbqWjJkRQlhdY5FxdyEDd4qMzJNFHb6H3mAkZXd+szs/NZ5L2Z2P3nAnYvjY8Tw1fxGa/uZ7KLkOGMhT8xcxfNxdhcz2580LGWjYa2r78w2Pdzh/PbnFufztX38jtzgXvUF//4uEEM2SlhkhhFXdr8hQaCgyooN1Hepyyim81qRYuvdeReU15BReI3LoANP54WPHM3T02IbZTWXX0Wg9GDxyFKrG7iODvqFFxtIryFwAQVPb3eX0yYVPWJOzhpLqO9O/vV28WTBmAZP9ZNdtIdqq1S0z//73vzszDyFED9GWIqMjPskvblVc6Y2muahUDviOCmVk1GP4jgq9U8hAwxiZe1tkzBjvLKrXDp9c+IT5WfPNChmA0upS5mfN55MLn7TreU3Z6fVUfZlD+Z6/UvVlDka9tPiInq/VxUxISAgfffRRZ+YiWqm0tJR58+YxZMgQ1Go1Op2OmJgYDh8+bIpRFMVsF+3OdOPGDV577TX8/Pzo27cv48ePJzc3t0vuLbqf5oqHjsQ15+8nLrEv61uCbjngW6dCsbA8kpdrG7fCaO1iee1YVE9v0LMmZw3GZlp9Gs+tzVnb7i6nin37+OaJyXybmMilX/6SbxMT+eaJyVTs29eu57uXwWDk4pnrnM0t5uKZ6xgMsi6V6B5a3c20atUqXn75ZXbt2sUHH3zAgAED7n9RL2E06ikry6W2thS12gutdjSK0nkzHuLi4qirqyM9PZ3AwEBKSko4cOAA16517JNue82dO5eTJ0/y0Ucf4ePjw5YtW5g8eTL5+fkMHjzYJjkJ22lt8dDmIuO2gqOl5P3f0zynV5vOVSgGPu1bR0Efg+mcAujcG2ZQtUlrF8trx6J6x0qPNWmRuZsRI8XVxRwrPcZo3eg2PXfFvn1cfPU1uGfhy/qSkobz77yN25NPtjnnRue+KuWLPxdQVVZrOtdPq+bRHw5naJhXu59XCGtodcvMSy+9xNdff83169cZNWoUGRkdHwDXE5SW7uWfhyZy7Ksfcyr/Fxz76sf889BESkv3dsr9ysrKOHjwIGvXrmXSpEn4+fkxZswYFi5cyNSpUwHw9/cH4JlnnkFRFNMxNOym/cgjj+Ds7ExgYCApKSnU19ebHlcUhQ0bNjBlyhT69u1LQEAAO3bsaDGfmzdv8vHHH5OamsrEiRMZNmwYS5cuJSAggA0bNnTK90B0b2MC+jPI3bnJhgGNFKC/q5pLLgr/vH4DfRtWnT73VSn7PjyJyz0NF65GhRnVfRh+686fNCOwZHpw28fl+I1vmLVk6RW0c1G9y9WXrRrXyKjXU7JqdZNCpuHBhnMlq1a3u8vp3FelZG48aVbIAFSV1ZK58STnvipt1/M2Mhj0fHfqBKf/+RnfnTqBQQZDizZq0wDggIAAPv30U9avX09cXBwjR47E0dH8KY4dO2bVBLuz0tK95J18mXsHCtbWlpB38mUeCnkPL68Yq95To9Gg0WjYtWsX48aNQ61WN4nJzc3Fy8uLzZs3Exsbi4NDQyvR3r17SUhI4Le//S2PPvoo586d48UXXwRgyZIlpusXL17MmjVreOedd/joo4+YNWsWISEhjBw5ssm96uvr0ev1TXa17tu3LwcPHrTmSxd2wkGlsGR6MD/bcuz2jkfmjEDx0H68/L/fAjBI7cSK4YOZ6qm1+LwGg5Ev/lwAgHJPoaGgYMTI92868Y1TLUYF5kT5t28KuMqhYfr19uehySu4fd/YNe0a/Ovp4mnVuEbVR45SX2xhDJHRSH1xMdVHjtJv7Jg2Pffd3/eWHNxeQMDDnqjaMaC74MtDfJr2gdn6P5r+A/n+7BcZPlZWYRat0+ap2RcuXODjjz+mf//+zJgxo8lXb2E06jlbsIyWZzzA2YLlGI3W/YTh6OhIWloa6enpaLVaoqKiWLRoESdOnDDFeHo2/CHUarXodDrT8cqVK1mwYAGJiYkEBgYSHR3N8uXL2bhxo9k94uPjmTt3LiNGjGD58uVERETw7rvvNpuPq6srkZGRLF++nEuXLqHX69myZQtffvklRUUdn34r7FNsyCA2JISjczcvco3ODtz6Xn8M3n1N54pr65h78jx/vVxm8TmLCsqatAzcTUHBzajigfqGP2vRwbr2v4Dgp2DmH8DtnmLIzafhfDvXmQn3CsfbxbtJMdZIQUHnoiPcK7xNz1t/uXUtOa2Nu9v9vu8AlddrKSooa/NzF3x5iIx1q5osZFh57QoZ61ZR8GX7BlnfzWgwUnOujOrjpdScK8Mo43x6pDa1zHz44Yf8f//f/8fkyZM5efKk6U2yN2oYI2NpNoWR2toiyspy8fAYZ9V7x8XFMXXqVL744gsOHz5MZmYmqamp/P73v2f27NktXnf06FFyc3NZuXKl6Zxer6empobq6mpcXFwAiIyMNLsuMjKS48ePt/i8H330EXPmzGHw4ME4ODgQHh7Oj370o17VSieaig0ZRHSwjpzCaxRX1LD4uyKuaBxAMX8jb5yuvbjgIrED3XFQmn+jr6qw/IbaSGNUGNSesTL3Cn6qYfq1FVcAdlA5sGDMAuZnzTe1JjVqLHBeH/M6Dm28h2Mr/xa3Nu5urf2+tzaukfku5s37R/oHDB091ny2WRvcPHmFst3n0JffMp1zcO+DdvpQ+oYMtHClsDetbpmJjY3l9ddfZ/369fzlL3/p1YUMQG1t6/qIWxvXVs7OzkRHR5OcnMyhQ4eYPXu2WVdRcwwGAykpKRw/ftz0lZeXR0FBQZNuonspLbzBAAwdOpTPPvuMyspKvvvuO3JycqirqyMgIKBdr030HA4qhcihA/Dyd+OKq2OTQqaREbhUW0d2WWWLz9XPrWmXanOqFGP7xso0R+UAAY/CQ//R8F8rbGUw2W8y6x5fh5eL+aBZbxdv1j2+rl3rzLhEPIKjTtfi9xdFwVGnwyXikTY/d2u/762Na3T/XczhxtUrXDx9qk3P2+jmyStc3XLarJAB0Jff4uqW09w8afne92MwGCgsLCQvL4/CwkIMBsP9LxKdptUtM3q9nhMnTvDAAw90Zj52Q61u3ej91sZ1VHBwsNlUbCcnJ/T3DPYLDw/nzJkzDBs2zOJzZWdn8/zzz5sdh4WF3TeHfv360a9fP65fv87evXtJTU1t24sQPVbprfr7B90nbtBwLf206ha7PIwYqXaAhYkPW227hM4y2W8yk3wncaz0GJerL+Pp4km4V3ibW2QaKQ4OeC9a2DBrSVHMBwLfLnC8Fy1EcWj789/v+w6g8VAzaLi2Tc/b6l3MWxl3N6PBSNnucxZjynb/C+fgASjtKHrz8/PJzMykoqLCdM7NzY3Y2FiCg4Pb/Hyi41pdzOzfv78z87A7Wu1o1GodtbUlND9uRkGt1qHVtm165f1cvXqV+Ph45syZQ2hoKK6urhw5coTU1FSzMUv+/v4cOHCAqKgo1Go1Hh4eJCcnM23aNHx9fYmPj0elUnHixAny8vJYsWKF6dodO3YQERHBhAkT2Lp1Kzk5OWzatKnFnPbu3YvRaOTBBx/km2++4Ve/+hUPPvggL7zwglVfu7BfXn1a96fGUpxKpfDoD4eTufFks48rKDwzJ4ThofYxTdhB5dDm6deWuD35JLzzNiWrVpsNBnb09sZ70cJ2T8u+3/cdYMLM4W0e/NvqXcxbGXe32sLyJi0y99KX11JbWI7zUG2bnjs/P5/t27c3OV9RUcH27duZOXNmhwuarl7uoyeQ7QzaSVEcGDE8+fZspuZnPIwYvtjqv4AajYaxY8fy1ltvce7cOerq6vD19SUpKYlFixaZ4t58803mz5/Phx9+yODBgzl//jwxMTHs2bOHZcuWkZqaipOTE0FBQcydO9fsHikpKWzbto2XXnoJnU7H1q1bLf7jLC8vZ+HChfz73/+mf//+xMXFsXLlSpycnKz62oX9GqfVMEjtRHFtXQulf8OspnFajcXnGRrmRey8kCbrnWg81EyYKeuduD35JK5PPNEwu+nyZRw9PXGJeKRdLTJ364zv+/13MW/YM2vwyFFtfm7DDcuFTFvjTPEGA5mZmRZjMjMzCQoKQqVq39aHpaV7OVuwzGxMplqtY8Tw5A7PjtUbjWSXVVJ6qx6vPo6M02paHKNmbxSjsQ2LPNihiooK3N3dKS8vx83NzeyxmpoaCgsLCQgIuO+YkZY094vXp4+OB/wX4jkwln5qB4vjTdrEaIRblaCvAwcn6KNpuY+8zU9tpLq+Gk0fDX/a8Sd+GPdDq+RtNBoxVFVjrK9DcXRC1c+l1c97v5+PwWBsmGlRUUs/t4Zm7vZMDW2OwaBvee+eDjAajNQWlmO4cQuVax/UAe7tauZujsFg4MKFC1RWVqLRaPDz82v3H9S7WetT4l8vlzH35PmG57zrfONHgV/5exPo4tyqP7Kd+bMXLbP2971xNlNLnpq/qF3Ts2vOlXHlw7z7xg1MeqhNLTOFhYWkp6ffNy4xMbFdYwZbWu6j8QNyR5b7+OvlMn5TcJGi2jrTudYui2Arlt6/7yUtMx3k5RWDp+dkyspyuV5ZREWtO4r6YW7gwI0rlTg5qPDROuPet0/HbnSzDMr/DYY7v4ionMD9Aeir7dBTV9RWUFRVRL2hYbxCaXUpZ6+fZVC/QbipLf8CWaIvL6euuBhj3Z2cFScnnHQ6HNzdO5RzZ65G2lnrXnTmzIrO6sO35qfEqZ5afh/i3+QPqtaxoTD6r/N3Vsa93x9ZlUph8INt734QHWPt73vjLub3/ntzHTCQSYnt//emDnDHwb2Pxa4mB3c16oC2/R2qrGx5gHp74u52/+U+FM4WLMfTc3KbP0w0fpC495kbl0X4fYh/ty1oWqvjH9sEiuKAyjmcSmUSKudws1+0Or2BC1erKb/ZtuZMMzfL4HqheSEDDcfXCxseb6eK2gq+u/GdqZBpVG+o57sb31FRW9HClZbpy8u59d13ZoUMgLGujlvffYe+vLzdOXfmaqSdte5FZ86saOzDv7uQgTt9+Pn5+e163sZPifcuQdC4KGR7Vrme6qnlSGQwHz/kT3rtVdadP4lffh7lt8x/T1q79oywf8PHjifpvU3MTF7FD175FTOTVzF3/aYOfXBQVAra6UMtxminB7a5VVSjsdwN2ta4u7VluY+20BuN/KbgooUV0RqWRWjLStzdkbTMWIHRaORSmeVN8y6V1eDm7NT2rhujsaFFxpLyf4Oze5u7nIxGI0VV5gvbnbxsPsivuKoY1z6ubcrbaDRSZ2k1UqCuuBiVm1ubvx+duRppZ6170ZkzKzqrD78zPyVW7d+P16rV9C8uZggQBpRq+7N+ZiJfhI256w73X3tG9AyNu5hbU9+QgQxIGNlMa6ga7fTAdrWG+vn54ebm1uSDw93c3Nzw8/Nr83N31nIf2WWVZi2h97p7WYQoD9c2PXd3Ii0zVlBVq6dOb3mNgTq9garadqwGfKuyaYvMvQx1DXFtVF1f3aRF5l51hjqq66vb9LyGquomLTL3MtbVYahq2/NC565G2lnrXrRlZkVbXbhwweIfVmhooblw4UKbnrezPiU2boZ479L7A8uukfLBWzz6Vc5dd7j/2jNCWNI3ZCC618cwMOkh+j/3IAOTHkL3+uh2d+uqVCpiY2MtxsTGxrZrrFpnLfdhjWUR7IEUM1ZQ38rFklobZ0Z/n0KmrXF3uV8h09a4Rsb61uXS2ri7ddZqpNB561501swK6Lw+/M74lGhpM0QVDcXLyzv+gOqefyf2/kdW2JaiUnAeqsXle144D9V2eMB9cHAwM2fObDIg1c3NrUPTshuX+7C0walaPajNy31YY1kEe2Df2XcTjq2swlsbZ8ahldObWxt3F0dV6378rY1rpDi2LpfWxt2ts1Yjhc5b90Ll2rrB362NM8ulk/rwO+NT4v02Q1QB3tev8tA3/8vXI+68Idj7H1nR8wQHBxMUFGTV2YOdtdyHtZZF6O6kZcYK+qkdcHKw/K10clDRT92Oqb19NA2zlixR3Z6m3UYuji73LVScVE64OLq06XlV/VxQ7rPGjOLUME27rRpXI7WkPauRwp11Lyxpz7oXjTMrLGnPzAq404dvSXv68DvjU2JrNzkcUH799h3Apwf8kRU9k0qlIiAggIceeoiAgACrLIPg5RXDQyHvoVZ7m51Xq3XtnpbtoCisGD4YaPqvufF4+fDBdj8uTYoZK1AUBR+t5XVqfLTO7Vu3RVEapl9b4v5Au9abURSFQf0sL/uu66drc96KouCks7xjsZOu7c8Ld1YjtaQ9q5E2PLcD35/9osWYSYkvtnm9mc6aWQGd14ff+Cnx9tG9jwJt/5TY2k0Or7p79Kg/skK0hZdXDFHjPyc8bCujgt8iPGwrUeM/69CCeY3LIujU5h8yB6mdesS0bJBF8zq8aN7dym/e4lJZjdlgYHtcZwYaWmR0/XQ2XWfG0s+nuXVmrLUKbHPrzHR03QtoaZ2Z9s+suFvXrjMziBHDF7f5j6tRr+ebJyZTX1LS7LgZA3DZYwA/WvFbdH3VLO/Gi3kJYY/sbQXgtiyaJ8WMFYsZaJiWXFWrp95gwFGlsrsVgOsN9TiqHHFxbP1Kvfd7XlkB+I7evAIw3JnNdPuJ7zygKBiNRi4uX4XL5Mnd/o+sEKLztaWYkW4mK1MUBY2zI1qXPmicHa1XyDQ8OahdKa2sZ94rv2SInx9qtRqdTkdMTAyHDx82y+PuXbRbk3c/p364q93p59SvTXl//vnnTJ8+HR8fnyb3VRQFB00/HNzdWf7f/8XgwYPp27cvjz/+OKdOtW2K870aVyMdMVrH4Ac9rLqcfeO6FyOjHsN3VKhVChmw/syKu3VGHz40dDl5eIxDp3sKD49xHdpvzO3JJxn8zts4epuPCXD09uaB375DdPwzRHm4SiEjhGgTmSZgBV3ddBcXF0ddXR3p6ekEBgZSUlLCgQMHuHbtWqfd05KqqioefvhhXnjhBeLi4pqNSU1NZd26daSlpTFixAhWrFhBdHQ0Z86cwdXVfhdqEm3XWZshCiF6MWMPV15ebgSM5eXlTR67efOmMT8/33jz5s12P/+e0uvG7/3zpNH7069MX9/750njntLrHci6ZdevXzcCxqysrBZj/Pz8jDTM6zMCRj8/P9NjGRkZxvDwcKNarTYGBAQYly5daqyrqzM9Dhjff/99Y2xsrNHZ2dno7+9v3L59e6vzA4w7d+40O2cwGIw6nc64Zs0a07mamhqju7u78Xe/+12Lz2WNn48QQgj7ZOn9+17SzdQBjZt33btUdGfuK6PRaNBoNOzatYva2uYXhsvNbViVdfPmzRQVFZmO9+7dS0JCAq+88gr5+fls3LiRtLQ0Vq5caXb94sWLiYuL4+uvvyYhIYFZs2Zx+vTpdudcWFhIcXExTz75pOmcWq3mscce49Ch9u11JGxLb9CTW5zL3/71N3KLc9Eb2rG6tRBCWIkUM+1kq827HB0dSUtLIz09Ha1WS1RUFIsWLeLEiROmGM/bU2C1Wi06nc50vHLlShYsWEBiYiKBgYFER0ezfPlyNm7caHaP+Ph45s6dy4gRI1i+fDkRERG8++677c65+PZCad73jJPw9vY2PSbsxycXPiHm4xjm7J3D61+8zpy9c4j5OIZPLnxi69SEEL2UFDPt1JbNu6wtLi6OS5cukZGRQUxMDFlZWYSHh5OWlmbxuqNHj7Js2TJT645GoyEpKYmioiKqq+/skxQZGWl2XWRkZIdaZhrdO6jYaDRad4C06HSfXPiE+VnzKakuMTtfWl3K/Kz5UtAIIWxCipl2svXmXc7OzkRHR5OcnMyhQ4eYPXs2S5YssXiNwWAgJSWF48ePm77y8vIoKCi479T0jhQdutsL6N3bClNaWtqktUZ0X3qDnjU5azA20x7ZeG5tzto2dTkZjXquX8+muDiD69ezMRqlu0oI0XZSzLRTd9u8Kzg4mKqqKtOxk5MTer35G0N4eDhnzpxh2LBhTb7unsabnZ1tdl12djZBQUHtzi0gIACdTsf+/ftN527dusVnn33G+PHtX4ROdK1jpceatMjczYiR4upijpUea9XzlZbu5Z+HJnLsqx9zKv8XHPvqx/zz0ERKS/daK2UhRC8hU7PbyVabd129epX4+HjmzJlDaGgorq6uHDlyhNTUVGbMmGGK8/f358CBA0RFRaFWq/Hw8CA5OZlp06bh6+tLfHw8KpWKEydOkJeXx4oVK0zX7tixg4iICCZMmMDWrVvJyclh06ZNLeZUWVnJN998YzouLCzk+PHj9O/fnyFDhqAoCq+99hqrVq1i+PDhDB8+nFWrVuHi4sKPfvQjq35/ROe5XN26vZVaE1dauvf2hnrm/3pqa0vIO/lyu/ehEUL0TtIy00622rxLo9EwduxY3nrrLSZOnEhISAiLFy8mKSmJ9evXm+LefPNN9u/fj6+vL2FhYQDExMSwZ88e9u/fz+jRoxk3bhzr1q1rsglhSkoK27ZtIzQ0lPT0dLZu3WpxSfwjR44QFhZmus/8+fMJCwsjOTnZFPPrX/+a1157jZdeeomIiAguXrzIvn37ZI0ZO+Lp0rq9le4XZzTqOVuwjHsLmduPAnC2YLl0OQkhWk22M+jgdgZ/vVzGbwoumg0G9lE72e2+MoqisHPnTp5++mlbp9Ip202I9tMb9MR8HENpdWmz42YUFLxdvMmMy8TBworJ169nc+yrH9/3fuFhW/HwGNehnIUQ9qst2xlIN1MHTfXUEjvQ3a427xKiPRxUDiwYs4D5WfNRUMwKGuV2e+TrY163WMgA1NaWtup+rY0TQgjpZrICB0UhysOVZ7w9ZF8Z0aNN9pvMusfX4eVivjO5t4s36x5fx2S/yfd9DrW6dbuatzZOCCGkZUaY6eG9jsIKJvtNZpLvJI6VHuNy9WU8XTwJ9wq/b4tMI612NGq1jtraEpofN6OgVuvQakdbNW8hRM8lxYwQvZFBDxcOQWUJaLzBbzy0YWdwB5UDo3XtKzYUxYERw5Nvz2ZSMC9oGlo1Rwxf3KHduYUQvYsUM0L0NvkZkPk6VFy6c87NB2LXQvBTXZKCl1cMD4W8x9mCZdTW3llMUa3WMWL4YpmWLYRoEylmhOhN8jNg+/M06d6pKGo4P/MPXVrQeHpOpqwsl9raUtRqL7Ta0dIiI4RoMylmhOgtDPqGFpkW13dRIHMBBE1tU5dTRyiKg0y/FkJ0mMxmEqK3uHDIvGupCSNUXGyIE0IIOyLFjBC9RWXL+yq1K04IIboJKWaE6C00rdyhvLVxQgjRTUgxY4dKS0uZN28eQ4YMQa1Wo9PpiImJ4fDhw6YYRVHYtWtXl+Tz+eefM336dHx8fFq871/+8hdiYmIYOHAgiqJw/PjxLslN3MVvfMOspSa7iTVSwG1wQ5wQQtgRKWasQG8wcvjcVf7n+EUOn7uK3tC5C8/FxcXx9ddfk56eztmzZ8nIyODxxx/n2rVrnXrfllRVVfHwww+bbXTZXExUVBRr1qzpwsyEGZVDw/RroMXtUWPXdNngXyGEsBa7ms20evVqFi1axKuvvsrbb79t63QAyDxZRMrufIrKa0znBrk7s2R6MLEhg6x+v7KyMg4ePEhWVhaPPfYYAH5+fowZM8YU4+/vD8Azzzxjevz8+fMA7N69m6VLl3Lq1Cl8fHxITEzkjTfewNGx4VdBURTef/99MjIyyMrKQqfTkZqaSnx8fIs5TZkyhSlTpljM+yc/+QmAKQ9hI8FPNUy/bnadmTVdNi27kcFg4MKFC1RWVqLRaPDz80Olks9YQoi2sZtiJjc3lw8++IDQ0FBbp2KSebKIn2051mSia3F5DT/bcowNCeFWL2g0Gg0ajYZdu3Yxbtw41Gp1k5jc3Fy8vLzYvHkzsbGxODg0fNLeu3cvCQkJ/Pa3v+XRRx/l3LlzvPjiiwAsWbLEdP3ixYtZs2YN77zzDh999BGzZs0iJCSEkSNHWvW1CBsJfqph+nUHVgC2hvz8fDIzM6moqDCdc3NzIzY2luDg4C7NRQhh3+ziI1BlZSU//vGP+fDDD/Hw8LB1OkBD11LK7vwWV+wASNmdb/UuJ0dHR9LS0khPT0er1RIVFcWiRYs4ceKEKcbT0xMArVaLTqczHa9cuZIFCxaQmJhIYGAg0dHRLF++nI0bN5rdIz4+nrlz5zJixAiWL19OREQE7777rlVfh7AxlQMEPAoP/UfDf21QyGzfvt2skAGoqKhg+/bt5Ofnd2k+Qgj7ZhfFzMsvv8zUqVOZPPn+O/J2lZzCa2ZdS/cyAkXlNeQUWn8cS1xcHJcuXSIjI4OYmBiysrIIDw8nLS3N4nVHjx5l2bJlptYdjUZDUlISRUVFVFdXm+IiIyPNrouMjOT06dNWfx2i/bp6nJY1GQwGMjMzLcZkZmZiMBi6KCMhhL3r9t1M27Zt49ixY+Tm5rYqvra2ltraWtPxvZ/8rKX0RsuFTHvi2srZ2Zno6Giio6NJTk5m7ty5LFmyhNmzZ7d4jcFgICUlhWeffbbZ57NEUVqaASO6WleP07K2Cxcu3PffZUVFBRcuXCAgIKCLshJC2LNu3TLz3Xff8eqrr7Jly5b7vtk2Wr16Ne7u7qYvX1/fTsnNy7V1+bQ2rqOCg4OpqqoyHTs5OaHX681iwsPDOXPmDMOGDWvydfegy+zsbLPrsrOzCQoK6twXIFqlcZzWva2CjeO0Mk8W2Siz1qusrLRqnBBCdOuWmaNHj1JaWsojjzxiOqfX6/n8889Zv349tbW1psGtjRYuXMj8+fNNxxUVFZ1S0IwJ6M8gd2eKy2uaHTejADp3Z8YE9Lfqfa9evUp8fDxz5swhNDQUV1dXjhw5QmpqKjNmzDDF+fv7c+DAAaKiolCr1Xh4eJCcnMy0adPw9fUlPj4elUrFiRMnyMvLY8WKFaZrd+zYQUREBBMmTGDr1q3k5OSwadOmFnOqrKzkm2++MR0XFhZy/Phx+vfvz5AhQwC4du0a3377LZcuNcygOXPmDAA6nQ6dTmfV71FPdb9xWgoN47Sig3U4qLpvS5pGo7FqnBBCdOuWmSeeeIK8vDyOHz9u+oqIiODHP/4xx48fb1LIAKjVatzc3My+OoODSmHJ9IYZFy2s2MGS6cFWf1PRaDSMHTuWt956i4kTJxISEsLixYtJSkoyW+flzTffZP/+/fj6+hIWFgZATEwMe/bsYf/+/YwePZpx48axbt06/Pz8zO6RkpLCtm3bCA0NJT09na1bt1qcXXLkyBHCwsJM95k/fz5hYWEkJyebYjIyMggLC2Pq1KkAPPfcc4SFhfG73/3Oat+bns6W47Ssyc/P777/Lt3c3Jr8XgohREsUo9FoPyMHgccff5zvfe97rV5npqKiAnd3d8rLy5v8Aa2pqaGwsJCAgIBWd2Pdy97HL9xLURR27tzJ008/betUrPLz6Un+5/hFXt12/L5x7zz3PWZ8b3DnJ9QBjbOZWjJz5kyZni1EL2fp/fte3bqbyR7EhgwiOlhHTuE1Sm/U4OXa0LXUnZv5hX3qbuO0OiI4OJiZM2fKOjNCCKuwu2ImKyvL1ik04aBSiBw6wNZpiB7OVuO0OktwcDBBQUGyArAQosPsrpgRncvOeh17lcZxWj/bcgwFzAqazhyn1ZlUKpVMvxZCdJh8BBLCjsSGDGJDQjg6d/OuJJ27c6dsnyGEEPZAWmaEsDMyTksIIcxJMSOEHZJxWkIIcYcUM0LYgN5oJLusktJb9Xj1cWScVoODbBkhhBDtIsWMEF3sr5fL+E3BRYpq60znBqmdWDF8MFM9tbZLTAgh7JQMABaiC/31chlzT543K2QAimvrmHvyPH+9XGabxGzEaDBSc66M6uOl1Jwrw2hHu38LIboPaZkRoovojUZ+U3DR4t5KiwsuEjvQvVd0Od08eYWy3efQl98ynXNw74N2+lD6hgy0YWZCCHsjLTN2qLS0lHnz5jFkyBDUajU6nY6YmBgOHz5silEUhV27dnVJPp9//jnTp0/Hx8en2fvW1dXx+uuv89BDD9GvXz98fHx4/vnnTZtO9hbZZZVNWmTuZgQu1daRXdbzd4u+efIKV7ecNitkAPTlt7i65TQ3T16xUWZCCHskxYw1GPRQ+AXk/b+G/xr0nXq7uLg4vv76a9LT0zl79iwZGRk8/vjjXLtmmw0Gq6qqePjhh802urxbdXU1x44dY/HixRw7doy//OUvnD17lqeeeqqLM7Wt0lv1Vo2zV0aDkbLd5yzGlO3+l3Q5CSFaTbqZOio/AzJfh4q7WhncfCB2LQRb/826rKyMgwcPkpWVxWOPPQY07EI8ZswYU4y/vz8AzzzzjOnx8+fPA7B7926WLl3KqVOn8PHxITExkTfeeANHx4ZfBUVReP/998nIyCArKwudTkdqairx8fEt5jRlyhSmTJnS4uPu7u7s37/f7Ny7777LmDFj+PbbbxkyZEibvw/2yKtP6/65tTbOXtUWljdpkbmXvryW2sJynIdquyYpIYRdk5aZjsjPgO3PmxcyABVFDefzM6x+S41Gg0ajYdeuXdTW1jYbk5ubC8DmzZspKioyHe/du5eEhAReeeUV8vPz2bhxI2lpaaxcudLs+sWLF5tafxISEpg1axanT5+26usoLy9HURS0Wq1Vn7c7G6fVMEjtREujYRTAR+3EOK2mK9PqcoYblguZtsYJIYQUM+1l0De0yLQ4nBPIXGD1LidHR0fS0tJIT09Hq9USFRXFokWLOHHihCnG09MTAK1Wi06nMx2vXLmSBQsWkJiYSGBgINHR0SxfvpyNGzea3SM+Pp65c+cyYsQIli9fTkREBO+++67VXkNNTQ0LFizgRz/60X23de9JHBSFFcMHAzQpaBqPlw8f3OMH/6pc+1g1TgghpJhprwuHmrbImDFCxcWGOCuLi4vj0qVLZGRkEBMTQ1ZWFuHh4aSlpVm87ujRoyxbtszUuqPRaEhKSqKoqIjq6mpTXGRkpNl1kZGRVmuZqaur47nnnsNgMPD+++9b5TntyVRPLb8P8UendjI7P0jtxO9D/HvFOjPqAHcc3C0XKg7uatQB7l2UkRDC3vXszvnOVFli3bg2cnZ2Jjo6mujoaJKTk5k7dy5Llixh9uzZLV5jMBhISUnh2Wefbfb5LFGs0FpQV1fHzJkzKSws5NNPP+1VrTJ3m+qpJXage69dAVhRKWinD+XqlpYLZO30QBTZa0oI0UpSzLSXxtu6cR0UHBxsNiXayckJvd68iys8PJwzZ84wbNgwi8+VnZ3N888/b3YcFhbWofwaC5mCggL+8Y9/MGBA795XyEFRiPJwtXUaNtM3ZCADEkY2s86MGu30QFlnRgjRJlLMtJff+IZZSxVFND9uRml43G+8VW979epV4uPjmTNnDqGhobi6unLkyBFSU1OZMWOGKc7f358DBw4QFRWFWq3Gw8OD5ORkpk2bhq+vL/Hx8ahUKk6cOEFeXh4rVqwwXbtjxw4iIiKYMGECW7duJScnh02bNrWYU2VlJd98843puLCwkOPHj9O/f3+GDBlCfX09//Ef/8GxY8fYs2cPer2e4uJiAPr370+fPvY3NsJo1FNWlkttbSlqtRda7WgUxcHWadmVviEDcQ4eQG1hOYYbt1C59kEd4C4tMkKINlOMRmOPXsyhoqICd3d3ysvLm3Rr1NTUUFhYSEBAwH27WZrVOJsJMC9obv8xnvkHq0/Prq2tZenSpezbt49z585RV1dnKk4WLVpE3759gYYp2PPnz+f8+fMMHjzYNDV77969LFu2jK+++gonJyeCgoKYO3cuSUlJDZkrCu+99x67du3i888/R6fTsWbNGp577rkWc8rKymLSpElNzicmJpKWlsb58+cJCAho9tp//OMfPP74480+1uGfTycpLd3L2YJl1NYWm86p1TpGDE/GyyvGhpkJIUTPYen9+15SzHT0zbLZdWYGQ+yaTllnprMpisLOnTt5+umnbZ1KtyxmSkv3knfyZZq2xjUUsA+FvCcFjRBCWEFbihnpZuqo4KcgaGrDrKXKkoYxMn7jQSVdDj2N0ajnbMEyWp6Or3C2YDmenpOly0kIIbqQFDPWoHKAgEdtnYXoZA1jZIotRBiprS2irCwXD49xXZaXEEL0dlLMCDM9vNexQ2prS60aJ4QQwjqkmBGildRqL6vGCTAY9Fw8fYrKsutotB4MHjkKlXTRCiHaSIoZIVpJqx2NWq2jtraElqbjq9U6tNrRXZ2aXSr48hCfpn1A5bUrpnOa/gP5/uwXGT7WuksaCCF6NtnOQIhWUhQHRgxPbjy691EARgxfLIN/W6Hgy0NkrFtlVsgAVF67Qsa6VRR8af1tQIQQPZcUM0K0gZdXDA+FvIdabb6ys1qtk2nZrWQw6Pk07QOLMf9I/wCDlTdpFUL0XNLNJEQbeXnF4Ok5WVYAbqeLp081aZG5142rV7h4+hS+o0K7KCshhD2TYkaIdlAUB5l+3U6VZdetGieEEFLMiF7JYDBw4cIFKisr0Wg0+Pn5oVJJr2tX0Gg9rBonhBDy19sOlZaWMm/ePIYMGYJarUan0xETE8Phw4dNMYqimO2i3Zk+//xzpk+fjo+PT4v3Xbp0KUFBQfTr1w8PDw8mT57Ml19+2SX53Ss/P5+3336b9PR0Pv74Y9LT03n77bfJz8+3ST69zeCRo9D0t7wrtuuAgQweOaqLMhJC2DspZqxAb9CTW5zL3/71N3KLc9F38sDFuLg4vv76a9LT0zl79iwZGRk8/vjjXLt2rVPv25Kqqioefvhh1q9f32LMiBEjWL9+PXl5eRw8eBB/f3+efPJJLl++3IWZNhQy27dvp6Kiwux8RUUF27dvl4KmC6hUDnx/9osWYyYlvijrzQghWk02muzgRoafXPiENTlrKKkuMZ3zdvFmwZgFTPab3KHcm1NWVoaHhwdZWVk89thjzcb4+/tz4cIF07Gfn59p1+zdu3ezdOlSTp06hY+PD4mJibzxxhs4Ojb0OCqKwvvvv09GRgZZWVnodDpSU1OJj49vVX6t3aiy8efyySef8MQTTzQbY+2NJg0GA2+//XaTQuZubm5uvPbaa9Ll1AWaW2fGdcBAJiXKOjNCCNlosst8cuET5mfNx3jPAmql1aXMz5rPusfXWb2g0Wg0aDQadu3axbhx41Cr1U1icnNz8fLyYvPmzcTGxuLg0PAJd+/evSQkJPDb3/6WRx99lHPnzvHiiw2fkJcsWWK6fvHixaxZs4Z33nmHjz76iFmzZhESEsLIkSOt8hpu3brFBx98gLu7Ow8//LBVnrM1Lly4YLGQgYZ/PBcuXCAgIKCLsuq9ho8dz9DRY2UFYCFEh8nHz3bSG/SsyVnTpJABTOfW5qy1epeTo6MjaWlppKeno9VqiYqKYtGiRZw4ccIU4+npCYBWq0Wn05mOV65cyYIFC0hMTCQwMJDo6GiWL1/Oxo0bze4RHx/P3LlzGTFiBMuXLyciIoJ33323w7nv2bMHjUaDs7Mzb731Fvv372fgQMtjJ6ypsrLSqnGi41QqB3xHhTIy6jF8R4VKISOEaBcpZtrpWOkxs66lexkxUlxdzLHSY1a/d1xcHJcuXSIjI4OYmBiysrIIDw8nLS3N4nVHjx5l2bJlptYdjUZDUlISRUVFVFdXm+IiIyPNrouMjOT06dMdznvSpEkcP36cQ4cOERsby8yZMykt7bpNGTUajVXjhBBCdA9SzLTT5erWDVxtbVxbOTs7Ex0dTXJyMocOHWL27NlmXUXNMRgMpKSkcPz4cdNXXl4eBQUF9x2Toij3Lt/fdv369WPYsGGMGzeOTZs24ejoyKZNmzr8vK3l5+d3335XNzc3/Pz8uigjIYQQ1iDFTDt5unhaNa6jgoODqaqqMh07OTmh15t3cYWHh3PmzBmGDRvW5OvuAa/Z2dlm12VnZxMUFGT1nI1GI7W1tVZ/3paoVCpiY2MtxsTGxsrgXyGEsDMyALidwr3C8XbxprS6tNlxMwoK3i7ehHuFW/W+V69eJT4+njlz5hAaGoqrqytHjhwhNTWVGTNmmOL8/f05cOAAUVFRqNVqPDw8SE5OZtq0afj6+hIfH49KpeLEiRPk5eWxYsUK07U7duwgIiKCCRMmsHXrVnJyciy2oFRWVvLNN9+YjgsLCzl+/Dj9+/dnyJAhVFVVsXLlSp566ikGDRrE1atXef/99/n3v//d6llS1hIcHMzMmTPJzMw0Gwzs5uZGbGwswcHBXZqPEEKIjpNipp0cVA4sGLOA+VnzUVDMChrl9g7Kr495HQcrD2jUaDSMHTuWt956i3PnzlFXV4evry9JSUksWrTIFPfmm28yf/58PvzwQwYPHsz58+eJiYlhz549LFu2jNTUVJycnAgKCmLu3Llm90hJSWHbtm289NJL6HQ6tm7davFN/siRI0yaNMl0PH/+fAASExNJS0vDwcGB//3f/yU9PZ0rV64wYMAARo8ezRdffMGoUV2/MFpwcDBBQUGyArAQQvQQss5MJ6wzo3PR8fqY1ztlnZnO1tp1YrqCtdeZEd2PwWCkqKCMqopa+rmpGTRci0rV8fFZQgj7J+vMdKHJfpOZ5DuJY6XHuFx9GU8XT8K9wq3eIiPMGQ1GagvLMdy4hcq1D+oAdxR5E7Qr574q5Ys/F1BVdmfcVD+tmkd/OJyhYV42zEwIYW+kmLECB5UDo3WjbZ1Gr3Hz5BXKdp9DX37LdM7BvQ/a6UPpG9J169aI9jv3VSmZG082OV9VVkvmxpPEzguRgkYI0WpSzAgz3b3X8ebJK1zd0nTNG335La5uOc2AhJFS0HRzBoORL/5cYDHm4PYCAh72lC4nIUSryIhHYTeMBiNlu89ZjCnb/S+Mhu5dkPV2RQVlZl1Lzam8XktRQVnXJCSEsHtSzAi7UVtYbta11Bx9eS21heVdlJFoj6qK1q0t1No4IYSQYkbYDcMNy4VMW+OEbfRza7o5akfihBBCihlhN1SufawaJ2xj0HAt/bSWCxWNR8M0bSGEaA0pZoTdUAe44+BuuVBxcFejDnDvooxEe6hUCo/+cLjFmAkzh8vgXyFEq0kxI+yGolLQTh9qMUY7PVDWm7EDQ8O8iJ0X0qSFRuOhlmnZQog2k6nZwq70DRnIgISRzawzo0Y7PVCmZduRoWFeBDzsKSsACyE6TFpm7FBpaSnz5s1jyJAhqNVqdDodMTExHD582BSjKAq7du3qknw+//xzpk+fjo+PT6vuO2/ePBRF4e23327X/fqGDET3+hgGJj1E/+ceZGDSQ+heHy2FjB1SqRQGP+jBiNE6Bj/oIYWMEKJdpGXGCox6PdVHjlJ/+TKOnp64RDyC4tB52xnExcVRV1dHeno6gYGBlJSUcODAAa5du9Zp97SkqqqKhx9+mBdeeIG4uDiLsbt27eLLL7/Ex8enQ/dUVArOQ7Udeg4hhBA9gxQzHVSxbx8lq1ZTX1xsOueo0+G9aCFuTz5p9fuVlZVx8OBBsrKyeOyxxwDw8/NjzJgxphh/f38AnnnmGdPj58+fB2D37t0sXbqUU6dO4ePjQ2JiIm+88QaOjg2/Coqi8P7775ORkUFWVhY6nY7U1FTi4+NbzGnKlClMmTLlvrlfvHiRn//85+zdu5epU6dSX3eLm5U3cHBwwMm5L4oin8qFEEK0nXQzdUDFvn1cfPU1s0IGoL6khIuvvkbFvn1Wv6dGo0Gj0bBr1y5qa5tfVCw3NxeAzZs3U1RUZDreu3cvCQkJvPLKK+Tn57Nx40bS0tJYuXKl2fWLFy8mLi6Or7/+moSEBGbNmsXp0023EGgLg8HAT37yE371q18x1M8PQ3091eXllJcUc+3SRa58e56aysoO3UMIIUTvJMVMOxn1ekpWrYbm9jK6fa5k1WqMer1V7+vo6EhaWhrp6elotVqioqJYtGgRJ06cMMV4enoCoNVq0el0puOVK1eyYMECEhMTCQwMJDo6muXLl7Nx40aze8THxzN37lxGjBjB8uXLiYiI4N133+1Q3mvXrsXR0ZEX58yhrKSIe79r+vp6ykqKpKARQgjRZlLMtFP1kaNNWmTMGI3UFxdTfeSo1e8dFxfHpUuXyMjIICYmhqysLMLDw0lLS7N43dGjR1m2bJmpdUej0ZCUlERRURHV1dWmuMjISLPrIiMjO9Qyc/ToUd555x02b95M5bUrFmNvXL3c7Te7FEII0b3ImJl2qr982apxbeXs7Ex0dDTR0dEkJyczd+5clixZwuzZs1u8xmAwkJKSwrPPPtvs81nSkfEsX3zxBaWlpfj5+ZnO6fV6Ulav4cO0dHI/+8ed8/X11NXcpE9fl3bfT9iPrh48L4Tombp1MbN69Wr+8pe/8L//+7/07duX8ePHs3btWh588EFbp4bj7a4ba8V1VHBwsNmUaCcnJ/T3dHGFh4dz5swZhg0bZvG5srOzef75582Ow8LC2p3bT37yEyZPnkxNdRWVVxtaZma9MIf/mDGDH/5H09lP9+YteqauHjwvhOi5unUx89lnn/Hyyy8zevRo6uvreeONN3jyySfJz8+nX79+Ns3NJeIRHHU66ktKmh83oyg4envjEvGIVe979epV4uPjmTNnDqGhobi6unLkyBFSU1OZMWOGKc7f358DBw4QFRWFWq3Gw8OD5ORkpk2bhq+vL/Hx8ahUKk6cOEFeXh4rVqwwXbtjxw4iIiKYMGECW7duJScnh02bNrWYU2VlJd98843puLCwkOPHj9O/f3+GDBnCgAEDGDBgALduVnPt0kUAHB2d8PT0ZFhgYJPnc5BP5j1e4+D5e//tNA6e5523paARQrRatx4zk5mZyezZsxk1ahQPP/wwmzdv5ttvv+XoUeuPQ2krxcEB70ULbx/c0wVz+9h70UKrN5lrNBrGjh3LW2+9xcSJEwkJCWHx4sUkJSWxfv16U9ybb77J/v378fX1NbWqxMTEsGfPHvbv38/o0aMZN24c69atM+v+AUhJSWHbtm2EhoaSnp7O1q1bCQ4ObjGnI0eOEBYWZrrP/PnzCQsLIzk52SzOybkvDo6W62cHR0ecnPu26Xsi7IutBs8LIXouxWhHoy2/+eYbhg8fTl5eHiEhIc3G1NbWmk1ZrqiowNfXl/Lyctzc3Mxia2pqKCwsJCAg4L5jRlrS05rKFUVh586dPP30053y/DWVlZSVFLX4uNZ7EM4aTUOsFX4+ovup+jKHbxMT7xs3JD2dfmPH3DdOCNEzVVRU4O7u3uz79726dTfT3YxGI/Pnz2fChAktFjLQMM4mJSWly/Jye/JJXJ94QgYxtpKzRoOWQdy4ehl9fb3pvIOjI64DPE2FjOi5bD14XgjR89hNMfPzn/+cEydOcPDgQYtxCxcuZP78+abjxpaZzqQ4OMgnyDZw1mhQ9+tHXc1N9Hq9rADcy3S3wfNCCPtnF8XM//k//4eMjAw+//xzHnjgAYuxarUatVrdRZn1PF3V66goiky/7qVsNXheCNFzdesBwEajkZ///Of85S9/4dNPPyUgIMDWKYnbjEYjt2rqqamq41ZNvSx0J1rNVoPnhRA9V7cuZl5++WW2bNnCH//4R1xdXSkuLqa4uJibN2/aOrVeraa6jqsXqygrqabiyk3KSqq5erGKmuo6W6cm7ITbk08y+J23cfT2Njvv6O3NYJmWLYRoo27dzbRhwwYAHn/8cbPzmzdvtrjSreg8NdV1VFxuWkwa9IaG857g7OJkg8yEvZHB80IIa+nWxYx0XXQvRqORymvN79TdqPJaLeq+jjKYV7SKDJ4XQlhDt+5mEt1LXa0eg95gMcagN1BXK4udCSGE6DpSzIhWM+hb11LW2jghhBDCGqSYEa2mcmhd11Fr44QQQghrkGLGDpWWljJv3jyGDBmCWq1Gp9MRExPD4cOHTTGKopjtom0NTmoHVA5Nf2UOf/lPEv7zh4SOeRBvf3f++vfdTWJmz56NoihmX+PGjbNqfkIIIXqnbj0A2F4YDEaKCsqoqqiln5uaQcO1qFSd1zoRFxdHXV0d6enpBAYGUlJSwoEDB7h27Vqn3RMaCiRNf3WT2UzV1dWMGhnCrPgfM+enP2lx8G9sbCybN282Hffp06dT8xVCCNE7SDHTQee+KuWLPxdQVXZnlk8/rZpHfzicoWFeVr9fWVkZBw8eJCsri8ceewwAPz8/xoy5MyPE398fgGeeecb0+Pnz5wHYvXs3S5cu5dSpU/j4+JCYmMgbb7yB4+3drBVF4f333ycjI4OsrCx0Oh2pqanEx8cDt6ddezbMWmocDPzEpGiiJ8eg6a9mzk9/0mLuja1IQjTSG/QcKz3G5erLeLp4Eu4VjoNKpmYLIdpGupk64NxXpWRuPGlWyABUldWSufEk574qtfo9NRoNGo2GXbt2me0Ofrfc3FygYT2eoqIi0/HevXtJSEjglVdeIT8/n40bN5KWlsbKlSvNrl+8eDFxcXF8/fXXJCQkMGvWLE6fPm163NnFiQGD+6H1dsFtYF+03i4MGNzvvuvLZGVl4eXlxYgRI0hKSqK01PrfH2E/PrnwCTEfxzBn7xxe/+J15uydQ8zHMXxy4RNbpyaEsDNSzLSTwWDkiz8XWIw5uL0Ag8G6M3scHR1JS0sjPT0drVZLVFQUixYt4sSJE6YYz9sb9Gm1WnQ6nel45cqVLFiwgMTERAIDA4mOjmb58uVs3LjR7B7x8fHMnTuXESNGsHz5ciIiInj33XfNYhRFoY+zI879nOjjfP91ZaZMmcLWrVv59NNPefPNN8nNzeX73/9+iwWZ6Nk+ufAJ87PmU1JdYna+tLqU+VnzpaARQrSJFDPtVFRQ1qRF5l6V12spKiiz+r3j4uK4dOkSGRkZxMTEkJWVRXh4OGlpaRavO3r0KMuWLTO17mg0GpKSkigqKqKqqsoUFxkZaXZdZGSkWctMe/zwhz9k6tSphISEMH36dP7+979z9uxZ/vrXv3boeYX90Rv0rMlZg5GmhX7jubU5a9EbZL0iIUTryJiZdqqqaF2LQmvj2srZ2Zno6Giio6NJTk5m7ty5LFmyxOI2DwaDgZSUFGZER1N/+TLG+nrTY8q336L38WnxWmuv6Dto0CD8/PwoKLDcuiV6nmOlx5q0yNzNiJHi6mKOlR5jtG50F2YmhLBX0jLTTv3c1FaN66jg4GCz1hUnJyf0evNPtuHh4fxvXh5DnJwI9PFh6JAhpi9Fr+fWd98BkJ2dbXZddnY2QUFBVs336tWrfPfddwwaNMiqzyu6v8vVl60aJ4QQ0jLTToOGa+mnVVvsatJ4NEzTtqarV68SHx/PnDlzCA0NxdXVlSNHjpCamsqMGTNMcf7+/hw4cICoqCjUajUeHh4sXryY6dOnM8jVlWeffBKVSsXJs2c5efYsS195xXTtjh07iIiIYMKECWzdupWcnBw2bdrUYk6VlZV88803puPCwkKOHz9O//79GTJkCJWVlSxdupS4uDgGDRrE+fPnWbRoEQMHDjTNuBK9h6eLp1XjhBBCWmbaSaVSePSHwy3GTJg53OrrzWg0GsaOHctbb73FxIkTCQkJYfHixSQlJbF+/XpT3Jtvvsn+/fvx9fUlLCwMgOgJj/Lx+vV8evgwj86axeM//jG//cMfGHJP99KSRYvYtm0boaGhpKens3XrVoKDg1vM6ciRI4SFhZnuM3/+fMLCwkhOTgbAwcGBvLw8ZsyYwYgRI0hMTGTEiBEcPnwYV1dXq35/RPcX7hWOt4s3Cs3/21BQ0LnoCPcK7+LMhBD2SjH28K2pKyoqcHd3p7y8HDc3N7PHampqKCwsJCAgAGdn53Y9f3PrzGg81EyY2TnrzHREfVkZdf/+t8UYl4ce4v9t2ULcj3/cRVm1zBo/H9E9Nc5mAswGAjcWOOseX8dkv8k2yU0I0T1Yev++l3QzddDQMC8CHvbs0hWA20txtLwOjClOFi0TnWyy32TWPb6ONTlrzAYDe7t48/qY16WQEUK0iRQzVqBSKQx+0MPWadyXqp8LipMTxro6i3GKc9cMWha922S/yUzynSQrAAshOkyKmV5EURScdDrTrKXm1JeV4eDu3oVZid7MQeUg06+FEB0mxUwv4+DuTh+grrjYrIVGcXLCSaeTQkYIIYTdkWKmF3Jwd0fl5oahqhpjfR2Ko1NDF5SVF8YTQgghuoIUM72Uoig4aPrZOg0hhBCiw6SYsVNGo5Hq+mrqDfU4qhxxcZSWFSGEEL2TFDN2qKK2gqKqIuoNd/ZWclQ5MqjfINzUlufiCyGEED2NFDN2pqK2gu9uNJ2NVG+o57sb3+GLrxQ0wn4Y9HDhEFSWgMYb/MaDTM0WQrSRFDN2xGg0UlRVZDGmuKoY1z6u0uUkur/8DMh8HSou3Tnn5gOxayH4KdvlJYSwO7I3kx1pHCNz9fJVUv6/FCZ/bzJhg8N4LPgxXox/keO5x6kz1FFdX42iKOzatatL8vr888+ZPn06Pj4+Fu97+vRpnnrqKdzd3XF1dWXcuHF8++23XZKj6GbyM2D78+aFDEBFUcP5/Azb5CWEsEvSMmMFBoOei6dPUVl2HY3Wg8EjR6HqhKbyxjEyv3jhF9TX17Nq/Soe8HuAq5evkv15NuXXy83iukpVVRUPP/wwL7zwAnFxcc3GnDt3jgkTJvCf//mfpKSk4O7uzunTp2XPpd7IoG9okaG5beGMgAKZCyBoqnQ5CSFaRYqZDir48hCfpn1A5bUrpnOa/gP5/uwXGT52vFXv5ahypKK8gmNfHmPzrs2MjmpYOdXH14eHwh8yxYU+GArAM888A4Cfnx/nz58HYPfu3SxdupRTp07h4+NDYmIib7zxBo6ODb8KiqLw/vvvk5GRQVZWFjqdjtTUVOLj41vMa8qUKUyZMsVi7m+88QY/+MEPSE1NNZ0LDAxs+zdB2L8Lh5q2yJgxQsXFhriAR7ssLSGE/ZJupg4o+PIQGetWmRUyAJXXrpCxbhUFXx6y6v1cHF1wc3XDpZ8Ln/79U27V3moS46RyIjcnF4DNmzdTVFREbm7D8d69e0lISOCVV14hPz+fjRs3kpaWxsqVK82eY/HixcTFxfH111+TkJDArFmzOH36dLvzNhgM/PWvf2XEiBHExMTg5eXF2LFju6wbTHQzlSX3j2lLnBCi15Nipp0MBj2fpn1gMeYf6R9gMOitdk9FUfB192Xluyv5nz//D5HDIkn4QQJvr3ibM6fOAKDrp8PLywsArVaLTqfD09MTgJUrV7JgwQISExMJDAwkOjqa5cuXs3HjRrP7xMfHM3fuXEaMGMHy5cuJiIjg3XffbXfepaWlVFZWsmbNGmJjY9m3bx/PPPMMzz77LJ999lm7n1fYKY23deOEEL2eFDPtdPH0qSYtMve6cfUKF0+fsup93dRuzPnRHL449QXvfvQuUd+PIvdQLjOfmMnnOz+3OC376NGjLFu2DI1GY/pKSkqiqKiI6upqU1xkZKTZdZGRkR1umQGYMWMGv/jFL/je977HggULmDZtGr/73e/a/bzCTvmNb5i1REsz7hRwG9wQJ4QQrSBjZtqpsuy6VePawk3txkODHmLojKHMnD4Tx+WOvPLTV1izYg0/S/pZi9cZDAZSUlJ49tlnmzx2v4G4HZnqPXDgQBwdHQkODjY7P3LkSA4ePNju5xV2SuXQMP16+/M0FDR3DwS+/XsWu0YG/wohWk1aZtpJo/WwalxbKYpCP6d+uKvd6efUj1GjRlFVVWV63MnJCb3evIsrPDycM2fOMGzYsCZfKtWdX4Xs7Gyz67KzswkKCmp3rn369GH06NGcOXPG7PzZs2fx8/Nr9/MKOxb8FMz8A7gNMj/v5tNwXtaZEUK0gbTMtNPgkaPQ9B9osavJdcBABo8c1fyDRiPcqgR9HTg4QR8NtKL14+rVq8THxzNnzhxCQ0NxdXXlyJEjpKamMmPGDFOcv78/Bw4cICoqCrVajYeHB8nJyUybNg1fX1/i4+NRqVScOHGCvLw8VqxYYbp2x44dREREMGHCBLZu3UpOTg6bNm1qMafKykq++eYb03FhYSHHjx+nf//+DBkyBIBf/epX/PCHP2TixIlMmjSJzMxMdu/eTVZW1n1fs+ihgp9qmH4tKwALITpIMRqNzS320GNUVFTg7u5OeXk5bm7m40lqamooLCwkICCgXeudNM5maslT8xc1Pz37ZhmU/xsMdXfOqZzA/QHoq7V4z9raWpYuXcq+ffs4d+4cdXV1puJk0aJF9O3bF2iYgj1//nzOnz/P4MGDTVOz9+7dy7Jly/jqq69wcnIiKCiIuXPnkpSUBDS0+Lz33nvs2rWLzz//HJ1Ox5o1a3juuedazCkrK4tJkyY1OZ+YmEhaWprp+P/+3//L6tWr+fe//82DDz5ISkqKWQF2r47+fIQQQtgvS+/f95JipoNvls2tM+M6YCCTEltYZ+ZmGVwvbPkJPQLuW9B0JkVR2LlzJ08//bTNcmgkxYwQQvRebSlmpJupg4aPHc/Q0WNbtwKw0djQImNJ+b/B2b1VXU5CCCGEkGLGKlQqB3xHhd4/8FaleddScwx1DXFqV+skJ4QQQvRwUsx0Jf19Cpm2xnWCHt7rKIQQogeSqdldycHJunFCCCGEkJaZLtVH0zBryVJXk+r2NG0hegG9wUhO4TVKb9Tg5erMmID+OKhkvJgQom2kmOlKitIw/drSbCb3B2Twr+gVMk8WkbI7n6LyGtO5Qe7OLJkeTGzIIAtXCiGEOelm6mp9tQ3Tr1X3dCWpnGw+LVuIrpJ5soifbTlmVsgAFJfX8LMtx8g8WWSjzIQQ9khaZmyhr7Zh+nU7VgAWwt7pDUZSdufT3FBzIw27M6Xszic6WCddTkKIVpGWGVtRlIbp1y79G/4rhYzoJXIKrzVpkbmbESgqryGn8FrXJSWEsGvSMtNORqORqlo99QYDjioV/dQOHdpZWojeovRGy4VMe+KEEEJaZtqh/OYt/rf4Bv+6Usm316r515VK/rf4BuU3b3XJ/UtLS5k3bx5DhgxBrVaj0+mIiYnh8OHD/3979xrV1JX2AfwfIgQxQLQCEYUAVYpAqWAci9CiqyI61dGWSVsqClUYO2OrljWdIpZwEy84UtdSUWypYaZYrasjRjsWGVuwjjJclEvFArUoVoOpchPRgMl5P/ByaspFLoEAeX5r5cM52eechxM0D3vv82y2DYfDQWZm5pDEc/bsWSxZsgS2trbdXpfD4XT52rFjx5DESIYPa/PeLU3R23aEEEI9M33U+KAV1++2aO/UMGB+boLiSh0eTeJjwjNPgTOIY/2BgYFoa2tDeno6nJyccPv2bZw5cwZ1dfrplr9//z6ee+45vPXWWwgMDOyyjUKhPaHz1KlTWL16dbftyej1O8cJmGRpitrGh13Om+EAEFq2P6ZNCCG9QclMHzAMg1sN2l3fxlcbYfadAtzm9toxDwAoLE0wfsnTGOs+UecxNDQ04Ny5c8jJyYGfnx8AQCQS4Xe/+x3bxsHBAQDwyiuvsO93rJp94sQJxMbG4vLly7C1tUVISAg2bdqEMWPafxU4HA5SUlIgl8uRk5MDoVCIpKQkSCSSbmNatGgRFi1a1GPcQqFQa/v48eOYN28enJyc+vTzk5GPa8RBzBJX/Pmzi+AAWglNx58AMUtcafIvIaTXaJipD+6r1GhTa9ht46uN4J+qgVGzdhE8TWMr7n52BQ++v/PbUwwYn88Hn89HZmYmVCpVl20KCgoAAAcPHoRCoWC3s7KyEBwcjHXr1qG8vBypqamQyWRITEzUOj46OhqBgYEoKSlBcHAwgoKCcOXKFZ39DLdv38ZXX32F1atX6+ycZGRZ6D4J+4K9ILTUHkoSWppiX7AX1ZkhhPQJ9cz0wSPNr4kMNAzMvmsfOunu78eGEz/B1FW3Q05jxoyBTCZDeHg49u/fDy8vL/j5+eGNN96Ah0f7YpdWVlYAAIFAoNUjkpiYiMjISISEhAAAnJyckJCQgL/97W+IiYlh20kkEoSFhQEAEhISkJ2djd27dyMlJUUnP0N6ejrMzc3x6quv6uR8ZGRa6D4J/q5CqgBMCBkw6pnpgzFGv96uMbfug9vc1m0iAwDqRhVU1Y06jyMwMBC3bt2CXC5HQEAAcnJy4OXlBZlM1uNxRUVFiI+PZ3t3+Hw+wsPDoVAo0NLy6zwgb29vreO8vb112jPz6aefYvny5TA1pQmeho5rxIH3009h6YzJ8H76KUpkCCH9Qj0zfTCOx4Ux1whtag2MWh716hjNvcF5wsnU1BT+/v7w9/eHVCpFWFgYYmJiEBoa2n0sGg3i4uK67BF5UmKhq8fOv/vuO1RUVODIkSM6OR8hhBBCPTN9wOFwYCto/9LXmPUuDzQyNxnMkFiurq64f/8+u21sbAy1Wq3VxsvLCxUVFZg6dWqnl9FjvU55eXlax+Xl5cHFxUUncaalpWHmzJl47rnndHI+QgghhHpm+shyrAlETwG3OByo+cYw6mGoiWvJA8/RUqfXv3v3LiQSCVatWgUPDw+Ym5ujsLAQSUlJWLp0KdvOwcEBZ86cgY+PD3g8HsaPHw+pVIrFixfDzs4OEokERkZGKC0tRVlZGTZv3swee/ToUYjFYvj6+iIjIwP5+flIS0vrNqbm5mb8+OOP7HZ1dTWKi4sxYcIE2Nvbs/ubmppw9OhR7Ny5U6f3hBBCiGGjnpl+sBxrAhdbC5j/3rHHOTOCJU46rzfD5/Mxe/ZsfPTRR3jxxRfh7u6O6OhohIeHY8+ePWy7nTt3Ijs7G3Z2dvD09AQABAQE4OTJk8jOzsasWbPw/PPPIzk5GSKRSOsacXFxOHz4MDw8PJCeno6MjAy4urp2G1NhYSE8PT3Z60RERMDT0xNSqVSr3eHDh8EwDIKCgnR1OwghhBBwGIbpqm7VqNHU1ARLS0s0NjbCwsJC672HDx+iuroajo6O/Z6M+uD7O2g4cRXqxl/nxnAteRAscRqUOjODjcPh4NixY1i2bJm+Q9HJ50MIIWRk6un7+7dGxDBTSkoKduzYAYVCATc3N+zatQsvvPCCvsMCAIx1nwhT16egqm6E5l4rjMxNwHO0HNQKwIQQQgj51bAfZjpy5Ag2bNiATZs24dKlS3jhhRewaNEi1NTU6Ds0FseIA9OnBTCbYQ3TpwWUyBBCCCFDaNgnM8nJyVi9ejXCwsIwffp07Nq1C3Z2dti3b5++QxuVGIYZFkNMhBBCSG8N62SmtbUVRUVFWLBggdb+BQsW4Pz583qKihBCCCHDybCeM3Pnzh2o1WrY2Nho7bexsUFtbW2Xx6hUKq01i5qamgY1RkIIIYTo17Dumenw2+qzDMN0W5F269atsLS0ZF92dnZDESIhhBBC9GRYJzMTJ04El8vt1AujVCo79dZ02LhxIxobG9nXjRs3hiJUQgghhOjJsE5mTExMMHPmTGRnZ2vtz87Oxpw5c7o8hsfjwcLCQutFCCGEkNFrWM+ZAdqrya5YsQJisRje3t44cOAAampq8Pbbb+s7NEIIIYQMA8M+mXn99ddx9+5dxMfHQ6FQwN3dHf/+9787leDvTkeB464mAre2tkKj0UCtVndalJHon1qthkajQXNzM1pbB2f1cUIIIcNTx/d2bxYqGPXLGfz888/dTgIWiUTYv38/Jk4cWcsO1NXVYf/+/Th//jzq6upgbm4OZ2dnhIeHw8PDAwAwa9Ys7NixA3Pnzh30eC5evIh//vOf+OGHH3Dnzp0ur9vS0oI9e/YgNzcXjY2NmDRpEl5//XX88Y9/7PHcd+7cwdtvv43r168P4k9ACCFkuLpx4wamTJnSY5th3zMzULa2trhx4wbMzc07PQFVV1eHH374Ac7Ozhg3bly/r6HRaFBTU4Pm5mbw+XzY29vDyGjwpiP5+fmhra0Nn332GZycnHD79m188803mDhxIrvYIwA4OTlpbQ+W2tpavPDCC1i/fj0kEkmn66rVarz22msoKyvDoUOH4ODggOzsbLzzzjuYPXs2/vCHP3R53ocPH+LatWsoLCyEiYnJoP8co0FTUxPs7Oxw48YNmi82iOg+Dx2610NnuN1rhmFw79492NraPrHtqE9mjIyMus3oOoYuuFwuuFxuv85fXl6Or7/+WmsYy8LCAgsXLuxxpen+amhowH//+1/k5OTAz88PQHvS4u3tzbZxcHAAAAQGBgJo74G6du0aAODEiROIjY3F5cuXYWtri5CQEGzatAljxrT/KnA4HKSkpEAulyMnJwdCoRBJSUmQSCTdxrR48WIsXryY3TYyMup0P8vKyrBy5Uq89NJLAICnn34aH3/8MS5evIhXXnmly/NyuVwYGRmBz+fTQpN9RJPfhwbd56FD93roDKd7bWlp2at2w/pppuGuvLwcX3zxRaf5OE1NTfjiiy9QXl6u82vy+Xzw+XxkZmZqFQd8XEFBAQDg4MGDUCgU7HZWVhaCg4Oxbt06lJeXIzU1FTKZDImJiVrHR0dHIzAwECUlJQgODkZQUBCuXLkyoLhnzJiBkydP4ubNm2AYBt9++y0qKysREBAwoPMSQgghlMz0k0ajwddff91jm6+//hoajUan1x0zZgxkMhnS09MhEAjg4+ODqKgolJaWsm2srKwAAAKBAEKhkN1OTExEZGQkQkJC4OTkBH9/fyQkJCA1NVXrGhKJBGFhYXB2dkZCQgLEYjF27949oLj/+te/Yvr06ZgyZQpMTEywcOFCpKSkwNfXd0DnJYQQQgw6mTExMYGlpWW31YR7cv369SculdDU1DQoE1cDAwNx69YtyOVyBAQEICcnB15eXpDJZD0eV1RUhPj4eLZ3h8/nIzw8HAqFAi0tLWy7x4esOrYH0jPD4XBw6tQp/O9//4NcLkdRURF27tyJv/zlL/jPf/7T7/OSzng8HmJiYsDj8fQdyqhG93no0L0eOiP5Xo/6OTM9MTExgUAg6Ndk3ebmZp226ytTU1P4+/vD398fUqkUYWFhiImJQWhoaLfHaDQaxMXF4dVXX+3yfD3pT8LXQaVSYdu2bTh27BhefvllAICHhweKi4vx97//HfPnz+/3uYk2Ho+H2NhYfYcx6tF9Hjp0r4fOSL7XBt0zMxB8Pl+n7QbK1dUV9+/fZ7eNjY071c7x8vJCRUUFpk6d2un1eEKXl5endVxeXh5cXFz6HVtbWxva2to6JY1cLlfnw3CEEEIMj0H3zAyESCSChYVFj0NNFhYWvS7u11t3796FRCLBqlWr4OHhAXNzcxQWFiIpKQlLly5l2zk4OODMmTPw8fEBj8fD+PHjIZVKsXjxYtjZ2UEikcDIyAilpaUoKyvD5s2b2WOPHj0KsVgMX19fZGRkID8/H2lpad3G1NzcjB9//JHdrq6uRnFxMSZMmAB7e3tYWFjAz88P77//PsaOHQuRSITc3Fz84x//QHJysk7vDyGEEMMz6ovm9eThw4eorq6Go6Njvx797XiaqTuvvfaazh/PVqlUiI2NxenTp3H16lW0tbWxyUlUVBTGjh0LoP0R7IiICFy7dg2TJ09mH83OyspCfHw8Ll26BGNjY7i4uCAsLAzh4eEA2oeT9u7di8zMTJw9exZCoRDbtm3DG2+80W1MOTk5mDdvXqf9ISEh7Dye2tpabNy4EadPn0ZdXR1EIhH+9Kc/4b333ut2CGugnw8hhBDDYLDJTEpKCjIyMiCVSjF58mSIRCKYm5v3+TxDXWdmsHE4HBw7dgzLli0b8LkUCgXq6+vx8OFDtl7MlClTep2YUDLTP1u3bkVUVBTWr1+PXbt26TucUefmzZv44IMPcOrUKTx48ADOzs5IS0vDzJkz9R3aqPLo0SPExsYiIyMDtbW1mDRpEkJDQ/Hhhx8OalHS0e7s2bPYsWMHioqKoFAoOv1/zzAM4uLicODAAdTX12P27NnYu3cv3Nzc9Bd0LxjkMNORI0ewYcMGpKWlYdKkSTAzM0NVVRXc3Nz6PIvb1dUVLi4uuH79OlsBWCQS0T82APfu3YO1tTXGjRsHhmFw8+ZNVFZWws3Nrd9FCknPCgoKcODAAXZZC6Jb9fX18PHxwbx583Dq1ClYW1vj6tWrEAgE+g5t1Nm+fTv279+P9PR0uLm5obCwEG+99RYsLS2xfv16fYc3Yt2/fx/PPfcc3nrrLbaw6uOSkpKQnJwMmUwGZ2dnbN68Gf7+/qioqOjXH/xDxSCTmeTkZKxevRoSiQTV1dWwtbVFS0sLfvnllyeu/9AVIyMjODo6DkKkI5uzs7PWtoODA0pKStDS0jKs/1GMVM3NzVi+fDk+/vhjrTlQRHe2b98OOzs7HDx4kN3XUXGb6NaFCxewdOlS9glIBwcHfP755ygsLNRzZCPbokWLsGjRoi7fYxgGu3btwqZNm9inXtPT02FjY4NDhw5hzZo1Qxlqnxhc90FrayuKioqwYMECrf0WFhaD9hj1SMIwjE6GmLrS8XRVx9IJRLfWrl2Ll19+mR51H0RyuRxisRgSiQTW1tbw9PTExx9/rO+wRiVfX1+cOXMGlZWVAICSkhKcO3cOv//97/Uc2ehVXV2N2tpare9HHo8HPz8/nD9/Xo+RPZnBfavcuXMHarUaNjY2WvuNjY3x6NEjPUU1+jEMgxs3boDP57OTlInuHD58GBcvXmSXriCD46effsK+ffsQERGBqKgo5OfnY926deDxeFi5cqW+wxtVPvjgAzQ2NsLFxQVcLhdqtRqJiYkICgrSd2ijVm1tLQB0+n60sbEZlAKwumRwyUyHgRSBI31XU1ODBw8eDKheDenajRs3sH79epw+fZomSg8yjUYDsViMLVu2AAA8PT1x+fJl7Nu3j5IZHTty5Ag+++wzHDp0CG5ubiguLsaGDRvYBXLJ4Pnt9yPDMMP+O9PgkpmJEyeCy+WyGWiHtrY2Gv4YJDU1NWhsbMQzzzwDExMTfYcz6hQVFUGpVGo9TaNWq3H27Fns2bMHKpWKJlzryKRJkzo9pTh9+nR8+eWXeopo9Hr//fcRGRnJloV49tlncf36dWzdupWSmUEiFAoBgH16rINSqezUWzPcGNycGRMTE8ycORPZ2dla+5uamoasWq+hYBgGNTU1qK+vh7Oz84hc72MkeOmll1BWVobi4mL2JRaLsXz5chQXF1Mio0M+Pj6oqKjQ2ldZWanz4pgEaGlpoarhQ8zR0RFCoVDr+7G1tRW5ubmYM2eOHiN7MoPsioiIiMCKFSvg4+ODZ599FgqFAq2trezq0kQ3ampqUFdXh6lTp4LL5aKtrQ1A+39I9Oi67pibm8Pd3V1r37hx4/DUU0912k8G5r333sOcOXOwZcsWvPbaa8jPz8eBAwdw4MABfYc26ixZsgSJiYmwt7eHm5sbLl26hOTkZKxatUrfoY1oT6rYvmHDBmzZsgXTpk3DtGnTsGXLFpiZmeHNN9/UY9RPZvBF86KjozFlypR+F80j3evuEUoHBwdMnDjxicdT0bz+mzt3LmbMmEFF8wbByZMnsXHjRlRVVcHR0RERERFsBW2iO/fu3UN0dDSOHTsGpVIJW1tbBAUFQSqV0nD1ADypYntH0bzU1FStonnD/Q8jg01mAPqyHO7o8yGEENIb1Nc/AimVSqxZswb29vbg8XgQCoUICAjAhQsX2DYcDgeZmZlDEs/Zs2exZMkS2Nradnvd27dvIzQ0FLa2tjAzM8PChQtRVVU1JPERQggZ3SiZ0QGGUaO+Pg+1tXLU1+eBYdSDer3AwECUlJQgPT0dlZWVkMvlmDt3Lurq6gb1ut3pKI+9Z8+eLt/vKMT3008/4fjx47h06RJEIhHmz5+P+/fvD3G0hBBCRhsaZhrgMIZSmYXKqnioVL8+6s3jCeE8TQpr6wBdhcpqaGjA+PHjkZOTAz8/vy7bODg4aBU4EolE7KrZJ06cQGxsLC5fvszWa9i0aRP7WDqHw0FKSgrkcjlycnIgFAqRlJQEiUTSq/i6WqiysrISzzzzDL7//nt2sTK1Wg1ra2ts374dYWFhXZ6LhpkIIYT0BvXMDIBSmYWy79dqJTIAoFLdRtn3a6FUZun8mnw+H3w+H5mZmVCpVF226agCe/DgQSgUCnY7KysLwcHBWLduHcrLy5GamgqZTIbExESt46Ojo9nen+DgYAQFBeHKlSv9jrkjzscTEi6XCxMTE5w7d67f5yWEEEIASmb6jWHUqKyKB9BVx1b7vsqqBJ0POY0ZMwYymQzp6ekQCATw8fFBVFQUSktL2TYdj5gLBAIIhUJ2OzExEZGRkQgJCYGTkxP8/f2RkJCA1NRUrWtIJBKEhYXB2dkZCQkJEIvF2L17d79jdnFxgUgkwsaNG1FfX4/W1lZs27YNtbW1UCgU/T4vIYQQAlAy028NDQWdemS0MVCpFGho0P1aOYGBgbh16xbkcjkCAgKQk5MDLy8vyGSyHo8rKipCfHw827vD5/MRHh4OhUKBlpYWtp23t7fWcd7e3gPqmTE2NsaXX36JyspKTJgwAWZmZsjJycGiRYuooBshhJABM8iiebqgUil12q6vTE1N4e/vD39/f0ilUoSFhSEmJgahoaHdHqPRaBAXF8cu7f7b8/VkoOtyzJw5E8XFxWhsbGQLFM6ePRtisXhA5yWEEEKoZ6afeDxrnbYbKFdXV60ng4yNjaFWaw9xeXl5oaKiAlOnTu30erwib15entZxeXl5Olsg0tLSElZWVqiqqkJhYSGWLl2qk/MSQggxXNQz008CwSzweEKoVLfR9bwZDng8IQSCWTq97t27dyGRSLBq1Sp4eHjA3NwchYWFSEpK0koMHBwccObMGfj4+IDH42H8+PGQSqVYvHgx7OzsIJFIYGRkhNLSUpSVlWHz5s3ssUePHoVYLIavry8yMjKQn5+PtLS0bmN6UnnsjnNaWVnB3t4eZWVlWL9+PZYtW4YFCxbo9P4QQggxPNQz008cDhfO06QdW799FwDgPC0aHI5u54Tw+XzMnj0bH330EV588UW4u7sjOjoa4eHhWnVedu7ciezsbNjZ2cHT0xMAEBAQgJMnTyI7OxuzZs3C888/j+Tk5E6L5MXFxeHw4cPw8PBAeno6MjIyOq0U/LjCwkJ4enqy14mIiICnpyekUinbRqFQYMWKFXBxccG6deuwYsUKfP7557q8NcRAqNVqzJkzB4GBgVr7GxsbYWdnhw8//FBPkRFC9IXqzAxKnZlJcJ4WPSh1ZgZbV3Vi9IXqzJDuVFVVYcaMGThw4ACWL18OAFi5ciVKSkpQUFBAa/cQYmBomGmArK0DYGU1//+fblKCx7OGQDBL5z0yhJBfTZs2DVu3bsW7776LefPmoaCgAIcPH0Z+fj4lMoQYIEpmdIDD4WL8+Of1HQYhBuXdd9/FsWPHsHLlSpSVlUEqlWLGjBn6DosQogeUzBAtBjzqSEYYDoeDffv2Yfr06Xj22WcRGRmp75AIIXpCE4AJISPWp59+CjMzM1RXV+Pnn3/WdziEED2hZIYQMiJduHABH330EY4fPw5vb2+sXr2aehYJMVCUzBBCRpwHDx4gJCQEa9aswfz58/HJJ5+goKCg0zpjhBDDQMkMIWTEiYyMhEajwfbt2wEA9vb22LlzJ95//31cu3ZNv8ERQoYcJTOEkBElNzcXe/fuhUwmw7hx49j94eHhmDNnDg03EWKA6GkmQsiI4ufnh0ePHnX5XlZW1hBHQwgZDqhnZgRSKpVYs2YN7O3twePxIBQKERAQgAsXLrBtOBwOMjMzhySerVu3YtasWTA3N4e1tTWWLVuGiooKrTYMwyA2Nha2trYYO3Ys5s6di8uXLw9JfIQQQkY3SmZGoMDAQJSUlCA9PR2VlZWQy+WYO3cu6urq9BJPbm4u1q5di7y8PGRnZ+PRo0dYsGCB1ireSUlJSE5Oxp49e1BQUAChUAh/f3/cu3dPLzETQggZRRgD9uDBA6a8vJx58ODBgM7zSKNhztU1Mf+qrWPO1TUxjzQaHUXYWX19PQOAycnJ6baNSCRi0L6UNwOAEYlE7HtyuZzx8vJieDwe4+joyMTGxjJtbW3s+wCYlJQUZuHChYypqSnj4ODAfPHFF32KUalUMgCY3NxchmEYRqPRMEKhkNm2bRvb5uHDh4ylpSWzf//+bs+jq8+HEELI6EY9MwP01S8NEF8oR2DxVfy5/DoCi69CfKEcX/3SMCjX4/P54PP5yMzMhEql6rJNQUEBAODgwYNQKBTsdlZWFoKDg7Fu3TqUl5cjNTUVMpkMiYmJWsdHR0ezvT/BwcEICgrClStXeh1jY2MjAGDChAkAgOrqatTW1mLBggVsGx6PBz8/P5w/f773PzwhhBDSBUpmBuCrXxoQ9v01KFRtWvtrVW0I+/7aoCQ0Y8aMgUwmQ3p6OgQCAXx8fBAVFYXS0lK2jZWVFQBAIBBAKBSy24mJiYiMjERISAicnJzg7++PhISETrU5JBIJwsLC4OzsjISEBIjFYuzevbtX8TEMg4iICPj6+sLd3R0AUFvbvqK4jY2NVlsbGxv2PUIIIaS/KJnpJzXD4MOqm+jqAdCOfdFVN6EehEdEAwMDcevWLcjlcgQEBCAnJwdeXl6QyWQ9HldUVIT4+Hi2d4fP5yM8PBwKhQItLS1sO29vb63jvL29e90z884776C0tBSff/55p/c4HI7WNsMwnfYRQgghfUXJTD/lNTR36pF5HAPglqoNeQ3Ng3J9U1NT+Pv7QyqV4vz58wgNDUVMTEyPx2g0GsTFxaG4uJh9lZWVoaqqCqampj0e25uk491334VcLse3336LKVOmsPuFQiEAdOqFUSqVnXprCCGEkL6iZKaflK1d17nob7uBcnV11Xp6yNjYGGq1WquNl5cXKioqMHXq1E4vI6NffxXy8vK0jsvLy4OLi0u312YYBu+88w7+9a9/4ZtvvoGjo6PW+46OjhAKhcjOzmb3tba2Ijc3F3PmzOnXz0sIIYR0oKJ5/WRt0rtb19t2vXX37l1IJBKsWrUKHh4eMDc3R2FhIZKSkrB06VK2nYODA86cOQMfHx/weDyMHz8eUqkUixcvhp2dHSQSCYyMjFBaWoqysjJs3ryZPfbo0aMQi8Xw9fVFRkYG8vPzkZaW1m1Ma9euxaFDh3D8+HGYm5uzPTCWlpYYO3YsOBwONmzYgC1btmDatGmYNm0atmzZAjMzM7z55ps6vT+EEEIMD4dhDLfu98OHD1FdXQ1HR8cnDrP8lpphIL5QjlpVW5fzZjgAJvGMUeDtCq4O54WoVCrExsbi9OnTuHr1Ktra2tjkJCoqCmPHjgUAnDhxAhEREbh27RomT57MrleTlZWF+Ph4XLp0CcbGxnBxcUFYWBjCw8Pb4+ZwsHfvXmRmZuLs2bMQCoXYtm0b3njjjW5j6m4I6uDBgwgNDQXQ3nsTFxeH1NRU1NfXY/bs2di7dy87SbgrA/l8CCGEGA5KZgbwZdnxNBMArYSm46v9E3cHvGwlGGiYQ4rD4eDYsWNYtmyZvkOhZIYQQkiv0JyZAXjZSoBP3B0g5Blr7Z/EMx6RiQwhhBAyEtGcmQF62UqAhRMtkdfQDGXrI1ibjMHzAr5Oh5YIIYQQ0j1KZnSAy+HAZ7y5vsPQCQMedSSEEDJC0TATIYQQQkY0SmYIIYQQMqJRMgMaWhmu6HMhhBDSGwadzBgbtz+F9Pi6RGT46PhcOj4nQgghpCsGPQGYy+VCIBBAqVQCAMzMzGjhw2GAYRi0tLRAqVRCIBCAy+XqOyRCCCHDmEEXzQPavzhra2vR0NCg71DIbwgEAgiFQkowCSGE9Mjgk5kOarUabW3dr4JNhpaxsTH1yBBCCOkVSmYIIYQQMqIZ9ARgQgghhIx8lMwQQgghZESjZIYQQgghIxolM4QQQggZ0SiZIYQQQsiIRskMIYQQQkY0SmYIIYQQMqL9HwWxeBNSa8uwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "class Pedestrian:\n",
    "    def __init__(self, position, goal, speed=0.5):\n",
    "        self.position = np.array(position, dtype=np.float64)\n",
    "        self.goal = np.array(goal, dtype=np.float64)\n",
    "        self.speed = speed\n",
    "\n",
    "    def update_position(self, others):\n",
    "        direction_to_goal = self.goal - self.position\n",
    "        norm_to_goal = np.linalg.norm(direction_to_goal)\n",
    "        \n",
    "        if norm_to_goal > 0:\n",
    "            unit_direction_to_goal = direction_to_goal / norm_to_goal\n",
    "        else:\n",
    "            unit_direction_to_goal = np.zeros_like(direction_to_goal)\n",
    "        \n",
    "        repulsion_force = np.zeros(2)\n",
    "        for other in others:\n",
    "            if other == self:\n",
    "                continue\n",
    "            diff = self.position - other.position\n",
    "            distance = np.linalg.norm(diff)\n",
    "            if distance < 1.0:\n",
    "                repulsion_force += diff / (distance + 1e-9)\n",
    "        \n",
    "        net_force = unit_direction_to_goal + 0.5 * repulsion_force\n",
    "        net_force_norm = np.linalg.norm(net_force)\n",
    "        \n",
    "        if net_force_norm > 0:\n",
    "            self.position += self.speed * (net_force / net_force_norm)\n",
    "\n",
    "class Simulation:\n",
    "    def __init__(self, pedestrians, steps):\n",
    "        self.pedestrians = pedestrians\n",
    "        self.steps = steps\n",
    "        self.positions = []\n",
    "\n",
    "    def run(self):\n",
    "        for _ in range(self.steps):\n",
    "            step_positions = []\n",
    "            for pedestrian in self.pedestrians:\n",
    "                pedestrian.update_position(self.pedestrians)\n",
    "                step_positions.append(pedestrian.position.copy())\n",
    "            self.positions.append(step_positions)\n",
    "\n",
    "    def plot(self):\n",
    "        for i, pos in enumerate(self.positions):\n",
    "            pos = np.array(pos)\n",
    "            plt.scatter(pos[:, 0], pos[:, 1], label=f'Step {i+1}')\n",
    "        plt.xlabel('X')\n",
    "        plt.ylabel('Y')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    p1 = Pedestrian([0, 0], [10, 10])\n",
    "    p2 = Pedestrian([0, 5], [10, 5])\n",
    "    p3 = Pedestrian([5, 0], [5, 10])\n",
    "    p4 = Pedestrian([5, 5], [10, 10])\n",
    "\n",
    "    pedestrians = [p1, p2, p3, p4]\n",
    "    simulation = Simulation(pedestrians, steps=20)\n",
    "\n",
    "    simulation.run()\n",
    "    simulation.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7bdbd5b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New position: 0.0005000000000000001, New velocity: 0.010000000000000002\n"
     ]
    }
   ],
   "source": [
    "class SocialForceModel:\n",
    "    def __init__(self, mass, initial_position, initial_velocity):\n",
    "        self.mass = mass\n",
    "        self.position = initial_position\n",
    "        self.velocity = initial_velocity\n",
    "\n",
    "    def calculate_force(self, goal_force, repulsion_force, attraction_force):\n",
    "        return goal_force + repulsion_force + attraction_force\n",
    "\n",
    "    def update_position(self, total_force, time_step):\n",
    "        acceleration = total_force / self.mass\n",
    "        self.position += self.velocity * time_step + 0.5 * acceleration * (time_step ** 2)\n",
    "        self.velocity += acceleration * time_step\n",
    "\n",
    "# Test case\n",
    "sfm = SocialForceModel(70, 0, 0)\n",
    "total_force = sfm.calculate_force(10, -5, 2)\n",
    "sfm.update_position(total_force, 0.1)\n",
    "print(f\"New position: {sfm.position}, New velocity: {sfm.velocity}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0db9d8d2",
   "metadata": {},
   "source": [
    "This class includes methods to calculate the total force acting on an individual and update their position accordingly. The test case demonstrates how to use the class to calculate a new position and velocity based on given forces.\n",
    "\n",
    "The Social Force Model is not just an academic exercise; it has real-world applications. Urban planners, for example, can use it to design more efficient and safer public spaces. Event organizers can employ it to manage large crowds and improve safety measures. It's a tool that invites us to delve deeper into the complexities of human behavior, offering both clarity and nuance in equal measure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2657dc7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New position of ind1: [5.35714286e-05 5.35714286e-05], New velocity of ind1: [0.00107143 0.00107143]\n",
      "New position of ind2: [1.000075 1.000075], New velocity of ind2: [0.00150002 0.00150002]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "class Individual:\n",
    "    def __init__(self, mass, initial_position, goal):\n",
    "        self.mass = float(mass)\n",
    "        self.position = np.array(initial_position, dtype=float)\n",
    "        self.velocity = np.zeros(2, dtype=float)\n",
    "        self.goal = np.array(goal, dtype=float)\n",
    "\n",
    "    def goal_force(self):\n",
    "        return (self.goal - self.position) * 0.1\n",
    "\n",
    "    def repulsion_force(self, other):\n",
    "        delta_pos = self.position - other.position\n",
    "        distance = np.linalg.norm(delta_pos)\n",
    "        return delta_pos / distance * 0.5 / distance\n",
    "\n",
    "    def obstacle_force(self, obstacle):\n",
    "        # Implement obstacle interaction (omitted for brevity)\n",
    "        return np.zeros(2, dtype=float)\n",
    "\n",
    "class SocialForceModel:\n",
    "    def __init__(self):\n",
    "        self.individuals = []\n",
    "        self.obstacles = []\n",
    "\n",
    "    def add_individual(self, individual):\n",
    "        self.individuals.append(individual)\n",
    "\n",
    "    def add_obstacle(self, obstacle):\n",
    "        self.obstacles.append(obstacle)\n",
    "\n",
    "    def update(self, time_step):\n",
    "        for ind in self.individuals:\n",
    "            total_force = ind.goal_force()\n",
    "            for other in self.individuals:\n",
    "                if ind != other:\n",
    "                    total_force += ind.repulsion_force(other)\n",
    "            for obs in self.obstacles:\n",
    "                total_force += ind.obstacle_force(obs)\n",
    "            \n",
    "            acceleration = total_force / ind.mass\n",
    "            ind.position += ind.velocity * time_step + 0.5 * acceleration * (time_step ** 2)\n",
    "            ind.velocity += acceleration * time_step\n",
    "\n",
    "# Test case\n",
    "sfm = SocialForceModel()\n",
    "ind1 = Individual(70, [0.0, 0.0], [10.0, 10.0])\n",
    "ind2 = Individual(70, [1.0, 1.0], [9.0, 9.0])\n",
    "sfm.add_individual(ind1)\n",
    "sfm.add_individual(ind2)\n",
    "sfm.update(0.1)\n",
    "\n",
    "print(f\"New position of ind1: {ind1.position}, New velocity of ind1: {ind1.velocity}\")\n",
    "print(f\"New position of ind2: {ind2.position}, New velocity of ind2: {ind2.velocity}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7761da00",
   "metadata": {},
   "source": [
    "In this example, the Individual class encapsulates the attributes and behaviors of each individual, including the calculation of different forces. The SocialForceModel class manages multiple individuals and obstacles, updating their positions based on the calculated forces.\n",
    "\n",
    "This is a more complex example that can be further extended to include additional features like varying individual attributes, more complex obstacle interactions, or even dynamic goals. It serves as a foundation for simulating more realistic scenarios, offering insights into the nuanced dynamics of crowd behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5e5afadf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "class Network:\n",
    "    \n",
    "    def create_source(self):\n",
    "        G = nx.DiGraph()\n",
    "        self.G = G\n",
    "        self.G.add_node(0)\n",
    "        self.G.nodes[0][\"Informed\"] = 1\n",
    "    \n",
    "    def add_node(self):\n",
    "        index = len(self.G.nodes)\n",
    "        self.G.add_node(index)\n",
    "        self.G.nodes[index][\"Informed\"] = 0\n",
    "        \n",
    "    def add_connection(self,node1,node2):\n",
    "        self.G.add_edge(node1,node2)\n",
    "        self.G[node1][node2][\"Connection\"] = nx.degree_centrality(self.G)[node1]\n",
    "    \n",
    "    def propagate_information(self, resistance):\n",
    "        for edge in list(self.G.edges):\n",
    "            rand = np.random.uniform(0, resistance)\n",
    "            if self.G.nodes[edge[0]][\"Informed\"] == 1 and rand < self.G[edge[0]][edge[1]][\"Connection\"]:\n",
    "                self.G.nodes[edge[1]][\"Informed\"] = 1\n",
    "\n",
    "# Create a network\n",
    "network = Network()\n",
    "network.create_source()\n",
    "\n",
    "for i in range(0, 100):\n",
    "    network.add_node()\n",
    "    \n",
    "nodes = list(network.G.nodes)\n",
    "for i in range(0, 300):\n",
    "    if i == 0: \n",
    "        node1 = nodes[0]\n",
    "    else:\n",
    "        node1 = np.random.choice(nodes)\n",
    "    node2 = np.random.choice(nodes)\n",
    "    \n",
    "    if node1 != node2:\n",
    "        network.add_connection(node1, node2)\n",
    "\n",
    "# Simulate the spread of information\n",
    "informed = []\n",
    "networks = [network.G.copy()]\n",
    "for i in range(0, 50):\n",
    "    network.propagate_information(0.3)\n",
    "    informed.append(sum(list(dict(network.G.nodes(data=\"Informed\")).values())))\n",
    "    networks.append(network.G.copy())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b83be5fb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "\n",
    "# Define a function to visualize the network\n",
    "def visualize_network(network):\n",
    "    pos = nx.kamada_kawai_layout(network)\n",
    "    color_dict = {0: \"red\", 1: \"green\"}\n",
    "    colors = [color_dict[network.nodes[node][\"Informed\"]] for node in network.nodes]\n",
    "    nx.draw(network, node_color=colors, arrowsize=20, pos=pos)\n",
    "\n",
    "\n",
    "# Create a network\n",
    "network = Network()\n",
    "network.create_source()\n",
    "\n",
    "for i in range(0, 100):\n",
    "    network.add_node()\n",
    "\n",
    "nodes = list(network.G.nodes)\n",
    "for i in range(0, 300):\n",
    "    if i == 0:\n",
    "        node1 = nodes[0]\n",
    "    else:\n",
    "        node1 = np.random.choice(nodes)\n",
    "    node2 = np.random.choice(nodes)\n",
    "\n",
    "    if node1 != node2:\n",
    "        network.add_connection(node1, node2)\n",
    "\n",
    "# Simulate the spread of information\n",
    "informed = []\n",
    "networks = [network.G.copy()]\n",
    "# for i in range(0, 50):\n",
    "#     network.propagate_information(0.3)\n",
    "#     informed.append(sum(list(dict(network.G.nodes(data=\"Informed\")).values())))\n",
    "#     networks.append(network.G.copy())\n",
    "\n",
    "# # Visualize the network at different time steps\n",
    "# # Visualize the network at different time steps\n",
    "# for i, network_state in enumerate(networks):\n",
    "#     print(f\"Time Step {i}\")\n",
    "#     visualize_network(network_state)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9388b9b2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time Step 0\n",
      "Time Step 1\n",
      "Time Step 2\n",
      "Time Step 3\n",
      "Time Step 4\n",
      "Time Step 5\n",
      "Time Step 6\n",
      "Time Step 7\n",
      "Time Step 8\n",
      "Time Step 9\n",
      "Time Step 10\n",
      "Time Step 11\n",
      "Time Step 12\n",
      "Time Step 13\n",
      "Time Step 14\n",
      "Time Step 15\n",
      "Time Step 16\n",
      "Time Step 17\n",
      "Time Step 18\n",
      "Time Step 19\n",
      "Time Step 20\n",
      "Time Step 21\n",
      "Time Step 22\n",
      "Time Step 23\n",
      "Time Step 24\n",
      "Time Step 25\n",
      "Time Step 26\n",
      "Time Step 27\n",
      "Time Step 28\n",
      "Time Step 29\n",
      "Time Step 30\n",
      "Time Step 31\n",
      "Time Step 32\n",
      "Time Step 33\n",
      "Time Step 34\n",
      "Time Step 35\n",
      "Time Step 36\n",
      "Time Step 37\n",
      "Time Step 38\n",
      "Time Step 39\n",
      "Time Step 40\n",
      "Time Step 41\n",
      "Time Step 42\n",
      "Time Step 43\n",
      "Time Step 44\n",
      "Time Step 45\n",
      "Time Step 46\n",
      "Time Step 47\n",
      "Time Step 48\n",
      "Time Step 49\n",
      "Time Step 50\n",
      "Time Step 51\n",
      "Time Step 52\n",
      "Time Step 53\n",
      "Time Step 54\n",
      "Time Step 55\n",
      "Time Step 56\n",
      "Time Step 57\n",
      "Time Step 58\n",
      "Time Step 59\n",
      "Time Step 60\n",
      "Time Step 61\n",
      "Time Step 62\n",
      "Time Step 63\n",
      "Time Step 64\n",
      "Time Step 65\n",
      "Time Step 66\n",
      "Time Step 67\n",
      "Time Step 68\n",
      "Time Step 69\n",
      "Time Step 70\n",
      "Time Step 71\n",
      "Time Step 72\n",
      "Time Step 73\n",
      "Time Step 74\n",
      "Time Step 75\n",
      "Time Step 76\n",
      "Time Step 77\n",
      "Time Step 78\n",
      "Time Step 79\n",
      "Time Step 80\n",
      "Time Step 81\n",
      "Time Step 82\n",
      "Time Step 83\n",
      "Time Step 84\n",
      "Time Step 85\n",
      "Time Step 86\n",
      "Time Step 87\n",
      "Time Step 88\n",
      "Time Step 89\n",
      "Time Step 90\n",
      "Time Step 91\n",
      "Time Step 92\n",
      "Time Step 93\n",
      "Time Step 94\n",
      "Time Step 95\n",
      "Time Step 96\n",
      "Time Step 97\n",
      "Time Step 98\n",
      "Time Step 99\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import shutil\n",
    "\n",
    "# ... (Your existing code)\n",
    "\n",
    "# Create a directory to store the frames\n",
    "import os\n",
    "os.makedirs(\"frames\", exist_ok=True)\n",
    "\n",
    "\n",
    "# Simulate the spread of information and visualize the network, then save frames\n",
    "for i in range(0, 100):\n",
    "    print(f\"Time Step {i}\")\n",
    "    network.propagate_information(0.3)\n",
    "    informed.append(sum(list(dict(network.G.nodes(data=\"Informed\")).values())))\n",
    "    \n",
    "    plt.clf()  # Clear the current figure\n",
    "    \n",
    "    visualize_network(network.G)\n",
    "    plt.savefig(f\"frames/frame_{i:03d}.png\", bbox_inches='tight')\n",
    "    plt.close()\n",
    "\n",
    "\n",
    "# Create a GIF from the saved frames\n",
    "images = [Image.open(f\"frames/frame_{i:03d}.png\") for i in range(50)]\n",
    "images[0].save(\"network_animation.gif\", save_all=True, append_images=images[1:], duration=100)\n",
    "\n",
    "\n",
    "# Delete the \"frames\" folder\n",
    "shutil.rmtree(\"frames\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03cdc14d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}