trabalho_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EQUACAO DE ADVECCAO-DIFUSAO: CRANK-NICOLSON\n",
      "\n",
      "JMAX= 21 TMAX= 1.00 T1= 1.00 T2= 0.00\n",
      "BETA= 0.50000 DELTA= 0.00000\n",
      "DELTA_T= 0.02500 DELTA_X= 0.20000\n",
      "S= 0.06250 ALPHA= 0.10000\n",
      "U= 1.00000 C= 0.12500\n",
      "\n",
      "t= 0.000 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.974 0.556 0.088 0.007 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.025 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.997 0.956 0.600 0.166 0.027 0.003 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.050 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.995 0.944 0.636 0.234 0.055 0.009 0.001 0.000 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.075 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.993 0.936 0.666 0.295 0.087 0.019 0.003 0.001 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.100 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.991 0.931 0.692 0.348 0.123 0.033 0.007 0.001 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.125 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.989 0.928 0.713 0.396 0.160 0.050 0.013 0.003 0.001 0.000 0.000 0.000 0.000 \n",
      "t= 0.150 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.998 0.987 0.927 0.733 0.438 0.197 0.069 0.020 0.005 0.001 0.000 0.000 0.000 0.000 \n",
      "t= 0.175 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.998 0.985 0.926 0.750 0.476 0.234 0.091 0.029 0.008 0.002 0.000 0.000 0.000 0.000 \n",
      "t= 0.200 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.997 0.984 0.927 0.765 0.511 0.270 0.115 0.040 0.012 0.003 0.001 0.000 0.000 0.000 \n",
      "t= 0.225 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.997 0.983 0.927 0.779 0.542 0.305 0.140 0.054 0.017 0.005 0.001 0.000 0.000 0.000 \n",
      "t= 0.250 TN = 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.997 0.982 0.929 0.791 0.570 0.338 0.166 0.068 0.024 0.007 0.002 0.001 0.000 0.000 \n",
      "...\n",
      "t= 0.500 TN = 1.000 1.000 1.000 1.000 1.000 1.000 0.999 0.995 0.982 0.948 0.876 0.755 0.594 0.421 0.267 0.152 0.078 0.036 0.015 0.006 0.000 \n",
      "\n",
      "t= 0.500 TE = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.998 0.987 0.943 0.829 0.624 0.376 0.171 0.057 0.013 0.002 0.000 0.000 0.000 \n",
      "\n",
      "EMQ= 0.0428\n",
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EQUACAO DE ADVECCAO-DIFUSAO: CRANK-NICOLSON\n",
      "\n",
      "JMAX= 21 TMAX= 1.00 T1= 1.00 T2= 0.00\n",
      "BETA= 0.50000 DELTA= 0.50000\n",
      "DELTA_T= 0.02500 DELTA_X= 0.20000\n",
      "S= 0.06250 ALPHA= 0.10000\n",
      "U= 1.00000 C= 0.12500\n",
      "\n",
      "t= 0.000 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.559 0.062 0.004 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.025 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.611 0.124 0.014 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.050 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.657 0.184 0.031 0.004 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.075 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.697 0.242 0.052 0.008 0.001 0.000 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.100 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.733 0.297 0.078 0.015 0.002 0.000 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.125 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.764 0.350 0.107 0.024 0.004 0.001 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.150 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.792 0.401 0.138 0.035 0.007 0.001 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.175 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.816 0.448 0.172 0.050 0.011 0.002 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.200 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.838 0.493 0.207 0.066 0.017 0.004 0.001 0.000 0.000 0.000 0.000 \n",
      "t= 0.225 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.857 0.534 0.243 0.085 0.024 0.006 0.001 0.000 0.000 0.000 0.000 \n",
      "t= 0.250 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.874 0.573 0.280 0.106 0.032 0.008 0.002 0.000 0.000 0.000 0.000 \n",
      "...\n",
      "t= 0.500 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.964 0.833 0.613 0.378 0.198 0.088 0.034 0.012 0.004 0.001 0.000 \n",
      "\n",
      "t= 0.500 TE = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.998 0.987 0.943 0.829 0.624 0.376 0.171 0.057 0.013 0.002 0.000 0.000 0.000 \n",
      "\n",
      "EMQ= 0.0120\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EQUACAO DE ADVECCAO-DIFUSAO: CRANK-NICOLSON\n",
      "\n",
      "JMAX= 21 TMAX= 1.00 T1= 1.00 T2= 0.00\n",
      "BETA= 0.50000 DELTA= 0.43750\n",
      "DELTA_T= 0.02500 DELTA_X= 0.20000\n",
      "S= 0.06250 ALPHA= 0.10000\n",
      "U= 1.00000 C= 0.12500\n",
      "\n",
      "t= 0.000 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.997 0.558 0.066 0.004 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.025 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.994 0.609 0.130 0.016 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.050 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.992 0.654 0.191 0.034 0.004 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.075 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.991 0.692 0.250 0.057 0.009 0.001 0.000 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.100 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.990 0.726 0.306 0.084 0.017 0.003 0.000 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.125 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.989 0.756 0.358 0.114 0.027 0.005 0.001 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.150 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.989 0.782 0.407 0.147 0.040 0.009 0.002 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.175 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.989 0.805 0.454 0.181 0.055 0.013 0.003 0.000 0.000 0.000 0.000 0.000 \n",
      "t= 0.200 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.989 0.826 0.497 0.217 0.072 0.019 0.004 0.001 0.000 0.000 0.000 0.000 \n",
      "t= 0.225 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.989 0.844 0.537 0.253 0.092 0.027 0.007 0.001 0.000 0.000 0.000 0.000 \n",
      "t= 0.250 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.989 0.860 0.574 0.289 0.114 0.036 0.010 0.002 0.000 0.000 0.000 0.000 \n",
      "...\n",
      "t= 0.500 TN = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.994 0.949 0.819 0.610 0.387 0.209 0.097 0.040 0.014 0.005 0.001 0.000 \n",
      "\n",
      "t= 0.500 TE = 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.998 0.987 0.943 0.829 0.624 0.376 0.171 0.057 0.013 0.002 0.000 0.000 0.000 \n",
      "\n",
      "EMQ= 0.0145\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4OBYuXJiprqOjY5Ztpv+D+s8//5jLEhMT+eGHHx76+o8SR04e9p516tQJg8GQoR7A559/jkqlomPHjha93oOio6MzLFEQHx/Pjz/+SM2aNfHx8THHsHv3biIiIsz1EhMT+eabbwgMDDSP8blx40aGtm1tbQkODkZRlAy/v/dr164dzs7OTJ06leTk5AzH0t/brN5rRVGYPXt2hvr3/97d/30/cuQI69evN/9Oi8JHrtyIAqFChQosWbKEF198kUqVKplXKFYUhaioKJYsWYJarc5yfM2DqlSpQtmyZRk3bhzR0dE4OzuzbNmyx7py07JlS/r168cXX3zBqVOn6NChA0ajka1bt9KyZUtGjBhBjRo1GDBgAN988w2xsbE0b96c3bt388MPP9C9e3datmyZ42tMnTqVzp0706RJEwYPHszNmzfNa4DcPx6pefPmvPLKK0ydOpWDBw/Srl07tFotp06d4vfff2f27Nn07Nkz29epXbs2AK+//jrt27dHo9HwwgsvPHa7lrKzsyM0NJQBAwZQv3591q5dy+rVq3n33XfNt5vyIqZ27dpha2tLly5deOWVV7h9+zbffvst3t7eXL58OdN7M2/ePD7++GPKly+Pt7c3rVq1ol27dpQpU4YhQ4bw5ptvotFoWLBgASVKlOD8+fO56q8lcTzOe9alSxdatmzJe++9x9mzZ6lRowbr16/nr7/+YvTo0eZE7VFVrFiRIUOGsGfPHkqWLMmCBQu4cuVKhiTtnXfe4ZdffqFjx468/vrreHh48MMPPxAVFcWyZcvMt5LbtWuHj48PjRs3pmTJkhw/fpwvv/ySzp07ZxozlM7FxYXPP/+cl156ibp169K7d2/c3d05dOgQSUlJ/PDDD1SuXJly5coxbtw4Ll26hIuLC8uWLctyHajp06fTsWNHGjZsyJAhQ7hz5w5z5szB1dVVtikpzJ74/CwhchAZGam89tprSvny5RU7OzvF3t5eqVy5svLqq68qBw8ezFB3wIABiqOjY5btHDlyRGnVqpXi5OSklChRQnn11VeVw4cPK4CycOFCc73cTgVXFEVJS0tTpk+frlSuXFmxtbVVSpQooXTs2FHZt2+fuY5er1cmTZqkBAUFKVqtVvH391fGjx+vJCcn56r/y5YtU6pUqaLodDolODhYWb58eZaxKIppjZDatWsr9vb2irOzs1KtWjXlrbfeUqKjo3N8jbS0NGXkyJFKiRIlFJVKlWmKc27aDQgIUDp37pypbUAZPnx4hrKoqCgFUKZPn24uS//enT59WmnXrp3i4OCglCxZUpkwYUKG9XDyIiZFUZSVK1cq1atXV+zs7JTAwEDl008/VRYsWJBpGndMTIzSuXNnxdnZWQEyTAvft2+fUr9+fcXW1lYpU6aMMnPmzGyngj9uHFmx5D1LSEhQ3njjDcXPz0/RarVKhQoVlOnTp2eYKq0oWX+/cpLet3Xr1inVq1dXdDqdUrlyZeX333/PVPf06dNKz549FTc3N8XOzk6pV6+esmrVqgx1vv76a6VZs2aKp6enec2qN998U4mLizPXyeo9VhTTe9moUSMFUAClXr16yi+//GI+fuzYMaVNmzaKk5OT4uXlpQwdOlQ5dOhQps8ARVGUjRs3Ko0bN1bs7e0VFxcXpUuXLsqxY8dy/b6Igkf2lhJCPHEDBw7kjz/+yHKGnMhaQXjPAgMDqVq1KqtWrbJaDA9KSEigatWq7Nu3Dy8vL2uHIwoIGXMjhBCi0HJ2duapp55i5cqV1g5FFCAy5kYIIUShNGPGDJydndm5c+dDx7SJ4kWSGyGEEIXSqlWriIiIoFatWvTu3dva4YgCRMbcCCGEEKJIkTE3QgghhChSJLkRQgghRJFS7MbcGI1G88Ju+b0HkBBCCCHyhqIoJCQk4Ofnl2lPwQcVu+QmOjo6067FQgghhCgcLly48NDV6otdcpO+pPeFCxdwcXHJ07b1ej3r1683LxFf1BT1/kHR76P0r/Ar6n2U/hV++dXH+Ph4/P39s92a437FLrlJvxXl4uKSL8mNg4MDLi4uRfKHtqj3D4p+H6V/hV9R76P0r/DL7z7mZkiJDCgWQgghRJEiyY0QQgghihRJboQQQghRpEhyI4QQQogiRZIbIYQQQhQpktwIIYQQokiR5EYIIYQQRYokN0IIIYQoUiS5EUIIIUSRIsmNEEIIIYoUqyY3//zzD126dMHPzw+VSsWKFSseek54eDhPPfUUOp2O8uXLs2jRonyPUwghhBCFh1WTm8TERGrUqMHcuXNzVT8qKorOnTvTsmVLDh48yOjRo3nppZdYt25dPkcqhBBCiMLCqhtnduzYkY4dO+a6/vz58wkKCuKzzz4DoEqVKmzbto3PP/+c9u3b51eYuZJw8yon//2HWxeOcGi7HhsbjVXjscTDtyAzSTMYuHXhCP/u0GOjebz+ZbXvmSrXkdx/juk/qrtfq1T3/n/vtVQZ6t+rp8rQBoDBYMBw+QDRB5T82fANVcYg73/cH6Uqm3rwQH0VqFWobGxR2WhQq9SoUKFSqVCjQq0y/f2SXm4wGNCkXCb5ZiSKjS0qlQoVKtR3/5+bDeny5o1Qg7MfqOXOuBAi7xWqXcEjIiJo06ZNhrL27dszevTobM9JSUkhJSXF/Dw+Ph4w7Vqq1+vzLLawtfP4IO0PcAbOLcuzdgscZ+BsEe4fgD1w/E9rR5G/QrO/WqpWFHMSqFMU3A0GPAxGPAwGPI2m/6c/v/9rN6PRog8UxbMChpYfoFTsmHW2+wjSf6fz8ne7oCnqfZT+FX751UdL2itUyU1MTAwlS5bMUFayZEni4+O5c+cO9vb2mc6ZOnUqkyZNylS+fv16HBwc8iy26EvR6EooedaeKN4s+UlSAEV19/+A8TEThfvPT1OpSFSruZiLi1gqRcHNaMTdYDQlPMa7/zc/N5iPeabpcb1xCps/+nPTsTxH/Xpx06nSY8V9vw0bNuRZWwVVUe+j9K/wy+s+JiUl5bpuoUpuHsX48eMZM2aM+Xl8fDz+/v60a9cOFxeXvHuhTp0YqNezYcMG2rZtmy+3NKxNXwj6pygKyt3MQEl/DijK3YThvuemOor5mKKY+hi2aTNNmzdHpdZgMCqkGZUM/89YZsx4zHDf10rGunf0BhKS07idnEZCShrxd/QkpJiexyenkXC3PDXN+Eh916pVVHC3JcTNhipOEOxgpKKdHnt9MkpKCsbkO6TdSeLEoUNULBuIWq9HSU3BmJKCkpKMok/FmJIMqab/K/pUkmOvExtzhluaFG642HAz/eFsw00XDTddtNx0tyXWXoWiUnFLo+GWRsOZXMTb0NabCWePUyoxkqanJmMs3w5Dyw/Au8oj9R8Kx8/o4yrqfZT+FX751cf0Oy+5UaiSGx8fH65cuZKh7MqVK7i4uGR51QZAp9Oh0+kylWu12nz7wcrPtguCotw/vd4GRy34uDlarY/Jd5OghGT93f+nEZ+sNz+Pv6O/lwwl67mVlMqJmAQSktM4diOFYzfu3YZVqaCslyshfq6ElHahcklHLrn5Ubtbp9z3T1EgOhqOHr33OHAUjh2Dux82BhXEOmnuJT9uttws58uNgBLcLOnMTXcdN+2M3FIlczPlFrf1t4lIvcozZUoz1rYMzx3bhDpyPerIDVCzN7QYD27+j/weFuWf0XRFvY/Sv8Ivr/toSVuFKrlp2LAha9asyVC2YcMGGjZsaKWIhMh7dloNdloNJZwzJ+XZURSFi7fucDQ6jiOX4jkaHcfR6HiuJqRw+loip68lsvJQ9N3aNnx56h9CSrlS1c+VED8XQkq54ONil/WAYpUKSpUyPdq1u/9F4dIlOHoUzdGjeB49iuexY6bk5/gtiLiVuS0bG6hYkXN1K/Bh2zT2p53jf2knWV+7A5MS0ih1ch0c/BkO/wH1hkLTseDgYdkbKIQo9qya3Ny+fZvIyEjz86ioKA4ePIiHhwdlypRh/PjxXLp0iR9//BGAV199lS+//JK33nqLwYMHs2nTJn777TdWr15trS4IUSCoVCr8PRzw93CgQ1Vfc/nVhGSORsdzLNqU8By+GMeFW3eIjksmOi6ZDcfuXQn1dLQl2M/FdJXHz4WqpVwJ8HBArc5mDI9KBaVLmx73z1ZUFLh48d5VnvSE59gxSEiAY8cIOHaMhT/CknGtmV0tll03DvOMjQNjO7zPc8c3ozq3HSK+hP2LocloqP8q2ObdGDkhRNFm1eRm7969tGzZ0vw8fWzMgAEDWLRoEZcvX+b8+fPm40FBQaxevZo33niD2bNnU7p0ab777jurTwMXoqDydrbDu5IdLSt5A6Z74ctWrqF0tQacvJrE0UumKzyR125zIzGVraeus/XUdfP5Xk46XmlWlr4NArC3zeX0f5UK/P1Njw4d7pUrCly4YEp0NmxA/cUX9J0eRrMKXnzwXh32p13kfyd/ZL1fPSbV+YZS276AK0cgbBLs/gZavAM1+4KmUF1wFkJYgVU/JVq0aIGiZD8vJKvVh1u0aMGBAwfyMSohijZ7G6gf5EGTivdmHibrDZyISTDfzjp6KY4TMQlcv53C5DXH+fqfMwxrUY7e9ctgp33ENY5UKihTxvTo2BH69YPBgylz8CALB4WyZERjZtdNZlfMbp65foSxLd+gZzKoN0+GuPPw9yjY8SW0mQCVn86z6eNCiKJHVtASQmCn1VDT340+9QOY0qMaf41owpFJ7Zneszr+HvZcv53CR6uO0Xz6Zn6MOEtKmuHxX7RWLdi9G6ZORW2ro++c7Sz7MJKn0nxISkvif7sm8/KVjVwatBI6fAL2HnDjFCztC9+3hbPbHz8GIUSRJMmNECJLWo2a5+r4s2lsC6Y+U41SbvZciU/hw7+O0mJ6OD/vOvfI09bvvYgW3nkHDh2CJk0oczaOhUM38s4OO+zUtuyK2UWP1b1Y6uaB8fUD0Owt0DrAxT2wqBP8/DxcOZo3HRZCFBmS3AghcqTVqHmxXhk2jWvO/7pXxcfFjstxybz35xFazghn6Z7z6A2PmeRUqgRbtsDcuagdnejzzV6WjT/JUykluJN2h493fczL/4zjYt0B8PpBqDME1DZwah3Mawx/vgqx5x/6MkKI4kGSGyFEruhsNPRrEED4my2Y2CWYEs46LsXe4e1lh2n92Rb+2HeRtMdJctRqGDbMNOC4Y0fKXEpk4aubeWezCnuV6SrOMyufYemlcIydZ8Dw3RDSA1Dg0C8wpzbqjR+gTUvIsz4LIQonSW6EEBax02oY2DiIrW+15P3OVfBysuX8zSTG/X6Itp//w4oDlzAYH2MrkjJlYPVqWLwYtYcnfX44zLK3j1E70cN8FWfo+qFctNXBc4tg6GYIagaGVDS75tHm2Ftw/VSe9VcIUfhIciOEeCR2Wg0vNS3LP2+1ZHzHyrg7aIm6nsjopQdp9/kW/j4UjfFRkxyVCvr2Na2N88IL+Mcks2DEP7yzTo+9ypbdMbt5ZuUz/HriV4x+NaH/Sui7HMWrEraGRDThH+dlV4UQhYwkN0KIx+Jga8Mrzcux9e1WvNm+Eq72Wk5fS2TkLwfoOHsraw9ffvQkx9sbfvkF/voLta8ffX45ybJxR6gd78KdtDtM3jXZdBXn9iUo35q0ZxagoEJ9cjVEH8zTfgohCg9JboQQecJJZ8PwluXZ9nZLxrStiLOdDSevJPDaz/vpPGcb64/G5LiuVY66djWNxRk6FP9rqSwYtYN3ViVhjzbjVRyvClx0v7sdS/jUvOucEKJQkeRGCJGnnO20vN66AtvebsXrrSvgpLPh+OV4Xl68j65fbmfTiSuPluS4ucE330BYGOqgsvT54wzL3jxK7VuO5qs4r256lR3erVBUGvgvFC7uzfP+CSEKPkluhBD5wtVey5i2Fdn2dkuGtyyHg62Gw5fiGLxoL92/2sHBC7GP1nCrVnD4MIwdi/+NNBaM2cX4P+OxR8veK3uZlvor/wU/baq7eXKe9UcIUXhIciOEyFduDra82b4yW99qySvNy2Kv1XDoQix9vt3JoUdNcBwcYMYM2LkTddVq9P7rPMvePELVG7akksoXTjamdXBOb4JzEXnaHyFEwSfJjRDiifB00jG+YxX+easljcp5kphqYMDC3Zy68hjr0tStC3v3wkcf4R8LU6cdQW1U+OfqHv6t1tVUR67eCFHsSHIjhHiiSjjr+KZ/HWr4uxGbpKfv97u4cDPp0Ru0tYUPPoADBwjwC6HrtlgAvtQZQGMLZ7fCmS15E7wQolCQ5EYI8cQ56WxYNLAuFUs6cSU+hX7f7+JqQvLjNRoSQtpvvzF07S1s0oxEXDvAnvuv3jzqTC0hRKEjyY0QwircHW1ZPKQ+pd3tOXsjif7f7yYuSf94jQYEkNS8K89uuQXAl+rbKDZ2cGEXRIblQdRCiMJAkhshhNWUdLHj55fqU8JZx4mYBAb/sIek1LTHavNUjx68dECHrd7I/lvHiKiefvXmY7l6I0QxIcmNEMKqAjwdWTykHi52Nuw7d4tXf9pPatqjb8Bp1Onwev8TeoXdBGBO6hUUrSNEH4CTa/MqbCFEASbJjRDC6ir7uLBwUD3stRr++e8abyw9+Fibbyo9ejAkvjL2KUaOJEYRXj193ZspYHyMncuFEIWCJDdCiAKhdoA73/SvjVajYvXhy7y/4vCjb9egUuH56Rz6bDRdvfky/gxGW2e4chhO/J2HUQshCiJJboQQBUbTCiWY/UIt1Cr4ZfcFPg09+eiNVa3KwNI9cEoy8F/aFdZX7WQq3zwVjIa8CVgIUSBJciOEKFA6VfNl6jPVAJi/5TTzwk8/cluuH06h/1bTGjpzrx8lzc4Vrh2Ho3/mSaxCiIJJkhshRIHTq24Z3utUBYBPQ0+wZNf5R2vIzY1+rd7E9XYaZzXxrKnY2lQePhUMjzcrSwhRcElyI4QokIY2K8vwluUAeG/FYf4+FP1I7TgNepXBh3QAfHXtX/T2HnAjEg7/lmexCiEKFkluhBAF1rh2lejboAyKAm8sPUj4yauWN6LR8EL/z/GM03PJLpUVpeuZyrd8CobHXDRQCFEgSXIjhCiwVCoVH3WtStcafqQZFV79aR97z960uB2HJi0ZGhMIwNexx0lxLAG3zsLBn/M2YCFEgSDJjRCiQFOrVXz2fA1aVipBst7IoEV7OBodZ3E7zw2bj8/NNK44KfzhUtFUuGU6pKXkccRCCGuT5EYIUeBpNWq+6lObeoEeJCSnMWDBbqKuJ1rUhm3pQF6xaQLAt/pzJDmWhPiLsP/H/AhZCGFFktwIIQoFe1sN3w2sQ7CvC9dvp9L3u11cjrtjURvdXppF6VsKN5zV/KIqaSr8ZwboLWtHCFGwSXIjhCg0XOy0/DikHmW9HLkUe4d+3+/mZmJqrs/X2jsyrMwLACxwuEGCow/cjoG9C/MrZCGEFUhyI4QoVLycdCx+qT6+rnZEXr3NwIW7SUjO/aynTt3HExRvQ7yjhp9u25kKt82EVMtucwkhCi5JboQQhU4pN3sWD6mPh6Mt/16MY+iPe0nW525LBY1aw/A6owH40UdPrJ03JF6D3d/mY8RCiCdJkhshRKFU3tuJHwfXw0lnw84zNxmx5AB6Q+52/G7boB+Vkl247aBh0Y27m3Nunw0pCfkYsRDiSZHkRghRaFUt5cp3A+qgs1Gz8fgV3v3zKMZcbCSuVqkZ0fJ9AJZU0HFd4wl3bsLO+fkcsRDiSZDkRghRqDUo68lXfZ7CRq1ixaHL/HlWjaI8PMNpXqED1fDljk7N9zfu7jMVMQfuxOZvwEKIfCfJjRCi0GtdpSSfPV8DlQr+iVGz6nDMQ89RqVSMaDMRgN+qOhNjcIHkOIiYm8/RCiHymyQ3QogioVvNUoxoURaAzzacytUA44Z+DantUIlUrZpvE+5e7dk5D5Is3+JBCFFwSHIjhCgyhjYJws1W4VJsMot2nH1ofZVKxYim7wCwvJobF5PsITXBNLhYCFFoSXIjhCgy7G01dC5jmjE1d1Nkrhb4q+NTh0ZedUizUTE/TWsq3P0N3H6EHciFEAWCJDdCiCKljpdCsK8zCSlpzN74X67OGVFvDAB/V3MlKlYL+iS5eiNEISbJjRCiSFGr4J0Opl2/f951ntPXbj/0nGolqtGidHOMahXztA6mwj3fQfzl/AxVCJFPJLkRQhQ5Dct60rqyN2lGhU/XnsjVOSNqjQRgbVVXTt5QQ1qyaVsGIUShI8mNEKJIGt+pMhq1ivXHrrDzzI2H1q/kUYn2ge0B+MrR1VS4bxHEXsjHKIUQ+UGSGyFEkVTe25kX6/kDMGXNcYy5WLp4WI1hqFGzqYozR6+rwZAKW2fkd6hCiDwmyY0Qosga3aYiTjob/r0Yx9//Rj+0flm3sjxd7mkA5ri6mQoP/AQ3o/IxSiFEXpPkRghRZHk56XitRTkApoWezNXCfq9WfxUblYbtFZ3Yf10DxjT4Z3p+hyqEyEOS3AghirQhTYLwdbXjUuwdFm4/+9D6/i7+dK/QA4A5nm4oAId+geuR+RqnECLvSHIjhCjS7LQa3mxfCYCvNkdy43bKQ895pforaNVa9pZ1ZNc1G1CMsOWT/A5VCJFHJLkRQhR53WuWomopF9PCfmGnHlrfx9GH5ys9D8AcH3fT1ZvDf8DV4/kbqBAiT0hyI4Qo8tRqFe92qgLkfmG/l6q9hJ3Gjn/L2LP1uhZQYPPUfI5UCJEXJLkRQhQLjcp50aaKNwajwie5WNjPy96LF6u8CMCX/p4YAY6vgpSE/A1UCPHYJLkRQhQb73SsgkatYkMuF/YbHDIYR60jx31sCTPoAAOc2pj/gQohHoskN0KIYqO8txO965UBYPLqhy/s52bnRr/gfgDMLeGOAWDbj/kcpRDicUlyI4QoVka1qYCTzobDl+JYeejhC/v1C+6Hi60Lp11tWevoANHbwZD2BCIVQjwqSW6EEMVKxoX9Tjx0YT8XWxcGVR0EwHcuLqBOgfMR+R6nEOLRSXIjhCh2hjQJws/Vjui4ZBZsf/jWCr0q9UKr1nLazpbTWhsIX/AEohRCPCpJboQQxY6dVsObHdIX9jvN9Ycs7Ods60xDv4YAbHR0gDMbQXn4RpxCCOuwenIzd+5cAgMDsbOzo379+uzevTvH+rNmzaJSpUrY29vj7+/PG2+8QXJy8hOKVghRVHSrUYpqpVy5nZLG7I0PX9ivTZk2AITZ24MmHq7/l98hCiEekVWTm6VLlzJmzBgmTJjA/v37qVGjBu3bt+fq1atZ1l+yZAnvvPMOEyZM4Pjx43z//fcsXbqUd9999wlHLoQo7O5f2G/J7vNEXs15Yb8W/i1Qq9Qct9NxwUYDG795EmEKIR6BVZObmTNnMnToUAYNGkRwcDDz58/HwcGBBQuyvp+9Y8cOGjduTO/evQkMDKRdu3a8+OKLD73aI4QQWWlYzpM2VUreXdgv560V3O3cqVOyDgCbHBzg2KonEaIQ4hHYWOuFU1NT2bdvH+PHjzeXqdVq2rRpQ0RE1jMRGjVqxE8//cTu3bupV68eZ86cYc2aNfTr1y/b10lJSSEl5d799Pj4eAD0ej16vT6PeoO5zfv/X9QU9f5B0e+j9C+zcW3Ls/nkVTYev8rWk1doUNYj27otS7dkd8xuNjo60D8uhrRb0eBU4rHjtoR8Dwu3ot4/yL8+WtKeSlGsMyouOjqaUqVKsWPHDho2bGguf+utt9iyZQu7du3K8rwvvviCcePGoSgKaWlpvPrqq8ybNy/b15k4cSKTJk3KVL5kyRIcHBwevyNCiELvjzNqtl5RU9pRYWw1A2pV1vXijfFMi58GwKbzF7mh7cLJKs8+wUiFKL6SkpLo3bs3cXFxuLi45FjXalduHkV4eDhTpkzhq6++on79+kRGRjJq1Cj+97//8cEHH2R5zvjx4xkzZoz5eXx8PP7+/rRr1+6hb46l9Ho9GzZsoG3btmi12jxtuyAo6v2Dot9H6V/W6iem0vrzrVxMNJBWqibda/plW3fturUcvnGYMAcHesX+S7lO3+dF6Lkm38PCraj3D/Kvj+l3XnLDasmNl5cXGo2GK1euZCi/cuUKPj4+WZ7zwQcf0K9fP1566SUAqlWrRmJiIi+//DLvvfceanXmIUQ6nQ6dTpepXKvV5tsPVn62XRAU9f5B0e+j9C8jHzctw1uWZ1roSWZujKRLzdLYaTVZ1m0b2JbDNw6z0dGBF25FoSUNtPZ5FXquyfewcCvq/YO876MlbVltQLGtrS21a9cmLCzMXGY0GgkLC8twm+p+SUlJmRIYjcb0AWSlu2tCiCJicOMgSrnZczkume+3Zb+wX/qU8L12OmJt1bBZ9poSoqCx6mypMWPG8O233/LDDz9w/PhxXnvtNRITExk0yLTUef/+/TMMOO7SpQvz5s3j119/JSoqig0bNvDBBx/QpUsXc5IjhBCPwk6r4c32poX95oVnv7Cfv4s/ldwrYVCpCHewh+2S3AhR0Fh1zE2vXr24du0aH374ITExMdSsWZPQ0FBKliwJwPnz5zNcqXn//fdRqVS8//77XLp0iRIlStClSxcmT55srS4IIYqQrjX8+H5bFIcvxTFr43983L1alvVaB7Tm5K2TbHR0oPvVY2A0Qha3xYUQ1mH138YRI0Zw7tw5UlJS2LVrF/Xr1zcfCw8PZ9GiRebnNjY2TJgwgcjISO7cucP58+eZO3cubm5uTz5wIUSRo1areK+zaWG/X3ZfIPJqQpb12pZpC8AOOzsSHRXY8ecTi1EI8XBWT26EEKIgaVDWk7bBpoX9pq45kWWdcm7lCHQJRK9WsdXBXlYrFqKAkeRGCCEe8E7HymjUKsJOXGVH5PVMx1UqFa3LtAZgg4M9xO570iEKIXIgyY0QQjygXAkn+tQvA8DkNccxGjPPxmwTYJo1tdXBnmT3NNgf/iRDFELkQJIbIYTIwqjWFXDW2XA0Op4/D1zKdDzEMwQfRx/uqNVE2NvB6jlWiFIIkRVJboQQIgueTjqGtSwPwIz1J7mTashwXKVSmde82ehgD5e3P/EYhRBZk+RGCCGyMahxoHlhvwXbMy/slz7uJtzBHr1nEhw/+IQjFEJkRZIbIYTIhp1Ww1sdTAv7fbU5kmsJGRf2q+VdCw87D+I1GvY42sOfs6wQpRDiQZLcCCFEDrpU96N6aVcSUw3M2vhfhmMatYZWZVoBEOZoD1EbrRGiEOIBktwIIUQO1GoV73YyLez3+96L3HhgW4b0cTdhDg4YPOIhKvKJxyiEyEiSGyGEeIgGZT2pXtqVVIORpXsvZDhWz6cezlonbthoOORmB79/YaUohRDpJLkRQohc6NsgAICfd57HcN+6N1qNlhb+LQHY6GgPx1ZZJT4hxD2S3AghRC50reGHm4OWS7F32HziaoZjrQNMs6bCHBxQXK7BhQtZNSGEeEIkuRFCiFyw02p4vo4/AD/uPJfhWCO/Rthr7IjW2nDMRwe/z7dGiEKIuyS5EUKIXOpbPwCVCv757xpR1xPN5fY29jQp3RSAMEcHOPCHtUIUQiDJjRBC5FoZTwdaVCwBwE8PXL25t1qxA+guweXLTzw+IYSJJDdCCGGB/g0DAfh974UMWzI0K90MrVpLlK2WM4G28MePVopQCCHJjRBCWKB5xRL4e9gTn5zGXwfvbajpZOtEA98GAGxwcoSIn60VohDFniQ3QghhAbVaRd/6pmnhP0acQ1HuTQtvG9AWMM2aQjkNV69m2YYQIn9JciOEEBZ6vo4/Ohs1xy7Hs/98rLm8hX8L1Kg4rrPlYkUdLP/NekEKUYxJciOEEBZyd7SlSw0/ABZHnL1XbudOHZ86AIS5OsDmH6wRnhDFniQ3QgjxCPo3NN2aWnM4huv37TfVOn3WlKMDJB+FGzesEp8QxZkkN0II8Qiql3ajhr+bab+pPfdWJG5dxrRa8UE7Hdeq2MKKP60VohDFliQ3QgjxiPqb95s6R5rBCEBJx5JU96oGwKYSjhAqt6aEeNIkuRFCiEfUubov7g5aouOSCbtvv6k2d2dNbXB0gNh9cOuWtUIUoliS5EYIIR6RnVbD83VN+03dv2Jx+mrFe+10xFa2gb//tkp8QhRXktwIIcRjSN9vauup65y5dhsAfxd/KrmVx6BSER7gBCsXWzlKIYoXSW6EEOIx+Hs40KqSNwCL77t60zqwHXB31tTl7RAfb5X4hCiOJLkRQojH1O/utPA/9l0kKTUNuHdraoe9HYkV1bBqldXiE6K4keRGCCEeU7MKJQj0dCAhOY0VB6IBKO9WnkBHP/QqFVsrO8Gfv1g5SiGKD0luhBDiManVKvo2SN9v6iyKoqBSqWgd1BGAjU4OcHoT3L5tzTCFKDYkuRFCiDzQs3ZpdDZqTsQksPecaep3mwDTral/HOxJLgesWWPFCIUoPiS5EUKIPODmYEu3mun7TZkGFod4huCj8+COWk1EDSf4QzbSFOJJkORGCCHySP+GgQCsPXKZawkpqFQq2qTfmnJzgMOhkJRkxQiFKB4kuRFCiDxStZQrtcq4oTco/Lr7PACt796aCnewRx+UBqGh1gxRiGJBkhshhMhD6buFL9l9njSDkVretfCwcSJeo2HPU87wx+9WjlCIok+SGyGEyEOdqvni6WjL5bhkNh6/gkatodXdqzdhJR0hYhUkJ1s5SiGKNkluhBAiD+lsNPS6u9/Uj3cHFrcJ6gBAmIMDhjKpsH691eITojiQ5EYIIfJY7/plUKtgx+kbRF5NoJ5PPZzVOm7YaDhUxwn++MPaIQpRpElyI4QQeay0uwOtKpcE4Ked59FqtDQv3RSAjQFOEPYXpKRYM0QhijRJboQQIh+kDyxetu8iiSlptCn3NGC6NaX43IGwMGuGJ0SRJsmNEELkgyblvQjyciQhJY0/D1yikV8j7FU2RGttOF7HQW5NCZGPJLkRQoh8cP9+U4sjzmGnsaOJd20ANlZ2gVV/gl5vzRCFKLIkuRFCiHzSs3Zp7LUaTl5JYHfUTdpU7AHc3UjT4zZs3mzlCIUomiS5EUKIfOJqr6V7LdN+Uz/uPEez0s3RoibKVsuZOvawbJmVIxSiaJLkRggh8lH6ral1R2JISrahgUcVADZUd4EVyyEtzZrhCVEkSXIjhBD5KMTPldoB7qQZFX7ZfYE2FZ8FIMzNEWxvwdatVo5QiKJHkhshhMhn9/abOkeT0q1QA8d1tlysbSezpoTIB5LcCCFEPutQ1QcvJ1uuxKew90wqdZwDAQir7QLLl4PBYN0AhShiJLkRQoh8prPR8ELdMgD8GHGW1hXuzprydoLUq7BjhzXDE6LIkeRGCCGegPT9pnaeuUmQSwsADtrpuFZTJ7emhMhjktwIIcQT4OdmT5sqpv2m1h5Morqd6etN9V1MU8KNRmuGJ0SRIsmNEEI8If0bBgKwfP8lmgV0AGCjvxPcjIadO60YmRBFi82jnpiUlMT58+dJTU3NUF69evXHDkoIIYqixuU9KVvCkTPXEklJaQz8wB57O2Kr2uK2ejU0amTtEIUoEiy+cnPt2jWefvppnJ2dCQkJoVatWhkeQgghsqZSqeh3d1G/v/elUknrhkGlIryxK6xfb+XohCg6LE5uRo8eTWxsLLt27cLe3p7Q0FB++OEHKlSowMqVK/MjRiGEKDKerV0aB1sNp67epqprQwDCyjnBwX1w44aVoxOiaLA4udm0aRMzZ86kTp06qNVqAgIC6Nu3L9OmTWPq1Kn5EaMQQhQZLnZautcqBcDZm6bkZoejPYkVbGDTJmuGJkSRYXFyk5iYiLe3NwDu7u5cu3YNgGrVqrF///68jU4IIYqg9FtTW4/b4q+yJ1WtYmsTF9iwwcqRCVE0WJzcVKpUiZMnTwJQo0YNvv76ay5dusT8+fPx9fW1OIC5c+cSGBiInZ0d9evXZ/fu3TnWj42NZfjw4fj6+qLT6ahYsSJr1qyx+HWFEMJaqvi6UDfQHYMRAtWmjTTDKzubxt0oipWjE6Lwszi5GTVqFJcvXwZgwoQJrF27ljJlyvDFF18wZcoUi9paunQpY8aMYcKECezfv58aNWrQvn17rl69mmX91NRU2rZty9mzZ/njjz84efIk3377LaVKlbK0G0IIYVX97k4LPxRdB4Ctbvak3T4PkZFWjEqIosHiqeB9+/Y1f127dm3OnTvHiRMnKFOmDF5eXha1NXPmTIYOHcqgQYMAmD9/PqtXr2bBggW88847meovWLCAmzdvsmPHDrRaLQCBgYE5vkZKSgopKSnm5/Hx8QDo9Xr0er1F8T5Ment53W5BUdT7B0W/j9K/gqN1RU+8nGy5dDOAUt4q4jUaDjZxplZoKMYcPtcKUx8fhfSv8MuvPlrSnkpRcn8NVK/XU7lyZVatWkWVKlUeKbh0qampODg48Mcff9C9e3dz+YABA4iNjeWvv/7KdE6nTp3w8PDAwcGBv/76ixIlStC7d2/efvttNBpNlq8zceJEJk2alKl8yZIlODg4PFYfhBDicaw5r2bdJTW1/T/jP6drDDwbS++1pdg9fry1QxOiwElKSqJ3797ExcXh4uKSY12LrtxotVqSk5MfK7h0169fx2AwULJkyQzlJUuW5MSJE1mec+bMGTZt2kSfPn1Ys2YNkZGRDBs2DL1ez4QJE7I8Z/z48YwZM8b8PD4+Hn9/f9q1a/fQN8dSer2eDRs20LZtW/OVpaKkqPcPin4fpX8Fy1PxyWz8bCtX4p4Cp3Vs8XVkzH/H6dSuHdhk/fFc2PpoKelf4ZdffUy/85IbFt+WGj58OJ9++infffcdNtn88uUXo9GIt7c333zzDRqNhtq1a3Pp0iWmT5+ebXKj0+nQ6XSZyrVabb79YOVn2wVBUe8fFP0+Sv8KBn9PLe2CS7L2WAPclFCidFoulNdT5sCBh65WXFj6+Kikf4VfXvfRkrYszk727NlDWFgY69evp1q1ajg6OmY4vnz58ly14+XlhUaj4cqVKxnKr1y5go+PT5bn+Pr6otVqM9yCqlKlCjExMaSmpmJra2thb4QQwrp61fVn7ZEY/JMdOWufRHgjF/pv2CBbMQjxGCyeLeXm5sazzz5L+/bt8fPzw9XVNcMjt2xtbalduzZhYWHmMqPRSFhYGA0bNszynMaNGxMZGYnxvt1z//vvP3x9fSWxEUIUSk0rlMDP1Q4loQIAW8o5wQbZikGIx2HxlZuFCxfm2YuPGTOGAQMGUKdOHerVq8esWbNITEw0z57q378/pUqVMq98/Nprr/Hll18yatQoRo4cyalTp5gyZQqvv/56nsUkhBBPkkatomcdf77/pxlq70Psd7Yj/vxeXOLjIY/HBQpRXDzZQTMP6NWrF9euXePDDz8kJiaGmjVrEhoaah5kfP78edTqexeX/P39WbduHW+88QbVq1enVKlSjBo1irfffttaXRBCiMf2XO3SzNlUivKpamJsjexo6kSH8HDo2tXaoQlRKFmc3AQFBaFSqbI9fubMGYvaGzFiBCNGjMjyWHh4eKayhg0bsnPnToteQwghCjJ/Dwcal/MiJcGPGI+LhFdzpsP69ZLcCPGILE5uRo8eneG5Xq/nwIEDhIaG8uabb+ZVXEIIUaw8X9efGX/VA4+LbPV2IG3xeuteWheiELP4d2fUqFFZls+dO5e9e/c+dkBCCFEctQsuyQcr6uNsWGZardjnInXOn4cyZawdmhCFjsWzpbLTsWNHli1bllfNCSFEsWKn1dCjVgBlEk2DiP+pK7uEC/Go8iy5+eOPP/Dw8Mir5oQQotjpVdeflNshAIQHOcH6dVaOSIjCyeLbUrVq1cowoFhRFGJiYrh27RpfffVVngYnhBDFSRVfF9ROXdAoEUTZaTl/JpwyRiOo8+zvUCGKBYuTm27dumVIbtRqNSVKlKBFixZUrlw5T4MTQoji5pl6Nfhzvw0nHQyE11LR/8ABqF3b2mEJUahYnNxMnDgxH8IQQggB0KWGHxu2BYLDacJC3ExbMUhyI4RFLL7WqdFouHr1aqbyGzduZNjzSQghhOVc7LSUKmFa3+ZfVy3x/6yxckRCFD4WJzeKomRZnpKSIvs7CSFEHujauCv+qUbSVCrCHU9BUpK1QxKiUMn1bakvvvgCAJVKxXfffYeTk5P5mMFg4J9//pExN0IIkQfqlfUkYE0JLtjeYF1td7r+8w906GDtsIQoNHKd3Hz++eeA6crN/PnzM9yCsrW1JTAwkPnz5+d9hEIIUcyoVCqqerdmW/JvHPBWk7YhFBtJboTItVwnN1FRUQC0bNmS5cuX4+7unm9BCSFEcfdM65f5ZeWvxGnUrLu8g87WDkiIQsTiMTebN2+WxEYIIfKZr1dJqqXYAbCukhEuX7ZyREIUHo+0L9vFixdZuXIl58+fJzU1NcOxmTNn5klgQghR3NX1rMe2O9s47ptK6vqN2A7oZ+2QhCgULE5uwsLC6Nq1K2XLluXEiRNUrVqVs2fPoigKTz31VH7EKIQQxVKPRkOZs3ErMTr4Y/8OektyI0SuWHxbavz48YwbN47Dhw9jZ2fHsmXLuHDhAs2bN+e5557LjxiFEKJYci9Vi+opRgA2uZ+HbJbiEEJkZHFyc/z4cfr37w+AjY0Nd+7cwcnJiY8++ohPP/00zwMUQohiS6WiiXNFAGJKXuPy7kNWDkiIwsHi5MbR0dE8zsbX15fTp0+bj12/fj3vIhNCCEH7ENMV8Yv2SSxZv9PK0QhROFic3DRo0IBt27YB0KlTJ8aOHcvkyZMZPHgwDRo0yPMAhRCiOCtTpQdBqXoMKhWblCMYjXJrSoiHsXhA8cyZM7l9+zYAkyZN4vbt2yxdupQKFSrITCkhhMhrtg40w40oElG5H2Pnf1esHZEQBZ5FyY3BYODixYtUr14dMN2iklWJhRAif7Us24YfLv7Fdcdr/LbxEO3KWjsiIQo2i25LaTQa2rVrx61bt/IrHiGEEA+oUa0frgYDiRrYHn+CpDRrRyREwWbxmJuqVaty5syZ/IhFCCFEFmxKVKRJsmmsjbdLBHuvqawckRAFm8XJzccff8y4ceNYtWoVly9fJj4+PsNDCCFEHlOpaOFeFYBUpyh2xSgosuaNENmyeEBxp06dAOjatSsq1b2/HhRFQaVSYTAY8i46IYQQADSu0Reb3e9xw1bPbcMtjkYnUCvQ09phCVEgWZzcbN68OT/iEEIIkQPn8m2p/c84dtnb4eu8i9/315LkRohsWJzcNG/ePD/iEEIIkRNbB5oZ3dnFHRycD/P3vzF88HRV7G011o5MiALH4jE3AFu3bqVv3740atSIS5cuAbB48WLz4n5CCCHyXotKTwNw3T6OhNTbrD1y2coRCVEwWZzcLFu2jPbt22Nvb8/+/ftJSUkBIC4ujilTpuR5gEIIIUzK1Oxzd7VicHI8zNI9F6wdkhAF0iPNlpo/fz7ffvstWq3WXN64cWP279+fp8EJIYS4j2c5mieZdgkv4bybXVE3OXs90cpBCVHwWJzcnDx5kmbNmmUqd3V1JTY2Ni9iEkIIkY3mzqYp4UmOFwEDv+2VqzdCPMji5MbHx4fIyMhM5du2baNsWVkTXAgh8lPV+oNwMRhIslHQ2J/jj30XSTMYrR2WEAWKxcnN0KFDGTVqFLt27UKlUhEdHc3PP//MuHHjeO211/IjRiGEEHdpgprTNCkZgNLOO7makEL4yWtWjkqIgsXiqeDvvPMORqOR1q1bk5SURLNmzdDpdIwbN46RI0fmR4xCCCHSae2pF2/HamewcTgGwNK9F2gTXNLKgQlRcFic3KhUKt577z3efPNNIiMjuX37NsHBwTg5OeVHfEIIIR5QxqEWNsohrtqnodJeZ9MJFVcTkvF2trN2aEIUCI+0zg2Ara0tzs7O+Pr6SmIjhBBP0C2/5jyVbFqGo4LnIQxGhWX7Llk5KiEKDouTm7S0ND744ANcXV0JDAwkMDAQV1dX3n//ffR6fX7EKIQQ4j5JupI0v20aROyi3QnA73svyGaaQtxlcXIzcuRIvvnmG6ZNm8aBAwc4cOAA06ZN4/vvv+f111/PjxiFEEI8oJldMACnHeJxsEvlzPVE9py9ZeWohCgYLB5zs2TJEn799Vc6duxoLqtevTr+/v68+OKLzJs3L08DFEIIkVnphoMI2v8uUbZaagecY+vJCizdc4F6QR7WDk0Iq7P4yo1OpyMwMDBTeVBQELa2tnkRkxBCiIdQgtvSPPEOAM76MADWHL5MfLIMDxDC4uRmxIgR/O9//zPvKQWQkpLC5MmTGTFiRJ4GJ4QQIhs2djRPdgPgX/VZypaw447ewN+Hoq0blxAFgMW3pQ4cOEBYWBilS5emRo0aABw6dIjU1FRat27NM888Y667fPnyvItUCCFEBjUD2uNyZxVxWg3PVk3kzGYNv+25QJ/6AdYOTQirsji5cXNz49lnn81Q5u/vn2cBCSGEyB2b1kNp+vsfrHZyRJu0CRt1Ow5djONETDyVfVysHZ4QVmNxcrNw4cL8iEMIIYSlvCvQItbAaifYfWM7bar0I/RoDEv3XGBClxBrRyeE1TzyIn5CCCGsr5G2CjaKwhltMq2qaQD488AlUtIMVo5MCOuxOLm5ceMGw4cPJzg4GC8vLzw8PDI8hBBCPDku9XubVyu+Y9yLj4sdsUl61h+9YuXIhLAei29L9evXj8jISIYMGULJkiVRqVT5EZcQQojcaPQczXe/y257O7Ye/YPn6sxmzqZIftt7gS41/KwdnRBWYXFys3XrVrZt22aeKSWEEMKKtPa0iHNiujfsM1xibHV35myCbZHXuXAzCX8PB2tHKMQTZ/FtqcqVK3Pnzp38iEUIIcQjKFO6JYGpetJUcC55P43KeaIo8Pu+i9YOTQirsDi5+eqrr3jvvffYsmULN27cID4+PsNDCCHEE9buFVokmf7o3HJ8Jb3qmpbn+GPvBQxG2UxTFD8WJzdubm7Ex8fTqlUrvL29cXd3x93dHTc3N9zd3fMjRiGEEDkpW5PmN0zbLmy9spPWVbxwtdcSHZfMtsjrVg5OiCfP4jE3ffr0QavVsmTJEhlQLIQQBURNYzlcDDeI08DJ2CN0r+nHDxHn+G3PBZpXLGHt8IR4oixObo4cOcKBAweoVKlSfsQjhBDiEdjUeoamMXNY7eTIlgvhPF/3JX6IOMf6YzFcjU/G28XO2iEK8cRYfFuqTp06XLhwIT9iEUII8ag6DKF5fBIAW/5bRYifK3UC3NEbFH7aec7KwQnxZFl85WbkyJGMGjWKN998k2rVqqHVajMcr169ep4FJ4QQIpccXWh8zQ4bX4Uz+utciL/AoMZB7D13i592nWdYy/LYaTXWjlKIJ8Li5KZXr14ADB482FymUqlQFAWVSoXBIEt+CyGENbh4N+Kp5D3strcj/GI4L4b0wc/Vjui4ZFYejOb5urLJsSgeLL4tFRUVlelx5swZ8/8fxdy5cwkMDMTOzo769euze/fuXJ3366+/olKp6N69+yO9rhBCFCktBtE8fUp41HpsNGr6NwoEYMH2KBRFpoWL4sHi5CYgICDHh6WWLl3KmDFjmDBhAvv376dGjRq0b9+eq1ev5nje2bNnGTduHE2bNrX4NYUQokiq34bmMckA7Lt+iITUBF6o64+9VsOJmAQiztywcoBCPBmPtCv44sWLady4MX5+fpw7ZxqoNmvWLP766y+L25o5cyZDhw5l0KBBBAcHM3/+fBwcHFiwYEG25xgMBvr06cOkSZMoW7bso3RBCCGKHpWKgNQyptWKUdgevR03B1uerV0KgAXbzlo3PiGeEIvH3MybN48PP/yQ0aNHM3nyZPMYGzc3N2bNmkW3bt1y3VZqair79u1j/Pjx5jK1Wk2bNm2IiIjI9ryPPvoIb29vhgwZwtatW3N8jZSUFFJSUszP01dR1uv16PX6XMeaG+nt5XW7BUVR7x8U/T5K/wq/h/VRXbE9LZKWsMhWy+Zzm2hdqjV96/nz087zhJ24QuSVOAIK8H5TRf17WNT7B/nXR0vaUykW3oQNDg5mypQpdO/eHWdnZw4dOkTZsmU5cuQILVq04Pr13K+GGR0dTalSpdixYwcNGzY0l7/11lts2bKFXbt2ZTpn27ZtvPDCCxw8eBAvLy8GDhxIbGwsK1asyPI1Jk6cyKRJkzKVL1myBAeHgvsLLoQQj8LuxlU8L73LoDI+OCq2vOX2HhqVhvnH1RyPVdPcx8gzQUZrhymExZKSkujduzdxcXG4uLjkWNfiKzdRUVHUqlUrU7lOpyMxMdHS5iySkJBAv379+Pbbb/Hy8srVOePHj2fMmDHm5/Hx8fj7+9OuXbuHvjmW0uv1bNiwgbZt22aaIl8UFPX+QdHvo/Sv8MtVH4d9gEspA/GaVErVLcVT3k/hXOE6g3/cz95bWj4f0hxnO4s//p+Iov49LOr9g/zroyX7V1r80x0UFMTBgwczDR4ODQ2lSpUqFrXl5eWFRqPhypUrGcqvXLmCj49PpvqnT5/m7NmzdOnSxVxmNJr+ArGxseHkyZOUK1cuwzk6nQ6dTpepLa1Wm28/WPnZdkFQ1PsHRb+P0r/CL8c+ejxFkzsnWOPkyPaY7dQvVZ+WVXwoV8KR09cSWXEohsFNgp5swBYq6t/Dot4/yPs+WtJWrgcUf/TRRyQlJTFmzBiGDx/O0qVLURSF3bt3M3nyZMaPH89bb71lUaC2trbUrl2bsLAwc5nRaCQsLCzDbap0lStX5vDhwxw8eND86Nq1Ky1btuTgwYP4+8saDkIIQaPe5l3Cw8+FmdchG9TYlNAs2nFWdgsXRVqur9xMmjSJV199lZdeegl7e3vef/998/0vPz8/Zs+ezQsvvGBxAGPGjGHAgAHUqVOHevXqMWvWLBITExk0aBAA/fv3p1SpUkydOhU7OzuqVq2a4Xw3NzeATOVCCFFstX2GxmHDsfVSiEo4z/Gbxwn2DOaZp0oxfd1Jzt9MYtOJq7QNLmntSIXIF7lObu4fd9ynTx/69OlDUlISt2/fxtvb+5ED6NWrF9euXePDDz8kJiaGmjVrEhoaSsmSpl+68+fPo1Y/0ox1IYQonhwccEksSaukJEKdHFkRuYJgz2AcbG14oZ4/X285w4JtUZLciCLLoqxBpVJleO7g4PBYiU26ESNGcO7cOVJSUti1axf169c3HwsPD2fRokXZnrto0aJsZ0oJIUSxVbYl3W6bJnmsObOGVEMqAP0bBqJRq4g4c4Pjl3M/QFOIwsSi5KZixYp4eHjk+BBCCFEAtBtMw+tJeKelEZcaR/iFcABKudnTIcQ0YWPh9ijrxSdEPrJottSkSZNwdXXNr1iEEELkldp10Xyqo2vpRL5zc2VF5AraBbYDYHCTQFYfvsyKg9G81aEyXk6ZZ5QKUZhZlNy88MILeXIbSgghRD5Tq6FMB7olrOU7N1e2R2/natJVvB28eaqMOzVKu3LoYhxLdp3n9dYVrB2tEHkq17elHhxvI4QQooDr/jKBl1KomZyCUTGy6swqgAzTwhfvPEdqmqxYLIqWXCc3Fu7SIIQQwtoaN4YzdnRPuA3AisgV5s/yTtV88XbWcS0hhdWHo60ZpRB5LtfJjdFolFtSQghRmGg0ULEL7RMSsTMaiYqL4t/r/wJga6Omf0PTSvMLt5+VP2BFkSILyAghRFH2TH+cIvW0ubti8V+Rf5kPvVivDLY2av69GMe+c7esFaEQeU6SGyGEKMqaNoUoe/OtqdCoUJLTkgHwdNLRo2YpABbItHBRhEhyI4QQRZmNDVTrQd3YZPz0aSToEwg7f28/v0FNAgEIPRLDxVtJVgpSiLwlyY0QQhR1PXuhPqqn690Vi1dErjAfquzjQqNynhgVWBxxzkoBCpG3JLkRQoiirkULOGNPt9umW1O7Lu/i8u3L5sOD704L/2X3eZJS06wRoRB5SpIbIYQo6rRaaNCd0ldSqXsnGQWFladXmg+3quxNgKcD8clpLNt/yYqBCpE3JLkRQojioOdzcEhv3kzzr9N/mad/q9UqBjYKBEz7TRmNMi1cFG6S3AghRHHQujVE2dE2MQkHo5ELCRfYf3W/+fBzdfxx1tlw5loiW05ds2KgQjw+SW6EEKI4sLWFVt1xOKOnfaJpVtT9A4uddDY8V8cfMC3qJ0RhJsmNEEIUFz17mm5NJZhuTa07u44k/b3p3wMbBaJSwT//XSPyaoK1ohTisUlyI4QQxUXbtnDBjqcSkimj13Mn7Q4bzm0wHy7j6UDbKiUBuXojCjdJboQQoriws4MOXVAdu3f15v5bU4B5t/Bl+y8Sm5T6pCMUIk9IciOEEMXJ3VtTXW8nolIU9l7Zy4WEC+bDDcp6UMXXhWS9kV92X8ihISEKLkluhBCiOOnQAa7Z4XNDT4Nk0x5T9695o1KpGNQ4EIAfI86iNxitEaUQj0WSGyGEKE7s7aFzZ/hXT/e7t6ZWRq7EqNxLYrrW8MPT0ZbLccmsOxpjrUiFeGSS3AghRHFz99ZUq6Q7OBuNRCdGsztmt/mwnVZDnwYBACzYJruFi8JHkhshhChuOnaEOzrszuvpkL5iceRfGar0bVAGrUbF/vOxHLwQa4UghXh0ktwIIURx4+RkGntzSE/3u8nNxnMbSUi9t7aNt7MdXar7AaYtGYQoTCS5EUKI4qhnTziip1pSKkGpepINyaw7uy5DlfRp4av/vcyV+GRrRCnEI5HkRgghiqOnnwbFFtVxPd1v3wYy35qqVtqVuoHupBkVFkecs0aUQjwSSW6EEKI4cnGB9u3hkJ4utxNRKwoHrx0kKi7jLajBd6/e/LzrHMl6gzUiFcJiktwIIURx1bMnnE6jxG1ofMd02+nBqzdtg0tSys2eW0l6/jp4yRpRCmExSW6EEKK46tIFbLRw4A7dE0y3pv4+/TcG470rNDYaNQMapU8LP4uiKFYJVQhLSHIjhBDFlbs7tGkDh/S0SLqDq8HI1TtXibgckaFarzplcLDVcPJKAjtO37BSsELkniQ3QghRnPXsCVeN2Mba0ul21ptpujpoefap0oBMCxeFgyQ3QghRnHXrBhoN7Iw3z5radH4TcSlxGaoNvLvfVNiJq0RdT3zSUQphEUluhBCiOPP0hFat4LCeKqkGKqakojfqWRu1NkO1ciWcaFmpBIoCP+w4a51YhcglSW6EEKK469kTkhRUMfbmFYsfvDUF9xb1+33vBeKT9U8yQiEsIsmNEEIUd927g1oNW67R+XYiNorC0RtHOXXrVIZqTSt4Ud7bicRUA7/tuWCdWIXIBUluhBCiuPP2hubN4b80PIw6miXdATKveaNSqcyL+v0QcRaDUaaFi4JJkhshhBCmW1NG4Jyd+dbU32f+Rm/MePupR61SuDlouXDzDuuPxlghUCEeTpIbIYQQ0KMHqFSw7gJNku7gYTByM/km2y5uy1DN3lZD3/qmRf2mrztJaprRGtEKkSNJboQQQoCvLzRpApeNaNUleDp9M83Tf2Wq+nLzsng52XLmeiKLdsi6N6LgkeRGCCGESc+epv+f1NAtwXRrasuFLdxMvpmhmoudlrfaVwbgi7BIriYkP9EwhXgYSW6EEEKYPPOM6f8rT1ExzUBwSgppShqrz6zOVLVn7dJUL+3K7ZQ0poeefMKBCpEzSW6EEEKYlC4NDRvCbQVsytH97tWbB2dNAajVKiZ0CQHg930XOXQh9klGKkSOJLkRQghxT/qtqUOpdEpMQqvAyVsnOX7jeKaqtQPc6VGrFAAT/z6KUaaGiwJCkhshhBD3PPus6f8rjuBq40DLxOxXLAZ4p2NlHGw1HDgfy4qDl55QkELkTJIbIYQQ9wQEQN26oFdAU8W85s3qqNWkGlIzVS/pYsfwluUB+GTtCW6npD3RcIXIiiQ3QgghMkq/NbUrgUZ3kvE2GIlLiWPLxS1ZVh/SJIgyHg5cTUhh7ubIJxioEFmT5EYIIURG6bemVu5D41KGpxMSgOxvTdlpNbzfuQoA32+N4uz1xCcRpRDZkuRGCCFERuXKQa1aYDAA925Nbb+0nWtJ17I8pW1wSZpW8CLVYOTj1ZkHHwvxJNlYO4CCymAwoNfrH17xPnq9HhsbG5KTkzEYDPkUmfUU9f5B0e+j9A+0Wi0ajeYJR1YI9ewJBw7AlqsE1UqjRnIKh+x0rDqzikFVB2WqrlKp+PDpYDrM3srG41f4579rNKtYwgqBCyHJTSaKohATE0NsbOwjnevj48OFCxdQqVR5H5yVFfX+QdHvo/TPxM3NDR8fnyL5HuSZnj3hvfdgzXZ4uiXd4o9wyE7HisgVDAwZmOV7V6GkM/0bBrBw+1k+WnWMtaOaotXIDQLx5Ely84D0xMbb2xsHBweLPvyMRiO3b9/GyckJtbro/UIX9f5B0e9jce+foigkJSVx9epVAHx9fZ90iIVHxYpQrRocPgzJZelwezfTPD05E3eGI9ePUK1EtSxPG92mIn8djCby6m1+jDjHkCZBTzhwISS5ycBgMJgTG09PT4vPNxqNpKamYmdnV2T/4SjK/YOi30fpH9jb2wNw9epVvL295RZVTnr2NCU3G87iXN+e1omJrHZyZEXkimyTG1d7LePaVeLdPw8za+N/dK/ph6eT7gkHLoq7ovfp9hjSx9g4ODhYORIhRH5K/x23dFxdsZM+JTx0E5RrT7cE007ha6PWkpyW/WaZver6E+LnQkJyGjPWy75T4smT5CYLch9eiKJNfsdzKTgYqlQBvR7iS1M/OQXfNCMJ+gQ2X9ic7Wma+/ad+nXPBY5cintSEQsBSHIjhBAiJ+lXb9YcRu1Siq4PWfMmXb0gD7rU8ENRYOLKoyiK7DslnhxJboQQQmTPfGtqHVTuQbe7a95EREcQkxiT46njO1bGTqtm77lbrDwUnd+RCmFWIJKbuXPnEhgYiJ2dHfXr12f37t3Z1v32229p2rQp7u7uuLu706ZNmxzrFxcDB5qmZj746NChQ560f/bsWTQaDYcPH86T9oQQhUS1alChAqSkwLUS+KelUTs5BQWFJSeW5Hiqn5s9w1qY9p2auuYESamy75R4Mqye3CxdupQxY8YwYcIE9u/fT40aNWjfvr15quaDwsPDefHFF9m8eTMRERH4+/vTrl07Ll2S3Wg7dOjA5cuXMzx++eUXa4clhCjMVKp7V2/+3gal6jAgLh6AxccWExUXlePpLzcrS2l3e2Lik5kXfjq/oxUCKADJzcyZMxk6dCiDBg0iODiY+fPn4+DgwIIFC7Ks//PPPzNs2DBq1qxJ5cqV+e677zAajYSFhT3hyAsenU6Hj49Phoe7uzvh4eHY2tqydetWc91p06bh7e3NlStXAAgNDaVJkya4ubnh6enJ008/zenT9z6IgoJMa1U0a9YMjUZDixYtANizZw9t27bFy8sLV1dXmjdvzv79+59cp4UQ+c887mYNVHmWFkl3aGqwIc2YxtRdU3McT2On1fBeJ9O+U1//c4YLN5OeRMSimLPqOjepqans27eP8ePHm8vUajVt2rQhIiIiV20kJSWh1+vx8PDI8nhKSgopKSnm5/Hxpr849Hp9pmmger0eRVEwGo0YjUZToaJAUu5+GRVFgcREFLUaY17MxnBwMP3VlMvXTo/9Qc2aNWPUqFH069ePAwcOcObMGT744AOWLl1KiRIlMBqNJCQkMHr0aKpXr87t27eZMGECPXr0YP/+/ajVanbu3EmDBg1YsWIFderUQafTYTQaiYuLo1+/fsyePRtFUZg5cyadOnXi5MmTODs7P/578ISlf0hn914WdtI/E6PRiKIo6PX6QrfOTfrn1hOdxl61KjZBQaiiokg7p0OjsWV89Hl2BQQQcTmC0DOhtCnTJtvTW1fypEGQOzujbvG/VUeZ+2LNbOtapX9PUFHvH+RfHy1pT6VYcQh7dHQ0pUqVYseOHTRs2NBc/tZbb7FlyxZ27dr10DaGDRvGunXrOHr0KHZ2dpmOT5w4kUmTJmUqX7JkSab1bGxsbPDx8cHf3x9bW1tTYWIibqVLW9izvBF78SI4Ouaq7rBhw/jtt98yvQdvvPEGY8eOJTU1lTZt2lC+fHmOHz9O/fr1mTVrVrbt3bhxg/Lly7N9+3aCg4M5f/48NWrU4J9//qFatawX7wLTPxqBgYF88803eTbeR4i8lpqayoULF4iJiSEtTcaB5EbwokVUWLGCi02aoHpWR6nYPXziU4Wf7RNxUbkwymUUOlX2i/VFJ8K0fzUoqBgebKCiq8yeEpZJSkqid+/exMXF4eLikmPdQr1C8SeffMKvv/5KeHh4lokNwPjx4xkzZoz5eXx8vHmczoNvTnJyMhcuXMDJyelee1b8q87FxSXXyY1Wq6VFixZ89dVXGco9PDzM/VyyZAk1a9YkICCAOXPm4Hhf26dOnWLChAns3r2b69evm//qvXnzJi4uLjg5OZnrOjs7m9cJuXLlCh988AFbtmzh6tWrGAwGkpKSuHHjxkN/+AoiRVFISEjI0MeiRPpnkpycjL29Pc2aNcv2s6Og0uv1bNiwgbZt26LVap/Y66pKlIAVKyh14ABpCzeg/NyJ0VdOEF6pBpdSbnKh1AVer/l6jm1csj/OT7susOG6KyOeb4BNFvtOWat/T0pR7x/kXx/T77zkhlWTGy8vLzQajXncR7orV67g4+OT47kzZszgk08+YePGjVSvXj3bejqdDp0u818TWq0205tuMBhQqVSo1ep7S7c7OcHt27nqj9FoJD4+HhcXlzxZ2l5twW0plUqFk5MTFStWzLbOzp07AVPCEhsbm+G2Ubdu3QgICODbb7/Fz88Po9FI1apVSUtLy/h+3H2t9OeDBg3ixo0bzJ49m4CAAHQ6HQ0bNkSv1xfK5f3Tk7r7+1iUSP9M1Go1KpUqy8+BwuKJx96wIfj7o7pwAe2RK1D/Vex2fsX4G7GMcIKfjv9Ejwo9KOtWNtsmxrWvzKrDMfx39Ta/H7hM/4aB2dYtzN+b3Cjq/YO876MlbVn1083W1pbatWtnGAycPjj4/ttUD5o2bRr/+9//CA0NpU6dOvkbpEplunpijUce/mV9+vRp3njjDb799lvq16/PgAEDzP8Q3Lhxg5MnT/L+++/TunVrqlSpwq1btzKcn36bzmAwZCjfvn07r7/+Op06dSIkJASdTsf169fzLG4hRAFx/6ypP/6Alu+Csy/Nr52lhX0p0pQ0puyakuPgYjcHW8a2Nf0B9tn6/7iVmPokIhfFkNX/dBszZgzffvstP/zwA8ePH+e1114jMTGRQYMGAdC/f/8MA44//fRTPvjgAxYsWEBgYCAxMTHExMRwO5dXV4qylJQU8/uR/rh+/ToGg4G+ffvSvn17Bg0axMKFC/n333/57LPPAHB3d8fT05NvvvmGyMhINm3alOFWHoC3tzf29vZs3LiRK1euEBdnWk69QoUKLF68mOPHj7Nr1y769Olj3phQCFHEpCc3K1cCttDhEwDejjyITm3LrphdrDu7LscmXqxXhso+zsTd0TNzw3/5HLAorqye3PTq1YsZM2bw4YcfUrNmTQ4ePEhoaCglS5YE4Pz581y+fNlcf968eaSmptKzZ098fX3NjxkzZlirCwVGaGhohvfE19eXJk2aMHnyZM6dO8fXX38NgK+vL9988w3vv/8+hw4dQq1W8+uvv7Jv3z6qVq3KG2+8wfTp0zO0bWNjw6xZs1i0aBGlS5emW7duAHz//ffcunWLp556in79+vH666/j7e39xPsuhHgCGjQAPz9ISIANGyC4G5RvQ+nUOwwxmiZoTN8znUR9YrZN2GjU5n2nft51juOXcz+OQojcsupsKWuIj4/H1dU1y9HWycnJREVFERQU9EiDDPN6zE1BU9T7B0W/j9I/k8f9XbcmvV7PmjVr6NSpk3XGbLz+OsyZAwMGwKJFcPMMfNWQFEMy3SvW4GLqLQaGDGRsnbE5NjPs532sORxDg7Ie/DK0gXkAuNX7l8+Kev8g//qY07/fDyp6n25CCCHyT/qtqb/+gtRU8CgLTcehU2D8NdPK8j8d+4nIW5E5NvNupyrobNTsPHOTtUdy3qNKCEtJciOEECL3GjeGkiUhNhbSJ4M0fh08K9Ds5mVa2nqbBhfvznlwcWl3B15pXg6AyauPk6w3ZFtXCEtJciOEECL3NJp7V2+mTDGt4m6jg86mCQpvnz6ITq1lT8we1katzbGp15qXw8/Vjkuxd/h6y5n8jlwUI5LcCCGEsMzbb5u2h9m2DX791VRWtjlUe55SaWkMTTEtoTZj7wxup2Y/k9XeVsP4u/tOzdsSyaXYO/keuigeJLkRQghhGX9/ePdd09dvvnlvodP2k0HnyqCLJymjdeXanWt8deir7NsBnq7uS70gD5L1RqauOZ7PgYviQpIbIYQQlhs7FoKC4NIlmDrVVObkDW0+xBYYH3MJgCXHl/DfrezXs1GpVEzoEoxaBav+vczuszefQPCiqJPkRgghhOXs7ODzz01fz5gBp0+bvq49CPyeokn8Tdpo3DEoBibvnJzj4OIQP1deqFcGgP+tPomxWC1QIvKDJDdCCCEeTdeu0K6daUp4+qrmag08/Tmo1LwVdQQ7tZb9V/ez6syqHJsa164SLnY2nIhJIOJq0dvUVTxZktwIIYR4NCoVzJoFNjamLRlCQ03lfjWh3sv4Ggy8kmSa4v3Z3s9ISE3ItikPR1veuLvv1KpzaqKuZ7/KsRAPI8mNyDOBgYHMmjUrX9qeOHEiNWvWLLDtCVFsValiWrUYYNQo01UcgJbvgZMP/S+fJdDGmRvJN/jqYM6Di/s2CKB6KReSDCoG/7CPq/HJ+Ry8KKokuSlCYmJiGDlyJGXLlkWn0+Hv70+XLl0y7Lpe0LRo0QKVSsWv6dNJ75o1axaBgYHm5+PGjSvQ/XgUMTEx9OjRAy8vL3Q6HUFBQXz77bf5+prJycmMGzeOEiVK4OTkxLPPPsuVK1dyPEdRFD788EN8fX2xt7enTZs2nDp1Kl/jFIXMhx+Ctzf89x988YWpzM4FOkw1DS6OPg/AkhNLOHnzZLbNaDVqvu5bCy+dwsXYZAYs3EN8sv4JdEAUNZLcFBFnz56ldu3abNq0ienTp3P48GFCQ0Np2bIlw4cPz/Y8vd76Hxx2dna8//77Ocbi5OSEp6fnE4wq/82YMYOjR4+yYcMGIiMjWb58OUFBQfn6mmPGjCE0NJSlS5eyZcsWoqOjeeaZZ3I8Z9q0aXzxxRfMnz+fXbt24ejoSPv27UlOlr+qxV2urvCJaYdwJk2C9M2OQ3pAudY0SkygLY4YFSOTd+U8uNjLScdrwQY8HW05fjmel3/cK6sXC4tJcvMQiqKQlJqW68edVINF9XN6WLKn6bBhw1CpVOzevZtnn32WihUrEhISwpgxY9i5c6e5nkqlYt68eXTt2hVHR0cmT56MwWBgyJAhBAUFYW9vT6VKlZg9e3aG9gcOHEiPHj2YM2cOpUqVwtPTk+HDh+eYkHz33Xe4ubk99IrLiy++SGxsbI5XLbK6jbRgwQJCQkLQ6XT4+voyYsQI87Hz58/TrVs3nJyccHFx4fnnn8/xCkV4eDj16tXD2dmZgIAAmjZtyrlz58zH582bR7ly5bC1taVSpUosXrw4w/kqlYrvvvuOHj164ODgQIUKFVi5cmWO/a5WrRqXLl1i+fLlREVFUbVqVdq0aZPjOY8jLi6OBQsWMHnyZFq1akXt2rVZuHAhO3bsyPAzcj9FUZg1axbvv/8+3bp1o3r16vz4449ER0ezYsWKfItVFEIDBkDduqY1b8aPN5WpVNBpOmh0vHX+P+zVWg5cPcDfZ/7OsSkvO/i+/1M46WzYeeYmY347iEGmUAkL2Fg7gILujt5A8IfrrPLaxz5qj4Ptw79FN2/eJDQ0lMmTJ+Po6JjpuJubW4bnEydO5JNPPmHWrFnY2NhgNBopXbo0v//+O56enuzYsYOXX34ZX19fnn/+efN54eHheHp6EhYWxpkzZ+jVqxc1a9Zk6NChmV5z2rRpTJs2jfXr11OvXr0c43dxceG9997jo48+YsCAAVn24UHz5s1jzJgxfPLJJ3Ts2JG4uDi2b98OmHaGTk9stmzZQlpaGsOHD6dXr16Eh4dnaistLY3u3bszdOhQfv75Z27evMmxY8fMuxT/+eefjBo1ilmzZtGmTRtWrVrFoEGDKF26NC1btjS3M2nSJKZNm8b06dOZM2cOffr04dy5c3h4eGTZhxYtWjB06FDGjh3L0aNH6dGjB71796Z3797Z9rtjx45s3bo12+MBAQEcPXo0y2P79u1Dr9fTokULc1nlypUpU6YMERERNGjQINM5UVFRxMTEZEi6XF1dqV+/PhEREbzwwgvZxiKKGbXatFt4gwbwww/w6qumrz3LQdOx+IRP4dX4O3zuZMNnez+jhX8LXGyz39k5xM+Fb/rVZsDC3aw5HIOX01EmdQ0x/14KkRNJboqAyMhIFEWhcuXKuarfu3dvBg0alKFs0qRJ5q+DgoKIiIjgt99+y5DcuLu7M336dNzd3QkODqZz586EhYVlSm7efvttFi9ezJYtWwgJCclVTMOGDWP27NnMnDmTDz744KH1P/74Y8aOHcuoUaPMZXXr1gUgLCyMw4cPExUVhb+/PwA//vgjISEh7Nmzx1wvXXx8PHFxcTz99NOUK1eOEiVKULduXdRq04XNGTNmMHDgQIYNGwZgvho2Y8aMDMnNwIEDefHFFwGYMmUKX3zxBbt376ZDhw6Z4jcYDPTo0YOffvoJNzc3GjduzPfff0/FihWpVasWVapUybLf3333HXfuZL9EvVarzfZYTEwMtra2uLq6ZigvWbIkMTFZ78qcXl6yZMlcnyOKsfr1YdAgWLgQRo6EXbtMSU+T0XD4N/pdi2SFaxWikm/y5YEvebf+uzk216i8F5/3qsnIXw7wY8Q5SjjpGNm6wpPpiyjUJLl5CHuthmMftc9VXaPRSEJ8As4uzuZ/GB/3tXPDkttXAHXq1MlUNnfuXBYsWMD58+e5c+cOqampmW4DBQcHo9Hci8nX15fDhw9nqPPZZ5+RmJjI3r17KVu2rLn8559/5pVXXjE/X7t2LU2bNjU/1+l0fPTRR4wcOZLXXnstx/ivXr1KdHQ0rVu3zvL48ePH8ff3Nyc26bG7ublx/PjxTMmNh4cHAwcOpH379rRp04YmTZrQr18/SpUqZW7v5ZdfznBO48aNM926q169uvlrR0dHXFxcuHr1apYx7ty5kyNHjmRIYkqWLEnZsmVZt25dtslNekxCFFhTp8KyZbB3LyxaBIMHmzfW1P7YjXcvnWWoTwmWnlxKj/I9qOKZ9c96uqer+3E9IYWJfx/jsw3/UcJZZ17wT4jsyJibh1CpVDjY2uT6YW+rsah+To/cXn6tUKECKpWKEydO5Kr+g7d9fv31V8aNG8eQIUNYv349Bw8eZNCgQaSmT+m868GrAiqVCqPRmKGsadOmGAwGfvvttwzlXbt25eDBg+ZHVglW3759CQgI4OOPP84xfnt7+4f20VILFy4kIiKCRo0asXz5cipXrpztOJTs5Ob9SXf58mWMRiMGQ8aBkoqicP369Wxfo2PHjjg5OWX7yOlKmY+PD6mpqcTFxWUov3LlCj4+Ptmek14nt+eIYq5kSZgwwfT1O+9AbKzp67ItoNpzNLhzhw4GW/PgYqOS9e/I/QY2DmJ4y3IAvPvnYdYflauGImeS3BQBHh4etG/fnrlz55KYmHnhq9j0D5dsbN++nUaNGjFs2DBq1apF+fLlOZ2+lLqF6tWrx9q1a5kyZQozZswwlzs7O1O+fHnzI6sERa1WM3XqVObNm8fZs2ezfQ1nZ2cCAwOzHahcpUoVLly4wIULF8xlx44dIzY2luDg4GzbrVWrFu+88w7r16+natWqLFmyxNxe+niedNu3b8+xrYepUKECBoOBiIgIc9m1a9c4efIk5cuXz/a87777LkOS+OBjzZo12Z5bu3ZttFotW7ZsMZedPHmS8+fP07BhwyzPCQoKwsfHJ8N7HR8fz65du7I9RwhGjIDKleHaNfjoo3vl7Uwba467FIW9Ssuha4f4K/KvXDU5rl0lnq9TGqMCI385wB7Zg0rkQJKbImLu3LkYDAbq1avHsmXLOHXqFMePH+eLL7546D9CFSpUYO/evaxbt47//vuPDz74gD179jxyLI0aNWLNmjVMmjTJ4kX9OnfuTP369fn6669zrDdx4kQ+++wzvvjiC06dOsX+/fuZM2cOAG3atKFatWr06dOH/fv3s3v3bvr370/z5s2zvGIUFRXF+PHjiYiI4Ny5c2zatIlTp06Zbw29+eabLFq0iHnz5nHq1ClmzpzJ8uXLGTdunEV9u1+NGjVo2bIlL7/8Mtu2bePQoUP07dsXT09PnnvuuWzPK1WqVIYk8cFHQEBAtue6uroyePBg3nvvPTZv3sy+ffsYNGgQDRs2zHIwMZiuPo0ePZqPP/6YlStXcvjwYfr374+fnx/du3d/5P6LIs7WFtJv286ZA8eOmb52LgmtP6CkwcCw2HgAPt/3OXEpcdk0dI9KpWJKj2q0ruxNSpqRIYv2cDIm+xWPRfEmyU0RUbZsWfbv30/Lli0ZO3YsVatWpW3btoSFhTFv3rwcz33llVd45pln6NWrF/Xr1+fGjRvmwbOPqkmTJqxevZr333/fnHTk1qeffvrQNVQGDBjArFmz+OqrrwgJCeHpp582LyynUqn466+/cHd3p1mzZrRp04ayZcuydOnSLNtycHDgxIkTPPvss1SuXJnRo0czbNgw8xih7t27M3v2bGbMmEFISAhff/01CxcuzDDr6FEsW7aMRo0a8fzzz9OqVSu0Wi3h4eG5mi32qGbOnEn79u157rnnaNasGT4+PixfvjxDncDAQCZOnGh+/tZbbzFy5Ehefvll6taty+3btwkNDcXOzi7f4hRFQLt20K0bpKWZVi5OHxtYZzD41aLPzWuUU9tzK+UWcw7k7jPCRqPmy95P8VQZN+KT0xiwYDeXYrMfYC+KL5Vi6WjUQi4+Ph5XV1fi4uJwcck4DTE5OZmoqCiCgoIe6YPbaDQSHx+Pi4tLngwoLmiKev+g6PfxYf1LSkrC09OTtWvXPnbyZg25/f497u+6Nen1etasWUOnTp1ynB1XIJw5A8HBkJICf/4J6Vf7og/At63YrdMyxLckKlT88vQvhHiG5Kp/sUmp9JwfQeTV25Qr4cgfrzbC3dH2yfXrMRSq798jyq8+5vTv94OK3qe3EOKRbd68mVatWhXKxEYUQGXLQvrt2zfegPRlDPxqQd2h1EtOoaNejYLClJ1TcjW4GMDNwZYfB9fD19WO09cSGfzDHpJS0/KpE6IwkuRGCGHWuXNnVq9ebe0wRFEyfjyUKgVnz8Jnn90rb/UeOJVk3OULOKq0/Hv9X/489Weum/Vzs+eHwfVwtddy4HwsI5YcQG/IXXIkij5JboQQQuQfR0dInzk5ZQqkz2K0c4UOU/E2GBh20zTzadb+WcSmxOa66YolnVkwsA46GzWbTlxl/PLDFq/7JYomSW6EEELkr169oGlT022pN9+8Vx7yDJRtyYuxtyiPLbEpscw9NNeipmsHeDC391No1Cr+2HeRaeuy33VcFB+S3AghhMhfKhV88YVpK4alSyF9jzeVyrRysUbHe5dNV3SWRy7nYtpFi5pvE1ySKT2qAjAv/DQLtkXlZfSiEJLkRgghRP6rWRPSt2B5/XXTFHG4u7HmGOokp/B0sgEFhb/u/EVCqmVr2PSqW4Y321cC4KNVx1h5KDoPgxeFjSQ3Qgghnoz//Q/c3eHwYbh/oc7Go8GjLGOvXsZZZcNlw2UGbxhM9G3LEpRhLcoxoKFpIcuxvx1k26nstzIRRZskN0IIIZ4MT09I3zvugw8gfR81rR10/gwvg5FvL13CBQdOx52m9+reHLl+JNfNq1QqPuwSQufqvugNCq8s3svhiw9f/VgUPZLcCCGEeHJefhmqV4dbt0wJTrpyraDqs4SkpLDgahIVXMpyI/kGg0IHEXYu633ksqJRq5j5fA0alfMkMdXAoEW7OXcj8557omiT5EbkmcDAQIv3ksqtiRMnUrNmzQLbnhAil2xsTIOLwXRr6sCBe8faT0Gxc6VS4nl+PBdFE6+aJBuSeSP8DX44+kOup3nrbDR83a82wb4uXL+dSr/vd3MtISUfOiMKKkluipCYmBhGjhxJ2bJl0el0+Pv706VLl2x3zy4IWrRogUql4tdff81QPmvWLAIDA83Px40bV6D78ShiYmLo0aMHXl5e6HQ6goKC+Pbbb/P1NZOTkxk3bhwlSpTAycmJZ599litXruR4zsCBA1GpVBkeHTp0yNc4RRHXvLlperiimAYXpyctzj6k9V1Jom0JnGLPMedgGL1K1ENBYcbeGXy882PSjLlbidjZTsuiwXXx97Dn/M0kBi7cTUKyPh87JQoSSW6KiLNnz1K7dm02bdrE9OnTOXz4MKGhobRs2ZLhw4dne55eb/1fdjs7O95///0cY3FycsLT0/MJRpX/ZsyYwdGjR9mwYQORkZEsX76coKCgfH3NMWPGEBoaytKlS9myZQvR0dE888wzDz2vQ4cOXL582fz45Zdf8jVOUQxMnw729rBtG9z/x03JELZUmogxqDk2+kTe2/0Hb7rWQIWK3/77jRGbRnA79XauXsLb2Y7Fg+vj6WjL0eh4Xv1pHylphnzqkChIJLl5GEWB1MTcP/RJltXP6WHBSpvDhg1DpVKxe/dunn32WSpWrEhISAhjxoxh586d5noqlYp58+bRtWtXHB0dmTx5MgaDgSFDhhAUFIS9vT2VKlVi9uzZGdofOHAgPXr0YM6cOZQqVQpPT0+GDx+eY0Ly3Xff4ebm9tArLi+++CKxsbE5XrXI6jbSggULCAkJQafT4evry4gRI8zHzp8/T7du3XBycsLFxYXnn38+xysU4eHh1KtXD2dnZwICAmjatCnnzp0zH583bx7lypXD1taWSpUqsXjx4gznq1QqvvvuO3r06IGDgwMVKlRg5cqVOfa7WrVqXLp0ieXLlxMVFUXVqlVp06ZNjuc8jri4OBYsWMDkyZNp1aoVtWvXZuHChezYsSPDz0hWdDodPj4+5oe7u3u+xSmKCX9/ePdd09dvvgm37yUsehtnDC8shUYjUQH9D/7N52o/7DQ6tl/aTv/Q/sQkxuTqZQK9HFk0qB6Othq2R97gma92cOhCbN73RxQoNtYOoMDTJ8EUv1xVVQNuefna70aDreNDq928eZPQ0FAmT56Mo2Pm+m5uGaOaOHEin3zyCbNmzcLGxgaj0Ujp0qX5/fff8fT0ZMeOHbz88sv4+vry/PPPm88LDw/H09OTsLAwzpw5Q69evahZsyZDhw7N9JrTpk1j2rRprF+/nnr16uUYv4uLC++99x4fffQRAwYMyLIPD5o3bx5jxozhk08+oWPHjsTFxbF9+3bAtDN0emKzZcsW0tLSGD58OL169SI8ffGw+6SlpdG9e3eGDh3Kzz//zM2bNzl27BgqlQqAP//8k1GjRjFr1izatGnDqlWrGDRoEKVLl6Zly5bmdiZNmsS0adOYPn06c+bMoU+fPpw7dw4PD48s+9CiRQuGDh3K2LFjOXr0KD169KB379707t0723537NiRrVu3Zns8ICCAo0ePZnls37596PX6DJtiVq5cmTJlyhAREUGDBg2ybTc8PBxvb2/c3d1p1aoVH3/8cZG7kiasYNw4WLAAoqJg6lSYPPneMbUNtPsYfGrAyhG0Ph3BwhJBjPBw49StU/Re3Zs5recQ4hny0JepVtqVb/rX4bWf9nE0Op7uX22nf4MAxrWvhLNd0dyZu7iT5KYIiIyMRFEUKleunKv6vXv3ZtCgQRnKJk2aZP46KCiIiIgIfvvttwzJjbu7O9OnT8fd3Z3g4GA6d+5MWFhYpuTm7bffZvHixWzZsoWQkId/8IDpytPs2bOZOXMmH9w/gyIbH3/8MWPHjmXUqFHmsrp16wIQFhbG4cOHiYqKwt/fH4Aff/yRkJAQ9uzZY66XLj4+nri4OJ5++mnKlStHiRIlqFu3Lmq16cLmjBkzGDhwIMOGDQMwXw2bMWNGhuRm4MCBvPjiiwBMmTKFL774gt27d2c5PsVgMNCjRw9++ukn3NzcaNy4Md9//z0VK1akVq1aVKlSJct+f/fdd9xJ31k5C1pt9h/UMTEx2Nra4urqmqG8ZMmSxMRk/1dwhw4deOaZZwgKCuL06dO8++67dOzYkYiICDQaTbbnCfFQdnbw+efQvbtp/6nBg6FMmYx1qj8HXhVgaV+qXotiyW1XhgdVJPLOFQaFDuLTpp/SskzLLJu/X+PyXoSNbcHk1cdYcTCaHyLOEXo0hgldQuhY1cf8x4woGiS5eRitg+kKSi4YjUbiExJwcXY2/8P42K+dC5ZuFFenTp1MZXPnzmXBggWcP3+eO3fukJqamuk2UHBwcIZ/zHx9fTl8+HCGOp999hmJiYns3buXsmXLmst//vlnXklfnRRYu3YtTZs2NT/X6XR89NFHjBw5ktdeey3H+K9evUp0dDStW7fO8vjx48fx9/c3Jzbpsbu5uXH8+PFMyY2HhwcDBw6kffv2tGnThiZNmtCvXz9KlSplbu/ll1/OcE7jxo0z3bqrXr26+WtHR0dcXFy4evVqljHu3LmTI0eOZEhiSpYsSdmyZVm3bl22yU16TE/SCy+8YP66WrVqVK9enXLlyhEeHp7t90CIXOvaFdq1g/XrYcwY+OOPzHX8asLL4fD7QPzObuXH43sZW7kuEckxjNo8irfrvU2fKn0e+lIlnHXMeqEWPWv78/6Kw5y9kcSwn/fTslIJPupWFX+P3H3mioJPxtw8jEplujWU24fWwbL6OT1y+ZdEhQoVUKlUnDhxIlf1H7zt8+uvvzJu3DiGDBnC+vXrOXjwIIMGDSI1NTVDvQevCqhUKoxGY4aypk2bYjAY+O233zKUd+3alYMHD5ofWSVYffv2JSAggI/TF/nKhr29/UP7aKmFCxcSERFBo0aNWL58OZUrV37oOJQH5eb9SXf58mWMRiMGQ8bBjYqicP169quqduzYEScnp2wfOV0p8/HxITU1lbi4jIuaXblyBR8fn4d1z6xs2bJ4eXkRGRmZ63OEyJZKBbNmmaaIr1yJat26rOs5ekG/P6H+azgrCnOP7+ZZtTsKCp/s/oQpu6bkeiZVkwpehI5uxuutyqPVqNh88hptP9/C/C2n0Ruy/p0VhYskN0WAh4cH7du3Z+7cuSQmZl6sKjY2Nsfzt2/fTqNGjRg2bBi1atWifPnynD59+pFiqVevHmvXrmXKlCnMmDHDXO7s7Ez58uXNj6wSFLVazdSpU5k3bx5nz57N9jWcnZ0JDAzMdqBylSpVuHDhAhcuXDCXHTt2jNjYWIKDg7Ntt1atWrzzzjusX7+eqlWrsmTJEnN76eN50m3fvj3Hth6mQoUKGAwGIiIizGXXrl3j5MmTlC9fPtvzvvvuuwxJ4oOPNWvWZHtu7dq10Wq1bNmyxVx28uRJzp8/T8OGDXMd+8WLF7lx4wa+vr65PkeIHFWpYpoSDmjGjkWV3UQFjRY6fgLdvkKr0THh9CHGJJtuQPxy4hdGbR5Fkj4pVy9pp9Uwpl0l1o5qRv0gD5L1Rj5Ze4Iuc7ax79ytPOmWsB5JboqIuXPnYjAYqFevHsuWLePUqVMcP36cL7744qH/cFWoUIG9e/eybt06/vvvPz744AP27NnzyLE0atSINWvWMGnSJIsX9evcuTP169fn6/v3ncnCxIkT+eyzz/jiiy84deoU+/fvZ86cOQC0adOGatWq0adPH/bv38/u3bvp378/zZs3z/KKUVRUFOPHjyciIoJz586xadMmTp06Zb419Oabb7Jo0SLmzZvHqVOnmDlzJsuXL2fcuHEW9e1+NWrUoGXLlrz88sts27aNQ4cO0bdvXzw9PXnuueeyPa9UqVIZksQHHwEBAdme6+rqyuDBg3nvvffYvHkz+/btY9CgQTRs2DDbwcS3b9/mzTffZOfOnZw9e5awsDC6detG+fLlad++/SP3X4hMPvwQvL1R/fcfZVevzrlurT4weC0qZz8GXT7DZzcT0als+OfiPwwIHcCVxJzXbrpfeW8nfn25AdN7VsfdQcuJmASenbeD8csPE5dk/aUyxKOR5KaIKFu2LPv376dly5aMHTuWqlWr0rZtW8LCwpg3b16O577yyis888wz9OrVi/r163Pjxg3z4NlH1aRJE1avXs37779vTjpy69NPPyU5OTnHOgMGDGDWrFl89dVXhISE8PTTT3Pq1CnAdDvor7/+wt3dnWbNmtGmTRvKli3L0qVLs2zLwcGBEydO8Oyzz1K5cmVGjx7NsGHDzGOEunfvzuzZs5kxYwYhISF8/fXXLFy4MMOso0exbNkyGjVqxPPPP0+rVq3QarWEh4fnarbYo5o5cybt27fnueeeo1mzZvj4+LB8+fIMdQIDA5k4cSIAGo2Gf//9l65du1KxYkWGDBlC7dq12bp1KzqdLt/iFMWQqyt88gkAVX76CfXUqZDTOlylasMrW6BMQ9rF3WDBpUt4qO04cfMEvdf05sTN3N2mB9NnxnN1/Akb24LnapcG4Jfd52k9M5y/Dl6yeFyjsD6VUsy+a/Hx8bi6uhIXF4eLi0uGY8nJyURFRREUFISdnZ3FbRuNRuLj43FxccmbAcUFTFHvHxT9Pj6sf0lJSXh6erJ27drHTt6sIbffv8f9XbcmvV7PmjVr6NSpU46z4woloxFjz56o//zT9LxGDfj+e6hdO/tz0lIh9B3Y+z0XbTQML1OWM0oK9jb2zGg+g2alm1kcxq4zN3j3z8Ocvma6zd+0ghf/61aVQK/H/8OjSH//7sqvPub07/eDit6ntxDikW3evJlWrVoVysRGFAFqNYZff2Xf6NEoHh5w6BDUrw9vvw3ZLYFgYwtPz4QusyltVLP4bCT1DRrupN1h5KaR/HLC8tW065f1ZM2opoxtWxFbGzVbT12n3ax/mBN2SlY4LiQkuRFCmHXu3JnVDxvvIER+Uqm42KIFaf/+a9p/ymCAadNMV3HuGwyfSe2BMGgNLg4lmXc+ih5JqRgVI1N2TeHT3Z9iMFqWlOhsNIxsXYH1o5vRtIIXqWlGPtvwH51mb2XnmRuP10eR7yS5EUIIUfB4e5v2nFqxAvz84NQpaNECXnsN4uOzPse/HrwcjrZUHSZdiWHULdOyBz8d/4nR4aNzPZPqfoFejvw4uB6zX6iJl5Mtp68l8sI3O3nz90PcTEx9eAPCKiS5EUIIUXB16wZHj0L6Sujz50NICGR3hdHFFwatQVWrHy/FxjH96nVsURF+IZyBoQM5efOkxSGoVCq61SxF2JgW9K5vWkH5930Xaf1ZOL/vvSADjgsgSW6EEEIUbG5u8M03EBYGZcvCxYvw9NPQpw9cu5a5vo0Ous6Bzp/R4U4q30dfxl1RcfzmcXr+3ZPn/36eJceXEJsca1EYrg5apvSoxrLXGlHZx5lbSXre/ONfXvhmJ/9dSciTroq8IcmNEEKIwqFVKzh8GMaOBbUaliyB4GD45Rd48OqJSgV1X4IBf1PTxpWfL16kbXIaNqg5fvM4U3dPpdXvrRgTPoZ/Lv6T69WNAWoHuPP3yCaM71gZe62GXVE3aff5P3ScvZXPN/zHkUtxcjXHyiS5EUIIUXg4OJg22dy5E6pVg+vXoXdv0x5VFy9mrh/QCF4Ox79ENWZejmbzufO8c+MmVRQb9EY9G85tYHjYcNr90Y6Z+2ZyJu5MrsLQatS80rwc699oRrvgkqhVcPxyPLPDTvH0nG00+XQzE1ceZXvkddnSwQpk40whhBCFT926sHcvfPop/O9/sGqVaTbVtGnw8sumKzvpXEvD4HVw8CfcjvxJn3Pb6RN/hpO2WlY4ObLaxY1rd66x8MhCFh5ZSPUS1elWrhsdgzr+v707j4uq6h84/pkZZoZdEBEwEcEF9w1F0dxJcNfMzMygn2kKbqFW9qSWTy6JlWk+mpn21M/S/D2iPWUYoWga4Z4ISGIImuKG7MgAc35/IJMji6ACOp3363VfMHfOufd8597LfDnnLthobCpthmt9Sza82JX0XB17z1wlIj6NA79f58+MfD7/5Tyf/3IeW3MzBrRqyFNtnOnpYVezn4sEyORGkiRJelxpNLBgATz9NLz8cklvzrRpJcNUGzdCixZ/lVWblwxTdXsZsi5Dwrd4xoXxemo0IekZHLC0YKe1NT9bWnDq2ilOXTvFiiMrGNhkICObj6SHSw+UiooHO+pbaXjGqzHPeDXmVmExB89e58f4NCITrnIjV8fOk5fYefISapWC5jZKshwv4tfOhYa2j9dNJB8XclhKemiaNm1a7WdJVdXbb79Np06dHtnlSZX77LPPGDRoUK2s68aNGzg7O3OxvCEKyTS1bQsHD5Y8XdzSEg4cgA4dSnpxiso5l8bWBbq/Av8TDq/Go/ZfzsD67Vlz9Ro/pV5k7o2bNNcVUlBcwO7k3bwS8Qp+//FjzYk1XMi6UHZ5dzFXq/Bt48SKZzpy+B++bJ/qw5Q+HjR1sKSwWJCQoWTBt/F4L41k9L8O8a+oJJKu5jz8z+VvTCY3JiQtLY0ZM2bg4eGBVqvF1dWV4cOHV/j07EdBv379UCgUbN261Wj+qlWraNq0qeH13LlzH+k47kdaWhqjR4+mQYMGaLVa3N3d+fTTT2t0nbdu3WLu3Lk4OjpibW3NmDFjuHKl8ocMBgYGolAojCZ/f/8y5aKiogzPpLp7nQsWLGDRokWGeXFxcYwZM4amTZuiUCiqlRQnJSXx0ksv0bhxY8PnNn78eI4ePQqAg4MDEydONFqf9DegUsGsWXD6NPj6wq1bJXc27tGj5E7HFan3BPSYBi9HwOzTNPD9JwE2nuz48zJf/5nGuKxsbIr1pOWmseHUBoaEDSHghwDCzoZV6b45KqWCbk3r8+aQ1uyb24/dM3oyrEkxHRqXPD7gRGoGK8IT8f1gPwNWRrFsdwLHUtIp1ssTkh+ETG5MxPnz5/Hy8mLv3r2EhoYSGxtLeHg4/fv3Jzg4uMJ6hZU9mK6WmJub89Zbb1XaFmtraxwcHGqxVTVv5cqVxMXFERERQVJSEjt27MDd3b1G1xkSEkJ4eDjbtm1j//79XLp0iaeffvqe9fz9/bl8+bJh+vrrv25pv379eq5evWp4rdPpeP/99w3b8//+7/+wtbWlV69ehjJ5eXl4eHiwfPlynJ2dq9z+o0eP4uXlxe+//84nn3xCfHw8YWFhtGrVijlz5hjKBQYGsmXLFtLT06u8bMlEuLvDjz/Cpk0ll5AfOwZdu5YMXxUUVF7XzhV6TofJkShmnaJd37d4S9uUfRcuEnr1Or3y8lEIwfGrx1n4y0L6bevHWwff4kjakSpdHaVQKGjR0JqnnhD855UexLw5kHdHtaNvS0fUKgV/XM/lkwN/MGZdNN2X/sTr/3eKn+KvcDX7lrz6qppkcnMPQgjyCvOqPOUX5VerfGVTdXbmoKAgFAoFhw8fZsyYMbRs2ZK2bdsSEhLCr7/+aiinUChYt24dI0aMwMrKiiVLllBcXMykSZNwd3fHwsICT09PPvroI6PlBwYGMnr0aNasWcMTTzyBg4MDwcHBlSYkGzduxM7O7p49LuPHjycjI6PSXovyhpE2bdpE27Zt0Wq1uLi4MH36dMN7qampjBw5Emtra2xtbXn22Wcr7aGIiorC29sbGxsb3Nzc6N27NykpKYb3161bR7NmzdBoNHh6evLll18a1VcoFGzcuJHRo0djaWlJixYt+PbbbyuNu3379vz555/s2LGD5ORk2rVrh6+vb6V1HkRmZiabNm1iyZIlDBgwAC8vLzZv3swvv/xitI+UR6vV4uzsbJjs7e0N77m6ujJixAjCwsKIi4tjwIABQMlnArB161aGDx9utLxu3boRGhrKc889V+WniwshCAwMpEWLFvz8888MHTqUZs2a0alTJxYtWsSuXbsMZdu2bUujRo0IK30Ao/T3olDASy9BfHzJ+ThFRfDuu9CpEyxeDP/5D5w5U/6QVSl7N+g1C6ZEoZ1xEv8e81ivcOHHC5eYlZ6BW2Eh+cX57Dq3i//Z8z/029aXgB8CWHhoIRtjNxKREkFieiL5RRU8EwtwsjXnhR5u/Pt/vDm+4Ck+fr4zIzs1wsbcjOs5OrYdvcDLXxzFe0kk7RbtYchHPxO05RjvhZ9h25FUYv64wZUsmfiUR55QfA/5Rfl0/6p7naw75vkYLNWW9yyXnp5OeHg4S5Yswcqq7FNr7ezsjF6//fbbLF++nFWrVmFmZoZer6dx48Zs374dBwcHfvnlF6ZMmYKLiwvPPvusoV5UVBQODg5ERkbyxx9/MG7cODp16sTk0juH3mHFihWsWLGCH3/8EW9v70rbb2tryz/+8Q8WL15MQEBAuTHcbd26dYSEhLB8+XIGDx5MZmYmhw4dAkqeDF2a2Ozfv5+ioiKCg4MZN24cUVFRZZZVVFTEqFGjmDx5suG//fj4eMOXc1hYGLNmzWLVqlX4+vry3XffGYZF+vfvb1jOO++8w4oVKwgNDWXNmjVMmDCBlJQU6tevX24M/fr1Y/LkycyZM4e4uDhGjx7N888/z/PPP19h3IMHD+bnn3+u8H03Nzfi4uLKfe/YsWMUFhYaPRSzVatWNGnShOjoaHr06FHhcqOiomjYsCH29vYMGDCAd99919CTNnToUJ588kl69OhBamoqBw8epHPnzoa6Bw8eZOLEiRUuu6pOnjxJXFwcX331VblP/Lazs0Ov/+uSW29vb37++WcmTZr0wOuWHlMuLiWJzH/+A8HBJQnNncOVajV4epacs3Pn1KwZmN3x9VjfHXqHQO8QnG+c4+W4MCbFhXHy2u/ssrEm3MqS9IKbpF+9yfGrx8s0w0lrj5utG03smtHY2pVrhddoldmKpvZN0apKknsbczXDOjRiWIdG6Ir0HE5O58f4NPb/fo0L6Xnk6oqJv5xF/OWyj56wUKtwc7CkqYMVbg1u/3SwxL2BFU425iiViof+0T7qHonkZu3atYSGhpKWlkbHjh1Zs2ZNpV+I27dvZ8GCBZw/f54WLVrw3nvvMWTIkFps8aMlKSkJIQStWrWqUvnnn3+el156yWjeO++8Y/jd3d2d6OhovvnmG6Pkxt7entDQUOzt7WnTpg1Dhw4lMjKyTHLz+uuv8+WXX7J//37atm1bpTYFBQXx0Ucf8cEHH7BgwYJ7ln/33XeZM2cOs2bNMszr1q0bAJGRkcTGxpKcnIyrqysAX3zxBW3btuXIkSOGcqWysrLIzMxk2LBhNGvWDEdHR7p162b4Al25ciWBgYEEBQUBGHrDVq5caZTcBAYGMn78eACWLl3K6tWrOXz4cLnnpxQXFzN69Gj+93//Fzs7O3r16sVnn31Gy5Yt6dy5M61bty437o0bN5Jf0dORAbVaXeF7aWlpaDQa6tWrZzTfycmJtLS0Cuv5+/vz9NNP4+7uzrlz53jzzTcZPHgw0dHRqFQqwsPDeeeddxg0aBCXLl1i5syZPPPMM0yfPp3s7GwyMzNp1KhRhcuvqrNnzwJUeT9v1KgRJ06ceOD1SiZgzBjo3x/+/e+S82/i4kp6dfLySs7ROX3auLxGU37S4+EBDs2gz1wUfebS+drvdI7fyetxO0jKOEeK2oxUtfr2TzNSzNRkq5RcKbjJlWs3OXztpGEVW77fggJwUdejiXUj3Op50MShFW713Gli24TuHo15skUDAAqKirl4M5/z13M5fyOPlBslP89fz+XizTzyC4s5k5bNmbSyd0nWmilxc7DEzcEK9wZWfyVBDpa41LNAZaKJT50nN9u2bSMkJIT169fTvXt3Vq1ahZ+fH4mJiTRs2LBM+V9++YXx48ezbNkyhg0bxldffcWoUaM4fvw47dq1e+jtszCzIOb5mCqV1ev1ZGdnY2NjU+5/lvez7qqobpdk165dy8xbu3YtmzZtIjU1lfz8fHQ6XZlhoDZt2qBSqQyvXVxciI2NNSrz/vvvk5uby9GjR/Hw8DDM37JlC6+88orh9Q8//EDv3r0Nr7VaLYsXL2bGjBlMmzat0vZfvXqVS5cuMXDgwHLfT0hIwNXV1ZDYlLbdzs6OhISEMslN/fr1CQwMxM/PD19fX5588kkmTpzIE088YVjelClTjOr06tWrzNBdhw4dDL9bWVlha2trdC7KnX799VdOnz5tlMQ4OTnh4eHBnj17KkxuSttUm5577jnD7+3bt6dDhw40a9aMqKgoBg4cSHJyMrt27SI+Pp6oqCi2bNnC6tWr0ev1hkTM3Lx6l7uWt79Udz+3sLAgL6/6D0qUTFT9+vDqq3+91ushNbUk0blzSkgoSXpiY0umO2m1ZZOeNqOweHIO7fWFtM+8CJmpkJEKGRcQGalkZp4nJecSqboMUsxUJUnP7SQoR6nkUmEml25m8uvNBDj/1/OyVICLmTVNzBtgp7HFUm2JldoaS7U1zZxtadfEBiutHVqNLbmFFmTkmXE9C65mwqWbxVy4oedi+i0KivT8fiWH36+UvRpLpVRgb6nBwUqDg7WG+lYaGlhrqX/7dcn8ktcNrLTYWpgZerQfdXWe3HzwwQdMnjzZ0JOwfv16vv/+ezZt2sQbb7xRpvxHH32Ev78/8+bNA+Cf//wnERERfPzxx6xfv/6ht0+hUFRpaAhKkpsisyIs1ZYPJbmpqhYtWqBQKDhz5kyVyt897LN161bmzp3L+++/j4+PDzY2NoSGhhITY5zU3d0roFAojIYBAHr37s3333/PN998Y7T9RowYQffufw3vlfcl/cILL7By5Ureffddoyul7mZhUbWkrzo2b97MzJkz+eGHH9ixYwdLliwhIiKi0qGau1Xl8yl1+fJl9Ho9xcXFmN3R/S2E4Pr16xWu40GGpZydndHpdGRmZmJra2uYf+XKlWqd1Ovh4UGDBg1ISkpi4MCBhmQ0Pj4eAI1Gw9y5c4GSK5cUCgU3b96s8vKh/P2ldP8+c+aM0bBXRdLT03F0dKzWeqW/EaUSmjYtmYYO/Wu+Xg8pKcYJT3x8yZSfD6dOlUx30mrB1RXMzUt+v/1TYW6OndYGO/OOdNSqwVJPsbaAa5kXcGxkRYY2j1R1Dilm+aSa6W4nPyU9P/lKJReLcriYcx+XiCsBR7BvAJZCgTlKtHoVaqFCWayCYjP0RWagNwNUIJQUZCu5nKXislCBUCGEEoEZ4vZrvTBDgQpzMw0WZhoszTRYqjVYqzXYaLTYarTUMzfHXmuOtVpNQdp5oO5GVOo0udHpdBw7doz58+cb5imVSnx9fYmOji63TnR0NCEhIUbz/Pz82LlzZ7nlCwoKKLjjDPmsrJLxysLCwjInwxYWFiKEQK/XV/ilVJnS/yxLl1Fb7OzsGDRoEGvXrmX69OllkpeMjAyj827uju/gwYP07NmTqVOnGuadO3fOUBaMe4dK4yudd+eyunXrRlBQEEOGDEGlUhmuYLGysjLqybl72aW/L1myhGeeecbQlrvXr9frsbKyomnTpvz000/07du3zOfh6enJhQsXSElJMfTexMfHk5GRQatWrSpse8eOHenQoQNBQUH4+/uzZcsWvL29ad26dZnzRg4ePEjr1q2N6pe331S0LzVr1ozi4mIOHTpk6MG6du0aiYmJzJw5s8L9Z8OGDfcclqqobufOnVGr1ezfv58JEyag1+tJTEwkNTWV7t27V3mfvXjxIjdu3MDJycmoTp8+fejTp4/RPDMzM9q0aUNcXFylJ0vffcyUt7906NCBNm3a8P777zN27Ngy/0BkZGQYhtyEEJw+fZq+ffuWG1fpPlBYWGjUG/k4KP279Shc6VgTHon4Gjcumfz8/pqn18P58yji40umhAQU8fFw5gyK/HxISqrSolVA6b8SDrenzgAKwFoBdkqEnYLrzmpSntByoYGGbHMVuRol+WoFuWoleWZKcs2U5KmU5CkU5CqV5CkV5CpKfhbf7l25pYBbCgEUg7K4ZKUVj1zfPz1w6/Z0W3MBwwpnPNTVVGefqNPk5vr16xQXF+Pk5GQ038nJqcJeiLS0tHLLV3TOwLJly4zOJyn1448/Ymlp3CNjZmaGs7MzOTk56HS66oRiJDu79p8Ou3z5cvz9/fH29mb+/Pm0bduWoqIioqKi2LRpk1EvTH5+viHJg5KrXb744gvCwsJwc3Nj27ZtHD58GDc3N6NksOj2lQWl8el0OoqKigxl9Ho9t27dol27dmzbto1nn32WoqKiSoeZioqK0Ol0hmX07t0bLy8vNmzYgKOjo2F+QUEBxcXFhtevvfYaISEh2Nra4uvrS05ODjExMUyZMgVvb2/atGljGL4sKipi7ty59OrVi5YtW5KVlWW0vJSUFD7//HMGDx6Ms7MzSUlJnD17lrFjx5KVlUVQUBAvvfQSrVq1ol+/foSHhxMWFsbOnTuNPse7P1chBLdu3TKaV8rd3Z3evXszZcoUVq1ahbW1NW+//Tb169fHz8+v3DoANjY22NhUfjv4iuoqFApeeOEF/vGPf2Bvb4+NjQ2vvfYa3bp1o02bNoZ63t7eLFy4kGHDhpGTk8N7773HiBEjcHJyIjk5mUWLFuHh4YGPj0+F67pTv379iIqKMjrPS6fTkZiYCJRs2z/++INDhw6Vm9TcafXq1YwePZpevXoxZ84cWrZsSU5ODuHh4ezbt4/vvy/p1r9y5YrhH6fy2qjT6cjPz+fAgQOG/fpxExERUddNqFGPbHxKJbRrVzIBFBdjefUq5hkZKAsLUep0qIqKUOp0KAsLURUWlsy/PanuMV9ZWIjqViGas4V4xhfSRqcDUYjizmFZIW6/FqAqmRRqUKhAqEGnFeRbKsmzUJBvoSDXXEG+uZI8cwV55kryNArytCXJUpFSQZFKQZESClW3XytLXhcpFRQqFRQpSn9XUnjnPMXteoqS18UKBUVAkQI0euVD34bVGWau82GpmjZ//nyjnp6srCxcXV0ZNGiQUdc8lNxs7MKFC1hbW1f7HAEo+TIrPeemtsclO3TowLFjx1i6dCkLFy7k8uXLODo60qVLF9atW2cUq4WFhdHrmTNnkpCQwKRJk1AoFDz33HMEBQURHh5uKKdWqw3DJ6XxaTQazMzMDGWUSiXm5ubY2tri5+fHf//7X4YNG4aVlZXRZdp3MjMzQ6PRGLUnNDSUJ598EqVSaZiv1WpRqVSG16XnY3z00UcsWLCABg0aMGbMGMP73377LTNnzmTo0KEolUr8/PxYvXp1uctr2LAhycnJBAYGGnokgoKCmDVrFkql0nCp+gcffMD8+fNxd3fns88+K3MS+92fq0KhMHwe5QkLC2PevHlMmjSJgoICevTowb59+3Bxcal0Wz+INWvWMGvWLAICAigoKDD0+N3ZxrNnz6LT6bC1tUWtVpOYmMiECRPIyMigUaNGPPXUUyxevLjKQz5Tp07F29sbIYShZ+X8+fP06dPHUObjjz/m448/pm/fvuzdu7fCZfXv35/Dhw+zdOlSXn31Va5fv46Liws+Pj6sXr0aGxsbsrOz2bdvH02aNCn3ZG4oOdYtLCzo06fPfR3rdamwsJCIiAieeuqpSk8gf1zJ+B6MAjC/Pdnfo2xNKSwsJPzHhx9jVf6ZMhB1qKCgQKhUKhEWFmY0/8UXXxQjRowot46rq6v48MMPjeYtXLhQdOjQoUrrzMzMFIDIzMws815+fr6Ij48X+fn5VVrW3YqLi8XNmzdFcXHxfdV/1Jl6fEKYfox1Fd8zzzwjli5dWuPrKY2ve/fuYsuWLRWWe9BjvS7pdDqxc+dOodPp6ropNULG9/irqRgr+/6+W53exE+j0eDl5WV0kze9Xk9kZCQ+Pj7l1vHx8SlzU7iIiIgKy0uSVPdCQ0OxtraulXXduHGD0aNHGy7LlyTp76fOh6VCQkIICAiga9eueHt7s2rVKnJzcw3j8y+++CJPPPEEy5YtA2DWrFn07duX999/n6FDh7J161aOHj3Khg0b6jIMSZIq0bRpU2bMeLgnF1bEwcGBefPmPTaXrEqS9PDVeXIzbtw4rl27xsKFC0lLS6NTp06Eh4cbThpOTU01uiqiZ8+efPXVV7z11lu8+eabtGjRgp07d9bIPW4kSZIkSXr81HlyAzB9+vQKTzgt73b5Y8eOZezYsTXcKkmSJEmSHkfywZnlEPIhZJJk0uQxLkmmTSY3dyi9ZE3esl2STFvpMW6KlxpLkvSIDEs9KlQqFXZ2dobnAVlaWlbrpES9Xo9Op+PWrVu1+viF2mLq8YHpx/h3j08IQV5eHlevXsXOzu6xuzuxJElVI5Obu5Q+Y6eiBx5WRghBfn4+FhYWJnmlhqnHB6Yfo4yvhJ2dXbWepyVJ0uNFJjd3USgUuLi40LBhw2o/26SwsJADBw7Qp08fk+zuNvX4wPRjlPGVDEXJHhtJMm0yuamASqWq9h9AlUpFUVER5ubmJvnFYerxgenHKOOTJOnvwPQG3SVJkiRJ+luTyY0kSZIkSSZFJjeSJEmSJJmUv905N6U376rWo9OrqLCwkLy8PLKyskxyvN/U4wPTj1HG9/gz9RhlfI+/moqx9Hu7Kjfh/NslN9nZ2QC4urrWcUskSZIkSaqu7Oxs6tWrV2kZhfib3Ydcr9dz6dIlbGxsHvp9PrKysnB1deXChQvY2to+1GU/Ckw9PjD9GGV8jz9Tj1HG9/irqRiFEGRnZ9OoUaN73oT0b9dzo1Qqady4cY2uw9bW1mR3WjD9+MD0Y5TxPf5MPUYZ3+OvJmK8V49NKXlCsSRJkiRJJkUmN5IkSZIkmRSZ3DxEWq2WRYsWodVq67opNcLU4wPTj1HG9/gz9RhlfI+/RyHGv90JxZIkSZIkmTbZcyNJkiRJkkmRyY0kSZIkSSZFJjeSJEmSJJkUmdxIkiRJkmRSZHLzAM6fP8+kSZNwd3fHwsKCZs2asWjRInQ6XaX1bt26RXBwMA4ODlhbWzNmzBiuXLlSS62uniVLltCzZ08sLS2xs7OrUp3AwEAUCoXR5O/vX7MNvU/3E58QgoULF+Li4oKFhQW+vr6cPXu2Zhv6ANLT05kwYQK2trbY2dkxadIkcnJyKq3Tr1+/Mttw6tSptdTiyq1du5amTZtibm5O9+7dOXz4cKXlt2/fTqtWrTA3N6d9+/bs3r27llp6/6oT4+eff15mW5mbm9dia6vnwIEDDB8+nEaNGqFQKNi5c+c960RFRdGlSxe0Wi3Nmzfn888/r/F23q/qxhcVFVVm+ykUCtLS0mqnwdW0bNkyunXrho2NDQ0bNmTUqFEkJibes15tH4cyuXkAZ86cQa/X88knnxAXF8eHH37I+vXrefPNNyut9+qrr/Lf//6X7du3s3//fi5dusTTTz9dS62uHp1Ox9ixY5k2bVq16vn7+3P58mXD9PXXX9dQCx/M/cS3YsUKVq9ezfr164mJicHKygo/Pz9u3bpVgy29fxMmTCAuLo6IiAi+++47Dhw4wJQpU+5Zb/LkyUbbcMWKFbXQ2spt27aNkJAQFi1axPHjx+nYsSN+fn5cvXq13PK//PIL48ePZ9KkSZw4cYJRo0YxatQoTp8+Xcstr7rqxggld4K9c1ulpKTUYourJzc3l44dO7J27doqlU9OTmbo0KH079+fkydPMnv2bF5++WX27NlTwy29P9WNr1RiYqLRNmzYsGENtfDB7N+/n+DgYH799VciIiIoLCxk0KBB5ObmVlinTo5DIT1UK1asEO7u7hW+n5GRIdRqtdi+fbthXkJCggBEdHR0bTTxvmzevFnUq1evSmUDAgLEyJEja7Q9D1tV49Pr9cLZ2VmEhoYa5mVkZAitViu+/vrrGmzh/YmPjxeAOHLkiGHeDz/8IBQKhfjzzz8rrNe3b18xa9asWmhh9Xh7e4vg4GDD6+LiYtGoUSOxbNmycss/++yzYujQoUbzunfvLl555ZUabeeDqG6M1Tk2HzWACAsLq7TMa6+9Jtq2bWs0b9y4ccLPz68GW/ZwVCW+ffv2CUDcvHmzVtr0sF29elUAYv/+/RWWqYvjUPbcPGSZmZnUr1+/wvePHTtGYWEhvr6+hnmtWrWiSZMmREdH10YTa0VUVBQNGzbE09OTadOmcePGjbpu0kORnJxMWlqa0farV68e3bt3fyS3X3R0NHZ2dnTt2tUwz9fXF6VSSUxMTKV1t2zZQoMGDWjXrh3z588nLy+vpptbKZ1Ox7Fjx4w+e6VSia+vb4WffXR0tFF5AD8/v0dyW8H9xQiQk5ODm5sbrq6ujBw5kri4uNpobq143Lbh/erUqRMuLi489dRTHDp0qK6bU2WZmZkAlX7v1cU2/Ns9OLMmJSUlsWbNGlauXFlhmbS0NDQaTZnzO5ycnB7ZMdbq8vf35+mnn8bd3Z1z587x5ptvMnjwYKKjo1GpVHXdvAdSuo2cnJyM5j+q2y8tLa1M97aZmRn169evtL3PP/88bm5uNGrUiFOnTvH666+TmJjIjh07arrJFbp+/TrFxcXlfvZnzpwpt05aWtpjs63g/mL09PRk06ZNdOjQgczMTFauXEnPnj2Ji4ur8YcE14aKtmFWVhb5+flYWFjUUcseDhcXF9avX0/Xrl0pKChg48aN9OvXj5iYGLp06VLXzauUXq9n9uzZ9OrVi3bt2lVYri6OQ9lzU4433nij3BO87pzu/kPz559/4u/vz9ixY5k8eXIdtbxq7ie+6njuuecYMWIE7du3Z9SoUXz33XccOXKEqKiohxdEJWo6vkdBTcc4ZcoU/Pz8aN++PRMmTOCLL74gLCyMc+fOPcQopIfBx8eHF198kU6dOtG3b1927NiBo6Mjn3zySV03TaoCT09PXnnlFby8vOjZsyebNm2iZ8+efPjhh3XdtHsKDg7m9OnTbN26ta6bUobsuSnHnDlzCAwMrLSMh4eH4fdLly7Rv39/evbsyYYNGyqt5+zsjE6nIyMjw6j35sqVKzg7Oz9Is6usuvE9KA8PDxo0aEBSUhIDBw58aMutSE3GV7qNrly5gouLi2H+lStX6NSp030t835UNUZnZ+cyJ6IWFRWRnp5erf2te/fuQEnvZLNmzard3oehQYMGqFSqMlcWVnbsODs7V6t8XbufGO+mVqvp3LkzSUlJNdHEWlfRNrS1tX3se20q4u3tzcGDB+u6GZWaPn264QKFe/UQ1sVxKJObcjg6OuLo6Filsn/++Sf9+/fHy8uLzZs3o1RW3hnm5eWFWq0mMjKSMWPGACVnyaempuLj4/PAba+K6sT3MFy8eJEbN24YJQM1qSbjc3d3x9nZmcjISEMyk5WVRUxMTLWvKHsQVY3Rx8eHjIwMjh07hpeXFwB79+5Fr9cbEpaqOHnyJECtbcPyaDQavLy8iIyMZNSoUUBJt3hkZCTTp08vt46Pjw+RkZHMnj3bMC8iIqLWjrXqup8Y71ZcXExsbCxDhgypwZbWHh8fnzKXDT/K2/BhOHnyZJ0ea5URQjBjxgzCwsKIiorC3d39nnXq5DissVOV/wYuXrwomjdvLgYOHCguXrwoLl++bJjuLOPp6SliYmIM86ZOnSqaNGki9u7dK44ePSp8fHyEj49PXYRwTykpKeLEiRPinXfeEdbW1uLEiRPixIkTIjs721DG09NT7NixQwghRHZ2tpg7d66Ijo4WycnJ4qeffhJdunQRLVq0ELdu3aqrMCpU3fiEEGL58uXCzs5O7Nq1S5w6dUqMHDlSuLu7i/z8/LoI4Z78/f1F586dRUxMjDh48KBo0aKFGD9+vOH9u/fRpKQksXjxYnH06FGRnJwsdu3aJTw8PESfPn3qKgSDrVu3Cq1WKz7//HMRHx8vpkyZIuzs7ERaWpoQQoiJEyeKN954w1D+0KFDwszMTKxcuVIkJCSIRYsWCbVaLWJjY+sqhHuqbozvvPOO2LNnjzh37pw4duyYeO6554S5ubmIi4urqxAqlZ2dbTjOAPHBBx+IEydOiJSUFCGEEG+88YaYOHGiofwff/whLC0txbx580RCQoJYu3atUKlUIjw8vK5CqFR14/vwww/Fzp07xdmzZ0VsbKyYNWuWUCqV4qeffqqrECo1bdo0Ua9ePREVFWX0nZeXl2co8ygchzK5eQCbN28WQLlTqeTkZAGIffv2Gebl5+eLoKAgYW9vLywtLcXo0aONEqJHSUBAQLnx3RkPIDZv3iyEECIvL08MGjRIODo6CrVaLdzc3MTkyZMNf5gfNdWNT4iSy8EXLFggnJychFarFQMHDhSJiYm13/gqunHjhhg/frywtrYWtra24qWXXjJK3u7eR1NTU0WfPn1E/fr1hVarFc2bNxfz5s0TmZmZdRSBsTVr1ogmTZoIjUYjvL29xa+//mp4r2/fviIgIMCo/DfffCNatmwpNBqNaNu2rfj+++9rucXVV50YZ8+ebSjr5OQkhgwZIo4fP14Hra6a0kuf755KYwoICBB9+/YtU6dTp05Co9EIDw8Po+PxUVPd+N577z3RrFkzYW5uLurXry/69esn9u7dWzeNr4KKvvPu3CaPwnGouN1YSZIkSZIkkyCvlpIkSZIkyaTI5EaSJEmSJJMikxtJkiRJkkyKTG4kSZIkSTIpMrmRJEmSJMmkyORGkiRJkiSTIpMbSZIkSZJMikxuJEmSJEkyKTK5kSTJJERFRaFQKMjIyKhynWeffZYmTZpw6NAhXnjhBY4cOVJzDZQkqdbI5EaSpFoVGBiIQqFAoVCg0Who3rw5ixcvpqio6IGW27NnTy5fvky9evWqVD4rK4vz58/z5ZdfMnv2bK5cuUKXLl0eqA2SJD0a5FPBJUmqdf7+/mzevJmCggJ2795NcHAwarWa+fPn3/cyNRoNzs7OVS5va2vL4cOHAWSPjSSZGNlzI0lSrdNqtTg7O+Pm5sa0adPw9fXl22+/5ebNm7z44ovY29tjaWnJ4MGDOXv2rKFeSkoKw4cPx97eHisrK9q2bcvu3buB8oelDh06RL9+/bC0tMTe3h4/Pz9u3rwJQHh4OE8++SR2dnY4ODgwbNgwzp07Z9TO2NhYBgwYgIWFBQ4ODkyZMoWcnJya/4AkSXogMrmRJKnOWVhYoNPpCAwM5OjRo3z77bdER0cjhGDIkCEUFhYCEBwcTEFBAQcOHCA2Npb33nsPa2vrcpd58uRJBg4cSJs2bYiOjubgwYMMHz6c4uJiAHJzcwkJCeHo0aNERkaiVCoZPXo0er3e8L6fnx/29vYcOXKE7du389NPPzF9+vTa+VAkSbp/NfrMcUmSpLsEBASIkSNHCiGE0Ov1IiIiQmi1WjFq1CgBiEOHDhnKXr9+XVhYWIhvvvlGCCFE+/btxdtvv13ucvft2ycAcfPmTSGEEOPHjxe9evWqcruuXbsmABEbGyuEEGLDhg3C3t5e5OTkGMp8//33QqlUirS0tOqELElSLZM9N5Ik1brvvvsOa2trzM3NGTx4MOPGjSMwMBAzMzO6d+9uKOfg4ICnpycJCQkAzJw5k3fffZdevXqxaNEiTp06VeE6SntuKnL27FnGjx+Ph4cHtra2NG3aFIDU1FQAEhIS6NixI1ZWVoY6vXr1Qq/Xk5iY+CDhS5JUw2RyI0lSrevfvz8nT57k7Nmz5Ofn8+9//xuFQnHPei+//DJ//PEHEydOJDY2lq5du7JmzZpyy1pYWFS6rOHDh5Oens6nn35KTEwMMTExAOh0uuoHJEnSI0UmN5Ik1TorKyuaN29OkyZNMDMruWizdevWFBUVGZIMgBs3bpCYmEibNm0M81xdXZk6dSo7duxgzpw5fPrpp+Wuo0OHDkRGRpb7Xuly33rrLQYOHEjr1q0NJxqXat26Nb/99hu5ubmGeYcOHUKpVOLp6XnfsUuSVPNkciNJ0iOhRYsWjBw5ksmTJ3Pw4EF+++03XnjhBZ544glGjhwJwOzZs9mzZw/JyckcP36cffv20bp163KXN3/+fI4cOUJQUBCnTp3izJkzrFu3juvXr2Nvb4+DgwMbNmwgKSmJvXv3EhISYlR/woQJmJubExAQwOnTp9m3bx8zZsxg4sSJODk51fjnIUnS/ZPJjSRJj4zNmzfj5eXFsGHD8PHxQQjB7t27UavVABQXFxMcHEzr1q3x9/enZcuW/Otf/yp3WS1btuTHH3/kt99+o2PHjrRu3Zpdu3ZhZmaGUqlk69atHDt2jHbt2vHqq68SGhpqVN/S0pI9e/aQnp5Ot27deOaZZxg4cCAff/xxjX8OkiQ9GIUQQtR1IyRJkmrShQsXmDhxIlFRUXXdFEmSaoHsuZEkyaQlJSWRlZXFkSNHypxXI0mSaZLJjSRJJm3p0qV06dKFAQMGYGdnV9fNkSSpFshhKUmSJEmSTIrsuZEkSZIkyaTI5EaSJEmSJJMikxtJkiRJkkyKTG4kSZIkSTIpMrmRJEmSJMmkyORGkiRJkiSTIpMbSZIkSZJMikxuJEmSJEkyKf8PT77/VsZdFKkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "  Trabalho 2 - Métodos Númericos para Equações Diferenciais\n",
    "  Aluno: Victor Luis Teixeira Reis - 202110049511\n",
    "\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def initial_condition(x):\n",
    "  \"\"\"Implementação da condição inicial do problema\"\"\"\n",
    "  if x < 0.0:\n",
    "    return T_1\n",
    "  elif x > 0.0:\n",
    "    return T_2\n",
    "  else:\n",
    "    return (T_1 + T_2)/2\n",
    "\n",
    "def analytic_solution (x, t):\n",
    "  \"\"\"Calcula a solução analítica do problema em uma posição x e instante t\"\"\"\n",
    "  serie_length = 10 # Números maiores do que esse tamanho não resultaram em grandes diferenças na aproximação\n",
    "  partial_sum = 0.0\n",
    "\n",
    "  for k in range(1, serie_length+1):\n",
    "    partial_sum += (1/(2*k-1))*np.exp((-alpha*((2*k-1)**2)*(np.pi**2)*t)/(L**2))*np.sin((2*k-1)*((np.pi*(x-u*t))/L))\n",
    "\n",
    "  return 0.5 - (2/np.pi)*partial_sum\n",
    "\n",
    "def plot_solution (x_grid, aprox_solution, exact_solution):\n",
    "  \"\"\"Plota um gráfico com uma linha representando a solução analítica e pontos representando a solução numérica\"\"\"\n",
    "  plt.plot(x_grid, exact_solution, label=\"Exata\", color=\"red\")\n",
    "  plt.scatter(x_grid, aprox_solution, label=\"Numérico\", marker=\"*\")\n",
    "\n",
    "  plt.xlabel('Posição')\n",
    "  plt.ylabel('Temperatura')\n",
    "  plt.title('Gráfico de temperatura por posição')\n",
    "  plt.grid(True) \n",
    "  plt.legend()\n",
    "  plt.show()\n",
    "\n",
    "\n",
    "def plot_all_solution (x_grid, exact_solution, solutions):\n",
    "  \"\"\"Plota um gráfico com várias linhas representando cada tipo de solução\"\"\"\n",
    "  plt.plot(x_grid, exact_solution, label=\"Exata\", color=\"red\")\n",
    "\n",
    "  for solution in solutions:\n",
    "    plt.plot(x_grid, solution[\"array\"], label=solution[\"label\"])\n",
    "\n",
    "  plt.xlabel('Posição')\n",
    "  plt.ylabel('Temperatura')\n",
    "  plt.title('Gráfico de temperatura por posição')\n",
    "  plt.grid(True) \n",
    "  plt.legend()\n",
    "  plt.show()\n",
    "\n",
    "def print_temperature (time, temperature, solution_type=\"TN\"):\n",
    "  \"\"\"Função para printar as temperaturas em cada posição da malha em um instante de tempo\"\"\"\n",
    "  temp_string = \"\"\n",
    "\n",
    "  for each_temp in temperature:\n",
    "    temp_string += f'{each_temp:.3f} '\n",
    "\n",
    "  print(f't= {time:.3f} {solution_type} = {temp_string}')\n",
    "\n",
    "def EMQ (aprox_solution, exact_solution, size):\n",
    "  \"\"\"Calcula o erro médio quadrado\"\"\"\n",
    "  emq_result = 0.0\n",
    "\n",
    "  for j in range(size):\n",
    "    emq_result += (aprox_solution[j] - exact_solution[j])**2\n",
    "\n",
    "  emq_result = emq_result / size\n",
    "  emq_result = np.sqrt(emq_result)\n",
    "\n",
    "  return emq_result\n",
    "\n",
    "\n",
    "def tdma(a, b, c, temperature, size, beta, delta):\n",
    "  \"\"\"Algoritmo de Thomas\"\"\"\n",
    "  d = np.zeros(size)\n",
    "  new_temperature = np.zeros(size)\n",
    "  p = np.zeros(size)\n",
    "  q = np.zeros(size)\n",
    "  \n",
    "  d[0] = temperature[0]\n",
    "  d[size-1] = temperature[size-1]\n",
    "\n",
    "  for j in range(1, size-1):\n",
    "    d[j] = (1-beta)*((1-delta)*C+S)*temperature[j-1] + (1-(1-beta)*((1-2*delta)*C+2*S))*temperature[j] + (1-beta)*(-delta*C+S)*temperature[j+1]\n",
    "\n",
    "  # Condições de contorno\n",
    "  p[0] = 0.0\n",
    "  q[0] = T_1\n",
    "  p[size-1] = 0.0\n",
    "  q[size-1] = T_2\n",
    "\n",
    "  for j in range(1, size-1):\n",
    "    p[j] = b[j] / (a[j] - c[j]*p[j-1])\n",
    "    q[j] = (d[j] + c[j]*q[j-1]) / (a[j] - c[j]*p[j-1])\n",
    "\n",
    "  new_temperature[size-1] = q[size-1]\n",
    "\n",
    "  for j in range(size-2, -1, -1):\n",
    "    new_temperature[j] = p[j]*new_temperature[j+1] + q[j]\n",
    "\n",
    "  return new_temperature\n",
    "    \n",
    "def solve_partial_equation(temperature, final_t, size, beta, delta):\n",
    "  \"\"\"Função principal para a resolução do problema\"\"\"\n",
    "\n",
    "  if beta == 0.5:\n",
    "    print(f'EQUACAO DE ADVECCAO-DIFUSAO: CRANK-NICOLSON\\n')\n",
    "  elif beta == 1.0:\n",
    "    print(f'EQUACAO DE ADVECCAO-DIFUSAO: TOTALMENTE IMPLÍCITO\\n')\n",
    "  elif beta == 0 and delta == 0.5:\n",
    "    print(f'EQUACAO DE ADVECCAO-DIFUSAO: FTCS (EXPLÍCITO)\\n')\n",
    "    \n",
    "    if 0 > C**2 or C**2 > 2*S or 2*S > 1:\n",
    "      return print(\"Condição de estabilidade não satisfeita (0 <= C² <= 2s <= 1)!\")\n",
    "    \n",
    "  elif beta == 0 and delta == 0:\n",
    "    print(f'EQUACAO DE ADVECCAO-DIFUSAO: UPWIND (EXPLÍCITO)\\n')\n",
    "\n",
    "    if C + 2*S > 1:\n",
    "      return print(\"Condição de estabilidade não satisfeita (C + 2s <= 1)!\")\n",
    "    \n",
    "  elif beta == 0 and delta == 0.5*(1-C):\n",
    "    print(f'EQUACAO DE ADVECCAO-DIFUSAO: LAX-WENDROFF (EXPLÍCITO)\\n')\n",
    "\n",
    "    if (C**2)+ 2*S > 1:\n",
    "      return print(\"Condição de estabilidade não satisfeita (C² + 2s <= 1)!\")\n",
    "  \n",
    "  print(f'JMAX= {size:.0f} TMAX= {T_1:.2f} T1= {T_1:.2f} T2= {T_2:.2f}')\n",
    "  print(f'BETA= {beta:.5f} DELTA= {delta:.5f}')\n",
    "  print(f'DELTA_T= {delta_t:.5f} DELTA_X= {delta_x:.5f}')\n",
    "  print(f'S= {S:.5f} ALPHA= {alpha:.5f}')\n",
    "  print(f'U= {u:.5f} C= {C:.5f}\\n')\n",
    "\n",
    "  # Expressões que calculam os elementos aj, bj, cj,\n",
    "  a_j = 1 + beta*((1-2*delta)*C + 2*S)\n",
    "  b_j = beta*(-delta*C + S)\n",
    "  c_j = beta*((1-delta)*C + S)\n",
    "  iteration = 0\n",
    "\n",
    "  a = np.array([a_j for _ in range(size)])\n",
    "  b = np.array([b_j for _ in range(size)])\n",
    "  c = np.array([c_j for _ in range(size)])\n",
    "\n",
    "  for j in np.arange(0.0, final_t+delta_t, delta_t):\n",
    "    temperature = tdma(a, b, c, temperature, size, beta, delta)\n",
    "\n",
    "    # Lógica para pular algumas linhas de impressão quando o resultado é muito extenso\n",
    "    if j == final_t and iteration >= max_print_size:\n",
    "      print(\"...\")\n",
    "      print_temperature(j, temperature)\n",
    "    \n",
    "    if iteration < max_print_size:\n",
    "      print_temperature(j, temperature)\n",
    "\n",
    "    iteration += 1\n",
    "\n",
    "  print() # Só para pular uma linha\n",
    "  print_temperature(final_t, exact_final_temperature, solution_type=\"TE\")\n",
    "\n",
    "  quadratic_error = EMQ(temperature, exact_final_temperature, size)\n",
    "\n",
    "  print(f'\\nEMQ= {quadratic_error:.4f}\\n')\n",
    "\n",
    "  plot_solution(x_grid, temperature, exact_final_temperature)\n",
    "\n",
    "  return temperature\n",
    "  \n",
    "#####################################################################\n",
    "#  Variáveis Gerais\n",
    "#####################################################################\n",
    "\n",
    "u = 1.0 # Velocidade de advecção\n",
    "alpha = 0.1 # Difusividade térmica\n",
    "delta_x, delta_t = 0.2, 0.025\n",
    "final_t = 0.5\n",
    "\n",
    "C = (u*delta_t)/delta_x\n",
    "S = (alpha*delta_t)/(delta_x**2)\n",
    "\n",
    "T_1 = 1.0\n",
    "T_2 = 0.0\n",
    "\n",
    "X_1 = -2.0\n",
    "X_2 = 2.0\n",
    "\n",
    "L = X_2 - X_1\n",
    "\n",
    "size = int((L/delta_x) + 1)\n",
    "x_grid = np.linspace(X_1, X_2, size)\n",
    "max_print_size = 11 # Número máximo de linhas que serão printadas antes de pular as linhas restantes\n",
    "\n",
    "initial_temperature = np.array([initial_condition(x) for x in x_grid])\n",
    "exact_final_temperature = np.array([analytic_solution(x, final_t) for x in x_grid])\n",
    "\n",
    "#####################################################################\n",
    "#  1 ° Caso (beta=0.0, u=0.0, delta_x=0.2449, delta_t=0.1, final_t=1.0)\n",
    "#####################################################################\n",
    "\n",
    "# ftcs_solution = solve_partial_equation(initial_temperature, final_t, size, 0.0, 0.5)\n",
    "# upwind_solution = solve_partial_equation(initial_temperature, final_t, size, 0.0, 0.0)\n",
    "# lax_wendroff_solution = solve_partial_equation(initial_temperature, final_t, size, 0.0, 0.5*(1-C))\n",
    "\n",
    "# solutions = [\n",
    "#   {\n",
    "#     \"label\": \"FTCS\",\n",
    "#     \"array\": ftcs_solution\n",
    "#   },\n",
    "#   {\n",
    "#     \"label\": \"Upwind\",\n",
    "#     \"array\": upwind_solution\n",
    "#   },\n",
    "#   {\n",
    "#     \"label\": \"Lax-Wendroff\",\n",
    "#     \"array\": lax_wendroff_solution\n",
    "#   }\n",
    "# ]\n",
    "# plot_all_solution(x_grid, exact_final_temperature, solutions)\n",
    "\n",
    "#####################################################################\n",
    "#  2 ° Caso (beta=0.0, u=0.5, delta_x=0.2, delta_t=0.05, final_t=1.0)\n",
    "#####################################################################\n",
    "\n",
    "# ftcs_solution = solve_partial_equation(initial_temperature, final_t, size, 0.0, 0.5)\n",
    "# upwind_solution = solve_partial_equation(initial_temperature, final_t, size, 0.0, 0.0)\n",
    "# lax_wendroff_solution = solve_partial_equation(initial_temperature, final_t, size, 0.0, 0.5*(1-C))\n",
    "\n",
    "# solutions = [\n",
    "#   {\n",
    "#     \"label\": \"FTCS\",\n",
    "#     \"array\": ftcs_solution\n",
    "#   },\n",
    "#   {\n",
    "#     \"label\": \"Upwind\",\n",
    "#     \"array\": upwind_solution\n",
    "#   },\n",
    "#   {\n",
    "#     \"label\": \"Lax-Wendroff\",\n",
    "#     \"array\": lax_wendroff_solution\n",
    "#   }\n",
    "# ]\n",
    "# plot_all_solution(x_grid, exact_final_temperature, solutions)\n",
    "\n",
    "\n",
    "#####################################################################\n",
    "#  3 ° Caso (beta=1.0, u=1.0, delta_x=0.2, delta_t=0.025, final_t=0.5)\n",
    "#####################################################################\n",
    "# total_implicit_solution_1 = solve_partial_equation(initial_temperature, final_t, size, 1.0, 0.0)\n",
    "# total_implicit_solution_2 = solve_partial_equation(initial_temperature, final_t, size, 1.0, 0.5)\n",
    "# total_implicit_solution_3 = solve_partial_equation(initial_temperature, final_t, size, 1.0, 0.5*(1-C))\n",
    "\n",
    "# solutions = [\n",
    "#   {\n",
    "#     \"label\": \"Totalmente Implícito δ = 0,0\",\n",
    "#     \"array\": total_implicit_solution_1\n",
    "#   },\n",
    "#   {\n",
    "#     \"label\": \"Totalmente Implícito δ = 0,5\",\n",
    "#     \"array\": total_implicit_solution_2\n",
    "#   },\n",
    "#   {\n",
    "#     \"label\": \"Totalmente Implícito δ = 0.5*(1-C)\",\n",
    "#     \"array\": total_implicit_solution_3\n",
    "#   }\n",
    "# ]\n",
    "# plot_all_solution(x_grid, exact_final_temperature, solutions)\n",
    "\n",
    "#####################################################################\n",
    "#  4 ° Caso (beta= 0.5, u=1.0, delta_x=0.2, delta_t=0.025, final_t=0.5)\n",
    "#####################################################################\n",
    "crank_solution_1 = solve_partial_equation(initial_temperature, final_t, size, 0.5, 0.0)\n",
    "crank_solution_2 = solve_partial_equation(initial_temperature, final_t, size, 0.5, 0.5)\n",
    "crank_solution_3 = solve_partial_equation(initial_temperature, final_t, size, 0.5, 0.5*(1-C))\n",
    "\n",
    "solutions = [\n",
    "  {\n",
    "    \"label\": \"Crank-Nicolson δ = 0,0\",\n",
    "    \"array\": crank_solution_1\n",
    "  },\n",
    "  {\n",
    "    \"label\": \"Crank-Nicolson δ = 0,5\",\n",
    "    \"array\": crank_solution_2\n",
    "  },\n",
    "  {\n",
    "    \"label\": \"Crank-Nicolson δ = 0.5*(1-C)\",\n",
    "    \"array\": crank_solution_3\n",
    "  }\n",
    "]\n",
    "\n",
    "plot_all_solution(x_grid, exact_final_temperature, solutions)\n"
   ]
  }
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