{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "from mplsoccer import Pitch, VerticalPitch\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "from functions import plot_heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.randint(1,101,500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>minute</th>\n",
       "      <th>second</th>\n",
       "      <th>teamId</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>period</th>\n",
       "      <th>type</th>\n",
       "      <th>outcome</th>\n",
       "      <th>playerId</th>\n",
       "      <th>endX</th>\n",
       "      <th>endY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Start</td>\n",
       "      <td>Successful</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Villarreal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Start</td>\n",
       "      <td>Successful</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>49.9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>18.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>59.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>32.2</td>\n",
       "      <td>58.1</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Unsuccessful</td>\n",
       "      <td>2.0</td>\n",
       "      <td>69.6</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Villarreal</td>\n",
       "      <td>34.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FirstHalf</td>\n",
       "      <td>Pass</td>\n",
       "      <td>Successful</td>\n",
       "      <td>8.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   minute  second             teamId     x     y     period   type  \\\n",
       "0       0     0.0  Manchester United   0.0   0.0  FirstHalf  Start   \n",
       "1       0     0.0         Villarreal   0.0   0.0  FirstHalf  Start   \n",
       "2       0     0.0  Manchester United  49.9  50.0  FirstHalf   Pass   \n",
       "3       0     3.0  Manchester United  32.2  58.1  FirstHalf   Pass   \n",
       "4       0    32.0         Villarreal  34.4   0.0  FirstHalf   Pass   \n",
       "\n",
       "        outcome  playerId  endX   endY  \n",
       "0    Successful       NaN   NaN    NaN  \n",
       "1    Successful       NaN   NaN    NaN  \n",
       "2    Successful      18.0  31.5   59.6  \n",
       "3  Unsuccessful       2.0  69.6  100.0  \n",
       "4    Successful       8.0  36.5    9.5  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('europaFinal.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x121f32790>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+F4eADQBotFnbPkYpa03sRaD/uv7KqF6nWDlk1HN0eS3reRCwAQClS3WBGaCLip78N22FOi9UL7rKvXXnM1X4O/bdv9Bjl4mADQCojSInOFbZM92EKnZX2f6Ptm+zfavtj9s+zPYxtj9ne2v/+9FVjrHOy8otSp0u2V6mRgXs97///Xr/+99f9TAANBDvH81Wdv91XU90nEaX20Nsr5f0NklLEXGmpBWSLpJ0maQbImKjpBv6t2uvyAVWJvVfn3Dczv1fkwxXsstaQeShvatLed46alTAXl5e1vLyctXDANBAXXr/aEIlL5XU/dcD4wI9VezaWilpte2VktZI2i7pQklX9++/WtJrKhpb6dXrbGvHuIA87XJ9mE2jAjYAYLw6VvJG9V83Wdkhe9ol+rpcvZakiLhP0m9LukfS/ZJ2RsRnJR0XEff397lf0nOrG+X88q562JSAvP2Bo6oewkIRsAGgfWpdyRsYPsGxyMVXUrVy5FW+h597UlsKlez66H8ic6GkUySdIGmt7Z+f4vGbbG+xvWXHjh3JxzdP9XraqxvmVa+LtnwMV7dTrCDy6NXfPmjb1l2zXx69KQjYANAibankNSW8NmWcHfATkr4VETsi4ruSPinpZZIesH28JPW/P5j34IjYHBFLEbG0bt26hQ06T5H+6zbKW0Hkzr03VDCSNAjYANAidavkpWgPGVVJLqP/etoqtjRbyL7poe9O/ZhRut4e0nePpJfaXmPbks6VdLukayVd3N/nYkmfWvTAWDlktOwSfW1DwAaAdmlEJa9t61+nrGRzifTpRcRXJP2ZpK9K+rp6+WazpHdLOs/2Vknn9W9jjLJWEJG6s0SfRMAGgLapbSWvbkZVwGepYku0i1QtIt4ZEadHxJkR8QsR8UREPBwR50bExv73RxY5pmmr121uD+nSEn0SARsAWqWplbwiJziWIXXIXjTaQ7pp0hrYdZNdQaToCY5N7r+WemeaAwBaJCLeKemdQ5ufUK+avTCz9l8v+gqOg5C967HDDjrew4+vmeq5zjzqSd26c1WysaG5UlSvsyuI5C3RN5Di8uZlXCI9bwWRgbZeIn2ACjYAYKHq2n896aTJRVSxp+m/pnqNWZURpnEgAjYAoLbmDbW7Hjts/1cRwyG7jFaRlCuIoB3a3Hst9fqvyzjBcffebyZ/zlQI2ACA5KZpDymj/zovVKcK2cAk07SHLCpcl7k6yDiT+q/btv71AAEbALAwk9pDUqzEUTRIjzOuXaQOV3ikPQR1V7T/uq0I2ACARsoL0inC9UA2ZFPFRlGpqtfTXiK9zqZpD2lD9VoiYAMAKjRve0i2x3qecL1m/dP7v7JGhewyTnjkAjPdkrI1ZJY1pss80XFQvd7+wFFjx9bG1UMGCNgAgKRG9V/XdfWQ4VA9fLtuaA9pvrqd1LiIVUUG/dep2kPqfIKjRMAGAFRkuHq9iN7l4Sr3qDCd3T5p+b5psIJIuxVpD6lbuC5bF9tDJAI2AAC58sJ31b3YVK+brW7helLletyJipMe0+X2EImADQBYgFnaQ+p0efK8KnbK8dF/jTYr0h7Spuq1RMAGACRUdP3rWdpDpr1seQqz9GNzqXTkmaZ6Pc8KItl1p4tI2X89XL0u4+IyUv37ryUCNgCghR5+fM3+r7qYt/+a9pB6m2Z5viYp2iYyTTvJcHtI26rXEgEbAFCy4faQMk9uzAvVdQrZ6Ka69V6Pk1cBnxSes/cP916nXj2kKQjYAIDaGdXfPCosl1mtrvuyfcAijArZo7aPag+Zt3rdhPYQSTr4ovAAgEaz/WxJH5Z0pqSQ9EZJd0r6E0knS/q2pJ+NiEcrGuJ+s1SvZwnSDz++Zv8KILseOyzp0nvzKnKCI+0hk9V13jepej3JpEr2qOp1F1HBBoD2eZ+kz0TE6ZLOknS7pMsk3RARGyXd0L+dVN4JjpPaQ/LUafWQaY06wZH1rxeiknkvpe2/nvUEx2ywnfZEx2HzPn64ej2qPaSNvdcDBGwAaBHbR0r6UUkfkaSIeDIi/lnShZKu7u92taTXVDPCZ+RVr5scrlGdus77sqrXdawMT1r3Wpp/7eumtIdIBGwAaJvvlbRD0v+0/Q+2P2x7raTjIuJ+Sep/f26Vg8zTtHB99+61hfZj9ZCFmGve295ke4vtLTt27FjcqBMY1eu8yCp2dt/BeCad3Njm6rVEwAaAtlkp6cWSfj8ifkDSbk3xsXiZQSPbHrKIy6IPq+NqIlxgJpm55n1EbI6IpYhYWrduXVljbKVBuJ5UvZ5Xk6rXEic5AkDbbJO0LSK+0r/9Z+oFjQdsHx8R99s+XtKDeQ+OiM2SNkvS0tJSLGLAUvOq13m4wEyl5pr3i5bXZ33HvvvnusBMnlRV7BOO2zn2/kG4Hq5eZ7X90ujDCNgA0CIR8U+277V9WkTcKelcSd/of10s6d3975+qcJgHKCNc3717bS1COyc3LkYd5/2o/utRITp1uE5p1qCeqj2kadVriYANAG10iaSP2l4l6S5Jb1CvJfATtt8k6R5J/6bsQQyvIDJQdnvIoDe6LiEbC1OLeT9O2SH6ob2rk176vOgxpdG94F1FwAaAlomImyUt5dxVuwV5hwPwYK3qrGl6p4ueeFgHrH+dVlXzvq2XSC8ir+86dXtIE6vXEic5AgAWoMj613nhetz2Rdlz3+R/KvP6r2kPQUrjLjW+6OrxQ3tXHxCupzn+NO0hTQ3XEgEbAFCRbPV6UoiuOmQD80rdHjJqLeyyV/MYfv5R4XrcfwiKaHK4lgjYAICGqFPIblIrCpDCcNU6Tx0vgFMVAjYAoFLTBOdJ+5Z9UmOqtbRZ/xplSlnFLhKsU2t69VoiYAMAFijFCiJFQ3aVK4jQf41FG27VSBGKJz1HGb3fbQjXUsNWEbnvvvu0d+9eXXrppVUPpVLLy8t61pMLu/4DGu5Z33lMy8uP83uzvKzVqxdbhRnH9lslfTQiHq16LHVy+JHfOeD2rscOy93vOUfsGVtNZnm++mLuj5fygjODgDzt0n2Lrli30UIDtu1NkjZJ0kknnbTIQwNA3XyPpBttf1XSlZKujwj+5zxkELhHBW00UifnfpHgXNbVDusSmCetINKW6rW04IA97yV4169fL0l63/vel3ZgDXPppZfqprseqHoYaIinDztSp37vcfze1KyCHxG/avvXJL1cvQtifMD2JyR9JCLa86/MkFt3rhrZJjJcvR6+bzhkT6piD6vTSZJd1ra5v3vvN5OvhT0qjG/duU8bj3omum3dtVIbD39mtY5tew7VhjVPJB3LKFxYZjx6sAGgIv2q3T/1v/ZJOlrSn9l+T6UDq6m8AL7o0ExIT4O5f7CyqtdN0abqtUTABoBK2H6b7ZskvUfS/5H0f0XEv5f0Ekn/d6WDq7FZQ3Z2n8FzrFn/9MzjoMd7dl2e+6NCdN72WQN3nSvLp62u3cVkS0PABoBqHCvpZyLiFRHxpxHxXUmKiKclvaraoaWRXYou5aoai65kzxLEi1y5ssNaP/el0f3Gd+y7/6Cv1OocsruCgA0AFYiIX4+Iu0fcd/uix9M0o0J2XtCmraNemPuLQciuVqOW6QMAtMfdu9fO1WqRd+KjND5QjzuRctbjSb31vW/duarwc52z6tVcbAYTFVl5ZPhEx6yiIXtRJ0Z2CRVsAEClBiuBzLIU36yBeZ7+ayriGDbqBL1Jy9LNYuvO/DA9j217Dp264j0plGdXO+kiAjYAtJDtFbb/wfZf928fY/tztrf2vx9d1dimqfQOjAvERUP2PNXrvONPqr6n6MM+5ogXzf0cQFGzBO2sUZX0LiJgA0A7XSop2896maQbImKjpBv6t5N65PGbx94/64mOg3C7Zv3TI4P24Ud+Z+I62sPPl9q0l4E/Z9WrSxlHl9XxP5ZlVLGHbd2Vtlo8b9AeVuTKlKnXEq8aARsAWsb2Bkk/KenDmc0XSrq6//PVkl6ziLFM6jO+e/daSdO1iUwK2kW2zWrwXIM2kUVUsTGVhf/HsohFhOwyTArZs/Rud2WpPgI2ALTP70r6ZUnZFHpcRNwvSf3vz61iYAOztIkMm1TNnlTVTnW8rGmr2Ein6v9YTrpQyqwhu+gyfqmr2Cl0uQ+bgA0ALWL7VZIejIibZnz8JttbbG/ZsWNH4tHlm6WKPTBtu0eK9pDh0D5PFbtImwh92IXV/j+WdalkTxPGp61iz9OH3aY2EQI2ALTLj0j6KdvflnSNpB+3/ceSHrB9vCT1vz+Y9+CI2BwRSxGxtG7duuSDS3nBmYGyeqonGbWaCFXsxavLfyyLXO47RcgetZLIuOC8ddfK/V95t+dRpFWkSB92mxCwAaBFIuLyiNgQESdLukjS30bEz0u6VtLF/d0ulvSpMo6fd6LjqD7sbJvIuCr2nvsm/1NVJGSnCOKD50hZxUYStf6P5bA7995QWjV7ODQXCdGpgvbApCr2uD7stlSxCdgA0A3vlnSe7a2SzuvfrkQZVWxpfIAuGq5X/dSZ+7+KPFeKKjaricyv6v9YzmqRQbvoY/IUWVFkVBW7q33YBGwAaKmI+HxEvKr/88MRcW5EbOx/f6Tq8Ulpq9jzGg7V40K2NHlFkeGQPU8Vmz7smS38P5ZF2kSGTQrZRU90rLuibSJtqGITsAEApVvUZcHzKtXztIaMCtlV9X1jsjr8x7KMkL0o87SKZKvY87SJSM0P2QRsAEBSky44Ix3YJpK3ZN88l0+fxbhq9bj7UlWxaROBNF3ILuOS6dM4dvXe/V+TzNom0uSQTcAGANTGoE0kzyLaREbJC9mznvA4K9pEmmWWKnZdTKpiD4fq7O1JVexsm0iRi840NWQTsAEACzHcJjLpZMeqerFHGReypeInPLKiCMapS6vItCZVsuc52bGJIZuADQCoXN7Jjk0y7SXU0Q0pq9h5JzrO2iaydee+A76mMS5ID+6b5RLqk6xd/fxGBW0CNgAguSJ92NMoWsWepcI9abWQSftyCXWMU7cTHvMCde62GU92HA7ZKdpEspoSsgnYAICFGdcmMm7JvmF77nvWAWF6+HaZRgXyaarYeW0iXDYdZUt9YuQJx+3c/1VEqjWxmxCyCdgAgEbIW1FkkcE6azhkjzrhMYsqdneVecJj0dA8ab8iz5NtDxkO1dmgPdxGkrqKLdU/ZBOwAQClGNUmkqqKXdSiAnjeCY/0YmNg2pCd1yYy6wVnyqhcF1FGL3ZWnUM2ARsA0BiLWhe7iGl6t4EypQrQKZ5nVBV7YFSbyCxV7DojYAMAai9bxU4ZsucNyXmPH+7FHoXl+rqnirWxq7ggTbbCXfRkx1nVtYpNwAYAVG5Sm8iwOlWys/JWFMm2iaTow+ZEx+6Ypk2k6Oogi9TlKjYBGwBQmqJ92EUM92LXJWSPq2JPi0umt1/ZJzzOur71ONle6qL913lV7C4hYAMAGmvXY4cd8FUHRdbFBuoqL5hv23PozM+XrWIXaRNpy4oiBGwAaBHbJ9r+X7Zvt32b7Uv724+x/TnbW/vfj656rMOKtIlMWlFkmqCd8iTFUc81qQ8b3TRNFTvlaiLTPs+sF5sZGFftTrUmdl0RsAGgXfZJ+s8R8f2SXirpLbbPkHSZpBsiYqOkG/q3UaJsm0jq5frow8a8BuE6VVifZFybSIoqdt0QsAGgRSLi/oj4av/nxyXdLmm9pAslXd3f7WpJr6lmhM+YpQ9bKrYudl3aRfJwwZn0mvjJzbxV7HksKlQPTGoTaSMCNgC0lO2TJf2ApK9IOi4i7pd6IVzSc6sb2fRGrSYyqzLWsM4+J33YC9e5T25ShuQyA3fRkyKHTVvFrlsfNgEbAFrI9uGS/lzSf4iIx6Z43CbbW2xv2bFjR3kDLCDbhz1s3qs7ol2a9MlNGx198cn7v6bR5j5sAjYAtIztQ9QL1x+NiE/2Nz9g+/j+/cdLejDvsRGxOSKWImJp3bp1ScYzaqm+PNkTHbtgnovN0Iedr0mf3FRxsmORx5S1fvY0fdhNR8AGgBaxbUkfkXR7RPxO5q5rJV3c//liSZ9a9NjmNdwmUrSKPdyusehLnLOSyOK04ZObpitSxS7ah93kkx0J2ADQLj8i6Rck/bjtm/tfr5T0bknn2d4q6Wh9oyMAACAASURBVLz+7crNeqLjAK0iGKjbJzeLsqgl++Y16qqObdWogH3qqafq1FNPrXoYABqoK+8fEfH/RYQj4oUR8aL+13UR8XBEnBsRG/vfH6l6rEUM92GnPtkxtUVXx2kT6WnyJzdVriayKLOe6Nhkjeouv+SSS6oeAoCG4v2jvR5+fA1tGBh8cvN124Om/19R75OaT9h+k6R7JP2bisZXC7NUqbfuWnlAS8f2B45KGpg3HrVyf8/36SuPP2iMp60+t5H/sWhUBRsA0H5lnui46ArzJF07qbMsbfvkZpwyr+w4yUN7V+duf/Tqb+f+3GUEbABAo+S1iXS5F5s2keabpk1klLJD9rY9h469/9Grv024ziBgAwBqbdx62E3Q5fCP+igSwLP7lLVUX1cQsAEAlZplJZEmV7HL+A8DVezmS3Gy4yJaRR7au1rbHziq9OM0HQEbAFC6aS42IzW3N/nJa2+tegjouLyQPW/w3rqrtybGpDaRUboYyAnYAIDay6v6Tqpi73rssFLHBNTVHfvu3x+qy6pqdzE0T4OADQBorLqui73nvgP/ea3rOFEvqdfEnvdS6qP6sEetJjLOLI9pMgI2AKByeX3Yw20io3qXJ11CPRt2F9nCseuxwxbaF04fNsqU1yZSpIrd1Uo3ARsA0BjThuy6tYlkx5/XZz7vpeOBRRlUpMcF6OH78nq4s1XyvIp7Ey8yIxGwAQALMu2JjtJ0JzuOa8MYbtkow6jqOO0hmEYdLp0+qk1kuIqdDdnDYTp7e7g9ZPA8bUbABgDUQtHq7bhl7rJhdlQVu+w2kT33PWvh7SHAog2HbOmZoJ0XrmddgaSpCNgAgFrLq2JPG7KlcqvYRUL7pPYQYBZVVbGlA0P2cJU6b9u0mtoeIhGwAQA1MqqKPU/IlhZXxR6uXk/THpKi/5oTHdshxaXTy5QXsqVnQvVwsM7uk33spP7rJiNgA0BH2D7f9p22l21fVsUYZunDHhgVsied+JhtFSljRZHB84xbmq/pl3tHvS26ii0dHLLzWkCGt0/Tez3ta6rbf0oI2ADQAbZXSPqgpAsknSHptbbPqHZU+Wap5FYVsocfX6T3mvYQFDFtYFxEO8W4kC09E6hHBe5Rz9W26rVEwAaArjhb0nJE3BURT0q6RtKFVQykSBV7XKvIqIA6qppdVsjOPm5ca0iR6jXL8yGFWUL2nXtv2P+VZzj8TgrZebbuWllq9bqOCNgA0A3rJd2bub2tv62RxlWBZw3Z0wTtvHA9fKy8sZRdvZ6nBQf1M0vbQ9Fwmheq5wnZeQF65PaWV68lAjYAdIVztsVBO9mbbG+xvWXHjh2lDWaeKvbAtNXsUSE7L2jnhe28+7Lh+uHH14w9qXHUWKlel68O5x8s0riK9Lj7BvfnmRSypWcC9biK9ajLr086/jh167+WpPav9A0AkHoV6xMztzdI2j68U0RslrRZkpaWlg4K4Iv2+Sf/SuesevXYfW566Lt6ybGH5N53685VOvOoJ/ffvnv3Wj1v7e6cPunvaM36pw/YMq6iPQjl41YMKbIsH+G6fJnzD85T7/fgRtvXRsQ3qh1ZMbv3flNrVz9/psfO2mpx594bdNrqcyfut3XnPm08qniUHA7Xw6G9Da0hA1SwAaAbbpS00fYptldJukjStVUOqGg7Q5EQOk01++7da3Or2Q/evmZ/RTtvzezsfbseO6xwuEblanP+QZPkhd28do6tO/dNrErn7ZOqNaSO1WuJCjYAdEJE7LP9VknXS1oh6cqIuK3iYemRx28utHZzkUq29EylOK+inVfNHmfXY6Pvy1bAJ4VrqteVyzv/4IeyO9jeJGmTJJ100kmLG1lB81Sx55FXyR4E49NXHn/A9kkhO+85ho/VJgRsAOiIiLhO0nVVj2NWRUO2NDpoD8JvXtDObx0ZrUjVepHhmhMcR5p4/kHdWqPy1ClkS72QPByyi0gZrutavZYI2ACAihWtYkvPBNMyg/bA89buPuj5RlW9p1kthMr1whU6/6AJqgzZkgpXs0fpSriWCNgAgBqYJmRL5QbtgSKXOZ+mai2VF66pXo+1//wDSfepd/7B66od0uyqCtnS+Gr2wHDYHtdr3ZYVQ/IQsAEAtTBtyJYODKzz9Ghng3Je2B6176jnz0O4rkZdzz+YRx1D9kDRkxebfin0SQjYAIDamCVkD0xT1Z50MuS0Jl1Ahp7rajX9/IM8VYds6eCWkWkfX0TTgvUAARsAUCvzhGxpuqp2NhiPWku7yGOLjGVaBGhMUmXIlqYP2m2vWmcRsAEAtTNvyB6YpaqdyqzhmmCNaQxCaB2C9sAgcLf1BMYiCNgAgFpKFbKl6U+KTHGsaRGsMY+qq9lZ86xp3YZwLRGwAQA1NgidqYO2lD5sE6xRtTpUs+fRlnAtEbABAA2QOmhLBwfiWQL3vCcvEq5RhmxQHRe2iwTaRYX1NoVriYANAGiQMoL2wCIvAEOwxqLMG1zLroq3LVgPELABAI2TDahlhO0yEa7RRGUE7baGa4mADQBouKaEbYI12qBo+0nR52grAjYAoDXqGLYJ1miraavaXQjWAwRsAEArVR22Cdboii4F56II2A21Ys8jWn1Hq676OrMVex6WJP48Rlix5xFJx1U9DKBSw2G3rMBNqAYgEbAb6dRTT616CLVy3337JEnr1xMi8x3HnOkI2++V9GpJT0r6pqQ3RMQ/9++7XNKbJD0l6W0RcX1lA62BvCA8a+gmVAMYRsBuoEsuuaTqIQCop89Jujwi9tn+75Iul/R222dIukjSCySdIOlvbH9fRDxV4Vhrh6AMIJVnVT0AAEAaEfHZiNjXv/llSRv6P18o6ZqIeCIiviVpWdLZVYwRALqAgA0A7fRGSZ/u/7xe0r2Z+7b1tx3E9ibbW2xv2bFjR8lDBIB2ImADQIPY/hvbt+Z8XZjZ5x2S9kn66GBTzlNF3vNHxOaIWIqIpXXr1qV/AUBitt9r+w7bt9j+C9vPztx3ue1l23fafkWV40S30IMNAA0SET8x7n7bF0t6laRzI2IQordJOjGz2wZJ28sZIbBwnHuA2qGCDQAtYft8SW+X9FMRsSdz17WSLrJ9qO1TJG2U9PdVjBFIjXMPUEdUsAGgPT4g6VBJn7MtSV+OiDdHxG22PyHpG+q1jryFKh5a6o2S/qT/83r1AvfAyHMPgNQI2ADQEhExcsHziHiXpHctcDhAMrb/RtL35Nz1joj4VH+fmc89sL1J0iZJOumkk+YeL0DABgAAtVb2uQcRsVnSZklaWlrKDeHANOjBBgAAjcW5B6gjKtgAAKDJOPcAtUPABgAAjcW5B6gjWkQAAACAhAjYAAAAQEIEbAAAACAhAjYAAACQEAEbAAAASIiADQAAACTkZy54tOAD2zsk3V1w92MlPVTicKrEa2smXlszzfLanhcR68oYTN1N+T49SdPmVZPGW9ZYOzn3E877Js2hgSaOWUo77iTzvrKAPQ3bWyJiqepxlIHX1ky8tmZq82uru6b92TdpvE0aa5c08e+liWOW6jluWkQAAACAhAjYAAAAQEJNCdibqx5AiXhtzcRra6Y2v7a6a9qffZPG26SxdkkT/16aOGaphuNuRA82AAAA0BRNqWADAAAAjVDrgG37fNt32l62fVnV45mH7RNt/y/bt9u+zfal/e3H2P6c7a3970dXPdZZ2V5h+x9s/3X/ditem+1n2/4z23f0//5+uEWv7T/25+Ottj9u+7CmvjbbV9p+0PatmW0jX4vty/vvLXfafkU1o+6Gur+X2/627a/bvtn2lv622vweMLebpe7zPavucz8zzsb9DtQ2YNteIemDki6QdIak19o+o9pRzWWfpP8cEd8v6aWS3tJ/PZdJuiEiNkq6oX+7qS6VdHvmdlte2/skfSYiTpd0lnqvsfGvzfZ6SW+TtBQRZ0paIekiNfe1XSXp/KFtua+l/7t3kaQX9B/zof57DhJr0Hv5j0XEizJLfdXp9+AqMbcboUHzPavOc3/gKjXsd6C2AVvS2ZKWI+KuiHhS0jWSLqx4TDOLiPsj4qv9nx9XL6StV+81Xd3f7WpJr6lmhPOxvUHST0r6cGZz41+b7SMl/aikj0hSRDwZEf+sFry2vpWSVtteKWmNpO1q6GuLiC9IemRo86jXcqGkayLiiYj4lqRl9d5zkF5T38tr83vA3G6Ups73rNrM/YEm/g7UOWCvl3Rv5va2/rbGs32ypB+Q9BVJx0XE/VIvhEt6bnUjm8vvSvplSU9ntrXhtX2vpB2S/me//eXDtteqBa8tIu6T9NuS7pF0v6SdEfFZteC1ZYx6La19f6mhJvxZh6TP2r7J9qb+trr/HjC366lpf/5NnPsDtf4dWLnoA07BOdsav+SJ7cMl/bmk/xARj9l5L7NZbL9K0oMRcZPtc6oeT2IrJb1Y0iUR8RXb71M9Pi6bW79f7UJJp0j6Z0l/avvnqx3VwrTy/aWmmvBn/SMRsd32cyV9zvYdVQ9oDk34826zpv35t2nuD9Ti76DOFextkk7M3N6g3sfXjWX7EPXC9Ucj4pP9zQ/YPr5///GSHqxqfHP4EUk/Zfvb6n0c9uO2/1jteG3bJG2LiK/0b/+ZeoG7Da/tJyR9KyJ2RMR3JX1S0svUjtc2MOq1tO79pcZq/2cdEdv73x+U9BfqfZxc998D5nY9NerPv6Fzf6DWvwN1Dtg3Stpo+xTbq9RrWL+24jHNzL1S9Uck3R4Rv5O561pJF/d/vljSpxY9tnlFxOURsSEiTlbv7+lvI+Ln1Y7X9k+S7rV9Wn/TuZK+oRa8NvVaQ15qe01/fp6r3rkBbXhtA6Ney7WSLrJ9qO1TJG2U9PcVjK8Lav1ebnut7SMGP0t6uaRbVf/fA+Z2PdV6vmc1eO4P1Pt3ICJq+yXplZL+UdI3Jb2j6vHM+Vr+hXofUdwi6eb+1yslPUe9s1+39r8fU/VY53yd50j66/7PrXhtkl4kaUv/7+4vJR3dotf2G5LuUO9N9Y8kHdrU1ybp4+r1kn9XvQrGm8a9Fknv6L+33CnpgqrH3+avOr+Xq3eexdf6X7cNxlen3wPmdrO+6jzfh8ZZ+7mfGWvjfge4kiMAAACQUJ1bRAAAAIDGIWADAAAACRGwAQAAgIQI2AAAAEBCBGwAAAAgIQI2AAAAkBABGwAAAEiIgA0AQJ/tH7R9i+3D+le6u832mVWPCygbcz8tLjQDAECG7d+SdJik1ZK2RcR/q3hIwEIw99MhYAMAkGF7laQbJX1H0ssi4qmKhwQsBHM/HVpEAAA40DGSDpd0hHrVPKArmPuJUMEGACDD9rWSrpF0iqTjI+KtFQ8JWAjmfjorqx4AAAB1Yfv1kvZFxMdsr5D0Rds/HhF/W/XYgDIx99Oigg0AAAAkRA82AAAAkBABGwAAAEiIgA0AAAAkRMAGAAAAEiJgAwAAAAkRsAEAAICECNgAAABAQgRsAAAAICECNgAAAJAQARsAAABIiIANAAAAJETABgAAABIiYCdgO2zvtv2uEp77UNu7bH/X9m+lfn50C3MVXcXcRxcx76tDwE7nrIh4x+CG7c2277T9tO1fHPfA/iS90vZjtv/J9n8a3BcRT0TE4ZI+Wt7Q0THzzNWftf1F23tsfz57H3MVDTDP3P9t21ttP277DtuvH9zH3EfNzTPv32P73n4+udv2/udh3o9HwC7P1yT9P5K+WmDf/yJpo6TnSfoxSb9s+/zyhgYcYJq5+oik35X07lJHBCzGNHN/t6RXSzpK0sWS3mf7ZSWODSjLNPP+I5JOj4gjJb1M0uts/0yZg2uLlVUPoK0i4oOSZPs7BXZ/vaQ3RMSjkh61/QeSflHSZ8obIdAzzVyNiL/p7/tLZY8LKNuUc/+dmZtfsf2/Jf2wpC+WNDygFFPO+zuHNj0t6dQyxtU2VLArZvtoSSeo9z/Kga9JekE1IwIAjGN7taQflHRb1WMBymb7Mtu7JG2TtFbSxyoeUiMQsKt3eP/7zsy2nZKOqGAsAIDJrlCvEHJ91QMByhYR71Yvk7xY0h/pwLyCEQjY1dvV/35kZtuRkh6vYCwAgDFsv1fSmZJ+NiKi6vEAixA9/yBpr6TfqHo8TUDArli/7/p+SWdlNp8lPnoEgFqx/RuSLpD08oh4rOrxABVYKen5VQ+iCQjYJbG9yvZhkizpENuH2X5W/75zbGcrH38o6VdtH237dEn/VtJVCx80OmmauWp7RX/flZKe1d/3kGpGDsxnyrl/uaTXSTovIh6uZsTA/IrOe9vPsv3v+tnEts+W9BZJN1Q3+uYgYJfns+p9lPIySZv7P/9o/74TJX0ps+87JX1T0t2S/k7SeyOCFUSwKNPM1V/o3//7kv5l/+c/WNhIgbSmmfv/VdJJkrb2L66xy/avLHKwQCLTzPufVi+fPC7pjyW9v/+FCUwL2fz6S908Ien3IuLXCuz/YUl/GhETT5CxfaikByQdIuk9EUHvE2bGXEVXMffRRcz76hCwAQAAgIQmtoj0L+H9oO1bR9xv279ne9n2LbZfnH6YQDHMV3QR8x5dxdxHXRXpwb5K0rjLdl+g3mW+N0rapF5vJlCVq8R8RfdcJeY9uukqMfdRQxMDdkR8QdIjY3a5UNIf9tdI/LKkZ9s+PtUAgWkwX9FFzHt0FXMfdZViFZH1ku7N3N7W3wbUEfMVXcS8R1cx91GJlQmewznbcs+ctL1JvY9otHbt2pecfvrpCQ6PJrrpppseioh1FRya+YqpVDhXU2LeY2pdmvvMewykmvcpAvY29dZNHNggaXvejhGxWb01F7W0tBRbtmxJcHg0ke27Kzo08xVTqXCupsS8x9S6NPeZ9xhINe9TtIhcK+n1/TN1XyppZ0Tcn+B5gTIwX9FFzHt0FXMflZhYwbb9cUnnSDrW9jb1rjp4iCRFxBWSrpP0SknLkvZIekNZgwUmYb6ii5j36CrmPupqYsCOiNdOuD/UuzY9UDnmK7qIeY+uYu6jrlK0iAAAAADoI2ADAAAACRGwAQAAgIQI2AAAAEBCBGwAAAAgIQI2AAAAkBABGwAAAEiIgA0AAAAkRMAGAAAAEiJgAwAAAAkRsAEAAICECNgAAABAQgRsAAAAICECNgAAAJAQARsAAABIiIANAAAAJETABgAAABIiYAMAAAAJEbABAACAhAjYAAAAQEIEbAAAACAhAjYAAACQEAEbAAAASIiADQAAACREwAYAAAASImADAAAACRGwAQAAgIQI2AAAAEBCBGwAAAAgIQI2AAAAkBABG61i+3zbd9petn1Zzv1H2f4r21+zfZvtN1QxTiA15j66iHmPuiJgozVsr5D0QUkXSDpD0mttnzG021skfSMizpJ0jqT/YXvVQgcKJMbcRxcx71FnBGy0ydmSliPiroh4UtI1ki4c2ickHWHbkg6X9IikfYsdJpAccx9dxLxHbRGw0SbrJd2bub2tvy3rA5K+X9J2SV+XdGlEPJ33ZLY32d5ie8uOHTvKGC+QSrK5z7xHgzDvUVsEbLSJc7bF0O1XSLpZ0gmSXiTpA7aPzHuyiNgcEUsRsbRu3bq0IwXSSjb3mfdoEOY9aouAjTbZJunEzO0N6lUtst4g6ZPRsyzpW5JOX9D4gLIw99FFzHvUFgEbbXKjpI22T+mfxHKRpGuH9rlH0rmSZPs4SadJumuhowTSY+6ji5j3qK2VVQ8ASCUi9tl+q6TrJa2QdGVE3Gb7zf37r5D0m5Kusv119T5efHtEPFTZoIEEmPvoIuY96oyAjVaJiOskXTe07YrMz9slvXzR4wLKxtxHFzHvUVe0iAAAAAAJEbABAACAhAjYAAAAQEIEbAAAACAhAjYAAACQEAEbAAAASIiADQAAACREwAYAAAASImADAAAACRGwAQAAgIQI2AAAAEBCBGwAAAAgIQI2AAAAkBABGwAAAEiIgA0AAAAkRMAGAAAAEiJgAwAAAAkRsAEAAICECNgAAABAQgRsAAAAICECNgAAAJAQARsAAABIiIANAAAAJETABgAAABIiYAMAAAAJEbABAACAhAjYAAAAQEIEbAAAACAhAjYAAACQEAEbrWL7fNt32l62fdmIfc6xfbPt22z/3aLHCJSBuY8uYt6jrlZWPQAgFdsrJH1Q0nmStkm60fa1EfGNzD7PlvQhSedHxD22n1vNaIF0mPvoIuY96owKNtrkbEnLEXFXRDwp6RpJFw7t8zpJn4yIeyQpIh5c8BiBMjD30UXMe9QWARttsl7SvZnb2/rbsr5P0tG2P2/7JtuvX9jogPIw99FFzHvUFi0iaBPnbIuh2yslvUTSuZJWS/qS7S9HxD8e9GT2JkmbJOmkk05KPFQgqWRzn3mPBmHeo7aoYKNNtkk6MXN7g6TtOft8JiJ2R8RDkr4g6ay8J4uIzRGxFBFL69atK2XAQCLJ5j7zHg3CvEdtEbDRJjdK2mj7FNurJF0k6dqhfT4l6V/aXml7jaQfknT7gscJpMbcRxcx71FbtIigNSJin+23Srpe0gpJV0bEbbbf3L//ioi43fZnJN0i6WlJH46IW6sbNTA/5j66iHmPOiNgo1Ui4jpJ1w1tu2Lo9nslvXeR4wLKxtxHFzHvUVeFWkQmLeRu+yjbf2X7a/2F3N+QfqgAAABA/U0M2JmF3C+QdIak19o+Y2i3t0j6RkScJekcSf+j3w8FAAAAdEqRCnaRhdxD0hG2LelwSY9I2pd0pAAAAEADFAnYRRZy/4Ck71dveZyvS7o0Ip5OMkIAAACgQYoE7CILub9C0s2STpD0IkkfsH3kQU9kb7K9xfaWHTt2TD1YAAAAoO6KBOwiC7m/QdIno2dZ0rcknT78RCzkDgAAgLYrErCLLOR+j3qXIZXt4ySdJumulAMFAAAAmmDiOthFFnKX9JuSrrL9dfVaSt7evyQpAAAA0CmFLjQzaSH3iNgu6eVphwYAAAA0T6ELzQAAAAAohoANAAAAJETABgAAABIiYAMAAAAJEbABAACAhAjYAAAAQEIEbAAAACAhAjYAAACQEAEbAAAASIiADQAAACREwAYAAAASImADAAAACRGwAQAAgIQI2AAAAEBCBGwAAAAgIQI2AAAAkBABGwAAAEiIgA0AAAAkRMAGAAAAEiJgAwAAAAkRsAEAAICECNhoFdvn277T9rLty8bs94O2n7L9rxc5PqAszH10EfMedUXARmvYXiHpg5IukHSGpNfaPmPEfv9d0vWLHSFQDuY+uoh5jzojYKNNzpa0HBF3RcSTkq6RdGHOfpdI+nNJDy5ycECJmPvoIuY9aouAjTZZL+nezO1t/W372V4v6aclXbHAcQFlY+6ji5j3qC0CNtrEOdti6PbvSnp7RDw18cnsTba32N6yY8eOJAMESpJs7jPv0SDMe9TWyqoHACS0TdKJmdsbJG0f2mdJ0jW2JelYSa+0vS8i/nL4ySJis6TNkrS0tDT8pg3USbK5z7xHgzDvUVsEbLTJjZI22j5F0n2SLpL0uuwOEXHK4GfbV0n667xwDTQMcx9dxLxHbRGw0RoRsc/2W9U7U3yFpCsj4jbbb+7fTw8eWom5jy5i3qPOCNholYi4TtJ1Q9ty32Qj4hcXMSZgEZj76CLmPeqKkxwBAACAhAjYAAAAQEIEbAAAACAhAjYAAACQEAEbAAAASIiADQAAACREwAYAAAASImADAAAACRGwAQAAgIQI2AAAAEBCBGwAAAAgIQI2AAAAkBABGwAAAEiIgA0AAAAkRMAGAAAAEiJgAwAAAAkRsAEAAICECNgAAABAQgRsAAAAICECNgAAAJAQARsAAABIiIANAAAAJETABgAAABIiYAMAAAAJEbABAACAhAjYAAAAQEIEbAAAACAhAjYAAACQEAEbAAAASIiADQAAACREwEar2D7f9p22l21flnP/z9m+pf/1RdtnVTFOIDXmPrqIeY+6ImCjNWyvkPRBSRdIOkPSa22fMbTbtyT9q4h4oaTflLR5saME0mPuo4uY96gzAjba5GxJyxFxV0Q8KekaSRdmd4iIL0bEo/2bX5a0YcFjBMrA3EcXMe9RWwRstMl6Sfdmbm/rbxvlTZI+PepO25tsb7G9ZceOHYmGCJQi2dxn3qNBmPeoLQI22sQ52yJ3R/vH1HuzffuoJ4uIzRGxFBFL69atSzREoBTJ5j7zHg3CvEdtrax6AEBC2ySdmLm9QdL24Z1sv1DShyVdEBEPL2hsQJmY++gi5j1qiwo22uRGSRttn2J7laSLJF2b3cH2SZI+KekXIuIfKxgjUAbmPrqIeY/aooKN1oiIfbbfKul6SSskXRkRt9l+c//+KyT9uqTnSPqQbUnaFxFLVY0ZSIG5jy5i3qPOCNholYi4TtJ1Q9uuyPz8S5J+adHjAsrG3EcXMe9RV7SIAAAAAAkRsAEAAICECNgAAABAQgRsAAAAICECNgAAAJAQARsAAABIiIANAAAAJETABgAAABIqFLBtn2/7TtvLti8bsc85tm+2fZvtv0s7TAAAAKAZJl7J0fYKSR+UdJ6kbZJutH1tRHwjs8+zJX1I0vkRcY/t55Y1YAAAAKDOilSwz5a0HBF3RcSTkq6RdOHQPq+T9MmIuEeSIuLBtMMEAAAAmqFIwF4v6d7M7W39bVnfJ+lo25+3fZPt16caIAAAANAkE1tEJDlnW+Q8z0sknStptaQv2f5yRPzjAU9kb5K0SZJOOumk6UcLAAAA1FyRCvY2SSdmbm+QtD1nn89ExO6IeEjSFySdNfxEEbE5IpYiYmndunWzjhkAAACorSIB+0ZJG22fYnuVpIskXTu0z6ck/UvbK22vkfRDkm5PO1QAAACg/ia2iETEPttvlXS9pBWSroyI22y/uX//FRFxu+3PSLpF0tOSPhwRt5Y5cAAAAKCOivRgKyKuk3Td0LYrhm6/V9J70w0NAAAAaB6u5AgAAAAkRMAGAAAAEiJg4Kk/uwAAIABJREFUAwAAAAkRsAEAAICECNgAAABAQgRsAAAAICECNgAAAJAQARsAAABIiIANAAAAJETABgAAABIiYAMAAAAJEbABAACAhAjYaBXb59u+0/ay7cty7rft3+vff4vtF1cxTiA15j66iHmPuiJgozVsr5D0QUkXSDpD0mttnzG02wWSNva/Nkn6/YUOEigBcx9dxLxHnRGw0SZnS1qOiLsi4klJ10i6cGifCyX9YfR8WdKzbR+/6IECiTH30UXMe9QWARttsl7SvZnb2/rbpt0HaBrmPrqIeY/aWln1AICEnLMtZtint6O9Sb2PFCXpCdu3zjG2eRwr6aGKjs3xpdMqPHZRyeZ+jea9VP3ffdePX/e5z7zn+GVIMu8J2GiTbZJOzNzeIGn7DPtIkiJis6TNkmR7S0QspRtqcVUem+P3jl/VsaeQbO7XZd5z/Hocv6pjF8S85/ilHD/F89Aigja5UdJG26fYXiXpIknXDu1zraTX988sf6mknRFx/6IHCiTG3EcXMe9RW1Sw0RoRsc/2WyVdL2mFpCsj4jbbb+7ff4Wk6yS9UtKypD2S3lDVeIFUmPvoIuY96oyAjVaJiOvUe0PNbrsi83NIessMT715zqHNo8pjc/zqj19ISXO/6tfO8bt9/ImY9xy/rsd3b+4t3tLSUmzZUvf2LpTF9k1V9lgBAACUhR5sAAAAICECNjptnsvsTnpsouP/XP+4t9j+ou2zMvd92/bXbd8861nPBY5/ju2d/WPcbPvXiz420fH/38yxb7X9lO1j+vfN9fptX2n7wVHLcZX9d18l5j3zvovzXmLuM/cXOPcjopKvl7zkJYHukrQlKpp7gy/1Tor5pqTvlbRK0tcknTG0zyslfVq9tVRfKukrRR+b6Pgvk3R0/+cLBsfv3/62pGNLfv3nSPrrWR6b4vhD+79a0t8mfP0/KunFkm4dcX9pf/fMe+Y98565z9xv99yngo0um+cyu0UeO/fxI+KLEfFo/+aX1VvDNZV5XsNCXv+Q10r6+JTHGCkiviDpkTG7lPl3XyXmPfO+i/NeYu4z9xc49wnY6LJ5LrOb4vK70z7Hm9T73/VASPqs7ZvcuwrZtIoe/4dtf832p22/YMrHpji+bK+RdL6kP89snvf1zzq+pl96mXnPvJ9lfE2f9xJzn7k/2/hmeu0s04cum+cyu4UvuT7n8Xs72j+m3pvtv8hs/pGI2G77uZI+Z/uO/v/QUx7/q5KeFxG7bL9S0l9K2jjN2Oc8/sCrJf2fiMhWH+Z9/bOOL8VrrxLzfvLxmfcHj6/p815i7jP3ZxvfTK+dCja6bJ7L7Ba+5Pqcx5ftF0r6sKQLI+LhwfaI2N7//qCkv1DvY6ykx4+IxyJiV//n6yQdYvvYomOf9/gZF2noo8IEr3/W8aV47VVi3jPvZxlf0+e9xNxn7s82vtlee8zYLD7vFyc5dpvqcZLjSkl3STpFz5y48IKhfX5SB5708PdFH5vo+CepdwWylw1tXyvpiMzPX5R0fgnH/x49s17+2ZLu6f9ZLOT19/c7Sr2+ubUpX3//sSdr9Akvpf3dM++Z98x75j5zv91znxYRdFbMcZndUY8t4fi/Luk5kj5kW5L2Re8CPcdJ+ov+tpWSPhYRnynh+P9a0r+3vU/SXkkXRe8dZ1GvX5J+WtJnI2J35uFzv37bH1fvjPljbW+T9E5Jh2SOXdrffZWY98x7dXDeS8x95v5i5z5XckQlzJUcAQBAS9GDDQAAACREwAYAAAASImADAID/v727D5asru88/v6Eh0QBZZSREB4c4o7AxCCQG0IkGzFUIpCNgBVTYBaQwhqpBRcqblZCaqMpdxPis64INQoLbBSKLKCsOwZZYpYYBR1geBhHdATEgQkzijoqljr43T/6XNK296Hv3NMP9973q6qrT5/f75zzPdPf6fre06d/P0ktssCWJEmSWmSBLUmSJLXIAluSJElqkQW2JEmS1CILbEmSJKlFFtiSJElSiyywJUmSpBZZYEuSJEktssCWJEmSWmSBLS0hSSrJ95P8twHs++eTfC/Jj5P817b3L82Hua+lyLwfHQtsael5aVX9OUCSFyf5eJJtSZ5MckuSQ6bbMMkfJflskqeS/GN3W1X9sKr2BD4y2PClnTaf3H9nkq8k+W6SLyU5c7LN3NeYm0/evz3J15NsT/K1JH8+2Wbez8wCW1ra9gZuBg4B9gU+D3x8hv5PAu8FLhl8aNJAzTX3vw/8AfBc4CzgfUleNuggpZbNNe+vAA6tqucALwNem+TVA49yEbDAlpawqvp8VV1RVU9W1Y+B9wCHJHn+NP3/b1VdDzw+1ECllu1E7r+lqr5UVT+pqjuBfwJ+c5gxS/O1E3n/YFV9v2vVT4B/M4xYFzoLbEndfhv4l6r65qgDkYas79xP8izg14ENA49KGqxZ8z7JRUm+B2wG9gA+OqzgFjILbEkAJDkAuBT4k1HHIg3TTuT+5cC9wC0DC0oasH7zvqouAfYCjgL+J/CdwUe38FlgSyLJcuBTwAer6tpRxyMNy1xzP8k7gJcAf1RVNej4pEGYa95Xxz3AD4C/HHR8i8Guow5A0mglWUbng/bmqmp9KCdpXM0195P8JXAi8PKq2j7o+KRBmOdn/q7Ai9qPavHxCra0hCV5Dp2vuf+5qi6aov24JNX1epckv0DnQ/bnkvxCkt2GF7HUjp3I/T8DXgv8rr9R0EI1l7xP8nNJ3pBkWTqOBs4Dbhtu1AuTBba0tJ1K58daZzcTBkw+DmraDwQ+19X/DDpfEV4G/Ntm+UPDDFhqyVxz/6+Ag4CvdPW9eMgxS/M117w/Ffgq8F3gb4H/3jw0CwtsaWn5IXBXkrcBVNXVVZWq2qOq9ux6PNr0fzld99tV1VVN/+7H6+CZWb2+DbwG+PFwT0ua1XxzP1X18z19/wrMfY21nc77ZkjKE6rqeU2fF1fVX03+9sC8n5n3YEtLSFX9whz7v34OfX9IZxIDaeyY+1qKzPvR8Qq2FpUkVybZmuSBadqT5P1JNiW5L8lRw45Rapt5L0njxQJbi81VwAkztJ8IrGweq+ncSywtdFdh3kvS2LDA1qJSVbcDT87Q5WTgmmZMzzuAvZPsN5zopMEw7yVpvFhga6nZH/h61+vNzTppMTPvJWmI/JGjlppMsW7K2diSrKbzdTp77LHHrx166KGDjEtj6q677vpGVS0fdRzzZN5rzhZJ7s/ZPvvsUytWrBh1GBqRtvLeAltLzWY643xOOgB4fKqOVbUGWAMwMTFR69atG3x0GjtJvjbqGFpg3mvOFknuz9mKFSsw75eutvLeW0S01NwMnNmMqnAM8J2q2jLqoKQBM+8laYi8gq1FJcm1wHHAPkk2A28BdgOoqsuBtcBJwCbgKeDs0UQqtce8l6TxYoGtRaWqTp+lvYDzhhSONBTmvSSNF28RkSRJI5XkwCSfTrIxyYYkF0zR59Akn0vywyT/qafthCQPNpMpXdS1/nlJbk3yleZ52TDOR7LAliRJo7YDeFNVHQYcA5yXZFVPnyeB/wi8s3tlkl2AS+lMqLQKOL1r24uA26pqJXBb81oaOAtsSZI0UlW1parubpa/C2ykZ6z2qtpaVV8Aftyz+dHApqp6qKp+BFxHZ3Ilmuerm+WrgVMGdArST7HAliRJYyPJCuBI4M4+N5lpIqV9J0fMaZ5fMM0xVydZl2Tdtm3bdiZs6adYYEuSpLGQZE/gBuDCqtre72ZTrJtyIqXpVNWaqpqoqonly5fc3DoaAAtsSZI0ckl2o1Ncf6SqbpzDpjNNpPREkv2a/e8HbG0jVmk2FtiSJGmkkgS4AthYVe+e4+ZfAFYmOTjJ7sBpdCZXonk+q1k+C/h4G/FKs3EcbEmSNGrHAmcA9ydZ36y7GDgIOhMmJflFYB3wHOAnSS4EVlXV9iTnA7cAuwBXVtWGZh+XANcnOQd4FHjN0M5IS5oFtiRJGqmq+gxT30vd3edf6Nz+MVXbWjozlvau/yZwfBsxSnPhLSKSJElSiyywJUmSpBZZYEuSJEkt6qvATnJCkgeTbEoy7TSjSX49ydNJ/rC9ECVJkqSFY9YCO8kuwKXAicAq4PQkq6bp9zd0fsUrSZIkLUn9XME+GthUVQ9V1Y+A64CTp+j3RjoDxDuIuyRJkpasfgrs/YGvd73e3Kx7RpL9gVOBy9sLTZIkSVp4+imwpxqXsnpevxd4c1U9PeOOktVJ1iVZt23btn5jlCRJkhaMfiaa2Qwc2PX6AODxnj4TwHWdmU7ZBzgpyY6q+lh3p6paA6wBmJiY6C3SJUmSpAWvnwL7C8DKJAcDjwGnAa/t7lBVB08uJ7kK+ERvcS1JkiQtBbMW2FW1I8n5dEYH2QW4sqo2JDm3afe+a0mSJKnRzxVsqmotsLZn3ZSFdVW9bv5hSZIkSQuTMzlKkiRJLbLAliRJI5XkwCSfTrIxyYYkF0zRJ0ne38wqfV+So5r1hyRZ3/XYnuTCpu2tSR7rajtp2OempamvW0QkSZIGaAfwpqq6O8lewF1Jbq2qL3b1ORFY2Tx+A7gM+I2qehA4Ap6ZVfox4Kau7d5TVe8cxklIk7yCLUmSRqqqtlTV3c3yd4GN9ExqR2cW6Wuq4w5g7yT79fQ5HvhqVX1t4EFLM7DAliRJYyPJCuBI4M6epllnlqYzlPC1PevOb24puTLJsmmO6UR4apUFtiRJGgtJ9gRuAC6squ29zVNs8sykdUl2B14F/F1X+2XAi+jcQrIFeNdUx62qNVU1UVUTy5cvn8cZSB0W2JIkaeSS7EanuP5IVd04RZfZZpY+Ebi7qp6YXFFVT1TV01X1E+BDwNHtRy79LAtsSZI0UkkCXAFsrKp3T9PtZuDMZjSRY4DvVNWWrvbT6bk9pOce7VOBB1oMW5qWo4hIkqRROxY4A7g/yfpm3cXAQfDM5HZrgZOATcBTwNmTGyd5NvC7wBt69vv2JEfQuZXkkSnapYGwwNaikuQE4H3ALsCHq+qSnvbnAn9L50N7V+CdVfU/hh6o1DJzXwtZVX2Gqe+x7u5TwHnTtD0FPH+K9We0EqA0R94iokWjGf/0Ujr34a0CTk+yqqfbecAXq+qlwHHAu5ofxkgLlrkvSePFAluLydHApqp6qKp+BFxHZ9zUbgXs1dzvtyfwJJ0JDqSFzNyXpDFiga3FpJ8xUj8AHEbnl+f3Axc0vy7/GY6LqgWktdw37yVp/iywtZjMOEZq45XAeuCX6IyL+oEkz5lqZ46LqgWktdw37yVp/iywtZjMNkYqdH51fmMz1e4m4GHg0CHFJw2KuS9JY8QCW4vJF4CVSQ5ufrx1Gp1xU7s9ChwPkGRf4BDgoaFGKbXP3JekMeIwfVo0qmpHkvOBW+gMVXZlVW1Icm7TfjnwNuCqJPfT+Vr9zVX1jZEFLbXA3Jek8WKBrUWlqtbSmYyge93lXcuPA7837LikQTP3JWl8eIuIJEmS1CILbEmSJKlFFtiSJElSiyywJUmSpBZZYEuSJEktssCWJEkjleTAJJ9OsjHJhiQXTNEnSd6fZFOS+5Ic1dX2SJL7k6xPsq5r/fOS3JrkK83zsmGdk5Y2C2xJkjRqO4A3VdVhwDHAeUlW9fQ5EVjZPFYDl/W0v6Kqjqiqia51FwG3VdVK4LbmtTRwFtiSJGmkqmpLVd3dLH8X2Ajs39PtZOCa6rgD2DvJfrPs+mTg6mb5auCUFsOWpmWBLUmSxkaSFcCRwJ09TfsDX+96vZl/LcIL+FSSu5Ks7uqzb1VtgU4RD7xgmmOuTrIuybpt27bN/yS05FlgS5KksZBkT+AG4MKq2t7bPMUm1TwfW1VH0bmN5Lwkvz2X41bVmqqaqKqJ5cuXzzluqZcFtiRJGrkku9Eprj9SVTdO0WUzcGDX6wOAxwGqavJ5K3ATcHTT54nJ20ia562DiV76aRbYkiRppJIEuALYWFXvnqbbzcCZzWgixwDfqaotSfZIsleznz2A3wMe6NrmrGb5LODjAzsJqcuuow5AkiQteccCZwD3J1nfrLsYOAigqi4H1gInAZuAp4Czm377Ajd1anR2BT5aVX/ftF0CXJ/kHOBR4DWDPxXJAluSJI1YVX2Gqe+x7u5TwHlTrH8IeOk023wTOL6NGKW58BYRSZIkqUUW2JIkSVKLLLAlSZKkFllgS5IkSS2ywJYkSZJaZIEtSZIktcgCW5IkSWqRBbYkSZLUIgtsSZIkqUUW2JIkSVKLLLAlSZKkFllgS5IkSS2ywJYkSZJaZIEtSZJGKsmBST6dZGOSDUkumKJPkrw/yaYk9yU5arZtk7w1yWNJ1jePk4Z5Xlq6dh11AJIkacnbAbypqu5OshdwV5Jbq+qLXX1OBFY2j98ALmueZ9v2PVX1zuGdiuQVbC0ySU5I8mBzheOiafoc11zJ2JDk/w07RmkQzH0tZFW1parubpa/C2wE9u/pdjJwTXXcAeydZL8+t5WGygJbi0aSXYBL6VzlWAWcnmRVT5+9gQ8Cr6qqXwFeM/RApZaZ+1pMkqwAjgTu7GnaH/h61+vN9BTS02x7fnNLyZVJlk1zzNVJ1iVZt23btnnFL4EFthaXo4FNVfVQVf0IuI7OFY9urwVurKpHAapq65BjlAbB3NeikGRP4Abgwqra3ts8xSY1y7aXAS8CjgC2AO+a6rhVtaaqJqpqYvny5fM8C8kCW4vLrFc3gBcDy5L8Y5K7kpw5tOikwTH3teAl2Y1OgfyRqrpxii6bgQO7Xh8APD7TtlX1RFU9XVU/AT5E549RaeAssLWYzHh1o7Er8GvA7wOvBP5LkhdPuTO/MtTC0Vrum/cahSQBrgA2VtW7p+l2M3BmM5rIMcB3qmrLTNsm2a/r5anAAwMIX/oZjiKixWTaqxs9fb5RVd8Hvp/kduClwJd7d1ZVa4A1ABMTE73FijROWst9814jcixwBnB/kvXNuouBgwCq6nJgLXASsAl4Cjh7pm2rai3w9iRH0PmD8xHgDYM/FckCW4vLF4CVSQ4GHgNOo3PfabePAx9IsiuwO50hnt4z1Cil9pn7WtCq6jNM/U1Md58CzpvLtlV1RisBSnNkga1Fo6p2JDkfuAXYBbiyqjYkObdpv7yqNib5e+A+4CfAh6vKrwy1oJn7kjReLLC1qDRfCa7tWXd5z+t3AO8YZlzSoJn7kjQ+/JGjJEmS1CILbEmSJKlFFtiSJElSiyywJUmSpBZZYEuSJEktssCWJEmSWmSBLUmSJLXIAluSJElqkQW2JEmS1CILbEmSJKlFFtiSJElSiyywJUnSSCU5MMmnk2xMsiHJBVP0SZL3J9mU5L4kR3W1nZDkwabtoq71z0tya5KvNM/LhnVOC83H7nmMYy/5Bw6+6P9w7CX/wMfueWzUIS1oFtiSJGnUdgBvqqrDgGOA85Ks6ulzIrCyeawGLgNIsgtwadO+Cji9a9uLgNuqaiVwW/NaPT52z2P82Y3389i3f0ABj337B/zZjfdbZM+DBbYkSRqpqtpSVXc3y98FNgL793Q7GbimOu4A9k6yH3A0sKmqHqqqHwHXNX0nt7m6Wb4aOGXAp7IgveOWB/nBj5/+qXU/+PHTvOOWB0cU0cJngS1JksZGkhXAkcCdPU37A1/ver25WTfdeoB9q2oLdIp44AXTHHN1knVJ1m3btm2+p7DgPP7tH8xpvWZngS1JksZCkj2BG4ALq2p7b/MUm9QM6/tWVWuqaqKqJpYvXz6XTReFX9r7WXNar9lZYEuSpJFLshud4vojVXXjFF02Awd2vT4AeHyG9QBPNLeR0DxvbTvuxeBPX3kIz9ptl59a96zdduFPX3nIiCJa+CywJUnSSCUJcAWwsarePU23m4Ezm9FEjgG+09z28QVgZZKDk+wOnNb0ndzmrGb5LODjAzuJBeyUI/fnr1/9q+y/97MIsP/ez+KvX/2rnHJk723w6teuow5AkiQteccCZwD3J1nfrLsYOAigqi4H1gInAZuAp4Czm7YdSc4HbgF2Aa6sqg3NPi4Brk9yDvAo8JrhnM7Cc8qR+1tQt8gCW5IkjVRVfYap76Xu7lPAedO0raVTgPeu/yZwfBsxSnPhLSKSJElSi/oqsKebIamr/Y+bWZXuS/LZJC9tP1RJkiRp/M1aYM8yQ9Kkh4GXV9XhwNuANW0HKkmSJC0E/VzBnmmGJACq6rNV9a3m5R10hsiRJEmSlpx+CuyZZkiayjnAJ+cTlCRJkrRQ9TOKSN8zJCV5BZ0C+7emaV8NrAY46KCD+gxRkiRJWjj6uYI90wxJz0hyOPBh4ORmWJyfsdSnIpUkSdLi10+BPdMMSQAkOQi4ETijqr7cfpiSJEnSwjDrLSLTzZCU5Nym/XLgL4DnAx/szHbKjqqaGFzYkiRJ0njqaybHqWZIagrryeXXA69vNzRJkiRp4XEmRy0qs02K1NXv15M8neQPhxmfNCjmviSNDwtsLRp9Too02e9v6Nz2JC145r4kjRcLbC0ms06K1HgjcAOwdZjBSQNk7kvSGLHA1mIy66RISfYHTgUuR1o8zH0teEmuTLI1yQPTtC9LclOS+5J8PslLmvWHJFnf9die5MKm7a1JHutqO2mY56SlywJbi0k/kyK9F3hzVT09686S1UnWJVm3bdu2VgKUBqS13DfvNUJXASfM0H4xsL6qDgfOBN4HUFUPVtURVXUE8GvAU8BNXdu9Z7K9GbRBGjgLbC0m/UyKNAFcl+QR4A/pDC15ylQ7c2IkLSCt5b55r1GpqtuBJ2fosgq4ren7JWBFkn17+hwPfLWqvjaYKKX+WGBrMZl1UqSqOriqVlTVCuB/Af+hqj42/FClVpn7WgruBV4NkORo4IV0/pjsdhpwbc+685vbSq5MsmyqHfvNjdpmga1Fo6p2AJOTIm0Erp+cFGlyYiRpMTL3tURcAixLsp7OD3bvAXZMNjZ/XL4K+LuubS4DXgQcAWwB3jXVjv3mRm3ra6IZaaGYbVKknvWvG0ZM0jCY+1rsqmo7cDZAOtNGP9w8Jp0I3F1VT3Rt88xykg8BnxhOtFrqvIItSZLGXpK9m6vU0Jk9+vam6J50Oj23hyTZr+vlqcCUI5RIbfMKtiRJGrkk1wLHAfsk2Qy8BdgNnvk25jDgmiRPA18Ezuna9tnA7wJv6Nnt25McQWdUnUemaJcGwgJbkiSNXFWdPkv754CV07Q9BTx/ivVntBOdNDfeIiJJkiS1yAJbkiRJapEFtiRJktQiC2xJkiSpRRbYkiRJUosssCVJkqQWWWBLkiRJLbLAliRJklpkgS1JkiS1yAJbkiRJapEFtiRJktQiC2xJkiSpRRbYkiRJUosssCVJ0sgluTLJ1iQPTNO+LMlNSe5L8vkkL+lqeyTJ/UnWJ1nXtf55SW5N8pXmedkwzkWywJYkSePgKuCEGdovBtZX1eHAmcD7etpfUVVHVNVE17qLgNuqaiVwW/NaGjgLbEmSNHJVdTvw5AxdVtEpkqmqLwErkuw7y25PBq5ulq8GTplvnFI/LLAlSdJCcC/waoAkRwMvBA5o2gr4VJK7kqzu2mbfqtoC0Dy/YKodJ1mdZF2Sddu2bRvYCWjpsMCWJEkLwSXAsiTrgTcC9wA7mrZjq+oo4ETgvCS/PZcdV9Waqpqoqonly5e3GrSWpl1HHYAkSdJsqmo7cDZAkgAPNw+q6vHmeWuSm4CjgduBJ5LsV1VbkuwHbB1J8FpyvIItSZLGXpK9k+zevHw9cHtVbU+yR5K9mj57AL8HTI5EcjNwVrN8FvDxYcaspcsr2JIkaeSSXAscB+yTZDPwFmA3gKq6HDgMuCbJ08AXgXOaTfcFbupc1GZX4KNV9fdN2yXA9UnOAR4FXjOcs9FSZ4EtSZJGrqpOn6X9c8DKKdY/BLx0mm2+CRzfSoDSHHiLiCRJktQiC2xJkiSpRRbYkiRJUosssCVJkqQWWWBLkiRJLbLA1qKS5IQkDybZlOSiKdr/OMl9zeOzSab85bm00Jj7kjQ+LLC1aCTZBbiUzlS5q4DTk6zq6fYw8PKqOhx4G7BmuFFK7TP3JWm8WGBrMTka2FRVD1XVj4DrgJO7O1TVZ6vqW83LO4ADhhyjNAjmviSNEQtsLSb7A1/ver25WTedc4BPTteYZHWSdUnWbdu2raUQpYFoLffNe0maPwtsLSaZYl1N2TF5BZ0i483T7ayq1lTVRFVNLF++vKUQpYFoLffNe0maP6dK12KyGTiw6/UBwOO9nZIcDnwYOLGZRlda6Mx9SRojXsHWYvIFYGWSg5PsDpwG3NzdIclBwI3AGVX15RHEKA2CuS9JY8QCW4tGVe0AzgduATYC11fVhiTnJjm36fYXwPOBDyZZn2TdiMKVWmPuazFIcmWSrUkemKZ9WZKbmqEmP5/kJc36A5N8OsnGJBuSXNC1zVuTPNbk/PokJw3rfLS0eYuIFpWqWgus7Vl3edfy64HXDzsuadDMfS0CVwEfAK6Zpv1iYH1VnZrkUDpDUx4P7ADeVFV3J9kLuCvJrVX1xWa791TVOwccu/RTvIItSZJGrqpuB56cocsq4Lam75eAFUn2raotVXV3s/67dL7FmWkUHWngLLAlSdJCcC/waoAkRwMvpGc89yQrgCOBO7tWn9/cVnJlkmXDCVVLnQW2JElaCC4BliVZD7wRuIfO7SEAJNkTuAG4sKq2N6svA14EHAFsAd411Y4d/11t8x5sSZI09pqi+WyAJAEebh4k2Y1Ocf2Rqrqxa5snJpeTfAj4xDT7XgOsAZiYmJhyDHlpLryCLUmSxl6SvZthKKHzg93bq2p7U2xfAWysqnf3bLNf18tTgSlHKJHa5hVsSZI0ckmuBY4D9kmyGXgLsBs8MyLOYcA1SZ4GvkhnRlKAY4EzgPub20cALm5G1nl7kiPozGz6CPCG4ZyNljoLbEmSNHJVdfos7Z8DVk6x/jNAptnmjHaik+YeMXgoAAAIKElEQVTGW0QkSZKkFllgS5IkSS2ywJYkSZJaZIEtSZIktcgCW5IkSWqRBbYkSZLUIgtsSZIkqUUW2JIkSVKLLLAlSZKkFllgS5IkSS2ywJYkSZJaZIEtSZIktcgCW5IkSWqRBbYkSRq5JFcm2ZrkgWnalyW5Kcl9ST6f5CVdbSckeTDJpiQXda1/XpJbk3yleV42jHORLLAlSdI4uAo4YYb2i4H1VXU4cCbwPoAkuwCXAicCq4DTk6xqtrkIuK2qVgK3Na+lgbPAliRJI1dVtwNPztBlFZ0imar6ErAiyb7A0cCmqnqoqn4EXAec3GxzMnB1s3w1cMogYpd6WWBLkqSF4F7g1QBJjgZeCBwA7A98vavf5mYdwL5VtQWgeX7B0KLVkmaBLUmSFoJLgGVJ1gNvBO4BdgCZom/NZcdJVidZl2Tdtm3b5h+plrxdRx2AJEnSbKpqO3A2QJIADzePZwMHdnU9AHi8WX4iyX5VtSXJfsDWafa9BlgDMDExMafiXJqKV7AlSdLYS7J3kt2bl68Hbm+K7i8AK5Mc3LSfBtzc9LsZOKtZPgv4+DBj1tLlFWxJkjRySa4FjgP2SbIZeAuwG0BVXQ4cBlyT5Gngi8A5TduOJOcDtwC7AFdW1YZmt5cA1yc5B3gUeM3wzkhLmQW2JEkauao6fZb2zwErp2lbC6ydYv03geNbCVCag75uEZluAPeu9iR5f9N+X5Kj2g9Vmp25qqXK3Jek8TFrgT3LAO6TTqTzV+VKYDVwWctxSrMyV7VUmfuSNF76uYI90wDuk04GrqmOO4C9m1/rSsNkrmqpMvclaYz0U2DPNID7XPpIg2auaqky9yVpjPTzI8d+BnDva5D3JKvpfDUJ8MMkD/Rx/EHZB/iGxx+ZQwawz9ZyFcYqX0f9Xi314w8iV9vm57THH4SFkPutu+uuu76R5Gst7GrU79/OWqhxQzuxv7CNQPopsDcz/QDuc+nzUwO5J1lXVRNzirZFHn/0xx/AblvLVRiffB2H92qpH39Ux54DP6c9/kCOP6pjj1JVLW9jP6N+/3bWQo0bxiv2fm4RmWkA90k3A2c2v1I/BvhOVW1pOVZpNuaqlipzX5LGyKxXsKcbwD3JuU375XTGnjwJ2AQ8RTOVqTRM5qqWKnNfksZLXxPNTDWAe/OBPblcwHlzPPaaOfZvm8dfhMcfUK7CaP+9FuV75fHb5ee0x1+Ex1/oFuq/30KNG8Yo9nQ+cyVJkiS1oa+ZHCVJkiT1ZyAF9nym7J1t25aO/8fNce9L8tkkL+1qeyTJ/UnW78wvqPs49nFJvtPsf32Sv+h325aO/6ddx34gydNJnte0zevcm31cmWTrdEN7Dfq934l4l2yu9nn8RZuvCy1X22Tem/dLMe8HJckuSe5J8okp2p6b5H8nuTfJhiRj89uH2XJpplwYpT7invbzY6iqqtUHnR/YfBX4ZWB34F5gVU+fk4BP0hmX9Rjgzn63ben4LwOWNcsnTh6/ef0IsM8Az/044BM7s20bx+/p/wfAP7Rx7l37+G3gKOCBadoH9t6bq+brYs1V8968N+/H9wH8CfDRaXLmYuBvmuXlwJPA7qOOuZ9cmi4XRv3oI+5pPz+G+RjEFez5TNnbz7bzPn5VfbaqvtW8vIPOeLBtmE/8Qzn3HqcD187xGDOqqtvpfIBMZ5Dv/Vwt5Vzt6/gD2nZn99Fqvi6wXG2TeW/eL8W8H4gkBwC/D3x4mi4F7JUkwJ50/u13DCm8+ZouF8bagD8/+jaIAns+U/a2MZXvXPdxDp2/0CYV8Kkkd6Uzo9kgjv2bzddFn0zyKzsZ93yOT5JnAycAN3Stns+5zzfGUUzjvJRzdS7HX6r5Ok652ibz3rzfmfgWet4PynuB/wz8ZJr2DwCH0ZnU6X7ggqqaru+wzZZL4/qez+X/QO/nx9D0NUzfHM1nyt6+p7Ge5/E7HZNX0PnH/62u1cdW1eNJXgDcmuRLzV/8bR37buCFVfW9JCcBHwNWziXueR5/0h8A/1xV3Vcy5nPu842xjfNvK5Z++iz0XO33+Es5X8cpV9tk3s9+fPP+Z+Nb6HnfuiT/DthaVXclOW6abq8E1gO/A7yIznv2T1W1fUhhzmS2XBrX97yv/wPTfH4MzSCuYM9nyt6+p7Ge5/FJcjidr3ROrqpvTq6vqseb563ATXS+Fmvt2FW1vaq+1yyvBXZLsk+/cc/3+F1Oo+drx3me+3xjbOP824qlnz4LPVf7Ov4Sz9dxytU2mffm/c7Et9DzfhCOBV6V5BE6t8z8TpK/7elzNnBjc5vFJuBh4NDhhjm1PnJpLN/zfv4PTPf5MVTV/s3nuwIPAQfzrz+E+JWePr/PT984//l+t23p+AfRmc3sZT3r9wD26lr+LHBCy8f+Rf51/PGjgUebf4ehnHvT77l07gPbo61z79n/Cqb/Ac3A3ntzdW7vl/m6cHLVvDfvzfvxfjD9D2MvA97aLO8LPMY8BxNoKd5Zc2m6XFgAcU/5+TH0WAf0D3AS8GU6vzj+82bducC5zXKAS5v2+4GJmbYdwPE/DHyLztc264F1zfpfbj407gU27Mzx+zj2+c2+76Vz8/3LhnnuzevXAdf1bDfvc2/2cy2wBfgxnb9+zxnme2+umq+LNVfNe/PevB/fB10Fds+/5y8Bn2r+LR8A/v2oY50pl/rNhTGPe8rPj2E/nMlRkiRJapEzOUqSJEktssCWJEmSWmSBLUmSJLXIAluSJElqkQW2JEmS1CILbEmSJKlFFtiSJElSiyywJUmSpBb9f8WEI4bdBbERAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,10),constrained_layout = True)\n",
    "\n",
    "gs = fig.add_gridspec(nrows = 3, ncols = 4)\n",
    "\n",
    "ax1 = fig.add_subplot(gs[0,0:2])\n",
    "ax1.set_title('[0,0]')\n",
    "boxplot = sns.boxplot(x = x, ax = ax1)\n",
    "\n",
    "ax3 = fig.add_subplot(gs[0,2])\n",
    "ax3.set_title('[0,2]')\n",
    "plot_heatmap(heat_df = df, teamId = 'Villarreal', p_type='Pass', ax=ax3)\n",
    "\n",
    "ax4 = fig.add_subplot(gs[0,3])\n",
    "ax4.set_title('[0,3]')\n",
    "plot_heatmap(heat_df = df, teamId = 'Manchester United', p_type='Pass', ax=ax4)\n",
    "\n",
    "ax5 = fig.add_subplot(gs[1:,0])\n",
    "ax5.set_title('[1,0]')\n",
    "\n",
    "ax6 = fig.add_subplot(gs[1,1])\n",
    "ax6.set_title('[1,1]')\n",
    "\n",
    "ax7 = fig.add_subplot(gs[1,2])\n",
    "ax7.set_title('[1,2]')\n",
    "\n",
    "ax8 = fig.add_subplot(gs[1,3])\n",
    "ax8.set_title('[1,3]')\n",
    "\n",
    "ax10 = fig.add_subplot(gs[2,1])\n",
    "ax10.set_title('[2,1]')\n",
    "\n",
    "ax11 = fig.add_subplot(gs[2,2])\n",
    "ax11.set_title('[2,2]')\n",
    "\n",
    "ax12 = fig.add_subplot(gs[2,3])\n",
    "ax12.set_title('[2,3]')\n",
    "ax12.scatter(5,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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