data_logistic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "345660fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to\n",
      "      ____              __\n",
      "     / __/__  ___ _____/ /__\n",
      "    _\\ \\/ _ \\/ _ `/ __/  '_/\n",
      "   /__ / .__/\\_,_/_/ /_/\\_\\   version 3.3.1\n",
      "      /_/\n",
      "\n",
      "Using Python version 3.9.12 (main, Apr  4 2022 05:22:27)\n",
      "Spark context Web UI available at http://jelly:4042\n",
      "Spark context available as 'sc' (master = local[*], app id = local-1671623668600).\n",
      "SparkSession available as 'spark'.\n",
      "successful\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "spark_name = os.environ.get('SPARK_HOME',None)\n",
    "if not spark_name:\n",
    "    raise ValueErrorError('spark环境没有配置好')\n",
    "sys.path.insert(0,os.path.join(spark_name,'python'))\n",
    "sys.path.insert(0,os.path.join(spark_name,'python/lib/py4j-0.10.7-src.zip'))\n",
    "exec(open(os.path.join(spark_name,'python/pyspark/shell.py')).read())\n",
    "import findspark\n",
    "findspark.init()\n",
    "import pyspark\n",
    "print(\"successful\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "484b867a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "successful\n"
     ]
    }
   ],
   "source": [
    "import findspark\n",
    "findspark.init()\n",
    "import pyspark\n",
    "print(\"successful\")\n",
    "import warnings#忽略版本等警告\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4b554495",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "# 构建SparkSession执行环境入口对象\n",
    "spark = SparkSession.builder. \\\n",
    "    appName('bigdata preana'). \\\n",
    "    master('local'). \\\n",
    "    getOrCreate()\n",
    "sc = spark.sparkContext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "950baff5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Records = 9226\n"
     ]
    }
   ],
   "source": [
    "dfread = spark.read.format('csv'). \\\n",
    "    option('sep', ','). \\\n",
    "        option('header', True). \\\n",
    "            option('encoding', 'GBK'). \\\n",
    "                load('D:\\pycharm\\pysparkProject_work\\\\bigdata_base\\data\\matches.csv',\n",
    "                     inferSchema=True)\n",
    "print('Total Records = {}'.format(dfread.count()))\n",
    "dfread = dfread.selectExpr(\"_c0 as index\",\"*\").drop(\"_c0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3a6a0f30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+---+----------+----------+--------------+----------+-----------+---------------+----------+--------------+----------+-----------+---------------+------+\n",
      "|index|win|firstBlood|firstTower|firstInhibitor|firstBaron|firstDragon|firstRiftHerald|towerKills|inhibitorKills|baronKills|dragonKills|riftHeraldKills|region|\n",
      "+-----+---+----------+----------+--------------+----------+-----------+---------------+----------+--------------+----------+-----------+---------------+------+\n",
      "|    0|  0|         0|         0|             0|         0|          0|              0|         0|             0|         0|          0|              0|   na1|\n",
      "|    1|  1|         1|         1|             0|         0|          1|              0|         1|             0|         0|          2|              0|   na1|\n",
      "|    2|  0|         0|         0|             0|         0|          0|              0|         0|             0|         0|          0|              0|   na1|\n",
      "|    3|  1|         1|         1|             1|         0|          1|              1|         8|             1|         0|          2|              1|   na1|\n",
      "|    4|  1|         1|         1|             1|         0|          1|              0|        10|             2|         0|          1|              0|   na1|\n",
      "+-----+---+----------+----------+--------------+----------+-----------+---------------+----------+--------------+----------+-----------+---------------+------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "dfread.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d51960d",
   "metadata": {},
   "source": [
    "dataframe的列不好处理，转成Pandas分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c88a4584",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.73186308, -0.99913326, -0.99244131, ..., -0.62371444,\n",
       "        -1.15938506, -0.94860297],\n",
       "       [-1.73148761,  1.00086749,  1.00761626, ..., -0.62371444,\n",
       "         0.33255811, -0.94860297],\n",
       "       [-1.73111214, -0.99913326, -0.99244131, ..., -0.62371444,\n",
       "        -1.15938506, -0.94860297],\n",
       "       ...,\n",
       "       [ 1.73111214,  1.00086749, -0.99244131, ...,  1.08992148,\n",
       "         1.0785297 ,  0.40637628],\n",
       "       [ 1.73148761,  1.00086749,  1.00761626, ...,  2.8035574 ,\n",
       "         1.82450128, -0.94860297],\n",
       "       [ 1.73186308, -0.99913326, -0.99244131, ..., -0.62371444,\n",
       "        -0.41341347,  0.40637628]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = dfread.drop('win').drop('region').toPandas()\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(df)\n",
    "scaled_data = scaler.transform(df)\n",
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b7123f39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0,\n",
       " 0.3711325291931783,\n",
       " 0.5222827276244917,\n",
       " 0.6249863766909862,\n",
       " 0.7169140092860143,\n",
       " 0.7983414196662131,\n",
       " 0.8718622589889559,\n",
       " 0.9191467963276541]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_var_ratio = []\n",
    "for n in range(0,8):\n",
    "    pca = PCA(n_components = n)\n",
    "    pca.fit(scaled_data)\n",
    "    pca.transform(scaled_data)\n",
    "    exp_var_ratio.append(sum(pca.explained_variance_ratio_))\n",
    "\n",
    "exp_var_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8ba8c672",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Ratio of Variance Explained')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AZ+PVwfhOC8Kfltn03wpkM0y66/Dj2lAQLetva5sBTdztG8vFRvqpfWATNpdDjahKD2XlHQAAAECDti0pV2l5pXql9dJyxV2SzCZDDwQs0zcFsZrYv5Vu6RmmMD9PNpdDnUN5BwAAANAgnSgs0Zc/HdEbG5MkSe09kiucd3a8Y0tvhTfzqrV8QFVQ3gEAAAA0GKVWmzYkHtPyHWlauzdLJVab/bXEonBFeyWe857EonBJUkATj1rLCVQV5R0AAABAvffLkTwtT0jX5z+mlzvKrWOQt4Z3b6l3Nh3Q3GOjtMDz3Gfe5x0bqVBfV8VG+jkjOlAplHcAAAAA9VJWQZE+33lEy3ek6dfMAvu4f2M33dAtWCOiQ9Sp5ZlNwML8PHXf4lLdk/y47mu+zL7b/LxjI7U2/1LNG3vJH573Djgb5R0AAABAvVFUatWavUe1PCFN3+7PltV2ZhXdzWLWoE4BGhEdov7tmsvVYi73vqujgjRvbIxmfOmmEQdj7eOhvq6aN/a/57wDdRXlHQAAAECdZhiGdqSc0PIdafrqpyPKLyqzv9Y9zFcjokN0XZcg+Xq6/eHnXB0VpMGdWmhbUq6yCooU0MRDsZF+rLijXqC8AwAAAKiT0k+c1qc70vTJjnQdyj5lH2/p46Hh0cG6MTpErZs3rtJnWswm9W7drLqjAjWO8g4AAACgzjhVXKZVuzO1fEeavj+UI+O3veUauVo0JKqFRsSEqHerZjKzWo6LDOUdAAAAgFPZbIa2JOVoeUK6Vu7OUGGJ1f5ar1Z+GhEdoiGXBKmxO/UFFy/+1w8AAADAKZKyT+mT326LTz9x2j4e0cxTN0aHaHj3YIX6eToxIVB3UN4BAAAA1Jq806X6atcRLU9I046UE/bxJu4uuq5rkEZEhygmvKlMJm6LB/4X5R0AAABAjSqz2rRxf7Y+3pGm+D1HVVJmkySZTVL/ds01IjpEgzsFysPV4uSkQN1FeQcAAABQI37NzNfyhDR99uMRHSsoto+3D2yiETHBGtYtWAHeHk5MCNQflHcAAAAA1SbnZLE+//GIlu9I0y9H8u3jfl5uur5rS90UE6LOLb25LR6oIso7AAAAgAtSXGbVul+z9HFCutYnZqnMduZ8N1eLSVd2CNCI6BBd3j5Abi5mJycF6i/KOwAAAIAqMwxDu9LytHxHmr746YhOFJbaX+sS4qMR0SG6vmtLNfVyc2JKoOGgvAMAAACotMy8In26M13Ld6TpQNZJ+3igt7uGdQ/WTdEhahvYxIkJgYaJ+1YAAABQr82dO1eRkZHy8PBQTEyMNm7c+IfzlyxZoq5du8rT01NBQUG6/fbblZOTU27O8uXL1alTJ7m7u6tTp0769NNPa/Ir1HmnS6z6bGe6xi3cqj7/XqtnV/2qA1kn5eFq1g3dWmrRHbHaPHWgpg3pSHEHaggr7wAAAKi3li5dqkmTJmnu3Lnq27ev5s+fryFDhmjPnj0KCws7Z/6mTZs0fvx4vfjiixo6dKjS09M1ceJE3XXXXfaC/v3332vUqFF6+umnNXz4cH366acaOXKkNm3apJ49e9b2V3QawzC0LSlXy3ekacXPmTpZXGZ/LTbCTyNignXNJUFq4uHqxJTAxcNkGIbh7BB1QX5+vnx8fJSXlydvb29nxwEAAEAl9OzZU9HR0Zo3b559rGPHjho2bJhmzpx5zvzZs2dr3rx5OnjwoH3s1Vdf1axZs5SamipJGjVqlPLz87Vy5Ur7nKuvvlpNmzbVBx98UIPfpm5IySnU8h1p+mRnmlJzT9vHQ/0a6cbuIRoRHaKwZp5OTAg0HFXpoay8AwAAoF4qKSlRQkKCpk6dWm48Li5OmzdvrvA9ffr00T/+8Q+tWLFCQ4YMUVZWlj7++GNde+219jnff/+9HnnkkXLvu+qqq/TSSy9V+3eoKwqKSrXi5wwtT0jXtsO59vHG7i665pIWGhEdoksj/GQ2c7wb4CyUdwAAANRL2dnZslqtCgwMLDceGBiozMzMCt/Tp08fLVmyRKNGjVJRUZHKysp0/fXX69VXX7XPyczMrNJn1ldWm6HvDmRr+Y40ff1LpopKbZIkk0m6rI2/RkSH6KrOLdTIzeLkpAAkyjsAAADqOZOp/GqwYRjnjJ21Z88ePfzww3riiSd01VVXKSMjQ1OmTNHEiRO1cOFChz6zvjmQVaCPE9L12c50ZeYX2cfbBDTWiOgQDe8erBY+Hk5MCKAilHcAAADUS/7+/rJYLOesiGdlZZ2zcn7WzJkz1bdvX02ZMkWS1KVLF3l5ealfv36aMWOGgoKC1KJFiyp9Zn1w/FSJvtx1RMsT0vRTWp593NfTVdd3bakR0SHqEuLTYH5BATRElHcAAADUS25uboqJiVF8fLyGDx9uH4+Pj9cNN9xQ4XsKCwvl4lL+r8AWy5nbws/u49y7d2/Fx8eXe+599erV6tOnT3V/hRpVarVp3a9ZWr4jTd/8mqVS65nv52I26fL2AbopJlhXdAiQuwu3xQP1AeUdAAAA9dajjz6qcePGqUePHurdu7cWLFiglJQUTZw4UZI0bdo0paena9GiRZKkoUOH6u6779a8efPst81PmjRJsbGxatmypSTpL3/5i/r3769nn31WN9xwgz7//HOtWbNGmzZtctr3rCzDMPTLkXx9nJCmL346otxTJfbXOrf01ojoEF3fraX8G7s7MSUAR1DeAQAAUG+NGjVKOTk5mj59ujIyMhQVFaUVK1YoPDxckpSRkaGUlBT7/AkTJqigoEBz5szR5MmT5evrqyuvvFLPPvusfU6fPn304Ycf6p///Kcef/xxtW7dWkuXLq3TZ7xn5Rfpsx/TtTwhXYlHC+zj/o3dNbx7S42ICVGHFhyHDNRnnPP+G855BwAAQH1SVGpV/J6jWr4jTd/uOybbb3+rd3MxK65ToEZEh6hfW3+5WMzODQrgvDjnHQAAAGiADMPQjpTj+jghXV/tOqKCojL7azHhTTUiOkTXdgmSTyNXJ6YEUBMo7wAAAEAdl3a8UJ/sSNcnO9J0OKfQPh7s20g3RgfrxugQRfp7OTEhgJpGeQcAAADqoFPFZVrxc4aW70jTlkO59nFPN4uGRAVpREywekU2k9nM8W7AxYDyDgAAANQRNpuh7w/laHlCmlbuztTpUqskyWSSerdqphHRIbo6qoW83PlrPHCx4d96AAAAwMkOHTup5TvS9OmOdB3JK7KPR/p7aUR0sIZHhyjYt5ETEwJwNso7AAAA4AR5haX6ctcRLd+Rpp0pJ+zj3h4uGtq1pW6MDlF0mK9MJm6LB0B5BwAAAGpNmdWmb/cf0/KEdMXvPaqSMpskyWI2aUC75hoRHaKBHQPk4WpxclIAdQ3lHQAAAKhhezPytTwhTZ/9eETZJ4vt4x1aNNFNMSG6vltLBTTxcGJCAHUd5R0AAACoAdkni/X5j0e0PCFNezLy7ePNvNx0Q7dgjYgJVueWPk5MCKA+obwDAAAA1aS4zKq1e7O0PCFN6/cdk9VmSJLcLGYN7BigEdEhGtC+uVwtZicnBVDfUN4BAACAC2AYhn5MPaHlO9L05U8Zyjtdan+ta6ivbooO1tCuLeXr6ebElADquzr7K7+5c+cqMjJSHh4eiomJ0caNG/9w/pIlS9S1a1d5enoqKChIt99+u3JycmopLQAAABoKq83Q9wdz9PmP6fr+YI599fz3MvJO67V1BzTohQ0aPnezFm9JUd7pUgX5eOj+y1trzaMD9PkDfTWudwTFHcAFq5Mr70uXLtWkSZM0d+5c9e3bV/Pnz9eQIUO0Z88ehYWFnTN/06ZNGj9+vF588UUNHTpU6enpmjhxou666y59+umnTvgGAAAAqI9W7c7QjC9/Vlref1fPQ3xc9c+hl+jqqCAVlpTp618ytTwhXd8dzJbxW6/3cDVrSFSQRkSHqHfrZrKYOd4NQPUyGYZR8a8Snahnz56Kjo7WvHnz7GMdO3bUsGHDNHPmzHPmz549W/PmzdPBgwftY6+++qpmzZql1NTUSl0zPz9fPj4+ysvLk7e394V/CQAAANQrq3Zn6L7FCRrovV33N1+q9h7JSiwK12vHRumb/EvVq5W/dqWd0KkSq/09PSP9NCImRNdcEqTG7nVyXQxAHVaVHlrnbpsvKSlRQkKC4uLiyo3HxcVp8+bNFb6nT58+SktL04oVK2QYho4ePaqPP/5Y11577XmvU1xcrPz8/HL/AAAA4OJktRma8eXPGui9XQvCn1a0V6K8LEWK9krUG+FP64om27Xt0FGdKrEqvJmnHhnUThv/eoWW3ttbI3uEUtwB1Lg6V96zs7NltVoVGBhYbjwwMFCZmZkVvqdPnz5asmSJRo0aJTc3N7Vo0UK+vr569dVXz3udmTNnysfHx/5PaGhotX4PAAAA1B/bknKVlleq+5svldlU/sZUs8nQAwHLZJWLnhzaSesfu1x/GdRWoX6eTkoL4GJU58r7WSZT+eeEDMM4Z+ysPXv26OGHH9YTTzyhhIQErVq1SklJSZo4ceJ5P3/atGnKy8uz/1PZ2+sBAADQ8OzJyJMktfdIrvD1s+NNvdzO+3dSAKhJde7+Hn9/f1kslnNW2bOyss5ZjT9r5syZ6tu3r6ZMmSJJ6tKli7y8vNSvXz/NmDFDQUFB57zH3d1d7u7u1f8FAAAAUC+UWW1a+2uWFm9J1sb92ZKkxKJwRXslnjM3sShckhTQxKNWMwLAWXVu5d3NzU0xMTGKj48vNx4fH68+ffpU+J7CwkKZzeW/isVikXRmxR4AAAA4Kyu/SK+s3a9+s9bp3vcS7MXd09XQa1mjZDPKr6zbDJPmHRupUF9XxUb6OSMyANS9lXdJevTRRzVu3Dj16NFDvXv31oIFC5SSkmK/DX7atGlKT0/XokWLJElDhw7V3XffrXnz5umqq65SRkaGJk2apNjYWLVs2dKZXwUAAAB1gGGcObt98dZkrf7lqMp+O7vdz8tNI3uEakzPMP1yJE/3LZbuSX5c9zVfZt9tft6xkVqbf6nmjb2EI+AAOE2dLO+jRo1STk6Opk+froyMDEVFRWnFihUKDz9zu1JGRoZSUlLs8ydMmKCCggLNmTNHkydPlq+vr6688ko9++yzzvoKAAAAqAPyTpdqeUKaFm9N1qFjp+zjl0Y01dhe4bo6qoXcXc7csRnq56l5Y2M040s3jTgYa58b6uuqeWPPnPMOAM5SJ895dwbOeQcAAGg4dqWd0OItyfripyMqKrVJkrzcLLoxOkRjeoWpQ4vz/33PajO0LSlXWQVFCmjiodhIP1bcAdSIqvTQOrnyDgAAAFTV6RKrvvzpiBZvTdautDz7eIcWTTS2V7iGdQ+u1HnsFrNJvVs3q8moAFBllHcAAADUawePndSSLSn6OCFV+UVlkiQ3i1nXdgnS2F5hig5ryvFuAOo9yjsAAADqnVKrTfF7jmrxlmRtPphjHw/1a6QxPcN1c0yImjXmWGAADQflHQAAAPVGRt5pfbA1RR9uT1VWQbEkyWySruwQqLG9wtS/bXOZeT4dQANEeQcAAECdZrMZ2nQgW+9tSdbavUf12ylv8m/srltiQzU6NkzBvo2cGxIAahjlHQAAAHXS8VMl+ighVUu2pig5p9A+3quVn8b2CldcpxZyczE7MSEA1B7KOwAAAOoMwzC0I+WElmxJ1lc/Z6ik7Mwxb03cXTQiJkRje4WpTUATJ6cEgNpHeQcAAIDTnSou0+c/HtF7W5K1NyPfPh4V7K2xPcN1fbeW8nTjr64ALl78FxAAAABOs+9ogRZvSdYnO9J1svjMMW/uLmYN7dpSY3uFq2uID8e8AYAo7wAAAKhlJWU2rfolU4u/T9a2w7n28Uh/L43pGaabYkLk6+nmxIQAUPdQ3gEAAFArUnML9cG2FC37IVXZJ0skSRazSYM7Bmpsr3D1ad2MY94A4Dwo7wAAAKgxVpuhDfuytHhLitYlZsn47Zi3QG933RIbptGXhqmFj4dzQwJAPUB5BwAAQLXLPlmsZT+k6v2tKUo7fto+flkbf43tFaaBHQPlauGYNwCoLMo7AAAAqoVhGNp++LgWb0nWyt0ZKrWeWWb3aeSqm2NCdGvPMLVq3tjJKQGgfqK8AwAA4IIUFJXq053pWrIlRYlHC+zj3UJ9NbZXuK7rEiQPV4sTEwJA/Ud5BwAAgEN+OZKnxVtS9PmP6SossUqSGrladEO3M8e8RQX7ODkhADQcPGgEAAAavLlz5yoyMlIeHh6KiYnRxo0bzzt3woQJMplM5/zTuXNn+5w33nhD/fr1U9OmTdW0aVMNGjRI27Ztq42v4nRFpVZ9siNNN879Tte+skkfbEtRYYlVbQIa68mhnbTl7wP17xFdKO4AUM1YeQcAAA3a0qVLNWnSJM2dO1d9+/bV/PnzNWTIEO3Zs0dhYWHnzH/55Zf173//2/5zWVmZunbtqptvvtk+tn79et1yyy3q06ePPDw8NGvWLMXFxemXX35RcHBwrXyv2pacc0pLtqboox9SdbywVJLkYjbpqqgWGtszXL1a+clk4pg3AKgpJsM4e2DHxS0/P18+Pj7Ky8uTt7e3s+MAAIBq0rNnT0VHR2vevHn2sY4dO2rYsGGaOXPmn77/s88+04033qikpCSFh4dXOMdqtapp06aaM2eOxo8fX23Zna3MatM3v2Zp8dYUfbvvmH082LeRbokN1chLQxXQhGPeAMBRVemhrLwDAIAGq6SkRAkJCZo6dWq58bi4OG3evLlSn7Fw4UINGjTovMVdkgoLC1VaWio/P78LyltXZOUX6cPtqfpgW4oy8ookSSaTNKBdc43tGa4rOgTIYmaVHQBqE+UdAAA0WNnZ2bJarQoMDCw3HhgYqMzMzD99f0ZGhlauXKn333//D+dNnTpVwcHBGjRo0AXldSbDMPT9oRwt3pKs1b8cVZntzM2Zfl5uurlHiMbEhiusmaeTUwLAxYvyDgAAGrzfP4ttGEalns9+55135Ovrq2HDhp13zqxZs/TBBx9o/fr18vCof7eQ550u1fKENC3ZmqyDx07Zx3uEN9XYXuEackkLubtwzBsAOBvlHQAANFj+/v6yWCznrLJnZWWdsxr/e4Zh6K233tK4cePk5uZW4ZzZs2frmWee0Zo1a9SlS5dqy10bdqWd0OItyfripyMqKrVJkrzcLBrWPVhje4WrYxB7AAFAXUJ5BwAADZabm5tiYmIUHx+v4cOH28fj4+N1ww03/OF7N2zYoAMHDujOO++s8PXnnntOM2bM0Ndff60ePXpUa+6acrrEqi93HdHiLcnalZZnH+/QoonG9ArX8O7BauzOXw8BoC7iv84AAKBBe/TRRzVu3Dj16NFDvXv31oIFC5SSkqKJEydKkqZNm6b09HQtWrSo3PsWLlyonj17Kioq6pzPnDVrlh5//HG9//77ioiIsK/sN27cWI0bN675L1VFB4+d1JItKfo4IVX5RWWSJDeLWddc0kJje4UrJrwpx7wBQB1HeQcAAA3aqFGjlJOTo+nTpysjI0NRUVFasWKFfff4jIwMpaSklHtPXl6eli9frpdffrnCz5w7d65KSkp00003lRv/17/+pSeffLJGvkdVlVptWrPnqN7bkqzNB3Ps46F+jXRrbLhG9ghRs8buTkwIAKgKznn/Dee8AwCAhiAj77Q+2JaqD7elKKugWJJkNklXdgjQmF7hGtC2ucwc8wYAdQLnvAMAAFxEbDZDmw5ka/GWZK39NUvW345582/splGXhuqW2DCFNOWYNwCozyjvAAAA9dTxUyX6KCFV729N0eGcQvt4z0g/je0Vrqs6t5Cbi9mJCQEA1YXyDgAAUI8YhqGdqWeOeftqV4ZKys4c89bE3UUjYkI0pmeY2gY2cXJKAEB1o7wDAADUA6eKy/T5j2eOeduTkW8f79zSW2N7heuGbi3l6cZf7QCgoeK/8AAAAHXYvqMFWrwlWZ/uSFdB8Zlj3txdzLquS0uN7RWmbqG+HPMGABcByjsAAEAdU1Jm06pfMrV4S7K2JeXaxyOaeWpMz3DdFBOipl5uTkwIAKhtlHcAAIA6Iu14od7fmqJlP6Qq+2SJJMliNmlQxwCN7RWuvq39OeYNAC5SlHcAAAAnstoMfbvvmBZvSdY3iVkyzpzypkBvd42+NEy3xIaphY+Hc0MCAJyO8g4AAOAE2SeLteyHM8e8pR0/bR/v26aZxvUK18COgXK1cMwbAOAMyjsAAEAtMQxDPyQf13vfJ2vl7gyVWs8ss/s0ctVNvx3z1qp5YyenBADURZR3AACAGlZQVKrPdqZr8ZYUJR4tsI93DfHR2F7hGtq1pTxcLU5MCACo6yjvAAAANWTPkXwt3pqsz3em61SJVZLk4WrWDV2DNbZXuC4J8XFyQgBAfUF5BwAAqAKrzdC2pFxlFRQpoImHYiP9ZPmfHeCLSq1a8XOGFm9J1o6UE/bx1s29NLZXuG6MDpFPI1cnJAcA1GeUdwAAgEpatTtDM778WWl5pfaxEB9X/XPoJeoY5G0/5u144ZnXXcwmXdW5hcb2ClevVn4ymTjmDQDgGMo7AABAJazanaH7FidooPd2vdJ6qdp7JCuxKFyvZY3SxMUlkv5bzFv6eOiW2DCNujRUAd4c8wYAuHAmwzh7mujFLT8/Xz4+PsrLy5O3t7ez4wAAgDrEajM04Nl4dTC+04Lwp2U2/fevTzbDpLsOP64NBdHq07aFxveO0BXtm8uFY94AAH+iKj2UP1UAAAD+xLakXKXller+5kvLFXdJMpsMPRCwTFa56P7L22hwp0CKOwCg2vEnCwAAwJ/YmpQjSWrvkVzh62fHswqKai0TAODiwjPvAAAAFbDZDK1LzNLrGw5q++HjkqTEonBFeyWeMzexKFySFNCE59sBADWD8g4AAPA/Ssps+uKnI1rw7UHtO3pSkuRilhq5SK8dG6U3PM995n3esZEK9XVVbKSfs2IDABo4yjsAAICkk8Vl+nBbihZuSlJG3pnb3xu7u2hMzzDdcVmkdqYc132LDd2T/Ljua77Mvtv8vGMjtTb/Us0be0m5894BAKhOlHcAAHBRO1ZQrHc2J+m975OVX1QmSWrexF139I3UmF5h8vZwlSRdHRWkeWNjNONLN404GGt/f6ivq+aNvURXRwU5JT8A4OJAeQcAABel5JxTWvDtIX2UkKaSMpskKdLfS/f0b6Xh3YPl4Wo55z1XRwVpcKcW2paUq6yCIgU08VBspB8r7gCAGkd5BwAAF5Wf0/L0+oaDWrk7Q7bfHl3vGuqr+wa00uBOLf60iFvMJvVu3awWkgIA8F+UdwAA0OAZhqFNB7L1+oaD+u5Ajn388vbNNXFAa/WM9JPJxOo5AKDuorwDAIAGq8xq04rdmZq/4aB+OZIv6czK+dAuQbp3QGt1DPJ2ckIAACqH8g4AABqc0yVWfZSQqjc2HlJq7mlJUiNXi0ZdGqq7+kUqpKmnkxMCAFA1lHcAANBgnCgs0aLvk/XO5sPKPVUiSfLzctNtvSM0vne4mnq5OTkhAACOobwDAIB6L/3Eab258ZCWbk9VYYlVkhTStJHu6d9KN8eEqpHbuTvHAwBQn1DeAQBAvfVrZr4WbDikL346orLfto7vGOStiQNa6dpLguRiMTs5IQAA1YPyDgAA6hXDMLQtKVevbziodYnH7ON9WjfTxAGt1a+tPzvHAwAaHMo7AACoF2w2Q6v3HNX8bw9qZ8oJSZLJJA2JaqF7+7dW11Bfp+YDAKAmUd4BAECdVlxm1Wc70zX/20M6dOyUJMnNxaybYkJ0T79WivD3cnJCAABqHuUdAADUSflFpXp/a4re2pSkrIJiSZK3h4vG9Q7XbX0iFNDEw8kJAQCoPZUq73fccYfDFzCZTFq4cKHD7wcAABeXrPwivfXdYS3ZkqyC4jJJUgtvD915WaRu6Rmmxu6sPQAALj4mwzCMP5tkNle8U+vZzWB+/xH/O24ymWS1Wi80Z43Lz8+Xj4+P8vLy5O3t7ew4AABcdA4dO6kF3x7SJzvSVWK1SZLaBDTWvf1b6YZuwXJzYed4AEDDUpUeWqlfXa9bt+6csWXLlmnevHnq06ePRo8erbCwMElSSkqKPvjgA33//fe67777NHLkSAe+AgAAuFjsTDmu+RsO6es9mTq7HhAT3lQTB7TWwA4BMpvZOR4AgEqV9wEDBpT7ecWKFZo/f77efPPNCm+pf/DBB/X222/r7rvv1jXXXFM9SQEAQINhGIbW7zum19cf1NakXPv4oI4BmjigtXpE+DkxHQAAdU+lbpv/vb59+8owDG3evPkP5/Xp00eS/nReXcBt8wAA1LxSq01f7Tqi+RsO6dfMAkmSi9mkYd2DdU//VmoX2MTJCQEAqD3Vftv87+3atUvXX3/9n86LjIzUF1984cglAABAA1JYUqal21P15sYkpZ84LUnycrPoltgw3dkvUkE+jZycEACAus2h8u7u7q4dO3b84RzDMLRjxw65u7s7FAwAANR/OSeL9e73yVr0/WGdKCyVJPk3dtPtfSM1tme4fDxdnZwQAID6waHyPnjwYC1btkwPP/ywnn32WTVqVP635adPn9bUqVO1b98+NqwDAOAilJpbqDc2HtKyH1JVVHpm5/jwZp66u18r3RQTIg9Xi5MTAgBQvzj0zHtycrJiY2OVnZ2tpk2b6pprrlFYWJhMJpOSk5O1YsUKHT9+XM2bN9eWLVsUERFRA9GrF8+8AwBw4X45kqf5Gw7pPz9nyGo781eMS4J9NHFAa10d1UIWdo4HAMCuKj3UofIuSYcOHdJ9992n+Pj4Cl8fOHCg5s2bpzZt2jjy8bWO8g4AgGMMw9D3B3M0b8NBbdyfbR/v19ZfEwe0Vp/WzWQyUdoBAPi9Gt+wTpJatWqlr7/+WocOHdJ3332nI0eOyDAMtWzZUn379lXr1q0d/WgAAFAPWG2GVu3O1PxvD2pXWp4kyWySruvSUvf0b6WoYB8nJwQAoOFwuLyf1apVK7Vq1ao6sgAA4BRz587Vc889p4yMDHXu3FkvvfSS+vXrd975xcXFmj59uhYvXqzMzEyFhIToH//4h+644w5J0htvvKFFixZp9+7dkqSYmBg988wzio2NrZXvU9OKSq1aviNNb3x7SIdzCiVJHq5mjewRqrv7tVKon6eTEwIA0PBccHmXpP379ys7O1vNmjVTu3btquMjAQCoFUuXLtWkSZM0d+5c9e3bV/Pnz9eQIUO0Z88ehYWFVfiekSNH6ujRo1q4cKHatGmjrKwslZWV2V9fv369brnlFvXp00ceHh6aNWuW4uLi9Msvvyg4OLi2vlq1yyss1eKtyXr7uyRlnyyRJPl6ump87wjd1jtczRpzwgwAADXF4WfeT58+rSeffFJvvvmmTpw4IUm67bbb9NZbb0mS3n77bb366qt666231K1bt+rKW2N45h0ALk49e/ZUdHS05s2bZx/r2LGjhg0bppkzZ54zf9WqVRo9erQOHTokPz+/Sl3DarWqadOmmjNnjsaPH19t2WtLRt5pvbUpSe9vTdGpEqskKdi3ke68LFKjLg2Vl3u1rAUAAHDRqUoPNTtygVOnTmnAgAGaPXu23N3dde211+r3vwPo37+/fvzxRy1dutSRSwAAUONKSkqUkJCguLi4cuNxcXHavHlzhe/54osv1KNHD82aNUvBwcFq166dHnvsMZ0+ffq81yksLFRpaWmly35dsf9ogR776Cf1n7VOb2xM0qkSqzq0aKIXR3XV+imX647LIinuAADUEof+xH322Wf1ww8/6J577tHLL78sd3d3mc3lfw/QunVrdejQQWvWrKlw5QIAAGfLzs6W1WpVYGBgufHAwEBlZmZW+J5Dhw5p06ZN8vDw0Keffqrs7Gzdf//9ys3Ntd999ntTp05VcHCwBg0aVO3foSb8cDhXr284qDV7s+xjPSP9NHFAa13evjk7xwMA4AQOlfelS5cqIiJCr732miwWy3nnhYeH68cff3Q0GwAAteL3ZdQwjPMWVJvNJpPJpCVLlsjH58xu6i+88IJuuukmvfbaa2rUqFG5+bNmzdIHH3yg9evXy8PDo2a+QDWw2Qx982uWXt9wUD8kH5ckmUxSXKdATRzQWt3Dmjo5IQAAFzeHyntKSoquu+66PyzukuTt7a3jx487FAwAgJrm7+8vi8Vyzip7VlbWOavxZwUFBSk4ONhe3KUzz8gbhqG0tDS1bdvWPj579mw988wzWrNmjbp06VIzX+IClZTZ9PmP6Vrw7SHtzzopSXKzmHVjdLDu7t9KrZs3dnJCAAAgOVjevby8lJ2d/afzkpKS1KxZM0cuAQBAjXNzc1NMTIzi4+M1fPhw+3h8fLxuuOGGCt/Tt29fffTRRzp58qQaNz5TbPft2yez2ayQkBD7vOeee04zZszQ119/rR49etTsF3HAyeIyfbgtRQs3JSkjr0iS1MTdRbf2CtMdfSMV6F137xIAAOBi5FB5j4mJ0aZNm5SamqrQ0NAK5/zyyy/auXPnef/yAwBAXfDoo49q3Lhx6tGjh3r37q0FCxYoJSVFEydOlCRNmzZN6enpWrRokSTp1ltv1dNPP63bb79dTz31lLKzszVlyhTdcccd9lvmZ82apccff1zvv/++IiIi7Cv7jRs3thd+ZzlWUKx3Nifpve+TlV905ni7gCbuuuOySN3aM0zeHq5OzQcAACrmUHl/8MEHFR8frxtvvFEffPCB2rRpU+715ORkjR8/XjabTQ8++GC1BAUAoCaMGjVKOTk5mj59ujIyMhQVFaUVK1YoPDxckpSRkaGUlBT7/MaNGys+Pl4PPfSQevTooWbNmmnkyJGaMWOGfc7cuXNVUlKim266qdy1/vWvf+nJJ5+sle/1e4ezT2nBxkP6OCFNJWU2SVIrfy/d07+VhkcHy93ljx+FAwAAzuXwOe+TJ0/Wiy++KJPJpI4dO2rv3r0KCgpSixYttGvXLpWVlelvf/tbvdlpnnPeAQAN0c9peXp9w0Gt3J0h229/4ncL9dXEAa0V1ylQZjM7xwMA4CxV6aEOl3dJWrZsmZ555hnt2rWr3Hj79u31z3/+U2PGjHH0o2sd5R0A0FAYhqGN+7P1+oaD2nwwxz5+RfvmmjigtWIj/TjuDQCAOqDWyvtZx44dU3JysqxWq0JCQhQcHHyhH1nrKO8AgPquzGrTf37O0PwNh7QnI1+SZDGbdH3Xlrp3QCt1aMGfbwAA1CVV6aEOPfP+e82bN1fz5s2r46MAAEAVnS6x6qOEVL2x8ZBSc09Lkhq5WjQ6NlR3XhapkKaeTk4IAAAuVLWUdwAAUPuOnyrRou+T9e73h5V7qkSS5Oflptt6R2h873A19XJzckIAAFBdHC7vWVlZmjt3rr799ltlZGSouLi4wnkmk0kHDx50OCAAACgv7Xih3tyYpKXbU3W61CpJCvVrpLv7tdLNMaFq5MbO8QAANDQOlfe9e/dqwIABysnJUTU8Mg8AACrh18x8zd9wSF/8dETW37aO7xTkrYmXt9Y1US3kYjE7OSEAAKgpDpX3KVOmKDs7WyNGjNDf//53tWvXTl5eXtWdDQCAi55hGNqalKvXNxzU+sRj9vG+bZpp4oDWuqyNPzvHAwBwEXCovG/cuFHt27fXsmXL+AsDAAA1wGYztHpPpl7fcEg/pp6QJJlN0pCoIN07oJW6hPg6NR8AAKhdDpV3wzB0ySWXUNwBAKhmxWVWfbojXQu+PaRD2ackSW4uZt0cE6K7+7VShD93ugEAcDFyqLz36NFDBw4cqO4sAABctPKLSrVkS4re+i5JxwrObALr7eGicb3DNaFPpJo3cXdyQgAA4EwOlfcnn3xSAwcO1LJlyzRy5MjqzgQAwEUjK79IC79L0vtbUlRQXCZJauHtobv6RWp0bJgau3OqKwAAuICj4v7yl79ozJgxWrFihQYPHqyQkJDz3kbfv39/hwMCANAQHTx2Ugs2HNKnO9NVYrVJktoGNNa9A1rr+q4t5ebCzvEAAOC/TIYDZ72ZzWaZTCb7MXF/9uy71Wp1LF0tys/Pl4+Pj/Ly8uTt7e3sOACAespqM7QtKVdZBUUKaOKh2Eg/Wcz//XNyR8pxzd9wUKv3HNXZP4F7hDfVxAGtdWWHAJnN7CcDAMDFoio91KGV9/Hjx7NZHQAAv7Nqd4ZmfPmz0vJK7WMhPq76x3WXyMPVonkbDmpbUq79tUEdAzVxQCv1iPBzRlwAAFCPOLTy3hCx8g4AuBCrdmfovsUJGui9Xfc3X6r2HslKLArXa1mjtLbgUklnfuntajFpWLdg3dO/ldoGNnFuaAAA4FRV6aGU999Q3gEAjrLaDA14Nl4djO+0IPxpmU3//aPVZph01+HHtfFkjMb3ba27+rVSkE8jJ6YFAAB1RVV6KLvhAABwgbYl5Sotr1T3N19arrhLktlk6IGAZSo1LBrUsQXFHQAAOKRSz7xPnz5dJpNJDzzwgPz8/DR9+vRKX8BkMunxxx93OCAAAHVdxonTkqT2HskVvn52PKugqNYyAQCAhqVSt82f3V1+7969ateu3Tm7zf/hBUwmdpsHADRIZVabPtmZrlkrf1X2qRJ90nqyor0Sz5mXcKqDRhycrQ/u7qXerZs5ISkAAKiLqn23+bfffluSFBQUVO5nAAAuRjabof/8nKEX1+zToWOnJEluZqteyxqlNyLOfeZ93rGRCvV1VWwku8oDAADHsGHdb1h5BwD8GcMwtGZvlp5fnahfMwskSU09XXXf5a0V6O2hSR/u1EDv7bqv+TL7bvPzjo3U2vxLNW9sjK6OCnLyNwAAAHVJjZ/zDgDAxcQwDG06kK3Zq/fpp9QTkqQm7i66u38r3d43Qk08XCVJ7i5mzfjSTSMOxtrfG+rrqnljL6G4AwCAC0J5BwDgD2w/nKvnvk7UtqRcSVIjV4sm9I3Qvf1bydfTrdzcq6OCNLhTC21LylVWQZECmngoNtJPFrPJGdEBAEADckHlfdOmTfr888+1f/9+FRQUVLiBnclk0tq1ay/kMgAA1LpdaSf0/Op92rDvmCTJzWLWmF5huv/yNmrexP2877OYTWxKBwAAqp1D5d0wDN15551699137YX997vPn/3ZZGK1AQBQfyRmFuiF+ER9/ctRSZKL2aSbe4TqoSvbqKUvZ7QDAADnMDvyptdff13vvPOOYmJiFB8frxtvvFGSlJiYqJUrV2rChAkym82aMmWKDh06VK2BAQCoCYezT+kvH+7U1S9/q69/OSqTSbqxe7DWTh6gmTdeQnEHAABO5dDK+zvvvCMvLy+tXLlSzZo10+LFiyVJbdu2Vdu2bXXVVVfpmmuu0ahRo9SnTx+Fh4dXa2gAAKpL+onTenXtfn2UkCar7cwdZNdc0kKPDGqntoFNnJwOAADgDIfK+969e9W7d281a3bmmb6zt8ZbrVZZLBZJ0k033aSYmBjNnj1bN9xwQzXFBQCgemQVFGnuuoN6f2uKSqw2SdIV7Ztrclx7RQX7ODkdAABAeQ7dNm+z2eTv72//2dPTU5J0/PjxcvPatm2rn3/+2aFgc+fOVWRkpDw8PBQTE6ONGzf+4fzi4mL94x//UHh4uNzd3dW6dWu99dZbDl0bANBwHT9Vopkr96r/rHV6Z/NhlVht6t2qmZbf11tv3x5LcQcAAHWSQyvvwcHBSktLs/989rb4nTt3avDgwfbxffv2ycWl6pdYunSpJk2apLlz56pv376aP3++hgwZoj179igsLKzC94wcOVJHjx7VwoUL1aZNG2VlZamsrKzK1wYANEwFRaV6c2OSFm5K0sniM38+dAv11ZSr2qtvG/8/eTcAAIBzmYyKznf7E7fccovi4+OVmZkpFxcX/fTTT+revbu6dOmiDz74QCEhIXr99df1t7/9TQMHDlR8fHyVPr9nz56Kjo7WvHnz7GMdO3bUsGHDNHPmzHPmr1q1SqNHj9ahQ4fk5+dX1a8jScrPz5ePj4/y8vLk7e3t0GcAAOqewpIyLfo+Wa9vOKgThaWSpI5B3nosrp2u7BDAqSgAAMBpqtJDHbpt/vrrr1dubq6++uorSVLXrl01evRo7dq1S1FRUfL19dXUqVPl4uKi//u//6vSZ5eUlCghIUFxcXHlxuPi4rR58+YK3/PFF1+oR48emjVrloKDg9WuXTs99thjOn369HmvU1xcrPz8/HL/AAAajuIyq975Lkn9Z63Xv1f+qhOFpWrd3Euv3Rqt/zx0mQZ2DKS4AwCAesOh2+ZvueUW3XjjjeVuiX/33XfVpUsXffbZZzp+/LjatWunv/71r4qNja3SZ2dnZ8tqtSowMLDceGBgoDIzMyt8z6FDh7Rp0yZ5eHjo008/VXZ2tu6//37l5uae97n3mTNn6qmnnqpSNgBA3VdqtWl5QppeWbtfR/KKJEmhfo00aWA73dCtpVwsDv3eGgAAwKkcum2+Jh05ckTBwcHavHmzevfubR//v//7P7333nv69ddfz3lPXFycNm7cqMzMTPn4nNlo6JNPPtFNN92kU6dOqVGjc8/mLS4uVnFxsf3n/Px8hYaGcts8ANRTVpuhr3Yd0Yvx+3Q4p1CS1MLbQw8NbKObY0Ll5kJpBwAAdUtVbpt3aOW9Jvn7+8tisZyzyp6VlXXOavxZQUFBCg4Othd36cwz8oZhKC0tTW3btj3nPe7u7nJ3d6/e8ACAWmcYhr7+5aheiE/UvqMnJUnNvNx03+WtNbZXuDxcLU5OCAAAcOHqXHl3c3NTTEyM4uPjNXz4cPt4fHz8ec+L79u3rz766COdPHlSjRs3lnRmp3uz2ayQkJBayQ0AqF2GYWjDvmN6fvU+/ZyeJ0ny9nDRvQNaa0KfCHm517k/4gAAABxWqb/ZtGrVyuELmEwmHTx4sErvefTRRzVu3Dj16NFDvXv31oIFC5SSkqKJEydKkqZNm6b09HQtWrRIknTrrbfq6aef1u23366nnnpK2dnZmjJliu64444Kb5kHANRvWw7l6PnVidp++LgkydPNojsvi9Rd/VrJp5Grk9MBAABUv0qV98OHD9dwjPJGjRqlnJwcTZ8+XRkZGYqKitKKFSvs58lnZGQoJSXFPr9x48aKj4/XQw89pB49eqhZs2YaOXKkZsyYUau5AQA168fUE3p+daI27s+WJLm5mDW+V7juu7y1mjXmUSgAANBw1bkN65yFc94BoO7am5Gv51fv05q9RyVJLmaTRseG6sEr2qqFj4eT0wEAADimXm9YBwDAWQePndSL8fv01a4MSZLZJN0YHaK/DGyrUD9PJ6cDAACoPZR3AECdk5pbqFfW7tfyHWmy/XZ/2HVdgjRpUDu1CWjs3HAAAABOcEHl/ejRo3rnnXe0adMmHTlyRJLUsmVLXXbZZRo/fryCgoKqJSQA4OJwNL9Ic745oA+3p6jUeqa1D+oYoEcHt1enljzSBAAALl4OP/O+aNEiPfjggzp16pR+/xEmk0menp565ZVXdPvtt1dL0JrGM+8A4Dw5J4v1+oaDWvR9sorLbJKky9r4a3JcO3UPa+rkdAAAADWjxp95X7lypW6//XaZzWaNHDlSt9xyiyIiImQYhlJSUvT+++/r448/1l133aUWLVpoyJAhDn0RAEDDlne6VG9uPKS3NiXpVIlVkhQT3lSPxbVX79bNnJwOAACg7nBo5f2yyy7Tli1b9MUXX+iaa66pcM6KFSs0dOhQ9enTRxs3brzgoDWNlXcAqD2nisv0zubDmr/hoPKLyiRJUcHemhzXXpe3ay6TyeTkhAAAADWvxlfef/zxR/Xr1++8xV2SrrnmGvXv31/bt2935BIAgAaoqNSqJVtTNG/9AWWfLJEktQ1orMlx7XRV5xaUdgAAgPNwqLx7eHioZcuWfzovKChIHh6cvwsAF7tSq03LfkjVq2sPKDO/SJIU3sxTjwxqp6FdW8piprQDAAD8EYfK+4ABA7R161bZbDaZzeYK51itVm3dulX9+/e/oIAAgPrLajP02c50vbx2v1JyCyVJLX089PDAthoREyJXS8V/hgAAAKA8h8r7v//9b8XGxuquu+7Siy++KB8fn3Kv5+fn65FHHtHx48c1c+bMagkKAKg/bDZDq37J1Avx+3Qg66Qkyb+xux64orVuiQ2Th6vFyQkBAADqF4c2rJs+fboOHDigxYsXq0mTJoqLi1N4eLgkKTk5WatXr9bJkyc1ZswYtWnTpvwFTSY9/vjj1ZO+GrFhHQBcOMMwtC4xS7O/3qc9GfmSJJ9Grpo4oLVu6xMuTzeHfmcMAADQIFWlhzpU3s1ms0wm0znnu1eGyWSS1Wqt8vtqGuUdAC7M5gPZmr06UTtSTkiSGru76M7LInVnv0h5e7g6NxwAAEAdVOO7zb/99tsOBQMANDwJycf1/OpEbT6YI0nycDXrtt4RundAa/l5uTk5HQAAQMPgUHm/7bbbqjsHANRZc+fO1XPPPaeMjAx17txZL730kvr161fh3PXr1+uKK644Z3zv3r3q0KGDJOmNN97QokWLtHv3bklSTEyMnnnmGcXGxtbcl6gBu9Pz9EL8Pn3za5YkydVi0q2xYXrgijYK8OakEQAAgOrEw4cA8AeWLl2qSZMmae7cuerbt6/mz5+vIUOGaM+ePQoLCzvv+xITE8vd+tS8eXP7/71+/Xrdcsst6tOnjzw8PDRr1izFxcXpl19+UXBwcI1+n+pwIKtAL8Tv04qfMyVJFrNJN0WH6KGBbRTS1NPJ6QAAABomh555f/755zV58uQ/nZebm6t7771XH330kUPhahPPvAOoSM+ePRUdHa158+bZxzp27Khhw4ZVeJrG2ZX348ePy9fXt1LXsFqtatq0qebMmaPx48dXV/Rql5JTqJfW7NNnP6bLZkgmk3R915aaNKidIv29nB0PAACg3qlKD3XogN0pU6Zo8ODBSk9PP++ctWvXqmvXrvrkk08cuQQAOF1JSYkSEhIUFxdXbjwuLk6bN2/+w/d2795dQUFBGjhwoNatW/eHcwsLC1VaWio/P78LzlwTMvJOa9onP+vK59frk51nivtVnQO18i/99PLo7hR3AACAWuDQbfNXXHGFvZy//vrruummm+yvlZaWaurUqXr55ZclSY899lj1JAWAWpadnS2r1arAwMBy44GBgcrMzKzwPUFBQVqwYIFiYmJUXFys9957TwMHDtT69evVv3//Ct8zdepUBQcHa9CgQdX+HS7EsYJizVt/UIu3JqukzCZJ6t+uuR6La6cuIb7ODQcAAHCRcai8r127VrNnz9Y///lPjRo1SuPHj9err76q5ORkjRkzRrt27VJYWJjeffddDRgwoLozA0CtMplM5X42DOOcsbPat2+v9u3b23/u3bu3UlNTNXv27ArL+6xZs/TBBx9o/fr18vCoG5u85RWWav63B/X2d4d1uvTM0Z6xkX56LK69YiPr5t0BAAAADZ3DG9Y99thjGjx4sG699VYtWrRIa9euVXZ2toqKinTrrbfqtddek4+PT3VmBYBa5e/vL4vFcs4qe1ZW1jmr8X+kV69eWrx48Tnjs2fP1jPPPKM1a9aoS5cuF5z3Qp0sLtNbm5L0xsZDKigqkyR1DfHR5Lj26tfW/7y/sAAAAEDNu6Dd5rt27aovv/xS0dHRSktLk8lk0rhx4/Tuu+9WVz4AcBo3NzfFxMQoPj5ew4cPt4/Hx8frhhtuqPTn7Ny5U0FBQeXGnnvuOc2YMUNff/21evToUW2ZHVFUatV73ydr3oaDyj1VIknq0KKJHh3cToM7BVLaAQAA6oALKu+fffaZ7rnnHuXn5ysmJka7d++2ry7NmTNHTZo0qZaQAOAsjz76qMaNG6cePXqod+/eWrBggVJSUjRx4kRJ0rRp05Senq5FixZJkl566SVFRESoc+fOKikp0eLFi7V8+XItX77c/pmzZs3S448/rvfff18RERH2lf3GjRurcePGtfbdSspsWro9Ra9+c0BZBcWSpFb+Xpo0uJ2uuyRIZjOlHQAAoK5wqLwXFhbq4Ycf1ttvvy1XV1fNmjVLkydP1u7duzVmzBi999572rhxoxYtWqTLLrusujMDQK0ZNWqUcnJyNH36dGVkZCgqKkorVqxQeHi4JCkjI0MpKSn2+SUlJXrssceUnp6uRo0aqXPnzvrPf/6ja665xj5n7ty5KikpKbfZpyT961//0pNPPlnj36nMatMnO9P18pr9Sj9xWpIU7NtIfxnUVjd2D5aLxaGDSAAAAFCDHDrnvV27djpw4IA6duyo999/X127drW/VlJSor/97W965ZVXZDabNWXKFD3zzDPVGromcM47gIbOZjP01c8Zeil+nw5ln5IkNW/iroeubKNRl4bK3cXi5IQAAAAXl6r0UIfKu9ls1oMPPqhZs2add3fk+Ph4TZgwQZmZmbJarVW9RK2jvANoqAzDUPyeo3ohfp9+zSyQJDX1dNV9l7fWuF4RauRGaQcAAHCGqvRQh26b/89//qMhQ4b84ZzBgwdr165duvfeex25BADgAhmGoU0HsjV79T79lHpCktTE3UV392+l2/tGqImHq3MDAgAAoNIcWnlviFh5B9CQbD+cq+e+TtS2pFxJUiNXi27vG6F7+reSr6ebk9MBAABAqoWV98oqKipSSUkJZRgAasmutBN6fvU+bdh3TJLk5mLW2J7huu/y1mrexN3J6QAAAOCoSm0p3KpVK/3tb3+r8LVHH31UH374YYWv3XffffLz83M8HQCgUhIzC3Tvez/o+jnfacO+Y3Ixm3RLbJjWP3a5nhjaieIOAABQz1Vq5f3w4cM6duxYha+99NJLmjBhgkaPHl3h69yVDwA1Jyn7lF5as09f/HREhiGZTNLwbsH6y6C2Cm/m5ex4AAAAqCY1ets8AKBmpJ84rVfW7NfHO9JktZ35Jek1l7TQI4PaqW1gEyenAwAAQHWjvANAPZJVUKS56w7q/a0pKrHaJElXtG+uyXHtFRXs4+R0AAAAqCmUdwCoB46fKtHr3x7Uu5sPq6j0TGnv3aqZHruqnWLC2VsEAACgoaO8A0AdVlBUqjc3JmnhpiSdLC6TJHUP89WUuPbq08bfyekAAABQWyjvAFAHFZaUadH3yXp9w0GdKCyVJHUK8tZjV7XTFe0DZDKZnJwQAAAAtanS5T0zM1PffvttlV7LzMx0PBkAXISKy6z6YGuK5qw7qOyTxZKk1s299Ojg9hoS1UJmM6UdAADgYmQyKnGWm9lsPu8qj2EYf/qa1Wq9sJS1ID8/Xz4+PsrLy5O3t7ez4wBooKw2Q9uScpVVUKSAJh6KjfSTxWxSqdWm5QlpemXtfh3JK5Ikhfo10qSB7TSse7AslHYAAIAGpyo9tFIr7/379+cWTQC4QKt2Z2jGlz8rLa/UPhbi46rBUcFa92uWDucUSpJaeHvooYFtdHNMqNxczM6KCwAAgDqkUuV9/fr1NRwDABq2VbszdN/iBA303q5XWi9Ve49kJRaFa07WKL393aWSTGrm5ab7Lm+tsb3C5eFqcXZkAAAA1CGVum3+YsBt8wBqitVmaMCz8epgfKcF4U/LbPrvf3Zthkl3HX5cO0t7asPfBsu7kasTkwIAAKA2VaWHcj8mANSwbUm5Sssr1f3Nl5Yr7pJkNhl6IGCZjhcZ+uVIvpMSAgAAoK6jvANADcsqOLMBXXuP5ApfPzt+dh4AAADwe5R3AKhh+afPbFCXWBRe4etnxwOaeNRaJgAAANQvlHcAqCGGYeid75L01Je/yKIyzckaJZtR/uQOm2HSvGMjFerrqthIPyclBQAAQF1HeQeAGlBQVKoH39+pJ7/cozKb1DXMX+sKLtU9yY8r4VQHnbQ2UsKpDron+XGtzb9U/7juEs5yBwAAwHlV6qg4AEDl7c3I1/1Ldigp+5RczCb949qOmtAnQl//kqkZX7ppxMFY+9xQX1fNG3uJro4KcmJiAAAA1HWUdwCoRst+SNXjn+1WcZlNLX08NGdMtKLDmkqSro4K0uBOLbQtKVdZBUUKaOKh2Eg/VtwBAADwpy64vP/444/avn27srOz1blzZ11//fWSpOLiYhUXF3NmOoCLQlGpVU98vlvLfkiTJA1o11wvjuomPy+3cvMsZpN6t27mjIgAAACoxxx+5n3v3r3q1auXYmJiNHHiRP3zn//UZ599Zn/9rbfeUtOmTbVq1arqyAkAdVZS9ikNe+07LfshTWaT9FhcO7094dJzijsAAADgKIfKe3Jysvr3769t27bphhtu0KxZs2QYRrk5o0ePlqurq5YvX14tQQGgLlrxc4aGvrpJv2YWyL+xmxbf2VMPXtlWZm6FBwAAQDVy6Lb5p556Srm5uXr33Xc1btw4SdKUKVPKzWnatKk6deqk77///sJTAkAdU1Jm0zMr9uqdzYclSbERfnr11u4K9OasdgAAAFQ/h8r7119/re7du9uL+/mEh4drw4YNDgUDgLoq/cRpPbBkh35MPSFJmjigtR6LaycXC6dvAgAAoGY4VN5zcnJ02WWX/ek8k8mkoqIiRy4BAHXSusQsPbL0R50oLJW3h4teGNlNgzoFOjsWAAAAGjiHyru/v7+SkpL+dN7evXsVHBzsyCUAoE6x2gy9GL9Pc9YdkCRdEuyjuWOiFern6eRkAAAAuBg4dI/ngAEDlJCQoO++++68c7766islJiZq8ODBDocDgLogq6BIY9/cai/u43qF6+P7elPcAQAAUGscKu/Tpk2Tq6urhg4dqoULF+rYsWP2106ePKnFixfr9ttvl6enpyZPnlxtYQGgtm09lKNrX9mk7w/lyNPNopdHd9PTw6Lk7mJxdjQAAABcREzG7894q6Tly5frtttu0+nTp//7YSaT/cg4Dw8PLV68WMOHD6+epDUsPz9fPj4+ysvLk7e3t7PjAHAym83Q/G8PafbqRFlthtoFNtbcMTFqE9DY2dEAAADQQFSlhzr0zLskjRgxQjExMXrppZe0Zs0aHT58WFarVSEhIRo0aJAmT56sNm3aOPrxAOA0JwpLNHnZT1r7a5Yk6cbuwZoxPEqebg7/JxMAAAC4IA6vvDc0rLwDkKSfUk/o/iU7lH7itNxczHrq+s4afWmoTCaTs6MBAACggamVlXcAaEgMw9DiLcl6+qu9KrHaFN7MU6/dGq2oYB9nRwMAAAAc27Bu8+bNuuOOO/T999//6ZytW7c6HA4AasPJ4jI9/OGPevzzX1RitemqzoH68qHLKO4AAACoMxxaeX/ttdf02Wef6YUXXjjvnI4dO+rDDz+U1WpVz549HQ4IADUpMbNA9y1J0KFjp+RiNmnqkA6687JIbpMHAABAneJQed+yZYu6d+8uX1/f885p2rSpoqOj//AseABwpuUJafrHZz+rqNSmFt4emnNrd/WI8HN2LAAAAOAcDpX3I0eOVGo1PSwsTDt37nTkEgBQY4pKrXrqy1/0wbZUSVK/tv56aVQ3NWvs7uRkAAAAQMUcKu9eXl7Kzs7+03nZ2dlyc3Nz5BIAUCOSc07pvsU7tCcjXyaTNGlgOz14ZRtZzNwmDwAAgLrLofLetWtXbdq0SWlpaQoJCalwTlpamjZu3KjY2NgLCggA1WXV7gxN+WiXCorL1MzLTS+N7qZ+bZs7OxYAAADwpxzabf6OO+5QUVGRhg4dWuFt8Tt37tT111+vkpIS3XHHHRccEgAuRKnVpqe/2qOJi3eooLhMPcKb6j8P96O4AwAAoN4wGYZhOPLGm2++WcuXL5fZbFZ0dLRat24tk8mkAwcOaMeOHbLZbBo+fLiWL19e3ZlrRH5+vnx8fJSXlydvb29nxwFQTTLyTuvB93cqIfm4JOme/q005ar2crU49LtLAAAAoNpUpYc6XN5tNpueeeYZvfDCCzpx4kS513x9ffXII4/o73//uywWiyMfX+so70DD8+2+Y5q09EflnipREw8Xzb65q67q3MLZsQAAAABJtVTezyotLdUPP/yg1NQzuzaHhoaqR48ecnV1vZCPrXWUd6DhsNoMvbx2v179Zr8MQ+rc0lvzxsQorJmns6MBAAAAdrVa3hsKyjvQMGSfLNakD3/UpgNnTsS4tWeYnriukzxc68ddQAAAALh4VKWHOrTbPADURdsP5+rB93foaH6xGrla9MyNURreveITMQAAAID6xOHyXlxcrA8++EDffvutMjIyVFxcXOE8k8mktWvXOhwQAP6MYRh6Y+MhPbsqUVaboTYBjTVvTLTaBjZxdjQAAACgWjhU3tPT0zVw4EDt379ff3bXvclkcigYAFRG3ulSPfbRT4rfc1SSdEO3lnpm+CXycufGIgAAADQcDv3tdsqUKdq3b5/69OmjyZMnq127dmrcuHF1ZwOAP/RzWp7ufz9Bqbmn5WYx64mhnTSmZxi/NAQAAECD41B5//rrrxUWFqY1a9bIw8OjujMBwB8yDEPvb0vRU1/sUYnVplC/Rpp7a4wuCfFxdjQAAACgRjhU3ouLi3XllVdS3AHUulPFZfrHpz/rsx+PSJIGdQzU8zd3lY9n/TqeEgAAAKgKh8r7JZdcorS0tOrOAgB/aP/RAt23ZIcOZJ2UxWzSX69qr3v6t+I2eQAAADR4Zkfe9Le//U3bt2/Xhg0bqjsPAFTos53pun7OdzqQdVIBTdz1wd29dO+A1hR3AAAAXBQcWnmPjo7W5MmTNXToUD366KMaPHiwQkJCzvuX6LCwsAsKCeDiVVRq1dNf7dGSrSmSpL5tmunl0d3l39jdyckAAACA2mMy/uystwqYzWaZTCYZhvGnq14mk0llZWUOB6wt+fn58vHxUV5enry9vZ0dB4CklJxC3f9+gnan58tkkh66oo3+MqidLGZW2wEAAFD/VaWHOrTy3r9/f25VBVCjVv+Sqckf/aSCojI19XTVS6O7a0C75s6OBQAAADiFQ+V9/fr11RwDAM4otdr03NeJWvDtIUlSdJiv5twarZa+jZycDAAAAHAeh8o7ANSEzLwiPfTBDm0/fFySdOdlkfrb1R3k5uLQ3poAAABAg0F5B1AnbNqfrb98uFM5p0rUxN1Fs27qoiGXBDk7FgAAAFAnXFB5Lyws1Lp167R//34VFBSoor3vTCaTHn/88Qu5DIAGzGYz9Oo3B/TS2n0yDKljkLfmjYlWhL+Xs6MBAAAAdYZDu81L0jvvvKNHHnlE+fn59rHf7z5/9mer1XrhSWsYu80DtS/nZLEmLf1RG/dnS5JGXxqqJ6/vLA9Xi5OTAQAAADWvxnebX7Nmje688075+Pjo73//u9atW6fvv/9e8+fP18GDB/Xpp59q//79evDBBxUTE+PQlwDQsCUk5+qBJTuVmV8kD1ezZgy7RDfFhDg7FgAAAFAnObTyPmTIEMXHxyshIUFdu3bV7bffrkWLFtlX2K1Wq6ZMmaIFCxZoy5YtioqKqvbg1Y2Vd6B2GIahhZuS9O+Vv6rMZqhVcy/NHROtDi349w4AAAAXl6r0UIe2cN6+fbt69eqlrl27Vvi6xWLR7NmzFRAQoH/961+OXAJAA5RfVKr7Fu/QjP/sVZnN0HVdgvTFg5dR3AEAAIA/4dBt8ydPnlRYWJj9Zw8PD0lSQUGBmjRpIkkym83q2bOn1q5dWw0xAdR3vxzJ0/1Ldig5p1CuFpMev66TxvUKL7dPBgAAAICKObTy3qJFC2VnZ5f7WZL27dtXbl5ubq5Onz59AfEA1HeGYejDbSkaPnezknMKFezbSB9N7KPxvSMo7gAAAEAlOVTeO3ToUK6o9+nTR4Zh6Nlnn7UfF7d582Z98803at++ffUkBVDvFJaUafJHP2nqJz+rpMymKzsE6D8PX6Zuob7OjgYAAADUKw7dNn/ttddq9erV2rJli3r16qWBAweqS5cuWr58uYKDgxUUFKTdu3fLZrNp0qRJ1RwZQH1wIOuk7l+SoH1HT8pskh67qr0m9m8ts5nVdgAAAKCqHCrv48ePV7t27RQUFCTpzPPt//nPf3TnnXdqzZo1yszMlI+Pj/76179q7Nix1RoYQN33xU9HNG35Lp0qsap5E3e9ekt39WrVzNmxAAAAgHrLoaPi/khhYaHy8vIUEBAgi8VSnR9dozgqDrhwxWVW/d9/9mrR98mSpF6t/PTKLd0V0MTDyckAAACAuqcqPdShlfc/4unpKU9Pz+r+WAB1XGpuoR58f4d+SsuTJD1wRWs9MqidXCwOba0BAAAA4H9Ue3kHcPFZu/eoHl32k/JOl8rX01UvjuymKzoEODsWAAAA0GBUqrzfcccdMplMeuaZZxQYGKg77rij0hcwmUxauHChwwEB1F1lVptmr96n1zcclCR1DfXV3DHRCvZt5ORkAAAAQMNSqWfezWazTCaT9u7dq3bt2slsrvxtsCaTSVar9YJC1gaeeQeqJiu/SA9+sFPbknIlSRP6ROjv13SUmwu3yQMAAACVUe3PvK9bt06SFBYWVu5nABenzQez9fAHPyr7ZLEau7vo2RFddG2XIGfHAgAAABqsat9tvr5i5R34czabobnrD+iF+H2yGVKHFk00d0y0WjVv7OxoAAAAQL1T47vN33jjjQoKCtJrr73mUEAA9c/xUyV6ZNmPWp94TJJ0c0yIpt8QpUZu9edISAAAAKC+cujh1BUrVignJ6e6swCoo3amHNe1r2zU+sRjcncxa9aILnru5q4UdwAAAKCWOLTyHhkZqVOnTlV3FgB1jGEYemfzYT2zYq9KrYYi/b302q3R6tSSR0sAAACA2uTQyvstt9yiDRs2KDMzs7rzAPXO3LlzFRkZKQ8PD8XExGjjxo3nnbtp0yb17dtXzZo1U6NGjdShQwe9+OKL5eZcfvnlMplM5/xz7bXX1vRXKaegqFQPvr9TT325R6VWQ9dc0kJfPNiX4g4AAAA4gUMr79OmTdPWrVs1YMAA/fvf/9Z1110nV1fX6s4G1HlLly7VpEmTNHfuXPXt21fz58/XkCFDtGfPHvvpDP/Ly8tLDz74oLp06SIvLy9t2rRJ9957r7y8vHTPPfdIkj755BOVlJTY35OTk6OuXbvq5ptvrrXvtTcjX/cv2aGk7FNytZj092s6akKfCJlMplrLAAAAAOC/HNptvlWrVrLZbEpNTT3zISaTAgIC5OHhce4FTCYdPHjwwpPWMHabhyN69uyp6OhozZs3zz7WsWNHDRs2TDNnzqzUZ9x4443y8vLSe++9V+HrL730kp544gllZGTIy8urWnL/kWU/pOrxz3aruMymlj4emjMmWtFhTWv8ugAAAMDFpsZ3mz98+HC5nw3D4BZ6XHRKSkqUkJCgqVOnlhuPi4vT5s2bK/UZO3fu1ObNmzVjxozzzlm4cKFGjx5d48X9dIlVT3y+Wx8lpEmSLm/fXC+O7KamXm41el0AAAAAf86h8m6z2ao7B1DvZGdny2q1KjAwsNx4YGDgn/4yKyQkRMeOHVNZWZmefPJJ3XXXXRXO27Ztm3bv3q2FCxdWW+6KHDp2Uvcv2aFfMwtkNkmPDm6n+y9vI7OZ2+QBAACAusCh8g7gv37/HLhhGH/6bPjGjRt18uRJbdmyRVOnTlWbNm10yy23nDNv4cKFioqKUmxsbLVm/l//2ZWhvy3fpZPFZfJv7KZXbumuPq39a+x6AAAAAKrOod3ma0NVdvD+X999951cXFzUrVu3mg2Ii56/v78sFss5q+xZWVnnrMb/XmRkpC655BLdfffdeuSRR/Tkk0+eM6ewsFAffvjheVflL1RJmU1PfvGLHnh/h04Wlyk20k//ebgfxR0AAACogy545b2goEAHDx5UQUGBzrf3Xf/+/av0mVXdwfusvLw8jR8/XgMHDtTRo0erdE2gqtzc3BQTE6P4+HgNHz7cPh4fH68bbrih0p9jGIaKi4vPGV+2bJmKi4s1duzYasn7v9JPnNYDS3box9QTkqSJA1rrsbh2crHU2d/nAQAAABc1h3abl6Tdu3dr0qRJWr9+/XlL+1lWq7VKn+3oDt6jR49W27ZtZbFY9Nlnn+nHH3+s9DXZbR6OWLp0qcaNG6fXX39dvXv31oIFC/TGG2/ol19+UXh4uKZNm6b09HQtWrRIkvTaa68pLCxMHTp0kHTm3PdJkybpoYceOmfTun79+ik4OFgffvhhtWZel5ilR5b+qBOFpfJp5KoXRnbVwI5/fKcAAAAAgOpX47vN79+/X5dddpny8/PVt29fZWRkKCkpSaNHj9ahQ4e0Y8cOlZWV6frrr5evr2+VPtvRHbzffvttHTx4UIsXL/7DnbvPKi4uLrfamZ+fX6WcgCSNGjVKOTk5mj59ujIyMhQVFaUVK1YoPDxckpSRkaGUlBT7fJvNpmnTpikpKUkuLi5q3bq1/v3vf+vee+8t97n79u3Tpk2btHr16mrLWma16cU1+/TaujNHN3YJ8dFrt0Yr1M+z2q4BAAAAoGY4VN5nzJihgoICvf3227rtttt0++23KykpSUuWLJEkHThwQHfeeaf27NmjLVu2VOmzHdnBe//+/Zo6dao2btwoF5fKfaWZM2fqqaeeqlI2oCL333+/7r///gpfe+edd8r9/NBDD+mhhx76089s167dn97RUhVZBUX6ywc/6vtDOZKkcb3C9c/rOsrdxVJt1wAAAABQcxx6wPWbb75Rx44dddttt1X4eps2bfT555/r2LFjevzxxx0KVtkdvK1Wq2699VY99dRTateuXaU/f9q0acrLy7P/k5qa6lBOoK7bcihH176ySd8fypGnm0Wv3NJdTw+LorgDAAAA9YhDK+9ZWVnq3bu3/WdXV1dJUlFRkTw8PCRJvr6+uvzyy/XVV19pzpw5lf7squ7gXVBQoB9++EE7d+7Ugw8+KOnMrcmGYcjFxUWrV6/WlVdeec773N3d5e7uXulcQH1jsxl6/duDmv11omyG1C6wseaOiVGbgMbOjgYAAACgihwq735+fioqKir3syQlJyerffv25eZmZWVV6bOruoO3t7e3fv7553Jjc+fO1TfffKOPP/5YkZGRVbo+0BCcKCzR5GU/ae2vZ/79u7F7sGYMj5Kn2wUfMAEAAADACRz6m3xkZKSSkpLsP3fr1k2GYejDDz/Uv/71L0lnnl1fv379Hx7tdj6PPvqoxo0bpx49eth38E5JSdHEiRMlqdwO3mazWVFRUeXeHxAQIA8Pj3PGgYvBT6kndP+SHUo/cVpuLmY9dX1njb40tMLHTgAAAADUDw6V97i4OD399NNKSkpSZGSkhg4dKn9/f02fPl179uxRSEiIPvnkE+Xl5dlvZa+Kqu7gDeDMvhDvbUnWjK/2qsRqU3gzT712a7Sign2cHQ0AAADABXLonPeDBw/qzTff1PDhwxUbGyvpzCZ2I0eOVG5urn3e4MGD9cUXX9SLZ8s55x312cniMk375Gd9+dMRSdJVnQP13M1d5e3h6uRkAAAAAM6nKj20UuV93LhxGj9+vAYNGvSHt96eOnVKGzdu1PHjx9WuXTvFxMRUPb2TUN5RXyVmFui+JQk6dOyUXMwmTR3SQXdeFslt8gAAAEAdV+3l3Ww2y2QyKTAwULfeeqvGjBmj7t27V1vguoDyjvpoeUKa/vHZzyoqtamFt4deG9NdMeF+zo4FAAAAoBKq0kMrdc77k08+qbZt2yozM1MvvPCCevTooaioKD377LOcjw44QVGpVVOX79Lkj35SUalN/dr66z8PX0ZxBwAAABqoKj3z/sMPP2jx4sVaunSpjh49KpPJJJPJpP79+2vcuHEaMWJEvV21ZuUd9cXh7FO6f8kO7cnIl8kkTRrYTg9e2UYWM7fJAwAAAPVJtd82/3s2m01r1qzRe++9p88//1wnT56UyWSSu7u7rr/+eo0dO1ZDhgyRxWJx+EvUNso76oNVuzM05aNdKiguUzMvN708ursua+vv7FgAAAAAHFDj5f1/nT59Wp9++qmWLFmi+Ph4lZWVyWQyqVmzZho1apTGjBmjXr16XcglagXlHXVZqdWmf6/8VQs3JUmSeoQ31Zxbo9XCx8PJyQAAAAA4qlbL+//Kzs7Whx9+qCVLlmjr1q322+rLysqq6xI1hvKOusBqM7QtKVdZBUUKaOKh2Eg/Hc0v0oPv79COlBOSpHv6t9KUq9rL1VKpLSsAAAAA1FFV6aEu1Xlhf39/DRw4UEeOHNGBAweUk5NTnR8PNGirdmdoxpc/Ky2v1D7W3MuswlKTTpVY1cTDRc/f3FVxnVs4MSUAAAAAZ6iW8p6RkaEPPvhAixcv1k8//SRJMgxDnTt31rhx46rjEkCDtmp3hu5bnKCB3tv1Suulau+RrMSicM3JGqVvSi5VqJ+nltzZS2HNPJ0dFQAAAIATOFzeT548qY8//lhLlizR+vXrZbPZZBiGWrRooVtuuUXjxo1Tt27dqjEq0DBZbYZmfPmzBnpv14Lwp2U2nXmSJdorUW9GPK27Dz+uRGtfBTdt5OSkAAAAAJylSuW9rKxMK1eu1OLFi/XVV1+pqKhIhmHI09NTw4YN07hx4zR48GCZzTyLC1TWtqRcpeWV6pXWS+3F/SyzydD9Acs04mCstiXlqnfrZk5KCQAAAMCZKlXev/vuOy1ZskQfffSRcnNzZRiGzGazrrzySvv57l5eXjWdFWiQsgqKJEntPZIrfP3s+Nl5AAAAAC4+lSrv/fr1k8lkkmEYioqK0rhx4zRmzBi1bNmypvMBDV5AkzPHvSUWhSvaK/Gc1xOLwsvNAwAAAHDxqVR5b9GihW699VaNGzdOXbt2relMwEUlNtJPIT6ueu3YKL3h+XS5W+dthknzjo1UqK+rYiP9nJgSAAAAgDNVqrynpaXxHDtQQyxmk/5+7SW6//0S3XX4cT0QsMy+2/y8YyO1Nv9SzRt7iSxmk7OjAgAAAHCSSpV3ijtQs8oMQ5JJG0/G6JuCWPt4qK+r5o29RFdHBTkvHAAAAACnq5Zz3gE4rqTMpudXn3nW/cGBHRQb2UxZBUUKaOKh2Eg/VtwBAAAAUN4BZ1u6PUXJOYXyb+yuu/q1kpc7/1oCAAAAKI/74QEnOlVcppfXHpAkPTywDcUdAAAAQIUo74ATvf1dkrJPFivMz1OjLw1zdhwAAAAAdVSlyvv06dP1xRdf1HQW4KJy/FSJ5m84JEmaHNdObi78Lg0AAABAxSrVFp588kl99tln9p8tFovuvPPOmsoEXBReW3dABcVl6hTkraFdWjo7DgAAAIA6rFLl3WKxqKSkxP6zYRgyDKPGQgENXfqJ01r0fbIk6a9Xt5eZHeUBAAAA/IFKlfegoCBt375dRUVFNZ0HuCi8FL9PJVaberXy04B2zZ0dBwAAAEAdV6mtrYcNG6Y5c+bI399fAQEBkqSPP/5Y69ev/9P3mkwmHTx48IJCAg3J/qMFWr4jTZL016s7yGRi1R0AAADAH6tUef/3v/8tSfr888+VnJwsk8mkkydP6uTJkzUaDmiIZn2dKJshXdU5UNFhTZ0dBwAAAEA9UKnb5j09PfXKK68oOTlZVqtVhmFowoQJstlslfoHwBkJybmK33NUZpM05ar2zo4DAAAAoJ5w6GyqAQMGqEOHDtWdBWjQDMPQsysTJUk3x4SqTUATJycCAAAAUF9U6rb531u3bl115wAavPWJx7TtcK7cXMz6y6C2zo4DAAAAoB5xqLz/r23btmnjxo06cuSITCaTgoKC1K9fP8XGxlZHPqBBsNkMPbvqV0nShD4RaunbyMmJAAAAANQnDpf3ffv2afz48dq+fbsk2c99P7tzdmxsrBYtWqS2bVlhBD7/KV2/ZhaoiYeL7r+8tbPjAAAAAKhnHCrvGRkZGjBggI4ePaqWLVvq5ptvVkREhCQpOTlZH330kbZu3arLL79cP/zwg4KCgqozM1CvlJTZ9PzqfZKkiQNay9fTzcmJAAAAANQ3DpX3GTNm6OjRo3rkkUc0c+ZMubmVLyPPPvuspk2bphdeeEHPPPOMXn311WoJC9RH729NVtrx02rexF23941wdhwAAAAA9ZDJOHu/exVERkbKw8NDe/fuPe8cwzDUqVMnFRUVKSkp6YJC1ob8/Hz5+PgoLy9P3t7ezo6DBuJkcZkGzFqnnFMlmjEsSmN7hTs7EgAAAIA6oio91KGj4jIyMhQdHf2Hc0wmk6Kjo5WRkeHIJYAG4c2Nh5RzqkSR/l4adWmos+MAAAAAqKccKu/e3t5KTU3903mpqamsYuOilXOyWG98e0iSNDmunVwtDv3rBgAAAACOlffevXtr8+bNWrly5XnnrFixQt9995369OnjcDigPpuz7oBOlVgVFeyta6LYtBEAAACA4xwq71OnTpXJZNKwYcN0++23Kz4+Xvv379eBAwcUHx+vCRMmaPjw4bJYLJo6dWp1ZwbqvNTcQi3ZkiJJ+tvVHWQ2m5ycCAAAAEB95tBu871799bbb7+te++9V++++64WLVpU7nXDMNSoUSMtWLBAvXr1qpagQH3y4pp9KrHa1LdNM/Vr29zZcQAAAADUcw6Vd0kaO3asLr/8cr3xxhvatGmTjhw5Iklq2bKl+vXrpzvvvFOhoWzQhYvPr5n5+nRnuiTpr1d1cHIaAAAAAA2Bw+VdkkJCQvTUU09VVxagQXhuVaIMQ7rmkhbqGurr7DgAAAAAGgC2vwaq0fbDuVr7a5YsZpMei2vv7DgAAAAAGgjKO1BNDMPQsyt/lSSN7BGqVs0bOzkRAAAAgIaC8g5Uk7V7s/RD8nG5u5g1aVBbZ8cBAAAA0IBQ3oFqYLUZmvX1mVX32/tGKtDbw8mJAAAAADQklHegGny6M137jp6Ut4eL7hvQ2tlxAAAAADQwlHfgAhWXWfVi/D5J0v1XtJGPp6uTEwEAAABoaCjvwAVavCVF6SdOK9DbXbf1jnB2HAAAAAAN0AWd835WTk6Ojhw5IpPJpKCgIDVr1qw6Phao8wqKSvXaugOSpEmD2qmRm8XJiQAAAAA0RBe08v7666+rU6dOCggIULdu3dS1a1cFBASoc+fOev3116srI1BnvfHtIeWeKlGr5l66OSbE2XEAAAAANFAOrbzbbDaNHDlSn376qQzDkK+vr8LDwyVJKSkp2rt3rx544AGtWbNGH330kUwmU7WGBuqCYwXFenNTkiRpSlx7uVh4CgUAAABAzXCobSxYsECffPKJ2rVrpy+++EK5ubnauXOndu7cqZycHH355Zdq3769Pv30Uy1YsKC6MwN1wpxv9quwxKquIT66OqqFs+MAAAAAaMBMhmEYVX1Tz549lZiYqMTERAUGBlY4JzMzU+3bt1eHDh20devWCw5a0/Lz8+Xj46O8vDx5e3s7Ow7quJScQg18Yb1KrYbev6un+rTxd3YkAAAAAPVMVXqoQyvve/bs0ZVXXnne4i5JLVq00MCBA7Vnzx5HLgHUac/HJ6rUaqhfW3+KOwAAAIAaV6MP6fKsOxqiX47k6fMfj0iS/nZ1ByenAQAAAHAxcKi8t2/fXuvWrVNOTs5552RnZ+ubb75R+/btHQ4H1EXPfZ0oSbquS5Cign2cnAYAAADAxcCh8n7bbbcpLy9PgwYN0oYNG855ff369Ro8eLDy8/M1YcKEC80I1BlbDuVofeIxuZhNeiyOX0wBAAAAqB0OHRV3//33a9WqVVq5cqWuvPJKtWjRQhERETKZTEpKSlJmZqYMw9A111yj+++/v7ozA05hGIb+vfJXSdLo2FBF+Hs5OREAAACAi4VD5d1isejLL7/Uiy++qFdeeUWpqanKyMiwvx4WFqaHHnpIjzzyiMxmzr5Gw7B6z1H9mHpCjVwtevjKts6OAwAAAOAi4tBRcb+XmpqqI0fObODVsmVLhYaGXnCw2sZRcfgjZVabrn55ow5kndQDV7TWlKvYqA4AAADAhalKD3Vo5f33QkND62VhByrrkx3pOpB1Ur6errp3QGtnxwEAAABwkeGeduBPFJVa9eKafZKkBy5vI28PVycnAgAAAHCxqdTK+x133CGTyaRnnnlGgYGBuuOOOyp9AZPJpIULFzocEHC2975PVkZekYJ8PDSud7iz4wAAAAC4CFXqmXez2SyTyaS9e/eqXbt2VdqEzmQyyWq1XlDI2sAz76hIflGp+s9apxOFpZo1ootGXsrjIQAAAACqR7U/875u3TpJZ3aR/9+fgYZu/oaDOlFYqjYBjXVjdLCz4wAAAAC4SFWqvA8YMOAPfwYaoqz8Ii3clCRJmnJVe7lY2CICAAAAgHM41Ea+/fZb7du370/n7d+/X99++60jlwCc7pVv9quo1KbuYb6K6xTo7DgAAAAALmIOlffLL79czz777J/OmzVrlq644gpHLgE41eHsU/pwW6ok6W9Xd5DJZHJyIgAAAAAXM4fvA67EPneVmgPURbNXJ6rMZujy9s3Vq1UzZ8cBAAAAcJGr0Yd4jxw5osaNG9fkJYBqtzs9T1/typAk/fWqDk5OAwAAAACV3LBOkhYtWlTu5wMHDpwzdlZZWZkSExO1Zs0a9erV68ISArXs2VW/SpJu6NZSnVpybCAAAAAA56vUOe/Sf896ryzDMOTh4aEvvvhCgwYNcjhgbeGcd0jS5gPZuvXNrXK1mLT20csV1szT2ZEAAAAANFDVfs67JD3xxBMymUwyDEPTp09Xt27ddMMNN1Q4183NTS1btlRcXJyCgoKqlh5wEsMw7Kvut8aGUdwBAAAA1BmVLu9PPvmk/f9+5513NGjQIP3rX/+qiUyAU6zanamf0vLk6WbRg1e2dXYcAAAAALCrdHn/X4cPH67mGIBzlVltem51oiTprssi1byJu5MTAQAAAMB/1ehu80B98VFCmg4dOyU/Lzfd3b+Vs+MAAAAAQDkOrbyftWnTJn3++efav3+/CgoKKjzX3WQyae3atRdyGaBGnS6x6qU1+yRJD1zRRk08XJ2cCAAAAADKc6i8G4ahO++8U++++669sJ/dzO6ssz9XZYd6wBne/f6wjuYXK9i3kcb2CnN2HAAAAAA4h0O3zb/++ut65513FBMTo/j4eN14442SpMTERK1cuVITJkyQ2WzWlClTdOjQoWoNDFSnvMJSzV13QJL0yOB2cnexODkRAAAAAJzLoZX3d955R15eXlq5cqWaNWumxYsXS5Latm2rtm3b6qqrrtI111yjUaNGqU+fPgoPD6/W0EB1mbfhoPKLytQ+sImGdw92dhwAAAAAqJBDK+979+5V79691axZM0my3xpvtVrtc2666SbFxMRo9uzZ1RATqH6ZeUV6+7skSdKUq9rLYuYRDwAAAAB1k0Pl3Wazyd/f3/6zp6enJOn48ePl5rVt21Y///zzBcQDas7La/eruMymHuFNNbBjgLPjAAAAAMB5OVTeg4ODlZaWZv/57G3xO3fuLDdv3759cnG5oA3tgRpx8NhJLfshVZL0tyEd2FgRAAAAQJ3mUHmPjo7Wnj17VFZWJkmKi4uTYRiaMmWK9u7dq4KCAj333HNKSEhQ9+7dqzUwUB2eX50oq83QwA4BujTCz9lxAAAAAOAPOVTer7/+euXm5uqrr76SJHXt2lWjR4/Wrl27FBUVJV9fX02dOlUuLi76v//7v2oNDFyon1JPaMXPmTKZpClXt3d2HAAAAAD4Uw7d037LLbfoxhtvLHdL/LvvvqsuXbros88+0/Hjx9WuXTv99a9/VWxsbLWFBarDrK9/lSQN7xasDi28nZwGAAAAAP6cyTAMw9kh6oL8/Hz5+PgoLy9P3t4UuoZq4/5jGrdwm9wsZq2dPEChfp7OjgQAAADgIlWVHurQbfOVlZWVpalTp9bkJYBKs9kMPbvqzKr7mF5hFHcAAAAA9UaNlPfU1FQ99NBDioyM1HPPPVcTlwCq7D8/Z2h3er683Cx68Io2zo4DAAAAAJVW6WfebTabPvzwQ3399dfKyspSQECAhgwZopEjR8psPvM7gNTUVD311FN677337DvRDx8+vGaSA1VQarXp+dWJkqS7+7dSs8buTk4EAAAAAJVXqfJeVlama665RmvXrtX/PiK/ePFiLVu2TJ988oneeustPfzwwzp9+rQk6YYbbtCTTz6pLl261ExyoAqWbk/V4ZxCNfNy0139Wjk7DgAAAABUSaXK+5w5c7RmzRp5eHhowoQJ6ty5swoKCrRy5Up9/vnnuueee7Rw4UIZhqG4uDg9++yz6tq1a01nByqlsKRML6/dL0l66Mo2auzu0CELAAAAAOA0lWoxS5culcVi0YYNG3TppZfax6dOnar77rtP8+fPl8lk0qxZs/TYY4/VWFjAEW9/d1jHCooV0rSRbu0Z7uw4AAAAAFBlldqwbu/everTp0+54n7WlClTJEnt27enuKPOOX6qRK+vPyhJmhzXTm4uNXrAAgAAAADUiEo1mYKCAkVERFT4WmRkpCSpW7du1ZUJqDbzNhxUQXGZOrRoohu6Bjs7DgAAAAA4pFLl3TAMWSyWCl8zmUySJA8Pj+pLBVSDIydO653NhyVJf7u6g8xmk3MDAQAAAICDuIcYDdbLa/arpMym2Eg/Xd6+ubPjAAAAAIDDTMb/nv12Hmaz2b7CXuULmEz2M9/rsvz8fPn4+CgvL0/e3t7OjoMLdCCrQHEvfiubIS2/r49iwps6OxIAAAAAlFOVHlrpM7Mq0fGr9X3AhXju60TZDGlwp0CKOwAAAIB6r1Ll3Waz1XQOoNrsSDmur385KrNJ+utV7Z0dBwAAAAAuGM+8o0ExDEPPrvxVkjQiOkRtA5s4OREAAAAAXDjKOxqUDfuOaWtSrtxczJo0uJ2z4wAAAABAtfj/9u48rKpyYf/4vZkRARUExBFRca5EM8wxZ8vUPDm8pVin0rIcyKFsOF5Zx7S09GiU5dF8LfOcHPKX8zwcLZXQJmfJGRFNQEyZnt8fvewTsjWHjWuD38918QfPetZa995reV3erL3XoryjxMjLM5qwYp8kqf99VVWxjK/FiQAAAADAOSjvKDH+3/cntedUuvy9PTS4TQ2r4wAAAACA01DeUSJk5eRp0qr9kqSBraqrrJ+XxYkAAAAAwHko7ygRvthxVEfPXVRwaW892TzC6jgAAAAA4FSUdxR7mZdzNHXtAUnS0LY1VMrrup6ACAAAAADFBuUdxd7MLUlKvZClqkGl1OfeKlbHAQAAAACno7yjWDuXmaUZmw5LkuLa15KnO6c0AAAAgJKHpoNibfr6g7pwOUf1wgPUtWG41XEAAAAAoEhQ3lFsHf/1ov532xFJ0qhOteXmZrM4EQAAAAAUDZct7x988IEiIiLk4+Oj6Ohobd68+apzFy5cqPbt26t8+fIKCAhQTEyMVq5ceRvTwgrvrT6grNw8xVQPUsuawVbHAQAAAIAi45Llff78+Ro2bJheeeUVJSYmqkWLFurcubOOHj3qcP6mTZvUvn17LVu2TAkJCWrTpo26du2qxMTE25wct8u+5AwtTDwuSRrVKUo2G1fdAQAAAJRcNmOMsTrElZo2bapGjRopPj7ePlanTh11795d48ePv65t1KtXT71799brr79+XfPT09MVGBiotLQ0BQQE3FRu3D5PfbpTa/acVqd6YfqwX7TVcQAAAADght1ID3W5K+9ZWVlKSEhQhw4dCox36NBBW7duva5t5OXlKSMjQ+XKlbvqnMuXLys9Pb3AD4qHnb+c05o9p+Vmk0Z0jLI6DgAAAAAUOZcr76mpqcrNzVVoaGiB8dDQUCUnJ1/XNiZNmqTMzEz16tXrqnPGjx+vwMBA+0/lypVvKTduD2OMJqzYK0nq1biyaoSUtjgRAAAAABQ9lyvv+a78DrMx5rq+1zxv3jyNHTtW8+fPV0hIyFXnvfzyy0pLS7P/HDt27JYzo+it35eiHb/8Km8PNw1tV9PqOAAAAABwW3hYHeBKwcHBcnd3L3SVPSUlpdDV+CvNnz9ff/3rX/Xvf/9b7dq1u+Zcb29veXt733Je3D65eUYTV+yTJA1oVk0VAn0tTgQAAAAAt4fLXXn38vJSdHS0Vq9eXWB89erVatas2VXXmzdvngYMGKDPP/9cDz74YFHHhAW+2nVCe5MzFODjoWdbR1odBwAAAABuG5e78i5JcXFx6tevnxo3bqyYmBjNmDFDR48e1aBBgyT9/pH3EydOaM6cOZJ+L+79+/fXlClTdN9999mv2vv6+iowMNCy1wHnuZyTq0mr9kuSBrWOVJlSXhYnAgAAAIDbxyXLe+/evXX27Fm98cYbOnXqlOrXr69ly5apatWqkqRTp04VeOb7Rx99pJycHA0ePFiDBw+2j8fGxmr27Nm3Oz6KwOffHtWJ878pxN9bTzSLsDoOAAAAANxWLvmcdyvwnHfXdeFyjlpNXK+zmVl6q0d9Pda0qtWRAAAAAOCWFevnvANX+njTYZ3NzFJEsJ96NeaRfgAAAADuPJR3uLTUC5f1yebDkqQRHaLk6c4pCwAAAODOQxOCS5u27qAys3LVoGKgOtcPszoOAAAAAFiC8g6XdezcRX327RFJ0uhOteXmZrM4EQAAAABYg/IOlzV59X5l5xo1rxGs5jWDrY4DAAAAAJahvMMl7TmVrsW7TkiSRnWKsjgNAAAAAFiL8g6X9M7KfTJGerBBBTWsVMbqOAAAAABgKco7XM72pHNatzdF7m42vdihltVxAAAAAMBylHe4FGOM3l6+R5LUu0llVS9f2uJEAAAAAGA9yjtcypo9Kfru6Hn5eLppaNuaVscBAAAAAJdAeYfLyM0zemflXknSE/dHKDTAx+JEAAAAAOAaKO9wGQu/O679py8o0NdTg1pFWh0HAAAAAFwG5R0u4VJ2rt5bvV+S9FzrSAX6elqcCAAAAABcB+UdLmHuN0d0Mu2SwgJ8FNusmtVxAAAAAMClUN5hufRL2Zq+/qAkaVi7mvLxdLc4EQAAAAC4Fso7LPfxpsP69WK2Isv76S/RlayOAwAAAAAuh/IOS6VkXNInm5MkSSM7RsnDnVMSAAAAAK5EU4Klpq07qN+yc3VX5TLqWC/M6jgAAAAA4JIo77DMkbOZ+vzbo5Kk0Z2iZLPZLE4EAAAAAK6J8g7LTFq1Xzl5Ri1rlVezyGCr4wAAAACAy6K8wxI/nkjTkt0nJUmjOkZZnAYAAAAAXBvlHZZ4Z+U+SVLXu8JVv2KgxWkAAAAAwLVR3nHbbTt0Vhv3n5GHm00vtq9ldRwAAAAAcHmUd9xWxhi9vWKvJKnvvVVULdjP4kQAAAAA4Poo77itVv6UrN3HzsvX010vtK1hdRwAAAAAKBYo77htcnLz7N91/2vzCIX4+1icCAAAAACKB8o7bpsF3x3XoTOZKlvKU8+0qm51HAAAAAAoNijvuC0uZefqvdUHJEmD29RQgI+nxYkAAAAAoPigvOO2mLPtFyWnX1J4oI8ev6+q1XEAAAAAoFihvKPIpf2WrenrD0mShrWvJR9Pd4sTAQAAAEDxQnlHkfto4yGl/ZatmiGl1bNRJavjAAAAAECxQ3lHkTqdfkn//E+SJGlkxyi5u9ksTgQAAAAAxQ/lHUVq6toDupSdp0ZVyqh93VCr4wAAAABAsUR5R5FJSs3UFzuOSZJGd6otm42r7gAAAABwMyjvKDLvrtqn3DyjNlHl1bR6kNVxAAAAAKDYoryjSPxwPE1Lvz8lm00a1am21XEAAAAAoFijvKNITFy5V5LU7a5w1akQYHEaAAAAACjeKO9wuv8cTNXmA6nydLfpxQ5RVscBAAAAgGKP8g6nMsZoworfr7o/1rSqKpcrZXEiAAAAACj+KO9wqmU/JOv742kq5eWu5x+oYXUcAAAAACgRKO9wmuzcPL27ap8k6akW1RVc2tviRAAAAABQMlDe4TT/3nlcSamZKufnpadbRFgdBwAAAABKDMo7nOK3rFy9v2a/JOn5NjXk7+NpcSIAAAAAKDko73CKWVuTlJJxWRXL+Oqx+6pYHQcAAAAAShTKO25Z2sVsfbjhkCQprn0teXu4W5wIAAAAAEoWyjtu2QcbDyr9Uo6iQv3V/Z6KVscBAAAAgBKH8o5bcirtN83+zy+SpFGdouTuZrM2EAAAAACUQJR33JIpaw7ock6eGlctqwdqh1gdBwAAAABKJMo7btrBlAv6185jkqSXOteWzcZVdwAAAAAoCpR33LRJq/Ypz0jt6oSocbVyVscBAAAAgBKL8o6bsuvYeS3/MVk2mzSyY22r4wAAAABAiUZ5xw0zxmjC8r2SpB73VFRUmL/FiQAAAACgZKO844ZtPpCqbYfPysvdTXHta1kdBwAAAABKPMo7bkhentGEFb9fdX/8vqqqVLaUxYkAAAAAoOSjvOOGfP3DKf10Ml2lvT00uE2k1XEAAAAA4I5Aecd1y87N06RV+yRJT7eorqDS3hYnAgAAAIA7A+Ud1+2LHcd05OxFBZf20lMtIqyOAwAAAAB3DMo7rsvFrBxNXXtAkvTCAzXl5+1hcSIAAAAAuHNQ3nFd/rklSWcyLqtyOV/1vbeK1XEAAAAA4I5Cecef+jUzSx9tPCxJerF9lLw8OG0AAAAA4HaiheFPfbDhoDIu56hOhQA9fFe41XEAAAAA4I5Decc1nTj/mz7ddkSSNKpTlNzcbBYnAgAAAIA7D+Ud1/T+6v3KyslT04hyal2rvNVxAAAAAOCORHnHVR04naEF3x2XJI3uXFs2G1fdAQAAAMAKlHdc1Tsr9ynPSB3qhqpRlbJWxwEAAACAOxblHQ4lHPlVq34+LTebNLJjlNVxAAAAAOCORnlHIcYYTVixV5LUs1El1Qz1tzgRAAAAANzZKO8oZMP+M9qedE5eHm4a3r6W1XEAAAAA4I5HeUcBeXlGE1fskyTFxlRVeBlfixMBAAAAACjvKGDJ7pPacypd/t4eeq51DavjAAAAAABEeccfZOXkadLq36+6D2xVXWX9vCxOBAAAAACQKO/4g3nbj+rYud9U3t9bTzaPsDoOAAAAAOD/UN4hScq8nKN/rDsgSRrStqZKeXlYnAgAAAAAkI/yDknSJ5uTlHohS1WDSqlPk8pWxwEAAAAA/AHlHTp74bJmbDokSXqxQ5Q83TktAAAAAMCV0NKg6esPKTMrV/XCA/RQgwpWxwEAAAAAXIHyfoc7/utFzf3miCRpdKfacnOzWZwIAAAAAHAlyvsdbvLq/crKzVOzyCC1qBlsdRwAAAAAgAOU9zvY3uR0LUo8IUka1am2bDauugMAAACAK6K838HeXblPxkid64fp7splrI4DAAAAALgKyvsdascv57RmT4rc3Wwa0THK6jgAAAAAgGugvN+BjDGasHyvJKlX40qKLF/a4kQAAAAAgGuhvN+B1u1N0c4jv8rbw01D29ayOg4AAAAA4E9Q3u8wuXlGE1fskyQNuL+awgJ9LE4EAAAAAPgzlPc7zOLEE9p3OkMBPh56rlUNq+MAAAAAAK4D5f0OcjknV5NX75ckDWodqcBSnhYnAgAAAABcD8r7HeSzb47qxPnfFBrgrSeaRVgdBwAAAABwnSjvReyDDz5QRESEfHx8FB0drc2bN19z/saNGxUdHS0fHx9Vr15dH374YaE5CxYsUN26deXt7a26detq0aJFf5oj41K2pq0/KEka2raWfL3cb+4FAQAAAABuO8p7EZo/f76GDRumV155RYmJiWrRooU6d+6so0ePOpyflJSkLl26qEWLFkpMTNSYMWM0ZMgQLViwwD5n27Zt6t27t/r166fdu3erX79+6tWrl7799ttrZvl4c5LOZWaperCfejWu5NTXCQAAAAAoWjZjjLE6hCtIT09XYGCg0tLSFBAQ4JRtNm3aVI0aNVJ8fLx9rE6dOurevbvGjx9faP7o0aO1ZMkS7dmzxz42aNAg7d69W9u2bZMk9e7dW+np6Vq+fLl9TqdOnVS2bFnNmzfPYY4zGZfV6p31upiVq+n/00gPNqzglNcHAAAAALh5N9JDufJeRLKyspSQkKAOHToUGO/QoYO2bt3qcJ1t27YVmt+xY0ft3LlT2dnZ15xztW1K0vT1B3UxK1cNKwWqS4Owm3k5AAAAAAALUd6LSGpqqnJzcxUaGlpgPDQ0VMnJyQ7XSU5Odjg/JydHqamp15xztW0ePXtRn317RJI0ulNt2Wy2m3o9AAAAAADrUN6L2JVl2RhzzQLtaP6V4zeyzcmr9yk716hFzWDdXyP4hrIDAAAAAFwD5b2IBAcHy93dvdAV8ZSUlEJXzvOFhYU5nO/h4aGgoKBrznG0zZ9Ppuur3SclSaM61r7p1wIAAAAAsBblvYh4eXkpOjpaq1evLjC+evVqNWvWzOE6MTExheavWrVKjRs3lqen5zXnONrmOyv3yhjpwYYV1KBS4K28HAAAAACAhTysDlCSxcXFqV+/fmrcuLFiYmI0Y8YMHT16VIMGDZIkvfzyyzpx4oTmzJkj6fc7y0+bNk1xcXF6+umntW3bNs2cObPAXeSHDh2qli1basKECerWrZu++uorrVmzRlu2bCmw728Pn9X6fWfk4WbTiA5Rt+9FAwAAAACcjvJehHr37q2zZ8/qjTfe0KlTp1S/fn0tW7ZMVatWlSSdOnWqwDPfIyIitGzZMg0fPlzTp09XeHi4pk6dqp49e9rnNGvWTF988YVeffVVvfbaa4qMjNT8+fPVtGlT+xxjjN5esff3DE0qKyLY7za9YgAAAABAUeA57/+nKJ7zbpWVPyVr4P8myMfTTRtHtlFogI/VkQAAAAAAV+A573ew3Dyjd1bukyQ9eX8ExR0AAAAASgDKewmz4LvjOphyQYG+nhrYKtLqOAAAAAAAJ6C8lyCXsnP1/ur9kqTBbSIV6OtpcSIAAAAAgDNQ3kuQ/912RCfTLqlCoI/6x1SzOg4AAAAAwEko7yVE+qVsTd9wUJI0rF1N+Xi6W5wIAAAAAOAslPcSYsbGwzp/MVuR5f3Us1Elq+MAAAAAAJyI8l4CpKRf0swtSZKkkR2j5OHOYQUAAACAkoSWVwJMXXdAv2Xn6u7KZdSxXpjVcQAAAAAATkZ5L+Z+Sc3UF9uPSZJGd6otm81mcSIAAAAAgLNR3ou5Sav3KyfPqFWt8oqJDLI6DgAAAACgCFDei7EfT6Tp/+0+Ken377oDAAAAAEomynsxNnHlPknSw3eFq37FQIvTAAAAAACKiofVAXBjcvOMtied038OntGm/WfkbpNe7FDL6lgAAAAAgCLkslfeP/jgA0VERMjHx0fR0dHavHnzNedv3LhR0dHR8vHxUfXq1fXhhx/epqS3z4ofT6nVhNXq+/E3mrb+kCTJz0vacyrd4mQAAAAAgKLkkuV9/vz5GjZsmF555RUlJiaqRYsW6ty5s44ePepwflJSkrp06aIWLVooMTFRY8aM0ZAhQ7RgwYLbnLzorPjxlJ6dm6Da5j9aGPmifqr3Fy2MfFFNvLfr2bkJWvHjKasjAgAAAACKiM0YY6wOcaWmTZuqUaNGio+Pt4/VqVNH3bt31/jx4wvNHz16tJYsWaI9e/bYxwYNGqTdu3dr27Zt17XP9PR0BQYGKi0tTQEBAbf+IpwoN8+o1YTVqm3+oxlVx8nN9t9DlmdseubIa9rndr82jGovdzceFQcAAAAAxcGN9FCXu/KelZWlhIQEdejQocB4hw4dtHXrVofrbNu2rdD8jh07aufOncrOzna4zuXLl5Wenl7gx1VtTzqn42nZeq78/ALFXZLcbEbPlv+Xjp3P1vakcxYlBAAAAAAUJZcr76mpqcrNzVVoaGiB8dDQUCUnJztcJzk52eH8nJwcpaamOlxn/PjxCgwMtP9UrlzZOS+gCKRkXJIkRfkccbg8fzx/HgAAAACgZHG58p7PZiv48W9jTKGxP5vvaDzfyy+/rLS0NPvPsWPHbjFx0Qnx95Ek7btU1eHy/PH8eQAAAACAksXlyntwcLDc3d0LXWVPSUkpdHU9X1hYmMP5Hh4eCgoKcriOt7e3AgICCvy4qnsjyqlSoKc+ONNbeabgHyPyjE3xZ3qpchlP3RtRzqKEAAAAAICi5HLl3cvLS9HR0Vq9enWB8dWrV6tZs2YO14mJiSk0f9WqVWrcuLE8PT2LLOvt4u5m06tdG2htehM9c+Q1JWTW1oVcXyVk1tYzR17T2vQmeuWhBtysDgAAAABKKA+rAzgSFxenfv36qXHjxoqJidGMGTN09OhRDRo0SNLvH3k/ceKE5syZI+n3O8tPmzZNcXFxevrpp7Vt2zbNnDlT8+bNs/JlOFWn+hUU/3i03vx/Xup56F77eOUynop/vIE61a9gYToAAAAAQFFyyfLeu3dvnT17Vm+88YZOnTql+vXra9myZapa9ffvdp86darAM98jIiK0bNkyDR8+XNOnT1d4eLimTp2qnj17WvUSikSn+hXUvm6YtiedU0rGJYX4++jeiHJccQcAAACAEs4ln/NuBVd+zjsAAAAAoOQp1s95BwAAAAAABVHeAQAAAABwcZR3AAAAAABcHOUdAAAAAAAXR3kHAAAAAMDFUd4BAAAAAHBxlHcAAAAAAFwc5R0AAAAAABdHeQcAAAAAwMVR3gEAAAAAcHGUdwAAAAAAXBzlHQAAAAAAF0d5BwAAAADAxVHeAQAAAABwcZR3AAAAAABcHOUdAAAAAAAXR3kHAAAAAMDFUd4BAAAAAHBxlHcAAAAAAFwc5R0AAAAAABfnYXUAV2GMkSSlp6dbnAQAAAAAcCfI75/5ffRaKO//JyMjQ5JUuXJli5MAAAAAAO4kGRkZCgwMvOYcm7mein8HyMvL08mTJ+Xv7y+bzWZ1nGtKT09X5cqVdezYMQUEBFgdB07G8S35OMYlH8e45OMYl3wc45KN41vyFZdjbIxRRkaGwsPD5eZ27W+1c+X9/7i5ualSpUpWx7ghAQEBLn0i4tZwfEs+jnHJxzEu+TjGJR/HuGTj+JZ8xeEY/9kV93zcsA4AAAAAABdHeQcAAAAAwMVR3oshb29v/e1vf5O3t7fVUVAEOL4lH8e45OMYl3wc45KPY1yycXxLvpJ4jLlhHQAAAAAALo4r7wAAAAAAuDjKOwAAAAAALo7yDgAAAACAi6O8AwAAAADg4ijvxcimTZvUtWtXhYeHy2azafHixVZHghONHz9eTZo0kb+/v0JCQtS9e3ft27fP6lhwovj4eDVs2FABAQEKCAhQTEyMli9fbnUsFJHx48fLZrNp2LBhVkeBk4wdO1Y2m63AT1hYmNWx4GQnTpzQ448/rqCgIJUqVUp33323EhISrI4FJ6lWrVqhf8c2m02DBw+2OhqcJCcnR6+++qoiIiLk6+ur6tWr64033lBeXp7V0W6Zh9UBcP0yMzN111136YknnlDPnj2tjgMn27hxowYPHqwmTZooJydHr7zyijp06KCff/5Zfn5+VseDE1SqVElvv/22atSoIUn69NNP1a1bNyUmJqpevXoWp4Mz7dixQzNmzFDDhg2tjgInq1evntasWWP/3d3d3cI0cLZff/1V999/v9q0aaPly5crJCREhw4dUpkyZayOBifZsWOHcnNz7b//+OOPat++vR599FELU8GZJkyYoA8//FCffvqp6tWrp507d+qJJ55QYGCghg4danW8W0J5L0Y6d+6szp07Wx0DRWTFihUFfp81a5ZCQkKUkJCgli1bWpQKztS1a9cCv7/11luKj4/XN998Q3kvQS5cuKDHHntMH3/8sd58802r48DJPDw8uNpegk2YMEGVK1fWrFmz7GPVqlWzLhCcrnz58gV+f/vttxUZGalWrVpZlAjOtm3bNnXr1k0PPvigpN//Dc+bN087d+60ONmt42PzgItKS0uTJJUrV87iJCgKubm5+uKLL5SZmamYmBir48CJBg8erAcffFDt2rWzOgqKwIEDBxQeHq6IiAj16dNHhw8ftjoSnGjJkiVq3LixHn30UYWEhOiee+7Rxx9/bHUsFJGsrCzNnTtXTz75pGw2m9Vx4CTNmzfX2rVrtX//fknS7t27tWXLFnXp0sXiZLeOK++ACzLGKC4uTs2bN1f9+vWtjgMn+uGHHxQTE6NLly6pdOnSWrRokerWrWt1LDjJF198oe+++047duywOgqKQNOmTTVnzhzVqlVLp0+f1ptvvqlmzZrpp59+UlBQkNXx4ASHDx9WfHy84uLiNGbMGG3fvl1DhgyRt7e3+vfvb3U8ONnixYt1/vx5DRgwwOoocKLRo0crLS1NtWvXlru7u3Jzc/XWW2+pb9++Vke7ZZR3wAU9//zz+v7777Vlyxaro8DJoqKitGvXLp0/f14LFixQbGysNm7cSIEvAY4dO6ahQ4dq1apV8vHxsToOisAfv7rWoEEDxcTEKDIyUp9++qni4uIsTAZnycvLU+PGjfX3v/9dknTPPffop59+Unx8POW9BJo5c6Y6d+6s8PBwq6PAiebPn6+5c+fq888/V7169bRr1y4NGzZM4eHhio2NtTreLaG8Ay7mhRde0JIlS7Rp0yZVqlTJ6jhwMi8vL/sN6xo3bqwdO3ZoypQp+uijjyxOhluVkJCglJQURUdH28dyc3O1adMmTZs2TZcvX+bmZiWMn5+fGjRooAMHDlgdBU5SoUKFQn9MrVOnjhYsWGBRIhSVI0eOaM2aNVq4cKHVUeBkI0eO1EsvvaQ+ffpI+v2PrUeOHNH48eMp7wCcwxijF154QYsWLdKGDRsUERFhdSTcBsYYXb582eoYcIK2bdvqhx9+KDD2xBNPqHbt2ho9ejTFvQS6fPmy9uzZoxYtWlgdBU5y//33F3pM6/79+1W1alWLEqGo5N8YOP+mZig5Ll68KDe3grd2c3d351FxuL0uXLiggwcP2n9PSkrSrl27VK5cOVWpUsXCZHCGwYMH6/PPP9dXX30lf39/JScnS5ICAwPl6+trcTo4w5gxY9S5c2dVrlxZGRkZ+uKLL7Rhw4ZCTxpA8eTv71/oHhV+fn4KCgri3hUlxIgRI9S1a1dVqVJFKSkpevPNN5Wenl7sr+Tgv4YPH65mzZrp73//u3r16qXt27drxowZmjFjhtXR4ER5eXmaNWuWYmNj5eFBHSppunbtqrfeektVqlRRvXr1lJiYqMmTJ+vJJ5+0OtotsxljjNUhcH02bNigNm3aFBqPjY3V7Nmzb38gONXV7nI6a9YsbqRSQvz1r3/V2rVrderUKQUGBqphw4YaPXq02rdvb3U0FJHWrVvr7rvv1vvvv291FDhBnz59tGnTJqWmpqp8+fK67777NG7cOO5ZUcJ8/fXXevnll3XgwAFFREQoLi5OTz/9tNWx4ESrVq1Sx44dtW/fPtWqVcvqOHCyjIwMvfbaa1q0aJFSUlIUHh6uvn376vXXX5eXl5fV8W4J5R0AAAAAABfHc94BAAAAAHBxlHcAAAAAAFwc5R0AAAAAABdHeQcAAAAAwMVR3gEAAAAAcHGUdwAAAAAAXBzlHQAAAAAAF0d5BwAAAADAxVHeAQBOYbPZZLPZVLZsWZ0/f97hnLFjx8pms+ntt9++veGuw4YNG2Sz2TRgwACrozhdbm6uXn/9dUVGRsrLy+uGX+fZs2c1btw4xcTEqHz58vL09FRwcLBatWqliRMn6syZM0UXHgAASKK8AwCc7Pz583rvvfesjoE/mDJlisaNG6dLly7pkUceUWxsrJo3b35d6y5ZskSRkZF6/fXXtXfvXkVHR+vRRx9VdHS0du3apdGjR6tGjRr66aefivhV4Haw2WyqVq2a1TEAAA54WB0AAFByuLm5ycPDQ++//76GDRumsmXLWh0JkhYvXixJ2rx5s6pXr37d661cuVI9evSQm5ubJk2apBdeeEGenp725VlZWZo7d67GjBnD1XcAAIoYV94BAE7j6empp556Sunp6Zo8ebLVcfB/jh8/Lkk3VNwvXryo/v37Ky8vT5988oni4uIKFHdJ8vLy0pNPPqmEhASu1gIAUMQo7wAApxozZoy8vb01ZcoUnTt37rrWad26tWw2m3755ZdCy3755RfZbDa1bt26wHj+9+dnz56thIQEde7cWWXKlFG5cuXUq1cve2HNzMzUyJEjVa1aNfn4+Kh+/fr68ssvr5nn1KlTGjBggEJDQ+Xr66tGjRppzpw5V51/5swZjRgxQlFRUfLx8VHZsmXVuXNnbdq0qdDcP363Pjk5WU899ZQqVapk/8TC9fj555/12GOPqUKFCvLy8lLFihXVv39/7du3r8C8AQMGyGazKSkpSdJ/70twtff6j+bMmaOUlBQ1bdpUsbGx15xbsWLFQuX97NmzGjlypGrWrCkfHx+VK1dOnTp10qpVqxxuI//j2jk5ORo3bpxq1KghX19f1alTR7NmzbLPW7dundq0aaOAgACVLVtW/fv319mzZwtt74/n1Ny5cxUdHa1SpUopJCREsbGxOnHihMMcOTk5+sc//qHo6GiVLl1apUuX1r333qv4+Hjl5uZecz+LFy/WfffdJz8/P5UrV059+/a1n4dXMsbo008/VcuWLVWmTBn5+vqqYcOGevfdd5WdnV1ofrVq1WSz2SRJn3zyiRo2bChfX1+FhYVp4MCBV73PRFZWlqZMmaImTZrI399ffn5+uvfeezVz5kwZY+zzZs+ebd/+kSNHCpwrf/y3d/bsWY0ZM0b16tVT6dKlFRgYqFq1aql///7avn27wwwAACcxAAA4gSTj7e1tjDHm+eefN5LMmDFjCsz529/+ZiSZ8ePHFxhv1aqVkWSSkpIKbTcpKclIMq1atXK4rUGDBhlvb29Tr14907NnT1OjRg0jydSqVcucP3/eNGnSxAQFBZmHHnrItG7d2thsNmOz2cyKFSsKbG/9+vVGkunataupUqWKCQ0NNb169TLt27c3Hh4eRpIZO3ZsoXx79uwxFStWNJJMZGSk6dGjh2nZsqXx8vIybm5u5rPPPnO4ny5duphKlSqZsLAw85e//MU89NBD5qOPPvrT93nNmjXG19fXSDKNGjUyffr0MXfffbeRZEqXLm02bdpkn/vxxx+b2NhY4+fnZySZ2NhY+8+ZM2euuZ+HHnrISDJTpkz500xXOn78uKlevbqRZKpUqWJ69+5tHnjgAePu7m4kmcmTJxdaR5KpWrWq6dGjh/H39zcdOnQwHTp0MN7e3kaS+ec//2n+/e9/Gw8PDxMdHW0eeeQR+/vevHlzk5eXV2B7+efU4MGDjc1mMy1btjR9+vQx1apVM5JMpUqVzLFjxwqsk5OTY7p06WIkmYCAANOtWzfTrVs34+/vbySZHj16mNzcXIf7GTlypHFzczP33nuveeSRR0zlypWNJFOzZk1z8eLFAuvk5uaaRx991L6ftm3bmm7dupmwsDD7uXHlfqpWrWrfj5eXl7n//vtN9+7dTUhIiJFkWrRoUeg9uHDhgmnRooWRZIKDg02nTp1Mly5dTNmyZY0kM3DgQPvczZs3m9jYWCPJ+Pn5FThX8v+9ZmRk2P991axZ0zzyyCPmkUceMY0bNzYeHh7mb3/72/WdIACAm0J5BwA4xR/L+4kTJ4yPj4/x9/c3qamp9jlFUd4lmffee88+npWVZdq1a2ckmbp165rWrVubc+fO2Zd/8sknRpJp2bJlge3ll2pJpn379ubChQv2Zdu3bzelS5c2bm5uJjEx0T6ek5Nj6tevby+5fyxP3333nQkKCjJ+fn7m9OnTDvfTo0cP89tvv139Tb3ChQsXTGhoqJFk4uPjCyybPHmyvZReunSpwLL84ncj8ovx5s2bb2g9Y/5b/Pv162eysrLs45s3bzalSpUy7u7uZvfu3QXWyX9P6tevX6BUr1u3zkgyFSpUMEFBQebLL7+0L0tLSzP16tUzksy6desKbC//nPLw8DBLly61j2dlZZnHHnvM/v7/0bvvvmskmQYNGhQ4ZidPnjRRUVFGkpk+fbrD/fj5+Zm1a9faxzMzM02zZs2MJDNz5swC60yYMMF+nqWkpNjHL1y4YLp27WokmWnTphVYJ/8YVqhQocA5eObMGXuh/uP+jTHm2WeftR+HjIwM+3hKSopp2rSpkWS+/vrrAuvk/xHFkVmzZhlJ5oUXXii07PTp0+aHH35wuB4AwDko7wAAp/hjeTfGmCFDhhhJ5qWXXrKPFUV5v7KEG2PMkiVLjCTj7u5uDhw4UGBZTk6OCQ4ONp6engWKZX6pttlsZu/evYW2OXr0aCPJPPPMM/axRYsWGUmmb9++Dt+T999/30gykyZNKrQfb29vc/z4cYfrXc0///lP+1VWR6Kjo40kM2/evALjN1PefXx8jCSH78W1HDp0yH5F+ddffy20PC4urtBVX2P+W96vLOHGGNOoUSMjyfTv37/QsilTphhJha765p9T//M//1NondTUVOPn52fc3NwKHIMqVao4LMHG/PecioqKcrifV199tdA6CxYssH/iIV92drYJDg42/v7+Dj/9kJycbLy9vU2DBg0KjOcfw08++aTQOpMmTSr0Hpw+fdp4enqaiIiIQn/MMcaYXbt22T9p8kfXKu/5f3RYtGiRw+UAgKLFd94BAEXipZdeko+Pj6ZNm6bU1NQi20/79u0LjeXfmK1atWqqUaNGgWXu7u6qVq2asrOzHea65557FBUVVWi8b9++kqQtW7bYx1avXi1J6t69u8Ns+Y9j27FjR6FljRo1UsWKFR2udzWbN2+WJD322GMOlz/++OMF5lkh//3p0qWLypQpU2h5v379JDnO6OXlpVatWhUazz+ejo51ZGSkpN/vU+BInz59Co0FBQWpffv2ysvL09atWyVJR48e1dGjRxUWFqYHHnig0DoPPfSQypQpo3379jm8s36HDh0KjdWqVatQtsTERKWmpqp58+YKDg4utE5oaKhq1qypH3/8Ub/99ttN72fjxo3Kzs5Wp06d5O3tXWidu+66S/7+/g7PzauJjo6W9Pt9Lb7++mtdunTputcFANw6yjsAoEhUqFBBgwYN0oULF/TOO+8U2X4cFWA/P7+rLvvj8suXLxdaVrVqVYfr5N+Q7eTJk/ax/Ju+9e7du8ANvvJ/GjduLEkO/0hQpUqVq7yiq8vf99Xu7O4o480KCgqSpBt+BNytZAwLC5ObW+H/mlzreF7rWErXfzz/LLfNZrNvy1H2SpUqFRorXbp0oWz558zy5csdnjM2m00//vijjDEOb/h4o/uJj4+/6n4yMjJu6A9rbdu21fDhw7V371517dpVgYGBatq0qV577bU/vQEiAODW8Zx3AECRGT16tD766CNNnz5dI0aMuKlt5OXlXXN5/h2yb3SZM+Tffbxz584KCQm56rzatWsXGvPx8bnp/f7Z63LG67777rt14sQJfffdd/ZPENyIq2XIH3e0/Ha8rnzmD3dav9F93Ez2fPnnTM2aNdWsWbNrznV0xfxG93PPPfeoYcOG17XO9Zg8ebIGDhyor776SmvXrtV//vMfbd++XRMnTtT8+fOv+ikUAMCto7wDAIpMWFiYnn32WU2ePFkTJ060XyW9kpeXlyTpwoULhZYdO3asSDNe6ciRI9ccDw8Pt4/lXwUdNGiQHn744SLPlr/v/Ee/XSk/Y4UKFW55Xw8++KCWLl2qefPmaciQIU7LmH+F1hkZr8eRI0ccltejR49K+m/eP8v9x3VuJXv+OVO/fn3Nnj37prdzvftp3bq1Jk+e7NRtR0VFadSoURo1apQuXbpk/+PcwIEDKe8AUIT42DwAoEiNHj1apUqV0gcffKDTp087nJNfhvbv319o2dWeC15Udu3a5TDHvHnzJEn333+/faxdu3aSpMWLF9+WbC1atJAkffbZZw6X54/nz7sV/fv3V/ny5fXNN9/o008/vebckydP2kt5/lX6pUuXOnz2+Ny5c52W8XrMnz+/0Ni5c+e0atUq2Ww2xcTESPr9awxVqlRRcnKy1q1bV2idpUuX6tdff1VUVJTKly9/03maNGmiwMBArV+/Xunp6Te9nT/Tpk0bubu76+uvv3b4fPqr8fT0VE5OznXP9/Hx0YsvvqgKFSooJSVFKSkpNxMXAHAdKO8AgCIVEhKi5557ThcvXrxqCcy/SdmkSZN08eJF+/iaNWv0/vvv346Ydnl5eRoyZEiBHAkJCZo+fbrc3Nw0cOBA+/hf/vIX1a5dW7Nnz9aECROUnZ1dYFtZWVlauHChfvjhB6dk69Wrl0JDQ7V582bNmDGjwLKpU6dqx44dqlSpknr06HHL+/Lz89Ps2bPl5uamp556Su+9916h15eTk6M5c+YoOjraXt6rV6+uBx98UBkZGRo6dGiBdbZt26b4+Hi5u7vrueeeu+WM1+Nf//qXVq5cWSDz8OHDlZmZqYcffrjAd8hfeOEFSdLw4cMLfNc/OTlZI0eOLDDnZnl7e2vEiBE6f/68evbs6fCTHt9//73DPzrciIoVK2rAgAE6cOCA+vXr5/C77Vu3btWyZcsKjIWHh+v06dMO//CyePFiffPNN4XGExMTdfr0afn7+6ts2bK3lBsAcHV8bB4AUORGjRql+Ph4ZWZmOlzet29fTZw4UVu3blWdOnXUpEkTHTt2TDt37lRcXJzefffd25b1oYce0vfff6/IyEi1bNlSaWlpWrdunbKzs/Xqq6/a77gtSR4eHlq0aJE6duyol156SVOmTFHDhg0VEBCgY8eOae/evTp//rwWLVqkBg0a3HI2Pz8/ffbZZ+ratasGDhyoGTNmqFatWtq7d68SExPl5+enzz//3OF3pW9Gly5d9OWXXyo2NlZxcXF64403dN9996lcuXI6e/asvv32W50/f15lypQp8J3/jz76SC1atNCcOXO0ceNGxcTE6MyZM9qwYYNyc3M1adIkp34P+1qeeeYZde7cWS1btlR4eLi++eYbJSUlKTw8XFOnTi0wd/jw4Vq3bp2WL1+umjVr6oEHHpAxRmvXrlVGRoa6d++uZ5999pYzjRkzRj///LPmzZunqKgoNWrUSFWqVFFqaqoOHz6spKQkdevWTb17976l/UydOlWHDx/WvHnz9PXXX+vuu+9WeHi4kpOTdfDgQZ04cUJDhw5Vly5d7Os8/PDD+sc//qFGjRqpWbNm8vHxUVRUlEaOHKkNGzZoypQpqlixou655x4FBATo5MmT2rJli/Ly8jRu3Dh5enre6tsDALgKrrwDAIpc+fLlNXjw4Ksu9/X11dq1a9W3b19lZGRo2bJlysvL0/z586+5XlEICgrStm3b1K5dO61fv14bNmxQ3bp1NWvWLI0bN67Q/Nq1a2vXrl0aO3asQkJCtGXLFi1dulRnzpxRy5YtNWvWLPvH652hbdu22rFjh/r27avjx4/ryy+/VHJysh5//HElJCQ4/ePoPXr00OHDhzV27FhFRUVp+/bt+te//qWdO3eqYcOGeuedd3To0CHVrVvXvk7FihW1Y8cOvfjii/Lw8NDChQuVkJCgtm3bauXKlYqLi3NqxmsZMWKEZs2apbS0NC1atEjp6enq16+fvv3220J3/Hd3d9eSJUs0ZcoUVa9eXStXrtSqVasUFRWl6dOn68svv3R4N/wb5ebmps8//1xffvml2rRpowMHDmjhwoX6+eefFRoaqrFjx2rChAm3vJ9SpUpp1apV+uSTT9SoUSP9+OOPWrRokQ4dOqTIyEhNnDix0I0kx48fr+eff145OTmaP3++Zs6cqaVLl0qSBgwYoBdffFHh4eHavn27FixYoKSkJHXp0kXr16/X0KFDbzkzAODqbOZqt1sFAAAoplq3bq2NGzcqKSnpqo9/AwCgOOHKOwAAAAAALo7yDgAAAACAi6O8AwAAAADg4vjOOwAAAAAALo4r7wAAAAAAuDjKOwAAAAAALo7yDgAAAACAi6O8AwAAAADg4ijvAAAAAAC4OMo7AAAAAAAujvIOAAAAAICLo7wDAAAAAODi/j+9Xak1sOmTxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "fig = plt.figure(figsize = (12, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "plt.plot(range(1,9), exp_var_ratio, marker = 'o', markerfacecolor = 'orange', markersize = 6)\n",
    "for i,j in zip(range(1,9),exp_var_ratio):\n",
    "    ax.annotate('{:.2f}'.format(j),xy=(i-.2,j+.03))\n",
    "plt.xlabel('Number of Componenets', size = 15)\n",
    "plt.ylabel('Ratio of Variance Explained', size = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d852f1f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "pca = PCA(n_components = 6)\n",
    "pca.fit(scaled_data)\n",
    "data_pca = pca.transform(scaled_data)\n",
    "match_df = dfread.toPandas()\n",
    "pca_corr = pd.DataFrame(pca.components_, columns = match_df.drop(['win', 'region'], axis = 1).columns)\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "sns.heatmap(pca_corr,  vmin = -1, vmax = 1, annot = True, lw = .5, linecolor = 'white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "95f85bc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bf5fd29",
   "metadata": {},
   "source": [
    "逻辑回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "86aed470",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.89      0.86      1360\n",
      "           1       0.89      0.83      0.86      1408\n",
      "\n",
      "    accuracy                           0.86      2768\n",
      "   macro avg       0.86      0.86      0.86      2768\n",
      "weighted avg       0.86      0.86      0.86      2768\n",
      "\n",
      "[[1215  145]\n",
      " [ 238 1170]]\n",
      "\n",
      "index             -0.000098\n",
      "firstBlood         0.022689\n",
      "firstTower         0.893513\n",
      "firstInhibitor     1.087886\n",
      "firstBaron         0.094260\n",
      "firstDragon        0.217383\n",
      "firstRiftHerald   -0.024516\n",
      "towerKills         0.379747\n",
      "inhibitorKills     0.741061\n",
      "baronKills        -0.196438\n",
      "dragonKills        0.146715\n",
      "riftHeraldKills   -0.035132\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "X = match_df.drop(['win', 'region'], axis = 1)\n",
    "y = match_df.win\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3)\n",
    "\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train, y_train)\n",
    "y_pred = log.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print()\n",
    "\n",
    "log_coeff_tot = pd.Series(log.coef_[0], index = match_df.drop(['win', 'region'], axis = 1).columns)\n",
    "print(log_coeff_tot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4d2b83f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.89      0.87       284\n",
      "           1       0.89      0.85      0.87       298\n",
      "\n",
      "    accuracy                           0.87       582\n",
      "   macro avg       0.87      0.87      0.87       582\n",
      "weighted avg       0.87      0.87      0.87       582\n",
      "\n",
      "[[254  30]\n",
      " [ 46 252]]\n",
      "\n",
      "index             -0.000039\n",
      "firstBlood         0.307773\n",
      "firstTower         1.310732\n",
      "firstInhibitor     1.331251\n",
      "firstBaron         0.122630\n",
      "firstDragon        0.697298\n",
      "firstRiftHerald   -0.170320\n",
      "towerKills         0.418017\n",
      "inhibitorKills     0.848805\n",
      "baronKills        -0.578585\n",
      "dragonKills        0.244195\n",
      "riftHeraldKills   -0.131832\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "X = match_df[match_df.region == 'na1'].drop(['win', 'region'], axis = 1)\n",
    "y = match_df[match_df.region == 'na1'].win\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3)\n",
    "\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train, y_train)\n",
    "y_pred = log.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print()\n",
    "\n",
    "log_coeff_na = pd.Series(log.coef_[0], index = match_df.drop(['win', 'region'], axis = 1).columns)\n",
    "print(log_coeff_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "64d75459",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.84      0.81       267\n",
      "           1       0.84      0.79      0.82       288\n",
      "\n",
      "    accuracy                           0.81       555\n",
      "   macro avg       0.82      0.82      0.81       555\n",
      "weighted avg       0.82      0.81      0.81       555\n",
      "\n",
      "[[224  43]\n",
      " [ 60 228]]\n",
      "\n",
      "index             -0.000468\n",
      "firstBlood         0.280399\n",
      "firstTower         1.092186\n",
      "firstInhibitor     1.286329\n",
      "firstBaron         0.265948\n",
      "firstDragon        0.191116\n",
      "firstRiftHerald    0.379472\n",
      "towerKills         0.425155\n",
      "inhibitorKills     0.368681\n",
      "baronKills        -0.200551\n",
      "dragonKills       -0.127123\n",
      "riftHeraldKills   -0.461576\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "X = match_df[match_df.region == 'br1'].drop(['win', 'region'], axis = 1)\n",
    "y = match_df[match_df.region == 'br1'].win\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3)\n",
    "\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train, y_train)\n",
    "y_pred = log.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print()\n",
    "\n",
    "log_coeff_br = pd.Series(log.coef_[0], index = match_df.drop(['win', 'region'], axis = 1).columns)\n",
    "print(log_coeff_br)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e9623236",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.86      0.82       295\n",
      "           1       0.85      0.78      0.81       304\n",
      "\n",
      "    accuracy                           0.82       599\n",
      "   macro avg       0.82      0.82      0.82       599\n",
      "weighted avg       0.82      0.82      0.82       599\n",
      "\n",
      "[[253  42]\n",
      " [ 68 236]]\n",
      "\n",
      "index             -0.000570\n",
      "firstBlood         0.473258\n",
      "firstTower         1.076918\n",
      "firstInhibitor     0.922980\n",
      "firstBaron        -0.178341\n",
      "firstDragon        0.356483\n",
      "firstRiftHerald    0.233211\n",
      "towerKills         0.408813\n",
      "inhibitorKills     0.646701\n",
      "baronKills        -0.099602\n",
      "dragonKills        0.368618\n",
      "riftHeraldKills   -0.441067\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "X = match_df[match_df.region == 'kr'].drop(['win', 'region'], axis = 1)\n",
    "y = match_df[match_df.region == 'kr'].win\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3)\n",
    "\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train, y_train)\n",
    "y_pred = log.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print()\n",
    "\n",
    "log_coeff_kr = pd.Series(log.coef_[0], index = match_df.drop(['win', 'region'], axis = 1).columns)\n",
    "print(log_coeff_kr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7f0c65e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.88      0.87       252\n",
      "           1       0.88      0.85      0.86       250\n",
      "\n",
      "    accuracy                           0.87       502\n",
      "   macro avg       0.87      0.87      0.87       502\n",
      "weighted avg       0.87      0.87      0.87       502\n",
      "\n",
      "[[222  30]\n",
      " [ 37 213]]\n",
      "\n",
      "index             -0.000475\n",
      "firstBlood         0.468171\n",
      "firstTower         0.933579\n",
      "firstInhibitor     1.121350\n",
      "firstBaron         0.155389\n",
      "firstDragon       -0.069355\n",
      "firstRiftHerald    0.027299\n",
      "towerKills         0.489585\n",
      "inhibitorKills     0.711914\n",
      "baronKills        -0.232451\n",
      "dragonKills       -0.044061\n",
      "riftHeraldKills   -0.196565\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "X = match_df[match_df.region == 'euw1'].drop(['win', 'region'], axis = 1)\n",
    "y = match_df[match_df.region == 'euw1'].win\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3)\n",
    "\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train, y_train)\n",
    "y_pred = log.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print()\n",
    "\n",
    "log_coeff_euw = pd.Series(log.coef_[0], index = match_df.drop(['win', 'region'], axis = 1).columns)\n",
    "print(log_coeff_euw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3ce61a66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.87      0.85       258\n",
      "           1       0.87      0.83      0.85       274\n",
      "\n",
      "    accuracy                           0.85       532\n",
      "   macro avg       0.85      0.85      0.85       532\n",
      "weighted avg       0.85      0.85      0.85       532\n",
      "\n",
      "[[225  33]\n",
      " [ 47 227]]\n",
      "\n",
      "index             -0.000364\n",
      "firstBlood         0.116354\n",
      "firstTower         0.145511\n",
      "firstInhibitor     0.309769\n",
      "firstBaron         0.007116\n",
      "firstDragon       -0.003903\n",
      "firstRiftHerald   -0.073720\n",
      "towerKills         0.660556\n",
      "inhibitorKills     0.313452\n",
      "baronKills        -0.014973\n",
      "dragonKills       -0.172414\n",
      "riftHeraldKills   -0.069030\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "X = match_df[match_df.region == 'eun1'].drop(['win', 'region'], axis = 1)\n",
    "y = match_df[match_df.region == 'eun1'].win\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3)\n",
    "\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train, y_train)\n",
    "y_pred = log.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print()\n",
    "\n",
    "log_coeff_eun = pd.Series(log.coef_[0], index = match_df.drop(['win', 'region'], axis = 1).columns)\n",
    "print(log_coeff_eun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "19a37565",
   "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>overall</th>\n",
       "      <th>na</th>\n",
       "      <th>br</th>\n",
       "      <th>kr</th>\n",
       "      <th>euw</th>\n",
       "      <th>eun</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <td>-0.000056</td>\n",
       "      <td>-0.000057</td>\n",
       "      <td>-0.000465</td>\n",
       "      <td>-0.000643</td>\n",
       "      <td>-0.000496</td>\n",
       "      <td>-0.000387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>firstBlood</th>\n",
       "      <td>0.155285</td>\n",
       "      <td>0.352765</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>0.472741</td>\n",
       "      <td>0.359047</td>\n",
       "      <td>0.439183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>firstTower</th>\n",
       "      <td>0.949051</td>\n",
       "      <td>1.442339</td>\n",
       "      <td>1.233045</td>\n",
       "      <td>0.519728</td>\n",
       "      <td>1.129837</td>\n",
       "      <td>0.495924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>firstInhibitor</th>\n",
       "      <td>1.149886</td>\n",
       "      <td>1.259752</td>\n",
       "      <td>1.441163</td>\n",
       "      <td>0.626625</td>\n",
       "      <td>1.482528</td>\n",
       "      <td>0.699291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>firstBaron</th>\n",
       "      <td>0.062520</td>\n",
       "      <td>0.130073</td>\n",
       "      <td>0.181838</td>\n",
       "      <td>-0.257803</td>\n",
       "      <td>0.292176</td>\n",
       "      <td>0.146625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>firstDragon</th>\n",
       "      <td>0.256334</td>\n",
       "      <td>0.774502</td>\n",
       "      <td>0.447680</td>\n",
       "      <td>0.153970</td>\n",
       "      <td>0.066093</td>\n",
       "      <td>0.291205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>firstRiftHerald</th>\n",
       "      <td>0.079523</td>\n",
       "      <td>-0.108139</td>\n",
       "      <td>0.447263</td>\n",
       "      <td>0.039781</td>\n",
       "      <td>0.115502</td>\n",
       "      <td>-0.365775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>towerKills</th>\n",
       "      <td>0.350033</td>\n",
       "      <td>0.462630</td>\n",
       "      <td>0.457028</td>\n",
       "      <td>0.431703</td>\n",
       "      <td>0.492825</td>\n",
       "      <td>0.567218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inhibitorKills</th>\n",
       "      <td>0.748038</td>\n",
       "      <td>0.716690</td>\n",
       "      <td>0.079818</td>\n",
       "      <td>0.788355</td>\n",
       "      <td>0.141359</td>\n",
       "      <td>0.428386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baronKills</th>\n",
       "      <td>-0.210058</td>\n",
       "      <td>-0.318264</td>\n",
       "      <td>0.011648</td>\n",
       "      <td>-0.279269</td>\n",
       "      <td>-0.358533</td>\n",
       "      <td>0.021375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dragonKills</th>\n",
       "      <td>0.157777</td>\n",
       "      <td>0.230313</td>\n",
       "      <td>-0.161270</td>\n",
       "      <td>0.393061</td>\n",
       "      <td>-0.057275</td>\n",
       "      <td>-0.076711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>riftHeraldKills</th>\n",
       "      <td>-0.006571</td>\n",
       "      <td>-0.193891</td>\n",
       "      <td>-0.505117</td>\n",
       "      <td>-0.005139</td>\n",
       "      <td>-0.187464</td>\n",
       "      <td>-0.174046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  overall        na        br        kr       euw       eun\n",
       "index           -0.000056 -0.000057 -0.000465 -0.000643 -0.000496 -0.000387\n",
       "firstBlood       0.155285  0.352765  0.093045  0.472741  0.359047  0.439183\n",
       "firstTower       0.949051  1.442339  1.233045  0.519728  1.129837  0.495924\n",
       "firstInhibitor   1.149886  1.259752  1.441163  0.626625  1.482528  0.699291\n",
       "firstBaron       0.062520  0.130073  0.181838 -0.257803  0.292176  0.146625\n",
       "firstDragon      0.256334  0.774502  0.447680  0.153970  0.066093  0.291205\n",
       "firstRiftHerald  0.079523 -0.108139  0.447263  0.039781  0.115502 -0.365775\n",
       "towerKills       0.350033  0.462630  0.457028  0.431703  0.492825  0.567218\n",
       "inhibitorKills   0.748038  0.716690  0.079818  0.788355  0.141359  0.428386\n",
       "baronKills      -0.210058 -0.318264  0.011648 -0.279269 -0.358533  0.021375\n",
       "dragonKills      0.157777  0.230313 -0.161270  0.393061 -0.057275 -0.076711\n",
       "riftHeraldKills -0.006571 -0.193891 -0.505117 -0.005139 -0.187464 -0.174046"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "win_conditions_dict = dict()\n",
    "win_conditions_dict['overall'] = log_coeff_tot\n",
    "# win_conditions_dict['na'] = log_coeff_na\n",
    "# win_conditions_dict['br'] = log_coeff_br\n",
    "# win_conditions_dict['kr'] = log_coeff_kr\n",
    "# win_conditions_dict['euw'] = log_coeff_euw\n",
    "# win_conditions_dict['eun'] = log_coeff_eun\n",
    "# win_conditions = pd.DataFrame(win_conditions_dict)\n",
    "win_conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2b0bd93a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "win_conditions.plot(kind = 'bar', figsize = (12, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e35d9bb2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}